diff --git a/3rdParty/my_ceres_vc14/CMake/CeresConfig.cmake b/3rdParty/CMake/CeresConfig.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/CeresConfig.cmake
rename to 3rdParty/CMake/CeresConfig.cmake
diff --git a/3rdParty/my_ceres_vc14/CMake/CeresConfigVersion.cmake b/3rdParty/CMake/CeresConfigVersion.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/CeresConfigVersion.cmake
rename to 3rdParty/CMake/CeresConfigVersion.cmake
diff --git a/3rdParty/my_ceres_vc14/CMake/CeresTargets-debug.cmake b/3rdParty/CMake/CeresTargets-debug.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/CeresTargets-debug.cmake
rename to 3rdParty/CMake/CeresTargets-debug.cmake
diff --git a/3rdParty/my_ceres_vc14/CMake/CeresTargets-release.cmake b/3rdParty/CMake/CeresTargets-release.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/CeresTargets-release.cmake
rename to 3rdParty/CMake/CeresTargets-release.cmake
diff --git a/3rdParty/my_ceres_vc14/CMake/CeresTargets.cmake b/3rdParty/CMake/CeresTargets.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/CeresTargets.cmake
rename to 3rdParty/CMake/CeresTargets.cmake
diff --git a/3rdParty/my_ceres_vc14/CMake/FindEigen.cmake b/3rdParty/CMake/FindEigen.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/FindEigen.cmake
rename to 3rdParty/CMake/FindEigen.cmake
diff --git a/3rdParty/my_ceres_vc14/CMake/FindGflags.cmake b/3rdParty/CMake/FindGflags.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/FindGflags.cmake
rename to 3rdParty/CMake/FindGflags.cmake
diff --git a/3rdParty/my_ceres_vc14/CMake/FindGlog.cmake b/3rdParty/CMake/FindGlog.cmake
similarity index 100%
rename from 3rdParty/my_ceres_vc14/CMake/FindGlog.cmake
rename to 3rdParty/CMake/FindGlog.cmake
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/CMakeLists.txt b/3rdParty/include/Eigen/CMakeLists.txt
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/CMakeLists.txt
rename to 3rdParty/include/Eigen/CMakeLists.txt
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Cholesky b/3rdParty/include/Eigen/Cholesky
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Cholesky
rename to 3rdParty/include/Eigen/Cholesky
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/CholmodSupport b/3rdParty/include/Eigen/CholmodSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/CholmodSupport
rename to 3rdParty/include/Eigen/CholmodSupport
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Core b/3rdParty/include/Eigen/Core
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Core
rename to 3rdParty/include/Eigen/Core
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Dense b/3rdParty/include/Eigen/Dense
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Dense
rename to 3rdParty/include/Eigen/Dense
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Eigen b/3rdParty/include/Eigen/Eigen
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Eigen
rename to 3rdParty/include/Eigen/Eigen
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Eigenvalues b/3rdParty/include/Eigen/Eigenvalues
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Eigenvalues
rename to 3rdParty/include/Eigen/Eigenvalues
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Geometry b/3rdParty/include/Eigen/Geometry
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Geometry
rename to 3rdParty/include/Eigen/Geometry
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Householder b/3rdParty/include/Eigen/Householder
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Householder
rename to 3rdParty/include/Eigen/Householder
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/IterativeLinearSolvers b/3rdParty/include/Eigen/IterativeLinearSolvers
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/IterativeLinearSolvers
rename to 3rdParty/include/Eigen/IterativeLinearSolvers
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Jacobi b/3rdParty/include/Eigen/Jacobi
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Jacobi
rename to 3rdParty/include/Eigen/Jacobi
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/LU b/3rdParty/include/Eigen/LU
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/LU
rename to 3rdParty/include/Eigen/LU
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/MetisSupport b/3rdParty/include/Eigen/MetisSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/MetisSupport
rename to 3rdParty/include/Eigen/MetisSupport
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/OrderingMethods b/3rdParty/include/Eigen/OrderingMethods
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/OrderingMethods
rename to 3rdParty/include/Eigen/OrderingMethods
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/PaStiXSupport b/3rdParty/include/Eigen/PaStiXSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/PaStiXSupport
rename to 3rdParty/include/Eigen/PaStiXSupport
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/PardisoSupport b/3rdParty/include/Eigen/PardisoSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/PardisoSupport
rename to 3rdParty/include/Eigen/PardisoSupport
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/QR b/3rdParty/include/Eigen/QR
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/QR
rename to 3rdParty/include/Eigen/QR
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/QtAlignedMalloc b/3rdParty/include/Eigen/QtAlignedMalloc
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/QtAlignedMalloc
rename to 3rdParty/include/Eigen/QtAlignedMalloc
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/SPQRSupport b/3rdParty/include/Eigen/SPQRSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/SPQRSupport
rename to 3rdParty/include/Eigen/SPQRSupport
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/SVD b/3rdParty/include/Eigen/SVD
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/SVD
rename to 3rdParty/include/Eigen/SVD
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/Sparse b/3rdParty/include/Eigen/Sparse
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/Sparse
rename to 3rdParty/include/Eigen/Sparse
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/SparseCholesky b/3rdParty/include/Eigen/SparseCholesky
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/SparseCholesky
rename to 3rdParty/include/Eigen/SparseCholesky
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/SparseCore b/3rdParty/include/Eigen/SparseCore
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/SparseCore
rename to 3rdParty/include/Eigen/SparseCore
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/SparseLU b/3rdParty/include/Eigen/SparseLU
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/SparseLU
rename to 3rdParty/include/Eigen/SparseLU
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/SparseQR b/3rdParty/include/Eigen/SparseQR
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/SparseQR
rename to 3rdParty/include/Eigen/SparseQR
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/StdDeque b/3rdParty/include/Eigen/StdDeque
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/StdDeque
rename to 3rdParty/include/Eigen/StdDeque
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/StdList b/3rdParty/include/Eigen/StdList
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/StdList
rename to 3rdParty/include/Eigen/StdList
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/StdVector b/3rdParty/include/Eigen/StdVector
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/StdVector
rename to 3rdParty/include/Eigen/StdVector
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/SuperLUSupport b/3rdParty/include/Eigen/SuperLUSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/SuperLUSupport
rename to 3rdParty/include/Eigen/SuperLUSupport
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/UmfPackSupport b/3rdParty/include/Eigen/UmfPackSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/UmfPackSupport
rename to 3rdParty/include/Eigen/UmfPackSupport
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Cholesky/LDLT.h b/3rdParty/include/Eigen/src/Cholesky/LDLT.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Cholesky/LDLT.h
rename to 3rdParty/include/Eigen/src/Cholesky/LDLT.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Cholesky/LLT.h b/3rdParty/include/Eigen/src/Cholesky/LLT.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Cholesky/LLT.h
rename to 3rdParty/include/Eigen/src/Cholesky/LLT.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Cholesky/LLT_LAPACKE.h b/3rdParty/include/Eigen/src/Cholesky/LLT_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Cholesky/LLT_LAPACKE.h
rename to 3rdParty/include/Eigen/src/Cholesky/LLT_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/CholmodSupport/CholmodSupport.h b/3rdParty/include/Eigen/src/CholmodSupport/CholmodSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/CholmodSupport/CholmodSupport.h
rename to 3rdParty/include/Eigen/src/CholmodSupport/CholmodSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Array.h b/3rdParty/include/Eigen/src/Core/Array.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Array.h
rename to 3rdParty/include/Eigen/src/Core/Array.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/ArrayBase.h b/3rdParty/include/Eigen/src/Core/ArrayBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/ArrayBase.h
rename to 3rdParty/include/Eigen/src/Core/ArrayBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/ArrayWrapper.h b/3rdParty/include/Eigen/src/Core/ArrayWrapper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/ArrayWrapper.h
rename to 3rdParty/include/Eigen/src/Core/ArrayWrapper.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Assign.h b/3rdParty/include/Eigen/src/Core/Assign.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Assign.h
rename to 3rdParty/include/Eigen/src/Core/Assign.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/AssignEvaluator.h b/3rdParty/include/Eigen/src/Core/AssignEvaluator.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/AssignEvaluator.h
rename to 3rdParty/include/Eigen/src/Core/AssignEvaluator.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Assign_MKL.h b/3rdParty/include/Eigen/src/Core/Assign_MKL.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Assign_MKL.h
rename to 3rdParty/include/Eigen/src/Core/Assign_MKL.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/BandMatrix.h b/3rdParty/include/Eigen/src/Core/BandMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/BandMatrix.h
rename to 3rdParty/include/Eigen/src/Core/BandMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Block.h b/3rdParty/include/Eigen/src/Core/Block.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Block.h
rename to 3rdParty/include/Eigen/src/Core/Block.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/BooleanRedux.h b/3rdParty/include/Eigen/src/Core/BooleanRedux.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/BooleanRedux.h
rename to 3rdParty/include/Eigen/src/Core/BooleanRedux.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CommaInitializer.h b/3rdParty/include/Eigen/src/Core/CommaInitializer.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CommaInitializer.h
rename to 3rdParty/include/Eigen/src/Core/CommaInitializer.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/ConditionEstimator.h b/3rdParty/include/Eigen/src/Core/ConditionEstimator.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/ConditionEstimator.h
rename to 3rdParty/include/Eigen/src/Core/ConditionEstimator.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CoreEvaluators.h b/3rdParty/include/Eigen/src/Core/CoreEvaluators.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CoreEvaluators.h
rename to 3rdParty/include/Eigen/src/Core/CoreEvaluators.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CoreIterators.h b/3rdParty/include/Eigen/src/Core/CoreIterators.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CoreIterators.h
rename to 3rdParty/include/Eigen/src/Core/CoreIterators.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseBinaryOp.h b/3rdParty/include/Eigen/src/Core/CwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseBinaryOp.h
rename to 3rdParty/include/Eigen/src/Core/CwiseBinaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseNullaryOp.h b/3rdParty/include/Eigen/src/Core/CwiseNullaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseNullaryOp.h
rename to 3rdParty/include/Eigen/src/Core/CwiseNullaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseTernaryOp.h b/3rdParty/include/Eigen/src/Core/CwiseTernaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseTernaryOp.h
rename to 3rdParty/include/Eigen/src/Core/CwiseTernaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseUnaryOp.h b/3rdParty/include/Eigen/src/Core/CwiseUnaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseUnaryOp.h
rename to 3rdParty/include/Eigen/src/Core/CwiseUnaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseUnaryView.h b/3rdParty/include/Eigen/src/Core/CwiseUnaryView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/CwiseUnaryView.h
rename to 3rdParty/include/Eigen/src/Core/CwiseUnaryView.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/DenseBase.h b/3rdParty/include/Eigen/src/Core/DenseBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/DenseBase.h
rename to 3rdParty/include/Eigen/src/Core/DenseBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/DenseCoeffsBase.h b/3rdParty/include/Eigen/src/Core/DenseCoeffsBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/DenseCoeffsBase.h
rename to 3rdParty/include/Eigen/src/Core/DenseCoeffsBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/DenseStorage.h b/3rdParty/include/Eigen/src/Core/DenseStorage.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/DenseStorage.h
rename to 3rdParty/include/Eigen/src/Core/DenseStorage.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Diagonal.h b/3rdParty/include/Eigen/src/Core/Diagonal.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Diagonal.h
rename to 3rdParty/include/Eigen/src/Core/Diagonal.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/DiagonalMatrix.h b/3rdParty/include/Eigen/src/Core/DiagonalMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/DiagonalMatrix.h
rename to 3rdParty/include/Eigen/src/Core/DiagonalMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/DiagonalProduct.h b/3rdParty/include/Eigen/src/Core/DiagonalProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/DiagonalProduct.h
rename to 3rdParty/include/Eigen/src/Core/DiagonalProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Dot.h b/3rdParty/include/Eigen/src/Core/Dot.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Dot.h
rename to 3rdParty/include/Eigen/src/Core/Dot.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/EigenBase.h b/3rdParty/include/Eigen/src/Core/EigenBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/EigenBase.h
rename to 3rdParty/include/Eigen/src/Core/EigenBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/ForceAlignedAccess.h b/3rdParty/include/Eigen/src/Core/ForceAlignedAccess.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/ForceAlignedAccess.h
rename to 3rdParty/include/Eigen/src/Core/ForceAlignedAccess.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Fuzzy.h b/3rdParty/include/Eigen/src/Core/Fuzzy.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Fuzzy.h
rename to 3rdParty/include/Eigen/src/Core/Fuzzy.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/GeneralProduct.h b/3rdParty/include/Eigen/src/Core/GeneralProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/GeneralProduct.h
rename to 3rdParty/include/Eigen/src/Core/GeneralProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/GenericPacketMath.h b/3rdParty/include/Eigen/src/Core/GenericPacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/GenericPacketMath.h
rename to 3rdParty/include/Eigen/src/Core/GenericPacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/GlobalFunctions.h b/3rdParty/include/Eigen/src/Core/GlobalFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/GlobalFunctions.h
rename to 3rdParty/include/Eigen/src/Core/GlobalFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/IO.h b/3rdParty/include/Eigen/src/Core/IO.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/IO.h
rename to 3rdParty/include/Eigen/src/Core/IO.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Inverse.h b/3rdParty/include/Eigen/src/Core/Inverse.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Inverse.h
rename to 3rdParty/include/Eigen/src/Core/Inverse.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Map.h b/3rdParty/include/Eigen/src/Core/Map.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Map.h
rename to 3rdParty/include/Eigen/src/Core/Map.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/MapBase.h b/3rdParty/include/Eigen/src/Core/MapBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/MapBase.h
rename to 3rdParty/include/Eigen/src/Core/MapBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/MathFunctions.h b/3rdParty/include/Eigen/src/Core/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/MathFunctionsImpl.h b/3rdParty/include/Eigen/src/Core/MathFunctionsImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/MathFunctionsImpl.h
rename to 3rdParty/include/Eigen/src/Core/MathFunctionsImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Matrix.h b/3rdParty/include/Eigen/src/Core/Matrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Matrix.h
rename to 3rdParty/include/Eigen/src/Core/Matrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/MatrixBase.h b/3rdParty/include/Eigen/src/Core/MatrixBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/MatrixBase.h
rename to 3rdParty/include/Eigen/src/Core/MatrixBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/NestByValue.h b/3rdParty/include/Eigen/src/Core/NestByValue.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/NestByValue.h
rename to 3rdParty/include/Eigen/src/Core/NestByValue.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/NoAlias.h b/3rdParty/include/Eigen/src/Core/NoAlias.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/NoAlias.h
rename to 3rdParty/include/Eigen/src/Core/NoAlias.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/NumTraits.h b/3rdParty/include/Eigen/src/Core/NumTraits.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/NumTraits.h
rename to 3rdParty/include/Eigen/src/Core/NumTraits.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/PermutationMatrix.h b/3rdParty/include/Eigen/src/Core/PermutationMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/PermutationMatrix.h
rename to 3rdParty/include/Eigen/src/Core/PermutationMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/PlainObjectBase.h b/3rdParty/include/Eigen/src/Core/PlainObjectBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/PlainObjectBase.h
rename to 3rdParty/include/Eigen/src/Core/PlainObjectBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Product.h b/3rdParty/include/Eigen/src/Core/Product.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Product.h
rename to 3rdParty/include/Eigen/src/Core/Product.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/ProductEvaluators.h b/3rdParty/include/Eigen/src/Core/ProductEvaluators.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/ProductEvaluators.h
rename to 3rdParty/include/Eigen/src/Core/ProductEvaluators.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Random.h b/3rdParty/include/Eigen/src/Core/Random.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Random.h
rename to 3rdParty/include/Eigen/src/Core/Random.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Redux.h b/3rdParty/include/Eigen/src/Core/Redux.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Redux.h
rename to 3rdParty/include/Eigen/src/Core/Redux.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Ref.h b/3rdParty/include/Eigen/src/Core/Ref.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Ref.h
rename to 3rdParty/include/Eigen/src/Core/Ref.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Replicate.h b/3rdParty/include/Eigen/src/Core/Replicate.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Replicate.h
rename to 3rdParty/include/Eigen/src/Core/Replicate.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/ReturnByValue.h b/3rdParty/include/Eigen/src/Core/ReturnByValue.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/ReturnByValue.h
rename to 3rdParty/include/Eigen/src/Core/ReturnByValue.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Reverse.h b/3rdParty/include/Eigen/src/Core/Reverse.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Reverse.h
rename to 3rdParty/include/Eigen/src/Core/Reverse.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Select.h b/3rdParty/include/Eigen/src/Core/Select.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Select.h
rename to 3rdParty/include/Eigen/src/Core/Select.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/SelfAdjointView.h b/3rdParty/include/Eigen/src/Core/SelfAdjointView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/SelfAdjointView.h
rename to 3rdParty/include/Eigen/src/Core/SelfAdjointView.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/SelfCwiseBinaryOp.h b/3rdParty/include/Eigen/src/Core/SelfCwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/SelfCwiseBinaryOp.h
rename to 3rdParty/include/Eigen/src/Core/SelfCwiseBinaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Solve.h b/3rdParty/include/Eigen/src/Core/Solve.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Solve.h
rename to 3rdParty/include/Eigen/src/Core/Solve.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/SolveTriangular.h b/3rdParty/include/Eigen/src/Core/SolveTriangular.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/SolveTriangular.h
rename to 3rdParty/include/Eigen/src/Core/SolveTriangular.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/SolverBase.h b/3rdParty/include/Eigen/src/Core/SolverBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/SolverBase.h
rename to 3rdParty/include/Eigen/src/Core/SolverBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/StableNorm.h b/3rdParty/include/Eigen/src/Core/StableNorm.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/StableNorm.h
rename to 3rdParty/include/Eigen/src/Core/StableNorm.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Stride.h b/3rdParty/include/Eigen/src/Core/Stride.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Stride.h
rename to 3rdParty/include/Eigen/src/Core/Stride.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Swap.h b/3rdParty/include/Eigen/src/Core/Swap.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Swap.h
rename to 3rdParty/include/Eigen/src/Core/Swap.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Transpose.h b/3rdParty/include/Eigen/src/Core/Transpose.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Transpose.h
rename to 3rdParty/include/Eigen/src/Core/Transpose.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Transpositions.h b/3rdParty/include/Eigen/src/Core/Transpositions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Transpositions.h
rename to 3rdParty/include/Eigen/src/Core/Transpositions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/TriangularMatrix.h b/3rdParty/include/Eigen/src/Core/TriangularMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/TriangularMatrix.h
rename to 3rdParty/include/Eigen/src/Core/TriangularMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/VectorBlock.h b/3rdParty/include/Eigen/src/Core/VectorBlock.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/VectorBlock.h
rename to 3rdParty/include/Eigen/src/Core/VectorBlock.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/VectorwiseOp.h b/3rdParty/include/Eigen/src/Core/VectorwiseOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/VectorwiseOp.h
rename to 3rdParty/include/Eigen/src/Core/VectorwiseOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/Visitor.h b/3rdParty/include/Eigen/src/Core/Visitor.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/Visitor.h
rename to 3rdParty/include/Eigen/src/Core/Visitor.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/Complex.h b/3rdParty/include/Eigen/src/Core/arch/AVX/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/Complex.h
rename to 3rdParty/include/Eigen/src/Core/arch/AVX/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/MathFunctions.h b/3rdParty/include/Eigen/src/Core/arch/AVX/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/arch/AVX/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/PacketMath.h b/3rdParty/include/Eigen/src/Core/arch/AVX/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/PacketMath.h
rename to 3rdParty/include/Eigen/src/Core/arch/AVX/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/TypeCasting.h b/3rdParty/include/Eigen/src/Core/arch/AVX/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX/TypeCasting.h
rename to 3rdParty/include/Eigen/src/Core/arch/AVX/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX512/MathFunctions.h b/3rdParty/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX512/PacketMath.h b/3rdParty/include/Eigen/src/Core/arch/AVX512/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AVX512/PacketMath.h
rename to 3rdParty/include/Eigen/src/Core/arch/AVX512/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AltiVec/Complex.h b/3rdParty/include/Eigen/src/Core/arch/AltiVec/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AltiVec/Complex.h
rename to 3rdParty/include/Eigen/src/Core/arch/AltiVec/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/3rdParty/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AltiVec/PacketMath.h b/3rdParty/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
rename to 3rdParty/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/Complex.h b/3rdParty/include/Eigen/src/Core/arch/CUDA/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/Complex.h
rename to 3rdParty/include/Eigen/src/Core/arch/CUDA/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/Half.h b/3rdParty/include/Eigen/src/Core/arch/CUDA/Half.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/Half.h
rename to 3rdParty/include/Eigen/src/Core/arch/CUDA/Half.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/MathFunctions.h b/3rdParty/include/Eigen/src/Core/arch/CUDA/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/arch/CUDA/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/PacketMath.h b/3rdParty/include/Eigen/src/Core/arch/CUDA/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/PacketMath.h
rename to 3rdParty/include/Eigen/src/Core/arch/CUDA/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/PacketMathHalf.h b/3rdParty/include/Eigen/src/Core/arch/CUDA/PacketMathHalf.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/PacketMathHalf.h
rename to 3rdParty/include/Eigen/src/Core/arch/CUDA/PacketMathHalf.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/TypeCasting.h b/3rdParty/include/Eigen/src/Core/arch/CUDA/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/CUDA/TypeCasting.h
rename to 3rdParty/include/Eigen/src/Core/arch/CUDA/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/Default/ConjHelper.h b/3rdParty/include/Eigen/src/Core/arch/Default/ConjHelper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/Default/ConjHelper.h
rename to 3rdParty/include/Eigen/src/Core/arch/Default/ConjHelper.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/Default/Settings.h b/3rdParty/include/Eigen/src/Core/arch/Default/Settings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/Default/Settings.h
rename to 3rdParty/include/Eigen/src/Core/arch/Default/Settings.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/NEON/Complex.h b/3rdParty/include/Eigen/src/Core/arch/NEON/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/NEON/Complex.h
rename to 3rdParty/include/Eigen/src/Core/arch/NEON/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/NEON/MathFunctions.h b/3rdParty/include/Eigen/src/Core/arch/NEON/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/NEON/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/arch/NEON/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/NEON/PacketMath.h b/3rdParty/include/Eigen/src/Core/arch/NEON/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/NEON/PacketMath.h
rename to 3rdParty/include/Eigen/src/Core/arch/NEON/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/Complex.h b/3rdParty/include/Eigen/src/Core/arch/SSE/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/Complex.h
rename to 3rdParty/include/Eigen/src/Core/arch/SSE/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/MathFunctions.h b/3rdParty/include/Eigen/src/Core/arch/SSE/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/arch/SSE/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/PacketMath.h b/3rdParty/include/Eigen/src/Core/arch/SSE/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/PacketMath.h
rename to 3rdParty/include/Eigen/src/Core/arch/SSE/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/TypeCasting.h b/3rdParty/include/Eigen/src/Core/arch/SSE/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/SSE/TypeCasting.h
rename to 3rdParty/include/Eigen/src/Core/arch/SSE/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/ZVector/Complex.h b/3rdParty/include/Eigen/src/Core/arch/ZVector/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/ZVector/Complex.h
rename to 3rdParty/include/Eigen/src/Core/arch/ZVector/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/ZVector/MathFunctions.h b/3rdParty/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
rename to 3rdParty/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/ZVector/PacketMath.h b/3rdParty/include/Eigen/src/Core/arch/ZVector/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/arch/ZVector/PacketMath.h
rename to 3rdParty/include/Eigen/src/Core/arch/ZVector/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/AssignmentFunctors.h b/3rdParty/include/Eigen/src/Core/functors/AssignmentFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/AssignmentFunctors.h
rename to 3rdParty/include/Eigen/src/Core/functors/AssignmentFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/BinaryFunctors.h b/3rdParty/include/Eigen/src/Core/functors/BinaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/BinaryFunctors.h
rename to 3rdParty/include/Eigen/src/Core/functors/BinaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/NullaryFunctors.h b/3rdParty/include/Eigen/src/Core/functors/NullaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/NullaryFunctors.h
rename to 3rdParty/include/Eigen/src/Core/functors/NullaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/StlFunctors.h b/3rdParty/include/Eigen/src/Core/functors/StlFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/StlFunctors.h
rename to 3rdParty/include/Eigen/src/Core/functors/StlFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/TernaryFunctors.h b/3rdParty/include/Eigen/src/Core/functors/TernaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/TernaryFunctors.h
rename to 3rdParty/include/Eigen/src/Core/functors/TernaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/UnaryFunctors.h b/3rdParty/include/Eigen/src/Core/functors/UnaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/functors/UnaryFunctors.h
rename to 3rdParty/include/Eigen/src/Core/functors/UnaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/3rdParty/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
rename to 3rdParty/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrix.h b/3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
rename to 3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
rename to 3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h b/3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h b/3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixVector.h b/3rdParty/include/Eigen/src/Core/products/GeneralMatrixVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixVector.h
rename to 3rdParty/include/Eigen/src/Core/products/GeneralMatrixVector.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h b/3rdParty/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/Parallelizer.h b/3rdParty/include/Eigen/src/Core/products/Parallelizer.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/Parallelizer.h
rename to 3rdParty/include/Eigen/src/Core/products/Parallelizer.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
rename to 3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h b/3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixVector.h b/3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
rename to 3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h b/3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointProduct.h b/3rdParty/include/Eigen/src/Core/products/SelfadjointProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointProduct.h
rename to 3rdParty/include/Eigen/src/Core/products/SelfadjointProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointRank2Update.h b/3rdParty/include/Eigen/src/Core/products/SelfadjointRank2Update.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/SelfadjointRank2Update.h
rename to 3rdParty/include/Eigen/src/Core/products/SelfadjointRank2Update.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixMatrix.h b/3rdParty/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
rename to 3rdParty/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h b/3rdParty/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixVector.h b/3rdParty/include/Eigen/src/Core/products/TriangularMatrixVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixVector.h
rename to 3rdParty/include/Eigen/src/Core/products/TriangularMatrixVector.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h b/3rdParty/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularSolverMatrix.h b/3rdParty/include/Eigen/src/Core/products/TriangularSolverMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularSolverMatrix.h
rename to 3rdParty/include/Eigen/src/Core/products/TriangularSolverMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h b/3rdParty/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
rename to 3rdParty/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularSolverVector.h b/3rdParty/include/Eigen/src/Core/products/TriangularSolverVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/products/TriangularSolverVector.h
rename to 3rdParty/include/Eigen/src/Core/products/TriangularSolverVector.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/BlasUtil.h b/3rdParty/include/Eigen/src/Core/util/BlasUtil.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/BlasUtil.h
rename to 3rdParty/include/Eigen/src/Core/util/BlasUtil.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Constants.h b/3rdParty/include/Eigen/src/Core/util/Constants.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Constants.h
rename to 3rdParty/include/Eigen/src/Core/util/Constants.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/DisableStupidWarnings.h b/3rdParty/include/Eigen/src/Core/util/DisableStupidWarnings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/DisableStupidWarnings.h
rename to 3rdParty/include/Eigen/src/Core/util/DisableStupidWarnings.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/ForwardDeclarations.h b/3rdParty/include/Eigen/src/Core/util/ForwardDeclarations.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/ForwardDeclarations.h
rename to 3rdParty/include/Eigen/src/Core/util/ForwardDeclarations.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/MKL_support.h b/3rdParty/include/Eigen/src/Core/util/MKL_support.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/MKL_support.h
rename to 3rdParty/include/Eigen/src/Core/util/MKL_support.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Macros.h b/3rdParty/include/Eigen/src/Core/util/Macros.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Macros.h
rename to 3rdParty/include/Eigen/src/Core/util/Macros.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Memory.h b/3rdParty/include/Eigen/src/Core/util/Memory.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Memory.h
rename to 3rdParty/include/Eigen/src/Core/util/Memory.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Meta.h b/3rdParty/include/Eigen/src/Core/util/Meta.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/Meta.h
rename to 3rdParty/include/Eigen/src/Core/util/Meta.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/NonMPL2.h b/3rdParty/include/Eigen/src/Core/util/NonMPL2.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/NonMPL2.h
rename to 3rdParty/include/Eigen/src/Core/util/NonMPL2.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/ReenableStupidWarnings.h b/3rdParty/include/Eigen/src/Core/util/ReenableStupidWarnings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/ReenableStupidWarnings.h
rename to 3rdParty/include/Eigen/src/Core/util/ReenableStupidWarnings.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/StaticAssert.h b/3rdParty/include/Eigen/src/Core/util/StaticAssert.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/StaticAssert.h
rename to 3rdParty/include/Eigen/src/Core/util/StaticAssert.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/XprHelper.h b/3rdParty/include/Eigen/src/Core/util/XprHelper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Core/util/XprHelper.h
rename to 3rdParty/include/Eigen/src/Core/util/XprHelper.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/3rdParty/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/ComplexSchur.h b/3rdParty/include/Eigen/src/Eigenvalues/ComplexSchur.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/ComplexSchur.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/ComplexSchur.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h b/3rdParty/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/EigenSolver.h b/3rdParty/include/Eigen/src/Eigenvalues/EigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/EigenSolver.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/EigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/3rdParty/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/3rdParty/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/3rdParty/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/3rdParty/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/RealQZ.h b/3rdParty/include/Eigen/src/Eigenvalues/RealQZ.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/RealQZ.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/RealQZ.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/RealSchur.h b/3rdParty/include/Eigen/src/Eigenvalues/RealSchur.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/RealSchur.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/RealSchur.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h b/3rdParty/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/3rdParty/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h b/3rdParty/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/Tridiagonalization.h b/3rdParty/include/Eigen/src/Eigenvalues/Tridiagonalization.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Eigenvalues/Tridiagonalization.h
rename to 3rdParty/include/Eigen/src/Eigenvalues/Tridiagonalization.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/AlignedBox.h b/3rdParty/include/Eigen/src/Geometry/AlignedBox.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/AlignedBox.h
rename to 3rdParty/include/Eigen/src/Geometry/AlignedBox.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/AngleAxis.h b/3rdParty/include/Eigen/src/Geometry/AngleAxis.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/AngleAxis.h
rename to 3rdParty/include/Eigen/src/Geometry/AngleAxis.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/EulerAngles.h b/3rdParty/include/Eigen/src/Geometry/EulerAngles.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/EulerAngles.h
rename to 3rdParty/include/Eigen/src/Geometry/EulerAngles.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Homogeneous.h b/3rdParty/include/Eigen/src/Geometry/Homogeneous.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Homogeneous.h
rename to 3rdParty/include/Eigen/src/Geometry/Homogeneous.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Hyperplane.h b/3rdParty/include/Eigen/src/Geometry/Hyperplane.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Hyperplane.h
rename to 3rdParty/include/Eigen/src/Geometry/Hyperplane.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/OrthoMethods.h b/3rdParty/include/Eigen/src/Geometry/OrthoMethods.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/OrthoMethods.h
rename to 3rdParty/include/Eigen/src/Geometry/OrthoMethods.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/ParametrizedLine.h b/3rdParty/include/Eigen/src/Geometry/ParametrizedLine.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/ParametrizedLine.h
rename to 3rdParty/include/Eigen/src/Geometry/ParametrizedLine.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Quaternion.h b/3rdParty/include/Eigen/src/Geometry/Quaternion.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Quaternion.h
rename to 3rdParty/include/Eigen/src/Geometry/Quaternion.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Rotation2D.h b/3rdParty/include/Eigen/src/Geometry/Rotation2D.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Rotation2D.h
rename to 3rdParty/include/Eigen/src/Geometry/Rotation2D.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/RotationBase.h b/3rdParty/include/Eigen/src/Geometry/RotationBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/RotationBase.h
rename to 3rdParty/include/Eigen/src/Geometry/RotationBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Scaling.h b/3rdParty/include/Eigen/src/Geometry/Scaling.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Scaling.h
rename to 3rdParty/include/Eigen/src/Geometry/Scaling.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Transform.h b/3rdParty/include/Eigen/src/Geometry/Transform.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Transform.h
rename to 3rdParty/include/Eigen/src/Geometry/Transform.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Translation.h b/3rdParty/include/Eigen/src/Geometry/Translation.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Translation.h
rename to 3rdParty/include/Eigen/src/Geometry/Translation.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Umeyama.h b/3rdParty/include/Eigen/src/Geometry/Umeyama.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/Umeyama.h
rename to 3rdParty/include/Eigen/src/Geometry/Umeyama.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/arch/Geometry_SSE.h b/3rdParty/include/Eigen/src/Geometry/arch/Geometry_SSE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Geometry/arch/Geometry_SSE.h
rename to 3rdParty/include/Eigen/src/Geometry/arch/Geometry_SSE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Householder/BlockHouseholder.h b/3rdParty/include/Eigen/src/Householder/BlockHouseholder.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Householder/BlockHouseholder.h
rename to 3rdParty/include/Eigen/src/Householder/BlockHouseholder.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Householder/Householder.h b/3rdParty/include/Eigen/src/Householder/Householder.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Householder/Householder.h
rename to 3rdParty/include/Eigen/src/Householder/Householder.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Householder/HouseholderSequence.h b/3rdParty/include/Eigen/src/Householder/HouseholderSequence.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Householder/HouseholderSequence.h
rename to 3rdParty/include/Eigen/src/Householder/HouseholderSequence.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/3rdParty/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
rename to 3rdParty/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/Jacobi/Jacobi.h b/3rdParty/include/Eigen/src/Jacobi/Jacobi.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/Jacobi/Jacobi.h
rename to 3rdParty/include/Eigen/src/Jacobi/Jacobi.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/LU/Determinant.h b/3rdParty/include/Eigen/src/LU/Determinant.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/LU/Determinant.h
rename to 3rdParty/include/Eigen/src/LU/Determinant.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/LU/FullPivLU.h b/3rdParty/include/Eigen/src/LU/FullPivLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/LU/FullPivLU.h
rename to 3rdParty/include/Eigen/src/LU/FullPivLU.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/LU/InverseImpl.h b/3rdParty/include/Eigen/src/LU/InverseImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/LU/InverseImpl.h
rename to 3rdParty/include/Eigen/src/LU/InverseImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/LU/PartialPivLU.h b/3rdParty/include/Eigen/src/LU/PartialPivLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/LU/PartialPivLU.h
rename to 3rdParty/include/Eigen/src/LU/PartialPivLU.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/LU/PartialPivLU_LAPACKE.h b/3rdParty/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
rename to 3rdParty/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/LU/arch/Inverse_SSE.h b/3rdParty/include/Eigen/src/LU/arch/Inverse_SSE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/LU/arch/Inverse_SSE.h
rename to 3rdParty/include/Eigen/src/LU/arch/Inverse_SSE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/MetisSupport/MetisSupport.h b/3rdParty/include/Eigen/src/MetisSupport/MetisSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/MetisSupport/MetisSupport.h
rename to 3rdParty/include/Eigen/src/MetisSupport/MetisSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/OrderingMethods/Amd.h b/3rdParty/include/Eigen/src/OrderingMethods/Amd.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/OrderingMethods/Amd.h
rename to 3rdParty/include/Eigen/src/OrderingMethods/Amd.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/OrderingMethods/Eigen_Colamd.h b/3rdParty/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
rename to 3rdParty/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/OrderingMethods/Ordering.h b/3rdParty/include/Eigen/src/OrderingMethods/Ordering.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/OrderingMethods/Ordering.h
rename to 3rdParty/include/Eigen/src/OrderingMethods/Ordering.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/PaStiXSupport/PaStiXSupport.h b/3rdParty/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
rename to 3rdParty/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/PardisoSupport/PardisoSupport.h b/3rdParty/include/Eigen/src/PardisoSupport/PardisoSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/PardisoSupport/PardisoSupport.h
rename to 3rdParty/include/Eigen/src/PardisoSupport/PardisoSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/QR/ColPivHouseholderQR.h b/3rdParty/include/Eigen/src/QR/ColPivHouseholderQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/QR/ColPivHouseholderQR.h
rename to 3rdParty/include/Eigen/src/QR/ColPivHouseholderQR.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h b/3rdParty/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
rename to 3rdParty/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/3rdParty/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
rename to 3rdParty/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/QR/FullPivHouseholderQR.h b/3rdParty/include/Eigen/src/QR/FullPivHouseholderQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/QR/FullPivHouseholderQR.h
rename to 3rdParty/include/Eigen/src/QR/FullPivHouseholderQR.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/QR/HouseholderQR.h b/3rdParty/include/Eigen/src/QR/HouseholderQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/QR/HouseholderQR.h
rename to 3rdParty/include/Eigen/src/QR/HouseholderQR.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/QR/HouseholderQR_LAPACKE.h b/3rdParty/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
rename to 3rdParty/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/3rdParty/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
rename to 3rdParty/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SVD/BDCSVD.h b/3rdParty/include/Eigen/src/SVD/BDCSVD.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SVD/BDCSVD.h
rename to 3rdParty/include/Eigen/src/SVD/BDCSVD.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SVD/JacobiSVD.h b/3rdParty/include/Eigen/src/SVD/JacobiSVD.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SVD/JacobiSVD.h
rename to 3rdParty/include/Eigen/src/SVD/JacobiSVD.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h b/3rdParty/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
rename to 3rdParty/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SVD/SVDBase.h b/3rdParty/include/Eigen/src/SVD/SVDBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SVD/SVDBase.h
rename to 3rdParty/include/Eigen/src/SVD/SVDBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SVD/UpperBidiagonalization.h b/3rdParty/include/Eigen/src/SVD/UpperBidiagonalization.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SVD/UpperBidiagonalization.h
rename to 3rdParty/include/Eigen/src/SVD/UpperBidiagonalization.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCholesky/SimplicialCholesky.h b/3rdParty/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
rename to 3rdParty/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/3rdParty/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
rename to 3rdParty/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/AmbiVector.h b/3rdParty/include/Eigen/src/SparseCore/AmbiVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/AmbiVector.h
rename to 3rdParty/include/Eigen/src/SparseCore/AmbiVector.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/CompressedStorage.h b/3rdParty/include/Eigen/src/SparseCore/CompressedStorage.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/CompressedStorage.h
rename to 3rdParty/include/Eigen/src/SparseCore/CompressedStorage.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/3rdParty/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
rename to 3rdParty/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/MappedSparseMatrix.h b/3rdParty/include/Eigen/src/SparseCore/MappedSparseMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/MappedSparseMatrix.h
rename to 3rdParty/include/Eigen/src/SparseCore/MappedSparseMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseAssign.h b/3rdParty/include/Eigen/src/SparseCore/SparseAssign.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseAssign.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseAssign.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseBlock.h b/3rdParty/include/Eigen/src/SparseCore/SparseBlock.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseBlock.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseBlock.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseColEtree.h b/3rdParty/include/Eigen/src/SparseCore/SparseColEtree.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseColEtree.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseColEtree.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseCompressedBase.h b/3rdParty/include/Eigen/src/SparseCore/SparseCompressedBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseCompressedBase.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseCompressedBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/3rdParty/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/3rdParty/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseDenseProduct.h b/3rdParty/include/Eigen/src/SparseCore/SparseDenseProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseDenseProduct.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseDenseProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseDiagonalProduct.h b/3rdParty/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseDot.h b/3rdParty/include/Eigen/src/SparseCore/SparseDot.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseDot.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseDot.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseFuzzy.h b/3rdParty/include/Eigen/src/SparseCore/SparseFuzzy.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseFuzzy.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseFuzzy.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseMap.h b/3rdParty/include/Eigen/src/SparseCore/SparseMap.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseMap.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseMap.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseMatrix.h b/3rdParty/include/Eigen/src/SparseCore/SparseMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseMatrix.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseMatrixBase.h b/3rdParty/include/Eigen/src/SparseCore/SparseMatrixBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseMatrixBase.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseMatrixBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparsePermutation.h b/3rdParty/include/Eigen/src/SparseCore/SparsePermutation.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparsePermutation.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparsePermutation.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseProduct.h b/3rdParty/include/Eigen/src/SparseCore/SparseProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseProduct.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseRedux.h b/3rdParty/include/Eigen/src/SparseCore/SparseRedux.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseRedux.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseRedux.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseRef.h b/3rdParty/include/Eigen/src/SparseCore/SparseRef.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseRef.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseRef.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseSelfAdjointView.h b/3rdParty/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseSolverBase.h b/3rdParty/include/Eigen/src/SparseCore/SparseSolverBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseSolverBase.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseSolverBase.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/3rdParty/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseTranspose.h b/3rdParty/include/Eigen/src/SparseCore/SparseTranspose.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseTranspose.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseTranspose.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseTriangularView.h b/3rdParty/include/Eigen/src/SparseCore/SparseTriangularView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseTriangularView.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseTriangularView.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseUtil.h b/3rdParty/include/Eigen/src/SparseCore/SparseUtil.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseUtil.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseUtil.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseVector.h b/3rdParty/include/Eigen/src/SparseCore/SparseVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseVector.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseVector.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseView.h b/3rdParty/include/Eigen/src/SparseCore/SparseView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/SparseView.h
rename to 3rdParty/include/Eigen/src/SparseCore/SparseView.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/TriangularSolver.h b/3rdParty/include/Eigen/src/SparseCore/TriangularSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseCore/TriangularSolver.h
rename to 3rdParty/include/Eigen/src/SparseCore/TriangularSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLUImpl.h b/3rdParty/include/Eigen/src/SparseLU/SparseLUImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLUImpl.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLUImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_Memory.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_Memory.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_Memory.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_Memory.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_Structs.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_Structs.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_Structs.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_Structs.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_Utils.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_Utils.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_Utils.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_Utils.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_column_bmod.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_column_dfs.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_gemm_kernel.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_pivotL.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_pivotL.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_pivotL.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_pivotL.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_pruneL.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_pruneL.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_pruneL.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_pruneL.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_relax_snode.h b/3rdParty/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
rename to 3rdParty/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SparseQR/SparseQR.h b/3rdParty/include/Eigen/src/SparseQR/SparseQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SparseQR/SparseQR.h
rename to 3rdParty/include/Eigen/src/SparseQR/SparseQR.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/StdDeque.h b/3rdParty/include/Eigen/src/StlSupport/StdDeque.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/StdDeque.h
rename to 3rdParty/include/Eigen/src/StlSupport/StdDeque.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/StdList.h b/3rdParty/include/Eigen/src/StlSupport/StdList.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/StdList.h
rename to 3rdParty/include/Eigen/src/StlSupport/StdList.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/StdVector.h b/3rdParty/include/Eigen/src/StlSupport/StdVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/StdVector.h
rename to 3rdParty/include/Eigen/src/StlSupport/StdVector.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/details.h b/3rdParty/include/Eigen/src/StlSupport/details.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/StlSupport/details.h
rename to 3rdParty/include/Eigen/src/StlSupport/details.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/SuperLUSupport/SuperLUSupport.h b/3rdParty/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
rename to 3rdParty/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/UmfPackSupport/UmfPackSupport.h b/3rdParty/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
rename to 3rdParty/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/misc/Image.h b/3rdParty/include/Eigen/src/misc/Image.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/misc/Image.h
rename to 3rdParty/include/Eigen/src/misc/Image.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/misc/Kernel.h b/3rdParty/include/Eigen/src/misc/Kernel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/misc/Kernel.h
rename to 3rdParty/include/Eigen/src/misc/Kernel.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/misc/RealSvd2x2.h b/3rdParty/include/Eigen/src/misc/RealSvd2x2.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/misc/RealSvd2x2.h
rename to 3rdParty/include/Eigen/src/misc/RealSvd2x2.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/misc/blas.h b/3rdParty/include/Eigen/src/misc/blas.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/misc/blas.h
rename to 3rdParty/include/Eigen/src/misc/blas.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/misc/lapack.h b/3rdParty/include/Eigen/src/misc/lapack.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/misc/lapack.h
rename to 3rdParty/include/Eigen/src/misc/lapack.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/misc/lapacke.h b/3rdParty/include/Eigen/src/misc/lapacke.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/misc/lapacke.h
rename to 3rdParty/include/Eigen/src/misc/lapacke.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/misc/lapacke_mangling.h b/3rdParty/include/Eigen/src/misc/lapacke_mangling.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/misc/lapacke_mangling.h
rename to 3rdParty/include/Eigen/src/misc/lapacke_mangling.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h b/3rdParty/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
rename to 3rdParty/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h b/3rdParty/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
rename to 3rdParty/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/plugins/BlockMethods.h b/3rdParty/include/Eigen/src/plugins/BlockMethods.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/plugins/BlockMethods.h
rename to 3rdParty/include/Eigen/src/plugins/BlockMethods.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/plugins/CommonCwiseBinaryOps.h b/3rdParty/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
rename to 3rdParty/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/plugins/CommonCwiseUnaryOps.h b/3rdParty/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
rename to 3rdParty/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h b/3rdParty/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
rename to 3rdParty/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h b/3rdParty/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
rename to 3rdParty/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/autodiff_cost_function.h b/3rdParty/include/ceres/autodiff_cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/autodiff_cost_function.h
rename to 3rdParty/include/ceres/autodiff_cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/autodiff_local_parameterization.h b/3rdParty/include/ceres/autodiff_local_parameterization.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/autodiff_local_parameterization.h
rename to 3rdParty/include/ceres/autodiff_local_parameterization.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/c_api.h b/3rdParty/include/ceres/c_api.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/c_api.h
rename to 3rdParty/include/ceres/c_api.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/ceres.h b/3rdParty/include/ceres/ceres.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/ceres.h
rename to 3rdParty/include/ceres/ceres.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/conditioned_cost_function.h b/3rdParty/include/ceres/conditioned_cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/conditioned_cost_function.h
rename to 3rdParty/include/ceres/conditioned_cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/context.h b/3rdParty/include/ceres/context.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/context.h
rename to 3rdParty/include/ceres/context.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/cost_function.h b/3rdParty/include/ceres/cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/cost_function.h
rename to 3rdParty/include/ceres/cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/cost_function_to_functor.h b/3rdParty/include/ceres/cost_function_to_functor.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/cost_function_to_functor.h
rename to 3rdParty/include/ceres/cost_function_to_functor.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/covariance.h b/3rdParty/include/ceres/covariance.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/covariance.h
rename to 3rdParty/include/ceres/covariance.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/crs_matrix.h b/3rdParty/include/ceres/crs_matrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/crs_matrix.h
rename to 3rdParty/include/ceres/crs_matrix.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/cubic_interpolation.h b/3rdParty/include/ceres/cubic_interpolation.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/cubic_interpolation.h
rename to 3rdParty/include/ceres/cubic_interpolation.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/dynamic_autodiff_cost_function.h b/3rdParty/include/ceres/dynamic_autodiff_cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/dynamic_autodiff_cost_function.h
rename to 3rdParty/include/ceres/dynamic_autodiff_cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/dynamic_cost_function.h b/3rdParty/include/ceres/dynamic_cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/dynamic_cost_function.h
rename to 3rdParty/include/ceres/dynamic_cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/dynamic_cost_function_to_functor.h b/3rdParty/include/ceres/dynamic_cost_function_to_functor.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/dynamic_cost_function_to_functor.h
rename to 3rdParty/include/ceres/dynamic_cost_function_to_functor.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/dynamic_numeric_diff_cost_function.h b/3rdParty/include/ceres/dynamic_numeric_diff_cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/dynamic_numeric_diff_cost_function.h
rename to 3rdParty/include/ceres/dynamic_numeric_diff_cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/evaluation_callback.h b/3rdParty/include/ceres/evaluation_callback.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/evaluation_callback.h
rename to 3rdParty/include/ceres/evaluation_callback.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/fpclassify.h b/3rdParty/include/ceres/fpclassify.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/fpclassify.h
rename to 3rdParty/include/ceres/fpclassify.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/gradient_checker.h b/3rdParty/include/ceres/gradient_checker.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/gradient_checker.h
rename to 3rdParty/include/ceres/gradient_checker.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/gradient_problem.h b/3rdParty/include/ceres/gradient_problem.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/gradient_problem.h
rename to 3rdParty/include/ceres/gradient_problem.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/gradient_problem_solver.h b/3rdParty/include/ceres/gradient_problem_solver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/gradient_problem_solver.h
rename to 3rdParty/include/ceres/gradient_problem_solver.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/autodiff.h b/3rdParty/include/ceres/internal/autodiff.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/autodiff.h
rename to 3rdParty/include/ceres/internal/autodiff.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/config.h b/3rdParty/include/ceres/internal/config.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/config.h
rename to 3rdParty/include/ceres/internal/config.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/disable_warnings.h b/3rdParty/include/ceres/internal/disable_warnings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/disable_warnings.h
rename to 3rdParty/include/ceres/internal/disable_warnings.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/eigen.h b/3rdParty/include/ceres/internal/eigen.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/eigen.h
rename to 3rdParty/include/ceres/internal/eigen.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/fixed_array.h b/3rdParty/include/ceres/internal/fixed_array.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/fixed_array.h
rename to 3rdParty/include/ceres/internal/fixed_array.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/macros.h b/3rdParty/include/ceres/internal/macros.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/macros.h
rename to 3rdParty/include/ceres/internal/macros.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/manual_constructor.h b/3rdParty/include/ceres/internal/manual_constructor.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/manual_constructor.h
rename to 3rdParty/include/ceres/internal/manual_constructor.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/numeric_diff.h b/3rdParty/include/ceres/internal/numeric_diff.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/numeric_diff.h
rename to 3rdParty/include/ceres/internal/numeric_diff.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/port.h b/3rdParty/include/ceres/internal/port.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/port.h
rename to 3rdParty/include/ceres/internal/port.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/reenable_warnings.h b/3rdParty/include/ceres/internal/reenable_warnings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/reenable_warnings.h
rename to 3rdParty/include/ceres/internal/reenable_warnings.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/scoped_ptr.h b/3rdParty/include/ceres/internal/scoped_ptr.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/scoped_ptr.h
rename to 3rdParty/include/ceres/internal/scoped_ptr.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/internal/variadic_evaluate.h b/3rdParty/include/ceres/internal/variadic_evaluate.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/internal/variadic_evaluate.h
rename to 3rdParty/include/ceres/internal/variadic_evaluate.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/iteration_callback.h b/3rdParty/include/ceres/iteration_callback.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/iteration_callback.h
rename to 3rdParty/include/ceres/iteration_callback.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/jet.h b/3rdParty/include/ceres/jet.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/jet.h
rename to 3rdParty/include/ceres/jet.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/local_parameterization.h b/3rdParty/include/ceres/local_parameterization.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/local_parameterization.h
rename to 3rdParty/include/ceres/local_parameterization.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/loss_function.h b/3rdParty/include/ceres/loss_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/loss_function.h
rename to 3rdParty/include/ceres/loss_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/normal_prior.h b/3rdParty/include/ceres/normal_prior.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/normal_prior.h
rename to 3rdParty/include/ceres/normal_prior.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/numeric_diff_cost_function.h b/3rdParty/include/ceres/numeric_diff_cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/numeric_diff_cost_function.h
rename to 3rdParty/include/ceres/numeric_diff_cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/numeric_diff_options.h b/3rdParty/include/ceres/numeric_diff_options.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/numeric_diff_options.h
rename to 3rdParty/include/ceres/numeric_diff_options.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/ordered_groups.h b/3rdParty/include/ceres/ordered_groups.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/ordered_groups.h
rename to 3rdParty/include/ceres/ordered_groups.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/problem.h b/3rdParty/include/ceres/problem.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/problem.h
rename to 3rdParty/include/ceres/problem.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/rotation.h b/3rdParty/include/ceres/rotation.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/rotation.h
rename to 3rdParty/include/ceres/rotation.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/sized_cost_function.h b/3rdParty/include/ceres/sized_cost_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/sized_cost_function.h
rename to 3rdParty/include/ceres/sized_cost_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/solver.h b/3rdParty/include/ceres/solver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/solver.h
rename to 3rdParty/include/ceres/solver.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/tiny_solver.h b/3rdParty/include/ceres/tiny_solver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/tiny_solver.h
rename to 3rdParty/include/ceres/tiny_solver.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/tiny_solver_autodiff_function.h b/3rdParty/include/ceres/tiny_solver_autodiff_function.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/tiny_solver_autodiff_function.h
rename to 3rdParty/include/ceres/tiny_solver_autodiff_function.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/tiny_solver_cost_function_adapter.h b/3rdParty/include/ceres/tiny_solver_cost_function_adapter.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/tiny_solver_cost_function_adapter.h
rename to 3rdParty/include/ceres/tiny_solver_cost_function_adapter.h
diff --git a/3rdParty/my_ceres_vc14/include/ceres/types.h b/3rdParty/include/ceres/types.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/ceres/types.h
rename to 3rdParty/include/ceres/types.h
diff --git a/3rdParty/my_ceres_vc17/include/ceres/version.h b/3rdParty/include/ceres/version.h
similarity index 85%
rename from 3rdParty/my_ceres_vc17/include/ceres/version.h
rename to 3rdParty/include/ceres/version.h
index 2d5d000..3ccd4c8 100644
--- a/3rdParty/my_ceres_vc17/include/ceres/version.h
+++ b/3rdParty/include/ceres/version.h
@@ -1,5 +1,5 @@
 // Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
+// Copyright 2015 Google Inc. All rights reserved.
 // http://ceres-solver.org/
 //
 // Redistribution and use in source and binary forms, with or without
@@ -31,8 +31,8 @@
 #ifndef CERES_PUBLIC_VERSION_H_
 #define CERES_PUBLIC_VERSION_H_
 
-#define CERES_VERSION_MAJOR 2
-#define CERES_VERSION_MINOR 2
+#define CERES_VERSION_MAJOR 1
+#define CERES_VERSION_MINOR 14
 #define CERES_VERSION_REVISION 0
 
 // Classic CPP stringifcation; the extra level of indirection allows the
@@ -41,9 +41,8 @@
 #define CERES_TO_STRING(x) CERES_TO_STRING_HELPER(x)
 
 // The Ceres version as a string; for example "1.9.0".
-#define CERES_VERSION_STRING                                    \
-  CERES_TO_STRING(CERES_VERSION_MAJOR)                          \
-  "." CERES_TO_STRING(CERES_VERSION_MINOR) "." CERES_TO_STRING( \
-      CERES_VERSION_REVISION)
+#define CERES_VERSION_STRING CERES_TO_STRING(CERES_VERSION_MAJOR) "." \
+                             CERES_TO_STRING(CERES_VERSION_MINOR) "." \
+                             CERES_TO_STRING(CERES_VERSION_REVISION)
 
 #endif  // CERES_PUBLIC_VERSION_H_
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Cholesky b/3rdParty/include/eigen3/Eigen/Cholesky
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Cholesky
rename to 3rdParty/include/eigen3/Eigen/Cholesky
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/CholmodSupport b/3rdParty/include/eigen3/Eigen/CholmodSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/CholmodSupport
rename to 3rdParty/include/eigen3/Eigen/CholmodSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Core b/3rdParty/include/eigen3/Eigen/Core
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Core
rename to 3rdParty/include/eigen3/Eigen/Core
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Dense b/3rdParty/include/eigen3/Eigen/Dense
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Dense
rename to 3rdParty/include/eigen3/Eigen/Dense
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Eigen b/3rdParty/include/eigen3/Eigen/Eigen
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Eigen
rename to 3rdParty/include/eigen3/Eigen/Eigen
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Eigenvalues b/3rdParty/include/eigen3/Eigen/Eigenvalues
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Eigenvalues
rename to 3rdParty/include/eigen3/Eigen/Eigenvalues
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Geometry b/3rdParty/include/eigen3/Eigen/Geometry
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Geometry
rename to 3rdParty/include/eigen3/Eigen/Geometry
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Householder b/3rdParty/include/eigen3/Eigen/Householder
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Householder
rename to 3rdParty/include/eigen3/Eigen/Householder
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/IterativeLinearSolvers b/3rdParty/include/eigen3/Eigen/IterativeLinearSolvers
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/IterativeLinearSolvers
rename to 3rdParty/include/eigen3/Eigen/IterativeLinearSolvers
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Jacobi b/3rdParty/include/eigen3/Eigen/Jacobi
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Jacobi
rename to 3rdParty/include/eigen3/Eigen/Jacobi
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/KLUSupport b/3rdParty/include/eigen3/Eigen/KLUSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/KLUSupport
rename to 3rdParty/include/eigen3/Eigen/KLUSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/LU b/3rdParty/include/eigen3/Eigen/LU
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/LU
rename to 3rdParty/include/eigen3/Eigen/LU
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/MetisSupport b/3rdParty/include/eigen3/Eigen/MetisSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/MetisSupport
rename to 3rdParty/include/eigen3/Eigen/MetisSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/OrderingMethods b/3rdParty/include/eigen3/Eigen/OrderingMethods
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/OrderingMethods
rename to 3rdParty/include/eigen3/Eigen/OrderingMethods
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/PaStiXSupport b/3rdParty/include/eigen3/Eigen/PaStiXSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/PaStiXSupport
rename to 3rdParty/include/eigen3/Eigen/PaStiXSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/PardisoSupport b/3rdParty/include/eigen3/Eigen/PardisoSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/PardisoSupport
rename to 3rdParty/include/eigen3/Eigen/PardisoSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/QR b/3rdParty/include/eigen3/Eigen/QR
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/QR
rename to 3rdParty/include/eigen3/Eigen/QR
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/QtAlignedMalloc b/3rdParty/include/eigen3/Eigen/QtAlignedMalloc
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/QtAlignedMalloc
rename to 3rdParty/include/eigen3/Eigen/QtAlignedMalloc
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/SPQRSupport b/3rdParty/include/eigen3/Eigen/SPQRSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/SPQRSupport
rename to 3rdParty/include/eigen3/Eigen/SPQRSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/SVD b/3rdParty/include/eigen3/Eigen/SVD
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/SVD
rename to 3rdParty/include/eigen3/Eigen/SVD
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/Sparse b/3rdParty/include/eigen3/Eigen/Sparse
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/Sparse
rename to 3rdParty/include/eigen3/Eigen/Sparse
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseCholesky b/3rdParty/include/eigen3/Eigen/SparseCholesky
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseCholesky
rename to 3rdParty/include/eigen3/Eigen/SparseCholesky
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseCore b/3rdParty/include/eigen3/Eigen/SparseCore
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseCore
rename to 3rdParty/include/eigen3/Eigen/SparseCore
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseLU b/3rdParty/include/eigen3/Eigen/SparseLU
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseLU
rename to 3rdParty/include/eigen3/Eigen/SparseLU
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseQR b/3rdParty/include/eigen3/Eigen/SparseQR
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/SparseQR
rename to 3rdParty/include/eigen3/Eigen/SparseQR
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/StdDeque b/3rdParty/include/eigen3/Eigen/StdDeque
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/StdDeque
rename to 3rdParty/include/eigen3/Eigen/StdDeque
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/StdList b/3rdParty/include/eigen3/Eigen/StdList
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/StdList
rename to 3rdParty/include/eigen3/Eigen/StdList
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/StdVector b/3rdParty/include/eigen3/Eigen/StdVector
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/StdVector
rename to 3rdParty/include/eigen3/Eigen/StdVector
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/SuperLUSupport b/3rdParty/include/eigen3/Eigen/SuperLUSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/SuperLUSupport
rename to 3rdParty/include/eigen3/Eigen/SuperLUSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/UmfPackSupport b/3rdParty/include/eigen3/Eigen/UmfPackSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/UmfPackSupport
rename to 3rdParty/include/eigen3/Eigen/UmfPackSupport
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Cholesky/LDLT.h b/3rdParty/include/eigen3/Eigen/src/Cholesky/LDLT.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Cholesky/LDLT.h
rename to 3rdParty/include/eigen3/Eigen/src/Cholesky/LDLT.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Cholesky/LLT.h b/3rdParty/include/eigen3/Eigen/src/Cholesky/LLT.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Cholesky/LLT.h
rename to 3rdParty/include/eigen3/Eigen/src/Cholesky/LLT.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h b/3rdParty/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ArithmeticSequence.h b/3rdParty/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Array.h b/3rdParty/include/eigen3/Eigen/src/Core/Array.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Array.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Array.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ArrayBase.h b/3rdParty/include/eigen3/Eigen/src/Core/ArrayBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ArrayBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/ArrayBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ArrayWrapper.h b/3rdParty/include/eigen3/Eigen/src/Core/ArrayWrapper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ArrayWrapper.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/ArrayWrapper.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Assign.h b/3rdParty/include/eigen3/Eigen/src/Core/Assign.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Assign.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Assign.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/AssignEvaluator.h b/3rdParty/include/eigen3/Eigen/src/Core/AssignEvaluator.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/AssignEvaluator.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/AssignEvaluator.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Assign_MKL.h b/3rdParty/include/eigen3/Eigen/src/Core/Assign_MKL.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Assign_MKL.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Assign_MKL.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/BandMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/BandMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/BandMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/BandMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Block.h b/3rdParty/include/eigen3/Eigen/src/Core/Block.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Block.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Block.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/BooleanRedux.h b/3rdParty/include/eigen3/Eigen/src/Core/BooleanRedux.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/BooleanRedux.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/BooleanRedux.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CommaInitializer.h b/3rdParty/include/eigen3/Eigen/src/Core/CommaInitializer.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CommaInitializer.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CommaInitializer.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ConditionEstimator.h b/3rdParty/include/eigen3/Eigen/src/Core/ConditionEstimator.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ConditionEstimator.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/ConditionEstimator.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CoreEvaluators.h b/3rdParty/include/eigen3/Eigen/src/Core/CoreEvaluators.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CoreEvaluators.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CoreEvaluators.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CoreIterators.h b/3rdParty/include/eigen3/Eigen/src/Core/CoreIterators.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CoreIterators.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CoreIterators.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h b/3rdParty/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h b/3rdParty/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h b/3rdParty/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h b/3rdParty/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseUnaryView.h b/3rdParty/include/eigen3/Eigen/src/Core/CwiseUnaryView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/CwiseUnaryView.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/CwiseUnaryView.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DenseBase.h b/3rdParty/include/eigen3/Eigen/src/Core/DenseBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DenseBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/DenseBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h b/3rdParty/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DenseStorage.h b/3rdParty/include/eigen3/Eigen/src/Core/DenseStorage.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DenseStorage.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/DenseStorage.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Diagonal.h b/3rdParty/include/eigen3/Eigen/src/Core/Diagonal.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Diagonal.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Diagonal.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DiagonalMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/DiagonalMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DiagonalMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/DiagonalMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DiagonalProduct.h b/3rdParty/include/eigen3/Eigen/src/Core/DiagonalProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/DiagonalProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/DiagonalProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Dot.h b/3rdParty/include/eigen3/Eigen/src/Core/Dot.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Dot.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Dot.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/EigenBase.h b/3rdParty/include/eigen3/Eigen/src/Core/EigenBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/EigenBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/EigenBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h b/3rdParty/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Fuzzy.h b/3rdParty/include/eigen3/Eigen/src/Core/Fuzzy.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Fuzzy.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Fuzzy.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/GeneralProduct.h b/3rdParty/include/eigen3/Eigen/src/Core/GeneralProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/GeneralProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/GeneralProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/GenericPacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/GenericPacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/GenericPacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/GenericPacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/GlobalFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/GlobalFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/GlobalFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/GlobalFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/IO.h b/3rdParty/include/eigen3/Eigen/src/Core/IO.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/IO.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/IO.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/IndexedView.h b/3rdParty/include/eigen3/Eigen/src/Core/IndexedView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/IndexedView.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/IndexedView.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Inverse.h b/3rdParty/include/eigen3/Eigen/src/Core/Inverse.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Inverse.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Inverse.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Map.h b/3rdParty/include/eigen3/Eigen/src/Core/Map.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Map.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Map.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MapBase.h b/3rdParty/include/eigen3/Eigen/src/Core/MapBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MapBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/MapBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h b/3rdParty/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Matrix.h b/3rdParty/include/eigen3/Eigen/src/Core/Matrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Matrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Matrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MatrixBase.h b/3rdParty/include/eigen3/Eigen/src/Core/MatrixBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/MatrixBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/MatrixBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/NestByValue.h b/3rdParty/include/eigen3/Eigen/src/Core/NestByValue.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/NestByValue.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/NestByValue.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/NoAlias.h b/3rdParty/include/eigen3/Eigen/src/Core/NoAlias.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/NoAlias.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/NoAlias.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/NumTraits.h b/3rdParty/include/eigen3/Eigen/src/Core/NumTraits.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/NumTraits.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/NumTraits.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h b/3rdParty/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/PermutationMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/PermutationMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/PermutationMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/PermutationMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/PlainObjectBase.h b/3rdParty/include/eigen3/Eigen/src/Core/PlainObjectBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/PlainObjectBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/PlainObjectBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Product.h b/3rdParty/include/eigen3/Eigen/src/Core/Product.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Product.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Product.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ProductEvaluators.h b/3rdParty/include/eigen3/Eigen/src/Core/ProductEvaluators.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ProductEvaluators.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/ProductEvaluators.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Random.h b/3rdParty/include/eigen3/Eigen/src/Core/Random.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Random.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Random.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Redux.h b/3rdParty/include/eigen3/Eigen/src/Core/Redux.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Redux.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Redux.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Ref.h b/3rdParty/include/eigen3/Eigen/src/Core/Ref.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Ref.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Ref.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Replicate.h b/3rdParty/include/eigen3/Eigen/src/Core/Replicate.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Replicate.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Replicate.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Reshaped.h b/3rdParty/include/eigen3/Eigen/src/Core/Reshaped.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Reshaped.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Reshaped.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ReturnByValue.h b/3rdParty/include/eigen3/Eigen/src/Core/ReturnByValue.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/ReturnByValue.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/ReturnByValue.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Reverse.h b/3rdParty/include/eigen3/Eigen/src/Core/Reverse.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Reverse.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Reverse.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Select.h b/3rdParty/include/eigen3/Eigen/src/Core/Select.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Select.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Select.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SelfAdjointView.h b/3rdParty/include/eigen3/Eigen/src/Core/SelfAdjointView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SelfAdjointView.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/SelfAdjointView.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h b/3rdParty/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Solve.h b/3rdParty/include/eigen3/Eigen/src/Core/Solve.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Solve.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Solve.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SolveTriangular.h b/3rdParty/include/eigen3/Eigen/src/Core/SolveTriangular.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SolveTriangular.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/SolveTriangular.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SolverBase.h b/3rdParty/include/eigen3/Eigen/src/Core/SolverBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/SolverBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/SolverBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/StableNorm.h b/3rdParty/include/eigen3/Eigen/src/Core/StableNorm.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/StableNorm.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/StableNorm.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/StlIterators.h b/3rdParty/include/eigen3/Eigen/src/Core/StlIterators.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/StlIterators.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/StlIterators.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Stride.h b/3rdParty/include/eigen3/Eigen/src/Core/Stride.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Stride.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Stride.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Swap.h b/3rdParty/include/eigen3/Eigen/src/Core/Swap.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Swap.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Swap.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Transpose.h b/3rdParty/include/eigen3/Eigen/src/Core/Transpose.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Transpose.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Transpose.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Transpositions.h b/3rdParty/include/eigen3/Eigen/src/Core/Transpositions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Transpositions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Transpositions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/TriangularMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/TriangularMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/TriangularMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/TriangularMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/VectorBlock.h b/3rdParty/include/eigen3/Eigen/src/Core/VectorBlock.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/VectorBlock.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/VectorBlock.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/VectorwiseOp.h b/3rdParty/include/eigen3/Eigen/src/Core/VectorwiseOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/VectorwiseOp.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/VectorwiseOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Visitor.h b/3rdParty/include/eigen3/Eigen/src/Core/Visitor.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/Visitor.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/Visitor.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/Half.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/Default/Half.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/Half.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/Default/Half.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/Settings.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/Default/Settings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/Settings.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/Default/Settings.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h b/3rdParty/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h b/3rdParty/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h b/3rdParty/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h b/3rdParty/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/StlFunctors.h b/3rdParty/include/eigen3/Eigen/src/Core/functors/StlFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/StlFunctors.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/functors/StlFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h b/3rdParty/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h b/3rdParty/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/3rdParty/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h b/3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/Parallelizer.h b/3rdParty/include/eigen3/Eigen/src/Core/products/Parallelizer.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/Parallelizer.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/Parallelizer.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h b/3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h b/3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h b/3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h b/3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h b/3rdParty/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h b/3rdParty/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h b/3rdParty/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/BlasUtil.h b/3rdParty/include/eigen3/Eigen/src/Core/util/BlasUtil.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/BlasUtil.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/BlasUtil.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h b/3rdParty/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Constants.h b/3rdParty/include/eigen3/Eigen/src/Core/util/Constants.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Constants.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/Constants.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h b/3rdParty/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h b/3rdParty/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h b/3rdParty/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/IntegralConstant.h b/3rdParty/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/MKL_support.h b/3rdParty/include/eigen3/Eigen/src/Core/util/MKL_support.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/MKL_support.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/MKL_support.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Macros.h b/3rdParty/include/eigen3/Eigen/src/Core/util/Macros.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Macros.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/Macros.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Memory.h b/3rdParty/include/eigen3/Eigen/src/Core/util/Memory.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Memory.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/Memory.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Meta.h b/3rdParty/include/eigen3/Eigen/src/Core/util/Meta.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/Meta.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/Meta.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/NonMPL2.h b/3rdParty/include/eigen3/Eigen/src/Core/util/NonMPL2.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/NonMPL2.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/NonMPL2.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h b/3rdParty/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h b/3rdParty/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/StaticAssert.h b/3rdParty/include/eigen3/Eigen/src/Core/util/StaticAssert.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/StaticAssert.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/StaticAssert.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h b/3rdParty/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/XprHelper.h b/3rdParty/include/eigen3/Eigen/src/Core/util/XprHelper.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Core/util/XprHelper.h
rename to 3rdParty/include/eigen3/Eigen/src/Core/util/XprHelper.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h b/3rdParty/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
rename to 3rdParty/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/AlignedBox.h b/3rdParty/include/eigen3/Eigen/src/Geometry/AlignedBox.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/AlignedBox.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/AlignedBox.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/AngleAxis.h b/3rdParty/include/eigen3/Eigen/src/Geometry/AngleAxis.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/AngleAxis.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/AngleAxis.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/EulerAngles.h b/3rdParty/include/eigen3/Eigen/src/Geometry/EulerAngles.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/EulerAngles.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/EulerAngles.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Homogeneous.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Homogeneous.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Homogeneous.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Homogeneous.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Hyperplane.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Hyperplane.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Hyperplane.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Hyperplane.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/OrthoMethods.h b/3rdParty/include/eigen3/Eigen/src/Geometry/OrthoMethods.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/OrthoMethods.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/OrthoMethods.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h b/3rdParty/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Quaternion.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Quaternion.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Quaternion.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Quaternion.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Rotation2D.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Rotation2D.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Rotation2D.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Rotation2D.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/RotationBase.h b/3rdParty/include/eigen3/Eigen/src/Geometry/RotationBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/RotationBase.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/RotationBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Scaling.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Scaling.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Scaling.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Scaling.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Transform.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Transform.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Transform.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Transform.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Translation.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Translation.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Translation.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Translation.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Umeyama.h b/3rdParty/include/eigen3/Eigen/src/Geometry/Umeyama.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/Umeyama.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/Umeyama.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h b/3rdParty/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h
rename to 3rdParty/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Householder/BlockHouseholder.h b/3rdParty/include/eigen3/Eigen/src/Householder/BlockHouseholder.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Householder/BlockHouseholder.h
rename to 3rdParty/include/eigen3/Eigen/src/Householder/BlockHouseholder.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Householder/Householder.h b/3rdParty/include/eigen3/Eigen/src/Householder/Householder.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Householder/Householder.h
rename to 3rdParty/include/eigen3/Eigen/src/Householder/Householder.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Householder/HouseholderSequence.h b/3rdParty/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
rename to 3rdParty/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
rename to 3rdParty/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Jacobi/Jacobi.h b/3rdParty/include/eigen3/Eigen/src/Jacobi/Jacobi.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/Jacobi/Jacobi.h
rename to 3rdParty/include/eigen3/Eigen/src/Jacobi/Jacobi.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h b/3rdParty/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/Determinant.h b/3rdParty/include/eigen3/Eigen/src/LU/Determinant.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/Determinant.h
rename to 3rdParty/include/eigen3/Eigen/src/LU/Determinant.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/FullPivLU.h b/3rdParty/include/eigen3/Eigen/src/LU/FullPivLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/FullPivLU.h
rename to 3rdParty/include/eigen3/Eigen/src/LU/FullPivLU.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/InverseImpl.h b/3rdParty/include/eigen3/Eigen/src/LU/InverseImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/InverseImpl.h
rename to 3rdParty/include/eigen3/Eigen/src/LU/InverseImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/PartialPivLU.h b/3rdParty/include/eigen3/Eigen/src/LU/PartialPivLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/PartialPivLU.h
rename to 3rdParty/include/eigen3/Eigen/src/LU/PartialPivLU.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/arch/InverseSize4.h b/3rdParty/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
rename to 3rdParty/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h b/3rdParty/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/OrderingMethods/Amd.h b/3rdParty/include/eigen3/Eigen/src/OrderingMethods/Amd.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/OrderingMethods/Amd.h
rename to 3rdParty/include/eigen3/Eigen/src/OrderingMethods/Amd.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h b/3rdParty/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h
rename to 3rdParty/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/OrderingMethods/Ordering.h b/3rdParty/include/eigen3/Eigen/src/OrderingMethods/Ordering.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/OrderingMethods/Ordering.h
rename to 3rdParty/include/eigen3/Eigen/src/OrderingMethods/Ordering.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h b/3rdParty/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h b/3rdParty/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h b/3rdParty/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h
rename to 3rdParty/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/3rdParty/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h
rename to 3rdParty/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h b/3rdParty/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
rename to 3rdParty/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/HouseholderQR.h b/3rdParty/include/eigen3/Eigen/src/QR/HouseholderQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/HouseholderQR.h
rename to 3rdParty/include/eigen3/Eigen/src/QR/HouseholderQR.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/3rdParty/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/BDCSVD.h b/3rdParty/include/eigen3/Eigen/src/SVD/BDCSVD.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/BDCSVD.h
rename to 3rdParty/include/eigen3/Eigen/src/SVD/BDCSVD.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/JacobiSVD.h b/3rdParty/include/eigen3/Eigen/src/SVD/JacobiSVD.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/JacobiSVD.h
rename to 3rdParty/include/eigen3/Eigen/src/SVD/JacobiSVD.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h b/3rdParty/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h
rename to 3rdParty/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/SVDBase.h b/3rdParty/include/eigen3/Eigen/src/SVD/SVDBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/SVDBase.h
rename to 3rdParty/include/eigen3/Eigen/src/SVD/SVDBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h b/3rdParty/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
rename to 3rdParty/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h b/3rdParty/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/3rdParty/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/AmbiVector.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/AmbiVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/AmbiVector.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/AmbiVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseAssign.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseAssign.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseAssign.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseAssign.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseBlock.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseDot.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseDot.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseDot.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseDot.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseMap.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseMap.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseMap.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseMap.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseProduct.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseRedux.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseRedux.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseRedux.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseRedux.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseRef.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseRef.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseRef.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseRef.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseUtil.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseUtil.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseUtil.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseUtil.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseVector.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseVector.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseView.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/SparseView.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/SparseView.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/SparseView.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h b/3rdParty/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h b/3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseQR/SparseQR.h b/3rdParty/include/eigen3/Eigen/src/SparseQR/SparseQR.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SparseQR/SparseQR.h
rename to 3rdParty/include/eigen3/Eigen/src/SparseQR/SparseQR.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/StdDeque.h b/3rdParty/include/eigen3/Eigen/src/StlSupport/StdDeque.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/StdDeque.h
rename to 3rdParty/include/eigen3/Eigen/src/StlSupport/StdDeque.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/StdList.h b/3rdParty/include/eigen3/Eigen/src/StlSupport/StdList.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/StdList.h
rename to 3rdParty/include/eigen3/Eigen/src/StlSupport/StdList.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/StdVector.h b/3rdParty/include/eigen3/Eigen/src/StlSupport/StdVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/StdVector.h
rename to 3rdParty/include/eigen3/Eigen/src/StlSupport/StdVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/details.h b/3rdParty/include/eigen3/Eigen/src/StlSupport/details.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/StlSupport/details.h
rename to 3rdParty/include/eigen3/Eigen/src/StlSupport/details.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h b/3rdParty/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h b/3rdParty/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
rename to 3rdParty/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/Image.h b/3rdParty/include/eigen3/Eigen/src/misc/Image.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/Image.h
rename to 3rdParty/include/eigen3/Eigen/src/misc/Image.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/Kernel.h b/3rdParty/include/eigen3/Eigen/src/misc/Kernel.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/Kernel.h
rename to 3rdParty/include/eigen3/Eigen/src/misc/Kernel.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/RealSvd2x2.h b/3rdParty/include/eigen3/Eigen/src/misc/RealSvd2x2.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/RealSvd2x2.h
rename to 3rdParty/include/eigen3/Eigen/src/misc/RealSvd2x2.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/blas.h b/3rdParty/include/eigen3/Eigen/src/misc/blas.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/blas.h
rename to 3rdParty/include/eigen3/Eigen/src/misc/blas.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/lapack.h b/3rdParty/include/eigen3/Eigen/src/misc/lapack.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/lapack.h
rename to 3rdParty/include/eigen3/Eigen/src/misc/lapack.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/lapacke.h b/3rdParty/include/eigen3/Eigen/src/misc/lapacke.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/lapacke.h
rename to 3rdParty/include/eigen3/Eigen/src/misc/lapacke.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/lapacke_mangling.h b/3rdParty/include/eigen3/Eigen/src/misc/lapacke_mangling.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/misc/lapacke_mangling.h
rename to 3rdParty/include/eigen3/Eigen/src/misc/lapacke_mangling.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h b/3rdParty/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h b/3rdParty/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/BlockMethods.h b/3rdParty/include/eigen3/Eigen/src/plugins/BlockMethods.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/BlockMethods.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/BlockMethods.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h b/3rdParty/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h b/3rdParty/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h b/3rdParty/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h b/3rdParty/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h b/3rdParty/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/ReshapedMethods.h b/3rdParty/include/eigen3/Eigen/src/plugins/ReshapedMethods.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/Eigen/src/plugins/ReshapedMethods.h
rename to 3rdParty/include/eigen3/Eigen/src/plugins/ReshapedMethods.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/signature_of_eigen3_matrix_library b/3rdParty/include/eigen3/signature_of_eigen3_matrix_library
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/signature_of_eigen3_matrix_library
rename to 3rdParty/include/eigen3/signature_of_eigen3_matrix_library
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/AdolcForward b/3rdParty/include/eigen3/unsupported/Eigen/AdolcForward
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/AdolcForward
rename to 3rdParty/include/eigen3/unsupported/Eigen/AdolcForward
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/AlignedVector3 b/3rdParty/include/eigen3/unsupported/Eigen/AlignedVector3
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/AlignedVector3
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/ArpackSupport b/3rdParty/include/eigen3/unsupported/Eigen/ArpackSupport
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/ArpackSupport
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/AutoDiff b/3rdParty/include/eigen3/unsupported/Eigen/AutoDiff
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/AutoDiff
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/BVH b/3rdParty/include/eigen3/unsupported/Eigen/BVH
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/BVH
rename to 3rdParty/include/eigen3/unsupported/Eigen/BVH
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/Tensor b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/Tensor
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/Tensor
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/Tensor
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/ThreadPool b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/ThreadPool
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/ThreadPool
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/ThreadPool
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
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rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
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rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h b/3rdParty/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/EulerAngles b/3rdParty/include/eigen3/unsupported/Eigen/EulerAngles
similarity index 100%
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/FFT b/3rdParty/include/eigen3/unsupported/Eigen/FFT
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/IterativeSolvers b/3rdParty/include/eigen3/unsupported/Eigen/IterativeSolvers
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/KroneckerProduct b/3rdParty/include/eigen3/unsupported/Eigen/KroneckerProduct
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rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/KroneckerProduct
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/LevenbergMarquardt b/3rdParty/include/eigen3/unsupported/Eigen/LevenbergMarquardt
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/LevenbergMarquardt
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/MPRealSupport b/3rdParty/include/eigen3/unsupported/Eigen/MPRealSupport
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/MatrixFunctions b/3rdParty/include/eigen3/unsupported/Eigen/MatrixFunctions
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/MoreVectorization b/3rdParty/include/eigen3/unsupported/Eigen/MoreVectorization
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/NonLinearOptimization b/3rdParty/include/eigen3/unsupported/Eigen/NonLinearOptimization
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/NumericalDiff b/3rdParty/include/eigen3/unsupported/Eigen/NumericalDiff
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/OpenGLSupport b/3rdParty/include/eigen3/unsupported/Eigen/OpenGLSupport
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/Polynomials b/3rdParty/include/eigen3/unsupported/Eigen/Polynomials
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/Skyline b/3rdParty/include/eigen3/unsupported/Eigen/Skyline
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/SparseExtra b/3rdParty/include/eigen3/unsupported/Eigen/SparseExtra
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/SpecialFunctions b/3rdParty/include/eigen3/unsupported/Eigen/SpecialFunctions
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diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/Splines b/3rdParty/include/eigen3/unsupported/Eigen/Splines
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/Splines
rename to 3rdParty/include/eigen3/unsupported/Eigen/Splines
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h b/3rdParty/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h b/3rdParty/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h b/3rdParty/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h b/3rdParty/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h b/3rdParty/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h b/3rdParty/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h b/3rdParty/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h b/3rdParty/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h b/3rdParty/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h b/3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h b/3rdParty/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h b/3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h b/3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h b/3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h b/3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h b/3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h b/3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h b/3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h b/3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/3rdParty/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h b/3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Splines/Spline.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Splines/Spline.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Splines/Spline.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Splines/Spline.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h
diff --git a/3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h b/3rdParty/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h
rename to 3rdParty/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h
diff --git a/3rdParty/my_ceres_vc14/include/gflags/gflags.h b/3rdParty/include/gflags/gflags.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/gflags/gflags.h
rename to 3rdParty/include/gflags/gflags.h
diff --git a/3rdParty/my_ceres_vc14/include/gflags/gflags_completions.h b/3rdParty/include/gflags/gflags_completions.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/gflags/gflags_completions.h
rename to 3rdParty/include/gflags/gflags_completions.h
diff --git a/3rdParty/my_ceres_vc14/include/gflags/gflags_declare.h b/3rdParty/include/gflags/gflags_declare.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/gflags/gflags_declare.h
rename to 3rdParty/include/gflags/gflags_declare.h
diff --git a/3rdParty/my_ceres_vc14/include/gflags/gflags_gflags.h b/3rdParty/include/gflags/gflags_gflags.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/gflags/gflags_gflags.h
rename to 3rdParty/include/gflags/gflags_gflags.h
diff --git a/3rdParty/my_ceres_vc14/include/glog/log_severity.h b/3rdParty/include/glog/log_severity.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/glog/log_severity.h
rename to 3rdParty/include/glog/log_severity.h
diff --git a/3rdParty/my_ceres_vc14/include/glog/logging.h b/3rdParty/include/glog/logging.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/glog/logging.h
rename to 3rdParty/include/glog/logging.h
diff --git a/3rdParty/my_ceres_vc14/include/glog/raw_logging.h b/3rdParty/include/glog/raw_logging.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/glog/raw_logging.h
rename to 3rdParty/include/glog/raw_logging.h
diff --git a/3rdParty/my_ceres_vc14/include/glog/stl_logging.h b/3rdParty/include/glog/stl_logging.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/glog/stl_logging.h
rename to 3rdParty/include/glog/stl_logging.h
diff --git a/3rdParty/my_ceres_vc14/include/glog/vlog_is_on.h b/3rdParty/include/glog/vlog_is_on.h
similarity index 100%
rename from 3rdParty/my_ceres_vc14/include/glog/vlog_is_on.h
rename to 3rdParty/include/glog/vlog_is_on.h
diff --git a/3rdParty/include/opencv2/calib3d.hpp b/3rdParty/include/opencv2/calib3d.hpp
new file mode 100644
index 0000000..b3709c8
--- /dev/null
+++ b/3rdParty/include/opencv2/calib3d.hpp
@@ -0,0 +1,4024 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CALIB3D_HPP
+#define OPENCV_CALIB3D_HPP
+
+#include "opencv2/core.hpp"
+#include "opencv2/features2d.hpp"
+#include "opencv2/core/affine.hpp"
+
+/**
+  @defgroup calib3d Camera Calibration and 3D Reconstruction
+
+The functions in this section use a so-called pinhole camera model. The view of a scene
+is obtained by projecting a scene's 3D point \f$P_w\f$ into the image plane using a perspective
+transformation which forms the corresponding pixel \f$p\f$. Both \f$P_w\f$ and \f$p\f$ are
+represented in homogeneous coordinates, i.e. as 3D and 2D homogeneous vector respectively. You will
+find a brief introduction to projective geometry, homogeneous vectors and homogeneous
+transformations at the end of this section's introduction. For more succinct notation, we often drop
+the 'homogeneous' and say vector instead of homogeneous vector.
+
+The distortion-free projective transformation given by a  pinhole camera model is shown below.
+
+\f[s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w,\f]
+
+where \f$P_w\f$ is a 3D point expressed with respect to the world coordinate system,
+\f$p\f$ is a 2D pixel in the image plane, \f$A\f$ is the camera intrinsic matrix,
+\f$R\f$ and \f$t\f$ are the rotation and translation that describe the change of coordinates from
+world to camera coordinate systems (or camera frame) and \f$s\f$ is the projective transformation's
+arbitrary scaling and not part of the camera model.
+
+The camera intrinsic matrix \f$A\f$ (notation used as in @cite Zhang2000 and also generally notated
+as \f$K\f$) projects 3D points given in the camera coordinate system to 2D pixel coordinates, i.e.
+
+\f[p = A P_c.\f]
+
+The camera intrinsic matrix \f$A\f$ is composed of the focal lengths \f$f_x\f$ and \f$f_y\f$, which are
+expressed in pixel units, and the principal point \f$(c_x, c_y)\f$, that is usually close to the
+image center:
+
+\f[A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1},\f]
+
+and thus
+
+\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} \vecthree{X_c}{Y_c}{Z_c}.\f]
+
+The matrix of intrinsic parameters does not depend on the scene viewed. So, once estimated, it can
+be re-used as long as the focal length is fixed (in case of a zoom lens). Thus, if an image from the
+camera is scaled by a factor, all of these parameters need to be scaled (multiplied/divided,
+respectively) by the same factor.
+
+The joint rotation-translation matrix \f$[R|t]\f$ is the matrix product of a projective
+transformation and a homogeneous transformation. The 3-by-4 projective transformation maps 3D points
+represented in camera coordinates to 2D points in the image plane and represented in normalized
+camera coordinates \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$:
+
+\f[Z_c \begin{bmatrix}
+x' \\
+y' \\
+1
+\end{bmatrix} = \begin{bmatrix}
+1 & 0 & 0 & 0 \\
+0 & 1 & 0 & 0 \\
+0 & 0 & 1 & 0
+\end{bmatrix}
+\begin{bmatrix}
+X_c \\
+Y_c \\
+Z_c \\
+1
+\end{bmatrix}.\f]
+
+The homogeneous transformation is encoded by the extrinsic parameters \f$R\f$ and \f$t\f$ and
+represents the change of basis from world coordinate system \f$w\f$ to the camera coordinate sytem
+\f$c\f$. Thus, given the representation of the point \f$P\f$ in world coordinates, \f$P_w\f$, we
+obtain \f$P\f$'s representation in the camera coordinate system, \f$P_c\f$, by
+
+\f[P_c = \begin{bmatrix}
+R & t \\
+0 & 1
+\end{bmatrix} P_w,\f]
+
+This homogeneous transformation is composed out of \f$R\f$, a 3-by-3 rotation matrix, and \f$t\f$, a
+3-by-1 translation vector:
+
+\f[\begin{bmatrix}
+R & t \\
+0 & 1
+\end{bmatrix} = \begin{bmatrix}
+r_{11} & r_{12} & r_{13} & t_x \\
+r_{21} & r_{22} & r_{23} & t_y \\
+r_{31} & r_{32} & r_{33} & t_z \\
+0 & 0 & 0 & 1
+\end{bmatrix},
+\f]
+
+and therefore
+
+\f[\begin{bmatrix}
+X_c \\
+Y_c \\
+Z_c \\
+1
+\end{bmatrix} = \begin{bmatrix}
+r_{11} & r_{12} & r_{13} & t_x \\
+r_{21} & r_{22} & r_{23} & t_y \\
+r_{31} & r_{32} & r_{33} & t_z \\
+0 & 0 & 0 & 1
+\end{bmatrix}
+\begin{bmatrix}
+X_w \\
+Y_w \\
+Z_w \\
+1
+\end{bmatrix}.\f]
+
+Combining the projective transformation and the homogeneous transformation, we obtain the projective
+transformation that maps 3D points in world coordinates into 2D points in the image plane and in
+normalized camera coordinates:
+
+\f[Z_c \begin{bmatrix}
+x' \\
+y' \\
+1
+\end{bmatrix} = \begin{bmatrix} R|t \end{bmatrix} \begin{bmatrix}
+X_w \\
+Y_w \\
+Z_w \\
+1
+\end{bmatrix} = \begin{bmatrix}
+r_{11} & r_{12} & r_{13} & t_x \\
+r_{21} & r_{22} & r_{23} & t_y \\
+r_{31} & r_{32} & r_{33} & t_z
+\end{bmatrix}
+\begin{bmatrix}
+X_w \\
+Y_w \\
+Z_w \\
+1
+\end{bmatrix},\f]
+
+with \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$. Putting the equations for instrincs and extrinsics together, we can write out
+\f$s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w\f$ as
+
+\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
+\begin{bmatrix}
+r_{11} & r_{12} & r_{13} & t_x \\
+r_{21} & r_{22} & r_{23} & t_y \\
+r_{31} & r_{32} & r_{33} & t_z
+\end{bmatrix}
+\begin{bmatrix}
+X_w \\
+Y_w \\
+Z_w \\
+1
+\end{bmatrix}.\f]
+
+If \f$Z_c \ne 0\f$, the transformation above is equivalent to the following,
+
+\f[\begin{bmatrix}
+u \\
+v
+\end{bmatrix} = \begin{bmatrix}
+f_x X_c/Z_c + c_x \\
+f_y Y_c/Z_c + c_y
+\end{bmatrix}\f]
+
+with
+
+\f[\vecthree{X_c}{Y_c}{Z_c} = \begin{bmatrix}
+R|t
+\end{bmatrix} \begin{bmatrix}
+X_w \\
+Y_w \\
+Z_w \\
+1
+\end{bmatrix}.\f]
+
+The following figure illustrates the pinhole camera model.
+
+![Pinhole camera model](pics/pinhole_camera_model.png)
+
+Real lenses usually have some distortion, mostly radial distortion, and slight tangential distortion.
+So, the above model is extended as:
+
+\f[\begin{bmatrix}
+u \\
+v
+\end{bmatrix} = \begin{bmatrix}
+f_x x'' + c_x \\
+f_y y'' + c_y
+\end{bmatrix}\f]
+
+where
+
+\f[\begin{bmatrix}
+x'' \\
+y''
+\end{bmatrix} = \begin{bmatrix}
+x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\
+y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
+\end{bmatrix}\f]
+
+with
+
+\f[r^2 = x'^2 + y'^2\f]
+
+and
+
+\f[\begin{bmatrix}
+x'\\
+y'
+\end{bmatrix} = \begin{bmatrix}
+X_c/Z_c \\
+Y_c/Z_c
+\end{bmatrix},\f]
+
+if \f$Z_c \ne 0\f$.
+
+The distortion parameters are the radial coefficients \f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$
+,\f$p_1\f$ and \f$p_2\f$ are the tangential distortion coefficients, and \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$,
+are the thin prism distortion coefficients. Higher-order coefficients are not considered in OpenCV.
+
+The next figures show two common types of radial distortion: barrel distortion
+(\f$ 1 + k_1 r^2 + k_2 r^4 + k_3 r^6 \f$ monotonically decreasing)
+and pincushion distortion (\f$ 1 + k_1 r^2 + k_2 r^4 + k_3 r^6 \f$ monotonically increasing).
+Radial distortion is always monotonic for real lenses,
+and if the estimator produces a non-monotonic result,
+this should be considered a calibration failure.
+More generally, radial distortion must be monotonic and the distortion function must be bijective.
+A failed estimation result may look deceptively good near the image center
+but will work poorly in e.g. AR/SFM applications.
+The optimization method used in OpenCV camera calibration does not include these constraints as
+the framework does not support the required integer programming and polynomial inequalities.
+See [issue #15992](https://github.com/opencv/opencv/issues/15992) for additional information.
+
+![](pics/distortion_examples.png)
+![](pics/distortion_examples2.png)
+
+In some cases, the image sensor may be tilted in order to focus an oblique plane in front of the
+camera (Scheimpflug principle). This can be useful for particle image velocimetry (PIV) or
+triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and
+\f$y''\f$. This distortion can be modeled in the following way, see e.g. @cite Louhichi07.
+
+\f[\begin{bmatrix}
+u \\
+v
+\end{bmatrix} = \begin{bmatrix}
+f_x x''' + c_x \\
+f_y y''' + c_y
+\end{bmatrix},\f]
+
+where
+
+\f[s\vecthree{x'''}{y'''}{1} =
+\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)}
+{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
+{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\f]
+
+and the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter
+\f$\tau_x\f$ and \f$\tau_y\f$, respectively,
+
+\f[
+R(\tau_x, \tau_y) =
+\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)}
+\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} =
+\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)}
+{0}{\cos(\tau_x)}{\sin(\tau_x)}
+{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}.
+\f]
+
+In the functions below the coefficients are passed or returned as
+
+\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f]
+
+vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion
+coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera
+parameters. And they remain the same regardless of the captured image resolution. If, for example, a
+camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion
+coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$,
+\f$c_x\f$, and \f$c_y\f$ need to be scaled appropriately.
+
+The functions below use the above model to do the following:
+
+-   Project 3D points to the image plane given intrinsic and extrinsic parameters.
+-   Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their
+projections.
+-   Estimate intrinsic and extrinsic camera parameters from several views of a known calibration
+pattern (every view is described by several 3D-2D point correspondences).
+-   Estimate the relative position and orientation of the stereo camera "heads" and compute the
+*rectification* transformation that makes the camera optical axes parallel.
+
+<B> Homogeneous Coordinates </B><br>
+Homogeneous Coordinates are a system of coordinates that are used in projective geometry. Their use
+allows to represent points at infinity by finite coordinates and simplifies formulas when compared
+to the cartesian counterparts, e.g. they have the advantage that affine transformations can be
+expressed as linear homogeneous transformation.
+
+One obtains the homogeneous vector \f$P_h\f$ by appending a 1 along an n-dimensional cartesian
+vector \f$P\f$ e.g. for a 3D cartesian vector the mapping \f$P \rightarrow P_h\f$ is:
+
+\f[\begin{bmatrix}
+X \\
+Y \\
+Z
+\end{bmatrix} \rightarrow \begin{bmatrix}
+X \\
+Y \\
+Z \\
+1
+\end{bmatrix}.\f]
+
+For the inverse mapping \f$P_h \rightarrow P\f$, one divides all elements of the homogeneous vector
+by its last element, e.g. for a 3D homogeneous vector one gets its 2D cartesian counterpart by:
+
+\f[\begin{bmatrix}
+X \\
+Y \\
+W
+\end{bmatrix} \rightarrow \begin{bmatrix}
+X / W \\
+Y / W
+\end{bmatrix},\f]
+
+if \f$W \ne 0\f$.
+
+Due to this mapping, all multiples \f$k P_h\f$, for \f$k \ne 0\f$, of a homogeneous point represent
+the same point \f$P_h\f$. An intuitive understanding of this property is that under a projective
+transformation, all multiples of \f$P_h\f$ are mapped to the same point. This is the physical
+observation one does for pinhole cameras, as all points along a ray through the camera's pinhole are
+projected to the same image point, e.g. all points along the red ray in the image of the pinhole
+camera model above would be mapped to the same image coordinate. This property is also the source
+for the scale ambiguity s in the equation of the pinhole camera model.
+
+As mentioned, by using homogeneous coordinates we can express any change of basis parameterized by
+\f$R\f$ and \f$t\f$ as a linear transformation, e.g. for the change of basis from coordinate system
+0 to coordinate system 1 becomes:
+
+\f[P_1 = R P_0 + t \rightarrow P_{h_1} = \begin{bmatrix}
+R & t \\
+0 & 1
+\end{bmatrix} P_{h_0}.\f]
+
+@note
+    -   Many functions in this module take a camera intrinsic matrix as an input parameter. Although all
+        functions assume the same structure of this parameter, they may name it differently. The
+        parameter's description, however, will be clear in that a camera intrinsic matrix with the structure
+        shown above is required.
+    -   A calibration sample for 3 cameras in a horizontal position can be found at
+        opencv_source_code/samples/cpp/3calibration.cpp
+    -   A calibration sample based on a sequence of images can be found at
+        opencv_source_code/samples/cpp/calibration.cpp
+    -   A calibration sample in order to do 3D reconstruction can be found at
+        opencv_source_code/samples/cpp/build3dmodel.cpp
+    -   A calibration example on stereo calibration can be found at
+        opencv_source_code/samples/cpp/stereo_calib.cpp
+    -   A calibration example on stereo matching can be found at
+        opencv_source_code/samples/cpp/stereo_match.cpp
+    -   (Python) A camera calibration sample can be found at
+        opencv_source_code/samples/python/calibrate.py
+
+  @{
+    @defgroup calib3d_fisheye Fisheye camera model
+
+    Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the
+    matrix X) The coordinate vector of P in the camera reference frame is:
+
+    \f[Xc = R X + T\f]
+
+    where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y
+    and z the 3 coordinates of Xc:
+
+    \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f]
+
+    The pinhole projection coordinates of P is [a; b] where
+
+    \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f]
+
+    Fisheye distortion:
+
+    \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f]
+
+    The distorted point coordinates are [x'; y'] where
+
+    \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f]
+
+    Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where:
+
+    \f[u = f_x (x' + \alpha y') + c_x \\
+    v = f_y y' + c_y\f]
+
+    @defgroup calib3d_c C API
+
+  @}
+ */
+
+namespace cv
+{
+
+//! @addtogroup calib3d
+//! @{
+
+//! type of the robust estimation algorithm
+enum { LMEDS  = 4,  //!< least-median of squares algorithm
+       RANSAC = 8,  //!< RANSAC algorithm
+       RHO    = 16, //!< RHO algorithm
+       USAC_DEFAULT  = 32, //!< USAC algorithm, default settings
+       USAC_PARALLEL = 33, //!< USAC, parallel version
+       USAC_FM_8PTS = 34,  //!< USAC, fundamental matrix 8 points
+       USAC_FAST = 35,     //!< USAC, fast settings
+       USAC_ACCURATE = 36, //!< USAC, accurate settings
+       USAC_PROSAC = 37,   //!< USAC, sorted points, runs PROSAC
+       USAC_MAGSAC = 38    //!< USAC, runs MAGSAC++
+     };
+
+enum SolvePnPMethod {
+    SOLVEPNP_ITERATIVE   = 0, //!< Pose refinement using non-linear Levenberg-Marquardt minimization scheme @cite Madsen04 @cite Eade13 \n
+                              //!< Initial solution for non-planar "objectPoints" needs at least 6 points and uses the DLT algorithm. \n
+                              //!< Initial solution for planar "objectPoints" needs at least 4 points and uses pose from homography decomposition.
+    SOLVEPNP_EPNP        = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp
+    SOLVEPNP_P3P         = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete
+    SOLVEPNP_DLS         = 3, //!< **Broken implementation. Using this flag will fallback to EPnP.** \n
+                              //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct
+    SOLVEPNP_UPNP        = 4, //!< **Broken implementation. Using this flag will fallback to EPnP.** \n
+                              //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive
+    SOLVEPNP_AP3P        = 5, //!< An Efficient Algebraic Solution to the Perspective-Three-Point Problem @cite Ke17
+    SOLVEPNP_IPPE        = 6, //!< Infinitesimal Plane-Based Pose Estimation @cite Collins14 \n
+                              //!< Object points must be coplanar.
+    SOLVEPNP_IPPE_SQUARE = 7, //!< Infinitesimal Plane-Based Pose Estimation @cite Collins14 \n
+                              //!< This is a special case suitable for marker pose estimation.\n
+                              //!< 4 coplanar object points must be defined in the following order:
+                              //!<   - point 0: [-squareLength / 2,  squareLength / 2, 0]
+                              //!<   - point 1: [ squareLength / 2,  squareLength / 2, 0]
+                              //!<   - point 2: [ squareLength / 2, -squareLength / 2, 0]
+                              //!<   - point 3: [-squareLength / 2, -squareLength / 2, 0]
+    SOLVEPNP_SQPNP       = 8, //!< SQPnP: A Consistently Fast and Globally OptimalSolution to the Perspective-n-Point Problem @cite Terzakis2020SQPnP
+#ifndef CV_DOXYGEN
+    SOLVEPNP_MAX_COUNT        //!< Used for count
+#endif
+};
+
+enum { CALIB_CB_ADAPTIVE_THRESH = 1,
+       CALIB_CB_NORMALIZE_IMAGE = 2,
+       CALIB_CB_FILTER_QUADS    = 4,
+       CALIB_CB_FAST_CHECK      = 8,
+       CALIB_CB_EXHAUSTIVE      = 16,
+       CALIB_CB_ACCURACY        = 32,
+       CALIB_CB_LARGER          = 64,
+       CALIB_CB_MARKER          = 128
+     };
+
+enum { CALIB_CB_SYMMETRIC_GRID  = 1,
+       CALIB_CB_ASYMMETRIC_GRID = 2,
+       CALIB_CB_CLUSTERING      = 4
+     };
+
+enum { CALIB_NINTRINSIC          = 18,
+       CALIB_USE_INTRINSIC_GUESS = 0x00001,
+       CALIB_FIX_ASPECT_RATIO    = 0x00002,
+       CALIB_FIX_PRINCIPAL_POINT = 0x00004,
+       CALIB_ZERO_TANGENT_DIST   = 0x00008,
+       CALIB_FIX_FOCAL_LENGTH    = 0x00010,
+       CALIB_FIX_K1              = 0x00020,
+       CALIB_FIX_K2              = 0x00040,
+       CALIB_FIX_K3              = 0x00080,
+       CALIB_FIX_K4              = 0x00800,
+       CALIB_FIX_K5              = 0x01000,
+       CALIB_FIX_K6              = 0x02000,
+       CALIB_RATIONAL_MODEL      = 0x04000,
+       CALIB_THIN_PRISM_MODEL    = 0x08000,
+       CALIB_FIX_S1_S2_S3_S4     = 0x10000,
+       CALIB_TILTED_MODEL        = 0x40000,
+       CALIB_FIX_TAUX_TAUY       = 0x80000,
+       CALIB_USE_QR              = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise
+       CALIB_FIX_TANGENT_DIST    = 0x200000,
+       // only for stereo
+       CALIB_FIX_INTRINSIC       = 0x00100,
+       CALIB_SAME_FOCAL_LENGTH   = 0x00200,
+       // for stereo rectification
+       CALIB_ZERO_DISPARITY      = 0x00400,
+       CALIB_USE_LU              = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise
+       CALIB_USE_EXTRINSIC_GUESS = (1 << 22)  //!< for stereoCalibrate
+     };
+
+//! the algorithm for finding fundamental matrix
+enum { FM_7POINT = 1, //!< 7-point algorithm
+       FM_8POINT = 2, //!< 8-point algorithm
+       FM_LMEDS  = 4, //!< least-median algorithm. 7-point algorithm is used.
+       FM_RANSAC = 8  //!< RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used.
+     };
+
+enum HandEyeCalibrationMethod
+{
+    CALIB_HAND_EYE_TSAI         = 0, //!< A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration @cite Tsai89
+    CALIB_HAND_EYE_PARK         = 1, //!< Robot Sensor Calibration: Solving AX = XB on the Euclidean Group @cite Park94
+    CALIB_HAND_EYE_HORAUD       = 2, //!< Hand-eye Calibration @cite Horaud95
+    CALIB_HAND_EYE_ANDREFF      = 3, //!< On-line Hand-Eye Calibration @cite Andreff99
+    CALIB_HAND_EYE_DANIILIDIS   = 4  //!< Hand-Eye Calibration Using Dual Quaternions @cite Daniilidis98
+};
+
+enum RobotWorldHandEyeCalibrationMethod
+{
+    CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0, //!< Solving the robot-world/hand-eye calibration problem using the kronecker product @cite Shah2013SolvingTR
+    CALIB_ROBOT_WORLD_HAND_EYE_LI   = 1  //!< Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product @cite Li2010SimultaneousRA
+};
+
+enum SamplingMethod { SAMPLING_UNIFORM, SAMPLING_PROGRESSIVE_NAPSAC, SAMPLING_NAPSAC,
+        SAMPLING_PROSAC };
+enum LocalOptimMethod {LOCAL_OPTIM_NULL, LOCAL_OPTIM_INNER_LO, LOCAL_OPTIM_INNER_AND_ITER_LO,
+        LOCAL_OPTIM_GC, LOCAL_OPTIM_SIGMA};
+enum ScoreMethod {SCORE_METHOD_RANSAC, SCORE_METHOD_MSAC, SCORE_METHOD_MAGSAC, SCORE_METHOD_LMEDS};
+enum NeighborSearchMethod { NEIGH_FLANN_KNN, NEIGH_GRID, NEIGH_FLANN_RADIUS };
+
+struct CV_EXPORTS_W_SIMPLE UsacParams
+{ // in alphabetical order
+    CV_WRAP UsacParams();
+    CV_PROP_RW double confidence;
+    CV_PROP_RW bool isParallel;
+    CV_PROP_RW int loIterations;
+    CV_PROP_RW LocalOptimMethod loMethod;
+    CV_PROP_RW int loSampleSize;
+    CV_PROP_RW int maxIterations;
+    CV_PROP_RW NeighborSearchMethod neighborsSearch;
+    CV_PROP_RW int randomGeneratorState;
+    CV_PROP_RW SamplingMethod sampler;
+    CV_PROP_RW ScoreMethod score;
+    CV_PROP_RW double threshold;
+};
+
+/** @brief Converts a rotation matrix to a rotation vector or vice versa.
+
+@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
+@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
+@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial
+derivatives of the output array components with respect to the input array components.
+
+\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r  \leftarrow r/ \theta \\ R =  \cos(\theta) I + (1- \cos{\theta} ) r r^T +  \sin(\theta) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f]
+
+Inverse transformation can be also done easily, since
+
+\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f]
+
+A rotation vector is a convenient and most compact representation of a rotation matrix (since any
+rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry
+optimization procedures like @ref calibrateCamera, @ref stereoCalibrate, or @ref solvePnP .
+
+@note More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinate
+can be found in:
+    - A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi @cite Gallego2014ACF
+
+@note Useful information on SE(3) and Lie Groups can be found in:
+    - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco @cite blanco2010tutorial
+    - Lie Groups for 2D and 3D Transformation, Ethan Eade @cite Eade17
+    - A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan @cite Sol2018AML
+ */
+CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() );
+
+
+
+/** Levenberg-Marquardt solver. Starting with the specified vector of parameters it
+    optimizes the target vector criteria "err"
+    (finds local minima of each target vector component absolute value).
+
+    When needed, it calls user-provided callback.
+*/
+class CV_EXPORTS LMSolver : public Algorithm
+{
+public:
+    class CV_EXPORTS Callback
+    {
+    public:
+        virtual ~Callback() {}
+        /**
+         computes error and Jacobian for the specified vector of parameters
+
+         @param param the current vector of parameters
+         @param err output vector of errors: err_i = actual_f_i - ideal_f_i
+         @param J output Jacobian: J_ij = d(err_i)/d(param_j)
+
+         when J=noArray(), it means that it does not need to be computed.
+         Dimensionality of error vector and param vector can be different.
+         The callback should explicitly allocate (with "create" method) each output array
+         (unless it's noArray()).
+        */
+        virtual bool compute(InputArray param, OutputArray err, OutputArray J) const = 0;
+    };
+
+    /**
+       Runs Levenberg-Marquardt algorithm using the passed vector of parameters as the start point.
+       The final vector of parameters (whether the algorithm converged or not) is stored at the same
+       vector. The method returns the number of iterations used. If it's equal to the previously specified
+       maxIters, there is a big chance the algorithm did not converge.
+
+       @param param initial/final vector of parameters.
+
+       Note that the dimensionality of parameter space is defined by the size of param vector,
+       and the dimensionality of optimized criteria is defined by the size of err vector
+       computed by the callback.
+    */
+    virtual int run(InputOutputArray param) const = 0;
+
+    /**
+       Sets the maximum number of iterations
+       @param maxIters the number of iterations
+    */
+    virtual void setMaxIters(int maxIters) = 0;
+    /**
+       Retrieves the current maximum number of iterations
+    */
+    virtual int getMaxIters() const = 0;
+
+    /**
+       Creates Levenberg-Marquard solver
+
+       @param cb callback
+       @param maxIters maximum number of iterations that can be further
+         modified using setMaxIters() method.
+    */
+    static Ptr<LMSolver> create(const Ptr<LMSolver::Callback>& cb, int maxIters);
+    static Ptr<LMSolver> create(const Ptr<LMSolver::Callback>& cb, int maxIters, double eps);
+};
+
+
+
+/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp
+An example program about pose estimation from coplanar points
+
+Check @ref tutorial_homography "the corresponding tutorial" for more details
+*/
+
+/** @brief Finds a perspective transformation between two planes.
+
+@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2
+or vector\<Point2f\> .
+@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
+a vector\<Point2f\> .
+@param method Method used to compute a homography matrix. The following methods are possible:
+-   **0** - a regular method using all the points, i.e., the least squares method
+-   @ref RANSAC - RANSAC-based robust method
+-   @ref LMEDS - Least-Median robust method
+-   @ref RHO - PROSAC-based robust method
+@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier
+(used in the RANSAC and RHO methods only). That is, if
+\f[\| \texttt{dstPoints} _i -  \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2  >  \texttt{ransacReprojThreshold}\f]
+then the point \f$i\f$ is considered as an outlier. If srcPoints and dstPoints are measured in pixels,
+it usually makes sense to set this parameter somewhere in the range of 1 to 10.
+@param mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input
+mask values are ignored.
+@param maxIters The maximum number of RANSAC iterations.
+@param confidence Confidence level, between 0 and 1.
+
+The function finds and returns the perspective transformation \f$H\f$ between the source and the
+destination planes:
+
+\f[s_i  \vecthree{x'_i}{y'_i}{1} \sim H  \vecthree{x_i}{y_i}{1}\f]
+
+so that the back-projection error
+
+\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f]
+
+is minimized. If the parameter method is set to the default value 0, the function uses all the point
+pairs to compute an initial homography estimate with a simple least-squares scheme.
+
+However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective
+transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
+you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
+random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix
+using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the
+computed homography (which is the number of inliers for RANSAC or the least median re-projection error for
+LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and
+the mask of inliers/outliers.
+
+Regardless of the method, robust or not, the computed homography matrix is refined further (using
+inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
+re-projection error even more.
+
+The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
+distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
+correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
+noise is rather small, use the default method (method=0).
+
+The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is
+determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an \f$H\f$ matrix
+cannot be estimated, an empty one will be returned.
+
+@sa
+getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
+perspectiveTransform
+ */
+CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints,
+                                 int method = 0, double ransacReprojThreshold = 3,
+                                 OutputArray mask=noArray(), const int maxIters = 2000,
+                                 const double confidence = 0.995);
+
+/** @overload */
+CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints,
+                               OutputArray mask, int method = 0, double ransacReprojThreshold = 3 );
+
+
+CV_EXPORTS_W Mat findHomography(InputArray srcPoints, InputArray dstPoints, OutputArray mask,
+                   const UsacParams &params);
+
+/** @brief Computes an RQ decomposition of 3x3 matrices.
+
+@param src 3x3 input matrix.
+@param mtxR Output 3x3 upper-triangular matrix.
+@param mtxQ Output 3x3 orthogonal matrix.
+@param Qx Optional output 3x3 rotation matrix around x-axis.
+@param Qy Optional output 3x3 rotation matrix around y-axis.
+@param Qz Optional output 3x3 rotation matrix around z-axis.
+
+The function computes a RQ decomposition using the given rotations. This function is used in
+#decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera
+and a rotation matrix.
+
+It optionally returns three rotation matrices, one for each axis, and the three Euler angles in
+degrees (as the return value) that could be used in OpenGL. Note, there is always more than one
+sequence of rotations about the three principal axes that results in the same orientation of an
+object, e.g. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angles
+are only one of the possible solutions.
+ */
+CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ,
+                                OutputArray Qx = noArray(),
+                                OutputArray Qy = noArray(),
+                                OutputArray Qz = noArray());
+
+/** @brief Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.
+
+@param projMatrix 3x4 input projection matrix P.
+@param cameraMatrix Output 3x3 camera intrinsic matrix \f$\cameramatrix{A}\f$.
+@param rotMatrix Output 3x3 external rotation matrix R.
+@param transVect Output 4x1 translation vector T.
+@param rotMatrixX Optional 3x3 rotation matrix around x-axis.
+@param rotMatrixY Optional 3x3 rotation matrix around y-axis.
+@param rotMatrixZ Optional 3x3 rotation matrix around z-axis.
+@param eulerAngles Optional three-element vector containing three Euler angles of rotation in
+degrees.
+
+The function computes a decomposition of a projection matrix into a calibration and a rotation
+matrix and the position of a camera.
+
+It optionally returns three rotation matrices, one for each axis, and three Euler angles that could
+be used in OpenGL. Note, there is always more than one sequence of rotations about the three
+principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned
+tree rotation matrices and corresponding three Euler angles are only one of the possible solutions.
+
+The function is based on RQDecomp3x3 .
+ */
+CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix,
+                                             OutputArray rotMatrix, OutputArray transVect,
+                                             OutputArray rotMatrixX = noArray(),
+                                             OutputArray rotMatrixY = noArray(),
+                                             OutputArray rotMatrixZ = noArray(),
+                                             OutputArray eulerAngles =noArray() );
+
+/** @brief Computes partial derivatives of the matrix product for each multiplied matrix.
+
+@param A First multiplied matrix.
+@param B Second multiplied matrix.
+@param dABdA First output derivative matrix d(A\*B)/dA of size
+\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ .
+@param dABdB Second output derivative matrix d(A\*B)/dB of size
+\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ .
+
+The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to
+the elements of each of the two input matrices. The function is used to compute the Jacobian
+matrices in #stereoCalibrate but can also be used in any other similar optimization function.
+ */
+CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB );
+
+/** @brief Combines two rotation-and-shift transformations.
+
+@param rvec1 First rotation vector.
+@param tvec1 First translation vector.
+@param rvec2 Second rotation vector.
+@param tvec2 Second translation vector.
+@param rvec3 Output rotation vector of the superposition.
+@param tvec3 Output translation vector of the superposition.
+@param dr3dr1 Optional output derivative of rvec3 with regard to rvec1
+@param dr3dt1 Optional output derivative of rvec3 with regard to tvec1
+@param dr3dr2 Optional output derivative of rvec3 with regard to rvec2
+@param dr3dt2 Optional output derivative of rvec3 with regard to tvec2
+@param dt3dr1 Optional output derivative of tvec3 with regard to rvec1
+@param dt3dt1 Optional output derivative of tvec3 with regard to tvec1
+@param dt3dr2 Optional output derivative of tvec3 with regard to rvec2
+@param dt3dt2 Optional output derivative of tvec3 with regard to tvec2
+
+The functions compute:
+
+\f[\begin{array}{l} \texttt{rvec3} =  \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} )  \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right )  \\ \texttt{tvec3} =  \mathrm{rodrigues} ( \texttt{rvec2} )  \cdot \texttt{tvec1} +  \texttt{tvec2} \end{array} ,\f]
+
+where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and
+\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details.
+
+Also, the functions can compute the derivatives of the output vectors with regards to the input
+vectors (see matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in
+your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
+function that contains a matrix multiplication.
+ */
+CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1,
+                             InputArray rvec2, InputArray tvec2,
+                             OutputArray rvec3, OutputArray tvec3,
+                             OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(),
+                             OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(),
+                             OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(),
+                             OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() );
+
+/** @brief Projects 3D points to an image plane.
+
+@param objectPoints Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3
+1-channel or 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is the number of points in the view.
+@param rvec The rotation vector (@ref Rodrigues) that, together with tvec, performs a change of
+basis from world to camera coordinate system, see @ref calibrateCamera for details.
+@param tvec The translation vector, see parameter description above.
+@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$ . If the vector is empty, the zero distortion coefficients are assumed.
+@param imagePoints Output array of image points, 1xN/Nx1 2-channel, or
+vector\<Point2f\> .
+@param jacobian Optional output 2Nx(10+\<numDistCoeffs\>) jacobian matrix of derivatives of image
+points with respect to components of the rotation vector, translation vector, focal lengths,
+coordinates of the principal point and the distortion coefficients. In the old interface different
+components of the jacobian are returned via different output parameters.
+@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the
+function assumes that the aspect ratio (\f$f_x / f_y\f$) is fixed and correspondingly adjusts the
+jacobian matrix.
+
+The function computes the 2D projections of 3D points to the image plane, given intrinsic and
+extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial
+derivatives of image points coordinates (as functions of all the input parameters) with respect to
+the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global
+optimization in @ref calibrateCamera, @ref solvePnP, and @ref stereoCalibrate. The function itself
+can also be used to compute a re-projection error, given the current intrinsic and extrinsic
+parameters.
+
+@note By setting rvec = tvec = \f$[0, 0, 0]\f$, or by setting cameraMatrix to a 3x3 identity matrix,
+or by passing zero distortion coefficients, one can get various useful partial cases of the
+function. This means, one can compute the distorted coordinates for a sparse set of points or apply
+a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.
+ */
+CV_EXPORTS_W void projectPoints( InputArray objectPoints,
+                                 InputArray rvec, InputArray tvec,
+                                 InputArray cameraMatrix, InputArray distCoeffs,
+                                 OutputArray imagePoints,
+                                 OutputArray jacobian = noArray(),
+                                 double aspectRatio = 0 );
+
+/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp
+An example program about homography from the camera displacement
+
+Check @ref tutorial_homography "the corresponding tutorial" for more details
+*/
+
+/** @brief Finds an object pose from 3D-2D point correspondences.
+
+@see @ref calib3d_solvePnP
+
+This function returns the rotation and the translation vectors that transform a 3D point expressed in the object
+coordinate frame to the camera coordinate frame, using different methods:
+- P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): need 4 input points to return a unique solution.
+- @ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
+- @ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation.
+Number of input points must be 4. Object points must be defined in the following order:
+  - point 0: [-squareLength / 2,  squareLength / 2, 0]
+  - point 1: [ squareLength / 2,  squareLength / 2, 0]
+  - point 2: [ squareLength / 2, -squareLength / 2, 0]
+  - point 3: [-squareLength / 2, -squareLength / 2, 0]
+- for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
+
+@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or
+1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
+@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
+where N is the number of points. vector\<Point2d\> can be also passed here.
+@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are
+assumed.
+@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from
+the model coordinate system to the camera coordinate system.
+@param tvec Output translation vector.
+@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
+the provided rvec and tvec values as initial approximations of the rotation and translation
+vectors, respectively, and further optimizes them.
+@param flags Method for solving a PnP problem: see @ref calib3d_solvePnP_flags
+
+More information about Perspective-n-Points is described in @ref calib3d_solvePnP
+
+@note
+   -   An example of how to use solvePnP for planar augmented reality can be found at
+        opencv_source_code/samples/python/plane_ar.py
+   -   If you are using Python:
+        - Numpy array slices won't work as input because solvePnP requires contiguous
+        arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
+        modules/calib3d/src/solvepnp.cpp version 2.4.9)
+        - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
+        to its calling of #undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
+        which requires 2-channel information.
+        - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
+        it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
+        np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
+   -   The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are
+       unstable and sometimes give completely wrong results. If you pass one of these two
+       flags, @ref SOLVEPNP_EPNP method will be used instead.
+   -   The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref SOLVEPNP_AP3P
+       methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
+       of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
+   -   With @ref SOLVEPNP_ITERATIVE method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
+       are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
+       global solution to converge.
+   -   With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
+   -   With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation.
+       Number of input points must be 4. Object points must be defined in the following order:
+         - point 0: [-squareLength / 2,  squareLength / 2, 0]
+         - point 1: [ squareLength / 2,  squareLength / 2, 0]
+         - point 2: [ squareLength / 2, -squareLength / 2, 0]
+         - point 3: [-squareLength / 2, -squareLength / 2, 0]
+    -  With @ref SOLVEPNP_SQPNP input points must be >= 3
+ */
+CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints,
+                            InputArray cameraMatrix, InputArray distCoeffs,
+                            OutputArray rvec, OutputArray tvec,
+                            bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE );
+
+/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
+
+@see @ref calib3d_solvePnP
+
+@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or
+1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
+@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
+where N is the number of points. vector\<Point2d\> can be also passed here.
+@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are
+assumed.
+@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from
+the model coordinate system to the camera coordinate system.
+@param tvec Output translation vector.
+@param useExtrinsicGuess Parameter used for @ref SOLVEPNP_ITERATIVE. If true (1), the function uses
+the provided rvec and tvec values as initial approximations of the rotation and translation
+vectors, respectively, and further optimizes them.
+@param iterationsCount Number of iterations.
+@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value
+is the maximum allowed distance between the observed and computed point projections to consider it
+an inlier.
+@param confidence The probability that the algorithm produces a useful result.
+@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints .
+@param flags Method for solving a PnP problem (see @ref solvePnP ).
+
+The function estimates an object pose given a set of object points, their corresponding image
+projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such
+a pose that minimizes reprojection error, that is, the sum of squared distances between the observed
+projections imagePoints and the projected (using @ref projectPoints ) objectPoints. The use of RANSAC
+makes the function resistant to outliers.
+
+@note
+   -   An example of how to use solvePNPRansac for object detection can be found at
+        opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
+   -   The default method used to estimate the camera pose for the Minimal Sample Sets step
+       is #SOLVEPNP_EPNP. Exceptions are:
+         - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
+         - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
+   -   The method used to estimate the camera pose using all the inliers is defined by the
+       flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case,
+       the method #SOLVEPNP_EPNP will be used instead.
+ */
+CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints,
+                                  InputArray cameraMatrix, InputArray distCoeffs,
+                                  OutputArray rvec, OutputArray tvec,
+                                  bool useExtrinsicGuess = false, int iterationsCount = 100,
+                                  float reprojectionError = 8.0, double confidence = 0.99,
+                                  OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE );
+
+
+/*
+Finds rotation and translation vector.
+If cameraMatrix is given then run P3P. Otherwise run linear P6P and output cameraMatrix too.
+*/
+CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints,
+                     InputOutputArray cameraMatrix, InputArray distCoeffs,
+                     OutputArray rvec, OutputArray tvec, OutputArray inliers,
+                     const UsacParams &params=UsacParams());
+
+/** @brief Finds an object pose from 3 3D-2D point correspondences.
+
+@see @ref calib3d_solvePnP
+
+@param objectPoints Array of object points in the object coordinate space, 3x3 1-channel or
+1x3/3x1 3-channel. vector\<Point3f\> can be also passed here.
+@param imagePoints Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel.
+ vector\<Point2f\> can be also passed here.
+@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are
+assumed.
+@param rvecs Output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points from
+the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions.
+@param tvecs Output translation vectors.
+@param flags Method for solving a P3P problem:
+-   @ref SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
+"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete).
+-   @ref SOLVEPNP_AP3P Method is based on the paper of T. Ke and S. Roumeliotis.
+"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
+
+The function estimates the object pose given 3 object points, their corresponding image
+projections, as well as the camera intrinsic matrix and the distortion coefficients.
+
+@note
+The solutions are sorted by reprojection errors (lowest to highest).
+ */
+CV_EXPORTS_W int solveP3P( InputArray objectPoints, InputArray imagePoints,
+                           InputArray cameraMatrix, InputArray distCoeffs,
+                           OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+                           int flags );
+
+/** @brief Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame
+to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
+
+@see @ref calib3d_solvePnP
+
+@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,
+where N is the number of points. vector\<Point3d\> can also be passed here.
+@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
+where N is the number of points. vector\<Point2d\> can also be passed here.
+@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are
+assumed.
+@param rvec Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from
+the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
+@param tvec Input/Output translation vector. Input values are used as an initial solution.
+@param criteria Criteria when to stop the Levenberg-Marquard iterative algorithm.
+
+The function refines the object pose given at least 3 object points, their corresponding image
+projections, an initial solution for the rotation and translation vector,
+as well as the camera intrinsic matrix and the distortion coefficients.
+The function minimizes the projection error with respect to the rotation and the translation vectors, according
+to a Levenberg-Marquardt iterative minimization @cite Madsen04 @cite Eade13 process.
+ */
+CV_EXPORTS_W void solvePnPRefineLM( InputArray objectPoints, InputArray imagePoints,
+                                    InputArray cameraMatrix, InputArray distCoeffs,
+                                    InputOutputArray rvec, InputOutputArray tvec,
+                                    TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 20, FLT_EPSILON));
+
+/** @brief Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame
+to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.
+
+@see @ref calib3d_solvePnP
+
+@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,
+where N is the number of points. vector\<Point3d\> can also be passed here.
+@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
+where N is the number of points. vector\<Point2d\> can also be passed here.
+@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are
+assumed.
+@param rvec Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from
+the model coordinate system to the camera coordinate system. Input values are used as an initial solution.
+@param tvec Input/Output translation vector. Input values are used as an initial solution.
+@param criteria Criteria when to stop the Levenberg-Marquard iterative algorithm.
+@param VVSlambda Gain for the virtual visual servoing control law, equivalent to the \f$\alpha\f$
+gain in the Damped Gauss-Newton formulation.
+
+The function refines the object pose given at least 3 object points, their corresponding image
+projections, an initial solution for the rotation and translation vector,
+as well as the camera intrinsic matrix and the distortion coefficients.
+The function minimizes the projection error with respect to the rotation and the translation vectors, using a
+virtual visual servoing (VVS) @cite Chaumette06 @cite Marchand16 scheme.
+ */
+CV_EXPORTS_W void solvePnPRefineVVS( InputArray objectPoints, InputArray imagePoints,
+                                     InputArray cameraMatrix, InputArray distCoeffs,
+                                     InputOutputArray rvec, InputOutputArray tvec,
+                                     TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 20, FLT_EPSILON),
+                                     double VVSlambda = 1);
+
+/** @brief Finds an object pose from 3D-2D point correspondences.
+
+@see @ref calib3d_solvePnP
+
+This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector>
+couple), depending on the number of input points and the chosen method:
+- P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
+- @ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
+- @ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation.
+Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
+  - point 0: [-squareLength / 2,  squareLength / 2, 0]
+  - point 1: [ squareLength / 2,  squareLength / 2, 0]
+  - point 2: [ squareLength / 2, -squareLength / 2, 0]
+  - point 3: [-squareLength / 2, -squareLength / 2, 0]
+- for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
+Only 1 solution is returned.
+
+@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or
+1xN/Nx1 3-channel, where N is the number of points. vector\<Point3d\> can be also passed here.
+@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
+where N is the number of points. vector\<Point2d\> can be also passed here.
+@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are
+assumed.
+@param rvecs Vector of output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points from
+the model coordinate system to the camera coordinate system.
+@param tvecs Vector of output translation vectors.
+@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
+the provided rvec and tvec values as initial approximations of the rotation and translation
+vectors, respectively, and further optimizes them.
+@param flags Method for solving a PnP problem: see @ref calib3d_solvePnP_flags
+@param rvec Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVE
+and useExtrinsicGuess is set to true.
+@param tvec Translation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVE
+and useExtrinsicGuess is set to true.
+@param reprojectionError Optional vector of reprojection error, that is the RMS error
+(\f$ \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \f$) between the input image points
+and the 3D object points projected with the estimated pose.
+
+More information is described in @ref calib3d_solvePnP
+
+@note
+   -   An example of how to use solvePnP for planar augmented reality can be found at
+        opencv_source_code/samples/python/plane_ar.py
+   -   If you are using Python:
+        - Numpy array slices won't work as input because solvePnP requires contiguous
+        arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
+        modules/calib3d/src/solvepnp.cpp version 2.4.9)
+        - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
+        to its calling of #undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
+        which requires 2-channel information.
+        - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
+        it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
+        np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
+   -   The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are
+       unstable and sometimes give completely wrong results. If you pass one of these two
+       flags, @ref SOLVEPNP_EPNP method will be used instead.
+   -   The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref SOLVEPNP_AP3P
+       methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
+       of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
+   -   With @ref SOLVEPNP_ITERATIVE method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
+       are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
+       global solution to converge.
+   -   With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
+   -   With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation.
+       Number of input points must be 4. Object points must be defined in the following order:
+         - point 0: [-squareLength / 2,  squareLength / 2, 0]
+         - point 1: [ squareLength / 2,  squareLength / 2, 0]
+         - point 2: [ squareLength / 2, -squareLength / 2, 0]
+         - point 3: [-squareLength / 2, -squareLength / 2, 0]
+ */
+CV_EXPORTS_W int solvePnPGeneric( InputArray objectPoints, InputArray imagePoints,
+                                  InputArray cameraMatrix, InputArray distCoeffs,
+                                  OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+                                  bool useExtrinsicGuess = false, SolvePnPMethod flags = SOLVEPNP_ITERATIVE,
+                                  InputArray rvec = noArray(), InputArray tvec = noArray(),
+                                  OutputArray reprojectionError = noArray() );
+
+/** @brief Finds an initial camera intrinsic matrix from 3D-2D point correspondences.
+
+@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern
+coordinate space. In the old interface all the per-view vectors are concatenated. See
+#calibrateCamera for details.
+@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the
+old interface all the per-view vectors are concatenated.
+@param imageSize Image size in pixels used to initialize the principal point.
+@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently.
+Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ .
+
+The function estimates and returns an initial camera intrinsic matrix for the camera calibration process.
+Currently, the function only supports planar calibration patterns, which are patterns where each
+object point has z-coordinate =0.
+ */
+CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints,
+                                     InputArrayOfArrays imagePoints,
+                                     Size imageSize, double aspectRatio = 1.0 );
+
+/** @brief Finds the positions of internal corners of the chessboard.
+
+@param image Source chessboard view. It must be an 8-bit grayscale or color image.
+@param patternSize Number of inner corners per a chessboard row and column
+( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
+@param corners Output array of detected corners.
+@param flags Various operation flags that can be zero or a combination of the following values:
+-   @ref CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black
+and white, rather than a fixed threshold level (computed from the average image brightness).
+-   @ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before
+applying fixed or adaptive thresholding.
+-   @ref CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter,
+square-like shape) to filter out false quads extracted at the contour retrieval stage.
+-   @ref CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners,
+and shortcut the call if none is found. This can drastically speed up the call in the
+degenerate condition when no chessboard is observed.
+
+The function attempts to determine whether the input image is a view of the chessboard pattern and
+locate the internal chessboard corners. The function returns a non-zero value if all of the corners
+are found and they are placed in a certain order (row by row, left to right in every row).
+Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example,
+a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black
+squares touch each other. The detected coordinates are approximate, and to determine their positions
+more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with
+different parameters if returned coordinates are not accurate enough.
+
+Sample usage of detecting and drawing chessboard corners: :
+@code
+    Size patternsize(8,6); //interior number of corners
+    Mat gray = ....; //source image
+    vector<Point2f> corners; //this will be filled by the detected corners
+
+    //CALIB_CB_FAST_CHECK saves a lot of time on images
+    //that do not contain any chessboard corners
+    bool patternfound = findChessboardCorners(gray, patternsize, corners,
+            CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
+            + CALIB_CB_FAST_CHECK);
+
+    if(patternfound)
+      cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
+        TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
+
+    drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
+@endcode
+@note The function requires white space (like a square-thick border, the wider the better) around
+the board to make the detection more robust in various environments. Otherwise, if there is no
+border and the background is dark, the outer black squares cannot be segmented properly and so the
+square grouping and ordering algorithm fails.
+
+Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.
+ */
+CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners,
+                                         int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE );
+
+/*
+   Checks whether the image contains chessboard of the specific size or not.
+   If yes, nonzero value is returned.
+*/
+CV_EXPORTS_W bool checkChessboard(InputArray img, Size size);
+
+/** @brief Finds the positions of internal corners of the chessboard using a sector based approach.
+
+@param image Source chessboard view. It must be an 8-bit grayscale or color image.
+@param patternSize Number of inner corners per a chessboard row and column
+( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
+@param corners Output array of detected corners.
+@param flags Various operation flags that can be zero or a combination of the following values:
+-   @ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection.
+-   @ref CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate.
+-   @ref CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects.
+-   @ref CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description).
+-   @ref CALIB_CB_MARKER The detected pattern must have a marker (see description).
+This should be used if an accurate camera calibration is required.
+@param meta Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).
+Each entry stands for one corner of the pattern and can have one of the following values:
+-   0 = no meta data attached
+-   1 = left-top corner of a black cell
+-   2 = left-top corner of a white cell
+-   3 = left-top corner of a black cell with a white marker dot
+-   4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)
+
+The function is analog to #findChessboardCorners but uses a localized radon
+transformation approximated by box filters being more robust to all sort of
+noise, faster on larger images and is able to directly return the sub-pixel
+position of the internal chessboard corners. The Method is based on the paper
+@cite duda2018 "Accurate Detection and Localization of Checkerboard Corners for
+Calibration" demonstrating that the returned sub-pixel positions are more
+accurate than the one returned by cornerSubPix allowing a precise camera
+calibration for demanding applications.
+
+In the case, the flags @ref CALIB_CB_LARGER or @ref CALIB_CB_MARKER are given,
+the result can be recovered from the optional meta array. Both flags are
+helpful to use calibration patterns exceeding the field of view of the camera.
+These oversized patterns allow more accurate calibrations as corners can be
+utilized, which are as close as possible to the image borders.  For a
+consistent coordinate system across all images, the optional marker (see image
+below) can be used to move the origin of the board to the location where the
+black circle is located.
+
+@note The function requires a white boarder with roughly the same width as one
+of the checkerboard fields around the whole board to improve the detection in
+various environments. In addition, because of the localized radon
+transformation it is beneficial to use round corners for the field corners
+which are located on the outside of the board. The following figure illustrates
+a sample checkerboard optimized for the detection. However, any other checkerboard
+can be used as well.
+
+Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.
+![Checkerboard](pics/checkerboard_radon.png)
+ */
+CV_EXPORTS_AS(findChessboardCornersSBWithMeta)
+bool findChessboardCornersSB(InputArray image,Size patternSize, OutputArray corners,
+                             int flags,OutputArray meta);
+/** @overload */
+CV_EXPORTS_W inline
+bool findChessboardCornersSB(InputArray image, Size patternSize, OutputArray corners,
+                             int flags = 0)
+{
+    return findChessboardCornersSB(image, patternSize, corners, flags, noArray());
+}
+
+/** @brief Estimates the sharpness of a detected chessboard.
+
+Image sharpness, as well as brightness, are a critical parameter for accuracte
+camera calibration. For accessing these parameters for filtering out
+problematic calibraiton images, this method calculates edge profiles by traveling from
+black to white chessboard cell centers. Based on this, the number of pixels is
+calculated required to transit from black to white. This width of the
+transition area is a good indication of how sharp the chessboard is imaged
+and should be below ~3.0 pixels.
+
+@param image Gray image used to find chessboard corners
+@param patternSize Size of a found chessboard pattern
+@param corners Corners found by #findChessboardCornersSB
+@param rise_distance Rise distance 0.8 means 10% ... 90% of the final signal strength
+@param vertical By default edge responses for horizontal lines are calculated
+@param sharpness Optional output array with a sharpness value for calculated edge responses (see description)
+
+The optional sharpness array is of type CV_32FC1 and has for each calculated
+profile one row with the following five entries:
+* 0 = x coordinate of the underlying edge in the image
+* 1 = y coordinate of the underlying edge in the image
+* 2 = width of the transition area (sharpness)
+* 3 = signal strength in the black cell (min brightness)
+* 4 = signal strength in the white cell (max brightness)
+
+@return Scalar(average sharpness, average min brightness, average max brightness,0)
+*/
+CV_EXPORTS_W Scalar estimateChessboardSharpness(InputArray image, Size patternSize, InputArray corners,
+                                                float rise_distance=0.8F,bool vertical=false,
+                                                OutputArray sharpness=noArray());
+
+
+//! finds subpixel-accurate positions of the chessboard corners
+CV_EXPORTS_W bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size );
+
+/** @brief Renders the detected chessboard corners.
+
+@param image Destination image. It must be an 8-bit color image.
+@param patternSize Number of inner corners per a chessboard row and column
+(patternSize = cv::Size(points_per_row,points_per_column)).
+@param corners Array of detected corners, the output of #findChessboardCorners.
+@param patternWasFound Parameter indicating whether the complete board was found or not. The
+return value of #findChessboardCorners should be passed here.
+
+The function draws individual chessboard corners detected either as red circles if the board was not
+found, or as colored corners connected with lines if the board was found.
+ */
+CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize,
+                                         InputArray corners, bool patternWasFound );
+
+/** @brief Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP
+
+@param image Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
+@param cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters.
+\f$\cameramatrix{A}\f$
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is empty, the zero distortion coefficients are assumed.
+@param rvec Rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from
+the model coordinate system to the camera coordinate system.
+@param tvec Translation vector.
+@param length Length of the painted axes in the same unit than tvec (usually in meters).
+@param thickness Line thickness of the painted axes.
+
+This function draws the axes of the world/object coordinate system w.r.t. to the camera frame.
+OX is drawn in red, OY in green and OZ in blue.
+ */
+CV_EXPORTS_W void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs,
+                                InputArray rvec, InputArray tvec, float length, int thickness=3);
+
+struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters
+{
+    CV_WRAP CirclesGridFinderParameters();
+    CV_PROP_RW cv::Size2f densityNeighborhoodSize;
+    CV_PROP_RW float minDensity;
+    CV_PROP_RW int kmeansAttempts;
+    CV_PROP_RW int minDistanceToAddKeypoint;
+    CV_PROP_RW int keypointScale;
+    CV_PROP_RW float minGraphConfidence;
+    CV_PROP_RW float vertexGain;
+    CV_PROP_RW float vertexPenalty;
+    CV_PROP_RW float existingVertexGain;
+    CV_PROP_RW float edgeGain;
+    CV_PROP_RW float edgePenalty;
+    CV_PROP_RW float convexHullFactor;
+    CV_PROP_RW float minRNGEdgeSwitchDist;
+
+    enum GridType
+    {
+      SYMMETRIC_GRID, ASYMMETRIC_GRID
+    };
+    GridType gridType;
+
+    CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.
+    CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from prediction. Used by CALIB_CB_CLUSTERING.
+};
+
+#ifndef DISABLE_OPENCV_3_COMPATIBILITY
+typedef CirclesGridFinderParameters CirclesGridFinderParameters2;
+#endif
+
+/** @brief Finds centers in the grid of circles.
+
+@param image grid view of input circles; it must be an 8-bit grayscale or color image.
+@param patternSize number of circles per row and column
+( patternSize = Size(points_per_row, points_per_colum) ).
+@param centers output array of detected centers.
+@param flags various operation flags that can be one of the following values:
+-   @ref CALIB_CB_SYMMETRIC_GRID uses symmetric pattern of circles.
+-   @ref CALIB_CB_ASYMMETRIC_GRID uses asymmetric pattern of circles.
+-   @ref CALIB_CB_CLUSTERING uses a special algorithm for grid detection. It is more robust to
+perspective distortions but much more sensitive to background clutter.
+@param blobDetector feature detector that finds blobs like dark circles on light background.
+                    If `blobDetector` is NULL then `image` represents Point2f array of candidates.
+@param parameters struct for finding circles in a grid pattern.
+
+The function attempts to determine whether the input image contains a grid of circles. If it is, the
+function locates centers of the circles. The function returns a non-zero value if all of the centers
+have been found and they have been placed in a certain order (row by row, left to right in every
+row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
+
+Sample usage of detecting and drawing the centers of circles: :
+@code
+    Size patternsize(7,7); //number of centers
+    Mat gray = ...; //source image
+    vector<Point2f> centers; //this will be filled by the detected centers
+
+    bool patternfound = findCirclesGrid(gray, patternsize, centers);
+
+    drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
+@endcode
+@note The function requires white space (like a square-thick border, the wider the better) around
+the board to make the detection more robust in various environments.
+ */
+CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
+                                   OutputArray centers, int flags,
+                                   const Ptr<FeatureDetector> &blobDetector,
+                                   const CirclesGridFinderParameters& parameters);
+
+/** @overload */
+CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
+                                   OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID,
+                                   const Ptr<FeatureDetector> &blobDetector = SimpleBlobDetector::create());
+
+/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration
+pattern.
+
+@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in
+the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
+vector contains as many elements as the number of pattern views. If the same calibration pattern
+is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
+possible to use partially occluded patterns or even different patterns in different views. Then,
+the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's
+XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig.
+In the old interface all the vectors of object points from different views are concatenated
+together.
+@param imagePoints In the new interface it is a vector of vectors of the projections of calibration
+pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
+objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal,
+respectively. In the old interface all the vectors of object points from different views are
+concatenated together.
+@param imageSize Size of the image used only to initialize the camera intrinsic matrix.
+@param cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix
+\f$\cameramatrix{A}\f$ . If @ref CALIB_USE_INTRINSIC_GUESS
+and/or @ref CALIB_FIX_ASPECT_RATIO, @ref CALIB_FIX_PRINCIPAL_POINT or @ref CALIB_FIX_FOCAL_LENGTH
+are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
+@param distCoeffs Input/output vector of distortion coefficients
+\f$\distcoeffs\f$.
+@param rvecs Output vector of rotation vectors (@ref Rodrigues ) estimated for each pattern view
+(e.g. std::vector<cv::Mat>>). That is, each i-th rotation vector together with the corresponding
+i-th translation vector (see the next output parameter description) brings the calibration pattern
+from the object coordinate space (in which object points are specified) to the camera coordinate
+space. In more technical terms, the tuple of the i-th rotation and translation vector performs
+a change of basis from object coordinate space to camera coordinate space. Due to its duality, this
+tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate
+space.
+@param tvecs Output vector of translation vectors estimated for each pattern view, see parameter
+describtion above.
+@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic
+parameters. Order of deviations values:
+\f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
+ s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
+@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic
+parameters. Order of deviations values: \f$(R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\f$ where M is
+the number of pattern views. \f$R_i, T_i\f$ are concatenated 1x3 vectors.
+ @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
+@param flags Different flags that may be zero or a combination of the following values:
+-   @ref CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of
+fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
+center ( imageSize is used), and focal distances are computed in a least-squares fashion.
+Note, that if intrinsic parameters are known, there is no need to use this function just to
+estimate extrinsic parameters. Use @ref solvePnP instead.
+-   @ref CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global
+optimization. It stays at the center or at a different location specified when
+ @ref CALIB_USE_INTRINSIC_GUESS is set too.
+-   @ref CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The
+ratio fx/fy stays the same as in the input cameraMatrix . When
+ @ref CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are
+ignored, only their ratio is computed and used further.
+-   @ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
+to zeros and stay zero.
+-   @ref CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if
+ @ref CALIB_USE_INTRINSIC_GUESS is set.
+-   @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 The corresponding radial distortion
+coefficient is not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is
+set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
+-   @ref CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the
+backward compatibility, this extra flag should be explicitly specified to make the
+calibration function use the rational model and return 8 coefficients or more.
+-   @ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the
+backward compatibility, this extra flag should be explicitly specified to make the
+calibration function use the thin prism model and return 12 coefficients or more.
+-   @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
+the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
+supplied distCoeffs matrix is used. Otherwise, it is set to 0.
+-   @ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the
+backward compatibility, this extra flag should be explicitly specified to make the
+calibration function use the tilted sensor model and return 14 coefficients.
+-   @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
+the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
+supplied distCoeffs matrix is used. Otherwise, it is set to 0.
+@param criteria Termination criteria for the iterative optimization algorithm.
+
+@return the overall RMS re-projection error.
+
+The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
+views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object
+points and their corresponding 2D projections in each view must be specified. That may be achieved
+by using an object with known geometry and easily detectable feature points. Such an object is
+called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
+a calibration rig (see @ref findChessboardCorners). Currently, initialization of intrinsic
+parameters (when @ref CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration
+patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
+be used as long as initial cameraMatrix is provided.
+
+The algorithm performs the following steps:
+
+-   Compute the initial intrinsic parameters (the option only available for planar calibration
+    patterns) or read them from the input parameters. The distortion coefficients are all set to
+    zeros initially unless some of CALIB_FIX_K? are specified.
+
+-   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
+    done using @ref solvePnP .
+
+-   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
+    that is, the total sum of squared distances between the observed feature points imagePoints and
+    the projected (using the current estimates for camera parameters and the poses) object points
+    objectPoints. See @ref projectPoints for details.
+
+@note
+    If you use a non-square (i.e. non-N-by-N) grid and @ref findChessboardCorners for calibration,
+    and @ref calibrateCamera returns bad values (zero distortion coefficients, \f$c_x\f$ and
+    \f$c_y\f$ very far from the image center, and/or large differences between \f$f_x\f$ and
+    \f$f_y\f$ (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols)
+    instead of using patternSize=cvSize(cols,rows) in @ref findChessboardCorners.
+
+@sa
+   calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate,
+   undistort
+ */
+CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints,
+                                     InputArrayOfArrays imagePoints, Size imageSize,
+                                     InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+                                     OutputArray stdDeviationsIntrinsics,
+                                     OutputArray stdDeviationsExtrinsics,
+                                     OutputArray perViewErrors,
+                                     int flags = 0, TermCriteria criteria = TermCriteria(
+                                        TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) );
+
+/** @overload */
+CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints,
+                                     InputArrayOfArrays imagePoints, Size imageSize,
+                                     InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+                                     int flags = 0, TermCriteria criteria = TermCriteria(
+                                        TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) );
+
+/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
+
+This function is an extension of #calibrateCamera with the method of releasing object which was
+proposed in @cite strobl2011iccv. In many common cases with inaccurate, unmeasured, roughly planar
+targets (calibration plates), this method can dramatically improve the precision of the estimated
+camera parameters. Both the object-releasing method and standard method are supported by this
+function. Use the parameter **iFixedPoint** for method selection. In the internal implementation,
+#calibrateCamera is a wrapper for this function.
+
+@param objectPoints Vector of vectors of calibration pattern points in the calibration pattern
+coordinate space. See #calibrateCamera for details. If the method of releasing object to be used,
+the identical calibration board must be used in each view and it must be fully visible, and all
+objectPoints[i] must be the same and all points should be roughly close to a plane. **The calibration
+target has to be rigid, or at least static if the camera (rather than the calibration target) is
+shifted for grabbing images.**
+@param imagePoints Vector of vectors of the projections of calibration pattern points. See
+#calibrateCamera for details.
+@param imageSize Size of the image used only to initialize the intrinsic camera matrix.
+@param iFixedPoint The index of the 3D object point in objectPoints[0] to be fixed. It also acts as
+a switch for calibration method selection. If object-releasing method to be used, pass in the
+parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will
+make standard calibration method selected. Usually the top-right corner point of the calibration
+board grid is recommended to be fixed when object-releasing method being utilized. According to
+\cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front
+and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and
+newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
+@param cameraMatrix Output 3x3 floating-point camera matrix. See #calibrateCamera for details.
+@param distCoeffs Output vector of distortion coefficients. See #calibrateCamera for details.
+@param rvecs Output vector of rotation vectors estimated for each pattern view. See #calibrateCamera
+for details.
+@param tvecs Output vector of translation vectors estimated for each pattern view.
+@param newObjPoints The updated output vector of calibration pattern points. The coordinates might
+be scaled based on three fixed points. The returned coordinates are accurate only if the above
+mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter
+is ignored with standard calibration method.
+@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
+See #calibrateCamera for details.
+@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
+See #calibrateCamera for details.
+@param stdDeviationsObjPoints Output vector of standard deviations estimated for refined coordinates
+of calibration pattern points. It has the same size and order as objectPoints[0] vector. This
+parameter is ignored with standard calibration method.
+ @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
+@param flags Different flags that may be zero or a combination of some predefined values. See
+#calibrateCamera for details. If the method of releasing object is used, the calibration time may
+be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially
+less precise and less stable in some rare cases.
+@param criteria Termination criteria for the iterative optimization algorithm.
+
+@return the overall RMS re-projection error.
+
+The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
+views. The algorithm is based on @cite Zhang2000, @cite BouguetMCT and @cite strobl2011iccv. See
+#calibrateCamera for other detailed explanations.
+@sa
+   calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
+ */
+CV_EXPORTS_AS(calibrateCameraROExtended) double calibrateCameraRO( InputArrayOfArrays objectPoints,
+                                     InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint,
+                                     InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+                                     OutputArray newObjPoints,
+                                     OutputArray stdDeviationsIntrinsics,
+                                     OutputArray stdDeviationsExtrinsics,
+                                     OutputArray stdDeviationsObjPoints,
+                                     OutputArray perViewErrors,
+                                     int flags = 0, TermCriteria criteria = TermCriteria(
+                                        TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) );
+
+/** @overload */
+CV_EXPORTS_W double calibrateCameraRO( InputArrayOfArrays objectPoints,
+                                     InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint,
+                                     InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+                                     OutputArray newObjPoints,
+                                     int flags = 0, TermCriteria criteria = TermCriteria(
+                                        TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) );
+
+/** @brief Computes useful camera characteristics from the camera intrinsic matrix.
+
+@param cameraMatrix Input camera intrinsic matrix that can be estimated by #calibrateCamera or
+#stereoCalibrate .
+@param imageSize Input image size in pixels.
+@param apertureWidth Physical width in mm of the sensor.
+@param apertureHeight Physical height in mm of the sensor.
+@param fovx Output field of view in degrees along the horizontal sensor axis.
+@param fovy Output field of view in degrees along the vertical sensor axis.
+@param focalLength Focal length of the lens in mm.
+@param principalPoint Principal point in mm.
+@param aspectRatio \f$f_y/f_x\f$
+
+The function computes various useful camera characteristics from the previously estimated camera
+matrix.
+
+@note
+   Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for
+    the chessboard pitch (it can thus be any value).
+ */
+CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize,
+                                           double apertureWidth, double apertureHeight,
+                                           CV_OUT double& fovx, CV_OUT double& fovy,
+                                           CV_OUT double& focalLength, CV_OUT Point2d& principalPoint,
+                                           CV_OUT double& aspectRatio );
+
+/** @brief Calibrates a stereo camera set up. This function finds the intrinsic parameters
+for each of the two cameras and the extrinsic parameters between the two cameras.
+
+@param objectPoints Vector of vectors of the calibration pattern points. The same structure as
+in @ref calibrateCamera. For each pattern view, both cameras need to see the same object
+points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be
+equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to
+be equal for each i.
+@param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
+observed by the first camera. The same structure as in @ref calibrateCamera.
+@param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
+observed by the second camera. The same structure as in @ref calibrateCamera.
+@param cameraMatrix1 Input/output camera intrinsic matrix for the first camera, the same as in
+@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
+@param distCoeffs1 Input/output vector of distortion coefficients, the same as in
+@ref calibrateCamera.
+@param cameraMatrix2 Input/output second camera intrinsic matrix for the second camera. See description for
+cameraMatrix1.
+@param distCoeffs2 Input/output lens distortion coefficients for the second camera. See
+description for distCoeffs1.
+@param imageSize Size of the image used only to initialize the camera intrinsic matrices.
+@param R Output rotation matrix. Together with the translation vector T, this matrix brings
+points given in the first camera's coordinate system to points in the second camera's
+coordinate system. In more technical terms, the tuple of R and T performs a change of basis
+from the first camera's coordinate system to the second camera's coordinate system. Due to its
+duality, this tuple is equivalent to the position of the first camera with respect to the
+second camera coordinate system.
+@param T Output translation vector, see description above.
+@param E Output essential matrix.
+@param F Output fundamental matrix.
+@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
+@param flags Different flags that may be zero or a combination of the following values:
+-   @ref CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F
+matrices are estimated.
+-   @ref CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters
+according to the specified flags. Initial values are provided by the user.
+-   @ref CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further.
+Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
+-   @ref CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization.
+-   @ref CALIB_FIX_FOCAL_LENGTH Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ .
+-   @ref CALIB_FIX_ASPECT_RATIO Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$
+.
+-   @ref CALIB_SAME_FOCAL_LENGTH Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ .
+-   @ref CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to
+zeros and fix there.
+-   @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 Do not change the corresponding radial
+distortion coefficient during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set,
+the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
+-   @ref CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward
+compatibility, this extra flag should be explicitly specified to make the calibration
+function use the rational model and return 8 coefficients. If the flag is not set, the
+function computes and returns only 5 distortion coefficients.
+-   @ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the
+backward compatibility, this extra flag should be explicitly specified to make the
+calibration function use the thin prism model and return 12 coefficients. If the flag is not
+set, the function computes and returns only 5 distortion coefficients.
+-   @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
+the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
+supplied distCoeffs matrix is used. Otherwise, it is set to 0.
+-   @ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the
+backward compatibility, this extra flag should be explicitly specified to make the
+calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
+set, the function computes and returns only 5 distortion coefficients.
+-   @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
+the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
+supplied distCoeffs matrix is used. Otherwise, it is set to 0.
+@param criteria Termination criteria for the iterative optimization algorithm.
+
+The function estimates the transformation between two cameras making a stereo pair. If one computes
+the poses of an object relative to the first camera and to the second camera,
+( \f$R_1\f$,\f$T_1\f$ ) and (\f$R_2\f$,\f$T_2\f$), respectively, for a stereo camera where the
+relative position and orientation between the two cameras are fixed, then those poses definitely
+relate to each other. This means, if the relative position and orientation (\f$R\f$,\f$T\f$) of the
+two cameras is known, it is possible to compute (\f$R_2\f$,\f$T_2\f$) when (\f$R_1\f$,\f$T_1\f$) is
+given. This is what the described function does. It computes (\f$R\f$,\f$T\f$) such that:
+
+\f[R_2=R R_1\f]
+\f[T_2=R T_1 + T.\f]
+
+Therefore, one can compute the coordinate representation of a 3D point for the second camera's
+coordinate system when given the point's coordinate representation in the first camera's coordinate
+system:
+
+\f[\begin{bmatrix}
+X_2 \\
+Y_2 \\
+Z_2 \\
+1
+\end{bmatrix} = \begin{bmatrix}
+R & T \\
+0 & 1
+\end{bmatrix} \begin{bmatrix}
+X_1 \\
+Y_1 \\
+Z_1 \\
+1
+\end{bmatrix}.\f]
+
+
+Optionally, it computes the essential matrix E:
+
+\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\f]
+
+where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ .
+And the function can also compute the fundamental matrix F:
+
+\f[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\f]
+
+Besides the stereo-related information, the function can also perform a full calibration of each of
+the two cameras. However, due to the high dimensionality of the parameter space and noise in the
+input data, the function can diverge from the correct solution. If the intrinsic parameters can be
+estimated with high accuracy for each of the cameras individually (for example, using
+#calibrateCamera ), you are recommended to do so and then pass @ref CALIB_FIX_INTRINSIC flag to the
+function along with the computed intrinsic parameters. Otherwise, if all the parameters are
+estimated at once, it makes sense to restrict some parameters, for example, pass
+ @ref CALIB_SAME_FOCAL_LENGTH and @ref CALIB_ZERO_TANGENT_DIST flags, which is usually a
+reasonable assumption.
+
+Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the
+points in all the available views from both cameras. The function returns the final value of the
+re-projection error.
+ */
+CV_EXPORTS_AS(stereoCalibrateExtended) double stereoCalibrate( InputArrayOfArrays objectPoints,
+                                     InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
+                                     InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
+                                     InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
+                                     Size imageSize, InputOutputArray R,InputOutputArray T, OutputArray E, OutputArray F,
+                                     OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC,
+                                     TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) );
+
+/// @overload
+CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints,
+                                     InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
+                                     InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
+                                     InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
+                                     Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F,
+                                     int flags = CALIB_FIX_INTRINSIC,
+                                     TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) );
+
+/** @brief Computes rectification transforms for each head of a calibrated stereo camera.
+
+@param cameraMatrix1 First camera intrinsic matrix.
+@param distCoeffs1 First camera distortion parameters.
+@param cameraMatrix2 Second camera intrinsic matrix.
+@param distCoeffs2 Second camera distortion parameters.
+@param imageSize Size of the image used for stereo calibration.
+@param R Rotation matrix from the coordinate system of the first camera to the second camera,
+see @ref stereoCalibrate.
+@param T Translation vector from the coordinate system of the first camera to the second camera,
+see @ref stereoCalibrate.
+@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix
+brings points given in the unrectified first camera's coordinate system to points in the rectified
+first camera's coordinate system. In more technical terms, it performs a change of basis from the
+unrectified first camera's coordinate system to the rectified first camera's coordinate system.
+@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix
+brings points given in the unrectified second camera's coordinate system to points in the rectified
+second camera's coordinate system. In more technical terms, it performs a change of basis from the
+unrectified second camera's coordinate system to the rectified second camera's coordinate system.
+@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
+camera, i.e. it projects points given in the rectified first camera coordinate system into the
+rectified first camera's image.
+@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
+camera, i.e. it projects points given in the rectified first camera coordinate system into the
+rectified second camera's image.
+@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see @ref reprojectImageTo3D).
+@param flags Operation flags that may be zero or @ref CALIB_ZERO_DISPARITY . If the flag is set,
+the function makes the principal points of each camera have the same pixel coordinates in the
+rectified views. And if the flag is not set, the function may still shift the images in the
+horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
+useful image area.
+@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default
+scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
+images are zoomed and shifted so that only valid pixels are visible (no black areas after
+rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
+pixels from the original images from the cameras are retained in the rectified images (no source
+image pixels are lost). Any intermediate value yields an intermediate result between
+those two extreme cases.
+@param newImageSize New image resolution after rectification. The same size should be passed to
+#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
+is passed (default), it is set to the original imageSize . Setting it to a larger value can help you
+preserve details in the original image, especially when there is a big radial distortion.
+@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels
+are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
+(see the picture below).
+@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels
+are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
+(see the picture below).
+
+The function computes the rotation matrices for each camera that (virtually) make both camera image
+planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
+the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate
+as input. As output, it provides two rotation matrices and also two projection matrices in the new
+coordinates. The function distinguishes the following two cases:
+
+-   **Horizontal stereo**: the first and the second camera views are shifted relative to each other
+    mainly along the x-axis (with possible small vertical shift). In the rectified images, the
+    corresponding epipolar lines in the left and right cameras are horizontal and have the same
+    y-coordinate. P1 and P2 look like:
+
+    \f[\texttt{P1} = \begin{bmatrix}
+                        f & 0 & cx_1 & 0 \\
+                        0 & f & cy & 0 \\
+                        0 & 0 & 1 & 0
+                     \end{bmatrix}\f]
+
+    \f[\texttt{P2} = \begin{bmatrix}
+                        f & 0 & cx_2 & T_x*f \\
+                        0 & f & cy & 0 \\
+                        0 & 0 & 1 & 0
+                     \end{bmatrix} ,\f]
+
+    where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if
+    @ref CALIB_ZERO_DISPARITY is set.
+
+-   **Vertical stereo**: the first and the second camera views are shifted relative to each other
+    mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar
+    lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:
+
+    \f[\texttt{P1} = \begin{bmatrix}
+                        f & 0 & cx & 0 \\
+                        0 & f & cy_1 & 0 \\
+                        0 & 0 & 1 & 0
+                     \end{bmatrix}\f]
+
+    \f[\texttt{P2} = \begin{bmatrix}
+                        f & 0 & cx & 0 \\
+                        0 & f & cy_2 & T_y*f \\
+                        0 & 0 & 1 & 0
+                     \end{bmatrix},\f]
+
+    where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if
+    @ref CALIB_ZERO_DISPARITY is set.
+
+As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera
+matrices. The matrices, together with R1 and R2 , can then be passed to #initUndistortRectifyMap to
+initialize the rectification map for each camera.
+
+See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through
+the corresponding image regions. This means that the images are well rectified, which is what most
+stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that
+their interiors are all valid pixels.
+
+![image](pics/stereo_undistort.jpg)
+ */
+CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1,
+                                 InputArray cameraMatrix2, InputArray distCoeffs2,
+                                 Size imageSize, InputArray R, InputArray T,
+                                 OutputArray R1, OutputArray R2,
+                                 OutputArray P1, OutputArray P2,
+                                 OutputArray Q, int flags = CALIB_ZERO_DISPARITY,
+                                 double alpha = -1, Size newImageSize = Size(),
+                                 CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 );
+
+/** @brief Computes a rectification transform for an uncalibrated stereo camera.
+
+@param points1 Array of feature points in the first image.
+@param points2 The corresponding points in the second image. The same formats as in
+#findFundamentalMat are supported.
+@param F Input fundamental matrix. It can be computed from the same set of point pairs using
+#findFundamentalMat .
+@param imgSize Size of the image.
+@param H1 Output rectification homography matrix for the first image.
+@param H2 Output rectification homography matrix for the second image.
+@param threshold Optional threshold used to filter out the outliers. If the parameter is greater
+than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
+for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are
+rejected prior to computing the homographies. Otherwise, all the points are considered inliers.
+
+The function computes the rectification transformations without knowing intrinsic parameters of the
+cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
+related difference from #stereoRectify is that the function outputs not the rectification
+transformations in the object (3D) space, but the planar perspective transformations encoded by the
+homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 .
+
+@note
+   While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
+    depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
+    it would be better to correct it before computing the fundamental matrix and calling this
+    function. For example, distortion coefficients can be estimated for each head of stereo camera
+    separately by using #calibrateCamera . Then, the images can be corrected using #undistort , or
+    just the point coordinates can be corrected with #undistortPoints .
+ */
+CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2,
+                                             InputArray F, Size imgSize,
+                                             OutputArray H1, OutputArray H2,
+                                             double threshold = 5 );
+
+//! computes the rectification transformations for 3-head camera, where all the heads are on the same line.
+CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1,
+                                      InputArray cameraMatrix2, InputArray distCoeffs2,
+                                      InputArray cameraMatrix3, InputArray distCoeffs3,
+                                      InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3,
+                                      Size imageSize, InputArray R12, InputArray T12,
+                                      InputArray R13, InputArray T13,
+                                      OutputArray R1, OutputArray R2, OutputArray R3,
+                                      OutputArray P1, OutputArray P2, OutputArray P3,
+                                      OutputArray Q, double alpha, Size newImgSize,
+                                      CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags );
+
+/** @brief Returns the new camera intrinsic matrix based on the free scaling parameter.
+
+@param cameraMatrix Input camera intrinsic matrix.
+@param distCoeffs Input vector of distortion coefficients
+\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are
+assumed.
+@param imageSize Original image size.
+@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are
+valid) and 1 (when all the source image pixels are retained in the undistorted image). See
+#stereoRectify for details.
+@param newImgSize Image size after rectification. By default, it is set to imageSize .
+@param validPixROI Optional output rectangle that outlines all-good-pixels region in the
+undistorted image. See roi1, roi2 description in #stereoRectify .
+@param centerPrincipalPoint Optional flag that indicates whether in the new camera intrinsic matrix the
+principal point should be at the image center or not. By default, the principal point is chosen to
+best fit a subset of the source image (determined by alpha) to the corrected image.
+@return new_camera_matrix Output new camera intrinsic matrix.
+
+The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter.
+By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original
+image pixels if there is valuable information in the corners alpha=1 , or get something in between.
+When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to
+"virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion
+coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to
+#initUndistortRectifyMap to produce the maps for #remap .
+ */
+CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs,
+                                            Size imageSize, double alpha, Size newImgSize = Size(),
+                                            CV_OUT Rect* validPixROI = 0,
+                                            bool centerPrincipalPoint = false);
+
+/** @brief Computes Hand-Eye calibration: \f$_{}^{g}\textrm{T}_c\f$
+
+@param[in] R_gripper2base Rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$).
+This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
+for all the transformations from gripper frame to robot base frame.
+@param[in] t_gripper2base Translation part extracted from the homogeneous matrix that transforms a point
+expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$).
+This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
+from gripper frame to robot base frame.
+@param[in] R_target2cam Rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$).
+This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
+for all the transformations from calibration target frame to camera frame.
+@param[in] t_target2cam Rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$).
+This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
+from calibration target frame to camera frame.
+@param[out] R_cam2gripper Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$).
+@param[out] t_cam2gripper Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
+expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$).
+@param[in] method One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod
+
+The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the
+rotation then the translation (separable solutions) and the following methods are implemented:
+  - R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89
+  - F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94
+  - R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95
+
+Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
+with the following implemented methods:
+  - N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99
+  - K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98
+
+The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye")
+mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.
+
+The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot
+end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting
+the suitable transformations to the function, see below.
+
+![](pics/hand-eye_figure.png)
+
+The calibration procedure is the following:
+  - a static calibration pattern is used to estimate the transformation between the target frame
+  and the camera frame
+  - the robot gripper is moved in order to acquire several poses
+  - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
+  instance the robot kinematics
+\f[
+    \begin{bmatrix}
+    X_b\\
+    Y_b\\
+    Z_b\\
+    1
+    \end{bmatrix}
+    =
+    \begin{bmatrix}
+    _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\
+    0_{1 \times 3} & 1
+    \end{bmatrix}
+    \begin{bmatrix}
+    X_g\\
+    Y_g\\
+    Z_g\\
+    1
+    \end{bmatrix}
+\f]
+  - for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using
+  for instance a pose estimation method (PnP) from 2D-3D point correspondences
+\f[
+    \begin{bmatrix}
+    X_c\\
+    Y_c\\
+    Z_c\\
+    1
+    \end{bmatrix}
+    =
+    \begin{bmatrix}
+    _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\
+    0_{1 \times 3} & 1
+    \end{bmatrix}
+    \begin{bmatrix}
+    X_t\\
+    Y_t\\
+    Z_t\\
+    1
+    \end{bmatrix}
+\f]
+
+The Hand-Eye calibration procedure returns the following homogeneous transformation
+\f[
+    \begin{bmatrix}
+    X_g\\
+    Y_g\\
+    Z_g\\
+    1
+    \end{bmatrix}
+    =
+    \begin{bmatrix}
+    _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\
+    0_{1 \times 3} & 1
+    \end{bmatrix}
+    \begin{bmatrix}
+    X_c\\
+    Y_c\\
+    Z_c\\
+    1
+    \end{bmatrix}
+\f]
+
+This problem is also known as solving the \f$\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\f$ equation:
+  - for an eye-in-hand configuration
+\f[
+    \begin{align*}
+    ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &=
+    \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\
+
+    (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &=
+    \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\
+
+    \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\
+    \end{align*}
+\f]
+
+  - for an eye-to-hand configuration
+\f[
+    \begin{align*}
+    ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &=
+    \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\
+
+    (^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &=
+    \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\
+
+    \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\
+    \end{align*}
+\f]
+
+\note
+Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration).
+\note
+A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation.
+So at least 3 different poses are required, but it is strongly recommended to use many more poses.
+
+ */
+CV_EXPORTS_W void calibrateHandEye( InputArrayOfArrays R_gripper2base, InputArrayOfArrays t_gripper2base,
+                                    InputArrayOfArrays R_target2cam, InputArrayOfArrays t_target2cam,
+                                    OutputArray R_cam2gripper, OutputArray t_cam2gripper,
+                                    HandEyeCalibrationMethod method=CALIB_HAND_EYE_TSAI );
+
+/** @brief Computes Robot-World/Hand-Eye calibration: \f$_{}^{w}\textrm{T}_b\f$ and \f$_{}^{c}\textrm{T}_g\f$
+
+@param[in] R_world2cam Rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the world frame to the camera frame (\f$_{}^{c}\textrm{T}_w\f$).
+This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
+for all the transformations from world frame to the camera frame.
+@param[in] t_world2cam Translation part extracted from the homogeneous matrix that transforms a point
+expressed in the world frame to the camera frame (\f$_{}^{c}\textrm{T}_w\f$).
+This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
+from world frame to the camera frame.
+@param[in] R_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the robot base frame to the gripper frame (\f$_{}^{g}\textrm{T}_b\f$).
+This is a vector (`vector<Mat>`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors,
+for all the transformations from robot base frame to the gripper frame.
+@param[in] t_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the robot base frame to the gripper frame (\f$_{}^{g}\textrm{T}_b\f$).
+This is a vector (`vector<Mat>`) that contains the `(3x1)` translation vectors for all the transformations
+from robot base frame to the gripper frame.
+@param[out] R_base2world Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the robot base frame to the world frame (\f$_{}^{w}\textrm{T}_b\f$).
+@param[out] t_base2world Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
+expressed in the robot base frame to the world frame (\f$_{}^{w}\textrm{T}_b\f$).
+@param[out] R_gripper2cam Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point
+expressed in the gripper frame to the camera frame (\f$_{}^{c}\textrm{T}_g\f$).
+@param[out] t_gripper2cam Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point
+expressed in the gripper frame to the camera frame (\f$_{}^{c}\textrm{T}_g\f$).
+@param[in] method One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod
+
+The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the
+rotation then the translation (separable solutions):
+  - M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product \cite Shah2013SolvingTR
+
+Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions),
+with the following implemented method:
+  - A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product \cite Li2010SimultaneousRA
+
+The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame
+and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.
+
+![](pics/robot-world_hand-eye_figure.png)
+
+The calibration procedure is the following:
+  - a static calibration pattern is used to estimate the transformation between the target frame
+  and the camera frame
+  - the robot gripper is moved in order to acquire several poses
+  - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for
+  instance the robot kinematics
+\f[
+    \begin{bmatrix}
+    X_g\\
+    Y_g\\
+    Z_g\\
+    1
+    \end{bmatrix}
+    =
+    \begin{bmatrix}
+    _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\
+    0_{1 \times 3} & 1
+    \end{bmatrix}
+    \begin{bmatrix}
+    X_b\\
+    Y_b\\
+    Z_b\\
+    1
+    \end{bmatrix}
+\f]
+  - for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using
+  for instance a pose estimation method (PnP) from 2D-3D point correspondences
+\f[
+    \begin{bmatrix}
+    X_c\\
+    Y_c\\
+    Z_c\\
+    1
+    \end{bmatrix}
+    =
+    \begin{bmatrix}
+    _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\
+    0_{1 \times 3} & 1
+    \end{bmatrix}
+    \begin{bmatrix}
+    X_w\\
+    Y_w\\
+    Z_w\\
+    1
+    \end{bmatrix}
+\f]
+
+The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations
+\f[
+    \begin{bmatrix}
+    X_w\\
+    Y_w\\
+    Z_w\\
+    1
+    \end{bmatrix}
+    =
+    \begin{bmatrix}
+    _{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\
+    0_{1 \times 3} & 1
+    \end{bmatrix}
+    \begin{bmatrix}
+    X_b\\
+    Y_b\\
+    Z_b\\
+    1
+    \end{bmatrix}
+\f]
+\f[
+    \begin{bmatrix}
+    X_c\\
+    Y_c\\
+    Z_c\\
+    1
+    \end{bmatrix}
+    =
+    \begin{bmatrix}
+    _{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\
+    0_{1 \times 3} & 1
+    \end{bmatrix}
+    \begin{bmatrix}
+    X_g\\
+    Y_g\\
+    Z_g\\
+    1
+    \end{bmatrix}
+\f]
+
+This problem is also known as solving the \f$\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}\f$ equation, with:
+  - \f$\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w\f$
+  - \f$\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b\f$
+  - \f$\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g\f$
+  - \f$\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b\f$
+
+\note
+At least 3 measurements are required (input vectors size must be greater or equal to 3).
+
+ */
+CV_EXPORTS_W void calibrateRobotWorldHandEye( InputArrayOfArrays R_world2cam, InputArrayOfArrays t_world2cam,
+                                              InputArrayOfArrays R_base2gripper, InputArrayOfArrays t_base2gripper,
+                                              OutputArray R_base2world, OutputArray t_base2world,
+                                              OutputArray R_gripper2cam, OutputArray t_gripper2cam,
+                                              RobotWorldHandEyeCalibrationMethod method=CALIB_ROBOT_WORLD_HAND_EYE_SHAH );
+
+/** @brief Converts points from Euclidean to homogeneous space.
+
+@param src Input vector of N-dimensional points.
+@param dst Output vector of N+1-dimensional points.
+
+The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of
+point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).
+ */
+CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst );
+
+/** @brief Converts points from homogeneous to Euclidean space.
+
+@param src Input vector of N-dimensional points.
+@param dst Output vector of N-1-dimensional points.
+
+The function converts points homogeneous to Euclidean space using perspective projection. That is,
+each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the
+output point coordinates will be (0,0,0,...).
+ */
+CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst );
+
+/** @brief Converts points to/from homogeneous coordinates.
+
+@param src Input array or vector of 2D, 3D, or 4D points.
+@param dst Output vector of 2D, 3D, or 4D points.
+
+The function converts 2D or 3D points from/to homogeneous coordinates by calling either
+#convertPointsToHomogeneous or #convertPointsFromHomogeneous.
+
+@note The function is obsolete. Use one of the previous two functions instead.
+ */
+CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst );
+
+/** @brief Calculates a fundamental matrix from the corresponding points in two images.
+
+@param points1 Array of N points from the first image. The point coordinates should be
+floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1 .
+@param method Method for computing a fundamental matrix.
+-   @ref FM_7POINT for a 7-point algorithm. \f$N = 7\f$
+-   @ref FM_8POINT for an 8-point algorithm. \f$N \ge 8\f$
+-   @ref FM_RANSAC for the RANSAC algorithm. \f$N \ge 8\f$
+-   @ref FM_LMEDS for the LMedS algorithm. \f$N \ge 8\f$
+@param ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
+line in pixels, beyond which the point is considered an outlier and is not used for computing the
+final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
+point localization, image resolution, and the image noise.
+@param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
+of confidence (probability) that the estimated matrix is correct.
+@param[out] mask optional output mask
+@param maxIters The maximum number of robust method iterations.
+
+The epipolar geometry is described by the following equation:
+
+\f[[p_2; 1]^T F [p_1; 1] = 0\f]
+
+where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the
+second images, respectively.
+
+The function calculates the fundamental matrix using one of four methods listed above and returns
+the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
+algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3
+matrices sequentially).
+
+The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the
+epipolar lines corresponding to the specified points. It can also be passed to
+#stereoRectifyUncalibrated to compute the rectification transformation. :
+@code
+    // Example. Estimation of fundamental matrix using the RANSAC algorithm
+    int point_count = 100;
+    vector<Point2f> points1(point_count);
+    vector<Point2f> points2(point_count);
+
+    // initialize the points here ...
+    for( int i = 0; i < point_count; i++ )
+    {
+        points1[i] = ...;
+        points2[i] = ...;
+    }
+
+    Mat fundamental_matrix =
+     findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
+@endcode
+ */
+CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,
+                                     int method, double ransacReprojThreshold, double confidence,
+                                     int maxIters, OutputArray mask = noArray() );
+
+/** @overload */
+CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,
+                                     int method = FM_RANSAC,
+                                     double ransacReprojThreshold = 3., double confidence = 0.99,
+                                     OutputArray mask = noArray() );
+
+/** @overload */
+CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2,
+                                   OutputArray mask, int method = FM_RANSAC,
+                                   double ransacReprojThreshold = 3., double confidence = 0.99 );
+
+
+CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,
+                        OutputArray mask, const UsacParams &params);
+
+/** @brief Calculates an essential matrix from the corresponding points in two images.
+
+@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
+be floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1 .
+@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+Note that this function assumes that points1 and points2 are feature points from cameras with the
+same camera intrinsic matrix. If this assumption does not hold for your use case, use
+#undistortPoints with `P = cv::NoArray()` for both cameras to transform image points
+to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When
+passing these coordinates, pass the identity matrix for this parameter.
+@param method Method for computing an essential matrix.
+-   @ref RANSAC for the RANSAC algorithm.
+-   @ref LMEDS for the LMedS algorithm.
+@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
+confidence (probability) that the estimated matrix is correct.
+@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
+line in pixels, beyond which the point is considered an outlier and is not used for computing the
+final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
+point localization, image resolution, and the image noise.
+@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
+for the other points. The array is computed only in the RANSAC and LMedS methods.
+@param maxIters The maximum number of robust method iterations.
+
+This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 .
+@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
+
+\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f]
+
+where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the
+second images, respectively. The result of this function may be passed further to
+#decomposeEssentialMat or  #recoverPose to recover the relative pose between cameras.
+ */
+CV_EXPORTS_W
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    InputArray cameraMatrix, int method = RANSAC,
+    double prob = 0.999, double threshold = 1.0,
+    int maxIters = 1000, OutputArray mask = noArray()
+);
+
+/** @overload */
+CV_EXPORTS
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    InputArray cameraMatrix, int method,
+    double prob, double threshold,
+    OutputArray mask
+);  // TODO remove from OpenCV 5.0
+
+/** @overload
+@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
+be floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1 .
+@param focal focal length of the camera. Note that this function assumes that points1 and points2
+are feature points from cameras with same focal length and principal point.
+@param pp principal point of the camera.
+@param method Method for computing a fundamental matrix.
+-   @ref RANSAC for the RANSAC algorithm.
+-   @ref LMEDS for the LMedS algorithm.
+@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
+line in pixels, beyond which the point is considered an outlier and is not used for computing the
+final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
+point localization, image resolution, and the image noise.
+@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
+confidence (probability) that the estimated matrix is correct.
+@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
+for the other points. The array is computed only in the RANSAC and LMedS methods.
+@param maxIters The maximum number of robust method iterations.
+
+This function differs from the one above that it computes camera intrinsic matrix from focal length and
+principal point:
+
+\f[A =
+\begin{bmatrix}
+f & 0 & x_{pp}  \\
+0 & f & y_{pp}  \\
+0 & 0 & 1
+\end{bmatrix}\f]
+ */
+CV_EXPORTS_W
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    double focal = 1.0, Point2d pp = Point2d(0, 0),
+    int method = RANSAC, double prob = 0.999,
+    double threshold = 1.0, int maxIters = 1000,
+    OutputArray mask = noArray()
+);
+
+/** @overload */
+CV_EXPORTS
+Mat findEssentialMat(
+    InputArray points1, InputArray points2,
+    double focal, Point2d pp,
+    int method, double prob,
+    double threshold, OutputArray mask
+);  // TODO remove from OpenCV 5.0
+
+/** @brief Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.
+
+@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
+be floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1 .
+@param cameraMatrix1 Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
+Note that this function assumes that points1 and points2 are feature points from cameras with the
+same camera matrix. If this assumption does not hold for your use case, use
+#undistortPoints with `P = cv::NoArray()` for both cameras to transform image points
+to normalized image coordinates, which are valid for the identity camera matrix. When
+passing these coordinates, pass the identity matrix for this parameter.
+@param cameraMatrix2 Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
+Note that this function assumes that points1 and points2 are feature points from cameras with the
+same camera matrix. If this assumption does not hold for your use case, use
+#undistortPoints with `P = cv::NoArray()` for both cameras to transform image points
+to normalized image coordinates, which are valid for the identity camera matrix. When
+passing these coordinates, pass the identity matrix for this parameter.
+@param distCoeffs1 Input vector of distortion coefficients
+\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
+@param distCoeffs2 Input vector of distortion coefficients
+\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
+@param method Method for computing an essential matrix.
+-   @ref RANSAC for the RANSAC algorithm.
+-   @ref LMEDS for the LMedS algorithm.
+@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
+confidence (probability) that the estimated matrix is correct.
+@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
+line in pixels, beyond which the point is considered an outlier and is not used for computing the
+final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
+point localization, image resolution, and the image noise.
+@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
+for the other points. The array is computed only in the RANSAC and LMedS methods.
+
+This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 .
+@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
+
+\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f]
+
+where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the
+second images, respectively. The result of this function may be passed further to
+#decomposeEssentialMat or  #recoverPose to recover the relative pose between cameras.
+ */
+CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
+                                 InputArray cameraMatrix1, InputArray distCoeffs1,
+                                 InputArray cameraMatrix2, InputArray distCoeffs2,
+                                 int method = RANSAC,
+                                 double prob = 0.999, double threshold = 1.0,
+                                 OutputArray mask = noArray() );
+
+
+CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
+                      InputArray cameraMatrix1, InputArray cameraMatrix2,
+                      InputArray dist_coeff1, InputArray dist_coeff2, OutputArray mask,
+                      const UsacParams &params);
+
+/** @brief Decompose an essential matrix to possible rotations and translation.
+
+@param E The input essential matrix.
+@param R1 One possible rotation matrix.
+@param R2 Another possible rotation matrix.
+@param t One possible translation.
+
+This function decomposes the essential matrix E using svd decomposition @cite HartleyZ00. In
+general, four possible poses exist for the decomposition of E. They are \f$[R_1, t]\f$,
+\f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$.
+
+If E gives the epipolar constraint \f$[p_2; 1]^T A^{-T} E A^{-1} [p_1; 1] = 0\f$ between the image
+points \f$p_1\f$ in the first image and \f$p_2\f$ in second image, then any of the tuples
+\f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$ is a change of basis from the first
+camera's coordinate system to the second camera's coordinate system. However, by decomposing E, one
+can only get the direction of the translation. For this reason, the translation t is returned with
+unit length.
+ */
+CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t );
+
+/** @brief Recovers the relative camera rotation and the translation from corresponding points in two images from two different cameras, using cheirality check. Returns the number of
+inliers that pass the check.
+
+@param points1 Array of N 2D points from the first image. The point coordinates should be
+floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1 .
+@param cameraMatrix1 Input/output camera matrix for the first camera, the same as in
+@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
+@param distCoeffs1 Input/output vector of distortion coefficients, the same as in
+@ref calibrateCamera.
+@param cameraMatrix2 Input/output camera matrix for the first camera, the same as in
+@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
+@param distCoeffs2 Input/output vector of distortion coefficients, the same as in
+@ref calibrateCamera.
+@param E The output essential matrix.
+@param R Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
+that performs a change of basis from the first camera's coordinate system to the second camera's
+coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
+described below.
+@param t Output translation vector. This vector is obtained by @ref decomposeEssentialMat and
+therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
+length.
+@param method Method for computing an essential matrix.
+-   @ref RANSAC for the RANSAC algorithm.
+-   @ref LMEDS for the LMedS algorithm.
+@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
+confidence (probability) that the estimated matrix is correct.
+@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
+line in pixels, beyond which the point is considered an outlier and is not used for computing the
+final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
+point localization, image resolution, and the image noise.
+@param mask Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
+inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to
+recover pose. In the output mask only inliers which pass the cheirality check.
+
+This function decomposes an essential matrix using @ref decomposeEssentialMat and then verifies
+possible pose hypotheses by doing cheirality check. The cheirality check means that the
+triangulated 3D points should have positive depth. Some details can be found in @cite Nister03.
+
+This function can be used to process the output E and mask from @ref findEssentialMat. In this
+scenario, points1 and points2 are the same input for findEssentialMat.:
+@code
+    // Example. Estimation of fundamental matrix using the RANSAC algorithm
+    int point_count = 100;
+    vector<Point2f> points1(point_count);
+    vector<Point2f> points2(point_count);
+
+    // initialize the points here ...
+    for( int i = 0; i < point_count; i++ )
+    {
+        points1[i] = ...;
+        points2[i] = ...;
+    }
+
+    // Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
+    Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;
+
+    // Output: Essential matrix, relative rotation and relative translation.
+    Mat E, R, t, mask;
+
+    recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);
+@endcode
+ */
+CV_EXPORTS_W int recoverPose( InputArray points1, InputArray points2,
+                            InputArray cameraMatrix1, InputArray distCoeffs1,
+                            InputArray cameraMatrix2, InputArray distCoeffs2,
+                            OutputArray E, OutputArray R, OutputArray t,
+                            int method = cv::RANSAC, double prob = 0.999, double threshold = 1.0,
+                            InputOutputArray mask = noArray());
+
+/** @brief Recovers the relative camera rotation and the translation from an estimated essential
+matrix and the corresponding points in two images, using cheirality check. Returns the number of
+inliers that pass the check.
+
+@param E The input essential matrix.
+@param points1 Array of N 2D points from the first image. The point coordinates should be
+floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1 .
+@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+Note that this function assumes that points1 and points2 are feature points from cameras with the
+same camera intrinsic matrix.
+@param R Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
+that performs a change of basis from the first camera's coordinate system to the second camera's
+coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
+described below.
+@param t Output translation vector. This vector is obtained by @ref decomposeEssentialMat and
+therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
+length.
+@param mask Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
+inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to
+recover pose. In the output mask only inliers which pass the cheirality check.
+
+This function decomposes an essential matrix using @ref decomposeEssentialMat and then verifies
+possible pose hypotheses by doing cheirality check. The cheirality check means that the
+triangulated 3D points should have positive depth. Some details can be found in @cite Nister03.
+
+This function can be used to process the output E and mask from @ref findEssentialMat. In this
+scenario, points1 and points2 are the same input for #findEssentialMat :
+@code
+    // Example. Estimation of fundamental matrix using the RANSAC algorithm
+    int point_count = 100;
+    vector<Point2f> points1(point_count);
+    vector<Point2f> points2(point_count);
+
+    // initialize the points here ...
+    for( int i = 0; i < point_count; i++ )
+    {
+        points1[i] = ...;
+        points2[i] = ...;
+    }
+
+    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
+    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
+
+    Mat E, R, t, mask;
+
+    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
+    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
+@endcode
+ */
+CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2,
+                            InputArray cameraMatrix, OutputArray R, OutputArray t,
+                            InputOutputArray mask = noArray() );
+
+/** @overload
+@param E The input essential matrix.
+@param points1 Array of N 2D points from the first image. The point coordinates should be
+floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1 .
+@param R Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
+that performs a change of basis from the first camera's coordinate system to the second camera's
+coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
+description below.
+@param t Output translation vector. This vector is obtained by @ref decomposeEssentialMat and
+therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
+length.
+@param focal Focal length of the camera. Note that this function assumes that points1 and points2
+are feature points from cameras with same focal length and principal point.
+@param pp principal point of the camera.
+@param mask Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
+inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to
+recover pose. In the output mask only inliers which pass the cheirality check.
+
+This function differs from the one above that it computes camera intrinsic matrix from focal length and
+principal point:
+
+\f[A =
+\begin{bmatrix}
+f & 0 & x_{pp}  \\
+0 & f & y_{pp}  \\
+0 & 0 & 1
+\end{bmatrix}\f]
+ */
+CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2,
+                            OutputArray R, OutputArray t,
+                            double focal = 1.0, Point2d pp = Point2d(0, 0),
+                            InputOutputArray mask = noArray() );
+
+/** @overload
+@param E The input essential matrix.
+@param points1 Array of N 2D points from the first image. The point coordinates should be
+floating-point (single or double precision).
+@param points2 Array of the second image points of the same size and format as points1.
+@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ .
+Note that this function assumes that points1 and points2 are feature points from cameras with the
+same camera intrinsic matrix.
+@param R Output rotation matrix. Together with the translation vector, this matrix makes up a tuple
+that performs a change of basis from the first camera's coordinate system to the second camera's
+coordinate system. Note that, in general, t can not be used for this tuple, see the parameter
+description below.
+@param t Output translation vector. This vector is obtained by @ref decomposeEssentialMat and
+therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit
+length.
+@param distanceThresh threshold distance which is used to filter out far away points (i.e. infinite
+points).
+@param mask Input/output mask for inliers in points1 and points2. If it is not empty, then it marks
+inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to
+recover pose. In the output mask only inliers which pass the cheirality check.
+@param triangulatedPoints 3D points which were reconstructed by triangulation.
+
+This function differs from the one above that it outputs the triangulated 3D point that are used for
+the cheirality check.
+ */
+CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2,
+                            InputArray cameraMatrix, OutputArray R, OutputArray t, double distanceThresh, InputOutputArray mask = noArray(),
+                            OutputArray triangulatedPoints = noArray());
+
+/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image.
+
+@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or
+vector\<Point2f\> .
+@param whichImage Index of the image (1 or 2) that contains the points .
+@param F Fundamental matrix that can be estimated using #findFundamentalMat or #stereoRectify .
+@param lines Output vector of the epipolar lines corresponding to the points in the other image.
+Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ .
+
+For every point in one of the two images of a stereo pair, the function finds the equation of the
+corresponding epipolar line in the other image.
+
+From the fundamental matrix definition (see #findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second
+image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as:
+
+\f[l^{(2)}_i = F p^{(1)}_i\f]
+
+And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as:
+
+\f[l^{(1)}_i = F^T p^{(2)}_i\f]
+
+Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ .
+ */
+CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage,
+                                             InputArray F, OutputArray lines );
+
+/** @brief This function reconstructs 3-dimensional points (in homogeneous coordinates) by using
+their observations with a stereo camera.
+
+@param projMatr1 3x4 projection matrix of the first camera, i.e. this matrix projects 3D points
+given in the world's coordinate system into the first image.
+@param projMatr2 3x4 projection matrix of the second camera, i.e. this matrix projects 3D points
+given in the world's coordinate system into the second image.
+@param projPoints1 2xN array of feature points in the first image. In the case of the c++ version,
+it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
+@param projPoints2 2xN array of corresponding points in the second image. In the case of the c++
+version, it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
+@param points4D 4xN array of reconstructed points in homogeneous coordinates. These points are
+returned in the world's coordinate system.
+
+@note
+   Keep in mind that all input data should be of float type in order for this function to work.
+
+@note
+   If the projection matrices from @ref stereoRectify are used, then the returned points are
+   represented in the first camera's rectified coordinate system.
+
+@sa
+   reprojectImageTo3D
+ */
+CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2,
+                                     InputArray projPoints1, InputArray projPoints2,
+                                     OutputArray points4D );
+
+/** @brief Refines coordinates of corresponding points.
+
+@param F 3x3 fundamental matrix.
+@param points1 1xN array containing the first set of points.
+@param points2 1xN array containing the second set of points.
+@param newPoints1 The optimized points1.
+@param newPoints2 The optimized points2.
+
+The function implements the Optimal Triangulation Method (see Multiple View Geometry for details).
+For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it
+computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric
+error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the
+geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint
+\f$newPoints2^T * F * newPoints1 = 0\f$ .
+ */
+CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2,
+                                  OutputArray newPoints1, OutputArray newPoints2 );
+
+/** @brief Filters off small noise blobs (speckles) in the disparity map
+
+@param img The input 16-bit signed disparity image
+@param newVal The disparity value used to paint-off the speckles
+@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not
+affected by the algorithm
+@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same
+blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
+disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
+account when specifying this parameter value.
+@param buf The optional temporary buffer to avoid memory allocation within the function.
+ */
+CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal,
+                                  int maxSpeckleSize, double maxDiff,
+                                  InputOutputArray buf = noArray() );
+
+//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by #stereoRectify)
+CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2,
+                                        int minDisparity, int numberOfDisparities,
+                                        int blockSize );
+
+//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm
+CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost,
+                                     int minDisparity, int numberOfDisparities,
+                                     int disp12MaxDisp = 1 );
+
+/** @brief Reprojects a disparity image to 3D space.
+
+@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
+floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no
+fractional bits. If the disparity is 16-bit signed format, as computed by @ref StereoBM or
+@ref StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before
+being used here.
+@param _3dImage Output 3-channel floating-point image of the same size as disparity. Each element of
+_3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one
+uses Q obtained by @ref stereoRectify, then the returned points are represented in the first
+camera's rectified coordinate system.
+@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with
+@ref stereoRectify.
+@param handleMissingValues Indicates, whether the function should handle missing values (i.e.
+points where the disparity was not computed). If handleMissingValues=true, then pixels with the
+minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
+to 3D points with a very large Z value (currently set to 10000).
+@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F
+depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
+
+The function transforms a single-channel disparity map to a 3-channel image representing a 3D
+surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
+computes:
+
+\f[\begin{bmatrix}
+X \\
+Y \\
+Z \\
+W
+\end{bmatrix} = Q \begin{bmatrix}
+x \\
+y \\
+\texttt{disparity} (x,y) \\
+z
+\end{bmatrix}.\f]
+
+@sa
+   To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.
+ */
+CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity,
+                                      OutputArray _3dImage, InputArray Q,
+                                      bool handleMissingValues = false,
+                                      int ddepth = -1 );
+
+/** @brief Calculates the Sampson Distance between two points.
+
+The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as:
+\f[
+sd( \texttt{pt1} , \texttt{pt2} )=
+\frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2}
+{((\texttt{F} \cdot \texttt{pt1})(0))^2 +
+((\texttt{F} \cdot \texttt{pt1})(1))^2 +
+((\texttt{F}^t \cdot \texttt{pt2})(0))^2 +
+((\texttt{F}^t \cdot \texttt{pt2})(1))^2}
+\f]
+The fundamental matrix may be calculated using the #findFundamentalMat function. See @cite HartleyZ00 11.4.3 for details.
+@param pt1 first homogeneous 2d point
+@param pt2 second homogeneous 2d point
+@param F fundamental matrix
+@return The computed Sampson distance.
+*/
+CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F);
+
+/** @brief Computes an optimal affine transformation between two 3D point sets.
+
+It computes
+\f[
+\begin{bmatrix}
+x\\
+y\\
+z\\
+\end{bmatrix}
+=
+\begin{bmatrix}
+a_{11} & a_{12} & a_{13}\\
+a_{21} & a_{22} & a_{23}\\
+a_{31} & a_{32} & a_{33}\\
+\end{bmatrix}
+\begin{bmatrix}
+X\\
+Y\\
+Z\\
+\end{bmatrix}
++
+\begin{bmatrix}
+b_1\\
+b_2\\
+b_3\\
+\end{bmatrix}
+\f]
+
+@param src First input 3D point set containing \f$(X,Y,Z)\f$.
+@param dst Second input 3D point set containing \f$(x,y,z)\f$.
+@param out Output 3D affine transformation matrix \f$3 \times 4\f$ of the form
+\f[
+\begin{bmatrix}
+a_{11} & a_{12} & a_{13} & b_1\\
+a_{21} & a_{22} & a_{23} & b_2\\
+a_{31} & a_{32} & a_{33} & b_3\\
+\end{bmatrix}
+\f]
+@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier).
+@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as
+an inlier.
+@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
+between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
+significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
+
+The function estimates an optimal 3D affine transformation between two 3D point sets using the
+RANSAC algorithm.
+ */
+CV_EXPORTS_W  int estimateAffine3D(InputArray src, InputArray dst,
+                                   OutputArray out, OutputArray inliers,
+                                   double ransacThreshold = 3, double confidence = 0.99);
+
+/** @brief Computes an optimal affine transformation between two 3D point sets.
+
+It computes \f$R,s,t\f$ minimizing \f$\sum{i} dst_i - c \cdot R \cdot src_i \f$
+where \f$R\f$ is a 3x3 rotation matrix, \f$t\f$ is a 3x1 translation vector and \f$s\f$ is a
+scalar size value. This is an implementation of the algorithm by Umeyama \cite umeyama1991least .
+The estimated affine transform has a homogeneous scale which is a subclass of affine
+transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3
+points each.
+
+@param src First input 3D point set.
+@param dst Second input 3D point set.
+@param scale If null is passed, the scale parameter c will be assumed to be 1.0.
+Else the pointed-to variable will be set to the optimal scale.
+@param force_rotation If true, the returned rotation will never be a reflection.
+This might be unwanted, e.g. when optimizing a transform between a right- and a
+left-handed coordinate system.
+@return 3D affine transformation matrix \f$3 \times 4\f$ of the form
+\f[T =
+\begin{bmatrix}
+R & t\\
+\end{bmatrix}
+\f]
+
+ */
+CV_EXPORTS_W   cv::Mat estimateAffine3D(InputArray src, InputArray dst,
+                                        CV_OUT double* scale = nullptr, bool force_rotation = true);
+
+/** @brief Computes an optimal translation between two 3D point sets.
+ *
+ * It computes
+ * \f[
+ * \begin{bmatrix}
+ * x\\
+ * y\\
+ * z\\
+ * \end{bmatrix}
+ * =
+ * \begin{bmatrix}
+ * X\\
+ * Y\\
+ * Z\\
+ * \end{bmatrix}
+ * +
+ * \begin{bmatrix}
+ * b_1\\
+ * b_2\\
+ * b_3\\
+ * \end{bmatrix}
+ * \f]
+ *
+ * @param src First input 3D point set containing \f$(X,Y,Z)\f$.
+ * @param dst Second input 3D point set containing \f$(x,y,z)\f$.
+ * @param out Output 3D translation vector \f$3 \times 1\f$ of the form
+ * \f[
+ * \begin{bmatrix}
+ * b_1 \\
+ * b_2 \\
+ * b_3 \\
+ * \end{bmatrix}
+ * \f]
+ * @param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier).
+ * @param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as
+ * an inlier.
+ * @param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
+ * between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
+ * significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
+ *
+ * The function estimates an optimal 3D translation between two 3D point sets using the
+ * RANSAC algorithm.
+ *  */
+CV_EXPORTS_W  int estimateTranslation3D(InputArray src, InputArray dst,
+                                        OutputArray out, OutputArray inliers,
+                                        double ransacThreshold = 3, double confidence = 0.99);
+
+/** @brief Computes an optimal affine transformation between two 2D point sets.
+
+It computes
+\f[
+\begin{bmatrix}
+x\\
+y\\
+\end{bmatrix}
+=
+\begin{bmatrix}
+a_{11} & a_{12}\\
+a_{21} & a_{22}\\
+\end{bmatrix}
+\begin{bmatrix}
+X\\
+Y\\
+\end{bmatrix}
++
+\begin{bmatrix}
+b_1\\
+b_2\\
+\end{bmatrix}
+\f]
+
+@param from First input 2D point set containing \f$(X,Y)\f$.
+@param to Second input 2D point set containing \f$(x,y)\f$.
+@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier).
+@param method Robust method used to compute transformation. The following methods are possible:
+-   @ref RANSAC - RANSAC-based robust method
+-   @ref LMEDS - Least-Median robust method
+RANSAC is the default method.
+@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
+a point as an inlier. Applies only to RANSAC.
+@param maxIters The maximum number of robust method iterations.
+@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
+between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
+significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
+@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
+Passing 0 will disable refining, so the output matrix will be output of robust method.
+
+@return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation
+could not be estimated. The returned matrix has the following form:
+\f[
+\begin{bmatrix}
+a_{11} & a_{12} & b_1\\
+a_{21} & a_{22} & b_2\\
+\end{bmatrix}
+\f]
+
+The function estimates an optimal 2D affine transformation between two 2D point sets using the
+selected robust algorithm.
+
+The computed transformation is then refined further (using only inliers) with the
+Levenberg-Marquardt method to reduce the re-projection error even more.
+
+@note
+The RANSAC method can handle practically any ratio of outliers but needs a threshold to
+distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
+correctly only when there are more than 50% of inliers.
+
+@sa estimateAffinePartial2D, getAffineTransform
+*/
+CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(),
+                                  int method = RANSAC, double ransacReprojThreshold = 3,
+                                  size_t maxIters = 2000, double confidence = 0.99,
+                                  size_t refineIters = 10);
+
+
+CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray pts1, InputArray pts2, OutputArray inliers,
+                     const UsacParams &params);
+
+/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between
+two 2D point sets.
+
+@param from First input 2D point set.
+@param to Second input 2D point set.
+@param inliers Output vector indicating which points are inliers.
+@param method Robust method used to compute transformation. The following methods are possible:
+-   @ref RANSAC - RANSAC-based robust method
+-   @ref LMEDS - Least-Median robust method
+RANSAC is the default method.
+@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
+a point as an inlier. Applies only to RANSAC.
+@param maxIters The maximum number of robust method iterations.
+@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
+between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
+significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
+@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
+Passing 0 will disable refining, so the output matrix will be output of robust method.
+
+@return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or
+empty matrix if transformation could not be estimated.
+
+The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to
+combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust
+estimation.
+
+The computed transformation is then refined further (using only inliers) with the
+Levenberg-Marquardt method to reduce the re-projection error even more.
+
+Estimated transformation matrix is:
+\f[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\
+                \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y
+\end{bmatrix} \f]
+Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ t_x, t_y \f$ are
+translations in \f$ x, y \f$ axes respectively.
+
+@note
+The RANSAC method can handle practically any ratio of outliers but need a threshold to
+distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
+correctly only when there are more than 50% of inliers.
+
+@sa estimateAffine2D, getAffineTransform
+*/
+CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(),
+                                  int method = RANSAC, double ransacReprojThreshold = 3,
+                                  size_t maxIters = 2000, double confidence = 0.99,
+                                  size_t refineIters = 10);
+
+/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp
+An example program with homography decomposition.
+
+Check @ref tutorial_homography "the corresponding tutorial" for more details.
+*/
+
+/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
+
+@param H The input homography matrix between two images.
+@param K The input camera intrinsic matrix.
+@param rotations Array of rotation matrices.
+@param translations Array of translation matrices.
+@param normals Array of plane normal matrices.
+
+This function extracts relative camera motion between two views of a planar object and returns up to
+four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of
+the homography matrix H is described in detail in @cite Malis.
+
+If the homography H, induced by the plane, gives the constraint
+\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] on the source image points
+\f$p_i\f$ and the destination image points \f$p'_i\f$, then the tuple of rotations[k] and
+translations[k] is a change of basis from the source camera's coordinate system to the destination
+camera's coordinate system. However, by decomposing H, one can only get the translation normalized
+by the (typically unknown) depth of the scene, i.e. its direction but with normalized length.
+
+If point correspondences are available, at least two solutions may further be invalidated, by
+applying positive depth constraint, i.e. all points must be in front of the camera.
+ */
+CV_EXPORTS_W int decomposeHomographyMat(InputArray H,
+                                        InputArray K,
+                                        OutputArrayOfArrays rotations,
+                                        OutputArrayOfArrays translations,
+                                        OutputArrayOfArrays normals);
+
+/** @brief Filters homography decompositions based on additional information.
+
+@param rotations Vector of rotation matrices.
+@param normals Vector of plane normal matrices.
+@param beforePoints Vector of (rectified) visible reference points before the homography is applied
+@param afterPoints Vector of (rectified) visible reference points after the homography is applied
+@param possibleSolutions Vector of int indices representing the viable solution set after filtering
+@param pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the #findHomography function
+
+This function is intended to filter the output of the #decomposeHomographyMat based on additional
+information as described in @cite Malis . The summary of the method: the #decomposeHomographyMat function
+returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the
+sets of points visible in the camera frame before and after the homography transformation is applied,
+we can determine which are the true potential solutions and which are the opposites by verifying which
+homographies are consistent with all visible reference points being in front of the camera. The inputs
+are left unchanged; the filtered solution set is returned as indices into the existing one.
+
+*/
+CV_EXPORTS_W void filterHomographyDecompByVisibleRefpoints(InputArrayOfArrays rotations,
+                                                           InputArrayOfArrays normals,
+                                                           InputArray beforePoints,
+                                                           InputArray afterPoints,
+                                                           OutputArray possibleSolutions,
+                                                           InputArray pointsMask = noArray());
+
+/** @brief The base class for stereo correspondence algorithms.
+ */
+class CV_EXPORTS_W StereoMatcher : public Algorithm
+{
+public:
+    enum { DISP_SHIFT = 4,
+           DISP_SCALE = (1 << DISP_SHIFT)
+         };
+
+    /** @brief Computes disparity map for the specified stereo pair
+
+    @param left Left 8-bit single-channel image.
+    @param right Right image of the same size and the same type as the left one.
+    @param disparity Output disparity map. It has the same size as the input images. Some algorithms,
+    like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
+    has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.
+     */
+    CV_WRAP virtual void compute( InputArray left, InputArray right,
+                                  OutputArray disparity ) = 0;
+
+    CV_WRAP virtual int getMinDisparity() const = 0;
+    CV_WRAP virtual void setMinDisparity(int minDisparity) = 0;
+
+    CV_WRAP virtual int getNumDisparities() const = 0;
+    CV_WRAP virtual void setNumDisparities(int numDisparities) = 0;
+
+    CV_WRAP virtual int getBlockSize() const = 0;
+    CV_WRAP virtual void setBlockSize(int blockSize) = 0;
+
+    CV_WRAP virtual int getSpeckleWindowSize() const = 0;
+    CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0;
+
+    CV_WRAP virtual int getSpeckleRange() const = 0;
+    CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0;
+
+    CV_WRAP virtual int getDisp12MaxDiff() const = 0;
+    CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0;
+};
+
+
+/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and
+contributed to OpenCV by K. Konolige.
+ */
+class CV_EXPORTS_W StereoBM : public StereoMatcher
+{
+public:
+    enum { PREFILTER_NORMALIZED_RESPONSE = 0,
+           PREFILTER_XSOBEL              = 1
+         };
+
+    CV_WRAP virtual int getPreFilterType() const = 0;
+    CV_WRAP virtual void setPreFilterType(int preFilterType) = 0;
+
+    CV_WRAP virtual int getPreFilterSize() const = 0;
+    CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0;
+
+    CV_WRAP virtual int getPreFilterCap() const = 0;
+    CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0;
+
+    CV_WRAP virtual int getTextureThreshold() const = 0;
+    CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0;
+
+    CV_WRAP virtual int getUniquenessRatio() const = 0;
+    CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0;
+
+    CV_WRAP virtual int getSmallerBlockSize() const = 0;
+    CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0;
+
+    CV_WRAP virtual Rect getROI1() const = 0;
+    CV_WRAP virtual void setROI1(Rect roi1) = 0;
+
+    CV_WRAP virtual Rect getROI2() const = 0;
+    CV_WRAP virtual void setROI2(Rect roi2) = 0;
+
+    /** @brief Creates StereoBM object
+
+    @param numDisparities the disparity search range. For each pixel algorithm will find the best
+    disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
+    shifted by changing the minimum disparity.
+    @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
+    (as the block is centered at the current pixel). Larger block size implies smoother, though less
+    accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
+    chance for algorithm to find a wrong correspondence.
+
+    The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
+    a specific stereo pair.
+     */
+    CV_WRAP static Ptr<StereoBM> create(int numDisparities = 0, int blockSize = 21);
+};
+
+/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original
+one as follows:
+
+-   By default, the algorithm is single-pass, which means that you consider only 5 directions
+instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the
+algorithm but beware that it may consume a lot of memory.
+-   The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the
+blocks to single pixels.
+-   Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi
+sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well.
+-   Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for
+example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness
+check, quadratic interpolation and speckle filtering).
+
+@note
+   -   (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
+        at opencv_source_code/samples/python/stereo_match.py
+ */
+class CV_EXPORTS_W StereoSGBM : public StereoMatcher
+{
+public:
+    enum
+    {
+        MODE_SGBM = 0,
+        MODE_HH   = 1,
+        MODE_SGBM_3WAY = 2,
+        MODE_HH4  = 3
+    };
+
+    CV_WRAP virtual int getPreFilterCap() const = 0;
+    CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0;
+
+    CV_WRAP virtual int getUniquenessRatio() const = 0;
+    CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0;
+
+    CV_WRAP virtual int getP1() const = 0;
+    CV_WRAP virtual void setP1(int P1) = 0;
+
+    CV_WRAP virtual int getP2() const = 0;
+    CV_WRAP virtual void setP2(int P2) = 0;
+
+    CV_WRAP virtual int getMode() const = 0;
+    CV_WRAP virtual void setMode(int mode) = 0;
+
+    /** @brief Creates StereoSGBM object
+
+    @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
+    rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
+    @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
+    zero. In the current implementation, this parameter must be divisible by 16.
+    @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
+    somewhere in the 3..11 range.
+    @param P1 The first parameter controlling the disparity smoothness. See below.
+    @param P2 The second parameter controlling the disparity smoothness. The larger the values are,
+    the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
+    between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
+    pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
+    P1 and P2 values are shown (like 8\*number_of_image_channels\*blockSize\*blockSize and
+    32\*number_of_image_channels\*blockSize\*blockSize , respectively).
+    @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
+    disparity check. Set it to a non-positive value to disable the check.
+    @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
+    computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
+    The result values are passed to the Birchfield-Tomasi pixel cost function.
+    @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
+    value should "win" the second best value to consider the found match correct. Normally, a value
+    within the 5-15 range is good enough.
+    @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
+    and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
+    50-200 range.
+    @param speckleRange Maximum disparity variation within each connected component. If you do speckle
+    filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
+    Normally, 1 or 2 is good enough.
+    @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
+    algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
+    huge for HD-size pictures. By default, it is set to false .
+
+    The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
+    set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
+    to a custom value.
+     */
+    CV_WRAP static Ptr<StereoSGBM> create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3,
+                                          int P1 = 0, int P2 = 0, int disp12MaxDiff = 0,
+                                          int preFilterCap = 0, int uniquenessRatio = 0,
+                                          int speckleWindowSize = 0, int speckleRange = 0,
+                                          int mode = StereoSGBM::MODE_SGBM);
+};
+
+
+//! cv::undistort mode
+enum UndistortTypes
+{
+    PROJ_SPHERICAL_ORTHO  = 0,
+    PROJ_SPHERICAL_EQRECT = 1
+};
+
+/** @brief Transforms an image to compensate for lens distortion.
+
+The function transforms an image to compensate radial and tangential lens distortion.
+
+The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap
+(with bilinear interpolation). See the former function for details of the transformation being
+performed.
+
+Those pixels in the destination image, for which there is no correspondent pixels in the source
+image, are filled with zeros (black color).
+
+A particular subset of the source image that will be visible in the corrected image can be regulated
+by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate
+newCameraMatrix depending on your requirements.
+
+The camera matrix and the distortion parameters can be determined using #calibrateCamera. If
+the resolution of images is different from the resolution used at the calibration stage, \f$f_x,
+f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain
+the same.
+
+@param src Input (distorted) image.
+@param dst Output (corrected) image that has the same size and type as src .
+@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
+@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as
+cameraMatrix but you may additionally scale and shift the result by using a different matrix.
+ */
+CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
+                             InputArray cameraMatrix,
+                             InputArray distCoeffs,
+                             InputArray newCameraMatrix = noArray() );
+
+/** @brief Computes the undistortion and rectification transformation map.
+
+The function computes the joint undistortion and rectification transformation and represents the
+result in the form of maps for #remap. The undistorted image looks like original, as if it is
+captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
+monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
+#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera,
+newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .
+
+Also, this new camera is oriented differently in the coordinate space, according to R. That, for
+example, helps to align two heads of a stereo camera so that the epipolar lines on both images
+become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
+
+The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That
+is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function
+computes the corresponding coordinates in the source image (that is, in the original image from
+camera). The following process is applied:
+\f[
+\begin{array}{l}
+x  \leftarrow (u - {c'}_x)/{f'}_x  \\
+y  \leftarrow (v - {c'}_y)/{f'}_y  \\
+{[X\,Y\,W]} ^T  \leftarrow R^{-1}*[x \, y \, 1]^T  \\
+x'  \leftarrow X/W  \\
+y'  \leftarrow Y/W  \\
+r^2  \leftarrow x'^2 + y'^2 \\
+x''  \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
++ 2p_1 x' y' + p_2(r^2 + 2 x'^2)  + s_1 r^2 + s_2 r^4\\
+y''  \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
++ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
+s\vecthree{x'''}{y'''}{1} =
+\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
+{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
+{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
+map_x(u,v)  \leftarrow x''' f_x + c_x  \\
+map_y(u,v)  \leftarrow y''' f_y + c_y
+\end{array}
+\f]
+where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+are the distortion coefficients.
+
+In case of a stereo camera, this function is called twice: once for each camera head, after
+#stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera
+was not calibrated, it is still possible to compute the rectification transformations directly from
+the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes
+homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
+space. R can be computed from H as
+\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
+where cameraMatrix can be chosen arbitrarily.
+
+@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
+@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
+computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation
+is assumed. In cvInitUndistortMap R assumed to be an identity matrix.
+@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
+@param size Undistorted image size.
+@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
+@param map1 The first output map.
+@param map2 The second output map.
+ */
+CV_EXPORTS_W
+void initUndistortRectifyMap(InputArray cameraMatrix, InputArray distCoeffs,
+                             InputArray R, InputArray newCameraMatrix,
+                             Size size, int m1type, OutputArray map1, OutputArray map2);
+
+/** @brief Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of
+#initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.
+
+The function computes the joint projection and inverse rectification transformation and represents the
+result in the form of maps for #remap. The projected image looks like a distorted version of the original which,
+once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix
+is usually equal to cameraMatrix, or it can be computed by
+#getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair,
+newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .
+
+The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs,
+this helps align the projector (in the same manner as #initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This
+allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair).
+
+The function builds the maps for the inverse mapping algorithm that is used by #remap. That
+is, for each pixel \f$(u, v)\f$ in the destination (projected and inverse-rectified) image, the function
+computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied:
+
+\f[
+\begin{array}{l}
+\text{newCameraMatrix}\\
+x  \leftarrow (u - {c'}_x)/{f'}_x  \\
+y  \leftarrow (v - {c'}_y)/{f'}_y  \\
+
+\\\text{Undistortion}
+\\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\
+r^2  \leftarrow x^2 + y^2 \\
+\theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\
+x' \leftarrow \frac{x}{\theta} \\
+y'  \leftarrow \frac{y}{\theta} \\
+
+\\\text{Rectification}\\
+{[X\,Y\,W]} ^T  \leftarrow R*[x' \, y' \, 1]^T  \\
+x''  \leftarrow X/W  \\
+y''  \leftarrow Y/W  \\
+
+\\\text{cameraMatrix}\\
+map_x(u,v)  \leftarrow x'' f_x + c_x  \\
+map_y(u,v)  \leftarrow y'' f_y + c_y
+\end{array}
+\f]
+where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+are the distortion coefficients vector distCoeffs.
+
+In case of a stereo-rectified projector-camera pair, this function is called for the projector while #initUndistortRectifyMap is called for the camera head.
+This is done after #stereoRectify, which in turn is called after #stereoCalibrate. If the projector-camera pair
+is not calibrated, it is still possible to compute the rectification transformations directly from
+the fundamental matrix using #stereoRectifyUncalibrated. For the projector and camera, the function computes
+homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
+space. R can be computed from H as
+\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
+where cameraMatrix can be chosen arbitrarily.
+
+@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
+@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2,
+computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation
+is assumed.
+@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
+@param size Distorted image size.
+@param m1type Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
+@param map1 The first output map for #remap.
+@param map2 The second output map for #remap.
+ */
+CV_EXPORTS_W
+void initInverseRectificationMap( InputArray cameraMatrix, InputArray distCoeffs,
+                           InputArray R, InputArray newCameraMatrix,
+                           const Size& size, int m1type, OutputArray map1, OutputArray map2 );
+
+//! initializes maps for #remap for wide-angle
+CV_EXPORTS
+float initWideAngleProjMap(InputArray cameraMatrix, InputArray distCoeffs,
+                           Size imageSize, int destImageWidth,
+                           int m1type, OutputArray map1, OutputArray map2,
+                           enum UndistortTypes projType = PROJ_SPHERICAL_EQRECT, double alpha = 0);
+static inline
+float initWideAngleProjMap(InputArray cameraMatrix, InputArray distCoeffs,
+                           Size imageSize, int destImageWidth,
+                           int m1type, OutputArray map1, OutputArray map2,
+                           int projType, double alpha = 0)
+{
+    return initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth,
+                                m1type, map1, map2, (UndistortTypes)projType, alpha);
+}
+
+/** @brief Returns the default new camera matrix.
+
+The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
+centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
+
+In the latter case, the new camera matrix will be:
+
+\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5  \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5  \\ 0 && 0 && 1 \end{bmatrix} ,\f]
+
+where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively.
+
+By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not
+move the principal point. However, when you work with stereo, it is important to move the principal
+points in both views to the same y-coordinate (which is required by most of stereo correspondence
+algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
+each view where the principal points are located at the center.
+
+@param cameraMatrix Input camera matrix.
+@param imgsize Camera view image size in pixels.
+@param centerPrincipalPoint Location of the principal point in the new camera matrix. The
+parameter indicates whether this location should be at the image center or not.
+ */
+CV_EXPORTS_W
+Mat getDefaultNewCameraMatrix(InputArray cameraMatrix, Size imgsize = Size(),
+                              bool centerPrincipalPoint = false);
+
+/** @brief Computes the ideal point coordinates from the observed point coordinates.
+
+The function is similar to #undistort and #initUndistortRectifyMap but it operates on a
+sparse set of points instead of a raster image. Also the function performs a reverse transformation
+to  #projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
+planar object, it does, up to a translation vector, if the proper R is specified.
+
+For each observed point coordinate \f$(u, v)\f$ the function computes:
+\f[
+\begin{array}{l}
+x^{"}  \leftarrow (u - c_x)/f_x  \\
+y^{"}  \leftarrow (v - c_y)/f_y  \\
+(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
+{[X\,Y\,W]} ^T  \leftarrow R*[x' \, y' \, 1]^T  \\
+x  \leftarrow X/W  \\
+y  \leftarrow Y/W  \\
+\text{only performed if P is specified:} \\
+u'  \leftarrow x {f'}_x + {c'}_x  \\
+v'  \leftarrow y {f'}_y + {c'}_y
+\end{array}
+\f]
+
+where *undistort* is an approximate iterative algorithm that estimates the normalized original
+point coordinates out of the normalized distorted point coordinates ("normalized" means that the
+coordinates do not depend on the camera matrix).
+
+The function can be used for both a stereo camera head or a monocular camera (when R is empty).
+@param src Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or
+vector\<Point2f\> ).
+@param dst Output ideal point coordinates (1xN/Nx1 2-channel or vector\<Point2f\> ) after undistortion and reverse perspective
+transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
+@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
+@param distCoeffs Input vector of distortion coefficients
+\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
+of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
+@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
+#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
+@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by
+#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
+ */
+CV_EXPORTS_W
+void undistortPoints(InputArray src, OutputArray dst,
+                     InputArray cameraMatrix, InputArray distCoeffs,
+                     InputArray R = noArray(), InputArray P = noArray());
+/** @overload
+    @note Default version of #undistortPoints does 5 iterations to compute undistorted points.
+ */
+CV_EXPORTS_AS(undistortPointsIter)
+void undistortPoints(InputArray src, OutputArray dst,
+                     InputArray cameraMatrix, InputArray distCoeffs,
+                     InputArray R, InputArray P, TermCriteria criteria);
+
+//! @} calib3d
+
+/** @brief The methods in this namespace use a so-called fisheye camera model.
+  @ingroup calib3d_fisheye
+*/
+namespace fisheye
+{
+//! @addtogroup calib3d_fisheye
+//! @{
+
+    enum{
+        CALIB_USE_INTRINSIC_GUESS   = 1 << 0,
+        CALIB_RECOMPUTE_EXTRINSIC   = 1 << 1,
+        CALIB_CHECK_COND            = 1 << 2,
+        CALIB_FIX_SKEW              = 1 << 3,
+        CALIB_FIX_K1                = 1 << 4,
+        CALIB_FIX_K2                = 1 << 5,
+        CALIB_FIX_K3                = 1 << 6,
+        CALIB_FIX_K4                = 1 << 7,
+        CALIB_FIX_INTRINSIC         = 1 << 8,
+        CALIB_FIX_PRINCIPAL_POINT   = 1 << 9,
+        CALIB_ZERO_DISPARITY        = 1 << 10,
+        CALIB_FIX_FOCAL_LENGTH      = 1 << 11
+    };
+
+    /** @brief Projects points using fisheye model
+
+    @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is
+    the number of points in the view.
+    @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
+    vector\<Point2f\>.
+    @param affine
+    @param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
+    @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
+    @param alpha The skew coefficient.
+    @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect
+    to components of the focal lengths, coordinates of the principal point, distortion coefficients,
+    rotation vector, translation vector, and the skew. In the old interface different components of
+    the jacobian are returned via different output parameters.
+
+    The function computes projections of 3D points to the image plane given intrinsic and extrinsic
+    camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
+    image points coordinates (as functions of all the input parameters) with respect to the particular
+    parameters, intrinsic and/or extrinsic.
+     */
+    CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine,
+        InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray());
+
+    /** @overload */
+    CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec,
+        InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray());
+
+    /** @brief Distorts 2D points using fisheye model.
+
+    @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
+    the number of points in the view.
+    @param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
+    @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
+    @param alpha The skew coefficient.
+    @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
+
+    Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity.
+    This means if you want to transform back points undistorted with #fisheye::undistortPoints you have to
+    multiply them with \f$P^{-1}\f$.
+     */
+    CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0);
+
+    /** @brief Undistorts 2D points using fisheye model
+
+    @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is the
+    number of points in the view.
+    @param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
+    @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
+    @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
+    1-channel or 1x1 3-channel
+    @param P New camera intrinsic matrix (3x3) or new projection matrix (3x4)
+    @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
+     */
+    CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted,
+        InputArray K, InputArray D, InputArray R = noArray(), InputArray P  = noArray());
+
+    /** @brief Computes undistortion and rectification maps for image transform by #remap. If D is empty zero
+    distortion is used, if R or P is empty identity matrixes are used.
+
+    @param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
+    @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
+    @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
+    1-channel or 1x1 3-channel
+    @param P New camera intrinsic matrix (3x3) or new projection matrix (3x4)
+    @param size Undistorted image size.
+    @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See #convertMaps
+    for details.
+    @param map1 The first output map.
+    @param map2 The second output map.
+     */
+    CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P,
+        const cv::Size& size, int m1type, OutputArray map1, OutputArray map2);
+
+    /** @brief Transforms an image to compensate for fisheye lens distortion.
+
+    @param distorted image with fisheye lens distortion.
+    @param undistorted Output image with compensated fisheye lens distortion.
+    @param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
+    @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
+    @param Knew Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you
+    may additionally scale and shift the result by using a different matrix.
+    @param new_size the new size
+
+    The function transforms an image to compensate radial and tangential lens distortion.
+
+    The function is simply a combination of #fisheye::initUndistortRectifyMap (with unity R ) and #remap
+    (with bilinear interpolation). See the former function for details of the transformation being
+    performed.
+
+    See below the results of undistortImage.
+       -   a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3,
+            k_4, k_5, k_6) of distortion were optimized under calibration)
+        -   b\) result of #fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2,
+            k_3, k_4) of fisheye distortion were optimized under calibration)
+        -   c\) original image was captured with fisheye lens
+
+    Pictures a) and b) almost the same. But if we consider points of image located far from the center
+    of image, we can notice that on image a) these points are distorted.
+
+    ![image](pics/fisheye_undistorted.jpg)
+     */
+    CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted,
+        InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size());
+
+    /** @brief Estimates new camera intrinsic matrix for undistortion or rectification.
+
+    @param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
+    @param image_size Size of the image
+    @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
+    @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
+    1-channel or 1x1 3-channel
+    @param P New camera intrinsic matrix (3x3) or new projection matrix (3x4)
+    @param balance Sets the new focal length in range between the min focal length and the max focal
+    length. Balance is in range of [0, 1].
+    @param new_size the new size
+    @param fov_scale Divisor for new focal length.
+     */
+    CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R,
+        OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0);
+
+    /** @brief Performs camera calibaration
+
+    @param objectPoints vector of vectors of calibration pattern points in the calibration pattern
+    coordinate space.
+    @param imagePoints vector of vectors of the projections of calibration pattern points.
+    imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
+    objectPoints[i].size() for each i.
+    @param image_size Size of the image used only to initialize the camera intrinsic matrix.
+    @param K Output 3x3 floating-point camera intrinsic matrix
+    \f$\cameramatrix{A}\f$ . If
+    @ref fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be
+    initialized before calling the function.
+    @param D Output vector of distortion coefficients \f$\distcoeffsfisheye\f$.
+    @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view.
+    That is, each k-th rotation vector together with the corresponding k-th translation vector (see
+    the next output parameter description) brings the calibration pattern from the model coordinate
+    space (in which object points are specified) to the world coordinate space, that is, a real
+    position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
+    @param tvecs Output vector of translation vectors estimated for each pattern view.
+    @param flags Different flags that may be zero or a combination of the following values:
+    -    @ref fisheye::CALIB_USE_INTRINSIC_GUESS  cameraMatrix contains valid initial values of
+    fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
+    center ( imageSize is used), and focal distances are computed in a least-squares fashion.
+    -    @ref fisheye::CALIB_RECOMPUTE_EXTRINSIC  Extrinsic will be recomputed after each iteration
+    of intrinsic optimization.
+    -    @ref fisheye::CALIB_CHECK_COND  The functions will check validity of condition number.
+    -    @ref fisheye::CALIB_FIX_SKEW  Skew coefficient (alpha) is set to zero and stay zero.
+    -    @ref fisheye::CALIB_FIX_K1,..., @ref fisheye::CALIB_FIX_K4 Selected distortion coefficients
+    are set to zeros and stay zero.
+    -    @ref fisheye::CALIB_FIX_PRINCIPAL_POINT  The principal point is not changed during the global
+optimization. It stays at the center or at a different location specified when @ref fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
+    -    @ref fisheye::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global
+optimization. It is the \f$max(width,height)/\pi\f$ or the provided \f$f_x\f$, \f$f_y\f$ when @ref fisheye::CALIB_USE_INTRINSIC_GUESS is set too.
+    @param criteria Termination criteria for the iterative optimization algorithm.
+     */
+    CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size,
+        InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0,
+            TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON));
+
+    /** @brief Stereo rectification for fisheye camera model
+
+    @param K1 First camera intrinsic matrix.
+    @param D1 First camera distortion parameters.
+    @param K2 Second camera intrinsic matrix.
+    @param D2 Second camera distortion parameters.
+    @param imageSize Size of the image used for stereo calibration.
+    @param R Rotation matrix between the coordinate systems of the first and the second
+    cameras.
+    @param tvec Translation vector between coordinate systems of the cameras.
+    @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera.
+    @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera.
+    @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
+    camera.
+    @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
+    camera.
+    @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
+    @param flags Operation flags that may be zero or @ref fisheye::CALIB_ZERO_DISPARITY . If the flag is set,
+    the function makes the principal points of each camera have the same pixel coordinates in the
+    rectified views. And if the flag is not set, the function may still shift the images in the
+    horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
+    useful image area.
+    @param newImageSize New image resolution after rectification. The same size should be passed to
+    #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
+    is passed (default), it is set to the original imageSize . Setting it to larger value can help you
+    preserve details in the original image, especially when there is a big radial distortion.
+    @param balance Sets the new focal length in range between the min focal length and the max focal
+    length. Balance is in range of [0, 1].
+    @param fov_scale Divisor for new focal length.
+     */
+    CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec,
+        OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(),
+        double balance = 0.0, double fov_scale = 1.0);
+
+    /** @brief Performs stereo calibration
+
+    @param objectPoints Vector of vectors of the calibration pattern points.
+    @param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
+    observed by the first camera.
+    @param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
+    observed by the second camera.
+    @param K1 Input/output first camera intrinsic matrix:
+    \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
+    any of @ref fisheye::CALIB_USE_INTRINSIC_GUESS , @ref fisheye::CALIB_FIX_INTRINSIC are specified,
+    some or all of the matrix components must be initialized.
+    @param D1 Input/output vector of distortion coefficients \f$\distcoeffsfisheye\f$ of 4 elements.
+    @param K2 Input/output second camera intrinsic matrix. The parameter is similar to K1 .
+    @param D2 Input/output lens distortion coefficients for the second camera. The parameter is
+    similar to D1 .
+    @param imageSize Size of the image used only to initialize camera intrinsic matrix.
+    @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems.
+    @param T Output translation vector between the coordinate systems of the cameras.
+    @param flags Different flags that may be zero or a combination of the following values:
+    -    @ref fisheye::CALIB_FIX_INTRINSIC  Fix K1, K2? and D1, D2? so that only R, T matrices
+    are estimated.
+    -    @ref fisheye::CALIB_USE_INTRINSIC_GUESS  K1, K2 contains valid initial values of
+    fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
+    center (imageSize is used), and focal distances are computed in a least-squares fashion.
+    -    @ref fisheye::CALIB_RECOMPUTE_EXTRINSIC  Extrinsic will be recomputed after each iteration
+    of intrinsic optimization.
+    -    @ref fisheye::CALIB_CHECK_COND  The functions will check validity of condition number.
+    -    @ref fisheye::CALIB_FIX_SKEW  Skew coefficient (alpha) is set to zero and stay zero.
+    -   @ref fisheye::CALIB_FIX_K1,..., @ref fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay
+    zero.
+    @param criteria Termination criteria for the iterative optimization algorithm.
+     */
+    CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
+                                  InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize,
+                                  OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC,
+                                  TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON));
+
+//! @} calib3d_fisheye
+} // end namespace fisheye
+
+} //end namespace cv
+
+#if 0 //def __cplusplus
+//////////////////////////////////////////////////////////////////////////////////////////
+class CV_EXPORTS CvLevMarq
+{
+public:
+    CvLevMarq();
+    CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria=
+              cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
+              bool completeSymmFlag=false );
+    ~CvLevMarq();
+    void init( int nparams, int nerrs, CvTermCriteria criteria=
+              cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
+              bool completeSymmFlag=false );
+    bool update( const CvMat*& param, CvMat*& J, CvMat*& err );
+    bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm );
+
+    void clear();
+    void step();
+    enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 };
+
+    cv::Ptr<CvMat> mask;
+    cv::Ptr<CvMat> prevParam;
+    cv::Ptr<CvMat> param;
+    cv::Ptr<CvMat> J;
+    cv::Ptr<CvMat> err;
+    cv::Ptr<CvMat> JtJ;
+    cv::Ptr<CvMat> JtJN;
+    cv::Ptr<CvMat> JtErr;
+    cv::Ptr<CvMat> JtJV;
+    cv::Ptr<CvMat> JtJW;
+    double prevErrNorm, errNorm;
+    int lambdaLg10;
+    CvTermCriteria criteria;
+    int state;
+    int iters;
+    bool completeSymmFlag;
+    int solveMethod;
+};
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/calib3d/calib3d.hpp b/3rdParty/include/opencv2/calib3d/calib3d.hpp
new file mode 100644
index 0000000..b3da45e
--- /dev/null
+++ b/3rdParty/include/opencv2/calib3d/calib3d.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/calib3d.hpp"
diff --git a/3rdParty/include/opencv2/calib3d/calib3d_c.h b/3rdParty/include/opencv2/calib3d/calib3d_c.h
new file mode 100644
index 0000000..e2af07b
--- /dev/null
+++ b/3rdParty/include/opencv2/calib3d/calib3d_c.h
@@ -0,0 +1,150 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CALIB3D_C_H
+#define OPENCV_CALIB3D_C_H
+
+#include "opencv2/core/types_c.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* Calculates fundamental matrix given a set of corresponding points */
+#define CV_FM_7POINT 1
+#define CV_FM_8POINT 2
+
+#define CV_LMEDS 4
+#define CV_RANSAC 8
+
+#define CV_FM_LMEDS_ONLY  CV_LMEDS
+#define CV_FM_RANSAC_ONLY CV_RANSAC
+#define CV_FM_LMEDS CV_LMEDS
+#define CV_FM_RANSAC CV_RANSAC
+
+enum
+{
+    CV_ITERATIVE = 0,
+    CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation"
+    CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem"
+    CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP"
+};
+
+#define CV_CALIB_CB_ADAPTIVE_THRESH  1
+#define CV_CALIB_CB_NORMALIZE_IMAGE  2
+#define CV_CALIB_CB_FILTER_QUADS     4
+#define CV_CALIB_CB_FAST_CHECK       8
+
+#define CV_CALIB_USE_INTRINSIC_GUESS  1
+#define CV_CALIB_FIX_ASPECT_RATIO     2
+#define CV_CALIB_FIX_PRINCIPAL_POINT  4
+#define CV_CALIB_ZERO_TANGENT_DIST    8
+#define CV_CALIB_FIX_FOCAL_LENGTH 16
+#define CV_CALIB_FIX_K1  32
+#define CV_CALIB_FIX_K2  64
+#define CV_CALIB_FIX_K3  128
+#define CV_CALIB_FIX_K4  2048
+#define CV_CALIB_FIX_K5  4096
+#define CV_CALIB_FIX_K6  8192
+#define CV_CALIB_RATIONAL_MODEL 16384
+#define CV_CALIB_THIN_PRISM_MODEL 32768
+#define CV_CALIB_FIX_S1_S2_S3_S4  65536
+#define CV_CALIB_TILTED_MODEL  262144
+#define CV_CALIB_FIX_TAUX_TAUY  524288
+#define CV_CALIB_FIX_TANGENT_DIST 2097152
+
+#define CV_CALIB_NINTRINSIC 18
+
+#define CV_CALIB_FIX_INTRINSIC  256
+#define CV_CALIB_SAME_FOCAL_LENGTH 512
+
+#define CV_CALIB_ZERO_DISPARITY 1024
+
+/* stereo correspondence parameters and functions */
+#define CV_STEREO_BM_NORMALIZED_RESPONSE  0
+#define CV_STEREO_BM_XSOBEL               1
+
+#ifdef __cplusplus
+} // extern "C"
+
+//////////////////////////////////////////////////////////////////////////////////////////
+class CV_EXPORTS CvLevMarq
+{
+public:
+    CvLevMarq();
+    CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria=
+              cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
+              bool completeSymmFlag=false );
+    ~CvLevMarq();
+    void init( int nparams, int nerrs, CvTermCriteria criteria=
+              cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
+              bool completeSymmFlag=false );
+    bool update( const CvMat*& param, CvMat*& J, CvMat*& err );
+    bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm );
+
+    void clear();
+    void step();
+    enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 };
+
+    cv::Ptr<CvMat> mask;
+    cv::Ptr<CvMat> prevParam;
+    cv::Ptr<CvMat> param;
+    cv::Ptr<CvMat> J;
+    cv::Ptr<CvMat> err;
+    cv::Ptr<CvMat> JtJ;
+    cv::Ptr<CvMat> JtJN;
+    cv::Ptr<CvMat> JtErr;
+    cv::Ptr<CvMat> JtJV;
+    cv::Ptr<CvMat> JtJW;
+    double prevErrNorm, errNorm;
+    int lambdaLg10;
+    CvTermCriteria criteria;
+    int state;
+    int iters;
+    bool completeSymmFlag;
+    int solveMethod;
+};
+
+#endif
+
+#endif /* OPENCV_CALIB3D_C_H */
diff --git a/3rdParty/include/opencv2/core.hpp b/3rdParty/include/opencv2/core.hpp
new file mode 100644
index 0000000..70ea4f8
--- /dev/null
+++ b/3rdParty/include/opencv2/core.hpp
@@ -0,0 +1,3344 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
+// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2015, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_HPP
+#define OPENCV_CORE_HPP
+
+#ifndef __cplusplus
+#  error core.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/base.hpp"
+#include "opencv2/core/cvstd.hpp"
+#include "opencv2/core/traits.hpp"
+#include "opencv2/core/matx.hpp"
+#include "opencv2/core/types.hpp"
+#include "opencv2/core/mat.hpp"
+#include "opencv2/core/persistence.hpp"
+
+/**
+@defgroup core Core functionality
+@{
+    @defgroup core_basic Basic structures
+    @defgroup core_c C structures and operations
+    @{
+        @defgroup core_c_glue Connections with C++
+    @}
+    @defgroup core_array Operations on arrays
+    @defgroup core_async Asynchronous API
+    @defgroup core_xml XML/YAML Persistence
+    @defgroup core_cluster Clustering
+    @defgroup core_utils Utility and system functions and macros
+    @{
+        @defgroup core_logging Logging facilities
+        @defgroup core_utils_sse SSE utilities
+        @defgroup core_utils_neon NEON utilities
+        @defgroup core_utils_vsx VSX utilities
+        @defgroup core_utils_softfloat Softfloat support
+        @defgroup core_utils_samples Utility functions for OpenCV samples
+    @}
+    @defgroup core_opengl OpenGL interoperability
+    @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters
+    @defgroup core_optim Optimization Algorithms
+    @defgroup core_directx DirectX interoperability
+    @defgroup core_eigen Eigen support
+    @defgroup core_opencl OpenCL support
+    @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability
+    @defgroup core_hal Hardware Acceleration Layer
+    @{
+        @defgroup core_hal_functions Functions
+        @defgroup core_hal_interface Interface
+        @defgroup core_hal_intrin Universal intrinsics
+        @{
+            @defgroup core_hal_intrin_impl Private implementation helpers
+        @}
+        @defgroup core_lowlevel_api Low-level API for external libraries / plugins
+    @}
+    @defgroup core_parallel Parallel Processing
+    @{
+        @defgroup core_parallel_backend Parallel backends API
+    @}
+@}
+ */
+
+namespace cv {
+
+//! @addtogroup core_utils
+//! @{
+
+/*! @brief Class passed to an error.
+
+This class encapsulates all or almost all necessary
+information about the error happened in the program. The exception is
+usually constructed and thrown implicitly via CV_Error and CV_Error_ macros.
+@see error
+ */
+class CV_EXPORTS Exception : public std::exception
+{
+public:
+    /*!
+     Default constructor
+     */
+    Exception();
+    /*!
+     Full constructor. Normally the constructor is not called explicitly.
+     Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used.
+    */
+    Exception(int _code, const String& _err, const String& _func, const String& _file, int _line);
+    virtual ~Exception() throw();
+
+    /*!
+     \return the error description and the context as a text string.
+    */
+    virtual const char *what() const throw() CV_OVERRIDE;
+    void formatMessage();
+
+    String msg; ///< the formatted error message
+
+    int code; ///< error code @see CVStatus
+    String err; ///< error description
+    String func; ///< function name. Available only when the compiler supports getting it
+    String file; ///< source file name where the error has occurred
+    int line; ///< line number in the source file where the error has occurred
+};
+
+/*! @brief Signals an error and raises the exception.
+
+By default the function prints information about the error to stderr,
+then it either stops if cv::setBreakOnError() had been called before or raises the exception.
+It is possible to alternate error processing by using #redirectError().
+@param exc the exception raisen.
+@deprecated drop this version
+ */
+CV_EXPORTS CV_NORETURN void error(const Exception& exc);
+
+enum SortFlags { SORT_EVERY_ROW    = 0, //!< each matrix row is sorted independently
+                 SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted
+                                        //!< independently; this flag and the previous one are
+                                        //!< mutually exclusive.
+                 SORT_ASCENDING    = 0, //!< each matrix row is sorted in the ascending
+                                        //!< order.
+                 SORT_DESCENDING   = 16 //!< each matrix row is sorted in the
+                                        //!< descending order; this flag and the previous one are also
+                                        //!< mutually exclusive.
+               };
+
+//! @} core_utils
+
+//! @addtogroup core
+//! @{
+
+//! Covariation flags
+enum CovarFlags {
+    /** The output covariance matrix is calculated as:
+       \f[\texttt{scale}   \cdot  [  \texttt{vects}  [0]-  \texttt{mean}  , \texttt{vects}  [1]-  \texttt{mean}  ,...]^T  \cdot  [ \texttt{vects}  [0]- \texttt{mean}  , \texttt{vects}  [1]- \texttt{mean}  ,...],\f]
+       The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used
+       for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for
+       face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true
+       covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of
+       the "scrambled" covariance matrix. */
+    COVAR_SCRAMBLED = 0,
+    /**The output covariance matrix is calculated as:
+        \f[\texttt{scale}   \cdot  [  \texttt{vects}  [0]-  \texttt{mean}  , \texttt{vects}  [1]-  \texttt{mean}  ,...]  \cdot  [ \texttt{vects}  [0]- \texttt{mean}  , \texttt{vects}  [1]- \texttt{mean}  ,...]^T,\f]
+        covar will be a square matrix of the same size as the total number of elements in each input
+        vector. One and only one of #COVAR_SCRAMBLED and #COVAR_NORMAL must be specified.*/
+    COVAR_NORMAL    = 1,
+    /** If the flag is specified, the function does not calculate mean from
+        the input vectors but, instead, uses the passed mean vector. This is useful if mean has been
+        pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In
+        this case, mean is not a mean vector of the input sub-set of vectors but rather the mean
+        vector of the whole set.*/
+    COVAR_USE_AVG   = 2,
+    /** If the flag is specified, the covariance matrix is scaled. In the
+        "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the
+        total number of elements in each input vector. By default (if the flag is not specified), the
+        covariance matrix is not scaled ( scale=1 ).*/
+    COVAR_SCALE     = 4,
+    /** If the flag is
+        specified, all the input vectors are stored as rows of the samples matrix. mean should be a
+        single-row vector in this case.*/
+    COVAR_ROWS      = 8,
+    /** If the flag is
+        specified, all the input vectors are stored as columns of the samples matrix. mean should be a
+        single-column vector in this case.*/
+    COVAR_COLS      = 16
+};
+
+//! @addtogroup core_cluster
+//!  @{
+
+//! k-Means flags
+enum KmeansFlags {
+    /** Select random initial centers in each attempt.*/
+    KMEANS_RANDOM_CENTERS     = 0,
+    /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/
+    KMEANS_PP_CENTERS         = 2,
+    /** During the first (and possibly the only) attempt, use the
+        user-supplied labels instead of computing them from the initial centers. For the second and
+        further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag
+        to specify the exact method.*/
+    KMEANS_USE_INITIAL_LABELS = 1
+};
+
+//! @} core_cluster
+
+//! @addtogroup core_array
+//! @{
+
+enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix.
+                   REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix.
+                   REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix.
+                   REDUCE_MIN = 3  //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix.
+                 };
+
+//! @} core_array
+
+/** @brief Swaps two matrices
+*/
+CV_EXPORTS void swap(Mat& a, Mat& b);
+/** @overload */
+CV_EXPORTS void swap( UMat& a, UMat& b );
+
+//! @} core
+
+//! @addtogroup core_array
+//! @{
+
+/** @brief Computes the source location of an extrapolated pixel.
+
+The function computes and returns the coordinate of a donor pixel corresponding to the specified
+extrapolated pixel when using the specified extrapolation border mode. For example, if you use
+cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and
+want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it
+looks like:
+@code{.cpp}
+    float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101),
+                              borderInterpolate(-5, img.cols, cv::BORDER_WRAP));
+@endcode
+Normally, the function is not called directly. It is used inside filtering functions and also in
+copyMakeBorder.
+@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len
+@param len Length of the array along the corresponding axis.
+@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and
+#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless
+of p and len.
+
+@sa copyMakeBorder
+*/
+CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType);
+
+/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
+An example using copyMakeBorder function.
+Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details
+*/
+
+/** @brief Forms a border around an image.
+
+The function copies the source image into the middle of the destination image. The areas to the
+left, to the right, above and below the copied source image will be filled with extrapolated
+pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but
+what other more complex functions, including your own, may do to simplify image boundary handling.
+
+The function supports the mode when src is already in the middle of dst . In this case, the
+function does not copy src itself but simply constructs the border, for example:
+
+@code{.cpp}
+    // let border be the same in all directions
+    int border=2;
+    // constructs a larger image to fit both the image and the border
+    Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());
+    // select the middle part of it w/o copying data
+    Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));
+    // convert image from RGB to grayscale
+    cvtColor(rgb, gray, COLOR_RGB2GRAY);
+    // form a border in-place
+    copyMakeBorder(gray, gray_buf, border, border,
+                   border, border, BORDER_REPLICATE);
+    // now do some custom filtering ...
+    ...
+@endcode
+@note When the source image is a part (ROI) of a bigger image, the function will try to use the
+pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as
+if src was not a ROI, use borderType | #BORDER_ISOLATED.
+
+@param src Source image.
+@param dst Destination image of the same type as src and the size Size(src.cols+left+right,
+src.rows+top+bottom) .
+@param top the top pixels
+@param bottom the bottom pixels
+@param left the left pixels
+@param right Parameter specifying how many pixels in each direction from the source image rectangle
+to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs
+to be built.
+@param borderType Border type. See borderInterpolate for details.
+@param value Border value if borderType==BORDER_CONSTANT .
+
+@sa  borderInterpolate
+*/
+CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst,
+                                 int top, int bottom, int left, int right,
+                                 int borderType, const Scalar& value = Scalar() );
+
+/** @brief Calculates the per-element sum of two arrays or an array and a scalar.
+
+The function add calculates:
+- Sum of two arrays when both input arrays have the same size and the same number of channels:
+\f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) +  \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
+- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of
+elements as `src1.channels()`:
+\f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) +  \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
+- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of
+elements as `src2.channels()`:
+\f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1} +  \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
+where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each
+channel is processed independently.
+
+The first function in the list above can be replaced with matrix expressions:
+@code{.cpp}
+    dst = src1 + src2;
+    dst += src1; // equivalent to add(dst, src1, dst);
+@endcode
+The input arrays and the output array can all have the same or different depths. For example, you
+can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit
+floating-point array. Depth of the output array is determined by the dtype parameter. In the second
+and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can
+be set to the default -1. In this case, the output array will have the same depth as the input
+array, be it src1, src2 or both.
+@note Saturation is not applied when the output array has the depth CV_32S. You may even get
+result of an incorrect sign in the case of overflow.
+@param src1 first input array or a scalar.
+@param src2 second input array or a scalar.
+@param dst output array that has the same size and number of channels as the input array(s); the
+depth is defined by dtype or src1/src2.
+@param mask optional operation mask - 8-bit single channel array, that specifies elements of the
+output array to be changed.
+@param dtype optional depth of the output array (see the discussion below).
+@sa subtract, addWeighted, scaleAdd, Mat::convertTo
+*/
+CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst,
+                      InputArray mask = noArray(), int dtype = -1);
+
+/** @brief Calculates the per-element difference between two arrays or array and a scalar.
+
+The function subtract calculates:
+- Difference between two arrays, when both input arrays have the same size and the same number of
+channels:
+    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) -  \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
+- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same
+number of elements as `src1.channels()`:
+    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) -  \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
+- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same
+number of elements as `src2.channels()`:
+    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1} -  \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
+- The reverse difference between a scalar and an array in the case of `SubRS`:
+    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src2} -  \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f]
+where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
+channel is processed independently.
+
+The first function in the list above can be replaced with matrix expressions:
+@code{.cpp}
+    dst = src1 - src2;
+    dst -= src1; // equivalent to subtract(dst, src1, dst);
+@endcode
+The input arrays and the output array can all have the same or different depths. For example, you
+can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of
+the output array is determined by dtype parameter. In the second and third cases above, as well as
+in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this
+case the output array will have the same depth as the input array, be it src1, src2 or both.
+@note Saturation is not applied when the output array has the depth CV_32S. You may even get
+result of an incorrect sign in the case of overflow.
+@param src1 first input array or a scalar.
+@param src2 second input array or a scalar.
+@param dst output array of the same size and the same number of channels as the input array.
+@param mask optional operation mask; this is an 8-bit single channel array that specifies elements
+of the output array to be changed.
+@param dtype optional depth of the output array
+@sa  add, addWeighted, scaleAdd, Mat::convertTo
+  */
+CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst,
+                           InputArray mask = noArray(), int dtype = -1);
+
+
+/** @brief Calculates the per-element scaled product of two arrays.
+
+The function multiply calculates the per-element product of two arrays:
+
+\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I)  \cdot \texttt{src2} (I))\f]
+
+There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul .
+
+For a not-per-element matrix product, see gemm .
+
+@note Saturation is not applied when the output array has the depth
+CV_32S. You may even get result of an incorrect sign in the case of
+overflow.
+@param src1 first input array.
+@param src2 second input array of the same size and the same type as src1.
+@param dst output array of the same size and type as src1.
+@param scale optional scale factor.
+@param dtype optional depth of the output array
+@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare,
+Mat::convertTo
+*/
+CV_EXPORTS_W void multiply(InputArray src1, InputArray src2,
+                           OutputArray dst, double scale = 1, int dtype = -1);
+
+/** @brief Performs per-element division of two arrays or a scalar by an array.
+
+The function cv::divide divides one array by another:
+\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f]
+or a scalar by an array when there is no src1 :
+\f[\texttt{dst(I) = saturate(scale/src2(I))}\f]
+
+Different channels of multi-channel arrays are processed independently.
+
+For integer types when src2(I) is zero, dst(I) will also be zero.
+
+@note In case of floating point data there is no special defined behavior for zero src2(I) values.
+Regular floating-point division is used.
+Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values).
+
+@note Saturation is not applied when the output array has the depth CV_32S. You may even get
+result of an incorrect sign in the case of overflow.
+@param src1 first input array.
+@param src2 second input array of the same size and type as src1.
+@param scale scalar factor.
+@param dst output array of the same size and type as src2.
+@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in
+case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth().
+@sa  multiply, add, subtract
+*/
+CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst,
+                         double scale = 1, int dtype = -1);
+
+/** @overload */
+CV_EXPORTS_W void divide(double scale, InputArray src2,
+                         OutputArray dst, int dtype = -1);
+
+/** @brief Calculates the sum of a scaled array and another array.
+
+The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY
+or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates
+the sum of a scaled array and another array:
+\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) +  \texttt{src2} (I)\f]
+The function can also be emulated with a matrix expression, for example:
+@code{.cpp}
+    Mat A(3, 3, CV_64F);
+    ...
+    A.row(0) = A.row(1)*2 + A.row(2);
+@endcode
+@param src1 first input array.
+@param alpha scale factor for the first array.
+@param src2 second input array of the same size and type as src1.
+@param dst output array of the same size and type as src1.
+@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo
+*/
+CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
+
+/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp
+Check @ref tutorial_trackbar "the corresponding tutorial" for more details
+*/
+
+/** @brief Calculates the weighted sum of two arrays.
+
+The function addWeighted calculates the weighted sum of two arrays as follows:
+\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} +  \texttt{src2} (I)* \texttt{beta} +  \texttt{gamma} )\f]
+where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
+channel is processed independently.
+The function can be replaced with a matrix expression:
+@code{.cpp}
+    dst = src1*alpha + src2*beta + gamma;
+@endcode
+@note Saturation is not applied when the output array has the depth CV_32S. You may even get
+result of an incorrect sign in the case of overflow.
+@param src1 first input array.
+@param alpha weight of the first array elements.
+@param src2 second input array of the same size and channel number as src1.
+@param beta weight of the second array elements.
+@param gamma scalar added to each sum.
+@param dst output array that has the same size and number of channels as the input arrays.
+@param dtype optional depth of the output array; when both input arrays have the same depth, dtype
+can be set to -1, which will be equivalent to src1.depth().
+@sa  add, subtract, scaleAdd, Mat::convertTo
+*/
+CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2,
+                              double beta, double gamma, OutputArray dst, int dtype = -1);
+
+/** @brief Scales, calculates absolute values, and converts the result to 8-bit.
+
+On each element of the input array, the function convertScaleAbs
+performs three operations sequentially: scaling, taking an absolute
+value, conversion to an unsigned 8-bit type:
+\f[\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} +  \texttt{beta} |)\f]
+In case of multi-channel arrays, the function processes each channel
+independently. When the output is not 8-bit, the operation can be
+emulated by calling the Mat::convertTo method (or by using matrix
+expressions) and then by calculating an absolute value of the result.
+For example:
+@code{.cpp}
+    Mat_<float> A(30,30);
+    randu(A, Scalar(-100), Scalar(100));
+    Mat_<float> B = A*5 + 3;
+    B = abs(B);
+    // Mat_<float> B = abs(A*5+3) will also do the job,
+    // but it will allocate a temporary matrix
+@endcode
+@param src input array.
+@param dst output array.
+@param alpha optional scale factor.
+@param beta optional delta added to the scaled values.
+@sa  Mat::convertTo, cv::abs(const Mat&)
+*/
+CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst,
+                                  double alpha = 1, double beta = 0);
+
+/** @brief Converts an array to half precision floating number.
+
+This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data.
+There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or
+CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error.
+The format of half precision floating point is defined in IEEE 754-2008.
+
+@param src input array.
+@param dst output array.
+*/
+CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst);
+
+/** @brief Performs a look-up table transform of an array.
+
+The function LUT fills the output array with values from the look-up table. Indices of the entries
+are taken from the input array. That is, the function processes each element of src as follows:
+\f[\texttt{dst} (I)  \leftarrow \texttt{lut(src(I) + d)}\f]
+where
+\f[d =  \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f]
+@param src input array of 8-bit elements.
+@param lut look-up table of 256 elements; in case of multi-channel input array, the table should
+either have a single channel (in this case the same table is used for all channels) or the same
+number of channels as in the input array.
+@param dst output array of the same size and number of channels as src, and the same depth as lut.
+@sa  convertScaleAbs, Mat::convertTo
+*/
+CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst);
+
+/** @brief Calculates the sum of array elements.
+
+The function cv::sum calculates and returns the sum of array elements,
+independently for each channel.
+@param src input array that must have from 1 to 4 channels.
+@sa  countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce
+*/
+CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src);
+
+/** @brief Counts non-zero array elements.
+
+The function returns the number of non-zero elements in src :
+\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f]
+@param src single-channel array.
+@sa  mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix
+*/
+CV_EXPORTS_W int countNonZero( InputArray src );
+
+/** @brief Returns the list of locations of non-zero pixels
+
+Given a binary matrix (likely returned from an operation such
+as threshold(), compare(), >, ==, etc, return all of
+the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y)
+For example:
+@code{.cpp}
+    cv::Mat binaryImage; // input, binary image
+    cv::Mat locations;   // output, locations of non-zero pixels
+    cv::findNonZero(binaryImage, locations);
+
+    // access pixel coordinates
+    Point pnt = locations.at<Point>(i);
+@endcode
+or
+@code{.cpp}
+    cv::Mat binaryImage; // input, binary image
+    vector<Point> locations;   // output, locations of non-zero pixels
+    cv::findNonZero(binaryImage, locations);
+
+    // access pixel coordinates
+    Point pnt = locations[i];
+@endcode
+@param src single-channel array
+@param idx the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input
+*/
+CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx );
+
+/** @brief Calculates an average (mean) of array elements.
+
+The function cv::mean calculates the mean value M of array elements,
+independently for each channel, and return it:
+\f[\begin{array}{l} N =  \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c =  \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f]
+When all the mask elements are 0's, the function returns Scalar::all(0)
+@param src input array that should have from 1 to 4 channels so that the result can be stored in
+Scalar_ .
+@param mask optional operation mask.
+@sa  countNonZero, meanStdDev, norm, minMaxLoc
+*/
+CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray());
+
+/** Calculates a mean and standard deviation of array elements.
+
+The function cv::meanStdDev calculates the mean and the standard deviation M
+of array elements independently for each channel and returns it via the
+output parameters:
+\f[\begin{array}{l} N =  \sum _{I, \texttt{mask} (I)  \ne 0} 1 \\ \texttt{mean} _c =  \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c =  \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c -  \texttt{mean} _c \right )^2}{N}} \end{array}\f]
+When all the mask elements are 0's, the function returns
+mean=stddev=Scalar::all(0).
+@note The calculated standard deviation is only the diagonal of the
+complete normalized covariance matrix. If the full matrix is needed, you
+can reshape the multi-channel array M x N to the single-channel array
+M\*N x mtx.channels() (only possible when the matrix is continuous) and
+then pass the matrix to calcCovarMatrix .
+@param src input array that should have from 1 to 4 channels so that the results can be stored in
+Scalar_ 's.
+@param mean output parameter: calculated mean value.
+@param stddev output parameter: calculated standard deviation.
+@param mask optional operation mask.
+@sa  countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
+*/
+CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev,
+                             InputArray mask=noArray());
+
+/** @brief Calculates the  absolute norm of an array.
+
+This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes.
+
+As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$.
+The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$
+is calculated as follows
+\f{align*}
+    \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\
+    \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\
+    \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2
+\f}
+and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is
+\f{align*}
+    \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\
+    \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\
+    \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5.
+\f}
+The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$.
+It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$.
+![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png)
+
+When the mask parameter is specified and it is not empty, the norm is
+
+If normType is not specified, #NORM_L2 is used.
+calculated only over the region specified by the mask.
+
+Multi-channel input arrays are treated as single-channel arrays, that is,
+the results for all channels are combined.
+
+Hamming norms can only be calculated with CV_8U depth arrays.
+
+@param src1 first input array.
+@param normType type of the norm (see #NormTypes).
+@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
+*/
+CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray());
+
+/** @brief Calculates an absolute difference norm or a relative difference norm.
+
+This version of cv::norm calculates the absolute difference norm
+or the relative difference norm of arrays src1 and src2.
+The type of norm to calculate is specified using #NormTypes.
+
+@param src1 first input array.
+@param src2 second input array of the same size and the same type as src1.
+@param normType type of the norm (see #NormTypes).
+@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
+*/
+CV_EXPORTS_W double norm(InputArray src1, InputArray src2,
+                         int normType = NORM_L2, InputArray mask = noArray());
+/** @overload
+@param src first input array.
+@param normType type of the norm (see #NormTypes).
+*/
+CV_EXPORTS double norm( const SparseMat& src, int normType );
+
+/** @brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric.
+
+This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB),
+between two input arrays src1 and src2. The arrays must have the same type.
+
+The PSNR is calculated as follows:
+
+\f[
+\texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) }
+\f]
+
+where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data)
+and MSE is the mean squared error between the two arrays.
+
+@param src1 first input array.
+@param src2 second input array of the same size as src1.
+@param R the maximum pixel value (255 by default)
+
+  */
+CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2, double R=255.);
+
+/** @brief naive nearest neighbor finder
+
+see http://en.wikipedia.org/wiki/Nearest_neighbor_search
+@todo document
+  */
+CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2,
+                                OutputArray dist, int dtype, OutputArray nidx,
+                                int normType = NORM_L2, int K = 0,
+                                InputArray mask = noArray(), int update = 0,
+                                bool crosscheck = false);
+
+/** @brief Normalizes the norm or value range of an array.
+
+The function cv::normalize normalizes scale and shift the input array elements so that
+\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f]
+(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
+\f[\min _I  \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I  \texttt{dst} (I)= \texttt{beta}\f]
+
+when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be
+normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this
+sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or
+min-max but modify the whole array, you can use norm and Mat::convertTo.
+
+In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this,
+the range transformation for sparse matrices is not allowed since it can shift the zero level.
+
+Possible usage with some positive example data:
+@code{.cpp}
+    vector<double> positiveData = { 2.0, 8.0, 10.0 };
+    vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax;
+
+    // Norm to probability (total count)
+    // sum(numbers) = 20.0
+    // 2.0      0.1     (2.0/20.0)
+    // 8.0      0.4     (8.0/20.0)
+    // 10.0     0.5     (10.0/20.0)
+    normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1);
+
+    // Norm to unit vector: ||positiveData|| = 1.0
+    // 2.0      0.15
+    // 8.0      0.62
+    // 10.0     0.77
+    normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2);
+
+    // Norm to max element
+    // 2.0      0.2     (2.0/10.0)
+    // 8.0      0.8     (8.0/10.0)
+    // 10.0     1.0     (10.0/10.0)
+    normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF);
+
+    // Norm to range [0.0;1.0]
+    // 2.0      0.0     (shift to left border)
+    // 8.0      0.75    (6.0/8.0)
+    // 10.0     1.0     (shift to right border)
+    normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);
+@endcode
+
+@param src input array.
+@param dst output array of the same size as src .
+@param alpha norm value to normalize to or the lower range boundary in case of the range
+normalization.
+@param beta upper range boundary in case of the range normalization; it is not used for the norm
+normalization.
+@param norm_type normalization type (see cv::NormTypes).
+@param dtype when negative, the output array has the same type as src; otherwise, it has the same
+number of channels as src and the depth =CV_MAT_DEPTH(dtype).
+@param mask optional operation mask.
+@sa norm, Mat::convertTo, SparseMat::convertTo
+*/
+CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0,
+                             int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray());
+
+/** @overload
+@param src input array.
+@param dst output array of the same size as src .
+@param alpha norm value to normalize to or the lower range boundary in case of the range
+normalization.
+@param normType normalization type (see cv::NormTypes).
+*/
+CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType );
+
+/** @brief Finds the global minimum and maximum in an array.
+
+The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The
+extremums are searched across the whole array or, if mask is not an empty array, in the specified
+array region.
+
+The function do not work with multi-channel arrays. If you need to find minimum or maximum
+elements across all the channels, use Mat::reshape first to reinterpret the array as
+single-channel. Or you may extract the particular channel using either extractImageCOI , or
+mixChannels , or split .
+@param src input single-channel array.
+@param minVal pointer to the returned minimum value; NULL is used if not required.
+@param maxVal pointer to the returned maximum value; NULL is used if not required.
+@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required.
+@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required.
+@param mask optional mask used to select a sub-array.
+@sa max, min, reduceArgMin, reduceArgMax, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape
+*/
+CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal,
+                            CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0,
+                            CV_OUT Point* maxLoc = 0, InputArray mask = noArray());
+
+/**
+ * @brief Finds indices of min elements along provided axis
+ *
+ * @note
+ *      - If input or output array is not continuous, this function will create an internal copy.
+ *      - NaN handling is left unspecified, see patchNaNs().
+ *      - The returned index is always in bounds of input matrix.
+ *
+ * @param src input single-channel array.
+ * @param dst output array of type CV_32SC1 with the same dimensionality as src,
+ * except for axis being reduced - it should be set to 1.
+ * @param lastIndex whether to get the index of first or last occurrence of min.
+ * @param axis axis to reduce along.
+ * @sa reduceArgMax, minMaxLoc, min, max, compare, reduce
+ */
+CV_EXPORTS_W void reduceArgMin(InputArray src, OutputArray dst, int axis, bool lastIndex = false);
+
+/**
+ * @brief Finds indices of max elements along provided axis
+ *
+ * @note
+ *      - If input or output array is not continuous, this function will create an internal copy.
+ *      - NaN handling is left unspecified, see patchNaNs().
+ *      - The returned index is always in bounds of input matrix.
+ *
+ * @param src input single-channel array.
+ * @param dst output array of type CV_32SC1 with the same dimensionality as src,
+ * except for axis being reduced - it should be set to 1.
+ * @param lastIndex whether to get the index of first or last occurrence of max.
+ * @param axis axis to reduce along.
+ * @sa reduceArgMin, minMaxLoc, min, max, compare, reduce
+ */
+CV_EXPORTS_W void reduceArgMax(InputArray src, OutputArray dst, int axis, bool lastIndex = false);
+
+/** @brief Finds the global minimum and maximum in an array
+
+The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The
+extremums are searched across the whole array or, if mask is not an empty array, in the specified
+array region. The function does not work with multi-channel arrays. If you need to find minimum or
+maximum elements across all the channels, use Mat::reshape first to reinterpret the array as
+single-channel. Or you may extract the particular channel using either extractImageCOI , or
+mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements
+only.
+@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is
+a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2
+dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be
+(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be
+(0,j1)/(0,j2)).
+@param src input single-channel array.
+@param minVal pointer to the returned minimum value; NULL is used if not required.
+@param maxVal pointer to the returned maximum value; NULL is used if not required.
+@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
+Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
+in each dimension are stored there sequentially.
+@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
+@param mask specified array region
+*/
+CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0,
+                          int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray());
+
+/** @overload
+@param a input single-channel array.
+@param minVal pointer to the returned minimum value; NULL is used if not required.
+@param maxVal pointer to the returned maximum value; NULL is used if not required.
+@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
+Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
+in each dimension are stored there sequentially.
+@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
+*/
+CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal,
+                          double* maxVal, int* minIdx = 0, int* maxIdx = 0);
+
+/** @brief Reduces a matrix to a vector.
+
+The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
+1D vectors and performing the specified operation on the vectors until a single row/column is
+obtained. For example, the function can be used to compute horizontal and vertical projections of a
+raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one.
+In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy.
+And multi-channel arrays are also supported in these two reduction modes.
+
+The following code demonstrates its usage for a single channel matrix.
+@snippet snippets/core_reduce.cpp example
+
+And the following code demonstrates its usage for a two-channel matrix.
+@snippet snippets/core_reduce.cpp example2
+
+@param src input 2D matrix.
+@param dst output vector. Its size and type is defined by dim and dtype parameters.
+@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to
+a single row. 1 means that the matrix is reduced to a single column.
+@param rtype reduction operation that could be one of #ReduceTypes
+@param dtype when negative, the output vector will have the same type as the input matrix,
+otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()).
+@sa repeat, reduceArgMin, reduceArgMax
+*/
+CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1);
+
+/** @brief Creates one multi-channel array out of several single-channel ones.
+
+The function cv::merge merges several arrays to make a single multi-channel array. That is, each
+element of the output array will be a concatenation of the elements of the input arrays, where
+elements of i-th input array are treated as mv[i].channels()-element vectors.
+
+The function cv::split does the reverse operation. If you need to shuffle channels in some other
+advanced way, use cv::mixChannels.
+
+The following example shows how to merge 3 single channel matrices into a single 3-channel matrix.
+@snippet snippets/core_merge.cpp example
+
+@param mv input array of matrices to be merged; all the matrices in mv must have the same
+size and the same depth.
+@param count number of input matrices when mv is a plain C array; it must be greater than zero.
+@param dst output array of the same size and the same depth as mv[0]; The number of channels will
+be equal to the parameter count.
+@sa  mixChannels, split, Mat::reshape
+*/
+CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst);
+
+/** @overload
+@param mv input vector of matrices to be merged; all the matrices in mv must have the same
+size and the same depth.
+@param dst output array of the same size and the same depth as mv[0]; The number of channels will
+be the total number of channels in the matrix array.
+  */
+CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst);
+
+/** @brief Divides a multi-channel array into several single-channel arrays.
+
+The function cv::split splits a multi-channel array into separate single-channel arrays:
+\f[\texttt{mv} [c](I) =  \texttt{src} (I)_c\f]
+If you need to extract a single channel or do some other sophisticated channel permutation, use
+mixChannels .
+
+The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices.
+@snippet snippets/core_split.cpp example
+
+@param src input multi-channel array.
+@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are
+reallocated, if needed.
+@sa merge, mixChannels, cvtColor
+*/
+CV_EXPORTS void split(const Mat& src, Mat* mvbegin);
+
+/** @overload
+@param m input multi-channel array.
+@param mv output vector of arrays; the arrays themselves are reallocated, if needed.
+*/
+CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv);
+
+/** @brief Copies specified channels from input arrays to the specified channels of
+output arrays.
+
+The function cv::mixChannels provides an advanced mechanism for shuffling image channels.
+
+cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels.
+
+In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R
+channels swapped) and a separate alpha-channel image:
+@code{.cpp}
+    Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) );
+    Mat bgr( bgra.rows, bgra.cols, CV_8UC3 );
+    Mat alpha( bgra.rows, bgra.cols, CV_8UC1 );
+
+    // forming an array of matrices is a quite efficient operation,
+    // because the matrix data is not copied, only the headers
+    Mat out[] = { bgr, alpha };
+    // bgra[0] -> bgr[2], bgra[1] -> bgr[1],
+    // bgra[2] -> bgr[0], bgra[3] -> alpha[0]
+    int from_to[] = { 0,2, 1,1, 2,0, 3,3 };
+    mixChannels( &bgra, 1, out, 2, from_to, 4 );
+@endcode
+@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and
+Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the
+function.
+@param src input array or vector of matrices; all of the matrices must have the same size and the
+same depth.
+@param nsrcs number of matrices in `src`.
+@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
+depth must be the same as in `src[0]`.
+@param ndsts number of matrices in `dst`.
+@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
+a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
+dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
+src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
+src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
+channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
+filled with zero .
+@param npairs number of index pairs in `fromTo`.
+@sa split, merge, extractChannel, insertChannel, cvtColor
+*/
+CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts,
+                            const int* fromTo, size_t npairs);
+
+/** @overload
+@param src input array or vector of matrices; all of the matrices must have the same size and the
+same depth.
+@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
+depth must be the same as in src[0].
+@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
+a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
+dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
+src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
+src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
+channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
+filled with zero .
+@param npairs number of index pairs in fromTo.
+*/
+CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
+                            const int* fromTo, size_t npairs);
+
+/** @overload
+@param src input array or vector of matrices; all of the matrices must have the same size and the
+same depth.
+@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
+depth must be the same as in src[0].
+@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
+a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
+dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
+src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
+src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
+channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
+filled with zero .
+*/
+CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
+                              const std::vector<int>& fromTo);
+
+/** @brief Extracts a single channel from src (coi is 0-based index)
+@param src input array
+@param dst output array
+@param coi index of channel to extract
+@sa mixChannels, split
+*/
+CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi);
+
+/** @brief Inserts a single channel to dst (coi is 0-based index)
+@param src input array
+@param dst output array
+@param coi index of channel for insertion
+@sa mixChannels, merge
+*/
+CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi);
+
+/** @brief Flips a 2D array around vertical, horizontal, or both axes.
+
+The function cv::flip flips the array in one of three different ways (row
+and column indices are 0-based):
+\f[\texttt{dst} _{ij} =
+\left\{
+\begin{array}{l l}
+\texttt{src} _{\texttt{src.rows}-i-1,j} & if\;  \texttt{flipCode} = 0 \\
+\texttt{src} _{i, \texttt{src.cols} -j-1} & if\;  \texttt{flipCode} > 0 \\
+\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\
+\end{array}
+\right.\f]
+The example scenarios of using the function are the following:
+*   Vertical flipping of the image (flipCode == 0) to switch between
+    top-left and bottom-left image origin. This is a typical operation
+    in video processing on Microsoft Windows\* OS.
+*   Horizontal flipping of the image with the subsequent horizontal
+    shift and absolute difference calculation to check for a
+    vertical-axis symmetry (flipCode \> 0).
+*   Simultaneous horizontal and vertical flipping of the image with
+    the subsequent shift and absolute difference calculation to check
+    for a central symmetry (flipCode \< 0).
+*   Reversing the order of point arrays (flipCode \> 0 or
+    flipCode == 0).
+@param src input array.
+@param dst output array of the same size and type as src.
+@param flipCode a flag to specify how to flip the array; 0 means
+flipping around the x-axis and positive value (for example, 1) means
+flipping around y-axis. Negative value (for example, -1) means flipping
+around both axes.
+@sa transpose , repeat , completeSymm
+*/
+CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode);
+
+enum RotateFlags {
+    ROTATE_90_CLOCKWISE = 0, //!<Rotate 90 degrees clockwise
+    ROTATE_180 = 1, //!<Rotate 180 degrees clockwise
+    ROTATE_90_COUNTERCLOCKWISE = 2, //!<Rotate 270 degrees clockwise
+};
+/** @brief Rotates a 2D array in multiples of 90 degrees.
+The function cv::rotate rotates the array in one of three different ways:
+*   Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE).
+*   Rotate by 180 degrees clockwise (rotateCode = ROTATE_180).
+*   Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE).
+@param src input array.
+@param dst output array of the same type as src.  The size is the same with ROTATE_180,
+and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE.
+@param rotateCode an enum to specify how to rotate the array; see the enum #RotateFlags
+@sa transpose , repeat , completeSymm, flip, RotateFlags
+*/
+CV_EXPORTS_W void rotate(InputArray src, OutputArray dst, int rotateCode);
+
+/** @brief Fills the output array with repeated copies of the input array.
+
+The function cv::repeat duplicates the input array one or more times along each of the two axes:
+\f[\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\f]
+The second variant of the function is more convenient to use with @ref MatrixExpressions.
+@param src input array to replicate.
+@param ny Flag to specify how many times the `src` is repeated along the
+vertical axis.
+@param nx Flag to specify how many times the `src` is repeated along the
+horizontal axis.
+@param dst output array of the same type as `src`.
+@sa cv::reduce
+*/
+CV_EXPORTS_W void repeat(InputArray src, int ny, int nx, OutputArray dst);
+
+/** @overload
+@param src input array to replicate.
+@param ny Flag to specify how many times the `src` is repeated along the
+vertical axis.
+@param nx Flag to specify how many times the `src` is repeated along the
+horizontal axis.
+  */
+CV_EXPORTS Mat repeat(const Mat& src, int ny, int nx);
+
+/** @brief Applies horizontal concatenation to given matrices.
+
+The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows).
+@code{.cpp}
+    cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
+                           cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
+                           cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
+
+    cv::Mat out;
+    cv::hconcat( matArray, 3, out );
+    //out:
+    //[1, 2, 3;
+    // 1, 2, 3;
+    // 1, 2, 3;
+    // 1, 2, 3]
+@endcode
+@param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
+@param nsrc number of matrices in src.
+@param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
+@sa cv::vconcat(const Mat*, size_t, OutputArray), @sa cv::vconcat(InputArrayOfArrays, OutputArray) and @sa cv::vconcat(InputArray, InputArray, OutputArray)
+*/
+CV_EXPORTS void hconcat(const Mat* src, size_t nsrc, OutputArray dst);
+/** @overload
+ @code{.cpp}
+    cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 4,
+                                                  2, 5,
+                                                  3, 6);
+    cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 7, 10,
+                                                  8, 11,
+                                                  9, 12);
+
+    cv::Mat C;
+    cv::hconcat(A, B, C);
+    //C:
+    //[1, 4, 7, 10;
+    // 2, 5, 8, 11;
+    // 3, 6, 9, 12]
+ @endcode
+ @param src1 first input array to be considered for horizontal concatenation.
+ @param src2 second input array to be considered for horizontal concatenation.
+ @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2.
+ */
+CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst);
+/** @overload
+ @code{.cpp}
+    std::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
+                                      cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
+                                      cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
+
+    cv::Mat out;
+    cv::hconcat( matrices, out );
+    //out:
+    //[1, 2, 3;
+    // 1, 2, 3;
+    // 1, 2, 3;
+    // 1, 2, 3]
+ @endcode
+ @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
+ @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
+same depth.
+ */
+CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst);
+
+/** @brief Applies vertical concatenation to given matrices.
+
+The function vertically concatenates two or more cv::Mat matrices (with the same number of cols).
+@code{.cpp}
+    cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
+                           cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
+                           cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
+
+    cv::Mat out;
+    cv::vconcat( matArray, 3, out );
+    //out:
+    //[1,   1,   1,   1;
+    // 2,   2,   2,   2;
+    // 3,   3,   3,   3]
+@endcode
+@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth.
+@param nsrc number of matrices in src.
+@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
+@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray)
+*/
+CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst);
+/** @overload
+ @code{.cpp}
+    cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 7,
+                                                  2, 8,
+                                                  3, 9);
+    cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 4, 10,
+                                                  5, 11,
+                                                  6, 12);
+
+    cv::Mat C;
+    cv::vconcat(A, B, C);
+    //C:
+    //[1, 7;
+    // 2, 8;
+    // 3, 9;
+    // 4, 10;
+    // 5, 11;
+    // 6, 12]
+ @endcode
+ @param src1 first input array to be considered for vertical concatenation.
+ @param src2 second input array to be considered for vertical concatenation.
+ @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2.
+ */
+CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst);
+/** @overload
+ @code{.cpp}
+    std::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
+                                      cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
+                                      cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
+
+    cv::Mat out;
+    cv::vconcat( matrices, out );
+    //out:
+    //[1,   1,   1,   1;
+    // 2,   2,   2,   2;
+    // 3,   3,   3,   3]
+ @endcode
+ @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth
+ @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
+same depth.
+ */
+CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst);
+
+/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2)
+Calculates the per-element bit-wise conjunction of two arrays or an
+array and a scalar.
+
+The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for:
+*   Two arrays when src1 and src2 have the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
+*   An array and a scalar when src2 is constructed from Scalar or has
+    the same number of elements as `src1.channels()`:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
+*   A scalar and an array when src1 is constructed from Scalar or has
+    the same number of elements as `src2.channels()`:
+    \f[\texttt{dst} (I) =  \texttt{src1}  \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
+In case of floating-point arrays, their machine-specific bit
+representations (usually IEEE754-compliant) are used for the operation.
+In case of multi-channel arrays, each channel is processed
+independently. In the second and third cases above, the scalar is first
+converted to the array type.
+@param src1 first input array or a scalar.
+@param src2 second input array or a scalar.
+@param dst output array that has the same size and type as the input
+arrays.
+@param mask optional operation mask, 8-bit single channel array, that
+specifies elements of the output array to be changed.
+*/
+CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2,
+                              OutputArray dst, InputArray mask = noArray());
+
+/** @brief Calculates the per-element bit-wise disjunction of two arrays or an
+array and a scalar.
+
+The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for:
+*   Two arrays when src1 and src2 have the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
+*   An array and a scalar when src2 is constructed from Scalar or has
+    the same number of elements as `src1.channels()`:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
+*   A scalar and an array when src1 is constructed from Scalar or has
+    the same number of elements as `src2.channels()`:
+    \f[\texttt{dst} (I) =  \texttt{src1}  \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
+In case of floating-point arrays, their machine-specific bit
+representations (usually IEEE754-compliant) are used for the operation.
+In case of multi-channel arrays, each channel is processed
+independently. In the second and third cases above, the scalar is first
+converted to the array type.
+@param src1 first input array or a scalar.
+@param src2 second input array or a scalar.
+@param dst output array that has the same size and type as the input
+arrays.
+@param mask optional operation mask, 8-bit single channel array, that
+specifies elements of the output array to be changed.
+*/
+CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2,
+                             OutputArray dst, InputArray mask = noArray());
+
+/** @brief Calculates the per-element bit-wise "exclusive or" operation on two
+arrays or an array and a scalar.
+
+The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or"
+operation for:
+*   Two arrays when src1 and src2 have the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
+*   An array and a scalar when src2 is constructed from Scalar or has
+    the same number of elements as `src1.channels()`:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
+*   A scalar and an array when src1 is constructed from Scalar or has
+    the same number of elements as `src2.channels()`:
+    \f[\texttt{dst} (I) =  \texttt{src1}  \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
+In case of floating-point arrays, their machine-specific bit
+representations (usually IEEE754-compliant) are used for the operation.
+In case of multi-channel arrays, each channel is processed
+independently. In the 2nd and 3rd cases above, the scalar is first
+converted to the array type.
+@param src1 first input array or a scalar.
+@param src2 second input array or a scalar.
+@param dst output array that has the same size and type as the input
+arrays.
+@param mask optional operation mask, 8-bit single channel array, that
+specifies elements of the output array to be changed.
+*/
+CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2,
+                              OutputArray dst, InputArray mask = noArray());
+
+/** @brief  Inverts every bit of an array.
+
+The function cv::bitwise_not calculates per-element bit-wise inversion of the input
+array:
+\f[\texttt{dst} (I) =  \neg \texttt{src} (I)\f]
+In case of a floating-point input array, its machine-specific bit
+representation (usually IEEE754-compliant) is used for the operation. In
+case of multi-channel arrays, each channel is processed independently.
+@param src input array.
+@param dst output array that has the same size and type as the input
+array.
+@param mask optional operation mask, 8-bit single channel array, that
+specifies elements of the output array to be changed.
+*/
+CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst,
+                              InputArray mask = noArray());
+
+/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar.
+
+The function cv::absdiff calculates:
+*   Absolute difference between two arrays when they have the same
+    size and type:
+    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{src2}(I)|)\f]
+*   Absolute difference between an array and a scalar when the second
+    array is constructed from Scalar or has as many elements as the
+    number of channels in `src1`:
+    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{src2} |)\f]
+*   Absolute difference between a scalar and an array when the first
+    array is constructed from Scalar or has as many elements as the
+    number of channels in `src2`:
+    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1} -  \texttt{src2}(I) |)\f]
+    where I is a multi-dimensional index of array elements. In case of
+    multi-channel arrays, each channel is processed independently.
+@note Saturation is not applied when the arrays have the depth CV_32S.
+You may even get a negative value in the case of overflow.
+@param src1 first input array or a scalar.
+@param src2 second input array or a scalar.
+@param dst output array that has the same size and type as input arrays.
+@sa cv::abs(const Mat&)
+*/
+CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst);
+
+/** @brief  This is an overloaded member function, provided for convenience (python)
+Copies the matrix to another one.
+When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data.
+@param src source matrix.
+@param dst Destination matrix. If it does not have a proper size or type before the operation, it is
+reallocated.
+@param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix
+elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels.
+*/
+
+void CV_EXPORTS_W copyTo(InputArray src, OutputArray dst, InputArray mask);
+/** @brief  Checks if array elements lie between the elements of two other arrays.
+
+The function checks the range as follows:
+-   For every element of a single-channel input array:
+    \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0\f]
+-   For two-channel arrays:
+    \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0  \land \texttt{lowerb} (I)_1  \leq \texttt{src} (I)_1 \leq  \texttt{upperb} (I)_1\f]
+-   and so forth.
+
+That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the
+specified 1D, 2D, 3D, ... box and 0 otherwise.
+
+When the lower and/or upper boundary parameters are scalars, the indexes
+(I) at lowerb and upperb in the above formulas should be omitted.
+@param src first input array.
+@param lowerb inclusive lower boundary array or a scalar.
+@param upperb inclusive upper boundary array or a scalar.
+@param dst output array of the same size as src and CV_8U type.
+*/
+CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb,
+                          InputArray upperb, OutputArray dst);
+
+/** @brief Performs the per-element comparison of two arrays or an array and scalar value.
+
+The function compares:
+*   Elements of two arrays when src1 and src2 have the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \,\texttt{cmpop}\, \texttt{src2} (I)\f]
+*   Elements of src1 with a scalar src2 when src2 is constructed from
+    Scalar or has a single element:
+    \f[\texttt{dst} (I) =  \texttt{src1}(I) \,\texttt{cmpop}\,  \texttt{src2}\f]
+*   src1 with elements of src2 when src1 is constructed from Scalar or
+    has a single element:
+    \f[\texttt{dst} (I) =  \texttt{src1}  \,\texttt{cmpop}\, \texttt{src2} (I)\f]
+When the comparison result is true, the corresponding element of output
+array is set to 255. The comparison operations can be replaced with the
+equivalent matrix expressions:
+@code{.cpp}
+    Mat dst1 = src1 >= src2;
+    Mat dst2 = src1 < 8;
+    ...
+@endcode
+@param src1 first input array or a scalar; when it is an array, it must have a single channel.
+@param src2 second input array or a scalar; when it is an array, it must have a single channel.
+@param dst output array of type ref CV_8U that has the same size and the same number of channels as
+    the input arrays.
+@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes)
+@sa checkRange, min, max, threshold
+*/
+CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop);
+
+/** @brief Calculates per-element minimum of two arrays or an array and a scalar.
+
+The function cv::min calculates the per-element minimum of two arrays:
+\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f]
+or array and a scalar:
+\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f]
+@param src1 first input array.
+@param src2 second input array of the same size and type as src1.
+@param dst output array of the same size and type as src1.
+@sa max, compare, inRange, minMaxLoc
+*/
+CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst);
+/** @overload
+needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
+*/
+CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst);
+/** @overload
+needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
+*/
+CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst);
+
+/** @brief Calculates per-element maximum of two arrays or an array and a scalar.
+
+The function cv::max calculates the per-element maximum of two arrays:
+\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f]
+or array and a scalar:
+\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f]
+@param src1 first input array.
+@param src2 second input array of the same size and type as src1 .
+@param dst output array of the same size and type as src1.
+@sa  min, compare, inRange, minMaxLoc, @ref MatrixExpressions
+*/
+CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst);
+/** @overload
+needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
+*/
+CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst);
+/** @overload
+needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
+*/
+CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst);
+
+/** @brief Calculates a square root of array elements.
+
+The function cv::sqrt calculates a square root of each input array element.
+In case of multi-channel arrays, each channel is processed
+independently. The accuracy is approximately the same as of the built-in
+std::sqrt .
+@param src input floating-point array.
+@param dst output array of the same size and type as src.
+*/
+CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst);
+
+/** @brief Raises every array element to a power.
+
+The function cv::pow raises every element of the input array to power :
+\f[\texttt{dst} (I) =  \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f]
+
+So, for a non-integer power exponent, the absolute values of input array
+elements are used. However, it is possible to get true values for
+negative values using some extra operations. In the example below,
+computing the 5th root of array src shows:
+@code{.cpp}
+    Mat mask = src < 0;
+    pow(src, 1./5, dst);
+    subtract(Scalar::all(0), dst, dst, mask);
+@endcode
+For some values of power, such as integer values, 0.5 and -0.5,
+specialized faster algorithms are used.
+
+Special values (NaN, Inf) are not handled.
+@param src input array.
+@param power exponent of power.
+@param dst output array of the same size and type as src.
+@sa sqrt, exp, log, cartToPolar, polarToCart
+*/
+CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst);
+
+/** @brief Calculates the exponent of every array element.
+
+The function cv::exp calculates the exponent of every element of the input
+array:
+\f[\texttt{dst} [I] = e^{ src(I) }\f]
+
+The maximum relative error is about 7e-6 for single-precision input and
+less than 1e-10 for double-precision input. Currently, the function
+converts denormalized values to zeros on output. Special values (NaN,
+Inf) are not handled.
+@param src input array.
+@param dst output array of the same size and type as src.
+@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude
+*/
+CV_EXPORTS_W void exp(InputArray src, OutputArray dst);
+
+/** @brief Calculates the natural logarithm of every array element.
+
+The function cv::log calculates the natural logarithm of every element of the input array:
+\f[\texttt{dst} (I) =  \log (\texttt{src}(I)) \f]
+
+Output on zero, negative and special (NaN, Inf) values is undefined.
+
+@param src input array.
+@param dst output array of the same size and type as src .
+@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude
+*/
+CV_EXPORTS_W void log(InputArray src, OutputArray dst);
+
+/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle.
+
+The function cv::polarToCart calculates the Cartesian coordinates of each 2D
+vector represented by the corresponding elements of magnitude and angle:
+\f[\begin{array}{l} \texttt{x} (I) =  \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) =  \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f]
+
+The relative accuracy of the estimated coordinates is about 1e-6.
+@param magnitude input floating-point array of magnitudes of 2D vectors;
+it can be an empty matrix (=Mat()), in this case, the function assumes
+that all the magnitudes are =1; if it is not empty, it must have the
+same size and type as angle.
+@param angle input floating-point array of angles of 2D vectors.
+@param x output array of x-coordinates of 2D vectors; it has the same
+size and type as angle.
+@param y output array of y-coordinates of 2D vectors; it has the same
+size and type as angle.
+@param angleInDegrees when true, the input angles are measured in
+degrees, otherwise, they are measured in radians.
+@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt
+*/
+CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle,
+                              OutputArray x, OutputArray y, bool angleInDegrees = false);
+
+/** @brief Calculates the magnitude and angle of 2D vectors.
+
+The function cv::cartToPolar calculates either the magnitude, angle, or both
+for every 2D vector (x(I),y(I)):
+\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f]
+
+The angles are calculated with accuracy about 0.3 degrees. For the point
+(0,0), the angle is set to 0.
+@param x array of x-coordinates; this must be a single-precision or
+double-precision floating-point array.
+@param y array of y-coordinates, that must have the same size and same type as x.
+@param magnitude output array of magnitudes of the same size and type as x.
+@param angle output array of angles that has the same size and type as
+x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees).
+@param angleInDegrees a flag, indicating whether the angles are measured
+in radians (which is by default), or in degrees.
+@sa Sobel, Scharr
+*/
+CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y,
+                              OutputArray magnitude, OutputArray angle,
+                              bool angleInDegrees = false);
+
+/** @brief Calculates the rotation angle of 2D vectors.
+
+The function cv::phase calculates the rotation angle of each 2D vector that
+is formed from the corresponding elements of x and y :
+\f[\texttt{angle} (I) =  \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f]
+
+The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 ,
+the corresponding angle(I) is set to 0.
+@param x input floating-point array of x-coordinates of 2D vectors.
+@param y input array of y-coordinates of 2D vectors; it must have the
+same size and the same type as x.
+@param angle output array of vector angles; it has the same size and
+same type as x .
+@param angleInDegrees when true, the function calculates the angle in
+degrees, otherwise, they are measured in radians.
+*/
+CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle,
+                        bool angleInDegrees = false);
+
+/** @brief Calculates the magnitude of 2D vectors.
+
+The function cv::magnitude calculates the magnitude of 2D vectors formed
+from the corresponding elements of x and y arrays:
+\f[\texttt{dst} (I) =  \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f]
+@param x floating-point array of x-coordinates of the vectors.
+@param y floating-point array of y-coordinates of the vectors; it must
+have the same size as x.
+@param magnitude output array of the same size and type as x.
+@sa cartToPolar, polarToCart, phase, sqrt
+*/
+CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude);
+
+/** @brief Checks every element of an input array for invalid values.
+
+The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \>
+-DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and
+maxVal. In case of multi-channel arrays, each channel is processed independently. If some values
+are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the
+function either returns false (when quiet=true) or throws an exception.
+@param a input array.
+@param quiet a flag, indicating whether the functions quietly return false when the array elements
+are out of range or they throw an exception.
+@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims
+elements.
+@param minVal inclusive lower boundary of valid values range.
+@param maxVal exclusive upper boundary of valid values range.
+*/
+CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0,
+                            double minVal = -DBL_MAX, double maxVal = DBL_MAX);
+
+/** @brief converts NaNs to the given number
+@param a input/output matrix (CV_32F type).
+@param val value to convert the NaNs
+*/
+CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0);
+
+/** @brief Performs generalized matrix multiplication.
+
+The function cv::gemm performs generalized matrix multiplication similar to the
+gemm functions in BLAS level 3. For example,
+`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`
+corresponds to
+\f[\texttt{dst} =  \texttt{alpha} \cdot \texttt{src1} ^T  \cdot \texttt{src2} +  \texttt{beta} \cdot \texttt{src3} ^T\f]
+
+In case of complex (two-channel) data, performed a complex matrix
+multiplication.
+
+The function can be replaced with a matrix expression. For example, the
+above call can be replaced with:
+@code{.cpp}
+    dst = alpha*src1.t()*src2 + beta*src3.t();
+@endcode
+@param src1 first multiplied input matrix that could be real(CV_32FC1,
+CV_64FC1) or complex(CV_32FC2, CV_64FC2).
+@param src2 second multiplied input matrix of the same type as src1.
+@param alpha weight of the matrix product.
+@param src3 third optional delta matrix added to the matrix product; it
+should have the same type as src1 and src2.
+@param beta weight of src3.
+@param dst output matrix; it has the proper size and the same type as
+input matrices.
+@param flags operation flags (cv::GemmFlags)
+@sa mulTransposed , transform
+*/
+CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
+                       InputArray src3, double beta, OutputArray dst, int flags = 0);
+
+/** @brief Calculates the product of a matrix and its transposition.
+
+The function cv::mulTransposed calculates the product of src and its
+transposition:
+\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f]
+if aTa=true , and
+\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f]
+otherwise. The function is used to calculate the covariance matrix. With
+zero delta, it can be used as a faster substitute for general matrix
+product A\*B when B=A'
+@param src input single-channel matrix. Note that unlike gemm, the
+function can multiply not only floating-point matrices.
+@param dst output square matrix.
+@param aTa Flag specifying the multiplication ordering. See the
+description below.
+@param delta Optional delta matrix subtracted from src before the
+multiplication. When the matrix is empty ( delta=noArray() ), it is
+assumed to be zero, that is, nothing is subtracted. If it has the same
+size as src , it is simply subtracted. Otherwise, it is "repeated" (see
+repeat ) to cover the full src and then subtracted. Type of the delta
+matrix, when it is not empty, must be the same as the type of created
+output matrix. See the dtype parameter description below.
+@param scale Optional scale factor for the matrix product.
+@param dtype Optional type of the output matrix. When it is negative,
+the output matrix will have the same type as src . Otherwise, it will be
+type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F .
+@sa calcCovarMatrix, gemm, repeat, reduce
+*/
+CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa,
+                                 InputArray delta = noArray(),
+                                 double scale = 1, int dtype = -1 );
+
+/** @brief Transposes a matrix.
+
+The function cv::transpose transposes the matrix src :
+\f[\texttt{dst} (i,j) =  \texttt{src} (j,i)\f]
+@note No complex conjugation is done in case of a complex matrix. It
+should be done separately if needed.
+@param src input array.
+@param dst output array of the same type as src.
+*/
+CV_EXPORTS_W void transpose(InputArray src, OutputArray dst);
+
+/** @brief Performs the matrix transformation of every array element.
+
+The function cv::transform performs the matrix transformation of every
+element of the array src and stores the results in dst :
+\f[\texttt{dst} (I) =  \texttt{m} \cdot \texttt{src} (I)\f]
+(when m.cols=src.channels() ), or
+\f[\texttt{dst} (I) =  \texttt{m} \cdot [ \texttt{src} (I); 1]\f]
+(when m.cols=src.channels()+1 )
+
+Every element of the N -channel array src is interpreted as N -element
+vector that is transformed using the M x N or M x (N+1) matrix m to
+M-element vector - the corresponding element of the output array dst .
+
+The function may be used for geometrical transformation of
+N -dimensional points, arbitrary linear color space transformation (such
+as various kinds of RGB to YUV transforms), shuffling the image
+channels, and so forth.
+@param src input array that must have as many channels (1 to 4) as
+m.cols or m.cols-1.
+@param dst output array of the same size and depth as src; it has as
+many channels as m.rows.
+@param m transformation 2x2 or 2x3 floating-point matrix.
+@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective
+*/
+CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m );
+
+/** @brief Performs the perspective matrix transformation of vectors.
+
+The function cv::perspectiveTransform transforms every element of src by
+treating it as a 2D or 3D vector, in the following way:
+\f[(x, y, z)  \rightarrow (x'/w, y'/w, z'/w)\f]
+where
+\f[(x', y', z', w') =  \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1  \end{bmatrix}\f]
+and
+\f[w =  \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f]
+
+Here a 3D vector transformation is shown. In case of a 2D vector
+transformation, the z component is omitted.
+
+@note The function transforms a sparse set of 2D or 3D vectors. If you
+want to transform an image using perspective transformation, use
+warpPerspective . If you have an inverse problem, that is, you want to
+compute the most probable perspective transformation out of several
+pairs of corresponding points, you can use getPerspectiveTransform or
+findHomography .
+@param src input two-channel or three-channel floating-point array; each
+element is a 2D/3D vector to be transformed.
+@param dst output array of the same size and type as src.
+@param m 3x3 or 4x4 floating-point transformation matrix.
+@sa  transform, warpPerspective, getPerspectiveTransform, findHomography
+*/
+CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m );
+
+/** @brief Copies the lower or the upper half of a square matrix to its another half.
+
+The function cv::completeSymm copies the lower or the upper half of a square matrix to
+its another half. The matrix diagonal remains unchanged:
+ - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i > j\f$ if
+    lowerToUpper=false
+ - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i < j\f$ if
+    lowerToUpper=true
+
+@param m input-output floating-point square matrix.
+@param lowerToUpper operation flag; if true, the lower half is copied to
+the upper half. Otherwise, the upper half is copied to the lower half.
+@sa flip, transpose
+*/
+CV_EXPORTS_W void completeSymm(InputOutputArray m, bool lowerToUpper = false);
+
+/** @brief Initializes a scaled identity matrix.
+
+The function cv::setIdentity initializes a scaled identity matrix:
+\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f]
+
+The function can also be emulated using the matrix initializers and the
+matrix expressions:
+@code
+    Mat A = Mat::eye(4, 3, CV_32F)*5;
+    // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]
+@endcode
+@param mtx matrix to initialize (not necessarily square).
+@param s value to assign to diagonal elements.
+@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=
+*/
+CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1));
+
+/** @brief Returns the determinant of a square floating-point matrix.
+
+The function cv::determinant calculates and returns the determinant of the
+specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the
+direct method is used. For larger matrices, the function uses LU
+factorization with partial pivoting.
+
+For symmetric positively-determined matrices, it is also possible to use
+eigen decomposition to calculate the determinant.
+@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and
+square size.
+@sa trace, invert, solve, eigen, @ref MatrixExpressions
+*/
+CV_EXPORTS_W double determinant(InputArray mtx);
+
+/** @brief Returns the trace of a matrix.
+
+The function cv::trace returns the sum of the diagonal elements of the
+matrix mtx .
+\f[\mathrm{tr} ( \texttt{mtx} ) =  \sum _i  \texttt{mtx} (i,i)\f]
+@param mtx input matrix.
+*/
+CV_EXPORTS_W Scalar trace(InputArray mtx);
+
+/** @brief Finds the inverse or pseudo-inverse of a matrix.
+
+The function cv::invert inverts the matrix src and stores the result in dst
+. When the matrix src is singular or non-square, the function calculates
+the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is
+minimal, where I is an identity matrix.
+
+In case of the #DECOMP_LU method, the function returns non-zero value if
+the inverse has been successfully calculated and 0 if src is singular.
+
+In case of the #DECOMP_SVD method, the function returns the inverse
+condition number of src (the ratio of the smallest singular value to the
+largest singular value) and 0 if src is singular. The SVD method
+calculates a pseudo-inverse matrix if src is singular.
+
+Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with
+non-singular square matrices that should also be symmetrical and
+positively defined. In this case, the function stores the inverted
+matrix in dst and returns non-zero. Otherwise, it returns 0.
+
+@param src input floating-point M x N matrix.
+@param dst output matrix of N x M size and the same type as src.
+@param flags inversion method (cv::DecompTypes)
+@sa solve, SVD
+*/
+CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU);
+
+/** @brief Solves one or more linear systems or least-squares problems.
+
+The function cv::solve solves a linear system or least-squares problem (the
+latter is possible with SVD or QR methods, or by specifying the flag
+#DECOMP_NORMAL ):
+\f[\texttt{dst} =  \arg \min _X \| \texttt{src1} \cdot \texttt{X} -  \texttt{src2} \|\f]
+
+If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1
+if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise,
+it returns 0. In the latter case, dst is not valid. Other methods find a
+pseudo-solution in case of a singular left-hand side part.
+
+@note If you want to find a unity-norm solution of an under-defined
+singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve
+will not do the work. Use SVD::solveZ instead.
+
+@param src1 input matrix on the left-hand side of the system.
+@param src2 input matrix on the right-hand side of the system.
+@param dst output solution.
+@param flags solution (matrix inversion) method (#DecompTypes)
+@sa invert, SVD, eigen
+*/
+CV_EXPORTS_W bool solve(InputArray src1, InputArray src2,
+                        OutputArray dst, int flags = DECOMP_LU);
+
+/** @brief Sorts each row or each column of a matrix.
+
+The function cv::sort sorts each matrix row or each matrix column in
+ascending or descending order. So you should pass two operation flags to
+get desired behaviour. If you want to sort matrix rows or columns
+lexicographically, you can use STL std::sort generic function with the
+proper comparison predicate.
+
+@param src input single-channel array.
+@param dst output array of the same size and type as src.
+@param flags operation flags, a combination of #SortFlags
+@sa sortIdx, randShuffle
+*/
+CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags);
+
+/** @brief Sorts each row or each column of a matrix.
+
+The function cv::sortIdx sorts each matrix row or each matrix column in the
+ascending or descending order. So you should pass two operation flags to
+get desired behaviour. Instead of reordering the elements themselves, it
+stores the indices of sorted elements in the output array. For example:
+@code
+    Mat A = Mat::eye(3,3,CV_32F), B;
+    sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING);
+    // B will probably contain
+    // (because of equal elements in A some permutations are possible):
+    // [[1, 2, 0], [0, 2, 1], [0, 1, 2]]
+@endcode
+@param src input single-channel array.
+@param dst output integer array of the same size as src.
+@param flags operation flags that could be a combination of cv::SortFlags
+@sa sort, randShuffle
+*/
+CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags);
+
+/** @brief Finds the real roots of a cubic equation.
+
+The function solveCubic finds the real roots of a cubic equation:
+-   if coeffs is a 4-element vector:
+\f[\texttt{coeffs} [0] x^3 +  \texttt{coeffs} [1] x^2 +  \texttt{coeffs} [2] x +  \texttt{coeffs} [3] = 0\f]
+-   if coeffs is a 3-element vector:
+\f[x^3 +  \texttt{coeffs} [0] x^2 +  \texttt{coeffs} [1] x +  \texttt{coeffs} [2] = 0\f]
+
+The roots are stored in the roots array.
+@param coeffs equation coefficients, an array of 3 or 4 elements.
+@param roots output array of real roots that has 1 or 3 elements.
+@return number of real roots. It can be 0, 1 or 2.
+*/
+CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots);
+
+/** @brief Finds the real or complex roots of a polynomial equation.
+
+The function cv::solvePoly finds real and complex roots of a polynomial equation:
+\f[\texttt{coeffs} [n] x^{n} +  \texttt{coeffs} [n-1] x^{n-1} + ... +  \texttt{coeffs} [1] x +  \texttt{coeffs} [0] = 0\f]
+@param coeffs array of polynomial coefficients.
+@param roots output (complex) array of roots.
+@param maxIters maximum number of iterations the algorithm does.
+*/
+CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300);
+
+/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix.
+
+The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric
+matrix src:
+@code
+    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
+@endcode
+
+@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix.
+
+@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical
+(src ^T^ == src).
+@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored
+in the descending order.
+@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the
+eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding
+eigenvalues.
+@sa eigenNonSymmetric, completeSymm , PCA
+*/
+CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues,
+                        OutputArray eigenvectors = noArray());
+
+/** @brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only).
+
+@note Assumes real eigenvalues.
+
+The function calculates eigenvalues and eigenvectors (optional) of the square matrix src:
+@code
+    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
+@endcode
+
+@param src input matrix (CV_32FC1 or CV_64FC1 type).
+@param eigenvalues output vector of eigenvalues (type is the same type as src).
+@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues.
+@sa eigen
+*/
+CV_EXPORTS_W void eigenNonSymmetric(InputArray src, OutputArray eigenvalues,
+                                    OutputArray eigenvectors);
+
+/** @brief Calculates the covariance matrix of a set of vectors.
+
+The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of
+the set of input vectors.
+@param samples samples stored as separate matrices
+@param nsamples number of samples
+@param covar output covariance matrix of the type ctype and square size.
+@param mean input or output (depending on the flags) array as the average value of the input vectors.
+@param flags operation flags as a combination of #CovarFlags
+@param ctype type of the matrixl; it equals 'CV_64F' by default.
+@sa PCA, mulTransposed, Mahalanobis
+@todo InputArrayOfArrays
+*/
+CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean,
+                                 int flags, int ctype = CV_64F);
+
+/** @overload
+@note use #COVAR_ROWS or #COVAR_COLS flag
+@param samples samples stored as rows/columns of a single matrix.
+@param covar output covariance matrix of the type ctype and square size.
+@param mean input or output (depending on the flags) array as the average value of the input vectors.
+@param flags operation flags as a combination of #CovarFlags
+@param ctype type of the matrixl; it equals 'CV_64F' by default.
+*/
+CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar,
+                                   InputOutputArray mean, int flags, int ctype = CV_64F);
+
+/** wrap PCA::operator() */
+CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
+                             OutputArray eigenvectors, int maxComponents = 0);
+
+/** wrap PCA::operator() and add eigenvalues output parameter */
+CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean,
+                                           OutputArray eigenvectors, OutputArray eigenvalues,
+                                           int maxComponents = 0);
+
+/** wrap PCA::operator() */
+CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
+                             OutputArray eigenvectors, double retainedVariance);
+
+/** wrap PCA::operator() and add eigenvalues output parameter */
+CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean,
+                                           OutputArray eigenvectors, OutputArray eigenvalues,
+                                           double retainedVariance);
+
+/** wrap PCA::project */
+CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean,
+                             InputArray eigenvectors, OutputArray result);
+
+/** wrap PCA::backProject */
+CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean,
+                                 InputArray eigenvectors, OutputArray result);
+
+/** wrap SVD::compute */
+CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 );
+
+/** wrap SVD::backSubst */
+CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt,
+                               InputArray rhs, OutputArray dst );
+
+/** @brief Calculates the Mahalanobis distance between two vectors.
+
+The function cv::Mahalanobis calculates and returns the weighted distance between two vectors:
+\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f]
+The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using
+the invert function (preferably using the #DECOMP_SVD method, as the most accurate).
+@param v1 first 1D input vector.
+@param v2 second 1D input vector.
+@param icovar inverse covariance matrix.
+*/
+CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar);
+
+/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.
+
+The function cv::dft performs one of the following:
+-   Forward the Fourier transform of a 1D vector of N elements:
+    \f[Y = F^{(N)}  \cdot X,\f]
+    where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$
+-   Inverse the Fourier transform of a 1D vector of N elements:
+    \f[\begin{array}{l} X'=  \left (F^{(N)} \right )^{-1}  \cdot Y =  \left (F^{(N)} \right )^*  \cdot y  \\ X = (1/N)  \cdot X, \end{array}\f]
+    where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$
+-   Forward the 2D Fourier transform of a M x N matrix:
+    \f[Y = F^{(M)}  \cdot X  \cdot F^{(N)}\f]
+-   Inverse the 2D Fourier transform of a M x N matrix:
+    \f[\begin{array}{l} X'=  \left (F^{(M)} \right )^*  \cdot Y  \cdot \left (F^{(N)} \right )^* \\ X =  \frac{1}{M \cdot N} \cdot X' \end{array}\f]
+
+In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input
+spectrum of the inverse Fourier transform can be represented in a packed format called *CCS*
+(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here
+is how 2D *CCS* spectrum looks:
+\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} &  \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2}  \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} &  \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2}  \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} &  \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2}  \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} &  Re Y_{M-3,1}  & Im Y_{M-3,1} &  \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2}  \\ Im Y_{M/2-1,0} &  Re Y_{M-2,1}  & Im Y_{M-2,1} &  \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2}  \\ Re Y_{M/2,0}  &  Re Y_{M-1,1} &  Im Y_{M-1,1} &  \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f]
+
+In case of 1D transform of a real vector, the output looks like the first row of the matrix above.
+
+So, the function chooses an operation mode depending on the flags and size of the input array:
+-   If #DFT_ROWS is set or the input array has a single row or single column, the function
+    performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set.
+    Otherwise, it performs a 2D transform.
+-   If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or
+    2D transform:
+    -   When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as
+        input.
+    -   When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as
+        input. In case of 2D transform, it uses the packed format as shown above. In case of a
+        single 1D transform, it looks like the first row of the matrix above. In case of
+        multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix
+        looks like the first row of the matrix above.
+-   If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the
+    output is a complex array of the same size as input. The function performs a forward or
+    inverse 1D or 2D transform of the whole input array or each row of the input array
+    independently, depending on the flags DFT_INVERSE and DFT_ROWS.
+-   When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT
+    is set, the output is a real array of the same size as input. The function performs a 1D or 2D
+    inverse transformation of the whole input array or each individual row, depending on the flags
+    #DFT_INVERSE and #DFT_ROWS.
+
+If #DFT_SCALE is set, the scaling is done after the transformation.
+
+Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed
+efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the
+current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize
+method.
+
+The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays:
+@code
+    void convolveDFT(InputArray A, InputArray B, OutputArray C)
+    {
+        // reallocate the output array if needed
+        C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
+        Size dftSize;
+        // calculate the size of DFT transform
+        dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
+        dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);
+
+        // allocate temporary buffers and initialize them with 0's
+        Mat tempA(dftSize, A.type(), Scalar::all(0));
+        Mat tempB(dftSize, B.type(), Scalar::all(0));
+
+        // copy A and B to the top-left corners of tempA and tempB, respectively
+        Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
+        A.copyTo(roiA);
+        Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
+        B.copyTo(roiB);
+
+        // now transform the padded A & B in-place;
+        // use "nonzeroRows" hint for faster processing
+        dft(tempA, tempA, 0, A.rows);
+        dft(tempB, tempB, 0, B.rows);
+
+        // multiply the spectrums;
+        // the function handles packed spectrum representations well
+        mulSpectrums(tempA, tempB, tempA);
+
+        // transform the product back from the frequency domain.
+        // Even though all the result rows will be non-zero,
+        // you need only the first C.rows of them, and thus you
+        // pass nonzeroRows == C.rows
+        dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);
+
+        // now copy the result back to C.
+        tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);
+
+        // all the temporary buffers will be deallocated automatically
+    }
+@endcode
+To optimize this sample, consider the following approaches:
+-   Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to
+    the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole
+    tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols)
+    rightmost columns of the matrices.
+-   This DFT-based convolution does not have to be applied to the whole big arrays, especially if B
+    is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts.
+    To do this, you need to split the output array C into multiple tiles. For each tile, estimate
+    which parts of A and B are required to calculate convolution in this tile. If the tiles in C are
+    too small, the speed will decrease a lot because of repeated work. In the ultimate case, when
+    each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution
+    algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and
+    there is also a slowdown because of bad cache locality. So, there is an optimal tile size
+    somewhere in the middle.
+-   If different tiles in C can be calculated in parallel and, thus, the convolution is done by
+    parts, the loop can be threaded.
+
+All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by
+using them, you can get the performance even better than with the above theoretically optimal
+implementation. Though, those two functions actually calculate cross-correlation, not convolution,
+so you need to "flip" the second convolution operand B vertically and horizontally using flip .
+@note
+-   An example using the discrete fourier transform can be found at
+    opencv_source_code/samples/cpp/dft.cpp
+-   (Python) An example using the dft functionality to perform Wiener deconvolution can be found
+    at opencv_source/samples/python/deconvolution.py
+-   (Python) An example rearranging the quadrants of a Fourier image can be found at
+    opencv_source/samples/python/dft.py
+@param src input array that could be real or complex.
+@param dst output array whose size and type depends on the flags .
+@param flags transformation flags, representing a combination of the #DftFlags
+@param nonzeroRows when the parameter is not zero, the function assumes that only the first
+nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the
+output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the
+rows more efficiently and save some time; this technique is very useful for calculating array
+cross-correlation or convolution using DFT.
+@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar ,
+magnitude , phase
+*/
+CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0);
+
+/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array.
+
+idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) .
+@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of
+dft or idft explicitly to make these transforms mutually inverse.
+@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize
+@param src input floating-point real or complex array.
+@param dst output array whose size and type depend on the flags.
+@param flags operation flags (see dft and #DftFlags).
+@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see
+the convolution sample in dft description.
+*/
+CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0);
+
+/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array.
+
+The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D
+floating-point array:
+-   Forward Cosine transform of a 1D vector of N elements:
+    \f[Y = C^{(N)}  \cdot X\f]
+    where
+    \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f]
+    and
+    \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*.
+-   Inverse Cosine transform of a 1D vector of N elements:
+    \f[X =  \left (C^{(N)} \right )^{-1}  \cdot Y =  \left (C^{(N)} \right )^T  \cdot Y\f]
+    (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ )
+-   Forward 2D Cosine transform of M x N matrix:
+    \f[Y = C^{(N)}  \cdot X  \cdot \left (C^{(N)} \right )^T\f]
+-   Inverse 2D Cosine transform of M x N matrix:
+    \f[X =  \left (C^{(N)} \right )^T  \cdot X  \cdot C^{(N)}\f]
+
+The function chooses the mode of operation by looking at the flags and size of the input array:
+-   If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it
+    is an inverse 1D or 2D transform.
+-   If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
+-   If the array is a single column or a single row, the function performs a 1D transform.
+-   If none of the above is true, the function performs a 2D transform.
+
+@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you
+can pad the array when necessary.
+Also, the function performance depends very much, and not monotonically, on the array size (see
+getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT
+of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as:
+@code
+    size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); }
+    N1 = getOptimalDCTSize(N);
+@endcode
+@param src input floating-point array.
+@param dst output array of the same size and type as src .
+@param flags transformation flags as a combination of cv::DftFlags (DCT_*)
+@sa dft , getOptimalDFTSize , idct
+*/
+CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0);
+
+/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array.
+
+idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).
+@param src input floating-point single-channel array.
+@param dst output array of the same size and type as src.
+@param flags operation flags.
+@sa  dct, dft, idft, getOptimalDFTSize
+*/
+CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0);
+
+/** @brief Performs the per-element multiplication of two Fourier spectrums.
+
+The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex
+matrices that are results of a real or complex Fourier transform.
+
+The function, together with dft and idft , may be used to calculate convolution (pass conjB=false )
+or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are
+simply multiplied (per element) with an optional conjugation of the second-array elements. When the
+arrays are real, they are assumed to be CCS-packed (see dft for details).
+@param a first input array.
+@param b second input array of the same size and type as src1 .
+@param c output array of the same size and type as src1 .
+@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
+each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
+@param conjB optional flag that conjugates the second input array before the multiplication (true)
+or not (false).
+*/
+CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c,
+                               int flags, bool conjB = false);
+
+/** @brief Returns the optimal DFT size for a given vector size.
+
+DFT performance is not a monotonic function of a vector size. Therefore, when you calculate
+convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to
+pad the input data with zeros to get a bit larger array that can be transformed much faster than the
+original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process.
+Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2)
+are also processed quite efficiently.
+
+The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize
+so that the DFT of a vector of size N can be processed efficiently. In the current implementation N
+= 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r.
+
+The function returns a negative number if vecsize is too large (very close to INT_MAX ).
+
+While the function cannot be used directly to estimate the optimal vector size for DCT transform
+(since the current DCT implementation supports only even-size vectors), it can be easily processed
+as getOptimalDFTSize((vecsize+1)/2)\*2.
+@param vecsize vector size.
+@sa dft , dct , idft , idct , mulSpectrums
+*/
+CV_EXPORTS_W int getOptimalDFTSize(int vecsize);
+
+/** @brief Returns the default random number generator.
+
+The function cv::theRNG returns the default random number generator. For each thread, there is a
+separate random number generator, so you can use the function safely in multi-thread environments.
+If you just need to get a single random number using this generator or initialize an array, you can
+use randu or randn instead. But if you are going to generate many random numbers inside a loop, it
+is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() .
+@sa RNG, randu, randn
+*/
+CV_EXPORTS RNG& theRNG();
+
+/** @brief Sets state of default random number generator.
+
+The function cv::setRNGSeed sets state of default random number generator to custom value.
+@param seed new state for default random number generator
+@sa RNG, randu, randn
+*/
+CV_EXPORTS_W void setRNGSeed(int seed);
+
+/** @brief Generates a single uniformly-distributed random number or an array of random numbers.
+
+Non-template variant of the function fills the matrix dst with uniformly-distributed
+random numbers from the specified range:
+\f[\texttt{low} _c  \leq \texttt{dst} (I)_c <  \texttt{high} _c\f]
+@param dst output array of random numbers; the array must be pre-allocated.
+@param low inclusive lower boundary of the generated random numbers.
+@param high exclusive upper boundary of the generated random numbers.
+@sa RNG, randn, theRNG
+*/
+CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high);
+
+/** @brief Fills the array with normally distributed random numbers.
+
+The function cv::randn fills the matrix dst with normally distributed random numbers with the specified
+mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the
+value range of the output array data type.
+@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.
+@param mean mean value (expectation) of the generated random numbers.
+@param stddev standard deviation of the generated random numbers; it can be either a vector (in
+which case a diagonal standard deviation matrix is assumed) or a square matrix.
+@sa RNG, randu
+*/
+CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev);
+
+/** @brief Shuffles the array elements randomly.
+
+The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and
+swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor .
+@param dst input/output numerical 1D array.
+@param iterFactor scale factor that determines the number of random swap operations (see the details
+below).
+@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used
+instead.
+@sa RNG, sort
+*/
+CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0);
+
+/** @brief Principal Component Analysis
+
+The class is used to calculate a special basis for a set of vectors. The
+basis will consist of eigenvectors of the covariance matrix calculated
+from the input set of vectors. The class %PCA can also transform
+vectors to/from the new coordinate space defined by the basis. Usually,
+in this new coordinate system, each vector from the original set (and
+any linear combination of such vectors) can be quite accurately
+approximated by taking its first few components, corresponding to the
+eigenvectors of the largest eigenvalues of the covariance matrix.
+Geometrically it means that you calculate a projection of the vector to
+a subspace formed by a few eigenvectors corresponding to the dominant
+eigenvalues of the covariance matrix. And usually such a projection is
+very close to the original vector. So, you can represent the original
+vector from a high-dimensional space with a much shorter vector
+consisting of the projected vector's coordinates in the subspace. Such a
+transformation is also known as Karhunen-Loeve Transform, or KLT.
+See http://en.wikipedia.org/wiki/Principal_component_analysis
+
+The sample below is the function that takes two matrices. The first
+function stores a set of vectors (a row per vector) that is used to
+calculate PCA. The second function stores another "test" set of vectors
+(a row per vector). First, these vectors are compressed with PCA, then
+reconstructed back, and then the reconstruction error norm is computed
+and printed for each vector. :
+
+@code{.cpp}
+using namespace cv;
+
+PCA compressPCA(const Mat& pcaset, int maxComponents,
+                const Mat& testset, Mat& compressed)
+{
+    PCA pca(pcaset, // pass the data
+            Mat(), // we do not have a pre-computed mean vector,
+                   // so let the PCA engine to compute it
+            PCA::DATA_AS_ROW, // indicate that the vectors
+                                // are stored as matrix rows
+                                // (use PCA::DATA_AS_COL if the vectors are
+                                // the matrix columns)
+            maxComponents // specify, how many principal components to retain
+            );
+    // if there is no test data, just return the computed basis, ready-to-use
+    if( !testset.data )
+        return pca;
+    CV_Assert( testset.cols == pcaset.cols );
+
+    compressed.create(testset.rows, maxComponents, testset.type());
+
+    Mat reconstructed;
+    for( int i = 0; i < testset.rows; i++ )
+    {
+        Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed;
+        // compress the vector, the result will be stored
+        // in the i-th row of the output matrix
+        pca.project(vec, coeffs);
+        // and then reconstruct it
+        pca.backProject(coeffs, reconstructed);
+        // and measure the error
+        printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2));
+    }
+    return pca;
+}
+@endcode
+@sa calcCovarMatrix, mulTransposed, SVD, dft, dct
+*/
+class CV_EXPORTS PCA
+{
+public:
+    enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows
+                 DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns
+                 USE_AVG     = 2  //!
+               };
+
+    /** @brief default constructor
+
+    The default constructor initializes an empty %PCA structure. The other
+    constructors initialize the structure and call PCA::operator()().
+    */
+    PCA();
+
+    /** @overload
+    @param data input samples stored as matrix rows or matrix columns.
+    @param mean optional mean value; if the matrix is empty (@c noArray()),
+    the mean is computed from the data.
+    @param flags operation flags; currently the parameter is only used to
+    specify the data layout (PCA::Flags)
+    @param maxComponents maximum number of components that %PCA should
+    retain; by default, all the components are retained.
+    */
+    PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0);
+
+    /** @overload
+    @param data input samples stored as matrix rows or matrix columns.
+    @param mean optional mean value; if the matrix is empty (noArray()),
+    the mean is computed from the data.
+    @param flags operation flags; currently the parameter is only used to
+    specify the data layout (PCA::Flags)
+    @param retainedVariance Percentage of variance that PCA should retain.
+    Using this parameter will let the PCA decided how many components to
+    retain but it will always keep at least 2.
+    */
+    PCA(InputArray data, InputArray mean, int flags, double retainedVariance);
+
+    /** @brief performs %PCA
+
+    The operator performs %PCA of the supplied dataset. It is safe to reuse
+    the same PCA structure for multiple datasets. That is, if the structure
+    has been previously used with another dataset, the existing internal
+    data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref
+    mean are allocated and computed.
+
+    The computed @ref eigenvalues are sorted from the largest to the smallest and
+    the corresponding @ref eigenvectors are stored as eigenvectors rows.
+
+    @param data input samples stored as the matrix rows or as the matrix
+    columns.
+    @param mean optional mean value; if the matrix is empty (noArray()),
+    the mean is computed from the data.
+    @param flags operation flags; currently the parameter is only used to
+    specify the data layout. (Flags)
+    @param maxComponents maximum number of components that PCA should
+    retain; by default, all the components are retained.
+    */
+    PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0);
+
+    /** @overload
+    @param data input samples stored as the matrix rows or as the matrix
+    columns.
+    @param mean optional mean value; if the matrix is empty (noArray()),
+    the mean is computed from the data.
+    @param flags operation flags; currently the parameter is only used to
+    specify the data layout. (PCA::Flags)
+    @param retainedVariance Percentage of variance that %PCA should retain.
+    Using this parameter will let the %PCA decided how many components to
+    retain but it will always keep at least 2.
+     */
+    PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance);
+
+    /** @brief Projects vector(s) to the principal component subspace.
+
+    The methods project one or more vectors to the principal component
+    subspace, where each vector projection is represented by coefficients in
+    the principal component basis. The first form of the method returns the
+    matrix that the second form writes to the result. So the first form can
+    be used as a part of expression while the second form can be more
+    efficient in a processing loop.
+    @param vec input vector(s); must have the same dimensionality and the
+    same layout as the input data used at %PCA phase, that is, if
+    DATA_AS_ROW are specified, then `vec.cols==data.cols`
+    (vector dimensionality) and `vec.rows` is the number of vectors to
+    project, and the same is true for the PCA::DATA_AS_COL case.
+    */
+    Mat project(InputArray vec) const;
+
+    /** @overload
+    @param vec input vector(s); must have the same dimensionality and the
+    same layout as the input data used at PCA phase, that is, if
+    DATA_AS_ROW are specified, then `vec.cols==data.cols`
+    (vector dimensionality) and `vec.rows` is the number of vectors to
+    project, and the same is true for the PCA::DATA_AS_COL case.
+    @param result output vectors; in case of PCA::DATA_AS_COL, the
+    output matrix has as many columns as the number of input vectors, this
+    means that `result.cols==vec.cols` and the number of rows match the
+    number of principal components (for example, `maxComponents` parameter
+    passed to the constructor).
+     */
+    void project(InputArray vec, OutputArray result) const;
+
+    /** @brief Reconstructs vectors from their PC projections.
+
+    The methods are inverse operations to PCA::project. They take PC
+    coordinates of projected vectors and reconstruct the original vectors.
+    Unless all the principal components have been retained, the
+    reconstructed vectors are different from the originals. But typically,
+    the difference is small if the number of components is large enough (but
+    still much smaller than the original vector dimensionality). As a
+    result, PCA is used.
+    @param vec coordinates of the vectors in the principal component
+    subspace, the layout and size are the same as of PCA::project output
+    vectors.
+     */
+    Mat backProject(InputArray vec) const;
+
+    /** @overload
+    @param vec coordinates of the vectors in the principal component
+    subspace, the layout and size are the same as of PCA::project output
+    vectors.
+    @param result reconstructed vectors; the layout and size are the same as
+    of PCA::project input vectors.
+     */
+    void backProject(InputArray vec, OutputArray result) const;
+
+    /** @brief write PCA objects
+
+    Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage
+     */
+    void write(FileStorage& fs) const;
+
+    /** @brief load PCA objects
+
+    Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode
+     */
+    void read(const FileNode& fn);
+
+    Mat eigenvectors; //!< eigenvectors of the covariation matrix
+    Mat eigenvalues; //!< eigenvalues of the covariation matrix
+    Mat mean; //!< mean value subtracted before the projection and added after the back projection
+};
+
+/** @example samples/cpp/pca.cpp
+An example using %PCA for dimensionality reduction while maintaining an amount of variance
+*/
+
+/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
+Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details
+*/
+
+/**
+@brief Linear Discriminant Analysis
+@todo document this class
+*/
+class CV_EXPORTS LDA
+{
+public:
+    /** @brief constructor
+    Initializes a LDA with num_components (default 0).
+    */
+    explicit LDA(int num_components = 0);
+
+    /** Initializes and performs a Discriminant Analysis with Fisher's
+     Optimization Criterion on given data in src and corresponding labels
+     in labels. If 0 (or less) number of components are given, they are
+     automatically determined for given data in computation.
+    */
+    LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0);
+
+    /** Serializes this object to a given filename.
+      */
+    void save(const String& filename) const;
+
+    /** Deserializes this object from a given filename.
+      */
+    void load(const String& filename);
+
+    /** Serializes this object to a given cv::FileStorage.
+      */
+    void save(FileStorage& fs) const;
+
+    /** Deserializes this object from a given cv::FileStorage.
+      */
+    void load(const FileStorage& node);
+
+    /** destructor
+      */
+    ~LDA();
+
+    /** Compute the discriminants for data in src (row aligned) and labels.
+      */
+    void compute(InputArrayOfArrays src, InputArray labels);
+
+    /** Projects samples into the LDA subspace.
+        src may be one or more row aligned samples.
+      */
+    Mat project(InputArray src);
+
+    /** Reconstructs projections from the LDA subspace.
+        src may be one or more row aligned projections.
+      */
+    Mat reconstruct(InputArray src);
+
+    /** Returns the eigenvectors of this LDA.
+      */
+    Mat eigenvectors() const { return _eigenvectors; }
+
+    /** Returns the eigenvalues of this LDA.
+      */
+    Mat eigenvalues() const { return _eigenvalues; }
+
+    static Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
+    static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
+
+protected:
+    int _num_components;
+    Mat _eigenvectors;
+    Mat _eigenvalues;
+    void lda(InputArrayOfArrays src, InputArray labels);
+};
+
+/** @brief Singular Value Decomposition
+
+Class for computing Singular Value Decomposition of a floating-point
+matrix. The Singular Value Decomposition is used to solve least-square
+problems, under-determined linear systems, invert matrices, compute
+condition numbers, and so on.
+
+If you want to compute a condition number of a matrix or an absolute value of
+its determinant, you do not need `u` and `vt`. You can pass
+flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u
+and vt must be computed, which is not necessary most of the time.
+
+@sa invert, solve, eigen, determinant
+*/
+class CV_EXPORTS SVD
+{
+public:
+    enum Flags {
+        /** allow the algorithm to modify the decomposed matrix; it can save space and speed up
+            processing. currently ignored. */
+        MODIFY_A = 1,
+        /** indicates that only a vector of singular values `w` is to be processed, while u and vt
+            will be set to empty matrices */
+        NO_UV    = 2,
+        /** when the matrix is not square, by default the algorithm produces u and vt matrices of
+            sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is
+            specified, u and vt will be full-size square orthogonal matrices.*/
+        FULL_UV  = 4
+    };
+
+    /** @brief the default constructor
+
+    initializes an empty SVD structure
+      */
+    SVD();
+
+    /** @overload
+    initializes an empty SVD structure and then calls SVD::operator()
+    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
+    @param flags operation flags (SVD::Flags)
+      */
+    SVD( InputArray src, int flags = 0 );
+
+    /** @brief the operator that performs SVD. The previously allocated u, w and vt are released.
+
+    The operator performs the singular value decomposition of the supplied
+    matrix. The u,`vt` , and the vector of singular values w are stored in
+    the structure. The same SVD structure can be reused many times with
+    different matrices. Each time, if needed, the previous u,`vt` , and w
+    are reclaimed and the new matrices are created, which is all handled by
+    Mat::create.
+    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
+    @param flags operation flags (SVD::Flags)
+      */
+    SVD& operator ()( InputArray src, int flags = 0 );
+
+    /** @brief decomposes matrix and stores the results to user-provided matrices
+
+    The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor
+    and SVD::operator(), they store the results to the user-provided
+    matrices:
+
+    @code{.cpp}
+    Mat A, w, u, vt;
+    SVD::compute(A, w, u, vt);
+    @endcode
+
+    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
+    @param w calculated singular values
+    @param u calculated left singular vectors
+    @param vt transposed matrix of right singular vectors
+    @param flags operation flags - see SVD::Flags.
+      */
+    static void compute( InputArray src, OutputArray w,
+                         OutputArray u, OutputArray vt, int flags = 0 );
+
+    /** @overload
+    computes singular values of a matrix
+    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
+    @param w calculated singular values
+    @param flags operation flags - see SVD::Flags.
+      */
+    static void compute( InputArray src, OutputArray w, int flags = 0 );
+
+    /** @brief performs back substitution
+      */
+    static void backSubst( InputArray w, InputArray u,
+                           InputArray vt, InputArray rhs,
+                           OutputArray dst );
+
+    /** @brief solves an under-determined singular linear system
+
+    The method finds a unit-length solution x of a singular linear system
+    A\*x = 0. Depending on the rank of A, there can be no solutions, a
+    single solution or an infinite number of solutions. In general, the
+    algorithm solves the following problem:
+    \f[dst =  \arg \min _{x:  \| x \| =1}  \| src  \cdot x  \|\f]
+    @param src left-hand-side matrix.
+    @param dst found solution.
+      */
+    static void solveZ( InputArray src, OutputArray dst );
+
+    /** @brief performs a singular value back substitution.
+
+    The method calculates a back substitution for the specified right-hand
+    side:
+
+    \f[\texttt{x} =  \texttt{vt} ^T  \cdot diag( \texttt{w} )^{-1}  \cdot \texttt{u} ^T  \cdot \texttt{rhs} \sim \texttt{A} ^{-1}  \cdot \texttt{rhs}\f]
+
+    Using this technique you can either get a very accurate solution of the
+    convenient linear system, or the best (in the least-squares terms)
+    pseudo-solution of an overdetermined linear system.
+
+    @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to
+    be solved, where A has been previously decomposed.
+
+    @param dst found solution of the system.
+
+    @note Explicit SVD with the further back substitution only makes sense
+    if you need to solve many linear systems with the same left-hand side
+    (for example, src ). If all you need is to solve a single system
+    (possibly with multiple rhs immediately available), simply call solve
+    add pass #DECOMP_SVD there. It does absolutely the same thing.
+      */
+    void backSubst( InputArray rhs, OutputArray dst ) const;
+
+    /** @todo document */
+    template<typename _Tp, int m, int n, int nm> static
+    void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt );
+
+    /** @todo document */
+    template<typename _Tp, int m, int n, int nm> static
+    void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w );
+
+    /** @todo document */
+    template<typename _Tp, int m, int n, int nm, int nb> static
+    void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst );
+
+    Mat u, w, vt;
+};
+
+/** @brief Random Number Generator
+
+Random number generator. It encapsulates the state (currently, a 64-bit
+integer) and has methods to return scalar random values and to fill
+arrays with random values. Currently it supports uniform and Gaussian
+(normal) distributions. The generator uses Multiply-With-Carry
+algorithm, introduced by G. Marsaglia (
+<http://en.wikipedia.org/wiki/Multiply-with-carry> ).
+Gaussian-distribution random numbers are generated using the Ziggurat
+algorithm ( <http://en.wikipedia.org/wiki/Ziggurat_algorithm> ),
+introduced by G. Marsaglia and W. W. Tsang.
+*/
+class CV_EXPORTS RNG
+{
+public:
+    enum { UNIFORM = 0,
+           NORMAL  = 1
+         };
+
+    /** @brief constructor
+
+    These are the RNG constructors. The first form sets the state to some
+    pre-defined value, equal to 2\*\*32-1 in the current implementation. The
+    second form sets the state to the specified value. If you passed state=0
+    , the constructor uses the above default value instead to avoid the
+    singular random number sequence, consisting of all zeros.
+    */
+    RNG();
+    /** @overload
+    @param state 64-bit value used to initialize the RNG.
+    */
+    RNG(uint64 state);
+    /**The method updates the state using the MWC algorithm and returns the
+    next 32-bit random number.*/
+    unsigned next();
+
+    /**Each of the methods updates the state using the MWC algorithm and
+    returns the next random number of the specified type. In case of integer
+    types, the returned number is from the available value range for the
+    specified type. In case of floating-point types, the returned value is
+    from [0,1) range.
+    */
+    operator uchar();
+    /** @overload */
+    operator schar();
+    /** @overload */
+    operator ushort();
+    /** @overload */
+    operator short();
+    /** @overload */
+    operator unsigned();
+    /** @overload */
+    operator int();
+    /** @overload */
+    operator float();
+    /** @overload */
+    operator double();
+
+    /** @brief returns a random integer sampled uniformly from [0, N).
+
+    The methods transform the state using the MWC algorithm and return the
+    next random number. The first form is equivalent to RNG::next . The
+    second form returns the random number modulo N , which means that the
+    result is in the range [0, N) .
+    */
+    unsigned operator ()();
+    /** @overload
+    @param N upper non-inclusive boundary of the returned random number.
+    */
+    unsigned operator ()(unsigned N);
+
+    /** @brief returns uniformly distributed integer random number from [a,b) range
+
+    The methods transform the state using the MWC algorithm and return the
+    next uniformly-distributed random number of the specified type, deduced
+    from the input parameter type, from the range [a, b) . There is a nuance
+    illustrated by the following sample:
+
+    @code{.cpp}
+    RNG rng;
+
+    // always produces 0
+    double a = rng.uniform(0, 1);
+
+    // produces double from [0, 1)
+    double a1 = rng.uniform((double)0, (double)1);
+
+    // produces float from [0, 1)
+    float b = rng.uniform(0.f, 1.f);
+
+    // produces double from [0, 1)
+    double c = rng.uniform(0., 1.);
+
+    // may cause compiler error because of ambiguity:
+    //  RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)?
+    double d = rng.uniform(0, 0.999999);
+    @endcode
+
+    The compiler does not take into account the type of the variable to
+    which you assign the result of RNG::uniform . The only thing that
+    matters to the compiler is the type of a and b parameters. So, if you
+    want a floating-point random number, but the range boundaries are
+    integer numbers, either put dots in the end, if they are constants, or
+    use explicit type cast operators, as in the a1 initialization above.
+    @param a lower inclusive boundary of the returned random number.
+    @param b upper non-inclusive boundary of the returned random number.
+    */
+    int uniform(int a, int b);
+    /** @overload */
+    float uniform(float a, float b);
+    /** @overload */
+    double uniform(double a, double b);
+
+    /** @brief Fills arrays with random numbers.
+
+    @param mat 2D or N-dimensional matrix; currently matrices with more than
+    4 channels are not supported by the methods, use Mat::reshape as a
+    possible workaround.
+    @param distType distribution type, RNG::UNIFORM or RNG::NORMAL.
+    @param a first distribution parameter; in case of the uniform
+    distribution, this is an inclusive lower boundary, in case of the normal
+    distribution, this is a mean value.
+    @param b second distribution parameter; in case of the uniform
+    distribution, this is a non-inclusive upper boundary, in case of the
+    normal distribution, this is a standard deviation (diagonal of the
+    standard deviation matrix or the full standard deviation matrix).
+    @param saturateRange pre-saturation flag; for uniform distribution only;
+    if true, the method will first convert a and b to the acceptable value
+    range (according to the mat datatype) and then will generate uniformly
+    distributed random numbers within the range [saturate(a), saturate(b)),
+    if saturateRange=false, the method will generate uniformly distributed
+    random numbers in the original range [a, b) and then will saturate them,
+    it means, for example, that
+    <tt>theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX)</tt> will likely
+    produce array mostly filled with 0's and 255's, since the range (0, 255)
+    is significantly smaller than [-DBL_MAX, DBL_MAX).
+
+    Each of the methods fills the matrix with the random values from the
+    specified distribution. As the new numbers are generated, the RNG state
+    is updated accordingly. In case of multiple-channel images, every
+    channel is filled independently, which means that RNG cannot generate
+    samples from the multi-dimensional Gaussian distribution with
+    non-diagonal covariance matrix directly. To do that, the method
+    generates samples from multi-dimensional standard Gaussian distribution
+    with zero mean and identity covariation matrix, and then transforms them
+    using transform to get samples from the specified Gaussian distribution.
+    */
+    void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false );
+
+    /** @brief Returns the next random number sampled from the Gaussian distribution
+    @param sigma standard deviation of the distribution.
+
+    The method transforms the state using the MWC algorithm and returns the
+    next random number from the Gaussian distribution N(0,sigma) . That is,
+    the mean value of the returned random numbers is zero and the standard
+    deviation is the specified sigma .
+    */
+    double gaussian(double sigma);
+
+    uint64 state;
+
+    bool operator ==(const RNG& other) const;
+};
+
+/** @brief Mersenne Twister random number generator
+
+Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c
+@todo document
+*/
+class CV_EXPORTS RNG_MT19937
+{
+public:
+    RNG_MT19937();
+    RNG_MT19937(unsigned s);
+    void seed(unsigned s);
+
+    unsigned next();
+
+    operator int();
+    operator unsigned();
+    operator float();
+    operator double();
+
+    unsigned operator ()(unsigned N);
+    unsigned operator ()();
+
+    /** @brief returns uniformly distributed integer random number from [a,b) range*/
+    int uniform(int a, int b);
+    /** @brief returns uniformly distributed floating-point random number from [a,b) range*/
+    float uniform(float a, float b);
+    /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/
+    double uniform(double a, double b);
+
+private:
+    enum PeriodParameters {N = 624, M = 397};
+    unsigned state[N];
+    int mti;
+};
+
+//! @} core_array
+
+//! @addtogroup core_cluster
+//!  @{
+
+/** @example samples/cpp/kmeans.cpp
+An example on K-means clustering
+*/
+
+/** @brief Finds centers of clusters and groups input samples around the clusters.
+
+The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters
+and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a
+0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix.
+
+@note
+-   (Python) An example on K-means clustering can be found at
+    opencv_source_code/samples/python/kmeans.py
+@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
+Examples of this array can be:
+-   Mat points(count, 2, CV_32F);
+-   Mat points(count, 1, CV_32FC2);
+-   Mat points(1, count, CV_32FC2);
+-   std::vector\<cv::Point2f\> points(sampleCount);
+@param K Number of clusters to split the set by.
+@param bestLabels Input/output integer array that stores the cluster indices for every sample.
+@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or
+the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster
+centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
+@param attempts Flag to specify the number of times the algorithm is executed using different
+initial labellings. The algorithm returns the labels that yield the best compactness (see the last
+function parameter).
+@param flags Flag that can take values of cv::KmeansFlags
+@param centers Output matrix of the cluster centers, one row per each cluster center.
+@return The function returns the compactness measure that is computed as
+\f[\sum _i  \| \texttt{samples} _i -  \texttt{centers} _{ \texttt{labels} _i} \| ^2\f]
+after every attempt. The best (minimum) value is chosen and the corresponding labels and the
+compactness value are returned by the function. Basically, you can use only the core of the
+function, set the number of attempts to 1, initialize labels each time using a custom algorithm,
+pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best
+(most-compact) clustering.
+*/
+CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels,
+                            TermCriteria criteria, int attempts,
+                            int flags, OutputArray centers = noArray() );
+
+//! @} core_cluster
+
+//! @addtogroup core_basic
+//! @{
+
+/////////////////////////////// Formatted output of cv::Mat ///////////////////////////
+
+/** @todo document */
+class CV_EXPORTS Formatted
+{
+public:
+    virtual const char* next() = 0;
+    virtual void reset() = 0;
+    virtual ~Formatted();
+};
+
+/** @todo document */
+class CV_EXPORTS Formatter
+{
+public:
+    enum FormatType {
+           FMT_DEFAULT = 0,
+           FMT_MATLAB  = 1,
+           FMT_CSV     = 2,
+           FMT_PYTHON  = 3,
+           FMT_NUMPY   = 4,
+           FMT_C       = 5
+         };
+
+    virtual ~Formatter();
+
+    virtual Ptr<Formatted> format(const Mat& mtx) const = 0;
+
+    virtual void set16fPrecision(int p = 4) = 0;
+    virtual void set32fPrecision(int p = 8) = 0;
+    virtual void set64fPrecision(int p = 16) = 0;
+    virtual void setMultiline(bool ml = true) = 0;
+
+    static Ptr<Formatter> get(Formatter::FormatType fmt = FMT_DEFAULT);
+
+};
+
+static inline
+String& operator << (String& out, Ptr<Formatted> fmtd)
+{
+    fmtd->reset();
+    for(const char* str = fmtd->next(); str; str = fmtd->next())
+        out += cv::String(str);
+    return out;
+}
+
+static inline
+String& operator << (String& out, const Mat& mtx)
+{
+    return out << Formatter::get()->format(mtx);
+}
+
+//////////////////////////////////////// Algorithm ////////////////////////////////////
+
+class CV_EXPORTS Algorithm;
+
+template<typename _Tp, typename _EnumTp = void> struct ParamType {};
+
+
+/** @brief This is a base class for all more or less complex algorithms in OpenCV
+
+especially for classes of algorithms, for which there can be multiple implementations. The examples
+are stereo correspondence (for which there are algorithms like block matching, semi-global block
+matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians
+models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck
+etc.).
+
+Here is example of SimpleBlobDetector use in your application via Algorithm interface:
+@snippet snippets/core_various.cpp Algorithm
+*/
+class CV_EXPORTS_W Algorithm
+{
+public:
+    Algorithm();
+    virtual ~Algorithm();
+
+    /** @brief Clears the algorithm state
+    */
+    CV_WRAP virtual void clear() {}
+
+    /** @brief Stores algorithm parameters in a file storage
+    */
+    virtual void write(FileStorage& fs) const { CV_UNUSED(fs); }
+
+    /** @brief simplified API for language bindings
+    * @overload
+    */
+    CV_WRAP void write(const Ptr<FileStorage>& fs, const String& name = String()) const;
+
+    /** @brief Reads algorithm parameters from a file storage
+    */
+    CV_WRAP virtual void read(const FileNode& fn) { CV_UNUSED(fn); }
+
+    /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
+    */
+    CV_WRAP virtual bool empty() const { return false; }
+
+    /** @brief Reads algorithm from the file node
+
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    @code
+    cv::FileStorage fsRead("example.xml", FileStorage::READ);
+    Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
+    @endcode
+    In order to make this method work, the derived class must overwrite Algorithm::read(const
+    FileNode& fn) and also have static create() method without parameters
+    (or with all the optional parameters)
+    */
+    template<typename _Tp> static Ptr<_Tp> read(const FileNode& fn)
+    {
+        Ptr<_Tp> obj = _Tp::create();
+        obj->read(fn);
+        return !obj->empty() ? obj : Ptr<_Tp>();
+    }
+
+    /** @brief Loads algorithm from the file
+
+    @param filename Name of the file to read.
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used)
+
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    @code
+    Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
+    @endcode
+    In order to make this method work, the derived class must overwrite Algorithm::read(const
+    FileNode& fn).
+    */
+    template<typename _Tp> static Ptr<_Tp> load(const String& filename, const String& objname=String())
+    {
+        FileStorage fs(filename, FileStorage::READ);
+        CV_Assert(fs.isOpened());
+        FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
+        if (fn.empty()) return Ptr<_Tp>();
+        Ptr<_Tp> obj = _Tp::create();
+        obj->read(fn);
+        return !obj->empty() ? obj : Ptr<_Tp>();
+    }
+
+    /** @brief Loads algorithm from a String
+
+    @param strModel The string variable containing the model you want to load.
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used)
+
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    @code
+    Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
+    @endcode
+    */
+    template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String())
+    {
+        FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
+        FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
+        Ptr<_Tp> obj = _Tp::create();
+        obj->read(fn);
+        return !obj->empty() ? obj : Ptr<_Tp>();
+    }
+
+    /** Saves the algorithm to a file.
+    In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
+    CV_WRAP virtual void save(const String& filename) const;
+
+    /** Returns the algorithm string identifier.
+    This string is used as top level xml/yml node tag when the object is saved to a file or string. */
+    CV_WRAP virtual String getDefaultName() const;
+
+protected:
+    void writeFormat(FileStorage& fs) const;
+};
+
+enum struct Param {
+    INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7,
+    UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12
+};
+
+
+
+template<> struct ParamType<bool>
+{
+    typedef bool const_param_type;
+    typedef bool member_type;
+
+    static const Param type = Param::BOOLEAN;
+};
+
+template<> struct ParamType<int>
+{
+    typedef int const_param_type;
+    typedef int member_type;
+
+    static const Param type = Param::INT;
+};
+
+template<> struct ParamType<double>
+{
+    typedef double const_param_type;
+    typedef double member_type;
+
+    static const Param type = Param::REAL;
+};
+
+template<> struct ParamType<String>
+{
+    typedef const String& const_param_type;
+    typedef String member_type;
+
+    static const Param type = Param::STRING;
+};
+
+template<> struct ParamType<Mat>
+{
+    typedef const Mat& const_param_type;
+    typedef Mat member_type;
+
+    static const Param type = Param::MAT;
+};
+
+template<> struct ParamType<std::vector<Mat> >
+{
+    typedef const std::vector<Mat>& const_param_type;
+    typedef std::vector<Mat> member_type;
+
+    static const Param type = Param::MAT_VECTOR;
+};
+
+template<> struct ParamType<Algorithm>
+{
+    typedef const Ptr<Algorithm>& const_param_type;
+    typedef Ptr<Algorithm> member_type;
+
+    static const Param type = Param::ALGORITHM;
+};
+
+template<> struct ParamType<float>
+{
+    typedef float const_param_type;
+    typedef float member_type;
+
+    static const Param type = Param::FLOAT;
+};
+
+template<> struct ParamType<unsigned>
+{
+    typedef unsigned const_param_type;
+    typedef unsigned member_type;
+
+    static const Param type = Param::UNSIGNED_INT;
+};
+
+template<> struct ParamType<uint64>
+{
+    typedef uint64 const_param_type;
+    typedef uint64 member_type;
+
+    static const Param type = Param::UINT64;
+};
+
+template<> struct ParamType<uchar>
+{
+    typedef uchar const_param_type;
+    typedef uchar member_type;
+
+    static const Param type = Param::UCHAR;
+};
+
+template<> struct ParamType<Scalar>
+{
+    typedef const Scalar& const_param_type;
+    typedef Scalar member_type;
+
+    static const Param type = Param::SCALAR;
+};
+
+template<typename _Tp>
+struct ParamType<_Tp, typename std::enable_if< std::is_enum<_Tp>::value >::type>
+{
+    typedef typename std::underlying_type<_Tp>::type const_param_type;
+    typedef typename std::underlying_type<_Tp>::type member_type;
+
+    static const Param type = Param::INT;
+};
+
+//! @} core_basic
+
+} //namespace cv
+
+#include "opencv2/core/operations.hpp"
+#include "opencv2/core/cvstd.inl.hpp"
+#include "opencv2/core/utility.hpp"
+#include "opencv2/core/optim.hpp"
+#include "opencv2/core/ovx.hpp"
+
+#endif /*OPENCV_CORE_HPP*/
diff --git a/3rdParty/include/opencv2/core/affine.hpp b/3rdParty/include/opencv2/core/affine.hpp
new file mode 100644
index 0000000..1806382
--- /dev/null
+++ b/3rdParty/include/opencv2/core/affine.hpp
@@ -0,0 +1,678 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_AFFINE3_HPP
+#define OPENCV_CORE_AFFINE3_HPP
+
+#ifdef __cplusplus
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+
+//! @addtogroup core
+//! @{
+
+    /** @brief Affine transform
+     *
+     * It represents a 4x4 homogeneous transformation matrix \f$T\f$
+     *
+     *  \f[T =
+     *  \begin{bmatrix}
+     *  R & t\\
+     *  0 & 1\\
+     *  \end{bmatrix}
+     *  \f]
+     *
+     *  where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector.
+     *
+     *  You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector,
+     *  which is converted to a 3x3 rotation matrix by the Rodrigues formula.
+     *
+     *  To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation
+     *  angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use
+     *
+     *  @code
+     *  cv::Vec3f r, t;
+     *  cv::Affine3f T(r, t);
+     *  @endcode
+     *
+     *  If you already have the rotation matrix \f$R\f$, then you can use
+     *
+     *  @code
+     *  cv::Matx33f R;
+     *  cv::Affine3f T(R, t);
+     *  @endcode
+     *
+     *  To extract the rotation matrix \f$R\f$ from \f$T\f$, use
+     *
+     *  @code
+     *  cv::Matx33f R = T.rotation();
+     *  @endcode
+     *
+     *  To extract the translation vector \f$t\f$ from \f$T\f$, use
+     *
+     *  @code
+     *  cv::Vec3f t = T.translation();
+     *  @endcode
+     *
+     *  To extract the rotation vector \f$r\f$ from \f$T\f$, use
+     *
+     *  @code
+     *  cv::Vec3f r = T.rvec();
+     *  @endcode
+     *
+     *  Note that since the mapping from rotation vectors to rotation matrices
+     *  is many to one. The returned rotation vector is not necessarily the one
+     *  you used before to set the matrix.
+     *
+     *  If you have two transformations \f$T = T_1 * T_2\f$, use
+     *
+     *  @code
+     *  cv::Affine3f T, T1, T2;
+     *  T = T2.concatenate(T1);
+     *  @endcode
+     *
+     *  To get the inverse transform of \f$T\f$, use
+     *
+     *  @code
+     *  cv::Affine3f T, T_inv;
+     *  T_inv = T.inv();
+     *  @endcode
+     *
+     */
+    template<typename T>
+    class Affine3
+    {
+    public:
+        typedef T float_type;
+        typedef Matx<float_type, 3, 3> Mat3;
+        typedef Matx<float_type, 4, 4> Mat4;
+        typedef Vec<float_type, 3> Vec3;
+
+       //! Default constructor. It represents a 4x4 identity matrix.
+        Affine3();
+
+        //! Augmented affine matrix
+        Affine3(const Mat4& affine);
+
+        /**
+         *  The resulting 4x4 matrix is
+         *
+         *  \f[
+         *  \begin{bmatrix}
+         *  R & t\\
+         *  0 & 1\\
+         *  \end{bmatrix}
+         *  \f]
+         *
+         * @param R 3x3 rotation matrix.
+         * @param t 3x1 translation vector.
+         */
+        Affine3(const Mat3& R, const Vec3& t = Vec3::all(0));
+
+        /**
+         * Rodrigues vector.
+         *
+         * The last row of the current matrix is set to [0,0,0,1].
+         *
+         * @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length
+         *             indicates the rotation angle in radian (using right hand rule).
+         * @param t 3x1 translation vector.
+         */
+        Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0));
+
+        /**
+         * Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix.
+         *
+         * The last row of the current matrix is set to [0,0,0,1] when data is not 4x4.
+         *
+         * @param data 1-channel matrix.
+         *             when it is 4x4, it is copied to the current matrix and t is not used.
+         *             When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used.
+         *             When it is 3x3, it is copied to the upper left 3x3 part of the current matrix.
+         *             When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used
+         *                             to compute a 3x3 rotation matrix.
+         * @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4.
+         */
+        explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0));
+
+        //! From 16-element array
+        explicit Affine3(const float_type* vals);
+
+        //! Create an 4x4 identity transform
+        static Affine3 Identity();
+
+        /**
+         * Rotation matrix.
+         *
+         * Copy the rotation matrix to the upper left 3x3 part of the current matrix.
+         * The remaining elements of the current matrix are not changed.
+         *
+         * @param R 3x3 rotation matrix.
+         *
+         */
+        void rotation(const Mat3& R);
+
+        /**
+         * Rodrigues vector.
+         *
+         * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
+         *
+         * @param rvec 3x1 rotation vector. The direction indicates the rotation axis and
+         *             its length indicates the rotation angle in radian (using the right thumb convention).
+         */
+        void rotation(const Vec3& rvec);
+
+        /**
+         * Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix.
+         *
+         * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
+         *
+         * @param data 1-channel matrix.
+         *             When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix.
+         *             When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula
+         *             is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix.
+         */
+        void rotation(const Mat& data);
+
+        /**
+         * Copy the 3x3 matrix L to the upper left part of the current matrix
+         *
+         * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
+         *
+         * @param L 3x3 matrix.
+         */
+        void linear(const Mat3& L);
+
+        /**
+         * Copy t to the first three elements of the last column of the current matrix
+         *
+         * It sets the upper right 3x1 part of the matrix. The remaining part is unaffected.
+         *
+         * @param t 3x1 translation vector.
+         */
+        void translation(const Vec3& t);
+
+        //! @return the upper left 3x3 part
+        Mat3 rotation() const;
+
+        //! @return the upper left 3x3 part
+        Mat3 linear() const;
+
+        //! @return the upper right 3x1 part
+        Vec3 translation() const;
+
+        //! Rodrigues vector.
+        //! @return a vector representing the upper left 3x3 rotation matrix of the current matrix.
+        //! @warning  Since the mapping between rotation vectors and rotation matrices is many to one,
+        //!           this function returns only one rotation vector that represents the current rotation matrix,
+        //!           which is not necessarily the same one set by `rotation(const Vec3& rvec)`.
+        Vec3 rvec() const;
+
+        //! @return the inverse of the current matrix.
+        Affine3 inv(int method = cv::DECOMP_SVD) const;
+
+        //! a.rotate(R) is equivalent to Affine(R, 0) * a;
+        Affine3 rotate(const Mat3& R) const;
+
+        //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a;
+        Affine3 rotate(const Vec3& rvec) const;
+
+        //! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix
+        Affine3 translate(const Vec3& t) const;
+
+        //! a.concatenate(affine) is equivalent to affine * a;
+        Affine3 concatenate(const Affine3& affine) const;
+
+        template <typename Y> operator Affine3<Y>() const;
+
+        template <typename Y> Affine3<Y> cast() const;
+
+        Mat4 matrix;
+
+#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
+        Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine);
+        Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine);
+        operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const;
+        operator Eigen::Transform<T, 3, Eigen::Affine>() const;
+#endif
+    };
+
+    template<typename T> static
+    Affine3<T> operator*(const Affine3<T>& affine1, const Affine3<T>& affine2);
+
+    //! V is a 3-element vector with member fields x, y and z
+    template<typename T, typename V> static
+    V operator*(const Affine3<T>& affine, const V& vector);
+
+    typedef Affine3<float> Affine3f;
+    typedef Affine3<double> Affine3d;
+
+    static Vec3f operator*(const Affine3f& affine, const Vec3f& vector);
+    static Vec3d operator*(const Affine3d& affine, const Vec3d& vector);
+
+    template<typename _Tp> class DataType< Affine3<_Tp> >
+    {
+    public:
+        typedef Affine3<_Tp>                               value_type;
+        typedef Affine3<typename DataType<_Tp>::work_type> work_type;
+        typedef _Tp                                        channel_type;
+
+        enum { generic_type = 0,
+               channels     = 16,
+               fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+               ,depth        = DataType<channel_type>::depth
+               ,type         = CV_MAKETYPE(depth, channels)
+#endif
+             };
+
+        typedef Vec<channel_type, channels> vec_type;
+    };
+
+    namespace traits {
+    template<typename _Tp>
+    struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; };
+    template<typename _Tp>
+    struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; };
+    } // namespace
+
+//! @} core
+
+}
+
+//! @cond IGNORED
+
+///////////////////////////////////////////////////////////////////////////////////
+// Implementation
+
+template<typename T> inline
+cv::Affine3<T>::Affine3()
+    : matrix(Mat4::eye())
+{}
+
+template<typename T> inline
+cv::Affine3<T>::Affine3(const Mat4& affine)
+    : matrix(affine)
+{}
+
+template<typename T> inline
+cv::Affine3<T>::Affine3(const Mat3& R, const Vec3& t)
+{
+    rotation(R);
+    translation(t);
+    matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
+    matrix.val[15] = 1;
+}
+
+template<typename T> inline
+cv::Affine3<T>::Affine3(const Vec3& _rvec, const Vec3& t)
+{
+    rotation(_rvec);
+    translation(t);
+    matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
+    matrix.val[15] = 1;
+}
+
+template<typename T> inline
+cv::Affine3<T>::Affine3(const cv::Mat& data, const Vec3& t)
+{
+    CV_Assert(data.type() == cv::traits::Type<T>::value);
+    CV_Assert(data.channels() == 1);
+
+    if (data.cols == 4 && data.rows == 4)
+    {
+        data.copyTo(matrix);
+        return;
+    }
+    else if (data.cols == 4 && data.rows == 3)
+    {
+        rotation(data(Rect(0, 0, 3, 3)));
+        translation(data(Rect(3, 0, 1, 3)));
+    }
+    else
+    {
+        rotation(data);
+        translation(t);
+    }
+
+    matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
+    matrix.val[15] = 1;
+}
+
+template<typename T> inline
+cv::Affine3<T>::Affine3(const float_type* vals) : matrix(vals)
+{}
+
+template<typename T> inline
+cv::Affine3<T> cv::Affine3<T>::Identity()
+{
+    return Affine3<T>(cv::Affine3<T>::Mat4::eye());
+}
+
+template<typename T> inline
+void cv::Affine3<T>::rotation(const Mat3& R)
+{
+    linear(R);
+}
+
+template<typename T> inline
+void cv::Affine3<T>::rotation(const Vec3& _rvec)
+{
+    double theta = norm(_rvec);
+
+    if (theta < DBL_EPSILON)
+        rotation(Mat3::eye());
+    else
+    {
+        double c = std::cos(theta);
+        double s = std::sin(theta);
+        double c1 = 1. - c;
+        double itheta = (theta != 0) ? 1./theta : 0.;
+
+        Point3_<T> r = _rvec*itheta;
+
+        Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z );
+        Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 );
+
+        // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x]
+        // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0]
+        Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x;
+
+        rotation(R);
+    }
+}
+
+//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix;
+template<typename T> inline
+void cv::Affine3<T>::rotation(const cv::Mat& data)
+{
+    CV_Assert(data.type() == cv::traits::Type<T>::value);
+    CV_Assert(data.channels() == 1);
+
+    if (data.cols == 3 && data.rows == 3)
+    {
+        Mat3 R;
+        data.copyTo(R);
+        rotation(R);
+    }
+    else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3))
+    {
+        Vec3 _rvec;
+        data.reshape(1, 3).copyTo(_rvec);
+        rotation(_rvec);
+    }
+    else
+        CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1");
+}
+
+template<typename T> inline
+void cv::Affine3<T>::linear(const Mat3& L)
+{
+    matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1];  matrix.val[ 2] = L.val[2];
+    matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4];  matrix.val[ 6] = L.val[5];
+    matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7];  matrix.val[10] = L.val[8];
+}
+
+template<typename T> inline
+void cv::Affine3<T>::translation(const Vec3& t)
+{
+    matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2];
+}
+
+template<typename T> inline
+typename cv::Affine3<T>::Mat3 cv::Affine3<T>::rotation() const
+{
+    return linear();
+}
+
+template<typename T> inline
+typename cv::Affine3<T>::Mat3 cv::Affine3<T>::linear() const
+{
+    typename cv::Affine3<T>::Mat3 R;
+    R.val[0] = matrix.val[0];  R.val[1] = matrix.val[1];  R.val[2] = matrix.val[ 2];
+    R.val[3] = matrix.val[4];  R.val[4] = matrix.val[5];  R.val[5] = matrix.val[ 6];
+    R.val[6] = matrix.val[8];  R.val[7] = matrix.val[9];  R.val[8] = matrix.val[10];
+    return R;
+}
+
+template<typename T> inline
+typename cv::Affine3<T>::Vec3 cv::Affine3<T>::translation() const
+{
+    return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]);
+}
+
+template<typename T> inline
+typename cv::Affine3<T>::Vec3 cv::Affine3<T>::rvec() const
+{
+    cv::Vec3d w;
+    cv::Matx33d u, vt, R = rotation();
+    cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A);
+    R = u * vt;
+
+    double rx = R.val[7] - R.val[5];
+    double ry = R.val[2] - R.val[6];
+    double rz = R.val[3] - R.val[1];
+
+    double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25);
+    double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5;
+    c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c;
+    double theta = std::acos(c);
+
+    if( s < 1e-5 )
+    {
+        if( c > 0 )
+            rx = ry = rz = 0;
+        else
+        {
+            double t;
+            t = (R.val[0] + 1) * 0.5;
+            rx = std::sqrt(std::max(t, 0.0));
+            t = (R.val[4] + 1) * 0.5;
+            ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0);
+            t = (R.val[8] + 1) * 0.5;
+            rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0);
+
+            if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) )
+                rz = -rz;
+            theta /= std::sqrt(rx*rx + ry*ry + rz*rz);
+            rx *= theta;
+            ry *= theta;
+            rz *= theta;
+        }
+    }
+    else
+    {
+        double vth = 1/(2*s);
+        vth *= theta;
+        rx *= vth; ry *= vth; rz *= vth;
+    }
+
+    return cv::Vec3d(rx, ry, rz);
+}
+
+template<typename T> inline
+cv::Affine3<T> cv::Affine3<T>::inv(int method) const
+{
+    return matrix.inv(method);
+}
+
+template<typename T> inline
+cv::Affine3<T> cv::Affine3<T>::rotate(const Mat3& R) const
+{
+    Mat3 Lc = linear();
+    Vec3 tc = translation();
+    Mat4 result;
+    result.val[12] = result.val[13] = result.val[14] = 0;
+    result.val[15] = 1;
+
+    for(int j = 0; j < 3; ++j)
+    {
+        for(int i = 0; i < 3; ++i)
+        {
+            float_type value = 0;
+            for(int k = 0; k < 3; ++k)
+                value += R(j, k) * Lc(k, i);
+            result(j, i) = value;
+        }
+
+        result(j, 3) = R.row(j).dot(tc.t());
+    }
+    return result;
+}
+
+template<typename T> inline
+cv::Affine3<T> cv::Affine3<T>::rotate(const Vec3& _rvec) const
+{
+    return rotate(Affine3f(_rvec).rotation());
+}
+
+template<typename T> inline
+cv::Affine3<T> cv::Affine3<T>::translate(const Vec3& t) const
+{
+    Mat4 m = matrix;
+    m.val[ 3] += t[0];
+    m.val[ 7] += t[1];
+    m.val[11] += t[2];
+    return m;
+}
+
+template<typename T> inline
+cv::Affine3<T> cv::Affine3<T>::concatenate(const Affine3<T>& affine) const
+{
+    return (*this).rotate(affine.rotation()).translate(affine.translation());
+}
+
+template<typename T> template <typename Y> inline
+cv::Affine3<T>::operator Affine3<Y>() const
+{
+    return Affine3<Y>(matrix);
+}
+
+template<typename T> template <typename Y> inline
+cv::Affine3<Y> cv::Affine3<T>::cast() const
+{
+    return Affine3<Y>(matrix);
+}
+
+template<typename T> inline
+cv::Affine3<T> cv::operator*(const cv::Affine3<T>& affine1, const cv::Affine3<T>& affine2)
+{
+    return affine2.concatenate(affine1);
+}
+
+template<typename T, typename V> inline
+V cv::operator*(const cv::Affine3<T>& affine, const V& v)
+{
+    const typename Affine3<T>::Mat4& m = affine.matrix;
+
+    V r;
+    r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3];
+    r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7];
+    r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11];
+    return r;
+}
+
+static inline
+cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v)
+{
+    const cv::Matx44f& m = affine.matrix;
+    cv::Vec3f r;
+    r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
+    r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
+    r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
+    return r;
+}
+
+static inline
+cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v)
+{
+    const cv::Matx44d& m = affine.matrix;
+    cv::Vec3d r;
+    r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
+    r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
+    r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
+    return r;
+}
+
+
+
+#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
+
+template<typename T> inline
+cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine)
+{
+    cv::Mat(4, 4, cv::traits::Type<T>::value, affine.matrix().data()).copyTo(matrix);
+}
+
+template<typename T> inline
+cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine)
+{
+    Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> a = affine;
+    cv::Mat(4, 4, cv::traits::Type<T>::value, a.matrix().data()).copyTo(matrix);
+}
+
+template<typename T> inline
+cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const
+{
+    Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> r;
+    cv::Mat hdr(4, 4, cv::traits::Type<T>::value, r.matrix().data());
+    cv::Mat(matrix, false).copyTo(hdr);
+    return r;
+}
+
+template<typename T> inline
+cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine>() const
+{
+    return this->operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>();
+}
+
+#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */
+
+//! @endcond
+
+#endif /* __cplusplus */
+
+#endif /* OPENCV_CORE_AFFINE3_HPP */
diff --git a/3rdParty/include/opencv2/core/async.hpp b/3rdParty/include/opencv2/core/async.hpp
new file mode 100644
index 0000000..54560c7
--- /dev/null
+++ b/3rdParty/include/opencv2/core/async.hpp
@@ -0,0 +1,105 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_ASYNC_HPP
+#define OPENCV_CORE_ASYNC_HPP
+
+#include <opencv2/core/mat.hpp>
+
+#ifdef CV_CXX11
+//#include <future>
+#include <chrono>
+#endif
+
+namespace cv {
+
+/** @addtogroup core_async
+
+@{
+*/
+
+
+/** @brief Returns result of asynchronous operations
+
+Object has attached asynchronous state.
+Assignment operator doesn't clone asynchronous state (it is shared between all instances).
+
+Result can be fetched via get() method only once.
+
+*/
+class CV_EXPORTS_W AsyncArray
+{
+public:
+    ~AsyncArray() CV_NOEXCEPT;
+    CV_WRAP AsyncArray() CV_NOEXCEPT;
+    AsyncArray(const AsyncArray& o) CV_NOEXCEPT;
+    AsyncArray& operator=(const AsyncArray& o) CV_NOEXCEPT;
+    CV_WRAP void release() CV_NOEXCEPT;
+
+    /** Fetch the result.
+    @param[out] dst destination array
+
+    Waits for result until container has valid result.
+    Throws exception if exception was stored as a result.
+
+    Throws exception on invalid container state.
+
+    @note Result or stored exception can be fetched only once.
+    */
+    CV_WRAP void get(OutputArray dst) const;
+
+    /** Retrieving the result with timeout
+    @param[out] dst destination array
+    @param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait
+
+    @returns true if result is ready, false if the timeout has expired
+
+    @note Result or stored exception can be fetched only once.
+    */
+    bool get(OutputArray dst, int64 timeoutNs) const;
+
+    CV_WRAP inline
+    bool get(OutputArray dst, double timeoutNs) const { return get(dst, (int64)timeoutNs); }
+
+    bool wait_for(int64 timeoutNs) const;
+
+    CV_WRAP inline
+    bool wait_for(double timeoutNs) const { return wait_for((int64)timeoutNs); }
+
+    CV_WRAP bool valid() const CV_NOEXCEPT;
+
+#ifdef CV_CXX11
+    inline AsyncArray(AsyncArray&& o) { p = o.p; o.p = NULL; }
+    inline AsyncArray& operator=(AsyncArray&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; }
+
+    template<typename _Rep, typename _Period>
+    inline bool get(OutputArray dst, const std::chrono::duration<_Rep, _Period>& timeout)
+    {
+        return get(dst, (int64)(std::chrono::nanoseconds(timeout).count()));
+    }
+
+    template<typename _Rep, typename _Period>
+    inline bool wait_for(const std::chrono::duration<_Rep, _Period>& timeout)
+    {
+        return wait_for((int64)(std::chrono::nanoseconds(timeout).count()));
+    }
+
+#if 0
+    std::future<Mat> getFutureMat() const;
+    std::future<UMat> getFutureUMat() const;
+#endif
+#endif
+
+
+    // PImpl
+    struct Impl; friend struct Impl;
+    inline void* _getImpl() const CV_NOEXCEPT { return p; }
+protected:
+    Impl* p;
+};
+
+
+//! @}
+} // namespace
+#endif // OPENCV_CORE_ASYNC_HPP
diff --git a/3rdParty/include/opencv2/core/base.hpp b/3rdParty/include/opencv2/core/base.hpp
new file mode 100644
index 0000000..21a61a4
--- /dev/null
+++ b/3rdParty/include/opencv2/core/base.hpp
@@ -0,0 +1,664 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2014, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_BASE_HPP
+#define OPENCV_CORE_BASE_HPP
+
+#ifndef __cplusplus
+#  error base.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/opencv_modules.hpp"
+
+#include <climits>
+#include <algorithm>
+
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/cvstd.hpp"
+
+namespace cv
+{
+
+//! @addtogroup core_utils
+//! @{
+
+namespace Error {
+//! error codes
+enum Code {
+    StsOk=                       0,  //!< everything is ok
+    StsBackTrace=               -1,  //!< pseudo error for back trace
+    StsError=                   -2,  //!< unknown /unspecified error
+    StsInternal=                -3,  //!< internal error (bad state)
+    StsNoMem=                   -4,  //!< insufficient memory
+    StsBadArg=                  -5,  //!< function arg/param is bad
+    StsBadFunc=                 -6,  //!< unsupported function
+    StsNoConv=                  -7,  //!< iteration didn't converge
+    StsAutoTrace=               -8,  //!< tracing
+    HeaderIsNull=               -9,  //!< image header is NULL
+    BadImageSize=              -10,  //!< image size is invalid
+    BadOffset=                 -11,  //!< offset is invalid
+    BadDataPtr=                -12,  //!<
+    BadStep=                   -13,  //!< image step is wrong, this may happen for a non-continuous matrix.
+    BadModelOrChSeq=           -14,  //!<
+    BadNumChannels=            -15,  //!< bad number of channels, for example, some functions accept only single channel matrices.
+    BadNumChannel1U=           -16,  //!<
+    BadDepth=                  -17,  //!< input image depth is not supported by the function
+    BadAlphaChannel=           -18,  //!<
+    BadOrder=                  -19,  //!< number of dimensions is out of range
+    BadOrigin=                 -20,  //!< incorrect input origin
+    BadAlign=                  -21,  //!< incorrect input align
+    BadCallBack=               -22,  //!<
+    BadTileSize=               -23,  //!<
+    BadCOI=                    -24,  //!< input COI is not supported
+    BadROISize=                -25,  //!< incorrect input roi
+    MaskIsTiled=               -26,  //!<
+    StsNullPtr=                -27,  //!< null pointer
+    StsVecLengthErr=           -28,  //!< incorrect vector length
+    StsFilterStructContentErr= -29,  //!< incorrect filter structure content
+    StsKernelStructContentErr= -30,  //!< incorrect transform kernel content
+    StsFilterOffsetErr=        -31,  //!< incorrect filter offset value
+    StsBadSize=                -201, //!< the input/output structure size is incorrect
+    StsDivByZero=              -202, //!< division by zero
+    StsInplaceNotSupported=    -203, //!< in-place operation is not supported
+    StsObjectNotFound=         -204, //!< request can't be completed
+    StsUnmatchedFormats=       -205, //!< formats of input/output arrays differ
+    StsBadFlag=                -206, //!< flag is wrong or not supported
+    StsBadPoint=               -207, //!< bad CvPoint
+    StsBadMask=                -208, //!< bad format of mask (neither 8uC1 nor 8sC1)
+    StsUnmatchedSizes=         -209, //!< sizes of input/output structures do not match
+    StsUnsupportedFormat=      -210, //!< the data format/type is not supported by the function
+    StsOutOfRange=             -211, //!< some of parameters are out of range
+    StsParseError=             -212, //!< invalid syntax/structure of the parsed file
+    StsNotImplemented=         -213, //!< the requested function/feature is not implemented
+    StsBadMemBlock=            -214, //!< an allocated block has been corrupted
+    StsAssert=                 -215, //!< assertion failed
+    GpuNotSupported=           -216, //!< no CUDA support
+    GpuApiCallError=           -217, //!< GPU API call error
+    OpenGlNotSupported=        -218, //!< no OpenGL support
+    OpenGlApiCallError=        -219, //!< OpenGL API call error
+    OpenCLApiCallError=        -220, //!< OpenCL API call error
+    OpenCLDoubleNotSupported=  -221,
+    OpenCLInitError=           -222, //!< OpenCL initialization error
+    OpenCLNoAMDBlasFft=        -223
+};
+} //Error
+
+//! @} core_utils
+
+//! @addtogroup core_array
+//! @{
+
+//! matrix decomposition types
+enum DecompTypes {
+    /** Gaussian elimination with the optimal pivot element chosen. */
+    DECOMP_LU       = 0,
+    /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix
+    src1 can be singular */
+    DECOMP_SVD      = 1,
+    /** eigenvalue decomposition; the matrix src1 must be symmetrical */
+    DECOMP_EIG      = 2,
+    /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively
+    defined */
+    DECOMP_CHOLESKY = 3,
+    /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */
+    DECOMP_QR       = 4,
+    /** while all the previous flags are mutually exclusive, this flag can be used together with
+    any of the previous; it means that the normal equations
+    \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are
+    solved instead of the original system
+    \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */
+    DECOMP_NORMAL   = 16
+};
+
+/** norm types
+
+src1 and src2 denote input arrays.
+*/
+
+enum NormTypes {
+                /**
+                \f[
+                norm =  \forkthree
+                {\|\texttt{src1}\|_{L_{\infty}} =  \max _I | \texttt{src1} (I)|}{if  \(\texttt{normType} = \texttt{NORM_INF}\) }
+                {\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} =  \max _I | \texttt{src1} (I) -  \texttt{src2} (I)|}{if  \(\texttt{normType} = \texttt{NORM_INF}\) }
+                {\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}}    }{\|\texttt{src2}\|_{L_{\infty}} }}{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) }
+                \f]
+                */
+                NORM_INF       = 1,
+                /**
+                \f[
+                norm =  \forkthree
+                {\| \texttt{src1} \| _{L_1} =  \sum _I | \texttt{src1} (I)|}{if  \(\texttt{normType} = \texttt{NORM_L1}\)}
+                { \| \texttt{src1} - \texttt{src2} \| _{L_1} =  \sum _I | \texttt{src1} (I) -  \texttt{src2} (I)|}{if  \(\texttt{normType} = \texttt{NORM_L1}\) }
+                { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) }
+                \f]*/
+                 NORM_L1        = 2,
+                 /**
+                 \f[
+                 norm =  \forkthree
+                 { \| \texttt{src1} \| _{L_2} =  \sqrt{\sum_I \texttt{src1}(I)^2} }{if  \(\texttt{normType} = \texttt{NORM_L2}\) }
+                 { \| \texttt{src1} - \texttt{src2} \| _{L_2} =  \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if  \(\texttt{normType} = \texttt{NORM_L2}\) }
+                 { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) }
+                 \f]
+                 */
+                 NORM_L2        = 4,
+                 /**
+                 \f[
+                 norm =  \forkthree
+                 { \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if  \(\texttt{normType} = \texttt{NORM_L2SQR}\)}
+                 { \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} =  \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if  \(\texttt{normType} = \texttt{NORM_L2SQR}\) }
+                 { \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2SQR}\) }
+                 \f]
+                 */
+                 NORM_L2SQR     = 5,
+                 /**
+                 In the case of one input array, calculates the Hamming distance of the array from zero,
+                 In the case of two input arrays, calculates the Hamming distance between the arrays.
+                 */
+                 NORM_HAMMING   = 6,
+                 /**
+                 Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will
+                 be added and treated as a single bit to be used in the same calculation as NORM_HAMMING.
+                 */
+                 NORM_HAMMING2  = 7,
+                 NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags
+                 NORM_RELATIVE  = 8, //!< flag
+                 NORM_MINMAX    = 32 //!< flag
+               };
+
+//! comparison types
+enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2.
+                CMP_GT = 1, //!< src1 is greater than src2.
+                CMP_GE = 2, //!< src1 is greater than or equal to src2.
+                CMP_LT = 3, //!< src1 is less than src2.
+                CMP_LE = 4, //!< src1 is less than or equal to src2.
+                CMP_NE = 5  //!< src1 is unequal to src2.
+              };
+
+//! generalized matrix multiplication flags
+enum GemmFlags { GEMM_1_T = 1, //!< transposes src1
+                 GEMM_2_T = 2, //!< transposes src2
+                 GEMM_3_T = 4 //!< transposes src3
+               };
+
+enum DftFlags {
+    /** performs an inverse 1D or 2D transform instead of the default forward
+        transform. */
+    DFT_INVERSE        = 1,
+    /** scales the result: divide it by the number of array elements. Normally, it is
+        combined with DFT_INVERSE. */
+    DFT_SCALE          = 2,
+    /** performs a forward or inverse transform of every individual row of the input
+        matrix; this flag enables you to transform multiple vectors simultaneously and can be used to
+        decrease the overhead (which is sometimes several times larger than the processing itself) to
+        perform 3D and higher-dimensional transformations and so forth.*/
+    DFT_ROWS           = 4,
+    /** performs a forward transformation of 1D or 2D real array; the result,
+        though being a complex array, has complex-conjugate symmetry (*CCS*, see the function
+        description below for details), and such an array can be packed into a real array of the same
+        size as input, which is the fastest option and which is what the function does by default;
+        however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) -
+        pass the flag to enable the function to produce a full-size complex output array. */
+    DFT_COMPLEX_OUTPUT = 16,
+    /** performs an inverse transformation of a 1D or 2D complex array; the
+        result is normally a complex array of the same size, however, if the input array has
+        conjugate-complex symmetry (for example, it is a result of forward transformation with
+        DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not
+        check whether the input is symmetrical or not, you can pass the flag and then the function
+        will assume the symmetry and produce the real output array (note that when the input is packed
+        into a real array and inverse transformation is executed, the function treats the input as a
+        packed complex-conjugate symmetrical array, and the output will also be a real array). */
+    DFT_REAL_OUTPUT    = 32,
+    /** specifies that input is complex input. If this flag is set, the input must have 2 channels.
+        On the other hand, for backwards compatibility reason, if input has 2 channels, input is
+        already considered complex. */
+    DFT_COMPLEX_INPUT  = 64,
+    /** performs an inverse 1D or 2D transform instead of the default forward transform. */
+    DCT_INVERSE        = DFT_INVERSE,
+    /** performs a forward or inverse transform of every individual row of the input
+        matrix. This flag enables you to transform multiple vectors simultaneously and can be used to
+        decrease the overhead (which is sometimes several times larger than the processing itself) to
+        perform 3D and higher-dimensional transforms and so forth.*/
+    DCT_ROWS           = DFT_ROWS
+};
+
+//! Various border types, image boundaries are denoted with `|`
+//! @see borderInterpolate, copyMakeBorder
+enum BorderTypes {
+    BORDER_CONSTANT    = 0, //!< `iiiiii|abcdefgh|iiiiiii`  with some specified `i`
+    BORDER_REPLICATE   = 1, //!< `aaaaaa|abcdefgh|hhhhhhh`
+    BORDER_REFLECT     = 2, //!< `fedcba|abcdefgh|hgfedcb`
+    BORDER_WRAP        = 3, //!< `cdefgh|abcdefgh|abcdefg`
+    BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba`
+    BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno`
+
+    BORDER_REFLECT101  = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
+    BORDER_DEFAULT     = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
+    BORDER_ISOLATED    = 16 //!< do not look outside of ROI
+};
+
+//! @} core_array
+
+//! @addtogroup core_utils
+//! @{
+
+/*! @brief Signals an error and raises the exception.
+
+By default the function prints information about the error to stderr,
+then it either stops if setBreakOnError() had been called before or raises the exception.
+It is possible to alternate error processing by using redirectError().
+@param _code - error code (Error::Code)
+@param _err - error description
+@param _func - function name. Available only when the compiler supports getting it
+@param _file - source file name where the error has occurred
+@param _line - line number in the source file where the error has occurred
+@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert
+ */
+CV_EXPORTS CV_NORETURN void error(int _code, const String& _err, const char* _func, const char* _file, int _line);
+
+#ifdef CV_STATIC_ANALYSIS
+
+// In practice, some macro are not processed correctly (noreturn is not detected).
+// We need to use simplified definition for them.
+#define CV_Error(code, msg) do { (void)(code); (void)(msg); abort(); } while (0)
+#define CV_Error_(code, args) do { (void)(code); (void)(cv::format args); abort(); } while (0)
+#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0)
+
+#else // CV_STATIC_ANALYSIS
+
+/** @brief Call the error handler.
+
+Currently, the error handler prints the error code and the error message to the standard
+error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that
+the execution stack and all the parameters can be analyzed by the debugger. In the Release
+configuration, the exception is thrown.
+
+@param code one of Error::Code
+@param msg error message
+*/
+#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ )
+
+/**  @brief Call the error handler.
+
+This macro can be used to construct an error message on-fly to include some dynamic information,
+for example:
+@code
+    // note the extra parentheses around the formatted text message
+    CV_Error_(Error::StsOutOfRange,
+    ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue));
+@endcode
+@param code one of Error::Code
+@param args printf-like formatted error message in parentheses
+*/
+#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ )
+
+/** @brief Checks a condition at runtime and throws exception if it fails
+
+The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
+raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
+configurations while CV_DbgAssert is only retained in the Debug configuration.
+*/
+#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
+
+#endif // CV_STATIC_ANALYSIS
+
+//! @cond IGNORED
+#if !defined(__OPENCV_BUILD)  // TODO: backward compatibility only
+#ifndef CV_ErrorNoReturn
+#define CV_ErrorNoReturn CV_Error
+#endif
+#ifndef CV_ErrorNoReturn_
+#define CV_ErrorNoReturn_ CV_Error_
+#endif
+#endif
+
+#define CV_Assert_1 CV_Assert
+#define CV_Assert_2( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_1( __VA_ARGS__ ))
+#define CV_Assert_3( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_2( __VA_ARGS__ ))
+#define CV_Assert_4( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_3( __VA_ARGS__ ))
+#define CV_Assert_5( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_4( __VA_ARGS__ ))
+#define CV_Assert_6( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_5( __VA_ARGS__ ))
+#define CV_Assert_7( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_6( __VA_ARGS__ ))
+#define CV_Assert_8( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_7( __VA_ARGS__ ))
+#define CV_Assert_9( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_8( __VA_ARGS__ ))
+#define CV_Assert_10( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_9( __VA_ARGS__ ))
+
+#define CV_Assert_N(...) do { __CV_EXPAND(__CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__)); } while(0)
+
+//! @endcond
+
+#if defined _DEBUG || defined CV_STATIC_ANALYSIS
+#  define CV_DbgAssert(expr) CV_Assert(expr)
+#else
+/** replaced with CV_Assert(expr) in Debug configuration */
+#  define CV_DbgAssert(expr)
+#endif
+
+/*
+ * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
+ * bit count of A exclusive XOR'ed with B
+ */
+struct CV_EXPORTS Hamming
+{
+    static const NormTypes normType = NORM_HAMMING;
+    typedef unsigned char ValueType;
+    typedef int ResultType;
+
+    /** this will count the bits in a ^ b
+     */
+    ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const;
+};
+
+typedef Hamming HammingLUT;
+
+/////////////////////////////////// inline norms ////////////////////////////////////
+
+template<typename _Tp> inline _Tp cv_abs(_Tp x) { return std::abs(x); }
+inline int cv_abs(uchar x) { return x; }
+inline int cv_abs(schar x) { return std::abs(x); }
+inline int cv_abs(ushort x) { return x; }
+inline int cv_abs(short x) { return std::abs(x); }
+
+template<typename _Tp, typename _AccTp> static inline
+_AccTp normL2Sqr(const _Tp* a, int n)
+{
+    _AccTp s = 0;
+    int i=0;
+#if CV_ENABLE_UNROLLED
+    for( ; i <= n - 4; i += 4 )
+    {
+        _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3];
+        s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
+    }
+#endif
+    for( ; i < n; i++ )
+    {
+        _AccTp v = a[i];
+        s += v*v;
+    }
+    return s;
+}
+
+template<typename _Tp, typename _AccTp> static inline
+_AccTp normL1(const _Tp* a, int n)
+{
+    _AccTp s = 0;
+    int i = 0;
+#if CV_ENABLE_UNROLLED
+    for(; i <= n - 4; i += 4 )
+    {
+        s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) +
+            (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]);
+    }
+#endif
+    for( ; i < n; i++ )
+        s += cv_abs(a[i]);
+    return s;
+}
+
+template<typename _Tp, typename _AccTp> static inline
+_AccTp normInf(const _Tp* a, int n)
+{
+    _AccTp s = 0;
+    for( int i = 0; i < n; i++ )
+        s = std::max(s, (_AccTp)cv_abs(a[i]));
+    return s;
+}
+
+template<typename _Tp, typename _AccTp> static inline
+_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n)
+{
+    _AccTp s = 0;
+    int i= 0;
+#if CV_ENABLE_UNROLLED
+    for(; i <= n - 4; i += 4 )
+    {
+        _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
+        s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
+    }
+#endif
+    for( ; i < n; i++ )
+    {
+        _AccTp v = _AccTp(a[i] - b[i]);
+        s += v*v;
+    }
+    return s;
+}
+
+static inline float normL2Sqr(const float* a, const float* b, int n)
+{
+    float s = 0.f;
+    for( int i = 0; i < n; i++ )
+    {
+        float v = a[i] - b[i];
+        s += v*v;
+    }
+    return s;
+}
+
+template<typename _Tp, typename _AccTp> static inline
+_AccTp normL1(const _Tp* a, const _Tp* b, int n)
+{
+    _AccTp s = 0;
+    int i= 0;
+#if CV_ENABLE_UNROLLED
+    for(; i <= n - 4; i += 4 )
+    {
+        _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
+        s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3);
+    }
+#endif
+    for( ; i < n; i++ )
+    {
+        _AccTp v = _AccTp(a[i] - b[i]);
+        s += std::abs(v);
+    }
+    return s;
+}
+
+inline float normL1(const float* a, const float* b, int n)
+{
+    float s = 0.f;
+    for( int i = 0; i < n; i++ )
+    {
+        s += std::abs(a[i] - b[i]);
+    }
+    return s;
+}
+
+inline int normL1(const uchar* a, const uchar* b, int n)
+{
+    int s = 0;
+    for( int i = 0; i < n; i++ )
+    {
+        s += std::abs(a[i] - b[i]);
+    }
+    return s;
+}
+
+template<typename _Tp, typename _AccTp> static inline
+_AccTp normInf(const _Tp* a, const _Tp* b, int n)
+{
+    _AccTp s = 0;
+    for( int i = 0; i < n; i++ )
+    {
+        _AccTp v0 = a[i] - b[i];
+        s = std::max(s, std::abs(v0));
+    }
+    return s;
+}
+
+/** @brief Computes the cube root of an argument.
+
+ The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly.
+ NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for
+ single-precision data.
+ @param val A function argument.
+ */
+CV_EXPORTS_W float cubeRoot(float val);
+
+/** @overload
+
+cubeRoot with argument of `double` type calls `std::cbrt(double)`
+*/
+static inline
+double cubeRoot(double val)
+{
+    return std::cbrt(val);
+}
+
+/** @brief Calculates the angle of a 2D vector in degrees.
+
+ The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured
+ in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.
+ @param x x-coordinate of the vector.
+ @param y y-coordinate of the vector.
+ */
+CV_EXPORTS_W float fastAtan2(float y, float x);
+
+/** proxy for hal::LU */
+CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
+/** proxy for hal::LU */
+CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
+/** proxy for hal::Cholesky */
+CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
+/** proxy for hal::Cholesky */
+CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
+
+////////////////// forward declarations for important OpenCV types //////////////////
+
+//! @cond IGNORED
+
+template<typename _Tp, int cn> class Vec;
+template<typename _Tp, int m, int n> class Matx;
+
+template<typename _Tp> class Complex;
+template<typename _Tp> class Point_;
+template<typename _Tp> class Point3_;
+template<typename _Tp> class Size_;
+template<typename _Tp> class Rect_;
+template<typename _Tp> class Scalar_;
+
+class CV_EXPORTS RotatedRect;
+class CV_EXPORTS Range;
+class CV_EXPORTS TermCriteria;
+class CV_EXPORTS KeyPoint;
+class CV_EXPORTS DMatch;
+class CV_EXPORTS RNG;
+
+class CV_EXPORTS Mat;
+class CV_EXPORTS MatExpr;
+
+class CV_EXPORTS UMat;
+
+class CV_EXPORTS SparseMat;
+typedef Mat MatND;
+
+template<typename _Tp> class Mat_;
+template<typename _Tp> class SparseMat_;
+
+class CV_EXPORTS MatConstIterator;
+class CV_EXPORTS SparseMatIterator;
+class CV_EXPORTS SparseMatConstIterator;
+template<typename _Tp> class MatIterator_;
+template<typename _Tp> class MatConstIterator_;
+template<typename _Tp> class SparseMatIterator_;
+template<typename _Tp> class SparseMatConstIterator_;
+
+namespace ogl
+{
+    class CV_EXPORTS Buffer;
+    class CV_EXPORTS Texture2D;
+    class CV_EXPORTS Arrays;
+}
+
+namespace cuda
+{
+    class CV_EXPORTS GpuMat;
+    class CV_EXPORTS HostMem;
+    class CV_EXPORTS Stream;
+    class CV_EXPORTS Event;
+}
+
+namespace cudev
+{
+    template <typename _Tp> class GpuMat_;
+}
+
+namespace ipp
+{
+CV_EXPORTS   unsigned long long getIppFeatures();
+CV_EXPORTS   void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL,
+                             int line = 0);
+CV_EXPORTS   int getIppStatus();
+CV_EXPORTS   String getIppErrorLocation();
+CV_EXPORTS_W bool   useIPP();
+CV_EXPORTS_W void   setUseIPP(bool flag);
+CV_EXPORTS_W String getIppVersion();
+
+// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results
+// but have internal accuracy differences which have too much direct or indirect impact on accuracy tests.
+CV_EXPORTS_W bool useIPP_NotExact();
+CV_EXPORTS_W void setUseIPP_NotExact(bool flag);
+#ifndef DISABLE_OPENCV_3_COMPATIBILITY
+static inline bool useIPP_NE() { return useIPP_NotExact(); }
+static inline void setUseIPP_NE(bool flag) { setUseIPP_NotExact(flag); }
+#endif
+
+} // ipp
+
+//! @endcond
+
+//! @} core_utils
+
+
+
+
+} // cv
+
+#include "opencv2/core/neon_utils.hpp"
+#include "opencv2/core/vsx_utils.hpp"
+#include "opencv2/core/check.hpp"
+
+#endif //OPENCV_CORE_BASE_HPP
diff --git a/3rdParty/include/opencv2/core/bindings_utils.hpp b/3rdParty/include/opencv2/core/bindings_utils.hpp
new file mode 100644
index 0000000..22a86ff
--- /dev/null
+++ b/3rdParty/include/opencv2/core/bindings_utils.hpp
@@ -0,0 +1,248 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_BINDINGS_UTILS_HPP
+#define OPENCV_CORE_BINDINGS_UTILS_HPP
+
+#include <opencv2/core/async.hpp>
+#include <opencv2/core/detail/async_promise.hpp>
+#include <opencv2/core/utils/logger.hpp>
+
+#include <stdexcept>
+
+namespace cv { namespace utils {
+//! @addtogroup core_utils
+//! @{
+
+CV_EXPORTS_W String dumpInputArray(InputArray argument);
+
+CV_EXPORTS_W String dumpInputArrayOfArrays(InputArrayOfArrays argument);
+
+CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument);
+
+CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument);
+
+CV_WRAP static inline
+String dumpBool(bool argument)
+{
+    return (argument) ? String("Bool: True") : String("Bool: False");
+}
+
+CV_WRAP static inline
+String dumpInt(int argument)
+{
+    return cv::format("Int: %d", argument);
+}
+
+CV_WRAP static inline
+String dumpSizeT(size_t argument)
+{
+    std::ostringstream oss("size_t: ", std::ios::ate);
+    oss << argument;
+    return oss.str();
+}
+
+CV_WRAP static inline
+String dumpFloat(float argument)
+{
+    return cv::format("Float: %.2f", argument);
+}
+
+CV_WRAP static inline
+String dumpDouble(double argument)
+{
+    return cv::format("Double: %.2f", argument);
+}
+
+CV_WRAP static inline
+String dumpCString(const char* argument)
+{
+    return cv::format("String: %s", argument);
+}
+
+CV_WRAP static inline
+String dumpString(const String& argument)
+{
+    return cv::format("String: %s", argument.c_str());
+}
+
+CV_WRAP static inline
+String testOverloadResolution(int value, const Point& point = Point(42, 24))
+{
+    return format("overload (int=%d, point=(x=%d, y=%d))", value, point.x,
+                  point.y);
+}
+
+CV_WRAP static inline
+String testOverloadResolution(const Rect& rect)
+{
+    return format("overload (rect=(x=%d, y=%d, w=%d, h=%d))", rect.x, rect.y,
+                  rect.width, rect.height);
+}
+
+CV_WRAP static inline
+String dumpRect(const Rect& argument)
+{
+    return format("rect: (x=%d, y=%d, w=%d, h=%d)", argument.x, argument.y,
+                  argument.width, argument.height);
+}
+
+CV_WRAP static inline
+String dumpTermCriteria(const TermCriteria& argument)
+{
+    return format("term_criteria: (type=%d, max_count=%d, epsilon=%lf",
+                  argument.type, argument.maxCount, argument.epsilon);
+}
+
+CV_WRAP static inline
+String dumpRotatedRect(const RotatedRect& argument)
+{
+    return format("rotated_rect: (c_x=%f, c_y=%f, w=%f, h=%f, a=%f)",
+                  argument.center.x, argument.center.y, argument.size.width,
+                  argument.size.height, argument.angle);
+}
+
+CV_WRAP static inline
+RotatedRect testRotatedRect(float x, float y, float w, float h, float angle)
+{
+    return RotatedRect(Point2f(x, y), Size2f(w, h), angle);
+}
+
+CV_WRAP static inline
+std::vector<RotatedRect> testRotatedRectVector(float x, float y, float w, float h, float angle)
+{
+    std::vector<RotatedRect> result;
+    for (int i = 0; i < 10; i++)
+        result.push_back(RotatedRect(Point2f(x + i, y + 2 * i), Size2f(w, h), angle + 10 * i));
+    return result;
+}
+
+CV_WRAP static inline
+String dumpRange(const Range& argument)
+{
+    if (argument == Range::all())
+    {
+        return "range: all";
+    }
+    else
+    {
+        return format("range: (s=%d, e=%d)", argument.start, argument.end);
+    }
+}
+
+CV_WRAP static inline
+int testOverwriteNativeMethod(int argument)
+{
+    return argument;
+}
+
+CV_WRAP static inline
+String testReservedKeywordConversion(int positional_argument, int lambda = 2, int from = 3)
+{
+    return format("arg=%d, lambda=%d, from=%d", positional_argument, lambda, from);
+}
+
+CV_EXPORTS_W String dumpVectorOfInt(const std::vector<int>& vec);
+
+CV_EXPORTS_W String dumpVectorOfDouble(const std::vector<double>& vec);
+
+CV_EXPORTS_W String dumpVectorOfRect(const std::vector<Rect>& vec);
+
+CV_WRAP static inline
+void generateVectorOfRect(size_t len, CV_OUT std::vector<Rect>& vec)
+{
+    vec.resize(len);
+    if (len > 0)
+    {
+        RNG rng(12345);
+        Mat tmp(static_cast<int>(len), 1, CV_32SC4);
+        rng.fill(tmp, RNG::UNIFORM, 10, 20);
+        tmp.copyTo(vec);
+    }
+}
+
+CV_WRAP static inline
+void generateVectorOfInt(size_t len, CV_OUT std::vector<int>& vec)
+{
+    vec.resize(len);
+    if (len > 0)
+    {
+        RNG rng(554433);
+        Mat tmp(static_cast<int>(len), 1, CV_32SC1);
+        rng.fill(tmp, RNG::UNIFORM, -10, 10);
+        tmp.copyTo(vec);
+    }
+}
+
+CV_WRAP static inline
+void generateVectorOfMat(size_t len, int rows, int cols, int dtype, CV_OUT std::vector<Mat>& vec)
+{
+    vec.resize(len);
+    if (len > 0)
+    {
+        RNG rng(65431);
+        for (size_t i = 0; i < len; ++i)
+        {
+            vec[i].create(rows, cols, dtype);
+            rng.fill(vec[i], RNG::UNIFORM, 0, 10);
+        }
+    }
+}
+
+CV_WRAP static inline
+void testRaiseGeneralException()
+{
+    throw std::runtime_error("exception text");
+}
+
+CV_WRAP static inline
+AsyncArray testAsyncArray(InputArray argument)
+{
+    AsyncPromise p;
+    p.setValue(argument);
+    return p.getArrayResult();
+}
+
+CV_WRAP static inline
+AsyncArray testAsyncException()
+{
+    AsyncPromise p;
+    try
+    {
+        CV_Error(Error::StsOk, "Test: Generated async error");
+    }
+    catch (const cv::Exception& e)
+    {
+        p.setException(e);
+    }
+    return p.getArrayResult();
+}
+
+namespace fs {
+    CV_EXPORTS_W cv::String getCacheDirectoryForDownloads();
+} // namespace fs
+
+//! @}  // core_utils
+}  // namespace cv::utils
+
+//! @cond IGNORED
+
+CV_WRAP static inline
+int setLogLevel(int level)
+{
+    // NB: Binding generators doesn't work with enums properly yet, so we define separate overload here
+    return cv::utils::logging::setLogLevel((cv::utils::logging::LogLevel)level);
+}
+
+CV_WRAP static inline
+int getLogLevel()
+{
+    return cv::utils::logging::getLogLevel();
+}
+
+//! @endcond IGNORED
+
+} // namespaces cv /  utils
+
+#endif // OPENCV_CORE_BINDINGS_UTILS_HPP
diff --git a/3rdParty/include/opencv2/core/bufferpool.hpp b/3rdParty/include/opencv2/core/bufferpool.hpp
new file mode 100644
index 0000000..4698e5d
--- /dev/null
+++ b/3rdParty/include/opencv2/core/bufferpool.hpp
@@ -0,0 +1,40 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
+
+#ifndef OPENCV_CORE_BUFFER_POOL_HPP
+#define OPENCV_CORE_BUFFER_POOL_HPP
+
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable: 4265)
+#endif
+
+namespace cv
+{
+
+//! @addtogroup core
+//! @{
+
+class BufferPoolController
+{
+protected:
+    ~BufferPoolController() { }
+public:
+    virtual size_t getReservedSize() const = 0;
+    virtual size_t getMaxReservedSize() const = 0;
+    virtual void setMaxReservedSize(size_t size) = 0;
+    virtual void freeAllReservedBuffers() = 0;
+};
+
+//! @}
+
+}
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+#endif // OPENCV_CORE_BUFFER_POOL_HPP
diff --git a/3rdParty/include/opencv2/core/check.hpp b/3rdParty/include/opencv2/core/check.hpp
new file mode 100644
index 0000000..d975223
--- /dev/null
+++ b/3rdParty/include/opencv2/core/check.hpp
@@ -0,0 +1,160 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_CHECK_HPP
+#define OPENCV_CORE_CHECK_HPP
+
+#include <opencv2/core/base.hpp>
+
+namespace cv {
+
+/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "<invalid depth>" */
+CV_EXPORTS const char* depthToString(int depth);
+
+/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "<invalid type>" */
+CV_EXPORTS const String typeToString(int type);
+
+
+//! @cond IGNORED
+namespace detail {
+
+/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */
+CV_EXPORTS const char* depthToString_(int depth);
+
+/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */
+CV_EXPORTS const cv::String typeToString_(int type);
+
+enum TestOp {
+  TEST_CUSTOM = 0,
+  TEST_EQ = 1,
+  TEST_NE = 2,
+  TEST_LE = 3,
+  TEST_LT = 4,
+  TEST_GE = 5,
+  TEST_GT = 6,
+  CV__LAST_TEST_OP
+};
+
+struct CheckContext {
+    const char* func;
+    const char* file;
+    int line;
+    enum TestOp testOp;
+    const char* message;
+    const char* p1_str;
+    const char* p2_str;
+};
+
+#ifndef CV__CHECK_FILENAME
+# define CV__CHECK_FILENAME __FILE__
+#endif
+
+#ifndef CV__CHECK_FUNCTION
+# if defined _MSC_VER
+#   define CV__CHECK_FUNCTION __FUNCSIG__
+# elif defined __GNUC__
+#   define CV__CHECK_FUNCTION __PRETTY_FUNCTION__
+# else
+#   define CV__CHECK_FUNCTION "<unknown>"
+# endif
+#endif
+
+#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__)
+#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \
+    static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \
+            { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, "" message, "" p1_str, "" p2_str }
+
+CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_<int> v1, const Size_<int> v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx);
+
+CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_<int> v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const std::string& v1, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx);
+
+
+#define CV__TEST_EQ(v1, v2) ((v1) == (v2))
+#define CV__TEST_NE(v1, v2) ((v1) != (v2))
+#define CV__TEST_LE(v1, v2) ((v1) <= (v2))
+#define CV__TEST_LT(v1, v2) ((v1) < (v2))
+#define CV__TEST_GE(v1, v2) ((v1) >= (v2))
+#define CV__TEST_GT(v1, v2) ((v1) > (v2))
+
+#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \
+    if(CV__TEST_##op((v1), (v2))) ; else { \
+        CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \
+        cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \
+    } \
+} while (0)
+
+#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \
+    if(!!(test_expr)) ; else { \
+        CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \
+        cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \
+    } \
+} while (0)
+
+} // namespace
+//! @endcond
+
+
+/// Supported values of these types: int, float, double
+#define CV_CheckEQ(v1, v2, msg)  CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
+#define CV_CheckNE(v1, v2, msg)  CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
+#define CV_CheckLE(v1, v2, msg)  CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
+#define CV_CheckLT(v1, v2, msg)  CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
+#define CV_CheckGE(v1, v2, msg)  CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
+#define CV_CheckGT(v1, v2, msg)  CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
+
+/// Check with additional "decoding" of type values in error message
+#define CV_CheckTypeEQ(t1, t2, msg)  CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg)
+/// Check with additional "decoding" of depth values in error message
+#define CV_CheckDepthEQ(d1, d2, msg)  CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg)
+
+#define CV_CheckChannelsEQ(c1, c2, msg)  CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg)
+
+/// Example: type == CV_8UC1 || type == CV_8UC3
+#define CV_CheckType(t, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg)
+
+/// Example: depth == CV_32F || depth == CV_64F
+#define CV_CheckDepth(t, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg)
+
+/// Example: v == A || v == B
+#define CV_Check(v, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
+
+/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1")
+// TODO define pretty-printers
+
+#ifndef NDEBUG
+#define CV_DbgCheck(v, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
+#define CV_DbgCheckEQ(v1, v2, msg)  CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckNE(v1, v2, msg)  CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckLE(v1, v2, msg)  CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckLT(v1, v2, msg)  CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckGE(v1, v2, msg)  CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckGT(v1, v2, msg)  CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
+#else
+#define CV_DbgCheck(v, test_expr, msg)  do { } while (0)
+#define CV_DbgCheckEQ(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckNE(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckLE(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckLT(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckGE(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckGT(v1, v2, msg)  do { } while (0)
+#endif
+
+} // namespace
+
+#endif // OPENCV_CORE_CHECK_HPP
diff --git a/3rdParty/include/opencv2/core/core.hpp b/3rdParty/include/opencv2/core/core.hpp
new file mode 100644
index 0000000..4389183
--- /dev/null
+++ b/3rdParty/include/opencv2/core/core.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/core.hpp"
diff --git a/3rdParty/include/opencv2/core/core_c.h b/3rdParty/include/opencv2/core/core_c.h
new file mode 100644
index 0000000..09ac1e7
--- /dev/null
+++ b/3rdParty/include/opencv2/core/core_c.h
@@ -0,0 +1,3125 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+
+#ifndef OPENCV_CORE_C_H
+#define OPENCV_CORE_C_H
+
+#include "opencv2/core/types_c.h"
+
+#ifdef __cplusplus
+#  ifdef _MSC_VER
+/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename'
+                          which is incompatible with C
+
+   It is OK to disable it because we only extend few plain structures with
+   C++ constructors for simpler interoperability with C++ API of the library
+*/
+#    pragma warning(disable:4190)
+#  elif defined __clang__ && __clang_major__ >= 3
+#    pragma GCC diagnostic ignored "-Wreturn-type-c-linkage"
+#  endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/** @addtogroup core_c
+    @{
+*/
+
+/****************************************************************************************\
+*          Array allocation, deallocation, initialization and access to elements         *
+\****************************************************************************************/
+
+/** `malloc` wrapper.
+   If there is no enough memory, the function
+   (as well as other OpenCV functions that call cvAlloc)
+   raises an error. */
+CVAPI(void*)  cvAlloc( size_t size );
+
+/** `free` wrapper.
+   Here and further all the memory releasing functions
+   (that all call cvFree) take double pointer in order to
+   to clear pointer to the data after releasing it.
+   Passing pointer to NULL pointer is Ok: nothing happens in this case
+*/
+CVAPI(void)   cvFree_( void* ptr );
+#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0)
+
+/** @brief Creates an image header but does not allocate the image data.
+
+@param size Image width and height
+@param depth Image depth (see cvCreateImage )
+@param channels Number of channels (see cvCreateImage )
+ */
+CVAPI(IplImage*)  cvCreateImageHeader( CvSize size, int depth, int channels );
+
+/** @brief Initializes an image header that was previously allocated.
+
+The returned IplImage\* points to the initialized header.
+@param image Image header to initialize
+@param size Image width and height
+@param depth Image depth (see cvCreateImage )
+@param channels Number of channels (see cvCreateImage )
+@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL
+@param align Alignment for image rows, typically 4 or 8 bytes
+ */
+CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth,
+                                   int channels, int origin CV_DEFAULT(0),
+                                   int align CV_DEFAULT(4));
+
+/** @brief Creates an image header and allocates the image data.
+
+This function call is equivalent to the following code:
+@code
+    header = cvCreateImageHeader(size, depth, channels);
+    cvCreateData(header);
+@endcode
+@param size Image width and height
+@param depth Bit depth of image elements. See IplImage for valid depths.
+@param channels Number of channels per pixel. See IplImage for details. This function only creates
+images with interleaved channels.
+ */
+CVAPI(IplImage*)  cvCreateImage( CvSize size, int depth, int channels );
+
+/** @brief Deallocates an image header.
+
+This call is an analogue of :
+@code
+    if(image )
+    {
+        iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI);
+        *image = 0;
+    }
+@endcode
+but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro).
+@param image Double pointer to the image header
+ */
+CVAPI(void)  cvReleaseImageHeader( IplImage** image );
+
+/** @brief Deallocates the image header and the image data.
+
+This call is a shortened form of :
+@code
+    if(*image )
+    {
+        cvReleaseData(*image);
+        cvReleaseImageHeader(image);
+    }
+@endcode
+@param image Double pointer to the image header
+*/
+CVAPI(void)  cvReleaseImage( IplImage** image );
+
+/** Creates a copy of IPL image (widthStep may differ) */
+CVAPI(IplImage*) cvCloneImage( const IplImage* image );
+
+/** @brief Sets the channel of interest in an IplImage.
+
+If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do
+*not* support the COI setting, so to process an individual image/matrix channel one may copy (via
+cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result
+back (via cvCopy or cvMerge) if needed.
+@param image A pointer to the image header
+@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected,
+etc. Note that the channel indices become 1-based.
+ */
+CVAPI(void)  cvSetImageCOI( IplImage* image, int coi );
+
+/** @brief Returns the index of the channel of interest.
+
+Returns the channel of interest of in an IplImage. Returned values correspond to the coi in
+cvSetImageCOI.
+@param image A pointer to the image header
+ */
+CVAPI(int)  cvGetImageCOI( const IplImage* image );
+
+/** @brief Sets an image Region Of Interest (ROI) for a given rectangle.
+
+If the original image ROI was NULL and the rect is not the whole image, the ROI structure is
+allocated.
+
+Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For
+example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the
+ROI, not the original image.
+@param image A pointer to the image header
+@param rect The ROI rectangle
+ */
+CVAPI(void)  cvSetImageROI( IplImage* image, CvRect rect );
+
+/** @brief Resets the image ROI to include the entire image and releases the ROI structure.
+
+This produces a similar result to the following, but in addition it releases the ROI structure. :
+@code
+    cvSetImageROI(image, cvRect(0, 0, image->width, image->height ));
+    cvSetImageCOI(image, 0);
+@endcode
+@param image A pointer to the image header
+ */
+CVAPI(void)  cvResetImageROI( IplImage* image );
+
+/** @brief Returns the image ROI.
+
+If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned.
+@param image A pointer to the image header
+ */
+CVAPI(CvRect) cvGetImageROI( const IplImage* image );
+
+/** @brief Creates a matrix header but does not allocate the matrix data.
+
+The function allocates a new matrix header and returns a pointer to it. The matrix data can then be
+allocated using cvCreateData or set explicitly to user-allocated data via cvSetData.
+@param rows Number of rows in the matrix
+@param cols Number of columns in the matrix
+@param type Type of the matrix elements, see cvCreateMat
+ */
+CVAPI(CvMat*)  cvCreateMatHeader( int rows, int cols, int type );
+
+#define CV_AUTOSTEP  0x7fffffff
+
+/** @brief Initializes a pre-allocated matrix header.
+
+This function is often used to process raw data with OpenCV matrix functions. For example, the
+following code computes the matrix product of two matrices, stored as ordinary arrays:
+@code
+    double a[] = { 1, 2, 3, 4,
+                   5, 6, 7, 8,
+                   9, 10, 11, 12 };
+
+    double b[] = { 1, 5, 9,
+                   2, 6, 10,
+                   3, 7, 11,
+                   4, 8, 12 };
+
+    double c[9];
+    CvMat Ma, Mb, Mc ;
+
+    cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a);
+    cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b);
+    cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c);
+
+    cvMatMulAdd(&Ma, &Mb, 0, &Mc);
+    // the c array now contains the product of a (3x4) and b (4x3)
+@endcode
+@param mat A pointer to the matrix header to be initialized
+@param rows Number of rows in the matrix
+@param cols Number of columns in the matrix
+@param type Type of the matrix elements, see cvCreateMat .
+@param data Optional: data pointer assigned to the matrix header
+@param step Optional: full row width in bytes of the assigned data. By default, the minimal
+possible step is used which assumes there are no gaps between subsequent rows of the matrix.
+ */
+CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols,
+                              int type, void* data CV_DEFAULT(NULL),
+                              int step CV_DEFAULT(CV_AUTOSTEP) );
+
+/** @brief Creates a matrix header and allocates the matrix data.
+
+The function call is equivalent to the following code:
+@code
+    CvMat* mat = cvCreateMatHeader(rows, cols, type);
+    cvCreateData(mat);
+@endcode
+@param rows Number of rows in the matrix
+@param cols Number of columns in the matrix
+@param type The type of the matrix elements in the form
+CV_\<bit depth\>\<S|U|F\>C\<number of channels\> , where S=signed, U=unsigned, F=float. For
+example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _
+32SC2 means the elements are 32-bit signed and there are 2 channels.
+ */
+CVAPI(CvMat*)  cvCreateMat( int rows, int cols, int type );
+
+/** @brief Deallocates a matrix.
+
+The function decrements the matrix data reference counter and deallocates matrix header. If the data
+reference counter is 0, it also deallocates the data. :
+@code
+    if(*mat )
+        cvDecRefData(*mat);
+    cvFree((void**)mat);
+@endcode
+@param mat Double pointer to the matrix
+ */
+CVAPI(void)  cvReleaseMat( CvMat** mat );
+
+/** @brief Decrements an array data reference counter.
+
+The function decrements the data reference counter in a CvMat or CvMatND if the reference counter
+
+pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current
+implementation the reference counter is not NULL only if the data was allocated using the
+cvCreateData function. The counter will be NULL in other cases such as: external data was assigned
+to the header using cvSetData, header is part of a larger matrix or image, or the header was
+converted from an image or n-dimensional matrix header.
+@param arr Pointer to an array header
+ */
+CV_INLINE  void  cvDecRefData( CvArr* arr )
+{
+    if( CV_IS_MAT( arr ))
+    {
+        CvMat* mat = (CvMat*)arr;
+        mat->data.ptr = NULL;
+        if( mat->refcount != NULL && --*mat->refcount == 0 )
+            cvFree( &mat->refcount );
+        mat->refcount = NULL;
+    }
+    else if( CV_IS_MATND( arr ))
+    {
+        CvMatND* mat = (CvMatND*)arr;
+        mat->data.ptr = NULL;
+        if( mat->refcount != NULL && --*mat->refcount == 0 )
+            cvFree( &mat->refcount );
+        mat->refcount = NULL;
+    }
+}
+
+/** @brief Increments array data reference counter.
+
+The function increments CvMat or CvMatND data reference counter and returns the new counter value if
+the reference counter pointer is not NULL, otherwise it returns zero.
+@param arr Array header
+ */
+CV_INLINE  int  cvIncRefData( CvArr* arr )
+{
+    int refcount = 0;
+    if( CV_IS_MAT( arr ))
+    {
+        CvMat* mat = (CvMat*)arr;
+        if( mat->refcount != NULL )
+            refcount = ++*mat->refcount;
+    }
+    else if( CV_IS_MATND( arr ))
+    {
+        CvMatND* mat = (CvMatND*)arr;
+        if( mat->refcount != NULL )
+            refcount = ++*mat->refcount;
+    }
+    return refcount;
+}
+
+
+/** Creates an exact copy of the input matrix (except, may be, step value) */
+CVAPI(CvMat*) cvCloneMat( const CvMat* mat );
+
+
+/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix.
+
+The function returns header, corresponding to a specified rectangle of the input array. In other
+
+words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is
+taken into account by the function so the sub-array of ROI is actually extracted.
+@param arr Input array
+@param submat Pointer to the resultant sub-array header
+@param rect Zero-based coordinates of the rectangle of interest
+ */
+CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect );
+#define cvGetSubArr cvGetSubRect
+
+/** @brief Returns array row or row span.
+
+The function returns the header, corresponding to a specified row/row span of the input array.
+cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1).
+@param arr Input array
+@param submat Pointer to the resulting sub-array header
+@param start_row Zero-based index of the starting row (inclusive) of the span
+@param end_row Zero-based index of the ending row (exclusive) of the span
+@param delta_row Index step in the row span. That is, the function extracts every delta_row -th
+row from start_row and up to (but not including) end_row .
+ */
+CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat,
+                        int start_row, int end_row,
+                        int delta_row CV_DEFAULT(1));
+
+/** @overload
+@param arr Input array
+@param submat Pointer to the resulting sub-array header
+@param row Zero-based index of the selected row
+*/
+CV_INLINE  CvMat*  cvGetRow( const CvArr* arr, CvMat* submat, int row )
+{
+    return cvGetRows( arr, submat, row, row + 1, 1 );
+}
+
+
+/** @brief Returns one of more array columns.
+
+The function returns the header, corresponding to a specified column span of the input array. That
+
+is, no data is copied. Therefore, any modifications of the submatrix will affect the original array.
+If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for
+cvGetCols(arr, submat, col, col+1).
+@param arr Input array
+@param submat Pointer to the resulting sub-array header
+@param start_col Zero-based index of the starting column (inclusive) of the span
+@param end_col Zero-based index of the ending column (exclusive) of the span
+ */
+CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat,
+                        int start_col, int end_col );
+
+/** @overload
+@param arr Input array
+@param submat Pointer to the resulting sub-array header
+@param col Zero-based index of the selected column
+*/
+CV_INLINE  CvMat*  cvGetCol( const CvArr* arr, CvMat* submat, int col )
+{
+    return cvGetCols( arr, submat, col, col + 1 );
+}
+
+/** @brief Returns one of array diagonals.
+
+The function returns the header, corresponding to a specified diagonal of the input array.
+@param arr Input array
+@param submat Pointer to the resulting sub-array header
+@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1
+corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so
+forth.
+ */
+CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat,
+                            int diag CV_DEFAULT(0));
+
+/** low-level scalar <-> raw data conversion functions */
+CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type,
+                              int extend_to_12 CV_DEFAULT(0) );
+
+CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar );
+
+/** @brief Creates a new matrix header but does not allocate the matrix data.
+
+The function allocates a header for a multi-dimensional dense array. The array data can further be
+allocated using cvCreateData or set explicitly to user-allocated data via cvSetData.
+@param dims Number of array dimensions
+@param sizes Array of dimension sizes
+@param type Type of array elements, see cvCreateMat
+ */
+CVAPI(CvMatND*)  cvCreateMatNDHeader( int dims, const int* sizes, int type );
+
+/** @brief Creates the header and allocates the data for a multi-dimensional dense array.
+
+This function call is equivalent to the following code:
+@code
+    CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type);
+    cvCreateData(mat);
+@endcode
+@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be
+changed at build time).
+@param sizes Array of dimension sizes.
+@param type Type of array elements, see cvCreateMat .
+ */
+CVAPI(CvMatND*)  cvCreateMatND( int dims, const int* sizes, int type );
+
+/** @brief Initializes a pre-allocated multi-dimensional array header.
+
+@param mat A pointer to the array header to be initialized
+@param dims The number of array dimensions
+@param sizes An array of dimension sizes
+@param type Type of array elements, see cvCreateMat
+@param data Optional data pointer assigned to the matrix header
+ */
+CVAPI(CvMatND*)  cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes,
+                                    int type, void* data CV_DEFAULT(NULL) );
+
+/** @brief Deallocates a multi-dimensional array.
+
+The function decrements the array data reference counter and releases the array header. If the
+reference counter reaches 0, it also deallocates the data. :
+@code
+    if(*mat )
+        cvDecRefData(*mat);
+    cvFree((void**)mat);
+@endcode
+@param mat Double pointer to the array
+ */
+CV_INLINE  void  cvReleaseMatND( CvMatND** mat )
+{
+    cvReleaseMat( (CvMat**)mat );
+}
+
+/** Creates a copy of CvMatND (except, may be, steps) */
+CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat );
+
+/** @brief Creates sparse array.
+
+The function allocates a multi-dimensional sparse array. Initially the array contain no elements,
+that is PtrND and other related functions will return 0 for every index.
+@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is
+practically unlimited (up to \f$2^{16}\f$ ).
+@param sizes Array of dimension sizes
+@param type Type of array elements. The same as for CvMat
+ */
+CVAPI(CvSparseMat*)  cvCreateSparseMat( int dims, const int* sizes, int type );
+
+/** @brief Deallocates sparse array.
+
+The function releases the sparse array and clears the array pointer upon exit.
+@param mat Double pointer to the array
+ */
+CVAPI(void)  cvReleaseSparseMat( CvSparseMat** mat );
+
+/** Creates a copy of CvSparseMat (except, may be, zero items) */
+CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat );
+
+/** @brief Initializes sparse array elements iterator.
+
+The function initializes iterator of sparse array elements and returns pointer to the first element,
+or NULL if the array is empty.
+@param mat Input array
+@param mat_iterator Initialized iterator
+ */
+CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat,
+                                              CvSparseMatIterator* mat_iterator );
+
+/** @brief Returns the next sparse matrix element
+
+The function moves iterator to the next sparse matrix element and returns pointer to it. In the
+current version there is no any particular order of the elements, because they are stored in the
+hash table. The sample below demonstrates how to iterate through the sparse matrix:
+@code
+    // print all the non-zero sparse matrix elements and compute their sum
+    double sum = 0;
+    int i, dims = cvGetDims(sparsemat);
+    CvSparseMatIterator it;
+    CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it);
+
+    for(; node != 0; node = cvGetNextSparseNode(&it))
+    {
+        int* idx = CV_NODE_IDX(array, node);
+        float val = *(float*)CV_NODE_VAL(array, node);
+        printf("M");
+        for(i = 0; i < dims; i++ )
+            printf("[%d]", idx[i]);
+        printf("=%g\n", val);
+
+        sum += val;
+    }
+
+    printf("nTotal sum = %g\n", sum);
+@endcode
+@param mat_iterator Sparse array iterator
+ */
+CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator )
+{
+    if( mat_iterator->node->next )
+        return mat_iterator->node = mat_iterator->node->next;
+    else
+    {
+        int idx;
+        for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ )
+        {
+            CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx];
+            if( node )
+            {
+                mat_iterator->curidx = idx;
+                return mat_iterator->node = node;
+            }
+        }
+        return NULL;
+    }
+}
+
+
+#define CV_MAX_ARR 10
+
+/** matrix iterator: used for n-ary operations on dense arrays */
+typedef struct CvNArrayIterator
+{
+    int count; /**< number of arrays */
+    int dims; /**< number of dimensions to iterate */
+    CvSize size; /**< maximal common linear size: { width = size, height = 1 } */
+    uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */
+    int stack[CV_MAX_DIM]; /**< for internal use */
+    CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the
+                                 matrices that are processed */
+}
+CvNArrayIterator;
+
+#define CV_NO_DEPTH_CHECK     1
+#define CV_NO_CN_CHECK        2
+#define CV_NO_SIZE_CHECK      4
+
+/** initializes iterator that traverses through several arrays simultaneously
+   (the function together with cvNextArraySlice is used for
+    N-ari element-wise operations) */
+CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs,
+                                 const CvArr* mask, CvMatND* stubs,
+                                 CvNArrayIterator* array_iterator,
+                                 int flags CV_DEFAULT(0) );
+
+/** returns zero value if iteration is finished, non-zero (slice length) otherwise */
+CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator );
+
+
+/** @brief Returns type of array elements.
+
+The function returns type of the array elements. In the case of IplImage the type is converted to
+CvMat-like representation. For example, if the image has been created as:
+@code
+    IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3);
+@endcode
+The code cvGetElemType(img) will return CV_8UC3.
+@param arr Input array
+ */
+CVAPI(int) cvGetElemType( const CvArr* arr );
+
+/** @brief Return number of array dimensions
+
+The function returns the array dimensionality and the array of dimension sizes. In the case of
+IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the
+following code calculates total number of array elements:
+@code
+    int sizes[CV_MAX_DIM];
+    int i, total = 1;
+    int dims = cvGetDims(arr, size);
+    for(i = 0; i < dims; i++ )
+        total *= sizes[i];
+@endcode
+@param arr Input array
+@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows
+(height) goes first, number of columns (width) next.
+ */
+CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) );
+
+
+/** @brief Returns array size along the specified dimension.
+
+@param arr Input array
+@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of
+columns; for images 0 means height, 1 means width)
+ */
+CVAPI(int) cvGetDimSize( const CvArr* arr, int index );
+
+
+/** @brief Return pointer to a particular array element.
+
+The functions return a pointer to a specific array element. Number of array dimension should match
+to the number of indices passed to the function except for cvPtr1D function that can be used for
+sequential access to 1D, 2D or nD dense arrays.
+
+The functions can be used for sparse arrays as well - if the requested node does not exist they
+create it and set it to zero.
+
+All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet
+, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range.
+@param arr Input array
+@param idx0 The first zero-based component of the element index
+@param type Optional output parameter: type of matrix elements
+ */
+CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL));
+/** @overload */
+CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) );
+/** @overload */
+CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2,
+                      int* type CV_DEFAULT(NULL));
+/** @overload
+@param arr Input array
+@param idx Array of the element indices
+@param type Optional output parameter: type of matrix elements
+@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter
+means that the requested element is created if it does not exist already.
+@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL,
+the function does not recalculate the node hash value, but takes it from the specified location.
+It is useful for speeding up pair-wise operations (TODO: provide an example)
+*/
+CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL),
+                      int create_node CV_DEFAULT(1),
+                      unsigned* precalc_hashval CV_DEFAULT(NULL));
+
+/** @brief Return a specific array element.
+
+The functions return a specific array element. In the case of a sparse array the functions return 0
+if the requested node does not exist (no new node is created by the functions).
+@param arr Input array
+@param idx0 The first zero-based component of the element index
+ */
+CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 );
+/** @overload */
+CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 );
+/** @overload */
+CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 );
+/** @overload
+@param arr Input array
+@param idx Array of the element indices
+*/
+CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx );
+
+/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array.
+
+Returns a specific element of a single-channel array. If the array has multiple channels, a runtime
+error is raised. Note that Get?D functions can be used safely for both single-channel and
+multiple-channel arrays though they are a bit slower.
+
+In the case of a sparse array the functions return 0 if the requested node does not exist (no new
+node is created by the functions).
+@param arr Input array. Must have a single channel.
+@param idx0 The first zero-based component of the element index
+ */
+CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 );
+/** @overload */
+CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 );
+/** @overload */
+CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 );
+/** @overload
+@param arr Input array. Must have a single channel.
+@param idx Array of the element indices
+*/
+CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx );
+
+/** @brief Change the particular array element.
+
+The functions assign the new value to a particular array element. In the case of a sparse array the
+functions create the node if it does not exist yet.
+@param arr Input array
+@param idx0 The first zero-based component of the element index
+@param value The assigned value
+ */
+CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value );
+/** @overload */
+CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value );
+/** @overload */
+CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value );
+/** @overload
+@param arr Input array
+@param idx Array of the element indices
+@param value The assigned value
+*/
+CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value );
+
+/** @brief Change a specific array element.
+
+The functions assign a new value to a specific element of a single-channel array. If the array has
+multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for
+both single-channel and multiple-channel arrays, though they are a bit slower.
+
+In the case of a sparse array the functions create the node if it does not yet exist.
+@param arr Input array
+@param idx0 The first zero-based component of the element index
+@param value The assigned value
+ */
+CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value );
+/** @overload */
+CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value );
+/** @overload */
+CVAPI(void) cvSetReal3D( CvArr* arr, int idx0,
+                        int idx1, int idx2, double value );
+/** @overload
+@param arr Input array
+@param idx Array of the element indices
+@param value The assigned value
+*/
+CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value );
+
+/** clears element of ND dense array,
+   in case of sparse arrays it deletes the specified node */
+CVAPI(void) cvClearND( CvArr* arr, const int* idx );
+
+/** @brief Returns matrix header for arbitrary array.
+
+The function returns a matrix header for the input array that can be a matrix - CvMat, an image -
+IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if
+allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of
+IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI
+and returns &header. Because COI is not supported by CvMat, it is returned separately.
+
+The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same
+code. Input array must have non-zero data pointer, otherwise the function will report an error.
+
+@note If the input array is IplImage with planar data layout and COI set, the function returns the
+pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures
+with planar data layout, even though OpenCV does not support such images.
+@param arr Input array
+@param header Pointer to CvMat structure used as a temporary buffer
+@param coi Optional output parameter for storing COI
+@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and
+returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or
+more than 2 dimensions). The CvMatND array must be continuous.
+@sa cvGetImage, cvarrToMat.
+ */
+CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header,
+                       int* coi CV_DEFAULT(NULL),
+                       int allowND CV_DEFAULT(0));
+
+/** @brief Returns image header for arbitrary array.
+
+The function returns the image header for the input array that can be a matrix (CvMat) or image
+(IplImage). In the case of an image the function simply returns the input pointer. In the case of
+CvMat it initializes an image_header structure with the parameters of the input matrix. Note that
+if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using
+this function, we will get different headers if the ROI is set in the original image.
+@param arr Input array
+@param image_header Pointer to IplImage structure used as a temporary buffer
+ */
+CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header );
+
+
+/** @brief Changes the shape of a multi-dimensional array without copying the data.
+
+The function is an advanced version of cvReshape that can work with multi-dimensional arrays as
+well (though it can work with ordinary images and matrices) and change the number of dimensions.
+
+Below are the two samples from the cvReshape description rewritten using cvReshapeMatND:
+@code
+    IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3);
+    IplImage gray_img_hdr, *gray_img;
+    gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0);
+    ...
+    int size[] = { 2, 2, 2 };
+    CvMatND* mat = cvCreateMatND(3, size, CV_32F);
+    CvMat row_header, *row;
+    row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0);
+@endcode
+In C, the header file for this function includes a convenient macro cvReshapeND that does away with
+the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples
+may be replaced as follow:
+@code
+    gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0);
+    ...
+    row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0);
+@endcode
+@param arr Input array
+@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND
+output headers
+@param header Output header to be filled
+@param new_cn New number of channels. new_cn = 0 means that the number of channels remains
+unchanged.
+@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions
+remains the same.
+@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the
+total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not
+used.
+ */
+CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr,
+                             int sizeof_header, CvArr* header,
+                             int new_cn, int new_dims, int* new_sizes );
+
+#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes )   \
+      cvReshapeMatND( (arr), sizeof(*(header)), (header),         \
+                      (new_cn), (new_dims), (new_sizes))
+
+/** @brief Changes shape of matrix/image without copying data.
+
+The function initializes the CvMat header so that it points to the same data as the original array
+but has a different shape - different number of channels, different number of rows, or both.
+
+The following example code creates one image buffer and two image headers, the first is for a
+320x240x3 image and the second is for a 960x240x1 image:
+@code
+    IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3);
+    CvMat gray_mat_hdr;
+    IplImage gray_img_hdr, *gray_img;
+    cvReshape(color_img, &gray_mat_hdr, 1);
+    gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr);
+@endcode
+And the next example converts a 3x3 matrix to a single 1x9 vector:
+@code
+    CvMat* mat = cvCreateMat(3, 3, CV_32F);
+    CvMat row_header, *row;
+    row = cvReshape(mat, &row_header, 0, 1);
+@endcode
+@param arr Input array
+@param header Output header to be filled
+@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains
+unchanged.
+@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains
+unchanged unless it needs to be changed according to new_cn value.
+*/
+CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header,
+                        int new_cn, int new_rows CV_DEFAULT(0) );
+
+/** Repeats source 2d array several times in both horizontal and
+   vertical direction to fill destination array */
+CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst );
+
+/** @brief Allocates array data
+
+The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of
+matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless
+CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used
+to allocate the data.
+@param arr Array header
+ */
+CVAPI(void)  cvCreateData( CvArr* arr );
+
+/** @brief Releases array data.
+
+The function releases the array data. In the case of CvMat or CvMatND it simply calls
+cvDecRefData(), that is the function can not deallocate external data. See also the note to
+cvCreateData .
+@param arr Array header
+ */
+CVAPI(void)  cvReleaseData( CvArr* arr );
+
+/** @brief Assigns user data to the array header.
+
+The function assigns user data to the array header. Header should be initialized before using
+cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader,
+cvInitImageHeader or cvInitMatNDHeader.
+@param arr Array header
+@param data User data
+@param step Full row length in bytes
+ */
+CVAPI(void)  cvSetData( CvArr* arr, void* data, int step );
+
+/** @brief Retrieves low-level information about the array.
+
+The function fills output variables with low-level information about the array data. All output
+
+parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with
+ROI set, the parameters of ROI are returned.
+
+The following example shows how to get access to array elements. It computes absolute values of the
+array elements :
+@code
+    float* data;
+    int step;
+    CvSize size;
+
+    cvGetRawData(array, (uchar**)&data, &step, &size);
+    step /= sizeof(data[0]);
+
+    for(int y = 0; y < size.height; y++, data += step )
+        for(int x = 0; x < size.width; x++ )
+            data[x] = (float)fabs(data[x]);
+@endcode
+@param arr Array header
+@param data Output pointer to the whole image origin or ROI origin if ROI is set
+@param step Output full row length in bytes
+@param roi_size Output ROI size
+ */
+CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data,
+                         int* step CV_DEFAULT(NULL),
+                         CvSize* roi_size CV_DEFAULT(NULL));
+
+/** @brief Returns size of matrix or image ROI.
+
+The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the
+input matrix or image. In the case of image the size of ROI is returned.
+@param arr array header
+ */
+CVAPI(CvSize) cvGetSize( const CvArr* arr );
+
+/** @brief Copies one array to another.
+
+The function copies selected elements from an input array to an output array:
+
+\f[\texttt{dst} (I)= \texttt{src} (I)  \quad \text{if} \quad \texttt{mask} (I)  \ne 0.\f]
+
+If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays
+must have the same type, the same number of dimensions, and the same size. The function can also
+copy sparse arrays (mask is not supported in this case).
+@param src The source array
+@param dst The destination array
+@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array
+to be changed
+ */
+CVAPI(void)  cvCopy( const CvArr* src, CvArr* dst,
+                     const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @brief Sets every element of an array to a given value.
+
+The function copies the scalar value to every selected element of the destination array:
+\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I)  \ne 0\f]
+If array arr is of IplImage type, then is ROI used, but COI must not be set.
+@param arr The destination array
+@param value Fill value
+@param mask Operation mask, 8-bit single channel array; specifies elements of the destination
+array to be changed
+ */
+CVAPI(void)  cvSet( CvArr* arr, CvScalar value,
+                    const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @brief Clears the array.
+
+The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage),
+cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the
+elements are removed.
+@param arr Array to be cleared
+ */
+CVAPI(void)  cvSetZero( CvArr* arr );
+#define cvZero  cvSetZero
+
+
+/** Splits a multi-channel array into the set of single-channel arrays or
+   extracts particular [color] plane */
+CVAPI(void)  cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1,
+                      CvArr* dst2, CvArr* dst3 );
+
+/** Merges a set of single-channel arrays into the single multi-channel array
+   or inserts one particular [color] plane to the array */
+CVAPI(void)  cvMerge( const CvArr* src0, const CvArr* src1,
+                      const CvArr* src2, const CvArr* src3,
+                      CvArr* dst );
+
+/** Copies several channels from input arrays to
+   certain channels of output arrays */
+CVAPI(void)  cvMixChannels( const CvArr** src, int src_count,
+                            CvArr** dst, int dst_count,
+                            const int* from_to, int pair_count );
+
+/** @brief Converts one array to another with optional linear transformation.
+
+The function has several different purposes, and thus has several different names. It copies one
+array to another with optional scaling, which is performed first, and/or optional type conversion,
+performed after:
+
+\f[\texttt{dst} (I) =  \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f]
+
+All the channels of multi-channel arrays are processed independently.
+
+The type of conversion is done with rounding and saturation, that is if the result of scaling +
+conversion can not be represented exactly by a value of the destination array element type, it is
+set to the nearest representable value on the real axis.
+@param src Source array
+@param dst Destination array
+@param scale Scale factor
+@param shift Value added to the scaled source array elements
+ */
+CVAPI(void)  cvConvertScale( const CvArr* src, CvArr* dst,
+                             double scale CV_DEFAULT(1),
+                             double shift CV_DEFAULT(0) );
+#define cvCvtScale cvConvertScale
+#define cvScale  cvConvertScale
+#define cvConvert( src, dst )  cvConvertScale( (src), (dst), 1, 0 )
+
+
+/** Performs linear transformation on every source array element,
+   stores absolute value of the result:
+   dst(x,y,c) = abs(scale*src(x,y,c)+shift).
+   destination array must have 8u type.
+   In other cases one may use cvConvertScale + cvAbsDiffS */
+CVAPI(void)  cvConvertScaleAbs( const CvArr* src, CvArr* dst,
+                                double scale CV_DEFAULT(1),
+                                double shift CV_DEFAULT(0) );
+#define cvCvtScaleAbs  cvConvertScaleAbs
+
+
+/** checks termination criteria validity and
+   sets eps to default_eps (if it is not set),
+   max_iter to default_max_iters (if it is not set)
+*/
+CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria,
+                                           double default_eps,
+                                           int default_max_iters );
+
+/****************************************************************************************\
+*                   Arithmetic, logic and comparison operations                          *
+\****************************************************************************************/
+
+/** dst(mask) = src1(mask) + src2(mask) */
+CVAPI(void)  cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst,
+                    const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(mask) = src(mask) + value */
+CVAPI(void)  cvAddS( const CvArr* src, CvScalar value, CvArr* dst,
+                     const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(mask) = src1(mask) - src2(mask) */
+CVAPI(void)  cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst,
+                    const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(mask) = src(mask) - value = src(mask) + (-value) */
+CV_INLINE  void  cvSubS( const CvArr* src, CvScalar value, CvArr* dst,
+                         const CvArr* mask CV_DEFAULT(NULL))
+{
+    cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]),
+            dst, mask );
+}
+
+/** dst(mask) = value - src(mask) */
+CVAPI(void)  cvSubRS( const CvArr* src, CvScalar value, CvArr* dst,
+                      const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(idx) = src1(idx) * src2(idx) * scale
+   (scaled element-wise multiplication of 2 arrays) */
+CVAPI(void)  cvMul( const CvArr* src1, const CvArr* src2,
+                    CvArr* dst, double scale CV_DEFAULT(1) );
+
+/** element-wise division/inversion with scaling:
+    dst(idx) = src1(idx) * scale / src2(idx)
+    or dst(idx) = scale / src2(idx) if src1 == 0 */
+CVAPI(void)  cvDiv( const CvArr* src1, const CvArr* src2,
+                    CvArr* dst, double scale CV_DEFAULT(1));
+
+/** dst = src1 * scale + src2 */
+CVAPI(void)  cvScaleAdd( const CvArr* src1, CvScalar scale,
+                         const CvArr* src2, CvArr* dst );
+#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C)
+
+/** dst = src1 * alpha + src2 * beta + gamma */
+CVAPI(void)  cvAddWeighted( const CvArr* src1, double alpha,
+                            const CvArr* src2, double beta,
+                            double gamma, CvArr* dst );
+
+/** @brief Calculates the dot product of two arrays in Euclidean metrics.
+
+The function calculates and returns the Euclidean dot product of two arrays.
+
+\f[src1  \bullet src2 =  \sum _I ( \texttt{src1} (I)  \texttt{src2} (I))\f]
+
+In the case of multiple channel arrays, the results for all channels are accumulated. In particular,
+cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can
+process multi-dimensional arrays, row by row, layer by layer, and so on.
+@param src1 The first source array
+@param src2 The second source array
+ */
+CVAPI(double)  cvDotProduct( const CvArr* src1, const CvArr* src2 );
+
+/** dst(idx) = src1(idx) & src2(idx) */
+CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2,
+                  CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(idx) = src(idx) & value */
+CVAPI(void) cvAndS( const CvArr* src, CvScalar value,
+                   CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(idx) = src1(idx) | src2(idx) */
+CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2,
+                 CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(idx) = src(idx) | value */
+CVAPI(void) cvOrS( const CvArr* src, CvScalar value,
+                  CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(idx) = src1(idx) ^ src2(idx) */
+CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2,
+                  CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(idx) = src(idx) ^ value */
+CVAPI(void) cvXorS( const CvArr* src, CvScalar value,
+                   CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
+
+/** dst(idx) = ~src(idx) */
+CVAPI(void) cvNot( const CvArr* src, CvArr* dst );
+
+/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */
+CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower,
+                      const CvArr* upper, CvArr* dst );
+
+/** dst(idx) = lower <= src(idx) < upper */
+CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower,
+                       CvScalar upper, CvArr* dst );
+
+#define CV_CMP_EQ   0
+#define CV_CMP_GT   1
+#define CV_CMP_GE   2
+#define CV_CMP_LT   3
+#define CV_CMP_LE   4
+#define CV_CMP_NE   5
+
+/** The comparison operation support single-channel arrays only.
+   Destination image should be 8uC1 or 8sC1 */
+
+/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */
+CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op );
+
+/** dst(idx) = src1(idx) _cmp_op_ value */
+CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op );
+
+/** dst(idx) = min(src1(idx),src2(idx)) */
+CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst );
+
+/** dst(idx) = max(src1(idx),src2(idx)) */
+CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst );
+
+/** dst(idx) = min(src(idx),value) */
+CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst );
+
+/** dst(idx) = max(src(idx),value) */
+CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst );
+
+/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */
+CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst );
+
+/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */
+CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value );
+#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0))
+
+/****************************************************************************************\
+*                                Math operations                                         *
+\****************************************************************************************/
+
+/** Does cartesian->polar coordinates conversion.
+   Either of output components (magnitude or angle) is optional */
+CVAPI(void)  cvCartToPolar( const CvArr* x, const CvArr* y,
+                            CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL),
+                            int angle_in_degrees CV_DEFAULT(0));
+
+/** Does polar->cartesian coordinates conversion.
+   Either of output components (magnitude or angle) is optional.
+   If magnitude is missing it is assumed to be all 1's */
+CVAPI(void)  cvPolarToCart( const CvArr* magnitude, const CvArr* angle,
+                            CvArr* x, CvArr* y,
+                            int angle_in_degrees CV_DEFAULT(0));
+
+/** Does powering: dst(idx) = src(idx)^power */
+CVAPI(void)  cvPow( const CvArr* src, CvArr* dst, double power );
+
+/** Does exponention: dst(idx) = exp(src(idx)).
+   Overflow is not handled yet. Underflow is handled.
+   Maximal relative error is ~7e-6 for single-precision input */
+CVAPI(void)  cvExp( const CvArr* src, CvArr* dst );
+
+/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))).
+   Logarithm of 0 gives large negative number(~-700)
+   Maximal relative error is ~3e-7 for single-precision output
+*/
+CVAPI(void)  cvLog( const CvArr* src, CvArr* dst );
+
+/** Fast arctangent calculation */
+CVAPI(float) cvFastArctan( float y, float x );
+
+/** Fast cubic root calculation */
+CVAPI(float)  cvCbrt( float value );
+
+#define  CV_CHECK_RANGE    1
+#define  CV_CHECK_QUIET    2
+/** Checks array values for NaNs, Infs or simply for too large numbers
+   (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set,
+   no runtime errors is raised (function returns zero value in case of "bad" values).
+   Otherwise cvError is called */
+CVAPI(int)  cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0),
+                        double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0));
+#define cvCheckArray cvCheckArr
+
+#define CV_RAND_UNI      0
+#define CV_RAND_NORMAL   1
+
+/** @brief Fills an array with random numbers and updates the RNG state.
+
+The function fills the destination array with uniformly or normally distributed random numbers.
+@param rng CvRNG state initialized by cvRNG
+@param arr The destination array
+@param dist_type Distribution type
+> -   **CV_RAND_UNI** uniform distribution
+> -   **CV_RAND_NORMAL** normal or Gaussian distribution
+@param param1 The first parameter of the distribution. In the case of a uniform distribution it is
+the inclusive lower boundary of the random numbers range. In the case of a normal distribution it
+is the mean value of the random numbers.
+@param param2 The second parameter of the distribution. In the case of a uniform distribution it
+is the exclusive upper boundary of the random numbers range. In the case of a normal distribution
+it is the standard deviation of the random numbers.
+@sa randu, randn, RNG::fill.
+ */
+CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type,
+                      CvScalar param1, CvScalar param2 );
+
+CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng,
+                           double iter_factor CV_DEFAULT(1.));
+
+#define CV_SORT_EVERY_ROW 0
+#define CV_SORT_EVERY_COLUMN 1
+#define CV_SORT_ASCENDING 0
+#define CV_SORT_DESCENDING 16
+
+CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL),
+                    CvArr* idxmat CV_DEFAULT(NULL),
+                    int flags CV_DEFAULT(0));
+
+/** Finds real roots of a cubic equation */
+CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots );
+
+/** Finds all real and complex roots of a polynomial equation */
+CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2,
+      int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100));
+
+/****************************************************************************************\
+*                                Matrix operations                                       *
+\****************************************************************************************/
+
+/** @brief Calculates the cross product of two 3D vectors.
+
+The function calculates the cross product of two 3D vectors:
+\f[\texttt{dst} =  \texttt{src1} \times \texttt{src2}\f]
+or:
+\f[\begin{array}{l} \texttt{dst} _1 =  \texttt{src1} _2  \texttt{src2} _3 -  \texttt{src1} _3  \texttt{src2} _2 \\ \texttt{dst} _2 =  \texttt{src1} _3  \texttt{src2} _1 -  \texttt{src1} _1  \texttt{src2} _3 \\ \texttt{dst} _3 =  \texttt{src1} _1  \texttt{src2} _2 -  \texttt{src1} _2  \texttt{src2} _1 \end{array}\f]
+@param src1 The first source vector
+@param src2 The second source vector
+@param dst The destination vector
+ */
+CVAPI(void)  cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst );
+
+/** Matrix transform: dst = A*B + C, C is optional */
+#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 )
+#define cvMatMul( src1, src2, dst )  cvMatMulAdd( (src1), (src2), NULL, (dst))
+
+#define CV_GEMM_A_T 1
+#define CV_GEMM_B_T 2
+#define CV_GEMM_C_T 4
+/** Extended matrix transform:
+   dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */
+CVAPI(void)  cvGEMM( const CvArr* src1, const CvArr* src2, double alpha,
+                     const CvArr* src3, double beta, CvArr* dst,
+                     int tABC CV_DEFAULT(0));
+#define cvMatMulAddEx cvGEMM
+
+/** Transforms each element of source array and stores
+   resultant vectors in destination array */
+CVAPI(void)  cvTransform( const CvArr* src, CvArr* dst,
+                          const CvMat* transmat,
+                          const CvMat* shiftvec CV_DEFAULT(NULL));
+#define cvMatMulAddS cvTransform
+
+/** Does perspective transform on every element of input array */
+CVAPI(void)  cvPerspectiveTransform( const CvArr* src, CvArr* dst,
+                                     const CvMat* mat );
+
+/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */
+CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order,
+                             const CvArr* delta CV_DEFAULT(NULL),
+                             double scale CV_DEFAULT(1.) );
+
+/** Transposes matrix. Square matrices can be transposed in-place */
+CVAPI(void)  cvTranspose( const CvArr* src, CvArr* dst );
+#define cvT cvTranspose
+
+/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */
+CVAPI(void)  cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) );
+
+/** Mirror array data around horizontal (flip=0),
+   vertical (flip=1) or both(flip=-1) axises:
+   cvFlip(src) flips images vertically and sequences horizontally (inplace) */
+CVAPI(void)  cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL),
+                     int flip_mode CV_DEFAULT(0));
+#define cvMirror cvFlip
+
+
+#define CV_SVD_MODIFY_A   1
+#define CV_SVD_U_T        2
+#define CV_SVD_V_T        4
+
+/** Performs Singular Value Decomposition of a matrix */
+CVAPI(void)   cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL),
+                     CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0));
+
+/** Performs Singular Value Back Substitution (solves A*X = B):
+   flags must be the same as in cvSVD */
+CVAPI(void)   cvSVBkSb( const CvArr* W, const CvArr* U,
+                        const CvArr* V, const CvArr* B,
+                        CvArr* X, int flags );
+
+#define CV_LU  0
+#define CV_SVD 1
+#define CV_SVD_SYM 2
+#define CV_CHOLESKY 3
+#define CV_QR  4
+#define CV_NORMAL 16
+
+/** Inverts matrix */
+CVAPI(double)  cvInvert( const CvArr* src, CvArr* dst,
+                         int method CV_DEFAULT(CV_LU));
+#define cvInv cvInvert
+
+/** Solves linear system (src1)*(dst) = (src2)
+   (returns 0 if src1 is a singular and CV_LU method is used) */
+CVAPI(int)  cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst,
+                     int method CV_DEFAULT(CV_LU));
+
+/** Calculates determinant of input matrix */
+CVAPI(double) cvDet( const CvArr* mat );
+
+/** Calculates trace of the matrix (sum of elements on the main diagonal) */
+CVAPI(CvScalar) cvTrace( const CvArr* mat );
+
+/** Finds eigen values and vectors of a symmetric matrix */
+CVAPI(void)  cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals,
+                        double eps CV_DEFAULT(0),
+                        int lowindex CV_DEFAULT(-1),
+                        int highindex CV_DEFAULT(-1));
+
+///* Finds selected eigen values and vectors of a symmetric matrix */
+//CVAPI(void)  cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals,
+//                                int lowindex, int highindex );
+
+/** Makes an identity matrix (mat_ij = i == j) */
+CVAPI(void)  cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) );
+
+/** Fills matrix with given range of numbers */
+CVAPI(CvArr*)  cvRange( CvArr* mat, double start, double end );
+
+/**   @anchor core_c_CovarFlags
+@name Flags for cvCalcCovarMatrix
+@see cvCalcCovarMatrix
+  @{
+*/
+
+/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */
+#define CV_COVAR_SCRAMBLED 0
+
+/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */
+#define CV_COVAR_NORMAL    1
+
+/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead
+   (useful for calculating covariance matrix by parts) */
+#define CV_COVAR_USE_AVG   2
+
+/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */
+#define CV_COVAR_SCALE     4
+
+/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */
+#define CV_COVAR_ROWS      8
+
+/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */
+#define CV_COVAR_COLS     16
+
+/** @} */
+
+/** Calculates covariation matrix for a set of vectors
+@see @ref core_c_CovarFlags "flags"
+*/
+CVAPI(void)  cvCalcCovarMatrix( const CvArr** vects, int count,
+                                CvArr* cov_mat, CvArr* avg, int flags );
+
+#define CV_PCA_DATA_AS_ROW 0
+#define CV_PCA_DATA_AS_COL 1
+#define CV_PCA_USE_AVG 2
+CVAPI(void)  cvCalcPCA( const CvArr* data, CvArr* mean,
+                        CvArr* eigenvals, CvArr* eigenvects, int flags );
+
+CVAPI(void)  cvProjectPCA( const CvArr* data, const CvArr* mean,
+                           const CvArr* eigenvects, CvArr* result );
+
+CVAPI(void)  cvBackProjectPCA( const CvArr* proj, const CvArr* mean,
+                               const CvArr* eigenvects, CvArr* result );
+
+/** Calculates Mahalanobis(weighted) distance */
+CVAPI(double)  cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat );
+#define cvMahalonobis  cvMahalanobis
+
+/****************************************************************************************\
+*                                    Array Statistics                                    *
+\****************************************************************************************/
+
+/** Finds sum of array elements */
+CVAPI(CvScalar)  cvSum( const CvArr* arr );
+
+/** Calculates number of non-zero pixels */
+CVAPI(int)  cvCountNonZero( const CvArr* arr );
+
+/** Calculates mean value of array elements */
+CVAPI(CvScalar)  cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) );
+
+/** Calculates mean and standard deviation of pixel values */
+CVAPI(void)  cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev,
+                       const CvArr* mask CV_DEFAULT(NULL) );
+
+/** Finds global minimum, maximum and their positions */
+CVAPI(void)  cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val,
+                          CvPoint* min_loc CV_DEFAULT(NULL),
+                          CvPoint* max_loc CV_DEFAULT(NULL),
+                          const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @anchor core_c_NormFlags
+  @name Flags for cvNorm and cvNormalize
+  @{
+*/
+#define CV_C            1
+#define CV_L1           2
+#define CV_L2           4
+#define CV_NORM_MASK    7
+#define CV_RELATIVE     8
+#define CV_DIFF         16
+#define CV_MINMAX       32
+
+#define CV_DIFF_C       (CV_DIFF | CV_C)
+#define CV_DIFF_L1      (CV_DIFF | CV_L1)
+#define CV_DIFF_L2      (CV_DIFF | CV_L2)
+#define CV_RELATIVE_C   (CV_RELATIVE | CV_C)
+#define CV_RELATIVE_L1  (CV_RELATIVE | CV_L1)
+#define CV_RELATIVE_L2  (CV_RELATIVE | CV_L2)
+/** @} */
+
+/** Finds norm, difference norm or relative difference norm for an array (or two arrays)
+@see ref core_c_NormFlags "flags"
+*/
+CVAPI(double)  cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL),
+                       int norm_type CV_DEFAULT(CV_L2),
+                       const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @see ref core_c_NormFlags "flags" */
+CVAPI(void)  cvNormalize( const CvArr* src, CvArr* dst,
+                          double a CV_DEFAULT(1.), double b CV_DEFAULT(0.),
+                          int norm_type CV_DEFAULT(CV_L2),
+                          const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @anchor core_c_ReduceFlags
+  @name Flags for cvReduce
+  @{
+*/
+#define CV_REDUCE_SUM 0
+#define CV_REDUCE_AVG 1
+#define CV_REDUCE_MAX 2
+#define CV_REDUCE_MIN 3
+/** @} */
+
+/** @see @ref core_c_ReduceFlags "flags" */
+CVAPI(void)  cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1),
+                       int op CV_DEFAULT(CV_REDUCE_SUM) );
+
+/****************************************************************************************\
+*                      Discrete Linear Transforms and Related Functions                  *
+\****************************************************************************************/
+
+/** @anchor core_c_DftFlags
+  @name Flags for cvDFT, cvDCT and cvMulSpectrums
+  @{
+  */
+#define CV_DXT_FORWARD  0
+#define CV_DXT_INVERSE  1
+#define CV_DXT_SCALE    2 /**< divide result by size of array */
+#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE)
+#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE
+#define CV_DXT_ROWS     4 /**< transform each row individually */
+#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */
+/** @} */
+
+/** Discrete Fourier Transform:
+    complex->complex,
+    real->ccs (forward),
+    ccs->real (inverse)
+@see core_c_DftFlags "flags"
+*/
+CVAPI(void)  cvDFT( const CvArr* src, CvArr* dst, int flags,
+                    int nonzero_rows CV_DEFAULT(0) );
+#define cvFFT cvDFT
+
+/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y))
+@see core_c_DftFlags "flags"
+*/
+CVAPI(void)  cvMulSpectrums( const CvArr* src1, const CvArr* src2,
+                             CvArr* dst, int flags );
+
+/** Finds optimal DFT vector size >= size0 */
+CVAPI(int)  cvGetOptimalDFTSize( int size0 );
+
+/** Discrete Cosine Transform
+@see core_c_DftFlags "flags"
+*/
+CVAPI(void)  cvDCT( const CvArr* src, CvArr* dst, int flags );
+
+/****************************************************************************************\
+*                              Dynamic data structures                                   *
+\****************************************************************************************/
+
+/** Calculates length of sequence slice (with support of negative indices). */
+CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq );
+
+
+/** Creates new memory storage.
+   block_size == 0 means that default,
+   somewhat optimal size, is used (currently, it is 64K) */
+CVAPI(CvMemStorage*)  cvCreateMemStorage( int block_size CV_DEFAULT(0));
+
+
+/** Creates a memory storage that will borrow memory blocks from parent storage */
+CVAPI(CvMemStorage*)  cvCreateChildMemStorage( CvMemStorage* parent );
+
+
+/** Releases memory storage. All the children of a parent must be released before
+   the parent. A child storage returns all the blocks to parent when it is released */
+CVAPI(void)  cvReleaseMemStorage( CvMemStorage** storage );
+
+
+/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos)
+   to reuse memory allocated for the storage - cvClearSeq,cvClearSet ...
+   do not free any memory.
+   A child storage returns all the blocks to the parent when it is cleared */
+CVAPI(void)  cvClearMemStorage( CvMemStorage* storage );
+
+/** Remember a storage "free memory" position */
+CVAPI(void)  cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos );
+
+/** Restore a storage "free memory" position */
+CVAPI(void)  cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos );
+
+/** Allocates continuous buffer of the specified size in the storage */
+CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size );
+
+/** Allocates string in memory storage */
+//CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr,
+//                                         int len CV_DEFAULT(-1) );
+
+/** Creates new empty sequence that will reside in the specified storage */
+CVAPI(CvSeq*)  cvCreateSeq( int seq_flags, size_t header_size,
+                            size_t elem_size, CvMemStorage* storage );
+
+/** Changes default size (granularity) of sequence blocks.
+   The default size is ~1Kbyte */
+CVAPI(void)  cvSetSeqBlockSize( CvSeq* seq, int delta_elems );
+
+
+/** Adds new element to the end of sequence. Returns pointer to the element */
+CVAPI(schar*)  cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL));
+
+
+/** Adds new element to the beginning of sequence. Returns pointer to it */
+CVAPI(schar*)  cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL));
+
+
+/** Removes the last element from sequence and optionally saves it */
+CVAPI(void)  cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL));
+
+
+/** Removes the first element from sequence and optioanally saves it */
+CVAPI(void)  cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL));
+
+
+#define CV_FRONT 1
+#define CV_BACK 0
+/** Adds several new elements to the end of sequence */
+CVAPI(void)  cvSeqPushMulti( CvSeq* seq, const void* elements,
+                             int count, int in_front CV_DEFAULT(0) );
+
+/** Removes several elements from the end of sequence and optionally saves them */
+CVAPI(void)  cvSeqPopMulti( CvSeq* seq, void* elements,
+                            int count, int in_front CV_DEFAULT(0) );
+
+/** Inserts a new element in the middle of sequence.
+   cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */
+CVAPI(schar*)  cvSeqInsert( CvSeq* seq, int before_index,
+                            const void* element CV_DEFAULT(NULL));
+
+/** Removes specified sequence element */
+CVAPI(void)  cvSeqRemove( CvSeq* seq, int index );
+
+
+/** Removes all the elements from the sequence. The freed memory
+   can be reused later only by the same sequence unless cvClearMemStorage
+   or cvRestoreMemStoragePos is called */
+CVAPI(void)  cvClearSeq( CvSeq* seq );
+
+
+/** Retrieves pointer to specified sequence element.
+   Negative indices are supported and mean counting from the end
+   (e.g -1 means the last sequence element) */
+CVAPI(schar*)  cvGetSeqElem( const CvSeq* seq, int index );
+
+/** Calculates index of the specified sequence element.
+   Returns -1 if element does not belong to the sequence */
+CVAPI(int)  cvSeqElemIdx( const CvSeq* seq, const void* element,
+                         CvSeqBlock** block CV_DEFAULT(NULL) );
+
+/** Initializes sequence writer. The new elements will be added to the end of sequence */
+CVAPI(void)  cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer );
+
+
+/** Combination of cvCreateSeq and cvStartAppendToSeq */
+CVAPI(void)  cvStartWriteSeq( int seq_flags, int header_size,
+                              int elem_size, CvMemStorage* storage,
+                              CvSeqWriter* writer );
+
+/** Closes sequence writer, updates sequence header and returns pointer
+   to the resultant sequence
+   (which may be useful if the sequence was created using cvStartWriteSeq))
+*/
+CVAPI(CvSeq*)  cvEndWriteSeq( CvSeqWriter* writer );
+
+
+/** Updates sequence header. May be useful to get access to some of previously
+   written elements via cvGetSeqElem or sequence reader */
+CVAPI(void)   cvFlushSeqWriter( CvSeqWriter* writer );
+
+
+/** Initializes sequence reader.
+   The sequence can be read in forward or backward direction */
+CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader,
+                           int reverse CV_DEFAULT(0) );
+
+
+/** Returns current sequence reader position (currently observed sequence element) */
+CVAPI(int)  cvGetSeqReaderPos( CvSeqReader* reader );
+
+
+/** Changes sequence reader position. It may seek to an absolute or
+   to relative to the current position */
+CVAPI(void)   cvSetSeqReaderPos( CvSeqReader* reader, int index,
+                                 int is_relative CV_DEFAULT(0));
+
+/** Copies sequence content to a continuous piece of memory */
+CVAPI(void*)  cvCvtSeqToArray( const CvSeq* seq, void* elements,
+                               CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) );
+
+/** Creates sequence header for array.
+   After that all the operations on sequences that do not alter the content
+   can be applied to the resultant sequence */
+CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size,
+                                       int elem_size, void* elements, int total,
+                                       CvSeq* seq, CvSeqBlock* block );
+
+/** Extracts sequence slice (with or without copying sequence elements) */
+CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice,
+                         CvMemStorage* storage CV_DEFAULT(NULL),
+                         int copy_data CV_DEFAULT(0));
+
+CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL))
+{
+    return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 );
+}
+
+/** Removes sequence slice */
+CVAPI(void)  cvSeqRemoveSlice( CvSeq* seq, CvSlice slice );
+
+/** Inserts a sequence or array into another sequence */
+CVAPI(void)  cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr );
+
+/** a < b ? -1 : a > b ? 1 : 0 */
+typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata );
+
+/** Sorts sequence in-place given element comparison function */
+CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) );
+
+/** Finds element in a [sorted] sequence */
+CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func,
+                           int is_sorted, int* elem_idx,
+                           void* userdata CV_DEFAULT(NULL) );
+
+/** Reverses order of sequence elements in-place */
+CVAPI(void) cvSeqInvert( CvSeq* seq );
+
+/** Splits sequence into one or more equivalence classes using the specified criteria */
+CVAPI(int)  cvSeqPartition( const CvSeq* seq, CvMemStorage* storage,
+                            CvSeq** labels, CvCmpFunc is_equal, void* userdata );
+
+/************ Internal sequence functions ************/
+CVAPI(void)  cvChangeSeqBlock( void* reader, int direction );
+CVAPI(void)  cvCreateSeqBlock( CvSeqWriter* writer );
+
+
+/** Creates a new set */
+CVAPI(CvSet*)  cvCreateSet( int set_flags, int header_size,
+                            int elem_size, CvMemStorage* storage );
+
+/** Adds new element to the set and returns pointer to it */
+CVAPI(int)  cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL),
+                      CvSetElem** inserted_elem CV_DEFAULT(NULL) );
+
+/** Fast variant of cvSetAdd */
+CV_INLINE  CvSetElem* cvSetNew( CvSet* set_header )
+{
+    CvSetElem* elem = set_header->free_elems;
+    if( elem )
+    {
+        set_header->free_elems = elem->next_free;
+        elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK;
+        set_header->active_count++;
+    }
+    else
+        cvSetAdd( set_header, NULL, &elem );
+    return elem;
+}
+
+/** Removes set element given its pointer */
+CV_INLINE  void cvSetRemoveByPtr( CvSet* set_header, void* elem )
+{
+    CvSetElem* _elem = (CvSetElem*)elem;
+    assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ );
+    _elem->next_free = set_header->free_elems;
+    _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG;
+    set_header->free_elems = _elem;
+    set_header->active_count--;
+}
+
+/** Removes element from the set by its index  */
+CVAPI(void)   cvSetRemove( CvSet* set_header, int index );
+
+/** Returns a set element by index. If the element doesn't belong to the set,
+   NULL is returned */
+CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx )
+{
+    CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx );
+    return elem && CV_IS_SET_ELEM( elem ) ? elem : 0;
+}
+
+/** Removes all the elements from the set */
+CVAPI(void)  cvClearSet( CvSet* set_header );
+
+/** Creates new graph */
+CVAPI(CvGraph*)  cvCreateGraph( int graph_flags, int header_size,
+                                int vtx_size, int edge_size,
+                                CvMemStorage* storage );
+
+/** Adds new vertex to the graph */
+CVAPI(int)  cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL),
+                           CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) );
+
+
+/** Removes vertex from the graph together with all incident edges */
+CVAPI(int)  cvGraphRemoveVtx( CvGraph* graph, int index );
+CVAPI(int)  cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx );
+
+
+/** Link two vertices specified by indices or pointers if they
+   are not connected or return pointer to already existing edge
+   connecting the vertices.
+   Functions return 1 if a new edge was created, 0 otherwise */
+CVAPI(int)  cvGraphAddEdge( CvGraph* graph,
+                            int start_idx, int end_idx,
+                            const CvGraphEdge* edge CV_DEFAULT(NULL),
+                            CvGraphEdge** inserted_edge CV_DEFAULT(NULL) );
+
+CVAPI(int)  cvGraphAddEdgeByPtr( CvGraph* graph,
+                               CvGraphVtx* start_vtx, CvGraphVtx* end_vtx,
+                               const CvGraphEdge* edge CV_DEFAULT(NULL),
+                               CvGraphEdge** inserted_edge CV_DEFAULT(NULL) );
+
+/** Remove edge connecting two vertices */
+CVAPI(void)  cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx );
+CVAPI(void)  cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx,
+                                     CvGraphVtx* end_vtx );
+
+/** Find edge connecting two vertices */
+CVAPI(CvGraphEdge*)  cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx );
+CVAPI(CvGraphEdge*)  cvFindGraphEdgeByPtr( const CvGraph* graph,
+                                           const CvGraphVtx* start_vtx,
+                                           const CvGraphVtx* end_vtx );
+#define cvGraphFindEdge cvFindGraphEdge
+#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr
+
+/** Remove all vertices and edges from the graph */
+CVAPI(void)  cvClearGraph( CvGraph* graph );
+
+
+/** Count number of edges incident to the vertex */
+CVAPI(int)  cvGraphVtxDegree( const CvGraph* graph, int vtx_idx );
+CVAPI(int)  cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx );
+
+
+/** Retrieves graph vertex by given index */
+#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx))
+
+/** Retrieves index of a graph vertex given its pointer */
+#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK)
+
+/** Retrieves index of a graph edge given its pointer */
+#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK)
+
+#define cvGraphGetVtxCount( graph ) ((graph)->active_count)
+#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count)
+
+#define  CV_GRAPH_VERTEX        1
+#define  CV_GRAPH_TREE_EDGE     2
+#define  CV_GRAPH_BACK_EDGE     4
+#define  CV_GRAPH_FORWARD_EDGE  8
+#define  CV_GRAPH_CROSS_EDGE    16
+#define  CV_GRAPH_ANY_EDGE      30
+#define  CV_GRAPH_NEW_TREE      32
+#define  CV_GRAPH_BACKTRACKING  64
+#define  CV_GRAPH_OVER          -1
+
+#define  CV_GRAPH_ALL_ITEMS    -1
+
+/** flags for graph vertices and edges */
+#define  CV_GRAPH_ITEM_VISITED_FLAG  (1 << 30)
+#define  CV_IS_GRAPH_VERTEX_VISITED(vtx) \
+    (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG)
+#define  CV_IS_GRAPH_EDGE_VISITED(edge) \
+    (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG)
+#define  CV_GRAPH_SEARCH_TREE_NODE_FLAG   (1 << 29)
+#define  CV_GRAPH_FORWARD_EDGE_FLAG       (1 << 28)
+
+typedef struct CvGraphScanner
+{
+    CvGraphVtx* vtx;       /* current graph vertex (or current edge origin) */
+    CvGraphVtx* dst;       /* current graph edge destination vertex */
+    CvGraphEdge* edge;     /* current edge */
+
+    CvGraph* graph;        /* the graph */
+    CvSeq*   stack;        /* the graph vertex stack */
+    int      index;        /* the lower bound of certainly visited vertices */
+    int      mask;         /* event mask */
+}
+CvGraphScanner;
+
+/** Creates new graph scanner. */
+CVAPI(CvGraphScanner*)  cvCreateGraphScanner( CvGraph* graph,
+                                             CvGraphVtx* vtx CV_DEFAULT(NULL),
+                                             int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS));
+
+/** Releases graph scanner. */
+CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner );
+
+/** Get next graph element */
+CVAPI(int)  cvNextGraphItem( CvGraphScanner* scanner );
+
+/** Creates a copy of graph */
+CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage );
+
+
+/** Does look-up transformation. Elements of the source array
+   (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */
+CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut );
+
+
+/******************* Iteration through the sequence tree *****************/
+typedef struct CvTreeNodeIterator
+{
+    const void* node;
+    int level;
+    int max_level;
+}
+CvTreeNodeIterator;
+
+CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator,
+                                   const void* first, int max_level );
+CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator );
+CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator );
+
+/** Inserts sequence into tree with specified "parent" sequence.
+   If parent is equal to frame (e.g. the most external contour),
+   then added contour will have null pointer to parent. */
+CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame );
+
+/** Removes contour from tree (together with the contour children). */
+CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame );
+
+/** Gathers pointers to all the sequences,
+   accessible from the `first`, to the single sequence */
+CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size,
+                              CvMemStorage* storage );
+
+/** The function implements the K-means algorithm for clustering an array of sample
+   vectors in a specified number of classes */
+#define CV_KMEANS_USE_INITIAL_LABELS    1
+CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels,
+                      CvTermCriteria termcrit, int attempts CV_DEFAULT(1),
+                      CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0),
+                      CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) );
+
+/****************************************************************************************\
+*                                    System functions                                    *
+\****************************************************************************************/
+
+/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */
+CVAPI(int)  cvUseOptimized( int on_off );
+
+typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader)
+                            (int,int,int,char*,char*,int,int,int,int,int,
+                            IplROI*,IplImage*,void*,IplTileInfo*);
+typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int);
+typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int);
+typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int);
+typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*);
+
+/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures.
+
+Normally, the function is not called directly. Instead, a simple macro
+CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers
+to IPL allocation functions. :
+@code
+    ...
+    CV_TURN_ON_IPL_COMPATIBILITY()
+    ...
+@endcode
+@param create_header pointer to a function, creating IPL image header.
+@param allocate_data pointer to a function, allocating IPL image data.
+@param deallocate pointer to a function, deallocating IPL image.
+@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest).
+@param clone_image pointer to a function, cloning an IPL image.
+ */
+CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header,
+                               Cv_iplAllocateImageData allocate_data,
+                               Cv_iplDeallocate deallocate,
+                               Cv_iplCreateROI create_roi,
+                               Cv_iplCloneImage clone_image );
+
+#define CV_TURN_ON_IPL_COMPATIBILITY()                                  \
+    cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage,         \
+                        iplDeallocate, iplCreateROI, iplCloneImage )
+
+/****************************************************************************************\
+*                                    Data Persistence                                    *
+\****************************************************************************************/
+
+#if 0
+/********************************** High-level functions ********************************/
+
+/** @brief Opens file storage for reading or writing data.
+
+The function opens file storage for reading or writing data. In the latter case, a new file is
+created or an existing file is rewritten. The type of the read or written file is determined by the
+filename extension: .xml for XML, .yml or .yaml for YAML and .json for JSON.
+
+At the same time, it also supports adding parameters like "example.xml?base64".
+
+The function returns a pointer to the CvFileStorage structure.
+If the file cannot be opened then the function returns NULL.
+@param filename Name of the file associated with the storage
+@param memstorage Memory storage used for temporary data and for
+:   storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory
+    storage is created and used.
+@param flags Can be one of the following:
+> -   **CV_STORAGE_READ** the storage is open for reading
+> -   **CV_STORAGE_WRITE** the storage is open for writing
+      (use **CV_STORAGE_WRITE | CV_STORAGE_WRITE_BASE64** to write rawdata in Base64)
+@param encoding
+ */
+CVAPI(CvFileStorage*)  cvOpenFileStorage( const char* filename, CvMemStorage* memstorage,
+                                          int flags, const char* encoding CV_DEFAULT(NULL) );
+
+/** @brief Releases file storage.
+
+The function closes the file associated with the storage and releases all the temporary structures.
+It must be called after all I/O operations with the storage are finished.
+@param fs Double pointer to the released file storage
+ */
+CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs );
+
+/** returns attribute value or 0 (NULL) if there is no such attribute */
+CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name );
+
+/** @brief Starts writing a new structure.
+
+The function starts writing a compound structure (collection) that can be a sequence or a map. After
+all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should
+be called. The function can be used to group some objects or to implement the write function for a
+some user object (see CvTypeInfo).
+@param fs File storage
+@param name Name of the written structure. The structure can be accessed by this name when the
+storage is read.
+@param struct_flags A combination one of the following values:
+-   **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ),
+    that is, its elements do not have a name.
+-   **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that
+    is, all its elements have names.
+One and only one of the two above flags must be specified
+-   **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that
+     the structure is written as a flow (not as a block), which is more compact. It is
+     recommended to use this flag for structures or arrays whose elements are all scalars.
+@param type_name Optional parameter - the object type name. In
+    case of XML it is written as a type_id attribute of the structure opening tag. In the case of
+    YAML it is written after a colon following the structure name (see the example in
+    CvFileStorage description). In case of JSON it is written as a name/value pair.
+    Mainly it is used with user objects. When the storage is read, the
+    encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ).
+@param attributes This parameter is not used in the current implementation
+ */
+CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name,
+                                int struct_flags, const char* type_name CV_DEFAULT(NULL),
+                                CvAttrList attributes CV_DEFAULT(cvAttrList()));
+
+/** @brief Finishes writing to a file node collection.
+@param fs File storage
+@sa cvStartWriteStruct.
+ */
+CVAPI(void) cvEndWriteStruct( CvFileStorage* fs );
+
+/** @brief Writes an integer value.
+
+The function writes a single integer value (with or without a name) to the file storage.
+@param fs File storage
+@param name Name of the written value. Should be NULL if and only if the parent structure is a
+sequence.
+@param value The written value
+ */
+CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value );
+
+/** @brief Writes a floating-point value.
+
+The function writes a single floating-point value (with or without a name) to file storage. Special
+values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf.
+
+The following example shows how to use the low-level writing functions to store custom structures,
+such as termination criteria, without registering a new type. :
+@code
+    void write_termcriteria( CvFileStorage* fs, const char* struct_name,
+                             CvTermCriteria* termcrit )
+    {
+        cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0));
+        cvWriteComment( fs, "termination criteria", 1 ); // just a description
+        if( termcrit->type & CV_TERMCRIT_ITER )
+            cvWriteInteger( fs, "max_iterations", termcrit->max_iter );
+        if( termcrit->type & CV_TERMCRIT_EPS )
+            cvWriteReal( fs, "accuracy", termcrit->epsilon );
+        cvEndWriteStruct( fs );
+    }
+@endcode
+@param fs File storage
+@param name Name of the written value. Should be NULL if and only if the parent structure is a
+sequence.
+@param value The written value
+*/
+CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value );
+
+/** @brief Writes a text string.
+
+The function writes a text string to file storage.
+@param fs File storage
+@param name Name of the written string . Should be NULL if and only if the parent structure is a
+sequence.
+@param str The written text string
+@param quote If non-zero, the written string is put in quotes, regardless of whether they are
+required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when
+the string starts with a digit or contains spaces).
+ */
+CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name,
+                           const char* str, int quote CV_DEFAULT(0) );
+
+/** @brief Writes a comment.
+
+The function writes a comment into file storage. The comments are skipped when the storage is read.
+@param fs File storage
+@param comment The written comment, single-line or multi-line
+@param eol_comment If non-zero, the function tries to put the comment at the end of current line.
+If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current
+line, the comment starts a new line.
+ */
+CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment,
+                            int eol_comment );
+
+/** @brief Writes an object to file storage.
+
+The function writes an object to file storage. First, the appropriate type info is found using
+cvTypeOf. Then, the write method associated with the type info is called.
+
+Attributes are used to customize the writing procedure. The standard types support the following
+attributes (all the dt attributes have the same format as in cvWriteRawData):
+
+-# CvSeq
+    -   **header_dt** description of user fields of the sequence header that follow CvSeq, or
+        CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or
+        point sequence)
+    -   **dt** description of the sequence elements.
+    -   **recursive** if the attribute is present and is not equal to "0" or "false", the whole
+        tree of sequences (contours) is stored.
+-# CvGraph
+    -   **header_dt** description of user fields of the graph header that follows CvGraph;
+    -   **vertex_dt** description of user fields of graph vertices
+    -   **edge_dt** description of user fields of graph edges (note that the edge weight is
+        always written, so there is no need to specify it explicitly)
+
+Below is the code that creates the YAML file shown in the CvFileStorage description:
+@code
+    #include "cxcore.h"
+
+    int main( int argc, char** argv )
+    {
+        CvMat* mat = cvCreateMat( 3, 3, CV_32F );
+        CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE );
+
+        cvSetIdentity( mat );
+        cvWrite( fs, "A", mat, cvAttrList(0,0) );
+
+        cvReleaseFileStorage( &fs );
+        cvReleaseMat( &mat );
+        return 0;
+    }
+@endcode
+@param fs File storage
+@param name Name of the written object. Should be NULL if and only if the parent structure is a
+sequence.
+@param ptr Pointer to the object
+@param attributes The attributes of the object. They are specific for each particular type (see
+the discussion below).
+ */
+CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr,
+                         CvAttrList attributes CV_DEFAULT(cvAttrList()));
+
+/** @brief Starts the next stream.
+
+The function finishes the currently written stream and starts the next stream. In the case of XML
+the file with multiple streams looks like this:
+@code{.xml}
+    <opencv_storage>
+    <!-- stream #1 data -->
+    </opencv_storage>
+    <opencv_storage>
+    <!-- stream #2 data -->
+    </opencv_storage>
+    ...
+@endcode
+The YAML file will look like this:
+@code{.yaml}
+    %YAML 1.0
+    # stream #1 data
+    ...
+    ---
+    # stream #2 data
+@endcode
+This is useful for concatenating files or for resuming the writing process.
+@param fs File storage
+ */
+CVAPI(void) cvStartNextStream( CvFileStorage* fs );
+
+/** @brief Writes multiple numbers.
+
+The function writes an array, whose elements consist of single or multiple numbers. The function
+call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single
+call is more efficient. Note that because none of the elements have a name, they should be written
+to a sequence rather than a map.
+@param fs File storage
+@param src Pointer to the written array
+@param len Number of the array elements to write
+@param dt Specification of each array element, see @ref format_spec "format specification"
+ */
+CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src,
+                                int len, const char* dt );
+
+/** @brief Writes multiple numbers in Base64.
+
+If either CV_STORAGE_WRITE_BASE64 or cv::FileStorage::WRITE_BASE64 is used,
+this function will be the same as cvWriteRawData. If neither, the main
+difference is that it outputs a sequence in Base64 encoding rather than
+in plain text.
+
+This function can only be used to write a sequence with a type "binary".
+
+@param fs File storage
+@param src Pointer to the written array
+@param len Number of the array elements to write
+@param dt Specification of each array element, see @ref format_spec "format specification"
+*/
+CVAPI(void) cvWriteRawDataBase64( CvFileStorage* fs, const void* src,
+                                 int len, const char* dt );
+
+/** @brief Returns a unique pointer for a given name.
+
+The function returns a unique pointer for each particular file node name. This pointer can be then
+passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares
+text strings by comparing pointers rather than the strings' content.
+
+Consider the following example where an array of points is encoded as a sequence of 2-entry maps:
+@code
+    points:
+      - { x: 10, y: 10 }
+      - { x: 20, y: 20 }
+      - { x: 30, y: 30 }
+      # ...
+@endcode
+Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. :
+@code
+    #include "cxcore.h"
+
+    int main( int argc, char** argv )
+    {
+        CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ );
+        CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 );
+        CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 );
+        CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" );
+
+        if( CV_NODE_IS_SEQ(points->tag) )
+        {
+            CvSeq* seq = points->data.seq;
+            int i, total = seq->total;
+            CvSeqReader reader;
+            cvStartReadSeq( seq, &reader, 0 );
+            for( i = 0; i < total; i++ )
+            {
+                CvFileNode* pt = (CvFileNode*)reader.ptr;
+    #if 1 // faster variant
+                CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 );
+                CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 );
+                assert( xnode && CV_NODE_IS_INT(xnode->tag) &&
+                        ynode && CV_NODE_IS_INT(ynode->tag));
+                int x = xnode->data.i; // or x = cvReadInt( xnode, 0 );
+                int y = ynode->data.i; // or y = cvReadInt( ynode, 0 );
+    #elif 1 // slower variant; does not use x_key & y_key
+                CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" );
+                CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" );
+                assert( xnode && CV_NODE_IS_INT(xnode->tag) &&
+                        ynode && CV_NODE_IS_INT(ynode->tag));
+                int x = xnode->data.i; // or x = cvReadInt( xnode, 0 );
+                int y = ynode->data.i; // or y = cvReadInt( ynode, 0 );
+    #else // the slowest yet the easiest to use variant
+                int x = cvReadIntByName( fs, pt, "x", 0 );
+                int y = cvReadIntByName( fs, pt, "y", 0 );
+    #endif
+                CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
+                printf("
+            }
+        }
+        cvReleaseFileStorage( &fs );
+        return 0;
+    }
+@endcode
+Please note that whatever method of accessing a map you are using, it is still much slower than
+using plain sequences; for example, in the above example, it is more efficient to encode the points
+as pairs of integers in a single numeric sequence.
+@param fs File storage
+@param name Literal node name
+@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated
+@param create_missing Flag that specifies, whether an absent key should be added into the hash table
+*/
+CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name,
+                                        int len CV_DEFAULT(-1),
+                                        int create_missing CV_DEFAULT(0));
+
+/** @brief Retrieves one of the top-level nodes of the file storage.
+
+The function returns one of the top-level file nodes. The top-level nodes do not have a name, they
+correspond to the streams that are stored one after another in the file storage. If the index is out
+of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by
+subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned.
+This function can be used as a base for recursive traversal of the file storage.
+@param fs File storage
+@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases,
+there is only one stream in the file; however, there can be several.
+ */
+CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs,
+                                     int stream_index CV_DEFAULT(0) );
+
+/** @brief Finds a node in a map or file storage.
+
+The function finds a file node. It is a faster version of cvGetFileNodeByName (see
+cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet.
+@param fs File storage
+@param map The parent map. If it is NULL, the function searches a top-level node. If both map and
+key are NULLs, the function returns the root file node - a map that contains top-level nodes.
+@param key Unique pointer to the node name, retrieved with cvGetHashedKey
+@param create_missing Flag that specifies whether an absent node should be added to the map
+ */
+CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map,
+                                 const CvStringHashNode* key,
+                                 int create_missing CV_DEFAULT(0) );
+
+/** @brief Finds a node in a map or file storage.
+
+The function finds a file node by name. The node is searched either in map or, if the pointer is
+NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or
+sequence reader) for sequences, it is possible to navigate through the file storage. To speed up
+multiple queries for a certain key (e.g., in the case of an array of structures) one may use a
+combination of cvGetHashedKey and cvGetFileNode.
+@param fs File storage
+@param map The parent map. If it is NULL, the function searches in all the top-level nodes
+(streams), starting with the first one.
+@param name The file node name
+ */
+CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs,
+                                       const CvFileNode* map,
+                                       const char* name );
+
+/** @brief Retrieves an integer value from a file node.
+
+The function returns an integer that is represented by the file node. If the file node is NULL, the
+default_value is returned (thus, it is convenient to call the function right after cvGetFileNode
+without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is
+returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer
+and returned. Otherwise the error is reported.
+@param node File node
+@param default_value The value that is returned if node is NULL
+ */
+CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) )
+{
+    return !node ? default_value :
+        CV_NODE_IS_INT(node->tag) ? node->data.i :
+        CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff;
+}
+
+/** @brief Finds a file node and returns its value.
+
+The function is a simple superposition of cvGetFileNodeByName and cvReadInt.
+@param fs File storage
+@param map The parent map. If it is NULL, the function searches a top-level node.
+@param name The node name
+@param default_value The value that is returned if the file node is not found
+ */
+CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map,
+                         const char* name, int default_value CV_DEFAULT(0) )
+{
+    return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value );
+}
+
+/** @brief Retrieves a floating-point value from a file node.
+
+The function returns a floating-point value that is represented by the file node. If the file node
+is NULL, the default_value is returned (thus, it is convenient to call the function right after
+cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL ,
+then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f
+is converted to floating-point and returned. Otherwise the result is not determined.
+@param node File node
+@param default_value The value that is returned if node is NULL
+ */
+CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) )
+{
+    return !node ? default_value :
+        CV_NODE_IS_INT(node->tag) ? (double)node->data.i :
+        CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300;
+}
+
+/** @brief Finds a file node and returns its value.
+
+The function is a simple superposition of cvGetFileNodeByName and cvReadReal .
+@param fs File storage
+@param map The parent map. If it is NULL, the function searches a top-level node.
+@param name The node name
+@param default_value The value that is returned if the file node is not found
+ */
+CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map,
+                        const char* name, double default_value CV_DEFAULT(0.) )
+{
+    return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value );
+}
+
+/** @brief Retrieves a text string from a file node.
+
+The function returns a text string that is represented by the file node. If the file node is NULL,
+the default_value is returned (thus, it is convenient to call the function right after
+cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then
+node-:math:\>data.str.ptr is returned. Otherwise the result is not determined.
+@param node File node
+@param default_value The value that is returned if node is NULL
+ */
+CV_INLINE const char* cvReadString( const CvFileNode* node,
+                        const char* default_value CV_DEFAULT(NULL) )
+{
+    return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0;
+}
+
+/** @brief Finds a file node by its name and returns its value.
+
+The function is a simple superposition of cvGetFileNodeByName and cvReadString .
+@param fs File storage
+@param map The parent map. If it is NULL, the function searches a top-level node.
+@param name The node name
+@param default_value The value that is returned if the file node is not found
+ */
+CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map,
+                        const char* name, const char* default_value CV_DEFAULT(NULL) )
+{
+    return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value );
+}
+
+
+/** @brief Decodes an object and returns a pointer to it.
+
+The function decodes a user object (creates an object in a native representation from the file
+storage subtree) and returns it. The object to be decoded must be an instance of a registered type
+that supports the read method (see CvTypeInfo). The type of the object is determined by the type
+name that is encoded in the file. If the object is a dynamic structure, it is created either in
+memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary
+memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is
+not a dynamic structure, it is created in a heap and should be released with a specialized function
+or by using the generic cvRelease.
+@param fs File storage
+@param node The root object node
+@param attributes Unused parameter
+ */
+CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node,
+                        CvAttrList* attributes CV_DEFAULT(NULL));
+
+/** @brief Finds an object by name and decodes it.
+
+The function is a simple superposition of cvGetFileNodeByName and cvRead.
+@param fs File storage
+@param map The parent map. If it is NULL, the function searches a top-level node.
+@param name The node name
+@param attributes Unused parameter
+ */
+CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map,
+                              const char* name, CvAttrList* attributes CV_DEFAULT(NULL) )
+{
+    return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes );
+}
+
+
+/** @brief Initializes the file node sequence reader.
+
+The function initializes the sequence reader to read data from a file node. The initialized reader
+can be then passed to cvReadRawDataSlice.
+@param fs File storage
+@param src The file node (a sequence) to read numbers from
+@param reader Pointer to the sequence reader
+ */
+CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src,
+                               CvSeqReader* reader );
+
+/** @brief Initializes file node sequence reader.
+
+The function reads one or more elements from the file node, representing a sequence, to a
+user-specified array. The total number of read sequence elements is a product of total and the
+number of components in each array element. For example, if dt=2if, the function will read total\*3
+sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read
+repeatedly by repositioning the reader using cvSetSeqReaderPos.
+@param fs File storage
+@param reader The sequence reader. Initialize it with cvStartReadRawData .
+@param count The number of elements to read
+@param dst Pointer to the destination array
+@param dt Specification of each array element. It has the same format as in cvWriteRawData .
+ */
+CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader,
+                               int count, void* dst, const char* dt );
+
+/** @brief Reads multiple numbers.
+
+The function reads elements from a file node that represents a sequence of scalars.
+@param fs File storage
+@param src The file node (a sequence) to read numbers from
+@param dst Pointer to the destination array
+@param dt Specification of each array element. It has the same format as in cvWriteRawData .
+ */
+CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src,
+                          void* dst, const char* dt );
+
+/** @brief Writes a file node to another file storage.
+
+The function writes a copy of a file node to file storage. Possible applications of the function are
+merging several file storages into one and conversion between XML, YAML and JSON formats.
+@param fs Destination file storage
+@param new_node_name New name of the file node in the destination file storage. To keep the
+existing name, use cvcvGetFileNodeName
+@param node The written node
+@param embed If the written node is a collection and this parameter is not zero, no extra level of
+hierarchy is created. Instead, all the elements of node are written into the currently written
+structure. Of course, map elements can only be embedded into another map, and sequence elements
+can only be embedded into another sequence.
+ */
+CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name,
+                            const CvFileNode* node, int embed );
+
+/** @brief Returns the name of a file node.
+
+The function returns the name of a file node or NULL, if the file node does not have a name or if
+node is NULL.
+@param node File node
+ */
+CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node );
+
+/*********************************** Adding own types ***********************************/
+
+/** @brief Registers a new type.
+
+The function registers a new type, which is described by info . The function creates a copy of the
+structure, so the user should delete it after calling the function.
+@param info Type info structure
+ */
+CVAPI(void) cvRegisterType( const CvTypeInfo* info );
+
+/** @brief Unregisters the type.
+
+The function unregisters a type with a specified name. If the name is unknown, it is possible to
+locate the type info by an instance of the type using cvTypeOf or by iterating the type list,
+starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName).
+@param type_name Name of an unregistered type
+ */
+CVAPI(void) cvUnregisterType( const char* type_name );
+
+/** @brief Returns the beginning of a type list.
+
+The function returns the first type in the list of registered types. Navigation through the list can
+be done via the prev and next fields of the CvTypeInfo structure.
+ */
+CVAPI(CvTypeInfo*) cvFirstType(void);
+
+/** @brief Finds a type by its name.
+
+The function finds a registered type by its name. It returns NULL if there is no type with the
+specified name.
+@param type_name Type name
+ */
+CVAPI(CvTypeInfo*) cvFindType( const char* type_name );
+
+/** @brief Returns the type of an object.
+
+The function finds the type of a given object. It iterates through the list of registered types and
+calls the is_instance function/method for every type info structure with that object until one of
+them returns non-zero or until the whole list has been traversed. In the latter case, the function
+returns NULL.
+@param struct_ptr The object pointer
+ */
+CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr );
+
+#endif
+
+/** @brief Releases an object.
+
+ The function finds the type of a given object and calls release with the double pointer.
+ @param struct_ptr Double pointer to the object
+ */
+CVAPI(void) cvRelease( void** struct_ptr );
+
+/** @brief Makes a clone of an object.
+
+The function finds the type of a given object and calls clone with the passed object. Of course, if
+you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific
+function, like cvCloneMat.
+@param struct_ptr The object to clone
+ */
+CVAPI(void*) cvClone( const void* struct_ptr );
+
+/*********************************** Measuring Execution Time ***************************/
+
+/** helper functions for RNG initialization and accurate time measurement:
+   uses internal clock counter on x86 */
+CVAPI(int64)  cvGetTickCount( void );
+CVAPI(double) cvGetTickFrequency( void );
+
+/*********************************** CPU capabilities ***********************************/
+
+CVAPI(int) cvCheckHardwareSupport(int feature);
+
+/*********************************** Multi-Threading ************************************/
+
+/** retrieve/set the number of threads used in OpenMP implementations */
+CVAPI(int)  cvGetNumThreads( void );
+CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) );
+/** get index of the thread being executed */
+CVAPI(int)  cvGetThreadNum( void );
+
+
+/********************************** Error Handling **************************************/
+
+/** Get current OpenCV error status */
+CVAPI(int) cvGetErrStatus( void );
+
+/** Sets error status silently */
+CVAPI(void) cvSetErrStatus( int status );
+
+#define CV_ErrModeLeaf     0   /* Print error and exit program */
+#define CV_ErrModeParent   1   /* Print error and continue */
+#define CV_ErrModeSilent   2   /* Don't print and continue */
+
+/** Retrieves current error processing mode */
+CVAPI(int)  cvGetErrMode( void );
+
+/** Sets error processing mode, returns previously used mode */
+CVAPI(int) cvSetErrMode( int mode );
+
+/** Sets error status and performs some additional actions (displaying message box,
+ writing message to stderr, terminating application etc.)
+ depending on the current error mode */
+CVAPI(void) cvError( int status, const char* func_name,
+                    const char* err_msg, const char* file_name, int line );
+
+/** Retrieves textual description of the error given its code */
+CVAPI(const char*) cvErrorStr( int status );
+
+/** Retrieves detailed information about the last error occurred */
+CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description,
+                        const char** filename, int* line );
+
+/** Maps IPP error codes to the counterparts from OpenCV */
+CVAPI(int) cvErrorFromIppStatus( int ipp_status );
+
+typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name,
+                                        const char* err_msg, const char* file_name, int line, void* userdata );
+
+/** Assigns a new error-handling function */
+CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler,
+                                       void* userdata CV_DEFAULT(NULL),
+                                       void** prev_userdata CV_DEFAULT(NULL) );
+
+/** Output nothing */
+CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg,
+                          const char* file_name, int line, void* userdata );
+
+/** Output to console(fprintf(stderr,...)) */
+CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg,
+                          const char* file_name, int line, void* userdata );
+
+/** Output to MessageBox(WIN32) */
+CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg,
+                          const char* file_name, int line, void* userdata );
+
+#define OPENCV_ERROR(status,func,context)                           \
+cvError((status),(func),(context),__FILE__,__LINE__)
+
+#define OPENCV_ASSERT(expr,func,context)                            \
+{if (! (expr))                                      \
+{OPENCV_ERROR(CV_StsInternal,(func),(context));}}
+
+#define OPENCV_CALL( Func )                                         \
+{                                                                   \
+Func;                                                           \
+}
+
+
+/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */
+#ifdef CV_NO_FUNC_NAMES
+#define CV_FUNCNAME( Name )
+#define cvFuncName ""
+#else
+#define CV_FUNCNAME( Name )  \
+static char cvFuncName[] = Name
+#endif
+
+
+/**
+ CV_ERROR macro unconditionally raises error with passed code and message.
+ After raising error, control will be transferred to the exit label.
+ */
+#define CV_ERROR( Code, Msg )                                       \
+{                                                                   \
+    cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ );        \
+    __CV_EXIT__;                                                   \
+}
+
+/**
+ CV_CHECK macro checks error status after CV (or IPL)
+ function call. If error detected, control will be transferred to the exit
+ label.
+ */
+#define CV_CHECK()                                                  \
+{                                                                   \
+    if( cvGetErrStatus() < 0 )                                      \
+        CV_ERROR( CV_StsBackTrace, "Inner function failed." );      \
+}
+
+
+/**
+ CV_CALL macro calls CV (or IPL) function, checks error status and
+ signals a error if the function failed. Useful in "parent node"
+ error processing mode
+ */
+#define CV_CALL( Func )                                             \
+{                                                                   \
+    Func;                                                           \
+    CV_CHECK();                                                     \
+}
+
+
+/** Runtime assertion macro */
+#define CV_ASSERT( Condition )                                          \
+{                                                                       \
+    if( !(Condition) )                                                  \
+        CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \
+}
+
+#define __CV_BEGIN__       {
+#define __CV_END__         goto exit; exit: ; }
+#define __CV_EXIT__        goto exit
+
+/** @} core_c */
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#ifdef __cplusplus
+
+#include "opencv2/core/utility.hpp"
+
+namespace cv
+{
+
+//! @addtogroup core_c_glue
+//! @{
+
+/////////////////////////////////////////// glue ///////////////////////////////////////////
+
+//! converts array (CvMat or IplImage) to cv::Mat
+CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false,
+                          bool allowND=true, int coiMode=0,
+                          AutoBuffer<double>* buf=0);
+
+static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0)
+{
+    return cvarrToMat(arr, copyData, true, coiMode);
+}
+
+
+//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it.
+CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1);
+//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage
+CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1);
+
+
+
+////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types //////
+
+template<> struct DefaultDeleter<CvMat>{ CV_EXPORTS void operator ()(CvMat* obj) const; };
+template<> struct DefaultDeleter<IplImage>{ CV_EXPORTS void operator ()(IplImage* obj) const; };
+template<> struct DefaultDeleter<CvMatND>{ CV_EXPORTS void operator ()(CvMatND* obj) const; };
+template<> struct DefaultDeleter<CvSparseMat>{ CV_EXPORTS void operator ()(CvSparseMat* obj) const; };
+template<> struct DefaultDeleter<CvMemStorage>{ CV_EXPORTS void operator ()(CvMemStorage* obj) const; };
+
+////////////// convenient wrappers for operating old-style dynamic structures //////////////
+
+template<typename _Tp> class SeqIterator;
+
+typedef Ptr<CvMemStorage> MemStorage;
+
+/*!
+ Template Sequence Class derived from CvSeq
+
+ The class provides more convenient access to sequence elements,
+ STL-style operations and iterators.
+
+ \note The class is targeted for simple data types,
+    i.e. no constructors or destructors
+    are called for the sequence elements.
+*/
+template<typename _Tp> class Seq
+{
+public:
+    typedef SeqIterator<_Tp> iterator;
+    typedef SeqIterator<_Tp> const_iterator;
+
+    //! the default constructor
+    Seq();
+    //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp.
+    Seq(const CvSeq* seq);
+    //! creates the empty sequence that resides in the specified storage
+    Seq(MemStorage& storage, int headerSize = sizeof(CvSeq));
+    //! returns read-write reference to the specified element
+    _Tp& operator [](int idx);
+    //! returns read-only reference to the specified element
+    const _Tp& operator[](int idx) const;
+    //! returns iterator pointing to the beginning of the sequence
+    SeqIterator<_Tp> begin() const;
+    //! returns iterator pointing to the element following the last sequence element
+    SeqIterator<_Tp> end() const;
+    //! returns the number of elements in the sequence
+    size_t size() const;
+    //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...)
+    int type() const;
+    //! returns the depth of sequence elements (CV_8U ... CV_64F)
+    int depth() const;
+    //! returns the number of channels in each sequence element
+    int channels() const;
+    //! returns the size of each sequence element
+    size_t elemSize() const;
+    //! returns index of the specified sequence element
+    size_t index(const _Tp& elem) const;
+    //! appends the specified element to the end of the sequence
+    void push_back(const _Tp& elem);
+    //! appends the specified element to the front of the sequence
+    void push_front(const _Tp& elem);
+    //! appends zero or more elements to the end of the sequence
+    void push_back(const _Tp* elems, size_t count);
+    //! appends zero or more elements to the front of the sequence
+    void push_front(const _Tp* elems, size_t count);
+    //! inserts the specified element to the specified position
+    void insert(int idx, const _Tp& elem);
+    //! inserts zero or more elements to the specified position
+    void insert(int idx, const _Tp* elems, size_t count);
+    //! removes element at the specified position
+    void remove(int idx);
+    //! removes the specified subsequence
+    void remove(const Range& r);
+
+    //! returns reference to the first sequence element
+    _Tp& front();
+    //! returns read-only reference to the first sequence element
+    const _Tp& front() const;
+    //! returns reference to the last sequence element
+    _Tp& back();
+    //! returns read-only reference to the last sequence element
+    const _Tp& back() const;
+    //! returns true iff the sequence contains no elements
+    bool empty() const;
+
+    //! removes all the elements from the sequence
+    void clear();
+    //! removes the first element from the sequence
+    void pop_front();
+    //! removes the last element from the sequence
+    void pop_back();
+    //! removes zero or more elements from the beginning of the sequence
+    void pop_front(_Tp* elems, size_t count);
+    //! removes zero or more elements from the end of the sequence
+    void pop_back(_Tp* elems, size_t count);
+
+    //! copies the whole sequence or the sequence slice to the specified vector
+    void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const;
+    //! returns the vector containing all the sequence elements
+    operator std::vector<_Tp>() const;
+
+    CvSeq* seq;
+};
+
+
+/*!
+ STL-style Sequence Iterator inherited from the CvSeqReader structure
+*/
+template<typename _Tp> class SeqIterator : public CvSeqReader
+{
+public:
+    //! the default constructor
+    SeqIterator();
+    //! the constructor setting the iterator to the beginning or to the end of the sequence
+    SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false);
+    //! positions the iterator within the sequence
+    void seek(size_t pos);
+    //! reports the current iterator position
+    size_t tell() const;
+    //! returns reference to the current sequence element
+    _Tp& operator *();
+    //! returns read-only reference to the current sequence element
+    const _Tp& operator *() const;
+    //! moves iterator to the next sequence element
+    SeqIterator& operator ++();
+    //! moves iterator to the next sequence element
+    SeqIterator operator ++(int) const;
+    //! moves iterator to the previous sequence element
+    SeqIterator& operator --();
+    //! moves iterator to the previous sequence element
+    SeqIterator operator --(int) const;
+
+    //! moves iterator forward by the specified offset (possibly negative)
+    SeqIterator& operator +=(int);
+    //! moves iterator backward by the specified offset (possibly negative)
+    SeqIterator& operator -=(int);
+
+    // this is index of the current element module seq->total*2
+    // (to distinguish between 0 and seq->total)
+    int index;
+};
+
+
+
+// bridge C++ => C Seq API
+CV_EXPORTS schar*  seqPush( CvSeq* seq, const void* element=0);
+CV_EXPORTS schar*  seqPushFront( CvSeq* seq, const void* element=0);
+CV_EXPORTS void  seqPop( CvSeq* seq, void* element=0);
+CV_EXPORTS void  seqPopFront( CvSeq* seq, void* element=0);
+CV_EXPORTS void  seqPopMulti( CvSeq* seq, void* elements,
+                              int count, int in_front=0 );
+CV_EXPORTS void  seqRemove( CvSeq* seq, int index );
+CV_EXPORTS void  clearSeq( CvSeq* seq );
+CV_EXPORTS schar*  getSeqElem( const CvSeq* seq, int index );
+CV_EXPORTS void  seqRemoveSlice( CvSeq* seq, CvSlice slice );
+CV_EXPORTS void  seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr );
+
+template<typename _Tp> inline Seq<_Tp>::Seq() : seq(0) {}
+template<typename _Tp> inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq)
+{
+    CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp));
+}
+
+template<typename _Tp> inline Seq<_Tp>::Seq( MemStorage& storage,
+                                             int headerSize )
+{
+    CV_Assert(headerSize >= (int)sizeof(CvSeq));
+    seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage);
+}
+
+template<typename _Tp> inline _Tp& Seq<_Tp>::operator [](int idx)
+{ return *(_Tp*)getSeqElem(seq, idx); }
+
+template<typename _Tp> inline const _Tp& Seq<_Tp>::operator [](int idx) const
+{ return *(_Tp*)getSeqElem(seq, idx); }
+
+template<typename _Tp> inline SeqIterator<_Tp> Seq<_Tp>::begin() const
+{ return SeqIterator<_Tp>(*this); }
+
+template<typename _Tp> inline SeqIterator<_Tp> Seq<_Tp>::end() const
+{ return SeqIterator<_Tp>(*this, true); }
+
+template<typename _Tp> inline size_t Seq<_Tp>::size() const
+{ return seq ? seq->total : 0; }
+
+template<typename _Tp> inline int Seq<_Tp>::type() const
+{ return seq ? CV_MAT_TYPE(seq->flags) : 0; }
+
+template<typename _Tp> inline int Seq<_Tp>::depth() const
+{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; }
+
+template<typename _Tp> inline int Seq<_Tp>::channels() const
+{ return seq ? CV_MAT_CN(seq->flags) : 0; }
+
+template<typename _Tp> inline size_t Seq<_Tp>::elemSize() const
+{ return seq ? seq->elem_size : 0; }
+
+template<typename _Tp> inline size_t Seq<_Tp>::index(const _Tp& elem) const
+{ return cvSeqElemIdx(seq, &elem); }
+
+template<typename _Tp> inline void Seq<_Tp>::push_back(const _Tp& elem)
+{ cvSeqPush(seq, &elem); }
+
+template<typename _Tp> inline void Seq<_Tp>::push_front(const _Tp& elem)
+{ cvSeqPushFront(seq, &elem); }
+
+template<typename _Tp> inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count)
+{ cvSeqPushMulti(seq, elem, (int)count, 0); }
+
+template<typename _Tp> inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count)
+{ cvSeqPushMulti(seq, elem, (int)count, 1); }
+
+template<typename _Tp> inline _Tp& Seq<_Tp>::back()
+{ return *(_Tp*)getSeqElem(seq, -1); }
+
+template<typename _Tp> inline const _Tp& Seq<_Tp>::back() const
+{ return *(const _Tp*)getSeqElem(seq, -1); }
+
+template<typename _Tp> inline _Tp& Seq<_Tp>::front()
+{ return *(_Tp*)getSeqElem(seq, 0); }
+
+template<typename _Tp> inline const _Tp& Seq<_Tp>::front() const
+{ return *(const _Tp*)getSeqElem(seq, 0); }
+
+template<typename _Tp> inline bool Seq<_Tp>::empty() const
+{ return !seq || seq->total == 0; }
+
+template<typename _Tp> inline void Seq<_Tp>::clear()
+{ if(seq) clearSeq(seq); }
+
+template<typename _Tp> inline void Seq<_Tp>::pop_back()
+{ seqPop(seq); }
+
+template<typename _Tp> inline void Seq<_Tp>::pop_front()
+{ seqPopFront(seq); }
+
+template<typename _Tp> inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count)
+{ seqPopMulti(seq, elem, (int)count, 0); }
+
+template<typename _Tp> inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count)
+{ seqPopMulti(seq, elem, (int)count, 1); }
+
+template<typename _Tp> inline void Seq<_Tp>::insert(int idx, const _Tp& elem)
+{ seqInsert(seq, idx, &elem); }
+
+template<typename _Tp> inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count)
+{
+    CvMat m = cvMat(1, count, DataType<_Tp>::type, elems);
+    seqInsertSlice(seq, idx, &m);
+}
+
+template<typename _Tp> inline void Seq<_Tp>::remove(int idx)
+{ seqRemove(seq, idx); }
+
+template<typename _Tp> inline void Seq<_Tp>::remove(const Range& r)
+{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); }
+
+template<typename _Tp> inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const
+{
+    size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start;
+    vec.resize(len);
+    if( seq && len )
+        cvCvtSeqToArray(seq, &vec[0], cvSlice(range));
+}
+
+template<typename _Tp> inline Seq<_Tp>::operator std::vector<_Tp>() const
+{
+    std::vector<_Tp> vec;
+    copyTo(vec);
+    return vec;
+}
+
+template<typename _Tp> inline SeqIterator<_Tp>::SeqIterator()
+{ memset(this, 0, sizeof(*this)); }
+
+template<typename _Tp> inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd)
+{
+    cvStartReadSeq(_seq.seq, this);
+    index = seekEnd ? _seq.seq->total : 0;
+}
+
+template<typename _Tp> inline void SeqIterator<_Tp>::seek(size_t pos)
+{
+    cvSetSeqReaderPos(this, (int)pos, false);
+    index = pos;
+}
+
+template<typename _Tp> inline size_t SeqIterator<_Tp>::tell() const
+{ return index; }
+
+template<typename _Tp> inline _Tp& SeqIterator<_Tp>::operator *()
+{ return *(_Tp*)ptr; }
+
+template<typename _Tp> inline const _Tp& SeqIterator<_Tp>::operator *() const
+{ return *(const _Tp*)ptr; }
+
+template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++()
+{
+    CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this);
+    if( ++index >= seq->total*2 )
+        index = 0;
+    return *this;
+}
+
+template<typename _Tp> inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const
+{
+    SeqIterator<_Tp> it = *this;
+    ++*this;
+    return it;
+}
+
+template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --()
+{
+    CV_PREV_SEQ_ELEM(sizeof(_Tp), *this);
+    if( --index < 0 )
+        index = seq->total*2-1;
+    return *this;
+}
+
+template<typename _Tp> inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const
+{
+    SeqIterator<_Tp> it = *this;
+    --*this;
+    return it;
+}
+
+template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta)
+{
+    cvSetSeqReaderPos(this, delta, 1);
+    index += delta;
+    int n = seq->total*2;
+    if( index < 0 )
+        index += n;
+    if( index >= n )
+        index -= n;
+    return *this;
+}
+
+template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta)
+{
+    return (*this += -delta);
+}
+
+template<typename _Tp> inline ptrdiff_t operator - (const SeqIterator<_Tp>& a,
+                                                    const SeqIterator<_Tp>& b)
+{
+    ptrdiff_t delta = a.index - b.index, n = a.seq->total;
+    if( delta > n || delta < -n )
+        delta += delta < 0 ? n : -n;
+    return delta;
+}
+
+template<typename _Tp> inline bool operator == (const SeqIterator<_Tp>& a,
+                                                const SeqIterator<_Tp>& b)
+{
+    return a.seq == b.seq && a.index == b.index;
+}
+
+template<typename _Tp> inline bool operator != (const SeqIterator<_Tp>& a,
+                                                const SeqIterator<_Tp>& b)
+{
+    return !(a == b);
+}
+
+//! @}
+
+} // cv
+
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/core/cuda.hpp b/3rdParty/include/opencv2/core/cuda.hpp
new file mode 100644
index 0000000..716b8bf
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda.hpp
@@ -0,0 +1,1270 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_CUDA_HPP
+#define OPENCV_CORE_CUDA_HPP
+
+#ifndef __cplusplus
+#  error cuda.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core.hpp"
+#include "opencv2/core/cuda_types.hpp"
+
+/**
+  @defgroup cuda CUDA-accelerated Computer Vision
+  @{
+    @defgroup cudacore Core part
+    @{
+      @defgroup cudacore_init Initialization and Information
+      @defgroup cudacore_struct Data Structures
+    @}
+  @}
+ */
+
+namespace cv { namespace cuda {
+
+//! @addtogroup cudacore_struct
+//! @{
+
+//===================================================================================
+// GpuMat
+//===================================================================================
+
+/** @brief Base storage class for GPU memory with reference counting.
+
+Its interface matches the Mat interface with the following limitations:
+
+-   no arbitrary dimensions support (only 2D)
+-   no functions that return references to their data (because references on GPU are not valid for
+    CPU)
+-   no expression templates technique support
+
+Beware that the latter limitation may lead to overloaded matrix operators that cause memory
+allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be
+passed directly to the kernel.
+
+@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are
+aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix.
+
+@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely
+on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory
+release function returns error if the CUDA context has been destroyed before.
+
+Some member functions are described as a "Blocking Call" while some are described as a
+"Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU
+operation is finished when the function returns. However, non-blocking functions are asynchronous to
+host. Those functions may return even if the GPU operation is not finished.
+
+Compared to their blocking counterpart, non-blocking functions accept Stream as an additional
+argument. If a non-default stream is passed, the GPU operation may overlap with operations in other
+streams.
+
+@sa Mat
+ */
+class CV_EXPORTS_W GpuMat
+{
+public:
+    class CV_EXPORTS_W Allocator
+    {
+    public:
+        virtual ~Allocator() {}
+
+        // allocator must fill data, step and refcount fields
+        virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0;
+        virtual void free(GpuMat* mat) = 0;
+    };
+
+    //! default allocator
+    CV_WRAP static GpuMat::Allocator* defaultAllocator();
+    CV_WRAP static void setDefaultAllocator(GpuMat::Allocator* allocator);
+
+    //! default constructor
+    CV_WRAP explicit GpuMat(GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
+
+    //! constructs GpuMat of the specified size and type
+    CV_WRAP GpuMat(int rows, int cols, int type, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
+    CV_WRAP GpuMat(Size size, int type, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
+
+    //! constructs GpuMat and fills it with the specified value _s
+    CV_WRAP GpuMat(int rows, int cols, int type, Scalar s, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
+    CV_WRAP GpuMat(Size size, int type, Scalar s, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
+
+    //! copy constructor
+    CV_WRAP GpuMat(const GpuMat& m);
+
+    //! constructor for GpuMat headers pointing to user-allocated data
+    GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP);
+    GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP);
+
+    //! creates a GpuMat header for a part of the bigger matrix
+    CV_WRAP GpuMat(const GpuMat& m, Range rowRange, Range colRange);
+    CV_WRAP GpuMat(const GpuMat& m, Rect roi);
+
+    //! builds GpuMat from host memory (Blocking call)
+    CV_WRAP explicit GpuMat(InputArray arr, GpuMat::Allocator* allocator = GpuMat::defaultAllocator());
+
+    //! destructor - calls release()
+    ~GpuMat();
+
+    //! assignment operators
+    GpuMat& operator =(const GpuMat& m);
+
+    //! allocates new GpuMat data unless the GpuMat already has specified size and type
+    CV_WRAP void create(int rows, int cols, int type);
+    CV_WRAP void create(Size size, int type);
+
+    //! decreases reference counter, deallocate the data when reference counter reaches 0
+    void release();
+
+    //! swaps with other smart pointer
+    CV_WRAP void swap(GpuMat& mat);
+
+    /** @brief Performs data upload to GpuMat (Blocking call)
+
+    This function copies data from host memory to device memory. As being a blocking call, it is
+    guaranteed that the copy operation is finished when this function returns.
+    */
+    CV_WRAP void upload(InputArray arr);
+
+    /** @brief Performs data upload to GpuMat (Non-Blocking call)
+
+    This function copies data from host memory to device memory. As being a non-blocking call, this
+    function may return even if the copy operation is not finished.
+
+    The copy operation may be overlapped with operations in other non-default streams if \p stream is
+    not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option.
+    */
+    CV_WRAP void upload(InputArray arr, Stream& stream);
+
+    /** @brief Performs data download from GpuMat (Blocking call)
+
+    This function copies data from device memory to host memory. As being a blocking call, it is
+    guaranteed that the copy operation is finished when this function returns.
+    */
+    CV_WRAP void download(OutputArray dst) const;
+
+    /** @brief Performs data download from GpuMat (Non-Blocking call)
+
+    This function copies data from device memory to host memory. As being a non-blocking call, this
+    function may return even if the copy operation is not finished.
+
+    The copy operation may be overlapped with operations in other non-default streams if \p stream is
+    not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option.
+    */
+    CV_WRAP void download(OutputArray dst, Stream& stream) const;
+
+    //! returns deep copy of the GpuMat, i.e. the data is copied
+    CV_WRAP GpuMat clone() const;
+
+    //! copies the GpuMat content to device memory (Blocking call)
+    CV_WRAP void copyTo(OutputArray dst) const;
+
+    //! copies the GpuMat content to device memory (Non-Blocking call)
+    CV_WRAP void copyTo(OutputArray dst, Stream& stream) const;
+
+    //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call)
+    CV_WRAP void copyTo(OutputArray dst, InputArray mask) const;
+
+    //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call)
+    CV_WRAP void copyTo(OutputArray dst, InputArray mask, Stream& stream) const;
+
+    //! sets some of the GpuMat elements to s (Blocking call)
+    CV_WRAP GpuMat& setTo(Scalar s);
+
+    //! sets some of the GpuMat elements to s (Non-Blocking call)
+    CV_WRAP GpuMat& setTo(Scalar s, Stream& stream);
+
+    //! sets some of the GpuMat elements to s, according to the mask (Blocking call)
+    CV_WRAP GpuMat& setTo(Scalar s, InputArray mask);
+
+    //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call)
+    CV_WRAP GpuMat& setTo(Scalar s, InputArray mask, Stream& stream);
+
+    //! converts GpuMat to another datatype (Blocking call)
+    CV_WRAP void convertTo(OutputArray dst, int rtype) const;
+
+    //! converts GpuMat to another datatype (Non-Blocking call)
+    CV_WRAP void convertTo(OutputArray dst, int rtype, Stream& stream) const;
+
+    //! converts GpuMat to another datatype with scaling (Blocking call)
+    CV_WRAP void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const;
+
+    //! converts GpuMat to another datatype with scaling (Non-Blocking call)
+    CV_WRAP void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const;
+
+    //! converts GpuMat to another datatype with scaling (Non-Blocking call)
+    CV_WRAP void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const;
+
+    CV_WRAP void assignTo(GpuMat& m, int type = -1) const;
+
+    //! returns pointer to y-th row
+    uchar* ptr(int y = 0);
+    const uchar* ptr(int y = 0) const;
+
+    //! template version of the above method
+    template<typename _Tp> _Tp* ptr(int y = 0);
+    template<typename _Tp> const _Tp* ptr(int y = 0) const;
+
+    template <typename _Tp> operator PtrStepSz<_Tp>() const;
+    template <typename _Tp> operator PtrStep<_Tp>() const;
+
+    //! returns a new GpuMat header for the specified row
+    CV_WRAP GpuMat row(int y) const;
+
+    //! returns a new GpuMat header for the specified column
+    CV_WRAP GpuMat col(int x) const;
+
+    //! ... for the specified row span
+    CV_WRAP GpuMat rowRange(int startrow, int endrow) const;
+    CV_WRAP GpuMat rowRange(Range r) const;
+
+    //! ... for the specified column span
+    CV_WRAP GpuMat colRange(int startcol, int endcol) const;
+    CV_WRAP GpuMat colRange(Range r) const;
+
+    //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.)
+    GpuMat operator ()(Range rowRange, Range colRange) const;
+    GpuMat operator ()(Rect roi) const;
+
+    //! creates alternative GpuMat header for the same data, with different
+    //! number of channels and/or different number of rows
+    CV_WRAP GpuMat reshape(int cn, int rows = 0) const;
+
+    //! locates GpuMat header within a parent GpuMat
+    CV_WRAP void locateROI(Size& wholeSize, Point& ofs) const;
+
+    //! moves/resizes the current GpuMat ROI inside the parent GpuMat
+    CV_WRAP GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright);
+
+    //! returns true iff the GpuMat data is continuous
+    //! (i.e. when there are no gaps between successive rows)
+    CV_WRAP bool isContinuous() const;
+
+    //! returns element size in bytes
+    CV_WRAP size_t elemSize() const;
+
+    //! returns the size of element channel in bytes
+    CV_WRAP size_t elemSize1() const;
+
+    //! returns element type
+    CV_WRAP int type() const;
+
+    //! returns element type
+    CV_WRAP int depth() const;
+
+    //! returns number of channels
+    CV_WRAP int channels() const;
+
+    //! returns step/elemSize1()
+    CV_WRAP size_t step1() const;
+
+    //! returns GpuMat size : width == number of columns, height == number of rows
+    CV_WRAP Size size() const;
+
+    //! returns true if GpuMat data is NULL
+    CV_WRAP bool empty() const;
+
+    // returns pointer to cuda memory
+    CV_WRAP void* cudaPtr() const;
+
+    //! internal use method: updates the continuity flag
+    CV_WRAP void updateContinuityFlag();
+
+    /*! includes several bit-fields:
+    - the magic signature
+    - continuity flag
+    - depth
+    - number of channels
+    */
+    int flags;
+
+    //! the number of rows and columns
+    int rows, cols;
+
+    //! a distance between successive rows in bytes; includes the gap if any
+    CV_PROP size_t step;
+
+    //! pointer to the data
+    uchar* data;
+
+    //! pointer to the reference counter;
+    //! when GpuMat points to user-allocated data, the pointer is NULL
+    int* refcount;
+
+    //! helper fields used in locateROI and adjustROI
+    uchar* datastart;
+    const uchar* dataend;
+
+    //! allocator
+    Allocator* allocator;
+};
+
+struct CV_EXPORTS_W GpuData
+{
+    explicit GpuData(size_t _size);
+     ~GpuData();
+
+    GpuData(const GpuData&) = delete;
+    GpuData& operator=(const GpuData&) = delete;
+
+    GpuData(GpuData&&) = delete;
+    GpuData& operator=(GpuData&&) = delete;
+
+    uchar* data;
+    size_t size;
+};
+
+class CV_EXPORTS_W GpuMatND
+{
+public:
+    using SizeArray = std::vector<int>;
+    using StepArray = std::vector<size_t>;
+    using IndexArray = std::vector<int>;
+
+    //! destructor
+    ~GpuMatND();
+
+    //! default constructor
+    GpuMatND();
+
+    /** @overload
+    @param size Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_16FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    */
+    GpuMatND(SizeArray size, int type);
+
+    /** @overload
+    @param size Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_16FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+    data, which means that no data is copied. This operation is very efficient and can be used to
+    process external data using OpenCV functions. The external data is not automatically deallocated, so
+    you should take care of it.
+    @param step Array of _size.size()-1 steps in case of a multi-dimensional array (the last step is always
+    set to the element size). If not specified, the matrix is assumed to be continuous.
+    */
+    GpuMatND(SizeArray size, int type, void* data, StepArray step = StepArray());
+
+    /** @brief Allocates GPU memory.
+    Suppose there is some GPU memory already allocated. In that case, this method may choose to reuse that
+    GPU memory under the specific condition: it must be of the same size and type, not externally allocated,
+    the GPU memory is continuous(i.e., isContinuous() is true), and is not a sub-matrix of another GpuMatND
+    (i.e., isSubmatrix() is false). In other words, this method guarantees that the GPU memory allocated by
+    this method is always continuous and is not a sub-region of another GpuMatND.
+    */
+    void create(SizeArray size, int type);
+
+    void release();
+
+    void swap(GpuMatND& m) noexcept;
+
+    /** @brief Creates a full copy of the array and the underlying data.
+    The method creates a full copy of the array. It mimics the behavior of Mat::clone(), i.e.
+    the original step is not taken into account. So, the array copy is a continuous array
+    occupying total()\*elemSize() bytes.
+    */
+    GpuMatND clone() const;
+
+    /** @overload
+    This overload is non-blocking, so it may return even if the copy operation is not finished.
+    */
+    GpuMatND clone(Stream& stream) const;
+
+    /** @brief Extracts a sub-matrix.
+    The operator makes a new header for the specified sub-array of \*this.
+    The operator is an O(1) operation, that is, no matrix data is copied.
+    @param ranges Array of selected ranges along each dimension.
+    */
+    GpuMatND operator()(const std::vector<Range>& ranges) const;
+
+    /** @brief Creates a GpuMat header for a 2D plane part of an n-dim matrix.
+    @note The returned GpuMat is constructed with the constructor for user-allocated data.
+    That is, It does not perform reference counting.
+    @note This function does not increment this GpuMatND's reference counter.
+    */
+    GpuMat createGpuMatHeader(IndexArray idx, Range rowRange, Range colRange) const;
+
+    /** @overload
+    Creates a GpuMat header if this GpuMatND is effectively 2D.
+    @note The returned GpuMat is constructed with the constructor for user-allocated data.
+    That is, It does not perform reference counting.
+    @note This function does not increment this GpuMatND's reference counter.
+    */
+    GpuMat createGpuMatHeader() const;
+
+    /** @brief Extracts a 2D plane part of an n-dim matrix.
+    It differs from createGpuMatHeader(IndexArray, Range, Range) in that it clones a part of this
+    GpuMatND to the returned GpuMat.
+    @note This operator does not increment this GpuMatND's reference counter;
+    */
+    GpuMat operator()(IndexArray idx, Range rowRange, Range colRange) const;
+
+    /** @brief Extracts a 2D plane part of an n-dim matrix if this GpuMatND is effectively 2D.
+    It differs from createGpuMatHeader() in that it clones a part of this GpuMatND.
+    @note This operator does not increment this GpuMatND's reference counter;
+    */
+    operator GpuMat() const;
+
+    GpuMatND(const GpuMatND&) = default;
+    GpuMatND& operator=(const GpuMatND&) = default;
+
+#if defined(__GNUC__) && __GNUC__ < 5
+    // error: function '...' defaulted on its first declaration with an exception-specification
+    // that differs from the implicit declaration '...'
+
+    GpuMatND(GpuMatND&&) = default;
+    GpuMatND& operator=(GpuMatND&&) = default;
+#else
+    GpuMatND(GpuMatND&&) noexcept = default;
+    GpuMatND& operator=(GpuMatND&&) noexcept = default;
+#endif
+
+    void upload(InputArray src);
+    void upload(InputArray src, Stream& stream);
+    void download(OutputArray dst) const;
+    void download(OutputArray dst, Stream& stream) const;
+
+    //! returns true iff the GpuMatND data is continuous
+    //! (i.e. when there are no gaps between successive rows)
+    bool isContinuous() const;
+
+    //! returns true if the matrix is a sub-matrix of another matrix
+    bool isSubmatrix() const;
+
+    //! returns element size in bytes
+    size_t elemSize() const;
+
+    //! returns the size of element channel in bytes
+    size_t elemSize1() const;
+
+    //! returns true if data is null
+    bool empty() const;
+
+    //! returns true if not empty and points to external(user-allocated) gpu memory
+    bool external() const;
+
+    //! returns pointer to the first byte of the GPU memory
+    uchar* getDevicePtr() const;
+
+    //! returns the total number of array elements
+    size_t total() const;
+
+    //! returns the size of underlying memory in bytes
+    size_t totalMemSize() const;
+
+    //! returns element type
+    int type() const;
+
+private:
+    //! internal use
+    void setFields(SizeArray size, int type, StepArray step = StepArray());
+
+public:
+    /*! includes several bit-fields:
+    - the magic signature
+    - continuity flag
+    - depth
+    - number of channels
+    */
+    int flags;
+
+    //! matrix dimensionality
+    int dims;
+
+    //! shape of this array
+    SizeArray size;
+
+    /*! step values
+    Their semantics is identical to the semantics of step for Mat.
+    */
+    StepArray step;
+
+private:
+    /*! internal use
+    If this GpuMatND holds external memory, this is empty.
+    */
+    std::shared_ptr<GpuData> data_;
+
+    /*! internal use
+    If this GpuMatND manages memory with reference counting, this value is
+    always equal to data_->data. If this GpuMatND holds external memory,
+    data_ is empty and data points to the external memory.
+    */
+    uchar* data;
+
+    /*! internal use
+    If this GpuMatND is a sub-matrix of a larger matrix, this value is the
+    difference of the first byte between the sub-matrix and the whole matrix.
+    */
+    size_t offset;
+};
+
+/** @brief Creates a continuous matrix.
+
+@param rows Row count.
+@param cols Column count.
+@param type Type of the matrix.
+@param arr Destination matrix. This parameter changes only if it has a proper type and area (
+\f$\texttt{rows} \times \texttt{cols}\f$ ).
+
+Matrix is called continuous if its elements are stored continuously, that is, without gaps at the
+end of each row.
+ */
+CV_EXPORTS_W void createContinuous(int rows, int cols, int type, OutputArray arr);
+
+/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type.
+
+@param rows Minimum desired number of rows.
+@param cols Minimum desired number of columns.
+@param type Desired matrix type.
+@param arr Destination matrix.
+
+The function does not reallocate memory if the matrix has proper attributes already.
+ */
+CV_EXPORTS_W void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr);
+
+/** @brief BufferPool for use with CUDA streams
+
+BufferPool utilizes Stream's allocator to create new buffers for GpuMat's. It is
+only useful when enabled with #setBufferPoolUsage.
+
+@code
+    setBufferPoolUsage(true);
+@endcode
+
+@note #setBufferPoolUsage must be called \em before any Stream declaration.
+
+Users may specify custom allocator for Stream and may implement their own stream based
+functions utilizing the same underlying GPU memory management.
+
+If custom allocator is not specified, BufferPool utilizes StackAllocator by
+default. StackAllocator allocates a chunk of GPU device memory beforehand,
+and when GpuMat is declared later on, it is given the pre-allocated memory.
+This kind of strategy reduces the number of calls for memory allocating APIs
+such as cudaMalloc or cudaMallocPitch.
+
+Below is an example that utilizes BufferPool with StackAllocator:
+
+@code
+    #include <opencv2/opencv.hpp>
+
+    using namespace cv;
+    using namespace cv::cuda
+
+    int main()
+    {
+        setBufferPoolUsage(true);                               // Tell OpenCV that we are going to utilize BufferPool
+        setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2);  // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks)
+
+        Stream stream1, stream2;                                // Each stream uses 1 stack
+        BufferPool pool1(stream1), pool2(stream2);
+
+        GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1);   // 16MB
+        GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3);   // 48MB, pool1 is now full
+
+        GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1);   // 1MB
+        GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3);   // 3MB
+
+        cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1);
+        cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2);
+    }
+@endcode
+
+If we allocate another GpuMat on pool1 in the above example, it will be carried out by
+the DefaultAllocator since the stack for pool1 is full.
+
+@code
+    GpuMat d_add1 = pool1.getBuffer(1024, 1024, CV_8UC1);   // Stack for pool1 is full, memory is allocated with DefaultAllocator
+@endcode
+
+If a third stream is declared in the above example, allocating with #getBuffer
+within that stream will also be carried out by the DefaultAllocator because we've run out of
+stacks.
+
+@code
+    Stream stream3;                                         // Only 2 stacks were allocated, we've run out of stacks
+    BufferPool pool3(stream3);
+    GpuMat d_src3 = pool3.getBuffer(1024, 1024, CV_8UC1);   // Memory is allocated with DefaultAllocator
+@endcode
+
+@warning When utilizing StackAllocator, deallocation order is important.
+
+Just like a stack, deallocation must be done in LIFO order. Below is an example of
+erroneous usage that violates LIFO rule. If OpenCV is compiled in Debug mode, this
+sample code will emit CV_Assert error.
+
+@code
+    int main()
+    {
+        setBufferPoolUsage(true);                               // Tell OpenCV that we are going to utilize BufferPool
+        Stream stream;                                          // A default size (10 MB) stack is allocated to this stream
+        BufferPool pool(stream);
+
+        GpuMat mat1 = pool.getBuffer(1024, 1024, CV_8UC1);      // Allocate mat1 (1MB)
+        GpuMat mat2 = pool.getBuffer(1024, 1024, CV_8UC1);      // Allocate mat2 (1MB)
+
+        mat1.release();                                         // erroneous usage : mat2 must be deallocated before mat1
+    }
+@endcode
+
+Since C++ local variables are destroyed in the reverse order of construction,
+the code sample below satisfies the LIFO rule. Local GpuMat's are deallocated
+and the corresponding memory is automatically returned to the pool for later usage.
+
+@code
+    int main()
+    {
+        setBufferPoolUsage(true);                               // Tell OpenCV that we are going to utilize BufferPool
+        setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2);  // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks)
+
+        Stream stream1, stream2;                                // Each stream uses 1 stack
+        BufferPool pool1(stream1), pool2(stream2);
+
+        for (int i = 0; i < 10; i++)
+        {
+            GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1);   // 16MB
+            GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3);   // 48MB, pool1 is now full
+
+            GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1);   // 1MB
+            GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3);   // 3MB
+
+            d_src1.setTo(Scalar(i), stream1);
+            d_src2.setTo(Scalar(i), stream2);
+
+            cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1);
+            cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2);
+                                                                    // The order of destruction of the local variables is:
+                                                                    //   d_dst2 => d_src2 => d_dst1 => d_src1
+                                                                    // LIFO rule is satisfied, this code runs without error
+        }
+    }
+@endcode
+ */
+class CV_EXPORTS_W BufferPool
+{
+public:
+
+    //! Gets the BufferPool for the given stream.
+    explicit BufferPool(Stream& stream);
+
+    //! Allocates a new GpuMat of given size and type.
+    CV_WRAP GpuMat getBuffer(int rows, int cols, int type);
+
+    //! Allocates a new GpuMat of given size and type.
+    CV_WRAP GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); }
+
+    //! Returns the allocator associated with the stream.
+    CV_WRAP Ptr<GpuMat::Allocator> getAllocator() const { return allocator_; }
+
+private:
+    Ptr<GpuMat::Allocator> allocator_;
+};
+
+//! BufferPool management (must be called before Stream creation)
+CV_EXPORTS_W void setBufferPoolUsage(bool on);
+CV_EXPORTS_W void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount);
+
+//===================================================================================
+// HostMem
+//===================================================================================
+
+/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA.
+
+Its interface is also Mat-like but with additional memory type parameters.
+
+-   **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous
+    uploading/downloading data from/to GPU.
+-   **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU
+    address space, if supported.
+-   **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are
+    used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache
+    utilization.
+
+@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2
+Pinned Memory APIs* document or *CUDA C Programming Guide*.
+ */
+class CV_EXPORTS_W HostMem
+{
+public:
+    enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 };
+
+    static MatAllocator* getAllocator(HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
+
+    CV_WRAP explicit HostMem(HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
+
+    HostMem(const HostMem& m);
+
+    CV_WRAP HostMem(int rows, int cols, int type, HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
+    CV_WRAP HostMem(Size size, int type, HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
+
+    //! creates from host memory with coping data
+    CV_WRAP explicit HostMem(InputArray arr, HostMem::AllocType alloc_type = HostMem::AllocType::PAGE_LOCKED);
+
+    ~HostMem();
+
+    HostMem& operator =(const HostMem& m);
+
+    //! swaps with other smart pointer
+    CV_WRAP void swap(HostMem& b);
+
+    //! returns deep copy of the matrix, i.e. the data is copied
+    CV_WRAP HostMem clone() const;
+
+    //! allocates new matrix data unless the matrix already has specified size and type.
+    CV_WRAP void create(int rows, int cols, int type);
+    void create(Size size, int type);
+
+    //! creates alternative HostMem header for the same data, with different
+    //! number of channels and/or different number of rows
+    CV_WRAP HostMem reshape(int cn, int rows = 0) const;
+
+    //! decrements reference counter and released memory if needed.
+    void release();
+
+    //! returns matrix header with disabled reference counting for HostMem data.
+    CV_WRAP Mat createMatHeader() const;
+
+    /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting
+    for it.
+
+    This can be done only if memory was allocated with the SHARED flag and if it is supported by the
+    hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which
+    eliminates an extra copy.
+     */
+    GpuMat createGpuMatHeader() const;
+
+    // Please see cv::Mat for descriptions
+    CV_WRAP bool isContinuous() const;
+    CV_WRAP size_t elemSize() const;
+    CV_WRAP size_t elemSize1() const;
+    CV_WRAP int type() const;
+    CV_WRAP int depth() const;
+    CV_WRAP int channels() const;
+    CV_WRAP size_t step1() const;
+    CV_WRAP Size size() const;
+    CV_WRAP bool empty() const;
+
+    // Please see cv::Mat for descriptions
+    int flags;
+    int rows, cols;
+    CV_PROP size_t step;
+
+    uchar* data;
+    int* refcount;
+
+    uchar* datastart;
+    const uchar* dataend;
+
+    AllocType alloc_type;
+};
+
+/** @brief Page-locks the memory of matrix and maps it for the device(s).
+
+@param m Input matrix.
+ */
+CV_EXPORTS_W void registerPageLocked(Mat& m);
+
+/** @brief Unmaps the memory of matrix and makes it pageable again.
+
+@param m Input matrix.
+ */
+CV_EXPORTS_W void unregisterPageLocked(Mat& m);
+
+//===================================================================================
+// Stream
+//===================================================================================
+
+/** @brief This class encapsulates a queue of asynchronous calls.
+
+@note Currently, you may face problems if an operation is enqueued twice with different data. Some
+functions use the constant GPU memory, and next call may update the memory before the previous one
+has been finished. But calling different operations asynchronously is safe because each operation
+has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are
+also safe.
+
+@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads.
+
+@code
+void thread1()
+{
+    cv::cuda::Stream stream1;
+    cv::cuda::func1(..., stream1);
+}
+
+void thread2()
+{
+    cv::cuda::Stream stream2;
+    cv::cuda::func2(..., stream2);
+}
+@endcode
+
+@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user.
+In multi-threading environment the stream objects must be passed explicitly (see previous note).
+ */
+class CV_EXPORTS_W Stream
+{
+    typedef void (Stream::*bool_type)() const;
+    void this_type_does_not_support_comparisons() const {}
+
+public:
+    typedef void (*StreamCallback)(int status, void* userData);
+
+    //! creates a new asynchronous stream
+    CV_WRAP Stream();
+
+    //! creates a new asynchronous stream with custom allocator
+    CV_WRAP Stream(const Ptr<GpuMat::Allocator>& allocator);
+
+    /** @brief creates a new Stream using the cudaFlags argument to determine the behaviors of the stream
+
+    @note The cudaFlags parameter is passed to the underlying api cudaStreamCreateWithFlags() and
+    supports the same parameter values.
+    @code
+        // creates an OpenCV cuda::Stream that manages an asynchronous, non-blocking,
+        // non-default CUDA stream
+        cv::cuda::Stream cvStream(cudaStreamNonBlocking);
+    @endcode
+     */
+    CV_WRAP Stream(const size_t cudaFlags);
+
+    /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false.
+    */
+    CV_WRAP bool queryIfComplete() const;
+
+    /** @brief Blocks the current CPU thread until all operations in the stream are complete.
+    */
+    CV_WRAP void waitForCompletion();
+
+    /** @brief Makes a compute stream wait on an event.
+    */
+    CV_WRAP void waitEvent(const Event& event);
+
+    /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have
+    completed.
+
+    @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization
+    that may depend on outstanding device work or other callbacks that are not mandated to run earlier.
+    Callbacks without a mandated order (in independent streams) execute in undefined order and may be
+    serialized.
+     */
+    void enqueueHostCallback(StreamCallback callback, void* userData);
+
+    //! return Stream object for default CUDA stream
+    CV_WRAP static Stream& Null();
+
+    //! returns true if stream object is not default (!= 0)
+    operator bool_type() const;
+
+    //! return Pointer to CUDA stream
+    CV_WRAP void* cudaPtr() const;
+
+    class Impl;
+
+private:
+    Ptr<Impl> impl_;
+    Stream(const Ptr<Impl>& impl);
+
+    friend struct StreamAccessor;
+    friend class BufferPool;
+    friend class DefaultDeviceInitializer;
+};
+
+class CV_EXPORTS_W Event
+{
+public:
+    enum CreateFlags
+    {
+        DEFAULT        = 0x00,  /**< Default event flag */
+        BLOCKING_SYNC  = 0x01,  /**< Event uses blocking synchronization */
+        DISABLE_TIMING = 0x02,  /**< Event will not record timing data */
+        INTERPROCESS   = 0x04   /**< Event is suitable for interprocess use. DisableTiming must be set */
+    };
+
+    CV_WRAP explicit Event(Event::CreateFlags flags = Event::CreateFlags::DEFAULT);
+
+    //! records an event
+    CV_WRAP void record(Stream& stream = Stream::Null());
+
+    //! queries an event's status
+    CV_WRAP bool queryIfComplete() const;
+
+    //! waits for an event to complete
+    CV_WRAP void waitForCompletion();
+
+    //! computes the elapsed time between events
+    CV_WRAP static float elapsedTime(const Event& start, const Event& end);
+
+    class Impl;
+
+private:
+    Ptr<Impl> impl_;
+    Event(const Ptr<Impl>& impl);
+
+    friend struct EventAccessor;
+};
+
+//! @} cudacore_struct
+
+//===================================================================================
+// Initialization & Info
+//===================================================================================
+
+//! @addtogroup cudacore_init
+//! @{
+
+/** @brief Returns the number of installed CUDA-enabled devices.
+
+Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support,
+this function returns 0. If the CUDA driver is not installed, or is incompatible, this function
+returns -1.
+ */
+CV_EXPORTS_W int getCudaEnabledDeviceCount();
+
+/** @brief Sets a device and initializes it for the current thread.
+
+@param device System index of a CUDA device starting with 0.
+
+If the call of this function is omitted, a default device is initialized at the fist CUDA usage.
+ */
+CV_EXPORTS_W void setDevice(int device);
+
+/** @brief Returns the current device index set by cuda::setDevice or initialized by default.
+ */
+CV_EXPORTS_W int getDevice();
+
+/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current
+process.
+
+Any subsequent API call to this device will reinitialize the device.
+ */
+CV_EXPORTS_W void resetDevice();
+
+/** @brief Enumeration providing CUDA computing features.
+ */
+enum FeatureSet
+{
+    FEATURE_SET_COMPUTE_10 = 10,
+    FEATURE_SET_COMPUTE_11 = 11,
+    FEATURE_SET_COMPUTE_12 = 12,
+    FEATURE_SET_COMPUTE_13 = 13,
+    FEATURE_SET_COMPUTE_20 = 20,
+    FEATURE_SET_COMPUTE_21 = 21,
+    FEATURE_SET_COMPUTE_30 = 30,
+    FEATURE_SET_COMPUTE_32 = 32,
+    FEATURE_SET_COMPUTE_35 = 35,
+    FEATURE_SET_COMPUTE_50 = 50,
+
+    GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11,
+    SHARED_ATOMICS = FEATURE_SET_COMPUTE_12,
+    NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13,
+    WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30,
+    DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35
+};
+
+//! checks whether current device supports the given feature
+CV_EXPORTS bool deviceSupports(FeatureSet feature_set);
+
+/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was
+built for.
+
+According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute
+capability can always be compiled to binary code of greater or equal compute capability".
+ */
+class CV_EXPORTS_W TargetArchs
+{
+public:
+    /** @brief The following method checks whether the module was built with the support of the given feature:
+
+    @param feature_set Features to be checked. See :ocvcuda::FeatureSet.
+     */
+    static bool builtWith(FeatureSet feature_set);
+
+    /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA
+    code for the given architecture(s):
+
+    @param major Major compute capability version.
+    @param minor Minor compute capability version.
+     */
+    CV_WRAP static bool has(int major, int minor);
+    CV_WRAP static bool hasPtx(int major, int minor);
+    CV_WRAP static bool hasBin(int major, int minor);
+
+    CV_WRAP static bool hasEqualOrLessPtx(int major, int minor);
+    CV_WRAP static bool hasEqualOrGreater(int major, int minor);
+    CV_WRAP static bool hasEqualOrGreaterPtx(int major, int minor);
+    CV_WRAP static bool hasEqualOrGreaterBin(int major, int minor);
+};
+
+/** @brief Class providing functionality for querying the specified GPU properties.
+ */
+class CV_EXPORTS_W DeviceInfo
+{
+public:
+    //! creates DeviceInfo object for the current GPU
+    CV_WRAP DeviceInfo();
+
+    /** @brief The constructors.
+
+    @param device_id System index of the CUDA device starting with 0.
+
+    Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it
+    constructs an object for the current device.
+     */
+    CV_WRAP DeviceInfo(int device_id);
+
+    /** @brief Returns system index of the CUDA device starting with 0.
+    */
+    CV_WRAP int deviceID() const;
+
+    //! ASCII string identifying device
+    const char* name() const;
+
+    //! global memory available on device in bytes
+    CV_WRAP size_t totalGlobalMem() const;
+
+    //! shared memory available per block in bytes
+    CV_WRAP size_t sharedMemPerBlock() const;
+
+    //! 32-bit registers available per block
+    CV_WRAP int regsPerBlock() const;
+
+    //! warp size in threads
+    CV_WRAP int warpSize() const;
+
+    //! maximum pitch in bytes allowed by memory copies
+    CV_WRAP size_t memPitch() const;
+
+    //! maximum number of threads per block
+    CV_WRAP int maxThreadsPerBlock() const;
+
+    //! maximum size of each dimension of a block
+    CV_WRAP Vec3i maxThreadsDim() const;
+
+    //! maximum size of each dimension of a grid
+    CV_WRAP Vec3i maxGridSize() const;
+
+    //! clock frequency in kilohertz
+    CV_WRAP int clockRate() const;
+
+    //! constant memory available on device in bytes
+    CV_WRAP size_t totalConstMem() const;
+
+    //! major compute capability
+    CV_WRAP int majorVersion() const;
+
+    //! minor compute capability
+    CV_WRAP int minorVersion() const;
+
+    //! alignment requirement for textures
+    CV_WRAP size_t textureAlignment() const;
+
+    //! pitch alignment requirement for texture references bound to pitched memory
+    CV_WRAP size_t texturePitchAlignment() const;
+
+    //! number of multiprocessors on device
+    CV_WRAP int multiProcessorCount() const;
+
+    //! specified whether there is a run time limit on kernels
+    CV_WRAP bool kernelExecTimeoutEnabled() const;
+
+    //! device is integrated as opposed to discrete
+    CV_WRAP bool integrated() const;
+
+    //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer
+    CV_WRAP bool canMapHostMemory() const;
+
+    enum ComputeMode
+    {
+        ComputeModeDefault,         /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */
+        ComputeModeExclusive,       /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */
+        ComputeModeProhibited,      /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */
+        ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */
+    };
+
+    //! compute mode
+    CV_WRAP DeviceInfo::ComputeMode computeMode() const;
+
+    //! maximum 1D texture size
+    CV_WRAP int maxTexture1D() const;
+
+    //! maximum 1D mipmapped texture size
+    CV_WRAP int maxTexture1DMipmap() const;
+
+    //! maximum size for 1D textures bound to linear memory
+    CV_WRAP int maxTexture1DLinear() const;
+
+    //! maximum 2D texture dimensions
+    CV_WRAP Vec2i maxTexture2D() const;
+
+    //! maximum 2D mipmapped texture dimensions
+    CV_WRAP Vec2i maxTexture2DMipmap() const;
+
+    //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory
+    CV_WRAP Vec3i maxTexture2DLinear() const;
+
+    //! maximum 2D texture dimensions if texture gather operations have to be performed
+    CV_WRAP Vec2i maxTexture2DGather() const;
+
+    //! maximum 3D texture dimensions
+    CV_WRAP Vec3i maxTexture3D() const;
+
+    //! maximum Cubemap texture dimensions
+    CV_WRAP int maxTextureCubemap() const;
+
+    //! maximum 1D layered texture dimensions
+    CV_WRAP Vec2i maxTexture1DLayered() const;
+
+    //! maximum 2D layered texture dimensions
+    CV_WRAP Vec3i maxTexture2DLayered() const;
+
+    //! maximum Cubemap layered texture dimensions
+    CV_WRAP Vec2i maxTextureCubemapLayered() const;
+
+    //! maximum 1D surface size
+    CV_WRAP int maxSurface1D() const;
+
+    //! maximum 2D surface dimensions
+    CV_WRAP Vec2i maxSurface2D() const;
+
+    //! maximum 3D surface dimensions
+    CV_WRAP Vec3i maxSurface3D() const;
+
+    //! maximum 1D layered surface dimensions
+    CV_WRAP Vec2i maxSurface1DLayered() const;
+
+    //! maximum 2D layered surface dimensions
+    CV_WRAP Vec3i maxSurface2DLayered() const;
+
+    //! maximum Cubemap surface dimensions
+    CV_WRAP int maxSurfaceCubemap() const;
+
+    //! maximum Cubemap layered surface dimensions
+    CV_WRAP Vec2i maxSurfaceCubemapLayered() const;
+
+    //! alignment requirements for surfaces
+    CV_WRAP size_t surfaceAlignment() const;
+
+    //! device can possibly execute multiple kernels concurrently
+    CV_WRAP bool concurrentKernels() const;
+
+    //! device has ECC support enabled
+    CV_WRAP bool ECCEnabled() const;
+
+    //! PCI bus ID of the device
+    CV_WRAP int pciBusID() const;
+
+    //! PCI device ID of the device
+    CV_WRAP int pciDeviceID() const;
+
+    //! PCI domain ID of the device
+    CV_WRAP int pciDomainID() const;
+
+    //! true if device is a Tesla device using TCC driver, false otherwise
+    CV_WRAP bool tccDriver() const;
+
+    //! number of asynchronous engines
+    CV_WRAP int asyncEngineCount() const;
+
+    //! device shares a unified address space with the host
+    CV_WRAP bool unifiedAddressing() const;
+
+    //! peak memory clock frequency in kilohertz
+    CV_WRAP int memoryClockRate() const;
+
+    //! global memory bus width in bits
+    CV_WRAP int memoryBusWidth() const;
+
+    //! size of L2 cache in bytes
+    CV_WRAP int l2CacheSize() const;
+
+    //! maximum resident threads per multiprocessor
+    CV_WRAP int maxThreadsPerMultiProcessor() const;
+
+    //! gets free and total device memory
+    CV_WRAP void queryMemory(size_t& totalMemory, size_t& freeMemory) const;
+    CV_WRAP size_t freeMemory() const;
+    CV_WRAP size_t totalMemory() const;
+
+    /** @brief Provides information on CUDA feature support.
+
+    @param feature_set Features to be checked. See cuda::FeatureSet.
+
+    This function returns true if the device has the specified CUDA feature. Otherwise, it returns false
+     */
+    bool supports(FeatureSet feature_set) const;
+
+    /** @brief Checks the CUDA module and device compatibility.
+
+    This function returns true if the CUDA module can be run on the specified device. Otherwise, it
+    returns false .
+     */
+    CV_WRAP bool isCompatible() const;
+
+private:
+    int device_id_;
+};
+
+CV_EXPORTS_W void printCudaDeviceInfo(int device);
+CV_EXPORTS_W void printShortCudaDeviceInfo(int device);
+
+/** @brief Converts an array to half precision floating number.
+
+@param _src input array.
+@param _dst output array.
+@param stream Stream for the asynchronous version.
+@sa convertFp16
+*/
+CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null());
+
+//! @} cudacore_init
+
+}} // namespace cv { namespace cuda {
+
+
+#include "opencv2/core/cuda.inl.hpp"
+
+#endif /* OPENCV_CORE_CUDA_HPP */
diff --git a/3rdParty/include/opencv2/core/cuda.inl.hpp b/3rdParty/include/opencv2/core/cuda.inl.hpp
new file mode 100644
index 0000000..3f2a0c7
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda.inl.hpp
@@ -0,0 +1,723 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_CUDAINL_HPP
+#define OPENCV_CORE_CUDAINL_HPP
+
+#include "opencv2/core/cuda.hpp"
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda {
+
+//===================================================================================
+// GpuMat
+//===================================================================================
+
+inline
+GpuMat::GpuMat(Allocator* allocator_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
+{}
+
+inline
+GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
+{
+    if (rows_ > 0 && cols_ > 0)
+        create(rows_, cols_, type_);
+}
+
+inline
+GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
+{
+    if (size_.height > 0 && size_.width > 0)
+        create(size_.height, size_.width, type_);
+}
+
+inline
+GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
+{
+    if (rows_ > 0 && cols_ > 0)
+    {
+        create(rows_, cols_, type_);
+        setTo(s_);
+    }
+}
+
+inline
+GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
+{
+    if (size_.height > 0 && size_.width > 0)
+    {
+        create(size_.height, size_.width, type_);
+        setTo(s_);
+    }
+}
+
+inline
+GpuMat::GpuMat(const GpuMat& m)
+    : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator)
+{
+    if (refcount)
+        CV_XADD(refcount, 1);
+}
+
+inline
+GpuMat::GpuMat(InputArray arr, Allocator* allocator_) :
+    flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
+{
+    upload(arr);
+}
+
+inline
+GpuMat::~GpuMat()
+{
+    release();
+}
+
+inline
+GpuMat& GpuMat::operator =(const GpuMat& m)
+{
+    if (this != &m)
+    {
+        GpuMat temp(m);
+        swap(temp);
+    }
+
+    return *this;
+}
+
+inline
+void GpuMat::create(Size size_, int type_)
+{
+    create(size_.height, size_.width, type_);
+}
+
+inline
+void GpuMat::swap(GpuMat& b)
+{
+    std::swap(flags, b.flags);
+    std::swap(rows, b.rows);
+    std::swap(cols, b.cols);
+    std::swap(step, b.step);
+    std::swap(data, b.data);
+    std::swap(datastart, b.datastart);
+    std::swap(dataend, b.dataend);
+    std::swap(refcount, b.refcount);
+    std::swap(allocator, b.allocator);
+}
+
+inline
+GpuMat GpuMat::clone() const
+{
+    GpuMat m;
+    copyTo(m);
+    return m;
+}
+
+inline
+void GpuMat::copyTo(OutputArray dst, InputArray mask) const
+{
+    copyTo(dst, mask, Stream::Null());
+}
+
+inline
+GpuMat& GpuMat::setTo(Scalar s)
+{
+    return setTo(s, Stream::Null());
+}
+
+inline
+GpuMat& GpuMat::setTo(Scalar s, InputArray mask)
+{
+    return setTo(s, mask, Stream::Null());
+}
+
+inline
+void GpuMat::convertTo(OutputArray dst, int rtype) const
+{
+    convertTo(dst, rtype, Stream::Null());
+}
+
+inline
+void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const
+{
+    convertTo(dst, rtype, alpha, beta, Stream::Null());
+}
+
+inline
+void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const
+{
+    convertTo(dst, rtype, alpha, 0.0, stream);
+}
+
+inline
+void GpuMat::assignTo(GpuMat& m, int _type) const
+{
+    if (_type < 0)
+        m = *this;
+    else
+        convertTo(m, _type);
+}
+
+inline
+uchar* GpuMat::ptr(int y)
+{
+    CV_DbgAssert( (unsigned)y < (unsigned)rows );
+    return data + step * y;
+}
+
+inline
+const uchar* GpuMat::ptr(int y) const
+{
+    CV_DbgAssert( (unsigned)y < (unsigned)rows );
+    return data + step * y;
+}
+
+template<typename _Tp> inline
+_Tp* GpuMat::ptr(int y)
+{
+    return (_Tp*)ptr(y);
+}
+
+template<typename _Tp> inline
+const _Tp* GpuMat::ptr(int y) const
+{
+    return (const _Tp*)ptr(y);
+}
+
+template <class T> inline
+GpuMat::operator PtrStepSz<T>() const
+{
+    return PtrStepSz<T>(rows, cols, (T*)data, step);
+}
+
+template <class T> inline
+GpuMat::operator PtrStep<T>() const
+{
+    return PtrStep<T>((T*)data, step);
+}
+
+inline
+GpuMat GpuMat::row(int y) const
+{
+    return GpuMat(*this, Range(y, y+1), Range::all());
+}
+
+inline
+GpuMat GpuMat::col(int x) const
+{
+    return GpuMat(*this, Range::all(), Range(x, x+1));
+}
+
+inline
+GpuMat GpuMat::rowRange(int startrow, int endrow) const
+{
+    return GpuMat(*this, Range(startrow, endrow), Range::all());
+}
+
+inline
+GpuMat GpuMat::rowRange(Range r) const
+{
+    return GpuMat(*this, r, Range::all());
+}
+
+inline
+GpuMat GpuMat::colRange(int startcol, int endcol) const
+{
+    return GpuMat(*this, Range::all(), Range(startcol, endcol));
+}
+
+inline
+GpuMat GpuMat::colRange(Range r) const
+{
+    return GpuMat(*this, Range::all(), r);
+}
+
+inline
+GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const
+{
+    return GpuMat(*this, rowRange_, colRange_);
+}
+
+inline
+GpuMat GpuMat::operator ()(Rect roi) const
+{
+    return GpuMat(*this, roi);
+}
+
+inline
+bool GpuMat::isContinuous() const
+{
+    return (flags & Mat::CONTINUOUS_FLAG) != 0;
+}
+
+inline
+size_t GpuMat::elemSize() const
+{
+    return CV_ELEM_SIZE(flags);
+}
+
+inline
+size_t GpuMat::elemSize1() const
+{
+    return CV_ELEM_SIZE1(flags);
+}
+
+inline
+int GpuMat::type() const
+{
+    return CV_MAT_TYPE(flags);
+}
+
+inline
+int GpuMat::depth() const
+{
+    return CV_MAT_DEPTH(flags);
+}
+
+inline
+int GpuMat::channels() const
+{
+    return CV_MAT_CN(flags);
+}
+
+inline
+size_t GpuMat::step1() const
+{
+    return step / elemSize1();
+}
+
+inline
+Size GpuMat::size() const
+{
+    return Size(cols, rows);
+}
+
+inline
+bool GpuMat::empty() const
+{
+    return data == 0;
+}
+
+inline
+void* GpuMat::cudaPtr() const
+{
+    return data;
+}
+
+static inline
+GpuMat createContinuous(int rows, int cols, int type)
+{
+    GpuMat m;
+    createContinuous(rows, cols, type, m);
+    return m;
+}
+
+static inline
+void createContinuous(Size size, int type, OutputArray arr)
+{
+    createContinuous(size.height, size.width, type, arr);
+}
+
+static inline
+GpuMat createContinuous(Size size, int type)
+{
+    GpuMat m;
+    createContinuous(size, type, m);
+    return m;
+}
+
+static inline
+void ensureSizeIsEnough(Size size, int type, OutputArray arr)
+{
+    ensureSizeIsEnough(size.height, size.width, type, arr);
+}
+
+static inline
+void swap(GpuMat& a, GpuMat& b)
+{
+    a.swap(b);
+}
+
+//===================================================================================
+// GpuMatND
+//===================================================================================
+
+inline
+GpuMatND::GpuMatND() :
+    flags(0), dims(0), data(nullptr), offset(0)
+{
+}
+
+inline
+GpuMatND::GpuMatND(SizeArray _size, int _type) :
+    flags(0), dims(0), data(nullptr), offset(0)
+{
+    create(std::move(_size), _type);
+}
+
+inline
+void GpuMatND::swap(GpuMatND& m) noexcept
+{
+    std::swap(*this, m);
+}
+
+inline
+bool GpuMatND::isContinuous() const
+{
+    return (flags & Mat::CONTINUOUS_FLAG) != 0;
+}
+
+inline
+bool GpuMatND::isSubmatrix() const
+{
+    return (flags & Mat::SUBMATRIX_FLAG) != 0;
+}
+
+inline
+size_t GpuMatND::elemSize() const
+{
+    return CV_ELEM_SIZE(flags);
+}
+
+inline
+size_t GpuMatND::elemSize1() const
+{
+    return CV_ELEM_SIZE1(flags);
+}
+
+inline
+bool GpuMatND::empty() const
+{
+    return data == nullptr;
+}
+
+inline
+bool GpuMatND::external() const
+{
+    return !empty() && data_.use_count() == 0;
+}
+
+inline
+uchar* GpuMatND::getDevicePtr() const
+{
+    return data + offset;
+}
+
+inline
+size_t GpuMatND::total() const
+{
+    size_t p = 1;
+    for(auto s : size)
+        p *= s;
+    return p;
+}
+
+inline
+size_t GpuMatND::totalMemSize() const
+{
+    return size[0] * step[0];
+}
+
+inline
+int GpuMatND::type() const
+{
+    return CV_MAT_TYPE(flags);
+}
+
+//===================================================================================
+// HostMem
+//===================================================================================
+
+inline
+HostMem::HostMem(AllocType alloc_type_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
+{
+}
+
+inline
+HostMem::HostMem(const HostMem& m)
+    : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type)
+{
+    if( refcount )
+        CV_XADD(refcount, 1);
+}
+
+inline
+HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
+{
+    if (rows_ > 0 && cols_ > 0)
+        create(rows_, cols_, type_);
+}
+
+inline
+HostMem::HostMem(Size size_, int type_, AllocType alloc_type_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
+{
+    if (size_.height > 0 && size_.width > 0)
+        create(size_.height, size_.width, type_);
+}
+
+inline
+HostMem::HostMem(InputArray arr, AllocType alloc_type_)
+    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
+{
+    arr.getMat().copyTo(*this);
+}
+
+inline
+HostMem::~HostMem()
+{
+    release();
+}
+
+inline
+HostMem& HostMem::operator =(const HostMem& m)
+{
+    if (this != &m)
+    {
+        HostMem temp(m);
+        swap(temp);
+    }
+
+    return *this;
+}
+
+inline
+void HostMem::swap(HostMem& b)
+{
+    std::swap(flags, b.flags);
+    std::swap(rows, b.rows);
+    std::swap(cols, b.cols);
+    std::swap(step, b.step);
+    std::swap(data, b.data);
+    std::swap(datastart, b.datastart);
+    std::swap(dataend, b.dataend);
+    std::swap(refcount, b.refcount);
+    std::swap(alloc_type, b.alloc_type);
+}
+
+inline
+HostMem HostMem::clone() const
+{
+    HostMem m(size(), type(), alloc_type);
+    createMatHeader().copyTo(m);
+    return m;
+}
+
+inline
+void HostMem::create(Size size_, int type_)
+{
+    create(size_.height, size_.width, type_);
+}
+
+inline
+Mat HostMem::createMatHeader() const
+{
+    return Mat(size(), type(), data, step);
+}
+
+inline
+bool HostMem::isContinuous() const
+{
+    return (flags & Mat::CONTINUOUS_FLAG) != 0;
+}
+
+inline
+size_t HostMem::elemSize() const
+{
+    return CV_ELEM_SIZE(flags);
+}
+
+inline
+size_t HostMem::elemSize1() const
+{
+    return CV_ELEM_SIZE1(flags);
+}
+
+inline
+int HostMem::type() const
+{
+    return CV_MAT_TYPE(flags);
+}
+
+inline
+int HostMem::depth() const
+{
+    return CV_MAT_DEPTH(flags);
+}
+
+inline
+int HostMem::channels() const
+{
+    return CV_MAT_CN(flags);
+}
+
+inline
+size_t HostMem::step1() const
+{
+    return step / elemSize1();
+}
+
+inline
+Size HostMem::size() const
+{
+    return Size(cols, rows);
+}
+
+inline
+bool HostMem::empty() const
+{
+    return data == 0;
+}
+
+static inline
+void swap(HostMem& a, HostMem& b)
+{
+    a.swap(b);
+}
+
+//===================================================================================
+// Stream
+//===================================================================================
+
+inline
+Stream::Stream(const Ptr<Impl>& impl)
+    : impl_(impl)
+{
+}
+
+//===================================================================================
+// Event
+//===================================================================================
+
+inline
+Event::Event(const Ptr<Impl>& impl)
+    : impl_(impl)
+{
+}
+
+//===================================================================================
+// Initialization & Info
+//===================================================================================
+
+inline
+bool TargetArchs::has(int major, int minor)
+{
+    return hasPtx(major, minor) || hasBin(major, minor);
+}
+
+inline
+bool TargetArchs::hasEqualOrGreater(int major, int minor)
+{
+    return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor);
+}
+
+inline
+DeviceInfo::DeviceInfo()
+{
+    device_id_ = getDevice();
+}
+
+inline
+DeviceInfo::DeviceInfo(int device_id)
+{
+    CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() );
+    device_id_ = device_id;
+}
+
+inline
+int DeviceInfo::deviceID() const
+{
+    return device_id_;
+}
+
+inline
+size_t DeviceInfo::freeMemory() const
+{
+    size_t _totalMemory = 0, _freeMemory = 0;
+    queryMemory(_totalMemory, _freeMemory);
+    return _freeMemory;
+}
+
+inline
+size_t DeviceInfo::totalMemory() const
+{
+    size_t _totalMemory = 0, _freeMemory = 0;
+    queryMemory(_totalMemory, _freeMemory);
+    return _totalMemory;
+}
+
+inline
+bool DeviceInfo::supports(FeatureSet feature_set) const
+{
+    int version = majorVersion() * 10 + minorVersion();
+    return version >= feature_set;
+}
+
+
+}} // namespace cv { namespace cuda {
+
+//===================================================================================
+// Mat
+//===================================================================================
+
+namespace cv {
+
+inline
+Mat::Mat(const cuda::GpuMat& m)
+    : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows)
+{
+    m.download(*this);
+}
+
+}
+
+//! @endcond
+
+#endif // OPENCV_CORE_CUDAINL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/block.hpp b/3rdParty/include/opencv2/core/cuda/block.hpp
new file mode 100644
index 0000000..c277f0e
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/block.hpp
@@ -0,0 +1,211 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP
+#define OPENCV_CUDA_DEVICE_BLOCK_HPP
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    struct Block
+    {
+        static __device__ __forceinline__ unsigned int id()
+        {
+            return blockIdx.x;
+        }
+
+        static __device__ __forceinline__ unsigned int stride()
+        {
+            return blockDim.x * blockDim.y * blockDim.z;
+        }
+
+        static __device__ __forceinline__ void sync()
+        {
+            __syncthreads();
+        }
+
+        static __device__ __forceinline__ int flattenedThreadId()
+        {
+            return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
+        }
+
+        template<typename It, typename T>
+        static __device__ __forceinline__ void fill(It beg, It end, const T& value)
+        {
+            int STRIDE = stride();
+            It t = beg + flattenedThreadId();
+
+            for(; t < end; t += STRIDE)
+                *t = value;
+        }
+
+        template<typename OutIt, typename T>
+        static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
+        {
+            int STRIDE = stride();
+            int tid = flattenedThreadId();
+            value += tid;
+
+            for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
+                *t = value;
+        }
+
+        template<typename InIt, typename OutIt>
+        static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
+        {
+            int STRIDE = stride();
+            InIt  t = beg + flattenedThreadId();
+            OutIt o = out + (t - beg);
+
+            for(; t < end; t += STRIDE, o += STRIDE)
+                *o = *t;
+        }
+
+        template<typename InIt, typename OutIt, class UnOp>
+        static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op)
+        {
+            int STRIDE = stride();
+            InIt  t = beg + flattenedThreadId();
+            OutIt o = out + (t - beg);
+
+            for(; t < end; t += STRIDE, o += STRIDE)
+                *o = op(*t);
+        }
+
+        template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
+        static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
+        {
+            int STRIDE = stride();
+            InIt1 t1 = beg1 + flattenedThreadId();
+            InIt2 t2 = beg2 + flattenedThreadId();
+            OutIt o  = out + (t1 - beg1);
+
+            for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
+                *o = op(*t1, *t2);
+        }
+
+        template<int CTA_SIZE, typename T, class BinOp>
+        static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
+        {
+            int tid = flattenedThreadId();
+            T val =  buffer[tid];
+
+            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
+            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
+            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
+            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }
+
+            if (tid < 32)
+            {
+                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
+                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
+                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
+                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
+                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
+                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
+            }
+        }
+
+        template<int CTA_SIZE, typename T, class BinOp>
+        static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
+        {
+            int tid = flattenedThreadId();
+            T val =  buffer[tid] = init;
+            __syncthreads();
+
+            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
+            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
+            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
+            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }
+
+            if (tid < 32)
+            {
+                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
+                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
+                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
+                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
+                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
+                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
+            }
+            __syncthreads();
+            return buffer[0];
+        }
+
+        template <typename T, class BinOp>
+        static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
+        {
+            int ftid = flattenedThreadId();
+            int sft = stride();
+
+            if (sft < n)
+            {
+                for (unsigned int i = sft + ftid; i < n; i += sft)
+                    data[ftid] = op(data[ftid], data[i]);
+
+                __syncthreads();
+
+                n = sft;
+            }
+
+            while (n > 1)
+            {
+                unsigned int half = n/2;
+
+                if (ftid < half)
+                    data[ftid] = op(data[ftid], data[n - ftid - 1]);
+
+                __syncthreads();
+
+                n = n - half;
+            }
+        }
+    };
+}}}
+
+//! @endcond
+
+#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */
diff --git a/3rdParty/include/opencv2/core/cuda/border_interpolate.hpp b/3rdParty/include/opencv2/core/cuda/border_interpolate.hpp
new file mode 100644
index 0000000..874f705
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/border_interpolate.hpp
@@ -0,0 +1,722 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_BORDER_INTERPOLATE_HPP
+#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP
+
+#include "saturate_cast.hpp"
+#include "vec_traits.hpp"
+#include "vec_math.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    //////////////////////////////////////////////////////////////
+    // BrdConstant
+
+    template <typename D> struct BrdRowConstant
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits<D>::all(0)) : width(width_), val(val_) {}
+
+        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
+        {
+            return x >= 0 ? saturate_cast<D>(data[x]) : val;
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
+        {
+            return x < width ? saturate_cast<D>(data[x]) : val;
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
+        {
+            return (x >= 0 && x < width) ? saturate_cast<D>(data[x]) : val;
+        }
+
+        int width;
+        D val;
+    };
+
+    template <typename D> struct BrdColConstant
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits<D>::all(0)) : height(height_), val(val_) {}
+
+        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
+        {
+            return y >= 0 ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
+        {
+            return y < height ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
+        {
+            return (y >= 0 && y < height) ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
+        }
+
+        int height;
+        D val;
+    };
+
+    template <typename D> struct BrdConstant
+    {
+        typedef D result_type;
+
+        __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits<D>::all(0)) : height(height_), width(width_), val(val_)
+        {
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
+        {
+            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(((const T*)((const uchar*)data + y * step))[x]) : val;
+        }
+
+        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
+        {
+            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
+        }
+
+        int height;
+        int width;
+        D val;
+    };
+
+    //////////////////////////////////////////////////////////////
+    // BrdReplicate
+
+    template <typename D> struct BrdRowReplicate
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {}
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return ::max(x, 0);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return ::min(x, last_col);
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_low(idx_col_high(x));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_low(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_high(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col(x)]);
+        }
+
+        int last_col;
+    };
+
+    template <typename D> struct BrdColReplicate
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {}
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return ::max(y, 0);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return ::min(y, last_row);
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_low(idx_row_high(y));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const T*)((const char*)data + idx_row_low(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const T*)((const char*)data + idx_row_high(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const T*)((const char*)data + idx_row(y) * step));
+        }
+
+        int last_row;
+    };
+
+    template <typename D> struct BrdReplicate
+    {
+        typedef D result_type;
+
+        __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return ::max(y, 0);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return ::min(y, last_row);
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_low(idx_row_high(y));
+        }
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return ::max(x, 0);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return ::min(x, last_col);
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_low(idx_col_high(x));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
+        }
+
+        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
+        {
+            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
+        }
+
+        int last_row;
+        int last_col;
+    };
+
+    //////////////////////////////////////////////////////////////
+    // BrdReflect101
+
+    template <typename D> struct BrdRowReflect101
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {}
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return ::abs(x) % (last_col + 1);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_low(idx_col_high(x));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_low(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_high(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col(x)]);
+        }
+
+        int last_col;
+    };
+
+    template <typename D> struct BrdColReflect101
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {}
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return ::abs(y) % (last_row + 1);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_low(idx_row_high(y));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
+        }
+
+        int last_row;
+    };
+
+    template <typename D> struct BrdReflect101
+    {
+        typedef D result_type;
+
+        __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return ::abs(y) % (last_row + 1);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_low(idx_row_high(y));
+        }
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return ::abs(x) % (last_col + 1);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_low(idx_col_high(x));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
+        }
+
+        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
+        {
+            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
+        }
+
+        int last_row;
+        int last_col;
+    };
+
+    //////////////////////////////////////////////////////////////
+    // BrdReflect
+
+    template <typename D> struct BrdRowReflect
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {}
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return (::abs(x) - (x < 0)) % (last_col + 1);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1);
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_high(::abs(x) - (x < 0));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_low(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_high(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col(x)]);
+        }
+
+        int last_col;
+    };
+
+    template <typename D> struct BrdColReflect
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {}
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return (::abs(y) - (y < 0)) % (last_row + 1);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1);
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_high(::abs(y) - (y < 0));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
+        }
+
+        int last_row;
+    };
+
+    template <typename D> struct BrdReflect
+    {
+        typedef D result_type;
+
+        __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return (::abs(y) - (y < 0)) % (last_row + 1);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/;
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_low(idx_row_high(y));
+        }
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return (::abs(x) - (x < 0)) % (last_col + 1);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return (last_col - ::abs(last_col - x) + (x > last_col));
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_low(idx_col_high(x));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
+        }
+
+        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
+        {
+            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
+        }
+
+        int last_row;
+        int last_col;
+    };
+
+    //////////////////////////////////////////////////////////////
+    // BrdWrap
+
+    template <typename D> struct BrdRowWrap
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {}
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return (x < width) * x + (x >= width) * (x % width);
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_high(idx_col_low(x));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_low(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col_high(x)]);
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
+        {
+            return saturate_cast<D>(data[idx_col(x)]);
+        }
+
+        int width;
+    };
+
+    template <typename D> struct BrdColWrap
+    {
+        typedef D result_type;
+
+        explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {}
+        template <typename U> __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {}
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return (y < height) * y + (y >= height) * (y % height);
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_high(idx_row_low(y));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
+        }
+
+        int height;
+    };
+
+    template <typename D> struct BrdWrap
+    {
+        typedef D result_type;
+
+        __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) :
+            height(height_), width(width_)
+        {
+        }
+        template <typename U>
+        __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) :
+            height(height_), width(width_)
+        {
+        }
+
+        __device__ __forceinline__ int idx_row_low(int y) const
+        {
+            return (y >= 0) ? y : (y - ((y - height + 1) / height) * height);
+        }
+
+        __device__ __forceinline__ int idx_row_high(int y) const
+        {
+            return (y < height) ? y : (y % height);
+        }
+
+        __device__ __forceinline__ int idx_row(int y) const
+        {
+            return idx_row_high(idx_row_low(y));
+        }
+
+        __device__ __forceinline__ int idx_col_low(int x) const
+        {
+            return (x >= 0) ? x : (x - ((x - width + 1) / width) * width);
+        }
+
+        __device__ __forceinline__ int idx_col_high(int x) const
+        {
+            return (x < width) ? x : (x % width);
+        }
+
+        __device__ __forceinline__ int idx_col(int x) const
+        {
+            return idx_col_high(idx_col_low(x));
+        }
+
+        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
+        {
+            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
+        }
+
+        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
+        {
+            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
+        }
+
+        int height;
+        int width;
+    };
+
+    //////////////////////////////////////////////////////////////
+    // BorderReader
+
+    template <typename Ptr2D, typename B> struct BorderReader
+    {
+        typedef typename B::result_type elem_type;
+        typedef typename Ptr2D::index_type index_type;
+
+        __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {}
+
+        __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const
+        {
+            return b.at(y, x, ptr);
+        }
+
+        Ptr2D ptr;
+        B b;
+    };
+
+    // under win32 there is some bug with templated types that passed as kernel parameters
+    // with this specialization all works fine
+    template <typename Ptr2D, typename D> struct BorderReader< Ptr2D, BrdConstant<D> >
+    {
+        typedef typename BrdConstant<D>::result_type elem_type;
+        typedef typename Ptr2D::index_type index_type;
+
+        __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant<D>& b) :
+            src(src_), height(b.height), width(b.width), val(b.val)
+        {
+        }
+
+        __device__ __forceinline__ D operator ()(index_type y, index_type x) const
+        {
+            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
+        }
+
+        Ptr2D src;
+        int height;
+        int width;
+        D val;
+    };
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_BORDER_INTERPOLATE_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/color.hpp b/3rdParty/include/opencv2/core/cuda/color.hpp
new file mode 100644
index 0000000..dcce280
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/color.hpp
@@ -0,0 +1,309 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_COLOR_HPP
+#define OPENCV_CUDA_COLOR_HPP
+
+#include "detail/color_detail.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements
+    // template <typename T> class ColorSpace1_to_ColorSpace2_traits
+    // {
+    //     typedef ... functor_type;
+    //     static __host__ __device__ functor_type create_functor();
+    // };
+
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3)
+    OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4)
+
+    #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5)
+    OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6)
+
+    #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5)
+    OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2)
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2)
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0)
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0)
+    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0)
+
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0)
+
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0)
+    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0)
+
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0)
+    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS
+
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0)
+
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0)
+    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0)
+
+    #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_COLOR_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/common.hpp b/3rdParty/include/opencv2/core/cuda/common.hpp
new file mode 100644
index 0000000..80b2ff0
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/common.hpp
@@ -0,0 +1,123 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_COMMON_HPP
+#define OPENCV_CUDA_COMMON_HPP
+
+#include <cuda_runtime.h>
+#include "opencv2/core/cuda_types.hpp"
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/base.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+#ifndef CV_PI_F
+    #ifndef CV_PI
+        #define CV_PI_F 3.14159265f
+    #else
+        #define CV_PI_F ((float)CV_PI)
+    #endif
+#endif
+
+namespace cv { namespace cuda {
+    static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func)
+    {
+        if (cudaSuccess != err)
+            cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line);
+    }
+}}
+
+#ifndef cudaSafeCall
+    #define cudaSafeCall(expr)  cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func)
+#endif
+
+namespace cv { namespace cuda
+{
+    template <typename T> static inline bool isAligned(const T* ptr, size_t size)
+    {
+        return reinterpret_cast<size_t>(ptr) % size == 0;
+    }
+
+    static inline bool isAligned(size_t step, size_t size)
+    {
+        return step % size == 0;
+    }
+}}
+
+namespace cv { namespace cuda
+{
+    namespace device
+    {
+        __host__ __device__ __forceinline__ int divUp(int total, int grain)
+        {
+            return (total + grain - 1) / grain;
+        }
+
+        template<class T> inline void bindTexture(const textureReference* tex, const PtrStepSz<T>& img)
+        {
+            cudaChannelFormatDesc desc = cudaCreateChannelDesc<T>();
+            cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) );
+        }
+
+        template<class T> inline void createTextureObjectPitch2D(cudaTextureObject_t* tex, PtrStepSz<T>& img, const cudaTextureDesc& texDesc)
+        {
+            cudaResourceDesc resDesc;
+            memset(&resDesc, 0, sizeof(resDesc));
+            resDesc.resType = cudaResourceTypePitch2D;
+            resDesc.res.pitch2D.devPtr = static_cast<void*>(img.ptr());
+            resDesc.res.pitch2D.height = img.rows;
+            resDesc.res.pitch2D.width = img.cols;
+            resDesc.res.pitch2D.pitchInBytes = img.step;
+            resDesc.res.pitch2D.desc = cudaCreateChannelDesc<T>();
+
+            cudaSafeCall( cudaCreateTextureObject(tex, &resDesc, &texDesc, NULL) );
+        }
+    }
+}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_COMMON_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/datamov_utils.hpp b/3rdParty/include/opencv2/core/cuda/datamov_utils.hpp
new file mode 100644
index 0000000..6820d0f
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/datamov_utils.hpp
@@ -0,0 +1,113 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_DATAMOV_UTILS_HPP
+#define OPENCV_CUDA_DATAMOV_UTILS_HPP
+
+#include "common.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200
+
+        // for Fermi memory space is detected automatically
+        template <typename T> struct ForceGlob
+        {
+            __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val)  { val = ptr[offset];  }
+        };
+
+    #else // __CUDA_ARCH__ >= 200
+
+        #if defined(_WIN64) || defined(__LP64__)
+            // 64-bit register modifier for inlined asm
+            #define OPENCV_CUDA_ASM_PTR "l"
+        #else
+            // 32-bit register modifier for inlined asm
+            #define OPENCV_CUDA_ASM_PTR "r"
+        #endif
+
+        template<class T> struct ForceGlob;
+
+        #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \
+            template <> struct ForceGlob<base_type> \
+            { \
+                __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
+                { \
+                    asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \
+                } \
+            };
+
+        #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \
+            template <> struct ForceGlob<base_type> \
+            { \
+                __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
+                { \
+                    asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast<uint*>(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \
+                } \
+            };
+
+            OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar,  u8)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar,  s8)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char,   b8)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB  (ushort, u16, h)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB  (short,  s16, h)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB  (uint,   u32, r)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB  (int,    s32, r)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB  (float,  f32, f)
+            OPENCV_CUDA_DEFINE_FORCE_GLOB  (double, f64, d)
+
+        #undef OPENCV_CUDA_DEFINE_FORCE_GLOB
+        #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B
+        #undef OPENCV_CUDA_ASM_PTR
+
+    #endif // __CUDA_ARCH__ >= 200
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_DATAMOV_UTILS_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/detail/color_detail.hpp b/3rdParty/include/opencv2/core/cuda/detail/color_detail.hpp
new file mode 100644
index 0000000..f4b4796
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/detail/color_detail.hpp
@@ -0,0 +1,2018 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_COLOR_DETAIL_HPP
+#define OPENCV_CUDA_COLOR_DETAIL_HPP
+
+#include "../common.hpp"
+#include "../vec_traits.hpp"
+#include "../saturate_cast.hpp"
+#include "../limits.hpp"
+#include "../functional.hpp"
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    #ifndef CV_DESCALE
+        #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n))
+    #endif
+
+    namespace color_detail
+    {
+        template<typename T> struct ColorChannel
+        {
+            typedef float worktype_f;
+            static __device__ __forceinline__ T max() { return numeric_limits<T>::max(); }
+            static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); }
+        };
+
+        template<> struct ColorChannel<float>
+        {
+            typedef float worktype_f;
+            static __device__ __forceinline__ float max() { return 1.f; }
+            static __device__ __forceinline__ float half() { return 0.5f; }
+        };
+
+        template <typename T> static __device__ __forceinline__ void setAlpha(typename TypeVec<T, 3>::vec_type& vec, T val)
+        {
+        }
+
+        template <typename T> static __device__ __forceinline__ void setAlpha(typename TypeVec<T, 4>::vec_type& vec, T val)
+        {
+            vec.w = val;
+        }
+
+        template <typename T> static __device__ __forceinline__ T getAlpha(const typename TypeVec<T, 3>::vec_type& vec)
+        {
+            return ColorChannel<T>::max();
+        }
+
+        template <typename T> static __device__ __forceinline__ T getAlpha(const typename TypeVec<T, 4>::vec_type& vec)
+        {
+            return vec.w;
+        }
+
+        //constants for conversion from/to RGB and Gray, YUV, YCrCb according to BT.601
+        constexpr float B2YF = 0.114f;
+        constexpr float G2YF = 0.587f;
+        constexpr float R2YF = 0.299f;
+
+        //to YCbCr
+        constexpr float YCBF = 0.564f; // == 1/2/(1-B2YF)
+        constexpr float YCRF = 0.713f; // == 1/2/(1-R2YF)
+        const     int   YCBI = 9241;  // == YCBF*16384
+        const     int   YCRI = 11682; // == YCRF*16384
+        //to YUV
+        constexpr float B2UF = 0.492f;
+        constexpr float R2VF = 0.877f;
+        const     int   B2UI = 8061;  // == B2UF*16384
+        const     int   R2VI = 14369; // == R2VF*16384
+        //from YUV
+        constexpr float U2BF = 2.032f;
+        constexpr float U2GF = -0.395f;
+        constexpr float V2GF = -0.581f;
+        constexpr float V2RF = 1.140f;
+        const     int   U2BI = 33292;
+        const     int   U2GI = -6472;
+        const     int   V2GI = -9519;
+        const     int   V2RI = 18678;
+        //from YCrCb
+        constexpr float CB2BF = 1.773f;
+        constexpr float CB2GF = -0.344f;
+        constexpr float CR2GF = -0.714f;
+        constexpr float CR2RF = 1.403f;
+        const     int   CB2BI = 29049;
+        const     int   CB2GI = -5636;
+        const     int   CR2GI = -11698;
+        const     int   CR2RI = 22987;
+
+        enum
+        {
+            yuv_shift  = 14,
+            xyz_shift  = 12,
+            gray_shift = 15,
+            R2Y        = 4899,
+            G2Y        = 9617,
+            B2Y        = 1868,
+            RY15 =  9798, // == R2YF*32768 + 0.5
+            GY15 = 19235, // == G2YF*32768 + 0.5
+            BY15 =  3735, // == B2YF*32768 + 0.5
+            BLOCK_SIZE = 256
+        };
+    }
+
+////////////////// Various 3/4-channel to 3/4-channel RGB transformations /////////////////
+
+    namespace color_detail
+    {
+        template <typename T, int scn, int dcn, int bidx> struct RGB2RGB
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                dst.x = (&src.x)[bidx];
+                dst.y = src.y;
+                dst.z = (&src.x)[bidx^2];
+                setAlpha(dst, getAlpha<T>(src));
+
+                return dst;
+            }
+
+            __host__ __device__ __forceinline__ RGB2RGB() {}
+            __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {}
+        };
+
+        template <> struct RGB2RGB<uchar, 4, 4, 2> : unary_function<uint, uint>
+        {
+            __device__ uint operator()(uint src) const
+            {
+                uint dst = 0;
+
+                dst |= (0xffu & (src >> 16));
+                dst |= (0xffu & (src >> 8)) << 8;
+                dst |= (0xffu & (src)) << 16;
+                dst |= (0xffu & (src >> 24)) << 24;
+
+                return dst;
+            }
+
+            __host__ __device__ __forceinline__ RGB2RGB() {}
+            __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2RGB<T, scn, dcn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB //////////
+
+    namespace color_detail
+    {
+        template <int green_bits, int bidx> struct RGB2RGB5x5Converter;
+        template<int bidx> struct RGB2RGB5x5Converter<6, bidx>
+        {
+            static __device__ __forceinline__ ushort cvt(const uchar3& src)
+            {
+                return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8));
+            }
+
+            static __device__ __forceinline__ ushort cvt(uint src)
+            {
+                uint b = 0xffu & (src >> (bidx * 8));
+                uint g = 0xffu & (src >> 8);
+                uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
+                return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8));
+            }
+        };
+
+        template<int bidx> struct RGB2RGB5x5Converter<5, bidx>
+        {
+            static __device__ __forceinline__ ushort cvt(const uchar3& src)
+            {
+                return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7));
+            }
+
+            static __device__ __forceinline__ ushort cvt(uint src)
+            {
+                uint b = 0xffu & (src >> (bidx * 8));
+                uint g = 0xffu & (src >> 8);
+                uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
+                uint a = 0xffu & (src >> 24);
+                return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000));
+            }
+        };
+
+        template<int scn, int bidx, int green_bits> struct RGB2RGB5x5;
+
+        template<int bidx, int green_bits> struct RGB2RGB5x5<3, bidx,green_bits> : unary_function<uchar3, ushort>
+        {
+            __device__ __forceinline__ ushort operator()(const uchar3& src) const
+            {
+                return RGB2RGB5x5Converter<green_bits, bidx>::cvt(src);
+            }
+
+            __host__ __device__ __forceinline__ RGB2RGB5x5() {}
+            __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {}
+        };
+
+        template<int bidx, int green_bits> struct RGB2RGB5x5<4, bidx,green_bits> : unary_function<uint, ushort>
+        {
+            __device__ __forceinline__ ushort operator()(uint src) const
+            {
+                return RGB2RGB5x5Converter<green_bits, bidx>::cvt(src);
+            }
+
+            __host__ __device__ __forceinline__ RGB2RGB5x5() {}
+            __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \
+    struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2RGB5x5<scn, bidx, green_bits> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        template <int green_bits, int bidx> struct RGB5x52RGBConverter;
+
+        template <int bidx> struct RGB5x52RGBConverter<5, bidx>
+        {
+            static __device__ __forceinline__ void cvt(uint src, uchar3& dst)
+            {
+                (&dst.x)[bidx] = src << 3;
+                dst.y = (src >> 2) & ~7;
+                (&dst.x)[bidx ^ 2] = (src >> 7) & ~7;
+            }
+
+            static __device__ __forceinline__ void cvt(uint src, uint& dst)
+            {
+                dst = 0;
+
+                dst |= (0xffu & (src << 3)) << (bidx * 8);
+                dst |= (0xffu & ((src >> 2) & ~7)) << 8;
+                dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8);
+                dst |= ((src & 0x8000) * 0xffu) << 24;
+            }
+        };
+
+        template <int bidx> struct RGB5x52RGBConverter<6, bidx>
+        {
+            static __device__ __forceinline__ void cvt(uint src, uchar3& dst)
+            {
+                (&dst.x)[bidx] = src << 3;
+                dst.y = (src >> 3) & ~3;
+                (&dst.x)[bidx ^ 2] = (src >> 8) & ~7;
+            }
+
+            static __device__ __forceinline__ void cvt(uint src, uint& dst)
+            {
+                dst = 0xffu << 24;
+
+                dst |= (0xffu & (src << 3)) << (bidx * 8);
+                dst |= (0xffu &((src >> 3) & ~3)) << 8;
+                dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8);
+            }
+        };
+
+        template <int dcn, int bidx, int green_bits> struct RGB5x52RGB;
+
+        template <int bidx, int green_bits> struct RGB5x52RGB<3, bidx, green_bits> : unary_function<ushort, uchar3>
+        {
+            __device__ __forceinline__ uchar3 operator()(ushort src) const
+            {
+                uchar3 dst;
+                RGB5x52RGBConverter<green_bits, bidx>::cvt(src, dst);
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB5x52RGB() {}
+            __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {}
+
+        };
+
+        template <int bidx, int green_bits> struct RGB5x52RGB<4, bidx, green_bits> : unary_function<ushort, uint>
+        {
+            __device__ __forceinline__ uint operator()(ushort src) const
+            {
+                uint dst;
+                RGB5x52RGBConverter<green_bits, bidx>::cvt(src, dst);
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB5x52RGB() {}
+            __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \
+    struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB5x52RGB<dcn, bidx, green_bits> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+///////////////////////////////// Grayscale to Color ////////////////////////////////
+
+    namespace color_detail
+    {
+        template <typename T, int dcn> struct Gray2RGB : unary_function<T, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(T src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                dst.z = dst.y = dst.x = src;
+                setAlpha(dst, ColorChannel<T>::max());
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ Gray2RGB() {}
+            __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {}
+        };
+
+        template <> struct Gray2RGB<uchar, 4> : unary_function<uchar, uint>
+        {
+            __device__ __forceinline__ uint operator()(uint src) const
+            {
+                uint dst = 0xffu << 24;
+
+                dst |= src;
+                dst |= src << 8;
+                dst |= src << 16;
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ Gray2RGB() {}
+            __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::Gray2RGB<T, dcn> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        template <int green_bits> struct Gray2RGB5x5Converter;
+        template<> struct Gray2RGB5x5Converter<6>
+        {
+            static __device__ __forceinline__ ushort cvt(uint t)
+            {
+                return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8));
+            }
+        };
+
+        template<> struct Gray2RGB5x5Converter<5>
+        {
+            static __device__ __forceinline__ ushort cvt(uint t)
+            {
+                t >>= 3;
+                return (ushort)(t | (t << 5) | (t << 10));
+            }
+        };
+
+        template<int green_bits> struct Gray2RGB5x5 : unary_function<uchar, ushort>
+        {
+            __device__ __forceinline__ ushort operator()(uint src) const
+            {
+                return Gray2RGB5x5Converter<green_bits>::cvt(src);
+            }
+
+            __host__ __device__ __forceinline__ Gray2RGB5x5() {}
+            __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \
+    struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::Gray2RGB5x5<green_bits> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+///////////////////////////////// Color to Grayscale ////////////////////////////////
+
+    namespace color_detail
+    {
+        template <int green_bits> struct RGB5x52GrayConverter;
+        template <> struct RGB5x52GrayConverter<6>
+        {
+            static __device__ __forceinline__ uchar cvt(uint t)
+            {
+                return (uchar)CV_DESCALE(((t << 3) & 0xf8) * BY15 + ((t >> 3) & 0xfc) * GY15 + ((t >> 8) & 0xf8) * RY15, gray_shift);
+            }
+        };
+
+        template <> struct RGB5x52GrayConverter<5>
+        {
+            static __device__ __forceinline__ uchar cvt(uint t)
+            {
+                return (uchar)CV_DESCALE(((t << 3) & 0xf8) * BY15 + ((t >> 2) & 0xf8) * GY15 + ((t >> 7) & 0xf8) * RY15, gray_shift);
+            }
+        };
+
+        template<int green_bits> struct RGB5x52Gray : unary_function<ushort, uchar>
+        {
+            __device__ __forceinline__ uchar operator()(uint src) const
+            {
+                return RGB5x52GrayConverter<green_bits>::cvt(src);
+            }
+            __host__ __device__ __forceinline__ RGB5x52Gray() {}
+            __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \
+    struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB5x52Gray<green_bits> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        template <int bidx, typename T> static __device__ __forceinline__ T RGB2GrayConvert(const T* src)
+        {
+            return (T)CV_DESCALE((unsigned)(src[bidx] * BY15 + src[1] * GY15 + src[bidx^2] * RY15), gray_shift);
+        }
+
+        template <int bidx> static __device__ __forceinline__ uchar RGB2GrayConvert(uint src)
+        {
+            uint b = 0xffu & (src >> (bidx * 8));
+            uint g = 0xffu & (src >> 8);
+            uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
+            return CV_DESCALE((uint)(b * BY15 + g * GY15 + r * RY15), gray_shift);
+        }
+
+        template <int bidx> static __device__ __forceinline__ float RGB2GrayConvert(const float* src)
+        {
+            return src[bidx] * B2YF + src[1] * G2YF + src[bidx^2] * R2YF;
+        }
+
+        template <typename T, int scn, int bidx> struct RGB2Gray : unary_function<typename TypeVec<T, scn>::vec_type, T>
+        {
+            __device__ __forceinline__ T operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                return RGB2GrayConvert<bidx>(&src.x);
+            }
+            __host__ __device__ __forceinline__ RGB2Gray() {}
+            __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {}
+        };
+
+        template <int bidx> struct RGB2Gray<uchar, 4, bidx> : unary_function<uint, uchar>
+        {
+            __device__ __forceinline__ uchar operator()(uint src) const
+            {
+                return RGB2GrayConvert<bidx>(src);
+            }
+            __host__ __device__ __forceinline__ RGB2Gray() {}
+            __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2Gray<T, scn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+///////////////////////////////////// RGB <-> YUV //////////////////////////////////////
+
+    namespace color_detail
+    {
+        __constant__ float c_RGB2YUVCoeffs_f[5] = { B2YF, G2YF, R2YF, B2UF, R2VF };
+        __constant__ int   c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, B2UI, R2VI };
+
+        template <int bidx, typename T, typename D> static __device__ void RGB2YUVConvert(const T* src, D& dst)
+        {
+            const int delta = ColorChannel<T>::half() * (1 << yuv_shift);
+
+            const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift);
+            const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift);
+            const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift);
+
+            dst.x = saturate_cast<T>(Y);
+            dst.y = saturate_cast<T>(Cr);
+            dst.z = saturate_cast<T>(Cb);
+        }
+
+        template <int bidx, typename D> static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst)
+        {
+            dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx];
+            dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel<float>::half();
+            dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel<float>::half();
+        }
+
+        template <typename T, int scn, int dcn, int bidx> struct RGB2YUV
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+                RGB2YUVConvert<bidx>(&src.x, dst);
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2YUV() {}
+            __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2YUV<T, scn, dcn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        __constant__ float c_YUV2RGBCoeffs_f[5] = { U2BF, U2GF, V2GF, V2RF };
+        __constant__ int   c_YUV2RGBCoeffs_i[5] = { U2BI, U2GI, V2GI, V2RI };
+
+        template <int bidx, typename T, typename D> static __device__ void YUV2RGBConvert(const T& src, D* dst)
+        {
+            const int b = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift);
+
+            const int g = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[2]
+                                             + (src.y - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift);
+
+            const int r = src.x + CV_DESCALE((src.y - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift);
+
+            dst[bidx] = saturate_cast<D>(b);
+            dst[1] = saturate_cast<D>(g);
+            dst[bidx^2] = saturate_cast<D>(r);
+        }
+
+        template <int bidx> static __device__ uint YUV2RGBConvert(uint src)
+        {
+            const int x = 0xff & (src);
+            const int y = 0xff & (src >> 8);
+            const int z = 0xff & (src >> 16);
+
+            const int b = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift);
+
+            const int g = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[2]
+                                         + (y - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift);
+
+            const int r = x + CV_DESCALE((y - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift);
+
+            uint dst = 0xffu << 24;
+
+            dst |= saturate_cast<uchar>(b) << (bidx * 8);
+            dst |= saturate_cast<uchar>(g) << 8;
+            dst |= saturate_cast<uchar>(r) << ((bidx ^ 2) * 8);
+
+            return dst;
+        }
+
+        template <int bidx, typename T> static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst)
+        {
+            dst[bidx] = src.x + (src.z - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[3];
+
+            dst[1] = src.x + (src.z - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[2]
+                     + (src.y - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[1];
+
+            dst[bidx^2] = src.x + (src.y - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[0];
+        }
+
+        template <typename T, int scn, int dcn, int bidx> struct YUV2RGB
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                YUV2RGBConvert<bidx>(src, &dst.x);
+                setAlpha(dst, ColorChannel<T>::max());
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ YUV2RGB() {}
+            __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {}
+        };
+
+        template <int bidx> struct YUV2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator ()(uint src) const
+            {
+                return YUV2RGBConvert<bidx>(src);
+            }
+            __host__ __device__ __forceinline__ YUV2RGB() {}
+            __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::YUV2RGB<T, scn, dcn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+///////////////////////////////////// RGB <-> YCrCb //////////////////////////////////////
+
+    namespace color_detail
+    {
+        __constant__ float c_RGB2YCrCbCoeffs_f[5] = {R2YF, G2YF, B2YF, YCRF, YCBF};
+        __constant__ int   c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, YCRI, YCBI};
+
+        template <int bidx, typename T, typename D> static __device__ void RGB2YCrCbConvert(const T* src, D& dst)
+        {
+            const int delta = ColorChannel<T>::half() * (1 << yuv_shift);
+
+            const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift);
+            const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift);
+            const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift);
+
+            dst.x = saturate_cast<T>(Y);
+            dst.y = saturate_cast<T>(Cr);
+            dst.z = saturate_cast<T>(Cb);
+        }
+
+        template <int bidx> static __device__ uint RGB2YCrCbConvert(uint src)
+        {
+            const int delta = ColorChannel<uchar>::half() * (1 << yuv_shift);
+
+            const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift);
+            const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift);
+            const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift);
+
+            uint dst = 0;
+
+            dst |= saturate_cast<uchar>(Y);
+            dst |= saturate_cast<uchar>(Cr) << 8;
+            dst |= saturate_cast<uchar>(Cb) << 16;
+
+            return dst;
+        }
+
+        template <int bidx, typename D> static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst)
+        {
+            dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx];
+            dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel<float>::half();
+            dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel<float>::half();
+        }
+
+        template <typename T, int scn, int dcn, int bidx> struct RGB2YCrCb
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+                RGB2YCrCbConvert<bidx>(&src.x, dst);
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2YCrCb() {}
+            __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {}
+        };
+
+        template <int bidx> struct RGB2YCrCb<uchar, 4, 4, bidx> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator ()(uint src) const
+            {
+                return RGB2YCrCbConvert<bidx>(src);
+            }
+
+            __host__ __device__ __forceinline__ RGB2YCrCb() {}
+            __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2YCrCb<T, scn, dcn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        __constant__ float c_YCrCb2RGBCoeffs_f[5] = {CR2RF, CR2GF, CB2GF, CB2BF};
+        __constant__ int   c_YCrCb2RGBCoeffs_i[5] = {CR2RI, CR2GI, CB2GI, CB2BI};
+
+        template <int bidx, typename T, typename D> static __device__ void YCrCb2RGBConvert(const T& src, D* dst)
+        {
+            const int b = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift);
+            const int g = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift);
+            const int r = src.x + CV_DESCALE((src.y - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift);
+
+            dst[bidx] = saturate_cast<D>(b);
+            dst[1] = saturate_cast<D>(g);
+            dst[bidx^2] = saturate_cast<D>(r);
+        }
+
+        template <int bidx> static __device__ uint YCrCb2RGBConvert(uint src)
+        {
+            const int x = 0xff & (src);
+            const int y = 0xff & (src >> 8);
+            const int z = 0xff & (src >> 16);
+
+            const int b = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift);
+            const int g = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift);
+            const int r = x + CV_DESCALE((y - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift);
+
+            uint dst = 0xffu << 24;
+
+            dst |= saturate_cast<uchar>(b) << (bidx * 8);
+            dst |= saturate_cast<uchar>(g) << 8;
+            dst |= saturate_cast<uchar>(r) << ((bidx ^ 2) * 8);
+
+            return dst;
+        }
+
+        template <int bidx, typename T> __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst)
+        {
+            dst[bidx] = src.x + (src.z - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[3];
+            dst[1] = src.x + (src.z - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[1];
+            dst[bidx^2] = src.x + (src.y - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[0];
+        }
+
+        template <typename T, int scn, int dcn, int bidx> struct YCrCb2RGB
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                YCrCb2RGBConvert<bidx>(src, &dst.x);
+                setAlpha(dst, ColorChannel<T>::max());
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ YCrCb2RGB() {}
+            __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {}
+        };
+
+        template <int bidx> struct YCrCb2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator ()(uint src) const
+            {
+                return YCrCb2RGBConvert<bidx>(src);
+            }
+            __host__ __device__ __forceinline__ YCrCb2RGB() {}
+            __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::YCrCb2RGB<T, scn, dcn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+////////////////////////////////////// RGB <-> XYZ ///////////////////////////////////////
+
+    namespace color_detail
+    {
+        __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f };
+        __constant__ int   c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 };
+
+        template <int bidx, typename T, typename D> static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst)
+        {
+            dst.z = saturate_cast<T>(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift));
+            dst.x = saturate_cast<T>(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift));
+            dst.y = saturate_cast<T>(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift));
+        }
+
+        template <int bidx> static __device__ __forceinline__ uint RGB2XYZConvert(uint src)
+        {
+            const uint b = 0xffu & (src >> (bidx * 8));
+            const uint g = 0xffu & (src >> 8);
+            const uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
+
+            const uint x = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift));
+            const uint y = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift));
+            const uint z = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift));
+
+            uint dst = 0;
+
+            dst |= x;
+            dst |= y << 8;
+            dst |= z << 16;
+
+            return dst;
+        }
+
+        template <int bidx, typename D> static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst)
+        {
+            dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2];
+            dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5];
+            dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8];
+        }
+
+        template <typename T, int scn, int dcn, int bidx> struct RGB2XYZ
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                RGB2XYZConvert<bidx>(&src.x, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2XYZ() {}
+            __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {}
+        };
+
+        template <int bidx> struct RGB2XYZ<uchar, 4, 4, bidx> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator()(uint src) const
+            {
+                return RGB2XYZConvert<bidx>(src);
+            }
+            __host__ __device__ __forceinline__ RGB2XYZ() {}
+            __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2XYZ<T, scn, dcn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f };
+        __constant__ int   c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 };
+
+        template <int bidx, typename T, typename D> static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst)
+        {
+            dst[bidx^2] = saturate_cast<D>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift));
+            dst[1]      = saturate_cast<D>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift));
+            dst[bidx]   = saturate_cast<D>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift));
+        }
+
+        template <int bidx> static __device__ __forceinline__ uint XYZ2RGBConvert(uint src)
+        {
+            const int x = 0xff & src;
+            const int y = 0xff & (src >> 8);
+            const int z = 0xff & (src >> 16);
+
+            const uint r = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift));
+            const uint g = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift));
+            const uint b = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift));
+
+            uint dst = 0xffu << 24;
+
+            dst |= b << (bidx * 8);
+            dst |= g << 8;
+            dst |= r << ((bidx ^ 2) * 8);
+
+            return dst;
+        }
+
+        template <int bidx, typename T> static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst)
+        {
+            dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2];
+            dst[1]      = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5];
+            dst[bidx]   = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8];
+        }
+
+        template <typename T, int scn, int dcn, int bidx> struct XYZ2RGB
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                XYZ2RGBConvert<bidx>(src, &dst.x);
+                setAlpha(dst, ColorChannel<T>::max());
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ XYZ2RGB() {}
+            __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {}
+        };
+
+        template <int bidx> struct XYZ2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator()(uint src) const
+            {
+                return XYZ2RGBConvert<bidx>(src);
+            }
+            __host__ __device__ __forceinline__ XYZ2RGB() {}
+            __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::XYZ2RGB<T, scn, dcn, bidx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+////////////////////////////////////// RGB <-> HSV ///////////////////////////////////////
+
+    namespace color_detail
+    {
+        __constant__ int c_HsvDivTable   [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096};
+        __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482};
+        __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685};
+
+        template <int bidx, int hr, typename D> static __device__ void RGB2HSVConvert(const uchar* src, D& dst)
+        {
+            const int hsv_shift = 12;
+            const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256;
+
+            int b = src[bidx], g = src[1], r = src[bidx^2];
+            int h, s, v = b;
+            int vmin = b, diff;
+            int vr, vg;
+
+            v = ::max(v, g);
+            v = ::max(v, r);
+            vmin = ::min(vmin, g);
+            vmin = ::min(vmin, r);
+
+            diff = v - vmin;
+            vr = (v == r) * -1;
+            vg = (v == g) * -1;
+
+            s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift;
+            h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff))));
+            h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift;
+            h += (h < 0) * hr;
+
+            dst.x = saturate_cast<uchar>(h);
+            dst.y = (uchar)s;
+            dst.z = (uchar)v;
+        }
+
+        template <int bidx, int hr> static __device__ uint RGB2HSVConvert(uint src)
+        {
+            const int hsv_shift = 12;
+            const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256;
+
+            const int b = 0xff & (src >> (bidx * 8));
+            const int g = 0xff & (src >> 8);
+            const int r = 0xff & (src >> ((bidx ^ 2) * 8));
+
+            int h, s, v = b;
+            int vmin = b, diff;
+            int vr, vg;
+
+            v = ::max(v, g);
+            v = ::max(v, r);
+            vmin = ::min(vmin, g);
+            vmin = ::min(vmin, r);
+
+            diff = v - vmin;
+            vr = (v == r) * -1;
+            vg = (v == g) * -1;
+
+            s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift;
+            h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff))));
+            h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift;
+            h += (h < 0) * hr;
+
+            uint dst = 0;
+
+            dst |= saturate_cast<uchar>(h);
+            dst |= (0xffu & s) << 8;
+            dst |= (0xffu & v) << 16;
+
+            return dst;
+        }
+
+        template <int bidx, int hr, typename D> static __device__ void RGB2HSVConvert(const float* src, D& dst)
+        {
+            const float hscale = hr * (1.f / 360.f);
+
+            float b = src[bidx], g = src[1], r = src[bidx^2];
+            float h, s, v;
+
+            float vmin, diff;
+
+            v = vmin = r;
+            v = fmax(v, g);
+            v = fmax(v, b);
+            vmin = fmin(vmin, g);
+            vmin = fmin(vmin, b);
+
+            diff = v - vmin;
+            s = diff / (float)(::fabs(v) + numeric_limits<float>::epsilon());
+            diff = (float)(60. / (diff + numeric_limits<float>::epsilon()));
+
+            h  = (v == r) * (g - b) * diff;
+            h += (v != r && v == g) * ((b - r) * diff + 120.f);
+            h += (v != r && v != g) * ((r - g) * diff + 240.f);
+            h += (h < 0) * 360.f;
+
+            dst.x = h * hscale;
+            dst.y = s;
+            dst.z = v;
+        }
+
+        template <typename T, int scn, int dcn, int bidx, int hr> struct RGB2HSV
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                RGB2HSVConvert<bidx, hr>(&src.x, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2HSV() {}
+            __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {}
+        };
+
+        template <int bidx, int hr> struct RGB2HSV<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator()(uint src) const
+            {
+                return RGB2HSVConvert<bidx, hr>(src);
+            }
+            __host__ __device__ __forceinline__ RGB2HSV() {}
+            __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HSV<T, scn, dcn, bidx, 180> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <typename T> struct name ## _full_traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HSV<T, scn, dcn, bidx, 256> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HSV<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _full_traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HSV<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} };
+
+        template <int bidx, int hr, typename T> static __device__ void HSV2RGBConvert(const T& src, float* dst)
+        {
+            const float hscale = 6.f / hr;
+
+            float h = src.x, s = src.y, v = src.z;
+            float b = v, g = v, r = v;
+
+            if (s != 0)
+            {
+                h *= hscale;
+
+                if( h < 0 )
+                    do h += 6; while( h < 0 );
+                else if( h >= 6 )
+                    do h -= 6; while( h >= 6 );
+
+                int sector = __float2int_rd(h);
+                h -= sector;
+
+                if ( (unsigned)sector >= 6u )
+                {
+                    sector = 0;
+                    h = 0.f;
+                }
+
+                float tab[4];
+                tab[0] = v;
+                tab[1] = v * (1.f - s);
+                tab[2] = v * (1.f - s * h);
+                tab[3] = v * (1.f - s * (1.f - h));
+
+                b = tab[c_HsvSectorData[sector][0]];
+                g = tab[c_HsvSectorData[sector][1]];
+                r = tab[c_HsvSectorData[sector][2]];
+            }
+
+            dst[bidx] = b;
+            dst[1] = g;
+            dst[bidx^2] = r;
+        }
+
+        template <int bidx, int HR, typename T> static __device__ void HSV2RGBConvert(const T& src, uchar* dst)
+        {
+            float3 buf;
+
+            buf.x = src.x;
+            buf.y = src.y * (1.f / 255.f);
+            buf.z = src.z * (1.f / 255.f);
+
+            HSV2RGBConvert<bidx, HR>(buf, &buf.x);
+
+            dst[0] = saturate_cast<uchar>(buf.x * 255.f);
+            dst[1] = saturate_cast<uchar>(buf.y * 255.f);
+            dst[2] = saturate_cast<uchar>(buf.z * 255.f);
+        }
+
+        template <int bidx, int hr> static __device__ uint HSV2RGBConvert(uint src)
+        {
+            float3 buf;
+
+            buf.x = src & 0xff;
+            buf.y = ((src >> 8) & 0xff) * (1.f/255.f);
+            buf.z = ((src >> 16) & 0xff) * (1.f/255.f);
+
+            HSV2RGBConvert<bidx, hr>(buf, &buf.x);
+
+            uint dst = 0xffu << 24;
+
+            dst |= saturate_cast<uchar>(buf.x * 255.f);
+            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
+            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
+
+            return dst;
+        }
+
+        template <typename T, int scn, int dcn, int bidx, int hr> struct HSV2RGB
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                HSV2RGBConvert<bidx, hr>(src, &dst.x);
+                setAlpha(dst, ColorChannel<T>::max());
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ HSV2RGB() {}
+            __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {}
+        };
+
+        template <int bidx, int hr> struct HSV2RGB<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator()(uint src) const
+            {
+                return HSV2RGBConvert<bidx, hr>(src);
+            }
+            __host__ __device__ __forceinline__ HSV2RGB() {}
+            __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::HSV2RGB<T, scn, dcn, bidx, 180> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <typename T> struct name ## _full_traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::HSV2RGB<T, scn, dcn, bidx, 255> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::HSV2RGB<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _full_traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::HSV2RGB<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+/////////////////////////////////////// RGB <-> HLS ////////////////////////////////////////
+
+    namespace color_detail
+    {
+        template <int bidx, int hr, typename D> static __device__ void RGB2HLSConvert(const float* src, D& dst)
+        {
+            const float hscale = hr * (1.f / 360.f);
+
+            float b = src[bidx], g = src[1], r = src[bidx^2];
+            float h = 0.f, s = 0.f, l;
+            float vmin, vmax, diff;
+
+            vmax = vmin = r;
+            vmax = fmax(vmax, g);
+            vmax = fmax(vmax, b);
+            vmin = fmin(vmin, g);
+            vmin = fmin(vmin, b);
+
+            diff = vmax - vmin;
+            l = (vmax + vmin) * 0.5f;
+
+            if (diff > numeric_limits<float>::epsilon())
+            {
+                s = (l < 0.5f) * diff / (vmax + vmin);
+                s += (l >= 0.5f) * diff / (2.0f - vmax - vmin);
+
+                diff = 60.f / diff;
+
+                h  = (vmax == r) * (g - b) * diff;
+                h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f);
+                h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f);
+                h += (h < 0.f) * 360.f;
+            }
+
+            dst.x = h * hscale;
+            dst.y = l;
+            dst.z = s;
+        }
+
+        template <int bidx, int hr, typename D> static __device__ void RGB2HLSConvert(const uchar* src, D& dst)
+        {
+            float3 buf;
+
+            buf.x = src[0] * (1.f / 255.f);
+            buf.y = src[1] * (1.f / 255.f);
+            buf.z = src[2] * (1.f / 255.f);
+
+            RGB2HLSConvert<bidx, hr>(&buf.x, buf);
+
+            dst.x = saturate_cast<uchar>(buf.x);
+            dst.y = saturate_cast<uchar>(buf.y*255.f);
+            dst.z = saturate_cast<uchar>(buf.z*255.f);
+        }
+
+        template <int bidx, int hr> static __device__ uint RGB2HLSConvert(uint src)
+        {
+            float3 buf;
+
+            buf.x = (0xff & src) * (1.f / 255.f);
+            buf.y = (0xff & (src >> 8)) * (1.f / 255.f);
+            buf.z = (0xff & (src >> 16)) * (1.f / 255.f);
+
+            RGB2HLSConvert<bidx, hr>(&buf.x, buf);
+
+            uint dst = 0xffu << 24;
+
+            dst |= saturate_cast<uchar>(buf.x);
+            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
+            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
+
+            return dst;
+        }
+
+        template <typename T, int scn, int dcn, int bidx, int hr> struct RGB2HLS
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                RGB2HLSConvert<bidx, hr>(&src.x, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2HLS() {}
+            __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {}
+        };
+
+        template <int bidx, int hr> struct RGB2HLS<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator()(uint src) const
+            {
+                return RGB2HLSConvert<bidx, hr>(src);
+            }
+            __host__ __device__ __forceinline__ RGB2HLS() {}
+            __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HLS<T, scn, dcn, bidx, 180> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <typename T> struct name ## _full_traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HLS<T, scn, dcn, bidx, 256> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HLS<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _full_traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2HLS<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} };
+
+        template <int bidx, int hr, typename T> static __device__ void HLS2RGBConvert(const T& src, float* dst)
+        {
+            const float hscale = 6.0f / hr;
+
+            float h = src.x, l = src.y, s = src.z;
+            float b = l, g = l, r = l;
+
+            if (s != 0)
+            {
+                float p2  = (l <= 0.5f) * l * (1 + s);
+                      p2 += (l > 0.5f) * (l + s - l * s);
+                float p1 = 2 * l - p2;
+
+                h *= hscale;
+
+                if( h < 0 )
+                    do h += 6; while( h < 0 );
+                else if( h >= 6 )
+                    do h -= 6; while( h >= 6 );
+
+                int sector;
+                sector = __float2int_rd(h);
+
+                h -= sector;
+
+                float tab[4];
+                tab[0] = p2;
+                tab[1] = p1;
+                tab[2] = p1 + (p2 - p1) * (1 - h);
+                tab[3] = p1 + (p2 - p1) * h;
+
+                b = tab[c_HlsSectorData[sector][0]];
+                g = tab[c_HlsSectorData[sector][1]];
+                r = tab[c_HlsSectorData[sector][2]];
+            }
+
+            dst[bidx] = b;
+            dst[1] = g;
+            dst[bidx^2] = r;
+        }
+
+        template <int bidx, int hr, typename T> static __device__ void HLS2RGBConvert(const T& src, uchar* dst)
+        {
+            float3 buf;
+
+            buf.x = src.x;
+            buf.y = src.y * (1.f / 255.f);
+            buf.z = src.z * (1.f / 255.f);
+
+            HLS2RGBConvert<bidx, hr>(buf, &buf.x);
+
+            dst[0] = saturate_cast<uchar>(buf.x * 255.f);
+            dst[1] = saturate_cast<uchar>(buf.y * 255.f);
+            dst[2] = saturate_cast<uchar>(buf.z * 255.f);
+        }
+
+        template <int bidx, int hr> static __device__ uint HLS2RGBConvert(uint src)
+        {
+            float3 buf;
+
+            buf.x = 0xff & src;
+            buf.y = (0xff & (src >> 8)) * (1.f / 255.f);
+            buf.z = (0xff & (src >> 16)) * (1.f / 255.f);
+
+            HLS2RGBConvert<bidx, hr>(buf, &buf.x);
+
+            uint dst = 0xffu << 24;
+
+            dst |= saturate_cast<uchar>(buf.x * 255.f);
+            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
+            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
+
+            return dst;
+        }
+
+        template <typename T, int scn, int dcn, int bidx, int hr> struct HLS2RGB
+            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
+            {
+                typename TypeVec<T, dcn>::vec_type dst;
+
+                HLS2RGBConvert<bidx, hr>(src, &dst.x);
+                setAlpha(dst, ColorChannel<T>::max());
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ HLS2RGB() {}
+            __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {}
+        };
+
+        template <int bidx, int hr> struct HLS2RGB<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
+        {
+            __device__ __forceinline__ uint operator()(uint src) const
+            {
+                return HLS2RGBConvert<bidx, hr>(src);
+            }
+            __host__ __device__ __forceinline__ HLS2RGB() {}
+            __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::HLS2RGB<T, scn, dcn, bidx, 180> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <typename T> struct name ## _full_traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::HLS2RGB<T, scn, dcn, bidx, 255> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::HLS2RGB<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    }; \
+    template <> struct name ## _full_traits<float> \
+    { \
+        typedef ::cv::cuda::device::color_detail::HLS2RGB<float, scn, dcn, bidx, 360> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+///////////////////////////////////// RGB <-> Lab /////////////////////////////////////
+
+    namespace color_detail
+    {
+        enum
+        {
+            LAB_CBRT_TAB_SIZE = 1024,
+            GAMMA_TAB_SIZE = 1024,
+            lab_shift = xyz_shift,
+            gamma_shift = 3,
+            lab_shift2 = (lab_shift + gamma_shift),
+            LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift))
+        };
+
+        __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040};
+
+        __device__ __forceinline__ int LabCbrt_b(int i)
+        {
+            float x = i * (1.f / (255.f * (1 << gamma_shift)));
+            return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x));
+        }
+
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst)
+        {
+            const int Lscale = (116 * 255 + 50) / 100;
+            const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100);
+
+            int B = blueIdx == 0 ? src.x : src.z;
+            int G = src.y;
+            int R = blueIdx == 0 ? src.z : src.x;
+
+            if (srgb)
+            {
+                B = c_sRGBGammaTab_b[B];
+                G = c_sRGBGammaTab_b[G];
+                R = c_sRGBGammaTab_b[R];
+            }
+            else
+            {
+                B <<= 3;
+                G <<= 3;
+                R <<= 3;
+            }
+
+            int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift));
+            int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift));
+            int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift));
+
+            int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2);
+            int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2);
+            int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2);
+
+            dst.x = saturate_cast<uchar>(L);
+            dst.y = saturate_cast<uchar>(a);
+            dst.z = saturate_cast<uchar>(b);
+        }
+
+        __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n)
+        {
+            int ix = ::min(::max(int(x), 0), n-1);
+            x -= ix;
+            tab += ix * 4;
+            return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0];
+        }
+
+        __constant__ float c_sRGBGammaTab[] = 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+
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst)
+        {
+            const float _1_3 = 1.0f / 3.0f;
+            const float _a = 16.0f / 116.0f;
+
+            float B = blueIdx == 0 ? src.x : src.z;
+            float G = src.y;
+            float R = blueIdx == 0 ? src.z : src.x;
+
+            if (srgb)
+            {
+                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
+                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
+                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
+            }
+
+            float X = B * 0.189828f + G * 0.376219f + R * 0.433953f;
+            float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f;
+            float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f;
+
+            float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a);
+            float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a);
+            float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a);
+
+            float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y);
+            float a = 500.f * (FX - FY);
+            float b = 200.f * (FY - FZ);
+
+            dst.x = L;
+            dst.y = a;
+            dst.z = b;
+        }
+
+        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct RGB2Lab;
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct RGB2Lab<uchar, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
+            {
+                typename TypeVec<uchar, dcn>::vec_type dst;
+
+                RGB2LabConvert_b<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2Lab() {}
+            __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {}
+        };
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct RGB2Lab<float, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
+            {
+                typename TypeVec<float, dcn>::vec_type dst;
+
+                RGB2LabConvert_f<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2Lab() {}
+            __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2Lab<T, scn, dcn, srgb, blueIdx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        __constant__ float c_sRGBInvGammaTab[] = 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+
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst)
+        {
+            const float lThresh = 0.008856f * 903.3f;
+            const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f;
+
+            float Y, fy;
+
+            if (src.x <= lThresh)
+            {
+                Y = src.x / 903.3f;
+                fy = 7.787f * Y + 16.0f / 116.0f;
+            }
+            else
+            {
+                fy = (src.x + 16.0f) / 116.0f;
+                Y = fy * fy * fy;
+            }
+
+            float X = src.y / 500.0f + fy;
+            float Z = fy - src.z / 200.0f;
+
+            if (X <= fThresh)
+                X = (X - 16.0f / 116.0f) / 7.787f;
+            else
+                X = X * X * X;
+
+            if (Z <= fThresh)
+                Z = (Z - 16.0f / 116.0f) / 7.787f;
+            else
+                Z = Z * Z * Z;
+
+            float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z;
+            float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z;
+            float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z;
+
+            if (srgb)
+            {
+                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
+                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
+                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
+            }
+
+            dst.x = blueIdx == 0 ? B : R;
+            dst.y = G;
+            dst.z = blueIdx == 0 ? R : B;
+            setAlpha(dst, ColorChannel<float>::max());
+        }
+
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst)
+        {
+            float3 srcf, dstf;
+
+            srcf.x = src.x * (100.f / 255.f);
+            srcf.y = src.y - 128;
+            srcf.z = src.z - 128;
+
+            Lab2RGBConvert_f<srgb, blueIdx>(srcf, dstf);
+
+            dst.x = saturate_cast<uchar>(dstf.x * 255.f);
+            dst.y = saturate_cast<uchar>(dstf.y * 255.f);
+            dst.z = saturate_cast<uchar>(dstf.z * 255.f);
+            setAlpha(dst, ColorChannel<uchar>::max());
+        }
+
+        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct Lab2RGB;
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct Lab2RGB<uchar, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
+            {
+                typename TypeVec<uchar, dcn>::vec_type dst;
+
+                Lab2RGBConvert_b<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ Lab2RGB() {}
+            __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {}
+        };
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct Lab2RGB<float, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
+            {
+                typename TypeVec<float, dcn>::vec_type dst;
+
+                Lab2RGBConvert_f<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ Lab2RGB() {}
+            __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::Lab2RGB<T, scn, dcn, srgb, blueIdx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+///////////////////////////////////// RGB <-> Luv /////////////////////////////////////
+
+    namespace color_detail
+    {
+        __constant__ float c_LabCbrtTab[] = 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+
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst)
+        {
+            const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3);
+            const float _un = 13 * (4 * 0.950456f * _d);
+            const float _vn = 13 * (9 * _d);
+
+            float B = blueIdx == 0 ? src.x : src.z;
+            float G = src.y;
+            float R = blueIdx == 0 ? src.z : src.x;
+
+            if (srgb)
+            {
+                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
+                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
+                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
+            }
+
+            float X = R * 0.412453f + G * 0.357580f + B * 0.180423f;
+            float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f;
+            float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f;
+
+            float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE);
+            L = 116.f * L - 16.f;
+
+            const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits<float>::epsilon());
+            float u = L * (X * d - _un);
+            float v = L * ((9 * 0.25f) * Y * d - _vn);
+
+            dst.x = L;
+            dst.y = u;
+            dst.z = v;
+        }
+
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst)
+        {
+            float3 srcf, dstf;
+
+            srcf.x = src.x * (1.f / 255.f);
+            srcf.y = src.y * (1.f / 255.f);
+            srcf.z = src.z * (1.f / 255.f);
+
+            RGB2LuvConvert_f<srgb, blueIdx>(srcf, dstf);
+
+            dst.x = saturate_cast<uchar>(dstf.x * 2.55f);
+            dst.y = saturate_cast<uchar>(dstf.y * 0.72033898305084743f + 96.525423728813564f);
+            dst.z = saturate_cast<uchar>(dstf.z * 0.9732824427480916f + 136.259541984732824f);
+        }
+
+        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct RGB2Luv;
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct RGB2Luv<uchar, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
+            {
+                typename TypeVec<uchar, dcn>::vec_type dst;
+
+                RGB2LuvConvert_b<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2Luv() {}
+            __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {}
+        };
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct RGB2Luv<float, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
+            {
+                typename TypeVec<float, dcn>::vec_type dst;
+
+                RGB2LuvConvert_f<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ RGB2Luv() {}
+            __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::RGB2Luv<T, scn, dcn, srgb, blueIdx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    namespace color_detail
+    {
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst)
+        {
+            const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3);
+            const float _un = 4 * 0.950456f * _d;
+            const float _vn = 9 * _d;
+
+            float L = src.x;
+            float u = src.y;
+            float v = src.z;
+
+            float Y = (L + 16.f) * (1.f / 116.f);
+            Y = Y * Y * Y;
+
+            float d = (1.f / 13.f) / L;
+            u = u * d + _un;
+            v = v * d + _vn;
+
+            float iv = 1.f / v;
+            float X = 2.25f * u * Y * iv;
+            float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv;
+
+            float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z;
+            float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z;
+            float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z;
+
+            if (srgb)
+            {
+                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
+                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
+                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
+            }
+
+            dst.x = blueIdx == 0 ? B : R;
+            dst.y = G;
+            dst.z = blueIdx == 0 ? R : B;
+            setAlpha(dst, ColorChannel<float>::max());
+        }
+
+        template <bool srgb, int blueIdx, typename T, typename D>
+        __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst)
+        {
+            float3 srcf, dstf;
+
+            srcf.x = src.x * (100.f / 255.f);
+            srcf.y = src.y * 1.388235294117647f - 134.f;
+            srcf.z = src.z * 1.027450980392157f - 140.f;
+
+            Luv2RGBConvert_f<srgb, blueIdx>(srcf, dstf);
+
+            dst.x = saturate_cast<uchar>(dstf.x * 255.f);
+            dst.y = saturate_cast<uchar>(dstf.y * 255.f);
+            dst.z = saturate_cast<uchar>(dstf.z * 255.f);
+            setAlpha(dst, ColorChannel<uchar>::max());
+        }
+
+        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct Luv2RGB;
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct Luv2RGB<uchar, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
+            {
+                typename TypeVec<uchar, dcn>::vec_type dst;
+
+                Luv2RGBConvert_b<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ Luv2RGB() {}
+            __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {}
+        };
+        template <int scn, int dcn, bool srgb, int blueIdx>
+        struct Luv2RGB<float, scn, dcn, srgb, blueIdx>
+            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
+        {
+            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
+            {
+                typename TypeVec<float, dcn>::vec_type dst;
+
+                Luv2RGBConvert_f<srgb, blueIdx>(src, dst);
+
+                return dst;
+            }
+            __host__ __device__ __forceinline__ Luv2RGB() {}
+            __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {}
+        };
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \
+    template <typename T> struct name ## _traits \
+    { \
+        typedef ::cv::cuda::device::color_detail::Luv2RGB<T, scn, dcn, srgb, blueIdx> functor_type; \
+        static __host__ __device__ __forceinline__ functor_type create_functor() \
+        { \
+            return functor_type(); \
+        } \
+    };
+
+    #undef CV_DESCALE
+
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_COLOR_DETAIL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/detail/reduce.hpp b/3rdParty/include/opencv2/core/cuda/detail/reduce.hpp
new file mode 100644
index 0000000..8af20b0
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/detail/reduce.hpp
@@ -0,0 +1,365 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_REDUCE_DETAIL_HPP
+#define OPENCV_CUDA_REDUCE_DETAIL_HPP
+
+#include <thrust/tuple.h>
+#include "../warp.hpp"
+#include "../warp_shuffle.hpp"
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    namespace reduce_detail
+    {
+        template <typename T> struct GetType;
+        template <typename T> struct GetType<T*>
+        {
+            typedef T type;
+        };
+        template <typename T> struct GetType<volatile T*>
+        {
+            typedef T type;
+        };
+        template <typename T> struct GetType<T&>
+        {
+            typedef T type;
+        };
+
+        template <unsigned int I, unsigned int N>
+        struct For
+        {
+            template <class PointerTuple, class ValTuple>
+            static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
+            {
+                thrust::get<I>(smem)[tid] = thrust::get<I>(val);
+
+                For<I + 1, N>::loadToSmem(smem, val, tid);
+            }
+            template <class PointerTuple, class ValTuple>
+            static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
+            {
+                thrust::get<I>(val) = thrust::get<I>(smem)[tid];
+
+                For<I + 1, N>::loadFromSmem(smem, val, tid);
+            }
+
+            template <class PointerTuple, class ValTuple, class OpTuple>
+            static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op)
+            {
+                typename GetType<typename thrust::tuple_element<I, PointerTuple>::type>::type reg = thrust::get<I>(smem)[tid + delta];
+                thrust::get<I>(smem)[tid] = thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
+
+                For<I + 1, N>::merge(smem, val, tid, delta, op);
+            }
+            template <class ValTuple, class OpTuple>
+            static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op)
+            {
+                typename GetType<typename thrust::tuple_element<I, ValTuple>::type>::type reg = shfl_down(thrust::get<I>(val), delta, width);
+                thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
+
+                For<I + 1, N>::mergeShfl(val, delta, width, op);
+            }
+        };
+        template <unsigned int N>
+        struct For<N, N>
+        {
+            template <class PointerTuple, class ValTuple>
+            static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int)
+            {
+            }
+            template <class PointerTuple, class ValTuple>
+            static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int)
+            {
+            }
+
+            template <class PointerTuple, class ValTuple, class OpTuple>
+            static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&)
+            {
+            }
+            template <class ValTuple, class OpTuple>
+            static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&)
+            {
+            }
+        };
+
+        template <typename T>
+        __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid)
+        {
+            smem[tid] = val;
+        }
+        template <typename T>
+        __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid)
+        {
+            val = smem[tid];
+        }
+        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
+        __device__ __forceinline__ void loadToSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+                                                       const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+                                                       unsigned int tid)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadToSmem(smem, val, tid);
+        }
+        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
+        __device__ __forceinline__ void loadFromSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+                                                         const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+                                                         unsigned int tid)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadFromSmem(smem, val, tid);
+        }
+
+        template <typename T, class Op>
+        __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op)
+        {
+            T reg = smem[tid + delta];
+            smem[tid] = val = op(val, reg);
+        }
+        template <typename T, class Op>
+        __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op)
+        {
+            T reg = shfl_down(val, delta, width);
+            val = op(val, reg);
+        }
+        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
+                  class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
+        __device__ __forceinline__ void merge(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+                                              const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+                                              unsigned int tid,
+                                              unsigned int delta,
+                                              const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::merge(smem, val, tid, delta, op);
+        }
+        template <typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
+                  class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
+        __device__ __forceinline__ void mergeShfl(const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+                                                  unsigned int delta,
+                                                  unsigned int width,
+                                                  const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9> >::value>::mergeShfl(val, delta, width, op);
+        }
+
+        template <unsigned int N> struct Generic
+        {
+            template <typename Pointer, typename Reference, class Op>
+            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
+            {
+                loadToSmem(smem, val, tid);
+                if (N >= 32)
+                    __syncthreads();
+
+                if (N >= 2048)
+                {
+                    if (tid < 1024)
+                        merge(smem, val, tid, 1024, op);
+
+                    __syncthreads();
+                }
+                if (N >= 1024)
+                {
+                    if (tid < 512)
+                        merge(smem, val, tid, 512, op);
+
+                    __syncthreads();
+                }
+                if (N >= 512)
+                {
+                    if (tid < 256)
+                        merge(smem, val, tid, 256, op);
+
+                    __syncthreads();
+                }
+                if (N >= 256)
+                {
+                    if (tid < 128)
+                        merge(smem, val, tid, 128, op);
+
+                    __syncthreads();
+                }
+                if (N >= 128)
+                {
+                    if (tid < 64)
+                        merge(smem, val, tid, 64, op);
+
+                    __syncthreads();
+                }
+                if (N >= 64)
+                {
+                    if (tid < 32)
+                        merge(smem, val, tid, 32, op);
+                }
+
+                if (tid < 16)
+                {
+                    merge(smem, val, tid, 16, op);
+                    merge(smem, val, tid, 8, op);
+                    merge(smem, val, tid, 4, op);
+                    merge(smem, val, tid, 2, op);
+                    merge(smem, val, tid, 1, op);
+                }
+            }
+        };
+
+        template <unsigned int I, typename Pointer, typename Reference, class Op>
+        struct Unroll
+        {
+            static __device__ void loopShfl(Reference val, Op op, unsigned int N)
+            {
+                mergeShfl(val, I, N, op);
+                Unroll<I / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
+            }
+            static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op)
+            {
+                merge(smem, val, tid, I, op);
+                Unroll<I / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
+            }
+        };
+        template <typename Pointer, typename Reference, class Op>
+        struct Unroll<0, Pointer, Reference, Op>
+        {
+            static __device__ void loopShfl(Reference, Op, unsigned int)
+            {
+            }
+            static __device__ void loop(Pointer, Reference, unsigned int, Op)
+            {
+            }
+        };
+
+        template <unsigned int N> struct WarpOptimized
+        {
+            template <typename Pointer, typename Reference, class Op>
+            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
+            {
+            #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+                CV_UNUSED(smem);
+                CV_UNUSED(tid);
+
+                Unroll<N / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
+            #else
+                loadToSmem(smem, val, tid);
+
+                if (tid < N / 2)
+                    Unroll<N / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
+            #endif
+            }
+        };
+
+        template <unsigned int N> struct GenericOptimized32
+        {
+            enum { M = N / 32 };
+
+            template <typename Pointer, typename Reference, class Op>
+            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
+            {
+                const unsigned int laneId = Warp::laneId();
+
+            #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+                Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize);
+
+                if (laneId == 0)
+                    loadToSmem(smem, val, tid / 32);
+            #else
+                loadToSmem(smem, val, tid);
+
+                if (laneId < 16)
+                    Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op);
+
+                __syncthreads();
+
+                if (laneId == 0)
+                    loadToSmem(smem, val, tid / 32);
+            #endif
+
+                __syncthreads();
+
+                loadFromSmem(smem, val, tid);
+
+                if (tid < 32)
+                {
+                #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+                    Unroll<M / 2, Pointer, Reference, Op>::loopShfl(val, op, M);
+                #else
+                    Unroll<M / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
+                #endif
+                }
+            }
+        };
+
+        template <bool val, class T1, class T2> struct StaticIf;
+        template <class T1, class T2> struct StaticIf<true, T1, T2>
+        {
+            typedef T1 type;
+        };
+        template <class T1, class T2> struct StaticIf<false, T1, T2>
+        {
+            typedef T2 type;
+        };
+
+        template <unsigned int N> struct IsPowerOf2
+        {
+            enum { value = ((N != 0) && !(N & (N - 1))) };
+        };
+
+        template <unsigned int N> struct Dispatcher
+        {
+            typedef typename StaticIf<
+                (N <= 32) && IsPowerOf2<N>::value,
+                WarpOptimized<N>,
+                typename StaticIf<
+                    (N <= 1024) && IsPowerOf2<N>::value,
+                    GenericOptimized32<N>,
+                    Generic<N>
+                >::type
+            >::type reductor;
+        };
+    }
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_REDUCE_DETAIL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/detail/reduce_key_val.hpp b/3rdParty/include/opencv2/core/cuda/detail/reduce_key_val.hpp
new file mode 100644
index 0000000..df37c17
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/detail/reduce_key_val.hpp
@@ -0,0 +1,502 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
+#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
+
+#include <thrust/tuple.h>
+#include "../warp.hpp"
+#include "../warp_shuffle.hpp"
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    namespace reduce_key_val_detail
+    {
+        template <typename T> struct GetType;
+        template <typename T> struct GetType<T*>
+        {
+            typedef T type;
+        };
+        template <typename T> struct GetType<volatile T*>
+        {
+            typedef T type;
+        };
+        template <typename T> struct GetType<T&>
+        {
+            typedef T type;
+        };
+
+        template <unsigned int I, unsigned int N>
+        struct For
+        {
+            template <class PointerTuple, class ReferenceTuple>
+            static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
+            {
+                thrust::get<I>(smem)[tid] = thrust::get<I>(data);
+
+                For<I + 1, N>::loadToSmem(smem, data, tid);
+            }
+            template <class PointerTuple, class ReferenceTuple>
+            static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
+            {
+                thrust::get<I>(data) = thrust::get<I>(smem)[tid];
+
+                For<I + 1, N>::loadFromSmem(smem, data, tid);
+            }
+
+            template <class ReferenceTuple>
+            static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width)
+            {
+                thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
+
+                For<I + 1, N>::copyShfl(val, delta, width);
+            }
+            template <class PointerTuple, class ReferenceTuple>
+            static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta)
+            {
+                thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
+
+                For<I + 1, N>::copy(svals, val, tid, delta);
+            }
+
+            template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
+            static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width)
+            {
+                typename GetType<typename thrust::tuple_element<I, KeyReferenceTuple>::type>::type reg = shfl_down(thrust::get<I>(key), delta, width);
+
+                if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
+                {
+                    thrust::get<I>(key) = reg;
+                    thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
+                }
+
+                For<I + 1, N>::mergeShfl(key, val, cmp, delta, width);
+            }
+            template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
+            static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key,
+                                         const ValPointerTuple& svals, const ValReferenceTuple& val,
+                                         const CmpTuple& cmp,
+                                         unsigned int tid, unsigned int delta)
+            {
+                typename GetType<typename thrust::tuple_element<I, KeyPointerTuple>::type>::type reg = thrust::get<I>(skeys)[tid + delta];
+
+                if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
+                {
+                    thrust::get<I>(skeys)[tid] = thrust::get<I>(key) = reg;
+                    thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
+                }
+
+                For<I + 1, N>::merge(skeys, key, svals, val, cmp, tid, delta);
+            }
+        };
+        template <unsigned int N>
+        struct For<N, N>
+        {
+            template <class PointerTuple, class ReferenceTuple>
+            static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
+            {
+            }
+            template <class PointerTuple, class ReferenceTuple>
+            static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
+            {
+            }
+
+            template <class ReferenceTuple>
+            static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int)
+            {
+            }
+            template <class PointerTuple, class ReferenceTuple>
+            static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int)
+            {
+            }
+
+            template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
+            static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int)
+            {
+            }
+            template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
+            static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&,
+                                         const ValPointerTuple&, const ValReferenceTuple&,
+                                         const CmpTuple&,
+                                         unsigned int, unsigned int)
+            {
+            }
+        };
+
+        //////////////////////////////////////////////////////
+        // loadToSmem
+
+        template <typename T>
+        __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid)
+        {
+            smem[tid] = data;
+        }
+        template <typename T>
+        __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid)
+        {
+            data = smem[tid];
+        }
+        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+        __device__ __forceinline__ void loadToSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
+                                                   const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
+                                                   unsigned int tid)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadToSmem(smem, data, tid);
+        }
+        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+        __device__ __forceinline__ void loadFromSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
+                                                     const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
+                                                     unsigned int tid)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadFromSmem(smem, data, tid);
+        }
+
+        //////////////////////////////////////////////////////
+        // copyVals
+
+        template <typename V>
+        __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width)
+        {
+            val = shfl_down(val, delta, width);
+        }
+        template <typename V>
+        __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta)
+        {
+            svals[tid] = val = svals[tid + delta];
+        }
+        template <typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+        __device__ __forceinline__ void copyValsShfl(const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                                     unsigned int delta,
+                                                     int width)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9> >::value>::copyShfl(val, delta, width);
+        }
+        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+        __device__ __forceinline__ void copyVals(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                                 unsigned int tid, unsigned int delta)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::copy(svals, val, tid, delta);
+        }
+
+        //////////////////////////////////////////////////////
+        // merge
+
+        template <typename K, typename V, class Cmp>
+        __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width)
+        {
+            K reg = shfl_down(key, delta, width);
+
+            if (cmp(reg, key))
+            {
+                key = reg;
+                copyValsShfl(val, delta, width);
+            }
+        }
+        template <typename K, typename V, class Cmp>
+        __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta)
+        {
+            K reg = skeys[tid + delta];
+
+            if (cmp(reg, key))
+            {
+                skeys[tid] = key = reg;
+                copyVals(svals, val, tid, delta);
+            }
+        }
+        template <typename K,
+                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+                  class Cmp>
+        __device__ __forceinline__ void mergeShfl(K& key,
+                                                  const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                                  const Cmp& cmp,
+                                                  unsigned int delta, int width)
+        {
+            K reg = shfl_down(key, delta, width);
+
+            if (cmp(reg, key))
+            {
+                key = reg;
+                copyValsShfl(val, delta, width);
+            }
+        }
+        template <typename K,
+                  typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+                  class Cmp>
+        __device__ __forceinline__ void merge(volatile K* skeys, K& key,
+                                              const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+                                              const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                              const Cmp& cmp, unsigned int tid, unsigned int delta)
+        {
+            K reg = skeys[tid + delta];
+
+            if (cmp(reg, key))
+            {
+                skeys[tid] = key = reg;
+                copyVals(svals, val, tid, delta);
+            }
+        }
+        template <typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
+                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+                  class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
+        __device__ __forceinline__ void mergeShfl(const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
+                                                  const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                                  const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
+                                                  unsigned int delta, int width)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9> >::value>::mergeShfl(key, val, cmp, delta, width);
+        }
+        template <typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
+                  typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
+                  typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+                  class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
+        __device__ __forceinline__ void merge(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
+                                              const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
+                                              const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+                                              const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                              const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
+                                              unsigned int tid, unsigned int delta)
+        {
+            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::merge(skeys, key, svals, val, cmp, tid, delta);
+        }
+
+        //////////////////////////////////////////////////////
+        // Generic
+
+        template <unsigned int N> struct Generic
+        {
+            template <class KP, class KR, class VP, class VR, class Cmp>
+            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+            {
+                loadToSmem(skeys, key, tid);
+                loadValsToSmem(svals, val, tid);
+                if (N >= 32)
+                    __syncthreads();
+
+                if (N >= 2048)
+                {
+                    if (tid < 1024)
+                        merge(skeys, key, svals, val, cmp, tid, 1024);
+
+                    __syncthreads();
+                }
+                if (N >= 1024)
+                {
+                    if (tid < 512)
+                        merge(skeys, key, svals, val, cmp, tid, 512);
+
+                    __syncthreads();
+                }
+                if (N >= 512)
+                {
+                    if (tid < 256)
+                        merge(skeys, key, svals, val, cmp, tid, 256);
+
+                    __syncthreads();
+                }
+                if (N >= 256)
+                {
+                    if (tid < 128)
+                        merge(skeys, key, svals, val, cmp, tid, 128);
+
+                    __syncthreads();
+                }
+                if (N >= 128)
+                {
+                    if (tid < 64)
+                        merge(skeys, key, svals, val, cmp, tid, 64);
+
+                    __syncthreads();
+                }
+                if (N >= 64)
+                {
+                    if (tid < 32)
+                        merge(skeys, key, svals, val, cmp, tid, 32);
+                }
+
+                if (tid < 16)
+                {
+                    merge(skeys, key, svals, val, cmp, tid, 16);
+                    merge(skeys, key, svals, val, cmp, tid, 8);
+                    merge(skeys, key, svals, val, cmp, tid, 4);
+                    merge(skeys, key, svals, val, cmp, tid, 2);
+                    merge(skeys, key, svals, val, cmp, tid, 1);
+                }
+            }
+        };
+
+        template <unsigned int I, class KP, class KR, class VP, class VR, class Cmp>
+        struct Unroll
+        {
+            static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N)
+            {
+                mergeShfl(key, val, cmp, I, N);
+                Unroll<I / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
+            }
+            static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+            {
+                merge(skeys, key, svals, val, cmp, tid, I);
+                Unroll<I / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+            }
+        };
+        template <class KP, class KR, class VP, class VR, class Cmp>
+        struct Unroll<0, KP, KR, VP, VR, Cmp>
+        {
+            static __device__ void loopShfl(KR, VR, Cmp, unsigned int)
+            {
+            }
+            static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp)
+            {
+            }
+        };
+
+        template <unsigned int N> struct WarpOptimized
+        {
+            template <class KP, class KR, class VP, class VR, class Cmp>
+            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+            {
+            #if 0 // __CUDA_ARCH__ >= 300
+                CV_UNUSED(skeys);
+                CV_UNUSED(svals);
+                CV_UNUSED(tid);
+
+                Unroll<N / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
+            #else
+                loadToSmem(skeys, key, tid);
+                loadToSmem(svals, val, tid);
+
+                if (tid < N / 2)
+                    Unroll<N / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+            #endif
+            }
+        };
+
+        template <unsigned int N> struct GenericOptimized32
+        {
+            enum { M = N / 32 };
+
+            template <class KP, class KR, class VP, class VR, class Cmp>
+            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+            {
+                const unsigned int laneId = Warp::laneId();
+
+            #if 0 // __CUDA_ARCH__ >= 300
+                Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize);
+
+                if (laneId == 0)
+                {
+                    loadToSmem(skeys, key, tid / 32);
+                    loadToSmem(svals, val, tid / 32);
+                }
+            #else
+                loadToSmem(skeys, key, tid);
+                loadToSmem(svals, val, tid);
+
+                if (laneId < 16)
+                    Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+
+                __syncthreads();
+
+                if (laneId == 0)
+                {
+                    loadToSmem(skeys, key, tid / 32);
+                    loadToSmem(svals, val, tid / 32);
+                }
+            #endif
+
+                __syncthreads();
+
+                loadFromSmem(skeys, key, tid);
+
+                if (tid < 32)
+                {
+                #if 0 // __CUDA_ARCH__ >= 300
+                    loadFromSmem(svals, val, tid);
+
+                    Unroll<M / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, M);
+                #else
+                    Unroll<M / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+                #endif
+                }
+            }
+        };
+
+        template <bool val, class T1, class T2> struct StaticIf;
+        template <class T1, class T2> struct StaticIf<true, T1, T2>
+        {
+            typedef T1 type;
+        };
+        template <class T1, class T2> struct StaticIf<false, T1, T2>
+        {
+            typedef T2 type;
+        };
+
+        template <unsigned int N> struct IsPowerOf2
+        {
+            enum { value = ((N != 0) && !(N & (N - 1))) };
+        };
+
+        template <unsigned int N> struct Dispatcher
+        {
+            typedef typename StaticIf<
+                (N <= 32) && IsPowerOf2<N>::value,
+                WarpOptimized<N>,
+                typename StaticIf<
+                    (N <= 1024) && IsPowerOf2<N>::value,
+                    GenericOptimized32<N>,
+                    Generic<N>
+                >::type
+            >::type reductor;
+        };
+    }
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/detail/transform_detail.hpp b/3rdParty/include/opencv2/core/cuda/detail/transform_detail.hpp
new file mode 100644
index 0000000..1919848
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/detail/transform_detail.hpp
@@ -0,0 +1,392 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_TRANSFORM_DETAIL_HPP
+#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP
+
+#include "../common.hpp"
+#include "../vec_traits.hpp"
+#include "../functional.hpp"
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    namespace transform_detail
+    {
+        //! Read Write Traits
+
+        template <typename T, typename D, int shift> struct UnaryReadWriteTraits
+        {
+            typedef typename TypeVec<T, shift>::vec_type read_type;
+            typedef typename TypeVec<D, shift>::vec_type write_type;
+        };
+
+        template <typename T1, typename T2, typename D, int shift> struct BinaryReadWriteTraits
+        {
+            typedef typename TypeVec<T1, shift>::vec_type read_type1;
+            typedef typename TypeVec<T2, shift>::vec_type read_type2;
+            typedef typename TypeVec<D, shift>::vec_type write_type;
+        };
+
+        //! Transform kernels
+
+        template <int shift> struct OpUnroller;
+        template <> struct OpUnroller<1>
+        {
+            template <typename T, typename D, typename UnOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src.x);
+            }
+
+            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src1.x, src2.x);
+            }
+        };
+        template <> struct OpUnroller<2>
+        {
+            template <typename T, typename D, typename UnOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src.x);
+                if (mask(y, x_shifted + 1))
+                    dst.y = op(src.y);
+            }
+
+            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src1.x, src2.x);
+                if (mask(y, x_shifted + 1))
+                    dst.y = op(src1.y, src2.y);
+            }
+        };
+        template <> struct OpUnroller<3>
+        {
+            template <typename T, typename D, typename UnOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src.x);
+                if (mask(y, x_shifted + 1))
+                    dst.y = op(src.y);
+                if (mask(y, x_shifted + 2))
+                    dst.z = op(src.z);
+            }
+
+            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src1.x, src2.x);
+                if (mask(y, x_shifted + 1))
+                    dst.y = op(src1.y, src2.y);
+                if (mask(y, x_shifted + 2))
+                    dst.z = op(src1.z, src2.z);
+            }
+        };
+        template <> struct OpUnroller<4>
+        {
+            template <typename T, typename D, typename UnOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src.x);
+                if (mask(y, x_shifted + 1))
+                    dst.y = op(src.y);
+                if (mask(y, x_shifted + 2))
+                    dst.z = op(src.z);
+                if (mask(y, x_shifted + 3))
+                    dst.w = op(src.w);
+            }
+
+            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.x = op(src1.x, src2.x);
+                if (mask(y, x_shifted + 1))
+                    dst.y = op(src1.y, src2.y);
+                if (mask(y, x_shifted + 2))
+                    dst.z = op(src1.z, src2.z);
+                if (mask(y, x_shifted + 3))
+                    dst.w = op(src1.w, src2.w);
+            }
+        };
+        template <> struct OpUnroller<8>
+        {
+            template <typename T, typename D, typename UnOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.a0 = op(src.a0);
+                if (mask(y, x_shifted + 1))
+                    dst.a1 = op(src.a1);
+                if (mask(y, x_shifted + 2))
+                    dst.a2 = op(src.a2);
+                if (mask(y, x_shifted + 3))
+                    dst.a3 = op(src.a3);
+                if (mask(y, x_shifted + 4))
+                    dst.a4 = op(src.a4);
+                if (mask(y, x_shifted + 5))
+                    dst.a5 = op(src.a5);
+                if (mask(y, x_shifted + 6))
+                    dst.a6 = op(src.a6);
+                if (mask(y, x_shifted + 7))
+                    dst.a7 = op(src.a7);
+            }
+
+            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
+            {
+                if (mask(y, x_shifted))
+                    dst.a0 = op(src1.a0, src2.a0);
+                if (mask(y, x_shifted + 1))
+                    dst.a1 = op(src1.a1, src2.a1);
+                if (mask(y, x_shifted + 2))
+                    dst.a2 = op(src1.a2, src2.a2);
+                if (mask(y, x_shifted + 3))
+                    dst.a3 = op(src1.a3, src2.a3);
+                if (mask(y, x_shifted + 4))
+                    dst.a4 = op(src1.a4, src2.a4);
+                if (mask(y, x_shifted + 5))
+                    dst.a5 = op(src1.a5, src2.a5);
+                if (mask(y, x_shifted + 6))
+                    dst.a6 = op(src1.a6, src2.a6);
+                if (mask(y, x_shifted + 7))
+                    dst.a7 = op(src1.a7, src2.a7);
+            }
+        };
+
+        template <typename T, typename D, typename UnOp, typename Mask>
+        static __global__ void transformSmart(const PtrStepSz<T> src_, PtrStep<D> dst_, const Mask mask, const UnOp op)
+        {
+            typedef TransformFunctorTraits<UnOp> ft;
+            typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::read_type read_type;
+            typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::write_type write_type;
+
+            const int x = threadIdx.x + blockIdx.x * blockDim.x;
+            const int y = threadIdx.y + blockIdx.y * blockDim.y;
+            const int x_shifted = x * ft::smart_shift;
+
+            if (y < src_.rows)
+            {
+                const T* src = src_.ptr(y);
+                D* dst = dst_.ptr(y);
+
+                if (x_shifted + ft::smart_shift - 1 < src_.cols)
+                {
+                    const read_type src_n_el = ((const read_type*)src)[x];
+                    OpUnroller<ft::smart_shift>::unroll(src_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y);
+                }
+                else
+                {
+                    for (int real_x = x_shifted; real_x < src_.cols; ++real_x)
+                    {
+                        if (mask(y, real_x))
+                            dst[real_x] = op(src[real_x]);
+                    }
+                }
+            }
+        }
+
+        template <typename T, typename D, typename UnOp, typename Mask>
+        __global__ static void transformSimple(const PtrStepSz<T> src, PtrStep<D> dst, const Mask mask, const UnOp op)
+        {
+            const int x = blockDim.x * blockIdx.x + threadIdx.x;
+            const int y = blockDim.y * blockIdx.y + threadIdx.y;
+
+            if (x < src.cols && y < src.rows && mask(y, x))
+            {
+                dst.ptr(y)[x] = op(src.ptr(y)[x]);
+            }
+        }
+
+        template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+        static __global__ void transformSmart(const PtrStepSz<T1> src1_, const PtrStep<T2> src2_, PtrStep<D> dst_,
+            const Mask mask, const BinOp op)
+        {
+            typedef TransformFunctorTraits<BinOp> ft;
+            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type1 read_type1;
+            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type2 read_type2;
+            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::write_type write_type;
+
+            const int x = threadIdx.x + blockIdx.x * blockDim.x;
+            const int y = threadIdx.y + blockIdx.y * blockDim.y;
+            const int x_shifted = x * ft::smart_shift;
+
+            if (y < src1_.rows)
+            {
+                const T1* src1 = src1_.ptr(y);
+                const T2* src2 = src2_.ptr(y);
+                D* dst = dst_.ptr(y);
+
+                if (x_shifted + ft::smart_shift - 1 < src1_.cols)
+                {
+                    const read_type1 src1_n_el = ((const read_type1*)src1)[x];
+                    const read_type2 src2_n_el = ((const read_type2*)src2)[x];
+
+                    OpUnroller<ft::smart_shift>::unroll(src1_n_el, src2_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y);
+                }
+                else
+                {
+                    for (int real_x = x_shifted; real_x < src1_.cols; ++real_x)
+                    {
+                        if (mask(y, real_x))
+                            dst[real_x] = op(src1[real_x], src2[real_x]);
+                    }
+                }
+            }
+        }
+
+        template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+        static __global__ void transformSimple(const PtrStepSz<T1> src1, const PtrStep<T2> src2, PtrStep<D> dst,
+            const Mask mask, const BinOp op)
+        {
+            const int x = blockDim.x * blockIdx.x + threadIdx.x;
+            const int y = blockDim.y * blockIdx.y + threadIdx.y;
+
+            if (x < src1.cols && y < src1.rows && mask(y, x))
+            {
+                const T1 src1_data = src1.ptr(y)[x];
+                const T2 src2_data = src2.ptr(y)[x];
+                dst.ptr(y)[x] = op(src1_data, src2_data);
+            }
+        }
+
+        template <bool UseSmart> struct TransformDispatcher;
+        template<> struct TransformDispatcher<false>
+        {
+            template <typename T, typename D, typename UnOp, typename Mask>
+            static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
+            {
+                typedef TransformFunctorTraits<UnOp> ft;
+
+                const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
+                const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1);
+
+                transformSimple<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
+                cudaSafeCall( cudaGetLastError() );
+
+                if (stream == 0)
+                    cudaSafeCall( cudaDeviceSynchronize() );
+            }
+
+            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+            static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
+            {
+                typedef TransformFunctorTraits<BinOp> ft;
+
+                const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
+                const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1);
+
+                transformSimple<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
+                cudaSafeCall( cudaGetLastError() );
+
+                if (stream == 0)
+                    cudaSafeCall( cudaDeviceSynchronize() );
+            }
+        };
+        template<> struct TransformDispatcher<true>
+        {
+            template <typename T, typename D, typename UnOp, typename Mask>
+            static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
+            {
+                typedef TransformFunctorTraits<UnOp> ft;
+
+                CV_StaticAssert(ft::smart_shift != 1, "");
+
+                if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) ||
+                    !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
+                {
+                    TransformDispatcher<false>::call(src, dst, op, mask, stream);
+                    return;
+                }
+
+                const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
+                const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1);
+
+                transformSmart<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
+                cudaSafeCall( cudaGetLastError() );
+
+                if (stream == 0)
+                    cudaSafeCall( cudaDeviceSynchronize() );
+            }
+
+            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+            static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
+            {
+                typedef TransformFunctorTraits<BinOp> ft;
+
+                CV_StaticAssert(ft::smart_shift != 1, "");
+
+                if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) ||
+                    !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) ||
+                    !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
+                {
+                    TransformDispatcher<false>::call(src1, src2, dst, op, mask, stream);
+                    return;
+                }
+
+                const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
+                const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1);
+
+                transformSmart<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
+                cudaSafeCall( cudaGetLastError() );
+
+                if (stream == 0)
+                    cudaSafeCall( cudaDeviceSynchronize() );
+            }
+        };
+    } // namespace transform_detail
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_TRANSFORM_DETAIL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/detail/type_traits_detail.hpp b/3rdParty/include/opencv2/core/cuda/detail/type_traits_detail.hpp
new file mode 100644
index 0000000..a78bd2c
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/detail/type_traits_detail.hpp
@@ -0,0 +1,191 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP
+#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP
+
+#include "../common.hpp"
+#include "../vec_traits.hpp"
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    namespace type_traits_detail
+    {
+        template <bool, typename T1, typename T2> struct Select { typedef T1 type; };
+        template <typename T1, typename T2> struct Select<false, T1, T2> { typedef T2 type; };
+
+        template <typename T> struct IsSignedIntergral { enum {value = 0}; };
+        template <> struct IsSignedIntergral<schar> { enum {value = 1}; };
+        template <> struct IsSignedIntergral<char1> { enum {value = 1}; };
+        template <> struct IsSignedIntergral<short> { enum {value = 1}; };
+        template <> struct IsSignedIntergral<short1> { enum {value = 1}; };
+        template <> struct IsSignedIntergral<int> { enum {value = 1}; };
+        template <> struct IsSignedIntergral<int1> { enum {value = 1}; };
+
+        template <typename T> struct IsUnsignedIntegral { enum {value = 0}; };
+        template <> struct IsUnsignedIntegral<uchar> { enum {value = 1}; };
+        template <> struct IsUnsignedIntegral<uchar1> { enum {value = 1}; };
+        template <> struct IsUnsignedIntegral<ushort> { enum {value = 1}; };
+        template <> struct IsUnsignedIntegral<ushort1> { enum {value = 1}; };
+        template <> struct IsUnsignedIntegral<uint> { enum {value = 1}; };
+        template <> struct IsUnsignedIntegral<uint1> { enum {value = 1}; };
+
+        template <typename T> struct IsIntegral { enum {value = IsSignedIntergral<T>::value || IsUnsignedIntegral<T>::value}; };
+        template <> struct IsIntegral<char> { enum {value = 1}; };
+        template <> struct IsIntegral<bool> { enum {value = 1}; };
+
+        template <typename T> struct IsFloat { enum {value = 0}; };
+        template <> struct IsFloat<float> { enum {value = 1}; };
+        template <> struct IsFloat<double> { enum {value = 1}; };
+
+        template <typename T> struct IsVec { enum {value = 0}; };
+        template <> struct IsVec<uchar1> { enum {value = 1}; };
+        template <> struct IsVec<uchar2> { enum {value = 1}; };
+        template <> struct IsVec<uchar3> { enum {value = 1}; };
+        template <> struct IsVec<uchar4> { enum {value = 1}; };
+        template <> struct IsVec<uchar8> { enum {value = 1}; };
+        template <> struct IsVec<char1> { enum {value = 1}; };
+        template <> struct IsVec<char2> { enum {value = 1}; };
+        template <> struct IsVec<char3> { enum {value = 1}; };
+        template <> struct IsVec<char4> { enum {value = 1}; };
+        template <> struct IsVec<char8> { enum {value = 1}; };
+        template <> struct IsVec<ushort1> { enum {value = 1}; };
+        template <> struct IsVec<ushort2> { enum {value = 1}; };
+        template <> struct IsVec<ushort3> { enum {value = 1}; };
+        template <> struct IsVec<ushort4> { enum {value = 1}; };
+        template <> struct IsVec<ushort8> { enum {value = 1}; };
+        template <> struct IsVec<short1> { enum {value = 1}; };
+        template <> struct IsVec<short2> { enum {value = 1}; };
+        template <> struct IsVec<short3> { enum {value = 1}; };
+        template <> struct IsVec<short4> { enum {value = 1}; };
+        template <> struct IsVec<short8> { enum {value = 1}; };
+        template <> struct IsVec<uint1> { enum {value = 1}; };
+        template <> struct IsVec<uint2> { enum {value = 1}; };
+        template <> struct IsVec<uint3> { enum {value = 1}; };
+        template <> struct IsVec<uint4> { enum {value = 1}; };
+        template <> struct IsVec<uint8> { enum {value = 1}; };
+        template <> struct IsVec<int1> { enum {value = 1}; };
+        template <> struct IsVec<int2> { enum {value = 1}; };
+        template <> struct IsVec<int3> { enum {value = 1}; };
+        template <> struct IsVec<int4> { enum {value = 1}; };
+        template <> struct IsVec<int8> { enum {value = 1}; };
+        template <> struct IsVec<float1> { enum {value = 1}; };
+        template <> struct IsVec<float2> { enum {value = 1}; };
+        template <> struct IsVec<float3> { enum {value = 1}; };
+        template <> struct IsVec<float4> { enum {value = 1}; };
+        template <> struct IsVec<float8> { enum {value = 1}; };
+        template <> struct IsVec<double1> { enum {value = 1}; };
+        template <> struct IsVec<double2> { enum {value = 1}; };
+        template <> struct IsVec<double3> { enum {value = 1}; };
+        template <> struct IsVec<double4> { enum {value = 1}; };
+        template <> struct IsVec<double8> { enum {value = 1}; };
+
+        template <class U> struct AddParameterType { typedef const U& type; };
+        template <class U> struct AddParameterType<U&> { typedef U& type; };
+        template <> struct AddParameterType<void> { typedef void type; };
+
+        template <class U> struct ReferenceTraits
+        {
+            enum { value = false };
+            typedef U type;
+        };
+        template <class U> struct ReferenceTraits<U&>
+        {
+            enum { value = true };
+            typedef U type;
+        };
+
+        template <class U> struct PointerTraits
+        {
+            enum { value = false };
+            typedef void type;
+        };
+        template <class U> struct PointerTraits<U*>
+        {
+            enum { value = true };
+            typedef U type;
+        };
+        template <class U> struct PointerTraits<U*&>
+        {
+            enum { value = true };
+            typedef U type;
+        };
+
+        template <class U> struct UnConst
+        {
+            typedef U type;
+            enum { value = 0 };
+        };
+        template <class U> struct UnConst<const U>
+        {
+            typedef U type;
+            enum { value = 1 };
+        };
+        template <class U> struct UnConst<const U&>
+        {
+            typedef U& type;
+            enum { value = 1 };
+        };
+
+        template <class U> struct UnVolatile
+        {
+            typedef U type;
+            enum { value = 0 };
+        };
+        template <class U> struct UnVolatile<volatile U>
+        {
+            typedef U type;
+            enum { value = 1 };
+        };
+        template <class U> struct UnVolatile<volatile U&>
+        {
+            typedef U& type;
+            enum { value = 1 };
+        };
+    } // namespace type_traits_detail
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/detail/vec_distance_detail.hpp b/3rdParty/include/opencv2/core/cuda/detail/vec_distance_detail.hpp
new file mode 100644
index 0000000..8283a99
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/detail/vec_distance_detail.hpp
@@ -0,0 +1,121 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP
+#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP
+
+#include "../datamov_utils.hpp"
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    namespace vec_distance_detail
+    {
+        template <int THREAD_DIM, int N> struct UnrollVecDiffCached
+        {
+            template <typename Dist, typename T1, typename T2>
+            static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind)
+            {
+                if (ind < len)
+                {
+                    T1 val1 = *vecCached++;
+
+                    T2 val2;
+                    ForceGlob<T2>::Load(vecGlob, ind, val2);
+
+                    dist.reduceIter(val1, val2);
+
+                    UnrollVecDiffCached<THREAD_DIM, N - 1>::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM);
+                }
+            }
+
+            template <typename Dist, typename T1, typename T2>
+            static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist)
+            {
+                T1 val1 = *vecCached++;
+
+                T2 val2;
+                ForceGlob<T2>::Load(vecGlob, 0, val2);
+                vecGlob += THREAD_DIM;
+
+                dist.reduceIter(val1, val2);
+
+                UnrollVecDiffCached<THREAD_DIM, N - 1>::calcWithoutCheck(vecCached, vecGlob, dist);
+            }
+        };
+        template <int THREAD_DIM> struct UnrollVecDiffCached<THREAD_DIM, 0>
+        {
+            template <typename Dist, typename T1, typename T2>
+            static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int)
+            {
+            }
+
+            template <typename Dist, typename T1, typename T2>
+            static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&)
+            {
+            }
+        };
+
+        template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN> struct VecDiffCachedCalculator;
+        template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, false>
+        {
+            template <typename Dist, typename T1, typename T2>
+            static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
+            {
+                UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcCheck(vecCached, vecGlob, len, dist, tid);
+            }
+        };
+        template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, true>
+        {
+            template <typename Dist, typename T1, typename T2>
+            static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
+            {
+                UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcWithoutCheck(vecCached, vecGlob + tid, dist);
+            }
+        };
+    } // namespace vec_distance_detail
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/dynamic_smem.hpp b/3rdParty/include/opencv2/core/cuda/dynamic_smem.hpp
new file mode 100644
index 0000000..42570c6
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/dynamic_smem.hpp
@@ -0,0 +1,88 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_DYNAMIC_SMEM_HPP
+#define OPENCV_CUDA_DYNAMIC_SMEM_HPP
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template<class T> struct DynamicSharedMem
+    {
+        __device__ __forceinline__ operator T*()
+        {
+            extern __shared__ int __smem[];
+            return (T*)__smem;
+        }
+
+        __device__ __forceinline__ operator const T*() const
+        {
+            extern __shared__ int __smem[];
+            return (T*)__smem;
+        }
+    };
+
+    // specialize for double to avoid unaligned memory access compile errors
+    template<> struct DynamicSharedMem<double>
+    {
+        __device__ __forceinline__ operator double*()
+        {
+            extern __shared__ double __smem_d[];
+            return (double*)__smem_d;
+        }
+
+        __device__ __forceinline__ operator const double*() const
+        {
+            extern __shared__ double __smem_d[];
+            return (double*)__smem_d;
+        }
+    };
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_DYNAMIC_SMEM_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/emulation.hpp b/3rdParty/include/opencv2/core/cuda/emulation.hpp
new file mode 100644
index 0000000..17dc117
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/emulation.hpp
@@ -0,0 +1,269 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_EMULATION_HPP_
+#define OPENCV_CUDA_EMULATION_HPP_
+
+#include "common.hpp"
+#include "warp_reduce.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    struct Emulation
+    {
+
+        static __device__ __forceinline__ int syncthreadsOr(int pred)
+        {
+#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
+                // just campilation stab
+                return 0;
+#else
+                return __syncthreads_or(pred);
+#endif
+        }
+
+        template<int CTA_SIZE>
+        static __forceinline__ __device__ int Ballot(int predicate)
+        {
+#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200)
+            return __ballot(predicate);
+#else
+            __shared__ volatile int cta_buffer[CTA_SIZE];
+
+            int tid = threadIdx.x;
+            cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0;
+            return warp_reduce(cta_buffer);
+#endif
+        }
+
+        struct smem
+        {
+            enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U };
+
+            template<typename T>
+            static __device__ __forceinline__ T atomicInc(T* address, T val)
+            {
+#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
+                T count;
+                unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
+                do
+                {
+                    count = *address & TAG_MASK;
+                    count = tag | (count + 1);
+                    *address = count;
+                } while (*address != count);
+
+                return (count & TAG_MASK) - 1;
+#else
+                return ::atomicInc(address, val);
+#endif
+            }
+
+            template<typename T>
+            static __device__ __forceinline__ T atomicAdd(T* address, T val)
+            {
+#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
+                T count;
+                unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
+                do
+                {
+                    count = *address & TAG_MASK;
+                    count = tag | (count + val);
+                    *address = count;
+                } while (*address != count);
+
+                return (count & TAG_MASK) - val;
+#else
+                return ::atomicAdd(address, val);
+#endif
+            }
+
+            template<typename T>
+            static __device__ __forceinline__ T atomicMin(T* address, T val)
+            {
+#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
+                T count = ::min(*address, val);
+                do
+                {
+                    *address = count;
+                } while (*address > count);
+
+                return count;
+#else
+                return ::atomicMin(address, val);
+#endif
+            }
+        }; // struct cmem
+
+        struct glob
+        {
+            static __device__ __forceinline__ int atomicAdd(int* address, int val)
+            {
+                return ::atomicAdd(address, val);
+            }
+            static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val)
+            {
+                return ::atomicAdd(address, val);
+            }
+            static __device__ __forceinline__ float atomicAdd(float* address, float val)
+            {
+            #if __CUDA_ARCH__ >= 200
+                return ::atomicAdd(address, val);
+            #else
+                int* address_as_i = (int*) address;
+                int old = *address_as_i, assumed;
+                do {
+                    assumed = old;
+                    old = ::atomicCAS(address_as_i, assumed,
+                        __float_as_int(val + __int_as_float(assumed)));
+                } while (assumed != old);
+                return __int_as_float(old);
+            #endif
+            }
+            static __device__ __forceinline__ double atomicAdd(double* address, double val)
+            {
+            #if __CUDA_ARCH__ >= 130
+                unsigned long long int* address_as_ull = (unsigned long long int*) address;
+                unsigned long long int old = *address_as_ull, assumed;
+                do {
+                    assumed = old;
+                    old = ::atomicCAS(address_as_ull, assumed,
+                        __double_as_longlong(val + __longlong_as_double(assumed)));
+                } while (assumed != old);
+                return __longlong_as_double(old);
+            #else
+                CV_UNUSED(address);
+                CV_UNUSED(val);
+                return 0.0;
+            #endif
+            }
+
+            static __device__ __forceinline__ int atomicMin(int* address, int val)
+            {
+                return ::atomicMin(address, val);
+            }
+            static __device__ __forceinline__ float atomicMin(float* address, float val)
+            {
+            #if __CUDA_ARCH__ >= 120
+                int* address_as_i = (int*) address;
+                int old = *address_as_i, assumed;
+                do {
+                    assumed = old;
+                    old = ::atomicCAS(address_as_i, assumed,
+                        __float_as_int(::fminf(val, __int_as_float(assumed))));
+                } while (assumed != old);
+                return __int_as_float(old);
+            #else
+                CV_UNUSED(address);
+                CV_UNUSED(val);
+                return 0.0f;
+            #endif
+            }
+            static __device__ __forceinline__ double atomicMin(double* address, double val)
+            {
+            #if __CUDA_ARCH__ >= 130
+                unsigned long long int* address_as_ull = (unsigned long long int*) address;
+                unsigned long long int old = *address_as_ull, assumed;
+                do {
+                    assumed = old;
+                    old = ::atomicCAS(address_as_ull, assumed,
+                        __double_as_longlong(::fmin(val, __longlong_as_double(assumed))));
+                } while (assumed != old);
+                return __longlong_as_double(old);
+            #else
+                CV_UNUSED(address);
+                CV_UNUSED(val);
+                return 0.0;
+            #endif
+            }
+
+            static __device__ __forceinline__ int atomicMax(int* address, int val)
+            {
+                return ::atomicMax(address, val);
+            }
+            static __device__ __forceinline__ float atomicMax(float* address, float val)
+            {
+            #if __CUDA_ARCH__ >= 120
+                int* address_as_i = (int*) address;
+                int old = *address_as_i, assumed;
+                do {
+                    assumed = old;
+                    old = ::atomicCAS(address_as_i, assumed,
+                        __float_as_int(::fmaxf(val, __int_as_float(assumed))));
+                } while (assumed != old);
+                return __int_as_float(old);
+            #else
+                CV_UNUSED(address);
+                CV_UNUSED(val);
+                return 0.0f;
+            #endif
+            }
+            static __device__ __forceinline__ double atomicMax(double* address, double val)
+            {
+            #if __CUDA_ARCH__ >= 130
+                unsigned long long int* address_as_ull = (unsigned long long int*) address;
+                unsigned long long int old = *address_as_ull, assumed;
+                do {
+                    assumed = old;
+                    old = ::atomicCAS(address_as_ull, assumed,
+                        __double_as_longlong(::fmax(val, __longlong_as_double(assumed))));
+                } while (assumed != old);
+                return __longlong_as_double(old);
+            #else
+                CV_UNUSED(address);
+                CV_UNUSED(val);
+                return 0.0;
+            #endif
+            }
+        };
+    }; //struct Emulation
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif /* OPENCV_CUDA_EMULATION_HPP_ */
diff --git a/3rdParty/include/opencv2/core/cuda/filters.hpp b/3rdParty/include/opencv2/core/cuda/filters.hpp
new file mode 100644
index 0000000..bf3147e
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/filters.hpp
@@ -0,0 +1,293 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_FILTERS_HPP
+#define OPENCV_CUDA_FILTERS_HPP
+
+#include "saturate_cast.hpp"
+#include "vec_traits.hpp"
+#include "vec_math.hpp"
+#include "type_traits.hpp"
+#include "nppdefs.h"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template <typename Ptr2D> struct PointFilter
+    {
+        typedef typename Ptr2D::elem_type elem_type;
+        typedef float index_type;
+
+        explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
+        : src(src_)
+        {
+            CV_UNUSED(fx);
+            CV_UNUSED(fy);
+        }
+
+        __device__ __forceinline__ elem_type operator ()(float y, float x) const
+        {
+            return src(__float2int_rz(y), __float2int_rz(x));
+        }
+
+        Ptr2D src;
+    };
+
+    template <typename Ptr2D> struct LinearFilter
+    {
+        typedef typename Ptr2D::elem_type elem_type;
+        typedef float index_type;
+
+        explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
+        : src(src_)
+        {
+            CV_UNUSED(fx);
+            CV_UNUSED(fy);
+        }
+        __device__ __forceinline__ elem_type operator ()(float y, float x) const
+        {
+            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
+
+            work_type out = VecTraits<work_type>::all(0);
+
+            const int x1 = __float2int_rd(x);
+            const int y1 = __float2int_rd(y);
+            if (x1 <= NPP_MIN_32S || x1 >= NPP_MAX_32S || y1 <= NPP_MIN_32S || y1 >= NPP_MAX_32S)
+            {
+                elem_type src_reg = src(y1, x1);
+                out = out + src_reg * 1.0f;
+                return saturate_cast<elem_type>(out);
+            }
+            const int x2 = x1 + 1;
+            const int y2 = y1 + 1;
+
+            elem_type src_reg = src(y1, x1);
+            out = out + src_reg * ((x2 - x) * (y2 - y));
+
+            src_reg = src(y1, x2);
+            out = out + src_reg * ((x - x1) * (y2 - y));
+
+            src_reg = src(y2, x1);
+            out = out + src_reg * ((x2 - x) * (y - y1));
+
+            src_reg = src(y2, x2);
+            out = out + src_reg * ((x - x1) * (y - y1));
+
+            return saturate_cast<elem_type>(out);
+        }
+
+        Ptr2D src;
+    };
+
+    template <typename Ptr2D> struct CubicFilter
+    {
+        typedef typename Ptr2D::elem_type elem_type;
+        typedef float index_type;
+        typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
+
+        explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
+        : src(src_)
+        {
+            CV_UNUSED(fx);
+            CV_UNUSED(fy);
+        }
+
+        static __device__ __forceinline__ float bicubicCoeff(float x_)
+        {
+            float x = fabsf(x_);
+            if (x <= 1.0f)
+            {
+                return x * x * (1.5f * x - 2.5f) + 1.0f;
+            }
+            else if (x < 2.0f)
+            {
+                return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
+            }
+            else
+            {
+                return 0.0f;
+            }
+        }
+
+        __device__ elem_type operator ()(float y, float x) const
+        {
+            const float xmin = ::ceilf(x - 2.0f);
+            const float xmax = ::floorf(x + 2.0f);
+
+            const float ymin = ::ceilf(y - 2.0f);
+            const float ymax = ::floorf(y + 2.0f);
+
+            work_type sum = VecTraits<work_type>::all(0);
+            float wsum = 0.0f;
+
+            for (float cy = ymin; cy <= ymax; cy += 1.0f)
+            {
+                for (float cx = xmin; cx <= xmax; cx += 1.0f)
+                {
+                    const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy);
+                    sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx));
+                    wsum += w;
+                }
+            }
+
+            work_type res = (!wsum)? VecTraits<work_type>::all(0) : sum / wsum;
+
+            return saturate_cast<elem_type>(res);
+        }
+
+        Ptr2D src;
+    };
+    // for integer scaling
+    template <typename Ptr2D> struct IntegerAreaFilter
+    {
+        typedef typename Ptr2D::elem_type elem_type;
+        typedef float index_type;
+
+        explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
+            : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {}
+
+        __device__ __forceinline__ elem_type operator ()(float y, float x) const
+        {
+            float fsx1 = x * scale_x;
+            float fsx2 = fsx1 + scale_x;
+
+            int sx1 = __float2int_ru(fsx1);
+            int sx2 = __float2int_rd(fsx2);
+
+            float fsy1 = y * scale_y;
+            float fsy2 = fsy1 + scale_y;
+
+            int sy1 = __float2int_ru(fsy1);
+            int sy2 = __float2int_rd(fsy2);
+
+            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
+            work_type out = VecTraits<work_type>::all(0.f);
+
+            for(int dy = sy1; dy < sy2; ++dy)
+                for(int dx = sx1; dx < sx2; ++dx)
+                {
+                    out = out + src(dy, dx) * scale;
+                }
+
+            return saturate_cast<elem_type>(out);
+        }
+
+        Ptr2D src;
+        float scale_x, scale_y ,scale;
+    };
+
+    template <typename Ptr2D> struct AreaFilter
+    {
+        typedef typename Ptr2D::elem_type elem_type;
+        typedef float index_type;
+
+        explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
+            : src(src_), scale_x(scale_x_), scale_y(scale_y_){}
+
+        __device__ __forceinline__ elem_type operator ()(float y, float x) const
+        {
+            float fsx1 = x * scale_x;
+            float fsx2 = fsx1 + scale_x;
+
+            int sx1 = __float2int_ru(fsx1);
+            int sx2 = __float2int_rd(fsx2);
+
+            float fsy1 = y * scale_y;
+            float fsy2 = fsy1 + scale_y;
+
+            int sy1 = __float2int_ru(fsy1);
+            int sy2 = __float2int_rd(fsy2);
+
+            float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1));
+
+            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
+            work_type out = VecTraits<work_type>::all(0.f);
+
+            for (int dy = sy1; dy < sy2; ++dy)
+            {
+                for (int dx = sx1; dx < sx2; ++dx)
+                    out = out + src(dy, dx) * scale;
+
+                if (sx1 > fsx1)
+                    out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale);
+
+                if (sx2 < fsx2)
+                    out = out + src(dy, sx2) * ((fsx2 -sx2) * scale);
+            }
+
+            if (sy1 > fsy1)
+                for (int dx = sx1; dx < sx2; ++dx)
+                    out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale);
+
+            if (sy2 < fsy2)
+                for (int dx = sx1; dx < sx2; ++dx)
+                    out = out + src(sy2, dx) * ((fsy2 -sy2) * scale);
+
+            if ((sy1 > fsy1) &&  (sx1 > fsx1))
+                out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale);
+
+            if ((sy1 > fsy1) &&  (sx2 < fsx2))
+                out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale);
+
+            if ((sy2 < fsy2) &&  (sx2 < fsx2))
+                out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale);
+
+            if ((sy2 < fsy2) &&  (sx1 > fsx1))
+                out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale);
+
+            return saturate_cast<elem_type>(out);
+        }
+
+        Ptr2D src;
+        float scale_x, scale_y;
+        int width, haight;
+    };
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_FILTERS_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/funcattrib.hpp b/3rdParty/include/opencv2/core/cuda/funcattrib.hpp
new file mode 100644
index 0000000..f582080
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/funcattrib.hpp
@@ -0,0 +1,79 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP
+#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP
+
+#include <cstdio>
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template<class Func>
+    void printFuncAttrib(Func& func)
+    {
+
+        cudaFuncAttributes attrs;
+        cudaFuncGetAttributes(&attrs, func);
+
+        printf("=== Function stats ===\n");
+        printf("Name: \n");
+        printf("sharedSizeBytes    = %d\n", attrs.sharedSizeBytes);
+        printf("constSizeBytes     = %d\n", attrs.constSizeBytes);
+        printf("localSizeBytes     = %d\n", attrs.localSizeBytes);
+        printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock);
+        printf("numRegs            = %d\n", attrs.numRegs);
+        printf("ptxVersion         = %d\n", attrs.ptxVersion);
+        printf("binaryVersion      = %d\n", attrs.binaryVersion);
+        printf("\n");
+        fflush(stdout);
+    }
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif  /* OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP */
diff --git a/3rdParty/include/opencv2/core/cuda/functional.hpp b/3rdParty/include/opencv2/core/cuda/functional.hpp
new file mode 100644
index 0000000..9f53d87
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/functional.hpp
@@ -0,0 +1,805 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_FUNCTIONAL_HPP
+#define OPENCV_CUDA_FUNCTIONAL_HPP
+
+#include <functional>
+#include "saturate_cast.hpp"
+#include "vec_traits.hpp"
+#include "type_traits.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    // Function Objects
+    template<typename Argument, typename Result> struct unary_function
+    {
+        typedef Argument argument_type;
+        typedef Result result_type;
+    };
+    template<typename Argument1, typename Argument2, typename Result> struct binary_function
+    {
+        typedef Argument1 first_argument_type;
+        typedef Argument2 second_argument_type;
+        typedef Result result_type;
+    };
+
+    // Arithmetic Operations
+    template <typename T> struct plus : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a + b;
+        }
+        __host__ __device__ __forceinline__ plus() {}
+        __host__ __device__ __forceinline__ plus(const plus&) {}
+    };
+
+    template <typename T> struct minus : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a - b;
+        }
+        __host__ __device__ __forceinline__ minus() {}
+        __host__ __device__ __forceinline__ minus(const minus&) {}
+    };
+
+    template <typename T> struct multiplies : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a * b;
+        }
+        __host__ __device__ __forceinline__ multiplies() {}
+        __host__ __device__ __forceinline__ multiplies(const multiplies&) {}
+    };
+
+    template <typename T> struct divides : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a / b;
+        }
+        __host__ __device__ __forceinline__ divides() {}
+        __host__ __device__ __forceinline__ divides(const divides&) {}
+    };
+
+    template <typename T> struct modulus : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a % b;
+        }
+        __host__ __device__ __forceinline__ modulus() {}
+        __host__ __device__ __forceinline__ modulus(const modulus&) {}
+    };
+
+    template <typename T> struct negate : unary_function<T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a) const
+        {
+            return -a;
+        }
+        __host__ __device__ __forceinline__ negate() {}
+        __host__ __device__ __forceinline__ negate(const negate&) {}
+    };
+
+    // Comparison Operations
+    template <typename T> struct equal_to : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a == b;
+        }
+        __host__ __device__ __forceinline__ equal_to() {}
+        __host__ __device__ __forceinline__ equal_to(const equal_to&) {}
+    };
+
+    template <typename T> struct not_equal_to : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a != b;
+        }
+        __host__ __device__ __forceinline__ not_equal_to() {}
+        __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {}
+    };
+
+    template <typename T> struct greater : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a > b;
+        }
+        __host__ __device__ __forceinline__ greater() {}
+        __host__ __device__ __forceinline__ greater(const greater&) {}
+    };
+
+    template <typename T> struct less : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a < b;
+        }
+        __host__ __device__ __forceinline__ less() {}
+        __host__ __device__ __forceinline__ less(const less&) {}
+    };
+
+    template <typename T> struct greater_equal : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a >= b;
+        }
+        __host__ __device__ __forceinline__ greater_equal() {}
+        __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {}
+    };
+
+    template <typename T> struct less_equal : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a <= b;
+        }
+        __host__ __device__ __forceinline__ less_equal() {}
+        __host__ __device__ __forceinline__ less_equal(const less_equal&) {}
+    };
+
+    // Logical Operations
+    template <typename T> struct logical_and : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a && b;
+        }
+        __host__ __device__ __forceinline__ logical_and() {}
+        __host__ __device__ __forceinline__ logical_and(const logical_and&) {}
+    };
+
+    template <typename T> struct logical_or : binary_function<T, T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
+                                                    typename TypeTraits<T>::ParameterType b) const
+        {
+            return a || b;
+        }
+        __host__ __device__ __forceinline__ logical_or() {}
+        __host__ __device__ __forceinline__ logical_or(const logical_or&) {}
+    };
+
+    template <typename T> struct logical_not : unary_function<T, bool>
+    {
+        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a) const
+        {
+            return !a;
+        }
+        __host__ __device__ __forceinline__ logical_not() {}
+        __host__ __device__ __forceinline__ logical_not(const logical_not&) {}
+    };
+
+    // Bitwise Operations
+    template <typename T> struct bit_and : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a & b;
+        }
+        __host__ __device__ __forceinline__ bit_and() {}
+        __host__ __device__ __forceinline__ bit_and(const bit_and&) {}
+    };
+
+    template <typename T> struct bit_or : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a | b;
+        }
+        __host__ __device__ __forceinline__ bit_or() {}
+        __host__ __device__ __forceinline__ bit_or(const bit_or&) {}
+    };
+
+    template <typename T> struct bit_xor : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
+                                                 typename TypeTraits<T>::ParameterType b) const
+        {
+            return a ^ b;
+        }
+        __host__ __device__ __forceinline__ bit_xor() {}
+        __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {}
+    };
+
+    template <typename T> struct bit_not : unary_function<T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType v) const
+        {
+            return ~v;
+        }
+        __host__ __device__ __forceinline__ bit_not() {}
+        __host__ __device__ __forceinline__ bit_not(const bit_not&) {}
+    };
+
+    // Generalized Identity Operations
+    template <typename T> struct identity : unary_function<T, T>
+    {
+        __device__ __forceinline__ typename TypeTraits<T>::ParameterType operator()(typename TypeTraits<T>::ParameterType x) const
+        {
+            return x;
+        }
+        __host__ __device__ __forceinline__ identity() {}
+        __host__ __device__ __forceinline__ identity(const identity&) {}
+    };
+
+    template <typename T1, typename T2> struct project1st : binary_function<T1, T2, T1>
+    {
+        __device__ __forceinline__ typename TypeTraits<T1>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
+        {
+            return lhs;
+        }
+        __host__ __device__ __forceinline__ project1st() {}
+        __host__ __device__ __forceinline__ project1st(const project1st&) {}
+    };
+
+    template <typename T1, typename T2> struct project2nd : binary_function<T1, T2, T2>
+    {
+        __device__ __forceinline__ typename TypeTraits<T2>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
+        {
+            return rhs;
+        }
+        __host__ __device__ __forceinline__ project2nd() {}
+        __host__ __device__ __forceinline__ project2nd(const project2nd&) {}
+    };
+
+    // Min/Max Operations
+
+#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \
+    template <> struct name<type> : binary_function<type, type, type> \
+    { \
+        __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \
+        __host__ __device__ __forceinline__ name() {}\
+        __host__ __device__ __forceinline__ name(const name&) {}\
+    };
+
+    template <typename T> struct maximum : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
+        {
+            return max(lhs, rhs);
+        }
+        __host__ __device__ __forceinline__ maximum() {}
+        __host__ __device__ __forceinline__ maximum(const maximum&) {}
+    };
+
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax)
+
+    template <typename T> struct minimum : binary_function<T, T, T>
+    {
+        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
+        {
+            return min(lhs, rhs);
+        }
+        __host__ __device__ __forceinline__ minimum() {}
+        __host__ __device__ __forceinline__ minimum(const minimum&) {}
+    };
+
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin)
+    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin)
+
+#undef OPENCV_CUDA_IMPLEMENT_MINMAX
+
+    // Math functions
+
+    template <typename T> struct abs_func : unary_function<T, T>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType x) const
+        {
+            return abs(x);
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<unsigned char> : unary_function<unsigned char, unsigned char>
+    {
+        __device__ __forceinline__ unsigned char operator ()(unsigned char x) const
+        {
+            return x;
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<signed char> : unary_function<signed char, signed char>
+    {
+        __device__ __forceinline__ signed char operator ()(signed char x) const
+        {
+            return ::abs((int)x);
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<char> : unary_function<char, char>
+    {
+        __device__ __forceinline__ char operator ()(char x) const
+        {
+            return ::abs((int)x);
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<unsigned short> : unary_function<unsigned short, unsigned short>
+    {
+        __device__ __forceinline__ unsigned short operator ()(unsigned short x) const
+        {
+            return x;
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<short> : unary_function<short, short>
+    {
+        __device__ __forceinline__ short operator ()(short x) const
+        {
+            return ::abs((int)x);
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<unsigned int> : unary_function<unsigned int, unsigned int>
+    {
+        __device__ __forceinline__ unsigned int operator ()(unsigned int x) const
+        {
+            return x;
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<int> : unary_function<int, int>
+    {
+        __device__ __forceinline__ int operator ()(int x) const
+        {
+            return ::abs(x);
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<float> : unary_function<float, float>
+    {
+        __device__ __forceinline__ float operator ()(float x) const
+        {
+            return ::fabsf(x);
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+    template <> struct abs_func<double> : unary_function<double, double>
+    {
+        __device__ __forceinline__ double operator ()(double x) const
+        {
+            return ::fabs(x);
+        }
+
+        __host__ __device__ __forceinline__ abs_func() {}
+        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
+    };
+
+#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \
+    template <typename T> struct name ## _func : unary_function<T, float> \
+    { \
+        __device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v) const \
+        { \
+            return func ## f(v); \
+        } \
+        __host__ __device__ __forceinline__ name ## _func() {} \
+        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
+    }; \
+    template <> struct name ## _func<double> : unary_function<double, double> \
+    { \
+        __device__ __forceinline__ double operator ()(double v) const \
+        { \
+            return func(v); \
+        } \
+        __host__ __device__ __forceinline__ name ## _func() {} \
+        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
+    };
+
+#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \
+    template <typename T> struct name ## _func : binary_function<T, T, float> \
+    { \
+        __device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v1, typename TypeTraits<T>::ParameterType v2) const \
+        { \
+            return func ## f(v1, v2); \
+        } \
+        __host__ __device__ __forceinline__ name ## _func() {} \
+        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
+    }; \
+    template <> struct name ## _func<double> : binary_function<double, double, double> \
+    { \
+        __device__ __forceinline__ double operator ()(double v1, double v2) const \
+        { \
+            return func(v1, v2); \
+        } \
+        __host__ __device__ __forceinline__ name ## _func() {} \
+        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
+    };
+
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh)
+    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh)
+
+    OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot)
+    OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2)
+    OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow)
+
+    #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR
+    #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE
+    #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR
+
+    template<typename T> struct hypot_sqr_func : binary_function<T, T, float>
+    {
+        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType src1, typename TypeTraits<T>::ParameterType src2) const
+        {
+            return src1 * src1 + src2 * src2;
+        }
+        __host__ __device__ __forceinline__ hypot_sqr_func() {}
+        __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {}
+    };
+
+    // Saturate Cast Functor
+    template <typename T, typename D> struct saturate_cast_func : unary_function<T, D>
+    {
+        __device__ __forceinline__ D operator ()(typename TypeTraits<T>::ParameterType v) const
+        {
+            return saturate_cast<D>(v);
+        }
+        __host__ __device__ __forceinline__ saturate_cast_func() {}
+        __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {}
+    };
+
+    // Threshold Functors
+    template <typename T> struct thresh_binary_func : unary_function<T, T>
+    {
+        __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
+
+        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
+        {
+            return (src > thresh) * maxVal;
+        }
+
+        __host__ __device__ __forceinline__ thresh_binary_func() {}
+        __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other)
+            : thresh(other.thresh), maxVal(other.maxVal) {}
+
+        T thresh;
+        T maxVal;
+    };
+
+    template <typename T> struct thresh_binary_inv_func : unary_function<T, T>
+    {
+        __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
+
+        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
+        {
+            return (src <= thresh) * maxVal;
+        }
+
+        __host__ __device__ __forceinline__ thresh_binary_inv_func() {}
+        __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other)
+            : thresh(other.thresh), maxVal(other.maxVal) {}
+
+        T thresh;
+        T maxVal;
+    };
+
+    template <typename T> struct thresh_trunc_func : unary_function<T, T>
+    {
+        explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);}
+
+        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
+        {
+            return minimum<T>()(src, thresh);
+        }
+
+        __host__ __device__ __forceinline__ thresh_trunc_func() {}
+        __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other)
+            : thresh(other.thresh) {}
+
+        T thresh;
+    };
+
+    template <typename T> struct thresh_to_zero_func : unary_function<T, T>
+    {
+        explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);}
+
+        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
+        {
+            return (src > thresh) * src;
+        }
+
+        __host__ __device__ __forceinline__ thresh_to_zero_func() {}
+       __host__  __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other)
+            : thresh(other.thresh) {}
+
+        T thresh;
+    };
+
+    template <typename T> struct thresh_to_zero_inv_func : unary_function<T, T>
+    {
+        explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);}
+
+        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
+        {
+            return (src <= thresh) * src;
+        }
+
+        __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {}
+        __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other)
+            : thresh(other.thresh) {}
+
+        T thresh;
+    };
+
+    // Function Object Adaptors
+    template <typename Predicate> struct unary_negate : unary_function<typename Predicate::argument_type, bool>
+    {
+      explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {}
+
+      __device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::argument_type>::ParameterType x) const
+      {
+          return !pred(x);
+      }
+
+      __host__ __device__ __forceinline__ unary_negate() {}
+      __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {}
+
+      Predicate pred;
+    };
+
+    template <typename Predicate> __host__ __device__ __forceinline__ unary_negate<Predicate> not1(const Predicate& pred)
+    {
+        return unary_negate<Predicate>(pred);
+    }
+
+    template <typename Predicate> struct binary_negate : binary_function<typename Predicate::first_argument_type, typename Predicate::second_argument_type, bool>
+    {
+        explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {}
+
+        __device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::first_argument_type>::ParameterType x,
+                                                   typename TypeTraits<typename Predicate::second_argument_type>::ParameterType y) const
+        {
+            return !pred(x,y);
+        }
+
+        __host__ __device__ __forceinline__ binary_negate() {}
+        __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {}
+
+        Predicate pred;
+    };
+
+    template <typename BinaryPredicate> __host__ __device__ __forceinline__ binary_negate<BinaryPredicate> not2(const BinaryPredicate& pred)
+    {
+        return binary_negate<BinaryPredicate>(pred);
+    }
+
+    template <typename Op> struct binder1st : unary_function<typename Op::second_argument_type, typename Op::result_type>
+    {
+        __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {}
+
+        __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits<typename Op::second_argument_type>::ParameterType a) const
+        {
+            return op(arg1, a);
+        }
+
+        __host__ __device__ __forceinline__ binder1st() {}
+        __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {}
+
+        Op op;
+        typename Op::first_argument_type arg1;
+    };
+
+    template <typename Op, typename T> __host__ __device__ __forceinline__ binder1st<Op> bind1st(const Op& op, const T& x)
+    {
+        return binder1st<Op>(op, typename Op::first_argument_type(x));
+    }
+
+    template <typename Op> struct binder2nd : unary_function<typename Op::first_argument_type, typename Op::result_type>
+    {
+        __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {}
+
+        __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits<typename Op::first_argument_type>::ParameterType a) const
+        {
+            return op(a, arg2);
+        }
+
+        __host__ __device__ __forceinline__ binder2nd() {}
+        __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {}
+
+        Op op;
+        typename Op::second_argument_type arg2;
+    };
+
+    template <typename Op, typename T> __host__ __device__ __forceinline__ binder2nd<Op> bind2nd(const Op& op, const T& x)
+    {
+        return binder2nd<Op>(op, typename Op::second_argument_type(x));
+    }
+
+    // Functor Traits
+    template <typename F> struct IsUnaryFunction
+    {
+        typedef char Yes;
+        struct No {Yes a[2];};
+
+        template <typename T, typename D> static Yes check(unary_function<T, D>);
+        static No check(...);
+
+        static F makeF();
+
+        enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
+    };
+
+    template <typename F> struct IsBinaryFunction
+    {
+        typedef char Yes;
+        struct No {Yes a[2];};
+
+        template <typename T1, typename T2, typename D> static Yes check(binary_function<T1, T2, D>);
+        static No check(...);
+
+        static F makeF();
+
+        enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
+    };
+
+    namespace functional_detail
+    {
+        template <size_t src_elem_size, size_t dst_elem_size> struct UnOpShift { enum { shift = 1 }; };
+        template <size_t src_elem_size> struct UnOpShift<src_elem_size, 1> { enum { shift = 4 }; };
+        template <size_t src_elem_size> struct UnOpShift<src_elem_size, 2> { enum { shift = 2 }; };
+
+        template <typename T, typename D> struct DefaultUnaryShift
+        {
+            enum { shift = UnOpShift<sizeof(T), sizeof(D)>::shift };
+        };
+
+        template <size_t src_elem_size1, size_t src_elem_size2, size_t dst_elem_size> struct BinOpShift { enum { shift = 1 }; };
+        template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 1> { enum { shift = 4 }; };
+        template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 2> { enum { shift = 2 }; };
+
+        template <typename T1, typename T2, typename D> struct DefaultBinaryShift
+        {
+            enum { shift = BinOpShift<sizeof(T1), sizeof(T2), sizeof(D)>::shift };
+        };
+
+        template <typename Func, bool unary = IsUnaryFunction<Func>::value> struct ShiftDispatcher;
+        template <typename Func> struct ShiftDispatcher<Func, true>
+        {
+            enum { shift = DefaultUnaryShift<typename Func::argument_type, typename Func::result_type>::shift };
+        };
+        template <typename Func> struct ShiftDispatcher<Func, false>
+        {
+            enum { shift = DefaultBinaryShift<typename Func::first_argument_type, typename Func::second_argument_type, typename Func::result_type>::shift };
+        };
+    }
+
+    template <typename Func> struct DefaultTransformShift
+    {
+        enum { shift = functional_detail::ShiftDispatcher<Func>::shift };
+    };
+
+    template <typename Func> struct DefaultTransformFunctorTraits
+    {
+        enum { simple_block_dim_x = 16 };
+        enum { simple_block_dim_y = 16 };
+
+        enum { smart_block_dim_x = 16 };
+        enum { smart_block_dim_y = 16 };
+        enum { smart_shift = DefaultTransformShift<Func>::shift };
+    };
+
+    template <typename Func> struct TransformFunctorTraits : DefaultTransformFunctorTraits<Func> {};
+
+#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \
+    template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type >
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_FUNCTIONAL_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/limits.hpp b/3rdParty/include/opencv2/core/cuda/limits.hpp
new file mode 100644
index 0000000..7e15ed6
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/limits.hpp
@@ -0,0 +1,128 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_LIMITS_HPP
+#define OPENCV_CUDA_LIMITS_HPP
+
+#include <limits.h>
+#include <float.h>
+#include "common.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+template <class T> struct numeric_limits;
+
+template <> struct numeric_limits<bool>
+{
+    __device__ __forceinline__ static bool min() { return false; }
+    __device__ __forceinline__ static bool max() { return true;  }
+    static const bool is_signed = false;
+};
+
+template <> struct numeric_limits<signed char>
+{
+    __device__ __forceinline__ static signed char min() { return SCHAR_MIN; }
+    __device__ __forceinline__ static signed char max() { return SCHAR_MAX; }
+    static const bool is_signed = true;
+};
+
+template <> struct numeric_limits<unsigned char>
+{
+    __device__ __forceinline__ static unsigned char min() { return 0; }
+    __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; }
+    static const bool is_signed = false;
+};
+
+template <> struct numeric_limits<short>
+{
+    __device__ __forceinline__ static short min() { return SHRT_MIN; }
+    __device__ __forceinline__ static short max() { return SHRT_MAX; }
+    static const bool is_signed = true;
+};
+
+template <> struct numeric_limits<unsigned short>
+{
+    __device__ __forceinline__ static unsigned short min() { return 0; }
+    __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; }
+    static const bool is_signed = false;
+};
+
+template <> struct numeric_limits<int>
+{
+    __device__ __forceinline__ static int min() { return INT_MIN; }
+    __device__ __forceinline__ static int max() { return INT_MAX; }
+    static const bool is_signed = true;
+};
+
+template <> struct numeric_limits<unsigned int>
+{
+    __device__ __forceinline__ static unsigned int min() { return 0; }
+    __device__ __forceinline__ static unsigned int max() { return UINT_MAX; }
+    static const bool is_signed = false;
+};
+
+template <> struct numeric_limits<float>
+{
+    __device__ __forceinline__ static float min() { return FLT_MIN; }
+    __device__ __forceinline__ static float max() { return FLT_MAX; }
+    __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; }
+    static const bool is_signed = true;
+};
+
+template <> struct numeric_limits<double>
+{
+    __device__ __forceinline__ static double min() { return DBL_MIN; }
+    __device__ __forceinline__ static double max() { return DBL_MAX; }
+    __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; }
+    static const bool is_signed = true;
+};
+}}} // namespace cv { namespace cuda { namespace cudev {
+
+//! @endcond
+
+#endif // OPENCV_CUDA_LIMITS_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/reduce.hpp b/3rdParty/include/opencv2/core/cuda/reduce.hpp
new file mode 100644
index 0000000..5de3650
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/reduce.hpp
@@ -0,0 +1,209 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_REDUCE_HPP
+#define OPENCV_CUDA_REDUCE_HPP
+
+#ifndef THRUST_DEBUG // eliminate -Wundef warning
+#define THRUST_DEBUG 0
+#endif
+
+#include <thrust/tuple.h>
+#include "detail/reduce.hpp"
+#include "detail/reduce_key_val.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template <int N, typename T, class Op>
+    __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op)
+    {
+        reduce_detail::Dispatcher<N>::reductor::template reduce<volatile T*, T&, const Op&>(smem, val, tid, op);
+    }
+    template <int N,
+              typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+              typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
+              class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
+    __device__ __forceinline__ void reduce(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+                                           const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+                                           unsigned int tid,
+                                           const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
+    {
+        reduce_detail::Dispatcher<N>::reductor::template reduce<
+                const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>&,
+                const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>&,
+                const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>&>(smem, val, tid, op);
+    }
+
+    template <unsigned int N, typename K, typename V, class Cmp>
+    __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp)
+    {
+        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&, volatile V*, V&, const Cmp&>(skeys, key, svals, val, tid, cmp);
+    }
+    template <unsigned int N,
+              typename K,
+              typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+              typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+              class Cmp>
+    __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key,
+                                                 const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                                 unsigned int tid, const Cmp& cmp)
+    {
+        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&,
+                const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
+                const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
+                const Cmp&>(skeys, key, svals, val, tid, cmp);
+    }
+    template <unsigned int N,
+              typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
+              typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
+              typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+              typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+              class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
+    __device__ __forceinline__ void reduceKeyVal(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
+                                                 const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
+                                                 const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+                                                 unsigned int tid,
+                                                 const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp)
+    {
+        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<
+                const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>&,
+                const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>&,
+                const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
+                const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
+                const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>&
+                >(skeys, key, svals, val, tid, cmp);
+    }
+
+    // smem_tuple
+
+    template <typename T0>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*>
+    smem_tuple(T0* t0)
+    {
+        return thrust::make_tuple((volatile T0*) t0);
+    }
+
+    template <typename T0, typename T1>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*>
+    smem_tuple(T0* t0, T1* t1)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1);
+    }
+
+    template <typename T0, typename T1, typename T2>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*>
+    smem_tuple(T0* t0, T1* t1, T2* t2)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2);
+    }
+
+    template <typename T0, typename T1, typename T2, typename T3>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*>
+    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3);
+    }
+
+    template <typename T0, typename T1, typename T2, typename T3, typename T4>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*>
+    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4);
+    }
+
+    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*>
+    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5);
+    }
+
+    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*>
+    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6);
+    }
+
+    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*>
+    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7);
+    }
+
+    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*>
+    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8);
+    }
+
+    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8, typename T9>
+    __device__ __forceinline__
+    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*, volatile T9*>
+    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9)
+    {
+        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9);
+    }
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_REDUCE_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/saturate_cast.hpp b/3rdParty/include/opencv2/core/cuda/saturate_cast.hpp
new file mode 100644
index 0000000..c3a3d1c
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/saturate_cast.hpp
@@ -0,0 +1,292 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_SATURATE_CAST_HPP
+#define OPENCV_CUDA_SATURATE_CAST_HPP
+
+#include "common.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); }
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); }
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); }
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); }
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); }
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); }
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); }
+    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); }
+
+    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(schar v)
+    {
+        uint res = 0;
+        int vi = v;
+        asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi));
+        return res;
+    }
+    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(short v)
+    {
+        uint res = 0;
+        asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(ushort v)
+    {
+        uint res = 0;
+        asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(int v)
+    {
+        uint res = 0;
+        asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(uint v)
+    {
+        uint res = 0;
+        asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(float v)
+    {
+        uint res = 0;
+        asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(double v)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
+        uint res = 0;
+        asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v));
+        return res;
+    #else
+        return saturate_cast<uchar>((float)v);
+    #endif
+    }
+
+    template<> __device__ __forceinline__ schar saturate_cast<schar>(uchar v)
+    {
+        uint res = 0;
+        uint vi = v;
+        asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi));
+        return res;
+    }
+    template<> __device__ __forceinline__ schar saturate_cast<schar>(short v)
+    {
+        uint res = 0;
+        asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ schar saturate_cast<schar>(ushort v)
+    {
+        uint res = 0;
+        asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ schar saturate_cast<schar>(int v)
+    {
+        uint res = 0;
+        asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ schar saturate_cast<schar>(uint v)
+    {
+        uint res = 0;
+        asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ schar saturate_cast<schar>(float v)
+    {
+        uint res = 0;
+        asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ schar saturate_cast<schar>(double v)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
+        uint res = 0;
+        asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v));
+        return res;
+    #else
+        return saturate_cast<schar>((float)v);
+    #endif
+    }
+
+    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(schar v)
+    {
+        ushort res = 0;
+        int vi = v;
+        asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi));
+        return res;
+    }
+    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(short v)
+    {
+        ushort res = 0;
+        asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(int v)
+    {
+        ushort res = 0;
+        asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(uint v)
+    {
+        ushort res = 0;
+        asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(float v)
+    {
+        ushort res = 0;
+        asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(double v)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
+        ushort res = 0;
+        asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v));
+        return res;
+    #else
+        return saturate_cast<ushort>((float)v);
+    #endif
+    }
+
+    template<> __device__ __forceinline__ short saturate_cast<short>(ushort v)
+    {
+        short res = 0;
+        asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ short saturate_cast<short>(int v)
+    {
+        short res = 0;
+        asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ short saturate_cast<short>(uint v)
+    {
+        short res = 0;
+        asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ short saturate_cast<short>(float v)
+    {
+        short res = 0;
+        asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ short saturate_cast<short>(double v)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
+        short res = 0;
+        asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v));
+        return res;
+    #else
+        return saturate_cast<short>((float)v);
+    #endif
+    }
+
+    template<> __device__ __forceinline__ int saturate_cast<int>(uint v)
+    {
+        int res = 0;
+        asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ int saturate_cast<int>(float v)
+    {
+        return __float2int_rn(v);
+    }
+    template<> __device__ __forceinline__ int saturate_cast<int>(double v)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
+        return __double2int_rn(v);
+    #else
+        return saturate_cast<int>((float)v);
+    #endif
+    }
+
+    template<> __device__ __forceinline__ uint saturate_cast<uint>(schar v)
+    {
+        uint res = 0;
+        int vi = v;
+        asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi));
+        return res;
+    }
+    template<> __device__ __forceinline__ uint saturate_cast<uint>(short v)
+    {
+        uint res = 0;
+        asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ uint saturate_cast<uint>(int v)
+    {
+        uint res = 0;
+        asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v));
+        return res;
+    }
+    template<> __device__ __forceinline__ uint saturate_cast<uint>(float v)
+    {
+        return __float2uint_rn(v);
+    }
+    template<> __device__ __forceinline__ uint saturate_cast<uint>(double v)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
+        return __double2uint_rn(v);
+    #else
+        return saturate_cast<uint>((float)v);
+    #endif
+    }
+}}}
+
+//! @endcond
+
+#endif /* OPENCV_CUDA_SATURATE_CAST_HPP */
diff --git a/3rdParty/include/opencv2/core/cuda/scan.hpp b/3rdParty/include/opencv2/core/cuda/scan.hpp
new file mode 100644
index 0000000..e128fb0
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/scan.hpp
@@ -0,0 +1,258 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_SCAN_HPP
+#define OPENCV_CUDA_SCAN_HPP
+
+#include "opencv2/core/cuda/common.hpp"
+#include "opencv2/core/cuda/utility.hpp"
+#include "opencv2/core/cuda/warp.hpp"
+#include "opencv2/core/cuda/warp_shuffle.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    enum ScanKind { EXCLUSIVE = 0,  INCLUSIVE = 1 };
+
+    template <ScanKind Kind, typename T, typename F> struct WarpScan
+    {
+        __device__ __forceinline__ WarpScan() {}
+        __device__ __forceinline__ WarpScan(const WarpScan& other) { CV_UNUSED(other); }
+
+        __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
+        {
+            const unsigned int lane = idx & 31;
+            F op;
+
+            if ( lane >=  1) ptr [idx ] = op(ptr [idx -  1], ptr [idx]);
+            if ( lane >=  2) ptr [idx ] = op(ptr [idx -  2], ptr [idx]);
+            if ( lane >=  4) ptr [idx ] = op(ptr [idx -  4], ptr [idx]);
+            if ( lane >=  8) ptr [idx ] = op(ptr [idx -  8], ptr [idx]);
+            if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
+
+            if( Kind == INCLUSIVE )
+                return ptr [idx];
+            else
+                return (lane > 0) ? ptr [idx - 1] : 0;
+        }
+
+        __device__ __forceinline__ unsigned int index(const unsigned int tid)
+        {
+            return tid;
+        }
+
+        __device__ __forceinline__ void init(volatile T *ptr){}
+
+        static const int warp_offset      = 0;
+
+        typedef WarpScan<INCLUSIVE, T, F>  merge;
+    };
+
+    template <ScanKind Kind , typename T, typename F> struct WarpScanNoComp
+    {
+        __device__ __forceinline__ WarpScanNoComp() {}
+        __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { CV_UNUSED(other); }
+
+        __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
+        {
+            const unsigned int lane = threadIdx.x & 31;
+            F op;
+
+            ptr [idx ] = op(ptr [idx -  1], ptr [idx]);
+            ptr [idx ] = op(ptr [idx -  2], ptr [idx]);
+            ptr [idx ] = op(ptr [idx -  4], ptr [idx]);
+            ptr [idx ] = op(ptr [idx -  8], ptr [idx]);
+            ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
+
+            if( Kind == INCLUSIVE )
+                return ptr [idx];
+            else
+                return (lane > 0) ? ptr [idx - 1] : 0;
+        }
+
+        __device__ __forceinline__ unsigned int index(const unsigned int tid)
+        {
+            return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask);
+        }
+
+        __device__ __forceinline__ void init(volatile T *ptr)
+        {
+            ptr[threadIdx.x] = 0;
+        }
+
+        static const int warp_smem_stride = 32 + 16 + 1;
+        static const int warp_offset      = 16;
+        static const int warp_log         = 5;
+        static const int warp_mask        = 31;
+
+        typedef WarpScanNoComp<INCLUSIVE, T, F> merge;
+    };
+
+    template <ScanKind Kind , typename T, typename Sc, typename F> struct BlockScan
+    {
+        __device__ __forceinline__ BlockScan() {}
+        __device__ __forceinline__ BlockScan(const BlockScan& other) { CV_UNUSED(other); }
+
+        __device__ __forceinline__ T operator()(volatile T *ptr)
+        {
+            const unsigned int tid  = threadIdx.x;
+            const unsigned int lane = tid & warp_mask;
+            const unsigned int warp = tid >> warp_log;
+
+            Sc scan;
+            typename Sc::merge merge_scan;
+            const unsigned int idx = scan.index(tid);
+
+            T val = scan(ptr, idx);
+            __syncthreads ();
+
+            if( warp == 0)
+                scan.init(ptr);
+            __syncthreads ();
+
+            if( lane == 31 )
+                ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx];
+            __syncthreads ();
+
+            if( warp == 0 )
+                merge_scan(ptr, idx);
+            __syncthreads();
+
+            if ( warp > 0)
+                val = ptr [scan.warp_offset + warp - 1] + val;
+            __syncthreads ();
+
+            ptr[idx] = val;
+            __syncthreads ();
+
+            return val ;
+        }
+
+        static const int warp_log  = 5;
+        static const int warp_mask = 31;
+    };
+
+    template <typename T>
+    __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
+    {
+    #if __CUDA_ARCH__ >= 300
+        const unsigned int laneId = cv::cuda::device::Warp::laneId();
+
+        // scan on shuffl functions
+        #pragma unroll
+        for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2)
+        {
+            const T n = cv::cuda::device::shfl_up(idata, i);
+            if (laneId >= i)
+                  idata += n;
+        }
+
+        return idata;
+    #else
+        unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1));
+        s_Data[pos] = 0;
+        pos += OPENCV_CUDA_WARP_SIZE;
+        s_Data[pos] = idata;
+
+        s_Data[pos] += s_Data[pos - 1];
+        s_Data[pos] += s_Data[pos - 2];
+        s_Data[pos] += s_Data[pos - 4];
+        s_Data[pos] += s_Data[pos - 8];
+        s_Data[pos] += s_Data[pos - 16];
+
+        return s_Data[pos];
+    #endif
+    }
+
+    template <typename T>
+    __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid)
+    {
+        return warpScanInclusive(idata, s_Data, tid) - idata;
+    }
+
+    template <int tiNumScanThreads, typename T>
+    __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
+    {
+        if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE)
+        {
+            //Bottom-level inclusive warp scan
+            T warpResult = warpScanInclusive(idata, s_Data, tid);
+
+            //Save top elements of each warp for exclusive warp scan
+            //sync to wait for warp scans to complete (because s_Data is being overwritten)
+            __syncthreads();
+            if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1))
+            {
+                s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult;
+            }
+
+            //wait for warp scans to complete
+            __syncthreads();
+
+            if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) )
+            {
+                //grab top warp elements
+                T val = s_Data[tid];
+                //calculate exclusive scan and write back to shared memory
+                s_Data[tid] = warpScanExclusive(val, s_Data, tid);
+            }
+
+            //return updated warp scans with exclusive scan results
+            __syncthreads();
+
+            return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE];
+        }
+        else
+        {
+            return warpScanInclusive(idata, s_Data, tid);
+        }
+    }
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_SCAN_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/simd_functions.hpp b/3rdParty/include/opencv2/core/cuda/simd_functions.hpp
new file mode 100644
index 0000000..3d8c2e0
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/simd_functions.hpp
@@ -0,0 +1,869 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+/*
+ * Copyright (c) 2013 NVIDIA Corporation. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ *   Redistributions of source code must retain the above copyright notice,
+ *   this list of conditions and the following disclaimer.
+ *
+ *   Redistributions in binary form must reproduce the above copyright notice,
+ *   this list of conditions and the following disclaimer in the documentation
+ *   and/or other materials provided with the distribution.
+ *
+ *   Neither the name of NVIDIA Corporation nor the names of its contributors
+ *   may be used to endorse or promote products derived from this software
+ *   without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+ * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ * POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP
+#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP
+
+#include "common.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    // 2
+
+    static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s;
+        s = a ^ b;          // sum bits
+        r = a + b;          // actual sum
+        s = s ^ r;          // determine carry-ins for each bit position
+        s = s & 0x00010000; // carry-in to high word (= carry-out from low word)
+        r = r - s;          // subtract out carry-out from low word
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s;
+        s = a ^ b;          // sum bits
+        r = a - b;          // actual sum
+        s = s ^ r;          // determine carry-ins for each bit position
+        s = s & 0x00010000; // borrow to high word
+        r = r + s;          // compensate for borrow from low word
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s, t, u, v;
+        s = a & 0x0000ffff; // extract low halfword
+        r = b & 0x0000ffff; // extract low halfword
+        u = ::max(r, s);    // maximum of low halfwords
+        v = ::min(r, s);    // minimum of low halfwords
+        s = a & 0xffff0000; // extract high halfword
+        r = b & 0xffff0000; // extract high halfword
+        t = ::max(r, s);    // maximum of high halfwords
+        s = ::min(r, s);    // minimum of high halfwords
+        r = u | t;          // maximum of both halfwords
+        s = v | s;          // minimum of both halfwords
+        r = r - s;          // |a - b| = max(a,b) - min(a,b);
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b)
+    {
+        unsigned int r, s;
+
+        // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
+        // (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
+        s = a ^ b;
+        r = a & b;
+        s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries
+        s = s >> 1;
+        s = r + s;
+
+        return s;
+    }
+
+    static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
+        // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
+        unsigned int s;
+        s = a ^ b;
+        r = a | b;
+        s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries
+        s = s >> 1;
+        r = r - s;
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        unsigned int c;
+        r = a ^ b;          // 0x0000 if a == b
+        c = r | 0x80008000; // set msbs, to catch carry out
+        r = r ^ c;          // extract msbs, msb = 1 if r < 0x8000
+        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
+        c = r & ~c;         // msb = 1, if r was 0x0000
+        r = c >> 15;        // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vseteq2(a, b);
+        c = r << 16;        // convert bool
+        r = c - r;          //  into mask
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        r = a ^ b;          // 0x0000 if a == b
+        c = r | 0x80008000; // set msbs, to catch carry out
+        r = r ^ c;          // extract msbs, msb = 1 if r < 0x8000
+        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
+        c = r & ~c;         // msb = 1, if r was 0x0000
+        r = c >> 15;        // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(b));
+        c = vavrg2(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
+        c = c & 0x80008000; // msb = carry-outs
+        r = c >> 15;        // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetge2(a, b);
+        c = r << 16;        // convert bool
+        r = c - r;          //  into mask
+    #else
+        asm("not.b32 %0, %0;" : "+r"(b));
+        c = vavrg2(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
+        c = c & 0x80008000; // msb = carry-outs
+        r = c >> 15;        // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(b));
+        c = vavg2(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
+        c = c & 0x80008000; // msbs = carry-outs
+        r = c >> 15;        // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetgt2(a, b);
+        c = r << 16;        // convert bool
+        r = c - r;          //  into mask
+    #else
+        asm("not.b32 %0, %0;" : "+r"(b));
+        c = vavg2(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
+        c = c & 0x80008000; // msbs = carry-outs
+        r = c >> 15;        // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavrg2(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
+        c = c & 0x80008000; // msb = carry-outs
+        r = c >> 15;        // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetle2(a, b);
+        c = r << 16;        // convert bool
+        r = c - r;          //  into mask
+    #else
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavrg2(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
+        c = c & 0x80008000; // msb = carry-outs
+        r = c >> 15;        // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavg2(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
+        c = c & 0x80008000; // msb = carry-outs
+        r = c >> 15;        // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetlt2(a, b);
+        c = r << 16;        // convert bool
+        r = c - r;          //  into mask
+    #else
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavg2(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
+        c = c & 0x80008000; // msb = carry-outs
+        r = c >> 15;        // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        unsigned int c;
+        r = a ^ b;          // 0x0000 if a == b
+        c = r | 0x80008000; // set msbs, to catch carry out
+        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
+        c = r | c;          // msb = 1, if r was not 0x0000
+        c = c & 0x80008000; // extract msbs
+        r = c >> 15;        // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetne2(a, b);
+        c = r << 16;        // convert bool
+        r = c - r;          //  into mask
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        r = a ^ b;          // 0x0000 if a == b
+        c = r | 0x80008000; // set msbs, to catch carry out
+        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
+        c = r | c;          // msb = 1, if r was not 0x0000
+        c = c & 0x80008000; // extract msbs
+        r = c >> 15;        // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s, t, u;
+        r = a & 0x0000ffff; // extract low halfword
+        s = b & 0x0000ffff; // extract low halfword
+        t = ::max(r, s);    // maximum of low halfwords
+        r = a & 0xffff0000; // extract high halfword
+        s = b & 0xffff0000; // extract high halfword
+        u = ::max(r, s);    // maximum of high halfwords
+        r = t | u;          // combine halfword maximums
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s, t, u;
+        r = a & 0x0000ffff; // extract low halfword
+        s = b & 0x0000ffff; // extract low halfword
+        t = ::min(r, s);    // minimum of low halfwords
+        r = a & 0xffff0000; // extract high halfword
+        s = b & 0xffff0000; // extract high halfword
+        u = ::min(r, s);    // minimum of high halfwords
+        r = t | u;          // combine halfword minimums
+    #endif
+
+        return r;
+    }
+
+    // 4
+
+    static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s, t;
+        s = a ^ b;          // sum bits
+        r = a & 0x7f7f7f7f; // clear msbs
+        t = b & 0x7f7f7f7f; // clear msbs
+        s = s & 0x80808080; // msb sum bits
+        r = r + t;          // add without msbs, record carry-out in msbs
+        r = r ^ s;          // sum of msb sum and carry-in bits, w/o carry-out
+    #endif /* __CUDA_ARCH__ >= 300 */
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s, t;
+        s = a ^ ~b;         // inverted sum bits
+        r = a | 0x80808080; // set msbs
+        t = b & 0x7f7f7f7f; // clear msbs
+        s = s & 0x80808080; // inverted msb sum bits
+        r = r - t;          // subtract w/o msbs, record inverted borrows in msb
+        r = r ^ s;          // combine inverted msb sum bits and borrows
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b)
+    {
+        unsigned int r, s;
+
+        // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
+        // (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
+        s = a ^ b;
+        r = a & b;
+        s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
+        s = s >> 1;
+        s = r + s;
+
+        return s;
+    }
+
+    static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
+        // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
+        unsigned int c;
+        c = a ^ b;
+        r = a | b;
+        c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
+        c = c >> 1;
+        r = r - c;
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        unsigned int c;
+        r = a ^ b;          // 0x00 if a == b
+        c = r | 0x80808080; // set msbs, to catch carry out
+        r = r ^ c;          // extract msbs, msb = 1 if r < 0x80
+        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
+        c = r & ~c;         // msb = 1, if r was 0x00
+        r = c >> 7;         // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b)
+    {
+        unsigned int r, t;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vseteq4(a, b);
+        t = r << 8;         // convert bool
+        r = t - r;          //  to mask
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        t = a ^ b;          // 0x00 if a == b
+        r = t | 0x80808080; // set msbs, to catch carry out
+        t = t ^ r;          // extract msbs, msb = 1 if t < 0x80
+        r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80
+        r = t & ~r;         // msb = 1, if t was 0x00
+        t = r >> 7;         // build mask
+        t = r - t;          //  from
+        r = t | r;          //   msbs
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavrg4(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
+        c = c & 0x80808080; // msb = carry-outs
+        r = c >> 7;         // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetle4(a, b);
+        c = r << 8;         // convert bool
+        r = c - r;          //  to mask
+    #else
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavrg4(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
+        c = c & 0x80808080; // msbs = carry-outs
+        r = c >> 7;         // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavg4(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
+        c = c & 0x80808080; // msb = carry-outs
+        r = c >> 7;         // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetlt4(a, b);
+        c = r << 8;         // convert bool
+        r = c - r;          //  to mask
+    #else
+        asm("not.b32 %0, %0;" : "+r"(a));
+        c = vavg4(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
+        c = c & 0x80808080; // msbs = carry-outs
+        r = c >> 7;         // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(b));
+        c = vavrg4(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
+        c = c & 0x80808080; // msb = carry-outs
+        r = c >> 7;         // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b)
+    {
+        unsigned int r, s;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetge4(a, b);
+        s = r << 8;         // convert bool
+        r = s - r;          //  to mask
+    #else
+        asm ("not.b32 %0,%0;" : "+r"(b));
+        r = vavrg4 (a, b);  // (a + ~b + 1) / 2 = (a - b) / 2
+        r = r & 0x80808080; // msb = carry-outs
+        s = r >> 7;         // build mask
+        s = r - s;          //  from
+        r = s | r;          //   msbs
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int c;
+        asm("not.b32 %0, %0;" : "+r"(b));
+        c = vavg4(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
+        c = c & 0x80808080; // msb = carry-outs
+        r = c >> 7;         // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetgt4(a, b);
+        c = r << 8;         // convert bool
+        r = c - r;          //  to mask
+    #else
+        asm("not.b32 %0, %0;" : "+r"(b));
+        c = vavg4(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
+        c = c & 0x80808080; // msb = carry-outs
+        r = c >> 7;         // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        unsigned int c;
+        r = a ^ b;          // 0x00 if a == b
+        c = r | 0x80808080; // set msbs, to catch carry out
+        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
+        c = r | c;          // msb = 1, if r was not 0x00
+        c = c & 0x80808080; // extract msbs
+        r = c >> 7;         // convert to bool
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b)
+    {
+        unsigned int r, c;
+
+    #if __CUDA_ARCH__ >= 300
+        r = vsetne4(a, b);
+        c = r << 8;         // convert bool
+        r = c - r;          //  to mask
+    #else
+        // inspired by Alan Mycroft's null-byte detection algorithm:
+        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
+        r = a ^ b;          // 0x00 if a == b
+        c = r | 0x80808080; // set msbs, to catch carry out
+        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
+        c = r | c;          // msb = 1, if r was not 0x00
+        c = c & 0x80808080; // extract msbs
+        r = c >> 7;         // convert
+        r = c - r;          //  msbs to
+        r = c | r;          //   mask
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s;
+        s = vcmpge4(a, b);  // mask = 0xff if a >= b
+        r = a ^ b;          //
+        s = (r &  s) ^ b;   // select a when a >= b, else select b => max(a,b)
+        r = s ^ r;          // select a when b >= a, else select b => min(a,b)
+        r = s - r;          // |a - b| = max(a,b) - min(a,b);
+    #endif
+
+        return r;
+    }
+
+    static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s;
+        s = vcmpge4(a, b);  // mask = 0xff if a >= b
+        r = a & s;          // select a when b >= a
+        s = b & ~s;         // select b when b < a
+        r = r | s;          // combine byte selections
+    #endif
+
+        return r;           // byte-wise unsigned maximum
+    }
+
+    static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b)
+    {
+        unsigned int r = 0;
+
+    #if __CUDA_ARCH__ >= 300
+        asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #elif __CUDA_ARCH__ >= 200
+        asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+        asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
+    #else
+        unsigned int s;
+        s = vcmpge4(b, a);  // mask = 0xff if a >= b
+        r = a & s;          // select a when b >= a
+        s = b & ~s;         // select b when b < a
+        r = r | s;          // combine byte selections
+    #endif
+
+        return r;
+    }
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_SIMD_FUNCTIONS_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/transform.hpp b/3rdParty/include/opencv2/core/cuda/transform.hpp
new file mode 100644
index 0000000..42aa6ea
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/transform.hpp
@@ -0,0 +1,75 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_TRANSFORM_HPP
+#define OPENCV_CUDA_TRANSFORM_HPP
+
+#include "common.hpp"
+#include "utility.hpp"
+#include "detail/transform_detail.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template <typename T, typename D, typename UnOp, typename Mask>
+    static inline void transform(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, const Mask& mask, cudaStream_t stream)
+    {
+        typedef TransformFunctorTraits<UnOp> ft;
+        transform_detail::TransformDispatcher<VecTraits<T>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream);
+    }
+
+    template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
+    static inline void transform(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, const Mask& mask, cudaStream_t stream)
+    {
+        typedef TransformFunctorTraits<BinOp> ft;
+        transform_detail::TransformDispatcher<VecTraits<T1>::cn == 1 && VecTraits<T2>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream);
+    }
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_TRANSFORM_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/type_traits.hpp b/3rdParty/include/opencv2/core/cuda/type_traits.hpp
new file mode 100644
index 0000000..8b7a3fd
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/type_traits.hpp
@@ -0,0 +1,90 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_TYPE_TRAITS_HPP
+#define OPENCV_CUDA_TYPE_TRAITS_HPP
+
+#include "detail/type_traits_detail.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template <typename T> struct IsSimpleParameter
+    {
+        enum {value = type_traits_detail::IsIntegral<T>::value || type_traits_detail::IsFloat<T>::value ||
+            type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<T>::type>::value};
+    };
+
+    template <typename T> struct TypeTraits
+    {
+        typedef typename type_traits_detail::UnConst<T>::type                                                NonConstType;
+        typedef typename type_traits_detail::UnVolatile<T>::type                                             NonVolatileType;
+        typedef typename type_traits_detail::UnVolatile<typename type_traits_detail::UnConst<T>::type>::type UnqualifiedType;
+        typedef typename type_traits_detail::PointerTraits<UnqualifiedType>::type                            PointeeType;
+        typedef typename type_traits_detail::ReferenceTraits<T>::type                                        ReferredType;
+
+        enum { isConst          = type_traits_detail::UnConst<T>::value };
+        enum { isVolatile       = type_traits_detail::UnVolatile<T>::value };
+
+        enum { isReference      = type_traits_detail::ReferenceTraits<UnqualifiedType>::value };
+        enum { isPointer        = type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<UnqualifiedType>::type>::value };
+
+        enum { isUnsignedInt    = type_traits_detail::IsUnsignedIntegral<UnqualifiedType>::value };
+        enum { isSignedInt      = type_traits_detail::IsSignedIntergral<UnqualifiedType>::value };
+        enum { isIntegral       = type_traits_detail::IsIntegral<UnqualifiedType>::value };
+        enum { isFloat          = type_traits_detail::IsFloat<UnqualifiedType>::value };
+        enum { isArith          = isIntegral || isFloat };
+        enum { isVec            = type_traits_detail::IsVec<UnqualifiedType>::value };
+
+        typedef typename type_traits_detail::Select<IsSimpleParameter<UnqualifiedType>::value,
+            T, typename type_traits_detail::AddParameterType<T>::type>::type ParameterType;
+    };
+}}}
+
+//! @endcond
+
+#endif // OPENCV_CUDA_TYPE_TRAITS_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/utility.hpp b/3rdParty/include/opencv2/core/cuda/utility.hpp
new file mode 100644
index 0000000..7f5db48
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/utility.hpp
@@ -0,0 +1,230 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_UTILITY_HPP
+#define OPENCV_CUDA_UTILITY_HPP
+
+#include "saturate_cast.hpp"
+#include "datamov_utils.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    struct CV_EXPORTS ThrustAllocator
+    {
+        typedef uchar value_type;
+        virtual ~ThrustAllocator();
+        virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0;
+        virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0;
+        static ThrustAllocator& getAllocator();
+        static void setAllocator(ThrustAllocator* allocator);
+    };
+    #define OPENCV_CUDA_LOG_WARP_SIZE        (5)
+    #define OPENCV_CUDA_WARP_SIZE            (1 << OPENCV_CUDA_LOG_WARP_SIZE)
+    #define OPENCV_CUDA_LOG_MEM_BANKS        ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla
+    #define OPENCV_CUDA_MEM_BANKS            (1 << OPENCV_CUDA_LOG_MEM_BANKS)
+
+    ///////////////////////////////////////////////////////////////////////////////
+    // swap
+
+    template <typename T> void __device__ __host__ __forceinline__ swap(T& a, T& b)
+    {
+        const T temp = a;
+        a = b;
+        b = temp;
+    }
+
+    ///////////////////////////////////////////////////////////////////////////////
+    // Mask Reader
+
+    struct SingleMask
+    {
+        explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {}
+        __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){}
+
+        __device__ __forceinline__ bool operator()(int y, int x) const
+        {
+            return mask.ptr(y)[x] != 0;
+        }
+
+        PtrStepb mask;
+    };
+
+    struct SingleMaskChannels
+    {
+        __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_)
+        : mask(mask_), channels(channels_) {}
+        __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_)
+            :mask(mask_.mask), channels(mask_.channels){}
+
+        __device__ __forceinline__ bool operator()(int y, int x) const
+        {
+            return mask.ptr(y)[x / channels] != 0;
+        }
+
+        PtrStepb mask;
+        int channels;
+    };
+
+    struct MaskCollection
+    {
+        explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_)
+            : maskCollection(maskCollection_) {}
+
+        __device__ __forceinline__ MaskCollection(const MaskCollection& masks_)
+            : maskCollection(masks_.maskCollection), curMask(masks_.curMask){}
+
+        __device__ __forceinline__ void next()
+        {
+            curMask = *maskCollection++;
+        }
+        __device__ __forceinline__ void setMask(int z)
+        {
+            curMask = maskCollection[z];
+        }
+
+        __device__ __forceinline__ bool operator()(int y, int x) const
+        {
+            uchar val;
+            return curMask.data == 0 || (ForceGlob<uchar>::Load(curMask.ptr(y), x, val), (val != 0));
+        }
+
+        const PtrStepb* maskCollection;
+        PtrStepb curMask;
+    };
+
+    struct WithOutMask
+    {
+        __host__ __device__ __forceinline__ WithOutMask(){}
+        __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){}
+
+        __device__ __forceinline__ void next() const
+        {
+        }
+        __device__ __forceinline__ void setMask(int) const
+        {
+        }
+
+        __device__ __forceinline__ bool operator()(int, int) const
+        {
+            return true;
+        }
+
+        __device__ __forceinline__ bool operator()(int, int, int) const
+        {
+            return true;
+        }
+
+        static __device__ __forceinline__ bool check(int, int)
+        {
+            return true;
+        }
+
+        static __device__ __forceinline__ bool check(int, int, int)
+        {
+            return true;
+        }
+    };
+
+    ///////////////////////////////////////////////////////////////////////////////
+    // Solve linear system
+
+    // solve 2x2 linear system Ax=b
+    template <typename T> __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2])
+    {
+        T det = A[0][0] * A[1][1] - A[1][0] * A[0][1];
+
+        if (det != 0)
+        {
+            double invdet = 1.0 / det;
+
+            x[0] = saturate_cast<T>(invdet * (b[0] * A[1][1] - b[1] * A[0][1]));
+
+            x[1] = saturate_cast<T>(invdet * (A[0][0] * b[1] - A[1][0] * b[0]));
+
+            return true;
+        }
+
+        return false;
+    }
+
+    // solve 3x3 linear system Ax=b
+    template <typename T> __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3])
+    {
+        T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1])
+              - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0])
+              + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]);
+
+        if (det != 0)
+        {
+            double invdet = 1.0 / det;
+
+            x[0] = saturate_cast<T>(invdet *
+                (b[0]    * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) -
+                 A[0][1] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) +
+                 A[0][2] * (b[1]    * A[2][1] - A[1][1] * b[2]   )));
+
+            x[1] = saturate_cast<T>(invdet *
+                (A[0][0] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) -
+                 b[0]    * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) +
+                 A[0][2] * (A[1][0] * b[2]    - b[1]    * A[2][0])));
+
+            x[2] = saturate_cast<T>(invdet *
+                (A[0][0] * (A[1][1] * b[2]    - b[1]    * A[2][1]) -
+                 A[0][1] * (A[1][0] * b[2]    - b[1]    * A[2][0]) +
+                 b[0]    * (A[1][0] * A[2][1] - A[1][1] * A[2][0])));
+
+            return true;
+        }
+
+        return false;
+    }
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_UTILITY_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/vec_distance.hpp b/3rdParty/include/opencv2/core/cuda/vec_distance.hpp
new file mode 100644
index 0000000..ef6e510
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/vec_distance.hpp
@@ -0,0 +1,232 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP
+#define OPENCV_CUDA_VEC_DISTANCE_HPP
+
+#include "reduce.hpp"
+#include "functional.hpp"
+#include "detail/vec_distance_detail.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template <typename T> struct L1Dist
+    {
+        typedef int value_type;
+        typedef int result_type;
+
+        __device__ __forceinline__ L1Dist() : mySum(0) {}
+
+        __device__ __forceinline__ void reduceIter(int val1, int val2)
+        {
+            mySum = __sad(val1, val2, mySum);
+        }
+
+        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
+        {
+            reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
+        }
+
+        __device__ __forceinline__ operator int() const
+        {
+            return mySum;
+        }
+
+        int mySum;
+    };
+    template <> struct L1Dist<float>
+    {
+        typedef float value_type;
+        typedef float result_type;
+
+        __device__ __forceinline__ L1Dist() : mySum(0.0f) {}
+
+        __device__ __forceinline__ void reduceIter(float val1, float val2)
+        {
+            mySum += ::fabs(val1 - val2);
+        }
+
+        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
+        {
+            reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
+        }
+
+        __device__ __forceinline__ operator float() const
+        {
+            return mySum;
+        }
+
+        float mySum;
+    };
+
+    struct L2Dist
+    {
+        typedef float value_type;
+        typedef float result_type;
+
+        __device__ __forceinline__ L2Dist() : mySum(0.0f) {}
+
+        __device__ __forceinline__ void reduceIter(float val1, float val2)
+        {
+            float reg = val1 - val2;
+            mySum += reg * reg;
+        }
+
+        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
+        {
+            reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
+        }
+
+        __device__ __forceinline__ operator float() const
+        {
+            return sqrtf(mySum);
+        }
+
+        float mySum;
+    };
+
+    struct HammingDist
+    {
+        typedef int value_type;
+        typedef int result_type;
+
+        __device__ __forceinline__ HammingDist() : mySum(0) {}
+
+        __device__ __forceinline__ void reduceIter(int val1, int val2)
+        {
+            mySum += __popc(val1 ^ val2);
+        }
+
+        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
+        {
+            reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
+        }
+
+        __device__ __forceinline__ operator int() const
+        {
+            return mySum;
+        }
+
+        int mySum;
+    };
+
+    // calc distance between two vectors in global memory
+    template <int THREAD_DIM, typename Dist, typename T1, typename T2>
+    __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
+    {
+        for (int i = tid; i < len; i += THREAD_DIM)
+        {
+            T1 val1;
+            ForceGlob<T1>::Load(vec1, i, val1);
+
+            T2 val2;
+            ForceGlob<T2>::Load(vec2, i, val2);
+
+            dist.reduceIter(val1, val2);
+        }
+
+        dist.reduceAll<THREAD_DIM>(smem, tid);
+    }
+
+    // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
+    template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
+    __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
+    {
+        vec_distance_detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
+
+        dist.reduceAll<THREAD_DIM>(smem, tid);
+    }
+
+    // calc distance between two vectors in global memory
+    template <int THREAD_DIM, typename T1> struct VecDiffGlobal
+    {
+        explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
+        {
+            vec1 = vec1_;
+        }
+
+        template <typename T2, typename Dist>
+        __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
+        {
+            calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
+        }
+
+        const T1* vec1;
+    };
+
+    // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
+    template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
+    {
+        template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
+        {
+            if (glob_tid < len)
+                smem[glob_tid] = vec1[glob_tid];
+            __syncthreads();
+
+            U* vec1ValsPtr = vec1Vals;
+
+            #pragma unroll
+            for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
+                *vec1ValsPtr++ = smem[i];
+
+            __syncthreads();
+        }
+
+        template <typename T2, typename Dist>
+        __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
+        {
+            calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
+        }
+
+        U vec1Vals[MAX_LEN / THREAD_DIM];
+    };
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_VEC_DISTANCE_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/vec_math.hpp b/3rdParty/include/opencv2/core/cuda/vec_math.hpp
new file mode 100644
index 0000000..80b1303
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/vec_math.hpp
@@ -0,0 +1,923 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_VECMATH_HPP
+#define OPENCV_CUDA_VECMATH_HPP
+
+#include "vec_traits.hpp"
+#include "saturate_cast.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+
+// saturate_cast
+
+namespace vec_math_detail
+{
+    template <int cn, typename VecD> struct SatCastHelper;
+    template <typename VecD> struct SatCastHelper<1, VecD>
+    {
+        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
+        {
+            typedef typename VecTraits<VecD>::elem_type D;
+            return VecTraits<VecD>::make(saturate_cast<D>(v.x));
+        }
+    };
+    template <typename VecD> struct SatCastHelper<2, VecD>
+    {
+        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
+        {
+            typedef typename VecTraits<VecD>::elem_type D;
+            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y));
+        }
+    };
+    template <typename VecD> struct SatCastHelper<3, VecD>
+    {
+        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
+        {
+            typedef typename VecTraits<VecD>::elem_type D;
+            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z));
+        }
+    };
+    template <typename VecD> struct SatCastHelper<4, VecD>
+    {
+        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
+        {
+            typedef typename VecTraits<VecD>::elem_type D;
+            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z), saturate_cast<D>(v.w));
+        }
+    };
+
+    template <typename VecD, typename VecS> static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v)
+    {
+        return SatCastHelper<VecTraits<VecD>::cn, VecD>::cast(v);
+    }
+}
+
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+template<typename T> static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
+
+// unary operators
+
+#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \
+    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \
+    { \
+        return VecTraits<output_type ## 1>::make(op (a.x)); \
+    } \
+    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \
+    { \
+        return VecTraits<output_type ## 2>::make(op (a.x), op (a.y)); \
+    } \
+    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \
+    { \
+        return VecTraits<output_type ## 3>::make(op (a.x), op (a.y), op (a.z)); \
+    } \
+    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \
+    { \
+        return VecTraits<output_type ## 4>::make(op (a.x), op (a.y), op (a.z), op (a.w)); \
+    }
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint)
+
+#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP
+
+// unary functions
+
+#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \
+    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \
+    { \
+        return VecTraits<output_type ## 1>::make(func (a.x)); \
+    } \
+    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \
+    { \
+        return VecTraits<output_type ## 2>::make(func (a.x), func (a.y)); \
+    } \
+    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \
+    { \
+        return VecTraits<output_type ## 3>::make(func (a.x), func (a.y), func (a.z)); \
+    } \
+    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \
+    { \
+        return VecTraits<output_type ## 4>::make(func (a.x), func (a.y), func (a.z), func (a.w)); \
+    }
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double)
+
+#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC
+
+// binary operators (vec & vec)
+
+#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \
+    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \
+    { \
+        return VecTraits<output_type ## 1>::make(a.x op b.x); \
+    } \
+    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \
+    { \
+        return VecTraits<output_type ## 2>::make(a.x op b.x, a.y op b.y); \
+    } \
+    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \
+    { \
+        return VecTraits<output_type ## 3>::make(a.x op b.x, a.y op b.y, a.z op b.z); \
+    } \
+    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \
+    { \
+        return VecTraits<output_type ## 4>::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \
+    }
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint)
+
+#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP
+
+// binary operators (vec & scalar)
+
+#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \
+    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 1>::make(a.x op s); \
+    } \
+    __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \
+    { \
+        return VecTraits<output_type ## 1>::make(s op b.x); \
+    } \
+    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 2>::make(a.x op s, a.y op s); \
+    } \
+    __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \
+    { \
+        return VecTraits<output_type ## 2>::make(s op b.x, s op b.y); \
+    } \
+    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 3>::make(a.x op s, a.y op s, a.z op s); \
+    } \
+    __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \
+    { \
+        return VecTraits<output_type ## 3>::make(s op b.x, s op b.y, s op b.z); \
+    } \
+    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 4>::make(a.x op s, a.y op s, a.z op s, a.w op s); \
+    } \
+    __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \
+    { \
+        return VecTraits<output_type ## 4>::make(s op b.x, s op b.y, s op b.z, s op b.w); \
+    }
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint)
+
+#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP
+
+// binary function (vec & vec)
+
+#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \
+    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \
+    { \
+        return VecTraits<output_type ## 1>::make(func (a.x, b.x)); \
+    } \
+    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \
+    { \
+        return VecTraits<output_type ## 2>::make(func (a.x, b.x), func (a.y, b.y)); \
+    } \
+    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \
+    { \
+        return VecTraits<output_type ## 3>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \
+    } \
+    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \
+    { \
+        return VecTraits<output_type ## 4>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \
+    }
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double)
+
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float)
+CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double)
+
+#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC
+
+// binary function (vec & scalar)
+
+#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \
+    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 1>::make(func ((output_type) a.x, (output_type) s)); \
+    } \
+    __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \
+    { \
+        return VecTraits<output_type ## 1>::make(func ((output_type) s, (output_type) b.x)); \
+    } \
+    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 2>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \
+    } \
+    __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \
+    { \
+        return VecTraits<output_type ## 2>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \
+    } \
+    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 3>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \
+    } \
+    __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \
+    { \
+        return VecTraits<output_type ## 3>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \
+    } \
+    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \
+    { \
+        return VecTraits<output_type ## 4>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \
+    } \
+    __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \
+    { \
+        return VecTraits<output_type ## 4>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \
+    }
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double)
+
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double)
+CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double)
+
+#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC
+
+}}} // namespace cv { namespace cuda { namespace device
+
+//! @endcond
+
+#endif // OPENCV_CUDA_VECMATH_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/vec_traits.hpp b/3rdParty/include/opencv2/core/cuda/vec_traits.hpp
new file mode 100644
index 0000000..b5ff281
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/vec_traits.hpp
@@ -0,0 +1,288 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_VEC_TRAITS_HPP
+#define OPENCV_CUDA_VEC_TRAITS_HPP
+
+#include "common.hpp"
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template<typename T, int N> struct TypeVec;
+
+    struct __align__(8) uchar8
+    {
+        uchar a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7)
+    {
+        uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+    struct __align__(8) char8
+    {
+        schar a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7)
+    {
+        char8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+    struct __align__(16) ushort8
+    {
+        ushort a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7)
+    {
+        ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+    struct __align__(16) short8
+    {
+        short a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7)
+    {
+        short8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+    struct __align__(32) uint8
+    {
+        uint a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7)
+    {
+        uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+    struct __align__(32) int8
+    {
+        int a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7)
+    {
+        int8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+    struct __align__(32) float8
+    {
+        float a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7)
+    {
+        float8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+    struct double8
+    {
+        double a0, a1, a2, a3, a4, a5, a6, a7;
+    };
+    static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7)
+    {
+        double8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
+        return val;
+    }
+
+#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \
+    template<> struct TypeVec<type, 1> { typedef type vec_type; }; \
+    template<> struct TypeVec<type ## 1, 1> { typedef type ## 1 vec_type; }; \
+    template<> struct TypeVec<type, 2> { typedef type ## 2 vec_type; }; \
+    template<> struct TypeVec<type ## 2, 2> { typedef type ## 2 vec_type; }; \
+    template<> struct TypeVec<type, 3> { typedef type ## 3 vec_type; }; \
+    template<> struct TypeVec<type ## 3, 3> { typedef type ## 3 vec_type; }; \
+    template<> struct TypeVec<type, 4> { typedef type ## 4 vec_type; }; \
+    template<> struct TypeVec<type ## 4, 4> { typedef type ## 4 vec_type; }; \
+    template<> struct TypeVec<type, 8> { typedef type ## 8 vec_type; }; \
+    template<> struct TypeVec<type ## 8, 8> { typedef type ## 8 vec_type; };
+
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar)
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char)
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort)
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short)
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int)
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint)
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float)
+    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double)
+
+    #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC
+
+    template<> struct TypeVec<schar, 1> { typedef schar vec_type; };
+    template<> struct TypeVec<schar, 2> { typedef char2 vec_type; };
+    template<> struct TypeVec<schar, 3> { typedef char3 vec_type; };
+    template<> struct TypeVec<schar, 4> { typedef char4 vec_type; };
+    template<> struct TypeVec<schar, 8> { typedef char8 vec_type; };
+
+    template<> struct TypeVec<bool, 1> { typedef uchar vec_type; };
+    template<> struct TypeVec<bool, 2> { typedef uchar2 vec_type; };
+    template<> struct TypeVec<bool, 3> { typedef uchar3 vec_type; };
+    template<> struct TypeVec<bool, 4> { typedef uchar4 vec_type; };
+    template<> struct TypeVec<bool, 8> { typedef uchar8 vec_type; };
+
+    template<typename T> struct VecTraits;
+
+#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \
+    template<> struct VecTraits<type> \
+    { \
+        typedef type elem_type; \
+        enum {cn=1}; \
+        static __device__ __host__ __forceinline__ type all(type v) {return v;} \
+        static __device__ __host__ __forceinline__ type make(type x) {return x;} \
+        static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \
+    }; \
+    template<> struct VecTraits<type ## 1> \
+    { \
+        typedef type elem_type; \
+        enum {cn=1}; \
+        static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \
+        static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \
+        static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \
+    }; \
+    template<> struct VecTraits<type ## 2> \
+    { \
+        typedef type elem_type; \
+        enum {cn=2}; \
+        static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \
+        static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \
+        static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \
+    }; \
+    template<> struct VecTraits<type ## 3> \
+    { \
+        typedef type elem_type; \
+        enum {cn=3}; \
+        static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \
+        static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \
+        static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \
+    }; \
+    template<> struct VecTraits<type ## 4> \
+    { \
+        typedef type elem_type; \
+        enum {cn=4}; \
+        static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \
+        static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \
+        static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \
+    }; \
+    template<> struct VecTraits<type ## 8> \
+    { \
+        typedef type elem_type; \
+        enum {cn=8}; \
+        static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \
+        static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \
+        static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \
+    };
+
+    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar)
+    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort)
+    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short)
+    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int)
+    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint)
+    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float)
+    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double)
+
+    #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS
+
+    template<> struct VecTraits<char>
+    {
+        typedef char elem_type;
+        enum {cn=1};
+        static __device__ __host__ __forceinline__ char all(char v) {return v;}
+        static __device__ __host__ __forceinline__ char make(char x) {return x;}
+        static __device__ __host__ __forceinline__ char make(const char* x) {return *x;}
+    };
+    template<> struct VecTraits<schar>
+    {
+        typedef schar elem_type;
+        enum {cn=1};
+        static __device__ __host__ __forceinline__ schar all(schar v) {return v;}
+        static __device__ __host__ __forceinline__ schar make(schar x) {return x;}
+        static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;}
+    };
+    template<> struct VecTraits<char1>
+    {
+        typedef schar elem_type;
+        enum {cn=1};
+        static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);}
+        static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);}
+        static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);}
+    };
+    template<> struct VecTraits<char2>
+    {
+        typedef schar elem_type;
+        enum {cn=2};
+        static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);}
+        static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);}
+        static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);}
+    };
+    template<> struct VecTraits<char3>
+    {
+        typedef schar elem_type;
+        enum {cn=3};
+        static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);}
+        static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);}
+        static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);}
+    };
+    template<> struct VecTraits<char4>
+    {
+        typedef schar elem_type;
+        enum {cn=4};
+        static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);}
+        static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);}
+        static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);}
+    };
+    template<> struct VecTraits<char8>
+    {
+        typedef schar elem_type;
+        enum {cn=8};
+        static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);}
+        static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);}
+        static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);}
+    };
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif // OPENCV_CUDA_VEC_TRAITS_HPP
diff --git a/3rdParty/include/opencv2/core/cuda/warp.hpp b/3rdParty/include/opencv2/core/cuda/warp.hpp
new file mode 100644
index 0000000..8af7e6a
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/warp.hpp
@@ -0,0 +1,139 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_DEVICE_WARP_HPP
+#define OPENCV_CUDA_DEVICE_WARP_HPP
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    struct Warp
+    {
+        enum
+        {
+            LOG_WARP_SIZE = 5,
+            WARP_SIZE     = 1 << LOG_WARP_SIZE,
+            STRIDE        = WARP_SIZE
+        };
+
+        /** \brief Returns the warp lane ID of the calling thread. */
+        static __device__ __forceinline__ unsigned int laneId()
+        {
+            unsigned int ret;
+            asm("mov.u32 %0, %%laneid;" : "=r"(ret) );
+            return ret;
+        }
+
+        template<typename It, typename T>
+        static __device__ __forceinline__ void fill(It beg, It end, const T& value)
+        {
+            for(It t = beg + laneId(); t < end; t += STRIDE)
+                *t = value;
+        }
+
+        template<typename InIt, typename OutIt>
+        static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out)
+        {
+            for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
+                *out = *t;
+            return out;
+        }
+
+        template<typename InIt, typename OutIt, class UnOp>
+        static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op)
+        {
+            for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
+                *out = op(*t);
+            return out;
+        }
+
+        template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
+        static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
+        {
+            unsigned int lane = laneId();
+
+            InIt1 t1 = beg1 + lane;
+            InIt2 t2 = beg2 + lane;
+            for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE)
+                *out = op(*t1, *t2);
+            return out;
+        }
+
+        template <class T, class BinOp>
+        static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op)
+        {
+            const unsigned int lane = laneId();
+
+            if (lane < 16)
+            {
+                T partial = ptr[lane];
+
+                ptr[lane] = partial = op(partial, ptr[lane + 16]);
+                ptr[lane] = partial = op(partial, ptr[lane + 8]);
+                ptr[lane] = partial = op(partial, ptr[lane + 4]);
+                ptr[lane] = partial = op(partial, ptr[lane + 2]);
+                ptr[lane] = partial = op(partial, ptr[lane + 1]);
+            }
+
+            return *ptr;
+        }
+
+        template<typename OutIt, typename T>
+        static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
+        {
+            unsigned int lane = laneId();
+            value += lane;
+
+            for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE)
+                *t = value;
+        }
+    };
+}}} // namespace cv { namespace cuda { namespace cudev
+
+//! @endcond
+
+#endif /* OPENCV_CUDA_DEVICE_WARP_HPP */
diff --git a/3rdParty/include/opencv2/core/cuda/warp_reduce.hpp b/3rdParty/include/opencv2/core/cuda/warp_reduce.hpp
new file mode 100644
index 0000000..530303d
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/warp_reduce.hpp
@@ -0,0 +1,76 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__
+#define OPENCV_CUDA_WARP_REDUCE_HPP__
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+    template <class T>
+    __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x)
+    {
+        const unsigned int lane = tid & 31; // index of thread in warp (0..31)
+
+        if (lane < 16)
+        {
+            T partial = ptr[tid];
+
+            ptr[tid] = partial = partial + ptr[tid + 16];
+            ptr[tid] = partial = partial + ptr[tid + 8];
+            ptr[tid] = partial = partial + ptr[tid + 4];
+            ptr[tid] = partial = partial + ptr[tid + 2];
+            ptr[tid] = partial = partial + ptr[tid + 1];
+        }
+
+        return ptr[tid - lane];
+    }
+}}} // namespace cv { namespace cuda { namespace cudev {
+
+//! @endcond
+
+#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */
diff --git a/3rdParty/include/opencv2/core/cuda/warp_shuffle.hpp b/3rdParty/include/opencv2/core/cuda/warp_shuffle.hpp
new file mode 100644
index 0000000..0da54ae
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda/warp_shuffle.hpp
@@ -0,0 +1,162 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CUDA_WARP_SHUFFLE_HPP
+#define OPENCV_CUDA_WARP_SHUFFLE_HPP
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+namespace cv { namespace cuda { namespace device
+{
+#if __CUDACC_VER_MAJOR__ >= 9
+#  define __shfl(x, y, z) __shfl_sync(0xFFFFFFFFU, x, y, z)
+#  define __shfl_up(x, y, z) __shfl_up_sync(0xFFFFFFFFU, x, y, z)
+#  define __shfl_down(x, y, z) __shfl_down_sync(0xFFFFFFFFU, x, y, z)
+#endif
+    template <typename T>
+    __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        return __shfl(val, srcLane, width);
+    #else
+        return T();
+    #endif
+    }
+    __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        return (unsigned int) __shfl((int) val, srcLane, width);
+    #else
+        return 0;
+    #endif
+    }
+    __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        int lo = __double2loint(val);
+        int hi = __double2hiint(val);
+
+        lo = __shfl(lo, srcLane, width);
+        hi = __shfl(hi, srcLane, width);
+
+        return __hiloint2double(hi, lo);
+    #else
+        return 0.0;
+    #endif
+    }
+
+    template <typename T>
+    __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        return __shfl_down(val, delta, width);
+    #else
+        return T();
+    #endif
+    }
+    __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        return (unsigned int) __shfl_down((int) val, delta, width);
+    #else
+        return 0;
+    #endif
+    }
+    __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        int lo = __double2loint(val);
+        int hi = __double2hiint(val);
+
+        lo = __shfl_down(lo, delta, width);
+        hi = __shfl_down(hi, delta, width);
+
+        return __hiloint2double(hi, lo);
+    #else
+        return 0.0;
+    #endif
+    }
+
+    template <typename T>
+    __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        return __shfl_up(val, delta, width);
+    #else
+        return T();
+    #endif
+    }
+    __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        return (unsigned int) __shfl_up((int) val, delta, width);
+    #else
+        return 0;
+    #endif
+    }
+    __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize)
+    {
+    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
+        int lo = __double2loint(val);
+        int hi = __double2hiint(val);
+
+        lo = __shfl_up(lo, delta, width);
+        hi = __shfl_up(hi, delta, width);
+
+        return __hiloint2double(hi, lo);
+    #else
+        return 0.0;
+    #endif
+    }
+}}}
+
+#  undef __shfl
+#  undef __shfl_up
+#  undef __shfl_down
+
+//! @endcond
+
+#endif // OPENCV_CUDA_WARP_SHUFFLE_HPP
diff --git a/3rdParty/include/opencv2/core/cuda_stream_accessor.hpp b/3rdParty/include/opencv2/core/cuda_stream_accessor.hpp
new file mode 100644
index 0000000..deaf356
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda_stream_accessor.hpp
@@ -0,0 +1,86 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP
+#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP
+
+#ifndef __cplusplus
+#  error cuda_stream_accessor.hpp header must be compiled as C++
+#endif
+
+/** @file cuda_stream_accessor.hpp
+ * This is only header file that depends on CUDA Runtime API. All other headers are independent.
+ */
+
+#include <cuda_runtime.h>
+#include "opencv2/core/cuda.hpp"
+
+namespace cv
+{
+    namespace cuda
+    {
+
+//! @addtogroup cudacore_struct
+//! @{
+
+        /** @brief Class that enables getting cudaStream_t from cuda::Stream
+         */
+        struct StreamAccessor
+        {
+            CV_EXPORTS static cudaStream_t getStream(const Stream& stream);
+            CV_EXPORTS static Stream wrapStream(cudaStream_t stream);
+        };
+
+        /** @brief Class that enables getting cudaEvent_t from cuda::Event
+         */
+        struct EventAccessor
+        {
+            CV_EXPORTS static cudaEvent_t getEvent(const Event& event);
+            CV_EXPORTS static Event wrapEvent(cudaEvent_t event);
+        };
+
+//! @}
+
+    }
+}
+
+#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP */
diff --git a/3rdParty/include/opencv2/core/cuda_types.hpp b/3rdParty/include/opencv2/core/cuda_types.hpp
new file mode 100644
index 0000000..b33f061
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cuda_types.hpp
@@ -0,0 +1,144 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_CUDA_TYPES_HPP
+#define OPENCV_CORE_CUDA_TYPES_HPP
+
+#ifndef __cplusplus
+#  error cuda_types.hpp header must be compiled as C++
+#endif
+
+#if defined(__OPENCV_BUILD) && defined(__clang__)
+#pragma clang diagnostic ignored "-Winconsistent-missing-override"
+#endif
+#if defined(__OPENCV_BUILD) && defined(__GNUC__) && __GNUC__ >= 5
+#pragma GCC diagnostic ignored "-Wsuggest-override"
+#endif
+
+/** @file
+ * @deprecated Use @ref cudev instead.
+ */
+
+//! @cond IGNORED
+
+#ifdef __CUDACC__
+    #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__
+#else
+    #define __CV_CUDA_HOST_DEVICE__
+#endif
+
+namespace cv
+{
+    namespace cuda
+    {
+
+        // Simple lightweight structures that encapsulates information about an image on device.
+        // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile
+
+        template <typename T> struct DevPtr
+        {
+            typedef T elem_type;
+            typedef int index_type;
+
+            enum { elem_size = sizeof(elem_type) };
+
+            T* data;
+
+            __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {}
+            __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {}
+
+            __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; }
+            __CV_CUDA_HOST_DEVICE__ operator       T*()       { return data; }
+            __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; }
+        };
+
+        template <typename T> struct PtrSz : public DevPtr<T>
+        {
+            __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {}
+            __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr<T>(data_), size(size_) {}
+
+            size_t size;
+        };
+
+        template <typename T> struct PtrStep : public DevPtr<T>
+        {
+            __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {}
+            __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr<T>(data_), step(step_) {}
+
+            size_t step;
+
+            __CV_CUDA_HOST_DEVICE__       T* ptr(int y = 0)       { return (      T*)( (      char*)(((DevPtr<T>*)this)->data) + y * step); }
+            __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)(((DevPtr<T>*)this)->data) + y * step); }
+
+            __CV_CUDA_HOST_DEVICE__       T& operator ()(int y, int x)       { return ptr(y)[x]; }
+            __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; }
+        };
+
+        template <typename T> struct PtrStepSz : public PtrStep<T>
+        {
+            __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {}
+            __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_)
+                : PtrStep<T>(data_, step_), cols(cols_), rows(rows_) {}
+
+            template <typename U>
+            explicit PtrStepSz(const PtrStepSz<U>& d) : PtrStep<T>((T*)d.data, d.step), cols(d.cols), rows(d.rows){}
+
+            int cols;
+            int rows;
+        };
+
+        typedef PtrStepSz<unsigned char> PtrStepSzb;
+        typedef PtrStepSz<unsigned short> PtrStepSzus;
+        typedef PtrStepSz<float> PtrStepSzf;
+        typedef PtrStepSz<int> PtrStepSzi;
+
+        typedef PtrStep<unsigned char> PtrStepb;
+        typedef PtrStep<unsigned short> PtrStepus;
+        typedef PtrStep<float> PtrStepf;
+        typedef PtrStep<int> PtrStepi;
+
+    }
+}
+
+//! @endcond
+
+#endif /* OPENCV_CORE_CUDA_TYPES_HPP */
diff --git a/3rdParty/include/opencv2/core/cv_cpu_dispatch.h b/3rdParty/include/opencv2/core/cv_cpu_dispatch.h
new file mode 100644
index 0000000..ab5a67d
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cv_cpu_dispatch.h
@@ -0,0 +1,368 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#if defined __OPENCV_BUILD \
+
+#include "cv_cpu_config.h"
+#include "cv_cpu_helper.h"
+
+#ifdef CV_CPU_DISPATCH_MODE
+#define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE)
+#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) {
+#define CV_CPU_OPTIMIZATION_NAMESPACE_END }
+#else
+#define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline
+#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline {
+#define CV_CPU_OPTIMIZATION_NAMESPACE_END }
+#define CV_CPU_BASELINE_MODE 1
+#endif
+
+
+#define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...)  /* done */
+#define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+#define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__))
+#define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros
+
+
+#if defined CV_ENABLE_INTRINSICS \
+    && !defined CV_DISABLE_OPTIMIZATION \
+    && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \
+
+#ifdef CV_CPU_COMPILE_SSE2
+#  include <emmintrin.h>
+#  define CV_MMX 1
+#  define CV_SSE 1
+#  define CV_SSE2 1
+#endif
+#ifdef CV_CPU_COMPILE_SSE3
+#  include <pmmintrin.h>
+#  define CV_SSE3 1
+#endif
+#ifdef CV_CPU_COMPILE_SSSE3
+#  include <tmmintrin.h>
+#  define CV_SSSE3 1
+#endif
+#ifdef CV_CPU_COMPILE_SSE4_1
+#  include <smmintrin.h>
+#  define CV_SSE4_1 1
+#endif
+#ifdef CV_CPU_COMPILE_SSE4_2
+#  include <nmmintrin.h>
+#  define CV_SSE4_2 1
+#endif
+#ifdef CV_CPU_COMPILE_POPCNT
+#  ifdef _MSC_VER
+#    include <nmmintrin.h>
+#    if defined(_M_X64)
+#      define CV_POPCNT_U64 (int)_mm_popcnt_u64
+#    endif
+#    define CV_POPCNT_U32 _mm_popcnt_u32
+#  else
+#    include <popcntintrin.h>
+#    if defined(__x86_64__)
+#      define CV_POPCNT_U64 __builtin_popcountll
+#    endif
+#    define CV_POPCNT_U32 __builtin_popcount
+#  endif
+#  define CV_POPCNT 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX
+#  include <immintrin.h>
+#  define CV_AVX 1
+#endif
+#ifdef CV_CPU_COMPILE_FP16
+#  if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM) || defined(_M_ARM64)
+#    include <arm_neon.h>
+#  else
+#    include <immintrin.h>
+#  endif
+#  define CV_FP16 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX2
+#  include <immintrin.h>
+#  define CV_AVX2 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX_512F
+#  include <immintrin.h>
+#  define CV_AVX_512F 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX512_COMMON
+#  define CV_AVX512_COMMON 1
+#  define CV_AVX_512CD 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX512_KNL
+#  define CV_AVX512_KNL 1
+#  define CV_AVX_512ER 1
+#  define CV_AVX_512PF 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX512_KNM
+#  define CV_AVX512_KNM 1
+#  define CV_AVX_5124FMAPS 1
+#  define CV_AVX_5124VNNIW 1
+#  define CV_AVX_512VPOPCNTDQ 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX512_SKX
+#  define CV_AVX512_SKX 1
+#  define CV_AVX_512VL 1
+#  define CV_AVX_512BW 1
+#  define CV_AVX_512DQ 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX512_CNL
+#  define CV_AVX512_CNL 1
+#  define CV_AVX_512IFMA 1
+#  define CV_AVX_512VBMI 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX512_CLX
+#  define CV_AVX512_CLX 1
+#  define CV_AVX_512VNNI 1
+#endif
+#ifdef CV_CPU_COMPILE_AVX512_ICL
+#  define CV_AVX512_ICL 1
+#  undef CV_AVX_512IFMA
+#  define CV_AVX_512IFMA 1
+#  undef CV_AVX_512VBMI
+#  define CV_AVX_512VBMI 1
+#  undef CV_AVX_512VNNI
+#  define CV_AVX_512VNNI 1
+#  define CV_AVX_512VBMI2 1
+#  define CV_AVX_512BITALG 1
+#  define CV_AVX_512VPOPCNTDQ 1
+#endif
+#ifdef CV_CPU_COMPILE_FMA3
+#  define CV_FMA3 1
+#endif
+
+#if defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) && (defined(CV_CPU_COMPILE_NEON) || !defined(_MSC_VER))
+# include <Intrin.h>
+# include <arm_neon.h>
+# define CV_NEON 1
+#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__))
+#  include <arm_neon.h>
+#  define CV_NEON 1
+#endif
+
+#if defined(__riscv) && defined(__riscv_vector) && defined(__riscv_vector_071)
+# include<riscv-vector.h>
+# define CV_RVV071 1
+#endif
+
+#if defined(__ARM_NEON__) || defined(__aarch64__)
+#  include <arm_neon.h>
+#endif
+
+#ifdef CV_CPU_COMPILE_VSX
+#  include <altivec.h>
+#  undef vector
+#  undef pixel
+#  undef bool
+#  define CV_VSX 1
+#endif
+
+#ifdef CV_CPU_COMPILE_VSX3
+#  define CV_VSX3 1
+#endif
+
+#ifdef CV_CPU_COMPILE_MSA
+#  include "hal/msa_macros.h"
+#  define CV_MSA 1
+#endif
+
+#ifdef __EMSCRIPTEN__
+#  define CV_WASM_SIMD 1
+#  include <wasm_simd128.h>
+#endif
+
+#if defined CV_CPU_COMPILE_RVV
+#  define CV_RVV 1
+#  include <riscv_vector.h>
+#endif
+
+#endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__
+
+#if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX
+struct VZeroUpperGuard {
+#ifdef __GNUC__
+    __attribute__((always_inline))
+#endif
+    inline VZeroUpperGuard() { _mm256_zeroupper(); }
+#ifdef __GNUC__
+    __attribute__((always_inline))
+#endif
+    inline ~VZeroUpperGuard() { _mm256_zeroupper(); }
+};
+#define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; CV_UNUSED(__vzeroupper_guard);
+#endif
+
+#ifdef __CV_AVX_GUARD
+#define CV_AVX_GUARD __CV_AVX_GUARD
+#else
+#define CV_AVX_GUARD
+#endif
+
+#endif // __OPENCV_BUILD
+
+
+
+#if !defined __OPENCV_BUILD /* Compatibility code */ \
+    && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */
+#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2)
+#  include <emmintrin.h>
+#  define CV_MMX 1
+#  define CV_SSE 1
+#  define CV_SSE2 1
+#elif defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) && (defined(CV_CPU_COMPILE_NEON) || !defined(_MSC_VER))
+# include <Intrin.h>
+# include <arm_neon.h>
+# define CV_NEON 1
+#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__))
+#  include <arm_neon.h>
+#  define CV_NEON 1
+#elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__)
+#  include <altivec.h>
+#  undef vector
+#  undef pixel
+#  undef bool
+#  define CV_VSX 1
+#endif
+
+#ifdef __F16C__
+#  include <immintrin.h>
+#  define CV_FP16 1
+#endif
+
+#endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code)
+
+
+
+#ifndef CV_MMX
+#  define CV_MMX 0
+#endif
+#ifndef CV_SSE
+#  define CV_SSE 0
+#endif
+#ifndef CV_SSE2
+#  define CV_SSE2 0
+#endif
+#ifndef CV_SSE3
+#  define CV_SSE3 0
+#endif
+#ifndef CV_SSSE3
+#  define CV_SSSE3 0
+#endif
+#ifndef CV_SSE4_1
+#  define CV_SSE4_1 0
+#endif
+#ifndef CV_SSE4_2
+#  define CV_SSE4_2 0
+#endif
+#ifndef CV_POPCNT
+#  define CV_POPCNT 0
+#endif
+#ifndef CV_AVX
+#  define CV_AVX 0
+#endif
+#ifndef CV_FP16
+#  define CV_FP16 0
+#endif
+#ifndef CV_AVX2
+#  define CV_AVX2 0
+#endif
+#ifndef CV_FMA3
+#  define CV_FMA3 0
+#endif
+#ifndef CV_AVX_512F
+#  define CV_AVX_512F 0
+#endif
+#ifndef CV_AVX_512BW
+#  define CV_AVX_512BW 0
+#endif
+#ifndef CV_AVX_512CD
+#  define CV_AVX_512CD 0
+#endif
+#ifndef CV_AVX_512DQ
+#  define CV_AVX_512DQ 0
+#endif
+#ifndef CV_AVX_512ER
+#  define CV_AVX_512ER 0
+#endif
+#ifndef CV_AVX_512IFMA
+#  define CV_AVX_512IFMA 0
+#endif
+#define CV_AVX_512IFMA512 CV_AVX_512IFMA // deprecated
+#ifndef CV_AVX_512PF
+#  define CV_AVX_512PF 0
+#endif
+#ifndef CV_AVX_512VBMI
+#  define CV_AVX_512VBMI 0
+#endif
+#ifndef CV_AVX_512VL
+#  define CV_AVX_512VL 0
+#endif
+#ifndef CV_AVX_5124FMAPS
+#  define CV_AVX_5124FMAPS 0
+#endif
+#ifndef CV_AVX_5124VNNIW
+#  define CV_AVX_5124VNNIW 0
+#endif
+#ifndef CV_AVX_512VPOPCNTDQ
+#  define CV_AVX_512VPOPCNTDQ 0
+#endif
+#ifndef CV_AVX_512VNNI
+#  define CV_AVX_512VNNI 0
+#endif
+#ifndef CV_AVX_512VBMI2
+#  define CV_AVX_512VBMI2 0
+#endif
+#ifndef CV_AVX_512BITALG
+#  define CV_AVX_512BITALG 0
+#endif
+#ifndef CV_AVX512_COMMON
+#  define CV_AVX512_COMMON 0
+#endif
+#ifndef CV_AVX512_KNL
+#  define CV_AVX512_KNL 0
+#endif
+#ifndef CV_AVX512_KNM
+#  define CV_AVX512_KNM 0
+#endif
+#ifndef CV_AVX512_SKX
+#  define CV_AVX512_SKX 0
+#endif
+#ifndef CV_AVX512_CNL
+#  define CV_AVX512_CNL 0
+#endif
+#ifndef CV_AVX512_CLX
+#  define CV_AVX512_CLX 0
+#endif
+#ifndef CV_AVX512_ICL
+#  define CV_AVX512_ICL 0
+#endif
+
+#ifndef CV_NEON
+#  define CV_NEON 0
+#endif
+
+#ifndef CV_RVV071
+#  define CV_RVV071 0
+#endif
+
+#ifndef CV_VSX
+#  define CV_VSX 0
+#endif
+
+#ifndef CV_VSX3
+#  define CV_VSX3 0
+#endif
+
+#ifndef CV_MSA
+#  define CV_MSA 0
+#endif
+
+#ifndef CV_WASM_SIMD
+#  define CV_WASM_SIMD 0
+#endif
+
+#ifndef CV_RVV
+#  define CV_RVV 0
+#endif
diff --git a/3rdParty/include/opencv2/core/cv_cpu_helper.h b/3rdParty/include/opencv2/core/cv_cpu_helper.h
new file mode 100644
index 0000000..39ae0b9
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cv_cpu_helper.h
@@ -0,0 +1,508 @@
+// AUTOGENERATED, DO NOT EDIT
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE
+#  define CV_TRY_SSE 1
+#  define CV_CPU_FORCE_SSE 1
+#  define CV_CPU_HAS_SUPPORT_SSE 1
+#  define CV_CPU_CALL_SSE(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_SSE_(fn, args) return (opt_SSE::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE
+#  define CV_TRY_SSE 1
+#  define CV_CPU_FORCE_SSE 0
+#  define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE))
+#  define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args)
+#  define CV_CPU_CALL_SSE_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args)
+#else
+#  define CV_TRY_SSE 0
+#  define CV_CPU_FORCE_SSE 0
+#  define CV_CPU_HAS_SUPPORT_SSE 0
+#  define CV_CPU_CALL_SSE(fn, args)
+#  define CV_CPU_CALL_SSE_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...)  CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2
+#  define CV_TRY_SSE2 1
+#  define CV_CPU_FORCE_SSE2 1
+#  define CV_CPU_HAS_SUPPORT_SSE2 1
+#  define CV_CPU_CALL_SSE2(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_SSE2_(fn, args) return (opt_SSE2::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2
+#  define CV_TRY_SSE2 1
+#  define CV_CPU_FORCE_SSE2 0
+#  define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2))
+#  define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args)
+#  define CV_CPU_CALL_SSE2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args)
+#else
+#  define CV_TRY_SSE2 0
+#  define CV_CPU_FORCE_SSE2 0
+#  define CV_CPU_HAS_SUPPORT_SSE2 0
+#  define CV_CPU_CALL_SSE2(fn, args)
+#  define CV_CPU_CALL_SSE2_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...)  CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3
+#  define CV_TRY_SSE3 1
+#  define CV_CPU_FORCE_SSE3 1
+#  define CV_CPU_HAS_SUPPORT_SSE3 1
+#  define CV_CPU_CALL_SSE3(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_SSE3_(fn, args) return (opt_SSE3::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3
+#  define CV_TRY_SSE3 1
+#  define CV_CPU_FORCE_SSE3 0
+#  define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3))
+#  define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args)
+#  define CV_CPU_CALL_SSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args)
+#else
+#  define CV_TRY_SSE3 0
+#  define CV_CPU_FORCE_SSE3 0
+#  define CV_CPU_HAS_SUPPORT_SSE3 0
+#  define CV_CPU_CALL_SSE3(fn, args)
+#  define CV_CPU_CALL_SSE3_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...)  CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3
+#  define CV_TRY_SSSE3 1
+#  define CV_CPU_FORCE_SSSE3 1
+#  define CV_CPU_HAS_SUPPORT_SSSE3 1
+#  define CV_CPU_CALL_SSSE3(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_SSSE3_(fn, args) return (opt_SSSE3::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3
+#  define CV_TRY_SSSE3 1
+#  define CV_CPU_FORCE_SSSE3 0
+#  define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3))
+#  define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args)
+#  define CV_CPU_CALL_SSSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args)
+#else
+#  define CV_TRY_SSSE3 0
+#  define CV_CPU_FORCE_SSSE3 0
+#  define CV_CPU_HAS_SUPPORT_SSSE3 0
+#  define CV_CPU_CALL_SSSE3(fn, args)
+#  define CV_CPU_CALL_SSSE3_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...)  CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1
+#  define CV_TRY_SSE4_1 1
+#  define CV_CPU_FORCE_SSE4_1 1
+#  define CV_CPU_HAS_SUPPORT_SSE4_1 1
+#  define CV_CPU_CALL_SSE4_1(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_SSE4_1_(fn, args) return (opt_SSE4_1::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1
+#  define CV_TRY_SSE4_1 1
+#  define CV_CPU_FORCE_SSE4_1 0
+#  define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1))
+#  define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args)
+#  define CV_CPU_CALL_SSE4_1_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args)
+#else
+#  define CV_TRY_SSE4_1 0
+#  define CV_CPU_FORCE_SSE4_1 0
+#  define CV_CPU_HAS_SUPPORT_SSE4_1 0
+#  define CV_CPU_CALL_SSE4_1(fn, args)
+#  define CV_CPU_CALL_SSE4_1_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...)  CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2
+#  define CV_TRY_SSE4_2 1
+#  define CV_CPU_FORCE_SSE4_2 1
+#  define CV_CPU_HAS_SUPPORT_SSE4_2 1
+#  define CV_CPU_CALL_SSE4_2(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_SSE4_2_(fn, args) return (opt_SSE4_2::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2
+#  define CV_TRY_SSE4_2 1
+#  define CV_CPU_FORCE_SSE4_2 0
+#  define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2))
+#  define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args)
+#  define CV_CPU_CALL_SSE4_2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args)
+#else
+#  define CV_TRY_SSE4_2 0
+#  define CV_CPU_FORCE_SSE4_2 0
+#  define CV_CPU_HAS_SUPPORT_SSE4_2 0
+#  define CV_CPU_CALL_SSE4_2(fn, args)
+#  define CV_CPU_CALL_SSE4_2_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...)  CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT
+#  define CV_TRY_POPCNT 1
+#  define CV_CPU_FORCE_POPCNT 1
+#  define CV_CPU_HAS_SUPPORT_POPCNT 1
+#  define CV_CPU_CALL_POPCNT(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_POPCNT_(fn, args) return (opt_POPCNT::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT
+#  define CV_TRY_POPCNT 1
+#  define CV_CPU_FORCE_POPCNT 0
+#  define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT))
+#  define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args)
+#  define CV_CPU_CALL_POPCNT_(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args)
+#else
+#  define CV_TRY_POPCNT 0
+#  define CV_CPU_FORCE_POPCNT 0
+#  define CV_CPU_HAS_SUPPORT_POPCNT 0
+#  define CV_CPU_CALL_POPCNT(fn, args)
+#  define CV_CPU_CALL_POPCNT_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...)  CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX
+#  define CV_TRY_AVX 1
+#  define CV_CPU_FORCE_AVX 1
+#  define CV_CPU_HAS_SUPPORT_AVX 1
+#  define CV_CPU_CALL_AVX(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX_(fn, args) return (opt_AVX::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX
+#  define CV_TRY_AVX 1
+#  define CV_CPU_FORCE_AVX 0
+#  define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX))
+#  define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args)
+#  define CV_CPU_CALL_AVX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args)
+#else
+#  define CV_TRY_AVX 0
+#  define CV_CPU_FORCE_AVX 0
+#  define CV_CPU_HAS_SUPPORT_AVX 0
+#  define CV_CPU_CALL_AVX(fn, args)
+#  define CV_CPU_CALL_AVX_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...)  CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16
+#  define CV_TRY_FP16 1
+#  define CV_CPU_FORCE_FP16 1
+#  define CV_CPU_HAS_SUPPORT_FP16 1
+#  define CV_CPU_CALL_FP16(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_FP16_(fn, args) return (opt_FP16::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16
+#  define CV_TRY_FP16 1
+#  define CV_CPU_FORCE_FP16 0
+#  define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16))
+#  define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args)
+#  define CV_CPU_CALL_FP16_(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args)
+#else
+#  define CV_TRY_FP16 0
+#  define CV_CPU_FORCE_FP16 0
+#  define CV_CPU_HAS_SUPPORT_FP16 0
+#  define CV_CPU_CALL_FP16(fn, args)
+#  define CV_CPU_CALL_FP16_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...)  CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2
+#  define CV_TRY_AVX2 1
+#  define CV_CPU_FORCE_AVX2 1
+#  define CV_CPU_HAS_SUPPORT_AVX2 1
+#  define CV_CPU_CALL_AVX2(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX2_(fn, args) return (opt_AVX2::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2
+#  define CV_TRY_AVX2 1
+#  define CV_CPU_FORCE_AVX2 0
+#  define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2))
+#  define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args)
+#  define CV_CPU_CALL_AVX2_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args)
+#else
+#  define CV_TRY_AVX2 0
+#  define CV_CPU_FORCE_AVX2 0
+#  define CV_CPU_HAS_SUPPORT_AVX2 0
+#  define CV_CPU_CALL_AVX2(fn, args)
+#  define CV_CPU_CALL_AVX2_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...)  CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3
+#  define CV_TRY_FMA3 1
+#  define CV_CPU_FORCE_FMA3 1
+#  define CV_CPU_HAS_SUPPORT_FMA3 1
+#  define CV_CPU_CALL_FMA3(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_FMA3_(fn, args) return (opt_FMA3::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3
+#  define CV_TRY_FMA3 1
+#  define CV_CPU_FORCE_FMA3 0
+#  define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3))
+#  define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args)
+#  define CV_CPU_CALL_FMA3_(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args)
+#else
+#  define CV_TRY_FMA3 0
+#  define CV_CPU_FORCE_FMA3 0
+#  define CV_CPU_HAS_SUPPORT_FMA3 0
+#  define CV_CPU_CALL_FMA3(fn, args)
+#  define CV_CPU_CALL_FMA3_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...)  CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX_512F
+#  define CV_TRY_AVX_512F 1
+#  define CV_CPU_FORCE_AVX_512F 1
+#  define CV_CPU_HAS_SUPPORT_AVX_512F 1
+#  define CV_CPU_CALL_AVX_512F(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX_512F_(fn, args) return (opt_AVX_512F::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX_512F
+#  define CV_TRY_AVX_512F 1
+#  define CV_CPU_FORCE_AVX_512F 0
+#  define CV_CPU_HAS_SUPPORT_AVX_512F (cv::checkHardwareSupport(CV_CPU_AVX_512F))
+#  define CV_CPU_CALL_AVX_512F(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args)
+#  define CV_CPU_CALL_AVX_512F_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args)
+#else
+#  define CV_TRY_AVX_512F 0
+#  define CV_CPU_FORCE_AVX_512F 0
+#  define CV_CPU_HAS_SUPPORT_AVX_512F 0
+#  define CV_CPU_CALL_AVX_512F(fn, args)
+#  define CV_CPU_CALL_AVX_512F_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX_512F(fn, args, mode, ...)  CV_CPU_CALL_AVX_512F(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_COMMON
+#  define CV_TRY_AVX512_COMMON 1
+#  define CV_CPU_FORCE_AVX512_COMMON 1
+#  define CV_CPU_HAS_SUPPORT_AVX512_COMMON 1
+#  define CV_CPU_CALL_AVX512_COMMON(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX512_COMMON_(fn, args) return (opt_AVX512_COMMON::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_COMMON
+#  define CV_TRY_AVX512_COMMON 1
+#  define CV_CPU_FORCE_AVX512_COMMON 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_COMMON (cv::checkHardwareSupport(CV_CPU_AVX512_COMMON))
+#  define CV_CPU_CALL_AVX512_COMMON(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_COMMON) return (opt_AVX512_COMMON::fn args)
+#  define CV_CPU_CALL_AVX512_COMMON_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_COMMON) return (opt_AVX512_COMMON::fn args)
+#else
+#  define CV_TRY_AVX512_COMMON 0
+#  define CV_CPU_FORCE_AVX512_COMMON 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_COMMON 0
+#  define CV_CPU_CALL_AVX512_COMMON(fn, args)
+#  define CV_CPU_CALL_AVX512_COMMON_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX512_COMMON(fn, args, mode, ...)  CV_CPU_CALL_AVX512_COMMON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_KNL
+#  define CV_TRY_AVX512_KNL 1
+#  define CV_CPU_FORCE_AVX512_KNL 1
+#  define CV_CPU_HAS_SUPPORT_AVX512_KNL 1
+#  define CV_CPU_CALL_AVX512_KNL(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX512_KNL_(fn, args) return (opt_AVX512_KNL::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_KNL
+#  define CV_TRY_AVX512_KNL 1
+#  define CV_CPU_FORCE_AVX512_KNL 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_KNL (cv::checkHardwareSupport(CV_CPU_AVX512_KNL))
+#  define CV_CPU_CALL_AVX512_KNL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNL) return (opt_AVX512_KNL::fn args)
+#  define CV_CPU_CALL_AVX512_KNL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNL) return (opt_AVX512_KNL::fn args)
+#else
+#  define CV_TRY_AVX512_KNL 0
+#  define CV_CPU_FORCE_AVX512_KNL 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_KNL 0
+#  define CV_CPU_CALL_AVX512_KNL(fn, args)
+#  define CV_CPU_CALL_AVX512_KNL_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX512_KNL(fn, args, mode, ...)  CV_CPU_CALL_AVX512_KNL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_KNM
+#  define CV_TRY_AVX512_KNM 1
+#  define CV_CPU_FORCE_AVX512_KNM 1
+#  define CV_CPU_HAS_SUPPORT_AVX512_KNM 1
+#  define CV_CPU_CALL_AVX512_KNM(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX512_KNM_(fn, args) return (opt_AVX512_KNM::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_KNM
+#  define CV_TRY_AVX512_KNM 1
+#  define CV_CPU_FORCE_AVX512_KNM 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_KNM (cv::checkHardwareSupport(CV_CPU_AVX512_KNM))
+#  define CV_CPU_CALL_AVX512_KNM(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNM) return (opt_AVX512_KNM::fn args)
+#  define CV_CPU_CALL_AVX512_KNM_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNM) return (opt_AVX512_KNM::fn args)
+#else
+#  define CV_TRY_AVX512_KNM 0
+#  define CV_CPU_FORCE_AVX512_KNM 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_KNM 0
+#  define CV_CPU_CALL_AVX512_KNM(fn, args)
+#  define CV_CPU_CALL_AVX512_KNM_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX512_KNM(fn, args, mode, ...)  CV_CPU_CALL_AVX512_KNM(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_SKX
+#  define CV_TRY_AVX512_SKX 1
+#  define CV_CPU_FORCE_AVX512_SKX 1
+#  define CV_CPU_HAS_SUPPORT_AVX512_SKX 1
+#  define CV_CPU_CALL_AVX512_SKX(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX512_SKX_(fn, args) return (opt_AVX512_SKX::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_SKX
+#  define CV_TRY_AVX512_SKX 1
+#  define CV_CPU_FORCE_AVX512_SKX 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_SKX (cv::checkHardwareSupport(CV_CPU_AVX512_SKX))
+#  define CV_CPU_CALL_AVX512_SKX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args)
+#  define CV_CPU_CALL_AVX512_SKX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args)
+#else
+#  define CV_TRY_AVX512_SKX 0
+#  define CV_CPU_FORCE_AVX512_SKX 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_SKX 0
+#  define CV_CPU_CALL_AVX512_SKX(fn, args)
+#  define CV_CPU_CALL_AVX512_SKX_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX512_SKX(fn, args, mode, ...)  CV_CPU_CALL_AVX512_SKX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CNL
+#  define CV_TRY_AVX512_CNL 1
+#  define CV_CPU_FORCE_AVX512_CNL 1
+#  define CV_CPU_HAS_SUPPORT_AVX512_CNL 1
+#  define CV_CPU_CALL_AVX512_CNL(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX512_CNL_(fn, args) return (opt_AVX512_CNL::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CNL
+#  define CV_TRY_AVX512_CNL 1
+#  define CV_CPU_FORCE_AVX512_CNL 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_CNL (cv::checkHardwareSupport(CV_CPU_AVX512_CNL))
+#  define CV_CPU_CALL_AVX512_CNL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CNL) return (opt_AVX512_CNL::fn args)
+#  define CV_CPU_CALL_AVX512_CNL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CNL) return (opt_AVX512_CNL::fn args)
+#else
+#  define CV_TRY_AVX512_CNL 0
+#  define CV_CPU_FORCE_AVX512_CNL 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_CNL 0
+#  define CV_CPU_CALL_AVX512_CNL(fn, args)
+#  define CV_CPU_CALL_AVX512_CNL_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX512_CNL(fn, args, mode, ...)  CV_CPU_CALL_AVX512_CNL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CLX
+#  define CV_TRY_AVX512_CLX 1
+#  define CV_CPU_FORCE_AVX512_CLX 1
+#  define CV_CPU_HAS_SUPPORT_AVX512_CLX 1
+#  define CV_CPU_CALL_AVX512_CLX(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX512_CLX_(fn, args) return (opt_AVX512_CLX::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CLX
+#  define CV_TRY_AVX512_CLX 1
+#  define CV_CPU_FORCE_AVX512_CLX 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_CLX (cv::checkHardwareSupport(CV_CPU_AVX512_CLX))
+#  define CV_CPU_CALL_AVX512_CLX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args)
+#  define CV_CPU_CALL_AVX512_CLX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args)
+#else
+#  define CV_TRY_AVX512_CLX 0
+#  define CV_CPU_FORCE_AVX512_CLX 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_CLX 0
+#  define CV_CPU_CALL_AVX512_CLX(fn, args)
+#  define CV_CPU_CALL_AVX512_CLX_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX512_CLX(fn, args, mode, ...)  CV_CPU_CALL_AVX512_CLX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_ICL
+#  define CV_TRY_AVX512_ICL 1
+#  define CV_CPU_FORCE_AVX512_ICL 1
+#  define CV_CPU_HAS_SUPPORT_AVX512_ICL 1
+#  define CV_CPU_CALL_AVX512_ICL(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_AVX512_ICL_(fn, args) return (opt_AVX512_ICL::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_ICL
+#  define CV_TRY_AVX512_ICL 1
+#  define CV_CPU_FORCE_AVX512_ICL 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_ICL (cv::checkHardwareSupport(CV_CPU_AVX512_ICL))
+#  define CV_CPU_CALL_AVX512_ICL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_ICL) return (opt_AVX512_ICL::fn args)
+#  define CV_CPU_CALL_AVX512_ICL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_ICL) return (opt_AVX512_ICL::fn args)
+#else
+#  define CV_TRY_AVX512_ICL 0
+#  define CV_CPU_FORCE_AVX512_ICL 0
+#  define CV_CPU_HAS_SUPPORT_AVX512_ICL 0
+#  define CV_CPU_CALL_AVX512_ICL(fn, args)
+#  define CV_CPU_CALL_AVX512_ICL_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_AVX512_ICL(fn, args, mode, ...)  CV_CPU_CALL_AVX512_ICL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON
+#  define CV_TRY_NEON 1
+#  define CV_CPU_FORCE_NEON 1
+#  define CV_CPU_HAS_SUPPORT_NEON 1
+#  define CV_CPU_CALL_NEON(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_NEON_(fn, args) return (opt_NEON::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON
+#  define CV_TRY_NEON 1
+#  define CV_CPU_FORCE_NEON 0
+#  define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON))
+#  define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args)
+#  define CV_CPU_CALL_NEON_(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args)
+#else
+#  define CV_TRY_NEON 0
+#  define CV_CPU_FORCE_NEON 0
+#  define CV_CPU_HAS_SUPPORT_NEON 0
+#  define CV_CPU_CALL_NEON(fn, args)
+#  define CV_CPU_CALL_NEON_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...)  CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_MSA
+#  define CV_TRY_MSA 1
+#  define CV_CPU_FORCE_MSA 1
+#  define CV_CPU_HAS_SUPPORT_MSA 1
+#  define CV_CPU_CALL_MSA(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_MSA_(fn, args) return (opt_MSA::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_MSA
+#  define CV_TRY_MSA 1
+#  define CV_CPU_FORCE_MSA 0
+#  define CV_CPU_HAS_SUPPORT_MSA (cv::checkHardwareSupport(CV_CPU_MSA))
+#  define CV_CPU_CALL_MSA(fn, args) if (CV_CPU_HAS_SUPPORT_MSA) return (opt_MSA::fn args)
+#  define CV_CPU_CALL_MSA_(fn, args) if (CV_CPU_HAS_SUPPORT_MSA) return (opt_MSA::fn args)
+#else
+#  define CV_TRY_MSA 0
+#  define CV_CPU_FORCE_MSA 0
+#  define CV_CPU_HAS_SUPPORT_MSA 0
+#  define CV_CPU_CALL_MSA(fn, args)
+#  define CV_CPU_CALL_MSA_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_MSA(fn, args, mode, ...)  CV_CPU_CALL_MSA(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX
+#  define CV_TRY_VSX 1
+#  define CV_CPU_FORCE_VSX 1
+#  define CV_CPU_HAS_SUPPORT_VSX 1
+#  define CV_CPU_CALL_VSX(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_VSX_(fn, args) return (opt_VSX::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX
+#  define CV_TRY_VSX 1
+#  define CV_CPU_FORCE_VSX 0
+#  define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX))
+#  define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args)
+#  define CV_CPU_CALL_VSX_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args)
+#else
+#  define CV_TRY_VSX 0
+#  define CV_CPU_FORCE_VSX 0
+#  define CV_CPU_HAS_SUPPORT_VSX 0
+#  define CV_CPU_CALL_VSX(fn, args)
+#  define CV_CPU_CALL_VSX_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...)  CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX3
+#  define CV_TRY_VSX3 1
+#  define CV_CPU_FORCE_VSX3 1
+#  define CV_CPU_HAS_SUPPORT_VSX3 1
+#  define CV_CPU_CALL_VSX3(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_VSX3_(fn, args) return (opt_VSX3::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX3
+#  define CV_TRY_VSX3 1
+#  define CV_CPU_FORCE_VSX3 0
+#  define CV_CPU_HAS_SUPPORT_VSX3 (cv::checkHardwareSupport(CV_CPU_VSX3))
+#  define CV_CPU_CALL_VSX3(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args)
+#  define CV_CPU_CALL_VSX3_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args)
+#else
+#  define CV_TRY_VSX3 0
+#  define CV_CPU_FORCE_VSX3 0
+#  define CV_CPU_HAS_SUPPORT_VSX3 0
+#  define CV_CPU_CALL_VSX3(fn, args)
+#  define CV_CPU_CALL_VSX3_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_VSX3(fn, args, mode, ...)  CV_CPU_CALL_VSX3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_RVV
+#  define CV_TRY_RVV 1
+#  define CV_CPU_FORCE_RVV 1
+#  define CV_CPU_HAS_SUPPORT_RVV 1
+#  define CV_CPU_CALL_RVV(fn, args) return (cpu_baseline::fn args)
+#  define CV_CPU_CALL_RVV_(fn, args) return (opt_RVV::fn args)
+#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_RVV
+#  define CV_TRY_RVV 1
+#  define CV_CPU_FORCE_RVV 0
+#  define CV_CPU_HAS_SUPPORT_RVV (cv::checkHardwareSupport(CV_CPU_RVV))
+#  define CV_CPU_CALL_RVV(fn, args) if (CV_CPU_HAS_SUPPORT_RVV) return (opt_RVV::fn args)
+#  define CV_CPU_CALL_RVV_(fn, args) if (CV_CPU_HAS_SUPPORT_RVV) return (opt_RVV::fn args)
+#else
+#  define CV_TRY_RVV 0
+#  define CV_CPU_FORCE_RVV 0
+#  define CV_CPU_HAS_SUPPORT_RVV 0
+#  define CV_CPU_CALL_RVV(fn, args)
+#  define CV_CPU_CALL_RVV_(fn, args)
+#endif
+#define __CV_CPU_DISPATCH_CHAIN_RVV(fn, args, mode, ...)  CV_CPU_CALL_RVV(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
+
+#define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args)
+#define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...)  CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */
diff --git a/3rdParty/include/opencv2/core/cvdef.h b/3rdParty/include/opencv2/core/cvdef.h
new file mode 100644
index 0000000..f785f32
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cvdef.h
@@ -0,0 +1,967 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_CVDEF_H
+#define OPENCV_CORE_CVDEF_H
+
+#include "opencv2/core/version.hpp"
+
+//! @addtogroup core_utils
+//! @{
+
+#ifdef OPENCV_INCLUDE_PORT_FILE  // User-provided header file with custom platform configuration
+#include OPENCV_INCLUDE_PORT_FILE
+#endif
+
+#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD
+#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \
+    (defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC))
+// Guard to prevent using of binary incompatible binaries / runtimes
+// https://github.com/opencv/opencv/pull/9161
+#define CV__DEBUG_NS_BEGIN namespace debug_build_guard {
+#define CV__DEBUG_NS_END }
+namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; }
+#endif
+#endif
+
+#ifndef CV__DEBUG_NS_BEGIN
+#define CV__DEBUG_NS_BEGIN
+#define CV__DEBUG_NS_END
+#endif
+
+
+#ifdef __OPENCV_BUILD
+#include "cvconfig.h"
+#endif
+
+#ifndef __CV_EXPAND
+#define __CV_EXPAND(x) x
+#endif
+
+#ifndef __CV_CAT
+#define __CV_CAT__(x, y) x ## y
+#define __CV_CAT_(x, y) __CV_CAT__(x, y)
+#define __CV_CAT(x, y) __CV_CAT_(x, y)
+#endif
+
+#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
+#define __CV_VA_NUM_ARGS(...) __CV_EXPAND(__CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0))
+
+#ifdef CV_Func
+// keep current value (through OpenCV port file)
+#elif defined __GNUC__ || (defined (__cpluscplus) && (__cpluscplus >= 201103))
+#define CV_Func __func__
+#elif defined __clang__ && (__clang_minor__ * 100 + __clang_major__ >= 305)
+#define CV_Func __func__
+#elif defined(__STDC_VERSION__) && (__STDC_VERSION >= 199901)
+#define CV_Func __func__
+#elif defined _MSC_VER
+#define CV_Func __FUNCTION__
+#elif defined(__INTEL_COMPILER) && (_INTEL_COMPILER >= 600)
+#define CV_Func __FUNCTION__
+#elif defined __IBMCPP__ && __IBMCPP__ >=500
+#define CV_Func __FUNCTION__
+#elif defined __BORLAND__ && (__BORLANDC__ >= 0x550)
+#define CV_Func __FUNC__
+#else
+#define CV_Func "<unknown>"
+#endif
+
+//! @cond IGNORED
+
+//////////////// static assert /////////////////
+#define CVAUX_CONCAT_EXP(a, b) a##b
+#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b)
+
+#if defined(__clang__)
+#  ifndef __has_extension
+#    define __has_extension __has_feature /* compatibility, for older versions of clang */
+#  endif
+#  if __has_extension(cxx_static_assert)
+#    define CV_StaticAssert(condition, reason)    static_assert((condition), reason " " #condition)
+#  elif __has_extension(c_static_assert)
+#    define CV_StaticAssert(condition, reason)    _Static_assert((condition), reason " " #condition)
+#  endif
+#elif defined(__GNUC__)
+#  if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L)
+#    define CV_StaticAssert(condition, reason)    static_assert((condition), reason " " #condition)
+#  endif
+#elif defined(_MSC_VER)
+#  if _MSC_VER >= 1600 /* MSVC 10 */
+#    define CV_StaticAssert(condition, reason)    static_assert((condition), reason " " #condition)
+#  endif
+#endif
+#ifndef CV_StaticAssert
+#  if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302)
+#    define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); })
+#  else
+namespace cv {
+     template <bool x> struct CV_StaticAssert_failed;
+     template <> struct CV_StaticAssert_failed<true> { enum { val = 1 }; };
+     template<int x> struct CV_StaticAssert_test {};
+}
+#    define CV_StaticAssert(condition, reason)\
+       typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast<bool>(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__)
+#  endif
+#endif
+
+// Suppress warning "-Wdeprecated-declarations" / C4996
+#if defined(_MSC_VER)
+    #define CV_DO_PRAGMA(x) __pragma(x)
+#elif defined(__GNUC__)
+    #define CV_DO_PRAGMA(x) _Pragma (#x)
+#else
+    #define CV_DO_PRAGMA(x)
+#endif
+
+#ifdef _MSC_VER
+#define CV_SUPPRESS_DEPRECATED_START \
+    CV_DO_PRAGMA(warning(push)) \
+    CV_DO_PRAGMA(warning(disable: 4996))
+#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop))
+#elif defined (__clang__) || ((__GNUC__)  && (__GNUC__*100 + __GNUC_MINOR__ > 405))
+#define CV_SUPPRESS_DEPRECATED_START \
+    CV_DO_PRAGMA(GCC diagnostic push) \
+    CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations")
+#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop)
+#else
+#define CV_SUPPRESS_DEPRECATED_START
+#define CV_SUPPRESS_DEPRECATED_END
+#endif
+
+#define CV_UNUSED(name) (void)name
+
+//! @endcond
+
+// undef problematic defines sometimes defined by system headers (windows.h in particular)
+#undef small
+#undef min
+#undef max
+#undef abs
+#undef Complex
+
+#if defined __cplusplus
+#include <limits>
+#else
+#include <limits.h>
+#endif
+
+#include "opencv2/core/hal/interface.h"
+
+#if defined __ICL
+#  define CV_ICC   __ICL
+#elif defined __ICC
+#  define CV_ICC   __ICC
+#elif defined __ECL
+#  define CV_ICC   __ECL
+#elif defined __ECC
+#  define CV_ICC   __ECC
+#elif defined __INTEL_COMPILER
+#  define CV_ICC   __INTEL_COMPILER
+#endif
+
+#ifndef CV_INLINE
+#  if defined __cplusplus
+#    define CV_INLINE static inline
+#  elif defined _MSC_VER
+#    define CV_INLINE __inline
+#  else
+#    define CV_INLINE static
+#  endif
+#endif
+
+#ifndef CV_ALWAYS_INLINE
+#if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))
+#define CV_ALWAYS_INLINE inline __attribute__((always_inline))
+#elif defined(_MSC_VER)
+#define CV_ALWAYS_INLINE __forceinline
+#else
+#define CV_ALWAYS_INLINE inline
+#endif
+#endif
+
+#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED)
+#  define CV_ENABLE_UNROLLED 0
+#else
+#  define CV_ENABLE_UNROLLED 1
+#endif
+
+#ifdef __GNUC__
+#  define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x)))
+#elif defined _MSC_VER
+#  define CV_DECL_ALIGNED(x) __declspec(align(x))
+#else
+#  define CV_DECL_ALIGNED(x)
+#endif
+
+/* CPU features and intrinsics support */
+#define CV_CPU_NONE             0
+#define CV_CPU_MMX              1
+#define CV_CPU_SSE              2
+#define CV_CPU_SSE2             3
+#define CV_CPU_SSE3             4
+#define CV_CPU_SSSE3            5
+#define CV_CPU_SSE4_1           6
+#define CV_CPU_SSE4_2           7
+#define CV_CPU_POPCNT           8
+#define CV_CPU_FP16             9
+#define CV_CPU_AVX              10
+#define CV_CPU_AVX2             11
+#define CV_CPU_FMA3             12
+
+#define CV_CPU_AVX_512F         13
+#define CV_CPU_AVX_512BW        14
+#define CV_CPU_AVX_512CD        15
+#define CV_CPU_AVX_512DQ        16
+#define CV_CPU_AVX_512ER        17
+#define CV_CPU_AVX_512IFMA512   18 // deprecated
+#define CV_CPU_AVX_512IFMA      18
+#define CV_CPU_AVX_512PF        19
+#define CV_CPU_AVX_512VBMI      20
+#define CV_CPU_AVX_512VL        21
+#define CV_CPU_AVX_512VBMI2     22
+#define CV_CPU_AVX_512VNNI      23
+#define CV_CPU_AVX_512BITALG    24
+#define CV_CPU_AVX_512VPOPCNTDQ 25
+#define CV_CPU_AVX_5124VNNIW    26
+#define CV_CPU_AVX_5124FMAPS    27
+
+#define CV_CPU_NEON             100
+
+#define CV_CPU_MSA              150
+
+#define CV_CPU_RISCVV           170
+
+#define CV_CPU_VSX              200
+#define CV_CPU_VSX3             201
+
+#define CV_CPU_RVV              210
+
+// CPU features groups
+#define CV_CPU_AVX512_SKX       256
+#define CV_CPU_AVX512_COMMON    257
+#define CV_CPU_AVX512_KNL       258
+#define CV_CPU_AVX512_KNM       259
+#define CV_CPU_AVX512_CNL       260
+#define CV_CPU_AVX512_CLX       261
+#define CV_CPU_AVX512_ICL       262
+
+// when adding to this list remember to update the following enum
+#define CV_HARDWARE_MAX_FEATURE 512
+
+/** @brief Available CPU features.
+*/
+enum CpuFeatures {
+    CPU_MMX             = 1,
+    CPU_SSE             = 2,
+    CPU_SSE2            = 3,
+    CPU_SSE3            = 4,
+    CPU_SSSE3           = 5,
+    CPU_SSE4_1          = 6,
+    CPU_SSE4_2          = 7,
+    CPU_POPCNT          = 8,
+    CPU_FP16            = 9,
+    CPU_AVX             = 10,
+    CPU_AVX2            = 11,
+    CPU_FMA3            = 12,
+
+    CPU_AVX_512F        = 13,
+    CPU_AVX_512BW       = 14,
+    CPU_AVX_512CD       = 15,
+    CPU_AVX_512DQ       = 16,
+    CPU_AVX_512ER       = 17,
+    CPU_AVX_512IFMA512  = 18, // deprecated
+    CPU_AVX_512IFMA     = 18,
+    CPU_AVX_512PF       = 19,
+    CPU_AVX_512VBMI     = 20,
+    CPU_AVX_512VL       = 21,
+    CPU_AVX_512VBMI2    = 22,
+    CPU_AVX_512VNNI     = 23,
+    CPU_AVX_512BITALG   = 24,
+    CPU_AVX_512VPOPCNTDQ= 25,
+    CPU_AVX_5124VNNIW   = 26,
+    CPU_AVX_5124FMAPS   = 27,
+
+    CPU_NEON            = 100,
+
+    CPU_MSA             = 150,
+
+    CPU_RISCVV          = 170,
+
+    CPU_VSX             = 200,
+    CPU_VSX3            = 201,
+
+    CPU_RVV             = 210,
+
+    CPU_AVX512_SKX      = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL
+    CPU_AVX512_COMMON   = 257, //!< Common instructions AVX-512F/CD for all CPUs that support AVX-512
+    CPU_AVX512_KNL      = 258, //!< Knights Landing with AVX-512F/CD/ER/PF
+    CPU_AVX512_KNM      = 259, //!< Knights Mill with AVX-512F/CD/ER/PF/4FMAPS/4VNNIW/VPOPCNTDQ
+    CPU_AVX512_CNL      = 260, //!< Cannon Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI
+    CPU_AVX512_CLX      = 261, //!< Cascade Lake with AVX-512F/CD/BW/DQ/VL/VNNI
+    CPU_AVX512_ICL      = 262, //!< Ice Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI/VBMI2/BITALG/VPOPCNTDQ
+
+    CPU_MAX_FEATURE     = 512  // see CV_HARDWARE_MAX_FEATURE
+};
+
+
+#include "cv_cpu_dispatch.h"
+
+#if !defined(CV_STRONG_ALIGNMENT) && defined(__arm__) && !(defined(__aarch64__) || defined(_M_ARM64))
+// int*, int64* should be propertly aligned pointers on ARMv7
+#define CV_STRONG_ALIGNMENT 1
+#endif
+#if !defined(CV_STRONG_ALIGNMENT)
+#define CV_STRONG_ALIGNMENT 0
+#endif
+
+/* fundamental constants */
+#define CV_PI   3.1415926535897932384626433832795
+#define CV_2PI  6.283185307179586476925286766559
+#define CV_LOG2 0.69314718055994530941723212145818
+
+#if defined __ARM_FP16_FORMAT_IEEE \
+    && !defined __CUDACC__
+#  define CV_FP16_TYPE 1
+#else
+#  define CV_FP16_TYPE 0
+#endif
+
+typedef union Cv16suf
+{
+    short i;
+    ushort u;
+#if CV_FP16_TYPE
+    __fp16 h;
+#endif
+}
+Cv16suf;
+
+typedef union Cv32suf
+{
+    int i;
+    unsigned u;
+    float f;
+}
+Cv32suf;
+
+typedef union Cv64suf
+{
+    int64 i;
+    uint64 u;
+    double f;
+}
+Cv64suf;
+
+#ifndef OPENCV_ABI_COMPATIBILITY
+#define OPENCV_ABI_COMPATIBILITY 400
+#endif
+
+#ifdef __OPENCV_BUILD
+#  define DISABLE_OPENCV_3_COMPATIBILITY
+#  define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY
+#endif
+
+#ifndef CV_EXPORTS
+# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) && defined(CVAPI_EXPORTS)
+#   define CV_EXPORTS __declspec(dllexport)
+# elif defined __GNUC__ && __GNUC__ >= 4 && (defined(CVAPI_EXPORTS) || defined(__APPLE__))
+#   define CV_EXPORTS __attribute__ ((visibility ("default")))
+# endif
+#endif
+
+#ifndef CV_EXPORTS
+# define CV_EXPORTS
+#endif
+
+#ifdef _MSC_VER
+#   define CV_EXPORTS_TEMPLATE
+#else
+#   define CV_EXPORTS_TEMPLATE CV_EXPORTS
+#endif
+
+#ifndef CV_DEPRECATED
+#  if defined(__GNUC__)
+#    define CV_DEPRECATED __attribute__ ((deprecated))
+#  elif defined(_MSC_VER)
+#    define CV_DEPRECATED __declspec(deprecated)
+#  else
+#    define CV_DEPRECATED
+#  endif
+#endif
+
+#ifndef CV_DEPRECATED_EXTERNAL
+#  if defined(__OPENCV_BUILD)
+#    define CV_DEPRECATED_EXTERNAL /* nothing */
+#  else
+#    define CV_DEPRECATED_EXTERNAL CV_DEPRECATED
+#  endif
+#endif
+
+
+#ifndef CV_EXTERN_C
+#  ifdef __cplusplus
+#    define CV_EXTERN_C extern "C"
+#  else
+#    define CV_EXTERN_C
+#  endif
+#endif
+
+/* special informative macros for wrapper generators */
+#define CV_EXPORTS_W CV_EXPORTS
+#define CV_EXPORTS_W_SIMPLE CV_EXPORTS
+#define CV_EXPORTS_AS(synonym) CV_EXPORTS
+#define CV_EXPORTS_W_MAP CV_EXPORTS
+#define CV_IN_OUT
+#define CV_OUT
+#define CV_PROP
+#define CV_PROP_RW
+#define CV_WRAP
+#define CV_WRAP_AS(synonym)
+#define CV_WRAP_MAPPABLE(mappable)
+#define CV_WRAP_PHANTOM(phantom_header)
+#define CV_WRAP_DEFAULT(val)
+
+/****************************************************************************************\
+*                                  Matrix type (Mat)                                     *
+\****************************************************************************************/
+
+#define CV_MAT_CN_MASK          ((CV_CN_MAX - 1) << CV_CN_SHIFT)
+#define CV_MAT_CN(flags)        ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1)
+#define CV_MAT_TYPE_MASK        (CV_DEPTH_MAX*CV_CN_MAX - 1)
+#define CV_MAT_TYPE(flags)      ((flags) & CV_MAT_TYPE_MASK)
+#define CV_MAT_CONT_FLAG_SHIFT  14
+#define CV_MAT_CONT_FLAG        (1 << CV_MAT_CONT_FLAG_SHIFT)
+#define CV_IS_MAT_CONT(flags)   ((flags) & CV_MAT_CONT_FLAG)
+#define CV_IS_CONT_MAT          CV_IS_MAT_CONT
+#define CV_SUBMAT_FLAG_SHIFT    15
+#define CV_SUBMAT_FLAG          (1 << CV_SUBMAT_FLAG_SHIFT)
+#define CV_IS_SUBMAT(flags)     ((flags) & CV_MAT_SUBMAT_FLAG)
+
+/** Size of each channel item,
+   0x28442211 = 0010 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */
+#define CV_ELEM_SIZE1(type) ((0x28442211 >> CV_MAT_DEPTH(type)*4) & 15)
+
+#define CV_ELEM_SIZE(type) (CV_MAT_CN(type)*CV_ELEM_SIZE1(type))
+
+#ifndef MIN
+#  define MIN(a,b)  ((a) > (b) ? (b) : (a))
+#endif
+
+#ifndef MAX
+#  define MAX(a,b)  ((a) < (b) ? (b) : (a))
+#endif
+
+///////////////////////////////////////// Enum operators ///////////////////////////////////////
+
+/**
+
+Provides compatibility operators for both classical and C++11 enum classes,
+as well as exposing the C++11 enum class members for backwards compatibility
+
+@code
+    // Provides operators required for flag enums
+    CV_ENUM_FLAGS(AccessFlag)
+
+    // Exposes the listed members of the enum class AccessFlag to the current namespace
+    CV_ENUM_CLASS_EXPOSE(AccessFlag, ACCESS_READ [, ACCESS_WRITE [, ...] ]);
+@endcode
+*/
+
+#define __CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST)                                              \
+static const EnumType MEMBER_CONST = EnumType::MEMBER_CONST;                                          \
+
+#define __CV_ENUM_CLASS_EXPOSE_2(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_1(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_CLASS_EXPOSE_3(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_2(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_CLASS_EXPOSE_4(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_3(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_CLASS_EXPOSE_5(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_4(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_CLASS_EXPOSE_6(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_5(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_CLASS_EXPOSE_7(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_6(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_CLASS_EXPOSE_8(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_7(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_CLASS_EXPOSE_9(EnumType, MEMBER_CONST, ...)                                         \
+__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST);                                                     \
+__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_8(EnumType, __VA_ARGS__));                                         \
+
+#define __CV_ENUM_FLAGS_LOGICAL_NOT(EnumType)                                                         \
+static inline bool operator!(const EnumType& val)                                                     \
+{                                                                                                     \
+    typedef std::underlying_type<EnumType>::type UnderlyingType;                                      \
+    return !static_cast<UnderlyingType>(val);                                                         \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_LOGICAL_NOT_EQ(Arg1Type, Arg2Type)                                            \
+static inline bool operator!=(const Arg1Type& a, const Arg2Type& b)                                   \
+{                                                                                                     \
+    return static_cast<int>(a) != static_cast<int>(b);                                                \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_LOGICAL_EQ(Arg1Type, Arg2Type)                                                \
+static inline bool operator==(const Arg1Type& a, const Arg2Type& b)                                   \
+{                                                                                                     \
+    return static_cast<int>(a) == static_cast<int>(b);                                                \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_BITWISE_NOT(EnumType)                                                         \
+static inline EnumType operator~(const EnumType& val)                                                 \
+{                                                                                                     \
+    typedef std::underlying_type<EnumType>::type UnderlyingType;                                      \
+    return static_cast<EnumType>(~static_cast<UnderlyingType>(val));                                  \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_BITWISE_OR(EnumType, Arg1Type, Arg2Type)                                      \
+static inline EnumType operator|(const Arg1Type& a, const Arg2Type& b)                                \
+{                                                                                                     \
+    typedef std::underlying_type<EnumType>::type UnderlyingType;                                      \
+    return static_cast<EnumType>(static_cast<UnderlyingType>(a) | static_cast<UnderlyingType>(b));    \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_BITWISE_AND(EnumType, Arg1Type, Arg2Type)                                     \
+static inline EnumType operator&(const Arg1Type& a, const Arg2Type& b)                                \
+{                                                                                                     \
+    typedef std::underlying_type<EnumType>::type UnderlyingType;                                      \
+    return static_cast<EnumType>(static_cast<UnderlyingType>(a) & static_cast<UnderlyingType>(b));    \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_BITWISE_XOR(EnumType, Arg1Type, Arg2Type)                                     \
+static inline EnumType operator^(const Arg1Type& a, const Arg2Type& b)                                \
+{                                                                                                     \
+    typedef std::underlying_type<EnumType>::type UnderlyingType;                                      \
+    return static_cast<EnumType>(static_cast<UnderlyingType>(a) ^ static_cast<UnderlyingType>(b));    \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_BITWISE_OR_EQ(EnumType, Arg1Type)                                             \
+static inline EnumType& operator|=(EnumType& _this, const Arg1Type& val)                              \
+{                                                                                                     \
+    _this = static_cast<EnumType>(static_cast<int>(_this) | static_cast<int>(val));                   \
+    return _this;                                                                                     \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_BITWISE_AND_EQ(EnumType, Arg1Type)                                            \
+static inline EnumType& operator&=(EnumType& _this, const Arg1Type& val)                              \
+{                                                                                                     \
+    _this = static_cast<EnumType>(static_cast<int>(_this) & static_cast<int>(val));                   \
+    return _this;                                                                                     \
+}                                                                                                     \
+
+#define __CV_ENUM_FLAGS_BITWISE_XOR_EQ(EnumType, Arg1Type)                                            \
+static inline EnumType& operator^=(EnumType& _this, const Arg1Type& val)                              \
+{                                                                                                     \
+    _this = static_cast<EnumType>(static_cast<int>(_this) ^ static_cast<int>(val));                   \
+    return _this;                                                                                     \
+}                                                                                                     \
+
+#define CV_ENUM_CLASS_EXPOSE(EnumType, ...)                                                           \
+__CV_EXPAND(__CV_CAT(__CV_ENUM_CLASS_EXPOSE_, __CV_VA_NUM_ARGS(__VA_ARGS__))(EnumType, __VA_ARGS__)); \
+
+#define CV_ENUM_FLAGS(EnumType)                                                                       \
+__CV_ENUM_FLAGS_LOGICAL_NOT      (EnumType)                                                           \
+__CV_ENUM_FLAGS_LOGICAL_EQ       (EnumType, int)                                                      \
+__CV_ENUM_FLAGS_LOGICAL_NOT_EQ   (EnumType, int)                                                      \
+                                                                                                      \
+__CV_ENUM_FLAGS_BITWISE_NOT      (EnumType)                                                           \
+__CV_ENUM_FLAGS_BITWISE_OR       (EnumType, EnumType, EnumType)                                       \
+__CV_ENUM_FLAGS_BITWISE_AND      (EnumType, EnumType, EnumType)                                       \
+__CV_ENUM_FLAGS_BITWISE_XOR      (EnumType, EnumType, EnumType)                                       \
+                                                                                                      \
+__CV_ENUM_FLAGS_BITWISE_OR_EQ    (EnumType, EnumType)                                                 \
+__CV_ENUM_FLAGS_BITWISE_AND_EQ   (EnumType, EnumType)                                                 \
+__CV_ENUM_FLAGS_BITWISE_XOR_EQ   (EnumType, EnumType)                                                 \
+
+/****************************************************************************************\
+*                                    static analysys                                     *
+\****************************************************************************************/
+
+// In practice, some macro are not processed correctly (noreturn is not detected).
+// We need to use simplified definition for them.
+#ifndef CV_STATIC_ANALYSIS
+# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__)
+#   define CV_STATIC_ANALYSIS 1
+# endif
+#else
+# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1)  // defined and not empty
+#   if 0 == CV_STATIC_ANALYSIS
+#     undef CV_STATIC_ANALYSIS
+#   endif
+# endif
+#endif
+
+/****************************************************************************************\
+*                                    Thread sanitizer                                    *
+\****************************************************************************************/
+#ifndef CV_THREAD_SANITIZER
+# if defined(__has_feature)
+#   if __has_feature(thread_sanitizer)
+#     define CV_THREAD_SANITIZER
+#   endif
+# endif
+#endif
+
+/****************************************************************************************\
+*          exchange-add operation for atomic operations on reference counters            *
+\****************************************************************************************/
+
+#ifdef CV_XADD
+  // allow to use user-defined macro
+#elif defined __GNUC__ || defined __clang__
+#  if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__)  && !defined __INTEL_COMPILER
+#    ifdef __ATOMIC_ACQ_REL
+#      define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL)
+#    else
+#      define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4)
+#    endif
+#  else
+#    if defined __ATOMIC_ACQ_REL && !defined __clang__
+       // version for gcc >= 4.7
+#      define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL)
+#    else
+#      define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta))
+#    endif
+#  endif
+#elif defined _MSC_VER && !defined RC_INVOKED
+#  include <intrin.h>
+#  define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta)
+#else
+  #ifdef OPENCV_FORCE_UNSAFE_XADD
+    CV_INLINE int CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; }
+  #else
+    #error "OpenCV: can't define safe CV_XADD macro for current platform (unsupported). Define CV_XADD macro through custom port header (see OPENCV_INCLUDE_PORT_FILE)"
+  #endif
+#endif
+
+
+/****************************************************************************************\
+*                                  CV_NORETURN attribute                                 *
+\****************************************************************************************/
+
+#ifndef CV_NORETURN
+#  if defined(__GNUC__)
+#    define CV_NORETURN __attribute__((__noreturn__))
+#  elif defined(_MSC_VER) && (_MSC_VER >= 1300)
+#    define CV_NORETURN __declspec(noreturn)
+#  else
+#    define CV_NORETURN /* nothing by default */
+#  endif
+#endif
+
+/****************************************************************************************\
+*                       CV_NODISCARD_STD attribute (C++17)                               *
+* encourages the compiler to issue a warning if the return value is discarded            *
+\****************************************************************************************/
+#ifndef CV_NODISCARD_STD
+#  ifndef __has_cpp_attribute
+//   workaround preprocessor non-compliance https://reviews.llvm.org/D57851
+#    define __has_cpp_attribute(__x) 0
+#  endif
+#  if __has_cpp_attribute(nodiscard)
+#    define CV_NODISCARD_STD [[nodiscard]]
+#  elif __cplusplus >= 201703L
+//   available when compiler is C++17 compliant
+#    define CV_NODISCARD_STD [[nodiscard]]
+#  elif defined(__INTEL_COMPILER)
+     // see above, available when C++17 is enabled
+#  elif defined(_MSC_VER) && _MSC_VER >= 1911 && _MSVC_LANG >= 201703L
+//   available with VS2017 v15.3+ with /std:c++17 or higher; works on functions and classes
+#    define CV_NODISCARD_STD [[nodiscard]]
+#  elif defined(__GNUC__) && (((__GNUC__ * 100) + __GNUC_MINOR__) >= 700) && (__cplusplus >= 201103L)
+//   available with GCC 7.0+; works on functions, works or silently fails on classes
+#    define CV_NODISCARD_STD [[nodiscard]]
+#  elif defined(__GNUC__) && (((__GNUC__ * 100) + __GNUC_MINOR__) >= 408) && (__cplusplus >= 201103L)
+//   available with GCC 4.8+ but it usually does nothing and can fail noisily -- therefore not used
+//   define CV_NODISCARD_STD [[gnu::warn_unused_result]]
+#  endif
+#endif
+#ifndef CV_NODISCARD_STD
+#  define CV_NODISCARD_STD /* nothing by default */
+#endif
+
+
+/****************************************************************************************\
+*                      CV_NODISCARD attribute (deprecated, GCC only)                     *
+* DONT USE: use instead the standard CV_NODISCARD_STD macro above                        *
+*           this legacy method silently fails to issue warning until some version        *
+*           after gcc 6.3.0. Yet with gcc 7+ you can use the above standard method       *
+*           which makes this method useless. Don't use it.                               *
+* @deprecated use instead CV_NODISCARD_STD                                               *
+\****************************************************************************************/
+#ifndef CV_NODISCARD
+#  if defined(__GNUC__)
+#    define CV_NODISCARD __attribute__((__warn_unused_result__))
+#  elif defined(__clang__) && defined(__has_attribute)
+#    if __has_attribute(__warn_unused_result__)
+#      define CV_NODISCARD __attribute__((__warn_unused_result__))
+#    endif
+#  endif
+#endif
+#ifndef CV_NODISCARD
+#  define CV_NODISCARD /* nothing by default */
+#endif
+
+
+/****************************************************************************************\
+*                                    C++ 11                                              *
+\****************************************************************************************/
+#ifndef CV_CXX11
+#  if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800)
+#    define CV_CXX11 1
+#  endif
+#else
+#  if CV_CXX11 == 0
+#    undef CV_CXX11
+#  endif
+#endif
+#ifndef CV_CXX11
+#  error "OpenCV 4.x+ requires enabled C++11 support"
+#endif
+
+#define CV_CXX_MOVE_SEMANTICS 1
+#define CV_CXX_MOVE(x) std::move(x)
+#define CV_CXX_STD_ARRAY 1
+#include <array>
+#ifndef CV_OVERRIDE
+#  define CV_OVERRIDE override
+#endif
+#ifndef CV_FINAL
+#  define CV_FINAL final
+#endif
+
+#ifndef CV_NOEXCEPT
+#  if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
+#    define CV_NOEXCEPT noexcept
+#  endif
+#endif
+#ifndef CV_NOEXCEPT
+#  define CV_NOEXCEPT
+#endif
+
+#ifndef CV_CONSTEXPR
+#  if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
+#    define CV_CONSTEXPR constexpr
+#  endif
+#endif
+#ifndef CV_CONSTEXPR
+#  define CV_CONSTEXPR
+#endif
+
+// Integer types portatibility
+#ifdef OPENCV_STDINT_HEADER
+#include OPENCV_STDINT_HEADER
+#elif defined(__cplusplus)
+#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */
+namespace cv {
+typedef signed char int8_t;
+typedef unsigned char uint8_t;
+typedef signed short int16_t;
+typedef unsigned short uint16_t;
+typedef signed int int32_t;
+typedef unsigned int uint32_t;
+typedef signed __int64 int64_t;
+typedef unsigned __int64 uint64_t;
+}
+#elif defined(_MSC_VER) || __cplusplus >= 201103L
+#include <cstdint>
+namespace cv {
+using std::int8_t;
+using std::uint8_t;
+using std::int16_t;
+using std::uint16_t;
+using std::int32_t;
+using std::uint32_t;
+using std::int64_t;
+using std::uint64_t;
+}
+#else
+#include <stdint.h>
+namespace cv {
+typedef ::int8_t int8_t;
+typedef ::uint8_t uint8_t;
+typedef ::int16_t int16_t;
+typedef ::uint16_t uint16_t;
+typedef ::int32_t int32_t;
+typedef ::uint32_t uint32_t;
+typedef ::int64_t int64_t;
+typedef ::uint64_t uint64_t;
+}
+#endif
+#else // pure C
+#include <stdint.h>
+#endif
+
+#ifdef __cplusplus
+namespace cv
+{
+
+class float16_t
+{
+public:
+#if CV_FP16_TYPE
+
+    float16_t() : h(0) {}
+    explicit float16_t(float x) { h = (__fp16)x; }
+    operator float() const { return (float)h; }
+    static float16_t fromBits(ushort w)
+    {
+        Cv16suf u;
+        u.u = w;
+        float16_t result;
+        result.h = u.h;
+        return result;
+    }
+    static float16_t zero()
+    {
+        float16_t result;
+        result.h = (__fp16)0;
+        return result;
+    }
+    ushort bits() const
+    {
+        Cv16suf u;
+        u.h = h;
+        return u.u;
+    }
+protected:
+    __fp16 h;
+
+#else
+    float16_t() : w(0) {}
+    explicit float16_t(float x)
+    {
+    #if CV_FP16
+        __m128 v = _mm_load_ss(&x);
+        w = (ushort)_mm_cvtsi128_si32(_mm_cvtps_ph(v, 0));
+    #else
+        Cv32suf in;
+        in.f = x;
+        unsigned sign = in.u & 0x80000000;
+        in.u ^= sign;
+
+        if( in.u >= 0x47800000 )
+            w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00);
+        else
+        {
+            if (in.u < 0x38800000)
+            {
+                in.f += 0.5f;
+                w = (ushort)(in.u - 0x3f000000);
+            }
+            else
+            {
+                unsigned t = in.u + 0xc8000fff;
+                w = (ushort)((t + ((in.u >> 13) & 1)) >> 13);
+            }
+        }
+
+        w = (ushort)(w | (sign >> 16));
+    #endif
+    }
+
+    operator float() const
+    {
+    #if CV_FP16
+        float f;
+        _mm_store_ss(&f, _mm_cvtph_ps(_mm_cvtsi32_si128(w)));
+        return f;
+    #else
+        Cv32suf out;
+
+        unsigned t = ((w & 0x7fff) << 13) + 0x38000000;
+        unsigned sign = (w & 0x8000) << 16;
+        unsigned e = w & 0x7c00;
+
+        out.u = t + (1 << 23);
+        out.u = (e >= 0x7c00 ? t + 0x38000000 :
+                 e == 0 ? (static_cast<void>(out.f -= 6.103515625e-05f), out.u) : t) | sign;
+        return out.f;
+    #endif
+    }
+
+    static float16_t fromBits(ushort b)
+    {
+        float16_t result;
+        result.w = b;
+        return result;
+    }
+    static float16_t zero()
+    {
+        float16_t result;
+        result.w = (ushort)0;
+        return result;
+    }
+    ushort bits() const { return w; }
+protected:
+    ushort w;
+
+#endif
+};
+
+}
+#endif
+
+//! @}
+
+#ifndef __cplusplus
+#include "opencv2/core/fast_math.hpp" // define cvRound(double)
+#endif
+
+#endif // OPENCV_CORE_CVDEF_H
diff --git a/3rdParty/include/opencv2/core/cvstd.hpp b/3rdParty/include/opencv2/core/cvstd.hpp
new file mode 100644
index 0000000..6ce9e4b
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cvstd.hpp
@@ -0,0 +1,190 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_CVSTD_HPP
+#define OPENCV_CORE_CVSTD_HPP
+
+#ifndef __cplusplus
+#  error cvstd.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core/cvdef.h"
+#include <cstddef>
+#include <cstring>
+#include <cctype>
+
+#include <string>
+
+// import useful primitives from stl
+#  include <algorithm>
+#  include <utility>
+#  include <cstdlib> //for abs(int)
+#  include <cmath>
+
+namespace cv
+{
+    static inline uchar abs(uchar a) { return a; }
+    static inline ushort abs(ushort a) { return a; }
+    static inline unsigned abs(unsigned a) { return a; }
+    static inline uint64 abs(uint64 a) { return a; }
+
+    using std::min;
+    using std::max;
+    using std::abs;
+    using std::swap;
+    using std::sqrt;
+    using std::exp;
+    using std::pow;
+    using std::log;
+}
+
+#include "cvstd_wrapper.hpp"
+
+namespace cv {
+
+//! @addtogroup core_utils
+//! @{
+
+//////////////////////////// memory management functions ////////////////////////////
+
+/** @brief Allocates an aligned memory buffer.
+
+The function allocates the buffer of the specified size and returns it. When the buffer size is 16
+bytes or more, the returned buffer is aligned to 16 bytes.
+@param bufSize Allocated buffer size.
+ */
+CV_EXPORTS void* fastMalloc(size_t bufSize);
+
+/** @brief Deallocates a memory buffer.
+
+The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the
+function does nothing. C version of the function clears the pointer *pptr* to avoid problems with
+double memory deallocation.
+@param ptr Pointer to the allocated buffer.
+ */
+CV_EXPORTS void fastFree(void* ptr);
+
+/*!
+  The STL-compliant memory Allocator based on cv::fastMalloc() and cv::fastFree()
+*/
+template<typename _Tp> class Allocator
+{
+public:
+    typedef _Tp value_type;
+    typedef value_type* pointer;
+    typedef const value_type* const_pointer;
+    typedef value_type& reference;
+    typedef const value_type& const_reference;
+    typedef size_t size_type;
+    typedef ptrdiff_t difference_type;
+    template<typename U> class rebind { typedef Allocator<U> other; };
+
+    explicit Allocator() {}
+    ~Allocator() {}
+    explicit Allocator(Allocator const&) {}
+    template<typename U>
+    explicit Allocator(Allocator<U> const&) {}
+
+    // address
+    pointer address(reference r) { return &r; }
+    const_pointer address(const_reference r) { return &r; }
+
+    pointer allocate(size_type count, const void* =0) { return reinterpret_cast<pointer>(fastMalloc(count * sizeof (_Tp))); }
+    void deallocate(pointer p, size_type) { fastFree(p); }
+
+    void construct(pointer p, const _Tp& v) { new(static_cast<void*>(p)) _Tp(v); }
+    void destroy(pointer p) { p->~_Tp(); }
+
+    size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); }
+};
+
+//! @} core_utils
+
+//! @endcond
+
+//! @addtogroup core_basic
+//! @{
+
+//////////////////////////////// string class ////////////////////////////////
+
+class CV_EXPORTS FileNode; //for string constructor from FileNode
+
+typedef std::string String;
+
+#ifndef OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS
+
+//! @cond IGNORED
+namespace details {
+// std::tolower is int->int
+static inline char char_tolower(char ch)
+{
+    return (char)std::tolower((int)ch);
+}
+// std::toupper is int->int
+static inline char char_toupper(char ch)
+{
+    return (char)std::toupper((int)ch);
+}
+} // namespace details
+//! @endcond
+
+static inline std::string toLowerCase(const std::string& str)
+{
+    std::string result(str);
+    std::transform(result.begin(), result.end(), result.begin(), details::char_tolower);
+    return result;
+}
+
+static inline std::string toUpperCase(const std::string& str)
+{
+    std::string result(str);
+    std::transform(result.begin(), result.end(), result.begin(), details::char_toupper);
+    return result;
+}
+
+#endif // OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS
+
+//! @} core_basic
+} // cv
+
+#endif //OPENCV_CORE_CVSTD_HPP
diff --git a/3rdParty/include/opencv2/core/cvstd.inl.hpp b/3rdParty/include/opencv2/core/cvstd.inl.hpp
new file mode 100644
index 0000000..37ad1e6
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cvstd.inl.hpp
@@ -0,0 +1,197 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_CVSTDINL_HPP
+#define OPENCV_CORE_CVSTDINL_HPP
+
+#include <complex>
+#include <ostream>
+#include <sstream>
+
+//! @cond IGNORED
+
+#ifdef _MSC_VER
+#pragma warning( push )
+#pragma warning( disable: 4127 )
+#endif
+
+namespace cv
+{
+
+template<typename _Tp> class DataType< std::complex<_Tp> >
+{
+public:
+    typedef std::complex<_Tp>  value_type;
+    typedef value_type         work_type;
+    typedef _Tp                channel_type;
+
+    enum { generic_type = 0,
+           depth        = DataType<channel_type>::depth,
+           channels     = 2,
+           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
+           type         = CV_MAKETYPE(depth, channels) };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+static inline
+std::ostream& operator << (std::ostream& out, Ptr<Formatted> fmtd)
+{
+    fmtd->reset();
+    for(const char* str = fmtd->next(); str; str = fmtd->next())
+        out << str;
+    return out;
+}
+
+static inline
+std::ostream& operator << (std::ostream& out, const Mat& mtx)
+{
+    return out << Formatter::get()->format(mtx);
+}
+
+static inline
+std::ostream& operator << (std::ostream& out, const UMat& m)
+{
+    return out << m.getMat(ACCESS_READ);
+}
+
+template<typename _Tp> static inline
+std::ostream& operator << (std::ostream& out, const Complex<_Tp>& c)
+{
+    return out << "(" << c.re << "," << c.im << ")";
+}
+
+template<typename _Tp> static inline
+std::ostream& operator << (std::ostream& out, const std::vector<Point_<_Tp> >& vec)
+{
+    return out << Formatter::get()->format(Mat(vec));
+}
+
+
+template<typename _Tp> static inline
+std::ostream& operator << (std::ostream& out, const std::vector<Point3_<_Tp> >& vec)
+{
+    return out << Formatter::get()->format(Mat(vec));
+}
+
+
+template<typename _Tp, int m, int n> static inline
+std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx)
+{
+    return out << Formatter::get()->format(Mat(matx));
+}
+
+template<typename _Tp> static inline
+std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p)
+{
+    out << "[" << p.x << ", " << p.y << "]";
+    return out;
+}
+
+template<typename _Tp> static inline
+std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p)
+{
+    out << "[" << p.x << ", " << p.y << ", " << p.z << "]";
+    return out;
+}
+
+template<typename _Tp, int n> static inline
+std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec)
+{
+    out << "[";
+    if (cv::traits::Depth<_Tp>::value <= CV_32S)
+    {
+        for (int i = 0; i < n - 1; ++i) {
+            out << (int)vec[i] << ", ";
+        }
+        out << (int)vec[n-1] << "]";
+    }
+    else
+    {
+        for (int i = 0; i < n - 1; ++i) {
+            out << vec[i] << ", ";
+        }
+        out << vec[n-1] << "]";
+    }
+
+    return out;
+}
+
+template<typename _Tp> static inline
+std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size)
+{
+    return out << "[" << size.width << " x " << size.height << "]";
+}
+
+template<typename _Tp> static inline
+std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect)
+{
+    return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]";
+}
+
+static inline std::ostream& operator << (std::ostream& out, const MatSize& msize)
+{
+    int i, dims = msize.dims();
+    for( i = 0; i < dims; i++ )
+    {
+        out << msize[i];
+        if( i < dims-1 )
+            out << " x ";
+    }
+    return out;
+}
+
+static inline std::ostream &operator<< (std::ostream &s, cv::Range &r)
+{
+    return s << "[" << r.start << " : " << r.end << ")";
+}
+
+} // cv
+
+#ifdef _MSC_VER
+#pragma warning( pop )
+#endif
+
+//! @endcond
+
+#endif // OPENCV_CORE_CVSTDINL_HPP
diff --git a/3rdParty/include/opencv2/core/cvstd_wrapper.hpp b/3rdParty/include/opencv2/core/cvstd_wrapper.hpp
new file mode 100644
index 0000000..25e0041
--- /dev/null
+++ b/3rdParty/include/opencv2/core/cvstd_wrapper.hpp
@@ -0,0 +1,154 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_CVSTD_WRAPPER_HPP
+#define OPENCV_CORE_CVSTD_WRAPPER_HPP
+
+#include "opencv2/core/cvdef.h"
+
+#include <string>
+#include <memory>  // std::shared_ptr
+#include <type_traits>  // std::enable_if
+
+namespace cv {
+
+using std::nullptr_t;
+
+//! @addtogroup core_basic
+//! @{
+
+#ifdef CV_DOXYGEN
+
+template <typename _Tp> using Ptr = std::shared_ptr<_Tp>;  // In ideal world it should look like this, but we need some compatibility workarounds below
+
+template<typename _Tp, typename ... A1> static inline
+Ptr<_Tp> makePtr(const A1&... a1) { return std::make_shared<_Tp>(a1...); }
+
+#else  // cv::Ptr with compatibility workarounds
+
+// It should be defined for C-API types only.
+// C++ types should use regular "delete" operator.
+template<typename Y> struct DefaultDeleter;
+#if 0
+{
+    void operator()(Y* p) const;
+};
+#endif
+
+namespace sfinae {
+template<typename C, typename Ret, typename... Args>
+struct has_parenthesis_operator
+{
+private:
+    template<typename T>
+    static CV_CONSTEXPR std::true_type has_parenthesis_operator_check(typename std::is_same<typename std::decay<decltype(std::declval<T>().operator()(std::declval<Args>()...))>::type, Ret>::type*);
+
+    template<typename> static CV_CONSTEXPR std::false_type has_parenthesis_operator_check(...);
+
+    typedef decltype(has_parenthesis_operator_check<C>(0)) type;
+
+public:
+#if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
+    static CV_CONSTEXPR bool value = type::value;
+#else
+    // support MSVS 2013
+    static const int value = type::value;
+#endif
+};
+} // namespace sfinae
+
+template <typename T, typename = void>
+struct has_custom_delete
+        : public std::false_type {};
+
+// Force has_custom_delete to std::false_type when NVCC is compiling CUDA source files
+#ifndef __CUDACC__
+template <typename T>
+struct has_custom_delete<T, typename std::enable_if< sfinae::has_parenthesis_operator<DefaultDeleter<T>, void, T*>::value >::type >
+        : public std::true_type {};
+#endif
+
+template<typename T>
+struct Ptr : public std::shared_ptr<T>
+{
+#if 0
+    using std::shared_ptr<T>::shared_ptr;  // GCC 5.x can't handle this
+#else
+    inline Ptr() CV_NOEXCEPT : std::shared_ptr<T>() {}
+    inline Ptr(nullptr_t) CV_NOEXCEPT : std::shared_ptr<T>(nullptr) {}
+    template<typename Y, typename D> inline Ptr(Y* p, D d) : std::shared_ptr<T>(p, d) {}
+    template<typename D> inline Ptr(nullptr_t, D d) : std::shared_ptr<T>(nullptr, d) {}
+
+    template<typename Y> inline Ptr(const Ptr<Y>& r, T* ptr) CV_NOEXCEPT : std::shared_ptr<T>(r, ptr) {}
+
+    inline Ptr(const Ptr<T>& o) CV_NOEXCEPT : std::shared_ptr<T>(o) {}
+    inline Ptr(Ptr<T>&& o) CV_NOEXCEPT : std::shared_ptr<T>(std::move(o)) {}
+
+    template<typename Y> inline Ptr(const Ptr<Y>& o) CV_NOEXCEPT : std::shared_ptr<T>(o) {}
+    template<typename Y> inline Ptr(Ptr<Y>&& o) CV_NOEXCEPT : std::shared_ptr<T>(std::move(o)) {}
+#endif
+    inline Ptr(const std::shared_ptr<T>& o) CV_NOEXCEPT : std::shared_ptr<T>(o) {}
+    inline Ptr(std::shared_ptr<T>&& o) CV_NOEXCEPT : std::shared_ptr<T>(std::move(o)) {}
+
+    // Overload with custom DefaultDeleter: Ptr<IplImage>(...)
+    template<typename Y>
+    inline Ptr(const std::true_type&, Y* ptr) : std::shared_ptr<T>(ptr, DefaultDeleter<Y>()) {}
+
+    // Overload without custom deleter: Ptr<std::string>(...);
+    template<typename Y>
+    inline Ptr(const std::false_type&, Y* ptr) : std::shared_ptr<T>(ptr) {}
+
+    template<typename Y = T>
+    inline Ptr(Y* ptr) : Ptr(has_custom_delete<Y>(), ptr) {}
+
+    // Overload with custom DefaultDeleter: Ptr<IplImage>(...)
+    template<typename Y>
+    inline void reset(const std::true_type&, Y* ptr) { std::shared_ptr<T>::reset(ptr, DefaultDeleter<Y>()); }
+
+    // Overload without custom deleter: Ptr<std::string>(...);
+    template<typename Y>
+    inline void reset(const std::false_type&, Y* ptr) { std::shared_ptr<T>::reset(ptr); }
+
+    template<typename Y>
+    inline void reset(Y* ptr) { Ptr<T>::reset(has_custom_delete<Y>(), ptr); }
+
+    template<class Y, class Deleter>
+    void reset(Y* ptr, Deleter d) { std::shared_ptr<T>::reset(ptr, d); }
+
+    void reset() CV_NOEXCEPT { std::shared_ptr<T>::reset(); }
+
+    Ptr& operator=(const Ptr& o) { std::shared_ptr<T>::operator =(o); return *this; }
+    template<typename Y> inline Ptr& operator=(const Ptr<Y>& o) { std::shared_ptr<T>::operator =(o); return *this; }
+
+    T* operator->() const CV_NOEXCEPT { return std::shared_ptr<T>::get();}
+    typename std::add_lvalue_reference<T>::type operator*() const CV_NOEXCEPT { return *std::shared_ptr<T>::get(); }
+
+    // OpenCV 3.x methods (not a part of standard C++ library)
+    inline void release() { std::shared_ptr<T>::reset(); }
+    inline operator T* () const { return std::shared_ptr<T>::get(); }
+    inline bool empty() const { return std::shared_ptr<T>::get() == nullptr; }
+
+    template<typename Y> inline
+    Ptr<Y> staticCast() const CV_NOEXCEPT { return std::static_pointer_cast<Y>(*this); }
+
+    template<typename Y> inline
+    Ptr<Y> constCast() const CV_NOEXCEPT { return std::const_pointer_cast<Y>(*this); }
+
+    template<typename Y> inline
+    Ptr<Y> dynamicCast() const CV_NOEXCEPT { return std::dynamic_pointer_cast<Y>(*this); }
+};
+
+template<typename _Tp, typename ... A1> static inline
+Ptr<_Tp> makePtr(const A1&... a1)
+{
+    static_assert( !has_custom_delete<_Tp>::value, "Can't use this makePtr with custom DefaultDeleter");
+    return (Ptr<_Tp>)std::make_shared<_Tp>(a1...);
+}
+
+#endif // CV_DOXYGEN
+
+//! @} core_basic
+} // cv
+
+#endif //OPENCV_CORE_CVSTD_WRAPPER_HPP
diff --git a/3rdParty/include/opencv2/core/detail/async_promise.hpp b/3rdParty/include/opencv2/core/detail/async_promise.hpp
new file mode 100644
index 0000000..6eb3fb5
--- /dev/null
+++ b/3rdParty/include/opencv2/core/detail/async_promise.hpp
@@ -0,0 +1,71 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_ASYNC_PROMISE_HPP
+#define OPENCV_CORE_ASYNC_PROMISE_HPP
+
+#include "../async.hpp"
+
+#include "exception_ptr.hpp"
+
+namespace cv {
+
+/** @addtogroup core_async
+@{
+*/
+
+
+/** @brief Provides result of asynchronous operations
+
+*/
+class CV_EXPORTS AsyncPromise
+{
+public:
+    ~AsyncPromise() CV_NOEXCEPT;
+    AsyncPromise() CV_NOEXCEPT;
+    explicit AsyncPromise(const AsyncPromise& o) CV_NOEXCEPT;
+    AsyncPromise& operator=(const AsyncPromise& o) CV_NOEXCEPT;
+    void release() CV_NOEXCEPT;
+
+    /** Returns associated AsyncArray
+    @note Can be called once
+    */
+    AsyncArray getArrayResult();
+
+    /** Stores asynchronous result.
+    @param[in] value result
+    */
+    void setValue(InputArray value);
+
+    // TODO "move" setters
+
+#if CV__EXCEPTION_PTR
+    /** Stores exception.
+    @param[in] exception exception to be raised in AsyncArray
+    */
+    void setException(std::exception_ptr exception);
+#endif
+
+    /** Stores exception.
+    @param[in] exception exception to be raised in AsyncArray
+    */
+    void setException(const cv::Exception& exception);
+
+#ifdef CV_CXX11
+    explicit AsyncPromise(AsyncPromise&& o) { p = o.p; o.p = NULL; }
+    AsyncPromise& operator=(AsyncPromise&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; }
+#endif
+
+
+    // PImpl
+    typedef struct AsyncArray::Impl Impl; friend struct AsyncArray::Impl;
+    inline void* _getImpl() const CV_NOEXCEPT { return p; }
+protected:
+    Impl* p;
+};
+
+
+//! @}
+} // namespace
+#endif // OPENCV_CORE_ASYNC_PROMISE_HPP
diff --git a/3rdParty/include/opencv2/core/detail/dispatch_helper.impl.hpp b/3rdParty/include/opencv2/core/detail/dispatch_helper.impl.hpp
new file mode 100644
index 0000000..d6ec676
--- /dev/null
+++ b/3rdParty/include/opencv2/core/detail/dispatch_helper.impl.hpp
@@ -0,0 +1,49 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_DETAIL_DISPATCH_HELPER_IMPL_HPP
+#define OPENCV_CORE_DETAIL_DISPATCH_HELPER_IMPL_HPP
+
+//! @cond IGNORED
+
+namespace cv {
+namespace detail {
+
+template<template<typename> class Functor, typename... Args>
+static inline void depthDispatch(const int depth, Args&&... args)
+{
+    switch (depth)
+    {
+        case CV_8U:
+            Functor<uint8_t>{}(std::forward<Args>(args)...);
+            break;
+        case CV_8S:
+            Functor<int8_t>{}(std::forward<Args>(args)...);
+            break;
+        case CV_16U:
+            Functor<uint16_t>{}(std::forward<Args>(args)...);
+            break;
+        case CV_16S:
+            Functor<int16_t>{}(std::forward<Args>(args)...);
+            break;
+        case CV_32S:
+            Functor<int32_t>{}(std::forward<Args>(args)...);
+            break;
+        case CV_32F:
+            Functor<float>{}(std::forward<Args>(args)...);
+            break;
+        case CV_64F:
+            Functor<double>{}(std::forward<Args>(args)...);
+            break;
+        case CV_16F:
+        default:
+            CV_Error(cv::Error::BadDepth, "Unsupported matrix type.");
+    };
+}
+
+}}
+
+//! @endcond
+
+#endif //OPENCV_CORE_DETAIL_DISPATCH_HELPER_IMPL_HPP
diff --git a/3rdParty/include/opencv2/core/detail/exception_ptr.hpp b/3rdParty/include/opencv2/core/detail/exception_ptr.hpp
new file mode 100644
index 0000000..d98ffc4
--- /dev/null
+++ b/3rdParty/include/opencv2/core/detail/exception_ptr.hpp
@@ -0,0 +1,27 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
+#define OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
+
+#ifndef CV__EXCEPTION_PTR
+#  if defined(__ANDROID__) && defined(ATOMIC_INT_LOCK_FREE) && ATOMIC_INT_LOCK_FREE < 2
+#    define CV__EXCEPTION_PTR 0  // Not supported, details: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=58938
+#  elif defined(CV_CXX11)
+#    define CV__EXCEPTION_PTR 1
+#  elif defined(_MSC_VER)
+#    define CV__EXCEPTION_PTR (_MSC_VER >= 1600)
+#  elif defined(__clang__)
+#    define CV__EXCEPTION_PTR 0  // C++11 only (see above)
+#  elif defined(__GNUC__) && defined(__GXX_EXPERIMENTAL_CXX0X__)
+#    define CV__EXCEPTION_PTR (__GXX_EXPERIMENTAL_CXX0X__ > 0)
+#  endif
+#endif
+#ifndef CV__EXCEPTION_PTR
+#  define CV__EXCEPTION_PTR 0
+#elif CV__EXCEPTION_PTR
+#  include <exception>  // std::exception_ptr
+#endif
+
+#endif // OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
diff --git a/3rdParty/include/opencv2/core/directx.hpp b/3rdParty/include/opencv2/core/directx.hpp
new file mode 100644
index 0000000..056a85a
--- /dev/null
+++ b/3rdParty/include/opencv2/core/directx.hpp
@@ -0,0 +1,184 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors as is and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the copyright holders or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_DIRECTX_HPP
+#define OPENCV_CORE_DIRECTX_HPP
+
+#include "mat.hpp"
+#include "ocl.hpp"
+
+#if !defined(__d3d11_h__)
+struct ID3D11Device;
+struct ID3D11Texture2D;
+#endif
+
+#if !defined(__d3d10_h__)
+struct ID3D10Device;
+struct ID3D10Texture2D;
+#endif
+
+#if !defined(_D3D9_H_)
+struct IDirect3DDevice9;
+struct IDirect3DDevice9Ex;
+struct IDirect3DSurface9;
+#endif
+
+
+namespace cv { namespace directx {
+
+namespace ocl {
+using namespace cv::ocl;
+
+//! @addtogroup core_directx
+// This section describes OpenCL and DirectX interoperability.
+//
+// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is
+// supported only on Windows.
+//
+// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource.
+//
+//! @{
+
+// TODO static functions in the Context class
+//! @brief Creates OpenCL context from D3D11 device
+//
+//! @param pD3D11Device - pointer to D3D11 device
+//! @return Returns reference to OpenCL Context
+CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device);
+
+//! @brief Creates OpenCL context from D3D10 device
+//
+//! @param pD3D10Device - pointer to D3D10 device
+//! @return Returns reference to OpenCL Context
+CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device);
+
+//! @brief Creates OpenCL context from Direct3DDevice9Ex device
+//
+//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device
+//! @return Returns reference to OpenCL Context
+CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex);
+
+//! @brief Creates OpenCL context from Direct3DDevice9 device
+//
+//! @param pDirect3DDevice9 - pointer to Direct3Device9 device
+//! @return Returns reference to OpenCL Context
+CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9);
+
+//! @}
+
+} // namespace cv::directx::ocl
+
+//! @addtogroup core_directx
+//! @{
+
+//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then
+//!        input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12.
+//
+//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to
+//!             pD3D11Texture2D
+//
+//! @param src - source InputArray
+//! @param pD3D11Texture2D - destination D3D11 texture
+CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D);
+
+//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then
+//!        data will be upsampled and color-converted to BGR format.
+//
+//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size.
+//!             function does memory copy from pD3D11Texture2D to dst
+//
+//! @param pD3D11Texture2D - source D3D11 texture
+//! @param dst             - destination OutputArray
+CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst);
+
+//! @brief Converts InputArray to ID3D10Texture2D
+//
+//! @note Note: function does memory copy from src to
+//!             pD3D10Texture2D
+//
+//! @param src             - source InputArray
+//! @param pD3D10Texture2D - destination D3D10 texture
+CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D);
+
+//! @brief Converts ID3D10Texture2D to OutputArray
+//
+//! @note Note: function does memory copy from pD3D10Texture2D
+//!             to dst
+//
+//! @param pD3D10Texture2D - source D3D10 texture
+//! @param dst             - destination OutputArray
+CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst);
+
+//! @brief Converts InputArray to IDirect3DSurface9
+//
+//! @note Note: function does memory copy from src to
+//!             pDirect3DSurface9
+//
+//! @param src                 - source InputArray
+//! @param pDirect3DSurface9   - destination D3D10 texture
+//! @param surfaceSharedHandle - shared handle
+CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL);
+
+//! @brief Converts IDirect3DSurface9 to OutputArray
+//
+//! @note Note: function does memory copy from pDirect3DSurface9
+//!             to dst
+//
+//! @param pDirect3DSurface9   - source D3D10 texture
+//! @param dst                 - destination OutputArray
+//! @param surfaceSharedHandle - shared handle
+CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL);
+
+//! @brief Get OpenCV type from DirectX type
+//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11
+//! @return OpenCV type or -1 if there is no equivalent
+CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11
+
+//! @brief Get OpenCV type from DirectX type
+//! @param iD3DFORMAT - enum D3DTYPE for D3D9
+//! @return OpenCV type or -1 if there is no equivalent
+CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9
+
+//! @}
+
+} } // namespace cv::directx
+
+#endif // OPENCV_CORE_DIRECTX_HPP
diff --git a/3rdParty/include/opencv2/core/dualquaternion.hpp b/3rdParty/include/opencv2/core/dualquaternion.hpp
new file mode 100644
index 0000000..1f644e9
--- /dev/null
+++ b/3rdParty/include/opencv2/core/dualquaternion.hpp
@@ -0,0 +1,979 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2020, Huawei Technologies Co., Ltd. All rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Author: Liangqian Kong <kongliangqian@huawei.com>
+//         Longbu Wang <wanglongbu@huawei.com>
+#ifndef OPENCV_CORE_DUALQUATERNION_HPP
+#define OPENCV_CORE_DUALQUATERNION_HPP
+
+#include <opencv2/core/quaternion.hpp>
+#include <opencv2/core/affine.hpp>
+
+namespace cv{
+//! @addtogroup core
+//! @{
+
+template <typename _Tp> class DualQuat;
+template <typename _Tp> std::ostream& operator<<(std::ostream&, const DualQuat<_Tp>&);
+
+/**
+ * Dual quaternions were introduced to describe rotation together with translation while ordinary
+ * quaternions can only describe rotation. It can be used for shortest path pose interpolation,
+ * local pose optimization or volumetric deformation. More details can be found
+ * - https://en.wikipedia.org/wiki/Dual_quaternion
+ * - ["A beginners guide to dual-quaternions: what they are, how they work, and how to use them for 3D character hierarchies", Ben Kenwright, 2012](https://borodust.org/public/shared/beginner_dual_quats.pdf)
+ * - ["Dual Quaternions", Yan-Bin Jia, 2013](http://web.cs.iastate.edu/~cs577/handouts/dual-quaternion.pdf)
+ * - ["Geometric Skinning with Approximate Dual Quaternion Blending", Kavan, 2008](https://www.cs.utah.edu/~ladislav/kavan08geometric/kavan08geometric)
+ * - http://rodolphe-vaillant.fr/?e=29
+ *
+ * A unit dual quaternion can be classically represented as:
+ * \f[
+ * \begin{equation}
+ * \begin{split}
+ * \sigma &= \left(r+\frac{\epsilon}{2}tr\right)\\
+ * &= [w, x, y, z, w\_, x\_, y\_, z\_]
+ * \end{split}
+ * \end{equation}
+ * \f]
+ * where \f$r, t\f$ represents the rotation (ordinary unit quaternion) and translation (pure ordinary quaternion) respectively.
+ *
+ * A general dual quaternions which consist of two quaternions is usually represented in form of:
+ * \f[
+ * \sigma = p + \epsilon q
+ * \f]
+ * where the introduced dual unit \f$\epsilon\f$ satisfies \f$\epsilon^2 = \epsilon^3 =...=0\f$, and \f$p, q\f$ are quaternions.
+ *
+ * Alternatively, dual quaternions can also be interpreted as four components which are all [dual numbers](https://www.cs.utah.edu/~ladislav/kavan08geometric/kavan08geometric):
+ * \f[
+ * \sigma = \hat{q}_w + \hat{q}_xi + \hat{q}_yj + \hat{q}_zk
+ * \f]
+ * If we set \f$\hat{q}_x, \hat{q}_y\f$ and \f$\hat{q}_z\f$ equal to 0, a dual quaternion is transformed to a dual number. see normalize().
+ *
+ * If you want to create a dual quaternion, you can use:
+ *
+ * ```
+ * using namespace cv;
+ * double angle = CV_PI;
+ *
+ * // create from eight number
+ * DualQuatd dq1(1, 2, 3, 4, 5, 6, 7, 8); //p = [1,2,3,4]. q=[5,6,7,8]
+ *
+ * // create from Vec
+ * Vec<double, 8> v{1,2,3,4,5,6,7,8};
+ * DualQuatd dq_v{v};
+ *
+ * // create from two quaternion
+ * Quatd p(1, 2, 3, 4);
+ * Quatd q(5, 6, 7, 8);
+ * DualQuatd dq2 = DualQuatd::createFromQuat(p, q);
+ *
+ * // create from an angle, an axis and a translation
+ * Vec3d axis{0, 0, 1};
+ * Vec3d trans{3, 4, 5};
+ * DualQuatd dq3 = DualQuatd::createFromAngleAxisTrans(angle, axis, trans);
+ *
+ * // If you already have an instance of class Affine3, then you can use
+ * Affine3d R = dq3.toAffine3();
+ * DualQuatd dq4 = DualQuatd::createFromAffine3(R);
+ *
+ * // or create directly by affine transformation matrix Rt
+ * // see createFromMat() in detail for the form of Rt
+ * Matx44d Rt = dq3.toMat();
+ * DualQuatd dq5 = DualQuatd::createFromMat(Rt);
+ *
+ * // Any rotation + translation movement can
+ * // be expressed as a rotation + translation around the same line in space (expressed by Plucker
+ * // coords), and here's a way to represent it this way.
+ * Vec3d axis{1, 1, 1}; // axis will be normalized in createFromPitch
+ * Vec3d trans{3, 4 ,5};
+ * axis = axis / std::sqrt(axis.dot(axis));// The formula for computing moment that I use below requires a normalized axis
+ * Vec3d moment = 1.0 / 2 * (trans.cross(axis) + axis.cross(trans.cross(axis)) *
+ *                            std::cos(rotation_angle / 2) / std::sin(rotation_angle / 2));
+ * double d = trans.dot(qaxis);
+ * DualQuatd dq6 = DualQuatd::createFromPitch(angle, d, axis, moment);
+ * ```
+ *
+ * A point \f$v=(x, y, z)\f$ in form of dual quaternion is \f$[1+\epsilon v]=[1,0,0,0,0,x,y,z]\f$.
+ * The transformation of a point \f$v_1\f$ to another point \f$v_2\f$ under the dual quaternion \f$\sigma\f$ is
+ * \f[
+ * 1 + \epsilon v_2 = \sigma * (1 + \epsilon v_1) * \sigma^{\star}
+ * \f]
+ * where \f$\sigma^{\star}=p^*-\epsilon q^*.\f$
+ *
+ * A line in the \f$Pl\ddot{u}cker\f$ coordinates \f$(\hat{l}, m)\f$ defined by the dual quaternion \f$l=\hat{l}+\epsilon m\f$.
+ * To transform a line, \f[l_2 = \sigma * l_1 * \sigma^*,\f] where \f$\sigma=r+\frac{\epsilon}{2}rt\f$ and
+ * \f$\sigma^*=p^*+\epsilon q^*\f$.
+ *
+ * To extract the Vec<double, 8> or Vec<float, 8>, see toVec();
+ *
+ * To extract the affine transformation matrix, see toMat();
+ *
+ * To extract the instance of Affine3, see toAffine3();
+ *
+ * If two quaternions \f$q_0, q_1\f$ are needed to be interpolated, you can use sclerp()
+ * ```
+ * DualQuatd::sclerp(q0, q1, t)
+ * ```
+ * or dqblend().
+ * ```
+ * DualQuatd::dqblend(q0, q1, t)
+ * ```
+ * With more than two dual quaternions to be blended, you can use generalize linear dual quaternion blending
+ * with the corresponding weights, i.e. gdqblend().
+ *
+ */
+template <typename _Tp>
+class CV_EXPORTS DualQuat{
+    static_assert(std::is_floating_point<_Tp>::value, "Dual quaternion only make sense with type of float or double");
+    using value_type = _Tp;
+
+public:
+    static constexpr _Tp CV_DUAL_QUAT_EPS = (_Tp)1.e-6;
+
+    DualQuat();
+
+    /**
+     * @brief create from eight same type numbers.
+     */
+    DualQuat(const _Tp w, const _Tp x, const _Tp y, const _Tp z, const _Tp w_, const _Tp x_, const _Tp y_, const _Tp z_);
+
+    /**
+     * @brief create from a double or float vector.
+     */
+    DualQuat(const Vec<_Tp, 8> &q);
+
+    _Tp w, x, y, z, w_, x_, y_, z_;
+
+    /**
+     * @brief create Dual Quaternion from two same type quaternions p and q.
+     * A Dual Quaternion \f$\sigma\f$ has the form:
+     * \f[\sigma = p + \epsilon q\f]
+     * where p and q are defined as follows:
+     * \f[\begin{equation}
+     *    \begin{split}
+     *    p &= w + x\boldsymbol{i} + y\boldsymbol{j} + z\boldsymbol{k}\\
+     *    q &= w\_ + x\_\boldsymbol{i} + y\_\boldsymbol{j} + z\_\boldsymbol{k}.
+     *    \end{split}
+     *   \end{equation}
+     * \f]
+     * The p and q are the real part and dual part respectively.
+     * @param realPart a quaternion, real part of dual quaternion.
+     * @param dualPart a quaternion, dual part of dual quaternion.
+     * @sa Quat
+    */
+    static DualQuat<_Tp> createFromQuat(const Quat<_Tp> &realPart, const Quat<_Tp> &dualPart);
+
+    /**
+     * @brief create a dual quaternion from a rotation angle \f$\theta\f$, a rotation axis
+     * \f$\boldsymbol{u}\f$ and a translation \f$\boldsymbol{t}\f$.
+     * It generates a dual quaternion \f$\sigma\f$ in the form of
+     * \f[\begin{equation}
+     *    \begin{split}
+     *    \sigma &= r + \frac{\epsilon}{2}\boldsymbol{t}r \\
+     *           &= [\cos(\frac{\theta}{2}), \boldsymbol{u}\sin(\frac{\theta}{2})]
+     *           + \frac{\epsilon}{2}[0, \boldsymbol{t}][[\cos(\frac{\theta}{2}),
+     *           \boldsymbol{u}\sin(\frac{\theta}{2})]]\\
+     *           &= \cos(\frac{\theta}{2}) + \boldsymbol{u}\sin(\frac{\theta}{2})
+     *           + \frac{\epsilon}{2}(-(\boldsymbol{t} \cdot \boldsymbol{u})\sin(\frac{\theta}{2})
+     *           + \boldsymbol{t}\cos(\frac{\theta}{2}) + \boldsymbol{u} \times \boldsymbol{t} \sin(\frac{\theta}{2})).
+     *    \end{split}
+     *    \end{equation}\f]
+     * @param angle rotation angle.
+     * @param axis rotation axis.
+     * @param translation a vector of length 3.
+     * @note Axis will be normalized in this function. And translation is applied
+     * after the rotation. Use @ref createFromQuat(r, r * t / 2) to create a dual quaternion
+     * which translation is applied before rotation.
+     * @sa Quat
+     */
+    static DualQuat<_Tp> createFromAngleAxisTrans(const _Tp angle, const Vec<_Tp, 3> &axis, const Vec<_Tp, 3> &translation);
+
+    /**
+     * @brief Transform this dual quaternion to an affine transformation matrix \f$M\f$.
+     * Dual quaternion consists of a rotation \f$r=[a,b,c,d]\f$ and a translation \f$t=[\Delta x,\Delta y,\Delta z]\f$. The
+     * affine transformation matrix \f$M\f$ has the form
+     * \f[
+     * \begin{bmatrix}
+     * 1-2(e_2^2 +e_3^2) &2(e_1e_2-e_0e_3) &2(e_0e_2+e_1e_3) &\Delta x\\
+     * 2(e_0e_3+e_1e_2)  &1-2(e_1^2+e_3^2) &2(e_2e_3-e_0e_1) &\Delta y\\
+     * 2(e_1e_3-e_0e_2)  &2(e_0e_1+e_2e_3) &1-2(e_1^2-e_2^2) &\Delta z\\
+     * 0&0&0&1
+     * \end{bmatrix}
+     * \f]
+     *  if A is a matrix consisting of  n points to be transformed, this could be achieved by
+     * \f[
+     *  new\_A = M * A
+     * \f]
+     * where A has the form
+     * \f[
+     * \begin{bmatrix}
+     * x_0& x_1& x_2&...&x_n\\
+     * y_0& y_1& y_2&...&y_n\\
+     * z_0& z_1& z_2&...&z_n\\
+     * 1&1&1&...&1
+     * \end{bmatrix}
+     * \f]
+     * where the same subscript represent the same point. The size of A should be \f$[4,n]\f$.
+     * and the same size for matrix new_A.
+     * @param _R 4x4 matrix that represents rotations and translation.
+     * @note Translation is applied after the rotation. Use createFromQuat(r, r * t / 2) to create
+     * a dual quaternion which translation is applied before rotation.
+     */
+    static DualQuat<_Tp> createFromMat(InputArray _R);
+
+    /**
+     * @brief create dual quaternion from an affine matrix. The definition of affine matrix can refer to  createFromMat()
+     */
+    static DualQuat<_Tp> createFromAffine3(const Affine3<_Tp> &R);
+
+    /**
+     * @brief A dual quaternion is a vector in form of
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * \sigma &=\boldsymbol{p} + \epsilon \boldsymbol{q}\\
+     * &= \cos\hat{\frac{\theta}{2}}+\overline{\hat{l}}\sin\frac{\hat{\theta}}{2}
+     * \end{split}
+     * \end{equation}
+     * \f]
+     * where \f$\hat{\theta}\f$ is dual angle and \f$\overline{\hat{l}}\f$ is dual axis:
+     * \f[
+     * \hat{\theta}=\theta + \epsilon d,\\
+     * \overline{\hat{l}}= \hat{l} +\epsilon m.
+     * \f]
+     * In this representation, \f$\theta\f$ is rotation angle and \f$(\hat{l},m)\f$ is the screw axis, d is the translation distance along the axis.
+     *
+     * @param angle rotation angle.
+     * @param d translation along the rotation axis.
+     * @param axis rotation axis represented by quaternion with w = 0.
+     * @param moment the moment of line, and it should be orthogonal to axis.
+     * @note Translation is applied after the rotation. Use createFromQuat(r, r * t / 2) to create
+     * a dual quaternion which translation is applied before rotation.
+     */
+    static DualQuat<_Tp> createFromPitch(const _Tp angle, const _Tp d, const Vec<_Tp, 3> &axis, const Vec<_Tp, 3> &moment);
+
+    /**
+     * @brief return a quaternion which represent the real part of dual quaternion.
+     * The definition of real part is in createFromQuat().
+     * @sa createFromQuat, getDualPart
+     */
+    Quat<_Tp> getRealPart() const;
+
+    /**
+     * @brief return a quaternion which represent the dual part of dual quaternion.
+     * The definition of dual part is in createFromQuat().
+     * @sa createFromQuat, getRealPart
+     */
+    Quat<_Tp> getDualPart() const;
+
+    /**
+     * @brief return the conjugate of a dual quaternion.
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * \sigma^* &= (p + \epsilon q)^*
+     *          &= (p^* + \epsilon q^*)
+     * \end{split}
+     * \end{equation}
+     * \f]
+     * @param dq a dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> conjugate(const DualQuat<T> &dq);
+
+    /**
+     * @brief return the conjugate of a dual quaternion.
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * \sigma^* &= (p + \epsilon q)^*
+     *          &= (p^* + \epsilon q^*)
+     * \end{split}
+     * \end{equation}
+     * \f]
+     */
+    DualQuat<_Tp> conjugate() const;
+
+    /**
+     * @brief return the rotation in quaternion form.
+     */
+    Quat<_Tp> getRotation(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the translation vector.
+     * The rotation \f$r\f$ in this dual quaternion \f$\sigma\f$ is applied before translation \f$t\f$.
+     * The dual quaternion \f$\sigma\f$ is defined as
+     * \f[\begin{equation}
+     * \begin{split}
+     * \sigma &= p + \epsilon q \\
+     *        &= r + \frac{\epsilon}{2}{t}r.
+     * \end{split}
+     * \end{equation}\f]
+     * Thus, the translation can be obtained as follows
+     * \f[t = 2qp^*.\f]
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, this dual quaternion assume to be a unit dual quaternion
+     * and this function will save some computations.
+     * @note This dual quaternion's translation is applied after the rotation.
+     */
+    Vec<_Tp, 3> getTranslation(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the norm \f$||\sigma||\f$ of dual quaternion \f$\sigma = p + \epsilon q\f$.
+     * \f[
+     *  \begin{equation}
+     *  \begin{split}
+     *  ||\sigma|| &= \sqrt{\sigma * \sigma^*} \\
+     *        &= ||p|| + \epsilon \frac{p \cdot q}{||p||}.
+     *  \end{split}
+     *  \end{equation}
+     *  \f]
+     * Generally speaking, the norm of a not unit dual
+     * quaternion is a dual number. For convenience, we return it in the form of a dual quaternion
+     * , i.e.
+     * \f[ ||\sigma|| = [||p||, 0, 0, 0, \frac{p \cdot q}{||p||}, 0, 0, 0].\f]
+     *
+     * @note The data type of dual number is dual quaternion.
+     */
+    DualQuat<_Tp> norm() const;
+
+    /**
+     * @brief return a normalized dual quaternion.
+     * A dual quaternion can be expressed as
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * \sigma &= p + \epsilon q\\
+     * &=||\sigma||\left(r+\frac{1}{2}tr\right)
+     * \end{split}
+     * \end{equation}
+     * \f]
+     * where \f$r, t\f$ represents the rotation (ordinary quaternion) and translation (pure ordinary quaternion) respectively,
+     * and \f$||\sigma||\f$ is the norm of dual quaternion(a dual number).
+     * A dual quaternion is unit if and only if
+     * \f[
+     * ||p||=1, p \cdot q=0
+     * \f]
+     * where \f$\cdot\f$ means dot product.
+     * The process of normalization is
+     * \f[
+     * \sigma_{u}=\frac{\sigma}{||\sigma||}
+     * \f]
+     * Next, we simply proof \f$\sigma_u\f$ is a unit dual quaternion:
+     * \f[
+     * \renewcommand{\Im}{\operatorname{Im}}
+     * \begin{equation}
+     * \begin{split}
+     * \sigma_{u}=\frac{\sigma}{||\sigma||}&=\frac{p + \epsilon q}{||p||+\epsilon\frac{p\cdot q}{||p||}}\\
+     * &=\frac{p}{||p||}+\epsilon\left(\frac{q}{||p||}-p\frac{p\cdot q}{||p||^3}\right)\\
+     * &=\frac{p}{||p||}+\epsilon\frac{1}{||p||^2}\left(qp^{*}-p\cdot q\right)\frac{p}{||p||}\\
+     * &=\frac{p}{||p||}+\epsilon\frac{1}{||p||^2}\Im(qp^*)\frac{p}{||p||}.\\
+     * \end{split}
+     * \end{equation}
+     * \f]
+     * As expected, the real part is a rotation and dual part is a pure quaternion.
+     */
+    DualQuat<_Tp> normalize() const;
+
+    /**
+     * @brief if \f$\sigma = p + \epsilon q\f$ is a dual quaternion, p is not zero,
+     * the inverse dual quaternion is
+     * \f[\sigma^{-1} = \frac{\sigma^*}{||\sigma||^2}, \f]
+     * or equivalentlly,
+     * \f[\sigma^{-1} = p^{-1} - \epsilon p^{-1}qp^{-1}.\f]
+     * @param dq a dual quaternion.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, dual quaternion dq assume to be a unit dual quaternion
+     * and this function will save some computations.
+     */
+    template <typename T>
+    friend DualQuat<T> inv(const DualQuat<T> &dq, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief if \f$\sigma = p + \epsilon q\f$ is a dual quaternion, p is not zero,
+     * the inverse dual quaternion is
+     * \f[\sigma^{-1} = \frac{\sigma^*}{||\sigma||^2}, \f]
+     * or equivalentlly,
+     * \f[\sigma^{-1} = p^{-1} - \epsilon p^{-1}qp^{-1}.\f]
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, this dual quaternion assume to be a unit dual quaternion
+     * and this function will save some computations.
+     */
+    DualQuat<_Tp> inv(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the dot product of two dual quaternion.
+     * @param p other dual quaternion.
+     */
+    _Tp dot(DualQuat<_Tp> p) const;
+
+    /**
+     ** @brief return the value of \f$p^t\f$ where p is a dual quaternion.
+     * This could be calculated as:
+     * \f[
+     * p^t = \exp(t\ln p)
+     * \f]
+     * @param dq a dual quaternion.
+     * @param t index of power function.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, dual quaternion dq assume to be a unit dual quaternion
+     * and this function will save some computations.
+     */
+    template <typename T>
+    friend DualQuat<T> power(const DualQuat<T> &dq, const T t, QuatAssumeType assumeUnit);
+
+    /**
+     ** @brief return the value of \f$p^t\f$ where p is a dual quaternion.
+     * This could be calculated as:
+     * \f[
+     * p^t = \exp(t\ln p)
+     * \f]
+     *
+     * @param t index of power function.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, this dual quaternion assume to be a unit dual quaternion
+     * and this function will save some computations.
+     */
+    DualQuat<_Tp> power(const _Tp t, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the value of \f$p^q\f$ where p and q are dual quaternions.
+     * This could be calculated as:
+     * \f[
+     * p^q = \exp(q\ln p)
+     * \f]
+     * @param p a dual quaternion.
+     * @param q a dual quaternion.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, dual quaternion p assume to be a dual unit quaternion
+     * and this function will save some computations.
+     */
+    template <typename T>
+    friend DualQuat<T> power(const DualQuat<T>& p, const DualQuat<T>& q, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief return the value of \f$p^q\f$ where p and q are dual quaternions.
+     * This could be calculated as:
+     * \f[
+     * p^q = \exp(q\ln p)
+     * \f]
+     *
+     * @param q a dual quaternion
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, this dual quaternion assume to be a dual unit quaternion
+     * and this function will save some computations.
+     */
+    DualQuat<_Tp> power(const DualQuat<_Tp>& q, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the value of exponential function value
+     * @param dq a dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> exp(const DualQuat<T> &dq);
+
+    /**
+     * @brief return the value of exponential function value
+     */
+    DualQuat<_Tp> exp() const;
+
+    /**
+     * @brief return the value of logarithm function value
+     *
+     * @param dq a dual quaternion.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, dual quaternion dq assume to be a unit dual quaternion
+     * and this function will save some computations.
+     */
+    template <typename T>
+    friend DualQuat<T> log(const DualQuat<T> &dq, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief return the value of logarithm function value
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, this dual quaternion assume to be a unit dual quaternion
+     * and this function will save some computations.
+     */
+    DualQuat<_Tp> log(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief Transform this dual quaternion to a vector.
+     */
+    Vec<_Tp, 8> toVec() const;
+
+    /**
+     * @brief Transform this dual quaternion to a affine transformation matrix
+     * the form of matrix, see createFromMat().
+     */
+    Matx<_Tp, 4, 4> toMat(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+      * @brief Transform this dual quaternion to a instance of Affine3.
+      */
+    Affine3<_Tp> toAffine3(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief The screw linear interpolation(ScLERP) is an extension of spherical linear interpolation of dual quaternion.
+     * If \f$\sigma_1\f$ and \f$\sigma_2\f$ are two dual quaternions representing the initial and final pose.
+     * The interpolation of ScLERP function can be defined as:
+     * \f[
+     * ScLERP(t;\sigma_1,\sigma_2) = \sigma_1 * (\sigma_1^{-1} * \sigma_2)^t, t\in[0,1]
+     * \f]
+     *
+     * @param q1 a dual quaternion represents a initial pose.
+     * @param q2 a dual quaternion represents a final pose.
+     * @param t interpolation parameter
+     * @param directChange if true, it always return the shortest path.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, this dual quaternion assume to be a unit dual quaternion
+     * and this function will save some computations.
+     *
+     * For example
+     * ```
+     * double angle1 = CV_PI / 2;
+     * Vec3d axis{0, 0, 1};
+     * Vec3d t(0, 0, 3);
+     * DualQuatd initial = DualQuatd::createFromAngleAxisTrans(angle1, axis, t);
+     * double angle2 = CV_PI;
+     * DualQuatd final = DualQuatd::createFromAngleAxisTrans(angle2, axis, t);
+     * DualQuatd inter = DualQuatd::sclerp(initial, final, 0.5);
+     * ```
+     */
+    static DualQuat<_Tp> sclerp(const DualQuat<_Tp> &q1, const DualQuat<_Tp> &q2, const _Tp t,
+                                bool directChange=true, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+    /**
+     * @brief The method of Dual Quaternion linear Blending(DQB) is to compute a transformation between dual quaternion
+     * \f$q_1\f$ and \f$q_2\f$ and can be defined as:
+     * \f[
+     * DQB(t;{\boldsymbol{q}}_1,{\boldsymbol{q}}_2)=
+     * \frac{(1-t){\boldsymbol{q}}_1+t{\boldsymbol{q}}_2}{||(1-t){\boldsymbol{q}}_1+t{\boldsymbol{q}}_2||}.
+     * \f]
+     * where \f$q_1\f$ and \f$q_2\f$ are unit dual quaternions representing the input transformations.
+     * If you want to use DQB that works for more than two rigid transformations, see @ref gdqblend
+     *
+     * @param q1 a unit dual quaternion representing the input transformations.
+     * @param q2 a unit dual quaternion representing the input transformations.
+     * @param t parameter \f$t\in[0,1]\f$.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, this dual quaternion assume to be a unit dual quaternion
+     * and this function will save some computations.
+     *
+     * @sa gdqblend
+     */
+    static DualQuat<_Tp> dqblend(const DualQuat<_Tp> &q1, const DualQuat<_Tp> &q2, const _Tp t,
+                                   QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+    /**
+     * @brief The generalized Dual Quaternion linear Blending works for more than two rigid transformations.
+     * If these transformations are expressed as unit dual quaternions \f$q_1,...,q_n\f$ with convex weights
+     * \f$w = (w_1,...,w_n)\f$, the generalized DQB is simply
+     * \f[
+     * gDQB(\boldsymbol{w};{\boldsymbol{q}}_1,...,{\boldsymbol{q}}_n)=\frac{w_1{\boldsymbol{q}}_1+...+w_n{\boldsymbol{q}}_n}
+     * {||w_1{\boldsymbol{q}}_1+...+w_n{\boldsymbol{q}}_n||}.
+     * \f]
+     * @param dualquat vector of dual quaternions
+     * @param weights vector of weights, the size of weights should be the same as dualquat, and the weights should
+     * satisfy \f$\sum_0^n w_{i} = 1\f$ and \f$w_i>0\f$.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, these dual quaternions assume to be unit quaternions
+     * and this function will save some computations.
+     * @note the type of weights' element should be the same as the date type of dual quaternion inside the dualquat.
+     */
+    template <int cn>
+    static DualQuat<_Tp> gdqblend(const Vec<DualQuat<_Tp>, cn> &dualquat, InputArray weights,
+                                QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+    /**
+     * @brief The generalized Dual Quaternion linear Blending works for more than two rigid transformations.
+     * If these transformations are expressed as unit dual quaternions \f$q_1,...,q_n\f$ with convex weights
+     * \f$w = (w_1,...,w_n)\f$, the generalized DQB is simply
+     * \f[
+     * gDQB(\boldsymbol{w};{\boldsymbol{q}}_1,...,{\boldsymbol{q}}_n)=\frac{w_1{\boldsymbol{q}}_1+...+w_n{\boldsymbol{q}}_n}
+     * {||w_1{\boldsymbol{q}}_1+...+w_n{\boldsymbol{q}}_n||}.
+     * \f]
+     * @param dualquat The dual quaternions which have 8 channels and 1 row or 1 col.
+     * @param weights vector of weights, the size of weights should be the same as dualquat, and the weights should
+     * satisfy \f$\sum_0^n w_{i} = 1\f$ and \f$w_i>0\f$.
+     * @param assumeUnit if @ref QUAT_ASSUME_UNIT, these dual quaternions assume to be unit quaternions
+     * and this function will save some computations.
+     * @note the type of weights' element should be the same as the date type of dual quaternion inside the dualquat.
+     */
+    static DualQuat<_Tp> gdqblend(InputArray dualquat, InputArray weights,
+                                QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+    /**
+     * @brief Return opposite dual quaternion \f$-p\f$
+     * which satisfies \f$p + (-p) = 0.\f$
+     *
+     * For example
+     * ```
+     * DualQuatd q{1, 2, 3, 4, 5, 6, 7, 8};
+     * std::cout << -q << std::endl; // [-1, -2, -3, -4, -5, -6, -7, -8]
+     * ```
+     */
+    DualQuat<_Tp> operator-() const;
+
+    /**
+     * @brief return true if two dual quaternions p and q are nearly equal, i.e. when the absolute
+     * value of each \f$p_i\f$ and \f$q_i\f$ is less than CV_DUAL_QUAT_EPS.
+     */
+    bool operator==(const DualQuat<_Tp>&) const;
+
+    /**
+     * @brief Subtraction operator of two dual quaternions p and q.
+     * It returns a new dual quaternion that each value is the sum of \f$p_i\f$ and \f$-q_i\f$.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * std::cout << p - q << std::endl; //[-4, -4, -4, -4, 4, -4, -4, -4]
+     * ```
+     */
+    DualQuat<_Tp> operator-(const DualQuat<_Tp>&) const;
+
+    /**
+     * @brief Subtraction assignment operator of two dual quaternions p and q.
+     * It subtracts right operand from the left operand and assign the result to left operand.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * p -= q; // equivalent to p = p - q
+     * std::cout << p << std::endl; //[-4, -4, -4, -4, 4, -4, -4, -4]
+     *
+     * ```
+     */
+    DualQuat<_Tp>& operator-=(const DualQuat<_Tp>&);
+
+    /**
+     * @brief Addition operator of two dual quaternions p and q.
+     * It returns a new dual quaternion that each value is the sum of \f$p_i\f$ and \f$q_i\f$.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * std::cout << p + q << std::endl; //[6, 8, 10, 12, 14, 16, 18, 20]
+     * ```
+     */
+    DualQuat<_Tp> operator+(const DualQuat<_Tp>&) const;
+
+    /**
+     * @brief Addition assignment operator of two dual quaternions p and q.
+     * It adds right operand to the left operand and assign the result to left operand.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * p += q; // equivalent to p = p + q
+     * std::cout << p << std::endl; //[6, 8, 10, 12, 14, 16, 18, 20]
+     *
+     * ```
+     */
+    DualQuat<_Tp>& operator+=(const DualQuat<_Tp>&);
+
+    /**
+     * @brief Multiplication assignment operator of two quaternions.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of dual quaternion multiplication:
+     * The dual quaternion can be written as an ordered pair of quaternions [A, B]. Thus
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * q &= [A, B][C, D]\\
+     * &=[AC, AD + BC]
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * p *= q;
+     * std::cout << p << std::endl; //[-60, 12, 30, 24, -216, 80, 124, 120]
+     * ```
+     */
+    DualQuat<_Tp>& operator*=(const DualQuat<_Tp>&);
+
+    /**
+     * @brief Multiplication assignment operator of a quaternions and a scalar.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of dual quaternion multiplication with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * s &= [w, x, y, z, w\_, x\_, y\_, z\_] * s\\
+     *  &=[w   s, x   s, y   s, z   s, w\_  \space  s, x\_  \space  s, y\_ \space  s, z\_ \space  s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double s = 2.0;
+     * p *= s;
+     * std::cout << p << std::endl; //[2, 4, 6, 8, 10, 12, 14, 16]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    DualQuat<_Tp> operator*=(const _Tp s);
+
+
+    /**
+     * @brief Multiplication operator of two dual quaternions q and p.
+     * Multiplies values on either side of the operator.
+     *
+     * Rule of dual quaternion multiplication:
+     * The dual quaternion can be written as an ordered pair of quaternions [A, B]. Thus
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * q &= [A, B][C, D]\\
+     * &=[AC, AD + BC]
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * std::cout << p * q << std::endl; //[-60, 12, 30, 24, -216, 80, 124, 120]
+     * ```
+     */
+    DualQuat<_Tp> operator*(const DualQuat<_Tp>&) const;
+
+    /**
+     * @brief Division operator of a dual quaternions and a scalar.
+     * It divides left operand with the right operand and assign the result to left operand.
+     *
+     * Rule of dual quaternion division with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / s &= [w, x, y, z, w\_, x\_, y\_, z\_] / s\\
+     * &=[w/s, x/s, y/s, z/s, w\_/s, x\_/s, y\_/s, z\_/s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double s = 2.0;
+     * p /= s; // equivalent to p = p / s
+     * std::cout << p << std::endl; //[0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4]
+     * ```
+     * @note the type of scalar should be equal to this dual quaternion.
+     */
+    DualQuat<_Tp> operator/(const _Tp s) const;
+
+    /**
+     * @brief Division operator of two dual quaternions p and q.
+     * Divides left hand operand by right hand operand.
+     *
+     * Rule of dual quaternion division with a dual quaternion:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / q &= p * q.inv()\\
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * std::cout << p / q << std::endl; // equivalent to p * q.inv()
+     * ```
+     */
+    DualQuat<_Tp> operator/(const DualQuat<_Tp>&) const;
+
+    /**
+     * @brief Division assignment operator of two dual quaternions p and q;
+     * It divides left operand with the right operand and assign the result to left operand.
+     *
+     * Rule of dual quaternion division with a quaternion:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / q&= p * q.inv()\\
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * DualQuatd q{5, 6, 7, 8, 9, 10, 11, 12};
+     * p /= q; // equivalent to p = p * q.inv()
+     * std::cout << p << std::endl;
+     * ```
+     */
+    DualQuat<_Tp>& operator/=(const DualQuat<_Tp>&);
+
+    /**
+     * @brief Division assignment operator of a dual quaternions and a scalar.
+     * It divides left operand with the right operand and assign the result to left operand.
+     *
+     * Rule of dual quaternion division with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / s &= [w, x, y, z, w\_, x\_, y\_ ,z\_] / s\\
+     * &=[w / s, x / s, y / s, z / s, w\_ / \space s, x\_ / \space s, y\_ / \space s, z\_ / \space s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double s = 2.0;;
+     * p /= s; // equivalent to p = p / s
+     * std::cout << p << std::endl; //[0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    Quat<_Tp>& operator/=(const _Tp s);
+
+    /**
+     * @brief Addition operator of a scalar and a dual quaternions.
+     * Adds right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double scalar = 2.0;
+     * std::cout << scalar + p << std::endl; //[3.0, 2, 3, 4, 5, 6, 7, 8]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> cv::operator+(const T s, const DualQuat<T>&);
+
+    /**
+     * @brief Addition operator of a dual quaternions and a scalar.
+     * Adds right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double scalar = 2.0;
+     * std::cout << p + scalar << std::endl; //[3.0, 2, 3, 4, 5, 6, 7, 8]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> cv::operator+(const DualQuat<T>&, const T s);
+
+    /**
+     * @brief Multiplication operator of a scalar and a dual quaternions.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of dual quaternion multiplication with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * s &= [w, x, y, z, w\_, x\_, y\_, z\_] * s\\
+     * &=[w s, x s, y s, z s, w\_ \space s, x\_ \space s, y\_ \space s, z\_ \space s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double s = 2.0;
+     * std::cout << s * p << std::endl; //[2, 4, 6, 8, 10, 12, 14, 16]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> cv::operator*(const T s, const DualQuat<T>&);
+
+    /**
+     * @brief Subtraction operator of a dual quaternion and a scalar.
+     * Subtracts right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double scalar = 2.0;
+     * std::cout << p - scalar << std::endl; //[-1, 2, 3, 4, 5, 6, 7, 8]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> cv::operator-(const DualQuat<T>&, const T s);
+
+    /**
+     * @brief Subtraction operator of a scalar and a dual quaternions.
+     * Subtracts right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double scalar = 2.0;
+     * std::cout << scalar - p << std::endl; //[1.0, -2, -3, -4, -5, -6, -7, -8]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> cv::operator-(const T s, const DualQuat<T>&);
+
+    /**
+     * @brief Multiplication operator of a dual quaternions and a scalar.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of dual quaternion multiplication with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * s &= [w, x, y, z, w\_, x\_, y\_, z\_] * s\\
+     * &=[w s, x s, y s, z s, w\_ \space s, x\_ \space s, y\_ \space s, z\_ \space s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * DualQuatd p{1, 2, 3, 4, 5, 6, 7, 8};
+     * double s = 2.0;
+     * std::cout << p * s << std::endl; //[2, 4, 6, 8, 10, 12, 14, 16]
+     * ```
+     * @note the type of scalar should be equal to the dual quaternion.
+     */
+    template <typename T>
+    friend DualQuat<T> cv::operator*(const DualQuat<T>&, const T s);
+
+    template <typename S>
+    friend std::ostream& cv::operator<<(std::ostream&, const DualQuat<S>&);
+
+};
+
+using DualQuatd = DualQuat<double>;
+using DualQuatf = DualQuat<float>;
+
+//! @} core
+}//namespace
+
+#include "dualquaternion.inl.hpp"
+
+#endif /* OPENCV_CORE_QUATERNION_HPP */
diff --git a/3rdParty/include/opencv2/core/dualquaternion.inl.hpp b/3rdParty/include/opencv2/core/dualquaternion.inl.hpp
new file mode 100644
index 0000000..6abb159
--- /dev/null
+++ b/3rdParty/include/opencv2/core/dualquaternion.inl.hpp
@@ -0,0 +1,487 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2020, Huawei Technologies Co., Ltd. All rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Author: Liangqian Kong <kongliangqian@huawei.com>
+//         Longbu Wang <wanglongbu@huawei.com>
+
+#ifndef OPENCV_CORE_DUALQUATERNION_INL_HPP
+#define OPENCV_CORE_DUALQUATERNION_INL_HPP
+
+#ifndef OPENCV_CORE_DUALQUATERNION_HPP
+#error This is not a standalone header. Include dualquaternion.hpp instead.
+#endif
+
+///////////////////////////////////////////////////////////////////////////////////////
+//Implementation
+namespace cv {
+
+template <typename T>
+DualQuat<T>::DualQuat():w(0), x(0), y(0), z(0), w_(0), x_(0), y_(0), z_(0){};
+
+template <typename T>
+DualQuat<T>::DualQuat(const T vw, const T vx, const T vy, const T vz, const T _w, const T _x, const T _y, const T _z):
+                      w(vw), x(vx), y(vy), z(vz), w_(_w), x_(_x), y_(_y), z_(_z){};
+
+template <typename T>
+DualQuat<T>::DualQuat(const Vec<T, 8> &q):w(q[0]), x(q[1]), y(q[2]), z(q[3]),
+                                          w_(q[4]), x_(q[5]), y_(q[6]), z_(q[7]){};
+
+template <typename T>
+DualQuat<T> DualQuat<T>::createFromQuat(const Quat<T> &realPart, const Quat<T> &dualPart)
+{
+    T w = realPart.w;
+    T x = realPart.x;
+    T y = realPart.y;
+    T z = realPart.z;
+    T w_ = dualPart.w;
+    T x_ = dualPart.x;
+    T y_ = dualPart.y;
+    T z_ = dualPart.z;
+    return DualQuat<T>(w, x, y, z, w_, x_, y_, z_);
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::createFromAngleAxisTrans(const T angle, const Vec<T, 3> &axis, const Vec<T, 3> &trans)
+{
+    Quat<T> r = Quat<T>::createFromAngleAxis(angle, axis);
+    Quat<T> t{0, trans[0], trans[1], trans[2]};
+    return createFromQuat(r, t * r * T(0.5));
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::createFromMat(InputArray _R)
+{
+    CV_CheckTypeEQ(_R.type(), cv::traits::Type<T>::value, "");
+    if (_R.size() != Size(4, 4))
+    {
+        CV_Error(Error::StsBadArg, "The input matrix must have 4 columns and 4 rows");
+    }
+    Mat R = _R.getMat();
+    Quat<T> r = Quat<T>::createFromRotMat(R.colRange(0, 3).rowRange(0, 3));
+    Quat<T> trans(0, R.at<T>(0, 3), R.at<T>(1, 3), R.at<T>(2, 3));
+    return createFromQuat(r, trans * r * T(0.5));
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::createFromAffine3(const Affine3<T> &R)
+{
+    return createFromMat(R.matrix);
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::createFromPitch(const T angle, const T d, const Vec<T, 3> &axis, const Vec<T, 3> &moment)
+{
+    T half_angle = angle * T(0.5), half_d = d * T(0.5);
+    Quat<T> qaxis = Quat<T>(0, axis[0], axis[1], axis[2]).normalize();
+    Quat<T> qmoment = Quat<T>(0, moment[0], moment[1], moment[2]);
+    qmoment -= qaxis * axis.dot(moment);
+    Quat<T> dual = -half_d * std::sin(half_angle) + std::sin(half_angle) * qmoment +
+        half_d * std::cos(half_angle) * qaxis;
+    return createFromQuat(Quat<T>::createFromAngleAxis(angle, axis), dual);
+}
+
+template <typename T>
+inline bool DualQuat<T>::operator==(const DualQuat<T> &q) const
+{
+    return (abs(w - q.w) < CV_DUAL_QUAT_EPS && abs(x - q.x) < CV_DUAL_QUAT_EPS &&
+            abs(y - q.y) < CV_DUAL_QUAT_EPS && abs(z - q.z) < CV_DUAL_QUAT_EPS &&
+            abs(w_ - q.w_) < CV_DUAL_QUAT_EPS && abs(x_ - q.x_) < CV_DUAL_QUAT_EPS &&
+            abs(y_ - q.y_) < CV_DUAL_QUAT_EPS && abs(z_ - q.z_) < CV_DUAL_QUAT_EPS);
+}
+
+template <typename T>
+inline Quat<T> DualQuat<T>::getRealPart() const
+{
+    return Quat<T>(w, x, y, z);
+}
+
+template <typename T>
+inline Quat<T> DualQuat<T>::getDualPart() const
+{
+    return Quat<T>(w_, x_, y_, z_);
+}
+
+template <typename T>
+inline DualQuat<T> conjugate(const DualQuat<T> &dq)
+{
+    return dq.conjugate();
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::conjugate() const
+{
+    return DualQuat<T>(w, -x, -y, -z, w_, -x_, -y_, -z_);
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::norm() const
+{
+    Quat<T> real = getRealPart();
+    T realNorm = real.norm();
+    Quat<T> dual = getDualPart();
+    if (realNorm < CV_DUAL_QUAT_EPS){
+        return DualQuat<T>(0, 0, 0, 0, 0, 0, 0, 0);
+    }
+    return DualQuat<T>(realNorm, 0, 0, 0, real.dot(dual) / realNorm, 0, 0, 0);
+}
+
+template <typename T>
+inline Quat<T> DualQuat<T>::getRotation(QuatAssumeType assumeUnit) const
+{
+    if (assumeUnit)
+    {
+        return getRealPart();
+    }
+    return getRealPart().normalize();
+}
+
+template <typename T>
+inline Vec<T, 3> DualQuat<T>::getTranslation(QuatAssumeType assumeUnit) const
+{
+    Quat<T> trans = T(2.0) * (getDualPart() * getRealPart().inv(assumeUnit));
+    return Vec<T, 3>{trans[1], trans[2], trans[3]};
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::normalize() const
+{
+    Quat<T> p = getRealPart();
+    Quat<T> q = getDualPart();
+    T p_norm = p.norm();
+    if (p_norm < CV_DUAL_QUAT_EPS)
+    {
+        CV_Error(Error::StsBadArg, "Cannot normalize this dual quaternion: the norm is too small.");
+    }
+    Quat<T> p_nr = p / p_norm;
+    Quat<T> q_nr = q / p_norm;
+    return createFromQuat(p_nr, q_nr - p_nr * p_nr.dot(q_nr));
+}
+
+template <typename T>
+inline T DualQuat<T>::dot(DualQuat<T> q) const
+{
+    return q.w * w + q.x * x + q.y * y + q.z * z + q.w_ * w_ + q.x_ * x_ + q.y_ * y_ + q.z_ * z_;
+}
+
+template <typename T>
+inline DualQuat<T> inv(const DualQuat<T> &dq, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT)
+{
+    return dq.inv(assumeUnit);
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::inv(QuatAssumeType assumeUnit) const
+{
+    Quat<T> real = getRealPart();
+    Quat<T> dual = getDualPart();
+    return createFromQuat(real.inv(assumeUnit), -real.inv(assumeUnit) * dual * real.inv(assumeUnit));
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::operator-(const DualQuat<T> &q) const
+{
+    return DualQuat<T>(w - q.w, x - q.x, y - q.y, z - q.z, w_ - q.w_, x_ - q.x_, y_ - q.y_, z_ - q.z_);
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::operator-() const
+{
+    return DualQuat<T>(-w, -x, -y, -z, -w_, -x_, -y_, -z_);
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::operator+(const DualQuat<T> &q) const
+{
+    return DualQuat<T>(w + q.w, x + q.x, y + q.y, z + q.z, w_ + q.w_, x_ + q.x_, y_ + q.y_, z_ + q.z_);
+}
+
+template <typename T>
+inline DualQuat<T>& DualQuat<T>::operator+=(const DualQuat<T> &q)
+{
+    *this = *this + q;
+    return *this;
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::operator*(const DualQuat<T> &q) const
+{
+    Quat<T> A = getRealPart();
+    Quat<T> B = getDualPart();
+    Quat<T> C = q.getRealPart();
+    Quat<T> D = q.getDualPart();
+    return DualQuat<T>::createFromQuat(A * C, A * D + B * C);
+}
+
+template <typename T>
+inline DualQuat<T>& DualQuat<T>::operator*=(const DualQuat<T> &q)
+{
+    *this = *this * q;
+    return *this;
+}
+
+template <typename T>
+inline DualQuat<T> operator+(const T a, const DualQuat<T> &q)
+{
+    return DualQuat<T>(a + q.w, q.x, q.y, q.z, q.w_, q.x_, q.y_, q.z_);
+}
+
+template <typename T>
+inline DualQuat<T> operator+(const DualQuat<T> &q, const T a)
+{
+    return DualQuat<T>(a + q.w, q.x, q.y, q.z, q.w_, q.x_, q.y_, q.z_);
+}
+
+template <typename T>
+inline DualQuat<T> operator-(const DualQuat<T> &q, const T a)
+{
+    return DualQuat<T>(q.w - a, q.x, q.y, q.z, q.w_, q.x_, q.y_, q.z_);
+}
+
+template <typename T>
+inline DualQuat<T>& DualQuat<T>::operator-=(const DualQuat<T> &q)
+{
+    *this = *this - q;
+    return *this;
+}
+
+template <typename T>
+inline DualQuat<T> operator-(const T a, const DualQuat<T> &q)
+{
+    return DualQuat<T>(a - q.w, -q.x, -q.y, -q.z, -q.w_, -q.x_, -q.y_, -q.z_);
+}
+
+template <typename T>
+inline DualQuat<T> operator*(const T a, const DualQuat<T> &q)
+{
+    return DualQuat<T>(q.w * a, q.x * a, q.y * a, q.z * a, q.w_ * a, q.x_ * a, q.y_ * a, q.z_ * a);
+}
+
+template <typename T>
+inline DualQuat<T> operator*(const DualQuat<T> &q, const T a)
+{
+    return DualQuat<T>(q.w * a, q.x * a, q.y * a, q.z * a, q.w_ * a, q.x_ * a, q.y_ * a, q.z_ * a);
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::operator/(const T a) const
+{
+    return DualQuat<T>(w / a, x / a, y / a, z / a, w_ / a, x_ / a, y_ / a, z_ / a);
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::operator/(const DualQuat<T> &q) const
+{
+    return *this * q.inv();
+}
+
+template <typename T>
+inline DualQuat<T>& DualQuat<T>::operator/=(const DualQuat<T> &q)
+{
+    *this = *this / q;
+    return *this;
+}
+
+template <typename T>
+std::ostream & operator<<(std::ostream &os, const DualQuat<T> &q)
+{
+    os << "DualQuat " << Vec<T, 8>{q.w, q.x, q.y, q.z, q.w_, q.x_, q.y_, q.z_};
+    return os;
+}
+
+template <typename T>
+inline DualQuat<T> exp(const DualQuat<T> &dq)
+{
+    return dq.exp();
+}
+
+namespace detail {
+
+template <typename _Tp>
+Matx<_Tp, 4, 4> jacob_exp(const Quat<_Tp> &q)
+{
+    _Tp nv = std::sqrt(q.x * q.x + q.y * q.y + q.z * q.z);
+    _Tp sinc_nv = abs(nv) < cv::DualQuat<_Tp>::CV_DUAL_QUAT_EPS ? _Tp(1.0) - nv * nv * _Tp(1.0/6.0) : std::sin(nv) / nv;
+    _Tp csiii_nv = abs(nv) < cv::DualQuat<_Tp>::CV_DUAL_QUAT_EPS ? -_Tp(1.0/3.0) : (std::cos(nv) - sinc_nv) / nv / nv;
+    Matx<_Tp, 4, 4> J_exp_quat {
+        std::cos(nv), -sinc_nv * q.x,  -sinc_nv * q.y,  -sinc_nv * q.z,
+        sinc_nv * q.x, csiii_nv * q.x * q.x + sinc_nv, csiii_nv * q.x * q.y, csiii_nv * q.x * q.z,
+        sinc_nv * q.y, csiii_nv * q.y * q.x, csiii_nv * q.y * q.y + sinc_nv, csiii_nv * q.y * q.z,
+        sinc_nv * q.z, csiii_nv * q.z * q.x, csiii_nv * q.z * q.y, csiii_nv * q.z * q.z + sinc_nv
+    };
+    return std::exp(q.w) * J_exp_quat;
+}
+
+} // namespace detail
+
+template <typename T>
+DualQuat<T> DualQuat<T>::exp() const
+{
+    Quat<T> real = getRealPart();
+    return createFromQuat(real.exp(), Quat<T>(detail::jacob_exp(real) * getDualPart().toVec()));
+}
+
+template <typename T>
+DualQuat<T> log(const DualQuat<T> &dq, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT)
+{
+    return dq.log(assumeUnit);
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::log(QuatAssumeType assumeUnit) const
+{
+    Quat<T> plog = getRealPart().log(assumeUnit);
+    Matx<T, 4, 4> jacob = detail::jacob_exp(plog);
+    return createFromQuat(plog, Quat<T>(jacob.inv() * getDualPart().toVec()));
+}
+
+template <typename T>
+inline DualQuat<T> power(const DualQuat<T> &dq, const T t, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT)
+{
+    return dq.power(t, assumeUnit);
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::power(const T t, QuatAssumeType assumeUnit) const
+{
+    return (t * log(assumeUnit)).exp();
+}
+
+template <typename T>
+inline DualQuat<T> power(const DualQuat<T> &p, const DualQuat<T> &q, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT)
+{
+    return p.power(q, assumeUnit);
+}
+
+template <typename T>
+inline DualQuat<T> DualQuat<T>::power(const DualQuat<T> &q, QuatAssumeType assumeUnit) const
+{
+    return (q * log(assumeUnit)).exp();
+}
+
+template <typename T>
+inline Vec<T, 8> DualQuat<T>::toVec() const
+{
+   return Vec<T, 8>(w, x, y, z, w_, x_, y_, z_);
+}
+
+template <typename T>
+Affine3<T> DualQuat<T>::toAffine3(QuatAssumeType assumeUnit) const
+{
+    return Affine3<T>(toMat(assumeUnit));
+}
+
+template <typename T>
+Matx<T, 4, 4> DualQuat<T>::toMat(QuatAssumeType assumeUnit) const
+{
+    Matx<T, 4, 4> rot44 = getRotation(assumeUnit).toRotMat4x4();
+    Vec<T, 3> translation = getTranslation(assumeUnit);
+    rot44(0, 3) = translation[0];
+    rot44(1, 3) = translation[1];
+    rot44(2, 3) = translation[2];
+    return rot44;
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::sclerp(const DualQuat<T> &q0, const DualQuat<T> &q1, const T t, bool directChange, QuatAssumeType assumeUnit)
+{
+    DualQuat<T> v0(q0), v1(q1);
+    if (!assumeUnit)
+    {
+        v0 = v0.normalize();
+        v1 = v1.normalize();
+    }
+    Quat<T> v0Real = v0.getRealPart();
+    Quat<T> v1Real = v1.getRealPart();
+    if (directChange && v1Real.dot(v0Real) < 0)
+    {
+        v0 = -v0;
+    }
+    DualQuat<T> v0inv1 = v0.inv() * v1;
+    return v0 * v0inv1.power(t, QUAT_ASSUME_UNIT);
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::dqblend(const DualQuat<T> &q1, const DualQuat<T> &q2, const T t, QuatAssumeType assumeUnit)
+{
+    DualQuat<T> v1(q1), v2(q2);
+    if (!assumeUnit)
+    {
+        v1 = v1.normalize();
+        v2 = v2.normalize();
+    }
+    if (v1.getRotation(assumeUnit).dot(v2.getRotation(assumeUnit)) < 0)
+    {
+        return ((1 - t) * v1 - t * v2).normalize();
+    }
+    return ((1 - t) * v1 + t * v2).normalize();
+}
+
+template <typename T>
+DualQuat<T> DualQuat<T>::gdqblend(InputArray _dualquat, InputArray _weight, QuatAssumeType assumeUnit)
+{
+    CV_CheckTypeEQ(_weight.type(), cv::traits::Type<T>::value, "");
+    CV_CheckTypeEQ(_dualquat.type(), CV_MAKETYPE(CV_MAT_DEPTH(cv::traits::Type<T>::value), 8), "");
+    Size dq_s = _dualquat.size();
+    if (dq_s != _weight.size() || (dq_s.height != 1 && dq_s.width != 1))
+    {
+        CV_Error(Error::StsBadArg, "The size of weight must be the same as dualquat, both of them should be (1, n) or (n, 1)");
+    }
+    Mat dualquat = _dualquat.getMat(), weight = _weight.getMat();
+    const int cn = std::max(dq_s.width, dq_s.height);
+    if (!assumeUnit)
+    {
+        for (int i = 0; i < cn; ++i)
+        {
+            dualquat.at<Vec<T, 8>>(i) = DualQuat<T>{dualquat.at<Vec<T, 8>>(i)}.normalize().toVec();
+        }
+    }
+    Vec<T, 8> dq_blend = dualquat.at<Vec<T, 8>>(0) * weight.at<T>(0);
+    Quat<T> q0 = DualQuat<T> {dualquat.at<Vec<T, 8>>(0)}.getRotation(assumeUnit);
+    for (int i = 1; i < cn; ++i)
+    {
+        T k = q0.dot(DualQuat<T>{dualquat.at<Vec<T, 8>>(i)}.getRotation(assumeUnit)) < 0 ? -1: 1;
+        dq_blend = dq_blend + dualquat.at<Vec<T, 8>>(i) * k * weight.at<T>(i);
+    }
+    return DualQuat<T>{dq_blend}.normalize();
+}
+
+template <typename T>
+template <int cn>
+DualQuat<T> DualQuat<T>::gdqblend(const Vec<DualQuat<T>, cn> &_dualquat, InputArray _weight, QuatAssumeType assumeUnit)
+{
+    Vec<DualQuat<T>, cn> dualquat(_dualquat);
+    if (cn == 0)
+    {
+        return DualQuat<T>(1, 0, 0, 0, 0, 0, 0, 0);
+    }
+    Mat dualquat_mat(cn, 1, CV_64FC(8));
+    for (int i = 0; i < cn ; ++i)
+    {
+        dualquat_mat.at<Vec<T, 8>>(i) = dualquat[i].toVec();
+    }
+    return gdqblend(dualquat_mat, _weight, assumeUnit);
+}
+
+} //namespace cv
+
+#endif /*OPENCV_CORE_DUALQUATERNION_INL_HPP*/
diff --git a/3rdParty/include/opencv2/core/eigen.hpp b/3rdParty/include/opencv2/core/eigen.hpp
new file mode 100644
index 0000000..51f4147
--- /dev/null
+++ b/3rdParty/include/opencv2/core/eigen.hpp
@@ -0,0 +1,402 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+
+#ifndef OPENCV_CORE_EIGEN_HPP
+#define OPENCV_CORE_EIGEN_HPP
+
+#ifndef EIGEN_WORLD_VERSION
+#error "Wrong usage of OpenCV's Eigen utility header. Include Eigen's headers first. See https://github.com/opencv/opencv/issues/17366"
+#endif
+
+#include "opencv2/core.hpp"
+
+#if defined _MSC_VER && _MSC_VER >= 1200
+#define NOMINMAX // fix https://github.com/opencv/opencv/issues/17548
+#pragma warning( disable: 4714 ) //__forceinline is not inlined
+#pragma warning( disable: 4127 ) //conditional expression is constant
+#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data
+#endif
+
+#if !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
+#if EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3 \
+    && defined(CV_CXX11) && defined(CV_CXX_STD_ARRAY)
+#include <unsupported/Eigen/CXX11/Tensor>
+#define OPENCV_EIGEN_TENSOR_SUPPORT 1
+#endif  // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
+#endif  // !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
+
+namespace cv
+{
+
+/** @addtogroup core_eigen
+These functions are provided for OpenCV-Eigen interoperability. They convert `Mat`
+objects to corresponding `Eigen::Matrix` objects and vice-versa. Consult the [Eigen
+documentation](https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html) for
+information about the `Matrix` template type.
+
+@note Using these functions requires the `Eigen/Dense` or similar header to be
+included before this header.
+*/
+//! @{
+
+#if defined(OPENCV_EIGEN_TENSOR_SUPPORT) || defined(CV_DOXYGEN)
+/** @brief Converts an Eigen::Tensor to a cv::Mat.
+
+The method converts an Eigen::Tensor with shape (H x W x C) to a cv::Mat where:
+ H = number of rows
+ W = number of columns
+ C = number of channels
+
+Usage:
+\code
+Eigen::Tensor<float, 3, Eigen::RowMajor> a_tensor(...);
+// populate tensor with values
+Mat a_mat;
+eigen2cv(a_tensor, a_mat);
+\endcode
+*/
+template <typename _Tp, int _layout> static inline
+void eigen2cv( const Eigen::Tensor<_Tp, 3, _layout> &src, OutputArray dst )
+{
+    if( !(_layout & Eigen::RowMajorBit) )
+    {
+        const std::array<int, 3> shuffle{2, 1, 0};
+        Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor = src.swap_layout().shuffle(shuffle);
+        Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), row_major_tensor.data());
+        _src.copyTo(dst);
+    }
+    else
+    {
+        Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), (void *)src.data());
+        _src.copyTo(dst);
+    }
+}
+
+/** @brief Converts a cv::Mat to an Eigen::Tensor.
+
+The method converts a cv::Mat to an Eigen Tensor with shape (H x W x C) where:
+ H = number of rows
+ W = number of columns
+ C = number of channels
+
+Usage:
+\code
+Mat a_mat(...);
+// populate Mat with values
+Eigen::Tensor<float, 3, Eigen::RowMajor> a_tensor(...);
+cv2eigen(a_mat, a_tensor);
+\endcode
+*/
+template <typename _Tp, int _layout> static inline
+void cv2eigen( const Mat &src, Eigen::Tensor<_Tp, 3, _layout> &dst )
+{
+    if( !(_layout & Eigen::RowMajorBit) )
+    {
+        Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor(src.rows, src.cols, src.channels());
+        Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), row_major_tensor.data());
+        if (src.type() == _dst.type())
+            src.copyTo(_dst);
+        else
+            src.convertTo(_dst, _dst.type());
+        const std::array<int, 3> shuffle{2, 1, 0};
+        dst = row_major_tensor.swap_layout().shuffle(shuffle);
+    }
+    else
+    {
+        dst.resize(src.rows, src.cols, src.channels());
+        Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), dst.data());
+        if (src.type() == _dst.type())
+            src.copyTo(_dst);
+        else
+            src.convertTo(_dst, _dst.type());
+    }
+}
+
+/** @brief Maps cv::Mat data to an Eigen::TensorMap.
+
+The method wraps an existing Mat data array with an Eigen TensorMap of shape (H x W x C) where:
+ H = number of rows
+ W = number of columns
+ C = number of channels
+
+Explicit instantiation of the return type is required.
+
+@note Caller should be aware of the lifetime of the cv::Mat instance and take appropriate safety measures.
+The cv::Mat instance will retain ownership of the data and the Eigen::TensorMap will lose access when the cv::Mat data is deallocated.
+
+The example below initializes a cv::Mat and produces an Eigen::TensorMap:
+\code
+float arr[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+Mat a_mat(2, 2, CV_32FC3, arr);
+Eigen::TensorMap<Eigen::Tensor<float, 3, Eigen::RowMajor>> a_tensormap = cv2eigen_tensormap<float>(a_mat);
+\endcode
+*/
+template <typename _Tp> static inline
+Eigen::TensorMap<Eigen::Tensor<_Tp, 3, Eigen::RowMajor>> cv2eigen_tensormap(InputArray src)
+{
+    Mat mat = src.getMat();
+    CV_CheckTypeEQ(mat.type(), CV_MAKETYPE(traits::Type<_Tp>::value, mat.channels()), "");
+    return Eigen::TensorMap<Eigen::Tensor<_Tp, 3, Eigen::RowMajor>>((_Tp *)mat.data, mat.rows, mat.cols, mat.channels());
+}
+#endif // OPENCV_EIGEN_TENSOR_SUPPORT
+
+template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
+void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, OutputArray dst )
+{
+    if( !(src.Flags & Eigen::RowMajorBit) )
+    {
+        Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value,
+              (void*)src.data(), src.outerStride()*sizeof(_Tp));
+        transpose(_src, dst);
+    }
+    else
+    {
+        Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value,
+                 (void*)src.data(), src.outerStride()*sizeof(_Tp));
+        _src.copyTo(dst);
+    }
+}
+
+// Matx case
+template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
+void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src,
+               Matx<_Tp, _rows, _cols>& dst )
+{
+    if( !(src.Flags & Eigen::RowMajorBit) )
+    {
+        dst = Matx<_Tp, _cols, _rows>(static_cast<const _Tp*>(src.data())).t();
+    }
+    else
+    {
+        dst = Matx<_Tp, _rows, _cols>(static_cast<const _Tp*>(src.data()));
+    }
+}
+
+template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
+void cv2eigen( const Mat& src,
+               Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
+{
+    CV_DbgAssert(src.rows == _rows && src.cols == _cols);
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        if( src.type() == _dst.type() )
+            transpose(src, _dst);
+        else if( src.cols == src.rows )
+        {
+            src.convertTo(_dst, _dst.type());
+            transpose(_dst, _dst);
+        }
+        else
+            Mat(src.t()).convertTo(_dst, _dst.type());
+    }
+    else
+    {
+        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        src.convertTo(_dst, _dst.type());
+    }
+}
+
+// Matx case
+template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
+void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
+               Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
+{
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        transpose(src, _dst);
+    }
+    else
+    {
+        const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        Mat(src).copyTo(_dst);
+    }
+}
+
+template<typename _Tp>  static inline
+void cv2eigen( const Mat& src,
+               Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
+{
+    dst.resize(src.rows, src.cols);
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
+             dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        if( src.type() == _dst.type() )
+            transpose(src, _dst);
+        else if( src.cols == src.rows )
+        {
+            src.convertTo(_dst, _dst.type());
+            transpose(_dst, _dst);
+        }
+        else
+            Mat(src.t()).convertTo(_dst, _dst.type());
+    }
+    else
+    {
+        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        src.convertTo(_dst, _dst.type());
+    }
+}
+
+// Matx case
+template<typename _Tp, int _rows, int _cols> static inline
+void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
+               Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
+{
+    dst.resize(_rows, _cols);
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
+             dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        transpose(src, _dst);
+    }
+    else
+    {
+        const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        Mat(src).copyTo(_dst);
+    }
+}
+
+template<typename _Tp> static inline
+void cv2eigen( const Mat& src,
+               Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
+{
+    CV_Assert(src.cols == 1);
+    dst.resize(src.rows);
+
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        if( src.type() == _dst.type() )
+            transpose(src, _dst);
+        else
+            Mat(src.t()).convertTo(_dst, _dst.type());
+    }
+    else
+    {
+        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        src.convertTo(_dst, _dst.type());
+    }
+}
+
+// Matx case
+template<typename _Tp, int _rows> static inline
+void cv2eigen( const Matx<_Tp, _rows, 1>& src,
+               Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
+{
+    dst.resize(_rows);
+
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(1, _rows, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        transpose(src, _dst);
+    }
+    else
+    {
+        const Mat _dst(_rows, 1, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        src.copyTo(_dst);
+    }
+}
+
+
+template<typename _Tp> static inline
+void cv2eigen( const Mat& src,
+               Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
+{
+    CV_Assert(src.rows == 1);
+    dst.resize(src.cols);
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        if( src.type() == _dst.type() )
+            transpose(src, _dst);
+        else
+            Mat(src.t()).convertTo(_dst, _dst.type());
+    }
+    else
+    {
+        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        src.convertTo(_dst, _dst.type());
+    }
+}
+
+//Matx
+template<typename _Tp, int _cols> static inline
+void cv2eigen( const Matx<_Tp, 1, _cols>& src,
+               Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
+{
+    dst.resize(_cols);
+    if( !(dst.Flags & Eigen::RowMajorBit) )
+    {
+        const Mat _dst(_cols, 1, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        transpose(src, _dst);
+    }
+    else
+    {
+        const Mat _dst(1, _cols, traits::Type<_Tp>::value,
+                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
+        Mat(src).copyTo(_dst);
+    }
+}
+
+//! @}
+
+} // cv
+
+#endif
diff --git a/3rdParty/include/opencv2/core/fast_math.hpp b/3rdParty/include/opencv2/core/fast_math.hpp
new file mode 100644
index 0000000..eb4fbe2
--- /dev/null
+++ b/3rdParty/include/opencv2/core/fast_math.hpp
@@ -0,0 +1,411 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_FAST_MATH_HPP
+#define OPENCV_CORE_FAST_MATH_HPP
+
+#include "opencv2/core/cvdef.h"
+
+//! @addtogroup core_utils
+//! @{
+
+/****************************************************************************************\
+*                                      fast math                                         *
+\****************************************************************************************/
+
+#ifdef __cplusplus
+#  include <cmath>
+#else
+#  ifdef __BORLANDC__
+#    include <fastmath.h>
+#  else
+#    include <math.h>
+#  endif
+#endif
+
+#if defined(__CUDACC__)
+  // nothing, intrinsics/asm code is not supported
+#else
+  #if ((defined _MSC_VER && defined _M_X64) \
+      || (defined __GNUC__ && defined __x86_64__ && defined __SSE2__)) \
+      && !defined(OPENCV_SKIP_INCLUDE_EMMINTRIN_H)
+    #include <emmintrin.h>
+  #endif
+
+  #if defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8 \
+      && !defined(OPENCV_SKIP_INCLUDE_ALTIVEC_H)
+    #include <altivec.h>
+    #undef vector
+    #undef bool
+    #undef pixel
+  #endif
+
+  #if defined(CV_INLINE_ROUND_FLT)
+    // user-specified version
+    // CV_INLINE_ROUND_DBL should be defined too
+  #elif defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__
+    // 1. general scheme
+    #define ARM_ROUND(_value, _asm_string) \
+        int res; \
+        float temp; \
+        CV_UNUSED(temp); \
+        __asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \
+        return res
+    // 2. version for double
+    #ifdef __clang__
+        #define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]")
+    #else
+        #define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]")
+    #endif
+    // 3. version for float
+    #define CV_INLINE_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]")
+  #elif defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8
+    // P8 and newer machines can convert fp32/64 to int quickly.
+    #define CV_INLINE_ROUND_DBL(value) \
+        int out; \
+        double temp; \
+        __asm__( "fctiw %[temp],%[in]\n\tmfvsrwz %[out],%[temp]\n\t" : [out] "=r" (out), [temp] "=d" (temp) : [in] "d" ((double)(value)) : ); \
+        return out;
+
+    // FP32 also works with FP64 routine above
+    #define CV_INLINE_ROUND_FLT(value) CV_INLINE_ROUND_DBL(value)
+  #endif
+
+  #ifdef CV_INLINE_ISINF_FLT
+    // user-specified version
+    // CV_INLINE_ISINF_DBL should be defined too
+  #elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class)
+    #define CV_INLINE_ISINF_DBL(value) return scalar_test_data_class(value, 0x30);
+    #define CV_INLINE_ISINF_FLT(value) CV_INLINE_ISINF_DBL(value)
+  #endif
+
+  #ifdef CV_INLINE_ISNAN_FLT
+    // user-specified version
+    // CV_INLINE_ISNAN_DBL should be defined too
+  #elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class)
+    #define CV_INLINE_ISNAN_DBL(value) return scalar_test_data_class(value, 0x40);
+    #define CV_INLINE_ISNAN_FLT(value) CV_INLINE_ISNAN_DBL(value)
+  #endif
+
+  #if !defined(OPENCV_USE_FASTMATH_BUILTINS) \
+    && ( \
+        defined(__x86_64__) || defined(__i686__) \
+        || defined(__arm__) \
+        || defined(__PPC64__) \
+    )
+    /* Let builtin C math functions when available. Dedicated hardware is available to
+       round and convert FP values. */
+    #define OPENCV_USE_FASTMATH_BUILTINS 1
+  #endif
+
+  /* Enable builtin math functions if possible, desired, and available.
+     Note, not all math functions inline equally. E.g lrint will not inline
+     without the -fno-math-errno option. */
+  #if defined(CV_ICC)
+    // nothing
+  #elif defined(OPENCV_USE_FASTMATH_BUILTINS) && OPENCV_USE_FASTMATH_BUILTINS
+    #if defined(__clang__)
+      #define CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS
+      #if !defined(CV_INLINE_ISNAN_DBL) && __has_builtin(__builtin_isnan)
+        #define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value);
+      #endif
+      #if !defined(CV_INLINE_ISNAN_FLT) && __has_builtin(__builtin_isnan)
+        #define CV_INLINE_ISNAN_FLT(value) return __builtin_isnan(value);
+      #endif
+      #if !defined(CV_INLINE_ISINF_DBL) && __has_builtin(__builtin_isinf)
+        #define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value);
+      #endif
+      #if !defined(CV_INLINE_ISINF_FLT) && __has_builtin(__builtin_isinf)
+        #define CV_INLINE_ISINF_FLT(value) return __builtin_isinf(value);
+      #endif
+    #elif defined(__GNUC__)
+      #define CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS
+      #if !defined(CV_INLINE_ISNAN_DBL)
+        #define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value);
+      #endif
+      #if !defined(CV_INLINE_ISNAN_FLT)
+        #define CV_INLINE_ISNAN_FLT(value) return __builtin_isnanf(value);
+      #endif
+      #if !defined(CV_INLINE_ISINF_DBL)
+        #define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value);
+      #endif
+      #if !defined(CV_INLINE_ISINF_FLT)
+        #define CV_INLINE_ISINF_FLT(value) return __builtin_isinff(value);
+      #endif
+    #elif defined(_MSC_VER)
+      #if !defined(CV_INLINE_ISNAN_DBL)
+        #define CV_INLINE_ISNAN_DBL(value) return isnan(value);
+      #endif
+      #if !defined(CV_INLINE_ISNAN_FLT)
+        #define CV_INLINE_ISNAN_FLT(value) return isnan(value);
+      #endif
+      #if !defined(CV_INLINE_ISINF_DBL)
+        #define CV_INLINE_ISINF_DBL(value) return isinf(value);
+      #endif
+      #if !defined(CV_INLINE_ISINF_FLT)
+        #define CV_INLINE_ISINF_FLT(value) return isinf(value);
+      #endif
+    #endif
+  #endif
+
+#endif // defined(__CUDACC__)
+
+/** @brief Rounds floating-point number to the nearest integer
+
+ @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
+ result is not defined.
+ */
+CV_INLINE int
+cvRound( double value )
+{
+#if defined CV_INLINE_ROUND_DBL
+    CV_INLINE_ROUND_DBL(value);
+#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
+    && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \
+    && !defined(__CUDACC__)
+    __m128d t = _mm_set_sd( value );
+    return _mm_cvtsd_si32(t);
+#elif defined _MSC_VER && defined _M_IX86
+    int t;
+    __asm
+    {
+        fld value;
+        fistp t;
+    }
+    return t;
+#elif defined CV_ICC || defined __GNUC__
+    return (int)(lrint(value));
+#else
+    /* it's ok if round does not comply with IEEE754 standard;
+       the tests should allow +/-1 difference when the tested functions use round */
+    return (int)(value + (value >= 0 ? 0.5 : -0.5));
+#endif
+}
+
+
+/** @brief Rounds floating-point number to the nearest integer not larger than the original.
+
+ The function computes an integer i such that:
+ \f[i \le \texttt{value} < i+1\f]
+ @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
+ result is not defined.
+ */
+CV_INLINE int cvFloor( double value )
+{
+#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
+    && ( \
+        defined(__PPC64__) \
+    )
+    return __builtin_floor(value);
+#else
+    int i = (int)value;
+    return i - (i > value);
+#endif
+}
+
+/** @brief Rounds floating-point number to the nearest integer not smaller than the original.
+
+ The function computes an integer i such that:
+ \f[i \le \texttt{value} < i+1\f]
+ @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
+ result is not defined.
+ */
+CV_INLINE int cvCeil( double value )
+{
+#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
+    && ( \
+        defined(__PPC64__) \
+    )
+    return __builtin_ceil(value);
+#else
+    int i = (int)value;
+    return i + (i < value);
+#endif
+}
+
+/** @brief Determines if the argument is Not A Number.
+
+ @param value The input floating-point value
+
+ The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0
+ otherwise. */
+CV_INLINE int cvIsNaN( double value )
+{
+#if defined CV_INLINE_ISNAN_DBL
+    CV_INLINE_ISNAN_DBL(value);
+#else
+    Cv64suf ieee754;
+    ieee754.f = value;
+    return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) +
+           ((unsigned)ieee754.u != 0) > 0x7ff00000;
+#endif
+}
+
+/** @brief Determines if the argument is Infinity.
+
+ @param value The input floating-point value
+
+ The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard)
+ and 0 otherwise. */
+CV_INLINE int cvIsInf( double value )
+{
+#if defined CV_INLINE_ISINF_DBL
+    CV_INLINE_ISINF_DBL(value);
+#elif defined(__x86_64__) || defined(_M_X64) || defined(__aarch64__) || defined(_M_ARM64) || defined(__PPC64__)
+    Cv64suf ieee754;
+    ieee754.f = value;
+    return (ieee754.u & 0x7fffffff00000000) ==
+                        0x7ff0000000000000;
+#else
+    Cv64suf ieee754;
+    ieee754.f = value;
+    return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 &&
+            (unsigned)ieee754.u == 0;
+#endif
+}
+
+#ifdef __cplusplus
+
+/** @overload */
+CV_INLINE int cvRound(float value)
+{
+#if defined CV_INLINE_ROUND_FLT
+    CV_INLINE_ROUND_FLT(value);
+#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
+    && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \
+    && !defined(__CUDACC__)
+    __m128 t = _mm_set_ss( value );
+    return _mm_cvtss_si32(t);
+#elif defined _MSC_VER && defined _M_IX86
+    int t;
+    __asm
+    {
+        fld value;
+        fistp t;
+    }
+    return t;
+#elif defined CV_ICC || defined __GNUC__
+    return (int)(lrintf(value));
+#else
+    /* it's ok if round does not comply with IEEE754 standard;
+     the tests should allow +/-1 difference when the tested functions use round */
+    return (int)(value + (value >= 0 ? 0.5f : -0.5f));
+#endif
+}
+
+/** @overload */
+CV_INLINE int cvRound( int value )
+{
+    return value;
+}
+
+/** @overload */
+CV_INLINE int cvFloor( float value )
+{
+#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
+    && ( \
+        defined(__PPC64__) \
+    )
+    return __builtin_floorf(value);
+#else
+    int i = (int)value;
+    return i - (i > value);
+#endif
+}
+
+/** @overload */
+CV_INLINE int cvFloor( int value )
+{
+    return value;
+}
+
+/** @overload */
+CV_INLINE int cvCeil( float value )
+{
+#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
+    && ( \
+        defined(__PPC64__) \
+    )
+    return __builtin_ceilf(value);
+#else
+    int i = (int)value;
+    return i + (i < value);
+#endif
+}
+
+/** @overload */
+CV_INLINE int cvCeil( int value )
+{
+    return value;
+}
+
+/** @overload */
+CV_INLINE int cvIsNaN( float value )
+{
+#if defined CV_INLINE_ISNAN_FLT
+    CV_INLINE_ISNAN_FLT(value);
+#else
+    Cv32suf ieee754;
+    ieee754.f = value;
+    return (ieee754.u & 0x7fffffff) > 0x7f800000;
+#endif
+}
+
+/** @overload */
+CV_INLINE int cvIsInf( float value )
+{
+#if defined CV_INLINE_ISINF_FLT
+    CV_INLINE_ISINF_FLT(value);
+#else
+    Cv32suf ieee754;
+    ieee754.f = value;
+    return (ieee754.u & 0x7fffffff) == 0x7f800000;
+#endif
+}
+
+#endif // __cplusplus
+
+//! @} core_utils
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/hal.hpp b/3rdParty/include/opencv2/core/hal/hal.hpp
new file mode 100644
index 0000000..0d68078
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/hal.hpp
@@ -0,0 +1,256 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HAL_HPP
+#define OPENCV_HAL_HPP
+
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/cvstd.hpp"
+#include "opencv2/core/hal/interface.h"
+
+namespace cv { namespace hal {
+
+//! @addtogroup core_hal_functions
+//! @{
+
+CV_EXPORTS int normHamming(const uchar* a, int n);
+CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n);
+
+CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize);
+CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize);
+
+CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
+CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
+CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
+CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
+CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags);
+CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags);
+CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors);
+CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors);
+
+CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
+                        float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
+                        int m_a, int n_a, int n_d, int flags);
+CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
+                        double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
+                        int m_a, int n_a, int n_d, int flags);
+CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
+                        float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
+                        int m_a, int n_a, int n_d, int flags);
+CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
+                        double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
+                        int m_a, int n_a, int n_d, int flags);
+
+CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n);
+CV_EXPORTS float normL1_(const float* a, const float* b, int n);
+CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n);
+
+CV_EXPORTS void exp32f(const float* src, float* dst, int n);
+CV_EXPORTS void exp64f(const double* src, double* dst, int n);
+CV_EXPORTS void log32f(const float* src, float* dst, int n);
+CV_EXPORTS void log64f(const double* src, double* dst, int n);
+
+CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
+CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees);
+CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n);
+CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n);
+CV_EXPORTS void sqrt32f(const float* src, float* dst, int len);
+CV_EXPORTS void sqrt64f(const double* src, double* dst, int len);
+CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len);
+CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len);
+
+CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn );
+CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn );
+CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn );
+CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn );
+
+CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn );
+CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn );
+CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn );
+CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn );
+
+CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
+
+CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
+
+CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
+
+CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
+
+CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
+
+CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
+
+CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
+CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
+CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
+CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
+CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
+CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
+CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
+
+CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
+
+CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
+
+CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
+CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
+
+CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars );
+CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars );
+CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars );
+CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars );
+CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars );
+CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars );
+CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars );
+
+CV_EXPORTS void cvt16f32f( const float16_t* src, float* dst, int len );
+CV_EXPORTS void cvt32f16f( const float* src, float16_t* dst, int len );
+
+CV_EXPORTS void addRNGBias32f( float* arr, const float* scaleBiasPairs, int len );
+CV_EXPORTS void addRNGBias64f( double* arr, const double* scaleBiasPairs, int len );
+
+struct CV_EXPORTS DFT1D
+{
+    static Ptr<DFT1D> create(int len, int count, int depth, int flags, bool * useBuffer = 0);
+    virtual void apply(const uchar *src, uchar *dst) = 0;
+    virtual ~DFT1D() {}
+};
+
+struct CV_EXPORTS DFT2D
+{
+    static Ptr<DFT2D> create(int width, int height, int depth,
+                             int src_channels, int dst_channels,
+                             int flags, int nonzero_rows = 0);
+    virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
+    virtual ~DFT2D() {}
+};
+
+struct CV_EXPORTS DCT2D
+{
+    static Ptr<DCT2D> create(int width, int height, int depth, int flags);
+    virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
+    virtual ~DCT2D() {}
+};
+
+//! @} core_hal
+
+//=============================================================================
+// for binary compatibility with 3.0
+
+//! @cond IGNORED
+
+CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
+CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
+CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
+CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
+
+CV_EXPORTS void exp(const float* src, float* dst, int n);
+CV_EXPORTS void exp(const double* src, double* dst, int n);
+CV_EXPORTS void log(const float* src, float* dst, int n);
+CV_EXPORTS void log(const double* src, double* dst, int n);
+
+CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
+CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n);
+CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n);
+CV_EXPORTS void sqrt(const float* src, float* dst, int len);
+CV_EXPORTS void sqrt(const double* src, double* dst, int len);
+CV_EXPORTS void invSqrt(const float* src, float* dst, int len);
+CV_EXPORTS void invSqrt(const double* src, double* dst, int len);
+
+//! @endcond
+
+}} //cv::hal
+
+#endif //OPENCV_HAL_HPP
diff --git a/3rdParty/include/opencv2/core/hal/interface.h b/3rdParty/include/opencv2/core/hal/interface.h
new file mode 100644
index 0000000..6f0a83d
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/interface.h
@@ -0,0 +1,190 @@
+#ifndef OPENCV_CORE_HAL_INTERFACE_H
+#define OPENCV_CORE_HAL_INTERFACE_H
+
+//! @addtogroup core_hal_interface
+//! @{
+
+//! @name Return codes
+//! @{
+#define CV_HAL_ERROR_OK 0
+#define CV_HAL_ERROR_NOT_IMPLEMENTED 1
+#define CV_HAL_ERROR_UNKNOWN -1
+//! @}
+
+#ifdef __cplusplus
+#include <cstddef>
+#else
+#include <stddef.h>
+#include <stdbool.h>
+#endif
+
+//! @name Data types
+//! primitive types
+//! - schar  - signed 1 byte integer
+//! - uchar  - unsigned 1 byte integer
+//! - short  - signed 2 byte integer
+//! - ushort - unsigned 2 byte integer
+//! - int    - signed 4 byte integer
+//! - uint   - unsigned 4 byte integer
+//! - int64  - signed 8 byte integer
+//! - uint64 - unsigned 8 byte integer
+//! @{
+#if !defined _MSC_VER && !defined __BORLANDC__
+#  if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__
+#    include <cstdint>
+#    ifdef __NEWLIB__
+        typedef unsigned int uint;
+#    else
+        typedef std::uint32_t uint;
+#    endif
+#  else
+#    include <stdint.h>
+     typedef uint32_t uint;
+#  endif
+#else
+   typedef unsigned uint;
+#endif
+
+typedef signed char schar;
+
+#ifndef __IPL_H__
+   typedef unsigned char uchar;
+   typedef unsigned short ushort;
+#endif
+
+#if defined _MSC_VER || defined __BORLANDC__
+   typedef __int64 int64;
+   typedef unsigned __int64 uint64;
+#  define CV_BIG_INT(n)   n##I64
+#  define CV_BIG_UINT(n)  n##UI64
+#else
+   typedef int64_t int64;
+   typedef uint64_t uint64;
+#  define CV_BIG_INT(n)   n##LL
+#  define CV_BIG_UINT(n)  n##ULL
+#endif
+
+#define CV_USRTYPE1 (void)"CV_USRTYPE1 support has been dropped in OpenCV 4.0"
+
+#define CV_CN_MAX     512
+#define CV_CN_SHIFT   3
+#define CV_DEPTH_MAX  (1 << CV_CN_SHIFT)
+
+#define CV_8U   0
+#define CV_8S   1
+#define CV_16U  2
+#define CV_16S  3
+#define CV_32S  4
+#define CV_32F  5
+#define CV_64F  6
+#define CV_16F  7
+
+#define CV_MAT_DEPTH_MASK       (CV_DEPTH_MAX - 1)
+#define CV_MAT_DEPTH(flags)     ((flags) & CV_MAT_DEPTH_MASK)
+
+#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT))
+#define CV_MAKE_TYPE CV_MAKETYPE
+
+#define CV_8UC1 CV_MAKETYPE(CV_8U,1)
+#define CV_8UC2 CV_MAKETYPE(CV_8U,2)
+#define CV_8UC3 CV_MAKETYPE(CV_8U,3)
+#define CV_8UC4 CV_MAKETYPE(CV_8U,4)
+#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n))
+
+#define CV_8SC1 CV_MAKETYPE(CV_8S,1)
+#define CV_8SC2 CV_MAKETYPE(CV_8S,2)
+#define CV_8SC3 CV_MAKETYPE(CV_8S,3)
+#define CV_8SC4 CV_MAKETYPE(CV_8S,4)
+#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n))
+
+#define CV_16UC1 CV_MAKETYPE(CV_16U,1)
+#define CV_16UC2 CV_MAKETYPE(CV_16U,2)
+#define CV_16UC3 CV_MAKETYPE(CV_16U,3)
+#define CV_16UC4 CV_MAKETYPE(CV_16U,4)
+#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n))
+
+#define CV_16SC1 CV_MAKETYPE(CV_16S,1)
+#define CV_16SC2 CV_MAKETYPE(CV_16S,2)
+#define CV_16SC3 CV_MAKETYPE(CV_16S,3)
+#define CV_16SC4 CV_MAKETYPE(CV_16S,4)
+#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n))
+
+#define CV_32SC1 CV_MAKETYPE(CV_32S,1)
+#define CV_32SC2 CV_MAKETYPE(CV_32S,2)
+#define CV_32SC3 CV_MAKETYPE(CV_32S,3)
+#define CV_32SC4 CV_MAKETYPE(CV_32S,4)
+#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n))
+
+#define CV_32FC1 CV_MAKETYPE(CV_32F,1)
+#define CV_32FC2 CV_MAKETYPE(CV_32F,2)
+#define CV_32FC3 CV_MAKETYPE(CV_32F,3)
+#define CV_32FC4 CV_MAKETYPE(CV_32F,4)
+#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n))
+
+#define CV_64FC1 CV_MAKETYPE(CV_64F,1)
+#define CV_64FC2 CV_MAKETYPE(CV_64F,2)
+#define CV_64FC3 CV_MAKETYPE(CV_64F,3)
+#define CV_64FC4 CV_MAKETYPE(CV_64F,4)
+#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n))
+
+#define CV_16FC1 CV_MAKETYPE(CV_16F,1)
+#define CV_16FC2 CV_MAKETYPE(CV_16F,2)
+#define CV_16FC3 CV_MAKETYPE(CV_16F,3)
+#define CV_16FC4 CV_MAKETYPE(CV_16F,4)
+#define CV_16FC(n) CV_MAKETYPE(CV_16F,(n))
+//! @}
+
+//! @name Comparison operation
+//! @sa cv::CmpTypes
+//! @{
+#define CV_HAL_CMP_EQ 0
+#define CV_HAL_CMP_GT 1
+#define CV_HAL_CMP_GE 2
+#define CV_HAL_CMP_LT 3
+#define CV_HAL_CMP_LE 4
+#define CV_HAL_CMP_NE 5
+//! @}
+
+//! @name Border processing modes
+//! @sa cv::BorderTypes
+//! @{
+#define CV_HAL_BORDER_CONSTANT 0
+#define CV_HAL_BORDER_REPLICATE 1
+#define CV_HAL_BORDER_REFLECT 2
+#define CV_HAL_BORDER_WRAP 3
+#define CV_HAL_BORDER_REFLECT_101 4
+#define CV_HAL_BORDER_TRANSPARENT 5
+#define CV_HAL_BORDER_ISOLATED 16
+//! @}
+
+//! @name DFT flags
+//! @{
+#define CV_HAL_DFT_INVERSE        1
+#define CV_HAL_DFT_SCALE          2
+#define CV_HAL_DFT_ROWS           4
+#define CV_HAL_DFT_COMPLEX_OUTPUT 16
+#define CV_HAL_DFT_REAL_OUTPUT    32
+#define CV_HAL_DFT_TWO_STAGE      64
+#define CV_HAL_DFT_STAGE_COLS    128
+#define CV_HAL_DFT_IS_CONTINUOUS 512
+#define CV_HAL_DFT_IS_INPLACE 1024
+//! @}
+
+//! @name SVD flags
+//! @{
+#define CV_HAL_SVD_NO_UV    1
+#define CV_HAL_SVD_SHORT_UV 2
+#define CV_HAL_SVD_MODIFY_A 4
+#define CV_HAL_SVD_FULL_UV  8
+//! @}
+
+//! @name Gemm flags
+//! @{
+#define CV_HAL_GEMM_1_T 1
+#define CV_HAL_GEMM_2_T 2
+#define CV_HAL_GEMM_3_T 4
+//! @}
+
+//! @}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin.hpp b/3rdParty/include/opencv2/core/hal/intrin.hpp
new file mode 100644
index 0000000..ac331f2
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin.hpp
@@ -0,0 +1,706 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HAL_INTRIN_HPP
+#define OPENCV_HAL_INTRIN_HPP
+
+#include <cmath>
+#include <float.h>
+#include <stdlib.h>
+#include "opencv2/core/cvdef.h"
+
+#define OPENCV_HAL_ADD(a, b) ((a) + (b))
+#define OPENCV_HAL_AND(a, b) ((a) & (b))
+#define OPENCV_HAL_NOP(a) (a)
+#define OPENCV_HAL_1ST(a, b) (a)
+
+namespace {
+inline unsigned int trailingZeros32(unsigned int value) {
+#if defined(_MSC_VER)
+#if (_MSC_VER < 1700) || defined(_M_ARM) || defined(_M_ARM64)
+    unsigned long index = 0;
+    _BitScanForward(&index, value);
+    return (unsigned int)index;
+#elif defined(__clang__)
+    // clang-cl doesn't export _tzcnt_u32 for non BMI systems
+    return value ? __builtin_ctz(value) : 32;
+#else
+    return _tzcnt_u32(value);
+#endif
+#elif defined(__GNUC__) || defined(__GNUG__)
+    return __builtin_ctz(value);
+#elif defined(__ICC) || defined(__INTEL_COMPILER)
+    return _bit_scan_forward(value);
+#elif defined(__clang__)
+    return llvm.cttz.i32(value, true);
+#else
+    static const int MultiplyDeBruijnBitPosition[32] = {
+        0, 1, 28, 2, 29, 14, 24, 3, 30, 22, 20, 15, 25, 17, 4, 8,
+        31, 27, 13, 23, 21, 19, 16, 7, 26, 12, 18, 6, 11, 5, 10, 9 };
+    return MultiplyDeBruijnBitPosition[((uint32_t)((value & -value) * 0x077CB531U)) >> 27];
+#endif
+}
+}
+
+// unlike HAL API, which is in cv::hal,
+// we put intrinsics into cv namespace to make its
+// access from within opencv code more accessible
+namespace cv {
+
+namespace hal {
+
+enum StoreMode
+{
+    STORE_UNALIGNED = 0,
+    STORE_ALIGNED = 1,
+    STORE_ALIGNED_NOCACHE = 2
+};
+
+}
+
+// TODO FIXIT: Don't use "God" traits. Split on separate cases.
+template<typename _Tp> struct V_TypeTraits
+{
+};
+
+#define CV_INTRIN_DEF_TYPE_TRAITS(type, int_type_, uint_type_, abs_type_, w_type_, q_type_, sum_type_) \
+    template<> struct V_TypeTraits<type> \
+    { \
+        typedef type value_type; \
+        typedef int_type_ int_type; \
+        typedef abs_type_ abs_type; \
+        typedef uint_type_ uint_type; \
+        typedef w_type_ w_type; \
+        typedef q_type_ q_type; \
+        typedef sum_type_ sum_type; \
+    \
+        static inline int_type reinterpret_int(type x) \
+        { \
+            union { type l; int_type i; } v; \
+            v.l = x; \
+            return v.i; \
+        } \
+    \
+        static inline type reinterpret_from_int(int_type x) \
+        { \
+            union { type l; int_type i; } v; \
+            v.i = x; \
+            return v.l; \
+        } \
+    }
+
+#define CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(type, int_type_, uint_type_, abs_type_, w_type_, sum_type_) \
+    template<> struct V_TypeTraits<type> \
+    { \
+        typedef type value_type; \
+        typedef int_type_ int_type; \
+        typedef abs_type_ abs_type; \
+        typedef uint_type_ uint_type; \
+        typedef w_type_ w_type; \
+        typedef sum_type_ sum_type; \
+    \
+        static inline int_type reinterpret_int(type x) \
+        { \
+            union { type l; int_type i; } v; \
+            v.l = x; \
+            return v.i; \
+        } \
+    \
+        static inline type reinterpret_from_int(int_type x) \
+        { \
+            union { type l; int_type i; } v; \
+            v.i = x; \
+            return v.l; \
+        } \
+    }
+
+CV_INTRIN_DEF_TYPE_TRAITS(uchar, schar, uchar, uchar, ushort, unsigned, unsigned);
+CV_INTRIN_DEF_TYPE_TRAITS(schar, schar, uchar, uchar, short, int, int);
+CV_INTRIN_DEF_TYPE_TRAITS(ushort, short, ushort, ushort, unsigned, uint64, unsigned);
+CV_INTRIN_DEF_TYPE_TRAITS(short, short, ushort, ushort, int, int64, int);
+CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(unsigned, int, unsigned, unsigned, uint64, unsigned);
+CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(int, int, unsigned, unsigned, int64, int);
+CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(float, int, unsigned, float, double, float);
+CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(uint64, int64, uint64, uint64, void, uint64);
+CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(int64, int64, uint64, uint64, void, int64);
+CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(double, int64, uint64, double, void, double);
+
+#ifndef CV_DOXYGEN
+
+#ifndef CV_CPU_OPTIMIZATION_HAL_NAMESPACE
+#ifdef CV_FORCE_SIMD128_CPP
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_EMULATOR_CPP
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_EMULATOR_CPP {
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
+#elif defined(CV_CPU_DISPATCH_MODE)
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE)
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) {
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
+#else
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline {
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
+#endif
+#endif // CV_CPU_OPTIMIZATION_HAL_NAMESPACE
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE;
+#endif
+}
+
+#ifdef CV_DOXYGEN
+#   undef CV_AVX2
+#   undef CV_SSE2
+#   undef CV_NEON
+#   undef CV_VSX
+#   undef CV_FP16
+#   undef CV_MSA
+#   undef CV_RVV
+#endif
+
+#if (CV_SSE2 || CV_NEON || CV_VSX || CV_MSA || CV_WASM_SIMD || CV_RVV071 || CV_RVV) && !defined(CV_FORCE_SIMD128_CPP)
+#define CV__SIMD_FORWARD 128
+#include "opencv2/core/hal/intrin_forward.hpp"
+#endif
+
+#if CV_SSE2 && !defined(CV_FORCE_SIMD128_CPP)
+
+#include "opencv2/core/hal/intrin_sse_em.hpp"
+#include "opencv2/core/hal/intrin_sse.hpp"
+
+#elif CV_NEON && !defined(CV_FORCE_SIMD128_CPP)
+
+#include "opencv2/core/hal/intrin_neon.hpp"
+
+#elif CV_RVV071 && !defined(CV_FORCE_SIMD128_CPP)
+#define CV_SIMD128_CPP 0
+#include "opencv2/core/hal/intrin_rvv071.hpp"
+
+#elif CV_VSX && !defined(CV_FORCE_SIMD128_CPP)
+
+#include "opencv2/core/hal/intrin_vsx.hpp"
+
+#elif CV_MSA && !defined(CV_FORCE_SIMD128_CPP)
+
+#include "opencv2/core/hal/intrin_msa.hpp"
+
+#elif CV_WASM_SIMD && !defined(CV_FORCE_SIMD128_CPP)
+#include "opencv2/core/hal/intrin_wasm.hpp"
+
+#elif CV_RVV && !defined(CV_FORCE_SIMD128_CPP)
+#include "opencv2/core/hal/intrin_rvv.hpp"
+
+#else
+
+#include "opencv2/core/hal/intrin_cpp.hpp"
+
+#endif
+
+// AVX2 can be used together with SSE2, so
+// we define those two sets of intrinsics at once.
+// Most of the intrinsics do not conflict (the proper overloaded variant is
+// resolved by the argument types, e.g. v_float32x4 ~ SSE2, v_float32x8 ~ AVX2),
+// but some of AVX2 intrinsics get v256_ prefix instead of v_, e.g. v256_load() vs v_load().
+// Correspondingly, the wide intrinsics (which are mapped to the "widest"
+// available instruction set) will get vx_ prefix
+// (and will be mapped to v256_ counterparts) (e.g. vx_load() => v256_load())
+#if CV_AVX2
+
+#define CV__SIMD_FORWARD 256
+#include "opencv2/core/hal/intrin_forward.hpp"
+#include "opencv2/core/hal/intrin_avx.hpp"
+
+#endif
+
+// AVX512 can be used together with SSE2 and AVX2, so
+// we define those sets of intrinsics at once.
+// For some of AVX512 intrinsics get v512_ prefix instead of v_, e.g. v512_load() vs v_load().
+// Wide intrinsics will be mapped to v512_ counterparts in this case(e.g. vx_load() => v512_load())
+#if CV_AVX512_SKX
+
+#define CV__SIMD_FORWARD 512
+#include "opencv2/core/hal/intrin_forward.hpp"
+#include "opencv2/core/hal/intrin_avx512.hpp"
+
+#endif
+
+//! @cond IGNORED
+
+namespace cv {
+
+#ifndef CV_DOXYGEN
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+#endif
+
+#ifndef CV_SIMD128
+#define CV_SIMD128 0
+#endif
+
+#ifndef CV_SIMD128_CPP
+#define CV_SIMD128_CPP 0
+#endif
+
+#ifndef CV_SIMD128_64F
+#define CV_SIMD128_64F 0
+#endif
+
+#ifndef CV_SIMD256
+#define CV_SIMD256 0
+#endif
+
+#ifndef CV_SIMD256_64F
+#define CV_SIMD256_64F 0
+#endif
+
+#ifndef CV_SIMD512
+#define CV_SIMD512 0
+#endif
+
+#ifndef CV_SIMD512_64F
+#define CV_SIMD512_64F 0
+#endif
+
+#ifndef CV_SIMD128_FP16
+#define CV_SIMD128_FP16 0
+#endif
+
+#ifndef CV_SIMD256_FP16
+#define CV_SIMD256_FP16 0
+#endif
+
+#ifndef CV_SIMD512_FP16
+#define CV_SIMD512_FP16 0
+#endif
+
+//==================================================================================================
+
+template<typename _Tp> struct V_RegTraits
+{
+};
+
+#define CV_DEF_REG_TRAITS(prefix, _reg, lane_type, suffix, _u_reg, _w_reg, _q_reg, _int_reg, _round_reg) \
+    template<> struct V_RegTraits<_reg> \
+    { \
+        typedef _reg reg; \
+        typedef _u_reg u_reg; \
+        typedef _w_reg w_reg; \
+        typedef _q_reg q_reg; \
+        typedef _int_reg int_reg; \
+        typedef _round_reg round_reg; \
+    }
+
+#if CV_SIMD128 || CV_SIMD128_CPP
+    CV_DEF_REG_TRAITS(v, v_uint8x16, uchar, u8, v_uint8x16, v_uint16x8, v_uint32x4, v_int8x16, void);
+    CV_DEF_REG_TRAITS(v, v_int8x16, schar, s8, v_uint8x16, v_int16x8, v_int32x4, v_int8x16, void);
+    CV_DEF_REG_TRAITS(v, v_uint16x8, ushort, u16, v_uint16x8, v_uint32x4, v_uint64x2, v_int16x8, void);
+    CV_DEF_REG_TRAITS(v, v_int16x8, short, s16, v_uint16x8, v_int32x4, v_int64x2, v_int16x8, void);
+    CV_DEF_REG_TRAITS(v, v_uint32x4, unsigned, u32, v_uint32x4, v_uint64x2, void, v_int32x4, void);
+    CV_DEF_REG_TRAITS(v, v_int32x4, int, s32, v_uint32x4, v_int64x2, void, v_int32x4, void);
+#if CV_SIMD128_64F || CV_SIMD128_CPP
+    CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, v_float64x2, void, v_int32x4, v_int32x4);
+#else
+    CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, void, void, v_int32x4, v_int32x4);
+#endif
+    CV_DEF_REG_TRAITS(v, v_uint64x2, uint64, u64, v_uint64x2, void, void, v_int64x2, void);
+    CV_DEF_REG_TRAITS(v, v_int64x2, int64, s64, v_uint64x2, void, void, v_int64x2, void);
+#if CV_SIMD128_64F
+    CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4);
+#endif
+#endif
+
+#if CV_SIMD256
+    CV_DEF_REG_TRAITS(v256, v_uint8x32, uchar, u8, v_uint8x32, v_uint16x16, v_uint32x8, v_int8x32, void);
+    CV_DEF_REG_TRAITS(v256, v_int8x32, schar, s8, v_uint8x32, v_int16x16, v_int32x8, v_int8x32, void);
+    CV_DEF_REG_TRAITS(v256, v_uint16x16, ushort, u16, v_uint16x16, v_uint32x8, v_uint64x4, v_int16x16, void);
+    CV_DEF_REG_TRAITS(v256, v_int16x16, short, s16, v_uint16x16, v_int32x8, v_int64x4, v_int16x16, void);
+    CV_DEF_REG_TRAITS(v256, v_uint32x8, unsigned, u32, v_uint32x8, v_uint64x4, void, v_int32x8, void);
+    CV_DEF_REG_TRAITS(v256, v_int32x8, int, s32, v_uint32x8, v_int64x4, void, v_int32x8, void);
+    CV_DEF_REG_TRAITS(v256, v_float32x8, float, f32, v_float32x8, v_float64x4, void, v_int32x8, v_int32x8);
+    CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void);
+    CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void);
+    CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8);
+#endif
+
+#if CV_SIMD512
+    CV_DEF_REG_TRAITS(v512, v_uint8x64, uchar, u8, v_uint8x64, v_uint16x32, v_uint32x16, v_int8x64, void);
+    CV_DEF_REG_TRAITS(v512, v_int8x64, schar, s8, v_uint8x64, v_int16x32, v_int32x16, v_int8x64, void);
+    CV_DEF_REG_TRAITS(v512, v_uint16x32, ushort, u16, v_uint16x32, v_uint32x16, v_uint64x8, v_int16x32, void);
+    CV_DEF_REG_TRAITS(v512, v_int16x32, short, s16, v_uint16x32, v_int32x16, v_int64x8, v_int16x32, void);
+    CV_DEF_REG_TRAITS(v512, v_uint32x16, unsigned, u32, v_uint32x16, v_uint64x8, void, v_int32x16, void);
+    CV_DEF_REG_TRAITS(v512, v_int32x16, int, s32, v_uint32x16, v_int64x8, void, v_int32x16, void);
+    CV_DEF_REG_TRAITS(v512, v_float32x16, float, f32, v_float32x16, v_float64x8, void, v_int32x16, v_int32x16);
+    CV_DEF_REG_TRAITS(v512, v_uint64x8, uint64, u64, v_uint64x8, void, void, v_int64x8, void);
+    CV_DEF_REG_TRAITS(v512, v_int64x8, int64, s64, v_uint64x8, void, void, v_int64x8, void);
+    CV_DEF_REG_TRAITS(v512, v_float64x8, double, f64, v_float64x8, void, void, v_int64x8, v_int32x16);
+#endif
+//! @endcond
+
+#if CV_SIMD512 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 512)
+#define CV__SIMD_NAMESPACE simd512
+namespace CV__SIMD_NAMESPACE {
+    #define CV_SIMD 1
+    #define CV_SIMD_64F CV_SIMD512_64F
+    #define CV_SIMD_FP16 CV_SIMD512_FP16
+    #define CV_SIMD_WIDTH 64
+//! @addtogroup core_hal_intrin
+//! @{
+    //! @brief Maximum available vector register capacity 8-bit unsigned integer values
+    typedef v_uint8x64    v_uint8;
+    //! @brief Maximum available vector register capacity 8-bit signed integer values
+    typedef v_int8x64     v_int8;
+    //! @brief Maximum available vector register capacity 16-bit unsigned integer values
+    typedef v_uint16x32   v_uint16;
+    //! @brief Maximum available vector register capacity 16-bit signed integer values
+    typedef v_int16x32    v_int16;
+    //! @brief Maximum available vector register capacity 32-bit unsigned integer values
+    typedef v_uint32x16   v_uint32;
+    //! @brief Maximum available vector register capacity 32-bit signed integer values
+    typedef v_int32x16    v_int32;
+    //! @brief Maximum available vector register capacity 64-bit unsigned integer values
+    typedef v_uint64x8    v_uint64;
+    //! @brief Maximum available vector register capacity 64-bit signed integer values
+    typedef v_int64x8     v_int64;
+    //! @brief Maximum available vector register capacity 32-bit floating point values (single precision)
+    typedef v_float32x16  v_float32;
+    #if CV_SIMD512_64F
+    //! @brief Maximum available vector register capacity 64-bit floating point values (double precision)
+    typedef v_float64x8   v_float64;
+    #endif
+//! @}
+
+    #define VXPREFIX(func) v512##func
+} // namespace
+using namespace CV__SIMD_NAMESPACE;
+#elif CV_SIMD256 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 256)
+#define CV__SIMD_NAMESPACE simd256
+namespace CV__SIMD_NAMESPACE {
+    #define CV_SIMD 1
+    #define CV_SIMD_64F CV_SIMD256_64F
+    #define CV_SIMD_FP16 CV_SIMD256_FP16
+    #define CV_SIMD_WIDTH 32
+//! @addtogroup core_hal_intrin
+//! @{
+    //! @brief Maximum available vector register capacity 8-bit unsigned integer values
+    typedef v_uint8x32   v_uint8;
+    //! @brief Maximum available vector register capacity 8-bit signed integer values
+    typedef v_int8x32    v_int8;
+    //! @brief Maximum available vector register capacity 16-bit unsigned integer values
+    typedef v_uint16x16  v_uint16;
+    //! @brief Maximum available vector register capacity 16-bit signed integer values
+    typedef v_int16x16   v_int16;
+    //! @brief Maximum available vector register capacity 32-bit unsigned integer values
+    typedef v_uint32x8   v_uint32;
+    //! @brief Maximum available vector register capacity 32-bit signed integer values
+    typedef v_int32x8    v_int32;
+    //! @brief Maximum available vector register capacity 64-bit unsigned integer values
+    typedef v_uint64x4   v_uint64;
+    //! @brief Maximum available vector register capacity 64-bit signed integer values
+    typedef v_int64x4    v_int64;
+    //! @brief Maximum available vector register capacity 32-bit floating point values (single precision)
+    typedef v_float32x8  v_float32;
+    #if CV_SIMD256_64F
+    //! @brief Maximum available vector register capacity 64-bit floating point values (double precision)
+    typedef v_float64x4  v_float64;
+    #endif
+//! @}
+
+    #define VXPREFIX(func) v256##func
+} // namespace
+using namespace CV__SIMD_NAMESPACE;
+#elif (CV_SIMD128 || CV_SIMD128_CPP) && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 128)
+#if defined CV_SIMD128_CPP
+#define CV__SIMD_NAMESPACE simd128_cpp
+#else
+#define CV__SIMD_NAMESPACE simd128
+#endif
+namespace CV__SIMD_NAMESPACE {
+    #define CV_SIMD CV_SIMD128
+    #define CV_SIMD_64F CV_SIMD128_64F
+    #define CV_SIMD_WIDTH 16
+//! @addtogroup core_hal_intrin
+//! @{
+    //! @brief Maximum available vector register capacity 8-bit unsigned integer values
+    typedef v_uint8x16  v_uint8;
+    //! @brief Maximum available vector register capacity 8-bit signed integer values
+    typedef v_int8x16   v_int8;
+    //! @brief Maximum available vector register capacity 16-bit unsigned integer values
+    typedef v_uint16x8  v_uint16;
+    //! @brief Maximum available vector register capacity 16-bit signed integer values
+    typedef v_int16x8   v_int16;
+    //! @brief Maximum available vector register capacity 32-bit unsigned integer values
+    typedef v_uint32x4  v_uint32;
+    //! @brief Maximum available vector register capacity 32-bit signed integer values
+    typedef v_int32x4   v_int32;
+    //! @brief Maximum available vector register capacity 64-bit unsigned integer values
+    typedef v_uint64x2  v_uint64;
+    //! @brief Maximum available vector register capacity 64-bit signed integer values
+    typedef v_int64x2   v_int64;
+    //! @brief Maximum available vector register capacity 32-bit floating point values (single precision)
+    typedef v_float32x4 v_float32;
+    #if CV_SIMD128_64F
+    //! @brief Maximum available vector register capacity 64-bit floating point values (double precision)
+    typedef v_float64x2 v_float64;
+    #endif
+//! @}
+
+    #define VXPREFIX(func) v##func
+} // namespace
+using namespace CV__SIMD_NAMESPACE;
+#endif
+
+namespace CV__SIMD_NAMESPACE {
+//! @addtogroup core_hal_intrin
+//! @{
+    //! @name Wide init with value
+    //! @{
+    //! @brief Create maximum available capacity vector with elements set to a specific value
+    inline v_uint8 vx_setall_u8(uchar v) { return VXPREFIX(_setall_u8)(v); }
+    inline v_int8 vx_setall_s8(schar v) { return VXPREFIX(_setall_s8)(v); }
+    inline v_uint16 vx_setall_u16(ushort v) { return VXPREFIX(_setall_u16)(v); }
+    inline v_int16 vx_setall_s16(short v) { return VXPREFIX(_setall_s16)(v); }
+    inline v_int32 vx_setall_s32(int v) { return VXPREFIX(_setall_s32)(v); }
+    inline v_uint32 vx_setall_u32(unsigned v) { return VXPREFIX(_setall_u32)(v); }
+    inline v_float32 vx_setall_f32(float v) { return VXPREFIX(_setall_f32)(v); }
+    inline v_int64 vx_setall_s64(int64 v) { return VXPREFIX(_setall_s64)(v); }
+    inline v_uint64 vx_setall_u64(uint64 v) { return VXPREFIX(_setall_u64)(v); }
+#if CV_SIMD_64F
+    inline v_float64 vx_setall_f64(double v) { return VXPREFIX(_setall_f64)(v); }
+#endif
+    //! @}
+
+    //! @name Wide init with zero
+    //! @{
+    //! @brief Create maximum available capacity vector with elements set to zero
+    inline v_uint8 vx_setzero_u8() { return VXPREFIX(_setzero_u8)(); }
+    inline v_int8 vx_setzero_s8() { return VXPREFIX(_setzero_s8)(); }
+    inline v_uint16 vx_setzero_u16() { return VXPREFIX(_setzero_u16)(); }
+    inline v_int16 vx_setzero_s16() { return VXPREFIX(_setzero_s16)(); }
+    inline v_int32 vx_setzero_s32() { return VXPREFIX(_setzero_s32)(); }
+    inline v_uint32 vx_setzero_u32() { return VXPREFIX(_setzero_u32)(); }
+    inline v_float32 vx_setzero_f32() { return VXPREFIX(_setzero_f32)(); }
+    inline v_int64 vx_setzero_s64() { return VXPREFIX(_setzero_s64)(); }
+    inline v_uint64 vx_setzero_u64() { return VXPREFIX(_setzero_u64)(); }
+#if CV_SIMD_64F
+    inline v_float64 vx_setzero_f64() { return VXPREFIX(_setzero_f64)(); }
+#endif
+    //! @}
+
+    //! @name Wide load from memory
+    //! @{
+    //! @brief Load maximum available capacity register contents from memory
+    inline v_uint8 vx_load(const uchar * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_int8 vx_load(const schar * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_uint16 vx_load(const ushort * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_int16 vx_load(const short * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_int32 vx_load(const int * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_uint32 vx_load(const unsigned * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_float32 vx_load(const float * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_int64 vx_load(const int64 * ptr) { return VXPREFIX(_load)(ptr); }
+    inline v_uint64 vx_load(const uint64 * ptr) { return VXPREFIX(_load)(ptr); }
+#if CV_SIMD_64F
+    inline v_float64 vx_load(const double * ptr) { return VXPREFIX(_load)(ptr); }
+#endif
+    //! @}
+
+    //! @name Wide load from memory(aligned)
+    //! @{
+    //! @brief Load maximum available capacity register contents from memory(aligned)
+    inline v_uint8 vx_load_aligned(const uchar * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_int8 vx_load_aligned(const schar * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_uint16 vx_load_aligned(const ushort * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_int16 vx_load_aligned(const short * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_int32 vx_load_aligned(const int * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_uint32 vx_load_aligned(const unsigned * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_float32 vx_load_aligned(const float * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_int64 vx_load_aligned(const int64 * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+    inline v_uint64 vx_load_aligned(const uint64 * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+#if CV_SIMD_64F
+    inline v_float64 vx_load_aligned(const double * ptr) { return VXPREFIX(_load_aligned)(ptr); }
+#endif
+    //! @}
+
+    //! @name Wide load lower half from memory
+    //! @{
+    //! @brief Load lower half of maximum available capacity register from memory
+    inline v_uint8 vx_load_low(const uchar * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_int8 vx_load_low(const schar * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_uint16 vx_load_low(const ushort * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_int16 vx_load_low(const short * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_int32 vx_load_low(const int * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_uint32 vx_load_low(const unsigned * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_float32 vx_load_low(const float * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_int64 vx_load_low(const int64 * ptr) { return VXPREFIX(_load_low)(ptr); }
+    inline v_uint64 vx_load_low(const uint64 * ptr) { return VXPREFIX(_load_low)(ptr); }
+#if CV_SIMD_64F
+    inline v_float64 vx_load_low(const double * ptr) { return VXPREFIX(_load_low)(ptr); }
+#endif
+    //! @}
+
+    //! @name Wide load halfs from memory
+    //! @{
+    //! @brief Load maximum available capacity register contents from two memory blocks
+    inline v_uint8 vx_load_halves(const uchar * ptr0, const uchar * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_int8 vx_load_halves(const schar * ptr0, const schar * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_uint16 vx_load_halves(const ushort * ptr0, const ushort * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_int16 vx_load_halves(const short * ptr0, const short * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_int32 vx_load_halves(const int * ptr0, const int * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_uint32 vx_load_halves(const unsigned * ptr0, const unsigned * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_float32 vx_load_halves(const float * ptr0, const float * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_int64 vx_load_halves(const int64 * ptr0, const int64 * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+    inline v_uint64 vx_load_halves(const uint64 * ptr0, const uint64 * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+#if CV_SIMD_64F
+    inline v_float64 vx_load_halves(const double * ptr0, const double * ptr1) { return VXPREFIX(_load_halves)(ptr0, ptr1); }
+#endif
+    //! @}
+
+    //! @name Wide LUT of elements
+    //! @{
+    //! @brief Load maximum available capacity register contents with array elements by provided indexes
+    inline v_uint8 vx_lut(const uchar * ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_int8 vx_lut(const schar * ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_uint16 vx_lut(const ushort * ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_int16 vx_lut(const short* ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_int32 vx_lut(const int* ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_uint32 vx_lut(const unsigned* ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_float32 vx_lut(const float* ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_int64 vx_lut(const int64 * ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+    inline v_uint64 vx_lut(const uint64 * ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+#if CV_SIMD_64F
+    inline v_float64 vx_lut(const double* ptr, const int* idx) { return VXPREFIX(_lut)(ptr, idx); }
+#endif
+    //! @}
+
+    //! @name Wide LUT of element pairs
+    //! @{
+    //! @brief Load maximum available capacity register contents with array element pairs by provided indexes
+    inline v_uint8 vx_lut_pairs(const uchar * ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_int8 vx_lut_pairs(const schar * ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_uint16 vx_lut_pairs(const ushort * ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_int16 vx_lut_pairs(const short* ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_int32 vx_lut_pairs(const int* ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_uint32 vx_lut_pairs(const unsigned* ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_float32 vx_lut_pairs(const float* ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_int64 vx_lut_pairs(const int64 * ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+    inline v_uint64 vx_lut_pairs(const uint64 * ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+#if CV_SIMD_64F
+    inline v_float64 vx_lut_pairs(const double* ptr, const int* idx) { return VXPREFIX(_lut_pairs)(ptr, idx); }
+#endif
+    //! @}
+
+    //! @name Wide LUT of element quads
+    //! @{
+    //! @brief Load maximum available capacity register contents with array element quads by provided indexes
+    inline v_uint8 vx_lut_quads(const uchar* ptr, const int* idx) { return VXPREFIX(_lut_quads)(ptr, idx); }
+    inline v_int8 vx_lut_quads(const schar* ptr, const int* idx) { return VXPREFIX(_lut_quads)(ptr, idx); }
+    inline v_uint16 vx_lut_quads(const ushort* ptr, const int* idx) { return VXPREFIX(_lut_quads)(ptr, idx); }
+    inline v_int16 vx_lut_quads(const short* ptr, const int* idx) { return VXPREFIX(_lut_quads)(ptr, idx); }
+    inline v_int32 vx_lut_quads(const int* ptr, const int* idx) { return VXPREFIX(_lut_quads)(ptr, idx); }
+    inline v_uint32 vx_lut_quads(const unsigned* ptr, const int* idx) { return VXPREFIX(_lut_quads)(ptr, idx); }
+    inline v_float32 vx_lut_quads(const float* ptr, const int* idx) { return VXPREFIX(_lut_quads)(ptr, idx); }
+    //! @}
+
+    //! @name Wide load with double expansion
+    //! @{
+    //! @brief Load maximum available capacity register contents from memory with double expand
+    inline v_uint16 vx_load_expand(const uchar * ptr) { return VXPREFIX(_load_expand)(ptr); }
+    inline v_int16 vx_load_expand(const schar * ptr) { return VXPREFIX(_load_expand)(ptr); }
+    inline v_uint32 vx_load_expand(const ushort * ptr) { return VXPREFIX(_load_expand)(ptr); }
+    inline v_int32 vx_load_expand(const short* ptr) { return VXPREFIX(_load_expand)(ptr); }
+    inline v_int64 vx_load_expand(const int* ptr) { return VXPREFIX(_load_expand)(ptr); }
+    inline v_uint64 vx_load_expand(const unsigned* ptr) { return VXPREFIX(_load_expand)(ptr); }
+    inline v_float32 vx_load_expand(const float16_t * ptr) { return VXPREFIX(_load_expand)(ptr); }
+    //! @}
+
+    //! @name Wide load with quad expansion
+    //! @{
+    //! @brief Load maximum available capacity register contents from memory with quad expand
+    inline v_uint32 vx_load_expand_q(const uchar * ptr) { return VXPREFIX(_load_expand_q)(ptr); }
+    inline v_int32 vx_load_expand_q(const schar * ptr) { return VXPREFIX(_load_expand_q)(ptr); }
+    //! @}
+
+    /** @brief SIMD processing state cleanup call */
+    inline void vx_cleanup() { VXPREFIX(_cleanup)(); }
+
+
+//! @cond IGNORED
+
+    // backward compatibility
+    template<typename _Tp, typename _Tvec> static inline
+    void vx_store(_Tp* dst, const _Tvec& v) { return v_store(dst, v); }
+    // backward compatibility
+    template<typename _Tp, typename _Tvec> static inline
+    void vx_store_aligned(_Tp* dst, const _Tvec& v) { return v_store_aligned(dst, v); }
+
+//! @endcond
+
+
+//! @}
+    #undef VXPREFIX
+} // namespace
+
+//! @cond IGNORED
+#ifndef CV_SIMD_64F
+#define CV_SIMD_64F 0
+#endif
+
+#ifndef CV_SIMD_FP16
+#define CV_SIMD_FP16 0  //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types
+#endif
+
+#ifndef CV_SIMD
+#define CV_SIMD 0
+#endif
+
+#include "simd_utils.impl.hpp"
+
+#ifndef CV_DOXYGEN
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+#endif
+
+} // cv::
+
+//! @endcond
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin_avx.hpp b/3rdParty/include/opencv2/core/hal/intrin_avx.hpp
new file mode 100644
index 0000000..979b616
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_avx.hpp
@@ -0,0 +1,3177 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+#ifndef OPENCV_HAL_INTRIN_AVX_HPP
+#define OPENCV_HAL_INTRIN_AVX_HPP
+
+#define CV_SIMD256 1
+#define CV_SIMD256_64F 1
+#define CV_SIMD256_FP16 0  // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1)
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+///////// Utils ////////////
+
+inline __m256i _v256_combine(const __m128i& lo, const __m128i& hi)
+{ return _mm256_inserti128_si256(_mm256_castsi128_si256(lo), hi, 1); }
+
+inline __m256 _v256_combine(const __m128& lo, const __m128& hi)
+{ return _mm256_insertf128_ps(_mm256_castps128_ps256(lo), hi, 1); }
+
+inline __m256d _v256_combine(const __m128d& lo, const __m128d& hi)
+{ return _mm256_insertf128_pd(_mm256_castpd128_pd256(lo), hi, 1); }
+
+inline int _v_cvtsi256_si32(const __m256i& a)
+{ return _mm_cvtsi128_si32(_mm256_castsi256_si128(a)); }
+
+inline __m256i _v256_shuffle_odd_64(const __m256i& v)
+{ return _mm256_permute4x64_epi64(v, _MM_SHUFFLE(3, 1, 2, 0)); }
+
+inline __m256d _v256_shuffle_odd_64(const __m256d& v)
+{ return _mm256_permute4x64_pd(v, _MM_SHUFFLE(3, 1, 2, 0)); }
+
+template<int imm>
+inline __m256i _v256_permute2x128(const __m256i& a, const __m256i& b)
+{ return _mm256_permute2x128_si256(a, b, imm); }
+
+template<int imm>
+inline __m256 _v256_permute2x128(const __m256& a, const __m256& b)
+{ return _mm256_permute2f128_ps(a, b, imm); }
+
+template<int imm>
+inline __m256d _v256_permute2x128(const __m256d& a, const __m256d& b)
+{ return _mm256_permute2f128_pd(a, b, imm); }
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v256_permute2x128(const _Tpvec& a, const _Tpvec& b)
+{ return _Tpvec(_v256_permute2x128<imm>(a.val, b.val)); }
+
+template<int imm>
+inline __m256i _v256_permute4x64(const __m256i& a)
+{ return _mm256_permute4x64_epi64(a, imm); }
+
+template<int imm>
+inline __m256d _v256_permute4x64(const __m256d& a)
+{ return _mm256_permute4x64_pd(a, imm); }
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v256_permute4x64(const _Tpvec& a)
+{ return _Tpvec(_v256_permute4x64<imm>(a.val)); }
+
+inline __m128i _v256_extract_high(const __m256i& v)
+{ return _mm256_extracti128_si256(v, 1); }
+
+inline __m128  _v256_extract_high(const __m256& v)
+{ return _mm256_extractf128_ps(v, 1); }
+
+inline __m128d _v256_extract_high(const __m256d& v)
+{ return _mm256_extractf128_pd(v, 1); }
+
+inline __m128i _v256_extract_low(const __m256i& v)
+{ return _mm256_castsi256_si128(v); }
+
+inline __m128  _v256_extract_low(const __m256& v)
+{ return _mm256_castps256_ps128(v); }
+
+inline __m128d _v256_extract_low(const __m256d& v)
+{ return _mm256_castpd256_pd128(v); }
+
+inline __m256i _v256_packs_epu32(const __m256i& a, const __m256i& b)
+{
+    const __m256i m = _mm256_set1_epi32(65535);
+    __m256i am = _mm256_min_epu32(a, m);
+    __m256i bm = _mm256_min_epu32(b, m);
+    return _mm256_packus_epi32(am, bm);
+}
+
+template<int i>
+inline int _v256_extract_epi8(const __m256i& a)
+{
+#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/))
+    return _mm256_extract_epi8(a, i);
+#else
+    __m128i b = _mm256_extractf128_si256(a, ((i) >> 4));
+    return _mm_extract_epi8(b, i & 15);  // SSE4.1
+#endif
+}
+
+template<int i>
+inline int _v256_extract_epi16(const __m256i& a)
+{
+#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/))
+    return _mm256_extract_epi16(a, i);
+#else
+    __m128i b = _mm256_extractf128_si256(a, ((i) >> 3));
+    return _mm_extract_epi16(b, i & 7);  // SSE2
+#endif
+}
+
+template<int i>
+inline int _v256_extract_epi32(const __m256i& a)
+{
+#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/))
+    return _mm256_extract_epi32(a, i);
+#else
+    __m128i b = _mm256_extractf128_si256(a, ((i) >> 2));
+    return _mm_extract_epi32(b, i & 3);  // SSE4.1
+#endif
+}
+
+template<int i>
+inline int64 _v256_extract_epi64(const __m256i& a)
+{
+#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/))
+    return _mm256_extract_epi64(a, i);
+#else
+    __m128i b = _mm256_extractf128_si256(a, ((i) >> 1));
+    return _mm_extract_epi64(b, i & 1);  // SSE4.1
+#endif
+}
+
+///////// Types ////////////
+
+struct v_uint8x32
+{
+    typedef uchar lane_type;
+    enum { nlanes = 32 };
+    __m256i val;
+
+    explicit v_uint8x32(__m256i v) : val(v) {}
+    v_uint8x32(uchar v0,  uchar v1,  uchar v2,  uchar v3,
+               uchar v4,  uchar v5,  uchar v6,  uchar v7,
+               uchar v8,  uchar v9,  uchar v10, uchar v11,
+               uchar v12, uchar v13, uchar v14, uchar v15,
+               uchar v16, uchar v17, uchar v18, uchar v19,
+               uchar v20, uchar v21, uchar v22, uchar v23,
+               uchar v24, uchar v25, uchar v26, uchar v27,
+               uchar v28, uchar v29, uchar v30, uchar v31)
+    {
+        val = _mm256_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3,
+            (char)v4,  (char)v5,  (char)v6 , (char)v7,  (char)v8,  (char)v9,
+            (char)v10, (char)v11, (char)v12, (char)v13, (char)v14, (char)v15,
+            (char)v16, (char)v17, (char)v18, (char)v19, (char)v20, (char)v21,
+            (char)v22, (char)v23, (char)v24, (char)v25, (char)v26, (char)v27,
+            (char)v28, (char)v29, (char)v30, (char)v31);
+    }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint8x32() {}
+
+    uchar get0() const { return (uchar)_v_cvtsi256_si32(val); }
+};
+
+struct v_int8x32
+{
+    typedef schar lane_type;
+    enum { nlanes = 32 };
+    __m256i val;
+
+    explicit v_int8x32(__m256i v) : val(v) {}
+    v_int8x32(schar v0,  schar v1,  schar v2,  schar v3,
+              schar v4,  schar v5,  schar v6,  schar v7,
+              schar v8,  schar v9,  schar v10, schar v11,
+              schar v12, schar v13, schar v14, schar v15,
+              schar v16, schar v17, schar v18, schar v19,
+              schar v20, schar v21, schar v22, schar v23,
+              schar v24, schar v25, schar v26, schar v27,
+              schar v28, schar v29, schar v30, schar v31)
+    {
+        val = _mm256_setr_epi8(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9,
+            v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20,
+            v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31);
+    }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int8x32() {}
+
+    schar get0() const { return (schar)_v_cvtsi256_si32(val); }
+};
+
+struct v_uint16x16
+{
+    typedef ushort lane_type;
+    enum { nlanes = 16 };
+    __m256i val;
+
+    explicit v_uint16x16(__m256i v) : val(v) {}
+    v_uint16x16(ushort v0,  ushort v1,  ushort v2,  ushort v3,
+                ushort v4,  ushort v5,  ushort v6,  ushort v7,
+                ushort v8,  ushort v9,  ushort v10, ushort v11,
+                ushort v12, ushort v13, ushort v14, ushort v15)
+    {
+        val = _mm256_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3,
+            (short)v4,  (short)v5,  (short)v6,  (short)v7,  (short)v8,  (short)v9,
+            (short)v10, (short)v11, (short)v12, (short)v13, (short)v14, (short)v15);
+    }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint16x16() {}
+
+    ushort get0() const { return (ushort)_v_cvtsi256_si32(val); }
+};
+
+struct v_int16x16
+{
+    typedef short lane_type;
+    enum { nlanes = 16 };
+    __m256i val;
+
+    explicit v_int16x16(__m256i v) : val(v) {}
+    v_int16x16(short v0,  short v1,  short v2,  short v3,
+               short v4,  short v5,  short v6,  short v7,
+               short v8,  short v9,  short v10, short v11,
+               short v12, short v13, short v14, short v15)
+    {
+        val = _mm256_setr_epi16(v0, v1, v2, v3, v4, v5, v6, v7,
+            v8, v9, v10, v11, v12, v13, v14, v15);
+    }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int16x16() {}
+
+    short get0() const { return (short)_v_cvtsi256_si32(val); }
+};
+
+struct v_uint32x8
+{
+    typedef unsigned lane_type;
+    enum { nlanes = 8 };
+    __m256i val;
+
+    explicit v_uint32x8(__m256i v) : val(v) {}
+    v_uint32x8(unsigned v0, unsigned v1, unsigned v2, unsigned v3,
+               unsigned v4, unsigned v5, unsigned v6, unsigned v7)
+    {
+        val = _mm256_setr_epi32((unsigned)v0, (unsigned)v1, (unsigned)v2,
+            (unsigned)v3, (unsigned)v4, (unsigned)v5, (unsigned)v6, (unsigned)v7);
+    }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint32x8() {}
+
+    unsigned get0() const { return (unsigned)_v_cvtsi256_si32(val); }
+};
+
+struct v_int32x8
+{
+    typedef int lane_type;
+    enum { nlanes = 8 };
+    __m256i val;
+
+    explicit v_int32x8(__m256i v) : val(v) {}
+    v_int32x8(int v0, int v1, int v2, int v3,
+              int v4, int v5, int v6, int v7)
+    {
+        val = _mm256_setr_epi32(v0, v1, v2, v3, v4, v5, v6, v7);
+    }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int32x8() {}
+
+    int get0() const { return _v_cvtsi256_si32(val); }
+};
+
+struct v_float32x8
+{
+    typedef float lane_type;
+    enum { nlanes = 8 };
+    __m256 val;
+
+    explicit v_float32x8(__m256 v) : val(v) {}
+    v_float32x8(float v0, float v1, float v2, float v3,
+                float v4, float v5, float v6, float v7)
+    {
+        val = _mm256_setr_ps(v0, v1, v2, v3, v4, v5, v6, v7);
+    }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_float32x8() {}
+
+    float get0() const { return _mm_cvtss_f32(_mm256_castps256_ps128(val)); }
+};
+
+struct v_uint64x4
+{
+    typedef uint64 lane_type;
+    enum { nlanes = 4 };
+    __m256i val;
+
+    explicit v_uint64x4(__m256i v) : val(v) {}
+    v_uint64x4(uint64 v0, uint64 v1, uint64 v2, uint64 v3)
+    { val = _mm256_setr_epi64x((int64)v0, (int64)v1, (int64)v2, (int64)v3); }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint64x4() {}
+
+    uint64 get0() const
+    {
+    #if defined __x86_64__ || defined _M_X64
+        return (uint64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val));
+    #else
+        int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val));
+        int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32)));
+        return (unsigned)a | ((uint64)(unsigned)b << 32);
+    #endif
+    }
+};
+
+struct v_int64x4
+{
+    typedef int64 lane_type;
+    enum { nlanes = 4 };
+    __m256i val;
+
+    explicit v_int64x4(__m256i v) : val(v) {}
+    v_int64x4(int64 v0, int64 v1, int64 v2, int64 v3)
+    { val = _mm256_setr_epi64x(v0, v1, v2, v3); }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int64x4() {}
+
+    int64 get0() const
+    {
+    #if defined __x86_64__ || defined _M_X64
+        return (int64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val));
+    #else
+        int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val));
+        int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32)));
+        return (int64)((unsigned)a | ((uint64)(unsigned)b << 32));
+    #endif
+    }
+};
+
+struct v_float64x4
+{
+    typedef double lane_type;
+    enum { nlanes = 4 };
+    __m256d val;
+
+    explicit v_float64x4(__m256d v) : val(v) {}
+    v_float64x4(double v0, double v1, double v2, double v3)
+    { val = _mm256_setr_pd(v0, v1, v2, v3); }
+    /* coverity[uninit_ctor]: suppress warning */
+    v_float64x4() {}
+
+    double get0() const { return _mm_cvtsd_f64(_mm256_castpd256_pd128(val)); }
+};
+
+//////////////// Load and store operations ///////////////
+
+#define OPENCV_HAL_IMPL_AVX_LOADSTORE(_Tpvec, _Tp)                    \
+    inline _Tpvec v256_load(const _Tp* ptr)                           \
+    { return _Tpvec(_mm256_loadu_si256((const __m256i*)ptr)); }       \
+    inline _Tpvec v256_load_aligned(const _Tp* ptr)                   \
+    { return _Tpvec(_mm256_load_si256((const __m256i*)ptr)); }        \
+    inline _Tpvec v256_load_low(const _Tp* ptr)                       \
+    {                                                                 \
+        __m128i v128 = _mm_loadu_si128((const __m128i*)ptr);          \
+        return _Tpvec(_mm256_castsi128_si256(v128));                  \
+    }                                                                 \
+    inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1)  \
+    {                                                                 \
+        __m128i vlo = _mm_loadu_si128((const __m128i*)ptr0);          \
+        __m128i vhi = _mm_loadu_si128((const __m128i*)ptr1);          \
+        return _Tpvec(_v256_combine(vlo, vhi));                       \
+    }                                                                 \
+    inline void v_store(_Tp* ptr, const _Tpvec& a)                    \
+    { _mm256_storeu_si256((__m256i*)ptr, a.val); }                    \
+    inline void v_store_aligned(_Tp* ptr, const _Tpvec& a)            \
+    { _mm256_store_si256((__m256i*)ptr, a.val); }                     \
+    inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a)    \
+    { _mm256_stream_si256((__m256i*)ptr, a.val); }                    \
+    inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \
+    { \
+        if( mode == hal::STORE_UNALIGNED ) \
+            _mm256_storeu_si256((__m256i*)ptr, a.val); \
+        else if( mode == hal::STORE_ALIGNED_NOCACHE )  \
+            _mm256_stream_si256((__m256i*)ptr, a.val); \
+        else \
+            _mm256_store_si256((__m256i*)ptr, a.val); \
+    } \
+    inline void v_store_low(_Tp* ptr, const _Tpvec& a)                \
+    { _mm_storeu_si128((__m128i*)ptr, _v256_extract_low(a.val)); }    \
+    inline void v_store_high(_Tp* ptr, const _Tpvec& a)               \
+    { _mm_storeu_si128((__m128i*)ptr, _v256_extract_high(a.val)); }
+
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint8x32,  uchar)
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int8x32,   schar)
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint16x16, ushort)
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int16x16,  short)
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint32x8,  unsigned)
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int32x8,   int)
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint64x4,  uint64)
+OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int64x4,   int64)
+
+#define OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(_Tpvec, _Tp, suffix, halfreg)   \
+    inline _Tpvec v256_load(const _Tp* ptr)                               \
+    { return _Tpvec(_mm256_loadu_##suffix(ptr)); }                        \
+    inline _Tpvec v256_load_aligned(const _Tp* ptr)                       \
+    { return _Tpvec(_mm256_load_##suffix(ptr)); }                         \
+    inline _Tpvec v256_load_low(const _Tp* ptr)                           \
+    {                                                                     \
+        return _Tpvec(_mm256_cast##suffix##128_##suffix##256              \
+                     (_mm_loadu_##suffix(ptr)));                          \
+    }                                                                     \
+    inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1)      \
+    {                                                                     \
+        halfreg vlo = _mm_loadu_##suffix(ptr0);                           \
+        halfreg vhi = _mm_loadu_##suffix(ptr1);                           \
+        return _Tpvec(_v256_combine(vlo, vhi));                           \
+    }                                                                     \
+    inline void v_store(_Tp* ptr, const _Tpvec& a)                        \
+    { _mm256_storeu_##suffix(ptr, a.val); }                               \
+    inline void v_store_aligned(_Tp* ptr, const _Tpvec& a)                \
+    { _mm256_store_##suffix(ptr, a.val); }                                \
+    inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a)        \
+    { _mm256_stream_##suffix(ptr, a.val); }                               \
+    inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \
+    { \
+        if( mode == hal::STORE_UNALIGNED ) \
+            _mm256_storeu_##suffix(ptr, a.val); \
+        else if( mode == hal::STORE_ALIGNED_NOCACHE )  \
+            _mm256_stream_##suffix(ptr, a.val); \
+        else \
+            _mm256_store_##suffix(ptr, a.val); \
+    } \
+    inline void v_store_low(_Tp* ptr, const _Tpvec& a)                    \
+    { _mm_storeu_##suffix(ptr, _v256_extract_low(a.val)); }               \
+    inline void v_store_high(_Tp* ptr, const _Tpvec& a)                   \
+    { _mm_storeu_##suffix(ptr, _v256_extract_high(a.val)); }
+
+OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float32x8, float,  ps, __m128)
+OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float64x4, double, pd, __m128d)
+
+#define OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, _Tpvecf, suffix, cast) \
+    inline _Tpvec v_reinterpret_as_##suffix(const _Tpvecf& a)   \
+    { return _Tpvec(cast(a.val)); }
+
+#define OPENCV_HAL_IMPL_AVX_INIT(_Tpvec, _Tp, suffix, ssuffix, ctype_s)          \
+    inline _Tpvec v256_setzero_##suffix()                                        \
+    { return _Tpvec(_mm256_setzero_si256()); }                                   \
+    inline _Tpvec v256_setall_##suffix(_Tp v)                                    \
+    { return _Tpvec(_mm256_set1_##ssuffix((ctype_s)v)); }                        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32,  suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32,   suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16,  suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8,  suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8,   suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4,  suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4,   suffix, OPENCV_HAL_NOP)        \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float32x8, suffix, _mm256_castps_si256)   \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float64x4, suffix, _mm256_castpd_si256)
+
+OPENCV_HAL_IMPL_AVX_INIT(v_uint8x32,  uchar,    u8,  epi8,   char)
+OPENCV_HAL_IMPL_AVX_INIT(v_int8x32,   schar,    s8,  epi8,   char)
+OPENCV_HAL_IMPL_AVX_INIT(v_uint16x16, ushort,   u16, epi16,  short)
+OPENCV_HAL_IMPL_AVX_INIT(v_int16x16,  short,    s16, epi16,  short)
+OPENCV_HAL_IMPL_AVX_INIT(v_uint32x8,  unsigned, u32, epi32,  int)
+OPENCV_HAL_IMPL_AVX_INIT(v_int32x8,   int,      s32, epi32,  int)
+OPENCV_HAL_IMPL_AVX_INIT(v_uint64x4,  uint64,   u64, epi64x, int64)
+OPENCV_HAL_IMPL_AVX_INIT(v_int64x4,   int64,    s64, epi64x, int64)
+
+#define OPENCV_HAL_IMPL_AVX_INIT_FLT(_Tpvec, _Tp, suffix, zsuffix, cast) \
+    inline _Tpvec v256_setzero_##suffix()                                \
+    { return _Tpvec(_mm256_setzero_##zsuffix()); }                       \
+    inline _Tpvec v256_setall_##suffix(_Tp v)                            \
+    { return _Tpvec(_mm256_set1_##zsuffix(v)); }                         \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32,   suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8,   suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4,   suffix, cast)
+
+OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float32x8, float,  f32, ps, _mm256_castsi256_ps)
+OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float64x4, double, f64, pd, _mm256_castsi256_pd)
+
+inline v_float32x8 v_reinterpret_as_f32(const v_float32x8& a)
+{ return a; }
+inline v_float32x8 v_reinterpret_as_f32(const v_float64x4& a)
+{ return v_float32x8(_mm256_castpd_ps(a.val)); }
+
+inline v_float64x4 v_reinterpret_as_f64(const v_float64x4& a)
+{ return a; }
+inline v_float64x4 v_reinterpret_as_f64(const v_float32x8& a)
+{ return v_float64x4(_mm256_castps_pd(a.val)); }
+
+/* Recombine */
+/*#define OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, perm)                    \
+    inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b)    \
+    { return _Tpvec(perm(a.val, b.val, 0x20)); }                     \
+    inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b)   \
+    { return _Tpvec(perm(a.val, b.val, 0x31)); }                     \
+    inline void v_recombine(const _Tpvec& a, const _Tpvec& b,        \
+                             _Tpvec& c, _Tpvec& d)                   \
+    { c = v_combine_low(a, b); d = v_combine_high(a, b); }
+
+#define OPENCV_HAL_IMPL_AVX_UNPACKS(_Tpvec, suffix)                  \
+    OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, _mm256_permute2x128_si256)   \
+    inline void v_zip(const _Tpvec& a0, const _Tpvec& a1,            \
+                             _Tpvec& b0, _Tpvec& b1)                 \
+    {                                                                \
+        __m256i v0 = _v256_shuffle_odd_64(a0.val);                   \
+        __m256i v1 = _v256_shuffle_odd_64(a1.val);                   \
+        b0.val = _mm256_unpacklo_##suffix(v0, v1);                   \
+        b1.val = _mm256_unpackhi_##suffix(v0, v1);                   \
+    }
+
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint8x32,  epi8)
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_int8x32,   epi8)
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint16x16, epi16)
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_int16x16,  epi16)
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint32x8,  epi32)
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_int32x8,   epi32)
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint64x4,  epi64)
+OPENCV_HAL_IMPL_AVX_UNPACKS(v_int64x4,   epi64)
+OPENCV_HAL_IMPL_AVX_COMBINE(v_float32x8, _mm256_permute2f128_ps)
+OPENCV_HAL_IMPL_AVX_COMBINE(v_float64x4, _mm256_permute2f128_pd)
+
+inline void v_zip(const v_float32x8& a0, const v_float32x8& a1, v_float32x8& b0, v_float32x8& b1)
+{
+    __m256 v0 = _mm256_unpacklo_ps(a0.val, a1.val);
+    __m256 v1 = _mm256_unpackhi_ps(a0.val, a1.val);
+    v_recombine(v_float32x8(v0), v_float32x8(v1), b0, b1);
+}
+
+inline void v_zip(const v_float64x4& a0, const v_float64x4& a1, v_float64x4& b0, v_float64x4& b1)
+{
+    __m256d v0 = _v_shuffle_odd_64(a0.val);
+    __m256d v1 = _v_shuffle_odd_64(a1.val);
+    b0.val = _mm256_unpacklo_pd(v0, v1);
+    b1.val = _mm256_unpackhi_pd(v0, v1);
+}*/
+
+//////////////// Variant Value reordering ///////////////
+
+// unpacks
+#define OPENCV_HAL_IMPL_AVX_UNPACK(_Tpvec, suffix)                 \
+    inline _Tpvec v256_unpacklo(const _Tpvec& a, const _Tpvec& b)  \
+    { return _Tpvec(_mm256_unpacklo_##suffix(a.val, b.val)); }     \
+    inline _Tpvec v256_unpackhi(const _Tpvec& a, const _Tpvec& b)  \
+    { return _Tpvec(_mm256_unpackhi_##suffix(a.val, b.val)); }
+
+OPENCV_HAL_IMPL_AVX_UNPACK(v_uint8x32,  epi8)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_int8x32,   epi8)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_uint16x16, epi16)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_int16x16,  epi16)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_uint32x8,  epi32)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_int32x8,   epi32)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_uint64x4,  epi64)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_int64x4,   epi64)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_float32x8, ps)
+OPENCV_HAL_IMPL_AVX_UNPACK(v_float64x4, pd)
+
+// blend
+#define OPENCV_HAL_IMPL_AVX_BLEND(_Tpvec, suffix)               \
+    template<int m>                                             \
+    inline _Tpvec v256_blend(const _Tpvec& a, const _Tpvec& b)  \
+    { return _Tpvec(_mm256_blend_##suffix(a.val, b.val, m)); }
+
+OPENCV_HAL_IMPL_AVX_BLEND(v_uint16x16, epi16)
+OPENCV_HAL_IMPL_AVX_BLEND(v_int16x16,  epi16)
+OPENCV_HAL_IMPL_AVX_BLEND(v_uint32x8,  epi32)
+OPENCV_HAL_IMPL_AVX_BLEND(v_int32x8,   epi32)
+OPENCV_HAL_IMPL_AVX_BLEND(v_float32x8, ps)
+OPENCV_HAL_IMPL_AVX_BLEND(v_float64x4, pd)
+
+template<int m>
+inline v_uint64x4 v256_blend(const v_uint64x4& a, const v_uint64x4& b)
+{
+    enum {M0 = m};
+    enum {M1 = (M0 | (M0 << 2)) & 0x33};
+    enum {M2 = (M1 | (M1 << 1)) & 0x55};
+    enum {MM =  M2 | (M2 << 1)};
+    return v_uint64x4(_mm256_blend_epi32(a.val, b.val, MM));
+}
+template<int m>
+inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b)
+{ return v_int64x4(v256_blend<m>(v_uint64x4(a.val), v_uint64x4(b.val)).val); }
+
+// shuffle
+// todo: emulate 64bit
+#define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin)  \
+    template<int m>                                  \
+    inline _Tpvec v256_shuffle(const _Tpvec& a)      \
+    { return _Tpvec(_mm256_##intrin(a.val, m)); }
+
+OPENCV_HAL_IMPL_AVX_SHUFFLE(v_uint32x8,  shuffle_epi32)
+OPENCV_HAL_IMPL_AVX_SHUFFLE(v_int32x8,   shuffle_epi32)
+OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float32x8, permute_ps)
+OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float64x4, permute_pd)
+
+template<typename _Tpvec>
+inline void v256_zip(const _Tpvec& a, const _Tpvec& b, _Tpvec& ab0, _Tpvec& ab1)
+{
+    ab0 = v256_unpacklo(a, b);
+    ab1 = v256_unpackhi(a, b);
+}
+
+template<typename _Tpvec>
+inline _Tpvec v256_combine_diagonal(const _Tpvec& a, const _Tpvec& b)
+{ return _Tpvec(_mm256_blend_epi32(a.val, b.val, 0xf0)); }
+
+inline v_float32x8 v256_combine_diagonal(const v_float32x8& a, const v_float32x8& b)
+{ return v256_blend<0xf0>(a, b); }
+
+inline v_float64x4 v256_combine_diagonal(const v_float64x4& a, const v_float64x4& b)
+{ return v256_blend<0xc>(a, b); }
+
+template<typename _Tpvec>
+inline _Tpvec v256_alignr_128(const _Tpvec& a, const _Tpvec& b)
+{ return v256_permute2x128<0x21>(a, b); }
+
+template<typename _Tpvec>
+inline _Tpvec v256_alignr_64(const _Tpvec& a, const _Tpvec& b)
+{ return _Tpvec(_mm256_alignr_epi8(a.val, b.val, 8)); }
+inline v_float64x4 v256_alignr_64(const v_float64x4& a, const v_float64x4& b)
+{ return v_float64x4(_mm256_shuffle_pd(b.val, a.val, _MM_SHUFFLE(0, 0, 1, 1))); }
+// todo: emulate float32
+
+template<typename _Tpvec>
+inline _Tpvec v256_swap_halves(const _Tpvec& a)
+{ return v256_permute2x128<1>(a, a); }
+
+template<typename _Tpvec>
+inline _Tpvec v256_reverse_64(const _Tpvec& a)
+{ return v256_permute4x64<_MM_SHUFFLE(0, 1, 2, 3)>(a); }
+
+// ZIP
+#define OPENCV_HAL_IMPL_AVX_ZIP(_Tpvec)                              \
+    inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b)    \
+    { return v256_permute2x128<0x20>(a, b); }                        \
+    inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b)   \
+    { return v256_permute2x128<0x31>(a, b); }                        \
+    inline void v_recombine(const _Tpvec& a, const _Tpvec& b,        \
+                             _Tpvec& c, _Tpvec& d)                   \
+    {                                                                \
+        _Tpvec a1b0 = v256_alignr_128(a, b);                         \
+        c = v256_combine_diagonal(a, a1b0);                          \
+        d = v256_combine_diagonal(a1b0, b);                          \
+    }                                                                \
+    inline void v_zip(const _Tpvec& a, const _Tpvec& b,              \
+                      _Tpvec& ab0, _Tpvec& ab1)                      \
+    {                                                                \
+        _Tpvec ab0ab2, ab1ab3;                                       \
+        v256_zip(a, b, ab0ab2, ab1ab3);                              \
+        v_recombine(ab0ab2, ab1ab3, ab0, ab1);                       \
+    }
+
+OPENCV_HAL_IMPL_AVX_ZIP(v_uint8x32)
+OPENCV_HAL_IMPL_AVX_ZIP(v_int8x32)
+OPENCV_HAL_IMPL_AVX_ZIP(v_uint16x16)
+OPENCV_HAL_IMPL_AVX_ZIP(v_int16x16)
+OPENCV_HAL_IMPL_AVX_ZIP(v_uint32x8)
+OPENCV_HAL_IMPL_AVX_ZIP(v_int32x8)
+OPENCV_HAL_IMPL_AVX_ZIP(v_uint64x4)
+OPENCV_HAL_IMPL_AVX_ZIP(v_int64x4)
+OPENCV_HAL_IMPL_AVX_ZIP(v_float32x8)
+OPENCV_HAL_IMPL_AVX_ZIP(v_float64x4)
+
+////////// Arithmetic, bitwise and comparison operations /////////
+
+/* Element-wise binary and unary operations */
+
+/** Arithmetics **/
+#define OPENCV_HAL_IMPL_AVX_BIN_OP(bin_op, _Tpvec, intrin)            \
+    inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b)  \
+    { return _Tpvec(intrin(a.val, b.val)); }                          \
+    inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b)    \
+    { a.val = intrin(a.val, b.val); return a; }
+
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint8x32,  _mm256_adds_epu8)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint8x32,  _mm256_subs_epu8)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int8x32,   _mm256_adds_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int8x32,   _mm256_subs_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint16x16, _mm256_adds_epu16)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint16x16, _mm256_subs_epu16)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int16x16,  _mm256_adds_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int16x16,  _mm256_subs_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint32x8,  _mm256_add_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint32x8,  _mm256_sub_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_uint32x8,  _mm256_mullo_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int32x8,   _mm256_add_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int32x8,   _mm256_sub_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_int32x8,   _mm256_mullo_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint64x4,  _mm256_add_epi64)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint64x4,  _mm256_sub_epi64)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int64x4,   _mm256_add_epi64)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int64x4,   _mm256_sub_epi64)
+
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float32x8, _mm256_add_ps)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float32x8, _mm256_sub_ps)
+OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float32x8, _mm256_mul_ps)
+OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float32x8, _mm256_div_ps)
+OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float64x4, _mm256_add_pd)
+OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float64x4, _mm256_sub_pd)
+OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float64x4, _mm256_mul_pd)
+OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float64x4, _mm256_div_pd)
+
+// saturating multiply 8-bit, 16-bit
+inline v_uint8x32 operator * (const v_uint8x32& a, const v_uint8x32& b)
+{
+    v_uint16x16 c, d;
+    v_mul_expand(a, b, c, d);
+    return v_pack(c, d);
+}
+inline v_int8x32 operator * (const v_int8x32& a, const v_int8x32& b)
+{
+    v_int16x16 c, d;
+    v_mul_expand(a, b, c, d);
+    return v_pack(c, d);
+}
+inline v_uint16x16 operator * (const v_uint16x16& a, const v_uint16x16& b)
+{
+    __m256i pl = _mm256_mullo_epi16(a.val, b.val);
+    __m256i ph = _mm256_mulhi_epu16(a.val, b.val);
+    __m256i p0 = _mm256_unpacklo_epi16(pl, ph);
+    __m256i p1 = _mm256_unpackhi_epi16(pl, ph);
+    return v_uint16x16(_v256_packs_epu32(p0, p1));
+}
+inline v_int16x16 operator * (const v_int16x16& a, const v_int16x16& b)
+{
+    __m256i pl = _mm256_mullo_epi16(a.val, b.val);
+    __m256i ph = _mm256_mulhi_epi16(a.val, b.val);
+    __m256i p0 = _mm256_unpacklo_epi16(pl, ph);
+    __m256i p1 = _mm256_unpackhi_epi16(pl, ph);
+    return v_int16x16(_mm256_packs_epi32(p0, p1));
+}
+inline v_uint8x32& operator *= (v_uint8x32& a, const v_uint8x32& b)
+{ a = a * b; return a; }
+inline v_int8x32& operator *= (v_int8x32& a, const v_int8x32& b)
+{ a = a * b; return a; }
+inline v_uint16x16& operator *= (v_uint16x16& a, const v_uint16x16& b)
+{ a = a * b; return a; }
+inline v_int16x16& operator *= (v_int16x16& a, const v_int16x16& b)
+{ a = a * b; return a; }
+
+/** Non-saturating arithmetics **/
+#define OPENCV_HAL_IMPL_AVX_BIN_FUNC(func, _Tpvec, intrin) \
+    inline _Tpvec func(const _Tpvec& a, const _Tpvec& b)   \
+    { return _Tpvec(intrin(a.val, b.val)); }
+
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint8x32,  _mm256_add_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int8x32,   _mm256_add_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint16x16, _mm256_add_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int16x16,  _mm256_add_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint8x32,  _mm256_sub_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int8x32,   _mm256_sub_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint16x16, _mm256_sub_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int16x16,  _mm256_sub_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_uint16x16, _mm256_mullo_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_int16x16,  _mm256_mullo_epi16)
+
+inline v_uint8x32 v_mul_wrap(const v_uint8x32& a, const v_uint8x32& b)
+{
+    __m256i ad = _mm256_srai_epi16(a.val, 8);
+    __m256i bd = _mm256_srai_epi16(b.val, 8);
+    __m256i p0 = _mm256_mullo_epi16(a.val, b.val); // even
+    __m256i p1 = _mm256_slli_epi16(_mm256_mullo_epi16(ad, bd), 8); // odd
+
+    const __m256i b01 = _mm256_set1_epi32(0xFF00FF00);
+    return v_uint8x32(_mm256_blendv_epi8(p0, p1, b01));
+}
+inline v_int8x32 v_mul_wrap(const v_int8x32& a, const v_int8x32& b)
+{
+    return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b)));
+}
+
+//  Multiply and expand
+inline void v_mul_expand(const v_uint8x32& a, const v_uint8x32& b,
+                         v_uint16x16& c, v_uint16x16& d)
+{
+    v_uint16x16 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int8x32& a, const v_int8x32& b,
+                         v_int16x16& c, v_int16x16& d)
+{
+    v_int16x16 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int16x16& a, const v_int16x16& b,
+                         v_int32x8& c, v_int32x8& d)
+{
+    v_int16x16 vhi = v_int16x16(_mm256_mulhi_epi16(a.val, b.val));
+
+    v_int16x16 v0, v1;
+    v_zip(v_mul_wrap(a, b), vhi, v0, v1);
+
+    c = v_reinterpret_as_s32(v0);
+    d = v_reinterpret_as_s32(v1);
+}
+
+inline void v_mul_expand(const v_uint16x16& a, const v_uint16x16& b,
+                         v_uint32x8& c, v_uint32x8& d)
+{
+    v_uint16x16 vhi = v_uint16x16(_mm256_mulhi_epu16(a.val, b.val));
+
+    v_uint16x16 v0, v1;
+    v_zip(v_mul_wrap(a, b), vhi, v0, v1);
+
+    c = v_reinterpret_as_u32(v0);
+    d = v_reinterpret_as_u32(v1);
+}
+
+inline void v_mul_expand(const v_uint32x8& a, const v_uint32x8& b,
+                         v_uint64x4& c, v_uint64x4& d)
+{
+    __m256i v0 = _mm256_mul_epu32(a.val, b.val);
+    __m256i v1 = _mm256_mul_epu32(_mm256_srli_epi64(a.val, 32), _mm256_srli_epi64(b.val, 32));
+    v_zip(v_uint64x4(v0), v_uint64x4(v1), c, d);
+}
+
+inline v_int16x16 v_mul_hi(const v_int16x16& a, const v_int16x16& b) { return v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); }
+inline v_uint16x16 v_mul_hi(const v_uint16x16& a, const v_uint16x16& b) { return v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); }
+
+/** Bitwise shifts **/
+#define OPENCV_HAL_IMPL_AVX_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai)  \
+    inline _Tpuvec operator << (const _Tpuvec& a, int imm)            \
+    { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); }             \
+    inline _Tpsvec operator << (const _Tpsvec& a, int imm)            \
+    { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); }             \
+    inline _Tpuvec operator >> (const _Tpuvec& a, int imm)            \
+    { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); }             \
+    inline _Tpsvec operator >> (const _Tpsvec& a, int imm)            \
+    { return _Tpsvec(srai(a.val, imm)); }                             \
+    template<int imm>                                                 \
+    inline _Tpuvec v_shl(const _Tpuvec& a)                            \
+    { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); }             \
+    template<int imm>                                                 \
+    inline _Tpsvec v_shl(const _Tpsvec& a)                            \
+    { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); }             \
+    template<int imm>                                                 \
+    inline _Tpuvec v_shr(const _Tpuvec& a)                            \
+    { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); }             \
+    template<int imm>                                                 \
+    inline _Tpsvec v_shr(const _Tpsvec& a)                            \
+    { return _Tpsvec(srai(a.val, imm)); }
+
+OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint16x16, v_int16x16, epi16, _mm256_srai_epi16)
+OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint32x8,  v_int32x8,  epi32, _mm256_srai_epi32)
+
+inline __m256i _mm256_srai_epi64xx(const __m256i a, int imm)
+{
+    __m256i d = _mm256_set1_epi64x((int64)1 << 63);
+    __m256i r = _mm256_srli_epi64(_mm256_add_epi64(a, d), imm);
+    return _mm256_sub_epi64(r, _mm256_srli_epi64(d, imm));
+}
+OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint64x4,  v_int64x4,  epi64, _mm256_srai_epi64xx)
+
+
+/** Bitwise logic **/
+#define OPENCV_HAL_IMPL_AVX_LOGIC_OP(_Tpvec, suffix, not_const)  \
+    OPENCV_HAL_IMPL_AVX_BIN_OP(&, _Tpvec, _mm256_and_##suffix)   \
+    OPENCV_HAL_IMPL_AVX_BIN_OP(|, _Tpvec, _mm256_or_##suffix)    \
+    OPENCV_HAL_IMPL_AVX_BIN_OP(^, _Tpvec, _mm256_xor_##suffix)   \
+    inline _Tpvec operator ~ (const _Tpvec& a)                   \
+    { return _Tpvec(_mm256_xor_##suffix(a.val, not_const)); }
+
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint8x32,   si256, _mm256_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int8x32,    si256, _mm256_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint16x16,  si256, _mm256_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int16x16,   si256, _mm256_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint32x8,   si256, _mm256_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int32x8,    si256, _mm256_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint64x4,   si256, _mm256_set1_epi64x(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int64x4,    si256, _mm256_set1_epi64x(-1))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float32x8,  ps,    _mm256_castsi256_ps(_mm256_set1_epi32(-1)))
+OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float64x4,  pd,    _mm256_castsi256_pd(_mm256_set1_epi32(-1)))
+
+/** Select **/
+#define OPENCV_HAL_IMPL_AVX_SELECT(_Tpvec, suffix)                               \
+    inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+    { return _Tpvec(_mm256_blendv_##suffix(b.val, a.val, mask.val)); }
+
+OPENCV_HAL_IMPL_AVX_SELECT(v_uint8x32,  epi8)
+OPENCV_HAL_IMPL_AVX_SELECT(v_int8x32,   epi8)
+OPENCV_HAL_IMPL_AVX_SELECT(v_uint16x16, epi8)
+OPENCV_HAL_IMPL_AVX_SELECT(v_int16x16,  epi8)
+OPENCV_HAL_IMPL_AVX_SELECT(v_uint32x8,  epi8)
+OPENCV_HAL_IMPL_AVX_SELECT(v_int32x8,   epi8)
+OPENCV_HAL_IMPL_AVX_SELECT(v_float32x8, ps)
+OPENCV_HAL_IMPL_AVX_SELECT(v_float64x4, pd)
+
+/** Comparison **/
+#define OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpvec)                     \
+    inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b)  \
+    { return ~(a == b); }                                         \
+    inline _Tpvec operator <  (const _Tpvec& a, const _Tpvec& b)  \
+    { return b > a; }                                             \
+    inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b)  \
+    { return ~(a < b); }                                          \
+    inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b)  \
+    { return b >= a; }
+
+#define OPENCV_HAL_IMPL_AVX_CMP_OP_INT(_Tpuvec, _Tpsvec, suffix, sbit)   \
+    inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b)      \
+    { return _Tpuvec(_mm256_cmpeq_##suffix(a.val, b.val)); }             \
+    inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b)       \
+    {                                                                    \
+        __m256i smask = _mm256_set1_##suffix(sbit);                      \
+        return _Tpuvec(_mm256_cmpgt_##suffix(                            \
+                       _mm256_xor_si256(a.val, smask),                   \
+                       _mm256_xor_si256(b.val, smask)));                 \
+    }                                                                    \
+    inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b)      \
+    { return _Tpsvec(_mm256_cmpeq_##suffix(a.val, b.val)); }             \
+    inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b)       \
+    { return _Tpsvec(_mm256_cmpgt_##suffix(a.val, b.val)); }             \
+    OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpuvec)                               \
+    OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpsvec)
+
+OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint8x32,  v_int8x32,  epi8,  (char)-128)
+OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint16x16, v_int16x16, epi16, (short)-32768)
+OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint32x8,  v_int32x8,  epi32, (int)0x80000000)
+
+#define OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(_Tpvec)                 \
+    inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+    { return _Tpvec(_mm256_cmpeq_epi64(a.val, b.val)); }         \
+    inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+    { return ~(a == b); }
+
+OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_uint64x4)
+OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_int64x4)
+
+#define OPENCV_HAL_IMPL_AVX_CMP_FLT(bin_op, imm8, _Tpvec, suffix)    \
+    inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+    { return _Tpvec(_mm256_cmp_##suffix(a.val, b.val, imm8)); }
+
+#define OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(_Tpvec, suffix)               \
+    OPENCV_HAL_IMPL_AVX_CMP_FLT(==, _CMP_EQ_OQ,  _Tpvec, suffix)     \
+    OPENCV_HAL_IMPL_AVX_CMP_FLT(!=, _CMP_NEQ_OQ, _Tpvec, suffix)     \
+    OPENCV_HAL_IMPL_AVX_CMP_FLT(<,  _CMP_LT_OQ,  _Tpvec, suffix)     \
+    OPENCV_HAL_IMPL_AVX_CMP_FLT(>,  _CMP_GT_OQ,  _Tpvec, suffix)     \
+    OPENCV_HAL_IMPL_AVX_CMP_FLT(<=, _CMP_LE_OQ,  _Tpvec, suffix)     \
+    OPENCV_HAL_IMPL_AVX_CMP_FLT(>=, _CMP_GE_OQ,  _Tpvec, suffix)
+
+OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float32x8, ps)
+OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float64x4, pd)
+
+inline v_float32x8 v_not_nan(const v_float32x8& a)
+{ return v_float32x8(_mm256_cmp_ps(a.val, a.val, _CMP_ORD_Q)); }
+inline v_float64x4 v_not_nan(const v_float64x4& a)
+{ return v_float64x4(_mm256_cmp_pd(a.val, a.val, _CMP_ORD_Q)); }
+
+/** min/max **/
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint8x32,  _mm256_min_epu8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint8x32,  _mm256_max_epu8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int8x32,   _mm256_min_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int8x32,   _mm256_max_epi8)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint16x16, _mm256_min_epu16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint16x16, _mm256_max_epu16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int16x16,  _mm256_min_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int16x16,  _mm256_max_epi16)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint32x8,  _mm256_min_epu32)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint32x8,  _mm256_max_epu32)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int32x8,   _mm256_min_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int32x8,   _mm256_max_epi32)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float32x8, _mm256_min_ps)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float32x8, _mm256_max_ps)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float64x4, _mm256_min_pd)
+OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float64x4, _mm256_max_pd)
+
+/** Rotate **/
+template<int imm>
+inline v_uint8x32 v_rotate_left(const v_uint8x32& a, const v_uint8x32& b)
+{
+    enum {IMM_R = (16 - imm) & 0xFF};
+    enum {IMM_R2 = (32 - imm) & 0xFF};
+
+    if (imm == 0)  return a;
+    if (imm == 32) return b;
+    if (imm > 32)  return v_uint8x32();
+
+    __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x03);
+    if (imm == 16) return v_uint8x32(swap);
+    if (imm < 16)  return v_uint8x32(_mm256_alignr_epi8(a.val, swap, IMM_R));
+    return v_uint8x32(_mm256_alignr_epi8(swap, b.val, IMM_R2)); // imm < 32
+}
+
+template<int imm>
+inline v_uint8x32 v_rotate_right(const v_uint8x32& a, const v_uint8x32& b)
+{
+    enum {IMM_L = (imm - 16) & 0xFF};
+
+    if (imm == 0)  return a;
+    if (imm == 32) return b;
+    if (imm > 32)  return v_uint8x32();
+
+    __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x21);
+    if (imm == 16) return v_uint8x32(swap);
+    if (imm < 16)  return v_uint8x32(_mm256_alignr_epi8(swap, a.val, imm));
+    return v_uint8x32(_mm256_alignr_epi8(b.val, swap, IMM_L));
+}
+
+template<int imm>
+inline v_uint8x32 v_rotate_left(const v_uint8x32& a)
+{
+    enum {IMM_L = (imm - 16) & 0xFF};
+    enum {IMM_R = (16 - imm) & 0xFF};
+
+    if (imm == 0) return a;
+    if (imm > 32) return v_uint8x32();
+
+    // ESAC control[3] ? [127:0] = 0
+    __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(0, 0, 2, 0));
+    if (imm == 16) return v_uint8x32(swapz);
+    if (imm < 16)  return v_uint8x32(_mm256_alignr_epi8(a.val, swapz, IMM_R));
+    return v_uint8x32(_mm256_slli_si256(swapz, IMM_L));
+}
+
+template<int imm>
+inline v_uint8x32 v_rotate_right(const v_uint8x32& a)
+{
+    enum {IMM_L = (imm - 16) & 0xFF};
+
+    if (imm == 0) return a;
+    if (imm > 32) return v_uint8x32();
+
+    // ESAC control[3] ? [127:0] = 0
+    __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(2, 0, 0, 1));
+    if (imm == 16) return v_uint8x32(swapz);
+    if (imm < 16)  return v_uint8x32(_mm256_alignr_epi8(swapz, a.val, imm));
+    return v_uint8x32(_mm256_srli_si256(swapz, IMM_L));
+}
+
+#define OPENCV_HAL_IMPL_AVX_ROTATE_CAST(intrin, _Tpvec, cast)     \
+    template<int imm>                                             \
+    inline _Tpvec intrin(const _Tpvec& a, const _Tpvec& b)        \
+    {                                                             \
+        enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)};  \
+        v_uint8x32 ret = intrin<IMMxW>(v_reinterpret_as_u8(a),    \
+                                       v_reinterpret_as_u8(b));   \
+        return _Tpvec(cast(ret.val));                             \
+    }                                                             \
+    template<int imm>                                             \
+    inline _Tpvec intrin(const _Tpvec& a)                         \
+    {                                                             \
+        enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)};  \
+        v_uint8x32 ret = intrin<IMMxW>(v_reinterpret_as_u8(a));   \
+        return _Tpvec(cast(ret.val));                             \
+    }
+
+#define OPENCV_HAL_IMPL_AVX_ROTATE(_Tpvec)                                  \
+    OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left,  _Tpvec, OPENCV_HAL_NOP) \
+    OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, _Tpvec, OPENCV_HAL_NOP)
+
+OPENCV_HAL_IMPL_AVX_ROTATE(v_int8x32)
+OPENCV_HAL_IMPL_AVX_ROTATE(v_uint16x16)
+OPENCV_HAL_IMPL_AVX_ROTATE(v_int16x16)
+OPENCV_HAL_IMPL_AVX_ROTATE(v_uint32x8)
+OPENCV_HAL_IMPL_AVX_ROTATE(v_int32x8)
+OPENCV_HAL_IMPL_AVX_ROTATE(v_uint64x4)
+OPENCV_HAL_IMPL_AVX_ROTATE(v_int64x4)
+
+OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left,  v_float32x8, _mm256_castsi256_ps)
+OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float32x8, _mm256_castsi256_ps)
+OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left,  v_float64x4, _mm256_castsi256_pd)
+OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float64x4, _mm256_castsi256_pd)
+
+/** Reverse **/
+inline v_uint8x32 v_reverse(const v_uint8x32 &a)
+{
+    static const __m256i perm = _mm256_setr_epi8(
+            15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
+            15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+    __m256i vec = _mm256_shuffle_epi8(a.val, perm);
+    return v_uint8x32(_mm256_permute2x128_si256(vec, vec, 1));
+}
+
+inline v_int8x32 v_reverse(const v_int8x32 &a)
+{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
+
+inline v_uint16x16 v_reverse(const v_uint16x16 &a)
+{
+    static const __m256i perm = _mm256_setr_epi8(
+            14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1,
+            14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1);
+    __m256i vec = _mm256_shuffle_epi8(a.val, perm);
+    return v_uint16x16(_mm256_permute2x128_si256(vec, vec, 1));
+}
+
+inline v_int16x16 v_reverse(const v_int16x16 &a)
+{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
+
+inline v_uint32x8 v_reverse(const v_uint32x8 &a)
+{
+    static const __m256i perm = _mm256_setr_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+    return v_uint32x8(_mm256_permutevar8x32_epi32(a.val, perm));
+}
+
+inline v_int32x8 v_reverse(const v_int32x8 &a)
+{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_float32x8 v_reverse(const v_float32x8 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x4 v_reverse(const v_uint64x4 &a)
+{
+    return v_uint64x4(_mm256_permute4x64_epi64(a.val, _MM_SHUFFLE(0, 1, 2, 3)));
+}
+
+inline v_int64x4 v_reverse(const v_int64x4 &a)
+{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
+
+inline v_float64x4 v_reverse(const v_float64x4 &a)
+{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
+
+////////// Reduce and mask /////////
+
+/** Reduce **/
+inline unsigned v_reduce_sum(const v_uint8x32& a)
+{
+    __m256i half = _mm256_sad_epu8(a.val, _mm256_setzero_si256());
+    __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half));
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter)));
+}
+inline int v_reduce_sum(const v_int8x32& a)
+{
+    __m256i half = _mm256_sad_epu8(_mm256_xor_si256(a.val, _mm256_set1_epi8((schar)-128)), _mm256_setzero_si256());
+    __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half));
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter))) - 4096;
+}
+#define OPENCV_HAL_IMPL_AVX_REDUCE_32(_Tpvec, sctype, func, intrin) \
+    inline sctype v_reduce_##func(const _Tpvec& a) \
+    { \
+        __m128i val = intrin(_v256_extract_low(a.val), _v256_extract_high(a.val)); \
+        val = intrin(val, _mm_srli_si128(val,8)); \
+        val = intrin(val, _mm_srli_si128(val,4)); \
+        val = intrin(val, _mm_srli_si128(val,2)); \
+        val = intrin(val, _mm_srli_si128(val,1)); \
+        return (sctype)_mm_cvtsi128_si32(val); \
+    }
+
+OPENCV_HAL_IMPL_AVX_REDUCE_32(v_uint8x32, uchar, min, _mm_min_epu8)
+OPENCV_HAL_IMPL_AVX_REDUCE_32(v_int8x32,  schar, min, _mm_min_epi8)
+OPENCV_HAL_IMPL_AVX_REDUCE_32(v_uint8x32, uchar, max, _mm_max_epu8)
+OPENCV_HAL_IMPL_AVX_REDUCE_32(v_int8x32,  schar, max, _mm_max_epi8)
+
+#define OPENCV_HAL_IMPL_AVX_REDUCE_16(_Tpvec, sctype, func, intrin) \
+    inline sctype v_reduce_##func(const _Tpvec& a)                  \
+    {                                                               \
+        __m128i v0 = _v256_extract_low(a.val);                      \
+        __m128i v1 = _v256_extract_high(a.val);                     \
+        v0 = intrin(v0, v1);                                        \
+        v0 = intrin(v0, _mm_srli_si128(v0, 8));                     \
+        v0 = intrin(v0, _mm_srli_si128(v0, 4));                     \
+        v0 = intrin(v0, _mm_srli_si128(v0, 2));                     \
+        return (sctype) _mm_cvtsi128_si32(v0);                      \
+    }
+
+OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, min, _mm_min_epu16)
+OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16,  short,  min, _mm_min_epi16)
+OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, max, _mm_max_epu16)
+OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16,  short,  max, _mm_max_epi16)
+
+#define OPENCV_HAL_IMPL_AVX_REDUCE_8(_Tpvec, sctype, func, intrin) \
+    inline sctype v_reduce_##func(const _Tpvec& a)                 \
+    {                                                              \
+        __m128i v0 = _v256_extract_low(a.val);                     \
+        __m128i v1 = _v256_extract_high(a.val);                    \
+        v0 = intrin(v0, v1);                                       \
+        v0 = intrin(v0, _mm_srli_si128(v0, 8));                    \
+        v0 = intrin(v0, _mm_srli_si128(v0, 4));                    \
+        return (sctype) _mm_cvtsi128_si32(v0);                     \
+    }
+
+OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, min, _mm_min_epu32)
+OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8,  int,      min, _mm_min_epi32)
+OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, max, _mm_max_epu32)
+OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8,  int,      max, _mm_max_epi32)
+
+#define OPENCV_HAL_IMPL_AVX_REDUCE_FLT(func, intrin)                  \
+    inline float v_reduce_##func(const v_float32x8& a)                \
+    {                                                                 \
+        __m128 v0 = _v256_extract_low(a.val);                         \
+        __m128 v1 = _v256_extract_high(a.val);                        \
+        v0 = intrin(v0, v1);                                          \
+        v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 3, 2))); \
+        v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 0, 1))); \
+        return _mm_cvtss_f32(v0);                                     \
+    }
+
+OPENCV_HAL_IMPL_AVX_REDUCE_FLT(min, _mm_min_ps)
+OPENCV_HAL_IMPL_AVX_REDUCE_FLT(max, _mm_max_ps)
+
+inline int v_reduce_sum(const v_int32x8& a)
+{
+    __m256i s0 = _mm256_hadd_epi32(a.val, a.val);
+            s0 = _mm256_hadd_epi32(s0, s0);
+
+    __m128i s1 = _v256_extract_high(s0);
+            s1 = _mm_add_epi32(_v256_extract_low(s0), s1);
+
+    return _mm_cvtsi128_si32(s1);
+}
+
+inline unsigned v_reduce_sum(const v_uint32x8& a)
+{ return v_reduce_sum(v_reinterpret_as_s32(a)); }
+
+inline int v_reduce_sum(const v_int16x16& a)
+{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); }
+inline unsigned v_reduce_sum(const v_uint16x16& a)
+{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); }
+
+inline float v_reduce_sum(const v_float32x8& a)
+{
+    __m256 s0 = _mm256_hadd_ps(a.val, a.val);
+           s0 = _mm256_hadd_ps(s0, s0);
+
+    __m128 s1 = _v256_extract_high(s0);
+           s1 = _mm_add_ps(_v256_extract_low(s0), s1);
+
+    return _mm_cvtss_f32(s1);
+}
+
+inline uint64 v_reduce_sum(const v_uint64x4& a)
+{
+    uint64 CV_DECL_ALIGNED(32) idx[2];
+    _mm_store_si128((__m128i*)idx, _mm_add_epi64(_v256_extract_low(a.val), _v256_extract_high(a.val)));
+    return idx[0] + idx[1];
+}
+inline int64 v_reduce_sum(const v_int64x4& a)
+{
+    int64 CV_DECL_ALIGNED(32) idx[2];
+    _mm_store_si128((__m128i*)idx, _mm_add_epi64(_v256_extract_low(a.val), _v256_extract_high(a.val)));
+    return idx[0] + idx[1];
+}
+inline double v_reduce_sum(const v_float64x4& a)
+{
+    __m256d s0 = _mm256_hadd_pd(a.val, a.val);
+    return _mm_cvtsd_f64(_mm_add_pd(_v256_extract_low(s0), _v256_extract_high(s0)));
+}
+
+inline v_float32x8 v_reduce_sum4(const v_float32x8& a, const v_float32x8& b,
+                                 const v_float32x8& c, const v_float32x8& d)
+{
+    __m256 ab = _mm256_hadd_ps(a.val, b.val);
+    __m256 cd = _mm256_hadd_ps(c.val, d.val);
+    return v_float32x8(_mm256_hadd_ps(ab, cd));
+}
+
+inline unsigned v_reduce_sad(const v_uint8x32& a, const v_uint8x32& b)
+{
+    __m256i half = _mm256_sad_epu8(a.val, b.val);
+    __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half));
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter)));
+}
+inline unsigned v_reduce_sad(const v_int8x32& a, const v_int8x32& b)
+{
+    __m256i half = _mm256_set1_epi8(0x7f);
+    half = _mm256_sad_epu8(_mm256_add_epi8(a.val, half), _mm256_add_epi8(b.val, half));
+    __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half));
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter)));
+}
+inline unsigned v_reduce_sad(const v_uint16x16& a, const v_uint16x16& b)
+{
+    v_uint32x8 l, h;
+    v_expand(v_add_wrap(a - b, b - a), l, h);
+    return v_reduce_sum(l + h);
+}
+inline unsigned v_reduce_sad(const v_int16x16& a, const v_int16x16& b)
+{
+    v_uint32x8 l, h;
+    v_expand(v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))), l, h);
+    return v_reduce_sum(l + h);
+}
+inline unsigned v_reduce_sad(const v_uint32x8& a, const v_uint32x8& b)
+{
+    return v_reduce_sum(v_max(a, b) - v_min(a, b));
+}
+inline unsigned v_reduce_sad(const v_int32x8& a, const v_int32x8& b)
+{
+    v_int32x8 m = a < b;
+    return v_reduce_sum(v_reinterpret_as_u32(((a - b) ^ m) - m));
+}
+inline float v_reduce_sad(const v_float32x8& a, const v_float32x8& b)
+{
+    return v_reduce_sum((a - b) & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff))));
+}
+
+/** Popcount **/
+inline v_uint8x32 v_popcount(const v_uint8x32& a)
+{
+    __m256i _popcnt_table = _mm256_setr_epi8(0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4,
+                                             0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4);
+    __m256i _popcnt_mask = _mm256_set1_epi8(0x0F);
+    return v_uint8x32(_mm256_add_epi8(_mm256_shuffle_epi8(_popcnt_table, _mm256_and_si256(                  a.val    , _popcnt_mask)),
+                                      _mm256_shuffle_epi8(_popcnt_table, _mm256_and_si256(_mm256_srli_epi16(a.val, 4), _popcnt_mask))));
+}
+inline v_uint16x16 v_popcount(const v_uint16x16& a)
+{
+    v_uint8x32 p = v_popcount(v_reinterpret_as_u8(a));
+    p += v_rotate_right<1>(p);
+    return v_reinterpret_as_u16(p) & v256_setall_u16(0x00ff);
+}
+inline v_uint32x8 v_popcount(const v_uint32x8& a)
+{
+    v_uint8x32 p = v_popcount(v_reinterpret_as_u8(a));
+    p += v_rotate_right<1>(p);
+    p += v_rotate_right<2>(p);
+    return v_reinterpret_as_u32(p) & v256_setall_u32(0x000000ff);
+}
+inline v_uint64x4 v_popcount(const v_uint64x4& a)
+{
+    return v_uint64x4(_mm256_sad_epu8(v_popcount(v_reinterpret_as_u8(a)).val, _mm256_setzero_si256()));
+}
+inline v_uint8x32 v_popcount(const v_int8x32& a)
+{ return v_popcount(v_reinterpret_as_u8(a)); }
+inline v_uint16x16 v_popcount(const v_int16x16& a)
+{ return v_popcount(v_reinterpret_as_u16(a)); }
+inline v_uint32x8 v_popcount(const v_int32x8& a)
+{ return v_popcount(v_reinterpret_as_u32(a)); }
+inline v_uint64x4 v_popcount(const v_int64x4& a)
+{ return v_popcount(v_reinterpret_as_u64(a)); }
+
+/** Mask **/
+inline int v_signmask(const v_int8x32& a)
+{ return _mm256_movemask_epi8(a.val); }
+inline int v_signmask(const v_uint8x32& a)
+{ return v_signmask(v_reinterpret_as_s8(a)); }
+
+inline int v_signmask(const v_int16x16& a)
+{ return v_signmask(v_pack(a, a)) & 0xFFFF; }
+inline int v_signmask(const v_uint16x16& a)
+{ return v_signmask(v_reinterpret_as_s16(a)); }
+
+inline int v_signmask(const v_float32x8& a)
+{ return _mm256_movemask_ps(a.val); }
+inline int v_signmask(const v_float64x4& a)
+{ return _mm256_movemask_pd(a.val); }
+
+inline int v_signmask(const v_int32x8& a)
+{ return v_signmask(v_reinterpret_as_f32(a)); }
+inline int v_signmask(const v_uint32x8& a)
+{ return v_signmask(v_reinterpret_as_f32(a)); }
+
+inline int v_signmask(const v_int64x4& a)
+{ return v_signmask(v_reinterpret_as_f64(a)); }
+inline int v_signmask(const v_uint64x4& a)
+{ return v_signmask(v_reinterpret_as_f64(a)); }
+
+inline int v_scan_forward(const v_int8x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); }
+inline int v_scan_forward(const v_uint8x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); }
+inline int v_scan_forward(const v_int16x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; }
+inline int v_scan_forward(const v_uint16x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; }
+inline int v_scan_forward(const v_int32x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_uint32x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_float32x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_int64x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+inline int v_scan_forward(const v_uint64x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+inline int v_scan_forward(const v_float64x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+
+/** Checks **/
+#define OPENCV_HAL_IMPL_AVX_CHECK(_Tpvec, allmask) \
+    inline bool v_check_all(const _Tpvec& a) { return v_signmask(a) == allmask; } \
+    inline bool v_check_any(const _Tpvec& a) { return v_signmask(a) != 0; }
+OPENCV_HAL_IMPL_AVX_CHECK(v_uint8x32, -1)
+OPENCV_HAL_IMPL_AVX_CHECK(v_int8x32, -1)
+OPENCV_HAL_IMPL_AVX_CHECK(v_uint32x8, 255)
+OPENCV_HAL_IMPL_AVX_CHECK(v_int32x8, 255)
+OPENCV_HAL_IMPL_AVX_CHECK(v_uint64x4, 15)
+OPENCV_HAL_IMPL_AVX_CHECK(v_int64x4, 15)
+OPENCV_HAL_IMPL_AVX_CHECK(v_float32x8, 255)
+OPENCV_HAL_IMPL_AVX_CHECK(v_float64x4, 15)
+
+#define OPENCV_HAL_IMPL_AVX_CHECK_SHORT(_Tpvec)  \
+    inline bool v_check_all(const _Tpvec& a) { return (v_signmask(v_reinterpret_as_s8(a)) & 0xaaaaaaaa) == 0xaaaaaaaa; } \
+    inline bool v_check_any(const _Tpvec& a) { return (v_signmask(v_reinterpret_as_s8(a)) & 0xaaaaaaaa) != 0; }
+OPENCV_HAL_IMPL_AVX_CHECK_SHORT(v_uint16x16)
+OPENCV_HAL_IMPL_AVX_CHECK_SHORT(v_int16x16)
+
+////////// Other math /////////
+
+/** Some frequent operations **/
+#if CV_FMA3
+#define OPENCV_HAL_IMPL_AVX_MULADD(_Tpvec, suffix)                            \
+    inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)    \
+    { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); }            \
+    inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \
+    { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); }
+#else
+#define OPENCV_HAL_IMPL_AVX_MULADD(_Tpvec, suffix)                                    \
+    inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)            \
+    { return _Tpvec(_mm256_add_##suffix(_mm256_mul_##suffix(a.val, b.val), c.val)); } \
+    inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)         \
+    { return _Tpvec(_mm256_add_##suffix(_mm256_mul_##suffix(a.val, b.val), c.val)); }
+#endif
+
+#define OPENCV_HAL_IMPL_AVX_MISC(_Tpvec, suffix)                              \
+    inline _Tpvec v_sqrt(const _Tpvec& x)                                     \
+    { return _Tpvec(_mm256_sqrt_##suffix(x.val)); }                           \
+    inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b)           \
+    { return v_fma(a, a, b * b); }                                            \
+    inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b)               \
+    { return v_sqrt(v_fma(a, a, b*b)); }
+
+OPENCV_HAL_IMPL_AVX_MULADD(v_float32x8, ps)
+OPENCV_HAL_IMPL_AVX_MULADD(v_float64x4, pd)
+OPENCV_HAL_IMPL_AVX_MISC(v_float32x8, ps)
+OPENCV_HAL_IMPL_AVX_MISC(v_float64x4, pd)
+
+inline v_int32x8 v_fma(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c)
+{
+    return a * b + c;
+}
+
+inline v_int32x8 v_muladd(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_float32x8 v_invsqrt(const v_float32x8& x)
+{
+    v_float32x8 half = x * v256_setall_f32(0.5);
+    v_float32x8 t  = v_float32x8(_mm256_rsqrt_ps(x.val));
+    // todo: _mm256_fnmsub_ps
+    t *= v256_setall_f32(1.5) - ((t * t) * half);
+    return t;
+}
+
+inline v_float64x4 v_invsqrt(const v_float64x4& x)
+{
+    return v256_setall_f64(1.) / v_sqrt(x);
+}
+
+/** Absolute values **/
+#define OPENCV_HAL_IMPL_AVX_ABS(_Tpvec, suffix)         \
+    inline v_u##_Tpvec v_abs(const v_##_Tpvec& x)       \
+    { return v_u##_Tpvec(_mm256_abs_##suffix(x.val)); }
+
+OPENCV_HAL_IMPL_AVX_ABS(int8x32,  epi8)
+OPENCV_HAL_IMPL_AVX_ABS(int16x16, epi16)
+OPENCV_HAL_IMPL_AVX_ABS(int32x8,  epi32)
+
+inline v_float32x8 v_abs(const v_float32x8& x)
+{ return x & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff))); }
+inline v_float64x4 v_abs(const v_float64x4& x)
+{ return x & v_float64x4(_mm256_castsi256_pd(_mm256_srli_epi64(_mm256_set1_epi64x(-1), 1))); }
+
+/** Absolute difference **/
+inline v_uint8x32 v_absdiff(const v_uint8x32& a, const v_uint8x32& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint16x16 v_absdiff(const v_uint16x16& a, const v_uint16x16& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint32x8 v_absdiff(const v_uint32x8& a, const v_uint32x8& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+inline v_uint8x32 v_absdiff(const v_int8x32& a, const v_int8x32& b)
+{
+    v_int8x32 d = v_sub_wrap(a, b);
+    v_int8x32 m = a < b;
+    return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m));
+}
+
+inline v_uint16x16 v_absdiff(const v_int16x16& a, const v_int16x16& b)
+{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); }
+
+inline v_uint32x8 v_absdiff(const v_int32x8& a, const v_int32x8& b)
+{
+    v_int32x8 d = a - b;
+    v_int32x8 m = a < b;
+    return v_reinterpret_as_u32((d ^ m) - m);
+}
+
+inline v_float32x8 v_absdiff(const v_float32x8& a, const v_float32x8& b)
+{ return v_abs(a - b); }
+
+inline v_float64x4 v_absdiff(const v_float64x4& a, const v_float64x4& b)
+{ return v_abs(a - b); }
+
+/** Saturating absolute difference **/
+inline v_int8x32 v_absdiffs(const v_int8x32& a, const v_int8x32& b)
+{
+    v_int8x32 d = a - b;
+    v_int8x32 m = a < b;
+    return (d ^ m) - m;
+}
+inline v_int16x16 v_absdiffs(const v_int16x16& a, const v_int16x16& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+////////// Conversions /////////
+
+/** Rounding **/
+inline v_int32x8 v_round(const v_float32x8& a)
+{ return v_int32x8(_mm256_cvtps_epi32(a.val)); }
+
+inline v_int32x8 v_round(const v_float64x4& a)
+{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvtpd_epi32(a.val))); }
+
+inline v_int32x8 v_round(const v_float64x4& a, const v_float64x4& b)
+{
+    __m128i ai = _mm256_cvtpd_epi32(a.val), bi = _mm256_cvtpd_epi32(b.val);
+    return v_int32x8(_v256_combine(ai, bi));
+}
+
+inline v_int32x8 v_trunc(const v_float32x8& a)
+{ return v_int32x8(_mm256_cvttps_epi32(a.val)); }
+
+inline v_int32x8 v_trunc(const v_float64x4& a)
+{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvttpd_epi32(a.val))); }
+
+inline v_int32x8 v_floor(const v_float32x8& a)
+{ return v_int32x8(_mm256_cvttps_epi32(_mm256_floor_ps(a.val))); }
+
+inline v_int32x8 v_floor(const v_float64x4& a)
+{ return v_trunc(v_float64x4(_mm256_floor_pd(a.val))); }
+
+inline v_int32x8 v_ceil(const v_float32x8& a)
+{ return v_int32x8(_mm256_cvttps_epi32(_mm256_ceil_ps(a.val))); }
+
+inline v_int32x8 v_ceil(const v_float64x4& a)
+{ return v_trunc(v_float64x4(_mm256_ceil_pd(a.val))); }
+
+/** To float **/
+inline v_float32x8 v_cvt_f32(const v_int32x8& a)
+{ return v_float32x8(_mm256_cvtepi32_ps(a.val)); }
+
+inline v_float32x8 v_cvt_f32(const v_float64x4& a)
+{ return v_float32x8(_mm256_castps128_ps256(_mm256_cvtpd_ps(a.val))); }
+
+inline v_float32x8 v_cvt_f32(const v_float64x4& a, const v_float64x4& b)
+{
+    __m128 af = _mm256_cvtpd_ps(a.val), bf = _mm256_cvtpd_ps(b.val);
+    return v_float32x8(_v256_combine(af, bf));
+}
+
+inline v_float64x4 v_cvt_f64(const v_int32x8& a)
+{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_low(a.val))); }
+
+inline v_float64x4 v_cvt_f64_high(const v_int32x8& a)
+{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_high(a.val))); }
+
+inline v_float64x4 v_cvt_f64(const v_float32x8& a)
+{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_low(a.val))); }
+
+inline v_float64x4 v_cvt_f64_high(const v_float32x8& a)
+{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_high(a.val))); }
+
+// from (Mysticial and wim) https://stackoverflow.com/q/41144668
+inline v_float64x4 v_cvt_f64(const v_int64x4& v)
+{
+    // constants encoded as floating-point
+    __m256i magic_i_lo   = _mm256_set1_epi64x(0x4330000000000000); // 2^52
+    __m256i magic_i_hi32 = _mm256_set1_epi64x(0x4530000080000000); // 2^84 + 2^63
+    __m256i magic_i_all  = _mm256_set1_epi64x(0x4530000080100000); // 2^84 + 2^63 + 2^52
+    __m256d magic_d_all  = _mm256_castsi256_pd(magic_i_all);
+
+    // Blend the 32 lowest significant bits of v with magic_int_lo
+    __m256i v_lo         = _mm256_blend_epi32(magic_i_lo, v.val, 0x55);
+    // Extract the 32 most significant bits of v
+    __m256i v_hi         = _mm256_srli_epi64(v.val, 32);
+    // Flip the msb of v_hi and blend with 0x45300000
+            v_hi         = _mm256_xor_si256(v_hi, magic_i_hi32);
+    // Compute in double precision
+    __m256d v_hi_dbl     = _mm256_sub_pd(_mm256_castsi256_pd(v_hi), magic_d_all);
+    // (v_hi - magic_d_all) + v_lo  Do not assume associativity of floating point addition
+    __m256d result       = _mm256_add_pd(v_hi_dbl, _mm256_castsi256_pd(v_lo));
+    return v_float64x4(result);
+}
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x32 v256_lut(const schar* tab, const int* idx)
+{
+    return v_int8x32(_mm256_setr_epi8(tab[idx[ 0]], tab[idx[ 1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]],
+                                      tab[idx[ 8]], tab[idx[ 9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]],
+                                      tab[idx[16]], tab[idx[17]], tab[idx[18]], tab[idx[19]], tab[idx[20]], tab[idx[21]], tab[idx[22]], tab[idx[23]],
+                                      tab[idx[24]], tab[idx[25]], tab[idx[26]], tab[idx[27]], tab[idx[28]], tab[idx[29]], tab[idx[30]], tab[idx[31]]));
+}
+inline v_int8x32 v256_lut_pairs(const schar* tab, const int* idx)
+{
+    return v_int8x32(_mm256_setr_epi16(*(const short*)(tab + idx[ 0]), *(const short*)(tab + idx[ 1]), *(const short*)(tab + idx[ 2]), *(const short*)(tab + idx[ 3]),
+                                       *(const short*)(tab + idx[ 4]), *(const short*)(tab + idx[ 5]), *(const short*)(tab + idx[ 6]), *(const short*)(tab + idx[ 7]),
+                                       *(const short*)(tab + idx[ 8]), *(const short*)(tab + idx[ 9]), *(const short*)(tab + idx[10]), *(const short*)(tab + idx[11]),
+                                       *(const short*)(tab + idx[12]), *(const short*)(tab + idx[13]), *(const short*)(tab + idx[14]), *(const short*)(tab + idx[15])));
+}
+inline v_int8x32 v256_lut_quads(const schar* tab, const int* idx)
+{
+    return v_int8x32(_mm256_i32gather_epi32((const int*)tab, _mm256_loadu_si256((const __m256i*)idx), 1));
+}
+inline v_uint8x32 v256_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v256_lut((const schar *)tab, idx)); }
+inline v_uint8x32 v256_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v256_lut_pairs((const schar *)tab, idx)); }
+inline v_uint8x32 v256_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v256_lut_quads((const schar *)tab, idx)); }
+
+inline v_int16x16 v256_lut(const short* tab, const int* idx)
+{
+    return v_int16x16(_mm256_setr_epi16(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]],
+                                        tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]));
+}
+inline v_int16x16 v256_lut_pairs(const short* tab, const int* idx)
+{
+    return v_int16x16(_mm256_i32gather_epi32((const int*)tab, _mm256_loadu_si256((const __m256i*)idx), 2));
+}
+inline v_int16x16 v256_lut_quads(const short* tab, const int* idx)
+{
+#if defined(__GNUC__)
+    return v_int16x16(_mm256_i32gather_epi64((const long long int*)tab, _mm_loadu_si128((const __m128i*)idx), 2));//Looks like intrinsic has wrong definition
+#else
+    return v_int16x16(_mm256_i32gather_epi64((const int64*)tab, _mm_loadu_si128((const __m128i*)idx), 2));
+#endif
+}
+inline v_uint16x16 v256_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v256_lut((const short *)tab, idx)); }
+inline v_uint16x16 v256_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v256_lut_pairs((const short *)tab, idx)); }
+inline v_uint16x16 v256_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v256_lut_quads((const short *)tab, idx)); }
+
+inline v_int32x8 v256_lut(const int* tab, const int* idx)
+{
+    return v_int32x8(_mm256_i32gather_epi32(tab, _mm256_loadu_si256((const __m256i*)idx), 4));
+}
+inline v_int32x8 v256_lut_pairs(const int* tab, const int* idx)
+{
+#if defined(__GNUC__)
+    return v_int32x8(_mm256_i32gather_epi64((const long long int*)tab, _mm_loadu_si128((const __m128i*)idx), 4));
+#else
+    return v_int32x8(_mm256_i32gather_epi64((const int64*)tab, _mm_loadu_si128((const __m128i*)idx), 4));
+#endif
+}
+inline v_int32x8 v256_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x8(_v256_combine(_mm_loadu_si128((const __m128i*)(tab + idx[0])), _mm_loadu_si128((const __m128i*)(tab + idx[1]))));
+}
+inline v_uint32x8 v256_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v256_lut((const int *)tab, idx)); }
+inline v_uint32x8 v256_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v256_lut_pairs((const int *)tab, idx)); }
+inline v_uint32x8 v256_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v256_lut_quads((const int *)tab, idx)); }
+
+inline v_int64x4 v256_lut(const int64* tab, const int* idx)
+{
+#if defined(__GNUC__)
+    return v_int64x4(_mm256_i32gather_epi64((const long long int*)tab, _mm_loadu_si128((const __m128i*)idx), 8));
+#else
+    return v_int64x4(_mm256_i32gather_epi64(tab, _mm_loadu_si128((const __m128i*)idx), 8));
+#endif
+}
+inline v_int64x4 v256_lut_pairs(const int64* tab, const int* idx)
+{
+    return v_int64x4(_v256_combine(_mm_loadu_si128((const __m128i*)(tab + idx[0])), _mm_loadu_si128((const __m128i*)(tab + idx[1]))));
+}
+inline v_uint64x4 v256_lut(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v256_lut((const int64 *)tab, idx)); }
+inline v_uint64x4 v256_lut_pairs(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v256_lut_pairs((const int64 *)tab, idx)); }
+
+inline v_float32x8 v256_lut(const float* tab, const int* idx)
+{
+    return v_float32x8(_mm256_i32gather_ps(tab, _mm256_loadu_si256((const __m256i*)idx), 4));
+}
+inline v_float32x8 v256_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v256_lut_pairs((const int *)tab, idx)); }
+inline v_float32x8 v256_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v256_lut_quads((const int *)tab, idx)); }
+
+inline v_float64x4 v256_lut(const double* tab, const int* idx)
+{
+    return v_float64x4(_mm256_i32gather_pd(tab, _mm_loadu_si128((const __m128i*)idx), 8));
+}
+inline v_float64x4 v256_lut_pairs(const double* tab, const int* idx) { return v_float64x4(_v256_combine(_mm_loadu_pd(tab + idx[0]), _mm_loadu_pd(tab + idx[1]))); }
+
+inline v_int32x8 v_lut(const int* tab, const v_int32x8& idxvec)
+{
+    return v_int32x8(_mm256_i32gather_epi32(tab, idxvec.val, 4));
+}
+
+inline v_uint32x8 v_lut(const unsigned* tab, const v_int32x8& idxvec)
+{
+    return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec));
+}
+
+inline v_float32x8 v_lut(const float* tab, const v_int32x8& idxvec)
+{
+    return v_float32x8(_mm256_i32gather_ps(tab, idxvec.val, 4));
+}
+
+inline v_float64x4 v_lut(const double* tab, const v_int32x8& idxvec)
+{
+    return v_float64x4(_mm256_i32gather_pd(tab, _mm256_castsi256_si128(idxvec.val), 8));
+}
+
+inline void v_lut_deinterleave(const float* tab, const v_int32x8& idxvec, v_float32x8& x, v_float32x8& y)
+{
+    int CV_DECL_ALIGNED(32) idx[8];
+    v_store_aligned(idx, idxvec);
+    __m128 z = _mm_setzero_ps();
+    __m128 xy01, xy45, xy23, xy67;
+    xy01 = _mm_loadl_pi(z, (const __m64*)(tab + idx[0]));
+    xy01 = _mm_loadh_pi(xy01, (const __m64*)(tab + idx[1]));
+    xy45 = _mm_loadl_pi(z, (const __m64*)(tab + idx[4]));
+    xy45 = _mm_loadh_pi(xy45, (const __m64*)(tab + idx[5]));
+    __m256 xy0145 = _v256_combine(xy01, xy45);
+    xy23 = _mm_loadl_pi(z, (const __m64*)(tab + idx[2]));
+    xy23 = _mm_loadh_pi(xy23, (const __m64*)(tab + idx[3]));
+    xy67 = _mm_loadl_pi(z, (const __m64*)(tab + idx[6]));
+    xy67 = _mm_loadh_pi(xy67, (const __m64*)(tab + idx[7]));
+    __m256 xy2367 = _v256_combine(xy23, xy67);
+
+    __m256 xxyy0145 = _mm256_unpacklo_ps(xy0145, xy2367);
+    __m256 xxyy2367 = _mm256_unpackhi_ps(xy0145, xy2367);
+
+    x = v_float32x8(_mm256_unpacklo_ps(xxyy0145, xxyy2367));
+    y = v_float32x8(_mm256_unpackhi_ps(xxyy0145, xxyy2367));
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x8& idxvec, v_float64x4& x, v_float64x4& y)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_low(idx, idxvec);
+    __m128d xy0 = _mm_loadu_pd(tab + idx[0]);
+    __m128d xy2 = _mm_loadu_pd(tab + idx[2]);
+    __m128d xy1 = _mm_loadu_pd(tab + idx[1]);
+    __m128d xy3 = _mm_loadu_pd(tab + idx[3]);
+    __m256d xy02 = _v256_combine(xy0, xy2);
+    __m256d xy13 = _v256_combine(xy1, xy3);
+
+    x = v_float64x4(_mm256_unpacklo_pd(xy02, xy13));
+    y = v_float64x4(_mm256_unpackhi_pd(xy02, xy13));
+}
+
+inline v_int8x32 v_interleave_pairs(const v_int8x32& vec)
+{
+    return v_int8x32(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0d0e0c0b090a08, 0x0705060403010200, 0x0f0d0e0c0b090a08, 0x0705060403010200)));
+}
+inline v_uint8x32 v_interleave_pairs(const v_uint8x32& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
+inline v_int8x32 v_interleave_quads(const v_int8x32& vec)
+{
+    return v_int8x32(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0b0e0a0d090c08, 0x0703060205010400, 0x0f0b0e0a0d090c08, 0x0703060205010400)));
+}
+inline v_uint8x32 v_interleave_quads(const v_uint8x32& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x16 v_interleave_pairs(const v_int16x16& vec)
+{
+    return v_int16x16(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0e0b0a0d0c0908, 0x0706030205040100, 0x0f0e0b0a0d0c0908, 0x0706030205040100)));
+}
+inline v_uint16x16 v_interleave_pairs(const v_uint16x16& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+inline v_int16x16 v_interleave_quads(const v_int16x16& vec)
+{
+    return v_int16x16(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0e07060d0c0504, 0x0b0a030209080100, 0x0f0e07060d0c0504, 0x0b0a030209080100)));
+}
+inline v_uint16x16 v_interleave_quads(const v_uint16x16& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x8 v_interleave_pairs(const v_int32x8& vec)
+{
+    return v_int32x8(_mm256_shuffle_epi32(vec.val, _MM_SHUFFLE(3, 1, 2, 0)));
+}
+inline v_uint32x8 v_interleave_pairs(const v_uint32x8& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x8 v_interleave_pairs(const v_float32x8& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+
+inline v_int8x32 v_pack_triplets(const v_int8x32& vec)
+{
+    return v_int8x32(_mm256_permutevar8x32_epi32(_mm256_shuffle_epi8(vec.val, _mm256_broadcastsi128_si256(_mm_set_epi64x(0xffffff0f0e0d0c0a, 0x0908060504020100))),
+                                                 _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000)));
+}
+inline v_uint8x32 v_pack_triplets(const v_uint8x32& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x16 v_pack_triplets(const v_int16x16& vec)
+{
+    return v_int16x16(_mm256_permutevar8x32_epi32(_mm256_shuffle_epi8(vec.val, _mm256_broadcastsi128_si256(_mm_set_epi64x(0xffff0f0e0d0c0b0a, 0x0908050403020100))),
+                                                  _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000)));
+}
+inline v_uint16x16 v_pack_triplets(const v_uint16x16& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x8 v_pack_triplets(const v_int32x8& vec)
+{
+    return v_int32x8(_mm256_permutevar8x32_epi32(vec.val, _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000)));
+}
+inline v_uint32x8 v_pack_triplets(const v_uint32x8& vec) { return v_reinterpret_as_u32(v_pack_triplets(v_reinterpret_as_s32(vec))); }
+inline v_float32x8 v_pack_triplets(const v_float32x8& vec)
+{
+    return v_float32x8(_mm256_permutevar8x32_ps(vec.val, _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000)));
+}
+
+////////// Matrix operations /////////
+
+//////// Dot Product ////////
+
+// 16 >> 32
+inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b)
+{ return v_int32x8(_mm256_madd_epi16(a.val, b.val)); }
+inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b, const v_int32x8& c)
+{ return v_dotprod(a, b) + c; }
+
+// 32 >> 64
+inline v_int64x4 v_dotprod(const v_int32x8& a, const v_int32x8& b)
+{
+    __m256i even = _mm256_mul_epi32(a.val, b.val);
+    __m256i odd = _mm256_mul_epi32(_mm256_srli_epi64(a.val, 32), _mm256_srli_epi64(b.val, 32));
+    return v_int64x4(_mm256_add_epi64(even, odd));
+}
+inline v_int64x4 v_dotprod(const v_int32x8& a, const v_int32x8& b, const v_int64x4& c)
+{ return v_dotprod(a, b) + c; }
+
+// 8 >> 32
+inline v_uint32x8 v_dotprod_expand(const v_uint8x32& a, const v_uint8x32& b)
+{
+    __m256i even_m = _mm256_set1_epi32(0xFF00FF00);
+    __m256i even_a = _mm256_blendv_epi8(a.val, _mm256_setzero_si256(), even_m);
+    __m256i odd_a  = _mm256_srli_epi16(a.val, 8);
+
+    __m256i even_b = _mm256_blendv_epi8(b.val, _mm256_setzero_si256(), even_m);
+    __m256i odd_b  = _mm256_srli_epi16(b.val, 8);
+
+    __m256i prod0  = _mm256_madd_epi16(even_a, even_b);
+    __m256i prod1  = _mm256_madd_epi16(odd_a, odd_b);
+    return v_uint32x8(_mm256_add_epi32(prod0, prod1));
+}
+inline v_uint32x8 v_dotprod_expand(const v_uint8x32& a, const v_uint8x32& b, const v_uint32x8& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int32x8 v_dotprod_expand(const v_int8x32& a, const v_int8x32& b)
+{
+    __m256i even_a = _mm256_srai_epi16(_mm256_bslli_epi128(a.val, 1), 8);
+    __m256i odd_a  = _mm256_srai_epi16(a.val, 8);
+
+    __m256i even_b = _mm256_srai_epi16(_mm256_bslli_epi128(b.val, 1), 8);
+    __m256i odd_b  = _mm256_srai_epi16(b.val, 8);
+
+    __m256i prod0  = _mm256_madd_epi16(even_a, even_b);
+    __m256i prod1  = _mm256_madd_epi16(odd_a, odd_b);
+    return v_int32x8(_mm256_add_epi32(prod0, prod1));
+}
+inline v_int32x8 v_dotprod_expand(const v_int8x32& a, const v_int8x32& b, const v_int32x8& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 16 >> 64
+inline v_uint64x4 v_dotprod_expand(const v_uint16x16& a, const v_uint16x16& b)
+{
+    __m256i mullo = _mm256_mullo_epi16(a.val, b.val);
+    __m256i mulhi = _mm256_mulhi_epu16(a.val, b.val);
+    __m256i mul0  = _mm256_unpacklo_epi16(mullo, mulhi);
+    __m256i mul1  = _mm256_unpackhi_epi16(mullo, mulhi);
+
+    __m256i p02   = _mm256_blend_epi32(mul0, _mm256_setzero_si256(), 0xAA);
+    __m256i p13   = _mm256_srli_epi64(mul0, 32);
+    __m256i p46   = _mm256_blend_epi32(mul1, _mm256_setzero_si256(), 0xAA);
+    __m256i p57   = _mm256_srli_epi64(mul1, 32);
+
+    __m256i p15_  = _mm256_add_epi64(p02, p13);
+    __m256i p9d_  = _mm256_add_epi64(p46, p57);
+
+    return v_uint64x4(_mm256_add_epi64(
+        _mm256_unpacklo_epi64(p15_, p9d_),
+        _mm256_unpackhi_epi64(p15_, p9d_)
+    ));
+}
+inline v_uint64x4 v_dotprod_expand(const v_uint16x16& a, const v_uint16x16& b, const v_uint64x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int64x4 v_dotprod_expand(const v_int16x16& a, const v_int16x16& b)
+{
+    __m256i prod = _mm256_madd_epi16(a.val, b.val);
+    __m256i sign = _mm256_srai_epi32(prod, 31);
+
+    __m256i lo = _mm256_unpacklo_epi32(prod, sign);
+    __m256i hi = _mm256_unpackhi_epi32(prod, sign);
+
+    return v_int64x4(_mm256_add_epi64(
+        _mm256_unpacklo_epi64(lo, hi),
+        _mm256_unpackhi_epi64(lo, hi)
+    ));
+}
+inline v_int64x4 v_dotprod_expand(const v_int16x16& a, const v_int16x16& b, const v_int64x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x4 v_dotprod_expand(const v_int32x8& a, const v_int32x8& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x4 v_dotprod_expand(const v_int32x8& a, const v_int32x8& b, const v_float64x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x8 v_dotprod_fast(const v_int16x16& a, const v_int16x16& b)
+{ return v_dotprod(a, b); }
+inline v_int32x8 v_dotprod_fast(const v_int16x16& a, const v_int16x16& b, const v_int32x8& c)
+{ return v_dotprod(a, b, c); }
+
+// 32 >> 64
+inline v_int64x4 v_dotprod_fast(const v_int32x8& a, const v_int32x8& b)
+{ return v_dotprod(a, b); }
+inline v_int64x4 v_dotprod_fast(const v_int32x8& a, const v_int32x8& b, const v_int64x4& c)
+{ return v_dotprod(a, b, c); }
+
+// 8 >> 32
+inline v_uint32x8 v_dotprod_expand_fast(const v_uint8x32& a, const v_uint8x32& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint32x8 v_dotprod_expand_fast(const v_uint8x32& a, const v_uint8x32& b, const v_uint32x8& c)
+{ return v_dotprod_expand(a, b, c); }
+
+inline v_int32x8 v_dotprod_expand_fast(const v_int8x32& a, const v_int8x32& b)
+{ return v_dotprod_expand(a, b); }
+inline v_int32x8 v_dotprod_expand_fast(const v_int8x32& a, const v_int8x32& b, const v_int32x8& c)
+{ return v_dotprod_expand(a, b, c); }
+
+// 16 >> 64
+inline v_uint64x4 v_dotprod_expand_fast(const v_uint16x16& a, const v_uint16x16& b)
+{
+    __m256i mullo = _mm256_mullo_epi16(a.val, b.val);
+    __m256i mulhi = _mm256_mulhi_epu16(a.val, b.val);
+    __m256i mul0  = _mm256_unpacklo_epi16(mullo, mulhi);
+    __m256i mul1  = _mm256_unpackhi_epi16(mullo, mulhi);
+
+    __m256i p02   = _mm256_blend_epi32(mul0, _mm256_setzero_si256(), 0xAA);
+    __m256i p13   = _mm256_srli_epi64(mul0, 32);
+    __m256i p46   = _mm256_blend_epi32(mul1, _mm256_setzero_si256(), 0xAA);
+    __m256i p57   = _mm256_srli_epi64(mul1, 32);
+
+    __m256i p15_  = _mm256_add_epi64(p02, p13);
+    __m256i p9d_  = _mm256_add_epi64(p46, p57);
+
+    return v_uint64x4(_mm256_add_epi64(p15_, p9d_));
+}
+inline v_uint64x4 v_dotprod_expand_fast(const v_uint16x16& a, const v_uint16x16& b, const v_uint64x4& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+inline v_int64x4 v_dotprod_expand_fast(const v_int16x16& a, const v_int16x16& b)
+{
+    __m256i prod = _mm256_madd_epi16(a.val, b.val);
+    __m256i sign = _mm256_srai_epi32(prod, 31);
+    __m256i lo = _mm256_unpacklo_epi32(prod, sign);
+    __m256i hi = _mm256_unpackhi_epi32(prod, sign);
+    return v_int64x4(_mm256_add_epi64(lo, hi));
+}
+inline v_int64x4 v_dotprod_expand_fast(const v_int16x16& a, const v_int16x16& b, const v_int64x4& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x4 v_dotprod_expand_fast(const v_int32x8& a, const v_int32x8& b)
+{ return v_dotprod_expand(a, b); }
+inline v_float64x4 v_dotprod_expand_fast(const v_int32x8& a, const v_int32x8& b, const v_float64x4& c)
+{ return v_dotprod_expand(a, b, c); }
+
+#define OPENCV_HAL_AVX_SPLAT2_PS(a, im) \
+    v_float32x8(_mm256_permute_ps(a.val, _MM_SHUFFLE(im, im, im, im)))
+
+inline v_float32x8 v_matmul(const v_float32x8& v, const v_float32x8& m0,
+                            const v_float32x8& m1, const v_float32x8& m2,
+                            const v_float32x8& m3)
+{
+    v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0);
+    v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1);
+    v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2);
+    v_float32x8 v37 = OPENCV_HAL_AVX_SPLAT2_PS(v, 3);
+    return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, v37 * m3)));
+}
+
+inline v_float32x8 v_matmuladd(const v_float32x8& v, const v_float32x8& m0,
+                               const v_float32x8& m1, const v_float32x8& m2,
+                               const v_float32x8& a)
+{
+    v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0);
+    v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1);
+    v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2);
+    return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, a)));
+}
+
+#define OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to)    \
+    inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1,              \
+                               const _Tpvec& a2, const _Tpvec& a3,              \
+                               _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3)  \
+    {                                                                           \
+        __m256i t0 = cast_from(_mm256_unpacklo_##suffix(a0.val, a1.val));       \
+        __m256i t1 = cast_from(_mm256_unpacklo_##suffix(a2.val, a3.val));       \
+        __m256i t2 = cast_from(_mm256_unpackhi_##suffix(a0.val, a1.val));       \
+        __m256i t3 = cast_from(_mm256_unpackhi_##suffix(a2.val, a3.val));       \
+        b0.val = cast_to(_mm256_unpacklo_epi64(t0, t1));                        \
+        b1.val = cast_to(_mm256_unpackhi_epi64(t0, t1));                        \
+        b2.val = cast_to(_mm256_unpacklo_epi64(t2, t3));                        \
+        b3.val = cast_to(_mm256_unpackhi_epi64(t2, t3));                        \
+    }
+
+OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_uint32x8,  epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_int32x8,   epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_float32x8, ps, _mm256_castps_si256, _mm256_castsi256_ps)
+
+//////////////// Value reordering ///////////////
+
+/* Expand */
+#define OPENCV_HAL_IMPL_AVX_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin)    \
+    inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
+    {                                                               \
+        b0.val = intrin(_v256_extract_low(a.val));                  \
+        b1.val = intrin(_v256_extract_high(a.val));                 \
+    }                                                               \
+    inline _Tpwvec v_expand_low(const _Tpvec& a)                    \
+    { return _Tpwvec(intrin(_v256_extract_low(a.val))); }           \
+    inline _Tpwvec v_expand_high(const _Tpvec& a)                   \
+    { return _Tpwvec(intrin(_v256_extract_high(a.val))); }          \
+    inline _Tpwvec v256_load_expand(const _Tp* ptr)                 \
+    {                                                               \
+        __m128i a = _mm_loadu_si128((const __m128i*)ptr);           \
+        return _Tpwvec(intrin(a));                                  \
+    }
+
+OPENCV_HAL_IMPL_AVX_EXPAND(v_uint8x32,  v_uint16x16, uchar,    _mm256_cvtepu8_epi16)
+OPENCV_HAL_IMPL_AVX_EXPAND(v_int8x32,   v_int16x16,  schar,    _mm256_cvtepi8_epi16)
+OPENCV_HAL_IMPL_AVX_EXPAND(v_uint16x16, v_uint32x8,  ushort,   _mm256_cvtepu16_epi32)
+OPENCV_HAL_IMPL_AVX_EXPAND(v_int16x16,  v_int32x8,   short,    _mm256_cvtepi16_epi32)
+OPENCV_HAL_IMPL_AVX_EXPAND(v_uint32x8,  v_uint64x4,  unsigned, _mm256_cvtepu32_epi64)
+OPENCV_HAL_IMPL_AVX_EXPAND(v_int32x8,   v_int64x4,   int,      _mm256_cvtepi32_epi64)
+
+#define OPENCV_HAL_IMPL_AVX_EXPAND_Q(_Tpvec, _Tp, intrin)   \
+    inline _Tpvec v256_load_expand_q(const _Tp* ptr)        \
+    {                                                       \
+        __m128i a = _mm_loadl_epi64((const __m128i*)ptr);   \
+        return _Tpvec(intrin(a));                           \
+    }
+
+OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_uint32x8, uchar, _mm256_cvtepu8_epi32)
+OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_int32x8,  schar, _mm256_cvtepi8_epi32)
+
+/* pack */
+// 16
+inline v_int8x32 v_pack(const v_int16x16& a, const v_int16x16& b)
+{ return v_int8x32(_v256_shuffle_odd_64(_mm256_packs_epi16(a.val, b.val))); }
+
+inline v_uint8x32 v_pack(const v_uint16x16& a, const v_uint16x16& b)
+{
+    __m256i t = _mm256_set1_epi16(255);
+    __m256i a1 = _mm256_min_epu16(a.val, t);
+    __m256i b1 = _mm256_min_epu16(b.val, t);
+    return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a1, b1)));
+}
+
+inline v_uint8x32 v_pack_u(const v_int16x16& a, const v_int16x16& b)
+{
+    return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a.val, b.val)));
+}
+
+inline void v_pack_store(schar* ptr, const v_int16x16& a)
+{ v_store_low(ptr, v_pack(a, a)); }
+
+inline void v_pack_store(uchar* ptr, const v_uint16x16& a)
+{
+    const __m256i m = _mm256_set1_epi16(255);
+    __m256i am = _mm256_min_epu16(a.val, m);
+            am =  _v256_shuffle_odd_64(_mm256_packus_epi16(am, am));
+    v_store_low(ptr, v_uint8x32(am));
+}
+
+inline void v_pack_u_store(uchar* ptr, const v_int16x16& a)
+{ v_store_low(ptr, v_pack_u(a, a)); }
+
+template<int n> inline
+v_uint8x32 v_rshr_pack(const v_uint16x16& a, const v_uint16x16& b)
+{
+    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
+    v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1)));
+    return v_pack_u(v_reinterpret_as_s16((a + delta) >> n),
+                    v_reinterpret_as_s16((b + delta) >> n));
+}
+
+template<int n> inline
+void v_rshr_pack_store(uchar* ptr, const v_uint16x16& a)
+{
+    v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1)));
+    v_pack_u_store(ptr, v_reinterpret_as_s16((a + delta) >> n));
+}
+
+template<int n> inline
+v_uint8x32 v_rshr_pack_u(const v_int16x16& a, const v_int16x16& b)
+{
+    v_int16x16 delta = v256_setall_s16((short)(1 << (n-1)));
+    return v_pack_u((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_u_store(uchar* ptr, const v_int16x16& a)
+{
+    v_int16x16 delta = v256_setall_s16((short)(1 << (n-1)));
+    v_pack_u_store(ptr, (a + delta) >> n);
+}
+
+template<int n> inline
+v_int8x32 v_rshr_pack(const v_int16x16& a, const v_int16x16& b)
+{
+    v_int16x16 delta = v256_setall_s16((short)(1 << (n-1)));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(schar* ptr, const v_int16x16& a)
+{
+    v_int16x16 delta = v256_setall_s16((short)(1 << (n-1)));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+// 32
+inline v_int16x16 v_pack(const v_int32x8& a, const v_int32x8& b)
+{ return v_int16x16(_v256_shuffle_odd_64(_mm256_packs_epi32(a.val, b.val))); }
+
+inline v_uint16x16 v_pack(const v_uint32x8& a, const v_uint32x8& b)
+{ return v_uint16x16(_v256_shuffle_odd_64(_v256_packs_epu32(a.val, b.val))); }
+
+inline v_uint16x16 v_pack_u(const v_int32x8& a, const v_int32x8& b)
+{ return v_uint16x16(_v256_shuffle_odd_64(_mm256_packus_epi32(a.val, b.val))); }
+
+inline void v_pack_store(short* ptr, const v_int32x8& a)
+{ v_store_low(ptr, v_pack(a, a)); }
+
+inline void v_pack_store(ushort* ptr, const v_uint32x8& a)
+{
+    const __m256i m = _mm256_set1_epi32(65535);
+    __m256i am = _mm256_min_epu32(a.val, m);
+            am = _v256_shuffle_odd_64(_mm256_packus_epi32(am, am));
+    v_store_low(ptr, v_uint16x16(am));
+}
+
+inline void v_pack_u_store(ushort* ptr, const v_int32x8& a)
+{ v_store_low(ptr, v_pack_u(a, a)); }
+
+
+template<int n> inline
+v_uint16x16 v_rshr_pack(const v_uint32x8& a, const v_uint32x8& b)
+{
+    // we assume that n > 0, and so the shifted 32-bit values can be treated as signed numbers.
+    v_uint32x8 delta = v256_setall_u32(1 << (n-1));
+    return v_pack_u(v_reinterpret_as_s32((a + delta) >> n),
+                    v_reinterpret_as_s32((b + delta) >> n));
+}
+
+template<int n> inline
+void v_rshr_pack_store(ushort* ptr, const v_uint32x8& a)
+{
+    v_uint32x8 delta = v256_setall_u32(1 << (n-1));
+    v_pack_u_store(ptr, v_reinterpret_as_s32((a + delta) >> n));
+}
+
+template<int n> inline
+v_uint16x16 v_rshr_pack_u(const v_int32x8& a, const v_int32x8& b)
+{
+    v_int32x8 delta = v256_setall_s32(1 << (n-1));
+    return v_pack_u((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_u_store(ushort* ptr, const v_int32x8& a)
+{
+    v_int32x8 delta = v256_setall_s32(1 << (n-1));
+    v_pack_u_store(ptr, (a + delta) >> n);
+}
+
+template<int n> inline
+v_int16x16 v_rshr_pack(const v_int32x8& a, const v_int32x8& b)
+{
+    v_int32x8 delta = v256_setall_s32(1 << (n-1));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(short* ptr, const v_int32x8& a)
+{
+    v_int32x8 delta = v256_setall_s32(1 << (n-1));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+// 64
+// Non-saturating pack
+inline v_uint32x8 v_pack(const v_uint64x4& a, const v_uint64x4& b)
+{
+    __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0));
+    __m256i b0 = _mm256_shuffle_epi32(b.val, _MM_SHUFFLE(0, 0, 2, 0));
+    __m256i ab = _mm256_unpacklo_epi64(a0, b0); // a0, a1, b0, b1, a2, a3, b2, b3
+    return v_uint32x8(_v256_shuffle_odd_64(ab));
+}
+
+inline v_int32x8 v_pack(const v_int64x4& a, const v_int64x4& b)
+{ return v_reinterpret_as_s32(v_pack(v_reinterpret_as_u64(a), v_reinterpret_as_u64(b))); }
+
+inline void v_pack_store(unsigned* ptr, const v_uint64x4& a)
+{
+    __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0));
+    v_store_low(ptr, v_uint32x8(_v256_shuffle_odd_64(a0)));
+}
+
+inline void v_pack_store(int* ptr, const v_int64x4& b)
+{ v_pack_store((unsigned*)ptr, v_reinterpret_as_u64(b)); }
+
+template<int n> inline
+v_uint32x8 v_rshr_pack(const v_uint64x4& a, const v_uint64x4& b)
+{
+    v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(unsigned* ptr, const v_uint64x4& a)
+{
+    v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+template<int n> inline
+v_int32x8 v_rshr_pack(const v_int64x4& a, const v_int64x4& b)
+{
+    v_int64x4 delta = v256_setall_s64((int64)1 << (n-1));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(int* ptr, const v_int64x4& a)
+{
+    v_int64x4 delta = v256_setall_s64((int64)1 << (n-1));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+// pack boolean
+inline v_uint8x32 v_pack_b(const v_uint16x16& a, const v_uint16x16& b)
+{
+    __m256i ab = _mm256_packs_epi16(a.val, b.val);
+    return v_uint8x32(_v256_shuffle_odd_64(ab));
+}
+
+inline v_uint8x32 v_pack_b(const v_uint32x8& a, const v_uint32x8& b,
+                           const v_uint32x8& c, const v_uint32x8& d)
+{
+    __m256i ab = _mm256_packs_epi32(a.val, b.val);
+    __m256i cd = _mm256_packs_epi32(c.val, d.val);
+
+    __m256i abcd = _v256_shuffle_odd_64(_mm256_packs_epi16(ab, cd));
+    return v_uint8x32(_mm256_shuffle_epi32(abcd, _MM_SHUFFLE(3, 1, 2, 0)));
+}
+
+inline v_uint8x32 v_pack_b(const v_uint64x4& a, const v_uint64x4& b, const v_uint64x4& c,
+                           const v_uint64x4& d, const v_uint64x4& e, const v_uint64x4& f,
+                           const v_uint64x4& g, const v_uint64x4& h)
+{
+    __m256i ab = _mm256_packs_epi32(a.val, b.val);
+    __m256i cd = _mm256_packs_epi32(c.val, d.val);
+    __m256i ef = _mm256_packs_epi32(e.val, f.val);
+    __m256i gh = _mm256_packs_epi32(g.val, h.val);
+
+    __m256i abcd = _mm256_packs_epi32(ab, cd);
+    __m256i efgh = _mm256_packs_epi32(ef, gh);
+    __m256i pkall = _v256_shuffle_odd_64(_mm256_packs_epi16(abcd, efgh));
+
+    __m256i rev = _mm256_alignr_epi8(pkall, pkall, 8);
+    return v_uint8x32(_mm256_unpacklo_epi16(pkall, rev));
+}
+
+/* Recombine */
+// its up there with load and store operations
+
+/* Extract */
+#define OPENCV_HAL_IMPL_AVX_EXTRACT(_Tpvec)                    \
+    template<int s>                                            \
+    inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b)  \
+    { return v_rotate_right<s>(a, b); }
+
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint8x32)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_int8x32)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint16x16)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_int16x16)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint32x8)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_int32x8)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint64x4)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_int64x4)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_float32x8)
+OPENCV_HAL_IMPL_AVX_EXTRACT(v_float64x4)
+
+template<int i>
+inline uchar v_extract_n(v_uint8x32 a)
+{
+    return (uchar)_v256_extract_epi8<i>(a.val);
+}
+
+template<int i>
+inline schar v_extract_n(v_int8x32 a)
+{
+    return (schar)v_extract_n<i>(v_reinterpret_as_u8(a));
+}
+
+template<int i>
+inline ushort v_extract_n(v_uint16x16 a)
+{
+    return (ushort)_v256_extract_epi16<i>(a.val);
+}
+
+template<int i>
+inline short v_extract_n(v_int16x16 a)
+{
+    return (short)v_extract_n<i>(v_reinterpret_as_u16(a));
+}
+
+template<int i>
+inline uint v_extract_n(v_uint32x8 a)
+{
+    return (uint)_v256_extract_epi32<i>(a.val);
+}
+
+template<int i>
+inline int v_extract_n(v_int32x8 a)
+{
+    return (int)v_extract_n<i>(v_reinterpret_as_u32(a));
+}
+
+template<int i>
+inline uint64 v_extract_n(v_uint64x4 a)
+{
+    return (uint64)_v256_extract_epi64<i>(a.val);
+}
+
+template<int i>
+inline int64 v_extract_n(v_int64x4 v)
+{
+    return (int64)v_extract_n<i>(v_reinterpret_as_u64(v));
+}
+
+template<int i>
+inline float v_extract_n(v_float32x8 v)
+{
+    union { uint iv; float fv; } d;
+    d.iv = v_extract_n<i>(v_reinterpret_as_u32(v));
+    return d.fv;
+}
+
+template<int i>
+inline double v_extract_n(v_float64x4 v)
+{
+    union { uint64 iv; double dv; } d;
+    d.iv = v_extract_n<i>(v_reinterpret_as_u64(v));
+    return d.dv;
+}
+
+template<int i>
+inline v_uint32x8 v_broadcast_element(v_uint32x8 a)
+{
+    static const __m256i perm = _mm256_set1_epi32((char)i);
+    return v_uint32x8(_mm256_permutevar8x32_epi32(a.val, perm));
+}
+
+template<int i>
+inline v_int32x8 v_broadcast_element(const v_int32x8 &a)
+{ return v_reinterpret_as_s32(v_broadcast_element<i>(v_reinterpret_as_u32(a))); }
+
+template<int i>
+inline v_float32x8 v_broadcast_element(const v_float32x8 &a)
+{ return v_reinterpret_as_f32(v_broadcast_element<i>(v_reinterpret_as_u32(a))); }
+
+
+///////////////////// load deinterleave /////////////////////////////
+
+inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b )
+{
+    __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 32));
+
+    const __m256i sh = _mm256_setr_epi8(0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15,
+                                               0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15);
+    __m256i p0 = _mm256_shuffle_epi8(ab0, sh);
+    __m256i p1 = _mm256_shuffle_epi8(ab1, sh);
+    __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16);
+    __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16);
+    __m256i a0 = _mm256_unpacklo_epi64(pl, ph);
+    __m256i b0 = _mm256_unpackhi_epi64(pl, ph);
+    a = v_uint8x32(a0);
+    b = v_uint8x32(b0);
+}
+
+inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b )
+{
+    __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 16));
+
+    const __m256i sh = _mm256_setr_epi8(0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15,
+                                               0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15);
+    __m256i p0 = _mm256_shuffle_epi8(ab0, sh);
+    __m256i p1 = _mm256_shuffle_epi8(ab1, sh);
+    __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16);
+    __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16);
+    __m256i a0 = _mm256_unpacklo_epi64(pl, ph);
+    __m256i b0 = _mm256_unpackhi_epi64(pl, ph);
+    a = v_uint16x16(a0);
+    b = v_uint16x16(b0);
+}
+
+inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b )
+{
+    __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 8));
+
+    enum { sh = 0+2*4+1*16+3*64 };
+    __m256i p0 = _mm256_shuffle_epi32(ab0, sh);
+    __m256i p1 = _mm256_shuffle_epi32(ab1, sh);
+    __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16);
+    __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16);
+    __m256i a0 = _mm256_unpacklo_epi64(pl, ph);
+    __m256i b0 = _mm256_unpackhi_epi64(pl, ph);
+    a = v_uint32x8(a0);
+    b = v_uint32x8(b0);
+}
+
+inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b )
+{
+    __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 4));
+
+    __m256i pl = _mm256_permute2x128_si256(ab0, ab1, 0 + 2*16);
+    __m256i ph = _mm256_permute2x128_si256(ab0, ab1, 1 + 3*16);
+    __m256i a0 = _mm256_unpacklo_epi64(pl, ph);
+    __m256i b0 = _mm256_unpackhi_epi64(pl, ph);
+    a = v_uint64x4(a0);
+    b = v_uint64x4(b0);
+}
+
+inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b, v_uint8x32& c )
+{
+    __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32));
+    __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64));
+
+    __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16);
+    __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16);
+
+    const __m256i m0 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0,
+                                               0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0);
+    const __m256i m1 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0,
+                                               -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1);
+
+    __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1);
+    __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0);
+    __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1);
+
+    const __m256i
+    sh_b = _mm256_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13,
+                            0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13),
+    sh_g = _mm256_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14,
+                            1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14),
+    sh_r = _mm256_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15,
+                            2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15);
+    b0 = _mm256_shuffle_epi8(b0, sh_b);
+    g0 = _mm256_shuffle_epi8(g0, sh_g);
+    r0 = _mm256_shuffle_epi8(r0, sh_r);
+
+    a = v_uint8x32(b0);
+    b = v_uint8x32(g0);
+    c = v_uint8x32(r0);
+}
+
+inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b, v_uint16x16& c )
+{
+    __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16));
+    __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32));
+
+    __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16);
+    __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16);
+
+    const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1,
+                                               0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0);
+    const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0,
+                                               -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0);
+    __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1);
+    __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1);
+    __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0);
+    const __m256i sh_b = _mm256_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11,
+                                                 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11);
+    const __m256i sh_g = _mm256_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13,
+                                                 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13);
+    const __m256i sh_r = _mm256_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15,
+                                                 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15);
+    b0 = _mm256_shuffle_epi8(b0, sh_b);
+    g0 = _mm256_shuffle_epi8(g0, sh_g);
+    r0 = _mm256_shuffle_epi8(r0, sh_r);
+
+    a = v_uint16x16(b0);
+    b = v_uint16x16(g0);
+    c = v_uint16x16(r0);
+}
+
+inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b, v_uint32x8& c )
+{
+    __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 8));
+    __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 16));
+
+    __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16);
+    __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16);
+
+    __m256i b0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_low, s02_high, 0x24), bgr1, 0x92);
+    __m256i g0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_high, s02_low, 0x92), bgr1, 0x24);
+    __m256i r0 = _mm256_blend_epi32(_mm256_blend_epi32(bgr1, s02_low, 0x24), s02_high, 0x92);
+
+    b0 = _mm256_shuffle_epi32(b0, 0x6c);
+    g0 = _mm256_shuffle_epi32(g0, 0xb1);
+    r0 = _mm256_shuffle_epi32(r0, 0xc6);
+
+    a = v_uint32x8(b0);
+    b = v_uint32x8(g0);
+    c = v_uint32x8(r0);
+}
+
+inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b, v_uint64x4& c )
+{
+    __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 4));
+    __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 8));
+
+    __m256i s01 = _mm256_blend_epi32(bgr0, bgr1, 0xf0);
+    __m256i s12 = _mm256_blend_epi32(bgr1, bgr2, 0xf0);
+    __m256i s20r = _mm256_permute4x64_epi64(_mm256_blend_epi32(bgr2, bgr0, 0xf0), 0x1b);
+    __m256i b0 = _mm256_unpacklo_epi64(s01, s20r);
+    __m256i g0 = _mm256_alignr_epi8(s12, s01, 8);
+    __m256i r0 = _mm256_unpackhi_epi64(s20r, s12);
+
+    a = v_uint64x4(b0);
+    b = v_uint64x4(g0);
+    c = v_uint64x4(r0);
+}
+
+inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b, v_uint8x32& c, v_uint8x32& d )
+{
+    __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32));
+    __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64));
+    __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 96));
+    const __m256i sh = _mm256_setr_epi8(0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15,
+                                               0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15);
+
+    __m256i p0 = _mm256_shuffle_epi8(bgr0, sh);
+    __m256i p1 = _mm256_shuffle_epi8(bgr1, sh);
+    __m256i p2 = _mm256_shuffle_epi8(bgr2, sh);
+    __m256i p3 = _mm256_shuffle_epi8(bgr3, sh);
+
+    __m256i p01l = _mm256_unpacklo_epi32(p0, p1);
+    __m256i p01h = _mm256_unpackhi_epi32(p0, p1);
+    __m256i p23l = _mm256_unpacklo_epi32(p2, p3);
+    __m256i p23h = _mm256_unpackhi_epi32(p2, p3);
+
+    __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16);
+    __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16);
+    __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16);
+    __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16);
+
+    __m256i b0 = _mm256_unpacklo_epi32(pll, plh);
+    __m256i g0 = _mm256_unpackhi_epi32(pll, plh);
+    __m256i r0 = _mm256_unpacklo_epi32(phl, phh);
+    __m256i a0 = _mm256_unpackhi_epi32(phl, phh);
+
+    a = v_uint8x32(b0);
+    b = v_uint8x32(g0);
+    c = v_uint8x32(r0);
+    d = v_uint8x32(a0);
+}
+
+inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b, v_uint16x16& c, v_uint16x16& d )
+{
+    __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16));
+    __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32));
+    __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 48));
+    const __m256i sh = _mm256_setr_epi8(0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15,
+                                               0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15);
+    __m256i p0 = _mm256_shuffle_epi8(bgr0, sh);
+    __m256i p1 = _mm256_shuffle_epi8(bgr1, sh);
+    __m256i p2 = _mm256_shuffle_epi8(bgr2, sh);
+    __m256i p3 = _mm256_shuffle_epi8(bgr3, sh);
+
+    __m256i p01l = _mm256_unpacklo_epi32(p0, p1);
+    __m256i p01h = _mm256_unpackhi_epi32(p0, p1);
+    __m256i p23l = _mm256_unpacklo_epi32(p2, p3);
+    __m256i p23h = _mm256_unpackhi_epi32(p2, p3);
+
+    __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16);
+    __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16);
+    __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16);
+    __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16);
+
+    __m256i b0 = _mm256_unpacklo_epi32(pll, plh);
+    __m256i g0 = _mm256_unpackhi_epi32(pll, plh);
+    __m256i r0 = _mm256_unpacklo_epi32(phl, phh);
+    __m256i a0 = _mm256_unpackhi_epi32(phl, phh);
+
+    a = v_uint16x16(b0);
+    b = v_uint16x16(g0);
+    c = v_uint16x16(r0);
+    d = v_uint16x16(a0);
+}
+
+inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b, v_uint32x8& c, v_uint32x8& d )
+{
+    __m256i p0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i p1 = _mm256_loadu_si256((const __m256i*)(ptr + 8));
+    __m256i p2 = _mm256_loadu_si256((const __m256i*)(ptr + 16));
+    __m256i p3 = _mm256_loadu_si256((const __m256i*)(ptr + 24));
+
+    __m256i p01l = _mm256_unpacklo_epi32(p0, p1);
+    __m256i p01h = _mm256_unpackhi_epi32(p0, p1);
+    __m256i p23l = _mm256_unpacklo_epi32(p2, p3);
+    __m256i p23h = _mm256_unpackhi_epi32(p2, p3);
+
+    __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16);
+    __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16);
+    __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16);
+    __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16);
+
+    __m256i b0 = _mm256_unpacklo_epi32(pll, plh);
+    __m256i g0 = _mm256_unpackhi_epi32(pll, plh);
+    __m256i r0 = _mm256_unpacklo_epi32(phl, phh);
+    __m256i a0 = _mm256_unpackhi_epi32(phl, phh);
+
+    a = v_uint32x8(b0);
+    b = v_uint32x8(g0);
+    c = v_uint32x8(r0);
+    d = v_uint32x8(a0);
+}
+
+inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b, v_uint64x4& c, v_uint64x4& d )
+{
+    __m256i bgra0 = _mm256_loadu_si256((const __m256i*)ptr);
+    __m256i bgra1 = _mm256_loadu_si256((const __m256i*)(ptr + 4));
+    __m256i bgra2 = _mm256_loadu_si256((const __m256i*)(ptr + 8));
+    __m256i bgra3 = _mm256_loadu_si256((const __m256i*)(ptr + 12));
+
+    __m256i l02 = _mm256_permute2x128_si256(bgra0, bgra2, 0 + 2*16);
+    __m256i h02 = _mm256_permute2x128_si256(bgra0, bgra2, 1 + 3*16);
+    __m256i l13 = _mm256_permute2x128_si256(bgra1, bgra3, 0 + 2*16);
+    __m256i h13 = _mm256_permute2x128_si256(bgra1, bgra3, 1 + 3*16);
+
+    __m256i b0 = _mm256_unpacklo_epi64(l02, l13);
+    __m256i g0 = _mm256_unpackhi_epi64(l02, l13);
+    __m256i r0 = _mm256_unpacklo_epi64(h02, h13);
+    __m256i a0 = _mm256_unpackhi_epi64(h02, h13);
+
+    a = v_uint64x4(b0);
+    b = v_uint64x4(g0);
+    c = v_uint64x4(r0);
+    d = v_uint64x4(a0);
+}
+
+///////////////////////////// store interleave /////////////////////////////////////
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x32& x, const v_uint8x32& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i xy_l = _mm256_unpacklo_epi8(x.val, y.val);
+    __m256i xy_h = _mm256_unpackhi_epi8(x.val, y.val);
+
+    __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16);
+    __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, xy0);
+        _mm256_stream_si256((__m256i*)(ptr + 32), xy1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, xy0);
+        _mm256_store_si256((__m256i*)(ptr + 32), xy1);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, xy0);
+        _mm256_storeu_si256((__m256i*)(ptr + 32), xy1);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x16& x, const v_uint16x16& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i xy_l = _mm256_unpacklo_epi16(x.val, y.val);
+    __m256i xy_h = _mm256_unpackhi_epi16(x.val, y.val);
+
+    __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16);
+    __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, xy0);
+        _mm256_stream_si256((__m256i*)(ptr + 16), xy1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, xy0);
+        _mm256_store_si256((__m256i*)(ptr + 16), xy1);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, xy0);
+        _mm256_storeu_si256((__m256i*)(ptr + 16), xy1);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x8& x, const v_uint32x8& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i xy_l = _mm256_unpacklo_epi32(x.val, y.val);
+    __m256i xy_h = _mm256_unpackhi_epi32(x.val, y.val);
+
+    __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16);
+    __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, xy0);
+        _mm256_stream_si256((__m256i*)(ptr + 8), xy1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, xy0);
+        _mm256_store_si256((__m256i*)(ptr + 8), xy1);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, xy0);
+        _mm256_storeu_si256((__m256i*)(ptr + 8), xy1);
+    }
+}
+
+inline void v_store_interleave( uint64* ptr, const v_uint64x4& x, const v_uint64x4& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i xy_l = _mm256_unpacklo_epi64(x.val, y.val);
+    __m256i xy_h = _mm256_unpackhi_epi64(x.val, y.val);
+
+    __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16);
+    __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, xy0);
+        _mm256_stream_si256((__m256i*)(ptr + 4), xy1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, xy0);
+        _mm256_store_si256((__m256i*)(ptr + 4), xy1);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, xy0);
+        _mm256_storeu_si256((__m256i*)(ptr + 4), xy1);
+    }
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x32& a, const v_uint8x32& b, const v_uint8x32& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    const __m256i sh_b = _mm256_setr_epi8(
+            0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5,
+            0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5);
+    const __m256i sh_g = _mm256_setr_epi8(
+            5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10,
+            5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10);
+    const __m256i sh_r = _mm256_setr_epi8(
+            10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15,
+            10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15);
+
+    __m256i b0 = _mm256_shuffle_epi8(a.val, sh_b);
+    __m256i g0 = _mm256_shuffle_epi8(b.val, sh_g);
+    __m256i r0 = _mm256_shuffle_epi8(c.val, sh_r);
+
+    const __m256i m0 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0,
+                                               0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0);
+    const __m256i m1 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0,
+                                               0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0);
+
+    __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1);
+    __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1);
+    __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1);
+
+    __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16);
+    __m256i bgr1 = _mm256_permute2x128_si256(p2, p0, 0 + 3*16);
+    __m256i bgr2 = _mm256_permute2x128_si256(p1, p2, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgr0);
+        _mm256_stream_si256((__m256i*)(ptr + 32), bgr1);
+        _mm256_stream_si256((__m256i*)(ptr + 64), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgr0);
+        _mm256_store_si256((__m256i*)(ptr + 32), bgr1);
+        _mm256_store_si256((__m256i*)(ptr + 64), bgr2);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgr0);
+        _mm256_storeu_si256((__m256i*)(ptr + 32), bgr1);
+        _mm256_storeu_si256((__m256i*)(ptr + 64), bgr2);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x16& a, const v_uint16x16& b, const v_uint16x16& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    const __m256i sh_b = _mm256_setr_epi8(
+         0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11,
+         0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11);
+    const __m256i sh_g = _mm256_setr_epi8(
+         10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5,
+         10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5);
+    const __m256i sh_r = _mm256_setr_epi8(
+         4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15,
+         4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15);
+
+    __m256i b0 = _mm256_shuffle_epi8(a.val, sh_b);
+    __m256i g0 = _mm256_shuffle_epi8(b.val, sh_g);
+    __m256i r0 = _mm256_shuffle_epi8(c.val, sh_r);
+
+    const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1,
+                                               0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0);
+    const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0,
+                                               -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0);
+
+    __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1);
+    __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1);
+    __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1);
+
+    __m256i bgr0 = _mm256_permute2x128_si256(p0, p2, 0 + 2*16);
+    //__m256i bgr1 = p1;
+    __m256i bgr2 = _mm256_permute2x128_si256(p0, p2, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgr0);
+        _mm256_stream_si256((__m256i*)(ptr + 16), p1);
+        _mm256_stream_si256((__m256i*)(ptr + 32), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgr0);
+        _mm256_store_si256((__m256i*)(ptr + 16), p1);
+        _mm256_store_si256((__m256i*)(ptr + 32), bgr2);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgr0);
+        _mm256_storeu_si256((__m256i*)(ptr + 16), p1);
+        _mm256_storeu_si256((__m256i*)(ptr + 32), bgr2);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x8& a, const v_uint32x8& b, const v_uint32x8& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i b0 = _mm256_shuffle_epi32(a.val, 0x6c);
+    __m256i g0 = _mm256_shuffle_epi32(b.val, 0xb1);
+    __m256i r0 = _mm256_shuffle_epi32(c.val, 0xc6);
+
+    __m256i p0 = _mm256_blend_epi32(_mm256_blend_epi32(b0, g0, 0x92), r0, 0x24);
+    __m256i p1 = _mm256_blend_epi32(_mm256_blend_epi32(g0, r0, 0x92), b0, 0x24);
+    __m256i p2 = _mm256_blend_epi32(_mm256_blend_epi32(r0, b0, 0x92), g0, 0x24);
+
+    __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16);
+    //__m256i bgr1 = p2;
+    __m256i bgr2 = _mm256_permute2x128_si256(p0, p1, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgr0);
+        _mm256_stream_si256((__m256i*)(ptr + 8), p2);
+        _mm256_stream_si256((__m256i*)(ptr + 16), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgr0);
+        _mm256_store_si256((__m256i*)(ptr + 8), p2);
+        _mm256_store_si256((__m256i*)(ptr + 16), bgr2);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgr0);
+        _mm256_storeu_si256((__m256i*)(ptr + 8), p2);
+        _mm256_storeu_si256((__m256i*)(ptr + 16), bgr2);
+    }
+}
+
+inline void v_store_interleave( uint64* ptr, const v_uint64x4& a, const v_uint64x4& b, const v_uint64x4& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i s01 = _mm256_unpacklo_epi64(a.val, b.val);
+    __m256i s12 = _mm256_unpackhi_epi64(b.val, c.val);
+    __m256i s20 = _mm256_blend_epi32(c.val, a.val, 0xcc);
+
+    __m256i bgr0 = _mm256_permute2x128_si256(s01, s20, 0 + 2*16);
+    __m256i bgr1 = _mm256_blend_epi32(s01, s12, 0x0f);
+    __m256i bgr2 = _mm256_permute2x128_si256(s20, s12, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgr0);
+        _mm256_stream_si256((__m256i*)(ptr + 4), bgr1);
+        _mm256_stream_si256((__m256i*)(ptr + 8), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgr0);
+        _mm256_store_si256((__m256i*)(ptr + 4), bgr1);
+        _mm256_store_si256((__m256i*)(ptr + 8), bgr2);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgr0);
+        _mm256_storeu_si256((__m256i*)(ptr + 4), bgr1);
+        _mm256_storeu_si256((__m256i*)(ptr + 8), bgr2);
+    }
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x32& a, const v_uint8x32& b,
+                                const v_uint8x32& c, const v_uint8x32& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i bg0 = _mm256_unpacklo_epi8(a.val, b.val);
+    __m256i bg1 = _mm256_unpackhi_epi8(a.val, b.val);
+    __m256i ra0 = _mm256_unpacklo_epi8(c.val, d.val);
+    __m256i ra1 = _mm256_unpackhi_epi8(c.val, d.val);
+
+    __m256i bgra0_ = _mm256_unpacklo_epi16(bg0, ra0);
+    __m256i bgra1_ = _mm256_unpackhi_epi16(bg0, ra0);
+    __m256i bgra2_ = _mm256_unpacklo_epi16(bg1, ra1);
+    __m256i bgra3_ = _mm256_unpackhi_epi16(bg1, ra1);
+
+    __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16);
+    __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16);
+    __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16);
+    __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgra0);
+        _mm256_stream_si256((__m256i*)(ptr + 32), bgra1);
+        _mm256_stream_si256((__m256i*)(ptr + 64), bgra2);
+        _mm256_stream_si256((__m256i*)(ptr + 96), bgra3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgra0);
+        _mm256_store_si256((__m256i*)(ptr + 32), bgra1);
+        _mm256_store_si256((__m256i*)(ptr + 64), bgra2);
+        _mm256_store_si256((__m256i*)(ptr + 96), bgra3);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgra0);
+        _mm256_storeu_si256((__m256i*)(ptr + 32), bgra1);
+        _mm256_storeu_si256((__m256i*)(ptr + 64), bgra2);
+        _mm256_storeu_si256((__m256i*)(ptr + 96), bgra3);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x16& a, const v_uint16x16& b,
+                                const v_uint16x16& c, const v_uint16x16& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i bg0 = _mm256_unpacklo_epi16(a.val, b.val);
+    __m256i bg1 = _mm256_unpackhi_epi16(a.val, b.val);
+    __m256i ra0 = _mm256_unpacklo_epi16(c.val, d.val);
+    __m256i ra1 = _mm256_unpackhi_epi16(c.val, d.val);
+
+    __m256i bgra0_ = _mm256_unpacklo_epi32(bg0, ra0);
+    __m256i bgra1_ = _mm256_unpackhi_epi32(bg0, ra0);
+    __m256i bgra2_ = _mm256_unpacklo_epi32(bg1, ra1);
+    __m256i bgra3_ = _mm256_unpackhi_epi32(bg1, ra1);
+
+    __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16);
+    __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16);
+    __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16);
+    __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgra0);
+        _mm256_stream_si256((__m256i*)(ptr + 16), bgra1);
+        _mm256_stream_si256((__m256i*)(ptr + 32), bgra2);
+        _mm256_stream_si256((__m256i*)(ptr + 48), bgra3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgra0);
+        _mm256_store_si256((__m256i*)(ptr + 16), bgra1);
+        _mm256_store_si256((__m256i*)(ptr + 32), bgra2);
+        _mm256_store_si256((__m256i*)(ptr + 48), bgra3);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgra0);
+        _mm256_storeu_si256((__m256i*)(ptr + 16), bgra1);
+        _mm256_storeu_si256((__m256i*)(ptr + 32), bgra2);
+        _mm256_storeu_si256((__m256i*)(ptr + 48), bgra3);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x8& a, const v_uint32x8& b,
+                                const v_uint32x8& c, const v_uint32x8& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i bg0 = _mm256_unpacklo_epi32(a.val, b.val);
+    __m256i bg1 = _mm256_unpackhi_epi32(a.val, b.val);
+    __m256i ra0 = _mm256_unpacklo_epi32(c.val, d.val);
+    __m256i ra1 = _mm256_unpackhi_epi32(c.val, d.val);
+
+    __m256i bgra0_ = _mm256_unpacklo_epi64(bg0, ra0);
+    __m256i bgra1_ = _mm256_unpackhi_epi64(bg0, ra0);
+    __m256i bgra2_ = _mm256_unpacklo_epi64(bg1, ra1);
+    __m256i bgra3_ = _mm256_unpackhi_epi64(bg1, ra1);
+
+    __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16);
+    __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16);
+    __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16);
+    __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgra0);
+        _mm256_stream_si256((__m256i*)(ptr + 8), bgra1);
+        _mm256_stream_si256((__m256i*)(ptr + 16), bgra2);
+        _mm256_stream_si256((__m256i*)(ptr + 24), bgra3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgra0);
+        _mm256_store_si256((__m256i*)(ptr + 8), bgra1);
+        _mm256_store_si256((__m256i*)(ptr + 16), bgra2);
+        _mm256_store_si256((__m256i*)(ptr + 24), bgra3);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgra0);
+        _mm256_storeu_si256((__m256i*)(ptr + 8), bgra1);
+        _mm256_storeu_si256((__m256i*)(ptr + 16), bgra2);
+        _mm256_storeu_si256((__m256i*)(ptr + 24), bgra3);
+    }
+}
+
+inline void v_store_interleave( uint64* ptr, const v_uint64x4& a, const v_uint64x4& b,
+                                const v_uint64x4& c, const v_uint64x4& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m256i bg0 = _mm256_unpacklo_epi64(a.val, b.val);
+    __m256i bg1 = _mm256_unpackhi_epi64(a.val, b.val);
+    __m256i ra0 = _mm256_unpacklo_epi64(c.val, d.val);
+    __m256i ra1 = _mm256_unpackhi_epi64(c.val, d.val);
+
+    __m256i bgra0 = _mm256_permute2x128_si256(bg0, ra0, 0 + 2*16);
+    __m256i bgra1 = _mm256_permute2x128_si256(bg1, ra1, 0 + 2*16);
+    __m256i bgra2 = _mm256_permute2x128_si256(bg0, ra0, 1 + 3*16);
+    __m256i bgra3 = _mm256_permute2x128_si256(bg1, ra1, 1 + 3*16);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm256_stream_si256((__m256i*)ptr, bgra0);
+        _mm256_stream_si256((__m256i*)(ptr + 4), bgra1);
+        _mm256_stream_si256((__m256i*)(ptr + 8), bgra2);
+        _mm256_stream_si256((__m256i*)(ptr + 12), bgra3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm256_store_si256((__m256i*)ptr, bgra0);
+        _mm256_store_si256((__m256i*)(ptr + 4), bgra1);
+        _mm256_store_si256((__m256i*)(ptr + 8), bgra2);
+        _mm256_store_si256((__m256i*)(ptr + 12), bgra3);
+    }
+    else
+    {
+        _mm256_storeu_si256((__m256i*)ptr, bgra0);
+        _mm256_storeu_si256((__m256i*)(ptr + 4), bgra1);
+        _mm256_storeu_si256((__m256i*)(ptr + 8), bgra2);
+        _mm256_storeu_si256((__m256i*)(ptr + 12), bgra3);
+    }
+}
+
+#define OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \
+{ \
+    _Tpvec1 a1, b1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \
+{ \
+    _Tpvec1 a1, b1, c1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \
+{ \
+    _Tpvec1 a1, b1, c1, d1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+    d0 = v_reinterpret_as_##suffix0(d1); \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                hal::StoreMode mode=hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, mode);      \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, const _Tpvec0& c0, \
+                                hal::StoreMode mode=hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode);  \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                const _Tpvec0& c0, const _Tpvec0& d0, \
+                                hal::StoreMode mode=hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \
+}
+
+OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int8x32, schar, s8, v_uint8x32, uchar, u8)
+OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int16x16, short, s16, v_uint16x16, ushort, u16)
+OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int32x8, int, s32, v_uint32x8, unsigned, u32)
+OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float32x8, float, f32, v_uint32x8, unsigned, u32)
+OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int64x4, int64, s64, v_uint64x4, uint64, u64)
+OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float64x4, double, f64, v_uint64x4, uint64, u64)
+
+//
+// FP16
+//
+
+inline v_float32x8 v256_load_expand(const float16_t* ptr)
+{
+#if CV_FP16
+    return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr)));
+#else
+    float CV_DECL_ALIGNED(32) buf[8];
+    for (int i = 0; i < 8; i++)
+        buf[i] = (float)ptr[i];
+    return v256_load_aligned(buf);
+#endif
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x8& a)
+{
+#if CV_FP16
+    __m128i ah = _mm256_cvtps_ph(a.val, 0);
+    _mm_storeu_si128((__m128i*)ptr, ah);
+#else
+    float CV_DECL_ALIGNED(32) buf[8];
+    v_store_aligned(buf, a);
+    for (int i = 0; i < 8; i++)
+        ptr[i] = float16_t(buf[i]);
+#endif
+}
+
+//
+// end of FP16
+//
+
+inline void v256_cleanup() { _mm256_zeroall(); }
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+} // cv::
+
+#endif // OPENCV_HAL_INTRIN_AVX_HPP
diff --git a/3rdParty/include/opencv2/core/hal/intrin_avx512.hpp b/3rdParty/include/opencv2/core/hal/intrin_avx512.hpp
new file mode 100644
index 0000000..d20d6dd
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_avx512.hpp
@@ -0,0 +1,3090 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+#ifndef OPENCV_HAL_INTRIN_AVX512_HPP
+#define OPENCV_HAL_INTRIN_AVX512_HPP
+
+#if defined(_MSC_VER) && (_MSC_VER < 1920/*MSVS2019*/)
+# pragma warning(disable:4146)  // unary minus operator applied to unsigned type, result still unsigned
+# pragma warning(disable:4309)  // 'argument': truncation of constant value
+# pragma warning(disable:4310)  // cast truncates constant value
+#endif
+
+#define CVT_ROUND_MODES_IMPLEMENTED 0
+
+#define CV_SIMD512 1
+#define CV_SIMD512_64F 1
+#define CV_SIMD512_FP16 0  // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1)
+
+#define _v512_set_epu64(a7, a6, a5, a4, a3, a2, a1, a0) _mm512_set_epi64((int64)(a7),(int64)(a6),(int64)(a5),(int64)(a4),(int64)(a3),(int64)(a2),(int64)(a1),(int64)(a0))
+#define _v512_set_epu32(a15, a14, a13, a12, a11, a10,  a9,  a8,  a7,  a6,  a5,  a4,  a3,  a2,  a1,  a0) \
+        _mm512_set_epi64(((int64)(a15)<<32)|(int64)(a14), ((int64)(a13)<<32)|(int64)(a12), ((int64)(a11)<<32)|(int64)(a10), ((int64)( a9)<<32)|(int64)( a8), \
+                         ((int64)( a7)<<32)|(int64)( a6), ((int64)( a5)<<32)|(int64)( a4), ((int64)( a3)<<32)|(int64)( a2), ((int64)( a1)<<32)|(int64)( a0))
+#define _v512_set_epu16(a31, a30, a29, a28, a27, a26, a25, a24, a23, a22, a21, a20, a19, a18, a17, a16, \
+                        a15, a14, a13, a12, a11, a10,  a9,  a8,  a7,  a6,  a5,  a4,  a3,  a2,  a1,  a0) \
+        _v512_set_epu32(((unsigned)(a31)<<16)|(unsigned)(a30), ((unsigned)(a29)<<16)|(unsigned)(a28), ((unsigned)(a27)<<16)|(unsigned)(a26), ((unsigned)(a25)<<16)|(unsigned)(a24), \
+                        ((unsigned)(a23)<<16)|(unsigned)(a22), ((unsigned)(a21)<<16)|(unsigned)(a20), ((unsigned)(a19)<<16)|(unsigned)(a18), ((unsigned)(a17)<<16)|(unsigned)(a16), \
+                        ((unsigned)(a15)<<16)|(unsigned)(a14), ((unsigned)(a13)<<16)|(unsigned)(a12), ((unsigned)(a11)<<16)|(unsigned)(a10), ((unsigned)( a9)<<16)|(unsigned)( a8), \
+                        ((unsigned)( a7)<<16)|(unsigned)( a6), ((unsigned)( a5)<<16)|(unsigned)( a4), ((unsigned)( a3)<<16)|(unsigned)( a2), ((unsigned)( a1)<<16)|(unsigned)( a0))
+#define _v512_set_epu8(a63, a62, a61, a60, a59, a58, a57, a56, a55, a54, a53, a52, a51, a50, a49, a48, \
+                       a47, a46, a45, a44, a43, a42, a41, a40, a39, a38, a37, a36, a35, a34, a33, a32, \
+                       a31, a30, a29, a28, a27, a26, a25, a24, a23, a22, a21, a20, a19, a18, a17, a16, \
+                       a15, a14, a13, a12, a11, a10,  a9,  a8,  a7,  a6,  a5,  a4,  a3,  a2,  a1,  a0) \
+        _v512_set_epu32(((unsigned)(a63)<<24)|((unsigned)(a62)<<16)|((unsigned)(a61)<<8)|(unsigned)(a60),((unsigned)(a59)<<24)|((unsigned)(a58)<<16)|((unsigned)(a57)<<8)|(unsigned)(a56), \
+                        ((unsigned)(a55)<<24)|((unsigned)(a54)<<16)|((unsigned)(a53)<<8)|(unsigned)(a52),((unsigned)(a51)<<24)|((unsigned)(a50)<<16)|((unsigned)(a49)<<8)|(unsigned)(a48), \
+                        ((unsigned)(a47)<<24)|((unsigned)(a46)<<16)|((unsigned)(a45)<<8)|(unsigned)(a44),((unsigned)(a43)<<24)|((unsigned)(a42)<<16)|((unsigned)(a41)<<8)|(unsigned)(a40), \
+                        ((unsigned)(a39)<<24)|((unsigned)(a38)<<16)|((unsigned)(a37)<<8)|(unsigned)(a36),((unsigned)(a35)<<24)|((unsigned)(a34)<<16)|((unsigned)(a33)<<8)|(unsigned)(a32), \
+                        ((unsigned)(a31)<<24)|((unsigned)(a30)<<16)|((unsigned)(a29)<<8)|(unsigned)(a28),((unsigned)(a27)<<24)|((unsigned)(a26)<<16)|((unsigned)(a25)<<8)|(unsigned)(a24), \
+                        ((unsigned)(a23)<<24)|((unsigned)(a22)<<16)|((unsigned)(a21)<<8)|(unsigned)(a20),((unsigned)(a19)<<24)|((unsigned)(a18)<<16)|((unsigned)(a17)<<8)|(unsigned)(a16), \
+                        ((unsigned)(a15)<<24)|((unsigned)(a14)<<16)|((unsigned)(a13)<<8)|(unsigned)(a12),((unsigned)(a11)<<24)|((unsigned)(a10)<<16)|((unsigned)( a9)<<8)|(unsigned)( a8), \
+                        ((unsigned)( a7)<<24)|((unsigned)( a6)<<16)|((unsigned)( a5)<<8)|(unsigned)( a4),((unsigned)( a3)<<24)|((unsigned)( a2)<<16)|((unsigned)( a1)<<8)|(unsigned)( a0))
+#define _v512_set_epi8(a63, a62, a61, a60, a59, a58, a57, a56, a55, a54, a53, a52, a51, a50, a49, a48, \
+                       a47, a46, a45, a44, a43, a42, a41, a40, a39, a38, a37, a36, a35, a34, a33, a32, \
+                       a31, a30, a29, a28, a27, a26, a25, a24, a23, a22, a21, a20, a19, a18, a17, a16, \
+                       a15, a14, a13, a12, a11, a10,  a9,  a8,  a7,  a6,  a5,  a4,  a3,  a2,  a1,  a0) \
+        _v512_set_epu8((uchar)(a63), (uchar)(a62), (uchar)(a61), (uchar)(a60), (uchar)(a59), (uchar)(a58), (uchar)(a57), (uchar)(a56), \
+                       (uchar)(a55), (uchar)(a54), (uchar)(a53), (uchar)(a52), (uchar)(a51), (uchar)(a50), (uchar)(a49), (uchar)(a48), \
+                       (uchar)(a47), (uchar)(a46), (uchar)(a45), (uchar)(a44), (uchar)(a43), (uchar)(a42), (uchar)(a41), (uchar)(a40), \
+                       (uchar)(a39), (uchar)(a38), (uchar)(a37), (uchar)(a36), (uchar)(a35), (uchar)(a34), (uchar)(a33), (uchar)(a32), \
+                       (uchar)(a31), (uchar)(a30), (uchar)(a29), (uchar)(a28), (uchar)(a27), (uchar)(a26), (uchar)(a25), (uchar)(a24), \
+                       (uchar)(a23), (uchar)(a22), (uchar)(a21), (uchar)(a20), (uchar)(a19), (uchar)(a18), (uchar)(a17), (uchar)(a16), \
+                       (uchar)(a15), (uchar)(a14), (uchar)(a13), (uchar)(a12), (uchar)(a11), (uchar)(a10), (uchar)( a9), (uchar)( a8), \
+                       (uchar)( a7), (uchar)( a6), (uchar)( a5), (uchar)( a4), (uchar)( a3), (uchar)( a2), (uchar)( a1), (uchar)( a0))
+
+#ifndef _mm512_cvtpd_pslo
+#ifdef _mm512_zextsi256_si512
+#define _mm512_cvtpd_pslo(a) _mm512_zextps256_ps512(_mm512_cvtpd_ps(a))
+#else
+//if preferred way to extend with zeros is unavailable
+#define _mm512_cvtpd_pslo(a) _mm512_castps256_ps512(_mm512_cvtpd_ps(a))
+#endif
+#endif
+///////// Utils ////////////
+
+namespace
+{
+
+inline __m512i _v512_combine(const __m256i& lo, const __m256i& hi)
+{ return _mm512_inserti32x8(_mm512_castsi256_si512(lo), hi, 1); }
+
+inline __m512 _v512_combine(const __m256& lo, const __m256& hi)
+{ return _mm512_insertf32x8(_mm512_castps256_ps512(lo), hi, 1); }
+
+inline __m512d _v512_combine(const __m256d& lo, const __m256d& hi)
+{ return _mm512_insertf64x4(_mm512_castpd256_pd512(lo), hi, 1); }
+
+inline int _v_cvtsi512_si32(const __m512i& a)
+{ return _mm_cvtsi128_si32(_mm512_castsi512_si128(a)); }
+
+inline __m256i _v512_extract_high(const __m512i& v)
+{ return _mm512_extracti32x8_epi32(v, 1); }
+
+inline __m256  _v512_extract_high(const __m512& v)
+{ return _mm512_extractf32x8_ps(v, 1); }
+
+inline __m256d _v512_extract_high(const __m512d& v)
+{ return _mm512_extractf64x4_pd(v, 1); }
+
+inline __m256i _v512_extract_low(const __m512i& v)
+{ return _mm512_castsi512_si256(v); }
+
+inline __m256  _v512_extract_low(const __m512& v)
+{ return _mm512_castps512_ps256(v); }
+
+inline __m256d _v512_extract_low(const __m512d& v)
+{ return _mm512_castpd512_pd256(v); }
+
+inline __m512i _v512_insert(const __m512i& a, const __m256i& b)
+{ return _mm512_inserti32x8(a, b, 0); }
+
+inline __m512 _v512_insert(const __m512& a, const __m256& b)
+{ return _mm512_insertf32x8(a, b, 0); }
+
+inline __m512d _v512_insert(const __m512d& a, const __m256d& b)
+{ return _mm512_insertf64x4(a, b, 0); }
+
+}
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+///////// Types ////////////
+
+struct v_uint8x64
+{
+    typedef uchar lane_type;
+    enum { nlanes = 64 };
+    __m512i val;
+
+    explicit v_uint8x64(__m512i v) : val(v) {}
+    v_uint8x64(uchar v0,  uchar v1,  uchar v2,  uchar v3,
+               uchar v4,  uchar v5,  uchar v6,  uchar v7,
+               uchar v8,  uchar v9,  uchar v10, uchar v11,
+               uchar v12, uchar v13, uchar v14, uchar v15,
+               uchar v16, uchar v17, uchar v18, uchar v19,
+               uchar v20, uchar v21, uchar v22, uchar v23,
+               uchar v24, uchar v25, uchar v26, uchar v27,
+               uchar v28, uchar v29, uchar v30, uchar v31,
+               uchar v32, uchar v33, uchar v34, uchar v35,
+               uchar v36, uchar v37, uchar v38, uchar v39,
+               uchar v40, uchar v41, uchar v42, uchar v43,
+               uchar v44, uchar v45, uchar v46, uchar v47,
+               uchar v48, uchar v49, uchar v50, uchar v51,
+               uchar v52, uchar v53, uchar v54, uchar v55,
+               uchar v56, uchar v57, uchar v58, uchar v59,
+               uchar v60, uchar v61, uchar v62, uchar v63)
+    {
+        val = _v512_set_epu8(v63, v62, v61, v60, v59, v58, v57, v56, v55, v54, v53, v52, v51, v50, v49, v48,
+                             v47, v46, v45, v44, v43, v42, v41, v40, v39, v38, v37, v36, v35, v34, v33, v32,
+                             v31, v30, v29, v28, v27, v26, v25, v24, v23, v22, v21, v20, v19, v18, v17, v16,
+                             v15, v14, v13, v12, v11, v10, v9,  v8,  v7,  v6,  v5,  v4,  v3,  v2,  v1,  v0);
+    }
+    v_uint8x64() {}
+
+    static inline v_uint8x64 zero() { return v_uint8x64(_mm512_setzero_si512()); }
+
+    uchar get0() const { return (uchar)_v_cvtsi512_si32(val); }
+};
+
+struct v_int8x64
+{
+    typedef schar lane_type;
+    enum { nlanes = 64 };
+    __m512i val;
+
+    explicit v_int8x64(__m512i v) : val(v) {}
+    v_int8x64(schar v0,  schar v1,  schar v2,  schar v3,
+              schar v4,  schar v5,  schar v6,  schar v7,
+              schar v8,  schar v9,  schar v10, schar v11,
+              schar v12, schar v13, schar v14, schar v15,
+              schar v16, schar v17, schar v18, schar v19,
+              schar v20, schar v21, schar v22, schar v23,
+              schar v24, schar v25, schar v26, schar v27,
+              schar v28, schar v29, schar v30, schar v31,
+              schar v32, schar v33, schar v34, schar v35,
+              schar v36, schar v37, schar v38, schar v39,
+              schar v40, schar v41, schar v42, schar v43,
+              schar v44, schar v45, schar v46, schar v47,
+              schar v48, schar v49, schar v50, schar v51,
+              schar v52, schar v53, schar v54, schar v55,
+              schar v56, schar v57, schar v58, schar v59,
+              schar v60, schar v61, schar v62, schar v63)
+    {
+        val = _v512_set_epi8(v63, v62, v61, v60, v59, v58, v57, v56, v55, v54, v53, v52, v51, v50, v49, v48,
+                             v47, v46, v45, v44, v43, v42, v41, v40, v39, v38, v37, v36, v35, v34, v33, v32,
+                             v31, v30, v29, v28, v27, v26, v25, v24, v23, v22, v21, v20, v19, v18, v17, v16,
+                             v15, v14, v13, v12, v11, v10, v9,  v8,  v7,  v6,  v5,  v4,  v3,  v2,  v1,  v0);
+    }
+    v_int8x64() {}
+
+    static inline v_int8x64 zero() { return v_int8x64(_mm512_setzero_si512()); }
+
+    schar get0() const { return (schar)_v_cvtsi512_si32(val); }
+};
+
+struct v_uint16x32
+{
+    typedef ushort lane_type;
+    enum { nlanes = 32 };
+    __m512i val;
+
+    explicit v_uint16x32(__m512i v) : val(v) {}
+    v_uint16x32(ushort v0,  ushort v1,  ushort v2,  ushort v3,
+                ushort v4,  ushort v5,  ushort v6,  ushort v7,
+                ushort v8,  ushort v9,  ushort v10, ushort v11,
+                ushort v12, ushort v13, ushort v14, ushort v15,
+                ushort v16, ushort v17, ushort v18, ushort v19,
+                ushort v20, ushort v21, ushort v22, ushort v23,
+                ushort v24, ushort v25, ushort v26, ushort v27,
+                ushort v28, ushort v29, ushort v30, ushort v31)
+    {
+        val = _v512_set_epu16(v31, v30, v29, v28, v27, v26, v25, v24, v23, v22, v21, v20, v19, v18, v17, v16,
+                              v15, v14, v13, v12, v11, v10, v9,  v8,  v7,  v6,  v5,  v4,  v3,  v2,  v1,  v0);
+    }
+    v_uint16x32() {}
+
+    static inline v_uint16x32 zero() { return v_uint16x32(_mm512_setzero_si512()); }
+
+    ushort get0() const { return (ushort)_v_cvtsi512_si32(val); }
+};
+
+struct v_int16x32
+{
+    typedef short lane_type;
+    enum { nlanes = 32 };
+    __m512i val;
+
+    explicit v_int16x32(__m512i v) : val(v) {}
+    v_int16x32(short v0,  short v1,  short v2,  short v3,  short v4,  short v5,  short v6,  short v7,
+               short v8,  short v9,  short v10, short v11, short v12, short v13, short v14, short v15,
+               short v16, short v17, short v18, short v19, short v20, short v21, short v22, short v23,
+               short v24, short v25, short v26, short v27, short v28, short v29, short v30, short v31)
+    {
+        val = _v512_set_epu16((ushort)v31, (ushort)v30, (ushort)v29, (ushort)v28, (ushort)v27, (ushort)v26, (ushort)v25, (ushort)v24,
+                              (ushort)v23, (ushort)v22, (ushort)v21, (ushort)v20, (ushort)v19, (ushort)v18, (ushort)v17, (ushort)v16,
+                              (ushort)v15, (ushort)v14, (ushort)v13, (ushort)v12, (ushort)v11, (ushort)v10, (ushort)v9 , (ushort)v8,
+                              (ushort)v7 , (ushort)v6 , (ushort)v5 , (ushort)v4 , (ushort)v3 , (ushort)v2 , (ushort)v1 , (ushort)v0);
+    }
+    v_int16x32() {}
+
+    static inline v_int16x32 zero() { return v_int16x32(_mm512_setzero_si512()); }
+
+    short get0() const { return (short)_v_cvtsi512_si32(val); }
+};
+
+struct v_uint32x16
+{
+    typedef unsigned lane_type;
+    enum { nlanes = 16 };
+    __m512i val;
+
+    explicit v_uint32x16(__m512i v) : val(v) {}
+    v_uint32x16(unsigned v0,  unsigned v1,  unsigned v2,  unsigned v3,
+                unsigned v4,  unsigned v5,  unsigned v6,  unsigned v7,
+                unsigned v8,  unsigned v9,  unsigned v10, unsigned v11,
+                unsigned v12, unsigned v13, unsigned v14, unsigned v15)
+    {
+        val = _mm512_setr_epi32((int)v0,  (int)v1,  (int)v2,  (int)v3, (int)v4,  (int)v5,  (int)v6,  (int)v7,
+                                (int)v8,  (int)v9,  (int)v10, (int)v11, (int)v12, (int)v13, (int)v14, (int)v15);
+    }
+    v_uint32x16() {}
+
+    static inline v_uint32x16 zero() { return v_uint32x16(_mm512_setzero_si512()); }
+
+    unsigned get0() const { return (unsigned)_v_cvtsi512_si32(val); }
+};
+
+struct v_int32x16
+{
+    typedef int lane_type;
+    enum { nlanes = 16 };
+    __m512i val;
+
+    explicit v_int32x16(__m512i v) : val(v) {}
+    v_int32x16(int v0, int v1, int v2,  int v3,  int v4,  int v5,  int v6,  int v7,
+               int v8, int v9, int v10, int v11, int v12, int v13, int v14, int v15)
+    {
+        val = _mm512_setr_epi32(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15);
+    }
+    v_int32x16() {}
+
+    static inline v_int32x16 zero() { return v_int32x16(_mm512_setzero_si512()); }
+
+    int get0() const { return _v_cvtsi512_si32(val); }
+};
+
+struct v_float32x16
+{
+    typedef float lane_type;
+    enum { nlanes = 16 };
+    __m512 val;
+
+    explicit v_float32x16(__m512 v) : val(v) {}
+    v_float32x16(float v0, float v1, float v2,  float v3,  float v4,  float v5,  float v6,  float v7,
+                 float v8, float v9, float v10, float v11, float v12, float v13, float v14, float v15)
+    {
+        val = _mm512_setr_ps(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15);
+    }
+    v_float32x16() {}
+
+    static inline v_float32x16 zero() { return v_float32x16(_mm512_setzero_ps()); }
+
+    float get0() const { return _mm_cvtss_f32(_mm512_castps512_ps128(val)); }
+};
+
+struct v_uint64x8
+{
+    typedef uint64 lane_type;
+    enum { nlanes = 8 };
+    __m512i val;
+
+    explicit v_uint64x8(__m512i v) : val(v) {}
+    v_uint64x8(uint64 v0, uint64 v1, uint64 v2, uint64 v3, uint64 v4, uint64 v5, uint64 v6, uint64 v7)
+    { val = _mm512_setr_epi64((int64)v0, (int64)v1, (int64)v2, (int64)v3, (int64)v4, (int64)v5, (int64)v6, (int64)v7); }
+    v_uint64x8() {}
+
+    static inline v_uint64x8 zero() { return v_uint64x8(_mm512_setzero_si512()); }
+
+    uint64 get0() const
+    {
+    #if defined __x86_64__ || defined _M_X64
+        return (uint64)_mm_cvtsi128_si64(_mm512_castsi512_si128(val));
+    #else
+        int a = _mm_cvtsi128_si32(_mm512_castsi512_si128(val));
+        int b = _mm_cvtsi128_si32(_mm512_castsi512_si128(_mm512_srli_epi64(val, 32)));
+        return (unsigned)a | ((uint64)(unsigned)b << 32);
+    #endif
+    }
+};
+
+struct v_int64x8
+{
+    typedef int64 lane_type;
+    enum { nlanes = 8 };
+    __m512i val;
+
+    explicit v_int64x8(__m512i v) : val(v) {}
+    v_int64x8(int64 v0, int64 v1, int64 v2, int64 v3, int64 v4, int64 v5, int64 v6, int64 v7)
+    { val = _mm512_setr_epi64(v0, v1, v2, v3, v4, v5, v6, v7); }
+    v_int64x8() {}
+
+    static inline v_int64x8 zero() { return v_int64x8(_mm512_setzero_si512()); }
+
+    int64 get0() const
+    {
+    #if defined __x86_64__ || defined _M_X64
+        return (int64)_mm_cvtsi128_si64(_mm512_castsi512_si128(val));
+    #else
+        int a = _mm_cvtsi128_si32(_mm512_castsi512_si128(val));
+        int b = _mm_cvtsi128_si32(_mm512_castsi512_si128(_mm512_srli_epi64(val, 32)));
+        return (int64)((unsigned)a | ((uint64)(unsigned)b << 32));
+    #endif
+    }
+};
+
+struct v_float64x8
+{
+    typedef double lane_type;
+    enum { nlanes = 8 };
+    __m512d val;
+
+    explicit v_float64x8(__m512d v) : val(v) {}
+    v_float64x8(double v0, double v1, double v2, double v3, double v4, double v5, double v6, double v7)
+    { val = _mm512_setr_pd(v0, v1, v2, v3, v4, v5, v6, v7); }
+    v_float64x8() {}
+
+    static inline v_float64x8 zero() { return v_float64x8(_mm512_setzero_pd()); }
+
+    double get0() const { return _mm_cvtsd_f64(_mm512_castpd512_pd128(val)); }
+};
+
+//////////////// Load and store operations ///////////////
+
+#define OPENCV_HAL_IMPL_AVX512_LOADSTORE(_Tpvec, _Tp)                    \
+    inline _Tpvec v512_load(const _Tp* ptr)                           \
+    { return _Tpvec(_mm512_loadu_si512((const __m512i*)ptr)); }       \
+    inline _Tpvec v512_load_aligned(const _Tp* ptr)                   \
+    { return _Tpvec(_mm512_load_si512((const __m512i*)ptr)); }        \
+    inline _Tpvec v512_load_low(const _Tp* ptr)                       \
+    {                                                                 \
+        __m256i v256 = _mm256_loadu_si256((const __m256i*)ptr);       \
+        return _Tpvec(_mm512_castsi256_si512(v256));                  \
+    }                                                                 \
+    inline _Tpvec v512_load_halves(const _Tp* ptr0, const _Tp* ptr1)  \
+    {                                                                 \
+        __m256i vlo = _mm256_loadu_si256((const __m256i*)ptr0);       \
+        __m256i vhi = _mm256_loadu_si256((const __m256i*)ptr1);       \
+        return _Tpvec(_v512_combine(vlo, vhi));                       \
+    }                                                                 \
+    inline void v_store(_Tp* ptr, const _Tpvec& a)                    \
+    { _mm512_storeu_si512((__m512i*)ptr, a.val); }                    \
+    inline void v_store_aligned(_Tp* ptr, const _Tpvec& a)            \
+    { _mm512_store_si512((__m512i*)ptr, a.val); }                     \
+    inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a)    \
+    { _mm512_stream_si512((__m512i*)ptr, a.val); }                    \
+    inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \
+    { \
+        if( mode == hal::STORE_UNALIGNED ) \
+            _mm512_storeu_si512((__m512i*)ptr, a.val); \
+        else if( mode == hal::STORE_ALIGNED_NOCACHE )  \
+            _mm512_stream_si512((__m512i*)ptr, a.val); \
+        else \
+            _mm512_store_si512((__m512i*)ptr, a.val); \
+    } \
+    inline void v_store_low(_Tp* ptr, const _Tpvec& a)                \
+    { _mm256_storeu_si256((__m256i*)ptr, _v512_extract_low(a.val)); }    \
+    inline void v_store_high(_Tp* ptr, const _Tpvec& a)               \
+    { _mm256_storeu_si256((__m256i*)ptr, _v512_extract_high(a.val)); }
+
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint8x64,  uchar)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int8x64,   schar)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint16x32, ushort)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int16x32,  short)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint32x16,  unsigned)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int32x16,   int)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint64x8,  uint64)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int64x8,   int64)
+
+#define OPENCV_HAL_IMPL_AVX512_LOADSTORE_FLT(_Tpvec, _Tp, suffix, halfreg)   \
+    inline _Tpvec v512_load(const _Tp* ptr)                               \
+    { return _Tpvec(_mm512_loadu_##suffix(ptr)); }                        \
+    inline _Tpvec v512_load_aligned(const _Tp* ptr)                       \
+    { return _Tpvec(_mm512_load_##suffix(ptr)); }                         \
+    inline _Tpvec v512_load_low(const _Tp* ptr)                           \
+    {                                                                     \
+        return _Tpvec(_mm512_cast##suffix##256_##suffix##512              \
+                     (_mm256_loadu_##suffix(ptr)));                       \
+    }                                                                     \
+    inline _Tpvec v512_load_halves(const _Tp* ptr0, const _Tp* ptr1)      \
+    {                                                                     \
+        halfreg vlo = _mm256_loadu_##suffix(ptr0);                        \
+        halfreg vhi = _mm256_loadu_##suffix(ptr1);                        \
+        return _Tpvec(_v512_combine(vlo, vhi));                           \
+    }                                                                     \
+    inline void v_store(_Tp* ptr, const _Tpvec& a)                        \
+    { _mm512_storeu_##suffix(ptr, a.val); }                               \
+    inline void v_store_aligned(_Tp* ptr, const _Tpvec& a)                \
+    { _mm512_store_##suffix(ptr, a.val); }                                \
+    inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a)        \
+    { _mm512_stream_##suffix(ptr, a.val); }                               \
+    inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \
+    { \
+        if( mode == hal::STORE_UNALIGNED ) \
+            _mm512_storeu_##suffix(ptr, a.val); \
+        else if( mode == hal::STORE_ALIGNED_NOCACHE )  \
+            _mm512_stream_##suffix(ptr, a.val); \
+        else \
+            _mm512_store_##suffix(ptr, a.val); \
+    } \
+    inline void v_store_low(_Tp* ptr, const _Tpvec& a)                    \
+    { _mm256_storeu_##suffix(ptr, _v512_extract_low(a.val)); }            \
+    inline void v_store_high(_Tp* ptr, const _Tpvec& a)                   \
+    { _mm256_storeu_##suffix(ptr, _v512_extract_high(a.val)); }
+
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_FLT(v_float32x16, float,  ps, __m256)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_FLT(v_float64x8, double, pd, __m256d)
+
+#define OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, _Tpvecf, suffix, cast) \
+    inline _Tpvec v_reinterpret_as_##suffix(const _Tpvecf& a)   \
+    { return _Tpvec(cast(a.val)); }
+
+#define OPENCV_HAL_IMPL_AVX512_INIT(_Tpvec, _Tp, suffix, ssuffix, ctype_s)         \
+    inline _Tpvec v512_setzero_##suffix()                                          \
+    { return _Tpvec(_mm512_setzero_si512()); }                                     \
+    inline _Tpvec v512_setall_##suffix(_Tp v)                                      \
+    { return _Tpvec(_mm512_set1_##ssuffix((ctype_s)v)); }                          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint8x64,   suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int8x64,    suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint16x32,  suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int16x32,   suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint32x16,  suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int32x16,   suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint64x8,   suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int64x8,    suffix, OPENCV_HAL_NOP)      \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_float32x16, suffix, _mm512_castps_si512) \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_float64x8,  suffix, _mm512_castpd_si512)
+
+OPENCV_HAL_IMPL_AVX512_INIT(v_uint8x64,  uchar,    u8,  epi8,   char)
+OPENCV_HAL_IMPL_AVX512_INIT(v_int8x64,   schar,    s8,  epi8,   char)
+OPENCV_HAL_IMPL_AVX512_INIT(v_uint16x32, ushort,   u16, epi16,  short)
+OPENCV_HAL_IMPL_AVX512_INIT(v_int16x32,  short,    s16, epi16,  short)
+OPENCV_HAL_IMPL_AVX512_INIT(v_uint32x16, unsigned, u32, epi32,  int)
+OPENCV_HAL_IMPL_AVX512_INIT(v_int32x16,  int,      s32, epi32,  int)
+OPENCV_HAL_IMPL_AVX512_INIT(v_uint64x8,  uint64,   u64, epi64,  int64)
+OPENCV_HAL_IMPL_AVX512_INIT(v_int64x8,   int64,    s64, epi64,  int64)
+
+#define OPENCV_HAL_IMPL_AVX512_INIT_FLT(_Tpvec, _Tp, suffix, zsuffix, cast) \
+    inline _Tpvec v512_setzero_##suffix()                                   \
+    { return _Tpvec(_mm512_setzero_##zsuffix()); }                          \
+    inline _Tpvec v512_setall_##suffix(_Tp v)                               \
+    { return _Tpvec(_mm512_set1_##zsuffix(v)); }                            \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint8x64,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int8x64,   suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint16x32, suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int16x32,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint32x16, suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int32x16,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint64x8,  suffix, cast)          \
+    OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int64x8,   suffix, cast)
+
+OPENCV_HAL_IMPL_AVX512_INIT_FLT(v_float32x16, float,  f32, ps, _mm512_castsi512_ps)
+OPENCV_HAL_IMPL_AVX512_INIT_FLT(v_float64x8,  double, f64, pd, _mm512_castsi512_pd)
+
+inline v_float32x16 v_reinterpret_as_f32(const v_float32x16& a)
+{ return a; }
+inline v_float32x16 v_reinterpret_as_f32(const v_float64x8& a)
+{ return v_float32x16(_mm512_castpd_ps(a.val)); }
+
+inline v_float64x8 v_reinterpret_as_f64(const v_float64x8& a)
+{ return a; }
+inline v_float64x8 v_reinterpret_as_f64(const v_float32x16& a)
+{ return v_float64x8(_mm512_castps_pd(a.val)); }
+
+// FP16
+inline v_float32x16 v512_load_expand(const float16_t* ptr)
+{
+    return v_float32x16(_mm512_cvtph_ps(_mm256_loadu_si256((const __m256i*)ptr)));
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x16& a)
+{
+    __m256i ah = _mm512_cvtps_ph(a.val, 0);
+    _mm256_storeu_si256((__m256i*)ptr, ah);
+}
+
+/* Recombine & ZIP */
+inline void v_zip(const v_int8x64& a, const v_int8x64& b, v_int8x64& ab0, v_int8x64& ab1)
+{
+#if CV_AVX_512VBMI
+    __m512i mask0 = _v512_set_epu8( 95,  31,  94,  30,  93,  29,  92,  28,  91,  27,  90,  26,  89,  25,  88,  24,
+                                    87,  23,  86,  22,  85,  21,  84,  20,  83,  19,  82,  18,  81,  17,  80,  16,
+                                    79,  15,  78,  14,  77,  13,  76,  12,  75,  11,  74,  10,  73,   9,  72,   8,
+                                    71,   7,  70,   6,  69,   5,  68,   4,  67,   3,  66,   2,  65,   1,  64,   0);
+    ab0 = v_int8x64(_mm512_permutex2var_epi8(a.val, mask0, b.val));
+    __m512i mask1 = _v512_set_epu8(127,  63, 126,  62, 125,  61, 124,  60, 123,  59, 122,  58, 121,  57, 120,  56,
+                                   119,  55, 118,  54, 117,  53, 116,  52, 115,  51, 114,  50, 113,  49, 112,  48,
+                                   111,  47, 110,  46, 109,  45, 108,  44, 107,  43, 106,  42, 105,  41, 104,  40,
+                                   103,  39, 102,  38, 101,  37, 100,  36,  99,  35,  98,  34,  97,  33,  96,  32);
+    ab1 = v_int8x64(_mm512_permutex2var_epi8(a.val, mask1, b.val));
+#else
+    __m512i low  = _mm512_unpacklo_epi8(a.val, b.val);
+    __m512i high = _mm512_unpackhi_epi8(a.val, b.val);
+    ab0 = v_int8x64(_mm512_permutex2var_epi64(low, _v512_set_epu64(11, 10, 3, 2,  9,  8, 1, 0), high));
+    ab1 = v_int8x64(_mm512_permutex2var_epi64(low, _v512_set_epu64(15, 14, 7, 6, 13, 12, 5, 4), high));
+#endif
+}
+inline void v_zip(const v_int16x32& a, const v_int16x32& b, v_int16x32& ab0, v_int16x32& ab1)
+{
+    __m512i mask0 = _v512_set_epu16(47, 15, 46, 14, 45, 13, 44, 12, 43, 11, 42, 10, 41,  9, 40,  8,
+                                    39,  7, 38,  6, 37,  5, 36,  4, 35,  3, 34,  2, 33,  1, 32,  0);
+    ab0 = v_int16x32(_mm512_permutex2var_epi16(a.val, mask0, b.val));
+    __m512i mask1 = _v512_set_epu16(63, 31, 62, 30, 61, 29, 60, 28, 59, 27, 58, 26, 57, 25, 56, 24,
+                                    55, 23, 54, 22, 53, 21, 52, 20, 51, 19, 50, 18, 49, 17, 48, 16);
+    ab1 = v_int16x32(_mm512_permutex2var_epi16(a.val, mask1, b.val));
+}
+inline void v_zip(const v_int32x16& a, const v_int32x16& b, v_int32x16& ab0, v_int32x16& ab1)
+{
+    __m512i mask0 = _v512_set_epu32(23,  7, 22,  6, 21,  5, 20,  4, 19,  3, 18,  2, 17, 1, 16, 0);
+    ab0 = v_int32x16(_mm512_permutex2var_epi32(a.val, mask0, b.val));
+    __m512i mask1 = _v512_set_epu32(31, 15, 30, 14, 29, 13, 28, 12, 27, 11, 26, 10, 25, 9, 24, 8);
+    ab1 = v_int32x16(_mm512_permutex2var_epi32(a.val, mask1, b.val));
+}
+inline void v_zip(const v_int64x8& a, const v_int64x8& b, v_int64x8& ab0, v_int64x8& ab1)
+{
+    __m512i mask0 = _v512_set_epu64(11, 3, 10, 2,  9, 1,  8, 0);
+    ab0 = v_int64x8(_mm512_permutex2var_epi64(a.val, mask0, b.val));
+    __m512i mask1 = _v512_set_epu64(15, 7, 14, 6, 13, 5, 12, 4);
+    ab1 = v_int64x8(_mm512_permutex2var_epi64(a.val, mask1, b.val));
+}
+
+inline void v_zip(const v_uint8x64&  a, const v_uint8x64&  b, v_uint8x64& ab0, v_uint8x64& ab1)
+{
+    v_int8x64 i0, i1;
+    v_zip(v_reinterpret_as_s8(a), v_reinterpret_as_s8(b), i0, i1);
+    ab0 = v_reinterpret_as_u8(i0);
+    ab1 = v_reinterpret_as_u8(i1);
+}
+inline void v_zip(const v_uint16x32&  a, const v_uint16x32&  b, v_uint16x32& ab0, v_uint16x32& ab1)
+{
+    v_int16x32 i0, i1;
+    v_zip(v_reinterpret_as_s16(a), v_reinterpret_as_s16(b), i0, i1);
+    ab0 = v_reinterpret_as_u16(i0);
+    ab1 = v_reinterpret_as_u16(i1);
+}
+inline void v_zip(const v_uint32x16&  a, const v_uint32x16&  b, v_uint32x16& ab0, v_uint32x16& ab1)
+{
+    v_int32x16 i0, i1;
+    v_zip(v_reinterpret_as_s32(a), v_reinterpret_as_s32(b), i0, i1);
+    ab0 = v_reinterpret_as_u32(i0);
+    ab1 = v_reinterpret_as_u32(i1);
+}
+inline void v_zip(const v_uint64x8&  a, const v_uint64x8&  b, v_uint64x8& ab0, v_uint64x8& ab1)
+{
+    v_int64x8 i0, i1;
+    v_zip(v_reinterpret_as_s64(a), v_reinterpret_as_s64(b), i0, i1);
+    ab0 = v_reinterpret_as_u64(i0);
+    ab1 = v_reinterpret_as_u64(i1);
+}
+inline void v_zip(const v_float32x16&  a, const v_float32x16&  b, v_float32x16& ab0, v_float32x16& ab1)
+{
+    v_int32x16 i0, i1;
+    v_zip(v_reinterpret_as_s32(a), v_reinterpret_as_s32(b), i0, i1);
+    ab0 = v_reinterpret_as_f32(i0);
+    ab1 = v_reinterpret_as_f32(i1);
+}
+inline void v_zip(const v_float64x8&  a, const v_float64x8&  b, v_float64x8& ab0, v_float64x8& ab1)
+{
+    v_int64x8 i0, i1;
+    v_zip(v_reinterpret_as_s64(a), v_reinterpret_as_s64(b), i0, i1);
+    ab0 = v_reinterpret_as_f64(i0);
+    ab1 = v_reinterpret_as_f64(i1);
+}
+
+#define OPENCV_HAL_IMPL_AVX512_COMBINE(_Tpvec, suffix)                                    \
+    inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b)                         \
+    { return _Tpvec(_v512_combine(_v512_extract_low(a.val), _v512_extract_low(b.val))); } \
+    inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b)                        \
+    { return _Tpvec(_v512_insert(b.val, _v512_extract_high(a.val))); }                    \
+    inline void v_recombine(const _Tpvec& a, const _Tpvec& b,                             \
+                                  _Tpvec& c, _Tpvec& d)                                   \
+    {                                                                                     \
+        c.val = _v512_combine(_v512_extract_low(a.val),_v512_extract_low(b.val));         \
+        d.val = _v512_insert(b.val,_v512_extract_high(a.val));                            \
+    }
+
+
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint8x64,   epi8)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_int8x64,    epi8)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint16x32,  epi16)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_int16x32,   epi16)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint32x16,  epi32)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_int32x16,   epi32)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint64x8,   epi64)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_int64x8,    epi64)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_float32x16, ps)
+OPENCV_HAL_IMPL_AVX512_COMBINE(v_float64x8,  pd)
+
+////////// Arithmetic, bitwise and comparison operations /////////
+
+/* Element-wise binary and unary operations */
+
+/** Non-saturating arithmetics **/
+#define OPENCV_HAL_IMPL_AVX512_BIN_FUNC(func, _Tpvec, intrin) \
+    inline _Tpvec func(const _Tpvec& a, const _Tpvec& b)      \
+    { return _Tpvec(intrin(a.val, b.val)); }
+
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_uint8x64, _mm512_add_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_int8x64, _mm512_add_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_uint16x32, _mm512_add_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_int16x32, _mm512_add_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_uint8x64, _mm512_sub_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_int8x64, _mm512_sub_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_uint16x32, _mm512_sub_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_int16x32, _mm512_sub_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_mul_wrap, v_uint16x32, _mm512_mullo_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_mul_wrap, v_int16x32, _mm512_mullo_epi16)
+
+inline v_uint8x64 v_mul_wrap(const v_uint8x64& a, const v_uint8x64& b)
+{
+    __m512i ad = _mm512_srai_epi16(a.val, 8);
+    __m512i bd = _mm512_srai_epi16(b.val, 8);
+    __m512i p0 = _mm512_mullo_epi16(a.val, b.val); // even
+    __m512i p1 = _mm512_slli_epi16(_mm512_mullo_epi16(ad, bd), 8); // odd
+    return v_uint8x64(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, p0, p1));
+}
+inline v_int8x64 v_mul_wrap(const v_int8x64& a, const v_int8x64& b)
+{
+    return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b)));
+}
+
+#define OPENCV_HAL_IMPL_AVX512_BIN_OP(bin_op, _Tpvec, intrin)            \
+    inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b)     \
+    { return _Tpvec(intrin(a.val, b.val)); }                             \
+    inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b)       \
+    { a.val = intrin(a.val, b.val); return a; }
+
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint32x16, _mm512_add_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint32x16, _mm512_sub_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int32x16, _mm512_add_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int32x16, _mm512_sub_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint64x8, _mm512_add_epi64)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint64x8, _mm512_sub_epi64)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int64x8, _mm512_add_epi64)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int64x8, _mm512_sub_epi64)
+
+OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_uint32x16, _mm512_mullo_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_int32x16, _mm512_mullo_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_uint64x8, _mm512_mullo_epi64)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_int64x8, _mm512_mullo_epi64)
+
+/** Saturating arithmetics **/
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint8x64,  _mm512_adds_epu8)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint8x64,  _mm512_subs_epu8)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int8x64,   _mm512_adds_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int8x64,   _mm512_subs_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint16x32, _mm512_adds_epu16)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint16x32, _mm512_subs_epu16)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int16x32,  _mm512_adds_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int16x32,  _mm512_subs_epi16)
+
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_float32x16, _mm512_add_ps)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_float32x16, _mm512_sub_ps)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_float32x16, _mm512_mul_ps)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(/, v_float32x16, _mm512_div_ps)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_float64x8, _mm512_add_pd)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_float64x8, _mm512_sub_pd)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_float64x8, _mm512_mul_pd)
+OPENCV_HAL_IMPL_AVX512_BIN_OP(/, v_float64x8, _mm512_div_pd)
+
+// saturating multiply
+inline v_uint8x64 operator * (const v_uint8x64& a, const v_uint8x64& b)
+{
+    v_uint16x32 c, d;
+    v_mul_expand(a, b, c, d);
+    return v_pack(c, d);
+}
+inline v_int8x64 operator * (const v_int8x64& a, const v_int8x64& b)
+{
+    v_int16x32 c, d;
+    v_mul_expand(a, b, c, d);
+    return v_pack(c, d);
+}
+inline v_uint16x32 operator * (const v_uint16x32& a, const v_uint16x32& b)
+{
+    __m512i pl = _mm512_mullo_epi16(a.val, b.val);
+    __m512i ph = _mm512_mulhi_epu16(a.val, b.val);
+    __m512i p0 = _mm512_unpacklo_epi16(pl, ph);
+    __m512i p1 = _mm512_unpackhi_epi16(pl, ph);
+
+    const __m512i m = _mm512_set1_epi32(65535);
+    return v_uint16x32(_mm512_packus_epi32(_mm512_min_epu32(p0, m), _mm512_min_epu32(p1, m)));
+}
+inline v_int16x32 operator * (const v_int16x32& a, const v_int16x32& b)
+{
+    __m512i pl = _mm512_mullo_epi16(a.val, b.val);
+    __m512i ph = _mm512_mulhi_epi16(a.val, b.val);
+    __m512i p0 = _mm512_unpacklo_epi16(pl, ph);
+    __m512i p1 = _mm512_unpackhi_epi16(pl, ph);
+    return v_int16x32(_mm512_packs_epi32(p0, p1));
+}
+
+inline v_uint8x64& operator *= (v_uint8x64& a, const v_uint8x64& b)
+{ a = a * b; return a; }
+inline v_int8x64& operator *= (v_int8x64& a, const v_int8x64& b)
+{ a = a * b; return a; }
+inline v_uint16x32& operator *= (v_uint16x32& a, const v_uint16x32& b)
+{ a = a * b; return a; }
+inline v_int16x32& operator *= (v_int16x32& a, const v_int16x32& b)
+{ a = a * b; return a; }
+
+inline v_int16x32 v_mul_hi(const v_int16x32& a, const v_int16x32& b) { return v_int16x32(_mm512_mulhi_epi16(a.val, b.val)); }
+inline v_uint16x32 v_mul_hi(const v_uint16x32& a, const v_uint16x32& b) { return v_uint16x32(_mm512_mulhi_epu16(a.val, b.val)); }
+
+//  Multiply and expand
+inline void v_mul_expand(const v_uint8x64& a, const v_uint8x64& b,
+                         v_uint16x32& c, v_uint16x32& d)
+{
+    v_uint16x32 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int8x64& a, const v_int8x64& b,
+                         v_int16x32& c, v_int16x32& d)
+{
+    v_int16x32 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int16x32& a, const v_int16x32& b,
+                         v_int32x16& c, v_int32x16& d)
+{
+    v_int16x32 v0, v1;
+    v_zip(v_mul_wrap(a, b), v_mul_hi(a, b), v0, v1);
+
+    c = v_reinterpret_as_s32(v0);
+    d = v_reinterpret_as_s32(v1);
+}
+
+inline void v_mul_expand(const v_uint16x32& a, const v_uint16x32& b,
+                         v_uint32x16& c, v_uint32x16& d)
+{
+    v_uint16x32 v0, v1;
+    v_zip(v_mul_wrap(a, b), v_mul_hi(a, b), v0, v1);
+
+    c = v_reinterpret_as_u32(v0);
+    d = v_reinterpret_as_u32(v1);
+}
+
+inline void v_mul_expand(const v_uint32x16& a, const v_uint32x16& b,
+                         v_uint64x8& c, v_uint64x8& d)
+{
+    v_zip(v_uint64x8(_mm512_mul_epu32(a.val, b.val)),
+          v_uint64x8(_mm512_mul_epu32(_mm512_srli_epi64(a.val, 32), _mm512_srli_epi64(b.val, 32))), c, d);
+}
+
+inline void v_mul_expand(const v_int32x16& a, const v_int32x16& b,
+    v_int64x8& c, v_int64x8& d)
+{
+    v_zip(v_int64x8(_mm512_mul_epi32(a.val, b.val)),
+          v_int64x8(_mm512_mul_epi32(_mm512_srli_epi64(a.val, 32), _mm512_srli_epi64(b.val, 32))), c, d);
+}
+
+/** Bitwise shifts **/
+#define OPENCV_HAL_IMPL_AVX512_SHIFT_OP(_Tpuvec, _Tpsvec, suffix) \
+    inline _Tpuvec operator << (const _Tpuvec& a, int imm)        \
+    { return _Tpuvec(_mm512_slli_##suffix(a.val, imm)); }         \
+    inline _Tpsvec operator << (const _Tpsvec& a, int imm)        \
+    { return _Tpsvec(_mm512_slli_##suffix(a.val, imm)); }         \
+    inline _Tpuvec operator >> (const _Tpuvec& a, int imm)        \
+    { return _Tpuvec(_mm512_srli_##suffix(a.val, imm)); }         \
+    inline _Tpsvec operator >> (const _Tpsvec& a, int imm)        \
+    { return _Tpsvec(_mm512_srai_##suffix(a.val, imm)); }         \
+    template<int imm>                                             \
+    inline _Tpuvec v_shl(const _Tpuvec& a)                        \
+    { return _Tpuvec(_mm512_slli_##suffix(a.val, imm)); }         \
+    template<int imm>                                             \
+    inline _Tpsvec v_shl(const _Tpsvec& a)                        \
+    { return _Tpsvec(_mm512_slli_##suffix(a.val, imm)); }         \
+    template<int imm>                                             \
+    inline _Tpuvec v_shr(const _Tpuvec& a)                        \
+    { return _Tpuvec(_mm512_srli_##suffix(a.val, imm)); }         \
+    template<int imm>                                             \
+    inline _Tpsvec v_shr(const _Tpsvec& a)                        \
+    { return _Tpsvec(_mm512_srai_##suffix(a.val, imm)); }
+
+OPENCV_HAL_IMPL_AVX512_SHIFT_OP(v_uint16x32, v_int16x32, epi16)
+OPENCV_HAL_IMPL_AVX512_SHIFT_OP(v_uint32x16, v_int32x16, epi32)
+OPENCV_HAL_IMPL_AVX512_SHIFT_OP(v_uint64x8,  v_int64x8,  epi64)
+
+
+/** Bitwise logic **/
+#define OPENCV_HAL_IMPL_AVX512_LOGIC_OP(_Tpvec, suffix, not_const) \
+    OPENCV_HAL_IMPL_AVX512_BIN_OP(&, _Tpvec, _mm512_and_##suffix)  \
+    OPENCV_HAL_IMPL_AVX512_BIN_OP(|, _Tpvec, _mm512_or_##suffix)   \
+    OPENCV_HAL_IMPL_AVX512_BIN_OP(^, _Tpvec, _mm512_xor_##suffix)  \
+    inline _Tpvec operator ~ (const _Tpvec& a)                     \
+    { return _Tpvec(_mm512_xor_##suffix(a.val, not_const)); }
+
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint8x64,   si512, _mm512_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int8x64,    si512, _mm512_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint16x32,  si512, _mm512_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int16x32,   si512, _mm512_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint32x16,  si512, _mm512_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int32x16,   si512, _mm512_set1_epi32(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint64x8,   si512, _mm512_set1_epi64(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int64x8,    si512, _mm512_set1_epi64(-1))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_float32x16, ps,    _mm512_castsi512_ps(_mm512_set1_epi32(-1)))
+OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_float64x8,  pd,    _mm512_castsi512_pd(_mm512_set1_epi32(-1)))
+
+/** Select **/
+#define OPENCV_HAL_IMPL_AVX512_SELECT(_Tpvec, suffix, zsuf)                      \
+    inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+    { return _Tpvec(_mm512_mask_blend_##suffix(_mm512_cmp_##suffix##_mask(mask.val, _mm512_setzero_##zsuf(), _MM_CMPINT_EQ), a.val, b.val)); }
+
+OPENCV_HAL_IMPL_AVX512_SELECT(v_uint8x64,   epi8, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_int8x64,    epi8, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_uint16x32, epi16, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_int16x32,  epi16, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_uint32x16, epi32, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_int32x16,  epi32, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_uint64x8,  epi64, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_int64x8,   epi64, si512)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_float32x16,   ps,    ps)
+OPENCV_HAL_IMPL_AVX512_SELECT(v_float64x8,    pd,    pd)
+
+/** Comparison **/
+#define OPENCV_HAL_IMPL_AVX512_CMP_INT(bin_op, imm8, _Tpvec, sufcmp, sufset, tval) \
+    inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b)               \
+    { return _Tpvec(_mm512_maskz_set1_##sufset(_mm512_cmp_##sufcmp##_mask(a.val, b.val, imm8), tval)); }
+
+#define OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(_Tpvec, sufcmp, sufset, tval)              \
+    OPENCV_HAL_IMPL_AVX512_CMP_INT(==, _MM_CMPINT_EQ,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_INT(!=, _MM_CMPINT_NE,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_INT(<,  _MM_CMPINT_LT,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_INT(>,  _MM_CMPINT_NLE, _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_INT(<=, _MM_CMPINT_LE,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_INT(>=, _MM_CMPINT_NLT, _Tpvec, sufcmp, sufset, tval)
+
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint8x64,   epu8,  epi8, (char)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int8x64,    epi8,  epi8, (char)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint16x32, epu16, epi16, (short)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int16x32,  epi16, epi16, (short)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint32x16, epu32, epi32, (int)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int32x16,  epi32, epi32, (int)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint64x8,  epu64, epi64, (int64)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int64x8,   epi64, epi64, (int64)-1)
+
+#define OPENCV_HAL_IMPL_AVX512_CMP_FLT(bin_op, imm8, _Tpvec, sufcmp, sufset, tval) \
+    inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b)               \
+    { return _Tpvec(_mm512_castsi512_##sufcmp(_mm512_maskz_set1_##sufset(_mm512_cmp_##sufcmp##_mask(a.val, b.val, imm8), tval))); }
+
+#define OPENCV_HAL_IMPL_AVX512_CMP_OP_FLT(_Tpvec, sufcmp, sufset, tval)           \
+    OPENCV_HAL_IMPL_AVX512_CMP_FLT(==, _CMP_EQ_OQ,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_FLT(!=, _CMP_NEQ_OQ, _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_FLT(<,  _CMP_LT_OQ,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_FLT(>,  _CMP_GT_OQ,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_FLT(<=, _CMP_LE_OQ,  _Tpvec, sufcmp, sufset, tval) \
+    OPENCV_HAL_IMPL_AVX512_CMP_FLT(>=, _CMP_GE_OQ,  _Tpvec, sufcmp, sufset, tval)
+
+OPENCV_HAL_IMPL_AVX512_CMP_OP_FLT(v_float32x16, ps, epi32, (int)-1)
+OPENCV_HAL_IMPL_AVX512_CMP_OP_FLT(v_float64x8,  pd, epi64, (int64)-1)
+
+inline v_float32x16 v_not_nan(const v_float32x16& a)
+{ return v_float32x16(_mm512_castsi512_ps(_mm512_maskz_set1_epi32(_mm512_cmp_ps_mask(a.val, a.val, _CMP_ORD_Q), (int)-1))); }
+inline v_float64x8 v_not_nan(const v_float64x8& a)
+{ return v_float64x8(_mm512_castsi512_pd(_mm512_maskz_set1_epi64(_mm512_cmp_pd_mask(a.val, a.val, _CMP_ORD_Q), (int64)-1))); }
+
+/** min/max **/
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint8x64,   _mm512_min_epu8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint8x64,   _mm512_max_epu8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int8x64,    _mm512_min_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int8x64,    _mm512_max_epi8)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint16x32,  _mm512_min_epu16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint16x32,  _mm512_max_epu16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int16x32,   _mm512_min_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int16x32,   _mm512_max_epi16)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint32x16,  _mm512_min_epu32)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint32x16,  _mm512_max_epu32)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int32x16,   _mm512_min_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int32x16,   _mm512_max_epi32)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint64x8,   _mm512_min_epu64)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint64x8,   _mm512_max_epu64)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int64x8,    _mm512_min_epi64)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int64x8,    _mm512_max_epi64)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_float32x16, _mm512_min_ps)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_float32x16, _mm512_max_ps)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_float64x8,  _mm512_min_pd)
+OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_float64x8,  _mm512_max_pd)
+
+/** Rotate **/
+namespace {
+    template<bool prec, int imm4, bool part, int imm32>
+    struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64&) { return v_int8x64(); }};
+    template<int imm4, int imm32>
+    struct _v_rotate_right<true, imm4, false, imm32> { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64& b)
+    {
+        return v_int8x64(_mm512_or_si512(_mm512_srli_epi32(_mm512_alignr_epi32(b.val, a.val, imm32    ),    imm4 *8),
+                                         _mm512_slli_epi32(_mm512_alignr_epi32(b.val, a.val, imm32 + 1), (4-imm4)*8)));
+    }};
+    template<int imm4>
+    struct _v_rotate_right<true, imm4, false, 15> { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64& b)
+    {
+        return v_int8x64(_mm512_or_si512(_mm512_srli_epi32(_mm512_alignr_epi32(b.val, a.val, 15),    imm4 *8),
+                                         _mm512_slli_epi32(                                b.val, (4-imm4)*8)));
+    }};
+    template<int imm4, int imm32>
+    struct _v_rotate_right<true, imm4, true, imm32> { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b)
+    {
+        return v_int8x64(_mm512_or_si512(_mm512_srli_epi32(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, imm32 - 16),    imm4 *8),
+                                         _mm512_slli_epi32(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, imm32 - 15), (4-imm4)*8)));
+    }};
+    template<int imm4>
+    struct _v_rotate_right<true, imm4, true, 31> { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b)
+    { return v_int8x64(_mm512_srli_epi32(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, 15), imm4*8)); }};
+    template<int imm32>
+    struct _v_rotate_right<false, 0, false, imm32> { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64& b)
+    { return v_int8x64(_mm512_alignr_epi32(b.val, a.val, imm32)); }};
+    template<>
+    struct _v_rotate_right<false, 0, false, 0> { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64&) { return a; }};
+    template<int imm32>
+    struct _v_rotate_right<false, 0, true, imm32> { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b)
+    { return v_int8x64(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, imm32 - 16)); }};
+    template<>
+    struct _v_rotate_right<false, 0, true, 16> { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b) { return b; }};
+    template<>
+    struct _v_rotate_right<false, 0, true, 32> { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64&) { return v_int8x64(); }};
+}
+template<int imm> inline v_int8x64 v_rotate_right(const v_int8x64& a, const v_int8x64& b)
+{
+    return imm >= 128 ? v_int8x64() :
+#if CV_AVX_512VBMI
+    v_int8x64(_mm512_permutex2var_epi8(a.val,
+    _v512_set_epu8(0x3f + imm, 0x3e + imm, 0x3d + imm, 0x3c + imm, 0x3b + imm, 0x3a + imm, 0x39 + imm, 0x38 + imm,
+                   0x37 + imm, 0x36 + imm, 0x35 + imm, 0x34 + imm, 0x33 + imm, 0x32 + imm, 0x31 + imm, 0x30 + imm,
+                   0x2f + imm, 0x2e + imm, 0x2d + imm, 0x2c + imm, 0x2b + imm, 0x2a + imm, 0x29 + imm, 0x28 + imm,
+                   0x27 + imm, 0x26 + imm, 0x25 + imm, 0x24 + imm, 0x23 + imm, 0x22 + imm, 0x21 + imm, 0x20 + imm,
+                   0x1f + imm, 0x1e + imm, 0x1d + imm, 0x1c + imm, 0x1b + imm, 0x1a + imm, 0x19 + imm, 0x18 + imm,
+                   0x17 + imm, 0x16 + imm, 0x15 + imm, 0x14 + imm, 0x13 + imm, 0x12 + imm, 0x11 + imm, 0x10 + imm,
+                   0x0f + imm, 0x0e + imm, 0x0d + imm, 0x0c + imm, 0x0b + imm, 0x0a + imm, 0x09 + imm, 0x08 + imm,
+                   0x07 + imm, 0x06 + imm, 0x05 + imm, 0x04 + imm, 0x03 + imm, 0x02 + imm, 0x01 + imm, 0x00 + imm), b.val));
+#else
+    _v_rotate_right<imm%4!=0, imm%4, (imm/4 > 15), imm/4>::eval(a, b);
+#endif
+}
+template<int imm>
+inline v_int8x64 v_rotate_left(const v_int8x64& a, const v_int8x64& b)
+{
+    if (imm == 0) return a;
+    if (imm == 64) return b;
+    if (imm >= 128) return v_int8x64();
+#if CV_AVX_512VBMI
+    return v_int8x64(_mm512_permutex2var_epi8(b.val,
+           _v512_set_epi8(0x7f - imm,0x7e - imm,0x7d - imm,0x7c - imm,0x7b - imm,0x7a - imm,0x79 - imm,0x78 - imm,
+                          0x77 - imm,0x76 - imm,0x75 - imm,0x74 - imm,0x73 - imm,0x72 - imm,0x71 - imm,0x70 - imm,
+                          0x6f - imm,0x6e - imm,0x6d - imm,0x6c - imm,0x6b - imm,0x6a - imm,0x69 - imm,0x68 - imm,
+                          0x67 - imm,0x66 - imm,0x65 - imm,0x64 - imm,0x63 - imm,0x62 - imm,0x61 - imm,0x60 - imm,
+                          0x5f - imm,0x5e - imm,0x5d - imm,0x5c - imm,0x5b - imm,0x5a - imm,0x59 - imm,0x58 - imm,
+                          0x57 - imm,0x56 - imm,0x55 - imm,0x54 - imm,0x53 - imm,0x52 - imm,0x51 - imm,0x50 - imm,
+                          0x4f - imm,0x4e - imm,0x4d - imm,0x4c - imm,0x4b - imm,0x4a - imm,0x49 - imm,0x48 - imm,
+                          0x47 - imm,0x46 - imm,0x45 - imm,0x44 - imm,0x43 - imm,0x42 - imm,0x41 - imm,0x40 - imm), a.val));
+#else
+    return imm < 64 ? v_rotate_right<64 - imm>(b, a) : v_rotate_right<128 - imm>(v512_setzero_s8(), b);
+#endif
+}
+template<int imm>
+inline v_int8x64 v_rotate_right(const v_int8x64& a)
+{
+    if (imm == 0) return a;
+    if (imm >= 64) return v_int8x64();
+#if CV_AVX_512VBMI
+    return v_int8x64(_mm512_maskz_permutexvar_epi8(0xFFFFFFFFFFFFFFFF >> imm,
+           _v512_set_epu8(0x3f + imm,0x3e + imm,0x3d + imm,0x3c + imm,0x3b + imm,0x3a + imm,0x39 + imm,0x38 + imm,
+                          0x37 + imm,0x36 + imm,0x35 + imm,0x34 + imm,0x33 + imm,0x32 + imm,0x31 + imm,0x30 + imm,
+                          0x2f + imm,0x2e + imm,0x2d + imm,0x2c + imm,0x2b + imm,0x2a + imm,0x29 + imm,0x28 + imm,
+                          0x27 + imm,0x26 + imm,0x25 + imm,0x24 + imm,0x23 + imm,0x22 + imm,0x21 + imm,0x20 + imm,
+                          0x1f + imm,0x1e + imm,0x1d + imm,0x1c + imm,0x1b + imm,0x1a + imm,0x19 + imm,0x18 + imm,
+                          0x17 + imm,0x16 + imm,0x15 + imm,0x14 + imm,0x13 + imm,0x12 + imm,0x11 + imm,0x10 + imm,
+                          0x0f + imm,0x0e + imm,0x0d + imm,0x0c + imm,0x0b + imm,0x0a + imm,0x09 + imm,0x08 + imm,
+                          0x07 + imm,0x06 + imm,0x05 + imm,0x04 + imm,0x03 + imm,0x02 + imm,0x01 + imm,0x00 + imm), a.val));
+#else
+    return v_rotate_right<imm>(a, v512_setzero_s8());
+#endif
+}
+template<int imm>
+inline v_int8x64 v_rotate_left(const v_int8x64& a)
+{
+    if (imm == 0) return a;
+    if (imm >= 64) return v_int8x64();
+#if CV_AVX_512VBMI
+    return v_int8x64(_mm512_maskz_permutexvar_epi8(0xFFFFFFFFFFFFFFFF << imm,
+           _v512_set_epi8(0x3f - imm,0x3e - imm,0x3d - imm,0x3c - imm,0x3b - imm,0x3a - imm,0x39 - imm,0x38 - imm,
+                          0x37 - imm,0x36 - imm,0x35 - imm,0x34 - imm,0x33 - imm,0x32 - imm,0x31 - imm,0x30 - imm,
+                          0x2f - imm,0x2e - imm,0x2d - imm,0x2c - imm,0x2b - imm,0x2a - imm,0x29 - imm,0x28 - imm,
+                          0x27 - imm,0x26 - imm,0x25 - imm,0x24 - imm,0x23 - imm,0x22 - imm,0x21 - imm,0x20 - imm,
+                          0x1f - imm,0x1e - imm,0x1d - imm,0x1c - imm,0x1b - imm,0x1a - imm,0x19 - imm,0x18 - imm,
+                          0x17 - imm,0x16 - imm,0x15 - imm,0x14 - imm,0x13 - imm,0x12 - imm,0x11 - imm,0x10 - imm,
+                          0x0f - imm,0x0e - imm,0x0d - imm,0x0c - imm,0x0b - imm,0x0a - imm,0x09 - imm,0x08 - imm,
+                          0x07 - imm,0x06 - imm,0x05 - imm,0x04 - imm,0x03 - imm,0x02 - imm,0x01 - imm,0x00 - imm), a.val));
+#else
+    return v_rotate_right<64 - imm>(v512_setzero_s8(), a);
+#endif
+}
+
+#define OPENCV_HAL_IMPL_AVX512_ROTATE_PM(_Tpvec, suffix)                                                                                   \
+template<int imm> inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b)                                                            \
+{ return v_reinterpret_as_##suffix(v_rotate_left<imm * sizeof(_Tpvec::lane_type)>(v_reinterpret_as_s8(a), v_reinterpret_as_s8(b))); }      \
+template<int imm> inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b)                                                           \
+{ return v_reinterpret_as_##suffix(v_rotate_right<imm * sizeof(_Tpvec::lane_type)>(v_reinterpret_as_s8(a), v_reinterpret_as_s8(b))); }     \
+template<int imm> inline _Tpvec v_rotate_left(const _Tpvec& a)                                                                             \
+{ return v_reinterpret_as_##suffix(v_rotate_left<imm * sizeof(_Tpvec::lane_type)>(v_reinterpret_as_s8(a))); }                              \
+template<int imm> inline _Tpvec v_rotate_right(const _Tpvec& a)                                                                            \
+{ return v_reinterpret_as_##suffix(v_rotate_right<imm * sizeof(_Tpvec::lane_type)>(v_reinterpret_as_s8(a))); }
+
+#define OPENCV_HAL_IMPL_AVX512_ROTATE_EC(_Tpvec, suffix)                                                                                   \
+template<int imm>                                                                                                                          \
+inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b)                                                                              \
+{                                                                                                                                          \
+    enum { SHIFT2 = (_Tpvec::nlanes - imm) };                                                                                              \
+    enum { MASK = ((1 << _Tpvec::nlanes) - 1) };                                                                                           \
+    if (imm == 0) return a;                                                                                                                \
+    if (imm == _Tpvec::nlanes) return b;                                                                                                   \
+    if (imm >= 2*_Tpvec::nlanes) return _Tpvec::zero();                                                                                    \
+    return _Tpvec(_mm512_mask_expand_##suffix(_mm512_maskz_compress_##suffix((MASK << SHIFT2)&MASK, b.val), (MASK << (imm))&MASK, a.val)); \
+}                                                                                                                                          \
+template<int imm>                                                                                                                          \
+inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b)                                                                             \
+{                                                                                                                                          \
+    enum { SHIFT2 = (_Tpvec::nlanes - imm) };                                                                                              \
+    enum { MASK = ((1 << _Tpvec::nlanes) - 1) };                                                                                           \
+    if (imm == 0) return a;                                                                                                                \
+    if (imm == _Tpvec::nlanes) return b;                                                                                                   \
+    if (imm >= 2*_Tpvec::nlanes) return _Tpvec::zero();                                                                                    \
+    return _Tpvec(_mm512_mask_expand_##suffix(_mm512_maskz_compress_##suffix((MASK << (imm))&MASK, a.val), (MASK << SHIFT2)&MASK, b.val)); \
+}                                                                                                                                          \
+template<int imm>                                                                                                                          \
+inline _Tpvec v_rotate_left(const _Tpvec& a)                                                                                               \
+{                                                                                                                                          \
+    if (imm == 0) return a;                                                                                                                \
+    if (imm >= _Tpvec::nlanes) return _Tpvec::zero();                                                                                      \
+    return _Tpvec(_mm512_maskz_expand_##suffix((1 << _Tpvec::nlanes) - (1 << (imm)), a.val));                                              \
+}                                                                                                                                          \
+template<int imm>                                                                                                                          \
+inline _Tpvec v_rotate_right(const _Tpvec& a)                                                                                              \
+{                                                                                                                                          \
+    if (imm == 0) return a;                                                                                                                \
+    if (imm >= _Tpvec::nlanes) return _Tpvec::zero();                                                                                      \
+    return _Tpvec(_mm512_maskz_compress_##suffix((1 << _Tpvec::nlanes) - (1 << (imm)), a.val));                                            \
+}
+
+OPENCV_HAL_IMPL_AVX512_ROTATE_PM(v_uint8x64,   u8)
+OPENCV_HAL_IMPL_AVX512_ROTATE_PM(v_uint16x32,  u16)
+OPENCV_HAL_IMPL_AVX512_ROTATE_PM(v_int16x32,   s16)
+OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_uint32x16,  epi32)
+OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_int32x16,   epi32)
+OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_uint64x8,   epi64)
+OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_int64x8,    epi64)
+OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_float32x16, ps)
+OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_float64x8,  pd)
+
+/** Reverse **/
+inline v_uint8x64 v_reverse(const v_uint8x64 &a)
+{
+#if CV_AVX_512VBMI
+    static const __m512i perm = _mm512_set_epi32(
+            0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f,
+            0x10111213, 0x14151617, 0x18191a1b, 0x1c1d1e1f,
+            0x20212223, 0x24252627, 0x28292a2b, 0x2c2d2e2f,
+            0x30313233, 0x34353637, 0x38393a3b, 0x3c3d3e3f);
+    return v_uint8x64(_mm512_permutexvar_epi8(perm, a.val));
+#else
+    static const __m512i shuf = _mm512_set_epi32(
+            0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f,
+            0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f,
+            0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f,
+            0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f);
+    static const __m512i perm = _mm512_set_epi64(1, 0, 3, 2, 5, 4, 7, 6);
+    __m512i vec = _mm512_shuffle_epi8(a.val, shuf);
+    return v_uint8x64(_mm512_permutexvar_epi64(perm, vec));
+#endif
+}
+
+inline v_int8x64 v_reverse(const v_int8x64 &a)
+{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
+
+inline v_uint16x32 v_reverse(const v_uint16x32 &a)
+{
+#if CV_AVX_512VBMI
+    static const __m512i perm = _mm512_set_epi32(
+            0x00000001, 0x00020003, 0x00040005, 0x00060007,
+            0x00080009, 0x000a000b, 0x000c000d, 0x000e000f,
+            0x00100011, 0x00120013, 0x00140015, 0x00160017,
+            0x00180019, 0x001a001b, 0x001c001d, 0x001e001f);
+    return v_uint16x32(_mm512_permutexvar_epi16(perm, a.val));
+#else
+    static const __m512i shuf = _mm512_set_epi32(
+            0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e,
+            0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e,
+            0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e,
+            0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e);
+    static const __m512i perm = _mm512_set_epi64(1, 0, 3, 2, 5, 4, 7, 6);
+    __m512i vec = _mm512_shuffle_epi8(a.val, shuf);
+    return v_uint16x32(_mm512_permutexvar_epi64(perm, vec));
+#endif
+}
+
+inline v_int16x32 v_reverse(const v_int16x32 &a)
+{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
+
+inline v_uint32x16 v_reverse(const v_uint32x16 &a)
+{
+    static const __m512i perm = _mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15);
+    return v_uint32x16(_mm512_permutexvar_epi32(perm, a.val));
+}
+
+inline v_int32x16 v_reverse(const v_int32x16 &a)
+{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_float32x16 v_reverse(const v_float32x16 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x8 v_reverse(const v_uint64x8 &a)
+{
+    static const __m512i perm = _mm512_set_epi64(0, 1, 2, 3, 4, 5, 6, 7);
+    return v_uint64x8(_mm512_permutexvar_epi64(perm, a.val));
+}
+
+inline v_int64x8 v_reverse(const v_int64x8 &a)
+{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
+
+inline v_float64x8 v_reverse(const v_float64x8 &a)
+{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
+
+////////// Reduce /////////
+
+/** Reduce **/
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64(a, b) a + b
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_8(sctype, func, _Tpvec, ifunc, scop)                                          \
+    inline sctype v_reduce_##func(const _Tpvec& a)                                                                  \
+    { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val));                           \
+      sctype CV_DECL_ALIGNED(64) idx[2];                                                                            \
+      _mm_store_si128((__m128i*)idx, _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1))); \
+      return scop(idx[0], idx[1]); }
+OPENCV_HAL_IMPL_AVX512_REDUCE_8(uint64, min, v_uint64x8, min_epu64, min)
+OPENCV_HAL_IMPL_AVX512_REDUCE_8(uint64, max, v_uint64x8, max_epu64, max)
+OPENCV_HAL_IMPL_AVX512_REDUCE_8(uint64, sum, v_uint64x8, add_epi64, OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64)
+OPENCV_HAL_IMPL_AVX512_REDUCE_8(int64,  min, v_int64x8,  min_epi64, min)
+OPENCV_HAL_IMPL_AVX512_REDUCE_8(int64,  max, v_int64x8,  max_epi64, max)
+OPENCV_HAL_IMPL_AVX512_REDUCE_8(int64,  sum, v_int64x8,  add_epi64, OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64)
+
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_8F(func, ifunc, scop)                                         \
+    inline double v_reduce_##func(const v_float64x8& a)                                             \
+    { __m256d half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val));           \
+      double CV_DECL_ALIGNED(64) idx[2];                                                            \
+      _mm_store_pd(idx, _mm_##ifunc(_mm256_castpd256_pd128(half), _mm256_extractf128_pd(half, 1))); \
+      return scop(idx[0], idx[1]); }
+OPENCV_HAL_IMPL_AVX512_REDUCE_8F(min, min_pd, min)
+OPENCV_HAL_IMPL_AVX512_REDUCE_8F(max, max_pd, max)
+OPENCV_HAL_IMPL_AVX512_REDUCE_8F(sum, add_pd, OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64)
+
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_16(sctype, func, _Tpvec, ifunc)                                 \
+    inline sctype v_reduce_##func(const _Tpvec& a)                                                    \
+    { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val));             \
+      __m128i quarter = _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 8));                                     \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 4));                                     \
+      return (sctype)_mm_cvtsi128_si32(quarter); }
+OPENCV_HAL_IMPL_AVX512_REDUCE_16(uint, min, v_uint32x16, min_epu32)
+OPENCV_HAL_IMPL_AVX512_REDUCE_16(uint, max, v_uint32x16, max_epu32)
+OPENCV_HAL_IMPL_AVX512_REDUCE_16(int,  min, v_int32x16,  min_epi32)
+OPENCV_HAL_IMPL_AVX512_REDUCE_16(int,  max, v_int32x16,  max_epi32)
+
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_16F(func, ifunc)                                            \
+    inline float v_reduce_##func(const v_float32x16& a)                                           \
+    { __m256 half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val));          \
+      __m128 quarter = _mm_##ifunc(_mm256_castps256_ps128(half), _mm256_extractf128_ps(half, 1)); \
+      quarter = _mm_##ifunc(quarter, _mm_permute_ps(quarter, _MM_SHUFFLE(0, 0, 3, 2)));           \
+      quarter = _mm_##ifunc(quarter, _mm_permute_ps(quarter, _MM_SHUFFLE(0, 0, 0, 1)));           \
+      return _mm_cvtss_f32(quarter); }
+OPENCV_HAL_IMPL_AVX512_REDUCE_16F(min, min_ps)
+OPENCV_HAL_IMPL_AVX512_REDUCE_16F(max, max_ps)
+
+inline float v_reduce_sum(const v_float32x16& a)
+{
+    __m256 half = _mm256_add_ps(_v512_extract_low(a.val), _v512_extract_high(a.val));
+    __m128 quarter = _mm_add_ps(_mm256_castps256_ps128(half), _mm256_extractf128_ps(half, 1));
+    quarter = _mm_hadd_ps(quarter, quarter);
+    return _mm_cvtss_f32(_mm_hadd_ps(quarter, quarter));
+}
+inline int v_reduce_sum(const v_int32x16& a)
+{
+    __m256i half = _mm256_add_epi32(_v512_extract_low(a.val), _v512_extract_high(a.val));
+    __m128i quarter = _mm_add_epi32(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1));
+    quarter = _mm_hadd_epi32(quarter, quarter);
+    return _mm_cvtsi128_si32(_mm_hadd_epi32(quarter, quarter));
+}
+inline uint v_reduce_sum(const v_uint32x16& a)
+{ return (uint)v_reduce_sum(v_reinterpret_as_s32(a)); }
+
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_32(sctype, func, _Tpvec, ifunc)                                 \
+    inline sctype v_reduce_##func(const _Tpvec& a)                                                    \
+    { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val));             \
+      __m128i quarter = _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 8));                                     \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 4));                                     \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 2));                                     \
+      return (sctype)_mm_cvtsi128_si32(quarter); }
+OPENCV_HAL_IMPL_AVX512_REDUCE_32(ushort, min, v_uint16x32, min_epu16)
+OPENCV_HAL_IMPL_AVX512_REDUCE_32(ushort, max, v_uint16x32, max_epu16)
+OPENCV_HAL_IMPL_AVX512_REDUCE_32(short,  min, v_int16x32,  min_epi16)
+OPENCV_HAL_IMPL_AVX512_REDUCE_32(short,  max, v_int16x32,  max_epi16)
+
+inline int v_reduce_sum(const v_int16x32& a)
+{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); }
+inline uint v_reduce_sum(const v_uint16x32& a)
+{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); }
+
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_64(sctype, func, _Tpvec, ifunc)                                 \
+    inline sctype v_reduce_##func(const _Tpvec& a)                                                    \
+    { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val));             \
+      __m128i quarter = _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 8));                                     \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 4));                                     \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 2));                                     \
+      quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 1));                                     \
+      return (sctype)_mm_cvtsi128_si32(quarter); }
+OPENCV_HAL_IMPL_AVX512_REDUCE_64(uchar, min, v_uint8x64, min_epu8)
+OPENCV_HAL_IMPL_AVX512_REDUCE_64(uchar, max, v_uint8x64, max_epu8)
+OPENCV_HAL_IMPL_AVX512_REDUCE_64(schar, min, v_int8x64,  min_epi8)
+OPENCV_HAL_IMPL_AVX512_REDUCE_64(schar, max, v_int8x64,  max_epi8)
+
+#define OPENCV_HAL_IMPL_AVX512_REDUCE_64_SUM(sctype, _Tpvec, suffix)                                    \
+    inline sctype v_reduce_sum(const _Tpvec& a)                                                         \
+    {   __m512i a16 = _mm512_add_epi16(_mm512_cvt##suffix##_epi16(_v512_extract_low(a.val)),            \
+                                       _mm512_cvt##suffix##_epi16(_v512_extract_high(a.val)));          \
+        a16 = _mm512_cvtepi16_epi32(_mm256_add_epi16(_v512_extract_low(a16), _v512_extract_high(a16))); \
+        __m256i a8 = _mm256_add_epi32(_v512_extract_low(a16), _v512_extract_high(a16));                 \
+        __m128i a4 = _mm_add_epi32(_mm256_castsi256_si128(a8), _mm256_extracti128_si256(a8, 1));        \
+        a4 = _mm_hadd_epi32(a4, a4);                                                                    \
+        return (sctype)_mm_cvtsi128_si32(_mm_hadd_epi32(a4, a4)); }
+OPENCV_HAL_IMPL_AVX512_REDUCE_64_SUM(uint, v_uint8x64, epu8)
+OPENCV_HAL_IMPL_AVX512_REDUCE_64_SUM(int,  v_int8x64,  epi8)
+
+inline v_float32x16 v_reduce_sum4(const v_float32x16& a, const v_float32x16& b,
+                                  const v_float32x16& c, const v_float32x16& d)
+{
+    __m256 abl = _mm256_hadd_ps(_v512_extract_low(a.val), _v512_extract_low(b.val));
+    __m256 abh = _mm256_hadd_ps(_v512_extract_high(a.val), _v512_extract_high(b.val));
+    __m256 cdl = _mm256_hadd_ps(_v512_extract_low(c.val), _v512_extract_low(d.val));
+    __m256 cdh = _mm256_hadd_ps(_v512_extract_high(c.val), _v512_extract_high(d.val));
+    return v_float32x16(_v512_combine(_mm256_hadd_ps(abl, cdl), _mm256_hadd_ps(abh, cdh)));
+}
+
+inline unsigned v_reduce_sad(const v_uint8x64& a, const v_uint8x64& b)
+{
+    __m512i val = _mm512_sad_epu8(a.val, b.val);
+    __m256i half = _mm256_add_epi32(_v512_extract_low(val), _v512_extract_high(val));
+    __m128i quarter = _mm_add_epi32(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1));
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter)));
+}
+inline unsigned v_reduce_sad(const v_int8x64& a, const v_int8x64& b)
+{
+    __m512i val = _mm512_set1_epi8(-128);
+    val = _mm512_sad_epu8(_mm512_add_epi8(a.val, val), _mm512_add_epi8(b.val, val));
+    __m256i half = _mm256_add_epi32(_v512_extract_low(val), _v512_extract_high(val));
+    __m128i quarter = _mm_add_epi32(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1));
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter)));
+}
+inline unsigned v_reduce_sad(const v_uint16x32& a, const v_uint16x32& b)
+{ return v_reduce_sum(v_add_wrap(a - b, b - a)); }
+inline unsigned v_reduce_sad(const v_int16x32& a, const v_int16x32& b)
+{ return v_reduce_sum(v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b)))); }
+inline unsigned v_reduce_sad(const v_uint32x16& a, const v_uint32x16& b)
+{ return v_reduce_sum(v_max(a, b) - v_min(a, b)); }
+inline unsigned v_reduce_sad(const v_int32x16& a, const v_int32x16& b)
+{ return v_reduce_sum(v_reinterpret_as_u32(v_max(a, b) - v_min(a, b))); }
+inline float v_reduce_sad(const v_float32x16& a, const v_float32x16& b)
+{ return v_reduce_sum((a - b) & v_float32x16(_mm512_castsi512_ps(_mm512_set1_epi32(0x7fffffff)))); }
+inline double v_reduce_sad(const v_float64x8& a, const v_float64x8& b)
+{ return v_reduce_sum((a - b) & v_float64x8(_mm512_castsi512_pd(_mm512_set1_epi64(0x7fffffffffffffff)))); }
+
+/** Popcount **/
+inline v_uint8x64 v_popcount(const v_int8x64& a)
+{
+#if CV_AVX_512BITALG
+    return v_uint8x64(_mm512_popcnt_epi8(a.val));
+#elif CV_AVX_512VBMI
+    __m512i _popcnt_table0 = _v512_set_epu8(7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4, 4, 3,
+                                            5, 4, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1,
+                                            5, 4, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1,
+                                            4, 3, 3, 2, 3, 2, 2, 1, 3, 2, 2, 1, 2, 1, 1, 0);
+    __m512i _popcnt_table1 = _v512_set_epu8(7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4, 4, 3,
+                                            6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2,
+                                            6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2,
+                                            5, 4, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1);
+    return v_uint8x64(_mm512_sub_epi8(_mm512_permutex2var_epi8(_popcnt_table0, a.val, _popcnt_table1), _mm512_movm_epi8(_mm512_movepi8_mask(a.val))));
+#else
+    __m512i _popcnt_table = _mm512_set4_epi32(0x04030302, 0x03020201, 0x03020201, 0x02010100);
+    __m512i _popcnt_mask = _mm512_set1_epi8(0x0F);
+
+    return v_uint8x64(_mm512_add_epi8(_mm512_shuffle_epi8(_popcnt_table, _mm512_and_si512(                  a.val,     _popcnt_mask)),
+                                      _mm512_shuffle_epi8(_popcnt_table, _mm512_and_si512(_mm512_srli_epi16(a.val, 4), _popcnt_mask))));
+#endif
+}
+inline v_uint16x32 v_popcount(const v_int16x32& a)
+{
+#if CV_AVX_512BITALG
+    return v_uint16x32(_mm512_popcnt_epi16(a.val));
+#elif CV_AVX_512VPOPCNTDQ
+    __m512i zero = _mm512_setzero_si512();
+    return v_uint16x32(_mm512_packs_epi32(_mm512_popcnt_epi32(_mm512_unpacklo_epi16(a.val, zero)),
+                                          _mm512_popcnt_epi32(_mm512_unpackhi_epi16(a.val, zero))));
+#else
+    v_uint8x64 p = v_popcount(v_reinterpret_as_s8(a));
+    p += v_rotate_right<1>(p);
+    return v_reinterpret_as_u16(p) & v512_setall_u16(0x00ff);
+#endif
+}
+inline v_uint32x16 v_popcount(const v_int32x16& a)
+{
+#if CV_AVX_512VPOPCNTDQ
+    return v_uint32x16(_mm512_popcnt_epi32(a.val));
+#else
+    v_uint8x64 p = v_popcount(v_reinterpret_as_s8(a));
+    p += v_rotate_right<1>(p);
+    p += v_rotate_right<2>(p);
+    return v_reinterpret_as_u32(p) & v512_setall_u32(0x000000ff);
+#endif
+}
+inline v_uint64x8 v_popcount(const v_int64x8& a)
+{
+#if CV_AVX_512VPOPCNTDQ
+    return v_uint64x8(_mm512_popcnt_epi64(a.val));
+#else
+    return v_uint64x8(_mm512_sad_epu8(v_popcount(v_reinterpret_as_s8(a)).val, _mm512_setzero_si512()));
+#endif
+}
+
+
+inline v_uint8x64  v_popcount(const v_uint8x64&  a) { return v_popcount(v_reinterpret_as_s8 (a)); }
+inline v_uint16x32 v_popcount(const v_uint16x32& a) { return v_popcount(v_reinterpret_as_s16(a)); }
+inline v_uint32x16 v_popcount(const v_uint32x16& a) { return v_popcount(v_reinterpret_as_s32(a)); }
+inline v_uint64x8  v_popcount(const v_uint64x8&  a) { return v_popcount(v_reinterpret_as_s64(a)); }
+
+
+////////// Other math /////////
+
+/** Some frequent operations **/
+#if CV_FMA3
+#define OPENCV_HAL_IMPL_AVX512_MULADD(_Tpvec, suffix)                         \
+    inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)    \
+    { return _Tpvec(_mm512_fmadd_##suffix(a.val, b.val, c.val)); }            \
+    inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \
+    { return _Tpvec(_mm512_fmadd_##suffix(a.val, b.val, c.val)); }
+#else
+#define OPENCV_HAL_IMPL_AVX512_MULADD(_Tpvec, suffix)                                 \
+    inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)            \
+    { return _Tpvec(_mm512_add_##suffix(_mm512_mul_##suffix(a.val, b.val), c.val)); } \
+    inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)         \
+    { return _Tpvec(_mm512_add_##suffix(_mm512_mul_##suffix(a.val, b.val), c.val)); }
+#endif
+
+#define OPENCV_HAL_IMPL_AVX512_MISC(_Tpvec, suffix)                           \
+    inline _Tpvec v_sqrt(const _Tpvec& x)                                     \
+    { return _Tpvec(_mm512_sqrt_##suffix(x.val)); }                           \
+    inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b)           \
+    { return v_fma(a, a, b * b); }                                            \
+    inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b)               \
+    { return v_sqrt(v_fma(a, a, b * b)); }
+
+OPENCV_HAL_IMPL_AVX512_MULADD(v_float32x16, ps)
+OPENCV_HAL_IMPL_AVX512_MULADD(v_float64x8,  pd)
+OPENCV_HAL_IMPL_AVX512_MISC(v_float32x16, ps)
+OPENCV_HAL_IMPL_AVX512_MISC(v_float64x8,  pd)
+
+inline v_int32x16 v_fma(const v_int32x16& a, const v_int32x16& b, const v_int32x16& c)
+{ return a * b + c; }
+inline v_int32x16 v_muladd(const v_int32x16& a, const v_int32x16& b, const v_int32x16& c)
+{ return v_fma(a, b, c); }
+
+inline v_float32x16 v_invsqrt(const v_float32x16& x)
+{
+#if CV_AVX_512ER
+    return v_float32x16(_mm512_rsqrt28_ps(x.val));
+#else
+    v_float32x16 half = x * v512_setall_f32(0.5);
+    v_float32x16 t  = v_float32x16(_mm512_rsqrt14_ps(x.val));
+    t *= v512_setall_f32(1.5) - ((t * t) * half);
+    return t;
+#endif
+}
+
+inline v_float64x8 v_invsqrt(const v_float64x8& x)
+{
+#if CV_AVX_512ER
+    return v_float64x8(_mm512_rsqrt28_pd(x.val));
+#else
+    return v512_setall_f64(1.) / v_sqrt(x);
+//    v_float64x8 half = x * v512_setall_f64(0.5);
+//    v_float64x8 t = v_float64x8(_mm512_rsqrt14_pd(x.val));
+//    t *= v512_setall_f64(1.5) - ((t * t) * half);
+//    t *= v512_setall_f64(1.5) - ((t * t) * half);
+//    return t;
+#endif
+}
+
+/** Absolute values **/
+#define OPENCV_HAL_IMPL_AVX512_ABS(_Tpvec, _Tpuvec, suffix) \
+    inline _Tpuvec v_abs(const _Tpvec& x)                   \
+    { return _Tpuvec(_mm512_abs_##suffix(x.val)); }
+
+OPENCV_HAL_IMPL_AVX512_ABS(v_int8x64,    v_uint8x64,    epi8)
+OPENCV_HAL_IMPL_AVX512_ABS(v_int16x32,   v_uint16x32,  epi16)
+OPENCV_HAL_IMPL_AVX512_ABS(v_int32x16,   v_uint32x16,  epi32)
+OPENCV_HAL_IMPL_AVX512_ABS(v_int64x8,    v_uint64x8,   epi64)
+
+inline v_float32x16 v_abs(const v_float32x16& x)
+{
+#ifdef _mm512_abs_pd
+    return v_float32x16(_mm512_abs_ps(x.val));
+#else
+    return v_float32x16(_mm512_castsi512_ps(_mm512_and_si512(_mm512_castps_si512(x.val),
+                        _v512_set_epu64(0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF,
+                                        0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF))));
+#endif
+}
+
+inline v_float64x8 v_abs(const v_float64x8& x)
+{
+#ifdef _mm512_abs_pd
+    #if defined __GNUC__ && (__GNUC__ < 7 || (__GNUC__ == 7 && __GNUC_MINOR__ <= 3) || (__GNUC__ == 8 && __GNUC_MINOR__ <= 2))
+        // Workaround for https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87476
+        return v_float64x8(_mm512_abs_pd(_mm512_castpd_ps(x.val)));
+    #else
+        return v_float64x8(_mm512_abs_pd(x.val));
+    #endif
+#else
+    return v_float64x8(_mm512_castsi512_pd(_mm512_and_si512(_mm512_castpd_si512(x.val),
+                       _v512_set_epu64(0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF,
+                                       0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF))));
+#endif
+}
+
+/** Absolute difference **/
+inline v_uint8x64 v_absdiff(const v_uint8x64& a, const v_uint8x64& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint16x32 v_absdiff(const v_uint16x32& a, const v_uint16x32& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint32x16 v_absdiff(const v_uint32x16& a, const v_uint32x16& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+inline v_uint8x64 v_absdiff(const v_int8x64& a, const v_int8x64& b)
+{
+    v_int8x64 d = v_sub_wrap(a, b);
+    v_int8x64 m = a < b;
+    return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m));
+}
+
+inline v_uint16x32 v_absdiff(const v_int16x32& a, const v_int16x32& b)
+{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); }
+
+inline v_uint32x16 v_absdiff(const v_int32x16& a, const v_int32x16& b)
+{
+    v_int32x16 d = a - b;
+    v_int32x16 m = a < b;
+    return v_reinterpret_as_u32((d ^ m) - m);
+}
+
+inline v_float32x16 v_absdiff(const v_float32x16& a, const v_float32x16& b)
+{ return v_abs(a - b); }
+
+inline v_float64x8 v_absdiff(const v_float64x8& a, const v_float64x8& b)
+{ return v_abs(a - b); }
+
+/** Saturating absolute difference **/
+inline v_int8x64 v_absdiffs(const v_int8x64& a, const v_int8x64& b)
+{
+    v_int8x64 d = a - b;
+    v_int8x64 m = a < b;
+    return (d ^ m) - m;
+}
+inline v_int16x32 v_absdiffs(const v_int16x32& a, const v_int16x32& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+////////// Conversions /////////
+
+/** Rounding **/
+inline v_int32x16 v_round(const v_float32x16& a)
+{ return v_int32x16(_mm512_cvtps_epi32(a.val)); }
+
+inline v_int32x16 v_round(const v_float64x8& a)
+{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvtpd_epi32(a.val))); }
+
+inline v_int32x16 v_round(const v_float64x8& a, const v_float64x8& b)
+{ return v_int32x16(_v512_combine(_mm512_cvtpd_epi32(a.val), _mm512_cvtpd_epi32(b.val))); }
+
+inline v_int32x16 v_trunc(const v_float32x16& a)
+{ return v_int32x16(_mm512_cvttps_epi32(a.val)); }
+
+inline v_int32x16 v_trunc(const v_float64x8& a)
+{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvttpd_epi32(a.val))); }
+
+#if CVT_ROUND_MODES_IMPLEMENTED
+inline v_int32x16 v_floor(const v_float32x16& a)
+{ return v_int32x16(_mm512_cvt_roundps_epi32(a.val, _MM_FROUND_TO_NEG_INF | _MM_FROUND_NO_EXC)); }
+
+inline v_int32x16 v_floor(const v_float64x8& a)
+{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvt_roundpd_epi32(a.val, _MM_FROUND_TO_NEG_INF | _MM_FROUND_NO_EXC))); }
+
+inline v_int32x16 v_ceil(const v_float32x16& a)
+{ return v_int32x16(_mm512_cvt_roundps_epi32(a.val, _MM_FROUND_TO_POS_INF | _MM_FROUND_NO_EXC)); }
+
+inline v_int32x16 v_ceil(const v_float64x8& a)
+{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvt_roundpd_epi32(a.val, _MM_FROUND_TO_POS_INF | _MM_FROUND_NO_EXC))); }
+#else
+inline v_int32x16 v_floor(const v_float32x16& a)
+{ return v_int32x16(_mm512_cvtps_epi32(_mm512_roundscale_ps(a.val, 1))); }
+
+inline v_int32x16 v_floor(const v_float64x8& a)
+{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvtpd_epi32(_mm512_roundscale_pd(a.val, 1)))); }
+
+inline v_int32x16 v_ceil(const v_float32x16& a)
+{ return v_int32x16(_mm512_cvtps_epi32(_mm512_roundscale_ps(a.val, 2))); }
+
+inline v_int32x16 v_ceil(const v_float64x8& a)
+{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvtpd_epi32(_mm512_roundscale_pd(a.val, 2)))); }
+#endif
+
+/** To float **/
+inline v_float32x16 v_cvt_f32(const v_int32x16& a)
+{ return v_float32x16(_mm512_cvtepi32_ps(a.val)); }
+
+inline v_float32x16 v_cvt_f32(const v_float64x8& a)
+{ return v_float32x16(_mm512_cvtpd_pslo(a.val)); }
+
+inline v_float32x16 v_cvt_f32(const v_float64x8& a, const v_float64x8& b)
+{ return v_float32x16(_v512_combine(_mm512_cvtpd_ps(a.val), _mm512_cvtpd_ps(b.val))); }
+
+inline v_float64x8 v_cvt_f64(const v_int32x16& a)
+{ return v_float64x8(_mm512_cvtepi32_pd(_v512_extract_low(a.val))); }
+
+inline v_float64x8 v_cvt_f64_high(const v_int32x16& a)
+{ return v_float64x8(_mm512_cvtepi32_pd(_v512_extract_high(a.val))); }
+
+inline v_float64x8 v_cvt_f64(const v_float32x16& a)
+{ return v_float64x8(_mm512_cvtps_pd(_v512_extract_low(a.val))); }
+
+inline v_float64x8 v_cvt_f64_high(const v_float32x16& a)
+{ return v_float64x8(_mm512_cvtps_pd(_v512_extract_high(a.val))); }
+
+// from (Mysticial and wim) https://stackoverflow.com/q/41144668
+inline v_float64x8 v_cvt_f64(const v_int64x8& v)
+{
+#if CV_AVX_512DQ
+    return v_float64x8(_mm512_cvtepi64_pd(v.val));
+#else
+    // constants encoded as floating-point
+    __m512i magic_i_lo   = _mm512_set1_epi64(0x4330000000000000); // 2^52
+    __m512i magic_i_hi32 = _mm512_set1_epi64(0x4530000080000000); // 2^84 + 2^63
+    __m512i magic_i_all  = _mm512_set1_epi64(0x4530000080100000); // 2^84 + 2^63 + 2^52
+    __m512d magic_d_all  = _mm512_castsi512_pd(magic_i_all);
+
+    // Blend the 32 lowest significant bits of v with magic_int_lo
+    __m512i v_lo         = _mm512_mask_blend_epi32(0x5555, magic_i_lo, v.val);
+    // Extract the 32 most significant bits of v
+    __m512i v_hi         = _mm512_srli_epi64(v.val, 32);
+    // Flip the msb of v_hi and blend with 0x45300000
+            v_hi         = _mm512_xor_si512(v_hi, magic_i_hi32);
+    // Compute in double precision
+    __m512d v_hi_dbl     = _mm512_sub_pd(_mm512_castsi512_pd(v_hi), magic_d_all);
+    // (v_hi - magic_d_all) + v_lo  Do not assume associativity of floating point addition
+    __m512d result       = _mm512_add_pd(v_hi_dbl, _mm512_castsi512_pd(v_lo));
+    return v_float64x8(result);
+#endif
+}
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x64 v512_lut(const schar* tab, const int* idx)
+{
+    __m128i p0 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx    ), (const int *)tab, 1));
+    __m128i p1 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 1), (const int *)tab, 1));
+    __m128i p2 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 2), (const int *)tab, 1));
+    __m128i p3 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 3), (const int *)tab, 1));
+    return v_int8x64(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_castsi128_si512(p0), p1, 1), p2, 2), p3, 3));
+}
+inline v_int8x64 v512_lut_pairs(const schar* tab, const int* idx)
+{
+    __m256i p0 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx    ), (const int *)tab, 1));
+    __m256i p1 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 1), (const int *)tab, 1));
+    return v_int8x64(_v512_combine(p0, p1));
+}
+inline v_int8x64 v512_lut_quads(const schar* tab, const int* idx)
+{
+    return v_int8x64(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx), (const int *)tab, 1));
+}
+inline v_uint8x64 v512_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v512_lut((const schar *)tab, idx)); }
+inline v_uint8x64 v512_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v512_lut_pairs((const schar *)tab, idx)); }
+inline v_uint8x64 v512_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v512_lut_quads((const schar *)tab, idx)); }
+
+inline v_int16x32 v512_lut(const short* tab, const int* idx)
+{
+    __m256i p0 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx    ), (const int *)tab, 2));
+    __m256i p1 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 1), (const int *)tab, 2));
+    return v_int16x32(_v512_combine(p0, p1));
+}
+inline v_int16x32 v512_lut_pairs(const short* tab, const int* idx)
+{
+    return v_int16x32(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx), (const int *)tab, 2));
+}
+inline v_int16x32 v512_lut_quads(const short* tab, const int* idx)
+{
+#if defined(__GNUC__)
+    return v_int16x32(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const long long int*)tab, 2));
+#else
+    return v_int16x32(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const int64*)tab, 2));
+#endif
+}
+inline v_uint16x32 v512_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v512_lut((const short *)tab, idx)); }
+inline v_uint16x32 v512_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v512_lut_pairs((const short *)tab, idx)); }
+inline v_uint16x32 v512_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v512_lut_quads((const short *)tab, idx)); }
+
+inline v_int32x16 v512_lut(const int* tab, const int* idx)
+{
+    return v_int32x16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx), tab, 4));
+}
+inline v_int32x16 v512_lut_pairs(const int* tab, const int* idx)
+{
+#if defined(__GNUC__)
+    return v_int32x16(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const long long int*)tab, 4));
+#else
+    return v_int32x16(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const int64*)tab, 4));
+#endif
+}
+inline v_int32x16 v512_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x16(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_castsi128_si512(
+                          _mm_loadu_si128((const __m128i*)(tab + idx[0]))),
+                          _mm_loadu_si128((const __m128i*)(tab + idx[1])), 1),
+                          _mm_loadu_si128((const __m128i*)(tab + idx[2])), 2),
+                          _mm_loadu_si128((const __m128i*)(tab + idx[3])), 3));
+}
+inline v_uint32x16 v512_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v512_lut((const int *)tab, idx)); }
+inline v_uint32x16 v512_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v512_lut_pairs((const int *)tab, idx)); }
+inline v_uint32x16 v512_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v512_lut_quads((const int *)tab, idx)); }
+
+inline v_int64x8 v512_lut(const int64* tab, const int* idx)
+{
+#if defined(__GNUC__)
+    return v_int64x8(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const long long int*)tab, 8));
+#else
+    return v_int64x8(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), tab , 8));
+#endif
+}
+inline v_int64x8 v512_lut_pairs(const int64* tab, const int* idx)
+{
+    return v_int64x8(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_castsi128_si512(
+                         _mm_loadu_si128((const __m128i*)(tab + idx[0]))),
+                         _mm_loadu_si128((const __m128i*)(tab + idx[1])), 1),
+                         _mm_loadu_si128((const __m128i*)(tab + idx[2])), 2),
+                         _mm_loadu_si128((const __m128i*)(tab + idx[3])), 3));
+}
+inline v_uint64x8 v512_lut(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v512_lut((const int64 *)tab, idx)); }
+inline v_uint64x8 v512_lut_pairs(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v512_lut_pairs((const int64 *)tab, idx)); }
+
+inline v_float32x16 v512_lut(const float* tab, const int* idx)
+{
+    return v_float32x16(_mm512_i32gather_ps(_mm512_loadu_si512((const __m512i*)idx), tab, 4));
+}
+inline v_float32x16 v512_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v512_lut_pairs((const int *)tab, idx)); }
+inline v_float32x16 v512_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v512_lut_quads((const int *)tab, idx)); }
+
+inline v_float64x8 v512_lut(const double* tab, const int* idx)
+{
+    return v_float64x8(_mm512_i32gather_pd(_mm256_loadu_si256((const __m256i*)idx), tab, 8));
+}
+inline v_float64x8 v512_lut_pairs(const double* tab, const int* idx)
+{
+        return v_float64x8(_mm512_insertf64x2(_mm512_insertf64x2(_mm512_insertf64x2(_mm512_castpd128_pd512(
+                               _mm_loadu_pd(tab + idx[0])),
+                               _mm_loadu_pd(tab + idx[1]), 1),
+                               _mm_loadu_pd(tab + idx[2]), 2),
+                               _mm_loadu_pd(tab + idx[3]), 3));
+}
+
+inline v_int32x16 v_lut(const int* tab, const v_int32x16& idxvec)
+{
+    return v_int32x16(_mm512_i32gather_epi32(idxvec.val, tab, 4));
+}
+
+inline v_uint32x16 v_lut(const unsigned* tab, const v_int32x16& idxvec)
+{
+    return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec));
+}
+
+inline v_float32x16 v_lut(const float* tab, const v_int32x16& idxvec)
+{
+    return v_float32x16(_mm512_i32gather_ps(idxvec.val, tab, 4));
+}
+
+inline v_float64x8 v_lut(const double* tab, const v_int32x16& idxvec)
+{
+    return v_float64x8(_mm512_i32gather_pd(_v512_extract_low(idxvec.val), tab, 8));
+}
+
+inline void v_lut_deinterleave(const float* tab, const v_int32x16& idxvec, v_float32x16& x, v_float32x16& y)
+{
+    x.val = _mm512_i32gather_ps(idxvec.val, tab, 4);
+    y.val = _mm512_i32gather_ps(idxvec.val, &tab[1], 4);
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x16& idxvec, v_float64x8& x, v_float64x8& y)
+{
+    x.val = _mm512_i32gather_pd(_v512_extract_low(idxvec.val), tab, 8);
+    y.val = _mm512_i32gather_pd(_v512_extract_low(idxvec.val), &tab[1], 8);
+}
+
+inline v_int8x64 v_interleave_pairs(const v_int8x64& vec)
+{
+    return v_int8x64(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0d0e0c, 0x0b090a08, 0x07050604, 0x03010200)));
+}
+inline v_uint8x64 v_interleave_pairs(const v_uint8x64& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
+inline v_int8x64 v_interleave_quads(const v_int8x64& vec)
+{
+    return v_int8x64(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0b0e0a, 0x0d090c08, 0x07030602, 0x05010400)));
+}
+inline v_uint8x64 v_interleave_quads(const v_uint8x64& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x32 v_interleave_pairs(const v_int16x32& vec)
+{
+    return v_int16x32(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0e0b0a, 0x0d0c0908, 0x07060302, 0x05040100)));
+}
+inline v_uint16x32 v_interleave_pairs(const v_uint16x32& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+inline v_int16x32 v_interleave_quads(const v_int16x32& vec)
+{
+    return v_int16x32(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0e0706, 0x0d0c0504, 0x0b0a0302, 0x09080100)));
+}
+inline v_uint16x32 v_interleave_quads(const v_uint16x32& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x16 v_interleave_pairs(const v_int32x16& vec)
+{
+    return v_int32x16(_mm512_shuffle_epi32(vec.val, _MM_PERM_ACBD));
+}
+inline v_uint32x16 v_interleave_pairs(const v_uint32x16& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x16 v_interleave_pairs(const v_float32x16& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+
+inline v_int8x64 v_pack_triplets(const v_int8x64& vec)
+{
+    return v_int8x64(_mm512_permutexvar_epi32(_v512_set_epu64(0x0000000f0000000f, 0x0000000f0000000f, 0x0000000e0000000d, 0x0000000c0000000a,
+                                                              0x0000000900000008, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000),
+                                              _mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0xffffff0f, 0x0e0d0c0a, 0x09080605, 0x04020100))));
+}
+inline v_uint8x64 v_pack_triplets(const v_uint8x64& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x32 v_pack_triplets(const v_int16x32& vec)
+{
+    return v_int16x32(_mm512_permutexvar_epi16(_v512_set_epu64(0x001f001f001f001f, 0x001f001f001f001f, 0x001e001d001c001a, 0x0019001800160015,
+                                                               0x0014001200110010, 0x000e000d000c000a, 0x0009000800060005, 0x0004000200010000), vec.val));
+}
+inline v_uint16x32 v_pack_triplets(const v_uint16x32& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x16 v_pack_triplets(const v_int32x16& vec)
+{
+    return v_int32x16(_mm512_permutexvar_epi32(_v512_set_epu64(0x0000000f0000000f, 0x0000000f0000000f, 0x0000000e0000000d, 0x0000000c0000000a,
+                                                               0x0000000900000008, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000), vec.val));
+}
+inline v_uint32x16 v_pack_triplets(const v_uint32x16& vec) { return v_reinterpret_as_u32(v_pack_triplets(v_reinterpret_as_s32(vec))); }
+inline v_float32x16 v_pack_triplets(const v_float32x16& vec)
+{
+    return v_float32x16(_mm512_permutexvar_ps(_v512_set_epu64(0x0000000f0000000f, 0x0000000f0000000f, 0x0000000e0000000d, 0x0000000c0000000a,
+                                                              0x0000000900000008, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000), vec.val));
+}
+
+////////// Matrix operations /////////
+
+//////// Dot Product ////////
+
+// 16 >> 32
+inline v_int32x16 v_dotprod(const v_int16x32& a, const v_int16x32& b)
+{ return v_int32x16(_mm512_madd_epi16(a.val, b.val)); }
+inline v_int32x16 v_dotprod(const v_int16x32& a, const v_int16x32& b, const v_int32x16& c)
+{ return v_dotprod(a, b) + c; }
+
+// 32 >> 64
+inline v_int64x8 v_dotprod(const v_int32x16& a, const v_int32x16& b)
+{
+    __m512i even = _mm512_mul_epi32(a.val, b.val);
+    __m512i odd = _mm512_mul_epi32(_mm512_srli_epi64(a.val, 32), _mm512_srli_epi64(b.val, 32));
+    return v_int64x8(_mm512_add_epi64(even, odd));
+}
+inline v_int64x8 v_dotprod(const v_int32x16& a, const v_int32x16& b, const v_int64x8& c)
+{ return v_dotprod(a, b) + c; }
+
+// 8 >> 32
+inline v_uint32x16 v_dotprod_expand(const v_uint8x64& a, const v_uint8x64& b)
+{
+    __m512i even_a = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, a.val, _mm512_setzero_si512());
+    __m512i odd_a  = _mm512_srli_epi16(a.val, 8);
+
+    __m512i even_b = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, b.val, _mm512_setzero_si512());
+    __m512i odd_b  = _mm512_srli_epi16(b.val, 8);
+
+    __m512i prod0  = _mm512_madd_epi16(even_a, even_b);
+    __m512i prod1  = _mm512_madd_epi16(odd_a, odd_b);
+    return v_uint32x16(_mm512_add_epi32(prod0, prod1));
+}
+inline v_uint32x16 v_dotprod_expand(const v_uint8x64& a, const v_uint8x64& b, const v_uint32x16& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int32x16 v_dotprod_expand(const v_int8x64& a, const v_int8x64& b)
+{
+    __m512i even_a = _mm512_srai_epi16(_mm512_bslli_epi128(a.val, 1), 8);
+    __m512i odd_a  = _mm512_srai_epi16(a.val, 8);
+
+    __m512i even_b = _mm512_srai_epi16(_mm512_bslli_epi128(b.val, 1), 8);
+    __m512i odd_b  = _mm512_srai_epi16(b.val, 8);
+
+    __m512i prod0  = _mm512_madd_epi16(even_a, even_b);
+    __m512i prod1  = _mm512_madd_epi16(odd_a, odd_b);
+    return v_int32x16(_mm512_add_epi32(prod0, prod1));
+}
+inline v_int32x16 v_dotprod_expand(const v_int8x64& a, const v_int8x64& b, const v_int32x16& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 16 >> 64
+inline v_uint64x8 v_dotprod_expand(const v_uint16x32& a, const v_uint16x32& b)
+{
+    __m512i mullo = _mm512_mullo_epi16(a.val, b.val);
+    __m512i mulhi = _mm512_mulhi_epu16(a.val, b.val);
+    __m512i mul0  = _mm512_unpacklo_epi16(mullo, mulhi);
+    __m512i mul1  = _mm512_unpackhi_epi16(mullo, mulhi);
+
+    __m512i p02   = _mm512_mask_blend_epi32(0xAAAA, mul0, _mm512_setzero_si512());
+    __m512i p13   = _mm512_srli_epi64(mul0, 32);
+    __m512i p46   = _mm512_mask_blend_epi32(0xAAAA, mul1, _mm512_setzero_si512());
+    __m512i p57   = _mm512_srli_epi64(mul1, 32);
+
+    __m512i p15_  = _mm512_add_epi64(p02, p13);
+    __m512i p9d_  = _mm512_add_epi64(p46, p57);
+
+    return v_uint64x8(_mm512_add_epi64(
+        _mm512_unpacklo_epi64(p15_, p9d_),
+        _mm512_unpackhi_epi64(p15_, p9d_)
+    ));
+}
+inline v_uint64x8 v_dotprod_expand(const v_uint16x32& a, const v_uint16x32& b, const v_uint64x8& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int64x8 v_dotprod_expand(const v_int16x32& a, const v_int16x32& b)
+{
+    __m512i prod = _mm512_madd_epi16(a.val, b.val);
+    __m512i even = _mm512_srai_epi64(_mm512_bslli_epi128(prod, 4), 32);
+    __m512i odd  = _mm512_srai_epi64(prod, 32);
+    return v_int64x8(_mm512_add_epi64(even, odd));
+}
+inline v_int64x8 v_dotprod_expand(const v_int16x32& a, const v_int16x32& b, const v_int64x8& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x8 v_dotprod_expand(const v_int32x16& a, const v_int32x16& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x8 v_dotprod_expand(const v_int32x16& a, const v_int32x16& b, const v_float64x8& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x16 v_dotprod_fast(const v_int16x32& a, const v_int16x32& b)
+{ return v_dotprod(a, b); }
+inline v_int32x16 v_dotprod_fast(const v_int16x32& a, const v_int16x32& b, const v_int32x16& c)
+{ return v_dotprod(a, b, c); }
+
+// 32 >> 64
+inline v_int64x8 v_dotprod_fast(const v_int32x16& a, const v_int32x16& b)
+{ return v_dotprod(a, b); }
+inline v_int64x8 v_dotprod_fast(const v_int32x16& a, const v_int32x16& b, const v_int64x8& c)
+{ return v_dotprod(a, b, c); }
+
+// 8 >> 32
+inline v_uint32x16 v_dotprod_expand_fast(const v_uint8x64& a, const v_uint8x64& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint32x16 v_dotprod_expand_fast(const v_uint8x64& a, const v_uint8x64& b, const v_uint32x16& c)
+{ return v_dotprod_expand(a, b, c); }
+
+inline v_int32x16 v_dotprod_expand_fast(const v_int8x64& a, const v_int8x64& b)
+{ return v_dotprod_expand(a, b); }
+inline v_int32x16 v_dotprod_expand_fast(const v_int8x64& a, const v_int8x64& b, const v_int32x16& c)
+{ return v_dotprod_expand(a, b, c); }
+
+// 16 >> 64
+inline v_uint64x8 v_dotprod_expand_fast(const v_uint16x32& a, const v_uint16x32& b)
+{
+    __m512i mullo = _mm512_mullo_epi16(a.val, b.val);
+    __m512i mulhi = _mm512_mulhi_epu16(a.val, b.val);
+    __m512i mul0  = _mm512_unpacklo_epi16(mullo, mulhi);
+    __m512i mul1  = _mm512_unpackhi_epi16(mullo, mulhi);
+
+    __m512i p02   = _mm512_mask_blend_epi32(0xAAAA, mul0, _mm512_setzero_si512());
+    __m512i p13   = _mm512_srli_epi64(mul0, 32);
+    __m512i p46   = _mm512_mask_blend_epi32(0xAAAA, mul1, _mm512_setzero_si512());
+    __m512i p57   = _mm512_srli_epi64(mul1, 32);
+
+    __m512i p15_  = _mm512_add_epi64(p02, p13);
+    __m512i p9d_  = _mm512_add_epi64(p46, p57);
+    return v_uint64x8(_mm512_add_epi64(p15_, p9d_));
+}
+inline v_uint64x8 v_dotprod_expand_fast(const v_uint16x32& a, const v_uint16x32& b, const v_uint64x8& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+inline v_int64x8 v_dotprod_expand_fast(const v_int16x32& a, const v_int16x32& b)
+{ return v_dotprod_expand(a, b); }
+inline v_int64x8 v_dotprod_expand_fast(const v_int16x32& a, const v_int16x32& b, const v_int64x8& c)
+{ return v_dotprod_expand(a, b, c); }
+
+// 32 >> 64f
+inline v_float64x8 v_dotprod_expand_fast(const v_int32x16& a, const v_int32x16& b)
+{ return v_dotprod_expand(a, b); }
+inline v_float64x8 v_dotprod_expand_fast(const v_int32x16& a, const v_int32x16& b, const v_float64x8& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+
+#define OPENCV_HAL_AVX512_SPLAT2_PS(a, im) \
+    v_float32x16(_mm512_permute_ps(a.val, _MM_SHUFFLE(im, im, im, im)))
+
+inline v_float32x16 v_matmul(const v_float32x16& v,
+                             const v_float32x16& m0, const v_float32x16& m1,
+                             const v_float32x16& m2, const v_float32x16& m3)
+{
+    v_float32x16 v04 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 0);
+    v_float32x16 v15 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 1);
+    v_float32x16 v26 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 2);
+    v_float32x16 v37 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 3);
+    return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, v37 * m3)));
+}
+
+inline v_float32x16 v_matmuladd(const v_float32x16& v,
+                                const v_float32x16& m0, const v_float32x16& m1,
+                                const v_float32x16& m2, const v_float32x16& a)
+{
+    v_float32x16 v04 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 0);
+    v_float32x16 v15 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 1);
+    v_float32x16 v26 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 2);
+    return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, a)));
+}
+
+#define OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \
+    inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1,              \
+                               const _Tpvec& a2, const _Tpvec& a3,              \
+                               _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3)  \
+    {                                                                           \
+        __m512i t0 = cast_from(_mm512_unpacklo_##suffix(a0.val, a1.val));       \
+        __m512i t1 = cast_from(_mm512_unpacklo_##suffix(a2.val, a3.val));       \
+        __m512i t2 = cast_from(_mm512_unpackhi_##suffix(a0.val, a1.val));       \
+        __m512i t3 = cast_from(_mm512_unpackhi_##suffix(a2.val, a3.val));       \
+        b0.val = cast_to(_mm512_unpacklo_epi64(t0, t1));                        \
+        b1.val = cast_to(_mm512_unpackhi_epi64(t0, t1));                        \
+        b2.val = cast_to(_mm512_unpacklo_epi64(t2, t3));                        \
+        b3.val = cast_to(_mm512_unpackhi_epi64(t2, t3));                        \
+    }
+
+OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(v_uint32x16,  epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(v_int32x16,   epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(v_float32x16, ps, _mm512_castps_si512, _mm512_castsi512_ps)
+
+//////////////// Value reordering ///////////////
+
+/* Expand */
+#define OPENCV_HAL_IMPL_AVX512_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \
+    inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
+    {                                                               \
+        b0.val = intrin(_v512_extract_low(a.val));                  \
+        b1.val = intrin(_v512_extract_high(a.val));                 \
+    }                                                               \
+    inline _Tpwvec v_expand_low(const _Tpvec& a)                    \
+    { return _Tpwvec(intrin(_v512_extract_low(a.val))); }           \
+    inline _Tpwvec v_expand_high(const _Tpvec& a)                   \
+    { return _Tpwvec(intrin(_v512_extract_high(a.val))); }          \
+    inline _Tpwvec v512_load_expand(const _Tp* ptr)                 \
+    {                                                               \
+        __m256i a = _mm256_loadu_si256((const __m256i*)ptr);        \
+        return _Tpwvec(intrin(a));                                  \
+    }
+
+OPENCV_HAL_IMPL_AVX512_EXPAND(v_uint8x64,  v_uint16x32, uchar,    _mm512_cvtepu8_epi16)
+OPENCV_HAL_IMPL_AVX512_EXPAND(v_int8x64,   v_int16x32,  schar,    _mm512_cvtepi8_epi16)
+OPENCV_HAL_IMPL_AVX512_EXPAND(v_uint16x32, v_uint32x16, ushort,   _mm512_cvtepu16_epi32)
+OPENCV_HAL_IMPL_AVX512_EXPAND(v_int16x32,  v_int32x16,  short,    _mm512_cvtepi16_epi32)
+OPENCV_HAL_IMPL_AVX512_EXPAND(v_uint32x16, v_uint64x8,  unsigned, _mm512_cvtepu32_epi64)
+OPENCV_HAL_IMPL_AVX512_EXPAND(v_int32x16,  v_int64x8,   int,      _mm512_cvtepi32_epi64)
+
+#define OPENCV_HAL_IMPL_AVX512_EXPAND_Q(_Tpvec, _Tp, intrin) \
+    inline _Tpvec v512_load_expand_q(const _Tp* ptr)         \
+    {                                                        \
+        __m128i a = _mm_loadu_si128((const __m128i*)ptr);    \
+        return _Tpvec(intrin(a));                            \
+    }
+
+OPENCV_HAL_IMPL_AVX512_EXPAND_Q(v_uint32x16, uchar, _mm512_cvtepu8_epi32)
+OPENCV_HAL_IMPL_AVX512_EXPAND_Q(v_int32x16,  schar, _mm512_cvtepi8_epi32)
+
+/* pack */
+// 16
+inline v_int8x64 v_pack(const v_int16x32& a, const v_int16x32& b)
+{ return v_int8x64(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packs_epi16(a.val, b.val))); }
+
+inline v_uint8x64 v_pack(const v_uint16x32& a, const v_uint16x32& b)
+{
+    const __m512i t = _mm512_set1_epi16(255);
+    return v_uint8x64(_v512_combine(_mm512_cvtepi16_epi8(_mm512_min_epu16(a.val, t)), _mm512_cvtepi16_epi8(_mm512_min_epu16(b.val, t))));
+}
+
+inline v_uint8x64 v_pack_u(const v_int16x32& a, const v_int16x32& b)
+{
+    return v_uint8x64(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packus_epi16(a.val, b.val)));
+}
+
+inline void v_pack_store(schar* ptr, const v_int16x32& a)
+{ v_store_low(ptr, v_pack(a, a)); }
+
+inline void v_pack_store(uchar* ptr, const v_uint16x32& a)
+{
+    const __m512i m = _mm512_set1_epi16(255);
+    _mm256_storeu_si256((__m256i*)ptr, _mm512_cvtepi16_epi8(_mm512_min_epu16(a.val, m)));
+}
+
+inline void v_pack_u_store(uchar* ptr, const v_int16x32& a)
+{ v_store_low(ptr, v_pack_u(a, a)); }
+
+template<int n> inline
+v_uint8x64 v_rshr_pack(const v_uint16x32& a, const v_uint16x32& b)
+{
+    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
+    v_uint16x32 delta = v512_setall_u16((short)(1 << (n-1)));
+    return v_pack_u(v_reinterpret_as_s16((a + delta) >> n),
+                    v_reinterpret_as_s16((b + delta) >> n));
+}
+
+template<int n> inline
+void v_rshr_pack_store(uchar* ptr, const v_uint16x32& a)
+{
+    v_uint16x32 delta = v512_setall_u16((short)(1 << (n-1)));
+    v_pack_u_store(ptr, v_reinterpret_as_s16((a + delta) >> n));
+}
+
+template<int n> inline
+v_uint8x64 v_rshr_pack_u(const v_int16x32& a, const v_int16x32& b)
+{
+    v_int16x32 delta = v512_setall_s16((short)(1 << (n-1)));
+    return v_pack_u((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_u_store(uchar* ptr, const v_int16x32& a)
+{
+    v_int16x32 delta = v512_setall_s16((short)(1 << (n-1)));
+    v_pack_u_store(ptr, (a + delta) >> n);
+}
+
+template<int n> inline
+v_int8x64 v_rshr_pack(const v_int16x32& a, const v_int16x32& b)
+{
+    v_int16x32 delta = v512_setall_s16((short)(1 << (n-1)));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(schar* ptr, const v_int16x32& a)
+{
+    v_int16x32 delta = v512_setall_s16((short)(1 << (n-1)));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+// 32
+inline v_int16x32 v_pack(const v_int32x16& a, const v_int32x16& b)
+{ return v_int16x32(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packs_epi32(a.val, b.val))); }
+
+inline v_uint16x32 v_pack(const v_uint32x16& a, const v_uint32x16& b)
+{
+    const __m512i m = _mm512_set1_epi32(65535);
+    return v_uint16x32(_v512_combine(_mm512_cvtepi32_epi16(_mm512_min_epu32(a.val, m)), _mm512_cvtepi32_epi16(_mm512_min_epu32(b.val, m))));
+}
+
+inline v_uint16x32 v_pack_u(const v_int32x16& a, const v_int32x16& b)
+{ return v_uint16x32(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packus_epi32(a.val, b.val))); }
+
+inline void v_pack_store(short* ptr, const v_int32x16& a)
+{ v_store_low(ptr, v_pack(a, a)); }
+
+inline void v_pack_store(ushort* ptr, const v_uint32x16& a)
+{
+    const __m512i m = _mm512_set1_epi32(65535);
+    _mm256_storeu_si256((__m256i*)ptr, _mm512_cvtepi32_epi16(_mm512_min_epu32(a.val, m)));
+}
+
+inline void v_pack_u_store(ushort* ptr, const v_int32x16& a)
+{ v_store_low(ptr, v_pack_u(a, a)); }
+
+
+template<int n> inline
+v_uint16x32 v_rshr_pack(const v_uint32x16& a, const v_uint32x16& b)
+{
+    v_uint32x16 delta = v512_setall_u32(1 << (n-1));
+    return v_pack_u(v_reinterpret_as_s32((a + delta) >> n),
+                    v_reinterpret_as_s32((b + delta) >> n));
+}
+
+template<int n> inline
+void v_rshr_pack_store(ushort* ptr, const v_uint32x16& a)
+{
+    v_uint32x16 delta = v512_setall_u32(1 << (n-1));
+    v_pack_u_store(ptr, v_reinterpret_as_s32((a + delta) >> n));
+}
+
+template<int n> inline
+v_uint16x32 v_rshr_pack_u(const v_int32x16& a, const v_int32x16& b)
+{
+    v_int32x16 delta = v512_setall_s32(1 << (n-1));
+    return v_pack_u((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_u_store(ushort* ptr, const v_int32x16& a)
+{
+    v_int32x16 delta = v512_setall_s32(1 << (n-1));
+    v_pack_u_store(ptr, (a + delta) >> n);
+}
+
+template<int n> inline
+v_int16x32 v_rshr_pack(const v_int32x16& a, const v_int32x16& b)
+{
+    v_int32x16 delta = v512_setall_s32(1 << (n-1));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(short* ptr, const v_int32x16& a)
+{
+    v_int32x16 delta = v512_setall_s32(1 << (n-1));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+// 64
+// Non-saturating pack
+inline v_uint32x16 v_pack(const v_uint64x8& a, const v_uint64x8& b)
+{ return v_uint32x16(_v512_combine(_mm512_cvtepi64_epi32(a.val), _mm512_cvtepi64_epi32(b.val))); }
+
+inline v_int32x16 v_pack(const v_int64x8& a, const v_int64x8& b)
+{ return v_reinterpret_as_s32(v_pack(v_reinterpret_as_u64(a), v_reinterpret_as_u64(b))); }
+
+inline void v_pack_store(unsigned* ptr, const v_uint64x8& a)
+{ _mm256_storeu_si256((__m256i*)ptr, _mm512_cvtepi64_epi32(a.val)); }
+
+inline void v_pack_store(int* ptr, const v_int64x8& b)
+{ v_pack_store((unsigned*)ptr, v_reinterpret_as_u64(b)); }
+
+template<int n> inline
+v_uint32x16 v_rshr_pack(const v_uint64x8& a, const v_uint64x8& b)
+{
+    v_uint64x8 delta = v512_setall_u64((uint64)1 << (n-1));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(unsigned* ptr, const v_uint64x8& a)
+{
+    v_uint64x8 delta = v512_setall_u64((uint64)1 << (n-1));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+template<int n> inline
+v_int32x16 v_rshr_pack(const v_int64x8& a, const v_int64x8& b)
+{
+    v_int64x8 delta = v512_setall_s64((int64)1 << (n-1));
+    return v_pack((a + delta) >> n, (b + delta) >> n);
+}
+
+template<int n> inline
+void v_rshr_pack_store(int* ptr, const v_int64x8& a)
+{
+    v_int64x8 delta = v512_setall_s64((int64)1 << (n-1));
+    v_pack_store(ptr, (a + delta) >> n);
+}
+
+// pack boolean
+inline v_uint8x64 v_pack_b(const v_uint16x32& a, const v_uint16x32& b)
+{ return v_uint8x64(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packs_epi16(a.val, b.val))); }
+
+inline v_uint8x64 v_pack_b(const v_uint32x16& a, const v_uint32x16& b,
+                           const v_uint32x16& c, const v_uint32x16& d)
+{
+    __m512i ab = _mm512_packs_epi32(a.val, b.val);
+    __m512i cd = _mm512_packs_epi32(c.val, d.val);
+
+    return v_uint8x64(_mm512_permutexvar_epi32(_v512_set_epu32(15, 11, 7, 3, 14, 10, 6, 2, 13, 9, 5, 1, 12, 8, 4, 0), _mm512_packs_epi16(ab, cd)));
+}
+
+inline v_uint8x64 v_pack_b(const v_uint64x8& a, const v_uint64x8& b, const v_uint64x8& c,
+                           const v_uint64x8& d, const v_uint64x8& e, const v_uint64x8& f,
+                           const v_uint64x8& g, const v_uint64x8& h)
+{
+    __m512i ab = _mm512_packs_epi32(a.val, b.val);
+    __m512i cd = _mm512_packs_epi32(c.val, d.val);
+    __m512i ef = _mm512_packs_epi32(e.val, f.val);
+    __m512i gh = _mm512_packs_epi32(g.val, h.val);
+
+    __m512i abcd = _mm512_packs_epi32(ab, cd);
+    __m512i efgh = _mm512_packs_epi32(ef, gh);
+
+    return v_uint8x64(_mm512_permutexvar_epi16(_v512_set_epu16(31, 23, 15, 7, 30, 22, 14, 6, 29, 21, 13, 5, 28, 20, 12, 4,
+                                                               27, 19, 11, 3, 26, 18, 10, 2, 25, 17,  9, 1, 24, 16,  8, 0), _mm512_packs_epi16(abcd, efgh)));
+}
+
+/* Recombine */
+// its up there with load and store operations
+
+/* Extract */
+#define OPENCV_HAL_IMPL_AVX512_EXTRACT(_Tpvec)                \
+    template<int s>                                           \
+    inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \
+    { return v_rotate_right<s>(a, b); }
+
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint8x64)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int8x64)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint16x32)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int16x32)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint32x16)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int32x16)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint64x8)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int64x8)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_float32x16)
+OPENCV_HAL_IMPL_AVX512_EXTRACT(v_float64x8)
+
+#define OPENCV_HAL_IMPL_AVX512_EXTRACT_N(_Tpvec, _Tp) \
+template<int i> inline _Tp v_extract_n(_Tpvec v) { return v_rotate_right<i>(v).get0(); }
+
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint8x64, uchar)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int8x64, schar)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint16x32, ushort)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int16x32, short)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint32x16, uint)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int32x16, int)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint64x8, uint64)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int64x8, int64)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_float32x16, float)
+OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_float64x8, double)
+
+template<int i>
+inline v_uint32x16 v_broadcast_element(v_uint32x16 a)
+{
+    static const __m512i perm = _mm512_set1_epi32((char)i);
+    return v_uint32x16(_mm512_permutexvar_epi32(perm, a.val));
+}
+
+template<int i>
+inline v_int32x16 v_broadcast_element(const v_int32x16 &a)
+{ return v_reinterpret_as_s32(v_broadcast_element<i>(v_reinterpret_as_u32(a))); }
+
+template<int i>
+inline v_float32x16 v_broadcast_element(const v_float32x16 &a)
+{ return v_reinterpret_as_f32(v_broadcast_element<i>(v_reinterpret_as_u32(a))); }
+
+
+///////////////////// load deinterleave /////////////////////////////
+
+inline void v_load_deinterleave( const uchar* ptr, v_uint8x64& a, v_uint8x64& b )
+{
+    __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 64));
+#if CV_AVX_512VBMI
+    __m512i mask0 = _v512_set_epu8(126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104, 102, 100, 98, 96,
+                                    94,  92,  90,  88,  86,  84,  82,  80,  78,  76,  74,  72,  70,  68, 66, 64,
+                                    62,  60,  58,  56,  54,  52,  50,  48,  46,  44,  42,  40,  38,  36, 34, 32,
+                                    30,  28,  26,  24,  22,  20,  18,  16,  14,  12,  10,   8,   6,   4,  2,  0);
+    __m512i mask1 = _v512_set_epu8(127, 125, 123, 121, 119, 117, 115, 113, 111, 109, 107, 105, 103, 101, 99, 97,
+                                    95,  93,  91,  89,  87,  85,  83,  81,  79,  77,  75,  73,  71,  69, 67, 65,
+                                    63,  61,  59,  57,  55,  53,  51,  49,  47,  45,  43,  41,  39,  37, 35, 33,
+                                    31,  29,  27,  25,  23,  21,  19,  17,  15,  13,  11,   9,   7,   5,  3,  1);
+    a = v_uint8x64(_mm512_permutex2var_epi8(ab0, mask0, ab1));
+    b = v_uint8x64(_mm512_permutex2var_epi8(ab0, mask1, ab1));
+#else
+    __m512i mask0 = _mm512_set4_epi32(0x0f0d0b09, 0x07050301, 0x0e0c0a08, 0x06040200);
+    __m512i a0b0 = _mm512_shuffle_epi8(ab0, mask0);
+    __m512i a1b1 = _mm512_shuffle_epi8(ab1, mask0);
+    __m512i mask1 = _v512_set_epu64(14, 12, 10, 8, 6, 4, 2, 0);
+    __m512i mask2 = _v512_set_epu64(15, 13, 11, 9, 7, 5, 3, 1);
+    a = v_uint8x64(_mm512_permutex2var_epi64(a0b0, mask1, a1b1));
+    b = v_uint8x64(_mm512_permutex2var_epi64(a0b0, mask2, a1b1));
+#endif
+}
+
+inline void v_load_deinterleave( const ushort* ptr, v_uint16x32& a, v_uint16x32& b )
+{
+    __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 32));
+    __m512i mask0 = _v512_set_epu16(62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32,
+                                    30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10,  8,  6,  4,  2,  0);
+    __m512i mask1 = _v512_set_epu16(63, 61, 59, 57, 55, 53, 51, 49, 47, 45, 43, 41, 39, 37, 35, 33,
+                                    31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11,  9,  7,  5,  3,  1);
+    a = v_uint16x32(_mm512_permutex2var_epi16(ab0, mask0, ab1));
+    b = v_uint16x32(_mm512_permutex2var_epi16(ab0, mask1, ab1));
+}
+
+inline void v_load_deinterleave( const unsigned* ptr, v_uint32x16& a, v_uint32x16& b )
+{
+    __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 16));
+    __m512i mask0 = _v512_set_epu32(30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0);
+    __m512i mask1 = _v512_set_epu32(31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1);
+    a = v_uint32x16(_mm512_permutex2var_epi32(ab0, mask0, ab1));
+    b = v_uint32x16(_mm512_permutex2var_epi32(ab0, mask1, ab1));
+}
+
+inline void v_load_deinterleave( const uint64* ptr, v_uint64x8& a, v_uint64x8& b )
+{
+    __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 8));
+    __m512i mask0 = _v512_set_epu64(14, 12, 10, 8, 6, 4, 2, 0);
+    __m512i mask1 = _v512_set_epu64(15, 13, 11, 9, 7, 5, 3, 1);
+    a = v_uint64x8(_mm512_permutex2var_epi64(ab0, mask0, ab1));
+    b = v_uint64x8(_mm512_permutex2var_epi64(ab0, mask1, ab1));
+}
+
+inline void v_load_deinterleave( const uchar* ptr, v_uint8x64& a, v_uint8x64& b, v_uint8x64& c )
+{
+    __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 64));
+    __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 128));
+
+#if CV_AVX_512VBMI2
+    __m512i mask0 = _v512_set_epu8(126, 123, 120, 117, 114, 111, 108, 105, 102,  99,  96,  93,  90,  87,  84, 81,
+                                    78,  75,  72,  69,  66,  63,  60,  57,  54,  51,  48,  45,  42,  39,  36, 33,
+                                    30,  27,  24,  21,  18,  15,  12,   9,   6,   3,   0,  62,  59,  56,  53, 50,
+                                    47,  44,  41,  38,  35,  32,  29,  26,  23,  20,  17,  14,  11,   8,   5,  2);
+    __m512i r0b01 = _mm512_permutex2var_epi8(bgr0, mask0, bgr1);
+    __m512i b1g12 = _mm512_permutex2var_epi8(bgr1, mask0, bgr2);
+    __m512i r12b2 = _mm512_permutex2var_epi8(bgr1,
+                    _v512_set_epu8(125, 122, 119, 116, 113, 110, 107, 104, 101,  98,  95,  92,  89,  86,  83, 80,
+                                    77,  74,  71,  68,  65, 127, 124, 121, 118, 115, 112, 109, 106, 103, 100, 97,
+                                    94,  91,  88,  85,  82,  79,  76,  73,  70,  67,  64,  61,  58,  55,  52, 49,
+                                    46,  43,  40,  37,  34,  31,  28,  25,  22,  19,  16,  13,  10,   7,   4,  1), bgr2);
+    a = v_uint8x64(_mm512_mask_compress_epi8(r12b2, 0xffffffffffe00000, r0b01));
+    b = v_uint8x64(_mm512_mask_compress_epi8(b1g12, 0x2492492492492492, bgr0));
+    c = v_uint8x64(_mm512_mask_expand_epi8(r0b01, 0xffffffffffe00000, r12b2));
+#elif CV_AVX_512VBMI
+    __m512i b0g0b1 = _mm512_mask_blend_epi8(0xb6db6db6db6db6db, bgr1, bgr0);
+    __m512i g1r1g2 = _mm512_mask_blend_epi8(0xb6db6db6db6db6db, bgr2, bgr1);
+    __m512i r2b2r0 = _mm512_mask_blend_epi8(0xb6db6db6db6db6db, bgr0, bgr2);
+    a = v_uint8x64(_mm512_permutex2var_epi8(b0g0b1, _v512_set_epu8(125, 122, 119, 116, 113, 110, 107, 104, 101,  98,  95,  92,  89,  86,  83,  80,
+                                                                    77,  74,  71,  68,  65,  63,  61,  60,  58,  57,  55,  54,  52,  51,  49,  48,
+                                                                    46,  45,  43,  42,  40,  39,  37,  36,  34,  33,  31,  30,  28,  27,  25,  24,
+                                                                    23,  21,  20,  18,  17,  15,  14,  12,  11,   9,   8,   6,   5,   3,   2,   0), bgr2));
+    b = v_uint8x64(_mm512_permutex2var_epi8(g1r1g2, _v512_set_epu8( 63,  61,  60,  58,  57,  55,  54,  52,  51,  49,  48,  46,  45,  43,  42,  40,
+                                                                    39,  37,  36,  34,  33,  31,  30,  28,  27,  25,  24,  23,  21,  20,  18,  17,
+                                                                    15,  14,  12,  11,   9,   8,   6,   5,   3,   2,   0, 126, 123, 120, 117, 114,
+                                                                   111, 108, 105, 102,  99,  96,  93,  90,  87,  84,  81,  78,  75,  72,  69,  66), bgr0));
+    c = v_uint8x64(_mm512_permutex2var_epi8(r2b2r0, _v512_set_epu8( 63,  60,  57,  54,  51,  48,  45,  42,  39,  36,  33,  30,  27,  24,  21,  18,
+                                                                    15,  12,   9,   6,   3,   0, 125, 122, 119, 116, 113, 110, 107, 104, 101,  98,
+                                                                    95,  92,  89,  86,  83,  80,  77,  74,  71,  68,  65,  62,  59,  56,  53,  50,
+                                                                    47,  44,  41,  38,  35,  32,  29,  26,  23,  20,  17,  14,  11,   8,   5,   2), bgr1));
+#else
+    __m512i mask0 = _v512_set_epu16(61, 58, 55, 52, 49, 46, 43, 40, 37, 34, 63, 60, 57, 54, 51, 48,
+                                    45, 42, 39, 36, 33, 30, 27, 24, 21, 18, 15, 12,  9,  6,  3,  0);
+    __m512i b01g1 = _mm512_permutex2var_epi16(bgr0, mask0, bgr1);
+    __m512i r12b2 = _mm512_permutex2var_epi16(bgr1, mask0, bgr2);
+    __m512i g20r0 = _mm512_permutex2var_epi16(bgr2, mask0, bgr0);
+
+    __m512i b0g0 = _mm512_mask_blend_epi32(0xf800, b01g1, r12b2);
+    __m512i r0b1 = _mm512_permutex2var_epi16(bgr1, _v512_set_epu16(42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 29, 26, 23, 20, 17,
+                                                                   14, 11,  8,  5,  2, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43), g20r0);
+    __m512i g1r1 = _mm512_alignr_epi32(r12b2, g20r0, 11);
+    a = v_uint8x64(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, b0g0, r0b1));
+    c = v_uint8x64(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, r0b1, g1r1));
+    b = v_uint8x64(_mm512_shuffle_epi8(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, g1r1, b0g0), _mm512_set4_epi32(0x0e0f0c0d, 0x0a0b0809, 0x06070405, 0x02030001)));
+#endif
+}
+
+inline void v_load_deinterleave( const ushort* ptr, v_uint16x32& a, v_uint16x32& b, v_uint16x32& c )
+{
+    __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 32));
+    __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 64));
+
+    __m512i mask0 = _v512_set_epu16(61, 58, 55, 52, 49, 46, 43, 40, 37, 34, 63, 60, 57, 54, 51, 48,
+                                    45, 42, 39, 36, 33, 30, 27, 24, 21, 18, 15, 12,  9,  6,  3,  0);
+    __m512i b01g1 = _mm512_permutex2var_epi16(bgr0, mask0, bgr1);
+    __m512i r12b2 = _mm512_permutex2var_epi16(bgr1, mask0, bgr2);
+    __m512i g20r0 = _mm512_permutex2var_epi16(bgr2, mask0, bgr0);
+
+    a = v_uint16x32(_mm512_mask_blend_epi32(0xf800, b01g1, r12b2));
+    b = v_uint16x32(_mm512_permutex2var_epi16(bgr1, _v512_set_epu16(42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 29, 26, 23, 20, 17,
+                                                                    14, 11,  8,  5,  2, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43), g20r0));
+    c = v_uint16x32(_mm512_alignr_epi32(r12b2, g20r0, 11));
+}
+
+inline void v_load_deinterleave( const unsigned* ptr, v_uint32x16& a, v_uint32x16& b, v_uint32x16& c )
+{
+    __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 16));
+    __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 32));
+
+    __m512i mask0 = _v512_set_epu32(29, 26, 23, 20, 17, 30, 27, 24, 21, 18, 15, 12, 9, 6, 3, 0);
+    __m512i b01r1 = _mm512_permutex2var_epi32(bgr0, mask0, bgr1);
+    __m512i g12b2 = _mm512_permutex2var_epi32(bgr1, mask0, bgr2);
+    __m512i r20g0 = _mm512_permutex2var_epi32(bgr2, mask0, bgr0);
+
+    a = v_uint32x16(_mm512_mask_blend_epi32(0xf800, b01r1, g12b2));
+    b = v_uint32x16(_mm512_alignr_epi32(g12b2, r20g0, 11));
+    c = v_uint32x16(_mm512_permutex2var_epi32(bgr1, _v512_set_epu32(21, 20, 19, 18, 17, 16, 13, 10, 7, 4, 1, 26, 25, 24, 23, 22), r20g0));
+}
+
+inline void v_load_deinterleave( const uint64* ptr, v_uint64x8& a, v_uint64x8& b, v_uint64x8& c )
+{
+    __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 8));
+    __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 16));
+
+    __m512i mask0 = _v512_set_epu64(13, 10, 15, 12, 9, 6, 3, 0);
+    __m512i b01g1 = _mm512_permutex2var_epi64(bgr0, mask0, bgr1);
+    __m512i r12b2 = _mm512_permutex2var_epi64(bgr1, mask0, bgr2);
+    __m512i g20r0 = _mm512_permutex2var_epi64(bgr2, mask0, bgr0);
+
+    a = v_uint64x8(_mm512_mask_blend_epi64(0xc0, b01g1, r12b2));
+    c = v_uint64x8(_mm512_alignr_epi64(r12b2, g20r0, 6));
+    b = v_uint64x8(_mm512_permutex2var_epi64(bgr1, _v512_set_epu64(10, 9, 8, 5, 2, 13, 12, 11), g20r0));
+}
+
+inline void v_load_deinterleave( const uchar* ptr, v_uint8x64& a, v_uint8x64& b, v_uint8x64& c, v_uint8x64& d )
+{
+    __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 64));
+    __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 128));
+    __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 192));
+
+#if CV_AVX_512VBMI
+    __m512i mask0 = _v512_set_epu8(126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104, 102, 100, 98, 96,
+                                    94,  92,  90,  88,  86,  84,  82,  80,  78,  76,  74,  72,  70,  68, 66, 64,
+                                    62,  60,  58,  56,  54,  52,  50,  48,  46,  44,  42,  40,  38,  36, 34, 32,
+                                    30,  28,  26,  24,  22,  20,  18,  16,  14,  12,  10,   8,   6,   4,  2,  0);
+    __m512i mask1 = _v512_set_epu8(127, 125, 123, 121, 119, 117, 115, 113, 111, 109, 107, 105, 103, 101, 99, 97,
+                                    95,  93,  91,  89,  87,  85,  83,  81,  79,  77,  75,  73,  71,  69, 67, 65,
+                                    63,  61,  59,  57,  55,  53,  51,  49,  47,  45,  43,  41,  39,  37, 35, 33,
+                                    31,  29,  27,  25,  23,  21,  19,  17,  15,  13,  11,   9,   7,   5,  3,  1);
+
+    __m512i br01 = _mm512_permutex2var_epi8(bgra0, mask0, bgra1);
+    __m512i ga01 = _mm512_permutex2var_epi8(bgra0, mask1, bgra1);
+    __m512i br23 = _mm512_permutex2var_epi8(bgra2, mask0, bgra3);
+    __m512i ga23 = _mm512_permutex2var_epi8(bgra2, mask1, bgra3);
+
+    a = v_uint8x64(_mm512_permutex2var_epi8(br01, mask0, br23));
+    c = v_uint8x64(_mm512_permutex2var_epi8(br01, mask1, br23));
+    b = v_uint8x64(_mm512_permutex2var_epi8(ga01, mask0, ga23));
+    d = v_uint8x64(_mm512_permutex2var_epi8(ga01, mask1, ga23));
+#else
+    __m512i mask = _mm512_set4_epi32(0x0f0b0703, 0x0e0a0602, 0x0d090501, 0x0c080400);
+    __m512i b0g0r0a0 = _mm512_shuffle_epi8(bgra0, mask);
+    __m512i b1g1r1a1 = _mm512_shuffle_epi8(bgra1, mask);
+    __m512i b2g2r2a2 = _mm512_shuffle_epi8(bgra2, mask);
+    __m512i b3g3r3a3 = _mm512_shuffle_epi8(bgra3, mask);
+
+    __m512i mask0 = _v512_set_epu32(30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0);
+    __m512i mask1 = _v512_set_epu32(31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1);
+
+    __m512i br01 = _mm512_permutex2var_epi32(b0g0r0a0, mask0, b1g1r1a1);
+    __m512i ga01 = _mm512_permutex2var_epi32(b0g0r0a0, mask1, b1g1r1a1);
+    __m512i br23 = _mm512_permutex2var_epi32(b2g2r2a2, mask0, b3g3r3a3);
+    __m512i ga23 = _mm512_permutex2var_epi32(b2g2r2a2, mask1, b3g3r3a3);
+
+    a = v_uint8x64(_mm512_permutex2var_epi32(br01, mask0, br23));
+    c = v_uint8x64(_mm512_permutex2var_epi32(br01, mask1, br23));
+    b = v_uint8x64(_mm512_permutex2var_epi32(ga01, mask0, ga23));
+    d = v_uint8x64(_mm512_permutex2var_epi32(ga01, mask1, ga23));
+#endif
+}
+
+inline void v_load_deinterleave( const ushort* ptr, v_uint16x32& a, v_uint16x32& b, v_uint16x32& c, v_uint16x32& d )
+{
+    __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 32));
+    __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 64));
+    __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 96));
+
+    __m512i mask0 = _v512_set_epu16(62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32,
+                                    30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10,  8,  6,  4,  2,  0);
+    __m512i mask1 = _v512_set_epu16(63, 61, 59, 57, 55, 53, 51, 49, 47, 45, 43, 41, 39, 37, 35, 33,
+                                    31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11,  9,  7,  5,  3,  1);
+
+    __m512i br01 = _mm512_permutex2var_epi16(bgra0, mask0, bgra1);
+    __m512i ga01 = _mm512_permutex2var_epi16(bgra0, mask1, bgra1);
+    __m512i br23 = _mm512_permutex2var_epi16(bgra2, mask0, bgra3);
+    __m512i ga23 = _mm512_permutex2var_epi16(bgra2, mask1, bgra3);
+
+    a = v_uint16x32(_mm512_permutex2var_epi16(br01, mask0, br23));
+    c = v_uint16x32(_mm512_permutex2var_epi16(br01, mask1, br23));
+    b = v_uint16x32(_mm512_permutex2var_epi16(ga01, mask0, ga23));
+    d = v_uint16x32(_mm512_permutex2var_epi16(ga01, mask1, ga23));
+}
+
+inline void v_load_deinterleave( const unsigned* ptr, v_uint32x16& a, v_uint32x16& b, v_uint32x16& c, v_uint32x16& d )
+{
+    __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 16));
+    __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 32));
+    __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 48));
+
+    __m512i mask0 = _v512_set_epu32(30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0);
+    __m512i mask1 = _v512_set_epu32(31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1);
+
+    __m512i br01 = _mm512_permutex2var_epi32(bgra0, mask0, bgra1);
+    __m512i ga01 = _mm512_permutex2var_epi32(bgra0, mask1, bgra1);
+    __m512i br23 = _mm512_permutex2var_epi32(bgra2, mask0, bgra3);
+    __m512i ga23 = _mm512_permutex2var_epi32(bgra2, mask1, bgra3);
+
+    a = v_uint32x16(_mm512_permutex2var_epi32(br01, mask0, br23));
+    c = v_uint32x16(_mm512_permutex2var_epi32(br01, mask1, br23));
+    b = v_uint32x16(_mm512_permutex2var_epi32(ga01, mask0, ga23));
+    d = v_uint32x16(_mm512_permutex2var_epi32(ga01, mask1, ga23));
+}
+
+inline void v_load_deinterleave( const uint64* ptr, v_uint64x8& a, v_uint64x8& b, v_uint64x8& c, v_uint64x8& d )
+{
+    __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr);
+    __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 8));
+    __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 16));
+    __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 24));
+
+    __m512i mask0 = _v512_set_epu64(14, 12, 10, 8, 6, 4, 2, 0);
+    __m512i mask1 = _v512_set_epu64(15, 13, 11, 9, 7, 5, 3, 1);
+
+    __m512i br01 = _mm512_permutex2var_epi64(bgra0, mask0, bgra1);
+    __m512i ga01 = _mm512_permutex2var_epi64(bgra0, mask1, bgra1);
+    __m512i br23 = _mm512_permutex2var_epi64(bgra2, mask0, bgra3);
+    __m512i ga23 = _mm512_permutex2var_epi64(bgra2, mask1, bgra3);
+
+    a = v_uint64x8(_mm512_permutex2var_epi64(br01, mask0, br23));
+    c = v_uint64x8(_mm512_permutex2var_epi64(br01, mask1, br23));
+    b = v_uint64x8(_mm512_permutex2var_epi64(ga01, mask0, ga23));
+    d = v_uint64x8(_mm512_permutex2var_epi64(ga01, mask1, ga23));
+}
+
+///////////////////////////// store interleave /////////////////////////////////////
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x64& x, const v_uint8x64& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint8x64 low, high;
+    v_zip(x, y, low, high);
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, low.val);
+        _mm512_stream_si512((__m512i*)(ptr + 64), high.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, low.val);
+        _mm512_store_si512((__m512i*)(ptr + 64), high.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, low.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 64), high.val);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x32& x, const v_uint16x32& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint16x32 low, high;
+    v_zip(x, y, low, high);
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, low.val);
+        _mm512_stream_si512((__m512i*)(ptr + 32), high.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, low.val);
+        _mm512_store_si512((__m512i*)(ptr + 32), high.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, low.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 32), high.val);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x16& x, const v_uint32x16& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint32x16 low, high;
+    v_zip(x, y, low, high);
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, low.val);
+        _mm512_stream_si512((__m512i*)(ptr + 16), high.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, low.val);
+        _mm512_store_si512((__m512i*)(ptr + 16), high.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, low.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 16), high.val);
+    }
+}
+
+inline void v_store_interleave( uint64* ptr, const v_uint64x8& x, const v_uint64x8& y,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint64x8 low, high;
+    v_zip(x, y, low, high);
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, low.val);
+        _mm512_stream_si512((__m512i*)(ptr + 8), high.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, low.val);
+        _mm512_store_si512((__m512i*)(ptr + 8), high.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, low.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 8), high.val);
+    }
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x64& a, const v_uint8x64& b, const v_uint8x64& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+#if CV_AVX_512VBMI
+    __m512i mask0 = _v512_set_epu8(127,  84,  20, 126,  83,  19, 125,  82,  18, 124,  81,  17, 123,  80,  16, 122,
+                                    79,  15, 121,  78,  14, 120,  77,  13, 119,  76,  12, 118,  75,  11, 117,  74,
+                                    10, 116,  73,   9, 115,  72,   8, 114,  71,   7, 113,  70,   6, 112,  69,   5,
+                                   111,  68,   4, 110,  67,   3, 109,  66,   2, 108,  65,   1, 107,  64,   0, 106);
+    __m512i mask1 = _v512_set_epu8( 21,  42, 105,  20,  41, 104,  19,  40, 103,  18,  39, 102,  17,  38, 101,  16,
+                                    37, 100,  15,  36,  99,  14,  35,  98,  13,  34,  97,  12,  33,  96,  11,  32,
+                                    95,  10,  31,  94,   9,  30,  93,   8,  29,  92,   7,  28,  91,   6,  27,  90,
+                                     5,  26,  89,   4,  25,  88,   3,  24,  87,   2,  23,  86,   1,  22,  85,   0);
+    __m512i mask2 = _v512_set_epu8(106, 127,  63, 105, 126,  62, 104, 125,  61, 103, 124,  60, 102, 123,  59, 101,
+                                   122,  58, 100, 121,  57,  99, 120,  56,  98, 119,  55,  97, 118,  54,  96, 117,
+                                    53,  95, 116,  52,  94, 115,  51,  93, 114,  50,  92, 113,  49,  91, 112,  48,
+                                    90, 111,  47,  89, 110,  46,  88, 109,  45,  87, 108,  44,  86, 107,  43,  85);
+    __m512i r2g0r0 = _mm512_permutex2var_epi8(b.val, mask0, c.val);
+    __m512i b0r1b1 = _mm512_permutex2var_epi8(a.val, mask1, c.val);
+    __m512i g1b2g2 = _mm512_permutex2var_epi8(a.val, mask2, b.val);
+
+    __m512i bgr0 = _mm512_mask_blend_epi8(0x9249249249249249, r2g0r0, b0r1b1);
+    __m512i bgr1 = _mm512_mask_blend_epi8(0x9249249249249249, b0r1b1, g1b2g2);
+    __m512i bgr2 = _mm512_mask_blend_epi8(0x9249249249249249, g1b2g2, r2g0r0);
+#else
+    __m512i g1g0 = _mm512_shuffle_epi8(b.val, _mm512_set4_epi32(0x0e0f0c0d, 0x0a0b0809, 0x06070405, 0x02030001));
+    __m512i b0g0 = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, a.val, g1g0);
+    __m512i r0b1 = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, c.val, a.val);
+    __m512i g1r1 = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, g1g0, c.val);
+
+    __m512i mask0 = _v512_set_epu16(42, 10, 31, 41,  9, 30, 40,  8, 29, 39,  7, 28, 38,  6, 27, 37,
+                                     5, 26, 36,  4, 25, 35,  3, 24, 34,  2, 23, 33,  1, 22, 32,  0);
+    __m512i mask1 = _v512_set_epu16(21, 52, 41, 20, 51, 40, 19, 50, 39, 18, 49, 38, 17, 48, 37, 16,
+                                    47, 36, 15, 46, 35, 14, 45, 34, 13, 44, 33, 12, 43, 32, 11, 42);
+    __m512i mask2 = _v512_set_epu16(63, 31, 20, 62, 30, 19, 61, 29, 18, 60, 28, 17, 59, 27, 16, 58,
+                                    26, 15, 57, 25, 14, 56, 24, 13, 55, 23, 12, 54, 22, 11, 53, 21);
+    __m512i b0g0b2 = _mm512_permutex2var_epi16(b0g0, mask0, r0b1);
+    __m512i r1b1r0 = _mm512_permutex2var_epi16(b0g0, mask1, g1r1);
+    __m512i g2r2g1 = _mm512_permutex2var_epi16(r0b1, mask2, g1r1);
+
+    __m512i bgr0 = _mm512_mask_blend_epi16(0x24924924, b0g0b2, r1b1r0);
+    __m512i bgr1 = _mm512_mask_blend_epi16(0x24924924, r1b1r0, g2r2g1);
+    __m512i bgr2 = _mm512_mask_blend_epi16(0x24924924, g2r2g1, b0g0b2);
+#endif
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgr0);
+        _mm512_stream_si512((__m512i*)(ptr + 64), bgr1);
+        _mm512_stream_si512((__m512i*)(ptr + 128), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgr0);
+        _mm512_store_si512((__m512i*)(ptr + 64), bgr1);
+        _mm512_store_si512((__m512i*)(ptr + 128), bgr2);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgr0);
+        _mm512_storeu_si512((__m512i*)(ptr + 64), bgr1);
+        _mm512_storeu_si512((__m512i*)(ptr + 128), bgr2);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x32& a, const v_uint16x32& b, const v_uint16x32& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m512i mask0 = _v512_set_epu16(42, 10, 31, 41,  9, 30, 40,  8, 29, 39,  7, 28, 38,  6, 27, 37,
+                                     5, 26, 36,  4, 25, 35,  3, 24, 34,  2, 23, 33,  1, 22, 32,  0);
+    __m512i mask1 = _v512_set_epu16(21, 52, 41, 20, 51, 40, 19, 50, 39, 18, 49, 38, 17, 48, 37, 16,
+                                    47, 36, 15, 46, 35, 14, 45, 34, 13, 44, 33, 12, 43, 32, 11, 42);
+    __m512i mask2 = _v512_set_epu16(63, 31, 20, 62, 30, 19, 61, 29, 18, 60, 28, 17, 59, 27, 16, 58,
+                                    26, 15, 57, 25, 14, 56, 24, 13, 55, 23, 12, 54, 22, 11, 53, 21);
+    __m512i b0g0b2 = _mm512_permutex2var_epi16(a.val, mask0, b.val);
+    __m512i r1b1r0 = _mm512_permutex2var_epi16(a.val, mask1, c.val);
+    __m512i g2r2g1 = _mm512_permutex2var_epi16(b.val, mask2, c.val);
+
+    __m512i bgr0 = _mm512_mask_blend_epi16(0x24924924, b0g0b2, r1b1r0);
+    __m512i bgr1 = _mm512_mask_blend_epi16(0x24924924, r1b1r0, g2r2g1);
+    __m512i bgr2 = _mm512_mask_blend_epi16(0x24924924, g2r2g1, b0g0b2);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgr0);
+        _mm512_stream_si512((__m512i*)(ptr + 32), bgr1);
+        _mm512_stream_si512((__m512i*)(ptr + 64), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgr0);
+        _mm512_store_si512((__m512i*)(ptr + 32), bgr1);
+        _mm512_store_si512((__m512i*)(ptr + 64), bgr2);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgr0);
+        _mm512_storeu_si512((__m512i*)(ptr + 32), bgr1);
+        _mm512_storeu_si512((__m512i*)(ptr + 64), bgr2);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x16& a, const v_uint32x16& b, const v_uint32x16& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m512i mask0 = _v512_set_epu32(26, 31, 15, 25, 30, 14, 24, 29, 13, 23, 28, 12, 22, 27, 11, 21);
+    __m512i mask1 = _v512_set_epu32(31, 10, 25, 30,  9, 24, 29,  8, 23, 28,  7, 22, 27,  6, 21, 26);
+    __m512i g1b2g2 = _mm512_permutex2var_epi32(a.val, mask0, b.val);
+    __m512i r2r1b1 = _mm512_permutex2var_epi32(a.val, mask1, c.val);
+
+    __m512i bgr0 = _mm512_mask_expand_epi32(_mm512_mask_expand_epi32(_mm512_maskz_expand_epi32(0x9249, a.val), 0x2492, b.val), 0x4924, c.val);
+    __m512i bgr1 = _mm512_mask_blend_epi32(0x9249, r2r1b1, g1b2g2);
+    __m512i bgr2 = _mm512_mask_blend_epi32(0x9249, g1b2g2, r2r1b1);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgr0);
+        _mm512_stream_si512((__m512i*)(ptr + 16), bgr1);
+        _mm512_stream_si512((__m512i*)(ptr + 32), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgr0);
+        _mm512_store_si512((__m512i*)(ptr + 16), bgr1);
+        _mm512_store_si512((__m512i*)(ptr + 32), bgr2);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgr0);
+        _mm512_storeu_si512((__m512i*)(ptr + 16), bgr1);
+        _mm512_storeu_si512((__m512i*)(ptr + 32), bgr2);
+    }
+}
+
+inline void v_store_interleave( uint64* ptr, const v_uint64x8& a, const v_uint64x8& b, const v_uint64x8& c,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    __m512i mask0 = _v512_set_epu64( 5, 12,  7,  4, 11,  6,  3, 10);
+    __m512i mask1 = _v512_set_epu64(15,  7,  4, 14,  6,  3, 13,  5);
+    __m512i r1b1b2 = _mm512_permutex2var_epi64(a.val, mask0, c.val);
+    __m512i g2r2g1 = _mm512_permutex2var_epi64(b.val, mask1, c.val);
+
+    __m512i bgr0 = _mm512_mask_expand_epi64(_mm512_mask_expand_epi64(_mm512_maskz_expand_epi64(0x49, a.val), 0x92, b.val), 0x24, c.val);
+    __m512i bgr1 = _mm512_mask_blend_epi64(0xdb, g2r2g1, r1b1b2);
+    __m512i bgr2 = _mm512_mask_blend_epi64(0xdb, r1b1b2, g2r2g1);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgr0);
+        _mm512_stream_si512((__m512i*)(ptr + 8), bgr1);
+        _mm512_stream_si512((__m512i*)(ptr + 16), bgr2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgr0);
+        _mm512_store_si512((__m512i*)(ptr + 8), bgr1);
+        _mm512_store_si512((__m512i*)(ptr + 16), bgr2);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgr0);
+        _mm512_storeu_si512((__m512i*)(ptr + 8), bgr1);
+        _mm512_storeu_si512((__m512i*)(ptr + 16), bgr2);
+    }
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x64& a, const v_uint8x64& b,
+                                const v_uint8x64& c, const v_uint8x64& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint8x64 br01, br23, ga01, ga23;
+    v_zip(a, c, br01, br23);
+    v_zip(b, d, ga01, ga23);
+    v_uint8x64 bgra0, bgra1, bgra2, bgra3;
+    v_zip(br01, ga01, bgra0, bgra1);
+    v_zip(br23, ga23, bgra2, bgra3);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgra0.val);
+        _mm512_stream_si512((__m512i*)(ptr + 64), bgra1.val);
+        _mm512_stream_si512((__m512i*)(ptr + 128), bgra2.val);
+        _mm512_stream_si512((__m512i*)(ptr + 192), bgra3.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgra0.val);
+        _mm512_store_si512((__m512i*)(ptr + 64), bgra1.val);
+        _mm512_store_si512((__m512i*)(ptr + 128), bgra2.val);
+        _mm512_store_si512((__m512i*)(ptr + 192), bgra3.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgra0.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 64), bgra1.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 128), bgra2.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 192), bgra3.val);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x32& a, const v_uint16x32& b,
+                                const v_uint16x32& c, const v_uint16x32& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint16x32 br01, br23, ga01, ga23;
+    v_zip(a, c, br01, br23);
+    v_zip(b, d, ga01, ga23);
+    v_uint16x32 bgra0, bgra1, bgra2, bgra3;
+    v_zip(br01, ga01, bgra0, bgra1);
+    v_zip(br23, ga23, bgra2, bgra3);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgra0.val);
+        _mm512_stream_si512((__m512i*)(ptr + 32), bgra1.val);
+        _mm512_stream_si512((__m512i*)(ptr + 64), bgra2.val);
+        _mm512_stream_si512((__m512i*)(ptr + 96), bgra3.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgra0.val);
+        _mm512_store_si512((__m512i*)(ptr + 32), bgra1.val);
+        _mm512_store_si512((__m512i*)(ptr + 64), bgra2.val);
+        _mm512_store_si512((__m512i*)(ptr + 96), bgra3.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgra0.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 32), bgra1.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 64), bgra2.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 96), bgra3.val);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x16& a, const v_uint32x16& b,
+                                const v_uint32x16& c, const v_uint32x16& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint32x16 br01, br23, ga01, ga23;
+    v_zip(a, c, br01, br23);
+    v_zip(b, d, ga01, ga23);
+    v_uint32x16 bgra0, bgra1, bgra2, bgra3;
+    v_zip(br01, ga01, bgra0, bgra1);
+    v_zip(br23, ga23, bgra2, bgra3);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgra0.val);
+        _mm512_stream_si512((__m512i*)(ptr + 16), bgra1.val);
+        _mm512_stream_si512((__m512i*)(ptr + 32), bgra2.val);
+        _mm512_stream_si512((__m512i*)(ptr + 48), bgra3.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgra0.val);
+        _mm512_store_si512((__m512i*)(ptr + 16), bgra1.val);
+        _mm512_store_si512((__m512i*)(ptr + 32), bgra2.val);
+        _mm512_store_si512((__m512i*)(ptr + 48), bgra3.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgra0.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 16), bgra1.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 32), bgra2.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 48), bgra3.val);
+    }
+}
+
+inline void v_store_interleave( uint64* ptr, const v_uint64x8& a, const v_uint64x8& b,
+                                const v_uint64x8& c, const v_uint64x8& d,
+                                hal::StoreMode mode=hal::STORE_UNALIGNED )
+{
+    v_uint64x8 br01, br23, ga01, ga23;
+    v_zip(a, c, br01, br23);
+    v_zip(b, d, ga01, ga23);
+    v_uint64x8 bgra0, bgra1, bgra2, bgra3;
+    v_zip(br01, ga01, bgra0, bgra1);
+    v_zip(br23, ga23, bgra2, bgra3);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm512_stream_si512((__m512i*)ptr, bgra0.val);
+        _mm512_stream_si512((__m512i*)(ptr + 8), bgra1.val);
+        _mm512_stream_si512((__m512i*)(ptr + 16), bgra2.val);
+        _mm512_stream_si512((__m512i*)(ptr + 24), bgra3.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm512_store_si512((__m512i*)ptr, bgra0.val);
+        _mm512_store_si512((__m512i*)(ptr + 8), bgra1.val);
+        _mm512_store_si512((__m512i*)(ptr + 16), bgra2.val);
+        _mm512_store_si512((__m512i*)(ptr + 24), bgra3.val);
+    }
+    else
+    {
+        _mm512_storeu_si512((__m512i*)ptr, bgra0.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 8), bgra1.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 16), bgra2.val);
+        _mm512_storeu_si512((__m512i*)(ptr + 24), bgra3.val);
+    }
+}
+
+#define OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \
+{ \
+    _Tpvec1 a1, b1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \
+{ \
+    _Tpvec1 a1, b1, c1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \
+{ \
+    _Tpvec1 a1, b1, c1, d1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+    d0 = v_reinterpret_as_##suffix0(d1); \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                hal::StoreMode mode=hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, mode);      \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, const _Tpvec0& c0, \
+                                hal::StoreMode mode=hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode);  \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                const _Tpvec0& c0, const _Tpvec0& d0, \
+                                hal::StoreMode mode=hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \
+}
+
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int8x64, schar, s8, v_uint8x64, uchar, u8)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int16x32, short, s16, v_uint16x32, ushort, u16)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int32x16, int, s32, v_uint32x16, unsigned, u32)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_float32x16, float, f32, v_uint32x16, unsigned, u32)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int64x8, int64, s64, v_uint64x8, uint64, u64)
+OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_float64x8, double, f64, v_uint64x8, uint64, u64)
+
+////////// Mask and checks /////////
+
+/** Mask **/
+inline int64 v_signmask(const v_int8x64& a) { return (int64)_mm512_movepi8_mask(a.val); }
+inline int v_signmask(const v_int16x32& a) { return (int)_mm512_cmp_epi16_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); }
+inline int v_signmask(const v_int32x16& a) { return (int)_mm512_cmp_epi32_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); }
+inline int v_signmask(const v_int64x8& a) { return (int)_mm512_cmp_epi64_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); }
+
+inline int64 v_signmask(const v_uint8x64& a) { return v_signmask(v_reinterpret_as_s8(a)); }
+inline int v_signmask(const v_uint16x32& a) { return v_signmask(v_reinterpret_as_s16(a)); }
+inline int v_signmask(const v_uint32x16& a) { return v_signmask(v_reinterpret_as_s32(a)); }
+inline int v_signmask(const v_uint64x8& a) { return v_signmask(v_reinterpret_as_s64(a)); }
+inline int v_signmask(const v_float32x16& a) { return v_signmask(v_reinterpret_as_s32(a)); }
+inline int v_signmask(const v_float64x8& a) { return v_signmask(v_reinterpret_as_s64(a)); }
+
+/** Checks **/
+inline bool v_check_all(const v_int8x64& a) { return !(bool)_mm512_cmp_epi8_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); }
+inline bool v_check_any(const v_int8x64& a) { return (bool)_mm512_movepi8_mask(a.val); }
+inline bool v_check_all(const v_int16x32& a) { return !(bool)_mm512_cmp_epi16_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); }
+inline bool v_check_any(const v_int16x32& a) { return (bool)_mm512_cmp_epi16_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); }
+inline bool v_check_all(const v_int32x16& a) { return !(bool)_mm512_cmp_epi32_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); }
+inline bool v_check_any(const v_int32x16& a) { return (bool)_mm512_cmp_epi32_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); }
+inline bool v_check_all(const v_int64x8& a) { return !(bool)_mm512_cmp_epi64_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); }
+inline bool v_check_any(const v_int64x8& a) { return (bool)_mm512_cmp_epi64_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); }
+
+inline bool v_check_all(const v_float32x16& a) { return v_check_all(v_reinterpret_as_s32(a)); }
+inline bool v_check_any(const v_float32x16& a) { return v_check_any(v_reinterpret_as_s32(a)); }
+inline bool v_check_all(const v_float64x8& a) { return v_check_all(v_reinterpret_as_s64(a)); }
+inline bool v_check_any(const v_float64x8& a) { return v_check_any(v_reinterpret_as_s64(a)); }
+inline bool v_check_all(const v_uint8x64& a) { return v_check_all(v_reinterpret_as_s8(a)); }
+inline bool v_check_all(const v_uint16x32& a) { return v_check_all(v_reinterpret_as_s16(a)); }
+inline bool v_check_all(const v_uint32x16& a) { return v_check_all(v_reinterpret_as_s32(a)); }
+inline bool v_check_all(const v_uint64x8& a) { return v_check_all(v_reinterpret_as_s64(a)); }
+inline bool v_check_any(const v_uint8x64& a) { return v_check_any(v_reinterpret_as_s8(a)); }
+inline bool v_check_any(const v_uint16x32& a) { return v_check_any(v_reinterpret_as_s16(a)); }
+inline bool v_check_any(const v_uint32x16& a) { return v_check_any(v_reinterpret_as_s32(a)); }
+inline bool v_check_any(const v_uint64x8& a) { return v_check_any(v_reinterpret_as_s64(a)); }
+
+inline int v_scan_forward(const v_int8x64& a)
+{
+    int64 mask = _mm512_movepi8_mask(a.val);
+    int mask32 = (int)mask;
+    return mask != 0 ? mask32 != 0 ? trailingZeros32(mask32) : 32 + trailingZeros32((int)(mask >> 32)) : 0;
+}
+inline int v_scan_forward(const v_uint8x64& a) { return v_scan_forward(v_reinterpret_as_s8(a)); }
+inline int v_scan_forward(const v_int16x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))); }
+inline int v_scan_forward(const v_uint16x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))); }
+inline int v_scan_forward(const v_int32x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 2; }
+inline int v_scan_forward(const v_uint32x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 2; }
+inline int v_scan_forward(const v_float32x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 2; }
+inline int v_scan_forward(const v_int64x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 4; }
+inline int v_scan_forward(const v_uint64x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 4; }
+inline int v_scan_forward(const v_float64x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 4; }
+
+inline void v512_cleanup() { _mm256_zeroall(); }
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+} // cv::
+
+#endif // OPENCV_HAL_INTRIN_AVX_HPP
diff --git a/3rdParty/include/opencv2/core/hal/intrin_cpp.hpp b/3rdParty/include/opencv2/core/hal/intrin_cpp.hpp
new file mode 100644
index 0000000..4622214
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_cpp.hpp
@@ -0,0 +1,3320 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HAL_INTRIN_CPP_HPP
+#define OPENCV_HAL_INTRIN_CPP_HPP
+
+#include <limits>
+#include <cstring>
+#include <algorithm>
+#include "opencv2/core/saturate.hpp"
+
+//! @cond IGNORED
+#define CV_SIMD128_CPP 1
+#if defined(CV_FORCE_SIMD128_CPP)
+#define CV_SIMD128 1
+#define CV_SIMD128_64F 1
+#endif
+#if defined(CV_DOXYGEN)
+#define CV_SIMD128 1
+#define CV_SIMD128_64F 1
+#define CV_SIMD256 1
+#define CV_SIMD256_64F 1
+#define CV_SIMD512 1
+#define CV_SIMD512_64F 1
+#else
+#define CV_SIMD256 0 // Explicitly disable SIMD256 and SIMD512 support for scalar intrinsic implementation
+#define CV_SIMD512 0 // to avoid warnings during compilation
+#endif
+//! @endcond
+
+namespace cv
+{
+
+#ifndef CV_DOXYGEN
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+#endif
+
+/** @addtogroup core_hal_intrin
+
+"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on
+different platforms. Currently a few different SIMD extensions on different architectures are supported.
+128 bit registers of various types support is implemented for a wide range of architectures
+including x86(__SSE/SSE2/SSE4.2__), ARM(__NEON__), PowerPC(__VSX__), MIPS(__MSA__).
+256 bit long registers are supported on x86(__AVX2__) and 512 bit long registers are supported on x86(__AVX512__).
+In case when there is no SIMD extension available during compilation, fallback C++ implementation of intrinsics
+will be chosen and code will work as expected although it could be slower.
+
+### Types
+
+There are several types representing packed values vector registers, each type is
+implemented as a structure based on a one SIMD register.
+
+- cv::v_uint8 and cv::v_int8: 8-bit integer values (unsigned/signed) - char
+- cv::v_uint16 and cv::v_int16: 16-bit integer values (unsigned/signed) - short
+- cv::v_uint32 and cv::v_int32: 32-bit integer values (unsigned/signed) - int
+- cv::v_uint64 and cv::v_int64: 64-bit integer values (unsigned/signed) - int64
+- cv::v_float32: 32-bit floating point values (signed) - float
+- cv::v_float64: 64-bit floating point values (signed) - double
+
+Exact bit length(and value quantity) of listed types is compile time deduced and depends on architecture SIMD
+capabilities chosen as available during compilation of the library. All the types contains __nlanes__ enumeration
+to check for exact value quantity of the type.
+
+In case the exact bit length of the type is important it is possible to use specific fixed length register types.
+
+There are several types representing 128-bit registers.
+
+- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char
+- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short
+- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsigned/signed) - int
+- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64
+- cv::v_float32x4: four 32-bit floating point values (signed) - float
+- cv::v_float64x2: two 64-bit floating point values (signed) - double
+
+There are several types representing 256-bit registers.
+
+- cv::v_uint8x32 and cv::v_int8x32: thirty two 8-bit integer values (unsigned/signed) - char
+- cv::v_uint16x16 and cv::v_int16x16: sixteen 16-bit integer values (unsigned/signed) - short
+- cv::v_uint32x8 and cv::v_int32x8: eight 32-bit integer values (unsigned/signed) - int
+- cv::v_uint64x4 and cv::v_int64x4: four 64-bit integer values (unsigned/signed) - int64
+- cv::v_float32x8: eight 32-bit floating point values (signed) - float
+- cv::v_float64x4: four 64-bit floating point values (signed) - double
+
+@note
+256 bit registers at the moment implemented for AVX2 SIMD extension only, if you want to use this type directly,
+don't forget to check the CV_SIMD256 preprocessor definition:
+@code
+#if CV_SIMD256
+//...
+#endif
+@endcode
+
+There are several types representing 512-bit registers.
+
+- cv::v_uint8x64 and cv::v_int8x64: sixty four 8-bit integer values (unsigned/signed) - char
+- cv::v_uint16x32 and cv::v_int16x32: thirty two 16-bit integer values (unsigned/signed) - short
+- cv::v_uint32x16 and cv::v_int32x16: sixteen 32-bit integer values (unsigned/signed) - int
+- cv::v_uint64x8 and cv::v_int64x8: eight 64-bit integer values (unsigned/signed) - int64
+- cv::v_float32x16: sixteen 32-bit floating point values (signed) - float
+- cv::v_float64x8: eight 64-bit floating point values (signed) - double
+@note
+512 bit registers at the moment implemented for AVX512 SIMD extension only, if you want to use this type directly,
+don't forget to check the CV_SIMD512 preprocessor definition.
+
+@note
+cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to
+check the CV_SIMD128_64F preprocessor definition.
+
+### Load and store operations
+
+These operations allow to set contents of the register explicitly or by loading it from some memory
+block and to save contents of the register to memory block.
+
+There are variable size register load operations that provide result of maximum available size
+depending on chosen platform capabilities.
+- Constructors:
+@ref v_reg::v_reg(const _Tp *ptr) "from memory",
+- Other create methods:
+vx_setall_s8, vx_setall_u8, ...,
+vx_setzero_u8, vx_setzero_s8, ...
+- Memory load operations:
+vx_load, vx_load_aligned, vx_load_low, vx_load_halves,
+- Memory operations with expansion of values:
+vx_load_expand, vx_load_expand_q
+
+Also there are fixed size register load/store operations.
+
+For 128 bit registers
+- Constructors:
+@ref v_reg::v_reg(const _Tp *ptr) "from memory",
+@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ...
+- Other create methods:
+@ref v_setall_s8, @ref v_setall_u8, ...,
+@ref v_setzero_u8, @ref v_setzero_s8, ...
+- Memory load operations:
+@ref v_load, @ref v_load_aligned, @ref v_load_low, @ref v_load_halves,
+- Memory operations with expansion of values:
+@ref v_load_expand, @ref v_load_expand_q
+
+For 256 bit registers(check CV_SIMD256 preprocessor definition)
+- Constructors:
+@ref v_reg::v_reg(const _Tp *ptr) "from memory",
+@ref v_reg::v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) "from four values", ...
+- Other create methods:
+@ref v256_setall_s8, @ref v256_setall_u8, ...,
+@ref v256_setzero_u8, @ref v256_setzero_s8, ...
+- Memory load operations:
+@ref v256_load, @ref v256_load_aligned, @ref v256_load_low, @ref v256_load_halves,
+- Memory operations with expansion of values:
+@ref v256_load_expand, @ref v256_load_expand_q
+
+For 512 bit registers(check CV_SIMD512 preprocessor definition)
+- Constructors:
+@ref v_reg::v_reg(const _Tp *ptr) "from memory",
+@ref v_reg::v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, _Tp s4, _Tp s5, _Tp s6, _Tp s7) "from eight values", ...
+- Other create methods:
+@ref v512_setall_s8, @ref v512_setall_u8, ...,
+@ref v512_setzero_u8, @ref v512_setzero_s8, ...
+- Memory load operations:
+@ref v512_load, @ref v512_load_aligned, @ref v512_load_low, @ref v512_load_halves,
+- Memory operations with expansion of values:
+@ref v512_load_expand, @ref v512_load_expand_q
+
+Store to memory operations are similar across different platform capabilities:
+@ref v_store, @ref v_store_aligned,
+@ref v_store_high, @ref v_store_low
+
+### Value reordering
+
+These operations allow to reorder or recombine elements in one or multiple vectors.
+
+- Interleave, deinterleave (2, 3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave
+- Expand: @ref v_expand, @ref v_expand_low, @ref v_expand_high
+- Pack: @ref v_pack, @ref v_pack_u, @ref v_pack_b, @ref v_rshr_pack, @ref v_rshr_pack_u,
+@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store
+- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high
+- Reverse: @ref v_reverse
+- Extract: @ref v_extract
+
+
+### Arithmetic, bitwise and comparison operations
+
+Element-wise binary and unary operations.
+
+- Arithmetics:
+@ref operator +(const v_reg &a, const v_reg &b) "+",
+@ref operator -(const v_reg &a, const v_reg &b) "-",
+@ref operator *(const v_reg &a, const v_reg &b) "*",
+@ref operator /(const v_reg &a, const v_reg &b) "/",
+@ref v_mul_expand
+
+- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap
+
+- Bitwise shifts:
+@ref operator <<(const v_reg &a, int s) "<<",
+@ref operator >>(const v_reg &a, int s) ">>",
+@ref v_shl, @ref v_shr
+
+- Bitwise logic:
+@ref operator &(const v_reg &a, const v_reg &b) "&",
+@ref operator |(const v_reg &a, const v_reg &b) "|",
+@ref operator ^(const v_reg &a, const v_reg &b) "^",
+@ref operator ~(const v_reg &a) "~"
+
+- Comparison:
+@ref operator >(const v_reg &a, const v_reg &b) ">",
+@ref operator >=(const v_reg &a, const v_reg &b) ">=",
+@ref operator <(const v_reg &a, const v_reg &b) "<",
+@ref operator <=(const v_reg &a, const v_reg &b) "<=",
+@ref operator ==(const v_reg &a, const v_reg &b) "==",
+@ref operator !=(const v_reg &a, const v_reg &b) "!="
+
+- min/max: @ref v_min, @ref v_max
+
+### Reduce and mask
+
+Most of these operations return only one value.
+
+- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum, @ref v_popcount
+- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select
+
+### Other math
+
+- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude
+- Absolute values: @ref v_abs, @ref v_absdiff, @ref v_absdiffs
+
+### Conversions
+
+Different type conversions and casts:
+
+- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc,
+- To float: @ref v_cvt_f32, @ref v_cvt_f64
+- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ...
+
+### Matrix operations
+
+In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_dotprod_fast,
+@ref v_dotprod_expand, @ref v_dotprod_expand_fast, @ref v_matmul, @ref v_transpose4x4
+
+### Usability
+
+Most operations are implemented only for some subset of the available types, following matrices
+shows the applicability of different operations to the types.
+
+Regular integers:
+
+| Operations\\Types | uint 8 | int 8 | uint 16 | int 16 | uint 32 | int 32 |
+|-------------------|:-:|:-:|:-:|:-:|:-:|:-:|
+|load, store        | x | x | x | x | x | x |
+|interleave         | x | x | x | x | x | x |
+|expand             | x | x | x | x | x | x |
+|expand_low         | x | x | x | x | x | x |
+|expand_high        | x | x | x | x | x | x |
+|expand_q           | x | x |   |   |   |   |
+|add, sub           | x | x | x | x | x | x |
+|add_wrap, sub_wrap | x | x | x | x |   |   |
+|mul_wrap           | x | x | x | x |   |   |
+|mul                | x | x | x | x | x | x |
+|mul_expand         | x | x | x | x | x |   |
+|compare            | x | x | x | x | x | x |
+|shift              |   |   | x | x | x | x |
+|dotprod            |   |   |   | x |   | x |
+|dotprod_fast       |   |   |   | x |   | x |
+|dotprod_expand     | x | x | x | x |   | x |
+|dotprod_expand_fast| x | x | x | x |   | x |
+|logical            | x | x | x | x | x | x |
+|min, max           | x | x | x | x | x | x |
+|absdiff            | x | x | x | x | x | x |
+|absdiffs           |   | x |   | x |   |   |
+|reduce             | x | x | x | x | x | x |
+|mask               | x | x | x | x | x | x |
+|pack               | x | x | x | x | x | x |
+|pack_u             | x |   | x |   |   |   |
+|pack_b             | x |   |   |   |   |   |
+|unpack             | x | x | x | x | x | x |
+|extract            | x | x | x | x | x | x |
+|rotate (lanes)     | x | x | x | x | x | x |
+|cvt_flt32          |   |   |   |   |   | x |
+|cvt_flt64          |   |   |   |   |   | x |
+|transpose4x4       |   |   |   |   | x | x |
+|reverse            | x | x | x | x | x | x |
+|extract_n          | x | x | x | x | x | x |
+|broadcast_element  |   |   |   |   | x | x |
+
+Big integers:
+
+| Operations\\Types | uint 64 | int 64 |
+|-------------------|:-:|:-:|
+|load, store        | x | x |
+|add, sub           | x | x |
+|shift              | x | x |
+|logical            | x | x |
+|reverse            | x | x |
+|extract            | x | x |
+|rotate (lanes)     | x | x |
+|cvt_flt64          |   | x |
+|extract_n          | x | x |
+
+Floating point:
+
+| Operations\\Types | float 32 | float 64 |
+|-------------------|:-:|:-:|
+|load, store        | x | x |
+|interleave         | x |   |
+|add, sub           | x | x |
+|mul                | x | x |
+|div                | x | x |
+|compare            | x | x |
+|min, max           | x | x |
+|absdiff            | x | x |
+|reduce             | x |   |
+|mask               | x | x |
+|unpack             | x | x |
+|cvt_flt32          |   | x |
+|cvt_flt64          | x |   |
+|sqrt, abs          | x | x |
+|float math         | x | x |
+|transpose4x4       | x |   |
+|extract            | x | x |
+|rotate (lanes)     | x | x |
+|reverse            | x | x |
+|extract_n          | x | x |
+|broadcast_element  | x |   |
+
+ @{ */
+
+template<typename _Tp, int n> struct v_reg
+{
+//! @cond IGNORED
+    typedef _Tp lane_type;
+    enum { nlanes = n };
+// !@endcond
+
+    /** @brief Constructor
+
+    Initializes register with data from memory
+    @param ptr pointer to memory block with data for register */
+    explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; }
+
+    /** @brief Constructor
+
+    Initializes register with two 64-bit values */
+    v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; }
+
+    /** @brief Constructor
+
+    Initializes register with four 32-bit values */
+    v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; }
+
+    /** @brief Constructor
+
+    Initializes register with eight 16-bit values */
+    v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3,
+           _Tp s4, _Tp s5, _Tp s6, _Tp s7)
+    {
+        s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3;
+        s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7;
+    }
+
+    /** @brief Constructor
+
+    Initializes register with sixteen 8-bit values */
+    v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3,
+           _Tp s4, _Tp s5, _Tp s6, _Tp s7,
+           _Tp s8, _Tp s9, _Tp s10, _Tp s11,
+           _Tp s12, _Tp s13, _Tp s14, _Tp s15)
+    {
+        s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3;
+        s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7;
+        s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11;
+        s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15;
+    }
+
+    /** @brief Default constructor
+
+    Does not initialize anything*/
+    v_reg() {}
+
+    /** @brief Copy constructor */
+    v_reg(const v_reg<_Tp, n> & r)
+    {
+        for( int i = 0; i < n; i++ )
+            s[i] = r.s[i];
+    }
+    /** @brief Access first value
+
+    Returns value of the first lane according to register type, for example:
+    @code{.cpp}
+    v_int32x4 r(1, 2, 3, 4);
+    int v = r.get0(); // returns 1
+    v_uint64x2 r(1, 2);
+    uint64_t v = r.get0(); // returns 1
+    @endcode
+    */
+    _Tp get0() const { return s[0]; }
+
+//! @cond IGNORED
+    _Tp get(const int i) const { return s[i]; }
+    v_reg<_Tp, n> high() const
+    {
+        v_reg<_Tp, n> c;
+        int i;
+        for( i = 0; i < n/2; i++ )
+        {
+            c.s[i] = s[i+(n/2)];
+            c.s[i+(n/2)] = 0;
+        }
+        return c;
+    }
+
+    static v_reg<_Tp, n> zero()
+    {
+        v_reg<_Tp, n> c;
+        for( int i = 0; i < n; i++ )
+            c.s[i] = (_Tp)0;
+        return c;
+    }
+
+    static v_reg<_Tp, n> all(_Tp s)
+    {
+        v_reg<_Tp, n> c;
+        for( int i = 0; i < n; i++ )
+            c.s[i] = s;
+        return c;
+    }
+
+    template<typename _Tp2, int n2> v_reg<_Tp2, n2> reinterpret_as() const
+    {
+        size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n);
+        v_reg<_Tp2, n2> c;
+        std::memcpy(&c.s[0], &s[0], bytes);
+        return c;
+    }
+
+    v_reg& operator=(const v_reg<_Tp, n> & r)
+    {
+        for( int i = 0; i < n; i++ )
+            s[i] = r.s[i];
+        return *this;
+    }
+
+    _Tp s[n];
+//! @endcond
+};
+
+/** @brief Sixteen 8-bit unsigned integer values */
+typedef v_reg<uchar, 16> v_uint8x16;
+/** @brief Sixteen 8-bit signed integer values */
+typedef v_reg<schar, 16> v_int8x16;
+/** @brief Eight 16-bit unsigned integer values */
+typedef v_reg<ushort, 8> v_uint16x8;
+/** @brief Eight 16-bit signed integer values */
+typedef v_reg<short, 8> v_int16x8;
+/** @brief Four 32-bit unsigned integer values */
+typedef v_reg<unsigned, 4> v_uint32x4;
+/** @brief Four 32-bit signed integer values */
+typedef v_reg<int, 4> v_int32x4;
+/** @brief Four 32-bit floating point values (single precision) */
+typedef v_reg<float, 4> v_float32x4;
+/** @brief Two 64-bit floating point values (double precision) */
+typedef v_reg<double, 2> v_float64x2;
+/** @brief Two 64-bit unsigned integer values */
+typedef v_reg<uint64, 2> v_uint64x2;
+/** @brief Two 64-bit signed integer values */
+typedef v_reg<int64, 2> v_int64x2;
+
+#if CV_SIMD256
+/** @brief Thirty two 8-bit unsigned integer values */
+typedef v_reg<uchar, 32> v_uint8x32;
+/** @brief Thirty two 8-bit signed integer values */
+typedef v_reg<schar, 32> v_int8x32;
+/** @brief Sixteen 16-bit unsigned integer values */
+typedef v_reg<ushort, 16> v_uint16x16;
+/** @brief Sixteen 16-bit signed integer values */
+typedef v_reg<short, 16> v_int16x16;
+/** @brief Eight 32-bit unsigned integer values */
+typedef v_reg<unsigned, 8> v_uint32x8;
+/** @brief Eight 32-bit signed integer values */
+typedef v_reg<int, 8> v_int32x8;
+/** @brief Eight 32-bit floating point values (single precision) */
+typedef v_reg<float, 8> v_float32x8;
+/** @brief Four 64-bit floating point values (double precision) */
+typedef v_reg<double, 4> v_float64x4;
+/** @brief Four 64-bit unsigned integer values */
+typedef v_reg<uint64, 4> v_uint64x4;
+/** @brief Four 64-bit signed integer values */
+typedef v_reg<int64, 4> v_int64x4;
+#endif
+
+#if CV_SIMD512
+/** @brief Sixty four 8-bit unsigned integer values */
+typedef v_reg<uchar, 64> v_uint8x64;
+/** @brief Sixty four 8-bit signed integer values */
+typedef v_reg<schar, 64> v_int8x64;
+/** @brief Thirty two 16-bit unsigned integer values */
+typedef v_reg<ushort, 32> v_uint16x32;
+/** @brief Thirty two 16-bit signed integer values */
+typedef v_reg<short, 32> v_int16x32;
+/** @brief Sixteen 32-bit unsigned integer values */
+typedef v_reg<unsigned, 16> v_uint32x16;
+/** @brief Sixteen 32-bit signed integer values */
+typedef v_reg<int, 16> v_int32x16;
+/** @brief Sixteen 32-bit floating point values (single precision) */
+typedef v_reg<float, 16> v_float32x16;
+/** @brief Eight 64-bit floating point values (double precision) */
+typedef v_reg<double, 8> v_float64x8;
+/** @brief Eight 64-bit unsigned integer values */
+typedef v_reg<uint64, 8> v_uint64x8;
+/** @brief Eight 64-bit signed integer values */
+typedef v_reg<int64, 8> v_int64x8;
+#endif
+
+enum {
+    simd128_width = 16,
+#if CV_SIMD256
+    simd256_width = 32,
+#endif
+#if CV_SIMD512
+    simd512_width = 64,
+    simdmax_width = simd512_width
+#elif CV_SIMD256
+    simdmax_width = simd256_width
+#else
+    simdmax_width = simd128_width
+#endif
+};
+
+/** @brief Add values
+
+For all types. */
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator+(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator+=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+
+/** @brief Subtract values
+
+For all types. */
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator-(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator-=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+
+/** @brief Multiply values
+
+For 16- and 32-bit integer types and floating types. */
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator*(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator*=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+
+/** @brief Divide values
+
+For floating types only. */
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator/(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator/=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+
+
+/** @brief Bitwise AND
+
+Only for integer types. */
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator&(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator&=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+
+/** @brief Bitwise OR
+
+Only for integer types. */
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator|(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator|=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+
+/** @brief Bitwise XOR
+
+Only for integer types.*/
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator^(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator^=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
+
+/** @brief Bitwise NOT
+
+Only for integer types.*/
+template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator~(const v_reg<_Tp, n>& a);
+
+
+#ifndef CV_DOXYGEN
+
+#define CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(macro_name, ...) \
+__CV_EXPAND(macro_name(uchar, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(schar, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(ushort, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(short, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(unsigned, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(int, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(uint64, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(int64, __VA_ARGS__)) \
+
+#define CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(macro_name, ...) \
+__CV_EXPAND(macro_name(float, __VA_ARGS__)) \
+__CV_EXPAND(macro_name(double, __VA_ARGS__)) \
+
+#define CV__HAL_INTRIN_EXPAND_WITH_ALL_TYPES(macro_name, ...) \
+CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(macro_name, __VA_ARGS__) \
+CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(macro_name, __VA_ARGS__) \
+
+#define CV__HAL_INTRIN_IMPL_BIN_OP_(_Tp, bin_op) \
+template<int n> inline \
+v_reg<_Tp, n> operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    v_reg<_Tp, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \
+    return c; \
+} \
+template<int n> inline \
+v_reg<_Tp, n>& operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    for( int i = 0; i < n; i++ ) \
+        a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \
+    return a; \
+}
+
+#define CV__HAL_INTRIN_IMPL_BIN_OP(bin_op) CV__HAL_INTRIN_EXPAND_WITH_ALL_TYPES(CV__HAL_INTRIN_IMPL_BIN_OP_, bin_op)
+
+CV__HAL_INTRIN_IMPL_BIN_OP(+)
+CV__HAL_INTRIN_IMPL_BIN_OP(-)
+CV__HAL_INTRIN_IMPL_BIN_OP(*)
+CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(CV__HAL_INTRIN_IMPL_BIN_OP_, /)
+
+#define CV__HAL_INTRIN_IMPL_BIT_OP_(_Tp, bit_op) \
+template<int n> CV_INLINE \
+v_reg<_Tp, n> operator bit_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    v_reg<_Tp, n> c; \
+    typedef typename V_TypeTraits<_Tp>::int_type itype; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \
+                                                        V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \
+    return c; \
+} \
+template<int n> CV_INLINE \
+v_reg<_Tp, n>& operator bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    typedef typename V_TypeTraits<_Tp>::int_type itype; \
+    for( int i = 0; i < n; i++ ) \
+        a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \
+                                                        V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \
+    return a; \
+}
+
+#define CV__HAL_INTRIN_IMPL_BIT_OP(bit_op) \
+CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(CV__HAL_INTRIN_IMPL_BIT_OP_, bit_op) \
+CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(CV__HAL_INTRIN_IMPL_BIT_OP_, bit_op) /* TODO: FIXIT remove this after masks refactoring */
+
+
+CV__HAL_INTRIN_IMPL_BIT_OP(&)
+CV__HAL_INTRIN_IMPL_BIT_OP(|)
+CV__HAL_INTRIN_IMPL_BIT_OP(^)
+
+#define CV__HAL_INTRIN_IMPL_BITWISE_NOT_(_Tp, dummy) \
+template<int n> CV_INLINE \
+v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) \
+{ \
+    v_reg<_Tp, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); \
+    return c; \
+} \
+
+CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(CV__HAL_INTRIN_IMPL_BITWISE_NOT_, ~)
+
+#endif  // !CV_DOXYGEN
+
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \
+template<typename _Tp, int n> inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \
+{ \
+    v_reg<_Tp2, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = cfunc(a.s[i]); \
+    return c; \
+}
+
+/** @brief Square root of elements
+
+Only for floating point types.*/
+OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp)
+
+//! @cond IGNORED
+OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp)
+OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp)
+OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp)
+OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp)
+//! @endcond
+
+/** @brief Absolute value of elements
+
+Only for floating point types.*/
+OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs,
+                          typename V_TypeTraits<_Tp>::abs_type)
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \
+template<typename _Tp, int n> inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    v_reg<_Tp, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = cfunc(a.s[i], b.s[i]); \
+    return c; \
+}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \
+template<typename _Tp, int n> inline _Tp func(const v_reg<_Tp, n>& a) \
+{ \
+    _Tp c = a.s[0]; \
+    for( int i = 1; i < n; i++ ) \
+        c = cfunc(c, a.s[i]); \
+    return c; \
+}
+
+/** @brief Choose min values for each pair
+
+Scheme:
+@code
+{A1 A2 ...}
+{B1 B2 ...}
+--------------
+{min(A1,B1) min(A2,B2) ...}
+@endcode
+For all types except 64-bit integer. */
+OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min)
+
+/** @brief Choose max values for each pair
+
+Scheme:
+@code
+{A1 A2 ...}
+{B1 B2 ...}
+--------------
+{max(A1,B1) max(A2,B2) ...}
+@endcode
+For all types except 64-bit integer. */
+OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max)
+
+/** @brief Find one min value
+
+Scheme:
+@code
+{A1 A2 A3 ...} => min(A1,A2,A3,...)
+@endcode
+For all types except 64-bit integer and 64-bit floating point types. */
+OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min)
+
+/** @brief Find one max value
+
+Scheme:
+@code
+{A1 A2 A3 ...} => max(A1,A2,A3,...)
+@endcode
+For all types except 64-bit integer and 64-bit floating point types. */
+OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max)
+
+static const unsigned char popCountTable[] =
+{
+    0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8,
+};
+/** @brief Count the 1 bits in the vector lanes and return result as corresponding unsigned type
+
+Scheme:
+@code
+{A1 A2 A3 ...} => {popcount(A1), popcount(A2), popcount(A3), ...}
+@endcode
+For all integer types. */
+template<typename _Tp, int n>
+inline v_reg<typename V_TypeTraits<_Tp>::abs_type, n> v_popcount(const v_reg<_Tp, n>& a)
+{
+    v_reg<typename V_TypeTraits<_Tp>::abs_type, n> b = v_reg<typename V_TypeTraits<_Tp>::abs_type, n>::zero();
+    for (int i = 0; i < n*(int)sizeof(_Tp); i++)
+        b.s[i/sizeof(_Tp)] += popCountTable[v_reinterpret_as_u8(a).s[i]];
+    return b;
+}
+
+
+//! @cond IGNORED
+template<typename _Tp, int n>
+inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+                      v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval )
+{
+    for( int i = 0; i < n; i++ )
+    {
+        minval.s[i] = std::min(a.s[i], b.s[i]);
+        maxval.s[i] = std::max(a.s[i], b.s[i]);
+    }
+}
+//! @endcond
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \
+template<typename _Tp, int n> \
+inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    typedef typename V_TypeTraits<_Tp>::int_type itype; \
+    v_reg<_Tp, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \
+    return c; \
+}
+
+/** @brief Less-than comparison
+
+For all types except 64-bit integer values. */
+OPENCV_HAL_IMPL_CMP_OP(<)
+
+/** @brief Greater-than comparison
+
+For all types except 64-bit integer values. */
+OPENCV_HAL_IMPL_CMP_OP(>)
+
+/** @brief Less-than or equal comparison
+
+For all types except 64-bit integer values. */
+OPENCV_HAL_IMPL_CMP_OP(<=)
+
+/** @brief Greater-than or equal comparison
+
+For all types except 64-bit integer values. */
+OPENCV_HAL_IMPL_CMP_OP(>=)
+
+/** @brief Equal comparison
+
+For all types except 64-bit integer values. */
+OPENCV_HAL_IMPL_CMP_OP(==)
+
+/** @brief Not equal comparison
+
+For all types except 64-bit integer values. */
+OPENCV_HAL_IMPL_CMP_OP(!=)
+
+template<int n>
+inline v_reg<float, n> v_not_nan(const v_reg<float, n>& a)
+{
+    typedef typename V_TypeTraits<float>::int_type itype;
+    v_reg<float, n> c;
+    for (int i = 0; i < n; i++)
+        c.s[i] = V_TypeTraits<float>::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i]));
+    return c;
+}
+template<int n>
+inline v_reg<double, n> v_not_nan(const v_reg<double, n>& a)
+{
+    typedef typename V_TypeTraits<double>::int_type itype;
+    v_reg<double, n> c;
+    for (int i = 0; i < n; i++)
+        c.s[i] = V_TypeTraits<double>::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i]));
+    return c;
+}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_ARITHM_OP(func, bin_op, cast_op, _Tp2) \
+template<typename _Tp, int n> \
+inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    typedef _Tp2 rtype; \
+    v_reg<rtype, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \
+    return c; \
+}
+
+/** @brief Add values without saturation
+
+For 8- and 16-bit integer values. */
+OPENCV_HAL_IMPL_ARITHM_OP(v_add_wrap, +, (_Tp), _Tp)
+
+/** @brief Subtract values without saturation
+
+For 8- and 16-bit integer values. */
+OPENCV_HAL_IMPL_ARITHM_OP(v_sub_wrap, -, (_Tp), _Tp)
+
+/** @brief Multiply values without saturation
+
+For 8- and 16-bit integer values. */
+OPENCV_HAL_IMPL_ARITHM_OP(v_mul_wrap, *, (_Tp), _Tp)
+
+//! @cond IGNORED
+template<typename T> inline T _absdiff(T a, T b)
+{
+    return a > b ? a - b : b - a;
+}
+//! @endcond
+
+/** @brief Absolute difference
+
+Returns \f$ |a - b| \f$ converted to corresponding unsigned type.
+Example:
+@code{.cpp}
+v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1}
+v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3}
+@endcode
+For 8-, 16-, 32-bit integer source types. */
+template<typename _Tp, int n>
+inline v_reg<typename V_TypeTraits<_Tp>::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b)
+{
+    typedef typename V_TypeTraits<_Tp>::abs_type rtype;
+    v_reg<rtype, n> c;
+    const rtype mask = (rtype)(std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0);
+    for( int i = 0; i < n; i++ )
+    {
+        rtype ua = a.s[i] ^ mask;
+        rtype ub = b.s[i] ^ mask;
+        c.s[i] = _absdiff(ua, ub);
+    }
+    return c;
+}
+
+/** @overload
+
+For 32-bit floating point values */
+template<int n> inline v_reg<float, n> v_absdiff(const v_reg<float, n>& a, const v_reg<float, n>& b)
+{
+    v_reg<float, n> c;
+    for( int i = 0; i < c.nlanes; i++ )
+        c.s[i] = _absdiff(a.s[i], b.s[i]);
+    return c;
+}
+
+/** @overload
+
+For 64-bit floating point values */
+template<int n> inline v_reg<double, n> v_absdiff(const v_reg<double, n>& a, const v_reg<double, n>& b)
+{
+    v_reg<double, n> c;
+    for( int i = 0; i < c.nlanes; i++ )
+        c.s[i] = _absdiff(a.s[i], b.s[i]);
+    return c;
+}
+
+/** @brief Saturating absolute difference
+
+Returns \f$ saturate(|a - b|) \f$ .
+For 8-, 16-bit signed integer source types. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_absdiffs(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < n; i++)
+        c.s[i] = saturate_cast<_Tp>(std::abs(a.s[i] - b.s[i]));
+    return c;
+}
+
+/** @brief Inversed square root
+
+Returns \f$ 1/sqrt(a) \f$
+For floating point types only. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a)
+{
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = 1.f/std::sqrt(a.s[i]);
+    return c;
+}
+
+/** @brief Magnitude
+
+Returns \f$ sqrt(a^2 + b^2) \f$
+For floating point types only. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]);
+    return c;
+}
+
+/** @brief Square of the magnitude
+
+Returns \f$ a^2 + b^2 \f$
+For floating point types only. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i];
+    return c;
+}
+
+/** @brief Multiply and add
+
+ Returns \f$ a*b + c \f$
+ For floating point types and signed 32bit int only. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_fma(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+                           const v_reg<_Tp, n>& c)
+{
+    v_reg<_Tp, n> d;
+    for( int i = 0; i < n; i++ )
+        d.s[i] = a.s[i]*b.s[i] + c.s[i];
+    return d;
+}
+
+/** @brief A synonym for v_fma */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+                              const v_reg<_Tp, n>& c)
+{
+    return v_fma(a, b, c);
+}
+
+/** @brief Dot product of elements
+
+Multiply values in two registers and sum adjacent result pairs.
+
+Scheme:
+@code
+  {A1 A2 ...} // 16-bit
+x {B1 B2 ...} // 16-bit
+-------------
+{A1B1+A2B2 ...} // 32-bit
+
+@endcode
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
+v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    v_reg<w_type, n/2> c;
+    for( int i = 0; i < (n/2); i++ )
+        c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1];
+    return c;
+}
+
+/** @brief Dot product of elements
+
+Same as cv::v_dotprod, but add a third element to the sum of adjacent pairs.
+Scheme:
+@code
+  {A1 A2 ...} // 16-bit
+x {B1 B2 ...} // 16-bit
+-------------
+  {A1B1+A2B2+C1 ...} // 32-bit
+@endcode
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
+v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+          const v_reg<typename V_TypeTraits<_Tp>::w_type, n / 2>& c)
+{
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    v_reg<w_type, n/2> s;
+    for( int i = 0; i < (n/2); i++ )
+        s.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1] + c.s[i];
+    return s;
+}
+
+/** @brief Fast Dot product of elements
+
+Same as cv::v_dotprod, but it may perform unorder sum between result pairs in some platforms,
+this intrinsic can be used if the sum among all lanes is only matters
+and also it should be yielding better performance on the affected platforms.
+
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
+v_dotprod_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{ return v_dotprod(a, b); }
+
+/** @brief Fast Dot product of elements
+
+Same as cv::v_dotprod_fast, but add a third element to the sum of adjacent pairs.
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
+v_dotprod_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+               const v_reg<typename V_TypeTraits<_Tp>::w_type, n / 2>& c)
+{ return v_dotprod(a, b, c); }
+
+/** @brief Dot product of elements and expand
+
+Multiply values in two registers and expand the sum of adjacent result pairs.
+
+Scheme:
+@code
+  {A1 A2 A3 A4 ...} // 8-bit
+x {B1 B2 B3 B4 ...} // 8-bit
+-------------
+  {A1B1+A2B2+A3B3+A4B4 ...} // 32-bit
+
+@endcode
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::q_type, n/4>
+v_dotprod_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    typedef typename V_TypeTraits<_Tp>::q_type q_type;
+    v_reg<q_type, n/4> s;
+    for( int i = 0; i < (n/4); i++ )
+        s.s[i] = (q_type)a.s[i*4    ]*b.s[i*4    ] + (q_type)a.s[i*4 + 1]*b.s[i*4 + 1] +
+                 (q_type)a.s[i*4 + 2]*b.s[i*4 + 2] + (q_type)a.s[i*4 + 3]*b.s[i*4 + 3];
+    return s;
+}
+
+/** @brief Dot product of elements
+
+Same as cv::v_dotprod_expand, but add a third element to the sum of adjacent pairs.
+Scheme:
+@code
+  {A1 A2 A3 A4 ...} // 8-bit
+x {B1 B2 B3 B4 ...} // 8-bit
+-------------
+  {A1B1+A2B2+A3B3+A4B4+C1 ...} // 32-bit
+@endcode
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::q_type, n/4>
+v_dotprod_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+                 const v_reg<typename V_TypeTraits<_Tp>::q_type, n / 4>& c)
+{
+    typedef typename V_TypeTraits<_Tp>::q_type q_type;
+    v_reg<q_type, n/4> s;
+    for( int i = 0; i < (n/4); i++ )
+        s.s[i] = (q_type)a.s[i*4    ]*b.s[i*4    ] + (q_type)a.s[i*4 + 1]*b.s[i*4 + 1] +
+                 (q_type)a.s[i*4 + 2]*b.s[i*4 + 2] + (q_type)a.s[i*4 + 3]*b.s[i*4 + 3] + c.s[i];
+    return s;
+}
+
+/** @brief Fast Dot product of elements and expand
+
+Multiply values in two registers and expand the sum of adjacent result pairs.
+
+Same as cv::v_dotprod_expand, but it may perform unorder sum between result pairs in some platforms,
+this intrinsic can be used if the sum among all lanes is only matters
+and also it should be yielding better performance on the affected platforms.
+
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::q_type, n/4>
+v_dotprod_expand_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{ return v_dotprod_expand(a, b); }
+
+/** @brief Fast Dot product of elements
+
+Same as cv::v_dotprod_expand_fast, but add a third element to the sum of adjacent pairs.
+*/
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::q_type, n/4>
+v_dotprod_expand_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+                      const v_reg<typename V_TypeTraits<_Tp>::q_type, n / 4>& c)
+{ return v_dotprod_expand(a, b, c); }
+
+/** @brief Multiply and expand
+
+Multiply values two registers and store results in two registers with wider pack type.
+Scheme:
+@code
+  {A B C D} // 32-bit
+x {E F G H} // 32-bit
+---------------
+{AE BF}         // 64-bit
+        {CG DH} // 64-bit
+@endcode
+Example:
+@code{.cpp}
+v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2}
+v_uint64x2 c, d; // results
+v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8}
+@endcode
+Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4).
+*/
+template<typename _Tp, int n> inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+                                                       v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& c,
+                                                       v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& d)
+{
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    for( int i = 0; i < (n/2); i++ )
+    {
+        c.s[i] = (w_type)a.s[i]*b.s[i];
+        d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)];
+    }
+}
+
+/** @brief Multiply and extract high part
+
+Multiply values two registers and store high part of the results.
+Implemented only for 16-bit source types (v_int16x8, v_uint16x8). Returns \f$ a*b >> 16 \f$
+*/
+template<typename _Tp, int n> inline v_reg<_Tp, n> v_mul_hi(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    v_reg<_Tp, n> c;
+    for (int i = 0; i < n; i++)
+        c.s[i] = (_Tp)(((w_type)a.s[i] * b.s[i]) >> sizeof(_Tp)*8);
+    return c;
+}
+
+//! @cond IGNORED
+template<typename _Tp, int n> inline void v_hsum(const v_reg<_Tp, n>& a,
+                                                 v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& c)
+{
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    for( int i = 0; i < (n/2); i++ )
+    {
+        c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1];
+    }
+}
+//! @endcond
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \
+template<typename _Tp, int n> inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \
+{ \
+    v_reg<_Tp, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = (_Tp)(a.s[i] shift_op imm); \
+    return c; \
+}
+
+/** @brief Bitwise shift left
+
+For 16-, 32- and 64-bit integer values. */
+OPENCV_HAL_IMPL_SHIFT_OP(<< )
+
+/** @brief Bitwise shift right
+
+For 16-, 32- and 64-bit integer values. */
+OPENCV_HAL_IMPL_SHIFT_OP(>> )
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(suffix,opA,opB) \
+template<int imm, typename _Tp, int n> inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a) \
+{ \
+    v_reg<_Tp, n> b; \
+    for (int i = 0; i < n; i++) \
+    { \
+        int sIndex = i opA imm; \
+        if (0 <= sIndex && sIndex < n) \
+        { \
+            b.s[i] = a.s[sIndex]; \
+        } \
+        else \
+        { \
+            b.s[i] = 0; \
+        } \
+    } \
+    return b; \
+} \
+template<int imm, typename _Tp, int n> inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    v_reg<_Tp, n> c; \
+    for (int i = 0; i < n; i++) \
+    { \
+        int aIndex = i opA imm; \
+        int bIndex = i opA imm opB n; \
+        if (0 <= bIndex && bIndex < n) \
+        { \
+            c.s[i] = b.s[bIndex]; \
+        } \
+        else if (0 <= aIndex && aIndex < n) \
+        { \
+            c.s[i] = a.s[aIndex]; \
+        } \
+        else \
+        { \
+            c.s[i] = 0; \
+        } \
+    } \
+    return c; \
+}
+
+/** @brief Element shift left among vector
+
+For all type */
+OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(left,  -, +)
+
+/** @brief Element shift right among vector
+
+For all type */
+OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(right, +, -)
+
+/** @brief Sum packed values
+
+Scheme:
+@code
+{A1 A2 A3 ...} => sum{A1,A2,A3,...}
+@endcode
+*/
+template<typename _Tp, int n> inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a)
+{
+    typename V_TypeTraits<_Tp>::sum_type c = a.s[0];
+    for( int i = 1; i < n; i++ )
+        c += a.s[i];
+    return c;
+}
+
+/** @brief Sums all elements of each input vector, returns the vector of sums
+
+ Scheme:
+ @code
+ result[0] = a[0] + a[1] + a[2] + a[3]
+ result[1] = b[0] + b[1] + b[2] + b[3]
+ result[2] = c[0] + c[1] + c[2] + c[3]
+ result[3] = d[0] + d[1] + d[2] + d[3]
+ @endcode
+*/
+template<int n> inline v_reg<float, n> v_reduce_sum4(const v_reg<float, n>& a, const v_reg<float, n>& b,
+    const v_reg<float, n>& c, const v_reg<float, n>& d)
+{
+    v_reg<float, n> r;
+    for(int i = 0; i < (n/4); i++)
+    {
+        r.s[i*4 + 0] = a.s[i*4 + 0] + a.s[i*4 + 1] + a.s[i*4 + 2] + a.s[i*4 + 3];
+        r.s[i*4 + 1] = b.s[i*4 + 0] + b.s[i*4 + 1] + b.s[i*4 + 2] + b.s[i*4 + 3];
+        r.s[i*4 + 2] = c.s[i*4 + 0] + c.s[i*4 + 1] + c.s[i*4 + 2] + c.s[i*4 + 3];
+        r.s[i*4 + 3] = d.s[i*4 + 0] + d.s[i*4 + 1] + d.s[i*4 + 2] + d.s[i*4 + 3];
+    }
+    return r;
+}
+
+/** @brief Sum absolute differences of values
+
+Scheme:
+@code
+{A1 A2 A3 ...} {B1 B2 B3 ...} => sum{ABS(A1-B1),abs(A2-B2),abs(A3-B3),...}
+@endcode
+For all types except 64-bit types.*/
+template<typename _Tp, int n> inline typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type v_reduce_sad(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type c = _absdiff(a.s[0], b.s[0]);
+    for (int i = 1; i < n; i++)
+        c += _absdiff(a.s[i], b.s[i]);
+    return c;
+}
+
+/** @brief Get negative values mask
+@deprecated v_signmask depends on a lane count heavily and therefore isn't universal enough
+
+Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes.
+Example:
+@code{.cpp}
+v_int32x4 r; // set to {-1, -1, 1, 1}
+int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011
+@endcode
+*/
+template<typename _Tp, int n> inline int v_signmask(const v_reg<_Tp, n>& a)
+{
+    int mask = 0;
+    for( int i = 0; i < n; i++ )
+        mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i;
+    return mask;
+}
+
+/** @brief Get first negative lane index
+
+Returned value is an index of first negative lane (undefined for input of all positive values)
+Example:
+@code{.cpp}
+v_int32x4 r; // set to {0, 0, -1, -1}
+int idx = v_heading_zeros(r); // idx = 2
+@endcode
+*/
+template <typename _Tp, int n> inline int v_scan_forward(const v_reg<_Tp, n>& a)
+{
+    for (int i = 0; i < n; i++)
+        if(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0)
+            return i;
+    return 0;
+}
+
+/** @brief Check if all packed values are less than zero
+
+Unsigned values will be casted to signed: `uchar 254 => char -2`.
+*/
+template<typename _Tp, int n> inline bool v_check_all(const v_reg<_Tp, n>& a)
+{
+    for( int i = 0; i < n; i++ )
+        if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 )
+            return false;
+    return true;
+}
+
+/** @brief Check if any of packed values is less than zero
+
+Unsigned values will be casted to signed: `uchar 254 => char -2`.
+*/
+template<typename _Tp, int n> inline bool v_check_any(const v_reg<_Tp, n>& a)
+{
+    for( int i = 0; i < n; i++ )
+        if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 )
+            return true;
+    return false;
+}
+
+/** @brief Per-element select (blend operation)
+
+Return value will be built by combining values _a_ and _b_ using the following scheme:
+    result[i] = mask[i] ? a[i] : b[i];
+
+@note: _mask_ element values are restricted to these values:
+- 0: select element from _b_
+- 0xff/0xffff/etc: select element from _a_
+(fully compatible with bitwise-based operator)
+*/
+template<typename _Tp, int n> inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask,
+                                                           const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    typedef V_TypeTraits<_Tp> Traits;
+    typedef typename Traits::int_type int_type;
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < n; i++ )
+    {
+        int_type m = Traits::reinterpret_int(mask.s[i]);
+        CV_DbgAssert(m == 0 || m == (~(int_type)0));  // restrict mask values: 0 or 0xff/0xffff/etc
+        c.s[i] = m ? a.s[i] : b.s[i];
+    }
+    return c;
+}
+
+/** @brief Expand values to the wider pack type
+
+Copy contents of register to two registers with 2x wider pack type.
+Scheme:
+@code
+ int32x4     int64x2 int64x2
+{A B C D} ==> {A B} , {C D}
+@endcode */
+template<typename _Tp, int n> inline void v_expand(const v_reg<_Tp, n>& a,
+                            v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& b0,
+                            v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& b1)
+{
+    for( int i = 0; i < (n/2); i++ )
+    {
+        b0.s[i] = a.s[i];
+        b1.s[i] = a.s[i+(n/2)];
+    }
+}
+
+/** @brief Expand lower values to the wider pack type
+
+Same as cv::v_expand, but return lower half of the vector.
+
+Scheme:
+@code
+ int32x4     int64x2
+{A B C D} ==> {A B}
+@endcode */
+template<typename _Tp, int n>
+inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
+v_expand_low(const v_reg<_Tp, n>& a)
+{
+    v_reg<typename V_TypeTraits<_Tp>::w_type, n/2> b;
+    for( int i = 0; i < (n/2); i++ )
+        b.s[i] = a.s[i];
+    return b;
+}
+
+/** @brief Expand higher values to the wider pack type
+
+Same as cv::v_expand_low, but expand higher half of the vector instead.
+
+Scheme:
+@code
+ int32x4     int64x2
+{A B C D} ==> {C D}
+@endcode */
+template<typename _Tp, int n>
+inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
+v_expand_high(const v_reg<_Tp, n>& a)
+{
+    v_reg<typename V_TypeTraits<_Tp>::w_type, n/2> b;
+    for( int i = 0; i < (n/2); i++ )
+        b.s[i] = a.s[i+(n/2)];
+    return b;
+}
+
+//! @cond IGNORED
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::int_type, n>
+    v_reinterpret_as_int(const v_reg<_Tp, n>& a)
+{
+    v_reg<typename V_TypeTraits<_Tp>::int_type, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]);
+    return c;
+}
+
+template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::uint_type, n>
+    v_reinterpret_as_uint(const v_reg<_Tp, n>& a)
+{
+    v_reg<typename V_TypeTraits<_Tp>::uint_type, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]);
+    return c;
+}
+//! @endcond
+
+/** @brief Interleave two vectors
+
+Scheme:
+@code
+  {A1 A2 A3 A4}
+  {B1 B2 B3 B4}
+---------------
+  {A1 B1 A2 B2} and {A3 B3 A4 B4}
+@endcode
+For all types except 64-bit.
+*/
+template<typename _Tp, int n> inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1,
+                                               v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 )
+{
+    int i;
+    for( i = 0; i < n/2; i++ )
+    {
+        b0.s[i*2] = a0.s[i];
+        b0.s[i*2+1] = a1.s[i];
+    }
+    for( ; i < n; i++ )
+    {
+        b1.s[i*2-n] = a0.s[i];
+        b1.s[i*2-n+1] = a1.s[i];
+    }
+}
+
+/** @brief Load register contents from memory
+
+@param ptr pointer to memory block with data
+@return register object
+
+@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc.
+
+@note Use vx_load version to get maximum available register length result
+
+@note Alignment requirement:
+if CV_STRONG_ALIGNMENT=1 then passed pointer must be aligned (`sizeof(lane type)` should be enough).
+Do not cast pointer types without runtime check for pointer alignment (like `uchar*` => `int*`).
+ */
+template<typename _Tp>
+inline v_reg<_Tp, simd128_width / sizeof(_Tp)> v_load(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    return v_reg<_Tp, simd128_width / sizeof(_Tp)>(ptr);
+}
+
+#if CV_SIMD256
+/** @brief Load 256-bit length register contents from memory
+
+@param ptr pointer to memory block with data
+@return register object
+
+@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x32, int ==> cv::v_int32x8, etc.
+
+@note Check CV_SIMD256 preprocessor definition prior to use.
+Use vx_load version to get maximum available register length result
+
+@note Alignment requirement:
+if CV_STRONG_ALIGNMENT=1 then passed pointer must be aligned (`sizeof(lane type)` should be enough).
+Do not cast pointer types without runtime check for pointer alignment (like `uchar*` => `int*`).
+ */
+template<typename _Tp>
+inline v_reg<_Tp, simd256_width / sizeof(_Tp)> v256_load(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    return v_reg<_Tp, simd256_width / sizeof(_Tp)>(ptr);
+}
+#endif
+
+#if CV_SIMD512
+/** @brief Load 512-bit length register contents from memory
+
+@param ptr pointer to memory block with data
+@return register object
+
+@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x64, int ==> cv::v_int32x16, etc.
+
+@note Check CV_SIMD512 preprocessor definition prior to use.
+Use vx_load version to get maximum available register length result
+
+@note Alignment requirement:
+if CV_STRONG_ALIGNMENT=1 then passed pointer must be aligned (`sizeof(lane type)` should be enough).
+Do not cast pointer types without runtime check for pointer alignment (like `uchar*` => `int*`).
+ */
+template<typename _Tp>
+inline v_reg<_Tp, simd512_width / sizeof(_Tp)> v512_load(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    return v_reg<_Tp, simd512_width / sizeof(_Tp)>(ptr);
+}
+#endif
+
+/** @brief Load register contents from memory (aligned)
+
+similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary in case of SIMD128, 32-byte - SIMD256, etc)
+
+@note Use vx_load_aligned version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd128_width / sizeof(_Tp)> v_load_aligned(const _Tp* ptr)
+{
+    CV_Assert(isAligned<sizeof(v_reg<_Tp, simd128_width / sizeof(_Tp)>)>(ptr));
+    return v_reg<_Tp, simd128_width / sizeof(_Tp)>(ptr);
+}
+
+#if CV_SIMD256
+/** @brief Load register contents from memory (aligned)
+
+similar to cv::v256_load, but source memory block should be aligned (to 32-byte boundary in case of SIMD256, 64-byte - SIMD512, etc)
+
+@note Check CV_SIMD256 preprocessor definition prior to use.
+Use vx_load_aligned version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd256_width / sizeof(_Tp)> v256_load_aligned(const _Tp* ptr)
+{
+    CV_Assert(isAligned<sizeof(v_reg<_Tp, simd256_width / sizeof(_Tp)>)>(ptr));
+    return v_reg<_Tp, simd256_width / sizeof(_Tp)>(ptr);
+}
+#endif
+
+#if CV_SIMD512
+/** @brief Load register contents from memory (aligned)
+
+similar to cv::v512_load, but source memory block should be aligned (to 64-byte boundary in case of SIMD512, etc)
+
+@note Check CV_SIMD512 preprocessor definition prior to use.
+Use vx_load_aligned version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd512_width / sizeof(_Tp)> v512_load_aligned(const _Tp* ptr)
+{
+    CV_Assert(isAligned<sizeof(v_reg<_Tp, simd512_width / sizeof(_Tp)>)>(ptr));
+    return v_reg<_Tp, simd512_width / sizeof(_Tp)>(ptr);
+}
+#endif
+
+/** @brief Load 64-bits of data to lower part (high part is undefined).
+
+@param ptr memory block containing data for first half (0..n/2)
+
+@code{.cpp}
+int lo[2] = { 1, 2 };
+v_int32x4 r = v_load_low(lo);
+@endcode
+
+@note Use vx_load_low version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd128_width / sizeof(_Tp)> v_load_low(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    v_reg<_Tp, simd128_width / sizeof(_Tp)> c;
+    for( int i = 0; i < c.nlanes/2; i++ )
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+
+#if CV_SIMD256
+/** @brief Load 128-bits of data to lower part (high part is undefined).
+
+@param ptr memory block containing data for first half (0..n/2)
+
+@code{.cpp}
+int lo[4] = { 1, 2, 3, 4 };
+v_int32x8 r = v256_load_low(lo);
+@endcode
+
+@note Check CV_SIMD256 preprocessor definition prior to use.
+Use vx_load_low version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd256_width / sizeof(_Tp)> v256_load_low(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    v_reg<_Tp, simd256_width / sizeof(_Tp)> c;
+    for (int i = 0; i < c.nlanes / 2; i++)
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+#endif
+
+#if CV_SIMD512
+/** @brief Load 256-bits of data to lower part (high part is undefined).
+
+@param ptr memory block containing data for first half (0..n/2)
+
+@code{.cpp}
+int lo[8] = { 1, 2, 3, 4, 5, 6, 7, 8 };
+v_int32x16 r = v512_load_low(lo);
+@endcode
+
+@note Check CV_SIMD512 preprocessor definition prior to use.
+Use vx_load_low version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd512_width / sizeof(_Tp)> v512_load_low(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    v_reg<_Tp, simd512_width / sizeof(_Tp)> c;
+    for (int i = 0; i < c.nlanes / 2; i++)
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+#endif
+
+/** @brief Load register contents from two memory blocks
+
+@param loptr memory block containing data for first half (0..n/2)
+@param hiptr memory block containing data for second half (n/2..n)
+
+@code{.cpp}
+int lo[2] = { 1, 2 }, hi[2] = { 3, 4 };
+v_int32x4 r = v_load_halves(lo, hi);
+@endcode
+
+@note Use vx_load_halves version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd128_width / sizeof(_Tp)> v_load_halves(const _Tp* loptr, const _Tp* hiptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(loptr));
+    CV_Assert(isAligned<sizeof(_Tp)>(hiptr));
+#endif
+    v_reg<_Tp, simd128_width / sizeof(_Tp)> c;
+    for( int i = 0; i < c.nlanes/2; i++ )
+    {
+        c.s[i] = loptr[i];
+        c.s[i+c.nlanes/2] = hiptr[i];
+    }
+    return c;
+}
+
+#if CV_SIMD256
+/** @brief Load register contents from two memory blocks
+
+@param loptr memory block containing data for first half (0..n/2)
+@param hiptr memory block containing data for second half (n/2..n)
+
+@code{.cpp}
+int lo[4] = { 1, 2, 3, 4 }, hi[4] = { 5, 6, 7, 8 };
+v_int32x8 r = v256_load_halves(lo, hi);
+@endcode
+
+@note Check CV_SIMD256 preprocessor definition prior to use.
+Use vx_load_halves version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd256_width / sizeof(_Tp)> v256_load_halves(const _Tp* loptr, const _Tp* hiptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(loptr));
+    CV_Assert(isAligned<sizeof(_Tp)>(hiptr));
+#endif
+    v_reg<_Tp, simd256_width / sizeof(_Tp)> c;
+    for (int i = 0; i < c.nlanes / 2; i++)
+    {
+        c.s[i] = loptr[i];
+        c.s[i + c.nlanes / 2] = hiptr[i];
+    }
+    return c;
+}
+#endif
+
+#if CV_SIMD512
+/** @brief Load register contents from two memory blocks
+
+@param loptr memory block containing data for first half (0..n/2)
+@param hiptr memory block containing data for second half (n/2..n)
+
+@code{.cpp}
+int lo[4] = { 1, 2, 3, 4, 5, 6, 7, 8 }, hi[4] = { 9, 10, 11, 12, 13, 14, 15, 16 };
+v_int32x16 r = v512_load_halves(lo, hi);
+@endcode
+
+@note Check CV_SIMD512 preprocessor definition prior to use.
+Use vx_load_halves version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<_Tp, simd512_width / sizeof(_Tp)> v512_load_halves(const _Tp* loptr, const _Tp* hiptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(loptr));
+    CV_Assert(isAligned<sizeof(_Tp)>(hiptr));
+#endif
+    v_reg<_Tp, simd512_width / sizeof(_Tp)> c;
+    for (int i = 0; i < c.nlanes / 2; i++)
+    {
+        c.s[i] = loptr[i];
+        c.s[i + c.nlanes / 2] = hiptr[i];
+    }
+    return c;
+}
+#endif
+
+/** @brief Load register contents from memory with double expand
+
+Same as cv::v_load, but result pack type will be 2x wider than memory type.
+
+@code{.cpp}
+short buf[4] = {1, 2, 3, 4}; // type is int16
+v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32
+@endcode
+For 8-, 16-, 32-bit integer source types.
+
+@note Use vx_load_expand version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<typename V_TypeTraits<_Tp>::w_type, simd128_width / sizeof(typename V_TypeTraits<_Tp>::w_type)>
+v_load_expand(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    v_reg<w_type, simd128_width / sizeof(w_type)> c;
+    for( int i = 0; i < c.nlanes; i++ )
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+
+#if CV_SIMD256
+/** @brief Load register contents from memory with double expand
+
+Same as cv::v256_load, but result pack type will be 2x wider than memory type.
+
+@code{.cpp}
+short buf[8] = {1, 2, 3, 4, 5, 6, 7, 8}; // type is int16
+v_int32x8 r = v256_load_expand(buf); // r = {1, 2, 3, 4, 5, 6, 7, 8} - type is int32
+@endcode
+For 8-, 16-, 32-bit integer source types.
+
+@note Check CV_SIMD256 preprocessor definition prior to use.
+Use vx_load_expand version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<typename V_TypeTraits<_Tp>::w_type, simd256_width / sizeof(typename V_TypeTraits<_Tp>::w_type)>
+v256_load_expand(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    v_reg<w_type, simd256_width / sizeof(w_type)> c;
+    for (int i = 0; i < c.nlanes; i++)
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+#endif
+
+#if CV_SIMD512
+/** @brief Load register contents from memory with double expand
+
+Same as cv::v512_load, but result pack type will be 2x wider than memory type.
+
+@code{.cpp}
+short buf[8] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; // type is int16
+v_int32x16 r = v512_load_expand(buf); // r = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16} - type is int32
+@endcode
+For 8-, 16-, 32-bit integer source types.
+
+@note Check CV_SIMD512 preprocessor definition prior to use.
+Use vx_load_expand version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<typename V_TypeTraits<_Tp>::w_type, simd512_width / sizeof(typename V_TypeTraits<_Tp>::w_type)>
+v512_load_expand(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    typedef typename V_TypeTraits<_Tp>::w_type w_type;
+    v_reg<w_type, simd512_width / sizeof(w_type)> c;
+    for (int i = 0; i < c.nlanes; i++)
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+#endif
+
+/** @brief Load register contents from memory with quad expand
+
+Same as cv::v_load_expand, but result type is 4 times wider than source.
+@code{.cpp}
+char buf[4] = {1, 2, 3, 4}; // type is int8
+v_int32x4 r = v_load_expand_q(buf); // r = {1, 2, 3, 4} - type is int32
+@endcode
+For 8-bit integer source types.
+
+@note Use vx_load_expand_q version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<typename V_TypeTraits<_Tp>::q_type, simd128_width / sizeof(typename V_TypeTraits<_Tp>::q_type)>
+v_load_expand_q(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    typedef typename V_TypeTraits<_Tp>::q_type q_type;
+    v_reg<q_type, simd128_width / sizeof(q_type)> c;
+    for( int i = 0; i < c.nlanes; i++ )
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+
+#if CV_SIMD256
+/** @brief Load register contents from memory with quad expand
+
+Same as cv::v256_load_expand, but result type is 4 times wider than source.
+@code{.cpp}
+char buf[8] = {1, 2, 3, 4, 5, 6, 7, 8}; // type is int8
+v_int32x8 r = v256_load_expand_q(buf); // r = {1, 2, 3, 4, 5, 6, 7, 8} - type is int32
+@endcode
+For 8-bit integer source types.
+
+@note Check CV_SIMD256 preprocessor definition prior to use.
+Use vx_load_expand_q version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<typename V_TypeTraits<_Tp>::q_type, simd256_width / sizeof(typename V_TypeTraits<_Tp>::q_type)>
+v256_load_expand_q(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    typedef typename V_TypeTraits<_Tp>::q_type q_type;
+    v_reg<q_type, simd256_width / sizeof(q_type)> c;
+    for (int i = 0; i < c.nlanes; i++)
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+#endif
+
+#if CV_SIMD512
+/** @brief Load register contents from memory with quad expand
+
+Same as cv::v512_load_expand, but result type is 4 times wider than source.
+@code{.cpp}
+char buf[16] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; // type is int8
+v_int32x16 r = v512_load_expand_q(buf); // r = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16} - type is int32
+@endcode
+For 8-bit integer source types.
+
+@note Check CV_SIMD512 preprocessor definition prior to use.
+Use vx_load_expand_q version to get maximum available register length result
+*/
+template<typename _Tp>
+inline v_reg<typename V_TypeTraits<_Tp>::q_type, simd512_width / sizeof(typename V_TypeTraits<_Tp>::q_type)>
+v512_load_expand_q(const _Tp* ptr)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    typedef typename V_TypeTraits<_Tp>::q_type q_type;
+    v_reg<q_type, simd512_width / sizeof(q_type)> c;
+    for (int i = 0; i < c.nlanes; i++)
+    {
+        c.s[i] = ptr[i];
+    }
+    return c;
+}
+#endif
+
+/** @brief Load and deinterleave (2 channels)
+
+Load data from memory deinterleave and store to 2 registers.
+Scheme:
+@code
+{A1 B1 A2 B2 ...} ==> {A1 A2 ...}, {B1 B2 ...}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n> inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a,
+                                                            v_reg<_Tp, n>& b)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    int i, i2;
+    for( i = i2 = 0; i < n; i++, i2 += 2 )
+    {
+        a.s[i] = ptr[i2];
+        b.s[i] = ptr[i2+1];
+    }
+}
+
+/** @brief Load and deinterleave (3 channels)
+
+Load data from memory deinterleave and store to 3 registers.
+Scheme:
+@code
+{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n> inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a,
+                                                            v_reg<_Tp, n>& b, v_reg<_Tp, n>& c)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    int i, i3;
+    for( i = i3 = 0; i < n; i++, i3 += 3 )
+    {
+        a.s[i] = ptr[i3];
+        b.s[i] = ptr[i3+1];
+        c.s[i] = ptr[i3+2];
+    }
+}
+
+/** @brief Load and deinterleave (4 channels)
+
+Load data from memory deinterleave and store to 4 registers.
+Scheme:
+@code
+{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n>
+inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a,
+                                v_reg<_Tp, n>& b, v_reg<_Tp, n>& c,
+                                v_reg<_Tp, n>& d)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    int i, i4;
+    for( i = i4 = 0; i < n; i++, i4 += 4 )
+    {
+        a.s[i] = ptr[i4];
+        b.s[i] = ptr[i4+1];
+        c.s[i] = ptr[i4+2];
+        d.s[i] = ptr[i4+3];
+    }
+}
+
+/** @brief Interleave and store (2 channels)
+
+Interleave and store data from 2 registers to memory.
+Scheme:
+@code
+{A1 A2 ...}, {B1 B2 ...} ==> {A1 B1 A2 B2 ...}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n>
+inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a,
+                               const v_reg<_Tp, n>& b,
+                               hal::StoreMode /*mode*/=hal::STORE_UNALIGNED)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    int i, i2;
+    for( i = i2 = 0; i < n; i++, i2 += 2 )
+    {
+        ptr[i2] = a.s[i];
+        ptr[i2+1] = b.s[i];
+    }
+}
+
+/** @brief Interleave and store (3 channels)
+
+Interleave and store data from 3 registers to memory.
+Scheme:
+@code
+{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} ==> {A1 B1 C1 A2 B2 C2 ...}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n>
+inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a,
+                                const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c,
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    int i, i3;
+    for( i = i3 = 0; i < n; i++, i3 += 3 )
+    {
+        ptr[i3] = a.s[i];
+        ptr[i3+1] = b.s[i];
+        ptr[i3+2] = c.s[i];
+    }
+}
+
+/** @brief Interleave and store (4 channels)
+
+Interleave and store data from 4 registers to memory.
+Scheme:
+@code
+{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n> inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a,
+                                                            const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c,
+                                                            const v_reg<_Tp, n>& d,
+                                                            hal::StoreMode /*mode*/=hal::STORE_UNALIGNED)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    int i, i4;
+    for( i = i4 = 0; i < n; i++, i4 += 4 )
+    {
+        ptr[i4] = a.s[i];
+        ptr[i4+1] = b.s[i];
+        ptr[i4+2] = c.s[i];
+        ptr[i4+3] = d.s[i];
+    }
+}
+
+/** @brief Store data to memory
+
+Store register contents to memory.
+Scheme:
+@code
+  REG {A B C D} ==> MEM {A B C D}
+@endcode
+Pointer can be unaligned. */
+template<typename _Tp, int n>
+inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    for( int i = 0; i < n; i++ )
+        ptr[i] = a.s[i];
+}
+
+template<typename _Tp, int n>
+inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    v_store(ptr, a);
+}
+
+/** @brief Store data to memory (lower half)
+
+Store lower half of register contents to memory.
+Scheme:
+@code
+  REG {A B C D} ==> MEM {A B}
+@endcode */
+template<typename _Tp, int n>
+inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    for( int i = 0; i < (n/2); i++ )
+        ptr[i] = a.s[i];
+}
+
+/** @brief Store data to memory (higher half)
+
+Store higher half of register contents to memory.
+Scheme:
+@code
+  REG {A B C D} ==> MEM {C D}
+@endcode */
+template<typename _Tp, int n>
+inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a)
+{
+#if CV_STRONG_ALIGNMENT
+    CV_Assert(isAligned<sizeof(_Tp)>(ptr));
+#endif
+    for( int i = 0; i < (n/2); i++ )
+        ptr[i] = a.s[i+(n/2)];
+}
+
+/** @brief Store data to memory (aligned)
+
+Store register contents to memory.
+Scheme:
+@code
+  REG {A B C D} ==> MEM {A B C D}
+@endcode
+Pointer __should__ be aligned by 16-byte boundary. */
+template<typename _Tp, int n>
+inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a)
+{
+    CV_Assert(isAligned<sizeof(v_reg<_Tp, n>)>(ptr));
+    v_store(ptr, a);
+}
+
+template<typename _Tp, int n>
+inline void v_store_aligned_nocache(_Tp* ptr, const v_reg<_Tp, n>& a)
+{
+    CV_Assert(isAligned<sizeof(v_reg<_Tp, n>)>(ptr));
+    v_store(ptr, a);
+}
+
+template<typename _Tp, int n>
+inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/)
+{
+    CV_Assert(isAligned<sizeof(v_reg<_Tp, n>)>(ptr));
+    v_store(ptr, a);
+}
+
+/** @brief Combine vector from first elements of two vectors
+
+Scheme:
+@code
+  {A1 A2 A3 A4}
+  {B1 B2 B3 B4}
+---------------
+  {A1 A2 B1 B2}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < (n/2); i++ )
+    {
+        c.s[i] = a.s[i];
+        c.s[i+(n/2)] = b.s[i];
+    }
+    return c;
+}
+
+/** @brief Combine vector from last elements of two vectors
+
+Scheme:
+@code
+  {A1 A2 A3 A4}
+  {B1 B2 B3 B4}
+---------------
+  {A3 A4 B3 B4}
+@endcode
+For all types except 64-bit. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < (n/2); i++ )
+    {
+        c.s[i] = a.s[i+(n/2)];
+        c.s[i+(n/2)] = b.s[i+(n/2)];
+    }
+    return c;
+}
+
+/** @brief Combine two vectors from lower and higher parts of two other vectors
+
+@code{.cpp}
+low = cv::v_combine_low(a, b);
+high = cv::v_combine_high(a, b);
+@endcode */
+template<typename _Tp, int n>
+inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
+                        v_reg<_Tp, n>& low, v_reg<_Tp, n>& high)
+{
+    for( int i = 0; i < (n/2); i++ )
+    {
+        low.s[i] = a.s[i];
+        low.s[i+(n/2)] = b.s[i];
+        high.s[i] = a.s[i+(n/2)];
+        high.s[i+(n/2)] = b.s[i+(n/2)];
+    }
+}
+
+/** @brief Vector reverse order
+
+Reverse the order of the vector
+Scheme:
+@code
+  REG {A1 ... An} ==> REG {An ... A1}
+@endcode
+For all types. */
+template<typename _Tp, int n>
+inline v_reg<_Tp, n> v_reverse(const v_reg<_Tp, n>& a)
+{
+    v_reg<_Tp, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = a.s[n-i-1];
+    return c;
+}
+
+/** @brief Vector extract
+
+Scheme:
+@code
+  {A1 A2 A3 A4}
+  {B1 B2 B3 B4}
+========================
+shift = 1  {A2 A3 A4 B1}
+shift = 2  {A3 A4 B1 B2}
+shift = 3  {A4 B1 B2 B3}
+@endcode
+Restriction: 0 <= shift < nlanes
+
+Usage:
+@code
+v_int32x4 a, b, c;
+c = v_extract<2>(a, b);
+@endcode
+For all types. */
+template<int s, typename _Tp, int n>
+inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    v_reg<_Tp, n> r;
+    const int shift = n - s;
+    int i = 0;
+    for (; i < shift; ++i)
+        r.s[i] = a.s[i+s];
+    for (; i < n; ++i)
+        r.s[i] = b.s[i-shift];
+    return r;
+}
+
+/** @brief Vector extract
+
+Scheme:
+Return the s-th element of v.
+Restriction: 0 <= s < nlanes
+
+Usage:
+@code
+v_int32x4 a;
+int r;
+r = v_extract_n<2>(a);
+@endcode
+For all types. */
+template<int s, typename _Tp, int n>
+inline _Tp v_extract_n(const v_reg<_Tp, n>& v)
+{
+    CV_DbgAssert(s >= 0 && s < n);
+    return v.s[s];
+}
+
+/** @brief Broadcast i-th element of vector
+
+Scheme:
+@code
+{ v[0] v[1] v[2] ... v[SZ] } => { v[i], v[i], v[i] ... v[i] }
+@endcode
+Restriction: 0 <= i < nlanes
+Supported types: 32-bit integers and floats (s32/u32/f32)
+ */
+template<int i, typename _Tp, int n>
+inline v_reg<_Tp, n> v_broadcast_element(const v_reg<_Tp, n>& a)
+{
+    CV_DbgAssert(i >= 0 && i < n);
+    return v_reg<_Tp, n>::all(a.s[i]);
+}
+
+/** @brief Round elements
+
+Rounds each value. Input type is float vector ==> output type is int vector.
+@note Only for floating point types.
+*/
+template<int n> inline v_reg<int, n> v_round(const v_reg<float, n>& a)
+{
+    v_reg<int, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = cvRound(a.s[i]);
+    return c;
+}
+
+/** @overload */
+template<int n> inline v_reg<int, n*2> v_round(const v_reg<double, n>& a, const v_reg<double, n>& b)
+{
+    v_reg<int, n*2> c;
+    for( int i = 0; i < n; i++ )
+    {
+        c.s[i] = cvRound(a.s[i]);
+        c.s[i+n] = cvRound(b.s[i]);
+    }
+    return c;
+}
+
+/** @brief Floor elements
+
+Floor each value. Input type is float vector ==> output type is int vector.
+@note Only for floating point types.
+*/
+template<int n> inline v_reg<int, n> v_floor(const v_reg<float, n>& a)
+{
+    v_reg<int, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = cvFloor(a.s[i]);
+    return c;
+}
+
+/** @brief Ceil elements
+
+Ceil each value. Input type is float vector ==> output type is int vector.
+@note Only for floating point types.
+*/
+template<int n> inline v_reg<int, n> v_ceil(const v_reg<float, n>& a)
+{
+    v_reg<int, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = cvCeil(a.s[i]);
+    return c;
+}
+
+/** @brief Truncate elements
+
+Truncate each value. Input type is float vector ==> output type is int vector.
+@note Only for floating point types.
+*/
+template<int n> inline v_reg<int, n> v_trunc(const v_reg<float, n>& a)
+{
+    v_reg<int, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = (int)(a.s[i]);
+    return c;
+}
+
+/** @overload */
+template<int n> inline v_reg<int, n*2> v_round(const v_reg<double, n>& a)
+{
+    v_reg<int, n*2> c;
+    for( int i = 0; i < n; i++ )
+    {
+        c.s[i] = cvRound(a.s[i]);
+        c.s[i+n] = 0;
+    }
+    return c;
+}
+
+/** @overload */
+template<int n> inline v_reg<int, n*2> v_floor(const v_reg<double, n>& a)
+{
+    v_reg<int, n*2> c;
+    for( int i = 0; i < n; i++ )
+    {
+        c.s[i] = cvFloor(a.s[i]);
+        c.s[i+n] = 0;
+    }
+    return c;
+}
+
+/** @overload */
+template<int n> inline v_reg<int, n*2> v_ceil(const v_reg<double, n>& a)
+{
+    v_reg<int, n*2> c;
+    for( int i = 0; i < n; i++ )
+    {
+        c.s[i] = cvCeil(a.s[i]);
+        c.s[i+n] = 0;
+    }
+    return c;
+}
+
+/** @overload */
+template<int n> inline v_reg<int, n*2> v_trunc(const v_reg<double, n>& a)
+{
+    v_reg<int, n*2> c;
+    for( int i = 0; i < n; i++ )
+    {
+        c.s[i] = (int)(a.s[i]);
+        c.s[i+n] = 0;
+    }
+    return c;
+}
+
+/** @brief Convert to float
+
+Supported input type is cv::v_int32. */
+template<int n> inline v_reg<float, n> v_cvt_f32(const v_reg<int, n>& a)
+{
+    v_reg<float, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = (float)a.s[i];
+    return c;
+}
+
+/** @brief Convert lower half to float
+
+Supported input type is cv::v_float64. */
+template<int n> inline v_reg<float, n*2> v_cvt_f32(const v_reg<double, n>& a)
+{
+    v_reg<float, n*2> c;
+    for( int i = 0; i < n; i++ )
+    {
+        c.s[i] = (float)a.s[i];
+        c.s[i+n] = 0;
+    }
+    return c;
+}
+
+/** @brief Convert to float
+
+Supported input type is cv::v_float64. */
+template<int n> inline v_reg<float, n*2> v_cvt_f32(const v_reg<double, n>& a, const v_reg<double, n>& b)
+{
+    v_reg<float, n*2> c;
+    for( int i = 0; i < n; i++ )
+    {
+        c.s[i] = (float)a.s[i];
+        c.s[i+n] = (float)b.s[i];
+    }
+    return c;
+}
+
+/** @brief Convert lower half to double
+
+Supported input type is cv::v_int32. */
+template<int n> CV_INLINE v_reg<double, n/2> v_cvt_f64(const v_reg<int, n>& a)
+{
+    v_reg<double, (n/2)> c;
+    for( int i = 0; i < (n/2); i++ )
+        c.s[i] = (double)a.s[i];
+    return c;
+}
+
+/** @brief Convert to double high part of vector
+
+Supported input type is cv::v_int32. */
+template<int n> CV_INLINE v_reg<double, (n/2)> v_cvt_f64_high(const v_reg<int, n>& a)
+{
+    v_reg<double, (n/2)> c;
+    for( int i = 0; i < (n/2); i++ )
+        c.s[i] = (double)a.s[i + (n/2)];
+    return c;
+}
+
+/** @brief Convert lower half to double
+
+Supported input type is cv::v_float32. */
+template<int n> CV_INLINE v_reg<double, (n/2)> v_cvt_f64(const v_reg<float, n>& a)
+{
+    v_reg<double, (n/2)> c;
+    for( int i = 0; i < (n/2); i++ )
+        c.s[i] = (double)a.s[i];
+    return c;
+}
+
+/** @brief Convert to double high part of vector
+
+Supported input type is cv::v_float32. */
+template<int n> CV_INLINE v_reg<double, (n/2)> v_cvt_f64_high(const v_reg<float, n>& a)
+{
+    v_reg<double, (n/2)> c;
+    for( int i = 0; i < (n/2); i++ )
+        c.s[i] = (double)a.s[i + (n/2)];
+    return c;
+}
+
+/** @brief Convert to double
+
+Supported input type is cv::v_int64. */
+template<int n> CV_INLINE v_reg<double, n> v_cvt_f64(const v_reg<int64, n>& a)
+{
+    v_reg<double, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = (double)a.s[i];
+    return c;
+}
+
+
+template<typename _Tp> inline v_reg<_Tp, simd128_width / sizeof(_Tp)> v_lut(const _Tp* tab, const int* idx)
+{
+    v_reg<_Tp, simd128_width / sizeof(_Tp)> c;
+    for (int i = 0; i < c.nlanes; i++)
+        c.s[i] = tab[idx[i]];
+    return c;
+}
+template<typename _Tp> inline v_reg<_Tp, simd128_width / sizeof(_Tp)> v_lut_pairs(const _Tp* tab, const int* idx)
+{
+    v_reg<_Tp, simd128_width / sizeof(_Tp)> c;
+    for (int i = 0; i < c.nlanes; i++)
+        c.s[i] = tab[idx[i / 2] + i % 2];
+    return c;
+}
+template<typename _Tp> inline v_reg<_Tp, simd128_width / sizeof(_Tp)> v_lut_quads(const _Tp* tab, const int* idx)
+{
+    v_reg<_Tp, simd128_width / sizeof(_Tp)> c;
+    for (int i = 0; i < c.nlanes; i++)
+        c.s[i] = tab[idx[i / 4] + i % 4];
+    return c;
+}
+
+template<int n> inline v_reg<int, n> v_lut(const int* tab, const v_reg<int, n>& idx)
+{
+    v_reg<int, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = tab[idx.s[i]];
+    return c;
+}
+
+template<int n> inline v_reg<unsigned, n> v_lut(const unsigned* tab, const v_reg<int, n>& idx)
+{
+    v_reg<int, n> c;
+    for (int i = 0; i < n; i++)
+        c.s[i] = tab[idx.s[i]];
+    return c;
+}
+
+template<int n> inline v_reg<float, n> v_lut(const float* tab, const v_reg<int, n>& idx)
+{
+    v_reg<float, n> c;
+    for( int i = 0; i < n; i++ )
+        c.s[i] = tab[idx.s[i]];
+    return c;
+}
+
+template<int n> inline v_reg<double, n/2> v_lut(const double* tab, const v_reg<int, n>& idx)
+{
+    v_reg<double, n/2> c;
+    for( int i = 0; i < n/2; i++ )
+        c.s[i] = tab[idx.s[i]];
+    return c;
+}
+
+
+template<int n> inline void v_lut_deinterleave(const float* tab, const v_reg<int, n>& idx,
+                                               v_reg<float, n>& x, v_reg<float, n>& y)
+{
+    for( int i = 0; i < n; i++ )
+    {
+        int j = idx.s[i];
+        x.s[i] = tab[j];
+        y.s[i] = tab[j+1];
+    }
+}
+
+template<int n> inline void v_lut_deinterleave(const double* tab, const v_reg<int, n*2>& idx,
+                                               v_reg<double, n>& x, v_reg<double, n>& y)
+{
+    for( int i = 0; i < n; i++ )
+    {
+        int j = idx.s[i];
+        x.s[i] = tab[j];
+        y.s[i] = tab[j+1];
+    }
+}
+
+template<typename _Tp, int n> inline v_reg<_Tp, n> v_interleave_pairs(const v_reg<_Tp, n>& vec)
+{
+    v_reg<_Tp, n> c;
+    for (int i = 0; i < n/4; i++)
+    {
+        c.s[4*i  ] = vec.s[4*i  ];
+        c.s[4*i+1] = vec.s[4*i+2];
+        c.s[4*i+2] = vec.s[4*i+1];
+        c.s[4*i+3] = vec.s[4*i+3];
+    }
+    return c;
+}
+
+template<typename _Tp, int n> inline v_reg<_Tp, n> v_interleave_quads(const v_reg<_Tp, n>& vec)
+{
+    v_reg<_Tp, n> c;
+    for (int i = 0; i < n/8; i++)
+    {
+        c.s[8*i  ] = vec.s[8*i  ];
+        c.s[8*i+1] = vec.s[8*i+4];
+        c.s[8*i+2] = vec.s[8*i+1];
+        c.s[8*i+3] = vec.s[8*i+5];
+        c.s[8*i+4] = vec.s[8*i+2];
+        c.s[8*i+5] = vec.s[8*i+6];
+        c.s[8*i+6] = vec.s[8*i+3];
+        c.s[8*i+7] = vec.s[8*i+7];
+    }
+    return c;
+}
+
+template<typename _Tp, int n> inline v_reg<_Tp, n> v_pack_triplets(const v_reg<_Tp, n>& vec)
+{
+    v_reg<_Tp, n> c;
+    for (int i = 0; i < n/4; i++)
+    {
+        c.s[3*i  ] = vec.s[4*i  ];
+        c.s[3*i+1] = vec.s[4*i+1];
+        c.s[3*i+2] = vec.s[4*i+2];
+    }
+    return c;
+}
+
+/** @brief Transpose 4x4 matrix
+
+Scheme:
+@code
+a0  {A1 A2 A3 A4}
+a1  {B1 B2 B3 B4}
+a2  {C1 C2 C3 C4}
+a3  {D1 D2 D3 D4}
+===============
+b0  {A1 B1 C1 D1}
+b1  {A2 B2 C2 D2}
+b2  {A3 B3 C3 D3}
+b3  {A4 B4 C4 D4}
+@endcode
+*/
+template<typename _Tp, int n>
+inline void v_transpose4x4( v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1,
+                            const v_reg<_Tp, n>& a2, const v_reg<_Tp, n>& a3,
+                            v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1,
+                            v_reg<_Tp, n>& b2, v_reg<_Tp, n>& b3 )
+{
+    for (int i = 0; i < n / 4; i++)
+    {
+        b0.s[0 + i*4] = a0.s[0 + i*4]; b0.s[1 + i*4] = a1.s[0 + i*4];
+        b0.s[2 + i*4] = a2.s[0 + i*4]; b0.s[3 + i*4] = a3.s[0 + i*4];
+        b1.s[0 + i*4] = a0.s[1 + i*4]; b1.s[1 + i*4] = a1.s[1 + i*4];
+        b1.s[2 + i*4] = a2.s[1 + i*4]; b1.s[3 + i*4] = a3.s[1 + i*4];
+        b2.s[0 + i*4] = a0.s[2 + i*4]; b2.s[1 + i*4] = a1.s[2 + i*4];
+        b2.s[2 + i*4] = a2.s[2 + i*4]; b2.s[3 + i*4] = a3.s[2 + i*4];
+        b3.s[0 + i*4] = a0.s[3 + i*4]; b3.s[1 + i*4] = a1.s[3 + i*4];
+        b3.s[2 + i*4] = a2.s[3 + i*4]; b3.s[3 + i*4] = a3.s[3 + i*4];
+    }
+}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, prefix, suffix) \
+inline _Tpvec prefix##_setzero_##suffix() { return _Tpvec::zero(); }
+
+//! @name Init with zero
+//! @{
+//! @brief Create new vector with zero elements
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, v, u8)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, v, s8)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, v, u16)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, v, s16)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, v, u32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, v, s32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, v, f32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, v, f64)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, v, u64)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, v, s64)
+
+#if CV_SIMD256
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x32, v256, u8)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x32, v256, s8)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x16, v256, u16)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x16, v256, s16)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x8, v256, u32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x8, v256, s32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x8, v256, f32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x4, v256, f64)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x4, v256, u64)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x4, v256, s64)
+#endif
+
+#if CV_SIMD512
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x64, v512, u8)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x64, v512, s8)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x32, v512, u16)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x32, v512, s16)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x16, v512, u32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x16, v512, s32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x16, v512, f32)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x8, v512, f64)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x8, v512, u64)
+OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x8, v512, s64)
+#endif
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, prefix, suffix) \
+inline _Tpvec prefix##_setall_##suffix(_Tp val) { return _Tpvec::all(val); }
+
+//! @name Init with value
+//! @{
+//! @brief Create new vector with elements set to a specific value
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, v, u8)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, v, s8)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, v, u16)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, v, s16)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, v, u32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, v, s32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, v, f32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, v, f64)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, v, u64)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, v, s64)
+
+#if CV_SIMD256
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x32, uchar, v256, u8)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x32, schar, v256, s8)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x16, ushort, v256, u16)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x16, short, v256, s16)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x8, unsigned, v256, u32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x8, int, v256, s32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x8, float, v256, f32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x4, double, v256, f64)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x4, uint64, v256, u64)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x4, int64, v256, s64)
+#endif
+
+#if CV_SIMD512
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x64, uchar, v512, u8)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x64, schar, v512, s8)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x32, ushort, v512, u16)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x32, short, v512, s16)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x16, unsigned, v512, u32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x16, int, v512, s32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x16, float, v512, f32)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x8, double, v512, f64)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x8, uint64, v512, u64)
+OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x8, int64, v512, s64)
+#endif
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tp, suffix) \
+template<typename _Tp0, int n0> inline v_reg<_Tp, n0*sizeof(_Tp0)/sizeof(_Tp)> \
+    v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \
+{ return a.template reinterpret_as<_Tp, n0*sizeof(_Tp0)/sizeof(_Tp)>(); }
+
+//! @name Reinterpret
+//! @{
+//! @brief Convert vector to different type without modifying underlying data.
+OPENCV_HAL_IMPL_C_REINTERPRET(uchar, u8)
+OPENCV_HAL_IMPL_C_REINTERPRET(schar, s8)
+OPENCV_HAL_IMPL_C_REINTERPRET(ushort, u16)
+OPENCV_HAL_IMPL_C_REINTERPRET(short, s16)
+OPENCV_HAL_IMPL_C_REINTERPRET(unsigned, u32)
+OPENCV_HAL_IMPL_C_REINTERPRET(int, s32)
+OPENCV_HAL_IMPL_C_REINTERPRET(float, f32)
+OPENCV_HAL_IMPL_C_REINTERPRET(double, f64)
+OPENCV_HAL_IMPL_C_REINTERPRET(uint64, u64)
+OPENCV_HAL_IMPL_C_REINTERPRET(int64, s64)
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_SHIFTL(_Tp) \
+template<int shift, int n> inline v_reg<_Tp, n> v_shl(const v_reg<_Tp, n>& a) \
+{ return a << shift; }
+
+//! @name Left shift
+//! @{
+//! @brief Shift left
+OPENCV_HAL_IMPL_C_SHIFTL(ushort)
+OPENCV_HAL_IMPL_C_SHIFTL(short)
+OPENCV_HAL_IMPL_C_SHIFTL(unsigned)
+OPENCV_HAL_IMPL_C_SHIFTL(int)
+OPENCV_HAL_IMPL_C_SHIFTL(uint64)
+OPENCV_HAL_IMPL_C_SHIFTL(int64)
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_SHIFTR(_Tp) \
+template<int shift, int n> inline v_reg<_Tp, n> v_shr(const v_reg<_Tp, n>& a) \
+{ return a >> shift; }
+
+//! @name Right shift
+//! @{
+//! @brief Shift right
+OPENCV_HAL_IMPL_C_SHIFTR(ushort)
+OPENCV_HAL_IMPL_C_SHIFTR(short)
+OPENCV_HAL_IMPL_C_SHIFTR(unsigned)
+OPENCV_HAL_IMPL_C_SHIFTR(int)
+OPENCV_HAL_IMPL_C_SHIFTR(uint64)
+OPENCV_HAL_IMPL_C_SHIFTR(int64)
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tp) \
+template<int shift, int n> inline v_reg<_Tp, n> v_rshr(const v_reg<_Tp, n>& a) \
+{ \
+    v_reg<_Tp, n> c; \
+    for( int i = 0; i < n; i++ ) \
+        c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (shift - 1))) >> shift); \
+    return c; \
+}
+
+//! @name Rounding shift
+//! @{
+//! @brief Rounding shift right
+OPENCV_HAL_IMPL_C_RSHIFTR(ushort)
+OPENCV_HAL_IMPL_C_RSHIFTR(short)
+OPENCV_HAL_IMPL_C_RSHIFTR(unsigned)
+OPENCV_HAL_IMPL_C_RSHIFTR(int)
+OPENCV_HAL_IMPL_C_RSHIFTR(uint64)
+OPENCV_HAL_IMPL_C_RSHIFTR(int64)
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_PACK(_Tp, _Tpn, pack_suffix, cast) \
+template<int n> inline v_reg<_Tpn, 2*n> v_##pack_suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    v_reg<_Tpn, 2*n> c; \
+    for( int i = 0; i < n; i++ ) \
+    { \
+        c.s[i] = cast<_Tpn>(a.s[i]); \
+        c.s[i+n] = cast<_Tpn>(b.s[i]); \
+    } \
+    return c; \
+}
+
+//! @name Pack
+//! @{
+//! @brief Pack values from two vectors to one
+//!
+//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also
+//! converts to corresponding unsigned type.
+//!
+//! - pack: for 16-, 32- and 64-bit integer input types
+//! - pack_u: for 16- and 32-bit signed integer input types
+//!
+//! @note All variants except 64-bit use saturation.
+OPENCV_HAL_IMPL_C_PACK(ushort, uchar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK(short, schar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK(unsigned, ushort, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK(int, short, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK(uint64, unsigned, pack, static_cast)
+OPENCV_HAL_IMPL_C_PACK(int64, int, pack, static_cast)
+OPENCV_HAL_IMPL_C_PACK(short, uchar, pack_u, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK(int, ushort, pack_u, saturate_cast)
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tp, _Tpn, pack_suffix, cast) \
+template<int shift, int n> inline v_reg<_Tpn, 2*n> v_rshr_##pack_suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
+{ \
+    v_reg<_Tpn, 2*n> c; \
+    for( int i = 0; i < n; i++ ) \
+    { \
+        c.s[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (shift - 1))) >> shift); \
+        c.s[i+n] = cast<_Tpn>((b.s[i] + ((_Tp)1 << (shift - 1))) >> shift); \
+    } \
+    return c; \
+}
+
+//! @name Pack with rounding shift
+//! @{
+//! @brief Pack values from two vectors to one with rounding shift
+//!
+//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower
+//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type.
+//!
+//! - pack: for 16-, 32- and 64-bit integer input types
+//! - pack_u: for 16- and 32-bit signed integer input types
+//!
+//! @note All variants except 64-bit use saturation.
+OPENCV_HAL_IMPL_C_RSHR_PACK(ushort, uchar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK(short, schar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK(unsigned, ushort, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK(int, short, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK(uint64, unsigned, pack, static_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK(int64, int, pack, static_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK(short, uchar, pack_u, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK(int, ushort, pack_u, saturate_cast)
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tp, _Tpn, pack_suffix, cast) \
+template<int n> inline void v_##pack_suffix##_store(_Tpn* ptr, const v_reg<_Tp, n>& a) \
+{ \
+    for( int i = 0; i < n; i++ ) \
+        ptr[i] = cast<_Tpn>(a.s[i]); \
+}
+
+//! @name Pack and store
+//! @{
+//! @brief Store values from the input vector into memory with pack
+//!
+//! Values will be stored into memory with conversion to narrower type.
+//! Variant with _u_ suffix converts to corresponding unsigned type.
+//!
+//! - pack: for 16-, 32- and 64-bit integer input types
+//! - pack_u: for 16- and 32-bit signed integer input types
+//!
+//! @note All variants except 64-bit use saturation.
+OPENCV_HAL_IMPL_C_PACK_STORE(ushort, uchar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK_STORE(short, schar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK_STORE(unsigned, ushort, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK_STORE(int, short, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK_STORE(uint64, unsigned, pack, static_cast)
+OPENCV_HAL_IMPL_C_PACK_STORE(int64, int, pack, static_cast)
+OPENCV_HAL_IMPL_C_PACK_STORE(short, uchar, pack_u, saturate_cast)
+OPENCV_HAL_IMPL_C_PACK_STORE(int, ushort, pack_u, saturate_cast)
+//! @}
+
+//! @brief Helper macro
+//! @ingroup core_hal_intrin_impl
+#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tp, _Tpn, pack_suffix, cast) \
+template<int shift, int n> inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const v_reg<_Tp, n>& a) \
+{ \
+    for( int i = 0; i < n; i++ ) \
+        ptr[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (shift - 1))) >> shift); \
+}
+
+//! @name Pack and store with rounding shift
+//! @{
+//! @brief Store values from the input vector into memory with pack
+//!
+//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into
+//! memory. Variant with _u_ suffix converts to unsigned type.
+//!
+//! - pack: for 16-, 32- and 64-bit integer input types
+//! - pack_u: for 16- and 32-bit signed integer input types
+//!
+//! @note All variants except 64-bit use saturation.
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(ushort, uchar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(short, schar, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(unsigned, ushort, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(int, short, pack, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(uint64, unsigned, pack, static_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(int64, int, pack, static_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(short, uchar, pack_u, saturate_cast)
+OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(int, ushort, pack_u, saturate_cast)
+//! @}
+
+//! @cond IGNORED
+template<typename _Tpm, typename _Tp, int n>
+inline void _pack_b(_Tpm* mptr, const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
+{
+    for (int i = 0; i < n; ++i)
+    {
+        mptr[i] = (_Tpm)a.s[i];
+        mptr[i + n] = (_Tpm)b.s[i];
+    }
+}
+//! @endcond
+
+//! @name Pack boolean values
+//! @{
+//! @brief Pack boolean values from multiple vectors to one unsigned 8-bit integer vector
+//!
+//! @note Must provide valid boolean values to guarantee same result for all architectures.
+
+/** @brief
+//! For 16-bit boolean values
+
+Scheme:
+@code
+a  {0xFFFF 0 0 0xFFFF 0 0xFFFF 0xFFFF 0}
+b  {0xFFFF 0 0xFFFF 0 0 0xFFFF 0 0xFFFF}
+===============
+{
+   0xFF 0 0 0xFF 0 0xFF 0xFF 0
+   0xFF 0 0xFF 0 0 0xFF 0 0xFF
+}
+@endcode */
+
+template<int n> inline v_reg<uchar, 2*n> v_pack_b(const v_reg<ushort, n>& a, const v_reg<ushort, n>& b)
+{
+    v_reg<uchar, 2*n> mask;
+    _pack_b(mask.s, a, b);
+    return mask;
+}
+
+/** @overload
+For 32-bit boolean values
+
+Scheme:
+@code
+a  {0xFFFF.. 0 0 0xFFFF..}
+b  {0 0xFFFF.. 0xFFFF.. 0}
+c  {0xFFFF.. 0 0xFFFF.. 0}
+d  {0 0xFFFF.. 0 0xFFFF..}
+===============
+{
+   0xFF 0 0 0xFF 0 0xFF 0xFF 0
+   0xFF 0 0xFF 0 0 0xFF 0 0xFF
+}
+@endcode */
+
+template<int n> inline v_reg<uchar, 4*n> v_pack_b(const v_reg<unsigned, n>& a, const v_reg<unsigned, n>& b,
+                                                  const v_reg<unsigned, n>& c, const v_reg<unsigned, n>& d)
+{
+    v_reg<uchar, 4*n> mask;
+    _pack_b(mask.s, a, b);
+    _pack_b(mask.s + 2*n, c, d);
+    return mask;
+}
+
+/** @overload
+For 64-bit boolean values
+
+Scheme:
+@code
+a  {0xFFFF.. 0}
+b  {0 0xFFFF..}
+c  {0xFFFF.. 0}
+d  {0 0xFFFF..}
+
+e  {0xFFFF.. 0}
+f  {0xFFFF.. 0}
+g  {0 0xFFFF..}
+h  {0 0xFFFF..}
+===============
+{
+   0xFF 0 0 0xFF 0xFF 0 0 0xFF
+   0xFF 0 0xFF 0 0 0xFF 0 0xFF
+}
+@endcode */
+template<int n> inline v_reg<uchar, 8*n> v_pack_b(const v_reg<uint64, n>& a, const v_reg<uint64, n>& b,
+                                                  const v_reg<uint64, n>& c, const v_reg<uint64, n>& d,
+                                                  const v_reg<uint64, n>& e, const v_reg<uint64, n>& f,
+                                                  const v_reg<uint64, n>& g, const v_reg<uint64, n>& h)
+{
+    v_reg<uchar, 8*n> mask;
+    _pack_b(mask.s, a, b);
+    _pack_b(mask.s + 2*n, c, d);
+    _pack_b(mask.s + 4*n, e, f);
+    _pack_b(mask.s + 6*n, g, h);
+    return mask;
+}
+//! @}
+
+/** @brief Matrix multiplication
+
+Scheme:
+@code
+{A0 A1 A2 A3}   |V0|
+{B0 B1 B2 B3}   |V1|
+{C0 C1 C2 C3}   |V2|
+{D0 D1 D2 D3} x |V3|
+====================
+{R0 R1 R2 R3}, where:
+R0 = A0V0 + B0V1 + C0V2 + D0V3,
+R1 = A1V0 + B1V1 + C1V2 + D1V3
+...
+@endcode
+*/
+template<int n>
+inline v_reg<float, n> v_matmul(const v_reg<float, n>& v,
+                                const v_reg<float, n>& a, const v_reg<float, n>& b,
+                                const v_reg<float, n>& c, const v_reg<float, n>& d)
+{
+    v_reg<float, n> res;
+    for (int i = 0; i < n / 4; i++)
+    {
+        res.s[0 + i*4] = v.s[0 + i*4] * a.s[0 + i*4] + v.s[1 + i*4] * b.s[0 + i*4] + v.s[2 + i*4] * c.s[0 + i*4] + v.s[3 + i*4] * d.s[0 + i*4];
+        res.s[1 + i*4] = v.s[0 + i*4] * a.s[1 + i*4] + v.s[1 + i*4] * b.s[1 + i*4] + v.s[2 + i*4] * c.s[1 + i*4] + v.s[3 + i*4] * d.s[1 + i*4];
+        res.s[2 + i*4] = v.s[0 + i*4] * a.s[2 + i*4] + v.s[1 + i*4] * b.s[2 + i*4] + v.s[2 + i*4] * c.s[2 + i*4] + v.s[3 + i*4] * d.s[2 + i*4];
+        res.s[3 + i*4] = v.s[0 + i*4] * a.s[3 + i*4] + v.s[1 + i*4] * b.s[3 + i*4] + v.s[2 + i*4] * c.s[3 + i*4] + v.s[3 + i*4] * d.s[3 + i*4];
+    }
+    return res;
+}
+
+/** @brief Matrix multiplication and add
+
+Scheme:
+@code
+{A0 A1 A2 A3}   |V0|   |D0|
+{B0 B1 B2 B3}   |V1|   |D1|
+{C0 C1 C2 C3} x |V2| + |D2|
+====================   |D3|
+{R0 R1 R2 R3}, where:
+R0 = A0V0 + B0V1 + C0V2 + D0,
+R1 = A1V0 + B1V1 + C1V2 + D1
+...
+@endcode
+*/
+template<int n>
+inline v_reg<float, n> v_matmuladd(const v_reg<float, n>& v,
+                                   const v_reg<float, n>& a, const v_reg<float, n>& b,
+                                   const v_reg<float, n>& c, const v_reg<float, n>& d)
+{
+    v_reg<float, n> res;
+    for (int i = 0; i < n / 4; i++)
+    {
+        res.s[0 + i * 4] = v.s[0 + i * 4] * a.s[0 + i * 4] + v.s[1 + i * 4] * b.s[0 + i * 4] + v.s[2 + i * 4] * c.s[0 + i * 4] + d.s[0 + i * 4];
+        res.s[1 + i * 4] = v.s[0 + i * 4] * a.s[1 + i * 4] + v.s[1 + i * 4] * b.s[1 + i * 4] + v.s[2 + i * 4] * c.s[1 + i * 4] + d.s[1 + i * 4];
+        res.s[2 + i * 4] = v.s[0 + i * 4] * a.s[2 + i * 4] + v.s[1 + i * 4] * b.s[2 + i * 4] + v.s[2 + i * 4] * c.s[2 + i * 4] + d.s[2 + i * 4];
+        res.s[3 + i * 4] = v.s[0 + i * 4] * a.s[3 + i * 4] + v.s[1 + i * 4] * b.s[3 + i * 4] + v.s[2 + i * 4] * c.s[3 + i * 4] + d.s[3 + i * 4];
+    }
+    return res;
+}
+
+
+template<int n> inline v_reg<double, n/2> v_dotprod_expand(const v_reg<int, n>& a, const v_reg<int, n>& b)
+{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_cvt_f64_high(a) * v_cvt_f64_high(b)); }
+template<int n> inline v_reg<double, n/2> v_dotprod_expand(const v_reg<int, n>& a, const v_reg<int, n>& b,
+                                                           const v_reg<double, n/2>& c)
+{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_fma(v_cvt_f64_high(a), v_cvt_f64_high(b), c)); }
+
+template<int n> inline v_reg<double, n/2> v_dotprod_expand_fast(const v_reg<int, n>& a, const v_reg<int, n>& b)
+{ return v_dotprod_expand(a, b); }
+template<int n> inline v_reg<double, n/2> v_dotprod_expand_fast(const v_reg<int, n>& a, const v_reg<int, n>& b,
+                                                                const v_reg<double, n/2>& c)
+{ return v_dotprod_expand(a, b, c); }
+
+////// FP16 support ///////
+
+inline v_reg<float, simd128_width / sizeof(float)>
+v_load_expand(const float16_t* ptr)
+{
+    v_reg<float, simd128_width / sizeof(float)> v;
+    for( int i = 0; i < v.nlanes; i++ )
+    {
+        v.s[i] = ptr[i];
+    }
+    return v;
+}
+#if CV_SIMD256
+inline v_reg<float, simd256_width / sizeof(float)>
+v256_load_expand(const float16_t* ptr)
+{
+    v_reg<float, simd256_width / sizeof(float)> v;
+    for (int i = 0; i < v.nlanes; i++)
+    {
+        v.s[i] = ptr[i];
+    }
+    return v;
+}
+#endif
+#if CV_SIMD512
+inline v_reg<float, simd512_width / sizeof(float)>
+v512_load_expand(const float16_t* ptr)
+{
+    v_reg<float, simd512_width / sizeof(float)> v;
+    for (int i = 0; i < v.nlanes; i++)
+    {
+        v.s[i] = ptr[i];
+    }
+    return v;
+}
+#endif
+
+template<int n> inline void
+v_pack_store(float16_t* ptr, const v_reg<float, n>& v)
+{
+    for( int i = 0; i < v.nlanes; i++ )
+    {
+        ptr[i] = float16_t(v.s[i]);
+    }
+}
+
+inline void v_cleanup() {}
+#if CV_SIMD256
+inline void v256_cleanup() {}
+#endif
+#if CV_SIMD512
+inline void v512_cleanup() {}
+#endif
+
+//! @}
+
+#ifndef CV_DOXYGEN
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+#endif
+}
+
+#if !defined(CV_DOXYGEN)
+#undef CV_SIMD256
+#undef CV_SIMD512
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin_forward.hpp b/3rdParty/include/opencv2/core/hal/intrin_forward.hpp
new file mode 100644
index 0000000..979f15a
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_forward.hpp
@@ -0,0 +1,191 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+#ifndef CV__SIMD_FORWARD
+#error "Need to pre-define forward width"
+#endif
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+/** Types **/
+#if CV__SIMD_FORWARD == 1024
+// [todo] 1024
+#error "1024-long ops not implemented yet"
+#elif CV__SIMD_FORWARD == 512
+// 512
+#define __CV_VX(fun)   v512_##fun
+#define __CV_V_UINT8   v_uint8x64
+#define __CV_V_INT8    v_int8x64
+#define __CV_V_UINT16  v_uint16x32
+#define __CV_V_INT16   v_int16x32
+#define __CV_V_UINT32  v_uint32x16
+#define __CV_V_INT32   v_int32x16
+#define __CV_V_UINT64  v_uint64x8
+#define __CV_V_INT64   v_int64x8
+#define __CV_V_FLOAT32 v_float32x16
+#define __CV_V_FLOAT64 v_float64x8
+struct v_uint8x64;
+struct v_int8x64;
+struct v_uint16x32;
+struct v_int16x32;
+struct v_uint32x16;
+struct v_int32x16;
+struct v_uint64x8;
+struct v_int64x8;
+struct v_float32x16;
+struct v_float64x8;
+#elif CV__SIMD_FORWARD == 256
+// 256
+#define __CV_VX(fun)   v256_##fun
+#define __CV_V_UINT8   v_uint8x32
+#define __CV_V_INT8    v_int8x32
+#define __CV_V_UINT16  v_uint16x16
+#define __CV_V_INT16   v_int16x16
+#define __CV_V_UINT32  v_uint32x8
+#define __CV_V_INT32   v_int32x8
+#define __CV_V_UINT64  v_uint64x4
+#define __CV_V_INT64   v_int64x4
+#define __CV_V_FLOAT32 v_float32x8
+#define __CV_V_FLOAT64 v_float64x4
+struct v_uint8x32;
+struct v_int8x32;
+struct v_uint16x16;
+struct v_int16x16;
+struct v_uint32x8;
+struct v_int32x8;
+struct v_uint64x4;
+struct v_int64x4;
+struct v_float32x8;
+struct v_float64x4;
+#else
+// 128
+#define __CV_VX(fun)   v_##fun
+#define __CV_V_UINT8   v_uint8x16
+#define __CV_V_INT8    v_int8x16
+#define __CV_V_UINT16  v_uint16x8
+#define __CV_V_INT16   v_int16x8
+#define __CV_V_UINT32  v_uint32x4
+#define __CV_V_INT32   v_int32x4
+#define __CV_V_UINT64  v_uint64x2
+#define __CV_V_INT64   v_int64x2
+#define __CV_V_FLOAT32 v_float32x4
+#define __CV_V_FLOAT64 v_float64x2
+struct v_uint8x16;
+struct v_int8x16;
+struct v_uint16x8;
+struct v_int16x8;
+struct v_uint32x4;
+struct v_int32x4;
+struct v_uint64x2;
+struct v_int64x2;
+struct v_float32x4;
+struct v_float64x2;
+#endif
+
+/** Value reordering **/
+
+// Expansion
+void v_expand(const __CV_V_UINT8&,  __CV_V_UINT16&, __CV_V_UINT16&);
+void v_expand(const __CV_V_INT8&,   __CV_V_INT16&,  __CV_V_INT16&);
+void v_expand(const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&);
+void v_expand(const __CV_V_INT16&,  __CV_V_INT32&,  __CV_V_INT32&);
+void v_expand(const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&);
+void v_expand(const __CV_V_INT32&,  __CV_V_INT64&,  __CV_V_INT64&);
+// Low Expansion
+__CV_V_UINT16 v_expand_low(const __CV_V_UINT8&);
+__CV_V_INT16  v_expand_low(const __CV_V_INT8&);
+__CV_V_UINT32 v_expand_low(const __CV_V_UINT16&);
+__CV_V_INT32  v_expand_low(const __CV_V_INT16&);
+__CV_V_UINT64 v_expand_low(const __CV_V_UINT32&);
+__CV_V_INT64  v_expand_low(const __CV_V_INT32&);
+// High Expansion
+__CV_V_UINT16 v_expand_high(const __CV_V_UINT8&);
+__CV_V_INT16  v_expand_high(const __CV_V_INT8&);
+__CV_V_UINT32 v_expand_high(const __CV_V_UINT16&);
+__CV_V_INT32  v_expand_high(const __CV_V_INT16&);
+__CV_V_UINT64 v_expand_high(const __CV_V_UINT32&);
+__CV_V_INT64  v_expand_high(const __CV_V_INT32&);
+// Load & Low Expansion
+__CV_V_UINT16 __CV_VX(load_expand)(const uchar*);
+__CV_V_INT16  __CV_VX(load_expand)(const schar*);
+__CV_V_UINT32 __CV_VX(load_expand)(const ushort*);
+__CV_V_INT32  __CV_VX(load_expand)(const short*);
+__CV_V_UINT64 __CV_VX(load_expand)(const uint*);
+__CV_V_INT64  __CV_VX(load_expand)(const int*);
+// Load lower 8-bit and expand into 32-bit
+__CV_V_UINT32 __CV_VX(load_expand_q)(const uchar*);
+__CV_V_INT32  __CV_VX(load_expand_q)(const schar*);
+
+// Saturating Pack
+__CV_V_UINT8  v_pack(const __CV_V_UINT16&, const __CV_V_UINT16&);
+__CV_V_INT8   v_pack(const __CV_V_INT16&,  const __CV_V_INT16&);
+__CV_V_UINT16 v_pack(const __CV_V_UINT32&, const __CV_V_UINT32&);
+__CV_V_INT16  v_pack(const __CV_V_INT32&,  const __CV_V_INT32&);
+// Non-saturating Pack
+__CV_V_UINT32 v_pack(const __CV_V_UINT64&, const __CV_V_UINT64&);
+__CV_V_INT32  v_pack(const __CV_V_INT64&,  const __CV_V_INT64&);
+// Pack signed integers with unsigned saturation
+__CV_V_UINT8  v_pack_u(const __CV_V_INT16&, const __CV_V_INT16&);
+__CV_V_UINT16 v_pack_u(const __CV_V_INT32&, const __CV_V_INT32&);
+
+/** Arithmetic, bitwise and comparison operations **/
+
+// Non-saturating multiply
+#if CV_VSX
+template<typename Tvec>
+Tvec v_mul_wrap(const Tvec& a, const Tvec& b);
+#else
+__CV_V_UINT8  v_mul_wrap(const __CV_V_UINT8&,  const __CV_V_UINT8&);
+__CV_V_INT8   v_mul_wrap(const __CV_V_INT8&,   const __CV_V_INT8&);
+__CV_V_UINT16 v_mul_wrap(const __CV_V_UINT16&, const __CV_V_UINT16&);
+__CV_V_INT16  v_mul_wrap(const __CV_V_INT16&,  const __CV_V_INT16&);
+#endif
+
+//  Multiply and expand
+#if CV_VSX
+template<typename Tvec, typename Twvec>
+void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d);
+#else
+void v_mul_expand(const __CV_V_UINT8&,  const __CV_V_UINT8&,  __CV_V_UINT16&, __CV_V_UINT16&);
+void v_mul_expand(const __CV_V_INT8&,   const __CV_V_INT8&,   __CV_V_INT16&,  __CV_V_INT16&);
+void v_mul_expand(const __CV_V_UINT16&, const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&);
+void v_mul_expand(const __CV_V_INT16&,  const __CV_V_INT16&,  __CV_V_INT32&,  __CV_V_INT32&);
+void v_mul_expand(const __CV_V_UINT32&, const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&);
+void v_mul_expand(const __CV_V_INT32&,  const __CV_V_INT32&,  __CV_V_INT64&,  __CV_V_INT64&);
+#endif
+
+// Conversions
+__CV_V_FLOAT32 v_cvt_f32(const __CV_V_INT32& a);
+__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a);
+__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a, const __CV_V_FLOAT64& b);
+__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT32& a);
+__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_INT32& a);
+__CV_V_FLOAT64 v_cvt_f64(const __CV_V_FLOAT32& a);
+__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_FLOAT32& a);
+__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT64& a);
+
+/** Cleanup **/
+#undef CV__SIMD_FORWARD
+#undef __CV_VX
+#undef __CV_V_UINT8
+#undef __CV_V_INT8
+#undef __CV_V_UINT16
+#undef __CV_V_INT16
+#undef __CV_V_UINT32
+#undef __CV_V_INT32
+#undef __CV_V_UINT64
+#undef __CV_V_INT64
+#undef __CV_V_FLOAT32
+#undef __CV_V_FLOAT64
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+} // cv::
\ No newline at end of file
diff --git a/3rdParty/include/opencv2/core/hal/intrin_msa.hpp b/3rdParty/include/opencv2/core/hal/intrin_msa.hpp
new file mode 100644
index 0000000..a1fbb09
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_msa.hpp
@@ -0,0 +1,1887 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_HAL_INTRIN_MSA_HPP
+#define OPENCV_HAL_INTRIN_MSA_HPP
+
+#include <algorithm>
+#include "opencv2/core/utility.hpp"
+
+namespace cv
+{
+
+//! @cond IGNORED
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+#define CV_SIMD128 1
+
+//MSA implements 128-bit wide vector registers shared with the 64-bit wide floating-point unit registers.
+//MSA and FPU can not be both present, unless the FPU has 64-bit floating-point registers.
+#define CV_SIMD128_64F 1
+
+struct v_uint8x16
+{
+    typedef uchar lane_type;
+    enum { nlanes = 16 };
+
+    v_uint8x16() {}
+    explicit v_uint8x16(v16u8 v) : val(v) {}
+    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
+               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
+    {
+        uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = msa_ld1q_u8(v);
+    }
+
+    uchar get0() const
+    {
+        return msa_getq_lane_u8(val, 0);
+    }
+
+    v16u8 val;
+};
+
+struct v_int8x16
+{
+    typedef schar lane_type;
+    enum { nlanes = 16 };
+
+    v_int8x16() {}
+    explicit v_int8x16(v16i8 v) : val(v) {}
+    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
+               schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
+    {
+        schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = msa_ld1q_s8(v);
+    }
+
+    schar get0() const
+    {
+        return msa_getq_lane_s8(val, 0);
+    }
+
+    v16i8 val;
+};
+
+struct v_uint16x8
+{
+    typedef ushort lane_type;
+    enum { nlanes = 8 };
+
+    v_uint16x8() {}
+    explicit v_uint16x8(v8u16 v) : val(v) {}
+    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
+    {
+        ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = msa_ld1q_u16(v);
+    }
+
+    ushort get0() const
+    {
+        return msa_getq_lane_u16(val, 0);
+    }
+
+    v8u16 val;
+};
+
+struct v_int16x8
+{
+    typedef short lane_type;
+    enum { nlanes = 8 };
+
+    v_int16x8() {}
+    explicit v_int16x8(v8i16 v) : val(v) {}
+    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
+    {
+        short v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = msa_ld1q_s16(v);
+    }
+
+    short get0() const
+    {
+        return msa_getq_lane_s16(val, 0);
+    }
+
+    v8i16 val;
+};
+
+struct v_uint32x4
+{
+    typedef unsigned int lane_type;
+    enum { nlanes = 4 };
+
+    v_uint32x4() {}
+    explicit v_uint32x4(v4u32 v) : val(v) {}
+    v_uint32x4(unsigned int v0, unsigned int v1, unsigned int v2, unsigned int v3)
+    {
+        unsigned int v[] = {v0, v1, v2, v3};
+        val = msa_ld1q_u32(v);
+    }
+
+    unsigned int get0() const
+    {
+        return msa_getq_lane_u32(val, 0);
+    }
+
+    v4u32 val;
+};
+
+struct v_int32x4
+{
+    typedef int lane_type;
+    enum { nlanes = 4 };
+
+    v_int32x4() {}
+    explicit v_int32x4(v4i32 v) : val(v) {}
+    v_int32x4(int v0, int v1, int v2, int v3)
+    {
+        int v[] = {v0, v1, v2, v3};
+        val = msa_ld1q_s32(v);
+    }
+
+    int get0() const
+    {
+        return msa_getq_lane_s32(val, 0);
+    }
+
+    v4i32 val;
+};
+
+struct v_float32x4
+{
+    typedef float lane_type;
+    enum { nlanes = 4 };
+
+    v_float32x4() {}
+    explicit v_float32x4(v4f32 v) : val(v) {}
+    v_float32x4(float v0, float v1, float v2, float v3)
+    {
+        float v[] = {v0, v1, v2, v3};
+        val = msa_ld1q_f32(v);
+    }
+
+    float get0() const
+    {
+        return msa_getq_lane_f32(val, 0);
+    }
+
+    v4f32 val;
+};
+
+struct v_uint64x2
+{
+    typedef uint64 lane_type;
+    enum { nlanes = 2 };
+
+    v_uint64x2() {}
+    explicit v_uint64x2(v2u64 v) : val(v) {}
+    v_uint64x2(uint64 v0, uint64 v1)
+    {
+        uint64 v[] = {v0, v1};
+        val = msa_ld1q_u64(v);
+    }
+
+    uint64 get0() const
+    {
+        return msa_getq_lane_u64(val, 0);
+    }
+
+    v2u64 val;
+};
+
+struct v_int64x2
+{
+    typedef int64 lane_type;
+    enum { nlanes = 2 };
+
+    v_int64x2() {}
+    explicit v_int64x2(v2i64 v) : val(v) {}
+    v_int64x2(int64 v0, int64 v1)
+    {
+        int64 v[] = {v0, v1};
+        val = msa_ld1q_s64(v);
+    }
+
+    int64 get0() const
+    {
+        return msa_getq_lane_s64(val, 0);
+    }
+
+    v2i64 val;
+};
+
+struct v_float64x2
+{
+    typedef double lane_type;
+    enum { nlanes = 2 };
+
+    v_float64x2() {}
+    explicit v_float64x2(v2f64 v) : val(v) {}
+    v_float64x2(double v0, double v1)
+    {
+        double v[] = {v0, v1};
+        val = msa_ld1q_f64(v);
+    }
+
+    double get0() const
+    {
+        return msa_getq_lane_f64(val, 0);
+    }
+
+    v2f64 val;
+};
+
+#define OPENCV_HAL_IMPL_MSA_INIT(_Tpv, _Tp, suffix) \
+inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(msa_dupq_n_##suffix((_Tp)0)); } \
+inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(msa_dupq_n_##suffix(v)); } \
+inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(MSA_TPV_REINTERPRET(v16u8, v.val)); } \
+inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(MSA_TPV_REINTERPRET(v16i8, v.val)); } \
+inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(MSA_TPV_REINTERPRET(v8u16, v.val)); } \
+inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(MSA_TPV_REINTERPRET(v8i16, v.val)); } \
+inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(MSA_TPV_REINTERPRET(v4u32, v.val)); } \
+inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(MSA_TPV_REINTERPRET(v4i32, v.val)); } \
+inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(MSA_TPV_REINTERPRET(v2u64, v.val)); } \
+inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(MSA_TPV_REINTERPRET(v2i64, v.val)); } \
+inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(MSA_TPV_REINTERPRET(v4f32, v.val)); } \
+inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(MSA_TPV_REINTERPRET(v2f64, v.val)); }
+
+OPENCV_HAL_IMPL_MSA_INIT(uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_MSA_INIT(int8x16, schar, s8)
+OPENCV_HAL_IMPL_MSA_INIT(uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_MSA_INIT(int16x8, short, s16)
+OPENCV_HAL_IMPL_MSA_INIT(uint32x4, unsigned int, u32)
+OPENCV_HAL_IMPL_MSA_INIT(int32x4, int, s32)
+OPENCV_HAL_IMPL_MSA_INIT(uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_MSA_INIT(int64x2, int64, s64)
+OPENCV_HAL_IMPL_MSA_INIT(float32x4, float, f32)
+OPENCV_HAL_IMPL_MSA_INIT(float64x2, double, f64)
+
+#define OPENCV_HAL_IMPL_MSA_PACK(_Tpvec, _Tpwvec, pack, mov, rshr) \
+inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \
+{ \
+    return _Tpvec(mov(a.val, b.val)); \
+} \
+template<int n> inline \
+_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \
+{ \
+    return _Tpvec(rshr(a.val, b.val, n)); \
+}
+
+OPENCV_HAL_IMPL_MSA_PACK(v_uint8x16, v_uint16x8, pack, msa_qpack_u16, msa_qrpackr_u16)
+OPENCV_HAL_IMPL_MSA_PACK(v_int8x16, v_int16x8, pack, msa_qpack_s16, msa_qrpackr_s16)
+OPENCV_HAL_IMPL_MSA_PACK(v_uint16x8, v_uint32x4, pack, msa_qpack_u32, msa_qrpackr_u32)
+OPENCV_HAL_IMPL_MSA_PACK(v_int16x8, v_int32x4, pack, msa_qpack_s32, msa_qrpackr_s32)
+OPENCV_HAL_IMPL_MSA_PACK(v_uint32x4, v_uint64x2, pack, msa_pack_u64, msa_rpackr_u64)
+OPENCV_HAL_IMPL_MSA_PACK(v_int32x4, v_int64x2, pack, msa_pack_s64, msa_rpackr_s64)
+OPENCV_HAL_IMPL_MSA_PACK(v_uint8x16, v_int16x8, pack_u, msa_qpacku_s16, msa_qrpackru_s16)
+OPENCV_HAL_IMPL_MSA_PACK(v_uint16x8, v_int32x4, pack_u, msa_qpacku_s32, msa_qrpackru_s32)
+
+#define OPENCV_HAL_IMPL_MSA_PACK_STORE(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \
+inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
+{ \
+    hreg a1 = mov(a.val); \
+    msa_st1_##suffix(ptr, a1); \
+} \
+template<int n> inline \
+void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
+{ \
+    hreg a1 = rshr(a.val, n); \
+    msa_st1_##suffix(ptr, a1); \
+}
+
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint8x16, uchar, v8u8, u8, v_uint16x8, pack, msa_qmovn_u16, msa_qrshrn_n_u16)
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_int8x16, schar, v8i8, s8, v_int16x8, pack, msa_qmovn_s16, msa_qrshrn_n_s16)
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint16x8, ushort, v4u16, u16, v_uint32x4, pack, msa_qmovn_u32, msa_qrshrn_n_u32)
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_int16x8, short, v4i16, s16, v_int32x4, pack, msa_qmovn_s32, msa_qrshrn_n_s32)
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint32x4, unsigned, v2u32, u32, v_uint64x2, pack, msa_movn_u64, msa_rshrn_n_u64)
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_int32x4, int, v2i32, s32, v_int64x2, pack, msa_movn_s64, msa_rshrn_n_s64)
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint8x16, uchar, v8u8, u8, v_int16x8, pack_u, msa_qmovun_s16, msa_qrshrun_n_s16)
+OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint16x8, ushort, v4u16, u16, v_int32x4, pack_u, msa_qmovun_s32, msa_qrshrun_n_s32)
+
+// pack boolean
+inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
+{
+    return v_uint8x16(msa_pack_u16(a.val, b.val));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
+                           const v_uint32x4& c, const v_uint32x4& d)
+{
+    return v_uint8x16(msa_pack_u16(msa_pack_u32(a.val, b.val), msa_pack_u32(c.val, d.val)));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
+                           const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
+                           const v_uint64x2& g, const v_uint64x2& h)
+{
+    v8u16 abcd = msa_pack_u32(msa_pack_u64(a.val, b.val), msa_pack_u64(c.val, d.val));
+    v8u16 efgh = msa_pack_u32(msa_pack_u64(e.val, f.val), msa_pack_u64(g.val, h.val));
+    return v_uint8x16(msa_pack_u16(abcd, efgh));
+}
+
+inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
+                            const v_float32x4& m1, const v_float32x4& m2,
+                            const v_float32x4& m3)
+{
+    v4f32 v0 = v.val;
+    v4f32 res = msa_mulq_lane_f32(m0.val, v0, 0);
+    res = msa_mlaq_lane_f32(res, m1.val, v0, 1);
+    res = msa_mlaq_lane_f32(res, m2.val, v0, 2);
+    res = msa_mlaq_lane_f32(res, m3.val, v0, 3);
+    return v_float32x4(res);
+}
+
+inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
+                               const v_float32x4& m1, const v_float32x4& m2,
+                               const v_float32x4& a)
+{
+    v4f32 v0 = v.val;
+    v4f32 res = msa_mulq_lane_f32(m0.val, v0, 0);
+    res = msa_mlaq_lane_f32(res, m1.val, v0, 1);
+    res = msa_mlaq_lane_f32(res, m2.val, v0, 2);
+    res = msa_addq_f32(res, a.val);
+    return v_float32x4(res);
+}
+
+#define OPENCV_HAL_IMPL_MSA_BIN_OP(bin_op, _Tpvec, intrin) \
+inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+} \
+inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
+{ \
+    a.val = intrin(a.val, b.val); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint8x16, msa_qaddq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint8x16, msa_qsubq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int8x16, msa_qaddq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int8x16, msa_qsubq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint16x8, msa_qaddq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint16x8, msa_qsubq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int16x8, msa_qaddq_s16)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int16x8, msa_qsubq_s16)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int32x4, msa_addq_s32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int32x4, msa_subq_s32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_int32x4, msa_mulq_s32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint32x4, msa_addq_u32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint32x4, msa_subq_u32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_uint32x4, msa_mulq_u32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_float32x4, msa_addq_f32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_float32x4, msa_subq_f32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_float32x4, msa_mulq_f32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int64x2, msa_addq_s64)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int64x2, msa_subq_s64)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint64x2, msa_addq_u64)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint64x2, msa_subq_u64)
+OPENCV_HAL_IMPL_MSA_BIN_OP(/, v_float32x4, msa_divq_f32)
+OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_float64x2, msa_addq_f64)
+OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_float64x2, msa_subq_f64)
+OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_float64x2, msa_mulq_f64)
+OPENCV_HAL_IMPL_MSA_BIN_OP(/, v_float64x2, msa_divq_f64)
+
+// saturating multiply 8-bit, 16-bit
+#define OPENCV_HAL_IMPL_MSA_MUL_SAT(_Tpvec, _Tpwvec)         \
+inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b)  \
+{                                                            \
+    _Tpwvec c, d;                                            \
+    v_mul_expand(a, b, c, d);                                \
+    return v_pack(c, d);                                     \
+}                                                            \
+inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b)      \
+{a = a * b; return a; }
+
+OPENCV_HAL_IMPL_MSA_MUL_SAT(v_int8x16,  v_int16x8)
+OPENCV_HAL_IMPL_MSA_MUL_SAT(v_uint8x16, v_uint16x8)
+OPENCV_HAL_IMPL_MSA_MUL_SAT(v_int16x8,  v_int32x4)
+OPENCV_HAL_IMPL_MSA_MUL_SAT(v_uint16x8, v_uint32x4)
+
+//  Multiply and expand
+inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b,
+                         v_int16x8& c, v_int16x8& d)
+{
+    v16i8 a_lo, a_hi, b_lo, b_hi;
+
+    ILVRL_B2_SB(a.val, msa_dupq_n_s8(0), a_lo, a_hi);
+    ILVRL_B2_SB(b.val, msa_dupq_n_s8(0), b_lo, b_hi);
+    c.val = msa_mulq_s16(msa_paddlq_s8(a_lo), msa_paddlq_s8(b_lo));
+    d.val = msa_mulq_s16(msa_paddlq_s8(a_hi), msa_paddlq_s8(b_hi));
+}
+
+inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b,
+                         v_uint16x8& c, v_uint16x8& d)
+{
+    v16u8 a_lo, a_hi, b_lo, b_hi;
+
+    ILVRL_B2_UB(a.val, msa_dupq_n_u8(0), a_lo, a_hi);
+    ILVRL_B2_UB(b.val, msa_dupq_n_u8(0), b_lo, b_hi);
+    c.val = msa_mulq_u16(msa_paddlq_u8(a_lo), msa_paddlq_u8(b_lo));
+    d.val = msa_mulq_u16(msa_paddlq_u8(a_hi), msa_paddlq_u8(b_hi));
+}
+
+inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b,
+                         v_int32x4& c, v_int32x4& d)
+{
+    v8i16 a_lo, a_hi, b_lo, b_hi;
+
+    ILVRL_H2_SH(a.val, msa_dupq_n_s16(0), a_lo, a_hi);
+    ILVRL_H2_SH(b.val, msa_dupq_n_s16(0), b_lo, b_hi);
+    c.val = msa_mulq_s32(msa_paddlq_s16(a_lo), msa_paddlq_s16(b_lo));
+    d.val = msa_mulq_s32(msa_paddlq_s16(a_hi), msa_paddlq_s16(b_hi));
+}
+
+inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b,
+                         v_uint32x4& c, v_uint32x4& d)
+{
+    v8u16 a_lo, a_hi, b_lo, b_hi;
+
+    ILVRL_H2_UH(a.val, msa_dupq_n_u16(0), a_lo, a_hi);
+    ILVRL_H2_UH(b.val, msa_dupq_n_u16(0), b_lo, b_hi);
+    c.val = msa_mulq_u32(msa_paddlq_u16(a_lo), msa_paddlq_u16(b_lo));
+    d.val = msa_mulq_u32(msa_paddlq_u16(a_hi), msa_paddlq_u16(b_hi));
+}
+
+inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b,
+                         v_uint64x2& c, v_uint64x2& d)
+{
+    v4u32 a_lo, a_hi, b_lo, b_hi;
+
+    ILVRL_W2_UW(a.val, msa_dupq_n_u32(0), a_lo, a_hi);
+    ILVRL_W2_UW(b.val, msa_dupq_n_u32(0), b_lo, b_hi);
+    c.val = msa_mulq_u64(msa_paddlq_u32(a_lo), msa_paddlq_u32(b_lo));
+    d.val = msa_mulq_u64(msa_paddlq_u32(a_hi), msa_paddlq_u32(b_hi));
+}
+
+inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b)
+{
+    v8i16 a_lo, a_hi, b_lo, b_hi;
+
+    ILVRL_H2_SH(a.val, msa_dupq_n_s16(0), a_lo, a_hi);
+    ILVRL_H2_SH(b.val, msa_dupq_n_s16(0), b_lo, b_hi);
+
+    return v_int16x8(msa_packr_s32(msa_mulq_s32(msa_paddlq_s16(a_lo), msa_paddlq_s16(b_lo)),
+                                   msa_mulq_s32(msa_paddlq_s16(a_hi), msa_paddlq_s16(b_hi)), 16));
+}
+
+inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v8u16 a_lo, a_hi, b_lo, b_hi;
+
+    ILVRL_H2_UH(a.val, msa_dupq_n_u16(0), a_lo, a_hi);
+    ILVRL_H2_UH(b.val, msa_dupq_n_u16(0), b_lo, b_hi);
+
+    return v_uint16x8(msa_packr_u32(msa_mulq_u32(msa_paddlq_u16(a_lo), msa_paddlq_u16(b_lo)),
+                                    msa_mulq_u32(msa_paddlq_u16(a_hi), msa_paddlq_u16(b_hi)), 16));
+}
+
+//////// Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
+{ return v_int32x4(msa_dotp_s_w(a.val, b.val)); }
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_int32x4(msa_dpadd_s_w(c.val , a.val, b.val)); }
+
+// 32 >> 64
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
+{ return v_int64x2(msa_dotp_s_d(a.val, b.val)); }
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{ return v_int64x2(msa_dpadd_s_d(c.val , a.val, b.val)); }
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
+{
+    v8u16 even_a = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8), 8);
+    v8u16 odd_a  = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8);
+    v8u16 even_b = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8), 8);
+    v8u16 odd_b  = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8);
+    v4u32 prod   = msa_dotp_u_w(even_a, even_b);
+    return v_uint32x4(msa_dpadd_u_w(prod, odd_a, odd_b));
+}
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{
+    v8u16 even_a = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8), 8);
+    v8u16 odd_a  = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8);
+    v8u16 even_b = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8), 8);
+    v8u16 odd_b  = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8);
+    v4u32 prod   = msa_dpadd_u_w(c.val, even_a, even_b);
+    return v_uint32x4(msa_dpadd_u_w(prod, odd_a, odd_b));
+}
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
+{
+    v8i16 prod = msa_dotp_s_h(a.val, b.val);
+    return v_int32x4(msa_hadd_s32(prod, prod));
+}
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b,
+                                  const v_int32x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v4u32 even_a = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16), 16);
+    v4u32 odd_a  = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16);
+    v4u32 even_b = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16), 16);
+    v4u32 odd_b  = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16);
+    v2u64 prod   = msa_dotp_u_d(even_a, even_b);
+    return v_uint64x2(msa_dpadd_u_d(prod, odd_a, odd_b));
+}
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b,
+                                   const v_uint64x2& c)
+{
+    v4u32 even_a = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16), 16);
+    v4u32 odd_a  = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16);
+    v4u32 even_b = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16), 16);
+    v4u32 odd_b  = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16);
+    v2u64 prod   = msa_dpadd_u_d(c.val, even_a, even_b);
+    return v_uint64x2(msa_dpadd_u_d(prod, odd_a, odd_b));
+}
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
+{
+    v4i32 prod = msa_dotp_s_w(a.val, b.val);
+    return v_int64x2(msa_hadd_s64(prod, prod));
+}
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
+{ return v_dotprod(a, b); }
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_dotprod(a, b, c); }
+
+// 32 >> 64
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_dotprod(a, b); }
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{ return v_dotprod(a, b, c); }
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{ return v_dotprod_expand(a, b, c); }
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
+{ return v_dotprod_expand(a, b); }
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{ return v_dotprod_expand(a, b, c); }
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
+{ return v_dotprod_expand(a, b); }
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+
+// 32 >> 64f
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_dotprod_expand(a, b); }
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+
+#define OPENCV_HAL_IMPL_MSA_LOGIC_OP(_Tpvec, _Tpv, suffix) \
+OPENCV_HAL_IMPL_MSA_BIN_OP(&, _Tpvec, msa_andq_##suffix)   \
+OPENCV_HAL_IMPL_MSA_BIN_OP(|, _Tpvec, msa_orrq_##suffix)   \
+OPENCV_HAL_IMPL_MSA_BIN_OP(^, _Tpvec, msa_eorq_##suffix)   \
+inline _Tpvec operator ~ (const _Tpvec& a) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_mvnq_u8(MSA_TPV_REINTERPRET(v16u8, a.val)))); \
+}
+
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint8x16, v16u8, u8)
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int8x16, v16i8, s8)
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint16x8, v8u16, u16)
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int16x8, v8i16, s16)
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint32x4, v4u32, u32)
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int32x4, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint64x2, v2u64, u64)
+OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int64x2, v2i64, s64)
+
+#define OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(bin_op, intrin) \
+inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \
+{ \
+    return v_float32x4(MSA_TPV_REINTERPRET(v4f32, intrin(MSA_TPV_REINTERPRET(v4i32, a.val), MSA_TPV_REINTERPRET(v4i32, b.val)))); \
+} \
+inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \
+{ \
+    a.val = MSA_TPV_REINTERPRET(v4f32, intrin(MSA_TPV_REINTERPRET(v4i32, a.val), MSA_TPV_REINTERPRET(v4i32, b.val))); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(&, msa_andq_s32)
+OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(|, msa_orrq_s32)
+OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(^, msa_eorq_s32)
+
+inline v_float32x4 operator ~ (const v_float32x4& a)
+{
+    return v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_mvnq_s32(MSA_TPV_REINTERPRET(v4i32, a.val))));
+}
+
+/* v_abs */
+#define OPENCV_HAL_IMPL_MSA_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \
+inline _Tpuvec v_abs(const _Tpsvec& a) \
+{ \
+    return v_reinterpret_as_##usuffix(_Tpsvec(msa_absq_##ssuffix(a.val))); \
+}
+
+OPENCV_HAL_IMPL_MSA_ABS(v_uint8x16, v_int8x16, u8, s8)
+OPENCV_HAL_IMPL_MSA_ABS(v_uint16x8, v_int16x8, u16, s16)
+OPENCV_HAL_IMPL_MSA_ABS(v_uint32x4, v_int32x4, u32, s32)
+
+/* v_abs(float), v_sqrt, v_invsqrt */
+#define OPENCV_HAL_IMPL_MSA_BASIC_FUNC(_Tpvec, func, intrin) \
+inline _Tpvec func(const _Tpvec& a) \
+{ \
+    return _Tpvec(intrin(a.val)); \
+}
+
+OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float32x4, v_abs, msa_absq_f32)
+OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float64x2, v_abs, msa_absq_f64)
+OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float32x4, v_sqrt, msa_sqrtq_f32)
+OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float32x4, v_invsqrt, msa_rsqrtq_f32)
+OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float64x2, v_sqrt, msa_sqrtq_f64)
+OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float64x2, v_invsqrt, msa_rsqrtq_f64)
+
+#define OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(bin_op, intrin) \
+inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \
+{ \
+    return v_float64x2(MSA_TPV_REINTERPRET(v2f64, intrin(MSA_TPV_REINTERPRET(v2i64, a.val), MSA_TPV_REINTERPRET(v2i64, b.val)))); \
+} \
+inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \
+{ \
+    a.val = MSA_TPV_REINTERPRET(v2f64, intrin(MSA_TPV_REINTERPRET(v2i64, a.val), MSA_TPV_REINTERPRET(v2i64, b.val))); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(&, msa_andq_s64)
+OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(|, msa_orrq_s64)
+OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(^, msa_eorq_s64)
+
+inline v_float64x2 operator ~ (const v_float64x2& a)
+{
+    return v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_mvnq_s32(MSA_TPV_REINTERPRET(v4i32, a.val))));
+}
+
+// TODO: exp, log, sin, cos
+
+#define OPENCV_HAL_IMPL_MSA_BIN_FUNC(_Tpvec, func, intrin) \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+}
+
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_min, msa_minq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_max, msa_maxq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_min, msa_minq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_max, msa_maxq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_min, msa_minq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_max, msa_maxq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_min, msa_minq_s16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_max, msa_maxq_s16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint32x4, v_min, msa_minq_u32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint32x4, v_max, msa_maxq_u32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int32x4, v_min, msa_minq_s32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int32x4, v_max, msa_maxq_s32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float32x4, v_min, msa_minq_f32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float32x4, v_max, msa_maxq_f32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float64x2, v_min, msa_minq_f64)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float64x2, v_max, msa_maxq_f64)
+
+#define OPENCV_HAL_IMPL_MSA_INT_CMP_OP(_Tpvec, _Tpv, suffix, not_suffix) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_ceqq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_mvnq_##not_suffix(msa_ceqq_##suffix(a.val, b.val)))); } \
+inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cltq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cgtq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cleq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cgeq_##suffix(a.val, b.val))); }
+
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint8x16, v16u8, u8, u8)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int8x16, v16i8, s8, u8)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint16x8, v8u16, u16, u16)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int16x8, v8i16, s16, u16)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint32x4, v4u32, u32, u32)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int32x4, v4i32, s32, u32)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_float32x4, v4f32, f32, u32)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint64x2, v2u64, u64, u64)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int64x2, v2i64, s64, u64)
+OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_float64x2, v2f64, f64, u64)
+
+inline v_float32x4 v_not_nan(const v_float32x4& a)
+{ return v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ceqq_f32(a.val, a.val))); }
+inline v_float64x2 v_not_nan(const v_float64x2& a)
+{ return v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_ceqq_f64(a.val, a.val))); }
+
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_add_wrap, msa_addq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_add_wrap, msa_addq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_add_wrap, msa_addq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_add_wrap, msa_addq_s16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_sub_wrap, msa_subq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_sub_wrap, msa_subq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_sub_wrap, msa_subq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_sub_wrap, msa_subq_s16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_mul_wrap, msa_mulq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_mul_wrap, msa_mulq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_mul_wrap, msa_mulq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_mul_wrap, msa_mulq_s16)
+
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_absdiff, msa_abdq_u8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_absdiff, msa_abdq_u16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint32x4, v_absdiff, msa_abdq_u32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float32x4, v_absdiff, msa_abdq_f32)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float64x2, v_absdiff, msa_abdq_f64)
+
+/** Saturating absolute difference **/
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_absdiffs, msa_qabdq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_absdiffs, msa_qabdq_s16)
+
+#define OPENCV_HAL_IMPL_MSA_BIN_FUNC2(_Tpvec, _Tpvec2, _Tpv, func, intrin) \
+inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec2(MSA_TPV_REINTERPRET(_Tpv, intrin(a.val, b.val))); \
+}
+
+OPENCV_HAL_IMPL_MSA_BIN_FUNC2(v_int8x16, v_uint8x16, v16u8, v_absdiff, msa_abdq_s8)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC2(v_int16x8, v_uint16x8, v8u16, v_absdiff, msa_abdq_s16)
+OPENCV_HAL_IMPL_MSA_BIN_FUNC2(v_int32x4, v_uint32x4, v4u32, v_absdiff, msa_abdq_s32)
+
+/* v_magnitude, v_sqr_magnitude, v_fma, v_muladd */
+inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    v_float32x4 x(msa_mlaq_f32(msa_mulq_f32(a.val, a.val), b.val, b.val));
+    return v_sqrt(x);
+}
+
+inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    return v_float32x4(msa_mlaq_f32(msa_mulq_f32(a.val, a.val), b.val, b.val));
+}
+
+inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return v_float32x4(msa_mlaq_f32(c.val, a.val, b.val));
+}
+
+inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_int32x4(msa_mlaq_s32(c.val, a.val, b.val));
+}
+
+inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    v_float64x2 x(msa_mlaq_f64(msa_mulq_f64(a.val, a.val), b.val, b.val));
+    return v_sqrt(x);
+}
+
+inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float64x2(msa_mlaq_f64(msa_mulq_f64(a.val, a.val), b.val, b.val));
+}
+
+inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_float64x2(msa_mlaq_f64(c.val, a.val, b.val));
+}
+
+inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_fma(a, b, c);
+}
+
+// trade efficiency for convenience
+#define OPENCV_HAL_IMPL_MSA_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \
+inline _Tpvec operator << (const _Tpvec& a, int n) \
+{ return _Tpvec(msa_shlq_##suffix(a.val, msa_dupq_n_##ssuffix((_Tps)n))); } \
+inline _Tpvec operator >> (const _Tpvec& a, int n) \
+{ return _Tpvec(msa_shrq_##suffix(a.val, msa_dupq_n_##ssuffix((_Tps)n))); } \
+template<int n> inline _Tpvec v_shl(const _Tpvec& a) \
+{ return _Tpvec(msa_shlq_n_##suffix(a.val, n)); } \
+template<int n> inline _Tpvec v_shr(const _Tpvec& a) \
+{ return _Tpvec(msa_shrq_n_##suffix(a.val, n)); } \
+template<int n> inline _Tpvec v_rshr(const _Tpvec& a) \
+{ return _Tpvec(msa_rshrq_n_##suffix(a.val, n)); }
+
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint8x16, u8, schar, s8)
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int8x16, s8, schar, s8)
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint16x8, u16, short, s16)
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int16x8, s16, short, s16)
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint32x4, u32, int, s32)
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int32x4, s32, int, s32)
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint64x2, u64, int64, s64)
+OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int64x2, s64, int64, s64)
+
+/* v_rotate_right, v_rotate_left */
+#define OPENCV_HAL_IMPL_MSA_ROTATE_OP(_Tpvec, _Tpv, _Tpvs, suffix) \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##suffix(0), n))); \
+} \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(msa_dupq_n_##suffix(0), MSA_TPV_REINTERPRET(_Tpvs, a.val), _Tpvec::nlanes - n))); \
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \
+{ \
+    return a; \
+} \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), MSA_TPV_REINTERPRET(_Tpvs, b.val), n))); \
+} \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, b.val), MSA_TPV_REINTERPRET(_Tpvs, a.val), _Tpvec::nlanes - n))); \
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    CV_UNUSED(b); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint8x16, v16u8, v16i8, s8)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int8x16, v16i8, v16i8, s8)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint16x8, v8u16, v8i16, s16)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int16x8, v8i16, v8i16, s16)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint32x4, v4u32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int32x4, v4i32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_float32x4, v4f32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint64x2, v2u64, v2i64, s64)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int64x2, v2i64, v2i64, s64)
+OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_float64x2, v2f64, v2i64, s64)
+
+#define OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(_Tpvec, _Tp, suffix) \
+inline _Tpvec v_load(const _Tp* ptr) \
+{ return _Tpvec(msa_ld1q_##suffix(ptr)); } \
+inline _Tpvec v_load_aligned(const _Tp* ptr) \
+{ return _Tpvec(msa_ld1q_##suffix(ptr)); } \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ return _Tpvec(msa_combine_##suffix(msa_ld1_##suffix(ptr), msa_dup_n_##suffix((_Tp)0))); } \
+inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
+{ return _Tpvec(msa_combine_##suffix(msa_ld1_##suffix(ptr0), msa_ld1_##suffix(ptr1))); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a) \
+{ msa_st1q_##suffix(ptr, a.val); } \
+inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
+{ msa_st1q_##suffix(ptr, a.val); } \
+inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \
+{ msa_st1q_##suffix(ptr, a.val); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \
+{ msa_st1q_##suffix(ptr, a.val); } \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
+{ \
+    int n  = _Tpvec::nlanes; \
+    for( int i = 0; i < (n/2); i++ ) \
+        ptr[i] = a.val[i]; \
+} \
+inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
+{ \
+    int n  = _Tpvec::nlanes; \
+    for( int i = 0; i < (n/2); i++ ) \
+        ptr[i] = a.val[i+(n/2)]; \
+}
+
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int8x16, schar, s8)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int16x8, short, s16)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int32x4, int, s32)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int64x2, int64, s64)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_float32x4, float, f32)
+OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_float64x2, double, f64)
+
+
+/** Reverse **/
+inline v_uint8x16 v_reverse(const v_uint8x16 &a)
+{
+    v_uint8x16 c = v_uint8x16((v16u8)__builtin_msa_vshf_b((v16i8)((v2i64){0x08090A0B0C0D0E0F, 0x0001020304050607}), msa_dupq_n_s8(0), (v16i8)a.val));
+    return c;
+}
+
+inline v_int8x16 v_reverse(const v_int8x16 &a)
+{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
+
+inline v_uint16x8 v_reverse(const v_uint16x8 &a)
+{
+    v_uint16x8 c = v_uint16x8((v8u16)__builtin_msa_vshf_h((v8i16)((v2i64){0x0004000500060007, 0x0000000100020003}), msa_dupq_n_s16(0), (v8i16)a.val));
+    return c;
+}
+
+inline v_int16x8 v_reverse(const v_int16x8 &a)
+{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
+
+inline v_uint32x4 v_reverse(const v_uint32x4 &a)
+{
+    v_uint32x4 c;
+    c.val[0] = a.val[3];
+    c.val[1] = a.val[2];
+    c.val[2] = a.val[1];
+    c.val[3] = a.val[0];
+    return c;
+}
+
+inline v_int32x4 v_reverse(const v_int32x4 &a)
+{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_float32x4 v_reverse(const v_float32x4 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x2 v_reverse(const v_uint64x2 &a)
+{
+    v_uint64x2 c;
+    c.val[0] = a.val[1];
+    c.val[1] = a.val[0];
+    return c;
+}
+
+inline v_int64x2 v_reverse(const v_int64x2 &a)
+{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
+
+inline v_float64x2 v_reverse(const v_float64x2 &a)
+{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
+
+
+#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_8U(func, cfunc) \
+inline unsigned short v_reduce_##func(const v_uint16x8& a) \
+{ \
+    v8u16 a_lo, a_hi; \
+    ILVRL_H2_UH(a.val, msa_dupq_n_u16(0), a_lo, a_hi); \
+    v4u32 b = msa_##func##q_u32(msa_paddlq_u16(a_lo), msa_paddlq_u16(a_hi)); \
+    v4u32 b_lo, b_hi; \
+    ILVRL_W2_UW(b, msa_dupq_n_u32(0), b_lo, b_hi); \
+    v2u64 c = msa_##func##q_u64(msa_paddlq_u32(b_lo), msa_paddlq_u32(b_hi)); \
+    return (unsigned short)cfunc(c[0], c[1]); \
+}
+
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_8U(max, std::max)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_8U(min, std::min)
+
+#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_8S(func, cfunc) \
+inline short v_reduce_##func(const v_int16x8& a) \
+{ \
+    v8i16 a_lo, a_hi; \
+    ILVRL_H2_SH(a.val, msa_dupq_n_s16(0), a_lo, a_hi); \
+    v4i32 b = msa_##func##q_s32(msa_paddlq_s16(a_lo), msa_paddlq_s16(a_hi)); \
+    v4i32 b_lo, b_hi; \
+    ILVRL_W2_SW(b, msa_dupq_n_s32(0), b_lo, b_hi); \
+    v2i64 c = msa_##func##q_s64(msa_paddlq_s32(b_lo), msa_paddlq_s32(b_hi)); \
+    return (short)cfunc(c[0], c[1]); \
+}
+
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_8S(max, std::max)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_8S(min, std::min)
+
+#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(_Tpvec, scalartype, func, cfunc) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    return (scalartype)cfunc(cfunc(a.val[0], a.val[1]), cfunc(a.val[2], a.val[3])); \
+}
+
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_int32x4, int, max, std::max)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_int32x4, int, min, std::min)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_float32x4, float, max, std::max)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_float32x4, float, min, std::min)
+
+
+#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(_Tpvec, scalartype, _Tpvec2, func) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    _Tpvec2 a1, a2; \
+    v_expand(a, a1, a2); \
+    return (scalartype)v_reduce_##func(v_##func(a1, a2)); \
+}
+
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_uint8x16, uchar, v_uint16x8, min)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_uint8x16, uchar, v_uint16x8, max)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_int8x16, char, v_int16x8, min)
+OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_int8x16, char, v_int16x8, max)
+
+
+
+#define OPENCV_HAL_IMPL_MSA_REDUCE_SUM(_Tpvec, scalartype, suffix) \
+inline scalartype v_reduce_sum(const _Tpvec& a) \
+{ \
+    return (scalartype)msa_sum_##suffix(a.val); \
+}
+
+OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint8x16, unsigned char, u8)
+OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int8x16, char, s8)
+OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint16x8, unsigned short, u16)
+OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int16x8, short, s16)
+OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int32x4, int, s32)
+OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_float32x4, float, f32)
+
+inline uint64 v_reduce_sum(const v_uint64x2& a)
+{ return (uint64)(msa_getq_lane_u64(a.val, 0) + msa_getq_lane_u64(a.val, 1)); }
+inline int64 v_reduce_sum(const v_int64x2& a)
+{ return (int64)(msa_getq_lane_s64(a.val, 0) + msa_getq_lane_s64(a.val, 1)); }
+inline double v_reduce_sum(const v_float64x2& a)
+{
+    return msa_getq_lane_f64(a.val, 0) + msa_getq_lane_f64(a.val, 1);
+}
+
+/* v_reduce_sum4, v_reduce_sad */
+inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
+                                 const v_float32x4& c, const v_float32x4& d)
+{
+    v4f32 u0 = msa_addq_f32(MSA_TPV_REINTERPRET(v4f32, msa_ilvevq_s32(MSA_TPV_REINTERPRET(v4i32, b.val), MSA_TPV_REINTERPRET(v4i32, a.val))),
+                            MSA_TPV_REINTERPRET(v4f32, msa_ilvodq_s32(MSA_TPV_REINTERPRET(v4i32, b.val), MSA_TPV_REINTERPRET(v4i32, a.val)))); // a0+a1 b0+b1 a2+a3 b2+b3
+    v4f32 u1 = msa_addq_f32(MSA_TPV_REINTERPRET(v4f32, msa_ilvevq_s32(MSA_TPV_REINTERPRET(v4i32, d.val), MSA_TPV_REINTERPRET(v4i32, c.val))),
+                            MSA_TPV_REINTERPRET(v4f32, msa_ilvodq_s32(MSA_TPV_REINTERPRET(v4i32, d.val), MSA_TPV_REINTERPRET(v4i32, c.val)))); // c0+c1 d0+d1 c2+c3 d2+d3
+
+    return v_float32x4(msa_addq_f32(MSA_TPV_REINTERPRET(v4f32, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, u1), MSA_TPV_REINTERPRET(v2i64, u0))),
+                                    MSA_TPV_REINTERPRET(v4f32, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, u1), MSA_TPV_REINTERPRET(v2i64, u0)))));
+}
+
+inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b)
+{
+    v16u8 t0 = msa_abdq_u8(a.val, b.val);
+    v8u16 t1 = msa_paddlq_u8(t0);
+    v4u32 t2 = msa_paddlq_u16(t1);
+    return msa_sum_u32(t2);
+}
+inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b)
+{
+    v16u8 t0 = MSA_TPV_REINTERPRET(v16u8, msa_abdq_s8(a.val, b.val));
+    v8u16 t1 = msa_paddlq_u8(t0);
+    v4u32 t2 = msa_paddlq_u16(t1);
+    return msa_sum_u32(t2);
+}
+inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v8u16 t0 = msa_abdq_u16(a.val, b.val);
+    v4u32 t1 = msa_paddlq_u16(t0);
+    return msa_sum_u32(t1);
+}
+inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b)
+{
+    v8u16 t0 = MSA_TPV_REINTERPRET(v8u16, msa_abdq_s16(a.val, b.val));
+    v4u32 t1 = msa_paddlq_u16(t0);
+    return msa_sum_u32(t1);
+}
+inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b)
+{
+    v4u32 t0 = msa_abdq_u32(a.val, b.val);
+    return msa_sum_u32(t0);
+}
+inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b)
+{
+    v4u32 t0 = MSA_TPV_REINTERPRET(v4u32, msa_abdq_s32(a.val, b.val));
+    return msa_sum_u32(t0);
+}
+inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b)
+{
+    v4f32 t0 = msa_abdq_f32(a.val, b.val);
+    return msa_sum_f32(t0);
+}
+
+/* v_popcount */
+#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE8(_Tpvec) \
+inline v_uint8x16 v_popcount(const _Tpvec& a) \
+{ \
+    v16u8 t = MSA_TPV_REINTERPRET(v16u8, msa_cntq_s8(MSA_TPV_REINTERPRET(v16i8, a.val))); \
+    return v_uint8x16(t); \
+}
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE8(v_uint8x16)
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE8(v_int8x16)
+
+#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE16(_Tpvec) \
+inline v_uint16x8 v_popcount(const _Tpvec& a) \
+{ \
+    v8u16 t = MSA_TPV_REINTERPRET(v8u16, msa_cntq_s16(MSA_TPV_REINTERPRET(v8i16, a.val))); \
+    return v_uint16x8(t); \
+}
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE16(v_uint16x8)
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE16(v_int16x8)
+
+#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE32(_Tpvec) \
+inline v_uint32x4 v_popcount(const _Tpvec& a) \
+{ \
+    v4u32 t = MSA_TPV_REINTERPRET(v4u32, msa_cntq_s32(MSA_TPV_REINTERPRET(v4i32, a.val))); \
+    return v_uint32x4(t); \
+}
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE32(v_uint32x4)
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE32(v_int32x4)
+
+#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE64(_Tpvec) \
+inline v_uint64x2 v_popcount(const _Tpvec& a) \
+{ \
+    v2u64 t = MSA_TPV_REINTERPRET(v2u64, msa_cntq_s64(MSA_TPV_REINTERPRET(v2i64, a.val))); \
+    return v_uint64x2(t); \
+}
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE64(v_uint64x2)
+OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE64(v_int64x2)
+
+inline int v_signmask(const v_uint8x16& a)
+{
+    v8i8 m0 = msa_create_s8(CV_BIG_UINT(0x0706050403020100));
+    v16u8 v0 = msa_shlq_u8(msa_shrq_n_u8(a.val, 7), msa_combine_s8(m0, m0));
+    v8u16 v1 = msa_paddlq_u8(v0);
+    v4u32 v2 = msa_paddlq_u16(v1);
+    v2u64 v3 = msa_paddlq_u32(v2);
+    return (int)msa_getq_lane_u64(v3, 0) + ((int)msa_getq_lane_u64(v3, 1) << 8);
+}
+inline int v_signmask(const v_int8x16& a)
+{ return v_signmask(v_reinterpret_as_u8(a)); }
+
+inline int v_signmask(const v_uint16x8& a)
+{
+    v4i16 m0 = msa_create_s16(CV_BIG_UINT(0x0003000200010000));
+    v8u16 v0 = msa_shlq_u16(msa_shrq_n_u16(a.val, 15), msa_combine_s16(m0, m0));
+    v4u32 v1 = msa_paddlq_u16(v0);
+    v2u64 v2 = msa_paddlq_u32(v1);
+    return (int)msa_getq_lane_u64(v2, 0) + ((int)msa_getq_lane_u64(v2, 1) << 4);
+}
+inline int v_signmask(const v_int16x8& a)
+{ return v_signmask(v_reinterpret_as_u16(a)); }
+
+inline int v_signmask(const v_uint32x4& a)
+{
+    v2i32 m0 = msa_create_s32(CV_BIG_UINT(0x0000000100000000));
+    v4u32 v0 = msa_shlq_u32(msa_shrq_n_u32(a.val, 31), msa_combine_s32(m0, m0));
+    v2u64 v1 = msa_paddlq_u32(v0);
+    return (int)msa_getq_lane_u64(v1, 0) + ((int)msa_getq_lane_u64(v1, 1) << 2);
+}
+inline int v_signmask(const v_int32x4& a)
+{ return v_signmask(v_reinterpret_as_u32(a)); }
+inline int v_signmask(const v_float32x4& a)
+{ return v_signmask(v_reinterpret_as_u32(a)); }
+
+inline int v_signmask(const v_uint64x2& a)
+{
+    v2u64 v0 = msa_shrq_n_u64(a.val, 63);
+    return (int)msa_getq_lane_u64(v0, 0) + ((int)msa_getq_lane_u64(v0, 1) << 1);
+}
+inline int v_signmask(const v_int64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+inline int v_signmask(const v_float64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+
+inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(a)); }
+
+#define OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(_Tpvec, _Tpvec2, suffix, shift) \
+inline bool v_check_all(const v_##_Tpvec& a) \
+{ \
+    _Tpvec2 v0 = msa_shrq_n_##suffix(msa_mvnq_##suffix(a.val), shift); \
+    v2u64 v1 = MSA_TPV_REINTERPRET(v2u64, v0); \
+    return (msa_getq_lane_u64(v1, 0) | msa_getq_lane_u64(v1, 1)) == 0; \
+} \
+inline bool v_check_any(const v_##_Tpvec& a) \
+{ \
+    _Tpvec2 v0 = msa_shrq_n_##suffix(a.val, shift); \
+    v2u64 v1 = MSA_TPV_REINTERPRET(v2u64, v0); \
+    return (msa_getq_lane_u64(v1, 0) | msa_getq_lane_u64(v1, 1)) != 0; \
+}
+
+OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint8x16, v16u8, u8, 7)
+OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint16x8, v8u16, u16, 15)
+OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint32x4, v4u32, u32, 31)
+OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint64x2, v2u64, u64, 63)
+
+inline bool v_check_all(const v_int8x16& a)
+{ return v_check_all(v_reinterpret_as_u8(a)); }
+inline bool v_check_all(const v_int16x8& a)
+{ return v_check_all(v_reinterpret_as_u16(a)); }
+inline bool v_check_all(const v_int32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+inline bool v_check_all(const v_float32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+
+inline bool v_check_any(const v_int8x16& a)
+{ return v_check_any(v_reinterpret_as_u8(a)); }
+inline bool v_check_any(const v_int16x8& a)
+{ return v_check_any(v_reinterpret_as_u16(a)); }
+inline bool v_check_any(const v_int32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+inline bool v_check_any(const v_float32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+
+inline bool v_check_all(const v_int64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+inline bool v_check_all(const v_float64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+inline bool v_check_any(const v_int64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+inline bool v_check_any(const v_float64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+
+/* v_select */
+#define OPENCV_HAL_IMPL_MSA_SELECT(_Tpvec, _Tpv, _Tpvu) \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_bslq_u8(MSA_TPV_REINTERPRET(_Tpvu, mask.val), \
+                  MSA_TPV_REINTERPRET(_Tpvu, b.val), MSA_TPV_REINTERPRET(_Tpvu, a.val)))); \
+}
+
+OPENCV_HAL_IMPL_MSA_SELECT(v_uint8x16, v16u8, v16u8)
+OPENCV_HAL_IMPL_MSA_SELECT(v_int8x16, v16i8, v16u8)
+OPENCV_HAL_IMPL_MSA_SELECT(v_uint16x8, v8u16, v16u8)
+OPENCV_HAL_IMPL_MSA_SELECT(v_int16x8, v8i16, v16u8)
+OPENCV_HAL_IMPL_MSA_SELECT(v_uint32x4, v4u32, v16u8)
+OPENCV_HAL_IMPL_MSA_SELECT(v_int32x4, v4i32, v16u8)
+OPENCV_HAL_IMPL_MSA_SELECT(v_float32x4, v4f32, v16u8)
+OPENCV_HAL_IMPL_MSA_SELECT(v_float64x2, v2f64, v16u8)
+
+#define OPENCV_HAL_IMPL_MSA_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix, ssuffix, _Tpv, _Tpvs) \
+inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
+{ \
+    _Tpv a_lo = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \
+    _Tpv a_hi = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \
+    b0.val = msa_paddlq_##suffix(a_lo); \
+    b1.val = msa_paddlq_##suffix(a_hi); \
+} \
+inline _Tpwvec v_expand_low(const _Tpvec& a) \
+{ \
+    _Tpv a_lo = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \
+    return _Tpwvec(msa_paddlq_##suffix(a_lo)); \
+} \
+inline _Tpwvec v_expand_high(const _Tpvec& a) \
+{ \
+    _Tpv a_hi = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \
+    return _Tpwvec(msa_paddlq_##suffix(a_hi)); \
+} \
+inline _Tpwvec v_load_expand(const _Tp* ptr) \
+{ \
+    return _Tpwvec(msa_movl_##suffix(msa_ld1_##suffix(ptr))); \
+}
+
+OPENCV_HAL_IMPL_MSA_EXPAND(v_uint8x16, v_uint16x8, uchar, u8, s8, v16u8, v16i8)
+OPENCV_HAL_IMPL_MSA_EXPAND(v_int8x16, v_int16x8, schar, s8, s8, v16i8, v16i8)
+OPENCV_HAL_IMPL_MSA_EXPAND(v_uint16x8, v_uint32x4, ushort, u16, s16, v8u16, v8i16)
+OPENCV_HAL_IMPL_MSA_EXPAND(v_int16x8, v_int32x4, short, s16, s16, v8i16, v8i16)
+OPENCV_HAL_IMPL_MSA_EXPAND(v_uint32x4, v_uint64x2, uint, u32, s32, v4u32, v4i32)
+OPENCV_HAL_IMPL_MSA_EXPAND(v_int32x4, v_int64x2, int, s32, s32, v4i32, v4i32)
+
+inline v_uint32x4 v_load_expand_q(const uchar* ptr)
+{
+    return v_uint32x4((v4u32){ptr[0], ptr[1], ptr[2], ptr[3]});
+}
+
+inline v_int32x4 v_load_expand_q(const schar* ptr)
+{
+    return v_int32x4((v4i32){ptr[0], ptr[1], ptr[2], ptr[3]});
+}
+
+/* v_zip, v_combine_low, v_combine_high, v_recombine */
+#define OPENCV_HAL_IMPL_MSA_UNPACKS(_Tpvec, _Tpv, _Tpvs, ssuffix) \
+inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \
+{ \
+    b0.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \
+    b1.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \
+} \
+inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val)))); \
+} \
+inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val)))); \
+} \
+inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \
+{ \
+    c.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val))); \
+    d.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val))); \
+}
+
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_uint8x16, v16u8, v16i8, s8)
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_int8x16, v16i8, v16i8, s8)
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_uint16x8, v8u16, v8i16, s16)
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_int16x8, v8i16, v8i16, s16)
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_uint32x4, v4u32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_int32x4, v4i32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_float32x4, v4f32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_UNPACKS(v_float64x2, v2f64, v2i64, s64)
+
+/* v_extract */
+#define OPENCV_HAL_IMPL_MSA_EXTRACT(_Tpvec, _Tpv, _Tpvs, suffix) \
+template <int s> \
+inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), MSA_TPV_REINTERPRET(_Tpvs, b.val), s))); \
+}
+
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint8x16, v16u8, v16i8, s8)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_int8x16, v16i8, v16i8, s8)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint16x8, v8u16, v8i16, s16)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_int16x8, v8i16, v8i16, s16)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint32x4, v4u32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_int32x4, v4i32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint64x2, v2u64, v2i64, s64)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_int64x2, v2i64, v2i64, s64)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_float32x4, v4f32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_EXTRACT(v_float64x2, v2f64, v2i64, s64)
+
+/* v_round, v_floor, v_ceil, v_trunc */
+inline v_int32x4 v_round(const v_float32x4& a)
+{
+    return v_int32x4(msa_cvttintq_s32_f32(a.val));
+}
+
+inline v_int32x4 v_floor(const v_float32x4& a)
+{
+    v4i32 a1 = msa_cvttintq_s32_f32(a.val);
+    return v_int32x4(msa_addq_s32(a1, MSA_TPV_REINTERPRET(v4i32, msa_cgtq_f32(msa_cvtfintq_f32_s32(a1), a.val))));
+}
+
+inline v_int32x4 v_ceil(const v_float32x4& a)
+{
+    v4i32 a1 = msa_cvttintq_s32_f32(a.val);
+    return v_int32x4(msa_subq_s32(a1, MSA_TPV_REINTERPRET(v4i32, msa_cgtq_f32(a.val, msa_cvtfintq_f32_s32(a1)))));
+}
+
+inline v_int32x4 v_trunc(const v_float32x4& a)
+{
+    return v_int32x4(msa_cvttruncq_s32_f32(a.val));
+}
+
+inline v_int32x4 v_round(const v_float64x2& a)
+{
+    return v_int32x4(msa_pack_s64(msa_cvttintq_s64_f64(a.val), msa_dupq_n_s64(0)));
+}
+
+inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_int32x4(msa_pack_s64(msa_cvttintq_s64_f64(a.val), msa_cvttintq_s64_f64(b.val)));
+}
+
+inline v_int32x4 v_floor(const v_float64x2& a)
+{
+    v2f64 a1 = msa_cvtrintq_f64(a.val);
+    return v_int32x4(msa_pack_s64(msa_addq_s64(msa_cvttruncq_s64_f64(a1), MSA_TPV_REINTERPRET(v2i64, msa_cgtq_f64(a1, a.val))), msa_dupq_n_s64(0)));
+}
+
+inline v_int32x4 v_ceil(const v_float64x2& a)
+{
+    v2f64 a1 = msa_cvtrintq_f64(a.val);
+    return v_int32x4(msa_pack_s64(msa_subq_s64(msa_cvttruncq_s64_f64(a1), MSA_TPV_REINTERPRET(v2i64, msa_cgtq_f64(a.val, a1))), msa_dupq_n_s64(0)));
+}
+
+inline v_int32x4 v_trunc(const v_float64x2& a)
+{
+    return v_int32x4(msa_pack_s64(msa_cvttruncq_s64_f64(a.val), msa_dupq_n_s64(0)));
+}
+
+#define OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(_Tpvec, _Tpv, _Tpvs, ssuffix) \
+inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \
+                           const _Tpvec& a2, const _Tpvec& a3, \
+                           _Tpvec& b0, _Tpvec& b1, \
+                           _Tpvec& b2, _Tpvec& b3) \
+{ \
+    _Tpv t00 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \
+    _Tpv t01 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \
+    _Tpv t10 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a3.val), MSA_TPV_REINTERPRET(_Tpvs, a2.val))); \
+    _Tpv t11 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a3.val), MSA_TPV_REINTERPRET(_Tpvs, a2.val))); \
+    b0.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, t10), MSA_TPV_REINTERPRET(v2i64, t00))); \
+    b1.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, t10), MSA_TPV_REINTERPRET(v2i64, t00))); \
+    b2.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, t11), MSA_TPV_REINTERPRET(v2i64, t01))); \
+    b3.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, t11), MSA_TPV_REINTERPRET(v2i64, t01))); \
+}
+
+OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(v_uint32x4, v4u32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(v_int32x4, v4i32, v4i32, s32)
+OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(v_float32x4, v4f32, v4i32, s32)
+
+#define OPENCV_HAL_IMPL_MSA_INTERLEAVED(_Tpvec, _Tp, suffix) \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \
+{ \
+    msa_ld2q_##suffix(ptr, &a.val, &b.val); \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \
+{ \
+    msa_ld3q_##suffix(ptr, &a.val, &b.val, &c.val); \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \
+                                v_##_Tpvec& c, v_##_Tpvec& d) \
+{ \
+    msa_ld4q_##suffix(ptr, &a.val, &b.val, &c.val, &d.val); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    msa_st2q_##suffix(ptr, a.val, b.val); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    msa_st3q_##suffix(ptr, a.val, b.val, c.val); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                const v_##_Tpvec& c, const v_##_Tpvec& d, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \
+{ \
+    msa_st4q_##suffix(ptr, a.val, b.val, c.val, d.val); \
+}
+
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(int8x16, schar, s8)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(int16x8, short, s16)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(int32x4, int, s32)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(float32x4, float, f32)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(int64x2, int64, s64)
+OPENCV_HAL_IMPL_MSA_INTERLEAVED(float64x2, double, f64)
+
+/* v_cvt_f32, v_cvt_f64, v_cvt_f64_high */
+inline v_float32x4 v_cvt_f32(const v_int32x4& a)
+{
+    return v_float32x4(msa_cvtfintq_f32_s32(a.val));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a)
+{
+    return v_float32x4(msa_cvtfq_f32_f64(a.val, msa_dupq_n_f64(0.0f)));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float32x4(msa_cvtfq_f32_f64(a.val, b.val));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int32x4& a)
+{
+    return v_float64x2(msa_cvtflq_f64_f32(msa_cvtfintq_f32_s32(a.val)));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
+{
+    return v_float64x2(msa_cvtfhq_f64_f32(msa_cvtfintq_f32_s32(a.val)));
+}
+
+inline v_float64x2 v_cvt_f64(const v_float32x4& a)
+{
+    return v_float64x2(msa_cvtflq_f64_f32(a.val));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
+{
+    return v_float64x2(msa_cvtfhq_f64_f32(a.val));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int64x2& a)
+{
+    return v_float64x2(msa_cvtfintq_f64_s64(a.val));
+}
+
+////////////// Lookup table access ////////////////////
+inline v_int8x16 v_lut(const schar* tab, const int* idx)
+{
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[ 0]],
+        tab[idx[ 1]],
+        tab[idx[ 2]],
+        tab[idx[ 3]],
+        tab[idx[ 4]],
+        tab[idx[ 5]],
+        tab[idx[ 6]],
+        tab[idx[ 7]],
+        tab[idx[ 8]],
+        tab[idx[ 9]],
+        tab[idx[10]],
+        tab[idx[11]],
+        tab[idx[12]],
+        tab[idx[13]],
+        tab[idx[14]],
+        tab[idx[15]]
+    };
+    return v_int8x16(msa_ld1q_s8(elems));
+}
+inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx)
+{
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[4]],
+        tab[idx[4] + 1],
+        tab[idx[5]],
+        tab[idx[5] + 1],
+        tab[idx[6]],
+        tab[idx[6] + 1],
+        tab[idx[7]],
+        tab[idx[7] + 1]
+    };
+    return v_int8x16(msa_ld1q_s8(elems));
+}
+inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
+{
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[0] + 2],
+        tab[idx[0] + 3],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[1] + 2],
+        tab[idx[1] + 3],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[2] + 2],
+        tab[idx[2] + 3],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[3] + 2],
+        tab[idx[3] + 3]
+    };
+    return v_int8x16(msa_ld1q_s8(elems));
+}
+inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((schar*)tab, idx)); }
+
+
+inline v_int16x8 v_lut(const short* tab, const int* idx)
+{
+    short CV_DECL_ALIGNED(32) elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]],
+        tab[idx[4]],
+        tab[idx[5]],
+        tab[idx[6]],
+        tab[idx[7]]
+    };
+    return v_int16x8(msa_ld1q_s16(elems));
+}
+inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
+{
+    short CV_DECL_ALIGNED(32) elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1]
+    };
+    return v_int16x8(msa_ld1q_s16(elems));
+}
+inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
+{
+    return v_int16x8(msa_combine_s16(msa_ld1_s16(tab + idx[0]), msa_ld1_s16(tab + idx[1])));
+}
+inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((short*)tab, idx)); }
+inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((short*)tab, idx)); }
+inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((short*)tab, idx)); }
+
+inline v_int32x4 v_lut(const int* tab, const int* idx)
+{
+    int CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_int32x4(msa_ld1q_s32(elems));
+}
+inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
+{
+    return v_int32x4(msa_combine_s32(msa_ld1_s32(tab + idx[0]), msa_ld1_s32(tab + idx[1])));
+}
+inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x4(msa_ld1q_s32(tab + idx[0]));
+}
+inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((int*)tab, idx)); }
+inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((int*)tab, idx)); }
+inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((int*)tab, idx)); }
+
+inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(msa_combine_s64(msa_create_s64(tab[idx[0]]), msa_create_s64(tab[idx[1]])));
+}
+inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(msa_ld1q_s64(tab + idx[0]));
+}
+inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); }
+inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); }
+
+inline v_float32x4 v_lut(const float* tab, const int* idx)
+{
+    float CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_float32x4(msa_ld1q_f32(elems));
+}
+inline v_float32x4 v_lut_pairs(const float* tab, const int* idx)
+{
+    uint64 CV_DECL_ALIGNED(32) elems[2] =
+    {
+        *(uint64*)(tab + idx[0]),
+        *(uint64*)(tab + idx[1])
+    };
+    return v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ld1q_u64(elems)));
+}
+inline v_float32x4 v_lut_quads(const float* tab, const int* idx)
+{
+    return v_float32x4(msa_ld1q_f32(tab + idx[0]));
+}
+
+inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
+{
+    unsigned CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[msa_getq_lane_s32(idxvec.val, 0)],
+        tab[msa_getq_lane_s32(idxvec.val, 1)],
+        tab[msa_getq_lane_s32(idxvec.val, 2)],
+        tab[msa_getq_lane_s32(idxvec.val, 3)]
+    };
+    return v_uint32x4(msa_ld1q_u32(elems));
+}
+
+inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+
+inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    v4f32 xy02 = msa_combine_f32(msa_ld1_f32(tab + idx[0]), msa_ld1_f32(tab + idx[2]));
+    v4f32 xy13 = msa_combine_f32(msa_ld1_f32(tab + idx[1]), msa_ld1_f32(tab + idx[3]));
+    x = v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ilvevq_s32(MSA_TPV_REINTERPRET(v4i32, xy13), MSA_TPV_REINTERPRET(v4i32, xy02))));
+    y = v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ilvodq_s32(MSA_TPV_REINTERPRET(v4i32, xy13), MSA_TPV_REINTERPRET(v4i32, xy02))));
+}
+
+inline v_int8x16 v_interleave_pairs(const v_int8x16& vec)
+{
+    v_int8x16 c = v_int8x16(__builtin_msa_vshf_b((v16i8)((v2i64){0x0705060403010200, 0x0F0D0E0C0B090A08}), msa_dupq_n_s8(0), vec.val));
+    return c;
+}
+inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec)
+{ return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
+inline v_int8x16 v_interleave_quads(const v_int8x16& vec)
+{
+    v_int8x16 c = v_int8x16(__builtin_msa_vshf_b((v16i8)((v2i64){0x0703060205010400, 0x0F0B0E0A0D090C08}), msa_dupq_n_s8(0), vec.val));
+    return c;
+}
+inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_interleave_pairs(const v_int16x8& vec)
+{
+    v_int16x8 c = v_int16x8(__builtin_msa_vshf_h((v8i16)((v2i64){0x0003000100020000, 0x0007000500060004}), msa_dupq_n_s16(0), vec.val));
+    return c;
+}
+
+inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+
+inline v_int16x8 v_interleave_quads(const v_int16x8& vec)
+{
+    v_int16x8 c = v_int16x8(__builtin_msa_vshf_h((v8i16)((v2i64){0x0005000100040000, 0x0007000300060002}), msa_dupq_n_s16(0), vec.val));
+    return c;
+}
+
+inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_interleave_pairs(const v_int32x4& vec)
+{
+    v_int32x4 c;
+    c.val[0] = vec.val[0];
+    c.val[1] = vec.val[2];
+    c.val[2] = vec.val[1];
+    c.val[3] = vec.val[3];
+    return c;
+}
+
+inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+    v_int8x16 c = v_int8x16(__builtin_msa_vshf_b((v16i8)((v2i64){0x0908060504020100, 0x131211100E0D0C0A}), msa_dupq_n_s8(0), vec.val));
+    return c;
+}
+
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+    v_int16x8 c = v_int16x8(__builtin_msa_vshf_h((v8i16)((v2i64){0x0004000200010000, 0x0009000800060005}), msa_dupq_n_s16(0), vec.val));
+    return c;
+}
+
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; }
+
+inline v_float64x2 v_lut(const double* tab, const int* idx)
+{
+    double CV_DECL_ALIGNED(32) elems[2] =
+    {
+        tab[idx[0]],
+        tab[idx[1]]
+    };
+    return v_float64x2(msa_ld1q_f64(elems));
+}
+
+inline v_float64x2 v_lut_pairs(const double* tab, const int* idx)
+{
+    return v_float64x2(msa_ld1q_f64(tab + idx[0]));
+}
+
+inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    return v_float64x2(tab[idx[0]], tab[idx[1]]);
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    v2f64 xy0 = msa_ld1q_f64(tab + idx[0]);
+    v2f64 xy1 = msa_ld1q_f64(tab + idx[1]);
+    x = v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_ilvevq_s64(MSA_TPV_REINTERPRET(v2i64, xy1), MSA_TPV_REINTERPRET(v2i64, xy0))));
+    y = v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_ilvodq_s64(MSA_TPV_REINTERPRET(v2i64, xy1), MSA_TPV_REINTERPRET(v2i64, xy0))));
+}
+
+template<int i, typename _Tp>
+inline typename _Tp::lane_type v_extract_n(const _Tp& a)
+{
+    return v_rotate_right<i>(a).get0();
+}
+
+template<int i>
+inline v_uint32x4 v_broadcast_element(const v_uint32x4& a)
+{
+    return v_setall_u32(v_extract_n<i>(a));
+}
+template<int i>
+inline v_int32x4 v_broadcast_element(const v_int32x4& a)
+{
+    return v_setall_s32(v_extract_n<i>(a));
+}
+template<int i>
+inline v_float32x4 v_broadcast_element(const v_float32x4& a)
+{
+    return v_setall_f32(v_extract_n<i>(a));
+}
+
+////// FP16 support ///////
+#if CV_FP16
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+#ifndef msa_ld1_f16
+    v4f16 v = (v4f16)msa_ld1_s16((const short*)ptr);
+#else
+    v4f16 v = msa_ld1_f16((const __fp16*)ptr);
+#endif
+    return v_float32x4(msa_cvt_f32_f16(v));
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    v4f16 hv = msa_cvt_f16_f32(v.val);
+
+#ifndef msa_st1_f16
+    msa_st1_s16((short*)ptr, (int16x4_t)hv);
+#else
+    msa_st1_f16((__fp16*)ptr, hv);
+#endif
+}
+#else
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    float buf[4];
+    for( int i = 0; i < 4; i++ )
+        buf[i] = (float)ptr[i];
+    return v_load(buf);
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    float buf[4];
+    v_store(buf, v);
+    for( int i = 0; i < 4; i++ )
+        ptr[i] = (float16_t)buf[i];
+}
+#endif
+
+inline void v_cleanup() {}
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin_neon.hpp b/3rdParty/include/opencv2/core/hal/intrin_neon.hpp
new file mode 100644
index 0000000..e17972a
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_neon.hpp
@@ -0,0 +1,2613 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HAL_INTRIN_NEON_HPP
+#define OPENCV_HAL_INTRIN_NEON_HPP
+
+#include <algorithm>
+#include "opencv2/core/utility.hpp"
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+#define CV_SIMD128 1
+#if defined(__aarch64__) || defined(_M_ARM64)
+#define CV_SIMD128_64F 1
+#else
+#define CV_SIMD128_64F 0
+#endif
+
+// The following macro checks if the code is being compiled for the
+// AArch64 execution state of Armv8, to enable the 128-bit
+// intrinsics. The macro `__ARM_64BIT_STATE` is the one recommended by
+// the Arm C Language Extension (ACLE) specifications [1] to check the
+// availability of 128-bit intrinsics, and it is supporrted by clang
+// and gcc. The macro `_M_ARM64` is the equivalent one for Microsoft
+// Visual Studio [2] .
+//
+// [1] https://developer.arm.com/documentation/101028/0012/13--Advanced-SIMD--Neon--intrinsics
+// [2] https://docs.microsoft.com/en-us/cpp/preprocessor/predefined-macros
+#if defined(__ARM_64BIT_STATE) || defined(_M_ARM64)
+#define CV_NEON_AARCH64 1
+#else
+#define CV_NEON_AARCH64 0
+#endif
+
+// TODO
+#define CV_NEON_DOT 0
+
+//////////// Utils ////////////
+
+#if CV_SIMD128_64F
+#define OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv, _Tpvx2, suffix) \
+    inline void _v128_unzip(const _Tpv& a, const _Tpv& b, _Tpv& c, _Tpv& d) \
+    { c = vuzp1q_##suffix(a, b); d = vuzp2q_##suffix(a, b); }
+#define OPENCV_HAL_IMPL_NEON_UNZIP_L(_Tpv, _Tpvx2, suffix) \
+    inline void _v128_unzip(const _Tpv&a, const _Tpv&b, _Tpv& c, _Tpv& d) \
+    { c = vuzp1_##suffix(a, b); d = vuzp2_##suffix(a, b); }
+#else
+#define OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv, _Tpvx2, suffix) \
+    inline void _v128_unzip(const _Tpv& a, const _Tpv& b, _Tpv& c, _Tpv& d) \
+    { _Tpvx2 ab = vuzpq_##suffix(a, b); c = ab.val[0]; d = ab.val[1]; }
+#define OPENCV_HAL_IMPL_NEON_UNZIP_L(_Tpv, _Tpvx2, suffix) \
+    inline void _v128_unzip(const _Tpv& a, const _Tpv& b, _Tpv& c, _Tpv& d) \
+    { _Tpvx2 ab = vuzp_##suffix(a, b); c = ab.val[0]; d = ab.val[1]; }
+#endif
+
+#if CV_SIMD128_64F
+#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \
+    template <typename T> static inline \
+    _Tpv vreinterpretq_##suffix##_f64(T a) { return (_Tpv) a; } \
+    template <typename T> static inline \
+    float64x2_t vreinterpretq_f64_##suffix(T a) { return (float64x2_t) a; }
+#else
+#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix)
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(_Tpv, _Tpvl, suffix) \
+    OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv##_t, _Tpv##x2_t, suffix) \
+    OPENCV_HAL_IMPL_NEON_UNZIP_L(_Tpvl##_t, _Tpvl##x2_t, suffix) \
+    OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv##_t, suffix)
+
+#define OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_I64(_Tpv, _Tpvl, suffix) \
+    OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv##_t, suffix)
+
+#define OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_F64(_Tpv, _Tpvl, suffix) \
+    OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv##_t, _Tpv##x2_t, suffix)
+
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(uint8x16, uint8x8,  u8)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(int8x16,  int8x8,   s8)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(uint16x8, uint16x4, u16)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(int16x8,  int16x4,  s16)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(uint32x4, uint32x2, u32)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(int32x4,  int32x2,  s32)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(float32x4, float32x2, f32)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_I64(uint64x2, uint64x1, u64)
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_I64(int64x2,  int64x1,  s64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_F64(float64x2, float64x1,f64)
+#endif
+
+//////////// Types ////////////
+
+struct v_uint8x16
+{
+    typedef uchar lane_type;
+    enum { nlanes = 16 };
+
+    v_uint8x16() {}
+    explicit v_uint8x16(uint8x16_t v) : val(v) {}
+    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
+               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
+    {
+        uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = vld1q_u8(v);
+    }
+    uchar get0() const
+    {
+        return vgetq_lane_u8(val, 0);
+    }
+
+    uint8x16_t val;
+};
+
+struct v_int8x16
+{
+    typedef schar lane_type;
+    enum { nlanes = 16 };
+
+    v_int8x16() {}
+    explicit v_int8x16(int8x16_t v) : val(v) {}
+    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
+               schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
+    {
+        schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = vld1q_s8(v);
+    }
+    schar get0() const
+    {
+        return vgetq_lane_s8(val, 0);
+    }
+
+    int8x16_t val;
+};
+
+struct v_uint16x8
+{
+    typedef ushort lane_type;
+    enum { nlanes = 8 };
+
+    v_uint16x8() {}
+    explicit v_uint16x8(uint16x8_t v) : val(v) {}
+    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
+    {
+        ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = vld1q_u16(v);
+    }
+    ushort get0() const
+    {
+        return vgetq_lane_u16(val, 0);
+    }
+
+    uint16x8_t val;
+};
+
+struct v_int16x8
+{
+    typedef short lane_type;
+    enum { nlanes = 8 };
+
+    v_int16x8() {}
+    explicit v_int16x8(int16x8_t v) : val(v) {}
+    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
+    {
+        short v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = vld1q_s16(v);
+    }
+    short get0() const
+    {
+        return vgetq_lane_s16(val, 0);
+    }
+
+    int16x8_t val;
+};
+
+struct v_uint32x4
+{
+    typedef unsigned lane_type;
+    enum { nlanes = 4 };
+
+    v_uint32x4() {}
+    explicit v_uint32x4(uint32x4_t v) : val(v) {}
+    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
+    {
+        unsigned v[] = {v0, v1, v2, v3};
+        val = vld1q_u32(v);
+    }
+    unsigned get0() const
+    {
+        return vgetq_lane_u32(val, 0);
+    }
+
+    uint32x4_t val;
+};
+
+struct v_int32x4
+{
+    typedef int lane_type;
+    enum { nlanes = 4 };
+
+    v_int32x4() {}
+    explicit v_int32x4(int32x4_t v) : val(v) {}
+    v_int32x4(int v0, int v1, int v2, int v3)
+    {
+        int v[] = {v0, v1, v2, v3};
+        val = vld1q_s32(v);
+    }
+    int get0() const
+    {
+        return vgetq_lane_s32(val, 0);
+    }
+    int32x4_t val;
+};
+
+struct v_float32x4
+{
+    typedef float lane_type;
+    enum { nlanes = 4 };
+
+    v_float32x4() {}
+    explicit v_float32x4(float32x4_t v) : val(v) {}
+    v_float32x4(float v0, float v1, float v2, float v3)
+    {
+        float v[] = {v0, v1, v2, v3};
+        val = vld1q_f32(v);
+    }
+    float get0() const
+    {
+        return vgetq_lane_f32(val, 0);
+    }
+    float32x4_t val;
+};
+
+struct v_uint64x2
+{
+    typedef uint64 lane_type;
+    enum { nlanes = 2 };
+
+    v_uint64x2() {}
+    explicit v_uint64x2(uint64x2_t v) : val(v) {}
+    v_uint64x2(uint64 v0, uint64 v1)
+    {
+        uint64 v[] = {v0, v1};
+        val = vld1q_u64(v);
+    }
+    uint64 get0() const
+    {
+        return vgetq_lane_u64(val, 0);
+    }
+    uint64x2_t val;
+};
+
+struct v_int64x2
+{
+    typedef int64 lane_type;
+    enum { nlanes = 2 };
+
+    v_int64x2() {}
+    explicit v_int64x2(int64x2_t v) : val(v) {}
+    v_int64x2(int64 v0, int64 v1)
+    {
+        int64 v[] = {v0, v1};
+        val = vld1q_s64(v);
+    }
+    int64 get0() const
+    {
+        return vgetq_lane_s64(val, 0);
+    }
+    int64x2_t val;
+};
+
+#if CV_SIMD128_64F
+struct v_float64x2
+{
+    typedef double lane_type;
+    enum { nlanes = 2 };
+
+    v_float64x2() {}
+    explicit v_float64x2(float64x2_t v) : val(v) {}
+    v_float64x2(double v0, double v1)
+    {
+        double v[] = {v0, v1};
+        val = vld1q_f64(v);
+    }
+    double get0() const
+    {
+        return vgetq_lane_f64(val, 0);
+    }
+    float64x2_t val;
+};
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \
+inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \
+inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \
+inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \
+inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \
+inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \
+inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \
+inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \
+inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \
+inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \
+inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \
+inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \
+inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); }
+
+OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8)
+OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16)
+OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32)
+OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64)
+OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32)
+#if CV_SIMD128_64F
+#define OPENCV_HAL_IMPL_NEON_INIT_64(_Tpv, suffix) \
+inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(vreinterpretq_f64_##suffix(v.val)); }
+OPENCV_HAL_IMPL_NEON_INIT(float64x2, double, f64)
+OPENCV_HAL_IMPL_NEON_INIT_64(uint8x16, u8)
+OPENCV_HAL_IMPL_NEON_INIT_64(int8x16, s8)
+OPENCV_HAL_IMPL_NEON_INIT_64(uint16x8, u16)
+OPENCV_HAL_IMPL_NEON_INIT_64(int16x8, s16)
+OPENCV_HAL_IMPL_NEON_INIT_64(uint32x4, u32)
+OPENCV_HAL_IMPL_NEON_INIT_64(int32x4, s32)
+OPENCV_HAL_IMPL_NEON_INIT_64(uint64x2, u64)
+OPENCV_HAL_IMPL_NEON_INIT_64(int64x2, s64)
+OPENCV_HAL_IMPL_NEON_INIT_64(float32x4, f32)
+OPENCV_HAL_IMPL_NEON_INIT_64(float64x2, f64)
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \
+inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \
+{ \
+    hreg a1 = mov(a.val), b1 = mov(b.val); \
+    return _Tpvec(vcombine_##suffix(a1, b1)); \
+} \
+inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
+{ \
+    hreg a1 = mov(a.val); \
+    vst1_##suffix(ptr, a1); \
+} \
+template<int n> inline \
+_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \
+{ \
+    hreg a1 = rshr(a.val, n); \
+    hreg b1 = rshr(b.val, n); \
+    return _Tpvec(vcombine_##suffix(a1, b1)); \
+} \
+template<int n> inline \
+void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
+{ \
+    hreg a1 = rshr(a.val, n); \
+    vst1_##suffix(ptr, a1); \
+}
+
+OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, pack, vqmovn_u16, vqrshrn_n_u16)
+OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, pack, vqmovn_s16, vqrshrn_n_s16)
+OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, pack, vqmovn_u32, vqrshrn_n_u32)
+OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, pack, vqmovn_s32, vqrshrn_n_s32)
+OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, pack, vmovn_u64, vrshrn_n_u64)
+OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, pack, vmovn_s64, vrshrn_n_s64)
+
+OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, pack_u, vqmovun_s16, vqrshrun_n_s16)
+OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, pack_u, vqmovun_s32, vqrshrun_n_s32)
+
+// pack boolean
+inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
+{
+    uint8x16_t ab = vcombine_u8(vmovn_u16(a.val), vmovn_u16(b.val));
+    return v_uint8x16(ab);
+}
+
+inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
+                           const v_uint32x4& c, const v_uint32x4& d)
+{
+    uint16x8_t nab = vcombine_u16(vmovn_u32(a.val), vmovn_u32(b.val));
+    uint16x8_t ncd = vcombine_u16(vmovn_u32(c.val), vmovn_u32(d.val));
+    return v_uint8x16(vcombine_u8(vmovn_u16(nab), vmovn_u16(ncd)));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
+                           const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
+                           const v_uint64x2& g, const v_uint64x2& h)
+{
+    uint32x4_t ab = vcombine_u32(vmovn_u64(a.val), vmovn_u64(b.val));
+    uint32x4_t cd = vcombine_u32(vmovn_u64(c.val), vmovn_u64(d.val));
+    uint32x4_t ef = vcombine_u32(vmovn_u64(e.val), vmovn_u64(f.val));
+    uint32x4_t gh = vcombine_u32(vmovn_u64(g.val), vmovn_u64(h.val));
+
+    uint16x8_t abcd = vcombine_u16(vmovn_u32(ab), vmovn_u32(cd));
+    uint16x8_t efgh = vcombine_u16(vmovn_u32(ef), vmovn_u32(gh));
+    return v_uint8x16(vcombine_u8(vmovn_u16(abcd), vmovn_u16(efgh)));
+}
+
+inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
+                            const v_float32x4& m1, const v_float32x4& m2,
+                            const v_float32x4& m3)
+{
+    float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val);
+    float32x4_t res = vmulq_lane_f32(m0.val, vl, 0);
+    res = vmlaq_lane_f32(res, m1.val, vl, 1);
+    res = vmlaq_lane_f32(res, m2.val, vh, 0);
+    res = vmlaq_lane_f32(res, m3.val, vh, 1);
+    return v_float32x4(res);
+}
+
+inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
+                               const v_float32x4& m1, const v_float32x4& m2,
+                               const v_float32x4& a)
+{
+    float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val);
+    float32x4_t res = vmulq_lane_f32(m0.val, vl, 0);
+    res = vmlaq_lane_f32(res, m1.val, vl, 1);
+    res = vmlaq_lane_f32(res, m2.val, vh, 0);
+    res = vaddq_f32(res, a.val);
+    return v_float32x4(res);
+}
+
+#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \
+inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+} \
+inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
+{ \
+    a.val = intrin(a.val, b.val); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float32x4, vdivq_f32)
+OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float64x2, vaddq_f64)
+OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float64x2, vsubq_f64)
+OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float64x2, vmulq_f64)
+OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float64x2, vdivq_f64)
+#else
+inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b)
+{
+    float32x4_t reciprocal = vrecpeq_f32(b.val);
+    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
+    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
+    return v_float32x4(vmulq_f32(a.val, reciprocal));
+}
+inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b)
+{
+    float32x4_t reciprocal = vrecpeq_f32(b.val);
+    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
+    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
+    a.val = vmulq_f32(a.val, reciprocal);
+    return a;
+}
+#endif
+
+// saturating multiply 8-bit, 16-bit
+#define OPENCV_HAL_IMPL_NEON_MUL_SAT(_Tpvec, _Tpwvec)            \
+    inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b)  \
+    {                                                            \
+        _Tpwvec c, d;                                            \
+        v_mul_expand(a, b, c, d);                                \
+        return v_pack(c, d);                                     \
+    }                                                            \
+    inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b)      \
+    { a = a * b; return a; }
+
+OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int8x16,  v_int16x8)
+OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint8x16, v_uint16x8)
+OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int16x8,  v_int32x4)
+OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint16x8, v_uint32x4)
+
+//  Multiply and expand
+inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b,
+                         v_int16x8& c, v_int16x8& d)
+{
+    c.val = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val));
+#if CV_NEON_AARCH64
+    d.val = vmull_high_s8(a.val, b.val);
+#else // #if CV_NEON_AARCH64
+    d.val = vmull_s8(vget_high_s8(a.val), vget_high_s8(b.val));
+#endif // #if CV_NEON_AARCH64
+}
+
+inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b,
+                         v_uint16x8& c, v_uint16x8& d)
+{
+    c.val = vmull_u8(vget_low_u8(a.val), vget_low_u8(b.val));
+#if CV_NEON_AARCH64
+    d.val = vmull_high_u8(a.val, b.val);
+#else // #if CV_NEON_AARCH64
+    d.val = vmull_u8(vget_high_u8(a.val), vget_high_u8(b.val));
+#endif // #if CV_NEON_AARCH64
+}
+
+inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b,
+                         v_int32x4& c, v_int32x4& d)
+{
+    c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val));
+#if CV_NEON_AARCH64
+    d.val = vmull_high_s16(a.val, b.val);
+#else // #if CV_NEON_AARCH64
+    d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val));
+#endif // #if CV_NEON_AARCH64
+}
+
+inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b,
+                         v_uint32x4& c, v_uint32x4& d)
+{
+    c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val));
+#if CV_NEON_AARCH64
+    d.val = vmull_high_u16(a.val, b.val);
+#else // #if CV_NEON_AARCH64
+    d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val));
+#endif // #if CV_NEON_AARCH64
+}
+
+inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b,
+                         v_uint64x2& c, v_uint64x2& d)
+{
+    c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val));
+#if CV_NEON_AARCH64
+    d.val = vmull_high_u32(a.val, b.val);
+#else // #if CV_NEON_AARCH64
+    d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val));
+#endif // #if CV_NEON_AARCH64
+}
+
+inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b)
+{
+    return v_int16x8(vcombine_s16(
+                                  vshrn_n_s32(vmull_s16( vget_low_s16(a.val),  vget_low_s16(b.val)), 16),
+                                  vshrn_n_s32(
+#if CV_NEON_AARCH64
+                                    vmull_high_s16(a.val, b.val)
+#else // #if CV_NEON_AARCH64
+                                    vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val))
+#endif // #if CV_NEON_AARCH64
+                                    , 16)
+                                 ));
+}
+inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b)
+{
+    return v_uint16x8(vcombine_u16(
+                                   vshrn_n_u32(vmull_u16( vget_low_u16(a.val),  vget_low_u16(b.val)), 16),
+                                   vshrn_n_u32(
+#if CV_NEON_AARCH64
+                                    vmull_high_u16(a.val, b.val)
+#else // #if CV_NEON_AARCH64
+                                    vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val))
+#endif // #if CV_NEON_AARCH64
+                                    , 16)
+                                  ));
+}
+
+//////// Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
+{
+    int16x8_t uzp1, uzp2;
+    _v128_unzip(a.val, b.val, uzp1, uzp2);
+    int16x4_t a0 = vget_low_s16(uzp1);
+    int16x4_t b0 = vget_high_s16(uzp1);
+    int16x4_t a1 = vget_low_s16(uzp2);
+    int16x4_t b1 = vget_high_s16(uzp2);
+    int32x4_t p = vmull_s16(a0, b0);
+    return v_int32x4(vmlal_s16(p, a1, b1));
+}
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{
+    int16x8_t uzp1, uzp2;
+    _v128_unzip(a.val, b.val, uzp1, uzp2);
+    int16x4_t a0 = vget_low_s16(uzp1);
+    int16x4_t b0 = vget_high_s16(uzp1);
+    int16x4_t a1 = vget_low_s16(uzp2);
+    int16x4_t b1 = vget_high_s16(uzp2);
+    int32x4_t p = vmlal_s16(c.val, a0, b0);
+    return v_int32x4(vmlal_s16(p, a1, b1));
+}
+
+// 32 >> 64
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
+{
+    int32x4_t uzp1, uzp2;
+    _v128_unzip(a.val, b.val, uzp1, uzp2);
+    int32x2_t a0 = vget_low_s32(uzp1);
+    int32x2_t b0 = vget_high_s32(uzp1);
+    int32x2_t a1 = vget_low_s32(uzp2);
+    int32x2_t b1 = vget_high_s32(uzp2);
+    int64x2_t p = vmull_s32(a0, b0);
+    return v_int64x2(vmlal_s32(p, a1, b1));
+}
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{
+    int32x4_t uzp1, uzp2;
+    _v128_unzip(a.val, b.val, uzp1, uzp2);
+    int32x2_t a0 = vget_low_s32(uzp1);
+    int32x2_t b0 = vget_high_s32(uzp1);
+    int32x2_t a1 = vget_low_s32(uzp2);
+    int32x2_t b1 = vget_high_s32(uzp2);
+    int64x2_t p = vmlal_s32(c.val, a0, b0);
+    return v_int64x2(vmlal_s32(p, a1, b1));
+}
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
+{
+#if CV_NEON_DOT
+    return v_uint32x4(vdotq_u32(vdupq_n_u32(0), a.val, b.val));
+#else
+    const uint8x16_t zero   = vreinterpretq_u8_u32(vdupq_n_u32(0));
+    const uint8x16_t mask   = vreinterpretq_u8_u32(vdupq_n_u32(0x00FF00FF));
+    const uint16x8_t zero32 = vreinterpretq_u16_u32(vdupq_n_u32(0));
+    const uint16x8_t mask32 = vreinterpretq_u16_u32(vdupq_n_u32(0x0000FFFF));
+
+    uint16x8_t even = vmulq_u16(vreinterpretq_u16_u8(vbslq_u8(mask, a.val, zero)),
+                                vreinterpretq_u16_u8(vbslq_u8(mask, b.val, zero)));
+    uint16x8_t odd  = vmulq_u16(vshrq_n_u16(vreinterpretq_u16_u8(a.val), 8),
+                                vshrq_n_u16(vreinterpretq_u16_u8(b.val), 8));
+
+    uint32x4_t s0 = vaddq_u32(vreinterpretq_u32_u16(vbslq_u16(mask32, even, zero32)),
+                              vreinterpretq_u32_u16(vbslq_u16(mask32, odd,  zero32)));
+    uint32x4_t s1 = vaddq_u32(vshrq_n_u32(vreinterpretq_u32_u16(even), 16),
+                              vshrq_n_u32(vreinterpretq_u32_u16(odd),  16));
+    return v_uint32x4(vaddq_u32(s0, s1));
+#endif
+}
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b,
+                                   const v_uint32x4& c)
+{
+#if CV_NEON_DOT
+    return v_uint32x4(vdotq_u32(c.val, a.val, b.val));
+#else
+    return v_dotprod_expand(a, b) + c;
+#endif
+}
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
+{
+#if CV_NEON_DOT
+    return v_int32x4(vdotq_s32(vdupq_n_s32(0), a.val, b.val));
+#else
+    int16x8_t p0  = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val));
+    int16x8_t p1  = vmull_s8(vget_high_s8(a.val), vget_high_s8(b.val));
+    int16x8_t uzp1, uzp2;
+    _v128_unzip(p0, p1, uzp1, uzp2);
+    int16x8_t sum = vaddq_s16(uzp1, uzp2);
+    int16x4_t uzpl1, uzpl2;
+    _v128_unzip(vget_low_s16(sum), vget_high_s16(sum), uzpl1, uzpl2);
+    return v_int32x4(vaddl_s16(uzpl1, uzpl2));
+#endif
+}
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b,
+                                  const v_int32x4& c)
+{
+#if CV_NEON_DOT
+    return v_int32x4(vdotq_s32(c.val, a.val, b.val));
+#else
+    return v_dotprod_expand(a, b) + c;
+#endif
+}
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
+{
+    const uint16x8_t zero = vreinterpretq_u16_u32(vdupq_n_u32(0));
+    const uint16x8_t mask = vreinterpretq_u16_u32(vdupq_n_u32(0x0000FFFF));
+
+    uint32x4_t even = vmulq_u32(vreinterpretq_u32_u16(vbslq_u16(mask, a.val, zero)),
+                                vreinterpretq_u32_u16(vbslq_u16(mask, b.val, zero)));
+    uint32x4_t odd  = vmulq_u32(vshrq_n_u32(vreinterpretq_u32_u16(a.val), 16),
+                                vshrq_n_u32(vreinterpretq_u32_u16(b.val), 16));
+    uint32x4_t uzp1, uzp2;
+    _v128_unzip(even, odd, uzp1, uzp2);
+    uint64x2_t s0  = vaddl_u32(vget_low_u32(uzp1), vget_high_u32(uzp1));
+    uint64x2_t s1  = vaddl_u32(vget_low_u32(uzp2), vget_high_u32(uzp2));
+    return v_uint64x2(vaddq_u64(s0, s1));
+}
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
+{
+    int32x4_t p0  = vmull_s16(vget_low_s16(a.val),  vget_low_s16(b.val));
+    int32x4_t p1  = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val));
+
+    int32x4_t uzp1, uzp2;
+    _v128_unzip(p0, p1, uzp1, uzp2);
+    int32x4_t sum = vaddq_s32(uzp1, uzp2);
+
+    int32x2_t uzpl1, uzpl2;
+    _v128_unzip(vget_low_s32(sum), vget_high_s32(sum), uzpl1, uzpl2);
+    return v_int64x2(vaddl_s32(uzpl1, uzpl2));
+}
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b,
+                                  const v_int64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 32 >> 64f
+#if CV_SIMD128_64F
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a,   const v_int32x4& b,
+                                    const v_float64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+#endif
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
+{
+#if CV_NEON_AARCH64
+    int32x4_t p = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val));
+    return v_int32x4(vmlal_high_s16(p, a.val, b.val));
+#else
+    int16x4_t a0 = vget_low_s16(a.val);
+    int16x4_t a1 = vget_high_s16(a.val);
+    int16x4_t b0 = vget_low_s16(b.val);
+    int16x4_t b1 = vget_high_s16(b.val);
+    int32x4_t p = vmull_s16(a0, b0);
+    return v_int32x4(vmlal_s16(p, a1, b1));
+#endif
+}
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{
+#if CV_NEON_AARCH64
+    int32x4_t p = vmlal_s16(c.val, vget_low_s16(a.val), vget_low_s16(b.val));
+    return v_int32x4(vmlal_high_s16(p, a.val, b.val));
+#else
+    int16x4_t a0 = vget_low_s16(a.val);
+    int16x4_t a1 = vget_high_s16(a.val);
+    int16x4_t b0 = vget_low_s16(b.val);
+    int16x4_t b1 = vget_high_s16(b.val);
+    int32x4_t p = vmlal_s16(c.val, a0, b0);
+    return v_int32x4(vmlal_s16(p, a1, b1));
+#endif
+}
+
+// 32 >> 64
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_NEON_AARCH64
+    int64x2_t p = vmull_s32(vget_low_s32(a.val), vget_low_s32(b.val));
+    return v_int64x2(vmlal_high_s32(p, a.val, b.val));
+#else
+    int32x2_t a0 = vget_low_s32(a.val);
+    int32x2_t a1 = vget_high_s32(a.val);
+    int32x2_t b0 = vget_low_s32(b.val);
+    int32x2_t b1 = vget_high_s32(b.val);
+    int64x2_t p = vmull_s32(a0, b0);
+    return v_int64x2(vmlal_s32(p, a1, b1));
+#endif
+}
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{
+#if CV_NEON_AARCH64
+    int64x2_t p = vmlal_s32(c.val, vget_low_s32(a.val), vget_low_s32(b.val));
+    return v_int64x2(vmlal_high_s32(p, a.val, b.val));
+#else
+    int32x2_t a0 = vget_low_s32(a.val);
+    int32x2_t a1 = vget_high_s32(a.val);
+    int32x2_t b0 = vget_low_s32(b.val);
+    int32x2_t b1 = vget_high_s32(b.val);
+    int64x2_t p = vmlal_s32(c.val, a0, b0);
+    return v_int64x2(vmlal_s32(p, a1, b1));
+#endif
+}
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
+{
+#if CV_NEON_DOT
+    return v_uint32x4(vdotq_u32(vdupq_n_u32(0), a.val, b.val));
+#else
+    uint16x8_t p0 = vmull_u8(vget_low_u8(a.val), vget_low_u8(b.val));
+    uint16x8_t p1 = vmull_u8(vget_high_u8(a.val), vget_high_u8(b.val));
+    uint32x4_t s0 = vaddl_u16(vget_low_u16(p0), vget_low_u16(p1));
+    uint32x4_t s1 = vaddl_u16(vget_high_u16(p0), vget_high_u16(p1));
+    return v_uint32x4(vaddq_u32(s0, s1));
+#endif
+}
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{
+#if CV_NEON_DOT
+    return v_uint32x4(vdotq_u32(c.val, a.val, b.val));
+#else
+    return v_dotprod_expand_fast(a, b) + c;
+#endif
+}
+
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
+{
+#if CV_NEON_DOT
+    return v_int32x4(vdotq_s32(vdupq_n_s32(0), a.val, b.val));
+#else
+    int16x8_t prod = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val));
+    prod = vmlal_s8(prod, vget_high_s8(a.val), vget_high_s8(b.val));
+    return v_int32x4(vaddl_s16(vget_low_s16(prod), vget_high_s16(prod)));
+#endif
+}
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{
+#if CV_NEON_DOT
+    return v_int32x4(vdotq_s32(c.val, a.val, b.val));
+#else
+    return v_dotprod_expand_fast(a, b) + c;
+#endif
+}
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
+{
+    uint32x4_t p0  = vmull_u16(vget_low_u16(a.val),  vget_low_u16(b.val));
+    uint32x4_t p1  = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val));
+    uint64x2_t s0  = vaddl_u32(vget_low_u32(p0), vget_high_u32(p0));
+    uint64x2_t s1  = vaddl_u32(vget_low_u32(p1), vget_high_u32(p1));
+    return v_uint64x2(vaddq_u64(s0, s1));
+}
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
+{
+    int32x4_t prod = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val));
+    prod = vmlal_s16(prod, vget_high_s16(a.val), vget_high_s16(b.val));
+    return v_int64x2(vaddl_s32(vget_low_s32(prod), vget_high_s32(prod)));
+}
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+// 32 >> 64f
+#if CV_SIMD128_64F
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod_fast(a, b)); }
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+#endif
+
+
+#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \
+    OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \
+    OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \
+    OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \
+    inline _Tpvec operator ~ (const _Tpvec& a) \
+    { \
+        return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \
+    }
+
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8)
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8)
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16)
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16)
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32)
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32)
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64)
+OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64)
+
+#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \
+inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \
+{ \
+    return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \
+} \
+inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \
+{ \
+    a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32)
+OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32)
+OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32)
+
+inline v_float32x4 operator ~ (const v_float32x4& a)
+{
+    return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val))));
+}
+
+#if CV_SIMD128_64F
+inline v_float32x4 v_sqrt(const v_float32x4& x)
+{
+    return v_float32x4(vsqrtq_f32(x.val));
+}
+
+inline v_float32x4 v_invsqrt(const v_float32x4& x)
+{
+    v_float32x4 one = v_setall_f32(1.0f);
+    return one / v_sqrt(x);
+}
+#else
+inline v_float32x4 v_sqrt(const v_float32x4& x)
+{
+    float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN));
+    float32x4_t e = vrsqrteq_f32(x1);
+    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e);
+    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e);
+    return v_float32x4(vmulq_f32(x.val, e));
+}
+
+inline v_float32x4 v_invsqrt(const v_float32x4& x)
+{
+    float32x4_t e = vrsqrteq_f32(x.val);
+    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e);
+    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e);
+    return v_float32x4(e);
+}
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \
+inline _Tpuvec v_abs(const _Tpsvec& a) { return v_reinterpret_as_##usuffix(_Tpsvec(vabsq_##ssuffix(a.val))); }
+
+OPENCV_HAL_IMPL_NEON_ABS(v_uint8x16, v_int8x16, u8, s8)
+OPENCV_HAL_IMPL_NEON_ABS(v_uint16x8, v_int16x8, u16, s16)
+OPENCV_HAL_IMPL_NEON_ABS(v_uint32x4, v_int32x4, u32, s32)
+
+inline v_float32x4 v_abs(v_float32x4 x)
+{ return v_float32x4(vabsq_f32(x.val)); }
+
+#if CV_SIMD128_64F
+#define OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(bin_op, intrin) \
+inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \
+{ \
+    return v_float64x2(vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val)))); \
+} \
+inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \
+{ \
+    a.val = vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val))); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(&, vandq_s64)
+OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(|, vorrq_s64)
+OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(^, veorq_s64)
+
+inline v_float64x2 operator ~ (const v_float64x2& a)
+{
+    return v_float64x2(vreinterpretq_f64_s32(vmvnq_s32(vreinterpretq_s32_f64(a.val))));
+}
+
+inline v_float64x2 v_sqrt(const v_float64x2& x)
+{
+    return v_float64x2(vsqrtq_f64(x.val));
+}
+
+inline v_float64x2 v_invsqrt(const v_float64x2& x)
+{
+    v_float64x2 one = v_setall_f64(1.0f);
+    return one / v_sqrt(x);
+}
+
+inline v_float64x2 v_abs(v_float64x2 x)
+{ return v_float64x2(vabsq_f64(x.val)); }
+#endif
+
+// TODO: exp, log, sin, cos
+
+#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+}
+
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_min, vminq_f64)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_max, vmaxq_f64)
+#endif
+
+#if CV_SIMD128_64F
+inline int64x2_t vmvnq_s64(int64x2_t a)
+{
+    int64x2_t vx = vreinterpretq_s64_u32(vdupq_n_u32(0xFFFFFFFF));
+    return veorq_s64(a, vx);
+}
+inline uint64x2_t vmvnq_u64(uint64x2_t a)
+{
+    uint64x2_t vx = vreinterpretq_u64_u32(vdupq_n_u32(0xFFFFFFFF));
+    return veorq_u64(a, vx);
+}
+#endif
+#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \
+inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \
+inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); }
+
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint64x2, OPENCV_HAL_NOP, u64, u64)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64)
+OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64)
+#endif
+
+inline v_float32x4 v_not_nan(const v_float32x4& a)
+{ return v_float32x4(vreinterpretq_f32_u32(vceqq_f32(a.val, a.val))); }
+#if CV_SIMD128_64F
+inline v_float64x2 v_not_nan(const v_float64x2& a)
+{ return v_float64x2(vreinterpretq_f64_u64(vceqq_f64(a.val, a.val))); }
+#endif
+
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_mul_wrap, vmulq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_mul_wrap, vmulq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_mul_wrap, vmulq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_mul_wrap, vmulq_s16)
+
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_absdiff, vabdq_f64)
+#endif
+
+/** Saturating absolute difference **/
+inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b)
+{ return v_int8x16(vqabsq_s8(vqsubq_s8(a.val, b.val))); }
+inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b)
+{ return v_int16x8(vqabsq_s16(vqsubq_s16(a.val, b.val))); }
+
+#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \
+inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec2(cast(intrin(a.val, b.val))); \
+}
+
+OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16)
+OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32)
+
+inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val));
+    return v_sqrt(x);
+}
+
+inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val));
+}
+
+inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+#if CV_SIMD128_64F
+    // ARMv8, which adds support for 64-bit floating-point (so CV_SIMD128_64F is defined),
+    // also adds FMA support both for single- and double-precision floating-point vectors
+    return v_float32x4(vfmaq_f32(c.val, a.val, b.val));
+#else
+    return v_float32x4(vmlaq_f32(c.val, a.val, b.val));
+#endif
+}
+
+inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_int32x4(vmlaq_s32(c.val, a.val, b.val));
+}
+
+inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    v_float64x2 x(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val)));
+    return v_sqrt(x);
+}
+
+inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float64x2(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val)));
+}
+
+inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_float64x2(vfmaq_f64(c.val, a.val, b.val));
+}
+
+inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_fma(a, b, c);
+}
+#endif
+
+// trade efficiency for convenience
+#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \
+inline _Tpvec operator << (const _Tpvec& a, int n) \
+{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \
+inline _Tpvec operator >> (const _Tpvec& a, int n) \
+{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \
+template<int n> inline _Tpvec v_shl(const _Tpvec& a) \
+{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \
+template<int n> inline _Tpvec v_shr(const _Tpvec& a) \
+{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \
+template<int n> inline _Tpvec v_rshr(const _Tpvec& a) \
+{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); }
+
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8)
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8)
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16)
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16)
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32)
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32)
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64)
+OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64)
+
+#define OPENCV_HAL_IMPL_NEON_ROTATE_OP(_Tpvec, suffix) \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a) \
+{ return _Tpvec(vextq_##suffix(a.val, vdupq_n_##suffix(0), n)); } \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a) \
+{ return _Tpvec(vextq_##suffix(vdupq_n_##suffix(0), a.val, _Tpvec::nlanes - n)); } \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \
+{ return a; } \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(vextq_##suffix(a.val, b.val, n)); } \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(vextq_##suffix(b.val, a.val, _Tpvec::nlanes - n)); } \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \
+{ CV_UNUSED(b); return a; }
+
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint8x16, u8)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int8x16, s8)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint16x8, u16)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int16x8, s16)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint32x4, u32)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int32x4, s32)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float32x4, f32)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint64x2, u64)
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int64x2, s64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float64x2, f64)
+#endif
+
+#if defined(__clang__) && defined(__aarch64__)
+// avoid LD2 instruction. details: https://github.com/opencv/opencv/issues/14863
+#define OPENCV_HAL_IMPL_NEON_LOAD_LOW_OP(_Tpvec, _Tp, suffix) \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ \
+typedef uint64 CV_DECL_ALIGNED(1) unaligned_uint64; \
+uint64 v = *(unaligned_uint64*)ptr; \
+return _Tpvec(v_reinterpret_as_##suffix(v_uint64x2(v, (uint64)123456))); \
+}
+#else
+#define OPENCV_HAL_IMPL_NEON_LOAD_LOW_OP(_Tpvec, _Tp, suffix) \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr), vdup_n_##suffix((_Tp)0))); }
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \
+inline _Tpvec v_load(const _Tp* ptr) \
+{ return _Tpvec(vld1q_##suffix(ptr)); } \
+inline _Tpvec v_load_aligned(const _Tp* ptr) \
+{ return _Tpvec(vld1q_##suffix(ptr)); } \
+OPENCV_HAL_IMPL_NEON_LOAD_LOW_OP(_Tpvec, _Tp, suffix) \
+inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
+{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a) \
+{ vst1q_##suffix(ptr, a.val); } \
+inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
+{ vst1q_##suffix(ptr, a.val); } \
+inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \
+{ vst1q_##suffix(ptr, a.val); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \
+{ vst1q_##suffix(ptr, a.val); } \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
+{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \
+inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
+{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); }
+
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64)
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64)
+#endif
+
+inline unsigned v_reduce_sum(const v_uint8x16& a)
+{
+#if CV_NEON_AARCH64
+    uint16_t t0 = vaddlvq_u8(a.val);
+    return t0;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(a.val));
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline int v_reduce_sum(const v_int8x16& a)
+{
+#if CV_NEON_AARCH64
+    int16_t t0 = vaddlvq_s8(a.val);
+    return t0;
+#else // #if CV_NEON_AARCH64
+    int32x4_t t0 = vpaddlq_s16(vpaddlq_s8(a.val));
+    int32x2_t t1 = vpadd_s32(vget_low_s32(t0), vget_high_s32(t0));
+    return vget_lane_s32(vpadd_s32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline unsigned v_reduce_sum(const v_uint16x8& a)
+{
+#if CV_NEON_AARCH64
+    uint32_t t0 = vaddlvq_u16(a.val);
+    return t0;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vpaddlq_u16(a.val);
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline int v_reduce_sum(const v_int16x8& a)
+{
+#if CV_NEON_AARCH64
+    int32_t t0 = vaddlvq_s16(a.val);
+    return t0;
+#else // #if CV_NEON_AARCH64
+    int32x4_t t0 = vpaddlq_s16(a.val);
+    int32x2_t t1 = vpadd_s32(vget_low_s32(t0), vget_high_s32(t0));
+    return vget_lane_s32(vpadd_s32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+
+#if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    return v##vectorfunc##vq_##suffix(a.val); \
+}
+#else // #if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \
+    a0 = vp##vectorfunc##_##suffix(a0, a0); \
+    a0 = vp##vectorfunc##_##suffix(a0, a0); \
+    return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \
+}
+#endif // #if CV_NEON_AARCH64
+
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_uint8x16, uint8x8, uchar, max, max, u8)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_uint8x16, uint8x8, uchar, min, min, u8)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_int8x16, int8x8, schar, max, max, s8)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_int8x16, int8x8, schar, min, min, s8)
+
+#if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    return v##vectorfunc##vq_##suffix(a.val); \
+}
+#else // #if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \
+    a0 = vp##vectorfunc##_##suffix(a0, a0); \
+    return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \
+}
+#endif // #if CV_NEON_AARCH64
+
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, ushort, max, max, u16)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, ushort, min, min, u16)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, max, max, s16)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, min, min, s16)
+
+#if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    return v##vectorfunc##vq_##suffix(a.val); \
+}
+#else // #if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \
+    return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, vget_high_##suffix(a.val)),0); \
+}
+#endif // #if CV_NEON_AARCH64
+
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, sum, add, u32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, max, max, u32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, min, min, u32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, sum, add, s32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, max, max, s32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, min, min, s32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, sum, add, f32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, max, max, f32)
+OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, min, min, f32)
+
+inline uint64 v_reduce_sum(const v_uint64x2& a)
+{
+#if CV_NEON_AARCH64
+    return vaddvq_u64(a.val);
+#else // #if CV_NEON_AARCH64
+    return vget_lane_u64(vadd_u64(vget_low_u64(a.val), vget_high_u64(a.val)),0);
+#endif // #if CV_NEON_AARCH64
+}
+inline int64 v_reduce_sum(const v_int64x2& a)
+{
+#if CV_NEON_AARCH64
+    return vaddvq_s64(a.val);
+#else // #if CV_NEON_AARCH64
+    return vget_lane_s64(vadd_s64(vget_low_s64(a.val), vget_high_s64(a.val)),0);
+#endif // #if CV_NEON_AARCH64
+}
+#if CV_SIMD128_64F
+inline double v_reduce_sum(const v_float64x2& a)
+{
+    return vaddvq_f64(a.val);
+}
+#endif
+
+inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
+                                 const v_float32x4& c, const v_float32x4& d)
+{
+#if CV_NEON_AARCH64
+    float32x4_t ab = vpaddq_f32(a.val, b.val); // a0+a1 a2+a3 b0+b1 b2+b3
+    float32x4_t cd = vpaddq_f32(c.val, d.val); // c0+c1 d0+d1 c2+c3 d2+d3
+    return v_float32x4(vpaddq_f32(ab, cd));  // sumA sumB sumC sumD
+#else // #if CV_NEON_AARCH64
+    float32x4x2_t ab = vtrnq_f32(a.val, b.val);
+    float32x4x2_t cd = vtrnq_f32(c.val, d.val);
+
+    float32x4_t u0 = vaddq_f32(ab.val[0], ab.val[1]); // a0+a1 b0+b1 a2+a3 b2+b3
+    float32x4_t u1 = vaddq_f32(cd.val[0], cd.val[1]); // c0+c1 d0+d1 c2+c3 d2+d3
+
+    float32x4_t v0 = vcombine_f32(vget_low_f32(u0), vget_low_f32(u1));
+    float32x4_t v1 = vcombine_f32(vget_high_f32(u0), vget_high_f32(u1));
+
+    return v_float32x4(vaddq_f32(v0, v1));
+#endif // #if CV_NEON_AARCH64
+}
+
+inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b)
+{
+#if CV_NEON_AARCH64
+    uint8x16_t t0 = vabdq_u8(a.val, b.val);
+    uint16_t t1 = vaddlvq_u8(t0);
+    return t1;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vabdq_u8(a.val, b.val)));
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b)
+{
+#if CV_NEON_AARCH64
+    uint8x16_t t0 = vreinterpretq_u8_s8(vabdq_s8(a.val, b.val));
+    uint16_t t1 = vaddlvq_u8(t0);
+    return t1;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vreinterpretq_u8_s8(vabdq_s8(a.val, b.val))));
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b)
+{
+#if CV_NEON_AARCH64
+    uint16x8_t t0 = vabdq_u16(a.val, b.val);
+    uint32_t t1 = vaddlvq_u16(t0);
+    return t1;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vpaddlq_u16(vabdq_u16(a.val, b.val));
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b)
+{
+#if CV_NEON_AARCH64
+    uint16x8_t t0 = vreinterpretq_u16_s16(vabdq_s16(a.val, b.val));
+    uint32_t t1 = vaddlvq_u16(t0);
+    return t1;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vpaddlq_u16(vreinterpretq_u16_s16(vabdq_s16(a.val, b.val)));
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b)
+{
+#if CV_NEON_AARCH64
+    uint32x4_t t0 = vabdq_u32(a.val, b.val);
+    uint32_t t1 = vaddvq_u32(t0);
+    return t1;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vabdq_u32(a.val, b.val);
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_NEON_AARCH64
+    uint32x4_t t0 = vreinterpretq_u32_s32(vabdq_s32(a.val, b.val));
+    uint32_t t1 = vaddvq_u32(t0);
+    return t1;
+#else // #if CV_NEON_AARCH64
+    uint32x4_t t0 = vreinterpretq_u32_s32(vabdq_s32(a.val, b.val));
+    uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0));
+    return vget_lane_u32(vpadd_u32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b)
+{
+#if CV_NEON_AARCH64
+    float32x4_t t0 = vabdq_f32(a.val, b.val);
+    return vaddvq_f32(t0);
+#else // #if CV_NEON_AARCH64
+    float32x4_t t0 = vabdq_f32(a.val, b.val);
+    float32x2_t t1 = vpadd_f32(vget_low_f32(t0), vget_high_f32(t0));
+    return vget_lane_f32(vpadd_f32(t1, t1), 0);
+#endif // #if CV_NEON_AARCH64
+}
+
+inline v_uint8x16 v_popcount(const v_uint8x16& a)
+{ return v_uint8x16(vcntq_u8(a.val)); }
+inline v_uint8x16 v_popcount(const v_int8x16& a)
+{ return v_uint8x16(vcntq_u8(vreinterpretq_u8_s8(a.val))); }
+inline v_uint16x8 v_popcount(const v_uint16x8& a)
+{ return v_uint16x8(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_u16(a.val)))); }
+inline v_uint16x8 v_popcount(const v_int16x8& a)
+{ return v_uint16x8(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_s16(a.val)))); }
+inline v_uint32x4 v_popcount(const v_uint32x4& a)
+{ return v_uint32x4(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_u32(a.val))))); }
+inline v_uint32x4 v_popcount(const v_int32x4& a)
+{ return v_uint32x4(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_s32(a.val))))); }
+inline v_uint64x2 v_popcount(const v_uint64x2& a)
+{ return v_uint64x2(vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_u64(a.val)))))); }
+inline v_uint64x2 v_popcount(const v_int64x2& a)
+{ return v_uint64x2(vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_s64(a.val)))))); }
+
+inline int v_signmask(const v_uint8x16& a)
+{
+#if CV_NEON_AARCH64
+    const int8x16_t signPosition = {0,1,2,3,4,5,6,7,0,1,2,3,4,5,6,7};
+    const uint8x16_t byteOrder = {0,8,1,9,2,10,3,11,4,12,5,13,6,14,7,15};
+    uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), signPosition);
+    uint8x16_t v1 = vqtbl1q_u8(v0, byteOrder);
+    uint32_t t0 = vaddlvq_u16(vreinterpretq_u16_u8(v1));
+    return t0;
+#else // #if CV_NEON_AARCH64
+    int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100));
+    uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0));
+    uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0)));
+    return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8);
+#endif // #if CV_NEON_AARCH64
+}
+
+inline int v_signmask(const v_int8x16& a)
+{ return v_signmask(v_reinterpret_as_u8(a)); }
+
+inline int v_signmask(const v_uint16x8& a)
+{
+#if CV_NEON_AARCH64
+    const int16x8_t signPosition = {0,1,2,3,4,5,6,7};
+    uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), signPosition);
+    uint32_t t0 = vaddlvq_u16(v0);
+    return t0;
+#else // #if CV_NEON_AARCH64
+    int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000));
+    uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0));
+    uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0));
+    return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4);
+#endif // #if CV_NEON_AARCH64
+}
+inline int v_signmask(const v_int16x8& a)
+{ return v_signmask(v_reinterpret_as_u16(a)); }
+
+inline int v_signmask(const v_uint32x4& a)
+{
+#if CV_NEON_AARCH64
+    const int32x4_t signPosition = {0,1,2,3};
+    uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), signPosition);
+    uint32_t t0 = vaddvq_u32(v0);
+    return t0;
+#else // #if CV_NEON_AARCH64
+    int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000));
+    uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0));
+    uint64x2_t v1 = vpaddlq_u32(v0);
+    return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2);
+#endif // #if CV_NEON_AARCH64
+}
+inline int v_signmask(const v_int32x4& a)
+{ return v_signmask(v_reinterpret_as_u32(a)); }
+inline int v_signmask(const v_float32x4& a)
+{ return v_signmask(v_reinterpret_as_u32(a)); }
+inline int v_signmask(const v_uint64x2& a)
+{
+#if CV_NEON_AARCH64
+    const int64x2_t signPosition = {0,1};
+    uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), signPosition);
+    uint64_t t0 = vaddvq_u64(v0);
+    return t0;
+#else // #if CV_NEON_AARCH64
+    int64x1_t m0 = vdup_n_s64(0);
+    uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), vcombine_s64(m0, m0));
+    return (int)vgetq_lane_u64(v0, 0) + ((int)vgetq_lane_u64(v0, 1) << 1);
+#endif // #if CV_NEON_AARCH64
+}
+inline int v_signmask(const v_int64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+#if CV_SIMD128_64F
+inline int v_signmask(const v_float64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+#endif
+
+inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(a)); }
+#if CV_SIMD128_64F
+inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(a)); }
+#endif
+
+#if CV_NEON_AARCH64
+    #define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \
+    inline bool v_check_all(const v_##_Tpvec& a) \
+    { \
+        return (vminvq_##suffix(a.val) >> shift) != 0; \
+    } \
+    inline bool v_check_any(const v_##_Tpvec& a) \
+    { \
+        return (vmaxvq_##suffix(a.val) >> shift) != 0; \
+    }
+#else // #if CV_NEON_AARCH64
+    #define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \
+    inline bool v_check_all(const v_##_Tpvec& a) \
+    { \
+        _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \
+        uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \
+        return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \
+    } \
+    inline bool v_check_any(const v_##_Tpvec& a) \
+    { \
+        _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \
+        uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \
+        return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \
+    }
+#endif // #if CV_NEON_AARCH64
+
+OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7)
+OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15)
+OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31)
+
+inline bool v_check_all(const v_uint64x2& a)
+{
+    uint64x2_t v0 = vshrq_n_u64(a.val, 63);
+    return (vgetq_lane_u64(v0, 0) & vgetq_lane_u64(v0, 1)) == 1;
+}
+inline bool v_check_any(const v_uint64x2& a)
+{
+    uint64x2_t v0 = vshrq_n_u64(a.val, 63);
+    return (vgetq_lane_u64(v0, 0) | vgetq_lane_u64(v0, 1)) != 0;
+}
+
+inline bool v_check_all(const v_int8x16& a)
+{ return v_check_all(v_reinterpret_as_u8(a)); }
+inline bool v_check_all(const v_int16x8& a)
+{ return v_check_all(v_reinterpret_as_u16(a)); }
+inline bool v_check_all(const v_int32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+inline bool v_check_all(const v_float32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+
+inline bool v_check_any(const v_int8x16& a)
+{ return v_check_any(v_reinterpret_as_u8(a)); }
+inline bool v_check_any(const v_int16x8& a)
+{ return v_check_any(v_reinterpret_as_u16(a)); }
+inline bool v_check_any(const v_int32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+inline bool v_check_any(const v_float32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+
+inline bool v_check_all(const v_int64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+inline bool v_check_any(const v_int64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+#if CV_SIMD128_64F
+inline bool v_check_all(const v_float64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+inline bool v_check_any(const v_float64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \
+}
+
+OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8)
+OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8)
+OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16)
+OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16)
+OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32)
+OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32)
+OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_SELECT(v_float64x2, f64, u64)
+#endif
+
+#if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \
+inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
+{ \
+    b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \
+    b1.val = vmovl_high_##suffix(a.val); \
+} \
+inline _Tpwvec v_expand_low(const _Tpvec& a) \
+{ \
+    return _Tpwvec(vmovl_##suffix(vget_low_##suffix(a.val))); \
+} \
+inline _Tpwvec v_expand_high(const _Tpvec& a) \
+{ \
+    return _Tpwvec(vmovl_high_##suffix(a.val)); \
+} \
+inline _Tpwvec v_load_expand(const _Tp* ptr) \
+{ \
+    return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \
+}
+#else
+#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \
+inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
+{ \
+    b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \
+    b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \
+} \
+inline _Tpwvec v_expand_low(const _Tpvec& a) \
+{ \
+    return _Tpwvec(vmovl_##suffix(vget_low_##suffix(a.val))); \
+} \
+inline _Tpwvec v_expand_high(const _Tpvec& a) \
+{ \
+    return _Tpwvec(vmovl_##suffix(vget_high_##suffix(a.val))); \
+} \
+inline _Tpwvec v_load_expand(const _Tp* ptr) \
+{ \
+    return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \
+}
+#endif
+
+OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8)
+OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8)
+OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16)
+OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16)
+OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32)
+OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32)
+
+inline v_uint32x4 v_load_expand_q(const uchar* ptr)
+{
+    typedef unsigned int CV_DECL_ALIGNED(1) unaligned_uint;
+    uint8x8_t v0 = vcreate_u8(*(unaligned_uint*)ptr);
+    uint16x4_t v1 = vget_low_u16(vmovl_u8(v0));
+    return v_uint32x4(vmovl_u16(v1));
+}
+
+inline v_int32x4 v_load_expand_q(const schar* ptr)
+{
+    typedef unsigned int CV_DECL_ALIGNED(1) unaligned_uint;
+    int8x8_t v0 = vcreate_s8(*(unaligned_uint*)ptr);
+    int16x4_t v1 = vget_low_s16(vmovl_s8(v0));
+    return v_int32x4(vmovl_s16(v1));
+}
+
+#if defined(__aarch64__) || defined(_M_ARM64)
+#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \
+inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \
+{ \
+    b0.val = vzip1q_##suffix(a0.val, a1.val); \
+    b1.val = vzip2q_##suffix(a0.val, a1.val); \
+} \
+inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \
+} \
+inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \
+} \
+inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \
+{ \
+    c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \
+    d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \
+}
+#else
+#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \
+inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \
+{ \
+    _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \
+    b0.val = p.val[0]; \
+    b1.val = p.val[1]; \
+} \
+inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \
+} \
+inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \
+} \
+inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \
+{ \
+    c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \
+    d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \
+}
+#endif
+
+OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8)
+OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8)
+OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16)
+OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16)
+OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32)
+OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32)
+OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_UNPACKS(float64x2, f64)
+#endif
+
+inline v_uint8x16 v_reverse(const v_uint8x16 &a)
+{
+    uint8x16_t vec = vrev64q_u8(a.val);
+    return v_uint8x16(vextq_u8(vec, vec, 8));
+}
+
+inline v_int8x16 v_reverse(const v_int8x16 &a)
+{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
+
+inline v_uint16x8 v_reverse(const v_uint16x8 &a)
+{
+    uint16x8_t vec = vrev64q_u16(a.val);
+    return v_uint16x8(vextq_u16(vec, vec, 4));
+}
+
+inline v_int16x8 v_reverse(const v_int16x8 &a)
+{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
+
+inline v_uint32x4 v_reverse(const v_uint32x4 &a)
+{
+    uint32x4_t vec = vrev64q_u32(a.val);
+    return v_uint32x4(vextq_u32(vec, vec, 2));
+}
+
+inline v_int32x4 v_reverse(const v_int32x4 &a)
+{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_float32x4 v_reverse(const v_float32x4 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x2 v_reverse(const v_uint64x2 &a)
+{
+    uint64x2_t vec = a.val;
+    uint64x1_t vec_lo = vget_low_u64(vec);
+    uint64x1_t vec_hi = vget_high_u64(vec);
+    return v_uint64x2(vcombine_u64(vec_hi, vec_lo));
+}
+
+inline v_int64x2 v_reverse(const v_int64x2 &a)
+{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_reverse(const v_float64x2 &a)
+{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \
+template <int s> \
+inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \
+}
+
+OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8)
+OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8)
+OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16)
+OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16)
+OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32)
+OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32)
+OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64)
+OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64)
+OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_EXTRACT(float64x2, f64)
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_EXTRACT_N(_Tpvec, _Tp, suffix) \
+template<int i> inline _Tp v_extract_n(_Tpvec v) { return vgetq_lane_##suffix(v.val, i); }
+
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int8x16, schar, s8)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int16x8, short, s16)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint32x4, uint, u32)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int32x4, int, s32)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int64x2, int64, s64)
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_float32x4, float, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_float64x2, double, f64)
+#endif
+
+#define OPENCV_HAL_IMPL_NEON_BROADCAST(_Tpvec, _Tp, suffix) \
+template<int i> inline _Tpvec v_broadcast_element(_Tpvec v) { _Tp t = v_extract_n<i>(v); return v_setall_##suffix(t); }
+
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_int8x16, schar, s8)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_int16x8, short, s16)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint32x4, uint, u32)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_int32x4, int, s32)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_int64x2, int64, s64)
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_float32x4, float, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_BROADCAST(v_float64x2, double, f64)
+#endif
+
+#if CV_SIMD128_64F
+inline v_int32x4 v_round(const v_float32x4& a)
+{
+    float32x4_t a_ = a.val;
+    int32x4_t result;
+    __asm__ ("fcvtns %0.4s, %1.4s"
+             : "=w"(result)
+             : "w"(a_)
+             : /* No clobbers */);
+    return v_int32x4(result);
+}
+#else
+inline v_int32x4 v_round(const v_float32x4& a)
+{
+    static const int32x4_t v_sign = vdupq_n_s32(1 << 31),
+        v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f));
+
+    int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val)));
+    return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition))));
+}
+#endif
+inline v_int32x4 v_floor(const v_float32x4& a)
+{
+    int32x4_t a1 = vcvtq_s32_f32(a.val);
+    uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val);
+    return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask)));
+}
+
+inline v_int32x4 v_ceil(const v_float32x4& a)
+{
+    int32x4_t a1 = vcvtq_s32_f32(a.val);
+    uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1));
+    return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask)));
+}
+
+inline v_int32x4 v_trunc(const v_float32x4& a)
+{ return v_int32x4(vcvtq_s32_f32(a.val)); }
+
+#if CV_SIMD128_64F
+inline v_int32x4 v_round(const v_float64x2& a)
+{
+    static const int32x2_t zero = vdup_n_s32(0);
+    return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero));
+}
+
+inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), vmovn_s64(vcvtaq_s64_f64(b.val))));
+}
+
+inline v_int32x4 v_floor(const v_float64x2& a)
+{
+    static const int32x2_t zero = vdup_n_s32(0);
+    int64x2_t a1 = vcvtq_s64_f64(a.val);
+    uint64x2_t mask = vcgtq_f64(vcvtq_f64_s64(a1), a.val);
+    a1 = vaddq_s64(a1, vreinterpretq_s64_u64(mask));
+    return v_int32x4(vcombine_s32(vmovn_s64(a1), zero));
+}
+
+inline v_int32x4 v_ceil(const v_float64x2& a)
+{
+    static const int32x2_t zero = vdup_n_s32(0);
+    int64x2_t a1 = vcvtq_s64_f64(a.val);
+    uint64x2_t mask = vcgtq_f64(a.val, vcvtq_f64_s64(a1));
+    a1 = vsubq_s64(a1, vreinterpretq_s64_u64(mask));
+    return v_int32x4(vcombine_s32(vmovn_s64(a1), zero));
+}
+
+inline v_int32x4 v_trunc(const v_float64x2& a)
+{
+    static const int32x2_t zero = vdup_n_s32(0);
+    return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero));
+}
+#endif
+
+#if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \
+inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \
+                         const v_##_Tpvec& a2, const v_##_Tpvec& a3, \
+                         v_##_Tpvec& b0, v_##_Tpvec& b1, \
+                         v_##_Tpvec& b2, v_##_Tpvec& b3) \
+{ \
+    /* -- Pass 1: 64b transpose */ \
+    _Tpvec##_t t0 = vreinterpretq_##suffix##32_##suffix##64( \
+                        vtrn1q_##suffix##64(vreinterpretq_##suffix##64_##suffix##32(a0.val), \
+                                            vreinterpretq_##suffix##64_##suffix##32(a2.val))); \
+    _Tpvec##_t t1 = vreinterpretq_##suffix##32_##suffix##64( \
+                        vtrn1q_##suffix##64(vreinterpretq_##suffix##64_##suffix##32(a1.val), \
+                                            vreinterpretq_##suffix##64_##suffix##32(a3.val))); \
+    _Tpvec##_t t2 = vreinterpretq_##suffix##32_##suffix##64( \
+                        vtrn2q_##suffix##64(vreinterpretq_##suffix##64_##suffix##32(a0.val), \
+                                            vreinterpretq_##suffix##64_##suffix##32(a2.val))); \
+    _Tpvec##_t t3 = vreinterpretq_##suffix##32_##suffix##64( \
+                        vtrn2q_##suffix##64(vreinterpretq_##suffix##64_##suffix##32(a1.val), \
+                                            vreinterpretq_##suffix##64_##suffix##32(a3.val))); \
+    /* -- Pass 2: 32b transpose */ \
+    b0.val = vtrn1q_##suffix##32(t0, t1); \
+    b1.val = vtrn2q_##suffix##32(t0, t1); \
+    b2.val = vtrn1q_##suffix##32(t2, t3); \
+    b3.val = vtrn2q_##suffix##32(t2, t3); \
+}
+
+OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u)
+OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s)
+OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f)
+#else // #if CV_NEON_AARCH64
+#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \
+inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \
+                         const v_##_Tpvec& a2, const v_##_Tpvec& a3, \
+                         v_##_Tpvec& b0, v_##_Tpvec& b1, \
+                         v_##_Tpvec& b2, v_##_Tpvec& b3) \
+{ \
+    /* m00 m01 m02 m03 */ \
+    /* m10 m11 m12 m13 */ \
+    /* m20 m21 m22 m23 */ \
+    /* m30 m31 m32 m33 */ \
+    _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \
+    _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \
+    /* m00 m10 m02 m12 */ \
+    /* m01 m11 m03 m13 */ \
+    /* m20 m30 m22 m32 */ \
+    /* m21 m31 m23 m33 */ \
+    b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \
+    b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \
+    b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \
+    b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \
+}
+
+OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32)
+OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32)
+OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32)
+#endif // #if CV_NEON_AARCH64
+
+#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \
+{ \
+    _Tpvec##x2_t v = vld2q_##suffix(ptr); \
+    a.val = v.val[0]; \
+    b.val = v.val[1]; \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \
+{ \
+    _Tpvec##x3_t v = vld3q_##suffix(ptr); \
+    a.val = v.val[0]; \
+    b.val = v.val[1]; \
+    c.val = v.val[2]; \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \
+                                v_##_Tpvec& c, v_##_Tpvec& d) \
+{ \
+    _Tpvec##x4_t v = vld4q_##suffix(ptr); \
+    a.val = v.val[0]; \
+    b.val = v.val[1]; \
+    c.val = v.val[2]; \
+    d.val = v.val[3]; \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    _Tpvec##x2_t v; \
+    v.val[0] = a.val; \
+    v.val[1] = b.val; \
+    vst2q_##suffix(ptr, v); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    _Tpvec##x3_t v; \
+    v.val[0] = a.val; \
+    v.val[1] = b.val; \
+    v.val[2] = c.val; \
+    vst3q_##suffix(ptr, v); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                const v_##_Tpvec& c, const v_##_Tpvec& d, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec##x4_t v; \
+    v.val[0] = a.val; \
+    v.val[1] = b.val; \
+    v.val[2] = c.val; \
+    v.val[3] = d.val; \
+    vst4q_##suffix(ptr, v); \
+}
+
+#define OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(tp, suffix) \
+inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b ) \
+{ \
+    tp##x1_t a0 = vld1_##suffix(ptr); \
+    tp##x1_t b0 = vld1_##suffix(ptr + 1); \
+    tp##x1_t a1 = vld1_##suffix(ptr + 2); \
+    tp##x1_t b1 = vld1_##suffix(ptr + 3); \
+    a = v_##tp##x2(vcombine_##suffix(a0, a1)); \
+    b = v_##tp##x2(vcombine_##suffix(b0, b1)); \
+} \
+ \
+inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, \
+                                 v_##tp##x2& b, v_##tp##x2& c ) \
+{ \
+    tp##x1_t a0 = vld1_##suffix(ptr); \
+    tp##x1_t b0 = vld1_##suffix(ptr + 1); \
+    tp##x1_t c0 = vld1_##suffix(ptr + 2); \
+    tp##x1_t a1 = vld1_##suffix(ptr + 3); \
+    tp##x1_t b1 = vld1_##suffix(ptr + 4); \
+    tp##x1_t c1 = vld1_##suffix(ptr + 5); \
+    a = v_##tp##x2(vcombine_##suffix(a0, a1)); \
+    b = v_##tp##x2(vcombine_##suffix(b0, b1)); \
+    c = v_##tp##x2(vcombine_##suffix(c0, c1)); \
+} \
+ \
+inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b, \
+                                 v_##tp##x2& c, v_##tp##x2& d ) \
+{ \
+    tp##x1_t a0 = vld1_##suffix(ptr); \
+    tp##x1_t b0 = vld1_##suffix(ptr + 1); \
+    tp##x1_t c0 = vld1_##suffix(ptr + 2); \
+    tp##x1_t d0 = vld1_##suffix(ptr + 3); \
+    tp##x1_t a1 = vld1_##suffix(ptr + 4); \
+    tp##x1_t b1 = vld1_##suffix(ptr + 5); \
+    tp##x1_t c1 = vld1_##suffix(ptr + 6); \
+    tp##x1_t d1 = vld1_##suffix(ptr + 7); \
+    a = v_##tp##x2(vcombine_##suffix(a0, a1)); \
+    b = v_##tp##x2(vcombine_##suffix(b0, b1)); \
+    c = v_##tp##x2(vcombine_##suffix(c0, c1)); \
+    d = v_##tp##x2(vcombine_##suffix(d0, d1)); \
+} \
+ \
+inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    vst1_##suffix(ptr, vget_low_##suffix(a.val)); \
+    vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \
+    vst1_##suffix(ptr + 2, vget_high_##suffix(a.val)); \
+    vst1_##suffix(ptr + 3, vget_high_##suffix(b.val)); \
+} \
+ \
+inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, \
+                                const v_##tp##x2& b, const v_##tp##x2& c, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    vst1_##suffix(ptr, vget_low_##suffix(a.val)); \
+    vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \
+    vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \
+    vst1_##suffix(ptr + 3, vget_high_##suffix(a.val)); \
+    vst1_##suffix(ptr + 4, vget_high_##suffix(b.val)); \
+    vst1_##suffix(ptr + 5, vget_high_##suffix(c.val)); \
+} \
+ \
+inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \
+                                const v_##tp##x2& c, const v_##tp##x2& d, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    vst1_##suffix(ptr, vget_low_##suffix(a.val)); \
+    vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \
+    vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \
+    vst1_##suffix(ptr + 3, vget_low_##suffix(d.val)); \
+    vst1_##suffix(ptr + 4, vget_high_##suffix(a.val)); \
+    vst1_##suffix(ptr + 5, vget_high_##suffix(b.val)); \
+    vst1_##suffix(ptr + 6, vget_high_##suffix(c.val)); \
+    vst1_##suffix(ptr + 7, vget_high_##suffix(d.val)); \
+}
+
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8)
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16)
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32)
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_NEON_INTERLEAVED(float64x2, double, f64)
+#endif
+
+OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(int64, s64)
+OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(uint64, u64)
+
+inline v_float32x4 v_cvt_f32(const v_int32x4& a)
+{
+    return v_float32x4(vcvtq_f32_s32(a.val));
+}
+
+#if CV_SIMD128_64F
+inline v_float32x4 v_cvt_f32(const v_float64x2& a)
+{
+    float32x2_t zero = vdup_n_f32(0.0f);
+    return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), zero));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), vcvt_f32_f64(b.val)));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int32x4& a)
+{
+    return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_low_s32(a.val))));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
+{
+    return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_high_s32(a.val))));
+}
+
+inline v_float64x2 v_cvt_f64(const v_float32x4& a)
+{
+    return v_float64x2(vcvt_f64_f32(vget_low_f32(a.val)));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
+{
+    return v_float64x2(vcvt_f64_f32(vget_high_f32(a.val)));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int64x2& a)
+{  return v_float64x2(vcvtq_f64_s64(a.val)); }
+
+#endif
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x16 v_lut(const schar* tab, const int* idx)
+{
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[ 0]],
+        tab[idx[ 1]],
+        tab[idx[ 2]],
+        tab[idx[ 3]],
+        tab[idx[ 4]],
+        tab[idx[ 5]],
+        tab[idx[ 6]],
+        tab[idx[ 7]],
+        tab[idx[ 8]],
+        tab[idx[ 9]],
+        tab[idx[10]],
+        tab[idx[11]],
+        tab[idx[12]],
+        tab[idx[13]],
+        tab[idx[14]],
+        tab[idx[15]]
+    };
+    return v_int8x16(vld1q_s8(elems));
+}
+inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx)
+{
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[4]],
+        tab[idx[4] + 1],
+        tab[idx[5]],
+        tab[idx[5] + 1],
+        tab[idx[6]],
+        tab[idx[6] + 1],
+        tab[idx[7]],
+        tab[idx[7] + 1]
+    };
+    return v_int8x16(vld1q_s8(elems));
+}
+inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
+{
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[0] + 2],
+        tab[idx[0] + 3],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[1] + 2],
+        tab[idx[1] + 3],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[2] + 2],
+        tab[idx[2] + 3],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[3] + 2],
+        tab[idx[3] + 3]
+    };
+    return v_int8x16(vld1q_s8(elems));
+}
+inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((schar*)tab, idx)); }
+
+inline v_int16x8 v_lut(const short* tab, const int* idx)
+{
+    short CV_DECL_ALIGNED(32) elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]],
+        tab[idx[4]],
+        tab[idx[5]],
+        tab[idx[6]],
+        tab[idx[7]]
+    };
+    return v_int16x8(vld1q_s16(elems));
+}
+inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
+{
+    short CV_DECL_ALIGNED(32) elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1]
+    };
+    return v_int16x8(vld1q_s16(elems));
+}
+inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
+{
+    return v_int16x8(vcombine_s16(vld1_s16(tab + idx[0]), vld1_s16(tab + idx[1])));
+}
+inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((short*)tab, idx)); }
+inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((short*)tab, idx)); }
+inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((short*)tab, idx)); }
+
+inline v_int32x4 v_lut(const int* tab, const int* idx)
+{
+    int CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_int32x4(vld1q_s32(elems));
+}
+inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
+{
+    return v_int32x4(vcombine_s32(vld1_s32(tab + idx[0]), vld1_s32(tab + idx[1])));
+}
+inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x4(vld1q_s32(tab + idx[0]));
+}
+inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((int*)tab, idx)); }
+inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((int*)tab, idx)); }
+inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((int*)tab, idx)); }
+
+inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(vcombine_s64(vcreate_s64(tab[idx[0]]), vcreate_s64(tab[idx[1]])));
+}
+inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(vld1q_s64(tab + idx[0]));
+}
+inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); }
+inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); }
+
+inline v_float32x4 v_lut(const float* tab, const int* idx)
+{
+    float CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_float32x4(vld1q_f32(elems));
+}
+inline v_float32x4 v_lut_pairs(const float* tab, const int* idx)
+{
+    typedef uint64 CV_DECL_ALIGNED(1) unaligned_uint64;
+
+    uint64 CV_DECL_ALIGNED(32) elems[2] =
+    {
+        *(unaligned_uint64*)(tab + idx[0]),
+        *(unaligned_uint64*)(tab + idx[1])
+    };
+    return v_float32x4(vreinterpretq_f32_u64(vld1q_u64(elems)));
+}
+inline v_float32x4 v_lut_quads(const float* tab, const int* idx)
+{
+    return v_float32x4(vld1q_f32(tab + idx[0]));
+}
+
+inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
+{
+    int CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[vgetq_lane_s32(idxvec.val, 0)],
+        tab[vgetq_lane_s32(idxvec.val, 1)],
+        tab[vgetq_lane_s32(idxvec.val, 2)],
+        tab[vgetq_lane_s32(idxvec.val, 3)]
+    };
+    return v_int32x4(vld1q_s32(elems));
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
+{
+    unsigned CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[vgetq_lane_s32(idxvec.val, 0)],
+        tab[vgetq_lane_s32(idxvec.val, 1)],
+        tab[vgetq_lane_s32(idxvec.val, 2)],
+        tab[vgetq_lane_s32(idxvec.val, 3)]
+    };
+    return v_uint32x4(vld1q_u32(elems));
+}
+
+inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
+{
+    float CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[vgetq_lane_s32(idxvec.val, 0)],
+        tab[vgetq_lane_s32(idxvec.val, 1)],
+        tab[vgetq_lane_s32(idxvec.val, 2)],
+        tab[vgetq_lane_s32(idxvec.val, 3)]
+    };
+    return v_float32x4(vld1q_f32(elems));
+}
+
+inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
+{
+    /*int CV_DECL_ALIGNED(32) idx[4];
+    v_store(idx, idxvec);
+
+    float32x4_t xy02 = vcombine_f32(vld1_f32(tab + idx[0]), vld1_f32(tab + idx[2]));
+    float32x4_t xy13 = vcombine_f32(vld1_f32(tab + idx[1]), vld1_f32(tab + idx[3]));
+
+    float32x4x2_t xxyy = vuzpq_f32(xy02, xy13);
+    x = v_float32x4(xxyy.val[0]);
+    y = v_float32x4(xxyy.val[1]);*/
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+    y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]);
+}
+
+inline v_int8x16 v_interleave_pairs(const v_int8x16& vec)
+{
+    return v_int8x16(vcombine_s8(vtbl1_s8(vget_low_s8(vec.val), vcreate_s8(0x0705060403010200)), vtbl1_s8(vget_high_s8(vec.val), vcreate_s8(0x0705060403010200))));
+}
+inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
+inline v_int8x16 v_interleave_quads(const v_int8x16& vec)
+{
+    return v_int8x16(vcombine_s8(vtbl1_s8(vget_low_s8(vec.val), vcreate_s8(0x0703060205010400)), vtbl1_s8(vget_high_s8(vec.val), vcreate_s8(0x0703060205010400))));
+}
+inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_interleave_pairs(const v_int16x8& vec)
+{
+    return v_int16x8(vreinterpretq_s16_s8(vcombine_s8(vtbl1_s8(vget_low_s8(vreinterpretq_s8_s16(vec.val)), vcreate_s8(0x0706030205040100)), vtbl1_s8(vget_high_s8(vreinterpretq_s8_s16(vec.val)), vcreate_s8(0x0706030205040100)))));
+}
+inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+inline v_int16x8 v_interleave_quads(const v_int16x8& vec)
+{
+    int16x4x2_t res = vzip_s16(vget_low_s16(vec.val), vget_high_s16(vec.val));
+    return v_int16x8(vcombine_s16(res.val[0], res.val[1]));
+}
+inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_interleave_pairs(const v_int32x4& vec)
+{
+    int32x2x2_t res = vzip_s32(vget_low_s32(vec.val), vget_high_s32(vec.val));
+    return v_int32x4(vcombine_s32(res.val[0], res.val[1]));
+}
+inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+    return v_int8x16(vextq_s8(vcombine_s8(vtbl1_s8(vget_low_s8(vec.val), vcreate_s8(0x0605040201000000)), vtbl1_s8(vget_high_s8(vec.val), vcreate_s8(0x0807060504020100))), vdupq_n_s8(0), 2));
+}
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+    return v_int16x8(vreinterpretq_s16_s8(vextq_s8(vcombine_s8(vtbl1_s8(vget_low_s8(vreinterpretq_s8_s16(vec.val)), vcreate_s8(0x0504030201000000)), vget_high_s8(vreinterpretq_s8_s16(vec.val))), vdupq_n_s8(0), 2)));
+}
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; }
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_lut(const double* tab, const int* idx)
+{
+    double CV_DECL_ALIGNED(32) elems[2] =
+    {
+        tab[idx[0]],
+        tab[idx[1]]
+    };
+    return v_float64x2(vld1q_f64(elems));
+}
+
+inline v_float64x2 v_lut_pairs(const double* tab, const int* idx)
+{
+    return v_float64x2(vld1q_f64(tab + idx[0]));
+}
+
+inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
+{
+    double CV_DECL_ALIGNED(32) elems[2] =
+    {
+        tab[vgetq_lane_s32(idxvec.val, 0)],
+        tab[vgetq_lane_s32(idxvec.val, 1)],
+    };
+    return v_float64x2(vld1q_f64(elems));
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    x = v_float64x2(tab[idx[0]], tab[idx[1]]);
+    y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]);
+}
+#endif
+
+////// FP16 support ///////
+#if CV_FP16
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    float16x4_t v =
+    #ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro
+        (float16x4_t)vld1_s16((const short*)ptr);
+    #else
+        vld1_f16((const __fp16*)ptr);
+    #endif
+    return v_float32x4(vcvt_f32_f16(v));
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    float16x4_t hv = vcvt_f16_f32(v.val);
+
+    #ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro
+        vst1_s16((short*)ptr, (int16x4_t)hv);
+    #else
+        vst1_f16((__fp16*)ptr, hv);
+    #endif
+}
+#else
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    const int N = 4;
+    float buf[N];
+    for( int i = 0; i < N; i++ ) buf[i] = (float)ptr[i];
+    return v_load(buf);
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    const int N = 4;
+    float buf[N];
+    v_store(buf, v);
+    for( int i = 0; i < N; i++ ) ptr[i] = float16_t(buf[i]);
+}
+#endif
+
+inline void v_cleanup() {}
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin_rvv.hpp b/3rdParty/include/opencv2/core/hal/intrin_rvv.hpp
new file mode 100644
index 0000000..fe6c077
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_rvv.hpp
@@ -0,0 +1,2887 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+// The original implementation has been contributed by Yin Zhang.
+// Copyright (C) 2020, Institute of Software, Chinese Academy of Sciences.
+
+#ifndef OPENCV_HAL_INTRIN_RVV_HPP
+#define OPENCV_HAL_INTRIN_RVV_HPP
+
+#include <algorithm>
+
+namespace cv
+{
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+#define CV_SIMD128 1
+#define CV_SIMD128_64F 1
+
+//////////// Unsupported native intrinsics in C++ ////////////
+// The following types have been defined in clang, but not in GCC yet.
+#ifndef __clang__
+
+struct vuint8mf2_t
+{
+    uchar val[8] = {0};
+    vuint8mf2_t() {}
+    vuint8mf2_t(const uchar* ptr)
+    {
+        for (int i = 0; i < 8; ++i)
+        {
+            val[i] = ptr[i];
+        }
+    }
+};
+struct vint8mf2_t
+{
+    schar val[8] = {0};
+    vint8mf2_t() {}
+    vint8mf2_t(const schar* ptr)
+    {
+        for (int i = 0; i < 8; ++i)
+        {
+            val[i] = ptr[i];
+        }
+    }
+};
+struct vuint16mf2_t
+{
+    ushort val[4] = {0};
+    vuint16mf2_t() {}
+    vuint16mf2_t(const ushort* ptr)
+    {
+        for (int i = 0; i < 4; ++i)
+        {
+            val[i] = ptr[i];
+        }
+    }
+};
+struct vint16mf2_t
+{
+    short val[4] = {0};
+    vint16mf2_t() {}
+    vint16mf2_t(const short* ptr)
+    {
+        for (int i = 0; i < 4; ++i)
+        {
+            val[i] = ptr[i];
+        }
+    }
+};
+struct vuint32mf2_t
+{
+    unsigned val[2] = {0};
+    vuint32mf2_t() {}
+    vuint32mf2_t(const unsigned* ptr)
+    {
+        val[0] = ptr[0];
+        val[1] = ptr[1];
+    }
+};
+struct vint32mf2_t
+{
+    int val[2] = {0};
+    vint32mf2_t() {}
+    vint32mf2_t(const int* ptr)
+    {
+        val[0] = ptr[0];
+        val[1] = ptr[1];
+    }
+};
+struct vfloat32mf2_t
+{
+    float val[2] = {0};
+    vfloat32mf2_t() {}
+    vfloat32mf2_t(const float* ptr)
+    {
+        val[0] = ptr[0];
+        val[1] = ptr[1];
+    }
+};
+struct vuint64mf2_t
+{
+    uint64 val[1] = {0};
+    vuint64mf2_t() {}
+    vuint64mf2_t(const uint64* ptr)
+    {
+        val[0] = ptr[0];
+    }
+};
+struct vint64mf2_t
+{
+    int64 val[1] = {0};
+    vint64mf2_t() {}
+    vint64mf2_t(const int64* ptr)
+    {
+        val[0] = ptr[0];
+    }
+};
+struct vfloat64mf2_t
+{
+    double val[1] = {0};
+    vfloat64mf2_t() {}
+    vfloat64mf2_t(const double* ptr)
+    {
+        val[0] = ptr[0];
+    }
+};
+struct vuint8mf4_t
+{
+    uchar val[4] = {0};
+    vuint8mf4_t() {}
+    vuint8mf4_t(const uchar* ptr)
+    {
+        for (int i = 0; i < 4; ++i)
+        {
+            val[i] = ptr[i];
+        }
+    }
+};
+struct vint8mf4_t
+{
+    schar val[4] = {0};
+    vint8mf4_t() {}
+    vint8mf4_t(const schar* ptr)
+    {
+        for (int i = 0; i < 4; ++i)
+        {
+            val[i] = ptr[i];
+        }
+    }
+};
+
+#define OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(_Tpvec, _Tp, suffix, width, n) \
+inline _Tpvec vle##width##_v_##suffix##mf2(const _Tp* ptr, size_t vl) \
+{ \
+    CV_UNUSED(vl); \
+    return _Tpvec(ptr); \
+} \
+inline void vse##width##_v_##suffix##mf2(_Tp* ptr, _Tpvec v, size_t vl) \
+{ \
+    CV_UNUSED(vl); \
+    for (int i = 0; i < n; ++i) \
+    { \
+            ptr[i] = v.val[i]; \
+    } \
+}
+
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vuint8mf2_t, uint8_t, u8, 8, 8)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vint8mf2_t, int8_t, i8, 8, 8)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vuint16mf2_t, uint16_t, u16, 16, 4)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vint16mf2_t, int16_t, i16, 16, 4)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vuint32mf2_t, uint32_t, u32, 32, 2)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vint32mf2_t, int32_t, i32, 32, 2)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vfloat32mf2_t, float32_t, f32, 32, 2)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vuint64mf2_t, uint64_t, u64, 64, 1)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vint64mf2_t, int64_t, i64, 64, 1)
+OPENCV_HAL_IMPL_RVV_NATIVE_LOADSTORE_MF2(vfloat64mf2_t, float64_t, f64, 64, 1)
+
+
+#define OPENCV_HAL_IMPL_RVV_NATIVE_WCVT(_Tpwvec, _Tpvec, _wTp, wcvt, suffix, width, n) \
+inline _Tpwvec wcvt (_Tpvec v, size_t vl) \
+{ \
+    _wTp tmp[n]; \
+    for (int i = 0; i < n; ++i) \
+    { \
+            tmp[i] = (_wTp)v.val[i]; \
+    } \
+    return vle##width##_v_##suffix##m1(tmp, vl); \
+}
+
+OPENCV_HAL_IMPL_RVV_NATIVE_WCVT(vuint16m1_t, vuint8mf2_t, ushort, vwcvtu_x_x_v_u16m1, u16, 16, 8)
+OPENCV_HAL_IMPL_RVV_NATIVE_WCVT(vint16m1_t, vint8mf2_t, short, vwcvt_x_x_v_i16m1, i16, 16, 8)
+OPENCV_HAL_IMPL_RVV_NATIVE_WCVT(vuint32m1_t, vuint16mf2_t, unsigned, vwcvtu_x_x_v_u32m1, u32, 32, 4)
+OPENCV_HAL_IMPL_RVV_NATIVE_WCVT(vint32m1_t, vint16mf2_t, int, vwcvt_x_x_v_i32m1, i32, 32, 4)
+OPENCV_HAL_IMPL_RVV_NATIVE_WCVT(vuint64m1_t, vuint32mf2_t, uint64, vwcvtu_x_x_v_u64m1, u64, 64, 2)
+OPENCV_HAL_IMPL_RVV_NATIVE_WCVT(vint64m1_t, vint32mf2_t, int64, vwcvt_x_x_v_i64m1, i64, 64, 2)
+
+inline vuint8mf4_t vle8_v_u8mf4 (const uint8_t *base, size_t vl)
+{
+    CV_UNUSED(vl);
+    return vuint8mf4_t(base);
+}
+inline vint8mf4_t vle8_v_i8mf4 (const int8_t *base, size_t vl)
+{
+    CV_UNUSED(vl);
+    return vint8mf4_t(base);
+}
+
+inline vuint16mf2_t vwcvtu_x_x_v_u16mf2 (vuint8mf4_t src, size_t vl)
+{
+    ushort tmp[4];
+    for (int i = 0; i < 4; ++i)
+    {
+            tmp[i] = (ushort)src.val[i];
+    }
+    return vle16_v_u16mf2(tmp, vl);
+}
+inline vint16mf2_t vwcvt_x_x_v_i16mf2 (vint8mf4_t src, size_t vl)
+{
+    short tmp[4];
+    for (int i = 0; i < 4; ++i)
+    {
+            tmp[i] = (short)src.val[i];
+    }
+    return vle16_v_i16mf2(tmp, vl);
+}
+#endif
+
+//////////// Types ////////////
+
+struct v_uint8x16
+{
+    typedef uchar lane_type;
+    enum { nlanes = 16 };
+
+    v_uint8x16() {}
+    explicit v_uint8x16(vuint8m1_t v)
+    {
+        vse8_v_u8m1(val, v, nlanes);
+    }
+    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
+               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
+    {
+        uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vuint8m1_t() const
+    {
+        return vle8_v_u8m1(val, nlanes);
+    }
+    uchar get0() const
+    {
+        return val[0];
+    }
+
+    uchar val[16];
+};
+
+struct v_int8x16
+{
+    typedef schar lane_type;
+    enum { nlanes = 16 };
+
+    v_int8x16() {}
+    explicit v_int8x16(vint8m1_t v)
+    {
+        vse8_v_i8m1(val, v, nlanes);
+    }
+    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
+               schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
+    {
+        schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vint8m1_t() const
+    {
+        return vle8_v_i8m1(val, nlanes);
+    }
+    schar get0() const
+    {
+        return val[0];
+    }
+
+    schar val[16];
+};
+
+struct v_uint16x8
+{
+    typedef ushort lane_type;
+    enum { nlanes = 8 };
+
+    v_uint16x8() {}
+    explicit v_uint16x8(vuint16m1_t v)
+    {
+        vse16_v_u16m1(val, v, nlanes);
+    }
+    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
+    {
+        ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vuint16m1_t() const
+    {
+        return vle16_v_u16m1(val, nlanes);
+    }
+    ushort get0() const
+    {
+        return val[0];
+    }
+
+    ushort val[8];
+};
+
+struct v_int16x8
+{
+    typedef short lane_type;
+    enum { nlanes = 8 };
+
+    v_int16x8() {}
+    explicit v_int16x8(vint16m1_t v)
+    {
+        vse16_v_i16m1(val, v, nlanes);
+    }
+    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
+    {
+        short v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vint16m1_t() const
+    {
+        return vle16_v_i16m1(val, nlanes);
+    }
+    short get0() const
+    {
+        return val[0];
+    }
+
+    short val[8];
+};
+
+struct v_uint32x4
+{
+    typedef unsigned lane_type;
+    enum { nlanes = 4 };
+
+    v_uint32x4() {}
+    explicit v_uint32x4(vuint32m1_t v)
+    {
+        vse32_v_u32m1(val, v, nlanes);
+    }
+    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
+    {
+        unsigned v[] = {v0, v1, v2, v3};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vuint32m1_t() const
+    {
+        return vle32_v_u32m1(val, nlanes);
+    }
+    unsigned get0() const
+    {
+        return val[0];
+    }
+
+    unsigned val[4];
+};
+
+struct v_int32x4
+{
+    typedef int lane_type;
+    enum { nlanes = 4 };
+
+    v_int32x4() {}
+    explicit v_int32x4(vint32m1_t v)
+    {
+        vse32_v_i32m1(val, v, nlanes);
+    }
+    v_int32x4(int v0, int v1, int v2, int v3)
+    {
+        int v[] = {v0, v1, v2, v3};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vint32m1_t() const
+    {
+        return vle32_v_i32m1(val, nlanes);
+    }
+    int get0() const
+    {
+        return val[0];
+    }
+    int val[4];
+};
+
+struct v_float32x4
+{
+    typedef float lane_type;
+    enum { nlanes = 4 };
+
+    v_float32x4() {}
+    explicit v_float32x4(vfloat32m1_t v)
+    {
+        vse32_v_f32m1(val, v, nlanes);
+    }
+    v_float32x4(float v0, float v1, float v2, float v3)
+    {
+        float v[] = {v0, v1, v2, v3};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vfloat32m1_t() const
+    {
+        return vle32_v_f32m1(val, nlanes);
+    }
+    float get0() const
+    {
+        return val[0];
+    }
+    float val[4];
+};
+
+struct v_uint64x2
+{
+    typedef uint64 lane_type;
+    enum { nlanes = 2 };
+
+    v_uint64x2() {}
+    explicit v_uint64x2(vuint64m1_t v)
+    {
+        vse64_v_u64m1(val, v, nlanes);
+    }
+    v_uint64x2(uint64 v0, uint64 v1)
+    {
+        uint64 v[] = {v0, v1};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vuint64m1_t() const
+    {
+        return vle64_v_u64m1(val, nlanes);
+    }
+    uint64 get0() const
+    {
+        return val[0];
+    }
+
+    uint64 val[2];
+};
+
+struct v_int64x2
+{
+    typedef int64 lane_type;
+    enum { nlanes = 2 };
+
+    v_int64x2() {}
+    explicit v_int64x2(vint64m1_t v)
+    {
+        vse64_v_i64m1(val, v, nlanes);
+    }
+    v_int64x2(int64 v0, int64 v1)
+    {
+        int64 v[] = {v0, v1};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vint64m1_t() const
+    {
+        return vle64_v_i64m1(val, nlanes);
+    }
+    int64 get0() const
+    {
+        return val[0];
+    }
+
+    int64 val[2];
+};
+
+#if CV_SIMD128_64F
+struct v_float64x2
+{
+    typedef double lane_type;
+    enum { nlanes = 2 };
+
+    v_float64x2() {}
+    explicit v_float64x2(vfloat64m1_t v)
+    {
+        vse64_v_f64m1(val, v, nlanes);
+    }
+    v_float64x2(double v0, double v1)
+    {
+        double v[] = {v0, v1};
+        for (int i = 0; i < nlanes; ++i)
+        {
+            val[i] = v[i];
+        }
+    }
+    operator vfloat64m1_t() const
+    {
+        return vle64_v_f64m1(val, nlanes);
+    }
+    double get0() const
+    {
+        return val[0];
+    }
+
+    double val[2];
+};
+#endif
+
+
+//////////// Initial ////////////
+
+#define OPENCV_HAL_IMPL_RVV_INIT_INTEGER(_Tpvec, _Tp, suffix1, suffix2, vl) \
+inline v_##_Tpvec v_setzero_##suffix1() \
+{ \
+    return v_##_Tpvec(vmv_v_x_##suffix2##m1(0, vl)); \
+} \
+inline v_##_Tpvec v_setall_##suffix1(_Tp v) \
+{ \
+    return v_##_Tpvec(vmv_v_x_##suffix2##m1(v, vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(uint8x16, uchar, u8, u8, 16)
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(int8x16, schar, s8, i8, 16)
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(uint16x8, ushort, u16, u16, 8)
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(int16x8, short, s16, i16, 8)
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(uint32x4, unsigned, u32, u32, 4)
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(int32x4, int, s32, i32, 4)
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(uint64x2, uint64, u64, u64, 2)
+OPENCV_HAL_IMPL_RVV_INIT_INTEGER(int64x2, int64, s64, i64, 2)
+
+#define OPENCV_HAL_IMPL_RVV_INIT_FP(_Tpv, _Tp, suffix, vl) \
+inline v_##_Tpv v_setzero_##suffix() \
+{ \
+    return v_##_Tpv(vfmv_v_f_##suffix##m1(0, vl)); \
+} \
+inline v_##_Tpv v_setall_##suffix(_Tp v) \
+{ \
+    return v_##_Tpv(vfmv_v_f_##suffix##m1(v, vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_INIT_FP(float32x4, float, f32, 4)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_INIT_FP(float64x2, double, f64, 2)
+#endif
+
+//////////// Reinterpret ////////////
+
+#define OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(_Tpvec, suffix) \
+inline v_##_Tpvec v_reinterpret_as_##suffix(const v_##_Tpvec& v) { return v; }
+
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(uint8x16, u8)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(int8x16, s8)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(uint16x8, u16)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(int16x8, s16)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(uint32x4, u32)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(int32x4, s32)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(float32x4, f32)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(uint64x2, u64)
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(int64x2, s64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_SELF_REINTERPRET(float64x2, f64)
+#endif
+
+#define OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(_Tpvec1, _Tpvec2, suffix1, suffix2, nsuffix1, nsuffix2) \
+inline v_##_Tpvec1 v_reinterpret_as_##suffix1(const v_##_Tpvec2& v) \
+{ \
+    return v_##_Tpvec1(vreinterpret_v_##nsuffix2##m1_##nsuffix1##m1(v));\
+} \
+inline v_##_Tpvec2 v_reinterpret_as_##suffix2(const v_##_Tpvec1& v) \
+{ \
+    return v_##_Tpvec2(vreinterpret_v_##nsuffix1##m1_##nsuffix2##m1(v));\
+}
+
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint8x16, int8x16, u8, s8, u8, i8)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint16x8, int16x8, u16, s16, u16, i16)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint32x4, int32x4, u32, s32, u32, i32)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint32x4, float32x4, u32, f32, u32, f32)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int32x4, float32x4, s32, f32, i32, f32)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint64x2, int64x2, u64, s64, u64, i64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint64x2, float64x2, u64, f64, u64, f64)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int64x2, float64x2, s64, f64, i64, f64)
+#endif
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint8x16, uint16x8, u8, u16, u8, u16)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint8x16, uint32x4, u8, u32, u8, u32)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint8x16, uint64x2, u8, u64, u8, u64)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint16x8, uint32x4, u16, u32, u16, u32)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint16x8, uint64x2, u16, u64, u16, u64)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(uint32x4, uint64x2, u32, u64, u32, u64)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int8x16, int16x8, s8, s16, i8, i16)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int8x16, int32x4, s8, s32, i8, i32)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int8x16, int64x2, s8, s64, i8, i64)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int16x8, int32x4, s16, s32, i16, i32)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int16x8, int64x2, s16, s64, i16, i64)
+OPENCV_HAL_IMPL_RVV_NATIVE_REINTERPRET(int32x4, int64x2, s32, s64, i32, i64)
+
+
+#define OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(_Tpvec1, _Tpvec2, suffix1, suffix2, nsuffix1, nsuffix2, width1, width2) \
+inline v_##_Tpvec1 v_reinterpret_as_##suffix1(const v_##_Tpvec2& v) \
+{ \
+    return v_##_Tpvec1(vreinterpret_v_##nsuffix1##width2##m1_##nsuffix1##width1##m1(vreinterpret_v_##nsuffix2##width2##m1_##nsuffix1##width2##m1(v)));\
+} \
+inline v_##_Tpvec2 v_reinterpret_as_##suffix2(const v_##_Tpvec1& v) \
+{ \
+    return v_##_Tpvec2(vreinterpret_v_##nsuffix1##width2##m1_##nsuffix2##width2##m1(vreinterpret_v_##nsuffix1##width1##m1_##nsuffix1##width2##m1(v)));\
+}
+
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint8x16, int16x8, u8, s16, u, i, 8, 16)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint8x16, int32x4, u8, s32, u, i, 8, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint8x16, int64x2, u8, s64, u, i, 8, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint16x8, int8x16, u16, s8, u, i, 16, 8)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint16x8, int32x4, u16, s32, u, i, 16, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint16x8, int64x2, u16, s64, u, i, 16, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint32x4, int8x16, u32, s8, u, i, 32, 8)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint32x4, int16x8, u32, s16, u, i, 32, 16)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint32x4, int64x2, u32, s64, u, i, 32, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint64x2, int8x16, u64, s8, u, i, 64, 8)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint64x2, int16x8, u64, s16, u, i, 64, 16)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint64x2, int32x4, u64, s32, u, i, 64, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint8x16, float32x4, u8, f32, u, f, 8, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint16x8, float32x4, u16, f32, u, f, 16, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint64x2, float32x4, u64, f32, u, f, 64, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(int8x16, float32x4, s8, f32, i, f, 8, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(int16x8, float32x4, s16, f32, i, f, 16, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(int64x2, float32x4, s64, f32, i, f, 64, 32)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint8x16, float64x2, u8, f64, u, f, 8, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint16x8, float64x2, u16, f64, u, f, 16, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(uint32x4, float64x2, u32, f64, u, f, 32, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(int8x16, float64x2, s8, f64, i, f, 8, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(int16x8, float64x2, s16, f64, i, f, 16, 64)
+OPENCV_HAL_IMPL_RVV_TWO_TIMES_REINTERPRET(int32x4, float64x2, s32, f64, i, f, 32, 64)
+
+// Three times reinterpret
+inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& v) \
+{ \
+    return v_float32x4(vreinterpret_v_u32m1_f32m1(vreinterpret_v_u64m1_u32m1(vreinterpret_v_f64m1_u64m1(v))));\
+} \
+inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& v) \
+{ \
+    return v_float64x2(vreinterpret_v_u64m1_f64m1(vreinterpret_v_u32m1_u64m1(vreinterpret_v_f32m1_u32m1(v))));\
+}
+
+////////////// Extract //////////////
+
+#define OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(_Tpvec, _Tp, suffix, vmv, vl) \
+template <int s> \
+inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vslidedown_vx_##suffix##m1(vmv_v_x_##suffix##m1(0, vl), a, s, vl), b, _Tpvec::nlanes - s, vl)); \
+} \
+template<int i> inline _Tp v_extract_n(_Tpvec v) \
+{ \
+    return _Tp(vmv(vslidedown_vx_##suffix##m1(vmv_v_x_##suffix##m1(0, vl), v, i, vl))); \
+}
+
+
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_uint8x16, uchar, u8, vmv_x_s_u8m1_u8, 16)
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_int8x16, schar, i8, vmv_x_s_i8m1_i8, 16)
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_uint16x8, ushort, u16, vmv_x_s_u16m1_u16, 8)
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_int16x8, short, i16, vmv_x_s_i16m1_i16, 8)
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_uint32x4, uint, u32, vmv_x_s_u32m1_u32, 4)
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_int32x4, int, i32, vmv_x_s_i32m1_i32, 4)
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_uint64x2, uint64, u64, vmv_x_s_u64m1_u64, 2)
+OPENCV_HAL_IMPL_RVV_EXTRACT_INTEGER(v_int64x2, int64, i64, vmv_x_s_i64m1_i64, 2)
+
+#define OPENCV_HAL_IMPL_RVV_EXTRACT_FP(_Tpvec, _Tp, suffix, vmv, vl) \
+template <int s> \
+inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vslidedown_vx_##suffix##m1(vfmv_v_f_##suffix##m1(0, vl), a, s, vl), b, _Tpvec::nlanes - s, vl)); \
+} \
+template<int i> inline _Tp v_extract_n(_Tpvec v) \
+{ \
+    return _Tp(vmv(vslidedown_vx_##suffix##m1(vfmv_v_f_##suffix##m1(0, vl), v, i, vl))); \
+}
+
+OPENCV_HAL_IMPL_RVV_EXTRACT_FP(v_float32x4, float, f32, vfmv_f_s_f32m1_f32, 4)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_EXTRACT_FP(v_float64x2, double, f64, vfmv_f_s_f64m1_f64, 2)
+#endif
+
+////////////// Load/Store //////////////
+
+#define OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(_Tpvec, _nTpvec, _Tp, hvl, vl, width, suffix, vmv) \
+inline _Tpvec v_load(const _Tp* ptr) \
+{ \
+    return _Tpvec(vle##width##_v_##suffix##m1(ptr, vl)); \
+} \
+inline _Tpvec v_load_aligned(const _Tp* ptr) \
+{ \
+    return _Tpvec(vle##width##_v_##suffix##m1(ptr, vl)); \
+} \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ \
+    _Tpvec res = _Tpvec(vle##width##_v_##suffix##m1(ptr, hvl)); \
+    return res; \
+} \
+inline void v_store(_Tp* ptr, const _Tpvec& a) \
+{ \
+    vse##width##_v_##suffix##m1(ptr, a, vl); \
+} \
+inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
+{ \
+    vse##width##_v_##suffix##m1(ptr, a, vl); \
+} \
+inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \
+{ \
+    vse##width##_v_##suffix##m1(ptr, a, vl); \
+} \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \
+{ \
+    vse##width##_v_##suffix##m1(ptr, a, vl); \
+} \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
+{ \
+    vse##width##_v_##suffix##m1(ptr, a, hvl); \
+} \
+inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
+{ \
+    vse##width##_v_##suffix##m1(ptr, vslidedown_vx_##suffix##m1(vmv(0, vl), a, hvl, vl), hvl); \
+}
+
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_uint8x16, vuint8m1_t, uchar, 8, 16, 8, u8, vmv_v_x_u8m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_int8x16, vint8m1_t, schar, 8, 16, 8, i8, vmv_v_x_i8m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_uint16x8, vuint16m1_t, ushort, 4, 8, 16, u16, vmv_v_x_u16m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_int16x8, vint16m1_t, short, 4, 8, 16, i16, vmv_v_x_i16m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_uint32x4, vuint32m1_t, unsigned, 2, 4, 32, u32, vmv_v_x_u32m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_int32x4, vint32m1_t, int, 2, 4, 32, i32, vmv_v_x_i32m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_uint64x2, vuint64m1_t, uint64, 1, 2, 64, u64, vmv_v_x_u64m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_int64x2, vint64m1_t, int64, 1, 2, 64, i64, vmv_v_x_i64m1)
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_float32x4, vfloat32m1_t, float, 2, 4, 32, f32, vfmv_v_f_f32m1)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_LOADSTORE_OP(v_float64x2, vfloat64m1_t, double, 1, 2, 64, f64, vfmv_v_f_f64m1)
+#endif
+
+inline v_int8x16 v_load_halves(const schar* ptr0, const schar* ptr1)
+{
+    schar elems[16] =
+    {
+        ptr0[0], ptr0[1], ptr0[2], ptr0[3], ptr0[4], ptr0[5], ptr0[6], ptr0[7],
+        ptr1[0], ptr1[1], ptr1[2], ptr1[3], ptr1[4], ptr1[5], ptr1[6], ptr1[7]
+    };
+    return v_int8x16(vle8_v_i8m1(elems, 16));
+}
+inline v_uint8x16 v_load_halves(const uchar* ptr0, const uchar* ptr1) { return v_reinterpret_as_u8(v_load_halves((schar*)ptr0, (schar*)ptr1)); }
+
+inline v_int16x8 v_load_halves(const short* ptr0, const short* ptr1)
+{
+    short elems[8] =
+    {
+        ptr0[0], ptr0[1], ptr0[2], ptr0[3], ptr1[0], ptr1[1], ptr1[2], ptr1[3]
+    };
+    return v_int16x8(vle16_v_i16m1(elems, 8));
+}
+inline v_uint16x8 v_load_halves(const ushort* ptr0, const ushort* ptr1) { return v_reinterpret_as_u16(v_load_halves((short*)ptr0, (short*)ptr1)); }
+
+inline v_int32x4 v_load_halves(const int* ptr0, const int* ptr1)
+{
+    int elems[4] =
+    {
+        ptr0[0], ptr0[1], ptr1[0], ptr1[1]
+    };
+    return v_int32x4(vle32_v_i32m1(elems, 4));
+}
+inline v_float32x4 v_load_halves(const float* ptr0, const float* ptr1)
+{
+    float elems[4] =
+    {
+        ptr0[0], ptr0[1], ptr1[0], ptr1[1]
+    };
+    return v_float32x4(vle32_v_f32m1(elems, 4));
+}
+inline v_uint32x4 v_load_halves(const unsigned* ptr0, const unsigned* ptr1) { return v_reinterpret_as_u32(v_load_halves((int*)ptr0, (int*)ptr1)); }
+
+inline v_int64x2 v_load_halves(const int64* ptr0, const int64* ptr1)
+{
+    int64 elems[2] =
+    {
+        ptr0[0], ptr1[0]
+    };
+    return v_int64x2(vle64_v_i64m1(elems, 2));
+}
+inline v_uint64x2 v_load_halves(const uint64* ptr0, const uint64* ptr1) { return v_reinterpret_as_u64(v_load_halves((int64*)ptr0, (int64*)ptr1)); }
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_load_halves(const double* ptr0, const double* ptr1)
+{
+    double elems[2] =
+    {
+        ptr0[0], ptr1[0]
+    };
+    return v_float64x2(vle64_v_f64m1(elems, 2));
+}
+#endif
+
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x16 v_lut(const schar* tab, const int* idx)
+{
+    schar elems[16] =
+    {
+        tab[idx[ 0]],
+        tab[idx[ 1]],
+        tab[idx[ 2]],
+        tab[idx[ 3]],
+        tab[idx[ 4]],
+        tab[idx[ 5]],
+        tab[idx[ 6]],
+        tab[idx[ 7]],
+        tab[idx[ 8]],
+        tab[idx[ 9]],
+        tab[idx[10]],
+        tab[idx[11]],
+        tab[idx[12]],
+        tab[idx[13]],
+        tab[idx[14]],
+        tab[idx[15]]
+    };
+    return v_int8x16(vle8_v_i8m1(elems, 16));
+}
+inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx)
+{
+    schar elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[4]],
+        tab[idx[4] + 1],
+        tab[idx[5]],
+        tab[idx[5] + 1],
+        tab[idx[6]],
+        tab[idx[6] + 1],
+        tab[idx[7]],
+        tab[idx[7] + 1]
+    };
+    return v_int8x16(vle8_v_i8m1(elems, 16));
+}
+inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
+{
+    schar elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[0] + 2],
+        tab[idx[0] + 3],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[1] + 2],
+        tab[idx[1] + 3],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[2] + 2],
+        tab[idx[2] + 3],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[3] + 2],
+        tab[idx[3] + 3]
+    };
+    return v_int8x16(vle8_v_i8m1(elems, 16));
+}
+inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((schar*)tab, idx)); }
+
+inline v_int16x8 v_lut(const short* tab, const int* idx)
+{
+    short elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]],
+        tab[idx[4]],
+        tab[idx[5]],
+        tab[idx[6]],
+        tab[idx[7]]
+    };
+    return v_int16x8(vle16_v_i16m1(elems, 8));
+}
+inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
+{
+    short elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1]
+    };
+    return v_int16x8(vle16_v_i16m1(elems, 8));
+}
+inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
+{
+    short elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[0] + 2],
+        tab[idx[0] + 3],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[1] + 2],
+        tab[idx[1] + 3]
+    };
+    return v_int16x8(vle16_v_i16m1(elems, 8));
+}
+inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((short*)tab, idx)); }
+inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((short*)tab, idx)); }
+inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((short*)tab, idx)); }
+
+inline v_int32x4 v_lut(const int* tab, const int* idx)
+{
+    int elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_int32x4(vle32_v_i32m1(elems, 4));
+}
+inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
+{
+    int elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1]
+    };
+    return v_int32x4(vle32_v_i32m1(elems, 4));
+}
+inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x4(vle32_v_i32m1(tab + idx[0], 4));
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((int*)tab, idx)); }
+inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((int*)tab, idx)); }
+inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((int*)tab, idx)); }
+
+inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
+{
+    int64_t elems[2] =
+    {
+        tab[idx[0]],
+        tab[idx[1]]
+    };
+    return v_int64x2(vle64_v_i64m1(elems, 2));
+}
+inline v_int64x2 v_lut_pairs(const int64* tab, const int* idx)
+{
+    return v_int64x2(vle64_v_i64m1(tab + idx[0], 2));
+}
+inline v_uint64x2 v_lut(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); }
+inline v_uint64x2 v_lut_pairs(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); }
+
+inline v_float32x4 v_lut(const float* tab, const int* idx)
+{
+    float elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_float32x4(vle32_v_f32m1(elems, 4));
+}
+inline v_float32x4 v_lut_pairs(const float* tab, const int* idx)
+{
+    float elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1]
+    };
+    return v_float32x4(vle32_v_f32m1(elems, 4));
+}
+inline v_float32x4 v_lut_quads(const float* tab, const int* idx)
+{
+    return v_float32x4(vle32_v_f32m1(tab + idx[0], 4));
+}
+
+inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
+{
+    int elems[4] =
+    {
+        tab[v_extract_n<0>(idxvec)],
+        tab[v_extract_n<1>(idxvec)],
+        tab[v_extract_n<2>(idxvec)],
+        tab[v_extract_n<3>(idxvec)]
+    };
+    return v_int32x4(vle32_v_i32m1(elems, 4));
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
+{
+    unsigned elems[4] =
+    {
+        tab[v_extract_n<0>(idxvec)],
+        tab[v_extract_n<1>(idxvec)],
+        tab[v_extract_n<2>(idxvec)],
+        tab[v_extract_n<3>(idxvec)]
+    };
+    return v_uint32x4(vle32_v_u32m1(elems, 4));
+}
+
+inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
+{
+    float elems[4] =
+    {
+        tab[v_extract_n<0>(idxvec)],
+        tab[v_extract_n<1>(idxvec)],
+        tab[v_extract_n<2>(idxvec)],
+        tab[v_extract_n<3>(idxvec)]
+    };
+    return v_float32x4(vle32_v_f32m1(elems, 4));
+}
+
+inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
+{
+    int idx[4];
+    v_store_aligned(idx, idxvec);
+
+    x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+    y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]);
+}
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_lut(const double* tab, const int* idx)
+{
+    double elems[2] =
+    {
+        tab[idx[0]],
+        tab[idx[1]]
+    };
+    return v_float64x2(vle64_v_f64m1(elems, 2));
+}
+
+inline v_float64x2 v_lut_pairs(const double* tab, const int* idx)
+{
+    return v_float64x2(vle64_v_f64m1(tab + idx[0], 2));
+}
+
+inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
+{
+    double elems[2] =
+    {
+        tab[v_extract_n<0>(idxvec)],
+        tab[v_extract_n<1>(idxvec)]
+    };
+    return v_float64x2(vle64_v_f64m1(elems, 2));
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
+{
+    int idx[4] = {0};
+    v_store_aligned(idx, idxvec);
+
+    x = v_float64x2(tab[idx[0]], tab[idx[1]]);
+    y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]);
+}
+#endif
+
+////////////// Pack boolean ////////////////////
+
+inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
+{
+    ushort ptr[16] = {0};
+    v_store(ptr, a);
+    v_store(ptr + 8, b);
+    return v_uint8x16(vnsrl_wx_u8m1(vle16_v_u16m2(ptr, 16), 0, 16));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
+                           const v_uint32x4& c, const v_uint32x4& d)
+{
+    unsigned ptr[16] = {0};
+    v_store(ptr, a);
+    v_store(ptr + 4, b);
+    v_store(ptr + 8, c);
+    v_store(ptr + 12, d);
+    return v_uint8x16(vnsrl_wx_u8m1(vnsrl_wx_u16m2(vle32_v_u32m4(ptr, 16), 0, 16), 0, 16));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
+                           const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
+                           const v_uint64x2& g, const v_uint64x2& h)
+{
+    uint64 ptr[16] = {0};
+    v_store(ptr, a);
+    v_store(ptr + 2, b);
+    v_store(ptr + 4, c);
+    v_store(ptr + 6, d);
+    v_store(ptr + 8, e);
+    v_store(ptr + 10, f);
+    v_store(ptr + 12, g);
+    v_store(ptr + 14, h);
+    return v_uint8x16(vnsrl_wx_u8m1(vnsrl_wx_u16m2(vnsrl_wx_u32m4(vle64_v_u64m8(ptr, 16), 0, 16), 0, 16), 0, 16));
+}
+
+////////////// Arithmetics //////////////
+#define OPENCV_HAL_IMPL_RVV_BIN_OP(bin_op, _Tpvec, intrin, vl) \
+inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a, b, vl)); \
+} \
+inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
+{ \
+    a = _Tpvec(intrin(a, b, vl)); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_uint8x16, vsaddu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_uint8x16, vssubu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_uint8x16, vdivu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_int8x16, vsadd_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_int8x16, vssub_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_int8x16, vdiv_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_uint16x8, vsaddu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_uint16x8, vssubu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_uint16x8, vdivu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_int16x8, vsadd_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_int16x8, vssub_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_int16x8, vdiv_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_uint32x4, vadd_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_uint32x4, vsub_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(*, v_uint32x4, vmul_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_uint32x4, vdivu_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_int32x4, vadd_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_int32x4, vsub_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(*, v_int32x4, vmul_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_int32x4, vdiv_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_float32x4, vfadd_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_float32x4, vfsub_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(*, v_float32x4, vfmul_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_float32x4, vfdiv_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_uint64x2, vadd_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_uint64x2, vsub_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(*, v_uint64x2, vmul_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_uint64x2, vdivu_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_int64x2, vadd_vv_i64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_int64x2, vsub_vv_i64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(*, v_int64x2, vmul_vv_i64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_int64x2, vdiv_vv_i64m1, 2)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_BIN_OP(+, v_float64x2, vfadd_vv_f64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(-, v_float64x2, vfsub_vv_f64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(*, v_float64x2, vfmul_vv_f64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_OP(/, v_float64x2, vfdiv_vv_f64m1, 2)
+#endif
+
+
+////////////// Bitwise logic //////////////
+
+#define OPENCV_HAL_IMPL_RVV_LOGIC_OP(_Tpvec, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_BIN_OP(&, _Tpvec, vand_vv_##suffix##m1, vl) \
+OPENCV_HAL_IMPL_RVV_BIN_OP(|, _Tpvec, vor_vv_##suffix##m1, vl) \
+OPENCV_HAL_IMPL_RVV_BIN_OP(^, _Tpvec, vxor_vv_##suffix##m1, vl) \
+inline _Tpvec operator ~ (const _Tpvec& a) \
+{ \
+    return _Tpvec(vnot_v_##suffix##m1(a, vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_uint8x16, u8, 16)
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_int8x16, i8, 16)
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_uint16x8, u16, 8)
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_int16x8, i16, 8)
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_uint32x4, u32, 4)
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_int32x4, i32, 4)
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_uint64x2, u64, 2)
+OPENCV_HAL_IMPL_RVV_LOGIC_OP(v_int64x2, i64, 2)
+
+#define OPENCV_HAL_IMPL_RVV_FLT_BIT_OP(bin_op, intrin) \
+inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \
+{ \
+    return v_float32x4(vreinterpret_v_i32m1_f32m1(intrin(vreinterpret_v_f32m1_i32m1(a), vreinterpret_v_f32m1_i32m1(b), 4))); \
+} \
+inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \
+{ \
+    a = v_float32x4(vreinterpret_v_i32m1_f32m1(intrin(vreinterpret_v_f32m1_i32m1(a), vreinterpret_v_f32m1_i32m1(b), 4))); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_RVV_FLT_BIT_OP(&, vand_vv_i32m1)
+OPENCV_HAL_IMPL_RVV_FLT_BIT_OP(|, vor_vv_i32m1)
+OPENCV_HAL_IMPL_RVV_FLT_BIT_OP(^, vxor_vv_i32m1)
+
+inline v_float32x4 operator ~ (const v_float32x4& a)
+{
+    return v_float32x4(vreinterpret_v_i32m1_f32m1(vnot_v_i32m1(vreinterpret_v_f32m1_i32m1(a), 4)));
+}
+
+#if CV_SIMD128_64F
+#define OPENCV_HAL_IMPL_RVV_FLT64_BIT_OP(bin_op, intrin) \
+inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \
+{ \
+    return v_float64x2(vreinterpret_v_i64m1_f64m1(intrin(vreinterpret_v_f64m1_i64m1(a), vreinterpret_v_f64m1_i64m1(b), 2))); \
+} \
+inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \
+{ \
+    a = v_float64x2(vreinterpret_v_i64m1_f64m1(intrin(vreinterpret_v_f64m1_i64m1(a), vreinterpret_v_f64m1_i64m1(b), 2))); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_RVV_FLT64_BIT_OP(&, vand_vv_i64m1)
+OPENCV_HAL_IMPL_RVV_FLT64_BIT_OP(|, vor_vv_i64m1)
+OPENCV_HAL_IMPL_RVV_FLT64_BIT_OP(^, vxor_vv_i64m1)
+
+inline v_float64x2 operator ~ (const v_float64x2& a)
+{
+    return v_float64x2(vreinterpret_v_i64m1_f64m1(vnot_v_i64m1(vreinterpret_v_f64m1_i64m1(a), 2)));
+}
+#endif
+
+////////////// Bitwise shifts //////////////
+
+#define OPENCV_HAL_IMPL_RVV_UNSIGNED_SHIFT_OP(_Tpvec, suffix, vl) \
+inline _Tpvec operator << (const _Tpvec& a, int n) \
+{ \
+    return _Tpvec(vsll_vx_##suffix##m1(a, uint8_t(n), vl)); \
+} \
+inline _Tpvec operator >> (const _Tpvec& a, int n) \
+{ \
+    return _Tpvec(vsrl_vx_##suffix##m1(a, uint8_t(n), vl)); \
+} \
+template<int n> inline _Tpvec v_shl(const _Tpvec& a) \
+{ \
+    return _Tpvec(vsll_vx_##suffix##m1(a, uint8_t(n), vl)); \
+} \
+template<int n> inline _Tpvec v_shr(const _Tpvec& a) \
+{ \
+    return _Tpvec(vsrl_vx_##suffix##m1(a, uint8_t(n), vl)); \
+}
+
+#define OPENCV_HAL_IMPL_RVV_SIGNED_SHIFT_OP(_Tpvec, suffix, vl) \
+inline _Tpvec operator << (const _Tpvec& a, int n) \
+{ \
+    return _Tpvec(vsll_vx_##suffix##m1(a, uint8_t(n), vl)); \
+} \
+inline _Tpvec operator >> (const _Tpvec& a, int n) \
+{ \
+    return _Tpvec(vsra_vx_##suffix##m1(a, uint8_t(n), vl)); \
+} \
+template<int n> inline _Tpvec v_shl(const _Tpvec& a) \
+{ \
+    return _Tpvec(vsll_vx_##suffix##m1(a, uint8_t(n), vl)); \
+} \
+template<int n> inline _Tpvec v_shr(const _Tpvec& a) \
+{ \
+    return _Tpvec(vsra_vx_##suffix##m1(a, uint8_t(n), vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_UNSIGNED_SHIFT_OP(v_uint8x16, u8, 16)
+OPENCV_HAL_IMPL_RVV_UNSIGNED_SHIFT_OP(v_uint16x8, u16, 8)
+OPENCV_HAL_IMPL_RVV_UNSIGNED_SHIFT_OP(v_uint32x4, u32, 4)
+OPENCV_HAL_IMPL_RVV_UNSIGNED_SHIFT_OP(v_uint64x2, u64, 2)
+OPENCV_HAL_IMPL_RVV_SIGNED_SHIFT_OP(v_int8x16, i8, 16)
+OPENCV_HAL_IMPL_RVV_SIGNED_SHIFT_OP(v_int16x8, i16, 8)
+OPENCV_HAL_IMPL_RVV_SIGNED_SHIFT_OP(v_int32x4, i32, 4)
+OPENCV_HAL_IMPL_RVV_SIGNED_SHIFT_OP(v_int64x2, i64, 2)
+
+
+////////////// Comparison //////////////
+
+#define OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, op, intrin, suffix, vl) \
+inline _Tpvec operator op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    uint64_t ones = -1; \
+    return _Tpvec(vmerge_vxm_##suffix##m1(intrin(a, b, vl), vmv_v_x_##suffix##m1(0, vl), ones, vl)); \
+}
+
+#define OPENCV_HAL_IMPL_RVV_FLOAT_CMP_OP(_Tpvec, op, intrin, suffix, vl) \
+inline _Tpvec operator op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    union { uint64 u; double d; } ones; ones.u = -1; \
+    return _Tpvec(vfmerge_vfm_##suffix##m1(intrin(a, b, vl), vfmv_v_f_##suffix##m1(0, vl), ones.d, vl)); \
+}
+
+#define OPENCV_HAL_IMPL_RVV_UNSIGNED_CMP(_Tpvec, suffix, width, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, ==, vmseq_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, !=, vmsne_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, <, vmsltu_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, >, vmsgtu_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, <=, vmsleu_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, >=, vmsgeu_vv_##suffix##m1_b##width, suffix, vl)
+
+#define OPENCV_HAL_IMPL_RVV_SIGNED_CMP(_Tpvec, suffix, width, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, ==, vmseq_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, !=, vmsne_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, <, vmslt_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, >, vmsgt_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, <=, vmsle_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_INT_CMP_OP(_Tpvec, >=, vmsge_vv_##suffix##m1_b##width, suffix, vl)
+
+#define OPENCV_HAL_IMPL_RVV_FLOAT_CMP(_Tpvec, suffix, width, vl) \
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP_OP(_Tpvec, ==, vmfeq_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP_OP(_Tpvec, !=, vmfne_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP_OP(_Tpvec, <, vmflt_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP_OP(_Tpvec, >, vmfgt_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP_OP(_Tpvec, <=, vmfle_vv_##suffix##m1_b##width, suffix, vl) \
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP_OP(_Tpvec, >=, vmfge_vv_##suffix##m1_b##width, suffix, vl)
+
+
+OPENCV_HAL_IMPL_RVV_UNSIGNED_CMP(v_uint8x16, u8, 8, 16)
+OPENCV_HAL_IMPL_RVV_UNSIGNED_CMP(v_uint16x8, u16, 16, 8)
+OPENCV_HAL_IMPL_RVV_UNSIGNED_CMP(v_uint32x4, u32, 32, 4)
+OPENCV_HAL_IMPL_RVV_UNSIGNED_CMP(v_uint64x2, u64, 64, 2)
+OPENCV_HAL_IMPL_RVV_SIGNED_CMP(v_int8x16, i8, 8, 16)
+OPENCV_HAL_IMPL_RVV_SIGNED_CMP(v_int16x8, i16, 16, 8)
+OPENCV_HAL_IMPL_RVV_SIGNED_CMP(v_int32x4, i32, 32, 4)
+OPENCV_HAL_IMPL_RVV_SIGNED_CMP(v_int64x2, i64, 64, 2)
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP(v_float32x4, f32, 32, 4)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_FLOAT_CMP(v_float64x2, f64, 64, 2)
+#endif
+
+inline v_float32x4 v_not_nan(const v_float32x4& a)
+{ return a == a; }
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_not_nan(const v_float64x2& a)
+{ return a == a; }
+#endif
+
+////////////// Min/Max //////////////
+
+#define OPENCV_HAL_IMPL_RVV_BIN_FUNC(_Tpvec, func, intrin, vl) \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a, b, vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint8x16, v_min, vminu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint8x16, v_max, vmaxu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int8x16, v_min, vmin_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int8x16, v_max, vmax_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint16x8, v_min, vminu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint16x8, v_max, vmaxu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int16x8, v_min, vmin_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int16x8, v_max, vmax_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint32x4, v_min, vminu_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint32x4, v_max, vmaxu_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int32x4, v_min, vmin_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int32x4, v_max, vmax_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_float32x4, v_min, vfmin_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_float32x4, v_max, vfmax_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint64x2, v_min, vminu_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint64x2, v_max, vmaxu_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int64x2, v_min, vmin_vv_i64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int64x2, v_max, vmax_vv_i64m1, 2)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_float64x2, v_min, vfmin_vv_f64m1, 2)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_float64x2, v_max, vfmax_vv_f64m1, 2)
+#endif
+
+////////////// Arithmetics wrap //////////////
+
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint8x16, v_add_wrap, vadd_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int8x16, v_add_wrap, vadd_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint16x8, v_add_wrap, vadd_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int16x8, v_add_wrap, vadd_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint8x16, v_sub_wrap, vsub_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int8x16, v_sub_wrap, vsub_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint16x8, v_sub_wrap, vsub_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int16x8, v_sub_wrap, vsub_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint8x16, v_mul_wrap, vmul_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int8x16, v_mul_wrap, vmul_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_uint16x8, v_mul_wrap, vmul_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_BIN_FUNC(v_int16x8, v_mul_wrap, vmul_vv_i16m1, 8)
+
+////////////// Reduce //////////////
+
+#define OPENCV_HAL_IMPL_RVV_REDUCE_SUM(_Tpvec, _wTpvec, _nwTpvec, scalartype, suffix, wsuffix, vl, red) \
+inline scalartype v_reduce_sum(const _Tpvec& a)  \
+{ \
+    _nwTpvec zero = vmv_v_x_##wsuffix##m1(0, vl); \
+    _nwTpvec res = vmv_v_x_##wsuffix##m1(0, vl); \
+    res = v##red##_vs_##suffix##m1_##wsuffix##m1(res, a, zero, vl); \
+    return (scalartype)(_wTpvec(res).get0()); \
+}
+
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_uint8x16, v_uint16x8, vuint16m1_t, unsigned, u8, u16, 16, wredsumu)
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_int8x16, v_int16x8, vint16m1_t, int, i8, i16, 16, wredsum)
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_uint16x8, v_uint32x4, vuint32m1_t, unsigned, u16, u32, 8, wredsumu)
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_int16x8, v_int32x4, vint32m1_t, int, i16, i32, 8, wredsum)
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_uint32x4, v_uint64x2, vuint64m1_t, unsigned, u32, u64, 4, wredsumu)
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_int32x4, v_int64x2, vint64m1_t, int, i32, i64, 4, wredsum)
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_uint64x2, v_uint64x2, vuint64m1_t, uint64, u64, u64, 2, redsum)
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM(v_int64x2, v_int64x2, vint64m1_t, int64, i64, i64, 2, redsum)
+
+#define OPENCV_HAL_IMPL_RVV_REDUCE_SUM_FP(_Tpvec, _wTpvec, _nwTpvec, scalartype, suffix, wsuffix, vl, red) \
+inline scalartype v_reduce_sum(const _Tpvec& a)  \
+{ \
+    _nwTpvec zero = vfmv_v_f_##wsuffix##m1(0, vl); \
+    _nwTpvec res = vfmv_v_f_##wsuffix##m1(0, vl); \
+    res = v##red##_vs_##suffix##m1_##wsuffix##m1(res, a, zero, vl); \
+    return (scalartype)(_wTpvec(res).get0()); \
+}
+
+// vfredsum for float has renamed to fredosum, also updated in GNU.
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM_FP(v_float32x4, v_float32x4, vfloat32m1_t, float, f32, f32, 4, fredosum)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_REDUCE_SUM_FP(v_float64x2, v_float64x2, vfloat64m1_t, double, f64, f64, 2, fredosum)
+#endif
+
+
+#define OPENCV_HAL_IMPL_RVV_REDUCE(_Tpvec, func, scalartype, suffix, vl, red) \
+inline scalartype v_reduce_##func(const _Tpvec& a)  \
+{ \
+    _Tpvec res = _Tpvec(v##red##_vs_##suffix##m1_##suffix##m1(a, a, a, vl)); \
+    return scalartype(res.get0()); \
+}
+
+OPENCV_HAL_IMPL_RVV_REDUCE(v_uint8x16, min, uchar, u8, 16, redminu)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_int8x16, min, schar, i8, 16, redmin)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_uint16x8, min, ushort, u16, 8, redminu)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_int16x8, min, short, i16, 8, redmin)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_uint32x4, min, unsigned, u32, 4, redminu)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_int32x4, min, int, i32, 4, redmin)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_float32x4, min, float, f32, 4, fredmin)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_uint8x16, max, uchar, u8, 16, redmaxu)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_int8x16, max, schar, i8, 16, redmax)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_uint16x8, max, ushort, u16, 8, redmaxu)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_int16x8, max, short, i16, 8, redmax)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_uint32x4, max, unsigned, u32, 4, redmaxu)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_int32x4, max, int, i32, 4, redmax)
+OPENCV_HAL_IMPL_RVV_REDUCE(v_float32x4, max, float, f32, 4, fredmax)
+
+
+inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
+                                 const v_float32x4& c, const v_float32x4& d)
+{
+    float elems[4] =
+    {
+        v_reduce_sum(a),
+        v_reduce_sum(b),
+        v_reduce_sum(c),
+        v_reduce_sum(d)
+    };
+    return v_float32x4(vle32_v_f32m1(elems, 4));
+}
+
+////////////// Square-Root //////////////
+
+inline v_float32x4 v_sqrt(const v_float32x4& x)
+{
+    return v_float32x4(vfsqrt_v_f32m1(x, 4));
+}
+
+inline v_float32x4 v_invsqrt(const v_float32x4& x)
+{
+    v_float32x4 one = v_setall_f32(1.0f);
+    return one / v_sqrt(x);
+}
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_sqrt(const v_float64x2& x)
+{
+    return v_float64x2(vfsqrt_v_f64m1(x, 4));
+}
+
+inline v_float64x2 v_invsqrt(const v_float64x2& x)
+{
+    v_float64x2 one = v_setall_f64(1.0f);
+    return one / v_sqrt(x);
+}
+#endif
+
+inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    v_float32x4 x(vfmacc_vv_f32m1(vfmul_vv_f32m1(a, a, 4), b, b, 4));
+    return v_sqrt(x);
+}
+
+inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    return v_float32x4(vfmacc_vv_f32m1(vfmul_vv_f32m1(a, a, 4), b, b, 4));
+}
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    v_float64x2 x(vfmacc_vv_f64m1(vfmul_vv_f64m1(a, a, 2), b, b, 2));
+    return v_sqrt(x);
+}
+
+inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float64x2(vfmacc_vv_f64m1(vfmul_vv_f64m1(a, a, 2), b, b, 2));
+}
+#endif
+
+////////////// Multiply-Add //////////////
+
+inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return v_float32x4(vfmacc_vv_f32m1(c, a, b, 4));
+}
+inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_int32x4(vmacc_vv_i32m1(c, a, b, 4));
+}
+
+inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_float64x2(vfmacc_vv_f64m1(c, a, b, 2));
+}
+
+inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_fma(a, b, c);
+}
+#endif
+
+////////////// Check all/any //////////////
+
+// use overloaded vcpop in clang, no casting like (vuint64m1_t) is needed.
+#ifndef __clang__
+#define OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(_Tpvec, suffix, shift, vl) \
+inline bool v_check_all(const _Tpvec& a) \
+{ \
+    v_uint64x2 v = v_uint64x2((vuint64m1_t)vsrl_vx_##suffix##m1(vnot_v_##suffix##m1(a, vl), shift, vl)); \
+    return (v.val[0] | v.val[1]) == 0; \
+} \
+inline bool v_check_any(const _Tpvec& a) \
+{ \
+    v_uint64x2 v = v_uint64x2((vuint64m1_t)vsrl_vx_##suffix##m1(a, shift, vl)); \
+    return (v.val[0] | v.val[1]) != 0; \
+}
+
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_uint8x16, u8, 7, 16)
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_uint16x8, u16, 15, 8)
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_uint32x4, u32, 31, 4)
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_uint64x2, u64, 63, 2)
+
+
+inline bool v_check_all(const v_int8x16& a)
+{ return v_check_all(v_reinterpret_as_u8(a)); }
+inline bool v_check_any(const v_int8x16& a)
+{ return v_check_any(v_reinterpret_as_u8(a)); }
+
+inline bool v_check_all(const v_int16x8& a)
+{ return v_check_all(v_reinterpret_as_u16(a)); }
+inline bool v_check_any(const v_int16x8& a)
+{ return v_check_any(v_reinterpret_as_u16(a)); }
+
+inline bool v_check_all(const v_int32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+inline bool v_check_any(const v_int32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+
+inline bool v_check_all(const v_float32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+inline bool v_check_any(const v_float32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+
+inline bool v_check_all(const v_int64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+inline bool v_check_any(const v_int64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+
+#if CV_SIMD128_64F
+inline bool v_check_all(const v_float64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+inline bool v_check_any(const v_float64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+#endif
+#else
+#define OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(_Tpvec, vl) \
+inline bool v_check_all(const _Tpvec& a) \
+{ \
+    return vcpop(vmslt(a, 0, vl), vl) == vl; \
+} \
+inline bool v_check_any(const _Tpvec& a) \
+{ \
+    return vcpop(vmslt(a, 0, vl), vl) != 0; \
+}
+
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_int8x16, 16)
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_int16x8, 8)
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_int32x4, 4)
+OPENCV_HAL_IMPL_RVV_CHECK_ALLANY(v_int64x2, 2)
+
+
+inline bool v_check_all(const v_uint8x16& a)
+{ return v_check_all(v_reinterpret_as_s8(a)); }
+inline bool v_check_any(const v_uint8x16& a)
+{ return v_check_any(v_reinterpret_as_s8(a)); }
+
+inline bool v_check_all(const v_uint16x8& a)
+{ return v_check_all(v_reinterpret_as_s16(a)); }
+inline bool v_check_any(const v_uint16x8& a)
+{ return v_check_any(v_reinterpret_as_s16(a)); }
+
+inline bool v_check_all(const v_uint32x4& a)
+{ return v_check_all(v_reinterpret_as_s32(a)); }
+inline bool v_check_any(const v_uint32x4& a)
+{ return v_check_any(v_reinterpret_as_s32(a)); }
+
+inline bool v_check_all(const v_float32x4& a)
+{ return v_check_all(v_reinterpret_as_s32(a)); }
+inline bool v_check_any(const v_float32x4& a)
+{ return v_check_any(v_reinterpret_as_s32(a)); }
+
+inline bool v_check_all(const v_uint64x2& a)
+{ return v_check_all(v_reinterpret_as_s64(a)); }
+inline bool v_check_any(const v_uint64x2& a)
+{ return v_check_any(v_reinterpret_as_s64(a)); }
+
+#if CV_SIMD128_64F
+inline bool v_check_all(const v_float64x2& a)
+{ return v_check_all(v_reinterpret_as_s64(a)); }
+inline bool v_check_any(const v_float64x2& a)
+{ return v_check_any(v_reinterpret_as_s64(a)); }
+#endif
+#endif
+////////////// abs //////////////
+
+#define OPENCV_HAL_IMPL_RVV_ABSDIFF(_Tpvec, abs) \
+inline _Tpvec v_##abs(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return v_max(a, b) - v_min(a, b); \
+}
+
+OPENCV_HAL_IMPL_RVV_ABSDIFF(v_uint8x16, absdiff)
+OPENCV_HAL_IMPL_RVV_ABSDIFF(v_uint16x8, absdiff)
+OPENCV_HAL_IMPL_RVV_ABSDIFF(v_uint32x4, absdiff)
+OPENCV_HAL_IMPL_RVV_ABSDIFF(v_float32x4, absdiff)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_ABSDIFF(v_float64x2, absdiff)
+#endif
+OPENCV_HAL_IMPL_RVV_ABSDIFF(v_int8x16, absdiffs)
+OPENCV_HAL_IMPL_RVV_ABSDIFF(v_int16x8, absdiffs)
+
+// use reinterpret instead of c-style casting.
+#ifndef __clang__
+#define OPENCV_HAL_IMPL_RVV_ABSDIFF_S(_Tpvec, _rTpvec, _nwTpvec, sub, rshr, vl) \
+inline _rTpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _rTpvec(rshr((_nwTpvec)sub(v_max(a, b), v_min(a, b), vl), 0, vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_ABSDIFF_S(v_int8x16, v_uint8x16, vuint16m2_t, vwsub_vv_i16m2, vnclipu_wx_u8m1, 16)
+OPENCV_HAL_IMPL_RVV_ABSDIFF_S(v_int16x8, v_uint16x8, vuint32m2_t, vwsub_vv_i32m2, vnclipu_wx_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_ABSDIFF_S(v_int32x4, v_uint32x4, vuint64m2_t, vwsub_vv_i64m2, vnclipu_wx_u32m1, 4)
+#else
+#define OPENCV_HAL_IMPL_RVV_ABSDIFF_S(_Tpvec, _rTpvec, _nwTpvec, sub, rshr, width, vl) \
+inline _rTpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _rTpvec(rshr(vreinterpret_u##width##m2(sub(v_max(a, b), v_min(a, b), vl)), 0, vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_ABSDIFF_S(v_int8x16, v_uint8x16, vuint16m2_t, vwsub_vv_i16m2, vnclipu_wx_u8m1, 16, 16)
+OPENCV_HAL_IMPL_RVV_ABSDIFF_S(v_int16x8, v_uint16x8, vuint32m2_t, vwsub_vv_i32m2, vnclipu_wx_u16m1, 32, 8)
+OPENCV_HAL_IMPL_RVV_ABSDIFF_S(v_int32x4, v_uint32x4, vuint64m2_t, vwsub_vv_i64m2, vnclipu_wx_u32m1, 64, 4)
+#endif
+#define OPENCV_HAL_IMPL_RVV_ABS(_Tprvec, _Tpvec, suffix) \
+inline _Tprvec v_abs(const _Tpvec& a) \
+{ \
+    return v_absdiff(a, v_setzero_##suffix()); \
+}
+
+OPENCV_HAL_IMPL_RVV_ABS(v_uint8x16, v_int8x16, s8)
+OPENCV_HAL_IMPL_RVV_ABS(v_uint16x8, v_int16x8, s16)
+OPENCV_HAL_IMPL_RVV_ABS(v_uint32x4, v_int32x4, s32)
+OPENCV_HAL_IMPL_RVV_ABS(v_float32x4, v_float32x4, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_ABS(v_float64x2, v_float64x2, f64)
+#endif
+
+
+#define OPENCV_HAL_IMPL_RVV_REDUCE_SAD(_Tpvec, scalartype) \
+inline scalartype v_reduce_sad(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return v_reduce_sum(v_absdiff(a, b)); \
+}
+
+OPENCV_HAL_IMPL_RVV_REDUCE_SAD(v_uint8x16, unsigned)
+OPENCV_HAL_IMPL_RVV_REDUCE_SAD(v_int8x16, unsigned)
+OPENCV_HAL_IMPL_RVV_REDUCE_SAD(v_uint16x8, unsigned)
+OPENCV_HAL_IMPL_RVV_REDUCE_SAD(v_int16x8, unsigned)
+OPENCV_HAL_IMPL_RVV_REDUCE_SAD(v_uint32x4, unsigned)
+OPENCV_HAL_IMPL_RVV_REDUCE_SAD(v_int32x4, unsigned)
+OPENCV_HAL_IMPL_RVV_REDUCE_SAD(v_float32x4, float)
+
+////////////// Select //////////////
+
+#define OPENCV_HAL_IMPL_RVV_SELECT(_Tpvec, merge, ne, vl) \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(merge(ne(mask, 0, vl), b, a, vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_SELECT(v_uint8x16, vmerge_vvm_u8m1, vmsne_vx_u8m1_b8, 16)
+OPENCV_HAL_IMPL_RVV_SELECT(v_int8x16, vmerge_vvm_i8m1, vmsne_vx_i8m1_b8, 16)
+OPENCV_HAL_IMPL_RVV_SELECT(v_uint16x8, vmerge_vvm_u16m1, vmsne_vx_u16m1_b16, 8)
+OPENCV_HAL_IMPL_RVV_SELECT(v_int16x8, vmerge_vvm_i16m1, vmsne_vx_i16m1_b16, 8)
+OPENCV_HAL_IMPL_RVV_SELECT(v_uint32x4, vmerge_vvm_u32m1, vmsne_vx_u32m1_b32, 4)
+OPENCV_HAL_IMPL_RVV_SELECT(v_int32x4, vmerge_vvm_i32m1, vmsne_vx_i32m1_b32, 4)
+OPENCV_HAL_IMPL_RVV_SELECT(v_float32x4, vmerge_vvm_f32m1, vmfne_vf_f32m1_b32, 4)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_SELECT(v_float64x2, vmerge_vvm_f64m1, vmfne_vf_f64m1_b64, 2)
+#endif
+
+////////////// Rotate shift //////////////
+
+#define OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(_Tpvec, suffix, vl) \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a) \
+{ \
+    return _Tpvec(vslidedown_vx_##suffix##m1(vmv_v_x_##suffix##m1(0, vl), a, n, vl)); \
+} \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vmv_v_x_##suffix##m1(0, vl), a, n, vl)); \
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \
+{ return a; } \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vslidedown_vx_##suffix##m1(vmv_v_x_##suffix##m1(0, vl), a, n, vl), b, _Tpvec::nlanes - n, vl)); \
+} \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vslidedown_vx_##suffix##m1(vmv_v_x_##suffix##m1(0, vl), b, _Tpvec::nlanes - n, vl), a, n, vl)); \
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \
+{ CV_UNUSED(b); return a; }
+
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_uint8x16, u8, 16)
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_int8x16, i8, 16)
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_uint16x8, u16, 8)
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_int16x8, i16, 8)
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_uint32x4, u32, 4)
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_int32x4, i32, 4)
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_uint64x2, u64, 2)
+OPENCV_HAL_IMPL_RVV_ROTATE_INTEGER(v_int64x2, i64, 2)
+
+#define OPENCV_HAL_IMPL_RVV_ROTATE_FP(_Tpvec, suffix, vl) \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a) \
+{ \
+    return _Tpvec(vslidedown_vx_##suffix##m1(vfmv_v_f_##suffix##m1(0, vl), a, n, vl)); \
+} \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vfmv_v_f_##suffix##m1(0, vl), a, n, vl)); \
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \
+{ return a; } \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vslidedown_vx_##suffix##m1(vfmv_v_f_##suffix##m1(0, vl), a, n, vl), b, _Tpvec::nlanes - n, vl)); \
+} \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vslideup_vx_##suffix##m1(vslidedown_vx_##suffix##m1(vfmv_v_f_##suffix##m1(0, vl), b, _Tpvec::nlanes - n, vl), a, n, vl)); \
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \
+{ CV_UNUSED(b); return a; }
+
+OPENCV_HAL_IMPL_RVV_ROTATE_FP(v_float32x4, f32, 4)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_ROTATE_FP(v_float64x2, f64, 2)
+#endif
+
+////////////// Convert to float //////////////
+
+inline v_float32x4 v_cvt_f32(const v_int32x4& a)
+{
+    return v_float32x4(vfcvt_f_x_v_f32m1(a, 4));
+}
+
+#if CV_SIMD128_64F
+inline v_float32x4 v_cvt_f32(const v_float64x2& a)
+{
+    double arr[4] = {a.val[0], a.val[1], 0, 0};
+    vfloat64m2_t tmp = vle64_v_f64m2(arr, 4);
+    return v_float32x4(vfncvt_f_f_w_f32m1(tmp, 4));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
+{
+    double arr[4] = {a.val[0], a.val[1], b.val[0], b.val[1]};
+    vfloat64m2_t tmp = vle64_v_f64m2(arr, 4);
+    return v_float32x4(vfncvt_f_f_w_f32m1(tmp, 4));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int32x4& a)
+{
+    double ptr[4] = {0};
+    vse64_v_f64m2(ptr, vfwcvt_f_x_v_f64m2(a, 4), 4);
+    double elems[2] =
+    {
+        ptr[0], ptr[1]
+    };
+    return v_float64x2(vle64_v_f64m1(elems, 2));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
+{
+    double ptr[4] = {0};
+    vse64_v_f64m2(ptr, vfwcvt_f_x_v_f64m2(a, 4), 4);
+    double elems[2] =
+    {
+        ptr[2], ptr[3]
+    };
+    return v_float64x2(vle64_v_f64m1(elems, 2));
+}
+
+inline v_float64x2 v_cvt_f64(const v_float32x4& a)
+{
+    double ptr[4] = {0};
+    vse64_v_f64m2(ptr, vfwcvt_f_f_v_f64m2(a, 4), 4);
+    double elems[2] =
+    {
+        ptr[0], ptr[1]
+    };
+    return v_float64x2(vle64_v_f64m1(elems, 2));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
+{
+    double ptr[4] = {0};
+    vse64_v_f64m2(ptr, vfwcvt_f_f_v_f64m2(a, 4), 4);
+    double elems[2] =
+    {
+        ptr[2], ptr[3]
+    };
+    return v_float64x2(vle64_v_f64m1(elems, 2));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int64x2& a)
+{
+    return v_float64x2(vfcvt_f_x_v_f64m1(a, 2));
+}
+#endif
+
+////////////// Broadcast //////////////
+
+#define OPENCV_HAL_IMPL_RVV_BROADCAST(_Tpvec, suffix) \
+template<int i> inline _Tpvec v_broadcast_element(_Tpvec v) \
+{ \
+    return v_setall_##suffix(v_extract_n<i>(v)); \
+}
+
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_uint8x16, u8)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_int8x16, s8)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_uint16x8, u16)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_int16x8, s16)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_uint32x4, u32)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_int32x4, s32)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_uint64x2, u64)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_int64x2, s64)
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_float32x4, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_BROADCAST(v_float64x2, f64)
+#endif
+
+////////////// Transpose4x4 //////////////
+
+#define OPENCV_HAL_IMPL_RVV_TRANSPOSE4x4(_Tpvec, _Tp, suffix) \
+inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \
+                         const v_##_Tpvec& a2, const v_##_Tpvec& a3, \
+                         v_##_Tpvec& b0, v_##_Tpvec& b1, \
+                         v_##_Tpvec& b2, v_##_Tpvec& b3) \
+{ \
+    _Tp elems0[4] = \
+    { \
+        v_extract_n<0>(a0), \
+        v_extract_n<0>(a1), \
+        v_extract_n<0>(a2), \
+        v_extract_n<0>(a3) \
+    }; \
+    b0 = v_load(elems0); \
+    _Tp elems1[4] = \
+    { \
+        v_extract_n<1>(a0), \
+        v_extract_n<1>(a1), \
+        v_extract_n<1>(a2), \
+        v_extract_n<1>(a3) \
+    }; \
+    b1 = v_load(elems1); \
+    _Tp elems2[4] = \
+    { \
+        v_extract_n<2>(a0), \
+        v_extract_n<2>(a1), \
+        v_extract_n<2>(a2), \
+        v_extract_n<2>(a3) \
+    }; \
+    b2 = v_load(elems2); \
+    _Tp elems3[4] = \
+    { \
+        v_extract_n<3>(a0), \
+        v_extract_n<3>(a1), \
+        v_extract_n<3>(a2), \
+        v_extract_n<3>(a3) \
+    }; \
+    b3 = v_load(elems3); \
+}
+
+OPENCV_HAL_IMPL_RVV_TRANSPOSE4x4(uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_RVV_TRANSPOSE4x4(int32x4, int, i32)
+OPENCV_HAL_IMPL_RVV_TRANSPOSE4x4(float32x4, float, f32)
+
+////////////// Reverse //////////////
+
+#define OPENCV_HAL_IMPL_RVV_REVERSE(_Tpvec, _Tp, suffix) \
+inline _Tpvec v_reverse(const _Tpvec& a)  \
+{ \
+    _Tp ptr[_Tpvec::nlanes] = {0}; \
+    _Tp ptra[_Tpvec::nlanes] = {0}; \
+    v_store(ptra, a); \
+    for (int i = 0; i < _Tpvec::nlanes; i++) \
+    { \
+        ptr[i] = ptra[_Tpvec::nlanes-i-1]; \
+    } \
+    return v_load(ptr); \
+}
+
+OPENCV_HAL_IMPL_RVV_REVERSE(v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_int8x16, schar, i8)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_int16x8, short, i16)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_int32x4, int, i32)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_float32x4, float, f32)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_RVV_REVERSE(v_int64x2, int64, i64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_REVERSE(v_float64x2, double, f64)
+#endif
+
+//////////// Value reordering ////////////
+
+#define OPENCV_HAL_IMPL_RVV_EXPAND(_Tpwvec, _Tp, _Tpvec, width, suffix, wcvt, vl) \
+inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
+{ \
+    _Tp lptr[_Tpvec::nlanes/2] = {0}; \
+    _Tp hptr[_Tpvec::nlanes/2] = {0}; \
+    v_store_low(lptr, a); \
+    v_store_high(hptr, a); \
+    b0 = _Tpwvec(wcvt(vle##width##_v_##suffix##mf2(lptr, vl), vl)); \
+    b1 = _Tpwvec(wcvt(vle##width##_v_##suffix##mf2(hptr, vl), vl)); \
+} \
+inline _Tpwvec v_expand_low(const _Tpvec& a) \
+{ \
+    _Tp lptr[_Tpvec::nlanes/2] = {0}; \
+    v_store_low(lptr, a); \
+    return _Tpwvec(wcvt(vle##width##_v_##suffix##mf2(lptr, vl), vl)); \
+} \
+inline _Tpwvec v_expand_high(const _Tpvec& a) \
+{ \
+    _Tp hptr[_Tpvec::nlanes/2] = {0}; \
+    v_store_high(hptr, a); \
+    return _Tpwvec(wcvt(vle##width##_v_##suffix##mf2(hptr, vl), vl)); \
+} \
+inline _Tpwvec v_load_expand(const _Tp* ptr) \
+{ \
+    return _Tpwvec(wcvt(vle##width##_v_##suffix##mf2(ptr, vl), vl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_EXPAND(v_uint16x8, uchar, v_uint8x16, 8, u8, vwcvtu_x_x_v_u16m1, 8)
+OPENCV_HAL_IMPL_RVV_EXPAND(v_int16x8, schar, v_int8x16, 8, i8, vwcvt_x_x_v_i16m1, 8)
+OPENCV_HAL_IMPL_RVV_EXPAND(v_uint32x4, ushort, v_uint16x8, 16, u16, vwcvtu_x_x_v_u32m1, 4)
+OPENCV_HAL_IMPL_RVV_EXPAND(v_int32x4, short, v_int16x8, 16, i16, vwcvt_x_x_v_i32m1, 4)
+OPENCV_HAL_IMPL_RVV_EXPAND(v_uint64x2, uint, v_uint32x4, 32, u32, vwcvtu_x_x_v_u64m1, 2)
+OPENCV_HAL_IMPL_RVV_EXPAND(v_int64x2, int, v_int32x4, 32, i32, vwcvt_x_x_v_i64m1, 2)
+
+inline v_uint32x4 v_load_expand_q(const uchar* ptr)
+{
+    return v_uint32x4(vwcvtu_x_x_v_u32m1(vwcvtu_x_x_v_u16mf2(vle8_v_u8mf4(ptr, 4), 4), 4));
+}
+
+inline v_int32x4 v_load_expand_q(const schar* ptr)
+{
+    return v_int32x4(vwcvt_x_x_v_i32m1(vwcvt_x_x_v_i16mf2(vle8_v_i8mf4(ptr, 4), 4), 4));
+}
+
+
+#define OPENCV_HAL_IMPL_RVV_PACK(_Tpvec, _Tp, _wTpvec, _wTp, hwidth, width, hsuffix, suffix, rshr, shr, hvl, vl) \
+inline _Tpvec v_pack(const _wTpvec& a, const _wTpvec& b) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, b); \
+    return _Tpvec(shr(vle##width##_v_##suffix##m2(arr, vl), 0, vl)); \
+} \
+inline void v_pack_store(_Tp* ptr, const _wTpvec& a) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, _wTpvec(vmv_v_x_##suffix##m1(0, hvl))); \
+    vse##hwidth##_v_##hsuffix##m1(ptr, shr(vle##width##_v_##suffix##m2(arr, vl), 0, vl), hvl); \
+} \
+template<int n> inline \
+_Tpvec v_rshr_pack(const _wTpvec& a, const _wTpvec& b) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, b); \
+    return _Tpvec(rshr(vle##width##_v_##suffix##m2(arr, vl), n, vl)); \
+} \
+template<int n> inline \
+void v_rshr_pack_store(_Tp* ptr, const _wTpvec& a) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, _wTpvec(vmv_v_x_##suffix##m1(0, hvl))); \
+    vse##hwidth##_v_##hsuffix##m1(ptr, _Tpvec(rshr(vle##width##_v_##suffix##m2(arr, vl), n, vl)), hvl); \
+}
+
+OPENCV_HAL_IMPL_RVV_PACK(v_uint8x16, uchar, v_uint16x8, ushort, 8, 16, u8, u16, vnclipu_wx_u8m1, vnclipu_wx_u8m1, 8, 16)
+OPENCV_HAL_IMPL_RVV_PACK(v_int8x16, schar, v_int16x8, short, 8, 16, i8, i16, vnclip_wx_i8m1, vnclip_wx_i8m1, 8, 16)
+OPENCV_HAL_IMPL_RVV_PACK(v_uint16x8, ushort, v_uint32x4, unsigned, 16, 32, u16, u32, vnclipu_wx_u16m1, vnclipu_wx_u16m1, 4, 8)
+OPENCV_HAL_IMPL_RVV_PACK(v_int16x8, short, v_int32x4, int, 16, 32, i16, i32, vnclip_wx_i16m1, vnclip_wx_i16m1, 4, 8)
+OPENCV_HAL_IMPL_RVV_PACK(v_uint32x4, unsigned, v_uint64x2, uint64, 32, 64, u32, u64, vnclipu_wx_u32m1, vnsrl_wx_u32m1, 2, 4)
+OPENCV_HAL_IMPL_RVV_PACK(v_int32x4, int, v_int64x2, int64, 32, 64, i32, i64, vnclip_wx_i32m1, vnsra_wx_i32m1, 2, 4)
+
+
+#define OPENCV_HAL_IMPL_RVV_PACK_U(_Tpvec, _Tp, _wTpvec, _wTp, hwidth, width, hsuffix, suffix, rshr, cast, hvl, vl) \
+inline _Tpvec v_pack_u(const _wTpvec& a, const _wTpvec& b) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, b); \
+    return _Tpvec(rshr(cast(vmax_vx_##suffix##m2(vle##width##_v_##suffix##m2(arr, vl), 0, vl)), 0, vl)); \
+} \
+inline void v_pack_u_store(_Tp* ptr, const _wTpvec& a) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, _wTpvec(vmv_v_x_##suffix##m1(0, hvl))); \
+    vse##hwidth##_v_##hsuffix##m1(ptr, rshr(cast(vmax_vx_##suffix##m2(vle##width##_v_##suffix##m2(arr, vl), 0, vl)), 0, vl), hvl); \
+} \
+template<int n> inline \
+_Tpvec v_rshr_pack_u(const _wTpvec& a, const _wTpvec& b) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, b); \
+    return _Tpvec(rshr(cast(vmax_vx_##suffix##m2(vle##width##_v_##suffix##m2(arr, vl), 0, vl)), n, vl)); \
+} \
+template<int n> inline \
+void v_rshr_pack_u_store(_Tp* ptr, const _wTpvec& a) \
+{ \
+    _wTp arr[_Tpvec::nlanes] = {0}; \
+    v_store(arr, a); \
+    v_store(arr + _wTpvec::nlanes, _wTpvec(vmv_v_x_##suffix##m1(0, hvl))); \
+    v_store(ptr, _Tpvec(rshr(cast(vmax_vx_##suffix##m2(vle##width##_v_##suffix##m2(arr, vl), 0, vl)), n, vl))); \
+}
+
+OPENCV_HAL_IMPL_RVV_PACK_U(v_uint8x16, uchar, v_int16x8, short, 8, 16, u8, i16, vnclipu_wx_u8m1, vreinterpret_v_i16m2_u16m2, 8, 16)
+OPENCV_HAL_IMPL_RVV_PACK_U(v_uint16x8, ushort, v_int32x4, int, 16, 32, u16, i32, vnclipu_wx_u16m1, vreinterpret_v_i32m2_u32m2, 4, 8)
+
+
+#define OPENCV_HAL_IMPL_RVV_UNPACKS(_Tpvec, _Tp, suffix) \
+inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \
+{ \
+    _Tp ptra0[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptra1[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb0[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb1[v_##_Tpvec::nlanes] = {0}; \
+    v_store(ptra0, a0); \
+    v_store(ptra1, a1); \
+    int i; \
+    for( i = 0; i < v_##_Tpvec::nlanes/2; i++ ) \
+    { \
+        ptrb0[i*2] = ptra0[i]; \
+        ptrb0[i*2+1] = ptra1[i]; \
+    } \
+    for( ; i < v_##_Tpvec::nlanes; i++ ) \
+    { \
+        ptrb1[i*2-v_##_Tpvec::nlanes] = ptra0[i]; \
+        ptrb1[i*2-v_##_Tpvec::nlanes+1] = ptra1[i]; \
+    } \
+    b0 = v_load(ptrb0); \
+    b1 = v_load(ptrb1); \
+} \
+inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    _Tp ptra[v_##_Tpvec::nlanes/2] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes/2] = {0}; \
+    v_store_low(ptra, a); \
+    v_store_low(ptrb, b); \
+    return v_load_halves(ptra, ptrb); \
+} \
+inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    _Tp ptra[v_##_Tpvec::nlanes/2] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes/2] = {0}; \
+    v_store_high(ptra, a); \
+    v_store_high(ptrb, b); \
+    return v_load_halves(ptra, ptrb); \
+} \
+inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \
+{ \
+    c = v_combine_low(a, b); \
+    d = v_combine_high(a, b); \
+}
+
+OPENCV_HAL_IMPL_RVV_UNPACKS(uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_RVV_UNPACKS(int8x16, schar, i8)
+OPENCV_HAL_IMPL_RVV_UNPACKS(uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_RVV_UNPACKS(int16x8, short, i16)
+OPENCV_HAL_IMPL_RVV_UNPACKS(uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_RVV_UNPACKS(int32x4, int, i32)
+OPENCV_HAL_IMPL_RVV_UNPACKS(float32x4, float, f32)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_UNPACKS(float64x2, double, f64)
+#endif
+
+
+#define OPENCV_HAL_IMPL_RVV_INTERLEAVED(_Tpvec, _Tp) \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \
+{ \
+    _Tp ptra[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes] = {0}; \
+    int i, i2; \
+    for( i = i2 = 0; i < v_##_Tpvec::nlanes; i++, i2 += 2 ) \
+    { \
+        ptra[i] = ptr[i2]; \
+        ptrb[i] = ptr[i2+1]; \
+    } \
+    a = v_load(ptra); \
+    b = v_load(ptrb); \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \
+{ \
+    _Tp ptra[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrc[v_##_Tpvec::nlanes] = {0}; \
+    int i, i3; \
+    for( i = i3 = 0; i < v_##_Tpvec::nlanes; i++, i3 += 3 ) \
+    { \
+        ptra[i] = ptr[i3]; \
+        ptrb[i] = ptr[i3+1]; \
+        ptrc[i] = ptr[i3+2]; \
+    } \
+    a = v_load(ptra); \
+    b = v_load(ptrb); \
+    c = v_load(ptrc); \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \
+                                v_##_Tpvec& c, v_##_Tpvec& d) \
+{ \
+    _Tp ptra[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrc[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrd[v_##_Tpvec::nlanes] = {0}; \
+    int i, i4; \
+    for( i = i4 = 0; i < v_##_Tpvec::nlanes; i++, i4 += 4 ) \
+    { \
+        ptra[i] = ptr[i4]; \
+        ptrb[i] = ptr[i4+1]; \
+        ptrc[i] = ptr[i4+2]; \
+        ptrd[i] = ptr[i4+3]; \
+    } \
+    a = v_load(ptra); \
+    b = v_load(ptrb); \
+    c = v_load(ptrc); \
+    d = v_load(ptrd); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    int i, i2; \
+    _Tp ptra[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes] = {0}; \
+    v_store(ptra, a); \
+    v_store(ptrb, b); \
+    for( i = i2 = 0; i < v_##_Tpvec::nlanes; i++, i2 += 2 ) \
+    { \
+        ptr[i2] = ptra[i]; \
+        ptr[i2+1] = ptrb[i]; \
+    } \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    int i, i3; \
+    _Tp ptra[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrc[v_##_Tpvec::nlanes] = {0}; \
+    v_store(ptra, a); \
+    v_store(ptrb, b); \
+    v_store(ptrc, c); \
+    for( i = i3 = 0; i < v_##_Tpvec::nlanes; i++, i3 += 3 ) \
+    { \
+        ptr[i3] = ptra[i]; \
+        ptr[i3+1] = ptrb[i]; \
+        ptr[i3+2] = ptrc[i]; \
+    } \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
+                                const v_##_Tpvec& c, const v_##_Tpvec& d, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \
+{ \
+    int i, i4; \
+    _Tp ptra[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrb[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrc[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrd[v_##_Tpvec::nlanes] = {0}; \
+    v_store(ptra, a); \
+    v_store(ptrb, b); \
+    v_store(ptrc, c); \
+    v_store(ptrd, d); \
+    for( i = i4 = 0; i < v_##_Tpvec::nlanes; i++, i4 += 4 ) \
+    { \
+        ptr[i4] = ptra[i]; \
+        ptr[i4+1] = ptrb[i]; \
+        ptr[i4+2] = ptrc[i]; \
+        ptr[i4+3] = ptrd[i]; \
+    } \
+} \
+inline v_##_Tpvec v_interleave_pairs(const v_##_Tpvec& vec) \
+{ \
+    _Tp ptr[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrvec[v_##_Tpvec::nlanes] = {0}; \
+    v_store(ptrvec, vec); \
+    for (int i = 0; i < v_##_Tpvec::nlanes/4; i++) \
+    { \
+        ptr[4*i  ] = ptrvec[4*i  ]; \
+        ptr[4*i+1] = ptrvec[4*i+2]; \
+        ptr[4*i+2] = ptrvec[4*i+1]; \
+        ptr[4*i+3] = ptrvec[4*i+3]; \
+    } \
+    return v_load(ptr); \
+} \
+inline v_##_Tpvec v_interleave_quads(const v_##_Tpvec& vec) \
+{ \
+    _Tp ptr[v_##_Tpvec::nlanes] = {0}; \
+    _Tp ptrvec[v_##_Tpvec::nlanes] = {0}; \
+    v_store(ptrvec, vec); \
+    for (int i = 0; i < v_##_Tpvec::nlanes/8; i++) \
+    { \
+        ptr[8*i  ] = ptrvec[4*i  ]; \
+        ptr[8*i+1] = ptrvec[4*i+4]; \
+        ptr[8*i+2] = ptrvec[4*i+1]; \
+        ptr[8*i+3] = ptrvec[4*i+5]; \
+        ptr[8*i+4] = ptrvec[4*i+2]; \
+        ptr[8*i+5] = ptrvec[4*i+6]; \
+        ptr[8*i+6] = ptrvec[4*i+3]; \
+        ptr[8*i+7] = ptrvec[4*i+7]; \
+    } \
+    return v_load(ptr); \
+}
+
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(uint8x16, uchar)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(int8x16, schar)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(uint16x8, ushort)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(int16x8, short)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(uint32x4, unsigned)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(int32x4, int)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(float32x4, float)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(uint64x2, uint64)
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(int64x2, int64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_INTERLEAVED(float64x2, double)
+#endif
+
+//////////// PopCount ////////////
+
+static const unsigned char popCountTable[] =
+{
+    0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8,
+};
+
+#define OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(_rTpvec, _Tpvec, _rTp, _Tp, suffix) \
+inline _rTpvec v_popcount(const _Tpvec& a) \
+{ \
+    uchar ptra[16] = {0}; \
+    v_store(ptra, v_reinterpret_as_u8(a)); \
+    _rTp ptr[_Tpvec::nlanes] = {0}; \
+    v_store(ptr, v_setzero_##suffix()); \
+    for (int i = 0; i < _Tpvec::nlanes*(int)sizeof(_Tp); i++) \
+        ptr[i/sizeof(_Tp)] += popCountTable[ptra[i]]; \
+    return v_load(ptr); \
+}
+
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint8x16, v_uint8x16, uchar, uchar, u8)
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint8x16, v_int8x16, uchar, schar, u8)
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint16x8, v_uint16x8, ushort, ushort, u16)
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint16x8, v_int16x8, ushort, short, u16)
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint32x4, v_uint32x4, unsigned, unsigned, u32)
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint32x4, v_int32x4, unsigned, int, u32)
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint64x2, v_uint64x2, uint64, uint64, u64)
+OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint64x2, v_int64x2, uint64, int64, u64)
+
+//////////// SignMask ////////////
+
+#define OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(_Tpvec, _Tp, suffix, vl, shift) \
+inline int v_signmask(const _Tpvec& a) \
+{ \
+    int mask = 0; \
+    _Tpvec tmp = _Tpvec(vsrl_vx_##suffix##m1(a, shift, vl)); \
+    for( int i = 0; i < _Tpvec::nlanes; i++ ) \
+        mask |= (int)(tmp.val[i]) << i; \
+    return mask; \
+}
+
+OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_uint8x16, uchar, u8, 16, 7)
+OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_uint16x8, ushort, u16, 8, 15)
+OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_uint32x4, unsigned, u32, 4, 31)
+OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_uint64x2, uint64, u64, 2, 63)
+
+inline int v_signmask(const v_int8x16& a)
+{ return v_signmask(v_reinterpret_as_u8(a)); }
+inline int v_signmask(const v_int16x8& a)
+{ return v_signmask(v_reinterpret_as_u16(a)); }
+inline int v_signmask(const v_int32x4& a)
+{ return v_signmask(v_reinterpret_as_u32(a)); }
+inline int v_signmask(const v_float32x4& a)
+{ return v_signmask(v_reinterpret_as_u32(a)); }
+inline int v_signmask(const v_int64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+#if CV_SIMD128_64F
+inline int v_signmask(const v_float64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+#endif
+
+
+//////////// Scan forward ////////////
+
+#define OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(_Tpvec, _Tp, suffix) \
+inline int v_scan_forward(const _Tpvec& a) \
+{ \
+    _Tp ptr[_Tpvec::nlanes] = {0}; \
+    v_store(ptr, v_reinterpret_as_##suffix(a)); \
+    for (int i = 0; i < _Tpvec::nlanes; i++) \
+        if(int(ptr[i]) < 0) \
+            return i; \
+    return 0; \
+}
+
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_int8x16, schar, s8)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_int16x8, short, s16)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_int32x4, int, s32)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_float32x4, float, f32)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_int64x2, int64, s64)
+#if CV_SIMD128_64F
+OPENCV_HAL_IMPL_RVV_SCAN_FORWOARD_OP(v_float64x2, double, f64)
+#endif
+
+//////////// Pack triplets ////////////
+
+// use reinterpret instead of c-style casting.
+#ifndef __clang__
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+    uint64 ptr[2] = {0x0908060504020100, 0xFFFFFFFF0E0D0C0A};
+    return v_int8x16((vint8m1_t)vrgather_vv_u8m1((vuint8m1_t)vint8m1_t(vec), (vuint8m1_t)vle64_v_u64m1(ptr, 2), 16));
+}
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec)
+{
+    return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec)));
+}
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+    uint64 ptr[2] = {0x0908060504020100, 0xFFFFFFFF0E0D0C0A};
+    return v_int16x8((vint16m1_t)vrgather_vv_u8m1((vuint8m1_t)vint16m1_t(vec), (vuint8m1_t)vle64_v_u64m1(ptr, 2), 16));
+}
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec)
+{
+    return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec)));
+}
+
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; }
+
+#else
+
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+    uint64 ptr[2] = {0x0908060504020100, 0xFFFFFFFF0E0D0C0A};
+    return v_int8x16(vreinterpret_i8m1(vrgather_vv_u8m1(v_reinterpret_as_u8(vec), vreinterpret_u8m1(vle64_v_u64m1(ptr, 2)), 16)));
+}
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec)
+{
+    return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec)));
+}
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+    uint64 ptr[2] = {0x0908060504020100, 0xFFFFFFFF0E0D0C0A};
+    return v_int16x8(v_reinterpret_as_s16(v_uint8x16(vrgather_vv_u8m1(v_reinterpret_as_u8(vec), vreinterpret_u8m1(vle64_v_u64m1(ptr, 2)), 16))));
+}
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec)
+{
+    return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec)));
+}
+
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; }
+
+#endif
+
+////// FP16 support ///////
+
+#if CV_FP16
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    return v_float32x4(vfwcvt_f_f_v_f32m1(vle16_v_f16mf2(ptr, 4), 4));
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    vse16_v_f16mf2(ptr, vfncvt_f_f_w_f16mf2(v, 4), 4);
+}
+#else
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    const int N = 4;
+    float buf[N];
+    for( int i = 0; i < N; i++ ) buf[i] = (float)ptr[i];
+    return v_load(buf);
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    const int N = 4;
+    float buf[N];
+    v_store(buf, v);
+    for( int i = 0; i < N; i++ ) ptr[i] = float16_t(buf[i]);
+}
+#endif
+
+////////////// Rounding //////////////
+
+inline v_int32x4 v_round(const v_float32x4& a)
+{
+    return v_int32x4(vfcvt_x_f_v_i32m1(a, 4));
+}
+
+inline v_int32x4 v_floor(const v_float32x4& a)
+{
+    v_float32x4 ZP5 = v_setall_f32(0.5f);
+    v_float32x4 t = a - ZP5;
+    return v_int32x4(vfcvt_x_f_v_i32m1(t, 4));
+}
+
+inline v_int32x4 v_ceil(const v_float32x4& a)
+{
+    v_float32x4 ZP5 = v_setall_f32(0.5f);
+    v_float32x4 t = a + ZP5;
+    return v_int32x4(vfcvt_x_f_v_i32m1(t, 4));
+}
+
+inline v_int32x4 v_trunc(const v_float32x4& a)
+{
+    return v_int32x4(vfcvt_rtz_x_f_v_i32m1(a, 4));
+}
+#if CV_SIMD128_64F
+inline v_int32x4 v_round(const v_float64x2& a)
+{
+    double arr[4] = {a.val[0], a.val[1], 0, 0};
+    vfloat64m2_t tmp = vle64_v_f64m2(arr, 4);
+    return v_int32x4(vfncvt_x_f_w_i32m1(tmp, 4));
+}
+
+inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
+{
+    double arr[4] = {a.val[0], a.val[1], b.val[0], b.val[1]};
+    vfloat64m2_t tmp = vle64_v_f64m2(arr, 4);
+    return v_int32x4(vfncvt_x_f_w_i32m1(tmp, 4));
+}
+
+inline v_int32x4 v_floor(const v_float64x2& a)
+{
+    double arr[4] = {a.val[0]-0.5f, a.val[1]-0.5f, 0, 0};
+    vfloat64m2_t tmp = vle64_v_f64m2(arr, 4);
+    return v_int32x4(vfncvt_x_f_w_i32m1(tmp, 4));
+}
+
+inline v_int32x4 v_ceil(const v_float64x2& a)
+{
+    double arr[4] = {a.val[0]+0.5f, a.val[1]+0.5f, 0, 0};
+    vfloat64m2_t tmp = vle64_v_f64m2(arr, 4);
+    return v_int32x4(vfncvt_x_f_w_i32m1(tmp, 4));
+}
+
+inline v_int32x4 v_trunc(const v_float64x2& a)
+{
+    double arr[4] = {a.val[0], a.val[1], 0, 0};
+    vfloat64m2_t tmp = vle64_v_f64m2(arr, 4);
+    return v_int32x4(vfncvt_rtz_x_f_w_i32m1(tmp, 4));
+}
+#endif
+
+
+//////// Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
+{
+    int ptr[8] = {0};
+    v_int32x4 t1, t2;
+    vse32_v_i32m2(ptr, vwmul_vv_i32m2(a, b, 8), 8);
+    v_load_deinterleave(ptr, t1, t2);
+    return t1 + t2;
+}
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{
+    int ptr[8] = {0};
+    v_int32x4 t1, t2;
+    vse32_v_i32m2(ptr, vwmul_vv_i32m2(a, b, 8), 8);
+    v_load_deinterleave(ptr, t1, t2);
+    return t1 + t2 + c;
+}
+
+// 32 >> 64
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
+{
+    int64 ptr[4] = {0};
+    v_int64x2 t1, t2;
+    vse64_v_i64m2(ptr, vwmul_vv_i64m2(a, b, 4), 4);
+    v_load_deinterleave(ptr, t1, t2);
+    return t1 + t2;
+}
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{
+    int64 ptr[4] = {0};
+    v_int64x2 t1, t2;
+    vse64_v_i64m2(ptr, vwmul_vv_i64m2(a, b, 4), 4);
+    v_load_deinterleave(ptr, t1, t2);
+    return t1 + t2 + c;
+}
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
+{
+    unsigned ptr[16] = {0};
+    v_uint32x4 t1, t2, t3, t4;
+    vse32_v_u32m4(ptr, vwcvtu_x_x_v_u32m4(vwmulu_vv_u16m2(a, b, 16), 16), 16);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4;
+}
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b,
+                                   const v_uint32x4& c)
+{
+    unsigned ptr[16] = {0};
+    v_uint32x4 t1, t2, t3, t4;
+    vse32_v_u32m4(ptr, vwcvtu_x_x_v_u32m4(vwmulu_vv_u16m2(a, b, 16), 16), 16);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4 + c;
+}
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
+{
+    int ptr[16] = {0};
+    v_int32x4 t1, t2, t3, t4;
+    vse32_v_i32m4(ptr, vwcvt_x_x_v_i32m4(vwmul_vv_i16m2(a, b, 16), 16), 16);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4;
+}
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b,
+                                  const v_int32x4& c)
+{
+    int ptr[16] = {0};
+    v_int32x4 t1, t2, t3, t4;
+    vse32_v_i32m4(ptr, vwcvt_x_x_v_i32m4(vwmul_vv_i16m2(a, b, 16), 16), 16);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4 + c;
+}
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
+{
+    uint64 ptr[8] = {0};
+    v_uint64x2 t1, t2, t3, t4;
+    vse64_v_u64m4(ptr, vwcvtu_x_x_v_u64m4(vwmulu_vv_u32m2(a, b, 8), 8), 8);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4;
+}
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{
+    uint64 ptr[8] = {0};
+    v_uint64x2 t1, t2, t3, t4;
+    vse64_v_u64m4(ptr, vwcvtu_x_x_v_u64m4(vwmulu_vv_u32m2(a, b, 8), 8), 8);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4 + c;
+}
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
+{
+    int64 ptr[8] = {0};
+    v_int64x2 t1, t2, t3, t4;
+    vse64_v_i64m4(ptr, vwcvt_x_x_v_i64m4(vwmul_vv_i32m2(a, b, 8), 8), 8);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4;
+}
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b,
+                                  const v_int64x2& c)
+{
+    int64 ptr[8] = {0};
+    v_int64x2 t1, t2, t3, t4;
+    vse64_v_i64m4(ptr, vwcvt_x_x_v_i64m4(vwmul_vv_i32m2(a, b, 8), 8), 8);
+    v_load_deinterleave(ptr, t1, t2, t3, t4);
+    return t1 + t2 + t3 + t4 + c;
+}
+
+// 32 >> 64f
+#if CV_SIMD128_64F
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a,   const v_int32x4& b,
+                                    const v_float64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+#endif
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
+{
+    int ptr[8] = {0};
+    vse32_v_i32m2(ptr, vwmul_vv_i32m2(a, b, 8), 8);
+    v_int32x4 t1 = v_load(ptr);
+    v_int32x4 t2 = v_load(ptr+4);
+    return t1 + t2;
+}
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{
+    int ptr[8] = {0};
+    vse32_v_i32m2(ptr, vwmul_vv_i32m2(a, b, 8), 8);
+    v_int32x4 t1 = v_load(ptr);
+    v_int32x4 t2 = v_load(ptr+4);
+    return t1 + t2 + c;
+}
+
+// 32 >> 64
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
+{
+    int64 ptr[4] = {0};
+    vse64_v_i64m2(ptr, vwmul_vv_i64m2(a, b, 4), 4);
+    v_int64x2 t1 = v_load(ptr);
+    v_int64x2 t2 = v_load(ptr+2);
+    return t1 + t2;
+}
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{
+    int64 ptr[4] = {0};
+    vse64_v_i64m2(ptr, vwmul_vv_i64m2(a, b, 4), 4);
+    v_int64x2 t1 = v_load(ptr);
+    v_int64x2 t2 = v_load(ptr+2);
+    return t1 + t2 + c;
+}
+
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
+{
+    unsigned ptr[16] = {0};
+    vse32_v_u32m4(ptr, vwcvtu_x_x_v_u32m4(vwmulu_vv_u16m2(a, b, 16), 16), 16);
+    v_uint32x4 t1 = v_load(ptr);
+    v_uint32x4 t2 = v_load(ptr+4);
+    v_uint32x4 t3 = v_load(ptr+8);
+    v_uint32x4 t4 = v_load(ptr+12);
+    return t1 + t2 + t3 + t4;
+}
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{
+    unsigned ptr[16] = {0};
+    vse32_v_u32m4(ptr, vwcvtu_x_x_v_u32m4(vwmulu_vv_u16m2(a, b, 16), 16), 16);
+    v_uint32x4 t1 = v_load(ptr);
+    v_uint32x4 t2 = v_load(ptr+4);
+    v_uint32x4 t3 = v_load(ptr+8);
+    v_uint32x4 t4 = v_load(ptr+12);
+    return t1 + t2 + t3 + t4 + c;
+}
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
+{
+    int ptr[16] = {0};
+    vse32_v_i32m4(ptr, vwcvt_x_x_v_i32m4(vwmul_vv_i16m2(a, b, 16), 16), 16);
+    v_int32x4 t1 = v_load(ptr);
+    v_int32x4 t2 = v_load(ptr+4);
+    v_int32x4 t3 = v_load(ptr+8);
+    v_int32x4 t4 = v_load(ptr+12);
+    return t1 + t2 + t3 + t4;
+}
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{
+    int ptr[16] = {0};
+    vse32_v_i32m4(ptr, vwcvt_x_x_v_i32m4(vwmul_vv_i16m2(a, b, 16), 16), 16);
+    v_int32x4 t1 = v_load(ptr);
+    v_int32x4 t2 = v_load(ptr+4);
+    v_int32x4 t3 = v_load(ptr+8);
+    v_int32x4 t4 = v_load(ptr+12);
+    return t1 + t2 + t3 + t4 + c;
+}
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
+{
+    uint64 ptr[8] = {0};
+    vse64_v_u64m4(ptr, vwcvtu_x_x_v_u64m4(vwmulu_vv_u32m2(a, b, 8), 8), 8);
+    v_uint64x2 t1 = v_load(ptr);
+    v_uint64x2 t2 = v_load(ptr+2);
+    v_uint64x2 t3 = v_load(ptr+4);
+    v_uint64x2 t4 = v_load(ptr+6);
+    return t1 + t2 + t3 + t4;
+}
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{
+    uint64 ptr[8] = {0};
+    vse64_v_u64m4(ptr, vwcvtu_x_x_v_u64m4(vwmulu_vv_u32m2(a, b, 8), 8), 8);
+    v_uint64x2 t1 = v_load(ptr);
+    v_uint64x2 t2 = v_load(ptr+2);
+    v_uint64x2 t3 = v_load(ptr+4);
+    v_uint64x2 t4 = v_load(ptr+6);
+    return t1 + t2 + t3 + t4 + c;
+}
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
+{
+    int64 ptr[8] = {0};
+    vse64_v_i64m4(ptr, vwcvt_x_x_v_i64m4(vwmul_vv_i32m2(a, b, 8), 8), 8);
+    v_int64x2 t1 = v_load(ptr);
+    v_int64x2 t2 = v_load(ptr+2);
+    v_int64x2 t3 = v_load(ptr+4);
+    v_int64x2 t4 = v_load(ptr+6);
+    return t1 + t2 + t3 + t4;
+}
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{
+    int64 ptr[8] = {0};
+    vse64_v_i64m4(ptr, vwcvt_x_x_v_i64m4(vwmul_vv_i32m2(a, b, 8), 8), 8);
+    v_int64x2 t1 = v_load(ptr);
+    v_int64x2 t2 = v_load(ptr+2);
+    v_int64x2 t3 = v_load(ptr+4);
+    v_int64x2 t4 = v_load(ptr+6);
+    return t1 + t2 + t3 + t4 + c;
+}
+
+// 32 >> 64f
+#if CV_SIMD128_64F
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod_fast(a, b)); }
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+#endif
+
+
+inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
+                            const v_float32x4& m1, const v_float32x4& m2,
+                            const v_float32x4& m3)
+{
+    vfloat32m1_t res = vfmul_vf_f32m1(m0, v_extract_n<0>(v), 4);
+    res = vfmacc_vf_f32m1(res, v_extract_n<1>(v), m1, 4);
+    res = vfmacc_vf_f32m1(res, v_extract_n<2>(v), m2, 4);
+    res = vfmacc_vf_f32m1(res, v_extract_n<3>(v), m3, 4);
+    return v_float32x4(res);
+}
+
+inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
+                               const v_float32x4& m1, const v_float32x4& m2,
+                               const v_float32x4& a)
+{
+    vfloat32m1_t res = vfmul_vf_f32m1(m0, v_extract_n<0>(v), 4);
+    res = vfmacc_vf_f32m1(res, v_extract_n<1>(v), m1, 4);
+    res = vfmacc_vf_f32m1(res, v_extract_n<2>(v), m2, 4);
+    return v_float32x4(res) + a;
+}
+
+#define OPENCV_HAL_IMPL_RVV_MUL_EXPAND(_Tpvec, _Tpwvec, _Tpw, suffix, wmul, width, vl, hvl) \
+inline void v_mul_expand(const _Tpvec& a, const _Tpvec& b, _Tpwvec& c, _Tpwvec& d) \
+{ \
+    _Tpw ptr[_Tpwvec::nlanes*2] = {0}; \
+    vse##width##_v_##suffix##m2(ptr, wmul(a, b, vl), vl); \
+    c = _Tpwvec(vle##width##_v_##suffix##m1(ptr, hvl)); \
+    d = _Tpwvec(vle##width##_v_##suffix##m1(ptr+_Tpwvec::nlanes, hvl)); \
+}
+
+OPENCV_HAL_IMPL_RVV_MUL_EXPAND(v_uint8x16, v_uint16x8, ushort, u16, vwmulu_vv_u16m2, 16, 16, 8)
+OPENCV_HAL_IMPL_RVV_MUL_EXPAND(v_int8x16, v_int16x8, short, i16, vwmul_vv_i16m2, 16, 16, 8)
+OPENCV_HAL_IMPL_RVV_MUL_EXPAND(v_uint16x8, v_uint32x4, unsigned, u32, vwmulu_vv_u32m2, 32, 8, 4)
+OPENCV_HAL_IMPL_RVV_MUL_EXPAND(v_int16x8, v_int32x4, int, i32, vwmul_vv_i32m2, 32, 8, 4)
+OPENCV_HAL_IMPL_RVV_MUL_EXPAND(v_uint32x4, v_uint64x2, uint64, u64, vwmulu_vv_u64m2, 64, 4, 2)
+
+
+inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b)
+{
+    return v_int16x8(vnsra_wx_i16m1(vwmul_vv_i32m2(a, b, 8), 16, 8));
+}
+inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b)
+{
+    return v_uint16x8(vnsrl_wx_u16m1(vwmulu_vv_u32m2(a, b, 8), 16, 8));
+}
+
+
+//////// Saturating Multiply ////////
+
+#define OPENCV_HAL_IMPL_RVV_MUL_SAT(_Tpvec, _wTpvec) \
+inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    _wTpvec c, d; \
+    v_mul_expand(a, b, c, d); \
+    return v_pack(c, d); \
+} \
+inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \
+{ \
+    a = a * b; \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_RVV_MUL_SAT(v_uint8x16, v_uint16x8)
+OPENCV_HAL_IMPL_RVV_MUL_SAT(v_int8x16, v_int16x8)
+OPENCV_HAL_IMPL_RVV_MUL_SAT(v_uint16x8, v_uint32x4)
+OPENCV_HAL_IMPL_RVV_MUL_SAT(v_int16x8, v_int32x4)
+
+
+inline void v_cleanup() {}
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+
+}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin_rvv071.hpp b/3rdParty/include/opencv2/core/hal/intrin_rvv071.hpp
new file mode 100644
index 0000000..2bdc622
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_rvv071.hpp
@@ -0,0 +1,2545 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+// Copyright (C) 2015, PingTouGe Semiconductor Co., Ltd., all rights reserved.
+
+#ifndef OPENCV_HAL_INTRIN_RISCVV_HPP
+#define OPENCV_HAL_INTRIN_RISCVV_HPP
+
+#include <float.h>
+#include <algorithm>
+#include "opencv2/core/utility.hpp"
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+#define CV_SIMD128 1
+#define CV_SIMD128_64F 1
+//////////// Types ////////////
+struct v_uint8x16
+{
+    typedef uchar lane_type;
+    enum { nlanes = 16 };
+
+    v_uint8x16() {}
+    explicit v_uint8x16(vuint8m1_t v) : val(v) {}
+    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
+               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
+    {
+        uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = (vuint8m1_t)vle_v_u8m1((unsigned char*)v, 16);
+    }
+    uchar get0() const
+    {
+        return vmv_x_s_u8m1_u8(val, 16);
+    }
+
+    vuint8m1_t val;
+};
+
+struct v_int8x16
+{
+    typedef schar lane_type;
+    enum { nlanes = 16 };
+
+    v_int8x16() {}
+    explicit v_int8x16(vint8m1_t v) : val(v) {}
+    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
+               schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
+    {
+        schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = (vint8m1_t)vle_v_i8m1((schar*)v, 16);
+    }
+    schar get0() const
+    {
+        return vmv_x_s_i8m1_i8(val, 16);
+    }
+
+    vint8m1_t val;
+};
+
+struct v_uint16x8
+{
+    typedef ushort lane_type;
+    enum { nlanes = 8 };
+
+    v_uint16x8() {}
+    explicit v_uint16x8(vuint16m1_t v) : val(v) {}
+    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
+    {
+        ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = (vuint16m1_t)vle_v_u16m1((unsigned short*)v, 8);
+    }
+    ushort get0() const
+    {
+        return vmv_x_s_u16m1_u16(val, 8);
+    }
+
+    vuint16m1_t val;
+};
+
+struct v_int16x8
+{
+    typedef short lane_type;
+    enum { nlanes = 8 };
+
+    v_int16x8() {}
+    explicit v_int16x8(vint16m1_t v) : val(v) {}
+    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
+    {
+        short v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = (vint16m1_t)vle_v_i16m1((signed short*)v, 8);
+    }
+    short get0() const
+    {
+        return vmv_x_s_i16m1_i16(val, 8);
+    }
+
+    vint16m1_t val;
+};
+
+struct v_uint32x4
+{
+    typedef unsigned lane_type;
+    enum { nlanes = 4 };
+
+    v_uint32x4() {}
+    explicit v_uint32x4(vuint32m1_t v) : val(v) {}
+    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
+    {
+        unsigned v[] = {v0, v1, v2, v3};
+        val = (vuint32m1_t)vle_v_u32m1((unsigned int*)v, 4);
+    }
+    unsigned get0() const
+    {
+        return vmv_x_s_u32m1_u32(val, 4);
+    }
+
+    vuint32m1_t val;
+};
+
+struct v_int32x4
+{
+    typedef int lane_type;
+    enum { nlanes = 4 };
+
+    v_int32x4() {}
+    explicit v_int32x4(vint32m1_t v) : val(v) {}
+    v_int32x4(int v0, int v1, int v2, int v3)
+    {
+        int v[] = {v0, v1, v2, v3};
+        val = (vint32m1_t)vle_v_i32m1((signed int*)v, 4);
+    }
+    int get0() const
+    {
+        return vmv_x_s_i32m1_i32(val, 4);
+    }
+    vint32m1_t val;
+};
+
+struct v_float32x4
+{
+    typedef float lane_type;
+    enum { nlanes = 4 };
+
+    v_float32x4() {}
+    explicit v_float32x4(vfloat32m1_t v) : val(v) {}
+    v_float32x4(float v0, float v1, float v2, float v3)
+    {
+        float v[] = {v0, v1, v2, v3};
+        val = (vfloat32m1_t)vle_v_f32m1((float*)v, 4);
+    }
+    float get0() const
+    {
+        return vfmv_f_s_f32m1_f32(val, 4);
+    }
+    vfloat32m1_t val;
+};
+
+struct v_uint64x2
+{
+    typedef uint64 lane_type;
+    enum { nlanes = 2 };
+
+    v_uint64x2() {}
+    explicit v_uint64x2(vuint64m1_t v) : val(v) {}
+    v_uint64x2(uint64 v0, uint64 v1)
+    {
+        uint64 v[] = {v0, v1};
+        val = (vuint64m1_t)vle_v_u64m1((unsigned long*)v, 2);
+    }
+    uint64 get0() const
+    {
+        return vmv_x_s_u64m1_u64(val, 2);
+    }
+    vuint64m1_t val;
+};
+
+struct v_int64x2
+{
+    typedef int64 lane_type;
+    enum { nlanes = 2 };
+
+    v_int64x2() {}
+    explicit v_int64x2(vint64m1_t v) : val(v) {}
+    v_int64x2(int64 v0, int64 v1)
+    {
+        int64 v[] = {v0, v1};
+        val = (vint64m1_t)vle_v_i64m1((long*)v, 2);
+    }
+    int64 get0() const
+    {
+        return vmv_x_s_i64m1_i64(val, 2);
+    }
+    vint64m1_t val;
+};
+
+struct v_float64x2
+{
+    typedef double lane_type;
+    enum { nlanes = 2 };
+
+    v_float64x2() {}
+    explicit v_float64x2(vfloat64m1_t v) : val(v) {}
+    v_float64x2(double v0, double v1)
+    {
+        double v[] = {v0, v1};
+        val = (vfloat64m1_t)vle_v_f64m1((double*)v, 2);
+    }
+    double get0() const
+    {
+        return vfmv_f_s_f64m1_f64(val, 2);
+    }
+    vfloat64m1_t val;
+};
+
+#define OPENCV_HAL_IMPL_RISCVV_INIT(_Tpv, _Tp, suffix) \
+inline _Tp##m1_t vreinterpretq_##suffix##_##suffix(_Tp##m1_t v) { return v; } \
+inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16((vuint8m1_t)(v.val)); } \
+inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16((vint8m1_t)(v.val)); } \
+inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8((vuint16m1_t)(v.val)); } \
+inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8((vint16m1_t)(v.val)); } \
+inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4((vuint32m1_t)(v.val)); } \
+inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4((vint32m1_t)(v.val)); } \
+inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2((vuint64m1_t)(v.val)); } \
+inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2((vint64m1_t)(v.val)); } \
+inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4((vfloat32m1_t)(v.val)); }\
+inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2((vfloat64m1_t)(v.val)); }
+
+
+OPENCV_HAL_IMPL_RISCVV_INIT(uint8x16, vuint8, u8)
+OPENCV_HAL_IMPL_RISCVV_INIT(int8x16, vint8, s8)
+OPENCV_HAL_IMPL_RISCVV_INIT(uint16x8, vuint16, u16)
+OPENCV_HAL_IMPL_RISCVV_INIT(int16x8, vint16, s16)
+OPENCV_HAL_IMPL_RISCVV_INIT(uint32x4, vuint32, u32)
+OPENCV_HAL_IMPL_RISCVV_INIT(int32x4, vint32, s32)
+OPENCV_HAL_IMPL_RISCVV_INIT(uint64x2, vuint64, u64)
+OPENCV_HAL_IMPL_RISCVV_INIT(int64x2, vint64, s64)
+OPENCV_HAL_IMPL_RISCVV_INIT(float64x2, vfloat64, f64)
+OPENCV_HAL_IMPL_RISCVV_INIT(float32x4, vfloat32, f32)
+#define OPENCV_HAL_IMPL_RISCVV_INIT_SET(__Tp, _Tp, suffix, len, num) \
+inline v_##_Tp##x##num v_setzero_##suffix() { return v_##_Tp##x##num((v##_Tp##m1_t){0}); }     \
+inline v_##_Tp##x##num v_setall_##suffix(__Tp v) { return v_##_Tp##x##num(vmv_v_x_##len##m1(v, num)); }
+
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(uchar, uint8, u8, u8, 16)
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(char, int8, s8, i8, 16)
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(ushort, uint16, u16, u16, 8)
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(short, int16, s16, i16, 8)
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(unsigned int, uint32, u32, u32, 4)
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(int, int32, s32, i32, 4)
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(unsigned long, uint64, u64, u64, 2)
+OPENCV_HAL_IMPL_RISCVV_INIT_SET(long, int64, s64, i64, 2)
+inline v_float32x4 v_setzero_f32() { return v_float32x4((vfloat32m1_t){0}); }
+inline v_float32x4 v_setall_f32(float v) { return v_float32x4(vfmv_v_f_f32m1(v, 4)); }
+
+inline v_float64x2 v_setzero_f64() { return v_float64x2(vfmv_v_f_f64m1(0, 2)); }
+inline v_float64x2 v_setall_f64(double v) { return v_float64x2(vfmv_v_f_f64m1(v, 2)); }
+
+
+#define OPENCV_HAL_IMPL_RISCVV_BIN_OP(bin_op, _Tpvec, intrin) \
+inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+} \
+inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
+{ \
+    a.val = intrin(a.val, b.val); \
+    return a; \
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_BIN_OPN(bin_op, _Tpvec, intrin, num) \
+inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val, num)); \
+} \
+inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
+{ \
+    a.val = intrin(a.val, b.val, num); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_uint8x16, vsaddu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_uint8x16, vssubu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_int8x16, vsadd_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_int8x16, vssub_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_uint16x8, vsaddu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_uint16x8, vssubu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_int16x8, vsadd_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_int16x8, vssub_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_int32x4, vsadd_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_int32x4, vssub_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(*, v_int32x4, vmul_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_uint32x4, vadd_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_uint32x4, vsub_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(*, v_uint32x4, vmul_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_int64x2, vsadd_vv_i64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_int64x2, vssub_vv_i64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_uint64x2, vadd_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_uint64x2, vsub_vv_u64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_float32x4, vfadd_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_float32x4, vfsub_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(*, v_float32x4, vfmul_vv_f32m1, 4)
+inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b)
+{
+    return v_float32x4(vfdiv_vv_f32m1(a.val, b.val, 4));
+}
+inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b)
+{
+    a.val = vfdiv_vv_f32m1(a.val, b.val, 4);
+    return a;
+}
+
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(+, v_float64x2, vfadd_vv_f64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(-, v_float64x2, vfsub_vv_f64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_BIN_OPN(*, v_float64x2, vfmul_vv_f64m1, 2)
+inline v_float64x2 operator / (const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float64x2(vfdiv_vv_f64m1(a.val, b.val, 2));
+}
+inline v_float64x2& operator /= (v_float64x2& a, const v_float64x2& b)
+{
+    a.val = vfdiv_vv_f64m1(a.val, b.val, 2);
+    return a;
+}
+// TODO: exp, log, sin, cos
+
+#define OPENCV_HAL_IMPL_RISCVV_BIN_FUNC(_Tpvec, func, intrin) \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(_Tpvec, func, intrin, num) \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val, num)); \
+}
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint8x16, v_min, vminu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint8x16, v_max, vmaxu_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int8x16, v_min, vmin_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int8x16, v_max, vmax_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint16x8, v_min, vminu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint16x8, v_max, vmaxu_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int16x8, v_min, vmin_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int16x8, v_max, vmax_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint32x4, v_min, vminu_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint32x4, v_max, vmaxu_vv_u32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int32x4, v_min, vmin_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int32x4, v_max, vmax_vv_i32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_float32x4, v_min, vfmin_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_float32x4, v_max, vfmax_vv_f32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_float64x2, v_min, vfmin_vv_f64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_float64x2, v_max, vfmax_vv_f64m1, 2)
+
+inline v_float32x4 v_sqrt(const v_float32x4& x)
+{
+    return v_float32x4(vfsqrt_v_f32m1(x.val, 4));
+}
+
+inline v_float32x4 v_invsqrt(const v_float32x4& x)
+{
+    return v_float32x4(vfrdiv_vf_f32m1(vfsqrt_v_f32m1(x.val, 4), 1, 4));
+}
+
+inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    v_float32x4 x(vfmacc_vv_f32m1(vfmul_vv_f32m1(a.val, a.val, 4), b.val, b.val, 4));
+    return v_sqrt(x);
+}
+
+inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b)
+{
+    return v_float32x4(vfmacc_vv_f32m1(vfmul_vv_f32m1(a.val, a.val, 4), b.val, b.val, 4));
+}
+
+inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return v_float32x4(vfmacc_vv_f32m1(c.val, a.val, b.val, 4));
+}
+
+inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_int32x4(vmacc_vv_i32m1(c.val, a.val, b.val, 4));
+}
+
+inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
+                            const v_float32x4& m1, const v_float32x4& m2,
+                            const v_float32x4& m3)
+{
+    vfloat32m1_t res = vfmul_vf_f32m1(m0.val, v.val[0], 4);//vmuli_f32(m0.val, v.val, 0);
+    res = vfmacc_vf_f32m1(res, v.val[1], m1.val, 4);//vmulai_f32(res, m1.val, v.val, 1);
+    res = vfmacc_vf_f32m1(res, v.val[2], m2.val, 4);//vmulai_f32(res, m1.val, v.val, 1);
+    res = vfmacc_vf_f32m1(res, v.val[3], m3.val, 4);//vmulai_f32(res, m1.val, v.val, 1);
+    return v_float32x4(res);
+}
+
+inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
+                               const v_float32x4& m1, const v_float32x4& m2,
+                               const v_float32x4& a)
+{
+    vfloat32m1_t res = vfmul_vf_f32m1(m0.val, v.val[0], 4);//vmuli_f32(m0.val, v.val, 0);
+    res = vfmacc_vf_f32m1(res, v.val[1], m1.val, 4);//vmulai_f32(res, m1.val, v.val, 1);
+    res = vfmacc_vf_f32m1(res, v.val[2], m2.val, 4);//vmulai_f32(res, m1.val, v.val, 1);
+    res = vfadd_vv_f32m1(res, a.val, 4);//vmulai_f32(res, m1.val, v.val, 1);
+    return v_float32x4(res);
+}
+
+inline v_float64x2 v_sqrt(const v_float64x2& x)
+{
+    return v_float64x2(vfsqrt_v_f64m1(x.val, 2));
+}
+
+inline v_float64x2 v_invsqrt(const v_float64x2& x)
+{
+    return v_float64x2(vfrdiv_vf_f64m1(vfsqrt_v_f64m1(x.val, 2), 1, 2));
+}
+
+inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    v_float64x2 x(vfmacc_vv_f64m1(vfmul_vv_f64m1(a.val, a.val, 2), b.val, b.val, 2));
+    return v_sqrt(x);
+}
+
+inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float64x2(vfmacc_vv_f64m1(vfmul_vv_f64m1(a.val, a.val, 2), b.val, b.val, 2));
+}
+
+inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_float64x2(vfmacc_vv_f64m1(c.val, a.val, b.val, 2));
+}
+
+inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return v_fma(a, b, c);
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(_Tpvec, suffix, num) \
+    OPENCV_HAL_IMPL_RISCVV_BIN_OPN(&, _Tpvec, vand_vv_##suffix, num) \
+    OPENCV_HAL_IMPL_RISCVV_BIN_OPN(|, _Tpvec, vor_vv_##suffix, num) \
+    OPENCV_HAL_IMPL_RISCVV_BIN_OPN(^, _Tpvec, vxor_vv_##suffix, num) \
+    inline _Tpvec operator ~ (const _Tpvec & a) \
+    { \
+        return _Tpvec(vnot_v_##suffix(a.val, num)); \
+    }
+
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_uint8x16, u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_uint16x8, u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_uint32x4, u32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_uint64x2, u64m1, 2)
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_int8x16,  i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_int16x8,  i16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_int32x4,  i32m1, 4)
+OPENCV_HAL_IMPL_RISCVV_LOGIC_OPN(v_int64x2,  i64m1, 2)
+
+#define OPENCV_HAL_IMPL_RISCVV_FLT_BIT_OP(bin_op, intrin) \
+inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \
+{ \
+    return v_float32x4(vfloat32m1_t(intrin(vint32m1_t(a.val), vint32m1_t(b.val), 4))); \
+} \
+inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \
+{ \
+    a.val = vfloat32m1_t(intrin(vint32m1_t(a.val), vint32m1_t(b.val), 4)); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_RISCVV_FLT_BIT_OP(&, vand_vv_i32m1)
+OPENCV_HAL_IMPL_RISCVV_FLT_BIT_OP(|, vor_vv_i32m1)
+OPENCV_HAL_IMPL_RISCVV_FLT_BIT_OP(^, vxor_vv_i32m1)
+
+inline v_float32x4 operator ~ (const v_float32x4& a)
+{
+    return v_float32x4((vfloat32m1_t)(vnot_v_i32m1((vint32m1_t)(a.val), 4)));
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_FLT_64BIT_OP(bin_op, intrin) \
+inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \
+{ \
+    return v_float64x2(vfloat64m1_t(intrin(vint64m1_t(a.val), vint64m1_t(b.val), 2))); \
+} \
+inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \
+{ \
+    a.val = vfloat64m1_t(intrin(vint64m1_t(a.val), vint64m1_t(b.val), 2)); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_RISCVV_FLT_64BIT_OP(&, vand_vv_i64m1)
+OPENCV_HAL_IMPL_RISCVV_FLT_64BIT_OP(|, vor_vv_i64m1)
+OPENCV_HAL_IMPL_RISCVV_FLT_64BIT_OP(^, vxor_vv_i64m1)
+
+inline v_float64x2 operator ~ (const v_float64x2& a)
+{
+    return v_float64x2((vfloat64m1_t)(vnot_v_i64m1((vint64m1_t)(a.val), 2)));
+}
+inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b)
+{
+    return v_int16x8(vmulh_vv_i16m1(a.val, b.val, 8));
+}
+inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b)
+{
+    return v_uint16x8(vmulhu_vv_u16m1(a.val, b.val, 8));
+}
+
+//#define OPENCV_HAL_IMPL_RISCVV_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \
+//inline _Tpuvec v_abs(const _Tpsvec& a) {    \
+//    E##xm1_t mask=vmflt_vf_e32xm1_f32m1(x.val, 0.0, 4);
+
+//OPENCV_HAL_IMPL_RISCVV_ABS(v_uint8x16, v_int8x16, u8, s8)
+//OPENCV_HAL_IMPL_RISCVV_ABS(v_uint16x8, v_int16x8, u16, s16)
+//OPENCV_HAL_IMPL_RISCVV_ABS(v_uint32x4, v_int32x4, u32, s32)
+
+inline v_uint32x4 v_abs(v_int32x4 x)
+{
+    vbool32_t mask=vmslt_vx_i32m1_b32(x.val, 0, 4);
+    return v_uint32x4((vuint32m1_t)vrsub_vx_i32m1_m(mask, x.val, x.val, 0, 4));
+}
+
+inline v_uint16x8 v_abs(v_int16x8 x)
+{
+    vbool16_t mask=vmslt_vx_i16m1_b16(x.val, 0, 8);
+    return v_uint16x8((vuint16m1_t)vrsub_vx_i16m1_m(mask, x.val, x.val, 0, 8));
+}
+
+inline v_uint8x16 v_abs(v_int8x16 x)
+{
+    vbool8_t mask=vmslt_vx_i8m1_b8(x.val, 0, 16);
+    return v_uint8x16((vuint8m1_t)vrsub_vx_i8m1_m(mask, x.val, x.val, 0, 16));
+}
+
+inline v_float32x4 v_abs(v_float32x4 x)
+{
+    return (v_float32x4)vfsgnjx_vv_f32m1(x.val, x.val, 4);
+}
+
+inline v_float64x2 v_abs(v_float64x2 x)
+{
+    return (v_float64x2)vfsgnjx_vv_f64m1(x.val, x.val, 2);
+}
+
+inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b)
+{
+    vfloat32m1_t ret = vfsub_vv_f32m1(a.val, b.val, 4);
+    return (v_float32x4)vfsgnjx_vv_f32m1(ret, ret, 4);
+}
+
+inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b)
+{
+    vfloat64m1_t ret = vfsub_vv_f64m1(a.val, b.val, 2);
+    return (v_float64x2)vfsgnjx_vv_f64m1(ret, ret, 2);
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_ABSDIFF_U(bit, num) \
+inline v_uint##bit##x##num v_absdiff(v_uint##bit##x##num a, v_uint##bit##x##num b){    \
+    vuint##bit##m1_t vmax = vmaxu_vv_u##bit##m1(a.val, b.val, num);    \
+    vuint##bit##m1_t vmin = vminu_vv_u##bit##m1(a.val, b.val, num);    \
+    return v_uint##bit##x##num(vsub_vv_u##bit##m1(vmax, vmin, num));\
+}
+
+OPENCV_HAL_IMPL_RISCVV_ABSDIFF_U(8, 16)
+OPENCV_HAL_IMPL_RISCVV_ABSDIFF_U(16, 8)
+OPENCV_HAL_IMPL_RISCVV_ABSDIFF_U(32, 4)
+
+/** Saturating absolute difference **/
+inline v_int8x16 v_absdiffs(v_int8x16 a, v_int8x16 b){
+    vint8m1_t vmax = vmax_vv_i8m1(a.val, b.val, 16);
+    vint8m1_t vmin = vmin_vv_i8m1(a.val, b.val, 16);
+    return v_int8x16(vssub_vv_i8m1(vmax, vmin, 16));
+}
+inline v_int16x8 v_absdiffs(v_int16x8 a, v_int16x8 b){
+    vint16m1_t vmax = vmax_vv_i16m1(a.val, b.val, 8);
+    vint16m1_t vmin = vmin_vv_i16m1(a.val, b.val, 8);
+    return v_int16x8(vssub_vv_i16m1(vmax, vmin, 8));
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_ABSDIFF(_Tpvec, _Tpv, num) \
+inline v_uint##_Tpvec v_absdiff(v_int##_Tpvec a, v_int##_Tpvec b){    \
+     vint##_Tpv##_t max = vmax_vv_i##_Tpv(a.val, b.val, num);\
+     vint##_Tpv##_t min = vmin_vv_i##_Tpv(a.val, b.val, num);\
+    return v_uint##_Tpvec((vuint##_Tpv##_t)vsub_vv_i##_Tpv(max, min, num));    \
+}
+
+OPENCV_HAL_IMPL_RISCVV_ABSDIFF(8x16, 8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_ABSDIFF(16x8, 16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_ABSDIFF(32x4, 32m1, 4)
+
+//  Multiply and expand
+inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b,
+                         v_int16x8& c, v_int16x8& d)
+{
+    vint16m2_t res = vundefined_i16m2();
+    res = vwmul_vv_i16m2(a.val, b.val, 16);
+    c.val = vget_i16m2_i16m1(res, 0);
+    d.val = vget_i16m2_i16m1(res, 1);
+}
+
+inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b,
+                         v_uint16x8& c, v_uint16x8& d)
+{
+    vuint16m2_t res = vundefined_u16m2();
+    res = vwmulu_vv_u16m2(a.val, b.val, 16);
+    c.val = vget_u16m2_u16m1(res, 0);
+    d.val = vget_u16m2_u16m1(res, 1);
+}
+
+inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b,
+                         v_int32x4& c, v_int32x4& d)
+{
+    vint32m2_t res = vundefined_i32m2();
+    res = vwmul_vv_i32m2(a.val, b.val, 8);
+    c.val = vget_i32m2_i32m1(res, 0);
+    d.val = vget_i32m2_i32m1(res, 1);
+}
+
+inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b,
+                         v_uint32x4& c, v_uint32x4& d)
+{
+    vuint32m2_t res = vundefined_u32m2();
+    res = vwmulu_vv_u32m2(a.val, b.val, 8);
+    c.val = vget_u32m2_u32m1(res, 0);
+    d.val = vget_u32m2_u32m1(res, 1);
+}
+
+inline void v_mul_expand(const v_int32x4& a, const v_int32x4& b,
+                         v_int64x2& c, v_int64x2& d)
+{
+    vint64m2_t res = vundefined_i64m2();
+    res = vwmul_vv_i64m2(a.val, b.val, 4);
+    c.val = vget_i64m2_i64m1(res, 0);
+    d.val = vget_i64m2_i64m1(res, 1);
+}
+
+inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b,
+                         v_uint64x2& c, v_uint64x2& d)
+{
+    vuint64m2_t res = vundefined_u64m2();
+    res = vwmulu_vv_u64m2(a.val, b.val, 4);
+    c.val = vget_u64m2_u64m1(res, 0);
+    d.val = vget_u64m2_u64m1(res, 1);
+}
+
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint8x16, v_add_wrap, vadd_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int8x16, v_add_wrap, vadd_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint16x8, v_add_wrap, vadd_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int16x8, v_add_wrap, vadd_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint8x16, v_sub_wrap, vsub_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int8x16, v_sub_wrap, vsub_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint16x8, v_sub_wrap, vsub_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int16x8, v_sub_wrap, vsub_vv_i16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint8x16, v_mul_wrap, vmul_vv_u8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int8x16, v_mul_wrap, vmul_vv_i8m1, 16)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_uint16x8, v_mul_wrap, vmul_vv_u16m1, 8)
+OPENCV_HAL_IMPL_RISCVV_BINN_FUNC(v_int16x8, v_mul_wrap, vmul_vv_i16m1, 8)
+//////// Dot Product ////////
+// 16 >> 32
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
+{
+    vint32m2_t res = vundefined_i32m2();
+    res = vwmul_vv_i32m2(a.val, b.val, 8);
+    res = vrgather_vv_i32m2(res, (vuint32m2_t){0, 2, 4, 6, 1, 3, 5, 7}, 8);
+    return v_int32x4(vadd_vv_i32m1(vget_i32m2_i32m1(res, 0), vget_i32m2_i32m1(res, 1), 4));
+}
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{
+    vint32m2_t res = vundefined_i32m2();
+    res = vwmul_vv_i32m2(a.val, b.val, 8);
+    res = vrgather_vv_i32m2(res, (vuint32m2_t){0, 2, 4, 6, 1, 3, 5, 7}, 8);
+    return v_int32x4(vadd_vv_i32m1(vadd_vv_i32m1(vget_i32m2_i32m1(res, 0),vget_i32m2_i32m1(res, 1), 4), c.val, 4));
+}
+
+// 32 >> 64
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
+{
+    vint64m2_t res = vundefined_i64m2();
+    res = vwmul_vv_i64m2(a.val, b.val, 4);
+    res = vrgather_vv_i64m2(res, (vuint64m2_t){0, 2, 1, 3}, 4);
+    return v_int64x2(vadd_vv_i64m1(vget_i64m2_i64m1(res, 0), vget_i64m2_i64m1(res, 1), 2));
+}
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{
+    vint64m2_t res = vundefined_i64m2();
+    res = vwmul_vv_i64m2(a.val, b.val, 4);
+    res = vrgather_vv_i64m2(res, (vuint64m2_t){0, 2, 1, 3}, 4);
+    return v_int64x2(vadd_vv_i64m1(vadd_vv_i64m1(vget_i64m2_i64m1(res, 0), vget_i64m2_i64m1(res, 1), 2), c.val, 2));
+}
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
+{
+    vuint16m2_t v1 = vundefined_u16m2();
+    vuint32m2_t v2 = vundefined_u32m2();
+    v1 = vwmulu_vv_u16m2(a.val, b.val, 16);
+    v1 = vrgather_vv_u16m2(v1, (vuint16m2_t){0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15}, 16);
+    v2 = vwaddu_vv_u32m2(vget_u16m2_u16m1(v1, 0), vget_u16m2_u16m1(v1, 1), 8);
+    return v_uint32x4(vadd_vv_u32m1(vget_u32m2_u32m1(v2, 0), vget_u32m2_u32m1(v2, 1), 4));
+}
+
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b,
+                                   const v_uint32x4& c)
+{
+    vuint16m2_t v1 = vundefined_u16m2();
+    vuint32m2_t v2 = vundefined_u32m2();
+    v1 = vwmulu_vv_u16m2(a.val, b.val, 16);
+    v1 = vrgather_vv_u16m2(v1, (vuint16m2_t){0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15}, 16);
+    v2 = vwaddu_vv_u32m2(vget_u16m2_u16m1(v1, 0), vget_u16m2_u16m1(v1, 1), 8);
+    return v_uint32x4(vadd_vv_u32m1(vadd_vv_u32m1(vget_u32m2_u32m1(v2, 0), vget_u32m2_u32m1(v2, 1), 4), c.val, 4));
+}
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
+{
+    vint16m2_t v1 = vundefined_i16m2();
+    vint32m2_t v2 = vundefined_i32m2();
+    v1 = vwmul_vv_i16m2(a.val, b.val, 16);
+    v1 = vrgather_vv_i16m2(v1, (vuint16m2_t){0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15}, 16);
+    v2 = vwadd_vv_i32m2(vget_i16m2_i16m1(v1, 0), vget_i16m2_i16m1(v1, 1), 8);
+    return v_int32x4(vadd_vv_i32m1(vget_i32m2_i32m1(v2, 0), vget_i32m2_i32m1(v2, 1), 4));
+}
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b,
+                                   const v_int32x4& c)
+{
+    vint16m2_t v1 = vundefined_i16m2();
+    vint32m2_t v2 = vundefined_i32m2();
+    v1 = vwmul_vv_i16m2(a.val, b.val, 16);
+    v1 = vrgather_vv_i16m2(v1, (vuint16m2_t){0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15}, 16);
+    v2 = vwadd_vv_i32m2(vget_i16m2_i16m1(v1, 0), vget_i16m2_i16m1(v1, 1), 8);
+    return v_int32x4(vadd_vv_i32m1(vadd_vv_i32m1(vget_i32m2_i32m1(v2, 0), vget_i32m2_i32m1(v2, 1), 4), c.val, 4));
+}
+
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
+{
+    vuint32m2_t v1 = vundefined_u32m2();
+    vuint64m2_t v2 = vundefined_u64m2();
+    v1 = vwmulu_vv_u32m2(a.val, b.val, 8);
+    v1 = vrgather_vv_u32m2(v1, (vuint32m2_t){0, 4, 1, 5, 2, 6, 3, 7}, 8);
+    v2 = vwaddu_vv_u64m2(vget_u32m2_u32m1(v1, 0), vget_u32m2_u32m1(v1, 1), 4);
+    return v_uint64x2(vadd_vv_u64m1(vget_u64m2_u64m1(v2, 0), vget_u64m2_u64m1(v2, 1), 2));
+}
+
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b,
+                                   const v_uint64x2& c)
+{
+    vuint32m2_t v1 = vundefined_u32m2();
+    vuint64m2_t v2 = vundefined_u64m2();
+    v1 = vwmulu_vv_u32m2(a.val, b.val, 8);
+    v1 = vrgather_vv_u32m2(v1, (vuint32m2_t){0, 4, 1, 5, 2, 6, 3, 7}, 8);
+    v2 = vwaddu_vv_u64m2(vget_u32m2_u32m1(v1, 0), vget_u32m2_u32m1(v1, 1), 4);
+    return v_uint64x2(vadd_vv_u64m1(vadd_vv_u64m1(vget_u64m2_u64m1(v2, 0), vget_u64m2_u64m1(v2, 1), 2), c.val, 2));
+}
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
+{
+    vint32m2_t v1 = vundefined_i32m2();
+    vint64m2_t v2 = vundefined_i64m2();
+    v1 = vwmul_vv_i32m2(a.val, b.val, 8);
+    v1 = vrgather_vv_i32m2(v1, (vuint32m2_t){0, 4, 1, 5, 2, 6, 3, 7}, 8);
+    v2 = vwadd_vv_i64m2(vget_i32m2_i32m1(v1, 0), vget_i32m2_i32m1(v1, 1), 4);
+    return v_int64x2(vadd_vv_i64m1(vget_i64m2_i64m1(v2, 0), vget_i64m2_i64m1(v2, 1), 2));
+}
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b,
+                                   const v_int64x2& c)
+{
+    vint32m2_t v1 = vundefined_i32m2();
+    vint64m2_t v2 = vundefined_i64m2();
+    v1 = vwmul_vv_i32m2(a.val, b.val, 8);
+    v1 = vrgather_vv_i32m2(v1, (vuint32m2_t){0, 4, 1, 5, 2, 6, 3, 7}, 8);
+    v2 = vwadd_vv_i64m2(vget_i32m2_i32m1(v1, 0), vget_i32m2_i32m1(v1, 1), 4);
+    return v_int64x2(vadd_vv_i64m1(vadd_vv_i64m1(vget_i64m2_i64m1(v2, 0), vget_i64m2_i64m1(v2, 1), 2), c.val, 2));
+}
+
+//////// Fast Dot Product ////////
+// 16 >> 32
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
+{
+    vint32m2_t v1 = vundefined_i32m2();
+    v1 = vwmul_vv_i32m2(a.val, b.val, 8);
+    return v_int32x4(vadd_vv_i32m1(vget_i32m2_i32m1(v1, 0), vget_i32m2_i32m1(v1, 1), 4));
+}
+
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{
+    vint32m2_t v1 = vundefined_i32m2();
+    v1 = vwmul_vv_i32m2(a.val, b.val, 8);
+    return v_int32x4(vadd_vv_i32m1(vadd_vv_i32m1(vget_i32m2_i32m1(v1, 0), vget_i32m2_i32m1(v1, 1), 4), c.val, 4));
+}
+
+// 32 >> 64
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
+{
+    vint64m2_t v1 = vundefined_i64m2();
+    v1 = vwmul_vv_i64m2(a.val, b.val, 4);
+    return v_int64x2(vadd_vv_i64m1(vget_i64m2_i64m1(v1, 0), vget_i64m2_i64m1(v1, 1), 2));
+}
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{
+    vint64m2_t v1 = vundefined_i64m2();
+    v1 = vwmul_vv_i64m2(a.val, b.val, 8);
+    return v_int64x2(vadd_vv_i64m1(vadd_vv_i64m1(vget_i64m2_i64m1(v1, 0), vget_i64m2_i64m1(v1, 1), 4), c.val, 4));
+}
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
+{
+    vuint16m2_t v1 = vundefined_u16m2();
+    vuint32m2_t v2 = vundefined_u32m2();
+    v1 = vwmulu_vv_u16m2(a.val, b.val, 16);
+    v2 = vwaddu_vv_u32m2(vget_u16m2_u16m1(v1, 0), vget_u16m2_u16m1(v1, 1), 8);
+    return v_uint32x4(vadd_vv_u32m1(vget_u32m2_u32m1(v2, 0), vget_u32m2_u32m1(v2, 1), 4));
+}
+
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{
+    vuint16m2_t v1 = vundefined_u16m2();
+    vuint32m2_t v2 = vundefined_u32m2();
+    v1 = vwmulu_vv_u16m2(a.val, b.val, 16);
+    v2 = vwaddu_vv_u32m2(vget_u16m2_u16m1(v1, 0), vget_u16m2_u16m1(v1, 1), 8);
+    return v_uint32x4(vadd_vv_u32m1(vadd_vv_u32m1(vget_u32m2_u32m1(v2, 0), vget_u32m2_u32m1(v2, 1), 4), c.val, 4));
+}
+
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
+{
+    vint16m2_t v1 = vundefined_i16m2();
+    vint32m2_t v2 = vundefined_i32m2();
+    v1 = vwmul_vv_i16m2(a.val, b.val, 16);
+    v2 = vwadd_vv_i32m2(vget_i16m2_i16m1(v1, 0), vget_i16m2_i16m1(v1, 1), 8);
+    return v_int32x4(vadd_vv_i32m1(vget_i32m2_i32m1(v2, 0), vget_i32m2_i32m1(v2, 1), 4));
+}
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{
+    vint16m2_t v1 = vundefined_i16m2();
+    vint32m2_t v2 = vundefined_i32m2();
+    v1 = vwmul_vv_i16m2(a.val, b.val, 16);
+    v2 = vwadd_vv_i32m2(vget_i16m2_i16m1(v1, 0), vget_i16m2_i16m1(v1, 1), 8);
+    return v_int32x4(vadd_vv_i32m1(vadd_vv_i32m1(vget_i32m2_i32m1(v2, 0), vget_i32m2_i32m1(v2, 1), 4), c.val, 4));
+}
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
+{
+    vuint32m2_t v1 = vundefined_u32m2();
+    vuint64m2_t v2 = vundefined_u64m2();
+    v1 = vwmulu_vv_u32m2(a.val, b.val, 8);
+    v2 = vwaddu_vv_u64m2(vget_u32m2_u32m1(v1, 0), vget_u32m2_u32m1(v1, 1), 4);
+    return v_uint64x2(vadd_vv_u64m1(vget_u64m2_u64m1(v2, 0), vget_u64m2_u64m1(v2, 1), 2));
+}
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{
+    vuint32m2_t v1 = vundefined_u32m2();
+    vuint64m2_t v2 = vundefined_u64m2();
+    v1 = vwmulu_vv_u32m2(a.val, b.val, 8);
+    v2 = vwaddu_vv_u64m2(vget_u32m2_u32m1(v1, 0), vget_u32m2_u32m1(v1, 1), 4);
+    return v_uint64x2(vadd_vv_u64m1(vadd_vv_u64m1(vget_u64m2_u64m1(v2, 0), vget_u64m2_u64m1(v2, 1), 2), c.val, 2));
+}
+
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
+{
+    vint32m2_t v1 = vundefined_i32m2();
+    vint64m2_t v2 = vundefined_i64m2();
+    v1 = vwmul_vv_i32m2(a.val, b.val, 8);
+    v2 = vwadd_vv_i64m2(vget_i32m2_i32m1(v1, 0), vget_i32m2_i32m1(v1, 1), 4);
+    return v_int64x2(vadd_vv_i64m1(vget_i64m2_i64m1(v2, 0), vget_i64m2_i64m1(v2, 1), 2));
+}
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{
+    vint32m2_t v1 = vundefined_i32m2();
+    vint64m2_t v2 = vundefined_i64m2();
+    v1 = vwmul_vv_i32m2(a.val, b.val, 8);
+    v2 = vwadd_vv_i64m2(vget_i32m2_i32m1(v1, 0), vget_i32m2_i32m1(v1, 1), 4);
+    return v_int64x2(vadd_vv_i64m1(vadd_vv_i64m1(vget_i64m2_i64m1(v2, 0), vget_i64m2_i64m1(v2, 1), 2), c.val, 2));
+}
+
+
+#define OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_W(_Tpvec, _Tpvec2, len, scalartype, func, intrin, num) \
+inline scalartype v_reduce_##func(const v_##_Tpvec##x##num& a) \
+{\
+    v##_Tpvec2##m1_t val = vmv_v_x_##len##m1(0, num); \
+    val = intrin(val, a.val, val, num);    \
+    return vmv_x_s_##len##m1_##len(val, num);    \
+}
+
+
+#define OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(_Tpvec, _Tpvec2, scalartype, func, funcu, num) \
+inline scalartype v_reduce_##func(const v_##_Tpvec##x##num& a) \
+{\
+    v##_Tpvec##m1_t val = (v##_Tpvec##m1_t)vmv_v_x_i8m1(0, num); \
+    val = v##funcu##_vs_##_Tpvec2##m1_##_Tpvec2##m1(val, a.val, a.val, num);    \
+    return val[0];    \
+}
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_W(int8, int16, i16, int, sum, vwredsum_vs_i8m1_i16m1, 16)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_W(int16, int32, i32, int, sum, vwredsum_vs_i16m1_i32m1, 8)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_W(int32, int64, i64, int, sum, vwredsum_vs_i32m1_i64m1, 4)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_W(uint8, uint16, u16, unsigned, sum, vwredsumu_vs_u8m1_u16m1, 16)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_W(uint16, uint32, u32, unsigned, sum, vwredsumu_vs_u16m1_u32m1, 8)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_W(uint32, uint64, u64, unsigned, sum, vwredsumu_vs_u32m1_u64m1, 4)
+inline float v_reduce_sum(const v_float32x4& a) \
+{\
+    vfloat32m1_t val = vfmv_v_f_f32m1(0.0, 4); \
+    val = vfredsum_vs_f32m1_f32m1(val, a.val, val, 4);    \
+    return vfmv_f_s_f32m1_f32(val, 4);    \
+}
+inline double v_reduce_sum(const v_float64x2& a) \
+{\
+    vfloat64m1_t val = vfmv_v_f_f64m1(0.0, 2); \
+    val = vfredsum_vs_f64m1_f64m1(val, a.val, val, 2);    \
+    return vfmv_f_s_f64m1_f64(val, 2);    \
+}
+inline uint64 v_reduce_sum(const v_uint64x2& a)
+{ return vext_x_v_u64m1_u64((vuint64m1_t)a.val, 0, 2)+vext_x_v_u64m1_u64((vuint64m1_t)a.val, 1, 2); }
+
+inline int64 v_reduce_sum(const v_int64x2& a)
+{ return vext_x_v_i64m1_i64((vint64m1_t)a.val, 0, 2)+vext_x_v_i64m1_i64((vint64m1_t)a.val, 1, 2); }
+
+#define OPENCV_HAL_IMPL_RISCVV_REDUCE_OP(func)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(int8,  i8, int, func, red##func, 16)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(int16, i16, int, func, red##func, 8)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(int32, i32, int, func, red##func, 4)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(int64, i64, int, func, red##func, 2)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(uint8,  u8, unsigned, func, red##func##u, 16)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(uint16, u16, unsigned, func, red##func##u, 8)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(uint32, u32, unsigned, func, red##func##u, 4)    \
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP_(float32, f32, float, func, fred##func, 4)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP(max)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_OP(min)
+
+inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
+                                 const v_float32x4& c, const v_float32x4& d)
+{
+    vfloat32m1_t a0 = vfmv_v_f_f32m1(0.0, 4);
+    vfloat32m1_t b0 = vfmv_v_f_f32m1(0.0, 4);
+    vfloat32m1_t c0 = vfmv_v_f_f32m1(0.0, 4);
+    vfloat32m1_t d0 = vfmv_v_f_f32m1(0.0, 4);
+    a0 = vfredsum_vs_f32m1_f32m1(a0, a.val, a0, 4);
+    b0 = vfredsum_vs_f32m1_f32m1(b0, b.val, b0, 4);
+    c0 = vfredsum_vs_f32m1_f32m1(c0, c.val, c0, 4);
+    d0 = vfredsum_vs_f32m1_f32m1(d0, d.val, d0, 4);
+    return v_float32x4(a0[0], b0[0], c0[0], d0[0]);
+}
+
+inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b)
+{
+    vfloat32m1_t a0 = vfmv_v_f_f32m1(0.0, 4);
+    vfloat32m1_t x = vfsub_vv_f32m1(a.val, b.val, 4);
+    vbool32_t mask=vmflt_vf_f32m1_b32(x, 0, 4);
+    vfloat32m1_t val = vfrsub_vf_f32m1_m(mask, x, x, 0, 4);
+    a0 = vfredsum_vs_f32m1_f32m1(a0, val, a0, 4);
+    return a0[0];
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_REDUCE_SAD(_Tpvec, _Tpvec2) \
+inline unsigned v_reduce_sad(const _Tpvec& a, const _Tpvec&b){    \
+    _Tpvec2 x = v_absdiff(a, b);    \
+    return v_reduce_sum(x);    \
+}
+
+OPENCV_HAL_IMPL_RISCVV_REDUCE_SAD(v_int8x16, v_uint8x16)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_SAD(v_uint8x16, v_uint8x16)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_SAD(v_int16x8, v_uint16x8)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_SAD(v_uint16x8, v_uint16x8)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_SAD(v_int32x4, v_uint32x4)
+OPENCV_HAL_IMPL_RISCVV_REDUCE_SAD(v_uint32x4, v_uint32x4)
+
+#define OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(_Tpvec, _Tp, _T, num, uv) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    vbool##_T##_t mask = vmseq_vv_##_Tp##_b##_T(a.val, b.val, num);    \
+    return _Tpvec(vmerge_vxm_##_Tp(mask, vmv_v_x_##_Tp(0, num), -1, num));    \
+} \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    vbool##_T##_t mask = vmsne_vv_##_Tp##_b##_T(a.val, b.val, num);    \
+    return _Tpvec(vmerge_vxm_##_Tp(mask, vmv_v_x_##_Tp(0, num), -1, num));    \
+} \
+inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    vbool##_T##_t mask = vmslt##uv##_Tp##_b##_T(a.val, b.val, num);    \
+    return _Tpvec(vmerge_vxm_##_Tp(mask, vmv_v_x_##_Tp(0, num), -1, num));    \
+} \
+inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    vbool##_T##_t mask = vmslt##uv##_Tp##_b##_T(b.val, a.val, num);    \
+    return _Tpvec(vmerge_vxm_##_Tp(mask, vmv_v_x_##_Tp(0, num), -1, num));    \
+} \
+inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    vbool##_T##_t mask = vmsle##uv##_Tp##_b##_T(a.val, b.val, num);    \
+    return _Tpvec(vmerge_vxm_##_Tp(mask, vmv_v_x_##_Tp(0, num), -1, num));    \
+} \
+inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    vbool##_T##_t mask = vmsle##uv##_Tp##_b##_T(b.val, a.val, num);    \
+    return _Tpvec(vmerge_vxm_##_Tp(mask, vmv_v_x_##_Tp(0, num), -1, num));    \
+} \
+
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_int8x16, i8m1,  8, 16, _vv_)
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_int16x8, i16m1, 16, 8, _vv_)
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_int32x4, i32m1, 32, 4, _vv_)
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_int64x2, i64m1, 64, 2, _vv_)
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_uint8x16, u8m1, 8, 16, u_vv_)
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_uint16x8, u16m1, 16, 8, u_vv_)
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_uint32x4, u32m1, 32, 4, u_vv_)
+OPENCV_HAL_IMPL_RISCVV_INT_CMP_OP(v_uint64x2, u64m1, 64, 2, u_vv_)
+
+//TODO: ==
+inline v_float32x4 operator == (const v_float32x4& a, const v_float32x4& b)
+{
+    vbool32_t mask = vmfeq_vv_f32m1_b32(a.val, b.val, 4);
+    vint32m1_t res = vmerge_vxm_i32m1(mask, vmv_v_x_i32m1(0.0, 4), -1, 4);
+    return v_float32x4((vfloat32m1_t)res);
+}
+inline v_float32x4 operator != (const v_float32x4& a, const v_float32x4& b)
+{
+    vbool32_t mask = vmfne_vv_f32m1_b32(a.val, b.val, 4);
+    vint32m1_t res = vmerge_vxm_i32m1(mask, vmv_v_x_i32m1(0.0, 4), -1, 4);
+    return v_float32x4((vfloat32m1_t)res);
+}
+inline v_float32x4 operator < (const v_float32x4& a, const v_float32x4& b)
+{
+    vbool32_t mask = vmflt_vv_f32m1_b32(a.val, b.val, 4);
+    vint32m1_t res = vmerge_vxm_i32m1(mask, vmv_v_x_i32m1(0.0, 4), -1, 4);
+    return v_float32x4((vfloat32m1_t)res);
+}
+inline v_float32x4 operator <= (const v_float32x4& a, const v_float32x4& b)
+{
+    vbool32_t mask = vmfle_vv_f32m1_b32(a.val, b.val, 4);
+    vint32m1_t res = vmerge_vxm_i32m1(mask, vmv_v_x_i32m1(0.0, 4), -1, 4);
+    return v_float32x4((vfloat32m1_t)res);
+}
+inline v_float32x4 operator > (const v_float32x4& a, const v_float32x4& b)
+{
+    vbool32_t mask = vmfgt_vv_f32m1_b32(a.val, b.val, 4);
+    vint32m1_t res = vmerge_vxm_i32m1(mask, vmv_v_x_i32m1(0.0, 4), -1, 4);
+    return v_float32x4((vfloat32m1_t)res);
+}
+inline v_float32x4 operator >= (const v_float32x4& a, const v_float32x4& b)
+{
+    vbool32_t mask = vmfge_vv_f32m1_b32(a.val, b.val, 4);
+    vint32m1_t res = vmerge_vxm_i32m1(mask, vmv_v_x_i32m1(0.0, 4), -1, 4);
+    return v_float32x4((vfloat32m1_t)res);
+}
+inline v_float32x4 v_not_nan(const v_float32x4& a)
+{
+    vbool32_t mask = vmford_vv_f32m1_b32(a.val, a.val, 4);
+    vint32m1_t res = vmerge_vxm_i32m1(mask, vmv_v_x_i32m1(0.0, 4), -1, 4);
+    return v_float32x4((vfloat32m1_t)res);
+}
+
+//TODO: ==
+inline v_float64x2 operator == (const v_float64x2& a, const v_float64x2& b)
+{
+    vbool64_t mask = vmfeq_vv_f64m1_b64(a.val, b.val, 2);
+    vint64m1_t res = vmerge_vxm_i64m1(mask, vmv_v_x_i64m1(0.0, 2), -1, 2);
+    return v_float64x2((vfloat64m1_t)res);
+}
+inline v_float64x2 operator != (const v_float64x2& a, const v_float64x2& b)
+{
+    vbool64_t mask = vmfne_vv_f64m1_b64(a.val, b.val, 2);
+    vint64m1_t res = vmerge_vxm_i64m1(mask, vmv_v_x_i64m1(0.0, 2), -1, 2);
+    return v_float64x2((vfloat64m1_t)res);
+}
+inline v_float64x2 operator < (const v_float64x2& a, const v_float64x2& b)
+{
+    vbool64_t mask = vmflt_vv_f64m1_b64(a.val, b.val, 2);
+    vint64m1_t res = vmerge_vxm_i64m1(mask, vmv_v_x_i64m1(0.0, 2), -1, 2);
+    return v_float64x2((vfloat64m1_t)res);
+}
+inline v_float64x2 operator <= (const v_float64x2& a, const v_float64x2& b)
+{
+    vbool64_t mask = vmfle_vv_f64m1_b64(a.val, b.val, 2);
+    vint64m1_t res = vmerge_vxm_i64m1(mask, vmv_v_x_i64m1(0.0, 2), -1, 2);
+    return v_float64x2((vfloat64m1_t)res);
+}
+inline v_float64x2 operator > (const v_float64x2& a, const v_float64x2& b)
+{
+    vbool64_t mask = vmfgt_vv_f64m1_b64(a.val, b.val, 2);
+    vint64m1_t res = vmerge_vxm_i64m1(mask, vmv_v_x_i64m1(0.0, 2), -1, 2);
+    return v_float64x2((vfloat64m1_t)res);
+}
+inline v_float64x2 operator >= (const v_float64x2& a, const v_float64x2& b)
+{
+    vbool64_t mask = vmfge_vv_f64m1_b64(a.val, b.val, 2);
+    vint64m1_t res = vmerge_vxm_i64m1(mask, vmv_v_x_i64m1(0.0, 2), -1, 2);
+    return v_float64x2((vfloat64m1_t)res);
+}
+inline v_float64x2 v_not_nan(const v_float64x2& a)
+{
+    vbool64_t mask = vmford_vv_f64m1_b64(a.val, a.val, 2);
+    vint64m1_t res = vmerge_vxm_i64m1(mask, vmv_v_x_i64m1(0.0, 2), -1, 2);
+    return v_float64x2((vfloat64m1_t)res);
+}
+#define OPENCV_HAL_IMPL_RISCVV_TRANSPOSE4x4(_Tp, _T) \
+inline void v_transpose4x4(const v_##_Tp##32x4& a0, const v_##_Tp##32x4& a1, \
+                         const v_##_Tp##32x4& a2, const v_##_Tp##32x4& a3, \
+                         v_##_Tp##32x4& b0, v_##_Tp##32x4& b1, \
+                         v_##_Tp##32x4& b2, v_##_Tp##32x4& b3) \
+{ \
+    v##_Tp##32m4_t val = vundefined_##_T##m4();    \
+    val = vset_##_T##m4(val, 0, a0.val);    \
+    val = vset_##_T##m4(val, 1, a1.val);    \
+    val = vset_##_T##m4(val, 2, a2.val);    \
+    val = vset_##_T##m4(val, 3, a3.val);   \
+    val = vrgather_vv_##_T##m4(val, (vuint32m4_t){0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15}, 16);    \
+    b0.val = vget_##_T##m4_##_T##m1(val, 0);   \
+    b1.val = vget_##_T##m4_##_T##m1(val, 1);   \
+    b2.val = vget_##_T##m4_##_T##m1(val, 2);   \
+    b3.val = vget_##_T##m4_##_T##m1(val, 3);   \
+}
+OPENCV_HAL_IMPL_RISCVV_TRANSPOSE4x4(uint, u32)
+OPENCV_HAL_IMPL_RISCVV_TRANSPOSE4x4(int, i32)
+OPENCV_HAL_IMPL_RISCVV_TRANSPOSE4x4(float, f32)
+
+
+#define OPENCV_HAL_IMPL_RISCVV_SHIFT_LEFT(_Tpvec, suffix, _T, num) \
+inline _Tpvec operator << (const _Tpvec& a, int n) \
+{ return _Tpvec((vsll_vx_##_T##m1(a.val, n, num))); } \
+template<int n> inline _Tpvec v_shl(const _Tpvec& a) \
+{ return _Tpvec((vsll_vx_##_T##m1(a.val, n, num))); }
+
+#define OPENCV_HAL_IMPL_RISCVV_SHIFT_RIGHT(_Tpvec, suffix, _T, num, intric) \
+inline _Tpvec operator >> (const _Tpvec& a, int n) \
+{ return _Tpvec((v##intric##_vx_##_T##m1(a.val, n, num))); } \
+template<int n> inline _Tpvec v_shr(const _Tpvec& a) \
+{ return _Tpvec((v##intric##_vx_##_T##m1(a.val, n, num))); }\
+template<int n> inline _Tpvec v_rshr(const _Tpvec& a) \
+{ return _Tpvec((v##intric##_vx_##_T##m1(vadd_vx_##_T##m1(a.val, 1<<(n-1), num), n, num))); }
+
+// trade efficiency for convenience
+#define OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(suffix, _T, num, intrin) \
+OPENCV_HAL_IMPL_RISCVV_SHIFT_LEFT(v_##suffix##x##num, suffix, _T, num) \
+OPENCV_HAL_IMPL_RISCVV_SHIFT_RIGHT(v_##suffix##x##num, suffix, _T, num, intrin)
+
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(uint8, u8, 16, srl)
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(uint16, u16, 8, srl)
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(uint32, u32, 4, srl)
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(uint64, u64, 2, srl)
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(int8, i8, 16, sra)
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(int16, i16, 8, sra)
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(int32, i32, 4, sra)
+OPENCV_HAL_IMPL_RISCVV_SHIFT_OP(int64, i64, 2, sra)
+
+#if 0
+#define VUP4(n) {0, 1, 2, 3}
+#define VUP8(n) {0, 1, 2, 3, 4, 5, 6, 7}
+#define VUP16(n) {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}
+#define VUP2(n) {0, 1}
+#endif
+#define OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(_Tpvec, suffix, _T, num, num2, vmv, len) \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a) \
+{    \
+    suffix##m1_t tmp = vmv##_##_T##m1(0, num);\
+        tmp = vslideup_vx_##_T##m1_m(vmset_m_##len(num), tmp, a.val, n, num);\
+        return _Tpvec(tmp);\
+} \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a) \
+{     \
+        return _Tpvec(vslidedown_vx_##_T##m1(a.val, n, num));\
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \
+{ return a; } \
+template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    suffix##m2_t tmp = vundefined_##_T##m2();    \
+    tmp = vset_##_T##m2(tmp, 0, a.val);          \
+    tmp = vset_##_T##m2(tmp, 1, b.val);          \
+        tmp = vslidedown_vx_##_T##m2(tmp, n, num2);\
+        return _Tpvec(vget_##_T##m2_##_T##m1(tmp, 0));\
+} \
+template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    suffix##m2_t tmp = vundefined_##_T##m2();    \
+    tmp = vset_##_T##m2(tmp, 0, b.val);    \
+    tmp = vset_##_T##m2(tmp, 1, a.val);    \
+        tmp = vslideup_vx_##_T##m2(tmp, n, num2);\
+        return _Tpvec(vget_##_T##m2_##_T##m1(tmp, 1));\
+} \
+template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    CV_UNUSED(b); return a; \
+}
+
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_uint8x16, vuint8, u8, 16, 32, vmv_v_x, b8)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_int8x16, vint8, i8, 16, 32, vmv_v_x, b8)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_uint16x8, vuint16, u16, 8, 16, vmv_v_x, b16)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_int16x8, vint16, i16, 8, 16, vmv_v_x, b16)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_uint32x4, vuint32, u32, 4, 8, vmv_v_x, b32)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_int32x4, vint32, i32, 4, 8, vmv_v_x, b32)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_uint64x2, vuint64, u64, 2, 4, vmv_v_x, b64)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_int64x2, vint64, i64, 2, 4, vmv_v_x, b64)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_float32x4, vfloat32, f32, 4, 8, vfmv_v_f, b32)
+OPENCV_HAL_IMPL_RISCVV_ROTATE_OP(v_float64x2, vfloat64, f64, 2, 4, vfmv_v_f, b64)
+
+#define OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(_Tpvec, _Tp, _Tp2, len, hnum, num) \
+inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
+{ \
+  typedef uint64 CV_DECL_ALIGNED(1) unaligned_uint64; \
+  vuint64m1_t tmp = {*(unaligned_uint64*)ptr0, *(unaligned_uint64*)ptr1};\
+    return _Tpvec(_Tp2##_t(tmp)); } \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ return _Tpvec(vle_v_##len(ptr, hnum)); }\
+inline _Tpvec v_load_aligned(const _Tp* ptr) \
+{ return _Tpvec(vle_v_##len(ptr, num)); } \
+inline _Tpvec v_load(const _Tp* ptr) \
+{ return _Tpvec((_Tp2##_t)vle_v_##len((const _Tp *)ptr, num)); } \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
+{ vse_v_##len(ptr, a.val, hnum);}\
+inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
+{ \
+  _Tp2##_t a0 = vslidedown_vx_##len(a.val, hnum, num);    \
+  vse_v_##len(ptr, a0, hnum);}\
+inline void v_store(_Tp* ptr, const _Tpvec& a) \
+{ vse_v_##len(ptr, a.val, num); } \
+inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
+{ vse_v_##len(ptr, a.val, num); } \
+inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \
+{ vse_v_##len(ptr, a.val, num); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \
+{ vse_v_##len(ptr, a.val, num); }
+
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_uint8x16, uchar, vuint8m1, u8m1, 8, 16)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_int8x16,  schar, vint8m1, i8m1, 8, 16)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_uint16x8, ushort, vuint16m1, u16m1, 4, 8)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_int16x8,  short,  vint16m1, i16m1, 4, 8)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_uint32x4, unsigned, vuint32m1, u32m1, 2, 4)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_int32x4,  int,     vint32m1, i32m1, 2, 4)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_uint64x2, unsigned long, vuint64m1, u64m1, 1, 2)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_int64x2,  long,     vint64m1, i64m1, 1, 2)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_float32x4, float, vfloat32m1, f32m1, 2, 4)
+OPENCV_HAL_IMPL_RISCVV_LOADSTORE_OP(v_float64x2, double, vfloat64m1, f64m1, 1, 2)
+
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x16 v_lut(const schar* tab, const int* idx)
+{
+#if 1
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[ 0]],
+        tab[idx[ 1]],
+        tab[idx[ 2]],
+        tab[idx[ 3]],
+        tab[idx[ 4]],
+        tab[idx[ 5]],
+        tab[idx[ 6]],
+        tab[idx[ 7]],
+        tab[idx[ 8]],
+        tab[idx[ 9]],
+        tab[idx[10]],
+        tab[idx[11]],
+        tab[idx[12]],
+        tab[idx[13]],
+        tab[idx[14]],
+        tab[idx[15]]
+    };
+    return v_int8x16(vle_v_i8m1(elems, 16));
+#else
+    int32xm4_t index32 = vlev_int32xm4(idx, 16);
+    vint16m2_t index16 = vnsra_vx_i16m2_int32xm4(index32, 0, 16);
+    vint8m1_t index = vnsra_vx_i8m1_i16m2(index16, 0, 16);
+    return v_int8x16(vlxbv_i8m1(tab, index, 16));
+#endif
+}
+
+inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx){
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[4]],
+        tab[idx[4] + 1],
+        tab[idx[5]],
+        tab[idx[5] + 1],
+        tab[idx[6]],
+        tab[idx[6] + 1],
+        tab[idx[7]],
+        tab[idx[7] + 1]
+    };
+    return v_int8x16(vle_v_i8m1(elems, 16));
+}
+inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
+{
+    schar CV_DECL_ALIGNED(32) elems[16] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[0] + 2],
+        tab[idx[0] + 3],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[1] + 2],
+        tab[idx[1] + 3],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[2] + 2],
+        tab[idx[2] + 3],
+        tab[idx[3]],
+        tab[idx[3] + 1],
+        tab[idx[3] + 2],
+        tab[idx[3] + 3]
+    };
+    return v_int8x16(vle_v_i8m1(elems, 16));
+}
+
+inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((schar*)tab, idx)); }
+inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((schar*)tab, idx)); }
+
+inline v_int16x8 v_lut(const short* tab, const int* idx)
+{
+    short CV_DECL_ALIGNED(32) elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]],
+        tab[idx[4]],
+        tab[idx[5]],
+        tab[idx[6]],
+        tab[idx[7]]
+    };
+    return v_int16x8(vle_v_i16m1(elems, 8));
+}
+inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
+{
+    short CV_DECL_ALIGNED(32) elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[2]],
+        tab[idx[2] + 1],
+        tab[idx[3]],
+        tab[idx[3] + 1]
+    };
+    return v_int16x8(vle_v_i16m1(elems, 8));
+}
+inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
+{
+    short CV_DECL_ALIGNED(32) elems[8] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[0] + 2],
+        tab[idx[0] + 3],
+        tab[idx[1]],
+        tab[idx[1] + 1],
+        tab[idx[1] + 2],
+        tab[idx[1] + 3]
+    };
+    return v_int16x8(vle_v_i16m1(elems, 8));
+}
+inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((short*)tab, idx)); }
+inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((short*)tab, idx)); }
+inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((short*)tab, idx)); }
+
+inline v_int32x4 v_lut(const int* tab, const int* idx)
+{
+    int CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_int32x4(vle_v_i32m1(elems, 4));
+}
+inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
+{
+    int CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[0] + 1],
+        tab[idx[1]],
+        tab[idx[1] + 1]
+    };
+    return v_int32x4(vle_v_i32m1(elems, 4));
+}
+inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x4(vle_v_i32m1(tab+idx[0], 4));
+}
+inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((int*)tab, idx)); }
+inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((int*)tab, idx)); }
+inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((int*)tab, idx)); }
+
+inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
+{
+    vint64m1_t res = {tab[idx[0]], tab[idx[1]]};
+    return v_int64x2(res);
+}
+inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(vle_v_i64m1(tab+idx[0], 2));
+}
+
+inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx)
+{
+    vuint64m1_t res = {tab[idx[0]], tab[idx[1]]};
+    return v_uint64x2(res);
+}
+inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx)
+{
+    return v_uint64x2(vle_v_u64m1(tab+idx[0], 2));
+}
+
+inline v_float32x4 v_lut(const float* tab, const int* idx)
+{
+    float CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[1]],
+        tab[idx[2]],
+        tab[idx[3]]
+    };
+    return v_float32x4(vle_v_f32m1(elems, 4));
+}
+inline v_float32x4 v_lut_pairs(const float* tab, const int* idx)
+{
+    float CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idx[0]],
+        tab[idx[0]+1],
+        tab[idx[1]],
+        tab[idx[1]+1]
+    };
+    return v_float32x4(vle_v_f32m1(elems, 4));
+}
+inline v_float32x4 v_lut_quads(const float* tab, const int* idx)
+{
+    return v_float32x4(vle_v_f32m1(tab + idx[0], 4));
+}
+inline v_float64x2 v_lut(const double* tab, const int* idx)
+{
+    vfloat64m1_t res = {tab[idx[0]], tab[idx[1]]};
+    return v_float64x2(res);
+}
+inline v_float64x2 v_lut_pairs(const double* tab, const int* idx)
+{
+    return v_float64x2(vle_v_f64m1(tab+idx[0], 2));
+}
+
+inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
+{
+    int CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idxvec.val[0]],
+        tab[idxvec.val[1]],
+        tab[idxvec.val[2]],
+        tab[idxvec.val[3]]
+    };
+    return v_int32x4(vle_v_i32m1(elems, 4));
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
+{
+    unsigned CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idxvec.val[0]],
+        tab[idxvec.val[1]],
+        tab[idxvec.val[2]],
+        tab[idxvec.val[3]]
+    };
+    return v_uint32x4(vle_v_u32m1(elems, 4));
+}
+
+inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
+{
+    float CV_DECL_ALIGNED(32) elems[4] =
+    {
+        tab[idxvec.val[0]],
+        tab[idxvec.val[1]],
+        tab[idxvec.val[2]],
+        tab[idxvec.val[3]]
+    };
+    return v_float32x4(vle_v_f32m1(elems, 4));
+}
+inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
+{
+    vfloat64m1_t res = {tab[idxvec.val[0]], tab[idxvec.val[1]]};
+    return v_float64x2(res);
+}
+inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
+{
+    vint32m1_t index_x = vmul_vx_i32m1(idxvec.val, 4, 4);
+    vint32m1_t index_y = vadd_vx_i32m1(index_x, 4, 4);
+
+    x.val = vlxe_v_f32m1(tab, index_x, 4);
+    y.val = vlxe_v_f32m1(tab, index_y, 4);
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+
+    x = v_float64x2(tab[idx[0]], tab[idx[1]]);
+    y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]);
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_PACKS(_Tp, _Tp2, _T2, num2, _T1, num, intrin, shr, _Type) \
+inline v_##_Tp##x##num v_pack(const v_##_Tp2##x##num2& a, const v_##_Tp2##x##num2& b) \
+{ \
+    v##_Tp2##m2_t  tmp = vundefined_##_T2##m2();    \
+    tmp = vset_##_T2##m2(tmp, 0, a.val);    \
+    tmp = vset_##_T2##m2(tmp, 1, b.val);    \
+    return v_##_Tp##x##num(shr##_##_T1##m1(tmp, 0, num)); \
+}\
+template<int n> inline \
+v_##_Tp##x##num v_rshr_pack(const v_##_Tp2##x##num2& a, const v_##_Tp2##x##num2& b) \
+{ \
+    v##_Tp2##m2_t  tmp = vundefined_##_T2##m2();    \
+    tmp = vset_##_T2##m2(tmp, 0, a.val);    \
+    tmp = vset_##_T2##m2(tmp, 1, b.val);    \
+    return v_##_Tp##x##num(intrin##_##_T1##m1(tmp, n, num)); \
+}\
+inline void v_pack_store(_Type* ptr, const v_##_Tp2##x##num2& a) \
+{ \
+    v##_Tp2##m2_t tmp = vundefined_##_T2##m2();    \
+    tmp = vset_##_T2##m2(tmp, 0, a.val);    \
+    tmp = vset_##_T2##m2(tmp, 1, vmv_v_x_##_T2##m1(0, num2));    \
+    asm("" ::: "memory");                                       \
+    vse_v_##_T1##m1(ptr, shr##_##_T1##m1(tmp, 0, num), num2); \
+}\
+template<int n> inline \
+void v_rshr_pack_store(_Type* ptr, const v_##_Tp2##x##num2& a) \
+{ \
+    v##_Tp2##m2_t tmp = vundefined_##_T2##m2();    \
+    tmp = vset_##_T2##m2(tmp, 0, a.val);    \
+    tmp = vset_##_T2##m2(tmp, 1, vmv_v_x_##_T2##m1(0, num2));    \
+    vse_v_##_T1##m1(ptr, intrin##_##_T1##m1(tmp, n, num), num2); \
+}
+OPENCV_HAL_IMPL_RISCVV_PACKS(int8, int16, i16, 8, i8, 16, vnclip_vx, vnclip_vx, signed char)
+OPENCV_HAL_IMPL_RISCVV_PACKS(int16, int32, i32, 4, i16, 8, vnclip_vx, vnclip_vx, signed short)
+OPENCV_HAL_IMPL_RISCVV_PACKS(int32, int64, i64, 2, i32, 4, vnclip_vx, vnsra_vx, int)
+OPENCV_HAL_IMPL_RISCVV_PACKS(uint8, uint16, u16, 8, u8, 16, vnclipu_vx, vnclipu_vx, unsigned char)
+OPENCV_HAL_IMPL_RISCVV_PACKS(uint16, uint32, u32, 4, u16, 8, vnclipu_vx, vnclipu_vx, unsigned short)
+OPENCV_HAL_IMPL_RISCVV_PACKS(uint32, uint64, u64, 2, u32, 4, vnclipu_vx, vnsrl_vx, unsigned int)
+
+// pack boolean
+inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
+{
+    vuint16m2_t tmp = vundefined_u16m2();    \
+    tmp = vset_u16m2(tmp, 0, a.val);    \
+    tmp = vset_u16m2(tmp, 1, b.val);    \
+    return v_uint8x16(vnsrl_vx_u8m1(tmp, 0, 16));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
+                           const v_uint32x4& c, const v_uint32x4& d)
+{
+    vuint32m4_t vabcd = vundefined_u32m4();    \
+    vuint16m2_t v16 = vundefined_u16m2();    \
+    vabcd = vset_u32m4(vabcd, 0, a.val);    \
+    vabcd = vset_u32m4(vabcd, 1, b.val);    \
+    vabcd = vset_u32m4(vabcd, 2, c.val);    \
+    vabcd = vset_u32m4(vabcd, 3, d.val);    \
+    v16 = vnsrl_vx_u16m2(vabcd, 0, 16);
+    return v_uint8x16(vnsrl_vx_u8m1(v16, 0, 16));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
+                           const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
+                           const v_uint64x2& g, const v_uint64x2& h)
+{
+    vuint64m8_t v64 = vundefined_u64m8();    \
+    vuint32m4_t v32 = vundefined_u32m4();    \
+    vuint16m2_t v16 = vundefined_u16m2();    \
+    v64 = vset_u64m8(v64, 0, a.val);    \
+    v64 = vset_u64m8(v64, 1, b.val);    \
+    v64 = vset_u64m8(v64, 2, c.val);    \
+    v64 = vset_u64m8(v64, 3, d.val);    \
+    v64 = vset_u64m8(v64, 4, e.val);    \
+    v64 = vset_u64m8(v64, 5, f.val);    \
+    v64 = vset_u64m8(v64, 6, g.val);    \
+    v64 = vset_u64m8(v64, 7, h.val);    \
+    v32 = vnsrl_vx_u32m4(v64, 0, 16);
+    v16 = vnsrl_vx_u16m2(v32, 0, 16);
+    return v_uint8x16(vnsrl_vx_u8m1(v16, 0, 16));
+}
+
+//inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) \
+//{ \
+//    int16xm2_u tmp;    \
+//    tmp.m1[0] = (vint16m1_t)a.val;    \
+//    tmp.m1[1] = (vint16m1_t)b.val;    \
+//    e8xm1_t mask = (e8xm1_t)vmsge_vx_e16xm2_i16m2(tmp.v, 0, 16);\
+//    return v_uint8x16(vnclipuvi_mask_u8m1_u16m2(vmv_v_x_u8m1(0, 16), (vuint16m2_t)tmp.v, 0, mask, 16));
+//}
+
+#define OPENCV_HAL_IMPL_RISCVV_PACK_U(tp1, num1, tp2, num2, _Tp) \
+inline v_uint##tp1##x##num1 v_pack_u(const v_int##tp2##x##num2& a, const v_int##tp2##x##num2& b) \
+{ \
+    vint##tp2##m2_t tmp = vundefined_##i##tp2##m2();    \
+    tmp = vset_##i##tp2##m2(tmp, 0, a.val);    \
+    tmp = vset_##i##tp2##m2(tmp, 1, b.val);    \
+    vint##tp2##m2_t val = vmax_vx_i##tp2##m2(tmp, 0, num1);\
+    return v_uint##tp1##x##num1(vnclipu_vx_u##tp1##m1((vuint##tp2##m2_t)val, 0, num1));    \
+} \
+inline void v_pack_u_store(_Tp* ptr, const v_int##tp2##x##num2& a) \
+{ \
+    vint##tp2##m2_t tmp = vundefined_##i##tp2##m2();    \
+    tmp = vset_##i##tp2##m2(tmp, 0, a.val);    \
+    vint##tp2##m2_t val = vmax_vx_i##tp2##m2(tmp, 0, num1);\
+    return vse_v_u##tp1##m1(ptr, vnclipu_vx_u##tp1##m1((vuint##tp2##m2_t)val, 0, num1), num2);    \
+} \
+template<int n> inline \
+v_uint##tp1##x##num1 v_rshr_pack_u(const v_int##tp2##x##num2& a, const v_int##tp2##x##num2& b) \
+{ \
+    vint##tp2##m2_t tmp = vundefined_##i##tp2##m2();    \
+    tmp = vset_##i##tp2##m2(tmp, 0, a.val);    \
+    tmp = vset_##i##tp2##m2(tmp, 1, b.val);    \
+    vint##tp2##m2_t val = vmax_vx_i##tp2##m2(tmp, 0, num1);\
+    return v_uint##tp1##x##num1(vnclipu_vx_u##tp1##m1((vuint##tp2##m2_t)val, n, num1));    \
+} \
+template<int n> inline \
+void v_rshr_pack_u_store(_Tp* ptr, const v_int##tp2##x##num2& a) \
+{ \
+    vint##tp2##m2_t tmp = vundefined_##i##tp2##m2();    \
+    tmp = vset_##i##tp2##m2(tmp, 0, a.val);    \
+    vint##tp2##m2_t val_ = vmax_vx_i##tp2##m2(tmp, 0, num1);\
+    vuint##tp1##m1_t val = vnclipu_vx_u##tp1##m1((vuint##tp2##m2_t)val_, n, num1);    \
+    return vse_v_u##tp1##m1(ptr, val, num2);\
+}
+OPENCV_HAL_IMPL_RISCVV_PACK_U(8, 16, 16, 8, unsigned char )
+OPENCV_HAL_IMPL_RISCVV_PACK_U(16, 8, 32, 4, unsigned short)
+
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wuninitialized"
+#endif
+
+// saturating multiply 8-bit, 16-bit
+#define OPENCV_HAL_IMPL_RISCVV_MUL_SAT(_Tpvec, _Tpwvec)            \
+    inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b)  \
+    {                                                            \
+        _Tpwvec c, d;                                            \
+        v_mul_expand(a, b, c, d);                                \
+        return v_pack(c, d);                                     \
+    }                                                            \
+    inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b)      \
+    { a = a * b; return a; }
+
+OPENCV_HAL_IMPL_RISCVV_MUL_SAT(v_int8x16,  v_int16x8)
+OPENCV_HAL_IMPL_RISCVV_MUL_SAT(v_uint8x16, v_uint16x8)
+OPENCV_HAL_IMPL_RISCVV_MUL_SAT(v_int16x8,  v_int32x4)
+OPENCV_HAL_IMPL_RISCVV_MUL_SAT(v_uint16x8, v_uint32x4)
+
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+static const signed char popCountTable[256] =
+{
+    0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8,
+};
+
+inline vuint8m1_t vcnt_u8(vuint8m1_t val){
+    vuint8m1_t v0 = val & 1;
+    return vlxe_v_u8m1((unsigned char*)popCountTable, val >> 1, 16)+v0;
+}
+
+inline v_uint8x16
+v_popcount(const v_uint8x16& a)
+{
+    return v_uint8x16(vcnt_u8(a.val));
+}
+
+inline v_uint8x16
+v_popcount(const v_int8x16& a)
+{
+    return v_uint8x16(vcnt_u8((vuint8m1_t)a.val));
+}
+
+inline v_uint16x8
+v_popcount(const v_uint16x8& a)
+{
+    vuint8m2_t tmp = vundefined_u8m2();
+    tmp = vset_u8m2(tmp, 0, vcnt_u8((vuint8m1_t)a.val));
+    vuint64m2_t mask = (vuint64m2_t){0x0E0C0A0806040200, 0, 0x0F0D0B0907050301, 0};
+    tmp = vrgather_vv_u8m2(tmp, (vuint8m2_t)mask, 32);    \
+    vuint16m2_t res = vwaddu_vv_u16m2(vget_u8m2_u8m1(tmp, 0), vget_u8m2_u8m1(tmp, 1), 8);
+    return v_uint16x8(vget_u16m2_u16m1(res, 0));
+}
+
+inline v_uint16x8
+v_popcount(const v_int16x8& a)
+{
+    vuint8m2_t tmp = vundefined_u8m2();
+    tmp = vset_u8m2(tmp, 0, vcnt_u8((vuint8m1_t)a.val));
+    vuint64m2_t mask = (vuint64m2_t){0x0E0C0A0806040200, 0, 0x0F0D0B0907050301, 0};
+    tmp = vrgather_vv_u8m2(tmp, (vuint8m2_t)mask, 32);    \
+    vuint16m2_t res = vwaddu_vv_u16m2(vget_u8m2_u8m1(tmp, 0), vget_u8m2_u8m1(tmp, 1), 8);
+    return v_uint16x8(vget_u16m2_u16m1(res, 0));
+}
+
+inline v_uint32x4
+v_popcount(const v_uint32x4& a)
+{
+    vuint8m2_t tmp = vundefined_u8m2();
+    tmp = vset_u8m2(tmp, 0, vcnt_u8((vuint8m1_t)a.val));
+    vuint64m2_t mask = (vuint64m2_t){0xFFFFFFFF0C080400, 0xFFFFFFFF0D090501,
+                     0xFFFFFFFF0E0A0602, 0xFFFFFFFF0F0B0703};
+    tmp = vrgather_vv_u8m2(tmp, (vuint8m2_t)mask, 32);    \
+    vuint16m2_t res_ = vwaddu_vv_u16m2(vget_u8m2_u8m1(tmp, 0), vget_u8m2_u8m1(tmp, 1), 16);
+    vuint32m2_t res  = vwaddu_vv_u32m2(vget_u16m2_u16m1(res_, 0), vget_u16m2_u16m1(res_, 1), 8);
+    return v_uint32x4(vget_u32m2_u32m1(res, 0));
+}
+
+inline v_uint32x4
+v_popcount(const v_int32x4& a)
+{
+    vuint8m2_t tmp = vundefined_u8m2();
+    tmp = vset_u8m2(tmp, 0, vcnt_u8((vuint8m1_t)a.val));
+    vuint64m2_t mask = (vuint64m2_t){0xFFFFFFFF0C080400, 0xFFFFFFFF0D090501,
+                     0xFFFFFFFF0E0A0602, 0xFFFFFFFF0F0B0703};
+    tmp = vrgather_vv_u8m2(tmp, (vuint8m2_t)mask, 32);    \
+    vuint16m2_t res_ = vwaddu_vv_u16m2(vget_u8m2_u8m1(tmp, 0), vget_u8m2_u8m1(tmp, 1), 16);
+    vuint32m2_t res  = vwaddu_vv_u32m2(vget_u16m2_u16m1(res_, 0), vget_u16m2_u16m1(res_, 1), 8);
+    return v_uint32x4(vget_u32m2_u32m1(res, 0));
+}
+
+inline v_uint64x2
+v_popcount(const v_uint64x2& a)
+{
+    vuint8m2_t tmp = vundefined_u8m2();
+    tmp = vset_u8m2(tmp, 0, vcnt_u8((vuint8m1_t)a.val));
+    vuint64m2_t mask = (vuint64m2_t){0x0706050403020100, 0x0000000000000000,
+                     0x0F0E0D0C0B0A0908, 0x0000000000000000};
+    tmp = vrgather_vv_u8m2(tmp, (vuint8m2_t)mask, 32);    \
+    vuint8m1_t zero = vmv_v_x_u8m1(0, 16);
+    vuint8m1_t res1 = zero;
+    vuint8m1_t res2 = zero;
+    res1 = vredsum_vs_u8m1_u8m1(res1, vget_u8m2_u8m1(tmp, 0), zero, 8);
+    res2 = vredsum_vs_u8m1_u8m1(res2, vget_u8m2_u8m1(tmp, 1), zero, 8);
+
+    return v_uint64x2((unsigned long)vmv_x_s_u8m1_u8(res1, 8), (unsigned long)vmv_x_s_u8m1_u8(res2, 8));
+}
+
+inline v_uint64x2
+v_popcount(const v_int64x2& a)
+{
+    vuint8m2_t tmp = vundefined_u8m2();
+    tmp = vset_u8m2(tmp, 0, vcnt_u8((vuint8m1_t)a.val));
+    vuint64m2_t mask = (vuint64m2_t){0x0706050403020100, 0x0000000000000000,
+                     0x0F0E0D0C0B0A0908, 0x0000000000000000};
+    tmp = vrgather_vv_u8m2(tmp, (vuint8m2_t)mask, 32);    \
+    vuint8m1_t zero = vmv_v_x_u8m1(0, 16);
+    vuint8m1_t res1 = zero;
+    vuint8m1_t res2 = zero;
+    res1 = vredsum_vs_u8m1_u8m1(res1, vget_u8m2_u8m1(tmp, 0), zero, 8);
+    res2 = vredsum_vs_u8m1_u8m1(res2, vget_u8m2_u8m1(tmp, 1), zero, 8);
+
+    return v_uint64x2((unsigned long)vmv_x_s_u8m1_u8(res1, 8), (unsigned long)vmv_x_s_u8m1_u8(res2, 8));
+}
+
+#define SMASK 1, 2, 4, 8, 16, 32, 64, 128
+inline int v_signmask(const v_uint8x16& a)
+{
+    vuint8m1_t t0  = vsrl_vx_u8m1(a.val, 7, 16);
+    vuint8m1_t m1  = (vuint8m1_t){SMASK, SMASK};
+    vuint16m2_t t1 = vwmulu_vv_u16m2(t0, m1, 16);
+    vuint32m1_t res = vmv_v_x_u32m1(0, 4);
+    vuint32m2_t t2 = vwmulu_vx_u32m2(vget_u16m2_u16m1(t1, 1), 256, 8);
+    res = vredsum_vs_u32m2_u32m1(res, t2, res, 8);
+    res = vwredsumu_vs_u16m1_u32m1(res, vget_u16m2_u16m1(t1, 0), res, 8);
+    return vmv_x_s_u32m1_u32(res, 8);
+}
+inline int v_signmask(const v_int8x16& a)
+{
+    vuint8m1_t t0 = vsrl_vx_u8m1((vuint8m1_t)a.val, 7, 16);
+    vuint8m1_t m1 = (vuint8m1_t){SMASK, SMASK};
+    vint16m2_t t1 = (vint16m2_t)vwmulu_vv_u16m2(t0, m1, 16);
+    vint32m1_t res = vmv_v_x_i32m1(0, 4);
+    vint32m2_t t2 = vwmul_vx_i32m2(vget_i16m2_i16m1(t1, 1), 256, 8);
+    res = vredsum_vs_i32m2_i32m1(res, t2, res, 8);
+    res = vwredsum_vs_i16m1_i32m1(res, vget_i16m2_i16m1(t1, 0), res, 8);
+    return vmv_x_s_i32m1_i32(res, 8);
+}
+
+inline int v_signmask(const v_int16x8& a)
+{
+    vint16m1_t t0 = (vint16m1_t)vsrl_vx_u16m1((vuint16m1_t)a.val, 15, 8);
+    vint16m1_t m1 = (vint16m1_t){SMASK};
+    vint16m1_t t1 = vmul_vv_i16m1(t0, m1, 8);
+    vint16m1_t res = vmv_v_x_i16m1(0, 8);
+    res = vredsum_vs_i16m1_i16m1(res, t1, res, 8);
+    return vmv_x_s_i16m1_i16(res, 8);
+}
+inline int v_signmask(const v_uint16x8& a)
+{
+    vint16m1_t t0 = (vint16m1_t)vsrl_vx_u16m1((vuint16m1_t)a.val, 15, 8);
+    vint16m1_t m1 = (vint16m1_t){SMASK};
+    vint16m1_t t1 = vmul_vv_i16m1(t0, m1, 8);
+    vint16m1_t res = vmv_v_x_i16m1(0, 8);
+    res = vredsum_vs_i16m1_i16m1(res, t1, res, 8);
+    return vmv_x_s_i16m1_i16(res, 8);
+}
+inline int v_signmask(const v_int32x4& a)
+{
+    vint32m1_t t0 = (vint32m1_t)vsrl_vx_u32m1((vuint32m1_t)a.val, 31, 4);
+    vint32m1_t m1 = (vint32m1_t){1, 2, 4, 8};
+    vint32m1_t res = vmv_v_x_i32m1(0, 4);
+    vint32m1_t t1 = vmul_vv_i32m1(t0, m1, 4);
+    res = vredsum_vs_i32m1_i32m1(res, t1, res, 4);
+    return vmv_x_s_i32m1_i32(res, 4);
+}
+inline int v_signmask(const v_uint32x4& a)
+{
+    vint32m1_t t0 = (vint32m1_t)vsrl_vx_u32m1(a.val, 31, 4);
+    vint32m1_t m1 = (vint32m1_t){1, 2, 4, 8};
+    vint32m1_t res = vmv_v_x_i32m1(0, 4);
+    vint32m1_t t1 = vmul_vv_i32m1(t0, m1, 4);
+    res = vredsum_vs_i32m1_i32m1(res, t1, res, 4);
+    return vmv_x_s_i32m1_i32(res, 4);
+}
+inline int v_signmask(const v_uint64x2& a)
+{
+    vuint64m1_t v0 = vsrl_vx_u64m1(a.val, 63, 2);
+    int res = (int)vext_x_v_u64m1_u64(v0, 0, 2) + ((int)vext_x_v_u64m1_u64(v0, 1, 2) << 1);
+    return res;
+}
+inline int v_signmask(const v_int64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+inline int v_signmask(const v_float64x2& a)
+{ return v_signmask(v_reinterpret_as_u64(a)); }
+inline int v_signmask(const v_float32x4& a)
+{
+    vint32m1_t t0 = (vint32m1_t)vsrl_vx_u32m1((vuint32m1_t)a.val, 31, 4);
+    vint32m1_t m1 = (vint32m1_t){1, 2, 4, 8};
+    vint32m1_t res = vmv_v_x_i32m1(0, 4);
+    vint32m1_t t1 = vmul_vv_i32m1(t0, m1, 4);
+    res = vredsum_vs_i32m1_i32m1(res, t1, res, 4);
+    return vmv_x_s_i32m1_i32(res, 4);
+}
+
+inline int v_scan_forward(const v_int8x16& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_uint8x16& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_int16x8& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_uint16x8& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_int32x4& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_uint32x4& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_float32x4& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_int64x2& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+inline int v_scan_forward(const v_uint64x2& a) {
+int val = v_signmask(a);
+if(val==0) return 0;
+else return trailingZeros32(val); }
+
+#define OPENCV_HAL_IMPL_RISCVV_CHECK_ALLANY(_Tpvec, suffix, _T, shift, num) \
+inline bool v_check_all(const v_##_Tpvec& a) \
+{ \
+    suffix##m1_t v0 = vsrl_vx_##_T(vnot_v_##_T(a.val, num), shift, num); \
+    vuint64m1_t v1 = vuint64m1_t(v0); \
+    return (v1[0] | v1[1]) == 0; \
+} \
+inline bool v_check_any(const v_##_Tpvec& a) \
+{ \
+    suffix##m1_t v0 = vsrl_vx_##_T(a.val, shift, num); \
+    vuint64m1_t v1 = vuint64m1_t(v0); \
+    return (v1[0] | v1[1]) != 0; \
+}
+
+OPENCV_HAL_IMPL_RISCVV_CHECK_ALLANY(uint8x16, vuint8,  u8m1, 7, 16)
+OPENCV_HAL_IMPL_RISCVV_CHECK_ALLANY(uint16x8, vuint16, u16m1, 15, 8)
+OPENCV_HAL_IMPL_RISCVV_CHECK_ALLANY(uint32x4, vuint32, u32m1, 31, 4)
+OPENCV_HAL_IMPL_RISCVV_CHECK_ALLANY(uint64x2, vuint64, u64m1, 63, 2)
+
+inline bool v_check_all(const v_int8x16& a)
+{ return v_check_all(v_reinterpret_as_u8(a)); }
+inline bool v_check_all(const v_int16x8& a)
+{ return v_check_all(v_reinterpret_as_u16(a)); }
+inline bool v_check_all(const v_int32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+inline bool v_check_all(const v_float32x4& a)
+{ return v_check_all(v_reinterpret_as_u32(a)); }
+inline bool v_check_all(const v_int64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+inline bool v_check_all(const v_float64x2& a)
+{ return v_check_all(v_reinterpret_as_u64(a)); }
+
+inline bool v_check_any(const v_int8x16& a)
+{ return v_check_any(v_reinterpret_as_u8(a)); }
+inline bool v_check_any(const v_int16x8& a)
+{ return v_check_any(v_reinterpret_as_u16(a)); }
+inline bool v_check_any(const v_int32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+inline bool v_check_any(const v_float32x4& a)
+{ return v_check_any(v_reinterpret_as_u32(a)); }
+inline bool v_check_any(const v_int64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+inline bool v_check_any(const v_float64x2& a)
+{ return v_check_any(v_reinterpret_as_u64(a)); }
+
+#define OPENCV_HAL_IMPL_RISCVV_SELECT(_Tpvec, suffix, _Tpvec2, num) \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(vmerge_vvm_##suffix(_Tpvec2(mask.val), b.val, a.val, num)); \
+}
+
+OPENCV_HAL_IMPL_RISCVV_SELECT(v_int8x16,  i8m1, vbool8_t, 16)
+OPENCV_HAL_IMPL_RISCVV_SELECT(v_int16x8,  i16m1, vbool16_t, 8)
+OPENCV_HAL_IMPL_RISCVV_SELECT(v_int32x4,  i32m1, vbool32_t, 4)
+OPENCV_HAL_IMPL_RISCVV_SELECT(v_uint8x16, u8m1, vbool8_t, 16)
+OPENCV_HAL_IMPL_RISCVV_SELECT(v_uint16x8, u16m1, vbool16_t, 8)
+OPENCV_HAL_IMPL_RISCVV_SELECT(v_uint32x4, u32m1, vbool32_t, 4)
+inline v_float32x4 v_select(const v_float32x4& mask, const v_float32x4& a, const v_float32x4& b)
+{
+    return v_float32x4((vfloat32m1_t)vmerge_vvm_u32m1((vbool32_t)mask.val, (vuint32m1_t)b.val, (vuint32m1_t)a.val, 4));
+}
+inline v_float64x2 v_select(const v_float64x2& mask, const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float64x2((vfloat64m1_t)vmerge_vvm_u64m1((vbool64_t)mask.val, (vuint64m1_t)b.val, (vuint64m1_t)a.val, 2));
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_EXPAND(add, _Tpvec, _Tpwvec, _Tp, _Tp1, num1, _Tp2, num2, _T1, _T2) \
+inline void v_expand(const _Tpvec& a, v_##_Tpwvec& b0, v_##_Tpwvec& b1) \
+{ \
+    _T1##_t b = vw##add##_vv_##_Tp2##m2(a.val, vmv_v_x_##_Tp1(0, num1), num1);    \
+    b0.val = vget_##_Tp2##m2_##_Tp2##m1(b, 0);  \
+    b1.val = vget_##_Tp2##m2_##_Tp2##m1(b, 1);  \
+} \
+inline v_##_Tpwvec v_expand_low(const _Tpvec& a) \
+{ \
+    _T1##_t b = vw##add##_vv_##_Tp2##m2(a.val, vmv_v_x_##_Tp1(0, num2), num2);    \
+    return v_##_Tpwvec(vget_##_Tp2##m2_##_Tp2##m1(b, 0)); \
+} \
+inline v_##_Tpwvec v_expand_high(const _Tpvec& a) \
+{ \
+    _T1##_t b = vw##add##_vv_##_Tp2##m2(a.val, vmv_v_x_##_Tp1(0, num1), num1);    \
+    return v_##_Tpwvec(vget_##_Tp2##m2_##_Tp2##m1(b, 1)); \
+} \
+inline v_##_Tpwvec v_load_expand(const _Tp* ptr) \
+{ \
+    _T2##_t val = vle##_v_##_Tp1(ptr, num2);    \
+    _T1##_t b = vw##add##_vv_##_Tp2##m2(val, vmv_v_x_##_Tp1(0, num2), num2);    \
+    return v_##_Tpwvec(vget_##_Tp2##m2_##_Tp2##m1(b, 0)); \
+}
+
+OPENCV_HAL_IMPL_RISCVV_EXPAND(addu, v_uint8x16, uint16x8, uchar, u8m1, 16, u16, 8, vuint16m2, vuint8m1)
+OPENCV_HAL_IMPL_RISCVV_EXPAND(addu, v_uint16x8, uint32x4, ushort,  u16m1, 8, u32, 4, vuint32m2, vuint16m1)
+OPENCV_HAL_IMPL_RISCVV_EXPAND(addu, v_uint32x4, uint64x2, uint,  u32m1, 4, u64, 2, vuint64m2, vuint32m1)
+OPENCV_HAL_IMPL_RISCVV_EXPAND(add, v_int8x16, int16x8, schar,  i8m1, 16, i16, 8, vint16m2, vint8m1)
+OPENCV_HAL_IMPL_RISCVV_EXPAND(add, v_int16x8, int32x4, short,  i16m1, 8, i32, 4, vint32m2, vint16m1)
+OPENCV_HAL_IMPL_RISCVV_EXPAND(add, v_int32x4, int64x2, int,  i32m1, 4, i64, 2, vint64m2, vint32m1)
+
+inline v_uint32x4 v_load_expand_q(const uchar* ptr)
+{
+    vuint16m2_t b = vundefined_u16m2();
+    vuint32m2_t c = vundefined_u32m2();
+    vuint8m1_t val = vle_v_u8m1(ptr, 4);    \
+    b = vwaddu_vv_u16m2(val, vmv_v_x_u8m1(0, 4), 4);    \
+    c = vwaddu_vv_u32m2(vget_u16m2_u16m1(b, 0), vmv_v_x_u16m1(0, 4), 4);    \
+    return v_uint32x4(vget_u32m2_u32m1(c, 0));
+}
+
+inline v_int32x4 v_load_expand_q(const schar* ptr)
+{
+    vint16m2_t b = vundefined_i16m2();
+    vint32m2_t c = vundefined_i32m2();
+    vint8m1_t val = vle_v_i8m1(ptr, 4);    \
+    b = vwadd_vv_i16m2(val, vmv_v_x_i8m1(0, 4), 4);    \
+    c = vwadd_vv_i32m2(vget_i16m2_i16m1(b, 0), vmv_v_x_i16m1(0, 4), 4);    \
+    return v_int32x4(vget_i32m2_i32m1(c, 0));
+}
+#define VITL_16 (vuint64m2_t){0x1303120211011000, 0x1707160615051404, 0x1B0B1A0A19091808, 0x1F0F1E0E1D0D1C0C}
+#define VITL_8 (vuint64m2_t){0x0009000100080000, 0x000B0003000A0002, 0x000D0005000C0004, 0x000F0007000E0006}
+#define VITL_4 (vuint64m2_t){0x0000000400000000, 0x0000000500000001, 0x0000000600000002, 0x0000000700000003}
+#define VITL_2 (vuint64m2_t){0, 2, 1, 3}
+#define LOW_4  0x0000000100000000, 0x0000000500000004
+#define LOW_8  0x0003000200010000, 0x000B000A00090008
+#define LOW_16 0x0706050403020100, 0x1716151413121110
+#define HIGH_4  0x0000000300000002, 0x0000000700000006
+#define HIGH_8  0x0007000600050004, 0x000F000E000D000C
+#define HIGH_16 0x0F0E0D0C0B0A0908,  0x1F1E1D1C1B1A1918
+#define OPENCV_HAL_IMPL_RISCVV_UNPACKS(_Tpvec, _Tp, _T, _UTp, _UT, num, num2, len, numh) \
+inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \
+{ \
+    v##_Tp##m2_t tmp = vundefined_##_T##m2();\
+    tmp = vset_##_T##m2(tmp, 0, a0.val); \
+    tmp = vset_##_T##m2(tmp, 1, a1.val); \
+    vuint64m2_t mask = VITL_##num;    \
+    tmp = (v##_Tp##m2_t)vrgather_vv_##_T##m2((v##_Tp##m2_t)tmp, (v##_UTp##m2_t)mask, num2);    \
+    b0.val = vget_##_T##m2_##_T##m1(tmp, 0); \
+    b1.val = vget_##_T##m2_##_T##m1(tmp, 1); \
+} \
+inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    v##_Tp##m1_t b0 = vslideup_vx_##_T##m1_m(vmset_m_##len(num), a.val, b.val, numh, num);    \
+    return v_##_Tpvec(b0);\
+} \
+inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \
+{ \
+    v##_Tp##m1_t b0 = vslidedown_vx_##_T##m1(b.val, numh, num);    \
+    v##_Tp##m1_t a0 = vslidedown_vx_##_T##m1(a.val, numh, num);    \
+    v##_Tp##m1_t b1 = vslideup_vx_##_T##m1_m(vmset_m_##len(num), a0, b0, numh, num);    \
+    return v_##_Tpvec(b1);\
+} \
+inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \
+{ \
+    c.val = vslideup_vx_##_T##m1_m(vmset_m_##len(num), a.val, b.val, numh, num);    \
+    v##_Tp##m1_t b0 = vslidedown_vx_##_T##m1(b.val, numh, num);    \
+    v##_Tp##m1_t a0 = vslidedown_vx_##_T##m1(a.val, numh, num);    \
+    d.val = vslideup_vx_##_T##m1_m(vmset_m_##len(num), a0, b0, numh, num);    \
+}
+
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(uint8x16, uint8, u8, uint8, u8, 16, 32, b8, 8)
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(int8x16, int8, i8, uint8, u8, 16, 32, b8, 8)
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(uint16x8, uint16, u16, uint16, u16, 8, 16, b16, 4)
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(int16x8, int16, i16, uint16, u16, 8, 16, b16, 4)
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(uint32x4, uint32, u32, uint32, u32, 4, 8, b32, 2)
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(int32x4, int32, i32, uint32, u32, 4, 8, b32, 2)
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(float32x4, float32, f32, uint32, u32, 4, 8, b32, 2)
+OPENCV_HAL_IMPL_RISCVV_UNPACKS(float64x2, float64, f64, uint64, u64, 2, 4, b64, 1)
+
+inline v_uint8x16 v_reverse(const v_uint8x16 &a)
+{
+    vuint64m1_t mask = (vuint64m1_t){0x08090A0B0C0D0E0F, 0x0001020304050607};
+    return v_uint8x16(vrgather_vv_u8m1(a.val, (vuint8m1_t)mask, 16));
+}
+inline v_int8x16 v_reverse(const v_int8x16 &a)
+{
+    vint64m1_t mask = (vint64m1_t){0x08090A0B0C0D0E0F, 0x0001020304050607};
+    return v_int8x16(vrgather_vv_i8m1(a.val, (vuint8m1_t)mask, 16));
+}
+
+inline v_uint16x8 v_reverse(const v_uint16x8 &a)
+{
+    vuint64m1_t mask = (vuint64m1_t){0x0004000500060007, 0x000000100020003};
+    return v_uint16x8(vrgather_vv_u16m1(a.val, (vuint16m1_t)mask, 8));
+}
+
+inline v_int16x8 v_reverse(const v_int16x8 &a)
+{
+    vint64m1_t mask = (vint64m1_t){0x0004000500060007, 0x000000100020003};
+    return v_int16x8(vrgather_vv_i16m1(a.val, (vuint16m1_t)mask, 8));
+}
+inline v_uint32x4 v_reverse(const v_uint32x4 &a)
+{
+    return v_uint32x4(vrgather_vv_u32m1(a.val, (vuint32m1_t){3, 2, 1, 0}, 4));
+}
+
+inline v_int32x4 v_reverse(const v_int32x4 &a)
+{
+    return v_int32x4(vrgather_vv_i32m1(a.val, (vuint32m1_t){3, 2, 1, 0}, 4));
+}
+
+inline v_float32x4 v_reverse(const v_float32x4 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x2 v_reverse(const v_uint64x2 &a)
+{
+    return v_uint64x2(a.val[1], a.val[0]);
+}
+
+inline v_int64x2 v_reverse(const v_int64x2 &a)
+{
+    return v_int64x2(a.val[1], a.val[0]);
+}
+
+inline v_float64x2 v_reverse(const v_float64x2 &a)
+{
+    return v_float64x2(a.val[1], a.val[0]);
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_EXTRACT(_Tpvec, suffix, size) \
+template <int n> \
+inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \
+{ return v_rotate_right<n>(a, b);}
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_uint8x16, u8, 0)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_int8x16, s8, 0)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_uint16x8, u16, 1)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_int16x8, s16, 1)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_uint32x4, u32, 2)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_int32x4, s32, 2)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_uint64x2, u64, 3)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_int64x2, s64, 3)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_float32x4, f32, 2)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT(v_float64x2, f64, 3)
+
+
+#define OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(_Tpvec, _Tp, suffix) \
+template<int i> inline _Tp v_extract_n(_Tpvec v) { return v.val[i]; }
+
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_int8x16, schar, s8)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_int16x8, short, s16)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_uint32x4, uint, u32)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_int32x4, int, s32)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_int64x2, int64, s64)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_float32x4, float, f32)
+OPENCV_HAL_IMPL_RISCVV_EXTRACT_N(v_float64x2, double, f64)
+
+#define OPENCV_HAL_IMPL_RISCVV_BROADCAST(_Tpvec, _Tp, num) \
+template<int i> inline _Tpvec v_broadcast_element(_Tpvec v) { return _Tpvec(vrgather_vx_##_Tp##m1(v.val, i, num)); }
+
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_uint8x16, u8, 16)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_int8x16, i8, 16)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_uint16x8, u16, 8)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_int16x8, i16, 8)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_uint32x4, u32, 4)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_int32x4, i32, 4)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_uint64x2, u64, 2)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_int64x2, i64, 2)
+OPENCV_HAL_IMPL_RISCVV_BROADCAST(v_float32x4, f32, 4)
+inline v_int32x4 v_round(const v_float32x4& a)
+{
+    __builtin_riscv_fsrm(0);
+    vint32m1_t nan = vand_vx_i32m1((vint32m1_t)a.val, 0x7f800000, 4);
+    vbool32_t mask = vmsne_vx_i32m1_b32(nan, 0x7f800000, 4);
+    vint32m1_t val = vfcvt_x_f_v_i32m1_m(mask, vmv_v_x_i32m1(0, 4), a.val, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+inline v_int32x4 v_floor(const v_float32x4& a)
+{
+    __builtin_riscv_fsrm(2);
+    vint32m1_t nan = vand_vx_i32m1((vint32m1_t)a.val, 0x7f800000, 4);
+    vbool32_t mask = vmsne_vx_i32m1_b32(nan, 0x7f800000, 4);
+    vint32m1_t val = vfcvt_x_f_v_i32m1_m(mask, vmv_v_x_i32m1(0, 4), a.val, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+
+inline v_int32x4 v_ceil(const v_float32x4& a)
+{
+    __builtin_riscv_fsrm(3);
+    vint32m1_t nan = vand_vx_i32m1((vint32m1_t)a.val, 0x7f800000, 4);
+    vbool32_t mask = vmsne_vx_i32m1_b32(nan, 0x7f800000, 4);
+    vint32m1_t val = vfcvt_x_f_v_i32m1_m(mask, vmv_v_x_i32m1(0, 4), a.val, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+
+inline v_int32x4 v_trunc(const v_float32x4& a)
+{
+    __builtin_riscv_fsrm(1);
+    vint32m1_t nan = vand_vx_i32m1((vint32m1_t)a.val, 0x7f800000, 4);
+    vbool32_t mask = vmsne_vx_i32m1_b32(nan, 0x7f800000, 4);
+    vint32m1_t val = vfcvt_x_f_v_i32m1_m(mask, vmv_v_x_i32m1(0, 4), a.val, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+
+inline v_int32x4 v_round(const v_float64x2& a)
+{
+    __builtin_riscv_fsrm(0);
+    vfloat64m2_t _val = vundefined_f64m2();
+    _val = vset_f64m2(_val, 0, a.val);
+    //_val = vset_f64m2(_val, 1, a.val);
+    _val = vset_f64m2(_val, 1, vfmv_v_f_f64m1(0, 2));
+    vint32m1_t val = vfncvt_x_f_v_i32m1(_val, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
+{
+    __builtin_riscv_fsrm(0);
+    vfloat64m2_t _val = vundefined_f64m2();
+    _val = vset_f64m2(_val, 0, a.val);
+    _val = vset_f64m2(_val, 1, b.val);
+    vint32m1_t val = vfncvt_x_f_v_i32m1(_val, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+inline v_int32x4 v_floor(const v_float64x2& a)
+{
+    __builtin_riscv_fsrm(2);
+    vfloat64m2_t _val = vundefined_f64m2();
+    _val = vset_f64m2(_val, 0, a.val);
+    vfloat32m1_t aval = vfncvt_f_f_v_f32m1(_val, 2);
+
+    vint32m1_t nan = vand_vx_i32m1((vint32m1_t)aval, 0x7f800000, 4);
+    vbool32_t mask = vmsne_vx_i32m1_b32(nan, 0x7f800000, 4);
+    vint32m1_t val = vfcvt_x_f_v_i32m1_m(mask, vmv_v_x_i32m1(0, 4), aval, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+
+inline v_int32x4 v_ceil(const v_float64x2& a)
+{
+    __builtin_riscv_fsrm(3);
+    vfloat64m2_t _val = vundefined_f64m2();
+    _val = vset_f64m2(_val, 0, a.val);
+    vfloat32m1_t aval = vfncvt_f_f_v_f32m1(_val, 2);
+
+    vint32m1_t nan = vand_vx_i32m1((vint32m1_t)aval, 0x7f800000, 4);
+    vbool32_t mask = vmsne_vx_i32m1_b32(nan, 0x7f800000, 4);
+    vint32m1_t val = vfcvt_x_f_v_i32m1_m(mask, vmv_v_x_i32m1(0, 4), aval, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+
+inline v_int32x4 v_trunc(const v_float64x2& a)
+{
+    __builtin_riscv_fsrm(1);
+    vfloat64m2_t _val = vundefined_f64m2();
+    _val = vset_f64m2(_val, 0, a.val);
+    vfloat32m1_t aval = vfncvt_f_f_v_f32m1(_val, 2);
+
+    vint32m1_t nan = vand_vx_i32m1((vint32m1_t)aval, 0x7f800000, 4);
+    vbool32_t mask = vmsne_vx_i32m1_b32(nan, 0x7f800000, 4);
+    vint32m1_t val = vfcvt_x_f_v_i32m1_m(mask, vmv_v_x_i32m1(0, 4), aval, 4);
+    __builtin_riscv_fsrm(0);
+    return v_int32x4(val);
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_LOAD_DEINTERLEAVED(intrin, _Tpvec, num, _Tp, _T)    \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec##x##num& a, v_##_Tpvec##x##num& b) \
+{ \
+    v##_Tpvec##m1x2_t ret = intrin##2e_v_##_T##m1x2(ptr, num);\
+    a.val = vget_##_T##m1x2_##_T##m1(ret, 0);  \
+    b.val = vget_##_T##m1x2_##_T##m1(ret, 1);  \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec##x##num& a, v_##_Tpvec##x##num& b, v_##_Tpvec##x##num& c) \
+{ \
+    v##_Tpvec##m1x3_t ret = intrin##3e_v_##_T##m1x3(ptr, num);\
+    a.val = vget_##_T##m1x3_##_T##m1(ret, 0);  \
+    b.val = vget_##_T##m1x3_##_T##m1(ret, 1);  \
+    c.val = vget_##_T##m1x3_##_T##m1(ret, 2);  \
+}\
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec##x##num& a, v_##_Tpvec##x##num& b, \
+                                v_##_Tpvec##x##num& c, v_##_Tpvec##x##num& d) \
+{ \
+    v##_Tpvec##m1x4_t ret = intrin##4e_v_##_T##m1x4(ptr, num);\
+    a.val = vget_##_T##m1x4_##_T##m1(ret, 0);  \
+    b.val = vget_##_T##m1x4_##_T##m1(ret, 1);  \
+    c.val = vget_##_T##m1x4_##_T##m1(ret, 2);  \
+    d.val = vget_##_T##m1x4_##_T##m1(ret, 3);  \
+} \
+
+#define OPENCV_HAL_IMPL_RISCVV_STORE_INTERLEAVED(intrin, _Tpvec, num, _Tp, _T)    \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec##x##num& a, const v_##_Tpvec##x##num& b, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    v##_Tpvec##m1x2_t ret = vundefined_##_T##m1x2();      \
+    ret = vset_##_T##m1x2(ret, 0, a.val);  \
+    ret = vset_##_T##m1x2(ret, 1, b.val);  \
+    intrin##2e_v_##_T##m1x2(ptr, ret, num); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec##x##num& a, const v_##_Tpvec##x##num& b, \
+                                const v_##_Tpvec##x##num& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    v##_Tpvec##m1x3_t ret = vundefined_##_T##m1x3();       \
+    ret = vset_##_T##m1x3(ret, 0, a.val);  \
+    ret = vset_##_T##m1x3(ret, 1, b.val);  \
+    ret = vset_##_T##m1x3(ret, 2, c.val);  \
+    intrin##3e_v_##_T##m1x3(ptr, ret, num); \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec##x##num& a, const v_##_Tpvec##x##num& b, \
+                                const v_##_Tpvec##x##num& c, const v_##_Tpvec##x##num& d, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \
+{ \
+    v##_Tpvec##m1x4_t ret = vundefined_##_T##m1x4();             \
+    ret = vset_##_T##m1x4(ret, 0, a.val);  \
+    ret = vset_##_T##m1x4(ret, 1, b.val);  \
+    ret = vset_##_T##m1x4(ret, 2, c.val);  \
+    ret = vset_##_T##m1x4(ret, 3, d.val);  \
+    intrin##4e_v_##_T##m1x4(ptr, ret, num); \
+}
+
+#define OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(_Tpvec, _Tp, num, ld, st, _T) \
+OPENCV_HAL_IMPL_RISCVV_LOAD_DEINTERLEAVED(ld, _Tpvec, num, _Tp, _T)    \
+OPENCV_HAL_IMPL_RISCVV_STORE_INTERLEAVED(st, _Tpvec, num, _Tp, _T)
+
+//OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(uint8, uchar, )
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(int8, schar, 16, vlseg, vsseg, i8)
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(int16, short, 8, vlseg, vsseg, i16)
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(int32, int, 4, vlseg, vsseg, i32)
+
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(uint8, unsigned char, 16, vlseg, vsseg, u8)
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(uint16, unsigned short, 8, vlseg, vsseg, u16)
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED(uint32, unsigned int, 4, vlseg, vsseg, u32)
+
+#define OPENCV_HAL_IMPL_RISCVV_INTERLEAVED_(_Tpvec, _Tp, num, _T) \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec##x##num& a, v_##_Tpvec##x##num& b) \
+{ \
+    v##_Tpvec##m1x2_t ret = vlseg2e_v_##_T##m1x2(ptr, num); \
+    a.val = vget_##_T##m1x2_##_T##m1(ret, 0);  \
+    b.val = vget_##_T##m1x2_##_T##m1(ret, 1);  \
+} \
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec##x##num& a, v_##_Tpvec##x##num& b, v_##_Tpvec##x##num& c) \
+{ \
+    v##_Tpvec##m1x3_t ret = vlseg3e_v_##_T##m1x3(ptr, num);    \
+    a.val = vget_##_T##m1x3_##_T##m1(ret, 0);  \
+    b.val = vget_##_T##m1x3_##_T##m1(ret, 1);  \
+    c.val = vget_##_T##m1x3_##_T##m1(ret, 2);  \
+}\
+inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec##x##num& a, v_##_Tpvec##x##num& b, \
+                                v_##_Tpvec##x##num& c, v_##_Tpvec##x##num& d) \
+{ \
+    v##_Tpvec##m1x4_t ret = vlseg4e_v_##_T##m1x4(ptr, num);    \
+    a.val = vget_##_T##m1x4_##_T##m1(ret, 0);  \
+    b.val = vget_##_T##m1x4_##_T##m1(ret, 1);  \
+    c.val = vget_##_T##m1x4_##_T##m1(ret, 2);  \
+    d.val = vget_##_T##m1x4_##_T##m1(ret, 3);  \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec##x##num& a, const v_##_Tpvec##x##num& b, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    v##_Tpvec##m1x2_t ret = vundefined_##_T##m1x2();    \
+    ret = vset_##_T##m1x2(ret, 0, a.val);  \
+    ret = vset_##_T##m1x2(ret, 1, b.val);  \
+    vsseg2e_v_##_T##m1x2(ptr, ret, num);    \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec##x##num& a, const v_##_Tpvec##x##num& b, \
+                                const v_##_Tpvec##x##num& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ \
+    v##_Tpvec##m1x3_t ret = vundefined_##_T##m1x3();    \
+    ret = vset_##_T##m1x3(ret, 0, a.val);  \
+    ret = vset_##_T##m1x3(ret, 1, b.val);  \
+    ret = vset_##_T##m1x3(ret, 2, c.val);  \
+    vsseg3e_v_##_T##m1x3(ptr, ret, num);    \
+} \
+inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec##x##num& a, const v_##_Tpvec##x##num& b, \
+                                const v_##_Tpvec##x##num& c, const v_##_Tpvec##x##num& d, \
+                                hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \
+{ \
+    v##_Tpvec##m1x4_t ret = vundefined_##_T##m1x4();    \
+    ret = vset_##_T##m1x4(ret, 0, a.val);  \
+    ret = vset_##_T##m1x4(ret, 1, b.val);  \
+    ret = vset_##_T##m1x4(ret, 2, c.val);  \
+    ret = vset_##_T##m1x4(ret, 3, d.val);  \
+    vsseg4e_v_##_T##m1x4(ptr, ret, num);    \
+}
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED_(float32, float, 4, f32)
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED_(float64, double, 2, f64)
+
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED_(uint64, unsigned long, 2, u64)
+OPENCV_HAL_IMPL_RISCVV_INTERLEAVED_(int64, long, 2, i64)
+
+inline v_float32x4 v_cvt_f32(const v_int32x4& a)
+{
+    return v_float32x4(vfcvt_f_x_v_f32m1(a.val, 4));
+}
+
+#if CV_SIMD128_64F
+inline v_float32x4 v_cvt_f32(const v_float64x2& a)
+{
+    vfloat64m2_t _val = vundefined_f64m2();
+    _val = vset_f64m2(_val, 0, a.val);
+    vfloat32m1_t aval = vfncvt_f_f_v_f32m1(_val, 2);
+    return v_float32x4(aval);
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
+{
+    vfloat64m2_t _val = vundefined_f64m2();
+    _val = vset_f64m2(_val, 0, a.val);
+    _val = vset_f64m2(_val, 1, b.val);
+    vfloat32m1_t aval = vfncvt_f_f_v_f32m1(_val, 4);
+    return v_float32x4(aval);
+}
+
+inline v_float64x2 v_cvt_f64(const v_int32x4& a)
+{
+    vfloat32m1_t val = vfcvt_f_x_v_f32m1(a.val, 4);
+    vfloat64m2_t _val = vfwcvt_f_f_v_f64m2(val, 4);
+    return v_float64x2(vget_f64m2_f64m1(_val, 0));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
+{
+    vfloat32m1_t val = vfcvt_f_x_v_f32m1(a.val, 4);
+    vfloat64m2_t _val = vfwcvt_f_f_v_f64m2(val, 4);
+    return v_float64x2(vget_f64m2_f64m1(_val, 1));
+}
+
+inline v_float64x2 v_cvt_f64(const v_float32x4& a)
+{
+    vfloat64m2_t _val  = vfwcvt_f_f_v_f64m2(a.val, 4);
+    return v_float64x2(vget_f64m2_f64m1(_val, 0));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
+{
+    vfloat64m2_t _val  = vfwcvt_f_f_v_f64m2(a.val, 4);
+    return v_float64x2(vget_f64m2_f64m1(_val, 1));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int64x2& a)
+{
+    return v_float64x2(vfcvt_f_x_v_f64m1(a.val, 2));
+}
+
+#endif
+inline v_int8x16 v_interleave_pairs(const v_int8x16& vec)
+{
+    vuint64m1_t m0 = {0x0705060403010200, 0x0F0D0E0C0B090A08};
+    return v_int8x16(vrgather_vv_i8m1(vec.val, (vuint8m1_t)m0, 16));
+}
+inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec)
+{
+    return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec)));
+}
+
+inline v_int8x16 v_interleave_quads(const v_int8x16& vec)
+{
+    vuint64m1_t m0 = {0x0703060205010400, 0x0F0B0E0A0D090C08};
+    return v_int8x16(vrgather_vv_i8m1(vec.val, (vuint8m1_t)m0, 16));
+}
+inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec)
+{
+    return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec)));
+}
+
+inline v_int16x8 v_interleave_pairs(const v_int16x8& vec)
+{
+    vuint64m1_t m0 = {0x0706030205040100, 0x0F0E0B0A0D0C0908};
+    return v_int16x8((vint16m1_t)vrgather_vv_u8m1((vuint8m1_t)vec.val, (vuint8m1_t)m0, 16));
+}
+inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+inline v_int16x8 v_interleave_quads(const v_int16x8& vec)
+{
+    vuint64m1_t m0 = {0x0B0A030209080100, 0x0F0E07060D0C0504};
+    return v_int16x8((vint16m1_t)vrgather_vv_u8m1((vuint8m1_t)(vec.val), (vuint8m1_t)m0, 16));
+}
+inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_interleave_pairs(const v_int32x4& vec)
+{
+    vuint64m1_t m0 = {0x0B0A090803020100, 0x0F0E0D0C07060504};
+    return v_int32x4((vint32m1_t)vrgather_vv_u8m1((vuint8m1_t)(vec.val), (vuint8m1_t)m0, 16));
+}
+inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+    vuint64m1_t m0 = {0x0908060504020100, 0xFFFFFFFF0E0D0C0A};
+    return v_int8x16((vint8m1_t)vrgather_vv_u8m1((vuint8m1_t)(vec.val), (vuint8m1_t)m0, 16));
+}
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+    vuint64m1_t m0 = {0x0908050403020100, 0xFFFFFFFF0D0C0B0A};
+    return v_int16x8((vint16m1_t)vrgather_vv_u8m1((vuint8m1_t)(vec.val), (vuint8m1_t)m0, 16));
+}
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; }
+
+#if CV_SIMD128_64F
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a,   const v_int32x4& b,
+                                    const v_float64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
+{
+    vint64m2_t v1 = vwmul_vv_i64m2(a.val, b.val, 4);
+    vfloat64m1_t res = vfcvt_f_x_v_f64m1(vadd_vv_i64m1(vget_i64m2_i64m1(v1, 0), vget_i64m2_i64m1(v1, 1), 2), 2);
+    return v_float64x2(res);
+}
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ v_float64x2 res = v_dotprod_expand_fast(a, b);
+  return res + c; }
+#endif
+////// FP16 support ///////
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    vfloat16m1_t v = vle_v_f16m1((__fp16*)ptr, 4);
+    vfloat32m2_t v32 = vfwcvt_f_f_v_f32m2(v, 4);
+    return v_float32x4(vget_f32m2_f32m1(v32, 0));
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    vfloat32m2_t v32 = vundefined_f32m2();
+    v32 = vset_f32m2(v32, 0, v.val);
+    vfloat16m1_t hv = vfncvt_f_f_v_f16m1(v32, 4);
+    vse_v_f16m1((__fp16*)ptr, hv, 4);
+}
+
+
+inline void v_cleanup() {}
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+}
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin_sse.hpp b/3rdParty/include/opencv2/core/hal/intrin_sse.hpp
new file mode 100644
index 0000000..443ee16
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_sse.hpp
@@ -0,0 +1,3467 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HAL_SSE_HPP
+#define OPENCV_HAL_SSE_HPP
+
+#include <algorithm>
+#include "opencv2/core/utility.hpp"
+
+#define CV_SIMD128 1
+#define CV_SIMD128_64F 1
+#define CV_SIMD128_FP16 0  // no native operations with FP16 type.
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+//
+// Compilation troubleshooting:
+// - MSVC: error C2719: 'a': formal parameter with requested alignment of 16 won't be aligned
+//   Replace parameter declaration to const reference:
+//   -v_int32x4 a
+//   +const v_int32x4& a
+//
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+///////// Types ////////////
+
+struct v_uint8x16
+{
+    typedef uchar lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 16 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint8x16() {}
+    explicit v_uint8x16(__m128i v) : val(v) {}
+    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
+               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
+    {
+        val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3,
+                            (char)v4, (char)v5, (char)v6, (char)v7,
+                            (char)v8, (char)v9, (char)v10, (char)v11,
+                            (char)v12, (char)v13, (char)v14, (char)v15);
+    }
+
+    uchar get0() const
+    {
+        return (uchar)_mm_cvtsi128_si32(val);
+    }
+
+    __m128i val;
+};
+
+struct v_int8x16
+{
+    typedef schar lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 16 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int8x16() {}
+    explicit v_int8x16(__m128i v) : val(v) {}
+    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
+              schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
+    {
+        val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3,
+                            (char)v4, (char)v5, (char)v6, (char)v7,
+                            (char)v8, (char)v9, (char)v10, (char)v11,
+                            (char)v12, (char)v13, (char)v14, (char)v15);
+    }
+
+    schar get0() const
+    {
+        return (schar)_mm_cvtsi128_si32(val);
+    }
+
+    __m128i val;
+};
+
+struct v_uint16x8
+{
+    typedef ushort lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 8 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint16x8() {}
+    explicit v_uint16x8(__m128i v) : val(v) {}
+    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
+    {
+        val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3,
+                             (short)v4, (short)v5, (short)v6, (short)v7);
+    }
+
+    ushort get0() const
+    {
+        return (ushort)_mm_cvtsi128_si32(val);
+    }
+
+    __m128i val;
+};
+
+struct v_int16x8
+{
+    typedef short lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 8 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int16x8() {}
+    explicit v_int16x8(__m128i v) : val(v) {}
+    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
+    {
+        val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3,
+                             (short)v4, (short)v5, (short)v6, (short)v7);
+    }
+
+    short get0() const
+    {
+        return (short)_mm_cvtsi128_si32(val);
+    }
+
+    __m128i val;
+};
+
+struct v_uint32x4
+{
+    typedef unsigned lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 4 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint32x4() {}
+    explicit v_uint32x4(__m128i v) : val(v) {}
+    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
+    {
+        val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3);
+    }
+
+    unsigned get0() const
+    {
+        return (unsigned)_mm_cvtsi128_si32(val);
+    }
+
+    __m128i val;
+};
+
+struct v_int32x4
+{
+    typedef int lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 4 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int32x4() {}
+    explicit v_int32x4(__m128i v) : val(v) {}
+    v_int32x4(int v0, int v1, int v2, int v3)
+    {
+        val = _mm_setr_epi32(v0, v1, v2, v3);
+    }
+
+    int get0() const
+    {
+        return _mm_cvtsi128_si32(val);
+    }
+
+    __m128i val;
+};
+
+struct v_float32x4
+{
+    typedef float lane_type;
+    typedef __m128 vector_type;
+    enum { nlanes = 4 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_float32x4() {}
+    explicit v_float32x4(__m128 v) : val(v) {}
+    v_float32x4(float v0, float v1, float v2, float v3)
+    {
+        val = _mm_setr_ps(v0, v1, v2, v3);
+    }
+
+    float get0() const
+    {
+        return _mm_cvtss_f32(val);
+    }
+
+    __m128 val;
+};
+
+struct v_uint64x2
+{
+    typedef uint64 lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 2 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_uint64x2() {}
+    explicit v_uint64x2(__m128i v) : val(v) {}
+    v_uint64x2(uint64 v0, uint64 v1)
+    {
+#if defined(_MSC_VER) && _MSC_VER >= 1920/*MSVS 2019*/ && defined(_M_X64) && !defined(__clang__)
+        val = _mm_setr_epi64x((int64_t)v0, (int64_t)v1);
+#elif defined(__GNUC__)
+        val = _mm_setr_epi64((__m64)v0, (__m64)v1);
+#else
+        val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32));
+#endif
+    }
+
+    uint64 get0() const
+    {
+    #if !defined(__x86_64__) && !defined(_M_X64)
+        int a = _mm_cvtsi128_si32(val);
+        int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32));
+        return (unsigned)a | ((uint64)(unsigned)b << 32);
+    #else
+        return (uint64)_mm_cvtsi128_si64(val);
+    #endif
+    }
+
+    __m128i val;
+};
+
+struct v_int64x2
+{
+    typedef int64 lane_type;
+    typedef __m128i vector_type;
+    enum { nlanes = 2 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_int64x2() {}
+    explicit v_int64x2(__m128i v) : val(v) {}
+    v_int64x2(int64 v0, int64 v1)
+    {
+#if defined(_MSC_VER) && _MSC_VER >= 1920/*MSVS 2019*/ && defined(_M_X64) && !defined(__clang__)
+        val = _mm_setr_epi64x((int64_t)v0, (int64_t)v1);
+#elif defined(__GNUC__)
+        val = _mm_setr_epi64((__m64)v0, (__m64)v1);
+#else
+        val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32));
+#endif
+    }
+
+    int64 get0() const
+    {
+    #if !defined(__x86_64__) && !defined(_M_X64)
+        int a = _mm_cvtsi128_si32(val);
+        int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32));
+        return (int64)((unsigned)a | ((uint64)(unsigned)b << 32));
+    #else
+        return _mm_cvtsi128_si64(val);
+    #endif
+    }
+
+    __m128i val;
+};
+
+struct v_float64x2
+{
+    typedef double lane_type;
+    typedef __m128d vector_type;
+    enum { nlanes = 2 };
+
+    /* coverity[uninit_ctor]: suppress warning */
+    v_float64x2() {}
+    explicit v_float64x2(__m128d v) : val(v) {}
+    v_float64x2(double v0, double v1)
+    {
+        val = _mm_setr_pd(v0, v1);
+    }
+
+    double get0() const
+    {
+        return _mm_cvtsd_f64(val);
+    }
+
+    __m128d val;
+};
+
+namespace hal_sse_internal
+{
+    template <typename to_sse_type, typename from_sse_type>
+    to_sse_type v_sse_reinterpret_as(const from_sse_type& val);
+
+#define OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(to_sse_type, from_sse_type, sse_cast_intrin) \
+    template<> inline \
+    to_sse_type v_sse_reinterpret_as(const from_sse_type& a) \
+    { return sse_cast_intrin(a); }
+
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128i, OPENCV_HAL_NOP)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128, _mm_castps_si128)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128d, _mm_castpd_si128)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128i, _mm_castsi128_ps)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128, OPENCV_HAL_NOP)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128d, _mm_castpd_ps)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128i, _mm_castsi128_pd)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128, _mm_castps_pd)
+    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128d, OPENCV_HAL_NOP)
+}
+
+#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \
+inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \
+inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \
+template<typename _Tpvec0> inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \
+{ return _Tpvec(cast(a.val)); }
+
+OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, schar, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, schar, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps)
+OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd)
+
+inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); }
+inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); }
+inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); }
+inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); }
+
+template<typename _Tpvec> inline
+v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); }
+template<typename _Tpvec> inline
+v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); }
+inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a)
+{ return v_float32x4(_mm_castsi128_ps(a.val)); }
+inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a)
+{ return v_float32x4(_mm_castsi128_ps(a.val)); }
+inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a)
+{ return v_float64x2(_mm_castsi128_pd(a.val)); }
+inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a)
+{ return v_float64x2(_mm_castsi128_pd(a.val)); }
+
+#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \
+inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \
+{ return _Tpvec(_mm_castps_si128(a.val)); } \
+inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \
+{ return _Tpvec(_mm_castpd_si128(a.val)); }
+
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8)
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8)
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16)
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16)
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32)
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32)
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64)
+OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64)
+
+inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; }
+inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; }
+inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); }
+inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); }
+
+//////////////// PACK ///////////////
+inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b)
+{
+    __m128i delta = _mm_set1_epi16(255);
+    return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)),
+                                       _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta))));
+}
+
+inline void v_pack_store(uchar* ptr, const v_uint16x8& a)
+{
+    __m128i delta = _mm_set1_epi16(255);
+    __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta));
+    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1));
+}
+
+inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b)
+{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); }
+
+inline void v_pack_u_store(uchar* ptr, const v_int16x8& a)
+{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); }
+
+template<int n> inline
+v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b)
+{
+    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
+    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
+    return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n),
+                                       _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n)));
+}
+
+template<int n> inline
+void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a)
+{
+    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
+    __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n);
+    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1));
+}
+
+template<int n> inline
+v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b)
+{
+    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
+    return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n),
+                                       _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n)));
+}
+
+template<int n> inline
+void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a)
+{
+    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
+    __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n);
+    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1));
+}
+
+inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b)
+{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); }
+
+inline void v_pack_store(schar* ptr, const v_int16x8& a)
+{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); }
+
+template<int n> inline
+v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b)
+{
+    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
+    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
+    return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n),
+                                     _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n)));
+}
+template<int n> inline
+void v_rshr_pack_store(schar* ptr, const v_int16x8& a)
+{
+    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
+    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
+    __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n);
+    _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1));
+}
+
+
+// byte-wise "mask ? a : b"
+inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b)
+{
+#if CV_SSE4_1
+    return _mm_blendv_epi8(b, a, mask);
+#else
+    return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask));
+#endif
+}
+
+inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b)
+{ return v_uint16x8(_v128_packs_epu32(a.val, b.val)); }
+
+inline void v_pack_store(ushort* ptr, const v_uint32x4& a)
+{
+    __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768);
+    __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32);
+    __m128i r = _mm_packs_epi32(a1, a1);
+    _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768)));
+}
+
+template<int n> inline
+v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b)
+{
+    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
+    __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32);
+    __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32);
+    return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768)));
+}
+
+template<int n> inline
+void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a)
+{
+    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
+    __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32);
+    __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
+    _mm_storel_epi64((__m128i*)ptr, a2);
+}
+
+inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_SSE4_1
+    return v_uint16x8(_mm_packus_epi32(a.val, b.val));
+#else
+    __m128i delta32 = _mm_set1_epi32(32768);
+
+    // preliminary saturate negative values to zero
+    __m128i a1 = _mm_and_si128(a.val, _mm_cmpgt_epi32(a.val, _mm_set1_epi32(0)));
+    __m128i b1 = _mm_and_si128(b.val, _mm_cmpgt_epi32(b.val, _mm_set1_epi32(0)));
+
+    __m128i r = _mm_packs_epi32(_mm_sub_epi32(a1, delta32), _mm_sub_epi32(b1, delta32));
+    return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768)));
+#endif
+}
+
+inline void v_pack_u_store(ushort* ptr, const v_int32x4& a)
+{
+#if CV_SSE4_1
+    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a.val, a.val));
+#else
+    __m128i delta32 = _mm_set1_epi32(32768);
+    __m128i a1 = _mm_sub_epi32(a.val, delta32);
+    __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
+    _mm_storel_epi64((__m128i*)ptr, r);
+#endif
+}
+
+template<int n> inline
+v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_SSE4_1
+    __m128i delta = _mm_set1_epi32(1 << (n - 1));
+    return v_uint16x8(_mm_packus_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n),
+                                       _mm_srai_epi32(_mm_add_epi32(b.val, delta), n)));
+#else
+    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
+    __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32);
+    __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
+    __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32);
+    __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768));
+    return v_uint16x8(_mm_unpacklo_epi64(a2, b2));
+#endif
+}
+
+template<int n> inline
+void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a)
+{
+#if CV_SSE4_1
+    __m128i delta = _mm_set1_epi32(1 << (n - 1));
+    __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n);
+    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a1, a1));
+#else
+    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
+    __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32);
+    __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
+    _mm_storel_epi64((__m128i*)ptr, a2);
+#endif
+}
+
+inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b)
+{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); }
+
+inline void v_pack_store(short* ptr, const v_int32x4& a)
+{
+    _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val));
+}
+
+template<int n> inline
+v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b)
+{
+    __m128i delta = _mm_set1_epi32(1 << (n-1));
+    return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n),
+                                     _mm_srai_epi32(_mm_add_epi32(b.val, delta), n)));
+}
+
+template<int n> inline
+void v_rshr_pack_store(short* ptr, const v_int32x4& a)
+{
+    __m128i delta = _mm_set1_epi32(1 << (n-1));
+    __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n);
+    _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1));
+}
+
+
+// [a0 0 | b0 0]  [a1 0 | b1 0]
+inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b)
+{
+    __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0
+    __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0
+    return v_uint32x4(_mm_unpacklo_epi32(v0, v1));
+}
+
+inline void v_pack_store(unsigned* ptr, const v_uint64x2& a)
+{
+    __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0));
+    _mm_storel_epi64((__m128i*)ptr, a1);
+}
+
+// [a0 0 | b0 0]  [a1 0 | b1 0]
+inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b)
+{
+    __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0
+    __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0
+    return v_int32x4(_mm_unpacklo_epi32(v0, v1));
+}
+
+inline void v_pack_store(int* ptr, const v_int64x2& a)
+{
+    __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0));
+    _mm_storel_epi64((__m128i*)ptr, a1);
+}
+
+template<int n> inline
+v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b)
+{
+    uint64 delta = (uint64)1 << (n-1);
+    v_uint64x2 delta2(delta, delta);
+    __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n);
+    __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n);
+    __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0
+    __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0
+    return v_uint32x4(_mm_unpacklo_epi32(v0, v1));
+}
+
+template<int n> inline
+void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a)
+{
+    uint64 delta = (uint64)1 << (n-1);
+    v_uint64x2 delta2(delta, delta);
+    __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n);
+    __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0));
+    _mm_storel_epi64((__m128i*)ptr, a2);
+}
+
+inline __m128i v_sign_epi64(__m128i a)
+{
+    return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1
+}
+
+inline __m128i v_srai_epi64(__m128i a, int imm)
+{
+    __m128i smask = v_sign_epi64(a);
+    return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask);
+}
+
+template<int n> inline
+v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b)
+{
+    int64 delta = (int64)1 << (n-1);
+    v_int64x2 delta2(delta, delta);
+    __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n);
+    __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n);
+    __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0
+    __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0
+    return v_int32x4(_mm_unpacklo_epi32(v0, v1));
+}
+
+template<int n> inline
+void v_rshr_pack_store(int* ptr, const v_int64x2& a)
+{
+    int64 delta = (int64)1 << (n-1);
+    v_int64x2 delta2(delta, delta);
+    __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n);
+    __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0));
+    _mm_storel_epi64((__m128i*)ptr, a2);
+}
+
+// pack boolean
+inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
+{
+    __m128i ab = _mm_packs_epi16(a.val, b.val);
+    return v_uint8x16(ab);
+}
+
+inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
+                           const v_uint32x4& c, const v_uint32x4& d)
+{
+    __m128i ab = _mm_packs_epi32(a.val, b.val);
+    __m128i cd = _mm_packs_epi32(c.val, d.val);
+    return v_uint8x16(_mm_packs_epi16(ab, cd));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
+                           const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
+                           const v_uint64x2& g, const v_uint64x2& h)
+{
+    __m128i ab = _mm_packs_epi32(a.val, b.val);
+    __m128i cd = _mm_packs_epi32(c.val, d.val);
+    __m128i ef = _mm_packs_epi32(e.val, f.val);
+    __m128i gh = _mm_packs_epi32(g.val, h.val);
+
+    __m128i abcd = _mm_packs_epi32(ab, cd);
+    __m128i efgh = _mm_packs_epi32(ef, gh);
+    return v_uint8x16(_mm_packs_epi16(abcd, efgh));
+}
+
+inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
+                            const v_float32x4& m1, const v_float32x4& m2,
+                            const v_float32x4& m3)
+{
+    __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val);
+    __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val);
+    __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val);
+    __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val);
+
+    return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3)));
+}
+
+inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
+                               const v_float32x4& m1, const v_float32x4& m2,
+                               const v_float32x4& a)
+{
+    __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val);
+    __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val);
+    __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val);
+
+    return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, a.val)));
+}
+
+#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \
+    inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+    { \
+        return _Tpvec(intrin(a.val, b.val)); \
+    } \
+    inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
+    { \
+        a.val = intrin(a.val, b.val); \
+        return a; \
+    }
+
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32)
+OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint32x4, _v128_mullo_epi32)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32)
+OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int32x4, _v128_mullo_epi32)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps)
+OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps)
+OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd)
+OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd)
+OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64)
+OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64)
+OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64)
+
+// saturating multiply 8-bit, 16-bit
+#define OPENCV_HAL_IMPL_SSE_MUL_SAT(_Tpvec, _Tpwvec)             \
+    inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b)  \
+    {                                                            \
+        _Tpwvec c, d;                                            \
+        v_mul_expand(a, b, c, d);                                \
+        return v_pack(c, d);                                     \
+    }                                                            \
+    inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b)      \
+    { a = a * b; return a; }
+
+OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint8x16, v_uint16x8)
+OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int8x16,  v_int16x8)
+OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint16x8, v_uint32x4)
+OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int16x8,  v_int32x4)
+
+//  Multiply and expand
+inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b,
+                         v_uint16x8& c, v_uint16x8& d)
+{
+    v_uint16x8 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b,
+                         v_int16x8& c, v_int16x8& d)
+{
+    v_int16x8 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b,
+                         v_int32x4& c, v_int32x4& d)
+{
+    __m128i v0 = _mm_mullo_epi16(a.val, b.val);
+    __m128i v1 = _mm_mulhi_epi16(a.val, b.val);
+    c.val = _mm_unpacklo_epi16(v0, v1);
+    d.val = _mm_unpackhi_epi16(v0, v1);
+}
+
+inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b,
+                         v_uint32x4& c, v_uint32x4& d)
+{
+    __m128i v0 = _mm_mullo_epi16(a.val, b.val);
+    __m128i v1 = _mm_mulhi_epu16(a.val, b.val);
+    c.val = _mm_unpacklo_epi16(v0, v1);
+    d.val = _mm_unpackhi_epi16(v0, v1);
+}
+
+inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b,
+                         v_uint64x2& c, v_uint64x2& d)
+{
+    __m128i c0 = _mm_mul_epu32(a.val, b.val);
+    __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32));
+    c.val = _mm_unpacklo_epi64(c0, c1);
+    d.val = _mm_unpackhi_epi64(c0, c1);
+}
+
+inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) { return v_int16x8(_mm_mulhi_epi16(a.val, b.val)); }
+inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) { return v_uint16x8(_mm_mulhi_epu16(a.val, b.val)); }
+
+//////// Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
+{ return v_int32x4(_mm_madd_epi16(a.val, b.val)); }
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_dotprod(a, b) + c; }
+
+// 32 >> 64
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_SSE4_1
+    __m128i even = _mm_mul_epi32(a.val, b.val);
+    __m128i odd = _mm_mul_epi32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32));
+    return v_int64x2(_mm_add_epi64(even, odd));
+#else
+    __m128i even_u = _mm_mul_epu32(a.val, b.val);
+    __m128i odd_u = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32));
+    // convert unsigned to signed high multiplication (from: Agner Fog(veclib) and H S Warren: Hacker's delight, 2003, p. 132)
+    __m128i a_sign = _mm_srai_epi32(a.val, 31);
+    __m128i b_sign = _mm_srai_epi32(b.val, 31);
+    // |x * sign of x
+    __m128i axb  = _mm_and_si128(a.val, b_sign);
+    __m128i bxa  = _mm_and_si128(b.val, a_sign);
+    // sum of sign corrections
+    __m128i ssum = _mm_add_epi32(bxa, axb);
+    __m128i even_ssum = _mm_slli_epi64(ssum, 32);
+    __m128i odd_ssum = _mm_and_si128(ssum, _mm_set_epi32(-1, 0, -1, 0));
+    // convert to signed and prod
+    return v_int64x2(_mm_add_epi64(_mm_sub_epi64(even_u, even_ssum), _mm_sub_epi64(odd_u, odd_ssum)));
+#endif
+}
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{ return v_dotprod(a, b) + c; }
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
+{
+    __m128i a0 = _mm_srli_epi16(_mm_slli_si128(a.val, 1), 8); // even
+    __m128i a1 = _mm_srli_epi16(a.val, 8); // odd
+    __m128i b0 = _mm_srli_epi16(_mm_slli_si128(b.val, 1), 8);
+    __m128i b1 = _mm_srli_epi16(b.val, 8);
+    __m128i p0 = _mm_madd_epi16(a0, b0);
+    __m128i p1 = _mm_madd_epi16(a1, b1);
+    return v_uint32x4(_mm_add_epi32(p0, p1));
+}
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
+{
+    __m128i a0 = _mm_srai_epi16(_mm_slli_si128(a.val, 1), 8); // even
+    __m128i a1 = _mm_srai_epi16(a.val, 8); // odd
+    __m128i b0 = _mm_srai_epi16(_mm_slli_si128(b.val, 1), 8);
+    __m128i b1 = _mm_srai_epi16(b.val, 8);
+    __m128i p0 = _mm_madd_epi16(a0, b0);
+    __m128i p1 = _mm_madd_epi16(a1, b1);
+    return v_int32x4(_mm_add_epi32(p0, p1));
+}
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v_uint32x4 c, d;
+    v_mul_expand(a, b, c, d);
+
+    v_uint64x2 c0, c1, d0, d1;
+    v_expand(c, c0, c1);
+    v_expand(d, d0, d1);
+
+    c0 += c1; d0 += d1;
+    return v_uint64x2(_mm_add_epi64(
+        _mm_unpacklo_epi64(c0.val, d0.val),
+        _mm_unpackhi_epi64(c0.val, d0.val)
+    ));
+}
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
+{
+    v_int32x4 prod = v_dotprod(a, b);
+    v_int64x2 c, d;
+    v_expand(prod, c, d);
+    return v_int64x2(_mm_add_epi64(
+        _mm_unpacklo_epi64(c.val, d.val),
+        _mm_unpackhi_epi64(c.val, d.val)
+    ));
+}
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_SSE4_1
+    return v_cvt_f64(v_dotprod(a, b));
+#else
+    v_float64x2 c = v_cvt_f64(a) * v_cvt_f64(b);
+    v_float64x2 d = v_cvt_f64_high(a) * v_cvt_f64_high(b);
+
+    return v_float64x2(_mm_add_pd(
+        _mm_unpacklo_pd(c.val, d.val),
+        _mm_unpackhi_pd(c.val, d.val)
+    ));
+#endif
+}
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
+{ return v_dotprod(a, b); }
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_dotprod(a, b) + c; }
+
+// 32 >> 64
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_dotprod(a, b); }
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{ return v_dotprod_fast(a, b) + c; }
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
+{
+    __m128i a0 = v_expand_low(a).val;
+    __m128i a1 = v_expand_high(a).val;
+    __m128i b0 = v_expand_low(b).val;
+    __m128i b1 = v_expand_high(b).val;
+    __m128i p0 = _mm_madd_epi16(a0, b0);
+    __m128i p1 = _mm_madd_epi16(a1, b1);
+    return v_uint32x4(_mm_add_epi32(p0, p1));
+}
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
+{
+#if CV_SSE4_1
+    __m128i a0 = _mm_cvtepi8_epi16(a.val);
+    __m128i a1 = v_expand_high(a).val;
+    __m128i b0 = _mm_cvtepi8_epi16(b.val);
+    __m128i b1 = v_expand_high(b).val;
+    __m128i p0 = _mm_madd_epi16(a0, b0);
+    __m128i p1 = _mm_madd_epi16(a1, b1);
+    return v_int32x4(_mm_add_epi32(p0, p1));
+#else
+    return v_dotprod_expand(a, b);
+#endif
+}
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v_uint32x4 c, d;
+    v_mul_expand(a, b, c, d);
+
+    v_uint64x2 c0, c1, d0, d1;
+    v_expand(c, c0, c1);
+    v_expand(d, d0, d1);
+
+    c0 += c1; d0 += d1;
+    return c0 + d0;
+}
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
+{
+    v_int32x4 prod = v_dotprod(a, b);
+    v_int64x2 c, d;
+    v_expand(prod, c, d);
+    return c + d;
+}
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+// 32 >> 64f
+v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c);
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_cvt_f64_high(a) * v_cvt_f64_high(b)); }
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a,   const v_int32x4& b, const v_float64x2& c)
+{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_fma(v_cvt_f64_high(a), v_cvt_f64_high(b), c)); }
+
+#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \
+    OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \
+    OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \
+    OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \
+    inline _Tpvec operator ~ (const _Tpvec& a) \
+    { \
+        return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \
+    }
+
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1)))
+OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1)))
+
+inline v_float32x4 v_sqrt(const v_float32x4& x)
+{ return v_float32x4(_mm_sqrt_ps(x.val)); }
+
+inline v_float32x4 v_invsqrt(const v_float32x4& x)
+{
+    const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f);
+    __m128 t = x.val;
+    __m128 h = _mm_mul_ps(t, _0_5);
+    t = _mm_rsqrt_ps(t);
+    t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h)));
+    return v_float32x4(t);
+}
+
+inline v_float64x2 v_sqrt(const v_float64x2& x)
+{ return v_float64x2(_mm_sqrt_pd(x.val)); }
+
+inline v_float64x2 v_invsqrt(const v_float64x2& x)
+{
+    const __m128d v_1 = _mm_set1_pd(1.);
+    return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val)));
+}
+
+#define OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(_Tpuvec, _Tpsvec, func, suffix, subWidth) \
+inline _Tpuvec v_abs(const _Tpsvec& x) \
+{ return _Tpuvec(_mm_##func##_ep##suffix(x.val, _mm_sub_ep##subWidth(_mm_setzero_si128(), x.val))); }
+
+OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint8x16, v_int8x16, min, u8, i8)
+OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint16x8, v_int16x8, max, i16, i16)
+inline v_uint32x4 v_abs(const v_int32x4& x)
+{
+    __m128i s = _mm_srli_epi32(x.val, 31);
+    __m128i f = _mm_srai_epi32(x.val, 31);
+    return v_uint32x4(_mm_add_epi32(_mm_xor_si128(x.val, f), s));
+}
+inline v_float32x4 v_abs(const v_float32x4& x)
+{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); }
+inline v_float64x2 v_abs(const v_float64x2& x)
+{
+    return v_float64x2(_mm_and_pd(x.val,
+        _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1))));
+}
+
+// TODO: exp, log, sin, cos
+
+#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+}
+
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd)
+
+inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b)
+{
+#if CV_SSE4_1
+    return v_int8x16(_mm_min_epi8(a.val, b.val));
+#else
+    __m128i delta = _mm_set1_epi8((char)-128);
+    return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta),
+                                                       _mm_xor_si128(b.val, delta))));
+#endif
+}
+inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b)
+{
+#if CV_SSE4_1
+    return v_int8x16(_mm_max_epi8(a.val, b.val));
+#else
+    __m128i delta = _mm_set1_epi8((char)-128);
+    return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta),
+                                                       _mm_xor_si128(b.val, delta))));
+#endif
+}
+inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b)
+{
+#if CV_SSE4_1
+    return v_uint16x8(_mm_min_epu16(a.val, b.val));
+#else
+    return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val)));
+#endif
+}
+inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b)
+{
+#if CV_SSE4_1
+    return v_uint16x8(_mm_max_epu16(a.val, b.val));
+#else
+    return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val));
+#endif
+}
+inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b)
+{
+#if CV_SSE4_1
+    return v_uint32x4(_mm_min_epu32(a.val, b.val));
+#else
+    __m128i delta = _mm_set1_epi32((int)0x80000000);
+    __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta));
+    return v_uint32x4(v_select_si128(mask, b.val, a.val));
+#endif
+}
+inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b)
+{
+#if CV_SSE4_1
+    return v_uint32x4(_mm_max_epu32(a.val, b.val));
+#else
+    __m128i delta = _mm_set1_epi32((int)0x80000000);
+    __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta));
+    return v_uint32x4(v_select_si128(mask, a.val, b.val));
+#endif
+}
+inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_SSE4_1
+    return v_int32x4(_mm_min_epi32(a.val, b.val));
+#else
+    return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val));
+#endif
+}
+inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b)
+{
+#if CV_SSE4_1
+    return v_int32x4(_mm_max_epi32(a.val, b.val));
+#else
+    return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val));
+#endif
+}
+
+#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \
+inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \
+{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \
+inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \
+{ \
+    __m128i not_mask = _mm_set1_epi32(-1); \
+    return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \
+} \
+inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \
+{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \
+inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \
+{ \
+    __m128i not_mask = _mm_set1_epi32(-1); \
+    return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \
+} \
+inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \
+{ \
+    __m128i smask = _mm_set1_##suffix(sbit); \
+    return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \
+} \
+inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \
+{ \
+    __m128i smask = _mm_set1_##suffix(sbit); \
+    return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \
+} \
+inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \
+{ \
+    __m128i smask = _mm_set1_##suffix(sbit); \
+    __m128i not_mask = _mm_set1_epi32(-1); \
+    __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \
+    return _Tpuvec(_mm_xor_si128(res, not_mask)); \
+} \
+inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \
+{ \
+    __m128i smask = _mm_set1_##suffix(sbit); \
+    __m128i not_mask = _mm_set1_epi32(-1); \
+    __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \
+    return _Tpuvec(_mm_xor_si128(res, not_mask)); \
+} \
+inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \
+{ \
+    return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \
+} \
+inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \
+{ \
+    return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \
+} \
+inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \
+{ \
+    __m128i not_mask = _mm_set1_epi32(-1); \
+    return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \
+} \
+inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \
+{ \
+    __m128i not_mask = _mm_set1_epi32(-1); \
+    return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \
+}
+
+OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128)
+OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768)
+OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000)
+
+#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \
+inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \
+inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \
+inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \
+inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); }
+
+OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps)
+OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd)
+
+#if CV_SSE4_1
+#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(_mm_cmpeq_epi64(a.val, b.val)); } \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ return ~(a == b); }
+#else
+#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ __m128i cmp = _mm_cmpeq_epi32(a.val, b.val); \
+  return _Tpvec(_mm_and_si128(cmp, _mm_shuffle_epi32(cmp, _MM_SHUFFLE(2, 3, 0, 1)))); } \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ return ~(a == b); }
+#endif
+
+OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_uint64x2)
+OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_int64x2)
+
+inline v_float32x4 v_not_nan(const v_float32x4& a)
+{ return v_float32x4(_mm_cmpord_ps(a.val, a.val)); }
+inline v_float64x2 v_not_nan(const v_float64x2& a)
+{ return v_float64x2(_mm_cmpord_pd(a.val, a.val)); }
+
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_mul_wrap, _mm_mullo_epi16)
+OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_mul_wrap, _mm_mullo_epi16)
+
+inline v_uint8x16 v_mul_wrap(const v_uint8x16& a, const v_uint8x16& b)
+{
+    __m128i ad = _mm_srai_epi16(a.val, 8);
+    __m128i bd = _mm_srai_epi16(b.val, 8);
+    __m128i p0 = _mm_mullo_epi16(a.val, b.val); // even
+    __m128i p1 = _mm_slli_epi16(_mm_mullo_epi16(ad, bd), 8); // odd
+    const __m128i b01 = _mm_set1_epi32(0xFF00FF00);
+    return v_uint8x16(_v128_blendv_epi8(p0, p1, b01));
+}
+inline v_int8x16 v_mul_wrap(const v_int8x16& a, const v_int8x16& b)
+{
+    return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b)));
+}
+
+/** Absolute difference **/
+
+inline v_uint8x16 v_absdiff(const v_uint8x16& a, const v_uint8x16& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint16x8 v_absdiff(const v_uint16x8& a, const v_uint16x8& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b)
+{
+    v_int8x16 d = v_sub_wrap(a, b);
+    v_int8x16 m = a < b;
+    return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m));
+}
+inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b)
+{
+    return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b)));
+}
+inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b)
+{
+    v_int32x4 d = a - b;
+    v_int32x4 m = a < b;
+    return v_reinterpret_as_u32((d ^ m) - m);
+}
+
+/** Saturating absolute difference **/
+inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b)
+{
+    v_int8x16 d = a - b;
+    v_int8x16 m = a < b;
+    return (d ^ m) - m;
+ }
+inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+
+inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return a * b + c;
+}
+
+inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+#if CV_FMA3
+    return v_float32x4(_mm_fmadd_ps(a.val, b.val, c.val));
+#else
+    return v_float32x4(_mm_add_ps(_mm_mul_ps(a.val, b.val), c.val));
+#endif
+}
+
+inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+#if CV_FMA3
+    return v_float64x2(_mm_fmadd_pd(a.val, b.val, c.val));
+#else
+    return v_float64x2(_mm_add_pd(_mm_mul_pd(a.val, b.val), c.val));
+#endif
+}
+
+#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \
+inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \
+    return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \
+} \
+inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    _Tpvec res = v_fma(a, a, b*b); \
+    return _Tpvec(_mm_sqrt_##suffix(res.val)); \
+} \
+inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return v_fma(a, a, b*b); \
+} \
+inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \
+{ \
+    return v_fma(a, b, c); \
+}
+
+OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff))
+OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1))
+
+#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \
+inline _Tpuvec operator << (const _Tpuvec& a, int imm) \
+{ \
+    return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \
+} \
+inline _Tpsvec operator << (const _Tpsvec& a, int imm) \
+{ \
+    return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \
+} \
+inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \
+{ \
+    return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \
+} \
+inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \
+{ \
+    return _Tpsvec(srai(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpuvec v_shl(const _Tpuvec& a) \
+{ \
+    return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpsvec v_shl(const _Tpsvec& a) \
+{ \
+    return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpuvec v_shr(const _Tpuvec& a) \
+{ \
+    return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpsvec v_shr(const _Tpsvec& a) \
+{ \
+    return _Tpsvec(srai(a.val, imm)); \
+}
+
+OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16)
+OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32)
+OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64)
+
+namespace hal_sse_internal
+{
+    template <int imm,
+        bool is_invalid = ((imm < 0) || (imm > 16)),
+        bool is_first = (imm == 0),
+        bool is_half = (imm == 8),
+        bool is_second = (imm == 16),
+        bool is_other = (((imm > 0) && (imm < 8)) || ((imm > 8) && (imm < 16)))>
+    class v_sse_palignr_u8_class;
+
+    template <int imm>
+    class v_sse_palignr_u8_class<imm, true, false, false, false, false>;
+
+    template <int imm>
+    class v_sse_palignr_u8_class<imm, false, true, false, false, false>
+    {
+    public:
+        inline __m128i operator()(const __m128i& a, const __m128i&) const
+        {
+            return a;
+        }
+    };
+
+    template <int imm>
+    class v_sse_palignr_u8_class<imm, false, false, true, false, false>
+    {
+    public:
+        inline __m128i operator()(const __m128i& a, const __m128i& b) const
+        {
+            return _mm_unpacklo_epi64(_mm_unpackhi_epi64(a, a), b);
+        }
+    };
+
+    template <int imm>
+    class v_sse_palignr_u8_class<imm, false, false, false, true, false>
+    {
+    public:
+        inline __m128i operator()(const __m128i&, const __m128i& b) const
+        {
+            return b;
+        }
+    };
+
+    template <int imm>
+    class v_sse_palignr_u8_class<imm, false, false, false, false, true>
+    {
+#if CV_SSSE3
+    public:
+        inline __m128i operator()(const __m128i& a, const __m128i& b) const
+        {
+            return _mm_alignr_epi8(b, a, imm);
+        }
+#else
+    public:
+        inline __m128i operator()(const __m128i& a, const __m128i& b) const
+        {
+            enum { imm2 = (sizeof(__m128i) - imm) };
+            return _mm_or_si128(_mm_srli_si128(a, imm), _mm_slli_si128(b, imm2));
+        }
+#endif
+    };
+
+    template <int imm>
+    inline __m128i v_sse_palignr_u8(const __m128i& a, const __m128i& b)
+    {
+        CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_sse_palignr_u8.");
+        return v_sse_palignr_u8_class<imm>()(a, b);
+    }
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_right(const _Tpvec &a)
+{
+    using namespace hal_sse_internal;
+    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
+    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
+        _mm_srli_si128(
+            v_sse_reinterpret_as<__m128i>(a.val), imm2)));
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_left(const _Tpvec &a)
+{
+    using namespace hal_sse_internal;
+    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
+    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
+        _mm_slli_si128(
+            v_sse_reinterpret_as<__m128i>(a.val), imm2)));
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b)
+{
+    using namespace hal_sse_internal;
+    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
+    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
+        v_sse_palignr_u8<imm2>(
+            v_sse_reinterpret_as<__m128i>(a.val),
+            v_sse_reinterpret_as<__m128i>(b.val))));
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b)
+{
+    using namespace hal_sse_internal;
+    enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) };
+    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
+        v_sse_palignr_u8<imm2>(
+            v_sse_reinterpret_as<__m128i>(b.val),
+            v_sse_reinterpret_as<__m128i>(a.val))));
+}
+
+#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \
+inline _Tpvec v_load(const _Tp* ptr) \
+{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \
+inline _Tpvec v_load_aligned(const _Tp* ptr) \
+{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ return _Tpvec(_mm_loadl_epi64((const __m128i*)ptr)); } \
+inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
+{ \
+    return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \
+                                     _mm_loadl_epi64((const __m128i*)ptr1))); \
+} \
+inline void v_store(_Tp* ptr, const _Tpvec& a) \
+{ _mm_storeu_si128((__m128i*)ptr, a.val); } \
+inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
+{ _mm_store_si128((__m128i*)ptr, a.val); } \
+inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \
+{ _mm_stream_si128((__m128i*)ptr, a.val); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \
+{ \
+    if( mode == hal::STORE_UNALIGNED ) \
+        _mm_storeu_si128((__m128i*)ptr, a.val); \
+    else if( mode == hal::STORE_ALIGNED_NOCACHE )  \
+        _mm_stream_si128((__m128i*)ptr, a.val); \
+    else \
+        _mm_store_si128((__m128i*)ptr, a.val); \
+} \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
+{ _mm_storel_epi64((__m128i*)ptr, a.val); } \
+inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
+{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); }
+
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64)
+
+#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \
+inline _Tpvec v_load(const _Tp* ptr) \
+{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \
+inline _Tpvec v_load_aligned(const _Tp* ptr) \
+{ return _Tpvec(_mm_load_##suffix(ptr)); } \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ return _Tpvec(_mm_castsi128_##suffix(_mm_loadl_epi64((const __m128i*)ptr))); } \
+inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
+{ \
+    return _Tpvec(_mm_castsi128_##suffix( \
+        _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \
+                           _mm_loadl_epi64((const __m128i*)ptr1)))); \
+} \
+inline void v_store(_Tp* ptr, const _Tpvec& a) \
+{ _mm_storeu_##suffix(ptr, a.val); } \
+inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
+{ _mm_store_##suffix(ptr, a.val); } \
+inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \
+{ _mm_stream_##suffix(ptr, a.val); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \
+{ \
+    if( mode == hal::STORE_UNALIGNED ) \
+        _mm_storeu_##suffix(ptr, a.val); \
+    else if( mode == hal::STORE_ALIGNED_NOCACHE )  \
+        _mm_stream_##suffix(ptr, a.val); \
+    else \
+        _mm_store_##suffix(ptr, a.val); \
+} \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
+{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \
+inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
+{ \
+    __m128i a1 = _mm_cast##suffix##_si128(a.val); \
+    _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \
+}
+
+OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd)
+
+inline unsigned v_reduce_sum(const v_uint8x16& a)
+{
+    __m128i half = _mm_sad_epu8(a.val, _mm_setzero_si128());
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half)));
+}
+inline int v_reduce_sum(const v_int8x16& a)
+{
+    __m128i half = _mm_set1_epi8((schar)-128);
+    half = _mm_sad_epu8(_mm_xor_si128(a.val, half), _mm_setzero_si128());
+    return _mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half))) - 2048;
+}
+#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_16(func) \
+inline schar v_reduce_##func(const v_int8x16& a) \
+{ \
+    __m128i val = a.val; \
+    __m128i smask = _mm_set1_epi8((schar)-128); \
+    val = _mm_xor_si128(val, smask); \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,8)); \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,4)); \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,2)); \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,1)); \
+    return (schar)_mm_cvtsi128_si32(val) ^ (schar)-128; \
+} \
+inline uchar v_reduce_##func(const v_uint8x16& a) \
+{ \
+    __m128i val = a.val; \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,8)); \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,4)); \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,2)); \
+    val = _mm_##func##_epu8(val, _mm_srli_si128(val,1)); \
+    return (uchar)_mm_cvtsi128_si32(val); \
+}
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_16(max)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_16(min)
+
+#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \
+inline scalartype v_reduce_##func(const v_##_Tpvec& a) \
+{ \
+    __m128i val = a.val; \
+    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \
+    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \
+    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \
+    return (scalartype)_mm_cvtsi128_si32(val); \
+} \
+inline unsigned scalartype v_reduce_##func(const v_u##_Tpvec& a) \
+{ \
+    __m128i val = a.val; \
+    __m128i smask = _mm_set1_epi16(sbit); \
+    val = _mm_xor_si128(val, smask); \
+    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \
+    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \
+    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \
+    return (unsigned scalartype)(_mm_cvtsi128_si32(val) ^  sbit); \
+}
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, max, epi16, (short)-32768)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, min, epi16, (short)-32768)
+
+#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, cast_from, cast_to, extract) \
+inline scalartype v_reduce_sum(const _Tpvec& a) \
+{ \
+    regtype val = a.val; \
+    val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 8))); \
+    val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 4))); \
+    return (scalartype)_mm_cvt##extract(val); \
+}
+
+#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    scalartype CV_DECL_ALIGNED(16) buf[4]; \
+    v_store_aligned(buf, a); \
+    scalartype s0 = scalar_func(buf[0], buf[1]); \
+    scalartype s1 = scalar_func(buf[2], buf[3]); \
+    return scalar_func(s0, s1); \
+}
+
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_uint32x4, unsigned, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_int32x4, int, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_float32x4, float, __m128, ps, _mm_castps_si128, _mm_castsi128_ps, ss_f32)
+
+inline int v_reduce_sum(const v_int16x8& a)
+{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); }
+inline unsigned v_reduce_sum(const v_uint16x8& a)
+{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); }
+
+inline uint64 v_reduce_sum(const v_uint64x2& a)
+{
+    uint64 CV_DECL_ALIGNED(32) idx[2];
+    v_store_aligned(idx, a);
+    return idx[0] + idx[1];
+}
+inline int64 v_reduce_sum(const v_int64x2& a)
+{
+    int64 CV_DECL_ALIGNED(32) idx[2];
+    v_store_aligned(idx, a);
+    return idx[0] + idx[1];
+}
+inline double v_reduce_sum(const v_float64x2& a)
+{
+    double CV_DECL_ALIGNED(32) idx[2];
+    v_store_aligned(idx, a);
+    return idx[0] + idx[1];
+}
+
+inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
+                                 const v_float32x4& c, const v_float32x4& d)
+{
+#if CV_SSE3
+    __m128 ab = _mm_hadd_ps(a.val, b.val);
+    __m128 cd = _mm_hadd_ps(c.val, d.val);
+    return v_float32x4(_mm_hadd_ps(ab, cd));
+#else
+    __m128 ac = _mm_add_ps(_mm_unpacklo_ps(a.val, c.val), _mm_unpackhi_ps(a.val, c.val));
+    __m128 bd = _mm_add_ps(_mm_unpacklo_ps(b.val, d.val), _mm_unpackhi_ps(b.val, d.val));
+    return v_float32x4(_mm_add_ps(_mm_unpacklo_ps(ac, bd), _mm_unpackhi_ps(ac, bd)));
+#endif
+}
+
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max)
+OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min)
+
+inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b)
+{
+    __m128i half = _mm_sad_epu8(a.val, b.val);
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half)));
+}
+inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b)
+{
+    __m128i half = _mm_set1_epi8(0x7f);
+    half = _mm_sad_epu8(_mm_add_epi8(a.val, half), _mm_add_epi8(b.val, half));
+    return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half)));
+}
+inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v_uint32x4 l, h;
+    v_expand(v_absdiff(a, b), l, h);
+    return v_reduce_sum(l + h);
+}
+inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b)
+{
+    v_uint32x4 l, h;
+    v_expand(v_absdiff(a, b), l, h);
+    return v_reduce_sum(l + h);
+}
+inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b)
+{
+    return v_reduce_sum(v_absdiff(a, b));
+}
+inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b)
+{
+    return v_reduce_sum(v_absdiff(a, b));
+}
+inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b)
+{
+    return v_reduce_sum(v_absdiff(a, b));
+}
+
+inline v_uint8x16 v_popcount(const v_uint8x16& a)
+{
+    __m128i m1 = _mm_set1_epi32(0x55555555);
+    __m128i m2 = _mm_set1_epi32(0x33333333);
+    __m128i m4 = _mm_set1_epi32(0x0f0f0f0f);
+    __m128i p = a.val;
+    p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 1), m1), _mm_and_si128(p, m1));
+    p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 2), m2), _mm_and_si128(p, m2));
+    p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 4), m4), _mm_and_si128(p, m4));
+    return v_uint8x16(p);
+}
+inline v_uint16x8 v_popcount(const v_uint16x8& a)
+{
+    v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a));
+    p += v_rotate_right<1>(p);
+    return v_reinterpret_as_u16(p) & v_setall_u16(0x00ff);
+}
+inline v_uint32x4 v_popcount(const v_uint32x4& a)
+{
+    v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a));
+    p += v_rotate_right<1>(p);
+    p += v_rotate_right<2>(p);
+    return v_reinterpret_as_u32(p) & v_setall_u32(0x000000ff);
+}
+inline v_uint64x2 v_popcount(const v_uint64x2& a)
+{
+    return v_uint64x2(_mm_sad_epu8(v_popcount(v_reinterpret_as_u8(a)).val, _mm_setzero_si128()));
+}
+inline v_uint8x16 v_popcount(const v_int8x16& a)
+{ return v_popcount(v_reinterpret_as_u8(a)); }
+inline v_uint16x8 v_popcount(const v_int16x8& a)
+{ return v_popcount(v_reinterpret_as_u16(a)); }
+inline v_uint32x4 v_popcount(const v_int32x4& a)
+{ return v_popcount(v_reinterpret_as_u32(a)); }
+inline v_uint64x2 v_popcount(const v_int64x2& a)
+{ return v_popcount(v_reinterpret_as_u64(a)); }
+
+#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, cast_op, allmask) \
+inline int v_signmask(const _Tpvec& a)   { return _mm_movemask_##suffix(cast_op(a.val)); } \
+inline bool v_check_all(const _Tpvec& a) { return _mm_movemask_##suffix(cast_op(a.val)) == allmask; } \
+inline bool v_check_any(const _Tpvec& a) { return _mm_movemask_##suffix(cast_op(a.val)) != 0; }
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, 65535)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, 65535)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, ps, _mm_castsi128_ps, 15)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, ps, _mm_castsi128_ps, 15)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint64x2, pd, _mm_castsi128_pd, 3)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int64x2, pd, _mm_castsi128_pd, 3)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, 15)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, 3)
+
+#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS_SHORT(_Tpvec) \
+inline int v_signmask(const _Tpvec& a) { return _mm_movemask_epi8(_mm_packs_epi16(a.val, a.val)) & 255; } \
+inline bool v_check_all(const _Tpvec& a) { return (_mm_movemask_epi8(a.val) & 0xaaaa) == 0xaaaa; } \
+inline bool v_check_any(const _Tpvec& a) { return (_mm_movemask_epi8(a.val) & 0xaaaa) != 0; }
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS_SHORT(v_uint16x8)
+OPENCV_HAL_IMPL_SSE_CHECK_SIGNS_SHORT(v_int16x8)
+
+inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); }
+inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); }
+inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; }
+inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; }
+inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+
+#if CV_SSE4_1
+#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, cast_ret, cast, suffix) \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(cast_ret(_mm_blendv_##suffix(cast(b.val), cast(a.val), cast(mask.val)))); \
+}
+
+OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
+OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
+OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
+OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
+OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, _mm_castps_si128, _mm_castsi128_ps, ps)
+OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, _mm_castps_si128, _mm_castsi128_ps, ps)
+// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, TBD, TBD, pd)
+// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, TBD, TBD, ps)
+OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, OPENCV_HAL_NOP, OPENCV_HAL_NOP, ps)
+OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, OPENCV_HAL_NOP, OPENCV_HAL_NOP, pd)
+
+#else // CV_SSE4_1
+
+#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \
+}
+
+OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128)
+OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128)
+OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128)
+OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128)
+OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128)
+OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128)
+// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128)
+// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128)
+OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps)
+OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd)
+#endif
+
+/* Expand */
+#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin)    \
+    inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
+    {                                                               \
+        b0.val = intrin(a.val);                                     \
+        b1.val = __CV_CAT(intrin, _high)(a.val);                    \
+    }                                                               \
+    inline _Tpwvec v_expand_low(const _Tpvec& a)                    \
+    { return _Tpwvec(intrin(a.val)); }                              \
+    inline _Tpwvec v_expand_high(const _Tpvec& a)                   \
+    { return _Tpwvec(__CV_CAT(intrin, _high)(a.val)); }             \
+    inline _Tpwvec v_load_expand(const _Tp* ptr)                    \
+    {                                                               \
+        __m128i a = _mm_loadl_epi64((const __m128i*)ptr);           \
+        return _Tpwvec(intrin(a));                                  \
+    }
+
+OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8,  uchar,    _v128_cvtepu8_epi16)
+OPENCV_HAL_IMPL_SSE_EXPAND(v_int8x16,  v_int16x8,   schar,    _v128_cvtepi8_epi16)
+OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4,  ushort,   _v128_cvtepu16_epi32)
+OPENCV_HAL_IMPL_SSE_EXPAND(v_int16x8,  v_int32x4,   short,    _v128_cvtepi16_epi32)
+OPENCV_HAL_IMPL_SSE_EXPAND(v_uint32x4, v_uint64x2,  unsigned, _v128_cvtepu32_epi64)
+OPENCV_HAL_IMPL_SSE_EXPAND(v_int32x4,  v_int64x2,   int,      _v128_cvtepi32_epi64)
+
+#define OPENCV_HAL_IMPL_SSE_EXPAND_Q(_Tpvec, _Tp, intrin)  \
+    inline _Tpvec v_load_expand_q(const _Tp* ptr)          \
+    {                                                      \
+        __m128i a = _mm_cvtsi32_si128(*(const int*)ptr);   \
+        return _Tpvec(intrin(a));                          \
+    }
+
+OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_uint32x4, uchar, _v128_cvtepu8_epi32)
+OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_int32x4,  schar, _v128_cvtepi8_epi32)
+
+#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \
+inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \
+{ \
+    b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \
+    b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \
+} \
+inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \
+    return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \
+} \
+inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \
+    return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \
+} \
+inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \
+{ \
+    __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \
+    c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \
+    d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \
+}
+
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps)
+OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd)
+
+inline v_uint8x16 v_reverse(const v_uint8x16 &a)
+{
+#if CV_SSSE3
+    static const __m128i perm = _mm_setr_epi8(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+    return v_uint8x16(_mm_shuffle_epi8(a.val, perm));
+#else
+    uchar CV_DECL_ALIGNED(32) d[16];
+    v_store_aligned(d, a);
+    return v_uint8x16(d[15], d[14], d[13], d[12], d[11], d[10], d[9], d[8], d[7], d[6], d[5], d[4], d[3], d[2], d[1], d[0]);
+#endif
+}
+
+inline v_int8x16 v_reverse(const v_int8x16 &a)
+{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
+
+inline v_uint16x8 v_reverse(const v_uint16x8 &a)
+{
+#if CV_SSSE3
+    static const __m128i perm = _mm_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1);
+    return v_uint16x8(_mm_shuffle_epi8(a.val, perm));
+#else
+    __m128i r = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 1, 2, 3));
+    r = _mm_shufflelo_epi16(r, _MM_SHUFFLE(2, 3, 0, 1));
+    r = _mm_shufflehi_epi16(r, _MM_SHUFFLE(2, 3, 0, 1));
+    return v_uint16x8(r);
+#endif
+}
+
+inline v_int16x8 v_reverse(const v_int16x8 &a)
+{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
+
+inline v_uint32x4 v_reverse(const v_uint32x4 &a)
+{
+    return v_uint32x4(_mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 1, 2, 3)));
+}
+
+inline v_int32x4 v_reverse(const v_int32x4 &a)
+{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_float32x4 v_reverse(const v_float32x4 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x2 v_reverse(const v_uint64x2 &a)
+{
+    return v_uint64x2(_mm_shuffle_epi32(a.val, _MM_SHUFFLE(1, 0, 3, 2)));
+}
+
+inline v_int64x2 v_reverse(const v_int64x2 &a)
+{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
+
+inline v_float64x2 v_reverse(const v_float64x2 &a)
+{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
+
+template<int s, typename _Tpvec>
+inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b)
+{
+    return v_rotate_right<s>(a, b);
+}
+
+inline v_int32x4 v_round(const v_float32x4& a)
+{ return v_int32x4(_mm_cvtps_epi32(a.val)); }
+
+inline v_int32x4 v_floor(const v_float32x4& a)
+{
+    __m128i a1 = _mm_cvtps_epi32(a.val);
+    __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val));
+    return v_int32x4(_mm_add_epi32(a1, mask));
+}
+
+inline v_int32x4 v_ceil(const v_float32x4& a)
+{
+    __m128i a1 = _mm_cvtps_epi32(a.val);
+    __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1)));
+    return v_int32x4(_mm_sub_epi32(a1, mask));
+}
+
+inline v_int32x4 v_trunc(const v_float32x4& a)
+{ return v_int32x4(_mm_cvttps_epi32(a.val)); }
+
+inline v_int32x4 v_round(const v_float64x2& a)
+{ return v_int32x4(_mm_cvtpd_epi32(a.val)); }
+
+inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
+{
+    __m128i ai = _mm_cvtpd_epi32(a.val), bi = _mm_cvtpd_epi32(b.val);
+    return v_int32x4(_mm_unpacklo_epi64(ai, bi));
+}
+
+inline v_int32x4 v_floor(const v_float64x2& a)
+{
+    __m128i a1 = _mm_cvtpd_epi32(a.val);
+    __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val));
+    mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0
+    return v_int32x4(_mm_add_epi32(a1, mask));
+}
+
+inline v_int32x4 v_ceil(const v_float64x2& a)
+{
+    __m128i a1 = _mm_cvtpd_epi32(a.val);
+    __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1)));
+    mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0
+    return v_int32x4(_mm_sub_epi32(a1, mask));
+}
+
+inline v_int32x4 v_trunc(const v_float64x2& a)
+{ return v_int32x4(_mm_cvttpd_epi32(a.val)); }
+
+#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \
+inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \
+                           const _Tpvec& a2, const _Tpvec& a3, \
+                           _Tpvec& b0, _Tpvec& b1, \
+                           _Tpvec& b2, _Tpvec& b3) \
+{ \
+    __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \
+    __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \
+    __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \
+    __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \
+\
+    b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \
+    b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \
+    b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \
+    b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \
+}
+
+OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
+OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps)
+
+// load deinterleave
+inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b)
+{
+    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
+    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16));
+
+    __m128i t10 = _mm_unpacklo_epi8(t00, t01);
+    __m128i t11 = _mm_unpackhi_epi8(t00, t01);
+
+    __m128i t20 = _mm_unpacklo_epi8(t10, t11);
+    __m128i t21 = _mm_unpackhi_epi8(t10, t11);
+
+    __m128i t30 = _mm_unpacklo_epi8(t20, t21);
+    __m128i t31 = _mm_unpackhi_epi8(t20, t21);
+
+    a.val = _mm_unpacklo_epi8(t30, t31);
+    b.val = _mm_unpackhi_epi8(t30, t31);
+}
+
+inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c)
+{
+#if CV_SSE4_1
+    const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0);
+    const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0);
+    __m128i s0 = _mm_loadu_si128((const __m128i*)ptr);
+    __m128i s1 = _mm_loadu_si128((const __m128i*)(ptr + 16));
+    __m128i s2 = _mm_loadu_si128((const __m128i*)(ptr + 32));
+    __m128i a0 = _mm_blendv_epi8(_mm_blendv_epi8(s0, s1, m0), s2, m1);
+    __m128i b0 = _mm_blendv_epi8(_mm_blendv_epi8(s1, s2, m0), s0, m1);
+    __m128i c0 = _mm_blendv_epi8(_mm_blendv_epi8(s2, s0, m0), s1, m1);
+    const __m128i sh_b = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13);
+    const __m128i sh_g = _mm_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14);
+    const __m128i sh_r = _mm_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15);
+    a0 = _mm_shuffle_epi8(a0, sh_b);
+    b0 = _mm_shuffle_epi8(b0, sh_g);
+    c0 = _mm_shuffle_epi8(c0, sh_r);
+    a.val = a0;
+    b.val = b0;
+    c.val = c0;
+#elif CV_SSSE3
+    const __m128i m0 = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14);
+    const __m128i m1 = _mm_alignr_epi8(m0, m0, 11);
+    const __m128i m2 = _mm_alignr_epi8(m0, m0, 6);
+
+    __m128i t0 = _mm_loadu_si128((const __m128i*)ptr);
+    __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 16));
+    __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 32));
+
+    __m128i s0 = _mm_shuffle_epi8(t0, m0);
+    __m128i s1 = _mm_shuffle_epi8(t1, m1);
+    __m128i s2 = _mm_shuffle_epi8(t2, m2);
+
+    t0 = _mm_alignr_epi8(s1, _mm_slli_si128(s0, 10), 5);
+    a.val = _mm_alignr_epi8(s2, t0, 5);
+
+    t1 = _mm_alignr_epi8(_mm_srli_si128(s1, 5), _mm_slli_si128(s0, 5), 6);
+    b.val = _mm_alignr_epi8(_mm_srli_si128(s2, 5), t1, 5);
+
+    t2 = _mm_alignr_epi8(_mm_srli_si128(s2, 10), s1, 11);
+    c.val = _mm_alignr_epi8(t2, s0, 11);
+#else
+    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
+    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16));
+    __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32));
+
+    __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01));
+    __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02);
+    __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02));
+
+    __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11));
+    __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12);
+    __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12));
+
+    __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21));
+    __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22);
+    __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22));
+
+    a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31));
+    b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32);
+    c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32));
+#endif
+}
+
+inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d)
+{
+    __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ...
+    __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ...
+    __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ...
+    __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ...
+
+    __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ...
+    __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ...
+    __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ...
+    __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ...
+
+    u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ...
+    u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ...
+    u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ...
+    u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ...
+
+    v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ...
+    v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ...
+    v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ...
+    v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ...
+
+    a.val = _mm_unpacklo_epi8(v0, v1);
+    b.val = _mm_unpackhi_epi8(v0, v1);
+    c.val = _mm_unpacklo_epi8(v2, v3);
+    d.val = _mm_unpackhi_epi8(v2, v3);
+}
+
+inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b)
+{
+    __m128i v0 = _mm_loadu_si128((__m128i*)(ptr));     // a0 b0 a1 b1 a2 b2 a3 b3
+    __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); // a4 b4 a5 b5 a6 b6 a7 b7
+
+    __m128i v2 = _mm_unpacklo_epi16(v0, v1); // a0 a4 b0 b4 a1 a5 b1 b5
+    __m128i v3 = _mm_unpackhi_epi16(v0, v1); // a2 a6 b2 b6 a3 a7 b3 b7
+    __m128i v4 = _mm_unpacklo_epi16(v2, v3); // a0 a2 a4 a6 b0 b2 b4 b6
+    __m128i v5 = _mm_unpackhi_epi16(v2, v3); // a1 a3 a5 a7 b1 b3 b5 b7
+
+    a.val = _mm_unpacklo_epi16(v4, v5); // a0 a1 a2 a3 a4 a5 a6 a7
+    b.val = _mm_unpackhi_epi16(v4, v5); // b0 b1 ab b3 b4 b5 b6 b7
+}
+
+inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c)
+{
+#if CV_SSE4_1
+    __m128i v0 = _mm_loadu_si128((__m128i*)(ptr));
+    __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8));
+    __m128i v2 = _mm_loadu_si128((__m128i*)(ptr + 16));
+    __m128i a0 = _mm_blend_epi16(_mm_blend_epi16(v0, v1, 0x92), v2, 0x24);
+    __m128i b0 = _mm_blend_epi16(_mm_blend_epi16(v2, v0, 0x92), v1, 0x24);
+    __m128i c0 = _mm_blend_epi16(_mm_blend_epi16(v1, v2, 0x92), v0, 0x24);
+
+    const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11);
+    const __m128i sh_b = _mm_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13);
+    const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15);
+    a0 = _mm_shuffle_epi8(a0, sh_a);
+    b0 = _mm_shuffle_epi8(b0, sh_b);
+    c0 = _mm_shuffle_epi8(c0, sh_c);
+
+    a.val = a0;
+    b.val = b0;
+    c.val = c0;
+#else
+    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
+    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8));
+    __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16));
+
+    __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01));
+    __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02);
+    __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02));
+
+    __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11));
+    __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12);
+    __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12));
+
+    a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21));
+    b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22);
+    c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22));
+#endif
+}
+
+inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d)
+{
+    __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1
+    __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ...
+    __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ...
+    __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ...
+
+    __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ...
+    __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ...
+    __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ...
+    __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ...
+
+    u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ...
+    u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ...
+    u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ...
+    u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ...
+
+    a.val = _mm_unpacklo_epi16(u0, u1);
+    b.val = _mm_unpackhi_epi16(u0, u1);
+    c.val = _mm_unpacklo_epi16(u2, u3);
+    d.val = _mm_unpackhi_epi16(u2, u3);
+}
+
+inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b)
+{
+    __m128i v0 = _mm_loadu_si128((__m128i*)(ptr));     // a0 b0 a1 b1
+    __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 4)); // a2 b2 a3 b3
+
+    __m128i v2 = _mm_unpacklo_epi32(v0, v1); // a0 a2 b0 b2
+    __m128i v3 = _mm_unpackhi_epi32(v0, v1); // a1 a3 b1 b3
+
+    a.val = _mm_unpacklo_epi32(v2, v3); // a0 a1 a2 a3
+    b.val = _mm_unpackhi_epi32(v2, v3); // b0 b1 ab b3
+}
+
+inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c)
+{
+    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
+    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4));
+    __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8));
+
+    __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01));
+    __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02);
+    __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02));
+
+    a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11));
+    b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12);
+    c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12));
+}
+
+inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d)
+{
+    v_uint32x4 s0(_mm_loadu_si128((const __m128i*)ptr));        // a0 b0 c0 d0
+    v_uint32x4 s1(_mm_loadu_si128((const __m128i*)(ptr + 4)));  // a1 b1 c1 d1
+    v_uint32x4 s2(_mm_loadu_si128((const __m128i*)(ptr + 8)));  // a2 b2 c2 d2
+    v_uint32x4 s3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3
+
+    v_transpose4x4(s0, s1, s2, s3, a, b, c, d);
+}
+
+inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b)
+{
+    __m128 u0 = _mm_loadu_ps(ptr);       // a0 b0 a1 b1
+    __m128 u1 = _mm_loadu_ps((ptr + 4)); // a2 b2 a3 b3
+
+    a.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); // a0 a1 a2 a3
+    b.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(3, 1, 3, 1)); // b0 b1 ab b3
+}
+
+inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c)
+{
+    __m128 t0 = _mm_loadu_ps(ptr + 0);
+    __m128 t1 = _mm_loadu_ps(ptr + 4);
+    __m128 t2 = _mm_loadu_ps(ptr + 8);
+
+    __m128 at12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 1, 0, 2));
+    a.val = _mm_shuffle_ps(t0, at12, _MM_SHUFFLE(2, 0, 3, 0));
+
+    __m128 bt01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 0, 0, 1));
+    __m128 bt12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 2, 0, 3));
+    b.val = _mm_shuffle_ps(bt01, bt12, _MM_SHUFFLE(2, 0, 2, 0));
+
+    __m128 ct01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 1, 0, 2));
+    c.val = _mm_shuffle_ps(ct01, t2, _MM_SHUFFLE(3, 0, 2, 0));
+}
+
+inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d)
+{
+    __m128 t0 = _mm_loadu_ps(ptr +  0);
+    __m128 t1 = _mm_loadu_ps(ptr +  4);
+    __m128 t2 = _mm_loadu_ps(ptr +  8);
+    __m128 t3 = _mm_loadu_ps(ptr + 12);
+    __m128 t02lo = _mm_unpacklo_ps(t0, t2);
+    __m128 t13lo = _mm_unpacklo_ps(t1, t3);
+    __m128 t02hi = _mm_unpackhi_ps(t0, t2);
+    __m128 t13hi = _mm_unpackhi_ps(t1, t3);
+    a.val = _mm_unpacklo_ps(t02lo, t13lo);
+    b.val = _mm_unpackhi_ps(t02lo, t13lo);
+    c.val = _mm_unpacklo_ps(t02hi, t13hi);
+    d.val = _mm_unpackhi_ps(t02hi, t13hi);
+}
+
+inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b)
+{
+    __m128i t0 = _mm_loadu_si128((const __m128i*)ptr);
+    __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2));
+
+    a = v_uint64x2(_mm_unpacklo_epi64(t0, t1));
+    b = v_uint64x2(_mm_unpackhi_epi64(t0, t1));
+}
+
+inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c)
+{
+    __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0, b0
+    __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0, a1
+    __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // b1, c1
+
+    t1 = _mm_shuffle_epi32(t1, 0x4e); // a1, c0
+
+    a = v_uint64x2(_mm_unpacklo_epi64(t0, t1));
+    b = v_uint64x2(_mm_unpacklo_epi64(_mm_unpackhi_epi64(t0, t0), t2));
+    c = v_uint64x2(_mm_unpackhi_epi64(t1, t2));
+}
+
+inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a,
+                                v_uint64x2& b, v_uint64x2& c, v_uint64x2& d)
+{
+    __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0
+    __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0 d0
+    __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // a1 b1
+    __m128i t3 = _mm_loadu_si128((const __m128i*)(ptr + 6)); // c1 d1
+
+    a = v_uint64x2(_mm_unpacklo_epi64(t0, t2));
+    b = v_uint64x2(_mm_unpackhi_epi64(t0, t2));
+    c = v_uint64x2(_mm_unpacklo_epi64(t1, t3));
+    d = v_uint64x2(_mm_unpackhi_epi64(t1, t3));
+}
+
+// store interleave
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
+                                hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128i v0 = _mm_unpacklo_epi8(a.val, b.val);
+    __m128i v1 = _mm_unpackhi_epi8(a.val, b.val);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 16), v1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 16), v1);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 16), v1);
+    }
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
+                                const v_uint8x16& c, hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+#if CV_SSE4_1
+    const __m128i sh_a = _mm_setr_epi8(0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5);
+    const __m128i sh_b = _mm_setr_epi8(5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10);
+    const __m128i sh_c = _mm_setr_epi8(10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15);
+    __m128i a0 = _mm_shuffle_epi8(a.val, sh_a);
+    __m128i b0 = _mm_shuffle_epi8(b.val, sh_b);
+    __m128i c0 = _mm_shuffle_epi8(c.val, sh_c);
+
+    const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0);
+    const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0);
+    __m128i v0 = _mm_blendv_epi8(_mm_blendv_epi8(a0, b0, m1), c0, m0);
+    __m128i v1 = _mm_blendv_epi8(_mm_blendv_epi8(b0, c0, m1), a0, m0);
+    __m128i v2 = _mm_blendv_epi8(_mm_blendv_epi8(c0, a0, m1), b0, m0);
+#elif CV_SSSE3
+    const __m128i m0 = _mm_setr_epi8(0, 6, 11, 1, 7, 12, 2, 8, 13, 3, 9, 14, 4, 10, 15, 5);
+    const __m128i m1 = _mm_setr_epi8(5, 11, 0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 4, 10);
+    const __m128i m2 = _mm_setr_epi8(10, 0, 5, 11, 1, 6, 12, 2, 7, 13, 3, 8, 14, 4, 9, 15);
+
+    __m128i t0 = _mm_alignr_epi8(b.val, _mm_slli_si128(a.val, 10), 5);
+    t0 = _mm_alignr_epi8(c.val, t0, 5);
+    __m128i v0 = _mm_shuffle_epi8(t0, m0);
+
+    __m128i t1 = _mm_alignr_epi8(_mm_srli_si128(b.val, 5), _mm_slli_si128(a.val, 5), 6);
+    t1 = _mm_alignr_epi8(_mm_srli_si128(c.val, 5), t1, 5);
+    __m128i v1 = _mm_shuffle_epi8(t1, m1);
+
+    __m128i t2 = _mm_alignr_epi8(_mm_srli_si128(c.val, 10), b.val, 11);
+    t2 = _mm_alignr_epi8(t2, a.val, 11);
+    __m128i v2 = _mm_shuffle_epi8(t2, m2);
+#else
+    __m128i z = _mm_setzero_si128();
+    __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val);
+    __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val);
+    __m128i c0 = _mm_unpacklo_epi8(c.val, z);
+    __m128i c1 = _mm_unpackhi_epi8(c.val, z);
+
+    __m128i p00 = _mm_unpacklo_epi16(ab0, c0);
+    __m128i p01 = _mm_unpackhi_epi16(ab0, c0);
+    __m128i p02 = _mm_unpacklo_epi16(ab1, c1);
+    __m128i p03 = _mm_unpackhi_epi16(ab1, c1);
+
+    __m128i p10 = _mm_unpacklo_epi32(p00, p01);
+    __m128i p11 = _mm_unpackhi_epi32(p00, p01);
+    __m128i p12 = _mm_unpacklo_epi32(p02, p03);
+    __m128i p13 = _mm_unpackhi_epi32(p02, p03);
+
+    __m128i p20 = _mm_unpacklo_epi64(p10, p11);
+    __m128i p21 = _mm_unpackhi_epi64(p10, p11);
+    __m128i p22 = _mm_unpacklo_epi64(p12, p13);
+    __m128i p23 = _mm_unpackhi_epi64(p12, p13);
+
+    p20 = _mm_slli_si128(p20, 1);
+    p22 = _mm_slli_si128(p22, 1);
+
+    __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8);
+    __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8);
+    __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8);
+    __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8);
+
+    __m128i p40 = _mm_unpacklo_epi64(p30, p31);
+    __m128i p41 = _mm_unpackhi_epi64(p30, p31);
+    __m128i p42 = _mm_unpacklo_epi64(p32, p33);
+    __m128i p43 = _mm_unpackhi_epi64(p32, p33);
+
+    __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10));
+    __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6));
+    __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2));
+#endif
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 16), v1);
+        _mm_stream_si128((__m128i*)(ptr + 32), v2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 16), v1);
+        _mm_store_si128((__m128i*)(ptr + 32), v2);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 16), v1);
+        _mm_storeu_si128((__m128i*)(ptr + 32), v2);
+    }
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
+                                const v_uint8x16& c, const v_uint8x16& d,
+                                hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    // a0 a1 a2 a3 ....
+    // b0 b1 b2 b3 ....
+    // c0 c1 c2 c3 ....
+    // d0 d1 d2 d3 ....
+    __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ...
+    __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ...
+    __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ...
+    __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ...
+
+    __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ...
+    __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ...
+    __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ...
+    __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ...
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 16), v1);
+        _mm_stream_si128((__m128i*)(ptr + 32), v2);
+        _mm_stream_si128((__m128i*)(ptr + 48), v3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 16), v1);
+        _mm_store_si128((__m128i*)(ptr + 32), v2);
+        _mm_store_si128((__m128i*)(ptr + 48), v3);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 16), v1);
+        _mm_storeu_si128((__m128i*)(ptr + 32), v2);
+        _mm_storeu_si128((__m128i*)(ptr + 48), v3);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b,
+                                hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128i v0 = _mm_unpacklo_epi16(a.val, b.val);
+    __m128i v1 = _mm_unpackhi_epi16(a.val, b.val);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 8), v1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 8), v1);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 8), v1);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x8& a,
+                                const v_uint16x8& b, const v_uint16x8& c,
+                                hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+#if CV_SSE4_1
+    const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11);
+    const __m128i sh_b = _mm_setr_epi8(10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5);
+    const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15);
+    __m128i a0 = _mm_shuffle_epi8(a.val, sh_a);
+    __m128i b0 = _mm_shuffle_epi8(b.val, sh_b);
+    __m128i c0 = _mm_shuffle_epi8(c.val, sh_c);
+
+    __m128i v0 = _mm_blend_epi16(_mm_blend_epi16(a0, b0, 0x92), c0, 0x24);
+    __m128i v1 = _mm_blend_epi16(_mm_blend_epi16(c0, a0, 0x92), b0, 0x24);
+    __m128i v2 = _mm_blend_epi16(_mm_blend_epi16(b0, c0, 0x92), a0, 0x24);
+#else
+    __m128i z = _mm_setzero_si128();
+    __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val);
+    __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val);
+    __m128i c0 = _mm_unpacklo_epi16(c.val, z);
+    __m128i c1 = _mm_unpackhi_epi16(c.val, z);
+
+    __m128i p10 = _mm_unpacklo_epi32(ab0, c0);
+    __m128i p11 = _mm_unpackhi_epi32(ab0, c0);
+    __m128i p12 = _mm_unpacklo_epi32(ab1, c1);
+    __m128i p13 = _mm_unpackhi_epi32(ab1, c1);
+
+    __m128i p20 = _mm_unpacklo_epi64(p10, p11);
+    __m128i p21 = _mm_unpackhi_epi64(p10, p11);
+    __m128i p22 = _mm_unpacklo_epi64(p12, p13);
+    __m128i p23 = _mm_unpackhi_epi64(p12, p13);
+
+    p20 = _mm_slli_si128(p20, 2);
+    p22 = _mm_slli_si128(p22, 2);
+
+    __m128i p30 = _mm_unpacklo_epi64(p20, p21);
+    __m128i p31 = _mm_unpackhi_epi64(p20, p21);
+    __m128i p32 = _mm_unpacklo_epi64(p22, p23);
+    __m128i p33 = _mm_unpackhi_epi64(p22, p23);
+
+    __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10));
+    __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6));
+    __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2));
+#endif
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 8), v1);
+        _mm_stream_si128((__m128i*)(ptr + 16), v2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 8), v1);
+        _mm_store_si128((__m128i*)(ptr + 16), v2);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 8), v1);
+        _mm_storeu_si128((__m128i*)(ptr + 16), v2);
+    }
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b,
+                                const v_uint16x8& c, const v_uint16x8& d,
+                                hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    // a0 a1 a2 a3 ....
+    // b0 b1 b2 b3 ....
+    // c0 c1 c2 c3 ....
+    // d0 d1 d2 d3 ....
+    __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ...
+    __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ...
+    __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ...
+    __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ...
+
+    __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ...
+    __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ...
+    __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ...
+    __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ...
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 8), v1);
+        _mm_stream_si128((__m128i*)(ptr + 16), v2);
+        _mm_stream_si128((__m128i*)(ptr + 24), v3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 8), v1);
+        _mm_store_si128((__m128i*)(ptr + 16), v2);
+        _mm_store_si128((__m128i*)(ptr + 24), v3);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 8), v1);
+        _mm_storeu_si128((__m128i*)(ptr + 16), v2);
+        _mm_storeu_si128((__m128i*)(ptr + 24), v3);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
+                                hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128i v0 = _mm_unpacklo_epi32(a.val, b.val);
+    __m128i v1 = _mm_unpackhi_epi32(a.val, b.val);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 4), v1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 4), v1);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 4), v1);
+    }
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
+                                const v_uint32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3;
+    v_transpose4x4(a, b, c, z, u0, u1, u2, u3);
+
+    __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12));
+    __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8));
+    __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4));
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 4), v1);
+        _mm_stream_si128((__m128i*)(ptr + 8), v2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 4), v1);
+        _mm_store_si128((__m128i*)(ptr + 8), v2);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 4), v1);
+        _mm_storeu_si128((__m128i*)(ptr + 8), v2);
+    }
+}
+
+inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
+                               const v_uint32x4& c, const v_uint32x4& d,
+                               hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    v_uint32x4 v0, v1, v2, v3;
+    v_transpose4x4(a, b, c, d, v0, v1, v2, v3);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0.val);
+        _mm_stream_si128((__m128i*)(ptr + 4), v1.val);
+        _mm_stream_si128((__m128i*)(ptr + 8), v2.val);
+        _mm_stream_si128((__m128i*)(ptr + 12), v3.val);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0.val);
+        _mm_store_si128((__m128i*)(ptr + 4), v1.val);
+        _mm_store_si128((__m128i*)(ptr + 8), v2.val);
+        _mm_store_si128((__m128i*)(ptr + 12), v3.val);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0.val);
+        _mm_storeu_si128((__m128i*)(ptr + 4), v1.val);
+        _mm_storeu_si128((__m128i*)(ptr + 8), v2.val);
+        _mm_storeu_si128((__m128i*)(ptr + 12), v3.val);
+    }
+}
+
+// 2-channel, float only
+inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b,
+                               hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128 v0 = _mm_unpacklo_ps(a.val, b.val); // a0 b0 a1 b1
+    __m128 v1 = _mm_unpackhi_ps(a.val, b.val); // a2 b2 a3 b3
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_ps(ptr, v0);
+        _mm_stream_ps(ptr + 4, v1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_ps(ptr, v0);
+        _mm_store_ps(ptr + 4, v1);
+    }
+    else
+    {
+        _mm_storeu_ps(ptr, v0);
+        _mm_storeu_ps(ptr + 4, v1);
+    }
+}
+
+inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b,
+                               const v_float32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128 u0 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(0, 0, 0, 0));
+    __m128 u1 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(1, 1, 0, 0));
+    __m128 v0 = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0));
+    __m128 u2 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(1, 1, 1, 1));
+    __m128 u3 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(2, 2, 2, 2));
+    __m128 v1 = _mm_shuffle_ps(u2, u3, _MM_SHUFFLE(2, 0, 2, 0));
+    __m128 u4 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(3, 3, 2, 2));
+    __m128 u5 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(3, 3, 3, 3));
+    __m128 v2 = _mm_shuffle_ps(u4, u5, _MM_SHUFFLE(2, 0, 2, 0));
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_ps(ptr, v0);
+        _mm_stream_ps(ptr + 4, v1);
+        _mm_stream_ps(ptr + 8, v2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_ps(ptr, v0);
+        _mm_store_ps(ptr + 4, v1);
+        _mm_store_ps(ptr + 8, v2);
+    }
+    else
+    {
+        _mm_storeu_ps(ptr, v0);
+        _mm_storeu_ps(ptr + 4, v1);
+        _mm_storeu_ps(ptr + 8, v2);
+    }
+}
+
+inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b,
+                               const v_float32x4& c, const v_float32x4& d,
+                               hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128 u0 = _mm_unpacklo_ps(a.val, c.val);
+    __m128 u1 = _mm_unpacklo_ps(b.val, d.val);
+    __m128 u2 = _mm_unpackhi_ps(a.val, c.val);
+    __m128 u3 = _mm_unpackhi_ps(b.val, d.val);
+    __m128 v0 = _mm_unpacklo_ps(u0, u1);
+    __m128 v2 = _mm_unpacklo_ps(u2, u3);
+    __m128 v1 = _mm_unpackhi_ps(u0, u1);
+    __m128 v3 = _mm_unpackhi_ps(u2, u3);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_ps(ptr, v0);
+        _mm_stream_ps(ptr + 4, v1);
+        _mm_stream_ps(ptr + 8, v2);
+        _mm_stream_ps(ptr + 12, v3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_ps(ptr, v0);
+        _mm_store_ps(ptr + 4, v1);
+        _mm_store_ps(ptr + 8, v2);
+        _mm_store_ps(ptr + 12, v3);
+    }
+    else
+    {
+        _mm_storeu_ps(ptr, v0);
+        _mm_storeu_ps(ptr + 4, v1);
+        _mm_storeu_ps(ptr + 8, v2);
+        _mm_storeu_ps(ptr + 12, v3);
+    }
+}
+
+inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b,
+                               hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128i v0 = _mm_unpacklo_epi64(a.val, b.val);
+    __m128i v1 = _mm_unpackhi_epi64(a.val, b.val);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 2), v1);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 2), v1);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 2), v1);
+    }
+}
+
+inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b,
+                               const v_uint64x2& c, hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128i v0 = _mm_unpacklo_epi64(a.val, b.val);
+    __m128i v1 = _mm_unpacklo_epi64(c.val, _mm_unpackhi_epi64(a.val, a.val));
+    __m128i v2 = _mm_unpackhi_epi64(b.val, c.val);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 2), v1);
+        _mm_stream_si128((__m128i*)(ptr + 4), v2);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 2), v1);
+        _mm_store_si128((__m128i*)(ptr + 4), v2);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 2), v1);
+        _mm_storeu_si128((__m128i*)(ptr + 4), v2);
+    }
+}
+
+inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b,
+                               const v_uint64x2& c, const v_uint64x2& d,
+                               hal::StoreMode mode = hal::STORE_UNALIGNED)
+{
+    __m128i v0 = _mm_unpacklo_epi64(a.val, b.val);
+    __m128i v1 = _mm_unpacklo_epi64(c.val, d.val);
+    __m128i v2 = _mm_unpackhi_epi64(a.val, b.val);
+    __m128i v3 = _mm_unpackhi_epi64(c.val, d.val);
+
+    if( mode == hal::STORE_ALIGNED_NOCACHE )
+    {
+        _mm_stream_si128((__m128i*)(ptr), v0);
+        _mm_stream_si128((__m128i*)(ptr + 2), v1);
+        _mm_stream_si128((__m128i*)(ptr + 4), v2);
+        _mm_stream_si128((__m128i*)(ptr + 6), v3);
+    }
+    else if( mode == hal::STORE_ALIGNED )
+    {
+        _mm_store_si128((__m128i*)(ptr), v0);
+        _mm_store_si128((__m128i*)(ptr + 2), v1);
+        _mm_store_si128((__m128i*)(ptr + 4), v2);
+        _mm_store_si128((__m128i*)(ptr + 6), v3);
+    }
+    else
+    {
+        _mm_storeu_si128((__m128i*)(ptr), v0);
+        _mm_storeu_si128((__m128i*)(ptr + 2), v1);
+        _mm_storeu_si128((__m128i*)(ptr + 4), v2);
+        _mm_storeu_si128((__m128i*)(ptr + 6), v3);
+    }
+}
+
+#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \
+{ \
+    _Tpvec1 a1, b1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \
+{ \
+    _Tpvec1 a1, b1, c1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \
+{ \
+    _Tpvec1 a1, b1, c1, d1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+    d0 = v_reinterpret_as_##suffix0(d1); \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                hal::StoreMode mode = hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, mode);      \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                const _Tpvec0& c0, hal::StoreMode mode = hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode);  \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                const _Tpvec0& c0, const _Tpvec0& d0, \
+                                hal::StoreMode mode = hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \
+}
+
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int64x2, int64, s64, v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float64x2, double, f64, v_uint64x2, uint64, u64)
+
+inline v_float32x4 v_cvt_f32(const v_int32x4& a)
+{
+    return v_float32x4(_mm_cvtepi32_ps(a.val));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a)
+{
+    return v_float32x4(_mm_cvtpd_ps(a.val));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
+{
+    return v_float32x4(_mm_movelh_ps(_mm_cvtpd_ps(a.val), _mm_cvtpd_ps(b.val)));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int32x4& a)
+{
+    return v_float64x2(_mm_cvtepi32_pd(a.val));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
+{
+    return v_float64x2(_mm_cvtepi32_pd(_mm_srli_si128(a.val,8)));
+}
+
+inline v_float64x2 v_cvt_f64(const v_float32x4& a)
+{
+    return v_float64x2(_mm_cvtps_pd(a.val));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
+{
+    return v_float64x2(_mm_cvtps_pd(_mm_movehl_ps(a.val, a.val)));
+}
+
+// from (Mysticial and wim) https://stackoverflow.com/q/41144668
+inline v_float64x2 v_cvt_f64(const v_int64x2& v)
+{
+    // constants encoded as floating-point
+    __m128i magic_i_hi32 = _mm_set1_epi64x(0x4530000080000000); // 2^84 + 2^63
+    __m128i magic_i_all  = _mm_set1_epi64x(0x4530000080100000); // 2^84 + 2^63 + 2^52
+    __m128d magic_d_all  = _mm_castsi128_pd(magic_i_all);
+    // Blend the 32 lowest significant bits of v with magic_int_lo
+#if CV_SSE4_1
+    __m128i magic_i_lo   = _mm_set1_epi64x(0x4330000000000000); // 2^52
+    __m128i v_lo         = _mm_blend_epi16(v.val, magic_i_lo, 0xcc);
+#else
+    __m128i magic_i_lo   = _mm_set1_epi32(0x43300000); // 2^52
+    __m128i v_lo         = _mm_unpacklo_epi32(_mm_shuffle_epi32(v.val, _MM_SHUFFLE(0, 0, 2, 0)), magic_i_lo);
+#endif
+    // Extract the 32 most significant bits of v
+    __m128i v_hi         = _mm_srli_epi64(v.val, 32);
+    // Flip the msb of v_hi and blend with 0x45300000
+            v_hi         = _mm_xor_si128(v_hi, magic_i_hi32);
+    // Compute in double precision
+    __m128d v_hi_dbl     = _mm_sub_pd(_mm_castsi128_pd(v_hi), magic_d_all);
+    // (v_hi - magic_d_all) + v_lo  Do not assume associativity of floating point addition
+    __m128d result       = _mm_add_pd(v_hi_dbl, _mm_castsi128_pd(v_lo));
+    return v_float64x2(result);
+}
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x16 v_lut(const schar* tab, const int* idx)
+{
+#if defined(_MSC_VER)
+    return v_int8x16(_mm_setr_epi8(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]],
+                                   tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]));
+#else
+    return v_int8x16(_mm_setr_epi64(
+                        _mm_setr_pi8(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]]),
+                        _mm_setr_pi8(tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]])
+                    ));
+#endif
+}
+inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx)
+{
+#if defined(_MSC_VER)
+    return v_int8x16(_mm_setr_epi16(*(const short*)(tab + idx[0]), *(const short*)(tab + idx[1]), *(const short*)(tab + idx[2]), *(const short*)(tab + idx[3]),
+                                    *(const short*)(tab + idx[4]), *(const short*)(tab + idx[5]), *(const short*)(tab + idx[6]), *(const short*)(tab + idx[7])));
+#else
+    return v_int8x16(_mm_setr_epi64(
+                        _mm_setr_pi16(*(const short*)(tab + idx[0]), *(const short*)(tab + idx[1]), *(const short*)(tab + idx[2]), *(const short*)(tab + idx[3])),
+                        _mm_setr_pi16(*(const short*)(tab + idx[4]), *(const short*)(tab + idx[5]), *(const short*)(tab + idx[6]), *(const short*)(tab + idx[7]))
+                    ));
+#endif
+}
+inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
+{
+#if defined(_MSC_VER)
+    return v_int8x16(_mm_setr_epi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1]),
+                                    *(const int*)(tab + idx[2]), *(const int*)(tab + idx[3])));
+#else
+    return v_int8x16(_mm_setr_epi64(
+                        _mm_setr_pi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1])),
+                        _mm_setr_pi32(*(const int*)(tab + idx[2]), *(const int*)(tab + idx[3]))
+                    ));
+#endif
+}
+inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((const schar *)tab, idx)); }
+inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((const schar *)tab, idx)); }
+inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((const schar *)tab, idx)); }
+
+inline v_int16x8 v_lut(const short* tab, const int* idx)
+{
+#if defined(_MSC_VER)
+    return v_int16x8(_mm_setr_epi16(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]],
+                                    tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]]));
+#else
+    return v_int16x8(_mm_setr_epi64(
+                        _mm_setr_pi16(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]),
+                        _mm_setr_pi16(tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]])
+                    ));
+#endif
+}
+inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
+{
+#if defined(_MSC_VER)
+    return v_int16x8(_mm_setr_epi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1]),
+                                    *(const int*)(tab + idx[2]), *(const int*)(tab + idx[3])));
+#else
+    return v_int16x8(_mm_setr_epi64(
+                        _mm_setr_pi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1])),
+                        _mm_setr_pi32(*(const int*)(tab + idx[2]), *(const int*)(tab + idx[3]))
+                    ));
+#endif
+}
+inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
+{
+    return v_int16x8(_mm_set_epi64x(*(const int64_t*)(tab + idx[1]), *(const int64_t*)(tab + idx[0])));
+}
+inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((const short *)tab, idx)); }
+inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((const short *)tab, idx)); }
+inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((const short *)tab, idx)); }
+
+inline v_int32x4 v_lut(const int* tab, const int* idx)
+{
+#if defined(_MSC_VER)
+    return v_int32x4(_mm_setr_epi32(tab[idx[0]], tab[idx[1]],
+                                    tab[idx[2]], tab[idx[3]]));
+#else
+    return v_int32x4(_mm_setr_epi64(
+                        _mm_setr_pi32(tab[idx[0]], tab[idx[1]]),
+                        _mm_setr_pi32(tab[idx[2]], tab[idx[3]])
+                    ));
+#endif
+}
+inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
+{
+    return v_int32x4(_mm_set_epi64x(*(const int64_t*)(tab + idx[1]), *(const int64_t*)(tab + idx[0])));
+}
+inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x4(_mm_loadu_si128((const __m128i*)(tab + idx[0])));
+}
+inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((const int *)tab, idx)); }
+inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((const int *)tab, idx)); }
+inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((const int *)tab, idx)); }
+
+inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(_mm_set_epi64x(tab[idx[1]], tab[idx[0]]));
+}
+inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(_mm_loadu_si128((const __m128i*)(tab + idx[0])));
+}
+inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); }
+inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); }
+
+inline v_float32x4 v_lut(const float* tab, const int* idx)
+{
+    return v_float32x4(_mm_setr_ps(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]));
+}
+inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_pairs((const int *)tab, idx)); }
+inline v_float32x4 v_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_quads((const int *)tab, idx)); }
+
+inline v_float64x2 v_lut(const double* tab, const int* idx)
+{
+    return v_float64x2(_mm_setr_pd(tab[idx[0]], tab[idx[1]]));
+}
+inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) { return v_float64x2(_mm_castsi128_pd(_mm_loadu_si128((const __m128i*)(tab + idx[0])))); }
+
+inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+    return v_int32x4(_mm_setr_epi32(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]));
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
+{
+    return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec));
+}
+
+inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+    return v_float32x4(_mm_setr_ps(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]));
+}
+
+inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
+{
+    int idx[2];
+    v_store_low(idx, idxvec);
+    return v_float64x2(_mm_setr_pd(tab[idx[0]], tab[idx[1]]));
+}
+
+// loads pairs from the table and deinterleaves them, e.g. returns:
+//   x = (tab[idxvec[0], tab[idxvec[1]], tab[idxvec[2]], tab[idxvec[3]]),
+//   y = (tab[idxvec[0]+1], tab[idxvec[1]+1], tab[idxvec[2]+1], tab[idxvec[3]+1])
+// note that the indices are float's indices, not the float-pair indices.
+// in theory, this function can be used to implement bilinear interpolation,
+// when idxvec are the offsets within the image.
+inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
+{
+    int CV_DECL_ALIGNED(32) idx[4];
+    v_store_aligned(idx, idxvec);
+    __m128 z = _mm_setzero_ps();
+    __m128 xy01 = _mm_loadl_pi(z, (__m64*)(tab + idx[0]));
+    __m128 xy23 = _mm_loadl_pi(z, (__m64*)(tab + idx[2]));
+    xy01 = _mm_loadh_pi(xy01, (__m64*)(tab + idx[1]));
+    xy23 = _mm_loadh_pi(xy23, (__m64*)(tab + idx[3]));
+    __m128 xxyy02 = _mm_unpacklo_ps(xy01, xy23);
+    __m128 xxyy13 = _mm_unpackhi_ps(xy01, xy23);
+    x = v_float32x4(_mm_unpacklo_ps(xxyy02, xxyy13));
+    y = v_float32x4(_mm_unpackhi_ps(xxyy02, xxyy13));
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
+{
+    int idx[2];
+    v_store_low(idx, idxvec);
+    __m128d xy0 = _mm_loadu_pd(tab + idx[0]);
+    __m128d xy1 = _mm_loadu_pd(tab + idx[1]);
+    x = v_float64x2(_mm_unpacklo_pd(xy0, xy1));
+    y = v_float64x2(_mm_unpackhi_pd(xy0, xy1));
+}
+
+inline v_int8x16 v_interleave_pairs(const v_int8x16& vec)
+{
+#if CV_SSSE3
+    return v_int8x16(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0d0e0c0b090a08, 0x0705060403010200)));
+#else
+    __m128i a = _mm_shufflelo_epi16(vec.val, _MM_SHUFFLE(3, 1, 2, 0));
+    a = _mm_shufflehi_epi16(a, _MM_SHUFFLE(3, 1, 2, 0));
+    a = _mm_shuffle_epi32(a, _MM_SHUFFLE(3, 1, 2, 0));
+    return v_int8x16(_mm_unpacklo_epi8(a, _mm_unpackhi_epi64(a, a)));
+#endif
+}
+inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
+inline v_int8x16 v_interleave_quads(const v_int8x16& vec)
+{
+#if CV_SSSE3
+    return v_int8x16(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0b0e0a0d090c08, 0x0703060205010400)));
+#else
+    __m128i a = _mm_shuffle_epi32(vec.val, _MM_SHUFFLE(3, 1, 2, 0));
+    return v_int8x16(_mm_unpacklo_epi8(a, _mm_unpackhi_epi64(a, a)));
+#endif
+}
+inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_interleave_pairs(const v_int16x8& vec)
+{
+#if CV_SSSE3
+    return v_int16x8(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0e0b0a0d0c0908, 0x0706030205040100)));
+#else
+    __m128i a = _mm_shufflelo_epi16(vec.val, _MM_SHUFFLE(3, 1, 2, 0));
+    return v_int16x8(_mm_shufflehi_epi16(a, _MM_SHUFFLE(3, 1, 2, 0)));
+#endif
+}
+inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+inline v_int16x8 v_interleave_quads(const v_int16x8& vec)
+{
+#if CV_SSSE3
+    return v_int16x8(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0e07060d0c0504, 0x0b0a030209080100)));
+#else
+    return v_int16x8(_mm_unpacklo_epi16(vec.val, _mm_unpackhi_epi64(vec.val, vec.val)));
+#endif
+}
+inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_interleave_pairs(const v_int32x4& vec)
+{
+    return v_int32x4(_mm_shuffle_epi32(vec.val, _MM_SHUFFLE(3, 1, 2, 0)));
+}
+inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+#if CV_SSSE3
+    return v_int8x16(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0xffffff0f0e0d0c0a, 0x0908060504020100)));
+#else
+    __m128i mask = _mm_set1_epi64x(0x00000000FFFFFFFF);
+    __m128i a = _mm_srli_si128(_mm_or_si128(_mm_andnot_si128(mask, vec.val), _mm_and_si128(mask, _mm_sll_epi32(vec.val, _mm_set_epi64x(0, 8)))), 1);
+    return v_int8x16(_mm_srli_si128(_mm_shufflelo_epi16(a, _MM_SHUFFLE(2, 1, 0, 3)), 2));
+#endif
+}
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+#if CV_SSSE3
+    return v_int16x8(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0xffff0f0e0d0c0b0a, 0x0908050403020100)));
+#else
+    return v_int16x8(_mm_srli_si128(_mm_shufflelo_epi16(vec.val, _MM_SHUFFLE(2, 1, 0, 3)), 2));
+#endif
+}
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; }
+
+template<int i>
+inline uchar v_extract_n(const v_uint8x16& v)
+{
+#if CV_SSE4_1
+    return (uchar)_mm_extract_epi8(v.val, i);
+#else
+    return v_rotate_right<i>(v).get0();
+#endif
+}
+
+template<int i>
+inline schar v_extract_n(const v_int8x16& v)
+{
+    return (schar)v_extract_n<i>(v_reinterpret_as_u8(v));
+}
+
+template<int i>
+inline ushort v_extract_n(const v_uint16x8& v)
+{
+    return (ushort)_mm_extract_epi16(v.val, i);
+}
+
+template<int i>
+inline short v_extract_n(const v_int16x8& v)
+{
+    return (short)v_extract_n<i>(v_reinterpret_as_u16(v));
+}
+
+template<int i>
+inline uint v_extract_n(const v_uint32x4& v)
+{
+#if CV_SSE4_1
+    return (uint)_mm_extract_epi32(v.val, i);
+#else
+    return v_rotate_right<i>(v).get0();
+#endif
+}
+
+template<int i>
+inline int v_extract_n(const v_int32x4& v)
+{
+    return (int)v_extract_n<i>(v_reinterpret_as_u32(v));
+}
+
+template<int i>
+inline uint64 v_extract_n(const v_uint64x2& v)
+{
+#ifdef CV__SIMD_NATIVE_mm_extract_epi64
+    return (uint64)_v128_extract_epi64<i>(v.val);
+#else
+    return v_rotate_right<i>(v).get0();
+#endif
+}
+
+template<int i>
+inline int64 v_extract_n(const v_int64x2& v)
+{
+    return (int64)v_extract_n<i>(v_reinterpret_as_u64(v));
+}
+
+template<int i>
+inline float v_extract_n(const v_float32x4& v)
+{
+    union { uint iv; float fv; } d;
+    d.iv = v_extract_n<i>(v_reinterpret_as_u32(v));
+    return d.fv;
+}
+
+template<int i>
+inline double v_extract_n(const v_float64x2& v)
+{
+    union { uint64 iv; double dv; } d;
+    d.iv = v_extract_n<i>(v_reinterpret_as_u64(v));
+    return d.dv;
+}
+
+template<int i>
+inline v_int32x4 v_broadcast_element(const v_int32x4& v)
+{
+    return v_int32x4(_mm_shuffle_epi32(v.val, _MM_SHUFFLE(i,i,i,i)));
+}
+
+template<int i>
+inline v_uint32x4 v_broadcast_element(const v_uint32x4& v)
+{
+    return v_uint32x4(_mm_shuffle_epi32(v.val, _MM_SHUFFLE(i,i,i,i)));
+}
+
+template<int i>
+inline v_float32x4 v_broadcast_element(const v_float32x4& v)
+{
+    return v_float32x4(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE((char)i,(char)i,(char)i,(char)i)));
+}
+
+////////////// FP16 support ///////////////////////////
+
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+#if CV_FP16
+    return v_float32x4(_mm_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr)));
+#else
+    const __m128i z = _mm_setzero_si128(), delta = _mm_set1_epi32(0x38000000);
+    const __m128i signmask = _mm_set1_epi32(0x80000000), maxexp = _mm_set1_epi32(0x7c000000);
+    const __m128 deltaf = _mm_castsi128_ps(_mm_set1_epi32(0x38800000));
+    __m128i bits = _mm_unpacklo_epi16(z, _mm_loadl_epi64((const __m128i*)ptr)); // h << 16
+    __m128i e = _mm_and_si128(bits, maxexp), sign = _mm_and_si128(bits, signmask);
+    __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_xor_si128(bits, sign), 3), delta); // ((h & 0x7fff) << 13) + delta
+    __m128i zt = _mm_castps_si128(_mm_sub_ps(_mm_castsi128_ps(_mm_add_epi32(t, _mm_set1_epi32(1 << 23))), deltaf));
+
+    t = _mm_add_epi32(t, _mm_and_si128(delta, _mm_cmpeq_epi32(maxexp, e)));
+    __m128i zmask = _mm_cmpeq_epi32(e, z);
+    __m128i ft = v_select_si128(zmask, zt, t);
+    return v_float32x4(_mm_castsi128_ps(_mm_or_si128(ft, sign)));
+#endif
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+#if CV_FP16
+    __m128i fp16_value = _mm_cvtps_ph(v.val, 0);
+    _mm_storel_epi64((__m128i*)ptr, fp16_value);
+#else
+    const __m128i signmask = _mm_set1_epi32(0x80000000);
+    const __m128i rval = _mm_set1_epi32(0x3f000000);
+
+    __m128i t = _mm_castps_si128(v.val);
+    __m128i sign = _mm_srai_epi32(_mm_and_si128(t, signmask), 16);
+    t = _mm_andnot_si128(signmask, t);
+
+    __m128i finitemask = _mm_cmpgt_epi32(_mm_set1_epi32(0x47800000), t);
+    __m128i isnan = _mm_cmpgt_epi32(t, _mm_set1_epi32(0x7f800000));
+    __m128i naninf = v_select_si128(isnan, _mm_set1_epi32(0x7e00), _mm_set1_epi32(0x7c00));
+    __m128i tinymask = _mm_cmpgt_epi32(_mm_set1_epi32(0x38800000), t);
+    __m128i tt = _mm_castps_si128(_mm_add_ps(_mm_castsi128_ps(t), _mm_castsi128_ps(rval)));
+    tt = _mm_sub_epi32(tt, rval);
+    __m128i odd = _mm_and_si128(_mm_srli_epi32(t, 13), _mm_set1_epi32(1));
+    __m128i nt = _mm_add_epi32(t, _mm_set1_epi32(0xc8000fff));
+    nt = _mm_srli_epi32(_mm_add_epi32(nt, odd), 13);
+    t = v_select_si128(tinymask, tt, nt);
+    t = v_select_si128(finitemask, t, naninf);
+    t = _mm_or_si128(t, sign);
+    t = _mm_packs_epi32(t, t);
+    _mm_storel_epi64((__m128i*)ptr, t);
+#endif
+}
+
+inline void v_cleanup() {}
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/intrin_sse_em.hpp b/3rdParty/include/opencv2/core/hal/intrin_sse_em.hpp
new file mode 100644
index 0000000..6fb0881
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_sse_em.hpp
@@ -0,0 +1,180 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+#ifndef OPENCV_HAL_INTRIN_SSE_EM_HPP
+#define OPENCV_HAL_INTRIN_SSE_EM_HPP
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+#define OPENCV_HAL_SSE_WRAP_1(fun, tp) \
+    inline tp _v128_##fun(const tp& a) \
+    { return _mm_##fun(a); }
+
+#define OPENCV_HAL_SSE_WRAP_2(fun, tp) \
+    inline tp _v128_##fun(const tp& a, const tp& b) \
+    { return _mm_##fun(a, b); }
+
+#define OPENCV_HAL_SSE_WRAP_3(fun, tp) \
+    inline tp _v128_##fun(const tp& a, const tp& b, const tp& c) \
+    { return _mm_##fun(a, b, c); }
+
+///////////////////////////// XOP /////////////////////////////
+
+// [todo] define CV_XOP
+#if 1 // CV_XOP
+inline __m128i _v128_comgt_epu32(const __m128i& a, const __m128i& b)
+{
+    const __m128i delta = _mm_set1_epi32((int)0x80000000);
+    return _mm_cmpgt_epi32(_mm_xor_si128(a, delta), _mm_xor_si128(b, delta));
+}
+// wrapping XOP
+#else
+OPENCV_HAL_SSE_WRAP_2(_v128_comgt_epu32, __m128i)
+#endif // !CV_XOP
+
+///////////////////////////// SSE4.1 /////////////////////////////
+
+#if !CV_SSE4_1
+
+/** Swizzle **/
+inline __m128i _v128_blendv_epi8(const __m128i& a, const __m128i& b, const __m128i& mask)
+{ return _mm_xor_si128(a, _mm_and_si128(_mm_xor_si128(b, a), mask)); }
+
+/** Convert **/
+// 8 >> 16
+inline __m128i _v128_cvtepu8_epi16(const __m128i& a)
+{
+    const __m128i z = _mm_setzero_si128();
+    return _mm_unpacklo_epi8(a, z);
+}
+inline __m128i _v128_cvtepi8_epi16(const __m128i& a)
+{ return _mm_srai_epi16(_mm_unpacklo_epi8(a, a), 8); }
+// 8 >> 32
+inline __m128i _v128_cvtepu8_epi32(const __m128i& a)
+{
+    const __m128i z = _mm_setzero_si128();
+    return _mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z);
+}
+inline __m128i _v128_cvtepi8_epi32(const __m128i& a)
+{
+    __m128i r = _mm_unpacklo_epi8(a, a);
+    r = _mm_unpacklo_epi8(r, r);
+    return _mm_srai_epi32(r, 24);
+}
+// 16 >> 32
+inline __m128i _v128_cvtepu16_epi32(const __m128i& a)
+{
+    const __m128i z = _mm_setzero_si128();
+    return _mm_unpacklo_epi16(a, z);
+}
+inline __m128i _v128_cvtepi16_epi32(const __m128i& a)
+{ return _mm_srai_epi32(_mm_unpacklo_epi16(a, a), 16); }
+// 32 >> 64
+inline __m128i _v128_cvtepu32_epi64(const __m128i& a)
+{
+    const __m128i z = _mm_setzero_si128();
+    return _mm_unpacklo_epi32(a, z);
+}
+inline __m128i _v128_cvtepi32_epi64(const __m128i& a)
+{ return _mm_unpacklo_epi32(a, _mm_srai_epi32(a, 31)); }
+
+/** Arithmetic **/
+inline __m128i _v128_mullo_epi32(const __m128i& a, const __m128i& b)
+{
+    __m128i c0 = _mm_mul_epu32(a, b);
+    __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a, 32), _mm_srli_epi64(b, 32));
+    __m128i d0 = _mm_unpacklo_epi32(c0, c1);
+    __m128i d1 = _mm_unpackhi_epi32(c0, c1);
+    return _mm_unpacklo_epi64(d0, d1);
+}
+
+/** Math **/
+inline __m128i _v128_min_epu32(const __m128i& a, const __m128i& b)
+{ return _v128_blendv_epi8(a, b, _v128_comgt_epu32(a, b)); }
+
+// wrapping SSE4.1
+#else
+OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi16, __m128i)
+OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi16, __m128i)
+OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi32, __m128i)
+OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi32, __m128i)
+OPENCV_HAL_SSE_WRAP_1(cvtepu16_epi32, __m128i)
+OPENCV_HAL_SSE_WRAP_1(cvtepi16_epi32, __m128i)
+OPENCV_HAL_SSE_WRAP_1(cvtepu32_epi64, __m128i)
+OPENCV_HAL_SSE_WRAP_1(cvtepi32_epi64, __m128i)
+OPENCV_HAL_SSE_WRAP_2(min_epu32, __m128i)
+OPENCV_HAL_SSE_WRAP_2(mullo_epi32, __m128i)
+OPENCV_HAL_SSE_WRAP_3(blendv_epi8, __m128i)
+#endif // !CV_SSE4_1
+
+///////////////////////////// Revolutionary /////////////////////////////
+
+/** Convert **/
+// 16 << 8
+inline __m128i _v128_cvtepu8_epi16_high(const __m128i& a)
+{
+    const __m128i z = _mm_setzero_si128();
+    return _mm_unpackhi_epi8(a, z);
+}
+inline __m128i _v128_cvtepi8_epi16_high(const __m128i& a)
+{ return _mm_srai_epi16(_mm_unpackhi_epi8(a, a), 8); }
+// 32 << 16
+inline __m128i _v128_cvtepu16_epi32_high(const __m128i& a)
+{
+    const __m128i z = _mm_setzero_si128();
+    return _mm_unpackhi_epi16(a, z);
+}
+inline __m128i _v128_cvtepi16_epi32_high(const __m128i& a)
+{ return _mm_srai_epi32(_mm_unpackhi_epi16(a, a), 16); }
+// 64 << 32
+inline __m128i _v128_cvtepu32_epi64_high(const __m128i& a)
+{
+    const __m128i z = _mm_setzero_si128();
+    return _mm_unpackhi_epi32(a, z);
+}
+inline __m128i _v128_cvtepi32_epi64_high(const __m128i& a)
+{ return _mm_unpackhi_epi32(a, _mm_srai_epi32(a, 31)); }
+
+/** Miscellaneous **/
+inline __m128i _v128_packs_epu32(const __m128i& a, const __m128i& b)
+{
+    const __m128i m = _mm_set1_epi32(65535);
+    __m128i am = _v128_min_epu32(a, m);
+    __m128i bm = _v128_min_epu32(b, m);
+#if CV_SSE4_1
+    return _mm_packus_epi32(am, bm);
+#else
+    const __m128i d = _mm_set1_epi32(32768), nd = _mm_set1_epi16(-32768);
+    am = _mm_sub_epi32(am, d);
+    bm = _mm_sub_epi32(bm, d);
+    am = _mm_packs_epi32(am, bm);
+    return _mm_sub_epi16(am, nd);
+#endif
+}
+
+template<int i>
+inline int64 _v128_extract_epi64(const __m128i& a)
+{
+#if defined(CV__SIMD_HAVE_mm_extract_epi64) || (CV_SSE4_1 && (defined(__x86_64__)/*GCC*/ || defined(_M_X64)/*MSVC*/))
+#define CV__SIMD_NATIVE_mm_extract_epi64 1
+    return _mm_extract_epi64(a, i);
+#else
+    CV_DECL_ALIGNED(16) int64 tmp[2];
+    _mm_store_si128((__m128i*)tmp, a);
+    return tmp[i];
+#endif
+}
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+} // cv::
+
+#endif // OPENCV_HAL_INTRIN_SSE_EM_HPP
diff --git a/3rdParty/include/opencv2/core/hal/intrin_vsx.hpp b/3rdParty/include/opencv2/core/hal/intrin_vsx.hpp
new file mode 100644
index 0000000..b198643
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_vsx.hpp
@@ -0,0 +1,1608 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+#ifndef OPENCV_HAL_VSX_HPP
+#define OPENCV_HAL_VSX_HPP
+
+#include <algorithm>
+#include "opencv2/core/utility.hpp"
+
+#define CV_SIMD128 1
+#define CV_SIMD128_64F 1
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+///////// Types ////////////
+
+struct v_uint8x16
+{
+    typedef uchar lane_type;
+    enum { nlanes = 16 };
+    vec_uchar16 val;
+
+    explicit v_uint8x16(const vec_uchar16& v) : val(v)
+    {}
+    v_uint8x16()
+    {}
+    v_uint8x16(vec_bchar16 v) : val(vec_uchar16_c(v))
+    {}
+    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
+               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
+        : val(vec_uchar16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15))
+    {}
+
+    static inline v_uint8x16 zero() { return v_uint8x16(vec_uchar16_z); }
+
+    uchar get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_int8x16
+{
+    typedef schar lane_type;
+    enum { nlanes = 16 };
+    vec_char16 val;
+
+    explicit v_int8x16(const vec_char16& v) : val(v)
+    {}
+    v_int8x16()
+    {}
+    v_int8x16(vec_bchar16 v) : val(vec_char16_c(v))
+    {}
+    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
+              schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
+        : val(vec_char16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15))
+    {}
+
+    static inline v_int8x16 zero() { return v_int8x16(vec_char16_z); }
+
+    schar get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_uint16x8
+{
+    typedef ushort lane_type;
+    enum { nlanes = 8 };
+    vec_ushort8 val;
+
+    explicit v_uint16x8(const vec_ushort8& v) : val(v)
+    {}
+    v_uint16x8()
+    {}
+    v_uint16x8(vec_bshort8 v) : val(vec_ushort8_c(v))
+    {}
+    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
+        : val(vec_ushort8_set(v0, v1, v2, v3, v4, v5, v6, v7))
+    {}
+
+    static inline v_uint16x8 zero() { return v_uint16x8(vec_ushort8_z); }
+
+    ushort get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_int16x8
+{
+    typedef short lane_type;
+    enum { nlanes = 8 };
+    vec_short8 val;
+
+    explicit v_int16x8(const vec_short8& v) : val(v)
+    {}
+    v_int16x8()
+    {}
+    v_int16x8(vec_bshort8 v) : val(vec_short8_c(v))
+    {}
+    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
+        : val(vec_short8_set(v0, v1, v2, v3, v4, v5, v6, v7))
+    {}
+
+    static inline v_int16x8 zero() { return v_int16x8(vec_short8_z); }
+
+    short get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_uint32x4
+{
+    typedef unsigned lane_type;
+    enum { nlanes = 4 };
+    vec_uint4 val;
+
+    explicit v_uint32x4(const vec_uint4& v) : val(v)
+    {}
+    v_uint32x4()
+    {}
+    v_uint32x4(vec_bint4 v) : val(vec_uint4_c(v))
+    {}
+    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) : val(vec_uint4_set(v0, v1, v2, v3))
+    {}
+
+    static inline v_uint32x4 zero() { return v_uint32x4(vec_uint4_z); }
+
+    uint get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_int32x4
+{
+    typedef int lane_type;
+    enum { nlanes = 4 };
+    vec_int4 val;
+
+    explicit v_int32x4(const vec_int4& v) : val(v)
+    {}
+    v_int32x4()
+    {}
+    v_int32x4(vec_bint4 v) : val(vec_int4_c(v))
+    {}
+    v_int32x4(int v0, int v1, int v2, int v3) : val(vec_int4_set(v0, v1, v2, v3))
+    {}
+
+    static inline v_int32x4 zero() { return v_int32x4(vec_int4_z); }
+
+    int get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_float32x4
+{
+    typedef float lane_type;
+    enum { nlanes = 4 };
+    vec_float4 val;
+
+    explicit v_float32x4(const vec_float4& v) : val(v)
+    {}
+    v_float32x4()
+    {}
+    v_float32x4(vec_bint4 v) : val(vec_float4_c(v))
+    {}
+    v_float32x4(float v0, float v1, float v2, float v3) : val(vec_float4_set(v0, v1, v2, v3))
+    {}
+
+    static inline v_float32x4 zero() { return v_float32x4(vec_float4_z); }
+
+    float get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_uint64x2
+{
+    typedef uint64 lane_type;
+    enum { nlanes = 2 };
+    vec_udword2 val;
+
+    explicit v_uint64x2(const vec_udword2& v) : val(v)
+    {}
+    v_uint64x2()
+    {}
+    v_uint64x2(vec_bdword2 v) : val(vec_udword2_c(v))
+    {}
+    v_uint64x2(uint64 v0, uint64 v1) : val(vec_udword2_set(v0, v1))
+    {}
+
+    static inline v_uint64x2 zero() { return v_uint64x2(vec_udword2_z); }
+
+    uint64 get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_int64x2
+{
+    typedef int64 lane_type;
+    enum { nlanes = 2 };
+    vec_dword2 val;
+
+    explicit v_int64x2(const vec_dword2& v) : val(v)
+    {}
+    v_int64x2()
+    {}
+    v_int64x2(vec_bdword2 v) : val(vec_dword2_c(v))
+    {}
+    v_int64x2(int64 v0, int64 v1) : val(vec_dword2_set(v0, v1))
+    {}
+
+    static inline v_int64x2 zero() { return v_int64x2(vec_dword2_z); }
+
+    int64 get0() const
+    { return vec_extract(val, 0); }
+};
+
+struct v_float64x2
+{
+    typedef double lane_type;
+    enum { nlanes = 2 };
+    vec_double2 val;
+
+    explicit v_float64x2(const vec_double2& v) : val(v)
+    {}
+    v_float64x2()
+    {}
+    v_float64x2(vec_bdword2 v) : val(vec_double2_c(v))
+    {}
+    v_float64x2(double v0, double v1) : val(vec_double2_set(v0, v1))
+    {}
+
+    static inline v_float64x2 zero() { return v_float64x2(vec_double2_z); }
+
+    double get0() const
+    { return vec_extract(val, 0); }
+};
+
+#define OPENCV_HAL_IMPL_VSX_EXTRACT_N(_Tpvec, _Tp) \
+template<int i> inline _Tp v_extract_n(VSX_UNUSED(_Tpvec v)) { return vec_extract(v.val, i); }
+
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint8x16, uchar)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int8x16, schar)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint16x8, ushort)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int16x8, short)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint32x4, uint)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int32x4, int)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint64x2, uint64)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int64x2, int64)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_float32x4, float)
+OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_float64x2, double)
+
+//////////////// Load and store operations ///////////////
+
+/*
+ * clang-5 aborted during parse "vec_xxx_c" only if it's
+ * inside a function template which is defined by preprocessor macro.
+ *
+ * if vec_xxx_c defined as C++ cast, clang-5 will pass it
+*/
+#define OPENCV_HAL_IMPL_VSX_INITVEC(_Tpvec, _Tp, suffix, cast)                        \
+inline _Tpvec v_setzero_##suffix() { return _Tpvec(vec_splats((_Tp)0)); }             \
+inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(vec_splats((_Tp)v));}          \
+template<typename _Tpvec0> inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0 &a)  \
+{ return _Tpvec((cast)a.val); }
+
+OPENCV_HAL_IMPL_VSX_INITVEC(v_uint8x16, uchar, u8, vec_uchar16)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_int8x16, schar, s8, vec_char16)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_uint16x8, ushort, u16, vec_ushort8)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_int16x8, short, s16, vec_short8)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_uint32x4, uint, u32, vec_uint4)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_int32x4, int, s32, vec_int4)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_uint64x2, uint64, u64, vec_udword2)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_int64x2, int64, s64, vec_dword2)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_float32x4, float, f32, vec_float4)
+OPENCV_HAL_IMPL_VSX_INITVEC(v_float64x2, double, f64, vec_double2)
+
+#define OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, ld, ld_a, st, st_a)    \
+inline _Tpvec v_load(const _Tp* ptr)                                        \
+{ return _Tpvec(ld(0, ptr)); }                                              \
+inline _Tpvec v_load_aligned(VSX_UNUSED(const _Tp* ptr))                    \
+{ return _Tpvec(ld_a(0, ptr)); }                                            \
+inline _Tpvec v_load_low(const _Tp* ptr)                                    \
+{ return _Tpvec(vec_ld_l8(ptr)); }                                          \
+inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1)               \
+{ return _Tpvec(vec_mergesqh(vec_ld_l8(ptr0), vec_ld_l8(ptr1))); }          \
+inline void v_store(_Tp* ptr, const _Tpvec& a)                              \
+{ st(a.val, 0, ptr); }                                                      \
+inline void v_store_aligned(VSX_UNUSED(_Tp* ptr), const _Tpvec& a)          \
+{ st_a(a.val, 0, ptr); }                                                    \
+inline void v_store_aligned_nocache(VSX_UNUSED(_Tp* ptr), const _Tpvec& a)  \
+{ st_a(a.val, 0, ptr); }                                                    \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode)         \
+{ if(mode == hal::STORE_UNALIGNED) st(a.val, 0, ptr); else st_a(a.val, 0, ptr); } \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a)                          \
+{ vec_st_l8(a.val, ptr); }                                                  \
+inline void v_store_high(_Tp* ptr, const _Tpvec& a)                         \
+{ vec_st_h8(a.val, ptr); }
+
+// working around gcc bug for aligned ld/st
+// if runtime check for vec_ld/st fail we failback to unaligned ld/st
+// https://github.com/opencv/opencv/issues/13211
+#ifdef CV_COMPILER_VSX_BROKEN_ALIGNED
+    #define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \
+    OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vsx_ld, vsx_st, vsx_st)
+#else
+    #define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \
+    OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vec_ld, vsx_st, vec_st)
+#endif
+
+OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint8x16,  uchar)
+OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int8x16,   schar)
+OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint16x8,  ushort)
+OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int16x8,   short)
+OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint32x4,  uint)
+OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int32x4,   int)
+OPENCV_HAL_IMPL_VSX_LOADSTORE(v_float32x4, float)
+
+OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_float64x2, double, vsx_ld,  vsx_ld,  vsx_st,  vsx_st)
+OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_uint64x2,  uint64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2)
+OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_int64x2,    int64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2)
+
+//////////////// Value reordering ///////////////
+
+/* de&interleave */
+#define OPENCV_HAL_IMPL_VSX_INTERLEAVE(_Tp, _Tpvec)                          \
+inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b)        \
+{ vec_ld_deinterleave(ptr, a.val, b.val);}                                   \
+inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a,                   \
+                                _Tpvec& b, _Tpvec& c)                        \
+{ vec_ld_deinterleave(ptr, a.val, b.val, c.val); }                           \
+inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b,        \
+                                                _Tpvec& c, _Tpvec& d)        \
+{ vec_ld_deinterleave(ptr, a.val, b.val, c.val, d.val); }                    \
+inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b,   \
+                               hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ vec_st_interleave(a.val, b.val, ptr); }                                    \
+inline void v_store_interleave(_Tp* ptr, const _Tpvec& a,                    \
+                               const _Tpvec& b, const _Tpvec& c,             \
+                               hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ vec_st_interleave(a.val, b.val, c.val, ptr); }                             \
+inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b,   \
+                                         const _Tpvec& c, const _Tpvec& d,   \
+                               hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \
+{ vec_st_interleave(a.val, b.val, c.val, d.val, ptr); }
+
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(uchar, v_uint8x16)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(schar, v_int8x16)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(ushort, v_uint16x8)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(short, v_int16x8)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint, v_uint32x4)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(int, v_int32x4)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(float, v_float32x4)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(double, v_float64x2)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(int64, v_int64x2)
+OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint64, v_uint64x2)
+
+/* Expand */
+#define OPENCV_HAL_IMPL_VSX_EXPAND(_Tpvec, _Tpwvec, _Tp, fl, fh)  \
+inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1)   \
+{                                                                 \
+    b0.val = fh(a.val);                                           \
+    b1.val = fl(a.val);                                           \
+}                                                                 \
+inline _Tpwvec v_expand_low(const _Tpvec& a)                      \
+{ return _Tpwvec(fh(a.val)); }                                    \
+inline _Tpwvec v_expand_high(const _Tpvec& a)                     \
+{ return _Tpwvec(fl(a.val)); }                                    \
+inline _Tpwvec v_load_expand(const _Tp* ptr)                      \
+{ return _Tpwvec(fh(vec_ld_l8(ptr))); }
+
+OPENCV_HAL_IMPL_VSX_EXPAND(v_uint8x16, v_uint16x8, uchar, vec_unpacklu, vec_unpackhu)
+OPENCV_HAL_IMPL_VSX_EXPAND(v_int8x16, v_int16x8, schar, vec_unpackl, vec_unpackh)
+OPENCV_HAL_IMPL_VSX_EXPAND(v_uint16x8, v_uint32x4, ushort, vec_unpacklu, vec_unpackhu)
+OPENCV_HAL_IMPL_VSX_EXPAND(v_int16x8, v_int32x4, short, vec_unpackl, vec_unpackh)
+OPENCV_HAL_IMPL_VSX_EXPAND(v_uint32x4, v_uint64x2, uint, vec_unpacklu, vec_unpackhu)
+OPENCV_HAL_IMPL_VSX_EXPAND(v_int32x4, v_int64x2, int, vec_unpackl, vec_unpackh)
+
+/* Load and zero expand a 4 byte value into the second dword, first is don't care. */
+#if !defined(CV_COMPILER_VSX_BROKEN_ASM)
+    #define _LXSIWZX(out, ptr, T) __asm__ ("lxsiwzx %x0, 0, %1\r\n" : "=wa"(out) : "r" (ptr) : "memory");
+#else
+    /* This is compiler-agnostic, but will introduce an unneeded splat on the critical path. */
+    #define _LXSIWZX(out, ptr, T) out = (T)vec_udword2_sp(*(uint32_t*)(ptr));
+#endif
+
+inline v_uint32x4 v_load_expand_q(const uchar* ptr)
+{
+    // Zero-extend the extra 24B instead of unpacking. Usually faster in small kernel
+    // Likewise note, value is zero extended and upper 4 bytes are zero'ed.
+    vec_uchar16 pmu = {8, 12, 12, 12, 9, 12, 12, 12, 10, 12, 12, 12, 11, 12, 12, 12};
+    vec_uchar16 out;
+
+    _LXSIWZX(out, ptr, vec_uchar16);
+    out = vec_perm(out, out, pmu);
+    return v_uint32x4((vec_uint4)out);
+}
+
+inline v_int32x4 v_load_expand_q(const schar* ptr)
+{
+    vec_char16 out;
+    vec_short8 outs;
+    vec_int4 outw;
+
+    _LXSIWZX(out, ptr, vec_char16);
+    outs = vec_unpackl(out);
+    outw = vec_unpackh(outs);
+    return v_int32x4(outw);
+}
+
+/* pack */
+#define OPENCV_HAL_IMPL_VSX_PACK(_Tpvec, _Tp, _Tpwvec, _Tpvn, _Tpdel, sfnc, pkfnc, addfnc, pack)    \
+inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b)                                          \
+{                                                                                                   \
+    return _Tpvec(pkfnc(a.val, b.val));                                                             \
+}                                                                                                   \
+inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a)                                            \
+{                                                                                                   \
+    vec_st_l8(pkfnc(a.val, a.val), ptr);                                                            \
+}                                                                                                   \
+template<int n>                                                                                     \
+inline _Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b)                                     \
+{                                                                                                   \
+    const __vector _Tpvn vn = vec_splats((_Tpvn)n);                                                 \
+    const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1)));                         \
+    return _Tpvec(pkfnc(sfnc(addfnc(a.val, delta), vn), sfnc(addfnc(b.val, delta), vn)));           \
+}                                                                                                   \
+template<int n>                                                                                     \
+inline void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a)                                       \
+{                                                                                                   \
+    const __vector _Tpvn vn = vec_splats((_Tpvn)n);                                                 \
+    const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1)));                         \
+    vec_st_l8(pkfnc(sfnc(addfnc(a.val, delta), vn), delta), ptr);                                   \
+}
+
+OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_uint16x8, unsigned short, unsigned short,
+                         vec_sr, vec_packs, vec_adds, pack)
+OPENCV_HAL_IMPL_VSX_PACK(v_int8x16, schar, v_int16x8, unsigned short, short,
+                         vec_sra, vec_packs, vec_adds, pack)
+
+OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_uint32x4, unsigned int, unsigned int,
+                         vec_sr, vec_packs, vec_add, pack)
+OPENCV_HAL_IMPL_VSX_PACK(v_int16x8, short, v_int32x4, unsigned int, int,
+                         vec_sra, vec_packs, vec_add, pack)
+
+OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_uint64x2, unsigned long long, unsigned long long,
+                         vec_sr, vec_pack, vec_add, pack)
+OPENCV_HAL_IMPL_VSX_PACK(v_int32x4, int, v_int64x2, unsigned long long, long long,
+                         vec_sra, vec_pack, vec_add, pack)
+
+OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_int16x8, unsigned short, short,
+                         vec_sra, vec_packsu, vec_adds, pack_u)
+OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_int32x4, unsigned int, int,
+                         vec_sra, vec_packsu, vec_add, pack_u)
+// Following variant is not implemented on other platforms:
+//OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_int64x2, unsigned long long, long long,
+//                         vec_sra, vec_packsu, vec_add, pack_u)
+
+// pack boolean
+inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
+{
+    vec_uchar16 ab = vec_pack(a.val, b.val);
+    return v_uint8x16(ab);
+}
+
+inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
+                           const v_uint32x4& c, const v_uint32x4& d)
+{
+    vec_ushort8 ab = vec_pack(a.val, b.val);
+    vec_ushort8 cd = vec_pack(c.val, d.val);
+    return v_uint8x16(vec_pack(ab, cd));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
+                           const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
+                           const v_uint64x2& g, const v_uint64x2& h)
+{
+    vec_uint4 ab = vec_pack(a.val, b.val);
+    vec_uint4 cd = vec_pack(c.val, d.val);
+    vec_uint4 ef = vec_pack(e.val, f.val);
+    vec_uint4 gh = vec_pack(g.val, h.val);
+
+    vec_ushort8 abcd = vec_pack(ab, cd);
+    vec_ushort8 efgh = vec_pack(ef, gh);
+    return v_uint8x16(vec_pack(abcd, efgh));
+}
+
+/* Recombine */
+template <typename _Tpvec>
+inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1)
+{
+    b0.val = vec_mergeh(a0.val, a1.val);
+    b1.val = vec_mergel(a0.val, a1.val);
+}
+
+template <typename _Tpvec>
+inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b)
+{ return _Tpvec(vec_mergesql(a.val, b.val)); }
+
+template <typename _Tpvec>
+inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b)
+{ return _Tpvec(vec_mergesqh(a.val, b.val)); }
+
+template <typename _Tpvec>
+inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d)
+{
+    c.val = vec_mergesqh(a.val, b.val);
+    d.val = vec_mergesql(a.val, b.val);
+}
+
+////////// Arithmetic, bitwise and comparison operations /////////
+
+/* Element-wise binary and unary operations */
+/** Arithmetics **/
+#define OPENCV_HAL_IMPL_VSX_BIN_OP(bin_op, _Tpvec, intrin)       \
+inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(intrin(a.val, b.val)); }                         \
+inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b)   \
+{ a.val = intrin(a.val, b.val); return a; }
+
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint8x16, vec_adds)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint8x16, vec_subs)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int8x16,  vec_adds)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int8x16, vec_subs)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint16x8, vec_adds)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint16x8, vec_subs)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int16x8, vec_adds)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int16x8, vec_subs)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint32x4, vec_add)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint32x4, vec_sub)
+OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint32x4, vec_mul)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int32x4, vec_add)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int32x4, vec_sub)
+OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int32x4, vec_mul)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float32x4, vec_add)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float32x4, vec_sub)
+OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float32x4, vec_mul)
+OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float32x4, vec_div)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float64x2, vec_add)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float64x2, vec_sub)
+OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float64x2, vec_mul)
+OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float64x2, vec_div)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint64x2, vec_add)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint64x2, vec_sub)
+OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int64x2, vec_add)
+OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int64x2, vec_sub)
+
+// saturating multiply
+#define OPENCV_HAL_IMPL_VSX_MUL_SAT(_Tpvec, _Tpwvec)             \
+    inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b)  \
+    {                                                            \
+        _Tpwvec c, d;                                            \
+        v_mul_expand(a, b, c, d);                                \
+        return v_pack(c, d);                                     \
+    }                                                            \
+    inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b)      \
+    { a = a * b; return a; }
+
+OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int8x16,  v_int16x8)
+OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint8x16, v_uint16x8)
+OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int16x8,  v_int32x4)
+OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint16x8, v_uint32x4)
+
+template<typename Tvec, typename Twvec>
+inline void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d)
+{
+    Twvec p0 = Twvec(vec_mule(a.val, b.val));
+    Twvec p1 = Twvec(vec_mulo(a.val, b.val));
+    v_zip(p0, p1, c, d);
+}
+
+inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b)
+{
+    vec_int4 p0 = vec_mule(a.val, b.val);
+    vec_int4 p1 = vec_mulo(a.val, b.val);
+    static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31};
+    return v_int16x8(vec_perm(vec_short8_c(p0), vec_short8_c(p1), perm));
+}
+inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b)
+{
+    vec_uint4 p0 = vec_mule(a.val, b.val);
+    vec_uint4 p1 = vec_mulo(a.val, b.val);
+    static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31};
+    return v_uint16x8(vec_perm(vec_ushort8_c(p0), vec_ushort8_c(p1), perm));
+}
+
+/** Non-saturating arithmetics **/
+#define OPENCV_HAL_IMPL_VSX_BIN_FUNC(func, intrin)    \
+template<typename _Tpvec>                             \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b)  \
+{ return _Tpvec(intrin(a.val, b.val)); }
+
+OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_add_wrap, vec_add)
+OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_sub_wrap, vec_sub)
+OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_mul_wrap, vec_mul)
+
+/** Bitwise shifts **/
+#define OPENCV_HAL_IMPL_VSX_SHIFT_OP(_Tpvec, shr, splfunc)   \
+inline _Tpvec operator << (const _Tpvec& a, int imm)         \
+{ return _Tpvec(vec_sl(a.val, splfunc(imm))); }              \
+inline _Tpvec operator >> (const _Tpvec& a, int imm)         \
+{ return _Tpvec(shr(a.val, splfunc(imm))); }                 \
+template<int imm> inline _Tpvec v_shl(const _Tpvec& a)       \
+{ return _Tpvec(vec_sl(a.val, splfunc(imm))); }              \
+template<int imm> inline _Tpvec v_shr(const _Tpvec& a)       \
+{ return _Tpvec(shr(a.val, splfunc(imm))); }
+
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint8x16, vec_sr, vec_uchar16_sp)
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint16x8, vec_sr, vec_ushort8_sp)
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint32x4, vec_sr, vec_uint4_sp)
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint64x2, vec_sr, vec_udword2_sp)
+// algebraic right shift
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int8x16, vec_sra, vec_uchar16_sp)
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int16x8, vec_sra, vec_ushort8_sp)
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int32x4, vec_sra, vec_uint4_sp)
+OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int64x2, vec_sra, vec_udword2_sp)
+
+/** Bitwise logic **/
+#define OPENCV_HAL_IMPL_VSX_LOGIC_OP(_Tpvec)    \
+OPENCV_HAL_IMPL_VSX_BIN_OP(&, _Tpvec, vec_and)  \
+OPENCV_HAL_IMPL_VSX_BIN_OP(|, _Tpvec, vec_or)   \
+OPENCV_HAL_IMPL_VSX_BIN_OP(^, _Tpvec, vec_xor)  \
+inline _Tpvec operator ~ (const _Tpvec& a)      \
+{ return _Tpvec(vec_not(a.val)); }
+
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint8x16)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int8x16)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint16x8)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int16x8)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint32x4)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int32x4)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint64x2)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int64x2)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float32x4)
+OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float64x2)
+
+/** Bitwise select **/
+#define OPENCV_HAL_IMPL_VSX_SELECT(_Tpvec, cast)                             \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(vec_sel(b.val, a.val, cast(mask.val))); }
+
+OPENCV_HAL_IMPL_VSX_SELECT(v_uint8x16, vec_bchar16_c)
+OPENCV_HAL_IMPL_VSX_SELECT(v_int8x16, vec_bchar16_c)
+OPENCV_HAL_IMPL_VSX_SELECT(v_uint16x8, vec_bshort8_c)
+OPENCV_HAL_IMPL_VSX_SELECT(v_int16x8, vec_bshort8_c)
+OPENCV_HAL_IMPL_VSX_SELECT(v_uint32x4, vec_bint4_c)
+OPENCV_HAL_IMPL_VSX_SELECT(v_int32x4, vec_bint4_c)
+OPENCV_HAL_IMPL_VSX_SELECT(v_float32x4, vec_bint4_c)
+OPENCV_HAL_IMPL_VSX_SELECT(v_float64x2, vec_bdword2_c)
+
+/** Comparison **/
+#define OPENCV_HAL_IMPL_VSX_INT_CMP_OP(_Tpvec)                 \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b)   \
+{ return _Tpvec(vec_cmpeq(a.val, b.val)); }                    \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b)   \
+{ return _Tpvec(vec_cmpne(a.val, b.val)); }                    \
+inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b)    \
+{ return _Tpvec(vec_cmplt(a.val, b.val)); }                    \
+inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b)    \
+{ return _Tpvec(vec_cmpgt(a.val, b.val)); }                    \
+inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b)   \
+{ return _Tpvec(vec_cmple(a.val, b.val)); }                    \
+inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b)   \
+{ return _Tpvec(vec_cmpge(a.val, b.val)); }
+
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint8x16)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int8x16)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint16x8)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int16x8)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint32x4)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int32x4)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float32x4)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2)
+OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2)
+
+inline v_float32x4 v_not_nan(const v_float32x4& a)
+{ return v_float32x4(vec_cmpeq(a.val, a.val)); }
+inline v_float64x2 v_not_nan(const v_float64x2& a)
+{ return v_float64x2(vec_cmpeq(a.val, a.val)); }
+
+/** min/max **/
+OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min)
+OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max)
+
+/** Rotate **/
+#define OPENCV_IMPL_VSX_ROTATE(_Tpvec, suffix, shf, cast)                       \
+template<int imm>                                                               \
+inline _Tpvec v_rotate_##suffix(const _Tpvec& a)                                \
+{                                                                               \
+    const int wd = imm * sizeof(typename _Tpvec::lane_type);                    \
+    if (wd > 15)                                                                \
+        return _Tpvec::zero();                                                  \
+    return _Tpvec((cast)shf(vec_uchar16_c(a.val), vec_uchar16_sp(wd << 3)));    \
+}
+
+#define OPENCV_IMPL_VSX_ROTATE_LR(_Tpvec, cast)     \
+OPENCV_IMPL_VSX_ROTATE(_Tpvec, left, vec_slo, cast) \
+OPENCV_IMPL_VSX_ROTATE(_Tpvec, right, vec_sro, cast)
+
+OPENCV_IMPL_VSX_ROTATE_LR(v_uint8x16, vec_uchar16)
+OPENCV_IMPL_VSX_ROTATE_LR(v_int8x16,  vec_char16)
+OPENCV_IMPL_VSX_ROTATE_LR(v_uint16x8, vec_ushort8)
+OPENCV_IMPL_VSX_ROTATE_LR(v_int16x8,  vec_short8)
+OPENCV_IMPL_VSX_ROTATE_LR(v_uint32x4, vec_uint4)
+OPENCV_IMPL_VSX_ROTATE_LR(v_int32x4,  vec_int4)
+OPENCV_IMPL_VSX_ROTATE_LR(v_float32x4, vec_float4)
+OPENCV_IMPL_VSX_ROTATE_LR(v_uint64x2, vec_udword2)
+OPENCV_IMPL_VSX_ROTATE_LR(v_int64x2,  vec_dword2)
+OPENCV_IMPL_VSX_ROTATE_LR(v_float64x2, vec_double2)
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b)
+{
+    enum { CV_SHIFT = 16 - imm * (sizeof(typename _Tpvec::lane_type)) };
+    if (CV_SHIFT == 16)
+        return a;
+#ifdef __IBMCPP__
+    return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT & 15));
+#else
+    return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT));
+#endif
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b)
+{
+    enum { CV_SHIFT = imm * (sizeof(typename _Tpvec::lane_type)) };
+    if (CV_SHIFT == 16)
+        return b;
+    return _Tpvec(vec_sld(a.val, b.val, CV_SHIFT));
+}
+
+#define OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, suffix, rg1, rg2)   \
+template<int imm>                                                 \
+inline _Tpvec v_rotate_##suffix(const _Tpvec& a, const _Tpvec& b) \
+{                                                                 \
+    if (imm == 1)                                                 \
+        return _Tpvec(vec_permi(rg1.val, rg2.val, 2));            \
+    return imm ? b : a;                                           \
+}
+
+#define OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(_Tpvec)    \
+OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, left,  b, a)  \
+OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, right, a, b)
+
+OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_float64x2)
+OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_uint64x2)
+OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_int64x2)
+
+/* Reverse */
+inline v_uint8x16 v_reverse(const v_uint8x16 &a)
+{
+    static const vec_uchar16 perm = {15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+    vec_uchar16 vec = (vec_uchar16)a.val;
+    return v_uint8x16(vec_perm(vec, vec, perm));
+}
+
+inline v_int8x16 v_reverse(const v_int8x16 &a)
+{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
+
+inline v_uint16x8 v_reverse(const v_uint16x8 &a)
+{
+    static const vec_uchar16 perm = {14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1};
+    vec_uchar16 vec = (vec_uchar16)a.val;
+    return v_reinterpret_as_u16(v_uint8x16(vec_perm(vec, vec, perm)));
+}
+
+inline v_int16x8 v_reverse(const v_int16x8 &a)
+{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
+
+inline v_uint32x4 v_reverse(const v_uint32x4 &a)
+{
+    static const vec_uchar16 perm = {12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3};
+    vec_uchar16 vec = (vec_uchar16)a.val;
+    return v_reinterpret_as_u32(v_uint8x16(vec_perm(vec, vec, perm)));
+}
+
+inline v_int32x4 v_reverse(const v_int32x4 &a)
+{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_float32x4 v_reverse(const v_float32x4 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x2 v_reverse(const v_uint64x2 &a)
+{
+    static const vec_uchar16 perm = {8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7};
+    vec_uchar16 vec = (vec_uchar16)a.val;
+    return v_reinterpret_as_u64(v_uint8x16(vec_perm(vec, vec, perm)));
+}
+
+inline v_int64x2 v_reverse(const v_int64x2 &a)
+{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
+
+inline v_float64x2 v_reverse(const v_float64x2 &a)
+{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
+
+/* Extract */
+template<int s, typename _Tpvec>
+inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b)
+{ return v_rotate_right<s>(a, b); }
+
+////////// Reduce and mask /////////
+
+/** Reduce **/
+inline uint v_reduce_sum(const v_uint8x16& a)
+{
+    const vec_uint4 zero4 = vec_uint4_z;
+    vec_uint4 sum4 = vec_sum4s(a.val, zero4);
+    return (uint)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3);
+}
+inline int v_reduce_sum(const v_int8x16& a)
+{
+    const vec_int4 zero4 = vec_int4_z;
+    vec_int4 sum4 = vec_sum4s(a.val, zero4);
+    return (int)vec_extract(vec_sums(sum4, zero4), 3);
+}
+inline int v_reduce_sum(const v_int16x8& a)
+{
+    const vec_int4 zero = vec_int4_z;
+    return saturate_cast<int>(vec_extract(vec_sums(vec_sum4s(a.val, zero), zero), 3));
+}
+inline uint v_reduce_sum(const v_uint16x8& a)
+{
+    const vec_int4 v4 = vec_int4_c(vec_unpackhu(vec_adds(a.val, vec_sld(a.val, a.val, 8))));
+    return saturate_cast<uint>(vec_extract(vec_sums(v4, vec_int4_z), 3));
+}
+
+#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(_Tpvec, _Tpvec2, scalartype, suffix, func) \
+inline scalartype v_reduce_##suffix(const _Tpvec& a)                               \
+{                                                                                  \
+    const _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8));                      \
+    return vec_extract(func(rs, vec_sld(rs, rs, 4)), 0);                           \
+}
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, sum, vec_add)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, max, vec_max)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, min, vec_min)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, sum, vec_add)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, max, vec_max)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, min, vec_min)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, sum, vec_add)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, max, vec_max)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, min, vec_min)
+
+inline uint64 v_reduce_sum(const v_uint64x2& a)
+{
+    return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0);
+}
+inline int64 v_reduce_sum(const v_int64x2& a)
+{
+    return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0);
+}
+inline double v_reduce_sum(const v_float64x2& a)
+{
+    return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0);
+}
+
+#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(_Tpvec, _Tpvec2, scalartype, suffix, func) \
+inline scalartype v_reduce_##suffix(const _Tpvec& a)                               \
+{                                                                                  \
+    _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8));                            \
+    rs = func(rs, vec_sld(rs, rs, 4));                                             \
+    return vec_extract(func(rs, vec_sld(rs, rs, 2)), 0);                           \
+}
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, max, vec_max)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, min, vec_min)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, max, vec_max)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, min, vec_min)
+
+#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(_Tpvec, _Tpvec2, scalartype, suffix, func) \
+inline scalartype v_reduce_##suffix(const _Tpvec& a)                               \
+{                                                                                  \
+    _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8));                            \
+    rs = func(rs, vec_sld(rs, rs, 4));                                             \
+    rs = func(rs, vec_sld(rs, rs, 2));                                             \
+    return vec_extract(func(rs, vec_sld(rs, rs, 1)), 0);                           \
+}
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_uint8x16, vec_uchar16, uchar, max, vec_max)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_uint8x16, vec_uchar16, uchar, min, vec_min)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_int8x16, vec_char16, schar, max, vec_max)
+OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_int8x16, vec_char16, schar, min, vec_min)
+
+inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
+                                 const v_float32x4& c, const v_float32x4& d)
+{
+    vec_float4 ac = vec_add(vec_mergel(a.val, c.val), vec_mergeh(a.val, c.val));
+    ac = vec_add(ac, vec_sld(ac, ac, 8));
+
+    vec_float4 bd = vec_add(vec_mergel(b.val, d.val), vec_mergeh(b.val, d.val));
+    bd = vec_add(bd, vec_sld(bd, bd, 8));
+    return v_float32x4(vec_mergeh(ac, bd));
+}
+
+inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b)
+{
+    const vec_uint4 zero4 = vec_uint4_z;
+    vec_uint4 sum4 = vec_sum4s(vec_absd(a.val, b.val), zero4);
+    return (unsigned)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3);
+}
+inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b)
+{
+    const vec_int4 zero4 = vec_int4_z;
+    vec_char16 ad = vec_abss(vec_subs(a.val, b.val));
+    vec_int4 sum4 = vec_sum4s(ad, zero4);
+    return (unsigned)vec_extract(vec_sums(sum4, zero4), 3);
+}
+inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b)
+{
+    vec_ushort8 ad = vec_absd(a.val, b.val);
+    VSX_UNUSED(vec_int4) sum = vec_sums(vec_int4_c(vec_unpackhu(ad)) + vec_int4_c(vec_unpacklu(ad)), vec_int4_z);
+    return (unsigned)vec_extract(sum, 3);
+}
+inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b)
+{
+    const vec_int4 zero4 = vec_int4_z;
+    vec_short8 ad = vec_abss(vec_subs(a.val, b.val));
+    vec_int4 sum4 = vec_sum4s(ad, zero4);
+    return (unsigned)vec_extract(vec_sums(sum4, zero4), 3);
+}
+inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b)
+{
+    const vec_uint4 ad = vec_absd(a.val, b.val);
+    const vec_uint4 rd = vec_add(ad, vec_sld(ad, ad, 8));
+    return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0);
+}
+inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b)
+{
+    vec_int4 ad = vec_abss(vec_sub(a.val, b.val));
+    return (unsigned)vec_extract(vec_sums(ad, vec_int4_z), 3);
+}
+inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b)
+{
+    const vec_float4 ad = vec_abs(vec_sub(a.val, b.val));
+    const vec_float4 rd = vec_add(ad, vec_sld(ad, ad, 8));
+    return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0);
+}
+
+/** Popcount **/
+inline v_uint8x16 v_popcount(const v_uint8x16& a)
+{ return v_uint8x16(vec_popcntu(a.val)); }
+inline v_uint8x16 v_popcount(const v_int8x16& a)
+{ return v_uint8x16(vec_popcntu(a.val)); }
+inline v_uint16x8 v_popcount(const v_uint16x8& a)
+{ return v_uint16x8(vec_popcntu(a.val)); }
+inline v_uint16x8 v_popcount(const v_int16x8& a)
+{ return v_uint16x8(vec_popcntu(a.val)); }
+inline v_uint32x4 v_popcount(const v_uint32x4& a)
+{ return v_uint32x4(vec_popcntu(a.val)); }
+inline v_uint32x4 v_popcount(const v_int32x4& a)
+{ return v_uint32x4(vec_popcntu(a.val)); }
+inline v_uint64x2 v_popcount(const v_uint64x2& a)
+{ return v_uint64x2(vec_popcntu(a.val)); }
+inline v_uint64x2 v_popcount(const v_int64x2& a)
+{ return v_uint64x2(vec_popcntu(a.val)); }
+
+/** Mask **/
+inline int v_signmask(const v_uint8x16& a)
+{
+    static const vec_uchar16 qperm = {120, 112, 104, 96, 88, 80, 72, 64, 56, 48, 40, 32, 24, 16, 8, 0};
+    return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2);
+}
+inline int v_signmask(const v_int8x16& a)
+{ return v_signmask(v_reinterpret_as_u8(a)); }
+
+inline int v_signmask(const v_int16x8& a)
+{
+    static const vec_uchar16 qperm = {112, 96, 80, 64, 48, 32, 16, 0, 128, 128, 128, 128, 128, 128, 128, 128};
+    return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2);
+}
+inline int v_signmask(const v_uint16x8& a)
+{ return v_signmask(v_reinterpret_as_s16(a)); }
+
+inline int v_signmask(const v_int32x4& a)
+{
+    static const vec_uchar16 qperm = {96, 64, 32, 0, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128};
+    return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2);
+}
+inline int v_signmask(const v_uint32x4& a)
+{ return v_signmask(v_reinterpret_as_s32(a)); }
+inline int v_signmask(const v_float32x4& a)
+{ return v_signmask(v_reinterpret_as_s32(a)); }
+
+inline int v_signmask(const v_int64x2& a)
+{
+    VSX_UNUSED(const vec_dword2) sv = vec_sr(a.val, vec_udword2_sp(63));
+    return (int)vec_extract(sv, 0) | (int)vec_extract(sv, 1) << 1;
+}
+inline int v_signmask(const v_uint64x2& a)
+{ return v_signmask(v_reinterpret_as_s64(a)); }
+inline int v_signmask(const v_float64x2& a)
+{ return v_signmask(v_reinterpret_as_s64(a)); }
+
+inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(a)); }
+inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(a)); }
+
+template<typename _Tpvec>
+inline bool v_check_all(const _Tpvec& a)
+{ return vec_all_lt(a.val, _Tpvec::zero().val); }
+inline bool v_check_all(const v_uint8x16& a)
+{ return v_check_all(v_reinterpret_as_s8(a)); }
+inline bool v_check_all(const v_uint16x8& a)
+{ return v_check_all(v_reinterpret_as_s16(a)); }
+inline bool v_check_all(const v_uint32x4& a)
+{ return v_check_all(v_reinterpret_as_s32(a)); }
+inline bool v_check_all(const v_uint64x2& a)
+{ return v_check_all(v_reinterpret_as_s64(a)); }
+inline bool v_check_all(const v_float32x4& a)
+{ return v_check_all(v_reinterpret_as_s32(a)); }
+inline bool v_check_all(const v_float64x2& a)
+{ return v_check_all(v_reinterpret_as_s64(a)); }
+
+template<typename _Tpvec>
+inline bool v_check_any(const _Tpvec& a)
+{ return vec_any_lt(a.val, _Tpvec::zero().val); }
+inline bool v_check_any(const v_uint8x16& a)
+{ return v_check_any(v_reinterpret_as_s8(a)); }
+inline bool v_check_any(const v_uint16x8& a)
+{ return v_check_any(v_reinterpret_as_s16(a)); }
+inline bool v_check_any(const v_uint32x4& a)
+{ return v_check_any(v_reinterpret_as_s32(a)); }
+inline bool v_check_any(const v_uint64x2& a)
+{ return v_check_any(v_reinterpret_as_s64(a)); }
+inline bool v_check_any(const v_float32x4& a)
+{ return v_check_any(v_reinterpret_as_s32(a)); }
+inline bool v_check_any(const v_float64x2& a)
+{ return v_check_any(v_reinterpret_as_s64(a)); }
+
+////////// Other math /////////
+
+/** Some frequent operations **/
+inline v_float32x4 v_sqrt(const v_float32x4& x)
+{ return v_float32x4(vec_sqrt(x.val)); }
+inline v_float64x2 v_sqrt(const v_float64x2& x)
+{ return v_float64x2(vec_sqrt(x.val)); }
+
+inline v_float32x4 v_invsqrt(const v_float32x4& x)
+{ return v_float32x4(vec_rsqrt(x.val)); }
+inline v_float64x2 v_invsqrt(const v_float64x2& x)
+{ return v_float64x2(vec_rsqrt(x.val)); }
+
+#define OPENCV_HAL_IMPL_VSX_MULADD(_Tpvec)                                  \
+inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b)                 \
+{ return _Tpvec(vec_sqrt(vec_madd(a.val, a.val, vec_mul(b.val, b.val)))); } \
+inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b)             \
+{ return _Tpvec(vec_madd(a.val, a.val, vec_mul(b.val, b.val))); }           \
+inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)      \
+{ return _Tpvec(vec_madd(a.val, b.val, c.val)); }                           \
+inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)   \
+{ return _Tpvec(vec_madd(a.val, b.val, c.val)); }
+
+OPENCV_HAL_IMPL_VSX_MULADD(v_float32x4)
+OPENCV_HAL_IMPL_VSX_MULADD(v_float64x2)
+
+inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{ return a * b + c; }
+
+// TODO: exp, log, sin, cos
+
+/** Absolute values **/
+inline v_uint8x16 v_abs(const v_int8x16& x)
+{ return v_uint8x16(vec_uchar16_c(vec_abs(x.val))); }
+
+inline v_uint16x8 v_abs(const v_int16x8& x)
+{ return v_uint16x8(vec_ushort8_c(vec_abs(x.val))); }
+
+inline v_uint32x4 v_abs(const v_int32x4& x)
+{ return v_uint32x4(vec_uint4_c(vec_abs(x.val))); }
+
+inline v_float32x4 v_abs(const v_float32x4& x)
+{ return v_float32x4(vec_abs(x.val)); }
+
+inline v_float64x2 v_abs(const v_float64x2& x)
+{ return v_float64x2(vec_abs(x.val)); }
+
+/** Absolute difference **/
+// unsigned
+OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_absdiff, vec_absd)
+
+inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b)
+{ return v_reinterpret_as_u8(v_sub_wrap(v_max(a, b), v_min(a, b))); }
+inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b)
+{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); }
+inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b)
+{ return v_reinterpret_as_u32(v_max(a, b) - v_min(a, b)); }
+
+inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b)
+{ return v_abs(a - b); }
+inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b)
+{ return v_abs(a - b); }
+
+/** Absolute difference for signed integers **/
+inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b)
+{ return v_int8x16(vec_abss(vec_subs(a.val, b.val))); }
+inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b)
+{ return v_int16x8(vec_abss(vec_subs(a.val, b.val))); }
+
+////////// Conversions /////////
+
+/** Rounding **/
+inline v_int32x4 v_round(const v_float32x4& a)
+{ return v_int32x4(vec_cts(vec_rint(a.val))); }
+
+inline v_int32x4 v_round(const v_float64x2& a)
+{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_int4_z)); }
+
+inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
+{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_ctso(vec_rint(b.val)))); }
+
+inline v_int32x4 v_floor(const v_float32x4& a)
+{ return v_int32x4(vec_cts(vec_floor(a.val))); }
+
+inline v_int32x4 v_floor(const v_float64x2& a)
+{ return v_int32x4(vec_mergesqo(vec_ctso(vec_floor(a.val)), vec_int4_z)); }
+
+inline v_int32x4 v_ceil(const v_float32x4& a)
+{ return v_int32x4(vec_cts(vec_ceil(a.val))); }
+
+inline v_int32x4 v_ceil(const v_float64x2& a)
+{ return v_int32x4(vec_mergesqo(vec_ctso(vec_ceil(a.val)), vec_int4_z)); }
+
+inline v_int32x4 v_trunc(const v_float32x4& a)
+{ return v_int32x4(vec_cts(a.val)); }
+
+inline v_int32x4 v_trunc(const v_float64x2& a)
+{ return v_int32x4(vec_mergesqo(vec_ctso(a.val), vec_int4_z)); }
+
+/** To float **/
+inline v_float32x4 v_cvt_f32(const v_int32x4& a)
+{ return v_float32x4(vec_ctf(a.val)); }
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a)
+{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_float4_z)); }
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
+{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_cvfo(b.val))); }
+
+inline v_float64x2 v_cvt_f64(const v_int32x4& a)
+{ return v_float64x2(vec_ctdo(vec_mergeh(a.val, a.val))); }
+
+inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
+{ return v_float64x2(vec_ctdo(vec_mergel(a.val, a.val))); }
+
+inline v_float64x2 v_cvt_f64(const v_float32x4& a)
+{ return v_float64x2(vec_cvfo(vec_mergeh(a.val, a.val))); }
+
+inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
+{ return v_float64x2(vec_cvfo(vec_mergel(a.val, a.val))); }
+
+inline v_float64x2 v_cvt_f64(const v_int64x2& a)
+{ return v_float64x2(vec_ctd(a.val)); }
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x16 v_lut(const schar* tab, const int* idx)
+{
+    return v_int8x16(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]],
+                     tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]);
+}
+inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx)
+{
+    return v_reinterpret_as_s8(v_int16x8(*(const short*)(tab+idx[0]), *(const short*)(tab+idx[1]), *(const short*)(tab+idx[2]), *(const short*)(tab+idx[3]),
+                                       *(const short*)(tab+idx[4]), *(const short*)(tab+idx[5]), *(const short*)(tab+idx[6]), *(const short*)(tab+idx[7])));
+}
+inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
+{
+    return v_reinterpret_as_s8(v_int32x4(*(const int*)(tab+idx[0]), *(const int*)(tab+idx[1]), *(const int*)(tab+idx[2]), *(const int*)(tab+idx[3])));
+}
+inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((const schar*)tab, idx)); }
+inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((const schar*)tab, idx)); }
+inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((const schar*)tab, idx)); }
+
+inline v_int16x8 v_lut(const short* tab, const int* idx)
+{
+    return v_int16x8(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]]);
+}
+inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
+{
+    return v_reinterpret_as_s16(v_int32x4(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1]), *(const int*)(tab + idx[2]), *(const int*)(tab + idx[3])));
+}
+inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
+{
+    return v_reinterpret_as_s16(v_int64x2(*(const int64*)(tab + idx[0]), *(const int64*)(tab + idx[1])));
+}
+inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((const short*)tab, idx)); }
+inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((const short*)tab, idx)); }
+inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((const short*)tab, idx)); }
+
+inline v_int32x4 v_lut(const int* tab, const int* idx)
+{
+    return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
+{
+    return v_reinterpret_as_s32(v_int64x2(*(const int64*)(tab + idx[0]), *(const int64*)(tab + idx[1])));
+}
+inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x4(vsx_ld(0, tab + idx[0]));
+}
+inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((const int*)tab, idx)); }
+inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((const int*)tab, idx)); }
+inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((const int*)tab, idx)); }
+
+inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(tab[idx[0]], tab[idx[1]]);
+}
+inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(vsx_ld2(0, tab + idx[0]));
+}
+inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); }
+inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); }
+
+inline v_float32x4 v_lut(const float* tab, const int* idx)
+{
+    return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_pairs((const int*)tab, idx)); }
+inline v_float32x4 v_lut_quads(const float* tab, const int* idx) { return v_load(tab + *idx); }
+
+inline v_float64x2 v_lut(const double* tab, const int* idx)
+{
+    return v_float64x2(tab[idx[0]], tab[idx[1]]);
+}
+inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) { return v_load(tab + *idx); }
+
+inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
+{
+    const int idx[4] = {
+        vec_extract(idxvec.val, 0),
+        vec_extract(idxvec.val, 1),
+        vec_extract(idxvec.val, 2),
+        vec_extract(idxvec.val, 3)
+    };
+    return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
+{
+    const int idx[4] = {
+        vec_extract(idxvec.val, 0),
+        vec_extract(idxvec.val, 1),
+        vec_extract(idxvec.val, 2),
+        vec_extract(idxvec.val, 3)
+    };
+    return v_uint32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+
+inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
+{
+    const int idx[4] = {
+        vec_extract(idxvec.val, 0),
+        vec_extract(idxvec.val, 1),
+        vec_extract(idxvec.val, 2),
+        vec_extract(idxvec.val, 3)
+    };
+    return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+
+inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
+{
+    const int idx[2] = {
+        vec_extract(idxvec.val, 0),
+        vec_extract(idxvec.val, 1)
+    };
+    return v_float64x2(tab[idx[0]], tab[idx[1]]);
+}
+
+inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
+{
+    vec_float4 xy0 = vec_ld_l8(tab + vec_extract(idxvec.val, 0));
+    vec_float4 xy1 = vec_ld_l8(tab + vec_extract(idxvec.val, 1));
+    vec_float4 xy2 = vec_ld_l8(tab + vec_extract(idxvec.val, 2));
+    vec_float4 xy3 = vec_ld_l8(tab + vec_extract(idxvec.val, 3));
+    vec_float4 xy02 = vec_mergeh(xy0, xy2); // x0, x2, y0, y2
+    vec_float4 xy13 = vec_mergeh(xy1, xy3); // x1, x3, y1, y3
+    x.val = vec_mergeh(xy02, xy13);
+    y.val = vec_mergel(xy02, xy13);
+}
+inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
+{
+    vec_double2 xy0 = vsx_ld(vec_extract(idxvec.val, 0), tab);
+    vec_double2 xy1 = vsx_ld(vec_extract(idxvec.val, 1), tab);
+    x.val = vec_mergeh(xy0, xy1);
+    y.val = vec_mergel(xy0, xy1);
+}
+
+inline v_int8x16 v_interleave_pairs(const v_int8x16& vec)
+{
+    static const vec_uchar16 perm = {0, 2, 1, 3, 4, 6, 5, 7, 8, 10, 9, 11, 12, 14, 13, 15};
+    return v_int8x16(vec_perm(vec.val, vec.val, perm));
+}
+inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec)
+{ return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
+
+inline v_int8x16 v_interleave_quads(const v_int8x16& vec)
+{
+    static const vec_uchar16 perm = {0, 4, 1, 5, 2, 6, 3, 7, 8, 12, 9, 13, 10, 14, 11, 15};
+    return v_int8x16(vec_perm(vec.val, vec.val, perm));
+}
+inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec)
+{ return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_interleave_pairs(const v_int16x8& vec)
+{
+    static const vec_uchar16 perm = {0,1, 4,5, 2,3, 6,7, 8,9, 12,13, 10,11, 14,15};
+    return v_int16x8(vec_perm(vec.val, vec.val, perm));
+}
+inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec)
+{ return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+
+inline v_int16x8 v_interleave_quads(const v_int16x8& vec)
+{
+    static const vec_uchar16 perm = {0,1, 8,9, 2,3, 10,11, 4,5, 12,13, 6,7, 14,15};
+    return v_int16x8(vec_perm(vec.val, vec.val, perm));
+}
+inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec)
+{ return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_interleave_pairs(const v_int32x4& vec)
+{
+    static const vec_uchar16 perm = {0,1,2,3, 8,9,10,11, 4,5,6,7, 12,13,14,15};
+    return v_int32x4(vec_perm(vec.val, vec.val, perm));
+}
+inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec)
+{ return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x4 v_interleave_pairs(const v_float32x4& vec)
+{ return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+    static const vec_uchar16 perm = {0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 15, 15, 15};
+    return v_int8x16(vec_perm(vec.val, vec.val, perm));
+}
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec)
+{ return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+    static const vec_uchar16 perm = {0,1, 2,3, 4,5, 8,9, 10,11, 12,13, 14,15, 14,15};
+    return v_int16x8(vec_perm(vec.val, vec.val, perm));
+}
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec)
+{ return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec)
+{ return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec)
+{ return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec)
+{ return vec; }
+
+/////// FP16 support ////////
+
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    vec_ushort8 vf16 = vec_ld_l8((const ushort*)ptr);
+#if CV_VSX3 && defined(vec_extract_fp_from_shorth)
+    return v_float32x4(vec_extract_fp_from_shorth(vf16));
+#elif CV_VSX3 && !defined(CV_COMPILER_VSX_BROKEN_ASM)
+    vec_float4 vf32;
+    __asm__ __volatile__ ("xvcvhpsp %x0,%x1" : "=wa" (vf32) : "wa" (vec_mergeh(vf16, vf16)));
+    return v_float32x4(vf32);
+#else
+    const vec_int4 z = vec_int4_z, delta = vec_int4_sp(0x38000000);
+    const vec_int4 signmask = vec_int4_sp(0x80000000);
+    const vec_int4 maxexp = vec_int4_sp(0x7c000000);
+    const vec_float4 deltaf = vec_float4_c(vec_int4_sp(0x38800000));
+
+    vec_int4 bits = vec_int4_c(vec_mergeh(vec_short8_c(z), vec_short8_c(vf16)));
+    vec_int4 e = vec_and(bits, maxexp), sign = vec_and(bits, signmask);
+    vec_int4 t = vec_add(vec_sr(vec_xor(bits, sign), vec_uint4_sp(3)), delta); // ((h & 0x7fff) << 13) + delta
+    vec_int4 zt = vec_int4_c(vec_sub(vec_float4_c(vec_add(t, vec_int4_sp(1 << 23))), deltaf));
+
+    t = vec_add(t, vec_and(delta, vec_cmpeq(maxexp, e)));
+    vec_bint4 zmask = vec_cmpeq(e, z);
+    vec_int4 ft = vec_sel(t, zt, zmask);
+    return v_float32x4(vec_float4_c(vec_or(ft, sign)));
+#endif
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+// fixme: Is there any builtin op or intrinsic that cover "xvcvsphp"?
+#if CV_VSX3 && !defined(CV_COMPILER_VSX_BROKEN_ASM)
+    vec_ushort8 vf16;
+    __asm__ __volatile__ ("xvcvsphp %x0,%x1" : "=wa" (vf16) : "wa" (v.val));
+    vec_st_l8(vec_mergesqe(vf16, vf16), ptr);
+#else
+    const vec_int4 signmask = vec_int4_sp(0x80000000);
+    const vec_int4 rval = vec_int4_sp(0x3f000000);
+
+    vec_int4 t = vec_int4_c(v.val);
+    vec_int4 sign = vec_sra(vec_and(t, signmask), vec_uint4_sp(16));
+    t = vec_and(vec_nor(signmask, signmask), t);
+
+    vec_bint4 finitemask = vec_cmpgt(vec_int4_sp(0x47800000), t);
+    vec_bint4 isnan = vec_cmpgt(t, vec_int4_sp(0x7f800000));
+    vec_int4 naninf = vec_sel(vec_int4_sp(0x7c00), vec_int4_sp(0x7e00), isnan);
+    vec_bint4 tinymask = vec_cmpgt(vec_int4_sp(0x38800000), t);
+    vec_int4 tt = vec_int4_c(vec_add(vec_float4_c(t), vec_float4_c(rval)));
+    tt = vec_sub(tt, rval);
+    vec_int4 odd = vec_and(vec_sr(t, vec_uint4_sp(13)), vec_int4_sp(1));
+    vec_int4 nt = vec_add(t, vec_int4_sp(0xc8000fff));
+    nt = vec_sr(vec_add(nt, odd), vec_uint4_sp(13));
+    t = vec_sel(nt, tt, tinymask);
+    t = vec_sel(naninf, t, finitemask);
+    t = vec_or(t, sign);
+    vec_st_l8(vec_packs(t, t), ptr);
+#endif
+}
+
+inline void v_cleanup() {}
+
+
+/** Reinterpret **/
+/** its up there with load and store operations **/
+
+////////// Matrix operations /////////
+
+//////// Dot Product ////////
+// 16 >> 32
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
+{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)); }
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_int32x4(vec_msum(a.val, b.val, c.val)); }
+
+// 32 >> 64
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
+{
+    vec_dword2 even = vec_mule(a.val, b.val);
+    vec_dword2 odd = vec_mulo(a.val, b.val);
+    return v_int64x2(vec_add(even, odd));
+}
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{ return v_dotprod(a, b) + c; }
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{ return v_uint32x4(vec_msum(a.val, b.val, c.val)); }
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
+{ return v_uint32x4(vec_msum(a.val, b.val, vec_uint4_z)); }
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
+{
+    const vec_ushort8 eight = vec_ushort8_sp(8);
+    vec_short8 a0 = vec_sra((vec_short8)vec_sld(a.val, a.val, 1), eight); // even
+    vec_short8 a1 = vec_sra((vec_short8)a.val, eight); // odd
+    vec_short8 b0 = vec_sra((vec_short8)vec_sld(b.val, b.val, 1), eight);
+    vec_short8 b1 = vec_sra((vec_short8)b.val, eight);
+    return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, vec_int4_z)));
+}
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{
+    const vec_ushort8 eight = vec_ushort8_sp(8);
+    vec_short8 a0 = vec_sra((vec_short8)vec_sld(a.val, a.val, 1), eight); // even
+    vec_short8 a1 = vec_sra((vec_short8)a.val, eight); // odd
+    vec_short8 b0 = vec_sra((vec_short8)vec_sld(b.val, b.val, 1), eight);
+    vec_short8 b1 = vec_sra((vec_short8)b.val, eight);
+    return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, c.val)));
+}
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
+{
+    const vec_uint4 zero = vec_uint4_z;
+    vec_uint4 even = vec_mule(a.val, b.val);
+    vec_uint4 odd  = vec_mulo(a.val, b.val);
+    vec_udword2 e0 = (vec_udword2)vec_mergee(even, zero);
+    vec_udword2 e1 = (vec_udword2)vec_mergeo(even, zero);
+    vec_udword2 o0 = (vec_udword2)vec_mergee(odd, zero);
+    vec_udword2 o1 = (vec_udword2)vec_mergeo(odd, zero);
+    vec_udword2 s0 = vec_add(e0, o0);
+    vec_udword2 s1 = vec_add(e1, o1);
+    return v_uint64x2(vec_add(s0, s1));
+}
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
+{
+    v_int32x4 prod = v_dotprod(a, b);
+    v_int64x2 c, d;
+    v_expand(prod, c, d);
+    return v_int64x2(vec_add(vec_mergeh(c.val, d.val), vec_mergel(c.val, d.val)));
+}
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
+{ return v_dotprod(a, b); }
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)) + c; }
+// 32 >> 64
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_dotprod(a, b); }
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{ return v_dotprod(a, b, c); }
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{ return v_uint32x4(vec_msum(a.val, b.val, vec_uint4_z)) + c; }
+
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
+{
+    vec_short8 a0 = vec_unpackh(a.val);
+    vec_short8 a1 = vec_unpackl(a.val);
+    vec_short8 b0 = vec_unpackh(b.val);
+    vec_short8 b1 = vec_unpackl(b.val);
+    return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, vec_int4_z)));
+}
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
+{
+    v_int32x4 prod = v_dotprod(a, b);
+    v_int64x2 c, d;
+    v_expand(prod, c, d);
+    return c + d;
+}
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand_fast(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_dotprod_expand(a, b); }
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+
+inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
+                            const v_float32x4& m1, const v_float32x4& m2,
+                            const v_float32x4& m3)
+{
+    const vec_float4 v0 = vec_splat(v.val, 0);
+    const vec_float4 v1 = vec_splat(v.val, 1);
+    const vec_float4 v2 = vec_splat(v.val, 2);
+    VSX_UNUSED(const vec_float4) v3 = vec_splat(v.val, 3);
+    return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, vec_mul(v3, m3.val)))));
+}
+
+inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
+                               const v_float32x4& m1, const v_float32x4& m2,
+                               const v_float32x4& a)
+{
+    const vec_float4 v0 = vec_splat(v.val, 0);
+    const vec_float4 v1 = vec_splat(v.val, 1);
+    const vec_float4 v2 = vec_splat(v.val, 2);
+    return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, a.val))));
+}
+
+#define OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(_Tpvec, _Tpvec2)                        \
+inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1,                   \
+                           const _Tpvec& a2, const _Tpvec& a3,                   \
+                           _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3)       \
+{                                                                                \
+    _Tpvec2 a02 = vec_mergeh(a0.val, a2.val);                                    \
+    _Tpvec2 a13 = vec_mergeh(a1.val, a3.val);                                    \
+    b0.val = vec_mergeh(a02, a13);                                               \
+    b1.val = vec_mergel(a02, a13);                                               \
+    a02 = vec_mergel(a0.val, a2.val);                                            \
+    a13 = vec_mergel(a1.val, a3.val);                                            \
+    b2.val  = vec_mergeh(a02, a13);                                              \
+    b3.val  = vec_mergel(a02, a13);                                              \
+}
+OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_uint32x4, vec_uint4)
+OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_int32x4, vec_int4)
+OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_float32x4, vec_float4)
+
+template<int i, typename Tvec>
+inline Tvec v_broadcast_element(const Tvec& v)
+{ return Tvec(vec_splat(v.val, i)); }
+
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+}
+
+#endif // OPENCV_HAL_VSX_HPP
diff --git a/3rdParty/include/opencv2/core/hal/intrin_wasm.hpp b/3rdParty/include/opencv2/core/hal/intrin_wasm.hpp
new file mode 100644
index 0000000..b4178af
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/intrin_wasm.hpp
@@ -0,0 +1,2782 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_HAL_INTRIN_WASM_HPP
+#define OPENCV_HAL_INTRIN_WASM_HPP
+
+#include <limits>
+#include <cstring>
+#include <algorithm>
+#include "opencv2/core/saturate.hpp"
+
+#define CV_SIMD128 1
+#define CV_SIMD128_64F 0 // Now all implementation of f64 use fallback, so disable it.
+#define CV_SIMD128_FP16 0
+
+namespace cv
+{
+
+//! @cond IGNORED
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
+
+#if (__EMSCRIPTEN_major__ * 1000000 + __EMSCRIPTEN_minor__ * 1000 + __EMSCRIPTEN_tiny__) < (1038046)
+// handle renames: https://github.com/emscripten-core/emscripten/pull/9440 (https://github.com/emscripten-core/emscripten/commit/755d5b46cb84d0aa120c10981b11d05646c29673)
+#define wasm_i32x4_trunc_saturate_f32x4 wasm_trunc_saturate_i32x4_f32x4
+#define wasm_u32x4_trunc_saturate_f32x4 wasm_trunc_saturate_u32x4_f32x4
+#define wasm_i64x2_trunc_saturate_f64x2 wasm_trunc_saturate_i64x2_f64x2
+#define wasm_u64x2_trunc_saturate_f64x2 wasm_trunc_saturate_u64x2_f64x2
+#define wasm_f32x4_convert_i32x4 wasm_convert_f32x4_i32x4
+#define wasm_f32x4_convert_u32x4 wasm_convert_f32x4_u32x4
+#define wasm_f64x2_convert_i64x2 wasm_convert_f64x2_i64x2
+#define wasm_f64x2_convert_u64x2 wasm_convert_f64x2_u64x2
+#endif // COMPATIBILITY: <1.38.46
+
+///////// Types ///////////
+
+struct v_uint8x16
+{
+    typedef uchar lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 16 };
+
+    v_uint8x16() {}
+    explicit v_uint8x16(v128_t v) : val(v) {}
+    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
+            uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
+    {
+        uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = wasm_v128_load(v);
+    }
+
+    uchar get0() const
+    {
+        return (uchar)wasm_i8x16_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_int8x16
+{
+    typedef schar lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 16 };
+
+    v_int8x16() {}
+    explicit v_int8x16(v128_t v) : val(v) {}
+    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
+            schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
+    {
+        schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
+        val = wasm_v128_load(v);
+    }
+
+    schar get0() const
+    {
+        return wasm_i8x16_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_uint16x8
+{
+    typedef ushort lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 8 };
+
+    v_uint16x8() {}
+    explicit v_uint16x8(v128_t v) : val(v) {}
+    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
+    {
+        ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = wasm_v128_load(v);
+    }
+
+    ushort get0() const
+    {
+        return (ushort)wasm_i16x8_extract_lane(val, 0);    // wasm_u16x8_extract_lane() unimplemented yet
+    }
+
+    v128_t val;
+};
+
+struct v_int16x8
+{
+    typedef short lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 8 };
+
+    v_int16x8() {}
+    explicit v_int16x8(v128_t v) : val(v) {}
+    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
+    {
+        short v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
+        val = wasm_v128_load(v);
+    }
+
+    short get0() const
+    {
+        return wasm_i16x8_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_uint32x4
+{
+    typedef unsigned lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 4 };
+
+    v_uint32x4() {}
+    explicit v_uint32x4(v128_t v) : val(v) {}
+    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
+    {
+        unsigned v[] = {v0, v1, v2, v3};
+        val = wasm_v128_load(v);
+    }
+
+    unsigned get0() const
+    {
+        return (unsigned)wasm_i32x4_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_int32x4
+{
+    typedef int lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 4 };
+
+    v_int32x4() {}
+    explicit v_int32x4(v128_t v) : val(v) {}
+    v_int32x4(int v0, int v1, int v2, int v3)
+    {
+        int v[] = {v0, v1, v2, v3};
+        val = wasm_v128_load(v);
+    }
+
+    int get0() const
+    {
+        return wasm_i32x4_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_float32x4
+{
+    typedef float lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 4 };
+
+    v_float32x4() {}
+    explicit v_float32x4(v128_t v) : val(v) {}
+    v_float32x4(float v0, float v1, float v2, float v3)
+    {
+        float v[] = {v0, v1, v2, v3};
+        val = wasm_v128_load(v);
+    }
+
+    float get0() const
+    {
+        return wasm_f32x4_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_uint64x2
+{
+    typedef uint64 lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 2 };
+
+    v_uint64x2() {}
+    explicit v_uint64x2(v128_t v) : val(v) {}
+    v_uint64x2(uint64 v0, uint64 v1)
+    {
+        uint64 v[] = {v0, v1};
+        val = wasm_v128_load(v);
+    }
+
+    uint64 get0() const
+    {
+        return (uint64)wasm_i64x2_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_int64x2
+{
+    typedef int64 lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 2 };
+
+    v_int64x2() {}
+    explicit v_int64x2(v128_t v) : val(v) {}
+    v_int64x2(int64 v0, int64 v1)
+    {
+        int64 v[] = {v0, v1};
+        val = wasm_v128_load(v);
+    }
+
+    int64 get0() const
+    {
+        return wasm_i64x2_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+struct v_float64x2
+{
+    typedef double lane_type;
+    typedef v128_t vector_type;
+    enum { nlanes = 2 };
+
+    v_float64x2() {}
+    explicit v_float64x2(v128_t v) : val(v) {}
+    v_float64x2(double v0, double v1)
+    {
+        double v[] = {v0, v1};
+        val = wasm_v128_load(v);
+    }
+
+    double get0() const
+    {
+        return wasm_f64x2_extract_lane(val, 0);
+    }
+
+    v128_t val;
+};
+
+namespace
+{
+#define OPENCV_HAL_IMPL_REINTERPRET_INT(ft, tt) \
+inline tt reinterpret_int(ft x) { union { ft l; tt i; } v; v.l = x; return v.i; }
+OPENCV_HAL_IMPL_REINTERPRET_INT(uchar, schar)
+OPENCV_HAL_IMPL_REINTERPRET_INT(schar, schar)
+OPENCV_HAL_IMPL_REINTERPRET_INT(ushort, short)
+OPENCV_HAL_IMPL_REINTERPRET_INT(short, short)
+OPENCV_HAL_IMPL_REINTERPRET_INT(unsigned, int)
+OPENCV_HAL_IMPL_REINTERPRET_INT(int, int)
+OPENCV_HAL_IMPL_REINTERPRET_INT(float, int)
+OPENCV_HAL_IMPL_REINTERPRET_INT(uint64, int64)
+OPENCV_HAL_IMPL_REINTERPRET_INT(int64, int64)
+OPENCV_HAL_IMPL_REINTERPRET_INT(double, int64)
+
+static const unsigned char popCountTable[] =
+{
+    0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+    4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8,
+};
+}  // namespace
+
+static v128_t wasm_unpacklo_i8x16(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 0,16,1,17,2,18,3,19,4,20,5,21,6,22,7,23);
+}
+
+static v128_t wasm_unpacklo_i16x8(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 0,1,16,17,2,3,18,19,4,5,20,21,6,7,22,23);
+}
+
+static v128_t wasm_unpacklo_i32x4(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 0,1,2,3,16,17,18,19,4,5,6,7,20,21,22,23);
+}
+
+static v128_t wasm_unpacklo_i64x2(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23);
+}
+
+static v128_t wasm_unpackhi_i8x16(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 8,24,9,25,10,26,11,27,12,28,13,29,14,30,15,31);
+}
+
+static v128_t wasm_unpackhi_i16x8(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 8,9,24,25,10,11,26,27,12,13,28,29,14,15,30,31);
+}
+
+static v128_t wasm_unpackhi_i32x4(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 8,9,10,11,24,25,26,27,12,13,14,15,28,29,30,31);
+}
+
+static v128_t wasm_unpackhi_i64x2(v128_t a, v128_t b) {
+    return wasm_v8x16_shuffle(a, b, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31);
+}
+
+/** Convert **/
+// 8 >> 16
+inline v128_t v128_cvtu8x16_i16x8(const v128_t& a)
+{
+    const v128_t z = wasm_i8x16_splat(0);
+    return wasm_unpacklo_i8x16(a, z);
+}
+inline v128_t v128_cvti8x16_i16x8(const v128_t& a)
+{ return wasm_i16x8_shr(wasm_unpacklo_i8x16(a, a), 8); }
+// 8 >> 32
+inline v128_t v128_cvtu8x16_i32x4(const v128_t& a)
+{
+    const v128_t z = wasm_i8x16_splat(0);
+    return wasm_unpacklo_i16x8(wasm_unpacklo_i8x16(a, z), z);
+}
+inline v128_t v128_cvti8x16_i32x4(const v128_t& a)
+{
+    v128_t r = wasm_unpacklo_i8x16(a, a);
+    r = wasm_unpacklo_i8x16(r, r);
+    return wasm_i32x4_shr(r, 24);
+}
+// 16 >> 32
+inline v128_t v128_cvtu16x8_i32x4(const v128_t& a)
+{
+    const v128_t z = wasm_i8x16_splat(0);
+    return wasm_unpacklo_i16x8(a, z);
+}
+inline v128_t v128_cvti16x8_i32x4(const v128_t& a)
+{ return wasm_i32x4_shr(wasm_unpacklo_i16x8(a, a), 16); }
+// 32 >> 64
+inline v128_t v128_cvtu32x4_i64x2(const v128_t& a)
+{
+    const v128_t z = wasm_i8x16_splat(0);
+    return wasm_unpacklo_i32x4(a, z);
+}
+inline v128_t v128_cvti32x4_i64x2(const v128_t& a)
+{ return wasm_unpacklo_i32x4(a, wasm_i32x4_shr(a, 31)); }
+
+// 16 << 8
+inline v128_t v128_cvtu8x16_i16x8_high(const v128_t& a)
+{
+    const v128_t z = wasm_i8x16_splat(0);
+    return wasm_unpackhi_i8x16(a, z);
+}
+inline v128_t v128_cvti8x16_i16x8_high(const v128_t& a)
+{ return wasm_i16x8_shr(wasm_unpackhi_i8x16(a, a), 8); }
+// 32 << 16
+inline v128_t v128_cvtu16x8_i32x4_high(const v128_t& a)
+{
+    const v128_t z = wasm_i8x16_splat(0);
+    return wasm_unpackhi_i16x8(a, z);
+}
+inline v128_t v128_cvti16x8_i32x4_high(const v128_t& a)
+{ return wasm_i32x4_shr(wasm_unpackhi_i16x8(a, a), 16); }
+// 64 << 32
+inline v128_t v128_cvtu32x4_i64x2_high(const v128_t& a)
+{
+    const v128_t z = wasm_i8x16_splat(0);
+    return wasm_unpackhi_i32x4(a, z);
+}
+inline v128_t v128_cvti32x4_i64x2_high(const v128_t& a)
+{ return wasm_unpackhi_i32x4(a, wasm_i32x4_shr(a, 31)); }
+
+#define OPENCV_HAL_IMPL_WASM_INITVEC(_Tpvec, _Tp, suffix, zsuffix, _Tps) \
+inline _Tpvec v_setzero_##suffix() { return _Tpvec(wasm_##zsuffix##_splat((_Tps)0)); } \
+inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(wasm_##zsuffix##_splat((_Tps)v)); } \
+template<typename _Tpvec0> inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \
+{ return _Tpvec(a.val); }
+
+OPENCV_HAL_IMPL_WASM_INITVEC(v_uint8x16, uchar, u8, i8x16, schar)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_int8x16, schar, s8, i8x16, schar)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_uint16x8, ushort, u16, i16x8, short)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_int16x8, short, s16, i16x8, short)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_uint32x4, unsigned, u32, i32x4, int)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_int32x4, int, s32, i32x4, int)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_float32x4, float, f32, f32x4, float)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_uint64x2, uint64, u64, i64x2, int64)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_int64x2, int64, s64, i64x2, int64)
+OPENCV_HAL_IMPL_WASM_INITVEC(v_float64x2, double, f64, f64x2, double)
+
+//////////////// PACK ///////////////
+inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u16x8_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u16x8_gt(b.val, maxval));
+    return v_uint8x16(wasm_v8x16_shuffle(a1, b1, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30));
+}
+inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b)
+{
+    v128_t maxval = wasm_i16x8_splat(127);
+    v128_t minval = wasm_i16x8_splat(-128);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i16x8_gt(b.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval));
+    v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i16x8_lt(b1, minval));
+    return v_int8x16(wasm_v8x16_shuffle(a2, b2, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30));
+}
+inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b)
+{
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u32x4_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u32x4_gt(b.val, maxval));
+    return v_uint16x8(wasm_v8x16_shuffle(a1, b1, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29));
+}
+inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b)
+{
+    v128_t maxval = wasm_i32x4_splat(32767);
+    v128_t minval = wasm_i32x4_splat(-32768);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i32x4_gt(b.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval));
+    v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i32x4_lt(b1, minval));
+    return v_int16x8(wasm_v8x16_shuffle(a2, b2, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29));
+}
+inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b)
+{
+    return v_uint32x4(wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27));
+}
+inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b)
+{
+    return v_int32x4(wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27));
+}
+inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b)
+{
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t minval = wasm_i16x8_splat(0);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i16x8_gt(b.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval));
+    v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i16x8_lt(b1, minval));
+    return v_uint8x16(wasm_v8x16_shuffle(a2, b2, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30));
+}
+inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b)
+{
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t minval = wasm_i32x4_splat(0);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i32x4_gt(b.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval));
+    v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i32x4_lt(b1, minval));
+    return v_uint16x8(wasm_v8x16_shuffle(a2, b2, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29));
+}
+
+template<int n>
+inline v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v128_t delta = wasm_i16x8_splat(((short)1 << (n-1)));
+    v128_t a1 = wasm_u16x8_shr(wasm_i16x8_add(a.val, delta), n);
+    v128_t b1 = wasm_u16x8_shr(wasm_i16x8_add(b.val, delta), n);
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u16x8_gt(a1, maxval));
+    v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_u16x8_gt(b1, maxval));
+    return v_uint8x16(wasm_v8x16_shuffle(a2, b2, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30));
+}
+template<int n>
+inline v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b)
+{
+    v128_t delta = wasm_i16x8_splat(((short)1 << (n-1)));
+    v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n);
+    v128_t b1 = wasm_i16x8_shr(wasm_i16x8_add(b.val, delta), n);
+    v128_t maxval = wasm_i16x8_splat(127);
+    v128_t minval = wasm_i16x8_splat(-128);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval));
+    v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i16x8_gt(b1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval));
+    v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i16x8_lt(b1, minval));
+    return v_int8x16(wasm_v8x16_shuffle(a3, b3, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30));
+}
+template<int n>
+inline v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b)
+{
+    v128_t delta = wasm_i32x4_splat(((int)1 << (n-1)));
+    v128_t a1 = wasm_u32x4_shr(wasm_i32x4_add(a.val, delta), n);
+    v128_t b1 = wasm_u32x4_shr(wasm_i32x4_add(b.val, delta), n);
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u32x4_gt(a1, maxval));
+    v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_u32x4_gt(b1, maxval));
+    return v_uint16x8(wasm_v8x16_shuffle(a2, b2, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29));
+}
+template<int n>
+inline v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b)
+{
+    v128_t delta = wasm_i32x4_splat(((int)1 << (n-1)));
+    v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n);
+    v128_t b1 = wasm_i32x4_shr(wasm_i32x4_add(b.val, delta), n);
+    v128_t maxval = wasm_i32x4_splat(32767);
+    v128_t minval = wasm_i16x8_splat(-32768);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval));
+    v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i32x4_gt(b1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval));
+    v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i32x4_lt(b1, minval));
+    return v_int16x8(wasm_v8x16_shuffle(a3, b3, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29));
+}
+template<int n>
+inline v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b)
+{
+    v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1)));
+    v128_t a1 = wasm_u64x2_shr(wasm_i64x2_add(a.val, delta), n);
+    v128_t b1 = wasm_u64x2_shr(wasm_i64x2_add(b.val, delta), n);
+    return v_uint32x4(wasm_v8x16_shuffle(a1, b1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27));
+}
+template<int n>
+inline v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b)
+{
+    v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1)));
+    v128_t a1 = wasm_i64x2_shr(wasm_i64x2_add(a.val, delta), n);
+    v128_t b1 = wasm_i64x2_shr(wasm_i64x2_add(b.val, delta), n);
+    return v_int32x4(wasm_v8x16_shuffle(a1, b1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27));
+}
+template<int n>
+inline v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b)
+{
+    v128_t delta = wasm_i16x8_splat(((short)1 << (n-1)));
+    v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n);
+    v128_t b1 = wasm_i16x8_shr(wasm_i16x8_add(b.val, delta), n);
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t minval = wasm_i16x8_splat(0);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval));
+    v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i16x8_gt(b1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval));
+    v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i16x8_lt(b1, minval));
+    return v_uint8x16(wasm_v8x16_shuffle(a3, b3, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30));
+}
+template<int n>
+inline v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b)
+{
+    v128_t delta = wasm_i32x4_splat(((int)1 << (n-1)));
+    v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n);
+    v128_t b1 = wasm_i32x4_shr(wasm_i32x4_add(b.val, delta), n);
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t minval = wasm_i16x8_splat(0);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval));
+    v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i32x4_gt(b1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval));
+    v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i32x4_lt(b1, minval));
+    return v_uint16x8(wasm_v8x16_shuffle(a3, b3, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29));
+}
+
+inline void v_pack_store(uchar* ptr, const v_uint16x8& a)
+{
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u16x8_gt(a.val, maxval));
+    v128_t r = wasm_v8x16_shuffle(a1, a1, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14);
+    uchar t_ptr[16];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<8; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+inline void v_pack_store(schar* ptr, const v_int16x8& a)
+{
+    v128_t maxval = wasm_i16x8_splat(127);
+    v128_t minval = wasm_i16x8_splat(-128);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a2, a2, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14);
+    schar t_ptr[16];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<8; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+inline void v_pack_store(ushort* ptr, const v_uint32x4& a)
+{
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u32x4_gt(a.val, maxval));
+    v128_t r = wasm_v8x16_shuffle(a1, a1, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13);
+    ushort t_ptr[8];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<4; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+inline void v_pack_store(short* ptr, const v_int32x4& a)
+{
+    v128_t maxval = wasm_i32x4_splat(32767);
+    v128_t minval = wasm_i32x4_splat(-32768);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a2, a2, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13);
+    short t_ptr[8];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<4; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+inline void v_pack_store(unsigned* ptr, const v_uint64x2& a)
+{
+    v128_t r = wasm_v8x16_shuffle(a.val, a.val, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11);
+    unsigned t_ptr[4];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<2; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+inline void v_pack_store(int* ptr, const v_int64x2& a)
+{
+    v128_t r = wasm_v8x16_shuffle(a.val, a.val, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11);
+    int t_ptr[4];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<2; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+inline void v_pack_u_store(uchar* ptr, const v_int16x8& a)
+{
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t minval = wasm_i16x8_splat(0);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a2, a2, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14);
+    uchar t_ptr[16];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<8; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+inline void v_pack_u_store(ushort* ptr, const v_int32x4& a)
+{
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t minval = wasm_i32x4_splat(0);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval));
+    v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a2, a2, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13);
+    ushort t_ptr[8];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<4; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+
+template<int n>
+inline void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a)
+{
+    v128_t delta = wasm_i16x8_splat((short)(1 << (n-1)));
+    v128_t a1 = wasm_u16x8_shr(wasm_i16x8_add(a.val, delta), n);
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u16x8_gt(a1, maxval));
+    v128_t r = wasm_v8x16_shuffle(a2, a2, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14);
+    uchar t_ptr[16];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<8; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+template<int n>
+inline void v_rshr_pack_store(schar* ptr, const v_int16x8& a)
+{
+    v128_t delta = wasm_i16x8_splat(((short)1 << (n-1)));
+    v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n);
+    v128_t maxval = wasm_i16x8_splat(127);
+    v128_t minval = wasm_i16x8_splat(-128);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a3, a3, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14);
+    schar t_ptr[16];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<8; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+template<int n>
+inline void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a)
+{
+    v128_t delta = wasm_i32x4_splat(((int)1 << (n-1)));
+    v128_t a1 = wasm_u32x4_shr(wasm_i32x4_add(a.val, delta), n);
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u32x4_gt(a1, maxval));
+    v128_t r = wasm_v8x16_shuffle(a2, a2, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13);
+    ushort t_ptr[8];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<4; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+template<int n>
+inline void v_rshr_pack_store(short* ptr, const v_int32x4& a)
+{
+    v128_t delta = wasm_i32x4_splat(((int)1 << (n-1)));
+    v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n);
+    v128_t maxval = wasm_i32x4_splat(32767);
+    v128_t minval = wasm_i32x4_splat(-32768);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a3, a3, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13);
+    short t_ptr[8];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<4; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+template<int n>
+inline void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a)
+{
+    v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1)));
+    v128_t a1 = wasm_u64x2_shr(wasm_i64x2_add(a.val, delta), n);
+    v128_t r = wasm_v8x16_shuffle(a1, a1, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11);
+    unsigned t_ptr[4];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<2; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+template<int n>
+inline void v_rshr_pack_store(int* ptr, const v_int64x2& a)
+{
+    v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1)));
+    v128_t a1 = wasm_i64x2_shr(wasm_i64x2_add(a.val, delta), n);
+    v128_t r = wasm_v8x16_shuffle(a1, a1, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11);
+    int t_ptr[4];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<2; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+template<int n>
+inline void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a)
+{
+    v128_t delta = wasm_i16x8_splat(((short)1 << (n-1)));
+    v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n);
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t minval = wasm_i16x8_splat(0);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a3, a3, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14);
+    uchar t_ptr[16];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<8; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+template<int n>
+inline void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a)
+{
+    v128_t delta = wasm_i32x4_splat(((int)1 << (n-1)));
+    v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n);
+    v128_t maxval = wasm_i32x4_splat(65535);
+    v128_t minval = wasm_i32x4_splat(0);
+    v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval));
+    v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval));
+    v128_t r = wasm_v8x16_shuffle(a3, a3, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13);
+    ushort t_ptr[8];
+    wasm_v128_store(t_ptr, r);
+    for (int i=0; i<4; ++i) {
+        ptr[i] = t_ptr[i];
+    }
+}
+
+inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v128_t maxval = wasm_i16x8_splat(255);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u16x8_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u16x8_gt(b.val, maxval));
+    return v_uint8x16(wasm_v8x16_shuffle(a1, b1, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b,
+                           const v_uint32x4& c, const v_uint32x4& d)
+{
+    v128_t maxval = wasm_i32x4_splat(255);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u32x4_gt(a.val, maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u32x4_gt(b.val, maxval));
+    v128_t c1 = wasm_v128_bitselect(maxval, c.val, wasm_u32x4_gt(c.val, maxval));
+    v128_t d1 = wasm_v128_bitselect(maxval, d.val, wasm_u32x4_gt(d.val, maxval));
+    v128_t ab = wasm_v8x16_shuffle(a1, b1, 0,4,8,12,16,20,24,28,0,4,8,12,16,20,24,28);
+    v128_t cd = wasm_v8x16_shuffle(c1, d1, 0,4,8,12,16,20,24,28,0,4,8,12,16,20,24,28);
+    return v_uint8x16(wasm_v8x16_shuffle(ab, cd, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23));
+}
+
+inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c,
+                           const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f,
+                           const v_uint64x2& g, const v_uint64x2& h)
+{
+    v128_t maxval = wasm_i32x4_splat(255);
+    v128_t a1 = wasm_v128_bitselect(maxval, a.val, ((__u64x2)(a.val) > (__u64x2)maxval));
+    v128_t b1 = wasm_v128_bitselect(maxval, b.val, ((__u64x2)(b.val) > (__u64x2)maxval));
+    v128_t c1 = wasm_v128_bitselect(maxval, c.val, ((__u64x2)(c.val) > (__u64x2)maxval));
+    v128_t d1 = wasm_v128_bitselect(maxval, d.val, ((__u64x2)(d.val) > (__u64x2)maxval));
+    v128_t e1 = wasm_v128_bitselect(maxval, e.val, ((__u64x2)(e.val) > (__u64x2)maxval));
+    v128_t f1 = wasm_v128_bitselect(maxval, f.val, ((__u64x2)(f.val) > (__u64x2)maxval));
+    v128_t g1 = wasm_v128_bitselect(maxval, g.val, ((__u64x2)(g.val) > (__u64x2)maxval));
+    v128_t h1 = wasm_v128_bitselect(maxval, h.val, ((__u64x2)(h.val) > (__u64x2)maxval));
+    v128_t ab = wasm_v8x16_shuffle(a1, b1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24);
+    v128_t cd = wasm_v8x16_shuffle(c1, d1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24);
+    v128_t ef = wasm_v8x16_shuffle(e1, f1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24);
+    v128_t gh = wasm_v8x16_shuffle(g1, h1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24);
+    v128_t abcd = wasm_v8x16_shuffle(ab, cd, 0,1,2,3,16,17,18,19,0,1,2,3,16,17,18,19);
+    v128_t efgh = wasm_v8x16_shuffle(ef, gh, 0,1,2,3,16,17,18,19,0,1,2,3,16,17,18,19);
+    return v_uint8x16(wasm_v8x16_shuffle(abcd, efgh, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23));
+}
+
+inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
+                            const v_float32x4& m1, const v_float32x4& m2,
+                            const v_float32x4& m3)
+{
+    v128_t v0 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 0));
+    v128_t v1 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 1));
+    v128_t v2 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 2));
+    v128_t v3 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 3));
+    v0 = wasm_f32x4_mul(v0, m0.val);
+    v1 = wasm_f32x4_mul(v1, m1.val);
+    v2 = wasm_f32x4_mul(v2, m2.val);
+    v3 = wasm_f32x4_mul(v3, m3.val);
+
+    return v_float32x4(wasm_f32x4_add(wasm_f32x4_add(v0, v1), wasm_f32x4_add(v2, v3)));
+}
+
+inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
+                               const v_float32x4& m1, const v_float32x4& m2,
+                               const v_float32x4& a)
+{
+    v128_t v0 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 0));
+    v128_t v1 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 1));
+    v128_t v2 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 2));
+    v0 = wasm_f32x4_mul(v0, m0.val);
+    v1 = wasm_f32x4_mul(v1, m1.val);
+    v2 = wasm_f32x4_mul(v2, m2.val);
+
+    return v_float32x4(wasm_f32x4_add(wasm_f32x4_add(v0, v1), wasm_f32x4_add(v2, a.val)));
+}
+
+#define OPENCV_HAL_IMPL_WASM_BIN_OP(bin_op, _Tpvec, intrin) \
+inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+} \
+inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
+{ \
+    a.val = intrin(a.val, b.val); \
+    return a; \
+}
+
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint8x16, wasm_u8x16_add_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint8x16, wasm_u8x16_sub_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int8x16, wasm_i8x16_add_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int8x16, wasm_i8x16_sub_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint16x8, wasm_u16x8_add_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint16x8, wasm_u16x8_sub_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int16x8, wasm_i16x8_add_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int16x8, wasm_i16x8_sub_saturate)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint32x4, wasm_i32x4_add)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint32x4, wasm_i32x4_sub)
+OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_uint32x4, wasm_i32x4_mul)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int32x4, wasm_i32x4_add)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int32x4, wasm_i32x4_sub)
+OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_int32x4, wasm_i32x4_mul)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_float32x4, wasm_f32x4_add)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_float32x4, wasm_f32x4_sub)
+OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_float32x4, wasm_f32x4_mul)
+OPENCV_HAL_IMPL_WASM_BIN_OP(/, v_float32x4, wasm_f32x4_div)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint64x2, wasm_i64x2_add)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint64x2, wasm_i64x2_sub)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int64x2, wasm_i64x2_add)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int64x2, wasm_i64x2_sub)
+OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_float64x2, wasm_f64x2_add)
+OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_float64x2, wasm_f64x2_sub)
+OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_float64x2, wasm_f64x2_mul)
+OPENCV_HAL_IMPL_WASM_BIN_OP(/, v_float64x2, wasm_f64x2_div)
+
+// saturating multiply 8-bit, 16-bit
+#define OPENCV_HAL_IMPL_WASM_MUL_SAT(_Tpvec, _Tpwvec)        \
+inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b)  \
+{                                                            \
+    _Tpwvec c, d;                                            \
+    v_mul_expand(a, b, c, d);                                \
+    return v_pack(c, d);                                     \
+}                                                            \
+inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b)      \
+{ a = a * b; return a; }
+
+OPENCV_HAL_IMPL_WASM_MUL_SAT(v_uint8x16, v_uint16x8)
+OPENCV_HAL_IMPL_WASM_MUL_SAT(v_int8x16,  v_int16x8)
+OPENCV_HAL_IMPL_WASM_MUL_SAT(v_uint16x8, v_uint32x4)
+OPENCV_HAL_IMPL_WASM_MUL_SAT(v_int16x8,  v_int32x4)
+
+//  Multiply and expand
+inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b,
+                         v_uint16x8& c, v_uint16x8& d)
+{
+    v_uint16x8 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b,
+                         v_int16x8& c, v_int16x8& d)
+{
+    v_int16x8 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c = v_mul_wrap(a0, b0);
+    d = v_mul_wrap(a1, b1);
+}
+
+inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b,
+                         v_int32x4& c, v_int32x4& d)
+{
+    v_int32x4 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c.val = wasm_i32x4_mul(a0.val, b0.val);
+    d.val = wasm_i32x4_mul(a1.val, b1.val);
+}
+
+inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b,
+                         v_uint32x4& c, v_uint32x4& d)
+{
+    v_uint32x4 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c.val = wasm_i32x4_mul(a0.val, b0.val);
+    d.val = wasm_i32x4_mul(a1.val, b1.val);
+}
+
+inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b,
+                         v_uint64x2& c, v_uint64x2& d)
+{
+    v_uint64x2 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    c.val = ((__u64x2)(a0.val) * (__u64x2)(b0.val));
+    d.val = ((__u64x2)(a1.val) * (__u64x2)(b1.val));
+}
+
+inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b)
+{
+    v_int32x4 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    v128_t c = wasm_i32x4_mul(a0.val, b0.val);
+    v128_t d = wasm_i32x4_mul(a1.val, b1.val);
+    return v_int16x8(wasm_v8x16_shuffle(c, d, 2,3,6,7,10,11,14,15,18,19,22,23,26,27,30,31));
+}
+inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v_uint32x4 a0, a1, b0, b1;
+    v_expand(a, a0, a1);
+    v_expand(b, b0, b1);
+    v128_t c = wasm_i32x4_mul(a0.val, b0.val);
+    v128_t d = wasm_i32x4_mul(a1.val, b1.val);
+    return v_uint16x8(wasm_v8x16_shuffle(c, d, 2,3,6,7,10,11,14,15,18,19,22,23,26,27,30,31));
+}
+
+//////// Dot Product ////////
+
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
+{
+    v128_t a0 = wasm_i32x4_shr(wasm_i32x4_shl(a.val, 16), 16);
+    v128_t a1 = wasm_i32x4_shr(a.val, 16);
+    v128_t b0 = wasm_i32x4_shr(wasm_i32x4_shl(b.val, 16), 16);
+    v128_t b1 = wasm_i32x4_shr(b.val, 16);
+    v128_t c = wasm_i32x4_mul(a0, b0);
+    v128_t d = wasm_i32x4_mul(a1, b1);
+    return v_int32x4(wasm_i32x4_add(c, d));
+}
+
+inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_dotprod(a, b) + c; }
+
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b)
+{
+    v128_t a0 = wasm_i64x2_shr(wasm_i64x2_shl(a.val, 32), 32);
+    v128_t a1 = wasm_i64x2_shr(a.val, 32);
+    v128_t b0 = wasm_i64x2_shr(wasm_i64x2_shl(b.val, 32), 32);
+    v128_t b1 = wasm_i64x2_shr(b.val, 32);
+    v128_t c = (v128_t)((__i64x2)a0 * (__i64x2)b0);
+    v128_t d = (v128_t)((__i64x2)a1 * (__i64x2)b1);
+    return v_int64x2(wasm_i64x2_add(c, d));
+}
+inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{
+    return v_dotprod(a, b) + c;
+}
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b)
+{
+    v128_t a0 = wasm_u16x8_shr(wasm_i16x8_shl(a.val, 8), 8);
+    v128_t a1 = wasm_u16x8_shr(a.val, 8);
+    v128_t b0 = wasm_u16x8_shr(wasm_i16x8_shl(b.val, 8), 8);
+    v128_t b1 = wasm_u16x8_shr(b.val, 8);
+    return v_uint32x4((
+        v_dotprod(v_int16x8(a0), v_int16x8(b0)) +
+        v_dotprod(v_int16x8(a1), v_int16x8(b1))).val
+    );
+}
+inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b)
+{
+    v128_t a0 = wasm_i16x8_shr(wasm_i16x8_shl(a.val, 8), 8);
+    v128_t a1 = wasm_i16x8_shr(a.val, 8);
+    v128_t b0 = wasm_i16x8_shr(wasm_i16x8_shl(b.val, 8), 8);
+    v128_t b1 = wasm_i16x8_shr(b.val, 8);
+    return v_int32x4(
+        v_dotprod(v_int16x8(a0), v_int16x8(b0)) +
+        v_dotprod(v_int16x8(a1), v_int16x8(b1))
+    );
+}
+inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v128_t a0 = wasm_u32x4_shr(wasm_i32x4_shl(a.val, 16), 16);
+    v128_t a1 = wasm_u32x4_shr(a.val, 16);
+    v128_t b0 = wasm_u32x4_shr(wasm_i32x4_shl(b.val, 16), 16);
+    v128_t b1 = wasm_u32x4_shr(b.val, 16);
+    return v_uint64x2((
+        v_dotprod(v_int32x4(a0), v_int32x4(b0)) +
+        v_dotprod(v_int32x4(a1), v_int32x4(b1))).val
+    );
+}
+inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b)
+{
+    v128_t a0 = wasm_i32x4_shr(wasm_i32x4_shl(a.val, 16), 16);
+    v128_t a1 = wasm_i32x4_shr(a.val, 16);
+    v128_t b0 = wasm_i32x4_shr(wasm_i32x4_shl(b.val, 16), 16);
+    v128_t b1 = wasm_i32x4_shr(b.val, 16);
+    return v_int64x2((
+        v_dotprod(v_int32x4(a0), v_int32x4(b0)) +
+        v_dotprod(v_int32x4(a1), v_int32x4(b1)))
+    );
+}
+
+inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+// 32 >> 64f
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b)
+{ return v_cvt_f64(v_dotprod(a, b)); }
+inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand(a, b) + c; }
+
+//////// Fast Dot Product ////////
+
+// 16 >> 32
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b)
+{ return v_dotprod(a, b); }
+inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
+{ return v_dotprod(a, b, c); }
+
+// 32 >> 64
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_dotprod(a, b); }
+inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c)
+{ return v_dotprod(a, b, c); }
+
+// 8 >> 32
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c)
+{ return v_dotprod_expand(a, b, c); }
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b)
+{ return v_dotprod_expand(a, b); }
+inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c)
+{ return v_dotprod_expand(a, b, c); }
+
+// 16 >> 64
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b)
+{ return v_dotprod_expand(a, b); }
+inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b)
+{ return v_dotprod_expand(a, b); }
+inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+
+// 32 >> 64f
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b)
+{ return v_dotprod_expand(a, b); }
+inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c)
+{ return v_dotprod_expand(a, b, c); }
+
+#define OPENCV_HAL_IMPL_WASM_LOGIC_OP(_Tpvec) \
+OPENCV_HAL_IMPL_WASM_BIN_OP(&, _Tpvec, wasm_v128_and) \
+OPENCV_HAL_IMPL_WASM_BIN_OP(|, _Tpvec, wasm_v128_or) \
+OPENCV_HAL_IMPL_WASM_BIN_OP(^, _Tpvec, wasm_v128_xor) \
+inline _Tpvec operator ~ (const _Tpvec& a) \
+{ \
+    return _Tpvec(wasm_v128_not(a.val)); \
+}
+
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint8x16)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int8x16)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint16x8)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int16x8)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint32x4)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int32x4)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint64x2)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int64x2)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_float32x4)
+OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_float64x2)
+
+inline v_float32x4 v_sqrt(const v_float32x4& x)
+{
+    return v_float32x4(wasm_f32x4_sqrt(x.val));
+}
+
+inline v_float32x4 v_invsqrt(const v_float32x4& x)
+{
+    const v128_t _1_0 = wasm_f32x4_splat(1.0);
+    return v_float32x4(wasm_f32x4_div(_1_0, wasm_f32x4_sqrt(x.val)));
+}
+
+inline v_float64x2 v_sqrt(const v_float64x2& x)
+{
+    return v_float64x2(wasm_f64x2_sqrt(x.val));
+}
+
+inline v_float64x2 v_invsqrt(const v_float64x2& x)
+{
+    const v128_t _1_0 = wasm_f64x2_splat(1.0);
+    return v_float64x2(wasm_f64x2_div(_1_0, wasm_f64x2_sqrt(x.val)));
+}
+
+#define OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(_Tpuvec, _Tpsvec, suffix, zsuffix, shiftWidth) \
+inline _Tpuvec v_abs(const _Tpsvec& x) \
+{ \
+    v128_t s = wasm_##suffix##_shr(x.val, shiftWidth); \
+    v128_t f = wasm_##zsuffix##_shr(x.val, shiftWidth); \
+    return _Tpuvec(wasm_##zsuffix##_add(wasm_v128_xor(x.val, f), s)); \
+}
+
+OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(v_uint8x16, v_int8x16, u8x16, i8x16, 7)
+OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(v_uint16x8, v_int16x8, u16x8, i16x8, 15)
+OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(v_uint32x4, v_int32x4, u32x4, i32x4, 31)
+
+inline v_float32x4 v_abs(const v_float32x4& x)
+{ return v_float32x4(wasm_f32x4_abs(x.val)); }
+inline v_float64x2 v_abs(const v_float64x2& x)
+{
+    return v_float64x2(wasm_f64x2_abs(x.val));
+}
+
+// TODO: exp, log, sin, cos
+
+#define OPENCV_HAL_IMPL_WASM_BIN_FUNC(_Tpvec, func, intrin) \
+inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(intrin(a.val, b.val)); \
+}
+
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float32x4, v_min, wasm_f32x4_min)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float32x4, v_max, wasm_f32x4_max)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float64x2, v_min, wasm_f64x2_min)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float64x2, v_max, wasm_f64x2_max)
+
+#define OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(_Tpvec, suffix) \
+inline _Tpvec v_min(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(wasm_v128_bitselect(b.val, a.val, wasm_##suffix##_gt(a.val, b.val))); \
+} \
+inline _Tpvec v_max(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(wasm_v128_bitselect(a.val, b.val, wasm_##suffix##_gt(a.val, b.val))); \
+}
+
+OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(v_int8x16, i8x16)
+OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(v_int16x8, i16x8)
+OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(v_int32x4, i32x4)
+
+#define OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(_Tpvec, suffix, deltaNum) \
+inline _Tpvec v_min(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    v128_t delta = wasm_##suffix##_splat(deltaNum); \
+    v128_t mask = wasm_##suffix##_gt(wasm_v128_xor(a.val, delta), wasm_v128_xor(b.val, delta)); \
+    return _Tpvec(wasm_v128_bitselect(b.val, a.val, mask)); \
+} \
+inline _Tpvec v_max(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    v128_t delta = wasm_##suffix##_splat(deltaNum); \
+    v128_t mask = wasm_##suffix##_gt(wasm_v128_xor(a.val, delta), wasm_v128_xor(b.val, delta)); \
+    return _Tpvec(wasm_v128_bitselect(a.val, b.val, mask)); \
+}
+
+OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(v_uint8x16, i8x16, (schar)0x80)
+OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(v_uint16x8, i16x8, (short)0x8000)
+OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(v_uint32x4, i32x4, (int)0x80000000)
+
+#define OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(_Tpvec, suffix, esuffix) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(wasm_##esuffix##_eq(a.val, b.val)); } \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(wasm_##esuffix##_ne(a.val, b.val)); } \
+inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(wasm_##suffix##_lt(a.val, b.val)); } \
+inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(wasm_##suffix##_gt(a.val, b.val)); } \
+inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(wasm_##suffix##_le(a.val, b.val)); } \
+inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
+{ return _Tpvec(wasm_##suffix##_ge(a.val, b.val)); }
+
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_uint8x16, u8x16, i8x16)
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_int8x16, i8x16, i8x16)
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_uint16x8, u16x8, i16x8)
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_int16x8, i16x8, i16x8)
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_uint32x4, u32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_int32x4, i32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_float32x4, f32x4, f32x4)
+OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_float64x2, f64x2, f64x2)
+
+#define OPENCV_HAL_IMPL_WASM_64BIT_CMP_OP(_Tpvec, cast) \
+inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
+{ return cast(v_reinterpret_as_f64(a) == v_reinterpret_as_f64(b)); } \
+inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
+{ return cast(v_reinterpret_as_f64(a) != v_reinterpret_as_f64(b)); }
+
+OPENCV_HAL_IMPL_WASM_64BIT_CMP_OP(v_uint64x2, v_reinterpret_as_u64)
+OPENCV_HAL_IMPL_WASM_64BIT_CMP_OP(v_int64x2, v_reinterpret_as_s64)
+
+inline v_float32x4 v_not_nan(const v_float32x4& a)
+{
+    v128_t z = wasm_i32x4_splat(0x7fffffff);
+    v128_t t = wasm_i32x4_splat(0x7f800000);
+    return v_float32x4(wasm_u32x4_lt(wasm_v128_and(a.val, z), t));
+}
+inline v_float64x2 v_not_nan(const v_float64x2& a)
+{
+    v128_t z = wasm_i64x2_splat(0x7fffffffffffffff);
+    v128_t t = wasm_i64x2_splat(0x7ff0000000000000);
+    return v_float64x2((__u64x2)(wasm_v128_and(a.val, z)) < (__u64x2)t);
+}
+
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint8x16, v_add_wrap, wasm_i8x16_add)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int8x16, v_add_wrap, wasm_i8x16_add)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint16x8, v_add_wrap, wasm_i16x8_add)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int16x8, v_add_wrap, wasm_i16x8_add)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint8x16, v_sub_wrap, wasm_i8x16_sub)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int8x16, v_sub_wrap, wasm_i8x16_sub)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint16x8, v_sub_wrap, wasm_i16x8_sub)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int16x8, v_sub_wrap, wasm_i16x8_sub)
+#if (__EMSCRIPTEN_major__ * 1000000 + __EMSCRIPTEN_minor__ * 1000 + __EMSCRIPTEN_tiny__) >= (1039012)
+// details: https://github.com/opencv/opencv/issues/18097 ( https://github.com/emscripten-core/emscripten/issues/12018 )
+// 1.39.12: https://github.com/emscripten-core/emscripten/commit/cd801d0f110facfd694212a3c8b2ed2ffcd630e2
+inline v_uint8x16 v_mul_wrap(const v_uint8x16& a, const v_uint8x16& b)
+{
+    uchar a_[16], b_[16];
+    wasm_v128_store(a_, a.val);
+    wasm_v128_store(b_, b.val);
+    for (int i = 0; i < 16; i++)
+        a_[i] = (uchar)(a_[i] * b_[i]);
+    return v_uint8x16(wasm_v128_load(a_));
+}
+inline v_int8x16 v_mul_wrap(const v_int8x16& a, const v_int8x16& b)
+{
+    schar a_[16], b_[16];
+    wasm_v128_store(a_, a.val);
+    wasm_v128_store(b_, b.val);
+    for (int i = 0; i < 16; i++)
+        a_[i] = (schar)(a_[i] * b_[i]);
+    return v_int8x16(wasm_v128_load(a_));
+}
+#else
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint8x16, v_mul_wrap, wasm_i8x16_mul)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int8x16, v_mul_wrap, wasm_i8x16_mul)
+#endif
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint16x8, v_mul_wrap, wasm_i16x8_mul)
+OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int16x8, v_mul_wrap, wasm_i16x8_mul)
+
+
+/** Absolute difference **/
+
+inline v_uint8x16 v_absdiff(const v_uint8x16& a, const v_uint8x16& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint16x8 v_absdiff(const v_uint16x8& a, const v_uint16x8& b)
+{ return v_add_wrap(a - b,  b - a); }
+inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b)
+{
+    v_int8x16 d = v_sub_wrap(a, b);
+    v_int8x16 m = a < b;
+    return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m));
+}
+inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b)
+{
+    return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b)));
+}
+inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b)
+{
+    v_int32x4 d = a - b;
+    v_int32x4 m = a < b;
+    return v_reinterpret_as_u32((d ^ m) - m);
+}
+
+/** Saturating absolute difference **/
+inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b)
+{
+    v_int8x16 d = a - b;
+    v_int8x16 m = a < b;
+    return (d ^ m) - m;
+ }
+inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b)
+{ return v_max(a, b) - v_min(a, b); }
+
+
+inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return a * b + c;
+}
+
+inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
+{
+    return v_fma(a, b, c);
+}
+
+inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
+{
+    return a * b + c;
+}
+
+inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
+{
+    return a * b + c;
+}
+
+inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b)
+{
+    v128_t absmask_vec = wasm_i32x4_splat(0x7fffffff);
+    return v_float32x4(wasm_v128_and(wasm_f32x4_sub(a.val, b.val), absmask_vec));
+}
+inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b)
+{
+    v128_t absmask_vec = wasm_u64x2_shr(wasm_i32x4_splat(-1), 1);
+    return v_float64x2(wasm_v128_and(wasm_f64x2_sub(a.val, b.val), absmask_vec));
+}
+
+#define OPENCV_HAL_IMPL_WASM_MISC_FLT_OP(_Tpvec, suffix) \
+inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    v128_t a_Square = wasm_##suffix##_mul(a.val, a.val); \
+    v128_t b_Square = wasm_##suffix##_mul(b.val, b.val); \
+    return _Tpvec(wasm_##suffix##_sqrt(wasm_##suffix##_add(a_Square, b_Square))); \
+} \
+inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    v128_t a_Square = wasm_##suffix##_mul(a.val, a.val); \
+    v128_t b_Square = wasm_##suffix##_mul(b.val, b.val); \
+    return _Tpvec(wasm_##suffix##_add(a_Square, b_Square)); \
+} \
+inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \
+{ \
+    return _Tpvec(wasm_##suffix##_add(wasm_##suffix##_mul(a.val, b.val), c.val)); \
+}
+
+OPENCV_HAL_IMPL_WASM_MISC_FLT_OP(v_float32x4, f32x4)
+OPENCV_HAL_IMPL_WASM_MISC_FLT_OP(v_float64x2, f64x2)
+
+#define OPENCV_HAL_IMPL_WASM_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, ssuffix) \
+inline _Tpuvec operator << (const _Tpuvec& a, int imm) \
+{ \
+    return _Tpuvec(wasm_##suffix##_shl(a.val, imm)); \
+} \
+inline _Tpsvec operator << (const _Tpsvec& a, int imm) \
+{ \
+    return _Tpsvec(wasm_##suffix##_shl(a.val, imm)); \
+} \
+inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \
+{ \
+    return _Tpuvec(wasm_##ssuffix##_shr(a.val, imm)); \
+} \
+inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \
+{ \
+    return _Tpsvec(wasm_##suffix##_shr(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpuvec v_shl(const _Tpuvec& a) \
+{ \
+    return _Tpuvec(wasm_##suffix##_shl(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpsvec v_shl(const _Tpsvec& a) \
+{ \
+    return _Tpsvec(wasm_##suffix##_shl(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpuvec v_shr(const _Tpuvec& a) \
+{ \
+    return _Tpuvec(wasm_##ssuffix##_shr(a.val, imm)); \
+} \
+template<int imm> \
+inline _Tpsvec v_shr(const _Tpsvec& a) \
+{ \
+    return _Tpsvec(wasm_##suffix##_shr(a.val, imm)); \
+}
+
+OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint8x16, v_int8x16, i8x16, u8x16)
+OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint16x8, v_int16x8, i16x8, u16x8)
+OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint32x4, v_int32x4, i32x4, u32x4)
+OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint64x2, v_int64x2, i64x2, u64x2)
+
+namespace hal_wasm_internal
+{
+    template <int imm,
+        bool is_invalid = ((imm < 0) || (imm > 16)),
+        bool is_first = (imm == 0),
+        bool is_second = (imm == 16),
+        bool is_other = (((imm > 0) && (imm < 16)))>
+    class v_wasm_palignr_u8_class;
+
+    template <int imm>
+    class v_wasm_palignr_u8_class<imm, true, false, false, false>;
+
+    template <int imm>
+    class v_wasm_palignr_u8_class<imm, false, true, false, false>
+    {
+    public:
+        inline v128_t operator()(const v128_t& a, const v128_t&) const
+        {
+            return a;
+        }
+    };
+
+    template <int imm>
+    class v_wasm_palignr_u8_class<imm, false, false, true, false>
+    {
+    public:
+        inline v128_t operator()(const v128_t&, const v128_t& b) const
+        {
+            return b;
+        }
+    };
+
+    template <int imm>
+    class v_wasm_palignr_u8_class<imm, false, false, false, true>
+    {
+    public:
+        inline v128_t operator()(const v128_t& a, const v128_t& b) const
+        {
+            enum { imm2 = (sizeof(v128_t) - imm) };
+            return wasm_v8x16_shuffle(a, b,
+                                      imm, imm+1, imm+2, imm+3,
+                                      imm+4, imm+5, imm+6, imm+7,
+                                      imm+8, imm+9, imm+10, imm+11,
+                                      imm+12, imm+13, imm+14, imm+15);
+        }
+    };
+
+    template <int imm>
+    inline v128_t v_wasm_palignr_u8(const v128_t& a, const v128_t& b)
+    {
+        CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_wasm_palignr_u8.");
+        return v_wasm_palignr_u8_class<imm>()(a, b);
+    }
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_right(const _Tpvec &a)
+{
+    using namespace hal_wasm_internal;
+    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
+    v128_t z = wasm_i8x16_splat(0);
+    return _Tpvec(v_wasm_palignr_u8<imm2>(a.val, z));
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_left(const _Tpvec &a)
+{
+    using namespace hal_wasm_internal;
+    enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) };
+    v128_t z = wasm_i8x16_splat(0);
+    return _Tpvec(v_wasm_palignr_u8<imm2>(z, a.val));
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b)
+{
+    using namespace hal_wasm_internal;
+    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
+    return _Tpvec(v_wasm_palignr_u8<imm2>(a.val, b.val));
+}
+
+template<int imm, typename _Tpvec>
+inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b)
+{
+    using namespace hal_wasm_internal;
+    enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) };
+    return _Tpvec(v_wasm_palignr_u8<imm2>(b.val, a.val));
+}
+
+#define OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(_Tpvec, _Tp) \
+inline _Tpvec v_load(const _Tp* ptr) \
+{ return _Tpvec(wasm_v128_load(ptr)); } \
+inline _Tpvec v_load_aligned(const _Tp* ptr) \
+{ return _Tpvec(wasm_v128_load(ptr)); } \
+inline _Tpvec v_load_low(const _Tp* ptr) \
+{ \
+    _Tp tmp[_Tpvec::nlanes] = {0}; \
+    for (int i=0; i<_Tpvec::nlanes/2; ++i) { \
+        tmp[i] = ptr[i]; \
+    } \
+    return _Tpvec(wasm_v128_load(tmp)); \
+} \
+inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
+{ \
+    _Tp tmp[_Tpvec::nlanes]; \
+    for (int i=0; i<_Tpvec::nlanes/2; ++i) { \
+        tmp[i] = ptr0[i]; \
+        tmp[i+_Tpvec::nlanes/2] = ptr1[i]; \
+    } \
+    return _Tpvec(wasm_v128_load(tmp)); \
+} \
+inline void v_store(_Tp* ptr, const _Tpvec& a) \
+{ wasm_v128_store(ptr, a.val); } \
+inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
+{ wasm_v128_store(ptr, a.val); } \
+inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \
+{ wasm_v128_store(ptr, a.val); } \
+inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \
+{ \
+    wasm_v128_store(ptr, a.val); \
+} \
+inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
+{ \
+    _Tpvec::lane_type a_[_Tpvec::nlanes]; \
+    wasm_v128_store(a_, a.val); \
+    for (int i = 0; i < (_Tpvec::nlanes / 2); i++) \
+        ptr[i] = a_[i]; \
+} \
+inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
+{ \
+    _Tpvec::lane_type a_[_Tpvec::nlanes]; \
+    wasm_v128_store(a_, a.val); \
+    for (int i = 0; i < (_Tpvec::nlanes / 2); i++) \
+        ptr[i] = a_[i + (_Tpvec::nlanes / 2)]; \
+}
+
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint8x16, uchar)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int8x16, schar)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint16x8, ushort)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int16x8, short)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint32x4, unsigned)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int32x4, int)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint64x2, uint64)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int64x2, int64)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_float32x4, float)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_float64x2, double)
+
+
+/** Reverse **/
+inline v_uint8x16 v_reverse(const v_uint8x16 &a)
+{ return v_uint8x16(wasm_v8x16_shuffle(a.val, a.val, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)); }
+
+inline v_int8x16 v_reverse(const v_int8x16 &a)
+{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); }
+
+inline v_uint16x8 v_reverse(const v_uint16x8 &a)
+{ return v_uint16x8(wasm_v8x16_shuffle(a.val, a.val, 14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1)); }
+
+inline v_int16x8 v_reverse(const v_int16x8 &a)
+{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); }
+
+inline v_uint32x4 v_reverse(const v_uint32x4 &a)
+{ return v_uint32x4(wasm_v8x16_shuffle(a.val, a.val, 12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3)); }
+
+inline v_int32x4 v_reverse(const v_int32x4 &a)
+{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_float32x4 v_reverse(const v_float32x4 &a)
+{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); }
+
+inline v_uint64x2 v_reverse(const v_uint64x2 &a)
+{ return v_uint64x2(wasm_v8x16_shuffle(a.val, a.val, 8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7)); }
+
+inline v_int64x2 v_reverse(const v_int64x2 &a)
+{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); }
+
+inline v_float64x2 v_reverse(const v_float64x2 &a)
+{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); }
+
+
+#define OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, esuffix) \
+inline scalartype v_reduce_sum(const _Tpvec& a) \
+{ \
+    regtype val = a.val; \
+    val = wasm_##suffix##_add(val, wasm_v8x16_shuffle(val, val, 8,9,10,11,12,13,14,15,0,1,2,3,4,5,6,7)); \
+    val = wasm_##suffix##_add(val, wasm_v8x16_shuffle(val, val, 4,5,6,7,8,9,10,11,12,13,14,15,0,1,2,3)); \
+    return (scalartype)wasm_##esuffix##_extract_lane(val, 0); \
+}
+
+OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(v_uint32x4, unsigned, v128_t, i32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(v_int32x4, int, v128_t, i32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(v_float32x4, float, v128_t, f32x4, f32x4)
+
+// To do: Optimize v_reduce_sum with wasm intrin.
+//        Now use fallback implementation as there is no widening op in wasm intrin.
+
+#define OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(_Tpvec, scalartype) \
+inline scalartype v_reduce_sum(const _Tpvec& a) \
+{ \
+    _Tpvec::lane_type a_[_Tpvec::nlanes]; \
+    wasm_v128_store(a_, a.val); \
+    scalartype c = a_[0]; \
+    for (int i = 1; i < _Tpvec::nlanes; i++) \
+        c += a_[i]; \
+    return c; \
+}
+
+OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_uint8x16, unsigned)
+OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_int8x16, int)
+OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_uint16x8, unsigned)
+OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_int16x8, int)
+
+
+#define OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(_Tpvec, scalartype, regtype, suffix, esuffix) \
+inline scalartype v_reduce_sum(const _Tpvec& a) \
+{ \
+    regtype val = a.val; \
+    val = wasm_##suffix##_add(val, wasm_v8x16_shuffle(val, val, 8,9,10,11,12,13,14,15,0,1,2,3,4,5,6,7)); \
+    return (scalartype)wasm_##esuffix##_extract_lane(val, 0); \
+}
+OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(v_uint64x2, uint64, v128_t, i64x2, i64x2)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(v_int64x2, int64,  v128_t, i64x2, i64x2)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(v_float64x2, double,  v128_t, f64x2,f64x2)
+
+inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
+                                 const v_float32x4& c, const v_float32x4& d)
+{
+    v128_t ac = wasm_f32x4_add(wasm_unpacklo_i32x4(a.val, c.val), wasm_unpackhi_i32x4(a.val, c.val));
+    v128_t bd = wasm_f32x4_add(wasm_unpacklo_i32x4(b.val, d.val), wasm_unpackhi_i32x4(b.val, d.val));
+    return v_float32x4(wasm_f32x4_add(wasm_unpacklo_i32x4(ac, bd), wasm_unpackhi_i32x4(ac, bd)));
+}
+
+#define OPENCV_HAL_IMPL_WASM_REDUCE_OP(_Tpvec, scalartype, func, scalar_func) \
+inline scalartype v_reduce_##func(const _Tpvec& a) \
+{ \
+    scalartype buf[_Tpvec::nlanes]; \
+    v_store(buf, a); \
+    scalartype tmp = buf[0]; \
+    for (int i=1; i<_Tpvec::nlanes; ++i) { \
+        tmp = scalar_func(tmp, buf[i]); \
+    } \
+    return tmp; \
+}
+
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint8x16, uchar, max, std::max)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint8x16, uchar, min, std::min)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int8x16, schar, max, std::max)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int8x16, schar, min, std::min)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint16x8, ushort, max, std::max)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint16x8, ushort, min, std::min)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int16x8, short, max, std::max)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int16x8, short, min, std::min)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint32x4, unsigned, max, std::max)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint32x4, unsigned, min, std::min)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int32x4, int, max, std::max)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int32x4, int, min, std::min)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_float32x4, float, max, std::max)
+OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_float32x4, float, min, std::min)
+
+inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b)
+{
+    v_uint16x8 l16, h16;
+    v_uint32x4 l16_l32, l16_h32, h16_l32, h16_h32;
+    v_expand(v_absdiff(a, b), l16, h16);
+    v_expand(l16, l16_l32, l16_h32);
+    v_expand(h16, h16_l32, h16_h32);
+    return v_reduce_sum(l16_l32+l16_h32+h16_l32+h16_h32);
+}
+inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b)
+{
+    v_uint16x8 l16, h16;
+    v_uint32x4 l16_l32, l16_h32, h16_l32, h16_h32;
+    v_expand(v_absdiff(a, b), l16, h16);
+    v_expand(l16, l16_l32, l16_h32);
+    v_expand(h16, h16_l32, h16_h32);
+    return v_reduce_sum(l16_l32+l16_h32+h16_l32+h16_h32);
+}
+inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b)
+{
+    v_uint32x4 l, h;
+    v_expand(v_absdiff(a, b), l, h);
+    return v_reduce_sum(l + h);
+}
+inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b)
+{
+    v_uint32x4 l, h;
+    v_expand(v_absdiff(a, b), l, h);
+    return v_reduce_sum(l + h);
+}
+inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b)
+{
+    return v_reduce_sum(v_absdiff(a, b));
+}
+inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b)
+{
+    return v_reduce_sum(v_absdiff(a, b));
+}
+inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b)
+{
+    return v_reduce_sum(v_absdiff(a, b));
+}
+
+inline v_uint8x16 v_popcount(const v_uint8x16& a)
+{
+    v128_t m1 = wasm_i32x4_splat(0x55555555);
+    v128_t m2 = wasm_i32x4_splat(0x33333333);
+    v128_t m4 = wasm_i32x4_splat(0x0f0f0f0f);
+    v128_t p = a.val;
+    p = wasm_i32x4_add(wasm_v128_and(wasm_u32x4_shr(p, 1), m1), wasm_v128_and(p, m1));
+    p = wasm_i32x4_add(wasm_v128_and(wasm_u32x4_shr(p, 2), m2), wasm_v128_and(p, m2));
+    p = wasm_i32x4_add(wasm_v128_and(wasm_u32x4_shr(p, 4), m4), wasm_v128_and(p, m4));
+    return v_uint8x16(p);
+}
+inline v_uint16x8 v_popcount(const v_uint16x8& a)
+{
+    v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a));
+    p += v_rotate_right<1>(p);
+    return v_reinterpret_as_u16(p) & v_setall_u16(0x00ff);
+}
+inline v_uint32x4 v_popcount(const v_uint32x4& a)
+{
+    v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a));
+    p += v_rotate_right<1>(p);
+    p += v_rotate_right<2>(p);
+    return v_reinterpret_as_u32(p) & v_setall_u32(0x000000ff);
+}
+inline v_uint64x2 v_popcount(const v_uint64x2& a)
+{
+    uint64 a_[2], b_[2] = { 0 };
+    wasm_v128_store(a_, a.val);
+    for (int i = 0; i < 16; i++)
+        b_[i / 8] += popCountTable[((uint8_t*)a_)[i]];
+    return v_uint64x2(wasm_v128_load(b_));
+}
+inline v_uint8x16 v_popcount(const v_int8x16& a)
+{ return v_popcount(v_reinterpret_as_u8(a)); }
+inline v_uint16x8 v_popcount(const v_int16x8& a)
+{ return v_popcount(v_reinterpret_as_u16(a)); }
+inline v_uint32x4 v_popcount(const v_int32x4& a)
+{ return v_popcount(v_reinterpret_as_u32(a)); }
+inline v_uint64x2 v_popcount(const v_int64x2& a)
+{ return v_popcount(v_reinterpret_as_u64(a)); }
+
+#define OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(_Tpvec, suffix, scalarType) \
+inline int v_signmask(const _Tpvec& a) \
+{ \
+    _Tpvec::lane_type a_[_Tpvec::nlanes]; \
+    wasm_v128_store(a_, a.val); \
+    int mask = 0; \
+    for (int i = 0; i < _Tpvec::nlanes; i++) \
+        mask |= (reinterpret_int(a_[i]) < 0) << i; \
+    return mask; \
+} \
+inline bool v_check_all(const _Tpvec& a) \
+{ return wasm_i8x16_all_true(wasm_##suffix##_lt(a.val, wasm_##suffix##_splat(0))); } \
+inline bool v_check_any(const _Tpvec& a) \
+{ return wasm_i8x16_any_true(wasm_##suffix##_lt(a.val, wasm_##suffix##_splat(0)));; }
+
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_uint8x16, i8x16, schar)
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_int8x16, i8x16, schar)
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_uint16x8, i16x8, short)
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_int16x8, i16x8, short)
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_uint32x4, i32x4, int)
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_int32x4, i32x4, int)
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_float32x4, i32x4, float)
+OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_float64x2, f64x2, double)
+
+#define OPENCV_HAL_IMPL_WASM_CHECK_ALL_ANY(_Tpvec, suffix, esuffix) \
+inline bool v_check_all(const _Tpvec& a) \
+{ \
+    v128_t masked = v_reinterpret_as_##esuffix(a).val; \
+    masked = wasm_i32x4_replace_lane(masked, 0, 0xffffffff); \
+    masked = wasm_i32x4_replace_lane(masked, 2, 0xffffffff); \
+    return wasm_i8x16_all_true(wasm_##suffix##_lt(masked, wasm_##suffix##_splat(0))); \
+} \
+inline bool v_check_any(const _Tpvec& a) \
+{ \
+    v128_t masked = v_reinterpret_as_##esuffix(a).val; \
+    masked = wasm_i32x4_replace_lane(masked, 0, 0x0); \
+    masked = wasm_i32x4_replace_lane(masked, 2, 0x0); \
+    return wasm_i8x16_any_true(wasm_##suffix##_lt(masked, wasm_##suffix##_splat(0))); \
+} \
+
+OPENCV_HAL_IMPL_WASM_CHECK_ALL_ANY(v_int64x2, i32x4, s32)
+OPENCV_HAL_IMPL_WASM_CHECK_ALL_ANY(v_uint64x2, i32x4, u32)
+
+
+inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); }
+inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); }
+inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; }
+inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; }
+inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; }
+inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; }
+
+#define OPENCV_HAL_IMPL_WASM_SELECT(_Tpvec) \
+inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(wasm_v128_bitselect(a.val, b.val, mask.val)); \
+}
+
+OPENCV_HAL_IMPL_WASM_SELECT(v_uint8x16)
+OPENCV_HAL_IMPL_WASM_SELECT(v_int8x16)
+OPENCV_HAL_IMPL_WASM_SELECT(v_uint16x8)
+OPENCV_HAL_IMPL_WASM_SELECT(v_int16x8)
+OPENCV_HAL_IMPL_WASM_SELECT(v_uint32x4)
+OPENCV_HAL_IMPL_WASM_SELECT(v_int32x4)
+OPENCV_HAL_IMPL_WASM_SELECT(v_uint64x2)
+OPENCV_HAL_IMPL_WASM_SELECT(v_int64x2)
+OPENCV_HAL_IMPL_WASM_SELECT(v_float32x4)
+OPENCV_HAL_IMPL_WASM_SELECT(v_float64x2)
+
+#define OPENCV_HAL_IMPL_WASM_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin)    \
+inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1)      \
+{                                                                    \
+    b0.val = intrin(a.val);                                          \
+    b1.val = __CV_CAT(intrin, _high)(a.val);                         \
+}                                                                    \
+inline _Tpwvec v_expand_low(const _Tpvec& a)                         \
+{ return _Tpwvec(intrin(a.val)); }                                   \
+inline _Tpwvec v_expand_high(const _Tpvec& a)                        \
+{ return _Tpwvec(__CV_CAT(intrin, _high)(a.val)); }                  \
+inline _Tpwvec v_load_expand(const _Tp* ptr)                         \
+{                                                                    \
+    v128_t a = wasm_v128_load(ptr);                                  \
+    return _Tpwvec(intrin(a));                                       \
+}
+
+OPENCV_HAL_IMPL_WASM_EXPAND(v_uint8x16, v_uint16x8, uchar, v128_cvtu8x16_i16x8)
+OPENCV_HAL_IMPL_WASM_EXPAND(v_int8x16,  v_int16x8,  schar, v128_cvti8x16_i16x8)
+OPENCV_HAL_IMPL_WASM_EXPAND(v_uint16x8, v_uint32x4, ushort, v128_cvtu16x8_i32x4)
+OPENCV_HAL_IMPL_WASM_EXPAND(v_int16x8,  v_int32x4,  short, v128_cvti16x8_i32x4)
+OPENCV_HAL_IMPL_WASM_EXPAND(v_uint32x4, v_uint64x2, unsigned, v128_cvtu32x4_i64x2)
+OPENCV_HAL_IMPL_WASM_EXPAND(v_int32x4,  v_int64x2,  int, v128_cvti32x4_i64x2)
+
+#define OPENCV_HAL_IMPL_WASM_EXPAND_Q(_Tpvec, _Tp, intrin)  \
+inline _Tpvec v_load_expand_q(const _Tp* ptr)               \
+{                                                           \
+    v128_t a = wasm_v128_load(ptr);                         \
+    return _Tpvec(intrin(a));                               \
+}
+
+OPENCV_HAL_IMPL_WASM_EXPAND_Q(v_uint32x4, uchar, v128_cvtu8x16_i32x4)
+OPENCV_HAL_IMPL_WASM_EXPAND_Q(v_int32x4, schar, v128_cvti8x16_i32x4)
+
+#define OPENCV_HAL_IMPL_WASM_UNPACKS(_Tpvec, suffix) \
+inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \
+{ \
+    b0.val = wasm_unpacklo_##suffix(a0.val, a1.val); \
+    b1.val = wasm_unpackhi_##suffix(a0.val, a1.val); \
+} \
+inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(wasm_unpacklo_i64x2(a.val, b.val)); \
+} \
+inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \
+{ \
+    return _Tpvec(wasm_unpackhi_i64x2(a.val, b.val)); \
+} \
+inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \
+{ \
+    c.val = wasm_unpacklo_i64x2(a.val, b.val); \
+    d.val = wasm_unpackhi_i64x2(a.val, b.val); \
+}
+
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_uint8x16, i8x16)
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_int8x16, i8x16)
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_uint16x8, i16x8)
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_int16x8, i16x8)
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_uint32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_int32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_float32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_UNPACKS(v_float64x2, i64x2)
+
+template<int s, typename _Tpvec>
+inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b)
+{
+    return v_rotate_right<s>(a, b);
+}
+
+inline v_int32x4 v_round(const v_float32x4& a)
+{
+    v128_t h = wasm_f32x4_splat(0.5);
+    return v_int32x4(wasm_i32x4_trunc_saturate_f32x4(wasm_f32x4_add(a.val, h)));
+}
+
+inline v_int32x4 v_floor(const v_float32x4& a)
+{
+    v128_t a1 = wasm_i32x4_trunc_saturate_f32x4(a.val);
+    v128_t mask = wasm_f32x4_lt(a.val, wasm_f32x4_convert_i32x4(a1));
+    return v_int32x4(wasm_i32x4_add(a1, mask));
+}
+
+inline v_int32x4 v_ceil(const v_float32x4& a)
+{
+    v128_t a1 = wasm_i32x4_trunc_saturate_f32x4(a.val);
+    v128_t mask = wasm_f32x4_gt(a.val, wasm_f32x4_convert_i32x4(a1));
+    return v_int32x4(wasm_i32x4_sub(a1, mask));
+}
+
+inline v_int32x4 v_trunc(const v_float32x4& a)
+{ return v_int32x4(wasm_i32x4_trunc_saturate_f32x4(a.val)); }
+
+#define OPENCV_HAL_IMPL_WASM_MATH_FUNC(func, cfunc) \
+inline v_int32x4 func(const v_float64x2& a) \
+{ \
+    double a_[2]; \
+    wasm_v128_store(a_, a.val); \
+    int c_[4]; \
+    c_[0] = cfunc(a_[0]); \
+    c_[1] = cfunc(a_[1]); \
+    c_[2] = 0; \
+    c_[3] = 0; \
+    return v_int32x4(wasm_v128_load(c_)); \
+}
+
+OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_round, cvRound)
+OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_floor, cvFloor)
+OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_ceil, cvCeil)
+OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_trunc, int)
+
+inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b)
+{
+    double a_[2], b_[2];
+    wasm_v128_store(a_, a.val);
+    wasm_v128_store(b_, b.val);
+    int c_[4];
+    c_[0] = cvRound(a_[0]);
+    c_[1] = cvRound(a_[1]);
+    c_[2] = cvRound(b_[0]);
+    c_[3] = cvRound(b_[1]);
+    return v_int32x4(wasm_v128_load(c_));
+}
+
+#define OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(_Tpvec, suffix) \
+inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \
+                           const _Tpvec& a2, const _Tpvec& a3, \
+                           _Tpvec& b0, _Tpvec& b1, \
+                           _Tpvec& b2, _Tpvec& b3) \
+{ \
+    v128_t t0 = wasm_unpacklo_##suffix(a0.val, a1.val); \
+    v128_t t1 = wasm_unpacklo_##suffix(a2.val, a3.val); \
+    v128_t t2 = wasm_unpackhi_##suffix(a0.val, a1.val); \
+    v128_t t3 = wasm_unpackhi_##suffix(a2.val, a3.val); \
+\
+    b0.val = wasm_unpacklo_i64x2(t0, t1); \
+    b1.val = wasm_unpackhi_i64x2(t0, t1); \
+    b2.val = wasm_unpacklo_i64x2(t2, t3); \
+    b3.val = wasm_unpackhi_i64x2(t2, t3); \
+}
+
+OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(v_uint32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(v_int32x4, i32x4)
+OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(v_float32x4, i32x4)
+
+// load deinterleave
+inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b)
+{
+    v128_t t00 = wasm_v128_load(ptr);
+    v128_t t01 = wasm_v128_load(ptr + 16);
+
+    a.val = wasm_v8x16_shuffle(t00, t01, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30);
+    b.val = wasm_v8x16_shuffle(t00, t01, 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31);
+}
+
+inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c)
+{
+    v128_t t00 = wasm_v128_load(ptr);
+    v128_t t01 = wasm_v128_load(ptr + 16);
+    v128_t t02 = wasm_v128_load(ptr + 32);
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,3,6,9,12,15,18,21,24,27,30,1,2,4,5,7);
+    v128_t t11 = wasm_v8x16_shuffle(t00, t01, 1,4,7,10,13,16,19,22,25,28,31,0,2,3,5,6);
+    v128_t t12 = wasm_v8x16_shuffle(t00, t01, 2,5,8,11,14,17,20,23,26,29,0,1,3,4,6,7);
+
+    a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,17,20,23,26,29);
+    b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,10,18,21,24,27,30);
+    c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,8,9,16,19,22,25,28,31);
+}
+
+inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d)
+{
+    v128_t u0 = wasm_v128_load(ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ...
+    v128_t u1 = wasm_v128_load(ptr + 16); // a4 b4 c4 d4 ...
+    v128_t u2 = wasm_v128_load(ptr + 32); // a8 b8 c8 d8 ...
+    v128_t u3 = wasm_v128_load(ptr + 48); // a12 b12 c12 d12 ...
+
+    v128_t v0 = wasm_v8x16_shuffle(u0, u1, 0,4,8,12,16,20,24,28,1,5,9,13,17,21,25,29);
+    v128_t v1 = wasm_v8x16_shuffle(u2, u3, 0,4,8,12,16,20,24,28,1,5,9,13,17,21,25,29);
+    v128_t v2 = wasm_v8x16_shuffle(u0, u1, 2,6,10,14,18,22,26,30,3,7,11,15,19,23,27,31);
+    v128_t v3 = wasm_v8x16_shuffle(u2, u3, 2,6,10,14,18,22,26,30,3,7,11,15,19,23,27,31);
+
+    a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23);
+    b.val = wasm_v8x16_shuffle(v0, v1, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31);
+    c.val = wasm_v8x16_shuffle(v2, v3, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23);
+    d.val = wasm_v8x16_shuffle(v2, v3, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31);
+}
+
+inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b)
+{
+    v128_t v0 = wasm_v128_load(ptr);     // a0 b0 a1 b1 a2 b2 a3 b3
+    v128_t v1 = wasm_v128_load(ptr + 8); // a4 b4 a5 b5 a6 b6 a7 b7
+
+    a.val = wasm_v8x16_shuffle(v0, v1, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29); // a0 a1 a2 a3 a4 a5 a6 a7
+    b.val = wasm_v8x16_shuffle(v0, v1, 2,3,6,7,10,11,14,15,18,19,22,23,26,27,30,31); // b0 b1 ab b3 b4 b5 b6 b7
+}
+
+inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c)
+{
+    v128_t t00 = wasm_v128_load(ptr);        // a0 b0 c0 a1 b1 c1 a2 b2
+    v128_t t01 = wasm_v128_load(ptr + 8);    // c2 a3 b3 c3 a4 b4 c4 a5
+    v128_t t02 = wasm_v128_load(ptr + 16);  // b5 c5 a6 b6 c6 a7 b7 c7
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,1,6,7,12,13,18,19,24,25,30,31,2,3,4,5);
+    v128_t t11 = wasm_v8x16_shuffle(t00, t01, 2,3,8,9,14,15,20,21,26,27,0,1,4,5,6,7);
+    v128_t t12 = wasm_v8x16_shuffle(t00, t01, 4,5,10,11,16,17,22,23,28,29,0,1,2,3,6,7);
+
+    a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,11,20,21,26,27);
+    b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,16,17,22,23,28,29);
+    c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,8,9,18,19,24,25,30,31);
+}
+
+inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d)
+{
+    v128_t u0 = wasm_v128_load(ptr); // a0 b0 c0 d0 a1 b1 c1 d1
+    v128_t u1 = wasm_v128_load(ptr + 8); // a2 b2 c2 d2 ...
+    v128_t u2 = wasm_v128_load(ptr + 16); // a4 b4 c4 d4 ...
+    v128_t u3 = wasm_v128_load(ptr + 24); // a6 b6 c6 d6 ...
+
+    v128_t v0 = wasm_v8x16_shuffle(u0, u1, 0,1,8,9,16,17,24,25,2,3,10,11,18,19,26,27); // a0 a1 a2 a3 b0 b1 b2 b3
+    v128_t v1 = wasm_v8x16_shuffle(u2, u3, 0,1,8,9,16,17,24,25,2,3,10,11,18,19,26,27); // a4 a5 a6 a7 b4 b5 b6 b7
+    v128_t v2 = wasm_v8x16_shuffle(u0, u1, 4,5,12,13,20,21,28,29,6,7,14,15,22,23,30,31); // c0 c1 c2 c3 d0 d1 d2 d3
+    v128_t v3 = wasm_v8x16_shuffle(u2, u3, 4,5,12,13,20,21,28,29,6,7,14,15,22,23,30,31); // c4 c5 c6 c7 d4 d5 d6 d7
+
+    a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23);
+    b.val = wasm_v8x16_shuffle(v0, v1, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31);
+    c.val = wasm_v8x16_shuffle(v2, v3, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23);
+    d.val = wasm_v8x16_shuffle(v2, v3, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31);
+}
+
+inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b)
+{
+    v128_t v0 = wasm_v128_load(ptr);     // a0 b0 a1 b1
+    v128_t v1 = wasm_v128_load(ptr + 4); // a2 b2 a3 b3
+
+    a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27); // a0 a1 a2 a3
+    b.val = wasm_v8x16_shuffle(v0, v1, 4,5,6,7,12,13,14,15,20,21,22,23,28,29,30,31); // b0 b1 b2 b3
+}
+
+inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c)
+{
+    v128_t t00 = wasm_v128_load(ptr);        // a0 b0 c0 a1
+    v128_t t01 = wasm_v128_load(ptr + 4);     // b2 c2 a3 b3
+    v128_t t02 = wasm_v128_load(ptr + 8);    // c3 a4 b4 c4
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,1,2,3,12,13,14,15,24,25,26,27,4,5,6,7);
+    v128_t t11 = wasm_v8x16_shuffle(t00, t01, 4,5,6,7,16,17,18,19,28,29,30,31,0,1,2,3);
+    v128_t t12 = wasm_v8x16_shuffle(t00, t01, 8,9,10,11,20,21,22,23,0,1,2,3,4,5,6,7);
+
+    a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,11,20,21,22,23);
+    b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,10,11,24,25,26,27);
+    c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,16,17,18,19,28,29,30,31);
+}
+
+inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d)
+{
+    v_uint32x4 s0(wasm_v128_load(ptr));      // a0 b0 c0 d0
+    v_uint32x4 s1(wasm_v128_load(ptr + 4));  // a1 b1 c1 d1
+    v_uint32x4 s2(wasm_v128_load(ptr + 8));  // a2 b2 c2 d2
+    v_uint32x4 s3(wasm_v128_load(ptr + 12)); // a3 b3 c3 d3
+
+    v_transpose4x4(s0, s1, s2, s3, a, b, c, d);
+}
+
+inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b)
+{
+    v128_t v0 = wasm_v128_load(ptr);       // a0 b0 a1 b1
+    v128_t v1 = wasm_v128_load((ptr + 4)); // a2 b2 a3 b3
+
+    a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27); // a0 a1 a2 a3
+    b.val = wasm_v8x16_shuffle(v0, v1, 4,5,6,7,12,13,14,15,20,21,22,23,28,29,30,31); // b0 b1 b2 b3
+}
+
+inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c)
+{
+    v128_t t00 = wasm_v128_load(ptr);        // a0 b0 c0 a1
+    v128_t t01 = wasm_v128_load(ptr + 4);     // b2 c2 a3 b3
+    v128_t t02 = wasm_v128_load(ptr + 8);    // c3 a4 b4 c4
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,1,2,3,12,13,14,15,24,25,26,27,4,5,6,7);
+    v128_t t11 = wasm_v8x16_shuffle(t00, t01, 4,5,6,7,16,17,18,19,28,29,30,31,0,1,2,3);
+    v128_t t12 = wasm_v8x16_shuffle(t00, t01, 8,9,10,11,20,21,22,23,0,1,2,3,4,5,6,7);
+
+    a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,11,20,21,22,23);
+    b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,10,11,24,25,26,27);
+    c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,16,17,18,19,28,29,30,31);
+}
+
+inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d)
+{
+    v_float32x4 s0(wasm_v128_load(ptr));      // a0 b0 c0 d0
+    v_float32x4 s1(wasm_v128_load(ptr + 4));  // a1 b1 c1 d1
+    v_float32x4 s2(wasm_v128_load(ptr + 8));  // a2 b2 c2 d2
+    v_float32x4 s3(wasm_v128_load(ptr + 12)); // a3 b3 c3 d3
+
+    v_transpose4x4(s0, s1, s2, s3, a, b, c, d);
+}
+
+inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b)
+{
+    v128_t t0 = wasm_v128_load(ptr);      // a0 b0
+    v128_t t1 = wasm_v128_load(ptr + 2);  // a1 b1
+
+    a.val = wasm_unpacklo_i64x2(t0, t1);
+    b.val = wasm_unpackhi_i64x2(t0, t1);
+}
+
+inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c)
+{
+    v128_t t0 = wasm_v128_load(ptr);     // a0, b0
+    v128_t t1 = wasm_v128_load(ptr + 2); // c0, a1
+    v128_t t2 = wasm_v128_load(ptr + 4); // b1, c1
+
+    a.val = wasm_v8x16_shuffle(t0, t1, 0,1,2,3,4,5,6,7,24,25,26,27,28,29,30,31);
+    b.val = wasm_v8x16_shuffle(t0, t2, 8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23);
+    c.val = wasm_v8x16_shuffle(t1, t2, 0,1,2,3,4,5,6,7,24,25,26,27,28,29,30,31);
+}
+
+inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a,
+                                v_uint64x2& b, v_uint64x2& c, v_uint64x2& d)
+{
+    v128_t t0 = wasm_v128_load(ptr);     // a0 b0
+    v128_t t1 = wasm_v128_load(ptr + 2); // c0 d0
+    v128_t t2 = wasm_v128_load(ptr + 4); // a1 b1
+    v128_t t3 = wasm_v128_load(ptr + 6); // c1 d1
+
+    a.val = wasm_unpacklo_i64x2(t0, t2);
+    b.val = wasm_unpackhi_i64x2(t0, t2);
+    c.val = wasm_unpacklo_i64x2(t1, t3);
+    d.val = wasm_unpackhi_i64x2(t1, t3);
+}
+
+// store interleave
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
+                                hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t v0 = wasm_unpacklo_i8x16(a.val, b.val);
+    v128_t v1 = wasm_unpackhi_i8x16(a.val, b.val);
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 16, v1);
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
+                                const v_uint8x16& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,16,0,1,17,0,2,18,0,3,19,0,4,20,0,5);
+    v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 21,0,6,22,0,7,23,0,8,24,0,9,25,0,10,26);
+    v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 0,11,27,0,12,28,0,13,29,0,14,30,0,15,31,0);
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,16,3,4,17,6,7,18,9,10,19,12,13,20,15);
+    v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 0,21,2,3,22,5,6,23,8,9,24,11,12,25,14,15);
+    v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 26,1,2,27,4,5,28,7,8,29,10,11,30,13,14,31);
+
+    wasm_v128_store(ptr, t10);
+    wasm_v128_store(ptr + 16, t11);
+    wasm_v128_store(ptr + 32, t12);
+}
+
+inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
+                                const v_uint8x16& c, const v_uint8x16& d,
+                                hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    // a0 a1 a2 a3 ....
+    // b0 b1 b2 b3 ....
+    // c0 c1 c2 c3 ....
+    // d0 d1 d2 d3 ....
+    v128_t u0 = wasm_unpacklo_i8x16(a.val, c.val); // a0 c0 a1 c1 ...
+    v128_t u1 = wasm_unpackhi_i8x16(a.val, c.val); // a8 c8 a9 c9 ...
+    v128_t u2 = wasm_unpacklo_i8x16(b.val, d.val); // b0 d0 b1 d1 ...
+    v128_t u3 = wasm_unpackhi_i8x16(b.val, d.val); // b8 d8 b9 d9 ...
+
+    v128_t v0 = wasm_unpacklo_i8x16(u0, u2); // a0 b0 c0 d0 ...
+    v128_t v1 = wasm_unpackhi_i8x16(u0, u2); // a4 b4 c4 d4 ...
+    v128_t v2 = wasm_unpacklo_i8x16(u1, u3); // a8 b8 c8 d8 ...
+    v128_t v3 = wasm_unpackhi_i8x16(u1, u3); // a12 b12 c12 d12 ...
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 16, v1);
+    wasm_v128_store(ptr + 32, v2);
+    wasm_v128_store(ptr + 48, v3);
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b,
+                                hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t v0 = wasm_unpacklo_i16x8(a.val, b.val);
+    v128_t v1 = wasm_unpackhi_i16x8(a.val, b.val);
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 8, v1);
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x8& a,
+                                const v_uint16x8& b, const v_uint16x8& c,
+                                hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,1,16,17,0,0,2,3,18,19,0,0,4,5,20,21);
+    v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 0,0,6,7,22,23,0,0,8,9,24,25,0,0,10,11);
+    v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 26,27,0,0,12,13,28,29,0,0,14,15,30,31,0,0);
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,2,3,16,17,6,7,8,9,18,19,12,13,14,15);
+    v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 20,21,2,3,4,5,22,23,8,9,10,11,24,25,14,15);
+    v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 0,1,26,27,4,5,6,7,28,29,10,11,12,13,30,31);
+
+    wasm_v128_store(ptr, t10);
+    wasm_v128_store(ptr + 8, t11);
+    wasm_v128_store(ptr + 16, t12);
+}
+
+inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b,
+                                const v_uint16x8& c, const v_uint16x8& d,
+                                hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    // a0 a1 a2 a3 ....
+    // b0 b1 b2 b3 ....
+    // c0 c1 c2 c3 ....
+    // d0 d1 d2 d3 ....
+    v128_t u0 = wasm_unpacklo_i16x8(a.val, c.val); // a0 c0 a1 c1 ...
+    v128_t u1 = wasm_unpackhi_i16x8(a.val, c.val); // a4 c4 a5 c5 ...
+    v128_t u2 = wasm_unpacklo_i16x8(b.val, d.val); // b0 d0 b1 d1 ...
+    v128_t u3 = wasm_unpackhi_i16x8(b.val, d.val); // b4 d4 b5 d5 ...
+
+    v128_t v0 = wasm_unpacklo_i16x8(u0, u2); // a0 b0 c0 d0 ...
+    v128_t v1 = wasm_unpackhi_i16x8(u0, u2); // a2 b2 c2 d2 ...
+    v128_t v2 = wasm_unpacklo_i16x8(u1, u3); // a4 b4 c4 d4 ...
+    v128_t v3 = wasm_unpackhi_i16x8(u1, u3); // a6 b6 c6 d6 ...
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 8, v1);
+    wasm_v128_store(ptr + 16, v2);
+    wasm_v128_store(ptr + 24, v3);
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
+                                hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t v0 = wasm_unpacklo_i32x4(a.val, b.val);
+    v128_t v1 = wasm_unpackhi_i32x4(a.val, b.val);
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 4, v1);
+}
+
+inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
+                                const v_uint32x4& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,16,17,18,19,0,0,0,0,4,5,6,7);
+    v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 20,21,22,23,0,0,0,0,8,9,10,11,24,25,26,27);
+    v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 0,0,0,0,12,13,14,15,28,29,30,31,0,0,0,0);
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,2,3,4,5,6,7,16,17,18,19,12,13,14,15);
+    v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 0,1,2,3,20,21,22,23,8,9,10,11,12,13,14,15);
+    v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 24,25,26,27,4,5,6,7,8,9,10,11,28,29,30,31);
+
+    wasm_v128_store(ptr, t10);
+    wasm_v128_store(ptr + 4, t11);
+    wasm_v128_store(ptr + 8, t12);
+}
+
+inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
+                               const v_uint32x4& c, const v_uint32x4& d,
+                               hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v_uint32x4 v0, v1, v2, v3;
+    v_transpose4x4(a, b, c, d, v0, v1, v2, v3);
+
+    wasm_v128_store(ptr, v0.val);
+    wasm_v128_store(ptr + 4, v1.val);
+    wasm_v128_store(ptr + 8, v2.val);
+    wasm_v128_store(ptr + 12, v3.val);
+}
+
+// 2-channel, float only
+inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b,
+                               hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t v0 = wasm_unpacklo_i32x4(a.val, b.val);
+    v128_t v1 = wasm_unpackhi_i32x4(a.val, b.val);
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 4, v1);
+}
+
+inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b,
+                               const v_float32x4& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,16,17,18,19,0,0,0,0,4,5,6,7);
+    v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 20,21,22,23,0,0,0,0,8,9,10,11,24,25,26,27);
+    v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 0,0,0,0,12,13,14,15,28,29,30,31,0,0,0,0);
+
+    v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,2,3,4,5,6,7,16,17,18,19,12,13,14,15);
+    v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 0,1,2,3,20,21,22,23,8,9,10,11,12,13,14,15);
+    v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 24,25,26,27,4,5,6,7,8,9,10,11,28,29,30,31);
+
+    wasm_v128_store(ptr, t10);
+    wasm_v128_store(ptr + 4, t11);
+    wasm_v128_store(ptr + 8, t12);
+}
+
+inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b,
+                               const v_float32x4& c, const v_float32x4& d,
+                               hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v_float32x4 v0, v1, v2, v3;
+    v_transpose4x4(a, b, c, d, v0, v1, v2, v3);
+
+    wasm_v128_store(ptr, v0.val);
+    wasm_v128_store(ptr + 4, v1.val);
+    wasm_v128_store(ptr + 8, v2.val);
+    wasm_v128_store(ptr + 12, v3.val);
+}
+
+inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b,
+                               hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t v0 = wasm_unpacklo_i64x2(a.val, b.val);
+    v128_t v1 = wasm_unpackhi_i64x2(a.val, b.val);
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 2, v1);
+}
+
+inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b,
+                               const v_uint64x2& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t v0 = wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23);
+    v128_t v1 = wasm_v8x16_shuffle(a.val, c.val, 16,17,18,19,20,21,22,23,8,9,10,11,12,13,14,15);
+    v128_t v2 = wasm_v8x16_shuffle(b.val, c.val, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31);
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 2, v1);
+    wasm_v128_store(ptr + 4, v2);
+}
+
+inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b,
+                               const v_uint64x2& c, const v_uint64x2& d,
+                               hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED)
+{
+    v128_t v0 = wasm_unpacklo_i64x2(a.val, b.val);
+    v128_t v1 = wasm_unpacklo_i64x2(c.val, d.val);
+    v128_t v2 = wasm_unpackhi_i64x2(a.val, b.val);
+    v128_t v3 = wasm_unpackhi_i64x2(c.val, d.val);
+
+    wasm_v128_store(ptr, v0);
+    wasm_v128_store(ptr + 2, v1);
+    wasm_v128_store(ptr + 4, v2);
+    wasm_v128_store(ptr + 6, v3);
+}
+
+#define OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \
+{ \
+    _Tpvec1 a1, b1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \
+{ \
+    _Tpvec1 a1, b1, c1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+} \
+inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \
+{ \
+    _Tpvec1 a1, b1, c1, d1; \
+    v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \
+    a0 = v_reinterpret_as_##suffix0(a1); \
+    b0 = v_reinterpret_as_##suffix0(b1); \
+    c0 = v_reinterpret_as_##suffix0(c1); \
+    d0 = v_reinterpret_as_##suffix0(d1); \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                hal::StoreMode mode = hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, mode);      \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                const _Tpvec0& c0, hal::StoreMode mode = hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode);  \
+} \
+inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \
+                                const _Tpvec0& c0, const _Tpvec0& d0, \
+                                hal::StoreMode mode = hal::STORE_UNALIGNED ) \
+{ \
+    _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \
+    _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \
+    _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \
+    _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \
+    v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \
+}
+
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int64x2, int64, s64, v_uint64x2, uint64, u64)
+OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_float64x2, double, f64, v_uint64x2, uint64, u64)
+
+inline v_float32x4 v_cvt_f32(const v_int32x4& a)
+{
+    return v_float32x4(wasm_f32x4_convert_i32x4(a.val));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a)
+{
+    double a_[2];
+    wasm_v128_store(a_, a.val);
+    float c_[4];
+    c_[0] = (float)(a_[0]);
+    c_[1] = (float)(a_[1]);
+    c_[2] = 0;
+    c_[3] = 0;
+    return v_float32x4(wasm_v128_load(c_));
+}
+
+inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b)
+{
+    double a_[2], b_[2];
+    wasm_v128_store(a_, a.val);
+    wasm_v128_store(b_, b.val);
+    float c_[4];
+    c_[0] = (float)(a_[0]);
+    c_[1] = (float)(a_[1]);
+    c_[2] = (float)(b_[0]);
+    c_[3] = (float)(b_[1]);
+    return v_float32x4(wasm_v128_load(c_));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int32x4& a)
+{
+#ifdef __wasm_unimplemented_simd128__
+    v128_t p = v128_cvti32x4_i64x2(a.val);
+    return v_float64x2(wasm_f64x2_convert_i64x2(p));
+#else
+    int a_[4];
+    wasm_v128_store(a_, a.val);
+    double c_[2];
+    c_[0] = (double)(a_[0]);
+    c_[1] = (double)(a_[1]);
+    return v_float64x2(wasm_v128_load(c_));
+#endif
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
+{
+#ifdef __wasm_unimplemented_simd128__
+    v128_t p = v128_cvti32x4_i64x2_high(a.val);
+    return v_float64x2(wasm_f64x2_convert_i64x2(p));
+#else
+    int a_[4];
+    wasm_v128_store(a_, a.val);
+    double c_[2];
+    c_[0] = (double)(a_[2]);
+    c_[1] = (double)(a_[3]);
+    return v_float64x2(wasm_v128_load(c_));
+#endif
+}
+
+inline v_float64x2 v_cvt_f64(const v_float32x4& a)
+{
+    float a_[4];
+    wasm_v128_store(a_, a.val);
+    double c_[2];
+    c_[0] = (double)(a_[0]);
+    c_[1] = (double)(a_[1]);
+    return v_float64x2(wasm_v128_load(c_));
+}
+
+inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
+{
+    float a_[4];
+    wasm_v128_store(a_, a.val);
+    double c_[2];
+    c_[0] = (double)(a_[2]);
+    c_[1] = (double)(a_[3]);
+    return v_float64x2(wasm_v128_load(c_));
+}
+
+inline v_float64x2 v_cvt_f64(const v_int64x2& a)
+{
+#ifdef __wasm_unimplemented_simd128__
+    return v_float64x2(wasm_f64x2_convert_i64x2(a.val));
+#else
+    int64 a_[2];
+    wasm_v128_store(a_, a.val);
+    double c_[2];
+    c_[0] = (double)(a_[0]);
+    c_[1] = (double)(a_[1]);
+    return v_float64x2(wasm_v128_load(c_));
+#endif
+}
+
+////////////// Lookup table access ////////////////////
+
+inline v_int8x16 v_lut(const schar* tab, const int* idx)
+{
+    return v_int8x16(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]],
+                     tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]);
+}
+inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx)
+{
+    return v_int8x16(tab[idx[0]], tab[idx[0]+1], tab[idx[1]], tab[idx[1]+1], tab[idx[2]], tab[idx[2]+1], tab[idx[3]], tab[idx[3]+1],
+                     tab[idx[4]], tab[idx[4]+1], tab[idx[5]], tab[idx[5]+1], tab[idx[6]], tab[idx[6]+1], tab[idx[7]], tab[idx[7]+1]);
+}
+inline v_int8x16 v_lut_quads(const schar* tab, const int* idx)
+{
+    return v_int8x16(tab[idx[0]], tab[idx[0]+1], tab[idx[0]+2], tab[idx[0]+3], tab[idx[1]], tab[idx[1]+1], tab[idx[1]+2], tab[idx[1]+3],
+                     tab[idx[2]], tab[idx[2]+1], tab[idx[2]+2], tab[idx[2]+3], tab[idx[3]], tab[idx[3]+1], tab[idx[3]+2], tab[idx[3]+3]);
+}
+inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((const schar *)tab, idx)); }
+inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((const schar *)tab, idx)); }
+inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((const schar *)tab, idx)); }
+
+inline v_int16x8 v_lut(const short* tab, const int* idx)
+{
+    return v_int16x8(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]],
+                     tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]]);
+}
+inline v_int16x8 v_lut_pairs(const short* tab, const int* idx)
+{
+    return v_int16x8(tab[idx[0]], tab[idx[0]+1], tab[idx[1]], tab[idx[1]+1],
+                     tab[idx[2]], tab[idx[2]+1], tab[idx[3]], tab[idx[3]+1]);
+}
+inline v_int16x8 v_lut_quads(const short* tab, const int* idx)
+{
+    return v_int16x8(tab[idx[0]], tab[idx[0]+1], tab[idx[0]+2], tab[idx[0]+3],
+                     tab[idx[1]], tab[idx[1]+1], tab[idx[1]+2], tab[idx[1]+3]);
+}
+inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((const short *)tab, idx)); }
+inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((const short *)tab, idx)); }
+inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((const short *)tab, idx)); }
+
+inline v_int32x4 v_lut(const int* tab, const int* idx)
+{
+    return v_int32x4(tab[idx[0]], tab[idx[1]],
+                     tab[idx[2]], tab[idx[3]]);
+}
+inline v_int32x4 v_lut_pairs(const int* tab, const int* idx)
+{
+    return v_int32x4(tab[idx[0]], tab[idx[0]+1],
+                     tab[idx[1]], tab[idx[1]+1]);
+}
+inline v_int32x4 v_lut_quads(const int* tab, const int* idx)
+{
+    return v_int32x4(wasm_v128_load(tab + idx[0]));
+}
+inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((const int *)tab, idx)); }
+inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((const int *)tab, idx)); }
+inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((const int *)tab, idx)); }
+
+inline v_int64x2 v_lut(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(tab[idx[0]], tab[idx[1]]);
+}
+inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx)
+{
+    return v_int64x2(wasm_v128_load(tab + idx[0]));
+}
+inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); }
+inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); }
+
+inline v_float32x4 v_lut(const float* tab, const int* idx)
+{
+    return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]);
+}
+inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_pairs((const int *)tab, idx)); }
+inline v_float32x4 v_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_quads((const int *)tab, idx)); }
+
+inline v_float64x2 v_lut(const double* tab, const int* idx)
+{
+    return v_float64x2(tab[idx[0]], tab[idx[1]]);
+}
+inline v_float64x2 v_lut_pairs(const double* tab, const int* idx)
+{
+    return v_float64x2(wasm_v128_load(tab + idx[0]));
+}
+
+inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec)
+{
+    return v_int32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)],
+                     tab[wasm_i32x4_extract_lane(idxvec.val, 1)],
+                     tab[wasm_i32x4_extract_lane(idxvec.val, 2)],
+                     tab[wasm_i32x4_extract_lane(idxvec.val, 3)]);
+}
+
+inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec)
+{
+    return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec));
+}
+
+inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec)
+{
+    return v_float32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)],
+                       tab[wasm_i32x4_extract_lane(idxvec.val, 1)],
+                       tab[wasm_i32x4_extract_lane(idxvec.val, 2)],
+                       tab[wasm_i32x4_extract_lane(idxvec.val, 3)]);
+}
+
+inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec)
+{
+    return v_float64x2(tab[wasm_i32x4_extract_lane(idxvec.val, 0)],
+                       tab[wasm_i32x4_extract_lane(idxvec.val, 1)]);
+}
+
+// loads pairs from the table and deinterleaves them, e.g. returns:
+//   x = (tab[idxvec[0], tab[idxvec[1]], tab[idxvec[2]], tab[idxvec[3]]),
+//   y = (tab[idxvec[0]+1], tab[idxvec[1]+1], tab[idxvec[2]+1], tab[idxvec[3]+1])
+// note that the indices are float's indices, not the float-pair indices.
+// in theory, this function can be used to implement bilinear interpolation,
+// when idxvec are the offsets within the image.
+inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y)
+{
+    x = v_float32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)],
+                    tab[wasm_i32x4_extract_lane(idxvec.val, 1)],
+                    tab[wasm_i32x4_extract_lane(idxvec.val, 2)],
+                    tab[wasm_i32x4_extract_lane(idxvec.val, 3)]);
+    y = v_float32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)+1],
+                    tab[wasm_i32x4_extract_lane(idxvec.val, 1)+1],
+                    tab[wasm_i32x4_extract_lane(idxvec.val, 2)+1],
+                    tab[wasm_i32x4_extract_lane(idxvec.val, 3)+1]);
+}
+
+inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y)
+{
+    v128_t xy0 = wasm_v128_load(tab + wasm_i32x4_extract_lane(idxvec.val, 0));
+    v128_t xy1 = wasm_v128_load(tab + wasm_i32x4_extract_lane(idxvec.val, 1));
+    x.val = wasm_unpacklo_i64x2(xy0, xy1);
+    y.val = wasm_unpacklo_i64x2(xy0, xy1);
+}
+
+inline v_int8x16 v_interleave_pairs(const v_int8x16& vec)
+{
+    return v_int8x16(wasm_v8x16_shuffle(vec.val, vec.val, 0,2,1,3,4,6,5,7,8,10,9,11,12,14,13,15));
+}
+inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); }
+inline v_int8x16 v_interleave_quads(const v_int8x16& vec)
+{
+    return v_int8x16(wasm_v8x16_shuffle(vec.val, vec.val, 0,4,1,5,2,6,3,7,8,12,9,13,10,14,11,15));
+}
+inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_interleave_pairs(const v_int16x8& vec)
+{
+    return v_int16x8(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,4,5,2,3,6,7,8,9,12,13,10,11,14,15));
+}
+inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); }
+inline v_int16x8 v_interleave_quads(const v_int16x8& vec)
+{
+    return v_int16x8(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,8,9,2,3,10,11,4,5,12,13,6,7,14,15));
+}
+inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_interleave_pairs(const v_int32x4& vec)
+{
+    return v_int32x4(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,3,8,9,10,11,4,5,6,7,12,13,14,15));
+}
+inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); }
+inline v_float32x4 v_interleave_pairs(const v_float32x4& vec)
+{
+    return v_float32x4(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,3,8,9,10,11,4,5,6,7,12,13,14,15));
+}
+
+inline v_int8x16 v_pack_triplets(const v_int8x16& vec)
+{
+    return v_int8x16(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,4,5,6,8,9,10,12,13,14,16,16,16,16));
+}
+inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); }
+
+inline v_int16x8 v_pack_triplets(const v_int16x8& vec)
+{
+    return v_int16x8(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,3,4,5,8,9,10,11,12,13,14,15,6,7));
+}
+inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); }
+
+inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; }
+inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; }
+inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; }
+
+template<int i, typename _Tp>
+inline typename _Tp::lane_type v_extract_n(const _Tp& a)
+{
+    return v_rotate_right<i>(a).get0();
+}
+
+template<int i>
+inline v_uint32x4 v_broadcast_element(const v_uint32x4& a)
+{
+    return v_setall_u32(v_extract_n<i>(a));
+}
+template<int i>
+inline v_int32x4 v_broadcast_element(const v_int32x4& a)
+{
+    return v_setall_s32(v_extract_n<i>(a));
+}
+template<int i>
+inline v_float32x4 v_broadcast_element(const v_float32x4& a)
+{
+    return v_setall_f32(v_extract_n<i>(a));
+}
+
+
+////////////// FP16 support ///////////////////////////
+
+inline v_float32x4 v_load_expand(const float16_t* ptr)
+{
+    float a[4];
+    for (int i = 0; i < 4; i++)
+        a[i] = ptr[i];
+    return v_float32x4(wasm_v128_load(a));
+}
+
+inline void v_pack_store(float16_t* ptr, const v_float32x4& v)
+{
+    double v_[4];
+    wasm_v128_store(v_, v.val);
+    ptr[0] = float16_t(v_[0]);
+    ptr[1] = float16_t(v_[1]);
+    ptr[2] = float16_t(v_[2]);
+    ptr[3] = float16_t(v_[3]);
+}
+
+inline void v_cleanup() {}
+
+CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
+
+//! @endcond
+
+}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/hal/msa_macros.h b/3rdParty/include/opencv2/core/hal/msa_macros.h
new file mode 100644
index 0000000..bd6ddb1
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/msa_macros.h
@@ -0,0 +1,1558 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_HAL_MSA_MACROS_H
+#define OPENCV_CORE_HAL_MSA_MACROS_H
+
+#ifdef __mips_msa
+#include "msa.h"
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* Define 64 bits vector types */
+typedef signed char v8i8 __attribute__ ((vector_size(8), aligned(8)));
+typedef unsigned char v8u8 __attribute__ ((vector_size(8), aligned(8)));
+typedef short v4i16 __attribute__ ((vector_size(8), aligned(8)));
+typedef unsigned short v4u16 __attribute__ ((vector_size(8), aligned(8)));
+typedef int v2i32 __attribute__ ((vector_size(8), aligned(8)));
+typedef unsigned int v2u32 __attribute__ ((vector_size(8), aligned(8)));
+typedef long long v1i64 __attribute__ ((vector_size(8), aligned(8)));
+typedef unsigned long long v1u64 __attribute__ ((vector_size(8), aligned(8)));
+typedef float v2f32 __attribute__ ((vector_size(8), aligned(8)));
+typedef double v1f64 __attribute__ ((vector_size(8), aligned(8)));
+
+
+/* Load values from the given memory a 64-bit vector. */
+#define msa_ld1_s8(__a)  (*((v8i8*)(__a)))
+#define msa_ld1_s16(__a) (*((v4i16*)(__a)))
+#define msa_ld1_s32(__a) (*((v2i32*)(__a)))
+#define msa_ld1_s64(__a) (*((v1i64*)(__a)))
+#define msa_ld1_u8(__a)  (*((v8u8*)(__a)))
+#define msa_ld1_u16(__a) (*((v4u16*)(__a)))
+#define msa_ld1_u32(__a) (*((v2u32*)(__a)))
+#define msa_ld1_u64(__a) (*((v1u64*)(__a)))
+#define msa_ld1_f32(__a) (*((v2f32*)(__a)))
+#define msa_ld1_f64(__a) (*((v1f64*)(__a)))
+
+/* Load values from the given memory address to a 128-bit vector */
+#define msa_ld1q_s8(__a)  ((v16i8)__builtin_msa_ld_b(__a, 0))
+#define msa_ld1q_s16(__a) ((v8i16)__builtin_msa_ld_h(__a, 0))
+#define msa_ld1q_s32(__a) ((v4i32)__builtin_msa_ld_w(__a, 0))
+#define msa_ld1q_s64(__a) ((v2i64)__builtin_msa_ld_d(__a, 0))
+#define msa_ld1q_u8(__a)  ((v16u8)__builtin_msa_ld_b(__a, 0))
+#define msa_ld1q_u16(__a) ((v8u16)__builtin_msa_ld_h(__a, 0))
+#define msa_ld1q_u32(__a) ((v4u32)__builtin_msa_ld_w(__a, 0))
+#define msa_ld1q_u64(__a) ((v2u64)__builtin_msa_ld_d(__a, 0))
+#define msa_ld1q_f32(__a) ((v4f32)__builtin_msa_ld_w(__a, 0))
+#define msa_ld1q_f64(__a) ((v2f64)__builtin_msa_ld_d(__a, 0))
+
+/* Store 64bits vector elements values to the given memory address. */
+#define msa_st1_s8(__a, __b)  (*((v8i8*)(__a)) = __b)
+#define msa_st1_s16(__a, __b) (*((v4i16*)(__a)) = __b)
+#define msa_st1_s32(__a, __b) (*((v2i32*)(__a)) = __b)
+#define msa_st1_s64(__a, __b) (*((v1i64*)(__a)) = __b)
+#define msa_st1_u8(__a, __b)  (*((v8u8*)(__a)) = __b)
+#define msa_st1_u16(__a, __b) (*((v4u16*)(__a)) = __b)
+#define msa_st1_u32(__a, __b) (*((v2u32*)(__a)) = __b)
+#define msa_st1_u64(__a, __b) (*((v1u64*)(__a)) = __b)
+#define msa_st1_f32(__a, __b) (*((v2f32*)(__a)) = __b)
+#define msa_st1_f64(__a, __b) (*((v1f64*)(__a)) = __b)
+
+/* Store the values of elements in the 128 bits vector __a to the given memory address __a. */
+#define msa_st1q_s8(__a, __b)  (__builtin_msa_st_b((v16i8)(__b), __a, 0))
+#define msa_st1q_s16(__a, __b) (__builtin_msa_st_h((v8i16)(__b), __a, 0))
+#define msa_st1q_s32(__a, __b) (__builtin_msa_st_w((v4i32)(__b), __a, 0))
+#define msa_st1q_s64(__a, __b) (__builtin_msa_st_d((v2i64)(__b), __a, 0))
+#define msa_st1q_u8(__a, __b)  (__builtin_msa_st_b((v16i8)(__b), __a, 0))
+#define msa_st1q_u16(__a, __b) (__builtin_msa_st_h((v8i16)(__b), __a, 0))
+#define msa_st1q_u32(__a, __b) (__builtin_msa_st_w((v4i32)(__b), __a, 0))
+#define msa_st1q_u64(__a, __b) (__builtin_msa_st_d((v2i64)(__b), __a, 0))
+#define msa_st1q_f32(__a, __b) (__builtin_msa_st_w((v4i32)(__b), __a, 0))
+#define msa_st1q_f64(__a, __b) (__builtin_msa_st_d((v2i64)(__b), __a, 0))
+
+/* Store the value of the element with the index __c in vector __a to the given memory address __a. */
+#define msa_st1_lane_s8(__a, __b, __c)   (*((int8_t*)(__a)) = __b[__c])
+#define msa_st1_lane_s16(__a, __b, __c)  (*((int16_t*)(__a)) = __b[__c])
+#define msa_st1_lane_s32(__a, __b, __c)  (*((int32_t*)(__a)) = __b[__c])
+#define msa_st1_lane_s64(__a, __b, __c)  (*((int64_t*)(__a)) = __b[__c])
+#define msa_st1_lane_u8(__a, __b, __c)   (*((uint8_t*)(__a)) = __b[__c])
+#define msa_st1_lane_u16(__a, __b, __c)  (*((uint16_t*)(__a)) = __b[__c])
+#define msa_st1_lane_u32(__a, __b, __c)  (*((uint32_t*)(__a)) = __b[__c])
+#define msa_st1_lane_u64(__a, __b, __c)  (*((uint64_t*)(__a)) = __b[__c])
+#define msa_st1_lane_f32(__a, __b, __c)  (*((float*)(__a)) = __b[__c])
+#define msa_st1_lane_f64(__a, __b, __c)  (*((double*)(__a)) = __b[__c])
+#define msa_st1q_lane_s8(__a, __b, __c)  (*((int8_t*)(__a)) = (int8_t)__builtin_msa_copy_s_b(__b, __c))
+#define msa_st1q_lane_s16(__a, __b, __c) (*((int16_t*)(__a)) = (int16_t)__builtin_msa_copy_s_h(__b, __c))
+#define msa_st1q_lane_s32(__a, __b, __c) (*((int32_t*)(__a)) = __builtin_msa_copy_s_w(__b, __c))
+#define msa_st1q_lane_s64(__a, __b, __c) (*((int64_t*)(__a)) = __builtin_msa_copy_s_d(__b, __c))
+#define msa_st1q_lane_u8(__a, __b, __c)  (*((uint8_t*)(__a)) = (uint8_t)__builtin_msa_copy_u_b((v16i8)(__b), __c))
+#define msa_st1q_lane_u16(__a, __b, __c) (*((uint16_t*)(__a)) = (uint16_t)__builtin_msa_copy_u_h((v8i16)(__b), __c))
+#define msa_st1q_lane_u32(__a, __b, __c) (*((uint32_t*)(__a)) = __builtin_msa_copy_u_w((v4i32)(__b), __c))
+#define msa_st1q_lane_u64(__a, __b, __c) (*((uint64_t*)(__a)) = __builtin_msa_copy_u_d((v2i64)(__b), __c))
+#define msa_st1q_lane_f32(__a, __b, __c) (*((float*)(__a)) = __b[__c])
+#define msa_st1q_lane_f64(__a, __b, __c) (*((double*)(__a)) = __b[__c])
+
+/* Duplicate elements for 64-bit doubleword vectors */
+#define msa_dup_n_s8(__a)  ((v8i8)__builtin_msa_copy_s_d((v2i64)__builtin_msa_fill_b((int32_t)(__a)), 0))
+#define msa_dup_n_s16(__a) ((v4i16)__builtin_msa_copy_s_d((v2i64)__builtin_msa_fill_h((int32_t)(__a)), 0))
+#define msa_dup_n_s32(__a) ((v2i32){__a, __a})
+#define msa_dup_n_s64(__a) ((v1i64){__a})
+#define msa_dup_n_u8(__a)  ((v8u8)__builtin_msa_copy_u_d((v2i64)__builtin_msa_fill_b((int32_t)(__a)), 0))
+#define msa_dup_n_u16(__a) ((v4u16)__builtin_msa_copy_u_d((v2i64)__builtin_msa_fill_h((int32_t)(__a)), 0))
+#define msa_dup_n_u32(__a) ((v2u32){__a, __a})
+#define msa_dup_n_u64(__a) ((v1u64){__a})
+#define msa_dup_n_f32(__a) ((v2f32){__a, __a})
+#define msa_dup_n_f64(__a) ((v1f64){__a})
+
+/* Duplicate elements for 128-bit quadword vectors */
+#define msa_dupq_n_s8(__a)  (__builtin_msa_fill_b((int32_t)(__a)))
+#define msa_dupq_n_s16(__a) (__builtin_msa_fill_h((int32_t)(__a)))
+#define msa_dupq_n_s32(__a) (__builtin_msa_fill_w((int32_t)(__a)))
+#define msa_dupq_n_s64(__a) (__builtin_msa_fill_d((int64_t)(__a)))
+#define msa_dupq_n_u8(__a)  ((v16u8)__builtin_msa_fill_b((int32_t)(__a)))
+#define msa_dupq_n_u16(__a) ((v8u16)__builtin_msa_fill_h((int32_t)(__a)))
+#define msa_dupq_n_u32(__a) ((v4u32)__builtin_msa_fill_w((int32_t)(__a)))
+#define msa_dupq_n_u64(__a) ((v2u64)__builtin_msa_fill_d((int64_t)(__a)))
+#define msa_dupq_n_f32(__a) ((v4f32){__a, __a, __a, __a})
+#define msa_dupq_n_f64(__a) ((v2f64){__a, __a})
+#define msa_dupq_lane_s8(__a, __b)  (__builtin_msa_splat_b(__a, __b))
+#define msa_dupq_lane_s16(__a, __b) (__builtin_msa_splat_h(__a, __b))
+#define msa_dupq_lane_s32(__a, __b) (__builtin_msa_splat_w(__a, __b))
+#define msa_dupq_lane_s64(__a, __b) (__builtin_msa_splat_d(__a, __b))
+#define msa_dupq_lane_u8(__a, __b)  ((v16u8)__builtin_msa_splat_b((v16i8)(__a), __b))
+#define msa_dupq_lane_u16(__a, __b) ((v8u16)__builtin_msa_splat_h((v8i16)(__a), __b))
+#define msa_dupq_lane_u32(__a, __b) ((v4u32)__builtin_msa_splat_w((v4i32)(__a), __b))
+#define msa_dupq_lane_u64(__a, __b) ((v2u64)__builtin_msa_splat_d((v2i64)(__a), __b))
+
+/* Create a 64 bits vector */
+#define msa_create_s8(__a)  ((v8i8)((uint64_t)(__a)))
+#define msa_create_s16(__a) ((v4i16)((uint64_t)(__a)))
+#define msa_create_s32(__a) ((v2i32)((uint64_t)(__a)))
+#define msa_create_s64(__a) ((v1i64)((uint64_t)(__a)))
+#define msa_create_u8(__a)  ((v8u8)((uint64_t)(__a)))
+#define msa_create_u16(__a) ((v4u16)((uint64_t)(__a)))
+#define msa_create_u32(__a) ((v2u32)((uint64_t)(__a)))
+#define msa_create_u64(__a) ((v1u64)((uint64_t)(__a)))
+#define msa_create_f32(__a) ((v2f32)((uint64_t)(__a)))
+#define msa_create_f64(__a) ((v1f64)((uint64_t)(__a)))
+
+/* Sign extends or zero extends each element in a 64 bits vector to twice its original length, and places the results in a 128 bits vector. */
+/*Transform v8i8 to v8i16*/
+#define msa_movl_s8(__a) \
+((v8i16){(__a)[0], (__a)[1], (__a)[2], (__a)[3], \
+         (__a)[4], (__a)[5], (__a)[6], (__a)[7]})
+
+/*Transform v8u8 to v8u16*/
+#define msa_movl_u8(__a) \
+((v8u16){(__a)[0], (__a)[1], (__a)[2], (__a)[3], \
+         (__a)[4], (__a)[5], (__a)[6], (__a)[7]})
+
+/*Transform v4i16 to v8i16*/
+#define msa_movl_s16(__a) ((v4i32){(__a)[0], (__a)[1], (__a)[2], (__a)[3]})
+
+/*Transform v2i32 to v4i32*/
+#define msa_movl_s32(__a) ((v2i64){(__a)[0], (__a)[1]})
+
+/*Transform v4u16 to v8u16*/
+#define msa_movl_u16(__a) ((v4u32){(__a)[0], (__a)[1], (__a)[2], (__a)[3]})
+
+/*Transform v2u32 to v4u32*/
+#define msa_movl_u32(__a) ((v2u64){(__a)[0], (__a)[1]})
+
+/* Copies the least significant half of each element of a 128 bits vector into the corresponding elements of a 64 bits vector. */
+#define msa_movn_s16(__a) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)(__a)); \
+  (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_movn_s32(__a) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)(__a)); \
+  (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_movn_s64(__a) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)(__a)); \
+  (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_movn_u16(__a) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)(__a)); \
+  (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_movn_u32(__a) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)(__a)); \
+  (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_movn_u64(__a) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)(__a)); \
+  (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+/* qmovn */
+#define msa_qmovn_s16(__a) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_s_h((v8i16)(__a), 7)); \
+  (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_qmovn_s32(__a) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_s_w((v4i32)(__a), 15)); \
+  (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_qmovn_s64(__a) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_s_d((v2i64)(__a), 31)); \
+  (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_qmovn_u16(__a) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_u_h((v8u16)(__a), 7)); \
+  (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_qmovn_u32(__a) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_u_w((v4u32)(__a), 15)); \
+  (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_qmovn_u64(__a) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_u_d((v2u64)(__a), 31)); \
+  (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+/* qmovun */
+#define msa_qmovun_s16(__a) \
+({ \
+  v8i16 __d = __builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)); \
+  v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_u_h((v8u16)__d, 7)); \
+  (v8u8)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+#define msa_qmovun_s32(__a) \
+({ \
+  v4i32 __d = __builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a)); \
+  v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_u_w((v4u32)__d, 15)); \
+  (v4u16)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+#define msa_qmovun_s64(__a) \
+({ \
+  v2i64 __d = __builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a)); \
+  v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \
+  (v2u32)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+/* Right shift elements in a 128 bits vector by an immediate value, and places the results in a 64 bits vector. */
+#define msa_shrn_n_s16(__a, __b) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srai_h((v8i16)(__a), (int)(__b))); \
+  (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_shrn_n_s32(__a, __b) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srai_w((v4i32)(__a), (int)(__b))); \
+  (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_shrn_n_s64(__a, __b) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srai_d((v2i64)(__a), (int)(__b))); \
+  (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_shrn_n_u16(__a, __b) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srli_h((v8i16)(__a), (int)(__b))); \
+  (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_shrn_n_u32(__a, __b) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srli_w((v4i32)(__a), (int)(__b))); \
+  (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_shrn_n_u64(__a, __b) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srli_d((v2i64)(__a), (int)(__b))); \
+  (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+/* Right shift elements in a 128 bits vector by an immediate value, and places the results in a 64 bits vector. */
+#define msa_rshrn_n_s16(__a, __b) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srari_h((v8i16)(__a), (int)__b)); \
+  (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_rshrn_n_s32(__a, __b) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srari_w((v4i32)(__a), (int)__b)); \
+  (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_rshrn_n_s64(__a, __b) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srari_d((v2i64)(__a), (int)__b)); \
+  (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \
+})
+
+#define msa_rshrn_n_u16(__a, __b) \
+({ \
+  v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srlri_h((v8i16)(__a), (int)__b)); \
+  (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_rshrn_n_u32(__a, __b) \
+({ \
+  v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srlri_w((v4i32)(__a), (int)__b)); \
+  (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+#define msa_rshrn_n_u64(__a, __b) \
+({ \
+  v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srlri_d((v2i64)(__a), (int)__b)); \
+  (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \
+})
+
+/* Right shift elements in a 128 bits vector by an immediate value, saturate the results and them in a 64 bits vector. */
+#define msa_qrshrn_n_s16(__a, __b) \
+({ \
+  v8i16 __d = __builtin_msa_sat_s_h(__builtin_msa_srari_h((v8i16)(__a), (int)(__b)), 7); \
+  v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__d); \
+  (v8i8)__builtin_msa_copy_s_d((v2i64)__e, 0); \
+})
+
+#define msa_qrshrn_n_s32(__a, __b) \
+({ \
+  v4i32 __d = __builtin_msa_sat_s_w(__builtin_msa_srari_w((v4i32)(__a), (int)(__b)), 15); \
+  v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__d); \
+  (v4i16)__builtin_msa_copy_s_d((v2i64)__e, 0); \
+})
+
+#define msa_qrshrn_n_s64(__a, __b) \
+({ \
+  v2i64 __d = __builtin_msa_sat_s_d(__builtin_msa_srari_d((v2i64)(__a), (int)(__b)), 31); \
+  v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__d); \
+  (v2i32)__builtin_msa_copy_s_d((v2i64)__e, 0); \
+})
+
+#define msa_qrshrn_n_u16(__a, __b) \
+({ \
+  v8u16 __d = __builtin_msa_sat_u_h((v8u16)__builtin_msa_srlri_h((v8i16)(__a), (int)(__b)), 7); \
+  v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__d); \
+  (v8u8)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+#define msa_qrshrn_n_u32(__a, __b) \
+({ \
+  v4u32 __d = __builtin_msa_sat_u_w((v4u32)__builtin_msa_srlri_w((v4i32)(__a), (int)(__b)), 15); \
+  v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__d); \
+  (v4u16)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+#define msa_qrshrn_n_u64(__a, __b) \
+({ \
+  v2u64 __d = __builtin_msa_sat_u_d((v2u64)__builtin_msa_srlri_d((v2i64)(__a), (int)(__b)), 31); \
+  v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__d); \
+  (v2u32)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+/* Right shift elements in a 128 bits vector by an immediate value, saturate the results and them in a 64 bits vector.
+   Input is signed and output is unsigned. */
+#define msa_qrshrun_n_s16(__a, __b) \
+({ \
+  v8i16 __d = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)), (int)(__b)); \
+  v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_u_h((v8u16)__d, 7)); \
+  (v8u8)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+#define msa_qrshrun_n_s32(__a, __b) \
+({ \
+  v4i32 __d = __builtin_msa_srlri_w(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a)), (int)(__b)); \
+  v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_u_w((v4u32)__d, 15)); \
+  (v4u16)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+#define msa_qrshrun_n_s64(__a, __b) \
+({ \
+  v2i64 __d = __builtin_msa_srlri_d(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a)), (int)(__b)); \
+  v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \
+  (v2u32)__builtin_msa_copy_u_d((v2i64)__e, 0); \
+})
+
+/* pack */
+#define msa_pack_s16(__a, __b) (__builtin_msa_pckev_b((v16i8)(__b), (v16i8)(__a)))
+#define msa_pack_s32(__a, __b) (__builtin_msa_pckev_h((v8i16)(__b), (v8i16)(__a)))
+#define msa_pack_s64(__a, __b) (__builtin_msa_pckev_w((v4i32)(__b), (v4i32)(__a)))
+#define msa_pack_u16(__a, __b) ((v16u8)__builtin_msa_pckev_b((v16i8)(__b), (v16i8)(__a)))
+#define msa_pack_u32(__a, __b) ((v8u16)__builtin_msa_pckev_h((v8i16)(__b), (v8i16)(__a)))
+#define msa_pack_u64(__a, __b) ((v4u32)__builtin_msa_pckev_w((v4i32)(__b), (v4i32)(__a)))
+
+/* qpack */
+#define msa_qpack_s16(__a, __b) \
+(__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_s_h((v8i16)(__b), 7), (v16i8)__builtin_msa_sat_s_h((v8i16)(__a), 7)))
+#define msa_qpack_s32(__a, __b) \
+(__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_s_w((v4i32)(__b), 15), (v8i16)__builtin_msa_sat_s_w((v4i32)(__a), 15)))
+#define msa_qpack_s64(__a, __b) \
+(__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_s_d((v2i64)(__b), 31), (v4i32)__builtin_msa_sat_s_d((v2i64)(__a), 31)))
+#define msa_qpack_u16(__a, __b) \
+((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)(__b), 7), (v16i8)__builtin_msa_sat_u_h((v8u16)(__a), 7)))
+#define msa_qpack_u32(__a, __b) \
+((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)(__b), 15), (v8i16)__builtin_msa_sat_u_w((v4u32)(__a), 15)))
+#define msa_qpack_u64(__a, __b) \
+((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)(__b), 31), (v4i32)__builtin_msa_sat_u_d((v2u64)(__a), 31)))
+
+/* qpacku */
+#define msa_qpacku_s16(__a, __b) \
+((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__b))), 7), \
+                              (v16i8)__builtin_msa_sat_u_h((v8u16)(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a))), 7)))
+#define msa_qpacku_s32(__a, __b) \
+((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__b))), 15), \
+                              (v8i16)__builtin_msa_sat_u_w((v4u32)(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a))), 15)))
+#define msa_qpacku_s64(__a, __b) \
+((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__b))), 31), \
+                              (v4i32)__builtin_msa_sat_u_d((v2u64)(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a))), 31)))
+
+/* packr */
+#define msa_packr_s16(__a, __b, __c) \
+(__builtin_msa_pckev_b((v16i8)__builtin_msa_srai_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srai_h((v8i16)(__a), (int)(__c))))
+#define msa_packr_s32(__a, __b, __c) \
+(__builtin_msa_pckev_h((v8i16)__builtin_msa_srai_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srai_w((v4i32)(__a), (int)(__c))))
+#define msa_packr_s64(__a, __b, __c) \
+(__builtin_msa_pckev_w((v4i32)__builtin_msa_srai_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srai_d((v2i64)(__a), (int)(__c))))
+#define msa_packr_u16(__a, __b, __c) \
+((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_srli_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srli_h((v8i16)(__a), (int)(__c))))
+#define msa_packr_u32(__a, __b, __c) \
+((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_srli_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srli_w((v4i32)(__a), (int)(__c))))
+#define msa_packr_u64(__a, __b, __c) \
+((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_srli_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srli_d((v2i64)(__a), (int)(__c))))
+
+/* rpackr */
+#define msa_rpackr_s16(__a, __b, __c) \
+(__builtin_msa_pckev_b((v16i8)__builtin_msa_srari_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srari_h((v8i16)(__a), (int)(__c))))
+#define msa_rpackr_s32(__a, __b, __c) \
+(__builtin_msa_pckev_h((v8i16)__builtin_msa_srari_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srari_w((v4i32)(__a), (int)(__c))))
+#define msa_rpackr_s64(__a, __b, __c) \
+(__builtin_msa_pckev_w((v4i32)__builtin_msa_srari_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srari_d((v2i64)(__a), (int)(__c))))
+#define msa_rpackr_u16(__a, __b, __c) \
+((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_srlri_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srlri_h((v8i16)(__a), (int)(__c))))
+#define msa_rpackr_u32(__a, __b, __c) \
+((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_srlri_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srlri_w((v4i32)(__a), (int)(__c))))
+#define msa_rpackr_u64(__a, __b, __c) \
+((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_srlri_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srlri_d((v2i64)(__a), (int)(__c))))
+
+/* qrpackr */
+#define msa_qrpackr_s16(__a, __b, __c) \
+(__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_s_h(__builtin_msa_srari_h((v8i16)(__b), (int)(__c)), 7), \
+                       (v16i8)__builtin_msa_sat_s_h(__builtin_msa_srari_h((v8i16)(__a), (int)(__c)), 7)))
+#define msa_qrpackr_s32(__a, __b, __c) \
+(__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_s_w(__builtin_msa_srari_w((v4i32)(__b), (int)(__c)), 15), \
+                       (v8i16)__builtin_msa_sat_s_w(__builtin_msa_srari_w((v4i32)(__a), (int)(__c)), 15)))
+#define msa_qrpackr_s64(__a, __b, __c) \
+(__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_s_d(__builtin_msa_srari_d((v2i64)(__b), (int)(__c)), 31), \
+                       (v4i32)__builtin_msa_sat_s_d(__builtin_msa_srari_d((v2i64)(__a), (int)(__c)), 31)))
+#define msa_qrpackr_u16(__a, __b, __c) \
+((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)__builtin_msa_srlri_h((v8i16)(__b), (int)(__c)), 7), \
+                              (v16i8)__builtin_msa_sat_u_h((v8u16)__builtin_msa_srlri_h((v8i16)(__a), (int)(__c)), 7)))
+#define msa_qrpackr_u32(__a, __b, __c) \
+((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)__builtin_msa_srlri_w((v4i32)(__b), (int)(__c)), 15), \
+                              (v8i16)__builtin_msa_sat_u_w((v4u32)__builtin_msa_srlri_w((v4i32)(__a), (int)(__c)), 15)))
+#define msa_qrpackr_u64(__a, __b, __c) \
+((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)__builtin_msa_srlri_d((v2i64)(__b), (int)(__c)), 31), \
+                              (v4i32)__builtin_msa_sat_u_d((v2u64)__builtin_msa_srlri_d((v2i64)(__a), (int)(__c)), 31)))
+
+/* qrpackru */
+#define msa_qrpackru_s16(__a, __b, __c) \
+({ \
+  v8i16 __d = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)), (int)(__c)); \
+  v8i16 __e = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__b)), (int)(__c)); \
+  (v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)__e, 7), (v16i8)__builtin_msa_sat_u_h((v8u16)__d, 7)); \
+})
+
+#define msa_qrpackru_s32(__a, __b, __c) \
+({ \
+  v4i32 __d = __builtin_msa_srlri_w(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a)), (int)(__c)); \
+  v4i32 __e = __builtin_msa_srlri_w(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__b)), (int)(__c)); \
+  (v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)__e, 15), (v8i16)__builtin_msa_sat_u_w((v4u32)__d, 15)); \
+})
+
+#define msa_qrpackru_s64(__a, __b, __c) \
+({ \
+  v2i64 __d = __builtin_msa_srlri_d(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a)), (int)(__c)); \
+  v2i64 __e = __builtin_msa_srlri_d(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__b)), (int)(__c)); \
+  (v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)__e, 31), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \
+})
+
+/* Minimum values between corresponding elements in the two vectors are written to the returned vector. */
+#define msa_minq_s8(__a, __b)  (__builtin_msa_min_s_b(__a, __b))
+#define msa_minq_s16(__a, __b) (__builtin_msa_min_s_h(__a, __b))
+#define msa_minq_s32(__a, __b) (__builtin_msa_min_s_w(__a, __b))
+#define msa_minq_s64(__a, __b) (__builtin_msa_min_s_d(__a, __b))
+#define msa_minq_u8(__a, __b)  ((v16u8)__builtin_msa_min_u_b(__a, __b))
+#define msa_minq_u16(__a, __b) ((v8u16)__builtin_msa_min_u_h(__a, __b))
+#define msa_minq_u32(__a, __b) ((v4u32)__builtin_msa_min_u_w(__a, __b))
+#define msa_minq_u64(__a, __b) ((v2u64)__builtin_msa_min_u_d(__a, __b))
+#define msa_minq_f32(__a, __b) (__builtin_msa_fmin_w(__a, __b))
+#define msa_minq_f64(__a, __b) (__builtin_msa_fmin_d(__a, __b))
+
+/* Maximum values between corresponding elements in the two vectors are written to the returned vector. */
+#define msa_maxq_s8(__a, __b)  (__builtin_msa_max_s_b(__a, __b))
+#define msa_maxq_s16(__a, __b) (__builtin_msa_max_s_h(__a, __b))
+#define msa_maxq_s32(__a, __b) (__builtin_msa_max_s_w(__a, __b))
+#define msa_maxq_s64(__a, __b) (__builtin_msa_max_s_d(__a, __b))
+#define msa_maxq_u8(__a, __b)  ((v16u8)__builtin_msa_max_u_b(__a, __b))
+#define msa_maxq_u16(__a, __b) ((v8u16)__builtin_msa_max_u_h(__a, __b))
+#define msa_maxq_u32(__a, __b) ((v4u32)__builtin_msa_max_u_w(__a, __b))
+#define msa_maxq_u64(__a, __b) ((v2u64)__builtin_msa_max_u_d(__a, __b))
+#define msa_maxq_f32(__a, __b) (__builtin_msa_fmax_w(__a, __b))
+#define msa_maxq_f64(__a, __b) (__builtin_msa_fmax_d(__a, __b))
+
+/* Vector type reinterpretion */
+#define MSA_TPV_REINTERPRET(_Tpv, Vec) ((_Tpv)(Vec))
+
+/* Add the odd elements in vector __a with the even elements in vector __b to double width elements in the returned vector. */
+/* v8i16 msa_hadd_s16 ((v16i8)__a, (v16i8)__b) */
+#define msa_hadd_s16(__a, __b) (__builtin_msa_hadd_s_h((v16i8)(__a), (v16i8)(__b)))
+/* v4i32 msa_hadd_s32 ((v8i16)__a, (v8i16)__b) */
+#define msa_hadd_s32(__a, __b) (__builtin_msa_hadd_s_w((v8i16)(__a), (v8i16)(__b)))
+/* v2i64 msa_hadd_s64 ((v4i32)__a, (v4i32)__b) */
+#define msa_hadd_s64(__a, __b) (__builtin_msa_hadd_s_d((v4i32)(__a), (v4i32)(__b)))
+
+/* Copy even elements in __a to the left half and even elements in __b to the right half and return the result vector. */
+#define msa_pckev_s8(__a, __b)  (__builtin_msa_pckev_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_pckev_s16(__a, __b) (__builtin_msa_pckev_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_pckev_s32(__a, __b) (__builtin_msa_pckev_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_pckev_s64(__a, __b) (__builtin_msa_pckev_d((v2i64)(__a), (v2i64)(__b)))
+
+/* Copy even elements in __a to the left half and even elements in __b to the right half and return the result vector. */
+#define msa_pckod_s8(__a, __b)  (__builtin_msa_pckod_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_pckod_s16(__a, __b) (__builtin_msa_pckod_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_pckod_s32(__a, __b) (__builtin_msa_pckod_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_pckod_s64(__a, __b) (__builtin_msa_pckod_d((v2i64)(__a), (v2i64)(__b)))
+
+#ifdef _MIPSEB
+#define LANE_IMM0_1(x)  (0b1 - ((x) & 0b1))
+#define LANE_IMM0_3(x)  (0b11 - ((x) & 0b11))
+#define LANE_IMM0_7(x)  (0b111 - ((x) & 0b111))
+#define LANE_IMM0_15(x) (0b1111 - ((x) & 0b1111))
+#else
+#define LANE_IMM0_1(x)  ((x) & 0b1)
+#define LANE_IMM0_3(x)  ((x) & 0b11)
+#define LANE_IMM0_7(x)  ((x) & 0b111)
+#define LANE_IMM0_15(x) ((x) & 0b1111)
+#endif
+
+#define msa_get_lane_u8(__a, __b)        ((uint8_t)(__a)[LANE_IMM0_7(__b)])
+#define msa_get_lane_s8(__a, __b)        ((int8_t)(__a)[LANE_IMM0_7(__b)])
+#define msa_get_lane_u16(__a, __b)       ((uint16_t)(__a)[LANE_IMM0_3(__b)])
+#define msa_get_lane_s16(__a, __b)       ((int16_t)(__a)[LANE_IMM0_3(__b)])
+#define msa_get_lane_u32(__a, __b)       ((uint32_t)(__a)[LANE_IMM0_1(__b)])
+#define msa_get_lane_s32(__a, __b)       ((int32_t)(__a)[LANE_IMM0_1(__b)])
+#define msa_get_lane_f32(__a, __b)       ((float)(__a)[LANE_IMM0_3(__b)])
+#define msa_get_lane_s64(__a, __b)       ((int64_t)(__a)[LANE_IMM0_1(__b)])
+#define msa_get_lane_u64(__a, __b)       ((uint64_t)(__a)[LANE_IMM0_1(__b)])
+#define msa_get_lane_f64(__a, __b)       ((double)(__a)[LANE_IMM0_1(__b)])
+#define msa_getq_lane_u8(__a, imm0_15)   ((uint8_t)__builtin_msa_copy_u_b((v16i8)(__a), imm0_15))
+#define msa_getq_lane_s8(__a, imm0_15)   ((int8_t)__builtin_msa_copy_s_b(__a, imm0_15))
+#define msa_getq_lane_u16(__a, imm0_7)   ((uint16_t)__builtin_msa_copy_u_h((v8i16)(__a), imm0_7))
+#define msa_getq_lane_s16(__a, imm0_7)   ((int16_t)__builtin_msa_copy_s_h(__a, imm0_7))
+#define msa_getq_lane_u32(__a, imm0_3)   __builtin_msa_copy_u_w((v4i32)(__a), imm0_3)
+#define msa_getq_lane_s32                __builtin_msa_copy_s_w
+#define msa_getq_lane_f32(__a, __b)      ((float)(__a)[LANE_IMM0_3(__b)])
+#define msa_getq_lane_f64(__a, __b)      ((double)(__a)[LANE_IMM0_1(__b)])
+#if (__mips == 64)
+#define msa_getq_lane_u64(__a, imm0_1)   __builtin_msa_copy_u_d((v2i64)(__a), imm0_1)
+#define msa_getq_lane_s64                __builtin_msa_copy_s_d
+#else
+#define msa_getq_lane_u64(__a, imm0_1)   ((uint64_t)(__a)[LANE_IMM0_1(imm0_1)])
+#define msa_getq_lane_s64(__a, imm0_1)   ((int64_t)(__a)[LANE_IMM0_1(imm0_1)])
+#endif
+
+/* combine */
+#if (__mips == 64)
+#define __COMBINE_64_64(__TYPE, a, b)    ((__TYPE)((v2u64){((v1u64)(a))[0], ((v1u64)(b))[0]}))
+#else
+#define __COMBINE_64_64(__TYPE, a, b)    ((__TYPE)((v4u32){((v2u32)(a))[0], ((v2u32)(a))[1],  \
+                                                           ((v2u32)(b))[0], ((v2u32)(b))[1]}))
+#endif
+
+/* v16i8 msa_combine_s8 (v8i8 __a, v8i8 __b) */
+#define msa_combine_s8(__a, __b)  __COMBINE_64_64(v16i8, __a, __b)
+
+/* v8i16 msa_combine_s16(v4i16 __a, v4i16 __b) */
+#define msa_combine_s16(__a, __b)  __COMBINE_64_64(v8i16, __a, __b)
+
+/* v4i32 msa_combine_s32(v2i32 __a, v2i32 __b) */
+#define msa_combine_s32(__a, __b)  __COMBINE_64_64(v4i32, __a, __b)
+
+/* v2i64 msa_combine_s64(v1i64 __a, v1i64 __b) */
+#define msa_combine_s64(__a, __b)  __COMBINE_64_64(v2i64, __a, __b)
+
+/* v4f32 msa_combine_f32(v2f32 __a, v2f32 __b) */
+#define msa_combine_f32(__a, __b)  __COMBINE_64_64(v4f32, __a, __b)
+
+/* v16u8 msa_combine_u8(v8u8 __a, v8u8 __b) */
+#define msa_combine_u8(__a, __b)  __COMBINE_64_64(v16u8, __a, __b)
+
+/* v8u16 msa_combine_u16(v4u16 __a, v4u16 __b) */
+#define msa_combine_u16(__a, __b)  __COMBINE_64_64(v8u16, __a, __b)
+
+/* v4u32 msa_combine_u32(v2u32 __a, v2u32 __b) */
+#define msa_combine_u32(__a, __b)  __COMBINE_64_64(v4u32, __a, __b)
+
+/* v2u64 msa_combine_u64(v1u64 __a, v1u64 __b) */
+#define msa_combine_u64(__a, __b)  __COMBINE_64_64(v2u64, __a, __b)
+
+/* v2f64 msa_combine_f64(v1f64 __a, v1f64 __b) */
+#define msa_combine_f64(__a, __b)  __COMBINE_64_64(v2f64, __a, __b)
+
+/* get_low, get_high */
+#if (__mips == 64)
+#define __GET_LOW(__TYPE, a)   ((__TYPE)((v1u64)(__builtin_msa_copy_u_d((v2i64)(a), 0))))
+#define __GET_HIGH(__TYPE, a)  ((__TYPE)((v1u64)(__builtin_msa_copy_u_d((v2i64)(a), 1))))
+#else
+#define __GET_LOW(__TYPE, a)   ((__TYPE)(((v2u64)(a))[0]))
+#define __GET_HIGH(__TYPE, a)  ((__TYPE)(((v2u64)(a))[1]))
+#endif
+
+/* v8i8 msa_get_low_s8(v16i8 __a) */
+#define msa_get_low_s8(__a)  __GET_LOW(v8i8, __a)
+
+/* v4i16 msa_get_low_s16(v8i16 __a) */
+#define msa_get_low_s16(__a)  __GET_LOW(v4i16, __a)
+
+/* v2i32 msa_get_low_s32(v4i32 __a) */
+#define msa_get_low_s32(__a)  __GET_LOW(v2i32, __a)
+
+/* v1i64 msa_get_low_s64(v2i64 __a) */
+#define msa_get_low_s64(__a)  __GET_LOW(v1i64, __a)
+
+/* v8u8 msa_get_low_u8(v16u8 __a) */
+#define msa_get_low_u8(__a)  __GET_LOW(v8u8, __a)
+
+/* v4u16 msa_get_low_u16(v8u16 __a) */
+#define msa_get_low_u16(__a)  __GET_LOW(v4u16, __a)
+
+/* v2u32 msa_get_low_u32(v4u32 __a) */
+#define msa_get_low_u32(__a)  __GET_LOW(v2u32, __a)
+
+/* v1u64 msa_get_low_u64(v2u64 __a) */
+#define msa_get_low_u64(__a)  __GET_LOW(v1u64, __a)
+
+/* v2f32 msa_get_low_f32(v4f32 __a) */
+#define msa_get_low_f32(__a)  __GET_LOW(v2f32, __a)
+
+/* v1f64 msa_get_low_f64(v2f64 __a) */
+#define msa_get_low_f64(__a)  __GET_LOW(v1f64, __a)
+
+/* v8i8 msa_get_high_s8(v16i8 __a) */
+#define msa_get_high_s8(__a)  __GET_HIGH(v8i8, __a)
+
+/* v4i16 msa_get_high_s16(v8i16 __a) */
+#define msa_get_high_s16(__a)  __GET_HIGH(v4i16, __a)
+
+/* v2i32 msa_get_high_s32(v4i32 __a) */
+#define msa_get_high_s32(__a)  __GET_HIGH(v2i32, __a)
+
+/* v1i64 msa_get_high_s64(v2i64 __a) */
+#define msa_get_high_s64(__a)  __GET_HIGH(v1i64, __a)
+
+/* v8u8 msa_get_high_u8(v16u8 __a) */
+#define msa_get_high_u8(__a)  __GET_HIGH(v8u8, __a)
+
+/* v4u16 msa_get_high_u16(v8u16 __a) */
+#define msa_get_high_u16(__a)  __GET_HIGH(v4u16, __a)
+
+/* v2u32 msa_get_high_u32(v4u32 __a) */
+#define msa_get_high_u32(__a)  __GET_HIGH(v2u32, __a)
+
+/* v1u64 msa_get_high_u64(v2u64 __a) */
+#define msa_get_high_u64(__a)  __GET_HIGH(v1u64, __a)
+
+/* v2f32 msa_get_high_f32(v4f32 __a) */
+#define msa_get_high_f32(__a)  __GET_HIGH(v2f32, __a)
+
+/* v1f64 msa_get_high_f64(v2f64 __a) */
+#define msa_get_high_f64(__a)  __GET_HIGH(v1f64, __a)
+
+/* ri = ai * b[lane] */
+/* v4f32 msa_mulq_lane_f32(v4f32 __a, v4f32 __b, const int __lane) */
+#define msa_mulq_lane_f32(__a, __b, __lane)  ((__a) * msa_getq_lane_f32(__b, __lane))
+
+/* ri = ai + bi * c[lane] */
+/* v4f32 msa_mlaq_lane_f32(v4f32 __a, v4f32 __b, v4f32 __c, const int __lane) */
+#define msa_mlaq_lane_f32(__a, __b, __c, __lane)  ((__a) + ((__b) * msa_getq_lane_f32(__c, __lane)))
+
+/* uint16_t msa_sum_u16(v8u16 __a)*/
+#define msa_sum_u16(__a)                         \
+({                                               \
+  v4u32 _b;                                      \
+  v2u64 _c;                                      \
+  _b = __builtin_msa_hadd_u_w(__a, __a);         \
+  _c = __builtin_msa_hadd_u_d(_b, _b);           \
+  (uint16_t)(_c[0] + _c[1]);                     \
+})
+
+/* int16_t msa_sum_s16(v8i16 __a) */
+#define msa_sum_s16(__a)                        \
+({                                              \
+  v4i32 _b;                                     \
+  v2i64 _c;                                     \
+  _b = __builtin_msa_hadd_s_w(__a, __a);        \
+  _c = __builtin_msa_hadd_s_d(_b, _b);          \
+  (int16_t)(_c[0] + _c[1]);                     \
+})
+
+
+/* uint32_t msa_sum_u32(v4u32 __a)*/
+#define msa_sum_u32(__a)                       \
+({                                             \
+  v2u64 _b;                                    \
+  _b = __builtin_msa_hadd_u_d(__a, __a);       \
+  (uint32_t)(_b[0] + _b[1]);                   \
+})
+
+/* int32_t  msa_sum_s32(v4i32 __a)*/
+#define msa_sum_s32(__a)                       \
+({                                             \
+  v2i64 _b;                                    \
+  _b = __builtin_msa_hadd_s_d(__a, __a);       \
+  (int32_t)(_b[0] + _b[1]);                    \
+})
+
+/* uint8_t msa_sum_u8(v16u8 __a)*/
+#define msa_sum_u8(__a)                        \
+({                                             \
+  v8u16 _b16;                                    \
+  v4u32 _c32;                                    \
+  _b16 = __builtin_msa_hadd_u_h(__a, __a);       \
+  _c32 = __builtin_msa_hadd_u_w(_b16, _b16);         \
+  (uint8_t)msa_sum_u32(_c32);                    \
+})
+
+/* int8_t msa_sum_s8(v16s8 __a)*/
+#define msa_sum_s8(__a)                        \
+({                                             \
+  v8i16 _b16;                                    \
+  v4i32 _c32;                                    \
+  _b16 = __builtin_msa_hadd_s_h(__a, __a);       \
+  _c32 = __builtin_msa_hadd_s_w(_b16, _b16);         \
+  (int8_t)msa_sum_s32(_c32);                     \
+})
+
+/* float msa_sum_f32(v4f32 __a)*/
+#define msa_sum_f32(__a)  ((__a)[0] + (__a)[1] + (__a)[2] + (__a)[3])
+
+/* v8u16 msa_paddlq_u8(v16u8 __a) */
+#define msa_paddlq_u8(__a)  (__builtin_msa_hadd_u_h(__a, __a))
+
+/* v8i16 msa_paddlq_s8(v16i8 __a) */
+#define msa_paddlq_s8(__a)  (__builtin_msa_hadd_s_h(__a, __a))
+
+/* v4u32 msa_paddlq_u16 (v8u16 __a)*/
+#define msa_paddlq_u16(__a)  (__builtin_msa_hadd_u_w(__a, __a))
+
+/* v4i32 msa_paddlq_s16 (v8i16 __a)*/
+#define msa_paddlq_s16(__a)  (__builtin_msa_hadd_s_w(__a, __a))
+
+/* v2u64 msa_paddlq_u32(v4u32 __a) */
+#define msa_paddlq_u32(__a)  (__builtin_msa_hadd_u_d(__a, __a))
+
+/* v2i64 msa_paddlq_s32(v4i32 __a) */
+#define msa_paddlq_s32(__a)  (__builtin_msa_hadd_s_d(__a, __a))
+
+#define V8U8_2_V8U16(x)   {(uint16_t)x[0], (uint16_t)x[1], (uint16_t)x[2], (uint16_t)x[3], \
+                           (uint16_t)x[4], (uint16_t)x[5], (uint16_t)x[6], (uint16_t)x[7]}
+#define V8U8_2_V8I16(x)   {(int16_t)x[0], (int16_t)x[1], (int16_t)x[2], (int16_t)x[3], \
+                           (int16_t)x[4], (int16_t)x[5], (int16_t)x[6], (int16_t)x[7]}
+#define V8I8_2_V8I16(x)   {(int16_t)x[0], (int16_t)x[1], (int16_t)x[2], (int16_t)x[3], \
+                           (int16_t)x[4], (int16_t)x[5], (int16_t)x[6], (int16_t)x[7]}
+#define V4U16_2_V4U32(x)  {(uint32_t)x[0], (uint32_t)x[1], (uint32_t)x[2], (uint32_t)x[3]}
+#define V4U16_2_V4I32(x)  {(int32_t)x[0], (int32_t)x[1], (int32_t)x[2], (int32_t)x[3]}
+#define V4I16_2_V4I32(x)  {(int32_t)x[0], (int32_t)x[1], (int32_t)x[2], (int32_t)x[3]}
+#define V2U32_2_V2U64(x)  {(uint64_t)x[0], (uint64_t)x[1]}
+#define V2U32_2_V2I64(x)  {(int64_t)x[0], (int64_t)x[1]}
+
+/* v8u16 msa_mull_u8(v8u8 __a, v8u8 __b) */
+#define msa_mull_u8(__a, __b)  ((v8u16)__builtin_msa_mulv_h((v8i16)V8U8_2_V8I16(__a), (v8i16)V8U8_2_V8I16(__b)))
+
+/* v8i16 msa_mull_s8(v8i8 __a, v8i8 __b)*/
+#define msa_mull_s8(__a, __b)  (__builtin_msa_mulv_h((v8i16)V8I8_2_V8I16(__a), (v8i16)V8I8_2_V8I16(__b)))
+
+/* v4u32 msa_mull_u16(v4u16 __a, v4u16 __b) */
+#define msa_mull_u16(__a, __b)  ((v4u32)__builtin_msa_mulv_w((v4i32)V4U16_2_V4I32(__a), (v4i32)V4U16_2_V4I32(__b)))
+
+/* v4i32 msa_mull_s16(v4i16 __a, v4i16 __b) */
+#define msa_mull_s16(__a, __b)  (__builtin_msa_mulv_w((v4i32)V4I16_2_V4I32(__a), (v4i32)V4I16_2_V4I32(__b)))
+
+/* v2u64 msa_mull_u32(v2u32 __a, v2u32 __b) */
+#define msa_mull_u32(__a, __b)  ((v2u64)__builtin_msa_mulv_d((v2i64)V2U32_2_V2I64(__a), (v2i64)V2U32_2_V2I64(__b)))
+
+/* bitwise and: __builtin_msa_and_v */
+#define msa_andq_u8(__a, __b)  ((v16u8)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_andq_s8(__a, __b)  ((v16i8)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_andq_u16(__a, __b) ((v8u16)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_andq_s16(__a, __b) ((v8i16)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_andq_u32(__a, __b) ((v4u32)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_andq_s32(__a, __b) ((v4i32)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_andq_u64(__a, __b) ((v2u64)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_andq_s64(__a, __b) ((v2i64)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b)))
+
+/* bitwise or: __builtin_msa_or_v */
+#define msa_orrq_u8(__a, __b)  ((v16u8)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_orrq_s8(__a, __b)  ((v16i8)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_orrq_u16(__a, __b) ((v8u16)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_orrq_s16(__a, __b) ((v8i16)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_orrq_u32(__a, __b) ((v4u32)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_orrq_s32(__a, __b) ((v4i32)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_orrq_u64(__a, __b) ((v2u64)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_orrq_s64(__a, __b) ((v2i64)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b)))
+
+/* bitwise xor: __builtin_msa_xor_v */
+#define msa_eorq_u8(__a, __b)  ((v16u8)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_eorq_s8(__a, __b)  ((v16i8)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_eorq_u16(__a, __b) ((v8u16)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_eorq_s16(__a, __b) ((v8i16)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_eorq_u32(__a, __b) ((v4u32)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_eorq_s32(__a, __b) ((v4i32)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_eorq_u64(__a, __b) ((v2u64)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+#define msa_eorq_s64(__a, __b) ((v2i64)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b)))
+
+/* bitwise not: v16u8 __builtin_msa_xori_b (v16u8, 0xff) */
+#define msa_mvnq_u8(__a)  ((v16u8)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+#define msa_mvnq_s8(__a)  ((v16i8)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+#define msa_mvnq_u16(__a) ((v8u16)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+#define msa_mvnq_s16(__a) ((v8i16)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+#define msa_mvnq_u32(__a) ((v4u32)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+#define msa_mvnq_s32(__a) ((v4i32)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+#define msa_mvnq_u64(__a) ((v2u64)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+#define msa_mvnq_s64(__a) ((v2i64)__builtin_msa_xori_b((v16u8)(__a), 0xFF))
+
+/* compare equal: ceq -> ri = ai == bi ? 1...1:0...0 */
+#define msa_ceqq_u8(__a, __b)  ((v16u8)__builtin_msa_ceq_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_ceqq_s8(__a, __b)  ((v16u8)__builtin_msa_ceq_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_ceqq_u16(__a, __b) ((v8u16)__builtin_msa_ceq_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_ceqq_s16(__a, __b) ((v8u16)__builtin_msa_ceq_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_ceqq_u32(__a, __b) ((v4u32)__builtin_msa_ceq_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_ceqq_s32(__a, __b) ((v4u32)__builtin_msa_ceq_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_ceqq_f32(__a, __b) ((v4u32)__builtin_msa_fceq_w((v4f32)(__a), (v4f32)(__b)))
+#define msa_ceqq_u64(__a, __b) ((v2u64)__builtin_msa_ceq_d((v2i64)(__a), (v2i64)(__b)))
+#define msa_ceqq_s64(__a, __b) ((v2u64)__builtin_msa_ceq_d((v2i64)(__a), (v2i64)(__b)))
+#define msa_ceqq_f64(__a, __b) ((v2u64)__builtin_msa_fceq_d((v2f64)(__a), (v2f64)(__b)))
+
+/* Compare less-than: clt -> ri = ai < bi ? 1...1:0...0 */
+#define msa_cltq_u8(__a, __b)  ((v16u8)__builtin_msa_clt_u_b((v16u8)(__a), (v16u8)(__b)))
+#define msa_cltq_s8(__a, __b)  ((v16u8)__builtin_msa_clt_s_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_cltq_u16(__a, __b) ((v8u16)__builtin_msa_clt_u_h((v8u16)(__a), (v8u16)(__b)))
+#define msa_cltq_s16(__a, __b) ((v8u16)__builtin_msa_clt_s_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_cltq_u32(__a, __b) ((v4u32)__builtin_msa_clt_u_w((v4u32)(__a), (v4u32)(__b)))
+#define msa_cltq_s32(__a, __b) ((v4u32)__builtin_msa_clt_s_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_cltq_f32(__a, __b) ((v4u32)__builtin_msa_fclt_w((v4f32)(__a), (v4f32)(__b)))
+#define msa_cltq_u64(__a, __b) ((v2u64)__builtin_msa_clt_u_d((v2u64)(__a), (v2u64)(__b)))
+#define msa_cltq_s64(__a, __b) ((v2u64)__builtin_msa_clt_s_d((v2i64)(__a), (v2i64)(__b)))
+#define msa_cltq_f64(__a, __b) ((v2u64)__builtin_msa_fclt_d((v2f64)(__a), (v2f64)(__b)))
+
+/* compare greater-than: cgt -> ri = ai > bi ? 1...1:0...0 */
+#define msa_cgtq_u8(__a, __b)  ((v16u8)__builtin_msa_clt_u_b((v16u8)(__b), (v16u8)(__a)))
+#define msa_cgtq_s8(__a, __b)  ((v16u8)__builtin_msa_clt_s_b((v16i8)(__b), (v16i8)(__a)))
+#define msa_cgtq_u16(__a, __b) ((v8u16)__builtin_msa_clt_u_h((v8u16)(__b), (v8u16)(__a)))
+#define msa_cgtq_s16(__a, __b) ((v8u16)__builtin_msa_clt_s_h((v8i16)(__b), (v8i16)(__a)))
+#define msa_cgtq_u32(__a, __b) ((v4u32)__builtin_msa_clt_u_w((v4u32)(__b), (v4u32)(__a)))
+#define msa_cgtq_s32(__a, __b) ((v4u32)__builtin_msa_clt_s_w((v4i32)(__b), (v4i32)(__a)))
+#define msa_cgtq_f32(__a, __b) ((v4u32)__builtin_msa_fclt_w((v4f32)(__b), (v4f32)(__a)))
+#define msa_cgtq_u64(__a, __b) ((v2u64)__builtin_msa_clt_u_d((v2u64)(__b), (v2u64)(__a)))
+#define msa_cgtq_s64(__a, __b) ((v2u64)__builtin_msa_clt_s_d((v2i64)(__b), (v2i64)(__a)))
+#define msa_cgtq_f64(__a, __b) ((v2u64)__builtin_msa_fclt_d((v2f64)(__b), (v2f64)(__a)))
+
+/* compare less-equal: cle -> ri = ai <= bi ? 1...1:0...0 */
+#define msa_cleq_u8(__a, __b)  ((v16u8)__builtin_msa_cle_u_b((v16u8)(__a), (v16u8)(__b)))
+#define msa_cleq_s8(__a, __b)  ((v16u8)__builtin_msa_cle_s_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_cleq_u16(__a, __b) ((v8u16)__builtin_msa_cle_u_h((v8u16)(__a), (v8u16)(__b)))
+#define msa_cleq_s16(__a, __b) ((v8u16)__builtin_msa_cle_s_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_cleq_u32(__a, __b) ((v4u32)__builtin_msa_cle_u_w((v4u32)(__a), (v4u32)(__b)))
+#define msa_cleq_s32(__a, __b) ((v4u32)__builtin_msa_cle_s_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_cleq_f32(__a, __b) ((v4u32)__builtin_msa_fcle_w((v4f32)(__a), (v4f32)(__b)))
+#define msa_cleq_u64(__a, __b) ((v2u64)__builtin_msa_cle_u_d((v2u64)(__a), (v2u64)(__b)))
+#define msa_cleq_s64(__a, __b) ((v2u64)__builtin_msa_cle_s_d((v2i64)(__a), (v2i64)(__b)))
+#define msa_cleq_f64(__a, __b) ((v2u64)__builtin_msa_fcle_d((v2f64)(__a), (v2f64)(__b)))
+
+/* compare greater-equal: cge -> ri = ai >= bi ? 1...1:0...0 */
+#define msa_cgeq_u8(__a, __b)  ((v16u8)__builtin_msa_cle_u_b((v16u8)(__b), (v16u8)(__a)))
+#define msa_cgeq_s8(__a, __b)  ((v16u8)__builtin_msa_cle_s_b((v16i8)(__b), (v16i8)(__a)))
+#define msa_cgeq_u16(__a, __b) ((v8u16)__builtin_msa_cle_u_h((v8u16)(__b), (v8u16)(__a)))
+#define msa_cgeq_s16(__a, __b) ((v8u16)__builtin_msa_cle_s_h((v8i16)(__b), (v8i16)(__a)))
+#define msa_cgeq_u32(__a, __b) ((v4u32)__builtin_msa_cle_u_w((v4u32)(__b), (v4u32)(__a)))
+#define msa_cgeq_s32(__a, __b) ((v4u32)__builtin_msa_cle_s_w((v4i32)(__b), (v4i32)(__a)))
+#define msa_cgeq_f32(__a, __b) ((v4u32)__builtin_msa_fcle_w((v4f32)(__b), (v4f32)(__a)))
+#define msa_cgeq_u64(__a, __b) ((v2u64)__builtin_msa_cle_u_d((v2u64)(__b), (v2u64)(__a)))
+#define msa_cgeq_s64(__a, __b) ((v2u64)__builtin_msa_cle_s_d((v2i64)(__b), (v2i64)(__a)))
+#define msa_cgeq_f64(__a, __b) ((v2u64)__builtin_msa_fcle_d((v2f64)(__b), (v2f64)(__a)))
+
+/* Shift Left Logical: shl -> ri = ai << bi; */
+#define msa_shlq_u8(__a, __b)  ((v16u8)__builtin_msa_sll_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_shlq_s8(__a, __b)  ((v16i8)__builtin_msa_sll_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_shlq_u16(__a, __b) ((v8u16)__builtin_msa_sll_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_shlq_s16(__a, __b) ((v8i16)__builtin_msa_sll_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_shlq_u32(__a, __b) ((v4u32)__builtin_msa_sll_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_shlq_s32(__a, __b) ((v4i32)__builtin_msa_sll_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_shlq_u64(__a, __b) ((v2u64)__builtin_msa_sll_d((v2i64)(__a), (v2i64)(__b)))
+#define msa_shlq_s64(__a, __b) ((v2i64)__builtin_msa_sll_d((v2i64)(__a), (v2i64)(__b)))
+
+/* Immediate Shift Left Logical: shl -> ri = ai << imm; */
+#define msa_shlq_n_u8(__a, __imm)  ((v16u8)__builtin_msa_slli_b((v16i8)(__a), __imm))
+#define msa_shlq_n_s8(__a, __imm)  ((v16i8)__builtin_msa_slli_b((v16i8)(__a), __imm))
+#define msa_shlq_n_u16(__a, __imm) ((v8u16)__builtin_msa_slli_h((v8i16)(__a), __imm))
+#define msa_shlq_n_s16(__a, __imm) ((v8i16)__builtin_msa_slli_h((v8i16)(__a), __imm))
+#define msa_shlq_n_u32(__a, __imm) ((v4u32)__builtin_msa_slli_w((v4i32)(__a), __imm))
+#define msa_shlq_n_s32(__a, __imm) ((v4i32)__builtin_msa_slli_w((v4i32)(__a), __imm))
+#define msa_shlq_n_u64(__a, __imm) ((v2u64)__builtin_msa_slli_d((v2i64)(__a), __imm))
+#define msa_shlq_n_s64(__a, __imm) ((v2i64)__builtin_msa_slli_d((v2i64)(__a), __imm))
+
+/* shift right: shrq -> ri = ai >> bi; */
+#define msa_shrq_u8(__a, __b)  ((v16u8)__builtin_msa_srl_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_shrq_s8(__a, __b)  ((v16i8)__builtin_msa_sra_b((v16i8)(__a), (v16i8)(__b)))
+#define msa_shrq_u16(__a, __b) ((v8u16)__builtin_msa_srl_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_shrq_s16(__a, __b) ((v8i16)__builtin_msa_sra_h((v8i16)(__a), (v8i16)(__b)))
+#define msa_shrq_u32(__a, __b) ((v4u32)__builtin_msa_srl_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_shrq_s32(__a, __b) ((v4i32)__builtin_msa_sra_w((v4i32)(__a), (v4i32)(__b)))
+#define msa_shrq_u64(__a, __b) ((v2u64)__builtin_msa_srl_d((v2i64)(__a), (v2i64)(__b)))
+#define msa_shrq_s64(__a, __b) ((v2i64)__builtin_msa_sra_d((v2i64)(__a), (v2i64)(__b)))
+
+/* Immediate Shift Right: shr -> ri = ai >> imm; */
+#define msa_shrq_n_u8(__a, __imm)  ((v16u8)__builtin_msa_srli_b((v16i8)(__a), __imm))
+#define msa_shrq_n_s8(__a, __imm)  ((v16i8)__builtin_msa_srai_b((v16i8)(__a), __imm))
+#define msa_shrq_n_u16(__a, __imm) ((v8u16)__builtin_msa_srli_h((v8i16)(__a), __imm))
+#define msa_shrq_n_s16(__a, __imm) ((v8i16)__builtin_msa_srai_h((v8i16)(__a), __imm))
+#define msa_shrq_n_u32(__a, __imm) ((v4u32)__builtin_msa_srli_w((v4i32)(__a), __imm))
+#define msa_shrq_n_s32(__a, __imm) ((v4i32)__builtin_msa_srai_w((v4i32)(__a), __imm))
+#define msa_shrq_n_u64(__a, __imm) ((v2u64)__builtin_msa_srli_d((v2i64)(__a), __imm))
+#define msa_shrq_n_s64(__a, __imm) ((v2i64)__builtin_msa_srai_d((v2i64)(__a), __imm))
+
+/* Immediate Shift Right Rounded: shr -> ri = ai >> (rounded)imm; */
+#define msa_rshrq_n_u8(__a, __imm)  ((v16u8)__builtin_msa_srlri_b((v16i8)(__a), __imm))
+#define msa_rshrq_n_s8(__a, __imm)  ((v16i8)__builtin_msa_srari_b((v16i8)(__a), __imm))
+#define msa_rshrq_n_u16(__a, __imm) ((v8u16)__builtin_msa_srlri_h((v8i16)(__a), __imm))
+#define msa_rshrq_n_s16(__a, __imm) ((v8i16)__builtin_msa_srari_h((v8i16)(__a), __imm))
+#define msa_rshrq_n_u32(__a, __imm) ((v4u32)__builtin_msa_srlri_w((v4i32)(__a), __imm))
+#define msa_rshrq_n_s32(__a, __imm) ((v4i32)__builtin_msa_srari_w((v4i32)(__a), __imm))
+#define msa_rshrq_n_u64(__a, __imm) ((v2u64)__builtin_msa_srlri_d((v2i64)(__a), __imm))
+#define msa_rshrq_n_s64(__a, __imm) ((v2i64)__builtin_msa_srari_d((v2i64)(__a), __imm))
+
+/* Vector saturating rounding shift left, qrshl -> ri = ai << bi; */
+#define msa_qrshrq_s32(a, b)  ((v4i32)__msa_srar_w((v4i32)(a), (v4i32)(b)))
+
+/* Rename the msa builtin func to unify the name style for intrin_msa.hpp */
+#define msa_qaddq_u8          __builtin_msa_adds_u_b
+#define msa_qaddq_s8          __builtin_msa_adds_s_b
+#define msa_qaddq_u16         __builtin_msa_adds_u_h
+#define msa_qaddq_s16         __builtin_msa_adds_s_h
+#define msa_qaddq_u32         __builtin_msa_adds_u_w
+#define msa_qaddq_s32         __builtin_msa_adds_s_w
+#define msa_qaddq_u64         __builtin_msa_adds_u_d
+#define msa_qaddq_s64         __builtin_msa_adds_s_d
+#define msa_addq_u8(a, b)     ((v16u8)__builtin_msa_addv_b((v16i8)(a), (v16i8)(b)))
+#define msa_addq_s8           __builtin_msa_addv_b
+#define msa_addq_u16(a, b)    ((v8u16)__builtin_msa_addv_h((v8i16)(a), (v8i16)(b)))
+#define msa_addq_s16          __builtin_msa_addv_h
+#define msa_addq_u32(a, b)    ((v4u32)__builtin_msa_addv_w((v4i32)(a), (v4i32)(b)))
+#define msa_addq_s32          __builtin_msa_addv_w
+#define msa_addq_f32          __builtin_msa_fadd_w
+#define msa_addq_u64(a, b)    ((v2u64)__builtin_msa_addv_d((v2i64)(a), (v2i64)(b)))
+#define msa_addq_s64          __builtin_msa_addv_d
+#define msa_addq_f64          __builtin_msa_fadd_d
+#define msa_qsubq_u8          __builtin_msa_subs_u_b
+#define msa_qsubq_s8          __builtin_msa_subs_s_b
+#define msa_qsubq_u16         __builtin_msa_subs_u_h
+#define msa_qsubq_s16         __builtin_msa_subs_s_h
+#define msa_subq_u8(a, b)     ((v16u8)__builtin_msa_subv_b((v16i8)(a), (v16i8)(b)))
+#define msa_subq_s8           __builtin_msa_subv_b
+#define msa_subq_u16(a, b)    ((v8u16)__builtin_msa_subv_h((v8i16)(a), (v8i16)(b)))
+#define msa_subq_s16          __builtin_msa_subv_h
+#define msa_subq_u32(a, b)    ((v4u32)__builtin_msa_subv_w((v4i32)(a), (v4i32)(b)))
+#define msa_subq_s32          __builtin_msa_subv_w
+#define msa_subq_f32          __builtin_msa_fsub_w
+#define msa_subq_u64(a, b)    ((v2u64)__builtin_msa_subv_d((v2i64)(a), (v2i64)(b)))
+#define msa_subq_s64          __builtin_msa_subv_d
+#define msa_subq_f64          __builtin_msa_fsub_d
+#define msa_mulq_u8(a, b)     ((v16u8)__builtin_msa_mulv_b((v16i8)(a), (v16i8)(b)))
+#define msa_mulq_s8(a, b)     ((v16i8)__builtin_msa_mulv_b((v16i8)(a), (v16i8)(b)))
+#define msa_mulq_u16(a, b)    ((v8u16)__builtin_msa_mulv_h((v8i16)(a), (v8i16)(b)))
+#define msa_mulq_s16(a, b)    ((v8i16)__builtin_msa_mulv_h((v8i16)(a), (v8i16)(b)))
+#define msa_mulq_u32(a, b)    ((v4u32)__builtin_msa_mulv_w((v4i32)(a), (v4i32)(b)))
+#define msa_mulq_s32(a, b)    ((v4i32)__builtin_msa_mulv_w((v4i32)(a), (v4i32)(b)))
+#define msa_mulq_u64(a, b)    ((v2u64)__builtin_msa_mulv_d((v2i64)(a), (v2i64)(b)))
+#define msa_mulq_s64(a, b)    ((v2i64)__builtin_msa_mulv_d((v2i64)(a), (v2i64)(b)))
+#define msa_mulq_f32          __builtin_msa_fmul_w
+#define msa_mulq_f64          __builtin_msa_fmul_d
+#define msa_divq_f32          __builtin_msa_fdiv_w
+#define msa_divq_f64          __builtin_msa_fdiv_d
+#define msa_dotp_s_h          __builtin_msa_dotp_s_h
+#define msa_dotp_s_w          __builtin_msa_dotp_s_w
+#define msa_dotp_s_d          __builtin_msa_dotp_s_d
+#define msa_dotp_u_h          __builtin_msa_dotp_u_h
+#define msa_dotp_u_w          __builtin_msa_dotp_u_w
+#define msa_dotp_u_d          __builtin_msa_dotp_u_d
+#define msa_dpadd_s_h         __builtin_msa_dpadd_s_h
+#define msa_dpadd_s_w         __builtin_msa_dpadd_s_w
+#define msa_dpadd_s_d         __builtin_msa_dpadd_s_d
+#define msa_dpadd_u_h         __builtin_msa_dpadd_u_h
+#define msa_dpadd_u_w         __builtin_msa_dpadd_u_w
+#define msa_dpadd_u_d         __builtin_msa_dpadd_u_d
+
+#define ILVRL_B2(RTYPE, in0, in1, low, hi) do {       \
+      low = (RTYPE)__builtin_msa_ilvr_b((v16i8)(in0), (v16i8)(in1));  \
+      hi  = (RTYPE)__builtin_msa_ilvl_b((v16i8)(in0), (v16i8)(in1));  \
+    } while (0)
+#define ILVRL_B2_UB(...) ILVRL_B2(v16u8, __VA_ARGS__)
+#define ILVRL_B2_SB(...) ILVRL_B2(v16i8, __VA_ARGS__)
+#define ILVRL_B2_UH(...) ILVRL_B2(v8u16, __VA_ARGS__)
+#define ILVRL_B2_SH(...) ILVRL_B2(v8i16, __VA_ARGS__)
+#define ILVRL_B2_SW(...) ILVRL_B2(v4i32, __VA_ARGS__)
+
+#define ILVRL_H2(RTYPE, in0, in1, low, hi) do {       \
+      low = (RTYPE)__builtin_msa_ilvr_h((v8i16)(in0), (v8i16)(in1));  \
+      hi  = (RTYPE)__builtin_msa_ilvl_h((v8i16)(in0), (v8i16)(in1));  \
+    } while (0)
+#define ILVRL_H2_UB(...) ILVRL_H2(v16u8, __VA_ARGS__)
+#define ILVRL_H2_SB(...) ILVRL_H2(v16i8, __VA_ARGS__)
+#define ILVRL_H2_UH(...) ILVRL_H2(v8u16, __VA_ARGS__)
+#define ILVRL_H2_SH(...) ILVRL_H2(v8i16, __VA_ARGS__)
+#define ILVRL_H2_SW(...) ILVRL_H2(v4i32, __VA_ARGS__)
+#define ILVRL_H2_UW(...) ILVRL_H2(v4u32, __VA_ARGS__)
+
+#define ILVRL_W2(RTYPE, in0, in1, low, hi) do {       \
+      low = (RTYPE)__builtin_msa_ilvr_w((v4i32)(in0), (v4i32)(in1));  \
+      hi  = (RTYPE)__builtin_msa_ilvl_w((v4i32)(in0), (v4i32)(in1));  \
+    } while (0)
+#define ILVRL_W2_UB(...) ILVRL_W2(v16u8, __VA_ARGS__)
+#define ILVRL_W2_SH(...) ILVRL_W2(v8i16, __VA_ARGS__)
+#define ILVRL_W2_SW(...) ILVRL_W2(v4i32, __VA_ARGS__)
+#define ILVRL_W2_UW(...) ILVRL_W2(v4u32, __VA_ARGS__)
+
+/* absq, qabsq (r = |a|;) */
+#define msa_absq_s8(a)        __builtin_msa_add_a_b(a, __builtin_msa_fill_b(0))
+#define msa_absq_s16(a)       __builtin_msa_add_a_h(a, __builtin_msa_fill_h(0))
+#define msa_absq_s32(a)       __builtin_msa_add_a_w(a, __builtin_msa_fill_w(0))
+#define msa_absq_s64(a)       __builtin_msa_add_a_d(a, __builtin_msa_fill_d(0))
+#define msa_absq_f32(a)       ((v4f32)__builtin_msa_bclri_w((v4u32)(a), 31))
+#define msa_absq_f64(a)       ((v2f64)__builtin_msa_bclri_d((v2u64)(a), 63))
+#define msa_qabsq_s8(a)       __builtin_msa_adds_a_b(a, __builtin_msa_fill_b(0))
+#define msa_qabsq_s16(a)      __builtin_msa_adds_a_h(a, __builtin_msa_fill_h(0))
+#define msa_qabsq_s32(a)      __builtin_msa_adds_a_w(a, __builtin_msa_fill_w(0))
+#define msa_qabsq_s64(a)      __builtin_msa_adds_a_d(a, __builtin_msa_fill_d(0))
+
+/* abdq, qabdq (r = |a - b|;) */
+#define msa_abdq_u8           __builtin_msa_asub_u_b
+#define msa_abdq_s8           __builtin_msa_asub_s_b
+#define msa_abdq_u16          __builtin_msa_asub_u_h
+#define msa_abdq_s16          __builtin_msa_asub_s_h
+#define msa_abdq_u32          __builtin_msa_asub_u_w
+#define msa_abdq_s32          __builtin_msa_asub_s_w
+#define msa_abdq_u64          __builtin_msa_asub_u_d
+#define msa_abdq_s64          __builtin_msa_asub_s_d
+#define msa_abdq_f32(a, b)    msa_absq_f32(__builtin_msa_fsub_w(a, b))
+#define msa_abdq_f64(a, b)    msa_absq_f64(__builtin_msa_fsub_d(a, b))
+#define msa_qabdq_s8(a, b)    msa_qabsq_s8(__builtin_msa_subs_s_b(a, b))
+#define msa_qabdq_s16(a, b)   msa_qabsq_s16(__builtin_msa_subs_s_h(a, b))
+#define msa_qabdq_s32(a, b)   msa_qabsq_s32(__builtin_msa_subs_s_w(a, b))
+#define msa_qabdq_s64(a, b)   msa_qabsq_s64(__builtin_msa_subs_s_d(a, b))
+
+/* sqrtq, rsqrtq */
+#define msa_sqrtq_f32         __builtin_msa_fsqrt_w
+#define msa_sqrtq_f64         __builtin_msa_fsqrt_d
+#define msa_rsqrtq_f32        __builtin_msa_frsqrt_w
+#define msa_rsqrtq_f64        __builtin_msa_frsqrt_d
+
+
+/* mlaq: r = a + b * c; */
+__extension__ extern __inline v4i32
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__))
+msa_mlaq_s32(v4i32 __a, v4i32 __b, v4i32 __c)
+{
+  __asm__ volatile("maddv.w %w[__a], %w[__b], %w[__c]\n"
+               // Outputs
+               : [__a] "+f"(__a)
+               // Inputs
+               : [__b] "f"(__b), [__c] "f"(__c));
+  return __a;
+}
+
+__extension__ extern __inline v2i64
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__))
+msa_mlaq_s64(v2i64 __a, v2i64 __b, v2i64 __c)
+{
+  __asm__ volatile("maddv.d %w[__a], %w[__b], %w[__c]\n"
+               // Outputs
+               : [__a] "+f"(__a)
+               // Inputs
+               : [__b] "f"(__b), [__c] "f"(__c));
+  return __a;
+}
+
+__extension__ extern __inline v4f32
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__))
+msa_mlaq_f32(v4f32 __a, v4f32 __b, v4f32 __c)
+{
+  __asm__ volatile("fmadd.w %w[__a], %w[__b], %w[__c]\n"
+               // Outputs
+               : [__a] "+f"(__a)
+               // Inputs
+               : [__b] "f"(__b), [__c] "f"(__c));
+  return __a;
+}
+
+__extension__ extern __inline v2f64
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__))
+msa_mlaq_f64(v2f64 __a, v2f64 __b, v2f64 __c)
+{
+  __asm__ volatile("fmadd.d %w[__a], %w[__b], %w[__c]\n"
+               // Outputs
+               : [__a] "+f"(__a)
+               // Inputs
+               : [__b] "f"(__b), [__c] "f"(__c));
+  return __a;
+}
+
+/* cntq */
+#define msa_cntq_s8           __builtin_msa_pcnt_b
+#define msa_cntq_s16          __builtin_msa_pcnt_h
+#define msa_cntq_s32          __builtin_msa_pcnt_w
+#define msa_cntq_s64          __builtin_msa_pcnt_d
+
+/* bslq (a: mask; r = b(if a == 0); r = c(if a == 1);) */
+#define msa_bslq_u8           __builtin_msa_bsel_v
+
+/* ilvrq, ilvlq (For EL only, ilvrq: b0, a0, b1, a1; ilvlq: b2, a2, b3, a3;) */
+#define msa_ilvrq_s8          __builtin_msa_ilvr_b
+#define msa_ilvrq_s16         __builtin_msa_ilvr_h
+#define msa_ilvrq_s32         __builtin_msa_ilvr_w
+#define msa_ilvrq_s64         __builtin_msa_ilvr_d
+#define msa_ilvlq_s8          __builtin_msa_ilvl_b
+#define msa_ilvlq_s16         __builtin_msa_ilvl_h
+#define msa_ilvlq_s32         __builtin_msa_ilvl_w
+#define msa_ilvlq_s64         __builtin_msa_ilvl_d
+
+/* ilvevq, ilvodq (ilvevq: b0, a0, b2, a2; ilvodq: b1, a1, b3, a3; ) */
+#define msa_ilvevq_s8         __builtin_msa_ilvev_b
+#define msa_ilvevq_s16        __builtin_msa_ilvev_h
+#define msa_ilvevq_s32        __builtin_msa_ilvev_w
+#define msa_ilvevq_s64        __builtin_msa_ilvev_d
+#define msa_ilvodq_s8         __builtin_msa_ilvod_b
+#define msa_ilvodq_s16        __builtin_msa_ilvod_h
+#define msa_ilvodq_s32        __builtin_msa_ilvod_w
+#define msa_ilvodq_s64        __builtin_msa_ilvod_d
+
+/* extq (r = (a || b); a concatenation b and get elements from index c) */
+#ifdef _MIPSEB
+#define msa_extq_s8(a, b, c)  \
+(__builtin_msa_vshf_b(__builtin_msa_subv_b((v16i8)((v2i64){0x1716151413121110, 0x1F1E1D1C1B1A1918}), __builtin_msa_fill_b(c)), a, b))
+#define msa_extq_s16(a, b, c) \
+(__builtin_msa_vshf_h(__builtin_msa_subv_h((v8i16)((v2i64){0x000B000A00090008, 0x000F000E000D000C}), __builtin_msa_fill_h(c)), a, b))
+#define msa_extq_s32(a, b, c) \
+(__builtin_msa_vshf_w(__builtin_msa_subv_w((v4i32)((v2i64){0x0000000500000004, 0x0000000700000006}), __builtin_msa_fill_w(c)), a, b))
+#define msa_extq_s64(a, b, c) \
+(__builtin_msa_vshf_d(__builtin_msa_subv_d((v2i64){0x0000000000000002, 0x0000000000000003}, __builtin_msa_fill_d(c)), a, b))
+#else
+#define msa_extq_s8(a, b, c)  \
+(__builtin_msa_vshf_b(__builtin_msa_addv_b((v16i8)((v2i64){0x0706050403020100, 0x0F0E0D0C0B0A0908}), __builtin_msa_fill_b(c)), b, a))
+#define msa_extq_s16(a, b, c) \
+(__builtin_msa_vshf_h(__builtin_msa_addv_h((v8i16)((v2i64){0x0003000200010000, 0x0007000600050004}), __builtin_msa_fill_h(c)), b, a))
+#define msa_extq_s32(a, b, c) \
+(__builtin_msa_vshf_w(__builtin_msa_addv_w((v4i32)((v2i64){0x0000000100000000, 0x0000000300000002}), __builtin_msa_fill_w(c)), b, a))
+#define msa_extq_s64(a, b, c) \
+(__builtin_msa_vshf_d(__builtin_msa_addv_d((v2i64){0x0000000000000000, 0x0000000000000001}, __builtin_msa_fill_d(c)), b, a))
+#endif /* _MIPSEB */
+
+/* cvttruncq, cvttintq, cvtrintq */
+#define msa_cvttruncq_u32_f32 __builtin_msa_ftrunc_u_w
+#define msa_cvttruncq_s32_f32 __builtin_msa_ftrunc_s_w
+#define msa_cvttruncq_u64_f64 __builtin_msa_ftrunc_u_d
+#define msa_cvttruncq_s64_f64 __builtin_msa_ftrunc_s_d
+#define msa_cvttintq_u32_f32  __builtin_msa_ftint_u_w
+#define msa_cvttintq_s32_f32  __builtin_msa_ftint_s_w
+#define msa_cvttintq_u64_f64  __builtin_msa_ftint_u_d
+#define msa_cvttintq_s64_f64  __builtin_msa_ftint_s_d
+#define msa_cvtrintq_f32      __builtin_msa_frint_w
+#define msa_cvtrintq_f64      __builtin_msa_frint_d
+
+/* cvtfintq, cvtfq */
+#define msa_cvtfintq_f32_u32  __builtin_msa_ffint_u_w
+#define msa_cvtfintq_f32_s32  __builtin_msa_ffint_s_w
+#define msa_cvtfintq_f64_u64  __builtin_msa_ffint_u_d
+#define msa_cvtfintq_f64_s64  __builtin_msa_ffint_s_d
+#define msa_cvtfq_f32_f64     __builtin_msa_fexdo_w
+#define msa_cvtflq_f64_f32    __builtin_msa_fexupr_d
+#define msa_cvtfhq_f64_f32    __builtin_msa_fexupl_d
+
+#define msa_addl_u8(a, b)     ((v8u16)__builtin_msa_addv_h((v8i16)V8U8_2_V8I16(a), (v8i16)V8U8_2_V8I16(b)))
+#define msa_addl_s8(a, b)     (__builtin_msa_addv_h((v8i16)V8I8_2_V8I16(a), (v8i16)V8I8_2_V8I16(b)))
+#define msa_addl_u16(a, b)    ((v4u32)__builtin_msa_addv_w((v4i32)V4U16_2_V4I32(a), (v4i32)V4U16_2_V4I32(b)))
+#define msa_addl_s16(a, b)    (__builtin_msa_addv_w((v4i32)V4I16_2_V4I32(a), (v4i32)V4I16_2_V4I32(b)))
+#define msa_subl_s16(a, b)    (__builtin_msa_subv_w((v4i32)V4I16_2_V4I32(a), (v4i32)V4I16_2_V4I32(b)))
+#define msa_recpeq_f32        __builtin_msa_frcp_w
+#define msa_recpsq_f32(a, b)  (__builtin_msa_fsub_w(msa_dupq_n_f32(2.0f), __builtin_msa_fmul_w(a, b)))
+
+#define MSA_INTERLEAVED_IMPL_LOAD2_STORE2(_Tp, _Tpv, _Tpvs, suffix, df, nlanes) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld2q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b) \
+{ \
+  _Tpv v0 = msa_ld1q_##suffix(ptr); \
+  _Tpv v1 = msa_ld1q_##suffix(ptr + nlanes); \
+  *a = (_Tpv)__builtin_msa_pckev_##df((_Tpvs)v1, (_Tpvs)v0); \
+  *b = (_Tpv)__builtin_msa_pckod_##df((_Tpvs)v1, (_Tpvs)v0); \
+} \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st2q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b) \
+{ \
+  msa_st1q_##suffix(ptr, (_Tpv)__builtin_msa_ilvr_##df((_Tpvs)b, (_Tpvs)a)); \
+  msa_st1q_##suffix(ptr + nlanes, (_Tpv)__builtin_msa_ilvl_##df((_Tpvs)b, (_Tpvs)a)); \
+}
+
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint8_t, v16u8, v16i8, u8, b, 16)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int8_t, v16i8, v16i8, s8, b, 16)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint16_t, v8u16, v8i16, u16, h, 8)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int16_t, v8i16, v8i16, s16, h, 8)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint32_t, v4u32, v4i32, u32, w, 4)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int32_t, v4i32, v4i32, s32, w, 4)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(float, v4f32, v4i32, f32, w, 4)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint64_t, v2u64, v2i64, u64, d, 2)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int64_t, v2i64, v2i64, s64, d, 2)
+MSA_INTERLEAVED_IMPL_LOAD2_STORE2(double, v2f64, v2i64, f64, d, 2)
+
+#ifdef _MIPSEB
+#define MSA_INTERLEAVED_IMPL_LOAD3_8(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \
+{ \
+  _Tpv v0 = msa_ld1q_##suffix(ptr); \
+  _Tpv v1 = msa_ld1q_##suffix(ptr + 16); \
+  _Tpv v2 = msa_ld1q_##suffix(ptr + 32); \
+  _Tpvs v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0704011F1F1F1F1F, 0x1F1C191613100D0A}), (_Tpvs)v0, (_Tpvs)v1); \
+  *a = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x1716150E0B080502, 0x1F1E1D1C1B1A1918}), v3, (_Tpvs)v2); \
+  v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0603001F1F1F1F1F, 0x1E1B1815120F0C09}), (_Tpvs)v0, (_Tpvs)v1); \
+  *b = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x1716150D0A070401, 0x1F1E1D1C1B1A1918}), v3, (_Tpvs)v2); \
+  v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x05021F1F1F1F1F1F, 0x1D1A1714110E0B08}), (_Tpvs)v0, (_Tpvs)v1); \
+  *c = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x17160F0C09060300, 0x1F1E1D1C1B1A1918}), v3, (_Tpvs)v2); \
+}
+#else
+#define MSA_INTERLEAVED_IMPL_LOAD3_8(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \
+{ \
+  _Tpv v0 = msa_ld1q_##suffix(ptr); \
+  _Tpv v1 = msa_ld1q_##suffix(ptr + 16); \
+  _Tpv v2 = msa_ld1q_##suffix(ptr + 32); \
+  _Tpvs v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x15120F0C09060300, 0x00000000001E1B18}), (_Tpvs)v1, (_Tpvs)v0); \
+  *a = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x0706050403020100, 0x1D1A1714110A0908}), (_Tpvs)v2, v3); \
+  v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x1613100D0A070401, 0x00000000001F1C19}), (_Tpvs)v1, (_Tpvs)v0); \
+  *b = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x0706050403020100, 0x1E1B1815120A0908}), (_Tpvs)v2, v3); \
+  v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x1714110E0B080502, 0x0000000000001D1A}), (_Tpvs)v1, (_Tpvs)v0); \
+  *c = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x0706050403020100, 0x1F1C191613100908}), (_Tpvs)v2, v3); \
+}
+#endif
+
+MSA_INTERLEAVED_IMPL_LOAD3_8(uint8_t, v16u8, v16i8, u8)
+MSA_INTERLEAVED_IMPL_LOAD3_8(int8_t, v16i8, v16i8, s8)
+
+#ifdef _MIPSEB
+#define MSA_INTERLEAVED_IMPL_LOAD3_16(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \
+{ \
+  _Tpv v0 = msa_ld1q_##suffix(ptr); \
+  _Tpv v1 = msa_ld1q_##suffix(ptr + 8); \
+  _Tpv v2 = msa_ld1q_##suffix(ptr + 16); \
+  _Tpvs v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x00030000000F000F, 0x000F000C00090006}), (_Tpvs)v1, (_Tpvs)v0); \
+  *a = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000A00050002, 0x000F000E000D000C}), (_Tpvs)v2, v3); \
+  v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0002000F000F000F, 0x000E000B00080005}), (_Tpvs)v1, (_Tpvs)v0); \
+  *b = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000700040001, 0x000F000E000D000C}), (_Tpvs)v2, v3); \
+  v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0001000F000F000F, 0x000D000A00070004}), (_Tpvs)v1, (_Tpvs)v0); \
+  *c = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000600030000, 0x000F000E000D000C}), (_Tpvs)v2, v3); \
+}
+#else
+#define MSA_INTERLEAVED_IMPL_LOAD3_16(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \
+{ \
+  _Tpv v0 = msa_ld1q_##suffix(ptr); \
+  _Tpv v1 = msa_ld1q_##suffix(ptr + 8); \
+  _Tpv v2 = msa_ld1q_##suffix(ptr + 16); \
+  _Tpvs v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0009000600030000, 0x00000000000F000C}), (_Tpvs)v1, (_Tpvs)v0); \
+  *a = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x0003000200010000, 0x000D000A00050004}), (_Tpvs)v2, v3); \
+  v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000A000700040001, 0x000000000000000D}), (_Tpvs)v1, (_Tpvs)v0); \
+  *b = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x0003000200010000, 0x000E000B00080004}), (_Tpvs)v2, v3); \
+  v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000800050002, 0x000000000000000E}), (_Tpvs)v1, (_Tpvs)v0); \
+  *c = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x0003000200010000, 0x000F000C00090004}), (_Tpvs)v2, v3); \
+}
+#endif
+
+MSA_INTERLEAVED_IMPL_LOAD3_16(uint16_t, v8u16, v8i16, u16)
+MSA_INTERLEAVED_IMPL_LOAD3_16(int16_t, v8i16, v8i16, s16)
+
+#define MSA_INTERLEAVED_IMPL_LOAD3_32(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \
+{ \
+  _Tpv v00 = msa_ld1q_##suffix(ptr); \
+  _Tpv v01 = msa_ld1q_##suffix(ptr + 4); \
+  _Tpv v02 = msa_ld1q_##suffix(ptr + 8); \
+  _Tpvs v10 = __builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v01, (v2i64)v01), (_Tpvs)v00); \
+  _Tpvs v11 = __builtin_msa_ilvr_w((_Tpvs)v02, (_Tpvs)__builtin_msa_ilvl_d((v2i64)v00, (v2i64)v00)); \
+  _Tpvs v12 = __builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v02, (v2i64)v02), (_Tpvs)v01); \
+  *a = (_Tpv)__builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v11, (v2i64)v11), v10); \
+  *b = (_Tpv)__builtin_msa_ilvr_w(v12, (_Tpvs)__builtin_msa_ilvl_d((v2i64)v10, (v2i64)v10)); \
+  *c = (_Tpv)__builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v12, (v2i64)v12), v11); \
+}
+
+MSA_INTERLEAVED_IMPL_LOAD3_32(uint32_t, v4u32, v4i32, u32)
+MSA_INTERLEAVED_IMPL_LOAD3_32(int32_t, v4i32, v4i32, s32)
+MSA_INTERLEAVED_IMPL_LOAD3_32(float, v4f32, v4i32, f32)
+
+#define MSA_INTERLEAVED_IMPL_LOAD3_64(_Tp, _Tpv, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \
+{ \
+  *((_Tp*)a) = *ptr;           *((_Tp*)b) = *(ptr + 1);     *((_Tp*)c) = *(ptr + 2);     \
+  *((_Tp*)a + 1) = *(ptr + 3); *((_Tp*)b + 1) = *(ptr + 4); *((_Tp*)c + 1) = *(ptr + 5); \
+}
+
+MSA_INTERLEAVED_IMPL_LOAD3_64(uint64_t, v2u64, u64)
+MSA_INTERLEAVED_IMPL_LOAD3_64(int64_t, v2i64, s64)
+MSA_INTERLEAVED_IMPL_LOAD3_64(double, v2f64, f64)
+
+#ifdef _MIPSEB
+#define MSA_INTERLEAVED_IMPL_STORE3_8(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \
+{ \
+  _Tpvs v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0F0E0D0C0B1F1F1F, 0x1F1E1D1C1B1A1F1F}), (_Tpvs)b, (_Tpvs)a); \
+  _Tpvs v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0D1C140C1B130B1A, 0x1F170F1E160E1D15}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0A09080706051F1F, 0x19181716151F1F1F}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x1D14071C13061B12, 0x170A1F16091E1508}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x04030201001F1F1F, 0x14131211101F1F1F}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x15021C14011B1300, 0x051F17041E16031D}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 32, (_Tpv)v1); \
+}
+#else
+#define MSA_INTERLEAVED_IMPL_STORE3_8(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \
+{ \
+  _Tpvs v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0000050403020100, 0x0000001413121110}), (_Tpvs)b, (_Tpvs)a); \
+  _Tpvs v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0A02110901100800, 0x05140C04130B0312}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0000000A09080706, 0x00001A1918171615}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x170A011609001508, 0x0D04190C03180B02}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0000000F0E0D0C0B, 0x0000001F1E1D1C1B}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x021C09011B08001A, 0x1F0C041E0B031D0A}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 32, (_Tpv)v1); \
+}
+#endif
+
+MSA_INTERLEAVED_IMPL_STORE3_8(uint8_t, v16u8, v16i8, u8)
+MSA_INTERLEAVED_IMPL_STORE3_8(int8_t, v16i8, v16i8, s8)
+
+#ifdef _MIPSEB
+#define MSA_INTERLEAVED_IMPL_STORE3_16(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \
+{ \
+  _Tpvs v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000700060005000F, 0x000F000E000D000F}), (_Tpvs)b, (_Tpvs)a); \
+  _Tpvs v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000A0006000D0009, 0x000F000B0007000E}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x00040003000F000F, 0x000C000B000A000F}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000E000A0003000D, 0x0005000F000B0004}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000200010000000F, 0x00090008000F000F}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0001000E00090000, 0x000B0002000F000A}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \
+}
+#else
+#define MSA_INTERLEAVED_IMPL_STORE3_16(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \
+{ \
+  _Tpvs v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0000000200010000, 0x0000000A00090008}), (_Tpvs)b, (_Tpvs)a); \
+  _Tpvs v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0001000800040000, 0x0006000200090005}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0000000500040003, 0x00000000000C000B}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B00040000000A, 0x0002000C00050001}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0000000000070006, 0x0000000F000E000D}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x00050000000D0004, 0x000F00060001000E}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \
+}
+#endif
+
+MSA_INTERLEAVED_IMPL_STORE3_16(uint16_t, v8u16, v8i16, u16)
+MSA_INTERLEAVED_IMPL_STORE3_16(int16_t, v8i16, v8i16, s16)
+
+#ifdef _MIPSEB
+#define MSA_INTERLEAVED_IMPL_STORE3_32(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \
+{ \
+  _Tpvs v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000300000007, 0x0000000700000006}), (_Tpvs)b, (_Tpvs)a); \
+  _Tpvs v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000300000006, 0x0000000700000005}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000200000001, 0x0000000500000007}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000700000004, 0x0000000500000002}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 4, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000007, 0x0000000400000007}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000500000000, 0x0000000100000007}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \
+}
+#else
+#define MSA_INTERLEAVED_IMPL_STORE3_32(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \
+{ \
+  _Tpvs v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000100000000, 0x0000000000000004}), (_Tpvs)b, (_Tpvs)a); \
+  _Tpvs v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000200000000, 0x0000000100000004}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000002, 0x0000000600000005}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000500000002, 0x0000000300000000}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 4, (_Tpv)v1); \
+  v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000003, 0x0000000000000007}), (_Tpvs)b, (_Tpvs)a); \
+  v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000006, 0x0000000700000002}), (_Tpvs)c, (_Tpvs)v0); \
+  msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \
+}
+#endif
+
+MSA_INTERLEAVED_IMPL_STORE3_32(uint32_t, v4u32, v4i32, u32)
+MSA_INTERLEAVED_IMPL_STORE3_32(int32_t, v4i32, v4i32, s32)
+MSA_INTERLEAVED_IMPL_STORE3_32(float, v4f32, v4i32, f32)
+
+#define MSA_INTERLEAVED_IMPL_STORE3_64(_Tp, _Tpv, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \
+{ \
+  *ptr = a[0];       *(ptr + 1) = b[0]; *(ptr + 2) = c[0]; \
+  *(ptr + 3) = a[1]; *(ptr + 4) = b[1]; *(ptr + 5) = c[1]; \
+}
+
+MSA_INTERLEAVED_IMPL_STORE3_64(uint64_t, v2u64, u64)
+MSA_INTERLEAVED_IMPL_STORE3_64(int64_t, v2i64, s64)
+MSA_INTERLEAVED_IMPL_STORE3_64(double, v2f64, f64)
+
+#define MSA_INTERLEAVED_IMPL_LOAD4_STORE4(_Tp, _Tpv, _Tpvs, suffix, df, nlanes) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld4q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c, _Tpv* d) \
+{ \
+  _Tpv v0 = msa_ld1q_##suffix(ptr); \
+  _Tpv v1 = msa_ld1q_##suffix(ptr + nlanes); \
+  _Tpv v2 = msa_ld1q_##suffix(ptr + nlanes * 2); \
+  _Tpv v3 = msa_ld1q_##suffix(ptr + nlanes * 3); \
+  _Tpvs t0 = __builtin_msa_pckev_##df((_Tpvs)v1, (_Tpvs)v0); \
+  _Tpvs t1 = __builtin_msa_pckev_##df((_Tpvs)v3, (_Tpvs)v2); \
+  _Tpvs t2 = __builtin_msa_pckod_##df((_Tpvs)v1, (_Tpvs)v0); \
+  _Tpvs t3 = __builtin_msa_pckod_##df((_Tpvs)v3, (_Tpvs)v2); \
+  *a = (_Tpv)__builtin_msa_pckev_##df(t1, t0); \
+  *b = (_Tpv)__builtin_msa_pckev_##df(t3, t2); \
+  *c = (_Tpv)__builtin_msa_pckod_##df(t1, t0); \
+  *d = (_Tpv)__builtin_msa_pckod_##df(t3, t2); \
+} \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st4q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c, const _Tpv d) \
+{ \
+  _Tpvs v0 = __builtin_msa_ilvr_##df((_Tpvs)c, (_Tpvs)a); \
+  _Tpvs v1 = __builtin_msa_ilvr_##df((_Tpvs)d, (_Tpvs)b); \
+  _Tpvs v2 = __builtin_msa_ilvl_##df((_Tpvs)c, (_Tpvs)a); \
+  _Tpvs v3 = __builtin_msa_ilvl_##df((_Tpvs)d, (_Tpvs)b); \
+  msa_st1q_##suffix(ptr, (_Tpv)__builtin_msa_ilvr_##df(v1, v0)); \
+  msa_st1q_##suffix(ptr + nlanes, (_Tpv)__builtin_msa_ilvl_##df(v1, v0)); \
+  msa_st1q_##suffix(ptr + 2 * nlanes, (_Tpv)__builtin_msa_ilvr_##df(v3, v2)); \
+  msa_st1q_##suffix(ptr + 3 * nlanes, (_Tpv)__builtin_msa_ilvl_##df(v3, v2)); \
+}
+
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4(uint8_t, v16u8, v16i8, u8, b, 16)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4(int8_t, v16i8, v16i8, s8, b, 16)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4(uint16_t, v8u16, v8i16, u16, h, 8)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4(int16_t, v8i16, v8i16, s16, h, 8)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4(uint32_t, v4u32, v4i32, u32, w, 4)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4(int32_t, v4i32, v4i32, s32, w, 4)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4(float, v4f32, v4i32, f32, w, 4)
+
+#define MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(_Tp, _Tpv, _Tpvs, suffix) \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_ld4q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c, _Tpv* d) \
+{ \
+  _Tpv v0 = msa_ld1q_##suffix(ptr); \
+  _Tpv v1 = msa_ld1q_##suffix(ptr + 2); \
+  _Tpv v2 = msa_ld1q_##suffix(ptr + 4); \
+  _Tpv v3 = msa_ld1q_##suffix(ptr + 6); \
+  *a = (_Tpv)__builtin_msa_ilvr_d((_Tpvs)v2, (_Tpvs)v0); \
+  *b = (_Tpv)__builtin_msa_ilvl_d((_Tpvs)v2, (_Tpvs)v0); \
+  *c = (_Tpv)__builtin_msa_ilvr_d((_Tpvs)v3, (_Tpvs)v1); \
+  *d = (_Tpv)__builtin_msa_ilvl_d((_Tpvs)v3, (_Tpvs)v1); \
+} \
+__extension__ extern __inline void \
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \
+msa_st4q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c, const _Tpv d) \
+{ \
+  msa_st1q_##suffix(ptr, (_Tpv)__builtin_msa_ilvr_d((_Tpvs)b, (_Tpvs)a)); \
+  msa_st1q_##suffix(ptr + 2, (_Tpv)__builtin_msa_ilvr_d((_Tpvs)d, (_Tpvs)c)); \
+  msa_st1q_##suffix(ptr + 4, (_Tpv)__builtin_msa_ilvl_d((_Tpvs)b, (_Tpvs)a)); \
+  msa_st1q_##suffix(ptr + 6, (_Tpv)__builtin_msa_ilvl_d((_Tpvs)d, (_Tpvs)c)); \
+}
+
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(uint64_t, v2u64, v2i64, u64)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(int64_t, v2i64, v2i64, s64)
+MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(double, v2f64, v2i64, f64)
+
+__extension__ extern __inline v8i16
+__attribute__ ((__always_inline__, __gnu_inline__, __artificial__))
+msa_qdmulhq_n_s16(v8i16 a, int16_t b)
+{
+  v8i16 a_lo, a_hi;
+  ILVRL_H2_SH(a, msa_dupq_n_s16(0), a_lo, a_hi);
+  return msa_packr_s32(msa_shlq_n_s32(msa_mulq_s32(msa_paddlq_s16(a_lo), msa_dupq_n_s32(b)), 1),
+                       msa_shlq_n_s32(msa_mulq_s32(msa_paddlq_s16(a_hi), msa_dupq_n_s32(b)), 1), 16);
+}
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#endif /*__mips_msa*/
+#endif /* OPENCV_CORE_MSA_MACROS_H */
diff --git a/3rdParty/include/opencv2/core/hal/simd_utils.impl.hpp b/3rdParty/include/opencv2/core/hal/simd_utils.impl.hpp
new file mode 100644
index 0000000..fff8f94
--- /dev/null
+++ b/3rdParty/include/opencv2/core/hal/simd_utils.impl.hpp
@@ -0,0 +1,146 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+// This header is not standalone. Don't include directly, use "intrin.hpp" instead.
+#ifdef OPENCV_HAL_INTRIN_HPP  // defined in intrin.hpp
+
+
+#if CV_SIMD128 || CV_SIMD128_CPP
+
+template<typename _T> struct Type2Vec128_Traits;
+#define CV_INTRIN_DEF_TYPE2VEC128_TRAITS(type_, vec_type_) \
+    template<> struct Type2Vec128_Traits<type_> \
+    { \
+        typedef vec_type_ vec_type; \
+    }
+
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uchar, v_uint8x16);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(schar, v_int8x16);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(ushort, v_uint16x8);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(short, v_int16x8);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(unsigned, v_uint32x4);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int, v_int32x4);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(float, v_float32x4);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uint64, v_uint64x2);
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int64, v_int64x2);
+#if CV_SIMD128_64F
+CV_INTRIN_DEF_TYPE2VEC128_TRAITS(double, v_float64x2);
+#endif
+
+template<typename _T> static inline
+typename Type2Vec128_Traits<_T>::vec_type v_setall(const _T& a);
+
+template<> inline Type2Vec128_Traits< uchar>::vec_type v_setall< uchar>(const  uchar& a) { return v_setall_u8(a); }
+template<> inline Type2Vec128_Traits< schar>::vec_type v_setall< schar>(const  schar& a) { return v_setall_s8(a); }
+template<> inline Type2Vec128_Traits<ushort>::vec_type v_setall<ushort>(const ushort& a) { return v_setall_u16(a); }
+template<> inline Type2Vec128_Traits< short>::vec_type v_setall< short>(const  short& a) { return v_setall_s16(a); }
+template<> inline Type2Vec128_Traits<  uint>::vec_type v_setall<  uint>(const   uint& a) { return v_setall_u32(a); }
+template<> inline Type2Vec128_Traits<   int>::vec_type v_setall<   int>(const    int& a) { return v_setall_s32(a); }
+template<> inline Type2Vec128_Traits<uint64>::vec_type v_setall<uint64>(const uint64& a) { return v_setall_u64(a); }
+template<> inline Type2Vec128_Traits< int64>::vec_type v_setall< int64>(const  int64& a) { return v_setall_s64(a); }
+template<> inline Type2Vec128_Traits< float>::vec_type v_setall< float>(const  float& a) { return v_setall_f32(a); }
+#if CV_SIMD128_64F
+template<> inline Type2Vec128_Traits<double>::vec_type v_setall<double>(const double& a) { return v_setall_f64(a); }
+#endif
+
+#endif  // SIMD128
+
+
+#if CV_SIMD256
+
+template<typename _T> struct Type2Vec256_Traits;
+#define CV_INTRIN_DEF_TYPE2VEC256_TRAITS(type_, vec_type_) \
+    template<> struct Type2Vec256_Traits<type_> \
+    { \
+        typedef vec_type_ vec_type; \
+    }
+
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uchar, v_uint8x32);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(schar, v_int8x32);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(ushort, v_uint16x16);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(short, v_int16x16);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(unsigned, v_uint32x8);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int, v_int32x8);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(float, v_float32x8);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uint64, v_uint64x4);
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int64, v_int64x4);
+#if CV_SIMD256_64F
+CV_INTRIN_DEF_TYPE2VEC256_TRAITS(double, v_float64x4);
+#endif
+
+template<typename _T> static inline
+typename Type2Vec256_Traits<_T>::vec_type v256_setall(const _T& a);
+
+template<> inline Type2Vec256_Traits< uchar>::vec_type v256_setall< uchar>(const  uchar& a) { return v256_setall_u8(a); }
+template<> inline Type2Vec256_Traits< schar>::vec_type v256_setall< schar>(const  schar& a) { return v256_setall_s8(a); }
+template<> inline Type2Vec256_Traits<ushort>::vec_type v256_setall<ushort>(const ushort& a) { return v256_setall_u16(a); }
+template<> inline Type2Vec256_Traits< short>::vec_type v256_setall< short>(const  short& a) { return v256_setall_s16(a); }
+template<> inline Type2Vec256_Traits<  uint>::vec_type v256_setall<  uint>(const   uint& a) { return v256_setall_u32(a); }
+template<> inline Type2Vec256_Traits<   int>::vec_type v256_setall<   int>(const    int& a) { return v256_setall_s32(a); }
+template<> inline Type2Vec256_Traits<uint64>::vec_type v256_setall<uint64>(const uint64& a) { return v256_setall_u64(a); }
+template<> inline Type2Vec256_Traits< int64>::vec_type v256_setall< int64>(const  int64& a) { return v256_setall_s64(a); }
+template<> inline Type2Vec256_Traits< float>::vec_type v256_setall< float>(const  float& a) { return v256_setall_f32(a); }
+#if CV_SIMD256_64F
+template<> inline Type2Vec256_Traits<double>::vec_type v256_setall<double>(const double& a) { return v256_setall_f64(a); }
+#endif
+
+#endif  // SIMD256
+
+
+#if CV_SIMD512
+
+template<typename _T> struct Type2Vec512_Traits;
+#define CV_INTRIN_DEF_TYPE2VEC512_TRAITS(type_, vec_type_) \
+    template<> struct Type2Vec512_Traits<type_> \
+    { \
+        typedef vec_type_ vec_type; \
+    }
+
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uchar, v_uint8x64);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(schar, v_int8x64);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(ushort, v_uint16x32);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(short, v_int16x32);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(unsigned, v_uint32x16);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int, v_int32x16);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(float, v_float32x16);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uint64, v_uint64x8);
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int64, v_int64x8);
+#if CV_SIMD512_64F
+CV_INTRIN_DEF_TYPE2VEC512_TRAITS(double, v_float64x8);
+#endif
+
+template<typename _T> static inline
+typename Type2Vec512_Traits<_T>::vec_type v512_setall(const _T& a);
+
+template<> inline Type2Vec512_Traits< uchar>::vec_type v512_setall< uchar>(const  uchar& a) { return v512_setall_u8(a); }
+template<> inline Type2Vec512_Traits< schar>::vec_type v512_setall< schar>(const  schar& a) { return v512_setall_s8(a); }
+template<> inline Type2Vec512_Traits<ushort>::vec_type v512_setall<ushort>(const ushort& a) { return v512_setall_u16(a); }
+template<> inline Type2Vec512_Traits< short>::vec_type v512_setall< short>(const  short& a) { return v512_setall_s16(a); }
+template<> inline Type2Vec512_Traits<  uint>::vec_type v512_setall<  uint>(const   uint& a) { return v512_setall_u32(a); }
+template<> inline Type2Vec512_Traits<   int>::vec_type v512_setall<   int>(const    int& a) { return v512_setall_s32(a); }
+template<> inline Type2Vec512_Traits<uint64>::vec_type v512_setall<uint64>(const uint64& a) { return v512_setall_u64(a); }
+template<> inline Type2Vec512_Traits< int64>::vec_type v512_setall< int64>(const  int64& a) { return v512_setall_s64(a); }
+template<> inline Type2Vec512_Traits< float>::vec_type v512_setall< float>(const  float& a) { return v512_setall_f32(a); }
+#if CV_SIMD512_64F
+template<> inline Type2Vec512_Traits<double>::vec_type v512_setall<double>(const double& a) { return v512_setall_f64(a); }
+#endif
+
+#endif  // SIMD512
+
+
+#if CV_SIMD_WIDTH == 16
+template<typename _T> static inline
+typename Type2Vec128_Traits<_T>::vec_type vx_setall(const _T& a) { return v_setall(a); }
+#elif CV_SIMD_WIDTH == 32
+template<typename _T> static inline
+typename Type2Vec256_Traits<_T>::vec_type vx_setall(const _T& a) { return v256_setall(a); }
+#elif CV_SIMD_WIDTH == 64
+template<typename _T> static inline
+typename Type2Vec512_Traits<_T>::vec_type vx_setall(const _T& a) { return v512_setall(a); }
+#else
+#error "Build configuration error, unsupported CV_SIMD_WIDTH"
+#endif
+
+
+#endif  // OPENCV_HAL_INTRIN_HPP
diff --git a/3rdParty/include/opencv2/core/mat.hpp b/3rdParty/include/opencv2/core/mat.hpp
new file mode 100644
index 0000000..1ef0eb5
--- /dev/null
+++ b/3rdParty/include/opencv2/core/mat.hpp
@@ -0,0 +1,3766 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_MAT_HPP
+#define OPENCV_CORE_MAT_HPP
+
+#ifndef __cplusplus
+#  error mat.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core/matx.hpp"
+#include "opencv2/core/types.hpp"
+
+#include "opencv2/core/bufferpool.hpp"
+
+#include <type_traits>
+
+namespace cv
+{
+
+//! @addtogroup core_basic
+//! @{
+
+enum AccessFlag { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25,
+    ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 };
+CV_ENUM_FLAGS(AccessFlag)
+__CV_ENUM_FLAGS_BITWISE_AND(AccessFlag, int, AccessFlag)
+
+CV__DEBUG_NS_BEGIN
+
+class CV_EXPORTS _OutputArray;
+
+//////////////////////// Input/Output Array Arguments /////////////////////////////////
+
+/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions.
+
+It is defined as:
+@code
+    typedef const _InputArray& InputArray;
+@endcode
+where _InputArray is a class that can be constructed from `Mat`, `Mat_<T>`, `Matx<T, m, n>`,
+`std::vector<T>`, `std::vector<std::vector<T> >`, `std::vector<Mat>`, `std::vector<Mat_<T> >`,
+`UMat`, `std::vector<UMat>` or `double`. It can also be constructed from a matrix expression.
+
+Since this is mostly implementation-level class, and its interface may change in future versions, we
+do not describe it in details. There are a few key things, though, that should be kept in mind:
+
+-   When you see in the reference manual or in OpenCV source code a function that takes
+    InputArray, it means that you can actually pass `Mat`, `Matx`, `vector<T>` etc. (see above the
+    complete list).
+-   Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or
+    simply cv::Mat() as you probably did before).
+-   The class is designed solely for passing parameters. That is, normally you *should not*
+    declare class members, local and global variables of this type.
+-   If you want to design your own function or a class method that can operate of arrays of
+    multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside
+    a function you should use _InputArray::getMat() method to construct a matrix header for the
+    array (without copying data). _InputArray::kind() can be used to distinguish Mat from
+    `vector<>` etc., but normally it is not needed.
+
+Here is how you can use a function that takes InputArray :
+@code
+    std::vector<Point2f> vec;
+    // points or a circle
+    for( int i = 0; i < 30; i++ )
+        vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)),
+                              (float)(100 - 30*sin(i*CV_PI*2/5))));
+    cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20));
+@endcode
+That is, we form an STL vector containing points, and apply in-place affine transformation to the
+vector using the 2x3 matrix created inline as `Matx<float, 2, 3>` instance.
+
+Here is how such a function can be implemented (for simplicity, we implement a very specific case of
+it, according to the assertion statement inside) :
+@code
+    void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m)
+    {
+        // get Mat headers for input arrays. This is O(1) operation,
+        // unless _src and/or _m are matrix expressions.
+        Mat src = _src.getMat(), m = _m.getMat();
+        CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) );
+
+        // [re]create the output array so that it has the proper size and type.
+        // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize.
+        _dst.create(src.size(), src.type());
+        Mat dst = _dst.getMat();
+
+        for( int i = 0; i < src.rows; i++ )
+            for( int j = 0; j < src.cols; j++ )
+            {
+                Point2f pt = src.at<Point2f>(i, j);
+                dst.at<Point2f>(i, j) = Point2f(m.at<float>(0, 0)*pt.x +
+                                                m.at<float>(0, 1)*pt.y +
+                                                m.at<float>(0, 2),
+                                                m.at<float>(1, 0)*pt.x +
+                                                m.at<float>(1, 1)*pt.y +
+                                                m.at<float>(1, 2));
+            }
+    }
+@endcode
+There is another related type, InputArrayOfArrays, which is currently defined as a synonym for
+InputArray:
+@code
+    typedef InputArray InputArrayOfArrays;
+@endcode
+It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate
+synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation
+level their use is similar, but _InputArray::getMat(idx) should be used to get header for the
+idx-th component of the outer vector and _InputArray::size().area() should be used to find the
+number of components (vectors/matrices) of the outer vector.
+
+In general, type support is limited to cv::Mat types. Other types are forbidden.
+But in some cases we need to support passing of custom non-general Mat types, like arrays of cv::KeyPoint, cv::DMatch, etc.
+This data is not intended to be interpreted as an image data, or processed somehow like regular cv::Mat.
+To pass such custom type use rawIn() / rawOut() / rawInOut() wrappers.
+Custom type is wrapped as Mat-compatible `CV_8UC<N>` values (N = sizeof(T), N <= CV_CN_MAX).
+ */
+class CV_EXPORTS _InputArray
+{
+public:
+    enum KindFlag {
+        KIND_SHIFT = 16,
+        FIXED_TYPE = 0x8000 << KIND_SHIFT,
+        FIXED_SIZE = 0x4000 << KIND_SHIFT,
+        KIND_MASK = 31 << KIND_SHIFT,
+
+        NONE              = 0 << KIND_SHIFT,
+        MAT               = 1 << KIND_SHIFT,
+        MATX              = 2 << KIND_SHIFT,
+        STD_VECTOR        = 3 << KIND_SHIFT,
+        STD_VECTOR_VECTOR = 4 << KIND_SHIFT,
+        STD_VECTOR_MAT    = 5 << KIND_SHIFT,
+#if OPENCV_ABI_COMPATIBILITY < 500
+        EXPR              = 6 << KIND_SHIFT,  //!< removed: https://github.com/opencv/opencv/pull/17046
+#endif
+        OPENGL_BUFFER     = 7 << KIND_SHIFT,
+        CUDA_HOST_MEM     = 8 << KIND_SHIFT,
+        CUDA_GPU_MAT      = 9 << KIND_SHIFT,
+        UMAT              =10 << KIND_SHIFT,
+        STD_VECTOR_UMAT   =11 << KIND_SHIFT,
+        STD_BOOL_VECTOR   =12 << KIND_SHIFT,
+        STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT,
+#if OPENCV_ABI_COMPATIBILITY < 500
+        STD_ARRAY         =14 << KIND_SHIFT,  //!< removed: https://github.com/opencv/opencv/issues/18897
+#endif
+        STD_ARRAY_MAT     =15 << KIND_SHIFT
+    };
+
+    _InputArray();
+    _InputArray(int _flags, void* _obj);
+    _InputArray(const Mat& m);
+    _InputArray(const MatExpr& expr);
+    _InputArray(const std::vector<Mat>& vec);
+    template<typename _Tp> _InputArray(const Mat_<_Tp>& m);
+    template<typename _Tp> _InputArray(const std::vector<_Tp>& vec);
+    _InputArray(const std::vector<bool>& vec);
+    template<typename _Tp> _InputArray(const std::vector<std::vector<_Tp> >& vec);
+    _InputArray(const std::vector<std::vector<bool> >&) = delete;  // not supported
+    template<typename _Tp> _InputArray(const std::vector<Mat_<_Tp> >& vec);
+    template<typename _Tp> _InputArray(const _Tp* vec, int n);
+    template<typename _Tp, int m, int n> _InputArray(const Matx<_Tp, m, n>& matx);
+    _InputArray(const double& val);
+    _InputArray(const cuda::GpuMat& d_mat);
+    _InputArray(const std::vector<cuda::GpuMat>& d_mat_array);
+    _InputArray(const ogl::Buffer& buf);
+    _InputArray(const cuda::HostMem& cuda_mem);
+    template<typename _Tp> _InputArray(const cudev::GpuMat_<_Tp>& m);
+    _InputArray(const UMat& um);
+    _InputArray(const std::vector<UMat>& umv);
+
+    template<typename _Tp, std::size_t _Nm> _InputArray(const std::array<_Tp, _Nm>& arr);
+    template<std::size_t _Nm> _InputArray(const std::array<Mat, _Nm>& arr);
+
+    template<typename _Tp> static _InputArray rawIn(const std::vector<_Tp>& vec);
+    template<typename _Tp, std::size_t _Nm> static _InputArray rawIn(const std::array<_Tp, _Nm>& arr);
+
+    Mat getMat(int idx=-1) const;
+    Mat getMat_(int idx=-1) const;
+    UMat getUMat(int idx=-1) const;
+    void getMatVector(std::vector<Mat>& mv) const;
+    void getUMatVector(std::vector<UMat>& umv) const;
+    void getGpuMatVector(std::vector<cuda::GpuMat>& gpumv) const;
+    cuda::GpuMat getGpuMat() const;
+    ogl::Buffer getOGlBuffer() const;
+
+    int getFlags() const;
+    void* getObj() const;
+    Size getSz() const;
+
+    _InputArray::KindFlag kind() const;
+    int dims(int i=-1) const;
+    int cols(int i=-1) const;
+    int rows(int i=-1) const;
+    Size size(int i=-1) const;
+    int sizend(int* sz, int i=-1) const;
+    bool sameSize(const _InputArray& arr) const;
+    size_t total(int i=-1) const;
+    int type(int i=-1) const;
+    int depth(int i=-1) const;
+    int channels(int i=-1) const;
+    bool isContinuous(int i=-1) const;
+    bool isSubmatrix(int i=-1) const;
+    bool empty() const;
+    void copyTo(const _OutputArray& arr) const;
+    void copyTo(const _OutputArray& arr, const _InputArray & mask) const;
+    size_t offset(int i=-1) const;
+    size_t step(int i=-1) const;
+    bool isMat() const;
+    bool isUMat() const;
+    bool isMatVector() const;
+    bool isUMatVector() const;
+    bool isMatx() const;
+    bool isVector() const;
+    bool isGpuMat() const;
+    bool isGpuMatVector() const;
+    ~_InputArray();
+
+protected:
+    int flags;
+    void* obj;
+    Size sz;
+
+    void init(int _flags, const void* _obj);
+    void init(int _flags, const void* _obj, Size _sz);
+};
+CV_ENUM_FLAGS(_InputArray::KindFlag)
+__CV_ENUM_FLAGS_BITWISE_AND(_InputArray::KindFlag, int, _InputArray::KindFlag)
+
+/** @brief This type is very similar to InputArray except that it is used for input/output and output function
+parameters.
+
+Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`,
+`vector<T>` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly
+create OutputArray instances* applies here too.
+
+If you want to make your function polymorphic (i.e. accept different arrays as output parameters),
+it is also not very difficult. Take the sample above as the reference. Note that
+_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee
+that the output array is properly allocated.
+
+Optional output parameters. If you do not need certain output array to be computed and returned to
+you, pass cv::noArray(), just like you would in the case of optional input array. At the
+implementation level, use _OutputArray::needed() to check if certain output array needs to be
+computed or not.
+
+There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper
+generators:
+@code
+    typedef OutputArray OutputArrayOfArrays;
+    typedef OutputArray InputOutputArray;
+    typedef OutputArray InputOutputArrayOfArrays;
+@endcode
+ */
+class CV_EXPORTS _OutputArray : public _InputArray
+{
+public:
+    enum DepthMask
+    {
+        DEPTH_MASK_8U = 1 << CV_8U,
+        DEPTH_MASK_8S = 1 << CV_8S,
+        DEPTH_MASK_16U = 1 << CV_16U,
+        DEPTH_MASK_16S = 1 << CV_16S,
+        DEPTH_MASK_32S = 1 << CV_32S,
+        DEPTH_MASK_32F = 1 << CV_32F,
+        DEPTH_MASK_64F = 1 << CV_64F,
+        DEPTH_MASK_16F = 1 << CV_16F,
+        DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1,
+        DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S,
+        DEPTH_MASK_ALL_16F = (DEPTH_MASK_16F<<1)-1,
+        DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F
+    };
+
+    _OutputArray();
+    _OutputArray(int _flags, void* _obj);
+    _OutputArray(Mat& m);
+    _OutputArray(std::vector<Mat>& vec);
+    _OutputArray(cuda::GpuMat& d_mat);
+    _OutputArray(std::vector<cuda::GpuMat>& d_mat);
+    _OutputArray(ogl::Buffer& buf);
+    _OutputArray(cuda::HostMem& cuda_mem);
+    template<typename _Tp> _OutputArray(cudev::GpuMat_<_Tp>& m);
+    template<typename _Tp> _OutputArray(std::vector<_Tp>& vec);
+    _OutputArray(std::vector<bool>& vec) = delete;  // not supported
+    template<typename _Tp> _OutputArray(std::vector<std::vector<_Tp> >& vec);
+    _OutputArray(std::vector<std::vector<bool> >&) = delete;  // not supported
+    template<typename _Tp> _OutputArray(std::vector<Mat_<_Tp> >& vec);
+    template<typename _Tp> _OutputArray(Mat_<_Tp>& m);
+    template<typename _Tp> _OutputArray(_Tp* vec, int n);
+    template<typename _Tp, int m, int n> _OutputArray(Matx<_Tp, m, n>& matx);
+    _OutputArray(UMat& m);
+    _OutputArray(std::vector<UMat>& vec);
+
+    _OutputArray(const Mat& m);
+    _OutputArray(const std::vector<Mat>& vec);
+    _OutputArray(const cuda::GpuMat& d_mat);
+    _OutputArray(const std::vector<cuda::GpuMat>& d_mat);
+    _OutputArray(const ogl::Buffer& buf);
+    _OutputArray(const cuda::HostMem& cuda_mem);
+    template<typename _Tp> _OutputArray(const cudev::GpuMat_<_Tp>& m);
+    template<typename _Tp> _OutputArray(const std::vector<_Tp>& vec);
+    template<typename _Tp> _OutputArray(const std::vector<std::vector<_Tp> >& vec);
+    template<typename _Tp> _OutputArray(const std::vector<Mat_<_Tp> >& vec);
+    template<typename _Tp> _OutputArray(const Mat_<_Tp>& m);
+    template<typename _Tp> _OutputArray(const _Tp* vec, int n);
+    template<typename _Tp, int m, int n> _OutputArray(const Matx<_Tp, m, n>& matx);
+    _OutputArray(const UMat& m);
+    _OutputArray(const std::vector<UMat>& vec);
+
+    template<typename _Tp, std::size_t _Nm> _OutputArray(std::array<_Tp, _Nm>& arr);
+    template<typename _Tp, std::size_t _Nm> _OutputArray(const std::array<_Tp, _Nm>& arr);
+    template<std::size_t _Nm> _OutputArray(std::array<Mat, _Nm>& arr);
+    template<std::size_t _Nm> _OutputArray(const std::array<Mat, _Nm>& arr);
+
+    template<typename _Tp> static _OutputArray rawOut(std::vector<_Tp>& vec);
+    template<typename _Tp, std::size_t _Nm> static _OutputArray rawOut(std::array<_Tp, _Nm>& arr);
+
+    bool fixedSize() const;
+    bool fixedType() const;
+    bool needed() const;
+    Mat& getMatRef(int i=-1) const;
+    UMat& getUMatRef(int i=-1) const;
+    cuda::GpuMat& getGpuMatRef() const;
+    std::vector<cuda::GpuMat>& getGpuMatVecRef() const;
+    ogl::Buffer& getOGlBufferRef() const;
+    cuda::HostMem& getHostMemRef() const;
+    void create(Size sz, int type, int i=-1, bool allowTransposed=false, _OutputArray::DepthMask fixedDepthMask=static_cast<_OutputArray::DepthMask>(0)) const;
+    void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, _OutputArray::DepthMask fixedDepthMask=static_cast<_OutputArray::DepthMask>(0)) const;
+    void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, _OutputArray::DepthMask fixedDepthMask=static_cast<_OutputArray::DepthMask>(0)) const;
+    void createSameSize(const _InputArray& arr, int mtype) const;
+    void release() const;
+    void clear() const;
+    void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const;
+
+    void assign(const UMat& u) const;
+    void assign(const Mat& m) const;
+
+    void assign(const std::vector<UMat>& v) const;
+    void assign(const std::vector<Mat>& v) const;
+
+    void move(UMat& u) const;
+    void move(Mat& m) const;
+};
+
+
+class CV_EXPORTS _InputOutputArray : public _OutputArray
+{
+public:
+    _InputOutputArray();
+    _InputOutputArray(int _flags, void* _obj);
+    _InputOutputArray(Mat& m);
+    _InputOutputArray(std::vector<Mat>& vec);
+    _InputOutputArray(cuda::GpuMat& d_mat);
+    _InputOutputArray(ogl::Buffer& buf);
+    _InputOutputArray(cuda::HostMem& cuda_mem);
+    template<typename _Tp> _InputOutputArray(cudev::GpuMat_<_Tp>& m);
+    template<typename _Tp> _InputOutputArray(std::vector<_Tp>& vec);
+    _InputOutputArray(std::vector<bool>& vec) = delete;  // not supported
+    template<typename _Tp> _InputOutputArray(std::vector<std::vector<_Tp> >& vec);
+    template<typename _Tp> _InputOutputArray(std::vector<Mat_<_Tp> >& vec);
+    template<typename _Tp> _InputOutputArray(Mat_<_Tp>& m);
+    template<typename _Tp> _InputOutputArray(_Tp* vec, int n);
+    template<typename _Tp, int m, int n> _InputOutputArray(Matx<_Tp, m, n>& matx);
+    _InputOutputArray(UMat& m);
+    _InputOutputArray(std::vector<UMat>& vec);
+
+    _InputOutputArray(const Mat& m);
+    _InputOutputArray(const std::vector<Mat>& vec);
+    _InputOutputArray(const cuda::GpuMat& d_mat);
+    _InputOutputArray(const std::vector<cuda::GpuMat>& d_mat);
+    _InputOutputArray(const ogl::Buffer& buf);
+    _InputOutputArray(const cuda::HostMem& cuda_mem);
+    template<typename _Tp> _InputOutputArray(const cudev::GpuMat_<_Tp>& m);
+    template<typename _Tp> _InputOutputArray(const std::vector<_Tp>& vec);
+    template<typename _Tp> _InputOutputArray(const std::vector<std::vector<_Tp> >& vec);
+    template<typename _Tp> _InputOutputArray(const std::vector<Mat_<_Tp> >& vec);
+    template<typename _Tp> _InputOutputArray(const Mat_<_Tp>& m);
+    template<typename _Tp> _InputOutputArray(const _Tp* vec, int n);
+    template<typename _Tp, int m, int n> _InputOutputArray(const Matx<_Tp, m, n>& matx);
+    _InputOutputArray(const UMat& m);
+    _InputOutputArray(const std::vector<UMat>& vec);
+
+    template<typename _Tp, std::size_t _Nm> _InputOutputArray(std::array<_Tp, _Nm>& arr);
+    template<typename _Tp, std::size_t _Nm> _InputOutputArray(const std::array<_Tp, _Nm>& arr);
+    template<std::size_t _Nm> _InputOutputArray(std::array<Mat, _Nm>& arr);
+    template<std::size_t _Nm> _InputOutputArray(const std::array<Mat, _Nm>& arr);
+
+    template<typename _Tp> static _InputOutputArray rawInOut(std::vector<_Tp>& vec);
+    template<typename _Tp, std::size_t _Nm> _InputOutputArray rawInOut(std::array<_Tp, _Nm>& arr);
+
+};
+
+/** Helper to wrap custom types. @see InputArray */
+template<typename _Tp> static inline _InputArray rawIn(_Tp& v);
+/** Helper to wrap custom types. @see InputArray */
+template<typename _Tp> static inline _OutputArray rawOut(_Tp& v);
+/** Helper to wrap custom types. @see InputArray */
+template<typename _Tp> static inline _InputOutputArray rawInOut(_Tp& v);
+
+CV__DEBUG_NS_END
+
+typedef const _InputArray& InputArray;
+typedef InputArray InputArrayOfArrays;
+typedef const _OutputArray& OutputArray;
+typedef OutputArray OutputArrayOfArrays;
+typedef const _InputOutputArray& InputOutputArray;
+typedef InputOutputArray InputOutputArrayOfArrays;
+
+CV_EXPORTS InputOutputArray noArray();
+
+/////////////////////////////////// MatAllocator //////////////////////////////////////
+
+//! Usage flags for allocator
+enum UMatUsageFlags
+{
+    USAGE_DEFAULT = 0,
+
+    // buffer allocation policy is platform and usage specific
+    USAGE_ALLOCATE_HOST_MEMORY = 1 << 0,
+    USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1,
+    USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY
+
+    __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint
+};
+
+struct CV_EXPORTS UMatData;
+
+/** @brief  Custom array allocator
+*/
+class CV_EXPORTS MatAllocator
+{
+public:
+    MatAllocator() {}
+    virtual ~MatAllocator() {}
+
+    // let's comment it off for now to detect and fix all the uses of allocator
+    //virtual void allocate(int dims, const int* sizes, int type, int*& refcount,
+    //                      uchar*& datastart, uchar*& data, size_t* step) = 0;
+    //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0;
+    virtual UMatData* allocate(int dims, const int* sizes, int type,
+                               void* data, size_t* step, AccessFlag flags, UMatUsageFlags usageFlags) const = 0;
+    virtual bool allocate(UMatData* data, AccessFlag accessflags, UMatUsageFlags usageFlags) const = 0;
+    virtual void deallocate(UMatData* data) const = 0;
+    virtual void map(UMatData* data, AccessFlag accessflags) const;
+    virtual void unmap(UMatData* data) const;
+    virtual void download(UMatData* data, void* dst, int dims, const size_t sz[],
+                          const size_t srcofs[], const size_t srcstep[],
+                          const size_t dststep[]) const;
+    virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[],
+                        const size_t dstofs[], const size_t dststep[],
+                        const size_t srcstep[]) const;
+    virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[],
+                      const size_t srcofs[], const size_t srcstep[],
+                      const size_t dstofs[], const size_t dststep[], bool sync) const;
+
+    // default implementation returns DummyBufferPoolController
+    virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const;
+};
+
+
+//////////////////////////////// MatCommaInitializer //////////////////////////////////
+
+/** @brief  Comma-separated Matrix Initializer
+
+ The class instances are usually not created explicitly.
+ Instead, they are created on "matrix << firstValue" operator.
+
+ The sample below initializes 2x2 rotation matrix:
+
+ \code
+ double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180);
+ Mat R = (Mat_<double>(2,2) << a, -b, b, a);
+ \endcode
+*/
+template<typename _Tp> class MatCommaInitializer_
+{
+public:
+    //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat
+    MatCommaInitializer_(Mat_<_Tp>* _m);
+    //! the operator that takes the next value and put it to the matrix
+    template<typename T2> MatCommaInitializer_<_Tp>& operator , (T2 v);
+    //! another form of conversion operator
+    operator Mat_<_Tp>() const;
+protected:
+    MatIterator_<_Tp> it;
+};
+
+
+/////////////////////////////////////// Mat ///////////////////////////////////////////
+
+// note that umatdata might be allocated together
+// with the matrix data, not as a separate object.
+// therefore, it does not have constructor or destructor;
+// it should be explicitly initialized using init().
+struct CV_EXPORTS UMatData
+{
+    enum MemoryFlag { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2,
+        DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24,
+        USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64,
+        ASYNC_CLEANUP=128
+    };
+    UMatData(const MatAllocator* allocator);
+    ~UMatData();
+
+    // provide atomic access to the structure
+    void lock();
+    void unlock();
+
+    bool hostCopyObsolete() const;
+    bool deviceCopyObsolete() const;
+    bool deviceMemMapped() const;
+    bool copyOnMap() const;
+    bool tempUMat() const;
+    bool tempCopiedUMat() const;
+    void markHostCopyObsolete(bool flag);
+    void markDeviceCopyObsolete(bool flag);
+    void markDeviceMemMapped(bool flag);
+
+    const MatAllocator* prevAllocator;
+    const MatAllocator* currAllocator;
+    int urefcount;
+    int refcount;
+    uchar* data;
+    uchar* origdata;
+    size_t size;
+
+    UMatData::MemoryFlag flags;
+    void* handle;
+    void* userdata;
+    int allocatorFlags_;
+    int mapcount;
+    UMatData* originalUMatData;
+    std::shared_ptr<void> allocatorContext;
+};
+CV_ENUM_FLAGS(UMatData::MemoryFlag)
+
+
+struct CV_EXPORTS MatSize
+{
+    explicit MatSize(int* _p) CV_NOEXCEPT;
+    int dims() const CV_NOEXCEPT;
+    Size operator()() const;
+    const int& operator[](int i) const;
+    int& operator[](int i);
+    operator const int*() const CV_NOEXCEPT;  // TODO OpenCV 4.0: drop this
+    bool operator == (const MatSize& sz) const CV_NOEXCEPT;
+    bool operator != (const MatSize& sz) const CV_NOEXCEPT;
+
+    int* p;
+};
+
+struct CV_EXPORTS MatStep
+{
+    MatStep() CV_NOEXCEPT;
+    explicit MatStep(size_t s) CV_NOEXCEPT;
+    const size_t& operator[](int i) const CV_NOEXCEPT;
+    size_t& operator[](int i) CV_NOEXCEPT;
+    operator size_t() const;
+    MatStep& operator = (size_t s);
+
+    size_t* p;
+    size_t buf[2];
+protected:
+    MatStep& operator = (const MatStep&);
+};
+
+/** @example samples/cpp/cout_mat.cpp
+An example demonstrating the serial out capabilities of cv::Mat
+*/
+
+ /** @brief n-dimensional dense array class \anchor CVMat_Details
+
+The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It
+can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel
+volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms
+may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array
+`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k<M.size[k]\f$, is
+computed as:
+\f[addr(M_{i_0,...,i_{M.dims-1}}) = M.data + M.step[0]*i_0 + M.step[1]*i_1 + ... + M.step[M.dims-1]*i_{M.dims-1}\f]
+In case of a 2-dimensional array, the above formula is reduced to:
+\f[addr(M_{i,j}) = M.data + M.step[0]*i + M.step[1]*j\f]
+Note that `M.step[i] >= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means
+that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane,
+and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() .
+
+So, the data layout in Mat is compatible with the majority of dense array types from the standard
+toolkits and SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others,
+that is, with any array that uses *steps* (or *strides*) to compute the position of a pixel.
+Due to this compatibility, it is possible to make a Mat header for user-allocated data and process
+it in-place using OpenCV functions.
+
+There are many different ways to create a Mat object. The most popular options are listed below:
+
+- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue])
+constructor. A new array of the specified size and type is allocated. type has the same meaning as
+in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2
+means a 2-channel (complex) floating-point array, and so on.
+@code
+    // make a 7x7 complex matrix filled with 1+3j.
+    Mat M(7,7,CV_32FC2,Scalar(1,3));
+    // and now turn M to a 100x60 15-channel 8-bit matrix.
+    // The old content will be deallocated
+    M.create(100,60,CV_8UC(15));
+@endcode
+As noted in the introduction to this chapter, create() allocates only a new array when the shape
+or type of the current array are different from the specified ones.
+
+- Create a multi-dimensional array:
+@code
+    // create a 100x100x100 8-bit array
+    int sz[] = {100, 100, 100};
+    Mat bigCube(3, sz, CV_8U, Scalar::all(0));
+@endcode
+It passes the number of dimensions =1 to the Mat constructor but the created array will be
+2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0
+when the array is empty).
+
+- Use a copy constructor or assignment operator where there can be an array or expression on the
+right side (see below). As noted in the introduction, the array assignment is an O(1) operation
+because it only copies the header and increases the reference counter. The Mat::clone() method can
+be used to get a full (deep) copy of the array when you need it.
+
+- Construct a header for a part of another array. It can be a single row, single column, several
+rows, several columns, rectangular region in the array (called a *minor* in algebra) or a
+diagonal. Such operations are also O(1) because the new header references the same data. You can
+actually modify a part of the array using this feature, for example:
+@code
+    // add the 5-th row, multiplied by 3 to the 3rd row
+    M.row(3) = M.row(3) + M.row(5)*3;
+    // now copy the 7-th column to the 1-st column
+    // M.col(1) = M.col(7); // this will not work
+    Mat M1 = M.col(1);
+    M.col(7).copyTo(M1);
+    // create a new 320x240 image
+    Mat img(Size(320,240),CV_8UC3);
+    // select a ROI
+    Mat roi(img, Rect(10,10,100,100));
+    // fill the ROI with (0,255,0) (which is green in RGB space);
+    // the original 320x240 image will be modified
+    roi = Scalar(0,255,0);
+@endcode
+Due to the additional datastart and dataend members, it is possible to compute a relative
+sub-array position in the main *container* array using locateROI():
+@code
+    Mat A = Mat::eye(10, 10, CV_32S);
+    // extracts A columns, 1 (inclusive) to 3 (exclusive).
+    Mat B = A(Range::all(), Range(1, 3));
+    // extracts B rows, 5 (inclusive) to 9 (exclusive).
+    // that is, C \~ A(Range(5, 9), Range(1, 3))
+    Mat C = B(Range(5, 9), Range::all());
+    Size size; Point ofs;
+    C.locateROI(size, ofs);
+    // size will be (width=10,height=10) and the ofs will be (x=1, y=5)
+@endcode
+As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted
+sub-matrices.
+
+- Make a header for user-allocated data. It can be useful to do the following:
+    -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or
+    a processing module for gstreamer, and so on). For example:
+    @code
+        Mat process_video_frame(const unsigned char* pixels,
+                                int width, int height, int step)
+        {
+            // wrap input buffer
+            Mat img(height, width, CV_8UC3, (unsigned char*)pixels, step);
+
+            Mat result;
+            GaussianBlur(img, result, Size(7, 7), 1.5, 1.5);
+
+            return result;
+        }
+    @endcode
+    -# Quickly initialize small matrices and/or get a super-fast element access.
+    @code
+        double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};
+        Mat M = Mat(3, 3, CV_64F, m).inv();
+    @endcode
+    .
+
+- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example:
+@code
+    // create a double-precision identity matrix and add it to M.
+    M += Mat::eye(M.rows, M.cols, CV_64F);
+@endcode
+
+- Use a comma-separated initializer:
+@code
+    // create a 3x3 double-precision identity matrix
+    Mat M = (Mat_<double>(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
+@endcode
+With this approach, you first call a constructor of the Mat class with the proper parameters, and
+then you just put `<< operator` followed by comma-separated values that can be constants,
+variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation
+errors.
+
+Once the array is created, it is automatically managed via a reference-counting mechanism. If the
+array header is built on top of user-allocated data, you should handle the data by yourself. The
+array data is deallocated when no one points to it. If you want to release the data pointed by a
+array header before the array destructor is called, use Mat::release().
+
+The next important thing to learn about the array class is element access. This manual already
+described how to compute an address of each array element. Normally, you are not required to use the
+formula directly in the code. If you know the array element type (which can be retrieved using the
+method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as:
+@code
+    M.at<double>(i,j) += 1.f;
+@endcode
+assuming that `M` is a double-precision floating-point array. There are several variants of the method
+at for a different number of dimensions.
+
+If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to
+the row first, and then just use the plain C operator [] :
+@code
+    // compute sum of positive matrix elements
+    // (assuming that M is a double-precision matrix)
+    double sum=0;
+    for(int i = 0; i < M.rows; i++)
+    {
+        const double* Mi = M.ptr<double>(i);
+        for(int j = 0; j < M.cols; j++)
+            sum += std::max(Mi[j], 0.);
+    }
+@endcode
+Some operations, like the one above, do not actually depend on the array shape. They just process
+elements of an array one by one (or elements from multiple arrays that have the same coordinates,
+for example, array addition). Such operations are called *element-wise*. It makes sense to check
+whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If
+yes, process them as a long single row:
+@code
+    // compute the sum of positive matrix elements, optimized variant
+    double sum=0;
+    int cols = M.cols, rows = M.rows;
+    if(M.isContinuous())
+    {
+        cols *= rows;
+        rows = 1;
+    }
+    for(int i = 0; i < rows; i++)
+    {
+        const double* Mi = M.ptr<double>(i);
+        for(int j = 0; j < cols; j++)
+            sum += std::max(Mi[j], 0.);
+    }
+@endcode
+In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is
+smaller, which is especially noticeable in case of small matrices.
+
+Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows:
+@code
+    // compute sum of positive matrix elements, iterator-based variant
+    double sum=0;
+    MatConstIterator_<double> it = M.begin<double>(), it_end = M.end<double>();
+    for(; it != it_end; ++it)
+        sum += std::max(*it, 0.);
+@endcode
+The matrix iterators are random-access iterators, so they can be passed to any STL algorithm,
+including std::sort().
+
+@note Matrix Expressions and arithmetic see MatExpr
+*/
+class CV_EXPORTS Mat
+{
+public:
+    /**
+    These are various constructors that form a matrix. As noted in the AutomaticAllocation, often
+    the default constructor is enough, and the proper matrix will be allocated by an OpenCV function.
+    The constructed matrix can further be assigned to another matrix or matrix expression or can be
+    allocated with Mat::create . In the former case, the old content is de-referenced.
+     */
+    Mat() CV_NOEXCEPT;
+
+    /** @overload
+    @param rows Number of rows in a 2D array.
+    @param cols Number of columns in a 2D array.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    */
+    Mat(int rows, int cols, int type);
+
+    /** @overload
+    @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
+    number of columns go in the reverse order.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+      */
+    Mat(Size size, int type);
+
+    /** @overload
+    @param rows Number of rows in a 2D array.
+    @param cols Number of columns in a 2D array.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+    the particular value after the construction, use the assignment operator
+    Mat::operator=(const Scalar& value) .
+    */
+    Mat(int rows, int cols, int type, const Scalar& s);
+
+    /** @overload
+    @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
+    number of columns go in the reverse order.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+    the particular value after the construction, use the assignment operator
+    Mat::operator=(const Scalar& value) .
+      */
+    Mat(Size size, int type, const Scalar& s);
+
+    /** @overload
+    @param ndims Array dimensionality.
+    @param sizes Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    */
+    Mat(int ndims, const int* sizes, int type);
+
+    /** @overload
+    @param sizes Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    */
+    Mat(const std::vector<int>& sizes, int type);
+
+    /** @overload
+    @param ndims Array dimensionality.
+    @param sizes Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+    the particular value after the construction, use the assignment operator
+    Mat::operator=(const Scalar& value) .
+    */
+    Mat(int ndims, const int* sizes, int type, const Scalar& s);
+
+    /** @overload
+    @param sizes Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+    the particular value after the construction, use the assignment operator
+    Mat::operator=(const Scalar& value) .
+    */
+    Mat(const std::vector<int>& sizes, int type, const Scalar& s);
+
+
+    /** @overload
+    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+    formed using such a constructor, you also modify the corresponding elements of m . If you want to
+    have an independent copy of the sub-array, use Mat::clone() .
+    */
+    Mat(const Mat& m);
+
+    /** @overload
+    @param rows Number of rows in a 2D array.
+    @param cols Number of columns in a 2D array.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+    data, which means that no data is copied. This operation is very efficient and can be used to
+    process external data using OpenCV functions. The external data is not automatically deallocated, so
+    you should take care of it.
+    @param step Number of bytes each matrix row occupies. The value should include the padding bytes at
+    the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
+    and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
+    */
+    Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP);
+
+    /** @overload
+    @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
+    number of columns go in the reverse order.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+    data, which means that no data is copied. This operation is very efficient and can be used to
+    process external data using OpenCV functions. The external data is not automatically deallocated, so
+    you should take care of it.
+    @param step Number of bytes each matrix row occupies. The value should include the padding bytes at
+    the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
+    and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
+    */
+    Mat(Size size, int type, void* data, size_t step=AUTO_STEP);
+
+    /** @overload
+    @param ndims Array dimensionality.
+    @param sizes Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+    data, which means that no data is copied. This operation is very efficient and can be used to
+    process external data using OpenCV functions. The external data is not automatically deallocated, so
+    you should take care of it.
+    @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always
+    set to the element size). If not specified, the matrix is assumed to be continuous.
+    */
+    Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0);
+
+    /** @overload
+    @param sizes Array of integers specifying an n-dimensional array shape.
+    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+    data, which means that no data is copied. This operation is very efficient and can be used to
+    process external data using OpenCV functions. The external data is not automatically deallocated, so
+    you should take care of it.
+    @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always
+    set to the element size). If not specified, the matrix is assumed to be continuous.
+    */
+    Mat(const std::vector<int>& sizes, int type, void* data, const size_t* steps=0);
+
+    /** @overload
+    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+    formed using such a constructor, you also modify the corresponding elements of m . If you want to
+    have an independent copy of the sub-array, use Mat::clone() .
+    @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range
+    end is exclusive. Use Range::all() to take all the rows.
+    @param colRange Range of the m columns to take. Use Range::all() to take all the columns.
+    */
+    Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all());
+
+    /** @overload
+    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+    formed using such a constructor, you also modify the corresponding elements of m . If you want to
+    have an independent copy of the sub-array, use Mat::clone() .
+    @param roi Region of interest.
+    */
+    Mat(const Mat& m, const Rect& roi);
+
+    /** @overload
+    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+    formed using such a constructor, you also modify the corresponding elements of m . If you want to
+    have an independent copy of the sub-array, use Mat::clone() .
+    @param ranges Array of selected ranges of m along each dimensionality.
+    */
+    Mat(const Mat& m, const Range* ranges);
+
+    /** @overload
+    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+    formed using such a constructor, you also modify the corresponding elements of m . If you want to
+    have an independent copy of the sub-array, use Mat::clone() .
+    @param ranges Array of selected ranges of m along each dimensionality.
+    */
+    Mat(const Mat& m, const std::vector<Range>& ranges);
+
+    /** @overload
+    @param vec STL vector whose elements form the matrix. The matrix has a single column and the number
+    of rows equal to the number of vector elements. Type of the matrix matches the type of vector
+    elements. The constructor can handle arbitrary types, for which there is a properly declared
+    DataType . This means that the vector elements must be primitive numbers or uni-type numerical
+    tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is
+    explicit. Since STL vectors are not automatically converted to Mat instances, you should write
+    Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements
+    will be added to the vector because it can potentially yield vector data reallocation, and, thus,
+    the matrix data pointer will be invalid.
+    @param copyData Flag to specify whether the underlying data of the STL vector should be copied
+    to (true) or shared with (false) the newly constructed matrix. When the data is copied, the
+    allocated buffer is managed using Mat reference counting mechanism. While the data is shared,
+    the reference counter is NULL, and you should not deallocate the data until the matrix is not
+    destructed.
+    */
+    template<typename _Tp> explicit Mat(const std::vector<_Tp>& vec, bool copyData=false);
+
+    /** @overload
+    */
+    template<typename _Tp, typename = typename std::enable_if<std::is_arithmetic<_Tp>::value>::type>
+    explicit Mat(const std::initializer_list<_Tp> list);
+
+    /** @overload
+    */
+    template<typename _Tp> explicit Mat(const std::initializer_list<int> sizes, const std::initializer_list<_Tp> list);
+
+    /** @overload
+    */
+    template<typename _Tp, size_t _Nm> explicit Mat(const std::array<_Tp, _Nm>& arr, bool copyData=false);
+
+    /** @overload
+    */
+    template<typename _Tp, int n> explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true);
+
+    /** @overload
+    */
+    template<typename _Tp, int m, int n> explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true);
+
+    /** @overload
+    */
+    template<typename _Tp> explicit Mat(const Point_<_Tp>& pt, bool copyData=true);
+
+    /** @overload
+    */
+    template<typename _Tp> explicit Mat(const Point3_<_Tp>& pt, bool copyData=true);
+
+    /** @overload
+    */
+    template<typename _Tp> explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer);
+
+    //! download data from GpuMat
+    explicit Mat(const cuda::GpuMat& m);
+
+    //! destructor - calls release()
+    ~Mat();
+
+    /** @brief assignment operators
+
+    These are available assignment operators. Since they all are very different, make sure to read the
+    operator parameters description.
+    @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that
+    no data is copied but the data is shared and the reference counter, if any, is incremented. Before
+    assigning new data, the old data is de-referenced via Mat::release .
+     */
+    Mat& operator = (const Mat& m);
+
+    /** @overload
+    @param expr Assigned matrix expression object. As opposite to the first form of the assignment
+    operation, the second form can reuse already allocated matrix if it has the right size and type to
+    fit the matrix expression result. It is automatically handled by the real function that the matrix
+    expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of
+    automatic C reallocation.
+    */
+    Mat& operator = (const MatExpr& expr);
+
+    //! retrieve UMat from Mat
+    UMat getUMat(AccessFlag accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const;
+
+    /** @brief Creates a matrix header for the specified matrix row.
+
+    The method makes a new header for the specified matrix row and returns it. This is an O(1)
+    operation, regardless of the matrix size. The underlying data of the new matrix is shared with the
+    original matrix. Here is the example of one of the classical basic matrix processing operations,
+    axpy, used by LU and many other algorithms:
+    @code
+        inline void matrix_axpy(Mat& A, int i, int j, double alpha)
+        {
+            A.row(i) += A.row(j)*alpha;
+        }
+    @endcode
+    @note In the current implementation, the following code does not work as expected:
+    @code
+        Mat A;
+        ...
+        A.row(i) = A.row(j); // will not work
+    @endcode
+    This happens because A.row(i) forms a temporary header that is further assigned to another header.
+    Remember that each of these operations is O(1), that is, no data is copied. Thus, the above
+    assignment is not true if you may have expected the j-th row to be copied to the i-th row. To
+    achieve that, you should either turn this simple assignment into an expression or use the
+    Mat::copyTo method:
+    @code
+        Mat A;
+        ...
+        // works, but looks a bit obscure.
+        A.row(i) = A.row(j) + 0;
+        // this is a bit longer, but the recommended method.
+        A.row(j).copyTo(A.row(i));
+    @endcode
+    @param y A 0-based row index.
+     */
+    Mat row(int y) const;
+
+    /** @brief Creates a matrix header for the specified matrix column.
+
+    The method makes a new header for the specified matrix column and returns it. This is an O(1)
+    operation, regardless of the matrix size. The underlying data of the new matrix is shared with the
+    original matrix. See also the Mat::row description.
+    @param x A 0-based column index.
+     */
+    Mat col(int x) const;
+
+    /** @brief Creates a matrix header for the specified row span.
+
+    The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and
+    Mat::col , this is an O(1) operation.
+    @param startrow An inclusive 0-based start index of the row span.
+    @param endrow An exclusive 0-based ending index of the row span.
+     */
+    Mat rowRange(int startrow, int endrow) const;
+
+    /** @overload
+    @param r Range structure containing both the start and the end indices.
+    */
+    Mat rowRange(const Range& r) const;
+
+    /** @brief Creates a matrix header for the specified column span.
+
+    The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and
+    Mat::col , this is an O(1) operation.
+    @param startcol An inclusive 0-based start index of the column span.
+    @param endcol An exclusive 0-based ending index of the column span.
+     */
+    Mat colRange(int startcol, int endcol) const;
+
+    /** @overload
+    @param r Range structure containing both the start and the end indices.
+    */
+    Mat colRange(const Range& r) const;
+
+    /** @brief Extracts a diagonal from a matrix
+
+    The method makes a new header for the specified matrix diagonal. The new matrix is represented as a
+    single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation.
+    @param d index of the diagonal, with the following values:
+    - `d=0` is the main diagonal.
+    - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set
+      immediately below the main one.
+    - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set
+      immediately above the main one.
+    For example:
+    @code
+        Mat m = (Mat_<int>(3,3) <<
+                    1,2,3,
+                    4,5,6,
+                    7,8,9);
+        Mat d0 = m.diag(0);
+        Mat d1 = m.diag(1);
+        Mat d_1 = m.diag(-1);
+    @endcode
+    The resulting matrices are
+    @code
+     d0 =
+       [1;
+        5;
+        9]
+     d1 =
+       [2;
+        6]
+     d_1 =
+       [4;
+        8]
+    @endcode
+     */
+    Mat diag(int d=0) const;
+
+    /** @brief creates a diagonal matrix
+
+    The method creates a square diagonal matrix from specified main diagonal.
+    @param d One-dimensional matrix that represents the main diagonal.
+     */
+    CV_NODISCARD_STD static Mat diag(const Mat& d);
+
+    /** @brief Creates a full copy of the array and the underlying data.
+
+    The method creates a full copy of the array. The original step[] is not taken into account. So, the
+    array copy is a continuous array occupying total()*elemSize() bytes.
+     */
+    CV_NODISCARD_STD Mat clone() const;
+
+    /** @brief Copies the matrix to another one.
+
+    The method copies the matrix data to another matrix. Before copying the data, the method invokes :
+    @code
+        m.create(this->size(), this->type());
+    @endcode
+    so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the
+    function does not handle the case of a partial overlap between the source and the destination
+    matrices.
+
+    When the operation mask is specified, if the Mat::create call shown above reallocates the matrix,
+    the newly allocated matrix is initialized with all zeros before copying the data.
+    @param m Destination matrix. If it does not have a proper size or type before the operation, it is
+    reallocated.
+     */
+    void copyTo( OutputArray m ) const;
+
+    /** @overload
+    @param m Destination matrix. If it does not have a proper size or type before the operation, it is
+    reallocated.
+    @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix
+    elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels.
+    */
+    void copyTo( OutputArray m, InputArray mask ) const;
+
+    /** @brief Converts an array to another data type with optional scaling.
+
+    The method converts source pixel values to the target data type. saturate_cast\<\> is applied at
+    the end to avoid possible overflows:
+
+    \f[m(x,y) = saturate \_ cast<rType>( \alpha (*this)(x,y) +  \beta )\f]
+    @param m output matrix; if it does not have a proper size or type before the operation, it is
+    reallocated.
+    @param rtype desired output matrix type or, rather, the depth since the number of channels are the
+    same as the input has; if rtype is negative, the output matrix will have the same type as the input.
+    @param alpha optional scale factor.
+    @param beta optional delta added to the scaled values.
+     */
+    void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const;
+
+    /** @brief Provides a functional form of convertTo.
+
+    This is an internally used method called by the @ref MatrixExpressions engine.
+    @param m Destination array.
+    @param type Desired destination array depth (or -1 if it should be the same as the source type).
+     */
+    void assignTo( Mat& m, int type=-1 ) const;
+
+    /** @brief Sets all or some of the array elements to the specified value.
+    @param s Assigned scalar converted to the actual array type.
+    */
+    Mat& operator = (const Scalar& s);
+
+    /** @brief Sets all or some of the array elements to the specified value.
+
+    This is an advanced variant of the Mat::operator=(const Scalar& s) operator.
+    @param value Assigned scalar converted to the actual array type.
+    @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix
+    elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels
+     */
+    Mat& setTo(InputArray value, InputArray mask=noArray());
+
+    /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data.
+
+    The method makes a new matrix header for \*this elements. The new matrix may have a different size
+    and/or different number of channels. Any combination is possible if:
+    -   No extra elements are included into the new matrix and no elements are excluded. Consequently,
+        the product rows\*cols\*channels() must stay the same after the transformation.
+    -   No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of
+        rows, or the operation changes the indices of elements row in some other way, the matrix must be
+        continuous. See Mat::isContinuous .
+
+    For example, if there is a set of 3D points stored as an STL vector, and you want to represent the
+    points as a 3xN matrix, do the following:
+    @code
+        std::vector<Point3f> vec;
+        ...
+        Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation
+                          reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel.
+                                      // Also, an O(1) operation
+                             t(); // finally, transpose the Nx3 matrix.
+                                  // This involves copying all the elements
+    @endcode
+    @param cn New number of channels. If the parameter is 0, the number of channels remains the same.
+    @param rows New number of rows. If the parameter is 0, the number of rows remains the same.
+     */
+    Mat reshape(int cn, int rows=0) const;
+
+    /** @overload */
+    Mat reshape(int cn, int newndims, const int* newsz) const;
+
+    /** @overload */
+    Mat reshape(int cn, const std::vector<int>& newshape) const;
+
+    /** @brief Transposes a matrix.
+
+    The method performs matrix transposition by means of matrix expressions. It does not perform the
+    actual transposition but returns a temporary matrix transposition object that can be further used as
+    a part of more complex matrix expressions or can be assigned to a matrix:
+    @code
+        Mat A1 = A + Mat::eye(A.size(), A.type())*lambda;
+        Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I)
+    @endcode
+     */
+    MatExpr t() const;
+
+    /** @brief Inverses a matrix.
+
+    The method performs a matrix inversion by means of matrix expressions. This means that a temporary
+    matrix inversion object is returned by the method and can be used further as a part of more complex
+    matrix expressions or can be assigned to a matrix.
+    @param method Matrix inversion method. One of cv::DecompTypes
+     */
+    MatExpr inv(int method=DECOMP_LU) const;
+
+    /** @brief Performs an element-wise multiplication or division of the two matrices.
+
+    The method returns a temporary object encoding per-element array multiplication, with optional
+    scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator.
+
+    Example:
+    @code
+        Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5)
+    @endcode
+    @param m Another array of the same type and the same size as \*this, or a matrix expression.
+    @param scale Optional scale factor.
+     */
+    MatExpr mul(InputArray m, double scale=1) const;
+
+    /** @brief Computes a cross-product of two 3-element vectors.
+
+    The method computes a cross-product of two 3-element vectors. The vectors must be 3-element
+    floating-point vectors of the same shape and size. The result is another 3-element vector of the
+    same shape and type as operands.
+    @param m Another cross-product operand.
+     */
+    Mat cross(InputArray m) const;
+
+    /** @brief Computes a dot-product of two vectors.
+
+    The method computes a dot-product of two matrices. If the matrices are not single-column or
+    single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D
+    vectors. The vectors must have the same size and type. If the matrices have more than one channel,
+    the dot products from all the channels are summed together.
+    @param m another dot-product operand.
+     */
+    double dot(InputArray m) const;
+
+    /** @brief Returns a zero array of the specified size and type.
+
+    The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant
+    array as a function parameter, part of a matrix expression, or as a matrix initializer:
+    @code
+        Mat A;
+        A = Mat::zeros(3, 3, CV_32F);
+    @endcode
+    In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix.
+    Otherwise, the existing matrix A is filled with zeros.
+    @param rows Number of rows.
+    @param cols Number of columns.
+    @param type Created matrix type.
+     */
+    CV_NODISCARD_STD static MatExpr zeros(int rows, int cols, int type);
+
+    /** @overload
+    @param size Alternative to the matrix size specification Size(cols, rows) .
+    @param type Created matrix type.
+    */
+    CV_NODISCARD_STD static MatExpr zeros(Size size, int type);
+
+    /** @overload
+    @param ndims Array dimensionality.
+    @param sz Array of integers specifying the array shape.
+    @param type Created matrix type.
+    */
+    CV_NODISCARD_STD static MatExpr zeros(int ndims, const int* sz, int type);
+
+    /** @brief Returns an array of all 1's of the specified size and type.
+
+    The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using
+    this method you can initialize an array with an arbitrary value, using the following Matlab idiom:
+    @code
+        Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3.
+    @endcode
+    The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it
+    just remembers the scale factor (3 in this case) and use it when actually invoking the matrix
+    initializer.
+    @note In case of multi-channels type, only the first channel will be initialized with 1's, the
+    others will be set to 0's.
+    @param rows Number of rows.
+    @param cols Number of columns.
+    @param type Created matrix type.
+     */
+    CV_NODISCARD_STD static MatExpr ones(int rows, int cols, int type);
+
+    /** @overload
+    @param size Alternative to the matrix size specification Size(cols, rows) .
+    @param type Created matrix type.
+    */
+    CV_NODISCARD_STD static MatExpr ones(Size size, int type);
+
+    /** @overload
+    @param ndims Array dimensionality.
+    @param sz Array of integers specifying the array shape.
+    @param type Created matrix type.
+    */
+    CV_NODISCARD_STD static MatExpr ones(int ndims, const int* sz, int type);
+
+    /** @brief Returns an identity matrix of the specified size and type.
+
+    The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to
+    Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently:
+    @code
+        // make a 4x4 diagonal matrix with 0.1's on the diagonal.
+        Mat A = Mat::eye(4, 4, CV_32F)*0.1;
+    @endcode
+    @note In case of multi-channels type, identity matrix will be initialized only for the first channel,
+    the others will be set to 0's
+    @param rows Number of rows.
+    @param cols Number of columns.
+    @param type Created matrix type.
+     */
+    CV_NODISCARD_STD static MatExpr eye(int rows, int cols, int type);
+
+    /** @overload
+    @param size Alternative matrix size specification as Size(cols, rows) .
+    @param type Created matrix type.
+    */
+    CV_NODISCARD_STD static MatExpr eye(Size size, int type);
+
+    /** @brief Allocates new array data if needed.
+
+    This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays
+    call this method for each output array. The method uses the following algorithm:
+
+    -# If the current array shape and the type match the new ones, return immediately. Otherwise,
+       de-reference the previous data by calling Mat::release.
+    -# Initialize the new header.
+    -# Allocate the new data of total()\*elemSize() bytes.
+    -# Allocate the new, associated with the data, reference counter and set it to 1.
+
+    Such a scheme makes the memory management robust and efficient at the same time and helps avoid
+    extra typing for you. This means that usually there is no need to explicitly allocate output arrays.
+    That is, instead of writing:
+    @code
+        Mat color;
+        ...
+        Mat gray(color.rows, color.cols, color.depth());
+        cvtColor(color, gray, COLOR_BGR2GRAY);
+    @endcode
+    you can simply write:
+    @code
+        Mat color;
+        ...
+        Mat gray;
+        cvtColor(color, gray, COLOR_BGR2GRAY);
+    @endcode
+    because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array
+    internally.
+    @param rows New number of rows.
+    @param cols New number of columns.
+    @param type New matrix type.
+     */
+    void create(int rows, int cols, int type);
+
+    /** @overload
+    @param size Alternative new matrix size specification: Size(cols, rows)
+    @param type New matrix type.
+    */
+    void create(Size size, int type);
+
+    /** @overload
+    @param ndims New array dimensionality.
+    @param sizes Array of integers specifying a new array shape.
+    @param type New matrix type.
+    */
+    void create(int ndims, const int* sizes, int type);
+
+    /** @overload
+    @param sizes Array of integers specifying a new array shape.
+    @param type New matrix type.
+    */
+    void create(const std::vector<int>& sizes, int type);
+
+    /** @brief Increments the reference counter.
+
+    The method increments the reference counter associated with the matrix data. If the matrix header
+    points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no
+    effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It
+    is called implicitly by the matrix assignment operator. The reference counter increment is an atomic
+    operation on the platforms that support it. Thus, it is safe to operate on the same matrices
+    asynchronously in different threads.
+     */
+    void addref();
+
+    /** @brief Decrements the reference counter and deallocates the matrix if needed.
+
+    The method decrements the reference counter associated with the matrix data. When the reference
+    counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers
+    are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the
+    reference counter is NULL, and the method has no effect in this case.
+
+    This method can be called manually to force the matrix data deallocation. But since this method is
+    automatically called in the destructor, or by any other method that changes the data pointer, it is
+    usually not needed. The reference counter decrement and check for 0 is an atomic operation on the
+    platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in
+    different threads.
+     */
+    void release();
+
+    //! internal use function, consider to use 'release' method instead; deallocates the matrix data
+    void deallocate();
+    //! internal use function; properly re-allocates _size, _step arrays
+    void copySize(const Mat& m);
+
+    /** @brief Reserves space for the certain number of rows.
+
+    The method reserves space for sz rows. If the matrix already has enough space to store sz rows,
+    nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method
+    emulates the corresponding method of the STL vector class.
+    @param sz Number of rows.
+     */
+    void reserve(size_t sz);
+
+    /** @brief Reserves space for the certain number of bytes.
+
+    The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes,
+    nothing happens. If matrix has to be reallocated its previous content could be lost.
+    @param sz Number of bytes.
+    */
+    void reserveBuffer(size_t sz);
+
+    /** @brief Changes the number of matrix rows.
+
+    The methods change the number of matrix rows. If the matrix is reallocated, the first
+    min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL
+    vector class.
+    @param sz New number of rows.
+     */
+    void resize(size_t sz);
+
+    /** @overload
+    @param sz New number of rows.
+    @param s Value assigned to the newly added elements.
+     */
+    void resize(size_t sz, const Scalar& s);
+
+    //! internal function
+    void push_back_(const void* elem);
+
+    /** @brief Adds elements to the bottom of the matrix.
+
+    The methods add one or more elements to the bottom of the matrix. They emulate the corresponding
+    method of the STL vector class. When elem is Mat , its type and the number of columns must be the
+    same as in the container matrix.
+    @param elem Added element(s).
+     */
+    template<typename _Tp> void push_back(const _Tp& elem);
+
+    /** @overload
+    @param elem Added element(s).
+    */
+    template<typename _Tp> void push_back(const Mat_<_Tp>& elem);
+
+    /** @overload
+    @param elem Added element(s).
+    */
+    template<typename _Tp> void push_back(const std::vector<_Tp>& elem);
+
+    /** @overload
+    @param m Added line(s).
+    */
+    void push_back(const Mat& m);
+
+    /** @brief Removes elements from the bottom of the matrix.
+
+    The method removes one or more rows from the bottom of the matrix.
+    @param nelems Number of removed rows. If it is greater than the total number of rows, an exception
+    is thrown.
+     */
+    void pop_back(size_t nelems=1);
+
+    /** @brief Locates the matrix header within a parent matrix.
+
+    After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange,
+    Mat::colRange, and others, the resultant submatrix points just to the part of the original big
+    matrix. However, each submatrix contains information (represented by datastart and dataend
+    fields) that helps reconstruct the original matrix size and the position of the extracted
+    submatrix within the original matrix. The method locateROI does exactly that.
+    @param wholeSize Output parameter that contains the size of the whole matrix containing *this*
+    as a part.
+    @param ofs Output parameter that contains an offset of *this* inside the whole matrix.
+     */
+    void locateROI( Size& wholeSize, Point& ofs ) const;
+
+    /** @brief Adjusts a submatrix size and position within the parent matrix.
+
+    The method is complimentary to Mat::locateROI . The typical use of these functions is to determine
+    the submatrix position within the parent matrix and then shift the position somehow. Typically, it
+    can be required for filtering operations when pixels outside of the ROI should be taken into
+    account. When all the method parameters are positive, the ROI needs to grow in all directions by the
+    specified amount, for example:
+    @code
+        A.adjustROI(2, 2, 2, 2);
+    @endcode
+    In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted
+    by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the
+    filtering with the 5x5 kernel.
+
+    adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the
+    adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is
+    located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not
+    be increased in the upward direction.
+
+    The function is used internally by the OpenCV filtering functions, like filter2D , morphological
+    operations, and so on.
+    @param dtop Shift of the top submatrix boundary upwards.
+    @param dbottom Shift of the bottom submatrix boundary downwards.
+    @param dleft Shift of the left submatrix boundary to the left.
+    @param dright Shift of the right submatrix boundary to the right.
+    @sa copyMakeBorder
+     */
+    Mat& adjustROI( int dtop, int dbottom, int dleft, int dright );
+
+    /** @brief Extracts a rectangular submatrix.
+
+    The operators make a new header for the specified sub-array of \*this . They are the most
+    generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example,
+    `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above,
+    the operators are O(1) operations, that is, no matrix data is copied.
+    @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To
+    select all the rows, use Range::all().
+    @param colRange Start and end column of the extracted submatrix. The upper boundary is not included.
+    To select all the columns, use Range::all().
+     */
+    Mat operator()( Range rowRange, Range colRange ) const;
+
+    /** @overload
+    @param roi Extracted submatrix specified as a rectangle.
+    */
+    Mat operator()( const Rect& roi ) const;
+
+    /** @overload
+    @param ranges Array of selected ranges along each array dimension.
+    */
+    Mat operator()( const Range* ranges ) const;
+
+    /** @overload
+    @param ranges Array of selected ranges along each array dimension.
+    */
+    Mat operator()(const std::vector<Range>& ranges) const;
+
+    template<typename _Tp> operator std::vector<_Tp>() const;
+    template<typename _Tp, int n> operator Vec<_Tp, n>() const;
+    template<typename _Tp, int m, int n> operator Matx<_Tp, m, n>() const;
+
+    template<typename _Tp, std::size_t _Nm> operator std::array<_Tp, _Nm>() const;
+
+    /** @brief Reports whether the matrix is continuous or not.
+
+    The method returns true if the matrix elements are stored continuously without gaps at the end of
+    each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous.
+    Matrices created with Mat::create are always continuous. But if you extract a part of the matrix
+    using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data,
+    such matrices may no longer have this property.
+
+    The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when
+    you construct a matrix header. Thus, the continuity check is a very fast operation, though
+    theoretically it could be done as follows:
+    @code
+        // alternative implementation of Mat::isContinuous()
+        bool myCheckMatContinuity(const Mat& m)
+        {
+            //return (m.flags & Mat::CONTINUOUS_FLAG) != 0;
+            return m.rows == 1 || m.step == m.cols*m.elemSize();
+        }
+    @endcode
+    The method is used in quite a few of OpenCV functions. The point is that element-wise operations
+    (such as arithmetic and logical operations, math functions, alpha blending, color space
+    transformations, and others) do not depend on the image geometry. Thus, if all the input and output
+    arrays are continuous, the functions can process them as very long single-row vectors. The example
+    below illustrates how an alpha-blending function can be implemented:
+    @code
+        template<typename T>
+        void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst)
+        {
+            const float alpha_scale = (float)std::numeric_limits<T>::max(),
+                        inv_scale = 1.f/alpha_scale;
+
+            CV_Assert( src1.type() == src2.type() &&
+                       src1.type() == CV_MAKETYPE(traits::Depth<T>::value, 4) &&
+                       src1.size() == src2.size());
+            Size size = src1.size();
+            dst.create(size, src1.type());
+
+            // here is the idiom: check the arrays for continuity and,
+            // if this is the case,
+            // treat the arrays as 1D vectors
+            if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() )
+            {
+                size.width *= size.height;
+                size.height = 1;
+            }
+            size.width *= 4;
+
+            for( int i = 0; i < size.height; i++ )
+            {
+                // when the arrays are continuous,
+                // the outer loop is executed only once
+                const T* ptr1 = src1.ptr<T>(i);
+                const T* ptr2 = src2.ptr<T>(i);
+                T* dptr = dst.ptr<T>(i);
+
+                for( int j = 0; j < size.width; j += 4 )
+                {
+                    float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale;
+                    dptr[j] = saturate_cast<T>(ptr1[j]*alpha + ptr2[j]*beta);
+                    dptr[j+1] = saturate_cast<T>(ptr1[j+1]*alpha + ptr2[j+1]*beta);
+                    dptr[j+2] = saturate_cast<T>(ptr1[j+2]*alpha + ptr2[j+2]*beta);
+                    dptr[j+3] = saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale);
+                }
+            }
+        }
+    @endcode
+    This approach, while being very simple, can boost the performance of a simple element-operation by
+    10-20 percents, especially if the image is rather small and the operation is quite simple.
+
+    Another OpenCV idiom in this function, a call of Mat::create for the destination array, that
+    allocates the destination array unless it already has the proper size and type. And while the newly
+    allocated arrays are always continuous, you still need to check the destination array because
+    Mat::create does not always allocate a new matrix.
+     */
+    bool isContinuous() const;
+
+    //! returns true if the matrix is a submatrix of another matrix
+    bool isSubmatrix() const;
+
+    /** @brief Returns the matrix element size in bytes.
+
+    The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 ,
+    the method returns 3\*sizeof(short) or 6.
+     */
+    size_t elemSize() const;
+
+    /** @brief Returns the size of each matrix element channel in bytes.
+
+    The method returns the matrix element channel size in bytes, that is, it ignores the number of
+    channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2.
+     */
+    size_t elemSize1() const;
+
+    /** @brief Returns the type of a matrix element.
+
+    The method returns a matrix element type. This is an identifier compatible with the CvMat type
+    system, like CV_16SC3 or 16-bit signed 3-channel array, and so on.
+     */
+    int type() const;
+
+    /** @brief Returns the depth of a matrix element.
+
+    The method returns the identifier of the matrix element depth (the type of each individual channel).
+    For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of
+    matrix types contains the following values:
+    -   CV_8U - 8-bit unsigned integers ( 0..255 )
+    -   CV_8S - 8-bit signed integers ( -128..127 )
+    -   CV_16U - 16-bit unsigned integers ( 0..65535 )
+    -   CV_16S - 16-bit signed integers ( -32768..32767 )
+    -   CV_32S - 32-bit signed integers ( -2147483648..2147483647 )
+    -   CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )
+    -   CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )
+     */
+    int depth() const;
+
+    /** @brief Returns the number of matrix channels.
+
+    The method returns the number of matrix channels.
+     */
+    int channels() const;
+
+    /** @brief Returns a normalized step.
+
+    The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an
+    arbitrary matrix element.
+     */
+    size_t step1(int i=0) const;
+
+    /** @brief Returns true if the array has no elements.
+
+    The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and
+    resize() methods `M.total() == 0` does not imply that `M.data == NULL`.
+     */
+    bool empty() const;
+
+    /** @brief Returns the total number of array elements.
+
+    The method returns the number of array elements (a number of pixels if the array represents an
+    image).
+     */
+    size_t total() const;
+
+    /** @brief Returns the total number of array elements.
+
+     The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim
+     */
+    size_t total(int startDim, int endDim=INT_MAX) const;
+
+    /**
+     * @param elemChannels Number of channels or number of columns the matrix should have.
+     *                     For a 2-D matrix, when the matrix has only 1 column, then it should have
+     *                     elemChannels channels; When the matrix has only 1 channel,
+     *                     then it should have elemChannels columns.
+     *                     For a 3-D matrix, it should have only one channel. Furthermore,
+     *                     if the number of planes is not one, then the number of rows
+     *                     within every plane has to be 1; if the number of rows within
+     *                     every plane is not 1, then the number of planes has to be 1.
+     * @param depth The depth the matrix should have. Set it to -1 when any depth is fine.
+     * @param requireContinuous Set it to true to require the matrix to be continuous
+     * @return -1 if the requirement is not satisfied.
+     *         Otherwise, it returns the number of elements in the matrix. Note
+     *         that an element may have multiple channels.
+     *
+     * The following code demonstrates its usage for a 2-d matrix:
+     * @snippet snippets/core_mat_checkVector.cpp example-2d
+     *
+     * The following code demonstrates its usage for a 3-d matrix:
+     * @snippet snippets/core_mat_checkVector.cpp example-3d
+     */
+    int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const;
+
+    /** @brief Returns a pointer to the specified matrix row.
+
+    The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in
+    Mat::isContinuous to know how to use these methods.
+    @param i0 A 0-based row index.
+     */
+    uchar* ptr(int i0=0);
+    /** @overload */
+    const uchar* ptr(int i0=0) const;
+
+    /** @overload
+    @param row Index along the dimension 0
+    @param col Index along the dimension 1
+    */
+    uchar* ptr(int row, int col);
+    /** @overload
+    @param row Index along the dimension 0
+    @param col Index along the dimension 1
+    */
+    const uchar* ptr(int row, int col) const;
+
+    /** @overload */
+    uchar* ptr(int i0, int i1, int i2);
+    /** @overload */
+    const uchar* ptr(int i0, int i1, int i2) const;
+
+    /** @overload */
+    uchar* ptr(const int* idx);
+    /** @overload */
+    const uchar* ptr(const int* idx) const;
+    /** @overload */
+    template<int n> uchar* ptr(const Vec<int, n>& idx);
+    /** @overload */
+    template<int n> const uchar* ptr(const Vec<int, n>& idx) const;
+
+    /** @overload */
+    template<typename _Tp> _Tp* ptr(int i0=0);
+    /** @overload */
+    template<typename _Tp> const _Tp* ptr(int i0=0) const;
+    /** @overload
+    @param row Index along the dimension 0
+    @param col Index along the dimension 1
+    */
+    template<typename _Tp> _Tp* ptr(int row, int col);
+    /** @overload
+    @param row Index along the dimension 0
+    @param col Index along the dimension 1
+    */
+    template<typename _Tp> const _Tp* ptr(int row, int col) const;
+    /** @overload */
+    template<typename _Tp> _Tp* ptr(int i0, int i1, int i2);
+    /** @overload */
+    template<typename _Tp> const _Tp* ptr(int i0, int i1, int i2) const;
+    /** @overload */
+    template<typename _Tp> _Tp* ptr(const int* idx);
+    /** @overload */
+    template<typename _Tp> const _Tp* ptr(const int* idx) const;
+    /** @overload */
+    template<typename _Tp, int n> _Tp* ptr(const Vec<int, n>& idx);
+    /** @overload */
+    template<typename _Tp, int n> const _Tp* ptr(const Vec<int, n>& idx) const;
+
+    /** @brief Returns a reference to the specified array element.
+
+    The template methods return a reference to the specified array element. For the sake of higher
+    performance, the index range checks are only performed in the Debug configuration.
+
+    Note that the variants with a single index (i) can be used to access elements of single-row or
+    single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and
+    B is an M x 1 integer matrix, you can simply write `A.at<float>(k+4)` and `B.at<int>(2*i+1)`
+    instead of `A.at<float>(0,k+4)` and `B.at<int>(2*i+1,0)`, respectively.
+
+    The example below initializes a Hilbert matrix:
+    @code
+        Mat H(100, 100, CV_64F);
+        for(int i = 0; i < H.rows; i++)
+            for(int j = 0; j < H.cols; j++)
+                H.at<double>(i,j)=1./(i+j+1);
+    @endcode
+
+    Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends
+    on the image from which you are trying to retrieve the data. The table below gives a better insight in this:
+     - If matrix is of type `CV_8U` then use `Mat.at<uchar>(y,x)`.
+     - If matrix is of type `CV_8S` then use `Mat.at<schar>(y,x)`.
+     - If matrix is of type `CV_16U` then use `Mat.at<ushort>(y,x)`.
+     - If matrix is of type `CV_16S` then use `Mat.at<short>(y,x)`.
+     - If matrix is of type `CV_32S`  then use `Mat.at<int>(y,x)`.
+     - If matrix is of type `CV_32F`  then use `Mat.at<float>(y,x)`.
+     - If matrix is of type `CV_64F` then use `Mat.at<double>(y,x)`.
+
+    @param i0 Index along the dimension 0
+     */
+    template<typename _Tp> _Tp& at(int i0=0);
+    /** @overload
+    @param i0 Index along the dimension 0
+    */
+    template<typename _Tp> const _Tp& at(int i0=0) const;
+    /** @overload
+    @param row Index along the dimension 0
+    @param col Index along the dimension 1
+    */
+    template<typename _Tp> _Tp& at(int row, int col);
+    /** @overload
+    @param row Index along the dimension 0
+    @param col Index along the dimension 1
+    */
+    template<typename _Tp> const _Tp& at(int row, int col) const;
+
+    /** @overload
+    @param i0 Index along the dimension 0
+    @param i1 Index along the dimension 1
+    @param i2 Index along the dimension 2
+    */
+    template<typename _Tp> _Tp& at(int i0, int i1, int i2);
+    /** @overload
+    @param i0 Index along the dimension 0
+    @param i1 Index along the dimension 1
+    @param i2 Index along the dimension 2
+    */
+    template<typename _Tp> const _Tp& at(int i0, int i1, int i2) const;
+
+    /** @overload
+    @param idx Array of Mat::dims indices.
+    */
+    template<typename _Tp> _Tp& at(const int* idx);
+    /** @overload
+    @param idx Array of Mat::dims indices.
+    */
+    template<typename _Tp> const _Tp& at(const int* idx) const;
+
+    /** @overload */
+    template<typename _Tp, int n> _Tp& at(const Vec<int, n>& idx);
+    /** @overload */
+    template<typename _Tp, int n> const _Tp& at(const Vec<int, n>& idx) const;
+
+    /** @overload
+    special versions for 2D arrays (especially convenient for referencing image pixels)
+    @param pt Element position specified as Point(j,i) .
+    */
+    template<typename _Tp> _Tp& at(Point pt);
+    /** @overload
+    special versions for 2D arrays (especially convenient for referencing image pixels)
+    @param pt Element position specified as Point(j,i) .
+    */
+    template<typename _Tp> const _Tp& at(Point pt) const;
+
+    /** @brief Returns the matrix iterator and sets it to the first matrix element.
+
+    The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very
+    similar to the use of bi-directional STL iterators. In the example below, the alpha blending
+    function is rewritten using the matrix iterators:
+    @code
+        template<typename T>
+        void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst)
+        {
+            typedef Vec<T, 4> VT;
+
+            const float alpha_scale = (float)std::numeric_limits<T>::max(),
+                        inv_scale = 1.f/alpha_scale;
+
+            CV_Assert( src1.type() == src2.type() &&
+                       src1.type() == traits::Type<VT>::value &&
+                       src1.size() == src2.size());
+            Size size = src1.size();
+            dst.create(size, src1.type());
+
+            MatConstIterator_<VT> it1 = src1.begin<VT>(), it1_end = src1.end<VT>();
+            MatConstIterator_<VT> it2 = src2.begin<VT>();
+            MatIterator_<VT> dst_it = dst.begin<VT>();
+
+            for( ; it1 != it1_end; ++it1, ++it2, ++dst_it )
+            {
+                VT pix1 = *it1, pix2 = *it2;
+                float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale;
+                *dst_it = VT(saturate_cast<T>(pix1[0]*alpha + pix2[0]*beta),
+                             saturate_cast<T>(pix1[1]*alpha + pix2[1]*beta),
+                             saturate_cast<T>(pix1[2]*alpha + pix2[2]*beta),
+                             saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale));
+            }
+        }
+    @endcode
+     */
+    template<typename _Tp> MatIterator_<_Tp> begin();
+    template<typename _Tp> MatConstIterator_<_Tp> begin() const;
+
+    /** @brief Same as begin() but for inverse traversal
+     */
+    template<typename _Tp> std::reverse_iterator<MatIterator_<_Tp>> rbegin();
+    template<typename _Tp> std::reverse_iterator<MatConstIterator_<_Tp>> rbegin() const;
+
+    /** @brief Returns the matrix iterator and sets it to the after-last matrix element.
+
+    The methods return the matrix read-only or read-write iterators, set to the point following the last
+    matrix element.
+     */
+    template<typename _Tp> MatIterator_<_Tp> end();
+    template<typename _Tp> MatConstIterator_<_Tp> end() const;
+
+    /** @brief Same as end() but for inverse traversal
+     */
+    template<typename _Tp> std::reverse_iterator< MatIterator_<_Tp>> rend();
+    template<typename _Tp> std::reverse_iterator< MatConstIterator_<_Tp>> rend() const;
+
+
+    /** @brief Runs the given functor over all matrix elements in parallel.
+
+    The operation passed as argument has to be a function pointer, a function object or a lambda(C++11).
+
+    Example 1. All of the operations below put 0xFF the first channel of all matrix elements:
+    @code
+        Mat image(1920, 1080, CV_8UC3);
+        typedef cv::Point3_<uint8_t> Pixel;
+
+        // first. raw pointer access.
+        for (int r = 0; r < image.rows; ++r) {
+            Pixel* ptr = image.ptr<Pixel>(r, 0);
+            const Pixel* ptr_end = ptr + image.cols;
+            for (; ptr != ptr_end; ++ptr) {
+                ptr->x = 255;
+            }
+        }
+
+        // Using MatIterator. (Simple but there are a Iterator's overhead)
+        for (Pixel &p : cv::Mat_<Pixel>(image)) {
+            p.x = 255;
+        }
+
+        // Parallel execution with function object.
+        struct Operator {
+            void operator ()(Pixel &pixel, const int * position) {
+                pixel.x = 255;
+            }
+        };
+        image.forEach<Pixel>(Operator());
+
+        // Parallel execution using C++11 lambda.
+        image.forEach<Pixel>([](Pixel &p, const int * position) -> void {
+            p.x = 255;
+        });
+    @endcode
+    Example 2. Using the pixel's position:
+    @code
+        // Creating 3D matrix (255 x 255 x 255) typed uint8_t
+        // and initialize all elements by the value which equals elements position.
+        // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3).
+
+        int sizes[] = { 255, 255, 255 };
+        typedef cv::Point3_<uint8_t> Pixel;
+
+        Mat_<Pixel> image = Mat::zeros(3, sizes, CV_8UC3);
+
+        image.forEach<Pixel>([&](Pixel& pixel, const int position[]) -> void {
+            pixel.x = position[0];
+            pixel.y = position[1];
+            pixel.z = position[2];
+        });
+    @endcode
+     */
+    template<typename _Tp, typename Functor> void forEach(const Functor& operation);
+    /** @overload */
+    template<typename _Tp, typename Functor> void forEach(const Functor& operation) const;
+
+    Mat(Mat&& m);
+    Mat& operator = (Mat&& m);
+
+    enum { MAGIC_VAL  = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG };
+    enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 };
+
+    /*! includes several bit-fields:
+         - the magic signature
+         - continuity flag
+         - depth
+         - number of channels
+     */
+    int flags;
+    //! the matrix dimensionality, >= 2
+    int dims;
+    //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
+    int rows, cols;
+    //! pointer to the data
+    uchar* data;
+
+    //! helper fields used in locateROI and adjustROI
+    const uchar* datastart;
+    const uchar* dataend;
+    const uchar* datalimit;
+
+    //! custom allocator
+    MatAllocator* allocator;
+    //! and the standard allocator
+    static MatAllocator* getStdAllocator();
+    static MatAllocator* getDefaultAllocator();
+    static void setDefaultAllocator(MatAllocator* allocator);
+
+    //! internal use method: updates the continuity flag
+    void updateContinuityFlag();
+
+    //! interaction with UMat
+    UMatData* u;
+
+    MatSize size;
+    MatStep step;
+
+protected:
+    template<typename _Tp, typename Functor> void forEach_impl(const Functor& operation);
+};
+
+
+///////////////////////////////// Mat_<_Tp> ////////////////////////////////////
+
+/** @brief Template matrix class derived from Mat
+
+@code{.cpp}
+    template<typename _Tp> class Mat_ : public Mat
+    {
+    public:
+        // ... some specific methods
+        //         and
+        // no new extra fields
+    };
+@endcode
+The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any
+extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to
+these two classes can be freely but carefully converted one to another. For example:
+@code{.cpp}
+    // create a 100x100 8-bit matrix
+    Mat M(100,100,CV_8U);
+    // this will be compiled fine. no any data conversion will be done.
+    Mat_<float>& M1 = (Mat_<float>&)M;
+    // the program is likely to crash at the statement below
+    M1(99,99) = 1.f;
+@endcode
+While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element
+access operations and if you know matrix type at the compilation time. Note that
+`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same
+and run at the same speed, but the latter is certainly shorter:
+@code{.cpp}
+    Mat_<double> M(20,20);
+    for(int i = 0; i < M.rows; i++)
+        for(int j = 0; j < M.cols; j++)
+            M(i,j) = 1./(i+j+1);
+    Mat E, V;
+    eigen(M,E,V);
+    cout << E.at<double>(0,0)/E.at<double>(M.rows-1,0);
+@endcode
+To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter:
+@code{.cpp}
+    // allocate a 320x240 color image and fill it with green (in RGB space)
+    Mat_<Vec3b> img(240, 320, Vec3b(0,255,0));
+    // now draw a diagonal white line
+    for(int i = 0; i < 100; i++)
+        img(i,i)=Vec3b(255,255,255);
+    // and now scramble the 2nd (red) channel of each pixel
+    for(int i = 0; i < img.rows; i++)
+        for(int j = 0; j < img.cols; j++)
+            img(i,j)[2] ^= (uchar)(i ^ j);
+@endcode
+Mat_ is fully compatible with C++11 range-based for loop. For example such loop
+can be used to safely apply look-up table:
+@code{.cpp}
+void applyTable(Mat_<uchar>& I, const uchar* const table)
+{
+    for(auto& pixel : I)
+    {
+        pixel = table[pixel];
+    }
+}
+@endcode
+ */
+template<typename _Tp> class Mat_ : public Mat
+{
+public:
+    typedef _Tp value_type;
+    typedef typename DataType<_Tp>::channel_type channel_type;
+    typedef MatIterator_<_Tp> iterator;
+    typedef MatConstIterator_<_Tp> const_iterator;
+
+    //! default constructor
+    Mat_() CV_NOEXCEPT;
+    //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type)
+    Mat_(int _rows, int _cols);
+    //! constructor that sets each matrix element to specified value
+    Mat_(int _rows, int _cols, const _Tp& value);
+    //! equivalent to Mat(_size, DataType<_Tp>::type)
+    explicit Mat_(Size _size);
+    //! constructor that sets each matrix element to specified value
+    Mat_(Size _size, const _Tp& value);
+    //! n-dim array constructor
+    Mat_(int _ndims, const int* _sizes);
+    //! n-dim array constructor that sets each matrix element to specified value
+    Mat_(int _ndims, const int* _sizes, const _Tp& value);
+    //! copy/conversion constructor. If m is of different type, it's converted
+    Mat_(const Mat& m);
+    //! copy constructor
+    Mat_(const Mat_& m);
+    //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type
+    Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP);
+    //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type
+    Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0);
+    //! selects a submatrix
+    Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all());
+    //! selects a submatrix
+    Mat_(const Mat_& m, const Rect& roi);
+    //! selects a submatrix, n-dim version
+    Mat_(const Mat_& m, const Range* ranges);
+    //! selects a submatrix, n-dim version
+    Mat_(const Mat_& m, const std::vector<Range>& ranges);
+    //! from a matrix expression
+    explicit Mat_(const MatExpr& e);
+    //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column
+    explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false);
+    template<int n> explicit Mat_(const Vec<typename DataType<_Tp>::channel_type, n>& vec, bool copyData=true);
+    template<int m, int n> explicit Mat_(const Matx<typename DataType<_Tp>::channel_type, m, n>& mtx, bool copyData=true);
+    explicit Mat_(const Point_<typename DataType<_Tp>::channel_type>& pt, bool copyData=true);
+    explicit Mat_(const Point3_<typename DataType<_Tp>::channel_type>& pt, bool copyData=true);
+    explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer);
+
+    Mat_(std::initializer_list<_Tp> values);
+    explicit Mat_(const std::initializer_list<int> sizes, const std::initializer_list<_Tp> values);
+
+    template <std::size_t _Nm> explicit Mat_(const std::array<_Tp, _Nm>& arr, bool copyData=false);
+
+    Mat_& operator = (const Mat& m);
+    Mat_& operator = (const Mat_& m);
+    //! set all the elements to s.
+    Mat_& operator = (const _Tp& s);
+    //! assign a matrix expression
+    Mat_& operator = (const MatExpr& e);
+
+    //! iterators; they are smart enough to skip gaps in the end of rows
+    iterator begin();
+    iterator end();
+    const_iterator begin() const;
+    const_iterator end() const;
+
+    //reverse iterators
+    std::reverse_iterator<iterator> rbegin();
+    std::reverse_iterator<iterator> rend();
+    std::reverse_iterator<const_iterator> rbegin() const;
+    std::reverse_iterator<const_iterator> rend() const;
+
+    //! template methods for for operation over all matrix elements.
+    // the operations take care of skipping gaps in the end of rows (if any)
+    template<typename Functor> void forEach(const Functor& operation);
+    template<typename Functor> void forEach(const Functor& operation) const;
+
+    //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type)
+    void create(int _rows, int _cols);
+    //! equivalent to Mat::create(_size, DataType<_Tp>::type)
+    void create(Size _size);
+    //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type)
+    void create(int _ndims, const int* _sizes);
+    //! equivalent to Mat::release()
+    void release();
+    //! cross-product
+    Mat_ cross(const Mat_& m) const;
+    //! data type conversion
+    template<typename T2> operator Mat_<T2>() const;
+    //! overridden forms of Mat::row() etc.
+    Mat_ row(int y) const;
+    Mat_ col(int x) const;
+    Mat_ diag(int d=0) const;
+    CV_NODISCARD_STD Mat_ clone() const;
+
+    //! overridden forms of Mat::elemSize() etc.
+    size_t elemSize() const;
+    size_t elemSize1() const;
+    int type() const;
+    int depth() const;
+    int channels() const;
+    size_t step1(int i=0) const;
+    //! returns step()/sizeof(_Tp)
+    size_t stepT(int i=0) const;
+
+    //! overridden forms of Mat::zeros() etc. Data type is omitted, of course
+    CV_NODISCARD_STD static MatExpr zeros(int rows, int cols);
+    CV_NODISCARD_STD static MatExpr zeros(Size size);
+    CV_NODISCARD_STD static MatExpr zeros(int _ndims, const int* _sizes);
+    CV_NODISCARD_STD static MatExpr ones(int rows, int cols);
+    CV_NODISCARD_STD static MatExpr ones(Size size);
+    CV_NODISCARD_STD static MatExpr ones(int _ndims, const int* _sizes);
+    CV_NODISCARD_STD static MatExpr eye(int rows, int cols);
+    CV_NODISCARD_STD static MatExpr eye(Size size);
+
+    //! some more overridden methods
+    Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright );
+    Mat_ operator()( const Range& rowRange, const Range& colRange ) const;
+    Mat_ operator()( const Rect& roi ) const;
+    Mat_ operator()( const Range* ranges ) const;
+    Mat_ operator()(const std::vector<Range>& ranges) const;
+
+    //! more convenient forms of row and element access operators
+    _Tp* operator [](int y);
+    const _Tp* operator [](int y) const;
+
+    //! returns reference to the specified element
+    _Tp& operator ()(const int* idx);
+    //! returns read-only reference to the specified element
+    const _Tp& operator ()(const int* idx) const;
+
+    //! returns reference to the specified element
+    template<int n> _Tp& operator ()(const Vec<int, n>& idx);
+    //! returns read-only reference to the specified element
+    template<int n> const _Tp& operator ()(const Vec<int, n>& idx) const;
+
+    //! returns reference to the specified element (1D case)
+    _Tp& operator ()(int idx0);
+    //! returns read-only reference to the specified element (1D case)
+    const _Tp& operator ()(int idx0) const;
+    //! returns reference to the specified element (2D case)
+    _Tp& operator ()(int row, int col);
+    //! returns read-only reference to the specified element (2D case)
+    const _Tp& operator ()(int row, int col) const;
+    //! returns reference to the specified element (3D case)
+    _Tp& operator ()(int idx0, int idx1, int idx2);
+    //! returns read-only reference to the specified element (3D case)
+    const _Tp& operator ()(int idx0, int idx1, int idx2) const;
+
+    _Tp& operator ()(Point pt);
+    const _Tp& operator ()(Point pt) const;
+
+    //! conversion to vector.
+    operator std::vector<_Tp>() const;
+
+    //! conversion to array.
+    template<std::size_t _Nm> operator std::array<_Tp, _Nm>() const;
+
+    //! conversion to Vec
+    template<int n> operator Vec<typename DataType<_Tp>::channel_type, n>() const;
+    //! conversion to Matx
+    template<int m, int n> operator Matx<typename DataType<_Tp>::channel_type, m, n>() const;
+
+    Mat_(Mat_&& m);
+    Mat_& operator = (Mat_&& m);
+
+    Mat_(Mat&& m);
+    Mat_& operator = (Mat&& m);
+
+    Mat_(MatExpr&& e);
+};
+
+typedef Mat_<uchar> Mat1b;
+typedef Mat_<Vec2b> Mat2b;
+typedef Mat_<Vec3b> Mat3b;
+typedef Mat_<Vec4b> Mat4b;
+
+typedef Mat_<short> Mat1s;
+typedef Mat_<Vec2s> Mat2s;
+typedef Mat_<Vec3s> Mat3s;
+typedef Mat_<Vec4s> Mat4s;
+
+typedef Mat_<ushort> Mat1w;
+typedef Mat_<Vec2w> Mat2w;
+typedef Mat_<Vec3w> Mat3w;
+typedef Mat_<Vec4w> Mat4w;
+
+typedef Mat_<int>   Mat1i;
+typedef Mat_<Vec2i> Mat2i;
+typedef Mat_<Vec3i> Mat3i;
+typedef Mat_<Vec4i> Mat4i;
+
+typedef Mat_<float> Mat1f;
+typedef Mat_<Vec2f> Mat2f;
+typedef Mat_<Vec3f> Mat3f;
+typedef Mat_<Vec4f> Mat4f;
+
+typedef Mat_<double> Mat1d;
+typedef Mat_<Vec2d> Mat2d;
+typedef Mat_<Vec3d> Mat3d;
+typedef Mat_<Vec4d> Mat4d;
+
+/** @todo document */
+class CV_EXPORTS UMat
+{
+public:
+    //! default constructor
+    UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT) CV_NOEXCEPT;
+    //! constructs 2D matrix of the specified size and type
+    // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
+    UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+    UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+    //! constructs 2D matrix and fills it with the specified value _s.
+    UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+    UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+
+    //! constructs n-dimensional matrix
+    UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+    UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+
+    //! copy constructor
+    UMat(const UMat& m);
+
+    //! creates a matrix header for a part of the bigger matrix
+    UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all());
+    UMat(const UMat& m, const Rect& roi);
+    UMat(const UMat& m, const Range* ranges);
+    UMat(const UMat& m, const std::vector<Range>& ranges);
+
+    // FIXIT copyData=false is not implemented, drop this in favor of cv::Mat (OpenCV 5.0)
+    //! builds matrix from std::vector with or without copying the data
+    template<typename _Tp> explicit UMat(const std::vector<_Tp>& vec, bool copyData=false);
+
+    //! destructor - calls release()
+    ~UMat();
+    //! assignment operators
+    UMat& operator = (const UMat& m);
+
+    Mat getMat(AccessFlag flags) const;
+
+    //! returns a new matrix header for the specified row
+    UMat row(int y) const;
+    //! returns a new matrix header for the specified column
+    UMat col(int x) const;
+    //! ... for the specified row span
+    UMat rowRange(int startrow, int endrow) const;
+    UMat rowRange(const Range& r) const;
+    //! ... for the specified column span
+    UMat colRange(int startcol, int endcol) const;
+    UMat colRange(const Range& r) const;
+    //! ... for the specified diagonal
+    //! (d=0 - the main diagonal,
+    //!  >0 - a diagonal from the upper half,
+    //!  <0 - a diagonal from the lower half)
+    UMat diag(int d=0) const;
+    //! constructs a square diagonal matrix which main diagonal is vector "d"
+    CV_NODISCARD_STD static UMat diag(const UMat& d, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat diag(const UMat& d) { return diag(d, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+
+    //! returns deep copy of the matrix, i.e. the data is copied
+    CV_NODISCARD_STD UMat clone() const;
+    //! copies the matrix content to "m".
+    // It calls m.create(this->size(), this->type()).
+    void copyTo( OutputArray m ) const;
+    //! copies those matrix elements to "m" that are marked with non-zero mask elements.
+    void copyTo( OutputArray m, InputArray mask ) const;
+    //! converts matrix to another datatype with optional scaling. See cvConvertScale.
+    void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const;
+
+    void assignTo( UMat& m, int type=-1 ) const;
+
+    //! sets every matrix element to s
+    UMat& operator = (const Scalar& s);
+    //! sets some of the matrix elements to s, according to the mask
+    UMat& setTo(InputArray value, InputArray mask=noArray());
+    //! creates alternative matrix header for the same data, with different
+    // number of channels and/or different number of rows. see cvReshape.
+    UMat reshape(int cn, int rows=0) const;
+    UMat reshape(int cn, int newndims, const int* newsz) const;
+
+    //! matrix transposition by means of matrix expressions
+    UMat t() const;
+    //! matrix inversion by means of matrix expressions
+    UMat inv(int method=DECOMP_LU) const;
+    //! per-element matrix multiplication by means of matrix expressions
+    UMat mul(InputArray m, double scale=1) const;
+
+    //! computes dot-product
+    double dot(InputArray m) const;
+
+    //! Matlab-style matrix initialization
+    CV_NODISCARD_STD static UMat zeros(int rows, int cols, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat zeros(Size size, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat zeros(int ndims, const int* sz, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat zeros(int rows, int cols, int type) { return zeros(rows, cols, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+    CV_NODISCARD_STD static UMat zeros(Size size, int type) { return zeros(size, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+    CV_NODISCARD_STD static UMat zeros(int ndims, const int* sz, int type) { return zeros(ndims, sz, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+    CV_NODISCARD_STD static UMat ones(int rows, int cols, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat ones(Size size, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat ones(int ndims, const int* sz, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat ones(int rows, int cols, int type) { return ones(rows, cols, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+    CV_NODISCARD_STD static UMat ones(Size size, int type) { return ones(size, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+    CV_NODISCARD_STD static UMat ones(int ndims, const int* sz, int type) { return ones(ndims, sz, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+    CV_NODISCARD_STD static UMat eye(int rows, int cols, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat eye(Size size, int type, UMatUsageFlags usageFlags /*= USAGE_DEFAULT*/);
+    CV_NODISCARD_STD static UMat eye(int rows, int cols, int type) { return eye(rows, cols, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+    CV_NODISCARD_STD static UMat eye(Size size, int type) { return eye(size, type, USAGE_DEFAULT); }  // OpenCV 5.0: remove abi compatibility overload
+
+    //! allocates new matrix data unless the matrix already has specified size and type.
+    // previous data is unreferenced if needed.
+    void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+    void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+    void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+    void create(const std::vector<int>& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+
+    //! increases the reference counter; use with care to avoid memleaks
+    void addref();
+    //! decreases reference counter;
+    // deallocates the data when reference counter reaches 0.
+    void release();
+
+    //! deallocates the matrix data
+    void deallocate();
+    //! internal use function; properly re-allocates _size, _step arrays
+    void copySize(const UMat& m);
+
+    //! locates matrix header within a parent matrix. See below
+    void locateROI( Size& wholeSize, Point& ofs ) const;
+    //! moves/resizes the current matrix ROI inside the parent matrix.
+    UMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
+    //! extracts a rectangular sub-matrix
+    // (this is a generalized form of row, rowRange etc.)
+    UMat operator()( Range rowRange, Range colRange ) const;
+    UMat operator()( const Rect& roi ) const;
+    UMat operator()( const Range* ranges ) const;
+    UMat operator()(const std::vector<Range>& ranges) const;
+
+    //! returns true iff the matrix data is continuous
+    // (i.e. when there are no gaps between successive rows).
+    // similar to CV_IS_MAT_CONT(cvmat->type)
+    bool isContinuous() const;
+
+    //! returns true if the matrix is a submatrix of another matrix
+    bool isSubmatrix() const;
+
+    //! returns element size in bytes,
+    // similar to CV_ELEM_SIZE(cvmat->type)
+    size_t elemSize() const;
+    //! returns the size of element channel in bytes.
+    size_t elemSize1() const;
+    //! returns element type, similar to CV_MAT_TYPE(cvmat->type)
+    int type() const;
+    //! returns element type, similar to CV_MAT_DEPTH(cvmat->type)
+    int depth() const;
+    //! returns element type, similar to CV_MAT_CN(cvmat->type)
+    int channels() const;
+    //! returns step/elemSize1()
+    size_t step1(int i=0) const;
+    //! returns true if matrix data is NULL
+    bool empty() const;
+    //! returns the total number of matrix elements
+    size_t total() const;
+
+    //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise
+    int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const;
+
+    UMat(UMat&& m);
+    UMat& operator = (UMat&& m);
+
+    /*! Returns the OpenCL buffer handle on which UMat operates on.
+        The UMat instance should be kept alive during the use of the handle to prevent the buffer to be
+        returned to the OpenCV buffer pool.
+     */
+    void* handle(AccessFlag accessFlags) const;
+    void ndoffset(size_t* ofs) const;
+
+    enum { MAGIC_VAL  = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG };
+    enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 };
+
+    /*! includes several bit-fields:
+         - the magic signature
+         - continuity flag
+         - depth
+         - number of channels
+     */
+    int flags;
+
+    //! the matrix dimensionality, >= 2
+    int dims;
+
+    //! number of rows in the matrix; -1 when the matrix has more than 2 dimensions
+    int rows;
+
+    //! number of columns in the matrix; -1 when the matrix has more than 2 dimensions
+    int cols;
+
+    //! custom allocator
+    MatAllocator* allocator;
+
+    //! usage flags for allocator; recommend do not set directly, instead set during construct/create/getUMat
+    UMatUsageFlags usageFlags;
+
+    //! and the standard allocator
+    static MatAllocator* getStdAllocator();
+
+    //! internal use method: updates the continuity flag
+    void updateContinuityFlag();
+
+    //! black-box container of UMat data
+    UMatData* u;
+
+    //! offset of the submatrix (or 0)
+    size_t offset;
+
+    //! dimensional size of the matrix; accessible in various formats
+    MatSize size;
+
+    //! number of bytes each matrix element/row/plane/dimension occupies
+    MatStep step;
+
+protected:
+};
+
+
+/////////////////////////// multi-dimensional sparse matrix //////////////////////////
+
+/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays.
+
+Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only
+non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its
+stored elements can actually become 0. It is up to you to detect such elements and delete them
+using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is
+filled so that the search time is O(1) in average (regardless of whether element is there or not).
+Elements can be accessed using the following methods:
+-   Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and
+    SparseMat::find), for example:
+    @code
+        const int dims = 5;
+        int size[5] = {10, 10, 10, 10, 10};
+        SparseMat sparse_mat(dims, size, CV_32F);
+        for(int i = 0; i < 1000; i++)
+        {
+            int idx[dims];
+            for(int k = 0; k < dims; k++)
+                idx[k] = rand() % size[k];
+            sparse_mat.ref<float>(idx) += 1.f;
+        }
+        cout << "nnz = " << sparse_mat.nzcount() << endl;
+    @endcode
+-   Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator.
+    That is, the iteration loop is familiar to STL users:
+    @code
+        // prints elements of a sparse floating-point matrix
+        // and the sum of elements.
+        SparseMatConstIterator_<float>
+            it = sparse_mat.begin<float>(),
+            it_end = sparse_mat.end<float>();
+        double s = 0;
+        int dims = sparse_mat.dims();
+        for(; it != it_end; ++it)
+        {
+            // print element indices and the element value
+            const SparseMat::Node* n = it.node();
+            printf("(");
+            for(int i = 0; i < dims; i++)
+                printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")");
+            printf(": %g\n", it.value<float>());
+            s += *it;
+        }
+        printf("Element sum is %g\n", s);
+    @endcode
+    If you run this loop, you will notice that elements are not enumerated in a logical order
+    (lexicographical, and so on). They come in the same order as they are stored in the hash table
+    (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering.
+    Note, however, that pointers to the nodes may become invalid when you add more elements to the
+    matrix. This may happen due to possible buffer reallocation.
+-   Combination of the above 2 methods when you need to process 2 or more sparse matrices
+    simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2
+    floating-point sparse matrices:
+    @code
+        double cross_corr(const SparseMat& a, const SparseMat& b)
+        {
+            const SparseMat *_a = &a, *_b = &b;
+            // if b contains less elements than a,
+            // it is faster to iterate through b
+            if(_a->nzcount() > _b->nzcount())
+                std::swap(_a, _b);
+            SparseMatConstIterator_<float> it = _a->begin<float>(),
+                                           it_end = _a->end<float>();
+            double ccorr = 0;
+            for(; it != it_end; ++it)
+            {
+                // take the next element from the first matrix
+                float avalue = *it;
+                const Node* anode = it.node();
+                // and try to find an element with the same index in the second matrix.
+                // since the hash value depends only on the element index,
+                // reuse the hash value stored in the node
+                float bvalue = _b->value<float>(anode->idx,&anode->hashval);
+                ccorr += avalue*bvalue;
+            }
+            return ccorr;
+        }
+    @endcode
+ */
+class CV_EXPORTS SparseMat
+{
+public:
+    typedef SparseMatIterator iterator;
+    typedef SparseMatConstIterator const_iterator;
+
+    enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 };
+
+    //! the sparse matrix header
+    struct CV_EXPORTS Hdr
+    {
+        Hdr(int _dims, const int* _sizes, int _type);
+        void clear();
+        int refcount;
+        int dims;
+        int valueOffset;
+        size_t nodeSize;
+        size_t nodeCount;
+        size_t freeList;
+        std::vector<uchar> pool;
+        std::vector<size_t> hashtab;
+        int size[MAX_DIM];
+    };
+
+    //! sparse matrix node - element of a hash table
+    struct CV_EXPORTS Node
+    {
+        //! hash value
+        size_t hashval;
+        //! index of the next node in the same hash table entry
+        size_t next;
+        //! index of the matrix element
+        int idx[MAX_DIM];
+    };
+
+    /** @brief Various SparseMat constructors.
+     */
+    SparseMat();
+
+    /** @overload
+    @param dims Array dimensionality.
+    @param _sizes Sparce matrix size on all dementions.
+    @param _type Sparse matrix data type.
+    */
+    SparseMat(int dims, const int* _sizes, int _type);
+
+    /** @overload
+    @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
+    to sparse representation.
+    */
+    SparseMat(const SparseMat& m);
+
+    /** @overload
+    @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
+    to sparse representation.
+    */
+    explicit SparseMat(const Mat& m);
+
+    //! the destructor
+    ~SparseMat();
+
+    //! assignment operator. This is O(1) operation, i.e. no data is copied
+    SparseMat& operator = (const SparseMat& m);
+    //! equivalent to the corresponding constructor
+    SparseMat& operator = (const Mat& m);
+
+    //! creates full copy of the matrix
+    CV_NODISCARD_STD SparseMat clone() const;
+
+    //! copies all the data to the destination matrix. All the previous content of m is erased
+    void copyTo( SparseMat& m ) const;
+    //! converts sparse matrix to dense matrix.
+    void copyTo( Mat& m ) const;
+    //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type
+    void convertTo( SparseMat& m, int rtype, double alpha=1 ) const;
+    //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling.
+    /*!
+        @param [out] m - output matrix; if it does not have a proper size or type before the operation,
+            it is reallocated
+        @param [in] rtype - desired output matrix type or, rather, the depth since the number of channels
+            are the same as the input has; if rtype is negative, the output matrix will have the
+            same type as the input.
+        @param [in] alpha - optional scale factor
+        @param [in] beta - optional delta added to the scaled values
+    */
+    void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const;
+
+    // not used now
+    void assignTo( SparseMat& m, int type=-1 ) const;
+
+    //! reallocates sparse matrix.
+    /*!
+        If the matrix already had the proper size and type,
+        it is simply cleared with clear(), otherwise,
+        the old matrix is released (using release()) and the new one is allocated.
+    */
+    void create(int dims, const int* _sizes, int _type);
+    //! sets all the sparse matrix elements to 0, which means clearing the hash table.
+    void clear();
+    //! manually increments the reference counter to the header.
+    void addref();
+    // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated.
+    void release();
+
+    //! converts sparse matrix to the old-style representation; all the elements are copied.
+    //operator CvSparseMat*() const;
+    //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements)
+    size_t elemSize() const;
+    //! returns elemSize()/channels()
+    size_t elemSize1() const;
+
+    //! returns type of sparse matrix elements
+    int type() const;
+    //! returns the depth of sparse matrix elements
+    int depth() const;
+    //! returns the number of channels
+    int channels() const;
+
+    //! returns the array of sizes, or NULL if the matrix is not allocated
+    const int* size() const;
+    //! returns the size of i-th matrix dimension (or 0)
+    int size(int i) const;
+    //! returns the matrix dimensionality
+    int dims() const;
+    //! returns the number of non-zero elements (=the number of hash table nodes)
+    size_t nzcount() const;
+
+    //! computes the element hash value (1D case)
+    size_t hash(int i0) const;
+    //! computes the element hash value (2D case)
+    size_t hash(int i0, int i1) const;
+    //! computes the element hash value (3D case)
+    size_t hash(int i0, int i1, int i2) const;
+    //! computes the element hash value (nD case)
+    size_t hash(const int* idx) const;
+
+    //!@{
+    /*!
+     specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case.
+     return pointer to the matrix element.
+      - if the element is there (it's non-zero), the pointer to it is returned
+      - if it's not there and createMissing=false, NULL pointer is returned
+      - if it's not there and createMissing=true, then the new element
+        is created and initialized with 0. Pointer to it is returned
+      - if the optional hashval pointer is not NULL, the element hash value is
+        not computed, but *hashval is taken instead.
+    */
+    //! returns pointer to the specified element (1D case)
+    uchar* ptr(int i0, bool createMissing, size_t* hashval=0);
+    //! returns pointer to the specified element (2D case)
+    uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0);
+    //! returns pointer to the specified element (3D case)
+    uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0);
+    //! returns pointer to the specified element (nD case)
+    uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0);
+    //!@}
+
+    //!@{
+    /*!
+     return read-write reference to the specified sparse matrix element.
+
+     `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`.
+     The methods always return a valid reference.
+     If the element did not exist, it is created and initialized with 0.
+    */
+    //! returns reference to the specified element (1D case)
+    template<typename _Tp> _Tp& ref(int i0, size_t* hashval=0);
+    //! returns reference to the specified element (2D case)
+    template<typename _Tp> _Tp& ref(int i0, int i1, size_t* hashval=0);
+    //! returns reference to the specified element (3D case)
+    template<typename _Tp> _Tp& ref(int i0, int i1, int i2, size_t* hashval=0);
+    //! returns reference to the specified element (nD case)
+    template<typename _Tp> _Tp& ref(const int* idx, size_t* hashval=0);
+    //!@}
+
+    //!@{
+    /*!
+     return value of the specified sparse matrix element.
+
+     `value<_Tp>(i0,...[,hashval])` is equivalent to
+     @code
+     { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); }
+     @endcode
+
+     That is, if the element did not exist, the methods return 0.
+     */
+    //! returns value of the specified element (1D case)
+    template<typename _Tp> _Tp value(int i0, size_t* hashval=0) const;
+    //! returns value of the specified element (2D case)
+    template<typename _Tp> _Tp value(int i0, int i1, size_t* hashval=0) const;
+    //! returns value of the specified element (3D case)
+    template<typename _Tp> _Tp value(int i0, int i1, int i2, size_t* hashval=0) const;
+    //! returns value of the specified element (nD case)
+    template<typename _Tp> _Tp value(const int* idx, size_t* hashval=0) const;
+    //!@}
+
+    //!@{
+    /*!
+     Return pointer to the specified sparse matrix element if it exists
+
+     `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`.
+
+     If the specified element does not exist, the methods return NULL.
+    */
+    //! returns pointer to the specified element (1D case)
+    template<typename _Tp> const _Tp* find(int i0, size_t* hashval=0) const;
+    //! returns pointer to the specified element (2D case)
+    template<typename _Tp> const _Tp* find(int i0, int i1, size_t* hashval=0) const;
+    //! returns pointer to the specified element (3D case)
+    template<typename _Tp> const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const;
+    //! returns pointer to the specified element (nD case)
+    template<typename _Tp> const _Tp* find(const int* idx, size_t* hashval=0) const;
+    //!@}
+
+    //! erases the specified element (2D case)
+    void erase(int i0, int i1, size_t* hashval=0);
+    //! erases the specified element (3D case)
+    void erase(int i0, int i1, int i2, size_t* hashval=0);
+    //! erases the specified element (nD case)
+    void erase(const int* idx, size_t* hashval=0);
+
+    //!@{
+    /*!
+       return the sparse matrix iterator pointing to the first sparse matrix element
+    */
+    //! returns the sparse matrix iterator at the matrix beginning
+    SparseMatIterator begin();
+    //! returns the sparse matrix iterator at the matrix beginning
+    template<typename _Tp> SparseMatIterator_<_Tp> begin();
+    //! returns the read-only sparse matrix iterator at the matrix beginning
+    SparseMatConstIterator begin() const;
+    //! returns the read-only sparse matrix iterator at the matrix beginning
+    template<typename _Tp> SparseMatConstIterator_<_Tp> begin() const;
+    //!@}
+    /*!
+       return the sparse matrix iterator pointing to the element following the last sparse matrix element
+    */
+    //! returns the sparse matrix iterator at the matrix end
+    SparseMatIterator end();
+    //! returns the read-only sparse matrix iterator at the matrix end
+    SparseMatConstIterator end() const;
+    //! returns the typed sparse matrix iterator at the matrix end
+    template<typename _Tp> SparseMatIterator_<_Tp> end();
+    //! returns the typed read-only sparse matrix iterator at the matrix end
+    template<typename _Tp> SparseMatConstIterator_<_Tp> end() const;
+
+    //! returns the value stored in the sparse martix node
+    template<typename _Tp> _Tp& value(Node* n);
+    //! returns the value stored in the sparse martix node
+    template<typename _Tp> const _Tp& value(const Node* n) const;
+
+    ////////////// some internal-use methods ///////////////
+    Node* node(size_t nidx);
+    const Node* node(size_t nidx) const;
+
+    uchar* newNode(const int* idx, size_t hashval);
+    void removeNode(size_t hidx, size_t nidx, size_t previdx);
+    void resizeHashTab(size_t newsize);
+
+    int flags;
+    Hdr* hdr;
+};
+
+
+
+///////////////////////////////// SparseMat_<_Tp> ////////////////////////////////////
+
+/** @brief Template sparse n-dimensional array class derived from SparseMat
+
+SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies
+notation of some operations:
+@code
+    int sz[] = {10, 20, 30};
+    SparseMat_<double> M(3, sz);
+    ...
+    M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9);
+@endcode
+ */
+template<typename _Tp> class SparseMat_ : public SparseMat
+{
+public:
+    typedef SparseMatIterator_<_Tp> iterator;
+    typedef SparseMatConstIterator_<_Tp> const_iterator;
+
+    //! the default constructor
+    SparseMat_();
+    //! the full constructor equivalent to SparseMat(dims, _sizes, DataType<_Tp>::type)
+    SparseMat_(int dims, const int* _sizes);
+    //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted
+    SparseMat_(const SparseMat& m);
+    //! the copy constructor. This is O(1) operation - no data is copied
+    SparseMat_(const SparseMat_& m);
+    //! converts dense matrix to the sparse form
+    SparseMat_(const Mat& m);
+    //! converts the old-style sparse matrix to the C++ class. All the elements are copied
+    //SparseMat_(const CvSparseMat* m);
+    //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted
+    SparseMat_& operator = (const SparseMat& m);
+    //! the assignment operator. This is O(1) operation - no data is copied
+    SparseMat_& operator = (const SparseMat_& m);
+    //! converts dense matrix to the sparse form
+    SparseMat_& operator = (const Mat& m);
+
+    //! makes full copy of the matrix. All the elements are duplicated
+    CV_NODISCARD_STD SparseMat_ clone() const;
+    //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type)
+    void create(int dims, const int* _sizes);
+    //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied
+    //operator CvSparseMat*() const;
+
+    //! returns type of the matrix elements
+    int type() const;
+    //! returns depth of the matrix elements
+    int depth() const;
+    //! returns the number of channels in each matrix element
+    int channels() const;
+
+    //! equivalent to SparseMat::ref<_Tp>(i0, hashval)
+    _Tp& ref(int i0, size_t* hashval=0);
+    //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval)
+    _Tp& ref(int i0, int i1, size_t* hashval=0);
+    //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval)
+    _Tp& ref(int i0, int i1, int i2, size_t* hashval=0);
+    //! equivalent to SparseMat::ref<_Tp>(idx, hashval)
+    _Tp& ref(const int* idx, size_t* hashval=0);
+
+    //! equivalent to SparseMat::value<_Tp>(i0, hashval)
+    _Tp operator()(int i0, size_t* hashval=0) const;
+    //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval)
+    _Tp operator()(int i0, int i1, size_t* hashval=0) const;
+    //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval)
+    _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const;
+    //! equivalent to SparseMat::value<_Tp>(idx, hashval)
+    _Tp operator()(const int* idx, size_t* hashval=0) const;
+
+    //! returns sparse matrix iterator pointing to the first sparse matrix element
+    SparseMatIterator_<_Tp> begin();
+    //! returns read-only sparse matrix iterator pointing to the first sparse matrix element
+    SparseMatConstIterator_<_Tp> begin() const;
+    //! returns sparse matrix iterator pointing to the element following the last sparse matrix element
+    SparseMatIterator_<_Tp> end();
+    //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element
+    SparseMatConstIterator_<_Tp> end() const;
+};
+
+
+
+////////////////////////////////// MatConstIterator //////////////////////////////////
+
+class CV_EXPORTS MatConstIterator
+{
+public:
+    typedef uchar* value_type;
+    typedef ptrdiff_t difference_type;
+    typedef const uchar** pointer;
+    typedef uchar* reference;
+
+    typedef std::random_access_iterator_tag iterator_category;
+
+    //! default constructor
+    MatConstIterator();
+    //! constructor that sets the iterator to the beginning of the matrix
+    MatConstIterator(const Mat* _m);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatConstIterator(const Mat* _m, int _row, int _col=0);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatConstIterator(const Mat* _m, Point _pt);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatConstIterator(const Mat* _m, const int* _idx);
+    //! copy constructor
+    MatConstIterator(const MatConstIterator& it);
+
+    //! copy operator
+    MatConstIterator& operator = (const MatConstIterator& it);
+    //! returns the current matrix element
+    const uchar* operator *() const;
+    //! returns the i-th matrix element, relative to the current
+    const uchar* operator [](ptrdiff_t i) const;
+
+    //! shifts the iterator forward by the specified number of elements
+    MatConstIterator& operator += (ptrdiff_t ofs);
+    //! shifts the iterator backward by the specified number of elements
+    MatConstIterator& operator -= (ptrdiff_t ofs);
+    //! decrements the iterator
+    MatConstIterator& operator --();
+    //! decrements the iterator
+    MatConstIterator operator --(int);
+    //! increments the iterator
+    MatConstIterator& operator ++();
+    //! increments the iterator
+    MatConstIterator operator ++(int);
+    //! returns the current iterator position
+    Point pos() const;
+    //! returns the current iterator position
+    void pos(int* _idx) const;
+
+    ptrdiff_t lpos() const;
+    void seek(ptrdiff_t ofs, bool relative = false);
+    void seek(const int* _idx, bool relative = false);
+
+    const Mat* m;
+    size_t elemSize;
+    const uchar* ptr;
+    const uchar* sliceStart;
+    const uchar* sliceEnd;
+};
+
+
+
+////////////////////////////////// MatConstIterator_ /////////////////////////////////
+
+/** @brief Matrix read-only iterator
+ */
+template<typename _Tp>
+class MatConstIterator_ : public MatConstIterator
+{
+public:
+    typedef _Tp value_type;
+    typedef ptrdiff_t difference_type;
+    typedef const _Tp* pointer;
+    typedef const _Tp& reference;
+
+    typedef std::random_access_iterator_tag iterator_category;
+
+    //! default constructor
+    MatConstIterator_();
+    //! constructor that sets the iterator to the beginning of the matrix
+    MatConstIterator_(const Mat_<_Tp>* _m);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatConstIterator_(const Mat_<_Tp>* _m, Point _pt);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx);
+    //! copy constructor
+    MatConstIterator_(const MatConstIterator_& it);
+
+    //! copy operator
+    MatConstIterator_& operator = (const MatConstIterator_& it);
+    //! returns the current matrix element
+    const _Tp& operator *() const;
+    //! returns the i-th matrix element, relative to the current
+    const _Tp& operator [](ptrdiff_t i) const;
+
+    //! shifts the iterator forward by the specified number of elements
+    MatConstIterator_& operator += (ptrdiff_t ofs);
+    //! shifts the iterator backward by the specified number of elements
+    MatConstIterator_& operator -= (ptrdiff_t ofs);
+    //! decrements the iterator
+    MatConstIterator_& operator --();
+    //! decrements the iterator
+    MatConstIterator_ operator --(int);
+    //! increments the iterator
+    MatConstIterator_& operator ++();
+    //! increments the iterator
+    MatConstIterator_ operator ++(int);
+    //! returns the current iterator position
+    Point pos() const;
+};
+
+
+
+//////////////////////////////////// MatIterator_ ////////////////////////////////////
+
+/** @brief Matrix read-write iterator
+*/
+template<typename _Tp>
+class MatIterator_ : public MatConstIterator_<_Tp>
+{
+public:
+    typedef _Tp* pointer;
+    typedef _Tp& reference;
+
+    typedef std::random_access_iterator_tag iterator_category;
+
+    //! the default constructor
+    MatIterator_();
+    //! constructor that sets the iterator to the beginning of the matrix
+    MatIterator_(Mat_<_Tp>* _m);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatIterator_(Mat_<_Tp>* _m, Point _pt);
+    //! constructor that sets the iterator to the specified element of the matrix
+    MatIterator_(Mat_<_Tp>* _m, const int* _idx);
+    //! copy constructor
+    MatIterator_(const MatIterator_& it);
+    //! copy operator
+    MatIterator_& operator = (const MatIterator_<_Tp>& it );
+
+    //! returns the current matrix element
+    _Tp& operator *() const;
+    //! returns the i-th matrix element, relative to the current
+    _Tp& operator [](ptrdiff_t i) const;
+
+    //! shifts the iterator forward by the specified number of elements
+    MatIterator_& operator += (ptrdiff_t ofs);
+    //! shifts the iterator backward by the specified number of elements
+    MatIterator_& operator -= (ptrdiff_t ofs);
+    //! decrements the iterator
+    MatIterator_& operator --();
+    //! decrements the iterator
+    MatIterator_ operator --(int);
+    //! increments the iterator
+    MatIterator_& operator ++();
+    //! increments the iterator
+    MatIterator_ operator ++(int);
+};
+
+
+
+/////////////////////////////// SparseMatConstIterator ///////////////////////////////
+
+/**  @brief Read-Only Sparse Matrix Iterator.
+
+ Here is how to use the iterator to compute the sum of floating-point sparse matrix elements:
+
+ \code
+ SparseMatConstIterator it = m.begin(), it_end = m.end();
+ double s = 0;
+ CV_Assert( m.type() == CV_32F );
+ for( ; it != it_end; ++it )
+    s += it.value<float>();
+ \endcode
+*/
+class CV_EXPORTS SparseMatConstIterator
+{
+public:
+    //! the default constructor
+    SparseMatConstIterator();
+    //! the full constructor setting the iterator to the first sparse matrix element
+    SparseMatConstIterator(const SparseMat* _m);
+    //! the copy constructor
+    SparseMatConstIterator(const SparseMatConstIterator& it);
+
+    //! the assignment operator
+    SparseMatConstIterator& operator = (const SparseMatConstIterator& it);
+
+    //! template method returning the current matrix element
+    template<typename _Tp> const _Tp& value() const;
+    //! returns the current node of the sparse matrix. it.node->idx is the current element index
+    const SparseMat::Node* node() const;
+
+    //! moves iterator to the previous element
+    SparseMatConstIterator& operator --();
+    //! moves iterator to the previous element
+    SparseMatConstIterator operator --(int);
+    //! moves iterator to the next element
+    SparseMatConstIterator& operator ++();
+    //! moves iterator to the next element
+    SparseMatConstIterator operator ++(int);
+
+    //! moves iterator to the element after the last element
+    void seekEnd();
+
+    const SparseMat* m;
+    size_t hashidx;
+    uchar* ptr;
+};
+
+
+
+////////////////////////////////// SparseMatIterator /////////////////////////////////
+
+/** @brief  Read-write Sparse Matrix Iterator
+
+ The class is similar to cv::SparseMatConstIterator,
+ but can be used for in-place modification of the matrix elements.
+*/
+class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator
+{
+public:
+    //! the default constructor
+    SparseMatIterator();
+    //! the full constructor setting the iterator to the first sparse matrix element
+    SparseMatIterator(SparseMat* _m);
+    //! the full constructor setting the iterator to the specified sparse matrix element
+    SparseMatIterator(SparseMat* _m, const int* idx);
+    //! the copy constructor
+    SparseMatIterator(const SparseMatIterator& it);
+
+    //! the assignment operator
+    SparseMatIterator& operator = (const SparseMatIterator& it);
+    //! returns read-write reference to the current sparse matrix element
+    template<typename _Tp> _Tp& value() const;
+    //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!)
+    SparseMat::Node* node() const;
+
+    //! moves iterator to the next element
+    SparseMatIterator& operator ++();
+    //! moves iterator to the next element
+    SparseMatIterator operator ++(int);
+};
+
+
+
+/////////////////////////////// SparseMatConstIterator_ //////////////////////////////
+
+/** @brief  Template Read-Only Sparse Matrix Iterator Class.
+
+ This is the derived from SparseMatConstIterator class that
+ introduces more convenient operator *() for accessing the current element.
+*/
+template<typename _Tp> class SparseMatConstIterator_ : public SparseMatConstIterator
+{
+public:
+
+    typedef std::forward_iterator_tag iterator_category;
+
+    //! the default constructor
+    SparseMatConstIterator_();
+    //! the full constructor setting the iterator to the first sparse matrix element
+    SparseMatConstIterator_(const SparseMat_<_Tp>* _m);
+    SparseMatConstIterator_(const SparseMat* _m);
+    //! the copy constructor
+    SparseMatConstIterator_(const SparseMatConstIterator_& it);
+
+    //! the assignment operator
+    SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it);
+    //! the element access operator
+    const _Tp& operator *() const;
+
+    //! moves iterator to the next element
+    SparseMatConstIterator_& operator ++();
+    //! moves iterator to the next element
+    SparseMatConstIterator_ operator ++(int);
+};
+
+
+
+///////////////////////////////// SparseMatIterator_ /////////////////////////////////
+
+/** @brief  Template Read-Write Sparse Matrix Iterator Class.
+
+ This is the derived from cv::SparseMatConstIterator_ class that
+ introduces more convenient operator *() for accessing the current element.
+*/
+template<typename _Tp> class SparseMatIterator_ : public SparseMatConstIterator_<_Tp>
+{
+public:
+
+    typedef std::forward_iterator_tag iterator_category;
+
+    //! the default constructor
+    SparseMatIterator_();
+    //! the full constructor setting the iterator to the first sparse matrix element
+    SparseMatIterator_(SparseMat_<_Tp>* _m);
+    SparseMatIterator_(SparseMat* _m);
+    //! the copy constructor
+    SparseMatIterator_(const SparseMatIterator_& it);
+
+    //! the assignment operator
+    SparseMatIterator_& operator = (const SparseMatIterator_& it);
+    //! returns the reference to the current element
+    _Tp& operator *() const;
+
+    //! moves the iterator to the next element
+    SparseMatIterator_& operator ++();
+    //! moves the iterator to the next element
+    SparseMatIterator_ operator ++(int);
+};
+
+
+
+/////////////////////////////////// NAryMatIterator //////////////////////////////////
+
+/** @brief n-ary multi-dimensional array iterator.
+
+Use the class to implement unary, binary, and, generally, n-ary element-wise operations on
+multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some
+may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of
+the iterators after each small operations may be a big overhead. In this case consider using
+NAryMatIterator to iterate through several matrices simultaneously as long as they have the same
+geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`,
+`it.planes[1]`,... will be the slices of the corresponding matrices.
+
+The example below illustrates how you can compute a normalized and threshold 3D color histogram:
+@code
+    void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb)
+    {
+        const int histSize[] = {N, N, N};
+
+        // make sure that the histogram has a proper size and type
+        hist.create(3, histSize, CV_32F);
+
+        // and clear it
+        hist = Scalar(0);
+
+        // the loop below assumes that the image
+        // is a 8-bit 3-channel. check it.
+        CV_Assert(image.type() == CV_8UC3);
+        MatConstIterator_<Vec3b> it = image.begin<Vec3b>(),
+                                 it_end = image.end<Vec3b>();
+        for( ; it != it_end; ++it )
+        {
+            const Vec3b& pix = *it;
+            hist.at<float>(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f;
+        }
+
+        minProb *= image.rows*image.cols;
+
+        // initialize iterator (the style is different from STL).
+        // after initialization the iterator will contain
+        // the number of slices or planes the iterator will go through.
+        // it simultaneously increments iterators for several matrices
+        // supplied as a null terminated list of pointers
+        const Mat* arrays[] = {&hist, 0};
+        Mat planes[1];
+        NAryMatIterator itNAry(arrays, planes, 1);
+        double s = 0;
+        // iterate through the matrix. on each iteration
+        // itNAry.planes[i] (of type Mat) will be set to the current plane
+        // of the i-th n-dim matrix passed to the iterator constructor.
+        for(int p = 0; p < itNAry.nplanes; p++, ++itNAry)
+        {
+            threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO);
+            s += sum(itNAry.planes[0])[0];
+        }
+
+        s = 1./s;
+        itNAry = NAryMatIterator(arrays, planes, 1);
+        for(int p = 0; p < itNAry.nplanes; p++, ++itNAry)
+            itNAry.planes[0] *= s;
+    }
+@endcode
+ */
+class CV_EXPORTS NAryMatIterator
+{
+public:
+    //! the default constructor
+    NAryMatIterator();
+    //! the full constructor taking arbitrary number of n-dim matrices
+    NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1);
+    //! the full constructor taking arbitrary number of n-dim matrices
+    NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1);
+    //! the separate iterator initialization method
+    void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1);
+
+    //! proceeds to the next plane of every iterated matrix
+    NAryMatIterator& operator ++();
+    //! proceeds to the next plane of every iterated matrix (postfix increment operator)
+    NAryMatIterator operator ++(int);
+
+    //! the iterated arrays
+    const Mat** arrays;
+    //! the current planes
+    Mat* planes;
+    //! data pointers
+    uchar** ptrs;
+    //! the number of arrays
+    int narrays;
+    //! the number of hyper-planes that the iterator steps through
+    size_t nplanes;
+    //! the size of each segment (in elements)
+    size_t size;
+protected:
+    int iterdepth;
+    size_t idx;
+};
+
+
+
+///////////////////////////////// Matrix Expressions /////////////////////////////////
+
+class CV_EXPORTS MatOp
+{
+public:
+    MatOp();
+    virtual ~MatOp();
+
+    virtual bool elementWise(const MatExpr& expr) const;
+    virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0;
+    virtual void roi(const MatExpr& expr, const Range& rowRange,
+                     const Range& colRange, MatExpr& res) const;
+    virtual void diag(const MatExpr& expr, int d, MatExpr& res) const;
+    virtual void augAssignAdd(const MatExpr& expr, Mat& m) const;
+    virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const;
+    virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const;
+    virtual void augAssignDivide(const MatExpr& expr, Mat& m) const;
+    virtual void augAssignAnd(const MatExpr& expr, Mat& m) const;
+    virtual void augAssignOr(const MatExpr& expr, Mat& m) const;
+    virtual void augAssignXor(const MatExpr& expr, Mat& m) const;
+
+    virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
+    virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const;
+
+    virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
+    virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const;
+
+    virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
+    virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const;
+
+    virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
+    virtual void divide(double s, const MatExpr& expr, MatExpr& res) const;
+
+    virtual void abs(const MatExpr& expr, MatExpr& res) const;
+
+    virtual void transpose(const MatExpr& expr, MatExpr& res) const;
+    virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
+    virtual void invert(const MatExpr& expr, int method, MatExpr& res) const;
+
+    virtual Size size(const MatExpr& expr) const;
+    virtual int type(const MatExpr& expr) const;
+};
+
+/** @brief Matrix expression representation
+@anchor MatrixExpressions
+This is a list of implemented matrix operations that can be combined in arbitrary complex
+expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a
+real-valued scalar ( double )):
+-   Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A`
+-   Scaling: `A*alpha`
+-   Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A`
+-   Matrix multiplication: `A*B`
+-   Transposition: `A.t()` (means A<sup>T</sup>)
+-   Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems:
+    `A.inv([method]) (~ A<sup>-1</sup>)`,   `A.inv([method])*B (~ X: AX=B)`
+-   Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of
+  `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose
+    elements are set to 255 (if the particular element or pair of elements satisfy the condition) or
+    0.
+-   Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of
+  `&`, `|`, `^`.
+-   Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)`
+-   Element-wise absolute value: `abs(A)`
+-   Cross-product, dot-product: `A.cross(B)`, `A.dot(B)`
+-   Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm,
+    mean, sum, countNonZero, trace, determinant, repeat, and others.
+-   Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated
+    initializers, matrix constructors and operators that extract sub-matrices (see Mat description).
+-   Mat_<destination_type>() constructors to cast the result to the proper type.
+@note Comma-separated initializers and probably some other operations may require additional
+explicit Mat() or Mat_<T>() constructor calls to resolve a possible ambiguity.
+
+Here are examples of matrix expressions:
+@code
+    // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD)
+    SVD svd(A);
+    Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t();
+
+    // compute the new vector of parameters in the Levenberg-Marquardt algorithm
+    x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err);
+
+    // sharpen image using "unsharp mask" algorithm
+    Mat blurred; double sigma = 1, threshold = 5, amount = 1;
+    GaussianBlur(img, blurred, Size(), sigma, sigma);
+    Mat lowContrastMask = abs(img - blurred) < threshold;
+    Mat sharpened = img*(1+amount) + blurred*(-amount);
+    img.copyTo(sharpened, lowContrastMask);
+@endcode
+*/
+class CV_EXPORTS MatExpr
+{
+public:
+    MatExpr();
+    explicit MatExpr(const Mat& m);
+
+    MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(),
+            const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar());
+
+    operator Mat() const;
+    template<typename _Tp> operator Mat_<_Tp>() const;
+
+    Size size() const;
+    int type() const;
+
+    MatExpr row(int y) const;
+    MatExpr col(int x) const;
+    MatExpr diag(int d = 0) const;
+    MatExpr operator()( const Range& rowRange, const Range& colRange ) const;
+    MatExpr operator()( const Rect& roi ) const;
+
+    MatExpr t() const;
+    MatExpr inv(int method = DECOMP_LU) const;
+    MatExpr mul(const MatExpr& e, double scale=1) const;
+    MatExpr mul(const Mat& m, double scale=1) const;
+
+    Mat cross(const Mat& m) const;
+    double dot(const Mat& m) const;
+
+    void swap(MatExpr& b);
+
+    const MatOp* op;
+    int flags;
+
+    Mat a, b, c;
+    double alpha, beta;
+    Scalar s;
+};
+
+//! @} core_basic
+
+//! @relates cv::MatExpr
+//! @{
+CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a);
+CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s);
+CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e);
+CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator + (const Mat& a, const Matx<_Tp, m, n>& b) { return a + Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator + (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) + b; }
+
+CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a);
+CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s);
+CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e);
+CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator - (const Mat& a, const Matx<_Tp, m, n>& b) { return a - Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator - (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) - b; }
+
+CV_EXPORTS MatExpr operator - (const Mat& m);
+CV_EXPORTS MatExpr operator - (const MatExpr& e);
+
+CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator * (const Mat& a, double s);
+CV_EXPORTS MatExpr operator * (double s, const Mat& a);
+CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator * (const MatExpr& e, double s);
+CV_EXPORTS MatExpr operator * (double s, const MatExpr& e);
+CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator * (const Mat& a, const Matx<_Tp, m, n>& b) { return a * Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator * (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) * b; }
+
+CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator / (const Mat& a, double s);
+CV_EXPORTS MatExpr operator / (double s, const Mat& a);
+CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator / (const MatExpr& e, double s);
+CV_EXPORTS MatExpr operator / (double s, const MatExpr& e);
+CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator / (const Mat& a, const Matx<_Tp, m, n>& b) { return a / Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator / (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) / b; }
+
+CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator < (const Mat& a, double s);
+CV_EXPORTS MatExpr operator < (double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator < (const Mat& a, const Matx<_Tp, m, n>& b) { return a < Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator < (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) < b; }
+
+CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator <= (const Mat& a, double s);
+CV_EXPORTS MatExpr operator <= (double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator <= (const Mat& a, const Matx<_Tp, m, n>& b) { return a <= Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator <= (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) <= b; }
+
+CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator == (const Mat& a, double s);
+CV_EXPORTS MatExpr operator == (double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator == (const Mat& a, const Matx<_Tp, m, n>& b) { return a == Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator == (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) == b; }
+
+CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator != (const Mat& a, double s);
+CV_EXPORTS MatExpr operator != (double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator != (const Mat& a, const Matx<_Tp, m, n>& b) { return a != Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator != (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) != b; }
+
+CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator >= (const Mat& a, double s);
+CV_EXPORTS MatExpr operator >= (double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator >= (const Mat& a, const Matx<_Tp, m, n>& b) { return a >= Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator >= (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) >= b; }
+
+CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator > (const Mat& a, double s);
+CV_EXPORTS MatExpr operator > (double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator > (const Mat& a, const Matx<_Tp, m, n>& b) { return a > Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator > (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) > b; }
+
+CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator & (const Mat& a, const Matx<_Tp, m, n>& b) { return a & Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator & (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) & b; }
+
+CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator | (const Mat& a, const Matx<_Tp, m, n>& b) { return a | Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator | (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) | b; }
+
+CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr operator ^ (const Mat& a, const Matx<_Tp, m, n>& b) { return a ^ Mat(b); }
+template<typename _Tp, int m, int n> static inline
+MatExpr operator ^ (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) ^ b; }
+
+CV_EXPORTS MatExpr operator ~(const Mat& m);
+
+CV_EXPORTS MatExpr min(const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr min(const Mat& a, double s);
+CV_EXPORTS MatExpr min(double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr min (const Mat& a, const Matx<_Tp, m, n>& b) { return min(a, Mat(b)); }
+template<typename _Tp, int m, int n> static inline
+MatExpr min (const Matx<_Tp, m, n>& a, const Mat& b) { return min(Mat(a), b); }
+
+CV_EXPORTS MatExpr max(const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr max(const Mat& a, double s);
+CV_EXPORTS MatExpr max(double s, const Mat& a);
+template<typename _Tp, int m, int n> static inline
+MatExpr max (const Mat& a, const Matx<_Tp, m, n>& b) { return max(a, Mat(b)); }
+template<typename _Tp, int m, int n> static inline
+MatExpr max (const Matx<_Tp, m, n>& a, const Mat& b) { return max(Mat(a), b); }
+
+/** @brief Calculates an absolute value of each matrix element.
+
+abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms:
+- C = abs(A-B) is equivalent to `absdiff(A, B, C)`
+- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)`
+- C = `Mat_<Vec<uchar,n> >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha,
+beta)`
+
+The output matrix has the same size and the same type as the input one except for the last case,
+where C is depth=CV_8U .
+@param m matrix.
+@sa @ref MatrixExpressions, absdiff, convertScaleAbs
+ */
+CV_EXPORTS MatExpr abs(const Mat& m);
+/** @overload
+@param e matrix expression.
+*/
+CV_EXPORTS MatExpr abs(const MatExpr& e);
+//! @} relates cv::MatExpr
+
+} // cv
+
+#include "opencv2/core/mat.inl.hpp"
+
+#endif // OPENCV_CORE_MAT_HPP
diff --git a/3rdParty/include/opencv2/core/mat.inl.hpp b/3rdParty/include/opencv2/core/mat.inl.hpp
new file mode 100644
index 0000000..886b82c
--- /dev/null
+++ b/3rdParty/include/opencv2/core/mat.inl.hpp
@@ -0,0 +1,3422 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_MATRIX_OPERATIONS_HPP
+#define OPENCV_CORE_MATRIX_OPERATIONS_HPP
+
+#ifndef __cplusplus
+#  error mat.inl.hpp header must be compiled as C++
+#endif
+
+#ifdef _MSC_VER
+#pragma warning( push )
+#pragma warning( disable: 4127 )
+#endif
+
+#if defined(CV_SKIP_DISABLE_CLANG_ENUM_WARNINGS)
+  // nothing
+#elif defined(CV_FORCE_DISABLE_CLANG_ENUM_WARNINGS)
+  #define CV_DISABLE_CLANG_ENUM_WARNINGS
+#elif defined(__clang__) && defined(__has_warning)
+  #if __has_warning("-Wdeprecated-enum-enum-conversion") && __has_warning("-Wdeprecated-anon-enum-enum-conversion")
+    #define CV_DISABLE_CLANG_ENUM_WARNINGS
+  #endif
+#endif
+#ifdef CV_DISABLE_CLANG_ENUM_WARNINGS
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wdeprecated-enum-enum-conversion"
+#pragma clang diagnostic ignored "-Wdeprecated-anon-enum-enum-conversion"
+#endif
+
+namespace cv
+{
+CV__DEBUG_NS_BEGIN
+
+
+//! @cond IGNORED
+
+////////////////////////// Custom (raw) type wrapper //////////////////////////
+
+template<typename _Tp> static inline
+int rawType()
+{
+    CV_StaticAssert(sizeof(_Tp) <= CV_CN_MAX, "sizeof(_Tp) is too large");
+    const int elemSize = sizeof(_Tp);
+    return (int)CV_MAKETYPE(CV_8U, elemSize);
+}
+
+//////////////////////// Input/Output Arrays ////////////////////////
+
+inline void _InputArray::init(int _flags, const void* _obj)
+{ flags = _flags; obj = (void*)_obj; }
+
+inline void _InputArray::init(int _flags, const void* _obj, Size _sz)
+{ flags = _flags; obj = (void*)_obj; sz = _sz; }
+
+inline void* _InputArray::getObj() const { return obj; }
+inline int _InputArray::getFlags() const { return flags; }
+inline Size _InputArray::getSz() const { return sz; }
+
+inline _InputArray::_InputArray() { init(0 + NONE, 0); }
+inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); }
+inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); }
+inline _InputArray::_InputArray(const std::vector<Mat>& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); }
+inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); }
+inline _InputArray::_InputArray(const std::vector<UMat>& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); }
+
+template<typename _Tp> inline
+_InputArray::_InputArray(const std::vector<_Tp>& vec)
+{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); }
+
+template<typename _Tp, std::size_t _Nm> inline
+_InputArray::_InputArray(const std::array<_Tp, _Nm>& arr)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, arr.data(), Size(1, _Nm)); }
+
+template<std::size_t _Nm> inline
+_InputArray::_InputArray(const std::array<Mat, _Nm>& arr)
+{ init(STD_ARRAY_MAT + ACCESS_READ, arr.data(), Size(1, _Nm)); }
+
+inline
+_InputArray::_InputArray(const std::vector<bool>& vec)
+{ init(FIXED_TYPE + STD_BOOL_VECTOR + traits::Type<bool>::value + ACCESS_READ, &vec); }
+
+template<typename _Tp> inline
+_InputArray::_InputArray(const std::vector<std::vector<_Tp> >& vec)
+{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); }
+
+template<typename _Tp> inline
+_InputArray::_InputArray(const std::vector<Mat_<_Tp> >& vec)
+{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_READ, &vec); }
+
+template<typename _Tp, int m, int n> inline
+_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, &mtx, Size(n, m)); }
+
+template<typename _Tp> inline
+_InputArray::_InputArray(const _Tp* vec, int n)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, vec, Size(n, 1)); }
+
+template<typename _Tp> inline
+_InputArray::_InputArray(const Mat_<_Tp>& m)
+{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_READ, &m); }
+
+inline _InputArray::_InputArray(const double& val)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); }
+
+inline _InputArray::_InputArray(const cuda::GpuMat& d_mat)
+{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); }
+
+inline _InputArray::_InputArray(const std::vector<cuda::GpuMat>& d_mat)
+{	init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);}
+
+inline _InputArray::_InputArray(const ogl::Buffer& buf)
+{ init(OPENGL_BUFFER + ACCESS_READ, &buf); }
+
+inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem)
+{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); }
+
+template<typename _Tp> inline
+_InputArray _InputArray::rawIn(const std::vector<_Tp>& vec)
+{
+    _InputArray v;
+    v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_READ;
+    v.obj = (void*)&vec;
+    return v;
+}
+
+template<typename _Tp, std::size_t _Nm> inline
+_InputArray _InputArray::rawIn(const std::array<_Tp, _Nm>& arr)
+{
+    _InputArray v;
+    v.flags = FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ;
+    v.obj = (void*)arr.data();
+    v.sz = Size(1, _Nm);
+    return v;
+}
+
+inline _InputArray::~_InputArray() {}
+
+inline Mat _InputArray::getMat(int i) const
+{
+    if( kind() == MAT && i < 0 )
+        return *(const Mat*)obj;
+    return getMat_(i);
+}
+
+inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; }
+inline bool _InputArray::isUMat() const  { return kind() == _InputArray::UMAT; }
+inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; }
+inline bool _InputArray::isUMatVector() const  { return kind() == _InputArray::STD_VECTOR_UMAT; }
+inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; }
+inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR ||
+                                                   kind() == _InputArray::STD_BOOL_VECTOR ||
+                                                   (kind() == _InputArray::MATX && (sz.width <= 1 || sz.height <= 1)); }
+inline bool _InputArray::isGpuMat() const { return kind() == _InputArray::CUDA_GPU_MAT; }
+inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; }
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+inline _OutputArray::_OutputArray() { init(NONE + ACCESS_WRITE, 0); }
+inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags + ACCESS_WRITE, _obj); }
+inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); }
+inline _OutputArray::_OutputArray(std::vector<Mat>& vec) { init(STD_VECTOR_MAT + ACCESS_WRITE, &vec); }
+inline _OutputArray::_OutputArray(UMat& m) { init(UMAT + ACCESS_WRITE, &m); }
+inline _OutputArray::_OutputArray(std::vector<UMat>& vec) { init(STD_VECTOR_UMAT + ACCESS_WRITE, &vec); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(std::vector<_Tp>& vec)
+{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
+
+template<typename _Tp, std::size_t _Nm> inline
+_OutputArray::_OutputArray(std::array<_Tp, _Nm>& arr)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
+
+template<std::size_t _Nm> inline
+_OutputArray::_OutputArray(std::array<Mat, _Nm>& arr)
+{ init(STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(std::vector<std::vector<_Tp> >& vec)
+{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(std::vector<Mat_<_Tp> >& vec)
+{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(Mat_<_Tp>& m)
+{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); }
+
+template<typename _Tp, int m, int n> inline
+_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(_Tp* vec, int n)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(const std::vector<_Tp>& vec)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
+
+template<typename _Tp, std::size_t _Nm> inline
+_OutputArray::_OutputArray(const std::array<_Tp, _Nm>& arr)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
+
+template<std::size_t _Nm> inline
+_OutputArray::_OutputArray(const std::array<Mat, _Nm>& arr)
+{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(const std::vector<std::vector<_Tp> >& vec)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(const std::vector<Mat_<_Tp> >& vec)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(const Mat_<_Tp>& m)
+{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); }
+
+template<typename _Tp, int m, int n> inline
+_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); }
+
+template<typename _Tp> inline
+_OutputArray::_OutputArray(const _Tp* vec, int n)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); }
+
+inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat)
+{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); }
+
+inline _OutputArray::_OutputArray(std::vector<cuda::GpuMat>& d_mat)
+{	init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);}
+
+inline _OutputArray::_OutputArray(ogl::Buffer& buf)
+{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); }
+
+inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem)
+{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); }
+
+inline _OutputArray::_OutputArray(const Mat& m)
+{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); }
+
+inline _OutputArray::_OutputArray(const std::vector<Mat>& vec)
+{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); }
+
+inline _OutputArray::_OutputArray(const UMat& m)
+{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); }
+
+inline _OutputArray::_OutputArray(const std::vector<UMat>& vec)
+{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); }
+
+inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat)
+{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); }
+
+
+inline _OutputArray::_OutputArray(const ogl::Buffer& buf)
+{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); }
+
+inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem)
+{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); }
+
+template<typename _Tp> inline
+_OutputArray _OutputArray::rawOut(std::vector<_Tp>& vec)
+{
+    _OutputArray v;
+    v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_WRITE;
+    v.obj = (void*)&vec;
+    return v;
+}
+
+template<typename _Tp, std::size_t _Nm> inline
+_OutputArray _OutputArray::rawOut(std::array<_Tp, _Nm>& arr)
+{
+    _OutputArray v;
+    v.flags = FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE;
+    v.obj = (void*)arr.data();
+    v.sz = Size(1, _Nm);
+    return v;
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////
+
+inline _InputOutputArray::_InputOutputArray() { init(0+ACCESS_RW, 0); }
+inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags+ACCESS_RW, _obj); }
+inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); }
+inline _InputOutputArray::_InputOutputArray(std::vector<Mat>& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); }
+inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); }
+inline _InputOutputArray::_InputOutputArray(std::vector<UMat>& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec)
+{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
+
+template<typename _Tp, std::size_t _Nm> inline
+_InputOutputArray::_InputOutputArray(std::array<_Tp, _Nm>& arr)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); }
+
+template<std::size_t _Nm> inline
+_InputOutputArray::_InputOutputArray(std::array<Mat, _Nm>& arr)
+{ init(STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(std::vector<std::vector<_Tp> >& vec)
+{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(std::vector<Mat_<_Tp> >& vec)
+{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m)
+{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); }
+
+template<typename _Tp, int m, int n> inline
+_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(_Tp* vec, int n)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
+
+template<typename _Tp, std::size_t _Nm> inline
+_InputOutputArray::_InputOutputArray(const std::array<_Tp, _Nm>& arr)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); }
+
+template<std::size_t _Nm> inline
+_InputOutputArray::_InputOutputArray(const std::array<Mat, _Nm>& arr)
+{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(const std::vector<std::vector<_Tp> >& vec)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(const std::vector<Mat_<_Tp> >& vec)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m)
+{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); }
+
+template<typename _Tp, int m, int n> inline
+_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); }
+
+template<typename _Tp> inline
+_InputOutputArray::_InputOutputArray(const _Tp* vec, int n)
+{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); }
+
+inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat)
+{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); }
+
+inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf)
+{ init(OPENGL_BUFFER + ACCESS_RW, &buf); }
+
+inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem)
+{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); }
+
+inline _InputOutputArray::_InputOutputArray(const Mat& m)
+{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); }
+
+inline _InputOutputArray::_InputOutputArray(const std::vector<Mat>& vec)
+{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); }
+
+inline _InputOutputArray::_InputOutputArray(const UMat& m)
+{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); }
+
+inline _InputOutputArray::_InputOutputArray(const std::vector<UMat>& vec)
+{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); }
+
+inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat)
+{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); }
+
+inline _InputOutputArray::_InputOutputArray(const std::vector<cuda::GpuMat>& d_mat)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);}
+
+template<> inline _InputOutputArray::_InputOutputArray(std::vector<cuda::GpuMat>& d_mat)
+{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);}
+
+inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf)
+{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); }
+
+inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem)
+{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); }
+
+template<typename _Tp> inline
+_InputOutputArray _InputOutputArray::rawInOut(std::vector<_Tp>& vec)
+{
+    _InputOutputArray v;
+    v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_RW;
+    v.obj = (void*)&vec;
+    return v;
+}
+
+template<typename _Tp, std::size_t _Nm> inline
+_InputOutputArray _InputOutputArray::rawInOut(std::array<_Tp, _Nm>& arr)
+{
+    _InputOutputArray v;
+    v.flags = FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW;
+    v.obj = (void*)arr.data();
+    v.sz = Size(1, _Nm);
+    return v;
+}
+
+
+template<typename _Tp> static inline _InputArray rawIn(_Tp& v) { return _InputArray::rawIn(v); }
+template<typename _Tp> static inline _OutputArray rawOut(_Tp& v) { return _OutputArray::rawOut(v); }
+template<typename _Tp> static inline _InputOutputArray rawInOut(_Tp& v) { return _InputOutputArray::rawInOut(v); }
+
+CV__DEBUG_NS_END
+
+//////////////////////////////////////////// Mat //////////////////////////////////////////
+
+template<typename _Tp> inline
+Mat::Mat(const std::vector<_Tp>& vec, bool copyData)
+    : flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()),
+      cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
+{
+    if(vec.empty())
+        return;
+    if( !copyData )
+    {
+        step[0] = step[1] = sizeof(_Tp);
+        datastart = data = (uchar*)&vec[0];
+        datalimit = dataend = datastart + rows * step[0];
+    }
+    else
+        Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this);
+}
+
+template<typename _Tp, typename> inline
+Mat::Mat(const std::initializer_list<_Tp> list)
+    : Mat()
+{
+    CV_Assert(list.size() != 0);
+    Mat((int)list.size(), 1, traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this);
+}
+
+template<typename _Tp> inline
+Mat::Mat(const std::initializer_list<int> sizes, const std::initializer_list<_Tp> list)
+    : Mat()
+{
+    size_t size_total = 1;
+    for(auto s : sizes)
+        size_total *= s;
+    CV_Assert(list.size() != 0);
+    CV_Assert(size_total == list.size());
+    Mat((int)sizes.size(), (int*)sizes.begin(), traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this);
+}
+
+template<typename _Tp, std::size_t _Nm> inline
+Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData)
+    : flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(2), rows((int)arr.size()),
+      cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
+{
+    if(arr.empty())
+        return;
+    if( !copyData )
+    {
+        step[0] = step[1] = sizeof(_Tp);
+        datastart = data = (uchar*)arr.data();
+        datalimit = dataend = datastart + rows * step[0];
+    }
+    else
+        Mat((int)arr.size(), 1, traits::Type<_Tp>::value, (uchar*)arr.data()).copyTo(*this);
+}
+
+template<typename _Tp, int n> inline
+Mat::Mat(const Vec<_Tp, n>& vec, bool copyData)
+    : flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0),
+      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
+{
+    if( !copyData )
+    {
+        step[0] = step[1] = sizeof(_Tp);
+        datastart = data = (uchar*)vec.val;
+        datalimit = dataend = datastart + rows * step[0];
+    }
+    else
+        Mat(n, 1, traits::Type<_Tp>::value, (void*)vec.val).copyTo(*this);
+}
+
+
+template<typename _Tp, int m, int n> inline
+Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData)
+    : flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0),
+      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
+{
+    if( !copyData )
+    {
+        step[0] = cols * sizeof(_Tp);
+        step[1] = sizeof(_Tp);
+        datastart = data = (uchar*)M.val;
+        datalimit = dataend = datastart + rows * step[0];
+    }
+    else
+        Mat(m, n, traits::Type<_Tp>::value, (uchar*)M.val).copyTo(*this);
+}
+
+template<typename _Tp> inline
+Mat::Mat(const Point_<_Tp>& pt, bool copyData)
+    : flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0),
+      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
+{
+    if( !copyData )
+    {
+        step[0] = step[1] = sizeof(_Tp);
+        datastart = data = (uchar*)&pt.x;
+        datalimit = dataend = datastart + rows * step[0];
+    }
+    else
+    {
+        create(2, 1, traits::Type<_Tp>::value);
+        ((_Tp*)data)[0] = pt.x;
+        ((_Tp*)data)[1] = pt.y;
+    }
+}
+
+template<typename _Tp> inline
+Mat::Mat(const Point3_<_Tp>& pt, bool copyData)
+    : flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0),
+      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
+{
+    if( !copyData )
+    {
+        step[0] = step[1] = sizeof(_Tp);
+        datastart = data = (uchar*)&pt.x;
+        datalimit = dataend = datastart + rows * step[0];
+    }
+    else
+    {
+        create(3, 1, traits::Type<_Tp>::value);
+        ((_Tp*)data)[0] = pt.x;
+        ((_Tp*)data)[1] = pt.y;
+        ((_Tp*)data)[2] = pt.z;
+    }
+}
+
+template<typename _Tp> inline
+Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer)
+    : flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0),
+      datastart(0), dataend(0), allocator(0), u(0), size(&rows)
+{
+    *this = commaInitializer.operator Mat_<_Tp>();
+}
+
+inline
+Mat Mat::row(int y) const
+{
+    return Mat(*this, Range(y, y + 1), Range::all());
+}
+
+inline
+Mat Mat::col(int x) const
+{
+    return Mat(*this, Range::all(), Range(x, x + 1));
+}
+
+inline
+Mat Mat::rowRange(int startrow, int endrow) const
+{
+    return Mat(*this, Range(startrow, endrow), Range::all());
+}
+
+inline
+Mat Mat::rowRange(const Range& r) const
+{
+    return Mat(*this, r, Range::all());
+}
+
+inline
+Mat Mat::colRange(int startcol, int endcol) const
+{
+    return Mat(*this, Range::all(), Range(startcol, endcol));
+}
+
+inline
+Mat Mat::colRange(const Range& r) const
+{
+    return Mat(*this, Range::all(), r);
+}
+
+inline
+Mat Mat::operator()( Range _rowRange, Range _colRange ) const
+{
+    return Mat(*this, _rowRange, _colRange);
+}
+
+inline
+Mat Mat::operator()( const Rect& roi ) const
+{
+    return Mat(*this, roi);
+}
+
+inline
+Mat Mat::operator()(const Range* ranges) const
+{
+    return Mat(*this, ranges);
+}
+
+inline
+Mat Mat::operator()(const std::vector<Range>& ranges) const
+{
+    return Mat(*this, ranges);
+}
+
+inline
+bool Mat::isContinuous() const
+{
+    return (flags & CONTINUOUS_FLAG) != 0;
+}
+
+inline
+bool Mat::isSubmatrix() const
+{
+    return (flags & SUBMATRIX_FLAG) != 0;
+}
+
+inline
+size_t Mat::elemSize() const
+{
+    size_t res = dims > 0 ? step.p[dims - 1] : 0;
+    CV_DbgAssert(res != 0);
+    return res;
+}
+
+inline
+size_t Mat::elemSize1() const
+{
+    return CV_ELEM_SIZE1(flags);
+}
+
+inline
+int Mat::type() const
+{
+    return CV_MAT_TYPE(flags);
+}
+
+inline
+int Mat::depth() const
+{
+    return CV_MAT_DEPTH(flags);
+}
+
+inline
+int Mat::channels() const
+{
+    return CV_MAT_CN(flags);
+}
+
+inline
+uchar* Mat::ptr(int y)
+{
+    CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) );
+    return data + step.p[0] * y;
+}
+
+inline
+const uchar* Mat::ptr(int y) const
+{
+    CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) );
+    return data + step.p[0] * y;
+}
+
+template<typename _Tp> inline
+_Tp* Mat::ptr(int y)
+{
+    CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) );
+    return (_Tp*)(data + step.p[0] * y);
+}
+
+template<typename _Tp> inline
+const _Tp* Mat::ptr(int y) const
+{
+    CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) );
+    return (const _Tp*)(data + step.p[0] * y);
+}
+
+inline
+uchar* Mat::ptr(int i0, int i1)
+{
+    CV_DbgAssert(dims >= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    return data + i0 * step.p[0] + i1 * step.p[1];
+}
+
+inline
+const uchar* Mat::ptr(int i0, int i1) const
+{
+    CV_DbgAssert(dims >= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    return data + i0 * step.p[0] + i1 * step.p[1];
+}
+
+template<typename _Tp> inline
+_Tp* Mat::ptr(int i0, int i1)
+{
+    CV_DbgAssert(dims >= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]);
+}
+
+template<typename _Tp> inline
+const _Tp* Mat::ptr(int i0, int i1) const
+{
+    CV_DbgAssert(dims >= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]);
+}
+
+inline
+uchar* Mat::ptr(int i0, int i1, int i2)
+{
+    CV_DbgAssert(dims >= 3);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
+    return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2];
+}
+
+inline
+const uchar* Mat::ptr(int i0, int i1, int i2) const
+{
+    CV_DbgAssert(dims >= 3);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
+    return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2];
+}
+
+template<typename _Tp> inline
+_Tp* Mat::ptr(int i0, int i1, int i2)
+{
+    CV_DbgAssert(dims >= 3);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
+    return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]);
+}
+
+template<typename _Tp> inline
+const _Tp* Mat::ptr(int i0, int i1, int i2) const
+{
+    CV_DbgAssert(dims >= 3);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
+    return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]);
+}
+
+inline
+uchar* Mat::ptr(const int* idx)
+{
+    int i, d = dims;
+    uchar* p = data;
+    CV_DbgAssert( d >= 1 && p );
+    for( i = 0; i < d; i++ )
+    {
+        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
+        p += idx[i] * step.p[i];
+    }
+    return p;
+}
+
+inline
+const uchar* Mat::ptr(const int* idx) const
+{
+    int i, d = dims;
+    uchar* p = data;
+    CV_DbgAssert( d >= 1 && p );
+    for( i = 0; i < d; i++ )
+    {
+        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
+        p += idx[i] * step.p[i];
+    }
+    return p;
+}
+
+template<typename _Tp> inline
+_Tp* Mat::ptr(const int* idx)
+{
+    int i, d = dims;
+    uchar* p = data;
+    CV_DbgAssert( d >= 1 && p );
+    for( i = 0; i < d; i++ )
+    {
+        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
+        p += idx[i] * step.p[i];
+    }
+    return (_Tp*)p;
+}
+
+template<typename _Tp> inline
+const _Tp* Mat::ptr(const int* idx) const
+{
+    int i, d = dims;
+    uchar* p = data;
+    CV_DbgAssert( d >= 1 && p );
+    for( i = 0; i < d; i++ )
+    {
+        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
+        p += idx[i] * step.p[i];
+    }
+    return (const _Tp*)p;
+}
+
+template<int n> inline
+uchar* Mat::ptr(const Vec<int, n>& idx)
+{
+    return Mat::ptr(idx.val);
+}
+
+template<int n> inline
+const uchar* Mat::ptr(const Vec<int, n>& idx) const
+{
+    return Mat::ptr(idx.val);
+}
+
+template<typename _Tp, int n> inline
+_Tp* Mat::ptr(const Vec<int, n>& idx)
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return Mat::ptr<_Tp>(idx.val);
+}
+
+template<typename _Tp, int n> inline
+const _Tp* Mat::ptr(const Vec<int, n>& idx) const
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return Mat::ptr<_Tp>(idx.val);
+}
+
+
+template<typename _Tp> inline
+_Tp& Mat::at(int i0, int i1)
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
+    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
+    return ((_Tp*)(data + step.p[0] * i0))[i1];
+}
+
+template<typename _Tp> inline
+const _Tp& Mat::at(int i0, int i1) const
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
+    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
+    return ((const _Tp*)(data + step.p[0] * i0))[i1];
+}
+
+template<typename _Tp> inline
+_Tp& Mat::at(Point pt)
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
+    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
+    return ((_Tp*)(data + step.p[0] * pt.y))[pt.x];
+}
+
+template<typename _Tp> inline
+const _Tp& Mat::at(Point pt) const
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
+    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
+    return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x];
+}
+
+template<typename _Tp> inline
+_Tp& Mat::at(int i0)
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1]));
+    CV_DbgAssert(elemSize() == sizeof(_Tp));
+    if( isContinuous() || size.p[0] == 1 )
+        return ((_Tp*)data)[i0];
+    if( size.p[1] == 1 )
+        return *(_Tp*)(data + step.p[0] * i0);
+    int i = i0 / cols, j = i0 - i * cols;
+    return ((_Tp*)(data + step.p[0] * i))[j];
+}
+
+template<typename _Tp> inline
+const _Tp& Mat::at(int i0) const
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1]));
+    CV_DbgAssert(elemSize() == sizeof(_Tp));
+    if( isContinuous() || size.p[0] == 1 )
+        return ((const _Tp*)data)[i0];
+    if( size.p[1] == 1 )
+        return *(const _Tp*)(data + step.p[0] * i0);
+    int i = i0 / cols, j = i0 - i * cols;
+    return ((const _Tp*)(data + step.p[0] * i))[j];
+}
+
+template<typename _Tp> inline
+_Tp& Mat::at(int i0, int i1, int i2)
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return *(_Tp*)ptr(i0, i1, i2);
+}
+
+template<typename _Tp> inline
+const _Tp& Mat::at(int i0, int i1, int i2) const
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return *(const _Tp*)ptr(i0, i1, i2);
+}
+
+template<typename _Tp> inline
+_Tp& Mat::at(const int* idx)
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return *(_Tp*)ptr(idx);
+}
+
+template<typename _Tp> inline
+const _Tp& Mat::at(const int* idx) const
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return *(const _Tp*)ptr(idx);
+}
+
+template<typename _Tp, int n> inline
+_Tp& Mat::at(const Vec<int, n>& idx)
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return *(_Tp*)ptr(idx.val);
+}
+
+template<typename _Tp, int n> inline
+const _Tp& Mat::at(const Vec<int, n>& idx) const
+{
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return *(const _Tp*)ptr(idx.val);
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp> Mat::begin() const
+{
+    if (empty())
+        return MatConstIterator_<_Tp>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this);
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatConstIterator_<_Tp>> Mat::rbegin() const
+{
+    if (empty())
+        return std::reverse_iterator<MatConstIterator_<_Tp>>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this);
+    it += total();
+    return std::reverse_iterator<MatConstIterator_<_Tp>> (it);
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp> Mat::end() const
+{
+    if (empty())
+        return MatConstIterator_<_Tp>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this);
+    it += total();
+    return it;
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatConstIterator_<_Tp>> Mat::rend() const
+{
+    if (empty())
+        return std::reverse_iterator<MatConstIterator_<_Tp>>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return std::reverse_iterator<MatConstIterator_<_Tp>>((const Mat_<_Tp>*)this);
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp> Mat::begin()
+{
+    if (empty())
+        return MatIterator_<_Tp>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return MatIterator_<_Tp>((Mat_<_Tp>*)this);
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatIterator_<_Tp>> Mat::rbegin()
+{
+    if (empty())
+        return std::reverse_iterator<MatIterator_<_Tp>>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    MatIterator_<_Tp> it((Mat_<_Tp>*)this);
+    it += total();
+    return std::reverse_iterator<MatIterator_<_Tp>>(it);
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp> Mat::end()
+{
+    if (empty())
+        return MatIterator_<_Tp>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    MatIterator_<_Tp> it((Mat_<_Tp>*)this);
+    it += total();
+    return it;
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatIterator_<_Tp>> Mat::rend()
+{
+    if (empty())
+        return std::reverse_iterator<MatIterator_<_Tp>>();
+    CV_DbgAssert( elemSize() == sizeof(_Tp) );
+    return std::reverse_iterator<MatIterator_<_Tp>>(MatIterator_<_Tp>((Mat_<_Tp>*)this));
+}
+
+template<typename _Tp, typename Functor> inline
+void Mat::forEach(const Functor& operation) {
+    this->forEach_impl<_Tp>(operation);
+}
+
+template<typename _Tp, typename Functor> inline
+void Mat::forEach(const Functor& operation) const {
+    // call as not const
+    (const_cast<Mat*>(this))->forEach<_Tp>(operation);
+}
+
+template<typename _Tp> inline
+Mat::operator std::vector<_Tp>() const
+{
+    std::vector<_Tp> v;
+    copyTo(v);
+    return v;
+}
+
+template<typename _Tp, std::size_t _Nm> inline
+Mat::operator std::array<_Tp, _Nm>() const
+{
+    std::array<_Tp, _Nm> v;
+    copyTo(v);
+    return v;
+}
+
+template<typename _Tp, int n> inline
+Mat::operator Vec<_Tp, n>() const
+{
+    CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) &&
+               rows + cols - 1 == n && channels() == 1 );
+
+    if( isContinuous() && type() == traits::Type<_Tp>::value )
+        return Vec<_Tp, n>((_Tp*)data);
+    Vec<_Tp, n> v;
+    Mat tmp(rows, cols, traits::Type<_Tp>::value, v.val);
+    convertTo(tmp, tmp.type());
+    return v;
+}
+
+template<typename _Tp, int m, int n> inline
+Mat::operator Matx<_Tp, m, n>() const
+{
+    CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 );
+
+    if( isContinuous() && type() == traits::Type<_Tp>::value )
+        return Matx<_Tp, m, n>((_Tp*)data);
+    Matx<_Tp, m, n> mtx;
+    Mat tmp(rows, cols, traits::Type<_Tp>::value, mtx.val);
+    convertTo(tmp, tmp.type());
+    return mtx;
+}
+
+template<typename _Tp> inline
+void Mat::push_back(const _Tp& elem)
+{
+    if( !data )
+    {
+        *this = Mat(1, 1, traits::Type<_Tp>::value, (void*)&elem).clone();
+        return;
+    }
+    CV_Assert(traits::Type<_Tp>::value == type() && cols == 1
+              /* && dims == 2 (cols == 1 implies dims == 2) */);
+    const uchar* tmp = dataend + step[0];
+    if( !isSubmatrix() && isContinuous() && tmp <= datalimit )
+    {
+        *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem;
+        dataend = tmp;
+    }
+    else
+        push_back_(&elem);
+}
+
+template<typename _Tp> inline
+void Mat::push_back(const Mat_<_Tp>& m)
+{
+    push_back((const Mat&)m);
+}
+
+template<> inline
+void Mat::push_back(const MatExpr& expr)
+{
+    push_back(static_cast<Mat>(expr));
+}
+
+
+template<typename _Tp> inline
+void Mat::push_back(const std::vector<_Tp>& v)
+{
+    push_back(Mat(v));
+}
+
+
+///////////////////////////// MatSize ////////////////////////////
+
+inline
+MatSize::MatSize(int* _p) CV_NOEXCEPT
+    : p(_p) {}
+
+inline
+int MatSize::dims() const CV_NOEXCEPT
+{
+    return (p - 1)[0];
+}
+
+inline
+Size MatSize::operator()() const
+{
+    CV_DbgAssert(dims() <= 2);
+    return Size(p[1], p[0]);
+}
+
+inline
+const int& MatSize::operator[](int i) const
+{
+    CV_DbgAssert(i < dims());
+#ifdef __OPENCV_BUILD
+    CV_DbgAssert(i >= 0);
+#endif
+    return p[i];
+}
+
+inline
+int& MatSize::operator[](int i)
+{
+    CV_DbgAssert(i < dims());
+#ifdef __OPENCV_BUILD
+    CV_DbgAssert(i >= 0);
+#endif
+    return p[i];
+}
+
+inline
+MatSize::operator const int*() const CV_NOEXCEPT
+{
+    return p;
+}
+
+inline
+bool MatSize::operator != (const MatSize& sz) const CV_NOEXCEPT
+{
+    return !(*this == sz);
+}
+
+
+
+///////////////////////////// MatStep ////////////////////////////
+
+inline
+MatStep::MatStep() CV_NOEXCEPT
+{
+    p = buf; p[0] = p[1] = 0;
+}
+
+inline
+MatStep::MatStep(size_t s) CV_NOEXCEPT
+{
+    p = buf; p[0] = s; p[1] = 0;
+}
+
+inline
+const size_t& MatStep::operator[](int i) const CV_NOEXCEPT
+{
+    return p[i];
+}
+
+inline
+size_t& MatStep::operator[](int i) CV_NOEXCEPT
+{
+    return p[i];
+}
+
+inline MatStep::operator size_t() const
+{
+    CV_DbgAssert( p == buf );
+    return buf[0];
+}
+
+inline MatStep& MatStep::operator = (size_t s)
+{
+    CV_DbgAssert( p == buf );
+    buf[0] = s;
+    return *this;
+}
+
+
+
+////////////////////////////// Mat_<_Tp> ////////////////////////////
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_() CV_NOEXCEPT
+    : Mat()
+{
+    flags = (flags & ~CV_MAT_TYPE_MASK) + traits::Type<_Tp>::value;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(int _rows, int _cols)
+    : Mat(_rows, _cols, traits::Type<_Tp>::value)
+{
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value)
+    : Mat(_rows, _cols, traits::Type<_Tp>::value)
+{
+    *this = value;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(Size _sz)
+    : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(Size _sz, const _Tp& value)
+    : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value)
+{
+    *this = value;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(int _dims, const int* _sz)
+    : Mat(_dims, _sz, traits::Type<_Tp>::value)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s)
+    : Mat(_dims, _sz, traits::Type<_Tp>::value, Scalar(_s))
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps)
+    : Mat(_dims, _sz, traits::Type<_Tp>::value, _data, _steps)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges)
+    : Mat(m, ranges)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const std::vector<Range>& ranges)
+    : Mat(m, ranges)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Mat& m)
+    : Mat()
+{
+    flags = (flags & ~CV_MAT_TYPE_MASK) + traits::Type<_Tp>::value;
+    *this = m;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Mat_& m)
+    : Mat(m)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps)
+    : Mat(_rows, _cols, traits::Type<_Tp>::value, _data, steps)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange)
+    : Mat(m, _rowRange, _colRange)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi)
+    : Mat(m, roi)
+{}
+
+template<typename _Tp> template<int n> inline
+Mat_<_Tp>::Mat_(const Vec<typename DataType<_Tp>::channel_type, n>& vec, bool copyData)
+    : Mat(n / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&vec)
+{
+    CV_Assert(n%DataType<_Tp>::channels == 0);
+    if( copyData )
+        *this = clone();
+}
+
+template<typename _Tp> template<int m, int n> inline
+Mat_<_Tp>::Mat_(const Matx<typename DataType<_Tp>::channel_type, m, n>& M, bool copyData)
+    : Mat(m, n / DataType<_Tp>::channels, traits::Type<_Tp>::value, (void*)&M)
+{
+    CV_Assert(n % DataType<_Tp>::channels == 0);
+    if( copyData )
+        *this = clone();
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Point_<typename DataType<_Tp>::channel_type>& pt, bool copyData)
+    : Mat(2 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt)
+{
+    CV_Assert(2 % DataType<_Tp>::channels == 0);
+    if( copyData )
+        *this = clone();
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const Point3_<typename DataType<_Tp>::channel_type>& pt, bool copyData)
+    : Mat(3 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt)
+{
+    CV_Assert(3 % DataType<_Tp>::channels == 0);
+    if( copyData )
+        *this = clone();
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer)
+    : Mat(commaInitializer)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData)
+    : Mat(vec, copyData)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(std::initializer_list<_Tp> list)
+    : Mat(list)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const std::initializer_list<int> sizes, std::initializer_list<_Tp> list)
+    : Mat(sizes, list)
+{}
+
+template<typename _Tp> template<std::size_t _Nm> inline
+Mat_<_Tp>::Mat_(const std::array<_Tp, _Nm>& arr, bool copyData)
+    : Mat(arr, copyData)
+{}
+
+template<typename _Tp> inline
+Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m)
+{
+    if (m.empty())
+    {
+        release();
+        return *this;
+    }
+    if( traits::Type<_Tp>::value == m.type() )
+    {
+        Mat::operator = (m);
+        return *this;
+    }
+    if( traits::Depth<_Tp>::value == m.depth() )
+    {
+        return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0));
+    }
+    CV_Assert(DataType<_Tp>::channels == m.channels() || m.empty());
+    m.convertTo(*this, type());
+    return *this;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m)
+{
+    Mat::operator=(m);
+    return *this;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s)
+{
+    typedef typename DataType<_Tp>::vec_type VT;
+    Mat::operator=(Scalar((const VT&)s));
+    return *this;
+}
+
+template<typename _Tp> inline
+void Mat_<_Tp>::create(int _rows, int _cols)
+{
+    Mat::create(_rows, _cols, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+void Mat_<_Tp>::create(Size _sz)
+{
+    Mat::create(_sz, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+void Mat_<_Tp>::create(int _dims, const int* _sz)
+{
+    Mat::create(_dims, _sz, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+void Mat_<_Tp>::release()
+{
+    Mat::release();
+    flags = (flags & ~CV_MAT_TYPE_MASK) + traits::Type<_Tp>::value;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const
+{
+    return Mat_<_Tp>(Mat::cross(m));
+}
+
+template<typename _Tp> template<typename T2> inline
+Mat_<_Tp>::operator Mat_<T2>() const
+{
+    return Mat_<T2>(static_cast<const Mat&>(*this));
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::row(int y) const
+{
+    return Mat_(*this, Range(y, y+1), Range::all());
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::col(int x) const
+{
+    return Mat_(*this, Range::all(), Range(x, x+1));
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::diag(int d) const
+{
+    return Mat_(Mat::diag(d));
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::clone() const
+{
+    return Mat_(Mat::clone());
+}
+
+template<typename _Tp> inline
+size_t Mat_<_Tp>::elemSize() const
+{
+    CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) );
+    return sizeof(_Tp);
+}
+
+template<typename _Tp> inline
+size_t Mat_<_Tp>::elemSize1() const
+{
+    CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels );
+    return sizeof(_Tp) / DataType<_Tp>::channels;
+}
+
+template<typename _Tp> inline
+int Mat_<_Tp>::type() const
+{
+    CV_DbgAssert( Mat::type() == traits::Type<_Tp>::value );
+    return traits::Type<_Tp>::value;
+}
+
+template<typename _Tp> inline
+int Mat_<_Tp>::depth() const
+{
+    CV_DbgAssert( Mat::depth() == traits::Depth<_Tp>::value );
+    return traits::Depth<_Tp>::value;
+}
+
+template<typename _Tp> inline
+int Mat_<_Tp>::channels() const
+{
+    CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels );
+    return DataType<_Tp>::channels;
+}
+
+template<typename _Tp> inline
+size_t Mat_<_Tp>::stepT(int i) const
+{
+    return step.p[i] / elemSize();
+}
+
+template<typename _Tp> inline
+size_t Mat_<_Tp>::step1(int i) const
+{
+    return step.p[i] / elemSize1();
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright )
+{
+    return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright));
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const
+{
+    return Mat_<_Tp>(*this, _rowRange, _colRange);
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const
+{
+    return Mat_<_Tp>(*this, roi);
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const
+{
+    return Mat_<_Tp>(*this, ranges);
+}
+
+template<typename _Tp> inline
+Mat_<_Tp> Mat_<_Tp>::operator()(const std::vector<Range>& ranges) const
+{
+    return Mat_<_Tp>(*this, ranges);
+}
+
+template<typename _Tp> inline
+_Tp* Mat_<_Tp>::operator [](int y)
+{
+    CV_DbgAssert( 0 <= y && y < size.p[0] );
+    return (_Tp*)(data + y*step.p[0]);
+}
+
+template<typename _Tp> inline
+const _Tp* Mat_<_Tp>::operator [](int y) const
+{
+    CV_DbgAssert( 0 <= y && y < size.p[0] );
+    return (const _Tp*)(data + y*step.p[0]);
+}
+
+template<typename _Tp> inline
+_Tp& Mat_<_Tp>::operator ()(int i0, int i1)
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    CV_DbgAssert(type() == traits::Type<_Tp>::value);
+    return ((_Tp*)(data + step.p[0] * i0))[i1];
+}
+
+template<typename _Tp> inline
+const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
+    CV_DbgAssert(type() == traits::Type<_Tp>::value);
+    return ((const _Tp*)(data + step.p[0] * i0))[i1];
+}
+
+template<typename _Tp> inline
+_Tp& Mat_<_Tp>::operator ()(Point pt)
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]);
+    CV_DbgAssert(type() == traits::Type<_Tp>::value);
+    return ((_Tp*)(data + step.p[0] * pt.y))[pt.x];
+}
+
+template<typename _Tp> inline
+const _Tp& Mat_<_Tp>::operator ()(Point pt) const
+{
+    CV_DbgAssert(dims <= 2);
+    CV_DbgAssert(data);
+    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
+    CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]);
+    CV_DbgAssert(type() == traits::Type<_Tp>::value);
+    return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x];
+}
+
+template<typename _Tp> inline
+_Tp& Mat_<_Tp>::operator ()(const int* idx)
+{
+    return Mat::at<_Tp>(idx);
+}
+
+template<typename _Tp> inline
+const _Tp& Mat_<_Tp>::operator ()(const int* idx) const
+{
+    return Mat::at<_Tp>(idx);
+}
+
+template<typename _Tp> template<int n> inline
+_Tp& Mat_<_Tp>::operator ()(const Vec<int, n>& idx)
+{
+    return Mat::at<_Tp>(idx);
+}
+
+template<typename _Tp> template<int n> inline
+const _Tp& Mat_<_Tp>::operator ()(const Vec<int, n>& idx) const
+{
+    return Mat::at<_Tp>(idx);
+}
+
+template<typename _Tp> inline
+_Tp& Mat_<_Tp>::operator ()(int i0)
+{
+    return this->at<_Tp>(i0);
+}
+
+template<typename _Tp> inline
+const _Tp& Mat_<_Tp>::operator ()(int i0) const
+{
+    return this->at<_Tp>(i0);
+}
+
+template<typename _Tp> inline
+_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2)
+{
+    return this->at<_Tp>(i0, i1, i2);
+}
+
+template<typename _Tp> inline
+const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const
+{
+    return this->at<_Tp>(i0, i1, i2);
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::operator std::vector<_Tp>() const
+{
+    std::vector<_Tp> v;
+    copyTo(v);
+    return v;
+}
+
+template<typename _Tp> template<std::size_t _Nm> inline
+Mat_<_Tp>::operator std::array<_Tp, _Nm>() const
+{
+    std::array<_Tp, _Nm> a;
+    copyTo(a);
+    return a;
+}
+
+template<typename _Tp> template<int n> inline
+Mat_<_Tp>::operator Vec<typename DataType<_Tp>::channel_type, n>() const
+{
+    CV_Assert(n % DataType<_Tp>::channels == 0);
+
+#if defined _MSC_VER
+    const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround)
+    return pMat->operator Vec<typename DataType<_Tp>::channel_type, n>();
+#else
+    return this->Mat::operator Vec<typename DataType<_Tp>::channel_type, n>();
+#endif
+}
+
+template<typename _Tp> template<int m, int n> inline
+Mat_<_Tp>::operator Matx<typename DataType<_Tp>::channel_type, m, n>() const
+{
+    CV_Assert(n % DataType<_Tp>::channels == 0);
+
+#if defined _MSC_VER
+    const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround)
+    Matx<typename DataType<_Tp>::channel_type, m, n> res = pMat->operator Matx<typename DataType<_Tp>::channel_type, m, n>();
+    return res;
+#else
+    Matx<typename DataType<_Tp>::channel_type, m, n> res = this->Mat::operator Matx<typename DataType<_Tp>::channel_type, m, n>();
+    return res;
+#endif
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp> Mat_<_Tp>::begin() const
+{
+    return Mat::begin<_Tp>();
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatConstIterator_<_Tp>> Mat_<_Tp>::rbegin() const
+{
+    return Mat::rbegin<_Tp>();
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp> Mat_<_Tp>::end() const
+{
+    return Mat::end<_Tp>();
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatConstIterator_<_Tp>> Mat_<_Tp>::rend() const
+{
+    return Mat::rend<_Tp>();
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp> Mat_<_Tp>::begin()
+{
+    return Mat::begin<_Tp>();
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatIterator_<_Tp>> Mat_<_Tp>::rbegin()
+{
+    return Mat::rbegin<_Tp>();
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp> Mat_<_Tp>::end()
+{
+    return Mat::end<_Tp>();
+}
+
+template<typename _Tp> inline
+std::reverse_iterator<MatIterator_<_Tp>> Mat_<_Tp>::rend()
+{
+    return Mat::rend<_Tp>();
+}
+
+template<typename _Tp> template<typename Functor> inline
+void Mat_<_Tp>::forEach(const Functor& operation) {
+    Mat::forEach<_Tp, Functor>(operation);
+}
+
+template<typename _Tp> template<typename Functor> inline
+void Mat_<_Tp>::forEach(const Functor& operation) const {
+    Mat::forEach<_Tp, Functor>(operation);
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(Mat_&& m)
+    : Mat(std::move(m))
+{
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m)
+{
+    Mat::operator = (std::move(m));
+    return *this;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(Mat&& m)
+    : Mat()
+{
+    flags = (flags & ~CV_MAT_TYPE_MASK) + traits::Type<_Tp>::value;
+    *this = std::move(m);
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m)
+{
+    if (m.empty())
+    {
+        release();
+        return *this;
+    }
+    if( traits::Type<_Tp>::value == m.type() )
+    {
+        Mat::operator = ((Mat&&)m);
+        return *this;
+    }
+    if( traits::Depth<_Tp>::value == m.depth() )
+    {
+        Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0));
+        return *this;
+    }
+    CV_DbgAssert(DataType<_Tp>::channels == m.channels());
+    m.convertTo(*this, type());
+    return *this;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(MatExpr&& e)
+    : Mat()
+{
+    flags = (flags & ~CV_MAT_TYPE_MASK) + traits::Type<_Tp>::value;
+    *this = Mat(e);
+}
+
+
+///////////////////////////// SparseMat /////////////////////////////
+
+inline
+SparseMat SparseMat::clone() const
+{
+    SparseMat temp;
+    this->copyTo(temp);
+    return temp;
+}
+
+inline
+size_t SparseMat::elemSize() const
+{
+    return CV_ELEM_SIZE(flags);
+}
+
+inline
+size_t SparseMat::elemSize1() const
+{
+    return CV_ELEM_SIZE1(flags);
+}
+
+inline
+int SparseMat::type() const
+{
+    return CV_MAT_TYPE(flags);
+}
+
+inline
+int SparseMat::depth() const
+{
+    return CV_MAT_DEPTH(flags);
+}
+
+inline
+int SparseMat::channels() const
+{
+    return CV_MAT_CN(flags);
+}
+
+inline
+const int* SparseMat::size() const
+{
+    return hdr ? hdr->size : 0;
+}
+
+inline
+int SparseMat::size(int i) const
+{
+    if( hdr )
+    {
+        CV_DbgAssert((unsigned)i < (unsigned)hdr->dims);
+        return hdr->size[i];
+    }
+    return 0;
+}
+
+inline
+int SparseMat::dims() const
+{
+    return hdr ? hdr->dims : 0;
+}
+
+inline
+size_t SparseMat::nzcount() const
+{
+    return hdr ? hdr->nodeCount : 0;
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat::ref(int i0, size_t* hashval)
+{
+    return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval);
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat::ref(int i0, int i1, size_t* hashval)
+{
+    return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval);
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval)
+{
+    return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval);
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat::ref(const int* idx, size_t* hashval)
+{
+    return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval);
+}
+
+template<typename _Tp> inline
+_Tp SparseMat::value(int i0, size_t* hashval) const
+{
+    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval);
+    return p ? *p : _Tp();
+}
+
+template<typename _Tp> inline
+_Tp SparseMat::value(int i0, int i1, size_t* hashval) const
+{
+    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval);
+    return p ? *p : _Tp();
+}
+
+template<typename _Tp> inline
+_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const
+{
+    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval);
+    return p ? *p : _Tp();
+}
+
+template<typename _Tp> inline
+_Tp SparseMat::value(const int* idx, size_t* hashval) const
+{
+    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval);
+    return p ? *p : _Tp();
+}
+
+template<typename _Tp> inline
+const _Tp* SparseMat::find(int i0, size_t* hashval) const
+{
+    return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval);
+}
+
+template<typename _Tp> inline
+const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const
+{
+    return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval);
+}
+
+template<typename _Tp> inline
+const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const
+{
+    return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval);
+}
+
+template<typename _Tp> inline
+const _Tp* SparseMat::find(const int* idx, size_t* hashval) const
+{
+    return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval);
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat::value(Node* n)
+{
+    return *(_Tp*)((uchar*)n + hdr->valueOffset);
+}
+
+template<typename _Tp> inline
+const _Tp& SparseMat::value(const Node* n) const
+{
+    return *(const _Tp*)((const uchar*)n + hdr->valueOffset);
+}
+
+inline
+SparseMat::Node* SparseMat::node(size_t nidx)
+{
+    return (Node*)(void*)&hdr->pool[nidx];
+}
+
+inline
+const SparseMat::Node* SparseMat::node(size_t nidx) const
+{
+    return (const Node*)(const void*)&hdr->pool[nidx];
+}
+
+inline
+SparseMatIterator SparseMat::begin()
+{
+    return SparseMatIterator(this);
+}
+
+inline
+SparseMatConstIterator SparseMat::begin() const
+{
+    return SparseMatConstIterator(this);
+}
+
+inline
+SparseMatIterator SparseMat::end()
+{
+    SparseMatIterator it(this);
+    it.seekEnd();
+    return it;
+}
+
+inline
+SparseMatConstIterator SparseMat::end() const
+{
+    SparseMatConstIterator it(this);
+    it.seekEnd();
+    return it;
+}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp> SparseMat::begin()
+{
+    return SparseMatIterator_<_Tp>(this);
+}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp> SparseMat::begin() const
+{
+    return SparseMatConstIterator_<_Tp>(this);
+}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp> SparseMat::end()
+{
+    SparseMatIterator_<_Tp> it(this);
+    it.seekEnd();
+    return it;
+}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp> SparseMat::end() const
+{
+    SparseMatConstIterator_<_Tp> it(this);
+    it.seekEnd();
+    return it;
+}
+
+
+
+///////////////////////////// SparseMat_ ////////////////////////////
+
+template<typename _Tp> inline
+SparseMat_<_Tp>::SparseMat_()
+{
+    flags = MAGIC_VAL + traits::Type<_Tp>::value;
+}
+
+template<typename _Tp> inline
+SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes)
+    : SparseMat(_dims, _sizes, traits::Type<_Tp>::value)
+{}
+
+template<typename _Tp> inline
+SparseMat_<_Tp>::SparseMat_(const SparseMat& m)
+{
+    if( m.type() == traits::Type<_Tp>::value )
+        *this = (const SparseMat_<_Tp>&)m;
+    else
+        m.convertTo(*this, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m)
+{
+    this->flags = m.flags;
+    this->hdr = m.hdr;
+    if( this->hdr )
+        CV_XADD(&this->hdr->refcount, 1);
+}
+
+template<typename _Tp> inline
+SparseMat_<_Tp>::SparseMat_(const Mat& m)
+{
+    SparseMat sm(m);
+    *this = sm;
+}
+
+template<typename _Tp> inline
+SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m)
+{
+    if( this != &m )
+    {
+        if( m.hdr ) CV_XADD(&m.hdr->refcount, 1);
+        release();
+        flags = m.flags;
+        hdr = m.hdr;
+    }
+    return *this;
+}
+
+template<typename _Tp> inline
+SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m)
+{
+    if( m.type() == traits::Type<_Tp>::value )
+        return (*this = (const SparseMat_<_Tp>&)m);
+    m.convertTo(*this, traits::Type<_Tp>::value);
+    return *this;
+}
+
+template<typename _Tp> inline
+SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m)
+{
+    return (*this = SparseMat(m));
+}
+
+template<typename _Tp> inline
+SparseMat_<_Tp> SparseMat_<_Tp>::clone() const
+{
+    SparseMat_<_Tp> m;
+    this->copyTo(m);
+    return m;
+}
+
+template<typename _Tp> inline
+void SparseMat_<_Tp>::create(int _dims, const int* _sizes)
+{
+    SparseMat::create(_dims, _sizes, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+int SparseMat_<_Tp>::type() const
+{
+    return traits::Type<_Tp>::value;
+}
+
+template<typename _Tp> inline
+int SparseMat_<_Tp>::depth() const
+{
+    return traits::Depth<_Tp>::value;
+}
+
+template<typename _Tp> inline
+int SparseMat_<_Tp>::channels() const
+{
+    return DataType<_Tp>::channels;
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval)
+{
+    return SparseMat::ref<_Tp>(i0, hashval);
+}
+
+template<typename _Tp> inline
+_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const
+{
+    return SparseMat::value<_Tp>(i0, hashval);
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval)
+{
+    return SparseMat::ref<_Tp>(i0, i1, hashval);
+}
+
+template<typename _Tp> inline
+_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const
+{
+    return SparseMat::value<_Tp>(i0, i1, hashval);
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval)
+{
+    return SparseMat::ref<_Tp>(i0, i1, i2, hashval);
+}
+
+template<typename _Tp> inline
+_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const
+{
+    return SparseMat::value<_Tp>(i0, i1, i2, hashval);
+}
+
+template<typename _Tp> inline
+_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval)
+{
+    return SparseMat::ref<_Tp>(idx, hashval);
+}
+
+template<typename _Tp> inline
+_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const
+{
+    return SparseMat::value<_Tp>(idx, hashval);
+}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin()
+{
+    return SparseMatIterator_<_Tp>(this);
+}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const
+{
+    return SparseMatConstIterator_<_Tp>(this);
+}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp> SparseMat_<_Tp>::end()
+{
+    SparseMatIterator_<_Tp> it(this);
+    it.seekEnd();
+    return it;
+}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const
+{
+    SparseMatConstIterator_<_Tp> it(this);
+    it.seekEnd();
+    return it;
+}
+
+
+
+////////////////////////// MatConstIterator /////////////////////////
+
+inline
+MatConstIterator::MatConstIterator()
+    : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0)
+{}
+
+inline
+MatConstIterator::MatConstIterator(const Mat* _m)
+    : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0)
+{
+    if( m && m->isContinuous() )
+    {
+        CV_Assert(!m->empty());
+        sliceStart = m->ptr();
+        sliceEnd = sliceStart + m->total()*elemSize;
+    }
+    seek((const int*)0);
+}
+
+inline
+MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col)
+    : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0)
+{
+    CV_Assert(m && m->dims <= 2);
+    if( m->isContinuous() )
+    {
+        CV_Assert(!m->empty());
+        sliceStart = m->ptr();
+        sliceEnd = sliceStart + m->total()*elemSize;
+    }
+    int idx[] = {_row, _col};
+    seek(idx);
+}
+
+inline
+MatConstIterator::MatConstIterator(const Mat* _m, Point _pt)
+    : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0)
+{
+    CV_Assert(m && m->dims <= 2);
+    if( m->isContinuous() )
+    {
+        CV_Assert(!m->empty());
+        sliceStart = m->ptr();
+        sliceEnd = sliceStart + m->total()*elemSize;
+    }
+    int idx[] = {_pt.y, _pt.x};
+    seek(idx);
+}
+
+inline
+MatConstIterator::MatConstIterator(const MatConstIterator& it)
+    : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd)
+{}
+
+inline
+MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it )
+{
+    m = it.m; elemSize = it.elemSize; ptr = it.ptr;
+    sliceStart = it.sliceStart; sliceEnd = it.sliceEnd;
+    return *this;
+}
+
+inline
+const uchar* MatConstIterator::operator *() const
+{
+    return ptr;
+}
+
+inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs)
+{
+    if( !m || ofs == 0 )
+        return *this;
+    ptrdiff_t ofsb = ofs*elemSize;
+    ptr += ofsb;
+    if( ptr < sliceStart || sliceEnd <= ptr )
+    {
+        ptr -= ofsb;
+        seek(ofs, true);
+    }
+    return *this;
+}
+
+inline
+MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs)
+{
+    return (*this += -ofs);
+}
+
+inline
+MatConstIterator& MatConstIterator::operator --()
+{
+    if( m && (ptr -= elemSize) < sliceStart )
+    {
+        ptr += elemSize;
+        seek(-1, true);
+    }
+    return *this;
+}
+
+inline
+MatConstIterator MatConstIterator::operator --(int)
+{
+    MatConstIterator b = *this;
+    *this += -1;
+    return b;
+}
+
+inline
+MatConstIterator& MatConstIterator::operator ++()
+{
+    if( m && (ptr += elemSize) >= sliceEnd )
+    {
+        ptr -= elemSize;
+        seek(1, true);
+    }
+    return *this;
+}
+
+inline MatConstIterator MatConstIterator::operator ++(int)
+{
+    MatConstIterator b = *this;
+    *this += 1;
+    return b;
+}
+
+
+static inline
+bool operator == (const MatConstIterator& a, const MatConstIterator& b)
+{
+    return a.m == b.m && a.ptr == b.ptr;
+}
+
+static inline
+bool operator != (const MatConstIterator& a, const MatConstIterator& b)
+{
+    return !(a == b);
+}
+
+static inline
+bool operator < (const MatConstIterator& a, const MatConstIterator& b)
+{
+    return a.ptr < b.ptr;
+}
+
+static inline
+bool operator > (const MatConstIterator& a, const MatConstIterator& b)
+{
+    return a.ptr > b.ptr;
+}
+
+static inline
+bool operator <= (const MatConstIterator& a, const MatConstIterator& b)
+{
+    return a.ptr <= b.ptr;
+}
+
+static inline
+bool operator >= (const MatConstIterator& a, const MatConstIterator& b)
+{
+    return a.ptr >= b.ptr;
+}
+
+static inline
+ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a)
+{
+    if( a.m != b.m )
+        return ((size_t)(-1) >> 1);
+    if( a.sliceEnd == b.sliceEnd )
+        return (b.ptr - a.ptr)/static_cast<ptrdiff_t>(b.elemSize);
+
+    return b.lpos() - a.lpos();
+}
+
+static inline
+MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs)
+{
+    MatConstIterator b = a;
+    return b += ofs;
+}
+
+static inline
+MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a)
+{
+    MatConstIterator b = a;
+    return b += ofs;
+}
+
+static inline
+MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs)
+{
+    MatConstIterator b = a;
+    return b += -ofs;
+}
+
+
+inline
+const uchar* MatConstIterator::operator [](ptrdiff_t i) const
+{
+    return *(*this + i);
+}
+
+
+
+///////////////////////// MatConstIterator_ /////////////////////////
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>::MatConstIterator_()
+{}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m)
+    : MatConstIterator(_m)
+{}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col)
+    : MatConstIterator(_m, _row, _col)
+{}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt)
+    : MatConstIterator(_m, _pt)
+{}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it)
+    : MatConstIterator(it)
+{}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it )
+{
+    MatConstIterator::operator = (it);
+    return *this;
+}
+
+template<typename _Tp> inline
+const _Tp& MatConstIterator_<_Tp>::operator *() const
+{
+    return *(_Tp*)(this->ptr);
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs)
+{
+    MatConstIterator::operator += (ofs);
+    return *this;
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs)
+{
+    return (*this += -ofs);
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --()
+{
+    MatConstIterator::operator --();
+    return *this;
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int)
+{
+    MatConstIterator_ b = *this;
+    MatConstIterator::operator --();
+    return b;
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++()
+{
+    MatConstIterator::operator ++();
+    return *this;
+}
+
+template<typename _Tp> inline
+MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int)
+{
+    MatConstIterator_ b = *this;
+    MatConstIterator::operator ++();
+    return b;
+}
+
+
+template<typename _Tp> inline
+Point MatConstIterator_<_Tp>::pos() const
+{
+    if( !m )
+        return Point();
+    CV_DbgAssert( m->dims <= 2 );
+    if( m->isContinuous() )
+    {
+        ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data;
+        int y = (int)(ofs / m->cols);
+        int x = (int)(ofs - (ptrdiff_t)y * m->cols);
+        return Point(x, y);
+    }
+    else
+    {
+        ptrdiff_t ofs = (uchar*)ptr - m->data;
+        int y = (int)(ofs / m->step);
+        int x = (int)((ofs - y * m->step)/sizeof(_Tp));
+        return Point(x, y);
+    }
+}
+
+
+template<typename _Tp> static inline
+bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b)
+{
+    return a.m == b.m && a.ptr == b.ptr;
+}
+
+template<typename _Tp> static inline
+bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b)
+{
+    return a.m != b.m || a.ptr != b.ptr;
+}
+
+template<typename _Tp> static inline
+MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs)
+{
+    MatConstIterator t = (const MatConstIterator&)a + ofs;
+    return (MatConstIterator_<_Tp>&)t;
+}
+
+template<typename _Tp> static inline
+MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a)
+{
+    MatConstIterator t = (const MatConstIterator&)a + ofs;
+    return (MatConstIterator_<_Tp>&)t;
+}
+
+template<typename _Tp> static inline
+MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs)
+{
+    MatConstIterator t = (const MatConstIterator&)a - ofs;
+    return (MatConstIterator_<_Tp>&)t;
+}
+
+template<typename _Tp> inline
+const _Tp& MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const
+{
+    return *(_Tp*)MatConstIterator::operator [](i);
+}
+
+
+
+//////////////////////////// MatIterator_ ///////////////////////////
+
+template<typename _Tp> inline
+MatIterator_<_Tp>::MatIterator_()
+    : MatConstIterator_<_Tp>()
+{}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m)
+    : MatConstIterator_<_Tp>(_m)
+{}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col)
+    : MatConstIterator_<_Tp>(_m, _row, _col)
+{}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt)
+    : MatConstIterator_<_Tp>(_m, _pt)
+{}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx)
+    : MatConstIterator_<_Tp>(_m, _idx)
+{}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>::MatIterator_(const MatIterator_& it)
+    : MatConstIterator_<_Tp>(it)
+{}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it )
+{
+    MatConstIterator::operator = (it);
+    return *this;
+}
+
+template<typename _Tp> inline
+_Tp& MatIterator_<_Tp>::operator *() const
+{
+    return *(_Tp*)(this->ptr);
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs)
+{
+    MatConstIterator::operator += (ofs);
+    return *this;
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs)
+{
+    MatConstIterator::operator += (-ofs);
+    return *this;
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>& MatIterator_<_Tp>::operator --()
+{
+    MatConstIterator::operator --();
+    return *this;
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int)
+{
+    MatIterator_ b = *this;
+    MatConstIterator::operator --();
+    return b;
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++()
+{
+    MatConstIterator::operator ++();
+    return *this;
+}
+
+template<typename _Tp> inline
+MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int)
+{
+    MatIterator_ b = *this;
+    MatConstIterator::operator ++();
+    return b;
+}
+
+template<typename _Tp> inline
+_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const
+{
+    return *(*this + i);
+}
+
+
+template<typename _Tp> static inline
+bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b)
+{
+    return a.m == b.m && a.ptr == b.ptr;
+}
+
+template<typename _Tp> static inline
+bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b)
+{
+    return a.m != b.m || a.ptr != b.ptr;
+}
+
+template<typename _Tp> static inline
+MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs)
+{
+    MatConstIterator t = (const MatConstIterator&)a + ofs;
+    return (MatIterator_<_Tp>&)t;
+}
+
+template<typename _Tp> static inline
+MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a)
+{
+    MatConstIterator t = (const MatConstIterator&)a + ofs;
+    return (MatIterator_<_Tp>&)t;
+}
+
+template<typename _Tp> static inline
+MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs)
+{
+    MatConstIterator t = (const MatConstIterator&)a - ofs;
+    return (MatIterator_<_Tp>&)t;
+}
+
+
+
+/////////////////////// SparseMatConstIterator //////////////////////
+
+inline
+SparseMatConstIterator::SparseMatConstIterator()
+    : m(0), hashidx(0), ptr(0)
+{}
+
+inline
+SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it)
+    : m(it.m), hashidx(it.hashidx), ptr(it.ptr)
+{}
+
+inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it)
+{
+    if( this != &it )
+    {
+        m = it.m;
+        hashidx = it.hashidx;
+        ptr = it.ptr;
+    }
+    return *this;
+}
+
+template<typename _Tp> inline
+const _Tp& SparseMatConstIterator::value() const
+{
+    return *(const _Tp*)ptr;
+}
+
+inline
+const SparseMat::Node* SparseMatConstIterator::node() const
+{
+    return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0;
+}
+
+inline
+SparseMatConstIterator SparseMatConstIterator::operator ++(int)
+{
+    SparseMatConstIterator it = *this;
+    ++*this;
+    return it;
+}
+
+inline
+void SparseMatConstIterator::seekEnd()
+{
+    if( m && m->hdr )
+    {
+        hashidx = m->hdr->hashtab.size();
+        ptr = 0;
+    }
+}
+
+
+static inline
+bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2)
+{
+    return it1.m == it2.m && it1.ptr == it2.ptr;
+}
+
+static inline
+bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2)
+{
+    return !(it1 == it2);
+}
+
+
+
+///////////////////////// SparseMatIterator /////////////////////////
+
+inline
+SparseMatIterator::SparseMatIterator()
+{}
+
+inline
+SparseMatIterator::SparseMatIterator(SparseMat* _m)
+    : SparseMatConstIterator(_m)
+{}
+
+inline
+SparseMatIterator::SparseMatIterator(const SparseMatIterator& it)
+    : SparseMatConstIterator(it)
+{}
+
+inline
+SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it)
+{
+    (SparseMatConstIterator&)*this = it;
+    return *this;
+}
+
+template<typename _Tp> inline
+_Tp& SparseMatIterator::value() const
+{
+    return *(_Tp*)ptr;
+}
+
+inline
+SparseMat::Node* SparseMatIterator::node() const
+{
+    return (SparseMat::Node*)SparseMatConstIterator::node();
+}
+
+inline
+SparseMatIterator& SparseMatIterator::operator ++()
+{
+    SparseMatConstIterator::operator ++();
+    return *this;
+}
+
+inline
+SparseMatIterator SparseMatIterator::operator ++(int)
+{
+    SparseMatIterator it = *this;
+    ++*this;
+    return it;
+}
+
+
+
+////////////////////// SparseMatConstIterator_ //////////////////////
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp>::SparseMatConstIterator_()
+{}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m)
+    : SparseMatConstIterator(_m)
+{}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m)
+    : SparseMatConstIterator(_m)
+{
+    CV_Assert( _m->type() == traits::Type<_Tp>::value );
+}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it)
+    : SparseMatConstIterator(it)
+{}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it)
+{
+    return reinterpret_cast<SparseMatConstIterator_<_Tp>&>
+         (*reinterpret_cast<SparseMatConstIterator*>(this) =
+           reinterpret_cast<const SparseMatConstIterator&>(it));
+}
+
+template<typename _Tp> inline
+const _Tp& SparseMatConstIterator_<_Tp>::operator *() const
+{
+    return *(const _Tp*)this->ptr;
+}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++()
+{
+    SparseMatConstIterator::operator ++();
+    return *this;
+}
+
+template<typename _Tp> inline
+SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int)
+{
+    SparseMatConstIterator_<_Tp> it = *this;
+    SparseMatConstIterator::operator ++();
+    return it;
+}
+
+
+
+///////////////////////// SparseMatIterator_ ////////////////////////
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp>::SparseMatIterator_()
+{}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m)
+    : SparseMatConstIterator_<_Tp>(_m)
+{}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m)
+    : SparseMatConstIterator_<_Tp>(_m)
+{}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it)
+    : SparseMatConstIterator_<_Tp>(it)
+{}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it)
+{
+    return reinterpret_cast<SparseMatIterator_<_Tp>&>
+         (*reinterpret_cast<SparseMatConstIterator*>(this) =
+           reinterpret_cast<const SparseMatConstIterator&>(it));
+}
+
+template<typename _Tp> inline
+_Tp& SparseMatIterator_<_Tp>::operator *() const
+{
+    return *(_Tp*)this->ptr;
+}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++()
+{
+    SparseMatConstIterator::operator ++();
+    return *this;
+}
+
+template<typename _Tp> inline
+SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int)
+{
+    SparseMatIterator_<_Tp> it = *this;
+    SparseMatConstIterator::operator ++();
+    return it;
+}
+
+
+
+//////////////////////// MatCommaInitializer_ ///////////////////////
+
+template<typename _Tp> inline
+MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m)
+    : it(_m)
+{}
+
+template<typename _Tp> template<typename T2> inline
+MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v)
+{
+    CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() );
+    *this->it = _Tp(v);
+    ++this->it;
+    return *this;
+}
+
+template<typename _Tp> inline
+MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const
+{
+    CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() );
+    return Mat_<_Tp>(*this->it.m);
+}
+
+
+template<typename _Tp, typename T2> static inline
+MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val)
+{
+    MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m);
+    return (commaInitializer, val);
+}
+
+
+
+///////////////////////// Matrix Expressions ////////////////////////
+
+inline
+Mat& Mat::operator = (const MatExpr& e)
+{
+    e.op->assign(e, *this);
+    return *this;
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>::Mat_(const MatExpr& e)
+{
+    e.op->assign(e, *this, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e)
+{
+    e.op->assign(e, *this, traits::Type<_Tp>::value);
+    return *this;
+}
+
+template<typename _Tp> inline
+MatExpr Mat_<_Tp>::zeros(int rows, int cols)
+{
+    return Mat::zeros(rows, cols, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+MatExpr Mat_<_Tp>::zeros(Size sz)
+{
+    return Mat::zeros(sz, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+MatExpr Mat_<_Tp>::ones(int rows, int cols)
+{
+    return Mat::ones(rows, cols, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+MatExpr Mat_<_Tp>::ones(Size sz)
+{
+    return Mat::ones(sz, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+MatExpr Mat_<_Tp>::eye(int rows, int cols)
+{
+    return Mat::eye(rows, cols, traits::Type<_Tp>::value);
+}
+
+template<typename _Tp> inline
+MatExpr Mat_<_Tp>::eye(Size sz)
+{
+    return Mat::eye(sz, traits::Type<_Tp>::value);
+}
+
+inline
+MatExpr::MatExpr()
+    : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s()
+{}
+
+inline
+MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b,
+                 const Mat& _c, double _alpha, double _beta, const Scalar& _s)
+    : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s)
+{}
+
+inline
+MatExpr::operator Mat() const
+{
+    Mat m;
+    op->assign(*this, m);
+    return m;
+}
+
+template<typename _Tp> inline
+MatExpr::operator Mat_<_Tp>() const
+{
+    Mat_<_Tp> m;
+    op->assign(*this, m, traits::Type<_Tp>::value);
+    return m;
+}
+
+
+template<typename _Tp> static inline
+MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b)
+{
+    return cv::min((const Mat&)a, (const Mat&)b);
+}
+
+template<typename _Tp> static inline
+MatExpr min(const Mat_<_Tp>& a, double s)
+{
+    return cv::min((const Mat&)a, s);
+}
+
+template<typename _Tp> static inline
+MatExpr min(double s, const Mat_<_Tp>& a)
+{
+    return cv::min((const Mat&)a, s);
+}
+
+template<typename _Tp> static inline
+MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b)
+{
+    return cv::max((const Mat&)a, (const Mat&)b);
+}
+
+template<typename _Tp> static inline
+MatExpr max(const Mat_<_Tp>& a, double s)
+{
+    return cv::max((const Mat&)a, s);
+}
+
+template<typename _Tp> static inline
+MatExpr max(double s, const Mat_<_Tp>& a)
+{
+    return cv::max((const Mat&)a, s);
+}
+
+template<typename _Tp> static inline
+MatExpr abs(const Mat_<_Tp>& m)
+{
+    return cv::abs((const Mat&)m);
+}
+
+
+static inline
+Mat& operator += (Mat& a, const MatExpr& b)
+{
+    b.op->augAssignAdd(b, a);
+    return a;
+}
+
+static inline
+const Mat& operator += (const Mat& a, const MatExpr& b)
+{
+    b.op->augAssignAdd(b, (Mat&)a);
+    return a;
+}
+
+template<typename _Tp> static inline
+Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignAdd(b, a);
+    return a;
+}
+
+template<typename _Tp> static inline
+const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignAdd(b, (Mat&)a);
+    return a;
+}
+
+static inline
+Mat& operator -= (Mat& a, const MatExpr& b)
+{
+    b.op->augAssignSubtract(b, a);
+    return a;
+}
+
+static inline
+const Mat& operator -= (const Mat& a, const MatExpr& b)
+{
+    b.op->augAssignSubtract(b, (Mat&)a);
+    return a;
+}
+
+template<typename _Tp> static inline
+Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignSubtract(b, a);
+    return a;
+}
+
+template<typename _Tp> static inline
+const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignSubtract(b, (Mat&)a);
+    return a;
+}
+
+static inline
+Mat& operator *= (Mat& a, const MatExpr& b)
+{
+    b.op->augAssignMultiply(b, a);
+    return a;
+}
+
+static inline
+const Mat& operator *= (const Mat& a, const MatExpr& b)
+{
+    b.op->augAssignMultiply(b, (Mat&)a);
+    return a;
+}
+
+template<typename _Tp> static inline
+Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignMultiply(b, a);
+    return a;
+}
+
+template<typename _Tp> static inline
+const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignMultiply(b, (Mat&)a);
+    return a;
+}
+
+static inline
+Mat& operator /= (Mat& a, const MatExpr& b)
+{
+    b.op->augAssignDivide(b, a);
+    return a;
+}
+
+static inline
+const Mat& operator /= (const Mat& a, const MatExpr& b)
+{
+    b.op->augAssignDivide(b, (Mat&)a);
+    return a;
+}
+
+template<typename _Tp> static inline
+Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignDivide(b, a);
+    return a;
+}
+
+template<typename _Tp> static inline
+const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b)
+{
+    b.op->augAssignDivide(b, (Mat&)a);
+    return a;
+}
+
+
+//////////////////////////////// UMat ////////////////////////////////
+
+template<typename _Tp> inline
+UMat::UMat(const std::vector<_Tp>& vec, bool copyData)
+: flags(MAGIC_VAL + traits::Type<_Tp>::value + CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()),
+cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows)
+{
+    if(vec.empty())
+        return;
+    if( !copyData )
+    {
+        // !!!TODO!!!
+        CV_Error(Error::StsNotImplemented, "");
+    }
+    else
+        Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this);
+}
+
+inline
+UMat UMat::row(int y) const
+{
+    return UMat(*this, Range(y, y + 1), Range::all());
+}
+
+inline
+UMat UMat::col(int x) const
+{
+    return UMat(*this, Range::all(), Range(x, x + 1));
+}
+
+inline
+UMat UMat::rowRange(int startrow, int endrow) const
+{
+    return UMat(*this, Range(startrow, endrow), Range::all());
+}
+
+inline
+UMat UMat::rowRange(const Range& r) const
+{
+    return UMat(*this, r, Range::all());
+}
+
+inline
+UMat UMat::colRange(int startcol, int endcol) const
+{
+    return UMat(*this, Range::all(), Range(startcol, endcol));
+}
+
+inline
+UMat UMat::colRange(const Range& r) const
+{
+    return UMat(*this, Range::all(), r);
+}
+
+inline
+UMat UMat::operator()( Range _rowRange, Range _colRange ) const
+{
+    return UMat(*this, _rowRange, _colRange);
+}
+
+inline
+UMat UMat::operator()( const Rect& roi ) const
+{
+    return UMat(*this, roi);
+}
+
+inline
+UMat UMat::operator()(const Range* ranges) const
+{
+    return UMat(*this, ranges);
+}
+
+inline
+UMat UMat::operator()(const std::vector<Range>& ranges) const
+{
+    return UMat(*this, ranges);
+}
+
+inline
+bool UMat::isContinuous() const
+{
+    return (flags & CONTINUOUS_FLAG) != 0;
+}
+
+inline
+bool UMat::isSubmatrix() const
+{
+    return (flags & SUBMATRIX_FLAG) != 0;
+}
+
+inline
+size_t UMat::elemSize() const
+{
+    size_t res = dims > 0 ? step.p[dims - 1] : 0;
+    CV_DbgAssert(res != 0);
+    return res;
+}
+
+inline
+size_t UMat::elemSize1() const
+{
+    return CV_ELEM_SIZE1(flags);
+}
+
+inline
+int UMat::type() const
+{
+    return CV_MAT_TYPE(flags);
+}
+
+inline
+int UMat::depth() const
+{
+    return CV_MAT_DEPTH(flags);
+}
+
+inline
+int UMat::channels() const
+{
+    return CV_MAT_CN(flags);
+}
+
+inline
+size_t UMat::step1(int i) const
+{
+    return step.p[i] / elemSize1();
+}
+
+
+inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; }
+inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; }
+inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; }
+inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; }
+inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; }
+inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; }
+
+inline void UMatData::markDeviceMemMapped(bool flag)
+{
+  if(flag)
+    flags |= DEVICE_MEM_MAPPED;
+  else
+    flags &= ~DEVICE_MEM_MAPPED;
+}
+
+inline void UMatData::markHostCopyObsolete(bool flag)
+{
+    if(flag)
+        flags |= HOST_COPY_OBSOLETE;
+    else
+        flags &= ~HOST_COPY_OBSOLETE;
+}
+inline void UMatData::markDeviceCopyObsolete(bool flag)
+{
+    if(flag)
+        flags |= DEVICE_COPY_OBSOLETE;
+    else
+        flags &= ~DEVICE_COPY_OBSOLETE;
+}
+
+//! @endcond
+
+static inline
+void swap(MatExpr& a, MatExpr& b) { a.swap(b); }
+
+} //cv
+
+#ifdef _MSC_VER
+#pragma warning( pop )
+#endif
+
+#ifdef CV_DISABLE_CLANG_ENUM_WARNINGS
+#undef CV_DISABLE_CLANG_ENUM_WARNINGS
+#pragma clang diagnostic pop
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/core/matx.hpp b/3rdParty/include/opencv2/core/matx.hpp
new file mode 100644
index 0000000..3c92e3a
--- /dev/null
+++ b/3rdParty/include/opencv2/core/matx.hpp
@@ -0,0 +1,1508 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_MATX_HPP
+#define OPENCV_CORE_MATX_HPP
+
+#ifndef __cplusplus
+#  error matx.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/base.hpp"
+#include "opencv2/core/traits.hpp"
+#include "opencv2/core/saturate.hpp"
+
+#include <initializer_list>
+
+namespace cv
+{
+
+//! @addtogroup core_basic
+//! @{
+
+////////////////////////////// Small Matrix ///////////////////////////
+
+//! @cond IGNORED
+// FIXIT Remove this (especially CV_EXPORTS modifier)
+struct CV_EXPORTS Matx_AddOp { Matx_AddOp() {} Matx_AddOp(const Matx_AddOp&) {} };
+struct CV_EXPORTS Matx_SubOp { Matx_SubOp() {} Matx_SubOp(const Matx_SubOp&) {} };
+struct CV_EXPORTS Matx_ScaleOp { Matx_ScaleOp() {} Matx_ScaleOp(const Matx_ScaleOp&) {} };
+struct CV_EXPORTS Matx_MulOp { Matx_MulOp() {} Matx_MulOp(const Matx_MulOp&) {} };
+struct CV_EXPORTS Matx_DivOp { Matx_DivOp() {} Matx_DivOp(const Matx_DivOp&) {} };
+struct CV_EXPORTS Matx_MatMulOp { Matx_MatMulOp() {} Matx_MatMulOp(const Matx_MatMulOp&) {} };
+struct CV_EXPORTS Matx_TOp { Matx_TOp() {} Matx_TOp(const Matx_TOp&) {} };
+//! @endcond
+
+/** @brief Template class for small matrices whose type and size are known at compilation time
+
+If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the
+M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are
+available. To do an operation on Matx that is not implemented, you can easily convert the matrix to
+Mat and backwards:
+@code{.cpp}
+    Matx33f m(1, 2, 3,
+              4, 5, 6,
+              7, 8, 9);
+    cout << sum(Mat(m*m.t())) << endl;
+@endcode
+Except of the plain constructor which takes a list of elements, Matx can be initialized from a C-array:
+@code{.cpp}
+    float values[] = { 1, 2, 3};
+    Matx31f m(values);
+@endcode
+In case if C++11 features are available, std::initializer_list can be also used to initialize Matx:
+@code{.cpp}
+    Matx31f m = { 1, 2, 3};
+@endcode
+ */
+template<typename _Tp, int m, int n> class Matx
+{
+public:
+    enum {
+           rows     = m,
+           cols     = n,
+           channels = rows*cols,
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           depth    = traits::Type<_Tp>::value,
+           type     = CV_MAKETYPE(depth, channels),
+#endif
+           shortdim = (m < n ? m : n)
+         };
+
+    typedef _Tp                           value_type;
+    typedef Matx<_Tp, m, n>               mat_type;
+    typedef Matx<_Tp, shortdim, 1> diag_type;
+
+    //! default constructor
+    Matx();
+
+    explicit Matx(_Tp v0); //!< 1x1 matrix
+    Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
+         _Tp v4, _Tp v5, _Tp v6, _Tp v7,
+         _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
+         _Tp v4, _Tp v5, _Tp v6, _Tp v7,
+         _Tp v8, _Tp v9, _Tp v10, _Tp v11,
+         _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix
+    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
+         _Tp v4, _Tp v5, _Tp v6, _Tp v7,
+         _Tp v8, _Tp v9, _Tp v10, _Tp v11,
+         _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix
+    explicit Matx(const _Tp* vals); //!< initialize from a plain array
+
+    Matx(std::initializer_list<_Tp>); //!< initialize from an initializer list
+
+    CV_NODISCARD_STD static Matx all(_Tp alpha);
+    CV_NODISCARD_STD static Matx zeros();
+    CV_NODISCARD_STD static Matx ones();
+    CV_NODISCARD_STD static Matx eye();
+    CV_NODISCARD_STD static Matx diag(const diag_type& d);
+    /** @brief Generates uniformly distributed random numbers
+    @param a Range boundary.
+    @param b The other range boundary (boundaries don't have to be ordered, the lower boundary is inclusive,
+    the upper one is exclusive).
+     */
+    CV_NODISCARD_STD static Matx randu(_Tp a, _Tp b);
+    /** @brief Generates normally distributed random numbers
+    @param a Mean value.
+    @param b Standard deviation.
+     */
+    CV_NODISCARD_STD static Matx randn(_Tp a, _Tp b);
+
+    //! dot product computed with the default precision
+    _Tp dot(const Matx<_Tp, m, n>& v) const;
+
+    //! dot product computed in double-precision arithmetics
+    double ddot(const Matx<_Tp, m, n>& v) const;
+
+    //! conversion to another data type
+    template<typename T2> operator Matx<T2, m, n>() const;
+
+    //! change the matrix shape
+    template<int m1, int n1> Matx<_Tp, m1, n1> reshape() const;
+
+    //! extract part of the matrix
+    template<int m1, int n1> Matx<_Tp, m1, n1> get_minor(int base_row, int base_col) const;
+
+    //! extract the matrix row
+    Matx<_Tp, 1, n> row(int i) const;
+
+    //! extract the matrix column
+    Matx<_Tp, m, 1> col(int i) const;
+
+    //! extract the matrix diagonal
+    diag_type diag() const;
+
+    //! transpose the matrix
+    Matx<_Tp, n, m> t() const;
+
+    //! invert the matrix
+    Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const;
+
+    //! solve linear system
+    template<int l> Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const;
+    Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const;
+
+    //! multiply two matrices element-wise
+    Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const;
+
+    //! divide two matrices element-wise
+    Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const;
+
+    //! element access
+    const _Tp& operator ()(int row, int col) const;
+    _Tp& operator ()(int row, int col);
+
+    //! 1D element access
+    const _Tp& operator ()(int i) const;
+    _Tp& operator ()(int i);
+
+    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp);
+    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp);
+    template<typename _T2> Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp);
+    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp);
+    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp);
+    template<int l> Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp);
+    Matx(const Matx<_Tp, n, m>& a, Matx_TOp);
+
+    _Tp val[m*n]; //< matrix elements
+};
+
+typedef Matx<float, 1, 2> Matx12f;
+typedef Matx<double, 1, 2> Matx12d;
+typedef Matx<float, 1, 3> Matx13f;
+typedef Matx<double, 1, 3> Matx13d;
+typedef Matx<float, 1, 4> Matx14f;
+typedef Matx<double, 1, 4> Matx14d;
+typedef Matx<float, 1, 6> Matx16f;
+typedef Matx<double, 1, 6> Matx16d;
+
+typedef Matx<float, 2, 1> Matx21f;
+typedef Matx<double, 2, 1> Matx21d;
+typedef Matx<float, 3, 1> Matx31f;
+typedef Matx<double, 3, 1> Matx31d;
+typedef Matx<float, 4, 1> Matx41f;
+typedef Matx<double, 4, 1> Matx41d;
+typedef Matx<float, 6, 1> Matx61f;
+typedef Matx<double, 6, 1> Matx61d;
+
+typedef Matx<float, 2, 2> Matx22f;
+typedef Matx<double, 2, 2> Matx22d;
+typedef Matx<float, 2, 3> Matx23f;
+typedef Matx<double, 2, 3> Matx23d;
+typedef Matx<float, 3, 2> Matx32f;
+typedef Matx<double, 3, 2> Matx32d;
+
+typedef Matx<float, 3, 3> Matx33f;
+typedef Matx<double, 3, 3> Matx33d;
+
+typedef Matx<float, 3, 4> Matx34f;
+typedef Matx<double, 3, 4> Matx34d;
+typedef Matx<float, 4, 3> Matx43f;
+typedef Matx<double, 4, 3> Matx43d;
+
+typedef Matx<float, 4, 4> Matx44f;
+typedef Matx<double, 4, 4> Matx44d;
+typedef Matx<float, 6, 6> Matx66f;
+typedef Matx<double, 6, 6> Matx66d;
+
+/*!
+  traits
+*/
+template<typename _Tp, int m, int n> class DataType< Matx<_Tp, m, n> >
+{
+public:
+    typedef Matx<_Tp, m, n>                               value_type;
+    typedef Matx<typename DataType<_Tp>::work_type, m, n> work_type;
+    typedef _Tp                                           channel_type;
+    typedef value_type                                    vec_type;
+
+    enum { generic_type = 0,
+           channels     = m * n,
+           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+};
+
+namespace traits {
+template<typename _Tp, int m, int n>
+struct Depth< Matx<_Tp, m, n> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp, int m, int n>
+struct Type< Matx<_Tp, m, n> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, n*m) }; };
+} // namespace
+
+
+/** @brief  Comma-separated Matrix Initializer
+*/
+template<typename _Tp, int m, int n> class MatxCommaInitializer
+{
+public:
+    MatxCommaInitializer(Matx<_Tp, m, n>* _mtx);
+    template<typename T2> MatxCommaInitializer<_Tp, m, n>& operator , (T2 val);
+    Matx<_Tp, m, n> operator *() const;
+
+    Matx<_Tp, m, n>* dst;
+    int idx;
+};
+
+/*
+ Utility methods
+*/
+template<typename _Tp, int m> static double determinant(const Matx<_Tp, m, m>& a);
+template<typename _Tp, int m, int n> static double trace(const Matx<_Tp, m, n>& a);
+template<typename _Tp, int m, int n> static double norm(const Matx<_Tp, m, n>& M);
+template<typename _Tp, int m, int n> static double norm(const Matx<_Tp, m, n>& M, int normType);
+
+
+
+/////////////////////// Vec (used as element of multi-channel images /////////////////////
+
+/** @brief Template class for short numerical vectors, a partial case of Matx
+
+This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you
+can perform basic arithmetical operations, access individual elements using [] operator etc. The
+vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which
+elements are dynamically allocated in the heap.
+
+The template takes 2 parameters:
+@tparam _Tp element type
+@tparam cn the number of elements
+
+In addition to the universal notation like Vec<float, 3>, you can use shorter aliases
+for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec<float, 3>.
+
+It is possible to convert Vec\<T,2\> to/from Point_, Vec\<T,3\> to/from Point3_ , and Vec\<T,4\>
+to CvScalar or Scalar_. Use operator[] to access the elements of Vec.
+
+All the expected vector operations are also implemented:
+-   v1 = v2 + v3
+-   v1 = v2 - v3
+-   v1 = v2 \* scale
+-   v1 = scale \* v2
+-   v1 = -v2
+-   v1 += v2 and other augmenting operations
+-   v1 == v2, v1 != v2
+-   norm(v1) (euclidean norm)
+The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details.
+*/
+template<typename _Tp, int cn> class Vec : public Matx<_Tp, cn, 1>
+{
+public:
+    typedef _Tp value_type;
+    enum {
+           channels = cn,
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           depth    = Matx<_Tp, cn, 1>::depth,
+           type     = CV_MAKETYPE(depth, channels),
+#endif
+           _dummy_enum_finalizer = 0
+         };
+
+    //! default constructor
+    Vec();
+
+    Vec(_Tp v0); //!< 1-element vector constructor
+    Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor
+    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor
+    explicit Vec(const _Tp* values);
+
+    Vec(std::initializer_list<_Tp>);
+
+    Vec(const Vec<_Tp, cn>& v);
+
+    static Vec all(_Tp alpha);
+
+    //! per-element multiplication
+    Vec mul(const Vec<_Tp, cn>& v) const;
+
+    //! conjugation (makes sense for complex numbers and quaternions)
+    Vec conj() const;
+
+    /*!
+      cross product of the two 3D vectors.
+
+      For other dimensionalities the exception is raised
+    */
+    Vec cross(const Vec& v) const;
+    //! conversion to another data type
+    template<typename T2> operator Vec<T2, cn>() const;
+
+    /*! element access */
+    const _Tp& operator [](int i) const;
+    _Tp& operator[](int i);
+    const _Tp& operator ()(int i) const;
+    _Tp& operator ()(int i);
+
+#ifdef CV_CXX11
+    Vec<_Tp, cn>& operator=(const Vec<_Tp, cn>& rhs) = default;
+#endif
+
+    Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp);
+    Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp);
+    template<typename _T2> Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp);
+};
+
+/** @name Shorter aliases for the most popular specializations of Vec<T,n>
+  @{
+*/
+typedef Vec<uchar, 2> Vec2b;
+typedef Vec<uchar, 3> Vec3b;
+typedef Vec<uchar, 4> Vec4b;
+
+typedef Vec<short, 2> Vec2s;
+typedef Vec<short, 3> Vec3s;
+typedef Vec<short, 4> Vec4s;
+
+typedef Vec<ushort, 2> Vec2w;
+typedef Vec<ushort, 3> Vec3w;
+typedef Vec<ushort, 4> Vec4w;
+
+typedef Vec<int, 2> Vec2i;
+typedef Vec<int, 3> Vec3i;
+typedef Vec<int, 4> Vec4i;
+typedef Vec<int, 6> Vec6i;
+typedef Vec<int, 8> Vec8i;
+
+typedef Vec<float, 2> Vec2f;
+typedef Vec<float, 3> Vec3f;
+typedef Vec<float, 4> Vec4f;
+typedef Vec<float, 6> Vec6f;
+
+typedef Vec<double, 2> Vec2d;
+typedef Vec<double, 3> Vec3d;
+typedef Vec<double, 4> Vec4d;
+typedef Vec<double, 6> Vec6d;
+/** @} */
+
+/*!
+  traits
+*/
+template<typename _Tp, int cn> class DataType< Vec<_Tp, cn> >
+{
+public:
+    typedef Vec<_Tp, cn>                               value_type;
+    typedef Vec<typename DataType<_Tp>::work_type, cn> work_type;
+    typedef _Tp                                        channel_type;
+    typedef value_type                                 vec_type;
+
+    enum { generic_type = 0,
+           channels     = cn,
+           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           depth        = DataType<channel_type>::depth,
+           type         = CV_MAKETYPE(depth, channels),
+#endif
+           _dummy_enum_finalizer = 0
+         };
+};
+
+namespace traits {
+template<typename _Tp, int cn>
+struct Depth< Vec<_Tp, cn> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp, int cn>
+struct Type< Vec<_Tp, cn> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, cn) }; };
+} // namespace
+
+
+/** @brief  Comma-separated Vec Initializer
+*/
+template<typename _Tp, int m> class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1>
+{
+public:
+    VecCommaInitializer(Vec<_Tp, m>* _vec);
+    template<typename T2> VecCommaInitializer<_Tp, m>& operator , (T2 val);
+    Vec<_Tp, m> operator *() const;
+};
+
+template<typename _Tp, int cn> static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v);
+
+//! @} core_basic
+
+//! @cond IGNORED
+
+///////////////////////////////////// helper classes /////////////////////////////////////
+namespace internal
+{
+
+template<typename _Tp, int m> struct Matx_DetOp
+{
+    double operator ()(const Matx<_Tp, m, m>& a) const
+    {
+        Matx<_Tp, m, m> temp = a;
+        double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0);
+        if( p == 0 )
+            return p;
+        for( int i = 0; i < m; i++ )
+            p *= temp(i, i);
+        return p;
+    }
+};
+
+template<typename _Tp> struct Matx_DetOp<_Tp, 1>
+{
+    double operator ()(const Matx<_Tp, 1, 1>& a) const
+    {
+        return a(0,0);
+    }
+};
+
+template<typename _Tp> struct Matx_DetOp<_Tp, 2>
+{
+    double operator ()(const Matx<_Tp, 2, 2>& a) const
+    {
+        return a(0,0)*a(1,1) - a(0,1)*a(1,0);
+    }
+};
+
+template<typename _Tp> struct Matx_DetOp<_Tp, 3>
+{
+    double operator ()(const Matx<_Tp, 3, 3>& a) const
+    {
+        return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) -
+            a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) +
+            a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1));
+    }
+};
+
+template<typename _Tp> Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v)
+{
+    return Vec<_Tp, 2>(v[0], -v[1]);
+}
+
+template<typename _Tp> Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v)
+{
+    return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]);
+}
+
+} // internal
+
+
+
+////////////////////////////////// Matx Implementation ///////////////////////////////////
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx()
+{
+    for(int i = 0; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0)
+{
+    val[0] = v0;
+    for(int i = 1; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1)
+{
+    CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements.");
+    val[0] = v0; val[1] = v1;
+    for(int i = 2; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2)
+{
+    CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2;
+    for(int i = 3; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
+{
+    CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    for(int i = 4; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4)
+{
+    CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4;
+    for(int i = 5; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5)
+{
+    CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5;
+    for(int i = 6; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6)
+{
+    CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5; val[6] = v6;
+    for(int i = 7; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7)
+{
+    CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
+    for(int i = 8; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8)
+{
+    CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
+    val[8] = v8;
+    for(int i = 9; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9)
+{
+    CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
+    val[8] = v8; val[9] = v9;
+    for(int i = 10; i < channels; i++) val[i] = _Tp(0);
+}
+
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11)
+{
+    CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
+    val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
+    for(int i = 12; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13)
+{
+    CV_StaticAssert(channels >= 14, "Matx should have at least 14 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
+    val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
+    val[12] = v12; val[13] = v13;
+    for (int i = 14; i < channels; i++) val[i] = _Tp(0);
+}
+
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15)
+{
+    CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements.");
+    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
+    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
+    val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
+    val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15;
+    for(int i = 16; i < channels; i++) val[i] = _Tp(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(const _Tp* values)
+{
+    for( int i = 0; i < channels; i++ ) val[i] = values[i];
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n>::Matx(std::initializer_list<_Tp> list)
+{
+    CV_DbgAssert(list.size() == channels);
+    int i = 0;
+    for(const auto& elem : list)
+    {
+        val[i++] = elem;
+    }
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha)
+{
+    Matx<_Tp, m, n> M;
+    for( int i = 0; i < m*n; i++ ) M.val[i] = alpha;
+    return M;
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros()
+{
+    return all(0);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n> Matx<_Tp,m,n>::ones()
+{
+    return all(1);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n> Matx<_Tp,m,n>::eye()
+{
+    Matx<_Tp,m,n> M;
+    for(int i = 0; i < shortdim; i++)
+        M(i,i) = 1;
+    return M;
+}
+
+template<typename _Tp, int m, int n> inline
+_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const
+{
+    _Tp s = 0;
+    for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i];
+    return s;
+}
+
+template<typename _Tp, int m, int n> inline
+double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const
+{
+    double s = 0;
+    for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i];
+    return s;
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d)
+{
+    Matx<_Tp,m,n> M;
+    for(int i = 0; i < shortdim; i++)
+        M(i,i) = d(i, 0);
+    return M;
+}
+
+template<typename _Tp, int m, int n> template<typename T2>
+inline Matx<_Tp, m, n>::operator Matx<T2, m, n>() const
+{
+    Matx<T2, m, n> M;
+    for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast<T2>(val[i]);
+    return M;
+}
+
+template<typename _Tp, int m, int n> template<int m1, int n1> inline
+Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const
+{
+    CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements");
+    return (const Matx<_Tp, m1, n1>&)*this;
+}
+
+template<typename _Tp, int m, int n>
+template<int m1, int n1> inline
+Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int base_row, int base_col) const
+{
+    CV_DbgAssert(0 <= base_row && base_row+m1 <= m && 0 <= base_col && base_col+n1 <= n);
+    Matx<_Tp, m1, n1> s;
+    for( int di = 0; di < m1; di++ )
+        for( int dj = 0; dj < n1; dj++ )
+            s(di, dj) = (*this)(base_row+di, base_col+dj);
+    return s;
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const
+{
+    CV_DbgAssert((unsigned)i < (unsigned)m);
+    return Matx<_Tp, 1, n>(&val[i*n]);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const
+{
+    CV_DbgAssert((unsigned)j < (unsigned)n);
+    Matx<_Tp, m, 1> v;
+    for( int i = 0; i < m; i++ )
+        v.val[i] = val[i*n + j];
+    return v;
+}
+
+template<typename _Tp, int m, int n> inline
+typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const
+{
+    diag_type d;
+    for( int i = 0; i < shortdim; i++ )
+        d.val[i] = val[i*n + i];
+    return d;
+}
+
+template<typename _Tp, int m, int n> inline
+const _Tp& Matx<_Tp, m, n>::operator()(int row_idx, int col_idx) const
+{
+    CV_DbgAssert( (unsigned)row_idx < (unsigned)m && (unsigned)col_idx < (unsigned)n );
+    return this->val[row_idx*n + col_idx];
+}
+
+template<typename _Tp, int m, int n> inline
+_Tp& Matx<_Tp, m, n>::operator ()(int row_idx, int col_idx)
+{
+    CV_DbgAssert( (unsigned)row_idx < (unsigned)m && (unsigned)col_idx < (unsigned)n );
+    return val[row_idx*n + col_idx];
+}
+
+template<typename _Tp, int m, int n> inline
+const _Tp& Matx<_Tp, m, n>::operator ()(int i) const
+{
+    CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row");
+    CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) );
+    return val[i];
+}
+
+template<typename _Tp, int m, int n> inline
+_Tp& Matx<_Tp, m, n>::operator ()(int i)
+{
+    CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row");
+    CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) );
+    return val[i];
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp)
+{
+    for( int i = 0; i < channels; i++ )
+        val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp)
+{
+    for( int i = 0; i < channels; i++ )
+        val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]);
+}
+
+template<typename _Tp, int m, int n> template<typename _T2> inline
+Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp)
+{
+    for( int i = 0; i < channels; i++ )
+        val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp)
+{
+    for( int i = 0; i < channels; i++ )
+        val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp)
+{
+    for( int i = 0; i < channels; i++ )
+        val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]);
+}
+
+template<typename _Tp, int m, int n> template<int l> inline
+Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp)
+{
+    for( int i = 0; i < m; i++ )
+        for( int j = 0; j < n; j++ )
+        {
+            _Tp s = 0;
+            for( int k = 0; k < l; k++ )
+                s += a(i, k) * b(k, j);
+            val[i*n + j] = s;
+        }
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp)
+{
+    for( int i = 0; i < m; i++ )
+        for( int j = 0; j < n; j++ )
+            val[i*n + j] = a(j, i);
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const
+{
+    return Matx<_Tp, m, n>(*this, a, Matx_MulOp());
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const
+{
+    return Matx<_Tp, m, n>(*this, a, Matx_DivOp());
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const
+{
+    return Matx<_Tp, n, m>(*this, Matx_TOp());
+}
+
+template<typename _Tp, int m, int n> inline
+Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const
+{
+    Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method);
+    return (Vec<_Tp, n>&)(x);
+}
+
+template<typename _Tp, int m> static inline
+double determinant(const Matx<_Tp, m, m>& a)
+{
+    return cv::internal::Matx_DetOp<_Tp, m>()(a);
+}
+
+template<typename _Tp, int m, int n> static inline
+double trace(const Matx<_Tp, m, n>& a)
+{
+    _Tp s = 0;
+    for( int i = 0; i < std::min(m, n); i++ )
+        s += a(i,i);
+    return s;
+}
+
+template<typename _Tp, int m, int n> static inline
+double norm(const Matx<_Tp, m, n>& M)
+{
+    return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n));
+}
+
+template<typename _Tp, int m, int n> static inline
+double norm(const Matx<_Tp, m, n>& M, int normType)
+{
+    switch(normType) {
+    case NORM_INF:
+        return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
+    case NORM_L1:
+        return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
+    case NORM_L2SQR:
+        return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
+    default:
+    case NORM_L2:
+        return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n));
+    }
+}
+
+
+
+//////////////////////////////// matx comma initializer //////////////////////////////////
+
+template<typename _Tp, typename _T2, int m, int n> static inline
+MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val)
+{
+    MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx);
+    return (commaInitializer, val);
+}
+
+template<typename _Tp, int m, int n> inline
+MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx)
+    : dst(_mtx), idx(0)
+{}
+
+template<typename _Tp, int m, int n> template<typename _T2> inline
+MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value)
+{
+    CV_DbgAssert( idx < m*n );
+    dst->val[idx++] = saturate_cast<_Tp>(value);
+    return *this;
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const
+{
+    CV_DbgAssert( idx == n*m );
+    return *dst;
+}
+
+
+
+/////////////////////////////////// Vec Implementation ///////////////////////////////////
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec() {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0)
+    : Matx<_Tp, cn, 1>(v0) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1)
+    : Matx<_Tp, cn, 1>(v0, v1) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2)
+    : Matx<_Tp, cn, 1>(v0, v1, v2) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13)
+    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(const _Tp* values)
+    : Matx<_Tp, cn, 1>(values) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(std::initializer_list<_Tp> list)
+    : Matx<_Tp, cn, 1>(list) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m)
+    : Matx<_Tp, cn, 1>(m.val) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op)
+    : Matx<_Tp, cn, 1>(a, b, op) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op)
+    : Matx<_Tp, cn, 1>(a, b, op) {}
+
+template<typename _Tp, int cn> template<typename _T2> inline
+Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op)
+    : Matx<_Tp, cn, 1>(a, alpha, op) {}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha)
+{
+    Vec v;
+    for( int i = 0; i < cn; i++ ) v.val[i] = alpha;
+    return v;
+}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const
+{
+    Vec<_Tp, cn> w;
+    for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]);
+    return w;
+}
+
+template<> inline
+Vec<float, 2> Vec<float, 2>::conj() const
+{
+    return cv::internal::conjugate(*this);
+}
+
+template<> inline
+Vec<double, 2> Vec<double, 2>::conj() const
+{
+    return cv::internal::conjugate(*this);
+}
+
+template<> inline
+Vec<float, 4> Vec<float, 4>::conj() const
+{
+    return cv::internal::conjugate(*this);
+}
+
+template<> inline
+Vec<double, 4> Vec<double, 4>::conj() const
+{
+    return cv::internal::conjugate(*this);
+}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const
+{
+    CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined");
+    return Vec<_Tp, cn>();
+}
+
+template<> inline
+Vec<float, 3> Vec<float, 3>::cross(const Vec<float, 3>& v) const
+{
+    return Vec<float,3>(this->val[1]*v.val[2] - this->val[2]*v.val[1],
+                     this->val[2]*v.val[0] - this->val[0]*v.val[2],
+                     this->val[0]*v.val[1] - this->val[1]*v.val[0]);
+}
+
+template<> inline
+Vec<double, 3> Vec<double, 3>::cross(const Vec<double, 3>& v) const
+{
+    return Vec<double,3>(this->val[1]*v.val[2] - this->val[2]*v.val[1],
+                     this->val[2]*v.val[0] - this->val[0]*v.val[2],
+                     this->val[0]*v.val[1] - this->val[1]*v.val[0]);
+}
+
+template<typename _Tp, int cn> template<typename T2> inline
+Vec<_Tp, cn>::operator Vec<T2, cn>() const
+{
+    Vec<T2, cn> v;
+    for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast<T2>(this->val[i]);
+    return v;
+}
+
+template<typename _Tp, int cn> inline
+const _Tp& Vec<_Tp, cn>::operator [](int i) const
+{
+    CV_DbgAssert( (unsigned)i < (unsigned)cn );
+    return this->val[i];
+}
+
+template<typename _Tp, int cn> inline
+_Tp& Vec<_Tp, cn>::operator [](int i)
+{
+    CV_DbgAssert( (unsigned)i < (unsigned)cn );
+    return this->val[i];
+}
+
+template<typename _Tp, int cn> inline
+const _Tp& Vec<_Tp, cn>::operator ()(int i) const
+{
+    CV_DbgAssert( (unsigned)i < (unsigned)cn );
+    return this->val[i];
+}
+
+template<typename _Tp, int cn> inline
+_Tp& Vec<_Tp, cn>::operator ()(int i)
+{
+    CV_DbgAssert( (unsigned)i < (unsigned)cn );
+    return this->val[i];
+}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v)
+{
+    double nv = norm(v);
+    return v * (nv ? 1./nv : 0.);
+}
+
+
+
+//////////////////////////////// vec comma initializer //////////////////////////////////
+
+
+template<typename _Tp, typename _T2, int cn> static inline
+VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val)
+{
+    VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec);
+    return (commaInitializer, val);
+}
+
+template<typename _Tp, int cn> inline
+VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec)
+    : MatxCommaInitializer<_Tp, cn, 1>(_vec)
+{}
+
+template<typename _Tp, int cn> template<typename _T2> inline
+VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value)
+{
+    CV_DbgAssert( this->idx < cn );
+    this->dst->val[this->idx++] = saturate_cast<_Tp>(value);
+    return *this;
+}
+
+template<typename _Tp, int cn> inline
+Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const
+{
+    CV_DbgAssert( this->idx == cn );
+    return *this->dst;
+}
+
+//! @endcond
+
+///////////////////////////// Matx out-of-class operators ////////////////////////////////
+
+//! @relates cv::Matx
+//! @{
+
+template<typename _Tp1, typename _Tp2, int m, int n> static inline
+Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b)
+{
+    for( int i = 0; i < m*n; i++ )
+        a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]);
+    return a;
+}
+
+template<typename _Tp1, typename _Tp2, int m, int n> static inline
+Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b)
+{
+    for( int i = 0; i < m*n; i++ )
+        a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]);
+    return a;
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
+{
+    return Matx<_Tp, m, n>(a, b, Matx_AddOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
+{
+    return Matx<_Tp, m, n>(a, b, Matx_SubOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha)
+{
+    for( int i = 0; i < m*n; i++ )
+        a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
+    return a;
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha)
+{
+    for( int i = 0; i < m*n; i++ )
+        a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
+    return a;
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha)
+{
+    for( int i = 0; i < m*n; i++ )
+        a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
+    return a;
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha)
+{
+    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha)
+{
+    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha)
+{
+    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a)
+{
+    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a)
+{
+    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a)
+{
+    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n>& operator /= (Matx<_Tp, m, n>& a, float alpha)
+{
+    for( int i = 0; i < m*n; i++ )
+        a.val[i] = a.val[i] / alpha;
+    return a;
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n>& operator /= (Matx<_Tp, m, n>& a, double alpha)
+{
+    for( int i = 0; i < m*n; i++ )
+        a.val[i] = a.val[i] / alpha;
+    return a;
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator / (const Matx<_Tp, m, n>& a, float alpha)
+{
+    return Matx<_Tp, m, n>(a, 1.f/alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator / (const Matx<_Tp, m, n>& a, double alpha)
+{
+    return Matx<_Tp, m, n>(a, 1./alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a)
+{
+    return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp());
+}
+
+template<typename _Tp, int m, int n, int l> static inline
+Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b)
+{
+    return Matx<_Tp, m, n>(a, b, Matx_MatMulOp());
+}
+
+template<typename _Tp, int m, int n> static inline
+Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b)
+{
+    Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp());
+    return (const Vec<_Tp, m>&)(c);
+}
+
+template<typename _Tp, int m, int n> static inline
+bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
+{
+    for( int i = 0; i < m*n; i++ )
+        if( a.val[i] != b.val[i] ) return false;
+    return true;
+}
+
+template<typename _Tp, int m, int n> static inline
+bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
+{
+    return !(a == b);
+}
+
+//! @}
+
+////////////////////////////// Vec out-of-class operators ////////////////////////////////
+
+//! @relates cv::Vec
+//! @{
+
+template<typename _Tp1, typename _Tp2, int cn> static inline
+Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b)
+{
+    for( int i = 0; i < cn; i++ )
+        a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]);
+    return a;
+}
+
+template<typename _Tp1, typename _Tp2, int cn> static inline
+Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b)
+{
+    for( int i = 0; i < cn; i++ )
+        a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]);
+    return a;
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b)
+{
+    return Vec<_Tp, cn>(a, b, Matx_AddOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b)
+{
+    return Vec<_Tp, cn>(a, b, Matx_SubOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha)
+{
+    for( int i = 0; i < cn; i++ )
+        a[i] = saturate_cast<_Tp>(a[i]*alpha);
+    return a;
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha)
+{
+    for( int i = 0; i < cn; i++ )
+        a[i] = saturate_cast<_Tp>(a[i]*alpha);
+    return a;
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha)
+{
+    for( int i = 0; i < cn; i++ )
+        a[i] = saturate_cast<_Tp>(a[i]*alpha);
+    return a;
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha)
+{
+    double ialpha = 1./alpha;
+    for( int i = 0; i < cn; i++ )
+        a[i] = saturate_cast<_Tp>(a[i]*ialpha);
+    return a;
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha)
+{
+    float ialpha = 1.f/alpha;
+    for( int i = 0; i < cn; i++ )
+        a[i] = saturate_cast<_Tp>(a[i]*ialpha);
+    return a;
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha)
+{
+    double ialpha = 1./alpha;
+    for( int i = 0; i < cn; i++ )
+        a[i] = saturate_cast<_Tp>(a[i]*ialpha);
+    return a;
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha)
+{
+    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a)
+{
+    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha)
+{
+    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a)
+{
+    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha)
+{
+    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a)
+{
+    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha)
+{
+    return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha)
+{
+    return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha)
+{
+    return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp());
+}
+
+template<typename _Tp, int cn> static inline
+Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a)
+{
+    Vec<_Tp,cn> t;
+    for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]);
+    return t;
+}
+
+template<typename _Tp> inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2)
+{
+    return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]),
+                       saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]),
+                       saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]),
+                       saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0]));
+}
+
+template<typename _Tp> inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2)
+{
+    v1 = v1 * v2;
+    return v1;
+}
+
+//! @}
+
+} // cv
+
+#endif // OPENCV_CORE_MATX_HPP
diff --git a/3rdParty/include/opencv2/core/neon_utils.hpp b/3rdParty/include/opencv2/core/neon_utils.hpp
new file mode 100644
index 0000000..573ba99
--- /dev/null
+++ b/3rdParty/include/opencv2/core/neon_utils.hpp
@@ -0,0 +1,128 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HAL_NEON_UTILS_HPP
+#define OPENCV_HAL_NEON_UTILS_HPP
+
+#include "opencv2/core/cvdef.h"
+
+//! @addtogroup core_utils_neon
+//! @{
+
+#if CV_NEON
+
+inline int32x2_t cv_vrnd_s32_f32(float32x2_t v)
+{
+    static int32x2_t v_sign = vdup_n_s32(1 << 31),
+        v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f));
+
+    int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v)));
+    return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition)));
+}
+
+inline int32x4_t cv_vrndq_s32_f32(float32x4_t v)
+{
+    static int32x4_t v_sign = vdupq_n_s32(1 << 31),
+        v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f));
+
+    int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v)));
+    return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition)));
+}
+
+inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v)
+{
+    static float32x2_t v_05 = vdup_n_f32(0.5f);
+    return vcvt_u32_f32(vadd_f32(v, v_05));
+}
+
+inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v)
+{
+    static float32x4_t v_05 = vdupq_n_f32(0.5f);
+    return vcvtq_u32_f32(vaddq_f32(v, v_05));
+}
+
+inline float32x4_t cv_vrecpq_f32(float32x4_t val)
+{
+    float32x4_t reciprocal = vrecpeq_f32(val);
+    reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
+    reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
+    return reciprocal;
+}
+
+inline float32x2_t cv_vrecp_f32(float32x2_t val)
+{
+    float32x2_t reciprocal = vrecpe_f32(val);
+    reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
+    reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
+    return reciprocal;
+}
+
+inline float32x4_t cv_vrsqrtq_f32(float32x4_t val)
+{
+    float32x4_t e = vrsqrteq_f32(val);
+    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
+    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
+    return e;
+}
+
+inline float32x2_t cv_vrsqrt_f32(float32x2_t val)
+{
+    float32x2_t e = vrsqrte_f32(val);
+    e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
+    e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
+    return e;
+}
+
+inline float32x4_t cv_vsqrtq_f32(float32x4_t val)
+{
+    return cv_vrecpq_f32(cv_vrsqrtq_f32(val));
+}
+
+inline float32x2_t cv_vsqrt_f32(float32x2_t val)
+{
+    return cv_vrecp_f32(cv_vrsqrt_f32(val));
+}
+
+#endif
+
+//! @}
+
+#endif // OPENCV_HAL_NEON_UTILS_HPP
diff --git a/3rdParty/include/opencv2/core/ocl.hpp b/3rdParty/include/opencv2/core/ocl.hpp
new file mode 100644
index 0000000..4503fa0
--- /dev/null
+++ b/3rdParty/include/opencv2/core/ocl.hpp
@@ -0,0 +1,917 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_OPENCL_HPP
+#define OPENCV_OPENCL_HPP
+
+#include "opencv2/core.hpp"
+#include <typeinfo>
+#include <typeindex>
+
+namespace cv { namespace ocl {
+
+//! @addtogroup core_opencl
+//! @{
+
+CV_EXPORTS_W bool haveOpenCL();
+CV_EXPORTS_W bool useOpenCL();
+CV_EXPORTS_W bool haveAmdBlas();
+CV_EXPORTS_W bool haveAmdFft();
+CV_EXPORTS_W void setUseOpenCL(bool flag);
+CV_EXPORTS_W void finish();
+
+CV_EXPORTS bool haveSVM();
+
+class CV_EXPORTS Context;
+class CV_EXPORTS_W_SIMPLE Device;
+class CV_EXPORTS Kernel;
+class CV_EXPORTS Program;
+class CV_EXPORTS ProgramSource;
+class CV_EXPORTS Queue;
+class CV_EXPORTS PlatformInfo;
+class CV_EXPORTS Image2D;
+
+class CV_EXPORTS_W_SIMPLE Device
+{
+public:
+    CV_WRAP Device() CV_NOEXCEPT;
+    explicit Device(void* d);
+    Device(const Device& d);
+    Device& operator = (const Device& d);
+    Device(Device&& d) CV_NOEXCEPT;
+    Device& operator = (Device&& d) CV_NOEXCEPT;
+    CV_WRAP ~Device();
+
+    void set(void* d);
+
+    enum
+    {
+        TYPE_DEFAULT     = (1 << 0),
+        TYPE_CPU         = (1 << 1),
+        TYPE_GPU         = (1 << 2),
+        TYPE_ACCELERATOR = (1 << 3),
+        TYPE_DGPU        = TYPE_GPU + (1 << 16),
+        TYPE_IGPU        = TYPE_GPU + (1 << 17),
+        TYPE_ALL         = 0xFFFFFFFF
+    };
+
+    CV_WRAP String name() const;
+    CV_WRAP String extensions() const;
+    CV_WRAP bool isExtensionSupported(const String& extensionName) const;
+    CV_WRAP String version() const;
+    CV_WRAP String vendorName() const;
+    CV_WRAP String OpenCL_C_Version() const;
+    CV_WRAP String OpenCLVersion() const;
+    CV_WRAP int deviceVersionMajor() const;
+    CV_WRAP int deviceVersionMinor() const;
+    CV_WRAP String driverVersion() const;
+    void* ptr() const;
+
+    CV_WRAP int type() const;
+
+    CV_WRAP int addressBits() const;
+    CV_WRAP bool available() const;
+    CV_WRAP bool compilerAvailable() const;
+    CV_WRAP bool linkerAvailable() const;
+
+    enum
+    {
+        FP_DENORM=(1 << 0),
+        FP_INF_NAN=(1 << 1),
+        FP_ROUND_TO_NEAREST=(1 << 2),
+        FP_ROUND_TO_ZERO=(1 << 3),
+        FP_ROUND_TO_INF=(1 << 4),
+        FP_FMA=(1 << 5),
+        FP_SOFT_FLOAT=(1 << 6),
+        FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7)
+    };
+    CV_WRAP int doubleFPConfig() const;
+    CV_WRAP int singleFPConfig() const;
+    CV_WRAP int halfFPConfig() const;
+
+    CV_WRAP bool endianLittle() const;
+    CV_WRAP bool errorCorrectionSupport() const;
+
+    enum
+    {
+        EXEC_KERNEL=(1 << 0),
+        EXEC_NATIVE_KERNEL=(1 << 1)
+    };
+    CV_WRAP int executionCapabilities() const;
+
+    CV_WRAP size_t globalMemCacheSize() const;
+
+    enum
+    {
+        NO_CACHE=0,
+        READ_ONLY_CACHE=1,
+        READ_WRITE_CACHE=2
+    };
+    CV_WRAP int globalMemCacheType() const;
+    CV_WRAP int globalMemCacheLineSize() const;
+    CV_WRAP size_t globalMemSize() const;
+
+    CV_WRAP size_t localMemSize() const;
+    enum
+    {
+        NO_LOCAL_MEM=0,
+        LOCAL_IS_LOCAL=1,
+        LOCAL_IS_GLOBAL=2
+    };
+    CV_WRAP int localMemType() const;
+    CV_WRAP bool hostUnifiedMemory() const;
+
+    CV_WRAP bool imageSupport() const;
+
+    CV_WRAP bool imageFromBufferSupport() const;
+    uint imagePitchAlignment() const;
+    uint imageBaseAddressAlignment() const;
+
+    /// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value)
+    CV_WRAP bool intelSubgroupsSupport() const;
+
+    CV_WRAP size_t image2DMaxWidth() const;
+    CV_WRAP size_t image2DMaxHeight() const;
+
+    CV_WRAP size_t image3DMaxWidth() const;
+    CV_WRAP size_t image3DMaxHeight() const;
+    CV_WRAP size_t image3DMaxDepth() const;
+
+    CV_WRAP size_t imageMaxBufferSize() const;
+    CV_WRAP size_t imageMaxArraySize() const;
+
+    enum
+    {
+        UNKNOWN_VENDOR=0,
+        VENDOR_AMD=1,
+        VENDOR_INTEL=2,
+        VENDOR_NVIDIA=3
+    };
+    CV_WRAP int vendorID() const;
+    // FIXIT
+    // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform.
+    // This method should use platform name instead of vendor name.
+    // After fix restore code in arithm.cpp: ocl_compare()
+    CV_WRAP inline bool isAMD() const { return vendorID() == VENDOR_AMD; }
+    CV_WRAP inline bool isIntel() const { return vendorID() == VENDOR_INTEL; }
+    CV_WRAP inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; }
+
+    CV_WRAP int maxClockFrequency() const;
+    CV_WRAP int maxComputeUnits() const;
+    CV_WRAP int maxConstantArgs() const;
+    CV_WRAP size_t maxConstantBufferSize() const;
+
+    CV_WRAP size_t maxMemAllocSize() const;
+    CV_WRAP size_t maxParameterSize() const;
+
+    CV_WRAP int maxReadImageArgs() const;
+    CV_WRAP int maxWriteImageArgs() const;
+    CV_WRAP int maxSamplers() const;
+
+    CV_WRAP size_t maxWorkGroupSize() const;
+    CV_WRAP int maxWorkItemDims() const;
+    void maxWorkItemSizes(size_t*) const;
+
+    CV_WRAP int memBaseAddrAlign() const;
+
+    CV_WRAP int nativeVectorWidthChar() const;
+    CV_WRAP int nativeVectorWidthShort() const;
+    CV_WRAP int nativeVectorWidthInt() const;
+    CV_WRAP int nativeVectorWidthLong() const;
+    CV_WRAP int nativeVectorWidthFloat() const;
+    CV_WRAP int nativeVectorWidthDouble() const;
+    CV_WRAP int nativeVectorWidthHalf() const;
+
+    CV_WRAP int preferredVectorWidthChar() const;
+    CV_WRAP int preferredVectorWidthShort() const;
+    CV_WRAP int preferredVectorWidthInt() const;
+    CV_WRAP int preferredVectorWidthLong() const;
+    CV_WRAP int preferredVectorWidthFloat() const;
+    CV_WRAP int preferredVectorWidthDouble() const;
+    CV_WRAP int preferredVectorWidthHalf() const;
+
+    CV_WRAP size_t printfBufferSize() const;
+    CV_WRAP size_t profilingTimerResolution() const;
+
+    CV_WRAP static const Device& getDefault();
+
+    /**
+     * @param d OpenCL handle (cl_device_id). clRetainDevice() is called on success.
+     *
+     * @note Ownership of the passed device is passed to OpenCV on success.
+     * The caller should additionally call `clRetainDevice` on it if it intends
+     * to continue using the device.
+      */
+    static Device fromHandle(void* d);
+
+    struct Impl;
+    inline Impl* getImpl() const { return (Impl*)p; }
+    inline bool empty() const { return !p; }
+protected:
+    Impl* p;
+};
+
+
+class CV_EXPORTS Context
+{
+public:
+    Context() CV_NOEXCEPT;
+    explicit Context(int dtype);  //!< @deprecated
+    ~Context();
+    Context(const Context& c);
+    Context& operator= (const Context& c);
+    Context(Context&& c) CV_NOEXCEPT;
+    Context& operator = (Context&& c) CV_NOEXCEPT;
+
+    /** @deprecated */
+    bool create();
+    /** @deprecated */
+    bool create(int dtype);
+
+    size_t ndevices() const;
+    Device& device(size_t idx) const;
+    Program getProg(const ProgramSource& prog,
+                    const String& buildopt, String& errmsg);
+    void unloadProg(Program& prog);
+
+
+    /** Get thread-local OpenCL context (initialize if necessary) */
+#if 0  // OpenCV 5.0
+    static Context& getDefault();
+#else
+    static Context& getDefault(bool initialize = true);
+#endif
+
+    /** @returns cl_context value */
+    void* ptr() const;
+
+    /**
+     * @brief Get OpenCL context property specified on context creation
+     * @param propertyId Property id (CL_CONTEXT_* as defined in cl_context_properties type)
+     * @returns Property value if property was specified on clCreateContext, or NULL if context created without the property
+     */
+    void* getOpenCLContextProperty(int propertyId) const;
+
+    bool useSVM() const;
+    void setUseSVM(bool enabled);
+
+    /**
+     * @param context OpenCL handle (cl_context). clRetainContext() is called on success
+     */
+    static Context fromHandle(void* context);
+    static Context fromDevice(const ocl::Device& device);
+    static Context create(const std::string& configuration);
+
+    void release();
+
+    class CV_EXPORTS UserContext {
+    public:
+        virtual ~UserContext();
+    };
+    template <typename T>
+    inline void setUserContext(const std::shared_ptr<T>& userContext) {
+        setUserContext(typeid(T), userContext);
+    }
+    template <typename T>
+    inline std::shared_ptr<T> getUserContext() {
+        return std::dynamic_pointer_cast<T>(getUserContext(typeid(T)));
+    }
+    void setUserContext(std::type_index typeId, const std::shared_ptr<UserContext>& userContext);
+    std::shared_ptr<UserContext> getUserContext(std::type_index typeId);
+
+    struct Impl;
+    inline Impl* getImpl() const { return (Impl*)p; }
+    inline bool empty() const { return !p; }
+// TODO OpenCV 5.0
+//protected:
+    Impl* p;
+};
+
+/** @deprecated */
+class CV_EXPORTS Platform
+{
+public:
+    Platform() CV_NOEXCEPT;
+    ~Platform();
+    Platform(const Platform& p);
+    Platform& operator = (const Platform& p);
+    Platform(Platform&& p) CV_NOEXCEPT;
+    Platform& operator = (Platform&& p) CV_NOEXCEPT;
+
+    void* ptr() const;
+
+    /** @deprecated */
+    static Platform& getDefault();
+
+    struct Impl;
+    inline Impl* getImpl() const { return (Impl*)p; }
+    inline bool empty() const { return !p; }
+protected:
+    Impl* p;
+};
+
+/** @brief Attaches OpenCL context to OpenCV
+@note
+  OpenCV will check if available OpenCL platform has platformName name, then assign context to
+  OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and
+  new command queue will be created.
+@param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime
+@param platformID ID of platform attached context was created for
+@param context OpenCL context to be attached to OpenCV
+@param deviceID ID of device, must be created from attached context
+*/
+CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID);
+
+/** @brief Convert OpenCL buffer to UMat
+@note
+  OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory
+  content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and
+  `clRetainMemObject` is called.
+@param cl_mem_buffer source clBuffer handle
+@param step num of bytes in single row
+@param rows number of rows
+@param cols number of cols
+@param type OpenCV type of image
+@param dst destination UMat
+*/
+CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst);
+
+/** @brief Convert OpenCL image2d_t to UMat
+@note
+  OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content
+  is copied from image to UMat with `clEnqueueCopyImageToBuffer` function.
+@param cl_mem_image source image2d_t handle
+@param dst destination UMat
+*/
+CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst);
+
+// TODO Move to internal header
+/// @deprecated
+void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device);
+
+class CV_EXPORTS Queue
+{
+public:
+    Queue() CV_NOEXCEPT;
+    explicit Queue(const Context& c, const Device& d=Device());
+    ~Queue();
+    Queue(const Queue& q);
+    Queue& operator = (const Queue& q);
+    Queue(Queue&& q) CV_NOEXCEPT;
+    Queue& operator = (Queue&& q) CV_NOEXCEPT;
+
+    bool create(const Context& c=Context(), const Device& d=Device());
+    void finish();
+    void* ptr() const;
+    static Queue& getDefault();
+
+    /// @brief Returns OpenCL command queue with enable profiling mode support
+    const Queue& getProfilingQueue() const;
+
+    struct Impl; friend struct Impl;
+    inline Impl* getImpl() const { return p; }
+    inline bool empty() const { return !p; }
+protected:
+    Impl* p;
+};
+
+
+class CV_EXPORTS KernelArg
+{
+public:
+    enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 };
+    KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0);
+    KernelArg() CV_NOEXCEPT;
+
+    static KernelArg Local(size_t localMemSize)
+    { return KernelArg(LOCAL, 0, 1, 1, 0, localMemSize); }
+    static KernelArg PtrWriteOnly(const UMat& m)
+    { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); }
+    static KernelArg PtrReadOnly(const UMat& m)
+    { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); }
+    static KernelArg PtrReadWrite(const UMat& m)
+    { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); }
+    static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1)
+    { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); }
+    static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1)
+    { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); }
+    static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1)
+    { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); }
+    static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1)
+    { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); }
+    static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1)
+    { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); }
+    static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1)
+    { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); }
+    static KernelArg Constant(const Mat& m);
+    template<typename _Tp> static KernelArg Constant(const _Tp* arr, size_t n)
+    { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); }
+
+    int flags;
+    UMat* m;
+    const void* obj;
+    size_t sz;
+    int wscale, iwscale;
+};
+
+
+class CV_EXPORTS Kernel
+{
+public:
+    Kernel() CV_NOEXCEPT;
+    Kernel(const char* kname, const Program& prog);
+    Kernel(const char* kname, const ProgramSource& prog,
+           const String& buildopts = String(), String* errmsg=0);
+    ~Kernel();
+    Kernel(const Kernel& k);
+    Kernel& operator = (const Kernel& k);
+    Kernel(Kernel&& k) CV_NOEXCEPT;
+    Kernel& operator = (Kernel&& k) CV_NOEXCEPT;
+
+    bool empty() const;
+    bool create(const char* kname, const Program& prog);
+    bool create(const char* kname, const ProgramSource& prog,
+                const String& buildopts, String* errmsg=0);
+
+    int set(int i, const void* value, size_t sz);
+    int set(int i, const Image2D& image2D);
+    int set(int i, const UMat& m);
+    int set(int i, const KernelArg& arg);
+    template<typename _Tp> int set(int i, const _Tp& value)
+    { return set(i, &value, sizeof(value)); }
+
+
+protected:
+    template<typename _Tp0> inline
+    int set_args_(int i, const _Tp0& a0) { return set(i, a0); }
+    template<typename _Tp0, typename... _Tps> inline
+    int set_args_(int i, const _Tp0& a0, const _Tps&... rest_args) { i = set(i, a0); return set_args_(i, rest_args...); }
+public:
+    /** @brief Setup OpenCL Kernel arguments.
+    Avoid direct using of set(i, ...) methods.
+    @code
+    bool ok = kernel
+        .args(
+            srcUMat, dstUMat,
+            (float)some_float_param
+        ).run(ndims, globalSize, localSize);
+    if (!ok) return false;
+    @endcode
+    */
+    template<typename... _Tps> inline
+    Kernel& args(const _Tps&... kernel_args) { set_args_(0, kernel_args...); return *this; }
+
+    /** @brief Run the OpenCL kernel (globalsize value may be adjusted)
+
+    @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3.
+    @param globalsize work items for each dimension. It is not the final globalsize passed to
+      OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding
+      value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The
+      adjusted values are greater than or equal to the original values.
+    @param localsize work-group size for each dimension.
+    @param sync specify whether to wait for OpenCL computation to finish before return.
+    @param q command queue
+
+    @note Use run_() if your kernel code doesn't support adjusted globalsize.
+    */
+    bool run(int dims, size_t globalsize[],
+             size_t localsize[], bool sync, const Queue& q=Queue());
+
+    /** @brief Run the OpenCL kernel
+     *
+     * @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3.
+     * @param globalsize work items for each dimension. This value is passed to OpenCL without changes.
+     * @param localsize work-group size for each dimension.
+     * @param sync specify whether to wait for OpenCL computation to finish before return.
+     * @param q command queue
+     */
+    bool run_(int dims, size_t globalsize[], size_t localsize[], bool sync, const Queue& q=Queue());
+
+    bool runTask(bool sync, const Queue& q=Queue());
+
+    /** @brief Similar to synchronized run_() call with returning of kernel execution time
+     *
+     * Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE)
+     * @return Execution time in nanoseconds or negative number on error
+     */
+    int64 runProfiling(int dims, size_t globalsize[], size_t localsize[], const Queue& q=Queue());
+
+    size_t workGroupSize() const;
+    size_t preferedWorkGroupSizeMultiple() const;
+    bool compileWorkGroupSize(size_t wsz[]) const;
+    size_t localMemSize() const;
+
+    void* ptr() const;
+    struct Impl;
+
+protected:
+    Impl* p;
+};
+
+class CV_EXPORTS Program
+{
+public:
+    Program() CV_NOEXCEPT;
+    Program(const ProgramSource& src,
+            const String& buildflags, String& errmsg);
+    Program(const Program& prog);
+    Program& operator = (const Program& prog);
+    Program(Program&& prog) CV_NOEXCEPT;
+    Program& operator = (Program&& prog) CV_NOEXCEPT;
+    ~Program();
+
+    bool create(const ProgramSource& src,
+                const String& buildflags, String& errmsg);
+
+    void* ptr() const;
+
+    /**
+     * @brief Query device-specific program binary.
+     *
+     * Returns RAW OpenCL executable binary without additional attachments.
+     *
+     * @sa ProgramSource::fromBinary
+     *
+     * @param[out] binary output buffer
+     */
+    void getBinary(std::vector<char>& binary) const;
+
+    struct Impl; friend struct Impl;
+    inline Impl* getImpl() const { return (Impl*)p; }
+    inline bool empty() const { return !p; }
+protected:
+    Impl* p;
+public:
+#ifndef OPENCV_REMOVE_DEPRECATED_API
+    // TODO Remove this
+    CV_DEPRECATED bool read(const String& buf, const String& buildflags); // removed, use ProgramSource instead
+    CV_DEPRECATED bool write(String& buf) const; // removed, use getBinary() method instead (RAW OpenCL binary)
+    CV_DEPRECATED const ProgramSource& source() const; // implementation removed
+    CV_DEPRECATED String getPrefix() const; // deprecated, implementation replaced
+    CV_DEPRECATED static String getPrefix(const String& buildflags); // deprecated, implementation replaced
+#endif
+};
+
+
+class CV_EXPORTS ProgramSource
+{
+public:
+    typedef uint64 hash_t; // deprecated
+
+    ProgramSource() CV_NOEXCEPT;
+    explicit ProgramSource(const String& module, const String& name, const String& codeStr, const String& codeHash);
+    explicit ProgramSource(const String& prog); // deprecated
+    explicit ProgramSource(const char* prog); // deprecated
+    ~ProgramSource();
+    ProgramSource(const ProgramSource& prog);
+    ProgramSource& operator = (const ProgramSource& prog);
+    ProgramSource(ProgramSource&& prog) CV_NOEXCEPT;
+    ProgramSource& operator = (ProgramSource&& prog) CV_NOEXCEPT;
+
+    const String& source() const; // deprecated
+    hash_t hash() const; // deprecated
+
+
+    /** @brief Describe OpenCL program binary.
+     * Do not call clCreateProgramWithBinary() and/or clBuildProgram().
+     *
+     * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies).
+     *
+     * This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version.
+     *
+     * @param module name of program owner module
+     * @param name unique name of program (module+name is used as key for OpenCL program caching)
+     * @param binary buffer address. See buffer lifetime requirement in description.
+     * @param size buffer size
+     * @param buildOptions additional program-related build options passed to clBuildProgram()
+     * @return created ProgramSource object
+     */
+    static ProgramSource fromBinary(const String& module, const String& name,
+            const unsigned char* binary, const size_t size,
+            const cv::String& buildOptions = cv::String());
+
+    /** @brief Describe OpenCL program in SPIR format.
+     * Do not call clCreateProgramWithBinary() and/or clBuildProgram().
+     *
+     * Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior)
+     *
+     * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies).
+     *
+     * Programs in this format are portable between OpenCL implementations with 'khr_spir' extension:
+     * https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/cl_khr_spir.html
+     * (but they are not portable between different platforms: 32-bit / 64-bit)
+     *
+     * Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'.
+     *
+     * @param module name of program owner module
+     * @param name unique name of program (module+name is used as key for OpenCL program caching)
+     * @param binary buffer address. See buffer lifetime requirement in description.
+     * @param size buffer size
+     * @param buildOptions additional program-related build options passed to clBuildProgram()
+     *        (these options are added automatically: '-x spir' and '-spir-std=1.2')
+     * @return created ProgramSource object.
+     */
+    static ProgramSource fromSPIR(const String& module, const String& name,
+            const unsigned char* binary, const size_t size,
+            const cv::String& buildOptions = cv::String());
+
+    //OpenCL 2.1+ only
+    //static Program fromSPIRV(const String& module, const String& name,
+    //        const unsigned char* binary, const size_t size,
+    //        const cv::String& buildOptions = cv::String());
+
+    struct Impl; friend struct Impl;
+    inline Impl* getImpl() const { return (Impl*)p; }
+    inline bool empty() const { return !p; }
+protected:
+    Impl* p;
+};
+
+class CV_EXPORTS PlatformInfo
+{
+public:
+    PlatformInfo() CV_NOEXCEPT;
+    /**
+     * @param id pointer cl_platform_id (cl_platform_id*)
+     */
+    explicit PlatformInfo(void* id);
+    ~PlatformInfo();
+
+    PlatformInfo(const PlatformInfo& i);
+    PlatformInfo& operator =(const PlatformInfo& i);
+    PlatformInfo(PlatformInfo&& i) CV_NOEXCEPT;
+    PlatformInfo& operator = (PlatformInfo&& i) CV_NOEXCEPT;
+
+    String name() const;
+    String vendor() const;
+
+    /// See CL_PLATFORM_VERSION
+    String version() const;
+    int versionMajor() const;
+    int versionMinor() const;
+
+    int deviceNumber() const;
+    void getDevice(Device& device, int d) const;
+
+    struct Impl;
+    bool empty() const { return !p; }
+protected:
+    Impl* p;
+};
+
+CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf);
+CV_EXPORTS const char* typeToStr(int t);
+CV_EXPORTS const char* memopTypeToStr(int t);
+CV_EXPORTS const char* vecopTypeToStr(int t);
+CV_EXPORTS const char* getOpenCLErrorString(int errorCode);
+CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL);
+CV_EXPORTS void getPlatfomsInfo(std::vector<PlatformInfo>& platform_info);
+
+
+enum OclVectorStrategy
+{
+    // all matrices have its own vector width
+    OCL_VECTOR_OWN = 0,
+    // all matrices have maximal vector width among all matrices
+    // (useful for cases when matrices have different data types)
+    OCL_VECTOR_MAX = 1,
+
+    // default strategy
+    OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN
+};
+
+CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
+                                         InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
+                                         InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
+                                         OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
+
+CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths,
+                                       InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
+                                       InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
+                                       InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
+                                       OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
+
+// with OCL_VECTOR_MAX strategy
+CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
+                                            InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
+                                            InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray());
+
+CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m);
+
+class CV_EXPORTS Image2D
+{
+public:
+    Image2D() CV_NOEXCEPT;
+
+    /**
+    @param src UMat object from which to get image properties and data
+    @param norm flag to enable the use of normalized channel data types
+    @param alias flag indicating that the image should alias the src UMat. If true, changes to the
+        image or src will be reflected in both objects.
+    */
+    explicit Image2D(const UMat &src, bool norm = false, bool alias = false);
+    Image2D(const Image2D & i);
+    ~Image2D();
+
+    Image2D & operator = (const Image2D & i);
+    Image2D(Image2D &&) CV_NOEXCEPT;
+    Image2D &operator=(Image2D &&) CV_NOEXCEPT;
+
+    /** Indicates if creating an aliased image should succeed.
+    Depends on the underlying platform and the dimensions of the UMat.
+    */
+    static bool canCreateAlias(const UMat &u);
+
+    /** Indicates if the image format is supported.
+    */
+    static bool isFormatSupported(int depth, int cn, bool norm);
+
+    void* ptr() const;
+protected:
+    struct Impl;
+    Impl* p;
+};
+
+class CV_EXPORTS Timer
+{
+public:
+    Timer(const Queue& q);
+    ~Timer();
+    void start();
+    void stop();
+
+    uint64 durationNS() const; //< duration in nanoseconds
+
+protected:
+    struct Impl;
+    Impl* const p;
+
+private:
+    Timer(const Timer&); // disabled
+    Timer& operator=(const Timer&); // disabled
+};
+
+CV_EXPORTS MatAllocator* getOpenCLAllocator();
+
+
+class CV_EXPORTS_W OpenCLExecutionContext
+{
+public:
+    OpenCLExecutionContext() = default;
+    ~OpenCLExecutionContext() = default;
+
+    OpenCLExecutionContext(const OpenCLExecutionContext&) = default;
+    OpenCLExecutionContext(OpenCLExecutionContext&&) = default;
+
+    OpenCLExecutionContext& operator=(const OpenCLExecutionContext&) = default;
+    OpenCLExecutionContext& operator=(OpenCLExecutionContext&&) = default;
+
+    /** Get associated ocl::Context */
+    Context& getContext() const;
+    /** Get the single default associated ocl::Device */
+    Device& getDevice() const;
+    /** Get the single ocl::Queue that is associated with the ocl::Context and
+     *  the single default ocl::Device
+     */
+    Queue& getQueue() const;
+
+    bool useOpenCL() const;
+    void setUseOpenCL(bool flag);
+
+    /** Get OpenCL execution context of current thread.
+     *
+     * Initialize OpenCL execution context if it is empty
+     * - create new
+     * - reuse context of the main thread (threadID = 0)
+     */
+    static OpenCLExecutionContext& getCurrent();
+
+    /** Get OpenCL execution context of current thread (can be empty) */
+    static OpenCLExecutionContext& getCurrentRef();
+
+    /** Bind this OpenCL execution context to current thread.
+     *
+     * Context can't be empty.
+     *
+     * @note clFinish is not called for queue of previous execution context
+     */
+    void bind() const;
+
+    /** Creates new execution context with same OpenCV context and device
+     *
+     * @param q OpenCL queue
+     */
+    OpenCLExecutionContext cloneWithNewQueue(const ocl::Queue& q) const;
+    /** @overload */
+    OpenCLExecutionContext cloneWithNewQueue() const;
+
+    /** @brief Creates OpenCL execution context
+     * OpenCV will check if available OpenCL platform has platformName name,
+     * then assign context to OpenCV.
+     * The deviceID device will be used as target device and a new command queue will be created.
+     *
+     * @note On success, ownership of one reference of the context and device is taken.
+     * The caller should additionally call `clRetainContext` and/or `clRetainDevice`
+     * to increase the reference count if it wishes to continue using them.
+     *
+     * @param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime
+     * @param platformID ID of platform attached context was created for (cl_platform_id)
+     * @param context OpenCL context to be attached to OpenCV (cl_context)
+     * @param deviceID OpenCL device (cl_device_id)
+     */
+    static OpenCLExecutionContext create(const std::string& platformName, void* platformID, void* context, void* deviceID);
+
+    /** @brief Creates OpenCL execution context
+     *
+     * @param context non-empty OpenCL context
+     * @param device non-empty OpenCL device (must be a part of context)
+     * @param queue non-empty OpenCL queue for provided context and device
+     */
+    static OpenCLExecutionContext create(const Context& context, const Device& device, const ocl::Queue& queue);
+    /** @overload */
+    static OpenCLExecutionContext create(const Context& context, const Device& device);
+
+    struct Impl;
+    inline bool empty() const { return !p; }
+    void release();
+protected:
+    std::shared_ptr<Impl> p;
+};
+
+class OpenCLExecutionContextScope
+{
+    OpenCLExecutionContext ctx_;
+public:
+    inline OpenCLExecutionContextScope(const OpenCLExecutionContext& ctx)
+    {
+        CV_Assert(!ctx.empty());
+        ctx_ = OpenCLExecutionContext::getCurrentRef();
+        ctx.bind();
+    }
+
+    inline ~OpenCLExecutionContextScope()
+    {
+        if (!ctx_.empty())
+        {
+            ctx_.bind();
+        }
+    }
+};
+
+#ifdef __OPENCV_BUILD
+namespace internal {
+
+CV_EXPORTS bool isOpenCLForced();
+#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition))
+
+CV_EXPORTS bool isPerformanceCheckBypassed();
+#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition))
+
+CV_EXPORTS bool isCLBuffer(UMat& u);
+
+} // namespace internal
+#endif
+
+//! @}
+
+}}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/ocl_genbase.hpp b/3rdParty/include/opencv2/core/ocl_genbase.hpp
new file mode 100644
index 0000000..5334cf1
--- /dev/null
+++ b/3rdParty/include/opencv2/core/ocl_genbase.hpp
@@ -0,0 +1,69 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_OPENCL_GENBASE_HPP
+#define OPENCV_OPENCL_GENBASE_HPP
+
+//! @cond IGNORED
+
+namespace cv {
+namespace ocl {
+
+class ProgramSource;
+
+namespace internal {
+
+struct CV_EXPORTS ProgramEntry
+{
+    const char* module;
+    const char* name;
+    const char* programCode;
+    const char* programHash;
+    ProgramSource* pProgramSource;
+
+    operator ProgramSource& () const;
+};
+
+} } } // namespace
+
+//! @endcond
+
+#endif
diff --git a/3rdParty/include/opencv2/core/opencl/ocl_defs.hpp b/3rdParty/include/opencv2/core/opencl/ocl_defs.hpp
new file mode 100644
index 0000000..14df750
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/ocl_defs.hpp
@@ -0,0 +1,82 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#ifndef OPENCV_CORE_OPENCL_DEFS_HPP
+#define OPENCV_CORE_OPENCL_DEFS_HPP
+
+#include "opencv2/core/utility.hpp"
+#include "cvconfig.h"
+
+namespace cv { namespace ocl {
+#ifdef HAVE_OPENCL
+/// Call is similar to useOpenCL() but doesn't try to load OpenCL runtime or create OpenCL context
+CV_EXPORTS bool isOpenCLActivated();
+#else
+static inline bool isOpenCLActivated() { return false; }
+#endif
+}} // namespace
+
+
+//#define CV_OPENCL_RUN_ASSERT
+
+#ifdef HAVE_OPENCL
+
+#ifdef CV_OPENCL_RUN_VERBOSE
+#define CV_OCL_RUN_(condition, func, ...)                                   \
+    {                                                                       \
+        if (cv::ocl::isOpenCLActivated() && (condition) && func)            \
+        {                                                                   \
+            printf("%s: OpenCL implementation is running\n", CV_Func);      \
+            fflush(stdout);                                                 \
+            CV_IMPL_ADD(CV_IMPL_OCL);                                       \
+            return __VA_ARGS__;                                             \
+        }                                                                   \
+        else                                                                \
+        {                                                                   \
+            printf("%s: Plain implementation is running\n", CV_Func);       \
+            fflush(stdout);                                                 \
+        }                                                                   \
+    }
+#elif defined CV_OPENCL_RUN_ASSERT
+#define CV_OCL_RUN_(condition, func, ...)                                   \
+    {                                                                       \
+        if (cv::ocl::isOpenCLActivated() && (condition))                    \
+        {                                                                   \
+            if(func)                                                        \
+            {                                                               \
+                CV_IMPL_ADD(CV_IMPL_OCL);                                   \
+            }                                                               \
+            else                                                            \
+            {                                                               \
+                CV_Error(cv::Error::StsAssert, #func);                      \
+            }                                                               \
+            return __VA_ARGS__;                                             \
+        }                                                                   \
+    }
+#else
+#define CV_OCL_RUN_(condition, func, ...)                                   \
+try \
+{ \
+    if (cv::ocl::isOpenCLActivated() && (condition) && func)                \
+    {                                                                       \
+        CV_IMPL_ADD(CV_IMPL_OCL);                                           \
+        return __VA_ARGS__;                                                 \
+    } \
+} \
+catch (const cv::Exception& e) \
+{ \
+    CV_UNUSED(e); /* TODO: Add some logging here */ \
+}
+#endif
+
+#else
+#define CV_OCL_RUN_(condition, func, ...)
+#endif
+
+#define CV_OCL_RUN(condition, func) CV_OCL_RUN_(condition, func)
+
+#endif // OPENCV_CORE_OPENCL_DEFS_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/opencl_info.hpp b/3rdParty/include/opencv2/core/opencl/opencl_info.hpp
new file mode 100644
index 0000000..3ead76e
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/opencl_info.hpp
@@ -0,0 +1,212 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include <iostream>
+
+#include <opencv2/core.hpp>
+#include <opencv2/core/ocl.hpp>
+
+#ifndef DUMP_CONFIG_PROPERTY
+#define DUMP_CONFIG_PROPERTY(...)
+#endif
+
+#ifndef DUMP_MESSAGE_STDOUT
+#define DUMP_MESSAGE_STDOUT(...) do { std::cout << __VA_ARGS__ << std::endl; } while (false)
+#endif
+
+namespace cv {
+
+namespace {
+static std::string bytesToStringRepr(size_t value)
+{
+    size_t b = value % 1024;
+    value /= 1024;
+
+    size_t kb = value % 1024;
+    value /= 1024;
+
+    size_t mb = value % 1024;
+    value /= 1024;
+
+    size_t gb = value;
+
+    std::ostringstream stream;
+
+    if (gb > 0)
+        stream << gb << " GB ";
+    if (mb > 0)
+        stream << mb << " MB ";
+    if (kb > 0)
+        stream << kb << " KB ";
+    if (b > 0)
+        stream << b << " B";
+
+    std::string s = stream.str();
+    if (s[s.size() - 1] == ' ')
+        s = s.substr(0, s.size() - 1);
+    return s;
+}
+
+static String getDeviceTypeString(const cv::ocl::Device& device)
+{
+    if (device.type() == cv::ocl::Device::TYPE_CPU) {
+        return "CPU";
+    }
+
+    if (device.type() == cv::ocl::Device::TYPE_GPU) {
+        if (device.hostUnifiedMemory()) {
+            return "iGPU";
+        } else {
+            return "dGPU";
+        }
+    }
+
+    return "unknown";
+}
+} // namespace
+
+static void dumpOpenCLInformation()
+{
+    using namespace cv::ocl;
+
+    try
+    {
+        if (!haveOpenCL() || !useOpenCL())
+        {
+            DUMP_MESSAGE_STDOUT("OpenCL is disabled");
+            DUMP_CONFIG_PROPERTY("cv_ocl", "disabled");
+            return;
+        }
+
+        std::vector<PlatformInfo> platforms;
+        cv::ocl::getPlatfomsInfo(platforms);
+        if (platforms.empty())
+        {
+            DUMP_MESSAGE_STDOUT("OpenCL is not available");
+            DUMP_CONFIG_PROPERTY("cv_ocl", "not available");
+            return;
+        }
+
+        DUMP_MESSAGE_STDOUT("OpenCL Platforms: ");
+        for (size_t i = 0; i < platforms.size(); i++)
+        {
+            const PlatformInfo* platform = &platforms[i];
+            DUMP_MESSAGE_STDOUT("    " << platform->name());
+            Device current_device;
+            for (int j = 0; j < platform->deviceNumber(); j++)
+            {
+                platform->getDevice(current_device, j);
+                String deviceTypeStr = getDeviceTypeString(current_device);
+                DUMP_MESSAGE_STDOUT( "        " << deviceTypeStr << ": " << current_device.name() << " (" << current_device.version() << ")");
+                DUMP_CONFIG_PROPERTY( cv::format("cv_ocl_platform_%d_device_%d", (int)i, j ),
+                    cv::format("(Platform=%s)(Type=%s)(Name=%s)(Version=%s)",
+                    platform->name().c_str(), deviceTypeStr.c_str(), current_device.name().c_str(), current_device.version().c_str()) );
+            }
+        }
+        const Device& device = Device::getDefault();
+        if (!device.available())
+            CV_Error(Error::OpenCLInitError, "OpenCL device is not available");
+
+        DUMP_MESSAGE_STDOUT("Current OpenCL device: ");
+
+        String deviceTypeStr = getDeviceTypeString(device);
+        DUMP_MESSAGE_STDOUT("    Type = " << deviceTypeStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceType", deviceTypeStr);
+
+        DUMP_MESSAGE_STDOUT("    Name = " << device.name());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceName", device.name());
+
+        DUMP_MESSAGE_STDOUT("    Version = " << device.version());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceVersion", device.version());
+
+        DUMP_MESSAGE_STDOUT("    Driver version = " << device.driverVersion());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_driverVersion", device.driverVersion());
+
+        DUMP_MESSAGE_STDOUT("    Address bits = " << device.addressBits());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_addressBits", device.addressBits());
+
+        DUMP_MESSAGE_STDOUT("    Compute units = " << device.maxComputeUnits());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_maxComputeUnits", device.maxComputeUnits());
+
+        DUMP_MESSAGE_STDOUT("    Max work group size = " << device.maxWorkGroupSize());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_maxWorkGroupSize", device.maxWorkGroupSize());
+
+        std::string localMemorySizeStr = bytesToStringRepr(device.localMemSize());
+        DUMP_MESSAGE_STDOUT("    Local memory size = " << localMemorySizeStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_localMemSize", device.localMemSize());
+
+        std::string maxMemAllocSizeStr = bytesToStringRepr(device.maxMemAllocSize());
+        DUMP_MESSAGE_STDOUT("    Max memory allocation size = " << maxMemAllocSizeStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_maxMemAllocSize", device.maxMemAllocSize());
+
+        const char* doubleSupportStr = device.doubleFPConfig() > 0 ? "Yes" : "No";
+        DUMP_MESSAGE_STDOUT("    Double support = " << doubleSupportStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_haveDoubleSupport", device.doubleFPConfig() > 0);
+
+        const char* halfSupportStr = device.halfFPConfig() > 0 ? "Yes" : "No";
+        DUMP_MESSAGE_STDOUT("    Half support = " << halfSupportStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_haveHalfSupport", device.halfFPConfig() > 0);
+
+        const char* isUnifiedMemoryStr = device.hostUnifiedMemory() ? "Yes" : "No";
+        DUMP_MESSAGE_STDOUT("    Host unified memory = " << isUnifiedMemoryStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_hostUnifiedMemory", device.hostUnifiedMemory());
+
+        DUMP_MESSAGE_STDOUT("    Device extensions:");
+        String extensionsStr = device.extensions();
+        size_t pos = 0;
+        while (pos < extensionsStr.size())
+        {
+            size_t pos2 = extensionsStr.find(' ', pos);
+            if (pos2 == String::npos)
+                pos2 = extensionsStr.size();
+            if (pos2 > pos)
+            {
+                String extensionName = extensionsStr.substr(pos, pos2 - pos);
+                DUMP_MESSAGE_STDOUT("        " << extensionName);
+            }
+            pos = pos2 + 1;
+        }
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_extensions", extensionsStr);
+
+        const char* haveAmdBlasStr = haveAmdBlas() ? "Yes" : "No";
+        DUMP_MESSAGE_STDOUT("    Has AMD Blas = " << haveAmdBlasStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdBlas", haveAmdBlas());
+
+        const char* haveAmdFftStr = haveAmdFft() ? "Yes" : "No";
+        DUMP_MESSAGE_STDOUT("    Has AMD Fft = " << haveAmdFftStr);
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdFft", haveAmdFft());
+
+
+        DUMP_MESSAGE_STDOUT("    Preferred vector width char = " << device.preferredVectorWidthChar());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthChar", device.preferredVectorWidthChar());
+
+        DUMP_MESSAGE_STDOUT("    Preferred vector width short = " << device.preferredVectorWidthShort());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthShort", device.preferredVectorWidthShort());
+
+        DUMP_MESSAGE_STDOUT("    Preferred vector width int = " << device.preferredVectorWidthInt());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthInt", device.preferredVectorWidthInt());
+
+        DUMP_MESSAGE_STDOUT("    Preferred vector width long = " << device.preferredVectorWidthLong());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthLong", device.preferredVectorWidthLong());
+
+        DUMP_MESSAGE_STDOUT("    Preferred vector width float = " << device.preferredVectorWidthFloat());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthFloat", device.preferredVectorWidthFloat());
+
+        DUMP_MESSAGE_STDOUT("    Preferred vector width double = " << device.preferredVectorWidthDouble());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthDouble", device.preferredVectorWidthDouble());
+
+        DUMP_MESSAGE_STDOUT("    Preferred vector width half = " << device.preferredVectorWidthHalf());
+        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthHalf", device.preferredVectorWidthHalf());
+    }
+    catch (...)
+    {
+        DUMP_MESSAGE_STDOUT("Exception. Can't dump OpenCL info");
+        DUMP_MESSAGE_STDOUT("OpenCL device not available");
+        DUMP_CONFIG_PROPERTY("cv_ocl", "not available");
+    }
+}
+#undef DUMP_MESSAGE_STDOUT
+#undef DUMP_CONFIG_PROPERTY
+
+} // namespace
diff --git a/3rdParty/include/opencv2/core/opencl/opencl_svm.hpp b/3rdParty/include/opencv2/core/opencl/opencl_svm.hpp
new file mode 100644
index 0000000..7453082
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/opencl_svm.hpp
@@ -0,0 +1,81 @@
+/* See LICENSE file in the root OpenCV directory */
+
+#ifndef OPENCV_CORE_OPENCL_SVM_HPP
+#define OPENCV_CORE_OPENCL_SVM_HPP
+
+//
+// Internal usage only (binary compatibility is not guaranteed)
+//
+#ifndef __OPENCV_BUILD
+#error Internal header file
+#endif
+
+#if defined(HAVE_OPENCL) && defined(HAVE_OPENCL_SVM)
+#include "runtime/opencl_core.hpp"
+#include "runtime/opencl_svm_20.hpp"
+#include "runtime/opencl_svm_hsa_extension.hpp"
+
+namespace cv { namespace ocl { namespace svm {
+
+struct SVMCapabilities
+{
+    enum Value
+    {
+        SVM_COARSE_GRAIN_BUFFER = (1 << 0),
+        SVM_FINE_GRAIN_BUFFER = (1 << 1),
+        SVM_FINE_GRAIN_SYSTEM = (1 << 2),
+        SVM_ATOMICS = (1 << 3),
+    };
+    int value_;
+
+    SVMCapabilities(int capabilities = 0) : value_(capabilities) { }
+    operator int() const { return value_; }
+
+    inline bool isNoSVMSupport() const { return value_ == 0; }
+    inline bool isSupportCoarseGrainBuffer() const { return (value_ & SVM_COARSE_GRAIN_BUFFER) != 0; }
+    inline bool isSupportFineGrainBuffer() const { return (value_ & SVM_FINE_GRAIN_BUFFER) != 0; }
+    inline bool isSupportFineGrainSystem() const { return (value_ & SVM_FINE_GRAIN_SYSTEM) != 0; }
+    inline bool isSupportAtomics() const { return (value_ & SVM_ATOMICS) != 0; }
+};
+
+CV_EXPORTS const SVMCapabilities getSVMCapabilitites(const ocl::Context& context);
+
+struct SVMFunctions
+{
+    clSVMAllocAMD_fn fn_clSVMAlloc;
+    clSVMFreeAMD_fn fn_clSVMFree;
+    clSetKernelArgSVMPointerAMD_fn fn_clSetKernelArgSVMPointer;
+    //clSetKernelExecInfoAMD_fn fn_clSetKernelExecInfo;
+    //clEnqueueSVMFreeAMD_fn fn_clEnqueueSVMFree;
+    clEnqueueSVMMemcpyAMD_fn fn_clEnqueueSVMMemcpy;
+    clEnqueueSVMMemFillAMD_fn fn_clEnqueueSVMMemFill;
+    clEnqueueSVMMapAMD_fn fn_clEnqueueSVMMap;
+    clEnqueueSVMUnmapAMD_fn fn_clEnqueueSVMUnmap;
+
+    inline SVMFunctions()
+        : fn_clSVMAlloc(NULL), fn_clSVMFree(NULL),
+          fn_clSetKernelArgSVMPointer(NULL), /*fn_clSetKernelExecInfo(NULL),*/
+          /*fn_clEnqueueSVMFree(NULL),*/ fn_clEnqueueSVMMemcpy(NULL), fn_clEnqueueSVMMemFill(NULL),
+          fn_clEnqueueSVMMap(NULL), fn_clEnqueueSVMUnmap(NULL)
+    {
+        // nothing
+    }
+
+    inline bool isValid() const
+    {
+        return fn_clSVMAlloc != NULL && fn_clSVMFree && fn_clSetKernelArgSVMPointer &&
+                /*fn_clSetKernelExecInfo && fn_clEnqueueSVMFree &&*/ fn_clEnqueueSVMMemcpy &&
+                fn_clEnqueueSVMMemFill && fn_clEnqueueSVMMap && fn_clEnqueueSVMUnmap;
+    }
+};
+
+// We should guarantee that SVMFunctions lifetime is not less than context's lifetime
+CV_EXPORTS const SVMFunctions* getSVMFunctions(const ocl::Context& context);
+
+CV_EXPORTS bool useSVM(UMatUsageFlags usageFlags);
+
+}}} //namespace cv::ocl::svm
+#endif
+
+#endif // OPENCV_CORE_OPENCL_SVM_HPP
+/* End of file. */
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_clblas.hpp b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_clblas.hpp
new file mode 100644
index 0000000..2749927
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_clblas.hpp
@@ -0,0 +1,602 @@
+//
+// AUTOGENERATED, DO NOT EDIT
+//
+#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
+#error "Invalid usage"
+#endif
+
+// generated by parser_clblas.py
+#define clblasCaxpy clblasCaxpy_
+#define clblasCcopy clblasCcopy_
+#define clblasCdotc clblasCdotc_
+#define clblasCdotu clblasCdotu_
+#define clblasCgbmv clblasCgbmv_
+#define clblasCgemm clblasCgemm_
+#define clblasCgemv clblasCgemv_
+#define clblasCgerc clblasCgerc_
+#define clblasCgeru clblasCgeru_
+#define clblasChbmv clblasChbmv_
+#define clblasChemm clblasChemm_
+#define clblasChemv clblasChemv_
+#define clblasCher clblasCher_
+#define clblasCher2 clblasCher2_
+#define clblasCher2k clblasCher2k_
+#define clblasCherk clblasCherk_
+#define clblasChpmv clblasChpmv_
+#define clblasChpr clblasChpr_
+#define clblasChpr2 clblasChpr2_
+#define clblasCrotg clblasCrotg_
+#define clblasCscal clblasCscal_
+#define clblasCsrot clblasCsrot_
+#define clblasCsscal clblasCsscal_
+#define clblasCswap clblasCswap_
+#define clblasCsymm clblasCsymm_
+#define clblasCsyr2k clblasCsyr2k_
+#define clblasCsyrk clblasCsyrk_
+#define clblasCtbmv clblasCtbmv_
+#define clblasCtbsv clblasCtbsv_
+#define clblasCtpmv clblasCtpmv_
+#define clblasCtpsv clblasCtpsv_
+#define clblasCtrmm clblasCtrmm_
+#define clblasCtrmv clblasCtrmv_
+#define clblasCtrsm clblasCtrsm_
+#define clblasCtrsv clblasCtrsv_
+#define clblasDasum clblasDasum_
+#define clblasDaxpy clblasDaxpy_
+#define clblasDcopy clblasDcopy_
+#define clblasDdot clblasDdot_
+#define clblasDgbmv clblasDgbmv_
+#define clblasDgemm clblasDgemm_
+#define clblasDgemv clblasDgemv_
+#define clblasDger clblasDger_
+#define clblasDnrm2 clblasDnrm2_
+#define clblasDrot clblasDrot_
+#define clblasDrotg clblasDrotg_
+#define clblasDrotm clblasDrotm_
+#define clblasDrotmg clblasDrotmg_
+#define clblasDsbmv clblasDsbmv_
+#define clblasDscal clblasDscal_
+#define clblasDspmv clblasDspmv_
+#define clblasDspr clblasDspr_
+#define clblasDspr2 clblasDspr2_
+#define clblasDswap clblasDswap_
+#define clblasDsymm clblasDsymm_
+#define clblasDsymv clblasDsymv_
+#define clblasDsyr clblasDsyr_
+#define clblasDsyr2 clblasDsyr2_
+#define clblasDsyr2k clblasDsyr2k_
+#define clblasDsyrk clblasDsyrk_
+#define clblasDtbmv clblasDtbmv_
+#define clblasDtbsv clblasDtbsv_
+#define clblasDtpmv clblasDtpmv_
+#define clblasDtpsv clblasDtpsv_
+#define clblasDtrmm clblasDtrmm_
+#define clblasDtrmv clblasDtrmv_
+#define clblasDtrsm clblasDtrsm_
+#define clblasDtrsv clblasDtrsv_
+#define clblasDzasum clblasDzasum_
+#define clblasDznrm2 clblasDznrm2_
+#define clblasGetVersion clblasGetVersion_
+#define clblasSasum clblasSasum_
+#define clblasSaxpy clblasSaxpy_
+#define clblasScasum clblasScasum_
+#define clblasScnrm2 clblasScnrm2_
+#define clblasScopy clblasScopy_
+#define clblasSdot clblasSdot_
+#define clblasSetup clblasSetup_
+#define clblasSgbmv clblasSgbmv_
+#define clblasSgemm clblasSgemm_
+#define clblasSgemv clblasSgemv_
+#define clblasSger clblasSger_
+#define clblasSnrm2 clblasSnrm2_
+#define clblasSrot clblasSrot_
+#define clblasSrotg clblasSrotg_
+#define clblasSrotm clblasSrotm_
+#define clblasSrotmg clblasSrotmg_
+#define clblasSsbmv clblasSsbmv_
+#define clblasSscal clblasSscal_
+#define clblasSspmv clblasSspmv_
+#define clblasSspr clblasSspr_
+#define clblasSspr2 clblasSspr2_
+#define clblasSswap clblasSswap_
+#define clblasSsymm clblasSsymm_
+#define clblasSsymv clblasSsymv_
+#define clblasSsyr clblasSsyr_
+#define clblasSsyr2 clblasSsyr2_
+#define clblasSsyr2k clblasSsyr2k_
+#define clblasSsyrk clblasSsyrk_
+#define clblasStbmv clblasStbmv_
+#define clblasStbsv clblasStbsv_
+#define clblasStpmv clblasStpmv_
+#define clblasStpsv clblasStpsv_
+#define clblasStrmm clblasStrmm_
+#define clblasStrmv clblasStrmv_
+#define clblasStrsm clblasStrsm_
+#define clblasStrsv clblasStrsv_
+#define clblasTeardown clblasTeardown_
+#define clblasZaxpy clblasZaxpy_
+#define clblasZcopy clblasZcopy_
+#define clblasZdotc clblasZdotc_
+#define clblasZdotu clblasZdotu_
+#define clblasZdrot clblasZdrot_
+#define clblasZdscal clblasZdscal_
+#define clblasZgbmv clblasZgbmv_
+#define clblasZgemm clblasZgemm_
+#define clblasZgemv clblasZgemv_
+#define clblasZgerc clblasZgerc_
+#define clblasZgeru clblasZgeru_
+#define clblasZhbmv clblasZhbmv_
+#define clblasZhemm clblasZhemm_
+#define clblasZhemv clblasZhemv_
+#define clblasZher clblasZher_
+#define clblasZher2 clblasZher2_
+#define clblasZher2k clblasZher2k_
+#define clblasZherk clblasZherk_
+#define clblasZhpmv clblasZhpmv_
+#define clblasZhpr clblasZhpr_
+#define clblasZhpr2 clblasZhpr2_
+#define clblasZrotg clblasZrotg_
+#define clblasZscal clblasZscal_
+#define clblasZswap clblasZswap_
+#define clblasZsymm clblasZsymm_
+#define clblasZsyr2k clblasZsyr2k_
+#define clblasZsyrk clblasZsyrk_
+#define clblasZtbmv clblasZtbmv_
+#define clblasZtbsv clblasZtbsv_
+#define clblasZtpmv clblasZtpmv_
+#define clblasZtpsv clblasZtpsv_
+#define clblasZtrmm clblasZtrmm_
+#define clblasZtrmv clblasZtrmv_
+#define clblasZtrsm clblasZtrsm_
+#define clblasZtrsv clblasZtrsv_
+#define clblasiCamax clblasiCamax_
+#define clblasiDamax clblasiDamax_
+#define clblasiSamax clblasiSamax_
+#define clblasiZamax clblasiZamax_
+
+#include <clBLAS.h>
+
+// generated by parser_clblas.py
+#undef clblasCaxpy
+//#define clblasCaxpy clblasCaxpy_pfn
+#undef clblasCcopy
+//#define clblasCcopy clblasCcopy_pfn
+#undef clblasCdotc
+//#define clblasCdotc clblasCdotc_pfn
+#undef clblasCdotu
+//#define clblasCdotu clblasCdotu_pfn
+#undef clblasCgbmv
+//#define clblasCgbmv clblasCgbmv_pfn
+#undef clblasCgemm
+#define clblasCgemm clblasCgemm_pfn
+#undef clblasCgemv
+//#define clblasCgemv clblasCgemv_pfn
+#undef clblasCgerc
+//#define clblasCgerc clblasCgerc_pfn
+#undef clblasCgeru
+//#define clblasCgeru clblasCgeru_pfn
+#undef clblasChbmv
+//#define clblasChbmv clblasChbmv_pfn
+#undef clblasChemm
+//#define clblasChemm clblasChemm_pfn
+#undef clblasChemv
+//#define clblasChemv clblasChemv_pfn
+#undef clblasCher
+//#define clblasCher clblasCher_pfn
+#undef clblasCher2
+//#define clblasCher2 clblasCher2_pfn
+#undef clblasCher2k
+//#define clblasCher2k clblasCher2k_pfn
+#undef clblasCherk
+//#define clblasCherk clblasCherk_pfn
+#undef clblasChpmv
+//#define clblasChpmv clblasChpmv_pfn
+#undef clblasChpr
+//#define clblasChpr clblasChpr_pfn
+#undef clblasChpr2
+//#define clblasChpr2 clblasChpr2_pfn
+#undef clblasCrotg
+//#define clblasCrotg clblasCrotg_pfn
+#undef clblasCscal
+//#define clblasCscal clblasCscal_pfn
+#undef clblasCsrot
+//#define clblasCsrot clblasCsrot_pfn
+#undef clblasCsscal
+//#define clblasCsscal clblasCsscal_pfn
+#undef clblasCswap
+//#define clblasCswap clblasCswap_pfn
+#undef clblasCsymm
+//#define clblasCsymm clblasCsymm_pfn
+#undef clblasCsyr2k
+//#define clblasCsyr2k clblasCsyr2k_pfn
+#undef clblasCsyrk
+//#define clblasCsyrk clblasCsyrk_pfn
+#undef clblasCtbmv
+//#define clblasCtbmv clblasCtbmv_pfn
+#undef clblasCtbsv
+//#define clblasCtbsv clblasCtbsv_pfn
+#undef clblasCtpmv
+//#define clblasCtpmv clblasCtpmv_pfn
+#undef clblasCtpsv
+//#define clblasCtpsv clblasCtpsv_pfn
+#undef clblasCtrmm
+//#define clblasCtrmm clblasCtrmm_pfn
+#undef clblasCtrmv
+//#define clblasCtrmv clblasCtrmv_pfn
+#undef clblasCtrsm
+//#define clblasCtrsm clblasCtrsm_pfn
+#undef clblasCtrsv
+//#define clblasCtrsv clblasCtrsv_pfn
+#undef clblasDasum
+//#define clblasDasum clblasDasum_pfn
+#undef clblasDaxpy
+//#define clblasDaxpy clblasDaxpy_pfn
+#undef clblasDcopy
+//#define clblasDcopy clblasDcopy_pfn
+#undef clblasDdot
+//#define clblasDdot clblasDdot_pfn
+#undef clblasDgbmv
+//#define clblasDgbmv clblasDgbmv_pfn
+#undef clblasDgemm
+#define clblasDgemm clblasDgemm_pfn
+#undef clblasDgemv
+//#define clblasDgemv clblasDgemv_pfn
+#undef clblasDger
+//#define clblasDger clblasDger_pfn
+#undef clblasDnrm2
+//#define clblasDnrm2 clblasDnrm2_pfn
+#undef clblasDrot
+//#define clblasDrot clblasDrot_pfn
+#undef clblasDrotg
+//#define clblasDrotg clblasDrotg_pfn
+#undef clblasDrotm
+//#define clblasDrotm clblasDrotm_pfn
+#undef clblasDrotmg
+//#define clblasDrotmg clblasDrotmg_pfn
+#undef clblasDsbmv
+//#define clblasDsbmv clblasDsbmv_pfn
+#undef clblasDscal
+//#define clblasDscal clblasDscal_pfn
+#undef clblasDspmv
+//#define clblasDspmv clblasDspmv_pfn
+#undef clblasDspr
+//#define clblasDspr clblasDspr_pfn
+#undef clblasDspr2
+//#define clblasDspr2 clblasDspr2_pfn
+#undef clblasDswap
+//#define clblasDswap clblasDswap_pfn
+#undef clblasDsymm
+//#define clblasDsymm clblasDsymm_pfn
+#undef clblasDsymv
+//#define clblasDsymv clblasDsymv_pfn
+#undef clblasDsyr
+//#define clblasDsyr clblasDsyr_pfn
+#undef clblasDsyr2
+//#define clblasDsyr2 clblasDsyr2_pfn
+#undef clblasDsyr2k
+//#define clblasDsyr2k clblasDsyr2k_pfn
+#undef clblasDsyrk
+//#define clblasDsyrk clblasDsyrk_pfn
+#undef clblasDtbmv
+//#define clblasDtbmv clblasDtbmv_pfn
+#undef clblasDtbsv
+//#define clblasDtbsv clblasDtbsv_pfn
+#undef clblasDtpmv
+//#define clblasDtpmv clblasDtpmv_pfn
+#undef clblasDtpsv
+//#define clblasDtpsv clblasDtpsv_pfn
+#undef clblasDtrmm
+//#define clblasDtrmm clblasDtrmm_pfn
+#undef clblasDtrmv
+//#define clblasDtrmv clblasDtrmv_pfn
+#undef clblasDtrsm
+//#define clblasDtrsm clblasDtrsm_pfn
+#undef clblasDtrsv
+//#define clblasDtrsv clblasDtrsv_pfn
+#undef clblasDzasum
+//#define clblasDzasum clblasDzasum_pfn
+#undef clblasDznrm2
+//#define clblasDznrm2 clblasDznrm2_pfn
+#undef clblasGetVersion
+//#define clblasGetVersion clblasGetVersion_pfn
+#undef clblasSasum
+//#define clblasSasum clblasSasum_pfn
+#undef clblasSaxpy
+//#define clblasSaxpy clblasSaxpy_pfn
+#undef clblasScasum
+//#define clblasScasum clblasScasum_pfn
+#undef clblasScnrm2
+//#define clblasScnrm2 clblasScnrm2_pfn
+#undef clblasScopy
+//#define clblasScopy clblasScopy_pfn
+#undef clblasSdot
+//#define clblasSdot clblasSdot_pfn
+#undef clblasSetup
+#define clblasSetup clblasSetup_pfn
+#undef clblasSgbmv
+//#define clblasSgbmv clblasSgbmv_pfn
+#undef clblasSgemm
+#define clblasSgemm clblasSgemm_pfn
+#undef clblasSgemv
+//#define clblasSgemv clblasSgemv_pfn
+#undef clblasSger
+//#define clblasSger clblasSger_pfn
+#undef clblasSnrm2
+//#define clblasSnrm2 clblasSnrm2_pfn
+#undef clblasSrot
+//#define clblasSrot clblasSrot_pfn
+#undef clblasSrotg
+//#define clblasSrotg clblasSrotg_pfn
+#undef clblasSrotm
+//#define clblasSrotm clblasSrotm_pfn
+#undef clblasSrotmg
+//#define clblasSrotmg clblasSrotmg_pfn
+#undef clblasSsbmv
+//#define clblasSsbmv clblasSsbmv_pfn
+#undef clblasSscal
+//#define clblasSscal clblasSscal_pfn
+#undef clblasSspmv
+//#define clblasSspmv clblasSspmv_pfn
+#undef clblasSspr
+//#define clblasSspr clblasSspr_pfn
+#undef clblasSspr2
+//#define clblasSspr2 clblasSspr2_pfn
+#undef clblasSswap
+//#define clblasSswap clblasSswap_pfn
+#undef clblasSsymm
+//#define clblasSsymm clblasSsymm_pfn
+#undef clblasSsymv
+//#define clblasSsymv clblasSsymv_pfn
+#undef clblasSsyr
+//#define clblasSsyr clblasSsyr_pfn
+#undef clblasSsyr2
+//#define clblasSsyr2 clblasSsyr2_pfn
+#undef clblasSsyr2k
+//#define clblasSsyr2k clblasSsyr2k_pfn
+#undef clblasSsyrk
+//#define clblasSsyrk clblasSsyrk_pfn
+#undef clblasStbmv
+//#define clblasStbmv clblasStbmv_pfn
+#undef clblasStbsv
+//#define clblasStbsv clblasStbsv_pfn
+#undef clblasStpmv
+//#define clblasStpmv clblasStpmv_pfn
+#undef clblasStpsv
+//#define clblasStpsv clblasStpsv_pfn
+#undef clblasStrmm
+//#define clblasStrmm clblasStrmm_pfn
+#undef clblasStrmv
+//#define clblasStrmv clblasStrmv_pfn
+#undef clblasStrsm
+//#define clblasStrsm clblasStrsm_pfn
+#undef clblasStrsv
+//#define clblasStrsv clblasStrsv_pfn
+#undef clblasTeardown
+#define clblasTeardown clblasTeardown_pfn
+#undef clblasZaxpy
+//#define clblasZaxpy clblasZaxpy_pfn
+#undef clblasZcopy
+//#define clblasZcopy clblasZcopy_pfn
+#undef clblasZdotc
+//#define clblasZdotc clblasZdotc_pfn
+#undef clblasZdotu
+//#define clblasZdotu clblasZdotu_pfn
+#undef clblasZdrot
+//#define clblasZdrot clblasZdrot_pfn
+#undef clblasZdscal
+//#define clblasZdscal clblasZdscal_pfn
+#undef clblasZgbmv
+//#define clblasZgbmv clblasZgbmv_pfn
+#undef clblasZgemm
+#define clblasZgemm clblasZgemm_pfn
+#undef clblasZgemv
+//#define clblasZgemv clblasZgemv_pfn
+#undef clblasZgerc
+//#define clblasZgerc clblasZgerc_pfn
+#undef clblasZgeru
+//#define clblasZgeru clblasZgeru_pfn
+#undef clblasZhbmv
+//#define clblasZhbmv clblasZhbmv_pfn
+#undef clblasZhemm
+//#define clblasZhemm clblasZhemm_pfn
+#undef clblasZhemv
+//#define clblasZhemv clblasZhemv_pfn
+#undef clblasZher
+//#define clblasZher clblasZher_pfn
+#undef clblasZher2
+//#define clblasZher2 clblasZher2_pfn
+#undef clblasZher2k
+//#define clblasZher2k clblasZher2k_pfn
+#undef clblasZherk
+//#define clblasZherk clblasZherk_pfn
+#undef clblasZhpmv
+//#define clblasZhpmv clblasZhpmv_pfn
+#undef clblasZhpr
+//#define clblasZhpr clblasZhpr_pfn
+#undef clblasZhpr2
+//#define clblasZhpr2 clblasZhpr2_pfn
+#undef clblasZrotg
+//#define clblasZrotg clblasZrotg_pfn
+#undef clblasZscal
+//#define clblasZscal clblasZscal_pfn
+#undef clblasZswap
+//#define clblasZswap clblasZswap_pfn
+#undef clblasZsymm
+//#define clblasZsymm clblasZsymm_pfn
+#undef clblasZsyr2k
+//#define clblasZsyr2k clblasZsyr2k_pfn
+#undef clblasZsyrk
+//#define clblasZsyrk clblasZsyrk_pfn
+#undef clblasZtbmv
+//#define clblasZtbmv clblasZtbmv_pfn
+#undef clblasZtbsv
+//#define clblasZtbsv clblasZtbsv_pfn
+#undef clblasZtpmv
+//#define clblasZtpmv clblasZtpmv_pfn
+#undef clblasZtpsv
+//#define clblasZtpsv clblasZtpsv_pfn
+#undef clblasZtrmm
+//#define clblasZtrmm clblasZtrmm_pfn
+#undef clblasZtrmv
+//#define clblasZtrmv clblasZtrmv_pfn
+#undef clblasZtrsm
+//#define clblasZtrsm clblasZtrsm_pfn
+#undef clblasZtrsv
+//#define clblasZtrsv clblasZtrsv_pfn
+#undef clblasiCamax
+//#define clblasiCamax clblasiCamax_pfn
+#undef clblasiDamax
+//#define clblasiDamax clblasiDamax_pfn
+#undef clblasiSamax
+//#define clblasiSamax clblasiSamax_pfn
+#undef clblasiZamax
+//#define clblasiZamax clblasiZamax_pfn
+
+// generated by parser_clblas.py
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCaxpy)(size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCgbmv)(clblasOrder order, clblasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+extern CL_RUNTIME_EXPORT clblasStatus (*clblasCgemm)(clblasOrder order, clblasTranspose transA, clblasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCgemv)(clblasOrder order, clblasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCgerc)(clblasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCgeru)(clblasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasChbmv)(clblasOrder order, clblasUplo uplo, size_t N, size_t K, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasChemm)(clblasOrder order, clblasSide side, clblasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasChemv)(clblasOrder order, clblasUplo uplo, size_t N, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, FloatComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCher)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCher2)(clblasOrder order, clblasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCher2k)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCherk)(clblasOrder order, clblasUplo uplo, clblasTranspose transA, size_t N, size_t K, float alpha, const cl_mem A, size_t offa, size_t lda, float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasChpmv)(clblasOrder order, clblasUplo uplo, size_t N, cl_float2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasChpr)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasChpr2)(clblasOrder order, clblasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCscal)(size_t N, cl_float2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCsrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCsscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCsymm)(clblasOrder order, clblasSide side, clblasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCsyr2k)(clblasOrder order, clblasUplo uplo, clblasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCsyrk)(clblasOrder order, clblasUplo uplo, clblasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtbmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtbsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtpmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtpsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtrmm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtrmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtrsm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasCtrsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDaxpy)(size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDgbmv)(clblasOrder order, clblasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+extern CL_RUNTIME_EXPORT clblasStatus (*clblasDgemm)(clblasOrder order, clblasTranspose transA, clblasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDgemv)(clblasOrder order, clblasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDger)(clblasOrder order, size_t M, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDrotg)(cl_mem DA, size_t offDA, cl_mem DB, size_t offDB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDrotmg)(cl_mem DD1, size_t offDD1, cl_mem DD2, size_t offDD2, cl_mem DX1, size_t offDX1, const cl_mem DY1, size_t offDY1, cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDsbmv)(clblasOrder order, clblasUplo uplo, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDspmv)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDspr)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDspr2)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDsymm)(clblasOrder order, clblasSide side, clblasUplo uplo, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDsymv)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDsyr)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDsyr2)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDsyr2k)(clblasOrder order, clblasUplo uplo, clblasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDsyrk)(clblasOrder order, clblasUplo uplo, clblasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtbmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtbsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtpmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtpsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtrmm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtrmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtrsm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDtrsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDzasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasDznrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSaxpy)(size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasScasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasScnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasScopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+extern CL_RUNTIME_EXPORT clblasStatus (*clblasSetup)();
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSgbmv)(clblasOrder order, clblasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+extern CL_RUNTIME_EXPORT clblasStatus (*clblasSgemm)(clblasOrder order, clblasTranspose transA, clblasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSgemv)(clblasOrder order, clblasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSger)(clblasOrder order, size_t M, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSrotg)(cl_mem SA, size_t offSA, cl_mem SB, size_t offSB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSrotmg)(cl_mem SD1, size_t offSD1, cl_mem SD2, size_t offSD2, cl_mem SX1, size_t offSX1, const cl_mem SY1, size_t offSY1, cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSsbmv)(clblasOrder order, clblasUplo uplo, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSspmv)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSspr)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSspr2)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSsymm)(clblasOrder order, clblasSide side, clblasUplo uplo, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSsymv)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSsyr)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSsyr2)(clblasOrder order, clblasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSsyr2k)(clblasOrder order, clblasUplo uplo, clblasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasSsyrk)(clblasOrder order, clblasUplo uplo, clblasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStbmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStbsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStpmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStpsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStrmm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStrmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStrsm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasStrsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+extern CL_RUNTIME_EXPORT void (*clblasTeardown)();
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZaxpy)(size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZdrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZdscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZgbmv)(clblasOrder order, clblasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+extern CL_RUNTIME_EXPORT clblasStatus (*clblasZgemm)(clblasOrder order, clblasTranspose transA, clblasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZgemv)(clblasOrder order, clblasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZgerc)(clblasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZgeru)(clblasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZhbmv)(clblasOrder order, clblasUplo uplo, size_t N, size_t K, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZhemm)(clblasOrder order, clblasSide side, clblasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZhemv)(clblasOrder order, clblasUplo uplo, size_t N, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, DoubleComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZher)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZher2)(clblasOrder order, clblasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZher2k)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZherk)(clblasOrder order, clblasUplo uplo, clblasTranspose transA, size_t N, size_t K, double alpha, const cl_mem A, size_t offa, size_t lda, double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZhpmv)(clblasOrder order, clblasUplo uplo, size_t N, cl_double2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZhpr)(clblasOrder order, clblasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZhpr2)(clblasOrder order, clblasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZscal)(size_t N, cl_double2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZsymm)(clblasOrder order, clblasSide side, clblasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZsyr2k)(clblasOrder order, clblasUplo uplo, clblasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZsyrk)(clblasOrder order, clblasUplo uplo, clblasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtbmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtbsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtpmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtpsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtrmm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtrmv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtrsm)(clblasOrder order, clblasSide side, clblasUplo uplo, clblasTranspose transA, clblasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasZtrsv)(clblasOrder order, clblasUplo uplo, clblasTranspose trans, clblasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasiCamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasiDamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasiSamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
+//extern CL_RUNTIME_EXPORT clblasStatus (*clblasiZamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_clfft.hpp b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_clfft.hpp
new file mode 100644
index 0000000..dff3b40
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_clfft.hpp
@@ -0,0 +1,146 @@
+//
+// AUTOGENERATED, DO NOT EDIT
+//
+#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
+#error "Invalid usage"
+#endif
+
+// generated by parser_clfft.py
+#define clfftBakePlan clfftBakePlan_
+#define clfftCopyPlan clfftCopyPlan_
+#define clfftCreateDefaultPlan clfftCreateDefaultPlan_
+#define clfftDestroyPlan clfftDestroyPlan_
+#define clfftEnqueueTransform clfftEnqueueTransform_
+#define clfftGetLayout clfftGetLayout_
+#define clfftGetPlanBatchSize clfftGetPlanBatchSize_
+#define clfftGetPlanContext clfftGetPlanContext_
+#define clfftGetPlanDim clfftGetPlanDim_
+#define clfftGetPlanDistance clfftGetPlanDistance_
+#define clfftGetPlanInStride clfftGetPlanInStride_
+#define clfftGetPlanLength clfftGetPlanLength_
+#define clfftGetPlanOutStride clfftGetPlanOutStride_
+#define clfftGetPlanPrecision clfftGetPlanPrecision_
+#define clfftGetPlanScale clfftGetPlanScale_
+#define clfftGetPlanTransposeResult clfftGetPlanTransposeResult_
+#define clfftGetResultLocation clfftGetResultLocation_
+#define clfftGetTmpBufSize clfftGetTmpBufSize_
+#define clfftGetVersion clfftGetVersion_
+#define clfftSetLayout clfftSetLayout_
+#define clfftSetPlanBatchSize clfftSetPlanBatchSize_
+#define clfftSetPlanCallback clfftSetPlanCallback_
+#define clfftSetPlanDim clfftSetPlanDim_
+#define clfftSetPlanDistance clfftSetPlanDistance_
+#define clfftSetPlanInStride clfftSetPlanInStride_
+#define clfftSetPlanLength clfftSetPlanLength_
+#define clfftSetPlanOutStride clfftSetPlanOutStride_
+#define clfftSetPlanPrecision clfftSetPlanPrecision_
+#define clfftSetPlanScale clfftSetPlanScale_
+#define clfftSetPlanTransposeResult clfftSetPlanTransposeResult_
+#define clfftSetResultLocation clfftSetResultLocation_
+#define clfftSetup clfftSetup_
+#define clfftTeardown clfftTeardown_
+
+#include <clFFT.h>
+
+// generated by parser_clfft.py
+#undef clfftBakePlan
+#define clfftBakePlan clfftBakePlan_pfn
+#undef clfftCopyPlan
+//#define clfftCopyPlan clfftCopyPlan_pfn
+#undef clfftCreateDefaultPlan
+#define clfftCreateDefaultPlan clfftCreateDefaultPlan_pfn
+#undef clfftDestroyPlan
+#define clfftDestroyPlan clfftDestroyPlan_pfn
+#undef clfftEnqueueTransform
+#define clfftEnqueueTransform clfftEnqueueTransform_pfn
+#undef clfftGetLayout
+//#define clfftGetLayout clfftGetLayout_pfn
+#undef clfftGetPlanBatchSize
+//#define clfftGetPlanBatchSize clfftGetPlanBatchSize_pfn
+#undef clfftGetPlanContext
+//#define clfftGetPlanContext clfftGetPlanContext_pfn
+#undef clfftGetPlanDim
+//#define clfftGetPlanDim clfftGetPlanDim_pfn
+#undef clfftGetPlanDistance
+//#define clfftGetPlanDistance clfftGetPlanDistance_pfn
+#undef clfftGetPlanInStride
+//#define clfftGetPlanInStride clfftGetPlanInStride_pfn
+#undef clfftGetPlanLength
+//#define clfftGetPlanLength clfftGetPlanLength_pfn
+#undef clfftGetPlanOutStride
+//#define clfftGetPlanOutStride clfftGetPlanOutStride_pfn
+#undef clfftGetPlanPrecision
+//#define clfftGetPlanPrecision clfftGetPlanPrecision_pfn
+#undef clfftGetPlanScale
+//#define clfftGetPlanScale clfftGetPlanScale_pfn
+#undef clfftGetPlanTransposeResult
+//#define clfftGetPlanTransposeResult clfftGetPlanTransposeResult_pfn
+#undef clfftGetResultLocation
+//#define clfftGetResultLocation clfftGetResultLocation_pfn
+#undef clfftGetTmpBufSize
+#define clfftGetTmpBufSize clfftGetTmpBufSize_pfn
+#undef clfftGetVersion
+#define clfftGetVersion clfftGetVersion_pfn
+#undef clfftSetLayout
+#define clfftSetLayout clfftSetLayout_pfn
+#undef clfftSetPlanBatchSize
+#define clfftSetPlanBatchSize clfftSetPlanBatchSize_pfn
+#undef clfftSetPlanCallback
+//#define clfftSetPlanCallback clfftSetPlanCallback_pfn
+#undef clfftSetPlanDim
+//#define clfftSetPlanDim clfftSetPlanDim_pfn
+#undef clfftSetPlanDistance
+#define clfftSetPlanDistance clfftSetPlanDistance_pfn
+#undef clfftSetPlanInStride
+#define clfftSetPlanInStride clfftSetPlanInStride_pfn
+#undef clfftSetPlanLength
+//#define clfftSetPlanLength clfftSetPlanLength_pfn
+#undef clfftSetPlanOutStride
+#define clfftSetPlanOutStride clfftSetPlanOutStride_pfn
+#undef clfftSetPlanPrecision
+#define clfftSetPlanPrecision clfftSetPlanPrecision_pfn
+#undef clfftSetPlanScale
+#define clfftSetPlanScale clfftSetPlanScale_pfn
+#undef clfftSetPlanTransposeResult
+//#define clfftSetPlanTransposeResult clfftSetPlanTransposeResult_pfn
+#undef clfftSetResultLocation
+#define clfftSetResultLocation clfftSetResultLocation_pfn
+#undef clfftSetup
+#define clfftSetup clfftSetup_pfn
+#undef clfftTeardown
+#define clfftTeardown clfftTeardown_pfn
+
+// generated by parser_clfft.py
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftBakePlan)(clfftPlanHandle plHandle, cl_uint numQueues, cl_command_queue* commQueueFFT, void (CL_CALLBACK* pfn_notify) (clfftPlanHandle plHandle, void* user_data), void* user_data);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftCopyPlan)(clfftPlanHandle* out_plHandle, cl_context new_context, clfftPlanHandle in_plHandle);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftCreateDefaultPlan)(clfftPlanHandle* plHandle, cl_context context, const clfftDim dim, const size_t* clLengths);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftDestroyPlan)(clfftPlanHandle* plHandle);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftEnqueueTransform)(clfftPlanHandle plHandle, clfftDirection dir, cl_uint numQueuesAndEvents, cl_command_queue* commQueues, cl_uint numWaitEvents, const cl_event* waitEvents, cl_event* outEvents, cl_mem* inputBuffers, cl_mem* outputBuffers, cl_mem tmpBuffer);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetLayout)(const clfftPlanHandle plHandle, clfftLayout* iLayout, clfftLayout* oLayout);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanBatchSize)(const clfftPlanHandle plHandle, size_t* batchSize);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanContext)(const clfftPlanHandle plHandle, cl_context* context);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanDim)(const clfftPlanHandle plHandle, clfftDim* dim, cl_uint* size);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanDistance)(const clfftPlanHandle plHandle, size_t* iDist, size_t* oDist);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanInStride)(const clfftPlanHandle plHandle, const clfftDim dim, size_t* clStrides);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanLength)(const clfftPlanHandle plHandle, const clfftDim dim, size_t* clLengths);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanOutStride)(const clfftPlanHandle plHandle, const clfftDim dim, size_t* clStrides);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanPrecision)(const clfftPlanHandle plHandle, clfftPrecision* precision);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanScale)(const clfftPlanHandle plHandle, clfftDirection dir, cl_float* scale);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetPlanTransposeResult)(const clfftPlanHandle plHandle, clfftResultTransposed* transposed);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetResultLocation)(const clfftPlanHandle plHandle, clfftResultLocation* placeness);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetTmpBufSize)(const clfftPlanHandle plHandle, size_t* buffersize);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetLayout)(clfftPlanHandle plHandle, clfftLayout iLayout, clfftLayout oLayout);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanBatchSize)(clfftPlanHandle plHandle, size_t batchSize);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanCallback)(clfftPlanHandle plHandle, const char* funcName, const char* funcString, int localMemSize, clfftCallbackType callbackType, cl_mem* userdata, int numUserdataBuffers);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanDim)(clfftPlanHandle plHandle, const clfftDim dim);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanDistance)(clfftPlanHandle plHandle, size_t iDist, size_t oDist);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanInStride)(clfftPlanHandle plHandle, const clfftDim dim, size_t* clStrides);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanLength)(clfftPlanHandle plHandle, const clfftDim dim, const size_t* clLengths);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanOutStride)(clfftPlanHandle plHandle, const clfftDim dim, size_t* clStrides);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanPrecision)(clfftPlanHandle plHandle, clfftPrecision precision);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanScale)(clfftPlanHandle plHandle, clfftDirection dir, cl_float scale);
+//extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetPlanTransposeResult)(clfftPlanHandle plHandle, clfftResultTransposed transposed);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetResultLocation)(clfftPlanHandle plHandle, clfftResultLocation placeness);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftSetup)(const clfftSetupData* setupData);
+extern CL_RUNTIME_EXPORT clfftStatus (*clfftTeardown)();
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp
new file mode 100644
index 0000000..28618a1
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp
@@ -0,0 +1,371 @@
+//
+// AUTOGENERATED, DO NOT EDIT
+//
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
+#error "Invalid usage"
+#endif
+
+// generated by parser_cl.py
+#define clBuildProgram clBuildProgram_
+#define clCompileProgram clCompileProgram_
+#define clCreateBuffer clCreateBuffer_
+#define clCreateCommandQueue clCreateCommandQueue_
+#define clCreateContext clCreateContext_
+#define clCreateContextFromType clCreateContextFromType_
+#define clCreateImage clCreateImage_
+#define clCreateImage2D clCreateImage2D_
+#define clCreateImage3D clCreateImage3D_
+#define clCreateKernel clCreateKernel_
+#define clCreateKernelsInProgram clCreateKernelsInProgram_
+#define clCreateProgramWithBinary clCreateProgramWithBinary_
+#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_
+#define clCreateProgramWithSource clCreateProgramWithSource_
+#define clCreateSampler clCreateSampler_
+#define clCreateSubBuffer clCreateSubBuffer_
+#define clCreateSubDevices clCreateSubDevices_
+#define clCreateUserEvent clCreateUserEvent_
+#define clEnqueueBarrier clEnqueueBarrier_
+#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_
+#define clEnqueueCopyBuffer clEnqueueCopyBuffer_
+#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_
+#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_
+#define clEnqueueCopyImage clEnqueueCopyImage_
+#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_
+#define clEnqueueFillBuffer clEnqueueFillBuffer_
+#define clEnqueueFillImage clEnqueueFillImage_
+#define clEnqueueMapBuffer clEnqueueMapBuffer_
+#define clEnqueueMapImage clEnqueueMapImage_
+#define clEnqueueMarker clEnqueueMarker_
+#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_
+#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_
+#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_
+#define clEnqueueNativeKernel clEnqueueNativeKernel_
+#define clEnqueueReadBuffer clEnqueueReadBuffer_
+#define clEnqueueReadBufferRect clEnqueueReadBufferRect_
+#define clEnqueueReadImage clEnqueueReadImage_
+#define clEnqueueTask clEnqueueTask_
+#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_
+#define clEnqueueWaitForEvents clEnqueueWaitForEvents_
+#define clEnqueueWriteBuffer clEnqueueWriteBuffer_
+#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_
+#define clEnqueueWriteImage clEnqueueWriteImage_
+#define clFinish clFinish_
+#define clFlush clFlush_
+#define clGetCommandQueueInfo clGetCommandQueueInfo_
+#define clGetContextInfo clGetContextInfo_
+#define clGetDeviceIDs clGetDeviceIDs_
+#define clGetDeviceInfo clGetDeviceInfo_
+#define clGetEventInfo clGetEventInfo_
+#define clGetEventProfilingInfo clGetEventProfilingInfo_
+#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_
+#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_
+#define clGetImageInfo clGetImageInfo_
+#define clGetKernelArgInfo clGetKernelArgInfo_
+#define clGetKernelInfo clGetKernelInfo_
+#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_
+#define clGetMemObjectInfo clGetMemObjectInfo_
+#define clGetPlatformIDs clGetPlatformIDs_
+#define clGetPlatformInfo clGetPlatformInfo_
+#define clGetProgramBuildInfo clGetProgramBuildInfo_
+#define clGetProgramInfo clGetProgramInfo_
+#define clGetSamplerInfo clGetSamplerInfo_
+#define clGetSupportedImageFormats clGetSupportedImageFormats_
+#define clLinkProgram clLinkProgram_
+#define clReleaseCommandQueue clReleaseCommandQueue_
+#define clReleaseContext clReleaseContext_
+#define clReleaseDevice clReleaseDevice_
+#define clReleaseEvent clReleaseEvent_
+#define clReleaseKernel clReleaseKernel_
+#define clReleaseMemObject clReleaseMemObject_
+#define clReleaseProgram clReleaseProgram_
+#define clReleaseSampler clReleaseSampler_
+#define clRetainCommandQueue clRetainCommandQueue_
+#define clRetainContext clRetainContext_
+#define clRetainDevice clRetainDevice_
+#define clRetainEvent clRetainEvent_
+#define clRetainKernel clRetainKernel_
+#define clRetainMemObject clRetainMemObject_
+#define clRetainProgram clRetainProgram_
+#define clRetainSampler clRetainSampler_
+#define clSetEventCallback clSetEventCallback_
+#define clSetKernelArg clSetKernelArg_
+#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_
+#define clSetUserEventStatus clSetUserEventStatus_
+#define clUnloadCompiler clUnloadCompiler_
+#define clUnloadPlatformCompiler clUnloadPlatformCompiler_
+#define clWaitForEvents clWaitForEvents_
+
+#if defined __APPLE__
+#define CL_SILENCE_DEPRECATION
+#include <OpenCL/cl.h>
+#else
+#include <CL/cl.h>
+#endif
+
+// generated by parser_cl.py
+#undef clBuildProgram
+#define clBuildProgram clBuildProgram_pfn
+#undef clCompileProgram
+#define clCompileProgram clCompileProgram_pfn
+#undef clCreateBuffer
+#define clCreateBuffer clCreateBuffer_pfn
+#undef clCreateCommandQueue
+#define clCreateCommandQueue clCreateCommandQueue_pfn
+#undef clCreateContext
+#define clCreateContext clCreateContext_pfn
+#undef clCreateContextFromType
+#define clCreateContextFromType clCreateContextFromType_pfn
+#undef clCreateImage
+#define clCreateImage clCreateImage_pfn
+#undef clCreateImage2D
+#define clCreateImage2D clCreateImage2D_pfn
+#undef clCreateImage3D
+#define clCreateImage3D clCreateImage3D_pfn
+#undef clCreateKernel
+#define clCreateKernel clCreateKernel_pfn
+#undef clCreateKernelsInProgram
+#define clCreateKernelsInProgram clCreateKernelsInProgram_pfn
+#undef clCreateProgramWithBinary
+#define clCreateProgramWithBinary clCreateProgramWithBinary_pfn
+#undef clCreateProgramWithBuiltInKernels
+#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_pfn
+#undef clCreateProgramWithSource
+#define clCreateProgramWithSource clCreateProgramWithSource_pfn
+#undef clCreateSampler
+#define clCreateSampler clCreateSampler_pfn
+#undef clCreateSubBuffer
+#define clCreateSubBuffer clCreateSubBuffer_pfn
+#undef clCreateSubDevices
+#define clCreateSubDevices clCreateSubDevices_pfn
+#undef clCreateUserEvent
+#define clCreateUserEvent clCreateUserEvent_pfn
+#undef clEnqueueBarrier
+#define clEnqueueBarrier clEnqueueBarrier_pfn
+#undef clEnqueueBarrierWithWaitList
+#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_pfn
+#undef clEnqueueCopyBuffer
+#define clEnqueueCopyBuffer clEnqueueCopyBuffer_pfn
+#undef clEnqueueCopyBufferRect
+#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_pfn
+#undef clEnqueueCopyBufferToImage
+#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_pfn
+#undef clEnqueueCopyImage
+#define clEnqueueCopyImage clEnqueueCopyImage_pfn
+#undef clEnqueueCopyImageToBuffer
+#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_pfn
+#undef clEnqueueFillBuffer
+#define clEnqueueFillBuffer clEnqueueFillBuffer_pfn
+#undef clEnqueueFillImage
+#define clEnqueueFillImage clEnqueueFillImage_pfn
+#undef clEnqueueMapBuffer
+#define clEnqueueMapBuffer clEnqueueMapBuffer_pfn
+#undef clEnqueueMapImage
+#define clEnqueueMapImage clEnqueueMapImage_pfn
+#undef clEnqueueMarker
+#define clEnqueueMarker clEnqueueMarker_pfn
+#undef clEnqueueMarkerWithWaitList
+#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_pfn
+#undef clEnqueueMigrateMemObjects
+#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_pfn
+#undef clEnqueueNDRangeKernel
+#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_pfn
+#undef clEnqueueNativeKernel
+#define clEnqueueNativeKernel clEnqueueNativeKernel_pfn
+#undef clEnqueueReadBuffer
+#define clEnqueueReadBuffer clEnqueueReadBuffer_pfn
+#undef clEnqueueReadBufferRect
+#define clEnqueueReadBufferRect clEnqueueReadBufferRect_pfn
+#undef clEnqueueReadImage
+#define clEnqueueReadImage clEnqueueReadImage_pfn
+#undef clEnqueueTask
+#define clEnqueueTask clEnqueueTask_pfn
+#undef clEnqueueUnmapMemObject
+#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_pfn
+#undef clEnqueueWaitForEvents
+#define clEnqueueWaitForEvents clEnqueueWaitForEvents_pfn
+#undef clEnqueueWriteBuffer
+#define clEnqueueWriteBuffer clEnqueueWriteBuffer_pfn
+#undef clEnqueueWriteBufferRect
+#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_pfn
+#undef clEnqueueWriteImage
+#define clEnqueueWriteImage clEnqueueWriteImage_pfn
+#undef clFinish
+#define clFinish clFinish_pfn
+#undef clFlush
+#define clFlush clFlush_pfn
+#undef clGetCommandQueueInfo
+#define clGetCommandQueueInfo clGetCommandQueueInfo_pfn
+#undef clGetContextInfo
+#define clGetContextInfo clGetContextInfo_pfn
+#undef clGetDeviceIDs
+#define clGetDeviceIDs clGetDeviceIDs_pfn
+#undef clGetDeviceInfo
+#define clGetDeviceInfo clGetDeviceInfo_pfn
+#undef clGetEventInfo
+#define clGetEventInfo clGetEventInfo_pfn
+#undef clGetEventProfilingInfo
+#define clGetEventProfilingInfo clGetEventProfilingInfo_pfn
+#undef clGetExtensionFunctionAddress
+#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_pfn
+#undef clGetExtensionFunctionAddressForPlatform
+#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_pfn
+#undef clGetImageInfo
+#define clGetImageInfo clGetImageInfo_pfn
+#undef clGetKernelArgInfo
+#define clGetKernelArgInfo clGetKernelArgInfo_pfn
+#undef clGetKernelInfo
+#define clGetKernelInfo clGetKernelInfo_pfn
+#undef clGetKernelWorkGroupInfo
+#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_pfn
+#undef clGetMemObjectInfo
+#define clGetMemObjectInfo clGetMemObjectInfo_pfn
+#undef clGetPlatformIDs
+#define clGetPlatformIDs clGetPlatformIDs_pfn
+#undef clGetPlatformInfo
+#define clGetPlatformInfo clGetPlatformInfo_pfn
+#undef clGetProgramBuildInfo
+#define clGetProgramBuildInfo clGetProgramBuildInfo_pfn
+#undef clGetProgramInfo
+#define clGetProgramInfo clGetProgramInfo_pfn
+#undef clGetSamplerInfo
+#define clGetSamplerInfo clGetSamplerInfo_pfn
+#undef clGetSupportedImageFormats
+#define clGetSupportedImageFormats clGetSupportedImageFormats_pfn
+#undef clLinkProgram
+#define clLinkProgram clLinkProgram_pfn
+#undef clReleaseCommandQueue
+#define clReleaseCommandQueue clReleaseCommandQueue_pfn
+#undef clReleaseContext
+#define clReleaseContext clReleaseContext_pfn
+#undef clReleaseDevice
+#define clReleaseDevice clReleaseDevice_pfn
+#undef clReleaseEvent
+#define clReleaseEvent clReleaseEvent_pfn
+#undef clReleaseKernel
+#define clReleaseKernel clReleaseKernel_pfn
+#undef clReleaseMemObject
+#define clReleaseMemObject clReleaseMemObject_pfn
+#undef clReleaseProgram
+#define clReleaseProgram clReleaseProgram_pfn
+#undef clReleaseSampler
+#define clReleaseSampler clReleaseSampler_pfn
+#undef clRetainCommandQueue
+#define clRetainCommandQueue clRetainCommandQueue_pfn
+#undef clRetainContext
+#define clRetainContext clRetainContext_pfn
+#undef clRetainDevice
+#define clRetainDevice clRetainDevice_pfn
+#undef clRetainEvent
+#define clRetainEvent clRetainEvent_pfn
+#undef clRetainKernel
+#define clRetainKernel clRetainKernel_pfn
+#undef clRetainMemObject
+#define clRetainMemObject clRetainMemObject_pfn
+#undef clRetainProgram
+#define clRetainProgram clRetainProgram_pfn
+#undef clRetainSampler
+#define clRetainSampler clRetainSampler_pfn
+#undef clSetEventCallback
+#define clSetEventCallback clSetEventCallback_pfn
+#undef clSetKernelArg
+#define clSetKernelArg clSetKernelArg_pfn
+#undef clSetMemObjectDestructorCallback
+#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_pfn
+#undef clSetUserEventStatus
+#define clSetUserEventStatus clSetUserEventStatus_pfn
+#undef clUnloadCompiler
+#define clUnloadCompiler clUnloadCompiler_pfn
+#undef clUnloadPlatformCompiler
+#define clUnloadPlatformCompiler clUnloadPlatformCompiler_pfn
+#undef clWaitForEvents
+#define clWaitForEvents clWaitForEvents_pfn
+
+// generated by parser_cl.py
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clBuildProgram)(cl_program, cl_uint, const cl_device_id*, const char*, void (CL_CALLBACK*) (cl_program, void*), void*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCompileProgram)(cl_program, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, const char**, void (CL_CALLBACK*) (cl_program, void*), void*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateBuffer)(cl_context, cl_mem_flags, size_t, void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_command_queue (CL_API_CALL*clCreateCommandQueue)(cl_context, cl_device_id, cl_command_queue_properties, cl_int*);
+extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContext)(const cl_context_properties*, cl_uint, const cl_device_id*, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContextFromType)(const cl_context_properties*, cl_device_type, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage)(cl_context, cl_mem_flags, const cl_image_format*, const cl_image_desc*, void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage2D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage3D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, size_t, size_t, void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_kernel (CL_API_CALL*clCreateKernel)(cl_program, const char*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateKernelsInProgram)(cl_program, cl_uint, cl_kernel*, cl_uint*);
+extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBinary)(cl_context, cl_uint, const cl_device_id*, const size_t*, const unsigned char**, cl_int*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBuiltInKernels)(cl_context, cl_uint, const cl_device_id*, const char*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithSource)(cl_context, cl_uint, const char**, const size_t*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_sampler (CL_API_CALL*clCreateSampler)(cl_context, cl_bool, cl_addressing_mode, cl_filter_mode, cl_int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateSubBuffer)(cl_mem, cl_mem_flags, cl_buffer_create_type, const void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateSubDevices)(cl_device_id, const cl_device_partition_property*, cl_uint, cl_device_id*, cl_uint*);
+extern CL_RUNTIME_EXPORT cl_event (CL_API_CALL*clCreateUserEvent)(cl_context, cl_int*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrier)(cl_command_queue);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrierWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBuffer)(cl_command_queue, cl_mem, cl_mem, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferRect)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferToImage)(cl_command_queue, cl_mem, cl_mem, size_t, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImage)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImageToBuffer)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, size_t, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillBuffer)(cl_command_queue, cl_mem, const void*, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillImage)(cl_command_queue, cl_mem, const void*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapBuffer)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, size_t, size_t, cl_uint, const cl_event*, cl_event*, cl_int*);
+extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapImage)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, const size_t*, const size_t*, size_t*, size_t*, cl_uint, const cl_event*, cl_event*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarker)(cl_command_queue, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarkerWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMigrateMemObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_mem_migration_flags, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNativeKernel)(cl_command_queue, void (CL_CALLBACK*) (void*), void*, size_t, cl_uint, const cl_mem*, const void**, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueTask)(cl_command_queue, cl_kernel, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueUnmapMemObject)(cl_command_queue, cl_mem, void*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWaitForEvents)(cl_command_queue, cl_uint, const cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFinish)(cl_command_queue);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFlush)(cl_command_queue);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetCommandQueueInfo)(cl_command_queue, cl_command_queue_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetContextInfo)(cl_context, cl_context_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceIDs)(cl_platform_id, cl_device_type, cl_uint, cl_device_id*, cl_uint*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceInfo)(cl_device_id, cl_device_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventInfo)(cl_event, cl_event_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventProfilingInfo)(cl_event, cl_profiling_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddress)(const char*);
+extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddressForPlatform)(cl_platform_id, const char*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetImageInfo)(cl_mem, cl_image_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelArgInfo)(cl_kernel, cl_uint, cl_kernel_arg_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelInfo)(cl_kernel, cl_kernel_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelWorkGroupInfo)(cl_kernel, cl_device_id, cl_kernel_work_group_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetMemObjectInfo)(cl_mem, cl_mem_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformIDs)(cl_uint, cl_platform_id*, cl_uint*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformInfo)(cl_platform_id, cl_platform_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramBuildInfo)(cl_program, cl_device_id, cl_program_build_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramInfo)(cl_program, cl_program_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSamplerInfo)(cl_sampler, cl_sampler_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSupportedImageFormats)(cl_context, cl_mem_flags, cl_mem_object_type, cl_uint, cl_image_format*, cl_uint*);
+extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clLinkProgram)(cl_context, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, void (CL_CALLBACK*) (cl_program, void*), void*, cl_int*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseCommandQueue)(cl_command_queue);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseContext)(cl_context);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseDevice)(cl_device_id);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseEvent)(cl_event);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseKernel)(cl_kernel);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseMemObject)(cl_mem);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseProgram)(cl_program);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseSampler)(cl_sampler);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainCommandQueue)(cl_command_queue);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainContext)(cl_context);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainDevice)(cl_device_id);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainEvent)(cl_event);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainKernel)(cl_kernel);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainMemObject)(cl_mem);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainProgram)(cl_program);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainSampler)(cl_sampler);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetEventCallback)(cl_event, cl_int, void (CL_CALLBACK*) (cl_event, cl_int, void*), void*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetKernelArg)(cl_kernel, cl_uint, size_t, const void*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetMemObjectDestructorCallback)(cl_mem, void (CL_CALLBACK*) (cl_mem, void*), void*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetUserEventStatus)(cl_event, cl_int);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadCompiler)();
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadPlatformCompiler)(cl_platform_id);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clWaitForEvents)(cl_uint, const cl_event*);
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp
new file mode 100644
index 0000000..216b22b
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp
@@ -0,0 +1,272 @@
+//
+// AUTOGENERATED, DO NOT EDIT
+//
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
+#error "Invalid usage"
+#endif
+
+// generated by parser_cl.py
+#undef clBuildProgram
+#define clBuildProgram clBuildProgram_fn
+inline cl_int clBuildProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, void (CL_CALLBACK*p4) (cl_program, void*), void* p5) { return clBuildProgram_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clCompileProgram
+#define clCompileProgram clCompileProgram_fn
+inline cl_int clCompileProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, const char** p6, void (CL_CALLBACK*p7) (cl_program, void*), void* p8) { return clCompileProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clCreateBuffer
+#define clCreateBuffer clCreateBuffer_fn
+inline cl_mem clCreateBuffer(cl_context p0, cl_mem_flags p1, size_t p2, void* p3, cl_int* p4) { return clCreateBuffer_pfn(p0, p1, p2, p3, p4); }
+#undef clCreateCommandQueue
+#define clCreateCommandQueue clCreateCommandQueue_fn
+inline cl_command_queue clCreateCommandQueue(cl_context p0, cl_device_id p1, cl_command_queue_properties p2, cl_int* p3) { return clCreateCommandQueue_pfn(p0, p1, p2, p3); }
+#undef clCreateContext
+#define clCreateContext clCreateContext_fn
+inline cl_context clCreateContext(const cl_context_properties* p0, cl_uint p1, const cl_device_id* p2, void (CL_CALLBACK*p3) (const char*, const void*, size_t, void*), void* p4, cl_int* p5) { return clCreateContext_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clCreateContextFromType
+#define clCreateContextFromType clCreateContextFromType_fn
+inline cl_context clCreateContextFromType(const cl_context_properties* p0, cl_device_type p1, void (CL_CALLBACK*p2) (const char*, const void*, size_t, void*), void* p3, cl_int* p4) { return clCreateContextFromType_pfn(p0, p1, p2, p3, p4); }
+#undef clCreateImage
+#define clCreateImage clCreateImage_fn
+inline cl_mem clCreateImage(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, const cl_image_desc* p3, void* p4, cl_int* p5) { return clCreateImage_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clCreateImage2D
+#define clCreateImage2D clCreateImage2D_fn
+inline cl_mem clCreateImage2D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, void* p6, cl_int* p7) { return clCreateImage2D_pfn(p0, p1, p2, p3, p4, p5, p6, p7); }
+#undef clCreateImage3D
+#define clCreateImage3D clCreateImage3D_fn
+inline cl_mem clCreateImage3D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, size_t p6, size_t p7, void* p8, cl_int* p9) { return clCreateImage3D_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); }
+#undef clCreateKernel
+#define clCreateKernel clCreateKernel_fn
+inline cl_kernel clCreateKernel(cl_program p0, const char* p1, cl_int* p2) { return clCreateKernel_pfn(p0, p1, p2); }
+#undef clCreateKernelsInProgram
+#define clCreateKernelsInProgram clCreateKernelsInProgram_fn
+inline cl_int clCreateKernelsInProgram(cl_program p0, cl_uint p1, cl_kernel* p2, cl_uint* p3) { return clCreateKernelsInProgram_pfn(p0, p1, p2, p3); }
+#undef clCreateProgramWithBinary
+#define clCreateProgramWithBinary clCreateProgramWithBinary_fn
+inline cl_program clCreateProgramWithBinary(cl_context p0, cl_uint p1, const cl_device_id* p2, const size_t* p3, const unsigned char** p4, cl_int* p5, cl_int* p6) { return clCreateProgramWithBinary_pfn(p0, p1, p2, p3, p4, p5, p6); }
+#undef clCreateProgramWithBuiltInKernels
+#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_fn
+inline cl_program clCreateProgramWithBuiltInKernels(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_int* p4) { return clCreateProgramWithBuiltInKernels_pfn(p0, p1, p2, p3, p4); }
+#undef clCreateProgramWithSource
+#define clCreateProgramWithSource clCreateProgramWithSource_fn
+inline cl_program clCreateProgramWithSource(cl_context p0, cl_uint p1, const char** p2, const size_t* p3, cl_int* p4) { return clCreateProgramWithSource_pfn(p0, p1, p2, p3, p4); }
+#undef clCreateSampler
+#define clCreateSampler clCreateSampler_fn
+inline cl_sampler clCreateSampler(cl_context p0, cl_bool p1, cl_addressing_mode p2, cl_filter_mode p3, cl_int* p4) { return clCreateSampler_pfn(p0, p1, p2, p3, p4); }
+#undef clCreateSubBuffer
+#define clCreateSubBuffer clCreateSubBuffer_fn
+inline cl_mem clCreateSubBuffer(cl_mem p0, cl_mem_flags p1, cl_buffer_create_type p2, const void* p3, cl_int* p4) { return clCreateSubBuffer_pfn(p0, p1, p2, p3, p4); }
+#undef clCreateSubDevices
+#define clCreateSubDevices clCreateSubDevices_fn
+inline cl_int clCreateSubDevices(cl_device_id p0, const cl_device_partition_property* p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clCreateSubDevices_pfn(p0, p1, p2, p3, p4); }
+#undef clCreateUserEvent
+#define clCreateUserEvent clCreateUserEvent_fn
+inline cl_event clCreateUserEvent(cl_context p0, cl_int* p1) { return clCreateUserEvent_pfn(p0, p1); }
+#undef clEnqueueBarrier
+#define clEnqueueBarrier clEnqueueBarrier_fn
+inline cl_int clEnqueueBarrier(cl_command_queue p0) { return clEnqueueBarrier_pfn(p0); }
+#undef clEnqueueBarrierWithWaitList
+#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_fn
+inline cl_int clEnqueueBarrierWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueBarrierWithWaitList_pfn(p0, p1, p2, p3); }
+#undef clEnqueueCopyBuffer
+#define clEnqueueCopyBuffer clEnqueueCopyBuffer_fn
+inline cl_int clEnqueueCopyBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueCopyBufferRect
+#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_fn
+inline cl_int clEnqueueCopyBufferRect(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, cl_uint p10, const cl_event* p11, cl_event* p12) { return clEnqueueCopyBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12); }
+#undef clEnqueueCopyBufferToImage
+#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_fn
+inline cl_int clEnqueueCopyBufferToImage(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBufferToImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueCopyImage
+#define clEnqueueCopyImage clEnqueueCopyImage_fn
+inline cl_int clEnqueueCopyImage(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueCopyImageToBuffer
+#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_fn
+inline cl_int clEnqueueCopyImageToBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImageToBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueFillBuffer
+#define clEnqueueFillBuffer clEnqueueFillBuffer_fn
+inline cl_int clEnqueueFillBuffer(cl_command_queue p0, cl_mem p1, const void* p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueFillBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueFillImage
+#define clEnqueueFillImage clEnqueueFillImage_fn
+inline cl_int clEnqueueFillImage(cl_command_queue p0, cl_mem p1, const void* p2, const size_t* p3, const size_t* p4, cl_uint p5, const cl_event* p6, cl_event* p7) { return clEnqueueFillImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7); }
+#undef clEnqueueMapBuffer
+#define clEnqueueMapBuffer clEnqueueMapBuffer_fn
+inline void* clEnqueueMapBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8, cl_int* p9) { return clEnqueueMapBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); }
+#undef clEnqueueMapImage
+#define clEnqueueMapImage clEnqueueMapImage_fn
+inline void* clEnqueueMapImage(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, const size_t* p4, const size_t* p5, size_t* p6, size_t* p7, cl_uint p8, const cl_event* p9, cl_event* p10, cl_int* p11) { return clEnqueueMapImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11); }
+#undef clEnqueueMarker
+#define clEnqueueMarker clEnqueueMarker_fn
+inline cl_int clEnqueueMarker(cl_command_queue p0, cl_event* p1) { return clEnqueueMarker_pfn(p0, p1); }
+#undef clEnqueueMarkerWithWaitList
+#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_fn
+inline cl_int clEnqueueMarkerWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueMarkerWithWaitList_pfn(p0, p1, p2, p3); }
+#undef clEnqueueMigrateMemObjects
+#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_fn
+inline cl_int clEnqueueMigrateMemObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_mem_migration_flags p3, cl_uint p4, const cl_event* p5, cl_event* p6) { return clEnqueueMigrateMemObjects_pfn(p0, p1, p2, p3, p4, p5, p6); }
+#undef clEnqueueNDRangeKernel
+#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_fn
+inline cl_int clEnqueueNDRangeKernel(cl_command_queue p0, cl_kernel p1, cl_uint p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueNDRangeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueNativeKernel
+#define clEnqueueNativeKernel clEnqueueNativeKernel_fn
+inline cl_int clEnqueueNativeKernel(cl_command_queue p0, void (CL_CALLBACK*p1) (void*), void* p2, size_t p3, cl_uint p4, const cl_mem* p5, const void** p6, cl_uint p7, const cl_event* p8, cl_event* p9) { return clEnqueueNativeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); }
+#undef clEnqueueReadBuffer
+#define clEnqueueReadBuffer clEnqueueReadBuffer_fn
+inline cl_int clEnqueueReadBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueReadBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueReadBufferRect
+#define clEnqueueReadBufferRect clEnqueueReadBufferRect_fn
+inline cl_int clEnqueueReadBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueReadBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); }
+#undef clEnqueueReadImage
+#define clEnqueueReadImage clEnqueueReadImage_fn
+inline cl_int clEnqueueReadImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueReadImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); }
+#undef clEnqueueTask
+#define clEnqueueTask clEnqueueTask_fn
+inline cl_int clEnqueueTask(cl_command_queue p0, cl_kernel p1, cl_uint p2, const cl_event* p3, cl_event* p4) { return clEnqueueTask_pfn(p0, p1, p2, p3, p4); }
+#undef clEnqueueUnmapMemObject
+#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_fn
+inline cl_int clEnqueueUnmapMemObject(cl_command_queue p0, cl_mem p1, void* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueUnmapMemObject_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clEnqueueWaitForEvents
+#define clEnqueueWaitForEvents clEnqueueWaitForEvents_fn
+inline cl_int clEnqueueWaitForEvents(cl_command_queue p0, cl_uint p1, const cl_event* p2) { return clEnqueueWaitForEvents_pfn(p0, p1, p2); }
+#undef clEnqueueWriteBuffer
+#define clEnqueueWriteBuffer clEnqueueWriteBuffer_fn
+inline cl_int clEnqueueWriteBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, const void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueWriteBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clEnqueueWriteBufferRect
+#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_fn
+inline cl_int clEnqueueWriteBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, const void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueWriteBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); }
+#undef clEnqueueWriteImage
+#define clEnqueueWriteImage clEnqueueWriteImage_fn
+inline cl_int clEnqueueWriteImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, const void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueWriteImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); }
+#undef clFinish
+#define clFinish clFinish_fn
+inline cl_int clFinish(cl_command_queue p0) { return clFinish_pfn(p0); }
+#undef clFlush
+#define clFlush clFlush_fn
+inline cl_int clFlush(cl_command_queue p0) { return clFlush_pfn(p0); }
+#undef clGetCommandQueueInfo
+#define clGetCommandQueueInfo clGetCommandQueueInfo_fn
+inline cl_int clGetCommandQueueInfo(cl_command_queue p0, cl_command_queue_info p1, size_t p2, void* p3, size_t* p4) { return clGetCommandQueueInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetContextInfo
+#define clGetContextInfo clGetContextInfo_fn
+inline cl_int clGetContextInfo(cl_context p0, cl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetContextInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetDeviceIDs
+#define clGetDeviceIDs clGetDeviceIDs_fn
+inline cl_int clGetDeviceIDs(cl_platform_id p0, cl_device_type p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clGetDeviceIDs_pfn(p0, p1, p2, p3, p4); }
+#undef clGetDeviceInfo
+#define clGetDeviceInfo clGetDeviceInfo_fn
+inline cl_int clGetDeviceInfo(cl_device_id p0, cl_device_info p1, size_t p2, void* p3, size_t* p4) { return clGetDeviceInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetEventInfo
+#define clGetEventInfo clGetEventInfo_fn
+inline cl_int clGetEventInfo(cl_event p0, cl_event_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetEventProfilingInfo
+#define clGetEventProfilingInfo clGetEventProfilingInfo_fn
+inline cl_int clGetEventProfilingInfo(cl_event p0, cl_profiling_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventProfilingInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetExtensionFunctionAddress
+#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_fn
+inline void* clGetExtensionFunctionAddress(const char* p0) { return clGetExtensionFunctionAddress_pfn(p0); }
+#undef clGetExtensionFunctionAddressForPlatform
+#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_fn
+inline void* clGetExtensionFunctionAddressForPlatform(cl_platform_id p0, const char* p1) { return clGetExtensionFunctionAddressForPlatform_pfn(p0, p1); }
+#undef clGetImageInfo
+#define clGetImageInfo clGetImageInfo_fn
+inline cl_int clGetImageInfo(cl_mem p0, cl_image_info p1, size_t p2, void* p3, size_t* p4) { return clGetImageInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetKernelArgInfo
+#define clGetKernelArgInfo clGetKernelArgInfo_fn
+inline cl_int clGetKernelArgInfo(cl_kernel p0, cl_uint p1, cl_kernel_arg_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelArgInfo_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clGetKernelInfo
+#define clGetKernelInfo clGetKernelInfo_fn
+inline cl_int clGetKernelInfo(cl_kernel p0, cl_kernel_info p1, size_t p2, void* p3, size_t* p4) { return clGetKernelInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetKernelWorkGroupInfo
+#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_fn
+inline cl_int clGetKernelWorkGroupInfo(cl_kernel p0, cl_device_id p1, cl_kernel_work_group_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelWorkGroupInfo_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clGetMemObjectInfo
+#define clGetMemObjectInfo clGetMemObjectInfo_fn
+inline cl_int clGetMemObjectInfo(cl_mem p0, cl_mem_info p1, size_t p2, void* p3, size_t* p4) { return clGetMemObjectInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetPlatformIDs
+#define clGetPlatformIDs clGetPlatformIDs_fn
+inline cl_int clGetPlatformIDs(cl_uint p0, cl_platform_id* p1, cl_uint* p2) { return clGetPlatformIDs_pfn(p0, p1, p2); }
+#undef clGetPlatformInfo
+#define clGetPlatformInfo clGetPlatformInfo_fn
+inline cl_int clGetPlatformInfo(cl_platform_id p0, cl_platform_info p1, size_t p2, void* p3, size_t* p4) { return clGetPlatformInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetProgramBuildInfo
+#define clGetProgramBuildInfo clGetProgramBuildInfo_fn
+inline cl_int clGetProgramBuildInfo(cl_program p0, cl_device_id p1, cl_program_build_info p2, size_t p3, void* p4, size_t* p5) { return clGetProgramBuildInfo_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clGetProgramInfo
+#define clGetProgramInfo clGetProgramInfo_fn
+inline cl_int clGetProgramInfo(cl_program p0, cl_program_info p1, size_t p2, void* p3, size_t* p4) { return clGetProgramInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetSamplerInfo
+#define clGetSamplerInfo clGetSamplerInfo_fn
+inline cl_int clGetSamplerInfo(cl_sampler p0, cl_sampler_info p1, size_t p2, void* p3, size_t* p4) { return clGetSamplerInfo_pfn(p0, p1, p2, p3, p4); }
+#undef clGetSupportedImageFormats
+#define clGetSupportedImageFormats clGetSupportedImageFormats_fn
+inline cl_int clGetSupportedImageFormats(cl_context p0, cl_mem_flags p1, cl_mem_object_type p2, cl_uint p3, cl_image_format* p4, cl_uint* p5) { return clGetSupportedImageFormats_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clLinkProgram
+#define clLinkProgram clLinkProgram_fn
+inline cl_program clLinkProgram(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, void (CL_CALLBACK*p6) (cl_program, void*), void* p7, cl_int* p8) { return clLinkProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
+#undef clReleaseCommandQueue
+#define clReleaseCommandQueue clReleaseCommandQueue_fn
+inline cl_int clReleaseCommandQueue(cl_command_queue p0) { return clReleaseCommandQueue_pfn(p0); }
+#undef clReleaseContext
+#define clReleaseContext clReleaseContext_fn
+inline cl_int clReleaseContext(cl_context p0) { return clReleaseContext_pfn(p0); }
+#undef clReleaseDevice
+#define clReleaseDevice clReleaseDevice_fn
+inline cl_int clReleaseDevice(cl_device_id p0) { return clReleaseDevice_pfn(p0); }
+#undef clReleaseEvent
+#define clReleaseEvent clReleaseEvent_fn
+inline cl_int clReleaseEvent(cl_event p0) { return clReleaseEvent_pfn(p0); }
+#undef clReleaseKernel
+#define clReleaseKernel clReleaseKernel_fn
+inline cl_int clReleaseKernel(cl_kernel p0) { return clReleaseKernel_pfn(p0); }
+#undef clReleaseMemObject
+#define clReleaseMemObject clReleaseMemObject_fn
+inline cl_int clReleaseMemObject(cl_mem p0) { return clReleaseMemObject_pfn(p0); }
+#undef clReleaseProgram
+#define clReleaseProgram clReleaseProgram_fn
+inline cl_int clReleaseProgram(cl_program p0) { return clReleaseProgram_pfn(p0); }
+#undef clReleaseSampler
+#define clReleaseSampler clReleaseSampler_fn
+inline cl_int clReleaseSampler(cl_sampler p0) { return clReleaseSampler_pfn(p0); }
+#undef clRetainCommandQueue
+#define clRetainCommandQueue clRetainCommandQueue_fn
+inline cl_int clRetainCommandQueue(cl_command_queue p0) { return clRetainCommandQueue_pfn(p0); }
+#undef clRetainContext
+#define clRetainContext clRetainContext_fn
+inline cl_int clRetainContext(cl_context p0) { return clRetainContext_pfn(p0); }
+#undef clRetainDevice
+#define clRetainDevice clRetainDevice_fn
+inline cl_int clRetainDevice(cl_device_id p0) { return clRetainDevice_pfn(p0); }
+#undef clRetainEvent
+#define clRetainEvent clRetainEvent_fn
+inline cl_int clRetainEvent(cl_event p0) { return clRetainEvent_pfn(p0); }
+#undef clRetainKernel
+#define clRetainKernel clRetainKernel_fn
+inline cl_int clRetainKernel(cl_kernel p0) { return clRetainKernel_pfn(p0); }
+#undef clRetainMemObject
+#define clRetainMemObject clRetainMemObject_fn
+inline cl_int clRetainMemObject(cl_mem p0) { return clRetainMemObject_pfn(p0); }
+#undef clRetainProgram
+#define clRetainProgram clRetainProgram_fn
+inline cl_int clRetainProgram(cl_program p0) { return clRetainProgram_pfn(p0); }
+#undef clRetainSampler
+#define clRetainSampler clRetainSampler_fn
+inline cl_int clRetainSampler(cl_sampler p0) { return clRetainSampler_pfn(p0); }
+#undef clSetEventCallback
+#define clSetEventCallback clSetEventCallback_fn
+inline cl_int clSetEventCallback(cl_event p0, cl_int p1, void (CL_CALLBACK*p2) (cl_event, cl_int, void*), void* p3) { return clSetEventCallback_pfn(p0, p1, p2, p3); }
+#undef clSetKernelArg
+#define clSetKernelArg clSetKernelArg_fn
+inline cl_int clSetKernelArg(cl_kernel p0, cl_uint p1, size_t p2, const void* p3) { return clSetKernelArg_pfn(p0, p1, p2, p3); }
+#undef clSetMemObjectDestructorCallback
+#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_fn
+inline cl_int clSetMemObjectDestructorCallback(cl_mem p0, void (CL_CALLBACK*p1) (cl_mem, void*), void* p2) { return clSetMemObjectDestructorCallback_pfn(p0, p1, p2); }
+#undef clSetUserEventStatus
+#define clSetUserEventStatus clSetUserEventStatus_fn
+inline cl_int clSetUserEventStatus(cl_event p0, cl_int p1) { return clSetUserEventStatus_pfn(p0, p1); }
+#undef clUnloadCompiler
+#define clUnloadCompiler clUnloadCompiler_fn
+inline cl_int clUnloadCompiler() { return clUnloadCompiler_pfn(); }
+#undef clUnloadPlatformCompiler
+#define clUnloadPlatformCompiler clUnloadPlatformCompiler_fn
+inline cl_int clUnloadPlatformCompiler(cl_platform_id p0) { return clUnloadPlatformCompiler_pfn(p0); }
+#undef clWaitForEvents
+#define clWaitForEvents clWaitForEvents_fn
+inline cl_int clWaitForEvents(cl_uint p0, const cl_event* p1) { return clWaitForEvents_pfn(p0, p1); }
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp
new file mode 100644
index 0000000..0b12aed
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp
@@ -0,0 +1,62 @@
+//
+// AUTOGENERATED, DO NOT EDIT
+//
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
+#error "Invalid usage"
+#endif
+
+// generated by parser_cl.py
+#define clCreateFromGLBuffer clCreateFromGLBuffer_
+#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_
+#define clCreateFromGLTexture clCreateFromGLTexture_
+#define clCreateFromGLTexture2D clCreateFromGLTexture2D_
+#define clCreateFromGLTexture3D clCreateFromGLTexture3D_
+#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_
+#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_
+#define clGetGLContextInfoKHR clGetGLContextInfoKHR_
+#define clGetGLObjectInfo clGetGLObjectInfo_
+#define clGetGLTextureInfo clGetGLTextureInfo_
+
+#if defined __APPLE__
+#include <OpenCL/cl_gl.h>
+#else
+#include <CL/cl_gl.h>
+#endif
+
+// generated by parser_cl.py
+#undef clCreateFromGLBuffer
+#define clCreateFromGLBuffer clCreateFromGLBuffer_pfn
+#undef clCreateFromGLRenderbuffer
+#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_pfn
+#undef clCreateFromGLTexture
+#define clCreateFromGLTexture clCreateFromGLTexture_pfn
+#undef clCreateFromGLTexture2D
+#define clCreateFromGLTexture2D clCreateFromGLTexture2D_pfn
+#undef clCreateFromGLTexture3D
+#define clCreateFromGLTexture3D clCreateFromGLTexture3D_pfn
+#undef clEnqueueAcquireGLObjects
+#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_pfn
+#undef clEnqueueReleaseGLObjects
+#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_pfn
+#undef clGetGLContextInfoKHR
+#define clGetGLContextInfoKHR clGetGLContextInfoKHR_pfn
+#undef clGetGLObjectInfo
+#define clGetGLObjectInfo clGetGLObjectInfo_pfn
+#undef clGetGLTextureInfo
+#define clGetGLTextureInfo clGetGLTextureInfo_pfn
+
+#ifdef cl_khr_gl_sharing
+
+// generated by parser_cl.py
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLBuffer)(cl_context, cl_mem_flags, cl_GLuint, int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLRenderbuffer)(cl_context, cl_mem_flags, cl_GLuint, cl_int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture2D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*);
+extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture3D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueAcquireGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReleaseGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLContextInfoKHR)(const cl_context_properties*, cl_gl_context_info, size_t, void*, size_t*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLObjectInfo)(cl_mem, cl_gl_object_type*, cl_GLuint*);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLTextureInfo)(cl_mem, cl_gl_texture_info, size_t, void*, size_t*);
+
+#endif // cl_khr_gl_sharing
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp
new file mode 100644
index 0000000..12f342b
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp
@@ -0,0 +1,42 @@
+//
+// AUTOGENERATED, DO NOT EDIT
+//
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
+#error "Invalid usage"
+#endif
+
+#ifdef cl_khr_gl_sharing
+
+// generated by parser_cl.py
+#undef clCreateFromGLBuffer
+#define clCreateFromGLBuffer clCreateFromGLBuffer_fn
+inline cl_mem clCreateFromGLBuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, int* p3) { return clCreateFromGLBuffer_pfn(p0, p1, p2, p3); }
+#undef clCreateFromGLRenderbuffer
+#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_fn
+inline cl_mem clCreateFromGLRenderbuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, cl_int* p3) { return clCreateFromGLRenderbuffer_pfn(p0, p1, p2, p3); }
+#undef clCreateFromGLTexture
+#define clCreateFromGLTexture clCreateFromGLTexture_fn
+inline cl_mem clCreateFromGLTexture(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clCreateFromGLTexture2D
+#define clCreateFromGLTexture2D clCreateFromGLTexture2D_fn
+inline cl_mem clCreateFromGLTexture2D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture2D_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clCreateFromGLTexture3D
+#define clCreateFromGLTexture3D clCreateFromGLTexture3D_fn
+inline cl_mem clCreateFromGLTexture3D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture3D_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clEnqueueAcquireGLObjects
+#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_fn
+inline cl_int clEnqueueAcquireGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueAcquireGLObjects_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clEnqueueReleaseGLObjects
+#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_fn
+inline cl_int clEnqueueReleaseGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueReleaseGLObjects_pfn(p0, p1, p2, p3, p4, p5); }
+#undef clGetGLContextInfoKHR
+#define clGetGLContextInfoKHR clGetGLContextInfoKHR_fn
+inline cl_int clGetGLContextInfoKHR(const cl_context_properties* p0, cl_gl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLContextInfoKHR_pfn(p0, p1, p2, p3, p4); }
+#undef clGetGLObjectInfo
+#define clGetGLObjectInfo clGetGLObjectInfo_fn
+inline cl_int clGetGLObjectInfo(cl_mem p0, cl_gl_object_type* p1, cl_GLuint* p2) { return clGetGLObjectInfo_pfn(p0, p1, p2); }
+#undef clGetGLTextureInfo
+#define clGetGLTextureInfo clGetGLTextureInfo_fn
+inline cl_int clGetGLTextureInfo(cl_mem p0, cl_gl_texture_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLTextureInfo_pfn(p0, p1, p2, p3, p4); }
+
+#endif // cl_khr_gl_sharing
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_clblas.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_clblas.hpp
new file mode 100644
index 0000000..ccddf8f
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_clblas.hpp
@@ -0,0 +1,53 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
+#define OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
+
+#ifdef HAVE_CLAMDBLAS
+
+#include "opencl_core.hpp"
+
+#include "autogenerated/opencl_clblas.hpp"
+
+#endif // HAVE_CLAMDBLAS
+
+#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_clfft.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_clfft.hpp
new file mode 100644
index 0000000..7f4af5e
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_clfft.hpp
@@ -0,0 +1,53 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
+#define OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
+
+#ifdef HAVE_CLAMDFFT
+
+#include "opencl_core.hpp"
+
+#include "autogenerated/opencl_clfft.hpp"
+
+#endif // HAVE_CLAMDFFT
+
+#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_core.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_core.hpp
new file mode 100644
index 0000000..0404b31
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_core.hpp
@@ -0,0 +1,84 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
+#define OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
+
+#ifdef HAVE_OPENCL
+
+#ifndef CL_RUNTIME_EXPORT
+#if (defined(BUILD_SHARED_LIBS) || defined(OPENCV_CORE_SHARED)) && (defined _WIN32 || defined WINCE) && \
+    !(defined(__OPENCV_BUILD) && defined(OPENCV_MODULE_IS_PART_OF_WORLD))
+#define CL_RUNTIME_EXPORT __declspec(dllimport)
+#else
+#define CL_RUNTIME_EXPORT
+#endif
+#endif
+
+#ifdef HAVE_OPENCL_SVM
+#define clSVMAlloc clSVMAlloc_
+#define clSVMFree clSVMFree_
+#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_
+#define clSetKernelExecInfo clSetKernelExecInfo_
+#define clEnqueueSVMFree clEnqueueSVMFree_
+#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_
+#define clEnqueueSVMMemFill clEnqueueSVMMemFill_
+#define clEnqueueSVMMap clEnqueueSVMMap_
+#define clEnqueueSVMUnmap clEnqueueSVMUnmap_
+#endif
+
+#include "autogenerated/opencl_core.hpp"
+
+#ifndef CL_DEVICE_DOUBLE_FP_CONFIG
+#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032
+#endif
+
+#ifndef CL_DEVICE_HALF_FP_CONFIG
+#define CL_DEVICE_HALF_FP_CONFIG 0x1033
+#endif
+
+#ifndef CL_VERSION_1_2
+#define CV_REQUIRE_OPENCL_1_2_ERROR CV_Error(cv::Error::OpenCLApiCallError, "OpenCV compiled without OpenCL v1.2 support, so we can't use functionality from OpenCL v1.2")
+#endif
+
+#endif // HAVE_OPENCL
+
+#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp
new file mode 100644
index 0000000..38fcae9
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp
@@ -0,0 +1,47 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
+#define OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
+
+#include "autogenerated/opencl_core_wrappers.hpp"
+
+#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_gl.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_gl.hpp
new file mode 100644
index 0000000..659c7d8
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_gl.hpp
@@ -0,0 +1,53 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
+#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
+
+#if defined HAVE_OPENCL && defined HAVE_OPENGL
+
+#include "opencl_core.hpp"
+
+#include "autogenerated/opencl_gl.hpp"
+
+#endif // defined HAVE_OPENCL && defined HAVE_OPENGL
+
+#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp
new file mode 100644
index 0000000..9700004
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp
@@ -0,0 +1,47 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
+#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
+
+#include "autogenerated/opencl_gl_wrappers.hpp"
+
+#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp
new file mode 100644
index 0000000..9636b19
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp
@@ -0,0 +1,48 @@
+/* See LICENSE file in the root OpenCV directory */
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP
+#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP
+
+#if defined(HAVE_OPENCL_SVM)
+#include "opencl_core.hpp"
+
+#include "opencl_svm_definitions.hpp"
+
+#undef clSVMAlloc
+#define clSVMAlloc clSVMAlloc_pfn
+#undef clSVMFree
+#define clSVMFree clSVMFree_pfn
+#undef clSetKernelArgSVMPointer
+#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_pfn
+#undef clSetKernelExecInfo
+//#define clSetKernelExecInfo clSetKernelExecInfo_pfn
+#undef clEnqueueSVMFree
+//#define clEnqueueSVMFree clEnqueueSVMFree_pfn
+#undef clEnqueueSVMMemcpy
+#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_pfn
+#undef clEnqueueSVMMemFill
+#define clEnqueueSVMMemFill clEnqueueSVMMemFill_pfn
+#undef clEnqueueSVMMap
+#define clEnqueueSVMMap clEnqueueSVMMap_pfn
+#undef clEnqueueSVMUnmap
+#define clEnqueueSVMUnmap clEnqueueSVMUnmap_pfn
+
+extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSVMAlloc)(cl_context context, cl_svm_mem_flags flags, size_t size, unsigned int alignment);
+extern CL_RUNTIME_EXPORT void (CL_API_CALL *clSVMFree)(cl_context context, void* svm_pointer);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clSetKernelArgSVMPointer)(cl_kernel kernel, cl_uint arg_index, const void* arg_value);
+//extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSetKernelExecInfo)(cl_kernel kernel, cl_kernel_exec_info param_name, size_t param_value_size, const void* param_value);
+//extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMFree)(cl_command_queue command_queue, cl_uint num_svm_pointers, void* svm_pointers[],
+//        void (CL_CALLBACK *pfn_free_func)(cl_command_queue queue, cl_uint num_svm_pointers, void* svm_pointers[], void* user_data), void* user_data,
+//        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemcpy)(cl_command_queue command_queue, cl_bool blocking_copy, void* dst_ptr, const void* src_ptr, size_t size,
+        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemFill)(cl_command_queue command_queue, void* svm_ptr, const void* pattern, size_t pattern_size, size_t size,
+        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMap)(cl_command_queue command_queue, cl_bool blocking_map, cl_map_flags map_flags, void* svm_ptr, size_t size,
+        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
+extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMUnmap)(cl_command_queue command_queue, void* svm_ptr,
+        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
+
+#endif // HAVE_OPENCL_SVM
+
+#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp
new file mode 100644
index 0000000..97c927b
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp
@@ -0,0 +1,42 @@
+/* See LICENSE file in the root OpenCV directory */
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP
+#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP
+
+#if defined(HAVE_OPENCL_SVM)
+#if defined(CL_VERSION_2_0)
+
+// OpenCL 2.0 contains SVM definitions
+
+#else
+
+typedef cl_bitfield cl_device_svm_capabilities;
+typedef cl_bitfield cl_svm_mem_flags;
+typedef cl_uint     cl_kernel_exec_info;
+
+//
+// TODO Add real values after OpenCL 2.0 release
+//
+
+#ifndef CL_DEVICE_SVM_CAPABILITIES
+#define CL_DEVICE_SVM_CAPABILITIES 0x1053
+
+#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER             (1 << 0)
+#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER               (1 << 1)
+#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM               (1 << 2)
+#define CL_DEVICE_SVM_ATOMICS                         (1 << 3)
+#endif
+
+#ifndef CL_MEM_SVM_FINE_GRAIN_BUFFER
+#define CL_MEM_SVM_FINE_GRAIN_BUFFER (1 << 10)
+#endif
+
+#ifndef CL_MEM_SVM_ATOMICS
+#define CL_MEM_SVM_ATOMICS (1 << 11)
+#endif
+
+
+#endif // CL_VERSION_2_0
+#endif // HAVE_OPENCL_SVM
+
+#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP
diff --git a/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp b/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp
new file mode 100644
index 0000000..497bc3d
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp
@@ -0,0 +1,166 @@
+/* See LICENSE file in the root OpenCV directory */
+
+#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP
+#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP
+
+#if defined(HAVE_OPENCL_SVM)
+#include "opencl_core.hpp"
+
+#ifndef CL_DEVICE_SVM_CAPABILITIES_AMD
+//
+//  Part of the file is an extract from the cl_ext.h file from AMD APP SDK package.
+//  Below is the original copyright.
+//
+/*******************************************************************************
+ * Copyright (c) 2008-2013 The Khronos Group Inc.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and/or associated documentation files (the
+ * "Materials"), to deal in the Materials without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Materials, and to
+ * permit persons to whom the Materials are furnished to do so, subject to
+ * the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Materials.
+ *
+ * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+ * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.
+ ******************************************************************************/
+
+/*******************************************
+ * Shared Virtual Memory (SVM) extension
+ *******************************************/
+typedef cl_bitfield                      cl_device_svm_capabilities_amd;
+typedef cl_bitfield                      cl_svm_mem_flags_amd;
+typedef cl_uint                          cl_kernel_exec_info_amd;
+
+/* cl_device_info */
+#define CL_DEVICE_SVM_CAPABILITIES_AMD                     0x1053
+#define CL_DEVICE_PREFERRED_PLATFORM_ATOMIC_ALIGNMENT_AMD  0x1054
+
+/* cl_device_svm_capabilities_amd */
+#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER_AMD             (1 << 0)
+#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER_AMD               (1 << 1)
+#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM_AMD               (1 << 2)
+#define CL_DEVICE_SVM_ATOMICS_AMD                         (1 << 3)
+
+/* cl_svm_mem_flags_amd */
+#define CL_MEM_SVM_FINE_GRAIN_BUFFER_AMD                  (1 << 10)
+#define CL_MEM_SVM_ATOMICS_AMD                            (1 << 11)
+
+/* cl_mem_info */
+#define CL_MEM_USES_SVM_POINTER_AMD                       0x1109
+
+/* cl_kernel_exec_info_amd */
+#define CL_KERNEL_EXEC_INFO_SVM_PTRS_AMD                  0x11B6
+#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM_AMD     0x11B7
+
+/* cl_command_type */
+#define CL_COMMAND_SVM_FREE_AMD                           0x1209
+#define CL_COMMAND_SVM_MEMCPY_AMD                         0x120A
+#define CL_COMMAND_SVM_MEMFILL_AMD                        0x120B
+#define CL_COMMAND_SVM_MAP_AMD                            0x120C
+#define CL_COMMAND_SVM_UNMAP_AMD                          0x120D
+
+typedef CL_API_ENTRY void*
+(CL_API_CALL * clSVMAllocAMD_fn)(
+    cl_context            /* context */,
+    cl_svm_mem_flags_amd  /* flags */,
+    size_t                /* size */,
+    unsigned int          /* alignment */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY void
+(CL_API_CALL * clSVMFreeAMD_fn)(
+    cl_context  /* context */,
+    void*       /* svm_pointer */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY cl_int
+(CL_API_CALL * clEnqueueSVMFreeAMD_fn)(
+    cl_command_queue /* command_queue */,
+    cl_uint          /* num_svm_pointers */,
+    void**           /* svm_pointers */,
+    void (CL_CALLBACK *)( /*pfn_free_func*/
+        cl_command_queue /* queue */,
+        cl_uint          /* num_svm_pointers */,
+        void**           /* svm_pointers */,
+        void*            /* user_data */),
+    void*             /* user_data */,
+    cl_uint           /* num_events_in_wait_list */,
+    const cl_event*   /* event_wait_list */,
+    cl_event*         /* event */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY cl_int
+(CL_API_CALL * clEnqueueSVMMemcpyAMD_fn)(
+    cl_command_queue /* command_queue */,
+    cl_bool          /* blocking_copy */,
+    void*            /* dst_ptr */,
+    const void*      /* src_ptr */,
+    size_t           /* size */,
+    cl_uint          /* num_events_in_wait_list */,
+    const cl_event*  /* event_wait_list */,
+    cl_event*        /* event */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY cl_int
+(CL_API_CALL * clEnqueueSVMMemFillAMD_fn)(
+    cl_command_queue /* command_queue */,
+    void*            /* svm_ptr */,
+    const void*      /* pattern */,
+    size_t           /* pattern_size */,
+    size_t           /* size */,
+    cl_uint          /* num_events_in_wait_list */,
+    const cl_event*  /* event_wait_list */,
+    cl_event*        /* event */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY cl_int
+(CL_API_CALL * clEnqueueSVMMapAMD_fn)(
+    cl_command_queue /* command_queue */,
+    cl_bool          /* blocking_map */,
+    cl_map_flags     /* map_flags */,
+    void*            /* svm_ptr */,
+    size_t           /* size */,
+    cl_uint          /* num_events_in_wait_list */,
+    const cl_event*  /* event_wait_list */,
+    cl_event*        /* event */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY cl_int
+(CL_API_CALL * clEnqueueSVMUnmapAMD_fn)(
+    cl_command_queue /* command_queue */,
+    void*            /* svm_ptr */,
+    cl_uint          /* num_events_in_wait_list */,
+    const cl_event*  /* event_wait_list */,
+    cl_event*        /* event */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY cl_int
+(CL_API_CALL * clSetKernelArgSVMPointerAMD_fn)(
+    cl_kernel     /* kernel */,
+    cl_uint       /* arg_index */,
+    const void *  /* arg_value */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+typedef CL_API_ENTRY cl_int
+(CL_API_CALL * clSetKernelExecInfoAMD_fn)(
+     cl_kernel                /* kernel */,
+     cl_kernel_exec_info_amd  /* param_name */,
+     size_t                   /* param_value_size */,
+     const void *             /* param_value */
+) CL_EXT_SUFFIX__VERSION_1_2;
+
+#endif
+
+#endif // HAVE_OPENCL_SVM
+
+#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP
diff --git a/3rdParty/include/opencv2/core/opengl.hpp b/3rdParty/include/opencv2/core/opengl.hpp
new file mode 100644
index 0000000..a311ce2
--- /dev/null
+++ b/3rdParty/include/opencv2/core/opengl.hpp
@@ -0,0 +1,725 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OPENGL_HPP
+#define OPENCV_CORE_OPENGL_HPP
+
+#ifndef __cplusplus
+#  error opengl.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core.hpp"
+#include "ocl.hpp"
+
+namespace cv { namespace ogl {
+
+/** @addtogroup core_opengl
+This section describes OpenGL interoperability.
+
+To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is
+supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not
+supported). For GTK backend gtkglext-1.0 library is required.
+
+To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can
+do this with namedWindow function or with other OpenGL toolkit (GLUT, for example).
+*/
+//! @{
+
+/////////////////// OpenGL Objects ///////////////////
+
+/** @brief Smart pointer for OpenGL buffer object with reference counting.
+
+Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL
+context. These can be used to store vertex data, pixel data retrieved from images or the
+framebuffer, and a variety of other things.
+
+ogl::Buffer has interface similar with Mat interface and represents 2D array memory.
+
+ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory.
+ */
+class CV_EXPORTS Buffer
+{
+public:
+    /** @brief The target defines how you intend to use the buffer object.
+    */
+    enum Target
+    {
+        ARRAY_BUFFER         = 0x8892, //!< The buffer will be used as a source for vertex data
+        ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example)
+        PIXEL_PACK_BUFFER    = 0x88EB, //!< The buffer will be used for reading from OpenGL textures
+        PIXEL_UNPACK_BUFFER  = 0x88EC  //!< The buffer will be used for writing to OpenGL textures
+    };
+
+    enum Access
+    {
+        READ_ONLY  = 0x88B8,
+        WRITE_ONLY = 0x88B9,
+        READ_WRITE = 0x88BA
+    };
+
+    /** @brief The constructors.
+
+    Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId
+    parameter), allocates memory for ogl::Buffer object or copies from host/device memory.
+     */
+    Buffer();
+
+    /** @overload
+    @param arows Number of rows in a 2D array.
+    @param acols Number of columns in a 2D array.
+    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
+    @param abufId Buffer object name.
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false);
+
+    /** @overload
+    @param asize 2D array size.
+    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
+    @param abufId Buffer object name.
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false);
+
+    /** @overload
+    @param arows Number of rows in a 2D array.
+    @param acols Number of columns in a 2D array.
+    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
+    @param target Buffer usage. See cv::ogl::Buffer::Target .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
+
+    /** @overload
+    @param asize 2D array size.
+    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
+    @param target Buffer usage. See cv::ogl::Buffer::Target .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
+
+    /** @overload
+    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ).
+    @param target Buffer usage. See cv::ogl::Buffer::Target .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
+
+    /** @brief Allocates memory for ogl::Buffer object.
+
+    @param arows Number of rows in a 2D array.
+    @param acols Number of columns in a 2D array.
+    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
+    @param target Buffer usage. See cv::ogl::Buffer::Target .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+     */
+    void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
+
+    /** @overload
+    @param asize 2D array size.
+    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
+    @param target Buffer usage. See cv::ogl::Buffer::Target .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
+
+    /** @brief Decrements the reference counter and destroys the buffer object if needed.
+
+    The function will call setAutoRelease(true) .
+     */
+    void release();
+
+    /** @brief Sets auto release mode.
+
+    The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was
+    bound to a window it could be released at any time (user can close a window). If object's destructor
+    is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy
+    OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context).
+    This function can force ogl::Buffer destructor to destroy OpenGL object.
+    @param flag Auto release mode (if true, release will be called in object's destructor).
+     */
+    void setAutoRelease(bool flag);
+
+    /** @brief Copies from host/device memory to OpenGL buffer.
+    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ).
+    @param target Buffer usage. See cv::ogl::Buffer::Target .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+     */
+    void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
+
+    /** @overload */
+    void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false);
+
+    /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object.
+
+    @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or
+    ogl::Buffer ).
+     */
+    void copyTo(OutputArray arr) const;
+
+    /** @overload */
+    void copyTo(OutputArray arr, cuda::Stream& stream) const;
+
+    /** @brief Creates a full copy of the buffer object and the underlying data.
+
+    @param target Buffer usage for destination buffer.
+    @param autoRelease Auto release mode for destination buffer.
+     */
+    Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const;
+
+    /** @brief Binds OpenGL buffer to the specified buffer binding point.
+
+    @param target Binding point. See cv::ogl::Buffer::Target .
+     */
+    void bind(Target target) const;
+
+    /** @brief Unbind any buffers from the specified binding point.
+
+    @param target Binding point. See cv::ogl::Buffer::Target .
+     */
+    static void unbind(Target target);
+
+    /** @brief Maps OpenGL buffer to host memory.
+
+    mapHost maps to the client's address space the entire data store of the buffer object. The data can
+    then be directly read and/or written relative to the returned pointer, depending on the specified
+    access policy.
+
+    A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used.
+
+    This operation can lead to memory transfers between host and device.
+
+    Only one buffer object can be mapped at a time.
+    @param access Access policy, indicating whether it will be possible to read from, write to, or both
+    read from and write to the buffer object's mapped data store. The symbolic constant must be
+    ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE .
+     */
+    Mat mapHost(Access access);
+
+    /** @brief Unmaps OpenGL buffer.
+    */
+    void unmapHost();
+
+    //! map to device memory (blocking)
+    cuda::GpuMat mapDevice();
+    void unmapDevice();
+
+    /** @brief Maps OpenGL buffer to CUDA device memory.
+
+    This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time.
+
+    A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used.
+     */
+    cuda::GpuMat mapDevice(cuda::Stream& stream);
+
+    /** @brief Unmaps OpenGL buffer.
+    */
+    void unmapDevice(cuda::Stream& stream);
+
+    int rows() const;
+    int cols() const;
+    Size size() const;
+    bool empty() const;
+
+    int type() const;
+    int depth() const;
+    int channels() const;
+    int elemSize() const;
+    int elemSize1() const;
+
+    //! get OpenGL opject id
+    unsigned int bufId() const;
+
+    class Impl;
+
+private:
+    Ptr<Impl> impl_;
+    int rows_;
+    int cols_;
+    int type_;
+};
+
+/** @brief Smart pointer for OpenGL 2D texture memory with reference counting.
+ */
+class CV_EXPORTS Texture2D
+{
+public:
+    /** @brief An Image Format describes the way that the images in Textures store their data.
+    */
+    enum Format
+    {
+        NONE            = 0,
+        DEPTH_COMPONENT = 0x1902, //!< Depth
+        RGB             = 0x1907, //!< Red, Green, Blue
+        RGBA            = 0x1908  //!< Red, Green, Blue, Alpha
+    };
+
+    /** @brief The constructors.
+
+    Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from
+    host/device memory.
+     */
+    Texture2D();
+
+    /** @overload */
+    Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false);
+
+    /** @overload */
+    Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false);
+
+    /** @overload
+    @param arows Number of rows.
+    @param acols Number of columns.
+    @param aformat Image format. See cv::ogl::Texture2D::Format .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    Texture2D(int arows, int acols, Format aformat, bool autoRelease = false);
+
+    /** @overload
+    @param asize 2D array size.
+    @param aformat Image format. See cv::ogl::Texture2D::Format .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    Texture2D(Size asize, Format aformat, bool autoRelease = false);
+
+    /** @overload
+    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ).
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    explicit Texture2D(InputArray arr, bool autoRelease = false);
+
+    /** @brief Allocates memory for ogl::Texture2D object.
+
+    @param arows Number of rows.
+    @param acols Number of columns.
+    @param aformat Image format. See cv::ogl::Texture2D::Format .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+     */
+    void create(int arows, int acols, Format aformat, bool autoRelease = false);
+    /** @overload
+    @param asize 2D array size.
+    @param aformat Image format. See cv::ogl::Texture2D::Format .
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+    */
+    void create(Size asize, Format aformat, bool autoRelease = false);
+
+    /** @brief Decrements the reference counter and destroys the texture object if needed.
+
+    The function will call setAutoRelease(true) .
+     */
+    void release();
+
+    /** @brief Sets auto release mode.
+
+    @param flag Auto release mode (if true, release will be called in object's destructor).
+
+    The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was
+    bound to a window it could be released at any time (user can close a window). If object's destructor
+    is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't
+    destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL
+    context). This function can force ogl::Texture2D destructor to destroy OpenGL object.
+     */
+    void setAutoRelease(bool flag);
+
+    /** @brief Copies from host/device memory to OpenGL texture.
+
+    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ).
+    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
+     */
+    void copyFrom(InputArray arr, bool autoRelease = false);
+
+    /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object.
+
+    @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or
+    ogl::Texture2D ).
+    @param ddepth Destination depth.
+    @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture).
+     */
+    void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const;
+
+    /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target.
+    */
+    void bind() const;
+
+    int rows() const;
+    int cols() const;
+    Size size() const;
+    bool empty() const;
+
+    Format format() const;
+
+    //! get OpenGL opject id
+    unsigned int texId() const;
+
+    class Impl;
+
+private:
+    Ptr<Impl> impl_;
+    int rows_;
+    int cols_;
+    Format format_;
+};
+
+/** @brief Wrapper for OpenGL Client-Side Vertex arrays.
+
+ogl::Arrays stores vertex data in ogl::Buffer objects.
+ */
+class CV_EXPORTS Arrays
+{
+public:
+    /** @brief Default constructor
+     */
+    Arrays();
+
+    /** @brief Sets an array of vertex coordinates.
+    @param vertex array with vertex coordinates, can be both host and device memory.
+    */
+    void setVertexArray(InputArray vertex);
+
+    /** @brief Resets vertex coordinates.
+    */
+    void resetVertexArray();
+
+    /** @brief Sets an array of vertex colors.
+    @param color array with vertex colors, can be both host and device memory.
+     */
+    void setColorArray(InputArray color);
+
+    /** @brief Resets vertex colors.
+    */
+    void resetColorArray();
+
+    /** @brief Sets an array of vertex normals.
+    @param normal array with vertex normals, can be both host and device memory.
+     */
+    void setNormalArray(InputArray normal);
+
+    /** @brief Resets vertex normals.
+    */
+    void resetNormalArray();
+
+    /** @brief Sets an array of vertex texture coordinates.
+    @param texCoord array with vertex texture coordinates, can be both host and device memory.
+     */
+    void setTexCoordArray(InputArray texCoord);
+
+    /** @brief Resets vertex texture coordinates.
+    */
+    void resetTexCoordArray();
+
+    /** @brief Releases all inner buffers.
+    */
+    void release();
+
+    /** @brief Sets auto release mode all inner buffers.
+    @param flag Auto release mode.
+     */
+    void setAutoRelease(bool flag);
+
+    /** @brief Binds all vertex arrays.
+    */
+    void bind() const;
+
+    /** @brief Returns the vertex count.
+    */
+    int size() const;
+    bool empty() const;
+
+private:
+    int size_;
+    Buffer vertex_;
+    Buffer color_;
+    Buffer normal_;
+    Buffer texCoord_;
+};
+
+/////////////////// Render Functions ///////////////////
+
+//! render mode
+enum RenderModes {
+    POINTS         = 0x0000,
+    LINES          = 0x0001,
+    LINE_LOOP      = 0x0002,
+    LINE_STRIP     = 0x0003,
+    TRIANGLES      = 0x0004,
+    TRIANGLE_STRIP = 0x0005,
+    TRIANGLE_FAN   = 0x0006,
+    QUADS          = 0x0007,
+    QUAD_STRIP     = 0x0008,
+    POLYGON        = 0x0009
+};
+
+/** @brief Render OpenGL texture or primitives.
+@param tex Texture to draw.
+@param wndRect Region of window, where to draw a texture (normalized coordinates).
+@param texRect Region of texture to draw (normalized coordinates).
+ */
+CV_EXPORTS void render(const Texture2D& tex,
+    Rect_<double> wndRect = Rect_<double>(0.0, 0.0, 1.0, 1.0),
+    Rect_<double> texRect = Rect_<double>(0.0, 0.0, 1.0, 1.0));
+
+/** @overload
+@param arr Array of privitives vertices.
+@param mode Render mode. One of cv::ogl::RenderModes
+@param color Color for all vertices. Will be used if arr doesn't contain color array.
+*/
+CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255));
+
+/** @overload
+@param arr Array of privitives vertices.
+@param indices Array of vertices indices (host or device memory).
+@param mode Render mode. One of cv::ogl::RenderModes
+@param color Color for all vertices. Will be used if arr doesn't contain color array.
+*/
+CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255));
+
+/////////////////// CL-GL Interoperability Functions ///////////////////
+
+namespace ocl {
+using namespace cv::ocl;
+
+// TODO static functions in the Context class
+/** @brief Creates OpenCL context from GL.
+@return Returns reference to OpenCL Context
+ */
+CV_EXPORTS Context& initializeContextFromGL();
+
+} // namespace cv::ogl::ocl
+
+/** @brief Converts InputArray to Texture2D object.
+@param src     - source InputArray.
+@param texture - destination Texture2D object.
+ */
+CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture);
+
+/** @brief Converts Texture2D object to OutputArray.
+@param texture - source Texture2D object.
+@param dst     - destination OutputArray.
+ */
+CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst);
+
+/** @brief Maps Buffer object to process on CL side (convert to UMat).
+
+Function creates CL buffer from GL one, and then constructs UMat that can be used
+to process buffer data with OpenCV functions. Note that in current implementation
+UMat constructed this way doesn't own corresponding GL buffer object, so it is
+the user responsibility to close down CL/GL buffers relationships by explicitly
+calling unmapGLBuffer() function.
+@param buffer      - source Buffer object.
+@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE).
+@return Returns UMat object
+ */
+CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, AccessFlag accessFlags = ACCESS_READ | ACCESS_WRITE);
+
+/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer).
+
+Function must be called explicitly by the user for each UMat previously constructed
+by the call to mapGLBuffer() function.
+@param u           - source UMat, created by mapGLBuffer().
+ */
+CV_EXPORTS void unmapGLBuffer(UMat& u);
+
+//! @}
+}} // namespace cv::ogl
+
+namespace cv { namespace cuda {
+
+/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability.
+
+This function should be explicitly called after OpenGL context creation and before any CUDA calls.
+@param device System index of a CUDA device starting with 0.
+@ingroup core_opengl
+ */
+CV_EXPORTS void setGlDevice(int device = 0);
+
+}}
+
+//! @cond IGNORED
+
+////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////
+
+inline
+cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0)
+{
+    create(arows, acols, atype, target, autoRelease);
+}
+
+inline
+cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0)
+{
+    create(asize, atype, target, autoRelease);
+}
+
+inline
+void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease)
+{
+    create(asize.height, asize.width, atype, target, autoRelease);
+}
+
+inline
+int cv::ogl::Buffer::rows() const
+{
+    return rows_;
+}
+
+inline
+int cv::ogl::Buffer::cols() const
+{
+    return cols_;
+}
+
+inline
+cv::Size cv::ogl::Buffer::size() const
+{
+    return Size(cols_, rows_);
+}
+
+inline
+bool cv::ogl::Buffer::empty() const
+{
+    return rows_ == 0 || cols_ == 0;
+}
+
+inline
+int cv::ogl::Buffer::type() const
+{
+    return type_;
+}
+
+inline
+int cv::ogl::Buffer::depth() const
+{
+    return CV_MAT_DEPTH(type_);
+}
+
+inline
+int cv::ogl::Buffer::channels() const
+{
+    return CV_MAT_CN(type_);
+}
+
+inline
+int cv::ogl::Buffer::elemSize() const
+{
+    return CV_ELEM_SIZE(type_);
+}
+
+inline
+int cv::ogl::Buffer::elemSize1() const
+{
+    return CV_ELEM_SIZE1(type_);
+}
+
+///////
+
+inline
+cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE)
+{
+    create(arows, acols, aformat, autoRelease);
+}
+
+inline
+cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE)
+{
+    create(asize, aformat, autoRelease);
+}
+
+inline
+void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease)
+{
+    create(asize.height, asize.width, aformat, autoRelease);
+}
+
+inline
+int cv::ogl::Texture2D::rows() const
+{
+    return rows_;
+}
+
+inline
+int cv::ogl::Texture2D::cols() const
+{
+    return cols_;
+}
+
+inline
+cv::Size cv::ogl::Texture2D::size() const
+{
+    return Size(cols_, rows_);
+}
+
+inline
+bool cv::ogl::Texture2D::empty() const
+{
+    return rows_ == 0 || cols_ == 0;
+}
+
+inline
+cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const
+{
+    return format_;
+}
+
+///////
+
+inline
+cv::ogl::Arrays::Arrays() : size_(0)
+{
+}
+
+inline
+int cv::ogl::Arrays::size() const
+{
+    return size_;
+}
+
+inline
+bool cv::ogl::Arrays::empty() const
+{
+    return size_ == 0;
+}
+
+//! @endcond
+
+#endif /* OPENCV_CORE_OPENGL_HPP */
diff --git a/3rdParty/include/opencv2/core/operations.hpp b/3rdParty/include/opencv2/core/operations.hpp
new file mode 100644
index 0000000..bde28c4
--- /dev/null
+++ b/3rdParty/include/opencv2/core/operations.hpp
@@ -0,0 +1,594 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_OPERATIONS_HPP
+#define OPENCV_CORE_OPERATIONS_HPP
+
+#ifndef __cplusplus
+#  error operations.hpp header must be compiled as C++
+#endif
+
+#include <cstdio>
+
+#if defined(__GNUC__) || defined(__clang__) // at least GCC 3.1+, clang 3.5+
+#  if defined(__MINGW_PRINTF_FORMAT)  // https://sourceforge.net/p/mingw-w64/wiki2/gnu%20printf/.
+#    define CV_FORMAT_PRINTF(string_idx, first_to_check) __attribute__ ((format (__MINGW_PRINTF_FORMAT, string_idx, first_to_check)))
+#  else
+#    define CV_FORMAT_PRINTF(string_idx, first_to_check) __attribute__ ((format (printf, string_idx, first_to_check)))
+#  endif
+#else
+#  define CV_FORMAT_PRINTF(A, B)
+#endif
+
+//! @cond IGNORED
+
+namespace cv
+{
+
+////////////////////////////// Matx methods depending on core API /////////////////////////////
+
+namespace internal
+{
+
+template<typename _Tp, int m, int n> struct Matx_FastInvOp
+{
+    bool operator()(const Matx<_Tp, m, n>& a, Matx<_Tp, n, m>& b, int method) const
+    {
+        return invert(a, b, method) != 0;
+    }
+};
+
+template<typename _Tp, int m> struct Matx_FastInvOp<_Tp, m, m>
+{
+    bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const
+    {
+        if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
+        {
+            Matx<_Tp, m, m> temp = a;
+
+            // assume that b is all 0's on input => make it a unity matrix
+            for (int i = 0; i < m; i++)
+                b(i, i) = (_Tp)1;
+
+            if (method == DECOMP_CHOLESKY)
+                return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m);
+
+            return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0;
+        }
+        else
+        {
+            return invert(a, b, method) != 0;
+        }
+    }
+};
+
+template<typename _Tp> struct Matx_FastInvOp<_Tp, 2, 2>
+{
+    bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int /*method*/) const
+    {
+        _Tp d = (_Tp)determinant(a);
+        if (d == 0)
+            return false;
+        d = 1/d;
+        b(1,1) = a(0,0)*d;
+        b(0,0) = a(1,1)*d;
+        b(0,1) = -a(0,1)*d;
+        b(1,0) = -a(1,0)*d;
+        return true;
+    }
+};
+
+template<typename _Tp> struct Matx_FastInvOp<_Tp, 3, 3>
+{
+    bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int /*method*/) const
+    {
+        _Tp d = (_Tp)determinant(a);
+        if (d == 0)
+            return false;
+        d = 1/d;
+        b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d;
+        b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d;
+        b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d;
+
+        b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d;
+        b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d;
+        b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d;
+
+        b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d;
+        b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d;
+        b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d;
+        return true;
+    }
+};
+
+
+template<typename _Tp, int m, int l, int n> struct Matx_FastSolveOp
+{
+    bool operator()(const Matx<_Tp, m, l>& a, const Matx<_Tp, m, n>& b,
+                    Matx<_Tp, l, n>& x, int method) const
+    {
+        return cv::solve(a, b, x, method);
+    }
+};
+
+template<typename _Tp, int m, int n> struct Matx_FastSolveOp<_Tp, m, m, n>
+{
+    bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b,
+                    Matx<_Tp, m, n>& x, int method) const
+    {
+        if (method == DECOMP_LU || method == DECOMP_CHOLESKY)
+        {
+            Matx<_Tp, m, m> temp = a;
+            x = b;
+            if( method == DECOMP_CHOLESKY )
+                return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n);
+
+            return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0;
+        }
+        else
+        {
+            return cv::solve(a, b, x, method);
+        }
+    }
+};
+
+template<typename _Tp> struct Matx_FastSolveOp<_Tp, 2, 2, 1>
+{
+    bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b,
+                    Matx<_Tp, 2, 1>& x, int) const
+    {
+        _Tp d = (_Tp)determinant(a);
+        if (d == 0)
+            return false;
+        d = 1/d;
+        x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d;
+        x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d;
+        return true;
+    }
+};
+
+template<typename _Tp> struct Matx_FastSolveOp<_Tp, 3, 3, 1>
+{
+    bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b,
+                    Matx<_Tp, 3, 1>& x, int) const
+    {
+        _Tp d = (_Tp)determinant(a);
+        if (d == 0)
+            return false;
+        d = 1/d;
+        x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) -
+                a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) +
+                a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2)));
+
+        x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) -
+                b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) +
+                a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0)));
+
+        x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) -
+                a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) +
+                b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0)));
+        return true;
+    }
+};
+
+} // internal
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b)
+{
+    Matx<_Tp,m,n> M;
+    cv::randu(M, Scalar(a), Scalar(b));
+    return M;
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b)
+{
+    Matx<_Tp,m,n> M;
+    cv::randn(M, Scalar(a), Scalar(b));
+    return M;
+}
+
+template<typename _Tp, int m, int n> inline
+Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const
+{
+    Matx<_Tp, n, m> b;
+    bool ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method);
+    if (p_is_ok) *p_is_ok = ok;
+    return ok ? b : Matx<_Tp, n, m>::zeros();
+}
+
+template<typename _Tp, int m, int n> template<int l> inline
+Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const
+{
+    Matx<_Tp, n, l> x;
+    bool ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method);
+    return ok ? x : Matx<_Tp, n, l>::zeros();
+}
+
+
+
+////////////////////////// Augmenting algebraic & logical operations //////////////////////////
+
+#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \
+    static inline A& operator op (A& a, const B& b) { cvop; return a; }
+
+#define CV_MAT_AUG_OPERATOR(op, cvop, A, B)   \
+    CV_MAT_AUG_OPERATOR1(op, cvop, A, B)      \
+    CV_MAT_AUG_OPERATOR1(op, cvop, const A, B)
+
+#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B)                   \
+    template<typename _Tp> CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \
+    template<typename _Tp> CV_MAT_AUG_OPERATOR1(op, cvop, const A, B)
+
+#define CV_MAT_AUG_OPERATOR_TN(op, cvop, A)                                \
+    template<typename _Tp, int m, int n> static inline A& operator op (A& a, const Matx<_Tp,m,n>& b) { cvop; return a; } \
+    template<typename _Tp, int m, int n> static inline const A& operator op (const A& a, const Matx<_Tp,m,n>& b) { cvop; return a; }
+
+CV_MAT_AUG_OPERATOR  (+=, cv::add(a, b, (const Mat&)a), Mat, Mat)
+CV_MAT_AUG_OPERATOR  (+=, cv::add(a, b, (const Mat&)a), Mat, Scalar)
+CV_MAT_AUG_OPERATOR_T(+=, cv::add(a, b, (const Mat&)a), Mat_<_Tp>, Mat)
+CV_MAT_AUG_OPERATOR_T(+=, cv::add(a, b, (const Mat&)a), Mat_<_Tp>, Scalar)
+CV_MAT_AUG_OPERATOR_T(+=, cv::add(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>)
+CV_MAT_AUG_OPERATOR_TN(+=, cv::add(a, Mat(b), (const Mat&)a), Mat)
+CV_MAT_AUG_OPERATOR_TN(+=, cv::add(a, Mat(b), (const Mat&)a), Mat_<_Tp>)
+
+CV_MAT_AUG_OPERATOR  (-=, cv::subtract(a, b, (const Mat&)a), Mat, Mat)
+CV_MAT_AUG_OPERATOR  (-=, cv::subtract(a, b, (const Mat&)a), Mat, Scalar)
+CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a, b, (const Mat&)a), Mat_<_Tp>, Mat)
+CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a, b, (const Mat&)a), Mat_<_Tp>, Scalar)
+CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>)
+CV_MAT_AUG_OPERATOR_TN(-=, cv::subtract(a, Mat(b), (const Mat&)a), Mat)
+CV_MAT_AUG_OPERATOR_TN(-=, cv::subtract(a, Mat(b), (const Mat&)a), Mat_<_Tp>)
+
+CV_MAT_AUG_OPERATOR  (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat)
+CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat)
+CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>)
+CV_MAT_AUG_OPERATOR  (*=, a.convertTo(a, -1, b), Mat, double)
+CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double)
+CV_MAT_AUG_OPERATOR_TN(*=, cv::gemm(a, Mat(b), 1, Mat(), 0, a, 0), Mat)
+CV_MAT_AUG_OPERATOR_TN(*=, cv::gemm(a, Mat(b), 1, Mat(), 0, a, 0), Mat_<_Tp>)
+
+CV_MAT_AUG_OPERATOR  (/=, cv::divide(a, b, (const Mat&)a), Mat, Mat)
+CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a, b, (const Mat&)a), Mat_<_Tp>, Mat)
+CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>)
+CV_MAT_AUG_OPERATOR  (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double)
+CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double)
+CV_MAT_AUG_OPERATOR_TN(/=, cv::divide(a, Mat(b), (const Mat&)a), Mat)
+CV_MAT_AUG_OPERATOR_TN(/=, cv::divide(a, Mat(b), (const Mat&)a), Mat_<_Tp>)
+
+CV_MAT_AUG_OPERATOR  (&=, cv::bitwise_and(a, b, (const Mat&)a), Mat, Mat)
+CV_MAT_AUG_OPERATOR  (&=, cv::bitwise_and(a, b, (const Mat&)a), Mat, Scalar)
+CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a, b, (const Mat&)a), Mat_<_Tp>, Mat)
+CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a, b, (const Mat&)a), Mat_<_Tp>, Scalar)
+CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>)
+CV_MAT_AUG_OPERATOR_TN(&=, cv::bitwise_and(a, Mat(b), (const Mat&)a), Mat)
+CV_MAT_AUG_OPERATOR_TN(&=, cv::bitwise_and(a, Mat(b), (const Mat&)a), Mat_<_Tp>)
+
+CV_MAT_AUG_OPERATOR  (|=, cv::bitwise_or(a, b, (const Mat&)a), Mat, Mat)
+CV_MAT_AUG_OPERATOR  (|=, cv::bitwise_or(a, b, (const Mat&)a), Mat, Scalar)
+CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a, b, (const Mat&)a), Mat_<_Tp>, Mat)
+CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a, b, (const Mat&)a), Mat_<_Tp>, Scalar)
+CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>)
+CV_MAT_AUG_OPERATOR_TN(|=, cv::bitwise_or(a, Mat(b), (const Mat&)a), Mat)
+CV_MAT_AUG_OPERATOR_TN(|=, cv::bitwise_or(a, Mat(b), (const Mat&)a), Mat_<_Tp>)
+
+CV_MAT_AUG_OPERATOR  (^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat, Mat)
+CV_MAT_AUG_OPERATOR  (^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat, Scalar)
+CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat_<_Tp>, Mat)
+CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat_<_Tp>, Scalar)
+CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>)
+CV_MAT_AUG_OPERATOR_TN(^=, cv::bitwise_xor(a, Mat(b), (const Mat&)a), Mat)
+CV_MAT_AUG_OPERATOR_TN(^=, cv::bitwise_xor(a, Mat(b), (const Mat&)a), Mat_<_Tp>)
+
+#undef CV_MAT_AUG_OPERATOR_TN
+#undef CV_MAT_AUG_OPERATOR_T
+#undef CV_MAT_AUG_OPERATOR
+#undef CV_MAT_AUG_OPERATOR1
+
+
+
+///////////////////////////////////////////// SVD /////////////////////////////////////////////
+
+inline SVD::SVD() {}
+inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); }
+inline void SVD::solveZ( InputArray m, OutputArray _dst )
+{
+    Mat mtx = m.getMat();
+    SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV));
+    _dst.create(svd.vt.cols, 1, svd.vt.type());
+    Mat dst = _dst.getMat();
+    svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst);
+}
+
+template<typename _Tp, int m, int n, int nm> inline void
+    SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt )
+{
+    CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
+    Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false);
+    SVD::compute(_a, _w, _u, _vt);
+    CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]);
+}
+
+template<typename _Tp, int m, int n, int nm> inline void
+SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w )
+{
+    CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
+    Mat _a(a, false), _w(w, false);
+    SVD::compute(_a, _w);
+    CV_Assert(_w.data == (uchar*)&w.val[0]);
+}
+
+template<typename _Tp, int m, int n, int nm, int nb> inline void
+SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u,
+                const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs,
+                Matx<_Tp, n, nb>& dst )
+{
+    CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
+    Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false);
+    SVD::backSubst(_w, _u, _vt, _rhs, _dst);
+    CV_Assert(_dst.data == (uchar*)&dst.val[0]);
+}
+
+
+
+/////////////////////////////////// Multiply-with-Carry RNG ///////////////////////////////////
+
+inline RNG::RNG()              { state = 0xffffffff; }
+inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; }
+
+inline RNG::operator uchar()    { return (uchar)next(); }
+inline RNG::operator schar()    { return (schar)next(); }
+inline RNG::operator ushort()   { return (ushort)next(); }
+inline RNG::operator short()    { return (short)next(); }
+inline RNG::operator int()      { return (int)next(); }
+inline RNG::operator unsigned() { return next(); }
+inline RNG::operator float()    { return next()*2.3283064365386962890625e-10f; }
+inline RNG::operator double()   { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; }
+
+inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); }
+inline unsigned RNG::operator ()()           { return next(); }
+
+inline int    RNG::uniform(int a, int b)       { return a == b ? a : (int)(next() % (b - a) + a); }
+inline float  RNG::uniform(float a, float b)   { return ((float)*this)*(b - a) + a; }
+inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; }
+
+inline bool RNG::operator ==(const RNG& other) const { return state == other.state; }
+
+inline unsigned RNG::next()
+{
+    state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32);
+    return (unsigned)state;
+}
+
+//! returns the next uniformly-distributed random number of the specified type
+template<typename _Tp> static inline _Tp randu()
+{
+  return (_Tp)theRNG();
+}
+
+///////////////////////////////// Formatted string generation /////////////////////////////////
+
+/** @brief Returns a text string formatted using the printf-like expression.
+
+The function acts like sprintf but forms and returns an STL string. It can be used to form an error
+message in the Exception constructor.
+@param fmt printf-compatible formatting specifiers.
+
+**Note**:
+|Type|Specifier|
+|-|-|
+|`const char*`|`%s`|
+|`char`|`%c`|
+|`float` / `double`|`%f`,`%g`|
+|`int`, `long`, `long long`|`%d`, `%ld`, ``%lld`|
+|`unsigned`, `unsigned long`, `unsigned long long`|`%u`, `%lu`, `%llu`|
+|`uint64` -> `uintmax_t`, `int64` -> `intmax_t`|`%ju`, `%jd`|
+|`size_t`|`%zu`|
+ */
+CV_EXPORTS String format( const char* fmt, ... ) CV_FORMAT_PRINTF(1, 2);
+
+///////////////////////////////// Formatted output of cv::Mat /////////////////////////////////
+
+static inline
+Ptr<Formatted> format(InputArray mtx, Formatter::FormatType fmt)
+{
+    return Formatter::get(fmt)->format(mtx.getMat());
+}
+
+static inline
+int print(Ptr<Formatted> fmtd, FILE* stream = stdout)
+{
+    int written = 0;
+    fmtd->reset();
+    for(const char* str = fmtd->next(); str; str = fmtd->next())
+        written += fputs(str, stream);
+
+    return written;
+}
+
+static inline
+int print(const Mat& mtx, FILE* stream = stdout)
+{
+    return print(Formatter::get()->format(mtx), stream);
+}
+
+static inline
+int print(const UMat& mtx, FILE* stream = stdout)
+{
+    return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream);
+}
+
+template<typename _Tp> static inline
+int print(const std::vector<Point_<_Tp> >& vec, FILE* stream = stdout)
+{
+    return print(Formatter::get()->format(Mat(vec)), stream);
+}
+
+template<typename _Tp> static inline
+int print(const std::vector<Point3_<_Tp> >& vec, FILE* stream = stdout)
+{
+    return print(Formatter::get()->format(Mat(vec)), stream);
+}
+
+template<typename _Tp, int m, int n> static inline
+int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout)
+{
+    return print(Formatter::get()->format(cv::Mat(matx)), stream);
+}
+
+//! @endcond
+
+/****************************************************************************************\
+*                                  Auxiliary algorithms                                  *
+\****************************************************************************************/
+
+/** @brief Splits an element set into equivalency classes.
+
+The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements
+into one or more equivalency classes, as described in
+<http://en.wikipedia.org/wiki/Disjoint-set_data_structure> . The function returns the number of
+equivalency classes.
+@param _vec Set of elements stored as a vector.
+@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is
+a 0-based cluster index of `vec[i]`.
+@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an
+instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The
+predicate returns true when the elements are certainly in the same class, and returns false if they
+may or may not be in the same class.
+@ingroup core_cluster
+*/
+template<typename _Tp, class _EqPredicate> int
+partition( const std::vector<_Tp>& _vec, std::vector<int>& labels,
+          _EqPredicate predicate=_EqPredicate())
+{
+    int i, j, N = (int)_vec.size();
+    const _Tp* vec = &_vec[0];
+
+    const int PARENT=0;
+    const int RANK=1;
+
+    std::vector<int> _nodes(N*2);
+    int (*nodes)[2] = (int(*)[2])&_nodes[0];
+
+    // The first O(N) pass: create N single-vertex trees
+    for(i = 0; i < N; i++)
+    {
+        nodes[i][PARENT]=-1;
+        nodes[i][RANK] = 0;
+    }
+
+    // The main O(N^2) pass: merge connected components
+    for( i = 0; i < N; i++ )
+    {
+        int root = i;
+
+        // find root
+        while( nodes[root][PARENT] >= 0 )
+            root = nodes[root][PARENT];
+
+        for( j = 0; j < N; j++ )
+        {
+            if( i == j || !predicate(vec[i], vec[j]))
+                continue;
+            int root2 = j;
+
+            while( nodes[root2][PARENT] >= 0 )
+                root2 = nodes[root2][PARENT];
+
+            if( root2 != root )
+            {
+                // unite both trees
+                int rank = nodes[root][RANK], rank2 = nodes[root2][RANK];
+                if( rank > rank2 )
+                    nodes[root2][PARENT] = root;
+                else
+                {
+                    nodes[root][PARENT] = root2;
+                    nodes[root2][RANK] += rank == rank2;
+                    root = root2;
+                }
+                CV_Assert( nodes[root][PARENT] < 0 );
+
+                int k = j, parent;
+
+                // compress the path from node2 to root
+                while( (parent = nodes[k][PARENT]) >= 0 )
+                {
+                    nodes[k][PARENT] = root;
+                    k = parent;
+                }
+
+                // compress the path from node to root
+                k = i;
+                while( (parent = nodes[k][PARENT]) >= 0 )
+                {
+                    nodes[k][PARENT] = root;
+                    k = parent;
+                }
+            }
+        }
+    }
+
+    // Final O(N) pass: enumerate classes
+    labels.resize(N);
+    int nclasses = 0;
+
+    for( i = 0; i < N; i++ )
+    {
+        int root = i;
+        while( nodes[root][PARENT] >= 0 )
+            root = nodes[root][PARENT];
+        // re-use the rank as the class label
+        if( nodes[root][RANK] >= 0 )
+            nodes[root][RANK] = ~nclasses++;
+        labels[i] = ~nodes[root][RANK];
+    }
+
+    return nclasses;
+}
+
+} // cv
+
+#endif
diff --git a/3rdParty/include/opencv2/core/optim.hpp b/3rdParty/include/opencv2/core/optim.hpp
new file mode 100644
index 0000000..f61a2b9
--- /dev/null
+++ b/3rdParty/include/opencv2/core/optim.hpp
@@ -0,0 +1,302 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the OpenCV Foundation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_OPTIM_HPP
+#define OPENCV_OPTIM_HPP
+
+#include "opencv2/core.hpp"
+
+namespace cv
+{
+
+/** @addtogroup core_optim
+The algorithms in this section minimize or maximize function value within specified constraints or
+without any constraints.
+@{
+*/
+
+/** @brief Basic interface for all solvers
+ */
+class CV_EXPORTS MinProblemSolver : public Algorithm
+{
+public:
+    /** @brief Represents function being optimized
+     */
+    class CV_EXPORTS Function
+    {
+    public:
+        virtual ~Function() {}
+        virtual int getDims() const = 0;
+        virtual double getGradientEps() const;
+        virtual double calc(const double* x) const = 0;
+        virtual void getGradient(const double* x,double* grad);
+    };
+
+    /** @brief Getter for the optimized function.
+
+    The optimized function is represented by Function interface, which requires derivatives to
+    implement the calc(double*) and getDim() methods to evaluate the function.
+
+    @return Smart-pointer to an object that implements Function interface - it represents the
+    function that is being optimized. It can be empty, if no function was given so far.
+     */
+    virtual Ptr<Function> getFunction() const = 0;
+
+    /** @brief Setter for the optimized function.
+
+    *It should be called at least once before the call to* minimize(), as default value is not usable.
+
+    @param f The new function to optimize.
+     */
+    virtual void setFunction(const Ptr<Function>& f) = 0;
+
+    /** @brief Getter for the previously set terminal criteria for this algorithm.
+
+    @return Deep copy of the terminal criteria used at the moment.
+     */
+    virtual TermCriteria getTermCriteria() const = 0;
+
+    /** @brief Set terminal criteria for solver.
+
+    This method *is not necessary* to be called before the first call to minimize(), as the default
+    value is sensible.
+
+    Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when
+    the function values at the vertices of simplex are within termcrit.epsilon range or simplex
+    becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes
+    first.
+    @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure.
+     */
+    virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
+
+    /** @brief actually runs the algorithm and performs the minimization.
+
+    The sole input parameter determines the centroid of the starting simplex (roughly, it tells
+    where to start), all the others (terminal criteria, initial step, function to be minimized) are
+    supposed to be set via the setters before the call to this method or the default values (not
+    always sensible) will be used.
+
+    @param x The initial point, that will become a centroid of an initial simplex. After the algorithm
+    will terminate, it will be set to the point where the algorithm stops, the point of possible
+    minimum.
+    @return The value of a function at the point found.
+     */
+    virtual double minimize(InputOutputArray x) = 0;
+};
+
+/** @brief This class is used to perform the non-linear non-constrained minimization of a function,
+
+defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as
+**downhill simplex method**. The basic idea about the method can be obtained from
+<http://en.wikipedia.org/wiki/Nelder-Mead_method>.
+
+It should be noted, that this method, although deterministic, is rather a heuristic and therefore
+may converge to a local minima, not necessary a global one. It is iterative optimization technique,
+which at each step uses an information about the values of a function evaluated only at `n+1`
+points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At
+each step new point is chosen to evaluate function at, obtained value is compared with previous
+ones and based on this information simplex changes it's shape , slowly moving to the local minimum.
+Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear
+Conjugate Gradient method (which is also implemented in optim).
+
+Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the
+function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so
+small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some
+defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon.
+
+@note DownhillSolver is a derivative of the abstract interface
+cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to
+encapsulate the functionality, common to all non-linear optimization algorithms in the optim
+module.
+
+@note term criteria should meet following condition:
+@code
+    termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
+@endcode
+ */
+class CV_EXPORTS DownhillSolver : public MinProblemSolver
+{
+public:
+    /** @brief Returns the initial step that will be used in downhill simplex algorithm.
+
+    @param step Initial step that will be used in algorithm. Note, that although corresponding setter
+    accepts column-vectors as well as row-vectors, this method will return a row-vector.
+    @see DownhillSolver::setInitStep
+     */
+    virtual void getInitStep(OutputArray step) const=0;
+
+    /** @brief Sets the initial step that will be used in downhill simplex algorithm.
+
+    Step, together with initial point (given in DownhillSolver::minimize) are two `n`-dimensional
+    vectors that are used to determine the shape of initial simplex. Roughly said, initial point
+    determines the position of a simplex (it will become simplex's centroid), while step determines the
+    spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are
+    the initial step and initial point respectively, the vertices of a simplex will be:
+    \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes
+    projections of the initial step of *n*-th coordinate (the result of projection is treated to be
+    vector given by \f$s_i:=e_i\cdot\left<e_i\cdot s\right>\f$, where \f$e_i\f$ form canonical basis)
+
+    @param step Initial step that will be used in algorithm. Roughly said, it determines the spread
+    (size in each dimension) of an initial simplex.
+     */
+    virtual void setInitStep(InputArray step)=0;
+
+    /** @brief This function returns the reference to the ready-to-use DownhillSolver object.
+
+    All the parameters are optional, so this procedure can be called even without parameters at
+    all. In this case, the default values will be used. As default value for terminal criteria are
+    the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep()
+    should be called upon the obtained object, if the respective parameters were not given to
+    create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out
+    and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely
+    equivalent (and will drop the same errors in the same way, should invalid input be detected).
+    @param f Pointer to the function that will be minimized, similarly to the one you submit via
+    MinProblemSolver::setFunction.
+    @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one
+    you submit via MinProblemSolver::setInitStep.
+    @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
+    MinProblemSolver::setTermCriteria.
+     */
+    static Ptr<DownhillSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<MinProblemSolver::Function>(),
+                                      InputArray initStep=Mat_<double>(1,1,0.0),
+                                      TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
+};
+
+/** @brief This class is used to perform the non-linear non-constrained minimization of a function
+with known gradient,
+
+defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**.
+The implementation was done based on the beautifully clear explanatory article [An Introduction to
+the Conjugate Gradient Method Without the Agonizing
+Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard
+Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for
+example <http://en.wikipedia.org/wiki/Conjugate_gradient_method>) for numerically solving the
+systems of linear equations.
+
+It should be noted, that this method, although deterministic, is rather a heuristic method and
+therefore may converge to a local minima, not necessary a global one. What is even more disastrous,
+most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between
+local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may
+converge to it. Another obvious restriction is that it should be possible to compute the gradient of
+a function at any point, thus it is preferable to have analytic expression for gradient and
+computational burden should be born by the user.
+
+The latter responsibility is accomplished via the getGradient method of a
+MinProblemSolver::Function interface (which represents function being optimized). This method takes
+point a point in *n*-dimensional space (first argument represents the array of coordinates of that
+point) and compute its gradient (it should be stored in the second argument as an array).
+
+@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface.
+
+@note term criteria should meet following condition:
+@code
+    termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
+    // or
+    termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0
+@endcode
+ */
+class CV_EXPORTS ConjGradSolver : public MinProblemSolver
+{
+public:
+    /** @brief This function returns the reference to the ready-to-use ConjGradSolver object.
+
+    All the parameters are optional, so this procedure can be called even without parameters at
+    all. In this case, the default values will be used. As default value for terminal criteria are
+    the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained
+    object, if the function was not given to create(). Otherwise, the two ways (submit it to
+    create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent
+    (and will drop the same errors in the same way, should invalid input be detected).
+    @param f Pointer to the function that will be minimized, similarly to the one you submit via
+    MinProblemSolver::setFunction.
+    @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
+    MinProblemSolver::setTermCriteria.
+    */
+    static Ptr<ConjGradSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<ConjGradSolver::Function>(),
+                                      TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
+};
+
+//! return codes for cv::solveLP() function
+enum SolveLPResult
+{
+    SOLVELP_UNBOUNDED    = -2, //!< problem is unbounded (target function can achieve arbitrary high values)
+    SOLVELP_UNFEASIBLE    = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed)
+    SOLVELP_SINGLE    = 0, //!< there is only one maximum for target function
+    SOLVELP_MULTI    = 1 //!< there are multiple maxima for target function - the arbitrary one is returned
+};
+
+/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).
+
+What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as:
+
+\f[\mbox{Maximize } c\cdot x\\
+ \mbox{Subject to:}\\
+ Ax\leq b\\
+ x\geq 0\f]
+
+Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1`
+column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints.
+
+Simplex algorithm is one of many algorithms that are designed to handle this sort of problems
+efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve
+any problem written as above in polynomial time, while simplex method degenerates to exponential
+time for some special cases), it is well-studied, easy to implement and is shown to work well for
+real-life purposes.
+
+The particular implementation is taken almost verbatim from **Introduction to Algorithms, third
+edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the
+Bland's rule <http://en.wikipedia.org/wiki/Bland%27s_rule> is used to prevent cycling.
+
+@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should
+contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted,
+in the latter case it is understood to correspond to \f$c^T\f$.
+@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above
+and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers.
+@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the
+formulation above. It will contain 64-bit floating point numbers.
+@return One of cv::SolveLPResult
+ */
+CV_EXPORTS_W int solveLP(InputArray Func, InputArray Constr, OutputArray z);
+
+//! @}
+
+}// cv
+
+#endif
diff --git a/3rdParty/include/opencv2/core/ovx.hpp b/3rdParty/include/opencv2/core/ovx.hpp
new file mode 100644
index 0000000..8bb7d54
--- /dev/null
+++ b/3rdParty/include/opencv2/core/ovx.hpp
@@ -0,0 +1,28 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+// Copyright (C) 2016, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+// OpenVX related definitions and declarations
+
+#pragma once
+#ifndef OPENCV_OVX_HPP
+#define OPENCV_OVX_HPP
+
+#include "cvdef.h"
+
+namespace cv
+{
+/// Check if use of OpenVX is possible
+CV_EXPORTS_W bool haveOpenVX();
+
+/// Check if use of OpenVX is enabled
+CV_EXPORTS_W bool useOpenVX();
+
+/// Enable/disable use of OpenVX
+CV_EXPORTS_W void setUseOpenVX(bool flag);
+} // namespace cv
+
+#endif // OPENCV_OVX_HPP
diff --git a/3rdParty/include/opencv2/core/parallel/backend/parallel_for.openmp.hpp b/3rdParty/include/opencv2/core/parallel/backend/parallel_for.openmp.hpp
new file mode 100644
index 0000000..b172cac
--- /dev/null
+++ b/3rdParty/include/opencv2/core/parallel/backend/parallel_for.openmp.hpp
@@ -0,0 +1,72 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_PARALLEL_FOR_OPENMP_HPP
+#define OPENCV_CORE_PARALLEL_FOR_OPENMP_HPP
+
+#include "opencv2/core/parallel/parallel_backend.hpp"
+
+#if !defined(_OPENMP) && !defined(OPENCV_SKIP_OPENMP_PRESENSE_CHECK)
+#error "This file must be compiled with enabled OpenMP"
+#endif
+
+#include <omp.h>
+
+namespace cv { namespace parallel { namespace openmp {
+
+/** OpenMP parallel_for API implementation
+ *
+ * @sa setParallelForBackend
+ * @ingroup core_parallel_backend
+ */
+class ParallelForBackend : public ParallelForAPI
+{
+protected:
+    int numThreads;
+    int numThreadsMax;
+public:
+    ParallelForBackend()
+    {
+        numThreads = 0;
+        numThreadsMax = omp_get_max_threads();
+    }
+
+    virtual ~ParallelForBackend() {}
+
+    virtual void parallel_for(int tasks, FN_parallel_for_body_cb_t body_callback, void* callback_data) CV_OVERRIDE
+    {
+#pragma omp parallel for schedule(dynamic) num_threads(numThreads > 0 ? numThreads : numThreadsMax)
+        for (int i = 0; i < tasks; ++i)
+            body_callback(i, i + 1, callback_data);
+    }
+
+    virtual int getThreadNum() const CV_OVERRIDE
+    {
+        return omp_get_thread_num();
+    }
+
+    virtual int getNumThreads() const CV_OVERRIDE
+    {
+        return numThreads > 0
+               ? numThreads
+               : numThreadsMax;
+    }
+
+    virtual int setNumThreads(int nThreads) CV_OVERRIDE
+    {
+        int oldNumThreads = numThreads;
+        numThreads = nThreads;
+        // nothing needed as numThreads is used in #pragma omp parallel for directly
+        return oldNumThreads;
+    }
+
+    const char* getName() const CV_OVERRIDE
+    {
+        return "openmp";
+    }
+};
+
+}}}  // namespace
+
+#endif  // OPENCV_CORE_PARALLEL_FOR_OPENMP_HPP
diff --git a/3rdParty/include/opencv2/core/parallel/backend/parallel_for.tbb.hpp b/3rdParty/include/opencv2/core/parallel/backend/parallel_for.tbb.hpp
new file mode 100644
index 0000000..04b0c4c
--- /dev/null
+++ b/3rdParty/include/opencv2/core/parallel/backend/parallel_for.tbb.hpp
@@ -0,0 +1,153 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_PARALLEL_FOR_TBB_HPP
+#define OPENCV_CORE_PARALLEL_FOR_TBB_HPP
+
+#include "opencv2/core/parallel/parallel_backend.hpp"
+#include <opencv2/core/utils/logger.hpp>
+
+#ifndef TBB_SUPPRESS_DEPRECATED_MESSAGES  // supress warning
+#define TBB_SUPPRESS_DEPRECATED_MESSAGES 1
+#endif
+#include "tbb/tbb.h"
+#if !defined(TBB_INTERFACE_VERSION)
+#error "Unknows/unsupported TBB version"
+#endif
+
+#if TBB_INTERFACE_VERSION >= 8000
+#include "tbb/task_arena.h"
+#endif
+
+namespace cv { namespace parallel { namespace tbb {
+
+using namespace ::tbb;
+
+#if TBB_INTERFACE_VERSION >= 8000
+static tbb::task_arena& getArena()
+{
+    static tbb::task_arena tbbArena(tbb::task_arena::automatic);
+    return tbbArena;
+}
+#else
+static tbb::task_scheduler_init& getScheduler()
+{
+    static tbb::task_scheduler_init tbbScheduler(tbb::task_scheduler_init::deferred);
+    return tbbScheduler;
+}
+#endif
+
+/** TBB parallel_for API implementation
+ *
+ * @sa setParallelForBackend
+ * @ingroup core_parallel_backend
+ */
+class ParallelForBackend : public ParallelForAPI
+{
+protected:
+    int numThreads;
+    int numThreadsMax;
+public:
+    ParallelForBackend()
+    {
+        CV_LOG_INFO(NULL, "Initializing TBB parallel backend: TBB_INTERFACE_VERSION=" << TBB_INTERFACE_VERSION);
+        numThreads = 0;
+#if TBB_INTERFACE_VERSION >= 8000
+        (void)getArena();
+#else
+        (void)getScheduler();
+#endif
+    }
+
+    virtual ~ParallelForBackend() {}
+
+    class CallbackProxy
+    {
+        const FN_parallel_for_body_cb_t& callback;
+        void* const callback_data;
+        const int tasks;
+    public:
+        inline CallbackProxy(int tasks_, FN_parallel_for_body_cb_t& callback_, void* callback_data_)
+            : callback(callback_), callback_data(callback_data_), tasks(tasks_)
+        {
+            // nothing
+        }
+
+        void operator()(const tbb::blocked_range<int>& range) const
+        {
+            this->callback(range.begin(), range.end(), callback_data);
+        }
+
+        void operator()() const
+        {
+            tbb::parallel_for(tbb::blocked_range<int>(0, tasks), *this);
+        }
+    };
+
+    virtual void parallel_for(int tasks, FN_parallel_for_body_cb_t body_callback, void* callback_data) CV_OVERRIDE
+    {
+        CallbackProxy task(tasks, body_callback, callback_data);
+#if TBB_INTERFACE_VERSION >= 8000
+        getArena().execute(task);
+#else
+        task();
+#endif
+    }
+
+    virtual int getThreadNum() const CV_OVERRIDE
+    {
+#if TBB_INTERFACE_VERSION >= 9100
+        return tbb::this_task_arena::current_thread_index();
+#elif TBB_INTERFACE_VERSION >= 8000
+        return tbb::task_arena::current_thread_index();
+#else
+        return 0;
+#endif
+    }
+
+    virtual int getNumThreads() const CV_OVERRIDE
+    {
+#if TBB_INTERFACE_VERSION >= 9100
+    return getArena().max_concurrency();
+#elif TBB_INTERFACE_VERSION >= 8000
+    return numThreads > 0
+        ? numThreads
+        : tbb::task_scheduler_init::default_num_threads();
+#else
+    return getScheduler().is_active()
+           ? numThreads
+           : tbb::task_scheduler_init::default_num_threads();
+#endif
+    }
+
+    virtual int setNumThreads(int nThreads) CV_OVERRIDE
+    {
+        int oldNumThreads = numThreads;
+        numThreads = nThreads;
+
+#if TBB_INTERFACE_VERSION >= 8000
+        auto& tbbArena = getArena();
+        if (tbbArena.is_active())
+            tbbArena.terminate();
+        if (numThreads > 0)
+            tbbArena.initialize(numThreads);
+#else
+        auto& tbbScheduler = getScheduler();
+        if (tbbScheduler.is_active())
+            tbbScheduler.terminate();
+        if (numThreads > 0)
+            tbbScheduler.initialize(numThreads);
+#endif
+        return oldNumThreads;
+    }
+
+    const char* getName() const CV_OVERRIDE
+    {
+        return "tbb";
+    }
+};
+
+}}}  // namespace
+
+#endif  // OPENCV_CORE_PARALLEL_FOR_TBB_HPP
diff --git a/3rdParty/include/opencv2/core/parallel/parallel_backend.hpp b/3rdParty/include/opencv2/core/parallel/parallel_backend.hpp
new file mode 100644
index 0000000..c3e8333
--- /dev/null
+++ b/3rdParty/include/opencv2/core/parallel/parallel_backend.hpp
@@ -0,0 +1,90 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_PARALLEL_BACKEND_HPP
+#define OPENCV_CORE_PARALLEL_BACKEND_HPP
+
+#include "opencv2/core/cvdef.h"
+#include <memory>
+
+namespace cv { namespace parallel {
+#ifndef CV_API_CALL
+#define CV_API_CALL
+#endif
+
+/** @addtogroup core_parallel_backend
+ * @{
+ * API below is provided to resolve problem of CPU resource over-subscription by multiple thread pools from different multi-threading frameworks.
+ * This is common problem for cases when OpenCV compiled threading framework is different from the Users Applications framework.
+ *
+ * Applications can replace OpenCV `parallel_for()` backend with own implementation (to reuse Application's thread pool).
+ *
+ *
+ * ### Backend API usage examples
+ *
+ * #### Intel TBB
+ *
+ * - include header with simple implementation of TBB backend:
+ *   @snippet parallel_backend/example-tbb.cpp tbb_include
+ * - execute backend replacement code:
+ *   @snippet parallel_backend/example-tbb.cpp tbb_backend
+ * - configuration of compiler/linker options is responsibility of Application's scripts
+ *
+ * #### OpenMP
+ *
+ * - include header with simple implementation of OpenMP backend:
+ *   @snippet parallel_backend/example-openmp.cpp openmp_include
+ * - execute backend replacement code:
+ *   @snippet parallel_backend/example-openmp.cpp openmp_backend
+ * - Configuration of compiler/linker options is responsibility of Application's scripts
+ *
+ *
+ * ### Plugins support
+ *
+ * Runtime configuration options:
+ * - change backend priority: `OPENCV_PARALLEL_PRIORITY_<backend>=9999`
+ * - disable backend: `OPENCV_PARALLEL_PRIORITY_<backend>=0`
+ * - specify list of backends with high priority (>100000): `OPENCV_PARALLEL_PRIORITY_LIST=TBB,OPENMP`. Unknown backends are registered as new plugins.
+ *
+ */
+
+/** Interface for parallel_for backends implementations
+ *
+ * @sa setParallelForBackend
+ */
+class CV_EXPORTS ParallelForAPI
+{
+public:
+    virtual ~ParallelForAPI();
+
+    typedef void (CV_API_CALL *FN_parallel_for_body_cb_t)(int start, int end, void* data);
+
+    virtual void parallel_for(int tasks, FN_parallel_for_body_cb_t body_callback, void* callback_data) = 0;
+
+    virtual int getThreadNum() const = 0;
+
+    virtual int getNumThreads() const = 0;
+
+    virtual int setNumThreads(int nThreads) = 0;
+
+    virtual const char* getName() const = 0;
+};
+
+/** @brief Replace OpenCV parallel_for backend
+ *
+ * Application can replace OpenCV `parallel_for()` backend with own implementation.
+ *
+ * @note This call is not thread-safe. Consider calling this function from the `main()` before any other OpenCV processing functions (and without any other created threads).
+ */
+CV_EXPORTS void setParallelForBackend(const std::shared_ptr<ParallelForAPI>& api, bool propagateNumThreads = true);
+
+/** @brief Change OpenCV parallel_for backend
+ *
+ * @note This call is not thread-safe. Consider calling this function from the `main()` before any other OpenCV processing functions (and without any other created threads).
+ */
+CV_EXPORTS_W bool setParallelForBackend(const std::string& backendName, bool propagateNumThreads = true);
+
+//! @}
+}}  // namespace
+#endif  // OPENCV_CORE_PARALLEL_BACKEND_HPP
diff --git a/3rdParty/include/opencv2/core/persistence.hpp b/3rdParty/include/opencv2/core/persistence.hpp
new file mode 100644
index 0000000..276f640
--- /dev/null
+++ b/3rdParty/include/opencv2/core/persistence.hpp
@@ -0,0 +1,1350 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_PERSISTENCE_HPP
+#define OPENCV_CORE_PERSISTENCE_HPP
+
+#ifndef CV_DOXYGEN
+/// Define to support persistence legacy formats
+#define CV__LEGACY_PERSISTENCE
+#endif
+
+#ifndef __cplusplus
+#  error persistence.hpp header must be compiled as C++
+#endif
+
+//! @addtogroup core_c
+//! @{
+
+/** @brief "black box" representation of the file storage associated with a file on disk.
+
+Several functions that are described below take CvFileStorage\* as inputs and allow the user to
+save or to load hierarchical collections that consist of scalar values, standard CXCore objects
+(such as matrices, sequences, graphs), and user-defined objects.
+
+OpenCV can read and write data in XML (<http://www.w3c.org/XML>), YAML (<http://www.yaml.org>) or
+JSON (<http://www.json.org/>) formats. Below is an example of 3x3 floating-point identity matrix A,
+stored in XML and YAML files
+using CXCore functions:
+XML:
+@code{.xml}
+    <?xml version="1.0">
+    <opencv_storage>
+    <A type_id="opencv-matrix">
+      <rows>3</rows>
+      <cols>3</cols>
+      <dt>f</dt>
+      <data>1. 0. 0. 0. 1. 0. 0. 0. 1.</data>
+    </A>
+    </opencv_storage>
+@endcode
+YAML:
+@code{.yaml}
+    %YAML:1.0
+    A: !!opencv-matrix
+      rows: 3
+      cols: 3
+      dt: f
+      data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.]
+@endcode
+As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses
+indentation for that purpose (similar to the Python programming language).
+
+The same functions can read and write data in both formats; the particular format is determined by
+the extension of the opened file, ".xml" for XML files, ".yml" or ".yaml" for YAML and ".json" for
+JSON.
+ */
+
+//! @} core_c
+
+#include "opencv2/core/types.hpp"
+#include "opencv2/core/mat.hpp"
+
+namespace cv {
+
+/** @addtogroup core_xml
+
+XML/YAML/JSON file storages.     {#xml_storage}
+=======================
+Writing to a file storage.
+--------------------------
+You can store and then restore various OpenCV data structures to/from XML (<http://www.w3c.org/XML>),
+YAML (<http://www.yaml.org>) or JSON (<http://www.json.org/>) formats. Also, it is possible to store
+and load arbitrarily complex data structures, which include OpenCV data structures, as well as
+primitive data types (integer and floating-point numbers and text strings) as their elements.
+
+Use the following procedure to write something to XML, YAML or JSON:
+-# Create new FileStorage and open it for writing. It can be done with a single call to
+FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor
+and then call FileStorage::open. Format of the file (XML, YAML or JSON) is determined from the filename
+extension (".xml", ".yml"/".yaml" and ".json", respectively)
+-# Write all the data you want using the streaming operator `<<`, just like in the case of STL
+streams.
+-# Close the file using FileStorage::release. FileStorage destructor also closes the file.
+
+Here is an example:
+@code
+    #include "opencv2/core.hpp"
+    #include <time.h>
+
+    using namespace cv;
+
+    int main(int, char** argv)
+    {
+        FileStorage fs("test.yml", FileStorage::WRITE);
+
+        fs << "frameCount" << 5;
+        time_t rawtime; time(&rawtime);
+        fs << "calibrationDate" << asctime(localtime(&rawtime));
+        Mat cameraMatrix = (Mat_<double>(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1);
+        Mat distCoeffs = (Mat_<double>(5,1) << 0.1, 0.01, -0.001, 0, 0);
+        fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs;
+        fs << "features" << "[";
+        for( int i = 0; i < 3; i++ )
+        {
+            int x = rand() % 640;
+            int y = rand() % 480;
+            uchar lbp = rand() % 256;
+
+            fs << "{:" << "x" << x << "y" << y << "lbp" << "[:";
+            for( int j = 0; j < 8; j++ )
+                fs << ((lbp >> j) & 1);
+            fs << "]" << "}";
+        }
+        fs << "]";
+        fs.release();
+        return 0;
+    }
+@endcode
+The sample above stores to YML an integer, a text string (calibration date), 2 matrices, and a custom
+structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here
+is output of the sample:
+@code{.yaml}
+%YAML:1.0
+frameCount: 5
+calibrationDate: "Fri Jun 17 14:09:29 2011\n"
+cameraMatrix: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ]
+distCoeffs: !!opencv-matrix
+   rows: 5
+   cols: 1
+   dt: d
+   data: [ 1.0000000000000001e-01, 1.0000000000000000e-02,
+       -1.0000000000000000e-03, 0., 0. ]
+features:
+   - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] }
+   - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] }
+   - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] }
+@endcode
+
+As an exercise, you can replace ".yml" with ".xml" or ".json" in the sample above and see, how the
+corresponding XML file will look like.
+
+Several things can be noted by looking at the sample code and the output:
+
+-   The produced YAML (and XML/JSON) consists of heterogeneous collections that can be nested. There are
+    2 types of collections: named collections (mappings) and unnamed collections (sequences). In mappings
+    each element has a name and is accessed by name. This is similar to structures and std::map in
+    C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by
+    indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python.
+    "Heterogeneous" means that elements of each single collection can have different types.
+
+    Top-level collection in YAML/XML/JSON is a mapping. Each matrix is stored as a mapping, and the matrix
+    elements are stored as a sequence. Then, there is a sequence of features, where each feature is
+    represented a mapping, and lbp value in a nested sequence.
+
+-   When you write to a mapping (a structure), you write element name followed by its value. When you
+    write to a sequence, you simply write the elements one by one. OpenCV data structures (such as
+    cv::Mat) are written in absolutely the same way as simple C data structures - using `<<`
+    operator.
+
+-   To write a mapping, you first write the special string `{` to the storage, then write the
+    elements as pairs (`fs << <element_name> << <element_value>`) and then write the closing
+    `}`.
+
+-   To write a sequence, you first write the special string `[`, then write the elements, then
+    write the closing `]`.
+
+-   In YAML/JSON (but not XML), mappings and sequences can be written in a compact Python-like inline
+    form. In the sample above matrix elements, as well as each feature, including its lbp value, is
+    stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the
+    opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the
+    data is written to XML, those extra `:` are ignored.
+
+Reading data from a file storage.
+---------------------------------
+To read the previously written XML, YAML or JSON file, do the following:
+-#  Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method.
+    In the current implementation the whole file is parsed and the whole representation of file
+    storage is built in memory as a hierarchy of file nodes (see FileNode)
+
+-#  Read the data you are interested in. Use FileStorage::operator [], FileNode::operator []
+    and/or FileNodeIterator.
+
+-#  Close the storage using FileStorage::release.
+
+Here is how to read the file created by the code sample above:
+@code
+    FileStorage fs2("test.yml", FileStorage::READ);
+
+    // first method: use (type) operator on FileNode.
+    int frameCount = (int)fs2["frameCount"];
+
+    String date;
+    // second method: use FileNode::operator >>
+    fs2["calibrationDate"] >> date;
+
+    Mat cameraMatrix2, distCoeffs2;
+    fs2["cameraMatrix"] >> cameraMatrix2;
+    fs2["distCoeffs"] >> distCoeffs2;
+
+    cout << "frameCount: " << frameCount << endl
+         << "calibration date: " << date << endl
+         << "camera matrix: " << cameraMatrix2 << endl
+         << "distortion coeffs: " << distCoeffs2 << endl;
+
+    FileNode features = fs2["features"];
+    FileNodeIterator it = features.begin(), it_end = features.end();
+    int idx = 0;
+    std::vector<uchar> lbpval;
+
+    // iterate through a sequence using FileNodeIterator
+    for( ; it != it_end; ++it, idx++ )
+    {
+        cout << "feature #" << idx << ": ";
+        cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";
+        // you can also easily read numerical arrays using FileNode >> std::vector operator.
+        (*it)["lbp"] >> lbpval;
+        for( int i = 0; i < (int)lbpval.size(); i++ )
+            cout << " " << (int)lbpval[i];
+        cout << ")" << endl;
+    }
+    fs2.release();
+@endcode
+
+Format specification    {#format_spec}
+--------------------
+`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types:
+-   `u` 8-bit unsigned number
+-   `c` 8-bit signed number
+-   `w` 16-bit unsigned number
+-   `s` 16-bit signed number
+-   `i` 32-bit signed number
+-   `f` single precision floating-point number
+-   `d` double precision floating-point number
+-   `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to
+    store structures with links between the elements.
+
+`count` is the optional counter of values of a given type. For example, `2if` means that each array
+element is a structure of 2 integers, followed by a single-precision floating-point number. The
+equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u`
+means that the array consists of bytes, and `2d` means the array consists of pairs of doubles.
+
+@see @ref samples/cpp/filestorage.cpp
+*/
+
+//! @{
+
+/** @example samples/cpp/filestorage.cpp
+A complete example using the FileStorage interface
+*/
+
+////////////////////////// XML & YAML I/O //////////////////////////
+
+class CV_EXPORTS FileNode;
+class CV_EXPORTS FileNodeIterator;
+
+/** @brief XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or
+reading data to/from a file.
+ */
+class CV_EXPORTS_W FileStorage
+{
+public:
+    //! file storage mode
+    enum Mode
+    {
+        READ        = 0, //!< value, open the file for reading
+        WRITE       = 1, //!< value, open the file for writing
+        APPEND      = 2, //!< value, open the file for appending
+        MEMORY      = 4, //!< flag, read data from source or write data to the internal buffer (which is
+        //!< returned by FileStorage::release)
+        FORMAT_MASK = (7<<3), //!< mask for format flags
+        FORMAT_AUTO = 0,      //!< flag, auto format
+        FORMAT_XML  = (1<<3), //!< flag, XML format
+        FORMAT_YAML = (2<<3), //!< flag, YAML format
+        FORMAT_JSON = (3<<3), //!< flag, JSON format
+
+        BASE64      = 64,     //!< flag, write rawdata in Base64 by default. (consider using WRITE_BASE64)
+        WRITE_BASE64 = BASE64 | WRITE, //!< flag, enable both WRITE and BASE64
+    };
+    enum State
+    {
+        UNDEFINED      = 0,
+        VALUE_EXPECTED = 1,
+        NAME_EXPECTED  = 2,
+        INSIDE_MAP     = 4
+    };
+
+    /** @brief The constructors.
+
+     The full constructor opens the file. Alternatively you can use the default constructor and then
+     call FileStorage::open.
+     */
+    CV_WRAP FileStorage();
+
+    /** @overload
+     @copydoc open()
+     */
+    CV_WRAP FileStorage(const String& filename, int flags, const String& encoding=String());
+
+    //! the destructor. calls release()
+    virtual ~FileStorage();
+
+    /** @brief Opens a file.
+
+     See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release
+     before opening the file.
+     @param filename Name of the file to open or the text string to read the data from.
+     Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON
+     respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both
+     FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify
+     the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters.
+     You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to
+     FileStorage::BASE64 flag.
+     @param flags Mode of operation. One of FileStorage::Mode
+     @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and
+     you should use 8-bit encoding instead of it.
+     */
+    CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String());
+
+    /** @brief Checks whether the file is opened.
+
+     @returns true if the object is associated with the current file and false otherwise. It is a
+     good practice to call this method after you tried to open a file.
+     */
+    CV_WRAP virtual bool isOpened() const;
+
+    /** @brief Closes the file and releases all the memory buffers.
+
+     Call this method after all I/O operations with the storage are finished.
+     */
+    CV_WRAP virtual void release();
+
+    /** @brief Closes the file and releases all the memory buffers.
+
+     Call this method after all I/O operations with the storage are finished. If the storage was
+     opened for writing data and FileStorage::WRITE was specified
+     */
+    CV_WRAP virtual String releaseAndGetString();
+
+    /** @brief Returns the first element of the top-level mapping.
+     @returns The first element of the top-level mapping.
+     */
+    CV_WRAP FileNode getFirstTopLevelNode() const;
+
+    /** @brief Returns the top-level mapping
+     @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file.
+     However, YAML supports multiple streams and so there can be several.
+     @returns The top-level mapping.
+     */
+    CV_WRAP FileNode root(int streamidx=0) const;
+
+    /** @brief Returns the specified element of the top-level mapping.
+     @param nodename Name of the file node.
+     @returns Node with the given name.
+     */
+    FileNode operator[](const String& nodename) const;
+
+    /** @overload */
+    CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const;
+
+    /**
+     * @brief Simplified writing API to use with bindings.
+     * @param name Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string.
+     * @param val Value of the written object.
+     */
+    CV_WRAP void write(const String& name, int val);
+    /// @overload
+    CV_WRAP void write(const String& name, double val);
+    /// @overload
+    CV_WRAP void write(const String& name, const String& val);
+    /// @overload
+    CV_WRAP void write(const String& name, const Mat& val);
+    /// @overload
+    CV_WRAP void write(const String& name, const std::vector<String>& val);
+
+    /** @brief Writes multiple numbers.
+
+     Writes one or more numbers of the specified format to the currently written structure. Usually it is
+     more convenient to use operator `<<` instead of this method.
+     @param fmt Specification of each array element, see @ref format_spec "format specification"
+     @param vec Pointer to the written array.
+     @param len Number of the uchar elements to write.
+     */
+    void writeRaw( const String& fmt, const void* vec, size_t len );
+
+    /** @brief Writes a comment.
+
+     The function writes a comment into file storage. The comments are skipped when the storage is read.
+     @param comment The written comment, single-line or multi-line
+     @param append If true, the function tries to put the comment at the end of current line.
+     Else if the comment is multi-line, or if it does not fit at the end of the current
+     line, the comment starts a new line.
+     */
+    CV_WRAP void writeComment(const String& comment, bool append = false);
+
+    /** @brief Starts to write a nested structure (sequence or a mapping).
+    @param name name of the structure. When writing to sequences (a.k.a. "arrays"), pass an empty string.
+    @param flags type of the structure (FileNode::MAP or FileNode::SEQ (both with optional FileNode::FLOW)).
+    @param typeName optional name of the type you store. The effect of setting this depends on the storage format.
+    I.e. if the format has a specification for storing type information, this parameter is used.
+    */
+    CV_WRAP void startWriteStruct(const String& name, int flags, const String& typeName=String());
+
+    /** @brief Finishes writing nested structure (should pair startWriteStruct())
+    */
+    CV_WRAP void endWriteStruct();
+
+    /** @brief Returns the normalized object name for the specified name of a file.
+    @param filename Name of a file
+    @returns The normalized object name.
+     */
+    static String getDefaultObjectName(const String& filename);
+
+    /** @brief Returns the current format.
+     * @returns The current format, see FileStorage::Mode
+     */
+    CV_WRAP int getFormat() const;
+
+    int state;
+    std::string elname;
+
+    class Impl;
+    Ptr<Impl> p;
+};
+
+/** @brief File Storage Node class.
+
+The node is used to store each and every element of the file storage opened for reading. When
+XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of
+nodes. Each node can be a "leaf" that is contain a single number or a string, or be a collection of
+other nodes. There can be named collections (mappings) where each element has a name and it is
+accessed by a name, and ordered collections (sequences) where elements do not have names but rather
+accessed by index. Type of the file node can be determined using FileNode::type method.
+
+Note that file nodes are only used for navigating file storages opened for reading. When a file
+storage is opened for writing, no data is stored in memory after it is written.
+ */
+class CV_EXPORTS_W_SIMPLE FileNode
+{
+public:
+    //! type of the file storage node
+    enum
+    {
+        NONE      = 0, //!< empty node
+        INT       = 1, //!< an integer
+        REAL      = 2, //!< floating-point number
+        FLOAT     = REAL, //!< synonym or REAL
+        STR       = 3, //!< text string in UTF-8 encoding
+        STRING    = STR, //!< synonym for STR
+        SEQ       = 4, //!< sequence
+        MAP       = 5, //!< mapping
+        TYPE_MASK = 7,
+
+        FLOW      = 8,  //!< compact representation of a sequence or mapping. Used only by YAML writer
+        UNIFORM   = 8,  //!< if set, means that all the collection elements are numbers of the same type (real's or int's).
+        //!< UNIFORM is used only when reading FileStorage; FLOW is used only when writing. So they share the same bit
+        EMPTY     = 16, //!< empty structure (sequence or mapping)
+        NAMED     = 32  //!< the node has a name (i.e. it is element of a mapping).
+    };
+    /** @brief The constructors.
+
+     These constructors are used to create a default file node, construct it from obsolete structures or
+     from the another file node.
+     */
+    CV_WRAP FileNode();
+
+    /** @overload
+     @param fs Pointer to the file storage structure.
+     @param blockIdx Index of the memory block where the file node is stored
+     @param ofs Offset in bytes from the beginning of the serialized storage
+
+     @deprecated
+     */
+    FileNode(const FileStorage* fs, size_t blockIdx, size_t ofs);
+
+    /** @overload
+     @param node File node to be used as initialization for the created file node.
+     */
+    FileNode(const FileNode& node);
+
+    FileNode& operator=(const FileNode& node);
+
+    /** @brief Returns element of a mapping node or a sequence node.
+     @param nodename Name of an element in the mapping node.
+     @returns Returns the element with the given identifier.
+     */
+    FileNode operator[](const String& nodename) const;
+
+    /** @overload
+     @param nodename Name of an element in the mapping node.
+     */
+    CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const;
+
+    /** @overload
+     @param i Index of an element in the sequence node.
+     */
+    CV_WRAP_AS(at) FileNode operator[](int i) const;
+
+    /** @brief Returns keys of a mapping node.
+     @returns Keys of a mapping node.
+     */
+    CV_WRAP std::vector<String> keys() const;
+
+    /** @brief Returns type of the node.
+     @returns Type of the node. See FileNode::Type
+     */
+    CV_WRAP int type() const;
+
+    //! returns true if the node is empty
+    CV_WRAP bool empty() const;
+    //! returns true if the node is a "none" object
+    CV_WRAP bool isNone() const;
+    //! returns true if the node is a sequence
+    CV_WRAP bool isSeq() const;
+    //! returns true if the node is a mapping
+    CV_WRAP bool isMap() const;
+    //! returns true if the node is an integer
+    CV_WRAP bool isInt() const;
+    //! returns true if the node is a floating-point number
+    CV_WRAP bool isReal() const;
+    //! returns true if the node is a text string
+    CV_WRAP bool isString() const;
+    //! returns true if the node has a name
+    CV_WRAP bool isNamed() const;
+    //! returns the node name or an empty string if the node is nameless
+    CV_WRAP std::string name() const;
+    //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise.
+    CV_WRAP size_t size() const;
+    //! returns raw size of the FileNode in bytes
+    CV_WRAP size_t rawSize() const;
+    //! returns the node content as an integer. If the node stores floating-point number, it is rounded.
+    operator int() const;
+    //! returns the node content as float
+    operator float() const;
+    //! returns the node content as double
+    operator double() const;
+    //! returns the node content as text string
+    inline operator std::string() const { return this->string(); }
+
+    static bool isMap(int flags);
+    static bool isSeq(int flags);
+    static bool isCollection(int flags);
+    static bool isEmptyCollection(int flags);
+    static bool isFlow(int flags);
+
+    uchar* ptr();
+    const uchar* ptr() const;
+
+    //! returns iterator pointing to the first node element
+    FileNodeIterator begin() const;
+    //! returns iterator pointing to the element following the last node element
+    FileNodeIterator end() const;
+
+    /** @brief Reads node elements to the buffer with the specified format.
+
+    Usually it is more convenient to use operator `>>` instead of this method.
+    @param fmt Specification of each array element. See @ref format_spec "format specification"
+    @param vec Pointer to the destination array.
+    @param len Number of bytes to read (buffer size limit). If it is greater than number of
+               remaining elements then all of them will be read.
+     */
+    void readRaw( const String& fmt, void* vec, size_t len ) const;
+
+    /** Internal method used when reading FileStorage.
+     Sets the type (int, real or string) and value of the previously created node.
+     */
+    void setValue( int type, const void* value, int len=-1 );
+
+    //! Simplified reading API to use with bindings.
+    CV_WRAP double real() const;
+    //! Simplified reading API to use with bindings.
+    CV_WRAP std::string string() const;
+    //! Simplified reading API to use with bindings.
+    CV_WRAP Mat mat() const;
+
+    //protected:
+    FileNode(FileStorage::Impl* fs, size_t blockIdx, size_t ofs);
+
+    FileStorage::Impl* fs;
+    size_t blockIdx;
+    size_t ofs;
+};
+
+
+/** @brief used to iterate through sequences and mappings.
+
+ A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a
+ sequence, stored in node. See the data reading sample in the beginning of the section.
+ */
+class CV_EXPORTS FileNodeIterator
+{
+public:
+    /** @brief The constructors.
+
+     These constructors are used to create a default iterator, set it to specific element in a file node
+     or construct it from another iterator.
+     */
+    FileNodeIterator();
+
+    /** @overload
+     @param node File node - the collection to iterate over;
+        it can be a scalar (equivalent to 1-element collection) or "none" (equivalent to empty collection).
+     @param seekEnd - true if iterator needs to be set after the last element of the node;
+        that is:
+            * node.begin() => FileNodeIterator(node, false)
+            * node.end() => FileNodeIterator(node, true)
+     */
+    FileNodeIterator(const FileNode& node, bool seekEnd);
+
+    /** @overload
+     @param it Iterator to be used as initialization for the created iterator.
+     */
+    FileNodeIterator(const FileNodeIterator& it);
+
+    FileNodeIterator& operator=(const FileNodeIterator& it);
+
+    //! returns the currently observed element
+    FileNode operator *() const;
+
+    //! moves iterator to the next node
+    FileNodeIterator& operator ++ ();
+    //! moves iterator to the next node
+    FileNodeIterator operator ++ (int);
+    //! moves iterator forward by the specified offset (possibly negative)
+    FileNodeIterator& operator += (int ofs);
+
+    /** @brief Reads node elements to the buffer with the specified format.
+
+    Usually it is more convenient to use operator `>>` instead of this method.
+    @param fmt Specification of each array element. See @ref format_spec "format specification"
+    @param vec Pointer to the destination array.
+    @param len Number of bytes to read (buffer size limit). If it is greater than number of
+               remaining elements then all of them will be read.
+     */
+    FileNodeIterator& readRaw( const String& fmt, void* vec,
+                               size_t len=(size_t)INT_MAX );
+
+    //! returns the number of remaining (not read yet) elements
+    size_t remaining() const;
+
+    bool equalTo(const FileNodeIterator& it) const;
+
+protected:
+    FileStorage::Impl* fs;
+    size_t blockIdx;
+    size_t ofs;
+    size_t blockSize;
+    size_t nodeNElems;
+    size_t idx;
+};
+
+//! @} core_xml
+
+/////////////////// XML & YAML I/O implementation //////////////////
+
+//! @relates cv::FileStorage
+//! @{
+
+CV_EXPORTS void write( FileStorage& fs, const String& name, int value );
+CV_EXPORTS void write( FileStorage& fs, const String& name, float value );
+CV_EXPORTS void write( FileStorage& fs, const String& name, double value );
+CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value );
+CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value );
+CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value );
+#ifdef CV__LEGACY_PERSISTENCE
+CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector<KeyPoint>& value);
+CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector<DMatch>& value);
+#endif
+
+CV_EXPORTS void writeScalar( FileStorage& fs, int value );
+CV_EXPORTS void writeScalar( FileStorage& fs, float value );
+CV_EXPORTS void writeScalar( FileStorage& fs, double value );
+CV_EXPORTS void writeScalar( FileStorage& fs, const String& value );
+
+//! @}
+
+//! @relates cv::FileNode
+//! @{
+
+CV_EXPORTS void read(const FileNode& node, int& value, int default_value);
+CV_EXPORTS void read(const FileNode& node, float& value, float default_value);
+CV_EXPORTS void read(const FileNode& node, double& value, double default_value);
+CV_EXPORTS void read(const FileNode& node, std::string& value, const std::string& default_value);
+CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() );
+CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() );
+#ifdef CV__LEGACY_PERSISTENCE
+CV_EXPORTS void read(const FileNode& node, std::vector<KeyPoint>& keypoints);
+CV_EXPORTS void read(const FileNode& node, std::vector<DMatch>& matches);
+#endif
+CV_EXPORTS void read(const FileNode& node, KeyPoint& value, const KeyPoint& default_value);
+CV_EXPORTS void read(const FileNode& node, DMatch& value, const DMatch& default_value);
+
+template<typename _Tp> static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value)
+{
+    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
+    value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
+}
+
+template<typename _Tp> static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value)
+{
+    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
+    value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
+                                                            saturate_cast<_Tp>(temp[2]));
+}
+
+template<typename _Tp> static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value)
+{
+    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
+    value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
+}
+
+template<typename _Tp> static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value)
+{
+    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
+    value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
+}
+
+template<typename _Tp> static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value)
+{
+    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
+    value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
+                                                          saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3]));
+}
+
+template<typename _Tp, int cn> static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value)
+{
+    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
+    value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]);
+}
+
+template<typename _Tp, int m, int n> static inline void read(const FileNode& node, Matx<_Tp, m, n>& value, const Matx<_Tp, m, n>& default_matx = Matx<_Tp, m, n>())
+{
+    Mat temp;
+    read(node, temp); // read as a Mat class
+
+    if (temp.empty())
+        value = default_matx;
+    else
+        value = Matx<_Tp, m, n>(temp);
+}
+
+template<typename _Tp> static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value)
+{
+    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
+    value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
+                                                            saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3]));
+}
+
+static inline void read(const FileNode& node, Range& value, const Range& default_value)
+{
+    Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end);
+    read(node, temp, default_temp);
+    value.start = temp.x; value.end = temp.y;
+}
+
+//! @}
+
+/** @brief Writes string to a file storage.
+@relates cv::FileStorage
+ */
+CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str);
+
+//! @cond IGNORED
+
+namespace internal
+{
+    class CV_EXPORTS WriteStructContext
+    {
+    public:
+        WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String());
+        ~WriteStructContext();
+    private:
+        FileStorage* fs;
+    };
+
+    template<typename _Tp, int numflag> class VecWriterProxy
+    {
+    public:
+        VecWriterProxy( FileStorage* _fs ) : fs(_fs) {}
+        void operator()(const std::vector<_Tp>& vec) const
+        {
+            size_t count = vec.size();
+            for (size_t i = 0; i < count; i++)
+                write(*fs, vec[i]);
+        }
+    private:
+        FileStorage* fs;
+    };
+
+    template<typename _Tp> class VecWriterProxy<_Tp, 1>
+    {
+    public:
+        VecWriterProxy( FileStorage* _fs ) : fs(_fs) {}
+        void operator()(const std::vector<_Tp>& vec) const
+        {
+            int _fmt = traits::SafeFmt<_Tp>::fmt;
+            char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' };
+            fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp));
+        }
+    private:
+        FileStorage* fs;
+    };
+
+    template<typename _Tp, int numflag> class VecReaderProxy
+    {
+    public:
+        VecReaderProxy( FileNodeIterator* _it ) : it(_it) {}
+        void operator()(std::vector<_Tp>& vec, size_t count) const
+        {
+            count = std::min(count, it->remaining());
+            vec.resize(count);
+            for (size_t i = 0; i < count; i++, ++(*it))
+                read(**it, vec[i], _Tp());
+        }
+    private:
+        FileNodeIterator* it;
+    };
+
+    template<typename _Tp> class VecReaderProxy<_Tp, 1>
+    {
+    public:
+        VecReaderProxy( FileNodeIterator* _it ) : it(_it) {}
+        void operator()(std::vector<_Tp>& vec, size_t count) const
+        {
+            size_t remaining = it->remaining();
+            size_t cn = DataType<_Tp>::channels;
+            int _fmt = traits::SafeFmt<_Tp>::fmt;
+            CV_Assert((_fmt >> 8) < 9);
+            char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' };
+            CV_Assert((remaining % cn) == 0);
+            size_t remaining1 = remaining / cn;
+            count = count > remaining1 ? remaining1 : count;
+            vec.resize(count);
+            it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp));
+        }
+    private:
+        FileNodeIterator* it;
+    };
+
+} // internal
+
+//! @endcond
+
+//! @relates cv::FileStorage
+//! @{
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const _Tp& value)
+{
+    write(fs, String(), value);
+}
+
+template<> inline
+void write( FileStorage& fs, const int& value )
+{
+    writeScalar(fs, value);
+}
+
+template<> inline
+void write( FileStorage& fs, const float& value )
+{
+    writeScalar(fs, value);
+}
+
+template<> inline
+void write( FileStorage& fs, const double& value )
+{
+    writeScalar(fs, value);
+}
+
+template<> inline
+void write( FileStorage& fs, const String& value )
+{
+    writeScalar(fs, value);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const Point_<_Tp>& pt )
+{
+    write(fs, pt.x);
+    write(fs, pt.y);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const Point3_<_Tp>& pt )
+{
+    write(fs, pt.x);
+    write(fs, pt.y);
+    write(fs, pt.z);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const Size_<_Tp>& sz )
+{
+    write(fs, sz.width);
+    write(fs, sz.height);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const Complex<_Tp>& c )
+{
+    write(fs, c.re);
+    write(fs, c.im);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const Rect_<_Tp>& r )
+{
+    write(fs, r.x);
+    write(fs, r.y);
+    write(fs, r.width);
+    write(fs, r.height);
+}
+
+template<typename _Tp, int cn> static inline
+void write(FileStorage& fs, const Vec<_Tp, cn>& v )
+{
+    for(int i = 0; i < cn; i++)
+        write(fs, v.val[i]);
+}
+
+template<typename _Tp, int m, int n> static inline
+void write(FileStorage& fs, const Matx<_Tp, m, n>& x )
+{
+    write(fs, Mat(x)); // write as a Mat class
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const Scalar_<_Tp>& s )
+{
+    write(fs, s.val[0]);
+    write(fs, s.val[1]);
+    write(fs, s.val[2]);
+    write(fs, s.val[3]);
+}
+
+static inline
+void write(FileStorage& fs, const Range& r )
+{
+    write(fs, r.start);
+    write(fs, r.end);
+}
+
+template<typename _Tp> static inline
+void write( FileStorage& fs, const std::vector<_Tp>& vec )
+{
+    cv::internal::VecWriterProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> w(&fs);
+    w(vec);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, pt);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, pt);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, sz);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const String& name, const Complex<_Tp>& c )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, c);
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, r);
+}
+
+template<typename _Tp, int cn> static inline
+void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, v);
+}
+
+template<typename _Tp, int m, int n> static inline
+void write(FileStorage& fs, const String& name, const Matx<_Tp, m, n>& x )
+{
+    write(fs, name, Mat(x)); // write as a Mat class
+}
+
+template<typename _Tp> static inline
+void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, s);
+}
+
+static inline
+void write(FileStorage& fs, const String& name, const Range& r )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, r);
+}
+
+static inline
+void write(FileStorage& fs, const String& name, const KeyPoint& kpt)
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, kpt.pt.x);
+    write(fs, kpt.pt.y);
+    write(fs, kpt.size);
+    write(fs, kpt.angle);
+    write(fs, kpt.response);
+    write(fs, kpt.octave);
+    write(fs, kpt.class_id);
+}
+
+static inline
+void write(FileStorage& fs, const String& name, const DMatch& m)
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
+    write(fs, m.queryIdx);
+    write(fs, m.trainIdx);
+    write(fs, m.imgIdx);
+    write(fs, m.distance);
+}
+
+template<typename _Tp, typename std::enable_if< std::is_enum<_Tp>::value >::type* = nullptr>
+static inline void write( FileStorage& fs, const String& name, const _Tp& val )
+{
+    write(fs, name, static_cast<int>(val));
+}
+
+template<typename _Tp> static inline
+void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0));
+    write(fs, vec);
+}
+
+template<typename _Tp> static inline
+void write( FileStorage& fs, const String& name, const std::vector< std::vector<_Tp> >& vec )
+{
+    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ);
+    for(size_t i = 0; i < vec.size(); i++)
+    {
+        cv::internal::WriteStructContext ws_(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0));
+        write(fs, vec[i]);
+    }
+}
+
+#ifdef CV__LEGACY_PERSISTENCE
+// This code is not needed anymore, but it is preserved here to keep source compatibility
+// Implementation is similar to templates instantiations
+static inline void write(FileStorage& fs, const KeyPoint& kpt) { write(fs, String(), kpt); }
+static inline void write(FileStorage& fs, const DMatch& m) { write(fs, String(), m); }
+static inline void write(FileStorage& fs, const std::vector<KeyPoint>& vec)
+{
+    cv::internal::VecWriterProxy<KeyPoint, 0> w(&fs);
+    w(vec);
+}
+static inline void write(FileStorage& fs, const std::vector<DMatch>& vec)
+{
+    cv::internal::VecWriterProxy<DMatch, 0> w(&fs);
+    w(vec);
+
+}
+#endif
+
+//! @} FileStorage
+
+//! @relates cv::FileNode
+//! @{
+
+static inline
+void read(const FileNode& node, bool& value, bool default_value)
+{
+    int temp;
+    read(node, temp, (int)default_value);
+    value = temp != 0;
+}
+
+static inline
+void read(const FileNode& node, uchar& value, uchar default_value)
+{
+    int temp;
+    read(node, temp, (int)default_value);
+    value = saturate_cast<uchar>(temp);
+}
+
+static inline
+void read(const FileNode& node, schar& value, schar default_value)
+{
+    int temp;
+    read(node, temp, (int)default_value);
+    value = saturate_cast<schar>(temp);
+}
+
+static inline
+void read(const FileNode& node, ushort& value, ushort default_value)
+{
+    int temp;
+    read(node, temp, (int)default_value);
+    value = saturate_cast<ushort>(temp);
+}
+
+static inline
+void read(const FileNode& node, short& value, short default_value)
+{
+    int temp;
+    read(node, temp, (int)default_value);
+    value = saturate_cast<short>(temp);
+}
+
+template<typename _Tp> static inline
+void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX )
+{
+    cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it);
+    r(vec, maxCount);
+}
+
+template<typename _Tp, typename std::enable_if< std::is_enum<_Tp>::value >::type* = nullptr>
+static inline void read(const FileNode& node, _Tp& value, const _Tp& default_value = static_cast<_Tp>(0))
+{
+    int temp;
+    read(node, temp, static_cast<int>(default_value));
+    value = static_cast<_Tp>(temp);
+}
+
+template<typename _Tp> static inline
+void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() )
+{
+    if(node.empty())
+        vec = default_value;
+    else
+    {
+        FileNodeIterator it = node.begin();
+        read( it, vec );
+    }
+}
+
+static inline
+void read( const FileNode& node, std::vector<KeyPoint>& vec, const std::vector<KeyPoint>& default_value )
+{
+    if(node.empty())
+        vec = default_value;
+    else
+        read(node, vec);
+}
+
+static inline
+void read( const FileNode& node, std::vector<DMatch>& vec, const std::vector<DMatch>& default_value )
+{
+    if(node.empty())
+        vec = default_value;
+    else
+        read(node, vec);
+}
+
+//! @} FileNode
+
+//! @relates cv::FileStorage
+//! @{
+
+/** @brief Writes data to a file storage.
+ */
+template<typename _Tp> static inline
+FileStorage& operator << (FileStorage& fs, const _Tp& value)
+{
+    if( !fs.isOpened() )
+        return fs;
+    if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP )
+        CV_Error( Error::StsError, "No element name has been given" );
+    write( fs, fs.elname, value );
+    if( fs.state & FileStorage::INSIDE_MAP )
+        fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP;
+    return fs;
+}
+
+/** @brief Writes data to a file storage.
+ */
+static inline
+FileStorage& operator << (FileStorage& fs, const char* str)
+{
+    return (fs << String(str));
+}
+
+/** @brief Writes data to a file storage.
+ */
+static inline
+FileStorage& operator << (FileStorage& fs, char* value)
+{
+    return (fs << String(value));
+}
+
+//! @} FileStorage
+
+//! @relates cv::FileNodeIterator
+//! @{
+
+/** @brief Reads data from a file storage.
+ */
+template<typename _Tp> static inline
+FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value)
+{
+    read( *it, value, _Tp());
+    return ++it;
+}
+
+/** @brief Reads data from a file storage.
+ */
+template<typename _Tp> static inline
+FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec)
+{
+    cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it);
+    r(vec, (size_t)INT_MAX);
+    return it;
+}
+
+//! @} FileNodeIterator
+
+//! @relates cv::FileNode
+//! @{
+
+/** @brief Reads data from a file storage.
+ */
+template<typename _Tp> static inline
+void operator >> (const FileNode& n, _Tp& value)
+{
+    read( n, value, _Tp());
+}
+
+/** @brief Reads data from a file storage.
+ */
+template<typename _Tp> static inline
+void operator >> (const FileNode& n, std::vector<_Tp>& vec)
+{
+    FileNodeIterator it = n.begin();
+    it >> vec;
+}
+
+/** @brief Reads KeyPoint from a file storage.
+*/
+//It needs special handling because it contains two types of fields, int & float.
+static inline
+void operator >> (const FileNode& n, KeyPoint& kpt)
+{
+    FileNodeIterator it = n.begin();
+    it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave >> kpt.class_id;
+}
+
+#ifdef CV__LEGACY_PERSISTENCE
+static inline
+void operator >> (const FileNode& n, std::vector<KeyPoint>& vec)
+{
+    read(n, vec);
+}
+static inline
+void operator >> (const FileNode& n, std::vector<DMatch>& vec)
+{
+    read(n, vec);
+}
+#endif
+
+/** @brief Reads DMatch from a file storage.
+*/
+//It needs special handling because it contains two types of fields, int & float.
+static inline
+void operator >> (const FileNode& n, DMatch& m)
+{
+    FileNodeIterator it = n.begin();
+    it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance;
+}
+
+//! @} FileNode
+
+//! @relates cv::FileNodeIterator
+//! @{
+
+CV_EXPORTS bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2);
+CV_EXPORTS bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2);
+
+static inline
+ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2)
+{
+    return it2.remaining() - it1.remaining();
+}
+
+static inline
+bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2)
+{
+    return it1.remaining() > it2.remaining();
+}
+
+//! @} FileNodeIterator
+
+} // cv
+
+#endif // OPENCV_CORE_PERSISTENCE_HPP
diff --git a/3rdParty/include/opencv2/core/quaternion.hpp b/3rdParty/include/opencv2/core/quaternion.hpp
new file mode 100644
index 0000000..8c21501
--- /dev/null
+++ b/3rdParty/include/opencv2/core/quaternion.hpp
@@ -0,0 +1,1696 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2020, Huawei Technologies Co., Ltd. All rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Author: Liangqian Kong <chargerKong@126.com>
+//         Longbu Wang <riskiest@gmail.com>
+#ifndef OPENCV_CORE_QUATERNION_HPP
+#define OPENCV_CORE_QUATERNION_HPP
+
+#include <opencv2/core.hpp>
+#include <opencv2/core/utils/logger.hpp>
+#include <iostream>
+namespace cv
+{
+//! @addtogroup core
+//! @{
+
+//! Unit quaternion flag
+enum QuatAssumeType
+{
+    /**
+     * This flag is specified by default.
+     * If this flag is specified, the input quaternions are assumed to be not unit quaternions.
+     * It can guarantee the correctness of the calculations,
+     * although the calculation speed will be slower than the flag QUAT_ASSUME_UNIT.
+     */
+    QUAT_ASSUME_NOT_UNIT,
+    /**
+     * If this flag is specified, the input quaternions are assumed to be unit quaternions which
+     * will save some computations. However, if this flag is specified without unit quaternion,
+     * the program correctness of the result will not be guaranteed.
+     */
+    QUAT_ASSUME_UNIT
+};
+
+class QuatEnum
+{
+public:
+    /** @brief Enum of Euler angles type.
+     *
+     * Without considering the possibility of using two different convertions for the definition of the rotation axes ,
+     * there exists twelve possible sequences of rotation axes, divided into two groups:
+     * - Proper Euler angles (Z-X-Z, X-Y-X, Y-Z-Y, Z-Y-Z, X-Z-X, Y-X-Y)
+     * - Tait–Bryan angles (X-Y-Z, Y-Z-X, Z-X-Y, X-Z-Y, Z-Y-X, Y-X-Z).
+     *
+     * The three elemental rotations may be [extrinsic](https://en.wikipedia.org/wiki/Euler_angles#Definition_by_extrinsic_rotations)
+     * (rotations about the axes *xyz* of the original coordinate system, which is assumed to remain motionless),
+     * or [intrinsic](https://en.wikipedia.org/wiki/Euler_angles#Definition_by_intrinsic_rotations)(rotations about the axes of the rotating coordinate system *XYZ*, solidary with the moving body, which changes its orientation after each elemental rotation).
+     *
+     *
+     * Extrinsic and intrinsic rotations are relevant.
+     *
+     * The definition of the Euler angles is as following,
+     * - \f$\theta_1 \f$ represents the first rotation angle,
+     * - \f$\theta_2 \f$ represents the second rotation angle,
+     * - \f$\theta_3 \f$ represents the third rotation angle.
+     *
+     * For intrinsic rotations in the order of X-Y-Z, the rotation matrix R can be calculated by:\f[R =X(\theta_1) Y(\theta_2) Z(\theta_3) \f]
+     * For extrinsic rotations in the order of X-Y-Z, the rotation matrix R can be calculated by:\f[R =Z({\theta_3}) Y({\theta_2}) X({\theta_1})\f]
+     * where
+     * \f[X({\theta})={\begin{bmatrix}1&0&0\\0&\cos {\theta_1} &-\sin {\theta_1} \\0&\sin {\theta_1} &\cos {\theta_1} \\\end{bmatrix}},
+     * Y({\theta})={\begin{bmatrix}\cos \theta_{2}&0&\sin \theta_{2}\\0&1 &0 \\\ -sin \theta_2& 0&\cos \theta_{2} \\\end{bmatrix}},
+     * Z({\theta})={\begin{bmatrix}\cos\theta_{3} &-\sin \theta_3&0\\\sin \theta_3 &\cos \theta_3 &0\\0&0&1\\\end{bmatrix}}.
+     * \f]
+     *
+     * The function is designed according to this set of conventions:
+     * - [Right handed](https://en.wikipedia.org/wiki/Right_hand_rule) reference frames are adopted, and the [right hand rule](https://en.wikipedia.org/wiki/Right_hand_rule) is used to determine the sign of angles.
+     * - Each matrix is meant to represent an [active rotation](https://en.wikipedia.org/wiki/Active_and_passive_transformation) (the composing and composed matrices
+     * are supposed to act on the coordinates of vectors defined in the initial fixed reference frame and give as a result the coordinates of a rotated vector defined in the same reference frame).
+     * - For \f$\theta_1\f$ and \f$\theta_3\f$, the valid range is (−π, π].
+     *
+     *   For \f$\theta_2\f$, the valid range is [−π/2, π/2] or [0, π].
+     *
+     *   For Tait–Bryan angles, the valid range of \f$\theta_2\f$ is [−π/2, π/2]. When transforming a quaternion to Euler angles, the solution of Euler angles is unique in condition of \f$ \theta_2 \in (−π/2, π/2)\f$ .
+     *   If \f$\theta_2 = −π/2 \f$ or \f$ \theta_2 = π/2\f$, there are infinite solutions. The common name for this situation is gimbal lock.
+     *   For Proper Euler angles,the valid range of \f$\theta_2\f$ is in [0, π]. The solutions of Euler angles are unique in condition of  \f$ \theta_2 \in (0, π)\f$ . If \f$\theta_2 =0 \f$ or \f$\theta_2 =π \f$,
+     *   there are infinite solutions and gimbal lock will occur.
+     */
+    enum EulerAnglesType
+    {
+        INT_XYZ, ///< Intrinsic rotations with the Euler angles type X-Y-Z
+        INT_XZY, ///< Intrinsic rotations with the Euler angles type X-Z-Y
+        INT_YXZ, ///< Intrinsic rotations with the Euler angles type Y-X-Z
+        INT_YZX, ///< Intrinsic rotations with the Euler angles type Y-Z-X
+        INT_ZXY, ///< Intrinsic rotations with the Euler angles type Z-X-Y
+        INT_ZYX, ///< Intrinsic rotations with the Euler angles type Z-Y-X
+        INT_XYX, ///< Intrinsic rotations with the Euler angles type X-Y-X
+        INT_XZX, ///< Intrinsic rotations with the Euler angles type X-Z-X
+        INT_YXY, ///< Intrinsic rotations with the Euler angles type Y-X-Y
+        INT_YZY, ///< Intrinsic rotations with the Euler angles type Y-Z-Y
+        INT_ZXZ, ///< Intrinsic rotations with the Euler angles type Z-X-Z
+        INT_ZYZ, ///< Intrinsic rotations with the Euler angles type Z-Y-Z
+
+        EXT_XYZ, ///< Extrinsic rotations with the Euler angles type X-Y-Z
+        EXT_XZY, ///< Extrinsic rotations with the Euler angles type X-Z-Y
+        EXT_YXZ, ///< Extrinsic rotations with the Euler angles type Y-X-Z
+        EXT_YZX, ///< Extrinsic rotations with the Euler angles type Y-Z-X
+        EXT_ZXY, ///< Extrinsic rotations with the Euler angles type Z-X-Y
+        EXT_ZYX, ///< Extrinsic rotations with the Euler angles type Z-Y-X
+        EXT_XYX, ///< Extrinsic rotations with the Euler angles type X-Y-X
+        EXT_XZX, ///< Extrinsic rotations with the Euler angles type X-Z-X
+        EXT_YXY, ///< Extrinsic rotations with the Euler angles type Y-X-Y
+        EXT_YZY,  ///< Extrinsic rotations with the Euler angles type Y-Z-Y
+        EXT_ZXZ, ///< Extrinsic rotations with the Euler angles type Z-X-Z
+        EXT_ZYZ, ///< Extrinsic rotations with the Euler angles type Z-Y-Z
+        #ifndef CV_DOXYGEN
+            EULER_ANGLES_MAX_VALUE
+        #endif
+    };
+
+};
+
+template <typename _Tp> class Quat;
+template <typename _Tp> std::ostream& operator<<(std::ostream&, const Quat<_Tp>&);
+
+/**
+ * Quaternion is a number system that extends the complex numbers. It can be expressed as a
+ * rotation in three-dimensional space.
+ * A quaternion is generally represented in the form:
+ *      \f[q = w + x\boldsymbol{i} + y\boldsymbol{j} + z\boldsymbol{k}\f]
+ *      \f[q = [w, x, y, z]\f]
+ *      \f[q = [w, \boldsymbol{v}] \f]
+ *      \f[q = ||q||[\cos\psi, u_x\sin\psi,u_y\sin\psi,  u_z\sin\psi].\f]
+ *      \f[q = ||q||[\cos\psi, \boldsymbol{u}\sin\psi]\f]
+ * where \f$\psi = \frac{\theta}{2}\f$, \f$\theta\f$ represents rotation angle,
+ * \f$\boldsymbol{u} = [u_x, u_y, u_z]\f$ represents normalized rotation axis,
+ * and \f$||q||\f$ represents the norm of \f$q\f$.
+ *
+ * A unit quaternion is usually represents rotation, which has the form:
+ *      \f[q = [\cos\psi, u_x\sin\psi,u_y\sin\psi,  u_z\sin\psi].\f]
+ *
+ * To create a quaternion representing the rotation around the axis \f$\boldsymbol{u}\f$
+ * with angle \f$\theta\f$, you can use
+ * ```
+ * using namespace cv;
+ * double angle = CV_PI;
+ * Vec3d axis = {0, 0, 1};
+ * Quatd q = Quatd::createFromAngleAxis(angle, axis);
+ * ```
+ *
+ * You can simply use four same type number to create a quaternion
+ * ```
+ * Quatd q(1, 2, 3, 4);
+ * ```
+ * Or use a Vec4d or Vec4f vector.
+ * ```
+ * Vec4d vec{1, 2, 3, 4};
+ * Quatd q(vec);
+ * ```
+ *
+ * ```
+ * Vec4f vec{1, 2, 3, 4};
+ * Quatf q(vec);
+ * ```
+ *
+ * If you already have a 3x3 rotation matrix R, then you can use
+ * ```
+ * Quatd q = Quatd::createFromRotMat(R);
+ * ```
+ *
+ * If you already have a rotation vector rvec which has the form of `angle * axis`, then you can use
+ * ```
+ * Quatd q = Quatd::createFromRvec(rvec);
+ * ```
+ *
+ * To extract the rotation matrix from quaternion, see toRotMat3x3()
+ *
+ * To extract the Vec4d or Vec4f, see toVec()
+ *
+ * To extract the rotation vector, see toRotVec()
+ *
+ * If there are two quaternions \f$q_0, q_1\f$ are needed to interpolate, you can use nlerp(), slerp() or spline()
+ * ```
+ * Quatd::nlerp(q0, q1, t)
+ *
+ * Quatd::slerp(q0, q1, t)
+ *
+ * Quatd::spline(q0, q0, q1, q1, t)
+ * ```
+ * spline can smoothly connect rotations of  multiple quaternions
+ *
+ * Three ways to get an element in Quaternion
+ * ```
+ * Quatf q(1,2,3,4);
+ * std::cout << q.w << std::endl; // w=1, x=2, y=3, z=4
+ * std::cout << q[0] << std::endl; // q[0]=1, q[1]=2, q[2]=3, q[3]=4
+ * std::cout << q.at(0) << std::endl;
+ * ```
+ */
+template <typename _Tp>
+class Quat
+{
+    static_assert(std::is_floating_point<_Tp>::value, "Quaternion only make sense with type of float or double");
+    using value_type = _Tp;
+public:
+    static constexpr _Tp CV_QUAT_EPS = (_Tp)1.e-6;
+    static constexpr _Tp CV_QUAT_CONVERT_THRESHOLD = (_Tp)1.e-6;
+
+    Quat();
+
+    /**
+     * @brief From Vec4d or Vec4f.
+     */
+    explicit Quat(const Vec<_Tp, 4> &coeff);
+
+    /**
+     * @brief from four numbers.
+     */
+    Quat(_Tp w, _Tp x, _Tp y, _Tp z);
+
+    /**
+     * @brief from an angle, axis. Axis will be normalized in this function. And
+     * it generates
+     * \f[q = [\cos\psi, u_x\sin\psi,u_y\sin\psi,  u_z\sin\psi].\f]
+     * where \f$\psi = \frac{\theta}{2}\f$, \f$\theta\f$ is the rotation angle.
+     */
+    static Quat<_Tp> createFromAngleAxis(const _Tp angle, const Vec<_Tp, 3> &axis);
+
+    /**
+     * @brief from a 3x3 rotation matrix.
+     */
+    static Quat<_Tp> createFromRotMat(InputArray R);
+
+    /**
+     * @brief from a rotation vector
+     * \f$r\f$ has the form \f$\theta \cdot \boldsymbol{u}\f$, where \f$\theta\f$
+     * represents rotation angle and \f$\boldsymbol{u}\f$ represents normalized rotation axis.
+     *
+     * Angle and axis could be easily derived as:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * \psi &= ||r||\\
+     * \boldsymbol{u} &= \frac{r}{\theta}
+     * \end{split}
+     * \end{equation}
+     * \f]
+     * Then a quaternion can be calculated by
+     *  \f[q = [\cos\psi, \boldsymbol{u}\sin\psi]\f]
+     *  where \f$\psi = \theta / 2 \f$
+     */
+    static Quat<_Tp> createFromRvec(InputArray rvec);
+
+     /**
+     * @brief
+     * from Euler angles
+     *
+     * A quaternion can be generated from Euler angles by combining the quaternion representations of the Euler rotations.
+     *
+     * For example, if we use intrinsic rotations in the order of X-Y-Z,\f$\theta_1 \f$ is rotation around the X-axis, \f$\theta_2 \f$ is rotation around the Y-axis,
+     * \f$\theta_3 \f$ is rotation around the Z-axis. The final quaternion q can be calculated by
+     *
+     * \f[ {q} = q_{X, \theta_1}  q_{Y, \theta_2} q_{Z, \theta_3}\f]
+     * where \f$ q_{X, \theta_1} \f$ is created from @ref createFromXRot,  \f$ q_{Y, \theta_2} \f$ is created from @ref createFromYRot,
+     *  \f$ q_{Z, \theta_3} \f$ is created from @ref createFromZRot.
+     * @param angles the Euler angles in a vector of length 3
+     * @param eulerAnglesType the convertion Euler angles type
+     */
+    static Quat<_Tp> createFromEulerAngles(const Vec<_Tp, 3> &angles, QuatEnum::EulerAnglesType eulerAnglesType);
+
+    /**
+     * @brief get a quaternion from a rotation about the Y-axis by \f$\theta\f$ .
+     * \f[q = \cos(\theta/2)+0 i+ sin(\theta/2) j +0k \f]
+     */
+    static Quat<_Tp> createFromYRot(const _Tp theta);
+
+    /**
+     * @brief get a quaternion from a rotation about the X-axis by \f$\theta\f$ .
+     * \f[q = \cos(\theta/2)+sin(\theta/2) i +0 j +0 k \f]
+     */
+    static Quat<_Tp> createFromXRot(const _Tp theta);
+
+    /**
+     * @brief get a quaternion from a rotation about the Z-axis by \f$\theta\f$.
+     * \f[q = \cos(\theta/2)+0 i +0 j +sin(\theta/2) k \f]
+     */
+    static Quat<_Tp> createFromZRot(const _Tp theta);
+
+    /**
+     * @brief a way to get element.
+     * @param index over a range [0, 3].
+     *
+     * A quaternion q
+     *
+     * q.at(0) is equivalent to q.w,
+     *
+     * q.at(1) is equivalent to q.x,
+     *
+     * q.at(2) is equivalent to q.y,
+     *
+     * q.at(3) is equivalent to q.z.
+     */
+    _Tp at(size_t index) const;
+
+    /**
+     * @brief return the conjugate of this quaternion.
+     * \f[q.conjugate() = (w, -x, -y, -z).\f]
+     */
+    Quat<_Tp> conjugate() const;
+
+    /**
+     *
+     * @brief return the value of exponential value.
+     * \f[\exp(q) = e^w (\cos||\boldsymbol{v}||+ \frac{v}{||\boldsymbol{v}||})\sin||\boldsymbol{v}||\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example:
+     * ```
+     * Quatd q{1,2,3,4};
+     * cout << exp(q) << endl;
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> exp(const Quat<T> &q);
+
+    /**
+     * @brief return the value of exponential value.
+     * \f[\exp(q) = e^w (\cos||\boldsymbol{v}||+ \frac{v}{||\boldsymbol{v}||}\sin||\boldsymbol{v}||)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q{1,2,3,4};
+     * cout << q.exp() << endl;
+     * ```
+     */
+    Quat<_Tp> exp() const;
+
+    /**
+     * @brief return the value of logarithm function.
+     * \f[\ln(q) = \ln||q|| + \frac{\boldsymbol{v}}{||\boldsymbol{v}||}\arccos\frac{w}{||q||}.\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, q assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd q1{1,2,3,4};
+     * cout << log(q1) << endl;
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> log(const Quat<T> &q, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief return the value of logarithm function.
+     *  \f[\ln(q) = \ln||q|| + \frac{\boldsymbol{v}}{||\boldsymbol{v}||}\arccos\frac{w}{||q||}\f].
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.log();
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * Quatd q1(1,2,3,4);
+     * q1.normalize().log(assumeUnit);
+     * ```
+     */
+    Quat<_Tp> log(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the value of power function with index \f$x\f$.
+     * \f[q^x = ||q||(cos(x\theta) + \boldsymbol{u}sin(x\theta))).\f]
+     * @param q a quaternion.
+     * @param x index of exponentiation.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, quaternion q assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * power(q, 2.0);
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * double angle = CV_PI;
+     * Vec3d axis{0, 0, 1};
+     * Quatd q1 = Quatd::createFromAngleAxis(angle, axis); //generate a unit quat by axis and angle
+     * power(q1, 2.0, assumeUnit);//This assumeUnit means q1 is a unit quaternion.
+     * ```
+     * @note the type of the index should be the same as the quaternion.
+     */
+    template <typename T>
+    friend Quat<T> power(const Quat<T> &q, const T x, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief return the value of power function with index \f$x\f$.
+     * \f[q^x = ||q||(\cos(x\theta) + \boldsymbol{u}\sin(x\theta))).\f]
+     * @param x index of exponentiation.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.power(2.0);
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * double angle = CV_PI;
+     * Vec3d axis{0, 0, 1};
+     * Quatd q1 = Quatd::createFromAngleAxis(angle, axis); //generate a unit quat by axis and angle
+     * q1.power(2.0, assumeUnit); //This assumeUnt means q1 is a unit quaternion
+     * ```
+     */
+    Quat<_Tp> power(const _Tp x, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return \f$\sqrt{q}\f$.
+     * @param q a quaternion.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, quaternion q assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatf q(1,2,3,4);
+     * sqrt(q);
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * q = {1,0,0,0};
+     * sqrt(q, assumeUnit); //This assumeUnit means q is a unit quaternion.
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> sqrt(const Quat<T> &q, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief return \f$\sqrt{q}\f$.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatf q(1,2,3,4);
+     * q.sqrt();
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * q = {1,0,0,0};
+     * q.sqrt(assumeUnit); //This assumeUnit means q is a unit quaternion
+     * ```
+     */
+    Quat<_Tp> sqrt(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the value of power function with quaternion \f$q\f$.
+     * \f[p^q = e^{q\ln(p)}.\f]
+     * @param p base quaternion of power function.
+     * @param q index quaternion of power function.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, quaternion \f$p\f$ assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd p(1,2,3,4);
+     * Quatd q(5,6,7,8);
+     * power(p, q);
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * p = p.normalize();
+     * power(p, q, assumeUnit); //This assumeUnit means p is a unit quaternion
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> power(const Quat<T> &p, const Quat<T> &q, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief return the value of power function with quaternion \f$q\f$.
+     * \f[p^q = e^{q\ln(p)}.\f]
+     * @param q index quaternion of power function.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd p(1,2,3,4);
+     * Quatd q(5,6,7,8);
+     * p.power(q);
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * p = p.normalize();
+     * p.power(q, assumeUnit); //This assumeUnit means p is a unit quaternion
+     * ```
+     */
+    Quat<_Tp> power(const Quat<_Tp> &q, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the crossProduct between \f$p = (a, b, c, d) = (a, \boldsymbol{u})\f$ and \f$q = (w, x, y, z) = (w, \boldsymbol{v})\f$.
+     * \f[p \times q = \frac{pq- qp}{2}\f]
+     * \f[p \times q = \boldsymbol{u} \times \boldsymbol{v}\f]
+     * \f[p \times q = (cz-dy)i + (dx-bz)j + (by-xc)k \f]
+     *
+     * For example
+     * ```
+     * Quatd q{1,2,3,4};
+     * Quatd p{5,6,7,8};
+     * crossProduct(p, q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> crossProduct(const Quat<T> &p, const Quat<T> &q);
+
+    /**
+     * @brief return the crossProduct between \f$p = (a, b, c, d) = (a, \boldsymbol{u})\f$ and \f$q = (w, x, y, z) = (w, \boldsymbol{v})\f$.
+     * \f[p \times q = \frac{pq- qp}{2}.\f]
+     * \f[p \times q = \boldsymbol{u} \times \boldsymbol{v}.\f]
+     * \f[p \times q = (cz-dy)i + (dx-bz)j + (by-xc)k. \f]
+     *
+     * For example
+     * ```
+     * Quatd q{1,2,3,4};
+     * Quatd p{5,6,7,8};
+     * p.crossProduct(q)
+     * ```
+     */
+    Quat<_Tp> crossProduct(const Quat<_Tp> &q) const;
+
+    /**
+     * @brief return the norm of quaternion.
+     * \f[||q|| = \sqrt{w^2 + x^2 + y^2 + z^2}.\f]
+     */
+    _Tp norm() const;
+
+    /**
+     * @brief return a normalized \f$p\f$.
+     * \f[p = \frac{q}{||q||}\f]
+     * where \f$p\f$ satisfies \f$(p.x)^2 + (p.y)^2 + (p.z)^2 + (p.w)^2 = 1.\f$
+     */
+    Quat<_Tp> normalize() const;
+
+    /**
+     * @brief return \f$q^{-1}\f$ which is an inverse of \f$q\f$
+     * which satisfies \f$q * q^{-1} = 1\f$.
+     * @param q a quaternion.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, quaternion q assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * inv(q);
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * q = q.normalize();
+     * inv(q, assumeUnit);//This assumeUnit means p is a unit quaternion
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> inv(const Quat<T> &q, QuatAssumeType assumeUnit);
+
+    /**
+     * @brief return \f$q^{-1}\f$ which is an inverse of \f$q\f$
+     * satisfying \f$q * q^{-1} = 1\f$.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, quaternion q assume to be a unit quaternion and this function will save some computations.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.inv();
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * q = q.normalize();
+     * q.inv(assumeUnit);  //assumeUnit means p is a unit quaternion
+     * ```
+     */
+    Quat<_Tp> inv(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return sinh value of quaternion q, sinh could be calculated as:
+     * \f[\sinh(p) = \sin(w)\cos(||\boldsymbol{v}||) + \cosh(w)\frac{v}{||\boldsymbol{v}||}\sin||\boldsymbol{v}||\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * sinh(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> sinh(const Quat<T> &q);
+
+    /**
+     * @brief return sinh value of this quaternion, sinh could be calculated as:
+     * \f$\sinh(p) = \sin(w)\cos(||\boldsymbol{v}||) + \cosh(w)\frac{v}{||\boldsymbol{v}||}\sin||\boldsymbol{v}||\f$
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.sinh();
+     * ```
+     */
+    Quat<_Tp> sinh() const;
+
+    /**
+     * @brief return cosh value of quaternion q, cosh could be calculated as:
+     * \f[\cosh(p) = \cosh(w) * \cos(||\boldsymbol{v}||) + \sinh(w)\frac{\boldsymbol{v}}{||\boldsymbol{v}||}\sin(||\boldsymbol{v}||)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * cosh(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> cosh(const Quat<T> &q);
+
+    /**
+     * @brief return cosh value of this quaternion, cosh could be calculated as:
+     * \f[\cosh(p) = \cosh(w) * \cos(||\boldsymbol{v}||) + \sinh(w)\frac{\boldsymbol{v}}{||\boldsymbol{v}||}sin(||\boldsymbol{v}||)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.cosh();
+     * ```
+     */
+    Quat<_Tp> cosh() const;
+
+    /**
+     * @brief return tanh value of quaternion q, tanh could be calculated as:
+     * \f[ \tanh(q) = \frac{\sinh(q)}{\cosh(q)}.\f]
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * tanh(q);
+     * ```
+     * @sa sinh, cosh
+     */
+    template <typename T>
+    friend Quat<T> tanh(const Quat<T> &q);
+
+    /**
+     * @brief return tanh value of this quaternion, tanh could be calculated as:
+     * \f[ \tanh(q) = \frac{\sinh(q)}{\cosh(q)}.\f]
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.tanh();
+     * ```
+     * @sa sinh, cosh
+     */
+    Quat<_Tp> tanh() const;
+
+    /**
+     * @brief return tanh value of quaternion q, sin could be calculated as:
+     * \f[\sin(p) = \sin(w) * \cosh(||\boldsymbol{v}||) + \cos(w)\frac{\boldsymbol{v}}{||\boldsymbol{v}||}\sinh(||\boldsymbol{v}||)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * sin(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> sin(const Quat<T> &q);
+
+    /**
+     * @brief return sin value of this quaternion, sin could be calculated as:
+     * \f[\sin(p) = \sin(w) * \cosh(||\boldsymbol{v}||) + \cos(w)\frac{\boldsymbol{v}}{||\boldsymbol{v}||}\sinh(||\boldsymbol{v}||)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.sin();
+     * ```
+     */
+    Quat<_Tp> sin() const;
+
+    /**
+     * @brief return sin value of quaternion q, cos could be calculated as:
+     * \f[\cos(p) = \cos(w) * \cosh(||\boldsymbol{v}||) - \sin(w)\frac{\boldsymbol{v}}{||\boldsymbol{v}||}\sinh(||\boldsymbol{v}||)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * cos(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> cos(const Quat<T> &q);
+
+    /**
+     * @brief return cos value of this quaternion, cos could be calculated as:
+     * \f[\cos(p) = \cos(w) * \cosh(||\boldsymbol{v}||) - \sin(w)\frac{\boldsymbol{v}}{||\boldsymbol{v}||}\sinh(||\boldsymbol{v}||)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.cos();
+     * ```
+     */
+    Quat<_Tp> cos() const;
+
+    /**
+     * @brief return tan value of quaternion q, tan could be calculated as:
+     * \f[\tan(q) = \frac{\sin(q)}{\cos(q)}.\f]
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * tan(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> tan(const Quat<T> &q);
+
+    /**
+     * @brief return tan value of this quaternion, tan could be calculated as:
+     * \f[\tan(q) = \frac{\sin(q)}{\cos(q)}.\f]
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.tan();
+     * ```
+     */
+    Quat<_Tp> tan() const;
+
+    /**
+     * @brief return arcsin value of quaternion q, arcsin could be calculated as:
+     * \f[\arcsin(q) = -\frac{\boldsymbol{v}}{||\boldsymbol{v}||}arcsinh(q\frac{\boldsymbol{v}}{||\boldsymbol{v}||})\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * asin(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> asin(const Quat<T> &q);
+
+    /**
+     * @brief return arcsin value of this quaternion, arcsin could be calculated as:
+     * \f[\arcsin(q) = -\frac{\boldsymbol{v}}{||\boldsymbol{v}||}arcsinh(q\frac{\boldsymbol{v}}{||\boldsymbol{v}||})\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.asin();
+     * ```
+     */
+    Quat<_Tp> asin() const;
+
+    /**
+     * @brief return arccos value of quaternion q, arccos could be calculated as:
+     * \f[\arccos(q) = -\frac{\boldsymbol{v}}{||\boldsymbol{v}||}arccosh(q)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * acos(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> acos(const Quat<T> &q);
+
+    /**
+     * @brief return arccos value of this quaternion, arccos could be calculated as:
+     * \f[\arccos(q) = -\frac{\boldsymbol{v}}{||\boldsymbol{v}||}arccosh(q)\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.acos();
+     * ```
+     */
+    Quat<_Tp> acos() const;
+
+    /**
+     * @brief return arctan value of quaternion q, arctan could be calculated as:
+     * \f[\arctan(q) = -\frac{\boldsymbol{v}}{||\boldsymbol{v}||}arctanh(q\frac{\boldsymbol{v}}{||\boldsymbol{v}||})\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * atan(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> atan(const Quat<T> &q);
+
+    /**
+     * @brief return arctan value of this quaternion, arctan could be calculated as:
+     * \f[\arctan(q) = -\frac{\boldsymbol{v}}{||\boldsymbol{v}||}arctanh(q\frac{\boldsymbol{v}}{||\boldsymbol{v}||})\f]
+     * where \f$\boldsymbol{v} = [x, y, z].\f$
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.atan();
+     * ```
+     */
+    Quat<_Tp> atan() const;
+
+    /**
+     * @brief return arcsinh value of quaternion q, arcsinh could be calculated as:
+     * \f[arcsinh(q) = \ln(q + \sqrt{q^2 + 1})\f].
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * asinh(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> asinh(const Quat<T> &q);
+
+    /**
+     * @brief return arcsinh value of this quaternion, arcsinh could be calculated as:
+     * \f[arcsinh(q) = \ln(q + \sqrt{q^2 + 1})\f].
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.asinh();
+     * ```
+     */
+    Quat<_Tp> asinh() const;
+
+    /**
+     * @brief return arccosh value of quaternion q, arccosh could be calculated as:
+     * \f[arccosh(q) = \ln(q + \sqrt{q^2 - 1})\f].
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * acosh(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> acosh(const Quat<T> &q);
+
+    /**
+     * @brief return arccosh value of this quaternion, arccosh could be calculated as:
+     * \f[arcosh(q) = \ln(q + \sqrt{q^2 - 1})\f].
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.acosh();
+     * ```
+     */
+    Quat<_Tp> acosh() const;
+
+    /**
+     * @brief return arctanh value of quaternion q, arctanh could be calculated as:
+     * \f[arctanh(q) = \frac{\ln(q + 1) - \ln(1 - q)}{2}\f].
+     * @param q a quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * atanh(q);
+     * ```
+     */
+    template <typename T>
+    friend Quat<T> atanh(const Quat<T> &q);
+
+    /**
+     * @brief return arctanh value of this quaternion, arctanh could be calculated as:
+     * \f[arcsinh(q) = \frac{\ln(q + 1) - \ln(1 - q)}{2}\f].
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.atanh();
+     * ```
+     */
+    Quat<_Tp> atanh() const;
+
+    /**
+     * @brief return true if this quaternion is a unit quaternion.
+     * @param eps tolerance scope of normalization. The eps could be defined as
+     *
+     * \f[eps = |1 - dotValue|\f] where \f[dotValue = (this.w^2 + this.x^2 + this,y^2 + this.z^2).\f]
+     * And this function will consider it is normalized when the dotValue over a range \f$[1-eps, 1+eps]\f$.
+     */
+    bool isNormal(_Tp eps=CV_QUAT_EPS) const;
+
+    /**
+     * @brief to throw an error if this quaternion is not a unit quaternion.
+     * @param eps tolerance scope of normalization.
+     * @sa isNormal
+     */
+    void assertNormal(_Tp eps=CV_QUAT_EPS) const;
+
+    /**
+     * @brief transform a quaternion to a 3x3 rotation matrix.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and
+     * this function will save some computations. Otherwise, this function will normalize this
+     * quaternion at first then do the transformation.
+     *
+     * @note Matrix A which is to be rotated should have the form
+     * \f[\begin{bmatrix}
+     * x_0& x_1& x_2&...&x_n\\
+     * y_0& y_1& y_2&...&y_n\\
+     * z_0& z_1& z_2&...&z_n
+     * \end{bmatrix}\f]
+     * where the same subscript represents a point. The shape of A assume to be [3, n]
+     * The points matrix A can be rotated by toRotMat3x3() * A.
+     * The result has 3 rows and n columns too.
+
+     * For example
+     * ```
+     * double angle = CV_PI;
+     * Vec3d axis{0,0,1};
+     * Quatd q_unit = Quatd::createFromAngleAxis(angle, axis); //quaternion could also be get by interpolation by two or more quaternions.
+     *
+     * //assume there is two points (1,0,0) and (1,0,1) to be rotated
+     * Mat pointsA = (Mat_<double>(2, 3) << 1,0,0,1,0,1);
+     * //change the shape
+     * pointsA = pointsA.t();
+     * // rotate 180 degrees around the z axis
+     * Mat new_point = q_unit.toRotMat3x3() * pointsA;
+     * // print two points
+     * cout << new_point << endl;
+     * ```
+     */
+    Matx<_Tp, 3, 3> toRotMat3x3(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief transform a quaternion to a 4x4 rotation matrix.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and
+     * this function will save some computations. Otherwise, this function will normalize this
+     * quaternion at first then do the transformation.
+     *
+     * The operations is similar as toRotMat3x3
+     * except that the points matrix should have the form
+     * \f[\begin{bmatrix}
+     * x_0& x_1& x_2&...&x_n\\
+     * y_0& y_1& y_2&...&y_n\\
+     * z_0& z_1& z_2&...&z_n\\
+     * 0&0&0&...&0
+     * \end{bmatrix}\f]
+     *
+     * @sa toRotMat3x3
+     */
+
+    Matx<_Tp, 4, 4> toRotMat4x4(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief transform the this quaternion to a Vec<T, 4>.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.toVec();
+     * ```
+     */
+    Vec<_Tp, 4> toVec() const;
+
+    /**
+     * @brief transform this quaternion to a Rotation vector.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and
+     * this function will save some computations.
+     * Rotation vector rVec is defined as:
+     * \f[ rVec = [\theta v_x, \theta v_y, \theta v_z]\f]
+     * where \f$\theta\f$ represents rotation angle, and \f$\boldsymbol{v}\f$ represents the normalized rotation axis.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.toRotVec();
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * q.normalize().toRotVec(assumeUnit); //answer is same as q.toRotVec().
+     * ```
+     */
+    Vec<_Tp, 3> toRotVec(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief get the angle of quaternion, it returns the rotation angle.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and
+     * this function will save some computations.
+     * \f[\psi = 2 *arccos(\frac{w}{||q||})\f]
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.getAngle();
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * q.normalize().getAngle(assumeUnit);//same as q.getAngle().
+     * ```
+     * @note It always return the value between \f$[0, 2\pi]\f$.
+     */
+    _Tp getAngle(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief get the axis of quaternion, it returns a vector of length 3.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, this quaternion assume to be a unit quaternion and
+     * this function will save some computations.
+     *
+     * the unit axis \f$\boldsymbol{u}\f$ is defined by
+     * \f[\begin{equation}
+     *    \begin{split}
+     *      \boldsymbol{v}
+     *      &= \boldsymbol{u} ||\boldsymbol{v}||\\
+     *      &= \boldsymbol{u}||q||sin(\frac{\theta}{2})
+     *    \end{split}
+     *    \end{equation}\f]
+     *  where \f$v=[x, y ,z]\f$ and \f$\theta\f$ represents rotation angle.
+     *
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * q.getAxis();
+     *
+     * QuatAssumeType assumeUnit = QUAT_ASSUME_UNIT;
+     * q.normalize().getAxis(assumeUnit);//same as q.getAxis()
+     * ```
+     */
+    Vec<_Tp, 3> getAxis(QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT) const;
+
+    /**
+     * @brief return the dot between quaternion \f$q\f$ and this quaternion.
+     *
+     * dot(p, q) is a good metric of how close the quaternions are.
+     * Indeed, consider the unit quaternion difference \f$p^{-1} * q\f$, its real part is dot(p, q).
+     * At the same time its real part is equal to \f$\cos(\beta/2)\f$ where \f$\beta\f$ is
+     * an angle of rotation between p and q, i.e.,
+     * Therefore, the closer dot(p, q) to 1,
+     * the smaller rotation between them.
+     * \f[p \cdot q = p.w \cdot q.w + p.x \cdot q.x + p.y \cdot q.y + p.z \cdot q.z\f]
+     * @param q the other quaternion.
+     *
+     * For example
+     * ```
+     * Quatd q(1,2,3,4);
+     * Quatd p(5,6,7,8);
+     * p.dot(q);
+     * ```
+     */
+    _Tp dot(Quat<_Tp> q) const;
+
+    /**
+     * @brief To calculate the interpolation from \f$q_0\f$ to \f$q_1\f$ by Linear Interpolation(Nlerp)
+     * For two quaternions, this interpolation curve can be displayed as:
+     * \f[Lerp(q_0, q_1, t) = (1 - t)q_0 + tq_1.\f]
+     * Obviously, the lerp will interpolate along a straight line if we think of \f$q_0\f$ and \f$q_1\f$ as a vector
+     * in a two-dimensional space. When \f$t = 0\f$, it returns \f$q_0\f$ and when \f$t= 1\f$, it returns \f$q_1\f$.
+     * \f$t\f$ should to be ranged in \f$[0, 1]\f$ normally.
+     * @param q0 a quaternion used in linear interpolation.
+     * @param q1 a quaternion used in linear interpolation.
+     * @param t percent of vector \f$\overrightarrow{q_0q_1}\f$ over a range [0, 1].
+     * @note it returns a non-unit quaternion.
+     */
+    static Quat<_Tp> lerp(const Quat<_Tp> &q0, const Quat &q1, const _Tp t);
+
+    /**
+     * @brief To calculate the interpolation from \f$q_0\f$ to \f$q_1\f$ by Normalized Linear Interpolation(Nlerp).
+     * it returns a normalized quaternion of Linear Interpolation(Lerp).
+     * \f[ Nlerp(q_0, q_1, t) = \frac{(1 - t)q_0 + tq_1}{||(1 - t)q_0 + tq_1||}.\f]
+     * The interpolation will always choose the shortest path but the constant speed is not guaranteed.
+     * @param q0 a quaternion used in normalized linear interpolation.
+     * @param q1 a quaternion used in normalized linear interpolation.
+     * @param t percent of vector \f$\overrightarrow{q_0q_1}\f$ over a range [0, 1].
+     * @param assumeUnit if QUAT_ASSUME_UNIT, all input quaternions assume to be unit quaternion. Otherwise, all inputs
+     quaternion will be normalized inside the function.
+     * @sa lerp
+     */
+    static Quat<_Tp> nlerp(const Quat<_Tp> &q0, const Quat &q1, const _Tp t, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+    /**
+     @brief To calculate the interpolation between \f$q_0\f$ and \f$q_1\f$ by Spherical Linear
+     Interpolation(Slerp), which can be defined as:
+    \f[ Slerp(q_0, q_1, t) = \frac{\sin((1-t)\theta)}{\sin(\theta)}q_0 + \frac{\sin(t\theta)}{\sin(\theta)}q_1\f]
+    where \f$\theta\f$ can be calculated as:
+    \f[\theta=cos^{-1}(q_0\cdot q_1)\f]
+    resulting from the both of their norm is unit.
+    @param q0 a quaternion used in Slerp.
+    @param q1 a quaternion used in Slerp.
+    @param t percent of angle between \f$q_0\f$ and \f$q_1\f$ over a range [0, 1].
+    @param assumeUnit if QUAT_ASSUME_UNIT, all input quaternions assume to be unit quaternions. Otherwise, all input
+    quaternions will be normalized inside the function.
+    @param directChange if QUAT_ASSUME_UNIT, the interpolation will choose the nearest path.
+    @note If the interpolation angle is small, the error between Nlerp and Slerp is not so large. To improve efficiency and
+    avoid zero division error, we use Nlerp instead of Slerp.
+    */
+    static Quat<_Tp> slerp(const Quat<_Tp> &q0, const Quat &q1, const _Tp t, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT, bool directChange=true);
+
+    /**
+     * @brief To calculate the interpolation between \f$q_0\f$,\f$q_1\f$,\f$q_2\f$,\f$q_3\f$  by Spherical and quadrangle(Squad). This could be defined as:
+     * \f[Squad(q_i, s_i, s_{i+1}, q_{i+1}, t) = Slerp(Slerp(q_i, q_{i+1}, t), Slerp(s_i, s_{i+1}, t), 2t(1-t))\f]
+     * where
+     * \f[s_i = q_i\exp(-\frac{\log(q^*_iq_{i+1}) + \log(q^*_iq_{i-1})}{4})\f]
+     *
+     * The Squad expression is analogous to the \f$B\acute{e}zier\f$ curve, but involves spherical linear
+     * interpolation instead of simple linear interpolation. Each \f$s_i\f$ needs to be calculated by three
+     * quaternions.
+     *
+     * @param q0 the first quaternion.
+     * @param s0 the second quaternion.
+     * @param s1 the third quaternion.
+     * @param q1 thr fourth quaternion.
+     * @param t interpolation parameter of quadratic and linear interpolation over a range \f$[0, 1]\f$.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, all input quaternions assume to be unit quaternion. Otherwise, all input
+     * quaternions will be normalized inside the function.
+     * @param directChange if QUAT_ASSUME_UNIT, squad will find the nearest path to interpolate.
+     * @sa interPoint, spline
+     */
+    static Quat<_Tp> squad(const Quat<_Tp> &q0, const Quat<_Tp> &s0,
+                            const Quat<_Tp> &s1, const Quat<_Tp> &q1,
+                            const _Tp t, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT,
+                            bool directChange=true);
+
+    /**
+     * @brief This is the part calculation of squad.
+     * To calculate the intermedia quaternion \f$s_i\f$ between each three quaternion
+     * \f[s_i = q_i\exp(-\frac{\log(q^*_iq_{i+1}) + \log(q^*_iq_{i-1})}{4}).\f]
+     * @param q0 the first quaternion.
+     * @param q1 the second quaternion.
+     * @param q2 the third quaternion.
+     * @param assumeUnit if QUAT_ASSUME_UNIT, all input quaternions assume to be unit quaternion. Otherwise, all input
+     * quaternions will be normalized inside the function.
+     * @sa squad
+     */
+    static Quat<_Tp> interPoint(const Quat<_Tp> &q0, const Quat<_Tp> &q1,
+                                 const Quat<_Tp> &q2, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+    /**
+     * @brief to calculate a quaternion which is the result of a \f$C^1\f$ continuous
+     * spline curve constructed by squad at the ratio t. Here, the interpolation values are
+     * between \f$q_1\f$ and \f$q_2\f$. \f$q_0\f$ and \f$q_2\f$ are used to ensure the \f$C^1\f$
+     * continuity. if t = 0, it returns \f$q_1\f$, if t = 1, it returns \f$q_2\f$.
+     * @param q0 the first input quaternion to ensure \f$C^1\f$ continuity.
+     * @param q1 the second input quaternion.
+     * @param q2 the third input quaternion.
+     * @param q3 the fourth input quaternion the same use of \f$q1\f$.
+     * @param t ratio over a range [0, 1].
+     * @param assumeUnit if QUAT_ASSUME_UNIT, \f$q_0, q_1, q_2, q_3\f$ assume to be unit quaternion. Otherwise, all input
+     * quaternions will be normalized inside the function.
+     *
+     * For example:
+     *
+     * If there are three double quaternions \f$v_0, v_1, v_2\f$ waiting to be interpolated.
+     *
+     * Interpolation between \f$v_0\f$ and \f$v_1\f$ with a ratio \f$t_0\f$ could be calculated as
+     * ```
+     * Quatd::spline(v0, v0, v1, v2, t0);
+     * ```
+     * Interpolation between \f$v_1\f$ and \f$v_2\f$ with a ratio \f$t_0\f$ could be calculated as
+     * ```
+     * Quatd::spline(v0, v1, v2, v2, t0);
+     * ```
+     * @sa squad, slerp
+     */
+    static Quat<_Tp> spline(const Quat<_Tp> &q0, const Quat<_Tp> &q1,
+                            const Quat<_Tp> &q2, const Quat<_Tp> &q3,
+                            const _Tp t, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+    /**
+     * @brief Return opposite quaternion \f$-p\f$
+     * which satisfies \f$p + (-p) = 0.\f$
+     *
+     * For example
+     * ```
+     * Quatd q{1, 2, 3, 4};
+     * std::cout << -q << std::endl; // [-1, -2, -3, -4]
+     * ```
+     */
+    Quat<_Tp> operator-() const;
+
+    /**
+     * @brief return true if two quaternions p and q are nearly equal, i.e. when the absolute
+     * value of each \f$p_i\f$ and \f$q_i\f$ is less than CV_QUAT_EPS.
+     */
+    bool operator==(const Quat<_Tp>&) const;
+
+    /**
+     * @brief Addition operator of two quaternions p and q.
+     * It returns a new quaternion that each value is the sum of \f$p_i\f$ and \f$q_i\f$.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * std::cout << p + q << std::endl; //[6, 8, 10, 12]
+     * ```
+     */
+    Quat<_Tp> operator+(const Quat<_Tp>&) const;
+
+    /**
+     * @brief Addition assignment operator of two quaternions p and q.
+     * It adds right operand to the left operand and assign the result to left operand.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * p += q; // equivalent to p = p + q
+     * std::cout << p << std::endl; //[6, 8, 10, 12]
+     *
+     * ```
+     */
+    Quat<_Tp>& operator+=(const Quat<_Tp>&);
+
+    /**
+     * @brief Subtraction operator of two quaternions p and q.
+     * It returns a new quaternion that each value is the sum of \f$p_i\f$ and \f$-q_i\f$.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * std::cout << p - q << std::endl; //[-4, -4, -4, -4]
+     * ```
+     */
+    Quat<_Tp> operator-(const Quat<_Tp>&) const;
+
+    /**
+     * @brief Subtraction assignment operator of two quaternions p and q.
+     * It subtracts right operand from the left operand and assign the result to left operand.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * p -= q; // equivalent to p = p - q
+     * std::cout << p << std::endl; //[-4, -4, -4, -4]
+     *
+     * ```
+     */
+    Quat<_Tp>& operator-=(const Quat<_Tp>&);
+
+    /**
+     * @brief Multiplication assignment operator of two quaternions q and p.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of quaternion multiplication:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * q &= [p_0, \boldsymbol{u}]*[q_0, \boldsymbol{v}]\\
+     * &=[p_0q_0 - \boldsymbol{u}\cdot \boldsymbol{v}, p_0\boldsymbol{v} + q_0\boldsymbol{u}+ \boldsymbol{u}\times \boldsymbol{v}].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     * where \f$\cdot\f$ means dot product and \f$\times \f$ means cross product.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * p *= q; // equivalent to p = p * q
+     * std::cout << p << std::endl; //[-60, 12, 30, 24]
+     * ```
+     */
+    Quat<_Tp>& operator*=(const Quat<_Tp>&);
+
+    /**
+     * @brief Multiplication assignment operator of a quaternions and a scalar.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of quaternion multiplication with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * s &= [w, x, y, z] * s\\
+     * &=[w * s, x * s, y * s, z * s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double s = 2.0;
+     * p *= s; // equivalent to p = p * s
+     * std::cout << p << std::endl; //[2.0, 4.0, 6.0, 8.0]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    Quat<_Tp>& operator*=(const _Tp s);
+
+    /**
+     * @brief Multiplication operator of two quaternions q and p.
+     * Multiplies values on either side of the operator.
+     *
+     * Rule of quaternion multiplication:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * q &= [p_0, \boldsymbol{u}]*[q_0, \boldsymbol{v}]\\
+     * &=[p_0q_0 - \boldsymbol{u}\cdot \boldsymbol{v}, p_0\boldsymbol{v} + q_0\boldsymbol{u}+ \boldsymbol{u}\times \boldsymbol{v}].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     * where \f$\cdot\f$ means dot product and \f$\times \f$ means cross product.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * std::cout << p * q << std::endl; //[-60, 12, 30, 24]
+     * ```
+     */
+    Quat<_Tp> operator*(const Quat<_Tp>&) const;
+
+    /**
+     * @brief Division operator of a quaternions and a scalar.
+     * It divides left operand with the right operand and assign the result to left operand.
+     *
+     * Rule of quaternion division with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / s &= [w, x, y, z] / s\\
+     * &=[w/s, x/s, y/s, z/s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double s = 2.0;
+     * p /= s; // equivalent to p = p / s
+     * std::cout << p << std::endl; //[0.5, 1, 1.5, 2]
+     * ```
+     * @note the type of scalar should be equal to this quaternion.
+     */
+    Quat<_Tp> operator/(const _Tp s) const;
+
+    /**
+     * @brief Division operator of two quaternions p and q.
+     * Divides left hand operand by right hand operand.
+     *
+     * Rule of quaternion division with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / q &= p * q.inv()\\
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * std::cout << p / q << std::endl; // equivalent to p * q.inv()
+     * ```
+     */
+    Quat<_Tp> operator/(const Quat<_Tp>&) const;
+
+    /**
+     * @brief Division assignment operator of a quaternions and a scalar.
+     * It divides left operand with the right operand and assign the result to left operand.
+     *
+     * Rule of quaternion division with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / s &= [w, x, y, z] / s\\
+     * &=[w / s, x / s, y / s, z / s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double s = 2.0;;
+     * p /= s; // equivalent to p = p / s
+     * std::cout << p << std::endl; //[0.5, 1.0, 1.5, 2.0]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    Quat<_Tp>& operator/=(const _Tp s);
+
+    /**
+     * @brief Division assignment operator of two quaternions p and q;
+     * It divides left operand with the right operand and assign the result to left operand.
+     *
+     * Rule of quaternion division with a quaternion:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p / q&= p * q.inv()\\
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * Quatd q{5, 6, 7, 8};
+     * p /= q; // equivalent to p = p * q.inv()
+     * std::cout << p << std::endl;
+     * ```
+     */
+    Quat<_Tp>& operator/=(const Quat<_Tp>&);
+
+    _Tp& operator[](std::size_t n);
+
+    const _Tp& operator[](std::size_t n) const;
+
+    /**
+     * @brief Subtraction operator of a scalar and a quaternions.
+     * Subtracts right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double scalar = 2.0;
+     * std::cout << scalar - p << std::endl; //[1.0, -2, -3, -4]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    template <typename T>
+    friend Quat<T> cv::operator-(const T s, const Quat<T>&);
+
+    /**
+     * @brief Subtraction operator of a quaternions and a scalar.
+     * Subtracts right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double scalar = 2.0;
+     * std::cout << p - scalar << std::endl; //[-1.0, 2, 3, 4]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    template <typename T>
+    friend Quat<T> cv::operator-(const Quat<T>&, const T s);
+
+    /**
+     * @brief Addition operator of a quaternions and a scalar.
+     * Adds right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double scalar = 2.0;
+     * std::cout << scalar + p << std::endl; //[3.0, 2, 3, 4]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    template <typename T>
+    friend Quat<T> cv::operator+(const T s, const Quat<T>&);
+
+    /**
+     * @brief Addition operator of a quaternions and a scalar.
+     * Adds right hand operand from left hand operand.
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double scalar = 2.0;
+     * std::cout << p + scalar << std::endl; //[3.0, 2, 3, 4]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    template <typename T>
+    friend Quat<T> cv::operator+(const Quat<T>&, const T s);
+
+    /**
+     * @brief Multiplication operator of a scalar and a quaternions.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of quaternion multiplication with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * s &= [w, x, y, z] * s\\
+     * &=[w * s, x * s, y * s, z * s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double s = 2.0;
+     * std::cout << s * p << std::endl; //[2.0, 4.0, 6.0, 8.0]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    template <typename T>
+    friend Quat<T> cv::operator*(const T s, const Quat<T>&);
+
+    /**
+     * @brief Multiplication operator of a quaternion and a scalar.
+     * It multiplies right operand with the left operand and assign the result to left operand.
+     *
+     * Rule of quaternion multiplication with a scalar:
+     * \f[
+     * \begin{equation}
+     * \begin{split}
+     * p * s &= [w, x, y, z] * s\\
+     * &=[w * s, x * s, y * s, z * s].
+     * \end{split}
+     * \end{equation}
+     * \f]
+     *
+     * For example
+     * ```
+     * Quatd p{1, 2, 3, 4};
+     * double s = 2.0;
+     * std::cout << p * s << std::endl; //[2.0, 4.0, 6.0, 8.0]
+     * ```
+     * @note the type of scalar should be equal to the quaternion.
+     */
+    template <typename T>
+    friend Quat<T> cv::operator*(const Quat<T>&, const T s);
+
+    template <typename S>
+    friend std::ostream& cv::operator<<(std::ostream&, const Quat<S>&);
+
+    /**
+     * @brief Transform a quaternion q to Euler angles.
+     *
+     *
+     * When transforming a quaternion \f$q = w + x\boldsymbol{i} + y\boldsymbol{j} + z\boldsymbol{k}\f$ to Euler angles, rotation matrix M can be calculated by:
+     * \f[ \begin{aligned} {M} &={\begin{bmatrix}1-2(y^{2}+z^{2})&2(xy-zx)&2(xz+yw)\\2(xy+zw)&1-2(x^{2}+z^{2})&2(yz-xw)\\2(xz-yw)&2(yz+xw)&1-2(x^{2}+y^{2})\end{bmatrix}}\end{aligned}.\f]
+     * On the other hand, the rotation matrix can be obtained from Euler angles.
+     * Using intrinsic rotations with Euler angles type XYZ as an example,
+     * \f$\theta_1 \f$, \f$\theta_2 \f$, \f$\theta_3 \f$ are three angles for Euler angles, the rotation matrix R can be calculated by:\f[R =X(\theta_1)Y(\theta_2)Z(\theta_3)
+     * ={\begin{bmatrix}\cos\theta_{2}\cos\theta_{3}&-\cos\theta_{2}\sin\theta_{3}&\sin\theta_{2}\\\cos\theta_{1}\sin\theta_{3}+\cos\theta_{3}\sin\theta_{1}\sin\theta_{2}&\cos\theta_{1}\cos\theta_{3}-\sin\theta_{1}\sin\theta_{2}\sin\theta_{3}&-\cos\theta_{2}\sin\theta_{1}\\\sin\theta_{1}\sin\theta_{3}-\cos\theta_{1}\cos\theta_{3}\sin\theta_{2}&\cos\theta_{3}\sin\theta_{1}+\cos\theta_{1}\sin\theta_{2}\sin\theta_{3}&\cos\theta_{1}\cos_{2}\end{bmatrix}}\f]
+     * Rotation matrix M and R are equal. As long as \f$ s_{2} \neq 1 \f$, by comparing each element of two matrices ,the solution is\f$\begin{cases} \theta_1 = \arctan2(-m_{23},m_{33})\\\theta_2 = arcsin(m_{13}) \\\theta_3 = \arctan2(-m_{12},m_{11}) \end{cases}\f$.
+     *
+     * When \f$ s_{2}=1\f$ or \f$ s_{2}=-1\f$, the gimbal lock occurs. The function will prompt "WARNING: Gimbal Lock will occur. Euler angles is non-unique. For intrinsic rotations, we set the third angle to 0, and for external rotation, we set the first angle to 0.".
+     *
+     * When \f$ s_{2}=1\f$ ,
+     * The rotation matrix R is \f$R = {\begin{bmatrix}0&0&1\\\sin(\theta_1+\theta_3)&\cos(\theta_1+\theta_3)&0\\-\cos(\theta_1+\theta_3)&\sin(\theta_1+\theta_3)&0\end{bmatrix}}\f$.
+     *
+     * The number of solutions is infinite with the condition \f$\begin{cases} \theta_1+\theta_3 = \arctan2(m_{21},m_{22})\\ \theta_2=\pi/2 \end{cases}\ \f$.
+     *
+     * We set \f$ \theta_3 = 0\f$, the solution is \f$\begin{cases} \theta_1=\arctan2(m_{21},m_{22})\\ \theta_2=\pi/2\\ \theta_3=0 \end{cases}\f$.
+     *
+     * When \f$ s_{2}=-1\f$,
+     * The rotation matrix R is \f$X_{1}Y_{2}Z_{3}={\begin{bmatrix}0&0&-1\\-\sin(\theta_1-\theta_3)&\cos(\theta_1-\theta_3)&0\\\cos(\theta_1-\theta_3)&\sin(\theta_1-\theta_3)&0\end{bmatrix}}\f$.
+     *
+     * The number of solutions is infinite with the condition \f$\begin{cases} \theta_1+\theta_3 = \arctan2(m_{32},m_{22})\\ \theta_2=\pi/2 \end{cases}\ \f$.
+     *
+     * We set \f$ \theta_3 = 0\f$, the solution is \f$ \begin{cases}\theta_1=\arctan2(m_{32},m_{22}) \\ \theta_2=-\pi/2\\  \theta_3=0\end{cases}\f$.
+     *
+     * Since \f$ sin \theta\in [-1,1] \f$ and \f$ cos \theta \in [-1,1] \f$, the unnormalized quaternion will cause computational troubles. For this reason, this function will normalize the quaternion at first and @ref QuatAssumeType is not needed.
+     *
+     * When the gimbal lock occurs, we set \f$\theta_3 = 0\f$ for intrinsic rotations or \f$\theta_1 = 0\f$ for extrinsic rotations.
+     *
+     * As a result, for every Euler angles type, we can get solution as shown in the following table.
+     * EulerAnglesType  | Ordinary | \f$\theta_2 = π/2\f$ | \f$\theta_2 = -π/2\f$
+     * ------------- | -------------| -------------| -------------
+     * INT_XYZ|\f$ \theta_1 = \arctan2(-m_{23},m_{33})\\\theta_2 = \arcsin(m_{13}) \\\theta_3= \arctan2(-m_{12},m_{11}) \f$|\f$ \theta_1=\arctan2(m_{21},m_{22})\\ \theta_2=\pi/2\\ \theta_3=0 \f$|\f$ \theta_1=\arctan2(m_{32},m_{22})\\ \theta_2=-\pi/2\\ \theta_3=0 \f$
+     * INT_XZY|\f$ \theta_1 = \arctan2(m_{32},m_{22})\\\theta_2 = -\arcsin(m_{12}) \\\theta_3= \arctan2(m_{13},m_{11}) \f$|\f$ \theta_1=\arctan2(m_{31},m_{33})\\ \theta_2=\pi/2\\ \theta_3=0 \f$|\f$ \theta_1=\arctan2(-m_{23},m_{33})\\ \theta_2=-\pi/2\\ \theta_3=0 \f$
+     * INT_YXZ|\f$ \theta_1 = \arctan2(m_{13},m_{33})\\\theta_2 = -\arcsin(m_{23}) \\\theta_3= \arctan2(m_{21},m_{22}) \f$|\f$ \theta_1=\arctan2(m_{12},m_{11})\\ \theta_2=\pi/2\\ \theta_3=0 \f$|\f$ \theta_1=\arctan2(-m_{12},m_{11})\\ \theta_2=-\pi/2\\ \theta_3=0 \f$
+     * INT_YZX|\f$ \theta_1 = \arctan2(-m_{31},m_{11})\\\theta_2 = \arcsin(m_{21}) \\\theta_3= \arctan2(-m_{23},m_{22}) \f$|\f$ \theta_1=\arctan2(m_{13},m_{33})\\ \theta_2=\pi/2\\ \theta_3=0 \f$|\f$ \theta_1=\arctan2(m_{13},m_{12})\\ \theta_2=-\pi/2\\ \theta_3=0 \f$
+     * INT_ZXY|\f$ \theta_1 = \arctan2(-m_{12},m_{22})\\\theta_2 = \arcsin(m_{32}) \\\theta_3= \arctan2(-m_{31},m_{33}) \f$|\f$ \theta_1=\arctan2(m_{21},m_{11})\\ \theta_2=\pi/2\\ \theta_3=0 \f$|\f$ \theta_1=\arctan2(m_{21},m_{11})\\ \theta_2=-\pi/2\\ \theta_3=0 \f$
+     * INT_ZYX|\f$ \theta_1 = \arctan2(m_{21},m_{11})\\\theta_2 = \arcsin(-m_{31}) \\\theta_3= \arctan2(m_{32},m_{33}) \f$|\f$ \theta_1=\arctan2(m_{23},m_{22})\\ \theta_2=\pi/2\\ \theta_3=0 \f$|\f$ \theta_1=\arctan2(-m_{12},m_{22})\\ \theta_2=-\pi/2\\ \theta_3=0 \f$
+     * EXT_XYZ|\f$ \theta_1 = \arctan2(m_{32},m_{33})\\\theta_2 = \arcsin(-m_{31}) \\\ \theta_3 = \arctan2(m_{21},m_{11})\f$|\f$ \theta_1= 0\\ \theta_2=\pi/2\\ \theta_3=\arctan2(m_{23},m_{22}) \f$|\f$ \theta_1=0\\ \theta_2=-\pi/2\\ \theta_3=\arctan2(-m_{12},m_{22}) \f$
+     * EXT_XZY|\f$ \theta_1 = \arctan2(-m_{23},m_{22})\\\theta_2 = \arcsin(m_{21}) \\\theta_3=  \arctan2(-m_{31},m_{11})\f$|\f$ \theta_1= 0\\ \theta_2=\pi/2\\ \theta_3=\arctan2(m_{13},m_{33}) \f$|\f$ \theta_1=0\\ \theta_2=-\pi/2\\ \theta_3=\arctan2(m_{13},m_{12}) \f$
+     * EXT_YXZ|\f$ \theta_1 = \arctan2(-m_{31},m_{33}) \\\theta_2 = \arcsin(m_{32}) \\\theta_3= \arctan2(-m_{12},m_{22})\f$|\f$ \theta_1= 0\\ \theta_2=\pi/2\\ \theta_3=\arctan2(m_{21},m_{11}) \f$|\f$ \theta_1=0\\ \theta_2=-\pi/2\\ \theta_3=\arctan2(m_{21},m_{11}) \f$
+     * EXT_YZX|\f$ \theta_1 = \arctan2(m_{13},m_{11})\\\theta_2 = -\arcsin(m_{12}) \\\theta_3= \arctan2(m_{32},m_{22})\f$|\f$ \theta_1= 0\\ \theta_2=\pi/2\\ \theta_3=\arctan2(m_{31},m_{33}) \f$|\f$ \theta_1=0\\ \theta_2=-\pi/2\\ \theta_3=\arctan2(-m_{23},m_{33}) \f$
+     * EXT_ZXY|\f$ \theta_1 = \arctan2(m_{21},m_{22})\\\theta_2 = -\arcsin(m_{23}) \\\theta_3= \arctan2(m_{13},m_{33})\f$|\f$ \theta_1= 0\\ \theta_2=\pi/2\\ \theta_3=\arctan2(m_{12},m_{11}) \f$|\f$ \theta_1= 0\\ \theta_2=-\pi/2\\ \theta_3=\arctan2(-m_{12},m_{11}) \f$
+     * EXT_ZYX|\f$ \theta_1 = \arctan2(-m_{12},m_{11})\\\theta_2 = \arcsin(m_{13}) \\\theta_3= \arctan2(-m_{23},m_{33})\f$|\f$ \theta_1=0\\ \theta_2=\pi/2\\ \theta_3=\arctan2(m_{21},m_{22}) \f$|\f$ \theta_1=0\\ \theta_2=-\pi/2\\ \theta_3=\arctan2(m_{32},m_{22}) \f$
+     *
+     *  EulerAnglesType  | Ordinary | \f$\theta_2 = 0\f$ | \f$\theta_2 = π\f$
+     * ------------- | -------------| -------------| -------------
+     * INT_XYX| \f$ \theta_1 = \arctan2(m_{21},-m_{31})\\\theta_2 =\arccos(m_{11}) \\\theta_3 = \arctan2(m_{12},m_{13}) \f$| \f$ \theta_1=\arctan2(m_{32},m_{33})\\ \theta_2=0\\ \theta_3=0 \f$| \f$ \theta_1=\arctan2(m_{23},m_{22})\\ \theta_2=\pi\\ \theta_3=0 \f$
+     * INT_XZX| \f$ \theta_1 = \arctan2(m_{31},m_{21})\\\theta_2 = \arccos(m_{11}) \\\theta_3 = \arctan2(m_{13},-m_{12}) \f$| \f$ \theta_1=\arctan2(m_{32},m_{33})\\ \theta_2=0\\ \theta_3=0 \f$| \f$ \theta_1=\arctan2(-m_{32},m_{33})\\ \theta_2=\pi\\ \theta_3=0 \f$
+     * INT_YXY| \f$ \theta_1 = \arctan2(m_{12},m_{32})\\\theta_2 = \arccos(m_{22}) \\\theta_3 = \arctan2(m_{21},-m_{23}) \f$| \f$ \theta_1=\arctan2(m_{13},m_{11})\\ \theta_2=0\\ \theta_3=0 \f$| \f$ \theta_1=\arctan2(-m_{31},m_{11})\\ \theta_2=\pi\\ \theta_3=0 \f$
+     * INT_YZY| \f$ \theta_1 = \arctan2(m_{32},-m_{12})\\\theta_2 = \arccos(m_{22}) \\\theta_3 =\arctan2(m_{23},m_{21}) \f$| \f$ \theta_1=\arctan2(m_{13},m_{11})\\ \theta_2=0\\ \theta_3=0 \f$| \f$ \theta_1=\arctan2(m_{13},-m_{11})\\ \theta_2=\pi\\ \theta_3=0 \f$
+     * INT_ZXZ| \f$ \theta_1 = \arctan2(-m_{13},m_{23})\\\theta_2 = \arccos(m_{33}) \\\theta_3 =\arctan2(m_{31},m_{32}) \f$| \f$ \theta_1=\arctan2(m_{21},m_{22})\\ \theta_2=0\\ \theta_3=0 \f$| \f$ \theta_1=\arctan2(m_{21},m_{11})\\ \theta_2=\pi\\ \theta_3=0 \f$
+     * INT_ZYZ| \f$ \theta_1 = \arctan2(m_{23},m_{13})\\\theta_2 = \arccos(m_{33}) \\\theta_3 = \arctan2(m_{32},-m_{31}) \f$| \f$ \theta_1=\arctan2(m_{21},m_{11})\\ \theta_2=0\\ \theta_3=0 \f$| \f$ \theta_1=\arctan2(m_{21},m_{11})\\ \theta_2=\pi\\ \theta_3=0 \f$
+     * EXT_XYX| \f$ \theta_1 = \arctan2(m_{12},m_{13}) \\\theta_2 = \arccos(m_{11}) \\\theta_3 = \arctan2(m_{21},-m_{31})\f$| \f$ \theta_1=0\\ \theta_2=0\\ \theta_3=\arctan2(m_{32},m_{33}) \f$| \f$ \theta_1= 0\\ \theta_2=\pi\\ \theta_3= \arctan2(m_{23},m_{22}) \f$
+     * EXT_XZX| \f$ \theta_1 = \arctan2(m_{13},-m_{12})\\\theta_2 = \arccos(m_{11}) \\\theta_3 = \arctan2(m_{31},m_{21})\f$| \f$ \theta_1= 0\\ \theta_2=0\\ \theta_3=\arctan2(m_{32},m_{33}) \f$| \f$ \theta_1= 0\\ \theta_2=\pi\\ \theta_3=\arctan2(-m_{32},m_{33}) \f$
+     * EXT_YXY| \f$ \theta_1 = \arctan2(m_{21},-m_{23})\\\theta_2 = \arccos(m_{22}) \\\theta_3 = \arctan2(m_{12},m_{32}) \f$| \f$ \theta_1= 0\\ \theta_2=0\\ \theta_3=\arctan2(m_{13},m_{11}) \f$| \f$ \theta_1= 0\\ \theta_2=\pi\\ \theta_3=\arctan2(-m_{31},m_{11}) \f$
+     * EXT_YZY| \f$ \theta_1 = \arctan2(m_{23},m_{21}) \\\theta_2 = \arccos(m_{22}) \\\theta_3 = \arctan2(m_{32},-m_{12}) \f$| \f$ \theta_1= 0\\ \theta_2=0\\ \theta_3=\arctan2(m_{13},m_{11}) \f$| \f$ \theta_1=0\\ \theta_2=\pi\\ \theta_3=\arctan2(m_{13},-m_{11}) \f$
+     * EXT_ZXZ| \f$ \theta_1 = \arctan2(m_{31},m_{32}) \\\theta_2 = \arccos(m_{33}) \\\theta_3 = \arctan2(-m_{13},m_{23})\f$| \f$ \theta_1=0\\ \theta_2=0\\ \theta_3=\arctan2(m_{21},m_{22}) \f$| \f$ \theta_1= 0\\ \theta_2=\pi\\ \theta_3=\arctan2(m_{21},m_{11}) \f$
+     * EXT_ZYZ| \f$ \theta_1 = \arctan2(m_{32},-m_{31})\\\theta_2 = \arccos(m_{33}) \\\theta_3 = \arctan2(m_{23},m_{13}) \f$| \f$ \theta_1=0\\ \theta_2=0\\ \theta_3=\arctan2(m_{21},m_{11}) \f$| \f$ \theta_1= 0\\ \theta_2=\pi\\ \theta_3=\arctan2(m_{21},m_{11}) \f$
+     *
+     * @param eulerAnglesType the convertion Euler angles type
+     */
+
+    Vec<_Tp, 3> toEulerAngles(QuatEnum::EulerAnglesType eulerAnglesType);
+
+    _Tp w, x, y, z;
+
+};
+
+template <typename T>
+Quat<T> inv(const Quat<T> &q, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+template <typename T>
+Quat<T> sinh(const Quat<T> &q);
+
+template <typename T>
+Quat<T> cosh(const Quat<T> &q);
+
+template <typename T>
+Quat<T> tanh(const Quat<T> &q);
+
+template <typename T>
+Quat<T> sin(const Quat<T> &q);
+
+template <typename T>
+Quat<T> cos(const Quat<T> &q);
+
+template <typename T>
+Quat<T> tan(const Quat<T> &q);
+
+template <typename T>
+Quat<T> asinh(const Quat<T> &q);
+
+template <typename T>
+Quat<T> acosh(const Quat<T> &q);
+
+template <typename T>
+Quat<T> atanh(const Quat<T> &q);
+
+template <typename T>
+Quat<T> asin(const Quat<T> &q);
+
+template <typename T>
+Quat<T> acos(const Quat<T> &q);
+
+template <typename T>
+Quat<T> atan(const Quat<T> &q);
+
+template <typename T>
+Quat<T> power(const Quat<T> &q, const Quat<T> &p, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+template <typename T>
+Quat<T> exp(const Quat<T> &q);
+
+template <typename T>
+Quat<T> log(const Quat<T> &q, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+template <typename T>
+Quat<T> power(const Quat<T>& q, const T x, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+template <typename T>
+Quat<T> crossProduct(const Quat<T> &p, const Quat<T> &q);
+
+template <typename S>
+Quat<S> sqrt(const Quat<S> &q, QuatAssumeType assumeUnit=QUAT_ASSUME_NOT_UNIT);
+
+template <typename T>
+Quat<T> operator*(const T, const Quat<T>&);
+
+template <typename T>
+Quat<T> operator*(const Quat<T>&, const T);
+
+template <typename S>
+std::ostream& operator<<(std::ostream&, const Quat<S>&);
+
+using Quatd = Quat<double>;
+using Quatf = Quat<float>;
+
+//! @} core
+}
+
+#include "opencv2/core/quaternion.inl.hpp"
+
+#endif /* OPENCV_CORE_QUATERNION_HPP */
diff --git a/3rdParty/include/opencv2/core/quaternion.inl.hpp b/3rdParty/include/opencv2/core/quaternion.inl.hpp
new file mode 100644
index 0000000..29a16d9
--- /dev/null
+++ b/3rdParty/include/opencv2/core/quaternion.inl.hpp
@@ -0,0 +1,1063 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2020, Huawei Technologies Co., Ltd. All rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Author: Liangqian Kong <chargerKong@126.com>
+//         Longbu Wang <riskiest@gmail.com>
+
+#ifndef OPENCV_CORE_QUATERNION_INL_HPP
+#define OPENCV_CORE_QUATERNION_INL_HPP
+
+#ifndef OPENCV_CORE_QUATERNION_HPP
+#erorr This is not a standalone header. Include quaternion.hpp instead.
+#endif
+
+//@cond IGNORE
+///////////////////////////////////////////////////////////////////////////////////////
+//Implementation
+namespace cv {
+
+template <typename T>
+Quat<T>::Quat() : w(0), x(0), y(0), z(0) {}
+
+template <typename T>
+Quat<T>::Quat(const Vec<T, 4> &coeff):w(coeff[0]), x(coeff[1]), y(coeff[2]), z(coeff[3]){}
+
+template <typename T>
+Quat<T>::Quat(const T qw, const T qx, const T qy, const T qz):w(qw), x(qx), y(qy), z(qz){}
+
+template <typename T>
+Quat<T> Quat<T>::createFromAngleAxis(const T angle, const Vec<T, 3> &axis)
+{
+    T w, x, y, z;
+    T vNorm = std::sqrt(axis.dot(axis));
+    if (vNorm < CV_QUAT_EPS)
+    {
+        CV_Error(Error::StsBadArg, "this quaternion does not represent a rotation");
+    }
+    const T angle_half = angle * T(0.5);
+    w = std::cos(angle_half);
+    const T sin_v = std::sin(angle_half);
+    const T sin_norm = sin_v / vNorm;
+    x = sin_norm * axis[0];
+    y = sin_norm * axis[1];
+    z = sin_norm * axis[2];
+    return Quat<T>(w, x, y, z);
+}
+
+template <typename T>
+Quat<T> Quat<T>::createFromRotMat(InputArray _R)
+{
+    CV_CheckTypeEQ(_R.type(), cv::traits::Type<T>::value, "");
+    if (_R.rows() != 3 || _R.cols() != 3)
+    {
+        CV_Error(Error::StsBadArg, "Cannot convert matrix to quaternion: rotation matrix should be a 3x3 matrix");
+    }
+    Matx<T, 3, 3> R;
+    _R.copyTo(R);
+
+    T S, w, x, y, z;
+    T trace = R(0, 0) + R(1, 1) + R(2, 2);
+    if (trace > 0)
+    {
+        S = std::sqrt(trace + 1) * T(2);
+        x = (R(1, 2) - R(2, 1)) / S;
+        y = (R(2, 0) - R(0, 2)) / S;
+        z = (R(0, 1) - R(1, 0)) / S;
+        w = -T(0.25) * S;
+    }
+    else if (R(0, 0) > R(1, 1) && R(0, 0) > R(2, 2))
+    {
+
+        S = std::sqrt(T(1.0) + R(0, 0) - R(1, 1) - R(2, 2)) * T(2);
+        x = -T(0.25) * S;
+        y = -(R(1, 0) + R(0, 1)) / S;
+        z = -(R(0, 2) + R(2, 0)) / S;
+        w = (R(1, 2) - R(2, 1)) / S;
+    }
+    else if (R(1, 1) > R(2, 2))
+    {
+        S = std::sqrt(T(1.0) - R(0, 0) + R(1, 1) - R(2, 2)) * T(2);
+        x = (R(0, 1) + R(1, 0)) / S;
+        y = T(0.25) * S;
+        z = (R(1, 2) + R(2, 1)) / S;
+        w = (R(0, 2) - R(2, 0)) / S;
+    }
+    else
+    {
+        S = std::sqrt(T(1.0) - R(0, 0) - R(1, 1) + R(2, 2)) * T(2);
+        x = (R(0, 2) + R(2, 0)) / S;
+        y = (R(1, 2) + R(2, 1)) / S;
+        z = T(0.25) * S;
+        w = -(R(0, 1) - R(1, 0)) / S;
+    }
+    return Quat<T> (w, x, y, z);
+}
+
+template <typename T>
+Quat<T> Quat<T>::createFromRvec(InputArray _rvec)
+{
+    if (!((_rvec.cols() == 1 && _rvec.rows() == 3) || (_rvec.cols() == 3 && _rvec.rows() == 1))) {
+        CV_Error(Error::StsBadArg, "Cannot convert rotation vector to quaternion: The length of rotation vector should be 3");
+    }
+    Vec<T, 3> rvec;
+    _rvec.copyTo(rvec);
+    T psi = std::sqrt(rvec.dot(rvec));
+    if (abs(psi) < CV_QUAT_EPS) {
+        return Quat<T> (1, 0, 0, 0);
+    }
+    Vec<T, 3> axis = rvec / psi;
+    return createFromAngleAxis(psi, axis);
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::operator-() const
+{
+    return Quat<T>(-w, -x, -y, -z);
+}
+
+
+template <typename T>
+inline bool Quat<T>::operator==(const Quat<T> &q) const
+{
+    return (abs(w - q.w) < CV_QUAT_EPS && abs(x - q.x) < CV_QUAT_EPS && abs(y - q.y) < CV_QUAT_EPS && abs(z - q.z) < CV_QUAT_EPS);
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::operator+(const Quat<T> &q1) const
+{
+    return Quat<T>(w + q1.w, x + q1.x, y + q1.y, z + q1.z);
+}
+
+template <typename T>
+inline Quat<T> operator+(const T a, const Quat<T>& q)
+{
+    return Quat<T>(q.w + a, q.x, q.y, q.z);
+}
+
+template <typename T>
+inline Quat<T> operator+(const Quat<T>& q, const T a)
+{
+    return Quat<T>(q.w + a, q.x, q.y, q.z);
+}
+
+template <typename T>
+inline Quat<T> operator-(const T a, const Quat<T>& q)
+{
+    return Quat<T>(a - q.w, -q.x, -q.y, -q.z);
+}
+
+template <typename T>
+inline Quat<T> operator-(const Quat<T>& q, const T a)
+{
+    return Quat<T>(q.w - a, q.x, q.y, q.z);
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::operator-(const Quat<T> &q1) const
+{
+    return Quat<T>(w - q1.w, x - q1.x, y - q1.y, z - q1.z);
+}
+
+template <typename T>
+inline Quat<T>& Quat<T>::operator+=(const Quat<T> &q1)
+{
+    w += q1.w;
+    x += q1.x;
+    y += q1.y;
+    z += q1.z;
+    return *this;
+}
+
+template <typename T>
+inline Quat<T>& Quat<T>::operator-=(const Quat<T> &q1)
+{
+    w -= q1.w;
+    x -= q1.x;
+    y -= q1.y;
+    z -= q1.z;
+    return *this;
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::operator*(const Quat<T> &q1) const
+{
+    Vec<T, 4> q{w, x, y, z};
+    Vec<T, 4> q2{q1.w, q1.x, q1.y, q1.z};
+    return Quat<T>(q * q2);
+}
+
+
+template <typename T>
+Quat<T> operator*(const Quat<T> &q1, const T a)
+{
+    return Quat<T>(a * q1.w, a * q1.x, a * q1.y, a * q1.z);
+}
+
+template <typename T>
+Quat<T> operator*(const T a, const Quat<T> &q1)
+{
+    return Quat<T>(a * q1.w, a * q1.x, a * q1.y, a * q1.z);
+}
+
+template <typename T>
+inline Quat<T>& Quat<T>::operator*=(const Quat<T> &q1)
+{
+    T qw, qx, qy, qz;
+    qw = w * q1.w - x * q1.x - y * q1.y - z * q1.z;
+    qx = x * q1.w + w * q1.x + y * q1.z - z * q1.y;
+    qy = y * q1.w + w * q1.y + z * q1.x - x * q1.z;
+    qz = z * q1.w + w * q1.z + x * q1.y - y * q1.x;
+    w = qw;
+    x = qx;
+    y = qy;
+    z = qz;
+    return *this;
+}
+
+template <typename T>
+inline Quat<T>& Quat<T>::operator/=(const Quat<T> &q1)
+{
+    Quat<T> q(*this * q1.inv());
+    w = q.w;
+    x = q.x;
+    y = q.y;
+    z = q.z;
+    return *this;
+}
+template <typename T>
+Quat<T>& Quat<T>::operator*=(const T q1)
+{
+    w *= q1;
+    x *= q1;
+    y *= q1;
+    z *= q1;
+    return *this;
+}
+
+template <typename T>
+inline Quat<T>& Quat<T>::operator/=(const T a)
+{
+    const T a_inv = 1.0 / a;
+    w *= a_inv;
+    x *= a_inv;
+    y *= a_inv;
+    z *= a_inv;
+    return *this;
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::operator/(const T a) const
+{
+    const T a_inv = T(1.0) / a;
+    return Quat<T>(w * a_inv, x * a_inv, y * a_inv, z * a_inv);
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::operator/(const Quat<T> &q) const
+{
+    return *this * q.inv();
+}
+
+template <typename T>
+inline const T& Quat<T>::operator[](std::size_t n) const
+{
+    switch (n) {
+        case 0:
+            return w;
+        case 1:
+            return x;
+        case 2:
+            return y;
+        case 3:
+            return z;
+        default:
+            CV_Error(Error::StsOutOfRange, "subscript exceeds the index range");
+    }
+}
+
+template <typename T>
+inline T& Quat<T>::operator[](std::size_t n)
+{
+    switch (n) {
+        case 0:
+            return w;
+        case 1:
+            return x;
+        case 2:
+            return y;
+        case 3:
+            return z;
+        default:
+            CV_Error(Error::StsOutOfRange, "subscript exceeds the index range");
+    }
+}
+
+template <typename T>
+std::ostream & operator<<(std::ostream &os, const Quat<T> &q)
+{
+    os << "Quat " << Vec<T, 4>{q.w, q.x, q.y, q.z};
+    return os;
+}
+
+template <typename T>
+inline T Quat<T>::at(size_t index) const
+{
+    return (*this)[index];
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::conjugate() const
+{
+    return Quat<T>(w, -x, -y, -z);
+}
+
+template <typename T>
+inline T Quat<T>::norm() const
+{
+    return std::sqrt(dot(*this));
+}
+
+template <typename T>
+Quat<T> exp(const Quat<T> &q)
+{
+    return q.exp();
+}
+
+template <typename T>
+Quat<T> Quat<T>::exp() const
+{
+    Vec<T, 3> v{x, y, z};
+    T normV = std::sqrt(v.dot(v));
+    T k = normV < CV_QUAT_EPS ? 1 : std::sin(normV) / normV;
+    return std::exp(w) * Quat<T>(std::cos(normV), v[0] * k, v[1] * k, v[2] * k);
+}
+
+template <typename T>
+Quat<T> log(const Quat<T> &q, QuatAssumeType assumeUnit)
+{
+    return q.log(assumeUnit);
+}
+
+template <typename T>
+Quat<T> Quat<T>::log(QuatAssumeType assumeUnit) const
+{
+    Vec<T, 3> v{x, y, z};
+    T vNorm = std::sqrt(v.dot(v));
+    if (assumeUnit)
+    {
+        T k = vNorm < CV_QUAT_EPS ? 1 : std::acos(w) / vNorm;
+        return Quat<T>(0, v[0] * k, v[1] * k, v[2] * k);
+    }
+    T qNorm = norm();
+    if (qNorm < CV_QUAT_EPS)
+    {
+        CV_Error(Error::StsBadArg, "Cannot apply this quaternion to log function: undefined");
+    }
+    T k = vNorm < CV_QUAT_EPS ? 1 : std::acos(w / qNorm) / vNorm;
+    return Quat<T>(std::log(qNorm), v[0] * k, v[1] * k, v[2] *k);
+}
+
+template <typename T>
+inline Quat<T> power(const Quat<T> &q1, const T alpha, QuatAssumeType assumeUnit)
+{
+    return q1.power(alpha, assumeUnit);
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::power(const T alpha, QuatAssumeType assumeUnit) const
+{
+    if (x * x + y * y + z * z > CV_QUAT_EPS)
+    {
+        T angle = getAngle(assumeUnit);
+        Vec<T, 3> axis = getAxis(assumeUnit);
+        if (assumeUnit)
+        {
+            return createFromAngleAxis(alpha * angle, axis);
+        }
+        return std::pow(norm(), alpha) * createFromAngleAxis(alpha * angle, axis);
+    }
+    else
+    {
+        return std::pow(norm(), alpha) * Quat<T>(w, x, y, z);
+    }
+}
+
+
+template <typename T>
+inline Quat<T> sqrt(const Quat<T> &q, QuatAssumeType assumeUnit)
+{
+    return q.sqrt(assumeUnit);
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::sqrt(QuatAssumeType assumeUnit) const
+{
+    return power(0.5, assumeUnit);
+}
+
+
+template <typename T>
+inline Quat<T> power(const Quat<T> &p, const Quat<T> &q, QuatAssumeType assumeUnit)
+{
+    return p.power(q, assumeUnit);
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::power(const Quat<T> &q, QuatAssumeType assumeUnit) const
+{
+    return cv::exp(q * log(assumeUnit));
+}
+
+template <typename T>
+inline T Quat<T>::dot(Quat<T> q1) const
+{
+    return w * q1.w + x * q1.x + y * q1.y + z * q1.z;
+}
+
+
+template <typename T>
+inline Quat<T> crossProduct(const Quat<T> &p, const Quat<T> &q)
+{
+    return p.crossProduct(q);
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::crossProduct(const Quat<T> &q) const
+{
+    return Quat<T> (0, y * q.z - z * q.y, z * q.x - x * q.z, x * q.y - q.x * y);
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::normalize() const
+{
+    T normVal = norm();
+    if (normVal < CV_QUAT_EPS)
+    {
+        CV_Error(Error::StsBadArg, "Cannot normalize this quaternion: the norm is too small.");
+    }
+    return Quat<T>(w / normVal, x / normVal, y / normVal, z / normVal) ;
+}
+
+template <typename T>
+inline Quat<T> inv(const Quat<T> &q, QuatAssumeType assumeUnit)
+{
+    return q.inv(assumeUnit);
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::inv(QuatAssumeType assumeUnit) const
+{
+    if (assumeUnit)
+    {
+        return conjugate();
+    }
+    T norm2 = dot(*this);
+    if (norm2 < CV_QUAT_EPS)
+    {
+        CV_Error(Error::StsBadArg, "This quaternion do not have inverse quaternion");
+    }
+    return conjugate() / norm2;
+}
+
+template <typename T>
+inline Quat<T> sinh(const Quat<T> &q)
+{
+    return q.sinh();
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::sinh() const
+{
+    Vec<T, 3> v{x, y ,z};
+    T vNorm = std::sqrt(v.dot(v));
+    T k = vNorm < CV_QUAT_EPS ? 1 : std::cosh(w) * std::sin(vNorm) / vNorm;
+    return Quat<T>(std::sinh(w) * std::cos(vNorm), v[0] * k, v[1] * k, v[2] * k);
+}
+
+
+template <typename T>
+inline Quat<T> cosh(const Quat<T> &q)
+{
+    return q.cosh();
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::cosh() const
+{
+    Vec<T, 3> v{x, y ,z};
+    T vNorm = std::sqrt(v.dot(v));
+    T k = vNorm < CV_QUAT_EPS ? 1 : std::sinh(w) * std::sin(vNorm) / vNorm;
+    return Quat<T>(std::cosh(w) * std::cos(vNorm), v[0] * k, v[1] * k, v[2] * k);
+}
+
+template <typename T>
+inline Quat<T> tanh(const Quat<T> &q)
+{
+    return q.tanh();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::tanh() const
+{
+    return sinh() * cosh().inv();
+}
+
+
+template <typename T>
+inline Quat<T> sin(const Quat<T> &q)
+{
+    return q.sin();
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::sin() const
+{
+    Vec<T, 3> v{x, y ,z};
+    T vNorm = std::sqrt(v.dot(v));
+    T k = vNorm < CV_QUAT_EPS ? 1 : std::cos(w) * std::sinh(vNorm) / vNorm;
+    return Quat<T>(std::sin(w) * std::cosh(vNorm), v[0] * k, v[1] * k, v[2] * k);
+}
+
+template <typename T>
+inline Quat<T> cos(const Quat<T> &q)
+{
+    return q.cos();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::cos() const
+{
+    Vec<T, 3> v{x, y ,z};
+    T vNorm = std::sqrt(v.dot(v));
+    T k = vNorm < CV_QUAT_EPS ? 1 : std::sin(w) * std::sinh(vNorm) / vNorm;
+    return Quat<T>(std::cos(w) * std::cosh(vNorm), -v[0] * k, -v[1] * k, -v[2] * k);
+}
+
+template <typename T>
+inline Quat<T> tan(const Quat<T> &q)
+{
+    return q.tan();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::tan() const
+{
+    return sin() * cos().inv();
+}
+
+template <typename T>
+inline Quat<T> asinh(const Quat<T> &q)
+{
+    return q.asinh();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::asinh() const
+{
+    return cv::log(*this + cv::power(*this * *this + Quat<T>(1, 0, 0, 0), 0.5));
+}
+
+template <typename T>
+inline Quat<T> acosh(const Quat<T> &q)
+{
+    return q.acosh();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::acosh() const
+{
+    return cv::log(*this + cv::power(*this * *this - Quat<T>(1,0,0,0), 0.5));
+}
+
+template <typename T>
+inline Quat<T> atanh(const Quat<T> &q)
+{
+    return q.atanh();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::atanh() const
+{
+    Quat<T> ident(1, 0, 0, 0);
+    Quat<T> c1 = (ident + *this).log();
+    Quat<T> c2 = (ident - *this).log();
+    return 0.5 * (c1 - c2);
+}
+
+template <typename T>
+inline Quat<T> asin(const Quat<T> &q)
+{
+    return q.asin();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::asin() const
+{
+    Quat<T> v(0, x, y, z);
+    T vNorm = v.norm();
+    T k = vNorm < CV_QUAT_EPS ? 1 : vNorm;
+    return -v / k * (*this * v / k).asinh();
+}
+
+template <typename T>
+inline Quat<T> acos(const Quat<T> &q)
+{
+    return q.acos();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::acos() const
+{
+    Quat<T> v(0, x, y, z);
+    T vNorm = v.norm();
+    T k = vNorm < CV_QUAT_EPS ? 1 : vNorm;
+    return -v / k * acosh();
+}
+
+template <typename T>
+inline Quat<T> atan(const Quat<T> &q)
+{
+    return q.atan();
+}
+
+template <typename T>
+inline Quat<T> Quat<T>::atan() const
+{
+    Quat<T> v(0, x, y, z);
+    T vNorm = v.norm();
+    T k = vNorm < CV_QUAT_EPS ? 1 : vNorm;
+    return -v / k * (*this * v / k).atanh();
+}
+
+template <typename T>
+inline T Quat<T>::getAngle(QuatAssumeType assumeUnit) const
+{
+    if (assumeUnit)
+    {
+        return 2 * std::acos(w);
+    }
+    if (norm() < CV_QUAT_EPS)
+    {
+        CV_Error(Error::StsBadArg, "This quaternion does not represent a rotation");
+    }
+    return 2 * std::acos(w / norm());
+}
+
+template <typename T>
+inline Vec<T, 3> Quat<T>::getAxis(QuatAssumeType assumeUnit) const
+{
+    T angle = getAngle(assumeUnit);
+    const T sin_v = std::sin(angle * 0.5);
+    if (assumeUnit)
+    {
+        return Vec<T, 3>{x, y, z} / sin_v;
+    }
+    return Vec<T, 3> {x, y, z} / (norm() * sin_v);
+}
+
+template <typename T>
+Matx<T, 4, 4> Quat<T>::toRotMat4x4(QuatAssumeType assumeUnit) const
+{
+    T a = w, b = x, c = y, d = z;
+    if (!assumeUnit)
+    {
+        Quat<T> qTemp = normalize();
+        a = qTemp.w;
+        b = qTemp.x;
+        c = qTemp.y;
+        d = qTemp.z;
+    }
+    Matx<T, 4, 4> R{
+        1 - 2 * (c * c + d * d), 2 * (b * c - a * d)    , 2 * (b * d + a * c)    , 0,
+        2 * (b * c + a * d)    , 1 - 2 * (b * b + d * d), 2 * (c * d - a * b)    , 0,
+        2 * (b * d - a * c)    , 2 * (c * d + a * b)    , 1 - 2 * (b * b + c * c), 0,
+        0                      , 0                      , 0                      , 1,
+    };
+    return R;
+}
+
+template <typename T>
+Matx<T, 3, 3> Quat<T>::toRotMat3x3(QuatAssumeType assumeUnit) const
+{
+    T a = w, b = x, c = y, d = z;
+    if (!assumeUnit)
+    {
+        Quat<T> qTemp = normalize();
+        a = qTemp.w;
+        b = qTemp.x;
+        c = qTemp.y;
+        d = qTemp.z;
+    }
+    Matx<T, 3, 3> R{
+        1 - 2 * (c * c + d * d), 2 * (b * c - a * d)    , 2 * (b * d + a * c),
+        2 * (b * c + a * d)    , 1 - 2 * (b * b + d * d), 2 * (c * d - a * b),
+        2 * (b * d - a * c)    , 2 * (c * d + a * b)    , 1 - 2 * (b * b + c * c)
+    };
+    return R;
+}
+
+template <typename T>
+Vec<T, 3> Quat<T>::toRotVec(QuatAssumeType assumeUnit) const
+{
+    T angle = getAngle(assumeUnit);
+    Vec<T, 3> axis = getAxis(assumeUnit);
+    return angle * axis;
+}
+
+template <typename T>
+Vec<T, 4> Quat<T>::toVec() const
+{
+    return Vec<T, 4>{w, x, y, z};
+}
+
+template <typename T>
+Quat<T> Quat<T>::lerp(const Quat<T> &q0, const Quat<T> &q1, const T t)
+{
+    return (1 - t) * q0 + t * q1;
+}
+
+template <typename T>
+Quat<T> Quat<T>::slerp(const Quat<T> &q0, const Quat<T> &q1, const T t, QuatAssumeType assumeUnit, bool directChange)
+{
+    Quatd v0(q0);
+    Quatd v1(q1);
+    if (!assumeUnit)
+    {
+        v0 = v0.normalize();
+        v1 = v1.normalize();
+    }
+    T cosTheta = v0.dot(v1);
+    constexpr T DOT_THRESHOLD = 0.995;
+    if (cosTheta > DOT_THRESHOLD)
+    {
+        return nlerp(v0, v1, t, QUAT_ASSUME_UNIT);
+    }
+
+    if (directChange && cosTheta < 0)
+    {
+        v0 = -v0;
+        cosTheta = -cosTheta;
+    }
+    T sinTheta = std::sqrt(1 - cosTheta * cosTheta);
+    T angle = atan2(sinTheta, cosTheta);
+    return (std::sin((1 - t) * angle) / (sinTheta) * v0 + std::sin(t * angle) / (sinTheta) * v1).normalize();
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::nlerp(const Quat<T> &q0, const Quat<T> &q1, const T t, QuatAssumeType assumeUnit)
+{
+    Quat<T> v0(q0), v1(q1);
+    if (v1.dot(v0) < 0)
+    {
+        v0 = -v0;
+    }
+    if (assumeUnit)
+    {
+        return ((1 - t) * v0 + t * v1).normalize();
+    }
+    v0 = v0.normalize();
+    v1 = v1.normalize();
+    return ((1 - t) * v0 + t * v1).normalize();
+}
+
+
+template <typename T>
+inline bool Quat<T>::isNormal(T eps) const
+{
+
+    double normVar = norm();
+    if ((normVar > 1 - eps) && (normVar < 1 + eps))
+        return true;
+    return false;
+}
+
+template <typename T>
+inline void Quat<T>::assertNormal(T eps) const
+{
+    if (!isNormal(eps))
+        CV_Error(Error::StsBadArg, "Quaternion should be normalized");
+}
+
+
+template <typename T>
+inline Quat<T> Quat<T>::squad(const Quat<T> &q0, const Quat<T> &q1,
+                            const Quat<T> &q2, const Quat<T> &q3,
+                            const T t, QuatAssumeType assumeUnit,
+                            bool directChange)
+{
+    Quat<T> v0(q0), v1(q1), v2(q2), v3(q3);
+    if (!assumeUnit)
+    {
+        v0 = v0.normalize();
+        v1 = v1.normalize();
+        v2 = v2.normalize();
+        v3 = v3.normalize();
+    }
+
+    Quat<T> c0 = slerp(v0, v3, t, assumeUnit, directChange);
+    Quat<T> c1 = slerp(v1, v2, t, assumeUnit, directChange);
+    return slerp(c0, c1, 2 * t * (1 - t), assumeUnit, directChange);
+}
+
+template <typename T>
+Quat<T> Quat<T>::interPoint(const Quat<T> &q0, const Quat<T> &q1,
+                            const Quat<T> &q2, QuatAssumeType assumeUnit)
+{
+    Quat<T> v0(q0), v1(q1), v2(q2);
+    if (!assumeUnit)
+    {
+        v0 = v0.normalize();
+        v1 = v1.normalize();
+        v2 = v2.normalize();
+    }
+    return v1 * cv::exp(-(cv::log(v1.conjugate() * v0, assumeUnit) + (cv::log(v1.conjugate() * v2, assumeUnit))) / 4);
+}
+
+template <typename T>
+Quat<T> Quat<T>::spline(const Quat<T> &q0, const Quat<T> &q1, const Quat<T> &q2, const Quat<T> &q3, const T t, QuatAssumeType assumeUnit)
+{
+    Quatd v0(q0), v1(q1), v2(q2), v3(q3);
+    if (!assumeUnit)
+    {
+        v0 = v0.normalize();
+        v1 = v1.normalize();
+        v2 = v2.normalize();
+        v3 = v3.normalize();
+    }
+    T cosTheta;
+    std::vector<Quat<T>> vec{v0, v1, v2, v3};
+    for (size_t i = 0; i < 3; ++i)
+    {
+        cosTheta = vec[i].dot(vec[i + 1]);
+        if (cosTheta < 0)
+        {
+            vec[i + 1] = -vec[i + 1];
+        }
+    }
+    Quat<T> s1 = interPoint(vec[0], vec[1], vec[2], QUAT_ASSUME_UNIT);
+    Quat<T> s2 = interPoint(vec[1], vec[2], vec[3], QUAT_ASSUME_UNIT);
+    return squad(vec[1], s1, s2, vec[2], t, assumeUnit, QUAT_ASSUME_NOT_UNIT);
+}
+
+namespace detail {
+
+template <typename T> static
+Quat<T> createFromAxisRot(int axis, const T theta)
+{
+    if (axis == 0)
+        return Quat<T>::createFromXRot(theta);
+    if (axis == 1)
+        return Quat<T>::createFromYRot(theta);
+    if (axis == 2)
+        return Quat<T>::createFromZRot(theta);
+    CV_Assert(0);
+}
+
+inline bool isIntAngleType(QuatEnum::EulerAnglesType eulerAnglesType)
+{
+    return eulerAnglesType < QuatEnum::EXT_XYZ;
+}
+
+inline bool isTaitBryan(QuatEnum::EulerAnglesType eulerAnglesType)
+{
+    return eulerAnglesType/6 == 1 || eulerAnglesType/6 == 3;
+}
+}  // namespace detail
+
+template <typename T>
+Quat<T> Quat<T>::createFromYRot(const T theta)
+{
+    return Quat<T>{std::cos(theta * 0.5f), 0, std::sin(theta * 0.5f), 0};
+}
+
+template <typename T>
+Quat<T> Quat<T>::createFromXRot(const T theta){
+    return Quat<T>{std::cos(theta * 0.5f), std::sin(theta * 0.5f), 0, 0};
+}
+
+template <typename T>
+Quat<T> Quat<T>::createFromZRot(const T theta){
+    return Quat<T>{std::cos(theta * 0.5f), 0, 0, std::sin(theta * 0.5f)};
+}
+
+
+template <typename T>
+Quat<T> Quat<T>::createFromEulerAngles(const Vec<T, 3> &angles, QuatEnum::EulerAnglesType eulerAnglesType) {
+    CV_Assert(eulerAnglesType < QuatEnum::EulerAnglesType::EULER_ANGLES_MAX_VALUE);
+    static const int rotationAxis[24][3] = {
+        {0, 1, 2}, ///< Intrinsic rotations with the Euler angles type X-Y-Z
+        {0, 2, 1}, ///< Intrinsic rotations with the Euler angles type X-Z-Y
+        {1, 0, 2}, ///< Intrinsic rotations with the Euler angles type Y-X-Z
+        {1, 2, 0}, ///< Intrinsic rotations with the Euler angles type Y-Z-X
+        {2, 0, 1}, ///< Intrinsic rotations with the Euler angles type Z-X-Y
+        {2, 1, 0}, ///< Intrinsic rotations with the Euler angles type Z-Y-X
+        {0, 1, 0}, ///< Intrinsic rotations with the Euler angles type X-Y-X
+        {0, 2, 0}, ///< Intrinsic rotations with the Euler angles type X-Z-X
+        {1, 0, 1}, ///< Intrinsic rotations with the Euler angles type Y-X-Y
+        {1, 2, 1}, ///< Intrinsic rotations with the Euler angles type Y-Z-Y
+        {2, 0, 2}, ///< Intrinsic rotations with the Euler angles type Z-X-Z
+        {2, 1, 2}, ///< Intrinsic rotations with the Euler angles type Z-Y-Z
+        {0, 1, 2}, ///< Extrinsic rotations with the Euler angles type X-Y-Z
+        {0, 2, 1}, ///< Extrinsic rotations with the Euler angles type X-Z-Y
+        {1, 0, 2}, ///< Extrinsic rotations with the Euler angles type Y-X-Z
+        {1, 2, 0}, ///< Extrinsic rotations with the Euler angles type Y-Z-X
+        {2, 0, 1}, ///< Extrinsic rotations with the Euler angles type Z-X-Y
+        {2, 1, 0}, ///< Extrinsic rotations with the Euler angles type Z-Y-X
+        {0, 1, 0}, ///< Extrinsic rotations with the Euler angles type X-Y-X
+        {0, 2, 0}, ///< Extrinsic rotations with the Euler angles type X-Z-X
+        {1, 0, 1}, ///< Extrinsic rotations with the Euler angles type Y-X-Y
+        {1, 2, 1}, ///< Extrinsic rotations with the Euler angles type Y-Z-Y
+        {2, 0, 2}, ///< Extrinsic rotations with the Euler angles type Z-X-Z
+        {2, 1, 2}  ///< Extrinsic rotations with the Euler angles type Z-Y-Z
+    };
+    Quat<T> q1 = detail::createFromAxisRot(rotationAxis[eulerAnglesType][0], angles(0));
+    Quat<T> q2 = detail::createFromAxisRot(rotationAxis[eulerAnglesType][1], angles(1));
+    Quat<T> q3 = detail::createFromAxisRot(rotationAxis[eulerAnglesType][2], angles(2));
+    if (detail::isIntAngleType(eulerAnglesType))
+    {
+        return q1 * q2 * q3;
+    }
+    else // (!detail::isIntAngleType<T>(eulerAnglesType))
+    {
+        return q3 * q2 * q1;
+    }
+}
+
+template <typename T>
+Vec<T, 3> Quat<T>::toEulerAngles(QuatEnum::EulerAnglesType eulerAnglesType){
+    CV_Assert(eulerAnglesType < QuatEnum::EulerAnglesType::EULER_ANGLES_MAX_VALUE);
+    Matx33d R = toRotMat3x3();
+    enum {
+        C_ZERO,
+        C_PI,
+        C_PI_2,
+        N_CONSTANTS,
+        R_0_0 = N_CONSTANTS, R_0_1, R_0_2,
+        R_1_0, R_1_1, R_1_2,
+        R_2_0, R_2_1, R_2_2
+    };
+    static const T constants_[N_CONSTANTS] = {
+        0,  // C_ZERO
+        (T)CV_PI,  // C_PI
+        (T)(CV_PI * 0.5)  // C_PI_2, -C_PI_2
+    };
+    static const int rotationR_[24][12] = {
+        {+R_0_2,    +R_1_0, +R_1_1, C_PI_2,     +R_2_1, +R_1_1, -C_PI_2,    -R_1_2, +R_2_2,    +R_0_2,    -R_0_1, +R_0_0},  // INT_XYZ
+        {+R_0_1,    -R_1_2, +R_2_2, -C_PI_2,    +R_2_0, +R_2_2, C_PI_2,     +R_2_1, +R_1_1,    -R_0_1,    +R_0_2, +R_0_0},  // INT_XZY
+        {+R_1_2,    -R_0_1, +R_0_0, -C_PI_2,    +R_0_1, +R_0_0, C_PI_2,     +R_0_2, +R_2_2,    -R_1_2,    +R_1_0, +R_1_1},  // INT_YXZ
+        {+R_1_0,    +R_0_2, +R_2_2, C_PI_2,     +R_0_2, +R_0_1, -C_PI_2,    -R_2_0, +R_0_0,    +R_1_0,    -R_1_2, +R_1_1},  // INT_YZX
+        {+R_2_1,    +R_1_0, +R_0_0, C_PI_2,     +R_1_0, +R_0_0, -C_PI_2,    -R_0_1, +R_1_1,    +R_2_1,    -R_2_0, +R_2_2},  // INT_ZXY
+        {+R_2_0,    -R_0_1, +R_1_1, -C_PI_2,    +R_1_2, +R_1_1, C_PI_2,     +R_1_0, +R_0_0,    -R_2_0,    +R_2_1, +R_2_2},  // INT_ZYX
+        {+R_0_0,    +R_2_1, +R_2_2, C_ZERO,     +R_1_2, +R_1_1, C_PI,       +R_1_0, -R_2_0,    +R_0_0,    +R_0_1, +R_0_2},  // INT_XYX
+        {+R_0_0,    +R_2_1, +R_2_2, C_ZERO,     -R_2_1, +R_2_2, C_PI,       +R_2_0, +R_1_0,    +R_0_0,    +R_0_2, -R_0_1},  // INT_XZX
+        {+R_1_1,    +R_0_2, +R_0_0, C_ZERO,     -R_2_0, +R_0_0, C_PI,       +R_0_1, +R_2_1,    +R_1_1,    +R_1_0, -R_1_2},  // INT_YXY
+        {+R_1_1,    +R_0_2, +R_0_0, C_ZERO,     +R_0_2, -R_0_0, C_PI,       +R_2_1, -R_0_1,    +R_1_1,    +R_1_2, +R_1_0},  // INT_YZY
+        {+R_2_2,    +R_1_0, +R_1_1, C_ZERO,     +R_1_0, +R_0_0, C_PI,       +R_0_2, -R_1_2,    +R_2_2,    +R_2_0, +R_2_1},  // INT_ZXZ
+        {+R_2_2,    +R_1_0, +R_0_0, C_ZERO,     +R_1_0, +R_0_0, C_PI,       +R_1_2, +R_0_2,    +R_2_2,    +R_2_1, -R_2_0},  // INT_ZYZ
+
+        {+R_2_0,    -C_PI_2, -R_0_1, +R_1_1,    C_PI_2,  +R_1_2, +R_1_1,    +R_2_1, +R_2_2,    -R_2_0,    +R_1_0, +R_0_0},  // EXT_XYZ
+        {+R_1_0,    C_PI_2,  +R_0_2, +R_2_2,    -C_PI_2, +R_0_2, +R_0_1,    -R_1_2, +R_1_1,    +R_1_0,    -R_2_0, +R_0_0},  // EXT_XZY
+        {+R_2_1,    C_PI_2,  +R_1_0, +R_0_0,    -C_PI_2, +R_1_0, +R_0_0,    -R_2_0, +R_2_2,    +R_2_1,    -R_0_1, +R_1_1},  // EXT_YXZ
+        {+R_0_2,    -C_PI_2, -R_1_2, +R_2_2,    C_PI_2,  +R_2_0, +R_2_2,    +R_0_2, +R_0_0,    -R_0_1,    +R_2_1, +R_1_1},  // EXT_YZX
+        {+R_1_2,    -C_PI_2, -R_0_1, +R_0_0,    C_PI_2,  +R_0_1, +R_0_0,    +R_1_0, +R_1_1,    -R_1_2,    +R_0_2, +R_2_2},  // EXT_ZXY
+        {+R_0_2,    C_PI_2,  +R_1_0, +R_1_1,    -C_PI_2, +R_2_1, +R_1_1,    -R_0_1, +R_0_0,    +R_0_2,    -R_1_2, +R_2_2},  // EXT_ZYX
+        {+R_0_0,    C_ZERO,  +R_2_1, +R_2_2,    C_PI,    +R_1_2, +R_1_1,    +R_0_1, +R_0_2,    +R_0_0,    +R_1_0, -R_2_0},  // EXT_XYX
+        {+R_0_0,    C_ZERO,  +R_2_1, +R_2_2,    C_PI,    +R_2_1, +R_2_2,    +R_0_2, -R_0_1,    +R_0_0,    +R_2_0, +R_1_0},  // EXT_XZX
+        {+R_1_1,    C_ZERO,  +R_0_2, +R_0_0,    C_PI,    -R_2_0, +R_0_0,    +R_1_0, -R_1_2,    +R_1_1,    +R_0_1, +R_2_1},  // EXT_YXY
+        {+R_1_1,    C_ZERO,  +R_0_2, +R_0_0,    C_PI,    +R_0_2, -R_0_0,    +R_1_2, +R_1_0,    +R_1_1,    +R_2_1, -R_0_1},  // EXT_YZY
+        {+R_2_2,    C_ZERO,  +R_1_0, +R_1_1,    C_PI,    +R_1_0, +R_0_0,    +R_2_0, +R_2_1,    +R_2_2,    +R_0_2, -R_1_2},  // EXT_ZXZ
+        {+R_2_2,    C_ZERO,  +R_1_0, +R_0_0,    C_PI,    +R_1_0, +R_0_0,    +R_2_1, -R_2_0,    +R_2_2,    +R_1_2, +R_0_2},  // EXT_ZYZ
+    };
+    T rotationR[12];
+    for (int i = 0; i < 12; i++)
+    {
+        int id = rotationR_[eulerAnglesType][i];
+        unsigned idx = std::abs(id);
+        T value = 0.0f;
+        if (idx < N_CONSTANTS)
+        {
+            value = constants_[idx];
+        }
+        else
+        {
+            unsigned r_idx = idx - N_CONSTANTS;
+            CV_DbgAssert(r_idx < 9);
+            value = R.val[r_idx];
+        }
+        bool isNegative = id < 0;
+        if (isNegative)
+            value = -value;
+        rotationR[i] = value;
+    }
+    Vec<T, 3> angles;
+    if (detail::isIntAngleType(eulerAnglesType))
+    {
+        if (abs(rotationR[0] - 1) < CV_QUAT_CONVERT_THRESHOLD)
+        {
+            CV_LOG_WARNING(NULL,"Gimbal Lock occurs. Euler angles are non-unique, we set the third angle to 0");
+            angles = {std::atan2(rotationR[1], rotationR[2]), rotationR[3], 0};
+            return angles;
+        }
+        else if(abs(rotationR[0] + 1) < CV_QUAT_CONVERT_THRESHOLD)
+        {
+            CV_LOG_WARNING(NULL,"Gimbal Lock occurs. Euler angles are non-unique, we set the third angle to 0");
+            angles = {std::atan2(rotationR[4], rotationR[5]), rotationR[6], 0};
+            return angles;
+        }
+    }
+    else // (!detail::isIntAngleType<T>(eulerAnglesType))
+    {
+        if (abs(rotationR[0] - 1) < CV_QUAT_CONVERT_THRESHOLD)
+        {
+            CV_LOG_WARNING(NULL,"Gimbal Lock occurs. Euler angles are non-unique, we set the first angle to 0");
+            angles = {0, rotationR[1], std::atan2(rotationR[2], rotationR[3])};
+            return angles;
+        }
+        else if (abs(rotationR[0] + 1) < CV_QUAT_CONVERT_THRESHOLD)
+        {
+            CV_LOG_WARNING(NULL,"Gimbal Lock occurs. Euler angles are non-unique, we set the first angle to 0");
+            angles = {0, rotationR[4], std::atan2(rotationR[5], rotationR[6])};
+            return angles;
+        }
+    }
+
+    angles(0) = std::atan2(rotationR[7], rotationR[8]);
+    if (detail::isTaitBryan(eulerAnglesType))
+        angles(1) = std::acos(rotationR[9]);
+    else
+        angles(1) = std::asin(rotationR[9]);
+    angles(2) = std::atan2(rotationR[10], rotationR[11]);
+    return angles;
+}
+
+}  // namepsace
+//! @endcond
+
+#endif /*OPENCV_CORE_QUATERNION_INL_HPP*/
diff --git a/3rdParty/include/opencv2/core/saturate.hpp b/3rdParty/include/opencv2/core/saturate.hpp
new file mode 100644
index 0000000..8127e3d
--- /dev/null
+++ b/3rdParty/include/opencv2/core/saturate.hpp
@@ -0,0 +1,179 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2014, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_SATURATE_HPP
+#define OPENCV_CORE_SATURATE_HPP
+
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/fast_math.hpp"
+
+namespace cv
+{
+
+//! @addtogroup core_utils
+//! @{
+
+/////////////// saturate_cast (used in image & signal processing) ///////////////////
+
+/** @brief Template function for accurate conversion from one primitive type to another.
+
+ The function saturate_cast resembles the standard C++ cast operations, such as static_cast\<T\>()
+ and others. It perform an efficient and accurate conversion from one primitive type to another
+ (see the introduction chapter). saturate in the name means that when the input value v is out of the
+ range of the target type, the result is not formed just by taking low bits of the input, but instead
+ the value is clipped. For example:
+ @code
+ uchar a = saturate_cast<uchar>(-100); // a = 0 (UCHAR_MIN)
+ short b = saturate_cast<short>(33333.33333); // b = 32767 (SHRT_MAX)
+ @endcode
+ Such clipping is done when the target type is unsigned char , signed char , unsigned short or
+ signed short . For 32-bit integers, no clipping is done.
+
+ When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit),
+ the floating-point value is first rounded to the nearest integer and then clipped if needed (when
+ the target type is 8- or 16-bit).
+
+ @param v Function parameter.
+ @sa add, subtract, multiply, divide, Mat::convertTo
+ */
+template<typename _Tp> static inline _Tp saturate_cast(uchar v)    { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(schar v)    { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(ushort v)   { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(short v)    { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(unsigned v) { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(int v)      { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(float v)    { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(double v)   { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(int64 v)    { return _Tp(v); }
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(uint64 v)   { return _Tp(v); }
+
+template<> inline uchar saturate_cast<uchar>(schar v)        { return (uchar)std::max((int)v, 0); }
+template<> inline uchar saturate_cast<uchar>(ushort v)       { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); }
+template<> inline uchar saturate_cast<uchar>(int v)          { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
+template<> inline uchar saturate_cast<uchar>(short v)        { return saturate_cast<uchar>((int)v); }
+template<> inline uchar saturate_cast<uchar>(unsigned v)     { return (uchar)std::min(v, (unsigned)UCHAR_MAX); }
+template<> inline uchar saturate_cast<uchar>(float v)        { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
+template<> inline uchar saturate_cast<uchar>(double v)       { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
+template<> inline uchar saturate_cast<uchar>(int64 v)        { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
+template<> inline uchar saturate_cast<uchar>(uint64 v)       { return (uchar)std::min(v, (uint64)UCHAR_MAX); }
+
+template<> inline schar saturate_cast<schar>(uchar v)        { return (schar)std::min((int)v, SCHAR_MAX); }
+template<> inline schar saturate_cast<schar>(ushort v)       { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); }
+template<> inline schar saturate_cast<schar>(int v)          { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); }
+template<> inline schar saturate_cast<schar>(short v)        { return saturate_cast<schar>((int)v); }
+template<> inline schar saturate_cast<schar>(unsigned v)     { return (schar)std::min(v, (unsigned)SCHAR_MAX); }
+template<> inline schar saturate_cast<schar>(float v)        { int iv = cvRound(v); return saturate_cast<schar>(iv); }
+template<> inline schar saturate_cast<schar>(double v)       { int iv = cvRound(v); return saturate_cast<schar>(iv); }
+template<> inline schar saturate_cast<schar>(int64 v)        { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); }
+template<> inline schar saturate_cast<schar>(uint64 v)       { return (schar)std::min(v, (uint64)SCHAR_MAX); }
+
+template<> inline ushort saturate_cast<ushort>(schar v)      { return (ushort)std::max((int)v, 0); }
+template<> inline ushort saturate_cast<ushort>(short v)      { return (ushort)std::max((int)v, 0); }
+template<> inline ushort saturate_cast<ushort>(int v)        { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
+template<> inline ushort saturate_cast<ushort>(unsigned v)   { return (ushort)std::min(v, (unsigned)USHRT_MAX); }
+template<> inline ushort saturate_cast<ushort>(float v)      { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
+template<> inline ushort saturate_cast<ushort>(double v)     { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
+template<> inline ushort saturate_cast<ushort>(int64 v)      { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
+template<> inline ushort saturate_cast<ushort>(uint64 v)     { return (ushort)std::min(v, (uint64)USHRT_MAX); }
+
+template<> inline short saturate_cast<short>(ushort v)       { return (short)std::min((int)v, SHRT_MAX); }
+template<> inline short saturate_cast<short>(int v)          { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); }
+template<> inline short saturate_cast<short>(unsigned v)     { return (short)std::min(v, (unsigned)SHRT_MAX); }
+template<> inline short saturate_cast<short>(float v)        { int iv = cvRound(v); return saturate_cast<short>(iv); }
+template<> inline short saturate_cast<short>(double v)       { int iv = cvRound(v); return saturate_cast<short>(iv); }
+template<> inline short saturate_cast<short>(int64 v)        { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); }
+template<> inline short saturate_cast<short>(uint64 v)       { return (short)std::min(v, (uint64)SHRT_MAX); }
+
+template<> inline int saturate_cast<int>(unsigned v)         { return (int)std::min(v, (unsigned)INT_MAX); }
+template<> inline int saturate_cast<int>(int64 v)            { return (int)((uint64)(v - INT_MIN) <= (uint64)UINT_MAX ? v : v > 0 ? INT_MAX : INT_MIN); }
+template<> inline int saturate_cast<int>(uint64 v)           { return (int)std::min(v, (uint64)INT_MAX); }
+template<> inline int saturate_cast<int>(float v)            { return cvRound(v); }
+template<> inline int saturate_cast<int>(double v)           { return cvRound(v); }
+
+template<> inline unsigned saturate_cast<unsigned>(schar v)  { return (unsigned)std::max(v, (schar)0); }
+template<> inline unsigned saturate_cast<unsigned>(short v)  { return (unsigned)std::max(v, (short)0); }
+template<> inline unsigned saturate_cast<unsigned>(int v)    { return (unsigned)std::max(v, (int)0); }
+template<> inline unsigned saturate_cast<unsigned>(int64 v)  { return (unsigned)((uint64)v <= (uint64)UINT_MAX ? v : v > 0 ? UINT_MAX : 0); }
+template<> inline unsigned saturate_cast<unsigned>(uint64 v) { return (unsigned)std::min(v, (uint64)UINT_MAX); }
+// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc.
+template<> inline unsigned saturate_cast<unsigned>(float v)  { return static_cast<unsigned>(cvRound(v)); }
+template<> inline unsigned saturate_cast<unsigned>(double v) { return static_cast<unsigned>(cvRound(v)); }
+
+template<> inline uint64 saturate_cast<uint64>(schar v)      { return (uint64)std::max(v, (schar)0); }
+template<> inline uint64 saturate_cast<uint64>(short v)      { return (uint64)std::max(v, (short)0); }
+template<> inline uint64 saturate_cast<uint64>(int v)        { return (uint64)std::max(v, (int)0); }
+template<> inline uint64 saturate_cast<uint64>(int64 v)      { return (uint64)std::max(v, (int64)0); }
+
+template<> inline int64 saturate_cast<int64>(uint64 v)       { return (int64)std::min(v, (uint64)LLONG_MAX); }
+
+/** @overload */
+template<typename _Tp> static inline _Tp saturate_cast(float16_t v) { return saturate_cast<_Tp>((float)v); }
+
+// in theory, we could use a LUT for 8u/8s->16f conversion,
+// but with hardware support for FP32->FP16 conversion the current approach is preferable
+template<> inline float16_t saturate_cast<float16_t>(uchar v)   { return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(schar v)   { return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(ushort v)  { return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(short v)   { return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(unsigned v){ return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(int v)     { return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(uint64 v)  { return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(int64 v)   { return float16_t((float)v); }
+template<> inline float16_t saturate_cast<float16_t>(float v)   { return float16_t(v); }
+template<> inline float16_t saturate_cast<float16_t>(double v)  { return float16_t((float)v); }
+
+//! @}
+
+} // cv
+
+#endif // OPENCV_CORE_SATURATE_HPP
diff --git a/3rdParty/include/opencv2/core/simd_intrinsics.hpp b/3rdParty/include/opencv2/core/simd_intrinsics.hpp
new file mode 100644
index 0000000..2658d92
--- /dev/null
+++ b/3rdParty/include/opencv2/core/simd_intrinsics.hpp
@@ -0,0 +1,87 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_SIMD_INTRINSICS_HPP
+#define OPENCV_CORE_SIMD_INTRINSICS_HPP
+
+/**
+Helper header to support SIMD intrinsics (universal intrinsics) in user code.
+Intrinsics documentation: https://docs.opencv.org/4.x/df/d91/group__core__hal__intrin.html
+
+
+Checks of target CPU instruction set based on compiler definitions don't work well enough.
+More reliable solutions require utilization of configuration systems (like CMake).
+
+So, probably you need to specify your own configuration.
+
+You can do that via CMake in this way:
+    add_definitions(/DOPENCV_SIMD_CONFIG_HEADER=opencv_simd_config_custom.hpp)
+or
+    add_definitions(/DOPENCV_SIMD_CONFIG_INCLUDE_DIR=1)
+
+Additionally you may need to add include directory to your files:
+    include_directories("${CMAKE_CURRENT_LIST_DIR}/opencv_config_${MYTARGET}")
+
+These files can be pre-generated for target configurations of your application
+or generated by CMake on the fly (use CMAKE_BINARY_DIR for that).
+
+Notes:
+- H/W capability checks are still responsibility of your application
+- runtime dispatching is not covered by this helper header
+*/
+
+#ifdef __OPENCV_BUILD
+#error "Use core/hal/intrin.hpp during OpenCV build"
+#endif
+
+#ifdef OPENCV_HAL_INTRIN_HPP
+#error "core/simd_intrinsics.hpp must be included before core/hal/intrin.hpp"
+#endif
+
+#include "opencv2/core/cvdef.h"
+
+#ifdef OPENCV_SIMD_CONFIG_HEADER
+#include CVAUX_STR(OPENCV_SIMD_CONFIG_HEADER)
+#elif defined(OPENCV_SIMD_CONFIG_INCLUDE_DIR)
+#include "opencv_simd_config.hpp"  // corresponding directory should be added via -I compiler parameter
+#else  // custom config headers
+
+#if (!defined(CV_AVX_512F) || !CV_AVX_512F) && (defined(__AVX512__) || defined(__AVX512F__))
+#  include <immintrin.h>
+#  undef CV_AVX_512F
+#  define CV_AVX_512F 1
+#  ifndef OPENCV_SIMD_DONT_ASSUME_SKX  // Skylake-X with AVX-512F/CD/BW/DQ/VL
+#    undef CV_AVX512_SKX
+#    define CV_AVX512_SKX 1
+#    undef CV_AVX_512CD
+#    define CV_AVX_512CD 1
+#    undef CV_AVX_512BW
+#    define CV_AVX_512BW 1
+#    undef CV_AVX_512DQ
+#    define CV_AVX_512DQ 1
+#    undef CV_AVX_512VL
+#    define CV_AVX_512VL 1
+#  endif
+#endif // AVX512
+
+// GCC/Clang: -mavx2
+// MSVC: /arch:AVX2
+#if defined __AVX2__
+#  include <immintrin.h>
+#  undef CV_AVX2
+#  define CV_AVX2 1
+#  if defined __F16C__
+#    undef CV_FP16
+#    define CV_FP16 1
+#  endif
+#endif
+
+#endif
+
+// SSE / NEON / VSX is handled by cv_cpu_dispatch.h compatibility block
+#include "cv_cpu_dispatch.h"
+
+#include "hal/intrin.hpp"
+
+#endif // OPENCV_CORE_SIMD_INTRINSICS_HPP
diff --git a/3rdParty/include/opencv2/core/softfloat.hpp b/3rdParty/include/opencv2/core/softfloat.hpp
new file mode 100644
index 0000000..485e15c
--- /dev/null
+++ b/3rdParty/include/opencv2/core/softfloat.hpp
@@ -0,0 +1,514 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+// This file is based on files from package issued with the following license:
+
+/*============================================================================
+
+This C header file is part of the SoftFloat IEEE Floating-Point Arithmetic
+Package, Release 3c, by John R. Hauser.
+
+Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the
+University of California.  All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+ 1. Redistributions of source code must retain the above copyright notice,
+    this list of conditions, and the following disclaimer.
+
+ 2. Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions, and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+ 3. Neither the name of the University nor the names of its contributors may
+    be used to endorse or promote products derived from this software without
+    specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY
+EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE
+DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+=============================================================================*/
+
+#pragma once
+#ifndef softfloat_h
+#define softfloat_h 1
+
+#include "cvdef.h"
+
+namespace cv
+{
+
+/** @addtogroup core_utils_softfloat
+
+  [SoftFloat](http://www.jhauser.us/arithmetic/SoftFloat.html) is a software implementation
+  of floating-point calculations according to IEEE 754 standard.
+  All calculations are done in integers, that's why they are machine-independent and bit-exact.
+  This library can be useful in accuracy-critical parts like look-up tables generation, tests, etc.
+  OpenCV contains a subset of SoftFloat partially rewritten to C++.
+
+  ### Types
+
+  There are two basic types: @ref softfloat and @ref softdouble.
+  These types are binary compatible with float and double types respectively
+  and support conversions to/from them.
+  Other types from original SoftFloat library like fp16 or fp128 were thrown away
+  as well as quiet/signaling NaN support, on-the-fly rounding mode switch
+  and exception flags (though exceptions can be implemented in the future).
+
+  ### Operations
+
+  Both types support the following:
+  - Construction from signed and unsigned 32-bit and 64 integers,
+  float/double or raw binary representation
+  - Conversions between each other, to float or double and to int
+  using @ref cvRound, @ref cvTrunc, @ref cvFloor, @ref cvCeil or a bunch of
+  saturate_cast functions
+  - Add, subtract, multiply, divide, remainder, square root, FMA with absolute precision
+  - Comparison operations
+  - Explicit sign, exponent and significand manipulation through get/set methods,
+ number state indicators (isInf, isNan, isSubnormal)
+  - Type-specific constants like eps, minimum/maximum value, best pi approximation, etc.
+  - min(), max(), abs(), exp(), log() and pow() functions
+
+*/
+//! @{
+
+struct softfloat;
+struct softdouble;
+
+struct CV_EXPORTS softfloat
+{
+public:
+    /** @brief Default constructor */
+    softfloat() { v = 0; }
+    /** @brief Copy constructor */
+    softfloat( const softfloat& c) { v = c.v; }
+    /** @brief Assign constructor */
+    softfloat& operator=( const softfloat& c )
+    {
+        if(&c != this) v = c.v;
+        return *this;
+    }
+    /** @brief Construct from raw
+
+    Builds new value from raw binary representation
+    */
+    static const softfloat fromRaw( const uint32_t a ) { softfloat x; x.v = a; return x; }
+
+    /** @brief Construct from integer */
+    explicit softfloat( const uint32_t );
+    explicit softfloat( const uint64_t );
+    explicit softfloat( const int32_t );
+    explicit softfloat( const int64_t );
+
+#ifdef CV_INT32_T_IS_LONG_INT
+    // for platforms with int32_t = long int
+    explicit softfloat( const int a ) { *this = softfloat(static_cast<int32_t>(a)); }
+#endif
+
+    /** @brief Construct from float */
+    explicit softfloat( const float a ) { Cv32suf s; s.f = a; v = s.u; }
+
+    /** @brief Type casts  */
+    operator softdouble() const;
+    operator float() const { Cv32suf s; s.u = v; return s.f; }
+
+    /** @brief Basic arithmetics */
+    softfloat operator + (const softfloat&) const;
+    softfloat operator - (const softfloat&) const;
+    softfloat operator * (const softfloat&) const;
+    softfloat operator / (const softfloat&) const;
+    softfloat operator - () const { softfloat x; x.v = v ^ (1U << 31); return x; }
+
+    /** @brief Remainder operator
+
+    A quote from original SoftFloat manual:
+
+    > The IEEE Standard remainder operation computes the value
+    > a - n * b, where n is the integer closest to a / b.
+    > If a / b is exactly halfway between two integers, n is the even integer
+    > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding.
+    > Depending on the relative magnitudes of the operands, the remainder functions
+    > can take considerably longer to execute than the other SoftFloat functions.
+    > This is an inherent characteristic of the remainder operation itself and is not a flaw
+    > in the SoftFloat implementation.
+    */
+    softfloat operator % (const softfloat&) const;
+
+    softfloat& operator += (const softfloat& a) { *this = *this + a; return *this; }
+    softfloat& operator -= (const softfloat& a) { *this = *this - a; return *this; }
+    softfloat& operator *= (const softfloat& a) { *this = *this * a; return *this; }
+    softfloat& operator /= (const softfloat& a) { *this = *this / a; return *this; }
+    softfloat& operator %= (const softfloat& a) { *this = *this % a; return *this; }
+
+    /** @brief Comparison operations
+
+     - Any operation with NaN produces false
+       + The only exception is when x is NaN: x != y for any y.
+     - Positive and negative zeros are equal
+    */
+    bool operator == ( const softfloat& ) const;
+    bool operator != ( const softfloat& ) const;
+    bool operator >  ( const softfloat& ) const;
+    bool operator >= ( const softfloat& ) const;
+    bool operator <  ( const softfloat& ) const;
+    bool operator <= ( const softfloat& ) const;
+
+    /** @brief NaN state indicator */
+    inline bool isNaN() const { return (v & 0x7fffffff)  > 0x7f800000; }
+    /** @brief Inf state indicator */
+    inline bool isInf() const { return (v & 0x7fffffff) == 0x7f800000; }
+    /** @brief Subnormal number indicator */
+    inline bool isSubnormal() const { return ((v >> 23) & 0xFF) == 0; }
+
+    /** @brief Get sign bit */
+    inline bool getSign() const { return (v >> 31) != 0; }
+    /** @brief Construct a copy with new sign bit */
+    inline softfloat setSign(bool sign) const { softfloat x; x.v = (v & ((1U << 31) - 1)) | ((uint32_t)sign << 31); return x; }
+    /** @brief Get 0-based exponent */
+    inline int getExp() const { return ((v >> 23) & 0xFF) - 127; }
+    /** @brief Construct a copy with new 0-based exponent */
+    inline softfloat setExp(int e) const { softfloat x; x.v = (v & 0x807fffff) | (((e + 127) & 0xFF) << 23 ); return x; }
+
+    /** @brief Get a fraction part
+
+    Returns a number 1 <= x < 2 with the same significand
+    */
+    inline softfloat getFrac() const
+    {
+        uint_fast32_t vv = (v & 0x007fffff) | (127 << 23);
+        return softfloat::fromRaw(vv);
+    }
+    /** @brief Construct a copy with provided significand
+
+    Constructs a copy of a number with significand taken from parameter
+    */
+    inline softfloat setFrac(const softfloat& s) const
+    {
+        softfloat x;
+        x.v = (v & 0xff800000) | (s.v & 0x007fffff);
+        return x;
+    }
+
+    /** @brief Zero constant */
+    static softfloat zero() { return softfloat::fromRaw( 0 ); }
+    /** @brief Positive infinity constant */
+    static softfloat  inf() { return softfloat::fromRaw( 0xFF << 23 ); }
+    /** @brief Default NaN constant */
+    static softfloat  nan() { return softfloat::fromRaw( 0x7fffffff ); }
+    /** @brief One constant */
+    static softfloat  one() { return softfloat::fromRaw(  127 << 23 ); }
+    /** @brief Smallest normalized value */
+    static softfloat  min() { return softfloat::fromRaw( 0x01 << 23 ); }
+    /** @brief Difference between 1 and next representable value */
+    static softfloat  eps() { return softfloat::fromRaw( (127 - 23) << 23 ); }
+    /** @brief Biggest finite value */
+    static softfloat  max() { return softfloat::fromRaw( (0xFF << 23) - 1 ); }
+    /** @brief Correct pi approximation */
+    static softfloat   pi() { return softfloat::fromRaw( 0x40490fdb ); }
+
+    uint32_t v;
+};
+
+/*----------------------------------------------------------------------------
+*----------------------------------------------------------------------------*/
+
+struct CV_EXPORTS softdouble
+{
+public:
+    /** @brief Default constructor */
+    softdouble() : v(0) { }
+    /** @brief Copy constructor */
+    softdouble( const softdouble& c) { v = c.v; }
+    /** @brief Assign constructor */
+    softdouble& operator=( const softdouble& c )
+    {
+        if(&c != this) v = c.v;
+        return *this;
+    }
+    /** @brief Construct from raw
+
+    Builds new value from raw binary representation
+    */
+    static softdouble fromRaw( const uint64_t a ) { softdouble x; x.v = a; return x; }
+
+    /** @brief Construct from integer */
+    explicit softdouble( const uint32_t );
+    explicit softdouble( const uint64_t );
+    explicit softdouble( const  int32_t );
+    explicit softdouble( const  int64_t );
+
+#ifdef CV_INT32_T_IS_LONG_INT
+    // for platforms with int32_t = long int
+    explicit softdouble( const int a ) { *this = softdouble(static_cast<int32_t>(a)); }
+#endif
+
+    /** @brief Construct from double */
+    explicit softdouble( const double a ) { Cv64suf s; s.f = a; v = s.u; }
+
+    /** @brief Type casts  */
+    operator softfloat() const;
+    operator double() const { Cv64suf s; s.u = v; return s.f; }
+
+    /** @brief Basic arithmetics */
+    softdouble operator + (const softdouble&) const;
+    softdouble operator - (const softdouble&) const;
+    softdouble operator * (const softdouble&) const;
+    softdouble operator / (const softdouble&) const;
+    softdouble operator - () const { softdouble x; x.v = v ^ (1ULL << 63); return x; }
+
+    /** @brief Remainder operator
+
+    A quote from original SoftFloat manual:
+
+    > The IEEE Standard remainder operation computes the value
+    > a - n * b, where n is the integer closest to a / b.
+    > If a / b is exactly halfway between two integers, n is the even integer
+    > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding.
+    > Depending on the relative magnitudes of the operands, the remainder functions
+    > can take considerably longer to execute than the other SoftFloat functions.
+    > This is an inherent characteristic of the remainder operation itself and is not a flaw
+    > in the SoftFloat implementation.
+    */
+    softdouble operator % (const softdouble&) const;
+
+    softdouble& operator += (const softdouble& a) { *this = *this + a; return *this; }
+    softdouble& operator -= (const softdouble& a) { *this = *this - a; return *this; }
+    softdouble& operator *= (const softdouble& a) { *this = *this * a; return *this; }
+    softdouble& operator /= (const softdouble& a) { *this = *this / a; return *this; }
+    softdouble& operator %= (const softdouble& a) { *this = *this % a; return *this; }
+
+    /** @brief Comparison operations
+
+     - Any operation with NaN produces false
+       + The only exception is when x is NaN: x != y for any y.
+     - Positive and negative zeros are equal
+    */
+    bool operator == ( const softdouble& ) const;
+    bool operator != ( const softdouble& ) const;
+    bool operator >  ( const softdouble& ) const;
+    bool operator >= ( const softdouble& ) const;
+    bool operator <  ( const softdouble& ) const;
+    bool operator <= ( const softdouble& ) const;
+
+    /** @brief NaN state indicator */
+    inline bool isNaN() const { return (v & 0x7fffffffffffffff)  > 0x7ff0000000000000; }
+    /** @brief Inf state indicator */
+    inline bool isInf() const { return (v & 0x7fffffffffffffff) == 0x7ff0000000000000; }
+    /** @brief Subnormal number indicator */
+    inline bool isSubnormal() const { return ((v >> 52) & 0x7FF) == 0; }
+
+    /** @brief Get sign bit */
+    inline bool getSign() const { return (v >> 63) != 0; }
+    /** @brief Construct a copy with new sign bit */
+    softdouble setSign(bool sign) const { softdouble x; x.v = (v & ((1ULL << 63) - 1)) | ((uint_fast64_t)(sign) << 63); return x; }
+    /** @brief Get 0-based exponent */
+    inline int getExp() const { return ((v >> 52) & 0x7FF) - 1023; }
+    /** @brief Construct a copy with new 0-based exponent */
+    inline softdouble setExp(int e) const
+    {
+        softdouble x;
+        x.v = (v & 0x800FFFFFFFFFFFFF) | ((uint_fast64_t)((e + 1023) & 0x7FF) << 52);
+        return x;
+    }
+
+    /** @brief Get a fraction part
+
+    Returns a number 1 <= x < 2 with the same significand
+    */
+    inline softdouble getFrac() const
+    {
+        uint_fast64_t vv = (v & 0x000FFFFFFFFFFFFF) | ((uint_fast64_t)(1023) << 52);
+        return softdouble::fromRaw(vv);
+    }
+    /** @brief Construct a copy with provided significand
+
+    Constructs a copy of a number with significand taken from parameter
+    */
+    inline softdouble setFrac(const softdouble& s) const
+    {
+        softdouble x;
+        x.v = (v & 0xFFF0000000000000) | (s.v & 0x000FFFFFFFFFFFFF);
+        return x;
+    }
+
+    /** @brief Zero constant */
+    static softdouble zero() { return softdouble::fromRaw( 0 ); }
+    /** @brief Positive infinity constant */
+    static softdouble  inf() { return softdouble::fromRaw( (uint_fast64_t)(0x7FF) << 52 ); }
+    /** @brief Default NaN constant */
+    static softdouble  nan() { return softdouble::fromRaw( CV_BIG_INT(0x7FFFFFFFFFFFFFFF) ); }
+    /** @brief One constant */
+    static softdouble  one() { return softdouble::fromRaw( (uint_fast64_t)( 1023) << 52 ); }
+    /** @brief Smallest normalized value */
+    static softdouble  min() { return softdouble::fromRaw( (uint_fast64_t)( 0x01) << 52 ); }
+    /** @brief Difference between 1 and next representable value */
+    static softdouble  eps() { return softdouble::fromRaw( (uint_fast64_t)( 1023 - 52 ) << 52 ); }
+    /** @brief Biggest finite value */
+    static softdouble  max() { return softdouble::fromRaw( ((uint_fast64_t)(0x7FF) << 52) - 1 ); }
+    /** @brief Correct pi approximation */
+    static softdouble   pi() { return softdouble::fromRaw( CV_BIG_INT(0x400921FB54442D18) ); }
+
+    uint64_t v;
+};
+
+/*----------------------------------------------------------------------------
+*----------------------------------------------------------------------------*/
+
+/** @brief Fused Multiplication and Addition
+
+Computes (a*b)+c with single rounding
+*/
+CV_EXPORTS softfloat  mulAdd( const softfloat&  a, const softfloat&  b, const softfloat & c);
+CV_EXPORTS softdouble mulAdd( const softdouble& a, const softdouble& b, const softdouble& c);
+
+/** @brief Square root */
+CV_EXPORTS softfloat  sqrt( const softfloat&  a );
+CV_EXPORTS softdouble sqrt( const softdouble& a );
+}
+
+/*----------------------------------------------------------------------------
+| Ported from OpenCV and added for usability
+*----------------------------------------------------------------------------*/
+
+/** @brief Truncates number to integer with minimum magnitude */
+CV_EXPORTS int cvTrunc(const cv::softfloat&  a);
+CV_EXPORTS int cvTrunc(const cv::softdouble& a);
+
+/** @brief Rounds a number to nearest even integer */
+CV_EXPORTS int cvRound(const cv::softfloat&  a);
+CV_EXPORTS int cvRound(const cv::softdouble& a);
+
+/** @brief Rounds a number to nearest even long long integer */
+CV_EXPORTS int64_t cvRound64(const cv::softdouble& a);
+
+/** @brief Rounds a number down to integer */
+CV_EXPORTS int cvFloor(const cv::softfloat&  a);
+CV_EXPORTS int cvFloor(const cv::softdouble& a);
+
+/** @brief Rounds number up to integer */
+CV_EXPORTS int  cvCeil(const cv::softfloat&  a);
+CV_EXPORTS int  cvCeil(const cv::softdouble& a);
+
+namespace cv
+{
+/** @brief Saturate casts */
+template<typename _Tp> static inline _Tp saturate_cast(softfloat  a) { return _Tp(a); }
+template<typename _Tp> static inline _Tp saturate_cast(softdouble a) { return _Tp(a); }
+
+template<> inline uchar saturate_cast<uchar>(softfloat  a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); }
+template<> inline uchar saturate_cast<uchar>(softdouble a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); }
+
+template<> inline schar saturate_cast<schar>(softfloat  a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); }
+template<> inline schar saturate_cast<schar>(softdouble a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); }
+
+template<> inline ushort saturate_cast<ushort>(softfloat  a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); }
+template<> inline ushort saturate_cast<ushort>(softdouble a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); }
+
+template<> inline short saturate_cast<short>(softfloat  a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); }
+template<> inline short saturate_cast<short>(softdouble a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); }
+
+template<> inline int saturate_cast<int>(softfloat  a) { return cvRound(a); }
+template<> inline int saturate_cast<int>(softdouble a) { return cvRound(a); }
+
+template<> inline int64_t saturate_cast<int64_t>(softfloat  a) { return cvRound(a); }
+template<> inline int64_t saturate_cast<int64_t>(softdouble a) { return cvRound64(a); }
+
+/** @brief Saturate cast to unsigned integer and unsigned long long integer
+We intentionally do not clip negative numbers, to make -1 become 0xffffffff etc.
+*/
+template<> inline unsigned saturate_cast<unsigned>(softfloat  a) { return cvRound(a); }
+template<> inline unsigned saturate_cast<unsigned>(softdouble a) { return cvRound(a); }
+
+template<> inline uint64_t saturate_cast<uint64_t>(softfloat  a) { return cvRound(a); }
+template<> inline uint64_t saturate_cast<uint64_t>(softdouble a) { return cvRound64(a); }
+
+/** @brief Min and Max functions */
+inline softfloat  min(const softfloat&  a, const softfloat&  b) { return (a > b) ? b : a; }
+inline softdouble min(const softdouble& a, const softdouble& b) { return (a > b) ? b : a; }
+
+inline softfloat  max(const softfloat&  a, const softfloat&  b) { return (a > b) ? a : b; }
+inline softdouble max(const softdouble& a, const softdouble& b) { return (a > b) ? a : b; }
+
+/** @brief Absolute value */
+inline softfloat  abs( softfloat  a) { softfloat  x; x.v = a.v & ((1U   << 31) - 1); return x; }
+inline softdouble abs( softdouble a) { softdouble x; x.v = a.v & ((1ULL << 63) - 1); return x; }
+
+/** @brief Exponent
+
+Special cases:
+- exp(NaN) is NaN
+- exp(-Inf) == 0
+- exp(+Inf) == +Inf
+*/
+CV_EXPORTS softfloat  exp( const softfloat&  a);
+CV_EXPORTS softdouble exp( const softdouble& a);
+
+/** @brief Natural logarithm
+
+Special cases:
+- log(NaN), log(x < 0) are NaN
+- log(0) == -Inf
+*/
+CV_EXPORTS softfloat  log( const softfloat&  a );
+CV_EXPORTS softdouble log( const softdouble& a );
+
+/** @brief Raising to the power
+
+Special cases:
+- x**NaN is NaN for any x
+- ( |x| == 1 )**Inf is NaN
+- ( |x|  > 1 )**+Inf or ( |x| < 1 )**-Inf is +Inf
+- ( |x|  > 1 )**-Inf or ( |x| < 1 )**+Inf is 0
+- x ** 0 == 1 for any x
+- x ** 1 == 1 for any x
+- NaN ** y is NaN for any other y
+- Inf**(y < 0) == 0
+- Inf ** y is +Inf for any other y
+- (x < 0)**y is NaN for any other y if x can't be correctly rounded to integer
+- 0 ** 0 == 1
+- 0 ** (y < 0) is +Inf
+- 0 ** (y > 0) is 0
+*/
+CV_EXPORTS softfloat  pow( const softfloat&  a, const softfloat&  b);
+CV_EXPORTS softdouble pow( const softdouble& a, const softdouble& b);
+
+/** @brief Cube root
+
+Special cases:
+- cbrt(NaN) is NaN
+- cbrt(+/-Inf) is +/-Inf
+*/
+CV_EXPORTS softfloat cbrt( const softfloat& a );
+
+/** @brief Sine
+
+Special cases:
+- sin(Inf) or sin(NaN) is NaN
+- sin(x) == x when sin(x) is close to zero
+*/
+CV_EXPORTS softdouble sin( const softdouble& a );
+
+/** @brief Cosine
+ *
+Special cases:
+- cos(Inf) or cos(NaN) is NaN
+- cos(x) == +/- 1 when cos(x) is close to +/- 1
+*/
+CV_EXPORTS softdouble cos( const softdouble& a );
+
+//! @} core_utils_softfloat
+
+} // cv::
+
+#endif
diff --git a/3rdParty/include/opencv2/core/sse_utils.hpp b/3rdParty/include/opencv2/core/sse_utils.hpp
new file mode 100644
index 0000000..0906583
--- /dev/null
+++ b/3rdParty/include/opencv2/core/sse_utils.hpp
@@ -0,0 +1,652 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_SSE_UTILS_HPP
+#define OPENCV_CORE_SSE_UTILS_HPP
+
+#ifndef __cplusplus
+#  error sse_utils.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core/cvdef.h"
+
+//! @addtogroup core_utils_sse
+//! @{
+
+#if CV_SSE2
+
+inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
+{
+    __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0);
+    __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0);
+    __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1);
+    __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1);
+
+    __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2);
+    __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2);
+    __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3);
+    __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3);
+
+    __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2);
+    __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2);
+    __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3);
+    __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3);
+
+    __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2);
+    __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2);
+    __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3);
+    __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3);
+
+    v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2);
+    v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2);
+    v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3);
+    v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3);
+}
+
+inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
+                                  __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
+{
+    __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1);
+    __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1);
+    __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0);
+    __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0);
+    __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1);
+    __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1);
+
+    __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3);
+    __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3);
+    __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4);
+    __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4);
+    __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5);
+    __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5);
+
+    __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3);
+    __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3);
+    __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4);
+    __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4);
+    __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5);
+    __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5);
+
+    __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3);
+    __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3);
+    __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4);
+    __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4);
+    __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5);
+    __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5);
+
+    v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3);
+    v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3);
+    v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4);
+    v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4);
+    v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5);
+    v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5);
+}
+
+inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
+                                  __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
+{
+    __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0);
+    __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0);
+    __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1);
+    __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1);
+    __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0);
+    __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0);
+    __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1);
+    __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1);
+
+    __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4);
+    __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4);
+    __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5);
+    __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5);
+    __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6);
+    __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6);
+    __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7);
+    __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7);
+
+    __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4);
+    __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4);
+    __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5);
+    __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5);
+    __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6);
+    __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6);
+    __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7);
+    __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7);
+
+    __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4);
+    __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4);
+    __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5);
+    __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5);
+    __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6);
+    __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6);
+    __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7);
+    __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7);
+
+    v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4);
+    v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4);
+    v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5);
+    v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5);
+    v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6);
+    v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6);
+    v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7);
+    v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7);
+}
+
+inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
+{
+    __m128i v_mask = _mm_set1_epi16(0x00ff);
+
+    __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
+    __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8));
+    __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
+    __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8));
+
+    __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask));
+    __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8));
+    __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask));
+    __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8));
+
+    __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
+    __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8));
+    __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
+    __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8));
+
+    __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
+    __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8));
+    __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
+    __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8));
+
+    v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
+    v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8));
+    v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
+    v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8));
+}
+
+inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
+                                __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
+{
+    __m128i v_mask = _mm_set1_epi16(0x00ff);
+
+    __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
+    __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8));
+    __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
+    __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8));
+    __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
+    __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8));
+
+    __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask));
+    __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8));
+    __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask));
+    __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8));
+    __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask));
+    __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8));
+
+    __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
+    __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8));
+    __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
+    __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8));
+    __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
+    __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8));
+
+    __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
+    __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8));
+    __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
+    __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8));
+    __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
+    __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8));
+
+    v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
+    v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8));
+    v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
+    v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8));
+    v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
+    v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8));
+}
+
+inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
+                                __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
+{
+    __m128i v_mask = _mm_set1_epi16(0x00ff);
+
+    __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
+    __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8));
+    __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
+    __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8));
+    __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
+    __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8));
+    __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask));
+    __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8));
+
+    __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask));
+    __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8));
+    __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask));
+    __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8));
+    __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask));
+    __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8));
+    __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask));
+    __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8));
+
+    __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
+    __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8));
+    __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
+    __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8));
+    __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
+    __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8));
+    __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask));
+    __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8));
+
+    __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
+    __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8));
+    __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
+    __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8));
+    __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
+    __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8));
+    __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask));
+    __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8));
+
+    v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
+    v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8));
+    v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
+    v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8));
+    v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
+    v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8));
+    v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask));
+    v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8));
+}
+
+inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
+{
+    __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0);
+    __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0);
+    __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1);
+    __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1);
+
+    __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2);
+    __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2);
+    __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3);
+    __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3);
+
+    __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2);
+    __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2);
+    __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3);
+    __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3);
+
+    v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2);
+    v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2);
+    v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3);
+    v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3);
+}
+
+inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
+                                   __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
+{
+    __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1);
+    __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1);
+    __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0);
+    __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0);
+    __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1);
+    __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1);
+
+    __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3);
+    __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3);
+    __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4);
+    __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4);
+    __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5);
+    __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5);
+
+    __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3);
+    __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3);
+    __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4);
+    __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4);
+    __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5);
+    __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5);
+
+    v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3);
+    v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3);
+    v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4);
+    v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4);
+    v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5);
+    v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5);
+}
+
+inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
+                                   __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
+{
+    __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0);
+    __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0);
+    __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1);
+    __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1);
+    __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0);
+    __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0);
+    __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1);
+    __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1);
+
+    __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4);
+    __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4);
+    __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5);
+    __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5);
+    __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6);
+    __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6);
+    __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7);
+    __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7);
+
+    __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4);
+    __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4);
+    __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5);
+    __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5);
+    __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6);
+    __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6);
+    __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7);
+    __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7);
+
+    v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4);
+    v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4);
+    v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5);
+    v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5);
+    v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6);
+    v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6);
+    v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7);
+    v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7);
+}
+
+#if CV_SSE4_1
+
+inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
+{
+    __m128i v_mask = _mm_set1_epi32(0x0000ffff);
+
+    __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
+    __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16));
+    __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
+    __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16));
+
+    __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
+    __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16));
+    __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
+    __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16));
+
+    __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
+    __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16));
+    __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
+    __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16));
+
+    v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
+    v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16));
+    v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
+    v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16));
+}
+
+inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
+                                 __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
+{
+    __m128i v_mask = _mm_set1_epi32(0x0000ffff);
+
+    __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
+    __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16));
+    __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
+    __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16));
+    __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
+    __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16));
+
+    __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
+    __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16));
+    __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
+    __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16));
+    __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
+    __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16));
+
+    __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
+    __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16));
+    __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
+    __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16));
+    __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
+    __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16));
+
+    v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
+    v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16));
+    v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
+    v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16));
+    v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
+    v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16));
+}
+
+inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
+                                 __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
+{
+    __m128i v_mask = _mm_set1_epi32(0x0000ffff);
+
+    __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
+    __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16));
+    __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
+    __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16));
+    __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
+    __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16));
+    __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask));
+    __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16));
+
+    __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
+    __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16));
+    __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
+    __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16));
+    __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
+    __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16));
+    __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask));
+    __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16));
+
+    __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
+    __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16));
+    __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
+    __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16));
+    __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
+    __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16));
+    __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask));
+    __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16));
+
+    v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
+    v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16));
+    v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
+    v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16));
+    v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
+    v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16));
+    v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask));
+    v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16));
+}
+
+#endif // CV_SSE4_1
+
+inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1)
+{
+    __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0);
+    __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0);
+    __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1);
+    __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1);
+
+    __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2);
+    __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2);
+    __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3);
+    __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3);
+
+    v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2);
+    v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2);
+    v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3);
+    v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3);
+}
+
+inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0,
+                                __m128 & v_g1, __m128 & v_b0, __m128 & v_b1)
+{
+    __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1);
+    __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1);
+    __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0);
+    __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0);
+    __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1);
+    __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1);
+
+    __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3);
+    __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3);
+    __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4);
+    __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4);
+    __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5);
+    __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5);
+
+    v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3);
+    v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3);
+    v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4);
+    v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4);
+    v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5);
+    v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5);
+}
+
+inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1,
+                                __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1)
+{
+    __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0);
+    __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0);
+    __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1);
+    __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1);
+    __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0);
+    __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0);
+    __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1);
+    __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1);
+
+    __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4);
+    __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4);
+    __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5);
+    __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5);
+    __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6);
+    __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6);
+    __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7);
+    __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7);
+
+    v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4);
+    v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4);
+    v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5);
+    v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5);
+    v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6);
+    v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6);
+    v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7);
+    v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7);
+}
+
+inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1)
+{
+    enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) };
+
+    __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo);
+    __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi);
+    __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo);
+    __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi);
+
+    __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo);
+    __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi);
+    __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo);
+    __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi);
+
+    v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo);
+    v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi);
+    v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo);
+    v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi);
+}
+
+inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0,
+                              __m128 & v_g1, __m128 & v_b0, __m128 & v_b1)
+{
+    enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) };
+
+    __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo);
+    __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi);
+    __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo);
+    __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi);
+    __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo);
+    __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi);
+
+    __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo);
+    __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi);
+    __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo);
+    __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi);
+    __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo);
+    __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi);
+
+    v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo);
+    v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi);
+    v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo);
+    v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi);
+    v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo);
+    v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi);
+}
+
+inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1,
+                              __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1)
+{
+    enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) };
+
+    __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo);
+    __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi);
+    __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo);
+    __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi);
+    __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo);
+    __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi);
+    __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo);
+    __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi);
+
+    __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo);
+    __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi);
+    __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo);
+    __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi);
+    __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo);
+    __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi);
+    __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo);
+    __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi);
+
+    v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo);
+    v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi);
+    v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo);
+    v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi);
+    v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo);
+    v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi);
+    v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo);
+    v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi);
+}
+
+#endif // CV_SSE2
+
+//! @}
+
+#endif //OPENCV_CORE_SSE_UTILS_HPP
diff --git a/3rdParty/include/opencv2/core/traits.hpp b/3rdParty/include/opencv2/core/traits.hpp
new file mode 100644
index 0000000..52ab083
--- /dev/null
+++ b/3rdParty/include/opencv2/core/traits.hpp
@@ -0,0 +1,417 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_TRAITS_HPP
+#define OPENCV_CORE_TRAITS_HPP
+
+#include "opencv2/core/cvdef.h"
+
+namespace cv
+{
+
+//#define OPENCV_TRAITS_ENABLE_DEPRECATED
+
+//! @addtogroup core_basic
+//! @{
+
+/** @brief Template "trait" class for OpenCV primitive data types.
+
+@note Deprecated. This is replaced by "single purpose" traits: traits::Type and traits::Depth
+
+A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed
+short, int, float, double, or a tuple of values of one of these types, where all the values in the
+tuple have the same type. Any primitive type from the list can be defined by an identifier in the
+form CV_\<bit-depth\>{U|S|F}C(\<number_of_channels\>), for example: uchar \~ CV_8UC1, 3-element
+floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a
+single instance of such a primitive data type is Vec. Multiple instances of such a type can be
+stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to
+store Vec instances.
+
+The DataType class is basically used to provide a description of such primitive data types without
+adding any fields or methods to the corresponding classes (and it is actually impossible to add
+anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not
+DataType itself that is used but its specialized versions, such as:
+@code
+    template<> class DataType<uchar>
+    {
+        typedef uchar value_type;
+        typedef int work_type;
+        typedef uchar channel_type;
+        enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U };
+    };
+    ...
+    template<typename _Tp> DataType<std::complex<_Tp> >
+    {
+        typedef std::complex<_Tp> value_type;
+        typedef std::complex<_Tp> work_type;
+        typedef _Tp channel_type;
+        // DataDepth is another helper trait class
+        enum { depth = DataDepth<_Tp>::value, channels=2,
+            fmt=(channels-1)*256+DataDepth<_Tp>::fmt,
+            type=CV_MAKETYPE(depth, channels) };
+    };
+    ...
+@endcode
+The main purpose of this class is to convert compilation-time type information to an
+OpenCV-compatible data type identifier, for example:
+@code
+    // allocates a 30x40 floating-point matrix
+    Mat A(30, 40, DataType<float>::type);
+
+    Mat B = Mat_<std::complex<double> >(3, 3);
+    // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2
+    cout << B.depth() << ", " << B.channels() << endl;
+@endcode
+So, such traits are used to tell OpenCV which data type you are working with, even if such a type is
+not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV
+defines the proper specialized template class DataType\<complex\<_Tp\> \> . This mechanism is also
+useful (and used in OpenCV this way) for generic algorithms implementations.
+
+@note Default values were dropped to stop confusing developers about using of unsupported types (see #7599)
+*/
+template<typename _Tp> class DataType
+{
+public:
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+    typedef _Tp         value_type;
+    typedef value_type  work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 1,
+           depth        = -1,
+           channels     = 1,
+           fmt          = 0,
+           type = CV_MAKETYPE(depth, channels)
+         };
+#endif
+};
+
+template<> class DataType<bool>
+{
+public:
+    typedef bool        value_type;
+    typedef int         work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_8U,
+           channels     = 1,
+           fmt          = (int)'u',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<uchar>
+{
+public:
+    typedef uchar       value_type;
+    typedef int         work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_8U,
+           channels     = 1,
+           fmt          = (int)'u',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<schar>
+{
+public:
+    typedef schar       value_type;
+    typedef int         work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_8S,
+           channels     = 1,
+           fmt          = (int)'c',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<char>
+{
+public:
+    typedef schar       value_type;
+    typedef int         work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_8S,
+           channels     = 1,
+           fmt          = (int)'c',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<ushort>
+{
+public:
+    typedef ushort      value_type;
+    typedef int         work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_16U,
+           channels     = 1,
+           fmt          = (int)'w',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<short>
+{
+public:
+    typedef short       value_type;
+    typedef int         work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_16S,
+           channels     = 1,
+           fmt          = (int)'s',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<int>
+{
+public:
+    typedef int         value_type;
+    typedef value_type  work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_32S,
+           channels     = 1,
+           fmt          = (int)'i',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<float>
+{
+public:
+    typedef float       value_type;
+    typedef value_type  work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_32F,
+           channels     = 1,
+           fmt          = (int)'f',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<double>
+{
+public:
+    typedef double      value_type;
+    typedef value_type  work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_64F,
+           channels     = 1,
+           fmt          = (int)'d',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+template<> class DataType<float16_t>
+{
+public:
+    typedef float16_t   value_type;
+    typedef float       work_type;
+    typedef value_type  channel_type;
+    typedef value_type  vec_type;
+    enum { generic_type = 0,
+           depth        = CV_16F,
+           channels     = 1,
+           fmt          = (int)'h',
+           type         = CV_MAKETYPE(depth, channels)
+         };
+};
+
+/** @brief A helper class for cv::DataType
+
+The class is specialized for each fundamental numerical data type supported by OpenCV. It provides
+DataDepth<T>::value constant.
+*/
+template<typename _Tp> class DataDepth
+{
+public:
+    enum
+    {
+        value = DataType<_Tp>::depth,
+        fmt   = DataType<_Tp>::fmt
+    };
+};
+
+
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+
+template<int _depth> class TypeDepth
+{
+#ifdef OPENCV_TRAITS_ENABLE_LEGACY_DEFAULTS
+    enum { depth = CV_USRTYPE1 };
+    typedef void value_type;
+#endif
+};
+
+template<> class TypeDepth<CV_8U>
+{
+    enum { depth = CV_8U };
+    typedef uchar value_type;
+};
+
+template<> class TypeDepth<CV_8S>
+{
+    enum { depth = CV_8S };
+    typedef schar value_type;
+};
+
+template<> class TypeDepth<CV_16U>
+{
+    enum { depth = CV_16U };
+    typedef ushort value_type;
+};
+
+template<> class TypeDepth<CV_16S>
+{
+    enum { depth = CV_16S };
+    typedef short value_type;
+};
+
+template<> class TypeDepth<CV_32S>
+{
+    enum { depth = CV_32S };
+    typedef int value_type;
+};
+
+template<> class TypeDepth<CV_32F>
+{
+    enum { depth = CV_32F };
+    typedef float value_type;
+};
+
+template<> class TypeDepth<CV_64F>
+{
+    enum { depth = CV_64F };
+    typedef double value_type;
+};
+
+template<> class TypeDepth<CV_16F>
+{
+    enum { depth = CV_16F };
+    typedef float16_t value_type;
+};
+
+#endif
+
+//! @}
+
+namespace traits {
+
+namespace internal {
+#define CV_CREATE_MEMBER_CHECK(X) \
+template<typename T> class CheckMember_##X { \
+    struct Fallback { int X; }; \
+    struct Derived : T, Fallback { }; \
+    template<typename U, U> struct Check; \
+    typedef char CV_NO[1]; \
+    typedef char CV_YES[2]; \
+    template<typename U> static CV_NO & func(Check<int Fallback::*, &U::X> *); \
+    template<typename U> static CV_YES & func(...); \
+public: \
+    typedef CheckMember_##X type; \
+    enum { value = sizeof(func<Derived>(0)) == sizeof(CV_YES) }; \
+};
+
+CV_CREATE_MEMBER_CHECK(fmt)
+CV_CREATE_MEMBER_CHECK(type)
+
+} // namespace internal
+
+
+template<typename T>
+struct Depth
+{ enum { value = DataType<T>::depth }; };
+
+template<typename T>
+struct Type
+{ enum { value = DataType<T>::type }; };
+
+/** Similar to traits::Type<T> but has value = -1 in case of unknown type (instead of compiler error) */
+template<typename T, bool available = internal::CheckMember_type< DataType<T> >::value >
+struct SafeType {};
+
+template<typename T>
+struct SafeType<T, false>
+{ enum { value = -1 }; };
+
+template<typename T>
+struct SafeType<T, true>
+{ enum { value = Type<T>::value }; };
+
+
+template<typename T, bool available = internal::CheckMember_fmt< DataType<T> >::value >
+struct SafeFmt {};
+
+template<typename T>
+struct SafeFmt<T, false>
+{ enum { fmt = 0 }; };
+
+template<typename T>
+struct SafeFmt<T, true>
+{ enum { fmt = DataType<T>::fmt }; };
+
+
+} // namespace
+
+} // cv
+
+#endif // OPENCV_CORE_TRAITS_HPP
diff --git a/3rdParty/include/opencv2/core/types.hpp b/3rdParty/include/opencv2/core/types.hpp
new file mode 100644
index 0000000..2867520
--- /dev/null
+++ b/3rdParty/include/opencv2/core/types.hpp
@@ -0,0 +1,2439 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_TYPES_HPP
+#define OPENCV_CORE_TYPES_HPP
+
+#ifndef __cplusplus
+#  error types.hpp header must be compiled as C++
+#endif
+
+#include <climits>
+#include <cfloat>
+#include <vector>
+#include <limits>
+
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/cvstd.hpp"
+#include "opencv2/core/matx.hpp"
+
+namespace cv
+{
+
+//! @addtogroup core_basic
+//! @{
+
+//////////////////////////////// Complex //////////////////////////////
+
+/** @brief  A complex number class.
+
+  The template class is similar and compatible with std::complex, however it provides slightly
+  more convenient access to the real and imaginary parts using through the simple field access, as opposite
+  to std::complex::real() and std::complex::imag().
+*/
+template<typename _Tp> class Complex
+{
+public:
+
+    //! default constructor
+    Complex();
+    Complex( _Tp _re, _Tp _im = 0 );
+
+    //! conversion to another data type
+    template<typename T2> operator Complex<T2>() const;
+    //! conjugation
+    Complex conj() const;
+
+    _Tp re, im; //< the real and the imaginary parts
+};
+
+typedef Complex<float> Complexf;
+typedef Complex<double> Complexd;
+
+template<typename _Tp> class DataType< Complex<_Tp> >
+{
+public:
+    typedef Complex<_Tp> value_type;
+    typedef value_type   work_type;
+    typedef _Tp          channel_type;
+
+    enum { generic_type = 0,
+           channels     = 2,
+           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+    };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<typename _Tp>
+struct Depth< Complex<_Tp> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp>
+struct Type< Complex<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; };
+} // namespace
+
+
+//////////////////////////////// Point_ ////////////////////////////////
+
+/** @brief Template class for 2D points specified by its coordinates `x` and `y`.
+
+An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is
+also a cast operator to convert point coordinates to the specified type. The conversion from
+floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion
+uses this operation for each of the coordinates. Besides the class members listed in the
+declaration above, the following operations on points are implemented:
+@code
+    pt1 = pt2 + pt3;
+    pt1 = pt2 - pt3;
+    pt1 = pt2 * a;
+    pt1 = a * pt2;
+    pt1 = pt2 / a;
+    pt1 += pt2;
+    pt1 -= pt2;
+    pt1 *= a;
+    pt1 /= a;
+    double value = norm(pt); // L2 norm
+    pt1 == pt2;
+    pt1 != pt2;
+@endcode
+For your convenience, the following type aliases are defined:
+@code
+    typedef Point_<int> Point2i;
+    typedef Point2i Point;
+    typedef Point_<float> Point2f;
+    typedef Point_<double> Point2d;
+@endcode
+Example:
+@code
+    Point2f a(0.3f, 0.f), b(0.f, 0.4f);
+    Point pt = (a + b)*10.f;
+    cout << pt.x << ", " << pt.y << endl;
+@endcode
+*/
+template<typename _Tp> class Point_
+{
+public:
+    typedef _Tp value_type;
+
+    //! default constructor
+    Point_();
+    Point_(_Tp _x, _Tp _y);
+#if (defined(__GNUC__) && __GNUC__ < 5) && !defined(__clang__)  // GCC 4.x bug. Details: https://github.com/opencv/opencv/pull/20837
+    Point_(const Point_& pt);
+    Point_(Point_&& pt) CV_NOEXCEPT = default;
+#elif OPENCV_ABI_COMPATIBILITY < 500
+    Point_(const Point_& pt) = default;
+    Point_(Point_&& pt) CV_NOEXCEPT = default;
+#endif
+    Point_(const Size_<_Tp>& sz);
+    Point_(const Vec<_Tp, 2>& v);
+
+#if (defined(__GNUC__) && __GNUC__ < 5) && !defined(__clang__)  // GCC 4.x bug. Details: https://github.com/opencv/opencv/pull/20837
+    Point_& operator = (const Point_& pt);
+    Point_& operator = (Point_&& pt) CV_NOEXCEPT = default;
+#elif OPENCV_ABI_COMPATIBILITY < 500
+    Point_& operator = (const Point_& pt) = default;
+    Point_& operator = (Point_&& pt) CV_NOEXCEPT = default;
+#endif
+    //! conversion to another data type
+    template<typename _Tp2> operator Point_<_Tp2>() const;
+
+    //! conversion to the old-style C structures
+    operator Vec<_Tp, 2>() const;
+
+    //! dot product
+    _Tp dot(const Point_& pt) const;
+    //! dot product computed in double-precision arithmetics
+    double ddot(const Point_& pt) const;
+    //! cross-product
+    double cross(const Point_& pt) const;
+    //! checks whether the point is inside the specified rectangle
+    bool inside(const Rect_<_Tp>& r) const;
+    _Tp x; //!< x coordinate of the point
+    _Tp y; //!< y coordinate of the point
+};
+
+typedef Point_<int> Point2i;
+typedef Point_<int64> Point2l;
+typedef Point_<float> Point2f;
+typedef Point_<double> Point2d;
+typedef Point2i Point;
+
+template<typename _Tp> class DataType< Point_<_Tp> >
+{
+public:
+    typedef Point_<_Tp>                               value_type;
+    typedef Point_<typename DataType<_Tp>::work_type> work_type;
+    typedef _Tp                                       channel_type;
+
+    enum { generic_type = 0,
+           channels     = 2,
+           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<typename _Tp>
+struct Depth< Point_<_Tp> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp>
+struct Type< Point_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; };
+} // namespace
+
+
+//////////////////////////////// Point3_ ////////////////////////////////
+
+/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`.
+
+An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to
+Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and
+comparison operations are also supported.
+
+The following Point3_\<\> aliases are available:
+@code
+    typedef Point3_<int> Point3i;
+    typedef Point3_<float> Point3f;
+    typedef Point3_<double> Point3d;
+@endcode
+@see cv::Point3i, cv::Point3f and cv::Point3d
+*/
+template<typename _Tp> class Point3_
+{
+public:
+    typedef _Tp value_type;
+
+    //! default constructor
+    Point3_();
+    Point3_(_Tp _x, _Tp _y, _Tp _z);
+#if OPENCV_ABI_COMPATIBILITY < 500
+    Point3_(const Point3_& pt) = default;
+    Point3_(Point3_&& pt) CV_NOEXCEPT = default;
+#endif
+    explicit Point3_(const Point_<_Tp>& pt);
+    Point3_(const Vec<_Tp, 3>& v);
+
+#if OPENCV_ABI_COMPATIBILITY < 500
+    Point3_& operator = (const Point3_& pt) = default;
+    Point3_& operator = (Point3_&& pt) CV_NOEXCEPT = default;
+#endif
+    //! conversion to another data type
+    template<typename _Tp2> operator Point3_<_Tp2>() const;
+    //! conversion to cv::Vec<>
+    operator Vec<_Tp, 3>() const;
+
+    //! dot product
+    _Tp dot(const Point3_& pt) const;
+    //! dot product computed in double-precision arithmetics
+    double ddot(const Point3_& pt) const;
+    //! cross product of the 2 3D points
+    Point3_ cross(const Point3_& pt) const;
+    _Tp x; //!< x coordinate of the 3D point
+    _Tp y; //!< y coordinate of the 3D point
+    _Tp z; //!< z coordinate of the 3D point
+};
+
+typedef Point3_<int> Point3i;
+typedef Point3_<float> Point3f;
+typedef Point3_<double> Point3d;
+
+template<typename _Tp> class DataType< Point3_<_Tp> >
+{
+public:
+    typedef Point3_<_Tp>                               value_type;
+    typedef Point3_<typename DataType<_Tp>::work_type> work_type;
+    typedef _Tp                                        channel_type;
+
+    enum { generic_type = 0,
+           channels     = 3,
+           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<typename _Tp>
+struct Depth< Point3_<_Tp> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp>
+struct Type< Point3_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 3) }; };
+} // namespace
+
+//////////////////////////////// Size_ ////////////////////////////////
+
+/** @brief Template class for specifying the size of an image or rectangle.
+
+The class includes two members called width and height. The structure can be converted to and from
+the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison
+operations as for Point_ is available.
+
+OpenCV defines the following Size_\<\> aliases:
+@code
+    typedef Size_<int> Size2i;
+    typedef Size2i Size;
+    typedef Size_<float> Size2f;
+@endcode
+*/
+template<typename _Tp> class Size_
+{
+public:
+    typedef _Tp value_type;
+
+    //! default constructor
+    Size_();
+    Size_(_Tp _width, _Tp _height);
+#if OPENCV_ABI_COMPATIBILITY < 500
+    Size_(const Size_& sz) = default;
+    Size_(Size_&& sz) CV_NOEXCEPT = default;
+#endif
+    Size_(const Point_<_Tp>& pt);
+
+#if OPENCV_ABI_COMPATIBILITY < 500
+    Size_& operator = (const Size_& sz) = default;
+    Size_& operator = (Size_&& sz) CV_NOEXCEPT = default;
+#endif
+    //! the area (width*height)
+    _Tp area() const;
+    //! aspect ratio (width/height)
+    double aspectRatio() const;
+    //! true if empty
+    bool empty() const;
+
+    //! conversion of another data type.
+    template<typename _Tp2> operator Size_<_Tp2>() const;
+
+    _Tp width; //!< the width
+    _Tp height; //!< the height
+};
+
+typedef Size_<int> Size2i;
+typedef Size_<int64> Size2l;
+typedef Size_<float> Size2f;
+typedef Size_<double> Size2d;
+typedef Size2i Size;
+
+template<typename _Tp> class DataType< Size_<_Tp> >
+{
+public:
+    typedef Size_<_Tp>                               value_type;
+    typedef Size_<typename DataType<_Tp>::work_type> work_type;
+    typedef _Tp                                      channel_type;
+
+    enum { generic_type = 0,
+           channels     = 2,
+           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<typename _Tp>
+struct Depth< Size_<_Tp> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp>
+struct Type< Size_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; };
+} // namespace
+
+//////////////////////////////// Rect_ ////////////////////////////////
+
+/** @brief Template class for 2D rectangles
+
+described by the following parameters:
+-   Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y
+    in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner.
+-   Rectangle width and height.
+
+OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the
+right and bottom boundaries are not. For example, the method Rect_::contains returns true if
+
+\f[x  \leq pt.x < x+width,
+      y  \leq pt.y < y+height\f]
+
+Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\<int\> ) is
+implemented as:
+@code
+    for(int y = roi.y; y < roi.y + roi.height; y++)
+        for(int x = roi.x; x < roi.x + roi.width; x++)
+        {
+            // ...
+        }
+@endcode
+In addition to the class members, the following operations on rectangles are implemented:
+-   \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset)
+-   \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a
+    certain amount)
+-   rect += point, rect -= point, rect += size, rect -= size (augmenting operations)
+-   rect = rect1 & rect2 (rectangle intersection)
+-   rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 )
+-   rect &= rect1, rect |= rect1 (and the corresponding augmenting operations)
+-   rect == rect1, rect != rect1 (rectangle comparison)
+
+This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$
+rect2):
+@code
+    template<typename _Tp> inline bool
+    operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2)
+    {
+        return (r1 & r2) == r1;
+    }
+@endcode
+For your convenience, the Rect_\<\> alias is available: cv::Rect
+*/
+template<typename _Tp> class Rect_
+{
+public:
+    typedef _Tp value_type;
+
+    //! default constructor
+    Rect_();
+    Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height);
+#if OPENCV_ABI_COMPATIBILITY < 500
+    Rect_(const Rect_& r) = default;
+    Rect_(Rect_&& r) CV_NOEXCEPT = default;
+#endif
+    Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz);
+    Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2);
+
+#if OPENCV_ABI_COMPATIBILITY < 500
+    Rect_& operator = (const Rect_& r) = default;
+    Rect_& operator = (Rect_&& r) CV_NOEXCEPT = default;
+#endif
+    //! the top-left corner
+    Point_<_Tp> tl() const;
+    //! the bottom-right corner
+    Point_<_Tp> br() const;
+
+    //! size (width, height) of the rectangle
+    Size_<_Tp> size() const;
+    //! area (width*height) of the rectangle
+    _Tp area() const;
+    //! true if empty
+    bool empty() const;
+
+    //! conversion to another data type
+    template<typename _Tp2> operator Rect_<_Tp2>() const;
+
+    //! checks whether the rectangle contains the point
+    bool contains(const Point_<_Tp>& pt) const;
+
+    _Tp x; //!< x coordinate of the top-left corner
+    _Tp y; //!< y coordinate of the top-left corner
+    _Tp width; //!< width of the rectangle
+    _Tp height; //!< height of the rectangle
+};
+
+typedef Rect_<int> Rect2i;
+typedef Rect_<float> Rect2f;
+typedef Rect_<double> Rect2d;
+typedef Rect2i Rect;
+
+template<typename _Tp> class DataType< Rect_<_Tp> >
+{
+public:
+    typedef Rect_<_Tp>                               value_type;
+    typedef Rect_<typename DataType<_Tp>::work_type> work_type;
+    typedef _Tp                                      channel_type;
+
+    enum { generic_type = 0,
+           channels     = 4,
+           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<typename _Tp>
+struct Depth< Rect_<_Tp> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp>
+struct Type< Rect_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; };
+} // namespace
+
+///////////////////////////// RotatedRect /////////////////////////////
+
+/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane.
+
+Each rectangle is specified by the center point (mass center), length of each side (represented by
+#Size2f structure) and the rotation angle in degrees.
+
+The sample below demonstrates how to use RotatedRect:
+@snippet snippets/core_various.cpp RotatedRect_demo
+![image](pics/rotatedrect.png)
+
+@sa CamShift, fitEllipse, minAreaRect, CvBox2D
+*/
+class CV_EXPORTS RotatedRect
+{
+public:
+    //! default constructor
+    RotatedRect();
+    /** full constructor
+    @param center The rectangle mass center.
+    @param size Width and height of the rectangle.
+    @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc.,
+    the rectangle becomes an up-right rectangle.
+    */
+    RotatedRect(const Point2f& center, const Size2f& size, float angle);
+    /**
+    Any 3 end points of the RotatedRect. They must be given in order (either clockwise or
+    anticlockwise).
+     */
+    RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3);
+
+    /** returns 4 vertices of the rectangle
+    @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight.
+    */
+    void points(Point2f pts[]) const;
+    //! returns the minimal up-right integer rectangle containing the rotated rectangle
+    Rect boundingRect() const;
+    //! returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images
+    Rect_<float> boundingRect2f() const;
+    //! returns the rectangle mass center
+    Point2f center;
+    //! returns width and height of the rectangle
+    Size2f size;
+    //! returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle.
+    float angle;
+};
+
+template<> class DataType< RotatedRect >
+{
+public:
+    typedef RotatedRect  value_type;
+    typedef value_type   work_type;
+    typedef float        channel_type;
+
+    enum { generic_type = 0,
+           channels     = (int)sizeof(value_type)/sizeof(channel_type), // 5
+           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<>
+struct Depth< RotatedRect > { enum { value = Depth<float>::value }; };
+template<>
+struct Type< RotatedRect > { enum { value = CV_MAKETYPE(Depth<float>::value, (int)sizeof(RotatedRect)/sizeof(float)) }; };
+} // namespace
+
+
+//////////////////////////////// Range /////////////////////////////////
+
+/** @brief Template class specifying a continuous subsequence (slice) of a sequence.
+
+The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes.
+Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an
+inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a
+half-opened interval is usually denoted as \f$[start,end)\f$ .
+
+The static method Range::all() returns a special variable that means "the whole sequence" or "the
+whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in
+OpenCV that take Range support this special Range::all() value. But, of course, in case of your own
+custom processing, you will probably have to check and handle it explicitly:
+@code
+    void my_function(..., const Range& r, ....)
+    {
+        if(r == Range::all()) {
+            // process all the data
+        }
+        else {
+            // process [r.start, r.end)
+        }
+    }
+@endcode
+*/
+class CV_EXPORTS Range
+{
+public:
+    Range();
+    Range(int _start, int _end);
+    int size() const;
+    bool empty() const;
+    static Range all();
+
+    int start, end;
+};
+
+template<> class DataType<Range>
+{
+public:
+    typedef Range      value_type;
+    typedef value_type work_type;
+    typedef int        channel_type;
+
+    enum { generic_type = 0,
+           channels     = 2,
+           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<>
+struct Depth< Range > { enum { value = Depth<int>::value }; };
+template<>
+struct Type< Range > { enum { value = CV_MAKETYPE(Depth<int>::value, 2) }; };
+} // namespace
+
+
+//////////////////////////////// Scalar_ ///////////////////////////////
+
+/** @brief Template class for a 4-element vector derived from Vec.
+
+Being derived from Vec\<_Tp, 4\> , Scalar\_ and Scalar can be used just as typical 4-element
+vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in
+OpenCV to pass pixel values.
+*/
+template<typename _Tp> class Scalar_ : public Vec<_Tp, 4>
+{
+public:
+    //! default constructor
+    Scalar_();
+    Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0);
+    Scalar_(_Tp v0);
+
+    Scalar_(const Scalar_& s);
+    Scalar_(Scalar_&& s) CV_NOEXCEPT;
+
+    Scalar_& operator=(const Scalar_& s);
+    Scalar_& operator=(Scalar_&& s) CV_NOEXCEPT;
+
+    template<typename _Tp2, int cn>
+    Scalar_(const Vec<_Tp2, cn>& v);
+
+    //! returns a scalar with all elements set to v0
+    static Scalar_<_Tp> all(_Tp v0);
+
+    //! conversion to another data type
+    template<typename T2> operator Scalar_<T2>() const;
+
+    //! per-element product
+    Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const;
+
+    //! returns (v0, -v1, -v2, -v3)
+    Scalar_<_Tp> conj() const;
+
+    //! returns true iff v1 == v2 == v3 == 0
+    bool isReal() const;
+};
+
+typedef Scalar_<double> Scalar;
+
+template<typename _Tp> class DataType< Scalar_<_Tp> >
+{
+public:
+    typedef Scalar_<_Tp>                               value_type;
+    typedef Scalar_<typename DataType<_Tp>::work_type> work_type;
+    typedef _Tp                                        channel_type;
+
+    enum { generic_type = 0,
+           channels     = 4,
+           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<typename _Tp>
+struct Depth< Scalar_<_Tp> > { enum { value = Depth<_Tp>::value }; };
+template<typename _Tp>
+struct Type< Scalar_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; };
+} // namespace
+
+
+/////////////////////////////// KeyPoint ////////////////////////////////
+
+/** @brief Data structure for salient point detectors.
+
+The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint
+detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc.
+
+The keypoint is characterized by the 2D position, scale (proportional to the diameter of the
+neighborhood that needs to be taken into account), orientation and some other parameters. The
+keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually
+represented as a feature vector). The keypoints representing the same object in different images
+can then be matched using %KDTree or another method.
+*/
+class CV_EXPORTS_W_SIMPLE KeyPoint
+{
+public:
+    //! the default constructor
+    CV_WRAP KeyPoint();
+    /**
+    @param pt x & y coordinates of the keypoint
+    @param size keypoint diameter
+    @param angle keypoint orientation
+    @param response keypoint detector response on the keypoint (that is, strength of the keypoint)
+    @param octave pyramid octave in which the keypoint has been detected
+    @param class_id object id
+     */
+    KeyPoint(Point2f pt, float size, float angle=-1, float response=0, int octave=0, int class_id=-1);
+    /**
+    @param x x-coordinate of the keypoint
+    @param y y-coordinate of the keypoint
+    @param size keypoint diameter
+    @param angle keypoint orientation
+    @param response keypoint detector response on the keypoint (that is, strength of the keypoint)
+    @param octave pyramid octave in which the keypoint has been detected
+    @param class_id object id
+     */
+    CV_WRAP KeyPoint(float x, float y, float size, float angle=-1, float response=0, int octave=0, int class_id=-1);
+
+    size_t hash() const;
+
+    /**
+    This method converts vector of keypoints to vector of points or the reverse, where each keypoint is
+    assigned the same size and the same orientation.
+
+    @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
+    @param points2f Array of (x,y) coordinates of each keypoint
+    @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
+    convert only specified keypoints)
+    */
+    CV_WRAP static void convert(const std::vector<KeyPoint>& keypoints,
+                                CV_OUT std::vector<Point2f>& points2f,
+                                const std::vector<int>& keypointIndexes=std::vector<int>());
+    /** @overload
+    @param points2f Array of (x,y) coordinates of each keypoint
+    @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
+    @param size keypoint diameter
+    @param response keypoint detector response on the keypoint (that is, strength of the keypoint)
+    @param octave pyramid octave in which the keypoint has been detected
+    @param class_id object id
+    */
+    CV_WRAP static void convert(const std::vector<Point2f>& points2f,
+                                CV_OUT std::vector<KeyPoint>& keypoints,
+                                float size=1, float response=1, int octave=0, int class_id=-1);
+
+    /**
+    This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint
+    regions' intersection and area of keypoint regions' union (considering keypoint region as circle).
+    If they don't overlap, we get zero. If they coincide at same location with same size, we get 1.
+    @param kp1 First keypoint
+    @param kp2 Second keypoint
+    */
+    CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2);
+
+    CV_PROP_RW Point2f pt; //!< coordinates of the keypoints
+    CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood
+    CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable);
+                            //!< it's in [0,360) degrees and measured relative to
+                            //!< image coordinate system, ie in clockwise.
+    CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling
+    CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted
+    CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to)
+};
+
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+template<> class DataType<KeyPoint>
+{
+public:
+    typedef KeyPoint      value_type;
+    typedef float         work_type;
+    typedef float         channel_type;
+
+    enum { generic_type = 0,
+           depth        = DataType<channel_type>::depth,
+           channels     = (int)(sizeof(value_type)/sizeof(channel_type)), // 7
+           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
+           type         = CV_MAKETYPE(depth, channels)
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+#endif
+
+
+//////////////////////////////// DMatch /////////////////////////////////
+
+/** @brief Class for matching keypoint descriptors
+
+query descriptor index, train descriptor index, train image index, and distance between
+descriptors.
+*/
+class CV_EXPORTS_W_SIMPLE DMatch
+{
+public:
+    CV_WRAP DMatch();
+    CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance);
+    CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance);
+
+    CV_PROP_RW int queryIdx; //!< query descriptor index
+    CV_PROP_RW int trainIdx; //!< train descriptor index
+    CV_PROP_RW int imgIdx;   //!< train image index
+
+    CV_PROP_RW float distance;
+
+    // less is better
+    bool operator<(const DMatch &m) const;
+};
+
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+template<> class DataType<DMatch>
+{
+public:
+    typedef DMatch      value_type;
+    typedef int         work_type;
+    typedef int         channel_type;
+
+    enum { generic_type = 0,
+           depth        = DataType<channel_type>::depth,
+           channels     = (int)(sizeof(value_type)/sizeof(channel_type)), // 4
+           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
+           type         = CV_MAKETYPE(depth, channels)
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+#endif
+
+
+///////////////////////////// TermCriteria //////////////////////////////
+
+/** @brief The class defining termination criteria for iterative algorithms.
+
+You can initialize it by default constructor and then override any parameters, or the structure may
+be fully initialized using the advanced variant of the constructor.
+*/
+class CV_EXPORTS TermCriteria
+{
+public:
+    /**
+      Criteria type, can be one of: COUNT, EPS or COUNT + EPS
+    */
+    enum Type
+    {
+        COUNT=1, //!< the maximum number of iterations or elements to compute
+        MAX_ITER=COUNT, //!< ditto
+        EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops
+    };
+
+    //! default constructor
+    TermCriteria();
+    /**
+    @param type The type of termination criteria, one of TermCriteria::Type
+    @param maxCount The maximum number of iterations or elements to compute.
+    @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops.
+    */
+    TermCriteria(int type, int maxCount, double epsilon);
+
+    inline bool isValid() const
+    {
+        const bool isCount = (type & COUNT) && maxCount > 0;
+        const bool isEps = (type & EPS) && !cvIsNaN(epsilon);
+        return isCount || isEps;
+    }
+
+    int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS
+    int maxCount; //!< the maximum number of iterations/elements
+    double epsilon; //!< the desired accuracy
+};
+
+
+//! @} core_basic
+
+///////////////////////// raster image moments //////////////////////////
+
+//! @addtogroup imgproc_shape
+//! @{
+
+/** @brief struct returned by cv::moments
+
+The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as:
+
+\f[\texttt{m} _{ji}= \sum _{x,y}  \left ( \texttt{array} (x,y)  \cdot x^j  \cdot y^i \right )\f]
+
+The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as:
+
+\f[\texttt{mu} _{ji}= \sum _{x,y}  \left ( \texttt{array} (x,y)  \cdot (x -  \bar{x} )^j  \cdot (y -  \bar{y} )^i \right )\f]
+
+where \f$(\bar{x}, \bar{y})\f$ is the mass center:
+
+\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f]
+
+The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as:
+
+\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f]
+
+@note
+\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$
+\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not
+stored.
+
+The moments of a contour are defined in the same way but computed using the Green's formula (see
+<http://en.wikipedia.org/wiki/Green_theorem>). So, due to a limited raster resolution, the moments
+computed for a contour are slightly different from the moments computed for the same rasterized
+contour.
+
+@note
+Since the contour moments are computed using Green formula, you may get seemingly odd results for
+contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours.
+ */
+class CV_EXPORTS_W_MAP Moments
+{
+public:
+    //! the default constructor
+    Moments();
+    //! the full constructor
+    Moments(double m00, double m10, double m01, double m20, double m11,
+            double m02, double m30, double m21, double m12, double m03 );
+    ////! the conversion from CvMoments
+    //Moments( const CvMoments& moments );
+    ////! the conversion to CvMoments
+    //operator CvMoments() const;
+
+    //! @name spatial moments
+    //! @{
+    CV_PROP_RW double  m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
+    //! @}
+
+    //! @name central moments
+    //! @{
+    CV_PROP_RW double  mu20, mu11, mu02, mu30, mu21, mu12, mu03;
+    //! @}
+
+    //! @name central normalized moments
+    //! @{
+    CV_PROP_RW double  nu20, nu11, nu02, nu30, nu21, nu12, nu03;
+    //! @}
+};
+
+template<> class DataType<Moments>
+{
+public:
+    typedef Moments     value_type;
+    typedef double      work_type;
+    typedef double      channel_type;
+
+    enum { generic_type = 0,
+           channels     = (int)(sizeof(value_type)/sizeof(channel_type)), // 24
+           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8)
+#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
+           ,depth        = DataType<channel_type>::depth
+           ,type         = CV_MAKETYPE(depth, channels)
+#endif
+         };
+
+    typedef Vec<channel_type, channels> vec_type;
+};
+
+namespace traits {
+template<>
+struct Depth< Moments > { enum { value = Depth<double>::value }; };
+template<>
+struct Type< Moments > { enum { value = CV_MAKETYPE(Depth<double>::value, (int)(sizeof(Moments)/sizeof(double))) }; };
+} // namespace
+
+//! @} imgproc_shape
+
+//! @cond IGNORED
+
+/////////////////////////////////////////////////////////////////////////
+///////////////////////////// Implementation ////////////////////////////
+/////////////////////////////////////////////////////////////////////////
+
+//////////////////////////////// Complex ////////////////////////////////
+
+template<typename _Tp> inline
+Complex<_Tp>::Complex()
+    : re(0), im(0) {}
+
+template<typename _Tp> inline
+Complex<_Tp>::Complex( _Tp _re, _Tp _im )
+    : re(_re), im(_im) {}
+
+template<typename _Tp> template<typename T2> inline
+Complex<_Tp>::operator Complex<T2>() const
+{
+    return Complex<T2>(saturate_cast<T2>(re), saturate_cast<T2>(im));
+}
+
+template<typename _Tp> inline
+Complex<_Tp> Complex<_Tp>::conj() const
+{
+    return Complex<_Tp>(re, -im);
+}
+
+
+template<typename _Tp> static inline
+bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    return a.re == b.re && a.im == b.im;
+}
+
+template<typename _Tp> static inline
+bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    return a.re != b.re || a.im != b.im;
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    return Complex<_Tp>( a.re + b.re, a.im + b.im );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    a.re += b.re; a.im += b.im;
+    return a;
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    return Complex<_Tp>( a.re - b.re, a.im - b.im );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    a.re -= b.re; a.im -= b.im;
+    return a;
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator - (const Complex<_Tp>& a)
+{
+    return Complex<_Tp>(-a.re, -a.im);
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b)
+{
+    return Complex<_Tp>( a.re*b, a.im*b );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a)
+{
+    return Complex<_Tp>( a.re*b, a.im*b );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b)
+{
+    return Complex<_Tp>( a.re + b, a.im );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b)
+{ return Complex<_Tp>( a.re - b, a.im ); }
+
+template<typename _Tp> static inline
+Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a)
+{
+    return Complex<_Tp>( a.re + b, a.im );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a)
+{
+    return Complex<_Tp>( b - a.re, -a.im );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b)
+{
+    a.re += b; return a;
+}
+
+template<typename _Tp> static inline
+Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b)
+{
+    a.re -= b; return a;
+}
+
+template<typename _Tp> static inline
+Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b)
+{
+    a.re *= b; a.im *= b; return a;
+}
+
+template<typename _Tp> static inline
+double abs(const Complex<_Tp>& a)
+{
+    return std::sqrt( (double)a.re*a.re + (double)a.im*a.im);
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    double t = 1./((double)b.re*b.re + (double)b.im*b.im);
+    return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t),
+                        (_Tp)((-a.re*b.im + a.im*b.re)*t) );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b)
+{
+    a = a / b;
+    return a;
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b)
+{
+    _Tp t = (_Tp)1/b;
+    return Complex<_Tp>( a.re*t, a.im*t );
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a)
+{
+    return Complex<_Tp>(b)/a;
+}
+
+template<typename _Tp> static inline
+Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b)
+{
+    _Tp t = (_Tp)1/b;
+    a.re *= t; a.im *= t; return a;
+}
+
+
+
+//////////////////////////////// 2D Point ///////////////////////////////
+
+template<typename _Tp> inline
+Point_<_Tp>::Point_()
+    : x(0), y(0) {}
+
+template<typename _Tp> inline
+Point_<_Tp>::Point_(_Tp _x, _Tp _y)
+    : x(_x), y(_y) {}
+
+#if (defined(__GNUC__) && __GNUC__ < 5) && !defined(__clang__)  // GCC 4.x bug. Details: https://github.com/opencv/opencv/pull/20837
+template<typename _Tp> inline
+Point_<_Tp>::Point_(const Point_& pt)
+    : x(pt.x), y(pt.y) {}
+#endif
+
+template<typename _Tp> inline
+Point_<_Tp>::Point_(const Size_<_Tp>& sz)
+    : x(sz.width), y(sz.height) {}
+
+template<typename _Tp> inline
+Point_<_Tp>::Point_(const Vec<_Tp,2>& v)
+    : x(v[0]), y(v[1]) {}
+
+#if (defined(__GNUC__) && __GNUC__ < 5) && !defined(__clang__)  // GCC 4.x bug. Details: https://github.com/opencv/opencv/pull/20837
+template<typename _Tp> inline
+Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt)
+{
+    x = pt.x; y = pt.y;
+    return *this;
+}
+#endif
+
+template<typename _Tp> template<typename _Tp2> inline
+Point_<_Tp>::operator Point_<_Tp2>() const
+{
+    return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y));
+}
+
+template<typename _Tp> inline
+Point_<_Tp>::operator Vec<_Tp, 2>() const
+{
+    return Vec<_Tp, 2>(x, y);
+}
+
+template<typename _Tp> inline
+_Tp Point_<_Tp>::dot(const Point_& pt) const
+{
+    return saturate_cast<_Tp>(x*pt.x + y*pt.y);
+}
+
+template<typename _Tp> inline
+double Point_<_Tp>::ddot(const Point_& pt) const
+{
+    return (double)x*(double)(pt.x) + (double)y*(double)(pt.y);
+}
+
+template<typename _Tp> inline
+double Point_<_Tp>::cross(const Point_& pt) const
+{
+    return (double)x*pt.y - (double)y*pt.x;
+}
+
+template<typename _Tp> inline bool
+Point_<_Tp>::inside( const Rect_<_Tp>& r ) const
+{
+    return r.contains(*this);
+}
+
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b)
+{
+    a.x += b.x;
+    a.y += b.y;
+    return a;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b)
+{
+    a.x -= b.x;
+    a.y -= b.y;
+    return a;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator *= (Point_<_Tp>& a, int b)
+{
+    a.x = saturate_cast<_Tp>(a.x * b);
+    a.y = saturate_cast<_Tp>(a.y * b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator *= (Point_<_Tp>& a, float b)
+{
+    a.x = saturate_cast<_Tp>(a.x * b);
+    a.y = saturate_cast<_Tp>(a.y * b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator *= (Point_<_Tp>& a, double b)
+{
+    a.x = saturate_cast<_Tp>(a.x * b);
+    a.y = saturate_cast<_Tp>(a.y * b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator /= (Point_<_Tp>& a, int b)
+{
+    a.x = saturate_cast<_Tp>(a.x / b);
+    a.y = saturate_cast<_Tp>(a.y / b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator /= (Point_<_Tp>& a, float b)
+{
+    a.x = saturate_cast<_Tp>(a.x / b);
+    a.y = saturate_cast<_Tp>(a.y / b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp>& operator /= (Point_<_Tp>& a, double b)
+{
+    a.x = saturate_cast<_Tp>(a.x / b);
+    a.y = saturate_cast<_Tp>(a.y / b);
+    return a;
+}
+
+template<typename _Tp> static inline
+double norm(const Point_<_Tp>& pt)
+{
+    return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y);
+}
+
+template<typename _Tp> static inline
+bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b)
+{
+    return a.x == b.x && a.y == b.y;
+}
+
+template<typename _Tp> static inline
+bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b)
+{
+    return a.x != b.x || a.y != b.y;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator - (const Point_<_Tp>& a)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator * (const Point_<_Tp>& a, int b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator * (int a, const Point_<_Tp>& b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator * (const Point_<_Tp>& a, float b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator * (float a, const Point_<_Tp>& b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator * (const Point_<_Tp>& a, double b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator * (double a, const Point_<_Tp>& b)
+{
+    return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) );
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b)
+{
+    Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y);
+    return Point_<_Tp>(tmp.val[0], tmp.val[1]);
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b)
+{
+    Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1);
+    return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]);
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator / (const Point_<_Tp>& a, int b)
+{
+    Point_<_Tp> tmp(a);
+    tmp /= b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator / (const Point_<_Tp>& a, float b)
+{
+    Point_<_Tp> tmp(a);
+    tmp /= b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+Point_<_Tp> operator / (const Point_<_Tp>& a, double b)
+{
+    Point_<_Tp> tmp(a);
+    tmp /= b;
+    return tmp;
+}
+
+
+template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<int>& pt);
+template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<int64>& pt);
+template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<float>& pt);
+template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<double>& pt);
+
+template<> inline int normL2Sqr<int>(const Point_<int>& pt) { return pt.dot(pt); }
+template<> inline int64 normL2Sqr<int64>(const Point_<int64>& pt) { return pt.dot(pt); }
+template<> inline float normL2Sqr<float>(const Point_<float>& pt) { return pt.dot(pt); }
+template<> inline double normL2Sqr<double>(const Point_<int>& pt) { return pt.dot(pt); }
+
+template<> inline double normL2Sqr<double>(const Point_<float>& pt) { return pt.ddot(pt); }
+template<> inline double normL2Sqr<double>(const Point_<double>& pt) { return pt.ddot(pt); }
+
+
+
+//////////////////////////////// 3D Point ///////////////////////////////
+
+template<typename _Tp> inline
+Point3_<_Tp>::Point3_()
+    : x(0), y(0), z(0) {}
+
+template<typename _Tp> inline
+Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z)
+    : x(_x), y(_y), z(_z) {}
+
+template<typename _Tp> inline
+Point3_<_Tp>::Point3_(const Point_<_Tp>& pt)
+    : x(pt.x), y(pt.y), z(_Tp()) {}
+
+template<typename _Tp> inline
+Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v)
+    : x(v[0]), y(v[1]), z(v[2]) {}
+
+template<typename _Tp> template<typename _Tp2> inline
+Point3_<_Tp>::operator Point3_<_Tp2>() const
+{
+    return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z));
+}
+
+template<typename _Tp> inline
+Point3_<_Tp>::operator Vec<_Tp, 3>() const
+{
+    return Vec<_Tp, 3>(x, y, z);
+}
+
+template<typename _Tp> inline
+_Tp Point3_<_Tp>::dot(const Point3_& pt) const
+{
+    return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z);
+}
+
+template<typename _Tp> inline
+double Point3_<_Tp>::ddot(const Point3_& pt) const
+{
+    return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z;
+}
+
+template<typename _Tp> inline
+Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const
+{
+    return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x);
+}
+
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b)
+{
+    a.x += b.x;
+    a.y += b.y;
+    a.z += b.z;
+    return a;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b)
+{
+    a.x -= b.x;
+    a.y -= b.y;
+    a.z -= b.z;
+    return a;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b)
+{
+    a.x = saturate_cast<_Tp>(a.x * b);
+    a.y = saturate_cast<_Tp>(a.y * b);
+    a.z = saturate_cast<_Tp>(a.z * b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b)
+{
+    a.x = saturate_cast<_Tp>(a.x * b);
+    a.y = saturate_cast<_Tp>(a.y * b);
+    a.z = saturate_cast<_Tp>(a.z * b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b)
+{
+    a.x = saturate_cast<_Tp>(a.x * b);
+    a.y = saturate_cast<_Tp>(a.y * b);
+    a.z = saturate_cast<_Tp>(a.z * b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b)
+{
+    a.x = saturate_cast<_Tp>(a.x / b);
+    a.y = saturate_cast<_Tp>(a.y / b);
+    a.z = saturate_cast<_Tp>(a.z / b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b)
+{
+    a.x = saturate_cast<_Tp>(a.x / b);
+    a.y = saturate_cast<_Tp>(a.y / b);
+    a.z = saturate_cast<_Tp>(a.z / b);
+    return a;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b)
+{
+    a.x = saturate_cast<_Tp>(a.x / b);
+    a.y = saturate_cast<_Tp>(a.y / b);
+    a.z = saturate_cast<_Tp>(a.z / b);
+    return a;
+}
+
+template<typename _Tp> static inline
+double norm(const Point3_<_Tp>& pt)
+{
+    return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z);
+}
+
+template<typename _Tp> static inline
+bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
+{
+    return a.x == b.x && a.y == b.y && a.z == b.z;
+}
+
+template<typename _Tp> static inline
+bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
+{
+    return a.x != b.x || a.y != b.y || a.z != b.z;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z));
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z));
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator - (const Point3_<_Tp>& a)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) );
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) );
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) );
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) );
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) );
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) );
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b)
+{
+    return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) );
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b)
+{
+    Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z);
+    return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]);
+}
+
+template<typename _Tp> static inline
+Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b)
+{
+    return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1);
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b)
+{
+    Point3_<_Tp> tmp(a);
+    tmp /= b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b)
+{
+    Point3_<_Tp> tmp(a);
+    tmp /= b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b)
+{
+    Point3_<_Tp> tmp(a);
+    tmp /= b;
+    return tmp;
+}
+
+
+
+////////////////////////////////// Size /////////////////////////////////
+
+template<typename _Tp> inline
+Size_<_Tp>::Size_()
+    : width(0), height(0) {}
+
+template<typename _Tp> inline
+Size_<_Tp>::Size_(_Tp _width, _Tp _height)
+    : width(_width), height(_height) {}
+
+template<typename _Tp> inline
+Size_<_Tp>::Size_(const Point_<_Tp>& pt)
+    : width(pt.x), height(pt.y) {}
+
+template<typename _Tp> template<typename _Tp2> inline
+Size_<_Tp>::operator Size_<_Tp2>() const
+{
+    return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height));
+}
+
+template<typename _Tp> inline
+_Tp Size_<_Tp>::area() const
+{
+    const _Tp result = width * height;
+    CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer
+        || width == 0 || result / width == height); // make sure the result fits in the return value
+    return result;
+}
+
+template<typename _Tp> inline
+double Size_<_Tp>::aspectRatio() const
+{
+    return width / static_cast<double>(height);
+}
+
+template<typename _Tp> inline
+bool Size_<_Tp>::empty() const
+{
+    return width <= 0 || height <= 0;
+}
+
+
+template<typename _Tp> static inline
+Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b)
+{
+    a.width *= b;
+    a.height *= b;
+    return a;
+}
+
+template<typename _Tp> static inline
+Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b)
+{
+    Size_<_Tp> tmp(a);
+    tmp *= b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b)
+{
+    a.width /= b;
+    a.height /= b;
+    return a;
+}
+
+template<typename _Tp> static inline
+Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b)
+{
+    Size_<_Tp> tmp(a);
+    tmp /= b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b)
+{
+    a.width += b.width;
+    a.height += b.height;
+    return a;
+}
+
+template<typename _Tp> static inline
+Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b)
+{
+    Size_<_Tp> tmp(a);
+    tmp += b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b)
+{
+    a.width -= b.width;
+    a.height -= b.height;
+    return a;
+}
+
+template<typename _Tp> static inline
+Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b)
+{
+    Size_<_Tp> tmp(a);
+    tmp -= b;
+    return tmp;
+}
+
+template<typename _Tp> static inline
+bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b)
+{
+    return a.width == b.width && a.height == b.height;
+}
+
+template<typename _Tp> static inline
+bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b)
+{
+    return !(a == b);
+}
+
+
+
+////////////////////////////////// Rect /////////////////////////////////
+
+template<typename _Tp> inline
+Rect_<_Tp>::Rect_()
+    : x(0), y(0), width(0), height(0) {}
+
+template<typename _Tp> inline
+Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height)
+    : x(_x), y(_y), width(_width), height(_height) {}
+
+template<typename _Tp> inline
+Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz)
+    : x(org.x), y(org.y), width(sz.width), height(sz.height) {}
+
+template<typename _Tp> inline
+Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2)
+{
+    x = std::min(pt1.x, pt2.x);
+    y = std::min(pt1.y, pt2.y);
+    width = std::max(pt1.x, pt2.x) - x;
+    height = std::max(pt1.y, pt2.y) - y;
+}
+
+template<typename _Tp> inline
+Point_<_Tp> Rect_<_Tp>::tl() const
+{
+    return Point_<_Tp>(x,y);
+}
+
+template<typename _Tp> inline
+Point_<_Tp> Rect_<_Tp>::br() const
+{
+    return Point_<_Tp>(x + width, y + height);
+}
+
+template<typename _Tp> inline
+Size_<_Tp> Rect_<_Tp>::size() const
+{
+    return Size_<_Tp>(width, height);
+}
+
+template<typename _Tp> inline
+_Tp Rect_<_Tp>::area() const
+{
+    const _Tp result = width * height;
+    CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer
+        || width == 0 || result / width == height); // make sure the result fits in the return value
+    return result;
+}
+
+template<typename _Tp> inline
+bool Rect_<_Tp>::empty() const
+{
+    return width <= 0 || height <= 0;
+}
+
+template<typename _Tp> template<typename _Tp2> inline
+Rect_<_Tp>::operator Rect_<_Tp2>() const
+{
+    return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height));
+}
+
+template<typename _Tp> inline
+bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const
+{
+    return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height;
+}
+
+
+template<typename _Tp> static inline
+Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b )
+{
+    a.x += b.x;
+    a.y += b.y;
+    return a;
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b )
+{
+    a.x -= b.x;
+    a.y -= b.y;
+    return a;
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b )
+{
+    a.width += b.width;
+    a.height += b.height;
+    return a;
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b )
+{
+    const _Tp width = a.width - b.width;
+    const _Tp height = a.height - b.height;
+    CV_DbgAssert(width >= 0 && height >= 0);
+    a.width = width;
+    a.height = height;
+    return a;
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )
+{
+    if (a.empty() || b.empty()) {
+        a = Rect();
+        return a;
+    }
+    const Rect_<_Tp>& Rx_min = (a.x < b.x) ? a : b;
+    const Rect_<_Tp>& Rx_max = (a.x < b.x) ? b : a;
+    const Rect_<_Tp>& Ry_min = (a.y < b.y) ? a : b;
+    const Rect_<_Tp>& Ry_max = (a.y < b.y) ? b : a;
+    // Looking at the formula below, we will compute Rx_min.width - (Rx_max.x - Rx_min.x)
+    // but we want to avoid overflows. Rx_min.width >= 0 and (Rx_max.x - Rx_min.x) >= 0
+    // by definition so the difference does not overflow. The only thing that can overflow
+    // is (Rx_max.x - Rx_min.x). And it can only overflow if Rx_min.x < 0.
+    // Let us first deal with the following case.
+    if ((Rx_min.x < 0 && Rx_min.x + Rx_min.width < Rx_max.x) ||
+        (Ry_min.y < 0 && Ry_min.y + Ry_min.height < Ry_max.y)) {
+        a = Rect();
+        return a;
+    }
+    // We now know that either Rx_min.x >= 0, or
+    // Rx_min.x < 0 && Rx_min.x + Rx_min.width >= Rx_max.x and therefore
+    // Rx_min.width >= (Rx_max.x - Rx_min.x) which means (Rx_max.x - Rx_min.x)
+    // is inferior to a valid int and therefore does not overflow.
+    a.width = std::min(Rx_min.width - (Rx_max.x - Rx_min.x), Rx_max.width);
+    a.height = std::min(Ry_min.height - (Ry_max.y - Ry_min.y), Ry_max.height);
+    a.x = Rx_max.x;
+    a.y = Ry_max.y;
+    if (a.empty())
+        a = Rect();
+    return a;
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )
+{
+    if (a.empty()) {
+        a = b;
+    }
+    else if (!b.empty()) {
+        _Tp x1 = std::min(a.x, b.x);
+        _Tp y1 = std::min(a.y, b.y);
+        a.width = std::max(a.x + a.width, b.x + b.width) - x1;
+        a.height = std::max(a.y + a.height, b.y + b.height) - y1;
+        a.x = x1;
+        a.y = y1;
+    }
+    return a;
+}
+
+template<typename _Tp> static inline
+bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
+{
+    return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height;
+}
+
+template<typename _Tp> static inline
+bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
+{
+    return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height;
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b)
+{
+    return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height );
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b)
+{
+    return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height );
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b)
+{
+    return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height );
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Size_<_Tp>& b)
+{
+    const _Tp width = a.width - b.width;
+    const _Tp height = a.height - b.height;
+    CV_DbgAssert(width >= 0 && height >= 0);
+    return Rect_<_Tp>( a.x, a.y, width, height );
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
+{
+    Rect_<_Tp> c = a;
+    return c &= b;
+}
+
+template<typename _Tp> static inline
+Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
+{
+    Rect_<_Tp> c = a;
+    return c |= b;
+}
+
+/**
+ * @brief measure dissimilarity between two sample sets
+ *
+ * computes the complement of the Jaccard Index as described in <https://en.wikipedia.org/wiki/Jaccard_index>.
+ * For rectangles this reduces to computing the intersection over the union.
+ */
+template<typename _Tp> static inline
+double jaccardDistance(const Rect_<_Tp>& a, const Rect_<_Tp>& b) {
+    _Tp Aa = a.area();
+    _Tp Ab = b.area();
+
+    if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) {
+        // jaccard_index = 1 -> distance = 0
+        return 0.0;
+    }
+
+    double Aab = (a & b).area();
+    // distance = 1 - jaccard_index
+    return 1.0 - Aab / (Aa + Ab - Aab);
+}
+
+////////////////////////////// RotatedRect //////////////////////////////
+
+inline
+RotatedRect::RotatedRect()
+    : center(), size(), angle(0) {}
+
+inline
+RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle)
+    : center(_center), size(_size), angle(_angle) {}
+
+///////////////////////////////// Range /////////////////////////////////
+
+inline
+Range::Range()
+    : start(0), end(0) {}
+
+inline
+Range::Range(int _start, int _end)
+    : start(_start), end(_end) {}
+
+inline
+int Range::size() const
+{
+    return end - start;
+}
+
+inline
+bool Range::empty() const
+{
+    return start == end;
+}
+
+inline
+Range Range::all()
+{
+    return Range(INT_MIN, INT_MAX);
+}
+
+
+static inline
+bool operator == (const Range& r1, const Range& r2)
+{
+    return r1.start == r2.start && r1.end == r2.end;
+}
+
+static inline
+bool operator != (const Range& r1, const Range& r2)
+{
+    return !(r1 == r2);
+}
+
+static inline
+bool operator !(const Range& r)
+{
+    return r.start == r.end;
+}
+
+static inline
+Range operator & (const Range& r1, const Range& r2)
+{
+    Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end));
+    r.end = std::max(r.end, r.start);
+    return r;
+}
+
+static inline
+Range& operator &= (Range& r1, const Range& r2)
+{
+    r1 = r1 & r2;
+    return r1;
+}
+
+static inline
+Range operator + (const Range& r1, int delta)
+{
+    return Range(r1.start + delta, r1.end + delta);
+}
+
+static inline
+Range operator + (int delta, const Range& r1)
+{
+    return Range(r1.start + delta, r1.end + delta);
+}
+
+static inline
+Range operator - (const Range& r1, int delta)
+{
+    return r1 + (-delta);
+}
+
+
+
+///////////////////////////////// Scalar ////////////////////////////////
+
+template<typename _Tp> inline
+Scalar_<_Tp>::Scalar_()
+{
+    this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0;
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
+{
+    this->val[0] = v0;
+    this->val[1] = v1;
+    this->val[2] = v2;
+    this->val[3] = v3;
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp>::Scalar_(const Scalar_<_Tp>& s) : Vec<_Tp, 4>(s) {
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp>::Scalar_(Scalar_<_Tp>&& s) CV_NOEXCEPT {
+    this->val[0] = std::move(s.val[0]);
+    this->val[1] = std::move(s.val[1]);
+    this->val[2] = std::move(s.val[2]);
+    this->val[3] = std::move(s.val[3]);
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp>& Scalar_<_Tp>::operator=(const Scalar_<_Tp>& s) {
+    this->val[0] = s.val[0];
+    this->val[1] = s.val[1];
+    this->val[2] = s.val[2];
+    this->val[3] = s.val[3];
+    return *this;
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp>& Scalar_<_Tp>::operator=(Scalar_<_Tp>&& s) CV_NOEXCEPT {
+    this->val[0] = std::move(s.val[0]);
+    this->val[1] = std::move(s.val[1]);
+    this->val[2] = std::move(s.val[2]);
+    this->val[3] = std::move(s.val[3]);
+    return *this;
+}
+
+template<typename _Tp> template<typename _Tp2, int cn> inline
+Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v)
+{
+    int i;
+    for( i = 0; i < (cn < 4 ? cn : 4); i++ )
+        this->val[i] = cv::saturate_cast<_Tp>(v.val[i]);
+    for( ; i < 4; i++ )
+        this->val[i] = 0;
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp>::Scalar_(_Tp v0)
+{
+    this->val[0] = v0;
+    this->val[1] = this->val[2] = this->val[3] = 0;
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0)
+{
+    return Scalar_<_Tp>(v0, v0, v0, v0);
+}
+
+
+template<typename _Tp> inline
+Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const
+{
+    return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale),
+                        saturate_cast<_Tp>(this->val[1] * a.val[1] * scale),
+                        saturate_cast<_Tp>(this->val[2] * a.val[2] * scale),
+                        saturate_cast<_Tp>(this->val[3] * a.val[3] * scale));
+}
+
+template<typename _Tp> inline
+Scalar_<_Tp> Scalar_<_Tp>::conj() const
+{
+    return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]),
+                        saturate_cast<_Tp>(-this->val[1]),
+                        saturate_cast<_Tp>(-this->val[2]),
+                        saturate_cast<_Tp>(-this->val[3]));
+}
+
+template<typename _Tp> inline
+bool Scalar_<_Tp>::isReal() const
+{
+    return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0;
+}
+
+
+template<typename _Tp> template<typename T2> inline
+Scalar_<_Tp>::operator Scalar_<T2>() const
+{
+    return Scalar_<T2>(saturate_cast<T2>(this->val[0]),
+                       saturate_cast<T2>(this->val[1]),
+                       saturate_cast<T2>(this->val[2]),
+                       saturate_cast<T2>(this->val[3]));
+}
+
+
+template<typename _Tp> static inline
+Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    a.val[0] += b.val[0];
+    a.val[1] += b.val[1];
+    a.val[2] += b.val[2];
+    a.val[3] += b.val[3];
+    return a;
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    a.val[0] -= b.val[0];
+    a.val[1] -= b.val[1];
+    a.val[2] -= b.val[2];
+    a.val[3] -= b.val[3];
+    return a;
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v )
+{
+    a.val[0] *= v;
+    a.val[1] *= v;
+    a.val[2] *= v;
+    a.val[3] *= v;
+    return a;
+}
+
+template<typename _Tp> static inline
+bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b )
+{
+    return a.val[0] == b.val[0] && a.val[1] == b.val[1] &&
+           a.val[2] == b.val[2] && a.val[3] == b.val[3];
+}
+
+template<typename _Tp> static inline
+bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b )
+{
+    return a.val[0] != b.val[0] || a.val[1] != b.val[1] ||
+           a.val[2] != b.val[2] || a.val[3] != b.val[3];
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    return Scalar_<_Tp>(a.val[0] + b.val[0],
+                        a.val[1] + b.val[1],
+                        a.val[2] + b.val[2],
+                        a.val[3] + b.val[3]);
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]),
+                        saturate_cast<_Tp>(a.val[1] - b.val[1]),
+                        saturate_cast<_Tp>(a.val[2] - b.val[2]),
+                        saturate_cast<_Tp>(a.val[3] - b.val[3]));
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha)
+{
+    return Scalar_<_Tp>(a.val[0] * alpha,
+                        a.val[1] * alpha,
+                        a.val[2] * alpha,
+                        a.val[3] * alpha);
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a)
+{
+    return a*alpha;
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator - (const Scalar_<_Tp>& a)
+{
+    return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]),
+                        saturate_cast<_Tp>(-a.val[1]),
+                        saturate_cast<_Tp>(-a.val[2]),
+                        saturate_cast<_Tp>(-a.val[3]));
+}
+
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]),
+                        saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]),
+                        saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]),
+                        saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0]));
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    a = a * b;
+    return a;
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha)
+{
+    return Scalar_<_Tp>(a.val[0] / alpha,
+                        a.val[1] / alpha,
+                        a.val[2] / alpha,
+                        a.val[3] / alpha);
+}
+
+template<typename _Tp> static inline
+Scalar_<float> operator / (const Scalar_<float>& a, float alpha)
+{
+    float s = 1 / alpha;
+    return Scalar_<float>(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s);
+}
+
+template<typename _Tp> static inline
+Scalar_<double> operator / (const Scalar_<double>& a, double alpha)
+{
+    double s = 1 / alpha;
+    return Scalar_<double>(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s);
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha)
+{
+    a = a / alpha;
+    return a;
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b)
+{
+    _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]);
+    return b.conj() * s;
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    return a * ((_Tp)1 / b);
+}
+
+template<typename _Tp> static inline
+Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
+{
+    a = a / b;
+    return a;
+}
+
+template<typename _Tp> static inline
+Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b)
+{
+    Matx<double, 4, 1> c((Matx<double, 4, 4>)a, b, Matx_MatMulOp());
+    return reinterpret_cast<const Scalar&>(c);
+}
+
+template<> inline
+Scalar operator * (const Matx<double, 4, 4>& a, const Scalar& b)
+{
+    Matx<double, 4, 1> c(a, b, Matx_MatMulOp());
+    return reinterpret_cast<const Scalar&>(c);
+}
+
+
+
+//////////////////////////////// KeyPoint ///////////////////////////////
+
+inline
+KeyPoint::KeyPoint()
+    : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {}
+
+inline
+KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id)
+    : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {}
+
+inline
+KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id)
+    : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {}
+
+
+
+///////////////////////////////// DMatch ////////////////////////////////
+
+inline
+DMatch::DMatch()
+    : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {}
+
+inline
+DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance)
+    : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {}
+
+inline
+DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance)
+    : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {}
+
+inline
+bool DMatch::operator < (const DMatch &m) const
+{
+    return distance < m.distance;
+}
+
+
+
+////////////////////////////// TermCriteria /////////////////////////////
+
+inline
+TermCriteria::TermCriteria()
+    : type(0), maxCount(0), epsilon(0) {}
+
+inline
+TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon)
+    : type(_type), maxCount(_maxCount), epsilon(_epsilon) {}
+
+//! @endcond
+
+} // cv
+
+#endif //OPENCV_CORE_TYPES_HPP
diff --git a/3rdParty/include/opencv2/core/types_c.h b/3rdParty/include/opencv2/core/types_c.h
new file mode 100644
index 0000000..32f3c8c
--- /dev/null
+++ b/3rdParty/include/opencv2/core/types_c.h
@@ -0,0 +1,2126 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_TYPES_H
+#define OPENCV_CORE_TYPES_H
+
+#ifdef CV__ENABLE_C_API_CTORS  // invalid C API ctors (must be removed)
+#if defined(_WIN32) && !defined(CV__SKIP_MESSAGE_MALFORMED_C_API_CTORS)
+#error "C API ctors don't work on Win32: https://github.com/opencv/opencv/issues/15990"
+#endif
+#endif
+
+//#define CV__VALIDATE_UNUNITIALIZED_VARS 1  // C++11 & GCC only
+
+#ifdef __cplusplus
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+#pragma GCC diagnostic ignored "-Wmissing-field-initializers"
+#define CV_STRUCT_INITIALIZER {0,}
+#else
+#if defined(__GNUC__) && __GNUC__ == 4  // GCC 4.x warns on "= {}" initialization, fixed in GCC 5.0
+#pragma GCC diagnostic ignored "-Wmissing-field-initializers"
+#endif
+#define CV_STRUCT_INITIALIZER {}
+#endif
+
+#else
+#define CV_STRUCT_INITIALIZER {0}
+#endif
+
+
+#ifdef HAVE_IPL
+#  ifndef __IPL_H__
+#    if defined _WIN32
+#      include <ipl.h>
+#    else
+#      include <ipl/ipl.h>
+#    endif
+#  endif
+#elif defined __IPL_H__
+#  define HAVE_IPL
+#endif
+
+#include "opencv2/core/cvdef.h"
+
+#ifndef SKIP_INCLUDES
+#include <assert.h>
+#include <stdlib.h>
+#include <string.h>
+#include <float.h>
+#endif // SKIP_INCLUDES
+
+#if defined _WIN32
+#  define CV_CDECL __cdecl
+#  define CV_STDCALL __stdcall
+#else
+#  define CV_CDECL
+#  define CV_STDCALL
+#endif
+
+#ifndef CV_DEFAULT
+#  ifdef __cplusplus
+#    define CV_DEFAULT(val) = val
+#  else
+#    define CV_DEFAULT(val)
+#  endif
+#endif
+
+#ifndef CV_EXTERN_C_FUNCPTR
+#  ifdef __cplusplus
+#    define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; }
+#  else
+#    define CV_EXTERN_C_FUNCPTR(x) typedef x
+#  endif
+#endif
+
+#ifndef CVAPI
+#  define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL
+#endif
+
+#ifndef CV_IMPL
+#  define CV_IMPL CV_EXTERN_C
+#endif
+
+#ifdef __cplusplus
+#  include "opencv2/core.hpp"
+#endif
+
+/** @addtogroup core_c
+    @{
+*/
+
+/** @brief This is the "metatype" used *only* as a function parameter.
+
+It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even
+CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4
+bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray.
+ */
+typedef void CvArr;
+
+typedef int CVStatus;
+
+/** @see cv::Error::Code */
+enum {
+ CV_StsOk=                       0,  /**< everything is ok                */
+ CV_StsBackTrace=               -1,  /**< pseudo error for back trace     */
+ CV_StsError=                   -2,  /**< unknown /unspecified error      */
+ CV_StsInternal=                -3,  /**< internal error (bad state)      */
+ CV_StsNoMem=                   -4,  /**< insufficient memory             */
+ CV_StsBadArg=                  -5,  /**< function arg/param is bad       */
+ CV_StsBadFunc=                 -6,  /**< unsupported function            */
+ CV_StsNoConv=                  -7,  /**< iter. didn't converge           */
+ CV_StsAutoTrace=               -8,  /**< tracing                         */
+ CV_HeaderIsNull=               -9,  /**< image header is NULL            */
+ CV_BadImageSize=              -10,  /**< image size is invalid           */
+ CV_BadOffset=                 -11,  /**< offset is invalid               */
+ CV_BadDataPtr=                -12,  /**/
+ CV_BadStep=                   -13,  /**< image step is wrong, this may happen for a non-continuous matrix */
+ CV_BadModelOrChSeq=           -14,  /**/
+ CV_BadNumChannels=            -15,  /**< bad number of channels, for example, some functions accept only single channel matrices */
+ CV_BadNumChannel1U=           -16,  /**/
+ CV_BadDepth=                  -17,  /**< input image depth is not supported by the function */
+ CV_BadAlphaChannel=           -18,  /**/
+ CV_BadOrder=                  -19,  /**< number of dimensions is out of range */
+ CV_BadOrigin=                 -20,  /**< incorrect input origin               */
+ CV_BadAlign=                  -21,  /**< incorrect input align                */
+ CV_BadCallBack=               -22,  /**/
+ CV_BadTileSize=               -23,  /**/
+ CV_BadCOI=                    -24,  /**< input COI is not supported           */
+ CV_BadROISize=                -25,  /**< incorrect input roi                  */
+ CV_MaskIsTiled=               -26,  /**/
+ CV_StsNullPtr=                -27,  /**< null pointer */
+ CV_StsVecLengthErr=           -28,  /**< incorrect vector length */
+ CV_StsFilterStructContentErr= -29,  /**< incorrect filter structure content */
+ CV_StsKernelStructContentErr= -30,  /**< incorrect transform kernel content */
+ CV_StsFilterOffsetErr=        -31,  /**< incorrect filter offset value */
+ CV_StsBadSize=                -201, /**< the input/output structure size is incorrect  */
+ CV_StsDivByZero=              -202, /**< division by zero */
+ CV_StsInplaceNotSupported=    -203, /**< in-place operation is not supported */
+ CV_StsObjectNotFound=         -204, /**< request can't be completed */
+ CV_StsUnmatchedFormats=       -205, /**< formats of input/output arrays differ */
+ CV_StsBadFlag=                -206, /**< flag is wrong or not supported */
+ CV_StsBadPoint=               -207, /**< bad CvPoint */
+ CV_StsBadMask=                -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/
+ CV_StsUnmatchedSizes=         -209, /**< sizes of input/output structures do not match */
+ CV_StsUnsupportedFormat=      -210, /**< the data format/type is not supported by the function*/
+ CV_StsOutOfRange=             -211, /**< some of parameters are out of range */
+ CV_StsParseError=             -212, /**< invalid syntax/structure of the parsed file */
+ CV_StsNotImplemented=         -213, /**< the requested function/feature is not implemented */
+ CV_StsBadMemBlock=            -214, /**< an allocated block has been corrupted */
+ CV_StsAssert=                 -215, /**< assertion failed   */
+ CV_GpuNotSupported=           -216, /**< no CUDA support    */
+ CV_GpuApiCallError=           -217, /**< GPU API call error */
+ CV_OpenGlNotSupported=        -218, /**< no OpenGL support  */
+ CV_OpenGlApiCallError=        -219, /**< OpenGL API call error */
+ CV_OpenCLApiCallError=        -220, /**< OpenCL API call error */
+ CV_OpenCLDoubleNotSupported=  -221,
+ CV_OpenCLInitError=           -222, /**< OpenCL initialization error */
+ CV_OpenCLNoAMDBlasFft=        -223
+};
+
+/****************************************************************************************\
+*                             Common macros and inline functions                         *
+\****************************************************************************************/
+
+#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t))
+
+/** min & max without jumps */
+#define  CV_IMIN(a, b)  ((a) ^ (((a)^(b)) & (((a) < (b)) - 1)))
+
+#define  CV_IMAX(a, b)  ((a) ^ (((a)^(b)) & (((a) > (b)) - 1)))
+
+/** absolute value without jumps */
+#ifndef __cplusplus
+#  define  CV_IABS(a)     (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0))
+#else
+#  define  CV_IABS(a)     abs(a)
+#endif
+#define  CV_CMP(a,b)    (((a) > (b)) - ((a) < (b)))
+#define  CV_SIGN(a)     CV_CMP((a),0)
+
+#define cvInvSqrt(value) ((float)(1./sqrt(value)))
+#define cvSqrt(value)  ((float)sqrt(value))
+
+
+/*************** Random number generation *******************/
+
+typedef uint64 CvRNG;
+
+#define CV_RNG_COEFF 4164903690U
+
+/** @brief Initializes a random number generator state.
+
+The function initializes a random number generator and returns the state. The pointer to the state
+can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current
+implementation a multiply-with-carry generator is used.
+@param seed 64-bit value used to initiate a random sequence
+@sa the C++ class RNG replaced CvRNG.
+ */
+CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1))
+{
+    CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1;
+    return rng;
+}
+
+/** @brief Returns a 32-bit unsigned integer and updates RNG.
+
+The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG
+state. It is similar to the rand() function from the C runtime library, except that OpenCV functions
+always generates a 32-bit random number, regardless of the platform.
+@param rng CvRNG state initialized by cvRNG.
+ */
+CV_INLINE unsigned cvRandInt( CvRNG* rng )
+{
+    uint64 temp = *rng;
+    temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32);
+    *rng = temp;
+    return (unsigned)temp;
+}
+
+/** @brief Returns a floating-point random number and updates RNG.
+
+The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not
+included).
+@param rng RNG state initialized by cvRNG
+ */
+CV_INLINE double cvRandReal( CvRNG* rng )
+{
+    return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */;
+}
+
+/****************************************************************************************\
+*                                  Image type (IplImage)                                 *
+\****************************************************************************************/
+
+#ifndef HAVE_IPL
+
+/*
+ * The following definitions (until #endif)
+ * is an extract from IPL headers.
+ * Copyright (c) 1995 Intel Corporation.
+ */
+#define IPL_DEPTH_SIGN 0x80000000
+
+#define IPL_DEPTH_1U     1
+#define IPL_DEPTH_8U     8
+#define IPL_DEPTH_16U   16
+#define IPL_DEPTH_32F   32
+
+#define IPL_DEPTH_8S  (IPL_DEPTH_SIGN| 8)
+#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16)
+#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32)
+
+#define IPL_DATA_ORDER_PIXEL  0
+#define IPL_DATA_ORDER_PLANE  1
+
+#define IPL_ORIGIN_TL 0
+#define IPL_ORIGIN_BL 1
+
+#define IPL_ALIGN_4BYTES   4
+#define IPL_ALIGN_8BYTES   8
+#define IPL_ALIGN_16BYTES 16
+#define IPL_ALIGN_32BYTES 32
+
+#define IPL_ALIGN_DWORD   IPL_ALIGN_4BYTES
+#define IPL_ALIGN_QWORD   IPL_ALIGN_8BYTES
+
+#define IPL_BORDER_CONSTANT   0
+#define IPL_BORDER_REPLICATE  1
+#define IPL_BORDER_REFLECT    2
+#define IPL_BORDER_WRAP       3
+
+#ifdef __cplusplus
+typedef struct _IplImage IplImage;
+CV_EXPORTS _IplImage cvIplImage(const cv::Mat& m);
+#endif
+
+/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV
+only supports a subset of possible IplImage formats, as outlined in the parameter list above.
+
+In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require
+that the image size or ROI size of all source and destination images match exactly. On the other
+hand, the Intel Image Processing Library processes the area of intersection between the source and
+destination images (or ROIs), allowing them to vary independently.
+*/
+typedef struct
+_IplImage
+{
+    int  nSize;             /**< sizeof(IplImage) */
+    int  ID;                /**< version (=0)*/
+    int  nChannels;         /**< Most of OpenCV functions support 1,2,3 or 4 channels */
+    int  alphaChannel;      /**< Ignored by OpenCV */
+    int  depth;             /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S,
+                               IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported.  */
+    char colorModel[4];     /**< Ignored by OpenCV */
+    char channelSeq[4];     /**< ditto */
+    int  dataOrder;         /**< 0 - interleaved color channels, 1 - separate color channels.
+                               cvCreateImage can only create interleaved images */
+    int  origin;            /**< 0 - top-left origin,
+                               1 - bottom-left origin (Windows bitmaps style).  */
+    int  align;             /**< Alignment of image rows (4 or 8).
+                               OpenCV ignores it and uses widthStep instead.    */
+    int  width;             /**< Image width in pixels.                           */
+    int  height;            /**< Image height in pixels.                          */
+    struct _IplROI *roi;    /**< Image ROI. If NULL, the whole image is selected. */
+    struct _IplImage *maskROI;      /**< Must be NULL. */
+    void  *imageId;                 /**< "           " */
+    struct _IplTileInfo *tileInfo;  /**< "           " */
+    int  imageSize;         /**< Image data size in bytes
+                               (==image->height*image->widthStep
+                               in case of interleaved data)*/
+    char *imageData;        /**< Pointer to aligned image data.         */
+    int  widthStep;         /**< Size of aligned image row in bytes.    */
+    int  BorderMode[4];     /**< Ignored by OpenCV.                     */
+    int  BorderConst[4];    /**< Ditto.                                 */
+    char *imageDataOrigin;  /**< Pointer to very origin of image data
+                               (not necessarily aligned) -
+                               needed for correct deallocation */
+
+#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    _IplImage()
+    {
+        memset(this, 0, sizeof(*this));  // valid for POD structure
+        nSize = sizeof(IplImage);
+    }
+    _IplImage(const cv::Mat& m) { *this = cvIplImage(m); }
+#endif
+}
+IplImage;
+
+CV_INLINE IplImage cvIplImage()
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    IplImage self = CV_STRUCT_INITIALIZER; self.nSize = sizeof(IplImage); return self;
+#else
+    return _IplImage();
+#endif
+}
+
+typedef struct _IplTileInfo IplTileInfo;
+
+typedef struct _IplROI
+{
+    int  coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/
+    int  xOffset;
+    int  yOffset;
+    int  width;
+    int  height;
+}
+IplROI;
+
+typedef struct _IplConvKernel
+{
+    int  nCols;
+    int  nRows;
+    int  anchorX;
+    int  anchorY;
+    int *values;
+    int  nShiftR;
+}
+IplConvKernel;
+
+typedef struct _IplConvKernelFP
+{
+    int  nCols;
+    int  nRows;
+    int  anchorX;
+    int  anchorY;
+    float *values;
+}
+IplConvKernelFP;
+
+#define IPL_IMAGE_HEADER 1
+#define IPL_IMAGE_DATA   2
+#define IPL_IMAGE_ROI    4
+
+#endif/*HAVE_IPL*/
+
+/** extra border mode */
+#define IPL_BORDER_REFLECT_101    4
+#define IPL_BORDER_TRANSPARENT    5
+
+#define IPL_IMAGE_MAGIC_VAL  ((int)sizeof(IplImage))
+#define CV_TYPE_NAME_IMAGE "opencv-image"
+
+#define CV_IS_IMAGE_HDR(img) \
+    ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage))
+
+#define CV_IS_IMAGE(img) \
+    (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL)
+
+/** for storing double-precision
+   floating point data in IplImage's */
+#define IPL_DEPTH_64F  64
+
+/** get reference to pixel at (col,row),
+   for multi-channel images (col) should be multiplied by number of channels */
+#define CV_IMAGE_ELEM( image, elemtype, row, col )       \
+    (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)])
+
+/****************************************************************************************\
+*                                  Matrix type (CvMat)                                   *
+\****************************************************************************************/
+
+#define CV_AUTO_STEP  0x7fffffff
+#define CV_WHOLE_ARR  cvSlice( 0, 0x3fffffff )
+
+#define CV_MAGIC_MASK       0xFFFF0000
+#define CV_MAT_MAGIC_VAL    0x42420000
+#define CV_TYPE_NAME_MAT    "opencv-matrix"
+
+#ifdef __cplusplus
+typedef struct CvMat CvMat;
+CV_INLINE CvMat cvMat(const cv::Mat& m);
+#endif
+
+/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column
+index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro:
+
+    uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j)
+    CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f;
+
+To access multiple-channel matrices, you can use
+CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx).
+
+@deprecated CvMat is now obsolete; consider using Mat instead.
+ */
+typedef struct CvMat
+{
+    int type;
+    int step;
+
+    /* for internal use only */
+    int* refcount;
+    int hdr_refcount;
+
+    union
+    {
+        uchar* ptr;
+        short* s;
+        int* i;
+        float* fl;
+        double* db;
+    } data;
+
+#ifdef __cplusplus
+    union
+    {
+        int rows;
+        int height;
+    };
+
+    union
+    {
+        int cols;
+        int width;
+    };
+#else
+    int rows;
+    int cols;
+#endif
+
+#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvMat() {}
+    CvMat(const cv::Mat& m) { *this = cvMat(m); }
+#endif
+}
+CvMat;
+
+
+#define CV_IS_MAT_HDR(mat) \
+    ((mat) != NULL && \
+    (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \
+    ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0)
+
+#define CV_IS_MAT_HDR_Z(mat) \
+    ((mat) != NULL && \
+    (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \
+    ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0)
+
+#define CV_IS_MAT(mat) \
+    (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL)
+
+#define CV_IS_MASK_ARR(mat) \
+    (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0)
+
+#define CV_ARE_TYPES_EQ(mat1, mat2) \
+    ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0)
+
+#define CV_ARE_CNS_EQ(mat1, mat2) \
+    ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0)
+
+#define CV_ARE_DEPTHS_EQ(mat1, mat2) \
+    ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0)
+
+#define CV_ARE_SIZES_EQ(mat1, mat2) \
+    ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols)
+
+#define CV_IS_MAT_CONST(mat)  \
+    (((mat)->rows|(mat)->cols) == 1)
+
+#define IPL2CV_DEPTH(depth) \
+    ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \
+    (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \
+    (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15)
+
+/** Inline constructor. No data is allocated internally!!!
+ * (Use together with cvCreateData, or use cvCreateMat instead to
+ * get a matrix with allocated data):
+ */
+CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL))
+{
+    CvMat m;
+
+    assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F );
+    type = CV_MAT_TYPE(type);
+    m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type;
+    m.cols = cols;
+    m.rows = rows;
+    m.step = m.cols*CV_ELEM_SIZE(type);
+    m.data.ptr = (uchar*)data;
+    m.refcount = NULL;
+    m.hdr_refcount = 0;
+
+    return m;
+}
+
+#ifdef __cplusplus
+
+CV_INLINE CvMat cvMat(const cv::Mat& m)
+{
+    CvMat self;
+    CV_DbgAssert(m.dims <= 2);
+    self = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data);
+    self.step = (int)m.step[0];
+    self.type = (self.type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG);
+    return self;
+}
+CV_INLINE CvMat cvMat()
+{
+#if !defined(CV__ENABLE_C_API_CTORS)
+    CvMat self = CV_STRUCT_INITIALIZER; return self;
+#else
+    return CvMat();
+#endif
+}
+CV_INLINE CvMat cvMat(const CvMat& m)
+{
+#if !defined(CV__ENABLE_C_API_CTORS)
+    CvMat self = CV_STRUCT_INITIALIZER; memcpy(&self, &m, sizeof(self)); return self;
+#else
+    return CvMat(m);
+#endif
+}
+
+#endif // __cplusplus
+
+
+#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size )  \
+    (assert( (unsigned)(row) < (unsigned)(mat).rows &&   \
+             (unsigned)(col) < (unsigned)(mat).cols ),   \
+     (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col))
+
+#define CV_MAT_ELEM_PTR( mat, row, col )                 \
+    CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) )
+
+#define CV_MAT_ELEM( mat, elemtype, row, col )           \
+    (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype)))
+
+/** @brief Returns the particular element of single-channel floating-point matrix.
+
+The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point
+matrices. It is faster because it is inline, it does fewer checks for array type and array element
+type, and it checks for the row and column ranges only in debug mode.
+@param mat Input matrix
+@param row The zero-based index of row
+@param col The zero-based index of column
+ */
+CV_INLINE  double  cvmGet( const CvMat* mat, int row, int col )
+{
+    int type;
+
+    type = CV_MAT_TYPE(mat->type);
+    assert( (unsigned)row < (unsigned)mat->rows &&
+            (unsigned)col < (unsigned)mat->cols );
+
+    if( type == CV_32FC1 )
+        return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col];
+    else
+    {
+        assert( type == CV_64FC1 );
+        return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col];
+    }
+}
+
+/** @brief Sets a specific element of a single-channel floating-point matrix.
+
+The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point
+matrices. It is faster because it is inline, it does fewer checks for array type and array element
+type, and it checks for the row and column ranges only in debug mode.
+@param mat The matrix
+@param row The zero-based index of row
+@param col The zero-based index of column
+@param value The new value of the matrix element
+ */
+CV_INLINE  void  cvmSet( CvMat* mat, int row, int col, double value )
+{
+    int type;
+    type = CV_MAT_TYPE(mat->type);
+    assert( (unsigned)row < (unsigned)mat->rows &&
+            (unsigned)col < (unsigned)mat->cols );
+
+    if( type == CV_32FC1 )
+        ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value;
+    else
+    {
+        assert( type == CV_64FC1 );
+        ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value;
+    }
+}
+
+
+CV_INLINE int cvIplDepth( int type )
+{
+    int depth = CV_MAT_DEPTH(type);
+    return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S ||
+           depth == CV_32S ? IPL_DEPTH_SIGN : 0);
+}
+
+
+/****************************************************************************************\
+*                       Multi-dimensional dense array (CvMatND)                          *
+\****************************************************************************************/
+
+#define CV_MATND_MAGIC_VAL    0x42430000
+#define CV_TYPE_NAME_MATND    "opencv-nd-matrix"
+
+#define CV_MAX_DIM            32
+
+#ifdef __cplusplus
+typedef struct CvMatND CvMatND;
+CV_EXPORTS CvMatND cvMatND(const cv::Mat& m);
+#endif
+
+/**
+  @deprecated consider using cv::Mat instead
+  */
+typedef struct
+CvMatND
+{
+    int type;
+    int dims;
+
+    int* refcount;
+    int hdr_refcount;
+
+    union
+    {
+        uchar* ptr;
+        float* fl;
+        double* db;
+        int* i;
+        short* s;
+    } data;
+
+    struct
+    {
+        int size;
+        int step;
+    }
+    dim[CV_MAX_DIM];
+
+#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvMatND() {}
+    CvMatND(const cv::Mat& m) { *this = cvMatND(m); }
+#endif
+}
+CvMatND;
+
+
+CV_INLINE CvMatND cvMatND()
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvMatND self = CV_STRUCT_INITIALIZER; return self;
+#else
+    return CvMatND();
+#endif
+}
+
+#define CV_IS_MATND_HDR(mat) \
+    ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL)
+
+#define CV_IS_MATND(mat) \
+    (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL)
+
+
+/****************************************************************************************\
+*                      Multi-dimensional sparse array (CvSparseMat)                      *
+\****************************************************************************************/
+
+#define CV_SPARSE_MAT_MAGIC_VAL    0x42440000
+#define CV_TYPE_NAME_SPARSE_MAT    "opencv-sparse-matrix"
+
+struct CvSet;
+
+typedef struct CvSparseMat
+{
+    int type;
+    int dims;
+    int* refcount;
+    int hdr_refcount;
+
+    struct CvSet* heap;
+    void** hashtable;
+    int hashsize;
+    int valoffset;
+    int idxoffset;
+    int size[CV_MAX_DIM];
+
+#ifdef __cplusplus
+    CV_EXPORTS void copyToSparseMat(cv::SparseMat& m) const;
+#endif
+}
+CvSparseMat;
+
+#ifdef __cplusplus
+CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m);
+#endif
+
+#define CV_IS_SPARSE_MAT_HDR(mat) \
+    ((mat) != NULL && \
+    (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL)
+
+#define CV_IS_SPARSE_MAT(mat) \
+    CV_IS_SPARSE_MAT_HDR(mat)
+
+/**************** iteration through a sparse array *****************/
+
+typedef struct CvSparseNode
+{
+    unsigned hashval;
+    struct CvSparseNode* next;
+}
+CvSparseNode;
+
+typedef struct CvSparseMatIterator
+{
+    CvSparseMat* mat;
+    CvSparseNode* node;
+    int curidx;
+}
+CvSparseMatIterator;
+
+#define CV_NODE_VAL(mat,node)   ((void*)((uchar*)(node) + (mat)->valoffset))
+#define CV_NODE_IDX(mat,node)   ((int*)((uchar*)(node) + (mat)->idxoffset))
+
+/****************************************************************************************\
+*                                         Histogram                                      *
+\****************************************************************************************/
+
+typedef int CvHistType;
+
+#define CV_HIST_MAGIC_VAL     0x42450000
+#define CV_HIST_UNIFORM_FLAG  (1 << 10)
+
+/** indicates whether bin ranges are set already or not */
+#define CV_HIST_RANGES_FLAG   (1 << 11)
+
+#define CV_HIST_ARRAY         0
+#define CV_HIST_SPARSE        1
+#define CV_HIST_TREE          CV_HIST_SPARSE
+
+/** should be used as a parameter only,
+   it turns to CV_HIST_UNIFORM_FLAG of hist->type */
+#define CV_HIST_UNIFORM       1
+
+typedef struct CvHistogram
+{
+    int     type;
+    CvArr*  bins;
+    float   thresh[CV_MAX_DIM][2];  /**< For uniform histograms.                      */
+    float** thresh2;                /**< For non-uniform histograms.                  */
+    CvMatND mat;                    /**< Embedded matrix header for array histograms. */
+}
+CvHistogram;
+
+#define CV_IS_HIST( hist ) \
+    ((hist) != NULL  && \
+     (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \
+     (hist)->bins != NULL)
+
+#define CV_IS_UNIFORM_HIST( hist ) \
+    (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0)
+
+#define CV_IS_SPARSE_HIST( hist ) \
+    CV_IS_SPARSE_MAT((hist)->bins)
+
+#define CV_HIST_HAS_RANGES( hist ) \
+    (((hist)->type & CV_HIST_RANGES_FLAG) != 0)
+
+/****************************************************************************************\
+*                      Other supplementary data type definitions                         *
+\****************************************************************************************/
+
+/*************************************** CvRect *****************************************/
+/** @sa Rect_ */
+typedef struct CvRect
+{
+    int x;
+    int y;
+    int width;
+    int height;
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvRect() __attribute__(( warning("Non-initialized variable") )) {};
+    template<typename _Tp> CvRect(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 4);
+        x = y = width = height = 0;
+        if (list.size() == 4)
+        {
+            x = list.begin()[0]; y = list.begin()[1]; width = list.begin()[2]; height = list.begin()[3];
+        }
+    };
+#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {}
+    template<typename _Tp>
+    CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast<int>(r.x)), y(cv::saturate_cast<int>(r.y)), width(cv::saturate_cast<int>(r.width)), height(cv::saturate_cast<int>(r.height)) {}
+#endif
+#ifdef __cplusplus
+    template<typename _Tp>
+    operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); }
+#endif
+}
+CvRect;
+
+/** constructs CvRect structure. */
+CV_INLINE  CvRect  cvRect( int x, int y, int width, int height )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvRect r = {x, y, width, height};
+#else
+    CvRect r(x, y , width, height);
+#endif
+    return r;
+}
+#ifdef __cplusplus
+CV_INLINE CvRect cvRect(const cv::Rect& rc) { return cvRect(rc.x, rc.y, rc.width, rc.height); }
+#endif
+
+CV_INLINE  IplROI  cvRectToROI( CvRect rect, int coi )
+{
+    IplROI roi;
+    roi.xOffset = rect.x;
+    roi.yOffset = rect.y;
+    roi.width = rect.width;
+    roi.height = rect.height;
+    roi.coi = coi;
+
+    return roi;
+}
+
+
+CV_INLINE  CvRect  cvROIToRect( IplROI roi )
+{
+    return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height );
+}
+
+/*********************************** CvTermCriteria *************************************/
+
+#define CV_TERMCRIT_ITER    1
+#define CV_TERMCRIT_NUMBER  CV_TERMCRIT_ITER
+#define CV_TERMCRIT_EPS     2
+
+/** @sa TermCriteria
+ */
+typedef struct CvTermCriteria
+{
+    int    type;  /**< may be combination of
+                     CV_TERMCRIT_ITER
+                     CV_TERMCRIT_EPS */
+    int    max_iter;
+    double epsilon;
+#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps)  {}
+    CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon)  {}
+#endif
+#ifdef __cplusplus
+    operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); }
+#endif
+}
+CvTermCriteria;
+
+CV_INLINE  CvTermCriteria  cvTermCriteria( int type, int max_iter, double epsilon )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvTermCriteria t = { type, max_iter, (float)epsilon};
+#else
+    CvTermCriteria t(type, max_iter, epsilon);
+#endif
+    return t;
+}
+#ifdef __cplusplus
+CV_INLINE CvTermCriteria cvTermCriteria(const cv::TermCriteria& t) { return cvTermCriteria(t.type, t.maxCount, t.epsilon); }
+#endif
+
+
+/******************************* CvPoint and variants ***********************************/
+
+typedef struct CvPoint
+{
+    int x;
+    int y;
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvPoint() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvPoint(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 2);
+        x = y = 0;
+        if (list.size() == 2)
+        {
+            x = list.begin()[0]; y = list.begin()[1];
+        }
+    };
+#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {}
+    template<typename _Tp>
+    CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {}
+#endif
+#ifdef __cplusplus
+    template<typename _Tp>
+    operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); }
+#endif
+}
+CvPoint;
+
+/** constructs CvPoint structure. */
+CV_INLINE  CvPoint  cvPoint( int x, int y )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvPoint p = {x, y};
+#else
+    CvPoint p(x, y);
+#endif
+    return p;
+}
+#ifdef __cplusplus
+CV_INLINE CvPoint cvPoint(const cv::Point& pt) { return cvPoint(pt.x, pt.y); }
+#endif
+
+typedef struct CvPoint2D32f
+{
+    float x;
+    float y;
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvPoint2D32f() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvPoint2D32f(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 2);
+        x = y = 0;
+        if (list.size() == 2)
+        {
+            x = list.begin()[0]; y = list.begin()[1];
+        }
+    };
+#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {}
+    template<typename _Tp>
+    CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {}
+#endif
+#ifdef __cplusplus
+    template<typename _Tp>
+    operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); }
+#endif
+}
+CvPoint2D32f;
+
+/** constructs CvPoint2D32f structure. */
+CV_INLINE  CvPoint2D32f  cvPoint2D32f( double x, double y )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvPoint2D32f p = { (float)x, (float)y };
+#else
+    CvPoint2D32f p((float)x, (float)y);
+#endif
+    return p;
+}
+
+#ifdef __cplusplus
+template<typename _Tp>
+CvPoint2D32f cvPoint2D32f(const cv::Point_<_Tp>& pt)
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvPoint2D32f p = { (float)pt.x, (float)pt.y };
+#else
+    CvPoint2D32f p((float)pt.x, (float)pt.y);
+#endif
+    return p;
+}
+#endif
+
+/** converts CvPoint to CvPoint2D32f. */
+CV_INLINE  CvPoint2D32f  cvPointTo32f( CvPoint point )
+{
+    return cvPoint2D32f( (float)point.x, (float)point.y );
+}
+
+/** converts CvPoint2D32f to CvPoint. */
+CV_INLINE  CvPoint  cvPointFrom32f( CvPoint2D32f point )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvPoint ipt = { cvRound(point.x), cvRound(point.y) };
+#else
+    CvPoint ipt(cvRound(point.x), cvRound(point.y));
+#endif
+    return ipt;
+}
+
+
+typedef struct CvPoint3D32f
+{
+    float x;
+    float y;
+    float z;
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvPoint3D32f() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvPoint3D32f(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 3);
+        x = y = z = 0;
+        if (list.size() == 3)
+        {
+            x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2];
+        }
+    };
+#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {}
+    template<typename _Tp>
+    CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {}
+#endif
+#ifdef __cplusplus
+    template<typename _Tp>
+    operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); }
+#endif
+}
+CvPoint3D32f;
+
+/** constructs CvPoint3D32f structure. */
+CV_INLINE  CvPoint3D32f  cvPoint3D32f( double x, double y, double z )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvPoint3D32f p = { (float)x, (float)y, (float)z };
+#else
+    CvPoint3D32f p((float)x, (float)y, (float)z);
+#endif
+    return p;
+}
+
+#ifdef __cplusplus
+template<typename _Tp>
+CvPoint3D32f cvPoint3D32f(const cv::Point3_<_Tp>& pt)
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvPoint3D32f p  = { (float)pt.x, (float)pt.y, (float)pt.z };
+#else
+    CvPoint3D32f p((float)pt.x, (float)pt.y, (float)pt.z);
+#endif
+    return p;
+}
+#endif
+
+
+typedef struct CvPoint2D64f
+{
+    double x;
+    double y;
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvPoint2D64f() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvPoint2D64f(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 2);
+        x = y = 0;
+        if (list.size() == 2)
+        {
+            x = list.begin()[0]; y = list.begin()[1];
+        }
+    };
+#endif
+}
+CvPoint2D64f;
+
+/** constructs CvPoint2D64f structure.*/
+CV_INLINE  CvPoint2D64f  cvPoint2D64f( double x, double y )
+{
+    CvPoint2D64f p = { x, y };
+    return p;
+}
+
+
+typedef struct CvPoint3D64f
+{
+    double x;
+    double y;
+    double z;
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvPoint3D64f() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvPoint3D64f(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 3);
+        x = y = z = 0;
+        if (list.size() == 3)
+        {
+            x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2];
+        }
+    };
+#endif
+}
+CvPoint3D64f;
+
+/** constructs CvPoint3D64f structure. */
+CV_INLINE  CvPoint3D64f  cvPoint3D64f( double x, double y, double z )
+{
+    CvPoint3D64f p = { x, y, z };
+    return p;
+}
+
+
+/******************************** CvSize's & CvBox **************************************/
+
+typedef struct CvSize
+{
+    int width;
+    int height;
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvSize() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvSize(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 2);
+        width = 0; height = 0;
+        if (list.size() == 2)
+        {
+            width = list.begin()[0]; height = list.begin()[1];
+        }
+    };
+#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvSize(int w = 0, int h = 0): width(w), height(h) {}
+    template<typename _Tp>
+    CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast<int>(sz.width)), height(cv::saturate_cast<int>(sz.height)) {}
+#endif
+#ifdef __cplusplus
+    template<typename _Tp>
+    operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); }
+#endif
+}
+CvSize;
+
+/** constructs CvSize structure. */
+CV_INLINE  CvSize  cvSize( int width, int height )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvSize s = { width, height };
+#else
+    CvSize s(width, height);
+#endif
+    return s;
+}
+
+#ifdef __cplusplus
+CV_INLINE CvSize cvSize(const cv::Size& sz)
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvSize s = { sz.width, sz.height };
+#else
+    CvSize s(sz.width, sz.height);
+#endif
+    return s;
+}
+#endif
+
+typedef struct CvSize2D32f
+{
+    float width;
+    float height;
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvSize2D32f() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvSize2D32f(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 2);
+        width = 0; height = 0;
+        if (list.size() == 2)
+        {
+            width = list.begin()[0]; height = list.begin()[1];
+        }
+    };
+#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {}
+    template<typename _Tp>
+    CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast<float>(sz.width)), height(cv::saturate_cast<float>(sz.height)) {}
+#endif
+#ifdef __cplusplus
+    template<typename _Tp>
+    operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); }
+#endif
+}
+CvSize2D32f;
+
+/** constructs CvSize2D32f structure. */
+CV_INLINE  CvSize2D32f  cvSize2D32f( double width, double height )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvSize2D32f s = { (float)width, (float)height };
+#else
+    CvSize2D32f s((float)width, (float)height);
+#endif
+    return s;
+}
+#ifdef __cplusplus
+template<typename _Tp>
+CvSize2D32f cvSize2D32f(const cv::Size_<_Tp>& sz)
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvSize2D32f s = { (float)sz.width, (float)sz.height };
+#else
+    CvSize2D32f s((float)sz.width, (float)sz.height);
+#endif
+    return s;
+}
+#endif
+
+/** @sa RotatedRect
+ */
+typedef struct CvBox2D
+{
+    CvPoint2D32f center;  /**< Center of the box.                          */
+    CvSize2D32f  size;    /**< Box width and length.                       */
+    float angle;          /**< Angle between the horizontal axis           */
+                          /**< and the first side (i.e. length) in degrees */
+
+#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {}
+    CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {}
+#endif
+#ifdef __cplusplus
+    operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); }
+#endif
+}
+CvBox2D;
+
+
+#ifdef __cplusplus
+CV_INLINE CvBox2D cvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0)
+{
+    CvBox2D self;
+    self.center = c;
+    self.size = s;
+    self.angle = a;
+    return self;
+}
+CV_INLINE CvBox2D cvBox2D(const cv::RotatedRect& rr)
+{
+    CvBox2D self;
+    self.center = cvPoint2D32f(rr.center);
+    self.size = cvSize2D32f(rr.size);
+    self.angle = rr.angle;
+    return self;
+}
+#endif
+
+
+/** Line iterator state: */
+typedef struct CvLineIterator
+{
+    /** Pointer to the current point: */
+    uchar* ptr;
+
+    /* Bresenham algorithm state: */
+    int  err;
+    int  plus_delta;
+    int  minus_delta;
+    int  plus_step;
+    int  minus_step;
+}
+CvLineIterator;
+
+
+
+/************************************* CvSlice ******************************************/
+#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff
+#define CV_WHOLE_SEQ  cvSlice(0, CV_WHOLE_SEQ_END_INDEX)
+
+typedef struct CvSlice
+{
+    int  start_index, end_index;
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvSlice() __attribute__(( warning("Non-initialized variable") )) {}
+    template<typename _Tp> CvSlice(const std::initializer_list<_Tp> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 2);
+        start_index = end_index = 0;
+        if (list.size() == 2)
+        {
+            start_index = list.begin()[0]; end_index = list.begin()[1];
+        }
+    };
+#endif
+#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__)
+    CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {}
+    CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); }
+    operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); }
+#endif
+}
+CvSlice;
+
+CV_INLINE  CvSlice  cvSlice( int start, int end )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__))
+    CvSlice slice = { start, end };
+#else
+    CvSlice slice(start, end);
+#endif
+    return slice;
+}
+
+#if defined(__cplusplus)
+CV_INLINE  CvSlice  cvSlice(const cv::Range& r)
+{
+    CvSlice slice = (r.start != INT_MIN && r.end != INT_MAX) ? cvSlice(r.start, r.end) : cvSlice(0, CV_WHOLE_SEQ_END_INDEX);
+    return slice;
+}
+#endif
+
+
+/************************************* CvScalar *****************************************/
+/** @sa Scalar_
+ */
+typedef struct CvScalar
+{
+    double val[4];
+
+#ifdef CV__VALIDATE_UNUNITIALIZED_VARS
+    CvScalar() __attribute__(( warning("Non-initialized variable") )) {}
+    CvScalar(const std::initializer_list<double> list)
+    {
+        CV_Assert(list.size() == 0 || list.size() == 4);
+        val[0] = val[1] = val[2] = val[3] = 0;
+        if (list.size() == 4)
+        {
+            val[0] = list.begin()[0]; val[1] = list.begin()[1]; val[2] = list.begin()[2]; val[3] = list.begin()[3];
+        }
+    };
+#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvScalar() {}
+    CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; }
+    template<typename _Tp>
+    CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; }
+    template<typename _Tp, int cn>
+    CvScalar(const cv::Vec<_Tp, cn>& v)
+    {
+        int i;
+        for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i];
+        for( ; i < 4; i++ ) val[i] = 0;
+    }
+#endif
+#ifdef __cplusplus
+    template<typename _Tp>
+    operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); }
+#endif
+}
+CvScalar;
+
+CV_INLINE  CvScalar  cvScalar( double val0, double val1 CV_DEFAULT(0),
+                               double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0))
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvScalar scalar = CV_STRUCT_INITIALIZER;
+#else
+    CvScalar scalar;
+#endif
+    scalar.val[0] = val0; scalar.val[1] = val1;
+    scalar.val[2] = val2; scalar.val[3] = val3;
+    return scalar;
+}
+
+#ifdef __cplusplus
+CV_INLINE CvScalar cvScalar()
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvScalar scalar = CV_STRUCT_INITIALIZER;
+#else
+    CvScalar scalar;
+#endif
+    scalar.val[0] = scalar.val[1] = scalar.val[2] = scalar.val[3] = 0;
+    return scalar;
+}
+CV_INLINE CvScalar cvScalar(const cv::Scalar& s)
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvScalar scalar = CV_STRUCT_INITIALIZER;
+#else
+    CvScalar scalar;
+#endif
+    scalar.val[0] = s.val[0];
+    scalar.val[1] = s.val[1];
+    scalar.val[2] = s.val[2];
+    scalar.val[3] = s.val[3];
+    return scalar;
+}
+#endif
+
+CV_INLINE  CvScalar  cvRealScalar( double val0 )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvScalar scalar = CV_STRUCT_INITIALIZER;
+#else
+    CvScalar scalar;
+#endif
+    scalar.val[0] = val0;
+    scalar.val[1] = scalar.val[2] = scalar.val[3] = 0;
+    return scalar;
+}
+
+CV_INLINE  CvScalar  cvScalarAll( double val0123 )
+{
+#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus))
+    CvScalar scalar = CV_STRUCT_INITIALIZER;
+#else
+    CvScalar scalar;
+#endif
+    scalar.val[0] = val0123;
+    scalar.val[1] = val0123;
+    scalar.val[2] = val0123;
+    scalar.val[3] = val0123;
+    return scalar;
+}
+
+/****************************************************************************************\
+*                                   Dynamic Data structures                              *
+\****************************************************************************************/
+
+/******************************** Memory storage ****************************************/
+
+typedef struct CvMemBlock
+{
+    struct CvMemBlock*  prev;
+    struct CvMemBlock*  next;
+}
+CvMemBlock;
+
+#define CV_STORAGE_MAGIC_VAL    0x42890000
+
+typedef struct CvMemStorage
+{
+    int signature;
+    CvMemBlock* bottom;           /**< First allocated block.                   */
+    CvMemBlock* top;              /**< Current memory block - top of the stack. */
+    struct  CvMemStorage* parent; /**< We get new blocks from parent as needed. */
+    int block_size;               /**< Block size.                              */
+    int free_space;               /**< Remaining free space in current block.   */
+}
+CvMemStorage;
+
+#define CV_IS_STORAGE(storage)  \
+    ((storage) != NULL &&       \
+    (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL)
+
+
+typedef struct CvMemStoragePos
+{
+    CvMemBlock* top;
+    int free_space;
+}
+CvMemStoragePos;
+
+
+/*********************************** Sequence *******************************************/
+
+typedef struct CvSeqBlock
+{
+    struct CvSeqBlock*  prev; /**< Previous sequence block.                   */
+    struct CvSeqBlock*  next; /**< Next sequence block.                       */
+    int    start_index;       /**< Index of the first element in the block +  */
+                              /**< sequence->first->start_index.              */
+    int    count;             /**< Number of elements in the block.           */
+    schar* data;              /**< Pointer to the first element of the block. */
+}
+CvSeqBlock;
+
+
+#define CV_TREE_NODE_FIELDS(node_type)                               \
+    int       flags;             /**< Miscellaneous flags.     */      \
+    int       header_size;       /**< Size of sequence header. */      \
+    struct    node_type* h_prev; /**< Previous sequence.       */      \
+    struct    node_type* h_next; /**< Next sequence.           */      \
+    struct    node_type* v_prev; /**< 2nd previous sequence.   */      \
+    struct    node_type* v_next  /**< 2nd next sequence.       */
+
+/**
+   Read/Write sequence.
+   Elements can be dynamically inserted to or deleted from the sequence.
+*/
+#define CV_SEQUENCE_FIELDS()                                              \
+    CV_TREE_NODE_FIELDS(CvSeq);                                           \
+    int       total;          /**< Total number of elements.            */  \
+    int       elem_size;      /**< Size of sequence element in bytes.   */  \
+    schar*    block_max;      /**< Maximal bound of the last block.     */  \
+    schar*    ptr;            /**< Current write pointer.               */  \
+    int       delta_elems;    /**< Grow seq this many at a time.        */  \
+    CvMemStorage* storage;    /**< Where the seq is stored.             */  \
+    CvSeqBlock* free_blocks;  /**< Free blocks list.                    */  \
+    CvSeqBlock* first;        /**< Pointer to the first sequence block. */
+
+typedef struct CvSeq
+{
+    CV_SEQUENCE_FIELDS()
+}
+CvSeq;
+
+#define CV_TYPE_NAME_SEQ             "opencv-sequence"
+#define CV_TYPE_NAME_SEQ_TREE        "opencv-sequence-tree"
+
+/*************************************** Set ********************************************/
+/** @brief Set
+  Order is not preserved. There can be gaps between sequence elements.
+  After the element has been inserted it stays in the same place all the time.
+  The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists.
+*/
+#define CV_SET_ELEM_FIELDS(elem_type)   \
+    int  flags;                         \
+    struct elem_type* next_free;
+
+typedef struct CvSetElem
+{
+    CV_SET_ELEM_FIELDS(CvSetElem)
+}
+CvSetElem;
+
+#define CV_SET_FIELDS()      \
+    CV_SEQUENCE_FIELDS()     \
+    CvSetElem* free_elems;   \
+    int active_count;
+
+typedef struct CvSet
+{
+    CV_SET_FIELDS()
+}
+CvSet;
+
+
+#define CV_SET_ELEM_IDX_MASK   ((1 << 26) - 1)
+#define CV_SET_ELEM_FREE_FLAG  (1 << (sizeof(int)*8-1))
+
+/** Checks whether the element pointed by ptr belongs to a set or not */
+#define CV_IS_SET_ELEM( ptr )  (((CvSetElem*)(ptr))->flags >= 0)
+
+/************************************* Graph ********************************************/
+
+/** @name Graph
+
+We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly,
+pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in
+another set. There is a singly-linked list of incoming/outcoming edges for each vertex.
+
+Each edge consists of:
+
+- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively).
+
+    A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not
+distinguished during search operations.
+
+- Two pointers to next edges for the starting and ending vertices, where next[0] points to the
+next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1]
+adjacency list.
+
+@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph
+@{
+*/
+#define CV_GRAPH_EDGE_FIELDS()      \
+    int flags;                      \
+    float weight;                   \
+    struct CvGraphEdge* next[2];    \
+    struct CvGraphVtx* vtx[2];
+
+
+#define CV_GRAPH_VERTEX_FIELDS()    \
+    int flags;                      \
+    struct CvGraphEdge* first;
+
+
+typedef struct CvGraphEdge
+{
+    CV_GRAPH_EDGE_FIELDS()
+}
+CvGraphEdge;
+
+typedef struct CvGraphVtx
+{
+    CV_GRAPH_VERTEX_FIELDS()
+}
+CvGraphVtx;
+
+typedef struct CvGraphVtx2D
+{
+    CV_GRAPH_VERTEX_FIELDS()
+    CvPoint2D32f* ptr;
+}
+CvGraphVtx2D;
+
+/**
+   Graph is "derived" from the set (this is set a of vertices)
+   and includes another set (edges)
+*/
+#define  CV_GRAPH_FIELDS()   \
+    CV_SET_FIELDS()          \
+    CvSet* edges;
+
+typedef struct CvGraph
+{
+    CV_GRAPH_FIELDS()
+}
+CvGraph;
+
+#define CV_TYPE_NAME_GRAPH "opencv-graph"
+
+/** @} */
+
+/*********************************** Chain/Contour *************************************/
+
+typedef struct CvChain
+{
+    CV_SEQUENCE_FIELDS()
+    CvPoint  origin;
+}
+CvChain;
+
+#define CV_CONTOUR_FIELDS()  \
+    CV_SEQUENCE_FIELDS()     \
+    CvRect rect;             \
+    int color;               \
+    int reserved[3];
+
+typedef struct CvContour
+{
+    CV_CONTOUR_FIELDS()
+}
+CvContour;
+
+typedef CvContour CvPoint2DSeq;
+
+/****************************************************************************************\
+*                                    Sequence types                                      *
+\****************************************************************************************/
+
+#define CV_SEQ_MAGIC_VAL             0x42990000
+
+#define CV_IS_SEQ(seq) \
+    ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL)
+
+#define CV_SET_MAGIC_VAL             0x42980000
+#define CV_IS_SET(set) \
+    ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL)
+
+#define CV_SEQ_ELTYPE_BITS           12
+#define CV_SEQ_ELTYPE_MASK           ((1 << CV_SEQ_ELTYPE_BITS) - 1)
+
+#define CV_SEQ_ELTYPE_POINT          CV_32SC2  /**< (x,y) */
+#define CV_SEQ_ELTYPE_CODE           CV_8UC1   /**< freeman code: 0..7 */
+#define CV_SEQ_ELTYPE_GENERIC        0
+#define CV_SEQ_ELTYPE_PTR            CV_MAKE_TYPE(CV_8U, 8 /*sizeof(void*)*/)
+#define CV_SEQ_ELTYPE_PPOINT         CV_SEQ_ELTYPE_PTR  /**< &(x,y) */
+#define CV_SEQ_ELTYPE_INDEX          CV_32SC1  /**< #(x,y) */
+#define CV_SEQ_ELTYPE_GRAPH_EDGE     0  /**< &next_o, &next_d, &vtx_o, &vtx_d */
+#define CV_SEQ_ELTYPE_GRAPH_VERTEX   0  /**< first_edge, &(x,y) */
+#define CV_SEQ_ELTYPE_TRIAN_ATR      0  /**< vertex of the binary tree   */
+#define CV_SEQ_ELTYPE_CONNECTED_COMP 0  /**< connected component  */
+#define CV_SEQ_ELTYPE_POINT3D        CV_32FC3  /**< (x,y,z)  */
+
+#define CV_SEQ_KIND_BITS        2
+#define CV_SEQ_KIND_MASK        (((1 << CV_SEQ_KIND_BITS) - 1)<<CV_SEQ_ELTYPE_BITS)
+
+/** types of sequences */
+#define CV_SEQ_KIND_GENERIC     (0 << CV_SEQ_ELTYPE_BITS)
+#define CV_SEQ_KIND_CURVE       (1 << CV_SEQ_ELTYPE_BITS)
+#define CV_SEQ_KIND_BIN_TREE    (2 << CV_SEQ_ELTYPE_BITS)
+
+/** types of sparse sequences (sets) */
+#define CV_SEQ_KIND_GRAPH       (1 << CV_SEQ_ELTYPE_BITS)
+#define CV_SEQ_KIND_SUBDIV2D    (2 << CV_SEQ_ELTYPE_BITS)
+
+#define CV_SEQ_FLAG_SHIFT       (CV_SEQ_KIND_BITS + CV_SEQ_ELTYPE_BITS)
+
+/** flags for curves */
+#define CV_SEQ_FLAG_CLOSED     (1 << CV_SEQ_FLAG_SHIFT)
+#define CV_SEQ_FLAG_SIMPLE     (0 << CV_SEQ_FLAG_SHIFT)
+#define CV_SEQ_FLAG_CONVEX     (0 << CV_SEQ_FLAG_SHIFT)
+#define CV_SEQ_FLAG_HOLE       (2 << CV_SEQ_FLAG_SHIFT)
+
+/** flags for graphs */
+#define CV_GRAPH_FLAG_ORIENTED (1 << CV_SEQ_FLAG_SHIFT)
+
+#define CV_GRAPH               CV_SEQ_KIND_GRAPH
+#define CV_ORIENTED_GRAPH      (CV_SEQ_KIND_GRAPH|CV_GRAPH_FLAG_ORIENTED)
+
+/** point sets */
+#define CV_SEQ_POINT_SET       (CV_SEQ_KIND_GENERIC| CV_SEQ_ELTYPE_POINT)
+#define CV_SEQ_POINT3D_SET     (CV_SEQ_KIND_GENERIC| CV_SEQ_ELTYPE_POINT3D)
+#define CV_SEQ_POLYLINE        (CV_SEQ_KIND_CURVE  | CV_SEQ_ELTYPE_POINT)
+#define CV_SEQ_POLYGON         (CV_SEQ_FLAG_CLOSED | CV_SEQ_POLYLINE )
+#define CV_SEQ_CONTOUR         CV_SEQ_POLYGON
+#define CV_SEQ_SIMPLE_POLYGON  (CV_SEQ_FLAG_SIMPLE | CV_SEQ_POLYGON  )
+
+/** chain-coded curves */
+#define CV_SEQ_CHAIN           (CV_SEQ_KIND_CURVE  | CV_SEQ_ELTYPE_CODE)
+#define CV_SEQ_CHAIN_CONTOUR   (CV_SEQ_FLAG_CLOSED | CV_SEQ_CHAIN)
+
+/** binary tree for the contour */
+#define CV_SEQ_POLYGON_TREE    (CV_SEQ_KIND_BIN_TREE  | CV_SEQ_ELTYPE_TRIAN_ATR)
+
+/** sequence of the connected components */
+#define CV_SEQ_CONNECTED_COMP  (CV_SEQ_KIND_GENERIC  | CV_SEQ_ELTYPE_CONNECTED_COMP)
+
+/** sequence of the integer numbers */
+#define CV_SEQ_INDEX           (CV_SEQ_KIND_GENERIC  | CV_SEQ_ELTYPE_INDEX)
+
+#define CV_SEQ_ELTYPE( seq )   ((seq)->flags & CV_SEQ_ELTYPE_MASK)
+#define CV_SEQ_KIND( seq )     ((seq)->flags & CV_SEQ_KIND_MASK )
+
+/** flag checking */
+#define CV_IS_SEQ_INDEX( seq )      ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \
+                                     (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC))
+
+#define CV_IS_SEQ_CURVE( seq )      (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE)
+#define CV_IS_SEQ_CLOSED( seq )     (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0)
+#define CV_IS_SEQ_CONVEX( seq )     0
+#define CV_IS_SEQ_HOLE( seq )       (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0)
+#define CV_IS_SEQ_SIMPLE( seq )     1
+
+/** type checking macros */
+#define CV_IS_SEQ_POINT_SET( seq ) \
+    ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2))
+
+#define CV_IS_SEQ_POINT_SUBSET( seq ) \
+    (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT)
+
+#define CV_IS_SEQ_POLYLINE( seq )   \
+    (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq))
+
+#define CV_IS_SEQ_POLYGON( seq )   \
+    (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq))
+
+#define CV_IS_SEQ_CHAIN( seq )   \
+    (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1)
+
+#define CV_IS_SEQ_CONTOUR( seq )   \
+    (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq)))
+
+#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \
+    (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq ))
+
+#define CV_IS_SEQ_POLYGON_TREE( seq ) \
+    (CV_SEQ_ELTYPE (seq) ==  CV_SEQ_ELTYPE_TRIAN_ATR &&    \
+    CV_SEQ_KIND( seq ) ==  CV_SEQ_KIND_BIN_TREE )
+
+#define CV_IS_GRAPH( seq )    \
+    (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH)
+
+#define CV_IS_GRAPH_ORIENTED( seq )   \
+    (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0)
+
+#define CV_IS_SUBDIV2D( seq )  \
+    (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D)
+
+/****************************************************************************************/
+/*                            Sequence writer & reader                                  */
+/****************************************************************************************/
+
+#define CV_SEQ_WRITER_FIELDS()                                     \
+    int          header_size;                                      \
+    CvSeq*       seq;        /**< the sequence written */            \
+    CvSeqBlock*  block;      /**< current block */                   \
+    schar*       ptr;        /**< pointer to free space */           \
+    schar*       block_min;  /**< pointer to the beginning of block*/\
+    schar*       block_max;  /**< pointer to the end of block */
+
+typedef struct CvSeqWriter
+{
+    CV_SEQ_WRITER_FIELDS()
+}
+CvSeqWriter;
+
+
+#define CV_SEQ_READER_FIELDS()                                      \
+    int          header_size;                                       \
+    CvSeq*       seq;        /**< sequence, beign read */             \
+    CvSeqBlock*  block;      /**< current block */                    \
+    schar*       ptr;        /**< pointer to element be read next */  \
+    schar*       block_min;  /**< pointer to the beginning of block */\
+    schar*       block_max;  /**< pointer to the end of block */      \
+    int          delta_index;/**< = seq->first->start_index   */      \
+    schar*       prev_elem;  /**< pointer to previous element */
+
+typedef struct CvSeqReader
+{
+    CV_SEQ_READER_FIELDS()
+}
+CvSeqReader;
+
+/****************************************************************************************/
+/*                                Operations on sequences                               */
+/****************************************************************************************/
+
+#define  CV_SEQ_ELEM( seq, elem_type, index )                    \
+/** assert gives some guarantee that <seq> parameter is valid */  \
+(   assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) &&      \
+    (seq)->elem_size == sizeof(elem_type)),                      \
+    (elem_type*)((seq)->first && (unsigned)index <               \
+    (unsigned)((seq)->first->count) ?                            \
+    (seq)->first->data + (index) * sizeof(elem_type) :           \
+    cvGetSeqElem( (CvSeq*)(seq), (index) )))
+#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) )
+
+/** Add element to sequence: */
+#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer )     \
+{                                                     \
+    if( (writer).ptr >= (writer).block_max )          \
+    {                                                 \
+        cvCreateSeqBlock( &writer);                   \
+    }                                                 \
+    memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\
+    (writer).ptr += (writer).seq->elem_size;          \
+}
+
+#define CV_WRITE_SEQ_ELEM( elem, writer )             \
+{                                                     \
+    assert( (writer).seq->elem_size == sizeof(elem)); \
+    if( (writer).ptr >= (writer).block_max )          \
+    {                                                 \
+        cvCreateSeqBlock( &writer);                   \
+    }                                                 \
+    assert( (writer).ptr <= (writer).block_max - sizeof(elem));\
+    memcpy((writer).ptr, &(elem), sizeof(elem));      \
+    (writer).ptr += sizeof(elem);                     \
+}
+
+
+/** Move reader position forward: */
+#define CV_NEXT_SEQ_ELEM( elem_size, reader )                 \
+{                                                             \
+    if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \
+    {                                                         \
+        cvChangeSeqBlock( &(reader), 1 );                     \
+    }                                                         \
+}
+
+
+/** Move reader position backward: */
+#define CV_PREV_SEQ_ELEM( elem_size, reader )                \
+{                                                            \
+    if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \
+    {                                                        \
+        cvChangeSeqBlock( &(reader), -1 );                   \
+    }                                                        \
+}
+
+/** Read element and move read position forward: */
+#define CV_READ_SEQ_ELEM( elem, reader )                       \
+{                                                              \
+    assert( (reader).seq->elem_size == sizeof(elem));          \
+    memcpy( &(elem), (reader).ptr, sizeof((elem)));            \
+    CV_NEXT_SEQ_ELEM( sizeof(elem), reader )                   \
+}
+
+/** Read element and move read position backward: */
+#define CV_REV_READ_SEQ_ELEM( elem, reader )                     \
+{                                                                \
+    assert( (reader).seq->elem_size == sizeof(elem));            \
+    memcpy(&(elem), (reader).ptr, sizeof((elem)));               \
+    CV_PREV_SEQ_ELEM( sizeof(elem), reader )                     \
+}
+
+
+#define CV_READ_CHAIN_POINT( _pt, reader )                              \
+{                                                                       \
+    (_pt) = (reader).pt;                                                \
+    if( (reader).ptr )                                                  \
+    {                                                                   \
+        CV_READ_SEQ_ELEM( (reader).code, (reader));                     \
+        assert( ((reader).code & ~7) == 0 );                            \
+        (reader).pt.x += (reader).deltas[(int)(reader).code][0];        \
+        (reader).pt.y += (reader).deltas[(int)(reader).code][1];        \
+    }                                                                   \
+}
+
+#define CV_CURRENT_POINT( reader )  (*((CvPoint*)((reader).ptr)))
+#define CV_PREV_POINT( reader )     (*((CvPoint*)((reader).prev_elem)))
+
+#define CV_READ_EDGE( pt1, pt2, reader )               \
+{                                                      \
+    assert( sizeof(pt1) == sizeof(CvPoint) &&          \
+            sizeof(pt2) == sizeof(CvPoint) &&          \
+            reader.seq->elem_size == sizeof(CvPoint)); \
+    (pt1) = CV_PREV_POINT( reader );                   \
+    (pt2) = CV_CURRENT_POINT( reader );                \
+    (reader).prev_elem = (reader).ptr;                 \
+    CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader));      \
+}
+
+/************ Graph macros ************/
+
+/** Return next graph edge for given vertex: */
+#define  CV_NEXT_GRAPH_EDGE( edge, vertex )                              \
+     (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)),  \
+      (edge)->next[(edge)->vtx[1] == (vertex)])
+
+
+
+/****************************************************************************************\
+*             Data structures for persistence (a.k.a serialization) functionality        *
+\****************************************************************************************/
+
+#if 0
+
+/** "black box" file storage */
+typedef struct CvFileStorage CvFileStorage;
+
+/** Storage flags: */
+#define CV_STORAGE_READ          0
+#define CV_STORAGE_WRITE         1
+#define CV_STORAGE_WRITE_TEXT    CV_STORAGE_WRITE
+#define CV_STORAGE_WRITE_BINARY  CV_STORAGE_WRITE
+#define CV_STORAGE_APPEND        2
+#define CV_STORAGE_MEMORY        4
+#define CV_STORAGE_FORMAT_MASK   (7<<3)
+#define CV_STORAGE_FORMAT_AUTO   0
+#define CV_STORAGE_FORMAT_XML    8
+#define CV_STORAGE_FORMAT_YAML  16
+#define CV_STORAGE_FORMAT_JSON  24
+#define CV_STORAGE_BASE64       64
+#define CV_STORAGE_WRITE_BASE64  (CV_STORAGE_BASE64 | CV_STORAGE_WRITE)
+
+/** @brief List of attributes. :
+
+In the current implementation, attributes are used to pass extra parameters when writing user
+objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type
+specification (type_id attribute).
+@see cvAttrList, cvAttrValue
+ */
+typedef struct CvAttrList
+{
+    const char** attr;         /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */
+    struct CvAttrList* next;   /**< Pointer to next chunk of the attributes list.                    */
+}
+CvAttrList;
+
+/** initializes CvAttrList structure */
+CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL),
+                                 CvAttrList* next CV_DEFAULT(NULL) )
+{
+    CvAttrList l;
+    l.attr = attr;
+    l.next = next;
+
+    return l;
+}
+
+struct CvTypeInfo;
+
+#define CV_NODE_NONE        0
+#define CV_NODE_INT         1
+#define CV_NODE_INTEGER     CV_NODE_INT
+#define CV_NODE_REAL        2
+#define CV_NODE_FLOAT       CV_NODE_REAL
+#define CV_NODE_STR         3
+#define CV_NODE_STRING      CV_NODE_STR
+#define CV_NODE_REF         4 /**< not used */
+#define CV_NODE_SEQ         5
+#define CV_NODE_MAP         6
+#define CV_NODE_TYPE_MASK   7
+
+#define CV_NODE_TYPE(flags)  ((flags) & CV_NODE_TYPE_MASK)
+
+/** file node flags */
+#define CV_NODE_FLOW        8 /**<Used only for writing structures in YAML format. */
+#define CV_NODE_USER        16
+#define CV_NODE_EMPTY       32
+#define CV_NODE_NAMED       64
+
+#define CV_NODE_IS_INT(flags)        (CV_NODE_TYPE(flags) == CV_NODE_INT)
+#define CV_NODE_IS_REAL(flags)       (CV_NODE_TYPE(flags) == CV_NODE_REAL)
+#define CV_NODE_IS_STRING(flags)     (CV_NODE_TYPE(flags) == CV_NODE_STRING)
+#define CV_NODE_IS_SEQ(flags)        (CV_NODE_TYPE(flags) == CV_NODE_SEQ)
+#define CV_NODE_IS_MAP(flags)        (CV_NODE_TYPE(flags) == CV_NODE_MAP)
+#define CV_NODE_IS_COLLECTION(flags) (CV_NODE_TYPE(flags) >= CV_NODE_SEQ)
+#define CV_NODE_IS_FLOW(flags)       (((flags) & CV_NODE_FLOW) != 0)
+#define CV_NODE_IS_EMPTY(flags)      (((flags) & CV_NODE_EMPTY) != 0)
+#define CV_NODE_IS_USER(flags)       (((flags) & CV_NODE_USER) != 0)
+#define CV_NODE_HAS_NAME(flags)      (((flags) & CV_NODE_NAMED) != 0)
+
+#define CV_NODE_SEQ_SIMPLE 256
+#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0)
+
+typedef struct CvString
+{
+    int len;
+    char* ptr;
+}
+CvString;
+
+/** All the keys (names) of elements in the read file storage
+   are stored in the hash to speed up the lookup operations: */
+typedef struct CvStringHashNode
+{
+    unsigned hashval;
+    CvString str;
+    struct CvStringHashNode* next;
+}
+CvStringHashNode;
+
+typedef struct CvGenericHash CvFileNodeHash;
+
+/** Basic element of the file storage - scalar or collection: */
+typedef struct CvFileNode
+{
+    int tag;
+    struct CvTypeInfo* info; /**< type information
+            (only for user-defined object, for others it is 0) */
+    union
+    {
+        double f; /**< scalar floating-point number */
+        int i;    /**< scalar integer number */
+        CvString str; /**< text string */
+        CvSeq* seq; /**< sequence (ordered collection of file nodes) */
+        CvFileNodeHash* map; /**< map (collection of named file nodes) */
+    } data;
+}
+CvFileNode;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr );
+typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr );
+typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node );
+typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name,
+                                      const void* struct_ptr, CvAttrList attributes );
+typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr );
+#ifdef __cplusplus
+}
+#endif
+
+/** @brief Type information
+
+The structure contains information about one of the standard or user-defined types. Instances of the
+type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there
+is a way to find the type info structure for a given object using the cvTypeOf function.
+Alternatively, type info can be found by type name using cvFindType, which is used when an object
+is read from file storage. The user can register a new type with cvRegisterType that adds the type
+information structure into the beginning of the type list. Thus, it is possible to create
+specialized types from generic standard types and override the basic methods.
+ */
+typedef struct CvTypeInfo
+{
+    int flags; /**< not used */
+    int header_size; /**< sizeof(CvTypeInfo) */
+    struct CvTypeInfo* prev; /**< previous registered type in the list */
+    struct CvTypeInfo* next; /**< next registered type in the list */
+    const char* type_name; /**< type name, written to file storage */
+    CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */
+    CvReleaseFunc release; /**< releases object (memory etc.) */
+    CvReadFunc read; /**< reads object from file storage */
+    CvWriteFunc write; /**< writes object to file storage */
+    CvCloneFunc clone; /**< creates a copy of the object */
+}
+CvTypeInfo;
+#endif
+
+/** @} */
+
+#endif /*OPENCV_CORE_TYPES_H*/
+
+/* End of file. */
diff --git a/3rdParty/include/opencv2/core/utility.hpp b/3rdParty/include/opencv2/core/utility.hpp
new file mode 100644
index 0000000..108c0d9
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utility.hpp
@@ -0,0 +1,1229 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2015, Itseez Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_UTILITY_H
+#define OPENCV_CORE_UTILITY_H
+
+#ifndef __cplusplus
+#  error utility.hpp header must be compiled as C++
+#endif
+
+#if defined(check)
+#  warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers.
+#endif
+
+#include "opencv2/core.hpp"
+#include <ostream>
+
+#include <functional>
+
+#if !defined(_M_CEE)
+#include <mutex>  // std::mutex, std::lock_guard
+#endif
+
+namespace cv
+{
+
+//! @addtogroup core_utils
+//! @{
+
+/** @brief  Automatically Allocated Buffer Class
+
+ The class is used for temporary buffers in functions and methods.
+ If a temporary buffer is usually small (a few K's of memory),
+ but its size depends on the parameters, it makes sense to create a small
+ fixed-size array on stack and use it if it's large enough. If the required buffer size
+ is larger than the fixed size, another buffer of sufficient size is allocated dynamically
+ and released after the processing. Therefore, in typical cases, when the buffer size is small,
+ there is no overhead associated with malloc()/free().
+ At the same time, there is no limit on the size of processed data.
+
+ This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and
+ the number of stack-allocated elements. Here is how the class is used:
+
+ \code
+ void my_func(const cv::Mat& m)
+ {
+    cv::AutoBuffer<float> buf(1000); // create automatic buffer containing 1000 floats
+
+    buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used,
+                          // otherwise the buffer of "m.rows" floats will be allocated
+                          // dynamically and deallocated in cv::AutoBuffer destructor
+    ...
+ }
+ \endcode
+*/
+#ifdef OPENCV_ENABLE_MEMORY_SANITIZER
+template<typename _Tp, size_t fixed_size = 0> class AutoBuffer
+#else
+template<typename _Tp, size_t fixed_size = 1024/sizeof(_Tp)+8> class AutoBuffer
+#endif
+{
+public:
+    typedef _Tp value_type;
+
+    //! the default constructor
+    AutoBuffer();
+    //! constructor taking the real buffer size
+    explicit AutoBuffer(size_t _size);
+
+    //! the copy constructor
+    AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf);
+    //! the assignment operator
+    AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf);
+
+    //! destructor. calls deallocate()
+    ~AutoBuffer();
+
+    //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used
+    void allocate(size_t _size);
+    //! deallocates the buffer if it was dynamically allocated
+    void deallocate();
+    //! resizes the buffer and preserves the content
+    void resize(size_t _size);
+    //! returns the current buffer size
+    size_t size() const;
+    //! returns pointer to the real buffer, stack-allocated or heap-allocated
+    inline _Tp* data() { return ptr; }
+    //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated
+    inline const _Tp* data() const { return ptr; }
+
+#if !defined(OPENCV_DISABLE_DEPRECATED_COMPATIBILITY) // use to .data() calls instead
+    //! returns pointer to the real buffer, stack-allocated or heap-allocated
+    operator _Tp* () { return ptr; }
+    //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated
+    operator const _Tp* () const { return ptr; }
+#else
+    //! returns a reference to the element at specified location. No bounds checking is performed in Release builds.
+    inline _Tp& operator[] (size_t i) { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; }
+    //! returns a reference to the element at specified location. No bounds checking is performed in Release builds.
+    inline const _Tp& operator[] (size_t i) const { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; }
+#endif
+
+protected:
+    //! pointer to the real buffer, can point to buf if the buffer is small enough
+    _Tp* ptr;
+    //! size of the real buffer
+    size_t sz;
+    //! pre-allocated buffer. At least 1 element to confirm C++ standard requirements
+    _Tp buf[(fixed_size > 0) ? fixed_size : 1];
+};
+
+/**  @brief Sets/resets the break-on-error mode.
+
+When the break-on-error mode is set, the default error handler issues a hardware exception, which
+can make debugging more convenient.
+
+\return the previous state
+ */
+CV_EXPORTS bool setBreakOnError(bool flag);
+
+extern "C" typedef int (*ErrorCallback)( int status, const char* func_name,
+                                       const char* err_msg, const char* file_name,
+                                       int line, void* userdata );
+
+
+/** @brief Sets the new error handler and the optional user data.
+
+  The function sets the new error handler, called from cv::error().
+
+  \param errCallback the new error handler. If NULL, the default error handler is used.
+  \param userdata the optional user data pointer, passed to the callback.
+  \param prevUserdata the optional output parameter where the previous user data pointer is stored
+
+  \return the previous error handler
+*/
+CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0);
+
+CV_EXPORTS String tempfile( const char* suffix = 0);
+CV_EXPORTS void glob(String pattern, std::vector<String>& result, bool recursive = false);
+
+/** @brief OpenCV will try to set the number of threads for the next parallel region.
+
+If threads == 0, OpenCV will disable threading optimizations and run all it's functions
+sequentially. Passing threads \< 0 will reset threads number to system default. This function must
+be called outside of parallel region.
+
+OpenCV will try to run its functions with specified threads number, but some behaviour differs from
+framework:
+-   `TBB` - User-defined parallel constructions will run with the same threads number, if
+    another is not specified. If later on user creates his own scheduler, OpenCV will use it.
+-   `OpenMP` - No special defined behaviour.
+-   `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its
+    functions sequentially.
+-   `GCD` - Supports only values \<= 0.
+-   `C=` - No special defined behaviour.
+@param nthreads Number of threads used by OpenCV.
+@sa getNumThreads, getThreadNum
+ */
+CV_EXPORTS_W void setNumThreads(int nthreads);
+
+/** @brief Returns the number of threads used by OpenCV for parallel regions.
+
+Always returns 1 if OpenCV is built without threading support.
+
+The exact meaning of return value depends on the threading framework used by OpenCV library:
+- `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is
+  any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns
+  default number of threads used by TBB library.
+- `OpenMP` - An upper bound on the number of threads that could be used to form a new team.
+- `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions.
+- `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility.
+- `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before
+  called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs,
+  available for the process.
+@sa setNumThreads, getThreadNum
+ */
+CV_EXPORTS_W int getNumThreads();
+
+/** @brief Returns the index of the currently executed thread within the current parallel region. Always
+returns 0 if called outside of parallel region.
+
+@deprecated Current implementation doesn't corresponding to this documentation.
+
+The exact meaning of the return value depends on the threading framework used by OpenCV library:
+- `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future.
+- `OpenMP` - The thread number, within the current team, of the calling thread.
+- `Concurrency` - An ID for the virtual processor that the current context is executing on (0
+  for master thread and unique number for others, but not necessary 1,2,3,...).
+- `GCD` - System calling thread's ID. Never returns 0 inside parallel region.
+- `C=` - The index of the current parallel task.
+@sa setNumThreads, getNumThreads
+ */
+CV_EXPORTS_W int getThreadNum();
+
+/** @brief Returns full configuration time cmake output.
+
+Returned value is raw cmake output including version control system revision, compiler version,
+compiler flags, enabled modules and third party libraries, etc. Output format depends on target
+architecture.
+ */
+CV_EXPORTS_W const String& getBuildInformation();
+
+/** @brief Returns library version string
+
+For example "3.4.1-dev".
+
+@sa getMajorVersion, getMinorVersion, getRevisionVersion
+*/
+CV_EXPORTS_W String getVersionString();
+
+/** @brief Returns major library version */
+CV_EXPORTS_W int getVersionMajor();
+
+/** @brief Returns minor library version */
+CV_EXPORTS_W int getVersionMinor();
+
+/** @brief Returns revision field of the library version */
+CV_EXPORTS_W int getVersionRevision();
+
+/** @brief Returns the number of ticks.
+
+The function returns the number of ticks after the certain event (for example, when the machine was
+turned on). It can be used to initialize RNG or to measure a function execution time by reading the
+tick count before and after the function call.
+@sa getTickFrequency, TickMeter
+ */
+CV_EXPORTS_W int64 getTickCount();
+
+/** @brief Returns the number of ticks per second.
+
+The function returns the number of ticks per second. That is, the following code computes the
+execution time in seconds:
+@code
+    double t = (double)getTickCount();
+    // do something ...
+    t = ((double)getTickCount() - t)/getTickFrequency();
+@endcode
+@sa getTickCount, TickMeter
+ */
+CV_EXPORTS_W double getTickFrequency();
+
+/** @brief a Class to measure passing time.
+
+The class computes passing time by counting the number of ticks per second. That is, the following code computes the
+execution time in seconds:
+@snippet snippets/core_various.cpp TickMeter_total
+
+It is also possible to compute the average time over multiple runs:
+@snippet snippets/core_various.cpp TickMeter_average
+
+@sa getTickCount, getTickFrequency
+*/
+class CV_EXPORTS_W TickMeter
+{
+public:
+    //! the default constructor
+    CV_WRAP TickMeter()
+    {
+        reset();
+    }
+
+    //! starts counting ticks.
+    CV_WRAP void start()
+    {
+        startTime = cv::getTickCount();
+    }
+
+    //! stops counting ticks.
+    CV_WRAP void stop()
+    {
+        int64 time = cv::getTickCount();
+        if (startTime == 0)
+            return;
+        ++counter;
+        sumTime += (time - startTime);
+        startTime = 0;
+    }
+
+    //! returns counted ticks.
+    CV_WRAP int64 getTimeTicks() const
+    {
+        return sumTime;
+    }
+
+    //! returns passed time in microseconds.
+    CV_WRAP double getTimeMicro() const
+    {
+        return getTimeMilli()*1e3;
+    }
+
+    //! returns passed time in milliseconds.
+    CV_WRAP double getTimeMilli() const
+    {
+        return getTimeSec()*1e3;
+    }
+
+    //! returns passed time in seconds.
+    CV_WRAP double getTimeSec()   const
+    {
+        return (double)getTimeTicks() / getTickFrequency();
+    }
+
+    //! returns internal counter value.
+    CV_WRAP int64 getCounter() const
+    {
+        return counter;
+    }
+
+    //! returns average FPS (frames per second) value.
+    CV_WRAP double getFPS() const
+    {
+        const double sec = getTimeSec();
+        if (sec < DBL_EPSILON)
+            return 0.;
+        return counter / sec;
+    }
+
+    //! returns average time in seconds
+    CV_WRAP double getAvgTimeSec() const
+    {
+        if (counter <= 0)
+            return 0.;
+        return getTimeSec() / counter;
+    }
+
+    //! returns average time in milliseconds
+    CV_WRAP double getAvgTimeMilli() const
+    {
+        return getAvgTimeSec() * 1e3;
+    }
+
+    //! resets internal values.
+    CV_WRAP void reset()
+    {
+        startTime = 0;
+        sumTime = 0;
+        counter = 0;
+    }
+
+private:
+    int64 counter;
+    int64 sumTime;
+    int64 startTime;
+};
+
+/** @brief output operator
+@code
+TickMeter tm;
+tm.start();
+// do something ...
+tm.stop();
+std::cout << tm;
+@endcode
+*/
+
+static inline
+std::ostream& operator << (std::ostream& out, const TickMeter& tm)
+{
+    return out << tm.getTimeSec() << "sec";
+}
+
+/** @brief Returns the number of CPU ticks.
+
+The function returns the current number of CPU ticks on some architectures (such as x86, x64,
+PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for
+very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU
+systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU
+with its own counter. So, theoretically (and practically) the subsequent calls to the function do
+not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU
+frequency depending on the load, the number of CPU clocks spent in some code cannot be directly
+converted to time units. Therefore, getTickCount is generally a preferable solution for measuring
+execution time.
+ */
+CV_EXPORTS_W int64 getCPUTickCount();
+
+/** @brief Returns true if the specified feature is supported by the host hardware.
+
+The function returns true if the host hardware supports the specified feature. When user calls
+setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until
+setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code
+in OpenCV.
+@param feature The feature of interest, one of cv::CpuFeatures
+ */
+CV_EXPORTS_W bool checkHardwareSupport(int feature);
+
+/** @brief Returns feature name by ID
+
+Returns empty string if feature is not defined
+*/
+CV_EXPORTS_W String getHardwareFeatureName(int feature);
+
+/** @brief Returns list of CPU features enabled during compilation.
+
+Returned value is a string containing space separated list of CPU features with following markers:
+
+- no markers - baseline features
+- prefix `*` - features enabled in dispatcher
+- suffix `?` - features enabled but not available in HW
+
+Example: `SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?`
+*/
+CV_EXPORTS_W std::string getCPUFeaturesLine();
+
+/** @brief Returns the number of logical CPUs available for the process.
+ */
+CV_EXPORTS_W int getNumberOfCPUs();
+
+
+/** @brief Aligns a pointer to the specified number of bytes.
+
+The function returns the aligned pointer of the same type as the input pointer:
+\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f]
+@param ptr Aligned pointer.
+@param n Alignment size that must be a power of two.
+ */
+template<typename _Tp> static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp))
+{
+    CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2
+    return (_Tp*)(((size_t)ptr + n-1) & -n);
+}
+
+/** @brief Aligns a buffer size to the specified number of bytes.
+
+The function returns the minimum number that is greater than or equal to sz and is divisible by n :
+\f[\texttt{(sz + n-1) & -n}\f]
+@param sz Buffer size to align.
+@param n Alignment size that must be a power of two.
+ */
+static inline size_t alignSize(size_t sz, int n)
+{
+    CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2
+    return (sz + n-1) & -n;
+}
+
+/** @brief Integer division with result round up.
+
+Use this function instead of `ceil((float)a / b)` expressions.
+
+@sa alignSize
+*/
+static inline int divUp(int a, unsigned int b)
+{
+    CV_DbgAssert(a >= 0);
+    return (a + b - 1) / b;
+}
+/** @overload */
+static inline size_t divUp(size_t a, unsigned int b)
+{
+    return (a + b - 1) / b;
+}
+
+/** @brief Round first value up to the nearest multiple of second value.
+
+Use this function instead of `ceil((float)a / b) * b` expressions.
+
+@sa divUp
+*/
+static inline int roundUp(int a, unsigned int b)
+{
+    CV_DbgAssert(a >= 0);
+    return a + b - 1 - (a + b -1) % b;
+}
+/** @overload */
+static inline size_t roundUp(size_t a, unsigned int b)
+{
+    return a + b - 1 - (a + b - 1) % b;
+}
+
+/** @brief Alignment check of passed values
+
+Usage: `isAligned<sizeof(int)>(...)`
+
+@note Alignment(N) must be a power of 2 (2**k, 2^k)
+*/
+template<int N, typename T> static inline
+bool isAligned(const T& data)
+{
+    CV_StaticAssert((N & (N - 1)) == 0, "");  // power of 2
+    return (((size_t)data) & (N - 1)) == 0;
+}
+/** @overload */
+template<int N> static inline
+bool isAligned(const void* p1)
+{
+    return isAligned<N>((size_t)p1);
+}
+/** @overload */
+template<int N> static inline
+bool isAligned(const void* p1, const void* p2)
+{
+    return isAligned<N>(((size_t)p1)|((size_t)p2));
+}
+/** @overload */
+template<int N> static inline
+bool isAligned(const void* p1, const void* p2, const void* p3)
+{
+    return isAligned<N>(((size_t)p1)|((size_t)p2)|((size_t)p3));
+}
+/** @overload */
+template<int N> static inline
+bool isAligned(const void* p1, const void* p2, const void* p3, const void* p4)
+{
+    return isAligned<N>(((size_t)p1)|((size_t)p2)|((size_t)p3)|((size_t)p4));
+}
+
+/** @brief Enables or disables the optimized code.
+
+The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2,
+and other instructions on the platforms that support it). It sets a global flag that is further
+checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only
+safe to call the function on the very top level in your application where you can be sure that no
+other OpenCV function is currently executed.
+
+By default, the optimized code is enabled unless you disable it in CMake. The current status can be
+retrieved using useOptimized.
+@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true)
+or not (onoff=false).
+ */
+CV_EXPORTS_W void setUseOptimized(bool onoff);
+
+/** @brief Returns the status of optimized code usage.
+
+The function returns true if the optimized code is enabled. Otherwise, it returns false.
+ */
+CV_EXPORTS_W bool useOptimized();
+
+static inline size_t getElemSize(int type) { return (size_t)CV_ELEM_SIZE(type); }
+
+/////////////////////////////// Parallel Primitives //////////////////////////////////
+
+/** @brief Base class for parallel data processors
+
+@ingroup core_parallel
+*/
+class CV_EXPORTS ParallelLoopBody
+{
+public:
+    virtual ~ParallelLoopBody();
+    virtual void operator() (const Range& range) const = 0;
+};
+
+/** @brief Parallel data processor
+
+@ingroup core_parallel
+*/
+CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.);
+
+//! @ingroup core_parallel
+class ParallelLoopBodyLambdaWrapper : public ParallelLoopBody
+{
+private:
+    std::function<void(const Range&)> m_functor;
+public:
+    inline
+    ParallelLoopBodyLambdaWrapper(std::function<void(const Range&)> functor)
+        : m_functor(functor)
+    {
+        // nothing
+    }
+
+    virtual void operator() (const cv::Range& range) const CV_OVERRIDE
+    {
+        m_functor(range);
+    }
+};
+
+//! @ingroup core_parallel
+static inline
+void parallel_for_(const Range& range, std::function<void(const Range&)> functor, double nstripes=-1.)
+{
+    parallel_for_(range, ParallelLoopBodyLambdaWrapper(functor), nstripes);
+}
+
+
+/////////////////////////////// forEach method of cv::Mat ////////////////////////////
+template<typename _Tp, typename Functor> inline
+void Mat::forEach_impl(const Functor& operation) {
+    if (false) {
+        operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast<int*>(0));
+        // If your compiler fails in this line.
+        // Please check that your functor signature is
+        //     (_Tp&, const int*)   <- multi-dimensional
+        //  or (_Tp&, void*)        <- in case you don't need current idx.
+    }
+
+    CV_Assert(!empty());
+    CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX);
+    const int LINES = static_cast<int>(this->total() / this->size[this->dims - 1]);
+
+    class PixelOperationWrapper :public ParallelLoopBody
+    {
+    public:
+        PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation)
+            : mat(frame), op(_operation) {}
+        virtual ~PixelOperationWrapper(){}
+        // ! Overloaded virtual operator
+        // convert range call to row call.
+        virtual void operator()(const Range &range) const CV_OVERRIDE
+        {
+            const int DIMS = mat->dims;
+            const int COLS = mat->size[DIMS - 1];
+            if (DIMS <= 2) {
+                for (int row = range.start; row < range.end; ++row) {
+                    this->rowCall2(row, COLS);
+                }
+            } else {
+                std::vector<int> idx(DIMS); /// idx is modified in this->rowCall
+                idx[DIMS - 2] = range.start - 1;
+
+                for (int line_num = range.start; line_num < range.end; ++line_num) {
+                    idx[DIMS - 2]++;
+                    for (int i = DIMS - 2; i >= 0; --i) {
+                        if (idx[i] >= mat->size[i]) {
+                            idx[i - 1] += idx[i] / mat->size[i];
+                            idx[i] %= mat->size[i];
+                            continue; // carry-over;
+                        }
+                        else {
+                            break;
+                        }
+                    }
+                    this->rowCall(&idx[0], COLS, DIMS);
+                }
+            }
+        }
+    private:
+        Mat_<_Tp>* const mat;
+        const Functor op;
+        // ! Call operator for each elements in this row.
+        inline void rowCall(int* const idx, const int COLS, const int DIMS) const {
+            int &col = idx[DIMS - 1];
+            col = 0;
+            _Tp* pixel = &(mat->template at<_Tp>(idx));
+
+            while (col < COLS) {
+                op(*pixel, const_cast<const int*>(idx));
+                pixel++; col++;
+            }
+            col = 0;
+        }
+        // ! Call operator for each elements in this row. 2d mat special version.
+        inline void rowCall2(const int row, const int COLS) const {
+            union Index{
+                int body[2];
+                operator const int*() const {
+                    return reinterpret_cast<const int*>(this);
+                }
+                int& operator[](const int i) {
+                    return body[i];
+                }
+            } idx = {{row, 0}};
+            // Special union is needed to avoid
+            // "error: array subscript is above array bounds [-Werror=array-bounds]"
+            // when call the functor `op` such that access idx[3].
+
+            _Tp* pixel = &(mat->template at<_Tp>(idx));
+            const _Tp* const pixel_end = pixel + COLS;
+            while(pixel < pixel_end) {
+                op(*pixel++, static_cast<const int*>(idx));
+                idx[1]++;
+            }
+        }
+        PixelOperationWrapper& operator=(const PixelOperationWrapper &) {
+            CV_Assert(false);
+            // We can not remove this implementation because Visual Studio warning C4822.
+            return *this;
+        }
+    };
+
+    parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast<Mat_<_Tp>*>(this), operation));
+}
+
+/////////////////////////// Synchronization Primitives ///////////////////////////////
+
+#if !defined(_M_CEE)
+#ifndef OPENCV_DISABLE_THREAD_SUPPORT
+typedef std::recursive_mutex Mutex;
+typedef std::lock_guard<cv::Mutex> AutoLock;
+#else // OPENCV_DISABLE_THREAD_SUPPORT
+// Custom (failing) implementation of `std::recursive_mutex`.
+struct Mutex {
+    void lock(){
+        CV_Error(cv::Error::StsNotImplemented,
+                 "cv::Mutex is disabled by OPENCV_DISABLE_THREAD_SUPPORT=ON");
+    }
+    void unlock(){
+        CV_Error(cv::Error::StsNotImplemented,
+                 "cv::Mutex is disabled by OPENCV_DISABLE_THREAD_SUPPORT=ON");
+    }
+};
+// Stub for cv::AutoLock when threads are disabled.
+struct AutoLock {
+    AutoLock(Mutex &) { }
+};
+#endif // OPENCV_DISABLE_THREAD_SUPPORT
+#endif // !defined(_M_CEE)
+
+
+/** @brief Designed for command line parsing
+
+The sample below demonstrates how to use CommandLineParser:
+@code
+    CommandLineParser parser(argc, argv, keys);
+    parser.about("Application name v1.0.0");
+
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    int N = parser.get<int>("N");
+    double fps = parser.get<double>("fps");
+    String path = parser.get<String>("path");
+
+    use_time_stamp = parser.has("timestamp");
+
+    String img1 = parser.get<String>(0);
+    String img2 = parser.get<String>(1);
+
+    int repeat = parser.get<int>(2);
+
+    if (!parser.check())
+    {
+        parser.printErrors();
+        return 0;
+    }
+@endcode
+
+### Keys syntax
+
+The keys parameter is a string containing several blocks, each one is enclosed in curly braces and
+describes one argument. Each argument contains three parts separated by the `|` symbol:
+
+-# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol)
+-# default value will be used if the argument was not provided (can be empty)
+-# help message (can be empty)
+
+For example:
+
+@code{.cpp}
+    const String keys =
+        "{help h usage ? |      | print this message   }"
+        "{@image1        |      | image1 for compare   }"
+        "{@image2        |<none>| image2 for compare   }"
+        "{@repeat        |1     | number               }"
+        "{path           |.     | path to file         }"
+        "{fps            | -1.0 | fps for output video }"
+        "{N count        |100   | count of objects     }"
+        "{ts timestamp   |      | use time stamp       }"
+        ;
+}
+@endcode
+
+Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method.
+Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their
+actual value instead.
+
+String keys like `get<String>("@image1")` return the empty string `""` by default - even with an empty default value.
+Use the special `<none>` default value to enforce that the returned string must not be empty. (like in `get<String>("@image2")`)
+
+### Usage
+
+For the described keys:
+
+@code{.sh}
+    # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true)
+    $ ./app -N=200 1.png 2.jpg 19 -ts
+
+    # Bad call
+    $ ./app -fps=aaa
+    ERRORS:
+    Parameter 'fps': can not convert: [aaa] to [double]
+@endcode
+ */
+class CV_EXPORTS CommandLineParser
+{
+public:
+
+    /** @brief Constructor
+
+    Initializes command line parser object
+
+    @param argc number of command line arguments (from main())
+    @param argv array of command line arguments (from main())
+    @param keys string describing acceptable command line parameters (see class description for syntax)
+    */
+    CommandLineParser(int argc, const char* const argv[], const String& keys);
+
+    /** @brief Copy constructor */
+    CommandLineParser(const CommandLineParser& parser);
+
+    /** @brief Assignment operator */
+    CommandLineParser& operator = (const CommandLineParser& parser);
+
+    /** @brief Destructor */
+    ~CommandLineParser();
+
+    /** @brief Returns application path
+
+    This method returns the path to the executable from the command line (`argv[0]`).
+
+    For example, if the application has been started with such a command:
+    @code{.sh}
+    $ ./bin/my-executable
+    @endcode
+    this method will return `./bin`.
+    */
+    String getPathToApplication() const;
+
+    /** @brief Access arguments by name
+
+    Returns argument converted to selected type. If the argument is not known or can not be
+    converted to selected type, the error flag is set (can be checked with @ref check).
+
+    For example, define:
+    @code{.cpp}
+    String keys = "{N count||}";
+    @endcode
+
+    Call:
+    @code{.sh}
+    $ ./my-app -N=20
+    # or
+    $ ./my-app --count=20
+    @endcode
+
+    Access:
+    @code{.cpp}
+    int N = parser.get<int>("N");
+    @endcode
+
+    @param name name of the argument
+    @param space_delete remove spaces from the left and right of the string
+    @tparam T the argument will be converted to this type if possible
+
+    @note You can access positional arguments by their `@`-prefixed name:
+    @code{.cpp}
+    parser.get<String>("@image");
+    @endcode
+     */
+    template <typename T>
+    T get(const String& name, bool space_delete = true) const
+    {
+        T val = T();
+        getByName(name, space_delete, ParamType<T>::type, (void*)&val);
+        return val;
+    }
+
+    /** @brief Access positional arguments by index
+
+    Returns argument converted to selected type. Indexes are counted from zero.
+
+    For example, define:
+    @code{.cpp}
+    String keys = "{@arg1||}{@arg2||}"
+    @endcode
+
+    Call:
+    @code{.sh}
+    ./my-app abc qwe
+    @endcode
+
+    Access arguments:
+    @code{.cpp}
+    String val_1 = parser.get<String>(0); // returns "abc", arg1
+    String val_2 = parser.get<String>(1); // returns "qwe", arg2
+    @endcode
+
+    @param index index of the argument
+    @param space_delete remove spaces from the left and right of the string
+    @tparam T the argument will be converted to this type if possible
+     */
+    template <typename T>
+    T get(int index, bool space_delete = true) const
+    {
+        T val = T();
+        getByIndex(index, space_delete, ParamType<T>::type, (void*)&val);
+        return val;
+    }
+
+    /** @brief Check if field was provided in the command line
+
+    @param name argument name to check
+    */
+    bool has(const String& name) const;
+
+    /** @brief Check for parsing errors
+
+    Returns false if error occurred while accessing the parameters (bad conversion, missing arguments,
+    etc.). Call @ref printErrors to print error messages list.
+     */
+    bool check() const;
+
+    /** @brief Set the about message
+
+    The about message will be shown when @ref printMessage is called, right before arguments table.
+     */
+    void about(const String& message);
+
+    /** @brief Print help message
+
+    This method will print standard help message containing the about message and arguments description.
+
+    @sa about
+    */
+    void printMessage() const;
+
+    /** @brief Print list of errors occurred
+
+    @sa check
+    */
+    void printErrors() const;
+
+protected:
+    void getByName(const String& name, bool space_delete, Param type, void* dst) const;
+    void getByIndex(int index, bool space_delete, Param type, void* dst) const;
+
+    struct Impl;
+    Impl* impl;
+};
+
+//! @} core_utils
+
+//! @cond IGNORED
+
+/////////////////////////////// AutoBuffer implementation ////////////////////////////////////////
+
+template<typename _Tp, size_t fixed_size> inline
+AutoBuffer<_Tp, fixed_size>::AutoBuffer()
+{
+    ptr = buf;
+    sz = fixed_size;
+}
+
+template<typename _Tp, size_t fixed_size> inline
+AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size)
+{
+    ptr = buf;
+    sz = fixed_size;
+    allocate(_size);
+}
+
+template<typename _Tp, size_t fixed_size> inline
+AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf )
+{
+    ptr = buf;
+    sz = fixed_size;
+    allocate(abuf.size());
+    for( size_t i = 0; i < sz; i++ )
+        ptr[i] = abuf.ptr[i];
+}
+
+template<typename _Tp, size_t fixed_size> inline AutoBuffer<_Tp, fixed_size>&
+AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf)
+{
+    if( this != &abuf )
+    {
+        deallocate();
+        allocate(abuf.size());
+        for( size_t i = 0; i < sz; i++ )
+            ptr[i] = abuf.ptr[i];
+    }
+    return *this;
+}
+
+template<typename _Tp, size_t fixed_size> inline
+AutoBuffer<_Tp, fixed_size>::~AutoBuffer()
+{ deallocate(); }
+
+template<typename _Tp, size_t fixed_size> inline void
+AutoBuffer<_Tp, fixed_size>::allocate(size_t _size)
+{
+    if(_size <= sz)
+    {
+        sz = _size;
+        return;
+    }
+    deallocate();
+    sz = _size;
+    if(_size > fixed_size)
+    {
+        ptr = new _Tp[_size];
+    }
+}
+
+template<typename _Tp, size_t fixed_size> inline void
+AutoBuffer<_Tp, fixed_size>::deallocate()
+{
+    if( ptr != buf )
+    {
+        delete[] ptr;
+        ptr = buf;
+        sz = fixed_size;
+    }
+}
+
+template<typename _Tp, size_t fixed_size> inline void
+AutoBuffer<_Tp, fixed_size>::resize(size_t _size)
+{
+    if(_size <= sz)
+    {
+        sz = _size;
+        return;
+    }
+    size_t i, prevsize = sz, minsize = MIN(prevsize, _size);
+    _Tp* prevptr = ptr;
+
+    ptr = _size > fixed_size ? new _Tp[_size] : buf;
+    sz = _size;
+
+    if( ptr != prevptr )
+        for( i = 0; i < minsize; i++ )
+            ptr[i] = prevptr[i];
+    for( i = prevsize; i < _size; i++ )
+        ptr[i] = _Tp();
+
+    if( prevptr != buf )
+        delete[] prevptr;
+}
+
+template<typename _Tp, size_t fixed_size> inline size_t
+AutoBuffer<_Tp, fixed_size>::size() const
+{ return sz; }
+
+//! @endcond
+
+
+// Basic Node class for tree building
+template<class OBJECT>
+class CV_EXPORTS Node
+{
+public:
+    Node()
+    {
+        m_pParent  = 0;
+    }
+    Node(OBJECT& payload) : m_payload(payload)
+    {
+        m_pParent  = 0;
+    }
+    ~Node()
+    {
+        removeChilds();
+        if (m_pParent)
+        {
+            int idx = m_pParent->findChild(this);
+            if (idx >= 0)
+                m_pParent->m_childs.erase(m_pParent->m_childs.begin() + idx);
+        }
+    }
+
+    Node<OBJECT>* findChild(OBJECT& payload) const
+    {
+        for(size_t i = 0; i < this->m_childs.size(); i++)
+        {
+            if(this->m_childs[i]->m_payload == payload)
+                return this->m_childs[i];
+        }
+        return NULL;
+    }
+
+    int findChild(Node<OBJECT> *pNode) const
+    {
+        for (size_t i = 0; i < this->m_childs.size(); i++)
+        {
+            if(this->m_childs[i] == pNode)
+                return (int)i;
+        }
+        return -1;
+    }
+
+    void addChild(Node<OBJECT> *pNode)
+    {
+        if(!pNode)
+            return;
+
+        CV_Assert(pNode->m_pParent == 0);
+        pNode->m_pParent = this;
+        this->m_childs.push_back(pNode);
+    }
+
+    void removeChilds()
+    {
+        for(size_t i = 0; i < m_childs.size(); i++)
+        {
+            m_childs[i]->m_pParent = 0; // avoid excessive parent vector trimming
+            delete m_childs[i];
+        }
+        m_childs.clear();
+    }
+
+    int getDepth()
+    {
+        int   count   = 0;
+        Node *pParent = m_pParent;
+        while(pParent) count++, pParent = pParent->m_pParent;
+        return count;
+    }
+
+public:
+    OBJECT                     m_payload;
+    Node<OBJECT>*              m_pParent;
+    std::vector<Node<OBJECT>*> m_childs;
+};
+
+
+namespace samples {
+
+//! @addtogroup core_utils_samples
+// This section describes utility functions for OpenCV samples.
+//
+// @note Implementation of these utilities is not thread-safe.
+//
+//! @{
+
+/** @brief Try to find requested data file
+
+Search directories:
+
+1. Directories passed via `addSamplesDataSearchPath()`
+2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable
+3. OPENCV_SAMPLES_DATA_PATH environment variable
+   If parameter value is not empty and nothing is found then stop searching.
+4. Detects build/install path based on:
+   a. current working directory (CWD)
+   b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage)
+5. Scan `<source>/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree.
+6. Scan `<install>/share/OpenCV` directory if install directory is detected.
+
+@see cv::utils::findDataFile
+
+@param relative_path Relative path to data file
+@param required Specify "file not found" handling.
+       If true, function prints information message and raises cv::Exception.
+       If false, function returns empty result
+@param silentMode Disables messages
+@return Returns path (absolute or relative to the current directory) or empty string if file is not found
+*/
+CV_EXPORTS_W cv::String findFile(const cv::String& relative_path, bool required = true, bool silentMode = false);
+
+CV_EXPORTS_W cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode = false);
+
+inline cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode)
+{
+    cv::String res = findFile(relative_path, false, silentMode);
+    if (res.empty())
+        return relative_path;
+    return res;
+}
+
+/** @brief Override search data path by adding new search location
+
+Use this only to override default behavior
+Passed paths are used in LIFO order.
+
+@param path Path to used samples data
+*/
+CV_EXPORTS_W void addSamplesDataSearchPath(const cv::String& path);
+
+/** @brief Append samples search data sub directory
+
+General usage is to add OpenCV modules name (`<opencv_contrib>/modules/<name>/samples/data` -> `<name>/samples/data` + `modules/<name>/samples/data`).
+Passed subdirectories are used in LIFO order.
+
+@param subdir samples data sub directory
+*/
+CV_EXPORTS_W void addSamplesDataSearchSubDirectory(const cv::String& subdir);
+
+//! @}
+} // namespace samples
+
+namespace utils {
+
+CV_EXPORTS int getThreadID();
+
+} // namespace
+
+} //namespace cv
+
+#ifdef CV_COLLECT_IMPL_DATA
+#include "opencv2/core/utils/instrumentation.hpp"
+#else
+/// Collect implementation data on OpenCV function call. Requires ENABLE_IMPL_COLLECTION build option.
+#define CV_IMPL_ADD(impl)
+#endif
+
+#endif //OPENCV_CORE_UTILITY_H
diff --git a/3rdParty/include/opencv2/core/utils/allocator_stats.hpp b/3rdParty/include/opencv2/core/utils/allocator_stats.hpp
new file mode 100644
index 0000000..79e9338
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/allocator_stats.hpp
@@ -0,0 +1,29 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_ALLOCATOR_STATS_HPP
+#define OPENCV_CORE_ALLOCATOR_STATS_HPP
+
+#include "../cvdef.h"
+
+namespace cv { namespace utils {
+
+class AllocatorStatisticsInterface
+{
+protected:
+    AllocatorStatisticsInterface() {}
+    virtual ~AllocatorStatisticsInterface() {}
+public:
+    virtual uint64_t getCurrentUsage() const = 0;
+    virtual uint64_t getTotalUsage() const = 0;
+    virtual uint64_t getNumberOfAllocations() const = 0;
+    virtual uint64_t getPeakUsage() const = 0;
+
+    /** set peak usage = current usage */
+    virtual void resetPeakUsage() = 0;
+};
+
+}} // namespace
+
+#endif // OPENCV_CORE_ALLOCATOR_STATS_HPP
diff --git a/3rdParty/include/opencv2/core/utils/allocator_stats.impl.hpp b/3rdParty/include/opencv2/core/utils/allocator_stats.impl.hpp
new file mode 100644
index 0000000..eb5ecde
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/allocator_stats.impl.hpp
@@ -0,0 +1,158 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP
+#define OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP
+
+#include "./allocator_stats.hpp"
+
+//#define OPENCV_DISABLE_ALLOCATOR_STATS
+
+#ifdef CV_CXX11
+
+#include <atomic>
+
+#ifndef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE
+#if defined(__GNUC__) && (\
+        (defined(__SIZEOF_POINTER__) && __SIZEOF_POINTER__ == 4) || \
+        (defined(__GCC_HAVE_SYNC_COMPARE_AND_SWAP_4) && !defined(__GCC_HAVE_SYNC_COMPARE_AND_SWAP_8)) \
+    )
+#define OPENCV_ALLOCATOR_STATS_COUNTER_TYPE int
+#endif
+#endif
+
+#ifndef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE
+#define OPENCV_ALLOCATOR_STATS_COUNTER_TYPE long long
+#endif
+
+#else  // CV_CXX11
+
+#ifndef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE
+#define OPENCV_ALLOCATOR_STATS_COUNTER_TYPE int  // CV_XADD supports int only
+#endif
+
+#endif  // CV_CXX11
+
+namespace cv { namespace utils {
+
+#ifdef CV__ALLOCATOR_STATS_LOG
+namespace {
+#endif
+
+class AllocatorStatistics : public AllocatorStatisticsInterface
+{
+#ifdef OPENCV_DISABLE_ALLOCATOR_STATS
+
+public:
+    AllocatorStatistics() {}
+    ~AllocatorStatistics() CV_OVERRIDE {}
+
+    uint64_t getCurrentUsage() const CV_OVERRIDE { return 0; }
+    uint64_t getTotalUsage() const CV_OVERRIDE { return 0; }
+    uint64_t getNumberOfAllocations() const CV_OVERRIDE { return 0; }
+    uint64_t getPeakUsage() const CV_OVERRIDE { return 0; }
+
+    /** set peak usage = current usage */
+    void resetPeakUsage() CV_OVERRIDE {};
+
+    void onAllocate(size_t /*sz*/) {}
+    void onFree(size_t /*sz*/) {}
+
+#elif defined(CV_CXX11)
+
+protected:
+    typedef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE counter_t;
+    std::atomic<counter_t> curr, total, total_allocs, peak;
+public:
+    AllocatorStatistics() {}
+    ~AllocatorStatistics() CV_OVERRIDE {}
+
+    uint64_t getCurrentUsage() const CV_OVERRIDE { return (uint64_t)curr.load(); }
+    uint64_t getTotalUsage() const CV_OVERRIDE { return (uint64_t)total.load(); }
+    uint64_t getNumberOfAllocations() const CV_OVERRIDE { return (uint64_t)total_allocs.load(); }
+    uint64_t getPeakUsage() const CV_OVERRIDE { return (uint64_t)peak.load(); }
+
+    /** set peak usage = current usage */
+    void resetPeakUsage() CV_OVERRIDE { peak.store(curr.load()); }
+
+    // Controller interface
+    void onAllocate(size_t sz)
+    {
+#ifdef CV__ALLOCATOR_STATS_LOG
+        CV__ALLOCATOR_STATS_LOG(cv::format("allocate: %lld (curr=%lld)", (long long int)sz, (long long int)curr.load()));
+#endif
+
+        counter_t new_curr = curr.fetch_add((counter_t)sz) + (counter_t)sz;
+
+        // peak = std::max((uint64_t)peak, new_curr);
+        auto prev_peak = peak.load();
+        while (prev_peak < new_curr)
+        {
+            if (peak.compare_exchange_weak(prev_peak, new_curr))
+                break;
+        }
+        // end of peak = max(...)
+
+        total += (counter_t)sz;
+        total_allocs++;
+    }
+    void onFree(size_t sz)
+    {
+#ifdef CV__ALLOCATOR_STATS_LOG
+        CV__ALLOCATOR_STATS_LOG(cv::format("free: %lld (curr=%lld)", (long long int)sz, (long long int)curr.load()));
+#endif
+        curr -= (counter_t)sz;
+    }
+
+#else  // non C++11
+
+protected:
+    typedef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE counter_t;
+    volatile counter_t curr, total, total_allocs, peak;  // overflow is possible, CV_XADD operates with 'int' only
+public:
+    AllocatorStatistics()
+        : curr(0), total(0), total_allocs(0), peak(0)
+    {}
+    ~AllocatorStatistics() CV_OVERRIDE {}
+
+    uint64_t getCurrentUsage() const CV_OVERRIDE { return (uint64_t)curr; }
+    uint64_t getTotalUsage() const CV_OVERRIDE { return (uint64_t)total; }
+    uint64_t getNumberOfAllocations() const CV_OVERRIDE { return (uint64_t)total_allocs; }
+    uint64_t getPeakUsage() const CV_OVERRIDE { return (uint64_t)peak; }
+
+    void resetPeakUsage() CV_OVERRIDE { peak = curr; }
+
+    // Controller interface
+    void onAllocate(size_t sz)
+    {
+#ifdef CV__ALLOCATOR_STATS_LOG
+        CV__ALLOCATOR_STATS_LOG(cv::format("allocate: %lld (curr=%lld)", (long long int)sz, (long long int)curr));
+#endif
+
+        counter_t new_curr = (counter_t)CV_XADD(&curr, (counter_t)sz) + (counter_t)sz;
+
+        peak = std::max((counter_t)peak, new_curr);  // non-thread safe
+
+        //CV_XADD(&total, (uint64_t)sz);  // overflow with int, non-reliable...
+        total += sz;
+
+        CV_XADD(&total_allocs, (counter_t)1);
+    }
+    void onFree(size_t sz)
+    {
+#ifdef CV__ALLOCATOR_STATS_LOG
+        CV__ALLOCATOR_STATS_LOG(cv::format("free: %lld (curr=%lld)", (long long int)sz, (long long int)curr));
+#endif
+        CV_XADD(&curr, (counter_t)-sz);
+    }
+#endif
+};
+
+#ifdef CV__ALLOCATOR_STATS_LOG
+} // namespace
+#endif
+
+}} // namespace
+
+#endif // OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP
diff --git a/3rdParty/include/opencv2/core/utils/filesystem.hpp b/3rdParty/include/opencv2/core/utils/filesystem.hpp
new file mode 100644
index 0000000..a98d220
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/filesystem.hpp
@@ -0,0 +1,82 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_UTILS_FILESYSTEM_HPP
+#define OPENCV_UTILS_FILESYSTEM_HPP
+
+namespace cv { namespace utils { namespace fs {
+
+
+CV_EXPORTS bool exists(const cv::String& path);
+CV_EXPORTS bool isDirectory(const cv::String& path);
+
+CV_EXPORTS void remove_all(const cv::String& path);
+
+
+CV_EXPORTS cv::String getcwd();
+
+/** @brief Converts path p to a canonical absolute path
+ * Symlinks are processed if there is support for them on running platform.
+ *
+ * @param path input path. Target file/directory should exist.
+ */
+CV_EXPORTS cv::String canonical(const cv::String& path);
+
+/** Join path components */
+CV_EXPORTS cv::String join(const cv::String& base, const cv::String& path);
+
+/** Get parent directory */
+CV_EXPORTS cv::String getParent(const cv::String &path);
+CV_EXPORTS std::wstring getParent(const std::wstring& path);
+
+/**
+ * Generate a list of all files that match the globbing pattern.
+ *
+ * Result entries are prefixed by base directory path.
+ *
+ * @param directory base directory
+ * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results
+ * @param[out] result result of globing.
+ * @param recursive scan nested directories too
+ * @param includeDirectories include directories into results list
+ */
+CV_EXPORTS void glob(const cv::String& directory, const cv::String& pattern,
+        CV_OUT std::vector<cv::String>& result,
+        bool recursive = false, bool includeDirectories = false);
+
+/**
+ * Generate a list of all files that match the globbing pattern.
+ *
+ * @param directory base directory
+ * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results
+ * @param[out] result globbing result with relative paths from base directory
+ * @param recursive scan nested directories too
+ * @param includeDirectories include directories into results list
+ */
+CV_EXPORTS void glob_relative(const cv::String& directory, const cv::String& pattern,
+        CV_OUT std::vector<cv::String>& result,
+        bool recursive = false, bool includeDirectories = false);
+
+
+CV_EXPORTS bool createDirectory(const cv::String& path);
+CV_EXPORTS bool createDirectories(const cv::String& path);
+
+#ifdef __OPENCV_BUILD
+// TODO
+//CV_EXPORTS cv::String getTempDirectory();
+
+/**
+ * @brief Returns directory to store OpenCV cache files
+ * Create sub-directory in common OpenCV cache directory if it doesn't exist.
+ * @param sub_directory_name name of sub-directory. NULL or "" value asks to return root cache directory.
+ * @param configuration_name optional name of configuration parameter name which overrides default behavior.
+ * @return Path to cache directory. Returns empty string if cache directories support is not available. Returns "disabled" if cache disabled by user.
+ */
+CV_EXPORTS cv::String getCacheDirectory(const char* sub_directory_name, const char* configuration_name = NULL);
+
+#endif
+
+}}} // namespace
+
+#endif // OPENCV_UTILS_FILESYSTEM_HPP
diff --git a/3rdParty/include/opencv2/core/utils/instrumentation.hpp b/3rdParty/include/opencv2/core/utils/instrumentation.hpp
new file mode 100644
index 0000000..3639867
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/instrumentation.hpp
@@ -0,0 +1,125 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_UTILS_INSTR_HPP
+#define OPENCV_UTILS_INSTR_HPP
+
+#include <opencv2/core/utility.hpp>
+#include <opencv2/core/utils/tls.hpp>
+
+namespace cv {
+
+//! @addtogroup core_utils
+//! @{
+
+#ifdef CV_COLLECT_IMPL_DATA
+CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays
+CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays
+// Get stored implementation flags and functions names arrays
+// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which function
+CV_EXPORTS int getImpl(std::vector<int> &impl, std::vector<String> &funName);
+
+CV_EXPORTS bool useCollection(); // return implementation collection state
+CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state
+
+#define CV_IMPL_PLAIN  0x01 // native CPU OpenCV implementation
+#define CV_IMPL_OCL    0x02 // OpenCL implementation
+#define CV_IMPL_IPP    0x04 // IPP implementation
+#define CV_IMPL_MT     0x10 // multithreaded implementation
+
+#undef CV_IMPL_ADD
+#define CV_IMPL_ADD(impl)                                                   \
+    if(cv::useCollection())                                                 \
+    {                                                                       \
+        cv::addImpl(impl, CV_Func);                                         \
+    }
+#endif
+
+// Instrumentation external interface
+namespace instr
+{
+
+#if !defined OPENCV_ABI_CHECK
+
+enum TYPE
+{
+    TYPE_GENERAL = 0,   // OpenCV API function, e.g. exported function
+    TYPE_MARKER,        // Information marker
+    TYPE_WRAPPER,       // Wrapper function for implementation
+    TYPE_FUN,           // Simple function call
+};
+
+enum IMPL
+{
+    IMPL_PLAIN = 0,
+    IMPL_IPP,
+    IMPL_OPENCL,
+};
+
+struct NodeDataTls
+{
+    NodeDataTls()
+    {
+        m_ticksTotal = 0;
+    }
+    uint64      m_ticksTotal;
+};
+
+class CV_EXPORTS NodeData
+{
+public:
+    NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN);
+    NodeData(NodeData &ref);
+    ~NodeData();
+    NodeData& operator=(const NodeData&);
+
+    cv::String          m_funName;
+    cv::instr::TYPE     m_instrType;
+    cv::instr::IMPL     m_implType;
+    const char*         m_fileName;
+    int                 m_lineNum;
+    void*               m_retAddress;
+    bool                m_alwaysExpand;
+    bool                m_funError;
+
+    volatile int         m_counter;
+    volatile uint64      m_ticksTotal;
+    TLSDataAccumulator<NodeDataTls> m_tls;
+    int                  m_threads;
+
+    // No synchronization
+    double getTotalMs()   const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; }
+    double getMeanMs()    const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; }
+};
+bool operator==(const NodeData& lhs, const NodeData& rhs);
+
+typedef Node<NodeData> InstrNode;
+
+CV_EXPORTS InstrNode* getTrace();
+
+#endif // !defined OPENCV_ABI_CHECK
+
+
+CV_EXPORTS bool       useInstrumentation();
+CV_EXPORTS void       setUseInstrumentation(bool flag);
+CV_EXPORTS void       resetTrace();
+
+enum FLAGS
+{
+    FLAGS_NONE              = 0,
+    FLAGS_MAPPING           = 0x01,
+    FLAGS_EXPAND_SAME_NAMES = 0x02,
+};
+
+CV_EXPORTS void       setFlags(FLAGS modeFlags);
+static inline void    setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); }
+CV_EXPORTS FLAGS      getFlags();
+
+} // namespace instr
+
+//! @}
+
+} // namespace
+
+#endif // OPENCV_UTILS_TLS_HPP
diff --git a/3rdParty/include/opencv2/core/utils/logger.defines.hpp b/3rdParty/include/opencv2/core/utils/logger.defines.hpp
new file mode 100644
index 0000000..7d73f02
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/logger.defines.hpp
@@ -0,0 +1,42 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_LOGGER_DEFINES_HPP
+#define OPENCV_LOGGER_DEFINES_HPP
+
+//! @addtogroup core_logging
+//! @{
+
+// Supported logging levels and their semantic
+#define CV_LOG_LEVEL_SILENT 0          //!< for using in setLogLevel() call
+#define CV_LOG_LEVEL_FATAL 1           //!< Fatal (critical) error (unrecoverable internal error)
+#define CV_LOG_LEVEL_ERROR 2           //!< Error message
+#define CV_LOG_LEVEL_WARN 3            //!< Warning message
+#define CV_LOG_LEVEL_INFO 4            //!< Info message
+#define CV_LOG_LEVEL_DEBUG 5           //!< Debug message. Disabled in the "Release" build.
+#define CV_LOG_LEVEL_VERBOSE 6         //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build.
+
+namespace cv {
+namespace utils {
+namespace logging {
+
+//! Supported logging levels and their semantic
+enum LogLevel {
+    LOG_LEVEL_SILENT = 0,              //!< for using in setLogVevel() call
+    LOG_LEVEL_FATAL = 1,               //!< Fatal (critical) error (unrecoverable internal error)
+    LOG_LEVEL_ERROR = 2,               //!< Error message
+    LOG_LEVEL_WARNING = 3,             //!< Warning message
+    LOG_LEVEL_INFO = 4,                //!< Info message
+    LOG_LEVEL_DEBUG = 5,               //!< Debug message. Disabled in the "Release" build.
+    LOG_LEVEL_VERBOSE = 6,             //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build.
+#ifndef CV_DOXYGEN
+    ENUM_LOG_LEVEL_FORCE_INT = INT_MAX
+#endif
+};
+
+}}} // namespace
+
+//! @}
+
+#endif // OPENCV_LOGGER_DEFINES_HPP
diff --git a/3rdParty/include/opencv2/core/utils/logger.hpp b/3rdParty/include/opencv2/core/utils/logger.hpp
new file mode 100644
index 0000000..accb860
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/logger.hpp
@@ -0,0 +1,218 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_LOGGER_HPP
+#define OPENCV_LOGGER_HPP
+
+#include <iostream>
+#include <sstream>
+#include <limits.h> // INT_MAX
+
+#include "logger.defines.hpp"
+#include "logtag.hpp"
+
+namespace cv {
+namespace utils {
+namespace logging {
+
+//! @addtogroup core_logging
+//! @{
+
+/** Set global logging level
+@return previous logging level
+*/
+CV_EXPORTS LogLevel setLogLevel(LogLevel logLevel);
+/** Get global logging level */
+CV_EXPORTS LogLevel getLogLevel();
+
+CV_EXPORTS void registerLogTag(cv::utils::logging::LogTag* plogtag);
+
+CV_EXPORTS void setLogTagLevel(const char* tag, cv::utils::logging::LogLevel level);
+
+CV_EXPORTS cv::utils::logging::LogLevel getLogTagLevel(const char* tag);
+
+namespace internal {
+
+/** Get global log tag */
+CV_EXPORTS cv::utils::logging::LogTag* getGlobalLogTag();
+
+/** Write log message */
+CV_EXPORTS void writeLogMessage(LogLevel logLevel, const char* message);
+
+/** Write log message */
+CV_EXPORTS void writeLogMessageEx(LogLevel logLevel, const char* tag, const char* file, int line, const char* func, const char* message);
+
+} // namespace
+
+struct LogTagAuto
+    : public LogTag
+{
+    inline LogTagAuto(const char* _name, LogLevel _level)
+        : LogTag(_name, _level)
+    {
+        registerLogTag(this);
+    }
+};
+
+/**
+ * \def CV_LOG_STRIP_LEVEL
+ *
+ * Define CV_LOG_STRIP_LEVEL=CV_LOG_LEVEL_[DEBUG|INFO|WARN|ERROR|FATAL|SILENT] to compile out anything at that and before that logging level
+ */
+#ifndef CV_LOG_STRIP_LEVEL
+# if defined NDEBUG
+#   define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG
+# else
+#   define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE
+# endif
+#endif
+
+#define CV_LOGTAG_PTR_CAST(expr) static_cast<const cv::utils::logging::LogTag*>(expr)
+
+// CV_LOGTAG_EXPAND_NAME is intended to be re-defined (undef and then define again)
+// to allows logging users to use a shorter name argument when calling
+// CV_LOG_WITH_TAG or its related macros such as CV_LOG_INFO.
+//
+// This macro is intended to modify the tag argument as a string (token), via
+// preprocessor token pasting or metaprogramming techniques. A typical usage
+// is to apply a prefix, such as
+// ...... #define CV_LOGTAG_EXPAND_NAME(tag) cv_logtag_##tag
+//
+// It is permitted to re-define to a hard-coded expression, ignoring the tag.
+// This would work identically like the CV_LOGTAG_FALLBACK macro.
+//
+// Important: When the logging macro is called with tag being NULL, a user-defined
+// CV_LOGTAG_EXPAND_NAME may expand it into cv_logtag_0, cv_logtag_NULL, or
+// cv_logtag_nullptr. Use with care. Also be mindful of C++ symbol redefinitions.
+//
+// If there is significant amount of logging code with tag being NULL, it is
+// recommended to use (re-define) CV_LOGTAG_FALLBACK to inject locally a default
+// tag at the beginning of a compilation unit, to minimize lines of code changes.
+//
+#define CV_LOGTAG_EXPAND_NAME(tag) tag
+
+// CV_LOGTAG_FALLBACK is intended to be re-defined (undef and then define again)
+// by any other compilation units to provide a log tag when the logging statement
+// does not specify one. The macro needs to expand into a C++ expression that can
+// be static_cast into (cv::utils::logging::LogTag*). Null (nullptr) is permitted.
+#define CV_LOGTAG_FALLBACK nullptr
+
+// CV_LOGTAG_GLOBAL is the tag used when a log tag is not specified in the logging
+// statement nor the compilation unit. The macro needs to expand into a C++
+// expression that can be static_cast into (cv::utils::logging::LogTag*). Must be
+// non-null. Do not re-define.
+#define CV_LOGTAG_GLOBAL cv::utils::logging::internal::getGlobalLogTag()
+
+#define CV_LOG_WITH_TAG(tag, msgLevel, extra_check0, extra_check1, ...) \
+    for(;;) { \
+        extra_check0; \
+        const auto cv_temp_msglevel = (cv::utils::logging::LogLevel)(msgLevel); \
+        if (cv_temp_msglevel >= (CV_LOG_STRIP_LEVEL)) break; \
+        auto cv_temp_logtagptr = CV_LOGTAG_PTR_CAST(CV_LOGTAG_EXPAND_NAME(tag)); \
+        if (!cv_temp_logtagptr) cv_temp_logtagptr = CV_LOGTAG_PTR_CAST(CV_LOGTAG_FALLBACK); \
+        if (!cv_temp_logtagptr) cv_temp_logtagptr = CV_LOGTAG_PTR_CAST(CV_LOGTAG_GLOBAL); \
+        if (cv_temp_logtagptr && (cv_temp_msglevel > cv_temp_logtagptr->level)) break; \
+        extra_check1; \
+        std::stringstream cv_temp_logstream; \
+        cv_temp_logstream << __VA_ARGS__; \
+        cv::utils::logging::internal::writeLogMessageEx( \
+            cv_temp_msglevel, \
+            (cv_temp_logtagptr ? cv_temp_logtagptr->name : nullptr), \
+            __FILE__, \
+            __LINE__, \
+            CV_Func, \
+            cv_temp_logstream.str().c_str()); \
+        break; \
+    }
+
+#define CV_LOG_FATAL(tag, ...)   CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_FATAL, , , __VA_ARGS__)
+#define CV_LOG_ERROR(tag, ...)   CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_ERROR, , , __VA_ARGS__)
+#define CV_LOG_WARNING(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_WARNING, , , __VA_ARGS__)
+#define CV_LOG_INFO(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_INFO, , , __VA_ARGS__)
+#define CV_LOG_DEBUG(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_DEBUG, , , __VA_ARGS__)
+#define CV_LOG_VERBOSE(tag, v, ...) CV_LOG_WITH_TAG(tag, (cv::utils::logging::LOG_LEVEL_VERBOSE + (int)(v)), , , __VA_ARGS__)
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO
+#undef CV_LOG_INFO
+#define CV_LOG_INFO(tag, ...)
+#endif
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG
+#undef CV_LOG_DEBUG
+#define CV_LOG_DEBUG(tag, ...)
+#endif
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE
+#undef CV_LOG_VERBOSE
+#define CV_LOG_VERBOSE(tag, v, ...)
+#endif
+
+//! @cond IGNORED
+#define CV__LOG_ONCE_CHECK_PRE \
+    static bool _cv_log_once_ ## __LINE__ = false; \
+    if (_cv_log_once_ ## __LINE__) break;
+
+#define CV__LOG_ONCE_CHECK_POST \
+    _cv_log_once_ ## __LINE__ = true;
+
+#define CV__LOG_IF_CHECK(logging_cond) \
+    if (!(logging_cond)) break;
+
+//! @endcond
+
+
+// CV_LOG_ONCE_XXX macros
+
+#define CV_LOG_ONCE_ERROR(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_ERROR, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, __VA_ARGS__)
+#define CV_LOG_ONCE_WARNING(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_WARNING, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, __VA_ARGS__)
+#define CV_LOG_ONCE_INFO(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_INFO, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, __VA_ARGS__)
+#define CV_LOG_ONCE_DEBUG(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_DEBUG, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, __VA_ARGS__)
+#define CV_LOG_ONCE_VERBOSE(tag, v, ...) CV_LOG_WITH_TAG(tag, (cv::utils::logging::LOG_LEVEL_VERBOSE + (int)(v)), CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, __VA_ARGS__)
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO
+#undef CV_LOG_ONCE_INFO
+#define CV_LOG_ONCE_INFO(tag, ...)
+#endif
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG
+#undef CV_LOG_ONCE_DEBUG
+#define CV_LOG_ONCE_DEBUG(tag, ...)
+#endif
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE
+#undef CV_LOG_ONCE_VERBOSE
+#define CV_LOG_ONCE_VERBOSE(tag, v, ...)
+#endif
+
+
+// CV_LOG_IF_XXX macros
+
+#define CV_LOG_IF_FATAL(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_FATAL, , CV__LOG_IF_CHECK(logging_cond), __VA_ARGS__)
+#define CV_LOG_IF_ERROR(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_ERROR, , CV__LOG_IF_CHECK(logging_cond), __VA_ARGS__)
+#define CV_LOG_IF_WARNING(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_WARNING, , CV__LOG_IF_CHECK(logging_cond), __VA_ARGS__)
+#define CV_LOG_IF_INFO(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_INFO, , CV__LOG_IF_CHECK(logging_cond), __VA_ARGS__)
+#define CV_LOG_IF_DEBUG(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_DEBUG, , CV__LOG_IF_CHECK(logging_cond), __VA_ARGS__)
+#define CV_LOG_IF_VERBOSE(tag, v, logging_cond, ...) CV_LOG_WITH_TAG(tag, (cv::utils::logging::LOG_LEVEL_VERBOSE + (int)(v)), , CV__LOG_IF_CHECK(logging_cond), __VA_ARGS__)
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO
+#undef CV_LOG_IF_INFO
+#define CV_LOG_IF_INFO(tag, logging_cond, ...)
+#endif
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG
+#undef CV_LOG_IF_DEBUG
+#define CV_LOG_IF_DEBUG(tag, logging_cond, ...)
+#endif
+
+#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE
+#undef CV_LOG_IF_VERBOSE
+#define CV_LOG_IF_VERBOSE(tag, v, logging_cond, ...)
+#endif
+
+
+//! @}
+
+}}} // namespace
+
+#endif // OPENCV_LOGGER_HPP
diff --git a/3rdParty/include/opencv2/core/utils/logtag.hpp b/3rdParty/include/opencv2/core/utils/logtag.hpp
new file mode 100644
index 0000000..4089720
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/logtag.hpp
@@ -0,0 +1,28 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_CORE_LOGTAG_HPP
+#define OPENCV_CORE_LOGTAG_HPP
+
+#include "opencv2/core/cvstd.hpp"
+#include "logger.defines.hpp"
+
+namespace cv {
+namespace utils {
+namespace logging {
+
+struct LogTag
+{
+    const char* name;
+    LogLevel level;
+
+    inline LogTag(const char* _name, LogLevel _level)
+        : name(_name)
+        , level(_level)
+    {}
+};
+
+}}}
+
+#endif
diff --git a/3rdParty/include/opencv2/core/utils/tls.hpp b/3rdParty/include/opencv2/core/utils/tls.hpp
new file mode 100644
index 0000000..124caeb
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/tls.hpp
@@ -0,0 +1,235 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_UTILS_TLS_HPP
+#define OPENCV_UTILS_TLS_HPP
+
+#ifndef OPENCV_CORE_UTILITY_H
+#error "tls.hpp must be included after opencv2/core/utility.hpp or opencv2/core.hpp"
+#endif
+
+namespace cv {
+
+//! @addtogroup core_utils
+//! @{
+
+namespace details { class TlsStorage; }
+
+/** TLS container base implementation
+ *
+ * Don't use directly.
+ *
+ * @sa TLSData, TLSDataAccumulator templates
+ */
+class CV_EXPORTS TLSDataContainer
+{
+protected:
+    TLSDataContainer();
+    virtual ~TLSDataContainer();
+
+    /// @deprecated use detachData() instead
+    void  gatherData(std::vector<void*> &data) const;
+    /// get TLS data and detach all data from threads (similar to cleanup() call)
+    void  detachData(std::vector<void*>& data);
+
+    void* getData() const;
+    void  release();
+
+protected:
+    virtual void* createDataInstance() const = 0;
+    virtual void  deleteDataInstance(void* pData) const = 0;
+
+private:
+    int key_;
+
+    friend class cv::details::TlsStorage;  // core/src/system.cpp
+
+public:
+    void cleanup(); //!< Release created TLS data container objects. It is similar to release() call, but it keeps TLS container valid.
+
+private:
+    // Disable copy/assign (noncopyable pattern)
+    TLSDataContainer(TLSDataContainer &) = delete;
+    TLSDataContainer& operator =(const TLSDataContainer &) = delete;
+};
+
+
+/** @brief Simple TLS data class
+ *
+ * @sa TLSDataAccumulator
+ */
+template <typename T>
+class TLSData : protected TLSDataContainer
+{
+public:
+    inline TLSData() {}
+    inline ~TLSData() { release(); }
+
+    inline T* get() const   { return (T*)getData(); }  //!< Get data associated with key
+    inline T& getRef() const { T* ptr = (T*)getData(); CV_DbgAssert(ptr); return *ptr; }  //!< Get data associated with key
+
+    /// Release associated thread data
+    inline void cleanup()
+    {
+        TLSDataContainer::cleanup();
+    }
+
+protected:
+    /// Wrapper to allocate data by template
+    virtual void* createDataInstance() const CV_OVERRIDE { return new T; }
+    /// Wrapper to release data by template
+    virtual void  deleteDataInstance(void* pData) const CV_OVERRIDE { delete (T*)pData; }
+};
+
+
+/// TLS data accumulator with gathering methods
+template <typename T>
+class TLSDataAccumulator : public TLSData<T>
+{
+    mutable cv::Mutex mutex;
+    mutable std::vector<T*> dataFromTerminatedThreads;
+    std::vector<T*> detachedData;
+    bool cleanupMode;
+public:
+    TLSDataAccumulator() : cleanupMode(false) {}
+    ~TLSDataAccumulator()
+    {
+        release();
+    }
+
+    /** @brief Get data from all threads
+     * @deprecated replaced by detachData()
+     *
+     * Lifetime of vector data is valid until next detachData()/cleanup()/release() calls
+     *
+     * @param[out] data result buffer (should be empty)
+     */
+    void gather(std::vector<T*> &data) const
+    {
+        CV_Assert(cleanupMode == false);  // state is not valid
+        CV_Assert(data.empty());
+        {
+            std::vector<void*> &dataVoid = reinterpret_cast<std::vector<void*>&>(data);
+            TLSDataContainer::gatherData(dataVoid);
+        }
+        {
+            AutoLock lock(mutex);
+            data.reserve(data.size() + dataFromTerminatedThreads.size());
+            for (typename std::vector<T*>::const_iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i)
+            {
+                data.push_back((T*)*i);
+            }
+        }
+    }
+
+    /** @brief Get and detach data from all threads
+     *
+     * Call cleanupDetachedData() when returned vector is not needed anymore.
+     *
+     * @return Vector with associated data. Content is preserved (including lifetime of attached data pointers) until next detachData()/cleanupDetachedData()/cleanup()/release() calls
+     */
+    std::vector<T*>& detachData()
+    {
+        CV_Assert(cleanupMode == false);  // state is not valid
+        std::vector<void*> dataVoid;
+        {
+            TLSDataContainer::detachData(dataVoid);
+        }
+        {
+            AutoLock lock(mutex);
+            detachedData.reserve(dataVoid.size() + dataFromTerminatedThreads.size());
+            for (typename std::vector<T*>::const_iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i)
+            {
+                detachedData.push_back((T*)*i);
+            }
+            dataFromTerminatedThreads.clear();
+            for (typename std::vector<void*>::const_iterator i = dataVoid.begin(); i != dataVoid.end(); ++i)
+            {
+                detachedData.push_back((T*)(void*)*i);
+            }
+        }
+        dataVoid.clear();
+        return detachedData;
+    }
+
+    /// Release associated thread data returned by detachData() call
+    void cleanupDetachedData()
+    {
+        AutoLock lock(mutex);
+        cleanupMode = true;
+        _cleanupDetachedData();
+        cleanupMode = false;
+    }
+
+    /// Release associated thread data
+    void cleanup()
+    {
+        cleanupMode = true;
+        TLSDataContainer::cleanup();
+
+        AutoLock lock(mutex);
+        _cleanupDetachedData();
+        _cleanupTerminatedData();
+        cleanupMode = false;
+    }
+
+    /// Release associated thread data and free TLS key
+    void release()
+    {
+        cleanupMode = true;
+        TLSDataContainer::release();
+        {
+            AutoLock lock(mutex);
+            _cleanupDetachedData();
+            _cleanupTerminatedData();
+        }
+    }
+
+protected:
+    // synchronized
+    void _cleanupDetachedData()
+    {
+        for (typename std::vector<T*>::iterator i = detachedData.begin(); i != detachedData.end(); ++i)
+        {
+            deleteDataInstance((T*)*i);
+        }
+        detachedData.clear();
+    }
+
+    // synchronized
+    void _cleanupTerminatedData()
+    {
+        for (typename std::vector<T*>::iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i)
+        {
+            deleteDataInstance((T*)*i);
+        }
+        dataFromTerminatedThreads.clear();
+    }
+
+protected:
+    virtual void* createDataInstance() const CV_OVERRIDE
+    {
+        // Note: we can collect all allocated data here, but this would require raced mutex locks
+        return new T;
+    }
+    virtual void  deleteDataInstance(void* pData) const CV_OVERRIDE
+    {
+        if (cleanupMode)
+        {
+            delete (T*)pData;
+        }
+        else
+        {
+            AutoLock lock(mutex);
+            dataFromTerminatedThreads.push_back((T*)pData);
+        }
+    }
+};
+
+
+//! @}
+
+} // namespace
+
+#endif // OPENCV_UTILS_TLS_HPP
diff --git a/3rdParty/include/opencv2/core/utils/trace.hpp b/3rdParty/include/opencv2/core/utils/trace.hpp
new file mode 100644
index 0000000..ef5d35b
--- /dev/null
+++ b/3rdParty/include/opencv2/core/utils/trace.hpp
@@ -0,0 +1,252 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_TRACE_HPP
+#define OPENCV_TRACE_HPP
+
+#include <opencv2/core/cvdef.h>
+
+namespace cv {
+namespace utils {
+namespace trace {
+
+//! @addtogroup core_logging
+//! @{
+
+//! Macro to trace function
+#define CV_TRACE_FUNCTION()
+
+#define CV_TRACE_FUNCTION_SKIP_NESTED()
+
+//! Trace code scope.
+//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "initialize".
+#define CV_TRACE_REGION(name_as_static_string_literal)
+//! mark completed of the current opened region and create new one
+//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "step1".
+#define CV_TRACE_REGION_NEXT(name_as_static_string_literal)
+
+//! Macro to trace argument value
+#define CV_TRACE_ARG(arg_id)
+
+//! Macro to trace argument value (expanded version)
+#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value)
+
+//! @cond IGNORED
+#define CV_TRACE_NS cv::utils::trace
+
+#if !defined(OPENCV_DISABLE_TRACE) && defined(__EMSCRIPTEN__)
+#define OPENCV_DISABLE_TRACE 1
+#endif
+
+namespace details {
+
+#ifndef __OPENCV_TRACE
+# if defined __OPENCV_BUILD && !defined __OPENCV_TESTS && !defined __OPENCV_APPS
+#   define __OPENCV_TRACE 1
+# else
+#   define __OPENCV_TRACE 0
+# endif
+#endif
+
+#ifndef CV_TRACE_FILENAME
+# define CV_TRACE_FILENAME __FILE__
+#endif
+
+#ifndef CV__TRACE_FUNCTION
+# if defined _MSC_VER
+#   define CV__TRACE_FUNCTION __FUNCSIG__
+# elif defined __GNUC__
+#   define CV__TRACE_FUNCTION __PRETTY_FUNCTION__
+# else
+#   define CV__TRACE_FUNCTION "<unknown>"
+# endif
+#endif
+
+//! Thread-local instance (usually allocated on stack)
+class CV_EXPORTS Region
+{
+public:
+    struct LocationExtraData;
+    struct LocationStaticStorage
+    {
+        LocationExtraData** ppExtra;   //< implementation specific data
+        const char* name;              //< region name (function name or other custom name)
+        const char* filename;          //< source code filename
+        int line;                      //< source code line
+        int flags;                     //< flags (implementation code path: Plain, IPP, OpenCL)
+    };
+
+    Region(const LocationStaticStorage& location);
+    inline ~Region()
+    {
+        if (implFlags != 0)
+            destroy();
+        CV_DbgAssert(implFlags == 0);
+        CV_DbgAssert(pImpl == NULL);
+    }
+
+    class Impl;
+    Impl* pImpl; // NULL if current region is not active
+    int implFlags; // see RegionFlag, 0 if region is ignored
+
+    bool isActive() const { return pImpl != NULL; }
+
+    void destroy();
+private:
+    Region(const Region&); // disabled
+    Region& operator= (const Region&); // disabled
+};
+
+//! Specify region flags
+enum RegionLocationFlag {
+    REGION_FLAG_FUNCTION = (1 << 0),             //< region is function (=1) / nested named region (=0)
+    REGION_FLAG_APP_CODE = (1 << 1),             //< region is Application code (=1) / OpenCV library code (=0)
+    REGION_FLAG_SKIP_NESTED = (1 << 2),          //< avoid processing of nested regions
+
+    REGION_FLAG_IMPL_IPP = (1 << 16),            //< region is part of IPP code path
+    REGION_FLAG_IMPL_OPENCL = (2 << 16),         //< region is part of OpenCL code path
+    REGION_FLAG_IMPL_OPENVX = (3 << 16),         //< region is part of OpenVX code path
+
+    REGION_FLAG_IMPL_MASK = (15 << 16),
+
+    REGION_FLAG_REGION_FORCE = (1 << 30),
+    REGION_FLAG_REGION_NEXT = (1 << 31),         //< close previous region (see #CV_TRACE_REGION_NEXT macro)
+
+    ENUM_REGION_FLAG_FORCE_INT = INT_MAX
+};
+
+struct CV_EXPORTS TraceArg {
+public:
+    struct ExtraData;
+    ExtraData** ppExtra;
+    const char* name;
+    int flags;
+};
+/** @brief Add meta information to current region (function)
+ * See CV_TRACE_ARG macro
+ * @param arg argument information structure (global static cache)
+ * @param value argument value (can by dynamic string literal in case of string, static allocation is not required)
+ */
+CV_EXPORTS void traceArg(const TraceArg& arg, const char* value);
+//! @overload
+CV_EXPORTS void traceArg(const TraceArg& arg, int value);
+//! @overload
+CV_EXPORTS void traceArg(const TraceArg& arg, int64 value);
+//! @overload
+CV_EXPORTS void traceArg(const TraceArg& arg, double value);
+
+#define CV__TRACE_LOCATION_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_, loc_id), __LINE__)
+#define CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_extra_, loc_id) , __LINE__)
+
+#define CV__TRACE_DEFINE_LOCATION_(loc_id, name, flags) \
+    static CV_TRACE_NS::details::Region::LocationExtraData* CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) = 0; \
+    static const CV_TRACE_NS::details::Region::LocationStaticStorage \
+        CV__TRACE_LOCATION_VARNAME(loc_id) = { &(CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id)), name, CV_TRACE_FILENAME, __LINE__, flags};
+
+#define CV__TRACE_DEFINE_LOCATION_FN(name, flags) CV__TRACE_DEFINE_LOCATION_(fn, name, ((flags) | CV_TRACE_NS::details::REGION_FLAG_FUNCTION))
+
+
+#define CV__TRACE_OPENCV_FUNCTION() \
+    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, 0); \
+    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
+
+#define CV__TRACE_OPENCV_FUNCTION_NAME(name) \
+    CV__TRACE_DEFINE_LOCATION_FN(name, 0); \
+    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
+
+#define CV__TRACE_APP_FUNCTION() \
+    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
+    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
+
+#define CV__TRACE_APP_FUNCTION_NAME(name) \
+    CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
+    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
+
+
+#define CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED() \
+    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \
+    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
+
+#define CV__TRACE_OPENCV_FUNCTION_NAME_SKIP_NESTED(name) \
+    CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \
+    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
+
+#define CV__TRACE_APP_FUNCTION_SKIP_NESTED() \
+    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED | CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
+    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
+
+
+#define CV__TRACE_REGION_(name_as_static_string_literal, flags) \
+    CV__TRACE_DEFINE_LOCATION_(region, name_as_static_string_literal, flags); \
+    CV_TRACE_NS::details::Region CVAUX_CONCAT(__region_, __LINE__)(CV__TRACE_LOCATION_VARNAME(region));
+
+#define CV__TRACE_REGION(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, 0)
+#define CV__TRACE_REGION_NEXT(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, CV_TRACE_NS::details::REGION_FLAG_REGION_NEXT)
+
+#define CV__TRACE_ARG_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_ ## arg_id, __LINE__)
+#define CV__TRACE_ARG_EXTRA_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_extra_ ## arg_id, __LINE__)
+
+#define CV__TRACE_DEFINE_ARG_(arg_id, name, flags) \
+    static CV_TRACE_NS::details::TraceArg::ExtraData* CV__TRACE_ARG_EXTRA_VARNAME(arg_id) = 0; \
+    static const CV_TRACE_NS::details::TraceArg \
+        CV__TRACE_ARG_VARNAME(arg_id) = { &(CV__TRACE_ARG_EXTRA_VARNAME(arg_id)), name, flags };
+
+#define CV__TRACE_ARG_VALUE(arg_id, arg_name, value) \
+        CV__TRACE_DEFINE_ARG_(arg_id, arg_name, 0); \
+        CV_TRACE_NS::details::traceArg((CV__TRACE_ARG_VARNAME(arg_id)), value);
+
+#define CV__TRACE_ARG(arg_id) CV_TRACE_ARG_VALUE(arg_id, #arg_id, (arg_id))
+
+} // namespace
+
+#ifndef OPENCV_DISABLE_TRACE
+#undef CV_TRACE_FUNCTION
+#undef CV_TRACE_FUNCTION_SKIP_NESTED
+#if __OPENCV_TRACE
+#define CV_TRACE_FUNCTION CV__TRACE_OPENCV_FUNCTION
+#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED
+#else
+#define CV_TRACE_FUNCTION CV__TRACE_APP_FUNCTION
+#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_APP_FUNCTION_SKIP_NESTED
+#endif
+
+#undef CV_TRACE_REGION
+#define CV_TRACE_REGION CV__TRACE_REGION
+
+#undef CV_TRACE_REGION_NEXT
+#define CV_TRACE_REGION_NEXT CV__TRACE_REGION_NEXT
+
+#undef CV_TRACE_ARG_VALUE
+#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) \
+        if (__region_fn.isActive()) \
+        { \
+            CV__TRACE_ARG_VALUE(arg_id, arg_name, value); \
+        }
+
+#undef CV_TRACE_ARG
+#define CV_TRACE_ARG CV__TRACE_ARG
+
+#endif // OPENCV_DISABLE_TRACE
+
+#ifdef OPENCV_TRACE_VERBOSE
+#define CV_TRACE_FUNCTION_VERBOSE CV_TRACE_FUNCTION
+#define CV_TRACE_REGION_VERBOSE CV_TRACE_REGION
+#define CV_TRACE_REGION_NEXT_VERBOSE CV_TRACE_REGION_NEXT
+#define CV_TRACE_ARG_VALUE_VERBOSE CV_TRACE_ARG_VALUE
+#define CV_TRACE_ARG_VERBOSE CV_TRACE_ARG
+#else
+#define CV_TRACE_FUNCTION_VERBOSE(...)
+#define CV_TRACE_REGION_VERBOSE(...)
+#define CV_TRACE_REGION_NEXT_VERBOSE(...)
+#define CV_TRACE_ARG_VALUE_VERBOSE(...)
+#define CV_TRACE_ARG_VERBOSE(...)
+#endif
+
+//! @endcond
+
+//! @}
+
+}}} // namespace
+
+#endif // OPENCV_TRACE_HPP
diff --git a/3rdParty/include/opencv2/core/va_intel.hpp b/3rdParty/include/opencv2/core/va_intel.hpp
new file mode 100644
index 0000000..b37ce75
--- /dev/null
+++ b/3rdParty/include/opencv2/core/va_intel.hpp
@@ -0,0 +1,75 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+// Copyright (C) 2015, Itseez, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#ifndef OPENCV_CORE_VA_INTEL_HPP
+#define OPENCV_CORE_VA_INTEL_HPP
+
+#ifndef __cplusplus
+#  error va_intel.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core.hpp"
+#include "ocl.hpp"
+
+#if defined(HAVE_VA)
+# include "va/va.h"
+#else  // HAVE_VA
+# if !defined(_VA_H_)
+    typedef void* VADisplay;
+    typedef unsigned int VASurfaceID;
+# endif // !_VA_H_
+#endif // HAVE_VA
+
+namespace cv { namespace va_intel {
+
+/** @addtogroup core_va_intel
+This section describes Intel VA-API/OpenCL (CL-VA) interoperability.
+
+To enable basic VA interoperability build OpenCV with libva library integration enabled: `-DWITH_VA=ON` (corresponding dev package should be installed).
+
+To enable advanced CL-VA interoperability support on Intel HW, enable option: `-DWITH_VA_INTEL=ON` (OpenCL integration should be enabled which is the default setting). Special runtime environment should be set up in order to use this feature: correct combination of [libva](https://github.com/intel/libva), [OpenCL runtime](https://github.com/intel/compute-runtime) and [media driver](https://github.com/intel/media-driver) should be installed.
+
+Check usage example for details: samples/va_intel/va_intel_interop.cpp
+*/
+//! @{
+
+/////////////////// CL-VA Interoperability Functions ///////////////////
+
+namespace ocl {
+using namespace cv::ocl;
+
+// TODO static functions in the Context class
+/** @brief Creates OpenCL context from VA.
+@param display    - VADisplay for which CL interop should be established.
+@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false.
+@return Returns reference to OpenCL Context
+ */
+CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true);
+
+} // namespace cv::va_intel::ocl
+
+/** @brief Converts InputArray to VASurfaceID object.
+@param display - VADisplay object.
+@param src     - source InputArray.
+@param surface - destination VASurfaceID object.
+@param size    - size of image represented by VASurfaceID object.
+ */
+CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size);
+
+/** @brief Converts VASurfaceID object to OutputArray.
+@param display - VADisplay object.
+@param surface - source VASurfaceID object.
+@param size    - size of image represented by VASurfaceID object.
+@param dst     - destination OutputArray.
+ */
+CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst);
+
+//! @}
+
+}} // namespace cv::va_intel
+
+#endif /* OPENCV_CORE_VA_INTEL_HPP */
diff --git a/3rdParty/include/opencv2/core/version.hpp b/3rdParty/include/opencv2/core/version.hpp
new file mode 100644
index 0000000..2fbd499
--- /dev/null
+++ b/3rdParty/include/opencv2/core/version.hpp
@@ -0,0 +1,26 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_VERSION_HPP
+#define OPENCV_VERSION_HPP
+
+#define CV_VERSION_MAJOR    4
+#define CV_VERSION_MINOR    5
+#define CV_VERSION_REVISION 5
+#define CV_VERSION_STATUS   ""
+
+#define CVAUX_STR_EXP(__A)  #__A
+#define CVAUX_STR(__A)      CVAUX_STR_EXP(__A)
+
+#define CVAUX_STRW_EXP(__A)  L ## #__A
+#define CVAUX_STRW(__A)      CVAUX_STRW_EXP(__A)
+
+#define CV_VERSION          CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS
+
+/* old  style version constants*/
+#define CV_MAJOR_VERSION    CV_VERSION_MAJOR
+#define CV_MINOR_VERSION    CV_VERSION_MINOR
+#define CV_SUBMINOR_VERSION CV_VERSION_REVISION
+
+#endif // OPENCV_VERSION_HPP
diff --git a/3rdParty/include/opencv2/core/vsx_utils.hpp b/3rdParty/include/opencv2/core/vsx_utils.hpp
new file mode 100644
index 0000000..68863ff
--- /dev/null
+++ b/3rdParty/include/opencv2/core/vsx_utils.hpp
@@ -0,0 +1,1042 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html
+
+#ifndef OPENCV_HAL_VSX_UTILS_HPP
+#define OPENCV_HAL_VSX_UTILS_HPP
+
+#include "opencv2/core/cvdef.h"
+
+#ifndef SKIP_INCLUDES
+#   include <assert.h>
+#endif
+
+//! @addtogroup core_utils_vsx
+//! @{
+#if CV_VSX
+
+#define __VSX_S16__(c, v) (c){v, v, v, v, v, v, v, v, v, v, v, v, v, v, v, v}
+#define __VSX_S8__(c, v)  (c){v, v, v, v, v, v, v, v}
+#define __VSX_S4__(c, v)  (c){v, v, v, v}
+#define __VSX_S2__(c, v)  (c){v, v}
+
+typedef __vector unsigned char vec_uchar16;
+#define vec_uchar16_set(...) (vec_uchar16){__VA_ARGS__}
+#define vec_uchar16_sp(c)    (__VSX_S16__(vec_uchar16, (unsigned char)c))
+#define vec_uchar16_c(v)     ((vec_uchar16)(v))
+#define vec_uchar16_z        vec_uchar16_sp(0)
+
+typedef __vector signed char vec_char16;
+#define vec_char16_set(...) (vec_char16){__VA_ARGS__}
+#define vec_char16_sp(c)    (__VSX_S16__(vec_char16, (signed char)c))
+#define vec_char16_c(v)     ((vec_char16)(v))
+#define vec_char16_z        vec_char16_sp(0)
+
+typedef __vector unsigned short vec_ushort8;
+#define vec_ushort8_set(...) (vec_ushort8){__VA_ARGS__}
+#define vec_ushort8_sp(c)    (__VSX_S8__(vec_ushort8, (unsigned short)c))
+#define vec_ushort8_c(v)     ((vec_ushort8)(v))
+#define vec_ushort8_z        vec_ushort8_sp(0)
+
+typedef __vector signed short vec_short8;
+#define vec_short8_set(...) (vec_short8){__VA_ARGS__}
+#define vec_short8_sp(c)    (__VSX_S8__(vec_short8, (signed short)c))
+#define vec_short8_c(v)     ((vec_short8)(v))
+#define vec_short8_z        vec_short8_sp(0)
+
+typedef __vector unsigned int vec_uint4;
+#define vec_uint4_set(...) (vec_uint4){__VA_ARGS__}
+#define vec_uint4_sp(c)    (__VSX_S4__(vec_uint4, (unsigned int)c))
+#define vec_uint4_c(v)     ((vec_uint4)(v))
+#define vec_uint4_z        vec_uint4_sp(0)
+
+typedef __vector signed int vec_int4;
+#define vec_int4_set(...)  (vec_int4){__VA_ARGS__}
+#define vec_int4_sp(c)     (__VSX_S4__(vec_int4, (signed int)c))
+#define vec_int4_c(v)      ((vec_int4)(v))
+#define vec_int4_z         vec_int4_sp(0)
+
+typedef __vector float vec_float4;
+#define vec_float4_set(...)  (vec_float4){__VA_ARGS__}
+#define vec_float4_sp(c)     (__VSX_S4__(vec_float4, c))
+#define vec_float4_c(v)      ((vec_float4)(v))
+#define vec_float4_z         vec_float4_sp(0)
+
+typedef __vector unsigned long long vec_udword2;
+#define vec_udword2_set(...) (vec_udword2){__VA_ARGS__}
+#define vec_udword2_sp(c)    (__VSX_S2__(vec_udword2, (unsigned long long)c))
+#define vec_udword2_c(v)     ((vec_udword2)(v))
+#define vec_udword2_z        vec_udword2_sp(0)
+
+typedef __vector signed long long vec_dword2;
+#define vec_dword2_set(...) (vec_dword2){__VA_ARGS__}
+#define vec_dword2_sp(c)    (__VSX_S2__(vec_dword2, (signed long long)c))
+#define vec_dword2_c(v)     ((vec_dword2)(v))
+#define vec_dword2_z        vec_dword2_sp(0)
+
+typedef  __vector double vec_double2;
+#define vec_double2_set(...) (vec_double2){__VA_ARGS__}
+#define vec_double2_c(v)     ((vec_double2)(v))
+#define vec_double2_sp(c)    (__VSX_S2__(vec_double2, c))
+#define vec_double2_z        vec_double2_sp(0)
+
+#define vec_bchar16           __vector __bool char
+#define vec_bchar16_set(...) (vec_bchar16){__VA_ARGS__}
+#define vec_bchar16_c(v)     ((vec_bchar16)(v))
+
+#define vec_bshort8           __vector __bool short
+#define vec_bshort8_set(...) (vec_bshort8){__VA_ARGS__}
+#define vec_bshort8_c(v)     ((vec_bshort8)(v))
+
+#define vec_bint4             __vector __bool int
+#define vec_bint4_set(...)   (vec_bint4){__VA_ARGS__}
+#define vec_bint4_c(v)       ((vec_bint4)(v))
+
+#define vec_bdword2            __vector __bool long long
+#define vec_bdword2_set(...)  (vec_bdword2){__VA_ARGS__}
+#define vec_bdword2_c(v)      ((vec_bdword2)(v))
+
+#define VSX_FINLINE(tp) extern inline tp __attribute__((always_inline))
+
+#define VSX_REDIRECT_1RG(rt, rg, fnm, fn2)   \
+VSX_FINLINE(rt) fnm(const rg& a) { return fn2(a); }
+
+#define VSX_REDIRECT_2RG(rt, rg, fnm, fn2)   \
+VSX_FINLINE(rt) fnm(const rg& a, const rg& b) { return fn2(a, b); }
+
+/*
+ * GCC VSX compatibility
+**/
+#if defined(__GNUG__) && !defined(__clang__)
+
+// inline asm helper
+#define VSX_IMPL_1RG(rt, rg, opc, fnm) \
+VSX_FINLINE(rt) fnm(const rg& a)       \
+{ rt rs; __asm__ __volatile__(#opc" %x0,%x1" : "=wa" (rs) : "wa" (a)); return rs; }
+
+#define VSX_IMPL_1VRG(rt, rg, opc, fnm) \
+VSX_FINLINE(rt) fnm(const rg& a)        \
+{ rt rs; __asm__ __volatile__(#opc" %0,%1" : "=v" (rs) : "v" (a)); return rs; }
+
+#define VSX_IMPL_2VRG_F(rt, rg, fopc, fnm)     \
+VSX_FINLINE(rt) fnm(const rg& a, const rg& b)  \
+{ rt rs; __asm__ __volatile__(fopc : "=v" (rs) : "v" (a), "v" (b)); return rs; }
+
+#define VSX_IMPL_2VRG(rt, rg, opc, fnm) VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%1,%2", fnm)
+
+#if __GNUG__ < 8
+
+    // Support for int4 -> dword2 expanding multiply was added in GCC 8.
+    #ifdef vec_mule
+        #undef vec_mule
+    #endif
+    #ifdef vec_mulo
+        #undef vec_mulo
+    #endif
+
+    VSX_REDIRECT_2RG(vec_ushort8,  vec_uchar16,  vec_mule, __builtin_vec_mule)
+    VSX_REDIRECT_2RG(vec_short8,  vec_char16,  vec_mule, __builtin_vec_mule)
+    VSX_REDIRECT_2RG(vec_int4,  vec_short8,  vec_mule, __builtin_vec_mule)
+    VSX_REDIRECT_2RG(vec_uint4,  vec_ushort8,  vec_mule, __builtin_vec_mule)
+    VSX_REDIRECT_2RG(vec_ushort8,  vec_uchar16,  vec_mulo, __builtin_vec_mulo)
+    VSX_REDIRECT_2RG(vec_short8,  vec_char16,  vec_mulo, __builtin_vec_mulo)
+    VSX_REDIRECT_2RG(vec_int4,  vec_short8,  vec_mulo, __builtin_vec_mulo)
+    VSX_REDIRECT_2RG(vec_uint4,  vec_ushort8,  vec_mulo, __builtin_vec_mulo)
+
+    // dword2 support arrived in ISA 2.07 and GCC 8+
+    VSX_IMPL_2VRG(vec_dword2,  vec_int4,  vmulosw, vec_mule)
+    VSX_IMPL_2VRG(vec_udword2, vec_uint4, vmulouw, vec_mule)
+    VSX_IMPL_2VRG(vec_dword2,  vec_int4,  vmulesw, vec_mulo)
+    VSX_IMPL_2VRG(vec_udword2, vec_uint4, vmuleuw, vec_mulo)
+
+#endif
+
+#if __GNUG__ < 7
+// up to GCC 6 vec_mul only supports precisions and llong
+#   ifdef vec_mul
+#       undef vec_mul
+#   endif
+/*
+ * there's no a direct instruction for supporting 8-bit, 16-bit multiplication in ISA 2.07,
+ * XLC Implement it by using instruction "multiply even", "multiply odd" and "permute"
+**/
+#   define VSX_IMPL_MULH(Tvec, cperm)                                        \
+    VSX_FINLINE(Tvec) vec_mul(const Tvec& a, const Tvec& b)                  \
+    {                                                                        \
+        static const vec_uchar16 ev_od = {cperm};                            \
+        return vec_perm((Tvec)vec_mule(a, b), (Tvec)vec_mulo(a, b), ev_od);  \
+    }
+    #define VSX_IMPL_MULH_P16 0, 16, 2, 18, 4, 20, 6, 22, 8, 24, 10, 26, 12, 28, 14, 30
+    VSX_IMPL_MULH(vec_char16,  VSX_IMPL_MULH_P16)
+    VSX_IMPL_MULH(vec_uchar16, VSX_IMPL_MULH_P16)
+    #define VSX_IMPL_MULH_P8 0, 1, 16, 17, 4, 5, 20, 21, 8, 9, 24, 25, 12, 13, 28, 29
+    VSX_IMPL_MULH(vec_short8,  VSX_IMPL_MULH_P8)
+    VSX_IMPL_MULH(vec_ushort8, VSX_IMPL_MULH_P8)
+    // vmuluwm can be used for unsigned or signed integers, that's what they said
+    VSX_IMPL_2VRG(vec_int4,  vec_int4,  vmuluwm, vec_mul)
+    VSX_IMPL_2VRG(vec_uint4, vec_uint4, vmuluwm, vec_mul)
+    // redirect to GCC builtin vec_mul, since it already supports precisions and llong
+    VSX_REDIRECT_2RG(vec_float4,  vec_float4,  vec_mul, __builtin_vec_mul)
+    VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mul, __builtin_vec_mul)
+    VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mul, __builtin_vec_mul)
+    VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mul, __builtin_vec_mul)
+#endif // __GNUG__ < 7
+
+#if __GNUG__ < 6
+/*
+ * Instruction "compare greater than or equal" in ISA 2.07 only supports single
+ * and double precision.
+ * In XLC and new versions of GCC implement integers by using instruction "greater than" and NOR.
+**/
+#   ifdef vec_cmpge
+#       undef vec_cmpge
+#   endif
+#   ifdef vec_cmple
+#       undef vec_cmple
+#   endif
+#   define vec_cmple(a, b) vec_cmpge(b, a)
+#   define VSX_IMPL_CMPGE(rt, rg, opc, fnm) \
+    VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%2,%1\n\t xxlnor %x0,%x0,%x0", fnm)
+
+    VSX_IMPL_CMPGE(vec_bchar16, vec_char16,  vcmpgtsb, vec_cmpge)
+    VSX_IMPL_CMPGE(vec_bchar16, vec_uchar16, vcmpgtub, vec_cmpge)
+    VSX_IMPL_CMPGE(vec_bshort8, vec_short8,  vcmpgtsh, vec_cmpge)
+    VSX_IMPL_CMPGE(vec_bshort8, vec_ushort8, vcmpgtuh, vec_cmpge)
+    VSX_IMPL_CMPGE(vec_bint4,   vec_int4,    vcmpgtsw, vec_cmpge)
+    VSX_IMPL_CMPGE(vec_bint4,   vec_uint4,   vcmpgtuw, vec_cmpge)
+    VSX_IMPL_CMPGE(vec_bdword2, vec_dword2,  vcmpgtsd, vec_cmpge)
+    VSX_IMPL_CMPGE(vec_bdword2, vec_udword2, vcmpgtud, vec_cmpge)
+
+// redirect to GCC builtin cmpge, since it already supports precisions
+    VSX_REDIRECT_2RG(vec_bint4,   vec_float4,  vec_cmpge, __builtin_vec_cmpge)
+    VSX_REDIRECT_2RG(vec_bdword2, vec_double2, vec_cmpge, __builtin_vec_cmpge)
+
+// up to gcc5 vec_nor doesn't support bool long long
+#   undef vec_nor
+    template<typename T>
+    VSX_REDIRECT_2RG(T, T, vec_nor, __builtin_vec_nor)
+
+    VSX_FINLINE(vec_bdword2) vec_nor(const vec_bdword2& a, const vec_bdword2& b)
+    { return vec_bdword2_c(__builtin_vec_nor(vec_dword2_c(a), vec_dword2_c(b))); }
+
+// vec_packs doesn't support double words in gcc4 and old versions of gcc5
+#   undef vec_packs
+    VSX_REDIRECT_2RG(vec_char16,  vec_short8,  vec_packs, __builtin_vec_packs)
+    VSX_REDIRECT_2RG(vec_uchar16, vec_ushort8, vec_packs, __builtin_vec_packs)
+    VSX_REDIRECT_2RG(vec_short8,  vec_int4,    vec_packs, __builtin_vec_packs)
+    VSX_REDIRECT_2RG(vec_ushort8, vec_uint4,   vec_packs, __builtin_vec_packs)
+
+    VSX_IMPL_2VRG_F(vec_int4,  vec_dword2,  "vpksdss %0,%2,%1", vec_packs)
+    VSX_IMPL_2VRG_F(vec_uint4, vec_udword2, "vpkudus %0,%2,%1", vec_packs)
+#endif // __GNUG__ < 6
+
+#if __GNUG__ < 5
+// vec_xxpermdi in gcc4 missing little-endian supports just like clang
+#   define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1)))
+// same as vec_xxpermdi
+#   undef vec_vbpermq
+    VSX_IMPL_2VRG(vec_udword2, vec_uchar16, vbpermq, vec_vbpermq)
+    VSX_IMPL_2VRG(vec_dword2,  vec_char16, vbpermq, vec_vbpermq)
+#else
+#   define vec_permi vec_xxpermdi
+#endif // __GNUG__ < 5
+
+// shift left double by word immediate
+#ifndef vec_sldw
+#   define vec_sldw __builtin_vsx_xxsldwi
+#endif
+
+// vector population count
+VSX_IMPL_1VRG(vec_uchar16, vec_uchar16, vpopcntb, vec_popcntu)
+VSX_IMPL_1VRG(vec_uchar16, vec_char16,  vpopcntb, vec_popcntu)
+VSX_IMPL_1VRG(vec_ushort8, vec_ushort8, vpopcnth, vec_popcntu)
+VSX_IMPL_1VRG(vec_ushort8, vec_short8,  vpopcnth, vec_popcntu)
+VSX_IMPL_1VRG(vec_uint4,   vec_uint4,   vpopcntw, vec_popcntu)
+VSX_IMPL_1VRG(vec_uint4,   vec_int4,    vpopcntw, vec_popcntu)
+VSX_IMPL_1VRG(vec_udword2, vec_udword2, vpopcntd, vec_popcntu)
+VSX_IMPL_1VRG(vec_udword2, vec_dword2,  vpopcntd, vec_popcntu)
+
+// converts between single and double-precision
+VSX_REDIRECT_1RG(vec_float4,  vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp)
+VSX_REDIRECT_1RG(vec_double2, vec_float4,  vec_cvfo, __builtin_vsx_xvcvspdp)
+
+// converts word and doubleword to double-precision
+#undef vec_ctd
+VSX_IMPL_1RG(vec_double2, vec_int4,    xvcvsxwdp, vec_ctdo)
+VSX_IMPL_1RG(vec_double2, vec_uint4,   xvcvuxwdp, vec_ctdo)
+VSX_IMPL_1RG(vec_double2, vec_dword2,  xvcvsxddp, vec_ctd)
+VSX_IMPL_1RG(vec_double2, vec_udword2, xvcvuxddp, vec_ctd)
+
+// converts word and doubleword to single-precision
+#undef vec_ctf
+VSX_IMPL_1RG(vec_float4, vec_int4,    xvcvsxwsp, vec_ctf)
+VSX_IMPL_1RG(vec_float4, vec_uint4,   xvcvuxwsp, vec_ctf)
+VSX_IMPL_1RG(vec_float4, vec_dword2,  xvcvsxdsp, vec_ctfo)
+VSX_IMPL_1RG(vec_float4, vec_udword2, xvcvuxdsp, vec_ctfo)
+
+// converts single and double precision to signed word
+#undef vec_cts
+VSX_IMPL_1RG(vec_int4,  vec_double2, xvcvdpsxws, vec_ctso)
+VSX_IMPL_1RG(vec_int4,  vec_float4,  xvcvspsxws, vec_cts)
+
+// converts single and double precision to unsigned word
+#undef vec_ctu
+VSX_IMPL_1RG(vec_uint4, vec_double2, xvcvdpuxws, vec_ctuo)
+VSX_IMPL_1RG(vec_uint4, vec_float4,  xvcvspuxws, vec_ctu)
+
+// converts single and double precision to signed doubleword
+#undef vec_ctsl
+VSX_IMPL_1RG(vec_dword2, vec_double2, xvcvdpsxds, vec_ctsl)
+VSX_IMPL_1RG(vec_dword2, vec_float4,  xvcvspsxds, vec_ctslo)
+
+// converts single and double precision to unsigned doubleword
+#undef vec_ctul
+VSX_IMPL_1RG(vec_udword2, vec_double2, xvcvdpuxds, vec_ctul)
+VSX_IMPL_1RG(vec_udword2, vec_float4,  xvcvspuxds, vec_ctulo)
+
+// just in case if GCC doesn't define it
+#ifndef vec_xl
+#   define vec_xl vec_vsx_ld
+#   define vec_xst vec_vsx_st
+#endif
+
+#endif // GCC VSX compatibility
+
+/*
+ * CLANG VSX compatibility
+**/
+#if defined(__clang__) && !defined(__IBMCPP__)
+
+/*
+ * CLANG doesn't support %x<n> in the inline asm template which fixes register number
+ * when using any of the register constraints wa, wd, wf
+ *
+ * For more explanation checkout PowerPC and IBM RS6000 in https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html
+ * Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837
+ *
+ * So we're not able to use inline asm and only use built-in functions that CLANG supports
+ * and use __builtin_convertvector if clang missing any of vector conversions built-in functions
+ *
+ * todo: clang asm template bug is fixed, need to reconsider the current workarounds.
+*/
+
+// convert vector helper
+#define VSX_IMPL_CONVERT(rt, rg, fnm) \
+VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); }
+
+#if __clang_major__ < 5
+// implement vec_permi in a dirty way
+#   define VSX_IMPL_CLANG_4_PERMI(Tvec)                                                 \
+    VSX_FINLINE(Tvec) vec_permi(const Tvec& a, const Tvec& b, unsigned const char c)    \
+    {                                                                                   \
+        switch (c)                                                                      \
+        {                                                                               \
+        case 0:                                                                         \
+            return vec_mergeh(a, b);                                                    \
+        case 1:                                                                         \
+            return vec_mergel(vec_mergeh(a, a), b);                                     \
+        case 2:                                                                         \
+            return vec_mergeh(vec_mergel(a, a), b);                                     \
+        default:                                                                        \
+            return vec_mergel(a, b);                                                    \
+        }                                                                               \
+    }
+    VSX_IMPL_CLANG_4_PERMI(vec_udword2)
+    VSX_IMPL_CLANG_4_PERMI(vec_dword2)
+    VSX_IMPL_CLANG_4_PERMI(vec_double2)
+
+// vec_xxsldwi is missing in clang 4
+#   define vec_xxsldwi(a, b, c) vec_sld(a, b, (c) * 4)
+#else
+// vec_xxpermdi is missing little-endian supports in clang 4 just like gcc4
+#   define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1)))
+#endif // __clang_major__ < 5
+
+// shift left double by word immediate
+#ifndef vec_sldw
+#   define vec_sldw vec_xxsldwi
+#endif
+
+// Implement vec_rsqrt since clang only supports vec_rsqrte
+#ifndef vec_rsqrt
+    VSX_FINLINE(vec_float4) vec_rsqrt(const vec_float4& a)
+    { return vec_div(vec_float4_sp(1), vec_sqrt(a)); }
+
+    VSX_FINLINE(vec_double2) vec_rsqrt(const vec_double2& a)
+    { return vec_div(vec_double2_sp(1), vec_sqrt(a)); }
+#endif
+
+// vec_promote missing support for doubleword
+VSX_FINLINE(vec_dword2) vec_promote(long long a, int b)
+{
+    vec_dword2 ret = vec_dword2_z;
+    ret[b & 1] = a;
+    return ret;
+}
+
+VSX_FINLINE(vec_udword2) vec_promote(unsigned long long a, int b)
+{
+    vec_udword2 ret = vec_udword2_z;
+    ret[b & 1] = a;
+    return ret;
+}
+
+// vec_popcnt should return unsigned but clang has different thought just like gcc in vec_vpopcnt
+#define VSX_IMPL_POPCNTU(Tvec, Tvec2, ucast)   \
+VSX_FINLINE(Tvec) vec_popcntu(const Tvec2& a)  \
+{ return ucast(vec_popcnt(a)); }
+VSX_IMPL_POPCNTU(vec_uchar16, vec_char16, vec_uchar16_c);
+VSX_IMPL_POPCNTU(vec_ushort8, vec_short8, vec_ushort8_c);
+VSX_IMPL_POPCNTU(vec_uint4,   vec_int4,   vec_uint4_c);
+VSX_IMPL_POPCNTU(vec_udword2, vec_dword2, vec_udword2_c);
+// redirect unsigned types
+VSX_REDIRECT_1RG(vec_uchar16, vec_uchar16, vec_popcntu, vec_popcnt)
+VSX_REDIRECT_1RG(vec_ushort8, vec_ushort8, vec_popcntu, vec_popcnt)
+VSX_REDIRECT_1RG(vec_uint4,   vec_uint4,   vec_popcntu, vec_popcnt)
+VSX_REDIRECT_1RG(vec_udword2, vec_udword2, vec_popcntu, vec_popcnt)
+
+// converts between single and double precision
+VSX_REDIRECT_1RG(vec_float4,  vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp)
+VSX_REDIRECT_1RG(vec_double2, vec_float4,  vec_cvfo, __builtin_vsx_xvcvspdp)
+
+// converts word and doubleword to double-precision
+#ifdef vec_ctd
+#   undef vec_ctd
+#endif
+VSX_REDIRECT_1RG(vec_double2, vec_int4,  vec_ctdo, __builtin_vsx_xvcvsxwdp)
+VSX_REDIRECT_1RG(vec_double2, vec_uint4, vec_ctdo, __builtin_vsx_xvcvuxwdp)
+
+VSX_IMPL_CONVERT(vec_double2, vec_dword2,  vec_ctd)
+VSX_IMPL_CONVERT(vec_double2, vec_udword2, vec_ctd)
+
+// converts word and doubleword to single-precision
+#if __clang_major__ > 4
+#   undef vec_ctf
+#endif
+VSX_IMPL_CONVERT(vec_float4, vec_int4,    vec_ctf)
+VSX_IMPL_CONVERT(vec_float4, vec_uint4,   vec_ctf)
+VSX_REDIRECT_1RG(vec_float4, vec_dword2,  vec_ctfo, __builtin_vsx_xvcvsxdsp)
+VSX_REDIRECT_1RG(vec_float4, vec_udword2, vec_ctfo, __builtin_vsx_xvcvuxdsp)
+
+// converts single and double precision to signed word
+#if __clang_major__ > 4
+#   undef vec_cts
+#endif
+VSX_REDIRECT_1RG(vec_int4,  vec_double2, vec_ctso, __builtin_vsx_xvcvdpsxws)
+VSX_IMPL_CONVERT(vec_int4,  vec_float4,  vec_cts)
+
+// converts single and double precision to unsigned word
+#if __clang_major__ > 4
+#   undef vec_ctu
+#endif
+VSX_REDIRECT_1RG(vec_uint4, vec_double2, vec_ctuo, __builtin_vsx_xvcvdpuxws)
+VSX_IMPL_CONVERT(vec_uint4, vec_float4,  vec_ctu)
+
+// converts single and double precision to signed doubleword
+#ifdef vec_ctsl
+#   undef vec_ctsl
+#endif
+VSX_IMPL_CONVERT(vec_dword2, vec_double2, vec_ctsl)
+// __builtin_convertvector unable to convert, xvcvspsxds is missing on it
+VSX_FINLINE(vec_dword2) vec_ctslo(const vec_float4& a)
+{ return vec_ctsl(vec_cvfo(a)); }
+
+// converts single and double precision to unsigned doubleword
+#ifdef vec_ctul
+#   undef vec_ctul
+#endif
+VSX_IMPL_CONVERT(vec_udword2, vec_double2, vec_ctul)
+// __builtin_convertvector unable to convert, xvcvspuxds is missing on it
+VSX_FINLINE(vec_udword2) vec_ctulo(const vec_float4& a)
+{ return vec_ctul(vec_cvfo(a)); }
+
+#endif // CLANG VSX compatibility
+
+/*
+ * Common GCC, CLANG compatibility
+**/
+#if defined(__GNUG__) && !defined(__IBMCPP__)
+
+#ifdef vec_cvf
+#   undef vec_cvf
+#endif
+
+#define VSX_IMPL_CONV_EVEN_4_2(rt, rg, fnm, fn2) \
+VSX_FINLINE(rt) fnm(const rg& a)                 \
+{ return fn2(vec_sldw(a, a, 1)); }
+
+VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_float4, vec_cvf,  vec_cvfo)
+VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_int4,   vec_ctd,  vec_ctdo)
+VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_uint4,  vec_ctd,  vec_ctdo)
+
+VSX_IMPL_CONV_EVEN_4_2(vec_dword2,  vec_float4, vec_ctsl, vec_ctslo)
+VSX_IMPL_CONV_EVEN_4_2(vec_udword2, vec_float4, vec_ctul, vec_ctulo)
+
+#define VSX_IMPL_CONV_EVEN_2_4(rt, rg, fnm, fn2) \
+VSX_FINLINE(rt) fnm(const rg& a)                 \
+{                                                \
+    rt v4 = fn2(a);                              \
+    return vec_sldw(v4, v4, 3);                  \
+}
+
+VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_double2, vec_cvf, vec_cvfo)
+VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_dword2,  vec_ctf, vec_ctfo)
+VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_udword2, vec_ctf, vec_ctfo)
+
+VSX_IMPL_CONV_EVEN_2_4(vec_int4,   vec_double2, vec_cts, vec_ctso)
+VSX_IMPL_CONV_EVEN_2_4(vec_uint4,  vec_double2, vec_ctu, vec_ctuo)
+
+// Only for Eigen!
+/*
+ * changing behavior of conversion intrinsics for gcc has effect on Eigen
+ * so we redefine old behavior again only on gcc, clang
+*/
+#if !defined(__clang__) || __clang_major__ > 4
+    // ignoring second arg since Eigen only truncates toward zero
+#   define VSX_IMPL_CONV_2VARIANT(rt, rg, fnm, fn2)     \
+    VSX_FINLINE(rt) fnm(const rg& a, int only_truncate) \
+    {                                                   \
+        assert(only_truncate == 0);                     \
+        CV_UNUSED(only_truncate);                       \
+        return fn2(a);                                  \
+    }
+    VSX_IMPL_CONV_2VARIANT(vec_int4,   vec_float4,  vec_cts, vec_cts)
+    VSX_IMPL_CONV_2VARIANT(vec_uint4,  vec_float4,  vec_ctu, vec_ctu)
+    VSX_IMPL_CONV_2VARIANT(vec_float4, vec_int4,    vec_ctf, vec_ctf)
+    VSX_IMPL_CONV_2VARIANT(vec_float4, vec_uint4,   vec_ctf, vec_ctf)
+    // define vec_cts for converting double precision to signed doubleword
+    // which isn't compatible with xlc but its okay since Eigen only uses it for gcc
+    VSX_IMPL_CONV_2VARIANT(vec_dword2, vec_double2, vec_cts, vec_ctsl)
+#endif // Eigen
+
+#endif // Common GCC, CLANG compatibility
+
+/*
+ * XLC VSX compatibility
+**/
+#if defined(__IBMCPP__)
+
+// vector population count
+#define vec_popcntu vec_popcnt
+
+// overload and redirect with setting second arg to zero
+// since we only support conversions without the second arg
+#define VSX_IMPL_OVERLOAD_Z2(rt, rg, fnm) \
+VSX_FINLINE(rt) fnm(const rg& a) { return fnm(a, 0); }
+
+VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_int4,    vec_ctd)
+VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_uint4,   vec_ctd)
+VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_dword2,  vec_ctd)
+VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_udword2, vec_ctd)
+
+VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_int4,    vec_ctf)
+VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_uint4,   vec_ctf)
+VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_dword2,  vec_ctf)
+VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_udword2, vec_ctf)
+
+VSX_IMPL_OVERLOAD_Z2(vec_int4,    vec_double2, vec_cts)
+VSX_IMPL_OVERLOAD_Z2(vec_int4,    vec_float4,  vec_cts)
+
+VSX_IMPL_OVERLOAD_Z2(vec_uint4,   vec_double2, vec_ctu)
+VSX_IMPL_OVERLOAD_Z2(vec_uint4,   vec_float4,  vec_ctu)
+
+VSX_IMPL_OVERLOAD_Z2(vec_dword2,  vec_double2, vec_ctsl)
+VSX_IMPL_OVERLOAD_Z2(vec_dword2,  vec_float4,  vec_ctsl)
+
+VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_double2, vec_ctul)
+VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_float4,  vec_ctul)
+
+// fixme: implement conversions of odd-numbered elements in a dirty way
+// since xlc doesn't support VSX registers operand in inline asm.
+#define VSX_IMPL_CONV_ODD_4_2(rt, rg, fnm, fn2) \
+VSX_FINLINE(rt) fnm(const rg& a) { return fn2(vec_sldw(a, a, 3)); }
+
+VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_float4, vec_cvfo,  vec_cvf)
+VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_int4,   vec_ctdo,  vec_ctd)
+VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_uint4,  vec_ctdo,  vec_ctd)
+
+VSX_IMPL_CONV_ODD_4_2(vec_dword2,  vec_float4, vec_ctslo, vec_ctsl)
+VSX_IMPL_CONV_ODD_4_2(vec_udword2, vec_float4, vec_ctulo, vec_ctul)
+
+#define VSX_IMPL_CONV_ODD_2_4(rt, rg, fnm, fn2)  \
+VSX_FINLINE(rt) fnm(const rg& a)                 \
+{                                                \
+    rt v4 = fn2(a);                              \
+    return vec_sldw(v4, v4, 1);                  \
+}
+
+VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_double2, vec_cvfo, vec_cvf)
+VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_dword2,  vec_ctfo, vec_ctf)
+VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_udword2, vec_ctfo, vec_ctf)
+
+VSX_IMPL_CONV_ODD_2_4(vec_int4,   vec_double2, vec_ctso, vec_cts)
+VSX_IMPL_CONV_ODD_2_4(vec_uint4,  vec_double2, vec_ctuo, vec_ctu)
+
+#endif // XLC VSX compatibility
+
+// ignore GCC warning that caused by -Wunused-but-set-variable in rare cases
+#if defined(__GNUG__) && !defined(__clang__)
+#   define VSX_UNUSED(Tvec) Tvec __attribute__((__unused__))
+#else // CLANG, XLC
+#   define VSX_UNUSED(Tvec) Tvec
+#endif
+
+// gcc can find his way in casting log int and XLC, CLANG ambiguous
+#if defined(__clang__) || defined(__IBMCPP__)
+    VSX_FINLINE(vec_udword2) vec_splats(uint64 v)
+    { return vec_splats((unsigned long long) v); }
+
+    VSX_FINLINE(vec_dword2) vec_splats(int64 v)
+    { return vec_splats((long long) v); }
+
+    VSX_FINLINE(vec_udword2) vec_promote(uint64 a, int b)
+    { return vec_promote((unsigned long long) a, b); }
+
+    VSX_FINLINE(vec_dword2) vec_promote(int64 a, int b)
+    { return vec_promote((long long) a, b); }
+#endif
+
+/*
+ * implement vsx_ld(offset, pointer), vsx_st(vector, offset, pointer)
+ * load and set using offset depend on the pointer type
+ *
+ * implement vsx_ldf(offset, pointer), vsx_stf(vector, offset, pointer)
+ * load and set using offset depend on fixed bytes size
+ *
+ * Note: In clang vec_xl and vec_xst fails to load unaligned addresses
+ * so we are using vec_vsx_ld, vec_vsx_st instead
+*/
+
+#if defined(__clang__) && !defined(__IBMCPP__)
+#   define vsx_ldf  vec_vsx_ld
+#   define vsx_stf  vec_vsx_st
+#else // GCC , XLC
+#   define vsx_ldf  vec_xl
+#   define vsx_stf  vec_xst
+#endif
+
+#define VSX_OFFSET(o, p) ((o) * sizeof(*(p)))
+#define vsx_ld(o, p) vsx_ldf(VSX_OFFSET(o, p), p)
+#define vsx_st(v, o, p) vsx_stf(v, VSX_OFFSET(o, p), p)
+
+/*
+ * implement vsx_ld2(offset, pointer), vsx_st2(vector, offset, pointer) to load and store double words
+ * In GCC vec_xl and vec_xst it maps to vec_vsx_ld, vec_vsx_st which doesn't support long long
+ * and in CLANG we are using vec_vsx_ld, vec_vsx_st because vec_xl, vec_xst fails to load unaligned addresses
+ *
+ * In XLC vec_xl and vec_xst fail to cast int64(long int) to long long
+*/
+#if (defined(__GNUG__) || defined(__clang__)) && !defined(__IBMCPP__)
+    VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p)
+    { return vec_udword2_c(vsx_ldf(VSX_OFFSET(o, p), (unsigned int*)p)); }
+
+    VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p)
+    { return vec_dword2_c(vsx_ldf(VSX_OFFSET(o, p), (int*)p)); }
+
+    VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p)
+    { vsx_stf(vec_uint4_c(vec), VSX_OFFSET(o, p), (unsigned int*)p); }
+
+    VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p)
+    { vsx_stf(vec_int4_c(vec), VSX_OFFSET(o, p), (int*)p); }
+#else // XLC
+    VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p)
+    { return vsx_ldf(VSX_OFFSET(o, p), (unsigned long long*)p); }
+
+    VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p)
+    { return vsx_ldf(VSX_OFFSET(o, p), (long long*)p); }
+
+    VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p)
+    { vsx_stf(vec, VSX_OFFSET(o, p), (unsigned long long*)p); }
+
+    VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p)
+    { vsx_stf(vec, VSX_OFFSET(o, p), (long long*)p); }
+#endif
+
+// Store lower 8 byte
+#define vec_st_l8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 0)
+
+// Store higher 8 byte
+#define vec_st_h8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 1)
+
+// Load 64-bits of integer data to lower part
+#define VSX_IMPL_LOAD_L8(Tvec, Tp)                  \
+VSX_FINLINE(Tvec) vec_ld_l8(const Tp *p)            \
+{ return ((Tvec)vec_promote(*((uint64*)p), 0)); }
+
+VSX_IMPL_LOAD_L8(vec_uchar16, uchar)
+VSX_IMPL_LOAD_L8(vec_char16,  schar)
+VSX_IMPL_LOAD_L8(vec_ushort8, ushort)
+VSX_IMPL_LOAD_L8(vec_short8,  short)
+VSX_IMPL_LOAD_L8(vec_uint4,   uint)
+VSX_IMPL_LOAD_L8(vec_int4,    int)
+VSX_IMPL_LOAD_L8(vec_float4,  float)
+VSX_IMPL_LOAD_L8(vec_udword2, uint64)
+VSX_IMPL_LOAD_L8(vec_dword2,  int64)
+VSX_IMPL_LOAD_L8(vec_double2, double)
+
+// logical not
+#define vec_not(a) vec_nor(a, a)
+
+// power9 yaya
+// not equal
+#ifndef vec_cmpne
+#   define vec_cmpne(a, b) vec_not(vec_cmpeq(a, b))
+#endif
+
+// absolute difference
+#ifndef vec_absd
+#   define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b))
+#endif
+
+/*
+ * Implement vec_unpacklu and vec_unpackhu
+ * since vec_unpackl, vec_unpackh only support signed integers
+**/
+#define VSX_IMPL_UNPACKU(rt, rg, zero)      \
+VSX_FINLINE(rt) vec_unpacklu(const rg& a)   \
+{ return (rt)(vec_mergel(a, zero)); }       \
+VSX_FINLINE(rt) vec_unpackhu(const rg& a)   \
+{ return (rt)(vec_mergeh(a, zero));  }
+
+VSX_IMPL_UNPACKU(vec_ushort8, vec_uchar16, vec_uchar16_z)
+VSX_IMPL_UNPACKU(vec_uint4,   vec_ushort8, vec_ushort8_z)
+VSX_IMPL_UNPACKU(vec_udword2, vec_uint4,   vec_uint4_z)
+
+/*
+ * Implement vec_mergesqe and vec_mergesqo
+ * Merges the sequence values of even and odd elements of two vectors
+*/
+#define VSX_IMPL_PERM(rt, fnm, ...)            \
+VSX_FINLINE(rt) fnm(const rt& a, const rt& b)  \
+{ static const vec_uchar16 perm = {__VA_ARGS__}; return vec_perm(a, b, perm); }
+
+// 16
+#define perm16_mergesqe 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30
+#define perm16_mergesqo 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31
+VSX_IMPL_PERM(vec_uchar16, vec_mergesqe, perm16_mergesqe)
+VSX_IMPL_PERM(vec_uchar16, vec_mergesqo, perm16_mergesqo)
+VSX_IMPL_PERM(vec_char16,  vec_mergesqe, perm16_mergesqe)
+VSX_IMPL_PERM(vec_char16,  vec_mergesqo, perm16_mergesqo)
+// 8
+#define perm8_mergesqe 0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29
+#define perm8_mergesqo 2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31
+VSX_IMPL_PERM(vec_ushort8, vec_mergesqe, perm8_mergesqe)
+VSX_IMPL_PERM(vec_ushort8, vec_mergesqo, perm8_mergesqo)
+VSX_IMPL_PERM(vec_short8,  vec_mergesqe, perm8_mergesqe)
+VSX_IMPL_PERM(vec_short8,  vec_mergesqo, perm8_mergesqo)
+// 4
+#define perm4_mergesqe 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27
+#define perm4_mergesqo 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
+VSX_IMPL_PERM(vec_uint4,  vec_mergesqe, perm4_mergesqe)
+VSX_IMPL_PERM(vec_uint4,  vec_mergesqo, perm4_mergesqo)
+VSX_IMPL_PERM(vec_int4,   vec_mergesqe, perm4_mergesqe)
+VSX_IMPL_PERM(vec_int4,   vec_mergesqo, perm4_mergesqo)
+VSX_IMPL_PERM(vec_float4, vec_mergesqe, perm4_mergesqe)
+VSX_IMPL_PERM(vec_float4, vec_mergesqo, perm4_mergesqo)
+// 2
+VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqe, vec_mergeh)
+VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqo, vec_mergel)
+VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesqe, vec_mergeh)
+VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesqo, vec_mergel)
+VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqe, vec_mergeh)
+VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqo, vec_mergel)
+
+/*
+ * Implement vec_mergesqh and vec_mergesql
+ * Merges the sequence most and least significant halves of two vectors
+*/
+#define VSX_IMPL_MERGESQHL(Tvec)                                    \
+VSX_FINLINE(Tvec) vec_mergesqh(const Tvec& a, const Tvec& b)        \
+{ return (Tvec)vec_mergeh(vec_udword2_c(a), vec_udword2_c(b)); }    \
+VSX_FINLINE(Tvec) vec_mergesql(const Tvec& a, const Tvec& b)        \
+{ return (Tvec)vec_mergel(vec_udword2_c(a), vec_udword2_c(b)); }
+VSX_IMPL_MERGESQHL(vec_uchar16)
+VSX_IMPL_MERGESQHL(vec_char16)
+VSX_IMPL_MERGESQHL(vec_ushort8)
+VSX_IMPL_MERGESQHL(vec_short8)
+VSX_IMPL_MERGESQHL(vec_uint4)
+VSX_IMPL_MERGESQHL(vec_int4)
+VSX_IMPL_MERGESQHL(vec_float4)
+VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqh, vec_mergeh)
+VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesql, vec_mergel)
+VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesqh, vec_mergeh)
+VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesql, vec_mergel)
+VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqh, vec_mergeh)
+VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesql, vec_mergel)
+
+
+// 2 and 4 channels interleave for all types except 2 lanes
+#define VSX_IMPL_ST_INTERLEAVE(Tp, Tvec)                                    \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr)  \
+{                                                                           \
+    vsx_stf(vec_mergeh(a, b), 0, ptr);                                      \
+    vsx_stf(vec_mergel(a, b), 16, ptr);                                     \
+}                                                                           \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,           \
+                                     const Tvec& c, const Tvec& d, Tp* ptr) \
+{                                                                           \
+    Tvec ac = vec_mergeh(a, c);                                             \
+    Tvec bd = vec_mergeh(b, d);                                             \
+    vsx_stf(vec_mergeh(ac, bd), 0, ptr);                                    \
+    vsx_stf(vec_mergel(ac, bd), 16, ptr);                                   \
+    ac = vec_mergel(a, c);                                                  \
+    bd = vec_mergel(b, d);                                                  \
+    vsx_stf(vec_mergeh(ac, bd), 32, ptr);                                   \
+    vsx_stf(vec_mergel(ac, bd), 48, ptr);                                   \
+}
+VSX_IMPL_ST_INTERLEAVE(uchar,  vec_uchar16)
+VSX_IMPL_ST_INTERLEAVE(schar,  vec_char16)
+VSX_IMPL_ST_INTERLEAVE(ushort, vec_ushort8)
+VSX_IMPL_ST_INTERLEAVE(short,  vec_short8)
+VSX_IMPL_ST_INTERLEAVE(uint,   vec_uint4)
+VSX_IMPL_ST_INTERLEAVE(int,    vec_int4)
+VSX_IMPL_ST_INTERLEAVE(float,  vec_float4)
+
+// 2 and 4 channels deinterleave for 16 lanes
+#define VSX_IMPL_ST_DINTERLEAVE_8(Tp, Tvec)                                 \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
+{                                                                           \
+    Tvec v0 = vsx_ld(0, ptr);                                               \
+    Tvec v1 = vsx_ld(16, ptr);                                              \
+    a = vec_mergesqe(v0, v1);                                               \
+    b = vec_mergesqo(v0, v1);                                               \
+}                                                                           \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
+                                       Tvec& c, Tvec& d)                    \
+{                                                                           \
+    Tvec v0 = vsx_ld(0, ptr);                                               \
+    Tvec v1 = vsx_ld(16, ptr);                                              \
+    Tvec v2 = vsx_ld(32, ptr);                                              \
+    Tvec v3 = vsx_ld(48, ptr);                                              \
+    Tvec m0 = vec_mergesqe(v0, v1);                                         \
+    Tvec m1 = vec_mergesqe(v2, v3);                                         \
+    a = vec_mergesqe(m0, m1);                                               \
+    c = vec_mergesqo(m0, m1);                                               \
+    m0 = vec_mergesqo(v0, v1);                                              \
+    m1 = vec_mergesqo(v2, v3);                                              \
+    b = vec_mergesqe(m0, m1);                                               \
+    d = vec_mergesqo(m0, m1);                                               \
+}
+VSX_IMPL_ST_DINTERLEAVE_8(uchar, vec_uchar16)
+VSX_IMPL_ST_DINTERLEAVE_8(schar, vec_char16)
+
+// 2 and 4 channels deinterleave for 8 lanes
+#define VSX_IMPL_ST_DINTERLEAVE_16(Tp, Tvec)                                \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
+{                                                                           \
+    Tvec v0 = vsx_ld(0, ptr);                                               \
+    Tvec v1 = vsx_ld(8, ptr);                                               \
+    a = vec_mergesqe(v0, v1);                                               \
+    b = vec_mergesqo(v0, v1);                                               \
+}                                                                           \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
+                                       Tvec& c, Tvec& d)                    \
+{                                                                           \
+    Tvec v0 = vsx_ld(0, ptr);                                               \
+    Tvec v1 = vsx_ld(8, ptr);                                               \
+    Tvec m0 = vec_mergeh(v0, v1);                                           \
+    Tvec m1 = vec_mergel(v0, v1);                                           \
+    Tvec ab0 = vec_mergeh(m0, m1);                                          \
+    Tvec cd0 = vec_mergel(m0, m1);                                          \
+    v0 = vsx_ld(16, ptr);                                                   \
+    v1 = vsx_ld(24, ptr);                                                   \
+    m0 = vec_mergeh(v0, v1);                                                \
+    m1 = vec_mergel(v0, v1);                                                \
+    Tvec ab1 = vec_mergeh(m0, m1);                                          \
+    Tvec cd1 = vec_mergel(m0, m1);                                          \
+    a = vec_mergesqh(ab0, ab1);                                             \
+    b = vec_mergesql(ab0, ab1);                                             \
+    c = vec_mergesqh(cd0, cd1);                                             \
+    d = vec_mergesql(cd0, cd1);                                             \
+}
+VSX_IMPL_ST_DINTERLEAVE_16(ushort, vec_ushort8)
+VSX_IMPL_ST_DINTERLEAVE_16(short,  vec_short8)
+
+// 2 and 4 channels deinterleave for 4 lanes
+#define VSX_IMPL_ST_DINTERLEAVE_32(Tp, Tvec)                                \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
+{                                                                           \
+    a = vsx_ld(0, ptr);                                                     \
+    b = vsx_ld(4, ptr);                                                     \
+    Tvec m0 = vec_mergeh(a, b);                                             \
+    Tvec m1 = vec_mergel(a, b);                                             \
+    a = vec_mergeh(m0, m1);                                                 \
+    b = vec_mergel(m0, m1);                                                 \
+}                                                                           \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
+                                       Tvec& c, Tvec& d)                    \
+{                                                                           \
+    Tvec v0 = vsx_ld(0, ptr);                                               \
+    Tvec v1 = vsx_ld(4, ptr);                                               \
+    Tvec v2 = vsx_ld(8, ptr);                                               \
+    Tvec v3 = vsx_ld(12, ptr);                                              \
+    Tvec m0 = vec_mergeh(v0, v2);                                           \
+    Tvec m1 = vec_mergeh(v1, v3);                                           \
+    a = vec_mergeh(m0, m1);                                                 \
+    b = vec_mergel(m0, m1);                                                 \
+    m0 = vec_mergel(v0, v2);                                                \
+    m1 = vec_mergel(v1, v3);                                                \
+    c = vec_mergeh(m0, m1);                                                 \
+    d = vec_mergel(m0, m1);                                                 \
+}
+VSX_IMPL_ST_DINTERLEAVE_32(uint,  vec_uint4)
+VSX_IMPL_ST_DINTERLEAVE_32(int,   vec_int4)
+VSX_IMPL_ST_DINTERLEAVE_32(float, vec_float4)
+
+// 2 and 4 channels interleave and deinterleave for 2 lanes
+#define VSX_IMPL_ST_D_INTERLEAVE_64(Tp, Tvec, ld_func, st_func)             \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr)  \
+{                                                                           \
+    st_func(vec_mergeh(a, b), 0, ptr);                                      \
+    st_func(vec_mergel(a, b), 2, ptr);                                      \
+}                                                                           \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,           \
+                                     const Tvec& c, const Tvec& d, Tp* ptr) \
+{                                                                           \
+    st_func(vec_mergeh(a, b), 0, ptr);                                      \
+    st_func(vec_mergeh(c, d), 2, ptr);                                      \
+    st_func(vec_mergel(a, b), 4, ptr);                                      \
+    st_func(vec_mergel(c, d), 6, ptr);                                      \
+}                                                                           \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
+{                                                                           \
+    Tvec m0 = ld_func(0, ptr);                                              \
+    Tvec m1 = ld_func(2, ptr);                                              \
+    a = vec_mergeh(m0, m1);                                                 \
+    b = vec_mergel(m0, m1);                                                 \
+}                                                                           \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
+                                       Tvec& c, Tvec& d)                    \
+{                                                                           \
+    Tvec v0 = ld_func(0, ptr);                                              \
+    Tvec v1 = ld_func(2, ptr);                                              \
+    Tvec v2 = ld_func(4, ptr);                                              \
+    Tvec v3 = ld_func(6, ptr);                                              \
+    a = vec_mergeh(v0, v2);                                                 \
+    b = vec_mergel(v0, v2);                                                 \
+    c = vec_mergeh(v1, v3);                                                 \
+    d = vec_mergel(v1, v3);                                                 \
+}
+VSX_IMPL_ST_D_INTERLEAVE_64(int64,  vec_dword2,  vsx_ld2, vsx_st2)
+VSX_IMPL_ST_D_INTERLEAVE_64(uint64, vec_udword2, vsx_ld2, vsx_st2)
+VSX_IMPL_ST_D_INTERLEAVE_64(double, vec_double2, vsx_ld,  vsx_st)
+
+/* 3 channels */
+#define VSX_IMPL_ST_INTERLEAVE_3CH_16(Tp, Tvec)                                                   \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,                                 \
+                                     const Tvec& c, Tp* ptr)                                      \
+{                                                                                                 \
+    static const vec_uchar16 a12 = {0, 16, 0, 1, 17, 0, 2, 18, 0, 3, 19, 0, 4, 20, 0, 5};         \
+    static const vec_uchar16 a123 = {0, 1, 16, 3, 4, 17, 6, 7, 18, 9, 10, 19, 12, 13, 20, 15};    \
+    vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr);                                       \
+    static const vec_uchar16 b12 = {21, 0, 6, 22, 0, 7, 23, 0, 8, 24, 0, 9, 25, 0, 10, 26};       \
+    static const vec_uchar16 b123 = {0, 21, 2, 3, 22, 5, 6, 23, 8, 9, 24, 11, 12, 25, 14, 15};    \
+    vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 16, ptr);                                      \
+    static const vec_uchar16 c12 = {0, 11, 27, 0, 12, 28, 0, 13, 29, 0, 14, 30, 0, 15, 31, 0};    \
+    static const vec_uchar16 c123 = {26, 1, 2, 27, 4, 5, 28, 7, 8, 29, 10, 11, 30, 13, 14, 31};   \
+    vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 32, ptr);                                      \
+}                                                                                                 \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c)                   \
+{                                                                                                 \
+    Tvec v1 = vsx_ld(0, ptr);                                                                     \
+    Tvec v2 = vsx_ld(16, ptr);                                                                    \
+    Tvec v3 = vsx_ld(32, ptr);                                                                    \
+    static const vec_uchar16 a12_perm = {0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0, 0, 0, 0, 0};  \
+    static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 20, 23, 26, 29};  \
+    a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm);                                      \
+    static const vec_uchar16 b12_perm = {1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 0, 0, 0, 0, 0}; \
+    static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 18, 21, 24, 27, 30};  \
+    b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm);                                      \
+    static const vec_uchar16 c12_perm = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0, 0, 0, 0, 0, 0};  \
+    static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 22, 25, 28, 31};  \
+    c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm);                                      \
+}
+VSX_IMPL_ST_INTERLEAVE_3CH_16(uchar, vec_uchar16)
+VSX_IMPL_ST_INTERLEAVE_3CH_16(schar, vec_char16)
+
+#define VSX_IMPL_ST_INTERLEAVE_3CH_8(Tp, Tvec)                                                    \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,                                 \
+                                     const Tvec& c, Tp* ptr)                                      \
+{                                                                                                 \
+    static const vec_uchar16 a12 = {0, 1, 16, 17, 0, 0, 2, 3, 18, 19, 0, 0, 4, 5, 20, 21};        \
+    static const vec_uchar16 a123 = {0, 1, 2, 3, 16, 17, 6, 7, 8, 9, 18, 19, 12, 13, 14, 15};     \
+    vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr);                                       \
+    static const vec_uchar16 b12 = {0, 0, 6, 7, 22, 23, 0, 0, 8, 9, 24, 25, 0, 0, 10, 11};        \
+    static const vec_uchar16 b123 = {20, 21, 2, 3, 4, 5, 22, 23, 8, 9, 10, 11, 24, 25, 14, 15};   \
+    vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 8, ptr);                                       \
+    static const vec_uchar16 c12 = {26, 27, 0, 0, 12, 13, 28, 29, 0, 0, 14, 15, 30, 31, 0, 0};    \
+    static const vec_uchar16 c123 = {0, 1, 26, 27, 4, 5, 6, 7, 28, 29, 10, 11, 12, 13, 30, 31};   \
+    vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 16, ptr);                                      \
+}                                                                                                 \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c)                   \
+{                                                                                                 \
+    Tvec v1 = vsx_ld(0, ptr);                                                                     \
+    Tvec v2 = vsx_ld(8, ptr);                                                                     \
+    Tvec v3 = vsx_ld(16, ptr);                                                                    \
+    static const vec_uchar16 a12_perm = {0, 1, 6, 7, 12, 13, 18, 19, 24, 25, 30, 31, 0, 0, 0, 0}; \
+    static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 21, 26, 27};  \
+    a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm);                                      \
+    static const vec_uchar16 b12_perm = {2, 3, 8, 9, 14, 15, 20, 21, 26, 27, 0, 0, 0, 0, 0, 0};   \
+    static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 17, 22, 23, 28, 29};  \
+    b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm);                                      \
+    static const vec_uchar16 c12_perm = {4, 5, 10, 11, 16, 17, 22, 23, 28, 29, 0, 0, 0, 0, 0, 0}; \
+    static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 18, 19, 24, 25, 30, 31};  \
+    c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm);                                      \
+}
+VSX_IMPL_ST_INTERLEAVE_3CH_8(ushort, vec_ushort8)
+VSX_IMPL_ST_INTERLEAVE_3CH_8(short,  vec_short8)
+
+#define VSX_IMPL_ST_INTERLEAVE_3CH_4(Tp, Tvec)                                                     \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,                                  \
+                                     const Tvec& c, Tp* ptr)                                       \
+{                                                                                                  \
+    Tvec hbc = vec_mergeh(b, c);                                                                   \
+    static const vec_uchar16 ahbc = {0, 1, 2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 4, 5, 6, 7};      \
+    vsx_st(vec_perm(a, hbc, ahbc), 0, ptr);                                                        \
+    Tvec lab = vec_mergel(a, b);                                                                   \
+    vsx_st(vec_sld(lab, hbc, 8), 4, ptr);                                                          \
+    static const vec_uchar16 clab = {8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, 12, 13, 14, 15};\
+    vsx_st(vec_perm(c, lab, clab), 8, ptr);                                                        \
+}                                                                                                  \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c)                    \
+{                                                                                                  \
+    Tvec v1 = vsx_ld(0, ptr);                                                                      \
+    Tvec v2 = vsx_ld(4, ptr);                                                                      \
+    Tvec v3 = vsx_ld(8, ptr);                                                                      \
+    static const vec_uchar16 flp = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31};   \
+    a = vec_perm(v1, vec_sld(v3, v2, 8), flp);                                                     \
+    static const vec_uchar16 flp2 = {28, 29, 30, 31, 0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19};  \
+    b = vec_perm(v2, vec_sld(v1, v3, 8), flp2);                                                    \
+    c = vec_perm(vec_sld(v2, v1, 8), v3, flp);                                                     \
+}
+VSX_IMPL_ST_INTERLEAVE_3CH_4(uint,  vec_uint4)
+VSX_IMPL_ST_INTERLEAVE_3CH_4(int,   vec_int4)
+VSX_IMPL_ST_INTERLEAVE_3CH_4(float, vec_float4)
+
+#define VSX_IMPL_ST_INTERLEAVE_3CH_2(Tp, Tvec, ld_func, st_func)     \
+VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,    \
+                                     const Tvec& c, Tp* ptr)         \
+{                                                                    \
+    st_func(vec_mergeh(a, b), 0, ptr);                               \
+    st_func(vec_permi(c, a, 1), 2, ptr);                             \
+    st_func(vec_mergel(b, c), 4, ptr);                               \
+}                                                                    \
+VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a,        \
+                                       Tvec& b, Tvec& c)             \
+{                                                                    \
+    Tvec v1 = ld_func(0, ptr);                                       \
+    Tvec v2 = ld_func(2, ptr);                                       \
+    Tvec v3 = ld_func(4, ptr);                                       \
+    a = vec_permi(v1, v2, 1);                                        \
+    b = vec_permi(v1, v3, 2);                                        \
+    c = vec_permi(v2, v3, 1);                                        \
+}
+VSX_IMPL_ST_INTERLEAVE_3CH_2(int64,  vec_dword2,  vsx_ld2, vsx_st2)
+VSX_IMPL_ST_INTERLEAVE_3CH_2(uint64, vec_udword2, vsx_ld2, vsx_st2)
+VSX_IMPL_ST_INTERLEAVE_3CH_2(double, vec_double2, vsx_ld,  vsx_st)
+
+#endif // CV_VSX
+
+//! @}
+
+#endif // OPENCV_HAL_VSX_UTILS_HPP
diff --git a/3rdParty/include/opencv2/cvconfig.h b/3rdParty/include/opencv2/cvconfig.h
new file mode 100644
index 0000000..922e589
--- /dev/null
+++ b/3rdParty/include/opencv2/cvconfig.h
@@ -0,0 +1,149 @@
+#ifndef OPENCV_CVCONFIG_H_INCLUDED
+#define OPENCV_CVCONFIG_H_INCLUDED
+
+/* OpenCV compiled as static or dynamic libs */
+#define BUILD_SHARED_LIBS
+
+/* OpenCV intrinsics optimized code */
+#define CV_ENABLE_INTRINSICS
+
+/* OpenCV additional optimized code */
+/* #undef CV_DISABLE_OPTIMIZATION */
+
+/* Compile for 'real' NVIDIA GPU architectures */
+#define CUDA_ARCH_BIN ""
+
+/* NVIDIA GPU features are used */
+#define CUDA_ARCH_FEATURES ""
+
+/* Compile for 'virtual' NVIDIA PTX architectures */
+#define CUDA_ARCH_PTX ""
+
+/* AMD's Basic Linear Algebra Subprograms Library*/
+/* #undef HAVE_CLAMDBLAS */
+
+/* AMD's OpenCL Fast Fourier Transform Library*/
+/* #undef HAVE_CLAMDFFT */
+
+/* Clp support */
+/* #undef HAVE_CLP */
+
+/* NVIDIA CUDA Runtime API*/
+/* #undef HAVE_CUDA */
+
+/* NVIDIA CUDA Basic Linear Algebra Subprograms (BLAS) API*/
+/* #undef HAVE_CUBLAS */
+
+/* NVIDIA CUDA Deep Neural Network (cuDNN) API*/
+/* #undef HAVE_CUDNN */
+
+/* NVIDIA CUDA Fast Fourier Transform (FFT) API*/
+/* #undef HAVE_CUFFT */
+
+/* DirectX */
+#define HAVE_DIRECTX
+#define HAVE_DIRECTX_NV12
+#define HAVE_D3D11
+#define HAVE_D3D10
+#define HAVE_D3D9
+
+/* Eigen Matrix & Linear Algebra Library */
+/* #undef HAVE_EIGEN */
+
+/* Geospatial Data Abstraction Library */
+/* #undef HAVE_GDAL */
+
+/* Halide support */
+/* #undef HAVE_HALIDE */
+
+/* Vulkan support */
+/* #undef HAVE_VULKAN */
+
+/* Define to 1 if you have the <inttypes.h> header file. */
+#define HAVE_INTTYPES_H 1
+
+/* Intel Integrated Performance Primitives */
+#define HAVE_IPP
+#define HAVE_IPP_ICV
+#define HAVE_IPP_IW
+#define HAVE_IPP_IW_LL
+
+/* JPEG-2000 codec */
+#define HAVE_OPENJPEG
+/* #undef HAVE_JASPER */
+
+/* IJG JPEG codec */
+#define HAVE_JPEG
+
+/* libpng/png.h needs to be included */
+/* #undef HAVE_LIBPNG_PNG_H */
+
+/* GDCM DICOM codec */
+/* #undef HAVE_GDCM */
+
+/* NVIDIA Video Decoding API*/
+/* #undef HAVE_NVCUVID */
+/* #undef HAVE_NVCUVID_HEADER */
+/* #undef HAVE_DYNLINK_NVCUVID_HEADER */
+
+/* NVIDIA Video Encoding API*/
+/* #undef HAVE_NVCUVENC */
+
+/* OpenCL Support */
+#define HAVE_OPENCL
+/* #undef HAVE_OPENCL_STATIC */
+/* #undef HAVE_OPENCL_SVM */
+
+/* NVIDIA OpenCL D3D Extensions support */
+#define HAVE_OPENCL_D3D11_NV
+
+/* OpenEXR codec */
+#define HAVE_OPENEXR
+
+/* OpenGL support*/
+/* #undef HAVE_OPENGL */
+
+/* PNG codec */
+#define HAVE_PNG
+
+/* Posix threads (pthreads) */
+/* #undef HAVE_PTHREAD */
+
+/* parallel_for with pthreads */
+/* #undef HAVE_PTHREADS_PF */
+
+/* Intel Threading Building Blocks */
+/* #undef HAVE_TBB */
+
+/* Ste||ar Group High Performance ParallelX */
+/* #undef HAVE_HPX */
+
+/* TIFF codec */
+#define HAVE_TIFF
+
+/* Define if your processor stores words with the most significant byte
+   first (like Motorola and SPARC, unlike Intel and VAX). */
+/* #undef WORDS_BIGENDIAN */
+
+/* VA library (libva) */
+/* #undef HAVE_VA */
+
+/* Intel VA-API/OpenCL */
+/* #undef HAVE_VA_INTEL */
+
+/* Lapack */
+/* #undef HAVE_LAPACK */
+
+/* Library was compiled with functions instrumentation */
+/* #undef ENABLE_INSTRUMENTATION */
+
+/* OpenVX */
+/* #undef HAVE_OPENVX */
+
+/* OpenCV trace utilities */
+#define OPENCV_TRACE
+
+/* Library QR-code decoding */
+#define HAVE_QUIRC
+
+#endif // OPENCV_CVCONFIG_H_INCLUDED
diff --git a/3rdParty/include/opencv2/dnn.hpp b/3rdParty/include/opencv2/dnn.hpp
new file mode 100644
index 0000000..97f2fe3
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn.hpp
@@ -0,0 +1,78 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_HPP
+#define OPENCV_DNN_HPP
+
+// This is an umbrella header to include into you project.
+// We are free to change headers layout in dnn subfolder, so please include
+// this header for future compatibility
+
+
+/** @defgroup dnn Deep Neural Network module
+  @{
+    This module contains:
+        - API for new layers creation, layers are building bricks of neural networks;
+        - set of built-in most-useful Layers;
+        - API to construct and modify comprehensive neural networks from layers;
+        - functionality for loading serialized networks models from different frameworks.
+
+    Functionality of this module is designed only for forward pass computations (i.e. network testing).
+    A network training is in principle not supported.
+  @}
+*/
+/** @example samples/dnn/classification.cpp
+Check @ref tutorial_dnn_googlenet "the corresponding tutorial" for more details
+*/
+/** @example samples/dnn/colorization.cpp
+*/
+/** @example samples/dnn/object_detection.cpp
+Check @ref tutorial_dnn_yolo "the corresponding tutorial" for more details
+*/
+/** @example samples/dnn/openpose.cpp
+*/
+/** @example samples/dnn/segmentation.cpp
+*/
+/** @example samples/dnn/text_detection.cpp
+*/
+#include <opencv2/dnn/dnn.hpp>
+
+#endif /* OPENCV_DNN_HPP */
diff --git a/3rdParty/include/opencv2/dnn/all_layers.hpp b/3rdParty/include/opencv2/dnn/all_layers.hpp
new file mode 100644
index 0000000..44b16f7
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/all_layers.hpp
@@ -0,0 +1,1011 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_DNN_ALL_LAYERS_HPP
+#define OPENCV_DNN_DNN_ALL_LAYERS_HPP
+#include <opencv2/dnn.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+
+/** @defgroup dnnLayerList Partial List of Implemented Layers
+  @{
+  This subsection of dnn module contains information about built-in layers and their descriptions.
+
+  Classes listed here, in fact, provides C++ API for creating instances of built-in layers.
+  In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
+  You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
+
+  Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
+  In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
+  - Convolution
+  - Deconvolution
+  - Pooling
+  - InnerProduct
+  - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal
+  - Softmax
+  - Reshape, Flatten, Slice, Split
+  - LRN
+  - MVN
+  - Dropout (since it does nothing on forward pass -))
+*/
+
+    class CV_EXPORTS BlankLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams &params);
+    };
+
+    /**
+     * Constant layer produces the same data blob at an every forward pass.
+     */
+    class CV_EXPORTS ConstLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams &params);
+    };
+
+    //! LSTM recurrent layer
+    class CV_EXPORTS LSTMLayer : public Layer
+    {
+    public:
+        /** Creates instance of LSTM layer */
+        static Ptr<LSTMLayer> create(const LayerParams& params);
+
+        /** @deprecated Use LayerParams::blobs instead.
+        @brief Set trained weights for LSTM layer.
+
+        LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights.
+
+        Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state.
+        Than current output and current cell state is computed as follows:
+        @f{eqnarray*}{
+        h_t &= o_t \odot tanh(c_t),               \\
+        c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\
+        @f}
+        where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned weights.
+
+        Gates are computed as follows:
+        @f{eqnarray*}{
+        i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\
+        f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\
+        o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\
+        g_t &= tanh   &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\
+        @f}
+        where @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices:
+        @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.
+
+        For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$
+        (i.e. @f$W_x@f$ is vertical concatenation of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$.
+        The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$
+        and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$.
+
+        @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$)
+        @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$)
+        @param b  is bias vector (i.e. according to above mentioned notation is @f$ b @f$)
+        */
+        CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
+
+        /** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape.
+          * @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used,
+          * where `Wh` is parameter from setWeights().
+          */
+        virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
+
+        /** @deprecated Use flag `produce_cell_output` in LayerParams.
+          * @brief Specifies either interpret first dimension of input blob as timestamp dimension either as sample.
+          *
+          * If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
+          * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
+          *
+          * If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`].
+          * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
+          */
+        CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0;
+
+        /** @deprecated Use flag `use_timestamp_dim` in LayerParams.
+         * @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output.
+         * @details Shape of the second output is the same as first output.
+         */
+        CV_DEPRECATED virtual void setProduceCellOutput(bool produce = false) = 0;
+
+        /* In common case it use single input with @f$x_t@f$ values to compute output(s) @f$h_t@f$ (and @f$c_t@f$).
+         * @param input should contain packed values @f$x_t@f$
+         * @param output contains computed outputs: @f$h_t@f$ (and @f$c_t@f$ if setProduceCellOutput() flag was set to true).
+         *
+         * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`],
+         * where `T` specifies number of timestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]).
+         *
+         * If setUseTimstampsDim() is set to false then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
+         * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]).
+        */
+
+        int inputNameToIndex(String inputName) CV_OVERRIDE;
+        int outputNameToIndex(const String& outputName) CV_OVERRIDE;
+    };
+
+    /** @brief GRU recurrent one-layer
+     *
+     * Accepts input sequence and computes the final hidden state for each element in the batch.
+     *
+     * - input[0] containing the features of the input sequence.
+     * input[0] should have shape [`T`, `N`, `data_dims`] where `T` is sequence length, `N` is batch size, `data_dims` is input size
+     * - output would have shape [`T`, `N`, `D` * `hidden_size`] where `D = 2` if layer is bidirectional otherwise `D = 1`
+     *
+     * Depends on the following attributes:
+     * - hidden_size - Number of neurons in the hidden layer
+     * - direction - RNN could be bidirectional or forward
+     *
+     * The final hidden state @f$ h_t @f$ computes by the following formulas:
+     *
+     @f{eqnarray*}{
+     r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
+     z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
+     n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\
+     h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)} \\
+     @f}
+     * Where @f$x_t@f$ is current input, @f$h_{(t-1)}@f$ is previous or initial hidden state.
+     *
+     * @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices:
+     * @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.
+     *
+     * @f$\odot@f$ is per-element multiply operation.
+    */
+    class CV_EXPORTS GRULayer : public Layer
+    {
+    public:
+        /** Creates instance of GRU layer */
+        static Ptr<GRULayer> create(const LayerParams& params);
+    };
+
+    /** @brief Classical recurrent layer
+
+    Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.
+
+    - input: should contain packed input @f$x_t@f$.
+    - output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).
+
+    input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively.
+
+    output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.
+
+    If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.
+    */
+    class CV_EXPORTS RNNLayer : public Layer
+    {
+    public:
+        /** Creates instance of RNNLayer */
+        static Ptr<RNNLayer> create(const LayerParams& params);
+
+        /** Setups learned weights.
+
+        Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows:
+        @f{eqnarray*}{
+        h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h),  \\
+        o_t &= tanh&(W_{ho} h_t + b_o),
+        @f}
+
+        @param Wxh is @f$ W_{xh} @f$ matrix
+        @param bh  is @f$ b_{h}  @f$ vector
+        @param Whh is @f$ W_{hh} @f$ matrix
+        @param Who is @f$ W_{xo} @f$ matrix
+        @param bo  is @f$ b_{o}  @f$ vector
+        */
+        virtual void setWeights(const Mat &Wxh, const Mat &bh, const Mat &Whh, const Mat &Who, const Mat &bo) = 0;
+
+        /** @brief If this flag is set to true then layer will produce @f$ h_t @f$ as second output.
+         * @details Shape of the second output is the same as first output.
+         */
+        virtual void setProduceHiddenOutput(bool produce = false) = 0;
+
+    };
+
+    class CV_EXPORTS BaseConvolutionLayer : public Layer
+    {
+    public:
+        CV_DEPRECATED_EXTERNAL Size kernel, stride, pad, dilation, adjustPad;
+        std::vector<size_t> adjust_pads;
+        std::vector<size_t> kernel_size, strides, dilations;
+        std::vector<size_t> pads_begin, pads_end;
+        String padMode;
+        int numOutput;
+    };
+
+    class CV_EXPORTS ConvolutionLayer : public BaseConvolutionLayer
+    {
+    public:
+        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ConvolutionLayerInt8 : public BaseConvolutionLayer
+    {
+    public:
+        int input_zp, output_zp;
+        float output_sc;
+        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS DeconvolutionLayer : public BaseConvolutionLayer
+    {
+    public:
+        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS LRNLayer : public Layer
+    {
+    public:
+        int type;
+
+        int size;
+        float alpha, beta, bias;
+        bool normBySize;
+
+        static Ptr<LRNLayer> create(const LayerParams& params);
+    };
+
+
+    /** @brief ArgMax/ArgMin layer
+     * @note returns indices as floats, which means the supported range is [-2^24; 2^24]
+     */
+    class CV_EXPORTS ArgLayer : public Layer
+    {
+    public:
+        static Ptr<ArgLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS PoolingLayer : public Layer
+    {
+    public:
+        int type;
+        std::vector<size_t> kernel_size, strides;
+        std::vector<size_t> pads_begin, pads_end;
+        bool globalPooling; //!< Flag is true if at least one of the axes is global pooled.
+        std::vector<bool> isGlobalPooling;
+        bool computeMaxIdx;
+        String padMode;
+        bool ceilMode;
+        // If true for average pooling with padding, divide an every output region
+        // by a whole kernel area. Otherwise exclude zero padded values and divide
+        // by number of real values.
+        bool avePoolPaddedArea;
+        // ROIPooling parameters.
+        Size pooledSize;
+        float spatialScale;
+        // PSROIPooling parameters.
+        int psRoiOutChannels;
+
+        static Ptr<PoolingLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS PoolingLayerInt8 : public PoolingLayer
+    {
+    public:
+        int input_zp, output_zp;
+        static Ptr<PoolingLayerInt8> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS SoftmaxLayer : public Layer
+    {
+    public:
+        bool logSoftMax;
+
+        static Ptr<SoftmaxLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS SoftmaxLayerInt8 : public SoftmaxLayer
+    {
+    public:
+        float output_sc;
+        int output_zp;
+        static Ptr<SoftmaxLayerInt8> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS InnerProductLayer : public Layer
+    {
+    public:
+        int axis;
+        static Ptr<InnerProductLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS InnerProductLayerInt8 : public InnerProductLayer
+    {
+    public:
+        int output_zp;
+        static Ptr<InnerProductLayerInt8> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS MVNLayer : public Layer
+    {
+    public:
+        float eps;
+        bool normVariance, acrossChannels;
+
+        static Ptr<MVNLayer> create(const LayerParams& params);
+    };
+
+    /* Reshaping */
+
+    class CV_EXPORTS ReshapeLayer : public Layer
+    {
+    public:
+        MatShape newShapeDesc;
+        Range newShapeRange;
+
+        static Ptr<ReshapeLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS FlattenLayer : public Layer
+    {
+    public:
+        static Ptr<FlattenLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS QuantizeLayer : public Layer
+    {
+    public:
+        float scale;
+        int zeropoint;
+        static Ptr<QuantizeLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS DequantizeLayer : public Layer
+    {
+    public:
+        float scale;
+        int zeropoint;
+        static Ptr<DequantizeLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS RequantizeLayer : public Layer
+    {
+    public:
+        float scale, shift;
+        static Ptr<RequantizeLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ConcatLayer : public Layer
+    {
+    public:
+        int axis;
+        /**
+         * @brief Add zero padding in case of concatenation of blobs with different
+         * spatial sizes.
+         *
+         * Details: https://github.com/torch/nn/blob/master/doc/containers.md#depthconcat
+         */
+        bool padding;
+        int paddingValue;
+
+        static Ptr<ConcatLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SplitLayer : public Layer
+    {
+    public:
+        int outputsCount; //!< Number of copies that will be produced (is ignored when negative).
+
+        static Ptr<SplitLayer> create(const LayerParams &params);
+    };
+
+    /**
+     * Slice layer has several modes:
+     * 1. Caffe mode
+     * @param[in] axis Axis of split operation
+     * @param[in] slice_point Array of split points
+     *
+     * Number of output blobs equals to number of split points plus one. The
+     * first blob is a slice on input from 0 to @p slice_point[0] - 1 by @p axis,
+     * the second output blob is a slice of input from @p slice_point[0] to
+     * @p slice_point[1] - 1 by @p axis and the last output blob is a slice of
+     * input from @p slice_point[-1] up to the end of @p axis size.
+     *
+     * 2. TensorFlow mode
+     * @param begin Vector of start indices
+     * @param size Vector of sizes
+     *
+     * More convenient numpy-like slice. One and only output blob
+     * is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]`
+     *
+     * 3. Torch mode
+     * @param axis Axis of split operation
+     *
+     * Split input blob on the equal parts by @p axis.
+     */
+    class CV_EXPORTS SliceLayer : public Layer
+    {
+    public:
+        /**
+         * @brief Vector of slice ranges.
+         *
+         * The first dimension equals number of output blobs.
+         * Inner vector has slice ranges for the first number of input dimensions.
+         */
+        std::vector<std::vector<Range> > sliceRanges;
+        std::vector<std::vector<int> > sliceSteps;
+        int axis;
+        int num_split;
+
+        static Ptr<SliceLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS PermuteLayer : public Layer
+    {
+    public:
+        static Ptr<PermuteLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * Permute channels of 4-dimensional input blob.
+     * @param group Number of groups to split input channels and pick in turns
+     *              into output blob.
+     *
+     * \f[ groupSize = \frac{number\ of\ channels}{group} \f]
+     * \f[ output(n, c, h, w) = input(n, groupSize \times (c \% group) + \lfloor \frac{c}{group} \rfloor, h, w) \f]
+     * Read more at https://arxiv.org/pdf/1707.01083.pdf
+     */
+    class CV_EXPORTS ShuffleChannelLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+
+        int group;
+    };
+
+    /**
+     * @brief Adds extra values for specific axes.
+     * @param paddings Vector of paddings in format
+     *                 @code
+     *                 [ pad_before, pad_after,  // [0]th dimension
+     *                   pad_before, pad_after,  // [1]st dimension
+     *                   ...
+     *                   pad_before, pad_after ] // [n]th dimension
+     *                 @endcode
+     *                 that represents number of padded values at every dimension
+     *                 starting from the first one. The rest of dimensions won't
+     *                 be padded.
+     * @param value Value to be padded. Defaults to zero.
+     * @param type Padding type: 'constant', 'reflect'
+     * @param input_dims Torch's parameter. If @p input_dims is not equal to the
+     *                   actual input dimensionality then the `[0]th` dimension
+     *                   is considered as a batch dimension and @p paddings are shifted
+     *                   to a one dimension. Defaults to `-1` that means padding
+     *                   corresponding to @p paddings.
+     */
+    class CV_EXPORTS PaddingLayer : public Layer
+    {
+    public:
+        static Ptr<PaddingLayer> create(const LayerParams& params);
+    };
+
+    /* Activations */
+    class CV_EXPORTS ActivationLayer : public Layer
+    {
+    public:
+        virtual void forwardSlice(const float* src, float* dst, int len,
+                                  size_t outPlaneSize, int cn0, int cn1) const {};
+        virtual void forwardSlice(const int* src, const int* lut, int* dst, int len,
+                                  size_t outPlaneSize, int cn0, int cn1) const {};
+        virtual void forwardSlice(const int8_t* src, const int8_t* lut, int8_t* dst, int len,
+                                  size_t outPlaneSize, int cn0, int cn1) const {};
+    };
+
+    class CV_EXPORTS ReLULayer : public ActivationLayer
+    {
+    public:
+        float negativeSlope;
+
+        static Ptr<ReLULayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ReLU6Layer : public ActivationLayer
+    {
+    public:
+        float minValue, maxValue;
+
+        static Ptr<ReLU6Layer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ELULayer : public ActivationLayer
+    {
+    public:
+        float alpha;
+
+        static Ptr<ELULayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS TanHLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<TanHLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SwishLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SwishLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS MishLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<MishLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SigmoidLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SigmoidLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS BNLLLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<BNLLLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AbsLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AbsLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS PowerLayer : public ActivationLayer
+    {
+    public:
+        float power, scale, shift;
+
+        static Ptr<PowerLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ExpLayer : public ActivationLayer
+    {
+    public:
+        float base, scale, shift;
+
+        static Ptr<ExpLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS CeilLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<CeilLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS FloorLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<FloorLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS LogLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<LogLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS RoundLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<RoundLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SqrtLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SqrtLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS NotLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<NotLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AcosLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AcosLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AcoshLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AcoshLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AsinLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AsinLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AsinhLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AsinhLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AtanLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AtanLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AtanhLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AtanhLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS CosLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<CosLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS CoshLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<CoshLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ErfLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<ErfLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS HardSwishLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<HardSwishLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SinLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SinLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SinhLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SinhLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SoftplusLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SoftplusLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SoftsignLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SoftsignLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS TanLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<TanLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS CeluLayer : public ActivationLayer
+    {
+    public:
+        float alpha;
+
+        static Ptr<CeluLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS HardSigmoidLayer : public ActivationLayer
+    {
+    public:
+        float alpha;
+        float beta;
+
+        static Ptr<HardSigmoidLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SeluLayer : public ActivationLayer
+    {
+    public:
+        float alpha;
+        float gamma;
+
+        static Ptr<SeluLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ThresholdedReluLayer : public ActivationLayer
+    {
+    public:
+        float alpha;
+
+        static Ptr<ThresholdedReluLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ActivationLayerInt8 : public ActivationLayer
+    {
+    public:
+        static Ptr<ActivationLayerInt8> create(const LayerParams &params);
+    };
+
+    /* Layers used in semantic segmentation */
+
+    class CV_EXPORTS CropLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams &params);
+    };
+
+    /** @brief Element wise operation on inputs
+
+    Extra optional parameters:
+    - "operation" as string. Values are "sum" (default), "prod", "max", "div", "min"
+    - "coeff" as float array. Specify weights of inputs for SUM operation
+    - "output_channels_mode" as string. Values are "same" (default, all input must have the same layout), "input_0", "input_0_truncate", "max_input_channels"
+    */
+    class CV_EXPORTS EltwiseLayer : public Layer
+    {
+    public:
+        static Ptr<EltwiseLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS EltwiseLayerInt8 : public Layer
+    {
+    public:
+        static Ptr<EltwiseLayerInt8> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS BatchNormLayer : public ActivationLayer
+    {
+    public:
+        bool hasWeights, hasBias;
+        float epsilon;
+
+        static Ptr<BatchNormLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS BatchNormLayerInt8 : public BatchNormLayer
+    {
+    public:
+        float input_sc, output_sc;
+        int input_zp, output_zp;
+        static Ptr<BatchNormLayerInt8> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS MaxUnpoolLayer : public Layer
+    {
+    public:
+        Size poolKernel;
+        Size poolPad;
+        Size poolStride;
+
+        static Ptr<MaxUnpoolLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ScaleLayer : public Layer
+    {
+    public:
+        bool hasBias;
+        int axis;
+        String mode;
+
+        static Ptr<ScaleLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ScaleLayerInt8 : public ScaleLayer
+    {
+    public:
+        float output_sc;
+        int output_zp;
+        static Ptr<ScaleLayerInt8> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ShiftLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ShiftLayerInt8 : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS CompareLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS DataAugmentationLayer : public Layer
+    {
+    public:
+        static Ptr<DataAugmentationLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS CorrelationLayer : public Layer
+    {
+    public:
+        static Ptr<CorrelationLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS AccumLayer : public Layer
+    {
+    public:
+        static Ptr<AccumLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS FlowWarpLayer : public Layer
+    {
+    public:
+        static Ptr<FlowWarpLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS PriorBoxLayer : public Layer
+    {
+    public:
+        static Ptr<PriorBoxLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ReorgLayer : public Layer
+    {
+    public:
+        static Ptr<ReorgLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS RegionLayer : public Layer
+    {
+    public:
+        float nmsThreshold;
+
+        static Ptr<RegionLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief Detection output layer.
+     *
+     * The layer size is: @f$ (1 \times 1 \times N \times 7) @f$
+     *    where N is [keep_top_k] parameter multiplied by batch size. Each row is:
+     *    [image_id, label, confidence, xmin, ymin, xmax, ymax]
+     *    where image_id is the index of image input in the batch.
+     */
+    class CV_EXPORTS DetectionOutputLayer : public Layer
+    {
+    public:
+        static Ptr<DetectionOutputLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief \f$ L_p \f$ - normalization layer.
+     * @param p Normalization factor. The most common `p = 1` for \f$ L_1 \f$ -
+     *          normalization or `p = 2` for \f$ L_2 \f$ - normalization or a custom one.
+     * @param eps Parameter \f$ \epsilon \f$ to prevent a division by zero.
+     * @param across_spatial If true, normalize an input across all non-batch dimensions.
+     *                       Otherwise normalize an every channel separately.
+     *
+     * Across spatial:
+     * @f[
+     * norm = \sqrt[p]{\epsilon + \sum_{x, y, c} |src(x, y, c)|^p } \\
+     * dst(x, y, c) = \frac{ src(x, y, c) }{norm}
+     * @f]
+     *
+     * Channel wise normalization:
+     * @f[
+     * norm(c) = \sqrt[p]{\epsilon + \sum_{x, y} |src(x, y, c)|^p } \\
+     * dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)}
+     * @f]
+     *
+     * Where `x, y` - spatial coordinates, `c` - channel.
+     *
+     * An every sample in the batch is normalized separately. Optionally,
+     * output is scaled by the trained parameters.
+     */
+    class CV_EXPORTS NormalizeBBoxLayer : public Layer
+    {
+    public:
+        float pnorm, epsilon;
+        CV_DEPRECATED_EXTERNAL bool acrossSpatial;
+
+        static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
+     *
+     * Layer is used to support TensorFlow's resize_nearest_neighbor and resize_bilinear ops.
+     */
+    class CV_EXPORTS ResizeLayer : public Layer
+    {
+    public:
+        static Ptr<ResizeLayer> create(const LayerParams& params);
+    };
+
+    /**
+     * @brief Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2
+     *
+     * It differs from @ref ResizeLayer in output shape and resize scales computations.
+     */
+    class CV_EXPORTS InterpLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS ProposalLayer : public Layer
+    {
+    public:
+        static Ptr<ProposalLayer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS CropAndResizeLayer : public Layer
+    {
+    public:
+        static Ptr<Layer> create(const LayerParams& params);
+    };
+
+    class CV_EXPORTS CumSumLayer : public Layer
+    {
+    public:
+        int exclusive;
+        int reverse;
+
+        static Ptr<CumSumLayer> create(const LayerParams& params);
+    };
+
+//! @}
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+#endif
diff --git a/3rdParty/include/opencv2/dnn/dict.hpp b/3rdParty/include/opencv2/dnn/dict.hpp
new file mode 100644
index 0000000..463d314
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/dict.hpp
@@ -0,0 +1,160 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include <opencv2/core.hpp>
+#include <map>
+#include <ostream>
+
+#include <opencv2/dnn/dnn.hpp>
+
+#ifndef OPENCV_DNN_DNN_DICT_HPP
+#define OPENCV_DNN_DNN_DICT_HPP
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+
+/** @brief This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64.
+ *  @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers.
+ */
+struct CV_EXPORTS_W DictValue
+{
+    DictValue(const DictValue &r);
+    DictValue(bool i)           : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i ? 1 : 0; }       //!< Constructs integer scalar
+    DictValue(int64 i = 0)      : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
+    CV_WRAP DictValue(int i)    : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
+    DictValue(unsigned p)       : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = p; }       //!< Constructs integer scalar
+    CV_WRAP DictValue(double p)         : type(Param::REAL), pd(new AutoBuffer<double,1>) { (*pd)[0] = p; }     //!< Constructs floating point scalar
+    CV_WRAP DictValue(const String &s)  : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< Constructs string scalar
+    DictValue(const char *s)            : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< @overload
+
+    template<typename TypeIter>
+    static DictValue arrayInt(TypeIter begin, int size);    //!< Constructs integer array
+    template<typename TypeIter>
+    static DictValue arrayReal(TypeIter begin, int size);   //!< Constructs floating point array
+    template<typename TypeIter>
+    static DictValue arrayString(TypeIter begin, int size); //!< Constructs array of strings
+
+    template<typename T>
+    T get(int idx = -1) const; //!< Tries to convert array element with specified index to requested type and returns its.
+
+    int size() const;
+
+    CV_WRAP bool isInt() const;
+    CV_WRAP bool isString() const;
+    CV_WRAP bool isReal() const;
+
+    CV_WRAP int getIntValue(int idx = -1) const;
+    CV_WRAP double getRealValue(int idx = -1) const;
+    CV_WRAP String getStringValue(int idx = -1) const;
+
+    DictValue &operator=(const DictValue &r);
+
+    friend std::ostream &operator<<(std::ostream &stream, const DictValue &dictv);
+
+    ~DictValue();
+
+private:
+
+    Param type;
+
+    union
+    {
+        AutoBuffer<int64, 1> *pi;
+        AutoBuffer<double, 1> *pd;
+        AutoBuffer<String, 1> *ps;
+        void *pv;
+    };
+
+    DictValue(Param _type, void *_p) : type(_type), pv(_p) {}
+    void release();
+};
+
+/** @brief This class implements name-value dictionary, values are instances of DictValue. */
+class CV_EXPORTS Dict
+{
+    typedef std::map<String, DictValue> _Dict;
+    _Dict dict;
+
+public:
+
+    //! Checks a presence of the @p key in the dictionary.
+    bool has(const String &key) const;
+
+    //! If the @p key in the dictionary then returns pointer to its value, else returns NULL.
+    DictValue *ptr(const String &key);
+
+    /** @overload */
+    const DictValue *ptr(const String &key) const;
+
+    //! If the @p key in the dictionary then returns its value, else an error will be generated.
+    const DictValue &get(const String &key) const;
+
+    /** @overload */
+    template <typename T>
+    T get(const String &key) const;
+
+    //! If the @p key in the dictionary then returns its value, else returns @p defaultValue.
+    template <typename T>
+    T get(const String &key, const T &defaultValue) const;
+
+    //! Sets new @p value for the @p key, or adds new key-value pair into the dictionary.
+    template<typename T>
+    const T &set(const String &key, const T &value);
+
+    //! Erase @p key from the dictionary.
+    void erase(const String &key);
+
+    friend std::ostream &operator<<(std::ostream &stream, const Dict &dict);
+
+    std::map<String, DictValue>::const_iterator begin() const;
+
+    std::map<String, DictValue>::const_iterator end() const;
+};
+
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+
+#endif
diff --git a/3rdParty/include/opencv2/dnn/dnn.hpp b/3rdParty/include/opencv2/dnn/dnn.hpp
new file mode 100644
index 0000000..d6b29cf
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/dnn.hpp
@@ -0,0 +1,1696 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_DNN_HPP
+#define OPENCV_DNN_DNN_HPP
+
+#include <vector>
+#include <opencv2/core.hpp>
+#include "opencv2/core/async.hpp"
+
+#include "../dnn/version.hpp"
+
+#include <opencv2/dnn/dict.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+
+    typedef std::vector<int> MatShape;
+
+    /**
+     * @brief Enum of computation backends supported by layers.
+     * @see Net::setPreferableBackend
+     */
+    enum Backend
+    {
+        //! DNN_BACKEND_DEFAULT equals to DNN_BACKEND_INFERENCE_ENGINE if
+        //! OpenCV is built with Intel's Inference Engine library or
+        //! DNN_BACKEND_OPENCV otherwise.
+        DNN_BACKEND_DEFAULT = 0,
+        DNN_BACKEND_HALIDE,
+        DNN_BACKEND_INFERENCE_ENGINE,            //!< Intel's Inference Engine computational backend
+                                                 //!< @sa setInferenceEngineBackendType
+        DNN_BACKEND_OPENCV,
+        DNN_BACKEND_VKCOM,
+        DNN_BACKEND_CUDA,
+        DNN_BACKEND_WEBNN,
+#ifdef __OPENCV_BUILD
+        DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = 1000000,     // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
+        DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019,      // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
+#endif
+    };
+
+    /**
+     * @brief Enum of target devices for computations.
+     * @see Net::setPreferableTarget
+     */
+    enum Target
+    {
+        DNN_TARGET_CPU = 0,
+        DNN_TARGET_OPENCL,
+        DNN_TARGET_OPENCL_FP16,
+        DNN_TARGET_MYRIAD,
+        DNN_TARGET_VULKAN,
+        DNN_TARGET_FPGA,  //!< FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.
+        DNN_TARGET_CUDA,
+        DNN_TARGET_CUDA_FP16,
+        DNN_TARGET_HDDL
+    };
+
+    CV_EXPORTS std::vector< std::pair<Backend, Target> > getAvailableBackends();
+    CV_EXPORTS_W std::vector<Target> getAvailableTargets(dnn::Backend be);
+
+    /**
+     * @brief Enables detailed logging of the DNN model loading with CV DNN API.
+     * @param[in] isDiagnosticsMode Indicates whether diagnostic mode should be set.
+     *
+     * Diagnostic mode provides detailed logging of the model loading stage to explore
+     * potential problems (ex.: not implemented layer type).
+     *
+     * @note In diagnostic mode series of assertions will be skipped, it can lead to the
+     * expected application crash.
+     */
+    CV_EXPORTS void enableModelDiagnostics(bool isDiagnosticsMode);
+
+    /** @brief This class provides all data needed to initialize layer.
+     *
+     * It includes dictionary with scalar params (which can be read by using Dict interface),
+     * blob params #blobs and optional meta information: #name and #type of layer instance.
+    */
+    class CV_EXPORTS LayerParams : public Dict
+    {
+    public:
+        //TODO: Add ability to name blob params
+        std::vector<Mat> blobs; //!< List of learned parameters stored as blobs.
+
+        String name; //!< Name of the layer instance (optional, can be used internal purposes).
+        String type; //!< Type name which was used for creating layer by layer factory (optional).
+    };
+
+   /**
+    * @brief Derivatives of this class encapsulates functions of certain backends.
+    */
+    class BackendNode
+    {
+    public:
+        BackendNode(int backendId);
+
+        virtual ~BackendNode(); //!< Virtual destructor to make polymorphism.
+
+        int backendId; //!< Backend identifier.
+    };
+
+    /**
+     * @brief Derivatives of this class wraps cv::Mat for different backends and targets.
+     */
+    class BackendWrapper
+    {
+    public:
+        BackendWrapper(int backendId, int targetId);
+
+        /**
+         * @brief Wrap cv::Mat for specific backend and target.
+         * @param[in] targetId Target identifier.
+         * @param[in] m cv::Mat for wrapping.
+         *
+         * Make CPU->GPU data transfer if it's require for the target.
+         */
+        BackendWrapper(int targetId, const cv::Mat& m);
+
+        /**
+         * @brief Make wrapper for reused cv::Mat.
+         * @param[in] base Wrapper of cv::Mat that will be reused.
+         * @param[in] shape Specific shape.
+         *
+         * Initialize wrapper from another one. It'll wrap the same host CPU
+         * memory and mustn't allocate memory on device(i.e. GPU). It might
+         * has different shape. Use in case of CPU memory reusing for reuse
+         * associated memory on device too.
+         */
+        BackendWrapper(const Ptr<BackendWrapper>& base, const MatShape& shape);
+
+        virtual ~BackendWrapper(); //!< Virtual destructor to make polymorphism.
+
+        /**
+         * @brief Transfer data to CPU host memory.
+         */
+        virtual void copyToHost() = 0;
+
+        /**
+         * @brief Indicate that an actual data is on CPU.
+         */
+        virtual void setHostDirty() = 0;
+
+        int backendId;  //!< Backend identifier.
+        int targetId;   //!< Target identifier.
+    };
+
+    class CV_EXPORTS ActivationLayer;
+
+    /** @brief This interface class allows to build new Layers - are building blocks of networks.
+     *
+     * Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs.
+     * Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros.
+     */
+    class CV_EXPORTS_W Layer : public Algorithm
+    {
+    public:
+
+        //! List of learned parameters must be stored here to allow read them by using Net::getParam().
+        CV_PROP_RW std::vector<Mat> blobs;
+
+        /** @brief Computes and sets internal parameters according to inputs, outputs and blobs.
+         *  @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
+         *  @param[in]  input  vector of already allocated input blobs
+         *  @param[out] output vector of already allocated output blobs
+         *
+         * If this method is called after network has allocated all memory for input and output blobs
+         * and before inferencing.
+         */
+        CV_DEPRECATED_EXTERNAL
+        virtual void finalize(const std::vector<Mat*> &input, std::vector<Mat> &output);
+
+        /** @brief Computes and sets internal parameters according to inputs, outputs and blobs.
+         *  @param[in]  inputs  vector of already allocated input blobs
+         *  @param[out] outputs vector of already allocated output blobs
+         *
+         * If this method is called after network has allocated all memory for input and output blobs
+         * and before inferencing.
+         */
+        CV_WRAP virtual void finalize(InputArrayOfArrays inputs, OutputArrayOfArrays outputs);
+
+        /** @brief Given the @p input blobs, computes the output @p blobs.
+         *  @deprecated Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead
+         *  @param[in]  input  the input blobs.
+         *  @param[out] output allocated output blobs, which will store results of the computation.
+         *  @param[out] internals allocated internal blobs
+         */
+        CV_DEPRECATED_EXTERNAL
+        virtual void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals);
+
+        /** @brief Given the @p input blobs, computes the output @p blobs.
+         *  @param[in]  inputs  the input blobs.
+         *  @param[out] outputs allocated output blobs, which will store results of the computation.
+         *  @param[out] internals allocated internal blobs
+         */
+        virtual void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals);
+
+        /** @brief Tries to quantize the given layer and compute the quantization parameters required for fixed point implementation.
+         *  @param[in] scales input and output scales.
+         *  @param[in] zeropoints input and output zeropoints.
+         *  @param[out] params Quantized parameters required for fixed point implementation of that layer.
+         *  @returns True if layer can be quantized.
+         */
+        virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
+                                 const std::vector<std::vector<int> > &zeropoints, LayerParams& params);
+
+        /** @brief Given the @p input blobs, computes the output @p blobs.
+         *  @param[in]  inputs  the input blobs.
+         *  @param[out] outputs allocated output blobs, which will store results of the computation.
+         *  @param[out] internals allocated internal blobs
+         */
+        void forward_fallback(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals);
+
+        /** @brief
+         * @overload
+         * @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
+         */
+        CV_DEPRECATED_EXTERNAL
+        void finalize(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs);
+
+        /** @brief
+         * @overload
+         * @deprecated Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead
+         */
+        CV_DEPRECATED std::vector<Mat> finalize(const std::vector<Mat> &inputs);
+
+        /** @brief Allocates layer and computes output.
+         *  @deprecated This method will be removed in the future release.
+         */
+        CV_DEPRECATED CV_WRAP void run(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs,
+                                       CV_IN_OUT std::vector<Mat> &internals);
+
+        /** @brief Returns index of input blob into the input array.
+         *  @param inputName label of input blob
+         *
+         * Each layer input and output can be labeled to easily identify them using "%<layer_name%>[.output_name]" notation.
+         * This method maps label of input blob to its index into input vector.
+         */
+        virtual int inputNameToIndex(String inputName);
+        /** @brief Returns index of output blob in output array.
+         *  @see inputNameToIndex()
+         */
+        CV_WRAP virtual int outputNameToIndex(const String& outputName);
+
+        /**
+         * @brief Ask layer if it support specific backend for doing computations.
+         * @param[in] backendId computation backend identifier.
+         * @see Backend
+         */
+        virtual bool supportBackend(int backendId);
+
+        /**
+         * @brief Returns Halide backend node.
+         * @param[in] inputs Input Halide buffers.
+         * @see BackendNode, BackendWrapper
+         *
+         * Input buffers should be exactly the same that will be used in forward invocations.
+         * Despite we can use Halide::ImageParam based on input shape only,
+         * it helps prevent some memory management issues (if something wrong,
+         * Halide tests will be failed).
+         */
+        virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs);
+
+        virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs);
+
+        virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs, const std::vector<Ptr<BackendNode> >& nodes);
+
+        virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &inputs);
+
+        virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> > &inputs, const std::vector<Ptr<BackendNode> >& nodes);
+
+        /**
+         * @brief Returns a CUDA backend node
+         *
+         * @param   context  void pointer to CSLContext object
+         * @param   inputs   layer inputs
+         * @param   outputs  layer outputs
+         */
+        virtual Ptr<BackendNode> initCUDA(
+            void *context,
+            const std::vector<Ptr<BackendWrapper>>& inputs,
+            const std::vector<Ptr<BackendWrapper>>& outputs
+        );
+
+       /**
+        * @brief Automatic Halide scheduling based on layer hyper-parameters.
+        * @param[in] node Backend node with Halide functions.
+        * @param[in] inputs Blobs that will be used in forward invocations.
+        * @param[in] outputs Blobs that will be used in forward invocations.
+        * @param[in] targetId Target identifier
+        * @see BackendNode, Target
+        *
+        * Layer don't use own Halide::Func members because we can have applied
+        * layers fusing. In this way the fused function should be scheduled.
+        */
+        virtual void applyHalideScheduler(Ptr<BackendNode>& node,
+                                          const std::vector<Mat*> &inputs,
+                                          const std::vector<Mat> &outputs,
+                                          int targetId) const;
+
+        /**
+         * @brief Implement layers fusing.
+         * @param[in] node Backend node of bottom layer.
+         * @see BackendNode
+         *
+         * Actual for graph-based backends. If layer attached successfully,
+         * returns non-empty cv::Ptr to node of the same backend.
+         * Fuse only over the last function.
+         */
+        virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node);
+
+        /**
+         * @brief Tries to attach to the layer the subsequent activation layer, i.e. do the layer fusion in a partial case.
+         * @param[in] layer The subsequent activation layer.
+         *
+         * Returns true if the activation layer has been attached successfully.
+         */
+        virtual bool setActivation(const Ptr<ActivationLayer>& layer);
+
+        /**
+         * @brief Try to fuse current layer with a next one
+         * @param[in] top Next layer to be fused.
+         * @returns True if fusion was performed.
+         */
+        virtual bool tryFuse(Ptr<Layer>& top);
+
+        /**
+         * @brief Returns parameters of layers with channel-wise multiplication and addition.
+         * @param[out] scale Channel-wise multipliers. Total number of values should
+         *                   be equal to number of channels.
+         * @param[out] shift Channel-wise offsets. Total number of values should
+         *                   be equal to number of channels.
+         *
+         * Some layers can fuse their transformations with further layers.
+         * In example, convolution + batch normalization. This way base layer
+         * use weights from layer after it. Fused layer is skipped.
+         * By default, @p scale and @p shift are empty that means layer has no
+         * element-wise multiplications or additions.
+         */
+        virtual void getScaleShift(Mat& scale, Mat& shift) const;
+
+        /**
+         * @brief Returns scale and zeropoint of layers
+         * @param[out] scale Output scale
+         * @param[out] zeropoint Output zeropoint
+         *
+         * By default, @p scale is 1 and @p zeropoint is 0.
+         */
+        virtual void getScaleZeropoint(float& scale, int& zeropoint) const;
+
+
+        /**
+         * @brief "Deattaches" all the layers, attached to particular layer.
+         */
+        virtual void unsetAttached();
+
+        virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
+                                     const int requiredOutputs,
+                                     std::vector<MatShape> &outputs,
+                                     std::vector<MatShape> &internals) const;
+
+        virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
+                               const std::vector<MatShape> &outputs) const {CV_UNUSED(inputs); CV_UNUSED(outputs); return 0;}
+
+        virtual bool updateMemoryShapes(const std::vector<MatShape> &inputs);
+
+        CV_PROP String name; //!< Name of the layer instance, can be used for logging or other internal purposes.
+        CV_PROP String type; //!< Type name which was used for creating layer by layer factory.
+        CV_PROP int preferableTarget; //!< prefer target for layer forwarding
+
+        Layer();
+        explicit Layer(const LayerParams &params);      //!< Initializes only #name, #type and #blobs fields.
+        void setParamsFrom(const LayerParams &params);  //!< Initializes only #name, #type and #blobs fields.
+        virtual ~Layer();
+    };
+
+    /** @brief This class allows to create and manipulate comprehensive artificial neural networks.
+     *
+     * Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances,
+     * and edges specify relationships between layers inputs and outputs.
+     *
+     * Each network layer has unique integer id and unique string name inside its network.
+     * LayerId can store either layer name or layer id.
+     *
+     * This class supports reference counting of its instances, i. e. copies point to the same instance.
+     */
+    class CV_EXPORTS_W_SIMPLE Net
+    {
+    public:
+
+        CV_WRAP Net();  //!< Default constructor.
+        CV_WRAP ~Net(); //!< Destructor frees the net only if there aren't references to the net anymore.
+
+        /** @brief Create a network from Intel's Model Optimizer intermediate representation (IR).
+         *  @param[in] xml XML configuration file with network's topology.
+         *  @param[in] bin Binary file with trained weights.
+         *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
+         *  backend.
+         */
+        CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin);
+
+        /** @brief Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).
+         *  @param[in] bufferModelConfig buffer with model's configuration.
+         *  @param[in] bufferWeights buffer with model's trained weights.
+         *  @returns Net object.
+         */
+        CV_WRAP static
+        Net readFromModelOptimizer(const std::vector<uchar>& bufferModelConfig, const std::vector<uchar>& bufferWeights);
+
+        /** @brief Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).
+         *  @param[in] bufferModelConfigPtr buffer pointer of model's configuration.
+         *  @param[in] bufferModelConfigSize buffer size of model's configuration.
+         *  @param[in] bufferWeightsPtr buffer pointer of model's trained weights.
+         *  @param[in] bufferWeightsSize buffer size of model's trained weights.
+         *  @returns Net object.
+         */
+        static
+        Net readFromModelOptimizer(const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize,
+                                            const uchar* bufferWeightsPtr, size_t bufferWeightsSize);
+
+        /** Returns true if there are no layers in the network. */
+        CV_WRAP bool empty() const;
+
+        /** @brief Dump net to String
+         *  @returns String with structure, hyperparameters, backend, target and fusion
+         *  Call method after setInput(). To see correct backend, target and fusion run after forward().
+         */
+        CV_WRAP String dump();
+        /** @brief Dump net structure, hyperparameters, backend, target and fusion to dot file
+         *  @param path   path to output file with .dot extension
+         *  @see dump()
+         */
+        CV_WRAP void dumpToFile(const String& path);
+        /** @brief Adds new layer to the net.
+         *  @param name   unique name of the adding layer.
+         *  @param type   typename of the adding layer (type must be registered in LayerRegister).
+         *  @param dtype  datatype of output blobs.
+         *  @param params parameters which will be used to initialize the creating layer.
+         *  @returns unique identifier of created layer, or -1 if a failure will happen.
+         */
+        int addLayer(const String &name, const String &type, const int &dtype, LayerParams &params);
+
+        /** @overload Datatype of output blobs set to default CV_32F */
+        int addLayer(const String &name, const String &type, LayerParams &params);
+
+        /** @brief Adds new layer and connects its first input to the first output of previously added layer.
+         *  @see addLayer()
+         */
+        int addLayerToPrev(const String &name, const String &type, const int &dtype, LayerParams &params);
+
+        /** @overload */
+        int addLayerToPrev(const String &name, const String &type, LayerParams &params);
+
+        /** @brief Converts string name of the layer to the integer identifier.
+         *  @returns id of the layer, or -1 if the layer wasn't found.
+         */
+        CV_WRAP int getLayerId(const String &layer);
+
+        CV_WRAP std::vector<String> getLayerNames() const;
+
+        /** @brief Container for strings and integers. */
+        typedef DictValue LayerId;
+
+        /** @brief Returns pointer to layer with specified id or name which the network use. */
+        CV_WRAP Ptr<Layer> getLayer(LayerId layerId);
+
+        /** @brief Returns pointers to input layers of specific layer. */
+        std::vector<Ptr<Layer> > getLayerInputs(LayerId layerId); // FIXIT: CV_WRAP
+
+        /** @brief Connects output of the first layer to input of the second layer.
+         *  @param outPin descriptor of the first layer output.
+         *  @param inpPin descriptor of the second layer input.
+         *
+         * Descriptors have the following template <DFN>&lt;layer_name&gt;[.input_number]</DFN>:
+         * - the first part of the template <DFN>layer_name</DFN> is string name of the added layer.
+         *   If this part is empty then the network input pseudo layer will be used;
+         * - the second optional part of the template <DFN>input_number</DFN>
+         *   is either number of the layer input, either label one.
+         *   If this part is omitted then the first layer input will be used.
+         *
+         *  @see setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex()
+         */
+        CV_WRAP void connect(String outPin, String inpPin);
+
+        /** @brief Connects #@p outNum output of the first layer to #@p inNum input of the second layer.
+         *  @param outLayerId identifier of the first layer
+         *  @param outNum number of the first layer output
+         *  @param inpLayerId identifier of the second layer
+         *  @param inpNum number of the second layer input
+         */
+        void connect(int outLayerId, int outNum, int inpLayerId, int inpNum);
+
+        /** @brief Sets outputs names of the network input pseudo layer.
+         *
+         * Each net always has special own the network input pseudo layer with id=0.
+         * This layer stores the user blobs only and don't make any computations.
+         * In fact, this layer provides the only way to pass user data into the network.
+         * As any other layer, this layer can label its outputs and this function provides an easy way to do this.
+         */
+        CV_WRAP void setInputsNames(const std::vector<String> &inputBlobNames);
+
+        /** @brief Specify shape of network input.
+         */
+        CV_WRAP void setInputShape(const String &inputName, const MatShape& shape);
+
+        /** @brief Runs forward pass to compute output of layer with name @p outputName.
+         *  @param outputName name for layer which output is needed to get
+         *  @return blob for first output of specified layer.
+         *  @details By default runs forward pass for the whole network.
+         */
+        CV_WRAP Mat forward(const String& outputName = String());
+
+        /** @brief Runs forward pass to compute output of layer with name @p outputName.
+         *  @param outputName name for layer which output is needed to get
+         *  @details By default runs forward pass for the whole network.
+         *
+         *  This is an asynchronous version of forward(const String&).
+         *  dnn::DNN_BACKEND_INFERENCE_ENGINE backend is required.
+         */
+        CV_WRAP AsyncArray forwardAsync(const String& outputName = String());
+
+        /** @brief Runs forward pass to compute output of layer with name @p outputName.
+         *  @param outputBlobs contains all output blobs for specified layer.
+         *  @param outputName name for layer which output is needed to get
+         *  @details If @p outputName is empty, runs forward pass for the whole network.
+         */
+        CV_WRAP void forward(OutputArrayOfArrays outputBlobs, const String& outputName = String());
+
+        /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames.
+         *  @param outputBlobs contains blobs for first outputs of specified layers.
+         *  @param outBlobNames names for layers which outputs are needed to get
+         */
+        CV_WRAP void forward(OutputArrayOfArrays outputBlobs,
+                             const std::vector<String>& outBlobNames);
+
+        /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames.
+         *  @param outputBlobs contains all output blobs for each layer specified in @p outBlobNames.
+         *  @param outBlobNames names for layers which outputs are needed to get
+         */
+        CV_WRAP_AS(forwardAndRetrieve) void forward(CV_OUT std::vector<std::vector<Mat> >& outputBlobs,
+                                                    const std::vector<String>& outBlobNames);
+
+        /** @brief Returns a quantized Net from a floating-point Net.
+         *  @param calibData Calibration data to compute the quantization parameters.
+         *  @param inputsDtype Datatype of quantized net's inputs. Can be CV_32F or CV_8S.
+         *  @param outputsDtype Datatype of quantized net's outputs. Can be CV_32F or CV_8S.
+         */
+        CV_WRAP Net quantize(InputArrayOfArrays calibData, int inputsDtype, int outputsDtype);
+
+        /** @brief Returns input scale and zeropoint for a quantized Net.
+         *  @param scales output parameter for returning input scales.
+         *  @param zeropoints output parameter for returning input zeropoints.
+         */
+        CV_WRAP void getInputDetails(CV_OUT std::vector<float>& scales, CV_OUT std::vector<int>& zeropoints) const;
+
+        /** @brief Returns output scale and zeropoint for a quantized Net.
+         *  @param scales output parameter for returning output scales.
+         *  @param zeropoints output parameter for returning output zeropoints.
+         */
+        CV_WRAP void getOutputDetails(CV_OUT std::vector<float>& scales, CV_OUT std::vector<int>& zeropoints) const;
+
+        /**
+         * @brief Compile Halide layers.
+         * @param[in] scheduler Path to YAML file with scheduling directives.
+         * @see setPreferableBackend
+         *
+         * Schedule layers that support Halide backend. Then compile them for
+         * specific target. For layers that not represented in scheduling file
+         * or if no manual scheduling used at all, automatic scheduling will be applied.
+         */
+        CV_WRAP void setHalideScheduler(const String& scheduler);
+
+        /**
+         * @brief Ask network to use specific computation backend where it supported.
+         * @param[in] backendId backend identifier.
+         * @see Backend
+         *
+         * If OpenCV is compiled with Intel's Inference Engine library, DNN_BACKEND_DEFAULT
+         * means DNN_BACKEND_INFERENCE_ENGINE. Otherwise it equals to DNN_BACKEND_OPENCV.
+         */
+        CV_WRAP void setPreferableBackend(int backendId);
+
+        /**
+         * @brief Ask network to make computations on specific target device.
+         * @param[in] targetId target identifier.
+         * @see Target
+         *
+         * List of supported combinations backend / target:
+         * |                        | DNN_BACKEND_OPENCV | DNN_BACKEND_INFERENCE_ENGINE | DNN_BACKEND_HALIDE |  DNN_BACKEND_CUDA |
+         * |------------------------|--------------------|------------------------------|--------------------|-------------------|
+         * | DNN_TARGET_CPU         |                  + |                            + |                  + |                   |
+         * | DNN_TARGET_OPENCL      |                  + |                            + |                  + |                   |
+         * | DNN_TARGET_OPENCL_FP16 |                  + |                            + |                    |                   |
+         * | DNN_TARGET_MYRIAD      |                    |                            + |                    |                   |
+         * | DNN_TARGET_FPGA        |                    |                            + |                    |                   |
+         * | DNN_TARGET_CUDA        |                    |                              |                    |                 + |
+         * | DNN_TARGET_CUDA_FP16   |                    |                              |                    |                 + |
+         * | DNN_TARGET_HDDL        |                    |                            + |                    |                   |
+         */
+        CV_WRAP void setPreferableTarget(int targetId);
+
+        /** @brief Sets the new input value for the network
+         *  @param blob        A new blob. Should have CV_32F or CV_8U depth.
+         *  @param name        A name of input layer.
+         *  @param scalefactor An optional normalization scale.
+         *  @param mean        An optional mean subtraction values.
+         *  @see connect(String, String) to know format of the descriptor.
+         *
+         *  If scale or mean values are specified, a final input blob is computed
+         *  as:
+         * \f[input(n,c,h,w) = scalefactor \times (blob(n,c,h,w) - mean_c)\f]
+         */
+        CV_WRAP void setInput(InputArray blob, const String& name = "",
+                              double scalefactor = 1.0, const Scalar& mean = Scalar());
+
+        /** @brief Sets the new value for the learned param of the layer.
+         *  @param layer name or id of the layer.
+         *  @param numParam index of the layer parameter in the Layer::blobs array.
+         *  @param blob the new value.
+         *  @see Layer::blobs
+         *  @note If shape of the new blob differs from the previous shape,
+         *  then the following forward pass may fail.
+        */
+        CV_WRAP void setParam(LayerId layer, int numParam, const Mat &blob);
+
+        /** @brief Returns parameter blob of the layer.
+         *  @param layer name or id of the layer.
+         *  @param numParam index of the layer parameter in the Layer::blobs array.
+         *  @see Layer::blobs
+         */
+        CV_WRAP Mat getParam(LayerId layer, int numParam = 0);
+
+        /** @brief Returns indexes of layers with unconnected outputs.
+         */
+        CV_WRAP std::vector<int> getUnconnectedOutLayers() const;
+
+        /** @brief Returns names of layers with unconnected outputs.
+         */
+        CV_WRAP std::vector<String> getUnconnectedOutLayersNames() const;
+
+        /** @brief Returns input and output shapes for all layers in loaded model;
+         *  preliminary inferencing isn't necessary.
+         *  @param netInputShapes shapes for all input blobs in net input layer.
+         *  @param layersIds output parameter for layer IDs.
+         *  @param inLayersShapes output parameter for input layers shapes;
+         * order is the same as in layersIds
+         *  @param outLayersShapes output parameter for output layers shapes;
+         * order is the same as in layersIds
+         */
+        CV_WRAP void getLayersShapes(const std::vector<MatShape>& netInputShapes,
+                                     CV_OUT std::vector<int>& layersIds,
+                                     CV_OUT std::vector<std::vector<MatShape> >& inLayersShapes,
+                                     CV_OUT std::vector<std::vector<MatShape> >& outLayersShapes) const;
+
+        /** @overload */
+        CV_WRAP void getLayersShapes(const MatShape& netInputShape,
+                                     CV_OUT std::vector<int>& layersIds,
+                                     CV_OUT std::vector<std::vector<MatShape> >& inLayersShapes,
+                                     CV_OUT std::vector<std::vector<MatShape> >& outLayersShapes) const;
+
+        /** @brief Returns input and output shapes for layer with specified
+         * id in loaded model; preliminary inferencing isn't necessary.
+         *  @param netInputShape shape input blob in net input layer.
+         *  @param layerId id for layer.
+         *  @param inLayerShapes output parameter for input layers shapes;
+         * order is the same as in layersIds
+         *  @param outLayerShapes output parameter for output layers shapes;
+         * order is the same as in layersIds
+         */
+        void getLayerShapes(const MatShape& netInputShape,
+                                    const int layerId,
+                                    CV_OUT std::vector<MatShape>& inLayerShapes,
+                                    CV_OUT std::vector<MatShape>& outLayerShapes) const; // FIXIT: CV_WRAP
+
+        /** @overload */
+        void getLayerShapes(const std::vector<MatShape>& netInputShapes,
+                                    const int layerId,
+                                    CV_OUT std::vector<MatShape>& inLayerShapes,
+                                    CV_OUT std::vector<MatShape>& outLayerShapes) const; // FIXIT: CV_WRAP
+
+        /** @brief Computes FLOP for whole loaded model with specified input shapes.
+         * @param netInputShapes vector of shapes for all net inputs.
+         * @returns computed FLOP.
+         */
+        CV_WRAP int64 getFLOPS(const std::vector<MatShape>& netInputShapes) const;
+        /** @overload */
+        CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const;
+        /** @overload */
+        CV_WRAP int64 getFLOPS(const int layerId,
+                               const std::vector<MatShape>& netInputShapes) const;
+        /** @overload */
+        CV_WRAP int64 getFLOPS(const int layerId,
+                               const MatShape& netInputShape) const;
+
+        /** @brief Returns list of types for layer used in model.
+         * @param layersTypes output parameter for returning types.
+         */
+        CV_WRAP void getLayerTypes(CV_OUT std::vector<String>& layersTypes) const;
+
+        /** @brief Returns count of layers of specified type.
+         * @param layerType type.
+         * @returns count of layers
+         */
+        CV_WRAP int getLayersCount(const String& layerType) const;
+
+        /** @brief Computes bytes number which are required to store
+         * all weights and intermediate blobs for model.
+         * @param netInputShapes vector of shapes for all net inputs.
+         * @param weights output parameter to store resulting bytes for weights.
+         * @param blobs output parameter to store resulting bytes for intermediate blobs.
+         */
+        void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const; // FIXIT: CV_WRAP
+        /** @overload */
+        CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
+        /** @overload */
+        CV_WRAP void getMemoryConsumption(const int layerId,
+                                          const std::vector<MatShape>& netInputShapes,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
+        /** @overload */
+        CV_WRAP void getMemoryConsumption(const int layerId,
+                                          const MatShape& netInputShape,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
+
+        /** @brief Computes bytes number which are required to store
+         * all weights and intermediate blobs for each layer.
+         * @param netInputShapes vector of shapes for all net inputs.
+         * @param layerIds output vector to save layer IDs.
+         * @param weights output parameter to store resulting bytes for weights.
+         * @param blobs output parameter to store resulting bytes for intermediate blobs.
+         */
+        void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
+                                          CV_OUT std::vector<int>& layerIds,
+                                          CV_OUT std::vector<size_t>& weights,
+                                          CV_OUT std::vector<size_t>& blobs) const; // FIXIT: CV_WRAP
+        /** @overload */
+        void getMemoryConsumption(const MatShape& netInputShape,
+                                          CV_OUT std::vector<int>& layerIds,
+                                          CV_OUT std::vector<size_t>& weights,
+                                          CV_OUT std::vector<size_t>& blobs) const; // FIXIT: CV_WRAP
+
+        /** @brief Enables or disables layer fusion in the network.
+         * @param fusion true to enable the fusion, false to disable. The fusion is enabled by default.
+         */
+        CV_WRAP void enableFusion(bool fusion);
+
+        /** @brief Returns overall time for inference and timings (in ticks) for layers.
+         *
+         * Indexes in returned vector correspond to layers ids. Some layers can be fused with others,
+         * in this case zero ticks count will be return for that skipped layers. Supported by DNN_BACKEND_OPENCV on DNN_TARGET_CPU only.
+         *
+         * @param[out] timings vector for tick timings for all layers.
+         * @return overall ticks for model inference.
+         */
+        CV_WRAP int64 getPerfProfile(CV_OUT std::vector<double>& timings);
+
+    private:
+        struct Impl;
+        Ptr<Impl> impl;
+    };
+
+    /** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
+    *  @param cfgFile      path to the .cfg file with text description of the network architecture.
+    *  @param darknetModel path to the .weights file with learned network.
+    *  @returns Network object that ready to do forward, throw an exception in failure cases.
+    *  @returns Net object.
+    */
+    CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
+
+    /** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
+     *  @param bufferCfg   A buffer contains a content of .cfg file with text description of the network architecture.
+     *  @param bufferModel A buffer contains a content of .weights file with learned network.
+     *  @returns Net object.
+     */
+    CV_EXPORTS_W Net readNetFromDarknet(const std::vector<uchar>& bufferCfg,
+                                        const std::vector<uchar>& bufferModel = std::vector<uchar>());
+
+    /** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
+     *  @param bufferCfg   A buffer contains a content of .cfg file with text description of the network architecture.
+     *  @param lenCfg      Number of bytes to read from bufferCfg
+     *  @param bufferModel A buffer contains a content of .weights file with learned network.
+     *  @param lenModel    Number of bytes to read from bufferModel
+     *  @returns Net object.
+     */
+    CV_EXPORTS Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg,
+                                      const char *bufferModel = NULL, size_t lenModel = 0);
+
+    /** @brief Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
+      * @param prototxt   path to the .prototxt file with text description of the network architecture.
+      * @param caffeModel path to the .caffemodel file with learned network.
+      * @returns Net object.
+      */
+    CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
+
+    /** @brief Reads a network model stored in Caffe model in memory.
+      * @param bufferProto buffer containing the content of the .prototxt file
+      * @param bufferModel buffer containing the content of the .caffemodel file
+      * @returns Net object.
+      */
+    CV_EXPORTS_W Net readNetFromCaffe(const std::vector<uchar>& bufferProto,
+                                      const std::vector<uchar>& bufferModel = std::vector<uchar>());
+
+    /** @brief Reads a network model stored in Caffe model in memory.
+      * @details This is an overloaded member function, provided for convenience.
+      * It differs from the above function only in what argument(s) it accepts.
+      * @param bufferProto buffer containing the content of the .prototxt file
+      * @param lenProto length of bufferProto
+      * @param bufferModel buffer containing the content of the .caffemodel file
+      * @param lenModel length of bufferModel
+      * @returns Net object.
+      */
+    CV_EXPORTS Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
+                                    const char *bufferModel = NULL, size_t lenModel = 0);
+
+    /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
+      * @param model  path to the .pb file with binary protobuf description of the network architecture
+      * @param config path to the .pbtxt file that contains text graph definition in protobuf format.
+      *               Resulting Net object is built by text graph using weights from a binary one that
+      *               let us make it more flexible.
+      * @returns Net object.
+      */
+    CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String());
+
+    /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
+      * @param bufferModel buffer containing the content of the pb file
+      * @param bufferConfig buffer containing the content of the pbtxt file
+      * @returns Net object.
+      */
+    CV_EXPORTS_W Net readNetFromTensorflow(const std::vector<uchar>& bufferModel,
+                                           const std::vector<uchar>& bufferConfig = std::vector<uchar>());
+
+    /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
+      * @details This is an overloaded member function, provided for convenience.
+      * It differs from the above function only in what argument(s) it accepts.
+      * @param bufferModel buffer containing the content of the pb file
+      * @param lenModel length of bufferModel
+      * @param bufferConfig buffer containing the content of the pbtxt file
+      * @param lenConfig length of bufferConfig
+      */
+    CV_EXPORTS Net readNetFromTensorflow(const char *bufferModel, size_t lenModel,
+                                         const char *bufferConfig = NULL, size_t lenConfig = 0);
+
+    /**
+     *  @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
+     *  @param model    path to the file, dumped from Torch by using torch.save() function.
+     *  @param isBinary specifies whether the network was serialized in ascii mode or binary.
+     *  @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
+     *  @returns Net object.
+     *
+     *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
+     *  which has various bit-length on different systems.
+     *
+     * The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object
+     * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
+     *
+     * List of supported layers (i.e. object instances derived from Torch nn.Module class):
+     * - nn.Sequential
+     * - nn.Parallel
+     * - nn.Concat
+     * - nn.Linear
+     * - nn.SpatialConvolution
+     * - nn.SpatialMaxPooling, nn.SpatialAveragePooling
+     * - nn.ReLU, nn.TanH, nn.Sigmoid
+     * - nn.Reshape
+     * - nn.SoftMax, nn.LogSoftMax
+     *
+     * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
+     */
+     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true, bool evaluate = true);
+
+     /**
+      * @brief Read deep learning network represented in one of the supported formats.
+      * @param[in] model Binary file contains trained weights. The following file
+      *                  extensions are expected for models from different frameworks:
+      *                  * `*.caffemodel` (Caffe, http://caffe.berkeleyvision.org/)
+      *                  * `*.pb` (TensorFlow, https://www.tensorflow.org/)
+      *                  * `*.t7` | `*.net` (Torch, http://torch.ch/)
+      *                  * `*.weights` (Darknet, https://pjreddie.com/darknet/)
+      *                  * `*.bin` (DLDT, https://software.intel.com/openvino-toolkit)
+      *                  * `*.onnx` (ONNX, https://onnx.ai/)
+      * @param[in] config Text file contains network configuration. It could be a
+      *                   file with the following extensions:
+      *                  * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)
+      *                  * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/)
+      *                  * `*.cfg` (Darknet, https://pjreddie.com/darknet/)
+      *                  * `*.xml` (DLDT, https://software.intel.com/openvino-toolkit)
+      * @param[in] framework Explicit framework name tag to determine a format.
+      * @returns Net object.
+      *
+      * This function automatically detects an origin framework of trained model
+      * and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow,
+      * @ref readNetFromTorch or @ref readNetFromDarknet. An order of @p model and @p config
+      * arguments does not matter.
+      */
+     CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = "");
+
+     /**
+      * @brief Read deep learning network represented in one of the supported formats.
+      * @details This is an overloaded member function, provided for convenience.
+      *          It differs from the above function only in what argument(s) it accepts.
+      * @param[in] framework    Name of origin framework.
+      * @param[in] bufferModel  A buffer with a content of binary file with weights
+      * @param[in] bufferConfig A buffer with a content of text file contains network configuration.
+      * @returns Net object.
+      */
+     CV_EXPORTS_W Net readNet(const String& framework, const std::vector<uchar>& bufferModel,
+                              const std::vector<uchar>& bufferConfig = std::vector<uchar>());
+
+    /** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
+     *  @warning This function has the same limitations as readNetFromTorch().
+     */
+    CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true);
+
+    /** @brief Load a network from Intel's Model Optimizer intermediate representation.
+     *  @param[in] xml XML configuration file with network's topology.
+     *  @param[in] bin Binary file with trained weights.
+     *  @returns Net object.
+     *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
+     *  backend.
+     */
+    CV_EXPORTS_W
+    Net readNetFromModelOptimizer(const String &xml, const String &bin);
+
+    /** @brief Load a network from Intel's Model Optimizer intermediate representation.
+     *  @param[in] bufferModelConfig Buffer contains XML configuration with network's topology.
+     *  @param[in] bufferWeights Buffer contains binary data with trained weights.
+     *  @returns Net object.
+     *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
+     *  backend.
+     */
+    CV_EXPORTS_W
+    Net readNetFromModelOptimizer(const std::vector<uchar>& bufferModelConfig, const std::vector<uchar>& bufferWeights);
+
+    /** @brief Load a network from Intel's Model Optimizer intermediate representation.
+     *  @param[in] bufferModelConfigPtr Pointer to buffer which contains XML configuration with network's topology.
+     *  @param[in] bufferModelConfigSize Binary size of XML configuration data.
+     *  @param[in] bufferWeightsPtr Pointer to buffer which contains binary data with trained weights.
+     *  @param[in] bufferWeightsSize Binary size of trained weights data.
+     *  @returns Net object.
+     *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
+     *  backend.
+     */
+    CV_EXPORTS
+    Net readNetFromModelOptimizer(const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize,
+                                           const uchar* bufferWeightsPtr, size_t bufferWeightsSize);
+
+    /** @brief Reads a network model <a href="https://onnx.ai/">ONNX</a>.
+     *  @param onnxFile path to the .onnx file with text description of the network architecture.
+     *  @returns Network object that ready to do forward, throw an exception in failure cases.
+     */
+    CV_EXPORTS_W Net readNetFromONNX(const String &onnxFile);
+
+    /** @brief Reads a network model from <a href="https://onnx.ai/">ONNX</a>
+     *         in-memory buffer.
+     *  @param buffer memory address of the first byte of the buffer.
+     *  @param sizeBuffer size of the buffer.
+     *  @returns Network object that ready to do forward, throw an exception
+     *        in failure cases.
+     */
+    CV_EXPORTS Net readNetFromONNX(const char* buffer, size_t sizeBuffer);
+
+    /** @brief Reads a network model from <a href="https://onnx.ai/">ONNX</a>
+     *         in-memory buffer.
+     *  @param buffer in-memory buffer that stores the ONNX model bytes.
+     *  @returns Network object that ready to do forward, throw an exception
+     *        in failure cases.
+     */
+    CV_EXPORTS_W Net readNetFromONNX(const std::vector<uchar>& buffer);
+
+    /** @brief Creates blob from .pb file.
+     *  @param path to the .pb file with input tensor.
+     *  @returns Mat.
+     */
+    CV_EXPORTS_W Mat readTensorFromONNX(const String& path);
+
+    /** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center,
+     *  subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.
+     *  @param image input image (with 1-, 3- or 4-channels).
+     *  @param size spatial size for output image
+     *  @param mean scalar with mean values which are subtracted from channels. Values are intended
+     *  to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.
+     *  @param scalefactor multiplier for @p image values.
+     *  @param swapRB flag which indicates that swap first and last channels
+     *  in 3-channel image is necessary.
+     *  @param crop flag which indicates whether image will be cropped after resize or not
+     *  @param ddepth Depth of output blob. Choose CV_32F or CV_8U.
+     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
+     *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
+     *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
+     *  @returns 4-dimensional Mat with NCHW dimensions order.
+     */
+    CV_EXPORTS_W Mat blobFromImage(InputArray image, double scalefactor=1.0, const Size& size = Size(),
+                                   const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false,
+                                   int ddepth=CV_32F);
+
+    /** @brief Creates 4-dimensional blob from image.
+     *  @details This is an overloaded member function, provided for convenience.
+     *           It differs from the above function only in what argument(s) it accepts.
+     */
+    CV_EXPORTS void blobFromImage(InputArray image, OutputArray blob, double scalefactor=1.0,
+                                  const Size& size = Size(), const Scalar& mean = Scalar(),
+                                  bool swapRB=false, bool crop=false, int ddepth=CV_32F);
+
+
+    /** @brief Creates 4-dimensional blob from series of images. Optionally resizes and
+     *  crops @p images from center, subtract @p mean values, scales values by @p scalefactor,
+     *  swap Blue and Red channels.
+     *  @param images input images (all with 1-, 3- or 4-channels).
+     *  @param size spatial size for output image
+     *  @param mean scalar with mean values which are subtracted from channels. Values are intended
+     *  to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.
+     *  @param scalefactor multiplier for @p images values.
+     *  @param swapRB flag which indicates that swap first and last channels
+     *  in 3-channel image is necessary.
+     *  @param crop flag which indicates whether image will be cropped after resize or not
+     *  @param ddepth Depth of output blob. Choose CV_32F or CV_8U.
+     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
+     *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
+     *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
+     *  @returns 4-dimensional Mat with NCHW dimensions order.
+     */
+    CV_EXPORTS_W Mat blobFromImages(InputArrayOfArrays images, double scalefactor=1.0,
+                                    Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false,
+                                    int ddepth=CV_32F);
+
+    /** @brief Creates 4-dimensional blob from series of images.
+     *  @details This is an overloaded member function, provided for convenience.
+     *           It differs from the above function only in what argument(s) it accepts.
+     */
+    CV_EXPORTS void blobFromImages(InputArrayOfArrays images, OutputArray blob,
+                                   double scalefactor=1.0, Size size = Size(),
+                                   const Scalar& mean = Scalar(), bool swapRB=false, bool crop=false,
+                                   int ddepth=CV_32F);
+
+    /** @brief Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure
+     *  (std::vector<cv::Mat>).
+     *  @param[in] blob_ 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from
+     *  which you would like to extract the images.
+     *  @param[out] images_ array of 2D Mat containing the images extracted from the blob in floating point precision
+     *  (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension
+     *  of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).
+     */
+    CV_EXPORTS_W void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_);
+
+    /** @brief Convert all weights of Caffe network to half precision floating point.
+     * @param src Path to origin model from Caffe framework contains single
+     *            precision floating point weights (usually has `.caffemodel` extension).
+     * @param dst Path to destination model with updated weights.
+     * @param layersTypes Set of layers types which parameters will be converted.
+     *                    By default, converts only Convolutional and Fully-Connected layers'
+     *                    weights.
+     *
+     * @note Shrinked model has no origin float32 weights so it can't be used
+     *       in origin Caffe framework anymore. However the structure of data
+     *       is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
+     *       So the resulting model may be used there.
+     */
+    CV_EXPORTS_W void shrinkCaffeModel(const String& src, const String& dst,
+                                       const std::vector<String>& layersTypes = std::vector<String>());
+
+    /** @brief Create a text representation for a binary network stored in protocol buffer format.
+     *  @param[in] model  A path to binary network.
+     *  @param[in] output A path to output text file to be created.
+     *
+     *  @note To reduce output file size, trained weights are not included.
+     */
+    CV_EXPORTS_W void writeTextGraph(const String& model, const String& output);
+
+    /** @brief Performs non maximum suppression given boxes and corresponding scores.
+
+     * @param bboxes a set of bounding boxes to apply NMS.
+     * @param scores a set of corresponding confidences.
+     * @param score_threshold a threshold used to filter boxes by score.
+     * @param nms_threshold a threshold used in non maximum suppression.
+     * @param indices the kept indices of bboxes after NMS.
+     * @param eta a coefficient in adaptive threshold formula: \f$nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\f$.
+     * @param top_k if `>0`, keep at most @p top_k picked indices.
+     */
+    CV_EXPORTS void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
+                               const float score_threshold, const float nms_threshold,
+                               CV_OUT std::vector<int>& indices,
+                               const float eta = 1.f, const int top_k = 0);
+
+    CV_EXPORTS_W void NMSBoxes(const std::vector<Rect2d>& bboxes, const std::vector<float>& scores,
+                               const float score_threshold, const float nms_threshold,
+                               CV_OUT std::vector<int>& indices,
+                               const float eta = 1.f, const int top_k = 0);
+
+    CV_EXPORTS_AS(NMSBoxesRotated) void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>& scores,
+                             const float score_threshold, const float nms_threshold,
+                             CV_OUT std::vector<int>& indices,
+                             const float eta = 1.f, const int top_k = 0);
+
+    /**
+     * @brief Enum of Soft NMS methods.
+     * @see softNMSBoxes
+     */
+    enum class SoftNMSMethod
+    {
+        SOFTNMS_LINEAR = 1,
+        SOFTNMS_GAUSSIAN = 2
+    };
+
+    /** @brief Performs soft non maximum suppression given boxes and corresponding scores.
+     * Reference: https://arxiv.org/abs/1704.04503
+     * @param bboxes a set of bounding boxes to apply Soft NMS.
+     * @param scores a set of corresponding confidences.
+     * @param updated_scores a set of corresponding updated confidences.
+     * @param score_threshold a threshold used to filter boxes by score.
+     * @param nms_threshold a threshold used in non maximum suppression.
+     * @param indices the kept indices of bboxes after NMS.
+     * @param top_k keep at most @p top_k picked indices.
+     * @param sigma parameter of Gaussian weighting.
+     * @param method Gaussian or linear.
+     * @see SoftNMSMethod
+     */
+    CV_EXPORTS_W void softNMSBoxes(const std::vector<Rect>& bboxes,
+                                   const std::vector<float>& scores,
+                                   CV_OUT std::vector<float>& updated_scores,
+                                   const float score_threshold,
+                                   const float nms_threshold,
+                                   CV_OUT std::vector<int>& indices,
+                                   size_t top_k = 0,
+                                   const float sigma = 0.5,
+                                   SoftNMSMethod method = SoftNMSMethod::SOFTNMS_GAUSSIAN);
+
+
+     /** @brief This class is presented high-level API for neural networks.
+      *
+      * Model allows to set params for preprocessing input image.
+      * Model creates net from file with trained weights and config,
+      * sets preprocessing input and runs forward pass.
+      */
+     class CV_EXPORTS_W_SIMPLE Model
+     {
+     public:
+         CV_DEPRECATED_EXTERNAL  // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
+         Model();
+
+         Model(const Model&) = default;
+         Model(Model&&) = default;
+         Model& operator=(const Model&) = default;
+         Model& operator=(Model&&) = default;
+
+         /**
+          * @brief Create model from deep learning network represented in one of the supported formats.
+          * An order of @p model and @p config arguments does not matter.
+          * @param[in] model Binary file contains trained weights.
+          * @param[in] config Text file contains network configuration.
+          */
+         CV_WRAP Model(const String& model, const String& config = "");
+
+         /**
+          * @brief Create model from deep learning network.
+          * @param[in] network Net object.
+          */
+         CV_WRAP Model(const Net& network);
+
+         /** @brief Set input size for frame.
+          *  @param[in] size New input size.
+          *  @note If shape of the new blob less than 0, then frame size not change.
+         */
+         CV_WRAP Model& setInputSize(const Size& size);
+
+         /** @overload
+         *  @param[in] width New input width.
+         *  @param[in] height New input height.
+         */
+         CV_WRAP inline
+         Model& setInputSize(int width, int height) { return setInputSize(Size(width, height)); }
+
+         /** @brief Set mean value for frame.
+          *  @param[in] mean Scalar with mean values which are subtracted from channels.
+         */
+         CV_WRAP Model& setInputMean(const Scalar& mean);
+
+         /** @brief Set scalefactor value for frame.
+          *  @param[in] scale Multiplier for frame values.
+         */
+         CV_WRAP Model& setInputScale(double scale);
+
+         /** @brief Set flag crop for frame.
+          *  @param[in] crop Flag which indicates whether image will be cropped after resize or not.
+         */
+         CV_WRAP Model& setInputCrop(bool crop);
+
+         /** @brief Set flag swapRB for frame.
+          *  @param[in] swapRB Flag which indicates that swap first and last channels.
+         */
+         CV_WRAP Model& setInputSwapRB(bool swapRB);
+
+         /** @brief Set preprocessing parameters for frame.
+         *  @param[in] size New input size.
+         *  @param[in] mean Scalar with mean values which are subtracted from channels.
+         *  @param[in] scale Multiplier for frame values.
+         *  @param[in] swapRB Flag which indicates that swap first and last channels.
+         *  @param[in] crop Flag which indicates whether image will be cropped after resize or not.
+         *  blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
+         */
+         CV_WRAP void setInputParams(double scale = 1.0, const Size& size = Size(),
+                                     const Scalar& mean = Scalar(), bool swapRB = false, bool crop = false);
+
+         /** @brief Given the @p input frame, create input blob, run net and return the output @p blobs.
+          *  @param[in]  frame  The input image.
+          *  @param[out] outs Allocated output blobs, which will store results of the computation.
+          */
+         CV_WRAP void predict(InputArray frame, OutputArrayOfArrays outs) const;
+
+
+         // ============================== Net proxy methods ==============================
+         // Never expose methods with network implementation details, like:
+         // - addLayer, addLayerToPrev, connect, setInputsNames, setInputShape, setParam, getParam
+         // - getLayer*, getUnconnectedOutLayers, getUnconnectedOutLayersNames, getLayersShapes
+         // - forward* methods, setInput
+
+         /// @sa Net::setPreferableBackend
+         CV_WRAP Model& setPreferableBackend(dnn::Backend backendId);
+         /// @sa Net::setPreferableTarget
+         CV_WRAP Model& setPreferableTarget(dnn::Target targetId);
+
+         CV_DEPRECATED_EXTERNAL
+         operator Net&() const { return getNetwork_(); }
+
+     //protected: - internal/tests usage only
+         Net& getNetwork_() const;
+         inline Net& getNetwork_() { return const_cast<const Model*>(this)->getNetwork_(); }
+
+         struct Impl;
+         inline Impl* getImpl() const { return impl.get(); }
+         inline Impl& getImplRef() const { CV_DbgAssert(impl); return *impl.get(); }
+     protected:
+         Ptr<Impl> impl;
+     };
+
+     /** @brief This class represents high-level API for classification models.
+      *
+      * ClassificationModel allows to set params for preprocessing input image.
+      * ClassificationModel creates net from file with trained weights and config,
+      * sets preprocessing input, runs forward pass and return top-1 prediction.
+      */
+     class CV_EXPORTS_W_SIMPLE ClassificationModel : public Model
+     {
+     public:
+         /**
+          * @brief Create classification model from network represented in one of the supported formats.
+          * An order of @p model and @p config arguments does not matter.
+          * @param[in] model Binary file contains trained weights.
+          * @param[in] config Text file contains network configuration.
+          */
+          CV_WRAP ClassificationModel(const String& model, const String& config = "");
+
+         /**
+          * @brief Create model from deep learning network.
+          * @param[in] network Net object.
+          */
+         CV_WRAP ClassificationModel(const Net& network);
+
+         /** @brief Given the @p input frame, create input blob, run net and return top-1 prediction.
+          *  @param[in]  frame  The input image.
+          */
+         std::pair<int, float> classify(InputArray frame);
+
+         /** @overload */
+         CV_WRAP void classify(InputArray frame, CV_OUT int& classId, CV_OUT float& conf);
+     };
+
+     /** @brief This class represents high-level API for keypoints models
+      *
+      * KeypointsModel allows to set params for preprocessing input image.
+      * KeypointsModel creates net from file with trained weights and config,
+      * sets preprocessing input, runs forward pass and returns the x and y coordinates of each detected keypoint
+      */
+     class CV_EXPORTS_W_SIMPLE KeypointsModel: public Model
+     {
+     public:
+         /**
+          * @brief Create keypoints model from network represented in one of the supported formats.
+          * An order of @p model and @p config arguments does not matter.
+          * @param[in] model Binary file contains trained weights.
+          * @param[in] config Text file contains network configuration.
+          */
+          CV_WRAP KeypointsModel(const String& model, const String& config = "");
+
+         /**
+          * @brief Create model from deep learning network.
+          * @param[in] network Net object.
+          */
+         CV_WRAP KeypointsModel(const Net& network);
+
+         /** @brief Given the @p input frame, create input blob, run net
+          *  @param[in]  frame  The input image.
+          *  @param thresh minimum confidence threshold to select a keypoint
+          *  @returns a vector holding the x and y coordinates of each detected keypoint
+          *
+          */
+         CV_WRAP std::vector<Point2f> estimate(InputArray frame, float thresh=0.5);
+     };
+
+     /** @brief This class represents high-level API for segmentation  models
+      *
+      * SegmentationModel allows to set params for preprocessing input image.
+      * SegmentationModel creates net from file with trained weights and config,
+      * sets preprocessing input, runs forward pass and returns the class prediction for each pixel.
+      */
+     class CV_EXPORTS_W_SIMPLE SegmentationModel: public Model
+     {
+     public:
+         /**
+          * @brief Create segmentation model from network represented in one of the supported formats.
+          * An order of @p model and @p config arguments does not matter.
+          * @param[in] model Binary file contains trained weights.
+          * @param[in] config Text file contains network configuration.
+          */
+          CV_WRAP SegmentationModel(const String& model, const String& config = "");
+
+         /**
+          * @brief Create model from deep learning network.
+          * @param[in] network Net object.
+          */
+         CV_WRAP SegmentationModel(const Net& network);
+
+         /** @brief Given the @p input frame, create input blob, run net
+          *  @param[in]  frame  The input image.
+          *  @param[out] mask Allocated class prediction for each pixel
+          */
+         CV_WRAP void segment(InputArray frame, OutputArray mask);
+     };
+
+     /** @brief This class represents high-level API for object detection networks.
+      *
+      * DetectionModel allows to set params for preprocessing input image.
+      * DetectionModel creates net from file with trained weights and config,
+      * sets preprocessing input, runs forward pass and return result detections.
+      * For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported.
+      */
+     class CV_EXPORTS_W_SIMPLE DetectionModel : public Model
+     {
+     public:
+         /**
+          * @brief Create detection model from network represented in one of the supported formats.
+          * An order of @p model and @p config arguments does not matter.
+          * @param[in] model Binary file contains trained weights.
+          * @param[in] config Text file contains network configuration.
+          */
+         CV_WRAP DetectionModel(const String& model, const String& config = "");
+
+         /**
+          * @brief Create model from deep learning network.
+          * @param[in] network Net object.
+          */
+         CV_WRAP DetectionModel(const Net& network);
+
+         CV_DEPRECATED_EXTERNAL  // avoid using in C++ code (need to fix bindings first)
+         DetectionModel();
+
+         /**
+          * @brief nmsAcrossClasses defaults to false,
+          * such that when non max suppression is used during the detect() function, it will do so per-class.
+          * This function allows you to toggle this behaviour.
+          * @param[in] value The new value for nmsAcrossClasses
+          */
+         CV_WRAP DetectionModel& setNmsAcrossClasses(bool value);
+
+         /**
+          * @brief Getter for nmsAcrossClasses. This variable defaults to false,
+          * such that when non max suppression is used during the detect() function, it will do so only per-class
+          */
+         CV_WRAP bool getNmsAcrossClasses();
+
+         /** @brief Given the @p input frame, create input blob, run net and return result detections.
+          *  @param[in]  frame  The input image.
+          *  @param[out] classIds Class indexes in result detection.
+          *  @param[out] confidences A set of corresponding confidences.
+          *  @param[out] boxes A set of bounding boxes.
+          *  @param[in] confThreshold A threshold used to filter boxes by confidences.
+          *  @param[in] nmsThreshold A threshold used in non maximum suppression.
+          */
+         CV_WRAP void detect(InputArray frame, CV_OUT std::vector<int>& classIds,
+                             CV_OUT std::vector<float>& confidences, CV_OUT std::vector<Rect>& boxes,
+                             float confThreshold = 0.5f, float nmsThreshold = 0.0f);
+     };
+
+
+/** @brief This class represents high-level API for text recognition networks.
+ *
+ * TextRecognitionModel allows to set params for preprocessing input image.
+ * TextRecognitionModel creates net from file with trained weights and config,
+ * sets preprocessing input, runs forward pass and return recognition result.
+ * For TextRecognitionModel, CRNN-CTC is supported.
+ */
+class CV_EXPORTS_W_SIMPLE TextRecognitionModel : public Model
+{
+public:
+    CV_DEPRECATED_EXTERNAL  // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
+    TextRecognitionModel();
+
+    /**
+     * @brief Create Text Recognition model from deep learning network
+     * Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
+     * @param[in] network Net object
+     */
+    CV_WRAP TextRecognitionModel(const Net& network);
+
+    /**
+     * @brief Create text recognition model from network represented in one of the supported formats
+     * Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
+     * @param[in] model Binary file contains trained weights
+     * @param[in] config Text file contains network configuration
+     */
+    CV_WRAP inline
+    TextRecognitionModel(const std::string& model, const std::string& config = "")
+        : TextRecognitionModel(readNet(model, config)) { /* nothing */ }
+
+    /**
+     * @brief Set the decoding method of translating the network output into string
+     * @param[in] decodeType The decoding method of translating the network output into string, currently supported type:
+     *    - `"CTC-greedy"` greedy decoding for the output of CTC-based methods
+     *    - `"CTC-prefix-beam-search"` Prefix beam search decoding for the output of CTC-based methods
+     */
+    CV_WRAP
+    TextRecognitionModel& setDecodeType(const std::string& decodeType);
+
+    /**
+     * @brief Get the decoding method
+     * @return the decoding method
+     */
+    CV_WRAP
+    const std::string& getDecodeType() const;
+
+    /**
+     * @brief Set the decoding method options for `"CTC-prefix-beam-search"` decode usage
+     * @param[in] beamSize Beam size for search
+     * @param[in] vocPruneSize Parameter to optimize big vocabulary search,
+     * only take top @p vocPruneSize tokens in each search step, @p vocPruneSize <= 0 stands for disable this prune.
+     */
+    CV_WRAP
+    TextRecognitionModel& setDecodeOptsCTCPrefixBeamSearch(int beamSize, int vocPruneSize = 0);
+
+    /**
+     * @brief Set the vocabulary for recognition.
+     * @param[in] vocabulary the associated vocabulary of the network.
+     */
+    CV_WRAP
+    TextRecognitionModel& setVocabulary(const std::vector<std::string>& vocabulary);
+
+    /**
+     * @brief Get the vocabulary for recognition.
+     * @return vocabulary the associated vocabulary
+     */
+    CV_WRAP
+    const std::vector<std::string>& getVocabulary() const;
+
+    /**
+     * @brief Given the @p input frame, create input blob, run net and return recognition result
+     * @param[in] frame The input image
+     * @return The text recognition result
+     */
+    CV_WRAP
+    std::string recognize(InputArray frame) const;
+
+    /**
+     * @brief Given the @p input frame, create input blob, run net and return recognition result
+     * @param[in] frame The input image
+     * @param[in] roiRects List of text detection regions of interest (cv::Rect, CV_32SC4). ROIs is be cropped as the network inputs
+     * @param[out] results A set of text recognition results.
+     */
+    CV_WRAP
+    void recognize(InputArray frame, InputArrayOfArrays roiRects, CV_OUT std::vector<std::string>& results) const;
+};
+
+
+/** @brief Base class for text detection networks
+ */
+class CV_EXPORTS_W_SIMPLE TextDetectionModel : public Model
+{
+protected:
+    CV_DEPRECATED_EXTERNAL  // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
+    TextDetectionModel();
+
+public:
+
+    /** @brief Performs detection
+     *
+     * Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.
+     *
+     * Each result is quadrangle's 4 points in this order:
+     * - bottom-left
+     * - top-left
+     * - top-right
+     * - bottom-right
+     *
+     * Use cv::getPerspectiveTransform function to retrive image region without perspective transformations.
+     *
+     * @note If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output.
+     *
+     * @param[in] frame The input image
+     * @param[out] detections array with detections' quadrangles (4 points per result)
+     * @param[out] confidences array with detection confidences
+     */
+    CV_WRAP
+    void detect(
+            InputArray frame,
+            CV_OUT std::vector< std::vector<Point> >& detections,
+            CV_OUT std::vector<float>& confidences
+    ) const;
+
+    /** @overload */
+    CV_WRAP
+    void detect(
+            InputArray frame,
+            CV_OUT std::vector< std::vector<Point> >& detections
+    ) const;
+
+    /** @brief Performs detection
+     *
+     * Given the input @p frame, prepare network input, run network inference, post-process network output and return result detections.
+     *
+     * Each result is rotated rectangle.
+     *
+     * @note Result may be inaccurate in case of strong perspective transformations.
+     *
+     * @param[in] frame the input image
+     * @param[out] detections array with detections' RotationRect results
+     * @param[out] confidences array with detection confidences
+     */
+    CV_WRAP
+    void detectTextRectangles(
+            InputArray frame,
+            CV_OUT std::vector<cv::RotatedRect>& detections,
+            CV_OUT std::vector<float>& confidences
+    ) const;
+
+    /** @overload */
+    CV_WRAP
+    void detectTextRectangles(
+            InputArray frame,
+            CV_OUT std::vector<cv::RotatedRect>& detections
+    ) const;
+};
+
+/** @brief This class represents high-level API for text detection DL networks compatible with EAST model.
+ *
+ * Configurable parameters:
+ * - (float) confThreshold - used to filter boxes by confidences, default: 0.5f
+ * - (float) nmsThreshold - used in non maximum suppression, default: 0.0f
+ */
+class CV_EXPORTS_W_SIMPLE TextDetectionModel_EAST : public TextDetectionModel
+{
+public:
+    CV_DEPRECATED_EXTERNAL  // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
+    TextDetectionModel_EAST();
+
+    /**
+     * @brief Create text detection algorithm from deep learning network
+     * @param[in] network Net object
+     */
+    CV_WRAP TextDetectionModel_EAST(const Net& network);
+
+    /**
+     * @brief Create text detection model from network represented in one of the supported formats.
+     * An order of @p model and @p config arguments does not matter.
+     * @param[in] model Binary file contains trained weights.
+     * @param[in] config Text file contains network configuration.
+     */
+    CV_WRAP inline
+    TextDetectionModel_EAST(const std::string& model, const std::string& config = "")
+        : TextDetectionModel_EAST(readNet(model, config)) { /* nothing */ }
+
+    /**
+     * @brief Set the detection confidence threshold
+     * @param[in] confThreshold A threshold used to filter boxes by confidences
+     */
+    CV_WRAP
+    TextDetectionModel_EAST& setConfidenceThreshold(float confThreshold);
+
+    /**
+     * @brief Get the detection confidence threshold
+     */
+    CV_WRAP
+    float getConfidenceThreshold() const;
+
+    /**
+     * @brief Set the detection NMS filter threshold
+     * @param[in] nmsThreshold A threshold used in non maximum suppression
+     */
+    CV_WRAP
+    TextDetectionModel_EAST& setNMSThreshold(float nmsThreshold);
+
+    /**
+     * @brief Get the detection confidence threshold
+     */
+    CV_WRAP
+    float getNMSThreshold() const;
+};
+
+/** @brief This class represents high-level API for text detection DL networks compatible with DB model.
+ *
+ * Related publications: @cite liao2020real
+ * Paper: https://arxiv.org/abs/1911.08947
+ * For more information about the hyper-parameters setting, please refer to https://github.com/MhLiao/DB
+ *
+ * Configurable parameters:
+ * - (float) binaryThreshold - The threshold of the binary map. It is usually set to 0.3.
+ * - (float) polygonThreshold - The threshold of text polygons. It is usually set to 0.5, 0.6, and 0.7. Default is 0.5f
+ * - (double) unclipRatio - The unclip ratio of the detected text region, which determines the output size. It is usually set to 2.0.
+ * - (int) maxCandidates - The max number of the output results.
+ */
+class CV_EXPORTS_W_SIMPLE TextDetectionModel_DB : public TextDetectionModel
+{
+public:
+    CV_DEPRECATED_EXTERNAL  // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
+    TextDetectionModel_DB();
+
+    /**
+     * @brief Create text detection algorithm from deep learning network.
+     * @param[in] network Net object.
+     */
+    CV_WRAP TextDetectionModel_DB(const Net& network);
+
+    /**
+     * @brief Create text detection model from network represented in one of the supported formats.
+     * An order of @p model and @p config arguments does not matter.
+     * @param[in] model Binary file contains trained weights.
+     * @param[in] config Text file contains network configuration.
+     */
+    CV_WRAP inline
+    TextDetectionModel_DB(const std::string& model, const std::string& config = "")
+        : TextDetectionModel_DB(readNet(model, config)) { /* nothing */ }
+
+    CV_WRAP TextDetectionModel_DB& setBinaryThreshold(float binaryThreshold);
+    CV_WRAP float getBinaryThreshold() const;
+
+    CV_WRAP TextDetectionModel_DB& setPolygonThreshold(float polygonThreshold);
+    CV_WRAP float getPolygonThreshold() const;
+
+    CV_WRAP TextDetectionModel_DB& setUnclipRatio(double unclipRatio);
+    CV_WRAP double getUnclipRatio() const;
+
+    CV_WRAP TextDetectionModel_DB& setMaxCandidates(int maxCandidates);
+    CV_WRAP int getMaxCandidates() const;
+};
+
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+
+#include <opencv2/dnn/layer.hpp>
+#include <opencv2/dnn/dnn.inl.hpp>
+
+/// @deprecated Include this header directly from application. Automatic inclusion will be removed
+#include <opencv2/dnn/utils/inference_engine.hpp>
+
+#endif  /* OPENCV_DNN_DNN_HPP */
diff --git a/3rdParty/include/opencv2/dnn/dnn.inl.hpp b/3rdParty/include/opencv2/dnn/dnn.inl.hpp
new file mode 100644
index 0000000..8312a41
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/dnn.inl.hpp
@@ -0,0 +1,412 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_DNN_INL_HPP
+#define OPENCV_DNN_DNN_INL_HPP
+
+#include <opencv2/dnn.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+template<typename TypeIter>
+DictValue DictValue::arrayInt(TypeIter begin, int size)
+{
+    DictValue res(Param::INT, new AutoBuffer<int64, 1>(size));
+    for (int j = 0; j < size; begin++, j++)
+        (*res.pi)[j] = *begin;
+    return res;
+}
+
+template<typename TypeIter>
+DictValue DictValue::arrayReal(TypeIter begin, int size)
+{
+    DictValue res(Param::REAL, new AutoBuffer<double, 1>(size));
+    for (int j = 0; j < size; begin++, j++)
+        (*res.pd)[j] = *begin;
+    return res;
+}
+
+template<typename TypeIter>
+DictValue DictValue::arrayString(TypeIter begin, int size)
+{
+    DictValue res(Param::STRING, new AutoBuffer<String, 1>(size));
+    for (int j = 0; j < size; begin++, j++)
+        (*res.ps)[j] = *begin;
+    return res;
+}
+
+template<>
+inline DictValue DictValue::get<DictValue>(int idx) const
+{
+    CV_Assert(idx == -1);
+    return *this;
+}
+
+template<>
+inline int64 DictValue::get<int64>(int idx) const
+{
+    CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size()));
+    idx = (idx == -1) ? 0 : idx;
+
+    if (type == Param::INT)
+    {
+        return (*pi)[idx];
+    }
+    else if (type == Param::REAL)
+    {
+        double doubleValue = (*pd)[idx];
+
+        double fracpart, intpart;
+        fracpart = std::modf(doubleValue, &intpart);
+        CV_Assert(fracpart == 0.0);
+
+        return (int64)doubleValue;
+    }
+    else if (type == Param::STRING)
+    {
+        return std::atoi((*ps)[idx].c_str());
+    }
+    else
+    {
+        CV_Assert(isInt() || isReal() || isString());
+        return 0;
+    }
+}
+
+template<>
+inline int DictValue::get<int>(int idx) const
+{
+    return (int)get<int64>(idx);
+}
+
+inline int DictValue::getIntValue(int idx) const
+{
+    return (int)get<int64>(idx);
+}
+
+template<>
+inline unsigned DictValue::get<unsigned>(int idx) const
+{
+    return (unsigned)get<int64>(idx);
+}
+
+template<>
+inline bool DictValue::get<bool>(int idx) const
+{
+    return (get<int64>(idx) != 0);
+}
+
+template<>
+inline double DictValue::get<double>(int idx) const
+{
+    CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size()));
+    idx = (idx == -1) ? 0 : idx;
+
+    if (type == Param::REAL)
+    {
+        return (*pd)[idx];
+    }
+    else if (type == Param::INT)
+    {
+        return (double)(*pi)[idx];
+    }
+    else if (type == Param::STRING)
+    {
+        return std::atof((*ps)[idx].c_str());
+    }
+    else
+    {
+        CV_Assert(isReal() || isInt() || isString());
+        return 0;
+    }
+}
+
+inline double DictValue::getRealValue(int idx) const
+{
+    return get<double>(idx);
+}
+
+template<>
+inline float DictValue::get<float>(int idx) const
+{
+    return (float)get<double>(idx);
+}
+
+template<>
+inline String DictValue::get<String>(int idx) const
+{
+    CV_Assert(isString());
+    CV_Assert((idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size()));
+    return (*ps)[(idx == -1) ? 0 : idx];
+}
+
+
+inline String DictValue::getStringValue(int idx) const
+{
+    return get<String>(idx);
+}
+
+inline void DictValue::release()
+{
+    switch (type)
+    {
+    case Param::INT:
+        delete pi;
+        break;
+    case Param::STRING:
+        delete ps;
+        break;
+    case Param::REAL:
+        delete pd;
+        break;
+    case Param::BOOLEAN:
+    case Param::MAT:
+    case Param::MAT_VECTOR:
+    case Param::ALGORITHM:
+    case Param::FLOAT:
+    case Param::UNSIGNED_INT:
+    case Param::UINT64:
+    case Param::UCHAR:
+    case Param::SCALAR:
+        break; // unhandled
+    }
+}
+
+inline DictValue::~DictValue()
+{
+    release();
+}
+
+inline DictValue & DictValue::operator=(const DictValue &r)
+{
+    if (&r == this)
+        return *this;
+
+    if (r.type == Param::INT)
+    {
+        AutoBuffer<int64, 1> *tmp = new AutoBuffer<int64, 1>(*r.pi);
+        release();
+        pi = tmp;
+    }
+    else if (r.type == Param::STRING)
+    {
+        AutoBuffer<String, 1> *tmp = new AutoBuffer<String, 1>(*r.ps);
+        release();
+        ps = tmp;
+    }
+    else if (r.type == Param::REAL)
+    {
+        AutoBuffer<double, 1> *tmp = new AutoBuffer<double, 1>(*r.pd);
+        release();
+        pd = tmp;
+    }
+
+    type = r.type;
+
+    return *this;
+}
+
+inline DictValue::DictValue(const DictValue &r)
+    : pv(NULL)
+{
+    type = r.type;
+
+    if (r.type == Param::INT)
+        pi = new AutoBuffer<int64, 1>(*r.pi);
+    else if (r.type == Param::STRING)
+        ps = new AutoBuffer<String, 1>(*r.ps);
+    else if (r.type == Param::REAL)
+        pd = new AutoBuffer<double, 1>(*r.pd);
+}
+
+inline bool DictValue::isString() const
+{
+    return (type == Param::STRING);
+}
+
+inline bool DictValue::isInt() const
+{
+    return (type == Param::INT);
+}
+
+inline bool DictValue::isReal() const
+{
+    return (type == Param::REAL || type == Param::INT);
+}
+
+inline int DictValue::size() const
+{
+    switch (type)
+    {
+    case Param::INT:
+        return (int)pi->size();
+    case Param::STRING:
+        return (int)ps->size();
+    case Param::REAL:
+        return (int)pd->size();
+    case Param::BOOLEAN:
+    case Param::MAT:
+    case Param::MAT_VECTOR:
+    case Param::ALGORITHM:
+    case Param::FLOAT:
+    case Param::UNSIGNED_INT:
+    case Param::UINT64:
+    case Param::UCHAR:
+    case Param::SCALAR:
+        break; // unhandled
+    }
+    CV_Error_(Error::StsInternal, ("Unhandled type (%d)", static_cast<int>(type)));
+}
+
+inline std::ostream &operator<<(std::ostream &stream, const DictValue &dictv)
+{
+    int i;
+
+    if (dictv.isInt())
+    {
+        for (i = 0; i < dictv.size() - 1; i++)
+            stream << dictv.get<int64>(i) << ", ";
+        stream << dictv.get<int64>(i);
+    }
+    else if (dictv.isReal())
+    {
+        for (i = 0; i < dictv.size() - 1; i++)
+            stream << dictv.get<double>(i) << ", ";
+        stream << dictv.get<double>(i);
+    }
+    else if (dictv.isString())
+    {
+        for (i = 0; i < dictv.size() - 1; i++)
+            stream << "\"" << dictv.get<String>(i) << "\", ";
+        stream << dictv.get<String>(i);
+    }
+
+    return stream;
+}
+
+/////////////////////////////////////////////////////////////////
+
+inline bool Dict::has(const String &key) const
+{
+    return dict.count(key) != 0;
+}
+
+inline DictValue *Dict::ptr(const String &key)
+{
+    _Dict::iterator i = dict.find(key);
+    return (i == dict.end()) ? NULL : &i->second;
+}
+
+inline const DictValue *Dict::ptr(const String &key) const
+{
+    _Dict::const_iterator i = dict.find(key);
+    return (i == dict.end()) ? NULL : &i->second;
+}
+
+inline const DictValue &Dict::get(const String &key) const
+{
+    _Dict::const_iterator i = dict.find(key);
+    if (i == dict.end())
+        CV_Error(Error::StsObjectNotFound, "Required argument \"" + key + "\" not found into dictionary");
+    return i->second;
+}
+
+template <typename T>
+inline T Dict::get(const String &key) const
+{
+    return this->get(key).get<T>();
+}
+
+template <typename T>
+inline T Dict::get(const String &key, const T &defaultValue) const
+{
+    _Dict::const_iterator i = dict.find(key);
+
+    if (i != dict.end())
+        return i->second.get<T>();
+    else
+        return defaultValue;
+}
+
+template<typename T>
+inline const T &Dict::set(const String &key, const T &value)
+{
+    _Dict::iterator i = dict.find(key);
+
+    if (i != dict.end())
+        i->second = DictValue(value);
+    else
+        dict.insert(std::make_pair(key, DictValue(value)));
+
+    return value;
+}
+
+inline void Dict::erase(const String &key)
+{
+    dict.erase(key);
+}
+
+inline std::ostream &operator<<(std::ostream &stream, const Dict &dict)
+{
+    Dict::_Dict::const_iterator it;
+    for (it = dict.dict.begin(); it != dict.dict.end(); it++)
+        stream << it->first << " : " << it->second << "\n";
+
+    return stream;
+}
+
+inline std::map<String, DictValue>::const_iterator Dict::begin() const
+{
+    return dict.begin();
+}
+
+inline std::map<String, DictValue>::const_iterator Dict::end() const
+{
+    return dict.end();
+}
+
+CV__DNN_INLINE_NS_END
+}
+}
+
+#endif
diff --git a/3rdParty/include/opencv2/dnn/layer.details.hpp b/3rdParty/include/opencv2/dnn/layer.details.hpp
new file mode 100644
index 0000000..1133da5
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/layer.details.hpp
@@ -0,0 +1,78 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+#ifndef OPENCV_DNN_LAYER_DETAILS_HPP
+#define OPENCV_DNN_LAYER_DETAILS_HPP
+
+#include <opencv2/dnn/layer.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+/** @brief Registers layer constructor in runtime.
+*   @param type string, containing type name of the layer.
+*   @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer.
+*   @details This macros must be placed inside the function code.
+*/
+#define CV_DNN_REGISTER_LAYER_FUNC(type, constructorFunc) \
+    cv::dnn::LayerFactory::registerLayer(#type, constructorFunc);
+
+/** @brief Registers layer class in runtime.
+ *  @param type string, containing type name of the layer.
+ *  @param class C++ class, derived from Layer.
+ *  @details This macros must be placed inside the function code.
+ */
+#define CV_DNN_REGISTER_LAYER_CLASS(type, class) \
+    cv::dnn::LayerFactory::registerLayer(#type, cv::dnn::details::_layerDynamicRegisterer<class>);
+
+/** @brief Registers layer constructor on module load time.
+*   @param type string, containing type name of the layer.
+*   @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer.
+*   @details This macros must be placed outside the function code.
+*/
+#define CV_DNN_REGISTER_LAYER_FUNC_STATIC(type, constructorFunc) \
+static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constructorFunc);
+
+/** @brief Registers layer class on module load time.
+ *  @param type string, containing type name of the layer.
+ *  @param class C++ class, derived from Layer.
+ *  @details This macros must be placed outside the function code.
+ */
+#define CV_DNN_REGISTER_LAYER_CLASS_STATIC(type, class)                         \
+Ptr<Layer> __LayerStaticRegisterer_func_##type(LayerParams &params) \
+    { return Ptr<Layer>(new class(params)); }                       \
+static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, __LayerStaticRegisterer_func_##type);
+
+namespace details {
+
+template<typename LayerClass>
+Ptr<Layer> _layerDynamicRegisterer(LayerParams &params)
+{
+    return Ptr<Layer>(LayerClass::create(params));
+}
+
+//allows automatically register created layer on module load time
+class _LayerStaticRegisterer
+{
+    String type;
+public:
+
+    _LayerStaticRegisterer(const String &layerType, LayerFactory::Constructor layerConstructor)
+    {
+        this->type = layerType;
+        LayerFactory::registerLayer(layerType, layerConstructor);
+    }
+
+    ~_LayerStaticRegisterer()
+    {
+        LayerFactory::unregisterLayer(type);
+    }
+};
+
+} // namespace
+CV__DNN_INLINE_NS_END
+}} // namespace
+
+#endif
diff --git a/3rdParty/include/opencv2/dnn/layer.hpp b/3rdParty/include/opencv2/dnn/layer.hpp
new file mode 100644
index 0000000..8500599
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/layer.hpp
@@ -0,0 +1,85 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_LAYER_HPP
+#define OPENCV_DNN_LAYER_HPP
+#include <opencv2/dnn.hpp>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+//! @addtogroup dnn
+//! @{
+//!
+//! @defgroup dnnLayerFactory Utilities for New Layers Registration
+//! @{
+
+/** @brief %Layer factory allows to create instances of registered layers. */
+class CV_EXPORTS LayerFactory
+{
+public:
+
+    //! Each Layer class must provide this function to the factory
+    typedef Ptr<Layer>(*Constructor)(LayerParams &params);
+
+    //! Registers the layer class with typename @p type and specified @p constructor. Thread-safe.
+    static void registerLayer(const String &type, Constructor constructor);
+
+    //! Unregisters registered layer with specified type name. Thread-safe.
+    static void unregisterLayer(const String &type);
+
+    /** @brief Creates instance of registered layer.
+     *  @param type type name of creating layer.
+     *  @param params parameters which will be used for layer initialization.
+     *  @note Thread-safe.
+     */
+    static Ptr<Layer> createLayerInstance(const String &type, LayerParams& params);
+
+private:
+    LayerFactory();
+};
+
+//! @}
+//! @}
+CV__DNN_INLINE_NS_END
+}
+}
+#endif
diff --git a/3rdParty/include/opencv2/dnn/shape_utils.hpp b/3rdParty/include/opencv2/dnn/shape_utils.hpp
new file mode 100644
index 0000000..4c610f6
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/shape_utils.hpp
@@ -0,0 +1,259 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_DNN_DNN_SHAPE_UTILS_HPP
+#define OPENCV_DNN_DNN_SHAPE_UTILS_HPP
+
+#include <opencv2/dnn/dnn.hpp>
+#include <opencv2/core/types_c.h>  // CV_MAX_DIM
+#include <iostream>
+#include <ostream>
+#include <sstream>
+
+namespace cv {
+namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+//Slicing
+
+struct _Range : public cv::Range
+{
+    _Range(const Range &r) : cv::Range(r) {}
+    _Range(int start_, int size_ = 1) : cv::Range(start_, start_ + size_) {}
+};
+
+static inline Mat slice(const Mat &m, const _Range &r0)
+{
+    Range ranges[CV_MAX_DIM];
+    for (int i = 1; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    return m(&ranges[0]);
+}
+
+static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1)
+{
+    CV_Assert(m.dims >= 2);
+    Range ranges[CV_MAX_DIM];
+    for (int i = 2; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    ranges[1] = r1;
+    return m(&ranges[0]);
+}
+
+static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2)
+{
+    CV_Assert(m.dims >= 3);
+    Range ranges[CV_MAX_DIM];
+    for (int i = 3; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    ranges[1] = r1;
+    ranges[2] = r2;
+    return m(&ranges[0]);
+}
+
+static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2, const _Range &r3)
+{
+    CV_Assert(m.dims >= 4);
+    Range ranges[CV_MAX_DIM];
+    for (int i = 4; i < m.dims; i++)
+        ranges[i] = Range::all();
+    ranges[0] = r0;
+    ranges[1] = r1;
+    ranges[2] = r2;
+    ranges[3] = r3;
+    return m(&ranges[0]);
+}
+
+static inline Mat getPlane(const Mat &m, int n, int cn)
+{
+    CV_Assert(m.dims > 2);
+    int sz[CV_MAX_DIM];
+    for(int i = 2; i < m.dims; i++)
+    {
+        sz[i-2] = m.size.p[i];
+    }
+    return Mat(m.dims - 2, sz, m.type(), (void*)m.ptr<float>(n, cn));
+}
+
+static inline MatShape shape(const int* dims, const int n)
+{
+    MatShape shape;
+    shape.assign(dims, dims + n);
+    return shape;
+}
+
+static inline MatShape shape(const Mat& mat)
+{
+    return shape(mat.size.p, mat.dims);
+}
+
+static inline MatShape shape(const MatSize& sz)
+{
+    return shape(sz.p, sz.dims());
+}
+
+static inline MatShape shape(const UMat& mat)
+{
+    return shape(mat.size.p, mat.dims);
+}
+
+#if 0  // issues with MatExpr wrapped into InputArray
+static inline
+MatShape shape(InputArray input)
+{
+    int sz[CV_MAX_DIM];
+    int ndims = input.sizend(sz);
+    return shape(sz, ndims);
+}
+#endif
+
+namespace {inline bool is_neg(int i) { return i < 0; }}
+
+static inline MatShape shape(int a0, int a1=-1, int a2=-1, int a3=-1)
+{
+    int dims[] = {a0, a1, a2, a3};
+    MatShape s = shape(dims, 4);
+    s.erase(std::remove_if(s.begin(), s.end(), is_neg), s.end());
+    return s;
+}
+
+static inline int total(const MatShape& shape, int start = -1, int end = -1)
+{
+    if (start == -1) start = 0;
+    if (end == -1) end = (int)shape.size();
+
+    if (shape.empty())
+        return 0;
+
+    int elems = 1;
+    CV_Assert(start <= (int)shape.size() && end <= (int)shape.size() &&
+              start <= end);
+    for(int i = start; i < end; i++)
+    {
+        elems *= shape[i];
+    }
+    return elems;
+}
+
+static inline MatShape concat(const MatShape& a, const MatShape& b)
+{
+    MatShape c = a;
+    c.insert(c.end(), b.begin(), b.end());
+
+    return c;
+}
+
+static inline std::string toString(const MatShape& shape, const String& name = "")
+{
+    std::ostringstream ss;
+    if (!name.empty())
+        ss << name << ' ';
+    ss << '[';
+    for(size_t i = 0, n = shape.size(); i < n; ++i)
+        ss << ' ' << shape[i];
+    ss << " ]";
+    return ss.str();
+}
+static inline void print(const MatShape& shape, const String& name = "")
+{
+    std::cout << toString(shape, name) << std::endl;
+}
+static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape)
+{
+    out << toString(shape);
+    return out;
+}
+
+/// @brief Converts axis from `[-dims; dims)` (similar to Python's slice notation) to `[0; dims)` range.
+static inline
+int normalize_axis(int axis, int dims)
+{
+    CV_Check(axis, axis >= -dims && axis < dims, "");
+    axis = (axis < 0) ? (dims + axis) : axis;
+    CV_DbgCheck(axis, axis >= 0 && axis < dims, "");
+    return axis;
+}
+
+static inline
+int normalize_axis(int axis, const MatShape& shape)
+{
+    return normalize_axis(axis, (int)shape.size());
+}
+
+static inline
+Range normalize_axis_range(const Range& r, int axisSize)
+{
+    if (r == Range::all())
+        return Range(0, axisSize);
+    CV_CheckGE(r.start, 0, "");
+    Range clamped(r.start,
+                  r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1);
+    CV_DbgCheckGE(clamped.start, 0, "");
+    CV_CheckLT(clamped.start, clamped.end, "");
+    CV_CheckLE(clamped.end, axisSize, "");
+    return clamped;
+}
+
+static inline
+bool isAllOnes(const MatShape &inputShape, int startPos, int endPos)
+{
+    CV_Assert(!inputShape.empty());
+
+    CV_CheckGE((int) inputShape.size(), startPos, "");
+    CV_CheckGE(startPos, 0, "");
+    CV_CheckLE(startPos, endPos, "");
+    CV_CheckLE((size_t)endPos, inputShape.size(), "");
+
+    for (size_t i = startPos; i < endPos; i++)
+    {
+        if (inputShape[i] != 1)
+            return false;
+    }
+    return true;
+}
+
+CV__DNN_INLINE_NS_END
+}
+}
+#endif
diff --git a/3rdParty/include/opencv2/dnn/utils/debug_utils.hpp b/3rdParty/include/opencv2/dnn/utils/debug_utils.hpp
new file mode 100644
index 0000000..71dd3ab
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/utils/debug_utils.hpp
@@ -0,0 +1,24 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_UTILS_DEBUG_UTILS_HPP
+#define OPENCV_DNN_UTILS_DEBUG_UTILS_HPP
+
+#include "../dnn.hpp"
+
+namespace cv { namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+/**
+ * @brief Skip model import after diagnostic run in readNet() functions.
+ * @param[in] skip Indicates whether to skip the import.
+ *
+ * This is an internal OpenCV function not intended for users.
+ */
+CV_EXPORTS void skipModelImport(bool skip);
+
+CV__DNN_INLINE_NS_END
+}} // namespace
+
+#endif // OPENCV_DNN_UTILS_DEBUG_UTILS_HPP
diff --git a/3rdParty/include/opencv2/dnn/utils/inference_engine.hpp b/3rdParty/include/opencv2/dnn/utils/inference_engine.hpp
new file mode 100644
index 0000000..333b1bf
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/utils/inference_engine.hpp
@@ -0,0 +1,76 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#ifndef OPENCV_DNN_UTILS_INF_ENGINE_HPP
+#define OPENCV_DNN_UTILS_INF_ENGINE_HPP
+
+#include "../dnn.hpp"
+
+namespace cv { namespace dnn {
+CV__DNN_INLINE_NS_BEGIN
+
+
+/* Values for 'OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE' parameter */
+#define CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API     "NN_BUILDER"
+#define CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH             "NGRAPH"
+
+/** @brief Returns Inference Engine internal backend API.
+ *
+ * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
+ *
+ * Default value is controlled through `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable).
+ */
+CV_EXPORTS_W cv::String getInferenceEngineBackendType();
+
+/** @brief Specify Inference Engine internal backend API.
+ *
+ * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
+ *
+ * @returns previous value of internal backend API
+ */
+CV_EXPORTS_W cv::String setInferenceEngineBackendType(const cv::String& newBackendType);
+
+
+/** @brief Release a Myriad device (binded by OpenCV).
+ *
+ * Single Myriad device cannot be shared across multiple processes which uses
+ * Inference Engine's Myriad plugin.
+ */
+CV_EXPORTS_W void resetMyriadDevice();
+
+
+/* Values for 'OPENCV_DNN_IE_VPU_TYPE' parameter */
+#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_UNSPECIFIED ""
+/// Intel(R) Movidius(TM) Neural Compute Stick, NCS (USB 03e7:2150), Myriad2 (https://software.intel.com/en-us/movidius-ncs)
+#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 "Myriad2"
+/// Intel(R) Neural Compute Stick 2, NCS2 (USB 03e7:2485), MyriadX (https://software.intel.com/ru-ru/neural-compute-stick)
+#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X "MyriadX"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE "ARM_COMPUTE"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86         "X86"
+
+
+/** @brief Returns Inference Engine VPU type.
+ *
+ * See values of `CV_DNN_INFERENCE_ENGINE_VPU_TYPE_*` macros.
+ */
+CV_EXPORTS_W cv::String getInferenceEngineVPUType();
+
+/** @brief Returns Inference Engine CPU type.
+ *
+ * Specify OpenVINO plugin: CPU or ARM.
+ */
+CV_EXPORTS_W cv::String getInferenceEngineCPUType();
+
+/** @brief Release a HDDL plugin.
+ */
+CV_EXPORTS_W void releaseHDDLPlugin();
+
+
+CV__DNN_INLINE_NS_END
+}} // namespace
+
+#endif // OPENCV_DNN_UTILS_INF_ENGINE_HPP
diff --git a/3rdParty/include/opencv2/dnn/version.hpp b/3rdParty/include/opencv2/dnn/version.hpp
new file mode 100644
index 0000000..a0a1754
--- /dev/null
+++ b/3rdParty/include/opencv2/dnn/version.hpp
@@ -0,0 +1,21 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_VERSION_HPP
+#define OPENCV_DNN_VERSION_HPP
+
+/// Use with major OpenCV version only.
+#define OPENCV_DNN_API_VERSION 20211220
+
+#if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_INLINE_NS
+#define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION)
+#define CV__DNN_INLINE_NS_BEGIN namespace CV__DNN_INLINE_NS {
+#define CV__DNN_INLINE_NS_END }
+namespace cv { namespace dnn { namespace CV__DNN_INLINE_NS { } using namespace CV__DNN_INLINE_NS; }}
+#else
+#define CV__DNN_INLINE_NS_BEGIN
+#define CV__DNN_INLINE_NS_END
+#endif
+
+#endif  // OPENCV_DNN_VERSION_HPP
diff --git a/3rdParty/include/opencv2/features2d.hpp b/3rdParty/include/opencv2/features2d.hpp
new file mode 100644
index 0000000..26c2b95
--- /dev/null
+++ b/3rdParty/include/opencv2/features2d.hpp
@@ -0,0 +1,1535 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_FEATURES_2D_HPP
+#define OPENCV_FEATURES_2D_HPP
+
+#include "opencv2/opencv_modules.hpp"
+#include "opencv2/core.hpp"
+
+#ifdef HAVE_OPENCV_FLANN
+#include "opencv2/flann/miniflann.hpp"
+#endif
+
+/**
+  @defgroup features2d 2D Features Framework
+  @{
+    @defgroup features2d_main Feature Detection and Description
+    @defgroup features2d_match Descriptor Matchers
+
+Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to
+easily switch between different algorithms solving the same problem. This section is devoted to
+matching descriptors that are represented as vectors in a multidimensional space. All objects that
+implement vector descriptor matchers inherit the DescriptorMatcher interface.
+
+    @defgroup features2d_draw Drawing Function of Keypoints and Matches
+    @defgroup features2d_category Object Categorization
+
+This section describes approaches based on local 2D features and used to categorize objects.
+
+    @defgroup feature2d_hal Hardware Acceleration Layer
+    @{
+        @defgroup features2d_hal_interface Interface
+    @}
+  @}
+ */
+
+namespace cv
+{
+
+//! @addtogroup features2d_main
+//! @{
+
+// //! writes vector of keypoints to the file storage
+// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);
+// //! reads vector of keypoints from the specified file storage node
+// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);
+
+/** @brief A class filters a vector of keypoints.
+
+ Because now it is difficult to provide a convenient interface for all usage scenarios of the
+ keypoints filter class, it has only several needed by now static methods.
+ */
+class CV_EXPORTS KeyPointsFilter
+{
+public:
+    KeyPointsFilter(){}
+
+    /*
+     * Remove keypoints within borderPixels of an image edge.
+     */
+    static void runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize );
+    /*
+     * Remove keypoints of sizes out of range.
+     */
+    static void runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize,
+                                   float maxSize=FLT_MAX );
+    /*
+     * Remove keypoints from some image by mask for pixels of this image.
+     */
+    static void runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask );
+    /*
+     * Remove duplicated keypoints.
+     */
+    static void removeDuplicated( std::vector<KeyPoint>& keypoints );
+    /*
+     * Remove duplicated keypoints and sort the remaining keypoints
+     */
+    static void removeDuplicatedSorted( std::vector<KeyPoint>& keypoints );
+
+    /*
+     * Retain the specified number of the best keypoints (according to the response)
+     */
+    static void retainBest( std::vector<KeyPoint>& keypoints, int npoints );
+};
+
+
+/************************************ Base Classes ************************************/
+
+/** @brief Abstract base class for 2D image feature detectors and descriptor extractors
+*/
+#ifdef __EMSCRIPTEN__
+class CV_EXPORTS_W Feature2D : public Algorithm
+#else
+class CV_EXPORTS_W Feature2D : public virtual Algorithm
+#endif
+{
+public:
+    virtual ~Feature2D();
+
+    /** @brief Detects keypoints in an image (first variant) or image set (second variant).
+
+    @param image Image.
+    @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
+    of keypoints detected in images[i] .
+    @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
+    matrix with non-zero values in the region of interest.
+     */
+    CV_WRAP virtual void detect( InputArray image,
+                                 CV_OUT std::vector<KeyPoint>& keypoints,
+                                 InputArray mask=noArray() );
+
+    /** @overload
+    @param images Image set.
+    @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
+    of keypoints detected in images[i] .
+    @param masks Masks for each input image specifying where to look for keypoints (optional).
+    masks[i] is a mask for images[i].
+    */
+    CV_WRAP virtual void detect( InputArrayOfArrays images,
+                         CV_OUT std::vector<std::vector<KeyPoint> >& keypoints,
+                         InputArrayOfArrays masks=noArray() );
+
+    /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set
+    (second variant).
+
+    @param image Image.
+    @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
+    computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
+    with several dominant orientations (for each orientation).
+    @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
+    descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
+    descriptor for keypoint j-th keypoint.
+     */
+    CV_WRAP virtual void compute( InputArray image,
+                                  CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints,
+                                  OutputArray descriptors );
+
+    /** @overload
+
+    @param images Image set.
+    @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
+    computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
+    with several dominant orientations (for each orientation).
+    @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
+    descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
+    descriptor for keypoint j-th keypoint.
+    */
+    CV_WRAP virtual void compute( InputArrayOfArrays images,
+                          CV_OUT CV_IN_OUT std::vector<std::vector<KeyPoint> >& keypoints,
+                          OutputArrayOfArrays descriptors );
+
+    /** Detects keypoints and computes the descriptors */
+    CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask,
+                                           CV_OUT std::vector<KeyPoint>& keypoints,
+                                           OutputArray descriptors,
+                                           bool useProvidedKeypoints=false );
+
+    CV_WRAP virtual int descriptorSize() const;
+    CV_WRAP virtual int descriptorType() const;
+    CV_WRAP virtual int defaultNorm() const;
+
+    CV_WRAP void write( const String& fileName ) const;
+
+    CV_WRAP void read( const String& fileName );
+
+    virtual void write( FileStorage&) const CV_OVERRIDE;
+
+    // see corresponding cv::Algorithm method
+    CV_WRAP virtual void read( const FileNode&) CV_OVERRIDE;
+
+    //! Return true if detector object is empty
+    CV_WRAP virtual bool empty() const CV_OVERRIDE;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+
+    // see corresponding cv::Algorithm method
+    CV_WRAP inline void write(const Ptr<FileStorage>& fs, const String& name = String()) const { Algorithm::write(fs, name); }
+};
+
+/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch
+between different algorithms solving the same problem. All objects that implement keypoint detectors
+inherit the FeatureDetector interface. */
+typedef Feature2D FeatureDetector;
+
+/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you
+to easily switch between different algorithms solving the same problem. This section is devoted to
+computing descriptors represented as vectors in a multidimensional space. All objects that implement
+the vector descriptor extractors inherit the DescriptorExtractor interface.
+ */
+typedef Feature2D DescriptorExtractor;
+
+
+/** @brief Class for implementing the wrapper which makes detectors and extractors to be affine invariant,
+described as ASIFT in @cite YM11 .
+*/
+class CV_EXPORTS_W AffineFeature : public Feature2D
+{
+public:
+    /**
+    @param backend The detector/extractor you want to use as backend.
+    @param maxTilt The highest power index of tilt factor. 5 is used in the paper as tilt sampling range n.
+    @param minTilt The lowest power index of tilt factor. 0 is used in the paper.
+    @param tiltStep Tilt sampling step \f$\delta_t\f$ in Algorithm 1 in the paper.
+    @param rotateStepBase Rotation sampling step factor b in Algorithm 1 in the paper.
+    */
+    CV_WRAP static Ptr<AffineFeature> create(const Ptr<Feature2D>& backend,
+        int maxTilt = 5, int minTilt = 0, float tiltStep = 1.4142135623730951f, float rotateStepBase = 72);
+
+    CV_WRAP virtual void setViewParams(const std::vector<float>& tilts, const std::vector<float>& rolls) = 0;
+    CV_WRAP virtual void getViewParams(std::vector<float>& tilts, std::vector<float>& rolls) const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+typedef AffineFeature AffineFeatureDetector;
+typedef AffineFeature AffineDescriptorExtractor;
+
+
+/** @brief Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform
+(SIFT) algorithm by D. Lowe @cite Lowe04 .
+*/
+class CV_EXPORTS_W SIFT : public Feature2D
+{
+public:
+    /**
+    @param nfeatures The number of best features to retain. The features are ranked by their scores
+    (measured in SIFT algorithm as the local contrast)
+
+    @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
+    number of octaves is computed automatically from the image resolution.
+
+    @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
+    (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
+
+    @note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
+    nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
+    this argument to 0.09.
+
+    @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
+    is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
+    filtered out (more features are retained).
+
+    @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
+    is captured with a weak camera with soft lenses, you might want to reduce the number.
+    */
+    CV_WRAP static Ptr<SIFT> create(int nfeatures = 0, int nOctaveLayers = 3,
+        double contrastThreshold = 0.04, double edgeThreshold = 10,
+        double sigma = 1.6);
+
+    /** @brief Create SIFT with specified descriptorType.
+    @param nfeatures The number of best features to retain. The features are ranked by their scores
+    (measured in SIFT algorithm as the local contrast)
+
+    @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
+    number of octaves is computed automatically from the image resolution.
+
+    @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
+    (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
+
+    @note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
+    nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
+    this argument to 0.09.
+
+    @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
+    is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
+    filtered out (more features are retained).
+
+    @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
+    is captured with a weak camera with soft lenses, you might want to reduce the number.
+
+    @param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported.
+    */
+    CV_WRAP static Ptr<SIFT> create(int nfeatures, int nOctaveLayers,
+        double contrastThreshold, double edgeThreshold,
+        double sigma, int descriptorType);
+
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+typedef SIFT SiftFeatureDetector;
+typedef SIFT SiftDescriptorExtractor;
+
+
+/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 .
+ */
+class CV_EXPORTS_W BRISK : public Feature2D
+{
+public:
+    /** @brief The BRISK constructor
+
+    @param thresh AGAST detection threshold score.
+    @param octaves detection octaves. Use 0 to do single scale.
+    @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
+    keypoint.
+     */
+    CV_WRAP static Ptr<BRISK> create(int thresh=30, int octaves=3, float patternScale=1.0f);
+
+    /** @brief The BRISK constructor for a custom pattern
+
+    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
+    keypoint scale 1).
+    @param numberList defines the number of sampling points on the sampling circle. Must be the same
+    size as radiusList..
+    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
+    scale 1).
+    @param dMin threshold for the long pairings used for orientation determination (in pixels for
+    keypoint scale 1).
+    @param indexChange index remapping of the bits. */
+    CV_WRAP static Ptr<BRISK> create(const std::vector<float> &radiusList, const std::vector<int> &numberList,
+        float dMax=5.85f, float dMin=8.2f, const std::vector<int>& indexChange=std::vector<int>());
+
+    /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves
+
+    @param thresh AGAST detection threshold score.
+    @param octaves detection octaves. Use 0 to do single scale.
+    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
+    keypoint scale 1).
+    @param numberList defines the number of sampling points on the sampling circle. Must be the same
+    size as radiusList..
+    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
+    scale 1).
+    @param dMin threshold for the long pairings used for orientation determination (in pixels for
+    keypoint scale 1).
+    @param indexChange index remapping of the bits. */
+    CV_WRAP static Ptr<BRISK> create(int thresh, int octaves, const std::vector<float> &radiusList,
+        const std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f,
+        const std::vector<int>& indexChange=std::vector<int>());
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+
+    /** @brief Set detection threshold.
+    @param threshold AGAST detection threshold score.
+    */
+    CV_WRAP virtual void setThreshold(int threshold) { CV_UNUSED(threshold); return; }
+    CV_WRAP virtual int getThreshold() const { return -1; }
+
+    /** @brief Set detection octaves.
+    @param octaves detection octaves. Use 0 to do single scale.
+    */
+    CV_WRAP virtual void setOctaves(int octaves) { CV_UNUSED(octaves); return; }
+    CV_WRAP virtual int getOctaves() const { return -1; }
+};
+
+/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
+
+described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
+the strongest features using FAST or Harris response, finds their orientation using first-order
+moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
+k-tuples) are rotated according to the measured orientation).
+ */
+class CV_EXPORTS_W ORB : public Feature2D
+{
+public:
+    enum ScoreType { HARRIS_SCORE=0, FAST_SCORE=1 };
+    static const int kBytes = 32;
+
+    /** @brief The ORB constructor
+
+    @param nfeatures The maximum number of features to retain.
+    @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
+    pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
+    will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
+    will mean that to cover certain scale range you will need more pyramid levels and so the speed
+    will suffer.
+    @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
+    input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
+    @param edgeThreshold This is size of the border where the features are not detected. It should
+    roughly match the patchSize parameter.
+    @param firstLevel The level of pyramid to put source image to. Previous layers are filled
+    with upscaled source image.
+    @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
+    default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
+    so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
+    random points (of course, those point coordinates are random, but they are generated from the
+    pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
+    rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
+    output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
+    denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
+    bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
+    @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
+    (the score is written to KeyPoint::score and is used to retain best nfeatures features);
+    FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
+    but it is a little faster to compute.
+    @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
+    pyramid layers the perceived image area covered by a feature will be larger.
+    @param fastThreshold the fast threshold
+     */
+    CV_WRAP static Ptr<ORB> create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31,
+        int firstLevel=0, int WTA_K=2, ORB::ScoreType scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20);
+
+    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
+    CV_WRAP virtual int getMaxFeatures() const = 0;
+
+    CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0;
+    CV_WRAP virtual double getScaleFactor() const = 0;
+
+    CV_WRAP virtual void setNLevels(int nlevels) = 0;
+    CV_WRAP virtual int getNLevels() const = 0;
+
+    CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0;
+    CV_WRAP virtual int getEdgeThreshold() const = 0;
+
+    CV_WRAP virtual void setFirstLevel(int firstLevel) = 0;
+    CV_WRAP virtual int getFirstLevel() const = 0;
+
+    CV_WRAP virtual void setWTA_K(int wta_k) = 0;
+    CV_WRAP virtual int getWTA_K() const = 0;
+
+    CV_WRAP virtual void setScoreType(ORB::ScoreType scoreType) = 0;
+    CV_WRAP virtual ORB::ScoreType getScoreType() const = 0;
+
+    CV_WRAP virtual void setPatchSize(int patchSize) = 0;
+    CV_WRAP virtual int getPatchSize() const = 0;
+
+    CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0;
+    CV_WRAP virtual int getFastThreshold() const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+/** @brief Maximally stable extremal region extractor
+
+The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki
+article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
+
+- there are two different implementation of %MSER: one for grey image, one for color image
+
+- the grey image algorithm is taken from: @cite nister2008linear ;  the paper claims to be faster
+than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
+
+- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower
+than grey image method ( 3~4 times )
+
+- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py
+*/
+class CV_EXPORTS_W MSER : public Feature2D
+{
+public:
+    /** @brief Full constructor for %MSER detector
+
+    @param delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
+    @param min_area prune the area which smaller than minArea
+    @param max_area prune the area which bigger than maxArea
+    @param max_variation prune the area have similar size to its children
+    @param min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
+    @param max_evolution  for color image, the evolution steps
+    @param area_threshold for color image, the area threshold to cause re-initialize
+    @param min_margin for color image, ignore too small margin
+    @param edge_blur_size for color image, the aperture size for edge blur
+     */
+    CV_WRAP static Ptr<MSER> create( int delta=5, int min_area=60, int max_area=14400,
+          double max_variation=0.25, double min_diversity=.2,
+          int max_evolution=200, double area_threshold=1.01,
+          double min_margin=0.003, int edge_blur_size=5 );
+
+    /** @brief Detect %MSER regions
+
+    @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3)
+    @param msers resulting list of point sets
+    @param bboxes resulting bounding boxes
+    */
+    CV_WRAP virtual void detectRegions( InputArray image,
+                                        CV_OUT std::vector<std::vector<Point> >& msers,
+                                        CV_OUT std::vector<Rect>& bboxes ) = 0;
+
+    CV_WRAP virtual void setDelta(int delta) = 0;
+    CV_WRAP virtual int getDelta() const = 0;
+
+    CV_WRAP virtual void setMinArea(int minArea) = 0;
+    CV_WRAP virtual int getMinArea() const = 0;
+
+    CV_WRAP virtual void setMaxArea(int maxArea) = 0;
+    CV_WRAP virtual int getMaxArea() const = 0;
+
+    CV_WRAP virtual void setPass2Only(bool f) = 0;
+    CV_WRAP virtual bool getPass2Only() const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+//! @} features2d_main
+
+//! @addtogroup features2d_main
+//! @{
+
+/** @brief Wrapping class for feature detection using the FAST method. :
+ */
+class CV_EXPORTS_W FastFeatureDetector : public Feature2D
+{
+public:
+    enum DetectorType
+    {
+        TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2
+    };
+    enum
+    {
+        THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002
+    };
+
+
+    CV_WRAP static Ptr<FastFeatureDetector> create( int threshold=10,
+                                                    bool nonmaxSuppression=true,
+                                                    FastFeatureDetector::DetectorType type=FastFeatureDetector::TYPE_9_16 );
+
+    CV_WRAP virtual void setThreshold(int threshold) = 0;
+    CV_WRAP virtual int getThreshold() const = 0;
+
+    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
+    CV_WRAP virtual bool getNonmaxSuppression() const = 0;
+
+    CV_WRAP virtual void setType(FastFeatureDetector::DetectorType type) = 0;
+    CV_WRAP virtual FastFeatureDetector::DetectorType getType() const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+/** @overload */
+CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
+                      int threshold, bool nonmaxSuppression=true );
+
+/** @brief Detects corners using the FAST algorithm
+
+@param image grayscale image where keypoints (corners) are detected.
+@param keypoints keypoints detected on the image.
+@param threshold threshold on difference between intensity of the central pixel and pixels of a
+circle around this pixel.
+@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
+(keypoints).
+@param type one of the three neighborhoods as defined in the paper:
+FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12,
+FastFeatureDetector::TYPE_5_8
+
+Detects corners using the FAST algorithm by @cite Rosten06 .
+
+@note In Python API, types are given as cv.FAST_FEATURE_DETECTOR_TYPE_5_8,
+cv.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner
+detection, use cv.FAST.detect() method.
+ */
+CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
+                      int threshold, bool nonmaxSuppression, FastFeatureDetector::DetectorType type );
+
+//! @} features2d_main
+
+//! @addtogroup features2d_main
+//! @{
+
+/** @brief Wrapping class for feature detection using the AGAST method. :
+ */
+class CV_EXPORTS_W AgastFeatureDetector : public Feature2D
+{
+public:
+    enum DetectorType
+    {
+        AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3,
+    };
+
+    enum
+    {
+        THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001,
+    };
+
+    CV_WRAP static Ptr<AgastFeatureDetector> create( int threshold=10,
+                                                     bool nonmaxSuppression=true,
+                                                     AgastFeatureDetector::DetectorType type = AgastFeatureDetector::OAST_9_16);
+
+    CV_WRAP virtual void setThreshold(int threshold) = 0;
+    CV_WRAP virtual int getThreshold() const = 0;
+
+    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
+    CV_WRAP virtual bool getNonmaxSuppression() const = 0;
+
+    CV_WRAP virtual void setType(AgastFeatureDetector::DetectorType type) = 0;
+    CV_WRAP virtual AgastFeatureDetector::DetectorType getType() const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+/** @overload */
+CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
+                      int threshold, bool nonmaxSuppression=true );
+
+/** @brief Detects corners using the AGAST algorithm
+
+@param image grayscale image where keypoints (corners) are detected.
+@param keypoints keypoints detected on the image.
+@param threshold threshold on difference between intensity of the central pixel and pixels of a
+circle around this pixel.
+@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
+(keypoints).
+@param type one of the four neighborhoods as defined in the paper:
+AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
+AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16
+
+For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results.
+The 32-bit binary tree tables were generated automatically from original code using perl script.
+The perl script and examples of tree generation are placed in features2d/doc folder.
+Detects corners using the AGAST algorithm by @cite mair2010_agast .
+
+ */
+CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
+                      int threshold, bool nonmaxSuppression, AgastFeatureDetector::DetectorType type );
+
+/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. :
+ */
+class CV_EXPORTS_W GFTTDetector : public Feature2D
+{
+public:
+    CV_WRAP static Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
+                                             int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
+    CV_WRAP static Ptr<GFTTDetector> create( int maxCorners, double qualityLevel, double minDistance,
+                                             int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 );
+    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
+    CV_WRAP virtual int getMaxFeatures() const = 0;
+
+    CV_WRAP virtual void setQualityLevel(double qlevel) = 0;
+    CV_WRAP virtual double getQualityLevel() const = 0;
+
+    CV_WRAP virtual void setMinDistance(double minDistance) = 0;
+    CV_WRAP virtual double getMinDistance() const = 0;
+
+    CV_WRAP virtual void setBlockSize(int blockSize) = 0;
+    CV_WRAP virtual int getBlockSize() const = 0;
+
+    CV_WRAP virtual void setHarrisDetector(bool val) = 0;
+    CV_WRAP virtual bool getHarrisDetector() const = 0;
+
+    CV_WRAP virtual void setK(double k) = 0;
+    CV_WRAP virtual double getK() const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+/** @brief Class for extracting blobs from an image. :
+
+The class implements a simple algorithm for extracting blobs from an image:
+
+1.  Convert the source image to binary images by applying thresholding with several thresholds from
+    minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between
+    neighboring thresholds.
+2.  Extract connected components from every binary image by findContours and calculate their
+    centers.
+3.  Group centers from several binary images by their coordinates. Close centers form one group that
+    corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter.
+4.  From the groups, estimate final centers of blobs and their radiuses and return as locations and
+    sizes of keypoints.
+
+This class performs several filtrations of returned blobs. You should set filterBy\* to true/false
+to turn on/off corresponding filtration. Available filtrations:
+
+-   **By color**. This filter compares the intensity of a binary image at the center of a blob to
+blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs
+and blobColor = 255 to extract light blobs.
+-   **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).
+-   **By circularity**. Extracted blobs have circularity
+(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and
+maxCircularity (exclusive).
+-   **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio
+between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
+-   **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between
+minConvexity (inclusive) and maxConvexity (exclusive).
+
+Default values of parameters are tuned to extract dark circular blobs.
+ */
+class CV_EXPORTS_W SimpleBlobDetector : public Feature2D
+{
+public:
+  struct CV_EXPORTS_W_SIMPLE Params
+  {
+      CV_WRAP Params();
+      CV_PROP_RW float thresholdStep;
+      CV_PROP_RW float minThreshold;
+      CV_PROP_RW float maxThreshold;
+      CV_PROP_RW size_t minRepeatability;
+      CV_PROP_RW float minDistBetweenBlobs;
+
+      CV_PROP_RW bool filterByColor;
+      CV_PROP_RW uchar blobColor;
+
+      CV_PROP_RW bool filterByArea;
+      CV_PROP_RW float minArea, maxArea;
+
+      CV_PROP_RW bool filterByCircularity;
+      CV_PROP_RW float minCircularity, maxCircularity;
+
+      CV_PROP_RW bool filterByInertia;
+      CV_PROP_RW float minInertiaRatio, maxInertiaRatio;
+
+      CV_PROP_RW bool filterByConvexity;
+      CV_PROP_RW float minConvexity, maxConvexity;
+
+      void read( const FileNode& fn );
+      void write( FileStorage& fs ) const;
+  };
+
+  CV_WRAP static Ptr<SimpleBlobDetector>
+    create(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
+  CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+//! @} features2d_main
+
+//! @addtogroup features2d_main
+//! @{
+
+/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .
+
+@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo
+F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision
+(ECCV), Fiorenze, Italy, October 2012.
+*/
+class CV_EXPORTS_W KAZE : public Feature2D
+{
+public:
+    enum DiffusivityType
+    {
+        DIFF_PM_G1 = 0,
+        DIFF_PM_G2 = 1,
+        DIFF_WEICKERT = 2,
+        DIFF_CHARBONNIER = 3
+    };
+
+    /** @brief The KAZE constructor
+
+    @param extended Set to enable extraction of extended (128-byte) descriptor.
+    @param upright Set to enable use of upright descriptors (non rotation-invariant).
+    @param threshold Detector response threshold to accept point
+    @param nOctaves Maximum octave evolution of the image
+    @param nOctaveLayers Default number of sublevels per scale level
+    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
+    DIFF_CHARBONNIER
+     */
+    CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,
+                                    float threshold = 0.001f,
+                                    int nOctaves = 4, int nOctaveLayers = 4,
+                                    KAZE::DiffusivityType diffusivity = KAZE::DIFF_PM_G2);
+
+    CV_WRAP virtual void setExtended(bool extended) = 0;
+    CV_WRAP virtual bool getExtended() const = 0;
+
+    CV_WRAP virtual void setUpright(bool upright) = 0;
+    CV_WRAP virtual bool getUpright() const = 0;
+
+    CV_WRAP virtual void setThreshold(double threshold) = 0;
+    CV_WRAP virtual double getThreshold() const = 0;
+
+    CV_WRAP virtual void setNOctaves(int octaves) = 0;
+    CV_WRAP virtual int getNOctaves() const = 0;
+
+    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
+    CV_WRAP virtual int getNOctaveLayers() const = 0;
+
+    CV_WRAP virtual void setDiffusivity(KAZE::DiffusivityType diff) = 0;
+    CV_WRAP virtual KAZE::DiffusivityType getDiffusivity() const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13.
+
+@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe.
+
+@note When you need descriptors use Feature2D::detectAndCompute, which
+provides better performance. When using Feature2D::detect followed by
+Feature2D::compute scale space pyramid is computed twice.
+
+@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm
+will use OpenCL.
+
+@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear
+Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In
+British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
+
+*/
+class CV_EXPORTS_W AKAZE : public Feature2D
+{
+public:
+    // AKAZE descriptor type
+    enum DescriptorType
+    {
+        DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation
+        DESCRIPTOR_KAZE = 3,
+        DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation
+        DESCRIPTOR_MLDB = 5
+    };
+
+    /** @brief The AKAZE constructor
+
+    @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
+    DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
+    @param descriptor_size Size of the descriptor in bits. 0 -\> Full size
+    @param descriptor_channels Number of channels in the descriptor (1, 2, 3)
+    @param threshold Detector response threshold to accept point
+    @param nOctaves Maximum octave evolution of the image
+    @param nOctaveLayers Default number of sublevels per scale level
+    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
+    DIFF_CHARBONNIER
+     */
+    CV_WRAP static Ptr<AKAZE> create(AKAZE::DescriptorType descriptor_type = AKAZE::DESCRIPTOR_MLDB,
+                                     int descriptor_size = 0, int descriptor_channels = 3,
+                                     float threshold = 0.001f, int nOctaves = 4,
+                                     int nOctaveLayers = 4, KAZE::DiffusivityType diffusivity = KAZE::DIFF_PM_G2);
+
+    CV_WRAP virtual void setDescriptorType(AKAZE::DescriptorType dtype) = 0;
+    CV_WRAP virtual AKAZE::DescriptorType getDescriptorType() const = 0;
+
+    CV_WRAP virtual void setDescriptorSize(int dsize) = 0;
+    CV_WRAP virtual int getDescriptorSize() const = 0;
+
+    CV_WRAP virtual void setDescriptorChannels(int dch) = 0;
+    CV_WRAP virtual int getDescriptorChannels() const = 0;
+
+    CV_WRAP virtual void setThreshold(double threshold) = 0;
+    CV_WRAP virtual double getThreshold() const = 0;
+
+    CV_WRAP virtual void setNOctaves(int octaves) = 0;
+    CV_WRAP virtual int getNOctaves() const = 0;
+
+    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
+    CV_WRAP virtual int getNOctaveLayers() const = 0;
+
+    CV_WRAP virtual void setDiffusivity(KAZE::DiffusivityType diff) = 0;
+    CV_WRAP virtual KAZE::DiffusivityType getDiffusivity() const = 0;
+    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
+};
+
+//! @} features2d_main
+
+/****************************************************************************************\
+*                                      Distance                                          *
+\****************************************************************************************/
+
+template<typename T>
+struct CV_EXPORTS Accumulator
+{
+    typedef T Type;
+};
+
+template<> struct Accumulator<unsigned char>  { typedef float Type; };
+template<> struct Accumulator<unsigned short> { typedef float Type; };
+template<> struct Accumulator<char>   { typedef float Type; };
+template<> struct Accumulator<short>  { typedef float Type; };
+
+/*
+ * Squared Euclidean distance functor
+ */
+template<class T>
+struct CV_EXPORTS SL2
+{
+    static const NormTypes normType = NORM_L2SQR;
+    typedef T ValueType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    ResultType operator()( const T* a, const T* b, int size ) const
+    {
+        return normL2Sqr<ValueType, ResultType>(a, b, size);
+    }
+};
+
+/*
+ * Euclidean distance functor
+ */
+template<class T>
+struct L2
+{
+    static const NormTypes normType = NORM_L2;
+    typedef T ValueType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    ResultType operator()( const T* a, const T* b, int size ) const
+    {
+        return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
+    }
+};
+
+/*
+ * Manhattan distance (city block distance) functor
+ */
+template<class T>
+struct L1
+{
+    static const NormTypes normType = NORM_L1;
+    typedef T ValueType;
+    typedef typename Accumulator<T>::Type ResultType;
+
+    ResultType operator()( const T* a, const T* b, int size ) const
+    {
+        return normL1<ValueType, ResultType>(a, b, size);
+    }
+};
+
+/****************************************************************************************\
+*                                  DescriptorMatcher                                     *
+\****************************************************************************************/
+
+//! @addtogroup features2d_match
+//! @{
+
+/** @brief Abstract base class for matching keypoint descriptors.
+
+It has two groups of match methods: for matching descriptors of an image with another image or with
+an image set.
+ */
+class CV_EXPORTS_W DescriptorMatcher : public Algorithm
+{
+public:
+   enum MatcherType
+    {
+        FLANNBASED            = 1,
+        BRUTEFORCE            = 2,
+        BRUTEFORCE_L1         = 3,
+        BRUTEFORCE_HAMMING    = 4,
+        BRUTEFORCE_HAMMINGLUT = 5,
+        BRUTEFORCE_SL2        = 6
+    };
+
+    virtual ~DescriptorMatcher();
+
+    /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor
+    collection.
+
+    If the collection is not empty, the new descriptors are added to existing train descriptors.
+
+    @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
+    train image.
+     */
+    CV_WRAP virtual void add( InputArrayOfArrays descriptors );
+
+    /** @brief Returns a constant link to the train descriptor collection trainDescCollection .
+     */
+    CV_WRAP const std::vector<Mat>& getTrainDescriptors() const;
+
+    /** @brief Clears the train descriptor collections.
+     */
+    CV_WRAP virtual void clear() CV_OVERRIDE;
+
+    /** @brief Returns true if there are no train descriptors in the both collections.
+     */
+    CV_WRAP virtual bool empty() const CV_OVERRIDE;
+
+    /** @brief Returns true if the descriptor matcher supports masking permissible matches.
+     */
+    CV_WRAP virtual bool isMaskSupported() const = 0;
+
+    /** @brief Trains a descriptor matcher
+
+    Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
+    train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher)
+    have an empty implementation of this method. Other matchers really train their inner structures (for
+    example, FlannBasedMatcher trains flann::Index ).
+     */
+    CV_WRAP virtual void train();
+
+    /** @brief Finds the best match for each descriptor from a query set.
+
+    @param queryDescriptors Query set of descriptors.
+    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
+    collection stored in the class object.
+    @param matches Matches. If a query descriptor is masked out in mask , no match is added for this
+    descriptor. So, matches size may be smaller than the query descriptors count.
+    @param mask Mask specifying permissible matches between an input query and train matrices of
+    descriptors.
+
+    In the first variant of this method, the train descriptors are passed as an input argument. In the
+    second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
+    used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
+    matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
+    mask.at\<uchar\>(i,j) is non-zero.
+     */
+    CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,
+                CV_OUT std::vector<DMatch>& matches, InputArray mask=noArray() ) const;
+
+    /** @brief Finds the k best matches for each descriptor from a query set.
+
+    @param queryDescriptors Query set of descriptors.
+    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
+    collection stored in the class object.
+    @param mask Mask specifying permissible matches between an input query and train matrices of
+    descriptors.
+    @param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
+    @param k Count of best matches found per each query descriptor or less if a query descriptor has
+    less than k possible matches in total.
+    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
+    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
+    the matches vector does not contain matches for fully masked-out query descriptors.
+
+    These extended variants of DescriptorMatcher::match methods find several best matches for each query
+    descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
+    for the details about query and train descriptors.
+     */
+    CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,
+                   CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
+                   InputArray mask=noArray(), bool compactResult=false ) const;
+
+    /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance.
+
+    @param queryDescriptors Query set of descriptors.
+    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
+    collection stored in the class object.
+    @param matches Found matches.
+    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
+    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
+    the matches vector does not contain matches for fully masked-out query descriptors.
+    @param maxDistance Threshold for the distance between matched descriptors. Distance means here
+    metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
+    in Pixels)!
+    @param mask Mask specifying permissible matches between an input query and train matrices of
+    descriptors.
+
+    For each query descriptor, the methods find such training descriptors that the distance between the
+    query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
+    returned in the distance increasing order.
+     */
+    CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,
+                      CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,
+                      InputArray mask=noArray(), bool compactResult=false ) const;
+
+    /** @overload
+    @param queryDescriptors Query set of descriptors.
+    @param matches Matches. If a query descriptor is masked out in mask , no match is added for this
+    descriptor. So, matches size may be smaller than the query descriptors count.
+    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query
+    descriptors and stored train descriptors from the i-th image trainDescCollection[i].
+    */
+    CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,
+                        InputArrayOfArrays masks=noArray() );
+    /** @overload
+    @param queryDescriptors Query set of descriptors.
+    @param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
+    @param k Count of best matches found per each query descriptor or less if a query descriptor has
+    less than k possible matches in total.
+    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query
+    descriptors and stored train descriptors from the i-th image trainDescCollection[i].
+    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
+    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
+    the matches vector does not contain matches for fully masked-out query descriptors.
+    */
+    CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
+                           InputArrayOfArrays masks=noArray(), bool compactResult=false );
+    /** @overload
+    @param queryDescriptors Query set of descriptors.
+    @param matches Found matches.
+    @param maxDistance Threshold for the distance between matched descriptors. Distance means here
+    metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
+    in Pixels)!
+    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query
+    descriptors and stored train descriptors from the i-th image trainDescCollection[i].
+    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
+    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
+    the matches vector does not contain matches for fully masked-out query descriptors.
+    */
+    CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,
+                      InputArrayOfArrays masks=noArray(), bool compactResult=false );
+
+
+    CV_WRAP void write( const String& fileName ) const
+    {
+        FileStorage fs(fileName, FileStorage::WRITE);
+        write(fs);
+    }
+
+    CV_WRAP void read( const String& fileName )
+    {
+        FileStorage fs(fileName, FileStorage::READ);
+        read(fs.root());
+    }
+    // Reads matcher object from a file node
+    // see corresponding cv::Algorithm method
+    CV_WRAP virtual void read( const FileNode& ) CV_OVERRIDE;
+    // Writes matcher object to a file storage
+    virtual void write( FileStorage& ) const CV_OVERRIDE;
+
+    /** @brief Clones the matcher.
+
+    @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object,
+    that is, copies both parameters and train data. If emptyTrainData is true, the method creates an
+    object copy with the current parameters but with empty train data.
+     */
+    CV_WRAP CV_NODISCARD_STD virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
+
+    /** @brief Creates a descriptor matcher of a given type with the default parameters (using default
+    constructor).
+
+    @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
+    supported:
+    -   `BruteForce` (it uses L2 )
+    -   `BruteForce-L1`
+    -   `BruteForce-Hamming`
+    -   `BruteForce-Hamming(2)`
+    -   `FlannBased`
+     */
+    CV_WRAP static Ptr<DescriptorMatcher> create( const String& descriptorMatcherType );
+
+    CV_WRAP static Ptr<DescriptorMatcher> create( const DescriptorMatcher::MatcherType& matcherType );
+
+
+    // see corresponding cv::Algorithm method
+    CV_WRAP inline void write(const Ptr<FileStorage>& fs, const String& name = String()) const { Algorithm::write(fs, name); }
+
+protected:
+    /**
+     * Class to work with descriptors from several images as with one merged matrix.
+     * It is used e.g. in FlannBasedMatcher.
+     */
+    class CV_EXPORTS DescriptorCollection
+    {
+    public:
+        DescriptorCollection();
+        DescriptorCollection( const DescriptorCollection& collection );
+        virtual ~DescriptorCollection();
+
+        // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here.
+        void set( const std::vector<Mat>& descriptors );
+        virtual void clear();
+
+        const Mat& getDescriptors() const;
+        const Mat getDescriptor( int imgIdx, int localDescIdx ) const;
+        const Mat getDescriptor( int globalDescIdx ) const;
+        void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const;
+
+        int size() const;
+
+    protected:
+        Mat mergedDescriptors;
+        std::vector<int> startIdxs;
+    };
+
+    //! In fact the matching is implemented only by the following two methods. These methods suppose
+    //! that the class object has been trained already. Public match methods call these methods
+    //! after calling train().
+    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
+        InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
+    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
+        InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
+
+    static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx );
+    static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx );
+
+    CV_NODISCARD_STD static Mat clone_op( Mat m ) { return m.clone(); }
+    void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const;
+
+    //! Collection of descriptors from train images.
+    std::vector<Mat> trainDescCollection;
+    std::vector<UMat> utrainDescCollection;
+};
+
+/** @brief Brute-force descriptor matcher.
+
+For each descriptor in the first set, this matcher finds the closest descriptor in the second set
+by trying each one. This descriptor matcher supports masking permissible matches of descriptor
+sets.
+ */
+class CV_EXPORTS_W BFMatcher : public DescriptorMatcher
+{
+public:
+    /** @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create()
+     *
+     *
+    */
+    CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false );
+
+    virtual ~BFMatcher() {}
+
+    virtual bool isMaskSupported() const CV_OVERRIDE { return true; }
+
+    /** @brief Brute-force matcher create method.
+    @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
+    preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
+    BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
+    description).
+    @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k
+    nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
+    k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
+    matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
+    pairs. Such technique usually produces best results with minimal number of outliers when there are
+    enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
+     */
+    CV_WRAP static Ptr<BFMatcher> create( int normType=NORM_L2, bool crossCheck=false ) ;
+
+    CV_NODISCARD_STD virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const CV_OVERRIDE;
+protected:
+    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
+        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
+    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
+        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
+
+    int normType;
+    bool crossCheck;
+};
+
+#if defined(HAVE_OPENCV_FLANN) || defined(CV_DOXYGEN)
+
+/** @brief Flann-based descriptor matcher.
+
+This matcher trains cv::flann::Index on a train descriptor collection and calls its nearest search
+methods to find the best matches. So, this matcher may be faster when matching a large train
+collection than the brute force matcher. FlannBasedMatcher does not support masking permissible
+matches of descriptor sets because flann::Index does not support this. :
+ */
+class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
+{
+public:
+    CV_WRAP FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=makePtr<flann::KDTreeIndexParams>(),
+                       const Ptr<flann::SearchParams>& searchParams=makePtr<flann::SearchParams>() );
+
+    virtual void add( InputArrayOfArrays descriptors ) CV_OVERRIDE;
+    virtual void clear() CV_OVERRIDE;
+
+    // Reads matcher object from a file node
+    virtual void read( const FileNode& ) CV_OVERRIDE;
+    // Writes matcher object to a file storage
+    virtual void write( FileStorage& ) const CV_OVERRIDE;
+
+    virtual void train() CV_OVERRIDE;
+    virtual bool isMaskSupported() const CV_OVERRIDE;
+
+    CV_WRAP static Ptr<FlannBasedMatcher> create();
+
+    CV_NODISCARD_STD virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const CV_OVERRIDE;
+protected:
+    static void convertToDMatches( const DescriptorCollection& descriptors,
+                                   const Mat& indices, const Mat& distances,
+                                   std::vector<std::vector<DMatch> >& matches );
+
+    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
+        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
+    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
+        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
+
+    Ptr<flann::IndexParams> indexParams;
+    Ptr<flann::SearchParams> searchParams;
+    Ptr<flann::Index> flannIndex;
+
+    DescriptorCollection mergedDescriptors;
+    int addedDescCount;
+};
+
+#endif
+
+//! @} features2d_match
+
+/****************************************************************************************\
+*                                   Drawing functions                                    *
+\****************************************************************************************/
+
+//! @addtogroup features2d_draw
+//! @{
+
+enum struct DrawMatchesFlags
+{
+  DEFAULT = 0, //!< Output image matrix will be created (Mat::create),
+               //!< i.e. existing memory of output image may be reused.
+               //!< Two source image, matches and single keypoints will be drawn.
+               //!< For each keypoint only the center point will be drawn (without
+               //!< the circle around keypoint with keypoint size and orientation).
+  DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create).
+                        //!< Matches will be drawn on existing content of output image.
+  NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn.
+  DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and
+                          //!< orientation will be drawn.
+};
+CV_ENUM_FLAGS(DrawMatchesFlags)
+
+/** @brief Draws keypoints.
+
+@param image Source image.
+@param keypoints Keypoints from the source image.
+@param outImage Output image. Its content depends on the flags value defining what is drawn in the
+output image. See possible flags bit values below.
+@param color Color of keypoints.
+@param flags Flags setting drawing features. Possible flags bit values are defined by
+DrawMatchesFlags. See details above in drawMatches .
+
+@note
+For Python API, flags are modified as cv.DRAW_MATCHES_FLAGS_DEFAULT,
+cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG,
+cv.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
+ */
+CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
+                               const Scalar& color=Scalar::all(-1), DrawMatchesFlags flags=DrawMatchesFlags::DEFAULT );
+
+/** @brief Draws the found matches of keypoints from two images.
+
+@param img1 First source image.
+@param keypoints1 Keypoints from the first source image.
+@param img2 Second source image.
+@param keypoints2 Keypoints from the second source image.
+@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i]
+has a corresponding point in keypoints2[matches[i]] .
+@param outImg Output image. Its content depends on the flags value defining what is drawn in the
+output image. See possible flags bit values below.
+@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1)
+, the color is generated randomly.
+@param singlePointColor Color of single keypoints (circles), which means that keypoints do not
+have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
+@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are
+drawn.
+@param flags Flags setting drawing features. Possible flags bit values are defined by
+DrawMatchesFlags.
+
+This function draws matches of keypoints from two images in the output image. Match is a line
+connecting two keypoints (circles). See cv::DrawMatchesFlags.
+ */
+CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
+                             InputArray img2, const std::vector<KeyPoint>& keypoints2,
+                             const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
+                             const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
+                             const std::vector<char>& matchesMask=std::vector<char>(), DrawMatchesFlags flags=DrawMatchesFlags::DEFAULT );
+
+/** @overload */
+CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
+                             InputArray img2, const std::vector<KeyPoint>& keypoints2,
+                             const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
+                             const int matchesThickness, const Scalar& matchColor=Scalar::all(-1),
+                             const Scalar& singlePointColor=Scalar::all(-1), const std::vector<char>& matchesMask=std::vector<char>(),
+                             DrawMatchesFlags flags=DrawMatchesFlags::DEFAULT );
+
+CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
+                             InputArray img2, const std::vector<KeyPoint>& keypoints2,
+                             const std::vector<std::vector<DMatch> >& matches1to2, InputOutputArray outImg,
+                             const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
+                             const std::vector<std::vector<char> >& matchesMask=std::vector<std::vector<char> >(), DrawMatchesFlags flags=DrawMatchesFlags::DEFAULT );
+
+//! @} features2d_draw
+
+/****************************************************************************************\
+*   Functions to evaluate the feature detectors and [generic] descriptor extractors      *
+\****************************************************************************************/
+
+CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
+                                         std::vector<KeyPoint>* keypoints1, std::vector<KeyPoint>* keypoints2,
+                                         float& repeatability, int& correspCount,
+                                         const Ptr<FeatureDetector>& fdetector=Ptr<FeatureDetector>() );
+
+CV_EXPORTS void computeRecallPrecisionCurve( const std::vector<std::vector<DMatch> >& matches1to2,
+                                             const std::vector<std::vector<uchar> >& correctMatches1to2Mask,
+                                             std::vector<Point2f>& recallPrecisionCurve );
+
+CV_EXPORTS float getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
+CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
+
+/****************************************************************************************\
+*                                     Bag of visual words                                *
+\****************************************************************************************/
+
+//! @addtogroup features2d_category
+//! @{
+
+/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors.
+
+For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka,
+Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :
+ */
+class CV_EXPORTS_W BOWTrainer
+{
+public:
+    BOWTrainer();
+    virtual ~BOWTrainer();
+
+    /** @brief Adds descriptors to a training set.
+
+    @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a
+    descriptor.
+
+    The training set is clustered using clustermethod to construct the vocabulary.
+     */
+    CV_WRAP void add( const Mat& descriptors );
+
+    /** @brief Returns a training set of descriptors.
+    */
+    CV_WRAP const std::vector<Mat>& getDescriptors() const;
+
+    /** @brief Returns the count of all descriptors stored in the training set.
+    */
+    CV_WRAP int descriptorsCount() const;
+
+    CV_WRAP virtual void clear();
+
+    /** @overload */
+    CV_WRAP virtual Mat cluster() const = 0;
+
+    /** @brief Clusters train descriptors.
+
+    @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
+    Descriptors are not added to the inner train descriptor set.
+
+    The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
+    variant of the method, train descriptors stored in the object are clustered. In the second variant,
+    input descriptors are clustered.
+     */
+    CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0;
+
+protected:
+    std::vector<Mat> descriptors;
+    int size;
+};
+
+/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. :
+ */
+class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer
+{
+public:
+    /** @brief The constructor.
+
+    @see cv::kmeans
+    */
+    CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
+                      int attempts=3, int flags=KMEANS_PP_CENTERS );
+    virtual ~BOWKMeansTrainer();
+
+    // Returns trained vocabulary (i.e. cluster centers).
+    CV_WRAP virtual Mat cluster() const CV_OVERRIDE;
+    CV_WRAP virtual Mat cluster( const Mat& descriptors ) const CV_OVERRIDE;
+
+protected:
+
+    int clusterCount;
+    TermCriteria termcrit;
+    int attempts;
+    int flags;
+};
+
+/** @brief Class to compute an image descriptor using the *bag of visual words*.
+
+Such a computation consists of the following steps:
+
+1.  Compute descriptors for a given image and its keypoints set.
+2.  Find the nearest visual words from the vocabulary for each keypoint descriptor.
+3.  Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words
+encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the
+vocabulary in the given image.
+ */
+class CV_EXPORTS_W BOWImgDescriptorExtractor
+{
+public:
+    /** @brief The constructor.
+
+    @param dextractor Descriptor extractor that is used to compute descriptors for an input image and
+    its keypoints.
+    @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary
+    for each keypoint descriptor of the image.
+     */
+    CV_WRAP BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
+                               const Ptr<DescriptorMatcher>& dmatcher );
+    /** @overload */
+    BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& dmatcher );
+    virtual ~BOWImgDescriptorExtractor();
+
+    /** @brief Sets a visual vocabulary.
+
+    @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the
+    vocabulary is a visual word (cluster center).
+     */
+    CV_WRAP void setVocabulary( const Mat& vocabulary );
+
+    /** @brief Returns the set vocabulary.
+    */
+    CV_WRAP const Mat& getVocabulary() const;
+
+    /** @brief Computes an image descriptor using the set visual vocabulary.
+
+    @param image Image, for which the descriptor is computed.
+    @param keypoints Keypoints detected in the input image.
+    @param imgDescriptor Computed output image descriptor.
+    @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
+    pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
+    returned if it is non-zero.
+    @param descriptors Descriptors of the image keypoints that are returned if they are non-zero.
+     */
+    void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
+                  std::vector<std::vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 );
+    /** @overload
+    @param keypointDescriptors Computed descriptors to match with vocabulary.
+    @param imgDescriptor Computed output image descriptor.
+    @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
+    pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
+    returned if it is non-zero.
+    */
+    void compute( InputArray keypointDescriptors, OutputArray imgDescriptor,
+                  std::vector<std::vector<int> >* pointIdxsOfClusters=0 );
+    // compute() is not constant because DescriptorMatcher::match is not constant
+
+    CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector<KeyPoint>& keypoints, CV_OUT Mat& imgDescriptor )
+    { compute(image,keypoints,imgDescriptor); }
+
+    /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
+    */
+    CV_WRAP int descriptorSize() const;
+
+    /** @brief Returns an image descriptor type.
+     */
+    CV_WRAP int descriptorType() const;
+
+protected:
+    Mat vocabulary;
+    Ptr<DescriptorExtractor> dextractor;
+    Ptr<DescriptorMatcher> dmatcher;
+};
+
+//! @} features2d_category
+
+//! @} features2d
+
+} /* namespace cv */
+
+#endif
diff --git a/3rdParty/include/opencv2/features2d/features2d.hpp b/3rdParty/include/opencv2/features2d/features2d.hpp
new file mode 100644
index 0000000..e81df0a
--- /dev/null
+++ b/3rdParty/include/opencv2/features2d/features2d.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/features2d.hpp"
diff --git a/3rdParty/include/opencv2/features2d/hal/interface.h b/3rdParty/include/opencv2/features2d/hal/interface.h
new file mode 100644
index 0000000..bc3b084
--- /dev/null
+++ b/3rdParty/include/opencv2/features2d/hal/interface.h
@@ -0,0 +1,33 @@
+#ifndef OPENCV_FEATURE2D_HAL_INTERFACE_H
+#define OPENCV_FEATURE2D_HAL_INTERFACE_H
+
+#include "opencv2/core/cvdef.h"
+//! @addtogroup features2d_hal_interface
+//! @{
+
+//! @name Fast feature detector types
+//! @sa cv::FastFeatureDetector
+//! @{
+#define CV_HAL_TYPE_5_8  0
+#define CV_HAL_TYPE_7_12 1
+#define CV_HAL_TYPE_9_16 2
+//! @}
+
+//! @name Key point
+//! @sa cv::KeyPoint
+//! @{
+struct CV_EXPORTS cvhalKeyPoint
+{
+    float x;
+    float y;
+    float size;
+    float angle;
+    float response;
+    int octave;
+    int class_id;
+};
+//! @}
+
+//! @}
+
+#endif
diff --git a/3rdParty/include/opencv2/flann.hpp b/3rdParty/include/opencv2/flann.hpp
new file mode 100644
index 0000000..0468903
--- /dev/null
+++ b/3rdParty/include/opencv2/flann.hpp
@@ -0,0 +1,629 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_FLANN_HPP
+#define OPENCV_FLANN_HPP
+
+#include "opencv2/core.hpp"
+#include "opencv2/flann/miniflann.hpp"
+#include "opencv2/flann/flann_base.hpp"
+
+/**
+@defgroup flann Clustering and Search in Multi-Dimensional Spaces
+
+This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate
+Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest
+neighbor search in large datasets and for high dimensional features. More information about FLANN
+can be found in @cite Muja2009 .
+*/
+
+namespace cvflann
+{
+    CV_EXPORTS flann_distance_t flann_distance_type();
+    CV_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order);
+}
+
+
+namespace cv
+{
+namespace flann
+{
+
+
+//! @addtogroup flann
+//! @{
+
+template <typename T> struct CvType {};
+template <> struct CvType<unsigned char> { static int type() { return CV_8U; } };
+template <> struct CvType<char> { static int type() { return CV_8S; } };
+template <> struct CvType<unsigned short> { static int type() { return CV_16U; } };
+template <> struct CvType<short> { static int type() { return CV_16S; } };
+template <> struct CvType<int> { static int type() { return CV_32S; } };
+template <> struct CvType<float> { static int type() { return CV_32F; } };
+template <> struct CvType<double> { static int type() { return CV_64F; } };
+
+
+// bring the flann parameters into this namespace
+using ::cvflann::get_param;
+using ::cvflann::print_params;
+
+// bring the flann distances into this namespace
+using ::cvflann::L2_Simple;
+using ::cvflann::L2;
+using ::cvflann::L1;
+using ::cvflann::MinkowskiDistance;
+using ::cvflann::MaxDistance;
+using ::cvflann::HammingLUT;
+using ::cvflann::Hamming;
+using ::cvflann::Hamming2;
+using ::cvflann::DNAmmingLUT;
+using ::cvflann::DNAmming2;
+using ::cvflann::HistIntersectionDistance;
+using ::cvflann::HellingerDistance;
+using ::cvflann::ChiSquareDistance;
+using ::cvflann::KL_Divergence;
+
+
+/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which
+the index is built.
+
+`Distance` functor specifies the metric to be used to calculate the distance between two points.
+There are several `Distance` functors that are readily available:
+
+cv::cvflann::L2_Simple - Squared Euclidean distance functor.
+This is the simpler, unrolled version. This is preferable for very low dimensionality data (eg 3D points)
+
+cv::flann::L2 - Squared Euclidean distance functor, optimized version.
+
+cv::flann::L1 - Manhattan distance functor, optimized version.
+
+cv::flann::MinkowskiDistance -  The Minkowsky distance functor.
+This is highly optimised with loop unrolling.
+The computation of squared root at the end is omitted for efficiency.
+
+cv::flann::MaxDistance - The max distance functor. It computes the
+maximum distance between two vectors. This distance is not a valid kdtree distance, it's not
+dimensionwise additive.
+
+cv::flann::HammingLUT -  %Hamming distance functor. It counts the bit
+differences between two strings using a lookup table implementation.
+
+cv::flann::Hamming - %Hamming distance functor. Population count is
+performed using library calls, if available. Lookup table implementation is used as a fallback.
+
+cv::flann::Hamming2 - %Hamming distance functor. Population count is
+implemented in 12 arithmetic operations (one of which is multiplication).
+
+cv::flann::DNAmmingLUT -  %Adaptation of the Hamming distance functor to DNA comparison.
+As the four bases A, C, G, T of the DNA (or A, G, C, U for RNA) can be coded on 2 bits,
+it counts the bits pairs differences between two sequences using a lookup table implementation.
+
+cv::flann::DNAmming2 - %Adaptation of the Hamming distance functor to DNA comparison.
+Bases differences count are vectorised thanks to arithmetic operations using standard
+registers (AVX2 and AVX-512 should come in a near future).
+
+cv::flann::HistIntersectionDistance - The histogram
+intersection distance functor.
+
+cv::flann::HellingerDistance - The Hellinger distance functor.
+
+cv::flann::ChiSquareDistance - The chi-square distance functor.
+
+cv::flann::KL_Divergence - The Kullback-Leibler divergence functor.
+
+Although the provided implementations cover a vast range of cases, it is also possible to use
+a custom implementation. The distance functor is a class whose `operator()` computes the distance
+between two features. If the distance is also a kd-tree compatible distance, it should also provide an
+`accum_dist()` method that computes the distance between individual feature dimensions.
+
+In addition to `operator()` and `accum_dist()`, a distance functor should also define the
+`ElementType` and the `ResultType` as the types of the elements it operates on and the type of the
+result it computes. If a distance functor can be used as a kd-tree distance (meaning that the full
+distance between a pair of features can be accumulated from the partial distances between the
+individual dimensions) a typedef `is_kdtree_distance` should be present inside the distance functor.
+If the distance is not a kd-tree distance, but it's a distance in a vector space (the individual
+dimensions of the elements it operates on can be accessed independently) a typedef
+`is_vector_space_distance` should be defined inside the functor. If neither typedef is defined, the
+distance is assumed to be a metric distance and will only be used with indexes operating on
+generic metric distances.
+ */
+template <typename Distance>
+class GenericIndex
+{
+public:
+        typedef typename Distance::ElementType ElementType;
+        typedef typename Distance::ResultType DistanceType;
+
+        /** @brief Constructs a nearest neighbor search index for a given dataset.
+
+        @param features Matrix of containing the features(points) to index. The size of the matrix is
+        num_features x feature_dimensionality and the data type of the elements in the matrix must
+        coincide with the type of the index.
+        @param params Structure containing the index parameters. The type of index that will be
+        constructed depends on the type of this parameter. See the description.
+        @param distance
+
+        The method constructs a fast search structure from a set of features using the specified algorithm
+        with specified parameters, as defined by params. params is a reference to one of the following class
+        IndexParams descendants:
+
+        - **LinearIndexParams** When passing an object of this type, the index will perform a linear,
+        brute-force search. :
+        @code
+        struct LinearIndexParams : public IndexParams
+        {
+        };
+        @endcode
+        - **KDTreeIndexParams** When passing an object of this type the index constructed will consist of
+        a set of randomized kd-trees which will be searched in parallel. :
+        @code
+        struct KDTreeIndexParams : public IndexParams
+        {
+            KDTreeIndexParams( int trees = 4 );
+        };
+        @endcode
+        - **HierarchicalClusteringIndexParams** When passing an object of this type the index constructed
+        will be a hierarchical tree of clusters, dividing each set of points into n clusters whose centers
+        are picked among the points without further refinement of their position.
+        This algorithm fits both floating, integer and binary vectors. :
+        @code
+        struct HierarchicalClusteringIndexParams : public IndexParams
+        {
+            HierarchicalClusteringIndexParams(
+                int branching = 32,
+                flann_centers_init_t centers_init = CENTERS_RANDOM,
+                int trees = 4,
+                int leaf_size = 100);
+
+        };
+        @endcode
+        - **KMeansIndexParams** When passing an object of this type the index constructed will be a
+        hierarchical k-means tree (one tree by default), dividing each set of points into n clusters
+        whose barycenters are refined iteratively.
+        Note that this algorithm has been extended to the support of binary vectors as an alternative
+        to LSH when knn search speed is the criterium. It will also outperform LSH when processing
+        directly (i.e. without the use of MCA/PCA) datasets whose points share mostly the same values
+        for most of the dimensions. It is recommended to set more than one tree with binary data. :
+        @code
+        struct KMeansIndexParams : public IndexParams
+        {
+            KMeansIndexParams(
+                int branching = 32,
+                int iterations = 11,
+                flann_centers_init_t centers_init = CENTERS_RANDOM,
+                float cb_index = 0.2,
+                int trees = 1);
+        };
+        @endcode
+        - **CompositeIndexParams** When using a parameters object of this type the index created
+        combines the randomized kd-trees and the hierarchical k-means tree. :
+        @code
+        struct CompositeIndexParams : public IndexParams
+        {
+            CompositeIndexParams(
+                int trees = 4,
+                int branching = 32,
+                int iterations = 11,
+                flann_centers_init_t centers_init = CENTERS_RANDOM,
+                float cb_index = 0.2 );
+        };
+        @endcode
+        - **LshIndexParams** When using a parameters object of this type the index created uses
+        multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
+        by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd
+        International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007).
+        This algorithm is designed for binary vectors. :
+        @code
+        struct LshIndexParams : public IndexParams
+        {
+            LshIndexParams(
+                int table_number,
+                int key_size,
+                int multi_probe_level );
+        };
+        @endcode
+        - **AutotunedIndexParams** When passing an object of this type the index created is
+        automatically tuned to offer the best performance, by choosing the optimal index type
+        (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. :
+        @code
+        struct AutotunedIndexParams : public IndexParams
+        {
+            AutotunedIndexParams(
+                float target_precision = 0.9,
+                float build_weight = 0.01,
+                float memory_weight = 0,
+                float sample_fraction = 0.1 );
+        };
+        @endcode
+        - **SavedIndexParams** This object type is used for loading a previously saved index from the
+        disk. :
+        @code
+        struct SavedIndexParams : public IndexParams
+        {
+            SavedIndexParams( String filename );
+        };
+        @endcode
+         */
+        GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance());
+
+        ~GenericIndex();
+
+        /** @brief Performs a K-nearest neighbor search for a given query point using the index.
+
+        @param query The query point
+        @param indices Vector that will contain the indices of the K-nearest neighbors found. It must have
+        at least knn size.
+        @param dists Vector that will contain the distances to the K-nearest neighbors found. It must have
+        at least knn size.
+        @param knn Number of nearest neighbors to search for.
+        @param params SearchParams
+         */
+        void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
+                       std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
+        void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);
+
+        /** @brief Performs a radius nearest neighbor search for a given query point using the index.
+
+        @param query The query point.
+        @param indices Vector that will contain the indices of the nearest neighbors found.
+        @param dists Vector that will contain the distances to the nearest neighbors found. It has the same
+        number of elements as indices.
+        @param radius The search radius.
+        @param params SearchParams
+
+        This function returns the number of nearest neighbors found.
+        */
+        int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
+                         std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
+        int radiusSearch(const Mat& query, Mat& indices, Mat& dists,
+                         DistanceType radius, const ::cvflann::SearchParams& params);
+
+        void save(String filename) { nnIndex->save(filename); }
+
+        int veclen() const { return nnIndex->veclen(); }
+
+        int size() const { return (int)nnIndex->size(); }
+
+        ::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); }
+
+        CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); }
+
+private:
+        ::cvflann::Index<Distance>* nnIndex;
+        Mat _dataset;
+};
+
+//! @cond IGNORED
+
+#define FLANN_DISTANCE_CHECK \
+    if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \
+        printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\
+        "the distance using cvflann::set_distance_type. This is no longer working as expected "\
+        "(cv::flann::Index always uses L2). You should create the index templated on the distance, "\
+        "for example for L1 distance use: GenericIndex< L1<float> > \n"); \
+    }
+
+
+template <typename Distance>
+GenericIndex<Distance>::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance)
+: _dataset(dataset)
+{
+    CV_Assert(dataset.type() == CvType<ElementType>::type());
+    CV_Assert(dataset.isContinuous());
+    ::cvflann::Matrix<ElementType> m_dataset((ElementType*)_dataset.ptr<ElementType>(0), _dataset.rows, _dataset.cols);
+
+    nnIndex = new ::cvflann::Index<Distance>(m_dataset, params, distance);
+
+    FLANN_DISTANCE_CHECK
+
+    nnIndex->buildIndex();
+}
+
+template <typename Distance>
+GenericIndex<Distance>::~GenericIndex()
+{
+    delete nnIndex;
+}
+
+template <typename Distance>
+void GenericIndex<Distance>::knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
+{
+    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
+    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
+    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
+
+    FLANN_DISTANCE_CHECK
+
+    nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
+}
+
+
+template <typename Distance>
+void GenericIndex<Distance>::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
+{
+    CV_Assert(queries.type() == CvType<ElementType>::type());
+    CV_Assert(queries.isContinuous());
+    ::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
+
+    CV_Assert(indices.type() == CV_32S);
+    CV_Assert(indices.isContinuous());
+    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
+
+    CV_Assert(dists.type() == CvType<DistanceType>::type());
+    CV_Assert(dists.isContinuous());
+    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
+
+    FLANN_DISTANCE_CHECK
+
+    nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
+}
+
+template <typename Distance>
+int GenericIndex<Distance>::radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
+{
+    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
+    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
+    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
+
+    FLANN_DISTANCE_CHECK
+
+    return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
+}
+
+template <typename Distance>
+int GenericIndex<Distance>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
+{
+    CV_Assert(query.type() == CvType<ElementType>::type());
+    CV_Assert(query.isContinuous());
+    ::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
+
+    CV_Assert(indices.type() == CV_32S);
+    CV_Assert(indices.isContinuous());
+    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
+
+    CV_Assert(dists.type() == CvType<DistanceType>::type());
+    CV_Assert(dists.isContinuous());
+    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
+
+    FLANN_DISTANCE_CHECK
+
+    return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
+}
+
+/**
+ * @deprecated Use GenericIndex class instead
+ */
+template <typename T>
+class Index_
+{
+public:
+    typedef typename L2<T>::ElementType ElementType;
+    typedef typename L2<T>::ResultType DistanceType;
+
+    CV_DEPRECATED Index_(const Mat& dataset, const ::cvflann::IndexParams& params)
+    {
+        printf("[WARNING] The cv::flann::Index_<T> class is deperecated, use cv::flann::GenericIndex<Distance> instead\n");
+
+        CV_Assert(dataset.type() == CvType<ElementType>::type());
+        CV_Assert(dataset.isContinuous());
+        ::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);
+
+        if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
+            nnIndex_L1 = NULL;
+            nnIndex_L2 = new ::cvflann::Index< L2<ElementType> >(m_dataset, params);
+        }
+        else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
+            nnIndex_L1 = new ::cvflann::Index< L1<ElementType> >(m_dataset, params);
+            nnIndex_L2 = NULL;
+        }
+        else {
+            printf("[ERROR] cv::flann::Index_<T> only provides backwards compatibility for the L1 and L2 distances. "
+                   "For other distance types you must use cv::flann::GenericIndex<Distance>\n");
+            CV_Assert(0);
+        }
+        if (nnIndex_L1) nnIndex_L1->buildIndex();
+        if (nnIndex_L2) nnIndex_L2->buildIndex();
+    }
+    CV_DEPRECATED ~Index_()
+    {
+        if (nnIndex_L1) delete nnIndex_L1;
+        if (nnIndex_L2) delete nnIndex_L2;
+    }
+
+    CV_DEPRECATED void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
+    {
+        ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
+        ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
+        ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
+
+        if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
+        if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
+    }
+    CV_DEPRECATED void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
+    {
+        CV_Assert(queries.type() == CvType<ElementType>::type());
+        CV_Assert(queries.isContinuous());
+        ::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
+
+        CV_Assert(indices.type() == CV_32S);
+        CV_Assert(indices.isContinuous());
+        ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
+
+        CV_Assert(dists.type() == CvType<DistanceType>::type());
+        CV_Assert(dists.isContinuous());
+        ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
+
+        if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
+        if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
+    }
+
+    CV_DEPRECATED int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
+    {
+        ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
+        ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
+        ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
+
+        if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
+        if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
+    }
+
+    CV_DEPRECATED int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
+    {
+        CV_Assert(query.type() == CvType<ElementType>::type());
+        CV_Assert(query.isContinuous());
+        ::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
+
+        CV_Assert(indices.type() == CV_32S);
+        CV_Assert(indices.isContinuous());
+        ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
+
+        CV_Assert(dists.type() == CvType<DistanceType>::type());
+        CV_Assert(dists.isContinuous());
+        ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
+
+        if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
+        if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
+    }
+
+    CV_DEPRECATED void save(String filename)
+    {
+        if (nnIndex_L1) nnIndex_L1->save(filename);
+        if (nnIndex_L2) nnIndex_L2->save(filename);
+    }
+
+    CV_DEPRECATED int veclen() const
+    {
+        if (nnIndex_L1) return nnIndex_L1->veclen();
+        if (nnIndex_L2) return nnIndex_L2->veclen();
+    }
+
+    CV_DEPRECATED int size() const
+    {
+        if (nnIndex_L1) return nnIndex_L1->size();
+        if (nnIndex_L2) return nnIndex_L2->size();
+    }
+
+    CV_DEPRECATED ::cvflann::IndexParams getParameters()
+    {
+        if (nnIndex_L1) return nnIndex_L1->getParameters();
+        if (nnIndex_L2) return nnIndex_L2->getParameters();
+
+    }
+
+    CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters()
+    {
+        if (nnIndex_L1) return nnIndex_L1->getIndexParameters();
+        if (nnIndex_L2) return nnIndex_L2->getIndexParameters();
+    }
+
+private:
+    // providing backwards compatibility for L2 and L1 distances (most common)
+    ::cvflann::Index< L2<ElementType> >* nnIndex_L2;
+    ::cvflann::Index< L1<ElementType> >* nnIndex_L1;
+};
+
+//! @endcond
+
+/** @brief Clusters features using hierarchical k-means algorithm.
+
+@param features The points to be clustered. The matrix must have elements of type
+Distance::ElementType.
+@param centers The centers of the clusters obtained. The matrix must have type
+Distance::CentersType. The number of rows in this matrix represents the number of clusters desired,
+however, because of the way the cut in the hierarchical tree is chosen, the number of clusters
+computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of
+clusters desired, where branching is the tree's branching factor (see description of the
+KMeansIndexParams).
+@param params Parameters used in the construction of the hierarchical k-means tree.
+@param d Distance to be used for clustering.
+
+The method clusters the given feature vectors by constructing a hierarchical k-means tree and
+choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters
+found.
+ */
+template <typename Distance>
+int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params,
+                           Distance d = Distance())
+{
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::CentersType CentersType;
+
+    CV_Assert(features.type() == CvType<ElementType>::type());
+    CV_Assert(features.isContinuous());
+    ::cvflann::Matrix<ElementType> m_features((ElementType*)features.ptr<ElementType>(0), features.rows, features.cols);
+
+    CV_Assert(centers.type() == CvType<CentersType>::type());
+    CV_Assert(centers.isContinuous());
+    ::cvflann::Matrix<CentersType> m_centers((CentersType*)centers.ptr<CentersType>(0), centers.rows, centers.cols);
+
+    return ::cvflann::hierarchicalClustering<Distance>(m_features, m_centers, params, d);
+}
+
+//! @cond IGNORED
+
+template <typename ELEM_TYPE, typename DIST_TYPE>
+CV_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params)
+{
+    printf("[WARNING] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> is deprecated, use "
+        "cv::flann::hierarchicalClustering<Distance> instead\n");
+
+    if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
+        return hierarchicalClustering< L2<ELEM_TYPE> >(features, centers, params);
+    }
+    else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
+        return hierarchicalClustering< L1<ELEM_TYPE> >(features, centers, params);
+    }
+    else {
+        printf("[ERROR] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> only provides backwards "
+        "compatibility for the L1 and L2 distances. "
+        "For other distance types you must use cv::flann::hierarchicalClustering<Distance>\n");
+        CV_Assert(0);
+    }
+}
+
+//! @endcond
+
+//! @} flann
+
+} } // namespace cv::flann
+
+#endif
diff --git a/3rdParty/include/opencv2/flann/all_indices.h b/3rdParty/include/opencv2/flann/all_indices.h
new file mode 100644
index 0000000..03877ab
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/all_indices.h
@@ -0,0 +1,162 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef OPENCV_FLANN_ALL_INDICES_H_
+#define OPENCV_FLANN_ALL_INDICES_H_
+
+//! @cond IGNORED
+
+#include "general.h"
+
+#include "nn_index.h"
+#include "kdtree_index.h"
+#include "kdtree_single_index.h"
+#include "kmeans_index.h"
+#include "composite_index.h"
+#include "linear_index.h"
+#include "hierarchical_clustering_index.h"
+#include "lsh_index.h"
+#include "autotuned_index.h"
+
+
+namespace cvflann
+{
+
+template<typename KDTreeCapability, typename VectorSpace, typename Distance>
+struct index_creator
+{
+    static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
+    {
+        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
+
+        NNIndex<Distance>* nnIndex;
+        switch (index_type) {
+        case FLANN_INDEX_LINEAR:
+            nnIndex = new LinearIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_KDTREE_SINGLE:
+            nnIndex = new KDTreeSingleIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_KDTREE:
+            nnIndex = new KDTreeIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_KMEANS:
+            nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_COMPOSITE:
+            nnIndex = new CompositeIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_AUTOTUNED:
+            nnIndex = new AutotunedIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_HIERARCHICAL:
+            nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_LSH:
+            nnIndex = new LshIndex<Distance>(dataset, params, distance);
+            break;
+        default:
+            FLANN_THROW(cv::Error::StsBadArg, "Unknown index type");
+        }
+
+        return nnIndex;
+    }
+};
+
+template<typename VectorSpace, typename Distance>
+struct index_creator<False,VectorSpace,Distance>
+{
+    static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
+    {
+        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
+
+        NNIndex<Distance>* nnIndex;
+        switch (index_type) {
+        case FLANN_INDEX_LINEAR:
+            nnIndex = new LinearIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_KMEANS:
+            nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_HIERARCHICAL:
+            nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_LSH:
+            nnIndex = new LshIndex<Distance>(dataset, params, distance);
+            break;
+        default:
+            FLANN_THROW(cv::Error::StsBadArg, "Unknown index type");
+        }
+
+        return nnIndex;
+    }
+};
+
+template<typename Distance>
+struct index_creator<False,False,Distance>
+{
+    static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
+    {
+        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
+
+        NNIndex<Distance>* nnIndex;
+        switch (index_type) {
+        case FLANN_INDEX_LINEAR:
+            nnIndex = new LinearIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_KMEANS:
+            nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_HIERARCHICAL:
+            nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
+            break;
+        case FLANN_INDEX_LSH:
+            nnIndex = new LshIndex<Distance>(dataset, params, distance);
+            break;
+        default:
+            FLANN_THROW(cv::Error::StsBadArg, "Unknown index type");
+        }
+
+        return nnIndex;
+    }
+};
+
+template<typename Distance>
+NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
+{
+    return index_creator<typename Distance::is_kdtree_distance,
+                         typename Distance::is_vector_space_distance,
+                         Distance>::create(dataset, params,distance);
+}
+
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_ALL_INDICES_H_ */
diff --git a/3rdParty/include/opencv2/flann/allocator.h b/3rdParty/include/opencv2/flann/allocator.h
new file mode 100644
index 0000000..d5870a0
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/allocator.h
@@ -0,0 +1,196 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_ALLOCATOR_H_
+#define OPENCV_FLANN_ALLOCATOR_H_
+
+//! @cond IGNORED
+
+#include <stdlib.h>
+#include <stdio.h>
+
+
+namespace cvflann
+{
+
+/**
+ * Allocates (using C's malloc) a generic type T.
+ *
+ * Params:
+ *     count = number of instances to allocate.
+ * Returns: pointer (of type T*) to memory buffer
+ */
+template <typename T>
+T* allocate(size_t count = 1)
+{
+    T* mem = (T*) ::malloc(sizeof(T)*count);
+    return mem;
+}
+
+
+/**
+ * Pooled storage allocator
+ *
+ * The following routines allow for the efficient allocation of storage in
+ * small chunks from a specified pool.  Rather than allowing each structure
+ * to be freed individually, an entire pool of storage is freed at once.
+ * This method has two advantages over just using malloc() and free().  First,
+ * it is far more efficient for allocating small objects, as there is
+ * no overhead for remembering all the information needed to free each
+ * object or consolidating fragmented memory.  Second, the decision about
+ * how long to keep an object is made at the time of allocation, and there
+ * is no need to track down all the objects to free them.
+ *
+ */
+
+const size_t     WORDSIZE=16;
+const  size_t     BLOCKSIZE=8192;
+
+class PooledAllocator
+{
+    /* We maintain memory alignment to word boundaries by requiring that all
+        allocations be in multiples of the machine wordsize.  */
+    /* Size of machine word in bytes.  Must be power of 2. */
+    /* Minimum number of bytes requested at a time from	the system.  Must be multiple of WORDSIZE. */
+
+
+    int     remaining;  /* Number of bytes left in current block of storage. */
+    void*   base;     /* Pointer to base of current block of storage. */
+    void*   loc;      /* Current location in block to next allocate memory. */
+    int     blocksize;
+
+
+public:
+    int     usedMemory;
+    int     wastedMemory;
+
+    /**
+        Default constructor. Initializes a new pool.
+     */
+    PooledAllocator(int blockSize = BLOCKSIZE)
+    {
+        blocksize = blockSize;
+        remaining = 0;
+        base = NULL;
+        loc = NULL;
+
+        usedMemory = 0;
+        wastedMemory = 0;
+    }
+
+    /**
+     * Destructor. Frees all the memory allocated in this pool.
+     */
+    ~PooledAllocator()
+    {
+        void* prev;
+
+        while (base != NULL) {
+            prev = *((void**) base); /* Get pointer to prev block. */
+            ::free(base);
+            base = prev;
+        }
+    }
+
+    /**
+     * Returns a pointer to a piece of new memory of the given size in bytes
+     * allocated from the pool.
+     */
+    void* allocateMemory(int size)
+    {
+        int blockSize;
+
+        /* Round size up to a multiple of wordsize.  The following expression
+            only works for WORDSIZE that is a power of 2, by masking last bits of
+            incremented size to zero.
+         */
+        size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
+
+        /* Check whether a new block must be allocated.  Note that the first word
+            of a block is reserved for a pointer to the previous block.
+         */
+        if (size > remaining) {
+
+            wastedMemory += remaining;
+
+            /* Allocate new storage. */
+            blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
+                        size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
+
+            // use the standard C malloc to allocate memory
+            void* m = ::malloc(blockSize);
+            if (!m) {
+                fprintf(stderr,"Failed to allocate memory.\n");
+                return NULL;
+            }
+
+            /* Fill first word of new block with pointer to previous block. */
+            ((void**) m)[0] = base;
+            base = m;
+
+            int shift = 0;
+            //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
+
+            remaining = blockSize - sizeof(void*) - shift;
+            loc = ((char*)m + sizeof(void*) + shift);
+        }
+        void* rloc = loc;
+        loc = (char*)loc + size;
+        remaining -= size;
+
+        usedMemory += size;
+
+        return rloc;
+    }
+
+    /**
+     * Allocates (using this pool) a generic type T.
+     *
+     * Params:
+     *     count = number of instances to allocate.
+     * Returns: pointer (of type T*) to memory buffer
+     */
+    template <typename T>
+    T* allocate(size_t count = 1)
+    {
+        T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count));
+        return mem;
+    }
+
+private:
+    PooledAllocator(const PooledAllocator &); // copy disabled
+    PooledAllocator& operator=(const PooledAllocator &); // assign disabled
+};
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_ALLOCATOR_H_
diff --git a/3rdParty/include/opencv2/flann/any.h b/3rdParty/include/opencv2/flann/any.h
new file mode 100644
index 0000000..4906fec
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/any.h
@@ -0,0 +1,332 @@
+#ifndef OPENCV_FLANN_ANY_H_
+#define OPENCV_FLANN_ANY_H_
+/*
+ * (C) Copyright Christopher Diggins 2005-2011
+ * (C) Copyright Pablo Aguilar 2005
+ * (C) Copyright Kevlin Henney 2001
+ *
+ * Distributed under the Boost Software License, Version 1.0. (See
+ * accompanying file LICENSE_1_0.txt or copy at
+ * http://www.boost.org/LICENSE_1_0.txt
+ *
+ * Adapted for FLANN by Marius Muja
+ */
+
+//! @cond IGNORED
+
+#include "defines.h"
+#include <stdexcept>
+#include <ostream>
+#include <typeinfo>
+
+namespace cvflann
+{
+
+namespace anyimpl
+{
+
+struct bad_any_cast
+{
+};
+
+struct empty_any
+{
+};
+
+inline std::ostream& operator <<(std::ostream& out, const empty_any&)
+{
+    out << "[empty_any]";
+    return out;
+}
+
+struct base_any_policy
+{
+    virtual void static_delete(void** x) = 0;
+    virtual void copy_from_value(void const* src, void** dest) = 0;
+    virtual void clone(void* const* src, void** dest) = 0;
+    virtual void move(void* const* src, void** dest) = 0;
+    virtual void* get_value(void** src) = 0;
+    virtual const void* get_value(void* const * src) = 0;
+    virtual ::size_t get_size() = 0;
+    virtual const std::type_info& type() = 0;
+    virtual void print(std::ostream& out, void* const* src) = 0;
+    virtual ~base_any_policy() {}
+};
+
+template<typename T>
+struct typed_base_any_policy : base_any_policy
+{
+    virtual ::size_t get_size() CV_OVERRIDE { return sizeof(T); }
+    virtual const std::type_info& type() CV_OVERRIDE { return typeid(T); }
+
+};
+
+template<typename T>
+struct small_any_policy CV_FINAL : typed_base_any_policy<T>
+{
+    virtual void static_delete(void**) CV_OVERRIDE { }
+    virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE
+    {
+        new (dest) T(* reinterpret_cast<T const*>(src));
+    }
+    virtual void clone(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; }
+    virtual void move(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; }
+    virtual void* get_value(void** src) CV_OVERRIDE { return reinterpret_cast<void*>(src); }
+    virtual const void* get_value(void* const * src) CV_OVERRIDE { return reinterpret_cast<const void*>(src); }
+    virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast<T const*>(src); }
+};
+
+template<typename T>
+struct big_any_policy CV_FINAL : typed_base_any_policy<T>
+{
+    virtual void static_delete(void** x) CV_OVERRIDE
+    {
+        if (* x) delete (* reinterpret_cast<T**>(x));
+        *x = NULL;
+    }
+    virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE
+    {
+        *dest = new T(*reinterpret_cast<T const*>(src));
+    }
+    virtual void clone(void* const* src, void** dest) CV_OVERRIDE
+    {
+        *dest = new T(**reinterpret_cast<T* const*>(src));
+    }
+    virtual void move(void* const* src, void** dest) CV_OVERRIDE
+    {
+        (*reinterpret_cast<T**>(dest))->~T();
+        **reinterpret_cast<T**>(dest) = **reinterpret_cast<T* const*>(src);
+    }
+    virtual void* get_value(void** src) CV_OVERRIDE { return *src; }
+    virtual const void* get_value(void* const * src) CV_OVERRIDE { return *src; }
+    virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast<T const*>(*src); }
+};
+
+template<> inline void big_any_policy<flann_centers_init_t>::print(std::ostream& out, void* const* src)
+{
+    out << int(*reinterpret_cast<flann_centers_init_t const*>(*src));
+}
+
+template<> inline void big_any_policy<flann_algorithm_t>::print(std::ostream& out, void* const* src)
+{
+    out << int(*reinterpret_cast<flann_algorithm_t const*>(*src));
+}
+
+template<> inline void big_any_policy<cv::String>::print(std::ostream& out, void* const* src)
+{
+    out << (*reinterpret_cast<cv::String const*>(*src)).c_str();
+}
+
+template<typename T>
+struct choose_policy
+{
+    typedef big_any_policy<T> type;
+};
+
+template<typename T>
+struct choose_policy<T*>
+{
+    typedef small_any_policy<T*> type;
+};
+
+struct any;
+
+/// Choosing the policy for an any type is illegal, but should never happen.
+/// This is designed to throw a compiler error.
+template<>
+struct choose_policy<any>
+{
+    typedef void type;
+};
+
+/// Specializations for small types.
+#define SMALL_POLICY(TYPE) \
+    template<> \
+    struct choose_policy<TYPE> { typedef small_any_policy<TYPE> type; \
+    }
+
+SMALL_POLICY(signed char);
+SMALL_POLICY(unsigned char);
+SMALL_POLICY(signed short);
+SMALL_POLICY(unsigned short);
+SMALL_POLICY(signed int);
+SMALL_POLICY(unsigned int);
+SMALL_POLICY(signed long);
+SMALL_POLICY(unsigned long);
+SMALL_POLICY(float);
+SMALL_POLICY(bool);
+
+#undef SMALL_POLICY
+
+template <typename T>
+class SinglePolicy
+{
+    SinglePolicy();
+    SinglePolicy(const SinglePolicy& other);
+    SinglePolicy& operator=(const SinglePolicy& other);
+
+public:
+    static base_any_policy* get_policy();
+};
+
+/// This function will return a different policy for each type.
+template <typename T>
+inline base_any_policy* SinglePolicy<T>::get_policy()
+{
+    static typename choose_policy<T>::type policy;
+    return &policy;
+}
+
+} // namespace anyimpl
+
+struct any
+{
+private:
+    // fields
+    anyimpl::base_any_policy* policy;
+    void* object;
+
+public:
+    /// Initializing constructor.
+    template <typename T>
+    any(const T& x)
+        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
+    {
+        assign(x);
+    }
+
+    /// Empty constructor.
+    any()
+        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
+    { }
+
+    /// Special initializing constructor for string literals.
+    any(const char* x)
+        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
+    {
+        assign(x);
+    }
+
+    /// Copy constructor.
+    any(const any& x)
+        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
+    {
+        assign(x);
+    }
+
+    /// Destructor.
+    ~any()
+    {
+        policy->static_delete(&object);
+    }
+
+    /// Assignment function from another any.
+    any& assign(const any& x)
+    {
+        reset();
+        policy = x.policy;
+        policy->clone(&x.object, &object);
+        return *this;
+    }
+
+    /// Assignment function.
+    template <typename T>
+    any& assign(const T& x)
+    {
+        reset();
+        policy = anyimpl::SinglePolicy<T>::get_policy();
+        policy->copy_from_value(&x, &object);
+        return *this;
+    }
+
+    /// Assignment operator.
+    template<typename T>
+    any& operator=(const T& x)
+    {
+        return assign(x);
+    }
+
+    /// Assignment operator. Template-based version above doesn't work as expected. We need regular assignment operator here.
+    any& operator=(const any& x)
+    {
+        return assign(x);
+    }
+
+    /// Assignment operator, specialed for literal strings.
+    /// They have types like const char [6] which don't work as expected.
+    any& operator=(const char* x)
+    {
+        return assign(x);
+    }
+
+    /// Utility functions
+    any& swap(any& x)
+    {
+        std::swap(policy, x.policy);
+        std::swap(object, x.object);
+        return *this;
+    }
+
+    /// Cast operator. You can only cast to the original type.
+    template<typename T>
+    T& cast()
+    {
+        if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
+        T* r = reinterpret_cast<T*>(policy->get_value(&object));
+        return *r;
+    }
+
+    /// Cast operator. You can only cast to the original type.
+    template<typename T>
+    const T& cast() const
+    {
+        if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
+        const T* r = reinterpret_cast<const T*>(policy->get_value(&object));
+        return *r;
+    }
+
+    /// Returns true if the any contains no value.
+    bool empty() const
+    {
+        return policy->type() == typeid(anyimpl::empty_any);
+    }
+
+    /// Frees any allocated memory, and sets the value to NULL.
+    void reset()
+    {
+        policy->static_delete(&object);
+        policy = anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy();
+    }
+
+    /// Returns true if the two types are the same.
+    bool compatible(const any& x) const
+    {
+        return policy->type() == x.policy->type();
+    }
+
+    /// Returns if the type is compatible with the policy
+    template<typename T>
+    bool has_type()
+    {
+        return policy->type() == typeid(T);
+    }
+
+    const std::type_info& type() const
+    {
+        return policy->type();
+    }
+
+    friend std::ostream& operator <<(std::ostream& out, const any& any_val);
+};
+
+inline std::ostream& operator <<(std::ostream& out, const any& any_val)
+{
+    any_val.policy->print(out,&any_val.object);
+    return out;
+}
+
+}
+
+//! @endcond
+
+#endif // OPENCV_FLANN_ANY_H_
diff --git a/3rdParty/include/opencv2/flann/autotuned_index.h b/3rdParty/include/opencv2/flann/autotuned_index.h
new file mode 100644
index 0000000..d90f739
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/autotuned_index.h
@@ -0,0 +1,594 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_
+#define OPENCV_FLANN_AUTOTUNED_INDEX_H_
+
+//! @cond IGNORED
+
+#include <sstream>
+
+#include "nn_index.h"
+#include "ground_truth.h"
+#include "index_testing.h"
+#include "sampling.h"
+#include "kdtree_index.h"
+#include "kdtree_single_index.h"
+#include "kmeans_index.h"
+#include "composite_index.h"
+#include "linear_index.h"
+#include "logger.h"
+
+namespace cvflann
+{
+
+template<typename Distance>
+NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance);
+
+
+struct AutotunedIndexParams : public IndexParams
+{
+    AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED;
+        // precision desired (used for autotuning, -1 otherwise)
+        (*this)["target_precision"] = target_precision;
+        // build tree time weighting factor
+        (*this)["build_weight"] = build_weight;
+        // index memory weighting factor
+        (*this)["memory_weight"] = memory_weight;
+        // what fraction of the dataset to use for autotuning
+        (*this)["sample_fraction"] = sample_fraction;
+    }
+};
+
+
+template <typename Distance>
+class AutotunedIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    AutotunedIndex(const Matrix<ElementType>& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) :
+        dataset_(inputData), distance_(d)
+    {
+        target_precision_ = get_param(params, "target_precision",0.8f);
+        build_weight_ =  get_param(params,"build_weight", 0.01f);
+        memory_weight_ = get_param(params, "memory_weight", 0.0f);
+        sample_fraction_ = get_param(params,"sample_fraction", 0.1f);
+        bestIndex_ = NULL;
+        speedup_ = 0;
+    }
+
+    AutotunedIndex(const AutotunedIndex&);
+    AutotunedIndex& operator=(const AutotunedIndex&);
+
+    virtual ~AutotunedIndex()
+    {
+        if (bestIndex_ != NULL) {
+            delete bestIndex_;
+            bestIndex_ = NULL;
+        }
+    }
+
+    /**
+     *          Method responsible with building the index.
+     */
+    virtual void buildIndex() CV_OVERRIDE
+    {
+        std::ostringstream stream;
+        bestParams_ = estimateBuildParams();
+        print_params(bestParams_, stream);
+        Logger::info("----------------------------------------------------\n");
+        Logger::info("Autotuned parameters:\n");
+        Logger::info("%s", stream.str().c_str());
+        Logger::info("----------------------------------------------------\n");
+
+        bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_);
+        bestIndex_->buildIndex();
+        speedup_ = estimateSearchParams(bestSearchParams_);
+        stream.str(std::string());
+        print_params(bestSearchParams_, stream);
+        Logger::info("----------------------------------------------------\n");
+        Logger::info("Search parameters:\n");
+        Logger::info("%s", stream.str().c_str());
+        Logger::info("----------------------------------------------------\n");
+    }
+
+    /**
+     *  Saves the index to a stream
+     */
+    virtual void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        save_value(stream, (int)bestIndex_->getType());
+        bestIndex_->saveIndex(stream);
+        save_value(stream, get_param<int>(bestSearchParams_, "checks"));
+    }
+
+    /**
+     *  Loads the index from a stream
+     */
+    virtual void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        int index_type;
+
+        load_value(stream, index_type);
+        IndexParams params;
+        params["algorithm"] = (flann_algorithm_t)index_type;
+        bestIndex_ = create_index_by_type<Distance>(dataset_, params, distance_);
+        bestIndex_->loadIndex(stream);
+        int checks;
+        load_value(stream, checks);
+        bestSearchParams_["checks"] = checks;
+    }
+
+    /**
+     *      Method that searches for nearest-neighbors
+     */
+    virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
+    {
+        int checks = get_param<int>(searchParams,"checks",FLANN_CHECKS_AUTOTUNED);
+        if (checks == FLANN_CHECKS_AUTOTUNED) {
+            bestIndex_->findNeighbors(result, vec, bestSearchParams_);
+        }
+        else {
+            bestIndex_->findNeighbors(result, vec, searchParams);
+        }
+    }
+
+
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return bestIndex_->getParameters();
+    }
+
+    SearchParams getSearchParameters() const
+    {
+        return bestSearchParams_;
+    }
+
+    float getSpeedup() const
+    {
+        return speedup_;
+    }
+
+
+    /**
+     *      Number of features in this index.
+     */
+    virtual size_t size() const CV_OVERRIDE
+    {
+        return bestIndex_->size();
+    }
+
+    /**
+     *  The length of each vector in this index.
+     */
+    virtual size_t veclen() const CV_OVERRIDE
+    {
+        return bestIndex_->veclen();
+    }
+
+    /**
+     * The amount of memory (in bytes) this index uses.
+     */
+    virtual int usedMemory() const CV_OVERRIDE
+    {
+        return bestIndex_->usedMemory();
+    }
+
+    /**
+     * Algorithm name
+     */
+    virtual flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_AUTOTUNED;
+    }
+
+private:
+
+    struct CostData
+    {
+        float searchTimeCost;
+        float buildTimeCost;
+        float memoryCost;
+        float totalCost;
+        IndexParams params;
+    };
+
+    void evaluate_kmeans(CostData& cost)
+    {
+        StartStopTimer t;
+        int checks;
+        const int nn = 1;
+
+        Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n",
+                     get_param<int>(cost.params,"iterations"),
+                     get_param<int>(cost.params,"branching"));
+        KMeansIndex<Distance> kmeans(sampledDataset_, cost.params, distance_);
+        // measure index build time
+        t.start();
+        kmeans.buildIndex();
+        t.stop();
+        float buildTime = (float)t.value;
+
+        // measure search time
+        float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
+
+        float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
+        cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory;
+        cost.searchTimeCost = searchTime;
+        cost.buildTimeCost = buildTime;
+        Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_);
+    }
+
+
+    void evaluate_kdtree(CostData& cost)
+    {
+        StartStopTimer t;
+        int checks;
+        const int nn = 1;
+
+        Logger::info("KDTree using params: trees=%d\n", get_param<int>(cost.params,"trees"));
+        KDTreeIndex<Distance> kdtree(sampledDataset_, cost.params, distance_);
+
+        t.start();
+        kdtree.buildIndex();
+        t.stop();
+        float buildTime = (float)t.value;
+
+        //measure search time
+        float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
+
+        float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
+        cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory;
+        cost.searchTimeCost = searchTime;
+        cost.buildTimeCost = buildTime;
+        Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime);
+    }
+
+
+    //    struct KMeansSimpleDownhillFunctor {
+    //
+    //        Autotune& autotuner;
+    //        KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {}
+    //
+    //        float operator()(int* params) {
+    //
+    //            float maxFloat = numeric_limits<float>::max();
+    //
+    //            if (params[0]<2) return maxFloat;
+    //            if (params[1]<0) return maxFloat;
+    //
+    //            CostData c;
+    //            c.params["algorithm"] = KMEANS;
+    //            c.params["centers-init"] = CENTERS_RANDOM;
+    //            c.params["branching"] = params[0];
+    //            c.params["max-iterations"] = params[1];
+    //
+    //            autotuner.evaluate_kmeans(c);
+    //
+    //            return c.timeCost;
+    //
+    //        }
+    //    };
+    //
+    //    struct KDTreeSimpleDownhillFunctor {
+    //
+    //        Autotune& autotuner;
+    //        KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {}
+    //
+    //        float operator()(int* params) {
+    //            float maxFloat = numeric_limits<float>::max();
+    //
+    //            if (params[0]<1) return maxFloat;
+    //
+    //            CostData c;
+    //            c.params["algorithm"] = KDTREE;
+    //            c.params["trees"] = params[0];
+    //
+    //            autotuner.evaluate_kdtree(c);
+    //
+    //            return c.timeCost;
+    //
+    //        }
+    //    };
+
+
+
+    void optimizeKMeans(std::vector<CostData>& costs)
+    {
+        Logger::info("KMEANS, Step 1: Exploring parameter space\n");
+
+        // explore kmeans parameters space using combinations of the parameters below
+        int maxIterations[] = { 1, 5, 10, 15 };
+        int branchingFactors[] = { 16, 32, 64, 128, 256 };
+
+        int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors);
+        costs.reserve(costs.size() + kmeansParamSpaceSize);
+
+        // evaluate kmeans for all parameter combinations
+        for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) {
+            for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) {
+                CostData cost;
+                cost.params["algorithm"] = FLANN_INDEX_KMEANS;
+                cost.params["centers_init"] = FLANN_CENTERS_RANDOM;
+                cost.params["iterations"] = maxIterations[i];
+                cost.params["branching"] = branchingFactors[j];
+
+                evaluate_kmeans(cost);
+                costs.push_back(cost);
+            }
+        }
+
+        //         Logger::info("KMEANS, Step 2: simplex-downhill optimization\n");
+        //
+        //         const int n = 2;
+        //         // choose initial simplex points as the best parameters so far
+        //         int kmeansNMPoints[n*(n+1)];
+        //         float kmeansVals[n+1];
+        //         for (int i=0;i<n+1;++i) {
+        //             kmeansNMPoints[i*n] = (int)kmeansCosts[i].params["branching"];
+        //             kmeansNMPoints[i*n+1] = (int)kmeansCosts[i].params["max-iterations"];
+        //             kmeansVals[i] = kmeansCosts[i].timeCost;
+        //         }
+        //         KMeansSimpleDownhillFunctor kmeans_cost_func(*this);
+        //         // run optimization
+        //         optimizeSimplexDownhill(kmeansNMPoints,n,kmeans_cost_func,kmeansVals);
+        //         // store results
+        //         for (int i=0;i<n+1;++i) {
+        //             kmeansCosts[i].params["branching"] = kmeansNMPoints[i*2];
+        //             kmeansCosts[i].params["max-iterations"] = kmeansNMPoints[i*2+1];
+        //             kmeansCosts[i].timeCost = kmeansVals[i];
+        //         }
+    }
+
+
+    void optimizeKDTree(std::vector<CostData>& costs)
+    {
+        Logger::info("KD-TREE, Step 1: Exploring parameter space\n");
+
+        // explore kd-tree parameters space using the parameters below
+        int testTrees[] = { 1, 4, 8, 16, 32 };
+
+        // evaluate kdtree for all parameter combinations
+        for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) {
+            CostData cost;
+            cost.params["algorithm"] = FLANN_INDEX_KDTREE;
+            cost.params["trees"] = testTrees[i];
+
+            evaluate_kdtree(cost);
+            costs.push_back(cost);
+        }
+
+        //         Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n");
+        //
+        //         const int n = 1;
+        //         // choose initial simplex points as the best parameters so far
+        //         int kdtreeNMPoints[n*(n+1)];
+        //         float kdtreeVals[n+1];
+        //         for (int i=0;i<n+1;++i) {
+        //             kdtreeNMPoints[i] = (int)kdtreeCosts[i].params["trees"];
+        //             kdtreeVals[i] = kdtreeCosts[i].timeCost;
+        //         }
+        //         KDTreeSimpleDownhillFunctor kdtree_cost_func(*this);
+        //         // run optimization
+        //         optimizeSimplexDownhill(kdtreeNMPoints,n,kdtree_cost_func,kdtreeVals);
+        //         // store results
+        //         for (int i=0;i<n+1;++i) {
+        //             kdtreeCosts[i].params["trees"] = kdtreeNMPoints[i];
+        //             kdtreeCosts[i].timeCost = kdtreeVals[i];
+        //         }
+    }
+
+    /**
+     *  Chooses the best nearest-neighbor algorithm and estimates the optimal
+     *  parameters to use when building the index (for a given precision).
+     *  Returns a dictionary with the optimal parameters.
+     */
+    IndexParams estimateBuildParams()
+    {
+        std::vector<CostData> costs;
+
+        int sampleSize = int(sample_fraction_ * dataset_.rows);
+        int testSampleSize = std::min(sampleSize / 10, 1000);
+
+        Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_);
+
+        // For a very small dataset, it makes no sense to build any fancy index, just
+        // use linear search
+        if (testSampleSize < 10) {
+            Logger::info("Choosing linear, dataset too small\n");
+            return LinearIndexParams();
+        }
+
+        // We use a fraction of the original dataset to speedup the autotune algorithm
+        sampledDataset_ = random_sample(dataset_, sampleSize);
+        // We use a cross-validation approach, first we sample a testset from the dataset
+        testDataset_ = random_sample(sampledDataset_, testSampleSize, true);
+
+        // We compute the ground truth using linear search
+        Logger::info("Computing ground truth... \n");
+        gt_matches_ = Matrix<int>(new int[testDataset_.rows], testDataset_.rows, 1);
+        StartStopTimer t;
+        t.start();
+        compute_ground_truth<Distance>(sampledDataset_, testDataset_, gt_matches_, 0, distance_);
+        t.stop();
+
+        CostData linear_cost;
+        linear_cost.searchTimeCost = (float)t.value;
+        linear_cost.buildTimeCost = 0;
+        linear_cost.memoryCost = 0;
+        linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR;
+
+        costs.push_back(linear_cost);
+
+        // Start parameter autotune process
+        Logger::info("Autotuning parameters...\n");
+
+        optimizeKMeans(costs);
+        optimizeKDTree(costs);
+
+        float bestTimeCost = costs[0].searchTimeCost;
+        for (size_t i = 0; i < costs.size(); ++i) {
+            float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost;
+            if (timeCost < bestTimeCost) {
+                bestTimeCost = timeCost;
+            }
+        }
+
+        float bestCost = costs[0].searchTimeCost / bestTimeCost;
+        IndexParams bestParams = costs[0].params;
+        if (bestTimeCost > 0) {
+            for (size_t i = 0; i < costs.size(); ++i) {
+                float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost +
+                                memory_weight_ * costs[i].memoryCost;
+                if (crtCost < bestCost) {
+                    bestCost = crtCost;
+                    bestParams = costs[i].params;
+                }
+            }
+        }
+
+        delete[] gt_matches_.data;
+        delete[] testDataset_.data;
+        delete[] sampledDataset_.data;
+
+        return bestParams;
+    }
+
+
+
+    /**
+     *  Estimates the search time parameters needed to get the desired precision.
+     *  Precondition: the index is built
+     *  Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search.
+     */
+    float estimateSearchParams(SearchParams& searchParams)
+    {
+        const int nn = 1;
+        const size_t SAMPLE_COUNT = 1000;
+
+        CV_Assert(bestIndex_ != NULL && "Requires a valid index"); // must have a valid index
+
+        float speedup = 0;
+
+        int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT);
+        if (samples > 0) {
+            Matrix<ElementType> testDataset = random_sample(dataset_, samples);
+
+            Logger::info("Computing ground truth\n");
+
+            // we need to compute the ground truth first
+            Matrix<int> gt_matches(new int[testDataset.rows], testDataset.rows, 1);
+            StartStopTimer t;
+            t.start();
+            compute_ground_truth<Distance>(dataset_, testDataset, gt_matches, 1, distance_);
+            t.stop();
+            float linear = (float)t.value;
+
+            int checks;
+            Logger::info("Estimating number of checks\n");
+
+            float searchTime;
+            float cb_index;
+            if (bestIndex_->getType() == FLANN_INDEX_KMEANS) {
+                Logger::info("KMeans algorithm, estimating cluster border factor\n");
+                KMeansIndex<Distance>* kmeans = (KMeansIndex<Distance>*)bestIndex_;
+                float bestSearchTime = -1;
+                float best_cb_index = -1;
+                int best_checks = -1;
+                for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) {
+                    kmeans->set_cb_index(cb_index);
+                    searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
+                    if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) {
+                        bestSearchTime = searchTime;
+                        best_cb_index = cb_index;
+                        best_checks = checks;
+                    }
+                }
+                searchTime = bestSearchTime;
+                cb_index = best_cb_index;
+                checks = best_checks;
+
+                kmeans->set_cb_index(best_cb_index);
+                Logger::info("Optimum cb_index: %g\n", cb_index);
+                bestParams_["cb_index"] = cb_index;
+            }
+            else {
+                searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
+            }
+
+            Logger::info("Required number of checks: %d \n", checks);
+            searchParams["checks"] = checks;
+
+            speedup = linear / searchTime;
+
+            delete[] gt_matches.data;
+            delete[] testDataset.data;
+        }
+
+        return speedup;
+    }
+
+private:
+    NNIndex<Distance>* bestIndex_;
+
+    IndexParams bestParams_;
+    SearchParams bestSearchParams_;
+
+    Matrix<ElementType> sampledDataset_;
+    Matrix<ElementType> testDataset_;
+    Matrix<int> gt_matches_;
+
+    float speedup_;
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    /**
+     * Index parameters
+     */
+    float target_precision_;
+    float build_weight_;
+    float memory_weight_;
+    float sample_fraction_;
+
+    Distance distance_;
+
+
+};
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */
diff --git a/3rdParty/include/opencv2/flann/composite_index.h b/3rdParty/include/opencv2/flann/composite_index.h
new file mode 100644
index 0000000..f1af41a
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/composite_index.h
@@ -0,0 +1,197 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_
+#define OPENCV_FLANN_COMPOSITE_INDEX_H_
+
+//! @cond IGNORED
+
+#include "nn_index.h"
+#include "kdtree_index.h"
+#include "kmeans_index.h"
+
+namespace cvflann
+{
+
+/**
+ * Index parameters for the CompositeIndex.
+ */
+struct CompositeIndexParams : public IndexParams
+{
+    CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
+                         flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KMEANS;
+        // number of randomized trees to use (for kdtree)
+        (*this)["trees"] = trees;
+        // branching factor
+        (*this)["branching"] = branching;
+        // max iterations to perform in one kmeans clustering (kmeans tree)
+        (*this)["iterations"] = iterations;
+        // algorithm used for picking the initial cluster centers for kmeans tree
+        (*this)["centers_init"] = centers_init;
+        // cluster boundary index. Used when searching the kmeans tree
+        (*this)["cb_index"] = cb_index;
+    }
+};
+
+
+/**
+ * This index builds a kd-tree index and a k-means index and performs nearest
+ * neighbour search both indexes. This gives a slight boost in search performance
+ * as some of the neighbours that are missed by one index are found by the other.
+ */
+template <typename Distance>
+class CompositeIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    /**
+     * Index constructor
+     * @param inputData dataset containing the points to index
+     * @param params Index parameters
+     * @param d Distance functor
+     * @return
+     */
+    CompositeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = CompositeIndexParams(),
+                   Distance d = Distance()) : index_params_(params)
+    {
+        kdtree_index_ = new KDTreeIndex<Distance>(inputData, params, d);
+        kmeans_index_ = new KMeansIndex<Distance>(inputData, params, d);
+
+    }
+
+    CompositeIndex(const CompositeIndex&);
+    CompositeIndex& operator=(const CompositeIndex&);
+
+    virtual ~CompositeIndex()
+    {
+        delete kdtree_index_;
+        delete kmeans_index_;
+    }
+
+    /**
+     * @return The index type
+     */
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_COMPOSITE;
+    }
+
+    /**
+     * @return Size of the index
+     */
+    size_t size() const CV_OVERRIDE
+    {
+        return kdtree_index_->size();
+    }
+
+    /**
+     * \returns The dimensionality of the features in this index.
+     */
+    size_t veclen() const CV_OVERRIDE
+    {
+        return kdtree_index_->veclen();
+    }
+
+    /**
+     * \returns The amount of memory (in bytes) used by the index.
+     */
+    int usedMemory() const CV_OVERRIDE
+    {
+        return kmeans_index_->usedMemory() + kdtree_index_->usedMemory();
+    }
+
+    /**
+     * \brief Builds the index
+     */
+    void buildIndex() CV_OVERRIDE
+    {
+        Logger::info("Building kmeans tree...\n");
+        kmeans_index_->buildIndex();
+        Logger::info("Building kdtree tree...\n");
+        kdtree_index_->buildIndex();
+    }
+
+    /**
+     * \brief Saves the index to a stream
+     * \param stream The stream to save the index to
+     */
+    void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        kmeans_index_->saveIndex(stream);
+        kdtree_index_->saveIndex(stream);
+    }
+
+    /**
+     * \brief Loads the index from a stream
+     * \param stream The stream from which the index is loaded
+     */
+    void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        kmeans_index_->loadIndex(stream);
+        kdtree_index_->loadIndex(stream);
+    }
+
+    /**
+     * \returns The index parameters
+     */
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return index_params_;
+    }
+
+    /**
+     * \brief Method that searches for nearest-neighbours
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
+    {
+        kmeans_index_->findNeighbors(result, vec, searchParams);
+        kdtree_index_->findNeighbors(result, vec, searchParams);
+    }
+
+private:
+    /** The k-means index */
+    KMeansIndex<Distance>* kmeans_index_;
+
+    /** The kd-tree index */
+    KDTreeIndex<Distance>* kdtree_index_;
+
+    /** The index parameters */
+    const IndexParams index_params_;
+};
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_
diff --git a/3rdParty/include/opencv2/flann/config.h b/3rdParty/include/opencv2/flann/config.h
new file mode 100644
index 0000000..c9342c0
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/config.h
@@ -0,0 +1,42 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef OPENCV_FLANN_CONFIG_H_
+#define OPENCV_FLANN_CONFIG_H_
+
+//! @cond IGNORED
+
+#ifdef FLANN_VERSION_
+#undef FLANN_VERSION_
+#endif
+#define FLANN_VERSION_ "1.6.10"
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_CONFIG_H_ */
diff --git a/3rdParty/include/opencv2/flann/defines.h b/3rdParty/include/opencv2/flann/defines.h
new file mode 100644
index 0000000..8ab8329
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/defines.h
@@ -0,0 +1,169 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef OPENCV_FLANN_DEFINES_H_
+#define OPENCV_FLANN_DEFINES_H_
+
+//! @cond IGNORED
+
+#include "config.h"
+
+#ifdef FLANN_EXPORT
+#undef FLANN_EXPORT
+#endif
+#ifdef _WIN32
+/* win32 dll export/import directives */
+ #ifdef FLANN_EXPORTS
+  #define FLANN_EXPORT __declspec(dllexport)
+ #elif defined(FLANN_STATIC)
+  #define FLANN_EXPORT
+ #else
+  #define FLANN_EXPORT __declspec(dllimport)
+ #endif
+#else
+/* unix needs nothing */
+ #define FLANN_EXPORT
+#endif
+
+
+#undef FLANN_PLATFORM_32_BIT
+#undef FLANN_PLATFORM_64_BIT
+#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64
+#define FLANN_PLATFORM_64_BIT
+#else
+#define FLANN_PLATFORM_32_BIT
+#endif
+
+
+#undef FLANN_ARRAY_LEN
+#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0]))
+
+namespace cvflann {
+
+/* Nearest neighbour index algorithms */
+enum flann_algorithm_t
+{
+    FLANN_INDEX_LINEAR = 0,
+    FLANN_INDEX_KDTREE = 1,
+    FLANN_INDEX_KMEANS = 2,
+    FLANN_INDEX_COMPOSITE = 3,
+    FLANN_INDEX_KDTREE_SINGLE = 4,
+    FLANN_INDEX_HIERARCHICAL = 5,
+    FLANN_INDEX_LSH = 6,
+    FLANN_INDEX_SAVED = 254,
+    FLANN_INDEX_AUTOTUNED = 255,
+
+    // deprecated constants, should use the FLANN_INDEX_* ones instead
+    LINEAR = 0,
+    KDTREE = 1,
+    KMEANS = 2,
+    COMPOSITE = 3,
+    KDTREE_SINGLE = 4,
+    SAVED = 254,
+    AUTOTUNED = 255
+};
+
+
+
+enum flann_centers_init_t
+{
+    FLANN_CENTERS_RANDOM = 0,
+    FLANN_CENTERS_GONZALES = 1,
+    FLANN_CENTERS_KMEANSPP = 2,
+    FLANN_CENTERS_GROUPWISE = 3,
+
+    // deprecated constants, should use the FLANN_CENTERS_* ones instead
+    CENTERS_RANDOM = 0,
+    CENTERS_GONZALES = 1,
+    CENTERS_KMEANSPP = 2
+};
+
+enum flann_log_level_t
+{
+    FLANN_LOG_NONE = 0,
+    FLANN_LOG_FATAL = 1,
+    FLANN_LOG_ERROR = 2,
+    FLANN_LOG_WARN = 3,
+    FLANN_LOG_INFO = 4
+};
+
+enum flann_distance_t
+{
+    FLANN_DIST_EUCLIDEAN = 1,
+    FLANN_DIST_L2 = 1,
+    FLANN_DIST_MANHATTAN = 2,
+    FLANN_DIST_L1 = 2,
+    FLANN_DIST_MINKOWSKI = 3,
+    FLANN_DIST_MAX   = 4,
+    FLANN_DIST_HIST_INTERSECT   = 5,
+    FLANN_DIST_HELLINGER = 6,
+    FLANN_DIST_CHI_SQUARE = 7,
+    FLANN_DIST_CS         = 7,
+    FLANN_DIST_KULLBACK_LEIBLER  = 8,
+    FLANN_DIST_KL                = 8,
+    FLANN_DIST_HAMMING          = 9,
+    FLANN_DIST_DNAMMING          = 10,
+
+    // deprecated constants, should use the FLANN_DIST_* ones instead
+    EUCLIDEAN = 1,
+    MANHATTAN = 2,
+    MINKOWSKI = 3,
+    MAX_DIST   = 4,
+    HIST_INTERSECT   = 5,
+    HELLINGER = 6,
+    CS         = 7,
+    KL         = 8,
+    KULLBACK_LEIBLER  = 8
+};
+
+enum flann_datatype_t
+{
+    FLANN_INT8 = 0,
+    FLANN_INT16 = 1,
+    FLANN_INT32 = 2,
+    FLANN_INT64 = 3,
+    FLANN_UINT8 = 4,
+    FLANN_UINT16 = 5,
+    FLANN_UINT32 = 6,
+    FLANN_UINT64 = 7,
+    FLANN_FLOAT32 = 8,
+    FLANN_FLOAT64 = 9
+};
+
+enum
+{
+    FLANN_CHECKS_UNLIMITED = -1,
+    FLANN_CHECKS_AUTOTUNED = -2
+};
+
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_DEFINES_H_ */
diff --git a/3rdParty/include/opencv2/flann/dist.h b/3rdParty/include/opencv2/flann/dist.h
new file mode 100644
index 0000000..608f8a5
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/dist.h
@@ -0,0 +1,1292 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_DIST_H_
+#define OPENCV_FLANN_DIST_H_
+
+//! @cond IGNORED
+
+#include <cmath>
+#include <cstdlib>
+#include <string.h>
+#ifdef _MSC_VER
+typedef unsigned __int32 uint32_t;
+typedef unsigned __int64 uint64_t;
+#else
+#include <stdint.h>
+#endif
+
+#include "defines.h"
+
+#if defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64))
+# include <Intrin.h>
+#endif
+
+#if defined(__ARM_NEON__) && !defined(__CUDACC__)
+# include "arm_neon.h"
+#endif
+
+namespace cvflann
+{
+
+template<typename T>
+inline T abs(T x) { return (x<0) ? -x : x; }
+
+template<>
+inline int abs<int>(int x) { return ::abs(x); }
+
+template<>
+inline float abs<float>(float x) { return fabsf(x); }
+
+template<>
+inline double abs<double>(double x) { return fabs(x); }
+
+
+template<typename TargetType>
+inline TargetType round(float x) { return static_cast<TargetType>(x); }
+
+template<>
+inline unsigned int round<unsigned int>(float x) { return static_cast<unsigned int>(x + 0.5f); }
+
+template<>
+inline unsigned short round<unsigned short>(float x) { return static_cast<unsigned short>(x + 0.5f); }
+
+template<>
+inline unsigned char round<unsigned char>(float x) { return static_cast<unsigned char>(x + 0.5f); }
+
+template<>
+inline long long round<long long>(float x) { return static_cast<long long>(x + 0.5f); }
+
+template<>
+inline long round<long>(float x) { return static_cast<long>(x + 0.5f); }
+
+template<>
+inline int round<int>(float x) { return static_cast<int>(x + 0.5f) - (x<0); }
+
+template<>
+inline short round<short>(float x) { return static_cast<short>(x + 0.5f) - (x<0); }
+
+template<>
+inline char round<char>(float x) { return static_cast<char>(x + 0.5f) - (x<0); }
+
+
+template<typename TargetType>
+inline TargetType round(double x) { return static_cast<TargetType>(x); }
+
+template<>
+inline unsigned int round<unsigned int>(double x) { return static_cast<unsigned int>(x + 0.5); }
+
+template<>
+inline unsigned short round<unsigned short>(double x) { return static_cast<unsigned short>(x + 0.5); }
+
+template<>
+inline unsigned char round<unsigned char>(double x) { return static_cast<unsigned char>(x + 0.5); }
+
+template<>
+inline long long round<long long>(double x) { return static_cast<long long>(x + 0.5); }
+
+template<>
+inline long round<long>(double x) { return static_cast<long>(x + 0.5); }
+
+template<>
+inline int round<int>(double x) { return static_cast<int>(x + 0.5) - (x<0); }
+
+template<>
+inline short round<short>(double x) { return static_cast<short>(x + 0.5) - (x<0); }
+
+template<>
+inline char round<char>(double x) { return static_cast<char>(x + 0.5) - (x<0); }
+
+
+template<typename T>
+struct Accumulator { typedef T Type; };
+template<>
+struct Accumulator<unsigned char>  { typedef float Type; };
+template<>
+struct Accumulator<unsigned short> { typedef float Type; };
+template<>
+struct Accumulator<unsigned int> { typedef float Type; };
+template<>
+struct Accumulator<char>   { typedef float Type; };
+template<>
+struct Accumulator<short>  { typedef float Type; };
+template<>
+struct Accumulator<int> { typedef float Type; };
+
+#undef True
+#undef False
+
+class True
+{
+public:
+    static const bool val = true;
+};
+
+class False
+{
+public:
+    static const bool val = false;
+};
+
+
+/*
+ * This is a "zero iterator". It basically behaves like a zero filled
+ * array to all algorithms that use arrays as iterators (STL style).
+ * It's useful when there's a need to compute the distance between feature
+ * and origin it and allows for better compiler optimisation than using a
+ * zero-filled array.
+ */
+template <typename T>
+struct ZeroIterator
+{
+
+    T operator*()
+    {
+        return 0;
+    }
+
+    T operator[](int)
+    {
+        return 0;
+    }
+
+    const ZeroIterator<T>& operator ++()
+    {
+        return *this;
+    }
+
+    ZeroIterator<T> operator ++(int)
+    {
+        return *this;
+    }
+
+    ZeroIterator<T>& operator+=(int)
+    {
+        return *this;
+    }
+
+};
+
+
+
+/**
+ * Squared Euclidean distance functor.
+ *
+ * This is the simpler, unrolled version. This is preferable for
+ * very low dimensionality data (eg 3D points)
+ */
+template<class T>
+struct L2_Simple
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff;
+        for(size_t i = 0; i < size; ++i ) {
+            diff = (ResultType)(*a++ - *b++);
+            result += diff*diff;
+        }
+        return result;
+    }
+
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return (a-b)*(a-b);
+    }
+};
+
+
+
+/**
+ * Squared Euclidean distance functor, optimized version
+ */
+template<class T>
+struct L2
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    /**
+     *  Compute the squared Euclidean distance between two vectors.
+     *
+     *	This is highly optimised, with loop unrolling, as it is one
+     *	of the most expensive inner loops.
+     *
+     *	The computation of squared root at the end is omitted for
+     *	efficiency.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = (ResultType)(a[0] - b[0]);
+            diff1 = (ResultType)(a[1] - b[1]);
+            diff2 = (ResultType)(a[2] - b[2]);
+            diff3 = (ResultType)(a[3] - b[3]);
+            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = (ResultType)(*a++ - *b++);
+            result += diff0 * diff0;
+        }
+        return result;
+    }
+
+    /**
+     *	Partial euclidean distance, using just one dimension. This is used by the
+     *	kd-tree when computing partial distances while traversing the tree.
+     *
+     *	Squared root is omitted for efficiency.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return (a-b)*(a-b);
+    }
+};
+
+
+/*
+ * Manhattan distance functor, optimized version
+ */
+template<class T>
+struct L1
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    /**
+     *  Compute the Manhattan (L_1) distance between two vectors.
+     *
+     *	This is highly optimised, with loop unrolling, as it is one
+     *	of the most expensive inner loops.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = (ResultType)abs(a[0] - b[0]);
+            diff1 = (ResultType)abs(a[1] - b[1]);
+            diff2 = (ResultType)abs(a[2] - b[2]);
+            diff3 = (ResultType)abs(a[3] - b[3]);
+            result += diff0 + diff1 + diff2 + diff3;
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = (ResultType)abs(*a++ - *b++);
+            result += diff0;
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return abs(a-b);
+    }
+};
+
+
+
+template<class T>
+struct MinkowskiDistance
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    int order;
+
+    MinkowskiDistance(int order_) : order(order_) {}
+
+    /**
+     *  Compute the Minkowsky (L_p) distance between two vectors.
+     *
+     *	This is highly optimised, with loop unrolling, as it is one
+     *	of the most expensive inner loops.
+     *
+     *	The computation of squared root at the end is omitted for
+     *	efficiency.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = (ResultType)abs(a[0] - b[0]);
+            diff1 = (ResultType)abs(a[1] - b[1]);
+            diff2 = (ResultType)abs(a[2] - b[2]);
+            diff3 = (ResultType)abs(a[3] - b[3]);
+            result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order);
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = (ResultType)abs(*a++ - *b++);
+            result += pow(diff0,order);
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return pow(static_cast<ResultType>(abs(a-b)),order);
+    }
+};
+
+
+
+template<class T>
+struct MaxDistance
+{
+    typedef False is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    /**
+     *  Compute the max distance (L_infinity) between two vectors.
+     *
+     *  This distance is not a valid kdtree distance, it's not dimensionwise additive.
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = abs(a[0] - b[0]);
+            diff1 = abs(a[1] - b[1]);
+            diff2 = abs(a[2] - b[2]);
+            diff3 = abs(a[3] - b[3]);
+            if (diff0>result) {result = diff0; }
+            if (diff1>result) {result = diff1; }
+            if (diff2>result) {result = diff2; }
+            if (diff3>result) {result = diff3; }
+            a += 4;
+            b += 4;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            diff0 = abs(*a++ - *b++);
+            result = (diff0>result) ? diff0 : result;
+        }
+        return result;
+    }
+
+    /* This distance functor is not dimension-wise additive, which
+     * makes it an invalid kd-tree distance, not implementing the accum_dist method */
+
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/**
+ * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
+ * bit count of A exclusive XOR'ed with B
+ */
+struct HammingLUT
+{
+    typedef False is_kdtree_distance;
+    typedef False is_vector_space_distance;
+
+    typedef unsigned char ElementType;
+    typedef int ResultType;
+    typedef ElementType CentersType;
+
+    /** this will count the bits in a ^ b
+     */
+    template<typename Iterator2>
+    ResultType operator()(const unsigned char* a, const Iterator2 b, size_t size) const
+    {
+        static const uchar popCountTable[] =
+        {
+            0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+            3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
+        };
+        ResultType result = 0;
+        const unsigned char* b2 = reinterpret_cast<const unsigned char*> (b);
+        for (size_t i = 0; i < size; i++) {
+            result += popCountTable[a[i] ^ b2[i]];
+        }
+        return result;
+    }
+
+
+    ResultType operator()(const unsigned char* a, const ZeroIterator<unsigned char> b, size_t size) const
+    {
+        (void)b;
+        static const uchar popCountTable[] =
+        {
+            0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
+            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
+            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
+            3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
+        };
+        ResultType result = 0;
+        for (size_t i = 0; i < size; i++) {
+            result += popCountTable[a[i]];
+        }
+        return result;
+    }
+};
+
+/**
+ * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set)
+ * That code was taken from brief.cpp in OpenCV
+ */
+template<class T>
+struct Hamming
+{
+    typedef False is_kdtree_distance;
+    typedef False is_vector_space_distance;
+
+
+    typedef T ElementType;
+    typedef int ResultType;
+    typedef ElementType CentersType;
+
+    template<typename Iterator1, typename Iterator2>
+    ResultType operator()(const Iterator1 a, const Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        ResultType result = 0;
+#if defined(__ARM_NEON__) && !defined(__CUDACC__)
+        {
+            const unsigned char* a2 = reinterpret_cast<const unsigned char*> (a);
+            const unsigned char* b2 = reinterpret_cast<const unsigned char*> (b);
+            uint32x4_t bits = vmovq_n_u32(0);
+            for (size_t i = 0; i < size; i += 16) {
+                uint8x16_t A_vec = vld1q_u8 (a2 + i);
+                uint8x16_t B_vec = vld1q_u8 (b2 + i);
+                uint8x16_t AxorB = veorq_u8 (A_vec, B_vec);
+                uint8x16_t bitsSet = vcntq_u8 (AxorB);
+                uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
+                uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
+                bits = vaddq_u32(bits, bitSet4);
+            }
+            uint64x2_t bitSet2 = vpaddlq_u32 (bits);
+            result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
+            result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
+        }
+#elif defined(__GNUC__)
+        {
+            //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll)
+            typedef unsigned long long pop_t;
+            const size_t modulo = size % sizeof(pop_t);
+            const pop_t* a2 = reinterpret_cast<const pop_t*> (a);
+            const pop_t* b2 = reinterpret_cast<const pop_t*> (b);
+            const pop_t* a2_end = a2 + (size / sizeof(pop_t));
+
+            for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2));
+
+            if (modulo) {
+                //in the case where size is not dividable by sizeof(size_t)
+                //need to mask off the bits at the end
+                pop_t a_final = 0, b_final = 0;
+                memcpy(&a_final, a2, modulo);
+                memcpy(&b_final, b2, modulo);
+                result += __builtin_popcountll(a_final ^ b_final);
+            }
+        }
+#else // NO NEON and NOT GNUC
+        HammingLUT lut;
+        result = lut(reinterpret_cast<const unsigned char*> (a),
+                     reinterpret_cast<const unsigned char*> (b), size);
+#endif
+        return result;
+    }
+
+
+    template<typename Iterator1>
+    ResultType operator()(const Iterator1 a, ZeroIterator<unsigned char> b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        (void)b;
+        ResultType result = 0;
+#if defined(__ARM_NEON__) && !defined(__CUDACC__)
+        {
+            const unsigned char* a2 = reinterpret_cast<const unsigned char*> (a);
+            uint32x4_t bits = vmovq_n_u32(0);
+            for (size_t i = 0; i < size; i += 16) {
+                uint8x16_t A_vec = vld1q_u8 (a2 + i);
+                uint8x16_t bitsSet = vcntq_u8 (A_vec);
+                uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
+                uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
+                bits = vaddq_u32(bits, bitSet4);
+            }
+            uint64x2_t bitSet2 = vpaddlq_u32 (bits);
+            result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
+            result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
+        }
+#elif defined(__GNUC__)
+        {
+            //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll)
+            typedef unsigned long long pop_t;
+            const size_t modulo = size % sizeof(pop_t);
+            const pop_t* a2 = reinterpret_cast<const pop_t*> (a);
+            const pop_t* a2_end = a2 + (size / sizeof(pop_t));
+
+            for (; a2 != a2_end; ++a2) result += __builtin_popcountll(*a2);
+
+            if (modulo) {
+                //in the case where size is not dividable by sizeof(size_t)
+                //need to mask off the bits at the end
+                pop_t a_final = 0;
+                memcpy(&a_final, a2, modulo);
+                result += __builtin_popcountll(a_final);
+            }
+        }
+#else // NO NEON and NOT GNUC
+        HammingLUT lut;
+        result = lut(reinterpret_cast<const unsigned char*> (a), b, size);
+#endif
+        return result;
+    }
+};
+
+template<typename T>
+struct Hamming2
+{
+    typedef False is_kdtree_distance;
+    typedef False is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef int ResultType;
+    typedef ElementType CentersType;
+
+    /** This is popcount_3() from:
+     * http://en.wikipedia.org/wiki/Hamming_weight */
+    unsigned int popcnt32(uint32_t n) const
+    {
+        n -= ((n >> 1) & 0x55555555);
+        n = (n & 0x33333333) + ((n >> 2) & 0x33333333);
+        return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24;
+    }
+
+#ifdef FLANN_PLATFORM_64_BIT
+    unsigned int popcnt64(uint64_t n) const
+    {
+        n -= ((n >> 1) & 0x5555555555555555);
+        n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333);
+        return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56;
+    }
+#endif
+
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(const Iterator1 a, const Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)");
+
+#ifdef FLANN_PLATFORM_64_BIT
+        const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
+        const uint64_t* pb = reinterpret_cast<const uint64_t*>(b);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt64(*pa ^ *pb);
+            ++pa;
+            ++pb;
+        }
+#else
+        const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
+        const uint32_t* pb = reinterpret_cast<const uint32_t*>(b);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt32(*pa ^ *pb);
+            ++pa;
+            ++pb;
+        }
+#endif
+        return result;
+    }
+
+
+    template <typename Iterator1>
+    ResultType operator()(const Iterator1 a, ZeroIterator<unsigned char> b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)");
+
+        (void)b;
+#ifdef FLANN_PLATFORM_64_BIT
+        const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt64(*pa);
+            ++pa;
+        }
+#else
+        const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt32(*pa);
+            ++pa;
+        }
+#endif
+        return result;
+    }
+
+private:
+#ifdef FLANN_PLATFORM_64_BIT
+    static const size_t long_word_size_ = sizeof(uint64_t)/sizeof(unsigned char);
+#else
+    static const size_t long_word_size_ = sizeof(uint32_t)/sizeof(unsigned char);
+#endif
+};
+
+
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+struct DNAmmingLUT
+{
+    typedef False is_kdtree_distance;
+    typedef False is_vector_space_distance;
+
+    typedef unsigned char ElementType;
+    typedef int ResultType;
+    typedef ElementType CentersType;
+
+    /** this will count the bits in a ^ b
+     */
+    template<typename Iterator2>
+    ResultType operator()(const unsigned char* a, const Iterator2 b, size_t size) const
+    {
+        static const uchar popCountTable[] =
+        {
+            0, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4
+        };
+        ResultType result = 0;
+        const unsigned char* b2 = reinterpret_cast<const unsigned char*> (b);
+        for (size_t i = 0; i < size; i++) {
+            result += popCountTable[a[i] ^ b2[i]];
+        }
+        return result;
+    }
+
+
+    ResultType operator()(const unsigned char* a, const ZeroIterator<unsigned char> b, size_t size) const
+    {
+        (void)b;
+        static const uchar popCountTable[] =
+        {
+            0, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
+            2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4
+        };
+        ResultType result = 0;
+        for (size_t i = 0; i < size; i++) {
+            result += popCountTable[a[i]];
+        }
+        return result;
+    }
+};
+
+
+template<typename T>
+struct DNAmming2
+{
+    typedef False is_kdtree_distance;
+    typedef False is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef int ResultType;
+    typedef ElementType CentersType;
+
+    /** This is popcount_3() from:
+     * http://en.wikipedia.org/wiki/Hamming_weight */
+    unsigned int popcnt32(uint32_t n) const
+    {
+        n = ((n >> 1) | n) & 0x55555555;
+        n = (n & 0x33333333) + ((n >> 2) & 0x33333333);
+        return (((n + (n >> 4))& 0x0F0F0F0F)* 0x01010101) >> 24;
+    }
+
+#ifdef FLANN_PLATFORM_64_BIT
+    unsigned int popcnt64(uint64_t n) const
+    {
+        n = ((n >> 1) | n) & 0x5555555555555555;
+        n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333);
+        return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56;
+    }
+#endif
+
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(const Iterator1 a, const Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)");
+
+#ifdef FLANN_PLATFORM_64_BIT
+        const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
+        const uint64_t* pb = reinterpret_cast<const uint64_t*>(b);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt64(*pa ^ *pb);
+            ++pa;
+            ++pb;
+        }
+#else
+        const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
+        const uint32_t* pb = reinterpret_cast<const uint32_t*>(b);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt32(*pa ^ *pb);
+            ++pa;
+            ++pb;
+        }
+#endif
+        return result;
+    }
+
+
+    template <typename Iterator1>
+    ResultType operator()(const Iterator1 a, ZeroIterator<unsigned char> b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)");
+
+        (void)b;
+#ifdef FLANN_PLATFORM_64_BIT
+        const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt64(*pa);
+            ++pa;
+        }
+#else
+        const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
+        ResultType result = 0;
+        size /= long_word_size_;
+        for(size_t i = 0; i < size; ++i ) {
+            result += popcnt32(*pa);
+            ++pa;
+        }
+#endif
+        return result;
+    }
+
+private:
+#ifdef FLANN_PLATFORM_64_BIT
+    static const size_t long_word_size_= sizeof(uint64_t)/sizeof(unsigned char);
+#else
+    static const size_t long_word_size_= sizeof(uint32_t)/sizeof(unsigned char);
+#endif
+};
+
+
+
+template<class T>
+struct HistIntersectionDistance
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    /**
+     *  Compute the histogram intersection distance
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType min0, min1, min2, min3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]);
+            min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]);
+            min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]);
+            min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]);
+            result += min0 + min1 + min2 + min3;
+            a += 4;
+            b += 4;
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
+        while (a < last) {
+            min0 = (ResultType)(*a < *b ? *a : *b);
+            result += min0;
+            ++a;
+            ++b;
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        return a<b ? a : b;
+    }
+};
+
+
+
+template<class T>
+struct HellingerDistance
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    /**
+     *  Compute the Hellinger distance
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType diff0, diff1, diff2, diff3;
+        Iterator1 last = a + size;
+        Iterator1 lastgroup = last - 3;
+
+        /* Process 4 items with each loop for efficiency. */
+        while (a < lastgroup) {
+            diff0 = sqrt(static_cast<ResultType>(a[0])) - sqrt(static_cast<ResultType>(b[0]));
+            diff1 = sqrt(static_cast<ResultType>(a[1])) - sqrt(static_cast<ResultType>(b[1]));
+            diff2 = sqrt(static_cast<ResultType>(a[2])) - sqrt(static_cast<ResultType>(b[2]));
+            diff3 = sqrt(static_cast<ResultType>(a[3])) - sqrt(static_cast<ResultType>(b[3]));
+            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
+            a += 4;
+            b += 4;
+        }
+        while (a < last) {
+            diff0 = sqrt(static_cast<ResultType>(*a++)) - sqrt(static_cast<ResultType>(*b++));
+            result += diff0 * diff0;
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        ResultType diff = sqrt(static_cast<ResultType>(a)) - sqrt(static_cast<ResultType>(b));
+        return diff * diff;
+    }
+};
+
+
+template<class T>
+struct ChiSquareDistance
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    /**
+     *  Compute the chi-square distance
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        ResultType sum, diff;
+        Iterator1 last = a + size;
+
+        while (a < last) {
+            sum = (ResultType)(*a + *b);
+            if (sum>0) {
+                diff = (ResultType)(*a - *b);
+                result += diff*diff/sum;
+            }
+            ++a;
+            ++b;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        ResultType result = ResultType();
+        ResultType sum, diff;
+
+        sum = (ResultType)(a+b);
+        if (sum>0) {
+            diff = (ResultType)(a-b);
+            result = diff*diff/sum;
+        }
+        return result;
+    }
+};
+
+
+template<class T>
+struct KL_Divergence
+{
+    typedef True is_kdtree_distance;
+    typedef True is_vector_space_distance;
+
+    typedef T ElementType;
+    typedef typename Accumulator<T>::Type ResultType;
+    typedef ResultType CentersType;
+
+    /**
+     *  Compute the Kullback-Leibler divergence
+     */
+    template <typename Iterator1, typename Iterator2>
+    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
+    {
+        ResultType result = ResultType();
+        Iterator1 last = a + size;
+
+        while (a < last) {
+            if ( *a != 0 && *b != 0 ) {
+                ResultType ratio = (ResultType)(*a / *b);
+                if (ratio>0) {
+                    result += *a * log(ratio);
+                }
+            }
+            ++a;
+            ++b;
+
+            if ((worst_dist>0)&&(result>worst_dist)) {
+                return result;
+            }
+        }
+        return result;
+    }
+
+    /**
+     * Partial distance, used by the kd-tree.
+     */
+    template <typename U, typename V>
+    inline ResultType accum_dist(const U& a, const V& b, int) const
+    {
+        ResultType result = ResultType();
+        if( a != 0 && b != 0 ) {
+            ResultType ratio = (ResultType)(a / b);
+            if (ratio>0) {
+                result = a * log(ratio);
+            }
+        }
+        return result;
+    }
+};
+
+
+/*
+ * Depending on processed distances, some of them are already squared (e.g. L2)
+ * and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure
+ * we are working on ^2 distances, thus following templates to ensure that.
+ */
+template <typename Distance, typename ElementType>
+struct squareDistance
+{
+    typedef typename Distance::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return dist*dist; }
+};
+
+
+template <typename ElementType>
+struct squareDistance<L2_Simple<ElementType>, ElementType>
+{
+    typedef typename L2_Simple<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return dist; }
+};
+
+template <typename ElementType>
+struct squareDistance<L2<ElementType>, ElementType>
+{
+    typedef typename L2<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return dist; }
+};
+
+
+template <typename ElementType>
+struct squareDistance<MinkowskiDistance<ElementType>, ElementType>
+{
+    typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return dist; }
+};
+
+template <typename ElementType>
+struct squareDistance<HellingerDistance<ElementType>, ElementType>
+{
+    typedef typename HellingerDistance<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return dist; }
+};
+
+template <typename ElementType>
+struct squareDistance<ChiSquareDistance<ElementType>, ElementType>
+{
+    typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return dist; }
+};
+
+
+template <typename Distance>
+typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist )
+{
+    typedef typename Distance::ElementType ElementType;
+
+    squareDistance<Distance, ElementType> dummy;
+    return dummy( dist );
+}
+
+
+/*
+ * ...a template to tell the user if the distance he is working with is actually squared
+ */
+
+template <typename Distance, typename ElementType>
+struct isSquareDist
+{
+    bool operator()() { return false; }
+};
+
+
+template <typename ElementType>
+struct isSquareDist<L2_Simple<ElementType>, ElementType>
+{
+    bool operator()() { return true; }
+};
+
+template <typename ElementType>
+struct isSquareDist<L2<ElementType>, ElementType>
+{
+    bool operator()() { return true; }
+};
+
+
+template <typename ElementType>
+struct isSquareDist<MinkowskiDistance<ElementType>, ElementType>
+{
+    bool operator()() { return true; }
+};
+
+template <typename ElementType>
+struct isSquareDist<HellingerDistance<ElementType>, ElementType>
+{
+    bool operator()() { return true; }
+};
+
+template <typename ElementType>
+struct isSquareDist<ChiSquareDistance<ElementType>, ElementType>
+{
+    bool operator()() { return true; }
+};
+
+
+template <typename Distance>
+bool isSquareDistance()
+{
+    typedef typename Distance::ElementType ElementType;
+
+    isSquareDist<Distance, ElementType> dummy;
+    return dummy();
+}
+
+/*
+ * ...and a template to ensure the user that he will process the normal distance,
+ * and not squared distance, without losing processing time calling sqrt(ensureSquareDistance)
+ * that will result in doing actually sqrt(dist*dist) for L1 distance for instance.
+ */
+template <typename Distance, typename ElementType>
+struct simpleDistance
+{
+    typedef typename Distance::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return dist; }
+};
+
+
+template <typename ElementType>
+struct simpleDistance<L2_Simple<ElementType>, ElementType>
+{
+    typedef typename L2_Simple<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return sqrt(dist); }
+};
+
+template <typename ElementType>
+struct simpleDistance<L2<ElementType>, ElementType>
+{
+    typedef typename L2<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return sqrt(dist); }
+};
+
+
+template <typename ElementType>
+struct simpleDistance<MinkowskiDistance<ElementType>, ElementType>
+{
+    typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return sqrt(dist); }
+};
+
+template <typename ElementType>
+struct simpleDistance<HellingerDistance<ElementType>, ElementType>
+{
+    typedef typename HellingerDistance<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return sqrt(dist); }
+};
+
+template <typename ElementType>
+struct simpleDistance<ChiSquareDistance<ElementType>, ElementType>
+{
+    typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
+    ResultType operator()( ResultType dist ) { return sqrt(dist); }
+};
+
+
+template <typename Distance>
+typename Distance::ResultType ensureSimpleDistance( typename Distance::ResultType dist )
+{
+    typedef typename Distance::ElementType ElementType;
+
+    simpleDistance<Distance, ElementType> dummy;
+    return dummy( dist );
+}
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_DIST_H_
diff --git a/3rdParty/include/opencv2/flann/dummy.h b/3rdParty/include/opencv2/flann/dummy.h
new file mode 100644
index 0000000..c176f2e
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/dummy.h
@@ -0,0 +1,16 @@
+
+#ifndef OPENCV_FLANN_DUMMY_H_
+#define OPENCV_FLANN_DUMMY_H_
+
+//! @cond IGNORED
+
+namespace cvflann
+{
+
+CV_DEPRECATED inline void dummyfunc() {}
+
+}
+
+//! @endcond
+
+#endif  /* OPENCV_FLANN_DUMMY_H_ */
diff --git a/3rdParty/include/opencv2/flann/dynamic_bitset.h b/3rdParty/include/opencv2/flann/dynamic_bitset.h
new file mode 100644
index 0000000..a00ce1b
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/dynamic_bitset.h
@@ -0,0 +1,163 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/***********************************************************************
+ * Author: Vincent Rabaud
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_
+#define OPENCV_FLANN_DYNAMIC_BITSET_H_
+
+//! @cond IGNORED
+
+#ifndef FLANN_USE_BOOST
+#  define FLANN_USE_BOOST 0
+#endif
+//#define FLANN_USE_BOOST 1
+#if FLANN_USE_BOOST
+#include <boost/dynamic_bitset.hpp>
+typedef boost::dynamic_bitset<> DynamicBitset;
+#else
+
+#include <limits.h>
+
+#include "dist.h"
+
+namespace cvflann {
+
+/** Class re-implementing the boost version of it
+ * This helps not depending on boost, it also does not do the bound checks
+ * and has a way to reset a block for speed
+ */
+class DynamicBitset
+{
+public:
+    /** default constructor
+     */
+    DynamicBitset() : size_(0)
+    {
+    }
+
+    /** only constructor we use in our code
+     * @param sz the size of the bitset (in bits)
+     */
+    DynamicBitset(size_t sz)
+    {
+        resize(sz);
+        reset();
+    }
+
+    /** Sets all the bits to 0
+     */
+    void clear()
+    {
+        std::fill(bitset_.begin(), bitset_.end(), 0);
+    }
+
+    /** @brief checks if the bitset is empty
+     * @return true if the bitset is empty
+     */
+    bool empty() const
+    {
+        return bitset_.empty();
+    }
+
+    /** set all the bits to 0
+     */
+    void reset()
+    {
+        std::fill(bitset_.begin(), bitset_.end(), 0);
+    }
+
+    /** @brief set one bit to 0
+     * @param index
+     */
+    void reset(size_t index)
+    {
+        bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_));
+    }
+
+    /** @brief sets a specific bit to 0, and more bits too
+     * This function is useful when resetting a given set of bits so that the
+     * whole bitset ends up being 0: if that's the case, we don't care about setting
+     * other bits to 0
+     * @param index
+     */
+    void reset_block(size_t index)
+    {
+        bitset_[index / cell_bit_size_] = 0;
+    }
+
+    /** resize the bitset so that it contains at least sz bits
+     * @param sz
+     */
+    void resize(size_t sz)
+    {
+        size_ = sz;
+        bitset_.resize(sz / cell_bit_size_ + 1);
+    }
+
+    /** set a bit to true
+     * @param index the index of the bit to set to 1
+     */
+    void set(size_t index)
+    {
+        bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_);
+    }
+
+    /** gives the number of contained bits
+     */
+    size_t size() const
+    {
+        return size_;
+    }
+
+    /** check if a bit is set
+     * @param index the index of the bit to check
+     * @return true if the bit is set
+     */
+    bool test(size_t index) const
+    {
+        return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0;
+    }
+
+private:
+    std::vector<size_t> bitset_;
+    size_t size_;
+    static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t);
+};
+
+} // namespace cvflann
+
+#endif
+
+//! @endcond
+
+#endif // OPENCV_FLANN_DYNAMIC_BITSET_H_
diff --git a/3rdParty/include/opencv2/flann/flann.hpp b/3rdParty/include/opencv2/flann/flann.hpp
new file mode 100644
index 0000000..227683f
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/flann.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/flann.hpp"
diff --git a/3rdParty/include/opencv2/flann/flann_base.hpp b/3rdParty/include/opencv2/flann/flann_base.hpp
new file mode 100644
index 0000000..258ec38
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/flann_base.hpp
@@ -0,0 +1,299 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_BASE_HPP_
+#define OPENCV_FLANN_BASE_HPP_
+
+//! @cond IGNORED
+
+#include <vector>
+#include <cstdio>
+
+#include "general.h"
+#include "matrix.h"
+#include "params.h"
+#include "saving.h"
+
+#include "all_indices.h"
+
+namespace cvflann
+{
+
+/**
+ * Sets the log level used for all flann functions
+ * @param level Verbosity level
+ */
+inline void log_verbosity(int level)
+{
+    if (level >= 0) {
+        Logger::setLevel(level);
+    }
+}
+
+/**
+ * (Deprecated) Index parameters for creating a saved index.
+ */
+struct SavedIndexParams : public IndexParams
+{
+    SavedIndexParams(cv::String filename)
+    {
+        (* this)["algorithm"] = FLANN_INDEX_SAVED;
+        (*this)["filename"] = filename;
+    }
+};
+
+
+template<typename Distance>
+NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const cv::String& filename, Distance distance)
+{
+    typedef typename Distance::ElementType ElementType;
+
+    FILE* fin = fopen(filename.c_str(), "rb");
+    if (fin == NULL) {
+        return NULL;
+    }
+    IndexHeader header = load_header(fin);
+    if (header.data_type != Datatype<ElementType>::type()) {
+        fclose(fin);
+        FLANN_THROW(cv::Error::StsError, "Datatype of saved index is different than of the one to be created.");
+    }
+    if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) {
+        fclose(fin);
+        FLANN_THROW(cv::Error::StsError, "The index saved belongs to a different dataset");
+    }
+
+    IndexParams params;
+    params["algorithm"] = header.index_type;
+    NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance);
+    nnIndex->loadIndex(fin);
+    fclose(fin);
+
+    return nnIndex;
+}
+
+
+template<typename Distance>
+class Index : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
+        : index_params_(params)
+    {
+        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
+        loaded_ = false;
+
+        if (index_type == FLANN_INDEX_SAVED) {
+            nnIndex_ = load_saved_index<Distance>(features, get_param<cv::String>(params,"filename"), distance);
+            loaded_ = true;
+        }
+        else {
+            nnIndex_ = create_index_by_type<Distance>(features, params, distance);
+        }
+    }
+
+    ~Index()
+    {
+        delete nnIndex_;
+    }
+
+    /**
+     * Builds the index.
+     */
+    void buildIndex() CV_OVERRIDE
+    {
+        if (!loaded_) {
+            nnIndex_->buildIndex();
+        }
+    }
+
+    void save(cv::String filename)
+    {
+        FILE* fout = fopen(filename.c_str(), "wb");
+        if (fout == NULL) {
+            FLANN_THROW(cv::Error::StsError, "Cannot open file");
+        }
+        save_header(fout, *nnIndex_);
+        saveIndex(fout);
+        fclose(fout);
+    }
+
+    /**
+     * \brief Saves the index to a stream
+     * \param stream The stream to save the index to
+     */
+    virtual void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        nnIndex_->saveIndex(stream);
+    }
+
+    /**
+     * \brief Loads the index from a stream
+     * \param stream The stream from which the index is loaded
+     */
+    virtual void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        nnIndex_->loadIndex(stream);
+    }
+
+    /**
+     * \returns number of features in this index.
+     */
+    size_t veclen() const CV_OVERRIDE
+    {
+        return nnIndex_->veclen();
+    }
+
+    /**
+     * \returns The dimensionality of the features in this index.
+     */
+    size_t size() const CV_OVERRIDE
+    {
+        return nnIndex_->size();
+    }
+
+    /**
+     * \returns The index type (kdtree, kmeans,...)
+     */
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return nnIndex_->getType();
+    }
+
+    /**
+     * \returns The amount of memory (in bytes) used by the index.
+     */
+    virtual int usedMemory() const CV_OVERRIDE
+    {
+        return nnIndex_->usedMemory();
+    }
+
+
+    /**
+     * \returns The index parameters
+     */
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return nnIndex_->getParameters();
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
+    {
+        nnIndex_->knnSearch(queries, indices, dists, knn, params);
+    }
+
+    /**
+     * \brief Perform radius search
+     * \param[in] query The query point
+     * \param[out] indices The indinces of the neighbors found within the given radius
+     * \param[out] dists The distances to the nearest neighbors found
+     * \param[in] radius The radius used for search
+     * \param[in] params Search parameters
+     * \returns Number of neighbors found
+     */
+    int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params) CV_OVERRIDE
+    {
+        return nnIndex_->radiusSearch(query, indices, dists, radius, params);
+    }
+
+    /**
+     * \brief Method that searches for nearest-neighbours
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
+    {
+        nnIndex_->findNeighbors(result, vec, searchParams);
+    }
+
+    /**
+     * \brief Returns actual index
+     */
+    CV_DEPRECATED NNIndex<Distance>* getIndex()
+    {
+        return nnIndex_;
+    }
+
+    /**
+     * \brief Returns index parameters.
+     * \deprecated use getParameters() instead.
+     */
+    CV_DEPRECATED  const IndexParams* getIndexParameters()
+    {
+        return &index_params_;
+    }
+
+private:
+    /** Pointer to actual index class */
+    NNIndex<Distance>* nnIndex_;
+    /** Indices if the index was loaded from a file */
+    bool loaded_;
+    /** Parameters passed to the index */
+    IndexParams index_params_;
+
+    Index(const Index &); // copy disabled
+    Index& operator=(const Index &); // assign disabled
+};
+
+/**
+ * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
+ * the clustering tree to return a flat clustering.
+ * @param[in] points Points to be clustered
+ * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
+ *  number of clusters requested.
+ * @param params Clustering parameters (The same as for cvflann::KMeansIndex)
+ * @param d Distance to be used for clustering (eg: cvflann::L2)
+ * @return number of clusters computed (can be different than clusters.rows and is the highest number
+ * of the form (branching-1)*K+1 smaller than clusters.rows).
+ */
+template <typename Distance>
+int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::CentersType>& centers,
+                           const KMeansIndexParams& params, Distance d = Distance())
+{
+    KMeansIndex<Distance> kmeans(points, params, d);
+    kmeans.buildIndex();
+
+    int clusterNum = kmeans.getClusterCenters(centers);
+    return clusterNum;
+}
+
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_BASE_HPP_ */
diff --git a/3rdParty/include/opencv2/flann/general.h b/3rdParty/include/opencv2/flann/general.h
new file mode 100644
index 0000000..29fa8be
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/general.h
@@ -0,0 +1,63 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_GENERAL_H_
+#define OPENCV_FLANN_GENERAL_H_
+
+#if CV_VERSION_MAJOR <= 4
+
+//! @cond IGNORED
+
+#include "opencv2/core.hpp"
+
+namespace cvflann
+{
+
+class FLANNException : public cv::Exception
+{
+public:
+    FLANNException(const char* message) : cv::Exception(0, message, "", __FILE__, __LINE__) { }
+
+    FLANNException(const cv::String& message) : cv::Exception(0, message, "", __FILE__, __LINE__) { }
+};
+
+}
+
+#define FLANN_THROW(TYPE, STR) throw FLANNException(STR)
+
+#else
+
+#define FLANN_THROW(TYPE, STR) CV_Error(TYPE, STR)
+
+#endif
+
+//! @endcond
+
+#endif  /* OPENCV_FLANN_GENERAL_H_ */
diff --git a/3rdParty/include/opencv2/flann/ground_truth.h b/3rdParty/include/opencv2/flann/ground_truth.h
new file mode 100644
index 0000000..17f2a8e
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/ground_truth.h
@@ -0,0 +1,98 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_GROUND_TRUTH_H_
+#define OPENCV_FLANN_GROUND_TRUTH_H_
+
+//! @cond IGNORED
+
+#include "dist.h"
+#include "matrix.h"
+
+
+namespace cvflann
+{
+
+template <typename Distance>
+void find_nearest(const Matrix<typename Distance::ElementType>& dataset, typename Distance::ElementType* query, int* matches, int nn,
+                  int skip = 0, Distance distance = Distance())
+{
+    typedef typename Distance::ResultType DistanceType;
+    int n = nn + skip;
+
+    std::vector<int> match(n);
+    std::vector<DistanceType> dists(n);
+
+    dists[0] = distance(dataset[0], query, dataset.cols);
+    match[0] = 0;
+    int dcnt = 1;
+
+    for (size_t i=1; i<dataset.rows; ++i) {
+        DistanceType tmp = distance(dataset[i], query, dataset.cols);
+
+        if (dcnt<n) {
+            match[dcnt] = (int)i;
+            dists[dcnt++] = tmp;
+        }
+        else if (tmp < dists[dcnt-1]) {
+            dists[dcnt-1] = tmp;
+            match[dcnt-1] = (int)i;
+        }
+
+        int j = dcnt-1;
+        // bubble up
+        while (j>=1 && dists[j]<dists[j-1]) {
+            std::swap(dists[j],dists[j-1]);
+            std::swap(match[j],match[j-1]);
+            j--;
+        }
+    }
+
+    for (int i=0; i<nn; ++i) {
+        matches[i] = match[i+skip];
+    }
+}
+
+
+template <typename Distance>
+void compute_ground_truth(const Matrix<typename Distance::ElementType>& dataset, const Matrix<typename Distance::ElementType>& testset, Matrix<int>& matches,
+                          int skip=0, Distance d = Distance())
+{
+    for (size_t i=0; i<testset.rows; ++i) {
+        find_nearest<Distance>(dataset, testset[i], matches[i], (int)matches.cols, skip, d);
+    }
+}
+
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_GROUND_TRUTH_H_
diff --git a/3rdParty/include/opencv2/flann/hdf5.h b/3rdParty/include/opencv2/flann/hdf5.h
new file mode 100644
index 0000000..7554384
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/hdf5.h
@@ -0,0 +1,235 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef OPENCV_FLANN_HDF5_H_
+#define OPENCV_FLANN_HDF5_H_
+
+//! @cond IGNORED
+
+#include <hdf5.h>
+
+#include "matrix.h"
+
+
+namespace cvflann
+{
+
+namespace
+{
+
+template<typename T>
+hid_t get_hdf5_type()
+{
+    throw FLANNException("Unsupported type for IO operations");
+}
+
+template<>
+hid_t get_hdf5_type<char>() { return H5T_NATIVE_CHAR; }
+template<>
+hid_t get_hdf5_type<unsigned char>() { return H5T_NATIVE_UCHAR; }
+template<>
+hid_t get_hdf5_type<short int>() { return H5T_NATIVE_SHORT; }
+template<>
+hid_t get_hdf5_type<unsigned short int>() { return H5T_NATIVE_USHORT; }
+template<>
+hid_t get_hdf5_type<int>() { return H5T_NATIVE_INT; }
+template<>
+hid_t get_hdf5_type<unsigned int>() { return H5T_NATIVE_UINT; }
+template<>
+hid_t get_hdf5_type<long>() { return H5T_NATIVE_LONG; }
+template<>
+hid_t get_hdf5_type<unsigned long>() { return H5T_NATIVE_ULONG; }
+template<>
+hid_t get_hdf5_type<float>() { return H5T_NATIVE_FLOAT; }
+template<>
+hid_t get_hdf5_type<double>() { return H5T_NATIVE_DOUBLE; }
+}
+
+
+#define CHECK_ERROR(x,y) if ((x)<0) throw FLANNException((y));
+
+template<typename T>
+void save_to_file(const cvflann::Matrix<T>& dataset, const String& filename, const String& name)
+{
+
+#if H5Eset_auto_vers == 2
+    H5Eset_auto( H5E_DEFAULT, NULL, NULL );
+#else
+    H5Eset_auto( NULL, NULL );
+#endif
+
+    herr_t status;
+    hid_t file_id;
+    file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT);
+    if (file_id < 0) {
+        file_id = H5Fcreate(filename.c_str(), H5F_ACC_EXCL, H5P_DEFAULT, H5P_DEFAULT);
+    }
+    CHECK_ERROR(file_id,"Error creating hdf5 file.");
+
+    hsize_t     dimsf[2];              // dataset dimensions
+    dimsf[0] = dataset.rows;
+    dimsf[1] = dataset.cols;
+
+    hid_t space_id = H5Screate_simple(2, dimsf, NULL);
+    hid_t memspace_id = H5Screate_simple(2, dimsf, NULL);
+
+    hid_t dataset_id;
+#if H5Dcreate_vers == 2
+    dataset_id = H5Dcreate2(file_id, name.c_str(), get_hdf5_type<T>(), space_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+#else
+    dataset_id = H5Dcreate(file_id, name.c_str(), get_hdf5_type<T>(), space_id, H5P_DEFAULT);
+#endif
+
+    if (dataset_id<0) {
+#if H5Dopen_vers == 2
+        dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
+#else
+        dataset_id = H5Dopen(file_id, name.c_str());
+#endif
+    }
+    CHECK_ERROR(dataset_id,"Error creating or opening dataset in file.");
+
+    status = H5Dwrite(dataset_id, get_hdf5_type<T>(), memspace_id, space_id, H5P_DEFAULT, dataset.data );
+    CHECK_ERROR(status, "Error writing to dataset");
+
+    H5Sclose(memspace_id);
+    H5Sclose(space_id);
+    H5Dclose(dataset_id);
+    H5Fclose(file_id);
+
+}
+
+
+template<typename T>
+void load_from_file(cvflann::Matrix<T>& dataset, const String& filename, const String& name)
+{
+    herr_t status;
+    hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT);
+    CHECK_ERROR(file_id,"Error opening hdf5 file.");
+
+    hid_t dataset_id;
+#if H5Dopen_vers == 2
+    dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
+#else
+    dataset_id = H5Dopen(file_id, name.c_str());
+#endif
+    CHECK_ERROR(dataset_id,"Error opening dataset in file.");
+
+    hid_t space_id = H5Dget_space(dataset_id);
+
+    hsize_t dims_out[2];
+    H5Sget_simple_extent_dims(space_id, dims_out, NULL);
+
+    dataset = cvflann::Matrix<T>(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]);
+
+    status = H5Dread(dataset_id, get_hdf5_type<T>(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]);
+    CHECK_ERROR(status, "Error reading dataset");
+
+    H5Sclose(space_id);
+    H5Dclose(dataset_id);
+    H5Fclose(file_id);
+}
+
+
+#ifdef HAVE_MPI
+
+namespace mpi
+{
+/**
+ * Loads a the hyperslice corresponding to this processor from a hdf5 file.
+ * @param flann_dataset Dataset where the data is loaded
+ * @param filename HDF5 file name
+ * @param name Name of dataset inside file
+ */
+template<typename T>
+void load_from_file(cvflann::Matrix<T>& dataset, const String& filename, const String& name)
+{
+    MPI_Comm comm  = MPI_COMM_WORLD;
+    MPI_Info info  = MPI_INFO_NULL;
+
+    int mpi_size, mpi_rank;
+    MPI_Comm_size(comm, &mpi_size);
+    MPI_Comm_rank(comm, &mpi_rank);
+
+    herr_t status;
+
+    hid_t plist_id = H5Pcreate(H5P_FILE_ACCESS);
+    H5Pset_fapl_mpio(plist_id, comm, info);
+    hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, plist_id);
+    CHECK_ERROR(file_id,"Error opening hdf5 file.");
+    H5Pclose(plist_id);
+    hid_t dataset_id;
+#if H5Dopen_vers == 2
+    dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
+#else
+    dataset_id = H5Dopen(file_id, name.c_str());
+#endif
+    CHECK_ERROR(dataset_id,"Error opening dataset in file.");
+
+    hid_t space_id = H5Dget_space(dataset_id);
+    hsize_t dims[2];
+    H5Sget_simple_extent_dims(space_id, dims, NULL);
+
+    hsize_t count[2];
+    hsize_t offset[2];
+
+    hsize_t item_cnt = dims[0]/mpi_size+(dims[0]%mpi_size==0 ? 0 : 1);
+    hsize_t cnt = (mpi_rank<mpi_size-1 ? item_cnt : dims[0]-item_cnt*(mpi_size-1));
+
+    count[0] = cnt;
+    count[1] = dims[1];
+    offset[0] = mpi_rank*item_cnt;
+    offset[1] = 0;
+
+    hid_t memspace_id = H5Screate_simple(2,count,NULL);
+
+    H5Sselect_hyperslab(space_id, H5S_SELECT_SET, offset, NULL, count, NULL);
+
+    dataset.rows = count[0];
+    dataset.cols = count[1];
+    dataset.data = new T[dataset.rows*dataset.cols];
+
+    plist_id = H5Pcreate(H5P_DATASET_XFER);
+    H5Pset_dxpl_mpio(plist_id, H5FD_MPIO_COLLECTIVE);
+    status = H5Dread(dataset_id, get_hdf5_type<T>(), memspace_id, space_id, plist_id, dataset.data);
+    CHECK_ERROR(status, "Error reading dataset");
+
+    H5Pclose(plist_id);
+    H5Sclose(space_id);
+    H5Sclose(memspace_id);
+    H5Dclose(dataset_id);
+    H5Fclose(file_id);
+}
+}
+#endif // HAVE_MPI
+} // namespace cvflann::mpi
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_HDF5_H_ */
diff --git a/3rdParty/include/opencv2/flann/heap.h b/3rdParty/include/opencv2/flann/heap.h
new file mode 100644
index 0000000..8cace20
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/heap.h
@@ -0,0 +1,244 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_HEAP_H_
+#define OPENCV_FLANN_HEAP_H_
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <vector>
+
+#include <unordered_map>
+
+namespace cvflann
+{
+
+// TODO: Define x > y operator and use std::greater<T> instead
+template <typename T>
+struct greater
+{
+    bool operator()(const T& x, const T& y) const
+    {
+        return y < x;
+    }
+};
+
+/**
+ * Priority Queue Implementation
+ *
+ * The priority queue is implemented with a heap.  A heap is a complete
+ * (full) binary tree in which each parent is less than both of its
+ * children, but the order of the children is unspecified.
+ */
+template <typename T>
+class Heap
+{
+    /**
+     * Storage array for the heap.
+     * Type T must be comparable.
+     */
+    std::vector<T> heap;
+public:
+    /**
+     * \brief Constructs a heap with a pre-allocated capacity
+     *
+     * \param capacity heap maximum capacity
+     */
+    Heap(const int capacity)
+    {
+        reserve(capacity);
+    }
+
+    /**
+     * \brief Move-constructs a heap from an external vector
+     *
+     * \param vec external vector
+     */
+    Heap(std::vector<T>&& vec)
+        : heap(std::move(vec))
+    {
+        std::make_heap(heap.begin(), heap.end(), greater<T>());
+    }
+
+    /**
+     *
+     * \returns heap size
+     */
+    int size() const
+    {
+        return (int)heap.size();
+    }
+
+    /**
+     *
+     * \returns heap capacity
+     */
+    int capacity() const
+    {
+        return (int)heap.capacity();
+    }
+
+    /**
+     * \brief Tests if the heap is empty
+     *
+     * \returns true is heap empty, false otherwise
+     */
+    bool empty()
+    {
+        return heap.empty();
+    }
+
+    /**
+     * \brief Clears the heap.
+     */
+    void clear()
+    {
+        heap.clear();
+    }
+
+    /**
+     * \brief Sets the heap maximum capacity.
+     *
+     * \param capacity heap maximum capacity
+     */
+    void reserve(const int capacity)
+    {
+        heap.reserve(capacity);
+    }
+
+    /**
+     * \brief Inserts a new element in the heap.
+     *
+     * We select the next empty leaf node, and then keep moving any larger
+     * parents down until the right location is found to store this element.
+     *
+     * \param value the new element to be inserted in the heap
+     */
+    void insert(T value)
+    {
+        /* If heap is full, then return without adding this element. */
+        if (size() == capacity()) {
+            return;
+        }
+
+        heap.push_back(value);
+        std::push_heap(heap.begin(), heap.end(), greater<T>());
+    }
+
+    /**
+     * \brief Returns the node of minimum value from the heap (top of the heap).
+     *
+     * \param[out] value parameter used to return the min element
+     * \returns false if heap empty
+     */
+    bool popMin(T& value)
+    {
+        if (empty()) {
+            return false;
+        }
+
+        value = heap[0];
+        std::pop_heap(heap.begin(), heap.end(), greater<T>());
+        heap.pop_back();
+
+        return true;  /* Return old last node. */
+    }
+
+    /**
+     * \brief Returns a shared heap for the given memory pool ID.
+     *
+     * It constructs the heap if it does not already exists.
+     *
+     * \param poolId a user-chosen hashable ID for identifying the heap.
+     *     For thread-safe operations, using current thread ID is a good choice.
+     * \param capacity heap maximum capacity
+     * \param iterThreshold remove heaps that were not reused for more than specified iterations count
+     *        if iterThreshold value is less 2, it will be internally adjusted to twice the number of CPU threads
+     * \returns pointer to the heap
+     */
+    template <typename HashableT>
+    static cv::Ptr<Heap<T>> getPooledInstance(
+        const HashableT& poolId, const int capacity, int iterThreshold = 0)
+    {
+        static cv::Mutex mutex;
+        const cv::AutoLock lock(mutex);
+
+        struct HeapMapValueType {
+            cv::Ptr<Heap<T>> heapPtr;
+            int iterCounter;
+        };
+        typedef std::unordered_map<HashableT, HeapMapValueType> HeapMapType;
+
+        static HeapMapType heapsPool;
+        typename HeapMapType::iterator heapIt = heapsPool.find(poolId);
+
+        if (heapIt == heapsPool.end())
+        {
+            // Construct the heap as it does not already exists
+            HeapMapValueType heapAndTimePair = {cv::makePtr<Heap<T>>(capacity), 0};
+            const std::pair<typename HeapMapType::iterator, bool>& emplaceResult = heapsPool.emplace(poolId, std::move(heapAndTimePair));
+            CV_CheckEQ(static_cast<int>(emplaceResult.second), 1, "Failed to insert the heap into its memory pool");
+            heapIt = emplaceResult.first;
+        }
+        else
+        {
+            CV_CheckEQ(heapIt->second.heapPtr.use_count(), 1, "Cannot modify a heap that is currently accessed by another caller");
+            heapIt->second.heapPtr->clear();
+            heapIt->second.heapPtr->reserve(capacity);
+            heapIt->second.iterCounter = 0;
+        }
+
+        if (iterThreshold <= 1) {
+            iterThreshold = 2 * cv::getNumThreads();
+        }
+
+        // Remove heaps that were not reused for more than given iterThreshold
+        typename HeapMapType::iterator cleanupIt = heapsPool.begin();
+        while (cleanupIt != heapsPool.end())
+        {
+            if (cleanupIt->second.iterCounter++ > iterThreshold)
+            {
+                CV_Assert(cleanupIt != heapIt);
+                cleanupIt = heapsPool.erase(cleanupIt);
+                continue;
+            }
+            ++cleanupIt;
+        }
+
+        return heapIt->second.heapPtr;
+    }
+};
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_HEAP_H_
diff --git a/3rdParty/include/opencv2/flann/hierarchical_clustering_index.h b/3rdParty/include/opencv2/flann/hierarchical_clustering_index.h
new file mode 100644
index 0000000..60662e7
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/hierarchical_clustering_index.h
@@ -0,0 +1,846 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
+#define OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <map>
+#include <limits>
+#include <cmath>
+
+#include "general.h"
+#include "nn_index.h"
+#include "dist.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+
+
+namespace cvflann
+{
+
+struct HierarchicalClusteringIndexParams : public IndexParams
+{
+    HierarchicalClusteringIndexParams(int branching = 32,
+                                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM,
+                                      int trees = 4, int leaf_size = 100)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL;
+        // The branching factor used in the hierarchical clustering
+        (*this)["branching"] = branching;
+        // Algorithm used for picking the initial cluster centers
+        (*this)["centers_init"] = centers_init;
+        // number of parallel trees to build
+        (*this)["trees"] = trees;
+        // maximum leaf size
+        (*this)["leaf_size"] = leaf_size;
+    }
+};
+
+
+/**
+ * Hierarchical index
+ *
+ * Contains a tree constructed through a hierarchical clustering
+ * and other information for indexing a set of points for nearest-neighbour matching.
+ */
+template <typename Distance>
+class HierarchicalClusteringIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+private:
+
+
+    typedef void (HierarchicalClusteringIndex::* centersAlgFunction)(int, int*, int, int*, int&);
+
+    /**
+     * The function used for choosing the cluster centers.
+     */
+    centersAlgFunction chooseCenters;
+
+
+
+    /**
+     * Chooses the initial centers in the k-means clustering in a random manner.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     *     indices_length = length of indices vector
+     *
+     */
+    void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
+    {
+        UniqueRandom r(indices_length);
+
+        int index;
+        for (index=0; index<k; ++index) {
+            bool duplicate = true;
+            int rnd;
+            while (duplicate) {
+                duplicate = false;
+                rnd = r.next();
+                if (rnd<0) {
+                    centers_length = index;
+                    return;
+                }
+
+                centers[index] = dsindices[rnd];
+
+                for (int j=0; j<index; ++j) {
+                    DistanceType sq = distance(dataset[centers[index]], dataset[centers[j]], dataset.cols);
+                    if (sq<1e-16) {
+                        duplicate = true;
+                    }
+                }
+            }
+        }
+
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using Gonzales' algorithm
+     * so that the centers are spaced apart from each other.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersGonzales(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        int rnd = rand_int(n);
+        CV_DbgAssert(rnd >=0 && rnd < n);
+
+        centers[0] = dsindices[rnd];
+
+        int index;
+        for (index=1; index<k; ++index) {
+
+            int best_index = -1;
+            DistanceType best_val = 0;
+            for (int j=0; j<n; ++j) {
+                DistanceType dist = distance(dataset[centers[0]],dataset[dsindices[j]],dataset.cols);
+                for (int i=1; i<index; ++i) {
+                    DistanceType tmp_dist = distance(dataset[centers[i]],dataset[dsindices[j]],dataset.cols);
+                    if (tmp_dist<dist) {
+                        dist = tmp_dist;
+                    }
+                }
+                if (dist>best_val) {
+                    best_val = dist;
+                    best_index = j;
+                }
+            }
+            if (best_index!=-1) {
+                centers[index] = dsindices[best_index];
+            }
+            else {
+                break;
+            }
+        }
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using the algorithm
+     * proposed in the KMeans++ paper:
+     * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
+     *
+     * Implementation of this function was converted from the one provided in Arthur's code.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        double currentPot = 0;
+        DistanceType* closestDistSq = new DistanceType[n];
+
+        // Choose one random center and set the closestDistSq values
+        int index = rand_int(n);
+        CV_DbgAssert(index >=0 && index < n);
+        centers[0] = dsindices[index];
+
+        // Computing distance^2 will have the advantage of even higher probability further to pick new centers
+        // far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article)
+        for (int i = 0; i < n; i++) {
+            closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
+            closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
+            currentPot += closestDistSq[i];
+        }
+
+
+        const int numLocalTries = 1;
+
+        // Choose each center
+        int centerCount;
+        for (centerCount = 1; centerCount < k; centerCount++) {
+
+            // Repeat several trials
+            double bestNewPot = -1;
+            int bestNewIndex = 0;
+            for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
+
+                // Choose our center - have to be slightly careful to return a valid answer even accounting
+                // for possible rounding errors
+                double randVal = rand_double(currentPot);
+                for (index = 0; index < n-1; index++) {
+                    if (randVal <= closestDistSq[index]) break;
+                    else randVal -= closestDistSq[index];
+                }
+
+                // Compute the new potential
+                double newPot = 0;
+                for (int i = 0; i < n; i++) {
+                    DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
+                    newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
+                }
+
+                // Store the best result
+                if ((bestNewPot < 0)||(newPot < bestNewPot)) {
+                    bestNewPot = newPot;
+                    bestNewIndex = index;
+                }
+            }
+
+            // Add the appropriate center
+            centers[centerCount] = dsindices[bestNewIndex];
+            currentPot = bestNewPot;
+            for (int i = 0; i < n; i++) {
+                DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols);
+                closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
+            }
+        }
+
+        centers_length = centerCount;
+
+        delete[] closestDistSq;
+    }
+
+
+    /**
+     * Chooses the initial centers in a way inspired by Gonzales (by Pierre-Emmanuel Viel):
+     * select the first point of the list as a candidate, then parse the points list. If another
+     * point is further than current candidate from the other centers, test if it is a good center
+     * of a local aggregation. If it is, replace current candidate by this point. And so on...
+     *
+     * Used with KMeansIndex that computes centers coordinates by averaging positions of clusters points,
+     * this doesn't make a real difference with previous methods. But used with HierarchicalClusteringIndex
+     * class that pick centers among existing points instead of computing the barycenters, there is a real
+     * improvement.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void GroupWiseCenterChooser(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
+    {
+        const float kSpeedUpFactor = 1.3f;
+
+        int n = indices_length;
+
+        DistanceType* closestDistSq = new DistanceType[n];
+
+        // Choose one random center and set the closestDistSq values
+        int index = rand_int(n);
+        CV_DbgAssert(index >=0 && index < n);
+        centers[0] = dsindices[index];
+
+        for (int i = 0; i < n; i++) {
+            closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
+        }
+
+
+        // Choose each center
+        int centerCount;
+        for (centerCount = 1; centerCount < k; centerCount++) {
+
+            // Repeat several trials
+            double bestNewPot = -1;
+            int bestNewIndex = 0;
+            DistanceType furthest = 0;
+            for (index = 0; index < n; index++) {
+
+                // We will test only the potential of the points further than current candidate
+                if( closestDistSq[index] > kSpeedUpFactor * (float)furthest ) {
+
+                    // Compute the new potential
+                    double newPot = 0;
+                    for (int i = 0; i < n; i++) {
+                        newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols)
+                                            , closestDistSq[i] );
+                    }
+
+                    // Store the best result
+                    if ((bestNewPot < 0)||(newPot <= bestNewPot)) {
+                        bestNewPot = newPot;
+                        bestNewIndex = index;
+                        furthest = closestDistSq[index];
+                    }
+                }
+            }
+
+            // Add the appropriate center
+            centers[centerCount] = dsindices[bestNewIndex];
+            for (int i = 0; i < n; i++) {
+                closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols)
+                                             , closestDistSq[i] );
+            }
+        }
+
+        centers_length = centerCount;
+
+        delete[] closestDistSq;
+    }
+
+
+public:
+
+
+    /**
+     * Index constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the hierarchical k-means algorithm
+     */
+    HierarchicalClusteringIndex(const Matrix<ElementType>& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(),
+                                Distance d = Distance())
+        : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d)
+    {
+        memoryCounter = 0;
+
+        size_ = dataset.rows;
+        veclen_ = dataset.cols;
+
+        branching_ = get_param(params,"branching",32);
+        centers_init_ = get_param(params,"centers_init", FLANN_CENTERS_RANDOM);
+        trees_ = get_param(params,"trees",4);
+        leaf_size_ = get_param(params,"leaf_size",100);
+
+        if (centers_init_==FLANN_CENTERS_RANDOM) {
+            chooseCenters = &HierarchicalClusteringIndex::chooseCentersRandom;
+        }
+        else if (centers_init_==FLANN_CENTERS_GONZALES) {
+            chooseCenters = &HierarchicalClusteringIndex::chooseCentersGonzales;
+        }
+        else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
+            chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp;
+        }
+        else if (centers_init_==FLANN_CENTERS_GROUPWISE) {
+            chooseCenters = &HierarchicalClusteringIndex::GroupWiseCenterChooser;
+        }
+        else {
+            FLANN_THROW(cv::Error::StsError, "Unknown algorithm for choosing initial centers.");
+        }
+
+        root = new NodePtr[trees_];
+        indices = new int*[trees_];
+
+        for (int i=0; i<trees_; ++i) {
+            root[i] = NULL;
+            indices[i] = NULL;
+        }
+    }
+
+    HierarchicalClusteringIndex(const HierarchicalClusteringIndex&);
+    HierarchicalClusteringIndex& operator=(const HierarchicalClusteringIndex&);
+
+    /**
+     * Index destructor.
+     *
+     * Release the memory used by the index.
+     */
+    virtual ~HierarchicalClusteringIndex()
+    {
+        if (root!=NULL) {
+            delete[] root;
+        }
+
+        if (indices!=NULL) {
+            free_indices();
+            delete[] indices;
+        }
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const CV_OVERRIDE
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const CV_OVERRIDE
+    {
+        return veclen_;
+    }
+
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const CV_OVERRIDE
+    {
+        return pool.usedMemory+pool.wastedMemory+memoryCounter;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex() CV_OVERRIDE
+    {
+        if (branching_<2) {
+            FLANN_THROW(cv::Error::StsError, "Branching factor must be at least 2");
+        }
+
+        free_indices();
+
+        for (int i=0; i<trees_; ++i) {
+            indices[i] = new int[size_];
+            for (size_t j=0; j<size_; ++j) {
+                indices[i][j] = (int)j;
+            }
+            root[i] = pool.allocate<Node>();
+            computeClustering(root[i], indices[i], (int)size_, branching_,0);
+        }
+    }
+
+
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_HIERARCHICAL;
+    }
+
+
+    void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        save_value(stream, branching_);
+        save_value(stream, trees_);
+        save_value(stream, centers_init_);
+        save_value(stream, leaf_size_);
+        save_value(stream, memoryCounter);
+        for (int i=0; i<trees_; ++i) {
+            save_value(stream, *indices[i], size_);
+            save_tree(stream, root[i], i);
+        }
+
+    }
+
+
+    void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        if (root!=NULL) {
+            delete[] root;
+        }
+
+        if (indices!=NULL) {
+            free_indices();
+            delete[] indices;
+        }
+
+        load_value(stream, branching_);
+        load_value(stream, trees_);
+        load_value(stream, centers_init_);
+        load_value(stream, leaf_size_);
+        load_value(stream, memoryCounter);
+
+        indices = new int*[trees_];
+        root = new NodePtr[trees_];
+        for (int i=0; i<trees_; ++i) {
+            indices[i] = new int[size_];
+            load_value(stream, *indices[i], size_);
+            load_tree(stream, root[i], i);
+        }
+
+        params["algorithm"] = getType();
+        params["branching"] = branching_;
+        params["trees"] = trees_;
+        params["centers_init"] = centers_init_;
+        params["leaf_size"] = leaf_size_;
+    }
+
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     searchParams = parameters that influence the search algorithm (checks)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
+    {
+
+        const int maxChecks = get_param(searchParams,"checks",32);
+        const bool explore_all_trees = get_param(searchParams,"explore_all_trees",false);
+
+        // Priority queue storing intermediate branches in the best-bin-first search
+        const cv::Ptr<Heap<BranchSt>>& heap = Heap<BranchSt>::getPooledInstance(cv::utils::getThreadID(), (int)size_);
+
+        std::vector<bool> checked(size_,false);
+        int checks = 0;
+        for (int i=0; i<trees_; ++i) {
+            findNN(root[i], result, vec, checks, maxChecks, heap, checked, explore_all_trees);
+            if (!explore_all_trees && (checks >= maxChecks) && result.full())
+                break;
+        }
+
+        BranchSt branch;
+        while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
+            NodePtr node = branch.node;
+            findNN(node, result, vec, checks, maxChecks, heap, checked, false);
+        }
+
+        CV_Assert(result.full());
+    }
+
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return params;
+    }
+
+
+private:
+
+    /**
+     * Structure representing a node in the hierarchical k-means tree.
+     */
+    struct Node
+    {
+        /**
+         * The cluster center index
+         */
+        int pivot;
+        /**
+         * The cluster size (number of points in the cluster)
+         */
+        int size;
+        /**
+         * Child nodes (only for non-terminal nodes)
+         */
+        Node** childs;
+        /**
+         * Node points (only for terminal nodes)
+         */
+        int* indices;
+        /**
+         * Level
+         */
+        int level;
+    };
+    typedef Node* NodePtr;
+
+
+
+    /**
+     * Alias definition for a nicer syntax.
+     */
+    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
+
+
+
+    void save_tree(FILE* stream, NodePtr node, int num)
+    {
+        save_value(stream, *node);
+        if (node->childs==NULL) {
+            int indices_offset = (int)(node->indices - indices[num]);
+            save_value(stream, indices_offset);
+        }
+        else {
+            for(int i=0; i<branching_; ++i) {
+                save_tree(stream, node->childs[i], num);
+            }
+        }
+    }
+
+
+    void load_tree(FILE* stream, NodePtr& node, int num)
+    {
+        node = pool.allocate<Node>();
+        load_value(stream, *node);
+        if (node->childs==NULL) {
+            int indices_offset;
+            load_value(stream, indices_offset);
+            node->indices = indices[num] + indices_offset;
+        }
+        else {
+            node->childs = pool.allocate<NodePtr>(branching_);
+            for(int i=0; i<branching_; ++i) {
+                load_tree(stream, node->childs[i], num);
+            }
+        }
+    }
+
+
+    /**
+     * Release the inner elements of indices[]
+     */
+    void free_indices()
+    {
+        if (indices!=NULL) {
+            for(int i=0; i<trees_; ++i) {
+                if (indices[i]!=NULL) {
+                    delete[] indices[i];
+                    indices[i] = NULL;
+                }
+            }
+        }
+    }
+
+
+    void computeLabels(int* dsindices, int indices_length,  int* centers, int centers_length, int* labels, DistanceType& cost)
+    {
+        cost = 0;
+        for (int i=0; i<indices_length; ++i) {
+            ElementType* point = dataset[dsindices[i]];
+            DistanceType dist = distance(point, dataset[centers[0]], veclen_);
+            labels[i] = 0;
+            for (int j=1; j<centers_length; ++j) {
+                DistanceType new_dist = distance(point, dataset[centers[j]], veclen_);
+                if (dist>new_dist) {
+                    labels[i] = j;
+                    dist = new_dist;
+                }
+            }
+            cost += dist;
+        }
+    }
+
+    /**
+     * The method responsible with actually doing the recursive hierarchical
+     * clustering
+     *
+     * Params:
+     *     node = the node to cluster
+     *     indices = indices of the points belonging to the current node
+     *     branching = the branching factor to use in the clustering
+     *
+     * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
+     */
+    void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level)
+    {
+        node->size = indices_length;
+        node->level = level;
+
+        if (indices_length < leaf_size_) { // leaf node
+            node->indices = dsindices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            return;
+        }
+
+        std::vector<int> centers(branching);
+        std::vector<int> labels(indices_length);
+
+        int centers_length;
+        (this->*chooseCenters)(branching, dsindices, indices_length, &centers[0], centers_length);
+
+        if (centers_length<branching) {
+            node->indices = dsindices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            return;
+        }
+
+
+        //	assign points to clusters
+        DistanceType cost;
+        computeLabels(dsindices, indices_length, &centers[0], centers_length, &labels[0], cost);
+
+        node->childs = pool.allocate<NodePtr>(branching);
+        int start = 0;
+        int end = start;
+        for (int i=0; i<branching; ++i) {
+            for (int j=0; j<indices_length; ++j) {
+                if (labels[j]==i) {
+                    std::swap(dsindices[j],dsindices[end]);
+                    std::swap(labels[j],labels[end]);
+                    end++;
+                }
+            }
+
+            node->childs[i] = pool.allocate<Node>();
+            node->childs[i]->pivot = centers[i];
+            node->childs[i]->indices = NULL;
+            computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1);
+            start=end;
+        }
+    }
+
+
+
+    /**
+     * Performs one descent in the hierarchical k-means tree. The branches not
+     * visited are stored in a priority queue.
+     *
+     * Params:
+     *      node = node to explore
+     *      result = container for the k-nearest neighbors found
+     *      vec = query points
+     *      checks = how many points in the dataset have been checked so far
+     *      maxChecks = maximum dataset points to checks
+     */
+
+
+    void findNN(NodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
+                const cv::Ptr<Heap<BranchSt>>& heap, std::vector<bool>& checked, bool explore_all_trees = false)
+    {
+        if (node->childs==NULL) {
+            if (!explore_all_trees && (checks>=maxChecks) && result.full()) {
+                return;
+            }
+            for (int i=0; i<node->size; ++i) {
+                int index = node->indices[i];
+                if (!checked[index]) {
+                    DistanceType dist = distance(dataset[index], vec, veclen_);
+                    result.addPoint(dist, index);
+                    checked[index] = true;
+                    ++checks;
+                }
+            }
+        }
+        else {
+            DistanceType* domain_distances = new DistanceType[branching_];
+            int best_index = 0;
+            domain_distances[best_index] = distance(vec, dataset[node->childs[best_index]->pivot], veclen_);
+            for (int i=1; i<branching_; ++i) {
+                domain_distances[i] = distance(vec, dataset[node->childs[i]->pivot], veclen_);
+                if (domain_distances[i]<domain_distances[best_index]) {
+                    best_index = i;
+                }
+            }
+            for (int i=0; i<branching_; ++i) {
+                if (i!=best_index) {
+                    heap->insert(BranchSt(node->childs[i],domain_distances[i]));
+                }
+            }
+            delete[] domain_distances;
+            findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked, explore_all_trees);
+        }
+    }
+
+private:
+
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset;
+
+    /**
+     * Parameters used by this index
+     */
+    IndexParams params;
+
+
+    /**
+     * Number of features in the dataset.
+     */
+    size_t size_;
+
+    /**
+     * Length of each feature.
+     */
+    size_t veclen_;
+
+    /**
+     * The root node in the tree.
+     */
+    NodePtr* root;
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    int** indices;
+
+
+    /**
+     * The distance
+     */
+    Distance distance;
+
+    /**
+     * Pooled memory allocator.
+     *
+     * Using a pooled memory allocator is more efficient
+     * than allocating memory directly when there is a large
+     * number small of memory allocations.
+     */
+    PooledAllocator pool;
+
+    /**
+     * Memory occupied by the index.
+     */
+    int memoryCounter;
+
+    /** index parameters */
+    int branching_;
+    int trees_;
+    flann_centers_init_t centers_init_;
+    int leaf_size_;
+
+
+};
+
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */
diff --git a/3rdParty/include/opencv2/flann/index_testing.h b/3rdParty/include/opencv2/flann/index_testing.h
new file mode 100644
index 0000000..207adef
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/index_testing.h
@@ -0,0 +1,321 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_INDEX_TESTING_H_
+#define OPENCV_FLANN_INDEX_TESTING_H_
+
+//! @cond IGNORED
+
+#include <cstring>
+#include <cmath>
+
+#include "matrix.h"
+#include "nn_index.h"
+#include "result_set.h"
+#include "logger.h"
+#include "timer.h"
+
+
+namespace cvflann
+{
+
+inline int countCorrectMatches(int* neighbors, int* groundTruth, int n)
+{
+    int count = 0;
+    for (int i=0; i<n; ++i) {
+        for (int k=0; k<n; ++k) {
+            if (neighbors[i]==groundTruth[k]) {
+                count++;
+                break;
+            }
+        }
+    }
+    return count;
+}
+
+
+template <typename Distance>
+typename Distance::ResultType computeDistanceRaport(const Matrix<typename Distance::ElementType>& inputData, typename Distance::ElementType* target,
+                                                    int* neighbors, int* groundTruth, int veclen, int n, const Distance& distance)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    DistanceType ret = 0;
+    for (int i=0; i<n; ++i) {
+        DistanceType den = distance(inputData[groundTruth[i]], target, veclen);
+        DistanceType num = distance(inputData[neighbors[i]], target, veclen);
+
+        if ((den==0)&&(num==0)) {
+            ret += 1;
+        }
+        else {
+            ret += num/den;
+        }
+    }
+
+    return ret;
+}
+
+template <typename Distance>
+float search_with_ground_truth(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                               const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches, int nn, int checks,
+                               float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    if (matches.cols<size_t(nn)) {
+        Logger::info("matches.cols=%d, nn=%d\n",matches.cols,nn);
+
+        FLANN_THROW(cv::Error::StsError, "Ground truth is not computed for as many neighbors as requested");
+    }
+
+    KNNResultSet<DistanceType> resultSet(nn+skipMatches);
+    SearchParams searchParams(checks);
+
+    std::vector<int> indices(nn+skipMatches);
+    std::vector<DistanceType> dists(nn+skipMatches);
+    int* neighbors = &indices[skipMatches];
+
+    int correct = 0;
+    DistanceType distR = 0;
+    StartStopTimer t;
+    int repeats = 0;
+    while (t.value<0.2) {
+        repeats++;
+        t.start();
+        correct = 0;
+        distR = 0;
+        for (size_t i = 0; i < testData.rows; i++) {
+            resultSet.init(&indices[0], &dists[0]);
+            index.findNeighbors(resultSet, testData[i], searchParams);
+
+            correct += countCorrectMatches(neighbors,matches[i], nn);
+            distR += computeDistanceRaport<Distance>(inputData, testData[i], neighbors, matches[i], (int)testData.cols, nn, distance);
+        }
+        t.stop();
+    }
+    time = float(t.value/repeats);
+
+    float precicion = (float)correct/(nn*testData.rows);
+
+    dist = distR/(testData.rows*nn);
+
+    Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n",
+                 checks, precicion, time, 1000.0 * time / testData.rows, dist);
+
+    return precicion;
+}
+
+
+template <typename Distance>
+float test_index_checks(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                        const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
+                        int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
+    Logger::info("---------------------------------------------------------\n");
+
+    float time = 0;
+    DistanceType dist = 0;
+    precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches);
+
+    return time;
+}
+
+template <typename Distance>
+float test_index_precision(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                           const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
+                           float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0)
+{
+    typedef typename Distance::ResultType DistanceType;
+    const float SEARCH_EPS = 0.001f;
+
+    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
+    Logger::info("---------------------------------------------------------\n");
+
+    int c2 = 1;
+    float p2;
+    int c1 = 1;
+    //float p1;
+    float time;
+    DistanceType dist;
+
+    p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+
+    if (p2>precision) {
+        Logger::info("Got as close as I can\n");
+        checks = c2;
+        return time;
+    }
+
+    while (p2<precision) {
+        c1 = c2;
+        //p1 = p2;
+        c2 *=2;
+        p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+    }
+
+    int cx;
+    float realPrecision;
+    if (fabs(p2-precision)>SEARCH_EPS) {
+        Logger::info("Start linear estimation\n");
+        // after we got to values in the vecinity of the desired precision
+        // use linear approximation get a better estimation
+
+        cx = (c1+c2)/2;
+        realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+        while (fabs(realPrecision-precision)>SEARCH_EPS) {
+
+            if (realPrecision<precision) {
+                c1 = cx;
+            }
+            else {
+                c2 = cx;
+            }
+            cx = (c1+c2)/2;
+            if (cx==c1) {
+                Logger::info("Got as close as I can\n");
+                break;
+            }
+            realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+        }
+
+        c2 = cx;
+        p2 = realPrecision;
+
+    }
+    else {
+        Logger::info("No need for linear estimation\n");
+        cx = c2;
+        realPrecision = p2;
+    }
+
+    checks = cx;
+    return time;
+}
+
+
+template <typename Distance>
+void test_index_precisions(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
+                           const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
+                           float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0)
+{
+    typedef typename Distance::ResultType DistanceType;
+
+    const float SEARCH_EPS = 0.001;
+
+    // make sure precisions array is sorted
+    std::sort(precisions, precisions+precisions_length);
+
+    int pindex = 0;
+    float precision = precisions[pindex];
+
+    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
+    Logger::info("---------------------------------------------------------\n");
+
+    int c2 = 1;
+    float p2;
+
+    int c1 = 1;
+    float p1;
+
+    float time;
+    DistanceType dist;
+
+    p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+
+    // if precision for 1 run down the tree is already
+    // better then some of the requested precisions, then
+    // skip those
+    while (precisions[pindex]<p2 && pindex<precisions_length) {
+        pindex++;
+    }
+
+    if (pindex==precisions_length) {
+        Logger::info("Got as close as I can\n");
+        return;
+    }
+
+    for (int i=pindex; i<precisions_length; ++i) {
+
+        precision = precisions[i];
+        while (p2<precision) {
+            c1 = c2;
+            p1 = p2;
+            c2 *=2;
+            p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
+            if ((maxTime> 0)&&(time > maxTime)&&(p2<precision)) return;
+        }
+
+        int cx;
+        float realPrecision;
+        if (fabs(p2-precision)>SEARCH_EPS) {
+            Logger::info("Start linear estimation\n");
+            // after we got to values in the vecinity of the desired precision
+            // use linear approximation get a better estimation
+
+            cx = (c1+c2)/2;
+            realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+            while (fabs(realPrecision-precision)>SEARCH_EPS) {
+
+                if (realPrecision<precision) {
+                    c1 = cx;
+                }
+                else {
+                    c2 = cx;
+                }
+                cx = (c1+c2)/2;
+                if (cx==c1) {
+                    Logger::info("Got as close as I can\n");
+                    break;
+                }
+                realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
+            }
+
+            c2 = cx;
+            p2 = realPrecision;
+
+        }
+        else {
+            Logger::info("No need for linear estimation\n");
+            cx = c2;
+            realPrecision = p2;
+        }
+
+    }
+}
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_INDEX_TESTING_H_
diff --git a/3rdParty/include/opencv2/flann/kdtree_index.h b/3rdParty/include/opencv2/flann/kdtree_index.h
new file mode 100644
index 0000000..8245f7d
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/kdtree_index.h
@@ -0,0 +1,636 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_KDTREE_INDEX_H_
+#define OPENCV_FLANN_KDTREE_INDEX_H_
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <map>
+#include <cstring>
+
+#include "nn_index.h"
+#include "dynamic_bitset.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+
+
+namespace cvflann
+{
+
+struct KDTreeIndexParams : public IndexParams
+{
+    KDTreeIndexParams(int trees = 4)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KDTREE;
+        (*this)["trees"] = trees;
+    }
+};
+
+
+/**
+ * Randomized kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template <typename Distance>
+class KDTreeIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+
+    /**
+     * KDTree constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the kdtree algorithm
+     */
+    KDTreeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeIndexParams(),
+                Distance d = Distance() ) :
+        dataset_(inputData), index_params_(params), distance_(d)
+    {
+        size_ = dataset_.rows;
+        veclen_ = dataset_.cols;
+
+        trees_ = get_param(index_params_,"trees",4);
+        tree_roots_ = new NodePtr[trees_];
+
+        // Create a permutable array of indices to the input vectors.
+        vind_.resize(size_);
+        for (size_t i = 0; i < size_; ++i) {
+            vind_[i] = int(i);
+        }
+
+        mean_ = new DistanceType[veclen_];
+        var_ = new DistanceType[veclen_];
+    }
+
+
+    KDTreeIndex(const KDTreeIndex&);
+    KDTreeIndex& operator=(const KDTreeIndex&);
+
+    /**
+     * Standard destructor
+     */
+    ~KDTreeIndex()
+    {
+        if (tree_roots_!=NULL) {
+            delete[] tree_roots_;
+        }
+        delete[] mean_;
+        delete[] var_;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex() CV_OVERRIDE
+    {
+        /* Construct the randomized trees. */
+        for (int i = 0; i < trees_; i++) {
+            /* Randomize the order of vectors to allow for unbiased sampling. */
+#ifndef OPENCV_FLANN_USE_STD_RAND
+            cv::randShuffle(vind_);
+#else
+            std::random_shuffle(vind_.begin(), vind_.end());
+#endif
+
+            tree_roots_[i] = divideTree(&vind_[0], int(size_) );
+        }
+    }
+
+
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_KDTREE;
+    }
+
+
+    void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        save_value(stream, trees_);
+        for (int i=0; i<trees_; ++i) {
+            save_tree(stream, tree_roots_[i]);
+        }
+    }
+
+
+
+    void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        load_value(stream, trees_);
+        if (tree_roots_!=NULL) {
+            delete[] tree_roots_;
+        }
+        tree_roots_ = new NodePtr[trees_];
+        for (int i=0; i<trees_; ++i) {
+            load_tree(stream,tree_roots_[i]);
+        }
+
+        index_params_["algorithm"] = getType();
+        index_params_["trees"] = tree_roots_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const CV_OVERRIDE
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const CV_OVERRIDE
+    {
+        return veclen_;
+    }
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const CV_OVERRIDE
+    {
+        return int(pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int));  // pool memory and vind array memory
+    }
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
+    {
+        const int maxChecks = get_param(searchParams,"checks", 32);
+        const float epsError = 1+get_param(searchParams,"eps",0.0f);
+        const bool explore_all_trees = get_param(searchParams,"explore_all_trees",false);
+
+        if (maxChecks==FLANN_CHECKS_UNLIMITED) {
+            getExactNeighbors(result, vec, epsError);
+        }
+        else {
+            getNeighbors(result, vec, maxChecks, epsError, explore_all_trees);
+        }
+    }
+
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return index_params_;
+    }
+
+private:
+
+
+    /*--------------------- Internal Data Structures --------------------------*/
+    struct Node
+    {
+        /**
+         * Dimension used for subdivision.
+         */
+        int divfeat;
+        /**
+         * The values used for subdivision.
+         */
+        DistanceType divval;
+        /**
+         * The child nodes.
+         */
+        Node* child1, * child2;
+    };
+    typedef Node* NodePtr;
+    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
+    typedef BranchSt* Branch;
+
+
+
+    void save_tree(FILE* stream, NodePtr tree)
+    {
+        save_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            save_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            save_tree(stream, tree->child2);
+        }
+    }
+
+
+    void load_tree(FILE* stream, NodePtr& tree)
+    {
+        tree = pool_.allocate<Node>();
+        load_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            load_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            load_tree(stream, tree->child2);
+        }
+    }
+
+
+    /**
+     * Create a tree node that subdivides the list of vecs from vind[first]
+     * to vind[last].  The routine is called recursively on each sublist.
+     * Place a pointer to this new tree node in the location pTree.
+     *
+     * Params: pTree = the new node to create
+     *                  first = index of the first vector
+     *                  last = index of the last vector
+     */
+    NodePtr divideTree(int* ind, int count)
+    {
+        NodePtr node = pool_.allocate<Node>(); // allocate memory
+
+        /* If too few exemplars remain, then make this a leaf node. */
+        if ( count == 1) {
+            node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
+            node->divfeat = *ind;    /* Store index of this vec. */
+        }
+        else {
+            int idx;
+            int cutfeat;
+            DistanceType cutval;
+            meanSplit(ind, count, idx, cutfeat, cutval);
+
+            node->divfeat = cutfeat;
+            node->divval = cutval;
+            node->child1 = divideTree(ind, idx);
+            node->child2 = divideTree(ind+idx, count-idx);
+        }
+
+        return node;
+    }
+
+
+    /**
+     * Choose which feature to use in order to subdivide this set of vectors.
+     * Make a random choice among those with the highest variance, and use
+     * its variance as the threshold value.
+     */
+    void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval)
+    {
+        memset(mean_,0,veclen_*sizeof(DistanceType));
+        memset(var_,0,veclen_*sizeof(DistanceType));
+
+        /* Compute mean values.  Only the first SAMPLE_MEAN values need to be
+            sampled to get a good estimate.
+         */
+        int cnt = std::min((int)SAMPLE_MEAN+1, count);
+        for (int j = 0; j < cnt; ++j) {
+            ElementType* v = dataset_[ind[j]];
+            for (size_t k=0; k<veclen_; ++k) {
+                mean_[k] += v[k];
+            }
+        }
+        for (size_t k=0; k<veclen_; ++k) {
+            mean_[k] /= cnt;
+        }
+
+        /* Compute variances (no need to divide by count). */
+        for (int j = 0; j < cnt; ++j) {
+            ElementType* v = dataset_[ind[j]];
+            for (size_t k=0; k<veclen_; ++k) {
+                DistanceType dist = v[k] - mean_[k];
+                var_[k] += dist * dist;
+            }
+        }
+        /* Select one of the highest variance indices at random. */
+        cutfeat = selectDivision(var_);
+        cutval = mean_[cutfeat];
+
+        int lim1, lim2;
+        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+        if (lim1>count/2) index = lim1;
+        else if (lim2<count/2) index = lim2;
+        else index = count/2;
+
+        /* If either list is empty, it means that all remaining features
+         * are identical. Split in the middle to maintain a balanced tree.
+         */
+        if ((lim1==count)||(lim2==0)) index = count/2;
+    }
+
+
+    /**
+     * Select the top RAND_DIM largest values from v and return the index of
+     * one of these selected at random.
+     */
+    int selectDivision(DistanceType* v)
+    {
+        int num = 0;
+        size_t topind[RAND_DIM];
+
+        /* Create a list of the indices of the top RAND_DIM values. */
+        for (size_t i = 0; i < veclen_; ++i) {
+            if ((num < RAND_DIM)||(v[i] > v[topind[num-1]])) {
+                /* Put this element at end of topind. */
+                if (num < RAND_DIM) {
+                    topind[num++] = i;            /* Add to list. */
+                }
+                else {
+                    topind[num-1] = i;         /* Replace last element. */
+                }
+                /* Bubble end value down to right location by repeated swapping. */
+                int j = num - 1;
+                while (j > 0  &&  v[topind[j]] > v[topind[j-1]]) {
+                    std::swap(topind[j], topind[j-1]);
+                    --j;
+                }
+            }
+        }
+        /* Select a random integer in range [0,num-1], and return that index. */
+        int rnd = rand_int(num);
+        return (int)topind[rnd];
+    }
+
+
+    /**
+     *  Subdivide the list of points by a plane perpendicular on axe corresponding
+     *  to the 'cutfeat' dimension at 'cutval' position.
+     *
+     *  On return:
+     *  dataset[ind[0..lim1-1]][cutfeat]<cutval
+     *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
+     *  dataset[ind[lim2..count]][cutfeat]>cutval
+     */
+    void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
+    {
+        /* Move vector indices for left subtree to front of list. */
+        int left = 0;
+        int right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        lim1 = left;
+        right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        lim2 = left;
+    }
+
+    /**
+     * Performs an exact nearest neighbor search. The exact search performs a full
+     * traversal of the tree.
+     */
+    void getExactNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, float epsError)
+    {
+        //		checkID -= 1;  /* Set a different unique ID for each search. */
+
+        if (trees_ > 1) {
+            fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search");
+        }
+        if (trees_>0) {
+            searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError);
+        }
+        CV_Assert(result.full());
+    }
+
+    /**
+     * Performs the approximate nearest-neighbor search. The search is approximate
+     * because the tree traversal is abandoned after a given number of descends in
+     * the tree.
+     */
+    void getNeighbors(ResultSet<DistanceType>& result, const ElementType* vec,
+                      int maxCheck, float epsError, bool explore_all_trees = false)
+    {
+        int i;
+        BranchSt branch;
+        int checkCount = 0;
+        DynamicBitset checked(size_);
+
+        // Priority queue storing intermediate branches in the best-bin-first search
+        const cv::Ptr<Heap<BranchSt>>& heap = Heap<BranchSt>::getPooledInstance(cv::utils::getThreadID(), (int)size_);
+
+        /* Search once through each tree down to root. */
+        for (i = 0; i < trees_; ++i) {
+            searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck,
+                        epsError, heap, checked, explore_all_trees);
+            if (!explore_all_trees && (checkCount >= maxCheck) && result.full())
+                break;
+        }
+
+        /* Keep searching other branches from heap until finished. */
+        while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) {
+            searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck,
+                        epsError, heap, checked, false);
+        }
+
+        CV_Assert(result.full());
+    }
+
+
+    /**
+     *  Search starting from a given node of the tree.  Based on any mismatches at
+     *  higher levels, all exemplars below this level must have a distance of
+     *  at least "mindistsq".
+     */
+    void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck,
+                     float epsError, const cv::Ptr<Heap<BranchSt>>& heap, DynamicBitset& checked, bool explore_all_trees = false)
+    {
+        if (result_set.worstDist()<mindist) {
+            //			printf("Ignoring branch, too far\n");
+            return;
+        }
+
+        /* If this is a leaf node, then do check and return. */
+        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+            /*  Do not check same node more than once when searching multiple trees.
+                Once a vector is checked, we set its location in vind to the
+                current checkID.
+             */
+            int index = node->divfeat;
+            if ( checked.test(index) ||
+                 (!explore_all_trees && (checkCount>=maxCheck) && result_set.full()) ) {
+                return;
+            }
+            checked.set(index);
+            checkCount++;
+
+            DistanceType dist = distance_(dataset_[index], vec, veclen_);
+            result_set.addPoint(dist,index);
+
+            return;
+        }
+
+        /* Which child branch should be taken first? */
+        ElementType val = vec[node->divfeat];
+        DistanceType diff = val - node->divval;
+        NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
+        NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
+
+        /* Create a branch record for the branch not taken.  Add distance
+            of this feature boundary (we don't attempt to correct for any
+            use of this feature in a parent node, which is unlikely to
+            happen and would have only a small effect).  Don't bother
+            adding more branches to heap after halfway point, as cost of
+            adding exceeds their value.
+         */
+
+        DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
+        //		if (2 * checkCount < maxCheck  ||  !result.full()) {
+        if ((new_distsq*epsError < result_set.worstDist())||  !result_set.full()) {
+            heap->insert( BranchSt(otherChild, new_distsq) );
+        }
+
+        /* Call recursively to search next level down. */
+        searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked);
+    }
+
+    /**
+     * Performs an exact search in the tree starting from a node.
+     */
+    void searchLevelExact(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError)
+    {
+        /* If this is a leaf node, then do check and return. */
+        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+            int index = node->divfeat;
+            DistanceType dist = distance_(dataset_[index], vec, veclen_);
+            result_set.addPoint(dist,index);
+            return;
+        }
+
+        /* Which child branch should be taken first? */
+        ElementType val = vec[node->divfeat];
+        DistanceType diff = val - node->divval;
+        NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
+        NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
+
+        /* Create a branch record for the branch not taken.  Add distance
+            of this feature boundary (we don't attempt to correct for any
+            use of this feature in a parent node, which is unlikely to
+            happen and would have only a small effect).  Don't bother
+            adding more branches to heap after halfway point, as cost of
+            adding exceeds their value.
+         */
+
+        DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
+
+        /* Call recursively to search next level down. */
+        searchLevelExact(result_set, vec, bestChild, mindist, epsError);
+
+        if (new_distsq*epsError<=result_set.worstDist()) {
+            searchLevelExact(result_set, vec, otherChild, new_distsq, epsError);
+        }
+    }
+
+
+private:
+
+    enum
+    {
+        /**
+         * To improve efficiency, only SAMPLE_MEAN random values are used to
+         * compute the mean and variance at each level when building a tree.
+         * A value of 100 seems to perform as well as using all values.
+         */
+        SAMPLE_MEAN = 100,
+        /**
+         * Top random dimensions to consider
+         *
+         * When creating random trees, the dimension on which to subdivide is
+         * selected at random from among the top RAND_DIM dimensions with the
+         * highest variance.  A value of 5 works well.
+         */
+        RAND_DIM=5
+    };
+
+
+    /**
+     * Number of randomized trees that are used
+     */
+    int trees_;
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    std::vector<int> vind_;
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    IndexParams index_params_;
+
+    size_t size_;
+    size_t veclen_;
+
+
+    DistanceType* mean_;
+    DistanceType* var_;
+
+
+    /**
+     * Array of k-d trees used to find neighbours.
+     */
+    NodePtr* tree_roots_;
+
+    /**
+     * Pooled memory allocator.
+     *
+     * Using a pooled memory allocator is more efficient
+     * than allocating memory directly when there is a large
+     * number small of memory allocations.
+     */
+    PooledAllocator pool_;
+
+    Distance distance_;
+
+
+};   // class KDTreeForest
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_KDTREE_INDEX_H_
diff --git a/3rdParty/include/opencv2/flann/kdtree_single_index.h b/3rdParty/include/opencv2/flann/kdtree_single_index.h
new file mode 100644
index 0000000..ed95c3d
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/kdtree_single_index.h
@@ -0,0 +1,645 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_
+#define OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <map>
+#include <cstring>
+
+#include "nn_index.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+
+namespace cvflann
+{
+
+struct KDTreeSingleIndexParams : public IndexParams
+{
+    KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true, int dim = -1)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE;
+        (*this)["leaf_max_size"] = leaf_max_size;
+        (*this)["reorder"] = reorder;
+        (*this)["dim"] = dim;
+    }
+};
+
+
+/**
+ * Randomized kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template <typename Distance>
+class KDTreeSingleIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+
+    /**
+     * KDTree constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the kdtree algorithm
+     */
+    KDTreeSingleIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeSingleIndexParams(),
+                      Distance d = Distance() ) :
+        dataset_(inputData), index_params_(params), distance_(d)
+    {
+        size_ = dataset_.rows;
+        dim_ = dataset_.cols;
+        root_node_ = 0;
+        int dim_param = get_param(params,"dim",-1);
+        if (dim_param>0) dim_ = dim_param;
+        leaf_max_size_ = get_param(params,"leaf_max_size",10);
+        reorder_ = get_param(params,"reorder",true);
+
+        // Create a permutable array of indices to the input vectors.
+        vind_.resize(size_);
+        for (size_t i = 0; i < size_; i++) {
+            vind_[i] = (int)i;
+        }
+    }
+
+    KDTreeSingleIndex(const KDTreeSingleIndex&);
+    KDTreeSingleIndex& operator=(const KDTreeSingleIndex&);
+
+    /**
+     * Standard destructor
+     */
+    ~KDTreeSingleIndex()
+    {
+        if (reorder_) delete[] data_.data;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex() CV_OVERRIDE
+    {
+        computeBoundingBox(root_bbox_);
+        root_node_ = divideTree(0, (int)size_, root_bbox_ );   // construct the tree
+
+        if (reorder_) {
+            delete[] data_.data;
+            data_ = cvflann::Matrix<ElementType>(new ElementType[size_*dim_], size_, dim_);
+            for (size_t i=0; i<size_; ++i) {
+                for (size_t j=0; j<dim_; ++j) {
+                    data_[i][j] = dataset_[vind_[i]][j];
+                }
+            }
+        }
+        else {
+            data_ = dataset_;
+        }
+    }
+
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_KDTREE_SINGLE;
+    }
+
+
+    void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        save_value(stream, size_);
+        save_value(stream, dim_);
+        save_value(stream, root_bbox_);
+        save_value(stream, reorder_);
+        save_value(stream, leaf_max_size_);
+        save_value(stream, vind_);
+        if (reorder_) {
+            save_value(stream, data_);
+        }
+        save_tree(stream, root_node_);
+    }
+
+
+    void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        load_value(stream, size_);
+        load_value(stream, dim_);
+        load_value(stream, root_bbox_);
+        load_value(stream, reorder_);
+        load_value(stream, leaf_max_size_);
+        load_value(stream, vind_);
+        if (reorder_) {
+            load_value(stream, data_);
+        }
+        else {
+            data_ = dataset_;
+        }
+        load_tree(stream, root_node_);
+
+
+        index_params_["algorithm"] = getType();
+        index_params_["leaf_max_size"] = leaf_max_size_;
+        index_params_["reorder"] = reorder_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const CV_OVERRIDE
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const CV_OVERRIDE
+    {
+        return dim_;
+    }
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const CV_OVERRIDE
+    {
+        return (int)(pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int));  // pool memory and vind array memory
+    }
+
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
+    {
+        CV_Assert(queries.cols == veclen());
+        CV_Assert(indices.rows >= queries.rows);
+        CV_Assert(dists.rows >= queries.rows);
+        CV_Assert(int(indices.cols) >= knn);
+        CV_Assert(int(dists.cols) >= knn);
+
+        KNNSimpleResultSet<DistanceType> resultSet(knn);
+        for (size_t i = 0; i < queries.rows; i++) {
+            resultSet.init(indices[i], dists[i]);
+            findNeighbors(resultSet, queries[i], params);
+        }
+    }
+
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return index_params_;
+    }
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
+    {
+        float epsError = 1+get_param(searchParams,"eps",0.0f);
+
+        std::vector<DistanceType> dists(dim_,0);
+        DistanceType distsq = computeInitialDistances(vec, dists);
+        searchLevel(result, vec, root_node_, distsq, dists, epsError);
+    }
+
+private:
+
+
+    /*--------------------- Internal Data Structures --------------------------*/
+    struct Node
+    {
+        /**
+         * Indices of points in leaf node
+         */
+        int left, right;
+        /**
+         * Dimension used for subdivision.
+         */
+        int divfeat;
+        /**
+         * The values used for subdivision.
+         */
+        DistanceType divlow, divhigh;
+        /**
+         * The child nodes.
+         */
+        Node* child1, * child2;
+    };
+    typedef Node* NodePtr;
+
+
+    struct Interval
+    {
+        DistanceType low, high;
+    };
+
+    typedef std::vector<Interval> BoundingBox;
+
+    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
+    typedef BranchSt* Branch;
+
+
+
+
+    void save_tree(FILE* stream, NodePtr tree)
+    {
+        save_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            save_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            save_tree(stream, tree->child2);
+        }
+    }
+
+
+    void load_tree(FILE* stream, NodePtr& tree)
+    {
+        tree = pool_.allocate<Node>();
+        load_value(stream, *tree);
+        if (tree->child1!=NULL) {
+            load_tree(stream, tree->child1);
+        }
+        if (tree->child2!=NULL) {
+            load_tree(stream, tree->child2);
+        }
+    }
+
+
+    void computeBoundingBox(BoundingBox& bbox)
+    {
+        bbox.resize(dim_);
+        for (size_t i=0; i<dim_; ++i) {
+            bbox[i].low = (DistanceType)dataset_[0][i];
+            bbox[i].high = (DistanceType)dataset_[0][i];
+        }
+        for (size_t k=1; k<dataset_.rows; ++k) {
+            for (size_t i=0; i<dim_; ++i) {
+                if (dataset_[k][i]<bbox[i].low) bbox[i].low = (DistanceType)dataset_[k][i];
+                if (dataset_[k][i]>bbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i];
+            }
+        }
+    }
+
+
+    /**
+     * Create a tree node that subdivides the list of vecs from vind[first]
+     * to vind[last].  The routine is called recursively on each sublist.
+     * Place a pointer to this new tree node in the location pTree.
+     *
+     * Params: pTree = the new node to create
+     *                  first = index of the first vector
+     *                  last = index of the last vector
+     */
+    NodePtr divideTree(int left, int right, BoundingBox& bbox)
+    {
+        NodePtr node = pool_.allocate<Node>(); // allocate memory
+
+        /* If too few exemplars remain, then make this a leaf node. */
+        if ( (right-left) <= leaf_max_size_) {
+            node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
+            node->left = left;
+            node->right = right;
+
+            // compute bounding-box of leaf points
+            for (size_t i=0; i<dim_; ++i) {
+                bbox[i].low = (DistanceType)dataset_[vind_[left]][i];
+                bbox[i].high = (DistanceType)dataset_[vind_[left]][i];
+            }
+            for (int k=left+1; k<right; ++k) {
+                for (size_t i=0; i<dim_; ++i) {
+                    if (bbox[i].low>dataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i];
+                    if (bbox[i].high<dataset_[vind_[k]][i]) bbox[i].high=(DistanceType)dataset_[vind_[k]][i];
+                }
+            }
+        }
+        else {
+            int idx;
+            int cutfeat;
+            DistanceType cutval;
+            middleSplit_(&vind_[0]+left, right-left, idx, cutfeat, cutval, bbox);
+
+            node->divfeat = cutfeat;
+
+            BoundingBox left_bbox(bbox);
+            left_bbox[cutfeat].high = cutval;
+            node->child1 = divideTree(left, left+idx, left_bbox);
+
+            BoundingBox right_bbox(bbox);
+            right_bbox[cutfeat].low = cutval;
+            node->child2 = divideTree(left+idx, right, right_bbox);
+
+            node->divlow = left_bbox[cutfeat].high;
+            node->divhigh = right_bbox[cutfeat].low;
+
+            for (size_t i=0; i<dim_; ++i) {
+                bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
+                bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
+            }
+        }
+
+        return node;
+    }
+
+    void computeMinMax(int* ind, int count, int dim, ElementType& min_elem, ElementType& max_elem)
+    {
+        min_elem = dataset_[ind[0]][dim];
+        max_elem = dataset_[ind[0]][dim];
+        for (int i=1; i<count; ++i) {
+            ElementType val = dataset_[ind[i]][dim];
+            if (val<min_elem) min_elem = val;
+            if (val>max_elem) max_elem = val;
+        }
+    }
+
+    void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
+    {
+        // find the largest span from the approximate bounding box
+        ElementType max_span = bbox[0].high-bbox[0].low;
+        cutfeat = 0;
+        cutval = (bbox[0].high+bbox[0].low)/2;
+        for (size_t i=1; i<dim_; ++i) {
+            ElementType span = bbox[i].high-bbox[i].low;
+            if (span>max_span) {
+                max_span = span;
+                cutfeat = i;
+                cutval = (bbox[i].high+bbox[i].low)/2;
+            }
+        }
+
+        // compute exact span on the found dimension
+        ElementType min_elem, max_elem;
+        computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+        cutval = (min_elem+max_elem)/2;
+        max_span = max_elem - min_elem;
+
+        // check if a dimension of a largest span exists
+        size_t k = cutfeat;
+        for (size_t i=0; i<dim_; ++i) {
+            if (i==k) continue;
+            ElementType span = bbox[i].high-bbox[i].low;
+            if (span>max_span) {
+                computeMinMax(ind, count, i, min_elem, max_elem);
+                span = max_elem - min_elem;
+                if (span>max_span) {
+                    max_span = span;
+                    cutfeat = i;
+                    cutval = (min_elem+max_elem)/2;
+                }
+            }
+        }
+        int lim1, lim2;
+        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+        if (lim1>count/2) index = lim1;
+        else if (lim2<count/2) index = lim2;
+        else index = count/2;
+    }
+
+
+    void middleSplit_(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
+    {
+        const float EPS=0.00001f;
+        DistanceType max_span = bbox[0].high-bbox[0].low;
+        for (size_t i=1; i<dim_; ++i) {
+            DistanceType span = bbox[i].high-bbox[i].low;
+            if (span>max_span) {
+                max_span = span;
+            }
+        }
+        DistanceType max_spread = -1;
+        cutfeat = 0;
+        for (size_t i=0; i<dim_; ++i) {
+            DistanceType span = bbox[i].high-bbox[i].low;
+            if (span>(DistanceType)((1-EPS)*max_span)) {
+                ElementType min_elem, max_elem;
+                computeMinMax(ind, count, (int)i, min_elem, max_elem);
+                DistanceType spread = (DistanceType)(max_elem-min_elem);
+                if (spread>max_spread) {
+                    cutfeat = (int)i;
+                    max_spread = spread;
+                }
+            }
+        }
+        // split in the middle
+        DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2;
+        ElementType min_elem, max_elem;
+        computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+
+        if (split_val<min_elem) cutval = (DistanceType)min_elem;
+        else if (split_val>max_elem) cutval = (DistanceType)max_elem;
+        else cutval = split_val;
+
+        int lim1, lim2;
+        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
+
+        if (lim1>count/2) index = lim1;
+        else if (lim2<count/2) index = lim2;
+        else index = count/2;
+    }
+
+
+    /**
+     *  Subdivide the list of points by a plane perpendicular on axe corresponding
+     *  to the 'cutfeat' dimension at 'cutval' position.
+     *
+     *  On return:
+     *  dataset[ind[0..lim1-1]][cutfeat]<cutval
+     *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
+     *  dataset[ind[lim2..count]][cutfeat]>cutval
+     */
+    void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
+    {
+        /* Move vector indices for left subtree to front of list. */
+        int left = 0;
+        int right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        /* If either list is empty, it means that all remaining features
+         * are identical. Split in the middle to maintain a balanced tree.
+         */
+        lim1 = left;
+        right = count-1;
+        for (;; ) {
+            while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
+            while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
+            if (left>right) break;
+            std::swap(ind[left], ind[right]); ++left; --right;
+        }
+        lim2 = left;
+    }
+
+    DistanceType computeInitialDistances(const ElementType* vec, std::vector<DistanceType>& dists)
+    {
+        DistanceType distsq = 0.0;
+
+        for (size_t i = 0; i < dim_; ++i) {
+            if (vec[i] < root_bbox_[i].low) {
+                dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i);
+                distsq += dists[i];
+            }
+            if (vec[i] > root_bbox_[i].high) {
+                dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i);
+                distsq += dists[i];
+            }
+        }
+
+        return distsq;
+    }
+
+    /**
+     * Performs an exact search in the tree starting from a node.
+     */
+    void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
+                     std::vector<DistanceType>& dists, const float epsError)
+    {
+        /* If this is a leaf node, then do check and return. */
+        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
+            DistanceType worst_dist = result_set.worstDist();
+            if (reorder_) {
+                for (int i=node->left; i<node->right; ++i) {
+                    DistanceType dist = distance_(vec, data_[i], dim_, worst_dist);
+                    if (dist<worst_dist) {
+                        result_set.addPoint(dist,vind_[i]);
+                    }
+                }
+            } else {
+                for (int i=node->left; i<node->right; ++i) {
+                    DistanceType dist = distance_(vec, data_[vind_[i]], dim_, worst_dist);
+                    if (dist<worst_dist) {
+                        result_set.addPoint(dist,vind_[i]);
+                    }
+                }
+            }
+            return;
+        }
+
+        /* Which child branch should be taken first? */
+        int idx = node->divfeat;
+        ElementType val = vec[idx];
+        DistanceType diff1 = val - node->divlow;
+        DistanceType diff2 = val - node->divhigh;
+
+        NodePtr bestChild;
+        NodePtr otherChild;
+        DistanceType cut_dist;
+        if ((diff1+diff2)<0) {
+            bestChild = node->child1;
+            otherChild = node->child2;
+            cut_dist = distance_.accum_dist(val, node->divhigh, idx);
+        }
+        else {
+            bestChild = node->child2;
+            otherChild = node->child1;
+            cut_dist = distance_.accum_dist( val, node->divlow, idx);
+        }
+
+        /* Call recursively to search next level down. */
+        searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
+
+        DistanceType dst = dists[idx];
+        mindistsq = mindistsq + cut_dist - dst;
+        dists[idx] = cut_dist;
+        if (mindistsq*epsError<=result_set.worstDist()) {
+            searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
+        }
+        dists[idx] = dst;
+    }
+
+private:
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    IndexParams index_params_;
+
+    int leaf_max_size_;
+    bool reorder_;
+
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    std::vector<int> vind_;
+
+    Matrix<ElementType> data_;
+
+    size_t size_;
+    size_t dim_;
+
+    /**
+     * Array of k-d trees used to find neighbours.
+     */
+    NodePtr root_node_;
+
+    BoundingBox root_bbox_;
+
+    /**
+     * Pooled memory allocator.
+     *
+     * Using a pooled memory allocator is more efficient
+     * than allocating memory directly when there is a large
+     * number small of memory allocations.
+     */
+    PooledAllocator pool_;
+
+    Distance distance_;
+};   // class KDTree
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_
diff --git a/3rdParty/include/opencv2/flann/kmeans_index.h b/3rdParty/include/opencv2/flann/kmeans_index.h
new file mode 100644
index 0000000..fd7fe2b
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/kmeans_index.h
@@ -0,0 +1,1819 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_KMEANS_INDEX_H_
+#define OPENCV_FLANN_KMEANS_INDEX_H_
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <map>
+#include <limits>
+#include <cmath>
+
+#include "general.h"
+#include "nn_index.h"
+#include "dist.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+#include "logger.h"
+
+#define BITS_PER_CHAR 8
+#define BITS_PER_BASE 2 // for DNA/RNA sequences
+#define BASE_PER_CHAR (BITS_PER_CHAR/BITS_PER_BASE)
+#define HISTOS_PER_BASE (1<<BITS_PER_BASE)
+
+
+namespace cvflann
+{
+
+struct KMeansIndexParams : public IndexParams
+{
+    KMeansIndexParams(int branching = 32, int iterations = 11,
+                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM,
+                      float cb_index = 0.2, int trees = 1 )
+    {
+        (*this)["algorithm"] = FLANN_INDEX_KMEANS;
+        // branching factor
+        (*this)["branching"] = branching;
+        // max iterations to perform in one kmeans clustering (kmeans tree)
+        (*this)["iterations"] = iterations;
+        // algorithm used for picking the initial cluster centers for kmeans tree
+        (*this)["centers_init"] = centers_init;
+        // cluster boundary index. Used when searching the kmeans tree
+        (*this)["cb_index"] = cb_index;
+        // number of kmeans trees to search in
+        (*this)["trees"] = trees;
+    }
+};
+
+
+/**
+ * Hierarchical kmeans index
+ *
+ * Contains a tree constructed through a hierarchical kmeans clustering
+ * and other information for indexing a set of points for nearest-neighbour matching.
+ */
+template <typename Distance>
+class KMeansIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+    typedef typename Distance::CentersType CentersType;
+
+    typedef typename Distance::is_kdtree_distance is_kdtree_distance;
+    typedef typename Distance::is_vector_space_distance is_vector_space_distance;
+
+
+
+    typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&);
+
+    /**
+     * The function used for choosing the cluster centers.
+     */
+    centersAlgFunction chooseCenters;
+
+
+
+    /**
+     * Chooses the initial centers in the k-means clustering in a random manner.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     *     indices_length = length of indices vector
+     *
+     */
+    void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        UniqueRandom r(indices_length);
+
+        int index;
+        for (index=0; index<k; ++index) {
+            bool duplicate = true;
+            int rnd;
+            while (duplicate) {
+                duplicate = false;
+                rnd = r.next();
+                if (rnd<0) {
+                    centers_length = index;
+                    return;
+                }
+
+                centers[index] = indices[rnd];
+
+                for (int j=0; j<index; ++j) {
+                    DistanceType sq = distance_(dataset_[centers[index]], dataset_[centers[j]], dataset_.cols);
+                    if (sq<1e-16) {
+                        duplicate = true;
+                    }
+                }
+            }
+        }
+
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using Gonzales' algorithm
+     * so that the centers are spaced apart from each other.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        int rnd = rand_int(n);
+        CV_DbgAssert(rnd >=0 && rnd < n);
+
+        centers[0] = indices[rnd];
+
+        int index;
+        for (index=1; index<k; ++index) {
+
+            int best_index = -1;
+            DistanceType best_val = 0;
+            for (int j=0; j<n; ++j) {
+                DistanceType dist = distance_(dataset_[centers[0]],dataset_[indices[j]],dataset_.cols);
+                for (int i=1; i<index; ++i) {
+                    DistanceType tmp_dist = distance_(dataset_[centers[i]],dataset_[indices[j]],dataset_.cols);
+                    if (tmp_dist<dist) {
+                        dist = tmp_dist;
+                    }
+                }
+                if (dist>best_val) {
+                    best_val = dist;
+                    best_index = j;
+                }
+            }
+            if (best_index!=-1) {
+                centers[index] = indices[best_index];
+            }
+            else {
+                break;
+            }
+        }
+        centers_length = index;
+    }
+
+
+    /**
+     * Chooses the initial centers in the k-means using the algorithm
+     * proposed in the KMeans++ paper:
+     * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
+     *
+     * Implementation of this function was converted from the one provided in Arthur's code.
+     *
+     * Params:
+     *     k = number of centers
+     *     vecs = the dataset of points
+     *     indices = indices in the dataset
+     * Returns:
+     */
+    void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
+    {
+        int n = indices_length;
+
+        double currentPot = 0;
+        DistanceType* closestDistSq = new DistanceType[n];
+
+        // Choose one random center and set the closestDistSq values
+        int index = rand_int(n);
+        CV_DbgAssert(index >=0 && index < n);
+        centers[0] = indices[index];
+
+        for (int i = 0; i < n; i++) {
+            closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+            closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
+            currentPot += closestDistSq[i];
+        }
+
+
+        const int numLocalTries = 1;
+
+        // Choose each center
+        int centerCount;
+        for (centerCount = 1; centerCount < k; centerCount++) {
+
+            // Repeat several trials
+            double bestNewPot = -1;
+            int bestNewIndex = -1;
+            for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
+
+                // Choose our center - have to be slightly careful to return a valid answer even accounting
+                // for possible rounding errors
+                double randVal = rand_double(currentPot);
+                for (index = 0; index < n-1; index++) {
+                    if (randVal <= closestDistSq[index]) break;
+                    else randVal -= closestDistSq[index];
+                }
+
+                // Compute the new potential
+                double newPot = 0;
+                for (int i = 0; i < n; i++) {
+                    DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+                    newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
+                }
+
+                // Store the best result
+                if ((bestNewPot < 0)||(newPot < bestNewPot)) {
+                    bestNewPot = newPot;
+                    bestNewIndex = index;
+                }
+            }
+
+            // Add the appropriate center
+            centers[centerCount] = indices[bestNewIndex];
+            currentPot = bestNewPot;
+            for (int i = 0; i < n; i++) {
+                DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
+                closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
+            }
+        }
+
+        centers_length = centerCount;
+
+        delete[] closestDistSq;
+    }
+
+
+
+public:
+
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_KMEANS;
+    }
+
+    template<class CentersContainerType>
+    class KMeansDistanceComputer : public cv::ParallelLoopBody
+    {
+    public:
+        KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
+            const int _branching, const int* _indices, const CentersContainerType& _dcenters,
+            const size_t _veclen, std::vector<int> &_new_centroids,
+            std::vector<DistanceType> &_sq_dists)
+            : distance(_distance)
+            , dataset(_dataset)
+            , branching(_branching)
+            , indices(_indices)
+            , dcenters(_dcenters)
+            , veclen(_veclen)
+            , new_centroids(_new_centroids)
+            , sq_dists(_sq_dists)
+        {
+        }
+
+        void operator()(const cv::Range& range) const CV_OVERRIDE
+        {
+            const int begin = range.start;
+            const int end = range.end;
+
+            for( int i = begin; i<end; ++i)
+            {
+                DistanceType sq_dist(distance(dataset[indices[i]], dcenters[0], veclen));
+                int new_centroid(0);
+                for (int j=1; j<branching; ++j) {
+                    DistanceType new_sq_dist = distance(dataset[indices[i]], dcenters[j], veclen);
+                    if (sq_dist>new_sq_dist) {
+                        new_centroid = j;
+                        sq_dist = new_sq_dist;
+                    }
+                }
+                sq_dists[i] = sq_dist;
+                new_centroids[i] = new_centroid;
+            }
+        }
+
+    private:
+        Distance distance;
+        const Matrix<ElementType>& dataset;
+        const int branching;
+        const int* indices;
+        const CentersContainerType& dcenters;
+        const size_t veclen;
+        std::vector<int> &new_centroids;
+        std::vector<DistanceType> &sq_dists;
+        KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; }
+    };
+
+    /**
+     * Index constructor
+     *
+     * Params:
+     *          inputData = dataset with the input features
+     *          params = parameters passed to the hierarchical k-means algorithm
+     */
+    KMeansIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KMeansIndexParams(),
+                Distance d = Distance())
+        : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d)
+    {
+        memoryCounter_ = 0;
+
+        size_ = dataset_.rows;
+        veclen_ = dataset_.cols;
+
+        branching_ = get_param(params,"branching",32);
+        trees_ = get_param(params,"trees",1);
+        iterations_ = get_param(params,"iterations",11);
+        if (iterations_<0) {
+            iterations_ = (std::numeric_limits<int>::max)();
+        }
+        centers_init_  = get_param(params,"centers_init",FLANN_CENTERS_RANDOM);
+
+        if (centers_init_==FLANN_CENTERS_RANDOM) {
+            chooseCenters = &KMeansIndex::chooseCentersRandom;
+        }
+        else if (centers_init_==FLANN_CENTERS_GONZALES) {
+            chooseCenters = &KMeansIndex::chooseCentersGonzales;
+        }
+        else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
+            chooseCenters = &KMeansIndex::chooseCentersKMeanspp;
+        }
+        else {
+            FLANN_THROW(cv::Error::StsBadArg, "Unknown algorithm for choosing initial centers.");
+        }
+        cb_index_ = 0.4f;
+
+        root_ = new KMeansNodePtr[trees_];
+        indices_ = new int*[trees_];
+
+        for (int i=0; i<trees_; ++i) {
+            root_[i] = NULL;
+            indices_[i] = NULL;
+        }
+    }
+
+
+    KMeansIndex(const KMeansIndex&);
+    KMeansIndex& operator=(const KMeansIndex&);
+
+
+    /**
+     * Index destructor.
+     *
+     * Release the memory used by the index.
+     */
+    virtual ~KMeansIndex()
+    {
+        if (root_ != NULL) {
+            free_centers();
+            delete[] root_;
+        }
+        if (indices_!=NULL) {
+            free_indices();
+            delete[] indices_;
+        }
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const CV_OVERRIDE
+    {
+        return size_;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const CV_OVERRIDE
+    {
+        return veclen_;
+    }
+
+
+    void set_cb_index( float index)
+    {
+        cb_index_ = index;
+    }
+
+    /**
+     * Computes the inde memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const CV_OVERRIDE
+    {
+        return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex() CV_OVERRIDE
+    {
+        if (branching_<2) {
+            FLANN_THROW(cv::Error::StsError, "Branching factor must be at least 2");
+        }
+
+        free_indices();
+
+        for (int i=0; i<trees_; ++i) {
+            indices_[i] = new int[size_];
+            for (size_t j=0; j<size_; ++j) {
+                indices_[i][j] = int(j);
+            }
+            root_[i] = pool_.allocate<KMeansNode>();
+            std::memset(root_[i], 0, sizeof(KMeansNode));
+
+            Distance* dummy = NULL;
+            computeNodeStatistics(root_[i], indices_[i], (unsigned int)size_, dummy);
+
+            computeClustering(root_[i], indices_[i], (int)size_, branching_,0);
+        }
+    }
+
+
+    void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        save_value(stream, branching_);
+        save_value(stream, iterations_);
+        save_value(stream, memoryCounter_);
+        save_value(stream, cb_index_);
+        save_value(stream, trees_);
+        for (int i=0; i<trees_; ++i) {
+            save_value(stream, *indices_[i], (int)size_);
+            save_tree(stream, root_[i], i);
+        }
+    }
+
+
+    void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        if (indices_!=NULL) {
+            free_indices();
+            delete[] indices_;
+        }
+        if (root_!=NULL) {
+            free_centers();
+        }
+
+        load_value(stream, branching_);
+        load_value(stream, iterations_);
+        load_value(stream, memoryCounter_);
+        load_value(stream, cb_index_);
+        load_value(stream, trees_);
+
+        indices_ = new int*[trees_];
+        for (int i=0; i<trees_; ++i) {
+            indices_[i] = new int[size_];
+            load_value(stream, *indices_[i], size_);
+            load_tree(stream, root_[i], i);
+        }
+
+        index_params_["algorithm"] = getType();
+        index_params_["branching"] = branching_;
+        index_params_["trees"] = trees_;
+        index_params_["iterations"] = iterations_;
+        index_params_["centers_init"] = centers_init_;
+        index_params_["cb_index"] = cb_index_;
+    }
+
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     searchParams = parameters that influence the search algorithm (checks, cb_index)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
+    {
+
+        const int maxChecks = get_param(searchParams,"checks",32);
+
+        if (maxChecks==FLANN_CHECKS_UNLIMITED) {
+            findExactNN(root_[0], result, vec);
+        }
+        else {
+            // Priority queue storing intermediate branches in the best-bin-first search
+            const cv::Ptr<Heap<BranchSt>>& heap = Heap<BranchSt>::getPooledInstance(cv::utils::getThreadID(), (int)size_);
+
+            int checks = 0;
+            for (int i=0; i<trees_; ++i) {
+                findNN(root_[i], result, vec, checks, maxChecks, heap);
+                if ((checks >= maxChecks) && result.full())
+                    break;
+            }
+
+            BranchSt branch;
+            while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
+                KMeansNodePtr node = branch.node;
+                findNN(node, result, vec, checks, maxChecks, heap);
+            }
+            CV_Assert(result.full());
+        }
+    }
+
+    /**
+     * Clustering function that takes a cut in the hierarchical k-means
+     * tree and return the clusters centers of that clustering.
+     * Params:
+     *     numClusters = number of clusters to have in the clustering computed
+     * Returns: number of cluster centers
+     */
+    int getClusterCenters(Matrix<CentersType>& centers)
+    {
+        int numClusters = centers.rows;
+        if (numClusters<1) {
+            FLANN_THROW(cv::Error::StsBadArg, "Number of clusters must be at least 1");
+        }
+
+        DistanceType variance;
+        KMeansNodePtr* clusters = new KMeansNodePtr[numClusters];
+
+        int clusterCount = getMinVarianceClusters(root_[0], clusters, numClusters, variance);
+
+        Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount);
+
+        for (int i=0; i<clusterCount; ++i) {
+            CentersType* center = clusters[i]->pivot;
+            for (size_t j=0; j<veclen_; ++j) {
+                centers[i][j] = center[j];
+            }
+        }
+        delete[] clusters;
+
+        return clusterCount;
+    }
+
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return index_params_;
+    }
+
+
+private:
+    /**
+     * Structure representing a node in the hierarchical k-means tree.
+     */
+    struct KMeansNode
+    {
+        /**
+         * The cluster center.
+         */
+        CentersType* pivot;
+        /**
+         * The cluster radius.
+         */
+        DistanceType radius;
+        /**
+         * The cluster mean radius.
+         */
+        DistanceType mean_radius;
+        /**
+         * The cluster variance.
+         */
+        DistanceType variance;
+        /**
+         * The cluster size (number of points in the cluster)
+         */
+        int size;
+        /**
+         * Child nodes (only for non-terminal nodes)
+         */
+        KMeansNode** childs;
+        /**
+         * Node points (only for terminal nodes)
+         */
+        int* indices;
+        /**
+         * Level
+         */
+        int level;
+    };
+    typedef KMeansNode* KMeansNodePtr;
+
+    /**
+     * Alias definition for a nicer syntax.
+     */
+    typedef BranchStruct<KMeansNodePtr, DistanceType> BranchSt;
+
+
+
+
+    void save_tree(FILE* stream, KMeansNodePtr node, int num)
+    {
+        save_value(stream, *node);
+        save_value(stream, *(node->pivot), (int)veclen_);
+        if (node->childs==NULL) {
+            int indices_offset = (int)(node->indices - indices_[num]);
+            save_value(stream, indices_offset);
+        }
+        else {
+            for(int i=0; i<branching_; ++i) {
+                save_tree(stream, node->childs[i], num);
+            }
+        }
+    }
+
+
+    void load_tree(FILE* stream, KMeansNodePtr& node, int num)
+    {
+        node = pool_.allocate<KMeansNode>();
+        load_value(stream, *node);
+        node->pivot = new CentersType[veclen_];
+        load_value(stream, *(node->pivot), (int)veclen_);
+        if (node->childs==NULL) {
+            int indices_offset;
+            load_value(stream, indices_offset);
+            node->indices = indices_[num] + indices_offset;
+        }
+        else {
+            node->childs = pool_.allocate<KMeansNodePtr>(branching_);
+            for(int i=0; i<branching_; ++i) {
+                load_tree(stream, node->childs[i], num);
+            }
+        }
+    }
+
+
+    /**
+     * Helper function
+     */
+    void free_centers(KMeansNodePtr node)
+    {
+        delete[] node->pivot;
+        if (node->childs!=NULL) {
+            for (int k=0; k<branching_; ++k) {
+                free_centers(node->childs[k]);
+            }
+        }
+    }
+
+    void free_centers()
+    {
+       if (root_ != NULL) {
+           for(int i=0; i<trees_; ++i) {
+               if (root_[i] != NULL) {
+                   free_centers(root_[i]);
+               }
+           }
+       }
+    }
+
+    /**
+     * Release the inner elements of indices[]
+     */
+    void free_indices()
+    {
+        if (indices_!=NULL) {
+            for(int i=0; i<trees_; ++i) {
+                if (indices_[i]!=NULL) {
+                    delete[] indices_[i];
+                    indices_[i] = NULL;
+                }
+            }
+        }
+    }
+
+    /**
+     * Computes the statistics of a node (mean, radius, variance).
+     *
+     * Params:
+     *     node = the node to use
+     *     indices = array of indices of the points belonging to the node
+     *     indices_length = number of indices in the array
+     */
+    void computeNodeStatistics(KMeansNodePtr node, int* indices, unsigned int indices_length)
+    {
+        DistanceType variance = 0;
+        CentersType* mean = new CentersType[veclen_];
+        memoryCounter_ += int(veclen_*sizeof(CentersType));
+
+        memset(mean,0,veclen_*sizeof(CentersType));
+
+        for (unsigned int i=0; i<indices_length; ++i) {
+            ElementType* vec = dataset_[indices[i]];
+            for (size_t j=0; j<veclen_; ++j) {
+                mean[j] += vec[j];
+            }
+            variance += distance_(vec, ZeroIterator<ElementType>(), veclen_);
+        }
+        float length = static_cast<float>(indices_length);
+        for (size_t j=0; j<veclen_; ++j) {
+            mean[j] = cvflann::round<CentersType>( mean[j] / static_cast<double>(indices_length) );
+        }
+        variance /= static_cast<DistanceType>( length );
+        variance -= distance_(mean, ZeroIterator<ElementType>(), veclen_);
+
+        DistanceType radius = 0;
+        for (unsigned int i=0; i<indices_length; ++i) {
+            DistanceType tmp = distance_(mean, dataset_[indices[i]], veclen_);
+            if (tmp>radius) {
+                radius = tmp;
+            }
+        }
+
+        node->variance = variance;
+        node->radius = radius;
+        node->pivot = mean;
+    }
+
+
+    void computeBitfieldNodeStatistics(KMeansNodePtr node, int* indices,
+                                       unsigned int indices_length)
+    {
+        const unsigned int accumulator_veclen = static_cast<unsigned int>(
+                                                veclen_*sizeof(CentersType)*BITS_PER_CHAR);
+
+        unsigned long long variance = 0ull;
+        CentersType* mean = new CentersType[veclen_];
+        memoryCounter_ += int(veclen_*sizeof(CentersType));
+        unsigned int* mean_accumulator = new unsigned int[accumulator_veclen];
+
+        memset(mean_accumulator, 0, sizeof(unsigned int)*accumulator_veclen);
+
+        for (unsigned int i=0; i<indices_length; ++i) {
+            variance += static_cast<unsigned long long>( ensureSquareDistance<Distance>(
+                        distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_)));
+            unsigned char* vec = (unsigned char*)dataset_[indices[i]];
+            for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
+                mean_accumulator[k]   += (vec[l])    & 0x01;
+                mean_accumulator[k+1] += (vec[l]>>1) & 0x01;
+                mean_accumulator[k+2] += (vec[l]>>2) & 0x01;
+                mean_accumulator[k+3] += (vec[l]>>3) & 0x01;
+                mean_accumulator[k+4] += (vec[l]>>4) & 0x01;
+                mean_accumulator[k+5] += (vec[l]>>5) & 0x01;
+                mean_accumulator[k+6] += (vec[l]>>6) & 0x01;
+                mean_accumulator[k+7] += (vec[l]>>7) & 0x01;
+            }
+        }
+        double cnt = static_cast<double>(indices_length);
+        unsigned char* char_mean = (unsigned char*)mean;
+        for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
+            char_mean[l] = static_cast<unsigned char>(
+                              (((int)(0.5 + (double)(mean_accumulator[k])   / cnt)))
+                            | (((int)(0.5 + (double)(mean_accumulator[k+1]) / cnt))<<1)
+                            | (((int)(0.5 + (double)(mean_accumulator[k+2]) / cnt))<<2)
+                            | (((int)(0.5 + (double)(mean_accumulator[k+3]) / cnt))<<3)
+                            | (((int)(0.5 + (double)(mean_accumulator[k+4]) / cnt))<<4)
+                            | (((int)(0.5 + (double)(mean_accumulator[k+5]) / cnt))<<5)
+                            | (((int)(0.5 + (double)(mean_accumulator[k+6]) / cnt))<<6)
+                            | (((int)(0.5 + (double)(mean_accumulator[k+7]) / cnt))<<7));
+        }
+        variance = static_cast<unsigned long long>(
+                    0.5 + static_cast<double>(variance) / static_cast<double>(indices_length));
+        variance -= static_cast<unsigned long long>(
+                    ensureSquareDistance<Distance>(
+                        distance_(mean, ZeroIterator<ElementType>(), veclen_)));
+
+        DistanceType radius = 0;
+        for (unsigned int i=0; i<indices_length; ++i) {
+            DistanceType tmp = distance_(mean, dataset_[indices[i]], veclen_);
+            if (tmp>radius) {
+                radius = tmp;
+            }
+        }
+
+        node->variance = static_cast<DistanceType>(variance);
+        node->radius = radius;
+        node->pivot = mean;
+
+        delete[] mean_accumulator;
+    }
+
+
+    void computeDnaNodeStatistics(KMeansNodePtr node, int* indices,
+                                       unsigned int indices_length)
+    {
+        const unsigned int histos_veclen = static_cast<unsigned int>(
+                    veclen_*sizeof(CentersType)*(HISTOS_PER_BASE*BASE_PER_CHAR));
+
+        unsigned long long variance = 0ull;
+        unsigned int* histograms = new unsigned int[histos_veclen];
+        memset(histograms, 0, sizeof(unsigned int)*histos_veclen);
+
+        for (unsigned int i=0; i<indices_length; ++i) {
+            variance += static_cast<unsigned long long>( ensureSquareDistance<Distance>(
+                        distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_)));
+
+            unsigned char* vec = (unsigned char*)dataset_[indices[i]];
+            for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
+                histograms[k +     ((vec[l])    & 0x03)]++;
+                histograms[k + 4 + ((vec[l]>>2) & 0x03)]++;
+                histograms[k + 8 + ((vec[l]>>4) & 0x03)]++;
+                histograms[k +12 + ((vec[l]>>6) & 0x03)]++;
+            }
+        }
+
+        CentersType* mean = new CentersType[veclen_];
+        memoryCounter_ += int(veclen_*sizeof(CentersType));
+        unsigned char* char_mean = (unsigned char*)mean;
+        unsigned int* h = histograms;
+        for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
+            char_mean[l] = (h[k] > h[k+1] ? h[k+2] > h[k+3] ? h[k]   > h[k+2] ? 0x00 : 0x10
+                                                            : h[k]   > h[k+3] ? 0x00 : 0x11
+                                          : h[k+2] > h[k+3] ? h[k+1] > h[k+2] ? 0x01 : 0x10
+                                                            : h[k+1] > h[k+3] ? 0x01 : 0x11)
+                         | (h[k+4]>h[k+5] ? h[k+6] > h[k+7] ? h[k+4] > h[k+6] ? 0x00   : 0x1000
+                                                            : h[k+4] > h[k+7] ? 0x00   : 0x1100
+                                          : h[k+6] > h[k+7] ? h[k+5] > h[k+6] ? 0x0100 : 0x1000
+                                                            : h[k+5] > h[k+7] ? 0x0100 : 0x1100)
+                         | (h[k+8]>h[k+9] ? h[k+10]>h[k+11] ? h[k+8] >h[k+10] ? 0x00   : 0x100000
+                                                            : h[k+8] >h[k+11] ? 0x00   : 0x110000
+                                          : h[k+10]>h[k+11] ? h[k+9] >h[k+10] ? 0x010000 : 0x100000
+                                                            : h[k+9] >h[k+11] ? 0x010000 : 0x110000)
+                         | (h[k+12]>h[k+13] ? h[k+14]>h[k+15] ? h[k+12] >h[k+14] ? 0x00   : 0x10000000
+                                                              : h[k+12] >h[k+15] ? 0x00   : 0x11000000
+                                            : h[k+14]>h[k+15] ? h[k+13] >h[k+14] ? 0x01000000 : 0x10000000
+                                                              : h[k+13] >h[k+15] ? 0x01000000 : 0x11000000);
+        }
+        variance = static_cast<unsigned long long>(
+                    0.5 + static_cast<double>(variance) / static_cast<double>(indices_length));
+        variance -= static_cast<unsigned long long>(
+                    ensureSquareDistance<Distance>(
+                        distance_(mean, ZeroIterator<ElementType>(), veclen_)));
+
+        DistanceType radius = 0;
+        for (unsigned int i=0; i<indices_length; ++i) {
+            DistanceType tmp = distance_(mean, dataset_[indices[i]], veclen_);
+            if (tmp>radius) {
+                radius = tmp;
+            }
+        }
+
+        node->variance = static_cast<DistanceType>(variance);
+        node->radius = radius;
+        node->pivot = mean;
+
+        delete[] histograms;
+    }
+
+
+    template<typename DistType>
+    void computeNodeStatistics(KMeansNodePtr node, int* indices,
+                               unsigned int indices_length,
+                               const DistType* identifier)
+    {
+        (void)identifier;
+        computeNodeStatistics(node, indices, indices_length);
+    }
+
+    void computeNodeStatistics(KMeansNodePtr node, int* indices,
+                               unsigned int indices_length,
+                               const cvflann::HammingLUT* identifier)
+    {
+        (void)identifier;
+        computeBitfieldNodeStatistics(node, indices, indices_length);
+    }
+
+    void computeNodeStatistics(KMeansNodePtr node, int* indices,
+                               unsigned int indices_length,
+                               const cvflann::Hamming<unsigned char>* identifier)
+    {
+        (void)identifier;
+        computeBitfieldNodeStatistics(node, indices, indices_length);
+    }
+
+    void computeNodeStatistics(KMeansNodePtr node, int* indices,
+                               unsigned int indices_length,
+                               const cvflann::Hamming2<unsigned char>* identifier)
+    {
+        (void)identifier;
+        computeBitfieldNodeStatistics(node, indices, indices_length);
+    }
+
+    void computeNodeStatistics(KMeansNodePtr node, int* indices,
+                               unsigned int indices_length,
+                               const cvflann::DNAmmingLUT* identifier)
+    {
+        (void)identifier;
+        computeDnaNodeStatistics(node, indices, indices_length);
+    }
+
+    void computeNodeStatistics(KMeansNodePtr node, int* indices,
+                               unsigned int indices_length,
+                               const cvflann::DNAmming2<unsigned char>* identifier)
+    {
+        (void)identifier;
+        computeDnaNodeStatistics(node, indices, indices_length);
+    }
+
+
+    void refineClustering(int* indices, int indices_length, int branching, CentersType** centers,
+                          std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
+    {
+        cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
+        Matrix<double> dcenters(dcenters_buf.data(), branching, veclen_);
+
+        bool converged = false;
+        int iteration = 0;
+        while (!converged && iteration<iterations_) {
+            converged = true;
+            iteration++;
+
+            // compute the new cluster centers
+            for (int i=0; i<branching; ++i) {
+                memset(dcenters[i],0,sizeof(double)*veclen_);
+                radiuses[i] = 0;
+            }
+            for (int i=0; i<indices_length; ++i) {
+                ElementType* vec = dataset_[indices[i]];
+                double* center = dcenters[belongs_to[i]];
+                for (size_t k=0; k<veclen_; ++k) {
+                    center[k] += vec[k];
+                }
+            }
+            for (int i=0; i<branching; ++i) {
+                int cnt = count[i];
+                for (size_t k=0; k<veclen_; ++k) {
+                    dcenters[i][k] /= cnt;
+                }
+            }
+
+            std::vector<int> new_centroids(indices_length);
+            std::vector<DistanceType> sq_dists(indices_length);
+
+            // reassign points to clusters
+            KMeansDistanceComputer<Matrix<double> > invoker(
+                        distance_, dataset_, branching, indices, dcenters, veclen_, new_centroids, sq_dists);
+            parallel_for_(cv::Range(0, (int)indices_length), invoker);
+
+            for (int i=0; i < (int)indices_length; ++i) {
+                DistanceType sq_dist(sq_dists[i]);
+                int new_centroid(new_centroids[i]);
+                if (sq_dist > radiuses[new_centroid]) {
+                    radiuses[new_centroid] = sq_dist;
+                }
+                if (new_centroid != belongs_to[i]) {
+                    count[belongs_to[i]]--;
+                    count[new_centroid]++;
+                    belongs_to[i] = new_centroid;
+                    converged = false;
+                }
+            }
+
+            for (int i=0; i<branching; ++i) {
+                // if one cluster converges to an empty cluster,
+                // move an element into that cluster
+                if (count[i]==0) {
+                    int j = (i+1)%branching;
+                    while (count[j]<=1) {
+                        j = (j+1)%branching;
+                    }
+
+                    for (int k=0; k<indices_length; ++k) {
+                        if (belongs_to[k]==j) {
+                            // for cluster j, we move the furthest element from the center to the empty cluster i
+                            if ( distance_(dataset_[indices[k]], dcenters[j], veclen_) == radiuses[j] ) {
+                                belongs_to[k] = i;
+                                count[j]--;
+                                count[i]++;
+                                break;
+                            }
+                        }
+                    }
+                    converged = false;
+                }
+            }
+        }
+
+       for (int i=0; i<branching; ++i) {
+           centers[i] = new CentersType[veclen_];
+           memoryCounter_ += (int)(veclen_*sizeof(CentersType));
+           for (size_t k=0; k<veclen_; ++k) {
+               centers[i][k] = (CentersType)dcenters[i][k];
+           }
+       }
+    }
+
+
+    void refineBitfieldClustering(int* indices, int indices_length, int branching, CentersType** centers,
+                                  std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
+    {
+        for (int i=0; i<branching; ++i) {
+            centers[i] = new CentersType[veclen_];
+            memoryCounter_ += (int)(veclen_*sizeof(CentersType));
+        }
+
+        const unsigned int accumulator_veclen = static_cast<unsigned int>(
+                                                veclen_*sizeof(ElementType)*BITS_PER_CHAR);
+        cv::AutoBuffer<unsigned int> dcenters_buf(branching*accumulator_veclen);
+        Matrix<unsigned int> dcenters(dcenters_buf.data(), branching, accumulator_veclen);
+
+        bool converged = false;
+        int iteration = 0;
+        while (!converged && iteration<iterations_) {
+            converged = true;
+            iteration++;
+
+            // compute the new cluster centers
+            for (int i=0; i<branching; ++i) {
+                memset(dcenters[i],0,sizeof(unsigned int)*accumulator_veclen);
+                radiuses[i] = 0;
+            }
+            for (int i=0; i<indices_length; ++i) {
+                unsigned char* vec = (unsigned char*)dataset_[indices[i]];
+                unsigned int* dcenter = dcenters[belongs_to[i]];
+                for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
+                    dcenter[k]   += (vec[l])    & 0x01;
+                    dcenter[k+1] += (vec[l]>>1) & 0x01;
+                    dcenter[k+2] += (vec[l]>>2) & 0x01;
+                    dcenter[k+3] += (vec[l]>>3) & 0x01;
+                    dcenter[k+4] += (vec[l]>>4) & 0x01;
+                    dcenter[k+5] += (vec[l]>>5) & 0x01;
+                    dcenter[k+6] += (vec[l]>>6) & 0x01;
+                    dcenter[k+7] += (vec[l]>>7) & 0x01;
+                }
+            }
+            for (int i=0; i<branching; ++i) {
+                double cnt = static_cast<double>(count[i]);
+                unsigned int* dcenter = dcenters[i];
+                unsigned char* charCenter = (unsigned char*)centers[i];
+                for (size_t k=0, l=0; k<accumulator_veclen; k+=BITS_PER_CHAR, ++l) {
+                    charCenter[l] = static_cast<unsigned char>(
+                                      (((int)(0.5 + (double)(dcenter[k])   / cnt)))
+                                    | (((int)(0.5 + (double)(dcenter[k+1]) / cnt))<<1)
+                                    | (((int)(0.5 + (double)(dcenter[k+2]) / cnt))<<2)
+                                    | (((int)(0.5 + (double)(dcenter[k+3]) / cnt))<<3)
+                                    | (((int)(0.5 + (double)(dcenter[k+4]) / cnt))<<4)
+                                    | (((int)(0.5 + (double)(dcenter[k+5]) / cnt))<<5)
+                                    | (((int)(0.5 + (double)(dcenter[k+6]) / cnt))<<6)
+                                    | (((int)(0.5 + (double)(dcenter[k+7]) / cnt))<<7));
+                }
+            }
+
+            std::vector<int> new_centroids(indices_length);
+            std::vector<DistanceType> dists(indices_length);
+
+            // reassign points to clusters
+            KMeansDistanceComputer<ElementType**> invoker(
+                        distance_, dataset_, branching, indices, centers, veclen_, new_centroids, dists);
+            parallel_for_(cv::Range(0, (int)indices_length), invoker);
+
+            for (int i=0; i < indices_length; ++i) {
+                DistanceType dist(dists[i]);
+                int new_centroid(new_centroids[i]);
+                if (dist > radiuses[new_centroid]) {
+                    radiuses[new_centroid] = dist;
+                }
+                if (new_centroid != belongs_to[i]) {
+                    count[belongs_to[i]]--;
+                    count[new_centroid]++;
+                    belongs_to[i] = new_centroid;
+                    converged = false;
+                }
+            }
+
+            for (int i=0; i<branching; ++i) {
+                // if one cluster converges to an empty cluster,
+                // move an element into that cluster
+                if (count[i]==0) {
+                    int j = (i+1)%branching;
+                    while (count[j]<=1) {
+                        j = (j+1)%branching;
+                    }
+
+                    for (int k=0; k<indices_length; ++k) {
+                        if (belongs_to[k]==j) {
+                            // for cluster j, we move the furthest element from the center to the empty cluster i
+                            if ( distance_(dataset_[indices[k]], centers[j], veclen_) == radiuses[j] ) {
+                                belongs_to[k] = i;
+                                count[j]--;
+                                count[i]++;
+                                break;
+                            }
+                        }
+                    }
+                    converged = false;
+                }
+            }
+        }
+    }
+
+
+    void refineDnaClustering(int* indices, int indices_length, int branching, CentersType** centers,
+                                  std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
+    {
+        for (int i=0; i<branching; ++i) {
+            centers[i] = new CentersType[veclen_];
+            memoryCounter_ += (int)(veclen_*sizeof(CentersType));
+        }
+
+        const unsigned int histos_veclen = static_cast<unsigned int>(
+                    veclen_*sizeof(CentersType)*(HISTOS_PER_BASE*BASE_PER_CHAR));
+        cv::AutoBuffer<unsigned int> histos_buf(branching*histos_veclen);
+        Matrix<unsigned int> histos(histos_buf.data(), branching, histos_veclen);
+
+        bool converged = false;
+        int iteration = 0;
+        while (!converged && iteration<iterations_) {
+            converged = true;
+            iteration++;
+
+            // compute the new cluster centers
+            for (int i=0; i<branching; ++i) {
+                memset(histos[i],0,sizeof(unsigned int)*histos_veclen);
+                radiuses[i] = 0;
+            }
+            for (int i=0; i<indices_length; ++i) {
+                unsigned char* vec = (unsigned char*)dataset_[indices[i]];
+                unsigned int* h = histos[belongs_to[i]];
+                for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
+                    h[k +     ((vec[l])    & 0x03)]++;
+                    h[k + 4 + ((vec[l]>>2) & 0x03)]++;
+                    h[k + 8 + ((vec[l]>>4) & 0x03)]++;
+                    h[k +12 + ((vec[l]>>6) & 0x03)]++;
+                }
+            }
+            for (int i=0; i<branching; ++i) {
+                unsigned int* h = histos[i];
+                unsigned char* charCenter = (unsigned char*)centers[i];
+                for (size_t k=0, l=0; k<histos_veclen; k+=HISTOS_PER_BASE*BASE_PER_CHAR, ++l) {
+                    charCenter[l]= (h[k] > h[k+1] ? h[k+2] > h[k+3] ? h[k]   > h[k+2] ? 0x00 : 0x10
+                                                                    : h[k]   > h[k+3] ? 0x00 : 0x11
+                                                  : h[k+2] > h[k+3] ? h[k+1] > h[k+2] ? 0x01 : 0x10
+                                                                    : h[k+1] > h[k+3] ? 0x01 : 0x11)
+                                 | (h[k+4]>h[k+5] ? h[k+6] > h[k+7] ? h[k+4] > h[k+6] ? 0x00   : 0x1000
+                                                                    : h[k+4] > h[k+7] ? 0x00   : 0x1100
+                                                  : h[k+6] > h[k+7] ? h[k+5] > h[k+6] ? 0x0100 : 0x1000
+                                                                    : h[k+5] > h[k+7] ? 0x0100 : 0x1100)
+                                 | (h[k+8]>h[k+9] ? h[k+10]>h[k+11] ? h[k+8] >h[k+10] ? 0x00   : 0x100000
+                                                                    : h[k+8] >h[k+11] ? 0x00   : 0x110000
+                                                  : h[k+10]>h[k+11] ? h[k+9] >h[k+10] ? 0x010000 : 0x100000
+                                                                    : h[k+9] >h[k+11] ? 0x010000 : 0x110000)
+                                 | (h[k+12]>h[k+13] ? h[k+14]>h[k+15] ? h[k+12] >h[k+14] ? 0x00   : 0x10000000
+                                                                      : h[k+12] >h[k+15] ? 0x00   : 0x11000000
+                                                    : h[k+14]>h[k+15] ? h[k+13] >h[k+14] ? 0x01000000 : 0x10000000
+                                                                      : h[k+13] >h[k+15] ? 0x01000000 : 0x11000000);
+                }
+            }
+
+            std::vector<int> new_centroids(indices_length);
+            std::vector<DistanceType> dists(indices_length);
+
+            // reassign points to clusters
+            KMeansDistanceComputer<ElementType**> invoker(
+                        distance_, dataset_, branching, indices, centers, veclen_, new_centroids, dists);
+            parallel_for_(cv::Range(0, (int)indices_length), invoker);
+
+            for (int i=0; i < indices_length; ++i) {
+                DistanceType dist(dists[i]);
+                int new_centroid(new_centroids[i]);
+                if (dist > radiuses[new_centroid]) {
+                    radiuses[new_centroid] = dist;
+                }
+                if (new_centroid != belongs_to[i]) {
+                    count[belongs_to[i]]--;
+                    count[new_centroid]++;
+                    belongs_to[i] = new_centroid;
+                    converged = false;
+                }
+            }
+
+            for (int i=0; i<branching; ++i) {
+                // if one cluster converges to an empty cluster,
+                // move an element into that cluster
+                if (count[i]==0) {
+                    int j = (i+1)%branching;
+                    while (count[j]<=1) {
+                        j = (j+1)%branching;
+                    }
+
+                    for (int k=0; k<indices_length; ++k) {
+                        if (belongs_to[k]==j) {
+                            // for cluster j, we move the furthest element from the center to the empty cluster i
+                            if ( distance_(dataset_[indices[k]], centers[j], veclen_) == radiuses[j] ) {
+                                belongs_to[k] = i;
+                                count[j]--;
+                                count[i]++;
+                                break;
+                            }
+                        }
+                    }
+                    converged = false;
+                }
+            }
+        }
+    }
+
+
+    void computeSubClustering(KMeansNodePtr node, int* indices, int indices_length,
+                              int branching, int level, CentersType** centers,
+                              std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
+    {
+        // compute kmeans clustering for each of the resulting clusters
+        node->childs = pool_.allocate<KMeansNodePtr>(branching);
+        int start = 0;
+        int end = start;
+        for (int c=0; c<branching; ++c) {
+            int s = count[c];
+
+            DistanceType variance = 0;
+            DistanceType mean_radius =0;
+            for (int i=0; i<indices_length; ++i) {
+                if (belongs_to[i]==c) {
+                    DistanceType d = distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_);
+                    variance += d;
+                    mean_radius += static_cast<DistanceType>( sqrt(d) );
+                    std::swap(indices[i],indices[end]);
+                    std::swap(belongs_to[i],belongs_to[end]);
+                    end++;
+                }
+            }
+            variance /= s;
+            mean_radius /= s;
+            variance -= distance_(centers[c], ZeroIterator<ElementType>(), veclen_);
+
+            node->childs[c] = pool_.allocate<KMeansNode>();
+            std::memset(node->childs[c], 0, sizeof(KMeansNode));
+            node->childs[c]->radius = radiuses[c];
+            node->childs[c]->pivot = centers[c];
+            node->childs[c]->variance = variance;
+            node->childs[c]->mean_radius = mean_radius;
+            computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
+            start=end;
+        }
+    }
+
+
+    void computeAnyBitfieldSubClustering(KMeansNodePtr node, int* indices, int indices_length,
+                              int branching, int level, CentersType** centers,
+                              std::vector<DistanceType>& radiuses, int* belongs_to, int* count)
+    {
+        // compute kmeans clustering for each of the resulting clusters
+        node->childs = pool_.allocate<KMeansNodePtr>(branching);
+        int start = 0;
+        int end = start;
+        for (int c=0; c<branching; ++c) {
+            int s = count[c];
+
+            unsigned long long variance = 0ull;
+            DistanceType mean_radius =0;
+            for (int i=0; i<indices_length; ++i) {
+                if (belongs_to[i]==c) {
+                    DistanceType d = distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_);
+                    variance += static_cast<unsigned long long>( ensureSquareDistance<Distance>(d) );
+                    mean_radius += ensureSimpleDistance<Distance>(d);
+                    std::swap(indices[i],indices[end]);
+                    std::swap(belongs_to[i],belongs_to[end]);
+                    end++;
+                }
+            }
+            mean_radius = static_cast<DistanceType>(
+                        0.5f + static_cast<float>(mean_radius) / static_cast<float>(s));
+            variance = static_cast<unsigned long long>(
+                        0.5 + static_cast<double>(variance) / static_cast<double>(s));
+            variance -= static_cast<unsigned long long>(
+                        ensureSquareDistance<Distance>(
+                            distance_(centers[c], ZeroIterator<ElementType>(), veclen_)));
+
+            node->childs[c] = pool_.allocate<KMeansNode>();
+            std::memset(node->childs[c], 0, sizeof(KMeansNode));
+            node->childs[c]->radius = radiuses[c];
+            node->childs[c]->pivot = centers[c];
+            node->childs[c]->variance = static_cast<DistanceType>(variance);
+            node->childs[c]->mean_radius = mean_radius;
+            computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
+            start=end;
+        }
+    }
+
+
+    template<typename DistType>
+    void refineAndSplitClustering(
+            KMeansNodePtr node, int* indices, int indices_length, int branching,
+            int level, CentersType** centers, std::vector<DistanceType>& radiuses,
+            int* belongs_to, int* count, const DistType* identifier)
+    {
+        (void)identifier;
+        refineClustering(indices, indices_length, branching, centers, radiuses, belongs_to, count);
+
+        computeSubClustering(node, indices, indices_length, branching,
+                             level, centers, radiuses, belongs_to, count);
+    }
+
+
+    /**
+     * The methods responsible with doing the recursive hierarchical clustering on
+     * binary vectors.
+     * As some might have heard that KMeans on binary data doesn't make sense,
+     * it's worth a little explanation why it actually fairly works. As
+     * with the Hierarchical Clustering algortihm, we seed several centers for the
+     * current node by picking some of its points. Then in a first pass each point
+     * of the node is then related to its closest center. Now let's have a look at
+     * the 5 central dimensions of the 9 following points:
+     *
+     * xxxxxx11100xxxxx (1)
+     * xxxxxx11010xxxxx (2)
+     * xxxxxx11001xxxxx (3)
+     * xxxxxx10110xxxxx (4)
+     * xxxxxx10101xxxxx (5)
+     * xxxxxx10011xxxxx (6)
+     * xxxxxx01110xxxxx (7)
+     * xxxxxx01101xxxxx (8)
+     * xxxxxx01011xxxxx (9)
+     * sum   _____
+     * of 1: 66555
+     *
+     * Even if the barycenter notion doesn't apply, we can set a center
+     * xxxxxx11111xxxxx that will better fit the five dimensions we are focusing
+     * on for these points.
+     *
+     * Note that convergence isn't ensured anymore. In practice, using Gonzales
+     * as seeding algorithm should be fine for getting convergence ("iterations"
+     * value can be set to -1). But with KMeans++ seeding you should definitely
+     * set a maximum number of iterations (but make it higher than the "iterations"
+     * default value of 11).
+     *
+     * Params:
+     *     node = the node to cluster
+     *     indices = indices of the points belonging to the current node
+     *     indices_length = number of points in the current node
+     *     branching = the branching factor to use in the clustering
+     *     level = 0 for the root node, it increases with the subdivision levels
+     *     centers = clusters centers to compute
+     *     radiuses = radiuses of clusters
+     *     belongs_to = LookUp Table returning, for a given indice id, the center id it belongs to
+     *     count = array storing the number of indices for a given center id
+     *     identifier = dummy pointer on an instance of Distance (use to branch correctly among templates)
+     */
+    void refineAndSplitClustering(
+            KMeansNodePtr node, int* indices, int indices_length, int branching,
+            int level, CentersType** centers, std::vector<DistanceType>& radiuses,
+            int* belongs_to, int* count, const cvflann::HammingLUT* identifier)
+    {
+        (void)identifier;
+        refineBitfieldClustering(
+                    indices, indices_length, branching, centers, radiuses, belongs_to, count);
+
+        computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
+                                        level, centers, radiuses, belongs_to, count);
+    }
+
+
+    void refineAndSplitClustering(
+            KMeansNodePtr node, int* indices, int indices_length, int branching,
+            int level, CentersType** centers, std::vector<DistanceType>& radiuses,
+            int* belongs_to, int* count, const cvflann::Hamming<unsigned char>* identifier)
+    {
+        (void)identifier;
+        refineBitfieldClustering(
+                    indices, indices_length, branching, centers, radiuses, belongs_to, count);
+
+        computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
+                                        level, centers, radiuses, belongs_to, count);
+    }
+
+
+    void refineAndSplitClustering(
+            KMeansNodePtr node, int* indices, int indices_length, int branching,
+            int level, CentersType** centers, std::vector<DistanceType>& radiuses,
+            int* belongs_to, int* count, const cvflann::Hamming2<unsigned char>* identifier)
+    {
+        (void)identifier;
+        refineBitfieldClustering(
+                    indices, indices_length, branching, centers, radiuses, belongs_to, count);
+
+        computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
+                                        level, centers, radiuses, belongs_to, count);
+    }
+
+
+    void refineAndSplitClustering(
+            KMeansNodePtr node, int* indices, int indices_length, int branching,
+            int level, CentersType** centers, std::vector<DistanceType>& radiuses,
+            int* belongs_to, int* count, const cvflann::DNAmmingLUT* identifier)
+    {
+        (void)identifier;
+        refineDnaClustering(
+                    indices, indices_length, branching, centers, radiuses, belongs_to, count);
+
+        computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
+                                        level, centers, radiuses, belongs_to, count);
+    }
+
+
+    void refineAndSplitClustering(
+            KMeansNodePtr node, int* indices, int indices_length, int branching,
+            int level, CentersType** centers, std::vector<DistanceType>& radiuses,
+            int* belongs_to, int* count, const cvflann::DNAmming2<unsigned char>* identifier)
+    {
+        (void)identifier;
+        refineDnaClustering(
+                    indices, indices_length, branching, centers, radiuses, belongs_to, count);
+
+        computeAnyBitfieldSubClustering(node, indices, indices_length, branching,
+                                        level, centers, radiuses, belongs_to, count);
+    }
+
+
+    /**
+     * The method responsible with actually doing the recursive hierarchical
+     * clustering
+     *
+     * Params:
+     *     node = the node to cluster
+     *     indices = indices of the points belonging to the current node
+     *     branching = the branching factor to use in the clustering
+     *
+     * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
+     */
+    void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level)
+    {
+        node->size = indices_length;
+        node->level = level;
+
+        if (indices_length < branching) {
+            node->indices = indices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            return;
+        }
+
+        cv::AutoBuffer<int> centers_idx_buf(branching);
+        int* centers_idx = centers_idx_buf.data();
+        int centers_length;
+        (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length);
+
+        if (centers_length<branching) {
+            node->indices = indices;
+            std::sort(node->indices,node->indices+indices_length);
+            node->childs = NULL;
+            return;
+        }
+
+
+        std::vector<DistanceType> radiuses(branching);
+        cv::AutoBuffer<int> count_buf(branching);
+        int* count = count_buf.data();
+        for (int i=0; i<branching; ++i) {
+            radiuses[i] = 0;
+            count[i] = 0;
+        }
+
+        //	assign points to clusters
+        cv::AutoBuffer<int> belongs_to_buf(indices_length);
+        int* belongs_to = belongs_to_buf.data();
+        for (int i=0; i<indices_length; ++i) {
+            DistanceType sq_dist = distance_(dataset_[indices[i]], dataset_[centers_idx[0]], veclen_);
+            belongs_to[i] = 0;
+            for (int j=1; j<branching; ++j) {
+                DistanceType new_sq_dist = distance_(dataset_[indices[i]], dataset_[centers_idx[j]], veclen_);
+                if (sq_dist>new_sq_dist) {
+                    belongs_to[i] = j;
+                    sq_dist = new_sq_dist;
+                }
+            }
+            if (sq_dist>radiuses[belongs_to[i]]) {
+                radiuses[belongs_to[i]] = sq_dist;
+            }
+            count[belongs_to[i]]++;
+        }
+
+        CentersType** centers = new CentersType*[branching];
+
+        Distance* dummy = NULL;
+        refineAndSplitClustering(node, indices, indices_length, branching, level,
+                                 centers, radiuses, belongs_to, count, dummy);
+
+        delete[] centers;
+    }
+
+
+    /**
+     * Performs one descent in the hierarchical k-means tree. The branches not
+     * visited are stored in a priority queue.
+     *
+     * Params:
+     *      node = node to explore
+     *      result = container for the k-nearest neighbors found
+     *      vec = query points
+     *      checks = how many points in the dataset have been checked so far
+     *      maxChecks = maximum dataset points to checks
+     */
+
+
+    void findNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
+                const cv::Ptr<Heap<BranchSt>>& heap)
+    {
+        // Ignore those clusters that are too far away
+        {
+            DistanceType bsq = distance_(vec, node->pivot, veclen_);
+            DistanceType rsq = node->radius;
+            DistanceType wsq = result.worstDist();
+
+            if (isSquareDistance<Distance>())
+            {
+                DistanceType val = bsq-rsq-wsq;
+                if ((val>0) && (val*val > 4*rsq*wsq))
+                    return;
+            }
+            else
+            {
+                if (bsq-rsq > wsq)
+                    return;
+            }
+        }
+
+        if (node->childs==NULL) {
+            if ((checks>=maxChecks) && result.full()) {
+                return;
+            }
+            checks += node->size;
+            for (int i=0; i<node->size; ++i) {
+                int index = node->indices[i];
+                DistanceType dist = distance_(dataset_[index], vec, veclen_);
+                result.addPoint(dist, index);
+            }
+        }
+        else {
+            DistanceType* domain_distances = new DistanceType[branching_];
+            int closest_center = exploreNodeBranches(node, vec, domain_distances, heap);
+            delete[] domain_distances;
+            findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap);
+        }
+    }
+
+    /**
+     * Helper function that computes the nearest childs of a node to a given query point.
+     * Params:
+     *     node = the node
+     *     q = the query point
+     *     distances = array with the distances to each child node.
+     * Returns:
+     */
+    int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, const cv::Ptr<Heap<BranchSt>>& heap)
+    {
+
+        int best_index = 0;
+        domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_);
+        for (int i=1; i<branching_; ++i) {
+            domain_distances[i] = distance_(q, node->childs[i]->pivot, veclen_);
+            if (domain_distances[i]<domain_distances[best_index]) {
+                best_index = i;
+            }
+        }
+
+        //		float* best_center = node->childs[best_index]->pivot;
+        for (int i=0; i<branching_; ++i) {
+            if (i != best_index) {
+                domain_distances[i] -= cvflann::round<DistanceType>(
+                                        cb_index_*node->childs[i]->variance );
+
+                //				float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q);
+                //				if (domain_distances[i]<dist_to_border) {
+                //					domain_distances[i] = dist_to_border;
+                //				}
+                heap->insert(BranchSt(node->childs[i],domain_distances[i]));
+            }
+        }
+
+        return best_index;
+    }
+
+
+    /**
+     * Function the performs exact nearest neighbor search by traversing the entire tree.
+     */
+    void findExactNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec)
+    {
+        // Ignore those clusters that are too far away
+        {
+            DistanceType bsq = distance_(vec, node->pivot, veclen_);
+            DistanceType rsq = node->radius;
+            DistanceType wsq = result.worstDist();
+
+            if (isSquareDistance<Distance>())
+            {
+                DistanceType val = bsq-rsq-wsq;
+                if ((val>0) && (val*val > 4*rsq*wsq))
+                    return;
+            }
+            else
+            {
+                if (bsq-rsq > wsq)
+                    return;
+            }
+        }
+
+
+        if (node->childs==NULL) {
+            for (int i=0; i<node->size; ++i) {
+                int index = node->indices[i];
+                DistanceType dist = distance_(dataset_[index], vec, veclen_);
+                result.addPoint(dist, index);
+            }
+        }
+        else {
+            int* sort_indices = new int[branching_];
+
+            getCenterOrdering(node, vec, sort_indices);
+
+            for (int i=0; i<branching_; ++i) {
+                findExactNN(node->childs[sort_indices[i]],result,vec);
+            }
+
+            delete[] sort_indices;
+        }
+    }
+
+
+    /**
+     * Helper function.
+     *
+     * I computes the order in which to traverse the child nodes of a particular node.
+     */
+    void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices)
+    {
+        DistanceType* domain_distances = new DistanceType[branching_];
+        for (int i=0; i<branching_; ++i) {
+            DistanceType dist = distance_(q, node->childs[i]->pivot, veclen_);
+
+            int j=0;
+            while (domain_distances[j]<dist && j<i)
+                j++;
+            for (int k=i; k>j; --k) {
+                domain_distances[k] = domain_distances[k-1];
+                sort_indices[k] = sort_indices[k-1];
+            }
+            domain_distances[j] = dist;
+            sort_indices[j] = i;
+        }
+        delete[] domain_distances;
+    }
+
+    /**
+     * Method that computes the squared distance from the query point q
+     * from inside region with center c to the border between this
+     * region and the region with center p
+     */
+    DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q)
+    {
+        DistanceType sum = 0;
+        DistanceType sum2 = 0;
+
+        for (int i=0; i<veclen_; ++i) {
+            DistanceType t = c[i]-p[i];
+            sum += t*(q[i]-(c[i]+p[i])/2);
+            sum2 += t*t;
+        }
+
+        return sum*sum/sum2;
+    }
+
+
+    /**
+     * Helper function the descends in the hierarchical k-means tree by splitting those clusters that minimize
+     * the overall variance of the clustering.
+     * Params:
+     *     root = root node
+     *     clusters = array with clusters centers (return value)
+     *     varianceValue = variance of the clustering (return value)
+     * Returns:
+     */
+    int getMinVarianceClusters(KMeansNodePtr root, KMeansNodePtr* clusters, int clusters_length, DistanceType& varianceValue)
+    {
+        int clusterCount = 1;
+        clusters[0] = root;
+
+        DistanceType meanVariance = root->variance*root->size;
+
+        while (clusterCount<clusters_length) {
+            DistanceType minVariance = (std::numeric_limits<DistanceType>::max)();
+            int splitIndex = -1;
+
+            for (int i=0; i<clusterCount; ++i) {
+                if (clusters[i]->childs != NULL) {
+
+                    DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size;
+
+                    for (int j=0; j<branching_; ++j) {
+                        variance += clusters[i]->childs[j]->variance*clusters[i]->childs[j]->size;
+                    }
+                    if (variance<minVariance) {
+                        minVariance = variance;
+                        splitIndex = i;
+                    }
+                }
+            }
+
+            if (splitIndex==-1) break;
+            if ( (branching_+clusterCount-1) > clusters_length) break;
+
+            meanVariance = minVariance;
+
+            // split node
+            KMeansNodePtr toSplit = clusters[splitIndex];
+            clusters[splitIndex] = toSplit->childs[0];
+            for (int i=1; i<branching_; ++i) {
+                clusters[clusterCount++] = toSplit->childs[i];
+            }
+        }
+
+        varianceValue = meanVariance/root->size;
+        return clusterCount;
+    }
+
+private:
+    /** The branching factor used in the hierarchical k-means clustering */
+    int branching_;
+
+    /** Number of kmeans trees (default is one) */
+    int trees_;
+
+    /** Maximum number of iterations to use when performing k-means clustering */
+    int iterations_;
+
+    /** Algorithm for choosing the cluster centers */
+    flann_centers_init_t centers_init_;
+
+    /**
+     * Cluster border index. This is used in the tree search phase when determining
+     * the closest cluster to explore next. A zero value takes into account only
+     * the cluster centres, a value greater then zero also take into account the size
+     * of the cluster.
+     */
+    float cb_index_;
+
+    /**
+     * The dataset used by this index
+     */
+    const Matrix<ElementType> dataset_;
+
+    /** Index parameters */
+    IndexParams index_params_;
+
+    /**
+     * Number of features in the dataset.
+     */
+    size_t size_;
+
+    /**
+     * Length of each feature.
+     */
+    size_t veclen_;
+
+    /**
+     * The root node in the tree.
+     */
+    KMeansNodePtr* root_;
+
+    /**
+     *  Array of indices to vectors in the dataset.
+     */
+    int** indices_;
+
+    /**
+     * The distance
+     */
+    Distance distance_;
+
+    /**
+     * Pooled memory allocator.
+     */
+    PooledAllocator pool_;
+
+    /**
+     * Memory occupied by the index.
+     */
+    int memoryCounter_;
+};
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_KMEANS_INDEX_H_
diff --git a/3rdParty/include/opencv2/flann/linear_index.h b/3rdParty/include/opencv2/flann/linear_index.h
new file mode 100644
index 0000000..6428c0d
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/linear_index.h
@@ -0,0 +1,135 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_LINEAR_INDEX_H_
+#define OPENCV_FLANN_LINEAR_INDEX_H_
+
+//! @cond IGNORED
+
+#include "nn_index.h"
+
+namespace cvflann
+{
+
+struct LinearIndexParams : public IndexParams
+{
+    LinearIndexParams()
+    {
+        (* this)["algorithm"] = FLANN_INDEX_LINEAR;
+    }
+};
+
+template <typename Distance>
+class LinearIndex : public NNIndex<Distance>
+{
+public:
+
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+
+    LinearIndex(const Matrix<ElementType>& inputData, const IndexParams& params = LinearIndexParams(),
+                Distance d = Distance()) :
+        dataset_(inputData), index_params_(params), distance_(d)
+    {
+    }
+
+    LinearIndex(const LinearIndex&);
+    LinearIndex& operator=(const LinearIndex&);
+
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_LINEAR;
+    }
+
+
+    size_t size() const CV_OVERRIDE
+    {
+        return dataset_.rows;
+    }
+
+    size_t veclen() const CV_OVERRIDE
+    {
+        return dataset_.cols;
+    }
+
+
+    int usedMemory() const CV_OVERRIDE
+    {
+        return 0;
+    }
+
+    void buildIndex() CV_OVERRIDE
+    {
+        /* nothing to do here for linear search */
+    }
+
+    void saveIndex(FILE*) CV_OVERRIDE
+    {
+        /* nothing to do here for linear search */
+    }
+
+
+    void loadIndex(FILE*) CV_OVERRIDE
+    {
+        /* nothing to do here for linear search */
+
+        index_params_["algorithm"] = getType();
+    }
+
+    void findNeighbors(ResultSet<DistanceType>& resultSet, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE
+    {
+        ElementType* data = dataset_.data;
+        for (size_t i = 0; i < dataset_.rows; ++i, data += dataset_.cols) {
+            DistanceType dist = distance_(data, vec, dataset_.cols);
+            resultSet.addPoint(dist, (int)i);
+        }
+    }
+
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return index_params_;
+    }
+
+private:
+    /** The dataset */
+    const Matrix<ElementType> dataset_;
+    /** Index parameters */
+    IndexParams index_params_;
+    /** Index distance */
+    Distance distance_;
+
+};
+
+}
+
+//! @endcond
+
+#endif // OPENCV_FLANN_LINEAR_INDEX_H_
diff --git a/3rdParty/include/opencv2/flann/logger.h b/3rdParty/include/opencv2/flann/logger.h
new file mode 100644
index 0000000..8911812
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/logger.h
@@ -0,0 +1,139 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_LOGGER_H
+#define OPENCV_FLANN_LOGGER_H
+
+//! @cond IGNORED
+
+#include <stdio.h>
+#include <stdarg.h>
+
+#include "defines.h"
+
+
+namespace cvflann
+{
+
+class Logger
+{
+    Logger() : stream(stdout), logLevel(FLANN_LOG_WARN) {}
+
+    ~Logger()
+    {
+        if ((stream!=NULL)&&(stream!=stdout)) {
+            fclose(stream);
+        }
+    }
+
+    static Logger& instance()
+    {
+        static Logger logger;
+        return logger;
+    }
+
+    void _setDestination(const char* name)
+    {
+        if (name==NULL) {
+            stream = stdout;
+        }
+        else {
+#ifdef _MSC_VER
+            if (fopen_s(&stream, name, "w") != 0)
+                stream = NULL;
+#else
+            stream = fopen(name,"w");
+#endif
+            if (stream == NULL) {
+                stream = stdout;
+            }
+        }
+    }
+
+    int _log(int level, const char* fmt, va_list arglist)
+    {
+        if (level > logLevel ) return -1;
+        int ret = vfprintf(stream, fmt, arglist);
+        return ret;
+    }
+
+public:
+    /**
+     * Sets the logging level. All messages with lower priority will be ignored.
+     * @param level Logging level
+     */
+    static void setLevel(int level) { instance().logLevel = level; }
+
+    /**
+     * Sets the logging destination
+     * @param name Filename or NULL for console
+     */
+    static void setDestination(const char* name) { instance()._setDestination(name); }
+
+    /**
+     * Print log message
+     * @param level Log level
+     * @param fmt Message format
+     * @return
+     */
+    static int log(int level, const char* fmt, ...)
+    {
+        va_list arglist;
+        va_start(arglist, fmt);
+        int ret = instance()._log(level,fmt,arglist);
+        va_end(arglist);
+        return ret;
+    }
+
+#define LOG_METHOD(NAME,LEVEL) \
+    static int NAME(const char* fmt, ...) \
+    { \
+        va_list ap; \
+        va_start(ap, fmt); \
+        int ret = instance()._log(LEVEL, fmt, ap); \
+        va_end(ap); \
+        return ret; \
+    }
+
+    LOG_METHOD(fatal, FLANN_LOG_FATAL)
+    LOG_METHOD(error, FLANN_LOG_ERROR)
+    LOG_METHOD(warn, FLANN_LOG_WARN)
+    LOG_METHOD(info, FLANN_LOG_INFO)
+
+private:
+    FILE* stream;
+    int logLevel;
+};
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_LOGGER_H
diff --git a/3rdParty/include/opencv2/flann/lsh_index.h b/3rdParty/include/opencv2/flann/lsh_index.h
new file mode 100644
index 0000000..b5e87f6
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/lsh_index.h
@@ -0,0 +1,403 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/***********************************************************************
+ * Author: Vincent Rabaud
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_LSH_INDEX_H_
+#define OPENCV_FLANN_LSH_INDEX_H_
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <cstring>
+#include <map>
+#include <vector>
+
+#include "nn_index.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "lsh_table.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable: 4702) //disable unreachable code
+#endif
+
+namespace cvflann
+{
+
+struct LshIndexParams : public IndexParams
+{
+    LshIndexParams(int table_number = 12, int key_size = 20, int multi_probe_level = 2)
+    {
+        (*this)["algorithm"] = FLANN_INDEX_LSH;
+        // The number of hash tables to use
+        (*this)["table_number"] = table_number;
+        // The length of the key in the hash tables
+        (*this)["key_size"] = key_size;
+        // Number of levels to use in multi-probe (0 for standard LSH)
+        (*this)["multi_probe_level"] = multi_probe_level;
+    }
+};
+
+/**
+ * Locality-sensitive hashing  index
+ *
+ * Contains the tables and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template<typename Distance>
+class LshIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    /** Constructor
+     * @param input_data dataset with the input features
+     * @param params parameters passed to the LSH algorithm
+     * @param d the distance used
+     */
+    LshIndex(const Matrix<ElementType>& input_data, const IndexParams& params = LshIndexParams(),
+             Distance d = Distance()) :
+        dataset_(input_data), index_params_(params), distance_(d)
+    {
+        // cv::flann::IndexParams sets integer params as 'int', so it is used with get_param
+        // in place of 'unsigned int'
+        table_number_ = get_param(index_params_,"table_number",12);
+        key_size_ = get_param(index_params_,"key_size",20);
+        multi_probe_level_ = get_param(index_params_,"multi_probe_level",2);
+
+        feature_size_ = (unsigned)dataset_.cols;
+        fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
+    }
+
+
+    LshIndex(const LshIndex&);
+    LshIndex& operator=(const LshIndex&);
+
+    /**
+     * Builds the index
+     */
+    void buildIndex() CV_OVERRIDE
+    {
+        tables_.resize(table_number_);
+        for (int i = 0; i < table_number_; ++i) {
+            lsh::LshTable<ElementType>& table = tables_[i];
+            table = lsh::LshTable<ElementType>(feature_size_, key_size_);
+
+            // Add the features to the table
+            table.add(dataset_);
+        }
+    }
+
+    flann_algorithm_t getType() const CV_OVERRIDE
+    {
+        return FLANN_INDEX_LSH;
+    }
+
+
+    void saveIndex(FILE* stream) CV_OVERRIDE
+    {
+        save_value(stream,table_number_);
+        save_value(stream,key_size_);
+        save_value(stream,multi_probe_level_);
+        save_value(stream, dataset_);
+    }
+
+    void loadIndex(FILE* stream) CV_OVERRIDE
+    {
+        load_value(stream, table_number_);
+        load_value(stream, key_size_);
+        load_value(stream, multi_probe_level_);
+        load_value(stream, dataset_);
+        // Building the index is so fast we can afford not storing it
+        buildIndex();
+
+        index_params_["algorithm"] = getType();
+        index_params_["table_number"] = table_number_;
+        index_params_["key_size"] = key_size_;
+        index_params_["multi_probe_level"] = multi_probe_level_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const CV_OVERRIDE
+    {
+        return dataset_.rows;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const CV_OVERRIDE
+    {
+        return feature_size_;
+    }
+
+    /**
+     * Computes the index memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const CV_OVERRIDE
+    {
+        return (int)(dataset_.rows * sizeof(int));
+    }
+
+
+    IndexParams getParameters() const CV_OVERRIDE
+    {
+        return index_params_;
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
+    {
+        CV_Assert(queries.cols == veclen());
+        CV_Assert(indices.rows >= queries.rows);
+        CV_Assert(dists.rows >= queries.rows);
+        CV_Assert(int(indices.cols) >= knn);
+        CV_Assert(int(dists.cols) >= knn);
+
+
+        KNNUniqueResultSet<DistanceType> resultSet(knn);
+        for (size_t i = 0; i < queries.rows; i++) {
+            resultSet.clear();
+            std::fill_n(indices[i], knn, -1);
+            std::fill_n(dists[i], knn, std::numeric_limits<DistanceType>::max());
+            findNeighbors(resultSet, queries[i], params);
+            if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn);
+            else resultSet.copy(indices[i], dists[i], knn);
+        }
+    }
+
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE
+    {
+        getNeighbors(vec, result);
+    }
+
+private:
+    /** Defines the comparator on score and index
+     */
+    typedef std::pair<float, unsigned int> ScoreIndexPair;
+    struct SortScoreIndexPairOnSecond
+    {
+        bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const
+        {
+            return left.second < right.second;
+        }
+    };
+
+    /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH
+     * @param key the key we build neighbors from
+     * @param lowest_index the lowest index of the bit set
+     * @param level the multi-probe level we are at
+     * @param xor_masks all the xor mask
+     */
+    void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level,
+                       std::vector<lsh::BucketKey>& xor_masks)
+    {
+        xor_masks.push_back(key);
+        if (level == 0) return;
+        for (int index = lowest_index - 1; index >= 0; --index) {
+            // Create a new key
+            lsh::BucketKey new_key = key | (1 << index);
+            fill_xor_mask(new_key, index, level - 1, xor_masks);
+        }
+    }
+
+    /** Performs the approximate nearest-neighbor search.
+     * @param vec the feature to analyze
+     * @param do_radius flag indicating if we check the radius too
+     * @param radius the radius if it is a radius search
+     * @param do_k flag indicating if we limit the number of nn
+     * @param k_nn the number of nearest neighbors
+     * @param checked_average used for debugging
+     */
+    void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn,
+                      float& /*checked_average*/)
+    {
+        static std::vector<ScoreIndexPair> score_index_heap;
+
+        if (do_k) {
+            unsigned int worst_score = std::numeric_limits<unsigned int>::max();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+            for (; table != table_end; ++table) {
+                size_t key = table->getKey(vec);
+                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+                for (; xor_mask != xor_mask_end; ++xor_mask) {
+                    size_t sub_key = key ^ (*xor_mask);
+                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
+                    if (bucket == 0) continue;
+
+                    // Go over each descriptor index
+                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                    DistanceType hamming_distance;
+
+                    // Process the rest of the candidates
+                    for (; training_index < last_training_index; ++training_index) {
+                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
+
+                        if (hamming_distance < worst_score) {
+                            // Insert the new element
+                            score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
+                            std::push_heap(score_index_heap.begin(), score_index_heap.end());
+
+                            if (score_index_heap.size() > (unsigned int)k_nn) {
+                                // Remove the highest distance value as we have too many elements
+                                std::pop_heap(score_index_heap.begin(), score_index_heap.end());
+                                score_index_heap.pop_back();
+                                // Keep track of the worst score
+                                worst_score = score_index_heap.front().first;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        else {
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+            for (; table != table_end; ++table) {
+                size_t key = table->getKey(vec);
+                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+                for (; xor_mask != xor_mask_end; ++xor_mask) {
+                    size_t sub_key = key ^ (*xor_mask);
+                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
+                    if (bucket == 0) continue;
+
+                    // Go over each descriptor index
+                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                    DistanceType hamming_distance;
+
+                    // Process the rest of the candidates
+                    for (; training_index < last_training_index; ++training_index) {
+                        // Compute the Hamming distance
+                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
+                        if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
+                    }
+                }
+            }
+        }
+    }
+
+    /** Performs the approximate nearest-neighbor search.
+     * This is a slower version than the above as it uses the ResultSet
+     * @param vec the feature to analyze
+     */
+    void getNeighbors(const ElementType* vec, ResultSet<DistanceType>& result)
+    {
+        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+        for (; table != table_end; ++table) {
+            size_t key = table->getKey(vec);
+            std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+            std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+            for (; xor_mask != xor_mask_end; ++xor_mask) {
+                size_t sub_key = key ^ (*xor_mask);
+                const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key);
+                if (bucket == 0) continue;
+
+                // Go over each descriptor index
+                std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                DistanceType hamming_distance;
+
+                // Process the rest of the candidates
+                for (; training_index < last_training_index; ++training_index) {
+                    // Compute the Hamming distance
+                    hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols);
+                    result.addPoint(hamming_distance, *training_index);
+                }
+            }
+        }
+    }
+
+    /** The different hash tables */
+    std::vector<lsh::LshTable<ElementType> > tables_;
+
+    /** The data the LSH tables where built from */
+    Matrix<ElementType> dataset_;
+
+    /** The size of the features (as ElementType[]) */
+    unsigned int feature_size_;
+
+    IndexParams index_params_;
+
+    /** table number */
+    int table_number_;
+    /** key size */
+    int key_size_;
+    /** How far should we look for neighbors in multi-probe LSH */
+    int multi_probe_level_;
+
+    /** The XOR masks to apply to a key to get the neighboring buckets */
+    std::vector<lsh::BucketKey> xor_masks_;
+
+    Distance distance_;
+};
+}
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+//! @endcond
+
+#endif //OPENCV_FLANN_LSH_INDEX_H_
diff --git a/3rdParty/include/opencv2/flann/lsh_table.h b/3rdParty/include/opencv2/flann/lsh_table.h
new file mode 100644
index 0000000..a189562
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/lsh_table.h
@@ -0,0 +1,525 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/***********************************************************************
+ * Author: Vincent Rabaud
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_LSH_TABLE_H_
+#define OPENCV_FLANN_LSH_TABLE_H_
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <iostream>
+#include <iomanip>
+#include <limits.h>
+// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP
+#ifdef __GXX_EXPERIMENTAL_CXX0X__
+#  define USE_UNORDERED_MAP 1
+#else
+#  define USE_UNORDERED_MAP 0
+#endif
+#if USE_UNORDERED_MAP
+#include <unordered_map>
+#else
+#include <map>
+#endif
+#include <math.h>
+#include <stddef.h>
+
+#include "dynamic_bitset.h"
+#include "matrix.h"
+
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable: 4702) //disable unreachable code
+#endif
+
+
+namespace cvflann
+{
+
+namespace lsh
+{
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** What is stored in an LSH bucket
+ */
+typedef uint32_t FeatureIndex;
+/** The id from which we can get a bucket back in an LSH table
+ */
+typedef unsigned int BucketKey;
+
+/** A bucket in an LSH table
+ */
+typedef std::vector<FeatureIndex> Bucket;
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** POD for stats about an LSH table
+ */
+struct LshStats
+{
+    std::vector<unsigned int> bucket_sizes_;
+    size_t n_buckets_;
+    size_t bucket_size_mean_;
+    size_t bucket_size_median_;
+    size_t bucket_size_min_;
+    size_t bucket_size_max_;
+    size_t bucket_size_std_dev;
+    /** Each contained vector contains three value: beginning/end for interval, number of elements in the bin
+     */
+    std::vector<std::vector<unsigned int> > size_histogram_;
+};
+
+/** Overload the << operator for LshStats
+ * @param out the streams
+ * @param stats the stats to display
+ * @return the streams
+ */
+inline std::ostream& operator <<(std::ostream& out, const LshStats& stats)
+{
+    int w = 20;
+    out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : "
+    << stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : "
+    << std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w)
+    << std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w)
+    << std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left)
+    << stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : "
+    << std::setiosflags(std::ios::left) << stats.bucket_size_max_;
+
+    // Display the histogram
+    out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : "
+    << std::setiosflags(std::ios::left);
+    for (std::vector<std::vector<unsigned int> >::const_iterator iterator = stats.size_histogram_.begin(), end =
+             stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ",  ";
+
+    return out;
+}
+
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Lsh hash table. As its key is a sub-feature, and as usually
+ * the size of it is pretty small, we keep it as a continuous memory array.
+ * The value is an index in the corpus of features (we keep it as an unsigned
+ * int for pure memory reasons, it could be a size_t)
+ */
+template<typename ElementType>
+class LshTable
+{
+public:
+    /** A container of all the feature indices. Optimized for space
+     */
+#if USE_UNORDERED_MAP
+    typedef std::unordered_map<BucketKey, Bucket> BucketsSpace;
+#else
+    typedef std::map<BucketKey, Bucket> BucketsSpace;
+#endif
+
+    /** A container of all the feature indices. Optimized for speed
+     */
+    typedef std::vector<Bucket> BucketsSpeed;
+
+    /** Default constructor
+     */
+    LshTable()
+    {
+        key_size_ = 0;
+        feature_size_ = 0;
+        speed_level_ = kArray;
+    }
+
+    /** Default constructor
+     * Create the mask and allocate the memory
+     * @param feature_size is the size of the feature (considered as a ElementType[])
+     * @param key_size is the number of bits that are turned on in the feature
+     */
+    LshTable(unsigned int feature_size, unsigned int key_size)
+    {
+        feature_size_ = feature_size;
+        CV_UNUSED(key_size);
+        CV_Error(cv::Error::StsUnsupportedFormat, "LSH is not implemented for that type" );
+    }
+
+    /** Add a feature to the table
+     * @param value the value to store for that feature
+     * @param feature the feature itself
+     */
+    void add(unsigned int value, const ElementType* feature)
+    {
+        // Add the value to the corresponding bucket
+        BucketKey key = (lsh::BucketKey)getKey(feature);
+
+        switch (speed_level_) {
+        case kArray:
+            // That means we get the buckets from an array
+            buckets_speed_[key].push_back(value);
+            break;
+        case kBitsetHash:
+            // That means we can check the bitset for the presence of a key
+            key_bitset_.set(key);
+            buckets_space_[key].push_back(value);
+            break;
+        case kHash:
+        {
+            // That means we have to check for the hash table for the presence of a key
+            buckets_space_[key].push_back(value);
+            break;
+        }
+        }
+    }
+
+    /** Add a set of features to the table
+     * @param dataset the values to store
+     */
+    void add(Matrix<ElementType> dataset)
+    {
+#if USE_UNORDERED_MAP
+        buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2);
+#endif
+        // Add the features to the table
+        for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]);
+        // Now that the table is full, optimize it for speed/space
+        optimize();
+    }
+
+    /** Get a bucket given the key
+     * @param key
+     * @return
+     */
+    inline const Bucket* getBucketFromKey(BucketKey key) const
+    {
+        // Generate other buckets
+        switch (speed_level_) {
+        case kArray:
+            // That means we get the buckets from an array
+            return &buckets_speed_[key];
+            break;
+        case kBitsetHash:
+            // That means we can check the bitset for the presence of a key
+            if (key_bitset_.test(key)) return &buckets_space_.find(key)->second;
+            else return 0;
+            break;
+        case kHash:
+        {
+            // That means we have to check for the hash table for the presence of a key
+            BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end();
+            bucket_it = buckets_space_.find(key);
+            // Stop here if that bucket does not exist
+            if (bucket_it == bucket_end) return 0;
+            else return &bucket_it->second;
+            break;
+        }
+        }
+        return 0;
+    }
+
+    /** Compute the sub-signature of a feature
+     */
+    size_t getKey(const ElementType* /*feature*/) const
+    {
+        CV_Error(cv::Error::StsUnsupportedFormat, "LSH is not implemented for that type" );
+        return 0;
+    }
+
+    /** Get statistics about the table
+     * @return
+     */
+    LshStats getStats() const;
+
+private:
+    /** defines the speed fo the implementation
+     * kArray uses a vector for storing data
+     * kBitsetHash uses a hash map but checks for the validity of a key with a bitset
+     * kHash uses a hash map only
+     */
+    enum SpeedLevel
+    {
+        kArray, kBitsetHash, kHash
+    };
+
+    /** Initialize some variables
+     */
+    void initialize(size_t key_size)
+    {
+        const size_t key_size_lower_bound = 1;
+        //a value (size_t(1) << key_size) must fit the size_t type so key_size has to be strictly less than size of size_t
+        const size_t key_size_upper_bound = (std::min)(sizeof(BucketKey) * CHAR_BIT + 1, sizeof(size_t) * CHAR_BIT);
+        if (key_size < key_size_lower_bound || key_size >= key_size_upper_bound)
+        {
+            CV_Error(cv::Error::StsBadArg, cv::format("Invalid key_size (=%d). Valid values for your system are %d <= key_size < %d.", (int)key_size, (int)key_size_lower_bound, (int)key_size_upper_bound));
+        }
+
+        speed_level_ = kHash;
+        key_size_ = (unsigned)key_size;
+    }
+
+    /** Optimize the table for speed/space
+     */
+    void optimize()
+    {
+        // If we are already using the fast storage, no need to do anything
+        if (speed_level_ == kArray) return;
+
+        // Use an array if it will be more than half full
+        if (buckets_space_.size() > ((size_t(1) << key_size_) / 2)) {
+            speed_level_ = kArray;
+            // Fill the array version of it
+            buckets_speed_.resize(size_t(1) << key_size_);
+            for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second;
+
+            // Empty the hash table
+            buckets_space_.clear();
+            return;
+        }
+
+        // If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two
+        // for the vector) or less than 512MB (key_size_ <= 30)
+        if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10
+             >= (size_t(1) << key_size_)) || (key_size_ <= 32)) {
+            speed_level_ = kBitsetHash;
+            key_bitset_.resize(size_t(1) << key_size_);
+            key_bitset_.reset();
+            // Try with the BucketsSpace
+            for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first);
+        }
+        else {
+            speed_level_ = kHash;
+            key_bitset_.clear();
+        }
+    }
+
+    /** The vector of all the buckets if they are held for speed
+     */
+    BucketsSpeed buckets_speed_;
+
+    /** The hash table of all the buckets in case we cannot use the speed version
+     */
+    BucketsSpace buckets_space_;
+
+    /** What is used to store the data */
+    SpeedLevel speed_level_;
+
+    /** If the subkey is small enough, it will keep track of which subkeys are set through that bitset
+     * That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset)
+     */
+    DynamicBitset key_bitset_;
+
+    /** The size of the sub-signature in bits
+     */
+    unsigned int key_size_;
+
+    unsigned int feature_size_;
+
+    // Members only used for the unsigned char specialization
+    /** The mask to apply to a feature to get the hash key
+     * Only used in the unsigned char case
+     */
+    std::vector<size_t> mask_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// Specialization for unsigned char
+
+template<>
+inline LshTable<unsigned char>::LshTable(unsigned int feature_size, unsigned int subsignature_size)
+{
+    feature_size_ = feature_size;
+    initialize(subsignature_size);
+    // Allocate the mask
+    mask_ = std::vector<size_t>((feature_size * sizeof(char) + sizeof(size_t) - 1) / sizeof(size_t), 0);
+
+    // A bit brutal but fast to code
+    std::vector<int> indices(feature_size * CHAR_BIT);
+    for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = (int)i;
+#ifndef OPENCV_FLANN_USE_STD_RAND
+    cv::randShuffle(indices);
+#else
+    std::random_shuffle(indices.begin(), indices.end());
+#endif
+
+    // Generate a random set of order of subsignature_size_ bits
+    for (unsigned int i = 0; i < key_size_; ++i) {
+        size_t index = indices[i];
+
+        // Set that bit in the mask
+        size_t divisor = CHAR_BIT * sizeof(size_t);
+        size_t idx = index / divisor; //pick the right size_t index
+        mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset
+    }
+
+    // Set to 1 if you want to display the mask for debug
+#if 0
+    {
+        size_t bcount = 0;
+        BOOST_FOREACH(size_t mask_block, mask_){
+            out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block
+                << std::endl;
+            bcount += __builtin_popcountll(mask_block);
+        }
+        out << "bit count : " << std::dec << bcount << std::endl;
+        out << "mask size : " << mask_.size() << std::endl;
+        return out;
+    }
+#endif
+}
+
+/** Return the Subsignature of a feature
+ * @param feature the feature to analyze
+ */
+template<>
+inline size_t LshTable<unsigned char>::getKey(const unsigned char* feature) const
+{
+    // no need to check if T is dividable by sizeof(size_t) like in the Hamming
+    // distance computation as we have a mask
+    // FIXIT: This is bad assumption, because we reading tail bytes after of the allocated features buffer
+    const size_t* feature_block_ptr = reinterpret_cast<const size_t*> ((const void*)feature);
+
+    // Figure out the subsignature of the feature
+    // Given the feature ABCDEF, and the mask 001011, the output will be
+    // 000CEF
+    size_t subsignature = 0;
+    size_t bit_index = 1;
+
+    for (unsigned i = 0; i < feature_size_; i += sizeof(size_t)) {
+        // get the mask and signature blocks
+        size_t feature_block;
+        if (i <= feature_size_ - sizeof(size_t))
+        {
+            feature_block = *feature_block_ptr;
+        }
+        else
+        {
+            size_t tmp = 0;
+            memcpy(&tmp, feature_block_ptr, feature_size_ - i); // preserve bytes order
+            feature_block = tmp;
+        }
+        size_t mask_block = mask_[i / sizeof(size_t)];
+        while (mask_block) {
+            // Get the lowest set bit in the mask block
+            size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block);
+            // Add it to the current subsignature if necessary
+            subsignature += (feature_block & lowest_bit) ? bit_index : 0;
+            // Reset the bit in the mask block
+            mask_block ^= lowest_bit;
+            // increment the bit index for the subsignature
+            bit_index <<= 1;
+        }
+        // Check the next feature block
+        ++feature_block_ptr;
+    }
+    return subsignature;
+}
+
+template<>
+inline LshStats LshTable<unsigned char>::getStats() const
+{
+    LshStats stats;
+    stats.bucket_size_mean_ = 0;
+    if ((buckets_speed_.empty()) && (buckets_space_.empty())) {
+        stats.n_buckets_ = 0;
+        stats.bucket_size_median_ = 0;
+        stats.bucket_size_min_ = 0;
+        stats.bucket_size_max_ = 0;
+        return stats;
+    }
+
+    if (!buckets_speed_.empty()) {
+        for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) {
+            stats.bucket_sizes_.push_back((lsh::FeatureIndex)pbucket->size());
+            stats.bucket_size_mean_ += pbucket->size();
+        }
+        stats.bucket_size_mean_ /= buckets_speed_.size();
+        stats.n_buckets_ = buckets_speed_.size();
+    }
+    else {
+        for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) {
+            stats.bucket_sizes_.push_back((lsh::FeatureIndex)x->second.size());
+            stats.bucket_size_mean_ += x->second.size();
+        }
+        stats.bucket_size_mean_ /= buckets_space_.size();
+        stats.n_buckets_ = buckets_space_.size();
+    }
+
+    std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end());
+
+    //  BOOST_FOREACH(int size, stats.bucket_sizes_)
+    //          std::cout << size << " ";
+    //  std::cout << std::endl;
+    stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2];
+    stats.bucket_size_min_ = stats.bucket_sizes_.front();
+    stats.bucket_size_max_ = stats.bucket_sizes_.back();
+
+    // TODO compute mean and std
+    /*float mean, stddev;
+       stats.bucket_size_mean_ = mean;
+       stats.bucket_size_std_dev = stddev;*/
+
+    // Include a histogram of the buckets
+    unsigned int bin_start = 0;
+    unsigned int bin_end = 20;
+    bool is_new_bin = true;
+    for (std::vector<unsigned int>::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator
+         != end; )
+        if (*iterator < bin_end) {
+            if (is_new_bin) {
+                stats.size_histogram_.push_back(std::vector<unsigned int>(3, 0));
+                stats.size_histogram_.back()[0] = bin_start;
+                stats.size_histogram_.back()[1] = bin_end - 1;
+                is_new_bin = false;
+            }
+            ++stats.size_histogram_.back()[2];
+            ++iterator;
+        }
+        else {
+            bin_start += 20;
+            bin_end += 20;
+            is_new_bin = true;
+        }
+
+    return stats;
+}
+
+// End the two namespaces
+}
+}
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_LSH_TABLE_H_ */
diff --git a/3rdParty/include/opencv2/flann/matrix.h b/3rdParty/include/opencv2/flann/matrix.h
new file mode 100644
index 0000000..fb871bd
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/matrix.h
@@ -0,0 +1,118 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_DATASET_H_
+#define OPENCV_FLANN_DATASET_H_
+
+//! @cond IGNORED
+
+#include <stdio.h>
+
+namespace cvflann
+{
+
+/**
+ * Class that implements a simple rectangular matrix stored in a memory buffer and
+ * provides convenient matrix-like access using the [] operators.
+ */
+template <typename T>
+class Matrix
+{
+public:
+    typedef T type;
+
+    size_t rows;
+    size_t cols;
+    size_t stride;
+    T* data;
+
+    Matrix() : rows(0), cols(0), stride(0), data(NULL)
+    {
+    }
+
+    Matrix(T* data_, size_t rows_, size_t cols_, size_t stride_ = 0) :
+        rows(rows_), cols(cols_),  stride(stride_), data(data_)
+    {
+        if (stride==0) stride = cols;
+    }
+
+    /**
+     * Convenience function for deallocating the storage data.
+     */
+    CV_DEPRECATED void free()
+    {
+        fprintf(stderr, "The cvflann::Matrix<T>::free() method is deprecated "
+                "and it does not do any memory deallocation any more.  You are"
+                "responsible for deallocating the matrix memory (by doing"
+                "'delete[] matrix.data' for example)");
+    }
+
+    /**
+     * Operator that return a (pointer to a) row of the data.
+     */
+    T* operator[](size_t index) const
+    {
+        return data+index*stride;
+    }
+};
+
+
+class UntypedMatrix
+{
+public:
+    size_t rows;
+    size_t cols;
+    void* data;
+    flann_datatype_t type;
+
+    UntypedMatrix(void* data_, long rows_, long cols_) :
+        rows(rows_), cols(cols_), data(data_)
+    {
+    }
+
+    ~UntypedMatrix()
+    {
+    }
+
+
+    template<typename T>
+    Matrix<T> as()
+    {
+        return Matrix<T>((T*)data, rows, cols);
+    }
+};
+
+
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_DATASET_H_
diff --git a/3rdParty/include/opencv2/flann/miniflann.hpp b/3rdParty/include/opencv2/flann/miniflann.hpp
new file mode 100644
index 0000000..b8df92d
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/miniflann.hpp
@@ -0,0 +1,185 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_MINIFLANN_HPP
+#define OPENCV_MINIFLANN_HPP
+
+//! @cond IGNORED
+
+#include "opencv2/core.hpp"
+#include "opencv2/flann/defines.h"
+
+namespace cv
+{
+
+namespace flann
+{
+
+enum FlannIndexType {
+    FLANN_INDEX_TYPE_8U = CV_8U,
+    FLANN_INDEX_TYPE_8S = CV_8S,
+    FLANN_INDEX_TYPE_16U = CV_16U,
+    FLANN_INDEX_TYPE_16S = CV_16S,
+    FLANN_INDEX_TYPE_32S = CV_32S,
+    FLANN_INDEX_TYPE_32F = CV_32F,
+    FLANN_INDEX_TYPE_64F = CV_64F,
+    FLANN_INDEX_TYPE_STRING,
+    FLANN_INDEX_TYPE_BOOL,
+    FLANN_INDEX_TYPE_ALGORITHM,
+    LAST_VALUE_FLANN_INDEX_TYPE = FLANN_INDEX_TYPE_ALGORITHM
+};
+
+struct CV_EXPORTS IndexParams
+{
+    IndexParams();
+    ~IndexParams();
+
+    String getString(const String& key, const String& defaultVal=String()) const;
+    int getInt(const String& key, int defaultVal=-1) const;
+    double getDouble(const String& key, double defaultVal=-1) const;
+
+    void setString(const String& key, const String& value);
+    void setInt(const String& key, int value);
+    void setDouble(const String& key, double value);
+    void setFloat(const String& key, float value);
+    void setBool(const String& key, bool value);
+    void setAlgorithm(int value);
+
+    // FIXIT: replace by void write(FileStorage& fs) const + read()
+    void getAll(std::vector<String>& names,
+                std::vector<FlannIndexType>& types,
+                std::vector<String>& strValues,
+                std::vector<double>& numValues) const;
+
+    void* params;
+
+private:
+    IndexParams(const IndexParams &); // copy disabled
+    IndexParams& operator=(const IndexParams &); // assign disabled
+};
+
+struct CV_EXPORTS KDTreeIndexParams : public IndexParams
+{
+    KDTreeIndexParams(int trees=4);
+};
+
+struct CV_EXPORTS LinearIndexParams : public IndexParams
+{
+    LinearIndexParams();
+};
+
+struct CV_EXPORTS CompositeIndexParams : public IndexParams
+{
+    CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
+                         cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f );
+};
+
+struct CV_EXPORTS AutotunedIndexParams : public IndexParams
+{
+    AutotunedIndexParams(float target_precision = 0.8f, float build_weight = 0.01f,
+                         float memory_weight = 0, float sample_fraction = 0.1f);
+};
+
+struct CV_EXPORTS HierarchicalClusteringIndexParams : public IndexParams
+{
+    HierarchicalClusteringIndexParams(int branching = 32,
+                      cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, int trees = 4, int leaf_size = 100 );
+};
+
+struct CV_EXPORTS KMeansIndexParams : public IndexParams
+{
+    KMeansIndexParams(int branching = 32, int iterations = 11,
+                      cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f );
+};
+
+struct CV_EXPORTS LshIndexParams : public IndexParams
+{
+    LshIndexParams(int table_number, int key_size, int multi_probe_level);
+};
+
+struct CV_EXPORTS SavedIndexParams : public IndexParams
+{
+    SavedIndexParams(const String& filename);
+};
+
+struct CV_EXPORTS SearchParams : public IndexParams
+{
+    SearchParams( int checks, float eps, bool sorted, bool explore_all_trees );
+    SearchParams( int checks = 32, float eps = 0, bool sorted = true );
+};
+
+class CV_EXPORTS_W Index
+{
+public:
+    CV_WRAP Index();
+    CV_WRAP Index(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2);
+    virtual ~Index();
+
+    CV_WRAP virtual void build(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2);
+    CV_WRAP virtual void knnSearch(InputArray query, OutputArray indices,
+                   OutputArray dists, int knn, const SearchParams& params=SearchParams());
+
+    CV_WRAP virtual int radiusSearch(InputArray query, OutputArray indices,
+                             OutputArray dists, double radius, int maxResults,
+                             const SearchParams& params=SearchParams());
+
+    CV_WRAP virtual void save(const String& filename) const;
+    CV_WRAP virtual bool load(InputArray features, const String& filename);
+    CV_WRAP virtual void release();
+    CV_WRAP cvflann::flann_distance_t getDistance() const;
+    CV_WRAP cvflann::flann_algorithm_t getAlgorithm() const;
+
+protected:
+    bool load_(const String& filename);
+
+    cvflann::flann_distance_t distType;
+    cvflann::flann_algorithm_t algo;
+    int featureType;
+    void* index;
+    Mat features_clone;  // index may store features pointer internally for searching, so avoid dangling pointers: https://github.com/opencv/opencv/issues/17553
+};
+
+} } // namespace cv::flann
+
+//! @endcond
+
+#endif
diff --git a/3rdParty/include/opencv2/flann/nn_index.h b/3rdParty/include/opencv2/flann/nn_index.h
new file mode 100644
index 0000000..23a1de7
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/nn_index.h
@@ -0,0 +1,180 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_NNINDEX_H
+#define OPENCV_FLANN_NNINDEX_H
+
+#include "matrix.h"
+#include "result_set.h"
+#include "params.h"
+
+//! @cond IGNORED
+
+namespace cvflann
+{
+
+/**
+ * Nearest-neighbour index base class
+ */
+template <typename Distance>
+class NNIndex
+{
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+public:
+
+    virtual ~NNIndex() {}
+
+    /**
+     * \brief Builds the index
+     */
+    virtual void buildIndex() = 0;
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
+    {
+        CV_Assert(queries.cols == veclen());
+        CV_Assert(indices.rows >= queries.rows);
+        CV_Assert(dists.rows >= queries.rows);
+        CV_Assert(int(indices.cols) >= knn);
+        CV_Assert(int(dists.cols) >= knn);
+
+#if 0
+        KNNResultSet<DistanceType> resultSet(knn);
+        for (size_t i = 0; i < queries.rows; i++) {
+            resultSet.init(indices[i], dists[i]);
+            findNeighbors(resultSet, queries[i], params);
+        }
+#else
+        KNNUniqueResultSet<DistanceType> resultSet(knn);
+        for (size_t i = 0; i < queries.rows; i++) {
+            resultSet.clear();
+            findNeighbors(resultSet, queries[i], params);
+            if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn);
+            else resultSet.copy(indices[i], dists[i], knn);
+        }
+#endif
+    }
+
+    /**
+     * \brief Perform radius search
+     * \param[in] query The query point
+     * \param[out] indices The indinces of the neighbors found within the given radius
+     * \param[out] dists The distances to the nearest neighbors found
+     * \param[in] radius The radius used for search
+     * \param[in] params Search parameters
+     * \returns Number of neighbors found
+     */
+    virtual int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
+    {
+        if (query.rows != 1) {
+            fprintf(stderr, "I can only search one feature at a time for range search\n");
+            return -1;
+        }
+        CV_Assert(query.cols == veclen());
+        CV_Assert(indices.cols == dists.cols);
+
+        int n = 0;
+        int* indices_ptr = NULL;
+        DistanceType* dists_ptr = NULL;
+        if (indices.cols > 0) {
+            n = (int)indices.cols;
+            indices_ptr = indices[0];
+            dists_ptr = dists[0];
+        }
+
+        RadiusUniqueResultSet<DistanceType> resultSet((DistanceType)radius);
+        resultSet.clear();
+        findNeighbors(resultSet, query[0], params);
+        if (n>0) {
+            if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n);
+            else resultSet.copy(indices_ptr, dists_ptr, n);
+        }
+
+        return (int)resultSet.size();
+    }
+
+    /**
+     * \brief Saves the index to a stream
+     * \param stream The stream to save the index to
+     */
+    virtual void saveIndex(FILE* stream) = 0;
+
+    /**
+     * \brief Loads the index from a stream
+     * \param stream The stream from which the index is loaded
+     */
+    virtual void loadIndex(FILE* stream) = 0;
+
+    /**
+     * \returns number of features in this index.
+     */
+    virtual size_t size() const = 0;
+
+    /**
+     * \returns The dimensionality of the features in this index.
+     */
+    virtual size_t veclen() const = 0;
+
+    /**
+     * \returns The amount of memory (in bytes) used by the index.
+     */
+    virtual int usedMemory() const = 0;
+
+    /**
+     * \returns The index type (kdtree, kmeans,...)
+     */
+    virtual flann_algorithm_t getType() const = 0;
+
+    /**
+     * \returns The index parameters
+     */
+    virtual IndexParams getParameters() const = 0;
+
+
+    /**
+     * \brief Method that searches for nearest-neighbours
+     */
+    virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) = 0;
+};
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_NNINDEX_H
diff --git a/3rdParty/include/opencv2/flann/object_factory.h b/3rdParty/include/opencv2/flann/object_factory.h
new file mode 100644
index 0000000..5cc45ad
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/object_factory.h
@@ -0,0 +1,95 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_OBJECT_FACTORY_H_
+#define OPENCV_FLANN_OBJECT_FACTORY_H_
+
+//! @cond IGNORED
+
+#include <map>
+
+namespace cvflann
+{
+
+class CreatorNotFound
+{
+};
+
+template<typename BaseClass,
+         typename UniqueIdType,
+         typename ObjectCreator = BaseClass* (*)()>
+class ObjectFactory
+{
+    typedef ObjectFactory<BaseClass,UniqueIdType,ObjectCreator> ThisClass;
+    typedef std::map<UniqueIdType, ObjectCreator> ObjectRegistry;
+
+    // singleton class, private constructor
+    ObjectFactory() {}
+
+public:
+
+    bool subscribe(UniqueIdType id, ObjectCreator creator)
+    {
+        if (object_registry.find(id) != object_registry.end()) return false;
+
+        object_registry[id] = creator;
+        return true;
+    }
+
+    bool unregister(UniqueIdType id)
+    {
+        return object_registry.erase(id) == 1;
+    }
+
+    ObjectCreator create(UniqueIdType id)
+    {
+        typename ObjectRegistry::const_iterator iter = object_registry.find(id);
+
+        if (iter == object_registry.end()) {
+            throw CreatorNotFound();
+        }
+
+        return iter->second;
+    }
+
+    static ThisClass& instance()
+    {
+        static ThisClass the_factory;
+        return the_factory;
+    }
+private:
+    ObjectRegistry object_registry;
+};
+
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_OBJECT_FACTORY_H_ */
diff --git a/3rdParty/include/opencv2/flann/params.h b/3rdParty/include/opencv2/flann/params.h
new file mode 100644
index 0000000..c9093cd
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/params.h
@@ -0,0 +1,116 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef OPENCV_FLANN_PARAMS_H_
+#define OPENCV_FLANN_PARAMS_H_
+
+//! @cond IGNORED
+
+#include "any.h"
+#include "general.h"
+#include <iostream>
+#include <map>
+
+
+namespace cvflann
+{
+
+typedef std::map<cv::String, any> IndexParams;
+
+struct SearchParams : public IndexParams
+{
+    SearchParams(int checks = 32, float eps = 0, bool sorted = true )
+    {
+        init(checks, eps, sorted, false);
+    }
+
+    SearchParams(int checks, float eps, bool sorted, bool explore_all_trees )
+    {
+        init(checks, eps, sorted, explore_all_trees);
+    }
+
+    void init(int checks = 32, float eps = 0, bool sorted = true, bool explore_all_trees = false )
+    {
+        // how many leafs to visit when searching for neighbours (-1 for unlimited)
+        (*this)["checks"] = checks;
+        // search for eps-approximate neighbours (default: 0)
+        (*this)["eps"] = eps;
+        // only for radius search, require neighbours sorted by distance (default: true)
+        (*this)["sorted"] = sorted;
+        // if false, search stops at the tree reaching the number of  max checks (original behavior).
+        // When true, we do a descent in each tree and. Like before the alternative paths
+        // stored in the heap are not be processed further when max checks is reached.
+        (*this)["explore_all_trees"] = explore_all_trees;
+    }
+};
+
+
+template<typename T>
+T get_param(const IndexParams& params, cv::String name, const T& default_value)
+{
+    IndexParams::const_iterator it = params.find(name);
+    if (it != params.end()) {
+        return it->second.cast<T>();
+    }
+    else {
+        return default_value;
+    }
+}
+
+template<typename T>
+T get_param(const IndexParams& params, cv::String name)
+{
+    IndexParams::const_iterator it = params.find(name);
+    if (it != params.end()) {
+        return it->second.cast<T>();
+    }
+    else {
+        FLANN_THROW(cv::Error::StsBadArg, cv::String("Missing parameter '")+name+cv::String("' in the parameters given"));
+    }
+}
+
+inline void print_params(const IndexParams& params, std::ostream& stream)
+{
+    IndexParams::const_iterator it;
+
+    for(it=params.begin(); it!=params.end(); ++it) {
+        stream << it->first << " : " << it->second << std::endl;
+    }
+}
+
+inline void print_params(const IndexParams& params)
+{
+    print_params(params, std::cout);
+}
+
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_PARAMS_H_ */
diff --git a/3rdParty/include/opencv2/flann/random.h b/3rdParty/include/opencv2/flann/random.h
new file mode 100644
index 0000000..2c1809c
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/random.h
@@ -0,0 +1,157 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_RANDOM_H
+#define OPENCV_FLANN_RANDOM_H
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <cstdlib>
+#include <vector>
+
+namespace cvflann
+{
+
+inline int rand()
+{
+#ifndef OPENCV_FLANN_USE_STD_RAND
+#   if INT_MAX == RAND_MAX
+    int v = cv::theRNG().next() & INT_MAX;
+#   else
+    int v = cv::theRNG().uniform(0, RAND_MAX + 1);
+#   endif
+#else
+    int v = std::rand();
+#endif // OPENCV_FLANN_USE_STD_RAND
+    return v;
+}
+
+/**
+ * Seeds the random number generator
+ *  @param seed Random seed
+ */
+inline void seed_random(unsigned int seed)
+{
+#ifndef OPENCV_FLANN_USE_STD_RAND
+    cv::theRNG() = cv::RNG(seed);
+#else
+    std::srand(seed);
+#endif
+}
+
+/*
+ * Generates a random double value.
+ */
+/**
+ * Generates a random double value.
+ * @param high Upper limit
+ * @param low Lower limit
+ * @return Random double value
+ */
+inline double rand_double(double high = 1.0, double low = 0)
+{
+    return low + ((high-low) * (rand() / (RAND_MAX + 1.0)));
+}
+
+/**
+ * Generates a random integer value.
+ * @param high Upper limit
+ * @param low Lower limit
+ * @return Random integer value
+ */
+inline int rand_int(int high = RAND_MAX, int low = 0)
+{
+    return low + (int) ( double(high-low) * (rand() / (RAND_MAX + 1.0)));
+}
+
+/**
+ * Random number generator that returns a distinct number from
+ * the [0,n) interval each time.
+ */
+class UniqueRandom
+{
+    std::vector<int> vals_;
+    int size_;
+    int counter_;
+
+public:
+    /**
+     * Constructor.
+     * @param n Size of the interval from which to generate
+     * @return
+     */
+    UniqueRandom(int n)
+    {
+        init(n);
+    }
+
+    /**
+     * Initializes the number generator.
+     * @param n the size of the interval from which to generate random numbers.
+     */
+    void init(int n)
+    {
+        // create and initialize an array of size n
+        vals_.resize(n);
+        size_ = n;
+        for (int i = 0; i < size_; ++i) vals_[i] = i;
+
+        // shuffle the elements in the array
+#ifndef OPENCV_FLANN_USE_STD_RAND
+        cv::randShuffle(vals_);
+#else
+        std::random_shuffle(vals_.begin(), vals_.end());
+#endif
+
+        counter_ = 0;
+    }
+
+    /**
+     * Return a distinct random integer in greater or equal to 0 and less
+     * than 'n' on each call. It should be called maximum 'n' times.
+     * Returns: a random integer
+     */
+    int next()
+    {
+        if (counter_ == size_) {
+            return -1;
+        }
+        else {
+            return vals_[counter_++];
+        }
+    }
+};
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_RANDOM_H
diff --git a/3rdParty/include/opencv2/flann/result_set.h b/3rdParty/include/opencv2/flann/result_set.h
new file mode 100644
index 0000000..47ad231
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/result_set.h
@@ -0,0 +1,548 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_RESULTSET_H
+#define OPENCV_FLANN_RESULTSET_H
+
+//! @cond IGNORED
+
+#include <algorithm>
+#include <cstring>
+#include <iostream>
+#include <limits>
+#include <set>
+#include <vector>
+
+namespace cvflann
+{
+
+/* This record represents a branch point when finding neighbors in
+    the tree.  It contains a record of the minimum distance to the query
+    point, as well as the node at which the search resumes.
+ */
+
+template <typename T, typename DistanceType>
+struct BranchStruct
+{
+    T node;           /* Tree node at which search resumes */
+    DistanceType mindist;     /* Minimum distance to query for all nodes below. */
+
+    BranchStruct() {}
+    BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {}
+
+    bool operator<(const BranchStruct<T, DistanceType>& rhs) const
+    {
+        return mindist<rhs.mindist;
+    }
+};
+
+
+template <typename DistanceType>
+class ResultSet
+{
+public:
+    virtual ~ResultSet() {}
+
+    virtual bool full() const = 0;
+
+    virtual void addPoint(DistanceType dist, int index) = 0;
+
+    virtual DistanceType worstDist() const = 0;
+
+};
+
+/**
+ * KNNSimpleResultSet does not ensure that the element it holds are unique.
+ * Is used in those cases where the nearest neighbour algorithm used does not
+ * attempt to insert the same element multiple times.
+ */
+template <typename DistanceType>
+class KNNSimpleResultSet : public ResultSet<DistanceType>
+{
+    int* indices;
+    DistanceType* dists;
+    int capacity;
+    int count;
+    DistanceType worst_distance_;
+
+public:
+    KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0)
+    {
+    }
+
+    void init(int* indices_, DistanceType* dists_)
+    {
+        indices = indices_;
+        dists = dists_;
+        count = 0;
+        worst_distance_ = (std::numeric_limits<DistanceType>::max)();
+        dists[capacity-1] = worst_distance_;
+    }
+
+    size_t size() const
+    {
+        return count;
+    }
+
+    bool full() const CV_OVERRIDE
+    {
+        return count == capacity;
+    }
+
+
+    void addPoint(DistanceType dist, int index) CV_OVERRIDE
+    {
+        if (dist >= worst_distance_) return;
+        int i;
+        for (i=count; i>0; --i) {
+#ifdef FLANN_FIRST_MATCH
+            if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) )
+#else
+            if (dists[i-1]>dist)
+#endif
+            {
+                if (i<capacity) {
+                    dists[i] = dists[i-1];
+                    indices[i] = indices[i-1];
+                }
+            }
+            else break;
+        }
+        if (count < capacity) ++count;
+        dists[i] = dist;
+        indices[i] = index;
+        worst_distance_ = dists[capacity-1];
+    }
+
+    DistanceType worstDist() const CV_OVERRIDE
+    {
+        return worst_distance_;
+    }
+};
+
+/**
+ * K-Nearest neighbour result set. Ensures that the elements inserted are unique
+ */
+template <typename DistanceType>
+class KNNResultSet : public ResultSet<DistanceType>
+{
+    int* indices;
+    DistanceType* dists;
+    int capacity;
+    int count;
+    DistanceType worst_distance_;
+
+public:
+    KNNResultSet(int capacity_)
+        : indices(NULL), dists(NULL), capacity(capacity_), count(0), worst_distance_(0)
+    {
+    }
+
+    void init(int* indices_, DistanceType* dists_)
+    {
+        indices = indices_;
+        dists = dists_;
+        count = 0;
+        worst_distance_ = (std::numeric_limits<DistanceType>::max)();
+        dists[capacity-1] = worst_distance_;
+    }
+
+    size_t size() const
+    {
+        return count;
+    }
+
+    bool full() const CV_OVERRIDE
+    {
+        return count == capacity;
+    }
+
+
+    void addPoint(DistanceType dist, int index) CV_OVERRIDE
+    {
+        CV_DbgAssert(indices);
+        CV_DbgAssert(dists);
+        if (dist >= worst_distance_) return;
+        int i;
+        for (i = count; i > 0; --i) {
+#ifdef FLANN_FIRST_MATCH
+            if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) )
+#else
+            if (dists[i-1]<=dist)
+#endif
+            {
+                // Check for duplicate indices
+                for (int j = i; dists[j] == dist && j--;) {
+                    if (indices[j] == index) {
+                        return;
+                    }
+                }
+                break;
+            }
+        }
+
+        if (count < capacity) ++count;
+        for (int j = count-1; j > i; --j) {
+            dists[j] = dists[j-1];
+            indices[j] = indices[j-1];
+        }
+        dists[i] = dist;
+        indices[i] = index;
+        worst_distance_ = dists[capacity-1];
+    }
+
+    DistanceType worstDist() const CV_OVERRIDE
+    {
+        return worst_distance_;
+    }
+};
+
+
+/**
+ * A result-set class used when performing a radius based search.
+ */
+template <typename DistanceType>
+class RadiusResultSet : public ResultSet<DistanceType>
+{
+    DistanceType radius;
+    int* indices;
+    DistanceType* dists;
+    size_t capacity;
+    size_t count;
+
+public:
+    RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) :
+        radius(radius_), indices(indices_), dists(dists_), capacity(capacity_)
+    {
+        init();
+    }
+
+    ~RadiusResultSet()
+    {
+    }
+
+    void init()
+    {
+        count = 0;
+    }
+
+    size_t size() const
+    {
+        return count;
+    }
+
+    bool full() const
+    {
+        return true;
+    }
+
+    void addPoint(DistanceType dist, int index)
+    {
+        if (dist<radius) {
+            if ((capacity>0)&&(count < capacity)) {
+                dists[count] = dist;
+                indices[count] = index;
+            }
+            count++;
+        }
+    }
+
+    DistanceType worstDist() const
+    {
+        return radius;
+    }
+
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors
+ * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
+ */
+template<typename DistanceType>
+class UniqueResultSet : public ResultSet<DistanceType>
+{
+public:
+    struct DistIndex
+    {
+        DistIndex(DistanceType dist, unsigned int index) :
+            dist_(dist), index_(index)
+        {
+        }
+        bool operator<(const DistIndex dist_index) const
+        {
+            return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);
+        }
+        DistanceType dist_;
+        unsigned int index_;
+    };
+
+    /** Default constructor */
+    UniqueResultSet() :
+        is_full_(false), worst_distance_(std::numeric_limits<DistanceType>::max())
+    {
+    }
+
+    /** Check the status of the set
+     * @return true if we have k NN
+     */
+    inline bool full() const CV_OVERRIDE
+    {
+        return is_full_;
+    }
+
+    /** Remove all elements in the set
+     */
+    virtual void clear() = 0;
+
+    /** Copy the set to two C arrays
+     * @param indices pointer to a C array of indices
+     * @param dist pointer to a C array of distances
+     * @param n_neighbors the number of neighbors to copy
+     */
+    virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const
+    {
+        if (n_neighbors < 0) {
+            for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
+                     dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) {
+                *indices = dist_index->index_;
+                *dist = dist_index->dist_;
+            }
+        }
+        else {
+            int i = 0;
+            for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
+                     dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) {
+                *indices = dist_index->index_;
+                *dist = dist_index->dist_;
+            }
+        }
+    }
+
+    /** Copy the set to two C arrays but sort it according to the distance first
+     * @param indices pointer to a C array of indices
+     * @param dist pointer to a C array of distances
+     * @param n_neighbors the number of neighbors to copy
+     */
+    virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const
+    {
+        copy(indices, dist, n_neighbors);
+    }
+
+    /** The number of neighbors in the set
+     * @return
+     */
+    size_t size() const
+    {
+        return dist_indices_.size();
+    }
+
+    /** The distance of the furthest neighbor
+     * If we don't have enough neighbors, it returns the max possible value
+     * @return
+     */
+    inline DistanceType worstDist() const CV_OVERRIDE
+    {
+        return worst_distance_;
+    }
+protected:
+    /** Flag to say if the set is full */
+    bool is_full_;
+
+    /** The worst distance found so far */
+    DistanceType worst_distance_;
+
+    /** The best candidates so far */
+    std::set<DistIndex> dist_indices_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors
+ * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
+ */
+template<typename DistanceType>
+class KNNUniqueResultSet : public UniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param capacity the number of neighbors to store at max
+     */
+    KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity)
+    {
+        this->is_full_ = false;
+        this->clear();
+    }
+
+    /** Add a possible candidate to the best neighbors
+     * @param dist distance for that neighbor
+     * @param index index of that neighbor
+     */
+    inline void addPoint(DistanceType dist, int index) CV_OVERRIDE
+    {
+        // Don't do anything if we are worse than the worst
+        if (dist >= worst_distance_) return;
+        dist_indices_.insert(DistIndex(dist, index));
+
+        if (is_full_) {
+            if (dist_indices_.size() > capacity_) {
+                dist_indices_.erase(*dist_indices_.rbegin());
+                worst_distance_ = dist_indices_.rbegin()->dist_;
+            }
+        }
+        else if (dist_indices_.size() == capacity_) {
+            is_full_ = true;
+            worst_distance_ = dist_indices_.rbegin()->dist_;
+        }
+    }
+
+    /** Remove all elements in the set
+     */
+    void clear() CV_OVERRIDE
+    {
+        dist_indices_.clear();
+        worst_distance_ = std::numeric_limits<DistanceType>::max();
+        is_full_ = false;
+    }
+
+protected:
+    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
+    using UniqueResultSet<DistanceType>::is_full_;
+    using UniqueResultSet<DistanceType>::worst_distance_;
+    using UniqueResultSet<DistanceType>::dist_indices_;
+
+    /** The number of neighbors to keep */
+    unsigned int capacity_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the radius nearest neighbors
+ * It is more accurate than RadiusResult as it is not limited in the number of neighbors
+ */
+template<typename DistanceType>
+class RadiusUniqueResultSet : public UniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param radius the maximum distance of a neighbor
+     */
+    RadiusUniqueResultSet(DistanceType radius) :
+        radius_(radius)
+    {
+        is_full_ = true;
+    }
+
+    /** Add a possible candidate to the best neighbors
+     * @param dist distance for that neighbor
+     * @param index index of that neighbor
+     */
+    void addPoint(DistanceType dist, int index) CV_OVERRIDE
+    {
+        if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index));
+    }
+
+    /** Remove all elements in the set
+     */
+    inline void clear() CV_OVERRIDE
+    {
+        dist_indices_.clear();
+    }
+
+
+    /** Check the status of the set
+     * @return alwys false
+     */
+    inline bool full() const CV_OVERRIDE
+    {
+        return true;
+    }
+
+    /** The distance of the furthest neighbor
+     * If we don't have enough neighbors, it returns the max possible value
+     * @return
+     */
+    inline DistanceType worstDist() const CV_OVERRIDE
+    {
+        return radius_;
+    }
+private:
+    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
+    using UniqueResultSet<DistanceType>::dist_indices_;
+    using UniqueResultSet<DistanceType>::is_full_;
+
+    /** The furthest distance a neighbor can be */
+    DistanceType radius_;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+/** Class that holds the k NN neighbors within a radius distance
+ */
+template<typename DistanceType>
+class KNNRadiusUniqueResultSet : public KNNUniqueResultSet<DistanceType>
+{
+public:
+    /** Constructor
+     * @param capacity the number of neighbors to store at max
+     * @param radius the maximum distance of a neighbor
+     */
+    KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius)
+    {
+        this->capacity_ = capacity;
+        this->radius_ = radius;
+        this->dist_indices_.reserve(capacity_);
+        this->clear();
+    }
+
+    /** Remove all elements in the set
+     */
+    void clear()
+    {
+        dist_indices_.clear();
+        worst_distance_ = radius_;
+        is_full_ = false;
+    }
+private:
+    using KNNUniqueResultSet<DistanceType>::dist_indices_;
+    using KNNUniqueResultSet<DistanceType>::is_full_;
+    using KNNUniqueResultSet<DistanceType>::worst_distance_;
+
+    /** The maximum number of neighbors to consider */
+    unsigned int capacity_;
+
+    /** The maximum distance of a neighbor */
+    DistanceType radius_;
+};
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_RESULTSET_H
diff --git a/3rdParty/include/opencv2/flann/sampling.h b/3rdParty/include/opencv2/flann/sampling.h
new file mode 100644
index 0000000..4e452b9
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/sampling.h
@@ -0,0 +1,84 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+
+#ifndef OPENCV_FLANN_SAMPLING_H_
+#define OPENCV_FLANN_SAMPLING_H_
+
+//! @cond IGNORED
+
+#include "matrix.h"
+#include "random.h"
+
+namespace cvflann
+{
+
+template<typename T>
+Matrix<T> random_sample(Matrix<T>& srcMatrix, long size, bool remove = false)
+{
+    Matrix<T> newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols);
+
+    T* src,* dest;
+    for (long i=0; i<size; ++i) {
+        long r = rand_int((int)(srcMatrix.rows-i));
+        dest = newSet[i];
+        src = srcMatrix[r];
+        std::copy(src, src+srcMatrix.cols, dest);
+        if (remove) {
+            src = srcMatrix[srcMatrix.rows-i-1];
+            dest = srcMatrix[r];
+            std::copy(src, src+srcMatrix.cols, dest);
+        }
+    }
+    if (remove) {
+        srcMatrix.rows -= size;
+    }
+    return newSet;
+}
+
+template<typename T>
+Matrix<T> random_sample(const Matrix<T>& srcMatrix, size_t size)
+{
+    UniqueRandom rand((int)srcMatrix.rows);
+    Matrix<T> newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols);
+
+    T* src,* dest;
+    for (size_t i=0; i<size; ++i) {
+        long r = rand.next();
+        dest = newSet[i];
+        src = srcMatrix[r];
+        std::copy(src, src+srcMatrix.cols, dest);
+    }
+    return newSet;
+}
+
+} // namespace
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_SAMPLING_H_ */
diff --git a/3rdParty/include/opencv2/flann/saving.h b/3rdParty/include/opencv2/flann/saving.h
new file mode 100644
index 0000000..8b3aeb7
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/saving.h
@@ -0,0 +1,191 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE NNIndexGOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_SAVING_H_
+#define OPENCV_FLANN_SAVING_H_
+
+//! @cond IGNORED
+
+#include <cstring>
+#include <vector>
+
+#include "general.h"
+#include "nn_index.h"
+
+#ifdef FLANN_SIGNATURE_
+#undef FLANN_SIGNATURE_
+#endif
+#define FLANN_SIGNATURE_ "FLANN_INDEX"
+
+namespace cvflann
+{
+
+template <typename T>
+struct Datatype {};
+template<>
+struct Datatype<char> { static flann_datatype_t type() { return FLANN_INT8; } };
+template<>
+struct Datatype<short> { static flann_datatype_t type() { return FLANN_INT16; } };
+template<>
+struct Datatype<int> { static flann_datatype_t type() { return FLANN_INT32; } };
+template<>
+struct Datatype<unsigned char> { static flann_datatype_t type() { return FLANN_UINT8; } };
+template<>
+struct Datatype<unsigned short> { static flann_datatype_t type() { return FLANN_UINT16; } };
+template<>
+struct Datatype<unsigned int> { static flann_datatype_t type() { return FLANN_UINT32; } };
+template<>
+struct Datatype<float> { static flann_datatype_t type() { return FLANN_FLOAT32; } };
+template<>
+struct Datatype<double> { static flann_datatype_t type() { return FLANN_FLOAT64; } };
+
+
+/**
+ * Structure representing the index header.
+ */
+struct IndexHeader
+{
+    char signature[16];
+    char version[16];
+    flann_datatype_t data_type;
+    flann_algorithm_t index_type;
+    size_t rows;
+    size_t cols;
+};
+
+/**
+ * Saves index header to stream
+ *
+ * @param stream - Stream to save to
+ * @param index - The index to save
+ */
+template<typename Distance>
+void save_header(FILE* stream, const NNIndex<Distance>& index)
+{
+    IndexHeader header;
+    memset(header.signature, 0, sizeof(header.signature));
+    strcpy(header.signature, FLANN_SIGNATURE_);
+    memset(header.version, 0, sizeof(header.version));
+    strcpy(header.version, FLANN_VERSION_);
+    header.data_type = Datatype<typename Distance::ElementType>::type();
+    header.index_type = index.getType();
+    header.rows = index.size();
+    header.cols = index.veclen();
+
+    std::fwrite(&header, sizeof(header),1,stream);
+}
+
+
+/**
+ *
+ * @param stream - Stream to load from
+ * @return Index header
+ */
+inline IndexHeader load_header(FILE* stream)
+{
+    IndexHeader header;
+    size_t read_size = fread(&header,sizeof(header),1,stream);
+
+    if (read_size!=(size_t)1) {
+        FLANN_THROW(cv::Error::StsError, "Invalid index file, cannot read");
+    }
+
+    if (strcmp(header.signature,FLANN_SIGNATURE_)!=0) {
+        FLANN_THROW(cv::Error::StsError, "Invalid index file, wrong signature");
+    }
+
+    return header;
+
+}
+
+
+template<typename T>
+void save_value(FILE* stream, const T& value, size_t count = 1)
+{
+    fwrite(&value, sizeof(value),count, stream);
+}
+
+template<typename T>
+void save_value(FILE* stream, const cvflann::Matrix<T>& value)
+{
+    fwrite(&value, sizeof(value),1, stream);
+    fwrite(value.data, sizeof(T),value.rows*value.cols, stream);
+}
+
+template<typename T>
+void save_value(FILE* stream, const std::vector<T>& value)
+{
+    size_t size = value.size();
+    fwrite(&size, sizeof(size_t), 1, stream);
+    fwrite(&value[0], sizeof(T), size, stream);
+}
+
+template<typename T>
+void load_value(FILE* stream, T& value, size_t count = 1)
+{
+    size_t read_cnt = fread(&value, sizeof(value), count, stream);
+    if (read_cnt != count) {
+        FLANN_THROW(cv::Error::StsParseError, "Cannot read from file");
+    }
+}
+
+template<typename T>
+void load_value(FILE* stream, cvflann::Matrix<T>& value)
+{
+    size_t read_cnt = fread(&value, sizeof(value), 1, stream);
+    if (read_cnt != 1) {
+        FLANN_THROW(cv::Error::StsParseError, "Cannot read from file");
+    }
+    value.data = new T[value.rows*value.cols];
+    read_cnt = fread(value.data, sizeof(T), value.rows*value.cols, stream);
+    if (read_cnt != (size_t)(value.rows*value.cols)) {
+        FLANN_THROW(cv::Error::StsParseError, "Cannot read from file");
+    }
+}
+
+
+template<typename T>
+void load_value(FILE* stream, std::vector<T>& value)
+{
+    size_t size;
+    size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
+    if (read_cnt!=1) {
+        FLANN_THROW(cv::Error::StsError, "Cannot read from file");
+    }
+    value.resize(size);
+    read_cnt = fread(&value[0], sizeof(T), size, stream);
+    if (read_cnt != size) {
+        FLANN_THROW(cv::Error::StsError, "Cannot read from file");
+    }
+}
+
+}
+
+//! @endcond
+
+#endif /* OPENCV_FLANN_SAVING_H_ */
diff --git a/3rdParty/include/opencv2/flann/simplex_downhill.h b/3rdParty/include/opencv2/flann/simplex_downhill.h
new file mode 100644
index 0000000..0297014
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/simplex_downhill.h
@@ -0,0 +1,190 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_SIMPLEX_DOWNHILL_H_
+#define OPENCV_FLANN_SIMPLEX_DOWNHILL_H_
+
+//! @cond IGNORED
+
+namespace cvflann
+{
+
+/**
+    Adds val to array vals (and point to array points) and keeping the arrays sorted by vals.
+ */
+template <typename T>
+void addValue(int pos, float val, float* vals, T* point, T* points, int n)
+{
+    vals[pos] = val;
+    for (int i=0; i<n; ++i) {
+        points[pos*n+i] = point[i];
+    }
+
+    // bubble down
+    int j=pos;
+    while (j>0 && vals[j]<vals[j-1]) {
+        swap(vals[j],vals[j-1]);
+        for (int i=0; i<n; ++i) {
+            swap(points[j*n+i],points[(j-1)*n+i]);
+        }
+        --j;
+    }
+}
+
+
+/**
+    Simplex downhill optimization function.
+    Preconditions: points is a 2D mattrix of size (n+1) x n
+                    func is the cost function taking n an array of n params and returning float
+                    vals is the cost function in the n+1 simplex points, if NULL it will be computed
+
+    Postcondition: returns optimum value and points[0..n] are the optimum parameters
+ */
+template <typename T, typename F>
+float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL )
+{
+    const int MAX_ITERATIONS = 10;
+
+    CV_DbgAssert(n>0);
+
+    T* p_o = new T[n];
+    T* p_r = new T[n];
+    T* p_e = new T[n];
+
+    int alpha = 1;
+
+    int iterations = 0;
+
+    bool ownVals = false;
+    if (vals == NULL) {
+        ownVals = true;
+        vals = new float[n+1];
+        for (int i=0; i<n+1; ++i) {
+            float val = func(points+i*n);
+            addValue(i, val, vals, points+i*n, points, n);
+        }
+    }
+    int nn = n*n;
+
+    while (true) {
+
+        if (iterations++ > MAX_ITERATIONS) break;
+
+        // compute average of simplex points (except the highest point)
+        for (int j=0; j<n; ++j) {
+            p_o[j] = 0;
+            for (int i=0; i<n; ++i) {
+                p_o[i] += points[j*n+i];
+            }
+        }
+        for (int i=0; i<n; ++i) {
+            p_o[i] /= n;
+        }
+
+        bool converged = true;
+        for (int i=0; i<n; ++i) {
+            if (p_o[i] != points[nn+i]) {
+                converged = false;
+            }
+        }
+        if (converged) break;
+
+        // trying a reflection
+        for (int i=0; i<n; ++i) {
+            p_r[i] = p_o[i] + alpha*(p_o[i]-points[nn+i]);
+        }
+        float val_r = func(p_r);
+
+        if ((val_r>=vals[0])&&(val_r<vals[n])) {
+            // reflection between second highest and lowest
+            // add it to the simplex
+            Logger::info("Choosing reflection\n");
+            addValue(n, val_r,vals, p_r, points, n);
+            continue;
+        }
+
+        if (val_r<vals[0]) {
+            // value is smaller than smallest in simplex
+
+            // expand some more to see if it drops further
+            for (int i=0; i<n; ++i) {
+                p_e[i] = 2*p_r[i]-p_o[i];
+            }
+            float val_e = func(p_e);
+
+            if (val_e<val_r) {
+                Logger::info("Choosing reflection and expansion\n");
+                addValue(n, val_e,vals,p_e,points,n);
+            }
+            else {
+                Logger::info("Choosing reflection\n");
+                addValue(n, val_r,vals,p_r,points,n);
+            }
+            continue;
+        }
+        if (val_r>=vals[n]) {
+            for (int i=0; i<n; ++i) {
+                p_e[i] = (p_o[i]+points[nn+i])/2;
+            }
+            float val_e = func(p_e);
+
+            if (val_e<vals[n]) {
+                Logger::info("Choosing contraction\n");
+                addValue(n,val_e,vals,p_e,points,n);
+                continue;
+            }
+        }
+        {
+            Logger::info("Full contraction\n");
+            for (int j=1; j<=n; ++j) {
+                for (int i=0; i<n; ++i) {
+                    points[j*n+i] = (points[j*n+i]+points[i])/2;
+                }
+                float val = func(points+j*n);
+                addValue(j,val,vals,points+j*n,points,n);
+            }
+        }
+    }
+
+    float bestVal = vals[0];
+
+    delete[] p_r;
+    delete[] p_o;
+    delete[] p_e;
+    if (ownVals) delete[] vals;
+
+    return bestVal;
+}
+
+}
+
+//! @endcond
+
+#endif //OPENCV_FLANN_SIMPLEX_DOWNHILL_H_
diff --git a/3rdParty/include/opencv2/flann/timer.h b/3rdParty/include/opencv2/flann/timer.h
new file mode 100644
index 0000000..7dc50a4
--- /dev/null
+++ b/3rdParty/include/opencv2/flann/timer.h
@@ -0,0 +1,99 @@
+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_TIMER_H
+#define OPENCV_FLANN_TIMER_H
+
+//! @cond IGNORED
+
+#include <time.h>
+#include "opencv2/core.hpp"
+#include "opencv2/core/utility.hpp"
+
+namespace cvflann
+{
+
+/**
+ * A start-stop timer class.
+ *
+ * Can be used to time portions of code.
+ */
+class StartStopTimer
+{
+    int64 startTime;
+
+public:
+    /**
+     * Value of the timer.
+     */
+    double value;
+
+
+    /**
+     * Constructor.
+     */
+    StartStopTimer()
+        : startTime(0)
+    {
+        reset();
+    }
+
+    /**
+     * Starts the timer.
+     */
+    void start()
+    {
+        startTime = cv::getTickCount();
+    }
+
+    /**
+     * Stops the timer and updates timer value.
+     */
+    void stop()
+    {
+        int64 stopTime = cv::getTickCount();
+        value += ( (double)stopTime - startTime) / cv::getTickFrequency();
+    }
+
+    /**
+     * Resets the timer value to 0.
+     */
+    void reset()
+    {
+        value = 0;
+    }
+
+};
+
+}
+
+//! @endcond
+
+#endif // FLANN_TIMER_H
diff --git a/3rdParty/include/opencv2/gapi.hpp b/3rdParty/include/opencv2/gapi.hpp
new file mode 100644
index 0000000..f10dfd4
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi.hpp
@@ -0,0 +1,42 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2021 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_HPP
+#define OPENCV_GAPI_HPP
+
+#include <memory>
+
+/** \defgroup gapi G-API framework
+@{
+    @defgroup gapi_main_classes G-API Main Classes
+    @defgroup gapi_data_objects G-API Data Types
+    @{
+      @defgroup gapi_meta_args G-API Metadata Descriptors
+    @}
+    @defgroup gapi_std_backends G-API Standard Backends
+    @defgroup gapi_compile_args G-API Graph Compilation Arguments
+    @defgroup gapi_serialization G-API Serialization functionality
+@}
+ */
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/garray.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+#include <opencv2/gapi/gopaque.hpp>
+#include <opencv2/gapi/gframe.hpp>
+#include <opencv2/gapi/gcomputation.hpp>
+#include <opencv2/gapi/gcompiled.hpp>
+#include <opencv2/gapi/gtyped.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/operators.hpp>
+
+// Include these files here to avoid cyclic dependency between
+// Desync & GKernel & GComputation & GStreamingCompiled.
+#include <opencv2/gapi/streaming/desync.hpp>
+#include <opencv2/gapi/streaming/format.hpp>
+
+#endif // OPENCV_GAPI_HPP
diff --git a/3rdParty/include/opencv2/gapi/core.hpp b/3rdParty/include/opencv2/gapi/core.hpp
new file mode 100644
index 0000000..791aa4c
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/core.hpp
@@ -0,0 +1,1991 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_CORE_HPP
+#define OPENCV_GAPI_CORE_HPP
+
+#include <math.h>
+
+#include <utility> // std::tuple
+
+#include <opencv2/imgproc.hpp>
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/streaming/format.hpp>
+
+/** \defgroup gapi_core G-API Core functionality
+@{
+    @defgroup gapi_math Graph API: Math operations
+    @defgroup gapi_pixelwise Graph API: Pixelwise operations
+    @defgroup gapi_matrixop Graph API: Operations on matrices
+    @defgroup gapi_transform Graph API: Image and channel composition functions
+@}
+ */
+
+namespace cv { namespace gapi {
+/**
+ * @brief This namespace contains G-API Operation Types for OpenCV
+ * Core module functionality.
+ */
+namespace core {
+    using GMat2 = std::tuple<GMat,GMat>;
+    using GMat3 = std::tuple<GMat,GMat,GMat>; // FIXME: how to avoid this?
+    using GMat4 = std::tuple<GMat,GMat,GMat,GMat>;
+    using GMatScalar  = std::tuple<GMat, GScalar>;
+
+    G_TYPED_KERNEL(GAdd, <GMat(GMat, GMat, int)>, "org.opencv.core.math.add") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc b, int ddepth) {
+            if (ddepth == -1)
+            {
+                // OpenCV: When the input arrays in add/subtract/multiply/divide
+                // functions have different depths, the output array depth must be
+                // explicitly specified!
+                // See artim_op() @ arithm.cpp
+                GAPI_Assert(a.chan == b.chan);
+                GAPI_Assert(a.depth == b.depth);
+                return a;
+            }
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GAddC, <GMat(GMat, GScalar, int)>, "org.opencv.core.math.addC") {
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc, int ddepth) {
+            GAPI_Assert(a.chan <= 4);
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GSub, <GMat(GMat, GMat, int)>, "org.opencv.core.math.sub") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc b, int ddepth) {
+            if (ddepth == -1)
+            {
+                // This macro should select a larger data depth from a and b
+                // considering the number of channels in the same
+                // FIXME!!! Clarify if it is valid for sub()
+                GAPI_Assert(a.chan == b.chan);
+                ddepth = std::max(a.depth, b.depth);
+            }
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GSubC, <GMat(GMat, GScalar, int)>, "org.opencv.core.math.subC") {
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc, int ddepth) {
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GSubRC,<GMat(GScalar, GMat, int)>, "org.opencv.core.math.subRC") {
+        static GMatDesc outMeta(GScalarDesc, GMatDesc b, int ddepth) {
+            return b.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GMul, <GMat(GMat, GMat, double, int)>, "org.opencv.core.math.mul") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc, double, int ddepth) {
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GMulCOld, <GMat(GMat, double, int)>, "org.opencv.core.math.mulCOld") {
+        static GMatDesc outMeta(GMatDesc a, double, int ddepth) {
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GMulC, <GMat(GMat, GScalar, int)>, "org.opencv.core.math.mulC"){
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc, int ddepth) {
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GMulS, <GMat(GMat, GScalar)>, "org.opencv.core.math.muls") {
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a;
+        }
+    }; // FIXME: Merge with MulC
+
+    G_TYPED_KERNEL(GDiv, <GMat(GMat, GMat, double, int)>, "org.opencv.core.math.div") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc b, double, int ddepth) {
+            if (ddepth == -1)
+            {
+                GAPI_Assert(a.depth == b.depth);
+                return b;
+            }
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GDivC, <GMat(GMat, GScalar, double, int)>, "org.opencv.core.math.divC") {
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc, double, int ddepth) {
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GDivRC, <GMat(GScalar, GMat, double, int)>, "org.opencv.core.math.divRC") {
+        static GMatDesc outMeta(GScalarDesc, GMatDesc b, double, int ddepth) {
+            return b.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GMean, <GScalar(GMat)>, "org.opencv.core.math.mean") {
+        static GScalarDesc outMeta(GMatDesc) {
+            return empty_scalar_desc();
+        }
+    };
+
+    G_TYPED_KERNEL_M(GPolarToCart, <GMat2(GMat, GMat, bool)>, "org.opencv.core.math.polarToCart") {
+        static std::tuple<GMatDesc, GMatDesc> outMeta(GMatDesc, GMatDesc a, bool) {
+            return std::make_tuple(a, a);
+        }
+    };
+
+    G_TYPED_KERNEL_M(GCartToPolar, <GMat2(GMat, GMat, bool)>, "org.opencv.core.math.cartToPolar") {
+        static std::tuple<GMatDesc, GMatDesc> outMeta(GMatDesc x, GMatDesc, bool) {
+            return std::make_tuple(x, x);
+        }
+    };
+
+    G_TYPED_KERNEL(GPhase, <GMat(GMat, GMat, bool)>, "org.opencv.core.math.phase") {
+        static GMatDesc outMeta(const GMatDesc &inx, const GMatDesc &, bool) {
+            return inx;
+        }
+    };
+
+    G_TYPED_KERNEL(GMask, <GMat(GMat,GMat)>, "org.opencv.core.pixelwise.mask") {
+        static GMatDesc outMeta(GMatDesc in, GMatDesc) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpGT, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpGT") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpGE, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpGE") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpLE, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpLE") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpLT, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpLT") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpEQ, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpEQ") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpNE, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.compare.cmpNE") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpGTScalar, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpGTScalar"){
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpGEScalar, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpGEScalar"){
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpLEScalar, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpLEScalar"){
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpLTScalar, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpLTScalar"){
+    static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpEQScalar, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpEQScalar"){
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GCmpNEScalar, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.compare.cmpNEScalar"){
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a.withDepth(CV_8U);
+        }
+    };
+
+    G_TYPED_KERNEL(GAnd, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.bitwise_and") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GAndS, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.bitwise_andS") {
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GOr, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.bitwise_or") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GOrS, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.bitwise_orS") {
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GXor, <GMat(GMat, GMat)>, "org.opencv.core.pixelwise.bitwise_xor") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GXorS, <GMat(GMat, GScalar)>, "org.opencv.core.pixelwise.bitwise_xorS") {
+        static GMatDesc outMeta(GMatDesc a, GScalarDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GNot, <GMat(GMat)>, "org.opencv.core.pixelwise.bitwise_not") {
+        static GMatDesc outMeta(GMatDesc a) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GSelect, <GMat(GMat, GMat, GMat)>, "org.opencv.core.pixelwise.select") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc, GMatDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GMin, <GMat(GMat, GMat)>, "org.opencv.core.matrixop.min") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GMax, <GMat(GMat, GMat)>, "org.opencv.core.matrixop.max") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GAbsDiff, <GMat(GMat, GMat)>, "org.opencv.core.matrixop.absdiff") {
+        static GMatDesc outMeta(GMatDesc a, GMatDesc) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GAbsDiffC, <GMat(GMat,GScalar)>, "org.opencv.core.matrixop.absdiffC") {
+        static GMatDesc outMeta(const GMatDesc& a, const GScalarDesc&) {
+            return a;
+        }
+    };
+
+    G_TYPED_KERNEL(GSum, <GScalar(GMat)>, "org.opencv.core.matrixop.sum") {
+        static GScalarDesc outMeta(GMatDesc) {
+            return empty_scalar_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GCountNonZero, <GOpaque<int>(GMat)>, "org.opencv.core.matrixop.countNonZero") {
+        static GOpaqueDesc outMeta(GMatDesc in) {
+            GAPI_Assert(in.chan == 1);
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GAddW, <GMat(GMat, double, GMat, double, double, int)>, "org.opencv.core.matrixop.addweighted") {
+        static GMatDesc outMeta(GMatDesc a, double, GMatDesc b, double, double, int ddepth) {
+            if (ddepth == -1)
+            {
+                // OpenCV: When the input arrays in add/subtract/multiply/divide
+                // functions have different depths, the output array depth must be
+                // explicitly specified!
+                // See artim_op() @ arithm.cpp
+                GAPI_Assert(a.chan == b.chan);
+                GAPI_Assert(a.depth == b.depth);
+                return a;
+            }
+            return a.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GNormL1, <GScalar(GMat)>, "org.opencv.core.matrixop.norml1") {
+        static GScalarDesc outMeta(GMatDesc) {
+            return empty_scalar_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GNormL2, <GScalar(GMat)>, "org.opencv.core.matrixop.norml2") {
+        static GScalarDesc outMeta(GMatDesc) {
+            return empty_scalar_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GNormInf, <GScalar(GMat)>, "org.opencv.core.matrixop.norminf") {
+        static GScalarDesc outMeta(GMatDesc) {
+            return empty_scalar_desc();
+        }
+    };
+
+    G_TYPED_KERNEL_M(GIntegral, <GMat2(GMat, int, int)>, "org.opencv.core.matrixop.integral") {
+        static std::tuple<GMatDesc, GMatDesc> outMeta(GMatDesc in, int sd, int sqd) {
+            return std::make_tuple(in.withSizeDelta(1,1).withDepth(sd),
+                                   in.withSizeDelta(1,1).withDepth(sqd));
+        }
+    };
+
+    G_TYPED_KERNEL(GThreshold, <GMat(GMat, GScalar, GScalar, int)>, "org.opencv.core.matrixop.threshold") {
+        static GMatDesc outMeta(GMatDesc in, GScalarDesc, GScalarDesc, int) {
+            return in;
+        }
+    };
+
+
+    G_TYPED_KERNEL_M(GThresholdOT, <GMatScalar(GMat, GScalar, int)>, "org.opencv.core.matrixop.thresholdOT") {
+        static std::tuple<GMatDesc,GScalarDesc> outMeta(GMatDesc in, GScalarDesc, int) {
+            return std::make_tuple(in, empty_scalar_desc());
+        }
+    };
+
+    G_TYPED_KERNEL(GInRange, <GMat(GMat, GScalar, GScalar)>, "org.opencv.core.matrixop.inrange") {
+        static GMatDesc outMeta(GMatDesc in, GScalarDesc, GScalarDesc) {
+            return in.withType(CV_8U, 1);
+        }
+    };
+
+    G_TYPED_KERNEL_M(GSplit3, <GMat3(GMat)>, "org.opencv.core.transform.split3") {
+        static std::tuple<GMatDesc, GMatDesc, GMatDesc> outMeta(GMatDesc in) {
+            const auto out_depth = in.depth;
+            const auto out_desc  = in.withType(out_depth, 1);
+            return std::make_tuple(out_desc, out_desc, out_desc);
+        }
+    };
+
+    G_TYPED_KERNEL_M(GSplit4, <GMat4(GMat)>,"org.opencv.core.transform.split4") {
+        static std::tuple<GMatDesc, GMatDesc, GMatDesc, GMatDesc> outMeta(GMatDesc in) {
+            const auto out_depth = in.depth;
+            const auto out_desc = in.withType(out_depth, 1);
+            return std::make_tuple(out_desc, out_desc, out_desc, out_desc);
+        }
+    };
+
+    G_TYPED_KERNEL(GResize, <GMat(GMat,Size,double,double,int)>, "org.opencv.core.transform.resize") {
+        static GMatDesc outMeta(GMatDesc in, Size sz, double fx, double fy, int /*interp*/) {
+            if (sz.width != 0 && sz.height != 0)
+            {
+                return in.withSize(sz);
+            }
+            else
+            {
+                int outSz_w = static_cast<int>(round(in.size.width  * fx));
+                int outSz_h = static_cast<int>(round(in.size.height * fy));
+                GAPI_Assert(outSz_w > 0 && outSz_h > 0);
+                return in.withSize(Size(outSz_w, outSz_h));
+            }
+        }
+    };
+
+    G_TYPED_KERNEL(GResizeP, <GMatP(GMatP,Size,int)>, "org.opencv.core.transform.resizeP") {
+        static GMatDesc outMeta(GMatDesc in, Size sz, int interp) {
+            GAPI_Assert(in.depth == CV_8U);
+            GAPI_Assert(in.chan == 3);
+            GAPI_Assert(in.planar);
+            GAPI_Assert(interp == cv::INTER_LINEAR);
+            return in.withSize(sz);
+        }
+    };
+
+    G_TYPED_KERNEL(GMerge3, <GMat(GMat,GMat,GMat)>, "org.opencv.core.transform.merge3") {
+        static GMatDesc outMeta(GMatDesc in, GMatDesc, GMatDesc) {
+            // Preserve depth and add channel component
+            return in.withType(in.depth, 3);
+        }
+    };
+
+    G_TYPED_KERNEL(GMerge4, <GMat(GMat,GMat,GMat,GMat)>, "org.opencv.core.transform.merge4") {
+        static GMatDesc outMeta(GMatDesc in, GMatDesc, GMatDesc, GMatDesc) {
+            // Preserve depth and add channel component
+            return in.withType(in.depth, 4);
+        }
+    };
+
+    G_TYPED_KERNEL(GRemap, <GMat(GMat, Mat, Mat, int, int, Scalar)>, "org.opencv.core.transform.remap") {
+        static GMatDesc outMeta(GMatDesc in, Mat m1, Mat, int, int, Scalar) {
+            return in.withSize(m1.size());
+        }
+    };
+
+    G_TYPED_KERNEL(GFlip, <GMat(GMat, int)>, "org.opencv.core.transform.flip") {
+        static GMatDesc outMeta(GMatDesc in, int) {
+            return in;
+        }
+    };
+
+    // TODO: eliminate the need in this kernel (streaming)
+    G_TYPED_KERNEL(GCrop, <GMat(GMat, Rect)>, "org.opencv.core.transform.crop") {
+        static GMatDesc outMeta(GMatDesc in, Rect rc) {
+            return in.withSize(Size(rc.width, rc.height));
+        }
+    };
+
+    G_TYPED_KERNEL(GConcatHor, <GMat(GMat, GMat)>, "org.opencv.imgproc.transform.concatHor") {
+        static GMatDesc outMeta(GMatDesc l, GMatDesc r) {
+            return l.withSizeDelta(+r.size.width, 0);
+        }
+    };
+
+    G_TYPED_KERNEL(GConcatVert, <GMat(GMat, GMat)>, "org.opencv.imgproc.transform.concatVert") {
+        static GMatDesc outMeta(GMatDesc t, GMatDesc b) {
+            return t.withSizeDelta(0, +b.size.height);
+        }
+    };
+
+    G_TYPED_KERNEL(GLUT, <GMat(GMat, Mat)>, "org.opencv.core.transform.LUT") {
+        static GMatDesc outMeta(GMatDesc in, Mat) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GConvertTo, <GMat(GMat, int, double, double)>, "org.opencv.core.transform.convertTo") {
+        static GMatDesc outMeta(GMatDesc in, int rdepth, double, double) {
+            return rdepth < 0 ? in : in.withDepth(rdepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GSqrt, <GMat(GMat)>, "org.opencv.core.math.sqrt") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GNormalize, <GMat(GMat, double, double, int, int)>, "org.opencv.core.normalize") {
+        static GMatDesc outMeta(GMatDesc in, double, double, int, int ddepth) {
+            // unlike opencv doesn't have a mask as a parameter
+            return (ddepth < 0 ? in : in.withDepth(ddepth));
+        }
+    };
+
+    G_TYPED_KERNEL(GWarpPerspective, <GMat(GMat, const Mat&, Size, int, int, const cv::Scalar&)>, "org.opencv.core.warpPerspective") {
+        static GMatDesc outMeta(GMatDesc in, const Mat&, Size dsize, int, int borderMode, const cv::Scalar&) {
+            GAPI_Assert((borderMode == cv::BORDER_CONSTANT || borderMode == cv::BORDER_REPLICATE) &&
+                        "cv::gapi::warpPerspective supports only cv::BORDER_CONSTANT and cv::BORDER_REPLICATE border modes");
+            return in.withType(in.depth, in.chan).withSize(dsize);
+        }
+    };
+
+    G_TYPED_KERNEL(GWarpAffine, <GMat(GMat, const Mat&, Size, int, int, const cv::Scalar&)>, "org.opencv.core.warpAffine") {
+        static GMatDesc outMeta(GMatDesc in, const Mat&, Size dsize, int, int border_mode, const cv::Scalar&) {
+            GAPI_Assert(border_mode != cv::BORDER_TRANSPARENT &&
+                        "cv::BORDER_TRANSPARENT mode is not supported in cv::gapi::warpAffine");
+            return in.withType(in.depth, in.chan).withSize(dsize);
+        }
+    };
+
+    G_TYPED_KERNEL(
+        GKMeansND,
+        <std::tuple<GOpaque<double>,GMat,GMat>(GMat,int,GMat,TermCriteria,int,KmeansFlags)>,
+        "org.opencv.core.kmeansND") {
+
+        static std::tuple<GOpaqueDesc,GMatDesc,GMatDesc>
+        outMeta(const GMatDesc& in, int K, const GMatDesc& bestLabels, const TermCriteria&, int,
+                KmeansFlags flags) {
+            GAPI_Assert(in.depth == CV_32F);
+            std::vector<int> amount_n_dim = detail::checkVector(in);
+            int amount = amount_n_dim[0], dim = amount_n_dim[1];
+            if (amount == -1)   // Mat with height != 1, width != 1, channels != 1 given
+            {                   // which means that kmeans will consider the following:
+                amount = in.size.height;
+                dim    = in.size.width * in.chan;
+            }
+            // kmeans sets these labels' sizes when no bestLabels given:
+            GMatDesc out_labels(CV_32S, 1, Size{1, amount});
+            // kmeans always sets these centers' sizes:
+            GMatDesc centers   (CV_32F, 1, Size{dim, K});
+            if (flags & KMEANS_USE_INITIAL_LABELS)
+            {
+                GAPI_Assert(bestLabels.depth == CV_32S);
+                int labels_amount = detail::checkVector(bestLabels, 1u);
+                GAPI_Assert(labels_amount == amount);
+                out_labels = bestLabels;  // kmeans preserves bestLabels' sizes if given
+            }
+            return std::make_tuple(empty_gopaque_desc(), out_labels, centers);
+        }
+    };
+
+    G_TYPED_KERNEL(
+        GKMeansNDNoInit,
+        <std::tuple<GOpaque<double>,GMat,GMat>(GMat,int,TermCriteria,int,KmeansFlags)>,
+        "org.opencv.core.kmeansNDNoInit") {
+
+        static std::tuple<GOpaqueDesc,GMatDesc,GMatDesc>
+        outMeta(const GMatDesc& in, int K, const TermCriteria&, int, KmeansFlags flags) {
+            GAPI_Assert( !(flags & KMEANS_USE_INITIAL_LABELS) );
+            GAPI_Assert(in.depth == CV_32F);
+            std::vector<int> amount_n_dim = detail::checkVector(in);
+            int amount = amount_n_dim[0], dim = amount_n_dim[1];
+            if (amount == -1) // Mat with height != 1, width != 1, channels != 1 given
+            {                   // which means that kmeans will consider the following:
+                amount = in.size.height;
+                dim    = in.size.width * in.chan;
+            }
+            GMatDesc out_labels(CV_32S, 1, Size{1, amount});
+            GMatDesc centers   (CV_32F, 1, Size{dim, K});
+            return std::make_tuple(empty_gopaque_desc(), out_labels, centers);
+        }
+    };
+
+    G_TYPED_KERNEL(GKMeans2D, <std::tuple<GOpaque<double>,GArray<int>,GArray<Point2f>>
+                               (GArray<Point2f>,int,GArray<int>,TermCriteria,int,KmeansFlags)>,
+                   "org.opencv.core.kmeans2D") {
+        static std::tuple<GOpaqueDesc,GArrayDesc,GArrayDesc>
+        outMeta(const GArrayDesc&,int,const GArrayDesc&,const TermCriteria&,int,KmeansFlags) {
+            return std::make_tuple(empty_gopaque_desc(), empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    G_TYPED_KERNEL(GKMeans3D, <std::tuple<GOpaque<double>,GArray<int>,GArray<Point3f>>
+                               (GArray<Point3f>,int,GArray<int>,TermCriteria,int,KmeansFlags)>,
+                   "org.opencv.core.kmeans3D") {
+        static std::tuple<GOpaqueDesc,GArrayDesc,GArrayDesc>
+        outMeta(const GArrayDesc&,int,const GArrayDesc&,const TermCriteria&,int,KmeansFlags) {
+            return std::make_tuple(empty_gopaque_desc(), empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    G_TYPED_KERNEL(GTranspose, <GMat(GMat)>, "org.opencv.core.transpose") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in.withSize({in.size.height, in.size.width});
+        }
+    };
+} // namespace core
+
+namespace streaming {
+
+// Operations for Streaming (declared in this header for convenience)
+G_TYPED_KERNEL(GSize, <GOpaque<Size>(GMat)>, "org.opencv.streaming.size") {
+    static GOpaqueDesc outMeta(const GMatDesc&) {
+        return empty_gopaque_desc();
+    }
+};
+
+G_TYPED_KERNEL(GSizeR, <GOpaque<Size>(GOpaque<Rect>)>, "org.opencv.streaming.sizeR") {
+    static GOpaqueDesc outMeta(const GOpaqueDesc&) {
+        return empty_gopaque_desc();
+    }
+};
+
+G_TYPED_KERNEL(GSizeMF, <GOpaque<Size>(GFrame)>, "org.opencv.streaming.sizeMF") {
+    static GOpaqueDesc outMeta(const GFrameDesc&) {
+        return empty_gopaque_desc();
+    }
+};
+} // namespace streaming
+
+//! @addtogroup gapi_math
+//! @{
+
+/** @brief Calculates the per-element sum of two matrices.
+
+The function add calculates sum of two matrices of the same size and the same number of channels:
+\f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) +  \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
+
+The function can be replaced with matrix expressions:
+    \f[\texttt{dst} =  \texttt{src1} + \texttt{src2}\f]
+
+The input matrices and the output matrix can all have the same or different depths. For example, you
+can add a 16-bit unsigned matrix to a 8-bit signed matrix and store the sum as a 32-bit
+floating-point matrix. Depth of the output matrix is determined by the ddepth parameter.
+If src1.depth() == src2.depth(), ddepth can be set to the default -1. In this case, the output matrix will have
+the same depth as the input matrices.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.add"
+@param src1 first input matrix.
+@param src2 second input matrix.
+@param ddepth optional depth of the output matrix.
+@sa sub, addWeighted
+*/
+GAPI_EXPORTS_W GMat add(const GMat& src1, const GMat& src2, int ddepth = -1);
+
+/** @brief Calculates the per-element sum of matrix and given scalar.
+
+The function addC adds a given scalar value to each element of given matrix.
+The function can be replaced with matrix expressions:
+
+    \f[\texttt{dst} =  \texttt{src1} + \texttt{c}\f]
+
+Depth of the output matrix is determined by the ddepth parameter.
+If ddepth is set to default -1, the depth of output matrix will be the same as the depth of input matrix.
+The matrices can be single or multi channel. Output matrix must have the same size and number of channels as the input matrix.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.addC"
+@param src1 first input matrix.
+@param c scalar value to be added.
+@param ddepth optional depth of the output matrix.
+@sa sub, addWeighted
+*/
+GAPI_EXPORTS_W GMat addC(const GMat& src1, const GScalar& c, int ddepth = -1);
+//! @overload
+GAPI_EXPORTS GMat addC(const GScalar& c, const GMat& src1, int ddepth = -1);
+
+/** @brief Calculates the per-element difference between two matrices.
+
+The function sub calculates difference between two matrices, when both matrices have the same size and the same number of
+channels:
+    \f[\texttt{dst}(I) =   \texttt{src1}(I) -  \texttt{src2}(I)\f]
+
+The function can be replaced with matrix expressions:
+\f[\texttt{dst} =   \texttt{src1} -  \texttt{src2}\f]
+
+The input matrices and the output matrix can all have the same or different depths. For example, you
+can subtract two 8-bit unsigned matrices store the result as a 16-bit signed matrix.
+Depth of the output matrix is determined by the ddepth parameter.
+If src1.depth() == src2.depth(), ddepth can be set to the default -1. In this case, the output matrix will have
+the same depth as the input matrices. The matrices can be single or multi channel.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.sub"
+@param src1 first input matrix.
+@param src2 second input matrix.
+@param ddepth optional depth of the output matrix.
+@sa  add, addC
+  */
+GAPI_EXPORTS GMat sub(const GMat& src1, const GMat& src2, int ddepth = -1);
+
+/** @brief Calculates the per-element difference between matrix and given scalar.
+
+The function can be replaced with matrix expressions:
+    \f[\texttt{dst} =  \texttt{src} - \texttt{c}\f]
+
+Depth of the output matrix is determined by the ddepth parameter.
+If ddepth is set to default -1, the depth of output matrix will be the same as the depth of input matrix.
+The matrices can be single or multi channel. Output matrix must have the same size as src.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.subC"
+@param src first input matrix.
+@param c scalar value to subtracted.
+@param ddepth optional depth of the output matrix.
+@sa  add, addC, subRC
+  */
+GAPI_EXPORTS GMat subC(const GMat& src, const GScalar& c, int ddepth = -1);
+
+/** @brief Calculates the per-element difference between given scalar and the matrix.
+
+The function can be replaced with matrix expressions:
+    \f[\texttt{dst} =  \texttt{c} - \texttt{src}\f]
+
+Depth of the output matrix is determined by the ddepth parameter.
+If ddepth is set to default -1, the depth of output matrix will be the same as the depth of input matrix.
+The matrices can be single or multi channel. Output matrix must have the same size as src.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.subRC"
+@param c scalar value to subtract from.
+@param src input matrix to be subtracted.
+@param ddepth optional depth of the output matrix.
+@sa  add, addC, subC
+  */
+GAPI_EXPORTS GMat subRC(const GScalar& c, const GMat& src, int ddepth = -1);
+
+/** @brief Calculates the per-element scaled product of two matrices.
+
+The function mul calculates the per-element product of two matrices:
+
+\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I)  \cdot \texttt{src2} (I))\f]
+
+If src1.depth() == src2.depth(), ddepth can be set to the default -1. In this case, the output matrix will have
+the same depth as the input matrices. The matrices can be single or multi channel.
+Output matrix must have the same size as input matrices.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.mul"
+@param src1 first input matrix.
+@param src2 second input matrix of the same size and the same depth as src1.
+@param scale optional scale factor.
+@param ddepth optional depth of the output matrix.
+@sa add, sub, div, addWeighted
+*/
+GAPI_EXPORTS GMat mul(const GMat& src1, const GMat& src2, double scale = 1.0, int ddepth = -1);
+
+/** @brief Multiplies matrix by scalar.
+
+The function mulC multiplies each element of matrix src by given scalar value:
+
+\f[\texttt{dst} (I)= \texttt{saturate} (  \texttt{src1} (I)  \cdot \texttt{multiplier} )\f]
+
+The matrices can be single or multi channel. Output matrix must have the same size as src.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.mulC"
+@param src input matrix.
+@param multiplier factor to be multiplied.
+@param ddepth optional depth of the output matrix. If -1, the depth of output matrix will be the same as input matrix depth.
+@sa add, sub, div, addWeighted
+*/
+GAPI_EXPORTS GMat mulC(const GMat& src, double multiplier, int ddepth = -1);
+//! @overload
+GAPI_EXPORTS GMat mulC(const GMat& src, const GScalar& multiplier, int ddepth = -1);   // FIXME: merge with mulc
+//! @overload
+GAPI_EXPORTS GMat mulC(const GScalar& multiplier, const GMat& src, int ddepth = -1);   // FIXME: merge with mulc
+
+/** @brief Performs per-element division of two matrices.
+
+The function divides one matrix by another:
+\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f]
+
+For integer types when src2(I) is zero, dst(I) will also be zero.
+Floating point case returns Inf/NaN (according to IEEE).
+
+Different channels of
+multi-channel matrices are processed independently.
+The matrices can be single or multi channel. Output matrix must have the same size and depth as src.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.div"
+@param src1 first input matrix.
+@param src2 second input matrix of the same size and depth as src1.
+@param scale scalar factor.
+@param ddepth optional depth of the output matrix; you can only pass -1 when src1.depth() == src2.depth().
+@sa  mul, add, sub
+*/
+GAPI_EXPORTS GMat div(const GMat& src1, const GMat& src2, double scale, int ddepth = -1);
+
+/** @brief Divides matrix by scalar.
+
+The function divC divides each element of matrix src by given scalar value:
+
+\f[\texttt{dst(I) = saturate(src(I)*scale/divisor)}\f]
+
+When divisor is zero, dst(I) will also be zero. Different channels of
+multi-channel matrices are processed independently.
+The matrices can be single or multi channel. Output matrix must have the same size and depth as src.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.divC"
+@param src input matrix.
+@param divisor number to be divided by.
+@param ddepth optional depth of the output matrix. If -1, the depth of output matrix will be the same as input matrix depth.
+@param scale scale factor.
+@sa add, sub, div, addWeighted
+*/
+GAPI_EXPORTS GMat divC(const GMat& src, const GScalar& divisor, double scale, int ddepth = -1);
+
+/** @brief Divides scalar by matrix.
+
+The function divRC divides given scalar by each element of matrix src and keep the division result in new matrix of the same size and type as src:
+
+\f[\texttt{dst(I) = saturate(divident*scale/src(I))}\f]
+
+When src(I) is zero, dst(I) will also be zero. Different channels of
+multi-channel matrices are processed independently.
+The matrices can be single or multi channel. Output matrix must have the same size and depth as src.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.divRC"
+@param src input matrix.
+@param divident number to be divided.
+@param ddepth optional depth of the output matrix. If -1, the depth of output matrix will be the same as input matrix depth.
+@param scale scale factor
+@sa add, sub, div, addWeighted
+*/
+GAPI_EXPORTS GMat divRC(const GScalar& divident, const GMat& src, double scale, int ddepth = -1);
+
+/** @brief Applies a mask to a matrix.
+
+The function mask set value from given matrix if the corresponding pixel value in mask matrix set to true,
+and set the matrix value to 0 otherwise.
+
+Supported src matrix data types are @ref CV_8UC1, @ref CV_16SC1, @ref CV_16UC1. Supported mask data type is @ref CV_8UC1.
+
+@note Function textual ID is "org.opencv.core.math.mask"
+@param src input matrix.
+@param mask input mask matrix.
+*/
+GAPI_EXPORTS GMat mask(const GMat& src, const GMat& mask);
+
+/** @brief Calculates an average (mean) of matrix elements.
+
+The function mean calculates the mean value M of matrix elements,
+independently for each channel, and return it.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.math.mean"
+@param src input matrix.
+@sa  countNonZero, min, max
+*/
+GAPI_EXPORTS_W GScalar mean(const GMat& src);
+
+/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle.
+
+The function polarToCart calculates the Cartesian coordinates of each 2D
+vector represented by the corresponding elements of magnitude and angle:
+\f[\begin{array}{l} \texttt{x} (I) =  \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) =  \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f]
+
+The relative accuracy of the estimated coordinates is about 1e-6.
+
+First output is a matrix of x-coordinates of 2D vectors.
+Second output is a matrix of y-coordinates of 2D vectors.
+Both output must have the same size and depth as input matrices.
+
+@note Function textual ID is "org.opencv.core.math.polarToCart"
+
+@param magnitude input floating-point @ref CV_32FC1 matrix (1xN) of magnitudes of 2D vectors;
+@param angle input floating-point @ref CV_32FC1 matrix (1xN) of angles of 2D vectors.
+@param angleInDegrees when true, the input angles are measured in
+degrees, otherwise, they are measured in radians.
+@sa cartToPolar, exp, log, pow, sqrt
+*/
+GAPI_EXPORTS std::tuple<GMat, GMat> polarToCart(const GMat& magnitude, const GMat& angle,
+                                              bool angleInDegrees = false);
+
+/** @brief Calculates the magnitude and angle of 2D vectors.
+
+The function cartToPolar calculates either the magnitude, angle, or both
+for every 2D vector (x(I),y(I)):
+\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f]
+
+The angles are calculated with accuracy about 0.3 degrees. For the point
+(0,0), the angle is set to 0.
+
+First output is a matrix of magnitudes of the same size and depth as input x.
+Second output is a matrix of angles that has the same size and depth as
+x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees).
+
+@note Function textual ID is "org.opencv.core.math.cartToPolar"
+
+@param x matrix of @ref CV_32FC1 x-coordinates.
+@param y array of @ref CV_32FC1 y-coordinates.
+@param angleInDegrees a flag, indicating whether the angles are measured
+in radians (which is by default), or in degrees.
+@sa polarToCart
+*/
+GAPI_EXPORTS std::tuple<GMat, GMat> cartToPolar(const GMat& x, const GMat& y,
+                                              bool angleInDegrees = false);
+
+/** @brief Calculates the rotation angle of 2D vectors.
+
+The function cv::phase calculates the rotation angle of each 2D vector that
+is formed from the corresponding elements of x and y :
+\f[\texttt{angle} (I) =  \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f]
+
+The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 ,
+the corresponding angle(I) is set to 0.
+@param x input floating-point array of x-coordinates of 2D vectors.
+@param y input array of y-coordinates of 2D vectors; it must have the
+same size and the same type as x.
+@param angleInDegrees when true, the function calculates the angle in
+degrees, otherwise, they are measured in radians.
+@return array of vector angles; it has the same size and same type as x.
+*/
+GAPI_EXPORTS GMat phase(const GMat& x, const GMat &y, bool angleInDegrees = false);
+
+/** @brief Calculates a square root of array elements.
+
+The function cv::gapi::sqrt calculates a square root of each input array element.
+In case of multi-channel arrays, each channel is processed
+independently. The accuracy is approximately the same as of the built-in
+std::sqrt .
+@param src input floating-point array.
+@return output array of the same size and type as src.
+*/
+GAPI_EXPORTS GMat sqrt(const GMat &src);
+
+//! @} gapi_math
+//!
+//! @addtogroup gapi_pixelwise
+//! @{
+
+/** @brief Performs the per-element comparison of two matrices checking if elements from first matrix are greater compare to elements in second.
+
+The function compares elements of two matrices src1 and src2 of the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  > \texttt{src2} (I)\f]
+
+When the comparison result is true, the corresponding element of output
+array is set to 255. The comparison operations can be replaced with the
+equivalent matrix expressions:
+\f[\texttt{dst} =   \texttt{src1} > \texttt{src2}\f]
+
+Output matrix of depth @ref CV_8U must have the same size and the same number of channels as
+    the input matrices/matrix.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpGT"
+@param src1 first input matrix.
+@param src2 second input matrix/scalar of the same depth as first input matrix.
+@sa min, max, threshold, cmpLE, cmpGE, cmpLT
+*/
+GAPI_EXPORTS GMat cmpGT(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpGTScalar"
+*/
+GAPI_EXPORTS GMat cmpGT(const GMat& src1, const GScalar& src2);
+
+/** @brief Performs the per-element comparison of two matrices checking if elements from first matrix are less than elements in second.
+
+The function compares elements of two matrices src1 and src2 of the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  < \texttt{src2} (I)\f]
+
+When the comparison result is true, the corresponding element of output
+array is set to 255. The comparison operations can be replaced with the
+equivalent matrix expressions:
+    \f[\texttt{dst} =   \texttt{src1} < \texttt{src2}\f]
+
+Output matrix of depth @ref CV_8U must have the same size and the same number of channels as
+    the input matrices/matrix.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpLT"
+@param src1 first input matrix.
+@param src2 second input matrix/scalar of the same depth as first input matrix.
+@sa min, max, threshold, cmpLE, cmpGE, cmpGT
+*/
+GAPI_EXPORTS GMat cmpLT(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpLTScalar"
+*/
+GAPI_EXPORTS GMat cmpLT(const GMat& src1, const GScalar& src2);
+
+/** @brief Performs the per-element comparison of two matrices checking if elements from first matrix are greater or equal compare to elements in second.
+
+The function compares elements of two matrices src1 and src2 of the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  >= \texttt{src2} (I)\f]
+
+When the comparison result is true, the corresponding element of output
+array is set to 255. The comparison operations can be replaced with the
+equivalent matrix expressions:
+    \f[\texttt{dst} =   \texttt{src1} >= \texttt{src2}\f]
+
+Output matrix of depth @ref CV_8U must have the same size and the same number of channels as
+    the input matrices.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpGE"
+@param src1 first input matrix.
+@param src2 second input matrix/scalar of the same depth as first input matrix.
+@sa min, max, threshold, cmpLE, cmpGT, cmpLT
+*/
+GAPI_EXPORTS GMat cmpGE(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpLGEcalar"
+*/
+GAPI_EXPORTS GMat cmpGE(const GMat& src1, const GScalar& src2);
+
+/** @brief Performs the per-element comparison of two matrices checking if elements from first matrix are less or equal compare to elements in second.
+
+The function compares elements of two matrices src1 and src2 of the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  <=  \texttt{src2} (I)\f]
+
+When the comparison result is true, the corresponding element of output
+array is set to 255. The comparison operations can be replaced with the
+equivalent matrix expressions:
+    \f[\texttt{dst} =   \texttt{src1} <= \texttt{src2}\f]
+
+Output matrix of depth @ref CV_8U must have the same size and the same number of channels as
+    the input matrices.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpLE"
+@param src1 first input matrix.
+@param src2 second input matrix/scalar of the same depth as first input matrix.
+@sa min, max, threshold, cmpGT, cmpGE, cmpLT
+*/
+GAPI_EXPORTS GMat cmpLE(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpLEScalar"
+*/
+GAPI_EXPORTS GMat cmpLE(const GMat& src1, const GScalar& src2);
+
+/** @brief Performs the per-element comparison of two matrices checking if elements from first matrix are equal to elements in second.
+
+The function compares elements of two matrices src1 and src2 of the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  ==  \texttt{src2} (I)\f]
+
+When the comparison result is true, the corresponding element of output
+array is set to 255. The comparison operations can be replaced with the
+equivalent matrix expressions:
+    \f[\texttt{dst} =   \texttt{src1} == \texttt{src2}\f]
+
+Output matrix of depth @ref CV_8U must have the same size and the same number of channels as
+    the input matrices.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpEQ"
+@param src1 first input matrix.
+@param src2 second input matrix/scalar of the same depth as first input matrix.
+@sa min, max, threshold, cmpNE
+*/
+GAPI_EXPORTS GMat cmpEQ(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpEQScalar"
+*/
+GAPI_EXPORTS GMat cmpEQ(const GMat& src1, const GScalar& src2);
+
+/** @brief Performs the per-element comparison of two matrices checking if elements from first matrix are not equal to elements in second.
+
+The function compares elements of two matrices src1 and src2 of the same size:
+    \f[\texttt{dst} (I) =  \texttt{src1} (I)  !=  \texttt{src2} (I)\f]
+
+When the comparison result is true, the corresponding element of output
+array is set to 255. The comparison operations can be replaced with the
+equivalent matrix expressions:
+    \f[\texttt{dst} =   \texttt{src1} != \texttt{src2}\f]
+
+Output matrix of depth @ref CV_8U must have the same size and the same number of channels as
+    the input matrices.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpNE"
+@param src1 first input matrix.
+@param src2 second input matrix/scalar of the same depth as first input matrix.
+@sa min, max, threshold, cmpEQ
+*/
+GAPI_EXPORTS GMat cmpNE(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.compare.cmpNEScalar"
+*/
+GAPI_EXPORTS GMat cmpNE(const GMat& src1, const GScalar& src2);
+
+/** @brief computes bitwise conjunction of the two matrixes (src1 & src2)
+Calculates the per-element bit-wise logical conjunction of two matrices of the same size.
+
+In case of floating-point matrices, their machine-specific bit
+representations (usually IEEE754-compliant) are used for the operation.
+In case of multi-channel matrices, each channel is processed
+independently. Output matrix must have the same size and depth as the input
+matrices.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.bitwise_and"
+
+@param src1 first input matrix.
+@param src2 second input matrix.
+*/
+GAPI_EXPORTS GMat bitwise_and(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.bitwise_andS"
+@param src1 first input matrix.
+@param src2 scalar, which will be per-lemenetly conjuncted with elements of src1.
+*/
+GAPI_EXPORTS GMat bitwise_and(const GMat& src1, const GScalar& src2);
+
+/** @brief computes bitwise disjunction of the two matrixes (src1 | src2)
+Calculates the per-element bit-wise logical disjunction of two matrices of the same size.
+
+In case of floating-point matrices, their machine-specific bit
+representations (usually IEEE754-compliant) are used for the operation.
+In case of multi-channel matrices, each channel is processed
+independently. Output matrix must have the same size and depth as the input
+matrices.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.bitwise_or"
+
+@param src1 first input matrix.
+@param src2 second input matrix.
+*/
+GAPI_EXPORTS GMat bitwise_or(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.bitwise_orS"
+@param src1 first input matrix.
+@param src2 scalar, which will be per-lemenetly disjuncted with elements of src1.
+*/
+GAPI_EXPORTS GMat bitwise_or(const GMat& src1, const GScalar& src2);
+
+
+/** @brief computes bitwise logical "exclusive or" of the two matrixes (src1 ^ src2)
+Calculates the per-element bit-wise logical "exclusive or" of two matrices of the same size.
+
+In case of floating-point matrices, their machine-specific bit
+representations (usually IEEE754-compliant) are used for the operation.
+In case of multi-channel matrices, each channel is processed
+independently. Output matrix must have the same size and depth as the input
+matrices.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.bitwise_xor"
+
+@param src1 first input matrix.
+@param src2 second input matrix.
+*/
+GAPI_EXPORTS GMat bitwise_xor(const GMat& src1, const GMat& src2);
+/** @overload
+@note Function textual ID is "org.opencv.core.pixelwise.bitwise_xorS"
+@param src1 first input matrix.
+@param src2 scalar, for which per-lemenet "logical or" operation on elements of src1 will be performed.
+*/
+GAPI_EXPORTS GMat bitwise_xor(const GMat& src1, const GScalar& src2);
+
+
+/** @brief Inverts every bit of an array.
+
+The function bitwise_not calculates per-element bit-wise inversion of the input
+matrix:
+\f[\texttt{dst} (I) =  \neg \texttt{src} (I)\f]
+
+In case of floating-point matrices, their machine-specific bit
+representations (usually IEEE754-compliant) are used for the operation.
+In case of multi-channel matrices, each channel is processed
+independently. Output matrix must have the same size and depth as the input
+matrix.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.bitwise_not"
+
+@param src input matrix.
+*/
+GAPI_EXPORTS GMat bitwise_not(const GMat& src);
+
+/** @brief Select values from either first or second of input matrices by given mask.
+The function set to the output matrix either the value from the first input matrix if corresponding value of mask matrix is 255,
+ or value from the second input matrix (if value of mask matrix set to 0).
+
+Input mask matrix must be of @ref CV_8UC1 type, two other inout matrices and output matrix should be of the same type. The size should
+be the same for all input and output matrices.
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.pixelwise.select"
+
+@param src1 first input matrix.
+@param src2 second input matrix.
+@param mask mask input matrix.
+*/
+GAPI_EXPORTS GMat select(const GMat& src1, const GMat& src2, const GMat& mask);
+
+//! @} gapi_pixelwise
+
+
+//! @addtogroup gapi_matrixop
+//! @{
+/** @brief Calculates per-element minimum of two matrices.
+
+The function min calculates the per-element minimum of two matrices of the same size, number of channels and depth:
+\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f]
+    where I is a multi-dimensional index of matrix elements. In case of
+    multi-channel matrices, each channel is processed independently.
+Output matrix must be of the same size and depth as src1.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.min"
+@param src1 first input matrix.
+@param src2 second input matrix of the same size and depth as src1.
+@sa max, cmpEQ, cmpLT, cmpLE
+*/
+GAPI_EXPORTS GMat min(const GMat& src1, const GMat& src2);
+
+/** @brief Calculates per-element maximum of two matrices.
+
+The function max calculates the per-element maximum of two matrices of the same size, number of channels and depth:
+\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f]
+    where I is a multi-dimensional index of matrix elements. In case of
+    multi-channel matrices, each channel is processed independently.
+Output matrix must be of the same size and depth as src1.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.max"
+@param src1 first input matrix.
+@param src2 second input matrix of the same size and depth as src1.
+@sa min, compare, cmpEQ, cmpGT, cmpGE
+*/
+GAPI_EXPORTS GMat max(const GMat& src1, const GMat& src2);
+
+/** @brief Calculates the per-element absolute difference between two matrices.
+
+The function absDiff calculates absolute difference between two matrices of the same size and depth:
+    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{src2}(I)|)\f]
+    where I is a multi-dimensional index of matrix elements. In case of
+    multi-channel matrices, each channel is processed independently.
+Output matrix must have the same size and depth as input matrices.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.absdiff"
+@param src1 first input matrix.
+@param src2 second input matrix.
+@sa abs
+*/
+GAPI_EXPORTS GMat absDiff(const GMat& src1, const GMat& src2);
+
+/** @brief Calculates absolute value of matrix elements.
+
+The function abs calculates absolute difference between matrix elements and given scalar value:
+    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{matC}(I)|)\f]
+    where matC is constructed from given scalar c and has the same sizes and depth as input matrix src.
+
+Output matrix must be of the same size and depth as src.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.absdiffC"
+@param src input matrix.
+@param c scalar to be subtracted.
+@sa min, max
+*/
+GAPI_EXPORTS GMat absDiffC(const GMat& src, const GScalar& c);
+
+/** @brief Calculates sum of all matrix elements.
+
+The function sum calculates sum of all matrix elements, independently for each channel.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.sum"
+@param src input matrix.
+@sa countNonZero, mean, min, max
+*/
+GAPI_EXPORTS GScalar sum(const GMat& src);
+
+/** @brief Counts non-zero array elements.
+
+The function returns the number of non-zero elements in src :
+\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f]
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.countNonZero"
+@param src input single-channel matrix.
+@sa  mean, min, max
+*/
+GAPI_EXPORTS GOpaque<int> countNonZero(const GMat& src);
+
+/** @brief Calculates the weighted sum of two matrices.
+
+The function addWeighted calculates the weighted sum of two matrices as follows:
+\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} +  \texttt{src2} (I)* \texttt{beta} +  \texttt{gamma} )\f]
+where I is a multi-dimensional index of array elements. In case of multi-channel matrices, each
+channel is processed independently.
+
+The function can be replaced with a matrix expression:
+    \f[\texttt{dst}(I) =  \texttt{alpha} * \texttt{src1}(I) - \texttt{beta} * \texttt{src2}(I) + \texttt{gamma} \f]
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.addweighted"
+@param src1 first input matrix.
+@param alpha weight of the first matrix elements.
+@param src2 second input matrix of the same size and channel number as src1.
+@param beta weight of the second matrix elements.
+@param gamma scalar added to each sum.
+@param ddepth optional depth of the output matrix.
+@sa  add, sub
+*/
+GAPI_EXPORTS GMat addWeighted(const GMat& src1, double alpha, const GMat& src2, double beta, double gamma, int ddepth = -1);
+
+/** @brief Calculates the  absolute L1 norm of a matrix.
+
+This version of normL1 calculates the absolute L1 norm of src.
+
+As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$.
+The \f$ L_{1} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$
+is calculated as follows
+\f{align*}
+    \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\
+\f}
+and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is
+\f{align*}
+    \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\
+\f}
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.norml1"
+@param src input matrix.
+@sa normL2, normInf
+*/
+GAPI_EXPORTS GScalar normL1(const GMat& src);
+
+/** @brief Calculates the absolute L2 norm of a matrix.
+
+This version of normL2 calculates the absolute L2 norm of src.
+
+As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$.
+The \f$ L_{2} \f$  norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$
+is calculated as follows
+\f{align*}
+    \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\
+\f}
+and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is
+\f{align*}
+    \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\
+\f}
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+@note Function textual ID is "org.opencv.core.matrixop.norml2"
+@param src input matrix.
+@sa normL1, normInf
+*/
+GAPI_EXPORTS GScalar normL2(const GMat& src);
+
+/** @brief Calculates the absolute infinite norm of a matrix.
+
+This version of normInf calculates the absolute infinite norm of src.
+
+As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$.
+The \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$
+is calculated as follows
+\f{align*}
+    \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2
+\f}
+and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is
+\f{align*}
+    \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5.
+\f}
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.norminf"
+@param src input matrix.
+@sa normL1, normL2
+*/
+GAPI_EXPORTS GScalar normInf(const GMat& src);
+
+/** @brief Calculates the integral of an image.
+
+The function calculates one or more integral images for the source image as follows:
+
+\f[\texttt{sum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)\f]
+
+\f[\texttt{sqsum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)^2\f]
+
+The function return integral image as \f$(W+1)\times (H+1)\f$ , 32-bit integer or floating-point (32f or 64f) and
+ integral image for squared pixel values; it is \f$(W+1)\times (H+)\f$, double-precision floating-point (64f) array.
+
+@note Function textual ID is "org.opencv.core.matrixop.integral"
+
+@param src input image.
+@param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
+CV_64F.
+@param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
+ */
+GAPI_EXPORTS std::tuple<GMat, GMat> integral(const GMat& src, int sdepth = -1, int sqdepth = -1);
+
+/** @brief Applies a fixed-level threshold to each matrix element.
+
+The function applies fixed-level thresholding to a single- or multiple-channel matrix.
+The function is typically used to get a bi-level (binary) image out of a grayscale image ( cmp functions could be also used for
+this purpose) or for removing a noise, that is, filtering out pixels with too small or too large
+values. There are several types of thresholding supported by the function. They are determined by
+type parameter.
+
+Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the
+above values. In these cases, the function determines the optimal threshold value using the Otsu's
+or Triangle algorithm and uses it instead of the specified thresh . The function returns the
+computed threshold value in addititon to thresholded matrix.
+The Otsu's and Triangle methods are implemented only for 8-bit matrices.
+
+Input image should be single channel only in case of cv::THRESH_OTSU or cv::THRESH_TRIANGLE flags.
+Output matrix must be of the same size and depth as src.
+
+@note Function textual ID is "org.opencv.core.matrixop.threshold"
+
+@param src input matrix (@ref CV_8UC1, @ref CV_8UC3, or @ref CV_32FC1).
+@param thresh threshold value.
+@param maxval maximum value to use with the cv::THRESH_BINARY and cv::THRESH_BINARY_INV thresholding
+types.
+@param type thresholding type (see the cv::ThresholdTypes).
+
+@sa min, max, cmpGT, cmpLE, cmpGE, cmpLT
+ */
+GAPI_EXPORTS GMat threshold(const GMat& src, const GScalar& thresh, const GScalar& maxval, int type);
+/** @overload
+This function applicable for all threshold types except CV_THRESH_OTSU and CV_THRESH_TRIANGLE
+@note Function textual ID is "org.opencv.core.matrixop.thresholdOT"
+*/
+GAPI_EXPORTS_W std::tuple<GMat, GScalar> threshold(const GMat& src, const GScalar& maxval, int type);
+
+/** @brief Applies a range-level threshold to each matrix element.
+
+The function applies range-level thresholding to a single- or multiple-channel matrix.
+It sets output pixel value to OxFF if the corresponding pixel value of input matrix is in specified range,or 0 otherwise.
+
+Input and output matrices must be CV_8UC1.
+
+@note Function textual ID is "org.opencv.core.matrixop.inRange"
+
+@param src input matrix (CV_8UC1).
+@param threshLow lower boundary value.
+@param threshUp upper boundary value.
+
+@sa threshold
+ */
+GAPI_EXPORTS GMat inRange(const GMat& src, const GScalar& threshLow, const GScalar& threshUp);
+
+//! @} gapi_matrixop
+
+//! @addtogroup gapi_transform
+//! @{
+/** @brief Resizes an image.
+
+The function resizes the image src down to or up to the specified size.
+
+Output image size will have the size dsize (when dsize is non-zero) or the size computed from
+src.size(), fx, and fy; the depth of output is the same as of src.
+
+If you want to resize src so that it fits the pre-created dst,
+you may call the function as follows:
+@code
+    // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
+    resize(src, dst, dst.size(), 0, 0, interpolation);
+@endcode
+If you want to decimate the image by factor of 2 in each direction, you can call the function this
+way:
+@code
+    // specify fx and fy and let the function compute the destination image size.
+    resize(src, dst, Size(), 0.5, 0.5, interpolation);
+@endcode
+To shrink an image, it will generally look best with cv::INTER_AREA interpolation, whereas to
+enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::INTER_LINEAR
+(faster but still looks OK).
+
+@note Function textual ID is "org.opencv.core.transform.resize"
+
+@param src input image.
+@param dsize output image size; if it equals zero, it is computed as:
+ \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
+ Either dsize or both fx and fy must be non-zero.
+@param fx scale factor along the horizontal axis; when it equals 0, it is computed as
+\f[\texttt{(double)dsize.width/src.cols}\f]
+@param fy scale factor along the vertical axis; when it equals 0, it is computed as
+\f[\texttt{(double)dsize.height/src.rows}\f]
+@param interpolation interpolation method, see cv::InterpolationFlags
+
+@sa  warpAffine, warpPerspective, remap, resizeP
+ */
+GAPI_EXPORTS_W GMat resize(const GMat& src, const Size& dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);
+
+/** @brief Resizes a planar image.
+
+The function resizes the image src down to or up to the specified size.
+Planar image memory layout is three planes laying in the memory contiguously,
+so the image height should be plane_height*plane_number, image type is @ref CV_8UC1.
+
+Output image size will have the size dsize, the depth of output is the same as of src.
+
+@note Function textual ID is "org.opencv.core.transform.resizeP"
+
+@param src input image, must be of @ref CV_8UC1 type;
+@param dsize output image size;
+@param interpolation interpolation method, only cv::INTER_LINEAR is supported at the moment
+
+@sa  warpAffine, warpPerspective, remap, resize
+ */
+GAPI_EXPORTS GMatP resizeP(const GMatP& src, const Size& dsize, int interpolation = cv::INTER_LINEAR);
+
+/** @brief Creates one 4-channel matrix out of 4 single-channel ones.
+
+The function merges several matrices to make a single multi-channel matrix. That is, each
+element of the output matrix will be a concatenation of the elements of the input matrices, where
+elements of i-th input matrix are treated as mv[i].channels()-element vectors.
+Output matrix must be of @ref CV_8UC4 type.
+
+The function split4 does the reverse operation.
+
+@note
+ - Function textual ID is "org.opencv.core.transform.merge4"
+
+@param src1 first input @ref CV_8UC1 matrix to be merged.
+@param src2 second input @ref CV_8UC1 matrix to be merged.
+@param src3 third input @ref CV_8UC1 matrix to be merged.
+@param src4 fourth input @ref CV_8UC1 matrix to be merged.
+@sa merge3, split4, split3
+*/
+GAPI_EXPORTS GMat merge4(const GMat& src1, const GMat& src2, const GMat& src3, const GMat& src4);
+
+/** @brief Creates one 3-channel matrix out of 3 single-channel ones.
+
+The function merges several matrices to make a single multi-channel matrix. That is, each
+element of the output matrix will be a concatenation of the elements of the input matrices, where
+elements of i-th input matrix are treated as mv[i].channels()-element vectors.
+Output matrix must be of @ref CV_8UC3 type.
+
+The function split3 does the reverse operation.
+
+@note
+ - Function textual ID is "org.opencv.core.transform.merge3"
+
+@param src1 first input @ref CV_8UC1 matrix to be merged.
+@param src2 second input @ref CV_8UC1 matrix to be merged.
+@param src3 third input @ref CV_8UC1 matrix to be merged.
+@sa merge4, split4, split3
+*/
+GAPI_EXPORTS GMat merge3(const GMat& src1, const GMat& src2, const GMat& src3);
+
+/** @brief Divides a 4-channel matrix into 4 single-channel matrices.
+
+The function splits a 4-channel matrix into 4 single-channel matrices:
+\f[\texttt{mv} [c](I) =  \texttt{src} (I)_c\f]
+
+All output matrices must be of @ref CV_8UC1 type.
+
+The function merge4 does the reverse operation.
+
+@note
+ - Function textual ID is "org.opencv.core.transform.split4"
+
+@param src input @ref CV_8UC4 matrix.
+@sa split3, merge3, merge4
+*/
+GAPI_EXPORTS std::tuple<GMat, GMat, GMat,GMat> split4(const GMat& src);
+
+/** @brief Divides a 3-channel matrix into 3 single-channel matrices.
+
+The function splits a 3-channel matrix into 3 single-channel matrices:
+\f[\texttt{mv} [c](I) =  \texttt{src} (I)_c\f]
+
+All output matrices must be of @ref CV_8UC1 type.
+
+The function merge3 does the reverse operation.
+
+@note
+ - Function textual ID is "org.opencv.core.transform.split3"
+
+@param src input @ref CV_8UC3 matrix.
+@sa split4, merge3, merge4
+*/
+GAPI_EXPORTS_W std::tuple<GMat, GMat, GMat> split3(const GMat& src);
+
+/** @brief Applies a generic geometrical transformation to an image.
+
+The function remap transforms the source image using the specified map:
+
+\f[\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\f]
+
+where values of pixels with non-integer coordinates are computed using one of available
+interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps
+in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in
+\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to
+convert from floating to fixed-point representations of a map is that they can yield much faster
+(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x),
+cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients.
+Output image must be of the same size and depth as input one.
+
+@note
+ - Function textual ID is "org.opencv.core.transform.remap"
+ - Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
+
+@param src Source image.
+@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2,
+CV_32FC1, or CV_32FC2.
+@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
+if map1 is (x,y) points), respectively.
+@param interpolation Interpolation method (see cv::InterpolationFlags). The methods #INTER_AREA
+and #INTER_LINEAR_EXACT are not supported by this function.
+@param borderMode Pixel extrapolation method (see cv::BorderTypes). When
+borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image that
+corresponds to the "outliers" in the source image are not modified by the function.
+@param borderValue Value used in case of a constant border. By default, it is 0.
+ */
+GAPI_EXPORTS GMat remap(const GMat& src, const Mat& map1, const Mat& map2,
+                      int interpolation, int borderMode = BORDER_CONSTANT,
+                      const Scalar& borderValue = Scalar());
+
+/** @brief Flips a 2D matrix around vertical, horizontal, or both axes.
+
+The function flips the matrix in one of three different ways (row
+and column indices are 0-based):
+\f[\texttt{dst} _{ij} =
+\left\{
+\begin{array}{l l}
+\texttt{src} _{\texttt{src.rows}-i-1,j} & if\;  \texttt{flipCode} = 0 \\
+\texttt{src} _{i, \texttt{src.cols} -j-1} & if\;  \texttt{flipCode} > 0 \\
+\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\
+\end{array}
+\right.\f]
+The example scenarios of using the function are the following:
+*   Vertical flipping of the image (flipCode == 0) to switch between
+    top-left and bottom-left image origin. This is a typical operation
+    in video processing on Microsoft Windows\* OS.
+*   Horizontal flipping of the image with the subsequent horizontal
+    shift and absolute difference calculation to check for a
+    vertical-axis symmetry (flipCode \> 0).
+*   Simultaneous horizontal and vertical flipping of the image with
+    the subsequent shift and absolute difference calculation to check
+    for a central symmetry (flipCode \< 0).
+*   Reversing the order of point arrays (flipCode \> 0 or
+    flipCode == 0).
+Output image must be of the same depth as input one, size should be correct for given flipCode.
+
+@note Function textual ID is "org.opencv.core.transform.flip"
+
+@param src input matrix.
+@param flipCode a flag to specify how to flip the array; 0 means
+flipping around the x-axis and positive value (for example, 1) means
+flipping around y-axis. Negative value (for example, -1) means flipping
+around both axes.
+@sa remap
+*/
+GAPI_EXPORTS GMat flip(const GMat& src, int flipCode);
+
+/** @brief Crops a 2D matrix.
+
+The function crops the matrix by given cv::Rect.
+
+Output matrix must be of the same depth as input one, size is specified by given rect size.
+
+@note Function textual ID is "org.opencv.core.transform.crop"
+
+@param src input matrix.
+@param rect a rect to crop a matrix to
+@sa resize
+*/
+GAPI_EXPORTS GMat crop(const GMat& src, const Rect& rect);
+
+/** @brief Applies horizontal concatenation to given matrices.
+
+The function horizontally concatenates two GMat matrices (with the same number of rows).
+@code{.cpp}
+    GMat A = { 1, 4,
+               2, 5,
+               3, 6 };
+    GMat B = { 7, 10,
+               8, 11,
+               9, 12 };
+
+    GMat C = gapi::concatHor(A, B);
+    //C:
+    //[1, 4, 7, 10;
+    // 2, 5, 8, 11;
+    // 3, 6, 9, 12]
+@endcode
+Output matrix must the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2.
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.imgproc.transform.concatHor"
+
+@param src1 first input matrix to be considered for horizontal concatenation.
+@param src2 second input matrix to be considered for horizontal concatenation.
+@sa concatVert
+*/
+GAPI_EXPORTS GMat concatHor(const GMat& src1, const GMat& src2);
+
+/** @overload
+The function horizontally concatenates given number of GMat matrices (with the same number of columns).
+Output matrix must the same number of columns and depth as the input matrices, and the sum of rows of input matrices.
+
+@param v vector of input matrices to be concatenated horizontally.
+*/
+GAPI_EXPORTS GMat concatHor(const std::vector<GMat> &v);
+
+/** @brief Applies vertical concatenation to given matrices.
+
+The function vertically concatenates two GMat matrices (with the same number of cols).
+ @code{.cpp}
+    GMat A = { 1, 7,
+               2, 8,
+               3, 9 };
+    GMat B = { 4, 10,
+               5, 11,
+               6, 12 };
+
+    GMat C = gapi::concatVert(A, B);
+    //C:
+    //[1, 7;
+    // 2, 8;
+    // 3, 9;
+    // 4, 10;
+    // 5, 11;
+    // 6, 12]
+ @endcode
+
+Output matrix must the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2.
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+
+@note Function textual ID is "org.opencv.imgproc.transform.concatVert"
+
+@param src1 first input matrix to be considered for vertical concatenation.
+@param src2 second input matrix to be considered for vertical concatenation.
+@sa concatHor
+*/
+GAPI_EXPORTS GMat concatVert(const GMat& src1, const GMat& src2);
+
+/** @overload
+The function vertically concatenates given number of GMat matrices (with the same number of columns).
+Output matrix must the same number of columns and depth as the input matrices, and the sum of rows of input matrices.
+
+@param v vector of input matrices to be concatenated vertically.
+*/
+GAPI_EXPORTS GMat concatVert(const std::vector<GMat> &v);
+
+
+/** @brief Performs a look-up table transform of a matrix.
+
+The function LUT fills the output matrix with values from the look-up table. Indices of the entries
+are taken from the input matrix. That is, the function processes each element of src as follows:
+\f[\texttt{dst} (I)  \leftarrow \texttt{lut(src(I))}\f]
+
+Supported matrix data types are @ref CV_8UC1.
+Output is a matrix of the same size and number of channels as src, and the same depth as lut.
+
+@note Function textual ID is "org.opencv.core.transform.LUT"
+
+@param src input matrix of 8-bit elements.
+@param lut look-up table of 256 elements; in case of multi-channel input array, the table should
+either have a single channel (in this case the same table is used for all channels) or the same
+number of channels as in the input matrix.
+*/
+GAPI_EXPORTS GMat LUT(const GMat& src, const Mat& lut);
+
+/** @brief Converts a matrix to another data depth with optional scaling.
+
+The method converts source pixel values to the target data depth. saturate_cast\<\> is applied at
+the end to avoid possible overflows:
+
+\f[m(x,y) = saturate \_ cast<rType>( \alpha (*this)(x,y) +  \beta )\f]
+Output matrix must be of the same size as input one.
+
+@note Function textual ID is "org.opencv.core.transform.convertTo"
+@param src input matrix to be converted from.
+@param rdepth desired output matrix depth or, rather, the depth since the number of channels are the
+same as the input has; if rdepth is negative, the output matrix will have the same depth as the input.
+@param alpha optional scale factor.
+@param beta optional delta added to the scaled values.
+ */
+GAPI_EXPORTS GMat convertTo(const GMat& src, int rdepth, double alpha=1, double beta=0);
+
+/** @brief Normalizes the norm or value range of an array.
+
+The function normalizes scale and shift the input array elements so that
+\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f]
+(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
+\f[\min _I  \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I  \texttt{dst} (I)= \texttt{beta}\f]
+when normType=NORM_MINMAX (for dense arrays only).
+
+@note Function textual ID is "org.opencv.core.normalize"
+
+@param src input array.
+@param alpha norm value to normalize to or the lower range boundary in case of the range
+normalization.
+@param beta upper range boundary in case of the range normalization; it is not used for the norm
+normalization.
+@param norm_type normalization type (see cv::NormTypes).
+@param ddepth when negative, the output array has the same type as src; otherwise, it has the same
+number of channels as src and the depth =ddepth.
+@sa norm, Mat::convertTo
+*/
+GAPI_EXPORTS GMat normalize(const GMat& src, double alpha, double beta,
+                            int norm_type, int ddepth = -1);
+
+/** @brief Applies a perspective transformation to an image.
+
+The function warpPerspective transforms the source image using the specified matrix:
+
+\f[\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
+     \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f]
+
+when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
+and then put in the formula above instead of M. The function cannot operate in-place.
+
+@param src input image.
+@param M \f$3\times 3\f$ transformation matrix.
+@param dsize size of the output image.
+@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the
+optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
+\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
+@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
+@param borderValue value used in case of a constant border; by default, it equals 0.
+
+@sa  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
+ */
+GAPI_EXPORTS GMat warpPerspective(const GMat& src, const Mat& M, const Size& dsize, int flags = cv::INTER_LINEAR,
+                                  int borderMode = cv::BORDER_CONSTANT, const Scalar& borderValue = Scalar());
+
+/** @brief Applies an affine transformation to an image.
+
+The function warpAffine transforms the source image using the specified matrix:
+
+\f[\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\f]
+
+when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted
+with #invertAffineTransform and then put in the formula above instead of M. The function cannot
+operate in-place.
+
+@param src input image.
+@param M \f$2\times 3\f$ transformation matrix.
+@param dsize size of the output image.
+@param flags combination of interpolation methods (see #InterpolationFlags) and the optional
+flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
+\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
+@param borderMode pixel extrapolation method (see #BorderTypes);
+borderMode=#BORDER_TRANSPARENT isn't supported
+@param borderValue value used in case of a constant border; by default, it is 0.
+
+@sa  warpPerspective, resize, remap, getRectSubPix, transform
+ */
+GAPI_EXPORTS GMat warpAffine(const GMat& src, const Mat& M, const Size& dsize, int flags = cv::INTER_LINEAR,
+                             int borderMode = cv::BORDER_CONSTANT, const Scalar& borderValue = Scalar());
+//! @} gapi_transform
+
+/** @brief Finds centers of clusters and groups input samples around the clusters.
+
+The function kmeans implements a k-means algorithm that finds the centers of K clusters
+and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$
+contains a 0-based cluster index for the \f$i^{th}\f$ sample.
+
+@note
+ - Function textual ID is "org.opencv.core.kmeansND"
+ - In case of an N-dimentional points' set given, input GMat can have the following traits:
+2 dimensions, a single row or column if there are N channels,
+or N columns if there is a single channel. Mat should have @ref CV_32F depth.
+ - Although, if GMat with height != 1, width != 1, channels != 1 given as data, n-dimensional
+samples are considered given in amount of A, where A = height, n = width * channels.
+ - In case of GMat given as data:
+     - the output labels are returned as 1-channel GMat with sizes
+width = 1, height = A, where A is samples amount, or width = bestLabels.width,
+height = bestLabels.height if bestLabels given;
+     - the cluster centers are returned as 1-channel GMat with sizes
+width = n, height = K, where n is samples' dimentionality and K is clusters' amount.
+ - As one of possible usages, if you want to control the initial labels for each attempt
+by yourself, you can utilize just the core of the function. To do that, set the number
+of attempts to 1, initialize labels each time using a custom algorithm, pass them with the
+( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best (most-compact) clustering.
+
+@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
+Function can take GArray<Point2f>, GArray<Point3f> for 2D and 3D cases or GMat for any
+dimentionality and channels.
+@param K Number of clusters to split the set by.
+@param bestLabels Optional input integer array that can store the supposed initial cluster indices
+for every sample. Used when ( flags = #KMEANS_USE_INITIAL_LABELS ) flag is set.
+@param criteria The algorithm termination criteria, that is, the maximum number of iterations
+and/or the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of
+the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
+@param attempts Flag to specify the number of times the algorithm is executed using different
+initial labellings. The algorithm returns the labels that yield the best compactness (see the first
+function return value).
+@param flags Flag that can take values of cv::KmeansFlags .
+
+@return
+ - Compactness measure that is computed as
+\f[\sum _i  \| \texttt{samples} _i -  \texttt{centers} _{ \texttt{labels} _i} \| ^2\f]
+after every attempt. The best (minimum) value is chosen and the corresponding labels and the
+compactness value are returned by the function.
+ - Integer array that stores the cluster indices for every sample.
+ - Array of the cluster centers.
+*/
+GAPI_EXPORTS std::tuple<GOpaque<double>,GMat,GMat>
+kmeans(const GMat& data, const int K, const GMat& bestLabels,
+       const TermCriteria& criteria, const int attempts, const KmeansFlags flags);
+
+/** @overload
+@note
+ - Function textual ID is "org.opencv.core.kmeansNDNoInit"
+ - #KMEANS_USE_INITIAL_LABELS flag must not be set while using this overload.
+ */
+GAPI_EXPORTS_W std::tuple<GOpaque<double>,GMat,GMat>
+kmeans(const GMat& data, const int K, const TermCriteria& criteria, const int attempts,
+       const KmeansFlags flags);
+
+/** @overload
+@note Function textual ID is "org.opencv.core.kmeans2D"
+ */
+GAPI_EXPORTS_W std::tuple<GOpaque<double>,GArray<int>,GArray<Point2f>>
+kmeans(const GArray<Point2f>& data, const int K, const GArray<int>& bestLabels,
+       const TermCriteria& criteria, const int attempts, const KmeansFlags flags);
+
+/** @overload
+@note Function textual ID is "org.opencv.core.kmeans3D"
+ */
+GAPI_EXPORTS std::tuple<GOpaque<double>,GArray<int>,GArray<Point3f>>
+kmeans(const GArray<Point3f>& data, const int K, const GArray<int>& bestLabels,
+       const TermCriteria& criteria, const int attempts, const KmeansFlags flags);
+
+
+/** @brief Transposes a matrix.
+
+The function transposes the matrix:
+\f[\texttt{dst} (i,j) =  \texttt{src} (j,i)\f]
+
+@note
+ - Function textual ID is "org.opencv.core.transpose"
+ - No complex conjugation is done in case of a complex matrix. It should be done separately if needed.
+
+@param src input array.
+*/
+GAPI_EXPORTS GMat transpose(const GMat& src);
+
+
+namespace streaming {
+/** @brief Gets dimensions from Mat.
+
+@note Function textual ID is "org.opencv.streaming.size"
+
+@param src Input tensor
+@return Size (tensor dimensions).
+*/
+GAPI_EXPORTS_W GOpaque<Size> size(const GMat& src);
+
+/** @overload
+Gets dimensions from rectangle.
+
+@note Function textual ID is "org.opencv.streaming.sizeR"
+
+@param r Input rectangle.
+@return Size (rectangle dimensions).
+*/
+GAPI_EXPORTS_W GOpaque<Size> size(const GOpaque<Rect>& r);
+
+/** @brief Gets dimensions from MediaFrame.
+
+@note Function textual ID is "org.opencv.streaming.sizeMF"
+
+@param src Input frame
+@return Size (frame dimensions).
+*/
+GAPI_EXPORTS GOpaque<Size> size(const GFrame& src);
+} //namespace streaming
+} //namespace gapi
+} //namespace cv
+
+#endif //OPENCV_GAPI_CORE_HPP
diff --git a/3rdParty/include/opencv2/gapi/cpu/core.hpp b/3rdParty/include/opencv2/gapi/cpu/core.hpp
new file mode 100644
index 0000000..ac08f91
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/cpu/core.hpp
@@ -0,0 +1,27 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_CPU_CORE_API_HPP
+#define OPENCV_GAPI_CPU_CORE_API_HPP
+
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+#include <opencv2/gapi/own/exports.hpp> // GAPI_EXPORTS
+
+namespace cv {
+namespace gapi {
+namespace core {
+namespace cpu {
+
+GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels();
+
+} // namespace cpu
+} // namespace core
+} // namespace gapi
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_CPU_CORE_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/cpu/gcpukernel.hpp b/3rdParty/include/opencv2/gapi/cpu/gcpukernel.hpp
new file mode 100644
index 0000000..48909a8
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/cpu/gcpukernel.hpp
@@ -0,0 +1,533 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GCPUKERNEL_HPP
+#define OPENCV_GAPI_GCPUKERNEL_HPP
+
+#include <functional>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/mat.hpp>
+#include <opencv2/gapi/gcommon.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/gmetaarg.hpp>
+#include <opencv2/gapi/util/compiler_hints.hpp> //suppress_unused_warning
+#include <opencv2/gapi/util/util.hpp>
+
+// FIXME: namespace scheme for backends?
+namespace cv {
+
+namespace gimpl
+{
+    // Forward-declare an internal class
+    class GCPUExecutable;
+} // namespace gimpl
+
+namespace gapi
+{
+/**
+ * @brief This namespace contains G-API CPU backend functions,
+ * structures, and symbols.
+ */
+namespace cpu
+{
+    /**
+     * \addtogroup gapi_std_backends
+     * @{
+     *
+     * @brief G-API backends available in this OpenCV version
+     *
+     * G-API backends play a corner stone role in G-API execution
+     * stack. Every backend is hardware-oriented and thus can run its
+     * kernels efficiently on the target platform.
+     *
+     * Backends are usually "black boxes" for G-API users -- on the API
+     * side, all backends are represented as different objects of the
+     * same class cv::gapi::GBackend.
+     * User can manipulate with backends by specifying which kernels to use.
+     *
+     * @sa @ref gapi_hld
+     */
+
+    /**
+     * @brief Get a reference to CPU (OpenCV) backend.
+     *
+     * This is the default backend in G-API at the moment, providing
+     * broader functional coverage but losing some graph model
+     * advantages. Provided mostly for reference and prototyping
+     * purposes.
+     *
+     * @sa gapi_std_backends
+     */
+    GAPI_EXPORTS cv::gapi::GBackend backend();
+    /** @} */
+
+    class GOCVFunctor;
+
+    //! @cond IGNORED
+    template<typename K, typename Callable>
+    GOCVFunctor ocv_kernel(const Callable& c);
+
+    template<typename K, typename Callable>
+    GOCVFunctor ocv_kernel(Callable& c);
+    //! @endcond
+
+} // namespace cpu
+} // namespace gapi
+
+// Represents arguments which are passed to a wrapped CPU function
+// FIXME: put into detail?
+class GAPI_EXPORTS GCPUContext
+{
+public:
+    // Generic accessor API
+    template<typename T>
+    const T& inArg(int input) { return m_args.at(input).get<T>(); }
+
+    // Syntax sugar
+    const cv::Mat&   inMat(int input);
+    cv::Mat&         outMatR(int output); // FIXME: Avoid cv::Mat m = ctx.outMatR()
+
+    const cv::Scalar& inVal(int input);
+    cv::Scalar& outValR(int output); // FIXME: Avoid cv::Scalar s = ctx.outValR()
+    cv::MediaFrame& outFrame(int output);
+    template<typename T> std::vector<T>& outVecR(int output) // FIXME: the same issue
+    {
+        return outVecRef(output).wref<T>();
+    }
+    template<typename T> T& outOpaqueR(int output) // FIXME: the same issue
+    {
+        return outOpaqueRef(output).wref<T>();
+    }
+
+    GArg state()
+    {
+        return m_state;
+    }
+
+protected:
+    detail::VectorRef& outVecRef(int output);
+    detail::OpaqueRef& outOpaqueRef(int output);
+
+    std::vector<GArg> m_args;
+    GArg m_state;
+
+    //FIXME: avoid conversion of arguments from internal representation to OpenCV one on each call
+    //to OCV kernel. (This can be achieved by a two single time conversions in GCPUExecutable::run,
+    //once on enter for input and output arguments, and once before return for output arguments only
+    std::unordered_map<std::size_t, GRunArgP> m_results;
+
+    friend class gimpl::GCPUExecutable;
+};
+
+class GAPI_EXPORTS GCPUKernel
+{
+public:
+    // This function is a kernel's execution entry point (does the processing work)
+    using RunF = std::function<void(GCPUContext &)>;
+    // This function is a stateful kernel's setup routine (configures state)
+    using SetupF = std::function<void(const GMetaArgs &, const GArgs &,
+                                      GArg &, const GCompileArgs &)>;
+
+    GCPUKernel();
+    GCPUKernel(const RunF& runF, const SetupF& setupF = nullptr);
+
+    RunF m_runF = nullptr;
+    SetupF m_setupF = nullptr;
+
+    bool m_isStateful = false;
+};
+
+// FIXME: This is an ugly ad-hoc implementation. TODO: refactor
+
+namespace detail
+{
+template<class T> struct get_in;
+template<> struct get_in<cv::GMat>
+{
+    static cv::Mat    get(GCPUContext &ctx, int idx) { return ctx.inMat(idx); }
+};
+template<> struct get_in<cv::GMatP>
+{
+    static cv::Mat    get(GCPUContext &ctx, int idx) { return get_in<cv::GMat>::get(ctx, idx); }
+};
+template<> struct get_in<cv::GFrame>
+{
+    static cv::MediaFrame get(GCPUContext &ctx, int idx) { return ctx.inArg<cv::MediaFrame>(idx); }
+};
+template<> struct get_in<cv::GScalar>
+{
+    static cv::Scalar get(GCPUContext &ctx, int idx) { return ctx.inVal(idx); }
+};
+template<typename U> struct get_in<cv::GArray<U> >
+{
+    static const std::vector<U>& get(GCPUContext &ctx, int idx) { return ctx.inArg<VectorRef>(idx).rref<U>(); }
+};
+template<typename U> struct get_in<cv::GOpaque<U> >
+{
+    static const U& get(GCPUContext &ctx, int idx) { return ctx.inArg<OpaqueRef>(idx).rref<U>(); }
+};
+
+//FIXME(dm): GArray<Mat>/GArray<GMat> conversion should be done more gracefully in the system
+template<> struct get_in<cv::GArray<cv::GMat> >: public get_in<cv::GArray<cv::Mat> >
+{
+};
+
+//FIXME(dm): GArray<Scalar>/GArray<GScalar> conversion should be done more gracefully in the system
+template<> struct get_in<cv::GArray<cv::GScalar> >: public get_in<cv::GArray<cv::Scalar> >
+{
+};
+
+// FIXME(dm): GArray<vector<U>>/GArray<GArray<U>> conversion should be done more gracefully in the system
+template<typename U> struct get_in<cv::GArray<cv::GArray<U>> >: public get_in<cv::GArray<std::vector<U>> >
+{
+};
+
+//FIXME(dm): GOpaque<Mat>/GOpaque<GMat> conversion should be done more gracefully in the system
+template<> struct get_in<cv::GOpaque<cv::GMat> >: public get_in<cv::GOpaque<cv::Mat> >
+{
+};
+
+//FIXME(dm): GOpaque<Scalar>/GOpaque<GScalar> conversion should be done more gracefully in the system
+template<> struct get_in<cv::GOpaque<cv::GScalar> >: public get_in<cv::GOpaque<cv::Mat> >
+{
+};
+
+template<class T> struct get_in
+{
+    static T get(GCPUContext &ctx, int idx) { return ctx.inArg<T>(idx); }
+};
+
+struct tracked_cv_mat{
+    tracked_cv_mat(cv::Mat& m) : r{m}, original_data{m.data} {}
+    cv::Mat r;
+    uchar* original_data;
+
+    operator cv::Mat& (){ return r;}
+    void validate() const{
+        if (r.data != original_data)
+        {
+            util::throw_error
+                (std::logic_error
+                 ("OpenCV kernel output parameter was reallocated. \n"
+                  "Incorrect meta data was provided ?"));
+        }
+    }
+};
+
+template<typename... Outputs>
+void postprocess(Outputs&... outs)
+{
+    struct
+    {
+        void operator()(tracked_cv_mat* bm) { bm->validate();  }
+        void operator()(...)                {                  }
+
+    } validate;
+    //dummy array to unfold parameter pack
+    int dummy[] = { 0, (validate(&outs), 0)... };
+    cv::util::suppress_unused_warning(dummy);
+}
+
+template<class T> struct get_out;
+template<> struct get_out<cv::GMat>
+{
+    static tracked_cv_mat get(GCPUContext &ctx, int idx)
+    {
+        auto& r = ctx.outMatR(idx);
+        return {r};
+    }
+};
+template<> struct get_out<cv::GMatP>
+{
+    static tracked_cv_mat get(GCPUContext &ctx, int idx)
+    {
+        return get_out<cv::GMat>::get(ctx, idx);
+    }
+};
+template<> struct get_out<cv::GScalar>
+{
+    static cv::Scalar& get(GCPUContext &ctx, int idx)
+    {
+        return ctx.outValR(idx);
+    }
+};
+template<> struct get_out<cv::GFrame>
+{
+    static cv::MediaFrame& get(GCPUContext &ctx, int idx)
+    {
+        return ctx.outFrame(idx);
+    }
+};
+template<typename U> struct get_out<cv::GArray<U>>
+{
+    static std::vector<U>& get(GCPUContext &ctx, int idx)
+    {
+        return ctx.outVecR<U>(idx);
+    }
+};
+
+//FIXME(dm): GArray<Mat>/GArray<GMat> conversion should be done more gracefully in the system
+template<> struct get_out<cv::GArray<cv::GMat> >: public get_out<cv::GArray<cv::Mat> >
+{
+};
+
+// FIXME(dm): GArray<vector<U>>/GArray<GArray<U>> conversion should be done more gracefully in the system
+template<typename U> struct get_out<cv::GArray<cv::GArray<U>> >: public get_out<cv::GArray<std::vector<U>> >
+{
+};
+
+template<typename U> struct get_out<cv::GOpaque<U>>
+{
+    static U& get(GCPUContext &ctx, int idx)
+    {
+        return ctx.outOpaqueR<U>(idx);
+    }
+};
+
+template<typename, typename>
+struct OCVSetupHelper;
+
+template<typename Impl, typename... Ins>
+struct OCVSetupHelper<Impl, std::tuple<Ins...>>
+{
+    // Using 'auto' return type and 'decltype' specifier in both 'setup_impl' versions
+    // to check existence of required 'Impl::setup' functions.
+    // While 'decltype' specifier accepts expression we pass expression with 'comma-operator'
+    // where first operand of comma-operator is call attempt to desired 'Impl::setup' and
+    // the second operand is 'void()' expression.
+    //
+    // SFINAE for 'Impl::setup' which accepts compile arguments.
+    template<int... IIs>
+    static auto setup_impl(const GMetaArgs &metaArgs, const GArgs &args,
+                           GArg &state, const GCompileArgs &compileArgs,
+                           detail::Seq<IIs...>) ->
+        decltype(Impl::setup(detail::get_in_meta<Ins>(metaArgs, args, IIs)...,
+                             std::declval<typename std::add_lvalue_reference<
+                                              std::shared_ptr<typename Impl::State>
+                                                                            >::type
+                                         >(),
+                            compileArgs)
+                 , void())
+    {
+        // TODO: unique_ptr <-> shared_ptr conversion ?
+        // To check: Conversion is possible only if the state which should be passed to
+        // 'setup' user callback isn't required to have previous value
+        std::shared_ptr<typename Impl::State> stPtr;
+        Impl::setup(detail::get_in_meta<Ins>(metaArgs, args, IIs)..., stPtr, compileArgs);
+        state = GArg(stPtr);
+    }
+
+    // SFINAE for 'Impl::setup' which doesn't accept compile arguments.
+    template<int... IIs>
+    static auto setup_impl(const GMetaArgs &metaArgs, const GArgs &args,
+                           GArg &state, const GCompileArgs &/* compileArgs */,
+                           detail::Seq<IIs...>) ->
+        decltype(Impl::setup(detail::get_in_meta<Ins>(metaArgs, args, IIs)...,
+                             std::declval<typename std::add_lvalue_reference<
+                                              std::shared_ptr<typename Impl::State>
+                                                                            >::type
+                                         >()
+                            )
+                 , void())
+    {
+        // The same comment as in 'setup' above.
+        std::shared_ptr<typename Impl::State> stPtr;
+        Impl::setup(detail::get_in_meta<Ins>(metaArgs, args, IIs)..., stPtr);
+        state = GArg(stPtr);
+    }
+
+    static void setup(const GMetaArgs &metaArgs, const GArgs &args,
+                      GArg& state, const GCompileArgs &compileArgs)
+    {
+        setup_impl(metaArgs, args, state, compileArgs,
+                   typename detail::MkSeq<sizeof...(Ins)>::type());
+    }
+};
+
+// OCVCallHelper is a helper class to call stateless OCV kernels and OCV kernel functors.
+template<typename, typename, typename>
+struct OCVCallHelper;
+
+// FIXME: probably can be simplified with std::apply or analogue.
+template<typename Impl, typename... Ins, typename... Outs>
+struct OCVCallHelper<Impl, std::tuple<Ins...>, std::tuple<Outs...>>
+{
+    template<typename... Inputs>
+    struct call_and_postprocess
+    {
+        template<typename... Outputs>
+        static void call(Inputs&&... ins, Outputs&&... outs)
+        {
+            //not using a std::forward on outs is deliberate in order to
+            //cause compilation error, by trying to bind rvalue references to lvalue references
+            Impl::run(std::forward<Inputs>(ins)..., outs...);
+            postprocess(outs...);
+        }
+
+        template<typename... Outputs>
+        static void call(Impl& impl, Inputs&&... ins, Outputs&&... outs)
+        {
+            impl(std::forward<Inputs>(ins)..., outs...);
+        }
+    };
+
+    template<int... IIs, int... OIs>
+    static void call_impl(GCPUContext &ctx, detail::Seq<IIs...>, detail::Seq<OIs...>)
+    {
+        //Make sure that OpenCV kernels do not reallocate memory for output parameters
+        //by comparing it's state (data ptr) before and after the call.
+        //This is done by converting each output Mat into tracked_cv_mat object, and binding
+        //them to parameters of ad-hoc function
+        call_and_postprocess<decltype(get_in<Ins>::get(ctx, IIs))...>
+            ::call(get_in<Ins>::get(ctx, IIs)..., get_out<Outs>::get(ctx, OIs)...);
+    }
+
+    template<int... IIs, int... OIs>
+    static void call_impl(cv::GCPUContext &ctx, Impl& impl,
+                          detail::Seq<IIs...>, detail::Seq<OIs...>)
+    {
+        call_and_postprocess<decltype(get_in<Ins>::get(ctx, IIs))...>
+            ::call(impl, get_in<Ins>::get(ctx, IIs)..., get_out<Outs>::get(ctx, OIs)...);
+    }
+
+    static void call(GCPUContext &ctx)
+    {
+        call_impl(ctx,
+                  typename detail::MkSeq<sizeof...(Ins)>::type(),
+                  typename detail::MkSeq<sizeof...(Outs)>::type());
+    }
+
+    // NB: Same as call but calling the object
+    // This necessary for kernel implementations that have a state
+    // and are represented as an object
+    static void callFunctor(cv::GCPUContext &ctx, Impl& impl)
+    {
+        call_impl(ctx, impl,
+                  typename detail::MkSeq<sizeof...(Ins)>::type(),
+                  typename detail::MkSeq<sizeof...(Outs)>::type());
+    }
+};
+
+// OCVStCallHelper is a helper class to call stateful OCV kernels.
+template<typename, typename, typename>
+struct OCVStCallHelper;
+
+template<typename Impl, typename... Ins, typename... Outs>
+struct OCVStCallHelper<Impl, std::tuple<Ins...>, std::tuple<Outs...>> :
+    OCVCallHelper<Impl, std::tuple<Ins...>, std::tuple<Outs...>>
+{
+    template<typename... Inputs>
+    struct call_and_postprocess
+    {
+        template<typename... Outputs>
+        static void call(typename Impl::State& st, Inputs&&... ins, Outputs&&... outs)
+        {
+            Impl::run(std::forward<Inputs>(ins)..., outs..., st);
+            postprocess(outs...);
+        }
+    };
+
+    template<int... IIs, int... OIs>
+    static void call_impl(GCPUContext &ctx, detail::Seq<IIs...>, detail::Seq<OIs...>)
+    {
+        auto& st = *ctx.state().get<std::shared_ptr<typename Impl::State>>();
+        call_and_postprocess<decltype(get_in<Ins>::get(ctx, IIs))...>
+            ::call(st, get_in<Ins>::get(ctx, IIs)..., get_out<Outs>::get(ctx, OIs)...);
+    }
+
+    static void call(GCPUContext &ctx)
+    {
+        call_impl(ctx,
+                  typename detail::MkSeq<sizeof...(Ins)>::type(),
+                  typename detail::MkSeq<sizeof...(Outs)>::type());
+    }
+};
+
+} // namespace detail
+
+template<class Impl, class K>
+class GCPUKernelImpl: public cv::detail::KernelTag
+{
+    using CallHelper = cv::detail::OCVCallHelper<Impl, typename K::InArgs, typename K::OutArgs>;
+
+public:
+    using API = K;
+
+    static cv::gapi::GBackend backend() { return cv::gapi::cpu::backend(); }
+    static cv::GCPUKernel      kernel() { return GCPUKernel(&CallHelper::call); }
+};
+
+template<class Impl, class K, class S>
+class GCPUStKernelImpl: public cv::detail::KernelTag
+{
+    using StSetupHelper = detail::OCVSetupHelper<Impl, typename K::InArgs>;
+    using StCallHelper  = detail::OCVStCallHelper<Impl, typename K::InArgs, typename K::OutArgs>;
+
+public:
+    using API = K;
+    using State = S;
+
+    static cv::gapi::GBackend backend() { return cv::gapi::cpu::backend(); }
+    static cv::GCPUKernel     kernel()  { return GCPUKernel(&StCallHelper::call,
+                                                            &StSetupHelper::setup); }
+};
+
+#define GAPI_OCV_KERNEL(Name, API) struct Name: public cv::GCPUKernelImpl<Name, API>
+
+// TODO: Reuse Anatoliy's logic for support of types with commas in macro.
+//       Retrieve the common part from Anatoliy's logic to the separate place.
+#define GAPI_OCV_KERNEL_ST(Name, API, State)                   \
+    struct Name: public cv::GCPUStKernelImpl<Name, API, State> \
+
+/// @private
+class gapi::cpu::GOCVFunctor : public gapi::GFunctor
+{
+public:
+    using Impl = std::function<void(GCPUContext &)>;
+    using Meta = cv::GKernel::M;
+
+    GOCVFunctor(const char* id, const Meta &meta, const Impl& impl)
+        : gapi::GFunctor(id), impl_{GCPUKernel(impl), meta}
+    {
+    }
+
+    GKernelImpl    impl()    const override { return impl_;                }
+    gapi::GBackend backend() const override { return gapi::cpu::backend(); }
+
+private:
+    GKernelImpl impl_;
+};
+
+//! @cond IGNORED
+template<typename K, typename Callable>
+gapi::cpu::GOCVFunctor gapi::cpu::ocv_kernel(Callable& c)
+{
+    using P = cv::detail::OCVCallHelper<Callable, typename K::InArgs, typename K::OutArgs>;
+    return GOCVFunctor{ K::id()
+                      , &K::getOutMeta
+                      , std::bind(&P::callFunctor, std::placeholders::_1, std::ref(c))
+                      };
+}
+
+template<typename K, typename Callable>
+gapi::cpu::GOCVFunctor gapi::cpu::ocv_kernel(const Callable& c)
+{
+    using P = cv::detail::OCVCallHelper<Callable, typename K::InArgs, typename K::OutArgs>;
+    return GOCVFunctor{ K::id()
+                      , &K::getOutMeta
+                      , std::bind(&P::callFunctor, std::placeholders::_1, c)
+                      };
+}
+//! @endcond
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GCPUKERNEL_HPP
diff --git a/3rdParty/include/opencv2/gapi/cpu/imgproc.hpp b/3rdParty/include/opencv2/gapi/cpu/imgproc.hpp
new file mode 100644
index 0000000..0b96db0
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/cpu/imgproc.hpp
@@ -0,0 +1,27 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_CPU_IMGPROC_API_HPP
+#define OPENCV_GAPI_CPU_IMGPROC_API_HPP
+
+#include <opencv2/core/cvdef.h>     // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+namespace imgproc {
+namespace cpu {
+
+GAPI_EXPORTS GKernelPackage kernels();
+
+} // namespace cpu
+} // namespace imgproc
+} // namespace gapi
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_CPU_IMGPROC_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/cpu/stereo.hpp b/3rdParty/include/opencv2/gapi/cpu/stereo.hpp
new file mode 100644
index 0000000..e2a2242
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/cpu/stereo.hpp
@@ -0,0 +1,48 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_CPU_STEREO_API_HPP
+#define OPENCV_GAPI_CPU_STEREO_API_HPP
+
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+namespace calib3d {
+namespace cpu {
+
+GAPI_EXPORTS GKernelPackage kernels();
+
+/** @brief Structure for the Stereo operation initialization parameters.*/
+struct GAPI_EXPORTS StereoInitParam {
+    StereoInitParam(int nD, int bS, double bL, double f):
+        numDisparities(nD), blockSize(bS), baseline(bL), focus(f) {}
+
+    StereoInitParam() = default;
+
+    int numDisparities = 0;
+    int blockSize = 21;
+    double baseline = 63.5;
+    double focus = 3.6;
+};
+
+} // namespace cpu
+} // namespace calib3d
+} // namespace gapi
+
+namespace detail {
+
+    template<> struct CompileArgTag<cv::gapi::calib3d::cpu::StereoInitParam> {
+    static const char* tag() {
+        return "org.opencv.stereoInit";
+    }
+};
+
+} // namespace detail
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_CPU_STEREO_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/cpu/video.hpp b/3rdParty/include/opencv2/gapi/cpu/video.hpp
new file mode 100644
index 0000000..d3c1f2e
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/cpu/video.hpp
@@ -0,0 +1,25 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+#ifndef OPENCV_GAPI_CPU_VIDEO_API_HPP
+#define OPENCV_GAPI_CPU_VIDEO_API_HPP
+
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+namespace video {
+namespace cpu {
+
+GAPI_EXPORTS GKernelPackage kernels();
+
+} // namespace cpu
+} // namespace video
+} // namespace gapi
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_CPU_VIDEO_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/fluid/core.hpp b/3rdParty/include/opencv2/gapi/fluid/core.hpp
new file mode 100644
index 0000000..9eceb82
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/fluid/core.hpp
@@ -0,0 +1,20 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_FLUID_CORE_HPP
+#define OPENCV_GAPI_FLUID_CORE_HPP
+
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+#include <opencv2/gapi/own/exports.hpp> // GAPI_EXPORTS
+
+namespace cv { namespace gapi { namespace core { namespace fluid {
+
+GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels();
+
+}}}}
+
+#endif // OPENCV_GAPI_FLUID_CORE_HPP
diff --git a/3rdParty/include/opencv2/gapi/fluid/gfluidbuffer.hpp b/3rdParty/include/opencv2/gapi/fluid/gfluidbuffer.hpp
new file mode 100644
index 0000000..551f0a3
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/fluid/gfluidbuffer.hpp
@@ -0,0 +1,154 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_FLUID_BUFFER_HPP
+#define OPENCV_GAPI_FLUID_BUFFER_HPP
+
+#include <list>
+#include <numeric> // accumulate
+#include <ostream> // ostream
+#include <cstdint> // uint8_t
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/gmat.hpp>
+
+#include <opencv2/gapi/util/optional.hpp>
+
+namespace cv {
+namespace gapi {
+namespace fluid {
+
+struct Border
+{
+    // This constructor is required to support existing kernels which are part of G-API
+    Border(int _type, cv::Scalar _val) : type(_type), value(_val) {}
+
+    int type;
+    cv::Scalar value;
+};
+
+using BorderOpt = util::optional<Border>;
+
+bool operator == (const Border& b1, const Border& b2);
+
+class GAPI_EXPORTS Buffer;
+
+class GAPI_EXPORTS View
+{
+public:
+    struct Cache
+    {
+        std::vector<const uint8_t*> m_linePtrs;
+        GMatDesc m_desc;
+        int m_border_size = 0;
+
+        inline const uint8_t* linePtr(int index) const
+        {
+            // "out_of_window" check:
+            // user must not request the lines which are outside of specified kernel window
+            GAPI_DbgAssert(index >= -m_border_size
+                        && index <  -m_border_size + static_cast<int>(m_linePtrs.size()));
+            return m_linePtrs[index + m_border_size];
+        }
+    };
+
+    const inline uint8_t* InLineB(int index) const // -(w-1)/2...0...+(w-1)/2 for Filters
+    {
+        return m_cache->linePtr(index);
+    }
+
+    template<typename T> const inline T* InLine(int i) const
+    {
+        const uint8_t* ptr = this->InLineB(i);
+        return reinterpret_cast<const T*>(ptr);
+    }
+
+    inline operator bool() const { return m_priv != nullptr; }
+    bool ready() const;
+    inline int length() const { return m_cache->m_desc.size.width; }
+    int y() const;
+
+    inline const GMatDesc& meta() const { return m_cache->m_desc; }
+
+    class GAPI_EXPORTS Priv;      // internal use only
+    Priv& priv();               // internal use only
+    const Priv& priv() const;   // internal use only
+
+    View();
+    View(std::unique_ptr<Priv>&& p);
+    View(View&& v);
+    View& operator=(View&& v);
+    ~View();
+
+private:
+    std::unique_ptr<Priv> m_priv;
+    const Cache* m_cache = nullptr;
+};
+
+class GAPI_EXPORTS Buffer
+{
+public:
+    struct Cache
+    {
+        std::vector<uint8_t*> m_linePtrs;
+        GMatDesc m_desc;
+    };
+
+    // Default constructor (executable creation stage,
+    // all following initialization performed in Priv::init())
+    Buffer();
+    // Scratch constructor (user kernels)
+    Buffer(const cv::GMatDesc &desc);
+
+    // Constructor for intermediate buffers (for tests)
+    Buffer(const cv::GMatDesc &desc,
+           int max_line_consumption, int border_size,
+           int skew,
+           int wlpi,
+           BorderOpt border);
+    // Constructor for in/out buffers (for tests)
+    Buffer(const cv::Mat &data, bool is_input);
+    ~Buffer();
+    Buffer& operator=(Buffer&&);
+
+    inline uint8_t* OutLineB(int index = 0)
+    {
+        return m_cache->m_linePtrs[index];
+    }
+
+    template<typename T> inline T* OutLine(int index = 0)
+    {
+        uint8_t* ptr = this->OutLineB(index);
+        return reinterpret_cast<T*>(ptr);
+    }
+
+    int y() const;
+
+    int linesReady() const;
+    void debug(std::ostream &os) const;
+    inline int length() const { return m_cache->m_desc.size.width; }
+    int lpi() const;  // LPI for WRITER
+
+    inline const GMatDesc& meta() const { return m_cache->m_desc; }
+
+    View mkView(int borderSize, bool ownStorage);
+    void addView(const View* v);
+
+    class GAPI_EXPORTS Priv;      // internal use only
+    Priv& priv();               // internal use only
+    const Priv& priv() const;   // internal use only
+
+private:
+    std::unique_ptr<Priv> m_priv;
+    const Cache* m_cache;
+};
+
+} // namespace cv::gapi::fluid
+} // namespace cv::gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_FLUID_BUFFER_HPP
diff --git a/3rdParty/include/opencv2/gapi/fluid/gfluidkernel.hpp b/3rdParty/include/opencv2/gapi/fluid/gfluidkernel.hpp
new file mode 100644
index 0000000..53b68c4
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/fluid/gfluidkernel.hpp
@@ -0,0 +1,442 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2019 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_FLUID_KERNEL_HPP
+#define OPENCV_GAPI_FLUID_KERNEL_HPP
+
+#include <vector>
+#include <functional>
+#include <map>
+#include <unordered_map>
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/gcommon.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/garg.hpp>
+
+#include <opencv2/gapi/fluid/gfluidbuffer.hpp>
+
+// FIXME: namespace scheme for backends?
+namespace cv {
+
+namespace gapi
+{
+/**
+ * @brief This namespace contains G-API Fluid backend functions, structures, and symbols.
+ */
+namespace fluid
+{
+    /**
+     * \addtogroup gapi_std_backends G-API Standard Backends
+     * @{
+     */
+    /**
+     * @brief Get a reference to Fluid backend.
+     *
+     * @sa gapi_std_backends
+     */
+    GAPI_EXPORTS cv::gapi::GBackend backend();
+    /** @} */
+} // namespace fluid
+} // namespace gapi
+
+
+class GAPI_EXPORTS GFluidKernel
+{
+public:
+    enum class Kind
+    {
+        Filter,
+        Resize,
+        YUV420toRGB //Color conversion of 4:2:0 chroma sub-sampling formats (NV12, I420 ..etc) to RGB
+    };
+
+    // This function is a generic "doWork" callback
+    using F = std::function<void(const cv::GArgs&, const std::vector<gapi::fluid::Buffer*> &)>;
+
+    // This function is a generic "initScratch" callback
+    using IS = std::function<void(const cv::GMetaArgs &, const cv::GArgs&, gapi::fluid::Buffer &)>;
+
+    // This function is a generic "resetScratch" callback
+    using RS = std::function<void(gapi::fluid::Buffer &)>;
+
+    // This function describes kernel metadata inference rule.
+    using M = std::function<GMetaArgs(const GMetaArgs &, const GArgs &)>;
+
+    // This function is a generic "getBorder" callback (extracts border-related data from kernel's input parameters)
+    using B = std::function<gapi::fluid::BorderOpt(const GMetaArgs&, const GArgs&)>;
+
+    // This function is a generic "getWindow" callback (extracts window-related data from kernel's input parameters)
+    using GW = std::function<int(const GMetaArgs&, const GArgs&)>;
+
+    // FIXME: move implementations out of header file
+    GFluidKernel() {}
+    GFluidKernel(Kind k, int l, bool scratch, const F& f, const IS &is, const RS &rs, const B& b, const GW& win)
+        : m_kind(k)
+        , m_lpi(l)
+        , m_scratch(scratch)
+        , m_f(f)
+        , m_is(is)
+        , m_rs(rs)
+        , m_b(b)
+        , m_gw(win) {}
+
+    Kind m_kind;
+    const int  m_lpi     = -1;
+    const bool m_scratch = false;
+
+    const F    m_f;
+    const IS   m_is;
+    const RS   m_rs;
+    const B    m_b;
+    const GW   m_gw;
+};
+
+// FIXME!!!
+// This is the temporary and experimental API
+// which should be replaced by runtime roi-based scheduling
+/** \addtogroup gapi_compile_args
+ * @{
+ */
+/**
+ * @brief This structure allows to control the output image region
+ * which Fluid backend will produce in the graph.
+ *
+ * This feature is useful for external tiling and parallelism, but
+ * will be deprecated in the future releases.
+ */
+struct GFluidOutputRois
+{
+    std::vector<cv::Rect> rois;
+};
+
+/**
+ * @brief This structure forces Fluid backend to generate multiple
+ * parallel output regions in the graph. These regions execute in parallel.
+ *
+ * This feature may be deprecated in the future releases.
+ */
+struct GFluidParallelOutputRois
+{
+    std::vector<GFluidOutputRois> parallel_rois;
+};
+
+/**
+ * @brief This structure allows to customize the way how Fluid executes
+ * parallel regions.
+ *
+ * For example, user can utilize his own threading runtime via this parameter.
+ * The `parallel_for` member functor is called by the Fluid runtime with the
+ * following arguments:
+ *
+ * @param size Size of the parallel range to process
+ * @param f A function which should be called for every integer index
+ *   in this range by the specified parallel_for implementation.
+ *
+ * This feature may be deprecated in the future releases.
+ */
+struct GFluidParallelFor
+{
+    //this function accepts:
+    // - size of the "parallel" range as the first argument
+    // - and a function to be called on the range items, designated by item index
+    std::function<void(std::size_t size, std::function<void(std::size_t index)>)> parallel_for;
+};
+/** @} gapi_compile_args */
+
+namespace detail
+{
+template<> struct CompileArgTag<GFluidOutputRois>
+{
+    static const char* tag() { return "gapi.fluid.outputRois"; }
+};
+
+template<> struct CompileArgTag<GFluidParallelFor>
+{
+    static const char* tag() { return "gapi.fluid.parallelFor"; }
+};
+
+template<> struct CompileArgTag<GFluidParallelOutputRois>
+{
+    static const char* tag() { return "gapi.fluid.parallelOutputRois"; }
+};
+
+} // namespace detail
+
+namespace detail
+{
+template<class T> struct fluid_get_in;
+template<> struct fluid_get_in<cv::GMat>
+{
+    static const cv::gapi::fluid::View& get(const cv::GArgs &in_args, int idx)
+    {
+        return *in_args[idx].unsafe_get<cv::gapi::fluid::View*>();
+    }
+};
+
+template<> struct fluid_get_in<cv::GScalar>
+{
+    // FIXME: change to return by reference when moved to own::Scalar
+    static const cv::Scalar get(const cv::GArgs &in_args, int idx)
+    {
+        return in_args[idx].unsafe_get<cv::Scalar>();
+    }
+};
+
+template<typename U> struct fluid_get_in<cv::GArray<U>>
+{
+    static const std::vector<U>& get(const cv::GArgs &in_args, int idx)
+    {
+        return in_args.at(idx).unsafe_get<cv::detail::VectorRef>().rref<U>();
+    }
+};
+
+template<typename U> struct fluid_get_in<cv::GOpaque<U>>
+{
+    static const U& get(const cv::GArgs &in_args, int idx)
+    {
+        return in_args.at(idx).unsafe_get<cv::detail::OpaqueRef>().rref<U>();
+    }
+};
+
+template<class T> struct fluid_get_in
+{
+    static const T& get(const cv::GArgs &in_args, int idx)
+    {
+        return in_args[idx].unsafe_get<T>();
+    }
+};
+
+template<bool, typename Impl, typename... Ins>
+struct scratch_helper;
+
+template<typename Impl, typename... Ins>
+struct scratch_helper<true, Impl, Ins...>
+{
+    // Init
+    template<int... IIs>
+    static void help_init_impl(const cv::GMetaArgs &metas,
+                               const cv::GArgs     &in_args,
+                               gapi::fluid::Buffer &scratch_buf,
+                               detail::Seq<IIs...>)
+    {
+        Impl::initScratch(get_in_meta<Ins>(metas, in_args, IIs)..., scratch_buf);
+    }
+
+    static void help_init(const cv::GMetaArgs &metas,
+                          const cv::GArgs     &in_args,
+                          gapi::fluid::Buffer &b)
+    {
+        help_init_impl(metas, in_args, b, typename detail::MkSeq<sizeof...(Ins)>::type());
+    }
+
+    // Reset
+    static void help_reset(gapi::fluid::Buffer &b)
+    {
+        Impl::resetScratch(b);
+    }
+};
+
+template<typename Impl, typename... Ins>
+struct scratch_helper<false, Impl, Ins...>
+{
+    static void help_init(const cv::GMetaArgs &,
+                          const cv::GArgs     &,
+                          gapi::fluid::Buffer &)
+    {
+        GAPI_Assert(false);
+    }
+    static void help_reset(gapi::fluid::Buffer &)
+    {
+        GAPI_Assert(false);
+    }
+};
+
+template<typename T> struct is_gmat_type
+{
+    static const constexpr bool value = std::is_same<cv::GMat, T>::value;
+};
+
+template<bool CallCustomGetBorder, typename Impl, typename... Ins>
+struct get_border_helper;
+
+template<typename Impl, typename... Ins>
+struct get_border_helper<true, Impl, Ins...>
+{
+    template<int... IIs>
+    static gapi::fluid::BorderOpt get_border_impl(const GMetaArgs &metas,
+                                                  const cv::GArgs &in_args,
+                                                  cv::detail::Seq<IIs...>)
+    {
+        return util::make_optional(Impl::getBorder(cv::detail::get_in_meta<Ins>(metas, in_args, IIs)...));
+    }
+
+    static gapi::fluid::BorderOpt help(const GMetaArgs &metas,
+                                       const cv::GArgs &in_args)
+    {
+        return get_border_impl(metas, in_args, typename detail::MkSeq<sizeof...(Ins)>::type());
+    }
+};
+
+template<typename Impl, typename... Ins>
+struct get_border_helper<false, Impl, Ins...>
+{
+    static gapi::fluid::BorderOpt help(const cv::GMetaArgs &,
+                                       const cv::GArgs     &)
+    {
+        return {};
+    }
+};
+
+template<bool CallCustomGetWindow, typename, typename... Ins>
+struct get_window_helper;
+
+template<typename Impl, typename... Ins>
+struct get_window_helper<true, Impl, Ins...>
+{
+    template<int... IIs>
+    static int get_window_impl(const GMetaArgs &metas,
+                               const cv::GArgs &in_args,
+                               cv::detail::Seq<IIs...>)
+    {
+        return Impl::getWindow(cv::detail::get_in_meta<Ins>(metas, in_args, IIs)...);
+    }
+
+    static int help(const GMetaArgs &metas, const cv::GArgs &in_args)
+    {
+        return get_window_impl(metas, in_args, typename detail::MkSeq<sizeof...(Ins)>::type());
+    }
+};
+
+template<typename Impl, typename... Ins>
+struct get_window_helper<false, Impl, Ins...>
+{
+    static int help(const cv::GMetaArgs &,
+                    const cv::GArgs     &)
+    {
+        return Impl::Window;
+    }
+};
+
+template<typename C, typename T>
+struct has_Window
+{
+private:
+    template<class U>
+    static constexpr auto Check(U*) -> typename std::is_same<decltype(U::Window), T>::type;
+
+    template<typename>
+    static constexpr std::false_type Check(...);
+
+    typedef decltype(Check<C>(0)) Result;
+
+public:
+    static constexpr bool value = Result::value;
+};
+
+template<bool hasWindow, typename Impl>
+struct callCustomGetBorder;
+
+template<typename Impl>
+struct callCustomGetBorder<true, Impl>
+{
+    static constexpr bool value = (Impl::Window != 1);
+};
+
+template<typename Impl>
+struct callCustomGetBorder<false, Impl>
+{
+    static constexpr bool value = true;
+};
+
+template<typename, typename, typename, bool UseScratch>
+struct FluidCallHelper;
+
+template<typename Impl, typename... Ins, typename... Outs, bool UseScratch>
+struct FluidCallHelper<Impl, std::tuple<Ins...>, std::tuple<Outs...>, UseScratch>
+{
+    static_assert(all_satisfy<is_gmat_type, Outs...>::value, "return type must be GMat");
+    static_assert(contains<GMat, Ins...>::value, "input must contain at least one GMat");
+
+    // Execution dispatcher ////////////////////////////////////////////////////
+    template<int... IIs, int... OIs>
+    static void call_impl(const cv::GArgs &in_args,
+                          const std::vector<gapi::fluid::Buffer*> &out_bufs,
+                          detail::Seq<IIs...>,
+                          detail::Seq<OIs...>)
+    {
+        Impl::run(fluid_get_in<Ins>::get(in_args, IIs)..., *out_bufs[OIs]...);
+    }
+
+    static void call(const cv::GArgs &in_args,
+                     const std::vector<gapi::fluid::Buffer*> &out_bufs)
+    {
+        constexpr int numOuts = (sizeof...(Outs)) + (UseScratch ? 1 : 0);
+        call_impl(in_args, out_bufs,
+                  typename detail::MkSeq<sizeof...(Ins)>::type(),
+                  typename detail::MkSeq<numOuts>::type());
+    }
+
+    // Scratch buffer initialization dispatcher ////////////////////////////////
+    static void init_scratch(const GMetaArgs &metas,
+                             const cv::GArgs &in_args,
+                             gapi::fluid::Buffer &b)
+    {
+        scratch_helper<UseScratch, Impl, Ins...>::help_init(metas, in_args, b);
+    }
+
+    // Scratch buffer reset dispatcher /////////////////////////////////////////
+    static void reset_scratch(gapi::fluid::Buffer &scratch_buf)
+    {
+        scratch_helper<UseScratch, Impl, Ins...>::help_reset(scratch_buf);
+    }
+
+    static gapi::fluid::BorderOpt getBorder(const GMetaArgs &metas, const cv::GArgs &in_args)
+    {
+        constexpr bool hasWindow = has_Window<Impl, const int>::value;
+
+        // User must provide "init" callback if Window != 1
+        // TODO: move to constexpr if when we enable C++17
+        return get_border_helper<callCustomGetBorder<hasWindow, Impl>::value, Impl, Ins...>::help(metas, in_args);
+    }
+
+    static int getWindow(const GMetaArgs &metas, const cv::GArgs &in_args)
+    {
+        constexpr bool callCustomGetWindow = !(has_Window<Impl, const int>::value);
+        return get_window_helper<callCustomGetWindow, Impl, Ins...>::help(metas, in_args);
+    }
+};
+} // namespace detail
+
+
+template<class Impl, class K, bool UseScratch>
+class GFluidKernelImpl : public cv::detail::KernelTag
+{
+    static const int LPI = 1;
+    static const auto Kind = GFluidKernel::Kind::Filter;
+    using P = detail::FluidCallHelper<Impl, typename K::InArgs, typename K::OutArgs, UseScratch>;
+
+public:
+    using API = K;
+
+    static GFluidKernel kernel()
+    {
+        // FIXME: call() and getOutMeta() needs to be renamed so it is clear these
+        // functions are internal wrappers, not user API
+        return GFluidKernel(Impl::Kind, Impl::LPI,
+                            UseScratch,
+                            &P::call, &P::init_scratch, &P::reset_scratch, &P::getBorder, &P::getWindow);
+    }
+
+    static cv::gapi::GBackend backend() { return cv::gapi::fluid::backend(); }
+};
+
+#define GAPI_FLUID_KERNEL(Name, API, Scratch) struct Name: public cv::GFluidKernelImpl<Name, API, Scratch>
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GCPUKERNEL_HPP
diff --git a/3rdParty/include/opencv2/gapi/fluid/imgproc.hpp b/3rdParty/include/opencv2/gapi/fluid/imgproc.hpp
new file mode 100644
index 0000000..dedfa9d
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/fluid/imgproc.hpp
@@ -0,0 +1,20 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_FLUID_IMGPROC_HPP
+#define OPENCV_GAPI_FLUID_IMGPROC_HPP
+
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+#include <opencv2/gapi/own/exports.hpp> // GAPI_EXPORTS
+
+namespace cv { namespace gapi { namespace imgproc { namespace fluid {
+
+GAPI_EXPORTS GKernelPackage kernels();
+
+}}}}
+
+#endif // OPENCV_GAPI_FLUID_IMGPROC_HPP
diff --git a/3rdParty/include/opencv2/gapi/garg.hpp b/3rdParty/include/opencv2/gapi/garg.hpp
new file mode 100644
index 0000000..bfe147f
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/garg.hpp
@@ -0,0 +1,309 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2021 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GARG_HPP
+#define OPENCV_GAPI_GARG_HPP
+
+#include <vector>
+#include <unordered_map>
+#include <type_traits>
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/own/mat.hpp>
+#include <opencv2/gapi/media.hpp>
+
+#include <opencv2/gapi/util/util.hpp>
+#include <opencv2/gapi/util/any.hpp>
+#include <opencv2/gapi/util/variant.hpp>
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+#include <opencv2/gapi/garray.hpp>
+#include <opencv2/gapi/gopaque.hpp>
+#include <opencv2/gapi/gframe.hpp>
+#include <opencv2/gapi/gtype_traits.hpp>
+#include <opencv2/gapi/gmetaarg.hpp>
+#include <opencv2/gapi/streaming/source.hpp>
+#include <opencv2/gapi/rmat.hpp>
+
+namespace cv {
+
+class GArg;
+
+namespace detail {
+    template<typename T>
+    using is_garg = std::is_same<GArg, typename std::decay<T>::type>;
+}
+
+// Parameter holder class for a node
+// Depending on platform capabilities, can either support arbitrary types
+// (as `boost::any`) or a limited number of types (as `boot::variant`).
+// FIXME: put into "details" as a user shouldn't use it in his code
+class GAPI_EXPORTS GArg
+{
+public:
+    GArg() {}
+
+    template<typename T, typename std::enable_if<!detail::is_garg<T>::value, int>::type = 0>
+    explicit GArg(const T &t)
+        : kind(detail::GTypeTraits<T>::kind)
+        , opaque_kind(detail::GOpaqueTraits<T>::kind)
+        , value(detail::wrap_gapi_helper<T>::wrap(t))
+    {
+    }
+
+    template<typename T, typename std::enable_if<!detail::is_garg<T>::value, int>::type = 0>
+    explicit GArg(T &&t)
+        : kind(detail::GTypeTraits<typename std::decay<T>::type>::kind)
+        , opaque_kind(detail::GOpaqueTraits<typename std::decay<T>::type>::kind)
+        , value(detail::wrap_gapi_helper<T>::wrap(t))
+    {
+    }
+
+    template<typename T> inline T& get()
+    {
+        return util::any_cast<typename std::remove_reference<T>::type>(value);
+    }
+
+    template<typename T> inline const T& get() const
+    {
+        return util::any_cast<typename std::remove_reference<T>::type>(value);
+    }
+
+    template<typename T> inline T& unsafe_get()
+    {
+        return util::unsafe_any_cast<typename std::remove_reference<T>::type>(value);
+    }
+
+    template<typename T> inline const T& unsafe_get() const
+    {
+        return util::unsafe_any_cast<typename std::remove_reference<T>::type>(value);
+    }
+
+    detail::ArgKind kind = detail::ArgKind::OPAQUE_VAL;
+    detail::OpaqueKind opaque_kind = detail::OpaqueKind::CV_UNKNOWN;
+
+protected:
+    util::any value;
+};
+
+using GArgs = std::vector<GArg>;
+
+// FIXME: Express as M<GProtoArg...>::type
+// FIXME: Move to a separate file!
+using GRunArgBase  = util::variant<
+#if !defined(GAPI_STANDALONE)
+    cv::UMat,
+#endif // !defined(GAPI_STANDALONE)
+    cv::RMat,
+    cv::gapi::wip::IStreamSource::Ptr,
+    cv::Mat,
+    cv::Scalar,
+    cv::detail::VectorRef,
+    cv::detail::OpaqueRef,
+    cv::MediaFrame
+    >;
+
+namespace detail {
+template<typename,typename>
+struct in_variant;
+
+template<typename T, typename... Types>
+struct in_variant<T, util::variant<Types...> >
+    : std::integral_constant<bool, cv::detail::contains<T, Types...>::value > {
+};
+} // namespace detail
+
+struct GAPI_EXPORTS GRunArg: public GRunArgBase
+{
+    // Metadata information here
+    using Meta = std::unordered_map<std::string, util::any>;
+    Meta meta;
+
+    // Mimic the old GRunArg semantics here, old of the times when
+    // GRunArg was an alias to variant<>
+    GRunArg();
+    GRunArg(const cv::GRunArg &arg);
+    GRunArg(cv::GRunArg &&arg);
+
+    GRunArg& operator= (const GRunArg &arg);
+    GRunArg& operator= (GRunArg &&arg);
+
+    template <typename T>
+    GRunArg(const T &t,
+            const Meta &m = Meta{},
+            typename std::enable_if< detail::in_variant<T, GRunArgBase>::value, int>::type = 0)
+        : GRunArgBase(t)
+        , meta(m)
+    {
+    }
+    template <typename T>
+    GRunArg(T &&t,
+            const Meta &m = Meta{},
+            typename std::enable_if< detail::in_variant<T, GRunArgBase>::value, int>::type = 0)
+        : GRunArgBase(std::move(t))
+        , meta(m)
+    {
+    }
+    template <typename T> auto operator= (const T &t)
+        -> typename std::enable_if< detail::in_variant<T, GRunArgBase>::value, cv::GRunArg>::type&
+    {
+        GRunArgBase::operator=(t);
+        return *this;
+    }
+    template <typename T> auto operator= (T&& t)
+        -> typename std::enable_if< detail::in_variant<T, GRunArgBase>::value, cv::GRunArg>::type&
+    {
+        GRunArgBase::operator=(std::move(t));
+        return *this;
+    }
+};
+using GRunArgs = std::vector<GRunArg>;
+
+// TODO: Think about the addition operator
+/**
+ * @brief This operator allows to complement the input vector at runtime.
+ *
+ * It's an ordinary overload of addition assignment operator.
+ *
+ * Example of usage:
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/dynamic_graph_snippets.cpp GRunArgs usage
+ *
+ */
+inline GRunArgs& operator += (GRunArgs &lhs, const GRunArgs &rhs)
+{
+    lhs.reserve(lhs.size() + rhs.size());
+    lhs.insert(lhs.end(), rhs.begin(), rhs.end());
+    return lhs;
+}
+
+namespace gapi
+{
+namespace wip
+{
+/**
+ * @brief This aggregate type represents all types which G-API can
+ * handle (via variant).
+ *
+ * It only exists to overcome C++ language limitations (where a
+ * `using`-defined class can't be forward-declared).
+ */
+struct GAPI_EXPORTS Data: public GRunArg
+{
+    using GRunArg::GRunArg;
+    template <typename T>
+    Data& operator= (const T& t) { GRunArg::operator=(t); return *this; }
+    template <typename T>
+    Data& operator= (T&& t) { GRunArg::operator=(std::move(t)); return *this; }
+};
+} // namespace wip
+} // namespace gapi
+
+using GRunArgP = util::variant<
+#if !defined(GAPI_STANDALONE)
+    cv::UMat*,
+#endif // !defined(GAPI_STANDALONE)
+    cv::Mat*,
+    cv::RMat*,
+    cv::Scalar*,
+    cv::MediaFrame*,
+    cv::detail::VectorRef,
+    cv::detail::OpaqueRef
+    >;
+using GRunArgsP = std::vector<GRunArgP>;
+
+// TODO: Think about the addition operator
+/**
+ * @brief This operator allows to complement the output vector at runtime.
+ *
+ * It's an ordinary overload of addition assignment operator.
+ *
+ * Example of usage:
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/dynamic_graph_snippets.cpp GRunArgsP usage
+ *
+ */
+inline GRunArgsP& operator += (GRunArgsP &lhs, const GRunArgsP &rhs)
+{
+    lhs.reserve(lhs.size() + rhs.size());
+    lhs.insert(lhs.end(), rhs.begin(), rhs.end());
+    return lhs;
+}
+
+namespace gapi
+{
+/**
+ * \addtogroup gapi_serialization
+ * @{
+ *
+ * @brief G-API functions and classes for serialization and deserialization.
+ */
+/** @brief Wraps deserialized output GRunArgs to GRunArgsP which can be used by GCompiled.
+ *
+ * Since it's impossible to get modifiable output arguments from deserialization
+ * it needs to be wrapped by this function.
+ *
+ * Example of usage:
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp bind after deserialization
+ *
+ * @param out_args deserialized GRunArgs.
+ * @return the same GRunArgs wrapped in GRunArgsP.
+ * @see deserialize
+ */
+GAPI_EXPORTS cv::GRunArgsP bind(cv::GRunArgs &out_args);
+/** @brief Wraps output GRunArgsP available during graph execution to GRunArgs which can be serialized.
+ *
+ * GRunArgsP is pointer-to-value, so to be serialized they need to be binded to real values
+ * which this function does.
+ *
+ * Example of usage:
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp bind before serialization
+ *
+ * @param out output GRunArgsP available during graph execution.
+ * @return the same GRunArgsP wrapped in serializable GRunArgs.
+ * @see serialize
+ */
+GAPI_EXPORTS cv::GRunArg   bind(cv::GRunArgP &out);     // FIXME: think more about it
+/** @} */
+}
+
+template<typename... Ts> inline GRunArgs gin(const Ts&... args)
+{
+    return GRunArgs{ GRunArg(detail::wrap_host_helper<Ts>::wrap_in(args))... };
+}
+
+template<typename... Ts> inline GRunArgsP gout(Ts&... args)
+{
+    return GRunArgsP{ GRunArgP(detail::wrap_host_helper<Ts>::wrap_out(args))... };
+}
+
+struct GTypeInfo;
+using GTypesInfo = std::vector<GTypeInfo>;
+
+// FIXME: Needed for python bridge, must be moved to more appropriate header
+namespace detail {
+struct ExtractArgsCallback
+{
+    cv::GRunArgs operator()(const cv::GTypesInfo& info) const { return c(info); }
+    using CallBackT = std::function<cv::GRunArgs(const cv::GTypesInfo& info)>;
+    CallBackT c;
+};
+
+struct ExtractMetaCallback
+{
+    cv::GMetaArgs operator()(const cv::GTypesInfo& info) const { return c(info); }
+    using CallBackT = std::function<cv::GMetaArgs(const cv::GTypesInfo& info)>;
+    CallBackT c;
+};
+
+void constructGraphOutputs(const cv::GTypesInfo &out_info,
+                           cv::GRunArgs         &args,
+                           cv::GRunArgsP        &outs);
+} // namespace detail
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GARG_HPP
diff --git a/3rdParty/include/opencv2/gapi/garray.hpp b/3rdParty/include/opencv2/gapi/garray.hpp
new file mode 100644
index 0000000..17b0333
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/garray.hpp
@@ -0,0 +1,440 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GARRAY_HPP
+#define OPENCV_GAPI_GARRAY_HPP
+
+#include <functional>
+#include <ostream>
+#include <vector>
+#include <memory>
+
+#include <opencv2/gapi/own/exports.hpp>
+#include <opencv2/gapi/opencv_includes.hpp>
+
+#include <opencv2/gapi/util/variant.hpp>
+#include <opencv2/gapi/util/throw.hpp>
+#include <opencv2/gapi/own/assert.hpp>
+
+#include <opencv2/gapi/gmat.hpp>    // flatten_g only!
+#include <opencv2/gapi/gscalar.hpp> // flatten_g only!
+
+namespace cv
+{
+// Forward declaration; GNode and GOrigin are an internal
+// (user-inaccessible) classes.
+class GNode;
+struct GOrigin;
+template<typename T> class GArray;
+
+/**
+ * \addtogroup gapi_meta_args
+ * @{
+ */
+struct GAPI_EXPORTS_W_SIMPLE GArrayDesc
+{
+    // FIXME: Body
+    // FIXME: Also implement proper operator== then
+    bool operator== (const GArrayDesc&) const { return true; }
+};
+template<typename U> GArrayDesc descr_of(const std::vector<U> &) { return {};}
+GAPI_EXPORTS_W inline GArrayDesc empty_array_desc() {return {}; }
+/** @} */
+
+std::ostream& operator<<(std::ostream& os, const cv::GArrayDesc &desc);
+
+namespace detail
+{
+    // ConstructVec is a callback which stores information about T and is used by
+    // G-API runtime to construct arrays in host memory (T remains opaque for G-API).
+    // ConstructVec is carried into G-API internals by GArrayU.
+    // Currently it is suitable for Host (CPU) plugins only, real offload may require
+    // more information for manual memory allocation on-device.
+    class VectorRef;
+    using ConstructVec = std::function<void(VectorRef&)>;
+
+    // This is the base struct for GArrayU type holder
+    struct TypeHintBase{virtual ~TypeHintBase() = default;};
+
+    // This class holds type of initial GArray to be checked from GArrayU
+    template <typename T>
+    struct TypeHint final : public TypeHintBase{};
+
+    // This class strips type information from GArray<T> and makes it usable
+    // in the G-API graph compiler (expression unrolling, graph generation, etc).
+    // Part of GProtoArg.
+    class GAPI_EXPORTS GArrayU
+    {
+    public:
+        GArrayU(const GNode &n, std::size_t out); // Operation result constructor
+
+        template <typename T>
+        bool holds() const;                       // Check if was created from GArray<T>
+
+        GOrigin& priv();                          // Internal use only
+        const GOrigin& priv() const;              // Internal use only
+
+    protected:
+        GArrayU();                                // Default constructor
+        GArrayU(const detail::VectorRef& vref);   // Constant value constructor
+        template<class> friend class cv::GArray;  //  (available to GArray<T> only)
+
+        void setConstructFcn(ConstructVec &&cv);  // Store T-aware constructor
+
+        template <typename T>
+        void specifyType();                       // Store type of initial GArray<T>
+
+        template <typename T>
+        void storeKind();
+
+        void setKind(cv::detail::OpaqueKind);
+
+        std::shared_ptr<GOrigin> m_priv;
+        std::shared_ptr<TypeHintBase> m_hint;
+    };
+
+    template <typename T>
+    bool GArrayU::holds() const{
+        GAPI_Assert(m_hint != nullptr);
+        using U = typename std::decay<T>::type;
+        return dynamic_cast<TypeHint<U>*>(m_hint.get()) != nullptr;
+    };
+
+    template <typename T>
+    void GArrayU::specifyType(){
+        m_hint.reset(new TypeHint<typename std::decay<T>::type>);
+    };
+
+    template <typename T>
+    void GArrayU::storeKind(){
+        setKind(cv::detail::GOpaqueTraits<T>::kind);
+    };
+
+    // This class represents a typed STL vector reference.
+    // Depending on origins, this reference may be either "just a" reference to
+    // an object created externally, OR actually own the underlying object
+    // (be value holder).
+    class BasicVectorRef
+    {
+    public:
+        // These fields are set by the derived class(es)
+        std::size_t    m_elemSize = 0ul;
+        cv::GArrayDesc m_desc;
+        virtual ~BasicVectorRef() {}
+
+        virtual void mov(BasicVectorRef &ref) = 0;
+        virtual const void* ptr() const = 0;
+        virtual std::size_t size() const = 0;
+    };
+
+    template<typename T> class VectorRefT final: public BasicVectorRef
+    {
+        using empty_t  = util::monostate;
+        using ro_ext_t = const std::vector<T> *;
+        using rw_ext_t =       std::vector<T> *;
+        using rw_own_t =       std::vector<T>  ;
+        util::variant<empty_t, ro_ext_t, rw_ext_t, rw_own_t> m_ref;
+
+        inline bool isEmpty() const { return util::holds_alternative<empty_t>(m_ref);  }
+        inline bool isROExt() const { return util::holds_alternative<ro_ext_t>(m_ref); }
+        inline bool isRWExt() const { return util::holds_alternative<rw_ext_t>(m_ref); }
+        inline bool isRWOwn() const { return util::holds_alternative<rw_own_t>(m_ref); }
+
+        void init(const std::vector<T>* vec = nullptr)
+        {
+            m_elemSize = sizeof(T);
+            if (vec) m_desc = cv::descr_of(*vec);
+        }
+
+    public:
+        VectorRefT() { init(); }
+        virtual ~VectorRefT() {}
+
+        explicit VectorRefT(const std::vector<T>& vec) : m_ref(&vec)      { init(&vec); }
+        explicit VectorRefT(std::vector<T>& vec)  : m_ref(&vec)           { init(&vec); }
+        explicit VectorRefT(std::vector<T>&& vec) : m_ref(std::move(vec)) { init(&vec); }
+
+        // Reset a VectorRefT. Called only for objects instantiated
+        // internally in G-API (e.g. temporary GArray<T>'s within a
+        // computation).  Reset here means both initialization
+        // (creating an object) and reset (discarding its existing
+        // content before the next execution).  Must never be called
+        // for external VectorRefTs.
+        void reset()
+        {
+            if (isEmpty())
+            {
+                std::vector<T> empty_vector;
+                m_desc = cv::descr_of(empty_vector);
+                m_ref  = std::move(empty_vector);
+                GAPI_Assert(isRWOwn());
+            }
+            else if (isRWOwn())
+            {
+                util::get<rw_own_t>(m_ref).clear();
+            }
+            else GAPI_Assert(false); // shouldn't be called in *EXT modes
+        }
+
+        // Obtain a WRITE reference to underlying object
+        // Used by CPU kernel API wrappers when a kernel execution frame
+        // is created
+        std::vector<T>& wref()
+        {
+            GAPI_Assert(isRWExt() || isRWOwn());
+            if (isRWExt()) return *util::get<rw_ext_t>(m_ref);
+            if (isRWOwn()) return  util::get<rw_own_t>(m_ref);
+            util::throw_error(std::logic_error("Impossible happened"));
+        }
+
+        // Obtain a READ reference to underlying object
+        // Used by CPU kernel API wrappers when a kernel execution frame
+        // is created
+        const std::vector<T>& rref() const
+        {
+            // ANY vector can be accessed for reading, even if it declared for
+            // output. Example -- a GComputation from [in] to [out1,out2]
+            // where [out2] is a result of operation applied to [out1]:
+            //
+            //            GComputation boundary
+            //            . . . . . . .
+            //            .           .
+            //     [in] ----> foo() ----> [out1]
+            //            .           .    :
+            //            .           . . .:. . .
+            //            .                V    .
+            //            .              bar() ---> [out2]
+            //            . . . . . . . . . . . .
+            //
+            if (isROExt()) return *util::get<ro_ext_t>(m_ref);
+            if (isRWExt()) return *util::get<rw_ext_t>(m_ref);
+            if (isRWOwn()) return  util::get<rw_own_t>(m_ref);
+            util::throw_error(std::logic_error("Impossible happened"));
+        }
+
+        virtual void mov(BasicVectorRef &v) override {
+            VectorRefT<T> *tv = dynamic_cast<VectorRefT<T>*>(&v);
+            GAPI_Assert(tv != nullptr);
+            wref() = std::move(tv->wref());
+        }
+
+        virtual const void* ptr() const override { return &rref(); }
+        virtual std::size_t size() const override { return rref().size(); }
+    };
+
+    // This class strips type information from VectorRefT<> and makes it usable
+    // in the G-API executables (carrying run-time data/information to kernels).
+    // Part of GRunArg.
+    // Its methods are typed proxies to VectorRefT<T>.
+    // VectorRef maintains "reference" semantics so two copies of VectoRef refer
+    // to the same underlying object.
+    // FIXME: Put a good explanation on why cv::OutputArray doesn't fit this role
+    class VectorRef
+    {
+        std::shared_ptr<BasicVectorRef> m_ref;
+        cv::detail::OpaqueKind m_kind = cv::detail::OpaqueKind::CV_UNKNOWN;
+
+        template<typename T> inline void check() const
+        {
+            GAPI_DbgAssert(dynamic_cast<VectorRefT<T>*>(m_ref.get()) != nullptr);
+            GAPI_Assert(sizeof(T) == m_ref->m_elemSize);
+        }
+
+    public:
+        VectorRef() = default;
+        template<typename T> explicit VectorRef(const std::vector<T>& vec)
+            : m_ref(new VectorRefT<T>(vec))
+            , m_kind(GOpaqueTraits<T>::kind)
+        {}
+        template<typename T> explicit VectorRef(std::vector<T>& vec)
+            : m_ref(new VectorRefT<T>(vec))
+            , m_kind(GOpaqueTraits<T>::kind)
+        {}
+        template<typename T> explicit VectorRef(std::vector<T>&& vec)
+            : m_ref(new VectorRefT<T>(std::move(vec)))
+            , m_kind(GOpaqueTraits<T>::kind)
+        {}
+
+        cv::detail::OpaqueKind getKind() const
+        {
+            return m_kind;
+        }
+
+        template<typename T> void reset()
+        {
+            if (!m_ref) m_ref.reset(new VectorRefT<T>());
+            check<T>();
+            storeKind<T>();
+            static_cast<VectorRefT<T>&>(*m_ref).reset();
+        }
+
+        template <typename T>
+        void storeKind()
+        {
+            m_kind = cv::detail::GOpaqueTraits<T>::kind;
+        }
+
+        template<typename T> std::vector<T>& wref()
+        {
+            check<T>();
+            return static_cast<VectorRefT<T>&>(*m_ref).wref();
+        }
+
+        template<typename T> const std::vector<T>& rref() const
+        {
+            check<T>();
+            return static_cast<VectorRefT<T>&>(*m_ref).rref();
+        }
+
+        // Check if was created for/from std::vector<T>
+        template <typename T> bool holds() const
+        {
+            if (!m_ref) return false;
+            using U = typename std::decay<T>::type;
+            return dynamic_cast<VectorRefT<U>*>(m_ref.get()) != nullptr;
+        }
+
+        void mov(VectorRef &v)
+        {
+            m_ref->mov(*v.m_ref);
+        }
+
+        cv::GArrayDesc descr_of() const
+        {
+            return m_ref->m_desc;
+        }
+
+        std::size_t size() const
+        {
+            return m_ref->size();
+        }
+
+        // May be used to uniquely identify this object internally
+        const void *ptr() const { return m_ref->ptr(); }
+    };
+
+    // Helper (FIXME: work-around?)
+    // stripping G types to their host types
+    // like cv::GArray<GMat> would still map to std::vector<cv::Mat>
+    // but not to std::vector<cv::GMat>
+#if defined(GAPI_STANDALONE)
+#  define FLATTEN_NS cv::gapi::own
+#else
+#  define FLATTEN_NS cv
+#endif
+    template<class T> struct flatten_g;
+    template<> struct flatten_g<cv::GMat>         { using type = FLATTEN_NS::Mat; };
+    template<> struct flatten_g<cv::GScalar>      { using type = FLATTEN_NS::Scalar; };
+    template<class T> struct flatten_g<GArray<T>> { using type = std::vector<T>; };
+    template<class T> struct flatten_g            { using type = T; };
+#undef FLATTEN_NS
+    // FIXME: the above mainly duplicates "ProtoToParam" thing from gtyped.hpp
+    // but I decided not to include gtyped here - probably worth moving that stuff
+    // to some common place? (DM)
+} // namespace detail
+
+/** \addtogroup gapi_data_objects
+ * @{
+ */
+/**
+ * @brief `cv::GArray<T>` template class represents a list of objects
+ * of class `T` in the graph.
+ *
+ * `cv::GArray<T>` describes a functional relationship between
+ * operations consuming and producing arrays of objects of class
+ * `T`. The primary purpose of `cv::GArray<T>` is to represent a
+ * dynamic list of objects -- where the size of the list is not known
+ * at the graph construction or compile time. Examples include: corner
+ * and feature detectors (`cv::GArray<cv::Point>`), object detection
+ * and tracking  results (`cv::GArray<cv::Rect>`). Programmers can use
+ * their own types with `cv::GArray<T>` in the custom operations.
+ *
+ * Similar to `cv::GScalar`, `cv::GArray<T>` may be value-initialized
+ * -- in this case a graph-constant value is associated with the object.
+ *
+ * `GArray<T>` is a virtual counterpart of `std::vector<T>`, which is
+ * usually used to represent the `GArray<T>` data in G-API during the
+ * execution.
+ *
+ * @sa `cv::GOpaque<T>`
+ */
+template<typename T> class GArray
+{
+public:
+    // Host type (or Flat type) - the type this GArray is actually
+    // specified to.
+    /// @private
+    using HT = typename detail::flatten_g<typename std::decay<T>::type>::type;
+
+    /**
+     * @brief Constructs a value-initialized `cv::GArray<T>`
+     *
+     * `cv::GArray<T>` objects  may have their values
+     * be associated at graph construction time. It is useful when
+     * some operation has a `cv::GArray<T>` input which doesn't change during
+     * the program execution, and is set only once. In this case,
+     * there is no need to declare such `cv::GArray<T>` as a graph input.
+     *
+     * @note The value of `cv::GArray<T>` may be overwritten by assigning some
+     * other `cv::GArray<T>` to the object using `operator=` -- on the
+     * assigment, the old association or value is discarded.
+     *
+     * @param v a std::vector<T> to associate with this
+     * `cv::GArray<T>` object. Vector data is copied into the
+     * `cv::GArray<T>` (no reference to the passed data is held).
+     */
+    explicit GArray(const std::vector<HT>& v) // Constant value constructor
+        : m_ref(detail::GArrayU(detail::VectorRef(v))) { putDetails(); }
+
+    /**
+     * @overload
+     * @brief Constructs a value-initialized `cv::GArray<T>`
+     *
+     * @param v a std::vector<T> to associate with this
+     * `cv::GArray<T>` object. Vector data is moved into the `cv::GArray<T>`.
+     */
+    explicit GArray(std::vector<HT>&& v)      // Move-constructor
+        : m_ref(detail::GArrayU(detail::VectorRef(std::move(v)))) { putDetails(); }
+
+    /**
+     * @brief Constructs an empty `cv::GArray<T>`
+     *
+     * Normally, empty G-API data objects denote a starting point of
+     * the graph. When an empty `cv::GArray<T>` is assigned to a result
+     * of some operation, it obtains a functional link to this
+     * operation (and is not empty anymore).
+     */
+    GArray() { putDetails(); }                // Empty constructor
+
+    /// @private
+    explicit GArray(detail::GArrayU &&ref)    // GArrayU-based constructor
+        : m_ref(ref) { putDetails(); }        //   (used by GCall, not for users)
+
+    /// @private
+    detail::GArrayU strip() const {
+        return m_ref;
+    }
+    /// @private
+    static void VCtor(detail::VectorRef& vref) {
+        vref.reset<HT>();
+    }
+
+private:
+    void putDetails() {
+        m_ref.setConstructFcn(&VCtor);
+        m_ref.specifyType<HT>();  // FIXME: to unify those 2 to avoid excessive dynamic_cast
+        m_ref.storeKind<HT>();    //
+    }
+
+    detail::GArrayU m_ref;
+};
+
+/** @} */
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GARRAY_HPP
diff --git a/3rdParty/include/opencv2/gapi/gasync_context.hpp b/3rdParty/include/opencv2/gapi/gasync_context.hpp
new file mode 100644
index 0000000..f49b598
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gasync_context.hpp
@@ -0,0 +1,63 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+#ifndef OPENCV_GAPI_GASYNC_CONTEXT_HPP
+#define OPENCV_GAPI_GASYNC_CONTEXT_HPP
+
+#if !defined(GAPI_STANDALONE)
+#  include <opencv2/core/cvdef.h>
+#else   // Without OpenCV
+#  include <opencv2/gapi/own/cvdefs.hpp>
+#endif // !defined(GAPI_STANDALONE)
+
+#include <opencv2/gapi/own/exports.hpp>
+
+namespace cv {
+namespace gapi{
+
+/**
+ * @brief This namespace contains experimental G-API functionality,
+ * functions or structures in this namespace are subjects to change or
+ * removal in the future releases. This namespace also contains
+ * functions which API is not stabilized yet.
+ */
+namespace wip {
+
+/**
+ * @brief A class to group async requests to cancel them in a single shot.
+ *
+ * GAsyncContext is passed as an argument to async() and async_apply() functions
+ */
+
+class GAPI_EXPORTS GAsyncContext{
+    std::atomic<bool> cancelation_requested = {false};
+public:
+    /**
+     * @brief Start cancellation process for an associated request.
+     *
+     * User still has to wait for each individual request (either via callback or according std::future object) to make sure it actually canceled.
+     *
+     * @return true if it was a first request to cancel the context
+     */
+    bool cancel();
+
+    /**
+    * @brief Returns true if cancellation was requested for this context.
+    *
+    * @return true if cancellation was requested for this context
+    */
+    bool isCanceled() const;
+};
+
+class GAPI_EXPORTS GAsyncCanceled : public std::exception {
+public:
+    virtual const char* what() const noexcept CV_OVERRIDE;
+};
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif //OPENCV_GAPI_GASYNC_CONTEXT_HPP
diff --git a/3rdParty/include/opencv2/gapi/gcall.hpp b/3rdParty/include/opencv2/gapi/gcall.hpp
new file mode 100644
index 0000000..8d1b8d6
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gcall.hpp
@@ -0,0 +1,78 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GCALL_HPP
+#define OPENCV_GAPI_GCALL_HPP
+
+#include <opencv2/gapi/garg.hpp>      // GArg
+#include <opencv2/gapi/gmat.hpp>      // GMat
+#include <opencv2/gapi/gscalar.hpp>   // GScalar
+#include <opencv2/gapi/gframe.hpp>    // GFrame
+#include <opencv2/gapi/garray.hpp>    // GArray<T>
+#include <opencv2/gapi/gopaque.hpp>   // GOpaque<T>
+
+namespace cv {
+
+struct GKernel;
+
+// The whole idea of this class is to represent an operation
+// which is applied to arguments. This is part of public API,
+// since it is what users should use to define kernel interfaces.
+
+class GAPI_EXPORTS GCall final
+{
+public:
+    class Priv;
+
+    explicit GCall(const GKernel &k);
+    ~GCall();
+
+    template<typename... Ts>
+    GCall& pass(Ts&&... args)
+    {
+        setArgs({cv::GArg(std::move(args))...});
+        return *this;
+    }
+
+    // A generic yield method - obtain a link to operator's particular GMat output
+    GMat    yield      (int output = 0);
+    GMatP   yieldP     (int output = 0);
+    GScalar yieldScalar(int output = 0);
+    GFrame  yieldFrame (int output = 0);
+
+    template<class T> GArray<T> yieldArray(int output = 0)
+    {
+        return GArray<T>(yieldArray(output));
+    }
+
+    template<class T> GOpaque<T> yieldOpaque(int output = 0)
+    {
+        return GOpaque<T>(yieldOpaque(output));
+    }
+
+    // Internal use only
+    Priv& priv();
+    const Priv& priv() const;
+
+    // GKernel and params can be modified, it's needed for infer<Generic>,
+    // because information about output shapes doesn't exist in compile time
+    GKernel& kernel();
+    cv::util::any& params();
+
+    void setArgs(std::vector<GArg> &&args);
+
+protected:
+    std::shared_ptr<Priv> m_priv;
+
+    // Public versions return a typed array or opaque, those are implementation details
+    detail::GArrayU yieldArray(int output = 0);
+    detail::GOpaqueU yieldOpaque(int output = 0);
+};
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GCALL_HPP
diff --git a/3rdParty/include/opencv2/gapi/gcommon.hpp b/3rdParty/include/opencv2/gapi/gcommon.hpp
new file mode 100644
index 0000000..dfbd9e3
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gcommon.hpp
@@ -0,0 +1,287 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GCOMMON_HPP
+#define OPENCV_GAPI_GCOMMON_HPP
+
+#include <functional>   // std::hash
+#include <vector>       // std::vector
+#include <type_traits>  // decay
+
+#include <opencv2/gapi/opencv_includes.hpp>
+
+#include <opencv2/gapi/util/any.hpp>
+#include <opencv2/gapi/util/optional.hpp>
+#include <opencv2/gapi/own/exports.hpp>
+#include <opencv2/gapi/own/assert.hpp>
+#include <opencv2/gapi/render/render_types.hpp>
+#include <opencv2/gapi/s11n/base.hpp>
+
+namespace cv {
+
+class GMat; // FIXME: forward declaration for GOpaqueTraits
+
+namespace detail
+{
+    // This is a trait-like structure to mark backend-specific compile arguments
+    // with tags
+    template<typename T> struct CompileArgTag;
+
+    // These structures are tags which separate kernels and transformations
+    struct KernelTag
+    {};
+    struct TransformTag
+    {};
+
+    // This enum is utilized mostly by GArray and GOpaque to store and recognize their internal data
+    // types (aka Host type). Also it is widely used during serialization routine.
+    enum class OpaqueKind: int
+    {
+        CV_UNKNOWN,    // Unknown, generic, opaque-to-GAPI data type unsupported in graph seriallization
+        CV_BOOL,       // bool user G-API data
+        CV_INT,        // int user G-API data
+        CV_INT64,      // int64_t user G-API data
+        CV_DOUBLE,     // double user G-API data
+        CV_FLOAT,      // float user G-API data
+        CV_UINT64,     // uint64_t user G-API data
+        CV_STRING,     // std::string user G-API data
+        CV_POINT,      // cv::Point user G-API data
+        CV_POINT2F,    // cv::Point2f user G-API data
+        CV_SIZE,       // cv::Size user G-API data
+        CV_RECT,       // cv::Rect user G-API data
+        CV_SCALAR,     // cv::Scalar user G-API data
+        CV_MAT,        // cv::Mat user G-API data
+        CV_DRAW_PRIM,  // cv::gapi::wip::draw::Prim user G-API data
+    };
+
+    // Type traits helper which simplifies the extraction of kind from type
+    template<typename T> struct GOpaqueTraits;
+    template<typename T> struct GOpaqueTraits    { static constexpr const OpaqueKind kind = OpaqueKind::CV_UNKNOWN; };
+    template<> struct GOpaqueTraits<int>         { static constexpr const OpaqueKind kind = OpaqueKind::CV_INT; };
+    template<> struct GOpaqueTraits<int64_t>     { static constexpr const OpaqueKind kind = OpaqueKind::CV_INT64; };
+    template<> struct GOpaqueTraits<double>      { static constexpr const OpaqueKind kind = OpaqueKind::CV_DOUBLE; };
+    template<> struct GOpaqueTraits<float>       { static constexpr const OpaqueKind kind = OpaqueKind::CV_FLOAT; };
+    template<> struct GOpaqueTraits<uint64_t>    { static constexpr const OpaqueKind kind = OpaqueKind::CV_UINT64; };
+    template<> struct GOpaqueTraits<bool>        { static constexpr const OpaqueKind kind = OpaqueKind::CV_BOOL; };
+    template<> struct GOpaqueTraits<std::string> { static constexpr const OpaqueKind kind = OpaqueKind::CV_STRING; };
+    template<> struct GOpaqueTraits<cv::Size>    { static constexpr const OpaqueKind kind = OpaqueKind::CV_SIZE; };
+    template<> struct GOpaqueTraits<cv::Scalar>  { static constexpr const OpaqueKind kind = OpaqueKind::CV_SCALAR; };
+    template<> struct GOpaqueTraits<cv::Point>   { static constexpr const OpaqueKind kind = OpaqueKind::CV_POINT; };
+    template<> struct GOpaqueTraits<cv::Point2f> { static constexpr const OpaqueKind kind = OpaqueKind::CV_POINT2F; };
+    template<> struct GOpaqueTraits<cv::Mat>     { static constexpr const OpaqueKind kind = OpaqueKind::CV_MAT; };
+    template<> struct GOpaqueTraits<cv::Rect>    { static constexpr const OpaqueKind kind = OpaqueKind::CV_RECT; };
+    template<> struct GOpaqueTraits<cv::GMat>    { static constexpr const OpaqueKind kind = OpaqueKind::CV_MAT; };
+    template<> struct GOpaqueTraits<cv::gapi::wip::draw::Prim>
+                                                 { static constexpr const OpaqueKind kind = OpaqueKind::CV_DRAW_PRIM; };
+    using GOpaqueTraitsArrayTypes = std::tuple<int, double, float, uint64_t, bool, std::string, cv::Size, cv::Scalar, cv::Point, cv::Point2f,
+                                               cv::Mat, cv::Rect, cv::gapi::wip::draw::Prim>;
+    // GOpaque is not supporting cv::Mat and cv::Scalar since there are GScalar and GMat types
+    using GOpaqueTraitsOpaqueTypes = std::tuple<int, double, float, uint64_t, bool, std::string, cv::Size, cv::Point, cv::Point2f, cv::Rect,
+                                                cv::gapi::wip::draw::Prim>;
+} // namespace detail
+
+// This definition is here because it is reused by both public(?) and internal
+// modules. Keeping it here wouldn't expose public details (e.g., API-level)
+// to components which are internal and operate on a lower-level entities
+// (e.g., compiler, backends).
+// FIXME: merge with ArgKind?
+// FIXME: replace with variant[format desc]?
+enum class GShape: int
+{
+    GMAT,
+    GSCALAR,
+    GARRAY,
+    GOPAQUE,
+    GFRAME,
+};
+
+namespace gapi {
+namespace s11n {
+namespace detail {
+template<typename T> struct wrap_serialize;
+} // namespace detail
+} // namespace s11n
+} // namespace gapi
+
+
+struct GCompileArg;
+
+namespace detail {
+    template<typename T>
+    using is_compile_arg = std::is_same<GCompileArg, typename std::decay<T>::type>;
+} // namespace detail
+
+// CompileArg is an unified interface over backend-specific compilation
+// information
+// FIXME: Move to a separate file?
+/** \addtogroup gapi_compile_args
+ * @{
+ *
+ * @brief Compilation arguments: data structures controlling the
+ * compilation process
+ *
+ * G-API comes with a number of graph compilation options which can be
+ * passed to cv::GComputation::apply() or
+ * cv::GComputation::compile(). Known compilation options are listed
+ * in this page, while extra backends may introduce their own
+ * compilation options (G-API transparently accepts _everything_ which
+ * can be passed to cv::compile_args(), it depends on underlying
+ * backends if an option would be interpreted or not).
+ *
+ * For example, if an example computation is executed like this:
+ *
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp graph_decl_apply
+ *
+ * Extra parameter specifying which kernels to compile with can be
+ * passed like this:
+ *
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp apply_with_param
+ */
+
+/**
+ * @brief Represents an arbitrary compilation argument.
+ *
+ * Any value can be wrapped into cv::GCompileArg, but only known ones
+ * (to G-API or its backends) can be interpreted correctly.
+ *
+ * Normally objects of this class shouldn't be created manually, use
+ * cv::compile_args() function which automatically wraps everything
+ * passed in (a variadic template parameter pack) into a vector of
+ * cv::GCompileArg objects.
+ */
+struct GCompileArg
+{
+public:
+    // NB: Required for pythnon bindings
+    GCompileArg() = default;
+
+    std::string tag;
+
+    // FIXME: use decay in GArg/other trait-based wrapper before leg is shot!
+    template<typename T, typename std::enable_if<!detail::is_compile_arg<T>::value, int>::type = 0>
+    explicit GCompileArg(T &&t)
+        : tag(detail::CompileArgTag<typename std::decay<T>::type>::tag())
+        , serializeF(cv::gapi::s11n::detail::has_S11N_spec<T>::value ?
+                     &cv::gapi::s11n::detail::wrap_serialize<T>::serialize :
+                     nullptr)
+        , arg(t)
+    {
+    }
+
+    template<typename T> T& get()
+    {
+        return util::any_cast<T>(arg);
+    }
+
+    template<typename T> const T& get() const
+    {
+        return util::any_cast<T>(arg);
+    }
+
+    void serialize(cv::gapi::s11n::IOStream& os) const
+    {
+        if (serializeF)
+        {
+            serializeF(os, *this);
+        }
+    }
+
+private:
+    std::function<void(cv::gapi::s11n::IOStream&, const GCompileArg&)> serializeF;
+    util::any arg;
+};
+
+using GCompileArgs = std::vector<GCompileArg>;
+
+inline cv::GCompileArgs& operator += (      cv::GCompileArgs &lhs,
+                                      const cv::GCompileArgs &rhs)
+{
+    lhs.reserve(lhs.size() + rhs.size());
+    lhs.insert(lhs.end(), rhs.begin(), rhs.end());
+    return lhs;
+}
+
+/**
+ * @brief Wraps a list of arguments (a parameter pack) into a vector of
+ *        compilation arguments (cv::GCompileArg).
+ */
+template<typename... Ts> GCompileArgs compile_args(Ts&&... args)
+{
+    return GCompileArgs{ GCompileArg(args)... };
+}
+
+namespace gapi
+{
+/**
+ * @brief Retrieves particular compilation argument by its type from
+ *        cv::GCompileArgs
+ */
+template<typename T>
+inline cv::util::optional<T> getCompileArg(const cv::GCompileArgs &args)
+{
+    for (auto &compile_arg : args)
+    {
+        if (compile_arg.tag == cv::detail::CompileArgTag<T>::tag())
+        {
+            return cv::util::optional<T>(compile_arg.get<T>());
+        }
+    }
+    return cv::util::optional<T>();
+}
+
+namespace s11n {
+namespace detail {
+template<typename T> struct wrap_serialize
+{
+    static void serialize(IOStream& os, const GCompileArg& arg)
+    {
+        using DT = typename std::decay<T>::type;
+        S11N<DT>::serialize(os, arg.get<DT>());
+    }
+};
+} // namespace detail
+} // namespace s11n
+} // namespace gapi
+
+/**
+ * @brief Ask G-API to dump compiled graph in Graphviz format under
+ * the given file name.
+ *
+ * Specifies a graph dump path (path to .dot file to be generated).
+ * G-API will dump a .dot file under specified path during a
+ * compilation process if this flag is passed.
+ */
+struct graph_dump_path
+{
+    std::string m_dump_path;
+};
+/** @} */
+
+namespace detail
+{
+    template<> struct CompileArgTag<cv::graph_dump_path>
+    {
+        static const char* tag() { return "gapi.graph_dump_path"; }
+    };
+}
+
+} // namespace cv
+
+// std::hash overload for GShape
+namespace std
+{
+template<> struct hash<cv::GShape>
+{
+    size_t operator() (cv::GShape sh) const
+    {
+        return std::hash<int>()(static_cast<int>(sh));
+    }
+};
+} // namespace std
+
+
+#endif // OPENCV_GAPI_GCOMMON_HPP
diff --git a/3rdParty/include/opencv2/gapi/gcompiled.hpp b/3rdParty/include/opencv2/gapi/gcompiled.hpp
new file mode 100644
index 0000000..ac36783
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gcompiled.hpp
@@ -0,0 +1,232 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GCOMPILED_HPP
+#define OPENCV_GAPI_GCOMPILED_HPP
+
+#include <vector>
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/own/assert.hpp>
+#include <opencv2/gapi/garg.hpp>
+
+namespace cv {
+
+// This class represents a compiled computation.
+// In theory (and ideally), it can be used w/o the rest of APIs.
+// In theory (and ideally), it can be serialized/deserialized.
+// It can enable scenarious like deployment to an autonomous devince, FuSa, etc.
+//
+// Currently GCompiled assumes all GMats you used to pass data to G-API
+// are valid and not destroyed while you use a GCompiled object.
+//
+// FIXME: In future, there should be a way to name I/O objects and specify it
+// to GCompiled externally (for example, when it is loaded on the target system).
+
+/**
+ * \addtogroup gapi_main_classes
+ * @{
+ */
+/**
+ * @brief Represents a compiled computation (graph). Can only be used
+ * with image / data formats & resolutions it was compiled for, with
+ * some exceptions.
+ *
+ * This class represents a product of graph compilation (calling
+ * cv::GComputation::compile()). Objects of this class actually do
+ * data processing, and graph execution is incapsulated into objects
+ * of this class. Execution model itself depends on kernels and
+ * backends which were using during the compilation, see @ref
+ * gapi_compile_args for details.
+ *
+ * In a general case, GCompiled objects can be applied to data only in
+ * that formats/resolutions they were compiled for (see @ref
+ * gapi_meta_args). However, if the underlying backends allow, a
+ * compiled object can be _reshaped_ to handle data (images) of
+ * different resolution, though formats and types must remain the same.
+ *
+ * GCompiled is very similar to `std::function<>` in its semantics --
+ * running it looks like a function call in the user code.
+ *
+ * At the moment, GCompiled objects are not reentrant -- generally,
+ * the objects are stateful since graph execution itself is a stateful
+ * process and this state is now maintained in GCompiled's own memory
+ * (not on the process stack).
+ *
+ * At the same time, two different GCompiled objects produced from the
+ * single cv::GComputation are completely independent and can be used
+ * concurrently.
+ *
+ * @sa GStreamingCompiled
+ */
+class GAPI_EXPORTS GCompiled
+{
+public:
+    /// @private
+    class GAPI_EXPORTS Priv;
+
+    /**
+     * @brief Constructs an empty object
+     */
+    GCompiled();
+
+    /**
+     * @brief Run the compiled computation, a generic version.
+     *
+     * @param ins vector of inputs to process.
+     * @param outs vector of outputs to produce.
+     *
+     * Input/output vectors must have the same number of elements as
+     * defined in the cv::GComputation protocol (at the moment of its
+     * construction). Shapes of elements also must conform to protocol
+     * (e.g. cv::Mat needs to be passed where cv::GMat has been
+     * declared as input, and so on). Run-time exception is generated
+     * otherwise.
+     *
+     * Objects in output vector may remain empty (like cv::Mat) --
+     * G-API will automatically initialize output objects to proper formats.
+     *
+     * @note Don't construct GRunArgs/GRunArgsP objects manually, use
+     * cv::gin()/cv::gout() wrappers instead.
+     */
+    void operator() (GRunArgs &&ins, GRunArgsP &&outs);          // Generic arg-to-arg
+#if !defined(GAPI_STANDALONE)
+
+    /**
+     * @brief Execute an unary computation
+     *
+     * @overload
+     * @param in input cv::Mat for unary computation
+     * @param out output cv::Mat for unary computation
+     * process.
+     */
+    void operator() (cv::Mat in, cv::Mat &out);                  // Unary overload
+
+    /**
+     * @brief Execute an unary computation
+     *
+     * @overload
+     * @param in input cv::Mat for unary computation
+     * @param out output cv::Scalar for unary computation
+     * process.
+     */
+    void operator() (cv::Mat in, cv::Scalar &out);               // Unary overload (scalar)
+
+    /**
+     * @brief Execute a binary computation
+     *
+     * @overload
+     * @param in1 first input cv::Mat for binary computation
+     * @param in2 second input cv::Mat for binary computation
+     * @param out output cv::Mat for binary computation
+     * process.
+     */
+    void operator() (cv::Mat in1, cv::Mat in2, cv::Mat &out);    // Binary overload
+
+    /**
+     * @brief Execute an binary computation
+     *
+     * @overload
+     * @param in1 first input cv::Mat for binary computation
+     * @param in2 second input cv::Mat for binary computation
+     * @param out output cv::Scalar for binary computation
+     * process.
+     */
+    void operator() (cv::Mat in1, cv::Mat in2, cv::Scalar &out); // Binary overload (scalar)
+
+    /**
+     * @brief Execute a computation with arbitrary number of
+     * inputs/outputs.
+     *
+     * @overload
+     * @param ins vector of input cv::Mat objects to process by the
+     * computation.
+     * @param outs vector of output cv::Mat objects to produce by the
+     * computation.
+     *
+     * Numbers of elements in ins/outs vectors must match numbers of
+     * inputs/outputs which were used to define the source GComputation.
+     */
+    void operator() (const std::vector<cv::Mat> &ins,            // Compatibility overload
+                     const std::vector<cv::Mat> &outs);
+#endif  // !defined(GAPI_STANDALONE)
+    /// @private
+    Priv& priv();
+
+    /**
+     * @brief Check if compiled object is valid (non-empty)
+     *
+     * @return true if the object is runnable (valid), false otherwise
+     */
+    explicit operator bool () const;
+
+    /**
+     * @brief Vector of metadata this graph was compiled for.
+     *
+     * @return Unless _reshape_ is not supported, return value is the
+     * same vector which was passed to cv::GComputation::compile() to
+     * produce this compiled object. Otherwise, it is the latest
+     * metadata vector passed to reshape() (if that call was
+     * successful).
+     */
+    const GMetaArgs& metas() const; // Meta passed to compile()
+
+    /**
+     * @brief Vector of metadata descriptions of graph outputs
+     *
+     * @return vector with formats/resolutions of graph's output
+     * objects, auto-inferred from input metadata vector by
+     * operations which form this computation.
+     *
+     * @note GCompiled objects produced from the same
+     * cv::GComputiation graph with different input metas may return
+     * different values in this vector.
+     */
+    const GMetaArgs& outMetas() const;
+
+    /**
+     * @brief Check if the underlying backends support reshape or not.
+     *
+     * @return true if supported, false otherwise.
+     */
+    bool canReshape() const;
+
+    /**
+     * @brief Reshape a compiled graph to support new image
+     * resolutions.
+     *
+     * Throws an exception if an error occurs.
+     *
+     * @param inMetas new metadata to reshape on. Vector size and
+     * metadata shapes must match the computation's protocol.
+     * @param args compilation arguments to use.
+     */
+    // FIXME: Why it requires compile args?
+    void reshape(const GMetaArgs& inMetas, const GCompileArgs& args);
+
+    /**
+     * @brief Prepare inner kernels states for a new video-stream.
+     *
+     * GCompiled objects may be used to process video streams frame by frame.
+     * In this case, a GCompiled is called on every image frame individually.
+     * Starting OpenCV 4.4, some kernels in the graph may have their internal
+     * states (see GAPI_OCV_KERNEL_ST for the OpenCV backend).
+     * In this case, if user starts processing another video stream with
+     * this GCompiled, this method needs to be called to let kernels re-initialize
+     * their internal states to a new video stream.
+     */
+    void prepareForNewStream();
+
+protected:
+    /// @private
+    std::shared_ptr<Priv> m_priv;
+};
+/** @} */
+
+}
+
+#endif // OPENCV_GAPI_GCOMPILED_HPP
diff --git a/3rdParty/include/opencv2/gapi/gcompiled_async.hpp b/3rdParty/include/opencv2/gapi/gcompiled_async.hpp
new file mode 100644
index 0000000..a0c2917
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gcompiled_async.hpp
@@ -0,0 +1,73 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GCOMPILED_ASYNC_HPP
+#define OPENCV_GAPI_GCOMPILED_ASYNC_HPP
+
+#include <future>           //for std::future
+#include <exception>        //for std::exception_ptr
+#include <functional>       //for std::function
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/own/exports.hpp>
+
+namespace cv {
+    //fwd declaration
+    class GCompiled;
+
+namespace gapi{
+namespace wip {
+    class GAsyncContext;
+    /**
+    These functions asynchronously (i.e. probably on a separate thread of execution) call GCompiled::operator() member function of their first argument with copies of rest of arguments (except callback) passed in.
+    The difference between the function is the way to get the completion notification (via callback or a waiting on std::future object)
+    If exception is occurred during execution of apply it is transferred to the callback (via function parameter) or passed to future (and will be thrown on call to std::future::get)
+
+    N.B. :
+    Input arguments are copied on call to async function (actually on call to cv::gin) and thus do not have to outlive the actual completion of asynchronous activity.
+    While output arguments are "captured" by reference(pointer) and therefore _must_ outlive the asynchronous activity
+    (i.e. live at least until callback is called or future is unblocked)
+
+    @param gcmpld       Compiled computation (graph) to start asynchronously
+    @param callback     Callback to be called when execution of gcmpld is done
+    @param ins          Input parameters for gcmpld
+    @param outs         Output parameters for gcmpld
+    */
+    GAPI_EXPORTS void                async(GCompiled& gcmpld, std::function<void(std::exception_ptr)>&& callback, GRunArgs &&ins, GRunArgsP &&outs);
+
+    /** @overload
+    @param gcmpld       Compiled computation (graph) to run asynchronously
+    @param callback     Callback to be called when execution of gcmpld is done
+    @param ins          Input parameters for gcmpld
+    @param outs         Output parameters for gcmpld
+    @param ctx          Context this request belongs to
+    @see   async GAsyncContext
+    */
+    GAPI_EXPORTS void                async(GCompiled& gcmpld, std::function<void(std::exception_ptr)>&& callback, GRunArgs &&ins, GRunArgsP &&outs, GAsyncContext& ctx);
+
+    /** @overload
+    @param gcmpld       Compiled computation (graph) to run asynchronously
+    @param ins          Input parameters for gcmpld
+    @param outs         Output parameters for gcmpld
+    @return             std::future<void> object to wait for completion of async operation
+    @see async
+    */
+    GAPI_EXPORTS std::future<void>   async(GCompiled& gcmpld, GRunArgs &&ins, GRunArgsP &&outs);
+
+    /**
+    @param gcmpld       Compiled computation (graph) to run asynchronously
+    @param ins          Input parameters for gcmpld
+    @param outs         Output parameters for gcmpld
+    @param ctx          Context this request belongs to
+    @return             std::future<void> object to wait for completion of async operation
+    @see   async GAsyncContext
+    */
+    GAPI_EXPORTS std::future<void>   async(GCompiled& gcmpld, GRunArgs &&ins, GRunArgsP &&outs, GAsyncContext& ctx);
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_GCOMPILED_ASYNC_HPP
diff --git a/3rdParty/include/opencv2/gapi/gcompoundkernel.hpp b/3rdParty/include/opencv2/gapi/gcompoundkernel.hpp
new file mode 100644
index 0000000..df0ce34
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gcompoundkernel.hpp
@@ -0,0 +1,139 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2019 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GCOMPOUNDKERNEL_HPP
+#define OPENCV_GAPI_GCOMPOUNDKERNEL_HPP
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/gcommon.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/garg.hpp>
+
+namespace cv {
+namespace gapi
+{
+namespace compound
+{
+    // FIXME User does not need to know about this function
+    // Needs that user may define compound kernels(as cpu kernels)
+    GAPI_EXPORTS cv::gapi::GBackend backend();
+} // namespace compound
+} // namespace gapi
+
+namespace detail
+{
+
+struct GCompoundContext
+{
+    explicit GCompoundContext(const GArgs& in_args);
+    template<typename T>
+    const T& inArg(int input) { return m_args.at(input).get<T>(); }
+
+    GArgs m_args;
+    GArgs m_results;
+};
+
+class GAPI_EXPORTS GCompoundKernel
+{
+// Compound kernel must use all of it's inputs
+public:
+    using F = std::function<void(GCompoundContext& ctx)>;
+
+    explicit GCompoundKernel(const F& f);
+    void apply(GCompoundContext& ctx);
+
+protected:
+    F m_f;
+};
+
+template<typename T> struct get_compound_in
+{
+    static T get(GCompoundContext &ctx, int idx) { return ctx.inArg<T>(idx); }
+};
+
+template<typename U> struct get_compound_in<cv::GArray<U>>
+{
+    static cv::GArray<U> get(GCompoundContext &ctx, int idx)
+    {
+        auto array = cv::GArray<U>();
+        ctx.m_args[idx] = GArg(array);
+        return array;
+    }
+};
+
+template<typename U> struct get_compound_in<cv::GOpaque<U>>
+{
+    static cv::GOpaque<U> get(GCompoundContext &ctx, int idx)
+    {
+        auto opaq = cv::GOpaque<U>();
+        ctx.m_args[idx] = GArg(opaq);
+        return opaq;
+    }
+};
+
+template<> struct get_compound_in<cv::GMatP>
+{
+    static cv::GMatP get(GCompoundContext &ctx, int idx)
+    {
+        auto mat = cv::GMatP();
+        ctx.m_args[idx] = GArg(mat);
+        return mat;
+    }
+};
+
+template<typename, typename, typename>
+struct GCompoundCallHelper;
+
+template<typename Impl, typename... Ins, typename... Outs>
+struct GCompoundCallHelper<Impl, std::tuple<Ins...>, std::tuple<Outs...> >
+{
+    template<int... IIs, int... OIs>
+    static void expand_impl(GCompoundContext &ctx, detail::Seq<IIs...>, detail::Seq<OIs...>)
+    {
+        auto result = Impl::expand(get_compound_in<Ins>::get(ctx, IIs)...);
+        auto tuple_return = tuple_wrap_helper<decltype(result)>::get(std::move(result));
+        ctx.m_results = { cv::GArg(std::get<OIs>(tuple_return))... };
+    }
+
+    static void expand(GCompoundContext &ctx)
+    {
+        expand_impl(ctx,
+                    typename detail::MkSeq<sizeof...(Ins)>::type(),
+                    typename detail::MkSeq<sizeof...(Outs)>::type());
+    }
+};
+
+template<class Impl, class K>
+class GCompoundKernelImpl: public cv::detail::GCompoundCallHelper<Impl, typename K::InArgs, typename K::OutArgs>,
+                           public cv::detail::KernelTag
+{
+    using P = cv::detail::GCompoundCallHelper<Impl, typename K::InArgs, typename K::OutArgs>;
+
+public:
+    using API = K;
+
+    static cv::gapi::GBackend backend() { return cv::gapi::compound::backend(); }
+    static GCompoundKernel    kernel()  { return GCompoundKernel(&P::expand);   }
+};
+
+} // namespace detail
+
+
+/**
+ * Declares a new compound kernel. See this
+ * [documentation chapter](@ref gapi_kernel_compound)
+ * on compound kernels for more details.
+ *
+ * @param Name type name for new kernel
+ * @param API the interface this kernel implements
+ */
+#define GAPI_COMPOUND_KERNEL(Name, API) \
+    struct Name: public cv::detail::GCompoundKernelImpl<Name, API>
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GCOMPOUNDKERNEL_HPP
diff --git a/3rdParty/include/opencv2/gapi/gcomputation.hpp b/3rdParty/include/opencv2/gapi/gcomputation.hpp
new file mode 100644
index 0000000..13944c7
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gcomputation.hpp
@@ -0,0 +1,580 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GCOMPUTATION_HPP
+#define OPENCV_GAPI_GCOMPUTATION_HPP
+
+#include <functional>
+
+#include <opencv2/gapi/util/util.hpp>
+#include <opencv2/gapi/gcommon.hpp>
+#include <opencv2/gapi/gproto.hpp>
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/gcompiled.hpp>
+#include <opencv2/gapi/gstreaming.hpp>
+
+namespace cv {
+
+namespace detail
+{
+    // FIXME: move to algorithm, cover with separate tests
+    // FIXME: replace with O(1) version (both memory and compilation time)
+    template<typename...>
+    struct last_type;
+
+    template<typename T>
+    struct last_type<T> { using type = T;};
+
+    template<typename T, typename... Ts>
+    struct last_type<T, Ts...> { using type = typename last_type<Ts...>::type; };
+
+    template<typename... Ts>
+    using last_type_t = typename last_type<Ts...>::type;
+}
+
+// Forward-declare the serialization objects
+namespace gapi {
+namespace s11n {
+    struct IIStream;
+    struct IOStream;
+} // namespace s11n
+} // namespace gapi
+
+/**
+ * \addtogroup gapi_main_classes
+ * @{
+ *
+ * @brief G-API classes for constructed and compiled graphs.
+ */
+/**
+ * @brief GComputation class represents a captured computation
+ * graph. GComputation objects form boundaries for expression code
+ * user writes with G-API, allowing to compile and execute it.
+ *
+ * G-API computations are defined with input/output data
+ * objects. G-API will track automatically which operations connect
+ * specified outputs to the inputs, forming up a call graph to be
+ * executed. The below example expresses calculation of Sobel operator
+ * for edge detection (\f$G = \sqrt{G_x^2 + G_y^2}\f$):
+ *
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp graph_def
+ *
+ * Full pipeline can be now captured with this object declaration:
+ *
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp graph_cap_full
+ *
+ * Input/output data objects on which a call graph should be
+ * reconstructed are passed using special wrappers cv::GIn and
+ * cv::GOut. G-API will track automatically which operations form a
+ * path from inputs to outputs and build the execution graph appropriately.
+ *
+ * Note that cv::GComputation doesn't take ownership on data objects
+ * it is defined. Moreover, multiple GComputation objects may be
+ * defined on the same expressions, e.g. a smaller pipeline which
+ * expects that image gradients are already pre-calculated may be
+ * defined like this:
+ *
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp graph_cap_sub
+ *
+ * The resulting graph would expect two inputs and produce one
+ * output. In this case, it doesn't matter if gx/gy data objects are
+ * results of cv::gapi::Sobel operators -- G-API will stop unrolling
+ * expressions and building the underlying graph one reaching this
+ * data objects.
+ *
+ * The way how GComputation is defined is important as its definition
+ * specifies graph _protocol_ -- the way how the graph should be
+ * used. Protocol is defined by number of inputs, number of outputs,
+ * and shapes of inputs and outputs.
+ *
+ * In the above example, sobelEdge expects one Mat on input and
+ * produces one Mat; while sobelEdgeSub expects two Mats on input and
+ * produces one Mat. GComputation's protocol defines how other
+ * computation methods should be used -- cv::GComputation::compile() and
+ * cv::GComputation::apply(). For example, if a graph is defined on
+ * two GMat inputs, two cv::Mat objects have to be passed to apply()
+ * for execution. GComputation checks protocol correctness in runtime
+ * so passing a different number of objects in apply() or passing
+ * cv::Scalar instead of cv::Mat there would compile well as a C++
+ * source but raise an exception in run-time. G-API also comes with a
+ * typed wrapper cv::GComputationT<> which introduces this type-checking in
+ * compile-time.
+ *
+ * cv::GComputation itself is a thin object which just captures what
+ * the graph is. The compiled graph (which actually process data) is
+ * represented by class GCompiled. Use compile() method to generate a
+ * compiled graph with given compile options. cv::GComputation can
+ * also be used to process data with implicit graph compilation
+ * on-the-fly, see apply() for details.
+ *
+ * GComputation is a reference-counted object -- once defined, all its
+ * copies will refer to the same instance.
+ *
+ * @sa GCompiled
+ */
+class GAPI_EXPORTS_W GComputation
+{
+public:
+    class Priv;
+    typedef std::function<GComputation()> Generator;
+
+    // Various constructors enable different ways to define a computation: /////
+    // 1. Generic constructors
+    /**
+     * @brief Define a computation using a generator function.
+     *
+     * Graph can be defined in-place directly at the moment of its
+     * construction with a lambda:
+     *
+     * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp graph_gen
+     *
+     * This may be useful since all temporary objects (cv::GMats) and
+     * namespaces can be localized to scope of lambda, without
+     * contaminating the parent scope with probably unnecessary objects
+     * and information.
+     *
+     * @param gen generator function which returns a cv::GComputation,
+     * see Generator.
+     */
+    GComputation(const Generator& gen);                // Generator
+                                                       // overload
+
+    /**
+     * @brief Generic GComputation constructor.
+     *
+     * Constructs a new graph with a given protocol, specified as a
+     * flow of operations connecting input/output objects. Throws if
+     * the passed boundaries are invalid, e.g. if there's no
+     * functional dependency (path) between given outputs and inputs.
+     *
+     * @param ins Input data vector.
+     * @param outs Output data vector.
+     *
+     * @note Don't construct GProtoInputArgs/GProtoOutputArgs objects
+     * directly, use cv::GIn()/cv::GOut() wrapper functions instead.
+     *
+     * @sa @ref gapi_data_objects
+     */
+    GAPI_WRAP GComputation(GProtoInputArgs &&ins,
+                           GProtoOutputArgs &&outs);             // Arg-to-arg overload
+
+    // 2. Syntax sugar and compatibility overloads
+    /**
+     * @brief Defines an unary (one input -- one output) computation
+     *
+     * @overload
+     * @param in input GMat of the defined unary computation
+     * @param out output GMat of the defined unary computation
+     */
+    GAPI_WRAP GComputation(GMat in, GMat out);  // Unary overload
+
+    /**
+     * @brief Defines an unary (one input -- one output) computation
+     *
+     * @overload
+     * @param in input GMat of the defined unary computation
+     * @param out output GScalar of the defined unary computation
+     */
+    GAPI_WRAP GComputation(GMat in, GScalar out);      // Unary overload (scalar)
+
+    /**
+     * @brief Defines a binary (two inputs -- one output) computation
+     *
+     * @overload
+     * @param in1 first input GMat of the defined binary computation
+     * @param in2 second input GMat of the defined binary computation
+     * @param out output GMat of the defined binary computation
+     */
+    GAPI_WRAP GComputation(GMat in1, GMat in2, GMat out);        // Binary overload
+
+    /**
+     * @brief Defines a binary (two inputs -- one output) computation
+     *
+     * @overload
+     * @param in1 first input GMat of the defined binary computation
+     * @param in2 second input GMat of the defined binary computation
+     * @param out output GScalar of the defined binary computation
+     */
+    GComputation(GMat in1, GMat in2, GScalar out);     // Binary
+                                                       // overload
+                                                       // (scalar)
+
+    /**
+     * @brief Defines a computation with arbitrary input/output number.
+     *
+     * @overload
+     * @param ins vector of inputs GMats for this computation
+     * @param outs vector of outputs GMats for this computation
+     *
+     * Use this overload for cases when number of computation
+     * inputs/outputs is not known in compile-time -- e.g. when graph
+     * is programmatically generated to build an image pyramid with
+     * the given number of levels, etc.
+     */
+    GComputation(const std::vector<GMat> &ins,         // Compatibility overload
+                 const std::vector<GMat> &outs);
+
+    // Various versions of apply(): ////////////////////////////////////////////
+    // 1. Generic apply()
+    /**
+     * @brief Compile graph on-the-fly and immediately execute it on
+     * the inputs data vectors.
+     *
+     * Number of input/output data objects must match GComputation's
+     * protocol, also types of host data objects (cv::Mat, cv::Scalar)
+     * must match the shapes of data objects from protocol (cv::GMat,
+     * cv::GScalar). If there's a mismatch, a run-time exception will
+     * be generated.
+     *
+     * Internally, a cv::GCompiled object is created for the given
+     * input format configuration, which then is executed on the input
+     * data immediately. cv::GComputation caches compiled objects
+     * produced within apply() -- if this method would be called next
+     * time with the same input parameters (image formats, image
+     * resolution, etc), the underlying compiled graph will be reused
+     * without recompilation. If new metadata doesn't match the cached
+     * one, the underlying compiled graph is regenerated.
+     *
+     * @note compile() always triggers a compilation process and
+     * produces a new GCompiled object regardless if a similar one has
+     * been cached via apply() or not.
+     *
+     * @param ins vector of input data to process. Don't create
+     * GRunArgs object manually, use cv::gin() wrapper instead.
+     * @param outs vector of output data to fill results in. cv::Mat
+     * objects may be empty in this vector, G-API will automatically
+     * initialize it with the required format & dimensions. Don't
+     * create GRunArgsP object manually, use cv::gout() wrapper instead.
+     * @param args a list of compilation arguments to pass to the
+     * underlying compilation process. Don't create GCompileArgs
+     * object manually, use cv::compile_args() wrapper instead.
+     *
+     * @sa @ref gapi_data_objects, @ref gapi_compile_args
+     */
+    void apply(GRunArgs &&ins, GRunArgsP &&outs, GCompileArgs &&args = {});       // Arg-to-arg overload
+
+    /// @private -- Exclude this function from OpenCV documentation
+    GAPI_WRAP GRunArgs apply(const cv::detail::ExtractArgsCallback  &callback,
+                                   GCompileArgs                    &&args = {});
+
+    /// @private -- Exclude this function from OpenCV documentation
+    void apply(const std::vector<cv::Mat>& ins,                                   // Compatibility overload
+               const std::vector<cv::Mat>& outs,
+               GCompileArgs &&args = {});
+
+    // 2. Syntax sugar and compatibility overloads
+#if !defined(GAPI_STANDALONE)
+    /**
+     * @brief Execute an unary computation (with compilation on the fly)
+     *
+     * @overload
+     * @param in input cv::Mat for unary computation
+     * @param out output cv::Mat for unary computation
+     * @param args compilation arguments for underlying compilation
+     * process.
+     */
+    void apply(cv::Mat in, cv::Mat &out, GCompileArgs &&args = {}); // Unary overload
+
+    /**
+     * @brief Execute an unary computation (with compilation on the fly)
+     *
+     * @overload
+     * @param in input cv::Mat for unary computation
+     * @param out output cv::Scalar for unary computation
+     * @param args compilation arguments for underlying compilation
+     * process.
+     */
+    void apply(cv::Mat in, cv::Scalar &out, GCompileArgs &&args = {}); // Unary overload (scalar)
+
+    /**
+     * @brief Execute a binary computation (with compilation on the fly)
+     *
+     * @overload
+     * @param in1 first input cv::Mat for binary computation
+     * @param in2 second input cv::Mat for binary computation
+     * @param out output cv::Mat for binary computation
+     * @param args compilation arguments for underlying compilation
+     * process.
+     */
+    void apply(cv::Mat in1, cv::Mat in2, cv::Mat &out, GCompileArgs &&args = {}); // Binary overload
+
+    /**
+     * @brief Execute an binary computation (with compilation on the fly)
+     *
+     * @overload
+     * @param in1 first input cv::Mat for binary computation
+     * @param in2 second input cv::Mat for binary computation
+     * @param out output cv::Scalar for binary computation
+     * @param args compilation arguments for underlying compilation
+     * process.
+     */
+    void apply(cv::Mat in1, cv::Mat in2, cv::Scalar &out, GCompileArgs &&args = {}); // Binary overload (scalar)
+
+    /**
+     * @brief Execute a computation with arbitrary number of
+     * inputs/outputs (with compilation on-the-fly).
+     *
+     * @overload
+     * @param ins vector of input cv::Mat objects to process by the
+     * computation.
+     * @param outs vector of output cv::Mat objects to produce by the
+     * computation.
+     * @param args compilation arguments for underlying compilation
+     * process.
+     *
+     * Numbers of elements in ins/outs vectors must match numbers of
+     * inputs/outputs which were used to define this GComputation.
+     */
+    void apply(const std::vector<cv::Mat>& ins,         // Compatibility overload
+                     std::vector<cv::Mat>& outs,
+               GCompileArgs &&args = {});
+#endif // !defined(GAPI_STANDALONE)
+    // Various versions of compile(): //////////////////////////////////////////
+    // 1. Generic compile() - requires metas to be passed as vector
+    /**
+     * @brief Compile the computation for specific input format(s).
+     *
+     * This method triggers compilation process and produces a new
+     * GCompiled object which then can process data of the given
+     * format. Passing data with different format to the compiled
+     * computation will generate a run-time exception.
+     *
+     * @param in_metas vector of input metadata configuration. Grab
+     * metadata from real data objects (like cv::Mat or cv::Scalar)
+     * using cv::descr_of(), or create it on your own.
+     * @param args compilation arguments for this compilation
+     * process. Compilation arguments directly affect what kind of
+     * executable object would be produced, e.g. which kernels (and
+     * thus, devices) would be used to execute computation.
+     *
+     * @return GCompiled, an executable computation compiled
+     * specifically for the given input parameters.
+     *
+     * @sa @ref gapi_compile_args
+     */
+    GCompiled compile(GMetaArgs &&in_metas, GCompileArgs &&args = {});
+
+    // 2. Syntax sugar - variadic list of metas, no extra compile args
+    // FIXME: SFINAE looks ugly in the generated documentation
+    /**
+     * @overload
+     *
+     * Takes a variadic parameter pack with metadata
+     * descriptors for which a compiled object needs to be produced.
+     *
+     * @return GCompiled, an executable computation compiled
+     * specifically for the given input parameters.
+     */
+    template<typename... Ts>
+    auto compile(const Ts&... metas) ->
+        typename std::enable_if<detail::are_meta_descrs<Ts...>::value, GCompiled>::type
+    {
+        return compile(GMetaArgs{GMetaArg(metas)...}, GCompileArgs());
+    }
+
+    // 3. Syntax sugar - variadic list of metas, extra compile args
+    // (seems optional parameters don't work well when there's an variadic template
+    // comes first)
+    //
+    // Ideally it should look like:
+    //
+    //     template<typename... Ts>
+    //     GCompiled compile(const Ts&... metas, GCompileArgs &&args)
+    //
+    // But not all compilers can handle this (and seems they shouldn't be able to).
+    // FIXME: SFINAE looks ugly in the generated documentation
+    /**
+     * @overload
+     *
+     * Takes a  variadic parameter pack with metadata
+     * descriptors for which a compiled object needs to be produced,
+     * followed by GCompileArgs object representing compilation
+     * arguments for this process.
+     *
+     * @return GCompiled, an executable computation compiled
+     * specifically for the given input parameters.
+     */
+    template<typename... Ts>
+    auto compile(const Ts&... meta_and_compile_args) ->
+        typename std::enable_if<detail::are_meta_descrs_but_last<Ts...>::value
+                                && std::is_same<GCompileArgs, detail::last_type_t<Ts...> >::value,
+                                GCompiled>::type
+    {
+        //FIXME: wrapping meta_and_compile_args into a tuple to unwrap them inside a helper function is the overkill
+        return compile(std::make_tuple(meta_and_compile_args...),
+                       typename detail::MkSeq<sizeof...(Ts)-1>::type());
+    }
+
+
+    // FIXME: Document properly in the Doxygen format
+    // Video-oriented pipeline compilation:
+    // 1. A generic version
+    /**
+     * @brief Compile the computation for streaming mode.
+     *
+     * This method triggers compilation process and produces a new
+     * GStreamingCompiled object which then can process video stream
+     * data of the given format. Passing a stream in a different
+     * format to the compiled computation will generate a run-time
+     * exception.
+     *
+     * @param in_metas vector of input metadata configuration. Grab
+     * metadata from real data objects (like cv::Mat or cv::Scalar)
+     * using cv::descr_of(), or create it on your own.
+     *
+     * @param args compilation arguments for this compilation
+     * process. Compilation arguments directly affect what kind of
+     * executable object would be produced, e.g. which kernels (and
+     * thus, devices) would be used to execute computation.
+     *
+     * @return GStreamingCompiled, a streaming-oriented executable
+     * computation compiled specifically for the given input
+     * parameters.
+     *
+     * @sa @ref gapi_compile_args
+     */
+    GAPI_WRAP GStreamingCompiled compileStreaming(GMetaArgs &&in_metas, GCompileArgs &&args = {});
+
+    /**
+     * @brief Compile the computation for streaming mode.
+     *
+     * This method triggers compilation process and produces a new
+     * GStreamingCompiled object which then can process video stream
+     * data in any format. Underlying mechanisms will be adjusted to
+     * every new input video stream automatically, but please note that
+     * _not all_ existing backends support this (see reshape()).
+     *
+     * @param args compilation arguments for this compilation
+     * process. Compilation arguments directly affect what kind of
+     * executable object would be produced, e.g. which kernels (and
+     * thus, devices) would be used to execute computation.
+     *
+     * @return GStreamingCompiled, a streaming-oriented executable
+     * computation compiled for any input image format.
+     *
+     * @sa @ref gapi_compile_args
+     */
+    GAPI_WRAP GStreamingCompiled compileStreaming(GCompileArgs &&args = {});
+
+    /// @private -- Exclude this function from OpenCV documentation
+    GAPI_WRAP GStreamingCompiled compileStreaming(const cv::detail::ExtractMetaCallback &callback,
+                                                        GCompileArgs                   &&args = {});
+
+    // 2. Direct metadata version
+    /**
+     * @overload
+     *
+     * Takes a variadic parameter pack with metadata
+     * descriptors for which a compiled object needs to be produced.
+     *
+     * @return GStreamingCompiled, a streaming-oriented executable
+     * computation compiled specifically for the given input
+     * parameters.
+     */
+    template<typename... Ts>
+    auto compileStreaming(const Ts&... metas) ->
+        typename std::enable_if<detail::are_meta_descrs<Ts...>::value, GStreamingCompiled>::type
+    {
+        return compileStreaming(GMetaArgs{GMetaArg(metas)...}, GCompileArgs());
+    }
+
+    // 2. Direct metadata + compile arguments version
+    /**
+     * @overload
+     *
+     * Takes a  variadic parameter pack with metadata
+     * descriptors for which a compiled object needs to be produced,
+     * followed by GCompileArgs object representing compilation
+     * arguments for this process.
+     *
+     * @return GStreamingCompiled, a streaming-oriented executable
+     * computation compiled specifically for the given input
+     * parameters.
+     */
+    template<typename... Ts>
+    auto compileStreaming(const Ts&... meta_and_compile_args) ->
+        typename std::enable_if<detail::are_meta_descrs_but_last<Ts...>::value
+                                && std::is_same<GCompileArgs, detail::last_type_t<Ts...> >::value,
+                                GStreamingCompiled>::type
+    {
+        //FIXME: wrapping meta_and_compile_args into a tuple to unwrap them inside a helper function is the overkill
+        return compileStreaming(std::make_tuple(meta_and_compile_args...),
+                                typename detail::MkSeq<sizeof...(Ts)-1>::type());
+    }
+
+    // Internal use only
+    /// @private
+    Priv& priv();
+    /// @private
+    const Priv& priv() const;
+    /// @private
+    explicit GComputation(cv::gapi::s11n::IIStream &);
+    /// @private
+    void serialize(cv::gapi::s11n::IOStream &) const;
+
+protected:
+
+    // 4. Helper methods for (3)
+    /// @private
+    template<typename... Ts, int... IIs>
+    GCompiled compile(const std::tuple<Ts...> &meta_and_compile_args, detail::Seq<IIs...>)
+    {
+        GMetaArgs meta_args = {GMetaArg(std::get<IIs>(meta_and_compile_args))...};
+        GCompileArgs comp_args = std::get<sizeof...(Ts)-1>(meta_and_compile_args);
+        return compile(std::move(meta_args), std::move(comp_args));
+    }
+    template<typename... Ts, int... IIs>
+    GStreamingCompiled compileStreaming(const std::tuple<Ts...> &meta_and_compile_args, detail::Seq<IIs...>)
+    {
+        GMetaArgs meta_args = {GMetaArg(std::get<IIs>(meta_and_compile_args))...};
+        GCompileArgs comp_args = std::get<sizeof...(Ts)-1>(meta_and_compile_args);
+        return compileStreaming(std::move(meta_args), std::move(comp_args));
+    }
+    void recompile(GMetaArgs&& in_metas, GCompileArgs &&args);
+    /// @private
+    std::shared_ptr<Priv> m_priv;
+};
+/** @} */
+
+namespace gapi
+{
+    // FIXME: all these standalone functions need to be added to some
+    // common documentation section
+    /**
+     * @brief Define an tagged island (subgraph) within a computation.
+     *
+     * Declare an Island tagged with `name` and defined from `ins` to `outs`
+     * (exclusively, as ins/outs are data objects, and regioning is done on
+     * operations level).
+     * Throws if any operation between `ins` and `outs` are already assigned
+     * to another island.
+     *
+     * Islands allow to partition graph into subgraphs, fine-tuning
+     * the way it is scheduled by the underlying executor.
+     *
+     * @param name name of the Island to create
+     * @param ins vector of input data objects where the subgraph
+     * begins
+     * @param outs vector of output data objects where the subgraph
+     * ends.
+     *
+     * The way how an island is defined is similar to how
+     * cv::GComputation is defined on input/output data objects.
+     * Same rules apply here as well -- if there's no functional
+     * dependency between inputs and outputs or there's not enough
+     * input data objects were specified to properly calculate all
+     * outputs, an exception is thrown.
+     *
+     * Use cv::GIn() / cv::GOut() to specify input/output vectors.
+     */
+    void GAPI_EXPORTS island(const std::string &name,
+                             GProtoInputArgs  &&ins,
+                             GProtoOutputArgs &&outs);
+} // namespace gapi
+
+} // namespace cv
+#endif // OPENCV_GAPI_GCOMPUTATION_HPP
diff --git a/3rdParty/include/opencv2/gapi/gcomputation_async.hpp b/3rdParty/include/opencv2/gapi/gcomputation_async.hpp
new file mode 100644
index 0000000..8af603e
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gcomputation_async.hpp
@@ -0,0 +1,69 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+#ifndef OPENCV_GAPI_GCOMPUTATION_ASYNC_HPP
+#define OPENCV_GAPI_GCOMPUTATION_ASYNC_HPP
+
+
+#include <future>                           //for std::future
+#include <exception>                        //for std::exception_ptr
+#include <functional>                       //for std::function
+#include <opencv2/gapi/garg.hpp>            //for GRunArgs, GRunArgsP
+#include <opencv2/gapi/gcommon.hpp>         //for GCompileArgs
+#include <opencv2/gapi/own/exports.hpp>
+
+
+namespace cv {
+    //fwd declaration
+    class GComputation;
+namespace gapi {
+namespace wip  {
+    class GAsyncContext;
+    /** In contrast to async() functions, these do call GComputation::apply() member function of the GComputation passed in.
+
+    @param gcomp        Computation (graph) to run asynchronously
+    @param callback     Callback to be called when execution of gcomp is done
+    @param ins          Input parameters for gcomp
+    @param outs         Output parameters for gcomp
+    @param args         Compile arguments to pass to GComputation::apply()
+    @see                async
+    */
+    GAPI_EXPORTS void                async_apply(GComputation& gcomp, std::function<void(std::exception_ptr)>&& callback, GRunArgs &&ins, GRunArgsP &&outs, GCompileArgs &&args = {});
+    /** @overload
+    @param gcomp        Computation (graph) to run asynchronously
+    @param callback     Callback to be called when execution of gcomp is done
+    @param ins          Input parameters for gcomp
+    @param outs         Output parameters for gcomp
+    @param args         Compile arguments to pass to GComputation::apply()
+    @param ctx          Context this request belongs to
+    @see                async_apply async GAsyncContext
+    */
+    GAPI_EXPORTS void                async_apply(GComputation& gcomp, std::function<void(std::exception_ptr)>&& callback, GRunArgs &&ins, GRunArgsP &&outs, GCompileArgs &&args, GAsyncContext& ctx);
+    /** @overload
+    @param gcomp        Computation (graph) to run asynchronously
+    @param ins          Input parameters for gcomp
+    @param outs         Output parameters for gcomp
+    @param args         Compile arguments to pass to GComputation::apply()
+    @return             std::future<void> object to wait for completion of async operation
+    @see                async_apply async
+    */
+    GAPI_EXPORTS std::future<void>   async_apply(GComputation& gcomp, GRunArgs &&ins, GRunArgsP &&outs, GCompileArgs &&args = {});
+    /** @overload
+    @param gcomp        Computation (graph) to run asynchronously
+    @param ins          Input parameters for gcomp
+    @param outs         Output parameters for gcomp
+    @param args         Compile arguments to pass to GComputation::apply()
+    @param ctx          Context this request belongs to
+    @return             std::future<void> object to wait for completion of async operation
+    @see                async_apply async GAsyncContext
+    */
+    GAPI_EXPORTS std::future<void>   async_apply(GComputation& gcomp, GRunArgs &&ins, GRunArgsP &&outs, GCompileArgs &&args,  GAsyncContext& ctx);
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+
+#endif //OPENCV_GAPI_GCOMPUTATION_ASYNC_HPP
diff --git a/3rdParty/include/opencv2/gapi/gframe.hpp b/3rdParty/include/opencv2/gapi/gframe.hpp
new file mode 100644
index 0000000..96913dc
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gframe.hpp
@@ -0,0 +1,112 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GFRAME_HPP
+#define OPENCV_GAPI_GFRAME_HPP
+
+#include <ostream>
+#include <memory>                 // std::shared_ptr
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/gcommon.hpp> // GShape
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/own/assert.hpp>
+
+// TODO GAPI_EXPORTS or so
+namespace cv
+{
+// Forward declaration; GNode and GOrigin are an internal
+// (user-inaccessible) classes.
+class GNode;
+struct GOrigin;
+
+/** \addtogroup gapi_data_objects
+ * @{
+ */
+/**
+ * @brief GFrame class represents an image or media frame in the graph.
+ *
+ * GFrame doesn't store any data itself, instead it describes a
+ * functional relationship between operations consuming and producing
+ * GFrame objects.
+ *
+ * GFrame is introduced to handle various media formats (e.g., NV12 or
+ * I420) under the same type. Various image formats may differ in the
+ * number of planes (e.g. two for NV12, three for I420) and the pixel
+ * layout inside. GFrame type allows to handle these media formats in
+ * the graph uniformly -- the graph structure will not change if the
+ * media format changes, e.g. a different camera or decoder is used
+ * with the same graph. G-API provides a number of operations which
+ * operate directly on GFrame, like `infer<>()` or
+ * renderFrame(); these operations are expected to handle different
+ * media formats inside. There is also a number of accessor
+ * operations like BGR(), Y(), UV() -- these operations provide
+ * access to frame's data in the familiar cv::GMat form, which can be
+ * used with the majority of the existing G-API operations. These
+ * accessor functions may perform color space converion on the fly if
+ * the image format of the GFrame they are applied to differs from the
+ * operation's semantic (e.g. the BGR() accessor is called on an NV12
+ * image frame).
+ *
+ * GFrame is a virtual counterpart of cv::MediaFrame.
+ *
+ * @sa cv::MediaFrame, cv::GFrameDesc, BGR(), Y(), UV(), infer<>().
+ */
+class GAPI_EXPORTS_W_SIMPLE GFrame
+{
+public:
+    /**
+     * @brief Constructs an empty GFrame
+     *
+     * Normally, empty G-API data objects denote a starting point of
+     * the graph. When an empty GFrame is assigned to a result of some
+     * operation, it obtains a functional link to this operation (and
+     * is not empty anymore).
+     */
+    GAPI_WRAP GFrame();                      // Empty constructor
+
+    /// @private
+    GFrame(const GNode &n, std::size_t out); // Operation result constructor
+    /// @private
+    GOrigin& priv();                         // Internal use only
+    /// @private
+    const GOrigin& priv()  const;            // Internal use only
+
+private:
+    std::shared_ptr<GOrigin> m_priv;
+};
+/** @} */
+
+enum class MediaFormat: int
+{
+    BGR = 0,
+    NV12,
+};
+
+/**
+ * \addtogroup gapi_meta_args
+ * @{
+ */
+struct GAPI_EXPORTS GFrameDesc
+{
+    MediaFormat fmt;
+    cv::Size size;
+
+    bool operator== (const GFrameDesc &) const;
+};
+static inline GFrameDesc empty_gframe_desc() { return GFrameDesc{}; }
+/** @} */
+
+class MediaFrame;
+GAPI_EXPORTS GFrameDesc descr_of(const MediaFrame &frame);
+
+GAPI_EXPORTS std::ostream& operator<<(std::ostream& os, const cv::GFrameDesc &desc);
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GFRAME_HPP
diff --git a/3rdParty/include/opencv2/gapi/gkernel.hpp b/3rdParty/include/opencv2/gapi/gkernel.hpp
new file mode 100644
index 0000000..4d3fbd8
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gkernel.hpp
@@ -0,0 +1,749 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2021 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GKERNEL_HPP
+#define OPENCV_GAPI_GKERNEL_HPP
+
+#include <functional>
+#include <iostream>
+#include <string>  // string
+#include <type_traits> // false_type, true_type
+#include <unordered_map> // map (for GKernelPackage)
+#include <utility> // tuple
+
+#include <opencv2/gapi/gcommon.hpp> // CompileArgTag
+#include <opencv2/gapi/util/util.hpp> // Seq
+#include <opencv2/gapi/gcall.hpp>
+#include <opencv2/gapi/garg.hpp>      // GArg
+#include <opencv2/gapi/gmetaarg.hpp>  // GMetaArg
+#include <opencv2/gapi/gtype_traits.hpp> // GTypeTraits
+#include <opencv2/gapi/util/compiler_hints.hpp> //suppress_unused_warning
+#include <opencv2/gapi/gtransform.hpp>
+
+namespace cv {
+
+struct GTypeInfo
+{
+    GShape                 shape;
+    cv::detail::OpaqueKind kind;
+    detail::HostCtor       ctor;
+};
+
+using GShapes    = std::vector<GShape>;
+using GKinds     = std::vector<cv::detail::OpaqueKind>;
+using GCtors     = std::vector<detail::HostCtor>;
+using GTypesInfo = std::vector<GTypeInfo>;
+
+// GKernel describes kernel API to the system
+// FIXME: add attributes of a kernel, (e.g. number and types
+// of inputs, etc)
+struct GAPI_EXPORTS GKernel
+{
+    using M = std::function<GMetaArgs(const GMetaArgs &, const GArgs &)>;
+
+    std::string name;       // kernel ID, defined by its API (signature)
+    std::string tag;        // some (implementation-specific) tag
+    M           outMeta;    // generic adaptor to API::outMeta(...)
+    GShapes     outShapes;  // types (shapes) kernel's outputs
+    GKinds      inKinds;    // kinds of kernel's inputs (fixme: below)
+    GCtors      outCtors;   // captured constructors for template output types
+};
+// TODO: It's questionable if inKinds should really be here. Instead,
+// this information could come from meta.
+
+// GKernelImpl describes particular kernel implementation to the system
+struct GAPI_EXPORTS GKernelImpl
+{
+    util::any         opaque;    // backend-specific opaque info
+    GKernel::M        outMeta;   // for deserialized graphs, the outMeta is taken here
+};
+
+template<typename, typename> class GKernelTypeM;
+
+namespace detail
+{
+    ////////////////////////////////////////////////////////////////////////////
+    // yield() is used in graph construction time as a generic method to obtain
+    // lazy "return value" of G-API operations
+    //
+    template<typename T> struct Yield;
+    template<> struct Yield<cv::GMat>
+    {
+        static inline cv::GMat yield(cv::GCall &call, int i) { return call.yield(i); }
+    };
+    template<> struct Yield<cv::GMatP>
+    {
+        static inline cv::GMatP yield(cv::GCall &call, int i) { return call.yieldP(i); }
+    };
+    template<> struct Yield<cv::GScalar>
+    {
+        static inline cv::GScalar yield(cv::GCall &call, int i) { return call.yieldScalar(i); }
+    };
+    template<typename U> struct Yield<cv::GArray<U> >
+    {
+        static inline cv::GArray<U> yield(cv::GCall &call, int i) { return call.yieldArray<U>(i); }
+    };
+    template<typename U> struct Yield<cv::GOpaque<U> >
+    {
+        static inline cv::GOpaque<U> yield(cv::GCall &call, int i) { return call.yieldOpaque<U>(i); }
+    };
+    template<> struct Yield<GFrame>
+    {
+        static inline cv::GFrame yield(cv::GCall &call, int i) { return call.yieldFrame(i); }
+    };
+
+    ////////////////////////////////////////////////////////////////////////////
+    // Helper classes which brings outputMeta() marshalling to kernel
+    // implementations
+    //
+    // 1. MetaType establishes G#Type -> G#Meta mapping between G-API dynamic
+    //    types and its metadata descriptor types.
+    //    This mapping is used to transform types to call outMeta() callback.
+    template<typename T> struct MetaType;
+    template<> struct MetaType<cv::GMat>    { using type = GMatDesc; };
+    template<> struct MetaType<cv::GMatP>   { using type = GMatDesc; };
+    template<> struct MetaType<cv::GFrame>  { using type = GFrameDesc; };
+    template<> struct MetaType<cv::GScalar> { using type = GScalarDesc; };
+    template<typename U> struct MetaType<cv::GArray<U> >  { using type = GArrayDesc; };
+    template<typename U> struct MetaType<cv::GOpaque<U> > { using type = GOpaqueDesc; };
+    template<typename T> struct MetaType    { using type = T; }; // opaque args passed as-is
+    // FIXME: Move it to type traits?
+
+    // 2. Hacky test based on MetaType to check if we operate on G-* type or not
+    template<typename T> using is_nongapi_type = std::is_same<T, typename MetaType<T>::type>;
+
+    // 3. Two ways to transform input arguments to its meta - for G-* and non-G* types:
+    template<typename T>
+    typename std::enable_if<!is_nongapi_type<T>::value, typename MetaType<T>::type>
+    ::type get_in_meta(const GMetaArgs &in_meta, const GArgs &, int idx)
+    {
+        return util::get<typename MetaType<T>::type>(in_meta.at(idx));
+    }
+
+    template<typename T>
+    typename std::enable_if<is_nongapi_type<T>::value, T>
+    ::type get_in_meta(const GMetaArgs &, const GArgs &in_args, int idx)
+    {
+        return in_args.at(idx).template get<T>();
+    }
+
+    // 4. The MetaHelper itself: an entity which generates outMeta() call
+    //    based on kernel signature, with arguments properly substituted.
+    // 4.1 - case for multiple return values
+    // FIXME: probably can be simplified with std::apply or analogue.
+    template<typename, typename, typename>
+    struct MetaHelper;
+
+    template<typename K, typename... Ins, typename... Outs>
+    struct MetaHelper<K, std::tuple<Ins...>, std::tuple<Outs...> >
+    {
+        template<int... IIs, int... OIs>
+        static GMetaArgs getOutMeta_impl(const GMetaArgs &in_meta,
+                                         const GArgs &in_args,
+                                         detail::Seq<IIs...>,
+                                         detail::Seq<OIs...>)
+        {
+            // FIXME: decay?
+            using R   = std::tuple<typename MetaType<Outs>::type...>;
+            const R r = K::outMeta( get_in_meta<Ins>(in_meta, in_args, IIs)... );
+            return GMetaArgs{ GMetaArg(std::get<OIs>(r))... };
+        }
+        // FIXME: help users identify how outMeta must look like (via default impl w/static_assert?)
+
+        static GMetaArgs getOutMeta(const GMetaArgs &in_meta,
+                                    const GArgs &in_args)
+        {
+            return getOutMeta_impl(in_meta,
+                                   in_args,
+                                   typename detail::MkSeq<sizeof...(Ins)>::type(),
+                                   typename detail::MkSeq<sizeof...(Outs)>::type());
+        }
+    };
+
+    // 4.1 - case for a single return value
+    // FIXME: How to avoid duplication here?
+    template<typename K, typename... Ins, typename Out>
+    struct MetaHelper<K, std::tuple<Ins...>, Out >
+    {
+        template<int... IIs>
+        static GMetaArgs getOutMeta_impl(const GMetaArgs &in_meta,
+                                         const GArgs &in_args,
+                                         detail::Seq<IIs...>)
+        {
+            // FIXME: decay?
+            using R = typename MetaType<Out>::type;
+            const R r = K::outMeta( get_in_meta<Ins>(in_meta, in_args, IIs)... );
+            return GMetaArgs{ GMetaArg(r) };
+        }
+        // FIXME: help users identify how outMeta must look like (via default impl w/static_assert?)
+
+        static GMetaArgs getOutMeta(const GMetaArgs &in_meta,
+                                    const GArgs &in_args)
+        {
+            return getOutMeta_impl(in_meta,
+                                   in_args,
+                                   typename detail::MkSeq<sizeof...(Ins)>::type());
+        }
+    };
+
+    ////////////////////////////////////////////////////////////////////////////
+    // Helper class to introduce tags to calls. By default there's no tag
+    struct NoTag {
+        static constexpr const char *tag() { return ""; }
+    };
+
+} // namespace detail
+
+// GKernelType and GKernelTypeM are base classes which implement typed ::on()
+// method based on kernel signature. GKernelTypeM stands for multiple-return-value kernels
+//
+// G_TYPED_KERNEL and G_TYPED_KERNEL_M macros inherit user classes from GKernelType and
+// GKernelTypeM respectively.
+
+template<typename K, typename... R, typename... Args>
+class GKernelTypeM<K, std::function<std::tuple<R...>(Args...)> >
+    : public detail::MetaHelper<K, std::tuple<Args...>, std::tuple<R...>>
+    , public detail::NoTag
+{
+    template<int... IIs>
+    static std::tuple<R...> yield(cv::GCall &call, detail::Seq<IIs...>)
+    {
+        return std::make_tuple(detail::Yield<R>::yield(call, IIs)...);
+    }
+
+public:
+    using InArgs  = std::tuple<Args...>;
+    using OutArgs = std::tuple<R...>;
+
+    // TODO: Args&&... here?
+    static std::tuple<R...> on(Args... args)
+    {
+        cv::GCall call(GKernel{ K::id()
+                              , K::tag()
+                              , &K::getOutMeta
+                              , {detail::GTypeTraits<R>::shape...}
+                              , {detail::GTypeTraits<Args>::op_kind...}
+                              , {detail::GObtainCtor<R>::get()...}});
+        call.pass(args...); // TODO: std::forward() here?
+        return yield(call, typename detail::MkSeq<sizeof...(R)>::type());
+    }
+};
+
+template<typename, typename> class GKernelType;
+
+template<typename K, typename R, typename... Args>
+class GKernelType<K, std::function<R(Args...)> >
+    : public detail::MetaHelper<K, std::tuple<Args...>, R>
+    , public detail::NoTag
+{
+public:
+    using InArgs  = std::tuple<Args...>;
+    using OutArgs = std::tuple<R>;
+
+    static R on(Args... args)
+    {
+        cv::GCall call(GKernel{ K::id()
+                              , K::tag()
+                              , &K::getOutMeta
+                              , {detail::GTypeTraits<R>::shape}
+                              , {detail::GTypeTraits<Args>::op_kind...}
+                              , {detail::GObtainCtor<R>::get()}});
+        call.pass(args...);
+        return detail::Yield<R>::yield(call, 0);
+    }
+};
+
+namespace detail {
+// This tiny class eliminates the semantic difference between
+// GKernelType and GKernelTypeM.
+template<typename, typename> class KernelTypeMedium;
+
+template<typename K, typename... R, typename... Args>
+class KernelTypeMedium<K, std::function<std::tuple<R...>(Args...)>> :
+    public cv::GKernelTypeM<K, std::function<std::tuple<R...>(Args...)>> {};
+
+template<typename K, typename R, typename... Args>
+class KernelTypeMedium<K, std::function<R(Args...)>> :
+    public cv::GKernelType<K, std::function<R(Args...)>> {};
+} // namespace detail
+
+} // namespace cv
+
+
+// FIXME: I don't know a better way so far. Feel free to suggest one
+// The problem is that every typed kernel should have ::id() but body
+// of the class is defined by user (with outMeta, other stuff)
+
+//! @cond IGNORED
+#define G_ID_HELPER_CLASS(Class)  Class##IdHelper
+
+#define G_ID_HELPER_BODY(Class, Id)                                         \
+    struct G_ID_HELPER_CLASS(Class)                                         \
+    {                                                                       \
+        static constexpr const char * id() {return Id;}                     \
+    };                                                                      \
+//! @endcond
+
+#define GET_G_TYPED_KERNEL(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, NAME, ...) NAME
+#define COMBINE_SIGNATURE(...) __VA_ARGS__
+// Ensure correct __VA_ARGS__ expansion on Windows
+#define __WRAP_VAARGS(x) x
+
+/**
+ * Helper for G_TYPED_KERNEL declares a new G-API Operation. See [Kernel API](@ref gapi_kernel_api)
+ * for more details.
+ *
+ * @param Class type name for this operation.
+ * @param API an `std::function<>`-like signature for the operation;
+ *        return type is a single value or a tuple of multiple values.
+ * @param Id string identifier for the operation. Must be unique.
+ */
+#define G_TYPED_KERNEL_HELPER(Class, API, Id)                                               \
+    G_ID_HELPER_BODY(Class, Id)                                                             \
+    struct Class final: public cv::detail::KernelTypeMedium<Class, std::function API >,     \
+                        public G_ID_HELPER_CLASS(Class)
+// {body} is to be defined by user
+
+#define G_TYPED_KERNEL_HELPER_2(Class, _1, _2, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2), Id)
+
+#define G_TYPED_KERNEL_HELPER_3(Class, _1, _2, _3, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3), Id)
+
+#define G_TYPED_KERNEL_HELPER_4(Class, _1, _2, _3, _4, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3, _4), Id)
+
+#define G_TYPED_KERNEL_HELPER_5(Class, _1, _2, _3, _4, _5, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3, _4, _5), Id)
+
+#define G_TYPED_KERNEL_HELPER_6(Class, _1, _2, _3, _4, _5, _6, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3, _4, _5, _6), Id)
+
+#define G_TYPED_KERNEL_HELPER_7(Class, _1, _2, _3, _4, _5, _6, _7, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3, _4, _5, _6, _7), Id)
+
+#define G_TYPED_KERNEL_HELPER_8(Class, _1, _2, _3, _4, _5, _6, _7, _8, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3, _4, _5, _6, _7, _8), Id)
+
+#define G_TYPED_KERNEL_HELPER_9(Class, _1, _2, _3, _4, _5, _6, _7, _8, _9, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3, _4, _5, _6, _7, _8, _9), Id)
+
+#define G_TYPED_KERNEL_HELPER_10(Class, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, Id) \
+G_TYPED_KERNEL_HELPER(Class, COMBINE_SIGNATURE(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10), Id)
+
+/**
+ * Declares a new G-API Operation. See [Kernel API](@ref gapi_kernel_api)
+ * for more details.
+ *
+ * @param Class type name for this operation.
+ */
+#define G_TYPED_KERNEL(Class, ...) __WRAP_VAARGS(GET_G_TYPED_KERNEL(__VA_ARGS__, \
+                                                 G_TYPED_KERNEL_HELPER_10, \
+                                                 G_TYPED_KERNEL_HELPER_9, \
+                                                 G_TYPED_KERNEL_HELPER_8, \
+                                                 G_TYPED_KERNEL_HELPER_7, \
+                                                 G_TYPED_KERNEL_HELPER_6, \
+                                                 G_TYPED_KERNEL_HELPER_5, \
+                                                 G_TYPED_KERNEL_HELPER_4, \
+                                                 G_TYPED_KERNEL_HELPER_3, \
+                                                 G_TYPED_KERNEL_HELPER_2, \
+                                                 G_TYPED_KERNEL_HELPER)(Class, __VA_ARGS__)) \
+
+/**
+ * Declares a new G-API Operation. See [Kernel API](@ref gapi_kernel_api) for more details.
+ *
+ * @deprecated This macro is deprecated in favor of `G_TYPED_KERNEL` that is used for declaring any
+ * G-API Operation.
+ *
+ * @param Class type name for this operation.
+ */
+#define G_TYPED_KERNEL_M G_TYPED_KERNEL
+
+#define G_API_OP   G_TYPED_KERNEL
+#define G_API_OP_M G_API_OP
+
+namespace cv
+{
+namespace gapi
+{
+    // Prework: model "Device" API before it gets to G-API headers.
+    // FIXME: Don't mix with internal Backends class!
+    /// @private
+    class GAPI_EXPORTS GBackend
+    {
+    public:
+        class Priv;
+
+        // TODO: make it template (call `new` within??)
+        GBackend();
+        explicit GBackend(std::shared_ptr<Priv> &&p);
+
+        Priv& priv();
+        const Priv& priv() const;
+        std::size_t hash() const;
+
+        bool operator== (const GBackend &rhs) const;
+
+    private:
+        std::shared_ptr<Priv> m_priv;
+    };
+
+    inline bool operator != (const GBackend &lhs, const GBackend &rhs)
+    {
+        return !(lhs == rhs);
+    }
+} // namespace gapi
+} // namespace cv
+
+namespace std
+{
+    template<> struct hash<cv::gapi::GBackend>
+    {
+        std::size_t operator() (const cv::gapi::GBackend &b) const
+        {
+            return b.hash();
+        }
+    };
+} // namespace std
+
+
+namespace cv {
+namespace gapi {
+    /// @private
+    class GFunctor
+    {
+    public:
+        virtual cv::GKernelImpl impl()       const = 0;
+        virtual cv::gapi::GBackend backend() const = 0;
+        const char* id()                     const { return m_id; }
+
+        virtual ~GFunctor() = default;
+    protected:
+        GFunctor(const char* id) : m_id(id) { };
+    private:
+        const char* m_id;
+    };
+
+    /** \addtogroup gapi_compile_args
+     * @{
+     */
+
+    // FIXME: Hide implementation
+    /**
+     * @brief A container class for heterogeneous kernel
+     * implementation collections and graph transformations.
+     *
+     * GKernelPackage is a special container class which stores kernel
+     * _implementations_ and graph _transformations_. Objects of this class
+     * are created and passed to cv::GComputation::compile() to specify
+     * which kernels to use and which transformations to apply in the
+     * compiled graph. GKernelPackage may contain kernels of
+     * different backends, e.g. be heterogeneous.
+     *
+     * The most easy way to create a kernel package is to use function
+     * cv::gapi::kernels(). This template functions takes kernel
+     * implementations in form of type list (variadic template) and
+     * generates a kernel package atop of that.
+     *
+     * Kernel packages can be also generated programmatically, starting
+     * with an empty package (created with the default constructor)
+     * and then by populating it with kernels via call to
+     * GKernelPackage::include(). Note this method is also a template
+     * one since G-API kernel and transformation implementations are _types_,
+     * not objects.
+     *
+     * Finally, two kernel packages can be combined into a new one
+     * with function cv::gapi::combine().
+     */
+    class GAPI_EXPORTS_W_SIMPLE GKernelPackage
+    {
+
+        /// @private
+        using M = std::unordered_map<std::string, std::pair<GBackend, GKernelImpl>>;
+
+        /// @private
+        M m_id_kernels;
+
+        /// @private
+        std::vector<GTransform> m_transformations;
+
+    protected:
+        /// @private
+        // Remove ALL implementations of the given API (identified by ID)
+        void removeAPI(const std::string &id);
+
+        /// @private
+        // Partial include() specialization for kernels
+        template <typename KImpl>
+        typename std::enable_if<(std::is_base_of<cv::detail::KernelTag, KImpl>::value), void>::type
+        includeHelper()
+        {
+            auto backend     = KImpl::backend();
+            auto kernel_id   = KImpl::API::id();
+            auto kernel_impl = GKernelImpl{KImpl::kernel(), &KImpl::API::getOutMeta};
+            removeAPI(kernel_id);
+
+            m_id_kernels[kernel_id] = std::make_pair(backend, kernel_impl);
+        }
+
+        /// @private
+        // Partial include() specialization for transformations
+        template <typename TImpl>
+        typename std::enable_if<(std::is_base_of<cv::detail::TransformTag, TImpl>::value), void>::type
+        includeHelper()
+        {
+            m_transformations.emplace_back(TImpl::transformation());
+        }
+
+    public:
+        void include(const GFunctor& functor)
+        {
+            m_id_kernels[functor.id()] = std::make_pair(functor.backend(), functor.impl());
+        }
+        /**
+         * @brief Returns total number of kernels
+         * in the package (across all backends included)
+         *
+         * @return a number of kernels in the package
+         */
+        std::size_t size() const;
+
+        /**
+         * @brief Returns vector of transformations included in the package
+         *
+         * @return vector of transformations included in the package
+         */
+        const std::vector<GTransform>& get_transformations() const;
+
+        /**
+         * @brief Returns vector of kernel ids included in the package
+         *
+         * @return vector of kernel ids included in the package
+         */
+        std::vector<std::string> get_kernel_ids() const;
+
+        /**
+         * @brief Test if a particular kernel _implementation_ KImpl is
+         * included in this kernel package.
+         *
+         * @sa includesAPI()
+         *
+         * @note cannot be applied to transformations
+         *
+         * @return true if there is such kernel, false otherwise.
+         */
+        template<typename KImpl>
+        bool includes() const
+        {
+            static_assert(std::is_base_of<cv::detail::KernelTag, KImpl>::value,
+                          "includes() can be applied to kernels only");
+
+            auto kernel_it = m_id_kernels.find(KImpl::API::id());
+            return kernel_it != m_id_kernels.end() &&
+                   kernel_it->second.first == KImpl::backend();
+        }
+
+        /**
+         * @brief Remove all kernels associated with the given backend
+         * from the package.
+         *
+         * Does nothing if there's no kernels of this backend in the package.
+         *
+         * @param backend backend which kernels to remove
+         */
+        void remove(const GBackend& backend);
+
+        /**
+         * @brief Remove all kernels implementing the given API from
+         * the package.
+         *
+         * Does nothing if there's no kernels implementing the given interface.
+         */
+        template<typename KAPI>
+        void remove()
+        {
+            removeAPI(KAPI::id());
+        }
+
+        // FIXME: Rename to includes() and distinguish API/impl case by
+        //     statically?
+        /**
+         * Check if package contains ANY implementation of a kernel API
+         * by API type.
+         */
+        template<typename KAPI>
+        bool includesAPI() const
+        {
+            return includesAPI(KAPI::id());
+        }
+
+        /// @private
+        bool includesAPI(const std::string &id) const;
+
+        // FIXME: The below comment is wrong, and who needs this function?
+        /**
+         * @brief Find a kernel (by its API)
+         *
+         * Returns implementation corresponding id.
+         * Throws if nothing found.
+         *
+         * @return Backend which hosts matching kernel implementation.
+         *
+         */
+        template<typename KAPI>
+        GBackend lookup() const
+        {
+            return lookup(KAPI::id()).first;
+        }
+
+        /// @private
+        std::pair<cv::gapi::GBackend, cv::GKernelImpl>
+        lookup(const std::string &id) const;
+
+        // FIXME: No overwrites allowed?
+        /**
+         * @brief Put a new kernel implementation or a new transformation
+         * KImpl into the package.
+         */
+        template<typename KImpl>
+        void include()
+        {
+            includeHelper<KImpl>();
+        }
+
+        /**
+         * @brief Adds a new kernel based on it's backend and id into the kernel package
+         *
+         * @param backend backend associated with the kernel
+         * @param kernel_id a name/id of the kernel
+         */
+        void include(const cv::gapi::GBackend& backend, const std::string& kernel_id)
+        {
+            removeAPI(kernel_id);
+            m_id_kernels[kernel_id] = std::make_pair(backend, GKernelImpl{{}, {}});
+        }
+
+        /**
+         * @brief Lists all backends which are included into package
+         *
+         * @return vector of backends
+         */
+        std::vector<GBackend> backends() const;
+
+        // TODO: Doxygen bug -- it wants me to place this comment
+        // here, not below.
+        /**
+         * @brief Create a new package based on `lhs` and `rhs`.
+         *
+         * @param lhs "Left-hand-side" package in the process
+         * @param rhs "Right-hand-side" package in the process
+         * @return a new kernel package.
+         */
+        friend GAPI_EXPORTS GKernelPackage combine(const GKernelPackage  &lhs,
+                                                   const GKernelPackage  &rhs);
+    };
+
+    /**
+     * @brief Create a kernel package object containing kernels
+     * and transformations specified in variadic template argument.
+     *
+     * In G-API, kernel implementations and transformations are _types_.
+     * Every backend has its own kernel API (like GAPI_OCV_KERNEL() and
+     * GAPI_FLUID_KERNEL()) but all of that APIs define a new type for
+     * each kernel implementation.
+     *
+     * Use this function to pass kernel implementations (defined in
+     * either way) and transformations to the system. Example:
+     *
+     * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp kernels_snippet
+     *
+     * Note that kernels() itself is a function returning object, not
+     * a type, so having `()` at the end is important -- it must be a
+     * function call.
+     */
+    template<typename... KK> GKernelPackage kernels()
+    {
+        // FIXME: currently there is no check that transformations' signatures are unique
+        // and won't be any intersection in graph compilation stage
+        static_assert(cv::detail::all_unique<typename KK::API...>::value, "Kernels API must be unique");
+
+        GKernelPackage pkg;
+
+        // For those who wonder - below is a trick to call a number of
+        // methods based on parameter pack (zeroes just help hiding these
+        // calls into a sequence which helps to expand this parameter pack).
+        // Just note that `f(),a` always equals to `a` (with f() called!)
+        // and parentheses are used to hide function call in the expanded sequence.
+        // Leading 0 helps to handle case when KK is an empty list (kernels<>()).
+        int unused[] = { 0, (pkg.include<KK>(), 0)... };
+        cv::util::suppress_unused_warning(unused);
+        return pkg;
+    };
+
+    template<typename... FF>
+    GKernelPackage kernels(FF&... functors)
+    {
+        GKernelPackage pkg;
+        int unused[] = { 0, (pkg.include(functors), 0)... };
+        cv::util::suppress_unused_warning(unused);
+        return pkg;
+    };
+
+    /** @} */
+
+    // FYI - this function is already commented above
+    GAPI_EXPORTS GKernelPackage combine(const GKernelPackage  &lhs,
+                                        const GKernelPackage  &rhs);
+
+    /**
+     * @brief Combines multiple G-API kernel packages into one
+     *
+     * @overload
+     *
+     * This function successively combines the passed kernel packages using a right fold.
+     * Calling `combine(a, b, c)` is equal to `combine(a, combine(b, c))`.
+     *
+     * @return The resulting kernel package
+     */
+    template<typename... Ps>
+    GKernelPackage combine(const GKernelPackage &a, const GKernelPackage &b, Ps&&... rest)
+    {
+        return combine(a, combine(b, rest...));
+    }
+
+    /** \addtogroup gapi_compile_args
+     * @{
+     */
+    /**
+     * @brief cv::gapi::use_only() is a special combinator which hints G-API to use only
+     * kernels specified in cv::GComputation::compile() (and not to extend kernels available by
+     * default with that package).
+     */
+    struct GAPI_EXPORTS use_only
+    {
+        GKernelPackage pkg;
+    };
+    /** @} */
+
+} // namespace gapi
+
+namespace detail
+{
+    template<> struct CompileArgTag<cv::gapi::GKernelPackage>
+    {
+        static const char* tag() { return "gapi.kernel_package"; }
+    };
+
+    template<> struct CompileArgTag<cv::gapi::use_only>
+    {
+        static const char* tag() { return "gapi.use_only"; }
+    };
+} // namespace detail
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GKERNEL_HPP
diff --git a/3rdParty/include/opencv2/gapi/gmat.hpp b/3rdParty/include/opencv2/gapi/gmat.hpp
new file mode 100644
index 0000000..7bea97b
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gmat.hpp
@@ -0,0 +1,279 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GMAT_HPP
+#define OPENCV_GAPI_GMAT_HPP
+
+#include <ostream>
+#include <memory>                 // std::shared_ptr
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/gcommon.hpp> // GShape
+
+#include <opencv2/gapi/own/assert.hpp>
+
+// TODO GAPI_EXPORTS or so
+namespace cv
+{
+// Forward declaration; GNode and GOrigin are an internal
+// (user-inaccessible) classes.
+class GNode;
+struct GOrigin;
+
+/** \addtogroup gapi_data_objects
+ * @{
+ *
+ * @brief G-API data objects used to build G-API expressions.
+ *
+ * These objects do not own any particular data (except compile-time
+ * associated values like with cv::GScalar or `cv::GArray<T>`) and are
+ * used only to construct graphs.
+ *
+ * Every graph in G-API starts and ends with data objects.
+ *
+ * Once constructed and compiled, G-API operates with regular host-side
+ * data instead. Refer to the below table to find the mapping between
+ * G-API and regular data types when passing input and output data
+ * structures to G-API:
+ *
+ *    G-API data type    | I/O data type
+ *    ------------------ | -------------
+ *    cv::GMat           | cv::Mat, cv::UMat, cv::RMat
+ *    cv::GScalar        | cv::Scalar
+ *    `cv::GArray<T>`    | std::vector<T>
+ *    `cv::GOpaque<T>`   | T
+ *    cv::GFrame         | cv::MediaFrame
+ */
+/**
+ * @brief GMat class represents image or tensor data in the
+ * graph.
+ *
+ * GMat doesn't store any data itself, instead it describes a
+ * functional relationship between operations consuming and producing
+ * GMat objects.
+ *
+ * GMat is a virtual counterpart of Mat and UMat, but it
+ * doesn't mean G-API use Mat or UMat objects internally to represent
+ * GMat objects -- the internal data representation may be
+ * backend-specific or optimized out at all.
+ *
+ * @sa Mat, GMatDesc
+ */
+class GAPI_EXPORTS_W_SIMPLE GMat
+{
+public:
+    /**
+     * @brief Constructs an empty GMat
+     *
+     * Normally, empty G-API data objects denote a starting point of
+     * the graph. When an empty GMat is assigned to a result of some
+     * operation, it obtains a functional link to this operation (and
+     * is not empty anymore).
+     */
+    GAPI_WRAP GMat();                       // Empty constructor
+
+    /// @private
+    GMat(const GNode &n, std::size_t out);  // Operation result constructor
+    /// @private
+    GOrigin& priv();                        // Internal use only
+    /// @private
+    const GOrigin& priv()  const;           // Internal use only
+
+private:
+    std::shared_ptr<GOrigin> m_priv;
+};
+
+class GAPI_EXPORTS GMatP : public GMat
+{
+public:
+    using GMat::GMat;
+};
+
+class RMat;
+
+/** @} */
+
+/**
+ * \addtogroup gapi_meta_args
+ * @{
+ */
+struct GAPI_EXPORTS_W_SIMPLE GMatDesc
+{
+    // FIXME: Default initializers in C++14
+    GAPI_PROP int depth;
+    GAPI_PROP int chan;
+    GAPI_PROP cv::Size size; // NB.: no multi-dimensional cases covered yet
+    GAPI_PROP bool planar;
+    GAPI_PROP std::vector<int> dims; // FIXME: Maybe it's real questionable to have it here
+
+    GAPI_WRAP GMatDesc(int d, int c, cv::Size s, bool p = false)
+        : depth(d), chan(c), size(s), planar(p) {}
+
+    GAPI_WRAP GMatDesc(int d, const std::vector<int> &dd)
+        : depth(d), chan(-1), size{-1,-1}, planar(false), dims(dd) {}
+
+    GAPI_WRAP GMatDesc(int d, std::vector<int> &&dd)
+        : depth(d), chan(-1), size{-1,-1}, planar(false), dims(std::move(dd)) {}
+
+    GAPI_WRAP GMatDesc() : GMatDesc(-1, -1, {-1,-1}) {}
+
+    inline bool operator== (const GMatDesc &rhs) const
+    {
+        return    depth  == rhs.depth
+               && chan   == rhs.chan
+               && size   == rhs.size
+               && planar == rhs.planar
+               && dims   == rhs.dims;
+    }
+
+    inline bool operator!= (const GMatDesc &rhs) const
+    {
+        return !(*this == rhs);
+    }
+
+    bool isND() const { return !dims.empty(); }
+
+    // Checks if the passed mat can be described by this descriptor
+    // (it handles the case when
+    // 1-channel mat can be reinterpreted as is (1-channel mat)
+    // and as a 3-channel planar mat with height divided by 3)
+    bool canDescribe(const cv::Mat& mat) const;
+
+    bool canDescribe(const cv::RMat& mat) const;
+
+    // Meta combinator: return a new GMatDesc which differs in size by delta
+    // (all other fields are taken unchanged from this GMatDesc)
+    // FIXME: a better name?
+    GAPI_WRAP GMatDesc withSizeDelta(cv::Size delta) const
+    {
+        GMatDesc desc(*this);
+        desc.size += delta;
+        return desc;
+    }
+    // Meta combinator: return a new GMatDesc which differs in size by delta
+    // (all other fields are taken unchanged from this GMatDesc)
+    //
+    // This is an overload.
+    GAPI_WRAP GMatDesc withSizeDelta(int dx, int dy) const
+    {
+        return withSizeDelta(cv::Size{dx,dy});
+    }
+
+    GAPI_WRAP GMatDesc withSize(cv::Size sz) const
+    {
+        GMatDesc desc(*this);
+        desc.size = sz;
+        return desc;
+    }
+
+    // Meta combinator: return a new GMatDesc with specified data depth.
+    // (all other fields are taken unchanged from this GMatDesc)
+    GAPI_WRAP GMatDesc withDepth(int ddepth) const
+    {
+        GAPI_Assert(CV_MAT_CN(ddepth) == 1 || ddepth == -1);
+        GMatDesc desc(*this);
+        if (ddepth != -1) desc.depth = ddepth;
+        return desc;
+    }
+
+    // Meta combinator: return a new GMatDesc with specified data depth
+    // and number of channels.
+    // (all other fields are taken unchanged from this GMatDesc)
+    GAPI_WRAP GMatDesc withType(int ddepth, int dchan) const
+    {
+        GAPI_Assert(CV_MAT_CN(ddepth) == 1 || ddepth == -1);
+        GMatDesc desc = withDepth(ddepth);
+        desc.chan = dchan;
+        return desc;
+    }
+
+    // Meta combinator: return a new GMatDesc with planar flag set
+    // (no size changes are performed, only channel interpretation is changed
+    // (interleaved -> planar)
+    GAPI_WRAP GMatDesc asPlanar() const
+    {
+        GAPI_Assert(planar == false);
+        GMatDesc desc(*this);
+        desc.planar = true;
+        return desc;
+    }
+
+    // Meta combinator: return a new GMatDesc
+    // reinterpreting 1-channel input as planar image
+    // (size height is divided by plane number)
+    GAPI_WRAP GMatDesc asPlanar(int planes) const
+    {
+        GAPI_Assert(planar == false);
+        GAPI_Assert(chan == 1);
+        GAPI_Assert(planes > 1);
+        GAPI_Assert(size.height % planes == 0);
+        GMatDesc desc(*this);
+        desc.size.height /=  planes;
+        desc.chan = planes;
+        return desc.asPlanar();
+    }
+
+    // Meta combinator: return a new GMatDesc with planar flag set to false
+    // (no size changes are performed, only channel interpretation is changed
+    // (planar -> interleaved)
+    GAPI_WRAP GMatDesc asInterleaved() const
+    {
+        GAPI_Assert(planar == true);
+        GMatDesc desc(*this);
+        desc.planar = false;
+        return desc;
+    }
+};
+
+static inline GMatDesc empty_gmat_desc() { return GMatDesc{-1,-1,{-1,-1}}; }
+
+namespace gapi { namespace detail {
+/** Checks GMatDesc fields if the passed matrix is a set of n-dimentional points.
+@param in GMatDesc to check.
+@param n expected dimensionality.
+@return the amount of points. In case input matrix can't be described as vector of points
+of expected dimensionality, returns -1.
+ */
+int checkVector(const GMatDesc& in, const size_t n);
+
+/** @overload
+
+Checks GMatDesc fields if the passed matrix can be described as a set of points of any
+dimensionality.
+
+@return array of two elements in form of std::vector<int>: the amount of points
+and their calculated dimensionality. In case input matrix can't be described as vector of points,
+returns {-1, -1}.
+ */
+std::vector<int> checkVector(const GMatDesc& in);
+}} // namespace gapi::detail
+
+#if !defined(GAPI_STANDALONE)
+GAPI_EXPORTS GMatDesc descr_of(const cv::UMat &mat);
+#endif // !defined(GAPI_STANDALONE)
+
+//Fwd declarations
+namespace gapi { namespace own {
+    class Mat;
+    GAPI_EXPORTS GMatDesc descr_of(const Mat &mat);
+}}//gapi::own
+
+GAPI_EXPORTS GMatDesc descr_of(const RMat &mat);
+
+#if !defined(GAPI_STANDALONE)
+GAPI_EXPORTS GMatDesc descr_of(const cv::Mat &mat);
+#else
+using gapi::own::descr_of;
+#endif
+
+/** @} */
+
+GAPI_EXPORTS std::ostream& operator<<(std::ostream& os, const cv::GMatDesc &desc);
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GMAT_HPP
diff --git a/3rdParty/include/opencv2/gapi/gmetaarg.hpp b/3rdParty/include/opencv2/gapi/gmetaarg.hpp
new file mode 100644
index 0000000..f21182c
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gmetaarg.hpp
@@ -0,0 +1,80 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GMETAARG_HPP
+#define OPENCV_GAPI_GMETAARG_HPP
+
+#include <vector>
+#include <type_traits>
+
+#include <opencv2/gapi/util/util.hpp>
+#include <opencv2/gapi/util/variant.hpp>
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+#include <opencv2/gapi/garray.hpp>
+#include <opencv2/gapi/gopaque.hpp>
+#include <opencv2/gapi/gframe.hpp>
+
+namespace cv
+{
+// FIXME: Rename to GMeta?
+// FIXME: user shouldn't deal with it - put to detail?
+// GMetaArg is an union type over descriptions of G-types which can serve as
+// GComputation's in/output slots.
+//
+// GMetaArg objects are passed as arguments to GComputation::compile()
+// to specify which data a compiled computation should be specialized on.
+// For manual compile(), user must supply this metadata, in case of apply()
+// this metadata is taken from arguments computation should operate on.
+//
+// The first type (monostate) is equal to "uninitialized"/"unresolved" meta.
+using GMetaArg = util::variant
+    < util::monostate
+    , GMatDesc
+    , GScalarDesc
+    , GArrayDesc
+    , GOpaqueDesc
+    , GFrameDesc
+    >;
+GAPI_EXPORTS std::ostream& operator<<(std::ostream& os, const GMetaArg &);
+
+using GMetaArgs = std::vector<GMetaArg>;
+
+namespace detail
+{
+    // These traits are used by GComputation::compile()
+
+    // FIXME: is_constructible<T> doesn't work as variant doesn't do any SFINAE
+    // in its current template constructor
+
+    template<typename T> struct is_meta_descr    : std::false_type {};
+    template<> struct is_meta_descr<GMatDesc>    : std::true_type {};
+    template<> struct is_meta_descr<GScalarDesc> : std::true_type {};
+    template<> struct is_meta_descr<GArrayDesc>  : std::true_type {};
+    template<> struct is_meta_descr<GOpaqueDesc> : std::true_type {};
+
+    template<typename... Ts>
+    using are_meta_descrs = all_satisfy<is_meta_descr, Ts...>;
+
+    template<typename... Ts>
+    using are_meta_descrs_but_last = all_satisfy<is_meta_descr, typename all_but_last<Ts...>::type>;
+
+} // namespace detail
+
+// Note: descr_of(std::vector<..>) returns a GArrayDesc, while
+//       descrs_of(std::vector<..>) returns an array of Meta args!
+class UMat;
+GAPI_EXPORTS cv::GMetaArgs descrs_of(const std::vector<cv::Mat> &vec);
+GAPI_EXPORTS cv::GMetaArgs descrs_of(const std::vector<cv::UMat> &vec);
+namespace gapi { namespace own {
+    GAPI_EXPORTS cv::GMetaArgs descrs_of(const std::vector<Mat> &vec);
+}} // namespace gapi::own
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GMETAARG_HPP
diff --git a/3rdParty/include/opencv2/gapi/gopaque.hpp b/3rdParty/include/opencv2/gapi/gopaque.hpp
new file mode 100644
index 0000000..f77795c
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gopaque.hpp
@@ -0,0 +1,369 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GOPAQUE_HPP
+#define OPENCV_GAPI_GOPAQUE_HPP
+
+#include <functional>
+#include <ostream>
+#include <memory>
+
+#include <opencv2/gapi/own/exports.hpp>
+#include <opencv2/gapi/opencv_includes.hpp>
+
+#include <opencv2/gapi/util/any.hpp>
+#include <opencv2/gapi/util/variant.hpp>
+#include <opencv2/gapi/util/throw.hpp>
+#include <opencv2/gapi/util/type_traits.hpp>
+#include <opencv2/gapi/own/assert.hpp>
+
+#include <opencv2/gapi/gcommon.hpp>  // OpaqueKind
+#include <opencv2/gapi/garray.hpp>  // TypeHintBase
+
+namespace cv
+{
+// Forward declaration; GNode and GOrigin are an internal
+// (user-inaccessible) classes.
+class GNode;
+struct GOrigin;
+template<typename T> class GOpaque;
+
+/**
+ * \addtogroup gapi_meta_args
+ * @{
+ */
+struct GAPI_EXPORTS_W_SIMPLE GOpaqueDesc
+{
+    // FIXME: Body
+    // FIXME: Also implement proper operator== then
+    bool operator== (const GOpaqueDesc&) const { return true; }
+};
+template<typename U> GOpaqueDesc descr_of(const U &) { return {};}
+GAPI_EXPORTS_W inline GOpaqueDesc empty_gopaque_desc() {return {}; }
+/** @} */
+
+std::ostream& operator<<(std::ostream& os, const cv::GOpaqueDesc &desc);
+
+namespace detail
+{
+    // ConstructOpaque is a callback which stores information about T and is used by
+    // G-API runtime to construct an object in host memory (T remains opaque for G-API).
+    // ConstructOpaque is carried into G-API internals by GOpaqueU.
+    // Currently it is suitable for Host (CPU) plugins only, real offload may require
+    // more information for manual memory allocation on-device.
+    class OpaqueRef;
+    using ConstructOpaque = std::function<void(OpaqueRef&)>;
+
+    // FIXME: garray.hpp already contains hint classes (for actual T type verification),
+    // need to think where it can be moved (currently opaque uses it from garray)
+
+    // This class strips type information from GOpaque<T> and makes it usable
+    // in the G-API graph compiler (expression unrolling, graph generation, etc).
+    // Part of GProtoArg.
+    class GAPI_EXPORTS GOpaqueU
+    {
+    public:
+        GOpaqueU(const GNode &n, std::size_t out); // Operation result constructor
+
+        template <typename T>
+        bool holds() const;                       // Check if was created from GOpaque<T>
+
+        GOrigin& priv();                          // Internal use only
+        const GOrigin& priv() const;              // Internal use only
+
+    protected:
+        GOpaqueU();                                // Default constructor
+        template<class> friend class cv::GOpaque;  // (available for GOpaque<T> only)
+
+        void setConstructFcn(ConstructOpaque &&cv);  // Store T-aware constructor
+
+        template <typename T>
+        void specifyType();                       // Store type of initial GOpaque<T>
+
+        template <typename T>
+        void storeKind();
+
+        void setKind(cv::detail::OpaqueKind);
+
+        std::shared_ptr<GOrigin> m_priv;
+        std::shared_ptr<TypeHintBase> m_hint;
+    };
+
+    template <typename T>
+    bool GOpaqueU::holds() const{
+        GAPI_Assert(m_hint != nullptr);
+        using U = util::decay_t<T>;
+        return dynamic_cast<TypeHint<U>*>(m_hint.get()) != nullptr;
+    };
+
+    template <typename T>
+    void GOpaqueU::specifyType(){
+        m_hint.reset(new TypeHint<util::decay_t<T>>);
+    };
+
+    template <typename T>
+    void GOpaqueU::storeKind(){
+        // FIXME: Add assert here on cv::Mat and cv::Scalar?
+        setKind(cv::detail::GOpaqueTraits<T>::kind);
+    };
+
+    // This class represents a typed object reference.
+    // Depending on origins, this reference may be either "just a" reference to
+    // an object created externally, OR actually own the underlying object
+    // (be value holder).
+    class BasicOpaqueRef
+    {
+    public:
+        cv::GOpaqueDesc m_desc;
+        virtual ~BasicOpaqueRef() {}
+
+        virtual void mov(BasicOpaqueRef &ref) = 0;
+        virtual const void* ptr() const = 0;
+        virtual void set(const cv::util::any &a) = 0;
+    };
+
+    template<typename T> class OpaqueRefT final: public BasicOpaqueRef
+    {
+        using empty_t  = util::monostate;
+        using ro_ext_t = const T *;
+        using rw_ext_t =       T *;
+        using rw_own_t =       T  ;
+        util::variant<empty_t, ro_ext_t, rw_ext_t, rw_own_t> m_ref;
+
+        inline bool isEmpty() const { return util::holds_alternative<empty_t>(m_ref);  }
+        inline bool isROExt() const { return util::holds_alternative<ro_ext_t>(m_ref); }
+        inline bool isRWExt() const { return util::holds_alternative<rw_ext_t>(m_ref); }
+        inline bool isRWOwn() const { return util::holds_alternative<rw_own_t>(m_ref); }
+
+        void init(const T* obj = nullptr)
+        {
+            if (obj) m_desc = cv::descr_of(*obj);
+        }
+
+    public:
+        OpaqueRefT() { init(); }
+        virtual ~OpaqueRefT() {}
+
+        explicit OpaqueRefT(const T&  obj) : m_ref(&obj)           { init(&obj); }
+        explicit OpaqueRefT(      T&  obj) : m_ref(&obj)           { init(&obj); }
+        explicit OpaqueRefT(      T&& obj) : m_ref(std::move(obj)) { init(&obj); }
+
+        // Reset a OpaqueRefT. Called only for objects instantiated
+        // internally in G-API (e.g. temporary GOpaque<T>'s within a
+        // computation).  Reset here means both initialization
+        // (creating an object) and reset (discarding its existing
+        // content before the next execution). Must never be called
+        // for external OpaqueRefTs.
+        void reset()
+        {
+            if (isEmpty())
+            {
+                T empty_obj{};
+                m_desc = cv::descr_of(empty_obj);
+                m_ref  = std::move(empty_obj);
+                GAPI_Assert(isRWOwn());
+            }
+            else if (isRWOwn())
+            {
+                util::get<rw_own_t>(m_ref) = {};
+            }
+            else GAPI_Assert(false); // shouldn't be called in *EXT modes
+        }
+
+        // Obtain a WRITE reference to underlying object
+        // Used by CPU kernel API wrappers when a kernel execution frame
+        // is created
+        T& wref()
+        {
+            GAPI_Assert(isRWExt() || isRWOwn());
+            if (isRWExt()) return *util::get<rw_ext_t>(m_ref);
+            if (isRWOwn()) return  util::get<rw_own_t>(m_ref);
+            util::throw_error(std::logic_error("Impossible happened"));
+        }
+
+        // Obtain a READ reference to underlying object
+        // Used by CPU kernel API wrappers when a kernel execution frame
+        // is created
+        const T& rref() const
+        {
+            // ANY object can be accessed for reading, even if it declared for
+            // output. Example -- a GComputation from [in] to [out1,out2]
+            // where [out2] is a result of operation applied to [out1]:
+            //
+            //            GComputation boundary
+            //            . . . . . . .
+            //            .           .
+            //     [in] ----> foo() ----> [out1]
+            //            .           .    :
+            //            .           . . .:. . .
+            //            .                V    .
+            //            .              bar() ---> [out2]
+            //            . . . . . . . . . . . .
+            //
+            if (isROExt()) return *util::get<ro_ext_t>(m_ref);
+            if (isRWExt()) return *util::get<rw_ext_t>(m_ref);
+            if (isRWOwn()) return  util::get<rw_own_t>(m_ref);
+            util::throw_error(std::logic_error("Impossible happened"));
+        }
+
+        virtual void mov(BasicOpaqueRef &v) override {
+            OpaqueRefT<T> *tv = dynamic_cast<OpaqueRefT<T>*>(&v);
+            GAPI_Assert(tv != nullptr);
+            wref() = std::move(tv->wref());
+        }
+
+        virtual const void* ptr() const override { return &rref(); }
+
+        virtual void set(const cv::util::any &a) override {
+            wref() = util::any_cast<T>(a);
+        }
+    };
+
+    // This class strips type information from OpaqueRefT<> and makes it usable
+    // in the G-API executables (carrying run-time data/information to kernels).
+    // Part of GRunArg.
+    // Its methods are typed proxies to OpaqueRefT<T>.
+    // OpaqueRef maintains "reference" semantics so two copies of OpaqueRef refer
+    // to the same underlying object.
+    class OpaqueRef
+    {
+        std::shared_ptr<BasicOpaqueRef> m_ref;
+        cv::detail::OpaqueKind m_kind = cv::detail::OpaqueKind::CV_UNKNOWN;
+
+        template<typename T> inline void check() const
+        {
+            GAPI_DbgAssert(dynamic_cast<OpaqueRefT<T>*>(m_ref.get()) != nullptr);
+        }
+
+    public:
+        OpaqueRef() = default;
+
+        template<
+            typename T,
+            typename = util::are_different_t<OpaqueRef, T>
+        >
+        // FIXME: probably won't work with const object
+        explicit OpaqueRef(T&& obj) :
+            m_ref(new OpaqueRefT<util::decay_t<T>>(std::forward<T>(obj))),
+            m_kind(GOpaqueTraits<util::decay_t<T>>::kind) {}
+
+        cv::detail::OpaqueKind getKind() const
+        {
+            return m_kind;
+        }
+
+        template<typename T> void reset()
+        {
+            if (!m_ref) m_ref.reset(new OpaqueRefT<T>());
+            check<T>();
+            storeKind<T>();
+            static_cast<OpaqueRefT<T>&>(*m_ref).reset();
+        }
+
+        template <typename T>
+        void storeKind()
+        {
+            m_kind = cv::detail::GOpaqueTraits<T>::kind;
+        }
+
+        template<typename T> T& wref()
+        {
+            check<T>();
+            return static_cast<OpaqueRefT<T>&>(*m_ref).wref();
+        }
+
+        template<typename T> const T& rref() const
+        {
+            check<T>();
+            return static_cast<OpaqueRefT<T>&>(*m_ref).rref();
+        }
+
+        void mov(OpaqueRef &v)
+        {
+            m_ref->mov(*v.m_ref);
+        }
+
+        cv::GOpaqueDesc descr_of() const
+        {
+            return m_ref->m_desc;
+        }
+
+        // May be used to uniquely identify this object internally
+        const void *ptr() const { return m_ref->ptr(); }
+
+        // Introduced for in-graph meta handling
+        OpaqueRef& operator= (const cv::util::any &a)
+        {
+            m_ref->set(a);
+            return *this;
+        }
+    };
+} // namespace detail
+
+/** \addtogroup gapi_data_objects
+ * @{
+ */
+/**
+ * @brief `cv::GOpaque<T>` template class represents an object of
+ * class `T` in the graph.
+ *
+ * `cv::GOpaque<T>` describes a functional relationship between operations
+ * consuming and producing object of class `T`. `cv::GOpaque<T>` is
+ * designed to extend G-API with user-defined data types, which are
+ * often required with user-defined operations. G-API can't apply any
+ * optimizations to user-defined types since these types are opaque to
+ * the framework. However, there is a number of G-API operations
+ * declared with `cv::GOpaque<T>` as a return type,
+ * e.g. cv::gapi::streaming::timestamp() or cv::gapi::streaming::size().
+ *
+ * @sa `cv::GArray<T>`
+ */
+template<typename T> class GOpaque
+{
+public:
+    // Host type (or Flat type) - the type this GOpaque is actually
+    // specified to.
+    /// @private
+    using HT = typename detail::flatten_g<util::decay_t<T>>::type;
+
+    /**
+     * @brief Constructs an empty `cv::GOpaque<T>`
+     *
+     * Normally, empty G-API data objects denote a starting point of
+     * the graph. When an empty `cv::GOpaque<T>` is assigned to a result
+     * of some operation, it obtains a functional link to this
+     * operation (and is not empty anymore).
+     */
+    GOpaque() { putDetails(); }              // Empty constructor
+
+    /// @private
+    explicit GOpaque(detail::GOpaqueU &&ref) // GOpaqueU-based constructor
+        : m_ref(ref) { putDetails(); }       // (used by GCall, not for users)
+
+    /// @private
+    detail::GOpaqueU strip() const {
+        return m_ref;
+    }
+    /// @private
+    static void Ctor(detail::OpaqueRef& ref) {
+        ref.reset<HT>();
+    }
+private:
+    void putDetails() {
+        m_ref.setConstructFcn(&Ctor);
+        m_ref.specifyType<HT>();
+        m_ref.storeKind<HT>();
+    }
+
+    detail::GOpaqueU m_ref;
+};
+
+/** @} */
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GOPAQUE_HPP
diff --git a/3rdParty/include/opencv2/gapi/gproto.hpp b/3rdParty/include/opencv2/gapi/gproto.hpp
new file mode 100644
index 0000000..a2b5d83
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gproto.hpp
@@ -0,0 +1,159 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GPROTO_HPP
+#define OPENCV_GAPI_GPROTO_HPP
+
+#include <type_traits>
+#include <vector>
+#include <ostream>
+
+#include <opencv2/gapi/util/variant.hpp>
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+#include <opencv2/gapi/garray.hpp>
+#include <opencv2/gapi/gopaque.hpp>
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/gmetaarg.hpp>
+
+namespace cv {
+
+// FIXME: user shouldn't deal with it - put to detail?
+// GProtoArg is an union type over G-types which can serve as
+// GComputation's in/output slots. In other words, GProtoArg
+// wraps any type which can serve as G-API exchange type.
+//
+// In Runtime, GProtoArgs are substituted with appropriate GRunArgs.
+//
+// GProtoArg objects are constructed in-place when user describes
+// (captures) computations, user doesn't interact with these types
+// directly.
+using GProtoArg = util::variant
+    < GMat
+    , GMatP
+    , GFrame
+    , GScalar
+    , detail::GArrayU  // instead of GArray<T>
+    , detail::GOpaqueU // instead of GOpaque<T>
+    >;
+
+using GProtoArgs = std::vector<GProtoArg>;
+
+namespace detail
+{
+template<typename... Ts> inline GProtoArgs packArgs(Ts... args)
+{
+    return GProtoArgs{ GProtoArg(wrap_gapi_helper<Ts>::wrap(args))... };
+}
+
+}
+
+template<class Tag>
+struct GIOProtoArgs
+{
+public:
+    // NB: Used by python wrapper
+    GIOProtoArgs() = default;
+    explicit GIOProtoArgs(const GProtoArgs& args) : m_args(args) {}
+    explicit GIOProtoArgs(GProtoArgs &&args)      : m_args(std::move(args)) {}
+
+    GProtoArgs m_args;
+
+    // TODO: Think about the addition operator
+    /**
+     * @brief This operator allows to complement the proto vectors at runtime.
+     *
+     * It's an ordinary overload of addition assignment operator.
+     *
+     * Example of usage:
+     * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/dynamic_graph_snippets.cpp  GIOProtoArgs usage
+     *
+     */
+    template<typename Tg>
+    friend GIOProtoArgs<Tg>& operator += (GIOProtoArgs<Tg> &lhs, const GIOProtoArgs<Tg> &rhs);
+};
+
+template<typename Tg>
+cv::GIOProtoArgs<Tg>& operator += (cv::GIOProtoArgs<Tg> &lhs, const cv::GIOProtoArgs<Tg> &rhs)
+{
+    lhs.m_args.reserve(lhs.m_args.size() + rhs.m_args.size());
+    lhs.m_args.insert(lhs.m_args.end(), rhs.m_args.begin(), rhs.m_args.end());
+    return lhs;
+}
+
+struct In_Tag{};
+struct Out_Tag{};
+
+using GProtoInputArgs  = GIOProtoArgs<In_Tag>;
+using GProtoOutputArgs = GIOProtoArgs<Out_Tag>;
+
+// Perfect forwarding
+template<typename... Ts> inline GProtoInputArgs GIn(Ts&&... ts)
+{
+    return GProtoInputArgs(detail::packArgs(std::forward<Ts>(ts)...));
+}
+
+template<typename... Ts> inline GProtoOutputArgs GOut(Ts&&... ts)
+{
+    return GProtoOutputArgs(detail::packArgs(std::forward<Ts>(ts)...));
+}
+
+namespace detail
+{
+    // Extract elements form tuple
+    // FIXME: Someday utilize a generic tuple_to_vec<> routine
+    template<typename... Ts, int... Indexes>
+    static GProtoOutputArgs getGOut_impl(const std::tuple<Ts...>& ts, detail::Seq<Indexes...>)
+    {
+        return GProtoOutputArgs{ detail::packArgs(std::get<Indexes>(ts)...)};
+    }
+}
+
+template<typename... Ts> inline GProtoOutputArgs GOut(const std::tuple<Ts...>& ts)
+{
+    // TODO: think of std::forward(ts)
+    return detail::getGOut_impl(ts, typename detail::MkSeq<sizeof...(Ts)>::type());
+}
+
+// Takes rvalue as input arg
+template<typename... Ts> inline GProtoOutputArgs GOut(std::tuple<Ts...>&& ts)
+{
+    // TODO: think of std::forward(ts)
+    return detail::getGOut_impl(ts, typename detail::MkSeq<sizeof...(Ts)>::type());
+}
+
+// Extract run-time arguments from node origin
+// Can be used to extract constant values associated with G-objects
+// (like GScalar) at graph construction time
+GRunArg value_of(const GOrigin &origin);
+
+// Transform run-time computation arguments into a collection of metadata
+// extracted from that arguments
+GMetaArg  GAPI_EXPORTS descr_of(const GRunArg  &arg );
+GMetaArgs GAPI_EXPORTS descr_of(const GRunArgs &args);
+
+// Transform run-time operation result argument into metadata extracted from that argument
+// Used to compare the metadata, which generated at compile time with the metadata result operation in run time
+GMetaArg GAPI_EXPORTS descr_of(const GRunArgP& argp);
+
+// Checks if run-time computation argument can be described by metadata
+bool GAPI_EXPORTS can_describe(const GMetaArg&  meta,  const GRunArg&  arg);
+bool GAPI_EXPORTS can_describe(const GMetaArgs& metas, const GRunArgs& args);
+
+// Checks if run-time computation result argument can be described by metadata.
+// Used to check if the metadata generated at compile time
+// coincides with output arguments passed to computation in cpu and ocl backends
+bool GAPI_EXPORTS can_describe(const GMetaArg&  meta,  const GRunArgP& argp);
+
+// Validates input arguments
+void GAPI_EXPORTS validate_input_arg(const GRunArg& arg);
+void GAPI_EXPORTS validate_input_args(const GRunArgs& args);
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GPROTO_HPP
diff --git a/3rdParty/include/opencv2/gapi/gpu/core.hpp b/3rdParty/include/opencv2/gapi/gpu/core.hpp
new file mode 100644
index 0000000..a7ee595
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gpu/core.hpp
@@ -0,0 +1,27 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GPU_CORE_API_HPP
+#define OPENCV_GAPI_GPU_CORE_API_HPP
+/** @file
+* @deprecated Use <opencv2/gapi/ocl/core.hpp> instead.
+*/
+
+#include <opencv2/gapi/ocl/core.hpp>
+
+namespace cv {
+namespace gapi {
+namespace core {
+namespace gpu {
+    using namespace ocl;
+} // namespace gpu
+} // namespace core
+} // namespace gapi
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_GPU_CORE_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/gpu/ggpukernel.hpp b/3rdParty/include/opencv2/gapi/gpu/ggpukernel.hpp
new file mode 100644
index 0000000..b52c21d
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gpu/ggpukernel.hpp
@@ -0,0 +1,18 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GGPUKERNEL_HPP
+#define OPENCV_GAPI_GGPUKERNEL_HPP
+/** @file
+* @deprecated Use <opencv2/gapi/ocl/goclkernel.hpp> instead.
+*/
+
+#include <opencv2/gapi/ocl/goclkernel.hpp>
+#define GAPI_GPU_KERNEL GAPI_OCL_KERNEL
+
+
+#endif // OPENCV_GAPI_GGPUKERNEL_HPP
diff --git a/3rdParty/include/opencv2/gapi/gpu/imgproc.hpp b/3rdParty/include/opencv2/gapi/gpu/imgproc.hpp
new file mode 100644
index 0000000..b0df7ae
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gpu/imgproc.hpp
@@ -0,0 +1,28 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GPU_IMGPROC_API_HPP
+#define OPENCV_GAPI_GPU_IMGPROC_API_HPP
+/** @file
+* @deprecated Use <opencv2/gapi/ocl/imgproc.hpp> instead.
+*/
+
+#include <opencv2/gapi/ocl/imgproc.hpp>
+
+
+namespace cv {
+namespace gapi {
+namespace imgproc {
+namespace gpu {
+    using namespace ocl;
+} // namespace gpu
+} // namespace imgproc
+} // namespace gapi
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_GPU_IMGPROC_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/gscalar.hpp b/3rdParty/include/opencv2/gapi/gscalar.hpp
new file mode 100644
index 0000000..7ebeded
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gscalar.hpp
@@ -0,0 +1,140 @@
+// This file is part of OpenCV project.
+
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GSCALAR_HPP
+#define OPENCV_GAPI_GSCALAR_HPP
+
+#include <ostream>
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/gcommon.hpp> // GShape
+#include <opencv2/gapi/util/optional.hpp>
+
+namespace cv
+{
+// Forward declaration; GNode and GOrigin are an internal
+// (user-inaccessible) classes.
+class GNode;
+struct GOrigin;
+
+/** \addtogroup gapi_data_objects
+ * @{
+ */
+/**
+ * @brief GScalar class represents cv::Scalar data in the graph.
+ *
+ * GScalar may be associated with a cv::Scalar value, which becomes
+ * its constant value bound in graph compile time. cv::GScalar describes a
+ * functional relationship between operations consuming and producing
+ * GScalar objects.
+ *
+ * GScalar is a virtual counterpart of cv::Scalar, which is usually used
+ * to represent the GScalar data in G-API during the execution.
+ *
+ * @sa Scalar
+ */
+class GAPI_EXPORTS_W_SIMPLE GScalar
+{
+public:
+    /**
+     * @brief Constructs an empty GScalar
+     *
+     * Normally, empty G-API data objects denote a starting point of
+     * the graph. When an empty GScalar is assigned to a result of some
+     * operation, it obtains a functional link to this operation (and
+     * is not empty anymore).
+     */
+    GAPI_WRAP GScalar();
+
+    /**
+     * @brief Constructs a value-initialized GScalar
+     *
+     * In contrast with GMat (which can be either an explicit graph input
+     * or a result of some operation), GScalars may have their values
+     * be associated at graph construction time. It is useful when
+     * some operation has a GScalar input which doesn't change during
+     * the program execution, and is set only once. In this case,
+     * there is no need to declare such GScalar as a graph input.
+     *
+     * @note The value of GScalar may be overwritten by assigning some
+     * other GScalar to the object using `operator=` -- on the
+     * assigment, the old GScalar value is discarded.
+     *
+     * @param s a cv::Scalar value to associate with this GScalar object.
+     */
+    explicit GScalar(const cv::Scalar& s);
+
+    /**
+     * @overload
+     * @brief Constructs a value-initialized GScalar
+     *
+     * @param s a cv::Scalar value to associate with this GScalar object.
+     */
+    explicit GScalar(cv::Scalar&& s);       // Constant value move-constructor from cv::Scalar
+
+    /**
+     * @overload
+     * @brief Constructs a value-initialized GScalar
+     *
+     * @param v0 A `double` value to associate with this GScalar. Note
+     *  that only the first component of a four-component cv::Scalar is
+     *  set to this value, with others remain zeros.
+     *
+     * This constructor overload is not marked `explicit` and can be
+     * used in G-API expression code like this:
+     *
+     * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp gscalar_implicit
+     *
+     * Here operator+(GMat,GScalar) is used to wrap cv::gapi::addC()
+     * and a value-initialized GScalar is created on the fly.
+     *
+     * @overload
+     */
+    GScalar(double v0);                                // Constant value constructor from double
+
+    /// @private
+    GScalar(const GNode &n, std::size_t out);          // Operation result constructor
+    /// @private
+    GOrigin& priv();                                   // Internal use only
+    /// @private
+    const GOrigin& priv()  const;                      // Internal use only
+
+private:
+    std::shared_ptr<GOrigin> m_priv;
+};
+
+/** @} */
+
+/**
+ * \addtogroup gapi_meta_args
+ * @{
+ */
+struct GAPI_EXPORTS_W_SIMPLE GScalarDesc
+{
+    // NB.: right now it is empty
+
+    inline bool operator== (const GScalarDesc &) const
+    {
+        return true; // NB: implement this method if GScalar meta appears
+    }
+
+    inline bool operator!= (const GScalarDesc &rhs) const
+    {
+        return !(*this == rhs);
+    }
+};
+
+GAPI_EXPORTS_W inline GScalarDesc empty_scalar_desc() { return GScalarDesc(); }
+
+GAPI_EXPORTS GScalarDesc descr_of(const cv::Scalar &scalar);
+
+std::ostream& operator<<(std::ostream& os, const cv::GScalarDesc &desc);
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GSCALAR_HPP
diff --git a/3rdParty/include/opencv2/gapi/gstreaming.hpp b/3rdParty/include/opencv2/gapi/gstreaming.hpp
new file mode 100644
index 0000000..890eb58
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gstreaming.hpp
@@ -0,0 +1,430 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2021 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GSTREAMING_COMPILED_HPP
+#define OPENCV_GAPI_GSTREAMING_COMPILED_HPP
+
+#include <memory>
+#include <vector>
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/own/assert.hpp>
+#include <opencv2/gapi/util/optional.hpp>
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/streaming/source.hpp>
+
+namespace cv {
+
+template<class T> using optional = cv::util::optional<T>;
+
+namespace detail {
+template<typename T> struct wref_spec {
+    using type = T;
+};
+template<typename T> struct wref_spec<std::vector<T> > {
+    using type = T;
+};
+
+template<typename RefHolder>
+struct OptRef {
+    struct OptHolder {
+        virtual void mov(RefHolder &h) = 0;
+        virtual void reset() = 0;
+        virtual ~OptHolder() = default;
+        using Ptr = std::shared_ptr<OptHolder>;
+    };
+    template<class T> struct Holder final: OptHolder {
+        std::reference_wrapper<cv::optional<T> > m_opt_ref;
+
+        explicit Holder(cv::optional<T>& opt) : m_opt_ref(std::ref(opt)) {
+        }
+        virtual void mov(RefHolder &h) override {
+            using U = typename wref_spec<T>::type;
+            m_opt_ref.get() = cv::util::make_optional(std::move(h.template wref<U>()));
+        }
+        virtual void reset() override {
+            m_opt_ref.get().reset();
+        }
+    };
+    template<class T>
+    explicit OptRef(cv::optional<T>& t) : m_opt{new Holder<T>(t)} {}
+    void mov(RefHolder &h) { m_opt->mov(h); }
+    void reset()           { m_opt->reset();}
+private:
+    typename OptHolder::Ptr m_opt;
+};
+using OptionalVectorRef = OptRef<cv::detail::VectorRef>;
+using OptionalOpaqueRef = OptRef<cv::detail::OpaqueRef>;
+} // namespace detail
+
+// TODO: Keep it in sync with GRunArgP (derive the type automatically?)
+using GOptRunArgP = util::variant<
+    optional<cv::Mat>*,
+    optional<cv::RMat>*,
+    optional<cv::MediaFrame>*,
+    optional<cv::Scalar>*,
+    cv::detail::OptionalVectorRef,
+    cv::detail::OptionalOpaqueRef
+>;
+using GOptRunArgsP = std::vector<GOptRunArgP>;
+
+using GOptRunArg = util::variant<
+    optional<cv::Mat>,
+    optional<cv::RMat>,
+    optional<cv::MediaFrame>,
+    optional<cv::Scalar>,
+    optional<cv::detail::VectorRef>,
+    optional<cv::detail::OpaqueRef>
+>;
+using GOptRunArgs = std::vector<GOptRunArg>;
+
+namespace detail {
+
+template<typename T> inline GOptRunArgP wrap_opt_arg(optional<T>& arg) {
+    // By default, T goes to an OpaqueRef. All other types are specialized
+    return GOptRunArgP{OptionalOpaqueRef(arg)};
+}
+
+template<typename T> inline GOptRunArgP wrap_opt_arg(optional<std::vector<T> >& arg) {
+    return GOptRunArgP{OptionalVectorRef(arg)};
+}
+
+template<> inline GOptRunArgP wrap_opt_arg(optional<cv::Mat> &m) {
+    return GOptRunArgP{&m};
+}
+
+template<> inline GOptRunArgP wrap_opt_arg(optional<cv::RMat> &m) {
+    return GOptRunArgP{&m};
+}
+
+template<> inline GOptRunArgP wrap_opt_arg(optional<cv::MediaFrame> &f) {
+    return GOptRunArgP{&f};
+}
+
+template<> inline GOptRunArgP wrap_opt_arg(optional<cv::Scalar> &s) {
+    return GOptRunArgP{&s};
+}
+
+} // namespace detail
+
+// Now cv::gout() may produce an empty vector (see "dynamic graphs"), so
+// there may be a conflict between these two. State here that Opt version
+// _must_ have at least one input for this overload
+template<typename T, typename... Ts>
+inline GOptRunArgsP gout(optional<T>&arg, optional<Ts>&... args)
+{
+    return GOptRunArgsP{ detail::wrap_opt_arg(arg), detail::wrap_opt_arg(args)... };
+}
+
+/**
+ * \addtogroup gapi_main_classes
+ * @{
+ */
+/**
+ * @brief Represents a computation (graph) compiled for streaming.
+ *
+ * This class represents a product of graph compilation (calling
+ * cv::GComputation::compileStreaming()). Objects of this class
+ * actually do stream processing, and the whole pipeline execution
+ * complexity is incapsulated into objects of this class. Execution
+ * model has two levels: at the very top, the execution of a
+ * heterogeneous graph is aggressively pipelined; at the very bottom
+ * the execution of every internal block is determined by its
+ * associated backend. Backends are selected based on kernel packages
+ * passed via compilation arguments ( see @ref gapi_compile_args,
+ * GNetworkPackage, GKernelPackage for details).
+ *
+ * GStreamingCompiled objects have a "player" semantics -- there are
+ * methods like start() and stop(). GStreamingCompiled has a full
+ * control over a videostream and so is stateful. You need to specify the
+ * input stream data using setSource() and then call start() to
+ * actually start processing. After that, use pull() or try_pull() to
+ * obtain next processed data frame from the graph in a blocking or
+ * non-blocking way, respectively.
+ *
+ * Currently a single GStreamingCompiled can process only one video
+ * streat at time. Produce multiple GStreamingCompiled objects to run the
+ * same graph on multiple video streams.
+ *
+ * @sa GCompiled
+ */
+class GAPI_EXPORTS_W_SIMPLE GStreamingCompiled
+{
+public:
+    class GAPI_EXPORTS Priv;
+    GAPI_WRAP GStreamingCompiled();
+
+    // FIXME: More overloads?
+    /**
+     * @brief Specify the input data to GStreamingCompiled for
+     * processing, a generic version.
+     *
+     * Use gin() to create an input parameter vector.
+     *
+     * Input vectors must have the same number of elements as defined
+     * in the cv::GComputation protocol (at the moment of its
+     * construction). Shapes of elements also must conform to protocol
+     * (e.g. cv::Mat needs to be passed where cv::GMat has been
+     * declared as input, and so on). Run-time exception is generated
+     * on type mismatch.
+     *
+     * In contrast with regular GCompiled, user can also pass an
+     * object of type GVideoCapture for a GMat parameter of the parent
+     * GComputation.  The compiled pipeline will start fetching data
+     * from that GVideoCapture and feeding it into the
+     * pipeline. Pipeline stops when a GVideoCapture marks end of the
+     * stream (or when stop() is called).
+     *
+     * Passing a regular Mat for a GMat parameter makes it "infinite"
+     * source -- pipeline may run forever feeding with this Mat until
+     * stopped explicitly.
+     *
+     * Currently only a single GVideoCapture is supported as input. If
+     * the parent GComputation is declared with multiple input GMat's,
+     * one of those can be specified as GVideoCapture but all others
+     * must be regular Mat objects.
+     *
+     * Throws if pipeline is already running. Use stop() and then
+     * setSource() to run the graph on a new video stream.
+     *
+     * @note This method is not thread-safe (with respect to the user
+     * side) at the moment. Protect the access if
+     * start()/stop()/setSource() may be called on the same object in
+     * multiple threads in your application.
+     *
+     * @param ins vector of inputs to process.
+     * @sa gin
+     */
+    void setSource(GRunArgs &&ins);
+
+    /// @private -- Exclude this function from OpenCV documentation
+    GAPI_WRAP void setSource(const cv::detail::ExtractArgsCallback& callback);
+
+    /**
+     * @brief Specify an input video stream for a single-input
+     * computation pipeline.
+     *
+     * Throws if pipeline is already running. Use stop() and then
+     * setSource() to run the graph on a new video stream.
+     *
+     * @overload
+     * @param s a shared pointer to IStreamSource representing the
+     * input video stream.
+     */
+    void setSource(const gapi::wip::IStreamSource::Ptr& s);
+
+    /**
+     * @brief Constructs and specifies an input video stream for a
+     * single-input computation pipeline with the given parameters.
+     *
+     * Throws if pipeline is already running. Use stop() and then
+     * setSource() to run the graph on a new video stream.
+     *
+     * @overload
+     * @param args arguments used to contruct and initialize a stream
+     * source.
+     */
+    template<typename T, typename... Args>
+    void setSource(Args&&... args) {
+        setSource(cv::gapi::wip::make_src<T>(std::forward<Args>(args)...));
+    }
+
+    /**
+     * @brief Start the pipeline execution.
+     *
+     * Use pull()/try_pull() to obtain data. Throws an exception if
+     * a video source was not specified.
+     *
+     * setSource() must be called first, even if the pipeline has been
+     * working already and then stopped (explicitly via stop() or due
+     * stream completion)
+     *
+     * @note This method is not thread-safe (with respect to the user
+     * side) at the moment. Protect the access if
+     * start()/stop()/setSource() may be called on the same object in
+     * multiple threads in your application.
+     */
+    GAPI_WRAP void start();
+
+    /**
+     * @brief Get the next processed frame from the pipeline.
+     *
+     * Use gout() to create an output parameter vector.
+     *
+     * Output vectors must have the same number of elements as defined
+     * in the cv::GComputation protocol (at the moment of its
+     * construction). Shapes of elements also must conform to protocol
+     * (e.g. cv::Mat needs to be passed where cv::GMat has been
+     * declared as output, and so on). Run-time exception is generated
+     * on type mismatch.
+     *
+     * This method writes new data into objects passed via output
+     * vector.  If there is no data ready yet, this method blocks. Use
+     * try_pull() if you need a non-blocking version.
+     *
+     * @param outs vector of output parameters to obtain.
+     * @return true if next result has been obtained,
+     *    false marks end of the stream.
+     */
+    bool pull(cv::GRunArgsP &&outs);
+
+    // NB: Used from python
+    /// @private -- Exclude this function from OpenCV documentation
+    GAPI_WRAP std::tuple<bool, cv::util::variant<cv::GRunArgs, cv::GOptRunArgs>> pull();
+
+    /**
+     * @brief Get some next available data from the pipeline.
+     *
+     * This method takes a vector of cv::optional object. An object is
+     * assigned to some value if this value is available (ready) at
+     * the time of the call, and resets the object to empty() if it is
+     * not.
+     *
+     * This is a blocking method which guarantees that some data has
+     * been written to the output vector on return.
+     *
+     * Using this method only makes sense if the graph has
+     * desynchronized parts (see cv::gapi::desync). If there is no
+     * desynchronized parts in the graph, the behavior of this
+     * method is identical to the regular pull() (all data objects are
+     * produced synchronously in the output vector).
+     *
+     * Use gout() to create an output parameter vector.
+     *
+     * Output vectors must have the same number of elements as defined
+     * in the cv::GComputation protocol (at the moment of its
+     * construction). Shapes of elements also must conform to protocol
+     * (e.g. cv::optional<cv::Mat> needs to be passed where cv::GMat
+     * has been declared as output, and so on). Run-time exception is
+     * generated on type mismatch.
+     *
+     * This method writes new data into objects passed via output
+     * vector.  If there is no data ready yet, this method blocks. Use
+     * try_pull() if you need a non-blocking version.
+     *
+     * @param outs vector of output parameters to obtain.
+     * @return true if next result has been obtained,
+     *    false marks end of the stream.
+     *
+     * @sa cv::gapi::desync
+     */
+    bool pull(cv::GOptRunArgsP &&outs);
+
+    /**
+     * @brief Try to get the next processed frame from the pipeline.
+     *
+     * Use gout() to create an output parameter vector.
+     *
+     * This method writes new data into objects passed via output
+     * vector.  If there is no data ready yet, the output vector
+     * remains unchanged and false is returned.
+     *
+     * @return true if data has been obtained, and false if it was
+     *    not. Note: false here doesn't mark the end of the stream.
+     */
+    bool try_pull(cv::GRunArgsP &&outs);
+
+    /**
+     * @brief Stop (abort) processing the pipeline.
+     *
+     * Note - it is not pause but a complete stop. Calling start()
+     * will cause G-API to start processing the stream from the early beginning.
+     *
+     * Throws if the pipeline is not running.
+     */
+    GAPI_WRAP void stop();
+
+    /**
+     * @brief Test if the pipeline is running.
+     *
+     * @note This method is not thread-safe (with respect to the user
+     * side) at the moment. Protect the access if
+     * start()/stop()/setSource() may be called on the same object in
+     * multiple threads in your application.
+     *
+     * @return true if the current stream is not over yet.
+     */
+    GAPI_WRAP bool running() const;
+
+    /// @private
+    Priv& priv();
+
+    /**
+     * @brief Check if compiled object is valid (non-empty)
+     *
+     * @return true if the object is runnable (valid), false otherwise
+     */
+    explicit operator bool () const;
+
+    /**
+     * @brief Vector of metadata this graph was compiled for.
+     *
+     * @return Unless _reshape_ is not supported, return value is the
+     * same vector which was passed to cv::GComputation::compile() to
+     * produce this compiled object. Otherwise, it is the latest
+     * metadata vector passed to reshape() (if that call was
+     * successful).
+     */
+    const GMetaArgs& metas() const; // Meta passed to compile()
+
+    /**
+     * @brief Vector of metadata descriptions of graph outputs
+     *
+     * @return vector with formats/resolutions of graph's output
+     * objects, auto-inferred from input metadata vector by
+     * operations which form this computation.
+     *
+     * @note GCompiled objects produced from the same
+     * cv::GComputiation graph with different input metas may return
+     * different values in this vector.
+     */
+    const GMetaArgs& outMetas() const;
+
+protected:
+    /// @private
+    std::shared_ptr<Priv> m_priv;
+};
+/** @} */
+
+namespace gapi {
+
+/**
+ * @brief This namespace contains G-API functions, structures, and
+ * symbols related to the Streaming execution mode.
+ *
+ * Some of the operations defined in this namespace (e.g. size(),
+ * BGR(), etc.) can be used in the traditional execution mode too.
+ */
+namespace streaming {
+/**
+ * @brief Specify queue capacity for streaming execution.
+ *
+ * In the streaming mode the pipeline steps are connected with queues
+ * and this compile argument controls every queue's size.
+ */
+struct GAPI_EXPORTS_W_SIMPLE queue_capacity
+{
+    GAPI_WRAP
+    explicit queue_capacity(size_t cap = 1) : capacity(cap) { };
+    GAPI_PROP_RW
+    size_t capacity;
+};
+/** @} */
+} // namespace streaming
+} // namespace gapi
+
+namespace detail
+{
+template<> struct CompileArgTag<cv::gapi::streaming::queue_capacity>
+{
+    static const char* tag() { return "gapi.queue_capacity"; }
+};
+}
+
+}
+
+#endif // OPENCV_GAPI_GSTREAMING_COMPILED_HPP
diff --git a/3rdParty/include/opencv2/gapi/gtransform.hpp b/3rdParty/include/opencv2/gapi/gtransform.hpp
new file mode 100644
index 0000000..109bc87
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gtransform.hpp
@@ -0,0 +1,103 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+#ifndef OPENCV_GAPI_GTRANSFORM_HPP
+#define OPENCV_GAPI_GTRANSFORM_HPP
+
+#include <functional>
+#include <type_traits>
+#include <utility>
+
+#include <opencv2/gapi/gcommon.hpp>
+#include <opencv2/gapi/util/util.hpp>
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/gtype_traits.hpp>
+#include <opencv2/gapi/util/compiler_hints.hpp>
+#include <opencv2/gapi/gcomputation.hpp>
+
+namespace cv
+{
+
+struct GAPI_EXPORTS GTransform
+{
+    // FIXME: consider another simplified
+    // class instead of GComputation
+    using F = std::function<GComputation()>;
+
+    std::string description;
+    F pattern;
+    F substitute;
+
+    GTransform(const std::string& d, const F &p, const F &s) : description(d), pattern(p), substitute(s) {}
+};
+
+namespace detail
+{
+
+template <typename, typename, typename>
+struct TransHelper;
+
+template <typename K, typename... Ins, typename Out>
+struct TransHelper<K, std::tuple<Ins...>, Out>
+{
+    template <typename Callable, int... IIs, int... OIs>
+    static GComputation invoke(Callable f, Seq<IIs...>, Seq<OIs...>)
+    {
+        const std::tuple<Ins...> ins;
+        const auto r = tuple_wrap_helper<Out>::get(f(std::get<IIs>(ins)...));
+        return GComputation(cv::GIn(std::get<IIs>(ins)...),
+                            cv::GOut(std::get<OIs>(r)...));
+    }
+
+    static GComputation get_pattern()
+    {
+        return invoke(K::pattern, typename MkSeq<sizeof...(Ins)>::type(),
+                      typename MkSeq<std::tuple_size<typename tuple_wrap_helper<Out>::type>::value>::type());
+    }
+    static GComputation get_substitute()
+    {
+        return invoke(K::substitute, typename MkSeq<sizeof...(Ins)>::type(),
+                      typename MkSeq<std::tuple_size<typename tuple_wrap_helper<Out>::type>::value>::type());
+    }
+};
+} // namespace detail
+
+template <typename, typename>
+class GTransformImpl;
+
+template <typename K, typename R, typename... Args>
+class GTransformImpl<K, std::function<R(Args...)>> : public cv::detail::TransHelper<K, std::tuple<Args...>, R>,
+                                                     public cv::detail::TransformTag
+{
+public:
+    // FIXME: currently there is no check that transformations' signatures are unique
+    // and won't be any intersection in graph compilation stage
+    using API = K;
+
+    static GTransform transformation()
+    {
+        return GTransform(K::descr(), &K::get_pattern, &K::get_substitute);
+    }
+};
+} // namespace cv
+
+#define G_DESCR_HELPER_CLASS(Class) Class##DescrHelper
+
+#define G_DESCR_HELPER_BODY(Class, Descr)                       \
+    namespace detail                                            \
+    {                                                           \
+    struct G_DESCR_HELPER_CLASS(Class)                          \
+    {                                                           \
+        static constexpr const char *descr() { return Descr; }; \
+    };                                                          \
+    }
+
+#define GAPI_TRANSFORM(Class, API, Descr)                                     \
+    G_DESCR_HELPER_BODY(Class, Descr)                                         \
+    struct Class final : public cv::GTransformImpl<Class, std::function API>, \
+                         public detail::G_DESCR_HELPER_CLASS(Class)
+
+#endif // OPENCV_GAPI_GTRANSFORM_HPP
diff --git a/3rdParty/include/opencv2/gapi/gtype_traits.hpp b/3rdParty/include/opencv2/gapi/gtype_traits.hpp
new file mode 100644
index 0000000..2b43421
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gtype_traits.hpp
@@ -0,0 +1,240 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GTYPE_TRAITS_HPP
+#define OPENCV_GAPI_GTYPE_TRAITS_HPP
+
+#include <vector>
+#include <type_traits>
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+#include <opencv2/gapi/garray.hpp>
+#include <opencv2/gapi/gopaque.hpp>
+#include <opencv2/gapi/gframe.hpp>
+#include <opencv2/gapi/streaming/source.hpp>
+#include <opencv2/gapi/media.hpp>
+#include <opencv2/gapi/gcommon.hpp>
+#include <opencv2/gapi/util/util.hpp>
+#include <opencv2/gapi/own/convert.hpp>
+
+namespace cv
+{
+namespace detail
+{
+    template<typename, typename = void>
+    struct contains_shape_field : std::false_type {};
+
+    template<typename TaggedTypeCandidate>
+    struct contains_shape_field<TaggedTypeCandidate,
+                                void_t<decltype(TaggedTypeCandidate::shape)>> :
+        std::is_same<typename std::decay<decltype(TaggedTypeCandidate::shape)>::type, GShape>
+    {};
+
+    template<typename Type>
+    struct has_gshape : contains_shape_field<Type> {};
+
+    // FIXME: These traits and enum and possible numerous switch(kind)
+    // block may be replaced with a special Handler<T> object or with
+    // a double dispatch
+    enum class ArgKind: int
+    {
+        OPAQUE_VAL,   // Unknown, generic, opaque-to-GAPI data type - STATIC
+                      // Note: OPAQUE is sometimes defined in Win sys headers
+#if !defined(OPAQUE) && !defined(CV_DOXYGEN)
+        OPAQUE = OPAQUE_VAL,  // deprecated value used for compatibility, use OPAQUE_VAL instead
+#endif
+        GOBJREF,      // <internal> reference to object
+        GMAT,         // a cv::GMat
+        GMATP,        // a cv::GMatP
+        GFRAME,       // a cv::GFrame
+        GSCALAR,      // a cv::GScalar
+        GARRAY,       // a cv::GArrayU  (note - exactly GArrayU,  not GArray<T>!)
+        GOPAQUE,      // a cv::GOpaqueU (note - exactly GOpaqueU, not GOpaque<T>!)
+    };
+
+    // Describe G-API types (G-types) with traits.  Mostly used by
+    // cv::GArg to store meta information about types passed into
+    // operation arguments. Please note that cv::GComputation is
+    // defined on GProtoArgs, not GArgs!
+    template<typename T> struct GTypeTraits;
+    template<typename T> struct GTypeTraits
+    {
+        static constexpr const ArgKind kind = ArgKind::OPAQUE_VAL;
+        static constexpr const OpaqueKind op_kind = OpaqueKind::CV_UNKNOWN;
+    };
+    template<>           struct GTypeTraits<cv::GMat>
+    {
+        static constexpr const ArgKind kind = ArgKind::GMAT;
+        static constexpr const GShape shape = GShape::GMAT;
+        static constexpr const OpaqueKind op_kind = OpaqueKind::CV_UNKNOWN;
+    };
+    template<>           struct GTypeTraits<cv::GMatP>
+    {
+        static constexpr const ArgKind kind = ArgKind::GMATP;
+        static constexpr const GShape shape = GShape::GMAT;
+        static constexpr const OpaqueKind op_kind = OpaqueKind::CV_UNKNOWN;
+    };
+    template<>           struct GTypeTraits<cv::GFrame>
+    {
+        static constexpr const ArgKind kind = ArgKind::GFRAME;
+        static constexpr const GShape shape = GShape::GFRAME;
+        static constexpr const OpaqueKind op_kind = OpaqueKind::CV_UNKNOWN;
+    };
+    template<>           struct GTypeTraits<cv::GScalar>
+    {
+        static constexpr const ArgKind kind = ArgKind::GSCALAR;
+        static constexpr const GShape shape = GShape::GSCALAR;
+        static constexpr const OpaqueKind op_kind = OpaqueKind::CV_UNKNOWN;
+    };
+    template<class T> struct GTypeTraits<cv::GArray<T> >
+    {
+        static constexpr const ArgKind kind = ArgKind::GARRAY;
+        static constexpr const GShape shape = GShape::GARRAY;
+        static constexpr const OpaqueKind op_kind = GOpaqueTraits<T>::kind;
+        using host_type  = std::vector<T>;
+        using strip_type = cv::detail::VectorRef;
+        static cv::detail::GArrayU   wrap_value(const cv::GArray<T>  &t) { return t.strip();}
+        static cv::detail::VectorRef wrap_in   (const std::vector<T> &t) { return detail::VectorRef(t); }
+        static cv::detail::VectorRef wrap_out  (      std::vector<T> &t) { return detail::VectorRef(t); }
+    };
+    template<class T> struct GTypeTraits<cv::GOpaque<T> >
+    {
+        static constexpr const ArgKind kind = ArgKind::GOPAQUE;
+        static constexpr const GShape shape = GShape::GOPAQUE;
+        static constexpr const OpaqueKind op_kind = GOpaqueTraits<T>::kind;
+        using host_type  = T;
+        using strip_type = cv::detail::OpaqueRef;
+        static cv::detail::GOpaqueU  wrap_value(const cv::GOpaque<T>  &t) { return t.strip();}
+        static cv::detail::OpaqueRef wrap_in   (const T &t) { return detail::OpaqueRef(t); }
+        static cv::detail::OpaqueRef wrap_out  (      T &t) { return detail::OpaqueRef(t); }
+    };
+
+    // Tests if Trait for type T requires extra marshalling ("custom wrap") or not.
+    // If Traits<T> has wrap_value() defined, it does.
+    template<class T> struct has_custom_wrap
+    {
+        template<class,class> class check;
+        template<typename C> static std::true_type  test(check<C, decltype(&GTypeTraits<C>::wrap_value)> *);
+        template<typename C> static std::false_type test(...);
+        using type = decltype(test<T>(nullptr));
+        static const constexpr bool value = std::is_same<std::true_type, decltype(test<T>(nullptr))>::value;
+    };
+
+    // Resolve a Host type back to its associated G-Type.
+    // FIXME: Probably it can be avoided
+    // FIXME: GMatP is not present here.
+    // (Actually these traits is used only to check
+    // if associated G-type has custom wrap functions
+    // and GMat behavior is correct for GMatP)
+    template<typename T> struct GTypeOf;
+#if !defined(GAPI_STANDALONE)
+    template<>           struct GTypeOf<cv::UMat>              { using type = cv::GMat;      };
+#endif // !defined(GAPI_STANDALONE)
+    template<>           struct GTypeOf<cv::Mat>               { using type = cv::GMat;      };
+    template<>           struct GTypeOf<cv::RMat>              { using type = cv::GMat;      };
+    template<>           struct GTypeOf<cv::Scalar>            { using type = cv::GScalar;   };
+    template<typename U> struct GTypeOf<std::vector<U> >       { using type = cv::GArray<U>; };
+    template<typename U> struct GTypeOf                        { using type = cv::GOpaque<U>;};
+    template<>           struct GTypeOf<cv::MediaFrame>        { using type = cv::GFrame;    };
+    // FIXME: This is not quite correct since IStreamSource may produce not only Mat but also Scalar
+    // and vector data. TODO: Extend the type dispatching on these types too.
+    template<>           struct GTypeOf<cv::gapi::wip::IStreamSource::Ptr> { using type = cv::GMat;};
+    template<class T> using g_type_of_t = typename GTypeOf<T>::type;
+
+    // Marshalling helper for G-types and its Host types. Helps G-API
+    // to store G types in internal generic containers for further
+    // processing. Implements the following callbacks:
+    //
+    // * wrap() - converts user-facing G-type into an internal one
+    //   for internal storage.
+    //   Used when G-API operation is instantiated (G<Kernel>::on(),
+    //   etc) during expressing a pipeline. Mostly returns input
+    //   value "as is" except the case when G-type is a template. For
+    //   template G-classes, calls custom wrap() from Traits.
+    //   The value returned by wrap() is then wrapped into GArg() and
+    //   stored in G-API metadata.
+    //
+    //   Example:
+    //   - cv::GMat arguments are passed as-is.
+    //   - integers, pointers, STL containers, user types are passed as-is.
+    //   - cv::GArray<T> is converted to cv::GArrayU.
+    //
+    // * wrap_in() / wrap_out() - convert Host type associated with
+    //   G-type to internal representation type.
+    //
+    //   - For "simple" (non-template) G-types, returns value as-is.
+    //     Example: cv::GMat has host type cv::Mat, when user passes a
+    //              cv::Mat, system stores it internally as cv::Mat.
+    //
+    //   - For "complex" (template) G-types, utilizes custom
+    //     wrap_in()/wrap_out() as described in Traits.
+    //     Example: cv::GArray<T> has host type std::vector<T>, when
+    //              user passes a std::vector<T>, system stores it
+    //              internally as VectorRef (with <T> stripped away).
+    template<typename T, class Custom = void> struct WrapValue
+    {
+        static auto wrap(const T& t) ->
+            typename std::remove_reference<T>::type
+        {
+            return static_cast<typename std::remove_reference<T>::type>(t);
+        }
+
+        template<typename U> static U  wrap_in (const U &u) { return  u;  }
+        template<typename U> static U* wrap_out(U &u)       { return &u;  }
+    };
+    template<typename T> struct WrapValue<T, typename std::enable_if<has_custom_wrap<T>::value>::type>
+    {
+        static auto wrap(const T& t) -> decltype(GTypeTraits<T>::wrap_value(t))
+        {
+            return GTypeTraits<T>::wrap_value(t);
+        }
+        template<typename U> static auto wrap_in (const U &u) -> typename GTypeTraits<T>::strip_type
+        {
+            static_assert(!(cv::detail::has_gshape<GTypeTraits<U>>::value
+                            || cv::detail::contains<typename std::decay<U>::type, GAPI_OWN_TYPES_LIST>::value),
+                          "gin/gout must not be used with G* classes or cv::gapi::own::*");
+            return GTypeTraits<T>::wrap_in(u);
+        }
+        template<typename U> static auto wrap_out(U &u) -> typename GTypeTraits<T>::strip_type
+        {
+            static_assert(!(cv::detail::has_gshape<GTypeTraits<U>>::value
+                            || cv::detail::contains<typename std::decay<U>::type, GAPI_OWN_TYPES_LIST>::value),
+                          "gin/gout must not be used with G* classses or cv::gapi::own::*");
+            return GTypeTraits<T>::wrap_out(u);
+        }
+    };
+
+    template<typename T> using wrap_gapi_helper = WrapValue<typename std::decay<T>::type>;
+    template<typename T> using wrap_host_helper = WrapValue<typename std::decay<g_type_of_t<T> >::type>;
+
+// Union type for various user-defined type constructors (GArray<T>,
+// GOpaque<T>, etc)
+//
+// TODO: Replace construct-only API with a more generic one (probably
+//    with bits of introspection)
+//
+// Not required for non-user-defined types (GMat, GScalar, etc)
+using HostCtor = util::variant
+    < util::monostate
+    , detail::ConstructVec
+    , detail::ConstructOpaque
+    >;
+
+template<typename T> struct GObtainCtor {
+    static HostCtor get() { return HostCtor{}; }
+};
+template<typename T> struct GObtainCtor<GArray<T> > {
+    static HostCtor get() { return HostCtor{ConstructVec{&GArray<T>::VCtor}}; };
+};
+template<typename T> struct GObtainCtor<GOpaque<T> > {
+    static HostCtor get() { return HostCtor{ConstructOpaque{&GOpaque<T>::Ctor}}; };
+};
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_GAPI_GTYPE_TRAITS_HPP
diff --git a/3rdParty/include/opencv2/gapi/gtyped.hpp b/3rdParty/include/opencv2/gapi/gtyped.hpp
new file mode 100644
index 0000000..c1c16d1
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/gtyped.hpp
@@ -0,0 +1,246 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GTYPED_HPP
+#define OPENCV_GAPI_GTYPED_HPP
+#if !defined(GAPI_STANDALONE)
+
+#include <vector>
+
+#include <opencv2/gapi/gcomputation.hpp>
+#include <opencv2/gapi/gcompiled.hpp>
+#include <opencv2/gapi/gproto.hpp>
+#include <opencv2/gapi/gcommon.hpp>
+
+namespace cv {
+
+namespace detail
+{
+    // FIXME: How to prevent coolhackers from extending it by their own types?
+    // FIXME: ...Should we care?
+    template<typename T> struct ProtoToParam;
+    template<> struct ProtoToParam<cv::GMat>    { using type = cv::Mat; };
+    template<> struct ProtoToParam<cv::GScalar> { using type = cv::Scalar; };
+    template<typename U> struct ProtoToParam<cv::GArray<U> >  { using type = std::vector<U>; };
+    template<> struct ProtoToParam<cv::GArray<cv::GMat>>      { using type = std::vector<cv::Mat>; };
+    template<typename U> struct ProtoToParam<cv::GOpaque<U> > { using type = U; };
+    template<typename T> using ProtoToParamT = typename ProtoToParam<T>::type;
+
+    template<typename T> struct ProtoToMeta;
+    template<> struct ProtoToMeta<cv::GMat>     { using type = cv::GMatDesc; };
+    template<> struct ProtoToMeta<cv::GScalar>  { using type = cv::GScalarDesc; };
+    template<typename U> struct ProtoToMeta<cv::GArray<U> >  { using type = cv::GArrayDesc; };
+    template<typename U> struct ProtoToMeta<cv::GOpaque<U> > { using type = cv::GOpaqueDesc; };
+    template<typename T> using ProtoToMetaT = typename ProtoToMeta<T>::type;
+
+    //workaround for MSVC 19.0 bug
+    template <typename T>
+    auto make_default()->decltype(T{}) {return {};}
+}; // detail
+
+/**
+ * @brief This class is a typed wrapper over a regular GComputation.
+ *
+ * `std::function<>`-like template parameter specifies the graph
+ *  signature so methods so the object's constructor, methods like
+ *  `apply()` and the derived `GCompiledT::operator()` also become
+ *  typed.
+ *
+ *  There is no need to use cv::gin() or cv::gout() modifiers with
+ *  objects of this class.  Instead, all input arguments are followed
+ *  by all output arguments in the order from the template argument
+ *  signature.
+ *
+ *  Refer to the following example. Regular (untyped) code is written this way:
+ *
+ *  @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp Untyped_Example
+ *
+ *  Here:
+ *
+ *  - cv::GComputation object is created with a lambda constructor
+ *    where it is defined as a two-input, one-output graph.
+ *
+ *  - Its method `apply()` in fact takes arbitrary number of arguments
+ *    (as vectors) so user can pass wrong number of inputs/outputs
+ *    here. C++ compiler wouldn't notice that since the cv::GComputation
+ *    API is polymorphic, and only a run-time error will be generated.
+ *
+ *  Now the same code written with typed API:
+ *
+ *  @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp Typed_Example
+ *
+ *  The key difference is:
+ *
+ *  - Now the constructor lambda *must take* parameters and *must
+ *    return* values as defined in the `GComputationT<>` signature.
+ *  - Its method `apply()` does not require any extra specifiers to
+ *    separate input arguments from the output ones
+ *  - A `GCompiledT` (compilation product) takes input/output
+ *    arguments with no extra specifiers as well.
+ */
+template<typename> class GComputationT;
+
+// Single return value implementation
+template<typename R, typename... Args> class GComputationT<R(Args...)>
+{
+public:
+    typedef std::function<R(Args...)> Gen;
+
+    class GCompiledT
+    {
+    private:
+        friend class GComputationT<R(Args...)>;
+
+        cv::GCompiled m_comp;
+
+        explicit GCompiledT(const cv::GCompiled &comp) : m_comp(comp) {}
+
+    public:
+        GCompiledT() {}
+
+        void operator()(detail::ProtoToParamT<Args>... inArgs,
+                        detail::ProtoToParamT<R> &outArg)
+        {
+            m_comp(cv::gin(inArgs...), cv::gout(outArg));
+        }
+
+        explicit operator bool() const
+        {
+            return static_cast<bool>(m_comp);
+        }
+    };
+
+private:
+    typedef std::pair<R, GProtoInputArgs > Captured;
+
+    Captured capture(const Gen& g, Args... args)
+    {
+        return Captured(g(args...), cv::GIn(args...));
+    }
+
+    Captured m_capture;
+    cv::GComputation m_comp;
+
+public:
+    GComputationT(const Gen &generator)
+        : m_capture(capture(generator, detail::make_default<Args>()...))
+        , m_comp(cv::GProtoInputArgs(std::move(m_capture.second)),
+                 cv::GOut(m_capture.first))
+    {
+    }
+
+    void apply(detail::ProtoToParamT<Args>... inArgs,
+               detail::ProtoToParamT<R> &outArg,
+               GCompileArgs &&args)
+    {
+        m_comp.apply(cv::gin(inArgs...), cv::gout(outArg), std::move(args));
+    }
+
+    void apply(detail::ProtoToParamT<Args>... inArgs,
+               detail::ProtoToParamT<R> &outArg)
+    {
+        apply(inArgs..., outArg, GCompileArgs());
+    }
+
+
+    GCompiledT compile(detail::ProtoToMetaT<Args>... inDescs)
+    {
+        GMetaArgs inMetas = { GMetaArg(inDescs)... };
+        return GCompiledT(m_comp.compile(std::move(inMetas), GCompileArgs()));
+    }
+
+    GCompiledT compile(detail::ProtoToMetaT<Args>... inDescs, GCompileArgs &&args)
+    {
+        GMetaArgs inMetas = { GMetaArg(inDescs)... };
+        return GCompiledT(m_comp.compile(std::move(inMetas), std::move(args)));
+    }
+};
+
+// Multiple (fixed) return value implementation. FIXME: How to avoid copy-paste?
+template<typename... R, typename... Args> class GComputationT<std::tuple<R...>(Args...)>
+{
+public:
+    typedef std::function<std::tuple<R...>(Args...)> Gen;
+
+    class GCompiledT
+    {
+    private:
+        friend class GComputationT<std::tuple<R...>(Args...)>;
+
+        cv::GCompiled m_comp;
+        explicit GCompiledT(const cv::GCompiled &comp) : m_comp(comp) {}
+
+    public:
+        GCompiledT() {}
+
+        void operator()(detail::ProtoToParamT<Args>... inArgs,
+                        detail::ProtoToParamT<R>&... outArgs)
+        {
+            m_comp(cv::gin(inArgs...), cv::gout(outArgs...));
+        }
+
+        explicit operator bool() const
+        {
+            return static_cast<bool>(m_comp);
+        }
+    };
+
+private:
+    typedef std::pair<GProtoArgs, GProtoArgs> Captured;
+
+    template<int... IIs>
+    Captured capture(GProtoArgs &&args, const std::tuple<R...> &rr, detail::Seq<IIs...>)
+    {
+        return Captured(cv::GOut(std::get<IIs>(rr)...).m_args, args);
+    }
+
+    Captured capture(const Gen& g, Args... args)
+    {
+        return capture(cv::GIn(args...).m_args, g(args...), typename detail::MkSeq<sizeof...(R)>::type());
+    }
+
+    Captured m_capture;
+    cv::GComputation m_comp;
+
+public:
+    GComputationT(const Gen &generator)
+        : m_capture(capture(generator, detail::make_default<Args>()...))
+        , m_comp(cv::GProtoInputArgs(std::move(m_capture.second)),
+                 cv::GProtoOutputArgs(std::move(m_capture.first)))
+    {
+    }
+
+    void apply(detail::ProtoToParamT<Args>... inArgs,
+               detail::ProtoToParamT<R>&... outArgs,
+               GCompileArgs &&args)
+    {
+        m_comp.apply(cv::gin(inArgs...), cv::gout(outArgs...), std::move(args));
+    }
+
+    void apply(detail::ProtoToParamT<Args>... inArgs,
+               detail::ProtoToParamT<R>&... outArgs)
+    {
+        apply(inArgs..., outArgs..., GCompileArgs());
+    }
+
+
+    GCompiledT compile(detail::ProtoToMetaT<Args>... inDescs)
+    {
+        GMetaArgs inMetas = { GMetaArg(inDescs)... };
+        return GCompiledT(m_comp.compile(std::move(inMetas), GCompileArgs()));
+    }
+
+    GCompiledT compile(detail::ProtoToMetaT<Args>... inDescs, GCompileArgs &&args)
+    {
+        GMetaArgs inMetas = { GMetaArg(inDescs)... };
+        return GCompiledT(m_comp.compile(std::move(inMetas), std::move(args)));
+    }
+};
+
+} // namespace cv
+#endif // !defined(GAPI_STANDALONE)
+#endif // OPENCV_GAPI_GTYPED_HPP
diff --git a/3rdParty/include/opencv2/gapi/imgproc.hpp b/3rdParty/include/opencv2/gapi/imgproc.hpp
new file mode 100644
index 0000000..5c4c6f7
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/imgproc.hpp
@@ -0,0 +1,1682 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_IMGPROC_HPP
+#define OPENCV_GAPI_IMGPROC_HPP
+
+#include <opencv2/imgproc.hpp>
+
+#include <utility> // std::tuple
+
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+
+
+/** \defgroup gapi_imgproc G-API Image processing functionality
+@{
+    @defgroup gapi_filters Graph API: Image filters
+    @defgroup gapi_colorconvert Graph API: Converting image from one color space to another
+    @defgroup gapi_feature Graph API: Image Feature Detection
+    @defgroup gapi_shape Graph API: Image Structural Analysis and Shape Descriptors
+@}
+ */
+
+namespace {
+void validateFindingContoursMeta(const int depth, const int chan, const int mode)
+{
+    GAPI_Assert(chan == 1);
+    switch (mode)
+    {
+    case cv::RETR_CCOMP:
+        GAPI_Assert(depth == CV_8U || depth == CV_32S);
+        break;
+    case cv::RETR_FLOODFILL:
+        GAPI_Assert(depth == CV_32S);
+        break;
+    default:
+        GAPI_Assert(depth == CV_8U);
+        break;
+    }
+}
+} // anonymous namespace
+
+namespace cv { namespace gapi {
+
+/**
+ * @brief This namespace contains G-API Operation Types for OpenCV
+ * ImgProc module functionality.
+ */
+namespace imgproc {
+    using GMat2 = std::tuple<GMat,GMat>;
+    using GMat3 = std::tuple<GMat,GMat,GMat>; // FIXME: how to avoid this?
+    using GFindContoursOutput = std::tuple<GArray<GArray<Point>>,GArray<Vec4i>>;
+
+    G_TYPED_KERNEL(GFilter2D, <GMat(GMat,int,Mat,Point,Scalar,int,Scalar)>,"org.opencv.imgproc.filters.filter2D") {
+        static GMatDesc outMeta(GMatDesc in, int ddepth, Mat, Point, Scalar, int, Scalar) {
+            return in.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GSepFilter, <GMat(GMat,int,Mat,Mat,Point,Scalar,int,Scalar)>, "org.opencv.imgproc.filters.sepfilter") {
+        static GMatDesc outMeta(GMatDesc in, int ddepth, Mat, Mat, Point, Scalar, int, Scalar) {
+            return in.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GBoxFilter, <GMat(GMat,int,Size,Point,bool,int,Scalar)>, "org.opencv.imgproc.filters.boxfilter") {
+        static GMatDesc outMeta(GMatDesc in, int ddepth, Size, Point, bool, int, Scalar) {
+            return in.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GBlur, <GMat(GMat,Size,Point,int,Scalar)>,         "org.opencv.imgproc.filters.blur"){
+        static GMatDesc outMeta(GMatDesc in, Size, Point, int, Scalar) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GGaussBlur, <GMat(GMat,Size,double,double,int,Scalar)>, "org.opencv.imgproc.filters.gaussianBlur") {
+        static GMatDesc outMeta(GMatDesc in, Size, double, double, int, Scalar) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GMedianBlur, <GMat(GMat,int)>, "org.opencv.imgproc.filters.medianBlur") {
+        static GMatDesc outMeta(GMatDesc in, int) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GErode, <GMat(GMat,Mat,Point,int,int,Scalar)>, "org.opencv.imgproc.filters.erode") {
+        static GMatDesc outMeta(GMatDesc in, Mat, Point, int, int, Scalar) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GDilate, <GMat(GMat,Mat,Point,int,int,Scalar)>, "org.opencv.imgproc.filters.dilate") {
+        static GMatDesc outMeta(GMatDesc in, Mat, Point, int, int, Scalar) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GMorphologyEx, <GMat(GMat,MorphTypes,Mat,Point,int,BorderTypes,Scalar)>,
+                   "org.opencv.imgproc.filters.morphologyEx") {
+        static GMatDesc outMeta(const GMatDesc &in, MorphTypes, Mat, Point, int,
+                                BorderTypes, Scalar) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GSobel, <GMat(GMat,int,int,int,int,double,double,int,Scalar)>, "org.opencv.imgproc.filters.sobel") {
+        static GMatDesc outMeta(GMatDesc in, int ddepth, int, int, int, double, double, int, Scalar) {
+            return in.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL_M(GSobelXY, <GMat2(GMat,int,int,int,double,double,int,Scalar)>, "org.opencv.imgproc.filters.sobelxy") {
+        static std::tuple<GMatDesc, GMatDesc> outMeta(GMatDesc in, int ddepth, int, int, double, double, int, Scalar) {
+            return std::make_tuple(in.withDepth(ddepth), in.withDepth(ddepth));
+        }
+    };
+
+    G_TYPED_KERNEL(GLaplacian, <GMat(GMat,int, int, double, double, int)>,
+                   "org.opencv.imgproc.filters.laplacian") {
+        static GMatDesc outMeta(GMatDesc in, int ddepth, int, double, double, int) {
+            return in.withDepth(ddepth);
+        }
+    };
+
+    G_TYPED_KERNEL(GBilateralFilter, <GMat(GMat,int, double, double, int)>,
+                   "org.opencv.imgproc.filters.bilateralfilter") {
+        static GMatDesc outMeta(GMatDesc in, int, double, double, int) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GEqHist, <GMat(GMat)>, "org.opencv.imgproc.equalizeHist"){
+        static GMatDesc outMeta(GMatDesc in) {
+            return in.withType(CV_8U, 1);
+        }
+    };
+
+    G_TYPED_KERNEL(GCanny, <GMat(GMat,double,double,int,bool)>, "org.opencv.imgproc.feature.canny"){
+        static GMatDesc outMeta(GMatDesc in, double, double, int, bool) {
+            return in.withType(CV_8U, 1);
+        }
+    };
+
+    G_TYPED_KERNEL(GGoodFeatures,
+                   <cv::GArray<cv::Point2f>(GMat,int,double,double,Mat,int,bool,double)>,
+                   "org.opencv.imgproc.feature.goodFeaturesToTrack") {
+        static GArrayDesc outMeta(GMatDesc, int, double, double, const Mat&, int, bool, double) {
+            return empty_array_desc();
+        }
+    };
+
+    using RetrMode = RetrievalModes;
+    using ContMethod = ContourApproximationModes;
+    G_TYPED_KERNEL(GFindContours, <GArray<GArray<Point>>(GMat,RetrMode,ContMethod,GOpaque<Point>)>,
+                   "org.opencv.imgproc.shape.findContours")
+    {
+        static GArrayDesc outMeta(GMatDesc in, RetrMode mode, ContMethod, GOpaqueDesc)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return empty_array_desc();
+        }
+    };
+
+    // FIXME oc: make default value offset = Point()
+    G_TYPED_KERNEL(GFindContoursNoOffset, <GArray<GArray<Point>>(GMat,RetrMode,ContMethod)>,
+                   "org.opencv.imgproc.shape.findContoursNoOffset")
+    {
+        static GArrayDesc outMeta(GMatDesc in, RetrMode mode, ContMethod)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return empty_array_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFindContoursH,<GFindContoursOutput(GMat,RetrMode,ContMethod,GOpaque<Point>)>,
+                   "org.opencv.imgproc.shape.findContoursH")
+    {
+        static std::tuple<GArrayDesc,GArrayDesc>
+        outMeta(GMatDesc in, RetrMode mode, ContMethod, GOpaqueDesc)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return std::make_tuple(empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    // FIXME oc: make default value offset = Point()
+    G_TYPED_KERNEL(GFindContoursHNoOffset,<GFindContoursOutput(GMat,RetrMode,ContMethod)>,
+                   "org.opencv.imgproc.shape.findContoursHNoOffset")
+    {
+        static std::tuple<GArrayDesc,GArrayDesc>
+        outMeta(GMatDesc in, RetrMode mode, ContMethod)
+        {
+            validateFindingContoursMeta(in.depth, in.chan, mode);
+            return std::make_tuple(empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    G_TYPED_KERNEL(GBoundingRectMat, <GOpaque<Rect>(GMat)>,
+                   "org.opencv.imgproc.shape.boundingRectMat") {
+        static GOpaqueDesc outMeta(GMatDesc in) {
+            if (in.depth == CV_8U)
+            {
+                GAPI_Assert(in.chan == 1);
+            }
+            else
+            {
+                GAPI_Assert (in.depth == CV_32S || in.depth == CV_32F);
+                int amount = detail::checkVector(in, 2u);
+                GAPI_Assert(amount != -1 &&
+                            "Input Mat can't be described as vector of 2-dimentional points");
+            }
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GBoundingRectVector32S, <GOpaque<Rect>(GArray<Point2i>)>,
+                   "org.opencv.imgproc.shape.boundingRectVector32S") {
+        static GOpaqueDesc outMeta(GArrayDesc) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GBoundingRectVector32F, <GOpaque<Rect>(GArray<Point2f>)>,
+                   "org.opencv.imgproc.shape.boundingRectVector32F") {
+        static GOpaqueDesc outMeta(GArrayDesc) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine2DMat, <GOpaque<Vec4f>(GMat,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine2DMat") {
+        static GOpaqueDesc outMeta(GMatDesc in,DistanceTypes,double,double,double) {
+            int amount = detail::checkVector(in, 2u);
+            GAPI_Assert(amount != -1 &&
+                        "Input Mat can't be described as vector of 2-dimentional points");
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine2DVector32S,
+                   <GOpaque<Vec4f>(GArray<Point2i>,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine2DVector32S") {
+        static GOpaqueDesc outMeta(GArrayDesc,DistanceTypes,double,double,double) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine2DVector32F,
+                   <GOpaque<Vec4f>(GArray<Point2f>,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine2DVector32F") {
+        static GOpaqueDesc outMeta(GArrayDesc,DistanceTypes,double,double,double) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine2DVector64F,
+                   <GOpaque<Vec4f>(GArray<Point2d>,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine2DVector64F") {
+        static GOpaqueDesc outMeta(GArrayDesc,DistanceTypes,double,double,double) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine3DMat, <GOpaque<Vec6f>(GMat,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine3DMat") {
+        static GOpaqueDesc outMeta(GMatDesc in,int,double,double,double) {
+            int amount = detail::checkVector(in, 3u);
+            GAPI_Assert(amount != -1 &&
+                        "Input Mat can't be described as vector of 3-dimentional points");
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine3DVector32S,
+                   <GOpaque<Vec6f>(GArray<Point3i>,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine3DVector32S") {
+        static GOpaqueDesc outMeta(GArrayDesc,DistanceTypes,double,double,double) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine3DVector32F,
+                   <GOpaque<Vec6f>(GArray<Point3f>,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine3DVector32F") {
+        static GOpaqueDesc outMeta(GArrayDesc,DistanceTypes,double,double,double) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GFitLine3DVector64F,
+                   <GOpaque<Vec6f>(GArray<Point3d>,DistanceTypes,double,double,double)>,
+                   "org.opencv.imgproc.shape.fitLine3DVector64F") {
+        static GOpaqueDesc outMeta(GArrayDesc,DistanceTypes,double,double,double) {
+            return empty_gopaque_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GBGR2RGB, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2rgb") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GRGB2YUV, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2yuv") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GYUV2RGB, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.yuv2rgb") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GBGR2I420, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2i420") {
+        static GMatDesc outMeta(GMatDesc in) {
+            GAPI_Assert(in.depth == CV_8U);
+            GAPI_Assert(in.chan == 3);
+            GAPI_Assert(in.size.height % 2 == 0);
+            return in.withType(in.depth, 1).withSize(Size(in.size.width, in.size.height * 3 / 2));
+        }
+    };
+
+    G_TYPED_KERNEL(GRGB2I420, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2i420") {
+        static GMatDesc outMeta(GMatDesc in) {
+            GAPI_Assert(in.depth == CV_8U);
+            GAPI_Assert(in.chan == 3);
+            GAPI_Assert(in.size.height % 2 == 0);
+            return in.withType(in.depth, 1).withSize(Size(in.size.width, in.size.height * 3 / 2));
+        }
+    };
+
+    G_TYPED_KERNEL(GI4202BGR, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.i4202bgr") {
+        static GMatDesc outMeta(GMatDesc in) {
+            GAPI_Assert(in.depth == CV_8U);
+            GAPI_Assert(in.chan == 1);
+            GAPI_Assert(in.size.height % 3 == 0);
+            return in.withType(in.depth, 3).withSize(Size(in.size.width, in.size.height * 2 / 3));
+        }
+    };
+
+    G_TYPED_KERNEL(GI4202RGB, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.i4202rgb") {
+        static GMatDesc outMeta(GMatDesc in) {
+            GAPI_Assert(in.depth == CV_8U);
+            GAPI_Assert(in.chan == 1);
+            GAPI_Assert(in.size.height % 3 == 0);
+            return in.withType(in.depth, 3).withSize(Size(in.size.width, in.size.height * 2 / 3));
+        }
+    };
+
+    G_TYPED_KERNEL(GNV12toRGB, <GMat(GMat, GMat)>, "org.opencv.imgproc.colorconvert.nv12torgb") {
+        static GMatDesc outMeta(GMatDesc in_y, GMatDesc in_uv) {
+            GAPI_Assert(in_y.chan == 1);
+            GAPI_Assert(in_uv.chan == 2);
+            GAPI_Assert(in_y.depth == CV_8U);
+            GAPI_Assert(in_uv.depth == CV_8U);
+            // UV size should be aligned with Y
+            GAPI_Assert(in_y.size.width == 2 * in_uv.size.width);
+            GAPI_Assert(in_y.size.height == 2 * in_uv.size.height);
+            return in_y.withType(CV_8U, 3); // type will be CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GNV12toBGR, <GMat(GMat, GMat)>, "org.opencv.imgproc.colorconvert.nv12tobgr") {
+        static GMatDesc outMeta(GMatDesc in_y, GMatDesc in_uv) {
+            GAPI_Assert(in_y.chan == 1);
+            GAPI_Assert(in_uv.chan == 2);
+            GAPI_Assert(in_y.depth == CV_8U);
+            GAPI_Assert(in_uv.depth == CV_8U);
+            // UV size should be aligned with Y
+            GAPI_Assert(in_y.size.width == 2 * in_uv.size.width);
+            GAPI_Assert(in_y.size.height == 2 * in_uv.size.height);
+            return in_y.withType(CV_8U, 3); // type will be CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GRGB2Lab, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2lab") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GBGR2LUV, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2luv") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GLUV2BGR, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.luv2bgr") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GYUV2BGR, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.yuv2bgr") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GBGR2YUV, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2yuv") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in; // type still remains CV_8UC3;
+        }
+    };
+
+    G_TYPED_KERNEL(GRGB2Gray, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.rgb2gray") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in.withType(CV_8U, 1);
+        }
+    };
+
+    G_TYPED_KERNEL(GRGB2GrayCustom, <GMat(GMat,float,float,float)>, "org.opencv.imgproc.colorconvert.rgb2graycustom") {
+        static GMatDesc outMeta(GMatDesc in, float, float, float) {
+            return in.withType(CV_8U, 1);
+        }
+    };
+
+    G_TYPED_KERNEL(GBGR2Gray, <GMat(GMat)>, "org.opencv.imgproc.colorconvert.bgr2gray") {
+        static GMatDesc outMeta(GMatDesc in) {
+            return in.withType(CV_8U, 1);
+        }
+    };
+
+    G_TYPED_KERNEL(GBayerGR2RGB, <cv::GMat(cv::GMat)>, "org.opencv.imgproc.colorconvert.bayergr2rgb") {
+        static cv::GMatDesc outMeta(cv::GMatDesc in) {
+            return in.withType(CV_8U, 3);
+        }
+    };
+
+    G_TYPED_KERNEL(GRGB2HSV, <cv::GMat(cv::GMat)>, "org.opencv.imgproc.colorconvert.rgb2hsv") {
+        static cv::GMatDesc outMeta(cv::GMatDesc in) {
+            return in;
+        }
+    };
+
+    G_TYPED_KERNEL(GRGB2YUV422, <cv::GMat(cv::GMat)>, "org.opencv.imgproc.colorconvert.rgb2yuv422") {
+        static cv::GMatDesc outMeta(cv::GMatDesc in) {
+            GAPI_Assert(in.depth == CV_8U);
+            GAPI_Assert(in.chan == 3);
+            return in.withType(in.depth, 2);
+        }
+    };
+
+    G_TYPED_KERNEL(GNV12toRGBp, <GMatP(GMat,GMat)>, "org.opencv.imgproc.colorconvert.nv12torgbp") {
+        static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
+            GAPI_Assert(inY.depth == CV_8U);
+            GAPI_Assert(inUV.depth == CV_8U);
+            GAPI_Assert(inY.chan == 1);
+            GAPI_Assert(inY.planar == false);
+            GAPI_Assert(inUV.chan == 2);
+            GAPI_Assert(inUV.planar == false);
+            GAPI_Assert(inY.size.width  == 2 * inUV.size.width);
+            GAPI_Assert(inY.size.height == 2 * inUV.size.height);
+            return inY.withType(CV_8U, 3).asPlanar();
+        }
+    };
+
+    G_TYPED_KERNEL(GNV12toGray, <GMat(GMat,GMat)>, "org.opencv.imgproc.colorconvert.nv12togray") {
+        static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
+            GAPI_Assert(inY.depth   == CV_8U);
+            GAPI_Assert(inUV.depth  == CV_8U);
+            GAPI_Assert(inY.chan    == 1);
+            GAPI_Assert(inY.planar  == false);
+            GAPI_Assert(inUV.chan   == 2);
+            GAPI_Assert(inUV.planar == false);
+
+            GAPI_Assert(inY.size.width  == 2 * inUV.size.width);
+            GAPI_Assert(inY.size.height == 2 * inUV.size.height);
+            return inY.withType(CV_8U, 1);
+        }
+    };
+
+    G_TYPED_KERNEL(GNV12toBGRp, <GMatP(GMat,GMat)>, "org.opencv.imgproc.colorconvert.nv12tobgrp") {
+        static GMatDesc outMeta(GMatDesc inY, GMatDesc inUV) {
+            GAPI_Assert(inY.depth == CV_8U);
+            GAPI_Assert(inUV.depth == CV_8U);
+            GAPI_Assert(inY.chan == 1);
+            GAPI_Assert(inY.planar == false);
+            GAPI_Assert(inUV.chan == 2);
+            GAPI_Assert(inUV.planar == false);
+            GAPI_Assert(inY.size.width  == 2 * inUV.size.width);
+            GAPI_Assert(inY.size.height == 2 * inUV.size.height);
+            return inY.withType(CV_8U, 3).asPlanar();
+        }
+    };
+
+} //namespace imgproc
+
+//! @addtogroup gapi_filters
+//! @{
+/** @brief Applies a separable linear filter to a matrix(image).
+
+The function applies a separable linear filter to the matrix. That is, first, every row of src is
+filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
+kernel kernelY. The final result is returned.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - In case of floating-point computation, rounding to nearest even is procedeed
+if hardware supports it (if not - to nearest value).
+ - Function textual ID is "org.opencv.imgproc.filters.sepfilter"
+@param src Source image.
+@param ddepth desired depth of the destination image (the following combinations of src.depth() and ddepth are supported:
+
+        src.depth() = CV_8U, ddepth = -1/CV_16S/CV_32F/CV_64F
+        src.depth() = CV_16U/CV_16S, ddepth = -1/CV_32F/CV_64F
+        src.depth() = CV_32F, ddepth = -1/CV_32F/CV_64F
+        src.depth() = CV_64F, ddepth = -1/CV_64F
+
+when ddepth=-1, the output image will have the same depth as the source)
+@param kernelX Coefficients for filtering each row.
+@param kernelY Coefficients for filtering each column.
+@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
+is at the kernel center.
+@param delta Value added to the filtered results before storing them.
+@param borderType Pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of constant border type
+@sa  boxFilter, gaussianBlur, medianBlur
+ */
+GAPI_EXPORTS GMat sepFilter(const GMat& src, int ddepth, const Mat& kernelX, const Mat& kernelY, const Point& anchor /*FIXME: = Point(-1,-1)*/,
+                            const Scalar& delta /*FIXME = GScalar(0)*/, int borderType = BORDER_DEFAULT,
+                            const Scalar& borderValue = Scalar(0));
+
+/** @brief Convolves an image with the kernel.
+
+The function applies an arbitrary linear filter to an image. When
+the aperture is partially outside the image, the function interpolates outlier pixel values
+according to the specified border mode.
+
+The function does actually compute correlation, not the convolution:
+
+\f[\texttt{dst} (x,y) =  \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}}  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f]
+
+That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
+the kernel using flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -
+anchor.y - 1)`.
+
+Supported matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+Output image must have the same size and number of channels an input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.filter2D"
+
+@param src input image.
+@param ddepth desired depth of the destination image
+@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
+matrix; if you want to apply different kernels to different channels, split the image into
+separate color planes using split and process them individually.
+@param anchor anchor of the kernel that indicates the relative position of a filtered point within
+the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
+is at the kernel center.
+@param delta optional value added to the filtered pixels before storing them in dst.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of constant border type
+@sa  sepFilter
+ */
+GAPI_EXPORTS GMat filter2D(const GMat& src, int ddepth, const Mat& kernel, const Point& anchor = Point(-1,-1), const Scalar& delta = Scalar(0),
+                           int borderType = BORDER_DEFAULT, const Scalar& borderValue = Scalar(0));
+
+
+/** @brief Blurs an image using the box filter.
+
+The function smooths an image using the kernel:
+
+\f[\texttt{K} =  \alpha \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1 \end{bmatrix}\f]
+
+where
+
+\f[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true}  \\1 & \texttt{otherwise} \end{cases}\f]
+
+Unnormalized box filter is useful for computing various integral characteristics over each pixel
+neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
+algorithms, and so on). If you need to compute pixel sums over variable-size windows, use cv::integral.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.boxfilter"
+
+@param src Source image.
+@param dtype the output image depth (-1 to set the input image data type).
+@param ksize blurring kernel size.
+@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
+is at the kernel center.
+@param normalize flag, specifying whether the kernel is normalized by its area or not.
+@param borderType Pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of constant border type
+@sa  sepFilter, gaussianBlur, medianBlur, integral
+ */
+GAPI_EXPORTS GMat boxFilter(const GMat& src, int dtype, const Size& ksize, const Point& anchor = Point(-1,-1),
+                            bool normalize = true, int borderType = BORDER_DEFAULT,
+                            const Scalar& borderValue = Scalar(0));
+
+/** @brief Blurs an image using the normalized box filter.
+
+The function smooths an image using the kernel:
+
+\f[\texttt{K} =  \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \end{bmatrix}\f]
+
+The call `blur(src, ksize, anchor, borderType)` is equivalent to `boxFilter(src, src.type(), ksize, anchor,
+true, borderType)`.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.blur"
+
+@param src Source image.
+@param ksize blurring kernel size.
+@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
+center.
+@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
+@param borderValue border value in case of constant border type
+@sa  boxFilter, bilateralFilter, GaussianBlur, medianBlur
+ */
+GAPI_EXPORTS GMat blur(const GMat& src, const Size& ksize, const Point& anchor = Point(-1,-1),
+                       int borderType = BORDER_DEFAULT, const Scalar& borderValue = Scalar(0));
+
+
+//GAPI_EXPORTS_W void blur( InputArray src, OutputArray dst,
+ //                       Size ksize, Point anchor = Point(-1,-1),
+ //                       int borderType = BORDER_DEFAULT );
+
+
+/** @brief Blurs an image using a Gaussian filter.
+
+The function filter2Ds the source image with the specified Gaussian kernel.
+Output image must have the same type and number of channels an input image.
+
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.gaussianBlur"
+
+@param src input image;
+@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
+positive and odd. Or, they can be zero's and then they are computed from sigma.
+@param sigmaX Gaussian kernel standard deviation in X direction.
+@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
+equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
+respectively (see cv::getGaussianKernel for details); to fully control the result regardless of
+possible future modifications of all this semantics, it is recommended to specify all of ksize,
+sigmaX, and sigmaY.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of constant border type
+@sa  sepFilter, boxFilter, medianBlur
+ */
+GAPI_EXPORTS GMat gaussianBlur(const GMat& src, const Size& ksize, double sigmaX, double sigmaY = 0,
+                               int borderType = BORDER_DEFAULT, const Scalar& borderValue = Scalar(0));
+
+/** @brief Blurs an image using the median filter.
+
+The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
+\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+The median filter uses cv::BORDER_REPLICATE internally to cope with border pixels, see cv::BorderTypes
+ - Function textual ID is "org.opencv.imgproc.filters.medianBlur"
+
+@param src input matrix (image)
+@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
+@sa  boxFilter, gaussianBlur
+ */
+GAPI_EXPORTS_W GMat medianBlur(const GMat& src, int ksize);
+
+/** @brief Erodes an image by using a specific structuring element.
+
+The function erodes the source image using the specified structuring element that determines the
+shape of a pixel neighborhood over which the minimum is taken:
+
+\f[\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
+
+Erosion can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.erode"
+
+@param src input image
+@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
+structuring element is used. Kernel can be created using getStructuringElement.
+@param anchor position of the anchor within the element; default value (-1, -1) means that the
+anchor is at the element center.
+@param iterations number of times erosion is applied.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of a constant border
+@sa  dilate, morphologyEx
+ */
+GAPI_EXPORTS GMat erode(const GMat& src, const Mat& kernel, const Point& anchor = Point(-1,-1), int iterations = 1,
+                        int borderType = BORDER_CONSTANT,
+                        const  Scalar& borderValue = morphologyDefaultBorderValue());
+
+/** @brief Erodes an image by using 3 by 3 rectangular structuring element.
+
+The function erodes the source image using the rectangular structuring element with rectangle center as an anchor.
+Erosion can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.erode"
+
+@param src input image
+@param iterations number of times erosion is applied.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of a constant border
+@sa  erode, dilate3x3
+ */
+GAPI_EXPORTS GMat erode3x3(const GMat& src, int iterations = 1,
+                           int borderType = BORDER_CONSTANT,
+                           const  Scalar& borderValue = morphologyDefaultBorderValue());
+
+/** @brief Dilates an image by using a specific structuring element.
+
+The function dilates the source image using the specified structuring element that determines the
+shape of a pixel neighborhood over which the maximum is taken:
+\f[\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
+
+Dilation can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.dilate"
+
+@param src input image.
+@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
+structuring element is used. Kernel can be created using getStructuringElement
+@param anchor position of the anchor within the element; default value (-1, -1) means that the
+anchor is at the element center.
+@param iterations number of times dilation is applied.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of a constant border
+@sa  erode, morphologyEx, getStructuringElement
+ */
+GAPI_EXPORTS GMat dilate(const GMat& src, const Mat& kernel, const Point& anchor = Point(-1,-1), int iterations = 1,
+                         int borderType = BORDER_CONSTANT,
+                         const  Scalar& borderValue = morphologyDefaultBorderValue());
+
+/** @brief Dilates an image by using 3 by 3 rectangular structuring element.
+
+The function dilates the source image using the specified structuring element that determines the
+shape of a pixel neighborhood over which the maximum is taken:
+\f[\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
+
+Dilation can be applied several (iterations) times. In case of multi-channel images, each channel is processed independently.
+Supported input matrix data types are @ref CV_8UC1, @ref CV_8UC3, @ref CV_16UC1, @ref CV_16SC1, and @ref CV_32FC1.
+Output image must have the same type, size, and number of channels as the input image.
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.dilate"
+
+@param src input image.
+@param iterations number of times dilation is applied.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of a constant border
+@sa  dilate, erode3x3
+ */
+
+GAPI_EXPORTS GMat dilate3x3(const GMat& src, int iterations = 1,
+                            int borderType = BORDER_CONSTANT,
+                            const  Scalar& borderValue = morphologyDefaultBorderValue());
+
+/** @brief Performs advanced morphological transformations.
+
+The function can perform advanced morphological transformations using an erosion and dilation as
+basic operations.
+
+Any of the operations can be done in-place. In case of multi-channel images, each channel is
+processed independently.
+
+@note
+ - Function textual ID is "org.opencv.imgproc.filters.morphologyEx"
+ - The number of iterations is the number of times erosion or dilatation operation will be
+applied. For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to
+apply successively: erode -> erode -> dilate -> dilate
+(and not erode -> dilate -> erode -> dilate).
+
+@param src Input image.
+@param op Type of a morphological operation, see #MorphTypes
+@param kernel Structuring element. It can be created using #getStructuringElement.
+@param anchor Anchor position within the element. Both negative values mean that the anchor is at
+the kernel center.
+@param iterations Number of times erosion and dilation are applied.
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@param borderValue Border value in case of a constant border. The default value has a special
+meaning.
+@sa  dilate, erode, getStructuringElement
+ */
+GAPI_EXPORTS GMat morphologyEx(const GMat &src, const MorphTypes op, const Mat &kernel,
+                               const Point       &anchor      = Point(-1,-1),
+                               const int          iterations  = 1,
+                               const BorderTypes  borderType  = BORDER_CONSTANT,
+                               const Scalar      &borderValue = morphologyDefaultBorderValue());
+
+/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
+
+In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
+calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
+kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
+or the second x- or y- derivatives.
+
+There is also the special value `ksize = FILTER_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
+filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
+
+\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
+
+for the x-derivative, or transposed for the y-derivative.
+
+The function calculates an image derivative by convolving the image with the appropriate kernel:
+
+\f[\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
+
+The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
+resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
+or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
+case corresponds to a kernel of:
+
+\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
+
+The second case corresponds to a kernel of:
+
+\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
+
+@note
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.sobel"
+
+@param src input image.
+@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
+    8-bit input images it will result in truncated derivatives.
+@param dx order of the derivative x.
+@param dy order of the derivative y.
+@param ksize size of the extended Sobel kernel; it must be odd.
+@param scale optional scale factor for the computed derivative values; by default, no scaling is
+applied (see cv::getDerivKernels for details).
+@param delta optional delta value that is added to the results prior to storing them in dst.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of constant border type
+@sa filter2D, gaussianBlur, cartToPolar
+ */
+GAPI_EXPORTS GMat Sobel(const GMat& src, int ddepth, int dx, int dy, int ksize = 3,
+                        double scale = 1, double delta = 0,
+                        int borderType = BORDER_DEFAULT,
+                        const Scalar& borderValue = Scalar(0));
+
+/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
+
+In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
+calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
+kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
+or the second x- or y- derivatives.
+
+There is also the special value `ksize = FILTER_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
+filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
+
+\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
+
+for the x-derivative, or transposed for the y-derivative.
+
+The function calculates an image derivative by convolving the image with the appropriate kernel:
+
+\f[\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
+
+The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
+resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
+or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
+case corresponds to a kernel of:
+
+\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
+
+The second case corresponds to a kernel of:
+
+\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
+
+@note
+ - First returned matrix correspons to dx derivative while the second one to dy.
+ - Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.
+ - Function textual ID is "org.opencv.imgproc.filters.sobelxy"
+
+@param src input image.
+@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
+    8-bit input images it will result in truncated derivatives.
+@param order order of the derivatives.
+@param ksize size of the extended Sobel kernel; it must be odd.
+@param scale optional scale factor for the computed derivative values; by default, no scaling is
+applied (see cv::getDerivKernels for details).
+@param delta optional delta value that is added to the results prior to storing them in dst.
+@param borderType pixel extrapolation method, see cv::BorderTypes
+@param borderValue border value in case of constant border type
+@sa filter2D, gaussianBlur, cartToPolar
+ */
+GAPI_EXPORTS std::tuple<GMat, GMat> SobelXY(const GMat& src, int ddepth, int order, int ksize = 3,
+                        double scale = 1, double delta = 0,
+                        int borderType = BORDER_DEFAULT,
+                        const Scalar& borderValue = Scalar(0));
+
+/** @brief Calculates the Laplacian of an image.
+
+The function calculates the Laplacian of the source image by adding up the second x and y
+derivatives calculated using the Sobel operator:
+
+\f[\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
+
+This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
+with the following \f$3 \times 3\f$ aperture:
+
+\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
+
+@note Function textual ID is "org.opencv.imgproc.filters.laplacian"
+
+@param src Source image.
+@param ddepth Desired depth of the destination image.
+@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
+details. The size must be positive and odd.
+@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
+applied. See #getDerivKernels for details.
+@param delta Optional delta value that is added to the results prior to storing them in dst .
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@return Destination image of the same size and the same number of channels as src.
+@sa  Sobel, Scharr
+ */
+GAPI_EXPORTS GMat Laplacian(const GMat& src, int ddepth, int ksize = 1,
+                            double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT);
+
+/** @brief Applies the bilateral filter to an image.
+
+The function applies bilateral filtering to the input image, as described in
+http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
+bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is
+very slow compared to most filters.
+
+_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\<
+10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very
+strong effect, making the image look "cartoonish".
+
+_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time
+applications, and perhaps d=9 for offline applications that need heavy noise filtering.
+
+This filter does not work inplace.
+
+@note Function textual ID is "org.opencv.imgproc.filters.bilateralfilter"
+
+@param src Source 8-bit or floating-point, 1-channel or 3-channel image.
+@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
+it is computed from sigmaSpace.
+@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
+farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
+in larger areas of semi-equal color.
+@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
+farther pixels will influence each other as long as their colors are close enough (see sigmaColor
+). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
+proportional to sigmaSpace.
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
+@return Destination image of the same size and type as src.
+ */
+GAPI_EXPORTS GMat bilateralFilter(const GMat& src, int d, double sigmaColor, double sigmaSpace,
+                                  int borderType = BORDER_DEFAULT);
+
+//! @} gapi_filters
+
+//! @addtogroup gapi_feature
+//! @{
+/** @brief Finds edges in an image using the Canny algorithm.
+
+The function finds edges in the input image and marks them in the output map edges using the
+Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The
+largest value is used to find initial segments of strong edges. See
+<http://en.wikipedia.org/wiki/Canny_edge_detector>
+
+@note Function textual ID is "org.opencv.imgproc.feature.canny"
+
+@param image 8-bit input image.
+@param threshold1 first threshold for the hysteresis procedure.
+@param threshold2 second threshold for the hysteresis procedure.
+@param apertureSize aperture size for the Sobel operator.
+@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
+\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
+L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
+L2gradient=false ).
+ */
+GAPI_EXPORTS GMat Canny(const GMat& image, double threshold1, double threshold2,
+                        int apertureSize = 3, bool L2gradient = false);
+
+/** @brief Determines strong corners on an image.
+
+The function finds the most prominent corners in the image or in the specified image region, as
+described in @cite Shi94
+
+-   Function calculates the corner quality measure at every source image pixel using the
+    #cornerMinEigenVal or #cornerHarris .
+-   Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
+    retained).
+-   The corners with the minimal eigenvalue less than
+    \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
+-   The remaining corners are sorted by the quality measure in the descending order.
+-   Function throws away each corner for which there is a stronger corner at a distance less than
+    maxDistance.
+
+The function can be used to initialize a point-based tracker of an object.
+
+@note
+ - If the function is called with different values A and B of the parameter qualityLevel , and
+A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
+with qualityLevel=B .
+ - Function textual ID is "org.opencv.imgproc.feature.goodFeaturesToTrack"
+
+@param image Input 8-bit or floating-point 32-bit, single-channel image.
+@param maxCorners Maximum number of corners to return. If there are more corners than are found,
+the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
+and all detected corners are returned.
+@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
+parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
+(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
+quality measure less than the product are rejected. For example, if the best corner has the
+quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
+less than 15 are rejected.
+@param minDistance Minimum possible Euclidean distance between the returned corners.
+@param mask Optional region of interest. If the image is not empty (it needs to have the type
+CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
+@param blockSize Size of an average block for computing a derivative covariation matrix over each
+pixel neighborhood. See cornerEigenValsAndVecs .
+@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
+or #cornerMinEigenVal.
+@param k Free parameter of the Harris detector.
+
+@return vector of detected corners.
+ */
+GAPI_EXPORTS_W GArray<Point2f> goodFeaturesToTrack(const GMat  &image,
+                                                       int    maxCorners,
+                                                       double qualityLevel,
+                                                       double minDistance,
+                                                 const Mat   &mask = Mat(),
+                                                       int    blockSize = 3,
+                                                       bool   useHarrisDetector = false,
+                                                       double k = 0.04);
+
+/** @brief Equalizes the histogram of a grayscale image.
+
+//! @} gapi_feature
+
+The function equalizes the histogram of the input image using the following algorithm:
+
+- Calculate the histogram \f$H\f$ for src .
+- Normalize the histogram so that the sum of histogram bins is 255.
+- Compute the integral of the histogram:
+\f[H'_i =  \sum _{0  \le j < i} H(j)\f]
+- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$
+
+The algorithm normalizes the brightness and increases the contrast of the image.
+@note
+ - The returned image is of the same size and type as input.
+ - Function textual ID is "org.opencv.imgproc.equalizeHist"
+
+@param src Source 8-bit single channel image.
+ */
+GAPI_EXPORTS GMat equalizeHist(const GMat& src);
+
+//! @addtogroup gapi_shape
+//! @{
+/** @brief Finds contours in a binary image.
+
+The function retrieves contours from the binary image using the algorithm @cite Suzuki85 .
+The contours are a useful tool for shape analysis and object detection and recognition.
+See squares.cpp in the OpenCV sample directory.
+
+@note Function textual ID is "org.opencv.imgproc.shape.findContours"
+
+@param src Input gray-scale image @ref CV_8UC1. Non-zero pixels are treated as 1's. Zero
+pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
+#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
+If mode equals to #RETR_CCOMP, the input can also be a 32-bit integer
+image of labels ( @ref CV_32SC1 ). If #RETR_FLOODFILL then @ref CV_32SC1 is supported only.
+@param mode Contour retrieval mode, see #RetrievalModes
+@param method Contour approximation method, see #ContourApproximationModes
+@param offset Optional offset by which every contour point is shifted. This is useful if the
+contours are extracted from the image ROI and then they should be analyzed in the whole image
+context.
+
+@return GArray of detected contours. Each contour is stored as a GArray of points.
+ */
+GAPI_EXPORTS GArray<GArray<Point>>
+findContours(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method,
+             const GOpaque<Point> &offset);
+
+// FIXME oc: make default value offset = Point()
+/** @overload
+@note Function textual ID is "org.opencv.imgproc.shape.findContoursNoOffset"
+ */
+GAPI_EXPORTS GArray<GArray<Point>>
+findContours(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method);
+
+/** @brief Finds contours and their hierarchy in a binary image.
+
+The function retrieves contours from the binary image using the algorithm @cite Suzuki85
+and calculates their hierarchy.
+The contours are a useful tool for shape analysis and object detection and recognition.
+See squares.cpp in the OpenCV sample directory.
+
+@note Function textual ID is "org.opencv.imgproc.shape.findContoursH"
+
+@param src Input gray-scale image @ref CV_8UC1. Non-zero pixels are treated as 1's. Zero
+pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
+#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
+If mode equals to #RETR_CCOMP, the input can also be a 32-bit integer
+image of labels ( @ref CV_32SC1 ). If #RETR_FLOODFILL -- @ref CV_32SC1 supports only.
+@param mode Contour retrieval mode, see #RetrievalModes
+@param method Contour approximation method, see #ContourApproximationModes
+@param offset Optional offset by which every contour point is shifted. This is useful if the
+contours are extracted from the image ROI and then they should be analyzed in the whole image
+context.
+
+@return
+ - GArray of detected contours. Each contour is stored as a GArray of points.
+ - Optional output GArray of cv::Vec4i, containing information about the image topology.
+It has as many elements as the number of contours. For each i-th contour contours[i], the elements
+hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based
+indices in contours of the next and previous contours at the same hierarchical level, the first
+child contour and the parent contour, respectively. If for the contour i there are no next,
+previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
+ */
+GAPI_EXPORTS std::tuple<GArray<GArray<Point>>,GArray<Vec4i>>
+findContoursH(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method,
+              const GOpaque<Point> &offset);
+
+// FIXME oc: make default value offset = Point()
+/** @overload
+@note Function textual ID is "org.opencv.imgproc.shape.findContoursHNoOffset"
+ */
+GAPI_EXPORTS std::tuple<GArray<GArray<Point>>,GArray<Vec4i>>
+findContoursH(const GMat &src, const RetrievalModes mode, const ContourApproximationModes method);
+
+/** @brief Calculates the up-right bounding rectangle of a point set or non-zero pixels
+of gray-scale image.
+
+The function calculates and returns the minimal up-right bounding rectangle for the specified
+point set or non-zero pixels of gray-scale image.
+
+@note
+ - Function textual ID is "org.opencv.imgproc.shape.boundingRectMat"
+ - In case of a 2D points' set given, Mat should be 2-dimensional, have a single row or column
+if there are 2 channels, or have 2 columns if there is a single channel. Mat should have either
+@ref CV_32S or @ref CV_32F depth
+
+@param src Input gray-scale image @ref CV_8UC1; or input set of @ref CV_32S or @ref CV_32F
+2D points stored in Mat.
+ */
+GAPI_EXPORTS_W GOpaque<Rect> boundingRect(const GMat& src);
+
+/** @overload
+
+Calculates the up-right bounding rectangle of a point set.
+
+@note Function textual ID is "org.opencv.imgproc.shape.boundingRectVector32S"
+
+@param src Input 2D point set, stored in std::vector<cv::Point2i>.
+ */
+GAPI_EXPORTS_W GOpaque<Rect> boundingRect(const GArray<Point2i>& src);
+
+/** @overload
+
+Calculates the up-right bounding rectangle of a point set.
+
+@note Function textual ID is "org.opencv.imgproc.shape.boundingRectVector32F"
+
+@param src Input 2D point set, stored in std::vector<cv::Point2f>.
+ */
+GAPI_EXPORTS GOpaque<Rect> boundingRect(const GArray<Point2f>& src);
+
+/** @brief Fits a line to a 2D point set.
+
+The function fits a line to a 2D point set by minimizing \f$\sum_i \rho(r_i)\f$ where
+\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance
+function, one of the following:
+-  DIST_L2
+\f[\rho (r) = r^2/2  \quad \text{(the simplest and the fastest least-squares method)}\f]
+- DIST_L1
+\f[\rho (r) = r\f]
+- DIST_L12
+\f[\rho (r) = 2  \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f]
+- DIST_FAIR
+\f[\rho \left (r \right ) = C^2  \cdot \left (  \frac{r}{C} -  \log{\left(1 + \frac{r}{C}\right)} \right )  \quad \text{where} \quad C=1.3998\f]
+- DIST_WELSCH
+\f[\rho \left (r \right ) =  \frac{C^2}{2} \cdot \left ( 1 -  \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right )  \quad \text{where} \quad C=2.9846\f]
+- DIST_HUBER
+\f[\rho (r) =  \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f]
+
+The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
+that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
+weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ .
+
+@note
+ - Function textual ID is "org.opencv.imgproc.shape.fitLine2DMat"
+ - In case of an N-dimentional points' set given, Mat should be 2-dimensional, have a single row
+or column if there are N channels, or have N columns if there is a single channel.
+
+@param src Input set of 2D points stored in one of possible containers: Mat,
+std::vector<cv::Point2i>, std::vector<cv::Point2f>, std::vector<cv::Point2d>.
+@param distType Distance used by the M-estimator, see #DistanceTypes. @ref DIST_USER
+and @ref DIST_C are not suppored.
+@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
+is chosen.
+@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the
+line). 1.0 would be a good default value for reps. If it is 0, a default value is chosen.
+@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for aeps.
+If it is 0, a default value is chosen.
+
+@return Output line parameters: a vector of 4 elements (like Vec4f) - (vx, vy, x0, y0),
+where (vx, vy) is a normalized vector collinear to the line and (x0, y0) is a point on the line.
+ */
+GAPI_EXPORTS GOpaque<Vec4f> fitLine2D(const GMat& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+/** @overload
+
+@note Function textual ID is "org.opencv.imgproc.shape.fitLine2DVector32S"
+
+ */
+GAPI_EXPORTS GOpaque<Vec4f> fitLine2D(const GArray<Point2i>& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+/** @overload
+
+@note Function textual ID is "org.opencv.imgproc.shape.fitLine2DVector32F"
+
+ */
+GAPI_EXPORTS GOpaque<Vec4f> fitLine2D(const GArray<Point2f>& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+/** @overload
+
+@note Function textual ID is "org.opencv.imgproc.shape.fitLine2DVector64F"
+
+ */
+GAPI_EXPORTS GOpaque<Vec4f> fitLine2D(const GArray<Point2d>& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+/** @brief Fits a line to a 3D point set.
+
+The function fits a line to a 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where
+\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance
+function, one of the following:
+-  DIST_L2
+\f[\rho (r) = r^2/2  \quad \text{(the simplest and the fastest least-squares method)}\f]
+- DIST_L1
+\f[\rho (r) = r\f]
+- DIST_L12
+\f[\rho (r) = 2  \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f]
+- DIST_FAIR
+\f[\rho \left (r \right ) = C^2  \cdot \left (  \frac{r}{C} -  \log{\left(1 + \frac{r}{C}\right)} \right )  \quad \text{where} \quad C=1.3998\f]
+- DIST_WELSCH
+\f[\rho \left (r \right ) =  \frac{C^2}{2} \cdot \left ( 1 -  \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right )  \quad \text{where} \quad C=2.9846\f]
+- DIST_HUBER
+\f[\rho (r) =  \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f]
+
+The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
+that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
+weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ .
+
+@note
+ - Function textual ID is "org.opencv.imgproc.shape.fitLine3DMat"
+ - In case of an N-dimentional points' set given, Mat should be 2-dimensional, have a single row
+or column if there are N channels, or have N columns if there is a single channel.
+
+@param src Input set of 3D points stored in one of possible containers: Mat,
+std::vector<cv::Point3i>, std::vector<cv::Point3f>, std::vector<cv::Point3d>.
+@param distType Distance used by the M-estimator, see #DistanceTypes. @ref DIST_USER
+and @ref DIST_C are not suppored.
+@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
+is chosen.
+@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the
+line). 1.0 would be a good default value for reps. If it is 0, a default value is chosen.
+@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for aeps.
+If it is 0, a default value is chosen.
+
+@return Output line parameters: a vector of 6 elements (like Vec6f) - (vx, vy, vz, x0, y0, z0),
+where (vx, vy, vz) is a normalized vector collinear to the line and (x0, y0, z0) is a point on
+the line.
+ */
+GAPI_EXPORTS GOpaque<Vec6f> fitLine3D(const GMat& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+/** @overload
+
+@note Function textual ID is "org.opencv.imgproc.shape.fitLine3DVector32S"
+
+ */
+GAPI_EXPORTS GOpaque<Vec6f> fitLine3D(const GArray<Point3i>& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+/** @overload
+
+@note Function textual ID is "org.opencv.imgproc.shape.fitLine3DVector32F"
+
+ */
+GAPI_EXPORTS GOpaque<Vec6f> fitLine3D(const GArray<Point3f>& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+/** @overload
+
+@note Function textual ID is "org.opencv.imgproc.shape.fitLine3DVector64F"
+
+ */
+GAPI_EXPORTS GOpaque<Vec6f> fitLine3D(const GArray<Point3d>& src, const DistanceTypes distType,
+                                      const double param = 0., const double reps = 0.,
+                                      const double aeps = 0.);
+
+//! @} gapi_shape
+
+//! @addtogroup gapi_colorconvert
+//! @{
+/** @brief Converts an image from BGR color space to RGB color space.
+
+The function converts an input image from BGR color space to RGB.
+The conventional ranges for B, G, and R channel values are 0 to 255.
+
+Output image is 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2rgb"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa RGB2BGR
+*/
+GAPI_EXPORTS_W GMat BGR2RGB(const GMat& src);
+
+/** @brief Converts an image from RGB color space to gray-scaled.
+
+The conventional ranges for R, G, and B channel values are 0 to 255.
+Resulting gray color value computed as
+\f[\texttt{dst} (I)= \texttt{0.299} * \texttt{src}(I).R + \texttt{0.587} * \texttt{src}(I).G  + \texttt{0.114} * \texttt{src}(I).B \f]
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2gray"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
+@sa RGB2YUV
+ */
+GAPI_EXPORTS_W GMat RGB2Gray(const GMat& src);
+
+/** @overload
+Resulting gray color value computed as
+\f[\texttt{dst} (I)= \texttt{rY} * \texttt{src}(I).R + \texttt{gY} * \texttt{src}(I).G  + \texttt{bY} * \texttt{src}(I).B \f]
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2graycustom"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
+@param rY float multiplier for R channel.
+@param gY float multiplier for G channel.
+@param bY float multiplier for B channel.
+@sa RGB2YUV
+ */
+GAPI_EXPORTS GMat RGB2Gray(const GMat& src, float rY, float gY, float bY);
+
+/** @brief Converts an image from BGR color space to gray-scaled.
+
+The conventional ranges for B, G, and R channel values are 0 to 255.
+Resulting gray color value computed as
+\f[\texttt{dst} (I)= \texttt{0.114} * \texttt{src}(I).B + \texttt{0.587} * \texttt{src}(I).G  + \texttt{0.299} * \texttt{src}(I).R \f]
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2gray"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
+@sa BGR2LUV
+ */
+GAPI_EXPORTS GMat BGR2Gray(const GMat& src);
+
+/** @brief Converts an image from RGB color space to YUV color space.
+
+The function converts an input image from RGB color space to YUV.
+The conventional ranges for R, G, and B channel values are 0 to 255.
+
+In case of linear transformations, the range does not matter. But in case of a non-linear
+transformation, an input RGB image should be normalized to the proper value range to get the correct
+results, like here, at RGB \f$\rightarrow\f$ Y\*u\*v\* transformation.
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2yuv"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa YUV2RGB, RGB2Lab
+*/
+GAPI_EXPORTS GMat RGB2YUV(const GMat& src);
+
+/** @brief Converts an image from BGR color space to I420 color space.
+
+The function converts an input image from BGR color space to I420.
+The conventional ranges for R, G, and B channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 1-channel image. @ref CV_8UC1.
+Width of I420 output image must be the same as width of input image.
+Height of I420 output image must be equal 3/2 from height of input image.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2i420"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa I4202BGR
+*/
+GAPI_EXPORTS GMat BGR2I420(const GMat& src);
+
+/** @brief Converts an image from RGB color space to I420 color space.
+
+The function converts an input image from RGB color space to I420.
+The conventional ranges for R, G, and B channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 1-channel image. @ref CV_8UC1.
+Width of I420 output image must be the same as width of input image.
+Height of I420 output image must be equal 3/2 from height of input image.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2i420"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa I4202RGB
+*/
+GAPI_EXPORTS GMat RGB2I420(const GMat& src);
+
+/** @brief Converts an image from I420 color space to BGR color space.
+
+The function converts an input image from I420 color space to BGR.
+The conventional ranges for B, G, and R channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image. @ref CV_8UC3.
+Width of BGR output image must be the same as width of input image.
+Height of BGR output image must be equal 2/3 from height of input image.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.i4202bgr"
+
+@param src input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@sa BGR2I420
+*/
+GAPI_EXPORTS GMat I4202BGR(const GMat& src);
+
+/** @brief Converts an image from I420 color space to BGR color space.
+
+The function converts an input image from I420 color space to BGR.
+The conventional ranges for B, G, and R channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image. @ref CV_8UC3.
+Width of RGB output image must be the same as width of input image.
+Height of RGB output image must be equal 2/3 from height of input image.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.i4202rgb"
+
+@param src input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@sa RGB2I420
+*/
+GAPI_EXPORTS GMat I4202RGB(const GMat& src);
+
+/** @brief Converts an image from BGR color space to LUV color space.
+
+The function converts an input image from BGR color space to LUV.
+The conventional ranges for B, G, and R channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2luv"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa RGB2Lab, RGB2LUV
+*/
+GAPI_EXPORTS GMat BGR2LUV(const GMat& src);
+
+/** @brief Converts an image from LUV color space to BGR color space.
+
+The function converts an input image from LUV color space to BGR.
+The conventional ranges for B, G, and R channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.luv2bgr"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa BGR2LUV
+*/
+GAPI_EXPORTS GMat LUV2BGR(const GMat& src);
+
+/** @brief Converts an image from YUV color space to BGR color space.
+
+The function converts an input image from YUV color space to BGR.
+The conventional ranges for B, G, and R channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.yuv2bgr"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa BGR2YUV
+*/
+GAPI_EXPORTS GMat YUV2BGR(const GMat& src);
+
+/** @brief Converts an image from BGR color space to YUV color space.
+
+The function converts an input image from BGR color space to YUV.
+The conventional ranges for B, G, and R channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.bgr2yuv"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@sa YUV2BGR
+*/
+GAPI_EXPORTS GMat BGR2YUV(const GMat& src);
+
+/** @brief Converts an image from RGB color space to Lab color space.
+
+The function converts an input image from BGR color space to Lab.
+The conventional ranges for R, G, and B channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC1.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2lab"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC1.
+@sa RGB2YUV, RGB2LUV
+*/
+GAPI_EXPORTS GMat RGB2Lab(const GMat& src);
+
+/** @brief Converts an image from YUV color space to RGB.
+The function converts an input image from YUV color space to RGB.
+The conventional ranges for Y, U, and V channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.yuv2rgb"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@sa RGB2Lab, RGB2YUV
+*/
+GAPI_EXPORTS GMat YUV2RGB(const GMat& src);
+
+/** @brief Converts an image from NV12 (YUV420p) color space to RGB.
+The function converts an input image from NV12 color space to RGB.
+The conventional ranges for Y, U, and V channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12torgb"
+
+@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
+
+@sa YUV2RGB, NV12toBGR
+*/
+GAPI_EXPORTS GMat NV12toRGB(const GMat& src_y, const GMat& src_uv);
+
+/** @brief Converts an image from NV12 (YUV420p) color space to gray-scaled.
+The function converts an input image from NV12 color space to gray-scaled.
+The conventional ranges for Y, U, and V channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 1-channel image @ref CV_8UC1.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12togray"
+
+@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
+
+@sa YUV2RGB, NV12toBGR
+*/
+GAPI_EXPORTS GMat NV12toGray(const GMat& src_y, const GMat& src_uv);
+
+/** @brief Converts an image from NV12 (YUV420p) color space to BGR.
+The function converts an input image from NV12 color space to RGB.
+The conventional ranges for Y, U, and V channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12tobgr"
+
+@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
+
+@sa YUV2BGR, NV12toRGB
+*/
+GAPI_EXPORTS GMat NV12toBGR(const GMat& src_y, const GMat& src_uv);
+
+/** @brief Converts an image from BayerGR color space to RGB.
+The function converts an input image from BayerGR color space to RGB.
+The conventional ranges for G, R, and B channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.bayergr2rgb"
+
+@param src_gr input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+
+@sa YUV2BGR, NV12toRGB
+*/
+GAPI_EXPORTS GMat BayerGR2RGB(const GMat& src_gr);
+
+/** @brief Converts an image from RGB color space to HSV.
+The function converts an input image from RGB color space to HSV.
+The conventional ranges for R, G, and B channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2hsv"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@sa YUV2BGR, NV12toRGB
+*/
+GAPI_EXPORTS GMat RGB2HSV(const GMat& src);
+
+/** @brief Converts an image from RGB color space to YUV422.
+The function converts an input image from RGB color space to YUV422.
+The conventional ranges for R, G, and B channel values are 0 to 255.
+
+Output image must be 8-bit unsigned 2-channel image @ref CV_8UC2.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.rgb2yuv422"
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+
+@sa YUV2BGR, NV12toRGB
+*/
+GAPI_EXPORTS GMat RGB2YUV422(const GMat& src);
+
+/** @brief Converts an image from NV12 (YUV420p) color space to RGB.
+The function converts an input image from NV12 color space to RGB.
+The conventional ranges for Y, U, and V channel values are 0 to 255.
+
+Output image must be 8-bit unsigned planar 3-channel image @ref CV_8UC1.
+Planar image memory layout is three planes laying in the memory contiguously,
+so the image height should be plane_height*plane_number,
+image type is @ref CV_8UC1.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12torgbp"
+
+@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
+
+@sa YUV2RGB, NV12toBGRp, NV12toRGB
+*/
+GAPI_EXPORTS GMatP NV12toRGBp(const GMat &src_y, const GMat &src_uv);
+
+/** @brief Converts an image from NV12 (YUV420p) color space to BGR.
+The function converts an input image from NV12 color space to BGR.
+The conventional ranges for Y, U, and V channel values are 0 to 255.
+
+Output image must be 8-bit unsigned planar 3-channel image @ref CV_8UC1.
+Planar image memory layout is three planes laying in the memory contiguously,
+so the image height should be plane_height*plane_number,
+image type is @ref CV_8UC1.
+
+@note Function textual ID is "org.opencv.imgproc.colorconvert.nv12torgbp"
+
+@param src_y input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@param src_uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
+
+@sa YUV2RGB, NV12toRGBp, NV12toBGR
+*/
+GAPI_EXPORTS GMatP NV12toBGRp(const GMat &src_y, const GMat &src_uv);
+
+//! @} gapi_colorconvert
+} //namespace gapi
+} //namespace cv
+
+#endif // OPENCV_GAPI_IMGPROC_HPP
diff --git a/3rdParty/include/opencv2/gapi/infer.hpp b/3rdParty/include/opencv2/gapi/infer.hpp
new file mode 100644
index 0000000..807c82d
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/infer.hpp
@@ -0,0 +1,714 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019-2021 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_INFER_HPP
+#define OPENCV_GAPI_INFER_HPP
+
+// FIXME: Inference API is currently only available in full mode
+#if !defined(GAPI_STANDALONE)
+
+#include <functional>
+#include <string>  // string
+#include <utility> // tuple
+#include <type_traits> // is_same, false_type
+
+#include <opencv2/gapi/util/util.hpp> // all_satisfy
+#include <opencv2/gapi/util/any.hpp>  // any<>
+#include <opencv2/gapi/gkernel.hpp>   // GKernelType[M], GBackend
+#include <opencv2/gapi/garg.hpp>      // GArg
+#include <opencv2/gapi/gcommon.hpp>   // CompileArgTag
+#include <opencv2/gapi/gmetaarg.hpp>  // GMetaArg
+
+namespace cv {
+
+template<typename, typename> class GNetworkType;
+
+namespace detail {
+
+// Infer ///////////////////////////////////////////////////////////////////////
+template<typename T>
+struct accepted_infer_types {
+    static constexpr const auto value =
+            std::is_same<typename std::decay<T>::type, cv::GMat>::value
+         || std::is_same<typename std::decay<T>::type, cv::GFrame>::value;
+};
+
+template<typename... Ts>
+using valid_infer_types = all_satisfy<accepted_infer_types, Ts...>;
+
+// Infer2 //////////////////////////////////////////////////////////////////////
+
+template<typename, typename>
+struct valid_infer2_types;
+
+// Terminal case 1 (50/50 success)
+template<typename T>
+struct valid_infer2_types< std::tuple<cv::GMat>, std::tuple<T> > {
+    // By default, Nets are limited to GMat argument types only
+    // for infer2, every GMat argument may translate to either
+    // GArray<GMat> or GArray<Rect>. GArray<> part is stripped
+    // already at this point.
+    static constexpr const auto value =
+            std::is_same<typename std::decay<T>::type, cv::GMat>::value
+         || std::is_same<typename std::decay<T>::type, cv::Rect>::value;
+};
+
+// Terminal case 2 (100% failure)
+template<typename... Ts>
+struct valid_infer2_types< std::tuple<>, std::tuple<Ts...> >
+    : public std::false_type {
+};
+
+// Terminal case 3 (100% failure)
+template<typename... Ns>
+struct valid_infer2_types< std::tuple<Ns...>, std::tuple<> >
+    : public std::false_type {
+};
+
+// Recursion -- generic
+template<typename... Ns, typename T, typename...Ts>
+struct valid_infer2_types< std::tuple<cv::GMat,Ns...>, std::tuple<T,Ts...> > {
+    static constexpr const auto value =
+           valid_infer2_types< std::tuple<cv::GMat>, std::tuple<T> >::value
+        && valid_infer2_types< std::tuple<Ns...>, std::tuple<Ts...> >::value;
+};
+
+// Struct stores network input/output names.
+// Used by infer<Generic>
+struct InOutInfo
+{
+    std::vector<std::string> in_names;
+    std::vector<std::string> out_names;
+};
+
+template <typename OutT>
+class GInferOutputsTyped
+{
+public:
+    GInferOutputsTyped() = default;
+    GInferOutputsTyped(std::shared_ptr<cv::GCall> call)
+        : m_priv(std::make_shared<Priv>(std::move(call)))
+    {
+    }
+
+    OutT at(const std::string& name)
+    {
+        auto it = m_priv->blobs.find(name);
+        if (it == m_priv->blobs.end()) {
+            // FIXME: Avoid modifying GKernel
+            auto shape = cv::detail::GTypeTraits<OutT>::shape;
+            m_priv->call->kernel().outShapes.push_back(shape);
+            m_priv->call->kernel().outCtors.emplace_back(cv::detail::GObtainCtor<OutT>::get());
+            auto out_idx = static_cast<int>(m_priv->blobs.size());
+            it = m_priv->blobs.emplace(name,
+                    cv::detail::Yield<OutT>::yield(*(m_priv->call), out_idx)).first;
+            m_priv->info->out_names.push_back(name);
+        }
+        return it->second;
+    }
+private:
+    struct Priv
+    {
+        Priv(std::shared_ptr<cv::GCall> c)
+            : call(std::move(c)), info(cv::util::any_cast<InOutInfo>(&call->params()))
+        {
+        }
+
+        std::shared_ptr<cv::GCall> call;
+        InOutInfo* info = nullptr;
+        std::unordered_map<std::string, OutT> blobs;
+    };
+
+    std::shared_ptr<Priv> m_priv;
+};
+
+template <typename... Ts>
+class GInferInputsTyped
+{
+public:
+    GInferInputsTyped()
+        : m_priv(std::make_shared<Priv>())
+    {
+    }
+
+    template <typename U>
+    GInferInputsTyped<Ts...>& setInput(const std::string& name, U in)
+    {
+        m_priv->blobs.emplace(std::piecewise_construct,
+                              std::forward_as_tuple(name),
+                              std::forward_as_tuple(in));
+        return *this;
+    }
+
+    using StorageT = cv::util::variant<Ts...>;
+    StorageT& operator[](const std::string& name) {
+        return m_priv->blobs[name];
+    }
+
+    using Map = std::unordered_map<std::string, StorageT>;
+    const Map& getBlobs() const {
+        return m_priv->blobs;
+    }
+
+private:
+    struct Priv
+    {
+        std::unordered_map<std::string, StorageT> blobs;
+    };
+
+    std::shared_ptr<Priv> m_priv;
+};
+
+template<typename InferT>
+std::shared_ptr<cv::GCall> makeCall(const std::string         &tag,
+                                    std::vector<cv::GArg>    &&args,
+                                    std::vector<std::string> &&names,
+                                    cv::GKinds               &&kinds) {
+    auto call = std::make_shared<cv::GCall>(GKernel{
+                InferT::id(),
+                tag,
+                InferT::getOutMeta,
+                {}, // outShape will be filled later
+                std::move(kinds),
+                {}, // outCtors will be filled later
+            });
+
+    call->setArgs(std::move(args));
+    call->params() = cv::detail::InOutInfo{std::move(names), {}};
+
+    return call;
+}
+
+} // namespace detail
+
+// TODO: maybe tuple_wrap_helper from util.hpp may help with this.
+// Multiple-return-value network definition (specialized base class)
+template<typename K, typename... R, typename... Args>
+class GNetworkType<K, std::function<std::tuple<R...>(Args...)> >
+{
+public:
+    using InArgs  = std::tuple<Args...>;
+    using OutArgs = std::tuple<R...>;
+
+    using Result  = OutArgs;
+    using API     = std::function<Result(Args...)>;
+
+    using ResultL = std::tuple< cv::GArray<R>... >;
+};
+
+// Single-return-value network definition (specialized base class)
+template<typename K, typename R, typename... Args>
+class GNetworkType<K, std::function<R(Args...)> >
+{
+public:
+    using InArgs  = std::tuple<Args...>;
+    using OutArgs = std::tuple<R>;
+
+    using Result  = R;
+    using API     = std::function<R(Args...)>;
+
+    using ResultL = cv::GArray<R>;
+};
+
+// InferAPI: Accepts either GMat or GFrame for very individual network's input
+template<class Net, class... Ts>
+struct InferAPI {
+    using type = typename std::enable_if
+        <    detail::valid_infer_types<Ts...>::value
+          && std::tuple_size<typename Net::InArgs>::value == sizeof...(Ts)
+        , std::function<typename Net::Result(Ts...)>
+        >::type;
+};
+
+// InferAPIRoi: Accepts a rectangle and either GMat or GFrame
+template<class Net, class T>
+struct InferAPIRoi {
+    using type = typename std::enable_if
+        <    detail::valid_infer_types<T>::value
+          && std::tuple_size<typename Net::InArgs>::value == 1u
+          , std::function<typename Net::Result(cv::GOpaque<cv::Rect>, T)>
+        >::type;
+};
+
+// InferAPIList: Accepts a list of rectangles and list of GMat/GFrames;
+// crops every input.
+template<class Net, class... Ts>
+struct InferAPIList {
+    using type = typename std::enable_if
+        <    detail::valid_infer_types<Ts...>::value
+          && std::tuple_size<typename Net::InArgs>::value == sizeof...(Ts)
+        , std::function<typename Net::ResultL(cv::GArray<cv::Rect>, Ts...)>
+        >::type;
+};
+
+// APIList2 is also template to allow different calling options
+// (GArray<cv::Rect> vs GArray<cv::GMat> per input)
+template<class Net, typename T, class... Ts>
+struct InferAPIList2 {
+    using type = typename std::enable_if
+        < detail::valid_infer_types<T>::value &&
+          cv::detail::valid_infer2_types< typename Net::InArgs
+                                        , std::tuple<Ts...> >::value,
+          std::function<typename Net::ResultL(T, cv::GArray<Ts>...)>
+        >::type;
+};
+
+// Base "Infer" kernel. Note - for whatever network, kernel ID
+// is always the same. Different inference calls are distinguished by
+// network _tag_ (an extra field in GCall)
+//
+// getOutMeta is a stub callback collected by G-API kernel subsystem
+// automatically. This is a rare case when this callback is defined by
+// a particular backend, not by a network itself.
+struct GInferBase {
+    static constexpr const char * id() {
+        return "org.opencv.dnn.infer";            // Universal stub
+    }
+    static GMetaArgs getOutMeta(const GMetaArgs &, const GArgs &) {
+        return GMetaArgs{};                       // One more universal stub
+    }
+};
+
+// Base "InferROI" kernel.
+// All notes from "Infer" kernel apply here as well.
+struct GInferROIBase {
+    static constexpr const char * id() {
+        return "org.opencv.dnn.infer-roi";        // Universal stub
+    }
+    static GMetaArgs getOutMeta(const GMetaArgs &, const GArgs &) {
+        return GMetaArgs{};                       // One more universal stub
+    }
+};
+
+// Base "Infer list" kernel.
+// All notes from "Infer" kernel apply here as well.
+struct GInferListBase {
+    static constexpr const char * id() {
+        return "org.opencv.dnn.infer-roi-list-1"; // Universal stub
+    }
+    static GMetaArgs getOutMeta(const GMetaArgs &, const GArgs &) {
+        return GMetaArgs{};                       // One more universal stub
+    }
+};
+
+// Base "Infer list 2" kernel.
+// All notes from "Infer" kernel apply here as well.
+struct GInferList2Base {
+    static constexpr const char * id() {
+        return "org.opencv.dnn.infer-roi-list-2"; // Universal stub
+    }
+    static GMetaArgs getOutMeta(const GMetaArgs &, const GArgs &) {
+        return GMetaArgs{};                       // One more universal stub
+    }
+};
+
+// A generic inference kernel. API (::on()) is fully defined by the Net
+// template parameter.
+// Acts as a regular kernel in graph (via KernelTypeMedium).
+template<typename Net, typename... Args>
+struct GInfer final
+    : public GInferBase
+    , public detail::KernelTypeMedium< GInfer<Net, Args...>
+                                     , typename InferAPI<Net, Args...>::type > {
+    using GInferBase::getOutMeta; // FIXME: name lookup conflict workaround?
+
+    static constexpr const char* tag() { return Net::tag(); }
+};
+
+// A specific roi-inference kernel. API (::on()) is fixed here and
+// verified against Net.
+template<typename Net, typename T>
+struct GInferROI final
+    : public GInferROIBase
+    , public detail::KernelTypeMedium< GInferROI<Net, T>
+                                     , typename InferAPIRoi<Net, T>::type > {
+    using GInferROIBase::getOutMeta; // FIXME: name lookup conflict workaround?
+
+    static constexpr const char* tag() { return Net::tag(); }
+};
+
+
+// A generic roi-list inference kernel. API (::on()) is derived from
+// the Net template parameter (see more in infer<> overload).
+template<typename Net, typename... Args>
+struct GInferList final
+    : public GInferListBase
+    , public detail::KernelTypeMedium< GInferList<Net, Args...>
+                                     , typename InferAPIList<Net, Args...>::type > {
+    using GInferListBase::getOutMeta; // FIXME: name lookup conflict workaround?
+
+    static constexpr const char* tag() { return Net::tag(); }
+};
+
+// An even more generic roi-list inference kernel. API (::on()) is
+// derived from the Net template parameter (see more in infer<>
+// overload).
+// Takes an extra variadic template list to reflect how this network
+// was called (with Rects or GMats as array parameters)
+template<typename Net, typename T, typename... Args>
+struct GInferList2 final
+    : public GInferList2Base
+    , public detail::KernelTypeMedium< GInferList2<Net, T, Args...>
+                                     , typename InferAPIList2<Net, T, Args...>::type > {
+    using GInferList2Base::getOutMeta; // FIXME: name lookup conflict workaround?
+
+    static constexpr const char* tag() { return Net::tag(); }
+};
+
+/**
+ * @brief G-API object used to collect network inputs
+ */
+using GInferInputs = cv::detail::GInferInputsTyped<cv::GMat, cv::GFrame>;
+
+/**
+ * @brief G-API object used to collect the list of network inputs
+ */
+using GInferListInputs = cv::detail::GInferInputsTyped<cv::GArray<cv::GMat>, cv::GArray<cv::Rect>>;
+
+/**
+ * @brief G-API object used to collect network outputs
+ */
+using GInferOutputs = cv::detail::GInferOutputsTyped<cv::GMat>;
+
+/**
+ * @brief G-API object used to collect the list of network outputs
+ */
+using GInferListOutputs = cv::detail::GInferOutputsTyped<cv::GArray<cv::GMat>>;
+
+namespace detail {
+void inline unpackBlobs(const cv::GInferInputs::Map& blobs,
+                        std::vector<cv::GArg>& args,
+                        std::vector<std::string>& names,
+                        cv::GKinds& kinds)
+{
+    for (auto&& p : blobs) {
+        names.emplace_back(p.first);
+        switch (p.second.index()) {
+            case cv::GInferInputs::StorageT::index_of<cv::GMat>():
+                args.emplace_back(cv::util::get<cv::GMat>(p.second));
+                kinds.emplace_back(cv::detail::OpaqueKind::CV_MAT);
+                break;
+            case cv::GInferInputs::StorageT::index_of<cv::GFrame>():
+                args.emplace_back(cv::util::get<cv::GFrame>(p.second));
+                kinds.emplace_back(cv::detail::OpaqueKind::CV_UNKNOWN);
+                break;
+            default:
+                GAPI_Assert(false);
+        }
+    }
+}
+
+template <typename InferType>
+struct InferROITraits;
+
+template <>
+struct InferROITraits<GInferROIBase>
+{
+    using outType = cv::GInferOutputs;
+    using inType  = cv::GOpaque<cv::Rect>;
+};
+
+template <>
+struct InferROITraits<GInferListBase>
+{
+    using outType = cv::GInferListOutputs;
+    using inType  = cv::GArray<cv::Rect>;
+};
+
+template<typename InferType>
+typename InferROITraits<InferType>::outType
+inferGenericROI(const std::string& tag,
+         const typename InferROITraits<InferType>::inType& in,
+         const cv::GInferInputs& inputs)
+{
+    std::vector<cv::GArg> args;
+    std::vector<std::string> names;
+    cv::GKinds kinds;
+
+    args.emplace_back(in);
+    kinds.emplace_back(cv::detail::OpaqueKind::CV_RECT);
+
+    unpackBlobs(inputs.getBlobs(), args, names, kinds);
+
+    auto call = cv::detail::makeCall<InferType>(tag,
+                                                std::move(args),
+                                                std::move(names),
+                                                std::move(kinds));
+
+    return {std::move(call)};
+}
+
+} // namespace detail
+} // namespace cv
+
+// FIXME: Probably the <API> signature makes a function/tuple/function round-trip
+#define G_API_NET(Class, API, Tag)                                      \
+    struct Class final: public cv::GNetworkType<Class, std::function API> { \
+        static constexpr const char * tag() { return Tag; }             \
+    }
+
+namespace cv {
+namespace gapi {
+
+/** @brief Calculates response for the specified network (template
+ *     parameter) for the specified region in the source image.
+ *     Currently expects a single-input network only.
+ *
+ * @tparam A network type defined with G_API_NET() macro.
+ * @param in input image where to take ROI from.
+ * @param roi an object describing the region of interest
+ *   in the source image. May be calculated in the same graph dynamically.
+ * @return an object of return type as defined in G_API_NET().
+ *   If a network has multiple return values (defined with a tuple), a tuple of
+ *   objects of appropriate type is returned.
+ * @sa  G_API_NET()
+ */
+template<typename Net, typename T>
+typename Net::Result infer(cv::GOpaque<cv::Rect> roi, T in) {
+    return GInferROI<Net, T>::on(roi, in);
+}
+
+/** @brief Calculates responses for the specified network (template
+ *     parameter) for every region in the source image.
+ *
+ * @tparam A network type defined with G_API_NET() macro.
+ * @param roi a list of rectangles describing regions of interest
+ *   in the source image. Usually an output of object detector or tracker.
+ * @param args network's input parameters as specified in G_API_NET() macro.
+ *   NOTE: verified to work reliably with 1-input topologies only.
+ * @return a list of objects of return type as defined in G_API_NET().
+ *   If a network has multiple return values (defined with a tuple), a tuple of
+ *   GArray<> objects is returned with the appropriate types inside.
+ * @sa  G_API_NET()
+ */
+template<typename Net, typename... Args>
+typename Net::ResultL infer(cv::GArray<cv::Rect> roi, Args&&... args) {
+    return GInferList<Net, Args...>::on(roi, std::forward<Args>(args)...);
+}
+
+/** @brief Calculates responses for the specified network (template
+ *     parameter) for every region in the source image, extended version.
+ *
+ * @tparam A network type defined with G_API_NET() macro.
+ * @param image A source image containing regions of interest
+ * @param args GArray<> objects of cv::Rect or cv::GMat, one per every
+ * network input:
+ * - If a cv::GArray<cv::Rect> is passed, the appropriate
+ *   regions are taken from `image` and preprocessed to this particular
+ *   network input;
+ * - If a cv::GArray<cv::GMat> is passed, the underlying data traited
+ *   as tensor (no automatic preprocessing happen).
+ * @return a list of objects of return type as defined in G_API_NET().
+ *   If a network has multiple return values (defined with a tuple), a tuple of
+ *   GArray<> objects is returned with the appropriate types inside.
+ * @sa  G_API_NET()
+ */
+
+template<typename Net, typename T, typename... Args>
+typename Net::ResultL infer2(T image, cv::GArray<Args>... args) {
+    // FIXME: Declared as "2" because in the current form it steals
+    // overloads from the regular infer
+    return GInferList2<Net, T, Args...>::on(image, args...);
+}
+
+/**
+ * @brief Calculates response for the specified network (template
+ *     parameter) given the input data.
+ *
+ * @tparam A network type defined with G_API_NET() macro.
+ * @param args network's input parameters as specified in G_API_NET() macro.
+ * @return an object of return type as defined in G_API_NET().
+ *   If a network has multiple return values (defined with a tuple), a tuple of
+ *   objects of appropriate type is returned.
+ * @sa  G_API_NET()
+ */
+template<typename Net, typename... Args>
+typename Net::Result infer(Args&&... args) {
+    return GInfer<Net, Args...>::on(std::forward<Args>(args)...);
+}
+
+/**
+ * @brief Generic network type: input and output layers are configured dynamically at runtime
+ *
+ * Unlike the network types defined with G_API_NET macro, this one
+ * doesn't fix number of network inputs and outputs at the compilation stage
+ * thus providing user with an opportunity to program them in runtime.
+ */
+struct Generic { };
+
+/**
+ * @brief Calculates response for generic network
+ *
+ * @param tag a network tag
+ * @param inputs networks's inputs
+ * @return a GInferOutputs
+ */
+template<typename T = Generic> cv::GInferOutputs
+infer(const std::string& tag, const cv::GInferInputs& inputs)
+{
+    std::vector<cv::GArg> args;
+    std::vector<std::string> names;
+    cv::GKinds kinds;
+
+    cv::detail::unpackBlobs(inputs.getBlobs(), args, names, kinds);
+
+    auto call = cv::detail::makeCall<GInferBase>(tag,
+                                                 std::move(args),
+                                                 std::move(names),
+                                                 std::move(kinds));
+
+    return cv::GInferOutputs{std::move(call)};
+}
+
+/** @brief Calculates response for the generic network
+ *     for the specified region in the source image.
+ *     Currently expects a single-input network only.
+ *
+ * @param tag a network tag
+ * @param roi a an object describing the region of interest
+ *   in the source image. May be calculated in the same graph dynamically.
+ * @param inputs networks's inputs
+ * @return a cv::GInferOutputs
+ */
+template<typename T = Generic> cv::GInferOutputs
+infer(const std::string& tag, const cv::GOpaque<cv::Rect>& roi, const cv::GInferInputs& inputs)
+{
+    return cv::detail::inferGenericROI<GInferROIBase>(tag, roi, inputs);
+}
+
+/** @brief Calculates responses for the specified network
+ *     for every region in the source image.
+ *
+ * @param tag a network tag
+ * @param rois a list of rectangles describing regions of interest
+ *   in the source image. Usually an output of object detector or tracker.
+ * @param inputs networks's inputs
+ * @return a cv::GInferListOutputs
+ */
+template<typename T = Generic> cv::GInferListOutputs
+infer(const std::string& tag, const cv::GArray<cv::Rect>& rois, const cv::GInferInputs& inputs)
+{
+    return cv::detail::inferGenericROI<GInferListBase>(tag, rois, inputs);
+}
+
+/** @brief Calculates responses for the specified network
+ *     for every region in the source image, extended version.
+ *
+ * @param tag a network tag
+ * @param in a source image containing regions of interest.
+ * @param inputs networks's inputs
+ * @return a cv::GInferListOutputs
+ */
+template<typename T = Generic, typename Input>
+typename std::enable_if<cv::detail::accepted_infer_types<Input>::value, cv::GInferListOutputs>::type
+infer2(const std::string& tag,
+       const Input& in,
+       const cv::GInferListInputs& inputs)
+{
+    std::vector<cv::GArg> args;
+    std::vector<std::string> names;
+    cv::GKinds kinds;
+
+    args.emplace_back(in);
+    auto k = cv::detail::GOpaqueTraits<Input>::kind;
+    kinds.emplace_back(k);
+
+    for (auto&& p : inputs.getBlobs()) {
+        names.emplace_back(p.first);
+        switch (p.second.index()) {
+            case cv::GInferListInputs::StorageT::index_of<cv::GArray<cv::GMat>>():
+                args.emplace_back(cv::util::get<cv::GArray<cv::GMat>>(p.second));
+                kinds.emplace_back(cv::detail::OpaqueKind::CV_MAT);
+                break;
+            case cv::GInferListInputs::StorageT::index_of<cv::GArray<cv::Rect>>():
+                args.emplace_back(cv::util::get<cv::GArray<cv::Rect>>(p.second));
+                kinds.emplace_back(cv::detail::OpaqueKind::CV_RECT);
+                break;
+            default:
+                GAPI_Assert(false);
+        }
+    }
+
+    auto call = cv::detail::makeCall<GInferList2Base>(tag,
+                                                      std::move(args),
+                                                      std::move(names),
+                                                      std::move(kinds));
+
+    return cv::GInferListOutputs{std::move(call)};
+}
+
+} // namespace gapi
+} // namespace cv
+
+#endif // GAPI_STANDALONE
+
+namespace cv {
+namespace gapi {
+
+// Note: the below code _is_ part of STANDALONE build,
+// just to make our compiler code compileable.
+
+// A type-erased form of network parameters.
+// Similar to how a type-erased GKernel is represented and used.
+/// @private
+struct GAPI_EXPORTS_W_SIMPLE GNetParam {
+    std::string tag;     // FIXME: const?
+    GBackend backend;    // Specifies the execution model
+    util::any params;    // Backend-interpreted parameter structure
+};
+
+/** \addtogroup gapi_compile_args
+ * @{
+ */
+/**
+ * @brief A container class for network configurations. Similar to
+ * GKernelPackage. Use cv::gapi::networks() to construct this object.
+ *
+ * @sa cv::gapi::networks
+ */
+struct GAPI_EXPORTS_W_SIMPLE GNetPackage {
+    GAPI_WRAP GNetPackage() = default;
+    GAPI_WRAP explicit GNetPackage(std::vector<GNetParam> nets);
+    explicit GNetPackage(std::initializer_list<GNetParam> ii);
+    std::vector<GBackend> backends() const;
+    std::vector<GNetParam> networks;
+};
+/** @} gapi_compile_args */
+} // namespace gapi
+
+namespace detail {
+template<typename T>
+gapi::GNetParam strip(T&& t) {
+    return gapi::GNetParam { t.tag()
+                           , t.backend()
+                           , t.params()
+                           };
+}
+
+template<> struct CompileArgTag<cv::gapi::GNetPackage> {
+    static const char* tag() { return "gapi.net_package"; }
+};
+
+} // namespace cv::detail
+
+namespace gapi {
+template<typename... Args>
+cv::gapi::GNetPackage networks(Args&&... args) {
+    return cv::gapi::GNetPackage({ cv::detail::strip(args)... });
+}
+
+inline cv::gapi::GNetPackage& operator += (      cv::gapi::GNetPackage& lhs,
+                                           const cv::gapi::GNetPackage& rhs) {
+    lhs.networks.reserve(lhs.networks.size() + rhs.networks.size());
+    lhs.networks.insert(lhs.networks.end(), rhs.networks.begin(), rhs.networks.end());
+    return lhs;
+}
+
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_INFER_HPP
diff --git a/3rdParty/include/opencv2/gapi/infer/bindings_ie.hpp b/3rdParty/include/opencv2/gapi/infer/bindings_ie.hpp
new file mode 100644
index 0000000..94272de
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/infer/bindings_ie.hpp
@@ -0,0 +1,70 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+#ifndef OPENCV_GAPI_INFER_BINDINGS_IE_HPP
+#define OPENCV_GAPI_INFER_BINDINGS_IE_HPP
+
+#include <opencv2/gapi/util/any.hpp>
+#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp>     // GKernelPackage
+#include <opencv2/gapi/infer/ie.hpp>    // Params
+
+#include <string>
+
+namespace cv {
+namespace gapi {
+namespace ie {
+
+// NB: Used by python wrapper
+// This class can be marked as SIMPLE, because it's implemented as pimpl
+class GAPI_EXPORTS_W_SIMPLE PyParams {
+public:
+    GAPI_WRAP
+    PyParams() = default;
+
+    GAPI_WRAP
+    PyParams(const std::string &tag,
+             const std::string &model,
+             const std::string &weights,
+             const std::string &device);
+
+    GAPI_WRAP
+    PyParams(const std::string &tag,
+             const std::string &model,
+             const std::string &device);
+
+    GAPI_WRAP
+    PyParams& constInput(const std::string &layer_name,
+                         const cv::Mat &data,
+                         TraitAs hint = TraitAs::TENSOR);
+
+    GAPI_WRAP
+    PyParams& cfgNumRequests(size_t nireq);
+
+    GAPI_WRAP
+    PyParams& cfgBatchSize(const size_t size);
+
+    GBackend      backend() const;
+    std::string   tag()     const;
+    cv::util::any params()  const;
+
+private:
+    std::shared_ptr<Params<cv::gapi::Generic>> m_priv;
+};
+
+GAPI_EXPORTS_W PyParams params(const std::string &tag,
+                               const std::string &model,
+                               const std::string &weights,
+                               const std::string &device);
+
+GAPI_EXPORTS_W PyParams params(const std::string &tag,
+                               const std::string &model,
+                               const std::string &device);
+} // namespace ie
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_INFER_BINDINGS_IE_HPP
diff --git a/3rdParty/include/opencv2/gapi/infer/ie.hpp b/3rdParty/include/opencv2/gapi/infer/ie.hpp
new file mode 100644
index 0000000..e6b7be5
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/infer/ie.hpp
@@ -0,0 +1,479 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019-2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_INFER_IE_HPP
+#define OPENCV_GAPI_INFER_IE_HPP
+
+#include <unordered_map>
+#include <unordered_set>
+#include <string>
+#include <array>
+#include <tuple> // tuple, tuple_size
+#include <map>
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/util/any.hpp>
+
+#include <opencv2/core/cvdef.h>     // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+#include <opencv2/gapi/infer.hpp>   // Generic
+
+namespace cv {
+namespace gapi {
+// FIXME: introduce a new sub-namespace for NN?
+
+/**
+ * @brief This namespace contains G-API OpenVINO backend functions,
+ * structures, and symbols.
+ */
+namespace ie {
+
+GAPI_EXPORTS cv::gapi::GBackend backend();
+
+/**
+ * Specifies how G-API and IE should trait input data
+ *
+ * In OpenCV, the same cv::Mat is used to represent both
+ * image and tensor data. Sometimes those are hardly distinguishable,
+ * so this extra parameter is used to give G-API a hint.
+ *
+ * This hint controls how G-API reinterprets the data when converting
+ * it to IE Blob format (and which layout/etc is assigned to this data).
+ */
+enum class TraitAs: int
+{
+    TENSOR, //!< G-API traits an associated cv::Mat as a raw tensor and passes dimensions as-is
+    IMAGE   //!< G-API traits an associated cv::Mat as an image so creates an "image" blob (NCHW/NHWC, etc)
+};
+
+using IEConfig = std::map<std::string, std::string>;
+
+namespace detail {
+struct ParamDesc {
+    std::string model_path;
+    std::string weights_path;
+    std::string device_id;
+
+    std::vector<std::string> input_names;
+    std::vector<std::string> output_names;
+
+    using ConstInput = std::pair<cv::Mat, TraitAs>;
+    std::unordered_map<std::string, ConstInput> const_inputs;
+
+    std::size_t num_in;
+    std::size_t num_out;
+
+    enum class Kind {Load, Import};
+    Kind kind;
+    bool is_generic;
+    IEConfig config;
+
+    std::map<std::string, std::vector<std::size_t>> reshape_table;
+    std::unordered_set<std::string> layer_names_to_reshape;
+
+    // NB: Number of asyncrhonious infer requests
+    size_t nireq;
+
+    // NB: An optional config to setup RemoteContext for IE
+    cv::util::any context_config;
+
+    // NB: batch_size can't be equal to 1 by default, because some of models
+    // have 2D (Layout::NC) input and if the first dimension not equal to 1
+    // net.setBatchSize(1) will overwrite it.
+    cv::optional<size_t> batch_size;
+};
+} // namespace detail
+
+// FIXME: this is probably a shared (reusable) thing
+template<typename Net>
+struct PortCfg {
+    using In = std::array
+        < std::string
+        , std::tuple_size<typename Net::InArgs>::value >;
+    using Out = std::array
+        < std::string
+        , std::tuple_size<typename Net::OutArgs>::value >;
+};
+
+/**
+ * @brief This structure provides functions
+ * that fill inference parameters for "OpenVINO Toolkit" model.
+ */
+template<typename Net> class Params {
+public:
+    /** @brief Class constructor.
+
+    Constructs Params based on model information and specifies default values for other
+    inference description parameters. Model is loaded and compiled using "OpenVINO Toolkit".
+
+    @param model Path to topology IR (.xml file).
+    @param weights Path to weights (.bin file).
+    @param device target device to use.
+    */
+    Params(const std::string &model,
+           const std::string &weights,
+           const std::string &device)
+        : desc{ model, weights, device, {}, {}, {}
+              , std::tuple_size<typename Net::InArgs>::value  // num_in
+              , std::tuple_size<typename Net::OutArgs>::value // num_out
+              , detail::ParamDesc::Kind::Load
+              , false
+              , {}
+              , {}
+              , {}
+              , 1u
+              , {}
+              , {}} {
+    };
+
+    /** @overload
+    Use this constructor to work with pre-compiled network.
+    Model is imported from a pre-compiled blob.
+
+    @param model Path to model.
+    @param device target device to use.
+    */
+    Params(const std::string &model,
+           const std::string &device)
+        : desc{ model, {}, device, {}, {}, {}
+              , std::tuple_size<typename Net::InArgs>::value  // num_in
+              , std::tuple_size<typename Net::OutArgs>::value // num_out
+              , detail::ParamDesc::Kind::Import
+              , false
+              , {}
+              , {}
+              , {}
+              , 1u
+              , {}
+              , {}} {
+    };
+
+    /** @brief Specifies sequence of network input layers names for inference.
+
+    The function is used to associate cv::gapi::infer<> inputs with the model inputs.
+    Number of names has to match the number of network inputs as defined in G_API_NET().
+    In case a network has only single input layer, there is no need to specify name manually.
+
+    @param layer_names std::array<std::string, N> where N is the number of inputs
+    as defined in the @ref G_API_NET. Contains names of input layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputLayers(const typename PortCfg<Net>::In &layer_names) {
+        desc.input_names.clear();
+        desc.input_names.reserve(layer_names.size());
+        std::copy(layer_names.begin(), layer_names.end(),
+                  std::back_inserter(desc.input_names));
+        return *this;
+    }
+
+    /** @brief Specifies sequence of network output layers names for inference.
+
+    The function is used to associate cv::gapi::infer<> outputs with the model outputs.
+    Number of names has to match the number of network outputs as defined in G_API_NET().
+    In case a network has only single output layer, there is no need to specify name manually.
+
+    @param layer_names std::array<std::string, N> where N is the number of outputs
+    as defined in the @ref G_API_NET. Contains names of output layers.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgOutputLayers(const typename PortCfg<Net>::Out &layer_names) {
+        desc.output_names.clear();
+        desc.output_names.reserve(layer_names.size());
+        std::copy(layer_names.begin(), layer_names.end(),
+                  std::back_inserter(desc.output_names));
+        return *this;
+    }
+
+    /** @brief Specifies a constant input.
+
+    The function is used to set a constant input. This input has to be
+    a preprocessed tensor if its type is TENSOR. Need to provide name of the
+    network layer which will receive provided data.
+
+    @param layer_name Name of network layer.
+    @param data cv::Mat that contains data which will be associated with network layer.
+    @param hint Input type @sa cv::gapi::ie::TraitAs.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& constInput(const std::string &layer_name,
+                            const cv::Mat &data,
+                            TraitAs hint = TraitAs::TENSOR) {
+        desc.const_inputs[layer_name] = {data, hint};
+        return *this;
+    }
+
+    /** @brief Specifies OpenVINO plugin configuration.
+
+    The function is used to set configuration for OpenVINO plugin. Some parameters
+    can be different for each plugin. Please follow https://docs.openvinotoolkit.org/latest/index.html
+    to check information about specific plugin.
+
+    @param cfg Map of pairs: (config parameter name, config parameter value).
+    @return reference to this parameter structure.
+    */
+       Params& pluginConfig(const IEConfig& cfg) {
+        desc.config = cfg;
+        return *this;
+    }
+
+    /** @overload
+    Function with a rvalue parameter.
+
+    @param cfg rvalue map of pairs: (config parameter name, config parameter value).
+    @return reference to this parameter structure.
+    */
+    Params& pluginConfig(IEConfig&& cfg) {
+        desc.config = std::move(cfg);
+        return *this;
+    }
+
+    /** @brief Specifies configuration for RemoteContext in InferenceEngine.
+
+    When RemoteContext is configured the backend imports the networks using the context.
+    It also expects cv::MediaFrames to be actually remote, to operate with blobs via the context.
+
+    @param ctx_cfg cv::util::any value which holds InferenceEngine::ParamMap.
+    @return reference to this parameter structure.
+    */
+    Params& cfgContextParams(const cv::util::any& ctx_cfg) {
+        desc.context_config = ctx_cfg;
+        return *this;
+    }
+
+    /** @overload
+    Function with an rvalue parameter.
+
+    @param ctx_cfg cv::util::any value which holds InferenceEngine::ParamMap.
+    @return reference to this parameter structure.
+    */
+    Params& cfgContextParams(cv::util::any&& ctx_cfg) {
+        desc.context_config = std::move(ctx_cfg);
+        return *this;
+    }
+
+    /** @brief Specifies number of asynchronous inference requests.
+
+    @param nireq Number of inference asynchronous requests.
+    @return reference to this parameter structure.
+    */
+    Params& cfgNumRequests(size_t nireq) {
+        GAPI_Assert(nireq > 0 && "Number of infer requests must be greater than zero!");
+        desc.nireq = nireq;
+        return *this;
+    }
+
+    /** @brief Specifies new input shapes for the network inputs.
+
+    The function is used to specify new input shapes for the network inputs.
+    Follow https://docs.openvinotoolkit.org/latest/classInferenceEngine_1_1networkNetwork.html
+    for additional information.
+
+    @param reshape_table Map of pairs: name of corresponding data and its dimension.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputReshape(const std::map<std::string, std::vector<std::size_t>>& reshape_table) {
+        desc.reshape_table = reshape_table;
+        return *this;
+    }
+
+    /** @overload */
+    Params<Net>& cfgInputReshape(std::map<std::string, std::vector<std::size_t>>&& reshape_table) {
+        desc.reshape_table = std::move(reshape_table);
+        return *this;
+    }
+
+    /** @overload
+
+    @param layer_name Name of layer.
+    @param layer_dims New dimensions for this layer.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputReshape(const std::string& layer_name, const std::vector<size_t>& layer_dims) {
+        desc.reshape_table.emplace(layer_name, layer_dims);
+        return *this;
+    }
+
+    /** @overload */
+    Params<Net>& cfgInputReshape(std::string&& layer_name, std::vector<size_t>&& layer_dims) {
+        desc.reshape_table.emplace(layer_name, layer_dims);
+        return *this;
+    }
+
+    /** @overload
+
+    @param layer_names set of names of network layers that will be used for network reshape.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputReshape(const std::unordered_set<std::string>& layer_names) {
+        desc.layer_names_to_reshape = layer_names;
+        return *this;
+    }
+
+    /** @overload
+
+    @param layer_names rvalue set of the selected layers will be reshaped automatically
+    its input image size.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgInputReshape(std::unordered_set<std::string>&& layer_names) {
+        desc.layer_names_to_reshape = std::move(layer_names);
+        return *this;
+    }
+
+    /** @brief Specifies the inference batch size.
+
+    The function is used to specify inference batch size.
+    Follow https://docs.openvinotoolkit.org/latest/classInferenceEngine_1_1CNNNetwork.html#a8e9d19270a48aab50cb5b1c43eecb8e9 for additional information
+
+    @param size batch size which will be used.
+    @return reference to this parameter structure.
+    */
+    Params<Net>& cfgBatchSize(const size_t size) {
+        desc.batch_size = cv::util::make_optional(size);
+        return *this;
+    }
+
+    // BEGIN(G-API's network parametrization API)
+    GBackend      backend()    const { return cv::gapi::ie::backend();  }
+    std::string   tag()        const { return Net::tag(); }
+    cv::util::any params()     const { return { desc }; }
+    // END(G-API's network parametrization API)
+
+protected:
+    detail::ParamDesc desc;
+};
+
+/*
+* @brief This structure provides functions for generic network type that
+* fill inference parameters.
+* @see struct Generic
+*/
+template<>
+class Params<cv::gapi::Generic> {
+public:
+    /** @brief Class constructor.
+
+    Constructs Params based on model information and sets default values for other
+    inference description parameters. Model is loaded and compiled using OpenVINO Toolkit.
+
+    @param tag string tag of the network for which these parameters are intended.
+    @param model path to topology IR (.xml file).
+    @param weights path to weights (.bin file).
+    @param device target device to use.
+    */
+    Params(const std::string &tag,
+           const std::string &model,
+           const std::string &weights,
+           const std::string &device)
+        : desc{ model, weights, device, {}, {}, {}, 0u, 0u,
+                detail::ParamDesc::Kind::Load, true, {}, {}, {}, 1u,
+                {}, {}},
+          m_tag(tag) {
+    };
+
+    /** @overload
+
+    This constructor for pre-compiled networks. Model is imported from pre-compiled
+    blob.
+
+    @param tag string tag of the network for which these parameters are intended.
+    @param model path to model.
+    @param device target device to use.
+    */
+    Params(const std::string &tag,
+           const std::string &model,
+           const std::string &device)
+        : desc{ model, {}, device, {}, {}, {}, 0u, 0u,
+                detail::ParamDesc::Kind::Import, true, {}, {}, {}, 1u,
+                {}, {}},
+          m_tag(tag) {
+    };
+
+    /** @see ie::Params::pluginConfig. */
+    Params& pluginConfig(const IEConfig& cfg) {
+        desc.config = cfg;
+        return *this;
+    }
+
+    /** @overload */
+    Params& pluginConfig(IEConfig&& cfg) {
+        desc.config = std::move(cfg);
+        return *this;
+    }
+
+    /** @see ie::Params::constInput. */
+    Params& constInput(const std::string &layer_name,
+                       const cv::Mat &data,
+                       TraitAs hint = TraitAs::TENSOR) {
+        desc.const_inputs[layer_name] = {data, hint};
+        return *this;
+    }
+
+    /** @see ie::Params::cfgNumRequests. */
+    Params& cfgNumRequests(size_t nireq) {
+        GAPI_Assert(nireq > 0 && "Number of infer requests must be greater than zero!");
+        desc.nireq = nireq;
+        return *this;
+    }
+
+    /** @see ie::Params::cfgInputReshape */
+    Params& cfgInputReshape(const std::map<std::string, std::vector<std::size_t>>&reshape_table) {
+        desc.reshape_table = reshape_table;
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgInputReshape(std::map<std::string, std::vector<std::size_t>> && reshape_table) {
+        desc.reshape_table = std::move(reshape_table);
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgInputReshape(std::string && layer_name, std::vector<size_t> && layer_dims) {
+        desc.reshape_table.emplace(layer_name, layer_dims);
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgInputReshape(const std::string & layer_name, const std::vector<size_t>&layer_dims) {
+        desc.reshape_table.emplace(layer_name, layer_dims);
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgInputReshape(std::unordered_set<std::string> && layer_names) {
+        desc.layer_names_to_reshape = std::move(layer_names);
+        return *this;
+    }
+
+    /** @overload */
+    Params& cfgInputReshape(const std::unordered_set<std::string>&layer_names) {
+        desc.layer_names_to_reshape = layer_names;
+        return *this;
+    }
+
+    /** @see ie::Params::cfgBatchSize */
+    Params& cfgBatchSize(const size_t size) {
+        desc.batch_size = cv::util::make_optional(size);
+        return *this;
+    }
+
+    // BEGIN(G-API's network parametrization API)
+    GBackend      backend()    const { return cv::gapi::ie::backend();  }
+    std::string   tag()        const { return m_tag; }
+    cv::util::any params()     const { return { desc }; }
+    // END(G-API's network parametrization API)
+
+protected:
+    detail::ParamDesc desc;
+    std::string m_tag;
+};
+
+} // namespace ie
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_INFER_IE_HPP
diff --git a/3rdParty/include/opencv2/gapi/infer/onnx.hpp b/3rdParty/include/opencv2/gapi/infer/onnx.hpp
new file mode 100644
index 0000000..bb5ef6c
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/infer/onnx.hpp
@@ -0,0 +1,284 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020-2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_INFER_ONNX_HPP
+#define OPENCV_GAPI_INFER_ONNX_HPP
+
+#include <unordered_map>
+#include <string>
+#include <array>
+#include <tuple> // tuple, tuple_size
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/util/any.hpp>
+
+#include <opencv2/core/cvdef.h>     // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+
+/**
+ * @brief This namespace contains G-API ONNX Runtime backend functions, structures, and symbols.
+ */
+namespace onnx {
+
+GAPI_EXPORTS cv::gapi::GBackend backend();
+
+enum class TraitAs: int {
+    TENSOR, //!< G-API traits an associated cv::Mat as a raw tensor
+            // and passes dimensions as-is
+    IMAGE   //!< G-API traits an associated cv::Mat as an image so
+            // creates an "image" blob (NCHW/NHWC, etc)
+};
+
+using PostProc = std::function<void(const std::unordered_map<std::string, cv::Mat> &,
+                                          std::unordered_map<std::string, cv::Mat> &)>;
+
+namespace detail {
+/**
+* @brief This structure contains description of inference parameters
+* which is specific to ONNX models.
+*/
+struct ParamDesc {
+    std::string model_path; //!< Path to model.
+
+    // NB: nun_* may differ from topology's real input/output port numbers
+    // (e.g. topology's partial execution)
+    std::size_t num_in;  //!< How many inputs are defined in the operation
+    std::size_t num_out; //!< How many outputs are defined in the operation
+
+    // NB: Here order follows the `Net` API
+    std::vector<std::string> input_names; //!< Names of input network layers.
+    std::vector<std::string> output_names; //!< Names of output network layers.
+
+    using ConstInput = std::pair<cv::Mat, TraitAs>;
+    std::unordered_map<std::string, ConstInput> const_inputs; //!< Map with pair of name of network layer and ConstInput which will be associated with this.
+
+    std::vector<cv::Scalar> mean; //!< Mean values for preprocessing.
+    std::vector<cv::Scalar> stdev; //!< Standard deviation values for preprocessing.
+
+    std::vector<cv::GMatDesc> out_metas; //!< Out meta information about your output (type, dimension).
+    PostProc custom_post_proc; //!< Post processing function.
+
+    std::vector<bool> normalize; //!< Vector of bool values that enabled or disabled normalize of input data.
+
+    std::vector<std::string> names_to_remap; //!< Names of output layers that will be processed in PostProc function.
+};
+} // namespace detail
+
+template<typename Net>
+struct PortCfg {
+    using In = std::array
+        < std::string
+        , std::tuple_size<typename Net::InArgs>::value >;
+    using Out = std::array
+        < std::string
+        , std::tuple_size<typename Net::OutArgs>::value >;
+    using NormCoefs = std::array
+        < cv::Scalar
+        , std::tuple_size<typename Net::InArgs>::value >;
+    using Normalize = std::array
+        < bool
+        , std::tuple_size<typename Net::InArgs>::value >;
+};
+
+/**
+ * Contains description of inference parameters and kit of functions that
+ * fill this parameters.
+ */
+template<typename Net> class Params {
+public:
+    /** @brief Class constructor.
+
+    Constructs Params based on model information and sets default values for other
+    inference description parameters.
+
+    @param model Path to model (.onnx file).
+    */
+    Params(const std::string &model) {
+        desc.model_path = model;
+        desc.num_in  = std::tuple_size<typename Net::InArgs>::value;
+        desc.num_out = std::tuple_size<typename Net::OutArgs>::value;
+    };
+
+    /** @brief Specifies sequence of network input layers names for inference.
+
+    The function is used to associate data of graph inputs with input layers of
+    network topology. Number of names has to match the number of network inputs. If a network
+    has only one input layer, there is no need to call it as the layer is
+    associated with input automatically but this doesn't prevent you from
+    doing it yourself. Count of names has to match to number of network inputs.
+
+    @param layer_names std::array<std::string, N> where N is the number of inputs
+    as defined in the @ref G_API_NET. Contains names of input layers.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgInputLayers(const typename PortCfg<Net>::In &layer_names) {
+        desc.input_names.assign(layer_names.begin(), layer_names.end());
+        return *this;
+    }
+
+    /** @brief Specifies sequence of output layers names for inference.
+
+     The function is used to associate data of graph outputs with output layers of
+    network topology. If a network has only one output layer, there is no need to call it
+    as the layer is associated with ouput automatically but this doesn't prevent
+    you from doing it yourself. Count of names has to match to number of network
+    outputs or you can set your own output but for this case you have to
+    additionally use @ref cfgPostProc function.
+
+    @param layer_names std::array<std::string, N> where N is the number of outputs
+    as defined in the @ref G_API_NET. Contains names of output layers.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgOutputLayers(const typename PortCfg<Net>::Out &layer_names) {
+        desc.output_names.assign(layer_names.begin(), layer_names.end());
+        return *this;
+    }
+
+    /** @brief Sets a constant input.
+
+    The function is used to set constant input. This input has to be
+    a prepared tensor since preprocessing is disabled for this case. You should
+    provide name of network layer which will receive provided data.
+
+    @param layer_name Name of network layer.
+    @param data cv::Mat that contains data which will be associated with network layer.
+    @param hint Type of input (TENSOR).
+    @return the reference on modified object.
+    */
+    Params<Net>& constInput(const std::string &layer_name,
+                            const cv::Mat &data,
+                            TraitAs hint = TraitAs::TENSOR) {
+        desc.const_inputs[layer_name] = {data, hint};
+        return *this;
+    }
+
+    /** @brief Specifies mean value and standard deviation for preprocessing.
+
+    The function is used to set mean value and standard deviation for preprocessing
+    of input data.
+
+    @param m std::array<cv::Scalar, N> where N is the number of inputs
+    as defined in the @ref G_API_NET. Contains mean values.
+    @param s std::array<cv::Scalar, N> where N is the number of inputs
+    as defined in the @ref G_API_NET. Contains standard deviation values.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgMeanStd(const typename PortCfg<Net>::NormCoefs &m,
+                            const typename PortCfg<Net>::NormCoefs &s) {
+        desc.mean.assign(m.begin(), m.end());
+        desc.stdev.assign(s.begin(), s.end());
+        return *this;
+    }
+
+    /** @brief Configures graph output and provides the post processing function from user.
+
+    The function is used when you work with networks with dynamic outputs.
+    Since we can't know dimensions of inference result needs provide them for
+    construction of graph output. This dimensions can differ from inference result.
+    So you have to provide @ref PostProc function that gets information from inference
+    result and fill output which is constructed by dimensions from out_metas.
+
+    @param out_metas Out meta information about your output (type, dimension).
+    @param remap_function Post processing function, which has two parameters. First is onnx
+    result, second is graph output. Both parameters is std::map that contain pair of
+    layer's name and cv::Mat.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgPostProc(const std::vector<cv::GMatDesc> &out_metas,
+                             const PostProc &remap_function) {
+        desc.out_metas        = out_metas;
+        desc.custom_post_proc = remap_function;
+        return *this;
+    }
+
+    /** @overload
+    Function with a rvalue parameters.
+
+    @param out_metas rvalue out meta information about your output (type, dimension).
+    @param remap_function rvalue post processing function, which has two parameters. First is onnx
+    result, second is graph output. Both parameters is std::map that contain pair of
+    layer's name and cv::Mat.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgPostProc(std::vector<cv::GMatDesc> &&out_metas,
+                             PostProc &&remap_function) {
+        desc.out_metas        = std::move(out_metas);
+        desc.custom_post_proc = std::move(remap_function);
+        return *this;
+    }
+
+    /** @overload
+    The function has additional parameter names_to_remap. This parameter provides
+    information about output layers which will be used for inference and post
+    processing function.
+
+    @param out_metas Out meta information.
+    @param remap_function Post processing function.
+    @param names_to_remap Names of output layers. network's inference will
+    be done on these layers. Inference's result will be processed in post processing
+    function using these names.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgPostProc(const std::vector<cv::GMatDesc> &out_metas,
+                             const PostProc &remap_function,
+                             const std::vector<std::string> &names_to_remap) {
+        desc.out_metas        = out_metas;
+        desc.custom_post_proc = remap_function;
+        desc.names_to_remap   = names_to_remap;
+        return *this;
+    }
+
+    /** @overload
+    Function with a rvalue parameters and additional parameter names_to_remap.
+
+    @param out_metas rvalue out meta information.
+    @param remap_function rvalue post processing function.
+    @param names_to_remap rvalue names of output layers. network's inference will
+    be done on these layers. Inference's result will be processed in post processing
+    function using these names.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgPostProc(std::vector<cv::GMatDesc> &&out_metas,
+                             PostProc &&remap_function,
+                             std::vector<std::string> &&names_to_remap) {
+        desc.out_metas        = std::move(out_metas);
+        desc.custom_post_proc = std::move(remap_function);
+        desc.names_to_remap   = std::move(names_to_remap);
+        return *this;
+    }
+
+    /** @brief Specifies normalize parameter for preprocessing.
+
+    The function is used to set normalize parameter for preprocessing of input data.
+
+    @param normalizations std::array<cv::Scalar, N> where N is the number of inputs
+    as defined in the @ref G_API_NET. Сontains bool values that enabled or disabled
+    normalize of input data.
+    @return the reference on modified object.
+    */
+    Params<Net>& cfgNormalize(const typename PortCfg<Net>::Normalize &normalizations) {
+        desc.normalize.assign(normalizations.begin(), normalizations.end());
+        return *this;
+    }
+
+    // BEGIN(G-API's network parametrization API)
+    GBackend      backend() const { return cv::gapi::onnx::backend(); }
+    std::string   tag()     const { return Net::tag(); }
+    cv::util::any params()  const { return { desc }; }
+    // END(G-API's network parametrization API)
+
+protected:
+    detail::ParamDesc desc;
+};
+
+} // namespace onnx
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_INFER_HPP
diff --git a/3rdParty/include/opencv2/gapi/infer/parsers.hpp b/3rdParty/include/opencv2/gapi/infer/parsers.hpp
new file mode 100644
index 0000000..c7308dd
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/infer/parsers.hpp
@@ -0,0 +1,138 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_PARSERS_HPP
+#define OPENCV_GAPI_PARSERS_HPP
+
+#include <utility> // std::tuple
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+
+namespace cv { namespace gapi {
+namespace nn {
+namespace parsers {
+    using GRects      = GArray<Rect>;
+    using GDetections = std::tuple<GArray<Rect>, GArray<int>>;
+
+    G_TYPED_KERNEL(GParseSSDBL, <GDetections(GMat, GOpaque<Size>, float, int)>,
+                   "org.opencv.nn.parsers.parseSSD_BL") {
+        static std::tuple<GArrayDesc,GArrayDesc> outMeta(const GMatDesc&, const GOpaqueDesc&, float, int) {
+            return std::make_tuple(empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    G_TYPED_KERNEL(GParseSSD, <GRects(GMat, GOpaque<Size>, float, bool, bool)>,
+                   "org.opencv.nn.parsers.parseSSD") {
+        static GArrayDesc outMeta(const GMatDesc&, const GOpaqueDesc&, float, bool, bool) {
+            return empty_array_desc();
+        }
+    };
+
+    G_TYPED_KERNEL(GParseYolo, <GDetections(GMat, GOpaque<Size>, float, float, std::vector<float>)>,
+                   "org.opencv.nn.parsers.parseYolo") {
+        static std::tuple<GArrayDesc, GArrayDesc> outMeta(const GMatDesc&, const GOpaqueDesc&,
+                                                          float, float, const std::vector<float>&) {
+            return std::make_tuple(empty_array_desc(), empty_array_desc());
+        }
+        static const std::vector<float>& defaultAnchors() {
+            static std::vector<float> anchors {
+                0.57273f, 0.677385f, 1.87446f, 2.06253f, 3.33843f, 5.47434f, 7.88282f, 3.52778f, 9.77052f, 9.16828f
+            };
+            return anchors;
+        }
+    };
+} // namespace parsers
+} // namespace nn
+
+/** @brief Parses output of SSD network.
+
+Extracts detection information (box, confidence, label) from SSD output and
+filters it by given confidence and label.
+
+@note Function textual ID is "org.opencv.nn.parsers.parseSSD_BL"
+
+@param in Input CV_32F tensor with {1,1,N,7} dimensions.
+@param inSz Size to project detected boxes to (size of the input image).
+@param confidenceThreshold If confidence of the
+detection is smaller than confidence threshold, detection is rejected.
+@param filterLabel If provided (!= -1), only detections with
+given label will get to the output.
+@return a tuple with a vector of detected boxes and a vector of appropriate labels.
+*/
+GAPI_EXPORTS_W std::tuple<GArray<Rect>, GArray<int>> parseSSD(const GMat& in,
+                                                              const GOpaque<Size>& inSz,
+                                                              const float confidenceThreshold = 0.5f,
+                                                              const int   filterLabel = -1);
+
+/** @brief Parses output of SSD network.
+
+Extracts detection information (box, confidence) from SSD output and
+filters it by given confidence and by going out of bounds.
+
+@note Function textual ID is "org.opencv.nn.parsers.parseSSD"
+
+@param in Input CV_32F tensor with {1,1,N,7} dimensions.
+@param inSz Size to project detected boxes to (size of the input image).
+@param confidenceThreshold If confidence of the
+detection is smaller than confidence threshold, detection is rejected.
+@param alignmentToSquare If provided true, bounding boxes are extended to squares.
+The center of the rectangle remains unchanged, the side of the square is
+the larger side of the rectangle.
+@param filterOutOfBounds If provided true, out-of-frame boxes are filtered.
+@return a vector of detected bounding boxes.
+*/
+GAPI_EXPORTS_W GArray<Rect> parseSSD(const GMat& in,
+                                     const GOpaque<Size>& inSz,
+                                     const float confidenceThreshold,
+                                     const bool alignmentToSquare,
+                                     const bool filterOutOfBounds);
+
+/** @brief Parses output of Yolo network.
+
+Extracts detection information (box, confidence, label) from Yolo output,
+filters it by given confidence and performs non-maximum supression for overlapping boxes.
+
+@note Function textual ID is "org.opencv.nn.parsers.parseYolo"
+
+@param in Input CV_32F tensor with {1,13,13,N} dimensions, N should satisfy:
+\f[\texttt{N} = (\texttt{num_classes} + \texttt{5}) * \texttt{5},\f]
+where num_classes - a number of classes Yolo network was trained with.
+@param inSz Size to project detected boxes to (size of the input image).
+@param confidenceThreshold If confidence of the
+detection is smaller than confidence threshold, detection is rejected.
+@param nmsThreshold Non-maximum supression threshold which controls minimum
+relative box intersection area required for rejecting the box with a smaller confidence.
+If 1.f, nms is not performed and no boxes are rejected.
+@param anchors Anchors Yolo network was trained with.
+@note The default anchor values are specified for YOLO v2 Tiny as described in Intel Open Model Zoo
+<a href="https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v2-tiny-tf/yolo-v2-tiny-tf.md">documentation</a>.
+@return a tuple with a vector of detected boxes and a vector of appropriate labels.
+*/
+GAPI_EXPORTS_W std::tuple<GArray<Rect>, GArray<int>> parseYolo(const GMat& in,
+                                                               const GOpaque<Size>& inSz,
+                                                               const float confidenceThreshold = 0.5f,
+                                                               const float nmsThreshold = 0.5f,
+                                                               const std::vector<float>& anchors
+                                                                   = nn::parsers::GParseYolo::defaultAnchors());
+
+} // namespace gapi
+} // namespace cv
+
+// Reimport parseSSD & parseYolo under their initial namespace
+namespace cv {
+namespace gapi {
+namespace streaming {
+
+using cv::gapi::parseSSD;
+using cv::gapi::parseYolo;
+
+} // namespace streaming
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_PARSERS_HPP
diff --git a/3rdParty/include/opencv2/gapi/media.hpp b/3rdParty/include/opencv2/gapi/media.hpp
new file mode 100644
index 0000000..19aaef3
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/media.hpp
@@ -0,0 +1,257 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+#ifndef OPENCV_GAPI_MEDIA_HPP
+#define OPENCV_GAPI_MEDIA_HPP
+
+#include <memory>     // unique_ptr<>, shared_ptr<>
+#include <array>      // array<>
+#include <functional> // function<>
+#include <utility>    // forward<>()
+
+#include <opencv2/gapi/gframe.hpp>
+#include <opencv2/gapi/util/any.hpp>
+
+// Forward declaration
+namespace cv {
+namespace gapi {
+namespace s11n {
+struct IOStream;
+struct IIStream;
+} // namespace s11n
+} // namespace gapi
+} // namespace cv
+
+namespace cv {
+
+/** \addtogroup gapi_data_structures
+ * @{
+ *
+ * @brief Extra G-API data structures used to pass input/output data
+ * to the graph for processing.
+ */
+/**
+ * @brief cv::MediaFrame class represents an image/media frame
+ * obtained from an external source.
+ *
+ * cv::MediaFrame represents image data as specified in
+ * cv::MediaFormat. cv::MediaFrame is designed to be a thin wrapper over some
+ * external memory of buffer; the class itself provides an uniform
+ * interface over such types of memory. cv::MediaFrame wraps data from
+ * a camera driver or from a media codec and provides an abstraction
+ * layer over this memory to G-API. MediaFrame defines a compact interface
+ * to access and manage the underlying data; the implementation is
+ * fully defined by the associated Adapter (which is usually
+ * user-defined).
+ *
+ * @sa cv::RMat
+ */
+class GAPI_EXPORTS MediaFrame {
+public:
+    /// This enum defines different types of cv::MediaFrame provided
+    /// access to the underlying data. Note that different flags can't
+    /// be combined in this version.
+    enum class Access {
+        R, ///< Access data for reading
+        W, ///< Access data for writing
+    };
+    class IAdapter;
+    class View;
+    using AdapterPtr = std::unique_ptr<IAdapter>;
+
+    /**
+     * @brief Constructs an empty MediaFrame
+     *
+     * The constructed object has no any data associated with it.
+     */
+    MediaFrame();
+
+    /**
+     * @brief Constructs a MediaFrame with the given
+     * Adapter. MediaFrame takes ownership over the passed adapter.
+     *
+     * @param p an unique pointer to instance of IAdapter derived class.
+     */
+    explicit MediaFrame(AdapterPtr &&p);
+
+    /**
+     * @overload
+     * @brief Constructs a MediaFrame with the given parameters for
+     * the Adapter. The adapter of type `T` is costructed on the fly.
+     *
+     * @param args list of arguments to construct an adapter of type
+     * `T`.
+     */
+    template<class T, class... Args> static cv::MediaFrame Create(Args&&... args);
+
+    /**
+     * @brief Obtain access to the underlying data with the given
+     * mode.
+     *
+     * Depending on the associated Adapter and the data wrapped, this
+     * method may be cheap (e.g., the underlying memory is local) or
+     * costly (if the underlying memory is external or device
+     * memory).
+     *
+     * @param mode an access mode flag
+     * @return a MediaFrame::View object. The views should be handled
+     * carefully, refer to the MediaFrame::View documentation for details.
+     */
+    View access(Access mode) const;
+
+    /**
+     * @brief Returns a media frame descriptor -- the information
+     * about the media format, dimensions, etc.
+     * @return a cv::GFrameDesc
+     */
+    cv::GFrameDesc desc() const;
+
+    // FIXME: design a better solution
+    // Should be used only if the actual adapter provides implementation
+    /// @private -- exclude from the OpenCV documentation for now.
+    cv::util::any blobParams() const;
+
+    /**
+     * @brief Casts and returns the associated MediaFrame adapter to
+     * the particular adapter type `T`, returns nullptr if the type is
+     * different.
+     *
+     * This method may be useful if the adapter type is known by the
+     * caller, and some lower level access to the memory is required.
+     * Depending on the memory type, it may be more efficient than
+     * access().
+     *
+     * @return a pointer to the adapter object, nullptr if the adapter
+     * type is different.
+     */
+    template<typename T> T* get() const {
+        static_assert(std::is_base_of<IAdapter, T>::value,
+                      "T is not derived from cv::MediaFrame::IAdapter!");
+        auto* adapter = getAdapter();
+        GAPI_Assert(adapter != nullptr);
+        return dynamic_cast<T*>(adapter);
+    }
+
+    /**
+     * @brief Serialize MediaFrame's data to a byte array.
+     *
+     * @note The actual logic is implemented by frame's adapter class.
+     * Does nothing by default.
+     *
+     * @param os Bytestream to store serialized MediaFrame data in.
+     */
+    void serialize(cv::gapi::s11n::IOStream& os) const;
+
+private:
+    struct Priv;
+    std::shared_ptr<Priv> m;
+    IAdapter* getAdapter() const;
+};
+
+template<class T, class... Args>
+inline cv::MediaFrame cv::MediaFrame::Create(Args&&... args) {
+    std::unique_ptr<T> ptr(new T(std::forward<Args>(args)...));
+    return cv::MediaFrame(std::move(ptr));
+}
+
+/**
+ * @brief Provides access to the MediaFrame's underlying data.
+ *
+ * This object contains the necessary information to access the pixel
+ * data of the associated MediaFrame: arrays of pointers and strides
+ * (distance between every plane row, in bytes) for every image
+ * plane, as defined in cv::MediaFormat.
+ * There may be up to four image planes in MediaFrame.
+ *
+ * Depending on the MediaFrame::Access flag passed in
+ * MediaFrame::access(), a MediaFrame::View may be read- or
+ * write-only.
+ *
+ * Depending on the MediaFrame::IAdapter implementation associated
+ * with the parent MediaFrame, writing to memory with
+ * MediaFrame::Access::R flag may have no effect or lead to
+ * undefined behavior. Same applies to reading the memory with
+ * MediaFrame::Access::W flag -- again, depending on the IAdapter
+ * implementation, the host-side buffer the view provides access to
+ * may have no current data stored in (so in-place editing of the
+ * buffer contents may not be possible).
+ *
+ * MediaFrame::View objects must be handled carefully, as an external
+ * resource associated with MediaFrame may be locked for the time the
+ * MediaFrame::View object exists. Obtaining MediaFrame::View should
+ * be seen as "map" and destroying it as "unmap" in the "map/unmap"
+ * idiom (applicable to OpenCL, device memory, remote
+ * memory).
+ *
+ * When a MediaFrame buffer is accessed for writing, and the memory
+ * under MediaFrame::View::Ptrs is altered, the data synchronization
+ * of a host-side and device/remote buffer is not guaranteed until the
+ * MediaFrame::View is destroyed. In other words, the real data on the
+ * device or in a remote target may be updated at the MediaFrame::View
+ * destruction only -- but it depends on the associated
+ * MediaFrame::IAdapter implementation.
+ */
+class GAPI_EXPORTS MediaFrame::View final {
+public:
+    static constexpr const size_t MAX_PLANES = 4;
+    using Ptrs     = std::array<void*, MAX_PLANES>;
+    using Strides  = std::array<std::size_t, MAX_PLANES>; // in bytes
+    using Callback = std::function<void()>;
+
+    /// @private
+    View(Ptrs&& ptrs, Strides&& strs, Callback &&cb = [](){});
+
+    /// @private
+    View(const View&) = delete;
+
+    /// @private
+    View(View&&) = default;
+
+    /// @private
+    View& operator = (const View&) = delete;
+
+    ~View();
+
+    Ptrs    ptr; ///< Array of image plane pointers
+    Strides stride; ///< Array of image plane strides, in bytes.
+
+private:
+    Callback m_cb;
+};
+
+/**
+ * @brief An interface class for MediaFrame data adapters.
+ *
+ * Implement this interface to wrap media data in the MediaFrame. It
+ * makes sense to implement this class if there is a custom
+ * cv::gapi::wip::IStreamSource defined -- in this case, a stream
+ * source can produce MediaFrame objects with this adapter and the
+ * media data may be passed to graph without any copy. For example, a
+ * GStreamer-based stream source can implement an adapter over
+ * `GstBuffer` and G-API will transparently use it in the graph.
+ */
+class GAPI_EXPORTS MediaFrame::IAdapter {
+public:
+    virtual ~IAdapter() = 0;
+    virtual cv::GFrameDesc meta() const = 0;
+    virtual MediaFrame::View access(MediaFrame::Access) = 0;
+    // FIXME: design a better solution
+    // The default implementation does nothing
+    virtual cv::util::any blobParams() const;
+    virtual void serialize(cv::gapi::s11n::IOStream&) {
+        GAPI_Assert(false && "Generic serialize method of MediaFrame::IAdapter does nothing by default. "
+                             "Please, implement it in derived class to properly serialize the object.");
+    }
+    virtual void deserialize(cv::gapi::s11n::IIStream&) {
+        GAPI_Assert(false && "Generic deserialize method of MediaFrame::IAdapter does nothing by default. "
+                             "Please, implement it in derived class to properly deserialize the object.");
+    }
+};
+/** @} */
+
+} //namespace cv
+
+#endif // OPENCV_GAPI_MEDIA_HPP
diff --git a/3rdParty/include/opencv2/gapi/ocl/core.hpp b/3rdParty/include/opencv2/gapi/ocl/core.hpp
new file mode 100644
index 0000000..6c75870
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/ocl/core.hpp
@@ -0,0 +1,27 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OCL_CORE_API_HPP
+#define OPENCV_GAPI_OCL_CORE_API_HPP
+
+#include <opencv2/core/cvdef.h>     // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+namespace core {
+namespace ocl {
+
+        GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels();
+
+} // namespace ocl
+} // namespace core
+} // namespace gapi
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_OCL_CORE_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/ocl/goclkernel.hpp b/3rdParty/include/opencv2/gapi/ocl/goclkernel.hpp
new file mode 100644
index 0000000..6a2f1df
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/ocl/goclkernel.hpp
@@ -0,0 +1,249 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GOCLKERNEL_HPP
+#define OPENCV_GAPI_GOCLKERNEL_HPP
+
+#include <vector>
+#include <functional>
+#include <map>
+#include <unordered_map>
+
+#include <opencv2/core/mat.hpp>
+#include <opencv2/gapi/gcommon.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/garg.hpp>
+
+// FIXME: namespace scheme for backends?
+namespace cv {
+
+namespace gimpl
+{
+    // Forward-declare an internal class
+    class GOCLExecutable;
+} // namespace gimpl
+
+namespace gapi
+{
+/**
+ * @brief This namespace contains G-API OpenCL backend functions, structures, and symbols.
+ */
+namespace ocl
+{
+    /**
+     * \addtogroup gapi_std_backends G-API Standard Backends
+     * @{
+     */
+    /**
+     * @brief Get a reference to OCL backend.
+     *
+     * At the moment, the OCL backend is built atop of OpenCV
+     * "Transparent API" (T-API), see cv::UMat for details.
+     *
+     * @sa gapi_std_backends
+     */
+    GAPI_EXPORTS cv::gapi::GBackend backend();
+    /** @} */
+} // namespace ocl
+} // namespace gapi
+
+
+// Represents arguments which are passed to a wrapped OCL function
+// FIXME: put into detail?
+class GAPI_EXPORTS GOCLContext
+{
+public:
+    // Generic accessor API
+    template<typename T>
+    const T& inArg(int input) { return m_args.at(input).get<T>(); }
+
+    // Syntax sugar
+    const cv::UMat&  inMat(int input);
+    cv::UMat&  outMatR(int output); // FIXME: Avoid cv::Mat m = ctx.outMatR()
+
+    const cv::Scalar& inVal(int input);
+    cv::Scalar& outValR(int output); // FIXME: Avoid cv::Scalar s = ctx.outValR()
+    template<typename T> std::vector<T>& outVecR(int output) // FIXME: the same issue
+    {
+        return outVecRef(output).wref<T>();
+    }
+    template<typename T> T& outOpaqueR(int output) // FIXME: the same issue
+    {
+        return outOpaqueRef(output).wref<T>();
+    }
+
+protected:
+    detail::VectorRef& outVecRef(int output);
+    detail::OpaqueRef& outOpaqueRef(int output);
+
+    std::vector<GArg> m_args;
+    std::unordered_map<std::size_t, GRunArgP> m_results;
+
+
+    friend class gimpl::GOCLExecutable;
+};
+
+class GAPI_EXPORTS GOCLKernel
+{
+public:
+    // This function is kernel's execution entry point (does the processing work)
+    using F = std::function<void(GOCLContext &)>;
+
+    GOCLKernel();
+    explicit GOCLKernel(const F& f);
+
+    void apply(GOCLContext &ctx);
+
+protected:
+    F m_f;
+};
+
+// FIXME: This is an ugly ad-hoc implementation. TODO: refactor
+
+namespace detail
+{
+template<class T> struct ocl_get_in;
+template<> struct ocl_get_in<cv::GMat>
+{
+    static cv::UMat    get(GOCLContext &ctx, int idx) { return ctx.inMat(idx); }
+};
+template<> struct ocl_get_in<cv::GScalar>
+{
+    static cv::Scalar get(GOCLContext &ctx, int idx) { return ctx.inVal(idx); }
+};
+template<typename U> struct ocl_get_in<cv::GArray<U> >
+{
+    static const std::vector<U>& get(GOCLContext &ctx, int idx) { return ctx.inArg<VectorRef>(idx).rref<U>(); }
+};
+template<typename U> struct ocl_get_in<cv::GOpaque<U> >
+{
+    static const U& get(GOCLContext &ctx, int idx) { return ctx.inArg<OpaqueRef>(idx).rref<U>(); }
+};
+template<class T> struct ocl_get_in
+{
+    static T get(GOCLContext &ctx, int idx) { return ctx.inArg<T>(idx); }
+};
+
+struct tracked_cv_umat{
+    //TODO Think if T - API could reallocate UMat to a proper size - how do we handle this ?
+    //tracked_cv_umat(cv::UMat& m) : r{(m)}, original_data{m.getMat(ACCESS_RW).data} {}
+    tracked_cv_umat(cv::UMat& m) : r(m), original_data{ nullptr } {}
+    cv::UMat &r; // FIXME: It was a value (not a reference) before.
+                 // Actually OCL backend should allocate its internal data!
+    uchar* original_data;
+
+    operator cv::UMat& (){ return r;}
+    void validate() const{
+        //if (r.getMat(ACCESS_RW).data != original_data)
+        //{
+        //    util::throw_error
+        //        (std::logic_error
+        //         ("OpenCV kernel output parameter was reallocated. \n"
+        //          "Incorrect meta data was provided ?"));
+        //}
+
+    }
+};
+
+template<typename... Outputs>
+void postprocess_ocl(Outputs&... outs)
+{
+    struct
+    {
+        void operator()(tracked_cv_umat* bm) { bm->validate(); }
+        void operator()(...) {                  }
+
+    } validate;
+    //dummy array to unfold parameter pack
+    int dummy[] = { 0, (validate(&outs), 0)... };
+    cv::util::suppress_unused_warning(dummy);
+}
+
+template<class T> struct ocl_get_out;
+template<> struct ocl_get_out<cv::GMat>
+{
+    static tracked_cv_umat get(GOCLContext &ctx, int idx)
+    {
+        auto& r = ctx.outMatR(idx);
+        return{ r };
+    }
+};
+template<> struct ocl_get_out<cv::GScalar>
+{
+    static cv::Scalar& get(GOCLContext &ctx, int idx)
+    {
+        return ctx.outValR(idx);
+    }
+};
+template<typename U> struct ocl_get_out<cv::GArray<U> >
+{
+    static std::vector<U>& get(GOCLContext &ctx, int idx) { return ctx.outVecR<U>(idx);  }
+};
+template<typename U> struct ocl_get_out<cv::GOpaque<U> >
+{
+    static U& get(GOCLContext &ctx, int idx) { return ctx.outOpaqueR<U>(idx);  }
+};
+
+template<typename, typename, typename>
+struct OCLCallHelper;
+
+// FIXME: probably can be simplified with std::apply or analogue.
+template<typename Impl, typename... Ins, typename... Outs>
+struct OCLCallHelper<Impl, std::tuple<Ins...>, std::tuple<Outs...> >
+{
+    template<typename... Inputs>
+    struct call_and_postprocess
+    {
+        template<typename... Outputs>
+        static void call(Inputs&&... ins, Outputs&&... outs)
+        {
+            //not using a std::forward on outs is deliberate in order to
+            //cause compilation error, by trying to bind rvalue references to lvalue references
+            Impl::run(std::forward<Inputs>(ins)..., outs...);
+
+            postprocess_ocl(outs...);
+        }
+    };
+
+    template<int... IIs, int... OIs>
+    static void call_impl(GOCLContext &ctx, detail::Seq<IIs...>, detail::Seq<OIs...>)
+    {
+        //TODO: Make sure that OpenCV kernels do not reallocate memory for output parameters
+        //by comparing it's state (data ptr) before and after the call.
+        //Convert own::Scalar to cv::Scalar before call kernel and run kernel
+        //convert cv::Scalar to own::Scalar after call kernel and write back results
+        call_and_postprocess<decltype(ocl_get_in<Ins>::get(ctx, IIs))...>::call(ocl_get_in<Ins>::get(ctx, IIs)..., ocl_get_out<Outs>::get(ctx, OIs)...);
+    }
+
+    static void call(GOCLContext &ctx)
+    {
+        call_impl(ctx,
+            typename detail::MkSeq<sizeof...(Ins)>::type(),
+            typename detail::MkSeq<sizeof...(Outs)>::type());
+    }
+};
+
+} // namespace detail
+
+template<class Impl, class K>
+class GOCLKernelImpl: public cv::detail::OCLCallHelper<Impl, typename K::InArgs, typename K::OutArgs>,
+                      public cv::detail::KernelTag
+{
+    using P = detail::OCLCallHelper<Impl, typename K::InArgs, typename K::OutArgs>;
+
+public:
+    using API = K;
+
+    static cv::gapi::GBackend backend()  { return cv::gapi::ocl::backend(); }
+    static cv::GOCLKernel     kernel()   { return GOCLKernel(&P::call);     }
+};
+
+#define GAPI_OCL_KERNEL(Name, API) struct Name: public cv::GOCLKernelImpl<Name, API>
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GOCLKERNEL_HPP
diff --git a/3rdParty/include/opencv2/gapi/ocl/imgproc.hpp b/3rdParty/include/opencv2/gapi/ocl/imgproc.hpp
new file mode 100644
index 0000000..1bb5911
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/ocl/imgproc.hpp
@@ -0,0 +1,27 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OCL_IMGPROC_API_HPP
+#define OPENCV_GAPI_OCL_IMGPROC_API_HPP
+
+#include <opencv2/core/cvdef.h>     // GAPI_EXPORTS
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+namespace imgproc {
+namespace ocl {
+
+    GAPI_EXPORTS GKernelPackage kernels();
+
+} // namespace ocl
+} // namespace imgproc
+} // namespace gapi
+} // namespace cv
+
+
+#endif // OPENCV_GAPI_OCL_IMGPROC_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/opencv_includes.hpp b/3rdParty/include/opencv2/gapi/opencv_includes.hpp
new file mode 100644
index 0000000..25a67d6
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/opencv_includes.hpp
@@ -0,0 +1,41 @@
+// This file is part of OpenCV project.
+
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OPENCV_INCLUDES_HPP
+#define OPENCV_GAPI_OPENCV_INCLUDES_HPP
+
+#if !defined(GAPI_STANDALONE)
+#  include <opencv2/core/mat.hpp>
+#  include <opencv2/core/cvdef.h>
+#  include <opencv2/core/types.hpp>
+#  include <opencv2/core/base.hpp>
+#define GAPI_OWN_TYPES_LIST     cv::gapi::own::Rect,                           \
+                                cv::gapi::own::Size,                           \
+                                cv::gapi::own::Point,                          \
+                                cv::gapi::own::Point2f,                        \
+                                cv::gapi::own::Scalar,                         \
+                                cv::gapi::own::Mat
+#else   // Without OpenCV
+#  include <opencv2/gapi/own/cvdefs.hpp>
+#  include <opencv2/gapi/own/types.hpp>  // cv::gapi::own::Rect/Size/Point
+#  include <opencv2/gapi/own/scalar.hpp> // cv::gapi::own::Scalar
+#  include <opencv2/gapi/own/mat.hpp>
+// replacement of cv's structures:
+namespace cv {
+    using Rect    = gapi::own::Rect;
+    using Size    = gapi::own::Size;
+    using Point   = gapi::own::Point;
+    using Point2f = gapi::own::Point2f;
+    using Scalar  = gapi::own::Scalar;
+    using Mat     = gapi::own::Mat;
+}  // namespace cv
+#define GAPI_OWN_TYPES_LIST     cv::gapi::own::VoidType
+
+#endif // !defined(GAPI_STANDALONE)
+
+#endif // OPENCV_GAPI_OPENCV_INCLUDES_HPP
diff --git a/3rdParty/include/opencv2/gapi/operators.hpp b/3rdParty/include/opencv2/gapi/operators.hpp
new file mode 100644
index 0000000..6794b44
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/operators.hpp
@@ -0,0 +1,70 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OPERATORS_HPP
+#define OPENCV_GAPI_OPERATORS_HPP
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+
+namespace cv
+{
+GAPI_EXPORTS cv::GMat operator+(const cv::GMat&    lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator+(const cv::GMat&    lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator+(const cv::GScalar& lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator-(const cv::GMat&    lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator-(const cv::GMat&    lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator-(const cv::GScalar& lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator*(const cv::GMat&    lhs, float              rhs);
+GAPI_EXPORTS cv::GMat operator*(float              lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator*(const cv::GMat&    lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator*(const cv::GScalar& lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator/(const cv::GMat&    lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator/(const cv::GScalar& lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator/(const cv::GMat&    lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator&(const cv::GMat&    lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator|(const cv::GMat&    lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator^(const cv::GMat&    lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator~(const cv::GMat&    lhs);
+
+GAPI_EXPORTS cv::GMat operator&(const cv::GScalar& lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator|(const cv::GScalar& lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator^(const cv::GScalar& lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator&(const cv::GMat& lhs, const cv::GScalar&    rhs);
+GAPI_EXPORTS cv::GMat operator|(const cv::GMat& lhs, const cv::GScalar&    rhs);
+GAPI_EXPORTS cv::GMat operator^(const cv::GMat& lhs, const cv::GScalar&    rhs);
+
+GAPI_EXPORTS cv::GMat operator>(const cv::GMat&    lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator>=(const cv::GMat&   lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator<(const cv::GMat&    lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator<=(const cv::GMat&   lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator==(const cv::GMat&   lhs, const cv::GMat&    rhs);
+GAPI_EXPORTS cv::GMat operator!=(const cv::GMat&   lhs, const cv::GMat&    rhs);
+
+GAPI_EXPORTS cv::GMat operator>(const cv::GMat&    lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator>=(const cv::GMat&   lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator<(const cv::GMat&    lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator<=(const cv::GMat&   lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator==(const cv::GMat&   lhs, const cv::GScalar& rhs);
+GAPI_EXPORTS cv::GMat operator!=(const cv::GMat&   lhs, const cv::GScalar& rhs);
+
+GAPI_EXPORTS cv::GMat operator>(const cv::GScalar&    lhs, const cv::GMat& rhs);
+GAPI_EXPORTS cv::GMat operator>=(const cv::GScalar&   lhs, const cv::GMat& rhs);
+GAPI_EXPORTS cv::GMat operator<(const cv::GScalar&    lhs, const cv::GMat& rhs);
+GAPI_EXPORTS cv::GMat operator<=(const cv::GScalar&   lhs, const cv::GMat& rhs);
+GAPI_EXPORTS cv::GMat operator==(const cv::GScalar&   lhs, const cv::GMat& rhs);
+GAPI_EXPORTS cv::GMat operator!=(const cv::GScalar&   lhs, const cv::GMat& rhs);
+} // cv
+
+#endif // OPENCV_GAPI_OPERATORS_HPP
diff --git a/3rdParty/include/opencv2/gapi/own/assert.hpp b/3rdParty/include/opencv2/gapi/own/assert.hpp
new file mode 100644
index 0000000..d50543f
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/assert.hpp
@@ -0,0 +1,55 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OWN_ASSERT_HPP
+#define OPENCV_GAPI_OWN_ASSERT_HPP
+
+#include <opencv2/gapi/util/compiler_hints.hpp>
+
+#define GAPI_DbgAssertNoOp(expr) {                  \
+    constexpr bool _assert_tmp = false && (expr);   \
+    cv::util::suppress_unused_warning(_assert_tmp); \
+}
+
+#if !defined(GAPI_STANDALONE)
+#include <opencv2/core/base.hpp>
+#define GAPI_Assert CV_Assert
+
+#if defined _DEBUG || defined CV_STATIC_ANALYSIS
+#  define GAPI_DbgAssert CV_DbgAssert
+#else
+#  define GAPI_DbgAssert(expr) GAPI_DbgAssertNoOp(expr)
+#endif
+
+#else
+#include <stdexcept>
+#include <sstream>
+#include <opencv2/gapi/util/throw.hpp>
+
+namespace detail
+{
+    [[noreturn]] inline void assert_abort(const char* str, int line, const char* file, const char* func)
+    {
+        std::stringstream ss;
+        ss << file << ":" << line << ": Assertion " << str << " in function " << func << " failed\n";
+        cv::util::throw_error(std::logic_error(ss.str()));
+    }
+}
+
+#define GAPI_Assert(expr) \
+{ if (!(expr)) ::detail::assert_abort(#expr, __LINE__, __FILE__, __func__); }
+
+
+#ifdef NDEBUG
+#  define GAPI_DbgAssert(expr) GAPI_DbgAssertNoOp(expr)
+#else
+#  define GAPI_DbgAssert(expr) GAPI_Assert(expr)
+#endif
+
+#endif // GAPI_STANDALONE
+
+#endif // OPENCV_GAPI_OWN_ASSERT_HPP
diff --git a/3rdParty/include/opencv2/gapi/own/convert.hpp b/3rdParty/include/opencv2/gapi/own/convert.hpp
new file mode 100644
index 0000000..1a8ecd8
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/convert.hpp
@@ -0,0 +1,55 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OWN_CONVERT_HPP
+#define OPENCV_GAPI_OWN_CONVERT_HPP
+
+#if !defined(GAPI_STANDALONE)
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/own/mat.hpp>
+
+namespace cv
+{
+    template<typename T>
+    std::vector<T> to_own(const cv::MatSize &sz) {
+        std::vector<T> result(sz.dims());
+        for (int i = 0; i < sz.dims(); i++) {
+            // Note: cv::MatSize is not iterable
+            result[i] = static_cast<T>(sz[i]);
+        }
+        return result;
+    }
+
+    cv::gapi::own::Mat to_own(Mat&&) = delete;
+
+    inline cv::gapi::own::Mat to_own(Mat const& m) {
+        return (m.dims == 2)
+            ?  cv::gapi::own::Mat{m.rows, m.cols, m.type(), m.data, m.step}
+            :  cv::gapi::own::Mat{to_own<int>(m.size), m.type(), m.data};
+    };
+
+namespace gapi
+{
+namespace own
+{
+
+    inline cv::Mat to_ocv(Mat const& m) {
+        return m.dims.empty()
+            ? cv::Mat{m.rows, m.cols, m.type(), m.data, m.step}
+            : cv::Mat{m.dims, m.type(), m.data};
+    }
+
+    cv::Mat to_ocv(Mat&&) = delete;
+
+} // namespace own
+} // namespace gapi
+} // namespace cv
+
+#endif // !defined(GAPI_STANDALONE)
+
+#endif // OPENCV_GAPI_OWN_CONVERT_HPP
diff --git a/3rdParty/include/opencv2/gapi/own/cvdefs.hpp b/3rdParty/include/opencv2/gapi/own/cvdefs.hpp
new file mode 100644
index 0000000..d3bef98
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/cvdefs.hpp
@@ -0,0 +1,166 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_CV_DEFS_HPP
+#define OPENCV_GAPI_CV_DEFS_HPP
+
+#if defined(GAPI_STANDALONE)
+// Simulate OpenCV definitions taken from various
+// OpenCV interface headers if G-API is built in a
+// standalone mode.
+
+// interface.h:
+
+typedef unsigned char uchar;
+typedef          char schar;
+
+typedef unsigned short ushort;
+
+#define CV_USRTYPE1 (void)"CV_USRTYPE1 support has been dropped in OpenCV 4.0"
+
+#define CV_CN_MAX     512
+#define CV_CN_SHIFT   3
+#define CV_DEPTH_MAX  (1 << CV_CN_SHIFT)
+
+#define CV_8U   0
+#define CV_8S   1
+#define CV_16U  2
+#define CV_16S  3
+#define CV_32S  4
+#define CV_32F  5
+#define CV_64F  6
+#define CV_16F  7
+
+#define CV_MAT_DEPTH_MASK       (CV_DEPTH_MAX - 1)
+#define CV_MAT_DEPTH(flags)     ((flags) & CV_MAT_DEPTH_MASK)
+
+#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT))
+#define CV_MAKE_TYPE CV_MAKETYPE
+
+#define CV_8UC1 CV_MAKETYPE(CV_8U,1)
+#define CV_8UC2 CV_MAKETYPE(CV_8U,2)
+#define CV_8UC3 CV_MAKETYPE(CV_8U,3)
+#define CV_8UC4 CV_MAKETYPE(CV_8U,4)
+#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n))
+
+#define CV_8SC1 CV_MAKETYPE(CV_8S,1)
+#define CV_8SC2 CV_MAKETYPE(CV_8S,2)
+#define CV_8SC3 CV_MAKETYPE(CV_8S,3)
+#define CV_8SC4 CV_MAKETYPE(CV_8S,4)
+#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n))
+
+#define CV_16UC1 CV_MAKETYPE(CV_16U,1)
+#define CV_16UC2 CV_MAKETYPE(CV_16U,2)
+#define CV_16UC3 CV_MAKETYPE(CV_16U,3)
+#define CV_16UC4 CV_MAKETYPE(CV_16U,4)
+#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n))
+
+#define CV_16SC1 CV_MAKETYPE(CV_16S,1)
+#define CV_16SC2 CV_MAKETYPE(CV_16S,2)
+#define CV_16SC3 CV_MAKETYPE(CV_16S,3)
+#define CV_16SC4 CV_MAKETYPE(CV_16S,4)
+#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n))
+
+#define CV_32SC1 CV_MAKETYPE(CV_32S,1)
+#define CV_32SC2 CV_MAKETYPE(CV_32S,2)
+#define CV_32SC3 CV_MAKETYPE(CV_32S,3)
+#define CV_32SC4 CV_MAKETYPE(CV_32S,4)
+#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n))
+
+#define CV_16FC1 CV_MAKETYPE(CV_16F,1)
+#define CV_16FC2 CV_MAKETYPE(CV_16F,2)
+#define CV_16FC3 CV_MAKETYPE(CV_16F,3)
+#define CV_16FC4 CV_MAKETYPE(CV_16F,4)
+#define CV_16FC(n) CV_MAKETYPE(CV_16F,(n))
+
+#define CV_32FC1 CV_MAKETYPE(CV_32F,1)
+#define CV_32FC2 CV_MAKETYPE(CV_32F,2)
+#define CV_32FC3 CV_MAKETYPE(CV_32F,3)
+#define CV_32FC4 CV_MAKETYPE(CV_32F,4)
+#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n))
+
+#define CV_64FC1 CV_MAKETYPE(CV_64F,1)
+#define CV_64FC2 CV_MAKETYPE(CV_64F,2)
+#define CV_64FC3 CV_MAKETYPE(CV_64F,3)
+#define CV_64FC4 CV_MAKETYPE(CV_64F,4)
+#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n))
+
+// cvdef.h:
+
+#ifndef CV_ALWAYS_INLINE
+#  if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))
+#    define CV_ALWAYS_INLINE inline __attribute__((always_inline))
+#  elif defined(_MSC_VER)
+#    define CV_ALWAYS_INLINE __forceinline
+#  else
+#    define CV_ALWAYS_INLINE inline
+#  endif
+#endif
+
+#define CV_MAT_CN_MASK          ((CV_CN_MAX - 1) << CV_CN_SHIFT)
+#define CV_MAT_CN(flags)        ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1)
+#define CV_MAT_TYPE_MASK        (CV_DEPTH_MAX*CV_CN_MAX - 1)
+#define CV_MAT_TYPE(flags)      ((flags) & CV_MAT_TYPE_MASK)
+#define CV_MAT_CONT_FLAG_SHIFT  14
+#define CV_MAT_CONT_FLAG        (1 << CV_MAT_CONT_FLAG_SHIFT)
+#define CV_IS_MAT_CONT(flags)   ((flags) & CV_MAT_CONT_FLAG)
+#define CV_IS_CONT_MAT          CV_IS_MAT_CONT
+#define CV_SUBMAT_FLAG_SHIFT    15
+#define CV_SUBMAT_FLAG          (1 << CV_SUBMAT_FLAG_SHIFT)
+#define CV_IS_SUBMAT(flags)     ((flags) & CV_MAT_SUBMAT_FLAG)
+
+//** Size of each channel item,
+//   0x8442211 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */
+#define CV_ELEM_SIZE1(type) \
+   ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15)
+
+#define CV_MAT_TYPE(flags)      ((flags) & CV_MAT_TYPE_MASK)
+
+/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */
+#define CV_ELEM_SIZE(type) \
+    (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3))
+
+#ifndef CV_OVERRIDE
+#  define CV_OVERRIDE override
+#endif
+
+// base.h:
+namespace cv
+{
+enum BorderTypes {
+    BORDER_CONSTANT    = 0, //!< `iiiiii|abcdefgh|iiiiiii`  with some specified `i`
+    BORDER_REPLICATE   = 1, //!< `aaaaaa|abcdefgh|hhhhhhh`
+    BORDER_REFLECT     = 2, //!< `fedcba|abcdefgh|hgfedcb`
+    BORDER_WRAP        = 3, //!< `cdefgh|abcdefgh|abcdefg`
+    BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba`
+    BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno`
+
+    BORDER_REFLECT101  = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
+    BORDER_DEFAULT     = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
+    BORDER_ISOLATED    = 16 //!< do not look outside of ROI
+};
+// imgproc.hpp:
+enum InterpolationFlags{
+    INTER_NEAREST        = 0,
+    INTER_LINEAR         = 1,
+    INTER_CUBIC          = 2,
+    INTER_AREA           = 3,
+    INTER_LANCZOS4       = 4,
+    INTER_LINEAR_EXACT   = 5,
+    INTER_MAX            = 7,
+};
+} // namespace cv
+
+static inline int cvFloor( double value )
+{
+    int i = (int)value;
+    return i - (i > value);
+}
+
+#endif //  defined(GAPI_STANDALONE)
+
+#endif //  OPENCV_GAPI_CV_DEFS_HPP
diff --git a/3rdParty/include/opencv2/gapi/own/exports.hpp b/3rdParty/include/opencv2/gapi/own/exports.hpp
new file mode 100644
index 0000000..c36f400
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/exports.hpp
@@ -0,0 +1,42 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OWN_TYPES_HPP
+#define OPENCV_GAPI_OWN_TYPES_HPP
+
+#   if defined(__OPENCV_BUILD)
+#       include <opencv2/core/base.hpp>
+#       define GAPI_EXPORTS CV_EXPORTS
+        /* special informative macros for wrapper generators */
+#       define GAPI_PROP CV_PROP
+#       define GAPI_PROP_RW CV_PROP_RW
+#       define GAPI_WRAP CV_WRAP
+#       define GAPI_EXPORTS_W_SIMPLE CV_EXPORTS_W_SIMPLE
+#       define GAPI_EXPORTS_W CV_EXPORTS_W
+#   else
+#       define GAPI_PROP
+#       define GAPI_PROP_RW
+#       define GAPI_WRAP
+#       define GAPI_EXPORTS
+#       define GAPI_EXPORTS_W_SIMPLE
+#       define GAPI_EXPORTS_W
+
+#if 0  // Note: the following version currently is not needed for non-OpenCV build
+#       if defined _WIN32
+#           define GAPI_EXPORTS __declspec(dllexport)
+#       elif defined __GNUC__ && __GNUC__ >= 4
+#           define GAPI_EXPORTS __attribute__ ((visibility ("default")))
+#       endif
+
+#       ifndef GAPI_EXPORTS
+#           define GAPI_EXPORTS
+#       endif
+#endif
+
+#   endif
+
+#endif // OPENCV_GAPI_OWN_TYPES_HPP
diff --git a/3rdParty/include/opencv2/gapi/own/mat.hpp b/3rdParty/include/opencv2/gapi/own/mat.hpp
new file mode 100644
index 0000000..ce9c0bf
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/mat.hpp
@@ -0,0 +1,354 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OWN_MAT_HPP
+#define OPENCV_GAPI_OWN_MAT_HPP
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/own/types.hpp>
+#include <opencv2/gapi/own/scalar.hpp>
+#include <opencv2/gapi/own/saturate.hpp>
+#include <opencv2/gapi/own/assert.hpp>
+
+#include <memory>                   //std::shared_ptr
+#include <cstring>                  //std::memcpy
+#include <numeric>                  //std::accumulate
+#include <vector>
+#include <opencv2/gapi/util/throw.hpp>
+
+namespace cv { namespace gapi { namespace own {
+    namespace detail {
+        template <typename T, unsigned char channels>
+        void assign_row(void* ptr, int cols, Scalar const& s)
+        {
+            auto p = static_cast<T*>(ptr);
+            for (int c = 0; c < cols; c++)
+            {
+                for (int ch = 0; ch < channels; ch++)
+                {
+                    p[c * channels + ch] = saturate<T>(s[ch], roundd);
+                }
+            }
+        }
+
+        inline size_t default_step(int type, int cols)
+        {
+            return CV_ELEM_SIZE(type) * cols;
+        }
+        //Matrix header, i.e. fields that are unique to each Mat object.
+        //Devoted class is needed to implement custom behavior on move (erasing state of moved from object)
+        struct MatHeader{
+            enum { AUTO_STEP = 0};
+            enum { TYPE_MASK = 0x00000FFF  };
+
+            MatHeader() = default;
+
+            MatHeader(int _rows, int _cols, int type, void* _data, size_t _step)
+            : flags((type & TYPE_MASK)), rows(_rows), cols(_cols), data((uchar*)_data), step(_step == AUTO_STEP ? detail::default_step(type, _cols) : _step)
+            {}
+
+            MatHeader(const std::vector<int> &_dims, int type, void* _data)
+            : flags((type & TYPE_MASK)), data((uchar*)_data), step(0), dims(_dims)
+            {}
+
+            MatHeader(const MatHeader& ) = default;
+            MatHeader(MatHeader&& src) : MatHeader(src) // reuse copy constructor here
+            {
+                MatHeader empty; //give it a name to call copy(not move) assignment below
+                src = empty;
+            }
+            MatHeader& operator=(const MatHeader& ) = default;
+            MatHeader& operator=(MatHeader&& src)
+            {
+                *this = src; //calling a copy assignment here, not move one
+                MatHeader empty; //give it a name to call copy(not move) assignment below
+                src = empty;
+                return *this;
+            }
+            /*! includes several bit-fields:
+                 - depth
+                 - number of channels
+             */
+            int flags = 0;
+
+            //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
+            int rows = 0, cols = 0;
+            //! pointer to the data
+            uchar* data = nullptr;
+            size_t step = 0;
+            //! dimensions (ND-case)
+            std::vector<int> dims;
+        };
+    } // namespace detail
+    //concise version of cv::Mat suitable for GAPI needs (used when no dependence on OpenCV is required)
+    class Mat : public detail::MatHeader{
+    public:
+
+        Mat() = default;
+
+        /** @overload
+        @param _rows Number of rows in a 2D array.
+        @param _cols Number of columns in a 2D array.
+        @param _type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+        CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+        @param _data Pointer to the user data. Matrix constructors that take data and step parameters do not
+        allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+        data, which means that no data is copied. This operation is very efficient and can be used to
+        process external data using OpenCV functions. The external data is not automatically deallocated, so
+        you should take care of it.
+        @param _step Number of bytes each matrix row occupies. The value should include the padding bytes at
+        the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
+        and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
+        */
+        Mat(int _rows, int _cols, int _type, void* _data, size_t _step = AUTO_STEP)
+        : MatHeader (_rows, _cols, _type, _data, _step)
+        {}
+
+        Mat(const std::vector<int> &_dims, int _type, void* _data)
+        : MatHeader (_dims, _type, _data)
+        {}
+
+        Mat(std::vector<int> &&_dims, int _type, void* _data)
+        : MatHeader (std::move(_dims), _type, _data)
+        {}
+
+        Mat(Mat const& src, const Rect& roi )
+        : Mat(src)
+        {
+           rows = roi.height;
+           cols = roi.width;
+           data = ptr(roi.y, roi.x);
+        }
+
+        Mat(Mat const& ) = default;
+        Mat(Mat&& ) = default;
+
+        Mat& operator=(Mat const& ) = default;
+        Mat& operator=(Mat&& ) = default;
+
+        /** @brief Sets all or some of the array elements to the specified value.
+        @param s Assigned scalar converted to the actual array type.
+        */
+        Mat& operator = (const Scalar& s)
+        {
+            constexpr unsigned max_channels = 4; //Scalar can't fit more than 4
+            using func_p_t = void (*)(void*, int, Scalar const&);
+            using detail::assign_row;
+            #define TABLE_ENTRY(type)  {assign_row<type, 1>, assign_row<type, 2>, assign_row<type, 3>, assign_row<type, 4>}
+            static constexpr func_p_t func_tbl[][max_channels] = {
+                    TABLE_ENTRY(uchar),
+                    TABLE_ENTRY(schar),
+                    TABLE_ENTRY(ushort),
+                    TABLE_ENTRY(short),
+                    TABLE_ENTRY(int),
+                    TABLE_ENTRY(float),
+                    TABLE_ENTRY(double)
+            };
+            #undef TABLE_ENTRY
+
+            static_assert(CV_8U == 0 && CV_8S == 1  && CV_16U == 2 && CV_16S == 3
+                       && CV_32S == 4 && CV_32F == 5 && CV_64F == 6,
+                       "OCV type ids used as indexes to array, thus exact numbers are important!"
+            );
+
+            const auto depth = static_cast<unsigned int>(this->depth());
+            GAPI_Assert(depth < sizeof(func_tbl)/sizeof(func_tbl[0]));
+
+            if (dims.empty())
+            {
+                const auto channels = static_cast<unsigned int>(this->channels());
+                GAPI_Assert(channels <= max_channels);
+
+                auto* f = func_tbl[depth][channels - 1];
+                for (int r = 0; r < rows; ++r)
+                {
+                    (*f)(static_cast<void *>(ptr(r)), cols, s );
+                }
+            }
+            else
+            {
+                auto* f = func_tbl[depth][0];
+                // FIXME: better to refactor assign_row to use std::size_t by default
+                (*f)(static_cast<void *>(data), static_cast<int>(total()), s);
+            }
+            return *this;
+        }
+
+        /** @brief Returns the matrix element size in bytes.
+
+        The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 ,
+        the method returns 3\*sizeof(short) or 6.
+         */
+        size_t elemSize() const
+        {
+            return CV_ELEM_SIZE(type());
+        }
+        /** @brief Returns the type of a matrix element.
+
+        The method returns a matrix element type. This is an identifier compatible with the CvMat type
+        system, like CV_16SC3 or 16-bit signed 3-channel array, and so on.
+         */
+        int type() const            {return CV_MAT_TYPE(flags);}
+
+        /** @brief Returns the depth of a matrix element.
+
+        The method returns the identifier of the matrix element depth (the type of each individual channel).
+        For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of
+        matrix types contains the following values:
+        -   CV_8U - 8-bit unsigned integers ( 0..255 )
+        -   CV_8S - 8-bit signed integers ( -128..127 )
+        -   CV_16U - 16-bit unsigned integers ( 0..65535 )
+        -   CV_16S - 16-bit signed integers ( -32768..32767 )
+        -   CV_32S - 32-bit signed integers ( -2147483648..2147483647 )
+        -   CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )
+        -   CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )
+         */
+        int depth() const           {return CV_MAT_DEPTH(flags);}
+
+        /** @brief Returns the number of matrix channels.
+
+        The method returns the number of matrix channels.
+        If matrix is N-dimensional, -1 is returned.
+         */
+        int channels() const        {return dims.empty() ? CV_MAT_CN(flags) : -1;}
+
+        /**
+        @param _rows New number of rows.
+        @param _cols New number of columns.
+        @param _type New matrix type.
+         */
+        void create(int _rows, int _cols, int _type)
+        {
+            create(Size{_cols, _rows}, _type);
+        }
+        /** @overload
+        @param _size Alternative new matrix size specification: Size(cols, rows)
+        @param _type New matrix type.
+        */
+        void create(Size _size, int _type)
+        {
+            GAPI_Assert(_size.height >= 0 && _size.width >= 0);
+            if (_size != Size{cols, rows} )
+            {
+                Mat tmp{_size.height, _size.width, _type, nullptr};
+                tmp.memory.reset(new uchar[ tmp.step * tmp.rows], [](uchar * p){delete[] p;});
+                tmp.data = tmp.memory.get();
+
+                *this = std::move(tmp);
+            }
+        }
+
+        void create(const std::vector<int> &_dims, int _type)
+        {
+            // FIXME: make a proper reallocation-on-demands
+            // WARNING: no tensor views, so no strides
+            Mat tmp{_dims, _type, nullptr};
+            // FIXME: this accumulate duplicates a lot
+            const auto sz = std::accumulate(_dims.begin(), _dims.end(), 1, std::multiplies<int>());
+            tmp.memory.reset(new uchar[CV_ELEM_SIZE(_type)*sz], [](uchar * p){delete[] p;});
+            tmp.data = tmp.memory.get();
+            *this = std::move(tmp);
+        }
+
+        /** @brief Creates a full copy of the matrix and the underlying data.
+
+        The method creates a full copy of the matrix. The original step[] is not taken into account.
+        So, the copy has a continuous buffer occupying total() * elemSize() bytes.
+         */
+        Mat clone() const
+        {
+            Mat m;
+            copyTo(m);
+            return m;
+        }
+
+        /** @brief Copies the matrix to another one.
+
+        The method copies the matrix data to another matrix. Before copying the data, the method invokes :
+        @code
+            m.create(this->size(), this->type());
+        @endcode
+        so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the
+        function does not handle the case of a partial overlap between the source and the destination
+        matrices.
+         */
+        void copyTo(Mat& dst) const
+        {
+            if (dims.empty())
+            {
+                dst.create(rows, cols, type());
+                for (int r = 0; r < rows; ++r)
+                {
+                    std::copy_n(ptr(r), detail::default_step(type(),cols), dst.ptr(r));
+                }
+            }
+            else
+            {
+                dst.create(dims, depth());
+                std::copy_n(data, total()*elemSize(), data);
+            }
+        }
+
+        /** @brief Returns true if the array has no elements.
+
+        The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and
+        resize() methods `M.total() == 0` does not imply that `M.data == NULL`.
+         */
+        bool empty() const
+        {
+            return data == 0 || total() == 0;
+        }
+
+        /** @brief Returns the total number of array elements.
+
+        The method returns the number of array elements (a number of pixels if the array represents an
+        image).
+         */
+        size_t total() const
+        {
+            return dims.empty()
+                 ? (static_cast<std::size_t>(rows) * cols)
+                 : std::accumulate(dims.begin(), dims.end(), static_cast<std::size_t>(1), std::multiplies<size_t>());
+        }
+
+        /** @overload
+        @param roi Extracted submatrix specified as a rectangle.
+        */
+        Mat operator()( const Rect& roi ) const
+        {
+            return Mat{*this, roi};
+        }
+
+
+        /** @brief Returns a pointer to the specified matrix row.
+
+        The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in
+        Mat::isContinuous to know how to use these methods.
+        @param row Index along the dimension 0
+        @param col Index along the dimension 1
+        */
+        uchar* ptr(int row, int col = 0)
+        {
+            return const_cast<uchar*>(const_cast<const Mat*>(this)->ptr(row,col));
+        }
+        /** @overload */
+        const uchar* ptr(int row, int col = 0) const
+        {
+            return data + step * row + CV_ELEM_SIZE(type()) * col;
+        }
+
+
+    private:
+        //actual memory allocated for storage, or nullptr if object is non owning view to over memory
+        std::shared_ptr<uchar> memory;
+    };
+
+} //namespace own
+} //namespace gapi
+} //namespace cv
+
+#endif /* OPENCV_GAPI_OWN_MAT_HPP */
diff --git a/3rdParty/include/opencv2/gapi/own/saturate.hpp b/3rdParty/include/opencv2/gapi/own/saturate.hpp
new file mode 100644
index 0000000..74eaecf
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/saturate.hpp
@@ -0,0 +1,83 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_OWN_SATURATE_HPP
+#define OPENCV_GAPI_OWN_SATURATE_HPP
+
+#include <math.h>
+
+#include <limits>
+
+#include <opencv2/gapi/own/assert.hpp>
+#include <opencv2/gapi/util/type_traits.hpp>
+
+namespace cv { namespace gapi { namespace own {
+//-----------------------------
+//
+// Numeric cast with saturation
+//
+//-----------------------------
+
+template<typename DST, typename SRC,
+         typename = cv::util::enable_if_t<!std::is_same<DST, SRC>::value &&
+                                           std::is_integral<DST>::value  &&
+                                           std::is_integral<SRC>::value>   >
+static CV_ALWAYS_INLINE DST saturate(SRC x)
+{
+    if (sizeof(DST) > sizeof(SRC))
+        return static_cast<DST>(x);
+
+    // compiler must recognize this saturation,
+    // so compile saturate<s16>(a + b) with adds
+    // instruction (e.g.: _mm_adds_epi16 if x86)
+    return x < std::numeric_limits<DST>::min()?
+               std::numeric_limits<DST>::min():
+           x > std::numeric_limits<DST>::max()?
+               std::numeric_limits<DST>::max():
+           static_cast<DST>(x);
+}
+template<typename T>
+static CV_ALWAYS_INLINE T saturate(T x)
+{
+    return x;
+}
+
+template<typename DST, typename SRC, typename R,
+         cv::util::enable_if_t<std::is_floating_point<DST>::value, bool> = true >
+static CV_ALWAYS_INLINE DST saturate(SRC x, R)
+{
+    return static_cast<DST>(x);
+}
+template<typename DST, typename SRC, typename R,
+         cv::util::enable_if_t<std::is_integral<DST>::value &&
+                               std::is_integral<SRC>::value   , bool> = true >
+static CV_ALWAYS_INLINE DST saturate(SRC x, R)
+{
+    return saturate<DST>(x);
+}
+// Note, that OpenCV rounds differently:
+// - like std::round() for add, subtract
+// - like std::rint() for multiply, divide
+template<typename DST, typename SRC, typename R,
+         cv::util::enable_if_t<std::is_integral<DST>::value &&
+                               std::is_floating_point<SRC>::value, bool> = true >
+static CV_ALWAYS_INLINE DST saturate(SRC x, R round)
+{
+    int ix = static_cast<int>(round(x));
+    return saturate<DST>(ix);
+}
+
+// explicit suffix 'd' for double type
+inline double  ceild(double x) { return ceil(x); }
+inline double floord(double x) { return floor(x); }
+inline double roundd(double x) { return round(x); }
+inline double  rintd(double x) { return rint(x); }
+
+} //namespace own
+} //namespace gapi
+} //namespace cv
+#endif /* OPENCV_GAPI_OWN_SATURATE_HPP */
diff --git a/3rdParty/include/opencv2/gapi/own/scalar.hpp b/3rdParty/include/opencv2/gapi/own/scalar.hpp
new file mode 100644
index 0000000..bda91c8
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/scalar.hpp
@@ -0,0 +1,47 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GAPI_OWN_SCALAR_HPP
+#define OPENCV_GAPI_GAPI_OWN_SCALAR_HPP
+
+#include <opencv2/gapi/own/exports.hpp>
+
+namespace cv
+{
+namespace gapi
+{
+namespace own
+{
+
+class GAPI_EXPORTS Scalar
+{
+public:
+    Scalar() = default;
+    explicit Scalar(double v0) { val[0] = v0; };
+    Scalar(double v0, double v1, double v2 = 0, double v3 = 0)
+        : val{v0, v1, v2, v3}
+    {
+    }
+
+    const double& operator[](int i) const { return val[i]; }
+          double& operator[](int i)       { return val[i]; }
+
+    static Scalar all(double v0) { return Scalar(v0, v0, v0, v0); }
+
+    double val[4] = {0};
+};
+
+inline bool operator==(const Scalar& lhs, const Scalar& rhs)
+{
+    return std::equal(std::begin(lhs.val), std::end(lhs.val), std::begin(rhs.val));
+}
+
+} // namespace own
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_GAPI_OWN_SCALAR_HPP
diff --git a/3rdParty/include/opencv2/gapi/own/types.hpp b/3rdParty/include/opencv2/gapi/own/types.hpp
new file mode 100644
index 0000000..3814366
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/own/types.hpp
@@ -0,0 +1,151 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_TYPES_HPP
+#define OPENCV_GAPI_TYPES_HPP
+
+#include <algorithm>              // std::max, std::min
+#include <ostream>
+
+namespace cv
+{
+namespace gapi
+{
+
+/**
+ * @brief This namespace contains G-API own data structures used in
+ * its standalone mode build.
+ */
+namespace own
+{
+
+class Point
+{
+public:
+    Point() = default;
+    Point(int _x, int _y) : x(_x),  y(_y) {}
+
+    int x = 0;
+    int y = 0;
+};
+
+class Point2f
+{
+public:
+    Point2f() = default;
+    Point2f(float _x, float _y) : x(_x),  y(_y) {}
+
+    float x = 0.f;
+    float y = 0.f;
+};
+
+class Rect
+{
+public:
+    Rect() = default;
+    Rect(int _x, int _y, int _width, int _height) : x(_x), y(_y),   width(_width),  height(_height) {}
+#if !defined(GAPI_STANDALONE)
+    Rect(const cv::Rect& other) : x(other.x), y(other.y), width(other.width), height(other.height) {}
+    inline Rect& operator=(const cv::Rect& other)
+    {
+        x = other.x;
+        y = other.x;
+        width  = other.width;
+        height = other.height;
+        return *this;
+    }
+#endif // !defined(GAPI_STANDALONE)
+
+    int x      = 0; //!< x coordinate of the top-left corner
+    int y      = 0; //!< y coordinate of the top-left corner
+    int width  = 0; //!< width of the rectangle
+    int height = 0; //!< height of the rectangle
+};
+
+inline bool operator==(const Rect& lhs, const Rect& rhs)
+{
+    return lhs.x == rhs.x && lhs.y == rhs.y && lhs.width == rhs.width && lhs.height == rhs.height;
+}
+
+inline bool operator!=(const Rect& lhs, const Rect& rhs)
+{
+    return !(lhs == rhs);
+}
+
+inline Rect& operator&=(Rect& lhs, const Rect& rhs)
+{
+    int x1 = std::max(lhs.x, rhs.x);
+    int y1 = std::max(lhs.y, rhs.y);
+    lhs.width  = std::min(lhs.x + lhs.width,  rhs.x + rhs.width) -  x1;
+    lhs.height = std::min(lhs.y + lhs.height, rhs.y + rhs.height) - y1;
+    lhs.x = x1;
+    lhs.y = y1;
+    if( lhs.width <= 0 || lhs.height <= 0 )
+        lhs = Rect();
+    return lhs;
+}
+
+inline const Rect operator&(const Rect& lhs, const Rect& rhs)
+{
+    Rect result = lhs;
+    return result &= rhs;
+}
+
+inline std::ostream& operator<<(std::ostream& o, const Rect& rect)
+{
+    return o << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]";
+}
+
+class Size
+{
+public:
+    Size() = default;
+    Size(int _width, int _height) : width(_width),  height(_height) {}
+#if !defined(GAPI_STANDALONE)
+    Size(const cv::Size& other) : width(other.width), height(other.height) {}
+    inline Size& operator=(const cv::Size& rhs)
+    {
+        width  = rhs.width;
+        height = rhs.height;
+        return *this;
+    }
+#endif // !defined(GAPI_STANDALONE)
+
+    int width  = 0;
+    int height = 0;
+};
+
+inline Size& operator+=(Size& lhs, const Size& rhs)
+{
+    lhs.width  += rhs.width;
+    lhs.height += rhs.height;
+    return lhs;
+}
+
+inline bool operator==(const Size& lhs, const Size& rhs)
+{
+    return lhs.width == rhs.width && lhs.height == rhs.height;
+}
+
+inline bool operator!=(const Size& lhs, const Size& rhs)
+{
+    return !(lhs == rhs);
+}
+
+
+inline std::ostream& operator<<(std::ostream& o, const Size& s)
+{
+    o << "[" << s.width << " x " << s.height << "]";
+    return o;
+}
+
+struct VoidType {};
+} // namespace own
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_TYPES_HPP
diff --git a/3rdParty/include/opencv2/gapi/plaidml/core.hpp b/3rdParty/include/opencv2/gapi/plaidml/core.hpp
new file mode 100644
index 0000000..3c63fed
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/plaidml/core.hpp
@@ -0,0 +1,20 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_PLAIDML_CORE_HPP
+#define OPENCV_GAPI_PLAIDML_CORE_HPP
+
+#include <opencv2/gapi/gkernel.hpp>     // GKernelPackage
+#include <opencv2/gapi/own/exports.hpp> // GAPI_EXPORTS
+
+namespace cv { namespace gapi { namespace core { namespace plaidml {
+
+GAPI_EXPORTS cv::gapi::GKernelPackage kernels();
+
+}}}}
+
+#endif // OPENCV_GAPI_PLAIDML_CORE_HPP
diff --git a/3rdParty/include/opencv2/gapi/plaidml/gplaidmlkernel.hpp b/3rdParty/include/opencv2/gapi/plaidml/gplaidmlkernel.hpp
new file mode 100644
index 0000000..e22ecc7
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/plaidml/gplaidmlkernel.hpp
@@ -0,0 +1,140 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+//
+
+
+#ifndef OPENCV_GAPI_GPLAIDMLKERNEL_HPP
+#define OPENCV_GAPI_GPLAIDMLKERNEL_HPP
+
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/garg.hpp>
+
+namespace plaidml
+{
+namespace edsl
+{
+    class Tensor;
+} // namespace edsl
+} // namespace plaidml
+
+namespace cv
+{
+namespace gapi
+{
+namespace plaidml
+{
+
+GAPI_EXPORTS cv::gapi::GBackend backend();
+
+} // namespace plaidml
+} // namespace gapi
+
+struct GPlaidMLContext
+{
+    // Generic accessor API
+    template<typename T>
+    const T& inArg(int input) { return m_args.at(input).get<T>(); }
+
+    // Syntax sugar
+    const plaidml::edsl::Tensor& inTensor(int input)
+    {
+        return inArg<plaidml::edsl::Tensor>(input);
+    }
+
+    plaidml::edsl::Tensor& outTensor(int output)
+    {
+        return *(m_results.at(output).get<plaidml::edsl::Tensor*>());
+    }
+
+    std::vector<GArg> m_args;
+    std::unordered_map<std::size_t, GArg> m_results;
+};
+
+class GAPI_EXPORTS GPlaidMLKernel
+{
+public:
+    using F = std::function<void(GPlaidMLContext &)>;
+
+    GPlaidMLKernel() = default;
+    explicit GPlaidMLKernel(const F& f) : m_f(f) {}
+
+    void apply(GPlaidMLContext &ctx) const
+    {
+        GAPI_Assert(m_f);
+        m_f(ctx);
+    }
+
+protected:
+    F m_f;
+};
+
+
+namespace detail
+{
+
+template<class T> struct plaidml_get_in;
+template<> struct plaidml_get_in<cv::GMat>
+{
+    static const plaidml::edsl::Tensor& get(GPlaidMLContext& ctx, int idx)
+    {
+        return ctx.inTensor(idx);
+    }
+};
+
+template<class T> struct plaidml_get_in
+{
+    static T get(GPlaidMLContext &ctx, int idx) { return ctx.inArg<T>(idx); }
+};
+
+template<class T> struct plaidml_get_out;
+template<> struct plaidml_get_out<cv::GMat>
+{
+    static plaidml::edsl::Tensor& get(GPlaidMLContext& ctx, int idx)
+    {
+        return ctx.outTensor(idx);
+    }
+};
+
+template<typename, typename, typename>
+struct PlaidMLCallHelper;
+
+template<typename Impl, typename... Ins, typename... Outs>
+struct PlaidMLCallHelper<Impl, std::tuple<Ins...>, std::tuple<Outs...> >
+{
+    template<int... IIs, int... OIs>
+    static void call_impl(GPlaidMLContext &ctx, detail::Seq<IIs...>, detail::Seq<OIs...>)
+    {
+        Impl::run(plaidml_get_in<Ins>::get(ctx, IIs)..., plaidml_get_out<Outs>::get(ctx, OIs)...);
+    }
+
+    static void call(GPlaidMLContext& ctx)
+    {
+        call_impl(ctx,
+                  typename detail::MkSeq<sizeof...(Ins)>::type(),
+                  typename detail::MkSeq<sizeof...(Outs)>::type());
+    }
+};
+
+} // namespace detail
+
+template<class Impl, class K>
+class GPlaidMLKernelImpl: public cv::detail::PlaidMLCallHelper<Impl, typename K::InArgs, typename K::OutArgs>,
+                          public cv::detail::KernelTag
+{
+    using P = detail::PlaidMLCallHelper<Impl, typename K::InArgs, typename K::OutArgs>;
+
+public:
+    using API = K;
+
+    static cv::gapi::GBackend backend()  { return cv::gapi::plaidml::backend(); }
+    static cv::GPlaidMLKernel kernel()   { return GPlaidMLKernel(&P::call);     }
+};
+
+#define GAPI_PLAIDML_KERNEL(Name, API) struct Name: public cv::GPlaidMLKernelImpl<Name, API>
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_GPLAIDMLKERNEL_HPP
diff --git a/3rdParty/include/opencv2/gapi/plaidml/plaidml.hpp b/3rdParty/include/opencv2/gapi/plaidml/plaidml.hpp
new file mode 100644
index 0000000..3207a8c
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/plaidml/plaidml.hpp
@@ -0,0 +1,53 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_PLAIDML_PLAIDML_HPP
+#define OPENCV_GAPI_PLAIDML_PLAIDML_HPP
+
+#include <string>
+#include <opencv2/gapi/gcommon.hpp> // CompileArgTag
+
+namespace cv
+{
+namespace gapi
+{
+
+/**
+ * @brief This namespace contains G-API PlaidML backend functions,
+ * structures, and symbols.
+ */
+namespace plaidml
+{
+
+/** \addtogroup gapi_compile_args
+ * @{
+ */
+/**
+ * @brief This structure represents the basic parameters for the experimental
+ * PlaidML backend.
+ */
+struct config
+{
+    std::string dev_id; //!< Device ID. Refer to PlaidML documentation for details.
+    std::string trg_id; //!< Target ID. Refer to PlaidML documentation for details.
+};
+/** @} gapi_compile_args */
+
+} // namespace plaidml
+} // namespace gapi
+
+namespace detail
+{
+    template<> struct CompileArgTag<cv::gapi::plaidml::config>
+    {
+        static const char* tag() { return "gapi.plaidml.config"; }
+    };
+} // namespace detail
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_PLAIDML_PLAIDML_HPP
diff --git a/3rdParty/include/opencv2/gapi/python/python.hpp b/3rdParty/include/opencv2/gapi/python/python.hpp
new file mode 100644
index 0000000..0e20bbb
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/python/python.hpp
@@ -0,0 +1,67 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_PYTHON_API_HPP
+#define OPENCV_GAPI_PYTHON_API_HPP
+
+#include <opencv2/gapi/gkernel.hpp>     // GKernelPackage
+#include <opencv2/gapi/own/exports.hpp> // GAPI_EXPORTS
+
+namespace cv {
+namespace gapi {
+
+/**
+ * @brief This namespace contains G-API Python backend functions,
+ * structures, and symbols.
+ *
+ * This functionality is required to enable G-API custom operations
+ * and kernels when using G-API from Python, no need to use it in the
+ * C++ form.
+ */
+namespace python {
+
+GAPI_EXPORTS cv::gapi::GBackend backend();
+
+struct GPythonContext
+{
+    const cv::GArgs      &ins;
+    const cv::GMetaArgs  &in_metas;
+    const cv::GTypesInfo &out_info;
+};
+
+using Impl = std::function<cv::GRunArgs(const GPythonContext&)>;
+
+class GAPI_EXPORTS GPythonKernel
+{
+public:
+    GPythonKernel() = default;
+    GPythonKernel(Impl run);
+
+    cv::GRunArgs operator()(const GPythonContext& ctx);
+private:
+    Impl m_run;
+};
+
+class GAPI_EXPORTS GPythonFunctor : public cv::gapi::GFunctor
+{
+public:
+    using Meta = cv::GKernel::M;
+
+    GPythonFunctor(const char* id, const Meta &meta, const Impl& impl);
+
+    GKernelImpl    impl()    const override;
+    gapi::GBackend backend() const override;
+
+private:
+    GKernelImpl impl_;
+};
+
+} // namespace python
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_PYTHON_API_HPP
diff --git a/3rdParty/include/opencv2/gapi/render.hpp b/3rdParty/include/opencv2/gapi/render.hpp
new file mode 100644
index 0000000..52e55b0
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/render.hpp
@@ -0,0 +1,14 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+#ifndef OPENCV_GAPI_RENDER_ROOT_HPP
+#define OPENCV_GAPI_RENDER_ROOT_HPP
+
+// This file is just a shortcut to render/render.hpp
+
+#include <opencv2/gapi/render/render.hpp>
+
+#endif // OPENCV_GAPI_RENDER_ROOT_HPP
diff --git a/3rdParty/include/opencv2/gapi/render/render.hpp b/3rdParty/include/opencv2/gapi/render/render.hpp
new file mode 100644
index 0000000..5375412
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/render/render.hpp
@@ -0,0 +1,196 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_RENDER_HPP
+#define OPENCV_GAPI_RENDER_HPP
+
+#include <opencv2/gapi/render/render_types.hpp>
+
+#include <opencv2/gapi.hpp>
+
+/** \defgroup gapi_draw G-API Drawing and composition functionality
+ *  @{
+ *
+ *  @brief Functions for in-graph drawing.
+ *
+ *  @note This is a Work in Progress functionality and APIs may
+ *  change in the future releases.
+ *
+ *  G-API can do some in-graph drawing with a generic operations and a
+ *  set of [rendering primitives](@ref gapi_draw_prims).
+ *  In contrast with traditional OpenCV, in G-API user need to form a
+ *  *rendering list* of primitives to draw. This list can be built
+ *  manually or generated within a graph. This list is passed to
+ *  [special operations or functions](@ref gapi_draw_api) where all
+ *  primitives are interpreted and applied to the image.
+ *
+ *  For example, in a complex pipeline a list of detected objects
+ *  can be translated in-graph to a list of cv::gapi::wip::draw::Rect
+ *  primitives to highlight those with bounding boxes, or a list of
+ *  detected faces can be translated in-graph to a list of
+ *  cv::gapi::wip::draw::Mosaic primitives to hide sensitive content
+ *  or protect privacy.
+ *
+ *  Like any other operations, rendering in G-API can be reimplemented
+ *  by different backends. Currently only an OpenCV-based backend is
+ *  available.
+ *
+ *  In addition to the graph-level operations, there are also regular
+ *  (immediate) OpenCV-like functions are available -- see
+ *  cv::gapi::wip::draw::render(). These functions are just wrappers
+ *  over regular G-API and build the rendering graphs on the fly, so
+ *  take compilation arguments as parameters.
+ *
+ *  Currently this API is more machine-oriented than human-oriented.
+ *  The main purpose is to translate a set of domain-specific objects
+ *  to a list of primitives to draw. For example, in order to generate
+ *  a picture like this:
+ *
+ *  ![](modules/gapi/doc/pics/render_example.png)
+ *
+ *  Rendering list needs to be generated as follows:
+ *
+ *  @include modules/gapi/samples/draw_example.cpp
+ *
+ *  @defgroup gapi_draw_prims Drawing primitives
+ *  @defgroup gapi_draw_api Drawing operations and functions
+ *  @}
+ */
+
+namespace cv
+{
+namespace gapi
+{
+namespace wip
+{
+namespace draw
+{
+
+using GMat2     = std::tuple<cv::GMat,cv::GMat>;
+using GMatDesc2 = std::tuple<cv::GMatDesc,cv::GMatDesc>;
+
+//! @addtogroup gapi_draw_api
+//! @{
+/** @brief The function renders on the input image passed drawing primitivies
+
+@param bgr input image: 8-bit unsigned 3-channel image @ref CV_8UC3.
+@param prims vector of drawing primitivies
+@param args graph compile time parameters
+*/
+void GAPI_EXPORTS_W render(cv::Mat& bgr,
+                           const Prims& prims,
+                           cv::GCompileArgs&& args = {});
+
+/** @brief The function renders on two NV12 planes passed drawing primitivies
+
+@param y_plane input image: 8-bit unsigned 1-channel image @ref CV_8UC1.
+@param uv_plane input image: 8-bit unsigned 2-channel image @ref CV_8UC2.
+@param prims vector of drawing primitivies
+@param args graph compile time parameters
+*/
+void GAPI_EXPORTS_W render(cv::Mat& y_plane,
+                           cv::Mat& uv_plane,
+                           const Prims& prims,
+                           cv::GCompileArgs&& args = {});
+
+/** @brief The function renders on the input media frame passed drawing primitivies
+
+@param frame input Media Frame :  @ref cv::MediaFrame.
+@param prims vector of drawing primitivies
+@param args graph compile time parameters
+*/
+void GAPI_EXPORTS render(cv::MediaFrame& frame,
+                         const Prims& prims,
+                         cv::GCompileArgs&& args = {});
+
+
+G_TYPED_KERNEL_M(GRenderNV12, <GMat2(cv::GMat,cv::GMat,cv::GArray<wip::draw::Prim>)>, "org.opencv.render.nv12")
+{
+     static GMatDesc2 outMeta(GMatDesc y_plane, GMatDesc uv_plane, GArrayDesc)
+     {
+         return std::make_tuple(y_plane, uv_plane);
+     }
+};
+
+G_TYPED_KERNEL(GRenderBGR, <cv::GMat(cv::GMat,cv::GArray<wip::draw::Prim>)>, "org.opencv.render.bgr")
+{
+     static GMatDesc outMeta(GMatDesc bgr, GArrayDesc)
+     {
+         return bgr;
+     }
+};
+
+G_TYPED_KERNEL(GRenderFrame, <cv::GFrame(cv::GFrame, cv::GArray<wip::draw::Prim>)>, "org.opencv.render.frame")
+{
+    static GFrameDesc outMeta(GFrameDesc desc, GArrayDesc)
+    {
+        return desc;
+    }
+};
+
+/** @brief Renders on 3 channels input
+
+Output image must be 8-bit unsigned planar 3-channel image
+
+@param src input image: 8-bit unsigned 3-channel image @ref CV_8UC3
+@param prims draw primitives
+*/
+GAPI_EXPORTS_W GMat render3ch(const GMat& src, const GArray<Prim>& prims);
+
+/** @brief Renders on two planes
+
+Output y image must be 8-bit unsigned planar 1-channel image @ref CV_8UC1
+uv image must be 8-bit unsigned planar 2-channel image @ref CV_8UC2
+
+@param y  input image: 8-bit unsigned 1-channel image @ref CV_8UC1
+@param uv input image: 8-bit unsigned 2-channel image @ref CV_8UC2
+@param prims draw primitives
+*/
+GAPI_EXPORTS_W GMat2 renderNV12(const GMat& y,
+                                const GMat& uv,
+                                const GArray<Prim>& prims);
+
+/** @brief Renders Media Frame
+
+Output media frame frame cv::MediaFrame
+
+@param m_frame input image: cv::MediaFrame @ref cv::MediaFrame
+@param prims draw primitives
+*/
+GAPI_EXPORTS GFrame renderFrame(const GFrame& m_frame,
+                                const GArray<Prim>& prims);
+
+//! @} gapi_draw_api
+
+} // namespace draw
+} // namespace wip
+
+/**
+ * @brief This namespace contains G-API CPU rendering backend functions,
+ * structures, and symbols. See @ref gapi_draw for details.
+ */
+namespace render
+{
+namespace ocv
+{
+    GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels();
+
+} // namespace ocv
+} // namespace render
+} // namespace gapi
+
+namespace detail
+{
+    template<> struct CompileArgTag<cv::gapi::wip::draw::freetype_font>
+    {
+        static const char* tag() { return "gapi.freetype_font"; }
+    };
+} // namespace detail
+
+} // namespace cv
+
+#endif // OPENCV_GAPI_RENDER_HPP
diff --git a/3rdParty/include/opencv2/gapi/render/render_types.hpp b/3rdParty/include/opencv2/gapi/render/render_types.hpp
new file mode 100644
index 0000000..6d70e3a
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/render/render_types.hpp
@@ -0,0 +1,359 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_RENDER_TYPES_HPP
+#define OPENCV_GAPI_RENDER_TYPES_HPP
+
+#include <string>
+#include <vector>
+
+#include <opencv2/gapi/opencv_includes.hpp>
+#include <opencv2/gapi/util/variant.hpp>
+#include <opencv2/gapi/own/exports.hpp>
+
+namespace cv
+{
+namespace gapi
+{
+namespace wip
+{
+namespace draw
+{
+
+/**
+ * @brief This structure specifies which FreeType font to use by FText primitives.
+ */
+struct freetype_font
+{
+    /*@{*/
+    std::string path; //!< The path to the font file (.ttf)
+    /*@{*/
+};
+
+//! @addtogroup gapi_draw_prims
+//! @{
+/**
+ * @brief This structure represents a text string to draw.
+ *
+ * Parameters match cv::putText().
+ */
+struct GAPI_EXPORTS_W_SIMPLE Text
+{
+    /**
+     * @brief Text constructor
+     *
+     * @param text_               The text string to be drawn
+     * @param org_                The bottom-left corner of the text string in the image
+     * @param ff_                 The font type, see #HersheyFonts
+     * @param fs_                 The font scale factor that is multiplied by the font-specific base size
+     * @param color_              The text color
+     * @param thick_              The thickness of the lines used to draw a text
+     * @param lt_                 The line type. See #LineTypes
+     * @param bottom_left_origin_ When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner
+     */
+    GAPI_WRAP
+    Text(const std::string& text_,
+         const cv::Point& org_,
+         int ff_,
+         double fs_,
+         const cv::Scalar& color_,
+         int thick_ = 1,
+         int lt_ = 8,
+         bool bottom_left_origin_ = false) :
+        text(text_), org(org_), ff(ff_), fs(fs_),
+        color(color_), thick(thick_), lt(lt_), bottom_left_origin(bottom_left_origin_)
+    {
+    }
+
+    GAPI_WRAP
+    Text() = default;
+
+    /*@{*/
+    GAPI_PROP_RW std::string text;               //!< The text string to be drawn
+    GAPI_PROP_RW cv::Point   org;                //!< The bottom-left corner of the text string in the image
+    GAPI_PROP_RW int         ff;                 //!< The font type, see #HersheyFonts
+    GAPI_PROP_RW double      fs;                 //!< The font scale factor that is multiplied by the font-specific base size
+    GAPI_PROP_RW cv::Scalar  color;              //!< The text color
+    GAPI_PROP_RW int         thick;              //!< The thickness of the lines used to draw a text
+    GAPI_PROP_RW int         lt;                 //!< The line type. See #LineTypes
+    GAPI_PROP_RW bool        bottom_left_origin; //!< When true, the image data origin is at the bottom-left corner. Otherwise, it is at the top-left corner
+    /*@{*/
+};
+
+/**
+ * @brief This structure represents a text string to draw using
+ * FreeType renderer.
+ *
+ * If OpenCV is built without FreeType support, this primitive will
+ * fail at the execution stage.
+ */
+struct FText
+{
+    /**
+     * @brief FText constructor
+     *
+     * @param text_ The text string to be drawn
+     * @param org_  The bottom-left corner of the text string in the image
+     * @param fh_   The height of text
+     * @param color_ The text color
+     */
+    FText(const std::wstring& text_,
+          const cv::Point& org_,
+          int fh_,
+          const cv::Scalar& color_) :
+        text(text_), org(org_), fh(fh_), color(color_)
+    {
+    }
+
+    FText() = default;
+
+    /*@{*/
+    std::wstring text;              //!< The text string to be drawn
+    cv::Point    org;               //!< The bottom-left corner of the text string in the image
+    int          fh;                //!< The height of text
+    cv::Scalar   color;             //!< The text color
+    /*@{*/
+};
+
+/**
+ * @brief This structure represents a rectangle to draw.
+ *
+ * Parameters match cv::rectangle().
+ */
+struct GAPI_EXPORTS_W_SIMPLE Rect
+{
+    /**
+     * @brief Rect constructor
+     *
+     * @param rect_   Coordinates of the rectangle
+     * @param color_  The bottom-left corner of the text string in the image
+     * @param thick_  The thickness of lines that make up the rectangle. Negative values, like #FILLED, mean that the function has to draw a filled rectangle
+     * @param lt_     The type of the line. See #LineTypes
+     * @param shift_  The number of fractional bits in the point coordinates
+     */
+    Rect(const cv::Rect& rect_,
+         const cv::Scalar& color_,
+         int thick_ = 1,
+         int lt_ = 8,
+         int shift_ = 0) :
+        rect(rect_), color(color_), thick(thick_), lt(lt_), shift(shift_)
+    {
+    }
+
+    GAPI_WRAP
+    Rect() = default;
+
+    /*@{*/
+    GAPI_PROP_RW cv::Rect   rect;  //!< Coordinates of the rectangle
+    GAPI_PROP_RW cv::Scalar color; //!< The rectangle color or brightness (grayscale image)
+    GAPI_PROP_RW int        thick; //!< The thickness of lines that make up the rectangle. Negative values, like #FILLED, mean that the function has to draw a filled rectangle
+    GAPI_PROP_RW int        lt;    //!< The type of the line. See #LineTypes
+    GAPI_PROP_RW int        shift; //!< The number of fractional bits in the point coordinates
+    /*@{*/
+};
+
+/**
+ * @brief This structure represents a circle to draw.
+ *
+ * Parameters match cv::circle().
+ */
+struct GAPI_EXPORTS_W_SIMPLE Circle
+{
+    /**
+     * @brief Circle constructor
+     *
+     * @param  center_ The center of the circle
+     * @param  radius_ The radius of the circle
+     * @param  color_  The color of the  circle
+     * @param  thick_  The thickness of the circle outline, if positive. Negative values, like #FILLED, mean that a filled circle is to be drawn
+     * @param  lt_     The Type of the circle boundary. See #LineTypes
+     * @param  shift_  The Number of fractional bits in the coordinates of the center and in the radius value
+     */
+    GAPI_WRAP
+    Circle(const cv::Point& center_,
+           int radius_,
+           const cv::Scalar& color_,
+           int thick_ = 1,
+           int lt_ = 8,
+           int shift_ = 0) :
+        center(center_), radius(radius_), color(color_), thick(thick_), lt(lt_), shift(shift_)
+    {
+    }
+
+    GAPI_WRAP
+    Circle() = default;
+
+    /*@{*/
+    GAPI_PROP_RW cv::Point  center; //!< The center of the circle
+    GAPI_PROP_RW int        radius; //!< The radius of the circle
+    GAPI_PROP_RW cv::Scalar color;  //!< The color of the  circle
+    GAPI_PROP_RW int        thick;  //!< The thickness of the circle outline, if positive. Negative values, like #FILLED, mean that a filled circle is to be drawn
+    GAPI_PROP_RW int        lt;     //!< The Type of the circle boundary. See #LineTypes
+    GAPI_PROP_RW int        shift;  //!< The Number of fractional bits in the coordinates of the center and in the radius value
+    /*@{*/
+};
+
+/**
+ * @brief This structure represents a line to draw.
+ *
+ * Parameters match cv::line().
+ */
+struct GAPI_EXPORTS_W_SIMPLE Line
+{
+    /**
+     * @brief Line constructor
+     *
+     * @param  pt1_    The first point of the line segment
+     * @param  pt2_    The second point of the line segment
+     * @param  color_  The line color
+     * @param  thick_  The thickness of line
+     * @param  lt_     The Type of the line. See #LineTypes
+     * @param  shift_  The number of fractional bits in the point coordinates
+    */
+    GAPI_WRAP
+    Line(const cv::Point& pt1_,
+         const cv::Point& pt2_,
+         const cv::Scalar& color_,
+         int thick_ = 1,
+         int lt_ = 8,
+         int shift_ = 0) :
+        pt1(pt1_), pt2(pt2_), color(color_), thick(thick_), lt(lt_), shift(shift_)
+    {
+    }
+
+    GAPI_WRAP
+    Line() = default;
+
+    /*@{*/
+    GAPI_PROP_RW cv::Point  pt1;    //!< The first point of the line segment
+    GAPI_PROP_RW cv::Point  pt2;    //!< The second point of the line segment
+    GAPI_PROP_RW cv::Scalar color;  //!< The line color
+    GAPI_PROP_RW int        thick;  //!< The thickness of line
+    GAPI_PROP_RW int        lt;     //!< The Type of the line. See #LineTypes
+    GAPI_PROP_RW int        shift;  //!< The number of fractional bits in the point coordinates
+    /*@{*/
+};
+
+/**
+ * @brief This structure represents a mosaicing operation.
+ *
+ * Mosaicing is a very basic method to obfuscate regions in the image.
+ */
+struct GAPI_EXPORTS_W_SIMPLE Mosaic
+{
+    /**
+     * @brief Mosaic constructor
+     *
+     * @param mos_    Coordinates of the mosaic
+     * @param cellSz_ Cell size (same for X, Y)
+     * @param decim_  Decimation (0 stands for no decimation)
+    */
+    Mosaic(const cv::Rect& mos_,
+           int cellSz_,
+           int decim_) :
+        mos(mos_), cellSz(cellSz_), decim(decim_)
+    {
+    }
+
+    GAPI_WRAP
+    Mosaic() : cellSz(0), decim(0) {}
+
+    /*@{*/
+    GAPI_PROP_RW cv::Rect mos;    //!< Coordinates of the mosaic
+    GAPI_PROP_RW int      cellSz; //!< Cell size (same for X, Y)
+    GAPI_PROP_RW int      decim;  //!< Decimation (0 stands for no decimation)
+    /*@{*/
+};
+
+/**
+ * @brief This structure represents an image to draw.
+ *
+ * Image is blended on a frame using the specified mask.
+ */
+struct GAPI_EXPORTS_W_SIMPLE Image
+{
+    /**
+     * @brief Mosaic constructor
+     *
+     * @param  org_   The bottom-left corner of the image
+     * @param  img_   Image to draw
+     * @param  alpha_ Alpha channel for image to draw (same size and number of channels)
+    */
+    GAPI_WRAP
+    Image(const cv::Point& org_,
+          const cv::Mat& img_,
+          const cv::Mat& alpha_) :
+        org(org_), img(img_), alpha(alpha_)
+    {
+    }
+
+    GAPI_WRAP
+    Image() = default;
+
+    /*@{*/
+    GAPI_PROP_RW cv::Point org;   //!< The bottom-left corner of the image
+    GAPI_PROP_RW cv::Mat   img;   //!< Image to draw
+    GAPI_PROP_RW cv::Mat   alpha; //!< Alpha channel for image to draw (same size and number of channels)
+    /*@{*/
+};
+
+/**
+ * @brief This structure represents a polygon to draw.
+ */
+struct GAPI_EXPORTS_W_SIMPLE Poly
+{
+    /**
+     * @brief Mosaic constructor
+     *
+     * @param points_ Points to connect
+     * @param color_  The line color
+     * @param thick_  The thickness of line
+     * @param lt_     The Type of the line. See #LineTypes
+     * @param shift_  The number of fractional bits in the point coordinate
+    */
+    GAPI_WRAP
+    Poly(const std::vector<cv::Point>& points_,
+         const cv::Scalar& color_,
+         int thick_ = 1,
+         int lt_ = 8,
+         int shift_ = 0) :
+        points(points_), color(color_), thick(thick_), lt(lt_), shift(shift_)
+    {
+    }
+
+    GAPI_WRAP
+    Poly() = default;
+
+    /*@{*/
+    GAPI_PROP_RW std::vector<cv::Point> points;  //!< Points to connect
+    GAPI_PROP_RW cv::Scalar             color;   //!< The line color
+    GAPI_PROP_RW int                    thick;   //!< The thickness of line
+    GAPI_PROP_RW int                    lt;      //!< The Type of the line. See #LineTypes
+    GAPI_PROP_RW int                    shift;   //!< The number of fractional bits in the point coordinate
+    /*@{*/
+};
+
+using Prim  = util::variant
+    < Text
+    , FText
+    , Rect
+    , Circle
+    , Line
+    , Mosaic
+    , Image
+    , Poly
+    >;
+
+using Prims = std::vector<Prim>;
+//! @} gapi_draw_prims
+
+} // namespace draw
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_RENDER_TYPES_HPP
diff --git a/3rdParty/include/opencv2/gapi/rmat.hpp b/3rdParty/include/opencv2/gapi/rmat.hpp
new file mode 100644
index 0000000..38668d6
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/rmat.hpp
@@ -0,0 +1,160 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+#ifndef OPENCV_GAPI_RMAT_HPP
+#define OPENCV_GAPI_RMAT_HPP
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/own/exports.hpp>
+
+// Forward declaration
+namespace cv {
+namespace gapi {
+namespace s11n {
+struct IOStream;
+struct IIStream;
+} // namespace s11n
+} // namespace gapi
+} // namespace cv
+
+namespace cv {
+
+// "Remote Mat", a general class which provides an abstraction layer over the data
+// storage and placement (host, remote device etc) and allows to access this data.
+//
+// The device specific implementation is hidden in the RMat::IAdapter class
+//
+// The basic flow is the following:
+// * Backend which is aware of the remote device:
+//   - Implements own AdapterT class which is derived from RMat::IAdapter
+//   - Wraps device memory into RMat via make_rmat utility function:
+//         cv::RMat rmat = cv::make_rmat<AdapterT>(args);
+//
+// * End user:
+//   - Writes the code which works with RMats without any knowledge of the remote device:
+//     void func(const cv::RMat& in_rmat, cv::RMat& out_rmat) {
+//         // Fetch input data from the device, get mapped memory for output
+//         cv::RMat::View  in_view =  in_rmat.access(Access::R);
+//         cv::RMat::View out_view = out_rmat.access(Access::W);
+//         performCalculations(in_view, out_view);
+//         // data from out_view is transferred to the device when out_view is destroyed
+//     }
+/** \addtogroup gapi_data_structures
+ * @{
+ */
+class GAPI_EXPORTS RMat
+{
+public:
+    // A lightweight wrapper on image data:
+    // - Doesn't own the memory;
+    // - Doesn't implement copy semantics (it's assumed that a view is created each time
+    // wrapped data is being accessed);
+    // - Has an optional callback which is called when the view is destroyed.
+    class GAPI_EXPORTS View
+    {
+    public:
+        using DestroyCallback = std::function<void()>;
+        using stepsT = std::vector<size_t>;
+
+        View() = default;
+        View(const GMatDesc& desc, uchar* data, const stepsT& steps = {}, DestroyCallback&& cb = nullptr);
+        View(const GMatDesc& desc, uchar* data, size_t step, DestroyCallback&& cb = nullptr);
+
+        View(const View&) = delete;
+        View& operator=(const View&) = delete;
+        View(View&&) = default;
+        View& operator=(View&& v);
+        ~View() { if (m_cb) m_cb(); }
+
+        cv::Size size() const { return m_desc.size; }
+        const std::vector<int>& dims() const { return m_desc.dims; }
+        int cols() const { return m_desc.size.width; }
+        int rows() const { return m_desc.size.height; }
+        int type() const;
+        int depth() const { return m_desc.depth; }
+        int chan() const { return m_desc.chan; }
+        size_t elemSize() const { return CV_ELEM_SIZE(type()); }
+
+        template<typename T = uchar> T* ptr(int y = 0) {
+            return reinterpret_cast<T*>(m_data + step()*y);
+        }
+        template<typename T = uchar> const T* ptr(int y = 0) const {
+            return reinterpret_cast<T*>(m_data + step()*y);
+        }
+        template<typename T = uchar> T* ptr(int y, int x) {
+            return reinterpret_cast<T*>(m_data + step()*y + step(1)*x);
+        }
+        template<typename T = uchar> const T* ptr(int y, int x) const {
+            return reinterpret_cast<const T*>(m_data + step()*y + step(1)*x);
+        }
+        size_t step(size_t i = 0) const { GAPI_DbgAssert(i<m_steps.size()); return m_steps[i]; }
+        const stepsT& steps() const { return m_steps; }
+
+    private:
+        GMatDesc m_desc;
+        uchar* m_data = nullptr;
+        stepsT m_steps = {0u};
+        DestroyCallback m_cb = nullptr;
+    };
+
+    enum class Access { R, W };
+    class IAdapter
+    // Adapter class is going to be deleted and renamed as IAdapter
+    {
+    public:
+        virtual ~IAdapter() = default;
+        virtual GMatDesc desc() const = 0;
+        // Implementation is responsible for setting the appropriate callback to
+        // the view when accessed for writing, to ensure that the data from the view
+        // is transferred to the device when the view is destroyed
+        virtual View access(Access) = 0;
+        virtual void serialize(cv::gapi::s11n::IOStream&) {
+            GAPI_Assert(false && "Generic serialize method of RMat::IAdapter does nothing by default. "
+                                 "Please, implement it in derived class to properly serialize the object.");
+        }
+        virtual void deserialize(cv::gapi::s11n::IIStream&) {
+            GAPI_Assert(false && "Generic deserialize method of RMat::IAdapter does nothing by default. "
+                                 "Please, implement it in derived class to properly deserialize the object.");
+        }
+    };
+    using Adapter = IAdapter; // Keep backward compatibility
+    using AdapterP = std::shared_ptr<IAdapter>;
+
+    RMat() = default;
+    RMat(AdapterP&& a) : m_adapter(std::move(a)) {}
+    GMatDesc desc() const { return m_adapter->desc(); }
+
+    // Note: When accessed for write there is no guarantee that returned view
+    // will contain actual snapshot of the mapped device memory
+    // (no guarantee that fetch from a device is performed). The only
+    // guaranty is that when the view is destroyed, its data will be
+    // transferred to the device
+    View access(Access a) const { return m_adapter->access(a); }
+
+    // Cast underlying RMat adapter to the particular adapter type,
+    // return nullptr if underlying type is different
+    template<typename T> T* get() const
+    {
+        static_assert(std::is_base_of<IAdapter, T>::value, "T is not derived from IAdapter!");
+        GAPI_Assert(m_adapter != nullptr);
+        return dynamic_cast<T*>(m_adapter.get());
+    }
+
+    void serialize(cv::gapi::s11n::IOStream& os) const {
+        m_adapter->serialize(os);
+    }
+
+private:
+    AdapterP m_adapter = nullptr;
+};
+
+template<typename T, typename... Ts>
+RMat make_rmat(Ts&&... args) { return { std::make_shared<T>(std::forward<Ts>(args)...) }; }
+/** @} */
+
+} //namespace cv
+
+#endif /* OPENCV_GAPI_RMAT_HPP */
diff --git a/3rdParty/include/opencv2/gapi/s11n.hpp b/3rdParty/include/opencv2/gapi/s11n.hpp
new file mode 100644
index 0000000..6863a5e
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/s11n.hpp
@@ -0,0 +1,505 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020-2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_S11N_HPP
+#define OPENCV_GAPI_S11N_HPP
+
+#include <vector>
+#include <map>
+#include <unordered_map>
+#include <opencv2/gapi/s11n/base.hpp>
+#include <opencv2/gapi/gcomputation.hpp>
+#include <opencv2/gapi/rmat.hpp>
+#include <opencv2/gapi/media.hpp>
+#include <opencv2/gapi/util/util.hpp>
+
+// FIXME: caused by deserialize_runarg
+#if (defined _WIN32 || defined _WIN64) && defined _MSC_VER
+#pragma warning(disable: 4702)
+#endif
+
+namespace cv {
+namespace gapi {
+
+/**
+* \addtogroup gapi_serialization
+* @{
+*/
+
+namespace detail {
+    GAPI_EXPORTS cv::GComputation getGraph(const std::vector<char> &bytes);
+
+    GAPI_EXPORTS cv::GMetaArgs getMetaArgs(const std::vector<char> &bytes);
+
+    GAPI_EXPORTS cv::GRunArgs getRunArgs(const std::vector<char> &bytes);
+
+    GAPI_EXPORTS std::vector<std::string> getVectorOfStrings(const std::vector<char> &bytes);
+
+    template<typename... Types>
+    cv::GCompileArgs getCompileArgs(const std::vector<char> &bytes);
+
+    template<typename... AdapterType>
+    cv::GRunArgs getRunArgsWithAdapters(const std::vector<char> &bytes);
+} // namespace detail
+
+/** @brief Serialize a graph represented by GComputation into an array of bytes.
+ *
+ * Check different overloads for more examples.
+ * @param c GComputation to serialize.
+ * @return serialized vector of bytes.
+ */
+GAPI_EXPORTS std::vector<char> serialize(const cv::GComputation &c);
+
+/** @overload
+ * @param ca GCompileArgs to serialize.
+ */
+GAPI_EXPORTS std::vector<char> serialize(const cv::GCompileArgs& ca);
+
+/** @overload
+ * @param ma GMetaArgs to serialize.
+ */
+GAPI_EXPORTS std::vector<char> serialize(const cv::GMetaArgs& ma);
+
+/** @overload
+ * @param ra GRunArgs to serialize.
+ */
+GAPI_EXPORTS std::vector<char> serialize(const cv::GRunArgs& ra);
+
+/** @overload
+ * @param vs std::vector<std::string> to serialize.
+ */
+GAPI_EXPORTS std::vector<char> serialize(const std::vector<std::string>& vs);
+
+/**
+ * @private
+ */
+template<typename T> static inline
+T deserialize(const std::vector<char> &bytes);
+
+/** @brief Deserialize GComputation from a byte array.
+ *
+ * Check different overloads for more examples.
+ * @param bytes serialized vector of bytes.
+ * @return deserialized GComputation object.
+ */
+template<> inline
+cv::GComputation deserialize(const std::vector<char> &bytes) {
+    return detail::getGraph(bytes);
+}
+
+/** @brief Deserialize GMetaArgs from a byte array.
+ *
+ * Check different overloads for more examples.
+ * @param bytes serialized vector of bytes.
+ * @return deserialized GMetaArgs object.
+ */
+template<> inline
+cv::GMetaArgs deserialize(const std::vector<char> &bytes) {
+    return detail::getMetaArgs(bytes);
+}
+
+/** @brief Deserialize GRunArgs from a byte array.
+ *
+ * Check different overloads for more examples.
+ * @param bytes serialized vector of bytes.
+ * @return deserialized GRunArgs object.
+ */
+template<> inline
+cv::GRunArgs deserialize(const std::vector<char> &bytes) {
+    return detail::getRunArgs(bytes);
+}
+
+/** @brief Deserialize std::vector<std::string> from a byte array.
+ *
+ * Check different overloads for more examples.
+ * @param bytes serialized vector of bytes.
+ * @return deserialized std::vector<std::string> object.
+ */
+template<> inline
+std::vector<std::string> deserialize(const std::vector<char> &bytes) {
+    return detail::getVectorOfStrings(bytes);
+}
+
+/**
+ * @brief Deserialize GCompileArgs which types were specified in the template from a byte array.
+ *
+ * @note cv::gapi::s11n::detail::S11N template specialization must be provided to make a custom type
+ * in GCompileArgs deserializable.
+ *
+ * @param bytes vector of bytes to deserialize GCompileArgs object from.
+ * @return GCompileArgs object.
+ * @see GCompileArgs cv::gapi::s11n::detail::S11N
+ */
+template<typename T, typename... Types> inline
+typename std::enable_if<std::is_same<T, GCompileArgs>::value, GCompileArgs>::
+type deserialize(const std::vector<char> &bytes) {
+    return detail::getCompileArgs<Types...>(bytes);
+}
+
+/**
+ * @brief Deserialize GRunArgs including RMat and MediaFrame objects if any from a byte array.
+ *
+ * Adapter types are specified in the template.
+ * @note To be used properly specified adapter types must overload their deserialize() method.
+ * @param bytes vector of bytes to deserialize GRunArgs object from.
+ * @return GRunArgs including RMat and MediaFrame objects if any.
+ * @see RMat MediaFrame
+ */
+template<typename T, typename AtLeastOneAdapterT, typename... AdapterTypes> inline
+typename std::enable_if<std::is_same<T, GRunArgs>::value, GRunArgs>::
+type deserialize(const std::vector<char> &bytes) {
+    return detail::getRunArgsWithAdapters<AtLeastOneAdapterT, AdapterTypes...>(bytes);
+}
+} // namespace gapi
+} // namespace cv
+
+namespace cv {
+namespace gapi {
+namespace s11n {
+
+/** @brief This structure is an interface for serialization routines.
+ *
+ * It's main purpose is to provide multiple overloads for operator<<()
+ * with basic C++ in addition to OpenCV/G-API types.
+ *
+ * This sctructure can be inherited and further extended with additional types.
+ *
+ * For example, it is utilized in cv::gapi::s11n::detail::S11N as input parameter
+ * in serialize() method.
+ */
+struct GAPI_EXPORTS IOStream {
+    virtual ~IOStream() = default;
+    // Define the native support for basic C++ types at the API level:
+    virtual IOStream& operator<< (bool) = 0;
+    virtual IOStream& operator<< (char) = 0;
+    virtual IOStream& operator<< (unsigned char) = 0;
+    virtual IOStream& operator<< (short) = 0;
+    virtual IOStream& operator<< (unsigned short) = 0;
+    virtual IOStream& operator<< (int) = 0;
+    virtual IOStream& operator<< (uint32_t) = 0;
+    virtual IOStream& operator<< (uint64_t) = 0;
+    virtual IOStream& operator<< (float) = 0;
+    virtual IOStream& operator<< (double) = 0;
+    virtual IOStream& operator<< (const std::string&) = 0;
+};
+
+/** @brief This structure is an interface for deserialization routines.
+ *
+ * It's main purpose is to provide multiple overloads for operator>>()
+ * with basic C++ in addition to OpenCV/G-API types.
+ *
+ * This structure can be inherited and further extended with additional types.
+ *
+ * For example, it is utilized in cv::gapi::s11n::detail::S11N as input parameter
+ * in deserialize() method.
+ */
+struct GAPI_EXPORTS IIStream {
+    virtual ~IIStream() = default;
+    virtual IIStream& operator>> (bool &) = 0;
+    virtual IIStream& operator>> (std::vector<bool>::reference) = 0;
+    virtual IIStream& operator>> (char &) = 0;
+    virtual IIStream& operator>> (unsigned char &) = 0;
+    virtual IIStream& operator>> (short &) = 0;
+    virtual IIStream& operator>> (unsigned short &) = 0;
+    virtual IIStream& operator>> (int &) = 0;
+    virtual IIStream& operator>> (float &) = 0;
+    virtual IIStream& operator>> (double &) = 0;
+    virtual IIStream& operator >> (uint32_t &) = 0;
+    virtual IIStream& operator >> (uint64_t &) = 0;
+    virtual IIStream& operator>> (std::string &) = 0;
+};
+
+namespace detail {
+GAPI_EXPORTS std::unique_ptr<IIStream> getInStream(const std::vector<char> &bytes);
+} // namespace detail
+
+////////////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////
+// S11N operators
+// Note: operators for basic types are defined in IIStream/IOStream
+
+// OpenCV types ////////////////////////////////////////////////////////////////
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::Point &pt);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::Point &pt);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::Point2f &pt);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::Point2f &pt);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::Size &sz);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::Size &sz);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::Rect &rc);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::Rect &rc);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::Scalar &s);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::Scalar &s);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::Mat &m);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::Mat &m);
+
+// FIXME: for GRunArgs serailization
+#if !defined(GAPI_STANDALONE)
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::UMat & um);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::UMat & um);
+#endif // !defined(GAPI_STANDALONE)
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::RMat &r);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::RMat &r);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::gapi::wip::IStreamSource::Ptr &issptr);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::gapi::wip::IStreamSource::Ptr &issptr);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::detail::VectorRef &vr);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::detail::VectorRef &vr);
+
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::detail::OpaqueRef &opr);
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::detail::OpaqueRef &opr);
+
+/// @private -- Exclude this function from OpenCV documentation
+GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::MediaFrame &mf);
+/// @private -- Exclude this function from OpenCV documentation
+GAPI_EXPORTS IIStream& operator>> (IIStream& is,       cv::MediaFrame &mf);
+
+// Generic STL types ////////////////////////////////////////////////////////////////
+template<typename K, typename V>
+IOStream& operator<< (IOStream& os, const std::map<K, V> &m) {
+    const uint32_t sz = static_cast<uint32_t>(m.size());
+    os << sz;
+    for (const auto& it : m) os << it.first << it.second;
+    return os;
+}
+template<typename K, typename V>
+IIStream& operator>> (IIStream& is, std::map<K, V> &m) {
+    m.clear();
+    uint32_t sz = 0u;
+    is >> sz;
+    for (std::size_t i = 0; i < sz; ++i) {
+        K k{};
+        V v{};
+        is >> k >> v;
+        m[k] = v;
+    }
+    return is;
+}
+
+template<typename K, typename V>
+IOStream& operator<< (IOStream& os, const std::unordered_map<K, V> &m) {
+    const uint32_t sz = static_cast<uint32_t>(m.size());
+    os << sz;
+    for (auto &&it : m) os << it.first << it.second;
+    return os;
+}
+template<typename K, typename V>
+IIStream& operator>> (IIStream& is, std::unordered_map<K, V> &m) {
+    m.clear();
+    uint32_t sz = 0u;
+    is >> sz;
+    for (std::size_t i = 0; i < sz; ++i) {
+        K k{};
+        V v{};
+        is >> k >> v;
+        m[k] = v;
+    }
+    return is;
+}
+
+template<typename T>
+IOStream& operator<< (IOStream& os, const std::vector<T> &ts) {
+    const uint32_t sz = static_cast<uint32_t>(ts.size());
+    os << sz;
+    for (auto &&v : ts) os << v;
+    return os;
+}
+template<typename T>
+IIStream& operator>> (IIStream& is, std::vector<T> &ts) {
+    uint32_t sz = 0u;
+    is >> sz;
+    if (sz == 0u) {
+        ts.clear();
+    }
+    else {
+        ts.resize(sz);
+        for (std::size_t i = 0; i < sz; ++i) is >> ts[i];
+    }
+    return is;
+}
+
+// Generic: variant serialization
+namespace detail {
+template<typename V>
+IOStream& put_v(IOStream&, const V&, std::size_t) {
+    GAPI_Assert(false && "variant>>: requested index is invalid");
+};
+
+template<typename V, typename X, typename... Xs>
+IOStream& put_v(IOStream& os, const V& v, std::size_t x) {
+    return (x == 0u)
+        ? os << cv::util::get<X>(v)
+        : put_v<V, Xs...>(os, v, x-1);
+}
+
+template<typename V>
+IIStream& get_v(IIStream&, V&, std::size_t, std::size_t) {
+    GAPI_Assert(false && "variant<<: requested index is invalid");
+}
+
+template<typename V, typename X, typename... Xs>
+IIStream& get_v(IIStream& is, V& v, std::size_t i, std::size_t gi) {
+    if (i == gi) {
+        X x{};
+        is >> x;
+        v = V{std::move(x)};
+        return is;
+    } else return get_v<V, Xs...>(is, v, i+1, gi);
+}
+} // namespace detail
+
+//! @overload
+template<typename... Ts>
+IOStream& operator<< (IOStream& os, const cv::util::variant<Ts...> &v) {
+    os << static_cast<uint32_t>(v.index());
+    return detail::put_v<cv::util::variant<Ts...>, Ts...>(os, v, v.index());
+}
+//! @overload
+template<typename... Ts>
+IIStream& operator>> (IIStream& is, cv::util::variant<Ts...> &v) {
+    int idx = -1;
+    is >> idx;
+    GAPI_Assert(idx >= 0 && idx < (int)sizeof...(Ts));
+    return detail::get_v<cv::util::variant<Ts...>, Ts...>(is, v, 0u, idx);
+}
+
+// FIXME: consider a better solution
+/// @private -- Exclude this function from OpenCV documentation
+template<typename... Ts>
+void getRunArgByIdx (IIStream& is, cv::util::variant<Ts...> &v, uint32_t idx) {
+    is = detail::get_v<cv::util::variant<Ts...>, Ts...>(is, v, 0u, idx);
+}
+} // namespace s11n
+
+namespace detail
+{
+template<typename T> struct try_deserialize_comparg;
+
+template<> struct try_deserialize_comparg<std::tuple<>> {
+static cv::util::optional<GCompileArg> exec(const std::string&, cv::gapi::s11n::IIStream&) {
+        return { };
+    }
+};
+
+template<typename T, typename... Types>
+struct try_deserialize_comparg<std::tuple<T, Types...>> {
+static cv::util::optional<GCompileArg> exec(const std::string& tag, cv::gapi::s11n::IIStream& is) {
+    if (tag == cv::detail::CompileArgTag<T>::tag()) {
+        static_assert(cv::gapi::s11n::detail::has_S11N_spec<T>::value,
+            "cv::gapi::deserialize<GCompileArgs, Types...> expects Types to have S11N "
+            "specializations with deserialization callbacks!");
+        return cv::util::optional<GCompileArg>(
+            GCompileArg { cv::gapi::s11n::detail::S11N<T>::deserialize(is) });
+    }
+    return try_deserialize_comparg<std::tuple<Types...>>::exec(tag, is);
+}
+};
+
+template<typename ...T>
+struct deserialize_arg_with_adapter;
+
+template<typename RA, typename TA>
+struct deserialize_arg_with_adapter<RA, TA> {
+static GRunArg exec(cv::gapi::s11n::IIStream& is) {
+    std::unique_ptr<TA> ptr(new TA);
+    ptr->deserialize(is);
+    return GRunArg { RA(std::move(ptr)) };
+}
+};
+
+template<typename RA>
+struct deserialize_arg_with_adapter<RA, void> {
+static GRunArg exec(cv::gapi::s11n::IIStream&) {
+    GAPI_Assert(false && "No suitable adapter class found during RMat/MediaFrame deserialization. "
+                         "Please, make sure you've passed them in cv::gapi::deserialize() template");
+    return GRunArg{};
+}
+};
+
+template<typename... Types>
+struct deserialize_runarg {
+static GRunArg exec(cv::gapi::s11n::IIStream& is, uint32_t idx) {
+    if (idx == GRunArg::index_of<RMat>()) {
+        // Type or void (if not found)
+        using TA = typename cv::util::find_adapter_impl<RMat::IAdapter, Types...>::type;
+        return deserialize_arg_with_adapter<RMat, TA>::exec(is);
+    } else if (idx == GRunArg::index_of<MediaFrame>()) {
+        // Type or void (if not found)
+        using TA = typename cv::util::find_adapter_impl<MediaFrame::IAdapter, Types...>::type;
+        return deserialize_arg_with_adapter<MediaFrame, TA>::exec(is);
+    } else { // not an adapter holding type runarg - use default deserialization
+        GRunArg arg;
+        getRunArgByIdx(is, arg, idx);
+        return arg;
+    }
+}
+};
+
+template<typename... Types>
+inline cv::util::optional<GCompileArg> tryDeserializeCompArg(const std::string& tag,
+                                                             const std::vector<char>& sArg) {
+    std::unique_ptr<cv::gapi::s11n::IIStream> pArgIs = cv::gapi::s11n::detail::getInStream(sArg);
+    return try_deserialize_comparg<std::tuple<Types...>>::exec(tag, *pArgIs);
+}
+
+template<typename... Types>
+cv::GCompileArgs getCompileArgs(const std::vector<char> &sArgs) {
+    cv::GCompileArgs args;
+
+    std::unique_ptr<cv::gapi::s11n::IIStream> pIs = cv::gapi::s11n::detail::getInStream(sArgs);
+    cv::gapi::s11n::IIStream& is = *pIs;
+
+    uint32_t sz = 0;
+    is >> sz;
+    for (uint32_t i = 0; i < sz; ++i) {
+        std::string tag;
+        is >> tag;
+
+        std::vector<char> sArg;
+        is >> sArg;
+
+        cv::util::optional<GCompileArg> dArg =
+            cv::gapi::detail::tryDeserializeCompArg<Types...>(tag, sArg);
+
+        if (dArg.has_value())
+        {
+            args.push_back(dArg.value());
+        }
+    }
+
+    return args;
+}
+
+template<typename... AdapterTypes>
+cv::GRunArgs getRunArgsWithAdapters(const std::vector<char> &bytes) {
+    std::unique_ptr<cv::gapi::s11n::IIStream> pIs = cv::gapi::s11n::detail::getInStream(bytes);
+    cv::gapi::s11n::IIStream& is = *pIs;
+    cv::GRunArgs args;
+
+    uint32_t sz = 0;
+    is >> sz;
+    for (uint32_t i = 0; i < sz; ++i) {
+        uint32_t idx = 0;
+        is >> idx;
+        args.push_back(cv::gapi::detail::deserialize_runarg<AdapterTypes...>::exec(is, idx));
+    }
+
+    return args;
+}
+} // namespace detail
+/** @} */
+
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_S11N_HPP
diff --git a/3rdParty/include/opencv2/gapi/s11n/base.hpp b/3rdParty/include/opencv2/gapi/s11n/base.hpp
new file mode 100644
index 0000000..6cea941
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/s11n/base.hpp
@@ -0,0 +1,80 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020-2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_S11N_BASE_HPP
+#define OPENCV_GAPI_S11N_BASE_HPP
+
+#include <opencv2/gapi/own/assert.hpp>
+#include <opencv2/gapi/own/exports.hpp>
+
+namespace cv {
+namespace gapi {
+
+/**
+ * @brief This namespace contains G-API serialization and
+ * deserialization functions and data structures.
+ */
+namespace s11n {
+struct IOStream;
+struct IIStream;
+
+namespace detail {
+
+//! @addtogroup gapi_serialization
+//! @{
+
+struct NotImplemented {
+};
+
+/** @brief This structure allows to implement serialization routines for custom types.
+ *
+ * The default S11N for custom types is not implemented.
+ *
+ * @note When providing an overloaded implementation for S11N with your type
+ * don't inherit it from NotImplemented structure.
+ *
+ * @note There are lots of overloaded >> and << operators for basic and OpenCV/G-API types
+ * which can be utilized when serializing a custom type.
+ *
+ * Example of usage:
+ * @snippet samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp S11N usage
+ *
+ */
+template<typename T>
+struct S11N: public NotImplemented {
+    /**
+     * @brief This function allows user to serialize their custom type.
+     *
+     * @note The default overload throws an exception if called. User need to
+     * properly overload the function to use it.
+     */
+    static void serialize(IOStream &, const T &) {
+        GAPI_Assert(false && "No serialization routine is provided!");
+    }
+    /**
+     * @brief This function allows user to deserialize their custom type.
+     *
+     * @note The default overload throws an exception if called. User need to
+     * properly overload the function to use it.
+     */
+    static T deserialize(IIStream &) {
+        GAPI_Assert(false && "No deserialization routine is provided!");
+    }
+};
+
+/// @private -- Exclude this struct from OpenCV documentation
+template<typename T> struct has_S11N_spec {
+    static constexpr bool value = !std::is_base_of<NotImplemented,
+                                        S11N<typename std::decay<T>::type>>::value;
+};
+//! @} gapi_serialization
+
+} // namespace detail
+} // namespace s11n
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_S11N_BASE_HPP
diff --git a/3rdParty/include/opencv2/gapi/stereo.hpp b/3rdParty/include/opencv2/gapi/stereo.hpp
new file mode 100644
index 0000000..dcf8f4d
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/stereo.hpp
@@ -0,0 +1,85 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distereoibution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_STEREO_HPP
+#define OPENCV_GAPI_STEREO_HPP
+
+#include <opencv2/gapi/gmat.hpp>
+#include <opencv2/gapi/gscalar.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+
+namespace cv {
+namespace gapi {
+
+/**
+ * The enum specified format of result that you get from @ref cv::gapi::stereo.
+ */
+enum class StereoOutputFormat {
+    DEPTH_FLOAT16, ///< Floating point 16 bit value, CV_16FC1.
+                   ///< This identifier is deprecated, use DEPTH_16F instead.
+    DEPTH_FLOAT32, ///< Floating point 32 bit value, CV_32FC1
+                   ///< This identifier is deprecated, use DEPTH_16F instead.
+    DISPARITY_FIXED16_11_5, ///< 16 bit signed: first bit for sign,
+                            ///< 10 bits for integer part,
+                            ///< 5 bits for fractional part.
+                            ///< This identifier is deprecated,
+                            ///< use DISPARITY_16Q_10_5 instead.
+    DISPARITY_FIXED16_12_4, ///< 16 bit signed: first bit for sign,
+                            ///< 11 bits for integer part,
+                            ///< 4 bits for fractional part.
+                            ///< This identifier is deprecated,
+                            ///< use DISPARITY_16Q_11_4 instead.
+    DEPTH_16F = DEPTH_FLOAT16, ///< Same as DEPTH_FLOAT16
+    DEPTH_32F = DEPTH_FLOAT32, ///< Same as DEPTH_FLOAT32
+    DISPARITY_16Q_10_5 = DISPARITY_FIXED16_11_5, ///< Same as DISPARITY_FIXED16_11_5
+    DISPARITY_16Q_11_4 = DISPARITY_FIXED16_12_4 ///< Same as DISPARITY_FIXED16_12_4
+};
+
+
+/**
+ * @brief This namespace contains G-API Operation Types for Stereo and
+ * related functionality.
+ */
+namespace calib3d {
+
+G_TYPED_KERNEL(GStereo, <GMat(GMat, GMat, const StereoOutputFormat)>, "org.opencv.stereo") {
+    static GMatDesc outMeta(const GMatDesc &left, const GMatDesc &right, const StereoOutputFormat of) {
+        GAPI_Assert(left.chan == 1);
+        GAPI_Assert(left.depth == CV_8U);
+
+        GAPI_Assert(right.chan == 1);
+        GAPI_Assert(right.depth == CV_8U);
+
+        switch(of) {
+            case StereoOutputFormat::DEPTH_FLOAT16:
+                return left.withDepth(CV_16FC1);
+            case StereoOutputFormat::DEPTH_FLOAT32:
+                return left.withDepth(CV_32FC1);
+            case StereoOutputFormat::DISPARITY_FIXED16_11_5:
+            case StereoOutputFormat::DISPARITY_FIXED16_12_4:
+                return left.withDepth(CV_16SC1);
+            default:
+                GAPI_Assert(false && "Unknown output format!");
+        }
+    }
+};
+
+} // namespace calib3d
+
+/** @brief Computes disparity/depth map for the specified stereo-pair.
+The function computes disparity or depth map depending on passed StereoOutputFormat argument.
+
+@param left 8-bit single-channel left image of @ref CV_8UC1 type.
+@param right 8-bit single-channel right image of @ref CV_8UC1 type.
+@param of enum to specified output kind: depth or disparity and corresponding type
+*/
+GAPI_EXPORTS GMat stereo(const GMat& left,
+                         const GMat& right,
+                         const StereoOutputFormat of = StereoOutputFormat::DEPTH_FLOAT32);
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_STEREO_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/cap.hpp b/3rdParty/include/opencv2/gapi/streaming/cap.hpp
new file mode 100644
index 0000000..aad6af6
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/cap.hpp
@@ -0,0 +1,126 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+#ifndef OPENCV_GAPI_STREAMING_CAP_HPP
+#define OPENCV_GAPI_STREAMING_CAP_HPP
+
+/**
+ * YOUR ATTENTION PLEASE!
+ *
+ * This is a header-only implementation of cv::VideoCapture-based
+ * Stream source.  It is not built by default with G-API as G-API
+ * doesn't depend on videoio module.
+ *
+ * If you want to use it in your application, please make sure
+ * videioio is available in your OpenCV package and is linked to your
+ * application.
+ *
+ * Note for developers: please don't put videoio dependency in G-API
+ * because of this file.
+ */
+#include <chrono>
+
+#include <opencv2/videoio.hpp>
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/streaming/meta.hpp>
+
+namespace cv {
+namespace gapi {
+namespace wip {
+
+/**
+ * @brief OpenCV's VideoCapture-based streaming source.
+ *
+ * This class implements IStreamSource interface.
+ * Its constructor takes the same parameters as cv::VideoCapture does.
+ *
+ * Please make sure that videoio OpenCV module is available before using
+ * this in your application (G-API doesn't depend on it directly).
+ *
+ * @note stream sources are passed to G-API via shared pointers, so
+ *  please gapi::make_src<> to create objects and ptr() to pass a
+ *  GCaptureSource to cv::gin().
+ */
+class GCaptureSource: public IStreamSource
+{
+public:
+    explicit GCaptureSource(int id) : cap(id) { prep(); }
+    explicit GCaptureSource(const std::string &path) : cap(path) { prep(); }
+
+    // TODO: Add more constructor overloads to make it
+    // fully compatible with VideoCapture's interface.
+
+protected:
+    cv::VideoCapture cap;
+    cv::Mat first;
+    bool first_pulled = false;
+    int64_t counter = 0;
+
+    void prep()
+    {
+        // Prepare first frame to report its meta to engine
+        // when needed
+        GAPI_Assert(first.empty());
+        cv::Mat tmp;
+        if (!cap.read(tmp))
+        {
+            GAPI_Assert(false && "Couldn't grab the very first frame");
+        }
+        // NOTE: Some decode/media VideoCapture backends continue
+        // owning the video buffer under cv::Mat so in order to
+        // process it safely in a highly concurrent pipeline, clone()
+        // is the only right way.
+        first = tmp.clone();
+    }
+
+    virtual bool pull(cv::gapi::wip::Data &data) override
+    {
+        if (!first_pulled)
+        {
+            GAPI_Assert(!first.empty());
+            first_pulled = true;
+            data = first; // no need to clone here since it was cloned already
+        }
+        else
+        {
+            if (!cap.isOpened()) return false;
+
+            cv::Mat frame;
+            if (!cap.read(frame))
+            {
+                // end-of-stream happened
+                return false;
+            }
+            // Same reason to clone as in prep()
+            data = frame.clone();
+        }
+        // Tag data with seq_id/ts
+        const auto now = std::chrono::system_clock::now();
+        const auto dur = std::chrono::duration_cast<std::chrono::microseconds>
+            (now.time_since_epoch());
+        data.meta[cv::gapi::streaming::meta_tag::timestamp] = int64_t{dur.count()};
+        data.meta[cv::gapi::streaming::meta_tag::seq_id]    = int64_t{counter++};
+        return true;
+    }
+
+    virtual GMetaArg descr_of() const override
+    {
+        GAPI_Assert(!first.empty());
+        return cv::GMetaArg{cv::descr_of(first)};
+    }
+};
+
+// NB: Overload for using from python
+GAPI_EXPORTS_W cv::Ptr<IStreamSource> inline make_capture_src(const std::string& path)
+{
+    return make_src<GCaptureSource>(path);
+}
+
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_STREAMING_CAP_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/desync.hpp b/3rdParty/include/opencv2/gapi/streaming/desync.hpp
new file mode 100644
index 0000000..1ed6e24
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/desync.hpp
@@ -0,0 +1,85 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020-2021 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GSTREAMING_DESYNC_HPP
+#define OPENCV_GAPI_GSTREAMING_DESYNC_HPP
+
+#include <tuple>
+
+#include <opencv2/gapi/util/util.hpp>
+#include <opencv2/gapi/gtype_traits.hpp>
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/gcall.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+
+namespace cv {
+namespace gapi {
+namespace streaming {
+
+namespace detail {
+struct GDesync {
+    static const char *id() {
+        return "org.opencv.streaming.desync";
+    }
+
+    // An universal yield for desync.
+    // Yields output objects according to the input Types...
+    // Reuses gkernel machinery.
+    // FIXME: This function can be generic and declared in gkernel.hpp
+    //        (it is there already, but a part of GKernelType[M]
+    template<typename... R, int... IIs>
+    static std::tuple<R...> yield(cv::GCall &call, cv::detail::Seq<IIs...>) {
+        return std::make_tuple(cv::detail::Yield<R>::yield(call, IIs)...);
+    }
+};
+
+template<typename G>
+G desync(const G &g) {
+    cv::GKernel k{
+          GDesync::id()                                     // kernel id
+        , ""                                                // kernel tag
+        , [](const GMetaArgs &a, const GArgs &) {return a;} // outMeta callback
+        , {cv::detail::GTypeTraits<G>::shape}               // output Shape
+        , {cv::detail::GTypeTraits<G>::op_kind}             // input data kinds
+        , {cv::detail::GObtainCtor<G>::get()}               // output template ctors
+    };
+    cv::GCall call(std::move(k));
+    call.pass(g);
+    return std::get<0>(GDesync::yield<G>(call, cv::detail::MkSeq<1>::type()));
+}
+} // namespace detail
+
+/**
+ * @brief Starts a desynchronized branch in the graph.
+ *
+ * This operation takes a single G-API data object and returns a
+ * graph-level "duplicate" of this object.
+ *
+ * Operations which use this data object can be desynchronized
+ * from the rest of the graph.
+ *
+ * This operation has no effect when a GComputation is compiled with
+ * regular cv::GComputation::compile(), since cv::GCompiled objects
+ * always produce their full output vectors.
+ *
+ * This operation only makes sense when a GComputation is compiled in
+ * straming mode with cv::GComputation::compileStreaming(). If this
+ * operation is used and there are desynchronized outputs, the user
+ * should use a special version of cv::GStreamingCompiled::pull()
+ * which produces an array of cv::util::optional<> objects.
+ *
+ * @note This feature is highly experimental now and is currently
+ * limited to a single GMat/GFrame argument only.
+ */
+GAPI_EXPORTS GMat desync(const GMat &g);
+GAPI_EXPORTS GFrame desync(const GFrame &f);
+
+} // namespace streaming
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_GSTREAMING_DESYNC_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/format.hpp b/3rdParty/include/opencv2/gapi/streaming/format.hpp
new file mode 100644
index 0000000..f7c3bd4
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/format.hpp
@@ -0,0 +1,94 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+#ifndef OPENCV_GAPI_GSTREAMING_FORMAT_HPP
+#define OPENCV_GAPI_GSTREAMING_FORMAT_HPP
+
+#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
+
+namespace cv {
+namespace gapi {
+namespace streaming {
+
+GAPI_EXPORTS cv::gapi::GKernelPackage kernels();
+
+G_API_OP(GBGR, <GMat(GFrame)>, "org.opencv.streaming.BGR")
+{
+    static GMatDesc outMeta(const GFrameDesc& in) { return GMatDesc{CV_8U, 3, in.size}; }
+};
+
+G_API_OP(GY, <GMat(GFrame)>, "org.opencv.streaming.Y") {
+    static GMatDesc outMeta(const GFrameDesc& frameDesc) {
+        return GMatDesc { CV_8U, 1, frameDesc.size , false };
+    }
+};
+
+G_API_OP(GUV, <GMat(GFrame)>, "org.opencv.streaming.UV") {
+    static GMatDesc outMeta(const GFrameDesc& frameDesc) {
+        return GMatDesc { CV_8U, 2, cv::Size(frameDesc.size.width / 2, frameDesc.size.height / 2),
+                          false };
+    }
+};
+
+/** @brief Gets bgr plane from input frame
+
+@note Function textual ID is "org.opencv.streaming.BGR"
+
+@param in Input frame
+@return Image in BGR format
+*/
+GAPI_EXPORTS cv::GMat BGR(const cv::GFrame& in);
+
+/** @brief Extracts Y plane from media frame.
+
+Output image is 8-bit 1-channel image of @ref CV_8UC1.
+
+@note Function textual ID is "org.opencv.streaming.Y"
+
+@param frame input media frame.
+*/
+GAPI_EXPORTS GMat Y(const cv::GFrame& frame);
+
+/** @brief Extracts UV plane from media frame.
+
+Output image is 8-bit 2-channel image of @ref CV_8UC2.
+
+@note Function textual ID is "org.opencv.streaming.UV"
+
+@param frame input media frame.
+*/
+GAPI_EXPORTS GMat UV(const cv::GFrame& frame);
+} // namespace streaming
+
+//! @addtogroup gapi_transform
+//! @{
+/** @brief Makes a copy of the input image. Note that this copy may be not real
+(no actual data copied). Use this function to maintain graph contracts,
+e.g when graph's input needs to be passed directly to output, like in Streaming mode.
+
+@note Function textual ID is "org.opencv.streaming.copy"
+
+@param in Input image
+@return Copy of the input
+*/
+GAPI_EXPORTS_W GMat copy(const GMat& in);
+
+/** @brief Makes a copy of the input frame. Note that this copy may be not real
+(no actual data copied). Use this function to maintain graph contracts,
+e.g when graph's input needs to be passed directly to output, like in Streaming mode.
+
+@note Function textual ID is "org.opencv.streaming.copy"
+
+@param in Input frame
+@return Copy of the input
+*/
+GAPI_EXPORTS GFrame copy(const GFrame& in);
+//! @} gapi_transform
+
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_GSTREAMING_FORMAT_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/gstreamer/gstreamerpipeline.hpp b/3rdParty/include/opencv2/gapi/streaming/gstreamer/gstreamerpipeline.hpp
new file mode 100644
index 0000000..83afc99
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/gstreamer/gstreamerpipeline.hpp
@@ -0,0 +1,47 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_STREAMING_GSTREAMER_GSTREAMERPIPELINE_HPP
+#define OPENCV_GAPI_STREAMING_GSTREAMER_GSTREAMERPIPELINE_HPP
+
+#include <opencv2/gapi/streaming/gstreamer/gstreamersource.hpp>
+#include <opencv2/gapi/own/exports.hpp>
+
+#include <string>
+#include <unordered_map>
+#include <memory>
+
+namespace cv {
+namespace gapi {
+namespace wip {
+namespace gst {
+
+class GAPI_EXPORTS GStreamerPipeline
+{
+public:
+    class Priv;
+
+    explicit GStreamerPipeline(const std::string& pipeline);
+    IStreamSource::Ptr getStreamingSource(const std::string& appsinkName,
+                                          const GStreamerSource::OutputType outputType =
+                                              GStreamerSource::OutputType::MAT);
+    virtual ~GStreamerPipeline();
+
+protected:
+    explicit GStreamerPipeline(std::unique_ptr<Priv> priv);
+
+    std::unique_ptr<Priv> m_priv;
+};
+
+} // namespace gst
+
+using GStreamerPipeline = gst::GStreamerPipeline;
+
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_STREAMING_GSTREAMER_GSTREAMERPIPELINE_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/gstreamer/gstreamersource.hpp b/3rdParty/include/opencv2/gapi/streaming/gstreamer/gstreamersource.hpp
new file mode 100644
index 0000000..b81bad3
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/gstreamer/gstreamersource.hpp
@@ -0,0 +1,89 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_STREAMING_GSTREAMER_GSTREAMERSOURCE_HPP
+#define OPENCV_GAPI_STREAMING_GSTREAMER_GSTREAMERSOURCE_HPP
+
+#include <opencv2/gapi/streaming/source.hpp>
+#include <opencv2/gapi/garg.hpp>
+
+#include <memory>
+
+namespace cv {
+namespace gapi {
+namespace wip {
+namespace gst {
+
+/**
+ * @brief OpenCV's GStreamer streaming source.
+ *        Streams cv::Mat-s/cv::MediaFrame from passed GStreamer pipeline.
+ *
+ * This class implements IStreamSource interface.
+ *
+ * To create GStreamerSource instance you need to pass 'pipeline' and, optionally, 'outputType'
+ * arguments into constructor.
+ * 'pipeline' should represent GStreamer pipeline in form of textual description.
+ * Almost any custom pipeline is supported which can be successfully ran via gst-launch.
+ * The only two limitations are:
+ *      - there should be __one__ appsink element in the pipeline to pass data to OpenCV app.
+ *        Pipeline can actually contain many sink elements, but it must have one and only one
+ *        appsink among them.
+ *
+ *      - data passed to appsink should be video-frame in NV12 format.
+ *
+ * 'outputType' is used to select type of output data to produce: 'cv::MediaFrame' or 'cv::Mat'.
+ * To produce 'cv::MediaFrame'-s you need to pass 'GStreamerSource::OutputType::FRAME' and,
+ * correspondingly, 'GStreamerSource::OutputType::MAT' to produce 'cv::Mat'-s.
+ * Please note, that in the last case, output 'cv::Mat' will be of BGR format, internal conversion
+ * from NV12 GStreamer data will happen.
+ * Default value for 'outputType' is 'GStreamerSource::OutputType::MAT'.
+ *
+ * @note Stream sources are passed to G-API via shared pointers, so please use gapi::make_src<>
+ *       to create objects and ptr() to pass a GStreamerSource to cv::gin().
+ *
+ * @note You need to build OpenCV with GStreamer support to use this class.
+ */
+
+class GStreamerPipelineFacade;
+
+class GAPI_EXPORTS GStreamerSource : public IStreamSource
+{
+public:
+    class Priv;
+
+    // Indicates what type of data should be produced by GStreamerSource: cv::MediaFrame or cv::Mat
+    enum class OutputType {
+        FRAME,
+        MAT
+    };
+
+    GStreamerSource(const std::string& pipeline,
+                    const GStreamerSource::OutputType outputType =
+                        GStreamerSource::OutputType::MAT);
+    GStreamerSource(std::shared_ptr<GStreamerPipelineFacade> pipeline,
+                    const std::string& appsinkName,
+                    const GStreamerSource::OutputType outputType =
+                        GStreamerSource::OutputType::MAT);
+
+    bool pull(cv::gapi::wip::Data& data) override;
+    GMetaArg descr_of() const override;
+    ~GStreamerSource() override;
+
+protected:
+    explicit GStreamerSource(std::unique_ptr<Priv> priv);
+
+    std::unique_ptr<Priv> m_priv;
+};
+
+} // namespace gst
+
+using GStreamerSource = gst::GStreamerSource;
+
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_STREAMING_GSTREAMER_GSTREAMERSOURCE_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/meta.hpp b/3rdParty/include/opencv2/gapi/streaming/meta.hpp
new file mode 100644
index 0000000..cbcfc3a
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/meta.hpp
@@ -0,0 +1,79 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_GSTREAMING_META_HPP
+#define OPENCV_GAPI_GSTREAMING_META_HPP
+
+#include <opencv2/gapi/gopaque.hpp>
+#include <opencv2/gapi/gcall.hpp>
+#include <opencv2/gapi/gkernel.hpp>
+#include <opencv2/gapi/gtype_traits.hpp>
+
+namespace cv {
+namespace gapi {
+namespace streaming {
+
+// FIXME: the name is debatable
+namespace meta_tag {
+static constexpr const char * timestamp = "org.opencv.gapi.meta.timestamp";
+static constexpr const char * seq_id    = "org.opencv.gapi.meta.seq_id";
+} // namespace meta_tag
+
+namespace detail {
+struct GMeta {
+    static const char *id() {
+        return "org.opencv.streaming.meta";
+    }
+    // A universal yield for meta(), same as in GDesync
+    template<typename... R, int... IIs>
+    static std::tuple<R...> yield(cv::GCall &call, cv::detail::Seq<IIs...>) {
+        return std::make_tuple(cv::detail::Yield<R>::yield(call, IIs)...);
+    }
+    // Also a universal outMeta stub here
+    static GMetaArgs getOutMeta(const GMetaArgs &args, const GArgs &) {
+        return args;
+    }
+};
+} // namespace detail
+
+template<typename T, typename G>
+cv::GOpaque<T> meta(G g, const std::string &tag) {
+    using O = cv::GOpaque<T>;
+    cv::GKernel k{
+          detail::GMeta::id()                    // kernel id
+        , tag                                    // kernel tag. Use meta tag here
+        , &detail::GMeta::getOutMeta             // outMeta callback
+        , {cv::detail::GTypeTraits<O>::shape}    // output Shape
+        , {cv::detail::GTypeTraits<G>::op_kind}  // input data kinds
+        , {cv::detail::GObtainCtor<O>::get()}    // output template ctors
+    };
+    cv::GCall call(std::move(k));
+    call.pass(g);
+    return std::get<0>(detail::GMeta::yield<O>(call, cv::detail::MkSeq<1>::type()));
+}
+
+template<typename G>
+cv::GOpaque<int64_t> timestamp(G g) {
+    return meta<int64_t>(g, meta_tag::timestamp);
+}
+
+template<typename G>
+cv::GOpaque<int64_t> seq_id(G g) {
+    return meta<int64_t>(g, meta_tag::seq_id);
+}
+
+template<typename G>
+cv::GOpaque<int64_t> seqNo(G g) {
+    // Old name, compatibility only
+    return seq_id(g);
+}
+
+} // namespace streaming
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_GSTREAMING_META_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/onevpl/cfg_params.hpp b/3rdParty/include/opencv2/gapi/streaming/onevpl/cfg_params.hpp
new file mode 100644
index 0000000..bfd9224
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/onevpl/cfg_params.hpp
@@ -0,0 +1,151 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_STREAMING_ONEVPL_CFG_PARAMS_HPP
+#define OPENCV_GAPI_STREAMING_ONEVPL_CFG_PARAMS_HPP
+
+#include <map>
+#include <memory>
+#include <string>
+
+#include <opencv2/gapi/streaming/source.hpp>
+#include <opencv2/gapi/util/variant.hpp>
+
+namespace cv {
+namespace gapi {
+namespace wip {
+namespace onevpl {
+
+/**
+ * @brief Public class is using for creation of onevpl::GSource instances.
+ *
+ * Class members availaible through methods @ref CfgParam::get_name() and @ref CfgParam::get_value() are used by
+ * onevpl::GSource inner logic to create or find oneVPL particular implementation
+ * (software/hardware, specific API version and etc.).
+ *
+ * @note Because oneVPL may provide several implementations which are satisfying with multiple (or single one) @ref CfgParam
+ * criteria therefore it is possible to configure `preferred` parameters. This kind of CfgParams are created
+ * using `is_major = false` argument in @ref CfgParam::create method and are not used by creating oneVPL particular implementations.
+ * Instead they fill out a "score table" to select preferrable implementation from available list. Implementation are satisfying
+ * with most of these optional params would be chosen.
+ * If no one optional CfgParam params were present then first of available oneVPL implementation would be applied.
+ * Please get on https://spec.oneapi.io/versions/latest/elements/oneVPL/source/API_ref/VPL_disp_api_func.html?highlight=mfxcreateconfig#mfxsetconfigfilterproperty
+ * for using OneVPL configuration. In this schema `mfxU8 *name` represents @ref CfgParam::get_name() and
+ * `mfxVariant value` is @ref CfgParam::get_value()
+ */
+struct GAPI_EXPORTS CfgParam {
+    using name_t = std::string;
+    using value_t = cv::util::variant<uint8_t, int8_t,
+                                      uint16_t, int16_t,
+                                      uint32_t, int32_t,
+                                      uint64_t, int64_t,
+                                      float_t,
+                                      double_t,
+                                      void*,
+                                      std::string>;
+    /**
+     * @brief frames_pool_size_name
+     *
+     * Special configuration parameter name for onevp::GSource:
+     *
+     * @note frames_pool_size_name allows to allocate surfaces pool appropriate size to keep
+     * decoded frames in accelerator memory ready before
+     * they would be consumed by onevp::GSource::pull operation. If you see
+     * a lot of WARNING about lack of free surface then it's time to increase
+     * frames_pool_size_name but be aware of accelerator free memory volume.
+     * If not set then MFX implementation use
+     * mfxFrameAllocRequest::NumFrameSuggested behavior
+     *
+     */
+    static constexpr const char *frames_pool_size_name() { return "frames_pool_size"; }
+    static CfgParam create_frames_pool_size(size_t value);
+
+    /**
+     * @brief acceleration_mode_name
+     *
+     * Special configuration parameter names for onevp::GSource:
+     *
+     * @note acceleration_mode_name allows to activate hardware acceleration &
+     * device memory management.
+     * Supported values:
+     * - MFX_ACCEL_MODE_VIA_D3D11   Will activate DX11 acceleration and will produces
+     * MediaFrames with data allocated in DX11 device memory
+     *
+     * If not set then MFX implementation will use default acceleration behavior:
+     * all decoding operation uses default GPU resources but MediaFrame produces
+     * data allocated by using host RAM
+     *
+     */
+    static constexpr const char *acceleration_mode_name() { return "mfxImplDescription.AccelerationMode"; }
+    static CfgParam create_acceleration_mode(uint32_t value);
+    static CfgParam create_acceleration_mode(const char* value);
+
+    /**
+     * @brief decoder_id_name
+     *
+     * Special configuration parameter names for onevp::GSource:
+     *
+     * @note decoder_id_name allows to specify VPL decoder type which MUST present
+     * in case of RAW video input data and MUST NOT present as CfgParam if video
+     * stream incapsulated into container(*.mp4, *.mkv and so on). In latter case
+     * onevp::GSource will determine it automatically
+     * Supported values:
+     * - MFX_CODEC_AVC
+     * - MFX_CODEC_HEVC
+     * - MFX_CODEC_MPEG2
+     * - MFX_CODEC_VC1
+     * - MFX_CODEC_CAPTURE
+     * - MFX_CODEC_VP9
+     * - MFX_CODEC_AV1
+     *
+     */
+    static constexpr const char *decoder_id_name() { return "mfxImplDescription.mfxDecoderDescription.decoder.CodecID"; }
+    static CfgParam create_decoder_id(uint32_t value);
+    static CfgParam create_decoder_id(const char* value);
+
+    static constexpr const char *implementation_name() { return "mfxImplDescription.Impl"; }
+    static CfgParam create_implementation(uint32_t value);
+    static CfgParam create_implementation(const char* value);
+
+    /**
+     * Create generic onevp::GSource configuration parameter.
+     *
+     *@param name           name of parameter.
+     *@param value          value of parameter.
+     *@param is_major       TRUE if parameter MUST be provided by OneVPL inner implementation, FALSE for optional (for resolve multiple available implementations).
+     *
+     */
+    template<typename ValueType>
+    static CfgParam create(const std::string& name, ValueType&& value, bool is_major = true) {
+        CfgParam param(name, CfgParam::value_t(std::forward<ValueType>(value)), is_major);
+        return param;
+    }
+
+    struct Priv;
+
+    const name_t& get_name() const;
+    const value_t& get_value() const;
+    bool is_major() const;
+    bool operator==(const CfgParam& rhs) const;
+    bool operator< (const CfgParam& rhs) const;
+    bool operator!=(const CfgParam& rhs) const;
+
+    CfgParam& operator=(const CfgParam& src);
+    CfgParam& operator=(CfgParam&& src);
+    CfgParam(const CfgParam& src);
+    CfgParam(CfgParam&& src);
+    ~CfgParam();
+private:
+    CfgParam(const std::string& param_name, value_t&& param_value, bool is_major_param);
+    std::shared_ptr<Priv> m_priv;
+};
+
+} //namespace onevpl
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_STREAMING_ONEVPL_CFG_PARAMS_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/onevpl/data_provider_interface.hpp b/3rdParty/include/opencv2/gapi/streaming/onevpl/data_provider_interface.hpp
new file mode 100644
index 0000000..c70e3db
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/onevpl/data_provider_interface.hpp
@@ -0,0 +1,105 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef GAPI_STREAMING_ONEVPL_ONEVPL_DATA_PROVIDER_INTERFACE_HPP
+#define GAPI_STREAMING_ONEVPL_ONEVPL_DATA_PROVIDER_INTERFACE_HPP
+#include <exception>
+#include <memory>
+#include <string>
+
+#include <opencv2/gapi/own/exports.hpp> // GAPI_EXPORTS
+namespace cv {
+namespace gapi {
+namespace wip {
+namespace onevpl {
+
+struct GAPI_EXPORTS DataProviderException : public std::exception {
+    DataProviderException(const std::string& descr);
+    DataProviderException(std::string&& descr);
+
+    virtual ~DataProviderException() = default;
+    virtual const char* what() const noexcept override;
+private:
+    std::string reason;
+};
+
+struct GAPI_EXPORTS DataProviderSystemErrorException final : public DataProviderException {
+    DataProviderSystemErrorException(int error_code, const std::string& desription = std::string());
+    ~DataProviderSystemErrorException() = default;
+};
+
+struct GAPI_EXPORTS DataProviderUnsupportedException final : public DataProviderException {
+    DataProviderUnsupportedException(const std::string& description);
+    ~DataProviderUnsupportedException() = default;
+};
+
+struct GAPI_EXPORTS DataProviderImplementationException : public DataProviderException {
+    DataProviderImplementationException(const std::string& description);
+    ~DataProviderImplementationException() = default;
+};
+/**
+ * @brief Public interface allows to customize extraction of video stream data
+ * used by onevpl::GSource instead of reading stream from file (by default).
+ *
+ * Interface implementation constructor MUST provide consistency and creates fully operable object.
+ * If error happened implementation MUST throw `DataProviderException` kind exceptions
+ *
+ * @note Interface implementation MUST manage stream and other constructed resources by itself to avoid any kind of leak.
+ * For simple interface implementation example please see `StreamDataProvider` in `tests/streaming/gapi_streaming_tests.cpp`
+ */
+struct GAPI_EXPORTS IDataProvider {
+    using Ptr = std::shared_ptr<IDataProvider>;
+    using mfx_codec_id_type = uint32_t;
+
+    /**
+     * NB: here is supposed to be forward declaration of mfxBitstream
+     * But according to current oneVPL implementation it is impossible to forward
+     * declare untagged struct mfxBitstream.
+     *
+     * IDataProvider makes sense only for HAVE_VPL is ON and to keep IDataProvider
+     * interface API/ABI compliant between core library and user application layer
+     * let's introduce wrapper mfx_bitstream which inherits mfxBitstream in private
+     * G-API code section and declare forward for wrapper mfx_bitstream here
+     */
+    struct mfx_bitstream;
+
+    virtual ~IDataProvider() = default;
+
+    /**
+     * The function is used by onevpl::GSource to extract codec id from data
+     *
+     */
+    virtual mfx_codec_id_type get_mfx_codec_id() const = 0;
+
+    /**
+     * The function is used by onevpl::GSource to extract binary data stream from @ref IDataProvider
+     * implementation.
+     *
+     * It MUST throw `DataProviderException` kind exceptions in fail cases.
+     * It MUST return MFX_ERR_MORE_DATA in EOF which considered as not-fail case.
+     *
+     * @param in_out_bitsream the input-output reference on MFX bitstream buffer which MUST be empty at the first request
+     * to allow implementation to allocate it by itself and to return back. Subsequent invocation of `fetch_bitstream_data`
+     * MUST use the previously used in_out_bitsream to avoid skipping rest of frames which haven't been consumed
+     * @return true for fetched data, false on EOF and throws exception on error
+     */
+    virtual bool fetch_bitstream_data(std::shared_ptr<mfx_bitstream> &in_out_bitsream) = 0;
+
+    /**
+     * The function is used by onevpl::GSource to check more binary data availability.
+     *
+     * It MUST return TRUE in case of EOF and NO_THROW exceptions.
+     *
+     * @return boolean value which detects end of stream
+     */
+    virtual bool empty() const = 0;
+};
+} // namespace onevpl
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // GAPI_STREAMING_ONEVPL_ONEVPL_DATA_PROVIDER_INTERFACE_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/onevpl/device_selector_interface.hpp b/3rdParty/include/opencv2/gapi/streaming/onevpl/device_selector_interface.hpp
new file mode 100644
index 0000000..04f8cae
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/onevpl/device_selector_interface.hpp
@@ -0,0 +1,102 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef GAPI_STREAMING_ONEVPL_DEVICE_SELECTOR_INTERFACE_HPP
+#define GAPI_STREAMING_ONEVPL_DEVICE_SELECTOR_INTERFACE_HPP
+
+#include <limits>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS
+
+namespace cv {
+namespace gapi {
+namespace wip {
+namespace onevpl {
+
+enum class AccelType: uint8_t {
+    HOST,
+    DX11,
+
+    LAST_VALUE = std::numeric_limits<uint8_t>::max()
+};
+
+GAPI_EXPORTS const char* to_cstring(AccelType type);
+
+struct IDeviceSelector;
+struct GAPI_EXPORTS Device {
+    friend struct IDeviceSelector;
+    using Ptr = void*;
+
+    ~Device();
+    const std::string& get_name() const;
+    Ptr get_ptr() const;
+    AccelType get_type() const;
+private:
+    Device(Ptr device_ptr, const std::string& device_name,
+           AccelType device_type);
+
+    std::string name;
+    Ptr ptr;
+    AccelType type;
+};
+
+struct GAPI_EXPORTS Context {
+    friend struct IDeviceSelector;
+    using Ptr = void*;
+
+    ~Context();
+    Ptr get_ptr() const;
+    AccelType get_type() const;
+private:
+    Context(Ptr ctx_ptr, AccelType ctx_type);
+    Ptr ptr;
+    AccelType type;
+};
+
+struct GAPI_EXPORTS IDeviceSelector {
+    using Ptr = std::shared_ptr<IDeviceSelector>;
+
+    struct GAPI_EXPORTS Score {
+        friend struct IDeviceSelector;
+        using Type = int16_t;
+        static constexpr Type MaxActivePriority = std::numeric_limits<Type>::max();
+        static constexpr Type MinActivePriority = 0;
+        static constexpr Type MaxPassivePriority = MinActivePriority - 1;
+        static constexpr Type MinPassivePriority = std::numeric_limits<Type>::min();
+
+        Score(Type val);
+        ~Score();
+
+        operator Type () const;
+        Type get() const;
+        friend bool operator< (Score lhs, Score rhs) {
+            return lhs.get() < rhs.get();
+        }
+    private:
+        Type value;
+    };
+
+    using DeviceScoreTable = std::map<Score, Device>;
+    using DeviceContexts = std::vector<Context>;
+
+    virtual ~IDeviceSelector();
+    virtual DeviceScoreTable select_devices() const = 0;
+    virtual DeviceContexts select_context() = 0;
+protected:
+    template<typename Entity, typename ...Args>
+    static Entity create(Args &&...args) {
+        return Entity(std::forward<Args>(args)...);
+    }
+};
+} // namespace onevpl
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // GAPI_STREAMING_ONEVPL_DEVICE_SELECTOR_INTERFACE_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/onevpl/source.hpp b/3rdParty/include/opencv2/gapi/streaming/onevpl/source.hpp
new file mode 100644
index 0000000..6334480
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/onevpl/source.hpp
@@ -0,0 +1,90 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_STREAMING_ONEVPL_ONEVPL_SOURCE_HPP
+#define OPENCV_GAPI_STREAMING_ONEVPL_ONEVPL_SOURCE_HPP
+
+#include <opencv2/gapi/garg.hpp>
+#include <opencv2/gapi/streaming/meta.hpp>
+#include <opencv2/gapi/streaming/source.hpp>
+#include <opencv2/gapi/streaming/onevpl/cfg_params.hpp>
+#include <opencv2/gapi/streaming/onevpl/data_provider_interface.hpp>
+#include <opencv2/gapi/streaming/onevpl/device_selector_interface.hpp>
+
+namespace cv {
+namespace gapi {
+namespace wip {
+namespace onevpl {
+using CfgParams = std::vector<CfgParam>;
+
+/**
+ * @brief G-API streaming source based on OneVPL implementation.
+ *
+ * This class implements IStreamSource interface.
+ * Its constructor takes source file path (in usual way) or @ref onevpl::IDataProvider
+ * interface implementation (for not file-based sources). It also allows to pass-through
+ * oneVPL configuration parameters by using several @ref onevpl::CfgParam.
+ *
+ * @note stream sources are passed to G-API via shared pointers, so
+ *  please gapi::make_onevpl_src<> to create objects and ptr() to pass a
+ *  GSource to cv::gin().
+ */
+class GAPI_EXPORTS GSource : public IStreamSource
+{
+public:
+    struct Priv;
+
+    GSource(const std::string& filePath,
+            const CfgParams& cfg_params = CfgParams{});
+
+    GSource(const std::string& filePath,
+            const CfgParams& cfg_params,
+            const std::string& device_id,
+            void* accel_device_ptr,
+            void* accel_ctx_ptr);
+
+    GSource(const std::string& filePath,
+            const CfgParams& cfg_params,
+            std::shared_ptr<IDeviceSelector> selector);
+
+
+    GSource(std::shared_ptr<IDataProvider> source,
+            const CfgParams& cfg_params = CfgParams{});
+
+    GSource(std::shared_ptr<IDataProvider> source,
+            const CfgParams& cfg_params,
+            const std::string& device_id,
+            void* accel_device_ptr,
+            void* accel_ctx_ptr);
+
+    GSource(std::shared_ptr<IDataProvider> source,
+            const CfgParams& cfg_params,
+            std::shared_ptr<IDeviceSelector> selector);
+
+    ~GSource() override;
+
+    bool pull(cv::gapi::wip::Data& data) override;
+    GMetaArg descr_of() const override;
+
+private:
+    explicit GSource(std::unique_ptr<Priv>&& impl);
+    std::unique_ptr<Priv> m_priv;
+};
+} // namespace onevpl
+
+using GVPLSource = onevpl::GSource;
+
+template<class... Args>
+GAPI_EXPORTS_W cv::Ptr<IStreamSource> inline make_onevpl_src(Args&&... args)
+{
+    return make_src<onevpl::GSource>(std::forward<Args>(args)...);
+}
+
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_STREAMING_ONEVPL_ONEVPL_SOURCE_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/source.hpp b/3rdParty/include/opencv2/gapi/streaming/source.hpp
new file mode 100644
index 0000000..6597cad
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/source.hpp
@@ -0,0 +1,62 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019 Intel Corporation
+
+#ifndef OPENCV_GAPI_STREAMING_SOURCE_HPP
+#define OPENCV_GAPI_STREAMING_SOURCE_HPP
+
+#include <memory>                      // shared_ptr
+#include <type_traits>                 // is_base_of
+
+#include <opencv2/gapi/gmetaarg.hpp>   // GMetaArg
+
+
+namespace cv {
+namespace gapi {
+namespace wip {
+    struct Data; // "forward-declaration" of GRunArg
+
+/**
+ * @brief Abstract streaming pipeline source.
+ *
+ * Implement this interface if you want customize the way how data is
+ * streaming into GStreamingCompiled.
+ *
+ * Objects implementing this interface can be passed to
+ * GStreamingCompiled using setSource() with cv::gin(). Regular
+ * compiled graphs (GCompiled) don't support input objects of this
+ * type.
+ *
+ * Default cv::VideoCapture-based implementation is available, see
+ * cv::gapi::wip::GCaptureSource.
+ *
+ * @note stream sources are passed to G-API via shared pointers, so
+ *  please use ptr() when passing a IStreamSource implementation to
+ *  cv::gin().
+ */
+class IStreamSource: public std::enable_shared_from_this<IStreamSource>
+{
+public:
+    using Ptr = std::shared_ptr<IStreamSource>;
+    Ptr ptr() { return shared_from_this(); }
+    virtual bool pull(Data &data) = 0;
+    virtual GMetaArg descr_of() const = 0;
+    virtual ~IStreamSource() = default;
+};
+
+template<class T, class... Args>
+IStreamSource::Ptr inline make_src(Args&&... args)
+{
+    static_assert(std::is_base_of<IStreamSource, T>::value,
+                  "T must implement the cv::gapi::IStreamSource interface!");
+    auto src_ptr = std::make_shared<T>(std::forward<Args>(args)...);
+    return src_ptr->ptr();
+}
+
+} // namespace wip
+} // namespace gapi
+} // namespace cv
+
+#endif // OPENCV_GAPI_STREAMING_SOURCE_HPP
diff --git a/3rdParty/include/opencv2/gapi/streaming/sync.hpp b/3rdParty/include/opencv2/gapi/streaming/sync.hpp
new file mode 100644
index 0000000..5801e6f
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/streaming/sync.hpp
@@ -0,0 +1,30 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2021 Intel Corporation
+
+#ifndef OPENCV_GAPI_STREAMING_SYNC_HPP
+#define OPENCV_GAPI_STREAMING_SYNC_HPP
+
+namespace cv {
+namespace gapi {
+namespace streaming {
+
+enum class sync_policy {
+    dont_sync,
+    drop
+};
+
+} // namespace streaming
+} // namespace gapi
+
+namespace detail {
+    template<> struct CompileArgTag<gapi::streaming::sync_policy> {
+        static const char* tag() { return "gapi.streaming.sync_policy"; }
+    };
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_GAPI_STREAMING_SYNC_HPP
diff --git a/3rdParty/include/opencv2/gapi/util/any.hpp b/3rdParty/include/opencv2/gapi/util/any.hpp
new file mode 100644
index 0000000..94451c7
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/any.hpp
@@ -0,0 +1,190 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_UTIL_ANY_HPP
+#define OPENCV_GAPI_UTIL_ANY_HPP
+
+#include <memory>
+#include <type_traits>
+#include <typeinfo>
+#include <utility>
+
+#include <opencv2/gapi/util/throw.hpp>
+
+#if defined(_MSC_VER)
+   // disable MSVC warning on "multiple copy constructors specified"
+#  pragma warning(disable: 4521)
+#endif
+
+namespace cv
+{
+
+namespace internal
+{
+    template <class T, class Source>
+    T down_cast(Source operand)
+    {
+#if defined(__GXX_RTTI) || defined(_CPPRTTI)
+       return dynamic_cast<T>(operand);
+#else
+#ifdef __GNUC__
+#warning used static cast instead of dynamic because RTTI is disabled
+#else
+#pragma message("WARNING: used static cast instead of dynamic because RTTI is disabled")
+#endif
+       return static_cast<T>(operand);
+#endif
+    }
+}
+
+namespace util
+{
+   class bad_any_cast : public std::bad_cast
+   {
+   public:
+       virtual const char* what() const noexcept override
+       {
+           return "Bad any cast";
+       }
+   };
+
+   //modeled against C++17 std::any
+
+   class any
+   {
+   private:
+      struct holder;
+      using holder_ptr = std::unique_ptr<holder>;
+      struct holder
+      {
+         virtual holder_ptr clone() = 0;
+         virtual ~holder() = default;
+      };
+
+      template <typename value_t>
+      struct holder_impl : holder
+      {
+         value_t v;
+         template<typename arg_t>
+         holder_impl(arg_t&& a) : v(std::forward<arg_t>(a)) {}
+         holder_ptr clone() override { return holder_ptr(new holder_impl (v));}
+      };
+
+      holder_ptr hldr;
+   public:
+      template<class value_t>
+      any(value_t&& arg) :  hldr(new holder_impl<typename std::decay<value_t>::type>( std::forward<value_t>(arg))) {}
+
+      any(any const& src) : hldr( src.hldr ? src.hldr->clone() : nullptr) {}
+      //simple hack in order not to write enable_if<not any> for the template constructor
+      any(any & src) : any (const_cast<any const&>(src)) {}
+
+      any()       = default;
+      any(any&& ) = default;
+
+      any& operator=(any&&) = default;
+
+      any& operator=(any const& src)
+      {
+         any copy(src);
+         swap(*this, copy);
+         return *this;
+      }
+
+      template<class value_t>
+      friend value_t* any_cast(any* operand);
+
+      template<class value_t>
+      friend const value_t* any_cast(const any* operand);
+
+      template<class value_t>
+      friend value_t& unsafe_any_cast(any& operand);
+
+      template<class value_t>
+      friend const value_t& unsafe_any_cast(const any& operand);
+
+      friend void swap(any & lhs, any& rhs)
+      {
+         swap(lhs.hldr, rhs.hldr);
+      }
+
+   };
+
+   template<class value_t>
+   value_t* any_cast(any* operand)
+   {
+      auto casted = internal::down_cast<any::holder_impl<typename std::decay<value_t>::type> *>(operand->hldr.get());
+      if (casted){
+         return & (casted->v);
+      }
+      return nullptr;
+   }
+
+   template<class value_t>
+   const value_t* any_cast(const any* operand)
+   {
+      auto casted = internal::down_cast<any::holder_impl<typename std::decay<value_t>::type> *>(operand->hldr.get());
+      if (casted){
+         return & (casted->v);
+      }
+      return nullptr;
+   }
+
+   template<class value_t>
+   value_t& any_cast(any& operand)
+   {
+      auto ptr = any_cast<value_t>(&operand);
+      if (ptr)
+      {
+         return *ptr;
+      }
+
+      throw_error(bad_any_cast());
+   }
+
+
+   template<class value_t>
+   const value_t& any_cast(const any& operand)
+   {
+      auto ptr = any_cast<value_t>(&operand);
+      if (ptr)
+      {
+         return *ptr;
+      }
+
+      throw_error(bad_any_cast());
+   }
+
+   template<class value_t>
+   inline value_t& unsafe_any_cast(any& operand)
+   {
+#ifdef DEBUG
+      return any_cast<value_t>(operand);
+#else
+      return static_cast<any::holder_impl<typename std::decay<value_t>::type> *>(operand.hldr.get())->v;
+#endif
+   }
+
+   template<class value_t>
+   inline const value_t& unsafe_any_cast(const any& operand)
+   {
+#ifdef DEBUG
+      return any_cast<value_t>(operand);
+#else
+      return static_cast<any::holder_impl<typename std::decay<value_t>::type> *>(operand.hldr.get())->v;
+#endif
+   }
+
+} // namespace util
+} // namespace cv
+
+#if defined(_MSC_VER)
+   // Enable "multiple copy constructors specified" back
+#  pragma warning(default: 4521)
+#endif
+
+#endif // OPENCV_GAPI_UTIL_ANY_HPP
diff --git a/3rdParty/include/opencv2/gapi/util/compiler_hints.hpp b/3rdParty/include/opencv2/gapi/util/compiler_hints.hpp
new file mode 100644
index 0000000..a41a971
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/compiler_hints.hpp
@@ -0,0 +1,19 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+#ifndef OPENCV_GAPI_UTIL_COMPILER_HINTS_HPP
+#define OPENCV_GAPI_UTIL_COMPILER_HINTS_HPP
+
+namespace cv
+{
+namespace util
+{
+    //! Utility template function to prevent "unused" warnings by various compilers.
+    template<typename T> void suppress_unused_warning( const T& ) {}
+} // namespace util
+} // namespace cv
+
+#endif /* OPENCV_GAPI_UTIL_COMPILER_HINTS_HPP */
diff --git a/3rdParty/include/opencv2/gapi/util/copy_through_move.hpp b/3rdParty/include/opencv2/gapi/util/copy_through_move.hpp
new file mode 100644
index 0000000..1a1121e
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/copy_through_move.hpp
@@ -0,0 +1,34 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+#ifndef OPENCV_GAPI_UTIL_COPY_THROUGH_MOVE_HPP
+#define OPENCV_GAPI_UTIL_COPY_THROUGH_MOVE_HPP
+
+#include <opencv2/gapi/util/type_traits.hpp> //decay_t
+
+namespace cv
+{
+namespace util
+{
+    //This is a tool to move initialize captures of a lambda in C++11
+    template<typename T>
+    struct copy_through_move_t{
+       T value;
+       const T& get() const {return value;}
+       T&       get()       {return value;}
+       copy_through_move_t(T&& g) : value(std::move(g)) {}
+       copy_through_move_t(copy_through_move_t&&) = default;
+       copy_through_move_t(copy_through_move_t const& lhs) : copy_through_move_t(std::move(const_cast<copy_through_move_t&>(lhs))) {}
+    };
+
+    template<typename T>
+    copy_through_move_t<util::decay_t<T>> copy_through_move(T&& t){
+        return std::forward<T>(t);
+    }
+} // namespace util
+} // namespace cv
+
+#endif /* OPENCV_GAPI_UTIL_COPY_THROUGH_MOVE_HPP */
diff --git a/3rdParty/include/opencv2/gapi/util/optional.hpp b/3rdParty/include/opencv2/gapi/util/optional.hpp
new file mode 100644
index 0000000..dca03ca
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/optional.hpp
@@ -0,0 +1,178 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_UTIL_OPTIONAL_HPP
+#define OPENCV_GAPI_UTIL_OPTIONAL_HPP
+
+#include <opencv2/gapi/util/variant.hpp>
+
+// A poor man's `optional` implementation, incompletely modeled against C++17 spec.
+namespace cv
+{
+namespace util
+{
+    class bad_optional_access: public std::exception
+    {
+    public:
+        virtual const char *what() const noexcept override
+        {
+            return "Bad optional access";
+        }
+    };
+
+    // TODO: nullopt_t
+
+    // Interface ///////////////////////////////////////////////////////////////
+    template<typename T> class optional
+    {
+    public:
+        // Constructors
+        // NB.: there were issues with Clang 3.8 when =default() was used
+        // instead {}
+        optional() {}
+        optional(const optional&) = default;
+        explicit optional(T&&) noexcept;
+        explicit optional(const T&) noexcept;
+        optional(optional&&) noexcept;
+        // TODO: optional(nullopt_t) noexcept;
+        // TODO: optional(const optional<U> &)
+        // TODO: optional(optional<U> &&)
+        // TODO: optional(Args&&...)
+        // TODO: optional(initializer_list<U>)
+        // TODO: optional(U&& value);
+
+        // Assignment
+        optional& operator=(const optional&) = default;
+        optional& operator=(optional&&);
+
+        // Observers
+        T* operator-> ();
+        const T* operator-> () const;
+        T& operator* ();
+        const T& operator* () const;
+        // TODO: && versions
+
+        operator bool() const noexcept;
+        bool has_value() const noexcept;
+
+        T& value();
+        const T& value() const;
+        // TODO: && versions
+
+        template<class U>
+        T value_or(U &&default_value) const;
+
+        void swap(optional &other) noexcept;
+        void reset() noexcept;
+        // TODO: emplace
+
+        // TODO: operator==, !=, <, <=, >, >=
+
+    private:
+        struct nothing {};
+        util::variant<nothing, T> m_holder;
+    };
+
+    template<class T>
+    optional<typename std::decay<T>::type> make_optional(T&& value);
+
+    // TODO: Args... and initializer_list versions
+
+    // Implementation //////////////////////////////////////////////////////////
+    template<class T> optional<T>::optional(T &&v) noexcept
+        : m_holder(std::move(v))
+    {
+    }
+
+    template<class T> optional<T>::optional(const T &v) noexcept
+        : m_holder(v)
+    {
+    }
+
+    template<class T> optional<T>::optional(optional&& rhs) noexcept
+        : m_holder(std::move(rhs.m_holder))
+    {
+        rhs.reset();
+    }
+
+    template<class T> optional<T>& optional<T>::operator=(optional&& rhs)
+    {
+        m_holder = std::move(rhs.m_holder);
+        rhs.reset();
+        return *this;
+    }
+
+    template<class T> T* optional<T>::operator-> ()
+    {
+        return & *(*this);
+    }
+
+    template<class T> const T* optional<T>::operator-> () const
+    {
+        return & *(*this);
+    }
+
+    template<class T> T& optional<T>::operator* ()
+    {
+        return this->value();
+    }
+
+    template<class T> const T& optional<T>::operator* () const
+    {
+        return this->value();
+    }
+
+    template<class T> optional<T>::operator bool() const noexcept
+    {
+        return this->has_value();
+    }
+
+    template<class T> bool optional<T>::has_value() const noexcept
+    {
+        return util::holds_alternative<T>(m_holder);
+    }
+
+    template<class T> T& optional<T>::value()
+    {
+        if (!this->has_value())
+            throw_error(bad_optional_access());
+        return util::get<T>(m_holder);
+    }
+
+    template<class T> const T& optional<T>::value() const
+    {
+        if (!this->has_value())
+            throw_error(bad_optional_access());
+        return util::get<T>(m_holder);
+    }
+
+    template<class T>
+    template<class U> T optional<T>::value_or(U &&default_value) const
+    {
+        return (this->has_value() ? this->value() : T(default_value));
+    }
+
+    template<class T> void optional<T>::swap(optional<T> &other) noexcept
+    {
+        m_holder.swap(other.m_holder);
+    }
+
+    template<class T> void optional<T>::reset() noexcept
+    {
+        if (this->has_value())
+            m_holder = nothing{};
+    }
+
+    template<class T>
+    optional<typename std::decay<T>::type> make_optional(T&& value)
+    {
+        return optional<typename std::decay<T>::type>(std::forward<T>(value));
+    }
+} // namespace util
+} // namespace cv
+
+#endif // OPENCV_GAPI_UTIL_OPTIONAL_HPP
diff --git a/3rdParty/include/opencv2/gapi/util/throw.hpp b/3rdParty/include/opencv2/gapi/util/throw.hpp
new file mode 100644
index 0000000..689bf58
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/throw.hpp
@@ -0,0 +1,36 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_UTIL_THROW_HPP
+#define OPENCV_GAPI_UTIL_THROW_HPP
+
+#include <utility>  // std::forward
+
+#if !defined(__EXCEPTIONS)
+#include <stdlib.h>
+#include <stdio.h>
+#endif
+
+namespace cv
+{
+namespace util
+{
+template <class ExceptionType>
+[[noreturn]] void throw_error(ExceptionType &&e)
+{
+#if defined(__EXCEPTIONS) || defined(_CPPUNWIND)
+    throw std::forward<ExceptionType>(e);
+#else
+    fprintf(stderr, "An exception thrown! %s\n" , e.what());
+    fflush(stderr);
+    abort();
+#endif
+}
+} // namespace util
+} // namespace cv
+
+#endif // OPENCV_GAPI_UTIL_THROW_HPP
diff --git a/3rdParty/include/opencv2/gapi/util/type_traits.hpp b/3rdParty/include/opencv2/gapi/util/type_traits.hpp
new file mode 100644
index 0000000..637f184
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/type_traits.hpp
@@ -0,0 +1,31 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_UTIL_TYPE_TRAITS_HPP
+#define OPENCV_GAPI_UTIL_TYPE_TRAITS_HPP
+
+#include <type_traits>
+
+namespace cv
+{
+namespace util
+{
+    //these are C++14 parts of type_traits :
+    template< bool B, class T = void >
+    using enable_if_t = typename std::enable_if<B,T>::type;
+
+    template<typename T>
+    using decay_t = typename std::decay<T>::type;
+
+    //this is not part of C++14 but still, of pretty common usage
+    template<class T, class U, class V = void>
+    using are_different_t = enable_if_t< !std::is_same<decay_t<T>, decay_t<U>>::value, V>;
+
+} // namespace cv
+} // namespace util
+
+#endif // OPENCV_GAPI_UTIL_TYPE_TRAITS_HPP
diff --git a/3rdParty/include/opencv2/gapi/util/util.hpp b/3rdParty/include/opencv2/gapi/util/util.hpp
new file mode 100644
index 0000000..3be46d7
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/util.hpp
@@ -0,0 +1,190 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018-2019 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_UTIL_HPP
+#define OPENCV_GAPI_UTIL_HPP
+
+#include <tuple>
+
+// \cond HIDDEN_SYMBOLS
+// This header file contains some generic utility functions which are
+// used in other G-API Public API headers.
+//
+// PLEASE don't put any stuff here if it is NOT used in public API headers!
+
+namespace cv
+{
+namespace detail
+{
+    // Recursive integer sequence type, useful for enumerating elements of
+    // template parameter packs.
+    template<int... I> struct Seq     { using next = Seq<I..., sizeof...(I)>; };
+    template<int Sz>   struct MkSeq   { using type = typename MkSeq<Sz-1>::type::next; };
+    template<>         struct MkSeq<0>{ using type = Seq<>; };
+
+    // Checks if elements of variadic template satisfy the given Predicate.
+    // Implemented via tuple, with an interface to accept plain type lists
+    template<template<class> class, typename, typename...> struct all_satisfy;
+
+    template<template<class> class F, typename T, typename... Ts>
+    struct all_satisfy<F, std::tuple<T, Ts...> >
+    {
+        static const constexpr bool value = F<T>::value
+            && all_satisfy<F, std::tuple<Ts...> >::value;
+    };
+    template<template<class> class F, typename T>
+    struct all_satisfy<F, std::tuple<T> >
+    {
+        static const constexpr bool value = F<T>::value;
+    };
+
+    template<template<class> class F, typename T, typename... Ts>
+    struct all_satisfy: public all_satisfy<F, std::tuple<T, Ts...> > {};
+
+    // Permute given tuple type C with given integer sequence II
+    // Sequence may be less than tuple C size.
+    template<class, class> struct permute_tuple;
+
+    template<class C, int... IIs>
+    struct permute_tuple<C, Seq<IIs...> >
+    {
+        using type = std::tuple< typename std::tuple_element<IIs, C>::type... >;
+    };
+
+    // Given T..., generates a type sequence of sizeof...(T)-1 elements
+    // which is T... without its last element
+    // Implemented via tuple, with an interface to accept plain type lists
+    template<typename T, typename... Ts> struct all_but_last;
+
+    template<typename T, typename... Ts>
+    struct all_but_last<std::tuple<T, Ts...> >
+    {
+        using C    = std::tuple<T, Ts...>;
+        using S    = typename MkSeq<std::tuple_size<C>::value - 1>::type;
+        using type = typename permute_tuple<C, S>::type;
+    };
+
+    template<typename T, typename... Ts>
+    struct all_but_last: public all_but_last<std::tuple<T, Ts...> > {};
+
+    template<typename... Ts>
+    using all_but_last_t = typename all_but_last<Ts...>::type;
+
+    // NB.: This is here because there's no constexpr std::max in C++11
+    template<std::size_t S0, std::size_t... SS> struct max_of_t
+    {
+        static constexpr const std::size_t rest  = max_of_t<SS...>::value;
+        static constexpr const std::size_t value = rest > S0 ? rest : S0;
+    };
+    template<std::size_t S> struct max_of_t<S>
+    {
+        static constexpr const std::size_t value = S;
+    };
+
+    template <typename...>
+    struct contains : std::false_type{};
+
+    template <typename T1, typename T2, typename... Ts>
+    struct contains<T1, T2, Ts...> : std::integral_constant<bool, std::is_same<T1, T2>::value ||
+                                                                  contains<T1, Ts...>::value> {};
+    template<typename T, typename... Types>
+    struct contains<T, std::tuple<Types...>> : std::integral_constant<bool, contains<T, Types...>::value> {};
+
+    template <typename...>
+    struct all_unique : std::true_type{};
+
+    template <typename T1, typename... Ts>
+    struct all_unique<T1, Ts...> : std::integral_constant<bool, !contains<T1, Ts...>::value &&
+                                                                 all_unique<Ts...>::value> {};
+
+    template<typename>
+    struct tuple_wrap_helper;
+
+    template<typename T> struct tuple_wrap_helper
+    {
+        using type = std::tuple<T>;
+        static type get(T&& obj) { return std::make_tuple(std::move(obj)); }
+    };
+
+    template<typename... Objs>
+    struct tuple_wrap_helper<std::tuple<Objs...>>
+    {
+        using type = std::tuple<Objs...>;
+        static type get(std::tuple<Objs...>&& objs) { return std::forward<std::tuple<Objs...>>(objs); }
+    };
+
+    template<typename... Ts>
+    struct make_void { typedef void type;};
+
+    template<typename... Ts>
+    using void_t = typename make_void<Ts...>::type;
+
+} // namespace detail
+
+namespace util
+{
+template<typename ...L>
+struct overload_lamba_set;
+
+template<typename L1>
+struct overload_lamba_set<L1> : public L1
+{
+    overload_lamba_set(L1&& lambda) : L1(std::move(lambda)) {}
+    overload_lamba_set(const L1& lambda) : L1(lambda) {}
+
+    using L1::operator();
+};
+
+template<typename L1, typename ...L>
+struct overload_lamba_set<L1, L...> : public L1, public overload_lamba_set<L...>
+{
+    using base_type = overload_lamba_set<L...>;
+    overload_lamba_set(L1 &&lambda1, L&& ...lambdas):
+        L1(std::move(lambda1)),
+        base_type(std::forward<L>(lambdas)...) {}
+
+    overload_lamba_set(const L1 &lambda1, L&& ...lambdas):
+        L1(lambda1),
+        base_type(std::forward<L>(lambdas)...) {}
+
+    using L1::operator();
+    using base_type::operator();
+};
+
+template<typename... L>
+overload_lamba_set<L...> overload_lambdas(L&& ...lambdas)
+{
+    return overload_lamba_set<L...>(std::forward<L>(lambdas)...);
+}
+
+template<typename ...T>
+struct find_adapter_impl;
+
+template<typename AdapterT, typename T>
+struct find_adapter_impl<AdapterT, T>
+{
+    using type = typename std::conditional<std::is_base_of<AdapterT, T>::value,
+                                           T,
+                                           void>::type;
+    static constexpr bool found = std::is_base_of<AdapterT, T>::value;
+};
+
+template<typename AdapterT, typename T, typename... Types>
+struct find_adapter_impl<AdapterT, T, Types...>
+{
+    using type = typename std::conditional<std::is_base_of<AdapterT, T>::value,
+                                           T,
+                                           typename find_adapter_impl<AdapterT, Types...>::type>::type;
+    static constexpr bool found = std::is_base_of<AdapterT, T>::value ||
+                                  find_adapter_impl<AdapterT, Types...>::found;
+};
+} // namespace util
+} // namespace cv
+
+// \endcond
+
+#endif //  OPENCV_GAPI_UTIL_HPP
diff --git a/3rdParty/include/opencv2/gapi/util/variant.hpp b/3rdParty/include/opencv2/gapi/util/variant.hpp
new file mode 100644
index 0000000..f412110
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/util/variant.hpp
@@ -0,0 +1,658 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2018 Intel Corporation
+
+
+#ifndef OPENCV_GAPI_UTIL_VARIANT_HPP
+#define OPENCV_GAPI_UTIL_VARIANT_HPP
+
+#include <array>
+#include <type_traits>
+
+#include <opencv2/gapi/util/compiler_hints.hpp>
+#include <opencv2/gapi/util/throw.hpp>
+#include <opencv2/gapi/util/util.hpp> // max_of_t
+#include <opencv2/gapi/util/type_traits.hpp>
+
+// A poor man's `variant` implementation, incompletely modeled against C++17 spec.
+namespace cv
+{
+namespace util
+{
+    namespace detail
+    {
+        template<std::size_t I, typename Target, typename First, typename... Remaining>
+        struct type_list_index_helper
+        {
+            static const constexpr bool is_same = std::is_same<Target, First>::value;
+            static const constexpr std::size_t value =
+                std::conditional<is_same, std::integral_constant<std::size_t, I>, type_list_index_helper<I + 1, Target, Remaining...>>::type::value;
+        };
+
+        template<std::size_t I, typename Target, typename First>
+        struct type_list_index_helper<I, Target, First>
+        {
+            static_assert(std::is_same<Target, First>::value, "Type not found");
+            static const constexpr std::size_t value = I;
+        };
+    }
+
+    template<typename Target, typename... Types>
+    struct type_list_index
+    {
+        static const constexpr std::size_t value = detail::type_list_index_helper<0, Target, Types...>::value;
+    };
+
+    template<std::size_t Index, class... Types >
+    struct type_list_element
+    {
+        using type = typename std::tuple_element<Index, std::tuple<Types...> >::type;
+    };
+
+    class bad_variant_access: public std::exception
+    {
+    public:
+        virtual const char *what() const noexcept override
+        {
+            return "Bad variant access";
+        }
+    };
+
+    // Interface ///////////////////////////////////////////////////////////////
+    struct monostate {};
+    inline bool operator==(const util::monostate&, const util::monostate&)
+    {
+        return true;
+    }
+
+    template<typename... Ts> // FIXME: no references, arrays, and void
+    class variant
+    {
+        // FIXME: Replace with std::aligned_union after gcc4.8 support is dropped
+        static constexpr const std::size_t S = cv::detail::max_of_t<sizeof(Ts)...>::value;
+        static constexpr const std::size_t A = cv::detail::max_of_t<alignof(Ts)...>::value;
+        using Memory = typename std::aligned_storage<S, A>::type[1];
+
+        template<typename T> struct cctr_h {
+            static void help(Memory memory, const Memory from) {
+                new (memory) T(*reinterpret_cast<const T*>(from));
+            }
+        };
+
+        template<typename T> struct mctr_h {
+            static void help(Memory memory, void *pval) {
+                new (memory) T(std::move(*reinterpret_cast<T*>(pval)));
+            }
+        };
+
+        //FIXME: unify with cctr_h and mctr_h
+        template<typename T> struct cnvrt_ctor_h {
+            static void help(Memory memory, void* from) {
+                using util::decay_t;
+                new (memory) decay_t<T>(std::forward<T>(*reinterpret_cast<decay_t<T>*>(from)));
+            }
+        };
+
+        template<typename T> struct copy_h {
+            static void help(Memory to, const Memory from) {
+                *reinterpret_cast<T*>(to) = *reinterpret_cast<const T*>(from);
+            }
+        };
+
+        template<typename T> struct move_h {
+            static void help(Memory to, Memory from) {
+                *reinterpret_cast<T*>(to) = std::move(*reinterpret_cast<T*>(from));
+            }
+        };
+
+        //FIXME: unify with copy_h and move_h
+        template<typename T> struct cnvrt_assign_h {
+            static void help(Memory to, void* from) {
+                using util::decay_t;
+                *reinterpret_cast<decay_t<T>*>(to) = std::forward<T>(*reinterpret_cast<decay_t<T>*>(from));
+            }
+        };
+
+        template<typename T> struct swap_h {
+            static void help(Memory to, Memory from) {
+                std::swap(*reinterpret_cast<T*>(to), *reinterpret_cast<T*>(from));
+            }
+        };
+
+        template<typename T> struct dtor_h {
+            static void help(Memory memory) {
+                (void) memory; // MSCV warning
+                reinterpret_cast<T*>(memory)->~T();
+            }
+        };
+
+        template<typename T> struct equal_h {
+            static bool help(const Memory lhs, const Memory rhs) {
+                const T& t_lhs = *reinterpret_cast<const T*>(lhs);
+                const T& t_rhs = *reinterpret_cast<const T*>(rhs);
+                return t_lhs == t_rhs;
+            }
+        };
+
+        typedef void (*CCtr) (Memory, const Memory);  // Copy c-tor (variant)
+        typedef void (*MCtr) (Memory, void*);         // Generic move c-tor
+        typedef void (*Copy) (Memory, const Memory);  // Copy assignment
+        typedef void (*Move) (Memory, Memory);        // Move assignment
+
+        typedef void (*Swap) (Memory, Memory);        // Swap
+        typedef void (*Dtor) (Memory);                // Destructor
+
+        using  cnvrt_assgn_t   = void (*) (Memory, void*);  // Converting assignment (via std::forward)
+        using  cnvrt_ctor_t    = void (*) (Memory, void*);  // Converting constructor (via std::forward)
+
+        typedef bool (*Equal)(const Memory, const Memory); // Equality test (external)
+
+        static constexpr std::array<CCtr, sizeof...(Ts)> cctrs(){ return {{(&cctr_h<Ts>::help)...}};}
+        static constexpr std::array<MCtr, sizeof...(Ts)> mctrs(){ return {{(&mctr_h<Ts>::help)...}};}
+        static constexpr std::array<Copy, sizeof...(Ts)> cpyrs(){ return {{(&copy_h<Ts>::help)...}};}
+        static constexpr std::array<Move, sizeof...(Ts)> mvers(){ return {{(&move_h<Ts>::help)...}};}
+        static constexpr std::array<Swap, sizeof...(Ts)> swprs(){ return {{(&swap_h<Ts>::help)...}};}
+        static constexpr std::array<Dtor, sizeof...(Ts)> dtors(){ return {{(&dtor_h<Ts>::help)...}};}
+
+        template<bool cond, typename T>
+        struct conditional_ref : std::conditional<cond, typename std::remove_reference<T>::type&, typename std::remove_reference<T>::type > {};
+
+        template<bool cond, typename T>
+        using conditional_ref_t = typename conditional_ref<cond, T>::type;
+
+
+        template<bool is_lvalue_arg>
+        static constexpr std::array<cnvrt_assgn_t, sizeof...(Ts)> cnvrt_assgnrs(){
+            return {{(&cnvrt_assign_h<conditional_ref_t<is_lvalue_arg,Ts>>::help)...}};
+        }
+
+        template<bool is_lvalue_arg>
+        static constexpr std::array<cnvrt_ctor_t, sizeof...(Ts)> cnvrt_ctors(){
+            return {{(&cnvrt_ctor_h<conditional_ref_t<is_lvalue_arg,Ts>>::help)...}};
+        }
+
+        std::size_t m_index = 0;
+
+    protected:
+        template<typename T, typename... Us> friend T& get(variant<Us...> &v);
+        template<typename T, typename... Us> friend const T& get(const variant<Us...> &v);
+        template<typename T, typename... Us> friend T* get_if(variant<Us...> *v) noexcept;
+        template<typename T, typename... Us> friend const T* get_if(const variant<Us...> *v) noexcept;
+
+        template<typename... Us> friend bool operator==(const variant<Us...> &lhs,
+                                                        const variant<Us...> &rhs);
+        Memory memory;
+
+    public:
+        // Constructors
+        variant() noexcept;
+        variant(const variant& other);
+        variant(variant&& other) noexcept;
+        // are_different_t is a SFINAE trick to avoid variant(T &&t) with T=variant
+        // for some reason, this version is called instead of variant(variant&& o) when
+        // variant is used in STL containers (examples: vector assignment).
+        template<
+            typename T,
+            typename = util::are_different_t<variant, T>
+        >
+        explicit variant(T&& t);
+        // template<class T, class... Args> explicit variant(Args&&... args);
+        // FIXME: other constructors
+
+        // Destructor
+        ~variant();
+
+        // Assignment
+        variant& operator=(const variant& rhs);
+        variant& operator=(variant &&rhs) noexcept;
+
+        // SFINAE trick to avoid operator=(T&&) with T=variant<>, see comment above
+        template<
+            typename T,
+            typename = util::are_different_t<variant, T>
+        >
+        variant& operator=(T&& t) noexcept;
+
+        // Observers
+        std::size_t index() const noexcept;
+        // FIXME: valueless_by_exception()
+
+        // Modifiers
+        // FIXME: emplace()
+        void swap(variant &rhs) noexcept;
+
+        // Non-C++17x!
+        template<typename T> static constexpr std::size_t index_of();
+    };
+
+    // FIMXE: visit
+    template<typename T, typename... Types>
+    T* get_if(util::variant<Types...>* v) noexcept;
+
+    template<typename T, typename... Types>
+    const T* get_if(const util::variant<Types...>* v) noexcept;
+
+    template<typename T, typename... Types>
+    T& get(util::variant<Types...> &v);
+
+    template<typename T, typename... Types>
+    const T& get(const util::variant<Types...> &v);
+
+    template<std::size_t Index, typename... Types>
+    typename util::type_list_element<Index, Types...>::type& get(util::variant<Types...> &v);
+
+    template<std::size_t Index, typename... Types>
+    const typename util::type_list_element<Index, Types...>::type& get(const util::variant<Types...> &v);
+
+    template<typename T, typename... Types>
+    bool holds_alternative(const util::variant<Types...> &v) noexcept;
+
+
+    // Visitor
+    namespace detail
+    {
+        struct visitor_interface {};
+
+        // Class `visitor_return_type_deduction_helper`
+        // introduces solution for deduction `return_type` in `visit` function in common way
+        // for both Lambda and class Visitor and keep one interface invocation point: `visit` only
+        // his helper class is required to unify return_type deduction mechanism because
+        // for Lambda it is possible to take type of `decltype(visitor(get<0>(var)))`
+        // but for class Visitor there is no operator() in base case,
+        // because it provides `operator() (std::size_t index, ...)`
+        // So `visitor_return_type_deduction_helper` expose `operator()`
+        // uses only for class Visitor only for deduction `return type` in visit()
+        template<typename R>
+        struct visitor_return_type_deduction_helper
+        {
+            using return_type = R;
+
+            // to be used in Lambda return type deduction context only
+            template<typename T>
+            return_type operator() (T&&);
+        };
+    }
+
+    // Special purpose `static_visitor` can receive additional arguments
+    template<typename R, typename Impl>
+    struct static_visitor : public detail::visitor_interface,
+                            public detail::visitor_return_type_deduction_helper<R> {
+
+        // assign responsibility for return type deduction to helper class
+        using return_type = typename detail::visitor_return_type_deduction_helper<R>::return_type;
+        using detail::visitor_return_type_deduction_helper<R>::operator();
+        friend Impl;
+
+        template<typename VariantValue, typename ...Args>
+        return_type operator() (std::size_t index, VariantValue&& value, Args&& ...args)
+        {
+            suppress_unused_warning(index);
+            return static_cast<Impl*>(this)-> visit(
+                                                std::forward<VariantValue>(value),
+                                                std::forward<Args>(args)...);
+        }
+    };
+
+    // Special purpose `static_indexed_visitor` can receive additional arguments
+    // And make forwarding current variant index as runtime function argument to its `Impl`
+    template<typename R, typename Impl>
+    struct static_indexed_visitor : public detail::visitor_interface,
+                                    public detail::visitor_return_type_deduction_helper<R> {
+
+        // assign responsibility for return type deduction to helper class
+        using return_type = typename detail::visitor_return_type_deduction_helper<R>::return_type;
+        using detail::visitor_return_type_deduction_helper<R>::operator();
+        friend Impl;
+
+        template<typename VariantValue, typename ...Args>
+        return_type operator() (std::size_t Index, VariantValue&& value, Args&& ...args)
+        {
+            return static_cast<Impl*>(this)-> visit(Index,
+                                                std::forward<VariantValue>(value),
+                                                std::forward<Args>(args)...);
+        }
+    };
+
+    template <class T>
+    struct variant_size;
+
+    template <class... Types>
+    struct variant_size<util::variant<Types...>>
+        : std::integral_constant<std::size_t, sizeof...(Types)> { };
+    // FIXME: T&&, const TT&& versions.
+
+    // Implementation //////////////////////////////////////////////////////////
+    template<typename... Ts>
+    variant<Ts...>::variant() noexcept
+    {
+        typedef typename std::tuple_element<0, std::tuple<Ts...> >::type TFirst;
+        new (memory) TFirst();
+    }
+
+    template<typename... Ts>
+    variant<Ts...>::variant(const variant &other)
+        : m_index(other.m_index)
+    {
+        (cctrs()[m_index])(memory, other.memory);
+    }
+
+    template<typename... Ts>
+    variant<Ts...>::variant(variant &&other) noexcept
+        : m_index(other.m_index)
+    {
+        (mctrs()[m_index])(memory, other.memory);
+    }
+
+    template<typename... Ts>
+    template<class T, typename>
+    variant<Ts...>::variant(T&& t)
+        : m_index(util::type_list_index<util::decay_t<T>, Ts...>::value)
+    {
+        const constexpr bool is_lvalue_arg =  std::is_lvalue_reference<T>::value;
+        (cnvrt_ctors<is_lvalue_arg>()[m_index])(memory, const_cast<util::decay_t<T> *>(&t));
+    }
+
+    template<typename... Ts>
+    variant<Ts...>::~variant()
+    {
+        (dtors()[m_index])(memory);
+    }
+
+    template<typename... Ts>
+    variant<Ts...>& variant<Ts...>::operator=(const variant<Ts...> &rhs)
+    {
+        if (m_index != rhs.m_index)
+        {
+            (dtors()[    m_index])(memory);
+            (cctrs()[rhs.m_index])(memory, rhs.memory);
+            m_index = rhs.m_index;
+        }
+        else
+        {
+            (cpyrs()[rhs.m_index])(memory, rhs.memory);
+        }
+        return *this;
+    }
+
+    template<typename... Ts>
+    variant<Ts...>& variant<Ts...>::operator=(variant<Ts...> &&rhs) noexcept
+    {
+        if (m_index != rhs.m_index)
+        {
+            (dtors()[    m_index])(memory);
+            (mctrs()[rhs.m_index])(memory, rhs.memory);
+            m_index = rhs.m_index;
+        }
+        else
+        {
+            (mvers()[rhs.m_index])(memory, rhs.memory);
+        }
+        return *this;
+    }
+
+    template<typename... Ts>
+    template<typename T, typename>
+    variant<Ts...>& variant<Ts...>::operator=(T&& t) noexcept
+    {
+        using decayed_t = util::decay_t<T>;
+        // FIXME: No version with implicit type conversion available!
+        const constexpr std::size_t t_index =
+            util::type_list_index<decayed_t, Ts...>::value;
+
+        const constexpr bool is_lvalue_arg =  std::is_lvalue_reference<T>::value;
+
+        if (t_index != m_index)
+        {
+            (dtors()[m_index])(memory);
+            (cnvrt_ctors<is_lvalue_arg>()[t_index])(memory, &t);
+            m_index = t_index;
+        }
+        else
+        {
+            (cnvrt_assgnrs<is_lvalue_arg>()[m_index])(memory, &t);
+        }
+        return *this;
+
+    }
+
+    template<typename... Ts>
+    std::size_t util::variant<Ts...>::index() const noexcept
+    {
+        return m_index;
+    }
+
+    template<typename... Ts>
+    void variant<Ts...>::swap(variant<Ts...> &rhs) noexcept
+    {
+        if (m_index == rhs.index())
+        {
+            (swprs()[m_index](memory, rhs.memory));
+        }
+        else
+        {
+            variant<Ts...> tmp(std::move(*this));
+            *this = std::move(rhs);
+            rhs   = std::move(tmp);
+        }
+    }
+
+    template<typename... Ts>
+    template<typename T>
+    constexpr std::size_t variant<Ts...>::index_of()
+    {
+        return util::type_list_index<T, Ts...>::value; // FIXME: tests!
+    }
+
+    template<typename T, typename... Types>
+    T* get_if(util::variant<Types...>* v) noexcept
+    {
+        const constexpr std::size_t t_index =
+            util::type_list_index<T, Types...>::value;
+
+        if (v && v->index() == t_index)
+            return (T*)(&v->memory);  // workaround for ICC 2019
+            // original code: return reinterpret_cast<T&>(v.memory);
+        return nullptr;
+    }
+
+    template<typename T, typename... Types>
+    const T* get_if(const util::variant<Types...>* v) noexcept
+    {
+        const constexpr std::size_t t_index =
+            util::type_list_index<T, Types...>::value;
+
+        if (v && v->index() == t_index)
+            return (const T*)(&v->memory);  // workaround for ICC 2019
+            // original code: return reinterpret_cast<const T&>(v.memory);
+        return nullptr;
+    }
+
+    template<typename T, typename... Types>
+    T& get(util::variant<Types...> &v)
+    {
+        if (auto* p = get_if<T>(&v))
+            return *p;
+        else
+            throw_error(bad_variant_access());
+    }
+
+    template<typename T, typename... Types>
+    const T& get(const util::variant<Types...> &v)
+    {
+        if (auto* p = get_if<T>(&v))
+            return *p;
+        else
+            throw_error(bad_variant_access());
+    }
+
+    template<std::size_t Index, typename... Types>
+    typename util::type_list_element<Index, Types...>::type& get(util::variant<Types...> &v)
+    {
+        using ReturnType = typename util::type_list_element<Index, Types...>::type;
+        return const_cast<ReturnType&>(get<Index, Types...>(static_cast<const util::variant<Types...> &>(v)));
+    }
+
+    template<std::size_t Index, typename... Types>
+    const typename util::type_list_element<Index, Types...>::type& get(const util::variant<Types...> &v)
+    {
+        static_assert(Index < sizeof...(Types),
+                      "`Index` it out of bound of `util::variant` type list");
+        using ReturnType = typename util::type_list_element<Index, Types...>::type;
+        return get<ReturnType>(v);
+    }
+
+    template<typename T, typename... Types>
+    bool holds_alternative(const util::variant<Types...> &v) noexcept
+    {
+        return v.index() == util::variant<Types...>::template index_of<T>();
+    }
+
+    template<typename... Us> bool operator==(const variant<Us...> &lhs,
+                                             const variant<Us...> &rhs)
+    {
+        using V = variant<Us...>;
+
+        // Instantiate table only here since it requires operator== for <Us...>
+        // <Us...> should have operator== only if this one is used, not in general
+        static const std::array<typename V::Equal, sizeof...(Us)> eqs = {
+            {(&V::template equal_h<Us>::help)...}
+        };
+        if (lhs.index() != rhs.index())
+            return false;
+        return (eqs[lhs.index()])(lhs.memory, rhs.memory);
+    }
+
+    template<typename... Us> bool operator!=(const variant<Us...> &lhs,
+                                             const variant<Us...> &rhs)
+    {
+        return !(lhs == rhs);
+    }
+
+namespace detail
+{
+    // terminate recursion implementation for `non-void` ReturnType
+    template<typename ReturnType, std::size_t CurIndex, std::size_t ElemCount,
+             typename Visitor, typename Variant, typename... VisitorArgs>
+    ReturnType apply_visitor_impl(Visitor&&, Variant&,
+                                  std::true_type, std::false_type,
+                                  VisitorArgs&& ...)
+    {
+        return {};
+    }
+
+    // terminate recursion implementation for `void` ReturnType
+    template<typename ReturnType, std::size_t CurIndex, std::size_t ElemCount,
+             typename Visitor, typename Variant, typename... VisitorArgs>
+    void apply_visitor_impl(Visitor&&, Variant&,
+                            std::true_type, std::true_type,
+                            VisitorArgs&& ...)
+    {
+    }
+
+    // Intermediate resursion processor for Lambda Visitors
+    template<typename ReturnType, std::size_t CurIndex, std::size_t ElemCount,
+             typename Visitor, typename Variant, bool no_return_value, typename... VisitorArgs>
+    typename std::enable_if<!std::is_base_of<visitor_interface, typename std::decay<Visitor>::type>::value, ReturnType>::type
+         apply_visitor_impl(Visitor&& visitor, Variant&& v, std::false_type not_processed,
+                                               std::integral_constant<bool, no_return_value> should_no_return,
+                                               VisitorArgs&& ...args)
+    {
+        static_assert(std::is_same<ReturnType, decltype(visitor(get<CurIndex>(v)))>::value,
+                      "Different `ReturnType`s detected! All `Visitor::visit` or `overload_lamba_set`"
+                      " must return the same type");
+        suppress_unused_warning(not_processed);
+        if (v.index() == CurIndex)
+        {
+            return visitor.operator()(get<CurIndex>(v), std::forward<VisitorArgs>(args)... );
+        }
+
+        using is_variant_processed_t = std::integral_constant<bool, CurIndex + 1 >= ElemCount>;
+        return apply_visitor_impl<ReturnType, CurIndex +1, ElemCount>(
+                                  std::forward<Visitor>(visitor),
+                                  std::forward<Variant>(v),
+                                  is_variant_processed_t{},
+                                  should_no_return,
+                                  std::forward<VisitorArgs>(args)...);
+    }
+
+    //Visual Studio 2014 compilation fix: cast visitor to base class before invoke operator()
+    template<std::size_t CurIndex, typename ReturnType, typename Visitor, class Value, typename... VisitorArgs>
+    typename std::enable_if<std::is_base_of<static_visitor<ReturnType, typename std::decay<Visitor>::type>,
+                                            typename std::decay<Visitor>::type>::value, ReturnType>::type
+    invoke_class_visitor(Visitor& visitor, Value&& v,  VisitorArgs&&...args)
+    {
+        return static_cast<static_visitor<ReturnType, typename std::decay<Visitor>::type>&>(visitor).operator() (CurIndex, std::forward<Value>(v), std::forward<VisitorArgs>(args)... );
+    }
+
+    //Visual Studio 2014 compilation fix: cast visitor to base class before invoke operator()
+    template<std::size_t CurIndex, typename ReturnType, typename Visitor, class Value, typename... VisitorArgs>
+    typename std::enable_if<std::is_base_of<static_indexed_visitor<ReturnType, typename std::decay<Visitor>::type>,
+                                            typename std::decay<Visitor>::type>::value, ReturnType>::type
+    invoke_class_visitor(Visitor& visitor, Value&& v,  VisitorArgs&&...args)
+    {
+        return static_cast<static_indexed_visitor<ReturnType, typename std::decay<Visitor>::type>&>(visitor).operator() (CurIndex, std::forward<Value>(v), std::forward<VisitorArgs>(args)... );
+    }
+
+    // Intermediate recursion processor for special case `visitor_interface` derived Visitors
+    template<typename ReturnType, std::size_t CurIndex, std::size_t ElemCount,
+             typename Visitor, typename Variant, bool no_return_value, typename... VisitorArgs>
+    typename std::enable_if<std::is_base_of<visitor_interface, typename std::decay<Visitor>::type>::value, ReturnType>::type
+         apply_visitor_impl(Visitor&& visitor, Variant&& v, std::false_type not_processed,
+                                               std::integral_constant<bool, no_return_value> should_no_return,
+                                               VisitorArgs&& ...args)
+    {
+        static_assert(std::is_same<ReturnType, decltype(visitor(get<CurIndex>(v)))>::value,
+                      "Different `ReturnType`s detected! All `Visitor::visit` or `overload_lamba_set`"
+                      " must return the same type");
+        suppress_unused_warning(not_processed);
+        if (v.index() == CurIndex)
+        {
+            return invoke_class_visitor<CurIndex, ReturnType>(visitor, get<CurIndex>(v), std::forward<VisitorArgs>(args)... );
+        }
+
+        using is_variant_processed_t = std::integral_constant<bool, CurIndex + 1 >= ElemCount>;
+        return apply_visitor_impl<ReturnType, CurIndex +1, ElemCount>(
+                                  std::forward<Visitor>(visitor),
+                                  std::forward<Variant>(v),
+                                  is_variant_processed_t{},
+                                  should_no_return,
+                                  std::forward<VisitorArgs>(args)...);
+    }
+} // namespace detail
+
+    template<typename Visitor, typename Variant, typename... VisitorArg>
+    auto visit(Visitor &visitor, const Variant& var, VisitorArg &&...args) -> decltype(visitor(get<0>(var)))
+    {
+        constexpr std::size_t varsize = util::variant_size<Variant>::value;
+        static_assert(varsize != 0, "utils::variant must contains one type at least ");
+        using is_variant_processed_t = std::false_type;
+
+        using ReturnType = decltype(visitor(get<0>(var)));
+        using return_t = std::is_same<ReturnType, void>;
+        return detail::apply_visitor_impl<ReturnType, 0, varsize, Visitor>(
+                                    std::forward<Visitor>(visitor),
+                                    var, is_variant_processed_t{},
+                                    return_t{},
+                                    std::forward<VisitorArg>(args)...);
+    }
+
+    template<typename Visitor, typename Variant>
+    auto visit(Visitor&& visitor, const Variant& var) -> decltype(visitor(get<0>(var)))
+    {
+        constexpr std::size_t varsize = util::variant_size<Variant>::value;
+        static_assert(varsize != 0, "utils::variant must contains one type at least ");
+        using is_variant_processed_t = std::false_type;
+
+        using ReturnType = decltype(visitor(get<0>(var)));
+        using return_t = std::is_same<ReturnType, void>;
+        return detail::apply_visitor_impl<ReturnType, 0, varsize, Visitor>(
+                                    std::forward<Visitor>(visitor),
+                                    var, is_variant_processed_t{},
+                                    return_t{});
+    }
+} // namespace util
+} // namespace cv
+
+#endif // OPENCV_GAPI_UTIL_VARIANT_HPP
diff --git a/3rdParty/include/opencv2/gapi/video.hpp b/3rdParty/include/opencv2/gapi/video.hpp
new file mode 100644
index 0000000..4dcc1d4
--- /dev/null
+++ b/3rdParty/include/opencv2/gapi/video.hpp
@@ -0,0 +1,364 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2020 Intel Corporation
+
+#ifndef OPENCV_GAPI_VIDEO_HPP
+#define OPENCV_GAPI_VIDEO_HPP
+
+#include <utility> // std::tuple
+
+#include <opencv2/gapi/gkernel.hpp>
+
+
+/** \defgroup gapi_video G-API Video processing functionality
+ */
+
+namespace cv { namespace gapi {
+
+/** @brief Structure for the Kalman filter's initialization parameters.*/
+
+struct GAPI_EXPORTS KalmanParams
+{
+    // initial state
+
+    //! corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
+    Mat state;
+    //! posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
+    Mat errorCov;
+
+    // dynamic system description
+
+    //! state transition matrix (A)
+    Mat transitionMatrix;
+    //! measurement matrix (H)
+    Mat measurementMatrix;
+    //! process noise covariance matrix (Q)
+    Mat processNoiseCov;
+    //! measurement noise covariance matrix (R)
+    Mat measurementNoiseCov;
+    //! control matrix (B) (Optional: not used if there's no control)
+    Mat controlMatrix;
+};
+
+/**
+ * @brief This namespace contains G-API Operations and functions for
+ * video-oriented algorithms, like optical flow and background subtraction.
+ */
+namespace  video
+{
+using GBuildPyrOutput  = std::tuple<GArray<GMat>, GScalar>;
+
+using GOptFlowLKOutput = std::tuple<cv::GArray<cv::Point2f>,
+                                    cv::GArray<uchar>,
+                                    cv::GArray<float>>;
+
+G_TYPED_KERNEL(GBuildOptFlowPyramid, <GBuildPyrOutput(GMat,Size,GScalar,bool,int,int,bool)>,
+               "org.opencv.video.buildOpticalFlowPyramid")
+{
+    static std::tuple<GArrayDesc,GScalarDesc>
+            outMeta(GMatDesc,const Size&,GScalarDesc,bool,int,int,bool)
+    {
+        return std::make_tuple(empty_array_desc(), empty_scalar_desc());
+    }
+};
+
+G_TYPED_KERNEL(GCalcOptFlowLK,
+               <GOptFlowLKOutput(GMat,GMat,cv::GArray<cv::Point2f>,cv::GArray<cv::Point2f>,Size,
+                                 GScalar,TermCriteria,int,double)>,
+               "org.opencv.video.calcOpticalFlowPyrLK")
+{
+    static std::tuple<GArrayDesc,GArrayDesc,GArrayDesc> outMeta(GMatDesc,GMatDesc,GArrayDesc,
+                                                                GArrayDesc,const Size&,GScalarDesc,
+                                                                const TermCriteria&,int,double)
+    {
+        return std::make_tuple(empty_array_desc(), empty_array_desc(), empty_array_desc());
+    }
+
+};
+
+G_TYPED_KERNEL(GCalcOptFlowLKForPyr,
+               <GOptFlowLKOutput(cv::GArray<cv::GMat>,cv::GArray<cv::GMat>,
+                                 cv::GArray<cv::Point2f>,cv::GArray<cv::Point2f>,Size,GScalar,
+                                 TermCriteria,int,double)>,
+               "org.opencv.video.calcOpticalFlowPyrLKForPyr")
+{
+    static std::tuple<GArrayDesc,GArrayDesc,GArrayDesc> outMeta(GArrayDesc,GArrayDesc,
+                                                                GArrayDesc,GArrayDesc,
+                                                                const Size&,GScalarDesc,
+                                                                const TermCriteria&,int,double)
+    {
+        return std::make_tuple(empty_array_desc(), empty_array_desc(), empty_array_desc());
+    }
+};
+
+enum BackgroundSubtractorType
+{
+    TYPE_BS_MOG2,
+    TYPE_BS_KNN
+};
+
+/** @brief Structure for the Background Subtractor operation's initialization parameters.*/
+
+struct BackgroundSubtractorParams
+{
+    //! Type of the Background Subtractor operation.
+    BackgroundSubtractorType operation = TYPE_BS_MOG2;
+
+    //! Length of the history.
+    int history = 500;
+
+    //! For MOG2: Threshold on the squared Mahalanobis distance between the pixel
+    //! and the model to decide whether a pixel is well described by
+    //! the background model.
+    //! For KNN: Threshold on the squared distance between the pixel and the sample
+    //! to decide whether a pixel is close to that sample.
+    double threshold = 16;
+
+    //! If true, the algorithm will detect shadows and mark them.
+    bool detectShadows = true;
+
+    //! The value between 0 and 1 that indicates how fast
+    //! the background model is learnt.
+    //! Negative parameter value makes the algorithm use some automatically
+    //! chosen learning rate.
+    double learningRate = -1;
+
+    //! default constructor
+    BackgroundSubtractorParams() {}
+
+    /** Full constructor
+    @param op MOG2/KNN Background Subtractor type.
+    @param histLength Length of the history.
+    @param thrshld For MOG2: Threshold on the squared Mahalanobis distance between
+    the pixel and the model to decide whether a pixel is well described by the background model.
+    For KNN: Threshold on the squared distance between the pixel and the sample to decide
+    whether a pixel is close to that sample.
+    @param detect If true, the algorithm will detect shadows and mark them. It decreases the
+    speed a bit, so if you do not need this feature, set the parameter to false.
+    @param lRate The value between 0 and 1 that indicates how fast the background model is learnt.
+    Negative parameter value makes the algorithm to use some automatically chosen learning rate.
+    */
+    BackgroundSubtractorParams(BackgroundSubtractorType op, int histLength,
+                               double thrshld, bool detect, double lRate) : operation(op),
+                                                                            history(histLength),
+                                                                            threshold(thrshld),
+                                                                            detectShadows(detect),
+                                                                            learningRate(lRate){}
+};
+
+G_TYPED_KERNEL(GBackgroundSubtractor, <GMat(GMat, BackgroundSubtractorParams)>,
+               "org.opencv.video.BackgroundSubtractor")
+{
+    static GMatDesc outMeta(const GMatDesc& in, const BackgroundSubtractorParams& bsParams)
+    {
+        GAPI_Assert(bsParams.history >= 0);
+        GAPI_Assert(bsParams.learningRate <= 1);
+        return in.withType(CV_8U, 1);
+    }
+};
+
+void checkParams(const cv::gapi::KalmanParams& kfParams,
+                 const cv::GMatDesc& measurement, const cv::GMatDesc& control = {});
+
+G_TYPED_KERNEL(GKalmanFilter, <GMat(GMat, GOpaque<bool>, GMat, KalmanParams)>,
+               "org.opencv.video.KalmanFilter")
+{
+    static GMatDesc outMeta(const GMatDesc& measurement, const GOpaqueDesc&,
+                            const GMatDesc& control, const KalmanParams& kfParams)
+    {
+        checkParams(kfParams, measurement, control);
+        return measurement.withSize(Size(1, kfParams.transitionMatrix.rows));
+    }
+};
+
+G_TYPED_KERNEL(GKalmanFilterNoControl, <GMat(GMat, GOpaque<bool>, KalmanParams)>, "org.opencv.video.KalmanFilterNoControl")
+{
+    static GMatDesc outMeta(const GMatDesc& measurement, const GOpaqueDesc&, const KalmanParams& kfParams)
+    {
+        checkParams(kfParams, measurement);
+        return measurement.withSize(Size(1, kfParams.transitionMatrix.rows));
+    }
+};
+} //namespace video
+
+//! @addtogroup gapi_video
+//! @{
+/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
+
+@note Function textual ID is "org.opencv.video.buildOpticalFlowPyramid"
+
+@param img                8-bit input image.
+@param winSize            window size of optical flow algorithm. Must be not less than winSize
+                          argument of calcOpticalFlowPyrLK. It is needed to calculate required
+                          padding for pyramid levels.
+@param maxLevel           0-based maximal pyramid level number.
+@param withDerivatives    set to precompute gradients for the every pyramid level. If pyramid is
+                          constructed without the gradients then calcOpticalFlowPyrLK will calculate
+                          them internally.
+@param pyrBorder          the border mode for pyramid layers.
+@param derivBorder        the border mode for gradients.
+@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
+                          to force data copying.
+
+@return
+ - output pyramid.
+ - number of levels in constructed pyramid. Can be less than maxLevel.
+ */
+GAPI_EXPORTS std::tuple<GArray<GMat>, GScalar>
+buildOpticalFlowPyramid(const GMat     &img,
+                        const Size     &winSize,
+                        const GScalar  &maxLevel,
+                              bool      withDerivatives    = true,
+                              int       pyrBorder          = BORDER_REFLECT_101,
+                              int       derivBorder        = BORDER_CONSTANT,
+                              bool      tryReuseInputImage = true);
+
+/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade
+method with pyramids.
+
+See @cite Bouguet00 .
+
+@note Function textual ID is "org.opencv.video.calcOpticalFlowPyrLK"
+
+@param prevImg first 8-bit input image (GMat) or pyramid (GArray<GMat>) constructed by
+buildOpticalFlowPyramid.
+@param nextImg second input image (GMat) or pyramid (GArray<GMat>) of the same size and the same
+type as prevImg.
+@param prevPts GArray of 2D points for which the flow needs to be found; point coordinates must be
+single-precision floating-point numbers.
+@param predPts GArray of 2D points initial for the flow search; make sense only when
+OPTFLOW_USE_INITIAL_FLOW flag is passed; in that case the vector must have the same size as in
+the input.
+@param winSize size of the search window at each pyramid level.
+@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
+level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
+algorithm will use as many levels as pyramids have but no more than maxLevel.
+@param criteria parameter, specifying the termination criteria of the iterative search algorithm
+(after the specified maximum number of iterations criteria.maxCount or when the search window
+moves by less than criteria.epsilon).
+@param flags operation flags:
+ -   **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
+     not set, then prevPts is copied to nextPts and is considered the initial estimate.
+ -   **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see
+     minEigThreshold description); if the flag is not set, then L1 distance between patches
+     around the original and a moved point, divided by number of pixels in a window, is used as a
+     error measure.
+@param minEigThresh the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
+optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided
+by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
+feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
+performance boost.
+
+@return
+ - GArray of 2D points (with single-precision floating-point coordinates)
+containing the calculated new positions of input features in the second image.
+ - status GArray (of unsigned chars); each element of the vector is set to 1 if
+the flow for the corresponding features has been found, otherwise, it is set to 0.
+ - GArray of errors (doubles); each element of the vector is set to an error for the
+corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
+found then the error is not defined (use the status parameter to find such cases).
+ */
+GAPI_EXPORTS std::tuple<GArray<Point2f>, GArray<uchar>, GArray<float>>
+calcOpticalFlowPyrLK(const GMat            &prevImg,
+                     const GMat            &nextImg,
+                     const GArray<Point2f> &prevPts,
+                     const GArray<Point2f> &predPts,
+                     const Size            &winSize      = Size(21, 21),
+                     const GScalar         &maxLevel     = 3,
+                     const TermCriteria    &criteria     = TermCriteria(TermCriteria::COUNT |
+                                                                        TermCriteria::EPS,
+                                                                        30, 0.01),
+                           int              flags        = 0,
+                           double           minEigThresh = 1e-4);
+
+/**
+@overload
+@note Function textual ID is "org.opencv.video.calcOpticalFlowPyrLKForPyr"
+*/
+GAPI_EXPORTS std::tuple<GArray<Point2f>, GArray<uchar>, GArray<float>>
+calcOpticalFlowPyrLK(const GArray<GMat>    &prevPyr,
+                     const GArray<GMat>    &nextPyr,
+                     const GArray<Point2f> &prevPts,
+                     const GArray<Point2f> &predPts,
+                     const Size            &winSize      = Size(21, 21),
+                     const GScalar         &maxLevel     = 3,
+                     const TermCriteria    &criteria     = TermCriteria(TermCriteria::COUNT |
+                                                                        TermCriteria::EPS,
+                                                                        30, 0.01),
+                           int              flags        = 0,
+                           double           minEigThresh = 1e-4);
+
+/** @brief Gaussian Mixture-based or K-nearest neighbours-based Background/Foreground Segmentation Algorithm.
+The operation generates a foreground mask.
+
+@return Output image is foreground mask, i.e. 8-bit unsigned 1-channel (binary) matrix @ref CV_8UC1.
+
+@note Functional textual ID is "org.opencv.video.BackgroundSubtractor"
+
+@param src input image: Floating point frame is used without scaling and should be in range [0,255].
+@param bsParams Set of initialization parameters for Background Subtractor kernel.
+*/
+GAPI_EXPORTS GMat BackgroundSubtractor(const GMat& src, const cv::gapi::video::BackgroundSubtractorParams& bsParams);
+
+/** @brief Standard Kalman filter algorithm <http://en.wikipedia.org/wiki/Kalman_filter>.
+
+@note Functional textual ID is "org.opencv.video.KalmanFilter"
+
+@param measurement input matrix: 32-bit or 64-bit float 1-channel matrix containing measurements.
+@param haveMeasurement dynamic input flag that indicates whether we get measurements
+at a particular iteration .
+@param control input matrix: 32-bit or 64-bit float 1-channel matrix contains control data
+for changing dynamic system.
+@param kfParams Set of initialization parameters for Kalman filter kernel.
+
+@return Output matrix is predicted or corrected state. They can be 32-bit or 64-bit float
+1-channel matrix @ref CV_32FC1 or @ref CV_64FC1.
+
+@details If measurement matrix is given (haveMeasurements == true), corrected state will
+be returned which corresponds to the pipeline
+cv::KalmanFilter::predict(control) -> cv::KalmanFilter::correct(measurement).
+Otherwise, predicted state will be returned which corresponds to the call of
+cv::KalmanFilter::predict(control).
+@sa cv::KalmanFilter
+*/
+GAPI_EXPORTS GMat KalmanFilter(const GMat& measurement, const GOpaque<bool>& haveMeasurement,
+                               const GMat& control, const cv::gapi::KalmanParams& kfParams);
+
+/** @overload
+The case of Standard Kalman filter algorithm when there is no control in a dynamic system.
+In this case the controlMatrix is empty and control vector is absent.
+
+@note Function textual ID is "org.opencv.video.KalmanFilterNoControl"
+
+@param measurement input matrix: 32-bit or 64-bit float 1-channel matrix containing measurements.
+@param haveMeasurement dynamic input flag that indicates whether we get measurements
+at a particular iteration.
+@param kfParams Set of initialization parameters for Kalman filter kernel.
+
+@return Output matrix is predicted or corrected state. They can be 32-bit or 64-bit float
+1-channel matrix @ref CV_32FC1 or @ref CV_64FC1.
+
+@sa cv::KalmanFilter
+ */
+GAPI_EXPORTS GMat KalmanFilter(const GMat& measurement, const GOpaque<bool>& haveMeasurement,
+                               const cv::gapi::KalmanParams& kfParams);
+
+//! @} gapi_video
+} //namespace gapi
+} //namespace cv
+
+
+namespace cv { namespace detail {
+template<> struct CompileArgTag<cv::gapi::video::BackgroundSubtractorParams>
+{
+    static const char* tag()
+    {
+        return "org.opencv.video.background_substractor_params";
+    }
+};
+}  // namespace detail
+}  // namespace cv
+
+#endif // OPENCV_GAPI_VIDEO_HPP
diff --git a/3rdParty/include/opencv2/highgui.hpp b/3rdParty/include/opencv2/highgui.hpp
new file mode 100644
index 0000000..7ae2395
--- /dev/null
+++ b/3rdParty/include/opencv2/highgui.hpp
@@ -0,0 +1,868 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HIGHGUI_HPP
+#define OPENCV_HIGHGUI_HPP
+
+#include "opencv2/core.hpp"
+#ifdef HAVE_OPENCV_IMGCODECS
+#include "opencv2/imgcodecs.hpp"
+#endif
+#ifdef HAVE_OPENCV_VIDEOIO
+#include "opencv2/videoio.hpp"
+#endif
+
+/**
+@defgroup highgui High-level GUI
+
+While OpenCV was designed for use in full-scale applications and can be used within functionally
+rich UI frameworks (such as Qt\*, WinForms\*, or Cocoa\*) or without any UI at all, sometimes there
+it is required to try functionality quickly and visualize the results. This is what the HighGUI
+module has been designed for.
+
+It provides easy interface to:
+
+-   Create and manipulate windows that can display images and "remember" their content (no need to
+    handle repaint events from OS).
+-   Add trackbars to the windows, handle simple mouse events as well as keyboard commands.
+
+@{
+    @defgroup highgui_window_flags Flags related creating and manipulating HighGUI windows and mouse events
+    @defgroup highgui_opengl OpenGL support
+    @defgroup highgui_qt Qt New Functions
+
+    ![image](pics/qtgui.png)
+
+    This figure explains new functionality implemented with Qt\* GUI. The new GUI provides a statusbar,
+    a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it.
+    If you cannot see the control panel, press Ctrl+P or right-click any Qt window and select **Display
+    properties window**.
+
+    -   To attach a trackbar, the window name parameter must be NULL.
+
+    -   To attach a buttonbar, a button must be created. If the last bar attached to the control panel
+        is a buttonbar, the new button is added to the right of the last button. If the last bar
+        attached to the control panel is a trackbar, or the control panel is empty, a new buttonbar is
+        created. Then, a new button is attached to it.
+
+    See below the example used to generate the figure:
+    @code
+        int main(int argc, char *argv[])
+        {
+
+            int value = 50;
+            int value2 = 0;
+
+
+            namedWindow("main1",WINDOW_NORMAL);
+            namedWindow("main2",WINDOW_AUTOSIZE | WINDOW_GUI_NORMAL);
+            createTrackbar( "track1", "main1", &value, 255,  NULL);
+
+            String nameb1 = "button1";
+            String nameb2 = "button2";
+
+            createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1);
+            createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0);
+            createTrackbar( "track2", NULL, &value2, 255, NULL);
+            createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0);
+            createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1);
+
+            setMouseCallback( "main2",on_mouse,NULL );
+
+            Mat img1 = imread("files/flower.jpg");
+            VideoCapture video;
+            video.open("files/hockey.avi");
+
+            Mat img2,img3;
+
+            while( waitKey(33) != 27 )
+            {
+                img1.convertTo(img2,-1,1,value);
+                video >> img3;
+
+                imshow("main1",img2);
+                imshow("main2",img3);
+            }
+
+            destroyAllWindows();
+
+            return 0;
+        }
+    @endcode
+
+
+    @defgroup highgui_winrt WinRT support
+
+    This figure explains new functionality implemented with WinRT GUI. The new GUI provides an Image control,
+    and a slider panel. Slider panel holds trackbars attached to it.
+
+    Sliders are attached below the image control. Every new slider is added below the previous one.
+
+    See below the example used to generate the figure:
+    @code
+        void sample_app::MainPage::ShowWindow()
+        {
+            static cv::String windowName("sample");
+            cv::winrt_initContainer(this->cvContainer);
+            cv::namedWindow(windowName); // not required
+
+            cv::Mat image = cv::imread("Assets/sample.jpg");
+            cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4);
+            cv::cvtColor(image, converted, COLOR_BGR2BGRA);
+            cv::imshow(windowName, converted); // this will create window if it hasn't been created before
+
+            int state = 42;
+            cv::TrackbarCallback callback = [](int pos, void* userdata)
+            {
+                if (pos == 0) {
+                    cv::destroyWindow(windowName);
+                }
+            };
+            cv::TrackbarCallback callbackTwin = [](int pos, void* userdata)
+            {
+                if (pos >= 70) {
+                    cv::destroyAllWindows();
+                }
+            };
+            cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback);
+            cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin);
+        }
+    @endcode
+
+    @defgroup highgui_c C API
+@}
+*/
+
+///////////////////////// graphical user interface //////////////////////////
+namespace cv
+{
+
+//! @addtogroup highgui
+//! @{
+
+//! @addtogroup highgui_window_flags
+//! @{
+
+//! Flags for cv::namedWindow
+enum WindowFlags {
+       WINDOW_NORMAL     = 0x00000000, //!< the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size.
+       WINDOW_AUTOSIZE   = 0x00000001, //!< the user cannot resize the window, the size is constrainted by the image displayed.
+       WINDOW_OPENGL     = 0x00001000, //!< window with opengl support.
+
+       WINDOW_FULLSCREEN = 1,          //!< change the window to fullscreen.
+       WINDOW_FREERATIO  = 0x00000100, //!< the image expends as much as it can (no ratio constraint).
+       WINDOW_KEEPRATIO  = 0x00000000, //!< the ratio of the image is respected.
+       WINDOW_GUI_EXPANDED=0x00000000, //!< status bar and tool bar
+       WINDOW_GUI_NORMAL = 0x00000010, //!< old fashious way
+    };
+
+//! Flags for cv::setWindowProperty / cv::getWindowProperty
+enum WindowPropertyFlags {
+       WND_PROP_FULLSCREEN   = 0, //!< fullscreen property    (can be WINDOW_NORMAL or WINDOW_FULLSCREEN).
+       WND_PROP_AUTOSIZE     = 1, //!< autosize property      (can be WINDOW_NORMAL or WINDOW_AUTOSIZE).
+       WND_PROP_ASPECT_RATIO = 2, //!< window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO).
+       WND_PROP_OPENGL       = 3, //!< opengl support.
+       WND_PROP_VISIBLE      = 4, //!< checks whether the window exists and is visible
+       WND_PROP_TOPMOST      = 5, //!< property to toggle normal window being topmost or not
+       WND_PROP_VSYNC        = 6  //!< enable or disable VSYNC (in OpenGL mode)
+     };
+
+//! Mouse Events see cv::MouseCallback
+enum MouseEventTypes {
+       EVENT_MOUSEMOVE      = 0, //!< indicates that the mouse pointer has moved over the window.
+       EVENT_LBUTTONDOWN    = 1, //!< indicates that the left mouse button is pressed.
+       EVENT_RBUTTONDOWN    = 2, //!< indicates that the right mouse button is pressed.
+       EVENT_MBUTTONDOWN    = 3, //!< indicates that the middle mouse button is pressed.
+       EVENT_LBUTTONUP      = 4, //!< indicates that left mouse button is released.
+       EVENT_RBUTTONUP      = 5, //!< indicates that right mouse button is released.
+       EVENT_MBUTTONUP      = 6, //!< indicates that middle mouse button is released.
+       EVENT_LBUTTONDBLCLK  = 7, //!< indicates that left mouse button is double clicked.
+       EVENT_RBUTTONDBLCLK  = 8, //!< indicates that right mouse button is double clicked.
+       EVENT_MBUTTONDBLCLK  = 9, //!< indicates that middle mouse button is double clicked.
+       EVENT_MOUSEWHEEL     = 10,//!< positive and negative values mean forward and backward scrolling, respectively.
+       EVENT_MOUSEHWHEEL    = 11 //!< positive and negative values mean right and left scrolling, respectively.
+     };
+
+//! Mouse Event Flags see cv::MouseCallback
+enum MouseEventFlags {
+       EVENT_FLAG_LBUTTON   = 1, //!< indicates that the left mouse button is down.
+       EVENT_FLAG_RBUTTON   = 2, //!< indicates that the right mouse button is down.
+       EVENT_FLAG_MBUTTON   = 4, //!< indicates that the middle mouse button is down.
+       EVENT_FLAG_CTRLKEY   = 8, //!< indicates that CTRL Key is pressed.
+       EVENT_FLAG_SHIFTKEY  = 16,//!< indicates that SHIFT Key is pressed.
+       EVENT_FLAG_ALTKEY    = 32 //!< indicates that ALT Key is pressed.
+     };
+
+//! @} highgui_window_flags
+
+//! @addtogroup highgui_qt
+//! @{
+
+//! Qt font weight
+enum QtFontWeights {
+        QT_FONT_LIGHT           = 25, //!< Weight of 25
+        QT_FONT_NORMAL          = 50, //!< Weight of 50
+        QT_FONT_DEMIBOLD        = 63, //!< Weight of 63
+        QT_FONT_BOLD            = 75, //!< Weight of 75
+        QT_FONT_BLACK           = 87  //!< Weight of 87
+     };
+
+//! Qt font style
+enum QtFontStyles {
+        QT_STYLE_NORMAL         = 0, //!< Normal font.
+        QT_STYLE_ITALIC         = 1, //!< Italic font.
+        QT_STYLE_OBLIQUE        = 2  //!< Oblique font.
+     };
+
+//! Qt "button" type
+enum QtButtonTypes {
+       QT_PUSH_BUTTON   = 0,    //!< Push button.
+       QT_CHECKBOX      = 1,    //!< Checkbox button.
+       QT_RADIOBOX      = 2,    //!< Radiobox button.
+       QT_NEW_BUTTONBAR = 1024  //!< Button should create a new buttonbar
+     };
+
+//! @} highgui_qt
+
+/** @brief Callback function for mouse events. see cv::setMouseCallback
+@param event one of the cv::MouseEventTypes constants.
+@param x The x-coordinate of the mouse event.
+@param y The y-coordinate of the mouse event.
+@param flags one of the cv::MouseEventFlags constants.
+@param userdata The optional parameter.
+ */
+typedef void (*MouseCallback)(int event, int x, int y, int flags, void* userdata);
+
+/** @brief Callback function for Trackbar see cv::createTrackbar
+@param pos current position of the specified trackbar.
+@param userdata The optional parameter.
+ */
+typedef void (*TrackbarCallback)(int pos, void* userdata);
+
+/** @brief Callback function defined to be called every frame. See cv::setOpenGlDrawCallback
+@param userdata The optional parameter.
+ */
+typedef void (*OpenGlDrawCallback)(void* userdata);
+
+/** @brief Callback function for a button created by cv::createButton
+@param state current state of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button.
+@param userdata The optional parameter.
+ */
+typedef void (*ButtonCallback)(int state, void* userdata);
+
+/** @brief Creates a window.
+
+The function namedWindow creates a window that can be used as a placeholder for images and
+trackbars. Created windows are referred to by their names.
+
+If a window with the same name already exists, the function does nothing.
+
+You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated
+memory usage. For a simple program, you do not really have to call these functions because all the
+resources and windows of the application are closed automatically by the operating system upon exit.
+
+@note
+
+Qt backend supports additional flags:
+ -   **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the
+     window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the
+     displayed image (see imshow ), and you cannot change the window size manually.
+ -   **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image
+     with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio.
+ -   **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window
+     without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI.
+By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED
+
+@param winname Name of the window in the window caption that may be used as a window identifier.
+@param flags Flags of the window. The supported flags are: (cv::WindowFlags)
+ */
+CV_EXPORTS_W void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE);
+
+/** @brief Destroys the specified window.
+
+The function destroyWindow destroys the window with the given name.
+
+@param winname Name of the window to be destroyed.
+ */
+CV_EXPORTS_W void destroyWindow(const String& winname);
+
+/** @brief Destroys all of the HighGUI windows.
+
+The function destroyAllWindows destroys all of the opened HighGUI windows.
+ */
+CV_EXPORTS_W void destroyAllWindows();
+
+CV_EXPORTS_W int startWindowThread();
+
+/** @brief Similar to #waitKey, but returns full key code.
+
+@note
+
+Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc
+
+*/
+CV_EXPORTS_W int waitKeyEx(int delay = 0);
+
+/** @brief Waits for a pressed key.
+
+The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay
+milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the
+function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is
+running on your computer at that time. It returns the code of the pressed key or -1 if no key was
+pressed before the specified time had elapsed. To check for a key press but not wait for it, use
+#pollKey.
+
+@note The functions #waitKey and #pollKey are the only methods in HighGUI that can fetch and handle
+GUI events, so one of them needs to be called periodically for normal event processing unless
+HighGUI is used within an environment that takes care of event processing.
+
+@note The function only works if there is at least one HighGUI window created and the window is
+active. If there are several HighGUI windows, any of them can be active.
+
+@param delay Delay in milliseconds. 0 is the special value that means "forever".
+ */
+CV_EXPORTS_W int waitKey(int delay = 0);
+
+/** @brief Polls for a pressed key.
+
+The function pollKey polls for a key event without waiting. It returns the code of the pressed key
+or -1 if no key was pressed since the last invocation. To wait until a key was pressed, use #waitKey.
+
+@note The functions #waitKey and #pollKey are the only methods in HighGUI that can fetch and handle
+GUI events, so one of them needs to be called periodically for normal event processing unless
+HighGUI is used within an environment that takes care of event processing.
+
+@note The function only works if there is at least one HighGUI window created and the window is
+active. If there are several HighGUI windows, any of them can be active.
+ */
+CV_EXPORTS_W int pollKey();
+
+/** @brief Displays an image in the specified window.
+
+The function imshow displays an image in the specified window. If the window was created with the
+cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution.
+Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth:
+
+-   If the image is 8-bit unsigned, it is displayed as is.
+-   If the image is 16-bit unsigned, the pixels are divided by 256. That is, the
+    value range [0,255\*256] is mapped to [0,255].
+-   If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the
+    value range [0,1] is mapped to [0,255].
+-   32-bit integer images are not processed anymore due to ambiguouty of required transform.
+    Convert to 8-bit unsigned matrix using a custom preprocessing specific to image's context.
+
+If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and
+cuda::GpuMat as input.
+
+If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE.
+
+If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow.
+
+@note This function should be followed by a call to cv::waitKey or cv::pollKey to perform GUI
+housekeeping tasks that are necessary to actually show the given image and make the window respond
+to mouse and keyboard events. Otherwise, it won't display the image and the window might lock up.
+For example, **waitKey(0)** will display the window infinitely until any keypress (it is suitable
+for image display). **waitKey(25)** will display a frame and wait approximately 25 ms for a key
+press (suitable for displaying a video frame-by-frame). To remove the window, use cv::destroyWindow.
+
+@note
+
+[__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard.
+
+[__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image.
+
+@param winname Name of the window.
+@param mat Image to be shown.
+ */
+CV_EXPORTS_W void imshow(const String& winname, InputArray mat);
+
+/** @brief Resizes the window to the specified size
+
+@note
+
+-   The specified window size is for the image area. Toolbars are not counted.
+-   Only windows created without cv::WINDOW_AUTOSIZE flag can be resized.
+
+@param winname Window name.
+@param width The new window width.
+@param height The new window height.
+ */
+CV_EXPORTS_W void resizeWindow(const String& winname, int width, int height);
+
+/** @overload
+@param winname Window name.
+@param size The new window size.
+*/
+CV_EXPORTS_W void resizeWindow(const String& winname, const cv::Size& size);
+
+/** @brief Moves the window to the specified position
+
+@param winname Name of the window.
+@param x The new x-coordinate of the window.
+@param y The new y-coordinate of the window.
+ */
+CV_EXPORTS_W void moveWindow(const String& winname, int x, int y);
+
+/** @brief Changes parameters of a window dynamically.
+
+The function setWindowProperty enables changing properties of a window.
+
+@param winname Name of the window.
+@param prop_id Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags)
+@param prop_value New value of the window property. The supported flags are: (cv::WindowFlags)
+ */
+CV_EXPORTS_W void setWindowProperty(const String& winname, int prop_id, double prop_value);
+
+/** @brief Updates window title
+@param winname Name of the window.
+@param title New title.
+*/
+CV_EXPORTS_W void setWindowTitle(const String& winname, const String& title);
+
+/** @brief Provides parameters of a window.
+
+The function getWindowProperty returns properties of a window.
+
+@param winname Name of the window.
+@param prop_id Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags)
+
+@sa setWindowProperty
+ */
+CV_EXPORTS_W double getWindowProperty(const String& winname, int prop_id);
+
+/** @brief Provides rectangle of image in the window.
+
+The function getWindowImageRect returns the client screen coordinates, width and height of the image rendering area.
+
+@param winname Name of the window.
+
+@sa resizeWindow moveWindow
+ */
+CV_EXPORTS_W Rect getWindowImageRect(const String& winname);
+
+/** @example samples/cpp/create_mask.cpp
+This program demonstrates using mouse events and how to make and use a mask image (black and white) .
+*/
+/** @brief Sets mouse handler for the specified window
+
+@param winname Name of the window.
+@param onMouse Callback function for mouse events. See OpenCV samples on how to specify and use the callback.
+@param userdata The optional parameter passed to the callback.
+ */
+CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0);
+
+/** @brief Gets the mouse-wheel motion delta, when handling mouse-wheel events cv::EVENT_MOUSEWHEEL and
+cv::EVENT_MOUSEHWHEEL.
+
+For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to
+a one notch rotation of the wheel or the threshold for action to be taken and one such action should
+occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may
+generate smaller values.
+
+For cv::EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling,
+respectively. For cv::EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and
+left scrolling, respectively.
+
+@note
+
+Mouse-wheel events are currently supported only on Windows.
+
+@param flags The mouse callback flags parameter.
+ */
+CV_EXPORTS int getMouseWheelDelta(int flags);
+
+/** @brief Allows users to select a ROI on the given image.
+
+The function creates a window and allows users to select a ROI using the mouse.
+Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect).
+
+@param windowName name of the window where selection process will be shown.
+@param img image to select a ROI.
+@param showCrosshair if true crosshair of selection rectangle will be shown.
+@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of
+selection rectangle will correspont to the initial mouse position.
+@return selected ROI or empty rect if selection canceled.
+
+@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...).
+After finish of work an empty callback will be set for the used window.
+ */
+CV_EXPORTS_W Rect selectROI(const String& windowName, InputArray img, bool showCrosshair = true, bool fromCenter = false);
+
+/** @overload
+ */
+CV_EXPORTS_W Rect selectROI(InputArray img, bool showCrosshair = true, bool fromCenter = false);
+
+/** @brief Allows users to select multiple ROIs on the given image.
+
+The function creates a window and allows users to select multiple ROIs using the mouse.
+Controls: use `space` or `enter` to finish current selection and start a new one,
+use `esc` to terminate multiple ROI selection process.
+
+@param windowName name of the window where selection process will be shown.
+@param img image to select a ROI.
+@param boundingBoxes selected ROIs.
+@param showCrosshair if true crosshair of selection rectangle will be shown.
+@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of
+selection rectangle will correspont to the initial mouse position.
+
+@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...).
+After finish of work an empty callback will be set for the used window.
+ */
+CV_EXPORTS_W void selectROIs(const String& windowName, InputArray img,
+                             CV_OUT std::vector<Rect>& boundingBoxes, bool showCrosshair = true, bool fromCenter = false);
+
+/** @brief Creates a trackbar and attaches it to the specified window.
+
+The function createTrackbar creates a trackbar (a slider or range control) with the specified name
+and range, assigns a variable value to be a position synchronized with the trackbar and specifies
+the callback function onChange to be called on the trackbar position change. The created trackbar is
+displayed in the specified window winname.
+
+@note
+
+[__Qt Backend Only__] winname can be empty if the trackbar should be attached to the
+control panel.
+
+Clicking the label of each trackbar enables editing the trackbar values manually.
+
+@param trackbarname Name of the created trackbar.
+@param winname Name of the window that will be used as a parent of the created trackbar.
+@param value Optional pointer to an integer variable whose value reflects the position of the
+slider. Upon creation, the slider position is defined by this variable.
+@param count Maximal position of the slider. The minimal position is always 0.
+@param onChange Pointer to the function to be called every time the slider changes position. This
+function should be prototyped as void Foo(int,void\*); , where the first parameter is the trackbar
+position and the second parameter is the user data (see the next parameter). If the callback is
+the NULL pointer, no callbacks are called, but only value is updated.
+@param userdata User data that is passed as is to the callback. It can be used to handle trackbar
+events without using global variables.
+ */
+CV_EXPORTS int createTrackbar(const String& trackbarname, const String& winname,
+                              int* value, int count,
+                              TrackbarCallback onChange = 0,
+                              void* userdata = 0);
+
+/** @brief Returns the trackbar position.
+
+The function returns the current position of the specified trackbar.
+
+@note
+
+[__Qt Backend Only__] winname can be empty if the trackbar is attached to the control
+panel.
+
+@param trackbarname Name of the trackbar.
+@param winname Name of the window that is the parent of the trackbar.
+ */
+CV_EXPORTS_W int getTrackbarPos(const String& trackbarname, const String& winname);
+
+/** @brief Sets the trackbar position.
+
+The function sets the position of the specified trackbar in the specified window.
+
+@note
+
+[__Qt Backend Only__] winname can be empty if the trackbar is attached to the control
+panel.
+
+@param trackbarname Name of the trackbar.
+@param winname Name of the window that is the parent of trackbar.
+@param pos New position.
+ */
+CV_EXPORTS_W void setTrackbarPos(const String& trackbarname, const String& winname, int pos);
+
+/** @brief Sets the trackbar maximum position.
+
+The function sets the maximum position of the specified trackbar in the specified window.
+
+@note
+
+[__Qt Backend Only__] winname can be empty if the trackbar is attached to the control
+panel.
+
+@param trackbarname Name of the trackbar.
+@param winname Name of the window that is the parent of trackbar.
+@param maxval New maximum position.
+ */
+CV_EXPORTS_W void setTrackbarMax(const String& trackbarname, const String& winname, int maxval);
+
+/** @brief Sets the trackbar minimum position.
+
+The function sets the minimum position of the specified trackbar in the specified window.
+
+@note
+
+[__Qt Backend Only__] winname can be empty if the trackbar is attached to the control
+panel.
+
+@param trackbarname Name of the trackbar.
+@param winname Name of the window that is the parent of trackbar.
+@param minval New minimum position.
+ */
+CV_EXPORTS_W void setTrackbarMin(const String& trackbarname, const String& winname, int minval);
+
+//! @addtogroup highgui_opengl OpenGL support
+//! @{
+
+/** @brief Displays OpenGL 2D texture in the specified window.
+
+@param winname Name of the window.
+@param tex OpenGL 2D texture data.
+ */
+CV_EXPORTS void imshow(const String& winname, const ogl::Texture2D& tex);
+
+/** @brief Sets a callback function to be called to draw on top of displayed image.
+
+The function setOpenGlDrawCallback can be used to draw 3D data on the window. See the example of
+callback function below:
+@code
+    void on_opengl(void* param)
+    {
+        glLoadIdentity();
+
+        glTranslated(0.0, 0.0, -1.0);
+
+        glRotatef( 55, 1, 0, 0 );
+        glRotatef( 45, 0, 1, 0 );
+        glRotatef( 0, 0, 0, 1 );
+
+        static const int coords[6][4][3] = {
+            { { +1, -1, -1 }, { -1, -1, -1 }, { -1, +1, -1 }, { +1, +1, -1 } },
+            { { +1, +1, -1 }, { -1, +1, -1 }, { -1, +1, +1 }, { +1, +1, +1 } },
+            { { +1, -1, +1 }, { +1, -1, -1 }, { +1, +1, -1 }, { +1, +1, +1 } },
+            { { -1, -1, -1 }, { -1, -1, +1 }, { -1, +1, +1 }, { -1, +1, -1 } },
+            { { +1, -1, +1 }, { -1, -1, +1 }, { -1, -1, -1 }, { +1, -1, -1 } },
+            { { -1, -1, +1 }, { +1, -1, +1 }, { +1, +1, +1 }, { -1, +1, +1 } }
+        };
+
+        for (int i = 0; i < 6; ++i) {
+                    glColor3ub( i*20, 100+i*10, i*42 );
+                    glBegin(GL_QUADS);
+                    for (int j = 0; j < 4; ++j) {
+                            glVertex3d(0.2 * coords[i][j][0], 0.2 * coords[i][j][1], 0.2 * coords[i][j][2]);
+                    }
+                    glEnd();
+        }
+    }
+@endcode
+
+@param winname Name of the window.
+@param onOpenGlDraw Pointer to the function to be called every frame. This function should be
+prototyped as void Foo(void\*) .
+@param userdata Pointer passed to the callback function.(__Optional__)
+ */
+CV_EXPORTS void setOpenGlDrawCallback(const String& winname, OpenGlDrawCallback onOpenGlDraw, void* userdata = 0);
+
+/** @brief Sets the specified window as current OpenGL context.
+
+@param winname Name of the window.
+ */
+CV_EXPORTS void setOpenGlContext(const String& winname);
+
+/** @brief Force window to redraw its context and call draw callback ( See cv::setOpenGlDrawCallback ).
+
+@param winname Name of the window.
+ */
+CV_EXPORTS void updateWindow(const String& winname);
+
+//! @} highgui_opengl
+
+//! @addtogroup highgui_qt
+//! @{
+
+/** @brief QtFont available only for Qt. See cv::fontQt
+ */
+struct QtFont
+{
+    const char* nameFont;  //!< Name of the font
+    Scalar      color;     //!< Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component])
+    int         font_face; //!< See cv::QtFontStyles
+    const int*  ascii;     //!< font data and metrics
+    const int*  greek;
+    const int*  cyrillic;
+    float       hscale, vscale;
+    float       shear;     //!< slope coefficient: 0 - normal, >0 - italic
+    int         thickness; //!< See cv::QtFontWeights
+    float       dx;        //!< horizontal interval between letters
+    int         line_type; //!< PointSize
+};
+
+/** @brief Creates the font to draw a text on an image.
+
+The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText .
+
+A basic usage of this function is the following: :
+@code
+    QtFont font = fontQt("Times");
+    addText( img1, "Hello World !", Point(50,50), font);
+@endcode
+
+@param nameFont Name of the font. The name should match the name of a system font (such as
+*Times*). If the font is not found, a default one is used.
+@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the
+font is set to a system-dependent default value. Generally, this is 12 points.
+@param color Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB
+for simplicity.
+@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control.
+@param style Font style. Available operation flags are : cv::QtFontStyles
+@param spacing Spacing between characters. It can be negative or positive.
+ */
+CV_EXPORTS QtFont fontQt(const String& nameFont, int pointSize = -1,
+                         Scalar color = Scalar::all(0), int weight = QT_FONT_NORMAL,
+                         int style = QT_STYLE_NORMAL, int spacing = 0);
+
+/** @brief Draws a text on the image.
+
+The function addText draws *text* on the image *img* using a specific font *font* (see example cv::fontQt
+)
+
+@param img 8-bit 3-channel image where the text should be drawn.
+@param text Text to write on an image.
+@param org Point(x,y) where the text should start on an image.
+@param font Font to use to draw a text.
+ */
+CV_EXPORTS void addText( const Mat& img, const String& text, Point org, const QtFont& font);
+
+/** @brief Draws a text on the image.
+
+@param img 8-bit 3-channel image where the text should be drawn.
+@param text Text to write on an image.
+@param org Point(x,y) where the text should start on an image.
+@param nameFont Name of the font. The name should match the name of a system font (such as
+*Times*). If the font is not found, a default one is used.
+@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the
+font is set to a system-dependent default value. Generally, this is 12 points.
+@param color Color of the font in BGRA where A = 255 is fully transparent.
+@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control.
+@param style Font style. Available operation flags are : cv::QtFontStyles
+@param spacing Spacing between characters. It can be negative or positive.
+ */
+CV_EXPORTS_W void addText(const Mat& img, const String& text, Point org, const String& nameFont, int pointSize = -1, Scalar color = Scalar::all(0),
+        int weight = QT_FONT_NORMAL, int style = QT_STYLE_NORMAL, int spacing = 0);
+
+/** @brief Displays a text on a window image as an overlay for a specified duration.
+
+The function displayOverlay displays useful information/tips on top of the window for a certain
+amount of time *delayms*. The function does not modify the image, displayed in the window, that is,
+after the specified delay the original content of the window is restored.
+
+@param winname Name of the window.
+@param text Overlay text to write on a window image.
+@param delayms The period (in milliseconds), during which the overlay text is displayed. If this
+function is called before the previous overlay text timed out, the timer is restarted and the text
+is updated. If this value is zero, the text never disappears.
+ */
+CV_EXPORTS_W void displayOverlay(const String& winname, const String& text, int delayms = 0);
+
+/** @brief Displays a text on the window statusbar during the specified period of time.
+
+The function displayStatusBar displays useful information/tips on top of the window for a certain
+amount of time *delayms* . This information is displayed on the window statusbar (the window must be
+created with the CV_GUI_EXPANDED flags).
+
+@param winname Name of the window.
+@param text Text to write on the window statusbar.
+@param delayms Duration (in milliseconds) to display the text. If this function is called before
+the previous text timed out, the timer is restarted and the text is updated. If this value is
+zero, the text never disappears.
+ */
+CV_EXPORTS_W void displayStatusBar(const String& winname, const String& text, int delayms = 0);
+
+/** @brief Saves parameters of the specified window.
+
+The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning
+location of the window windowName.
+
+@param windowName Name of the window.
+ */
+CV_EXPORTS void saveWindowParameters(const String& windowName);
+
+/** @brief Loads parameters of the specified window.
+
+The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning
+location of the window windowName.
+
+@param windowName Name of the window.
+ */
+CV_EXPORTS void loadWindowParameters(const String& windowName);
+
+CV_EXPORTS  int startLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]);
+
+CV_EXPORTS  void stopLoop();
+
+/** @brief Attaches a button to the control panel.
+
+The function createButton attaches a button to the control panel. Each button is added to a
+buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the
+control panel before, or if the last element attached to the control panel was a trackbar or if the
+QT_NEW_BUTTONBAR flag is added to the type.
+
+See below various examples of the cv::createButton function call: :
+@code
+    createButton("",callbackButton);//create a push button "button 0", that will call callbackButton.
+    createButton("button2",callbackButton,NULL,QT_CHECKBOX,0);
+    createButton("button3",callbackButton,&value);
+    createButton("button5",callbackButton1,NULL,QT_RADIOBOX);
+    createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1);
+    createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON|QT_NEW_BUTTONBAR);// create a push button in a new row
+@endcode
+
+@param  bar_name Name of the button.
+@param on_change Pointer to the function to be called every time the button changes its state.
+This function should be prototyped as void Foo(int state,\*void); . *state* is the current state
+of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button.
+@param userdata Pointer passed to the callback function.
+@param type Optional type of the button. Available types are: (cv::QtButtonTypes)
+@param initial_button_state Default state of the button. Use for checkbox and radiobox. Its
+value could be 0 or 1. (__Optional__)
+*/
+CV_EXPORTS int createButton( const String& bar_name, ButtonCallback on_change,
+                             void* userdata = 0, int type = QT_PUSH_BUTTON,
+                             bool initial_button_state = false);
+
+//! @} highgui_qt
+
+//! @} highgui
+
+} // cv
+
+#endif
diff --git a/3rdParty/include/opencv2/highgui/highgui.hpp b/3rdParty/include/opencv2/highgui/highgui.hpp
new file mode 100644
index 0000000..160c9cf
--- /dev/null
+++ b/3rdParty/include/opencv2/highgui/highgui.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/highgui.hpp"
diff --git a/3rdParty/include/opencv2/highgui/highgui_c.h b/3rdParty/include/opencv2/highgui/highgui_c.h
new file mode 100644
index 0000000..e508e14
--- /dev/null
+++ b/3rdParty/include/opencv2/highgui/highgui_c.h
@@ -0,0 +1,251 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_HIGHGUI_H
+#define OPENCV_HIGHGUI_H
+
+#include "opencv2/core/core_c.h"
+#include "opencv2/imgproc/imgproc_c.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/** @addtogroup highgui_c
+  @{
+  */
+
+/****************************************************************************************\
+*                                  Basic GUI functions                                   *
+\****************************************************************************************/
+//YV
+//-----------New for Qt
+/* For font */
+enum {  CV_FONT_LIGHT           = 25,//QFont::Light,
+        CV_FONT_NORMAL          = 50,//QFont::Normal,
+        CV_FONT_DEMIBOLD        = 63,//QFont::DemiBold,
+        CV_FONT_BOLD            = 75,//QFont::Bold,
+        CV_FONT_BLACK           = 87 //QFont::Black
+};
+
+enum {  CV_STYLE_NORMAL         = 0,//QFont::StyleNormal,
+        CV_STYLE_ITALIC         = 1,//QFont::StyleItalic,
+        CV_STYLE_OBLIQUE        = 2 //QFont::StyleOblique
+};
+/* ---------*/
+
+//for color cvScalar(blue_component, green_component, red_component[, alpha_component])
+//and alpha= 0 <-> 0xFF (not transparent <-> transparent)
+CVAPI(CvFont) cvFontQt(const char* nameFont, int pointSize CV_DEFAULT(-1), CvScalar color CV_DEFAULT(cvScalarAll(0)), int weight CV_DEFAULT(CV_FONT_NORMAL),  int style CV_DEFAULT(CV_STYLE_NORMAL), int spacing CV_DEFAULT(0));
+
+CVAPI(void) cvAddText(const CvArr* img, const char* text, CvPoint org, CvFont *arg2);
+
+CVAPI(void) cvDisplayOverlay(const char* name, const char* text, int delayms CV_DEFAULT(0));
+CVAPI(void) cvDisplayStatusBar(const char* name, const char* text, int delayms CV_DEFAULT(0));
+
+CVAPI(void) cvSaveWindowParameters(const char* name);
+CVAPI(void) cvLoadWindowParameters(const char* name);
+CVAPI(int) cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]);
+CVAPI(void) cvStopLoop( void );
+
+typedef void (CV_CDECL *CvButtonCallback)(int state, void* userdata);
+enum {CV_PUSH_BUTTON = 0, CV_CHECKBOX = 1, CV_RADIOBOX = 2};
+CVAPI(int) cvCreateButton( const char* button_name CV_DEFAULT(NULL),CvButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL) , int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0));
+//----------------------
+
+
+/* this function is used to set some external parameters in case of X Window */
+CVAPI(int) cvInitSystem( int argc, char** argv );
+
+CVAPI(int) cvStartWindowThread( void );
+
+// ---------  YV ---------
+enum
+{
+    //These 3 flags are used by cvSet/GetWindowProperty
+    CV_WND_PROP_FULLSCREEN = 0, //to change/get window's fullscreen property
+    CV_WND_PROP_AUTOSIZE   = 1, //to change/get window's autosize property
+    CV_WND_PROP_ASPECTRATIO= 2, //to change/get window's aspectratio property
+    CV_WND_PROP_OPENGL     = 3, //to change/get window's opengl support
+    CV_WND_PROP_VISIBLE    = 4,
+
+    //These 2 flags are used by cvNamedWindow and cvSet/GetWindowProperty
+    CV_WINDOW_NORMAL       = 0x00000000, //the user can resize the window (no constraint)  / also use to switch a fullscreen window to a normal size
+    CV_WINDOW_AUTOSIZE     = 0x00000001, //the user cannot resize the window, the size is constrainted by the image displayed
+    CV_WINDOW_OPENGL       = 0x00001000, //window with opengl support
+
+    //Those flags are only for Qt
+    CV_GUI_EXPANDED         = 0x00000000, //status bar and tool bar
+    CV_GUI_NORMAL           = 0x00000010, //old fashious way
+
+    //These 3 flags are used by cvNamedWindow and cvSet/GetWindowProperty
+    CV_WINDOW_FULLSCREEN   = 1,//change the window to fullscreen
+    CV_WINDOW_FREERATIO    = 0x00000100,//the image expends as much as it can (no ratio constraint)
+    CV_WINDOW_KEEPRATIO    = 0x00000000//the ration image is respected.
+};
+
+/* create window */
+CVAPI(int) cvNamedWindow( const char* name, int flags CV_DEFAULT(CV_WINDOW_AUTOSIZE) );
+
+/* Set and Get Property of the window */
+CVAPI(void) cvSetWindowProperty(const char* name, int prop_id, double prop_value);
+CVAPI(double) cvGetWindowProperty(const char* name, int prop_id);
+
+/* display image within window (highgui windows remember their content) */
+CVAPI(void) cvShowImage( const char* name, const CvArr* image );
+
+/* resize/move window */
+CVAPI(void) cvResizeWindow( const char* name, int width, int height );
+CVAPI(void) cvMoveWindow( const char* name, int x, int y );
+
+
+/* destroy window and all the trackers associated with it */
+CVAPI(void) cvDestroyWindow( const char* name );
+
+CVAPI(void) cvDestroyAllWindows(void);
+
+/* get native window handle (HWND in case of Win32 and Widget in case of X Window) */
+CVAPI(void*) cvGetWindowHandle( const char* name );
+
+/* get name of highgui window given its native handle */
+CVAPI(const char*) cvGetWindowName( void* window_handle );
+
+
+typedef void (CV_CDECL *CvTrackbarCallback)(int pos);
+
+/* create trackbar and display it on top of given window, set callback */
+CVAPI(int) cvCreateTrackbar( const char* trackbar_name, const char* window_name,
+                             int* value, int count, CvTrackbarCallback on_change CV_DEFAULT(NULL));
+
+typedef void (CV_CDECL *CvTrackbarCallback2)(int pos, void* userdata);
+
+CVAPI(int) cvCreateTrackbar2( const char* trackbar_name, const char* window_name,
+                              int* value, int count, CvTrackbarCallback2 on_change,
+                              void* userdata CV_DEFAULT(0));
+
+/* retrieve or set trackbar position */
+CVAPI(int) cvGetTrackbarPos( const char* trackbar_name, const char* window_name );
+CVAPI(void) cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos );
+CVAPI(void) cvSetTrackbarMax(const char* trackbar_name, const char* window_name, int maxval);
+CVAPI(void) cvSetTrackbarMin(const char* trackbar_name, const char* window_name, int minval);
+
+enum
+{
+    CV_EVENT_MOUSEMOVE      =0,
+    CV_EVENT_LBUTTONDOWN    =1,
+    CV_EVENT_RBUTTONDOWN    =2,
+    CV_EVENT_MBUTTONDOWN    =3,
+    CV_EVENT_LBUTTONUP      =4,
+    CV_EVENT_RBUTTONUP      =5,
+    CV_EVENT_MBUTTONUP      =6,
+    CV_EVENT_LBUTTONDBLCLK  =7,
+    CV_EVENT_RBUTTONDBLCLK  =8,
+    CV_EVENT_MBUTTONDBLCLK  =9,
+    CV_EVENT_MOUSEWHEEL     =10,
+    CV_EVENT_MOUSEHWHEEL    =11
+};
+
+enum
+{
+    CV_EVENT_FLAG_LBUTTON   =1,
+    CV_EVENT_FLAG_RBUTTON   =2,
+    CV_EVENT_FLAG_MBUTTON   =4,
+    CV_EVENT_FLAG_CTRLKEY   =8,
+    CV_EVENT_FLAG_SHIFTKEY  =16,
+    CV_EVENT_FLAG_ALTKEY    =32
+};
+
+
+#define CV_GET_WHEEL_DELTA(flags) ((short)((flags >> 16) & 0xffff)) // upper 16 bits
+
+typedef void (CV_CDECL *CvMouseCallback )(int event, int x, int y, int flags, void* param);
+
+/* assign callback for mouse events */
+CVAPI(void) cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse,
+                                void* param CV_DEFAULT(NULL));
+
+/* wait for key event infinitely (delay<=0) or for "delay" milliseconds */
+CVAPI(int) cvWaitKey(int delay CV_DEFAULT(0));
+
+// OpenGL support
+
+typedef void (CV_CDECL *CvOpenGlDrawCallback)(void* userdata);
+CVAPI(void) cvSetOpenGlDrawCallback(const char* window_name, CvOpenGlDrawCallback callback, void* userdata CV_DEFAULT(NULL));
+
+CVAPI(void) cvSetOpenGlContext(const char* window_name);
+CVAPI(void) cvUpdateWindow(const char* window_name);
+
+
+/****************************************************************************************\
+
+*                              Obsolete functions/synonyms                               *
+\****************************************************************************************/
+
+#define cvAddSearchPath(path)
+#define cvvInitSystem cvInitSystem
+#define cvvNamedWindow cvNamedWindow
+#define cvvShowImage cvShowImage
+#define cvvResizeWindow cvResizeWindow
+#define cvvDestroyWindow cvDestroyWindow
+#define cvvCreateTrackbar cvCreateTrackbar
+#define cvvAddSearchPath cvAddSearchPath
+#define cvvWaitKey(name) cvWaitKey(0)
+#define cvvWaitKeyEx(name,delay) cvWaitKey(delay)
+#define HG_AUTOSIZE CV_WINDOW_AUTOSIZE
+#define set_preprocess_func cvSetPreprocessFuncWin32
+#define set_postprocess_func cvSetPostprocessFuncWin32
+
+#if defined _WIN32
+
+CVAPI(void) cvSetPreprocessFuncWin32_(const void* callback);
+CVAPI(void) cvSetPostprocessFuncWin32_(const void* callback);
+#define cvSetPreprocessFuncWin32(callback) cvSetPreprocessFuncWin32_((const void*)(callback))
+#define cvSetPostprocessFuncWin32(callback) cvSetPostprocessFuncWin32_((const void*)(callback))
+
+#endif
+
+/** @} highgui_c */
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/imgcodecs.hpp b/3rdParty/include/opencv2/imgcodecs.hpp
new file mode 100644
index 0000000..d7ff9a1
--- /dev/null
+++ b/3rdParty/include/opencv2/imgcodecs.hpp
@@ -0,0 +1,330 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_IMGCODECS_HPP
+#define OPENCV_IMGCODECS_HPP
+
+#include "opencv2/core.hpp"
+
+/**
+  @defgroup imgcodecs Image file reading and writing
+  @{
+    @defgroup imgcodecs_c C API
+    @defgroup imgcodecs_flags Flags used for image file reading and writing
+    @defgroup imgcodecs_ios iOS glue
+    @defgroup imgcodecs_macosx MacOS(OSX) glue
+  @}
+*/
+
+//////////////////////////////// image codec ////////////////////////////////
+namespace cv
+{
+
+//! @addtogroup imgcodecs
+//! @{
+
+//! @addtogroup imgcodecs_flags
+//! @{
+
+//! Imread flags
+enum ImreadModes {
+       IMREAD_UNCHANGED            = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). Ignore EXIF orientation.
+       IMREAD_GRAYSCALE            = 0,  //!< If set, always convert image to the single channel grayscale image (codec internal conversion).
+       IMREAD_COLOR                = 1,  //!< If set, always convert image to the 3 channel BGR color image.
+       IMREAD_ANYDEPTH             = 2,  //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit.
+       IMREAD_ANYCOLOR             = 4,  //!< If set, the image is read in any possible color format.
+       IMREAD_LOAD_GDAL            = 8,  //!< If set, use the gdal driver for loading the image.
+       IMREAD_REDUCED_GRAYSCALE_2  = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2.
+       IMREAD_REDUCED_COLOR_2      = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2.
+       IMREAD_REDUCED_GRAYSCALE_4  = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4.
+       IMREAD_REDUCED_COLOR_4      = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4.
+       IMREAD_REDUCED_GRAYSCALE_8  = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8.
+       IMREAD_REDUCED_COLOR_8      = 65, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8.
+       IMREAD_IGNORE_ORIENTATION   = 128 //!< If set, do not rotate the image according to EXIF's orientation flag.
+     };
+
+//! Imwrite flags
+enum ImwriteFlags {
+       IMWRITE_JPEG_QUALITY        = 1,  //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95.
+       IMWRITE_JPEG_PROGRESSIVE    = 2,  //!< Enable JPEG features, 0 or 1, default is False.
+       IMWRITE_JPEG_OPTIMIZE       = 3,  //!< Enable JPEG features, 0 or 1, default is False.
+       IMWRITE_JPEG_RST_INTERVAL   = 4,  //!< JPEG restart interval, 0 - 65535, default is 0 - no restart.
+       IMWRITE_JPEG_LUMA_QUALITY   = 5,  //!< Separate luma quality level, 0 - 100, default is 0 - don't use.
+       IMWRITE_JPEG_CHROMA_QUALITY = 6,  //!< Separate chroma quality level, 0 - 100, default is 0 - don't use.
+       IMWRITE_PNG_COMPRESSION     = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. If specified, strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). Default value is 1 (best speed setting).
+       IMWRITE_PNG_STRATEGY        = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_RLE.
+       IMWRITE_PNG_BILEVEL         = 18, //!< Binary level PNG, 0 or 1, default is 0.
+       IMWRITE_PXM_BINARY          = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1.
+       IMWRITE_EXR_TYPE            = (3 << 4) + 0, /* 48 */ //!< override EXR storage type (FLOAT (FP32) is default)
+       IMWRITE_EXR_COMPRESSION     = (3 << 4) + 1, /* 49 */ //!< override EXR compression type (ZIP_COMPRESSION = 3 is default)
+       IMWRITE_WEBP_QUALITY        = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used.
+       IMWRITE_PAM_TUPLETYPE       = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format
+       IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values
+       IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI
+       IMWRITE_TIFF_YDPI = 258, //!< For TIFF, use to specify the Y direction DPI
+       IMWRITE_TIFF_COMPRESSION = 259, //!< For TIFF, use to specify the image compression scheme. See libtiff for integer constants corresponding to compression formats. Note, for images whose depth is CV_32F, only libtiff's SGILOG compression scheme is used. For other supported depths, the compression scheme can be specified by this flag; LZW compression is the default.
+       IMWRITE_JPEG2000_COMPRESSION_X1000 = 272 //!< For JPEG2000, use to specify the target compression rate (multiplied by 1000). The value can be from 0 to 1000. Default is 1000.
+     };
+
+enum ImwriteEXRTypeFlags {
+       /*IMWRITE_EXR_TYPE_UNIT = 0, //!< not supported */
+       IMWRITE_EXR_TYPE_HALF = 1,   //!< store as HALF (FP16)
+       IMWRITE_EXR_TYPE_FLOAT = 2   //!< store as FP32 (default)
+     };
+
+enum ImwriteEXRCompressionFlags {
+       IMWRITE_EXR_COMPRESSION_NO    = 0, //!< no compression
+       IMWRITE_EXR_COMPRESSION_RLE   = 1, //!< run length encoding
+       IMWRITE_EXR_COMPRESSION_ZIPS  = 2, //!< zlib compression, one scan line at a time
+       IMWRITE_EXR_COMPRESSION_ZIP   = 3, //!< zlib compression, in blocks of 16 scan lines
+       IMWRITE_EXR_COMPRESSION_PIZ   = 4, //!< piz-based wavelet compression
+       IMWRITE_EXR_COMPRESSION_PXR24 = 5, //!< lossy 24-bit float compression
+       IMWRITE_EXR_COMPRESSION_B44   = 6, //!< lossy 4-by-4 pixel block compression, fixed compression rate
+       IMWRITE_EXR_COMPRESSION_B44A  = 7, //!< lossy 4-by-4 pixel block compression, flat fields are compressed more
+       IMWRITE_EXR_COMPRESSION_DWAA  = 8, //!< lossy DCT based compression, in blocks of 32 scanlines. More efficient for partial buffer access. Supported since OpenEXR 2.2.0.
+       IMWRITE_EXR_COMPRESSION_DWAB  = 9, //!< lossy DCT based compression, in blocks of 256 scanlines. More efficient space wise and faster to decode full frames than DWAA_COMPRESSION. Supported since OpenEXR 2.2.0.
+     };
+
+//! Imwrite PNG specific flags used to tune the compression algorithm.
+/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage.
+
+-   The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY.
+-   IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data.
+-   The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately.
+-   IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications.
+*/
+enum ImwritePNGFlags {
+       IMWRITE_PNG_STRATEGY_DEFAULT      = 0, //!< Use this value for normal data.
+       IMWRITE_PNG_STRATEGY_FILTERED     = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better.
+       IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match).
+       IMWRITE_PNG_STRATEGY_RLE          = 3, //!< Use this value to limit match distances to one (run-length encoding).
+       IMWRITE_PNG_STRATEGY_FIXED        = 4  //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications.
+     };
+
+//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file.
+enum ImwritePAMFlags {
+       IMWRITE_PAM_FORMAT_NULL = 0,
+       IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1,
+       IMWRITE_PAM_FORMAT_GRAYSCALE = 2,
+       IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3,
+       IMWRITE_PAM_FORMAT_RGB = 4,
+       IMWRITE_PAM_FORMAT_RGB_ALPHA = 5,
+     };
+
+//! @} imgcodecs_flags
+
+/** @brief Loads an image from a file.
+
+@anchor imread
+
+The function imread loads an image from the specified file and returns it. If the image cannot be
+read (because of missing file, improper permissions, unsupported or invalid format), the function
+returns an empty matrix ( Mat::data==NULL ).
+
+Currently, the following file formats are supported:
+
+-   Windows bitmaps - \*.bmp, \*.dib (always supported)
+-   JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Note* section)
+-   JPEG 2000 files - \*.jp2 (see the *Note* section)
+-   Portable Network Graphics - \*.png (see the *Note* section)
+-   WebP - \*.webp (see the *Note* section)
+-   Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported)
+-   PFM files - \*.pfm (see the *Note* section)
+-   Sun rasters - \*.sr, \*.ras (always supported)
+-   TIFF files - \*.tiff, \*.tif (see the *Note* section)
+-   OpenEXR Image files - \*.exr (see the *Note* section)
+-   Radiance HDR - \*.hdr, \*.pic (always supported)
+-   Raster and Vector geospatial data supported by GDAL (see the *Note* section)
+
+@note
+-   The function determines the type of an image by the content, not by the file extension.
+-   In the case of color images, the decoded images will have the channels stored in **B G R** order.
+-   When using IMREAD_GRAYSCALE, the codec's internal grayscale conversion will be used, if available.
+    Results may differ to the output of cvtColor()
+-   On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg,
+    libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs,
+    and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware
+    that currently these native image loaders give images with different pixel values because of
+    the color management embedded into MacOSX.
+-   On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for
+    codecs supplied with an OS image. Install the relevant packages (do not forget the development
+    files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn
+    on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake.
+-   In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image,
+    then the [GDAL](http://www.gdal.org) driver will be used in order to decode the image, supporting
+    the following formats: [Raster](http://www.gdal.org/formats_list.html),
+    [Vector](http://www.gdal.org/ogr_formats.html).
+-   If EXIF information is embedded in the image file, the EXIF orientation will be taken into account
+    and thus the image will be rotated accordingly except if the flags @ref IMREAD_IGNORE_ORIENTATION
+    or @ref IMREAD_UNCHANGED are passed.
+-   Use the IMREAD_UNCHANGED flag to keep the floating point values from PFM image.
+-   By default number of pixels must be less than 2^30. Limit can be set using system
+    variable OPENCV_IO_MAX_IMAGE_PIXELS
+
+@param filename Name of file to be loaded.
+@param flags Flag that can take values of cv::ImreadModes
+*/
+CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR );
+
+/** @brief Loads a multi-page image from a file.
+
+The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects.
+@param filename Name of file to be loaded.
+@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR.
+@param mats A vector of Mat objects holding each page, if more than one.
+@sa cv::imread
+*/
+CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector<Mat>& mats, int flags = IMREAD_ANYCOLOR);
+
+/** @brief Loads a of images of a multi-page image from a file.
+
+The function imreadmulti loads a specified range from a multi-page image from the specified file into a vector of Mat objects.
+@param filename Name of file to be loaded.
+@param start Start index of the image to load
+@param count Count number of images to load
+@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR.
+@param mats A vector of Mat objects holding each page, if more than one.
+@sa cv::imread
+*/
+CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector<Mat>& mats, int start, int count, int flags = IMREAD_ANYCOLOR);
+
+/** @brief Returns the number of images inside the give file
+
+The function imcount will return the number of pages in a multi-page image, or 1 for single-page images
+@param filename Name of file to be loaded.
+@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR.
+*/
+CV_EXPORTS_W size_t imcount(const String& filename, int flags = IMREAD_ANYCOLOR);
+
+/** @brief Saves an image to a specified file.
+
+The function imwrite saves the image to the specified file. The image format is chosen based on the
+filename extension (see cv::imread for the list of extensions). In general, only 8-bit
+single-channel or 3-channel (with 'BGR' channel order) images
+can be saved using this function, with these exceptions:
+
+- 16-bit unsigned (CV_16U) images can be saved in the case of PNG, JPEG 2000, and TIFF formats
+- 32-bit float (CV_32F) images can be saved in PFM, TIFF, OpenEXR, and Radiance HDR formats;
+  3-channel (CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding
+  (4 bytes per pixel)
+- PNG images with an alpha channel can be saved using this function. To do this, create
+8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels
+should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below).
+- Multiple images (vector of Mat) can be saved in TIFF format (see the code sample below).
+
+If the image format is not supported, the image will be converted to 8-bit unsigned (CV_8U) and saved that way.
+
+If the format, depth or channel order is different, use
+Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O
+functions to save the image to XML or YAML format.
+
+The sample below shows how to create a BGRA image, how to set custom compression parameters and save it to a PNG file.
+It also demonstrates how to save multiple images in a TIFF file:
+@include snippets/imgcodecs_imwrite.cpp
+@param filename Name of the file.
+@param img (Mat or vector of Mat) Image or Images to be saved.
+@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags
+*/
+CV_EXPORTS_W bool imwrite( const String& filename, InputArray img,
+              const std::vector<int>& params = std::vector<int>());
+
+/// @overload multi-image overload for bindings
+CV_WRAP static inline
+bool imwritemulti(const String& filename, InputArrayOfArrays img,
+                  const std::vector<int>& params = std::vector<int>())
+{
+    return imwrite(filename, img, params);
+}
+
+/** @brief Reads an image from a buffer in memory.
+
+The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or
+contains invalid data, the function returns an empty matrix ( Mat::data==NULL ).
+
+See cv::imread for the list of supported formats and flags description.
+
+@note In the case of color images, the decoded images will have the channels stored in **B G R** order.
+@param buf Input array or vector of bytes.
+@param flags The same flags as in cv::imread, see cv::ImreadModes.
+*/
+CV_EXPORTS_W Mat imdecode( InputArray buf, int flags );
+
+/** @overload
+@param buf
+@param flags
+@param dst The optional output placeholder for the decoded matrix. It can save the image
+reallocations when the function is called repeatedly for images of the same size.
+*/
+CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst);
+
+/** @brief Encodes an image into a memory buffer.
+
+The function imencode compresses the image and stores it in the memory buffer that is resized to fit the
+result. See cv::imwrite for the list of supported formats and flags description.
+
+@param ext File extension that defines the output format.
+@param img Image to be written.
+@param buf Output buffer resized to fit the compressed image.
+@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags.
+*/
+CV_EXPORTS_W bool imencode( const String& ext, InputArray img,
+                            CV_OUT std::vector<uchar>& buf,
+                            const std::vector<int>& params = std::vector<int>());
+
+/** @brief Returns true if the specified image can be decoded by OpenCV
+
+@param filename File name of the image
+*/
+CV_EXPORTS_W bool haveImageReader( const String& filename );
+
+/** @brief Returns true if an image with the specified filename can be encoded by OpenCV
+
+ @param filename File name of the image
+ */
+CV_EXPORTS_W bool haveImageWriter( const String& filename );
+
+
+//! @} imgcodecs
+
+} // cv
+
+#endif //OPENCV_IMGCODECS_HPP
diff --git a/3rdParty/include/opencv2/imgcodecs/imgcodecs.hpp b/3rdParty/include/opencv2/imgcodecs/imgcodecs.hpp
new file mode 100644
index 0000000..a3cd232
--- /dev/null
+++ b/3rdParty/include/opencv2/imgcodecs/imgcodecs.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/imgcodecs.hpp"
diff --git a/3rdParty/include/opencv2/imgcodecs/imgcodecs_c.h b/3rdParty/include/opencv2/imgcodecs/imgcodecs_c.h
new file mode 100644
index 0000000..c78b3f7
--- /dev/null
+++ b/3rdParty/include/opencv2/imgcodecs/imgcodecs_c.h
@@ -0,0 +1 @@
+#error "This header with legacy C API declarations has been removed from OpenCV. Legacy constants are available from legacy/constants_c.h file."
diff --git a/3rdParty/include/opencv2/imgcodecs/ios.h b/3rdParty/include/opencv2/imgcodecs/ios.h
new file mode 100644
index 0000000..5f17218
--- /dev/null
+++ b/3rdParty/include/opencv2/imgcodecs/ios.h
@@ -0,0 +1,59 @@
+
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#import <UIKit/UIKit.h>
+#import <Accelerate/Accelerate.h>
+#import <AVFoundation/AVFoundation.h>
+#import <ImageIO/ImageIO.h>
+#include "opencv2/core.hpp"
+
+//! @addtogroup imgcodecs_ios
+//! @{
+
+CV_EXPORTS CGImageRef MatToCGImage(const cv::Mat& image) CF_RETURNS_RETAINED;
+CV_EXPORTS void CGImageToMat(const CGImageRef image, cv::Mat& m, bool alphaExist = false);
+CV_EXPORTS UIImage* MatToUIImage(const cv::Mat& image);
+CV_EXPORTS void UIImageToMat(const UIImage* image,
+                             cv::Mat& m, bool alphaExist = false);
+
+//! @}
diff --git a/3rdParty/include/opencv2/imgcodecs/legacy/constants_c.h b/3rdParty/include/opencv2/imgcodecs/legacy/constants_c.h
new file mode 100644
index 0000000..de7be4f
--- /dev/null
+++ b/3rdParty/include/opencv2/imgcodecs/legacy/constants_c.h
@@ -0,0 +1,54 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_IMGCODECS_LEGACY_CONSTANTS_H
+#define OPENCV_IMGCODECS_LEGACY_CONSTANTS_H
+
+/* duplicate of "ImreadModes" enumeration for better compatibility with OpenCV 3.x */
+enum
+{
+/* 8bit, color or not */
+    CV_LOAD_IMAGE_UNCHANGED  =-1,
+/* 8bit, gray */
+    CV_LOAD_IMAGE_GRAYSCALE  =0,
+/* ?, color */
+    CV_LOAD_IMAGE_COLOR      =1,
+/* any depth, ? */
+    CV_LOAD_IMAGE_ANYDEPTH   =2,
+/* ?, any color */
+    CV_LOAD_IMAGE_ANYCOLOR   =4,
+/* ?, no rotate */
+    CV_LOAD_IMAGE_IGNORE_ORIENTATION  =128
+};
+
+/* duplicate of "ImwriteFlags" enumeration for better compatibility with OpenCV 3.x */
+enum
+{
+    CV_IMWRITE_JPEG_QUALITY =1,
+    CV_IMWRITE_JPEG_PROGRESSIVE =2,
+    CV_IMWRITE_JPEG_OPTIMIZE =3,
+    CV_IMWRITE_JPEG_RST_INTERVAL =4,
+    CV_IMWRITE_JPEG_LUMA_QUALITY =5,
+    CV_IMWRITE_JPEG_CHROMA_QUALITY =6,
+    CV_IMWRITE_PNG_COMPRESSION =16,
+    CV_IMWRITE_PNG_STRATEGY =17,
+    CV_IMWRITE_PNG_BILEVEL =18,
+    CV_IMWRITE_PNG_STRATEGY_DEFAULT =0,
+    CV_IMWRITE_PNG_STRATEGY_FILTERED =1,
+    CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY =2,
+    CV_IMWRITE_PNG_STRATEGY_RLE =3,
+    CV_IMWRITE_PNG_STRATEGY_FIXED =4,
+    CV_IMWRITE_PXM_BINARY =32,
+    CV_IMWRITE_EXR_TYPE = 48,
+    CV_IMWRITE_WEBP_QUALITY =64,
+    CV_IMWRITE_PAM_TUPLETYPE = 128,
+    CV_IMWRITE_PAM_FORMAT_NULL = 0,
+    CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1,
+    CV_IMWRITE_PAM_FORMAT_GRAYSCALE = 2,
+    CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3,
+    CV_IMWRITE_PAM_FORMAT_RGB = 4,
+    CV_IMWRITE_PAM_FORMAT_RGB_ALPHA = 5,
+};
+
+#endif // OPENCV_IMGCODECS_LEGACY_CONSTANTS_H
diff --git a/3rdParty/include/opencv2/imgcodecs/macosx.h b/3rdParty/include/opencv2/imgcodecs/macosx.h
new file mode 100644
index 0000000..cfb0770
--- /dev/null
+++ b/3rdParty/include/opencv2/imgcodecs/macosx.h
@@ -0,0 +1,20 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#if !defined(__APPLE__) || !defined(__MACH__)
+#error This header should be used in macOS ObjC/Swift projects.
+#endif
+
+#import <AppKit/AppKit.h>
+#include "opencv2/core.hpp"
+
+//! @addtogroup imgcodecs_macosx
+//! @{
+
+CV_EXPORTS CGImageRef MatToCGImage(const cv::Mat& image) CF_RETURNS_RETAINED;
+CV_EXPORTS void CGImageToMat(const CGImageRef image, cv::Mat& m, bool alphaExist = false);
+CV_EXPORTS NSImage* MatToNSImage(const cv::Mat& image);
+CV_EXPORTS void NSImageToMat(const NSImage* image, cv::Mat& m, bool alphaExist = false);
+
+//! @}
diff --git a/3rdParty/include/opencv2/imgproc.hpp b/3rdParty/include/opencv2/imgproc.hpp
new file mode 100644
index 0000000..eacec61
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc.hpp
@@ -0,0 +1,4994 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_IMGPROC_HPP
+#define OPENCV_IMGPROC_HPP
+
+#include "opencv2/core.hpp"
+
+/**
+  @defgroup imgproc Image Processing
+
+This module includes image-processing functions.
+
+  @{
+    @defgroup imgproc_filter Image Filtering
+
+Functions and classes described in this section are used to perform various linear or non-linear
+filtering operations on 2D images (represented as Mat's). It means that for each pixel location
+\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to
+compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of
+morphological operations, it is the minimum or maximum values, and so on. The computed response is
+stored in the destination image at the same location \f$(x,y)\f$. It means that the output image
+will be of the same size as the input image. Normally, the functions support multi-channel arrays,
+in which case every channel is processed independently. Therefore, the output image will also have
+the same number of channels as the input one.
+
+Another common feature of the functions and classes described in this section is that, unlike
+simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
+example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when
+processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
+of the image. You can let these pixels be the same as the left-most image pixels ("replicated
+border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
+border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method.
+For details, see #BorderTypes
+
+@anchor filter_depths
+### Depth combinations
+Input depth (src.depth()) | Output depth (ddepth)
+--------------------------|----------------------
+CV_8U                     | -1/CV_16S/CV_32F/CV_64F
+CV_16U/CV_16S             | -1/CV_32F/CV_64F
+CV_32F                    | -1/CV_32F/CV_64F
+CV_64F                    | -1/CV_64F
+
+@note when ddepth=-1, the output image will have the same depth as the source.
+
+    @defgroup imgproc_transform Geometric Image Transformations
+
+The functions in this section perform various geometrical transformations of 2D images. They do not
+change the image content but deform the pixel grid and map this deformed grid to the destination
+image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from
+destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the
+functions compute coordinates of the corresponding "donor" pixel in the source image and copy the
+pixel value:
+
+\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f]
+
+In case when you specify the forward mapping \f$\left<g_x, g_y\right>: \texttt{src} \rightarrow
+\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping
+\f$\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula.
+
+The actual implementations of the geometrical transformations, from the most generic remap and to
+the simplest and the fastest resize, need to solve two main problems with the above formula:
+
+- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the
+previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both
+of them may fall outside of the image. In this case, an extrapolation method needs to be used.
+OpenCV provides the same selection of extrapolation methods as in the filtering functions. In
+addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in
+the destination image will not be modified at all.
+
+- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point
+numbers. This means that \f$\left<f_x, f_y\right>\f$ can be either an affine or perspective
+transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional
+coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the
+nearest integer coordinates and the corresponding pixel can be used. This is called a
+nearest-neighbor interpolation. However, a better result can be achieved by using more
+sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) ,
+where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y),
+f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the
+interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See
+resize for details.
+
+@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images.
+
+    @defgroup imgproc_misc Miscellaneous Image Transformations
+    @defgroup imgproc_draw Drawing Functions
+
+Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be
+rendered with antialiasing (implemented only for 8-bit images for now). All the functions include
+the parameter color that uses an RGB value (that may be constructed with the Scalar constructor )
+for color images and brightness for grayscale images. For color images, the channel ordering is
+normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a
+color using the Scalar constructor, it should look like:
+
+\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f]
+
+If you are using your own image rendering and I/O functions, you can use any channel ordering. The
+drawing functions process each channel independently and do not depend on the channel order or even
+on the used color space. The whole image can be converted from BGR to RGB or to a different color
+space using cvtColor .
+
+If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also,
+many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means
+that the coordinates can be passed as fixed-point numbers encoded as integers. The number of
+fractional bits is specified by the shift parameter and the real point coordinates are calculated as
+\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is
+especially effective when rendering antialiased shapes.
+
+@note The functions do not support alpha-transparency when the target image is 4-channel. In this
+case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint
+semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main
+image.
+
+    @defgroup imgproc_color_conversions Color Space Conversions
+    @defgroup imgproc_colormap ColorMaps in OpenCV
+
+The human perception isn't built for observing fine changes in grayscale images. Human eyes are more
+sensitive to observing changes between colors, so you often need to recolor your grayscale images to
+get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your
+computer vision application.
+
+In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample
+code reads the path to an image from command line, applies a Jet colormap on it and shows the
+result:
+
+@include snippets/imgproc_applyColorMap.cpp
+
+@see #ColormapTypes
+
+    @defgroup imgproc_subdiv2d Planar Subdivision
+
+The Subdiv2D class described in this section is used to perform various planar subdivision on
+a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles
+using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram.
+In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi
+diagram with red lines.
+
+![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png)
+
+The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast
+location of points on the plane, building special graphs (such as NNG,RNG), and so forth.
+
+    @defgroup imgproc_hist Histograms
+    @defgroup imgproc_shape Structural Analysis and Shape Descriptors
+    @defgroup imgproc_motion Motion Analysis and Object Tracking
+    @defgroup imgproc_feature Feature Detection
+    @defgroup imgproc_object Object Detection
+    @defgroup imgproc_segmentation Image Segmentation
+    @defgroup imgproc_c C API
+    @defgroup imgproc_hal Hardware Acceleration Layer
+    @{
+        @defgroup imgproc_hal_functions Functions
+        @defgroup imgproc_hal_interface Interface
+    @}
+  @}
+*/
+
+namespace cv
+{
+
+/** @addtogroup imgproc
+@{
+*/
+
+//! @addtogroup imgproc_filter
+//! @{
+
+enum SpecialFilter {
+    FILTER_SCHARR = -1
+};
+
+//! type of morphological operation
+enum MorphTypes{
+    MORPH_ERODE    = 0, //!< see #erode
+    MORPH_DILATE   = 1, //!< see #dilate
+    MORPH_OPEN     = 2, //!< an opening operation
+                        //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f]
+    MORPH_CLOSE    = 3, //!< a closing operation
+                        //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f]
+    MORPH_GRADIENT = 4, //!< a morphological gradient
+                        //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f]
+    MORPH_TOPHAT   = 5, //!< "top hat"
+                        //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f]
+    MORPH_BLACKHAT = 6, //!< "black hat"
+                        //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f]
+    MORPH_HITMISS  = 7  //!< "hit or miss"
+                        //!<   .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation
+};
+
+//! shape of the structuring element
+enum MorphShapes {
+    MORPH_RECT    = 0, //!< a rectangular structuring element:  \f[E_{ij}=1\f]
+    MORPH_CROSS   = 1, //!< a cross-shaped structuring element:
+                       //!< \f[E_{ij} = \begin{cases} 1 & \texttt{if } {i=\texttt{anchor.y } {or } {j=\texttt{anchor.x}}} \\0 & \texttt{otherwise} \end{cases}\f]
+    MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed
+                      //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height)
+};
+
+//! @} imgproc_filter
+
+//! @addtogroup imgproc_transform
+//! @{
+
+//! interpolation algorithm
+enum InterpolationFlags{
+    /** nearest neighbor interpolation */
+    INTER_NEAREST        = 0,
+    /** bilinear interpolation */
+    INTER_LINEAR         = 1,
+    /** bicubic interpolation */
+    INTER_CUBIC          = 2,
+    /** resampling using pixel area relation. It may be a preferred method for image decimation, as
+    it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST
+    method. */
+    INTER_AREA           = 3,
+    /** Lanczos interpolation over 8x8 neighborhood */
+    INTER_LANCZOS4       = 4,
+    /** Bit exact bilinear interpolation */
+    INTER_LINEAR_EXACT = 5,
+    /** Bit exact nearest neighbor interpolation. This will produce same results as
+    the nearest neighbor method in PIL, scikit-image or Matlab. */
+    INTER_NEAREST_EXACT  = 6,
+    /** mask for interpolation codes */
+    INTER_MAX            = 7,
+    /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the
+    source image, they are set to zero */
+    WARP_FILL_OUTLIERS   = 8,
+    /** flag, inverse transformation
+
+    For example, #linearPolar or #logPolar transforms:
+    - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$
+    - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$
+    */
+    WARP_INVERSE_MAP     = 16
+};
+
+/** \brief Specify the polar mapping mode
+@sa warpPolar
+*/
+enum WarpPolarMode
+{
+    WARP_POLAR_LINEAR = 0, ///< Remaps an image to/from polar space.
+    WARP_POLAR_LOG = 256   ///< Remaps an image to/from semilog-polar space.
+};
+
+enum InterpolationMasks {
+       INTER_BITS      = 5,
+       INTER_BITS2     = INTER_BITS * 2,
+       INTER_TAB_SIZE  = 1 << INTER_BITS,
+       INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE
+     };
+
+//! @} imgproc_transform
+
+//! @addtogroup imgproc_misc
+//! @{
+
+//! Distance types for Distance Transform and M-estimators
+//! @see distanceTransform, fitLine
+enum DistanceTypes {
+    DIST_USER    = -1,  //!< User defined distance
+    DIST_L1      = 1,   //!< distance = |x1-x2| + |y1-y2|
+    DIST_L2      = 2,   //!< the simple euclidean distance
+    DIST_C       = 3,   //!< distance = max(|x1-x2|,|y1-y2|)
+    DIST_L12     = 4,   //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))
+    DIST_FAIR    = 5,   //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998
+    DIST_WELSCH  = 6,   //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846
+    DIST_HUBER   = 7    //!< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345
+};
+
+//! Mask size for distance transform
+enum DistanceTransformMasks {
+    DIST_MASK_3       = 3, //!< mask=3
+    DIST_MASK_5       = 5, //!< mask=5
+    DIST_MASK_PRECISE = 0  //!<
+};
+
+//! type of the threshold operation
+//! ![threshold types](pics/threshold.png)
+enum ThresholdTypes {
+    THRESH_BINARY     = 0, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{maxval}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
+    THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f]
+    THRESH_TRUNC      = 2, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
+    THRESH_TOZERO     = 3, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
+    THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
+    THRESH_MASK       = 7,
+    THRESH_OTSU       = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value
+    THRESH_TRIANGLE   = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value
+};
+
+//! adaptive threshold algorithm
+//! @see adaptiveThreshold
+enum AdaptiveThresholdTypes {
+    /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times
+    \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */
+    ADAPTIVE_THRESH_MEAN_C     = 0,
+    /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian
+    window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$
+    minus C . The default sigma (standard deviation) is used for the specified blockSize . See
+    #getGaussianKernel*/
+    ADAPTIVE_THRESH_GAUSSIAN_C = 1
+};
+
+//! class of the pixel in GrabCut algorithm
+enum GrabCutClasses {
+    GC_BGD    = 0,  //!< an obvious background pixels
+    GC_FGD    = 1,  //!< an obvious foreground (object) pixel
+    GC_PR_BGD = 2,  //!< a possible background pixel
+    GC_PR_FGD = 3   //!< a possible foreground pixel
+};
+
+//! GrabCut algorithm flags
+enum GrabCutModes {
+    /** The function initializes the state and the mask using the provided rectangle. After that it
+    runs iterCount iterations of the algorithm. */
+    GC_INIT_WITH_RECT  = 0,
+    /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT
+    and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are
+    automatically initialized with GC_BGD .*/
+    GC_INIT_WITH_MASK  = 1,
+    /** The value means that the algorithm should just resume. */
+    GC_EVAL            = 2,
+    /** The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model */
+    GC_EVAL_FREEZE_MODEL = 3
+};
+
+//! distanceTransform algorithm flags
+enum DistanceTransformLabelTypes {
+    /** each connected component of zeros in src (as well as all the non-zero pixels closest to the
+    connected component) will be assigned the same label */
+    DIST_LABEL_CCOMP = 0,
+    /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */
+    DIST_LABEL_PIXEL = 1
+};
+
+//! floodfill algorithm flags
+enum FloodFillFlags {
+    /** If set, the difference between the current pixel and seed pixel is considered. Otherwise,
+    the difference between neighbor pixels is considered (that is, the range is floating). */
+    FLOODFILL_FIXED_RANGE = 1 << 16,
+    /** If set, the function does not change the image ( newVal is ignored), and only fills the
+    mask with the value specified in bits 8-16 of flags as described above. This option only make
+    sense in function variants that have the mask parameter. */
+    FLOODFILL_MASK_ONLY   = 1 << 17
+};
+
+//! @} imgproc_misc
+
+//! @addtogroup imgproc_shape
+//! @{
+
+//! connected components statistics
+enum ConnectedComponentsTypes {
+    CC_STAT_LEFT   = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding
+                        //!< box in the horizontal direction.
+    CC_STAT_TOP    = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding
+                        //!< box in the vertical direction.
+    CC_STAT_WIDTH  = 2, //!< The horizontal size of the bounding box
+    CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box
+    CC_STAT_AREA   = 4, //!< The total area (in pixels) of the connected component
+#ifndef CV_DOXYGEN
+    CC_STAT_MAX    = 5 //!< Max enumeration value. Used internally only for memory allocation
+#endif
+};
+
+//! connected components algorithm
+enum ConnectedComponentsAlgorithmsTypes {
+    CCL_DEFAULT   = -1, //!< Spaghetti @cite Bolelli2019 algorithm for 8-way connectivity, Spaghetti4C @cite Bolelli2021 algorithm for 4-way connectivity.
+    CCL_WU        = 0,  //!< SAUF @cite Wu2009 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for SAUF.
+    CCL_GRANA     = 1,  //!< BBDT @cite Grana2010 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for both BBDT and SAUF.
+    CCL_BOLELLI   = 2,  //!< Spaghetti @cite Bolelli2019 algorithm for 8-way connectivity, Spaghetti4C @cite Bolelli2021 algorithm for 4-way connectivity. The parallel implementation described in @cite Bolelli2017 is available for both Spaghetti and Spaghetti4C.
+    CCL_SAUF      = 3,  //!< Same as CCL_WU. It is preferable to use the flag with the name of the algorithm (CCL_SAUF) rather than the one with the name of the first author (CCL_WU).
+    CCL_BBDT      = 4,  //!< Same as CCL_GRANA. It is preferable to use the flag with the name of the algorithm (CCL_BBDT) rather than the one with the name of the first author (CCL_GRANA).
+    CCL_SPAGHETTI = 5,  //!< Same as CCL_BOLELLI. It is preferable to use the flag with the name of the algorithm (CCL_SPAGHETTI) rather than the one with the name of the first author (CCL_BOLELLI).
+};
+
+//! mode of the contour retrieval algorithm
+enum RetrievalModes {
+    /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for
+    all the contours. */
+    RETR_EXTERNAL  = 0,
+    /** retrieves all of the contours without establishing any hierarchical relationships. */
+    RETR_LIST      = 1,
+    /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top
+    level, there are external boundaries of the components. At the second level, there are
+    boundaries of the holes. If there is another contour inside a hole of a connected component, it
+    is still put at the top level. */
+    RETR_CCOMP     = 2,
+    /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/
+    RETR_TREE      = 3,
+    RETR_FLOODFILL = 4 //!<
+};
+
+//! the contour approximation algorithm
+enum ContourApproximationModes {
+    /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and
+    (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is,
+    max(abs(x1-x2),abs(y2-y1))==1. */
+    CHAIN_APPROX_NONE      = 1,
+    /** compresses horizontal, vertical, and diagonal segments and leaves only their end points.
+    For example, an up-right rectangular contour is encoded with 4 points. */
+    CHAIN_APPROX_SIMPLE    = 2,
+    /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
+    CHAIN_APPROX_TC89_L1   = 3,
+    /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
+    CHAIN_APPROX_TC89_KCOS = 4
+};
+
+/** @brief Shape matching methods
+
+\f$A\f$ denotes object1,\f$B\f$ denotes object2
+
+\f$\begin{array}{l} m^A_i =  \mathrm{sign} (h^A_i)  \cdot \log{h^A_i} \\ m^B_i =  \mathrm{sign} (h^B_i)  \cdot \log{h^B_i} \end{array}\f$
+
+and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively.
+*/
+enum ShapeMatchModes {
+    CONTOURS_MATCH_I1  =1, //!< \f[I_1(A,B) =  \sum _{i=1...7}  \left |  \frac{1}{m^A_i} -  \frac{1}{m^B_i} \right |\f]
+    CONTOURS_MATCH_I2  =2, //!< \f[I_2(A,B) =  \sum _{i=1...7}  \left | m^A_i - m^B_i  \right |\f]
+    CONTOURS_MATCH_I3  =3  //!< \f[I_3(A,B) =  \max _{i=1...7}  \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f]
+};
+
+//! @} imgproc_shape
+
+//! @addtogroup imgproc_feature
+//! @{
+
+//! Variants of a Hough transform
+enum HoughModes {
+
+    /** classical or standard Hough transform. Every line is represented by two floating-point
+    numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line,
+    and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must
+    be (the created sequence will be) of CV_32FC2 type */
+    HOUGH_STANDARD      = 0,
+    /** probabilistic Hough transform (more efficient in case if the picture contains a few long
+    linear segments). It returns line segments rather than the whole line. Each segment is
+    represented by starting and ending points, and the matrix must be (the created sequence will
+    be) of the CV_32SC4 type. */
+    HOUGH_PROBABILISTIC = 1,
+    /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as
+    HOUGH_STANDARD. */
+    HOUGH_MULTI_SCALE   = 2,
+    HOUGH_GRADIENT      = 3, //!< basically *21HT*, described in @cite Yuen90
+    HOUGH_GRADIENT_ALT  = 4, //!< variation of HOUGH_GRADIENT to get better accuracy
+};
+
+//! Variants of Line Segment %Detector
+enum LineSegmentDetectorModes {
+    LSD_REFINE_NONE = 0, //!< No refinement applied
+    LSD_REFINE_STD  = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations.
+    LSD_REFINE_ADV  = 2  //!< Advanced refinement. Number of false alarms is calculated, lines are
+                         //!< refined through increase of precision, decrement in size, etc.
+};
+
+//! @} imgproc_feature
+
+/** Histogram comparison methods
+  @ingroup imgproc_hist
+*/
+enum HistCompMethods {
+    /** Correlation
+    \f[d(H_1,H_2) =  \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f]
+    where
+    \f[\bar{H_k} =  \frac{1}{N} \sum _J H_k(J)\f]
+    and \f$N\f$ is a total number of histogram bins. */
+    HISTCMP_CORREL        = 0,
+    /** Chi-Square
+    \f[d(H_1,H_2) =  \sum _I  \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */
+    HISTCMP_CHISQR        = 1,
+    /** Intersection
+    \f[d(H_1,H_2) =  \sum _I  \min (H_1(I), H_2(I))\f] */
+    HISTCMP_INTERSECT     = 2,
+    /** Bhattacharyya distance
+    (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.)
+    \f[d(H_1,H_2) =  \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */
+    HISTCMP_BHATTACHARYYA = 3,
+    HISTCMP_HELLINGER     = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA
+    /** Alternative Chi-Square
+    \f[d(H_1,H_2) =  2 * \sum _I  \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f]
+    This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */
+    HISTCMP_CHISQR_ALT    = 4,
+    /** Kullback-Leibler divergence
+    \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */
+    HISTCMP_KL_DIV        = 5
+};
+
+/** the color conversion codes
+@see @ref imgproc_color_conversions
+@ingroup imgproc_color_conversions
+ */
+enum ColorConversionCodes {
+    COLOR_BGR2BGRA     = 0, //!< add alpha channel to RGB or BGR image
+    COLOR_RGB2RGBA     = COLOR_BGR2BGRA,
+
+    COLOR_BGRA2BGR     = 1, //!< remove alpha channel from RGB or BGR image
+    COLOR_RGBA2RGB     = COLOR_BGRA2BGR,
+
+    COLOR_BGR2RGBA     = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)
+    COLOR_RGB2BGRA     = COLOR_BGR2RGBA,
+
+    COLOR_RGBA2BGR     = 3,
+    COLOR_BGRA2RGB     = COLOR_RGBA2BGR,
+
+    COLOR_BGR2RGB      = 4,
+    COLOR_RGB2BGR      = COLOR_BGR2RGB,
+
+    COLOR_BGRA2RGBA    = 5,
+    COLOR_RGBA2BGRA    = COLOR_BGRA2RGBA,
+
+    COLOR_BGR2GRAY     = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"
+    COLOR_RGB2GRAY     = 7,
+    COLOR_GRAY2BGR     = 8,
+    COLOR_GRAY2RGB     = COLOR_GRAY2BGR,
+    COLOR_GRAY2BGRA    = 9,
+    COLOR_GRAY2RGBA    = COLOR_GRAY2BGRA,
+    COLOR_BGRA2GRAY    = 10,
+    COLOR_RGBA2GRAY    = 11,
+
+    COLOR_BGR2BGR565   = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)
+    COLOR_RGB2BGR565   = 13,
+    COLOR_BGR5652BGR   = 14,
+    COLOR_BGR5652RGB   = 15,
+    COLOR_BGRA2BGR565  = 16,
+    COLOR_RGBA2BGR565  = 17,
+    COLOR_BGR5652BGRA  = 18,
+    COLOR_BGR5652RGBA  = 19,
+
+    COLOR_GRAY2BGR565  = 20, //!< convert between grayscale to BGR565 (16-bit images)
+    COLOR_BGR5652GRAY  = 21,
+
+    COLOR_BGR2BGR555   = 22,  //!< convert between RGB/BGR and BGR555 (16-bit images)
+    COLOR_RGB2BGR555   = 23,
+    COLOR_BGR5552BGR   = 24,
+    COLOR_BGR5552RGB   = 25,
+    COLOR_BGRA2BGR555  = 26,
+    COLOR_RGBA2BGR555  = 27,
+    COLOR_BGR5552BGRA  = 28,
+    COLOR_BGR5552RGBA  = 29,
+
+    COLOR_GRAY2BGR555  = 30, //!< convert between grayscale and BGR555 (16-bit images)
+    COLOR_BGR5552GRAY  = 31,
+
+    COLOR_BGR2XYZ      = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"
+    COLOR_RGB2XYZ      = 33,
+    COLOR_XYZ2BGR      = 34,
+    COLOR_XYZ2RGB      = 35,
+
+    COLOR_BGR2YCrCb    = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"
+    COLOR_RGB2YCrCb    = 37,
+    COLOR_YCrCb2BGR    = 38,
+    COLOR_YCrCb2RGB    = 39,
+
+    COLOR_BGR2HSV      = 40, //!< convert RGB/BGR to HSV (hue saturation value) with H range 0..180 if 8 bit image, @ref color_convert_rgb_hsv "color conversions"
+    COLOR_RGB2HSV      = 41,
+
+    COLOR_BGR2Lab      = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
+    COLOR_RGB2Lab      = 45,
+
+    COLOR_BGR2Luv      = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
+    COLOR_RGB2Luv      = 51,
+    COLOR_BGR2HLS      = 52, //!< convert RGB/BGR to HLS (hue lightness saturation) with H range 0..180 if 8 bit image, @ref color_convert_rgb_hls "color conversions"
+    COLOR_RGB2HLS      = 53,
+
+    COLOR_HSV2BGR      = 54, //!< backward conversions HSV to RGB/BGR with H range 0..180 if 8 bit image
+    COLOR_HSV2RGB      = 55,
+
+    COLOR_Lab2BGR      = 56,
+    COLOR_Lab2RGB      = 57,
+    COLOR_Luv2BGR      = 58,
+    COLOR_Luv2RGB      = 59,
+    COLOR_HLS2BGR      = 60, //!< backward conversions HLS to RGB/BGR with H range 0..180 if 8 bit image
+    COLOR_HLS2RGB      = 61,
+
+    COLOR_BGR2HSV_FULL = 66, //!< convert RGB/BGR to HSV (hue saturation value) with H range 0..255 if 8 bit image, @ref color_convert_rgb_hsv "color conversions"
+    COLOR_RGB2HSV_FULL = 67,
+    COLOR_BGR2HLS_FULL = 68, //!< convert RGB/BGR to HLS (hue lightness saturation) with H range 0..255 if 8 bit image, @ref color_convert_rgb_hls "color conversions"
+    COLOR_RGB2HLS_FULL = 69,
+
+    COLOR_HSV2BGR_FULL = 70, //!< backward conversions HSV to RGB/BGR with H range 0..255 if 8 bit image
+    COLOR_HSV2RGB_FULL = 71,
+    COLOR_HLS2BGR_FULL = 72, //!< backward conversions HLS to RGB/BGR with H range 0..255 if 8 bit image
+    COLOR_HLS2RGB_FULL = 73,
+
+    COLOR_LBGR2Lab     = 74,
+    COLOR_LRGB2Lab     = 75,
+    COLOR_LBGR2Luv     = 76,
+    COLOR_LRGB2Luv     = 77,
+
+    COLOR_Lab2LBGR     = 78,
+    COLOR_Lab2LRGB     = 79,
+    COLOR_Luv2LBGR     = 80,
+    COLOR_Luv2LRGB     = 81,
+
+    COLOR_BGR2YUV      = 82, //!< convert between RGB/BGR and YUV
+    COLOR_RGB2YUV      = 83,
+    COLOR_YUV2BGR      = 84,
+    COLOR_YUV2RGB      = 85,
+
+    //! YUV 4:2:0 family to RGB
+    COLOR_YUV2RGB_NV12  = 90,
+    COLOR_YUV2BGR_NV12  = 91,
+    COLOR_YUV2RGB_NV21  = 92,
+    COLOR_YUV2BGR_NV21  = 93,
+    COLOR_YUV420sp2RGB  = COLOR_YUV2RGB_NV21,
+    COLOR_YUV420sp2BGR  = COLOR_YUV2BGR_NV21,
+
+    COLOR_YUV2RGBA_NV12 = 94,
+    COLOR_YUV2BGRA_NV12 = 95,
+    COLOR_YUV2RGBA_NV21 = 96,
+    COLOR_YUV2BGRA_NV21 = 97,
+    COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
+    COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
+
+    COLOR_YUV2RGB_YV12  = 98,
+    COLOR_YUV2BGR_YV12  = 99,
+    COLOR_YUV2RGB_IYUV  = 100,
+    COLOR_YUV2BGR_IYUV  = 101,
+    COLOR_YUV2RGB_I420  = COLOR_YUV2RGB_IYUV,
+    COLOR_YUV2BGR_I420  = COLOR_YUV2BGR_IYUV,
+    COLOR_YUV420p2RGB   = COLOR_YUV2RGB_YV12,
+    COLOR_YUV420p2BGR   = COLOR_YUV2BGR_YV12,
+
+    COLOR_YUV2RGBA_YV12 = 102,
+    COLOR_YUV2BGRA_YV12 = 103,
+    COLOR_YUV2RGBA_IYUV = 104,
+    COLOR_YUV2BGRA_IYUV = 105,
+    COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
+    COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
+    COLOR_YUV420p2RGBA  = COLOR_YUV2RGBA_YV12,
+    COLOR_YUV420p2BGRA  = COLOR_YUV2BGRA_YV12,
+
+    COLOR_YUV2GRAY_420  = 106,
+    COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
+    COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
+    COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
+    COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
+    COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
+    COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
+    COLOR_YUV420p2GRAY  = COLOR_YUV2GRAY_420,
+
+    //! YUV 4:2:2 family to RGB
+    COLOR_YUV2RGB_UYVY = 107,
+    COLOR_YUV2BGR_UYVY = 108,
+    //COLOR_YUV2RGB_VYUY = 109,
+    //COLOR_YUV2BGR_VYUY = 110,
+    COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
+    COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
+    COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
+    COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
+
+    COLOR_YUV2RGBA_UYVY = 111,
+    COLOR_YUV2BGRA_UYVY = 112,
+    //COLOR_YUV2RGBA_VYUY = 113,
+    //COLOR_YUV2BGRA_VYUY = 114,
+    COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
+    COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
+    COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
+    COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
+
+    COLOR_YUV2RGB_YUY2 = 115,
+    COLOR_YUV2BGR_YUY2 = 116,
+    COLOR_YUV2RGB_YVYU = 117,
+    COLOR_YUV2BGR_YVYU = 118,
+    COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
+    COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
+    COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
+    COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
+
+    COLOR_YUV2RGBA_YUY2 = 119,
+    COLOR_YUV2BGRA_YUY2 = 120,
+    COLOR_YUV2RGBA_YVYU = 121,
+    COLOR_YUV2BGRA_YVYU = 122,
+    COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
+    COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
+    COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
+    COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
+
+    COLOR_YUV2GRAY_UYVY = 123,
+    COLOR_YUV2GRAY_YUY2 = 124,
+    //CV_YUV2GRAY_VYUY    = CV_YUV2GRAY_UYVY,
+    COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
+    COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
+    COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
+    COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
+    COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
+
+    //! alpha premultiplication
+    COLOR_RGBA2mRGBA    = 125,
+    COLOR_mRGBA2RGBA    = 126,
+
+    //! RGB to YUV 4:2:0 family
+    COLOR_RGB2YUV_I420  = 127,
+    COLOR_BGR2YUV_I420  = 128,
+    COLOR_RGB2YUV_IYUV  = COLOR_RGB2YUV_I420,
+    COLOR_BGR2YUV_IYUV  = COLOR_BGR2YUV_I420,
+
+    COLOR_RGBA2YUV_I420 = 129,
+    COLOR_BGRA2YUV_I420 = 130,
+    COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
+    COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
+    COLOR_RGB2YUV_YV12  = 131,
+    COLOR_BGR2YUV_YV12  = 132,
+    COLOR_RGBA2YUV_YV12 = 133,
+    COLOR_BGRA2YUV_YV12 = 134,
+
+    //! Demosaicing, see @ref color_convert_bayer "color conversions" for additional information
+    COLOR_BayerBG2BGR = 46, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2BGR = 47, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2BGR = 48, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2BGR = 49, //!< equivalent to GBRG Bayer pattern
+
+    COLOR_BayerRGGB2BGR = COLOR_BayerBG2BGR,
+    COLOR_BayerGRBG2BGR = COLOR_BayerGB2BGR,
+    COLOR_BayerBGGR2BGR = COLOR_BayerRG2BGR,
+    COLOR_BayerGBRG2BGR = COLOR_BayerGR2BGR,
+
+    COLOR_BayerRGGB2RGB = COLOR_BayerBGGR2BGR,
+    COLOR_BayerGRBG2RGB = COLOR_BayerGBRG2BGR,
+    COLOR_BayerBGGR2RGB = COLOR_BayerRGGB2BGR,
+    COLOR_BayerGBRG2RGB = COLOR_BayerGRBG2BGR,
+
+    COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, //!< equivalent to GBRG Bayer pattern
+
+    COLOR_BayerBG2GRAY = 86, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2GRAY = 87, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2GRAY = 88, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2GRAY = 89, //!< equivalent to GBRG Bayer pattern
+
+    COLOR_BayerRGGB2GRAY = COLOR_BayerBG2GRAY,
+    COLOR_BayerGRBG2GRAY = COLOR_BayerGB2GRAY,
+    COLOR_BayerBGGR2GRAY = COLOR_BayerRG2GRAY,
+    COLOR_BayerGBRG2GRAY = COLOR_BayerGR2GRAY,
+
+    //! Demosaicing using Variable Number of Gradients
+    COLOR_BayerBG2BGR_VNG = 62, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2BGR_VNG = 63, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2BGR_VNG = 64, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2BGR_VNG = 65, //!< equivalent to GBRG Bayer pattern
+
+    COLOR_BayerRGGB2BGR_VNG = COLOR_BayerBG2BGR_VNG,
+    COLOR_BayerGRBG2BGR_VNG = COLOR_BayerGB2BGR_VNG,
+    COLOR_BayerBGGR2BGR_VNG = COLOR_BayerRG2BGR_VNG,
+    COLOR_BayerGBRG2BGR_VNG = COLOR_BayerGR2BGR_VNG,
+
+    COLOR_BayerRGGB2RGB_VNG = COLOR_BayerBGGR2BGR_VNG,
+    COLOR_BayerGRBG2RGB_VNG = COLOR_BayerGBRG2BGR_VNG,
+    COLOR_BayerBGGR2RGB_VNG = COLOR_BayerRGGB2BGR_VNG,
+    COLOR_BayerGBRG2RGB_VNG = COLOR_BayerGRBG2BGR_VNG,
+
+    COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, //!< equivalent to GBRG Bayer pattern
+
+    //! Edge-Aware Demosaicing
+    COLOR_BayerBG2BGR_EA  = 135, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2BGR_EA  = 136, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2BGR_EA  = 137, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2BGR_EA  = 138, //!< equivalent to GBRG Bayer pattern
+
+    COLOR_BayerRGGB2BGR_EA  = COLOR_BayerBG2BGR_EA,
+    COLOR_BayerGRBG2BGR_EA  = COLOR_BayerGB2BGR_EA,
+    COLOR_BayerBGGR2BGR_EA  = COLOR_BayerRG2BGR_EA,
+    COLOR_BayerGBRG2BGR_EA  = COLOR_BayerGR2BGR_EA,
+
+    COLOR_BayerRGGB2RGB_EA  = COLOR_BayerBGGR2BGR_EA,
+    COLOR_BayerGRBG2RGB_EA  = COLOR_BayerGBRG2BGR_EA,
+    COLOR_BayerBGGR2RGB_EA  = COLOR_BayerRGGB2BGR_EA,
+    COLOR_BayerGBRG2RGB_EA  = COLOR_BayerGRBG2BGR_EA,
+
+    COLOR_BayerBG2RGB_EA  = COLOR_BayerRG2BGR_EA, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2RGB_EA  = COLOR_BayerGR2BGR_EA, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2RGB_EA  = COLOR_BayerBG2BGR_EA, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2RGB_EA  = COLOR_BayerGB2BGR_EA, //!< equivalent to GBRG Bayer pattern
+
+    //! Demosaicing with alpha channel
+    COLOR_BayerBG2BGRA = 139, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2BGRA = 140, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2BGRA = 141, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2BGRA = 142, //!< equivalent to GBRG Bayer pattern
+
+    COLOR_BayerRGGB2BGRA = COLOR_BayerBG2BGRA,
+    COLOR_BayerGRBG2BGRA = COLOR_BayerGB2BGRA,
+    COLOR_BayerBGGR2BGRA = COLOR_BayerRG2BGRA,
+    COLOR_BayerGBRG2BGRA = COLOR_BayerGR2BGRA,
+
+    COLOR_BayerRGGB2RGBA = COLOR_BayerBGGR2BGRA,
+    COLOR_BayerGRBG2RGBA = COLOR_BayerGBRG2BGRA,
+    COLOR_BayerBGGR2RGBA = COLOR_BayerRGGB2BGRA,
+    COLOR_BayerGBRG2RGBA = COLOR_BayerGRBG2BGRA,
+
+    COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA, //!< equivalent to RGGB Bayer pattern
+    COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA, //!< equivalent to GRBG Bayer pattern
+    COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA, //!< equivalent to BGGR Bayer pattern
+    COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA, //!< equivalent to GBRG Bayer pattern
+
+    COLOR_COLORCVT_MAX  = 143
+};
+
+//! @addtogroup imgproc_shape
+//! @{
+
+//! types of intersection between rectangles
+enum RectanglesIntersectTypes {
+    INTERSECT_NONE = 0, //!< No intersection
+    INTERSECT_PARTIAL  = 1, //!< There is a partial intersection
+    INTERSECT_FULL  = 2 //!< One of the rectangle is fully enclosed in the other
+};
+
+/** types of line
+@ingroup imgproc_draw
+*/
+enum LineTypes {
+    FILLED  = -1,
+    LINE_4  = 4, //!< 4-connected line
+    LINE_8  = 8, //!< 8-connected line
+    LINE_AA = 16 //!< antialiased line
+};
+
+/** Only a subset of Hershey fonts <https://en.wikipedia.org/wiki/Hershey_fonts> are supported
+@ingroup imgproc_draw
+*/
+enum HersheyFonts {
+    FONT_HERSHEY_SIMPLEX        = 0, //!< normal size sans-serif font
+    FONT_HERSHEY_PLAIN          = 1, //!< small size sans-serif font
+    FONT_HERSHEY_DUPLEX         = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX)
+    FONT_HERSHEY_COMPLEX        = 3, //!< normal size serif font
+    FONT_HERSHEY_TRIPLEX        = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX)
+    FONT_HERSHEY_COMPLEX_SMALL  = 5, //!< smaller version of FONT_HERSHEY_COMPLEX
+    FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font
+    FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX
+    FONT_ITALIC                 = 16 //!< flag for italic font
+};
+
+/** Possible set of marker types used for the cv::drawMarker function
+@ingroup imgproc_draw
+*/
+enum MarkerTypes
+{
+    MARKER_CROSS = 0,           //!< A crosshair marker shape
+    MARKER_TILTED_CROSS = 1,    //!< A 45 degree tilted crosshair marker shape
+    MARKER_STAR = 2,            //!< A star marker shape, combination of cross and tilted cross
+    MARKER_DIAMOND = 3,         //!< A diamond marker shape
+    MARKER_SQUARE = 4,          //!< A square marker shape
+    MARKER_TRIANGLE_UP = 5,     //!< An upwards pointing triangle marker shape
+    MARKER_TRIANGLE_DOWN = 6    //!< A downwards pointing triangle marker shape
+};
+
+/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform
+*/
+class CV_EXPORTS_W GeneralizedHough : public Algorithm
+{
+public:
+    //! set template to search
+    CV_WRAP virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0;
+    CV_WRAP virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
+
+    //! find template on image
+    CV_WRAP virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0;
+    CV_WRAP virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0;
+
+    //! Canny low threshold.
+    CV_WRAP virtual void setCannyLowThresh(int cannyLowThresh) = 0;
+    CV_WRAP virtual int getCannyLowThresh() const = 0;
+
+    //! Canny high threshold.
+    CV_WRAP virtual void setCannyHighThresh(int cannyHighThresh) = 0;
+    CV_WRAP virtual int getCannyHighThresh() const = 0;
+
+    //! Minimum distance between the centers of the detected objects.
+    CV_WRAP virtual void setMinDist(double minDist) = 0;
+    CV_WRAP virtual double getMinDist() const = 0;
+
+    //! Inverse ratio of the accumulator resolution to the image resolution.
+    CV_WRAP virtual void setDp(double dp) = 0;
+    CV_WRAP virtual double getDp() const = 0;
+
+    //! Maximal size of inner buffers.
+    CV_WRAP virtual void setMaxBufferSize(int maxBufferSize) = 0;
+    CV_WRAP virtual int getMaxBufferSize() const = 0;
+};
+
+/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform
+
+Detects position only without translation and rotation @cite Ballard1981 .
+*/
+class CV_EXPORTS_W GeneralizedHoughBallard : public GeneralizedHough
+{
+public:
+    //! R-Table levels.
+    CV_WRAP virtual void setLevels(int levels) = 0;
+    CV_WRAP virtual int getLevels() const = 0;
+
+    //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.
+    CV_WRAP virtual void setVotesThreshold(int votesThreshold) = 0;
+    CV_WRAP virtual int getVotesThreshold() const = 0;
+};
+
+/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform
+
+Detects position, translation and rotation @cite Guil1999 .
+*/
+class CV_EXPORTS_W GeneralizedHoughGuil : public GeneralizedHough
+{
+public:
+    //! Angle difference in degrees between two points in feature.
+    CV_WRAP virtual void setXi(double xi) = 0;
+    CV_WRAP virtual double getXi() const = 0;
+
+    //! Feature table levels.
+    CV_WRAP virtual void setLevels(int levels) = 0;
+    CV_WRAP virtual int getLevels() const = 0;
+
+    //! Maximal difference between angles that treated as equal.
+    CV_WRAP virtual void setAngleEpsilon(double angleEpsilon) = 0;
+    CV_WRAP virtual double getAngleEpsilon() const = 0;
+
+    //! Minimal rotation angle to detect in degrees.
+    CV_WRAP virtual void setMinAngle(double minAngle) = 0;
+    CV_WRAP virtual double getMinAngle() const = 0;
+
+    //! Maximal rotation angle to detect in degrees.
+    CV_WRAP virtual void setMaxAngle(double maxAngle) = 0;
+    CV_WRAP virtual double getMaxAngle() const = 0;
+
+    //! Angle step in degrees.
+    CV_WRAP virtual void setAngleStep(double angleStep) = 0;
+    CV_WRAP virtual double getAngleStep() const = 0;
+
+    //! Angle votes threshold.
+    CV_WRAP virtual void setAngleThresh(int angleThresh) = 0;
+    CV_WRAP virtual int getAngleThresh() const = 0;
+
+    //! Minimal scale to detect.
+    CV_WRAP virtual void setMinScale(double minScale) = 0;
+    CV_WRAP virtual double getMinScale() const = 0;
+
+    //! Maximal scale to detect.
+    CV_WRAP virtual void setMaxScale(double maxScale) = 0;
+    CV_WRAP virtual double getMaxScale() const = 0;
+
+    //! Scale step.
+    CV_WRAP virtual void setScaleStep(double scaleStep) = 0;
+    CV_WRAP virtual double getScaleStep() const = 0;
+
+    //! Scale votes threshold.
+    CV_WRAP virtual void setScaleThresh(int scaleThresh) = 0;
+    CV_WRAP virtual int getScaleThresh() const = 0;
+
+    //! Position votes threshold.
+    CV_WRAP virtual void setPosThresh(int posThresh) = 0;
+    CV_WRAP virtual int getPosThresh() const = 0;
+};
+
+//! @} imgproc_shape
+
+//! @addtogroup imgproc_hist
+//! @{
+
+/** @brief Base class for Contrast Limited Adaptive Histogram Equalization.
+*/
+class CV_EXPORTS_W CLAHE : public Algorithm
+{
+public:
+    /** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization.
+
+    @param src Source image of type CV_8UC1 or CV_16UC1.
+    @param dst Destination image.
+     */
+    CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0;
+
+    /** @brief Sets threshold for contrast limiting.
+
+    @param clipLimit threshold value.
+    */
+    CV_WRAP virtual void setClipLimit(double clipLimit) = 0;
+
+    //! Returns threshold value for contrast limiting.
+    CV_WRAP virtual double getClipLimit() const = 0;
+
+    /** @brief Sets size of grid for histogram equalization. Input image will be divided into
+    equally sized rectangular tiles.
+
+    @param tileGridSize defines the number of tiles in row and column.
+    */
+    CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0;
+
+    //!@brief Returns Size defines the number of tiles in row and column.
+    CV_WRAP virtual Size getTilesGridSize() const = 0;
+
+    CV_WRAP virtual void collectGarbage() = 0;
+};
+
+//! @} imgproc_hist
+
+//! @addtogroup imgproc_subdiv2d
+//! @{
+
+class CV_EXPORTS_W Subdiv2D
+{
+public:
+    /** Subdiv2D point location cases */
+    enum { PTLOC_ERROR        = -2, //!< Point location error
+           PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect
+           PTLOC_INSIDE       = 0, //!< Point inside some facet
+           PTLOC_VERTEX       = 1, //!< Point coincides with one of the subdivision vertices
+           PTLOC_ON_EDGE      = 2  //!< Point on some edge
+         };
+
+    /** Subdiv2D edge type navigation (see: getEdge()) */
+    enum { NEXT_AROUND_ORG   = 0x00,
+           NEXT_AROUND_DST   = 0x22,
+           PREV_AROUND_ORG   = 0x11,
+           PREV_AROUND_DST   = 0x33,
+           NEXT_AROUND_LEFT  = 0x13,
+           NEXT_AROUND_RIGHT = 0x31,
+           PREV_AROUND_LEFT  = 0x20,
+           PREV_AROUND_RIGHT = 0x02
+         };
+
+    /** creates an empty Subdiv2D object.
+    To create a new empty Delaunay subdivision you need to use the #initDelaunay function.
+     */
+    CV_WRAP Subdiv2D();
+
+    /** @overload
+
+    @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision.
+
+    The function creates an empty Delaunay subdivision where 2D points can be added using the function
+    insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime
+    error is raised.
+     */
+    CV_WRAP Subdiv2D(Rect rect);
+
+    /** @brief Creates a new empty Delaunay subdivision
+
+    @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision.
+
+     */
+    CV_WRAP void initDelaunay(Rect rect);
+
+    /** @brief Insert a single point into a Delaunay triangulation.
+
+    @param pt Point to insert.
+
+    The function inserts a single point into a subdivision and modifies the subdivision topology
+    appropriately. If a point with the same coordinates exists already, no new point is added.
+    @returns the ID of the point.
+
+    @note If the point is outside of the triangulation specified rect a runtime error is raised.
+     */
+    CV_WRAP int insert(Point2f pt);
+
+    /** @brief Insert multiple points into a Delaunay triangulation.
+
+    @param ptvec Points to insert.
+
+    The function inserts a vector of points into a subdivision and modifies the subdivision topology
+    appropriately.
+     */
+    CV_WRAP void insert(const std::vector<Point2f>& ptvec);
+
+    /** @brief Returns the location of a point within a Delaunay triangulation.
+
+    @param pt Point to locate.
+    @param edge Output edge that the point belongs to or is located to the right of it.
+    @param vertex Optional output vertex the input point coincides with.
+
+    The function locates the input point within the subdivision and gives one of the triangle edges
+    or vertices.
+
+    @returns an integer which specify one of the following five cases for point location:
+    -  The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of
+       edges of the facet.
+    -  The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge.
+    -  The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and
+       vertex will contain a pointer to the vertex.
+    -  The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT
+       and no pointers are filled.
+    -  One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error
+       processing mode is selected, #PTLOC_ERROR is returned.
+     */
+    CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex);
+
+    /** @brief Finds the subdivision vertex closest to the given point.
+
+    @param pt Input point.
+    @param nearestPt Output subdivision vertex point.
+
+    The function is another function that locates the input point within the subdivision. It finds the
+    subdivision vertex that is the closest to the input point. It is not necessarily one of vertices
+    of the facet containing the input point, though the facet (located using locate() ) is used as a
+    starting point.
+
+    @returns vertex ID.
+     */
+    CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0);
+
+    /** @brief Returns a list of all edges.
+
+    @param edgeList Output vector.
+
+    The function gives each edge as a 4 numbers vector, where each two are one of the edge
+    vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3].
+     */
+    CV_WRAP void getEdgeList(CV_OUT std::vector<Vec4f>& edgeList) const;
+
+    /** @brief Returns a list of the leading edge ID connected to each triangle.
+
+    @param leadingEdgeList Output vector.
+
+    The function gives one edge ID for each triangle.
+     */
+    CV_WRAP void getLeadingEdgeList(CV_OUT std::vector<int>& leadingEdgeList) const;
+
+    /** @brief Returns a list of all triangles.
+
+    @param triangleList Output vector.
+
+    The function gives each triangle as a 6 numbers vector, where each two are one of the triangle
+    vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5].
+     */
+    CV_WRAP void getTriangleList(CV_OUT std::vector<Vec6f>& triangleList) const;
+
+    /** @brief Returns a list of all Voronoi facets.
+
+    @param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector.
+    @param facetList Output vector of the Voronoi facets.
+    @param facetCenters Output vector of the Voronoi facets center points.
+
+     */
+    CV_WRAP void getVoronoiFacetList(const std::vector<int>& idx, CV_OUT std::vector<std::vector<Point2f> >& facetList,
+                                     CV_OUT std::vector<Point2f>& facetCenters);
+
+    /** @brief Returns vertex location from vertex ID.
+
+    @param vertex vertex ID.
+    @param firstEdge Optional. The first edge ID which is connected to the vertex.
+    @returns vertex (x,y)
+
+     */
+    CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const;
+
+    /** @brief Returns one of the edges related to the given edge.
+
+    @param edge Subdivision edge ID.
+    @param nextEdgeType Parameter specifying which of the related edges to return.
+    The following values are possible:
+    -   NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge)
+    -   NEXT_AROUND_DST next around the edge vertex ( eDnext )
+    -   PREV_AROUND_ORG previous around the edge origin (reversed eRnext )
+    -   PREV_AROUND_DST previous around the edge destination (reversed eLnext )
+    -   NEXT_AROUND_LEFT next around the left facet ( eLnext )
+    -   NEXT_AROUND_RIGHT next around the right facet ( eRnext )
+    -   PREV_AROUND_LEFT previous around the left facet (reversed eOnext )
+    -   PREV_AROUND_RIGHT previous around the right facet (reversed eDnext )
+
+    ![sample output](pics/quadedge.png)
+
+    @returns edge ID related to the input edge.
+     */
+    CV_WRAP int getEdge( int edge, int nextEdgeType ) const;
+
+    /** @brief Returns next edge around the edge origin.
+
+    @param edge Subdivision edge ID.
+
+    @returns an integer which is next edge ID around the edge origin: eOnext on the
+    picture above if e is the input edge).
+     */
+    CV_WRAP int nextEdge(int edge) const;
+
+    /** @brief Returns another edge of the same quad-edge.
+
+    @param edge Subdivision edge ID.
+    @param rotate Parameter specifying which of the edges of the same quad-edge as the input
+    one to return. The following values are possible:
+    -   0 - the input edge ( e on the picture below if e is the input edge)
+    -   1 - the rotated edge ( eRot )
+    -   2 - the reversed edge (reversed e (in green))
+    -   3 - the reversed rotated edge (reversed eRot (in green))
+
+    @returns one of the edges ID of the same quad-edge as the input edge.
+     */
+    CV_WRAP int rotateEdge(int edge, int rotate) const;
+    CV_WRAP int symEdge(int edge) const;
+
+    /** @brief Returns the edge origin.
+
+    @param edge Subdivision edge ID.
+    @param orgpt Output vertex location.
+
+    @returns vertex ID.
+     */
+    CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const;
+
+    /** @brief Returns the edge destination.
+
+    @param edge Subdivision edge ID.
+    @param dstpt Output vertex location.
+
+    @returns vertex ID.
+     */
+    CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const;
+
+protected:
+    int newEdge();
+    void deleteEdge(int edge);
+    int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0);
+    void deletePoint(int vtx);
+    void setEdgePoints( int edge, int orgPt, int dstPt );
+    void splice( int edgeA, int edgeB );
+    int connectEdges( int edgeA, int edgeB );
+    void swapEdges( int edge );
+    int isRightOf(Point2f pt, int edge) const;
+    void calcVoronoi();
+    void clearVoronoi();
+    void checkSubdiv() const;
+
+    struct CV_EXPORTS Vertex
+    {
+        Vertex();
+        Vertex(Point2f pt, bool isvirtual, int firstEdge=0);
+        bool isvirtual() const;
+        bool isfree() const;
+
+        int firstEdge;
+        int type;
+        Point2f pt;
+    };
+
+    struct CV_EXPORTS QuadEdge
+    {
+        QuadEdge();
+        QuadEdge(int edgeidx);
+        bool isfree() const;
+
+        int next[4];
+        int pt[4];
+    };
+
+    //! All of the vertices
+    std::vector<Vertex> vtx;
+    //! All of the edges
+    std::vector<QuadEdge> qedges;
+    int freeQEdge;
+    int freePoint;
+    bool validGeometry;
+
+    int recentEdge;
+    //! Top left corner of the bounding rect
+    Point2f topLeft;
+    //! Bottom right corner of the bounding rect
+    Point2f bottomRight;
+};
+
+//! @} imgproc_subdiv2d
+
+//! @addtogroup imgproc_feature
+//! @{
+
+/** @example samples/cpp/lsd_lines.cpp
+An example using the LineSegmentDetector
+\image html building_lsd.png "Sample output image" width=434 height=300
+*/
+
+/** @brief Line segment detector class
+
+following the algorithm described at @cite Rafael12 .
+
+@note Implementation has been removed from OpenCV version 3.4.6 to 3.4.15 and version 4.1.0 to 4.5.3 due original code license conflict.
+restored again after [Computation of a NFA](https://github.com/rafael-grompone-von-gioi/binomial_nfa) code published under the MIT license.
+*/
+class CV_EXPORTS_W LineSegmentDetector : public Algorithm
+{
+public:
+
+    /** @brief Finds lines in the input image.
+
+    This is the output of the default parameters of the algorithm on the above shown image.
+
+    ![image](pics/building_lsd.png)
+
+    @param image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
+    `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
+    @param lines A vector of Vec4f elements specifying the beginning and ending point of a line. Where
+    Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
+    oriented depending on the gradient.
+    @param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
+    @param prec Vector of precisions with which the lines are found.
+    @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
+    bigger the value, logarithmically better the detection.
+    - -1 corresponds to 10 mean false alarms
+    - 0 corresponds to 1 mean false alarm
+    - 1 corresponds to 0.1 mean false alarms
+    This vector will be calculated only when the objects type is #LSD_REFINE_ADV.
+    */
+    CV_WRAP virtual void detect(InputArray image, OutputArray lines,
+                        OutputArray width = noArray(), OutputArray prec = noArray(),
+                        OutputArray nfa = noArray()) = 0;
+
+    /** @brief Draws the line segments on a given image.
+    @param image The image, where the lines will be drawn. Should be bigger or equal to the image,
+    where the lines were found.
+    @param lines A vector of the lines that needed to be drawn.
+     */
+    CV_WRAP virtual void drawSegments(InputOutputArray image, InputArray lines) = 0;
+
+    /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
+
+    @param size The size of the image, where lines1 and lines2 were found.
+    @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
+    @param lines2 The second group of lines. They visualized in red color.
+    @param image Optional image, where the lines will be drawn. The image should be color(3-channel)
+    in order for lines1 and lines2 to be drawn in the above mentioned colors.
+     */
+    CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray image = noArray()) = 0;
+
+    virtual ~LineSegmentDetector() { }
+};
+
+/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it.
+
+The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
+to edit those, as to tailor it for their own application.
+
+@param refine The way found lines will be refined, see #LineSegmentDetectorModes
+@param scale The scale of the image that will be used to find the lines. Range (0..1].
+@param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
+@param quant Bound to the quantization error on the gradient norm.
+@param ang_th Gradient angle tolerance in degrees.
+@param log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen.
+@param density_th Minimal density of aligned region points in the enclosing rectangle.
+@param n_bins Number of bins in pseudo-ordering of gradient modulus.
+ */
+CV_EXPORTS_W Ptr<LineSegmentDetector> createLineSegmentDetector(
+    int refine = LSD_REFINE_STD, double scale = 0.8,
+    double sigma_scale = 0.6, double quant = 2.0, double ang_th = 22.5,
+    double log_eps = 0, double density_th = 0.7, int n_bins = 1024);
+
+//! @} imgproc_feature
+
+//! @addtogroup imgproc_filter
+//! @{
+
+/** @brief Returns Gaussian filter coefficients.
+
+The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter
+coefficients:
+
+\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f]
+
+where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$.
+
+Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
+smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
+You may also use the higher-level GaussianBlur.
+@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive.
+@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
+`sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`.
+@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
+@sa  sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
+ */
+CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F );
+
+/** @brief Returns filter coefficients for computing spatial image derivatives.
+
+The function computes and returns the filter coefficients for spatial image derivatives. When
+`ksize=FILTER_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel
+kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to
+
+@param kx Output matrix of row filter coefficients. It has the type ktype .
+@param ky Output matrix of column filter coefficients. It has the type ktype .
+@param dx Derivative order in respect of x.
+@param dy Derivative order in respect of y.
+@param ksize Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
+@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
+Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are
+going to filter floating-point images, you are likely to use the normalized kernels. But if you
+compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve
+all the fractional bits, you may want to set normalize=false .
+@param ktype Type of filter coefficients. It can be CV_32f or CV_64F .
+ */
+CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
+                                   int dx, int dy, int ksize,
+                                   bool normalize = false, int ktype = CV_32F );
+
+/** @brief Returns Gabor filter coefficients.
+
+For more details about gabor filter equations and parameters, see: [Gabor
+Filter](http://en.wikipedia.org/wiki/Gabor_filter).
+
+@param ksize Size of the filter returned.
+@param sigma Standard deviation of the gaussian envelope.
+@param theta Orientation of the normal to the parallel stripes of a Gabor function.
+@param lambd Wavelength of the sinusoidal factor.
+@param gamma Spatial aspect ratio.
+@param psi Phase offset.
+@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
+ */
+CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
+                                 double gamma, double psi = CV_PI*0.5, int ktype = CV_64F );
+
+//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
+static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
+
+/** @brief Returns a structuring element of the specified size and shape for morphological operations.
+
+The function constructs and returns the structuring element that can be further passed to #erode,
+#dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
+the structuring element.
+
+@param shape Element shape that could be one of #MorphShapes
+@param ksize Size of the structuring element.
+@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the
+anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor
+position. In other cases the anchor just regulates how much the result of the morphological
+operation is shifted.
+ */
+CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1));
+
+/** @example samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp
+Sample code for simple filters
+![Sample screenshot](Smoothing_Tutorial_Result_Median_Filter.jpg)
+Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details
+ */
+
+/** @brief Blurs an image using the median filter.
+
+The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
+\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
+In-place operation is supported.
+
+@note The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes
+
+@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be
+CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
+@param dst destination array of the same size and type as src.
+@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
+@sa  bilateralFilter, blur, boxFilter, GaussianBlur
+ */
+CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize );
+
+/** @brief Blurs an image using a Gaussian filter.
+
+The function convolves the source image with the specified Gaussian kernel. In-place filtering is
+supported.
+
+@param src input image; the image can have any number of channels, which are processed
+independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
+@param dst output image of the same size and type as src.
+@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
+positive and odd. Or, they can be zero's and then they are computed from sigma.
+@param sigmaX Gaussian kernel standard deviation in X direction.
+@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
+equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
+respectively (see #getGaussianKernel for details); to fully control the result regardless of
+possible future modifications of all this semantics, it is recommended to specify all of ksize,
+sigmaX, and sigmaY.
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+
+@sa  sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
+ */
+CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize,
+                                double sigmaX, double sigmaY = 0,
+                                int borderType = BORDER_DEFAULT );
+
+/** @brief Applies the bilateral filter to an image.
+
+The function applies bilateral filtering to the input image, as described in
+http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
+bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is
+very slow compared to most filters.
+
+_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\<
+10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very
+strong effect, making the image look "cartoonish".
+
+_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time
+applications, and perhaps d=9 for offline applications that need heavy noise filtering.
+
+This filter does not work inplace.
+@param src Source 8-bit or floating-point, 1-channel or 3-channel image.
+@param dst Destination image of the same size and type as src .
+@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
+it is computed from sigmaSpace.
+@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
+farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
+in larger areas of semi-equal color.
+@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
+farther pixels will influence each other as long as their colors are close enough (see sigmaColor
+). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
+proportional to sigmaSpace.
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
+ */
+CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
+                                   double sigmaColor, double sigmaSpace,
+                                   int borderType = BORDER_DEFAULT );
+
+/** @brief Blurs an image using the box filter.
+
+The function smooths an image using the kernel:
+
+\f[\texttt{K} =  \alpha \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1 \end{bmatrix}\f]
+
+where
+
+\f[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true}  \\1 & \texttt{otherwise}\end{cases}\f]
+
+Unnormalized box filter is useful for computing various integral characteristics over each pixel
+neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
+algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.
+
+@param src input image.
+@param dst output image of the same size and type as src.
+@param ddepth the output image depth (-1 to use src.depth()).
+@param ksize blurring kernel size.
+@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
+center.
+@param normalize flag, specifying whether the kernel is normalized by its area or not.
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa  blur, bilateralFilter, GaussianBlur, medianBlur, integral
+ */
+CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
+                             Size ksize, Point anchor = Point(-1,-1),
+                             bool normalize = true,
+                             int borderType = BORDER_DEFAULT );
+
+/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter.
+
+For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring
+pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$.
+
+The unnormalized square box filter can be useful in computing local image statistics such as the the local
+variance and standard deviation around the neighborhood of a pixel.
+
+@param src input image
+@param dst output image of the same size and type as src
+@param ddepth the output image depth (-1 to use src.depth())
+@param ksize kernel size
+@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
+center.
+@param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa boxFilter
+*/
+CV_EXPORTS_W void sqrBoxFilter( InputArray src, OutputArray dst, int ddepth,
+                                Size ksize, Point anchor = Point(-1, -1),
+                                bool normalize = true,
+                                int borderType = BORDER_DEFAULT );
+
+/** @brief Blurs an image using the normalized box filter.
+
+The function smooths an image using the kernel:
+
+\f[\texttt{K} =  \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \end{bmatrix}\f]
+
+The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize,
+anchor, true, borderType)`.
+
+@param src input image; it can have any number of channels, which are processed independently, but
+the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
+@param dst output image of the same size and type as src.
+@param ksize blurring kernel size.
+@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
+center.
+@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa  boxFilter, bilateralFilter, GaussianBlur, medianBlur
+ */
+CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
+                        Size ksize, Point anchor = Point(-1,-1),
+                        int borderType = BORDER_DEFAULT );
+
+/** @brief Convolves an image with the kernel.
+
+The function applies an arbitrary linear filter to an image. In-place operation is supported. When
+the aperture is partially outside the image, the function interpolates outlier pixel values
+according to the specified border mode.
+
+The function does actually compute correlation, not the convolution:
+
+\f[\texttt{dst} (x,y) =  \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}}  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f]
+
+That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
+the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -
+anchor.y - 1)`.
+
+The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or
+larger) and the direct algorithm for small kernels.
+
+@param src input image.
+@param dst output image of the same size and the same number of channels as src.
+@param ddepth desired depth of the destination image, see @ref filter_depths "combinations"
+@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
+matrix; if you want to apply different kernels to different channels, split the image into
+separate color planes using split and process them individually.
+@param anchor anchor of the kernel that indicates the relative position of a filtered point within
+the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
+is at the kernel center.
+@param delta optional value added to the filtered pixels before storing them in dst.
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa  sepFilter2D, dft, matchTemplate
+ */
+CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth,
+                            InputArray kernel, Point anchor = Point(-1,-1),
+                            double delta = 0, int borderType = BORDER_DEFAULT );
+
+/** @brief Applies a separable linear filter to an image.
+
+The function applies a separable linear filter to the image. That is, first, every row of src is
+filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
+kernel kernelY. The final result shifted by delta is stored in dst .
+
+@param src Source image.
+@param dst Destination image of the same size and the same number of channels as src .
+@param ddepth Destination image depth, see @ref filter_depths "combinations"
+@param kernelX Coefficients for filtering each row.
+@param kernelY Coefficients for filtering each column.
+@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
+is at the kernel center.
+@param delta Value added to the filtered results before storing them.
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa  filter2D, Sobel, GaussianBlur, boxFilter, blur
+ */
+CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
+                               InputArray kernelX, InputArray kernelY,
+                               Point anchor = Point(-1,-1),
+                               double delta = 0, int borderType = BORDER_DEFAULT );
+
+/** @example samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp
+Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
+![Sample screenshot](Sobel_Derivatives_Tutorial_Result.jpg)
+Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details
+*/
+
+/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
+
+In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
+calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
+kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
+or the second x- or y- derivatives.
+
+There is also the special value `ksize = #FILTER_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
+filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
+
+\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
+
+for the x-derivative, or transposed for the y-derivative.
+
+The function calculates an image derivative by convolving the image with the appropriate kernel:
+
+\f[\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
+
+The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
+resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
+or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
+case corresponds to a kernel of:
+
+\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
+
+The second case corresponds to a kernel of:
+
+\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
+
+@param src input image.
+@param dst output image of the same size and the same number of channels as src .
+@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
+    8-bit input images it will result in truncated derivatives.
+@param dx order of the derivative x.
+@param dy order of the derivative y.
+@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
+@param scale optional scale factor for the computed derivative values; by default, no scaling is
+applied (see #getDerivKernels for details).
+@param delta optional delta value that is added to the results prior to storing them in dst.
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
+ */
+CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
+                         int dx, int dy, int ksize = 3,
+                         double scale = 1, double delta = 0,
+                         int borderType = BORDER_DEFAULT );
+
+/** @brief Calculates the first order image derivative in both x and y using a Sobel operator
+
+Equivalent to calling:
+
+@code
+Sobel( src, dx, CV_16SC1, 1, 0, 3 );
+Sobel( src, dy, CV_16SC1, 0, 1, 3 );
+@endcode
+
+@param src input image.
+@param dx output image with first-order derivative in x.
+@param dy output image with first-order derivative in y.
+@param ksize size of Sobel kernel. It must be 3.
+@param borderType pixel extrapolation method, see #BorderTypes.
+                  Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.
+
+@sa Sobel
+ */
+
+CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx,
+                                   OutputArray dy, int ksize = 3,
+                                   int borderType = BORDER_DEFAULT );
+
+/** @brief Calculates the first x- or y- image derivative using Scharr operator.
+
+The function computes the first x- or y- spatial image derivative using the Scharr operator. The
+call
+
+\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f]
+
+is equivalent to
+
+\f[\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\f]
+
+@param src input image.
+@param dst output image of the same size and the same number of channels as src.
+@param ddepth output image depth, see @ref filter_depths "combinations"
+@param dx order of the derivative x.
+@param dy order of the derivative y.
+@param scale optional scale factor for the computed derivative values; by default, no scaling is
+applied (see #getDerivKernels for details).
+@param delta optional delta value that is added to the results prior to storing them in dst.
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa  cartToPolar
+ */
+CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
+                          int dx, int dy, double scale = 1, double delta = 0,
+                          int borderType = BORDER_DEFAULT );
+
+/** @example samples/cpp/laplace.cpp
+An example using Laplace transformations for edge detection
+*/
+
+/** @brief Calculates the Laplacian of an image.
+
+The function calculates the Laplacian of the source image by adding up the second x and y
+derivatives calculated using the Sobel operator:
+
+\f[\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
+
+This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
+with the following \f$3 \times 3\f$ aperture:
+
+\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
+
+@param src Source image.
+@param dst Destination image of the same size and the same number of channels as src .
+@param ddepth Desired depth of the destination image.
+@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
+details. The size must be positive and odd.
+@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
+applied. See #getDerivKernels for details.
+@param delta Optional delta value that is added to the results prior to storing them in dst .
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@sa  Sobel, Scharr
+ */
+CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
+                             int ksize = 1, double scale = 1, double delta = 0,
+                             int borderType = BORDER_DEFAULT );
+
+//! @} imgproc_filter
+
+//! @addtogroup imgproc_feature
+//! @{
+
+/** @example samples/cpp/edge.cpp
+This program demonstrates usage of the Canny edge detector
+
+Check @ref tutorial_canny_detector "the corresponding tutorial" for more details
+*/
+
+/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 .
+
+The function finds edges in the input image and marks them in the output map edges using the
+Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The
+largest value is used to find initial segments of strong edges. See
+<http://en.wikipedia.org/wiki/Canny_edge_detector>
+
+@param image 8-bit input image.
+@param edges output edge map; single channels 8-bit image, which has the same size as image .
+@param threshold1 first threshold for the hysteresis procedure.
+@param threshold2 second threshold for the hysteresis procedure.
+@param apertureSize aperture size for the Sobel operator.
+@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
+\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
+L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
+L2gradient=false ).
+ */
+CV_EXPORTS_W void Canny( InputArray image, OutputArray edges,
+                         double threshold1, double threshold2,
+                         int apertureSize = 3, bool L2gradient = false );
+
+/** \overload
+
+Finds edges in an image using the Canny algorithm with custom image gradient.
+
+@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
+@param dy 16-bit y derivative of input image (same type as dx).
+@param edges output edge map; single channels 8-bit image, which has the same size as image .
+@param threshold1 first threshold for the hysteresis procedure.
+@param threshold2 second threshold for the hysteresis procedure.
+@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
+\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
+L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
+L2gradient=false ).
+ */
+CV_EXPORTS_W void Canny( InputArray dx, InputArray dy,
+                         OutputArray edges,
+                         double threshold1, double threshold2,
+                         bool L2gradient = false );
+
+/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection.
+
+The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal
+eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms
+of the formulae in the cornerEigenValsAndVecs description.
+
+@param src Input single-channel 8-bit or floating-point image.
+@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
+src .
+@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
+@param ksize Aperture parameter for the Sobel operator.
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
+ */
+CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst,
+                                     int blockSize, int ksize = 3,
+                                     int borderType = BORDER_DEFAULT );
+
+/** @brief Harris corner detector.
+
+The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
+cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance
+matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it
+computes the following characteristic:
+
+\f[\texttt{dst} (x,y) =  \mathrm{det} M^{(x,y)} - k  \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f]
+
+Corners in the image can be found as the local maxima of this response map.
+
+@param src Input single-channel 8-bit or floating-point image.
+@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
+size as src .
+@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
+@param ksize Aperture parameter for the Sobel operator.
+@param k Harris detector free parameter. See the formula above.
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
+ */
+CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
+                                int ksize, double k,
+                                int borderType = BORDER_DEFAULT );
+
+/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection.
+
+For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize
+neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as:
+
+\f[M =  \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 &  \sum _{S(p)}dI/dx dI/dy  \\ \sum _{S(p)}dI/dx dI/dy &  \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f]
+
+where the derivatives are computed using the Sobel operator.
+
+After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as
+\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where
+
+-   \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$
+-   \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$
+-   \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$
+
+The output of the function can be used for robust edge or corner detection.
+
+@param src Input single-channel 8-bit or floating-point image.
+@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
+@param blockSize Neighborhood size (see details below).
+@param ksize Aperture parameter for the Sobel operator.
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
+
+@sa  cornerMinEigenVal, cornerHarris, preCornerDetect
+ */
+CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
+                                          int blockSize, int ksize,
+                                          int borderType = BORDER_DEFAULT );
+
+/** @brief Calculates a feature map for corner detection.
+
+The function calculates the complex spatial derivative-based function of the source image
+
+\f[\texttt{dst} = (D_x  \texttt{src} )^2  \cdot D_{yy}  \texttt{src} + (D_y  \texttt{src} )^2  \cdot D_{xx}  \texttt{src} - 2 D_x  \texttt{src} \cdot D_y  \texttt{src} \cdot D_{xy}  \texttt{src}\f]
+
+where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image
+derivatives, and \f$D_{xy}\f$ is the mixed derivative.
+
+The corners can be found as local maximums of the functions, as shown below:
+@code
+    Mat corners, dilated_corners;
+    preCornerDetect(image, corners, 3);
+    // dilation with 3x3 rectangular structuring element
+    dilate(corners, dilated_corners, Mat(), 1);
+    Mat corner_mask = corners == dilated_corners;
+@endcode
+
+@param src Source single-channel 8-bit of floating-point image.
+@param dst Output image that has the type CV_32F and the same size as src .
+@param ksize %Aperture size of the Sobel .
+@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.
+ */
+CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
+                                   int borderType = BORDER_DEFAULT );
+
+/** @brief Refines the corner locations.
+
+The function iterates to find the sub-pixel accurate location of corners or radial saddle
+points as described in @cite forstner1987fast, and as shown on the figure below.
+
+![image](pics/cornersubpix.png)
+
+Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$
+to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$
+subject to image and measurement noise. Consider the expression:
+
+\f[\epsilon _i = {DI_{p_i}}^T  \cdot (q - p_i)\f]
+
+where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The
+value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up
+with \f$\epsilon_i\f$ set to zero:
+
+\f[\sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T) \cdot q -  \sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T  \cdot p_i)\f]
+
+where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first
+gradient term \f$G\f$ and the second gradient term \f$b\f$ gives:
+
+\f[q = G^{-1}  \cdot b\f]
+
+The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates
+until the center stays within a set threshold.
+
+@param image Input single-channel, 8-bit or float image.
+@param corners Initial coordinates of the input corners and refined coordinates provided for
+output.
+@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) ,
+then a \f$(5*2+1) \times (5*2+1) = 11 \times 11\f$ search window is used.
+@param zeroZone Half of the size of the dead region in the middle of the search zone over which
+the summation in the formula below is not done. It is used sometimes to avoid possible
+singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such
+a size.
+@param criteria Criteria for termination of the iterative process of corner refinement. That is,
+the process of corner position refinement stops either after criteria.maxCount iterations or when
+the corner position moves by less than criteria.epsilon on some iteration.
+ */
+CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
+                                Size winSize, Size zeroZone,
+                                TermCriteria criteria );
+
+/** @brief Determines strong corners on an image.
+
+The function finds the most prominent corners in the image or in the specified image region, as
+described in @cite Shi94
+
+-   Function calculates the corner quality measure at every source image pixel using the
+    #cornerMinEigenVal or #cornerHarris .
+-   Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
+    retained).
+-   The corners with the minimal eigenvalue less than
+    \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
+-   The remaining corners are sorted by the quality measure in the descending order.
+-   Function throws away each corner for which there is a stronger corner at a distance less than
+    maxDistance.
+
+The function can be used to initialize a point-based tracker of an object.
+
+@note If the function is called with different values A and B of the parameter qualityLevel , and
+A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
+with qualityLevel=B .
+
+@param image Input 8-bit or floating-point 32-bit, single-channel image.
+@param corners Output vector of detected corners.
+@param maxCorners Maximum number of corners to return. If there are more corners than are found,
+the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
+and all detected corners are returned.
+@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
+parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
+(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
+quality measure less than the product are rejected. For example, if the best corner has the
+quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
+less than 15 are rejected.
+@param minDistance Minimum possible Euclidean distance between the returned corners.
+@param mask Optional region of interest. If the image is not empty (it needs to have the type
+CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
+@param blockSize Size of an average block for computing a derivative covariation matrix over each
+pixel neighborhood. See cornerEigenValsAndVecs .
+@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
+or #cornerMinEigenVal.
+@param k Free parameter of the Harris detector.
+
+@sa  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
+ */
+
+CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
+                                     int maxCorners, double qualityLevel, double minDistance,
+                                     InputArray mask = noArray(), int blockSize = 3,
+                                     bool useHarrisDetector = false, double k = 0.04 );
+
+CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
+                                     int maxCorners, double qualityLevel, double minDistance,
+                                     InputArray mask, int blockSize,
+                                     int gradientSize, bool useHarrisDetector = false,
+                                     double k = 0.04 );
+
+/** @brief Same as above, but returns also quality measure of the detected corners.
+
+@param image Input 8-bit or floating-point 32-bit, single-channel image.
+@param corners Output vector of detected corners.
+@param maxCorners Maximum number of corners to return. If there are more corners than are found,
+the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
+and all detected corners are returned.
+@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
+parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
+(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
+quality measure less than the product are rejected. For example, if the best corner has the
+quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
+less than 15 are rejected.
+@param minDistance Minimum possible Euclidean distance between the returned corners.
+@param mask Region of interest. If the image is not empty (it needs to have the type
+CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
+@param cornersQuality Output vector of quality measure of the detected corners.
+@param blockSize Size of an average block for computing a derivative covariation matrix over each
+pixel neighborhood. See cornerEigenValsAndVecs .
+@param gradientSize Aperture parameter for the Sobel operator used for derivatives computation.
+See cornerEigenValsAndVecs .
+@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
+or #cornerMinEigenVal.
+@param k Free parameter of the Harris detector.
+ */
+CV_EXPORTS CV_WRAP_AS(goodFeaturesToTrackWithQuality) void goodFeaturesToTrack(
+        InputArray image, OutputArray corners,
+        int maxCorners, double qualityLevel, double minDistance,
+        InputArray mask, OutputArray cornersQuality, int blockSize = 3,
+        int gradientSize = 3, bool useHarrisDetector = false, double k = 0.04);
+
+/** @example samples/cpp/tutorial_code/ImgTrans/houghlines.cpp
+An example using the Hough line detector
+![Sample input image](Hough_Lines_Tutorial_Original_Image.jpg) ![Output image](Hough_Lines_Tutorial_Result.jpg)
+*/
+
+/** @brief Finds lines in a binary image using the standard Hough transform.
+
+The function implements the standard or standard multi-scale Hough transform algorithm for line
+detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
+transform.
+
+@param image 8-bit, single-channel binary source image. The image may be modified by the function.
+@param lines Output vector of lines. Each line is represented by a 2 or 3 element vector
+\f$(\rho, \theta)\f$ or \f$(\rho, \theta, \textrm{votes})\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of
+the image). \f$\theta\f$ is the line rotation angle in radians (
+\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ).
+\f$\textrm{votes}\f$ is the value of accumulator.
+@param rho Distance resolution of the accumulator in pixels.
+@param theta Angle resolution of the accumulator in radians.
+@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
+votes ( \f$>\texttt{threshold}\f$ ).
+@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho .
+The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
+rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these
+parameters should be positive.
+@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
+@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines.
+Must fall between 0 and max_theta.
+@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines.
+Must fall between min_theta and CV_PI.
+ */
+CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines,
+                              double rho, double theta, int threshold,
+                              double srn = 0, double stn = 0,
+                              double min_theta = 0, double max_theta = CV_PI );
+
+/** @brief Finds line segments in a binary image using the probabilistic Hough transform.
+
+The function implements the probabilistic Hough transform algorithm for line detection, described
+in @cite Matas00
+
+See the line detection example below:
+@include snippets/imgproc_HoughLinesP.cpp
+This is a sample picture the function parameters have been tuned for:
+
+![image](pics/building.jpg)
+
+And this is the output of the above program in case of the probabilistic Hough transform:
+
+![image](pics/houghp.png)
+
+@param image 8-bit, single-channel binary source image. The image may be modified by the function.
+@param lines Output vector of lines. Each line is represented by a 4-element vector
+\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected
+line segment.
+@param rho Distance resolution of the accumulator in pixels.
+@param theta Angle resolution of the accumulator in radians.
+@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
+votes ( \f$>\texttt{threshold}\f$ ).
+@param minLineLength Minimum line length. Line segments shorter than that are rejected.
+@param maxLineGap Maximum allowed gap between points on the same line to link them.
+
+@sa LineSegmentDetector
+ */
+CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines,
+                               double rho, double theta, int threshold,
+                               double minLineLength = 0, double maxLineGap = 0 );
+
+/** @brief Finds lines in a set of points using the standard Hough transform.
+
+The function finds lines in a set of points using a modification of the Hough transform.
+@include snippets/imgproc_HoughLinesPointSet.cpp
+@param point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2.
+@param lines Output vector of found lines. Each vector is encoded as a vector<Vec3d> \f$(votes, rho, theta)\f$.
+The larger the value of 'votes', the higher the reliability of the Hough line.
+@param lines_max Max count of Hough lines.
+@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
+votes ( \f$>\texttt{threshold}\f$ ).
+@param min_rho Minimum value for \f$\rho\f$ for the accumulator (Note: \f$\rho\f$ can be negative. The absolute value \f$|\rho|\f$ is the distance of a line to the origin.).
+@param max_rho Maximum value for \f$\rho\f$ for the accumulator.
+@param rho_step Distance resolution of the accumulator.
+@param min_theta Minimum angle value of the accumulator in radians.
+@param max_theta Maximum angle value of the accumulator in radians.
+@param theta_step Angle resolution of the accumulator in radians.
+ */
+CV_EXPORTS_W void HoughLinesPointSet( InputArray point, OutputArray lines, int lines_max, int threshold,
+                                      double min_rho, double max_rho, double rho_step,
+                                      double min_theta, double max_theta, double theta_step );
+
+/** @example samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp
+An example using the Hough circle detector
+*/
+
+/** @brief Finds circles in a grayscale image using the Hough transform.
+
+The function finds circles in a grayscale image using a modification of the Hough transform.
+
+Example: :
+@include snippets/imgproc_HoughLinesCircles.cpp
+
+@note Usually the function detects the centers of circles well. However, it may fail to find correct
+radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if
+you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number
+to return centers only without radius search, and find the correct radius using an additional procedure.
+
+It also helps to smooth image a bit unless it's already soft. For example,
+GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.
+
+@param image 8-bit, single-channel, grayscale input image.
+@param circles Output vector of found circles. Each vector is encoded as  3 or 4 element
+floating-point vector \f$(x, y, radius)\f$ or \f$(x, y, radius, votes)\f$ .
+@param method Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
+@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
+dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
+half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5,
+unless some small very circles need to be detected.
+@param minDist Minimum distance between the centers of the detected circles. If the parameter is
+too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
+too large, some circles may be missed.
+@param param1 First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT,
+it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
+Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value
+shough normally be higher, such as 300 or normally exposed and contrasty images.
+@param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT, it is the
+accumulator threshold for the circle centers at the detection stage. The smaller it is, the more
+false circles may be detected. Circles, corresponding to the larger accumulator values, will be
+returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle "perfectness" measure.
+The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine.
+If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less.
+But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
+@param minRadius Minimum circle radius.
+@param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, #HOUGH_GRADIENT returns
+centers without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.
+
+@sa fitEllipse, minEnclosingCircle
+ */
+CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
+                               int method, double dp, double minDist,
+                               double param1 = 100, double param2 = 100,
+                               int minRadius = 0, int maxRadius = 0 );
+
+//! @} imgproc_feature
+
+//! @addtogroup imgproc_filter
+//! @{
+
+/** @example samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp
+Advanced morphology Transformations sample code
+![Sample screenshot](Morphology_2_Tutorial_Result.jpg)
+Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details
+*/
+
+/** @brief Erodes an image by using a specific structuring element.
+
+The function erodes the source image using the specified structuring element that determines the
+shape of a pixel neighborhood over which the minimum is taken:
+
+\f[\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
+
+The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In
+case of multi-channel images, each channel is processed independently.
+
+@param src input image; the number of channels can be arbitrary, but the depth should be one of
+CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
+@param dst output image of the same size and type as src.
+@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
+structuring element is used. Kernel can be created using #getStructuringElement.
+@param anchor position of the anchor within the element; default value (-1, -1) means that the
+anchor is at the element center.
+@param iterations number of times erosion is applied.
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@param borderValue border value in case of a constant border
+@sa  dilate, morphologyEx, getStructuringElement
+ */
+CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
+                         Point anchor = Point(-1,-1), int iterations = 1,
+                         int borderType = BORDER_CONSTANT,
+                         const Scalar& borderValue = morphologyDefaultBorderValue() );
+
+/** @example samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp
+Erosion and Dilation sample code
+![Sample Screenshot-Erosion](Morphology_1_Tutorial_Erosion_Result.jpg)![Sample Screenshot-Dilation](Morphology_1_Tutorial_Dilation_Result.jpg)
+Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details
+*/
+
+/** @brief Dilates an image by using a specific structuring element.
+
+The function dilates the source image using the specified structuring element that determines the
+shape of a pixel neighborhood over which the maximum is taken:
+\f[\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
+
+The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In
+case of multi-channel images, each channel is processed independently.
+
+@param src input image; the number of channels can be arbitrary, but the depth should be one of
+CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
+@param dst output image of the same size and type as src.
+@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
+structuring element is used. Kernel can be created using #getStructuringElement
+@param anchor position of the anchor within the element; default value (-1, -1) means that the
+anchor is at the element center.
+@param iterations number of times dilation is applied.
+@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
+@param borderValue border value in case of a constant border
+@sa  erode, morphologyEx, getStructuringElement
+ */
+CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel,
+                          Point anchor = Point(-1,-1), int iterations = 1,
+                          int borderType = BORDER_CONSTANT,
+                          const Scalar& borderValue = morphologyDefaultBorderValue() );
+
+/** @brief Performs advanced morphological transformations.
+
+The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
+basic operations.
+
+Any of the operations can be done in-place. In case of multi-channel images, each channel is
+processed independently.
+
+@param src Source image. The number of channels can be arbitrary. The depth should be one of
+CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
+@param dst Destination image of the same size and type as source image.
+@param op Type of a morphological operation, see #MorphTypes
+@param kernel Structuring element. It can be created using #getStructuringElement.
+@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
+kernel center.
+@param iterations Number of times erosion and dilation are applied.
+@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
+@param borderValue Border value in case of a constant border. The default value has a special
+meaning.
+@sa  dilate, erode, getStructuringElement
+@note The number of iterations is the number of times erosion or dilatation operation will be applied.
+For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
+successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
+ */
+CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst,
+                                int op, InputArray kernel,
+                                Point anchor = Point(-1,-1), int iterations = 1,
+                                int borderType = BORDER_CONSTANT,
+                                const Scalar& borderValue = morphologyDefaultBorderValue() );
+
+//! @} imgproc_filter
+
+//! @addtogroup imgproc_transform
+//! @{
+
+/** @brief Resizes an image.
+
+The function resize resizes the image src down to or up to the specified size. Note that the
+initial dst type or size are not taken into account. Instead, the size and type are derived from
+the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst,
+you may call the function as follows:
+@code
+    // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
+    resize(src, dst, dst.size(), 0, 0, interpolation);
+@endcode
+If you want to decimate the image by factor of 2 in each direction, you can call the function this
+way:
+@code
+    // specify fx and fy and let the function compute the destination image size.
+    resize(src, dst, Size(), 0.5, 0.5, interpolation);
+@endcode
+To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to
+enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR
+(faster but still looks OK).
+
+@param src input image.
+@param dst output image; it has the size dsize (when it is non-zero) or the size computed from
+src.size(), fx, and fy; the type of dst is the same as of src.
+@param dsize output image size; if it equals zero (`None` in Python), it is computed as:
+ \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
+ Either dsize or both fx and fy must be non-zero.
+@param fx scale factor along the horizontal axis; when it equals 0, it is computed as
+\f[\texttt{(double)dsize.width/src.cols}\f]
+@param fy scale factor along the vertical axis; when it equals 0, it is computed as
+\f[\texttt{(double)dsize.height/src.rows}\f]
+@param interpolation interpolation method, see #InterpolationFlags
+
+@sa  warpAffine, warpPerspective, remap
+ */
+CV_EXPORTS_W void resize( InputArray src, OutputArray dst,
+                          Size dsize, double fx = 0, double fy = 0,
+                          int interpolation = INTER_LINEAR );
+
+/** @brief Applies an affine transformation to an image.
+
+The function warpAffine transforms the source image using the specified matrix:
+
+\f[\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\f]
+
+when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted
+with #invertAffineTransform and then put in the formula above instead of M. The function cannot
+operate in-place.
+
+@param src input image.
+@param dst output image that has the size dsize and the same type as src .
+@param M \f$2\times 3\f$ transformation matrix.
+@param dsize size of the output image.
+@param flags combination of interpolation methods (see #InterpolationFlags) and the optional
+flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
+\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
+@param borderMode pixel extrapolation method (see #BorderTypes); when
+borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
+the "outliers" in the source image are not modified by the function.
+@param borderValue value used in case of a constant border; by default, it is 0.
+
+@sa  warpPerspective, resize, remap, getRectSubPix, transform
+ */
+CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
+                              InputArray M, Size dsize,
+                              int flags = INTER_LINEAR,
+                              int borderMode = BORDER_CONSTANT,
+                              const Scalar& borderValue = Scalar());
+
+/** @example samples/cpp/warpPerspective_demo.cpp
+An example program shows using cv::getPerspectiveTransform and cv::warpPerspective for image warping
+*/
+
+/** @brief Applies a perspective transformation to an image.
+
+The function warpPerspective transforms the source image using the specified matrix:
+
+\f[\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
+     \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f]
+
+when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
+and then put in the formula above instead of M. The function cannot operate in-place.
+
+@param src input image.
+@param dst output image that has the size dsize and the same type as src .
+@param M \f$3\times 3\f$ transformation matrix.
+@param dsize size of the output image.
+@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the
+optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
+\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
+@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
+@param borderValue value used in case of a constant border; by default, it equals 0.
+
+@sa  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
+ */
+CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst,
+                                   InputArray M, Size dsize,
+                                   int flags = INTER_LINEAR,
+                                   int borderMode = BORDER_CONSTANT,
+                                   const Scalar& borderValue = Scalar());
+
+/** @brief Applies a generic geometrical transformation to an image.
+
+The function remap transforms the source image using the specified map:
+
+\f[\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\f]
+
+where values of pixels with non-integer coordinates are computed using one of available
+interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps
+in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in
+\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to
+convert from floating to fixed-point representations of a map is that they can yield much faster
+(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x),
+cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients.
+
+This function cannot operate in-place.
+
+@param src Source image.
+@param dst Destination image. It has the same size as map1 and the same type as src .
+@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
+CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point
+representation to fixed-point for speed.
+@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
+if map1 is (x,y) points), respectively.
+@param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA
+and #INTER_LINEAR_EXACT are not supported by this function.
+@param borderMode Pixel extrapolation method (see #BorderTypes). When
+borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that
+corresponds to the "outliers" in the source image are not modified by the function.
+@param borderValue Value used in case of a constant border. By default, it is 0.
+@note
+Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
+ */
+CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
+                         InputArray map1, InputArray map2,
+                         int interpolation, int borderMode = BORDER_CONSTANT,
+                         const Scalar& borderValue = Scalar());
+
+/** @brief Converts image transformation maps from one representation to another.
+
+The function converts a pair of maps for remap from one representation to another. The following
+options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are
+supported:
+
+- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the
+most frequently used conversion operation, in which the original floating-point maps (see remap )
+are converted to a more compact and much faster fixed-point representation. The first output array
+contains the rounded coordinates and the second array (created only when nninterpolation=false )
+contains indices in the interpolation tables.
+
+- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but
+the original maps are stored in one 2-channel matrix.
+
+- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
+as the originals.
+
+@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
+@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
+respectively.
+@param dstmap1 The first output map that has the type dstmap1type and the same size as src .
+@param dstmap2 The second output map.
+@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
+CV_32FC2 .
+@param nninterpolation Flag indicating whether the fixed-point maps are used for the
+nearest-neighbor or for a more complex interpolation.
+
+@sa  remap, undistort, initUndistortRectifyMap
+ */
+CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2,
+                               OutputArray dstmap1, OutputArray dstmap2,
+                               int dstmap1type, bool nninterpolation = false );
+
+/** @brief Calculates an affine matrix of 2D rotation.
+
+The function calculates the following matrix:
+
+\f[\begin{bmatrix} \alpha &  \beta & (1- \alpha )  \cdot \texttt{center.x} -  \beta \cdot \texttt{center.y} \\ - \beta &  \alpha &  \beta \cdot \texttt{center.x} + (1- \alpha )  \cdot \texttt{center.y} \end{bmatrix}\f]
+
+where
+
+\f[\begin{array}{l} \alpha =  \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta =  \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f]
+
+The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
+
+@param center Center of the rotation in the source image.
+@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the
+coordinate origin is assumed to be the top-left corner).
+@param scale Isotropic scale factor.
+
+@sa  getAffineTransform, warpAffine, transform
+ */
+CV_EXPORTS_W Mat getRotationMatrix2D(Point2f center, double angle, double scale);
+
+/** @sa getRotationMatrix2D */
+CV_EXPORTS Matx23d getRotationMatrix2D_(Point2f center, double angle, double scale);
+
+inline
+Mat getRotationMatrix2D(Point2f center, double angle, double scale)
+{
+    return Mat(getRotationMatrix2D_(center, angle, scale), true);
+}
+
+/** @brief Calculates an affine transform from three pairs of the corresponding points.
+
+The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that:
+
+\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
+
+where
+
+\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f]
+
+@param src Coordinates of triangle vertices in the source image.
+@param dst Coordinates of the corresponding triangle vertices in the destination image.
+
+@sa  warpAffine, transform
+ */
+CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
+
+/** @brief Inverts an affine transformation.
+
+The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M:
+
+\f[\begin{bmatrix} a_{11} & a_{12} & b_1  \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f]
+
+The result is also a \f$2 \times 3\f$ matrix of the same type as M.
+
+@param M Original affine transformation.
+@param iM Output reverse affine transformation.
+ */
+CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM );
+
+/** @brief Calculates a perspective transform from four pairs of the corresponding points.
+
+The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that:
+
+\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
+
+where
+
+\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f]
+
+@param src Coordinates of quadrangle vertices in the source image.
+@param dst Coordinates of the corresponding quadrangle vertices in the destination image.
+@param solveMethod method passed to cv::solve (#DecompTypes)
+
+@sa  findHomography, warpPerspective, perspectiveTransform
+ */
+CV_EXPORTS_W Mat getPerspectiveTransform(InputArray src, InputArray dst, int solveMethod = DECOMP_LU);
+
+/** @overload */
+CV_EXPORTS Mat getPerspectiveTransform(const Point2f src[], const Point2f dst[], int solveMethod = DECOMP_LU);
+
+
+CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst );
+
+/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy.
+
+The function getRectSubPix extracts pixels from src:
+
+\f[patch(x, y) = src(x +  \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y +  \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f]
+
+where the values of the pixels at non-integer coordinates are retrieved using bilinear
+interpolation. Every channel of multi-channel images is processed independently. Also
+the image should be a single channel or three channel image. While the center of the
+rectangle must be inside the image, parts of the rectangle may be outside.
+
+@param image Source image.
+@param patchSize Size of the extracted patch.
+@param center Floating point coordinates of the center of the extracted rectangle within the
+source image. The center must be inside the image.
+@param patch Extracted patch that has the size patchSize and the same number of channels as src .
+@param patchType Depth of the extracted pixels. By default, they have the same depth as src .
+
+@sa  warpAffine, warpPerspective
+ */
+CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize,
+                                 Point2f center, OutputArray patch, int patchType = -1 );
+
+/** @example samples/cpp/polar_transforms.cpp
+An example using the cv::linearPolar and cv::logPolar operations
+*/
+
+/** @brief Remaps an image to semilog-polar coordinates space.
+
+@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);
+
+@internal
+Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"):
+\f[\begin{array}{l}
+  dst( \rho , \phi ) = src(x,y) \\
+  dst.size() \leftarrow src.size()
+\end{array}\f]
+
+where
+\f[\begin{array}{l}
+  I = (dx,dy) = (x - center.x,y - center.y) \\
+  \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\
+  \phi = Kangle \cdot \texttt{angle} (I) \\
+\end{array}\f]
+
+and
+\f[\begin{array}{l}
+  M = src.cols / log_e(maxRadius) \\
+  Kangle = src.rows / 2\Pi \\
+\end{array}\f]
+
+The function emulates the human "foveal" vision and can be used for fast scale and
+rotation-invariant template matching, for object tracking and so forth.
+@param src Source image
+@param dst Destination image. It will have same size and type as src.
+@param center The transformation center; where the output precision is maximal
+@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too.
+@param flags A combination of interpolation methods, see #InterpolationFlags
+
+@note
+-   The function can not operate in-place.
+-   To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
+
+@sa cv::linearPolar
+@endinternal
+*/
+CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst,
+                            Point2f center, double M, int flags );
+
+/** @brief Remaps an image to polar coordinates space.
+
+@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)
+
+@internal
+Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"):
+\f[\begin{array}{l}
+  dst( \rho , \phi ) = src(x,y) \\
+  dst.size() \leftarrow src.size()
+\end{array}\f]
+
+where
+\f[\begin{array}{l}
+  I = (dx,dy) = (x - center.x,y - center.y) \\
+  \rho = Kmag \cdot \texttt{magnitude} (I) ,\\
+  \phi = angle \cdot \texttt{angle} (I)
+\end{array}\f]
+
+and
+\f[\begin{array}{l}
+  Kx = src.cols / maxRadius \\
+  Ky = src.rows / 2\Pi
+\end{array}\f]
+
+
+@param src Source image
+@param dst Destination image. It will have same size and type as src.
+@param center The transformation center;
+@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
+@param flags A combination of interpolation methods, see #InterpolationFlags
+
+@note
+-   The function can not operate in-place.
+-   To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
+
+@sa cv::logPolar
+@endinternal
+*/
+CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst,
+                               Point2f center, double maxRadius, int flags );
+
+
+/** \brief Remaps an image to polar or semilog-polar coordinates space
+
+@anchor polar_remaps_reference_image
+![Polar remaps reference](pics/polar_remap_doc.png)
+
+Transform the source image using the following transformation:
+\f[
+dst(\rho , \phi ) = src(x,y)
+\f]
+
+where
+\f[
+\begin{array}{l}
+\vec{I} = (x - center.x, \;y - center.y) \\
+\phi = Kangle \cdot \texttt{angle} (\vec{I}) \\
+\rho = \left\{\begin{matrix}
+Klin \cdot \texttt{magnitude} (\vec{I}) & default \\
+Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\
+\end{matrix}\right.
+\end{array}
+\f]
+
+and
+\f[
+\begin{array}{l}
+Kangle = dsize.height / 2\Pi \\
+Klin = dsize.width / maxRadius \\
+Klog = dsize.width / log_e(maxRadius) \\
+\end{array}
+\f]
+
+
+\par Linear vs semilog mapping
+
+Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode.
+
+Linear is the default mode.
+
+The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision)
+in contrast to peripheral vision where acuity is minor.
+
+\par Option on `dsize`:
+
+- if both values in `dsize <=0 ` (default),
+the destination image will have (almost) same area of source bounding circle:
+\f[\begin{array}{l}
+dsize.area  \leftarrow (maxRadius^2 \cdot \Pi) \\
+dsize.width = \texttt{cvRound}(maxRadius) \\
+dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\
+\end{array}\f]
+
+
+- if only `dsize.height <= 0`,
+the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`:
+\f[\begin{array}{l}
+dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\
+\end{array}
+\f]
+
+- if both values in `dsize > 0 `,
+the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`.
+
+
+\par Reverse mapping
+
+You can get reverse mapping adding #WARP_INVERSE_MAP to `flags`
+\snippet polar_transforms.cpp InverseMap
+
+In addiction, to calculate the original coordinate from a polar mapped coordinate \f$(rho, phi)->(x, y)\f$:
+\snippet polar_transforms.cpp InverseCoordinate
+
+@param src Source image.
+@param dst Destination image. It will have same type as src.
+@param dsize The destination image size (see description for valid options).
+@param center The transformation center.
+@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
+@param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode.
+            - Add #WARP_POLAR_LINEAR to select linear polar mapping (default)
+            - Add #WARP_POLAR_LOG to select semilog polar mapping
+            - Add #WARP_INVERSE_MAP for reverse mapping.
+@note
+-  The function can not operate in-place.
+-  To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
+-  This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
+
+@sa cv::remap
+*/
+CV_EXPORTS_W void warpPolar(InputArray src, OutputArray dst, Size dsize,
+                            Point2f center, double maxRadius, int flags);
+
+
+//! @} imgproc_transform
+
+//! @addtogroup imgproc_misc
+//! @{
+
+/** @brief Calculates the integral of an image.
+
+The function calculates one or more integral images for the source image as follows:
+
+\f[\texttt{sum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)\f]
+
+\f[\texttt{sqsum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)^2\f]
+
+\f[\texttt{tilted} (X,Y) =  \sum _{y<Y,abs(x-X+1) \leq Y-y-1}  \texttt{image} (x,y)\f]
+
+Using these integral images, you can calculate sum, mean, and standard deviation over a specific
+up-right or rotated rectangular region of the image in a constant time, for example:
+
+\f[\sum _{x_1 \leq x < x_2,  \, y_1  \leq y < y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\f]
+
+It makes possible to do a fast blurring or fast block correlation with a variable window size, for
+example. In case of multi-channel images, sums for each channel are accumulated independently.
+
+As a practical example, the next figure shows the calculation of the integral of a straight
+rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the
+original image are shown, as well as the relative pixels in the integral images sum and tilted .
+
+![integral calculation example](pics/integral.png)
+
+@param src input image as \f$W \times H\f$, 8-bit or floating-point (32f or 64f).
+@param sum integral image as \f$(W+1)\times (H+1)\f$ , 32-bit integer or floating-point (32f or 64f).
+@param sqsum integral image for squared pixel values; it is \f$(W+1)\times (H+1)\f$, double-precision
+floating-point (64f) array.
+@param tilted integral for the image rotated by 45 degrees; it is \f$(W+1)\times (H+1)\f$ array with
+the same data type as sum.
+@param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
+CV_64F.
+@param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
+ */
+CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
+                                        OutputArray sqsum, OutputArray tilted,
+                                        int sdepth = -1, int sqdepth = -1 );
+
+/** @overload */
+CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 );
+
+/** @overload */
+CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
+                                        OutputArray sqsum, int sdepth = -1, int sqdepth = -1 );
+
+//! @} imgproc_misc
+
+//! @addtogroup imgproc_motion
+//! @{
+
+/** @brief Adds an image to the accumulator image.
+
+The function adds src or some of its elements to dst :
+
+\f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
+
+The function supports multi-channel images. Each channel is processed independently.
+
+The function cv::accumulate can be used, for example, to collect statistics of a scene background
+viewed by a still camera and for the further foreground-background segmentation.
+
+@param src Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
+@param dst %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
+@param mask Optional operation mask.
+
+@sa  accumulateSquare, accumulateProduct, accumulateWeighted
+ */
+CV_EXPORTS_W void accumulate( InputArray src, InputOutputArray dst,
+                              InputArray mask = noArray() );
+
+/** @brief Adds the square of a source image to the accumulator image.
+
+The function adds the input image src or its selected region, raised to a power of 2, to the
+accumulator dst :
+
+\f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)^2  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
+
+The function supports multi-channel images. Each channel is processed independently.
+
+@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
+@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
+floating-point.
+@param mask Optional operation mask.
+
+@sa  accumulateSquare, accumulateProduct, accumulateWeighted
+ */
+CV_EXPORTS_W void accumulateSquare( InputArray src, InputOutputArray dst,
+                                    InputArray mask = noArray() );
+
+/** @brief Adds the per-element product of two input images to the accumulator image.
+
+The function adds the product of two images or their selected regions to the accumulator dst :
+
+\f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src1} (x,y)  \cdot \texttt{src2} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
+
+The function supports multi-channel images. Each channel is processed independently.
+
+@param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
+@param src2 Second input image of the same type and the same size as src1 .
+@param dst %Accumulator image with the same number of channels as input images, 32-bit or 64-bit
+floating-point.
+@param mask Optional operation mask.
+
+@sa  accumulate, accumulateSquare, accumulateWeighted
+ */
+CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2,
+                                     InputOutputArray dst, InputArray mask=noArray() );
+
+/** @brief Updates a running average.
+
+The function calculates the weighted sum of the input image src and the accumulator dst so that dst
+becomes a running average of a frame sequence:
+
+\f[\texttt{dst} (x,y)  \leftarrow (1- \texttt{alpha} )  \cdot \texttt{dst} (x,y) +  \texttt{alpha} \cdot \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
+
+That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
+The function supports multi-channel images. Each channel is processed independently.
+
+@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
+@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
+floating-point.
+@param alpha Weight of the input image.
+@param mask Optional operation mask.
+
+@sa  accumulate, accumulateSquare, accumulateProduct
+ */
+CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst,
+                                      double alpha, InputArray mask = noArray() );
+
+/** @brief The function is used to detect translational shifts that occur between two images.
+
+The operation takes advantage of the Fourier shift theorem for detecting the translational shift in
+the frequency domain. It can be used for fast image registration as well as motion estimation. For
+more information please see <http://en.wikipedia.org/wiki/Phase_correlation>
+
+Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
+with getOptimalDFTSize.
+
+The function performs the following equations:
+- First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
+image to remove possible edge effects. This window is cached until the array size changes to speed
+up processing time.
+- Next it computes the forward DFTs of each source array:
+\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f]
+where \f$\mathcal{F}\f$ is the forward DFT.
+- It then computes the cross-power spectrum of each frequency domain array:
+\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f]
+- Next the cross-correlation is converted back into the time domain via the inverse DFT:
+\f[r = \mathcal{F}^{-1}\{R\}\f]
+- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
+achieve sub-pixel accuracy.
+\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f]
+- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5
+centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single
+peak) and will be smaller when there are multiple peaks.
+
+@param src1 Source floating point array (CV_32FC1 or CV_64FC1)
+@param src2 Source floating point array (CV_32FC1 or CV_64FC1)
+@param window Floating point array with windowing coefficients to reduce edge effects (optional).
+@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).
+@returns detected phase shift (sub-pixel) between the two arrays.
+
+@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
+ */
+CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2,
+                                    InputArray window = noArray(), CV_OUT double* response = 0);
+
+/** @brief This function computes a Hanning window coefficients in two dimensions.
+
+See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function)
+for more information.
+
+An example is shown below:
+@code
+    // create hanning window of size 100x100 and type CV_32F
+    Mat hann;
+    createHanningWindow(hann, Size(100, 100), CV_32F);
+@endcode
+@param dst Destination array to place Hann coefficients in
+@param winSize The window size specifications (both width and height must be > 1)
+@param type Created array type
+ */
+CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
+
+/** @brief Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.
+
+The function cv::divSpectrums performs the per-element division of the first array by the second array.
+The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.
+
+@param a first input array.
+@param b second input array of the same size and type as src1 .
+@param c output array of the same size and type as src1 .
+@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
+each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
+@param conjB optional flag that conjugates the second input array before the multiplication (true)
+or not (false).
+*/
+CV_EXPORTS_W void divSpectrums(InputArray a, InputArray b, OutputArray c,
+                               int flags, bool conjB = false);
+
+//! @} imgproc_motion
+
+//! @addtogroup imgproc_misc
+//! @{
+
+/** @brief Applies a fixed-level threshold to each array element.
+
+The function applies fixed-level thresholding to a multiple-channel array. The function is typically
+used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for
+this purpose) or for removing a noise, that is, filtering out pixels with too small or too large
+values. There are several types of thresholding supported by the function. They are determined by
+type parameter.
+
+Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the
+above values. In these cases, the function determines the optimal threshold value using the Otsu's
+or Triangle algorithm and uses it instead of the specified thresh.
+
+@note Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images.
+
+@param src input array (multiple-channel, 8-bit or 32-bit floating point).
+@param dst output array of the same size  and type and the same number of channels as src.
+@param thresh threshold value.
+@param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding
+types.
+@param type thresholding type (see #ThresholdTypes).
+@return the computed threshold value if Otsu's or Triangle methods used.
+
+@sa  adaptiveThreshold, findContours, compare, min, max
+ */
+CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
+                               double thresh, double maxval, int type );
+
+
+/** @brief Applies an adaptive threshold to an array.
+
+The function transforms a grayscale image to a binary image according to the formulae:
+-   **THRESH_BINARY**
+    \f[dst(x,y) =  \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
+-   **THRESH_BINARY_INV**
+    \f[dst(x,y) =  \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
+where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
+
+The function can process the image in-place.
+
+@param src Source 8-bit single-channel image.
+@param dst Destination image of the same size and the same type as src.
+@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
+@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes.
+The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
+@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV,
+see #ThresholdTypes.
+@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
+pixel: 3, 5, 7, and so on.
+@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
+is positive but may be zero or negative as well.
+
+@sa  threshold, blur, GaussianBlur
+ */
+CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
+                                     double maxValue, int adaptiveMethod,
+                                     int thresholdType, int blockSize, double C );
+
+//! @} imgproc_misc
+
+//! @addtogroup imgproc_filter
+//! @{
+
+/** @example samples/cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp
+An example using pyrDown and pyrUp functions
+*/
+
+/** @brief Blurs an image and downsamples it.
+
+By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in
+any case, the following conditions should be satisfied:
+
+\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f]
+
+The function performs the downsampling step of the Gaussian pyramid construction. First, it
+convolves the source image with the kernel:
+
+\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1  \\ 4 & 16 & 24 & 16 & 4  \\ 6 & 24 & 36 & 24 & 6  \\ 4 & 16 & 24 & 16 & 4  \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f]
+
+Then, it downsamples the image by rejecting even rows and columns.
+
+@param src input image.
+@param dst output image; it has the specified size and the same type as src.
+@param dstsize size of the output image.
+@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
+ */
+CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst,
+                           const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
+
+/** @brief Upsamples an image and then blurs it.
+
+By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any
+case, the following conditions should be satisfied:
+
+\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq  ( \texttt{dstsize.width}   \mod  2)  \\ | \texttt{dstsize.height} -src.rows*2| \leq  ( \texttt{dstsize.height}   \mod  2) \end{array}\f]
+
+The function performs the upsampling step of the Gaussian pyramid construction, though it can
+actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
+injecting even zero rows and columns and then convolves the result with the same kernel as in
+pyrDown multiplied by 4.
+
+@param src input image.
+@param dst output image. It has the specified size and the same type as src .
+@param dstsize size of the output image.
+@param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)
+ */
+CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst,
+                         const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
+
+/** @brief Constructs the Gaussian pyramid for an image.
+
+The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
+pyrDown to the previously built pyramid layers, starting from `dst[0]==src`.
+
+@param src Source image. Check pyrDown for the list of supported types.
+@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the
+same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on.
+@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative.
+@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
+ */
+CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst,
+                              int maxlevel, int borderType = BORDER_DEFAULT );
+
+//! @} imgproc_filter
+
+//! @addtogroup imgproc_hist
+//! @{
+
+/** @example samples/cpp/demhist.cpp
+An example for creating histograms of an image
+*/
+
+/** @brief Calculates a histogram of a set of arrays.
+
+The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used
+to increment a histogram bin are taken from the corresponding input arrays at the same location. The
+sample below shows how to compute a 2D Hue-Saturation histogram for a color image. :
+@include snippets/imgproc_calcHist.cpp
+
+@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
+size. Each of them can have an arbitrary number of channels.
+@param nimages Number of source images.
+@param channels List of the dims channels used to compute the histogram. The first array channels
+are numerated from 0 to images[0].channels()-1 , the second array channels are counted from
+images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.
+@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size
+as images[i] . The non-zero mask elements mark the array elements counted in the histogram.
+@param hist Output histogram, which is a dense or sparse dims -dimensional array.
+@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
+(equal to 32 in the current OpenCV version).
+@param histSize Array of histogram sizes in each dimension.
+@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the
+histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower
+(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary
+\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a
+uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform (
+uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:
+\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$
+. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not
+counted in the histogram.
+@param uniform Flag indicating whether the histogram is uniform or not (see above).
+@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning
+when it is allocated. This feature enables you to compute a single histogram from several sets of
+arrays, or to update the histogram in time.
+*/
+CV_EXPORTS void calcHist( const Mat* images, int nimages,
+                          const int* channels, InputArray mask,
+                          OutputArray hist, int dims, const int* histSize,
+                          const float** ranges, bool uniform = true, bool accumulate = false );
+
+/** @overload
+
+this variant uses %SparseMat for output
+*/
+CV_EXPORTS void calcHist( const Mat* images, int nimages,
+                          const int* channels, InputArray mask,
+                          SparseMat& hist, int dims,
+                          const int* histSize, const float** ranges,
+                          bool uniform = true, bool accumulate = false );
+
+/** @overload */
+CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
+                            const std::vector<int>& channels,
+                            InputArray mask, OutputArray hist,
+                            const std::vector<int>& histSize,
+                            const std::vector<float>& ranges,
+                            bool accumulate = false );
+
+/** @brief Calculates the back projection of a histogram.
+
+The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to
+#calcHist , at each location (x, y) the function collects the values from the selected channels
+in the input images and finds the corresponding histogram bin. But instead of incrementing it, the
+function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of
+statistics, the function computes probability of each element value in respect with the empirical
+probability distribution represented by the histogram. See how, for example, you can find and track
+a bright-colored object in a scene:
+
+- Before tracking, show the object to the camera so that it covers almost the whole frame.
+Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant
+colors in the object.
+
+- When tracking, calculate a back projection of a hue plane of each input video frame using that
+pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make
+sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels.
+
+- Find connected components in the resulting picture and choose, for example, the largest
+component.
+
+This is an approximate algorithm of the CamShift color object tracker.
+
+@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
+size. Each of them can have an arbitrary number of channels.
+@param nimages Number of source images.
+@param channels The list of channels used to compute the back projection. The number of channels
+must match the histogram dimensionality. The first array channels are numerated from 0 to
+images[0].channels()-1 , the second array channels are counted from images[0].channels() to
+images[0].channels() + images[1].channels()-1, and so on.
+@param hist Input histogram that can be dense or sparse.
+@param backProject Destination back projection array that is a single-channel array of the same
+size and depth as images[0] .
+@param ranges Array of arrays of the histogram bin boundaries in each dimension. See #calcHist .
+@param scale Optional scale factor for the output back projection.
+@param uniform Flag indicating whether the histogram is uniform or not (see above).
+
+@sa calcHist, compareHist
+ */
+CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
+                                 const int* channels, InputArray hist,
+                                 OutputArray backProject, const float** ranges,
+                                 double scale = 1, bool uniform = true );
+
+/** @overload */
+CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
+                                 const int* channels, const SparseMat& hist,
+                                 OutputArray backProject, const float** ranges,
+                                 double scale = 1, bool uniform = true );
+
+/** @overload */
+CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector<int>& channels,
+                                   InputArray hist, OutputArray dst,
+                                   const std::vector<float>& ranges,
+                                   double scale );
+
+/** @brief Compares two histograms.
+
+The function cv::compareHist compares two dense or two sparse histograms using the specified method.
+
+The function returns \f$d(H_1, H_2)\f$ .
+
+While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable
+for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling
+problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms
+or more general sparse configurations of weighted points, consider using the #EMD function.
+
+@param H1 First compared histogram.
+@param H2 Second compared histogram of the same size as H1 .
+@param method Comparison method, see #HistCompMethods
+ */
+CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method );
+
+/** @overload */
+CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
+
+/** @brief Equalizes the histogram of a grayscale image.
+
+The function equalizes the histogram of the input image using the following algorithm:
+
+- Calculate the histogram \f$H\f$ for src .
+- Normalize the histogram so that the sum of histogram bins is 255.
+- Compute the integral of the histogram:
+\f[H'_i =  \sum _{0  \le j < i} H(j)\f]
+- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$
+
+The algorithm normalizes the brightness and increases the contrast of the image.
+
+@param src Source 8-bit single channel image.
+@param dst Destination image of the same size and type as src .
+ */
+CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
+
+/** @brief Creates a smart pointer to a cv::CLAHE class and initializes it.
+
+@param clipLimit Threshold for contrast limiting.
+@param tileGridSize Size of grid for histogram equalization. Input image will be divided into
+equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
+ */
+CV_EXPORTS_W Ptr<CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
+
+/** @brief Computes the "minimal work" distance between two weighted point configurations.
+
+The function computes the earth mover distance and/or a lower boundary of the distance between the
+two weighted point configurations. One of the applications described in @cite RubnerSept98,
+@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
+problem that is solved using some modification of a simplex algorithm, thus the complexity is
+exponential in the worst case, though, on average it is much faster. In the case of a real metric
+the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
+to determine roughly whether the two signatures are far enough so that they cannot relate to the
+same object.
+
+@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix.
+Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
+a single column (weights only) if the user-defined cost matrix is used. The weights must be
+non-negative and have at least one non-zero value.
+@param signature2 Second signature of the same format as signature1 , though the number of rows
+may be different. The total weights may be different. In this case an extra "dummy" point is added
+to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
+value.
+@param distType Used metric. See #DistanceTypes.
+@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix
+is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
+@param lowerBound Optional input/output parameter: lower boundary of a distance between the two
+signatures that is a distance between mass centers. The lower boundary may not be calculated if
+the user-defined cost matrix is used, the total weights of point configurations are not equal, or
+if the signatures consist of weights only (the signature matrices have a single column). You
+**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or
+equal to \*lowerBound (it means that the signatures are far enough), the function does not
+calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
+return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
+should be set to 0.
+@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is
+a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 .
+ */
+CV_EXPORTS float EMD( InputArray signature1, InputArray signature2,
+                      int distType, InputArray cost=noArray(),
+                      float* lowerBound = 0, OutputArray flow = noArray() );
+
+CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature2,
+                      int distType, InputArray cost=noArray(),
+                      CV_IN_OUT Ptr<float> lowerBound = Ptr<float>(), OutputArray flow = noArray() );
+
+//! @} imgproc_hist
+
+//! @addtogroup imgproc_segmentation
+//! @{
+
+/** @example samples/cpp/watershed.cpp
+An example using the watershed algorithm
+*/
+
+/** @brief Performs a marker-based image segmentation using the watershed algorithm.
+
+The function implements one of the variants of watershed, non-parametric marker-based segmentation
+algorithm, described in @cite Meyer92 .
+
+Before passing the image to the function, you have to roughly outline the desired regions in the
+image markers with positive (\>0) indices. So, every region is represented as one or more connected
+components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary
+mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of
+the future image regions. All the other pixels in markers , whose relation to the outlined regions
+is not known and should be defined by the algorithm, should be set to 0's. In the function output,
+each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the
+regions.
+
+@note Any two neighbor connected components are not necessarily separated by a watershed boundary
+(-1's pixels); for example, they can touch each other in the initial marker image passed to the
+function.
+
+@param image Input 8-bit 3-channel image.
+@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same
+size as image .
+
+@sa findContours
+ */
+CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
+
+//! @} imgproc_segmentation
+
+//! @addtogroup imgproc_filter
+//! @{
+
+/** @brief Performs initial step of meanshift segmentation of an image.
+
+The function implements the filtering stage of meanshift segmentation, that is, the output of the
+function is the filtered "posterized" image with color gradients and fine-grain texture flattened.
+At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes
+meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is
+considered:
+
+\f[(x,y): X- \texttt{sp} \le x  \le X+ \texttt{sp} , Y- \texttt{sp} \le y  \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)||   \le \texttt{sr}\f]
+
+where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively
+(though, the algorithm does not depend on the color space used, so any 3-component color space can
+be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector
+(R',G',B') are found and they act as the neighborhood center on the next iteration:
+
+\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f]
+
+After the iterations over, the color components of the initial pixel (that is, the pixel from where
+the iterations started) are set to the final value (average color at the last iteration):
+
+\f[I(X,Y) <- (R*,G*,B*)\f]
+
+When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
+run on the smallest layer first. After that, the results are propagated to the larger layer and the
+iterations are run again only on those pixels where the layer colors differ by more than sr from the
+lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
+results will be actually different from the ones obtained by running the meanshift procedure on the
+whole original image (i.e. when maxLevel==0).
+
+@param src The source 8-bit, 3-channel image.
+@param dst The destination image of the same format and the same size as the source.
+@param sp The spatial window radius.
+@param sr The color window radius.
+@param maxLevel Maximum level of the pyramid for the segmentation.
+@param termcrit Termination criteria: when to stop meanshift iterations.
+ */
+CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
+                                         double sp, double sr, int maxLevel = 1,
+                                         TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) );
+
+//! @}
+
+//! @addtogroup imgproc_segmentation
+//! @{
+
+/** @example samples/cpp/grabcut.cpp
+An example using the GrabCut algorithm
+![Sample Screenshot](grabcut_output1.jpg)
+*/
+
+/** @brief Runs the GrabCut algorithm.
+
+The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
+
+@param img Input 8-bit 3-channel image.
+@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
+mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
+@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
+"obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT .
+@param bgdModel Temporary array for the background model. Do not modify it while you are
+processing the same image.
+@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
+processing the same image.
+@param iterCount Number of iterations the algorithm should make before returning the result. Note
+that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or
+mode==GC_EVAL .
+@param mode Operation mode that could be one of the #GrabCutModes
+ */
+CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
+                           InputOutputArray bgdModel, InputOutputArray fgdModel,
+                           int iterCount, int mode = GC_EVAL );
+
+//! @} imgproc_segmentation
+
+//! @addtogroup imgproc_misc
+//! @{
+
+/** @example samples/cpp/distrans.cpp
+An example on using the distance transform
+*/
+
+/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image.
+
+The function cv::distanceTransform calculates the approximate or precise distance from every binary
+image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
+
+When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the
+algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library.
+
+In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function
+finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
+diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall
+distance is calculated as a sum of these basic distances. Since the distance function should be
+symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all
+the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the
+same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated
+precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a
+relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV
+uses the values suggested in the original paper:
+- DIST_L1: `a = 1, b = 2`
+- DIST_L2:
+    - `3 x 3`: `a=0.955, b=1.3693`
+    - `5 x 5`: `a=1, b=1.4, c=2.1969`
+- DIST_C: `a = 1, b = 1`
+
+Typically, for a fast, coarse distance estimation #DIST_L2, a \f$3\times 3\f$ mask is used. For a
+more accurate distance estimation #DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used.
+Note that both the precise and the approximate algorithms are linear on the number of pixels.
+
+This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$
+but also identifies the nearest connected component consisting of zero pixels
+(labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the
+component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function
+automatically finds connected components of zero pixels in the input image and marks them with
+distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and
+marks all the zero pixels with distinct labels.
+
+In this mode, the complexity is still linear. That is, the function provides a very fast way to
+compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
+approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported
+yet.
+
+@param src 8-bit, single-channel (binary) source image.
+@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
+single-channel image of the same size as src.
+@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
+CV_32SC1 and the same size as src.
+@param distanceType Type of distance, see #DistanceTypes
+@param maskSize Size of the distance transform mask, see #DistanceTransformMasks.
+#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type,
+the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times
+5\f$ or any larger aperture.
+@param labelType Type of the label array to build, see #DistanceTransformLabelTypes.
+ */
+CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst,
+                                     OutputArray labels, int distanceType, int maskSize,
+                                     int labelType = DIST_LABEL_CCOMP );
+
+/** @overload
+@param src 8-bit, single-channel (binary) source image.
+@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
+single-channel image of the same size as src .
+@param distanceType Type of distance, see #DistanceTypes
+@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the
+#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives
+the same result as \f$5\times 5\f$ or any larger aperture.
+@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for
+the first variant of the function and distanceType == #DIST_L1.
+*/
+CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst,
+                                     int distanceType, int maskSize, int dstType=CV_32F);
+
+/** @brief Fills a connected component with the given color.
+
+The function cv::floodFill fills a connected component starting from the seed point with the specified
+color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
+pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if:
+
+- in case of a grayscale image and floating range
+\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\f]
+
+
+- in case of a grayscale image and fixed range
+\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f]
+
+
+- in case of a color image and floating range
+\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f]
+\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f]
+and
+\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f]
+
+
+- in case of a color image and fixed range
+\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f]
+\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f]
+and
+\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f]
+
+
+where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the
+component. That is, to be added to the connected component, a color/brightness of the pixel should
+be close enough to:
+- Color/brightness of one of its neighbors that already belong to the connected component in case
+of a floating range.
+- Color/brightness of the seed point in case of a fixed range.
+
+Use these functions to either mark a connected component with the specified color in-place, or build
+a mask and then extract the contour, or copy the region to another image, and so on.
+
+@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
+function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
+the details below.
+@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
+taller than image. Since this is both an input and output parameter, you must take responsibility
+of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example,
+an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
+mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags
+as described below. Additionally, the function fills the border of the mask with ones to simplify
+internal processing. It is therefore possible to use the same mask in multiple calls to the function
+to make sure the filled areas do not overlap.
+@param seedPoint Starting point.
+@param newVal New value of the repainted domain pixels.
+@param loDiff Maximal lower brightness/color difference between the currently observed pixel and
+one of its neighbors belonging to the component, or a seed pixel being added to the component.
+@param upDiff Maximal upper brightness/color difference between the currently observed pixel and
+one of its neighbors belonging to the component, or a seed pixel being added to the component.
+@param rect Optional output parameter set by the function to the minimum bounding rectangle of the
+repainted domain.
+@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of
+4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
+connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
+will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
+the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest
+neighbours and fill the mask with a value of 255. The following additional options occupy higher
+bits and therefore may be further combined with the connectivity and mask fill values using
+bit-wise or (|), see #FloodFillFlags.
+
+@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the
+pixel \f$(x+1, y+1)\f$ in the mask .
+
+@sa findContours
+ */
+CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask,
+                            Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0,
+                            Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
+                            int flags = 4 );
+
+/** @example samples/cpp/ffilldemo.cpp
+An example using the FloodFill technique
+*/
+
+/** @overload
+
+variant without `mask` parameter
+*/
+CV_EXPORTS int floodFill( InputOutputArray image,
+                          Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0,
+                          Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
+                          int flags = 4 );
+
+//! Performs linear blending of two images:
+//! \f[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \f]
+//! @param src1 It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer.
+//! @param src2 It has the same type and size as src1.
+//! @param weights1 It has a type of CV_32FC1 and the same size with src1.
+//! @param weights2 It has a type of CV_32FC1 and the same size with src1.
+//! @param dst It is created if it does not have the same size and type with src1.
+CV_EXPORTS_W void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst);
+
+//! @} imgproc_misc
+
+//! @addtogroup imgproc_color_conversions
+//! @{
+
+/** @brief Converts an image from one color space to another.
+
+The function converts an input image from one color space to another. In case of a transformation
+to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note
+that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the
+bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue
+component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and
+sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
+
+The conventional ranges for R, G, and B channel values are:
+-   0 to 255 for CV_8U images
+-   0 to 65535 for CV_16U images
+-   0 to 1 for CV_32F images
+
+In case of linear transformations, the range does not matter. But in case of a non-linear
+transformation, an input RGB image should be normalized to the proper value range to get the correct
+results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a
+32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
+have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor ,
+you need first to scale the image down:
+@code
+    img *= 1./255;
+    cvtColor(img, img, COLOR_BGR2Luv);
+@endcode
+If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many
+applications, this will not be noticeable but it is recommended to use 32-bit images in applications
+that need the full range of colors or that convert an image before an operation and then convert
+back.
+
+If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
+range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
+
+@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
+floating-point.
+@param dst output image of the same size and depth as src.
+@param code color space conversion code (see #ColorConversionCodes).
+@param dstCn number of channels in the destination image; if the parameter is 0, the number of the
+channels is derived automatically from src and code.
+
+@see @ref imgproc_color_conversions
+ */
+CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 );
+
+/** @brief Converts an image from one color space to another where the source image is
+stored in two planes.
+
+This function only supports YUV420 to RGB conversion as of now.
+
+@param src1: 8-bit image (#CV_8U) of the Y plane.
+@param src2: image containing interleaved U/V plane.
+@param dst: output image.
+@param code: Specifies the type of conversion. It can take any of the following values:
+- #COLOR_YUV2BGR_NV12
+- #COLOR_YUV2RGB_NV12
+- #COLOR_YUV2BGRA_NV12
+- #COLOR_YUV2RGBA_NV12
+- #COLOR_YUV2BGR_NV21
+- #COLOR_YUV2RGB_NV21
+- #COLOR_YUV2BGRA_NV21
+- #COLOR_YUV2RGBA_NV21
+*/
+CV_EXPORTS_W void cvtColorTwoPlane( InputArray src1, InputArray src2, OutputArray dst, int code );
+
+/** @brief main function for all demosaicing processes
+
+@param src input image: 8-bit unsigned or 16-bit unsigned.
+@param dst output image of the same size and depth as src.
+@param code Color space conversion code (see the description below).
+@param dstCn number of channels in the destination image; if the parameter is 0, the number of the
+channels is derived automatically from src and code.
+
+The function can do the following transformations:
+
+-   Demosaicing using bilinear interpolation
+
+    #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR
+
+    #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY
+
+-   Demosaicing using Variable Number of Gradients.
+
+    #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG
+
+-   Edge-Aware Demosaicing.
+
+    #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA
+
+-   Demosaicing with alpha channel
+
+    #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA
+
+@sa cvtColor
+*/
+CV_EXPORTS_W void demosaicing(InputArray src, OutputArray dst, int code, int dstCn = 0);
+
+//! @} imgproc_color_conversions
+
+//! @addtogroup imgproc_shape
+//! @{
+
+/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape.
+
+The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
+results are returned in the structure cv::Moments.
+
+@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
+\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ).
+@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
+used for images only.
+@returns moments.
+
+@note Only applicable to contour moments calculations from Python bindings: Note that the numpy
+type for the input array should be either np.int32 or np.float32.
+
+@sa  contourArea, arcLength
+ */
+CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false );
+
+/** @brief Calculates seven Hu invariants.
+
+The function calculates seven Hu invariants (introduced in @cite Hu62; see also
+<http://en.wikipedia.org/wiki/Image_moment>) defined as:
+
+\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f]
+
+where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ .
+
+These values are proved to be invariants to the image scale, rotation, and reflection except the
+seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of
+infinite image resolution. In case of raster images, the computed Hu invariants for the original and
+transformed images are a bit different.
+
+@param moments Input moments computed with moments .
+@param hu Output Hu invariants.
+
+@sa matchShapes
+ */
+CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
+
+/** @overload */
+CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu );
+
+//! @} imgproc_shape
+
+//! @addtogroup imgproc_object
+//! @{
+
+//! type of the template matching operation
+enum TemplateMatchModes {
+    TM_SQDIFF        = 0, /*!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f]
+                               with mask:
+                               \f[R(x,y)= \sum _{x',y'} \left( (T(x',y')-I(x+x',y+y')) \cdot
+                                  M(x',y') \right)^2\f] */
+    TM_SQDIFF_NORMED = 1, /*!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{
+                                  x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f]
+                               with mask:
+                               \f[R(x,y)= \frac{\sum _{x',y'} \left( (T(x',y')-I(x+x',y+y')) \cdot
+                                  M(x',y') \right)^2}{\sqrt{\sum_{x',y'} \left( T(x',y') \cdot
+                                  M(x',y') \right)^2 \cdot \sum_{x',y'} \left( I(x+x',y+y') \cdot
+                                  M(x',y') \right)^2}}\f] */
+    TM_CCORR         = 2, /*!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f]
+                               with mask:
+                               \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y') \cdot M(x',y')
+                                  ^2)\f] */
+    TM_CCORR_NORMED  = 3, /*!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{
+                                  \sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f]
+                               with mask:
+                               \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y') \cdot
+                                  M(x',y')^2)}{\sqrt{\sum_{x',y'} \left( T(x',y') \cdot M(x',y')
+                                  \right)^2 \cdot \sum_{x',y'} \left( I(x+x',y+y') \cdot M(x',y')
+                                  \right)^2}}\f] */
+    TM_CCOEFF        = 4, /*!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f]
+                               where
+                               \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{
+                                  x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h)
+                                  \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f]
+                               with mask:
+                               \f[\begin{array}{l} T'(x',y')=M(x',y') \cdot \left( T(x',y') -
+                                  \frac{1}{\sum _{x'',y''} M(x'',y'')} \cdot \sum _{x'',y''}
+                                  (T(x'',y'') \cdot M(x'',y'')) \right) \\ I'(x+x',y+y')=M(x',y')
+                                  \cdot \left( I(x+x',y+y') - \frac{1}{\sum _{x'',y''} M(x'',y'')}
+                                  \cdot \sum _{x'',y''} (I(x+x'',y+y'') \cdot M(x'',y'')) \right)
+                                  \end{array} \f] */
+    TM_CCOEFF_NORMED = 5  /*!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{
+                                  \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2}
+                                  }\f] */
+};
+
+/** @example samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp
+An example using Template Matching algorithm
+*/
+
+/** @brief Compares a template against overlapped image regions.
+
+The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against
+templ using the specified method and stores the comparison results in result . #TemplateMatchModes
+describes the formulae for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$
+template, \f$R\f$ result, \f$M\f$ the optional mask ). The summation is done over template and/or
+the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$
+
+After the function finishes the comparison, the best matches can be found as global minimums (when
+#TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the
+#minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
+the denominator is done over all of the channels and separate mean values are used for each channel.
+That is, the function can take a color template and a color image. The result will still be a
+single-channel image, which is easier to analyze.
+
+@param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
+@param templ Searched template. It must be not greater than the source image and have the same
+data type.
+@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
+is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ .
+@param method Parameter specifying the comparison method, see #TemplateMatchModes
+@param mask Optional mask. It must have the same size as templ. It must either have the same number
+            of channels as template or only one channel, which is then used for all template and
+            image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask,
+            meaning only elements where mask is nonzero are used and are kept unchanged independent
+            of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are
+            used as weights. The exact formulas are documented in #TemplateMatchModes.
+ */
+CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
+                                 OutputArray result, int method, InputArray mask = noArray() );
+
+//! @}
+
+//! @addtogroup imgproc_shape
+//! @{
+
+/** @example samples/cpp/connected_components.cpp
+This program demonstrates connected components and use of the trackbar
+*/
+
+/** @brief computes the connected components labeled image of boolean image
+
+image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
+represents the background label. ltype specifies the output label image type, an important
+consideration based on the total number of labels or alternatively the total number of pixels in
+the source image. ccltype specifies the connected components labeling algorithm to use, currently
+Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms
+are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces
+a row major ordering of labels while Spaghetti and BBDT do not.
+This function uses parallel version of the algorithms if at least one allowed
+parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
+
+@param image the 8-bit single-channel image to be labeled
+@param labels destination labeled image
+@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
+@param ltype output image label type. Currently CV_32S and CV_16U are supported.
+@param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
+*/
+CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels,
+                                                                        int connectivity, int ltype, int ccltype);
+
+
+/** @overload
+
+@param image the 8-bit single-channel image to be labeled
+@param labels destination labeled image
+@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
+@param ltype output image label type. Currently CV_32S and CV_16U are supported.
+*/
+CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels,
+                                     int connectivity = 8, int ltype = CV_32S);
+
+
+/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label
+
+image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
+represents the background label. ltype specifies the output label image type, an important
+consideration based on the total number of labels or alternatively the total number of pixels in
+the source image. ccltype specifies the connected components labeling algorithm to use, currently
+Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu's (SAUF) @cite Wu2009 algorithms
+are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces
+a row major ordering of labels while Spaghetti and BBDT do not.
+This function uses parallel version of the algorithms (statistics included) if at least one allowed
+parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
+
+@param image the 8-bit single-channel image to be labeled
+@param labels destination labeled image
+@param stats statistics output for each label, including the background label.
+Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
+#ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
+@param centroids centroid output for each label, including the background label. Centroids are
+accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
+@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
+@param ltype output image label type. Currently CV_32S and CV_16U are supported.
+@param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes).
+*/
+CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels,
+                                                                                          OutputArray stats, OutputArray centroids,
+                                                                                          int connectivity, int ltype, int ccltype);
+
+/** @overload
+@param image the 8-bit single-channel image to be labeled
+@param labels destination labeled image
+@param stats statistics output for each label, including the background label.
+Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
+#ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
+@param centroids centroid output for each label, including the background label. Centroids are
+accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
+@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
+@param ltype output image label type. Currently CV_32S and CV_16U are supported.
+*/
+CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels,
+                                              OutputArray stats, OutputArray centroids,
+                                              int connectivity = 8, int ltype = CV_32S);
+
+
+/** @brief Finds contours in a binary image.
+
+The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
+are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
+OpenCV sample directory.
+@note Since opencv 3.2 source image is not modified by this function.
+
+@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
+pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
+#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
+If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
+@param contours Detected contours. Each contour is stored as a vector of points (e.g.
+std::vector<std::vector<cv::Point> >).
+@param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
+as many elements as the number of contours. For each i-th contour contours[i], the elements
+hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
+in contours of the next and previous contours at the same hierarchical level, the first child
+contour and the parent contour, respectively. If for the contour i there are no next, previous,
+parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
+@note In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.
+@param mode Contour retrieval mode, see #RetrievalModes
+@param method Contour approximation method, see #ContourApproximationModes
+@param offset Optional offset by which every contour point is shifted. This is useful if the
+contours are extracted from the image ROI and then they should be analyzed in the whole image
+context.
+ */
+CV_EXPORTS_W void findContours( InputArray image, OutputArrayOfArrays contours,
+                              OutputArray hierarchy, int mode,
+                              int method, Point offset = Point());
+
+/** @overload */
+CV_EXPORTS void findContours( InputArray image, OutputArrayOfArrays contours,
+                              int mode, int method, Point offset = Point());
+
+/** @example samples/cpp/squares.cpp
+A program using pyramid scaling, Canny, contours and contour simplification to find
+squares in a list of images (pic1-6.png). Returns sequence of squares detected on the image.
+*/
+
+/** @example samples/tapi/squares.cpp
+A program using pyramid scaling, Canny, contours and contour simplification to find
+squares in the input image.
+*/
+
+/** @brief Approximates a polygonal curve(s) with the specified precision.
+
+The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less
+vertices so that the distance between them is less or equal to the specified precision. It uses the
+Douglas-Peucker algorithm <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm>
+
+@param curve Input vector of a 2D point stored in std::vector or Mat
+@param approxCurve Result of the approximation. The type should match the type of the input curve.
+@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance
+between the original curve and its approximation.
+@param closed If true, the approximated curve is closed (its first and last vertices are
+connected). Otherwise, it is not closed.
+ */
+CV_EXPORTS_W void approxPolyDP( InputArray curve,
+                                OutputArray approxCurve,
+                                double epsilon, bool closed );
+
+/** @brief Calculates a contour perimeter or a curve length.
+
+The function computes a curve length or a closed contour perimeter.
+
+@param curve Input vector of 2D points, stored in std::vector or Mat.
+@param closed Flag indicating whether the curve is closed or not.
+ */
+CV_EXPORTS_W double arcLength( InputArray curve, bool closed );
+
+/** @brief Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
+
+The function calculates and returns the minimal up-right bounding rectangle for the specified point set or
+non-zero pixels of gray-scale image.
+
+@param array Input gray-scale image or 2D point set, stored in std::vector or Mat.
+ */
+CV_EXPORTS_W Rect boundingRect( InputArray array );
+
+/** @brief Calculates a contour area.
+
+The function computes a contour area. Similarly to moments , the area is computed using the Green
+formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
+#drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong
+results for contours with self-intersections.
+
+Example:
+@code
+    vector<Point> contour;
+    contour.push_back(Point2f(0, 0));
+    contour.push_back(Point2f(10, 0));
+    contour.push_back(Point2f(10, 10));
+    contour.push_back(Point2f(5, 4));
+
+    double area0 = contourArea(contour);
+    vector<Point> approx;
+    approxPolyDP(contour, approx, 5, true);
+    double area1 = contourArea(approx);
+
+    cout << "area0 =" << area0 << endl <<
+            "area1 =" << area1 << endl <<
+            "approx poly vertices" << approx.size() << endl;
+@endcode
+@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat.
+@param oriented Oriented area flag. If it is true, the function returns a signed area value,
+depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can
+determine orientation of a contour by taking the sign of an area. By default, the parameter is
+false, which means that the absolute value is returned.
+ */
+CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false );
+
+/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
+
+The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a
+specified point set. Developer should keep in mind that the returned RotatedRect can contain negative
+indices when data is close to the containing Mat element boundary.
+
+@param points Input vector of 2D points, stored in std::vector\<\> or Mat
+ */
+CV_EXPORTS_W RotatedRect minAreaRect( InputArray points );
+
+/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
+
+The function finds the four vertices of a rotated rectangle. This function is useful to draw the
+rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please
+visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information.
+
+@param box The input rotated rectangle. It may be the output of
+@param points The output array of four vertices of rectangles.
+ */
+CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points);
+
+/** @brief Finds a circle of the minimum area enclosing a 2D point set.
+
+The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.
+
+@param points Input vector of 2D points, stored in std::vector\<\> or Mat
+@param center Output center of the circle.
+@param radius Output radius of the circle.
+ */
+CV_EXPORTS_W void minEnclosingCircle( InputArray points,
+                                      CV_OUT Point2f& center, CV_OUT float& radius );
+
+/** @example samples/cpp/minarea.cpp
+*/
+
+/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area.
+
+The function finds a triangle of minimum area enclosing the given set of 2D points and returns its
+area. The output for a given 2D point set is shown in the image below. 2D points are depicted in
+*red* and the enclosing triangle in *yellow*.
+
+![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png)
+
+The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's
+@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal
+enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function
+takes a 2D point set as input an additional preprocessing step of computing the convex hull of the
+2D point set is required. The complexity of the #convexHull function is \f$O(n log(n))\f$ which is higher
+than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$.
+
+@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat
+@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth
+of the OutputArray must be CV_32F.
+ */
+CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle );
+
+/** @brief Compares two shapes.
+
+The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments)
+
+@param contour1 First contour or grayscale image.
+@param contour2 Second contour or grayscale image.
+@param method Comparison method, see #ShapeMatchModes
+@param parameter Method-specific parameter (not supported now).
+ */
+CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
+                                 int method, double parameter );
+
+/** @example samples/cpp/convexhull.cpp
+An example using the convexHull functionality
+*/
+
+/** @brief Finds the convex hull of a point set.
+
+The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82
+that has *O(N logN)* complexity in the current implementation.
+
+@param points Input 2D point set, stored in std::vector or Mat.
+@param hull Output convex hull. It is either an integer vector of indices or vector of points. In
+the first case, the hull elements are 0-based indices of the convex hull points in the original
+array (since the set of convex hull points is a subset of the original point set). In the second
+case, hull elements are the convex hull points themselves.
+@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise.
+Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing
+to the right, and its Y axis pointing upwards.
+@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function
+returns convex hull points. Otherwise, it returns indices of the convex hull points. When the
+output array is std::vector, the flag is ignored, and the output depends on the type of the
+vector: std::vector\<int\> implies returnPoints=false, std::vector\<Point\> implies
+returnPoints=true.
+
+@note `points` and `hull` should be different arrays, inplace processing isn't supported.
+
+Check @ref tutorial_hull "the corresponding tutorial" for more details.
+
+useful links:
+
+https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/
+ */
+CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
+                              bool clockwise = false, bool returnPoints = true );
+
+/** @brief Finds the convexity defects of a contour.
+
+The figure below displays convexity defects of a hand contour:
+
+![image](pics/defects.png)
+
+@param contour Input contour.
+@param convexhull Convex hull obtained using convexHull that should contain indices of the contour
+points that make the hull.
+@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java
+interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i):
+(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices
+in the original contour of the convexity defect beginning, end and the farthest point, and
+fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the
+farthest contour point and the hull. That is, to get the floating-point value of the depth will be
+fixpt_depth/256.0.
+ */
+CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects );
+
+/** @brief Tests a contour convexity.
+
+The function tests whether the input contour is convex or not. The contour must be simple, that is,
+without self-intersections. Otherwise, the function output is undefined.
+
+@param contour Input vector of 2D points, stored in std::vector\<\> or Mat
+ */
+CV_EXPORTS_W bool isContourConvex( InputArray contour );
+
+/** @example samples/cpp/intersectExample.cpp
+Examples of how intersectConvexConvex works
+*/
+
+/** @brief Finds intersection of two convex polygons
+
+@param p1 First polygon
+@param p2 Second polygon
+@param p12 Output polygon describing the intersecting area
+@param handleNested When true, an intersection is found if one of the polygons is fully enclosed in the other.
+When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge
+of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.
+
+@returns Absolute value of area of intersecting polygon
+
+@note intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't.
+ */
+CV_EXPORTS_W float intersectConvexConvex( InputArray p1, InputArray p2,
+                                          OutputArray p12, bool handleNested = true );
+
+/** @example samples/cpp/fitellipse.cpp
+An example using the fitEllipse technique
+*/
+
+/** @brief Fits an ellipse around a set of 2D points.
+
+The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of
+all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95
+is used. Developer should keep in mind that it is possible that the returned
+ellipse/rotatedRect data contains negative indices, due to the data points being close to the
+border of the containing Mat element.
+
+@param points Input 2D point set, stored in std::vector\<\> or Mat
+ */
+CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
+
+/** @brief Fits an ellipse around a set of 2D points.
+
+ The function calculates the ellipse that fits a set of 2D points.
+ It returns the rotated rectangle in which the ellipse is inscribed.
+ The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used.
+
+ For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$,
+ which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$.
+ However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$,
+ the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines,
+ quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
+ If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used.
+ The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves
+ by imposing the condition that \f$ A^T ( D_x^T D_x  +   D_y^T D_y) A = 1 \f$ where
+ the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with
+ respect to x and y. The matrices are formed row by row applying the following to
+ each of the points in the set:
+ \f{align*}{
+ D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} &
+ D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} &
+ D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\}
+ \f}
+ The AMS method minimizes the cost function
+ \f{equation*}{
+ \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x +  D_y^T D_y) A^T }
+ \f}
+
+ The minimum cost is found by solving the generalized eigenvalue problem.
+
+ \f{equation*}{
+ D^T D A = \lambda  \left( D_x^T D_x +  D_y^T D_y\right) A
+ \f}
+
+ @param points Input 2D point set, stored in std::vector\<\> or Mat
+ */
+CV_EXPORTS_W RotatedRect fitEllipseAMS( InputArray points );
+
+
+/** @brief Fits an ellipse around a set of 2D points.
+
+ The function calculates the ellipse that fits a set of 2D points.
+ It returns the rotated rectangle in which the ellipse is inscribed.
+ The Direct least square (Direct) method by @cite Fitzgibbon1999 is used.
+
+ For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$,
+ which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$.
+ However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$,
+ the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines,
+ quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
+ The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$.
+ The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality
+ and as the coefficients can be arbitrarily scaled is not overly restrictive.
+
+ \f{equation*}{
+ \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix}
+ 0 & 0  & 2  & 0  & 0  &  0  \\
+ 0 & -1  & 0  & 0  & 0  &  0 \\
+ 2 & 0  & 0  & 0  & 0  &  0 \\
+ 0 & 0  & 0  & 0  & 0  &  0 \\
+ 0 & 0  & 0  & 0  & 0  &  0 \\
+ 0 & 0  & 0  & 0  & 0  &  0
+ \end{matrix} \right)
+ \f}
+
+ The minimum cost is found by solving the generalized eigenvalue problem.
+
+ \f{equation*}{
+ D^T D A = \lambda  \left( C\right) A
+ \f}
+
+ The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution
+ with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients
+
+ \f{equation*}{
+ A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}}  \mathbf{u}
+ \f}
+ The scaling factor guarantees that  \f$A^T C A =1\f$.
+
+ @param points Input 2D point set, stored in std::vector\<\> or Mat
+ */
+CV_EXPORTS_W RotatedRect fitEllipseDirect( InputArray points );
+
+/** @brief Fits a line to a 2D or 3D point set.
+
+The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where
+\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one
+of the following:
+-  DIST_L2
+\f[\rho (r) = r^2/2  \quad \text{(the simplest and the fastest least-squares method)}\f]
+- DIST_L1
+\f[\rho (r) = r\f]
+- DIST_L12
+\f[\rho (r) = 2  \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f]
+- DIST_FAIR
+\f[\rho \left (r \right ) = C^2  \cdot \left (  \frac{r}{C} -  \log{\left(1 + \frac{r}{C}\right)} \right )  \quad \text{where} \quad C=1.3998\f]
+- DIST_WELSCH
+\f[\rho \left (r \right ) =  \frac{C^2}{2} \cdot \left ( 1 -  \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right )  \quad \text{where} \quad C=2.9846\f]
+- DIST_HUBER
+\f[\rho (r) =  \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f]
+
+The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
+that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
+weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ .
+
+@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat.
+@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements
+(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and
+(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like
+Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line
+and (x0, y0, z0) is a point on the line.
+@param distType Distance used by the M-estimator, see #DistanceTypes
+@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
+is chosen.
+@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line).
+@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.
+ */
+CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
+                           double param, double reps, double aeps );
+
+/** @brief Performs a point-in-contour test.
+
+The function determines whether the point is inside a contour, outside, or lies on an edge (or
+coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge)
+value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively.
+Otherwise, the return value is a signed distance between the point and the nearest contour edge.
+
+See below a sample output of the function where each image pixel is tested against the contour:
+
+![sample output](pics/pointpolygon.png)
+
+@param contour Input contour.
+@param pt Point tested against the contour.
+@param measureDist If true, the function estimates the signed distance from the point to the
+nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
+ */
+CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );
+
+/** @brief Finds out if there is any intersection between two rotated rectangles.
+
+If there is then the vertices of the intersecting region are returned as well.
+
+Below are some examples of intersection configurations. The hatched pattern indicates the
+intersecting region and the red vertices are returned by the function.
+
+![intersection examples](pics/intersection.png)
+
+@param rect1 First rectangle
+@param rect2 Second rectangle
+@param intersectingRegion The output array of the vertices of the intersecting region. It returns
+at most 8 vertices. Stored as std::vector\<cv::Point2f\> or cv::Mat as Mx1 of type CV_32FC2.
+@returns One of #RectanglesIntersectTypes
+ */
+CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion  );
+
+/** @brief Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.
+*/
+CV_EXPORTS_W Ptr<GeneralizedHoughBallard> createGeneralizedHoughBallard();
+
+/** @brief Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.
+*/
+CV_EXPORTS_W Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();
+
+//! @} imgproc_shape
+
+//! @addtogroup imgproc_colormap
+//! @{
+
+//! GNU Octave/MATLAB equivalent colormaps
+enum ColormapTypes
+{
+    COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg)
+    COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg)
+    COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg)
+    COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg)
+    COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg)
+    COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg)
+    COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg)
+    COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg)
+    COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg)
+    COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg)
+    COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg)
+    COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg)
+    COLORMAP_PARULA = 12, //!< ![parula](pics/colormaps/colorscale_parula.jpg)
+    COLORMAP_MAGMA = 13, //!< ![magma](pics/colormaps/colorscale_magma.jpg)
+    COLORMAP_INFERNO = 14, //!< ![inferno](pics/colormaps/colorscale_inferno.jpg)
+    COLORMAP_PLASMA = 15, //!< ![plasma](pics/colormaps/colorscale_plasma.jpg)
+    COLORMAP_VIRIDIS = 16, //!< ![viridis](pics/colormaps/colorscale_viridis.jpg)
+    COLORMAP_CIVIDIS = 17, //!< ![cividis](pics/colormaps/colorscale_cividis.jpg)
+    COLORMAP_TWILIGHT = 18, //!< ![twilight](pics/colormaps/colorscale_twilight.jpg)
+    COLORMAP_TWILIGHT_SHIFTED = 19, //!< ![twilight shifted](pics/colormaps/colorscale_twilight_shifted.jpg)
+    COLORMAP_TURBO = 20, //!< ![turbo](pics/colormaps/colorscale_turbo.jpg)
+    COLORMAP_DEEPGREEN = 21  //!< ![deepgreen](pics/colormaps/colorscale_deepgreen.jpg)
+};
+
+/** @example samples/cpp/falsecolor.cpp
+An example using applyColorMap function
+*/
+
+/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image.
+
+@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
+@param dst The result is the colormapped source image. Note: Mat::create is called on dst.
+@param colormap The colormap to apply, see #ColormapTypes
+*/
+CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
+
+/** @brief Applies a user colormap on a given image.
+
+@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
+@param dst The result is the colormapped source image. Note: Mat::create is called on dst.
+@param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256
+*/
+CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, InputArray userColor);
+
+//! @} imgproc_colormap
+
+//! @addtogroup imgproc_draw
+//! @{
+
+
+/** OpenCV color channel order is BGR[A] */
+#define CV_RGB(r, g, b)  cv::Scalar((b), (g), (r), 0)
+
+/** @brief Draws a line segment connecting two points.
+
+The function line draws the line segment between pt1 and pt2 points in the image. The line is
+clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
+or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
+lines are drawn using Gaussian filtering.
+
+@param img Image.
+@param pt1 First point of the line segment.
+@param pt2 Second point of the line segment.
+@param color Line color.
+@param thickness Line thickness.
+@param lineType Type of the line. See #LineTypes.
+@param shift Number of fractional bits in the point coordinates.
+ */
+CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
+                     int thickness = 1, int lineType = LINE_8, int shift = 0);
+
+/** @brief Draws an arrow segment pointing from the first point to the second one.
+
+The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
+
+@param img Image.
+@param pt1 The point the arrow starts from.
+@param pt2 The point the arrow points to.
+@param color Line color.
+@param thickness Line thickness.
+@param line_type Type of the line. See #LineTypes
+@param shift Number of fractional bits in the point coordinates.
+@param tipLength The length of the arrow tip in relation to the arrow length
+ */
+CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
+                     int thickness=1, int line_type=8, int shift=0, double tipLength=0.1);
+
+/** @brief Draws a simple, thick, or filled up-right rectangle.
+
+The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
+are pt1 and pt2.
+
+@param img Image.
+@param pt1 Vertex of the rectangle.
+@param pt2 Vertex of the rectangle opposite to pt1 .
+@param color Rectangle color or brightness (grayscale image).
+@param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED,
+mean that the function has to draw a filled rectangle.
+@param lineType Type of the line. See #LineTypes
+@param shift Number of fractional bits in the point coordinates.
+ */
+CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2,
+                          const Scalar& color, int thickness = 1,
+                          int lineType = LINE_8, int shift = 0);
+
+/** @overload
+
+use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and
+r.br()-Point(1,1)` are opposite corners
+*/
+CV_EXPORTS_W void rectangle(InputOutputArray img, Rect rec,
+                          const Scalar& color, int thickness = 1,
+                          int lineType = LINE_8, int shift = 0);
+
+/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_2.cpp
+An example using drawing functions
+*/
+
+/** @brief Draws a circle.
+
+The function cv::circle draws a simple or filled circle with a given center and radius.
+@param img Image where the circle is drawn.
+@param center Center of the circle.
+@param radius Radius of the circle.
+@param color Circle color.
+@param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED,
+mean that a filled circle is to be drawn.
+@param lineType Type of the circle boundary. See #LineTypes
+@param shift Number of fractional bits in the coordinates of the center and in the radius value.
+ */
+CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius,
+                       const Scalar& color, int thickness = 1,
+                       int lineType = LINE_8, int shift = 0);
+
+/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector.
+
+The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
+arc, or a filled ellipse sector. The drawing code uses general parametric form.
+A piecewise-linear curve is used to approximate the elliptic arc
+boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
+#ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first
+variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and
+`endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains
+the meaning of the parameters to draw the blue arc.
+
+![Parameters of Elliptic Arc](pics/ellipse.svg)
+
+@param img Image.
+@param center Center of the ellipse.
+@param axes Half of the size of the ellipse main axes.
+@param angle Ellipse rotation angle in degrees.
+@param startAngle Starting angle of the elliptic arc in degrees.
+@param endAngle Ending angle of the elliptic arc in degrees.
+@param color Ellipse color.
+@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
+a filled ellipse sector is to be drawn.
+@param lineType Type of the ellipse boundary. See #LineTypes
+@param shift Number of fractional bits in the coordinates of the center and values of axes.
+ */
+CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes,
+                        double angle, double startAngle, double endAngle,
+                        const Scalar& color, int thickness = 1,
+                        int lineType = LINE_8, int shift = 0);
+
+/** @overload
+@param img Image.
+@param box Alternative ellipse representation via RotatedRect. This means that the function draws
+an ellipse inscribed in the rotated rectangle.
+@param color Ellipse color.
+@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
+a filled ellipse sector is to be drawn.
+@param lineType Type of the ellipse boundary. See #LineTypes
+*/
+CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color,
+                        int thickness = 1, int lineType = LINE_8);
+
+/* ----------------------------------------------------------------------------------------- */
+/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */
+/* ----------------------------------------------------------------------------------------- */
+
+/** @brief Draws a marker on a predefined position in an image.
+
+The function cv::drawMarker draws a marker on a given position in the image. For the moment several
+marker types are supported, see #MarkerTypes for more information.
+
+@param img Image.
+@param position The point where the crosshair is positioned.
+@param color Line color.
+@param markerType The specific type of marker you want to use, see #MarkerTypes
+@param thickness Line thickness.
+@param line_type Type of the line, See #LineTypes
+@param markerSize The length of the marker axis [default = 20 pixels]
+ */
+CV_EXPORTS_W void drawMarker(InputOutputArray img, Point position, const Scalar& color,
+                             int markerType = MARKER_CROSS, int markerSize=20, int thickness=1,
+                             int line_type=8);
+
+/* ----------------------------------------------------------------------------------------- */
+/* END OF MARKER SECTION */
+/* ----------------------------------------------------------------------------------------- */
+
+/** @brief Fills a convex polygon.
+
+The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the
+function #fillPoly . It can fill not only convex polygons but any monotonic polygon without
+self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
+twice at the most (though, its top-most and/or the bottom edge could be horizontal).
+
+@param img Image.
+@param points Polygon vertices.
+@param color Polygon color.
+@param lineType Type of the polygon boundaries. See #LineTypes
+@param shift Number of fractional bits in the vertex coordinates.
+ */
+CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points,
+                                 const Scalar& color, int lineType = LINE_8,
+                                 int shift = 0);
+
+/** @overload */
+CV_EXPORTS void fillConvexPoly(InputOutputArray img, const Point* pts, int npts,
+                               const Scalar& color, int lineType = LINE_8,
+                               int shift = 0);
+
+/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp
+An example using drawing functions
+Check @ref tutorial_random_generator_and_text "the corresponding tutorial" for more details
+*/
+
+/** @brief Fills the area bounded by one or more polygons.
+
+The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
+complex areas, for example, areas with holes, contours with self-intersections (some of their
+parts), and so forth.
+
+@param img Image.
+@param pts Array of polygons where each polygon is represented as an array of points.
+@param color Polygon color.
+@param lineType Type of the polygon boundaries. See #LineTypes
+@param shift Number of fractional bits in the vertex coordinates.
+@param offset Optional offset of all points of the contours.
+ */
+CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts,
+                           const Scalar& color, int lineType = LINE_8, int shift = 0,
+                           Point offset = Point() );
+
+/** @overload */
+CV_EXPORTS void fillPoly(InputOutputArray img, const Point** pts,
+                         const int* npts, int ncontours,
+                         const Scalar& color, int lineType = LINE_8, int shift = 0,
+                         Point offset = Point() );
+
+/** @brief Draws several polygonal curves.
+
+@param img Image.
+@param pts Array of polygonal curves.
+@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
+the function draws a line from the last vertex of each curve to its first vertex.
+@param color Polyline color.
+@param thickness Thickness of the polyline edges.
+@param lineType Type of the line segments. See #LineTypes
+@param shift Number of fractional bits in the vertex coordinates.
+
+The function cv::polylines draws one or more polygonal curves.
+ */
+CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
+                            bool isClosed, const Scalar& color,
+                            int thickness = 1, int lineType = LINE_8, int shift = 0 );
+
+/** @overload */
+CV_EXPORTS void polylines(InputOutputArray img, const Point* const* pts, const int* npts,
+                          int ncontours, bool isClosed, const Scalar& color,
+                          int thickness = 1, int lineType = LINE_8, int shift = 0 );
+
+/** @example samples/cpp/contours2.cpp
+An example program illustrates the use of cv::findContours and cv::drawContours
+\image html WindowsQtContoursOutput.png "Screenshot of the program"
+*/
+
+/** @example samples/cpp/segment_objects.cpp
+An example using drawContours to clean up a background segmentation result
+*/
+
+/** @brief Draws contours outlines or filled contours.
+
+The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area
+bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve
+connected components from the binary image and label them: :
+@include snippets/imgproc_drawContours.cpp
+
+@param image Destination image.
+@param contours All the input contours. Each contour is stored as a point vector.
+@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
+@param color Color of the contours.
+@param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
+thickness=#FILLED ), the contour interiors are drawn.
+@param lineType Line connectivity. See #LineTypes
+@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
+some of the contours (see maxLevel ).
+@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn.
+If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function
+draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This
+parameter is only taken into account when there is hierarchy available.
+@param offset Optional contour shift parameter. Shift all the drawn contours by the specified
+\f$\texttt{offset}=(dx,dy)\f$ .
+@note When thickness=#FILLED, the function is designed to handle connected components with holes correctly
+even when no hierarchy data is provided. This is done by analyzing all the outlines together
+using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
+contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
+of contours, or iterate over the collection using contourIdx parameter.
+ */
+CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
+                              int contourIdx, const Scalar& color,
+                              int thickness = 1, int lineType = LINE_8,
+                              InputArray hierarchy = noArray(),
+                              int maxLevel = INT_MAX, Point offset = Point() );
+
+/** @brief Clips the line against the image rectangle.
+
+The function cv::clipLine calculates a part of the line segment that is entirely within the specified
+rectangle. It returns false if the line segment is completely outside the rectangle. Otherwise,
+it returns true .
+@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
+@param pt1 First line point.
+@param pt2 Second line point.
+ */
+CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2);
+
+/** @overload
+@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
+@param pt1 First line point.
+@param pt2 Second line point.
+*/
+CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2);
+
+/** @overload
+@param imgRect Image rectangle.
+@param pt1 First line point.
+@param pt2 Second line point.
+*/
+CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2);
+
+/** @brief Approximates an elliptic arc with a polyline.
+
+The function ellipse2Poly computes the vertices of a polyline that approximates the specified
+elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped.
+
+@param center Center of the arc.
+@param axes Half of the size of the ellipse main axes. See #ellipse for details.
+@param angle Rotation angle of the ellipse in degrees. See #ellipse for details.
+@param arcStart Starting angle of the elliptic arc in degrees.
+@param arcEnd Ending angle of the elliptic arc in degrees.
+@param delta Angle between the subsequent polyline vertices. It defines the approximation
+accuracy.
+@param pts Output vector of polyline vertices.
+ */
+CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle,
+                                int arcStart, int arcEnd, int delta,
+                                CV_OUT std::vector<Point>& pts );
+
+/** @overload
+@param center Center of the arc.
+@param axes Half of the size of the ellipse main axes. See #ellipse for details.
+@param angle Rotation angle of the ellipse in degrees. See #ellipse for details.
+@param arcStart Starting angle of the elliptic arc in degrees.
+@param arcEnd Ending angle of the elliptic arc in degrees.
+@param delta Angle between the subsequent polyline vertices. It defines the approximation accuracy.
+@param pts Output vector of polyline vertices.
+*/
+CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle,
+                             int arcStart, int arcEnd, int delta,
+                             CV_OUT std::vector<Point2d>& pts);
+
+/** @brief Draws a text string.
+
+The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered
+using the specified font are replaced by question marks. See #getTextSize for a text rendering code
+example.
+
+@param img Image.
+@param text Text string to be drawn.
+@param org Bottom-left corner of the text string in the image.
+@param fontFace Font type, see #HersheyFonts.
+@param fontScale Font scale factor that is multiplied by the font-specific base size.
+@param color Text color.
+@param thickness Thickness of the lines used to draw a text.
+@param lineType Line type. See #LineTypes
+@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise,
+it is at the top-left corner.
+ */
+CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org,
+                         int fontFace, double fontScale, Scalar color,
+                         int thickness = 1, int lineType = LINE_8,
+                         bool bottomLeftOrigin = false );
+
+/** @brief Calculates the width and height of a text string.
+
+The function cv::getTextSize calculates and returns the size of a box that contains the specified text.
+That is, the following code renders some text, the tight box surrounding it, and the baseline: :
+@code
+    String text = "Funny text inside the box";
+    int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX;
+    double fontScale = 2;
+    int thickness = 3;
+
+    Mat img(600, 800, CV_8UC3, Scalar::all(0));
+
+    int baseline=0;
+    Size textSize = getTextSize(text, fontFace,
+                                fontScale, thickness, &baseline);
+    baseline += thickness;
+
+    // center the text
+    Point textOrg((img.cols - textSize.width)/2,
+                  (img.rows + textSize.height)/2);
+
+    // draw the box
+    rectangle(img, textOrg + Point(0, baseline),
+              textOrg + Point(textSize.width, -textSize.height),
+              Scalar(0,0,255));
+    // ... and the baseline first
+    line(img, textOrg + Point(0, thickness),
+         textOrg + Point(textSize.width, thickness),
+         Scalar(0, 0, 255));
+
+    // then put the text itself
+    putText(img, text, textOrg, fontFace, fontScale,
+            Scalar::all(255), thickness, 8);
+@endcode
+
+@param text Input text string.
+@param fontFace Font to use, see #HersheyFonts.
+@param fontScale Font scale factor that is multiplied by the font-specific base size.
+@param thickness Thickness of lines used to render the text. See #putText for details.
+@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text
+point.
+@return The size of a box that contains the specified text.
+
+@see putText
+ */
+CV_EXPORTS_W Size getTextSize(const String& text, int fontFace,
+                            double fontScale, int thickness,
+                            CV_OUT int* baseLine);
+
+
+/** @brief Calculates the font-specific size to use to achieve a given height in pixels.
+
+@param fontFace Font to use, see cv::HersheyFonts.
+@param pixelHeight Pixel height to compute the fontScale for
+@param thickness Thickness of lines used to render the text.See putText for details.
+@return The fontSize to use for cv::putText
+
+@see cv::putText
+*/
+CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace,
+                                           const int pixelHeight,
+                                           const int thickness = 1);
+
+/** @brief Line iterator
+
+The class is used to iterate over all the pixels on the raster line
+segment connecting two specified points.
+
+The class LineIterator is used to get each pixel of a raster line. It
+can be treated as versatile implementation of the Bresenham algorithm
+where you can stop at each pixel and do some extra processing, for
+example, grab pixel values along the line or draw a line with an effect
+(for example, with XOR operation).
+
+The number of pixels along the line is stored in LineIterator::count.
+The method LineIterator::pos returns the current position in the image:
+
+@code{.cpp}
+// grabs pixels along the line (pt1, pt2)
+// from 8-bit 3-channel image to the buffer
+LineIterator it(img, pt1, pt2, 8);
+LineIterator it2 = it;
+vector<Vec3b> buf(it.count);
+
+for(int i = 0; i < it.count; i++, ++it)
+    buf[i] = *(const Vec3b*)*it;
+
+// alternative way of iterating through the line
+for(int i = 0; i < it2.count; i++, ++it2)
+{
+    Vec3b val = img.at<Vec3b>(it2.pos());
+    CV_Assert(buf[i] == val);
+}
+@endcode
+*/
+class CV_EXPORTS LineIterator
+{
+public:
+    /** @brief initializes the iterator
+
+    creates iterators for the line connecting pt1 and pt2
+    the line will be clipped on the image boundaries
+    the line is 8-connected or 4-connected
+    If leftToRight=true, then the iteration is always done
+    from the left-most point to the right most,
+    not to depend on the ordering of pt1 and pt2 parameters;
+    */
+    LineIterator( const Mat& img, Point pt1, Point pt2,
+                  int connectivity = 8, bool leftToRight = false )
+    {
+        init(&img, Rect(0, 0, img.cols, img.rows), pt1, pt2, connectivity, leftToRight);
+        ptmode = false;
+    }
+    LineIterator( Point pt1, Point pt2,
+                  int connectivity = 8, bool leftToRight = false )
+    {
+        init(0, Rect(std::min(pt1.x, pt2.x),
+                     std::min(pt1.y, pt2.y),
+                     std::max(pt1.x, pt2.x) - std::min(pt1.x, pt2.x) + 1,
+                     std::max(pt1.y, pt2.y) - std::min(pt1.y, pt2.y) + 1),
+             pt1, pt2, connectivity, leftToRight);
+        ptmode = true;
+    }
+    LineIterator( Size boundingAreaSize, Point pt1, Point pt2,
+                  int connectivity = 8, bool leftToRight = false )
+    {
+        init(0, Rect(0, 0, boundingAreaSize.width, boundingAreaSize.height),
+             pt1, pt2, connectivity, leftToRight);
+        ptmode = true;
+    }
+    LineIterator( Rect boundingAreaRect, Point pt1, Point pt2,
+                  int connectivity = 8, bool leftToRight = false )
+    {
+        init(0, boundingAreaRect, pt1, pt2, connectivity, leftToRight);
+        ptmode = true;
+    }
+    void init(const Mat* img, Rect boundingAreaRect, Point pt1, Point pt2, int connectivity, bool leftToRight);
+
+    /** @brief returns pointer to the current pixel
+    */
+    uchar* operator *();
+    /** @brief prefix increment operator (++it). shifts iterator to the next pixel
+    */
+    LineIterator& operator ++();
+    /** @brief postfix increment operator (it++). shifts iterator to the next pixel
+    */
+    LineIterator operator ++(int);
+    /** @brief returns coordinates of the current pixel
+    */
+    Point pos() const;
+
+    uchar* ptr;
+    const uchar* ptr0;
+    int step, elemSize;
+    int err, count;
+    int minusDelta, plusDelta;
+    int minusStep, plusStep;
+    int minusShift, plusShift;
+    Point p;
+    bool ptmode;
+};
+
+//! @cond IGNORED
+
+// === LineIterator implementation ===
+
+inline
+uchar* LineIterator::operator *()
+{
+    return ptmode ? 0 : ptr;
+}
+
+inline
+LineIterator& LineIterator::operator ++()
+{
+    int mask = err < 0 ? -1 : 0;
+    err += minusDelta + (plusDelta & mask);
+    if(!ptmode)
+    {
+        ptr += minusStep + (plusStep & mask);
+    }
+    else
+    {
+        p.x += minusShift + (plusShift & mask);
+        p.y += minusStep + (plusStep & mask);
+    }
+    return *this;
+}
+
+inline
+LineIterator LineIterator::operator ++(int)
+{
+    LineIterator it = *this;
+    ++(*this);
+    return it;
+}
+
+inline
+Point LineIterator::pos() const
+{
+    if(!ptmode)
+    {
+        size_t offset = (size_t)(ptr - ptr0);
+        int y = (int)(offset/step);
+        int x = (int)((offset - (size_t)y*step)/elemSize);
+        return Point(x, y);
+    }
+    return p;
+}
+
+//! @endcond
+
+//! @} imgproc_draw
+
+//! @} imgproc
+
+} // cv
+
+
+#include "./imgproc/segmentation.hpp"
+
+
+#endif
diff --git a/3rdParty/include/opencv2/imgproc/bindings.hpp b/3rdParty/include/opencv2/imgproc/bindings.hpp
new file mode 100644
index 0000000..c69527a
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/bindings.hpp
@@ -0,0 +1,34 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_IMGPROC_BINDINGS_HPP
+#define OPENCV_IMGPROC_BINDINGS_HPP
+
+// This file contains special overloads for OpenCV bindings
+// No need to use these functions in C++ code.
+
+namespace cv {
+
+/** @brief Finds lines in a binary image using the standard Hough transform and get accumulator.
+ *
+ * @note This function is for bindings use only. Use original function in C++ code
+ *
+ * @sa HoughLines
+ */
+CV_WRAP static inline
+void HoughLinesWithAccumulator(
+        InputArray image, OutputArray lines,
+        double rho, double theta, int threshold,
+        double srn = 0, double stn = 0,
+        double min_theta = 0, double max_theta = CV_PI
+)
+{
+    std::vector<Vec3f> lines_acc;
+    HoughLines(image, lines_acc, rho, theta, threshold, srn, stn, min_theta, max_theta);
+    Mat(lines_acc).copyTo(lines);
+}
+
+}  // namespace
+
+#endif  // OPENCV_IMGPROC_BINDINGS_HPP
diff --git a/3rdParty/include/opencv2/imgproc/detail/gcgraph.hpp b/3rdParty/include/opencv2/imgproc/detail/gcgraph.hpp
new file mode 100644
index 0000000..f17c6e7
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/detail/gcgraph.hpp
@@ -0,0 +1,395 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_IMGPROC_DETAIL_GCGRAPH_HPP
+#define OPENCV_IMGPROC_DETAIL_GCGRAPH_HPP
+
+//! @cond IGNORED
+
+namespace cv { namespace detail {
+template <class TWeight> class GCGraph
+{
+public:
+    GCGraph();
+    GCGraph( unsigned int vtxCount, unsigned int edgeCount );
+    ~GCGraph();
+    void create( unsigned int vtxCount, unsigned int edgeCount );
+    int addVtx();
+    void addEdges( int i, int j, TWeight w, TWeight revw );
+    void addTermWeights( int i, TWeight sourceW, TWeight sinkW );
+    TWeight maxFlow();
+    bool inSourceSegment( int i );
+private:
+    class Vtx
+    {
+    public:
+        Vtx *next; // initialized and used in maxFlow() only
+        int parent;
+        int first;
+        int ts;
+        int dist;
+        TWeight weight;
+        uchar t;
+    };
+    class Edge
+    {
+    public:
+        int dst;
+        int next;
+        TWeight weight;
+    };
+
+    std::vector<Vtx> vtcs;
+    std::vector<Edge> edges;
+    TWeight flow;
+};
+
+template <class TWeight>
+GCGraph<TWeight>::GCGraph()
+{
+    flow = 0;
+}
+template <class TWeight>
+GCGraph<TWeight>::GCGraph( unsigned int vtxCount, unsigned int edgeCount )
+{
+    create( vtxCount, edgeCount );
+}
+template <class TWeight>
+GCGraph<TWeight>::~GCGraph()
+{
+}
+template <class TWeight>
+void GCGraph<TWeight>::create( unsigned int vtxCount, unsigned int edgeCount )
+{
+    vtcs.reserve( vtxCount );
+    edges.reserve( edgeCount + 2 );
+    flow = 0;
+}
+
+template <class TWeight>
+int GCGraph<TWeight>::addVtx()
+{
+    Vtx v;
+    memset( &v, 0, sizeof(Vtx));
+    vtcs.push_back(v);
+    return (int)vtcs.size() - 1;
+}
+
+template <class TWeight>
+void GCGraph<TWeight>::addEdges( int i, int j, TWeight w, TWeight revw )
+{
+    CV_Assert( i>=0 && i<(int)vtcs.size() );
+    CV_Assert( j>=0 && j<(int)vtcs.size() );
+    CV_Assert( w>=0 && revw>=0 );
+    CV_Assert( i != j );
+
+    if( !edges.size() )
+        edges.resize( 2 );
+
+    Edge fromI, toI;
+    fromI.dst = j;
+    fromI.next = vtcs[i].first;
+    fromI.weight = w;
+    vtcs[i].first = (int)edges.size();
+    edges.push_back( fromI );
+
+    toI.dst = i;
+    toI.next = vtcs[j].first;
+    toI.weight = revw;
+    vtcs[j].first = (int)edges.size();
+    edges.push_back( toI );
+}
+
+template <class TWeight>
+void GCGraph<TWeight>::addTermWeights( int i, TWeight sourceW, TWeight sinkW )
+{
+    CV_Assert( i>=0 && i<(int)vtcs.size() );
+
+    TWeight dw = vtcs[i].weight;
+    if( dw > 0 )
+        sourceW += dw;
+    else
+        sinkW -= dw;
+    flow += (sourceW < sinkW) ? sourceW : sinkW;
+    vtcs[i].weight = sourceW - sinkW;
+}
+
+template <class TWeight>
+TWeight GCGraph<TWeight>::maxFlow()
+{
+    CV_Assert(!vtcs.empty());
+    CV_Assert(!edges.empty());
+    const int TERMINAL = -1, ORPHAN = -2;
+    Vtx stub, *nilNode = &stub, *first = nilNode, *last = nilNode;
+    int curr_ts = 0;
+    stub.next = nilNode;
+    Vtx *vtxPtr = &vtcs[0];
+    Edge *edgePtr = &edges[0];
+
+    std::vector<Vtx*> orphans;
+
+    // initialize the active queue and the graph vertices
+    for( int i = 0; i < (int)vtcs.size(); i++ )
+    {
+        Vtx* v = vtxPtr + i;
+        v->ts = 0;
+        if( v->weight != 0 )
+        {
+            last = last->next = v;
+            v->dist = 1;
+            v->parent = TERMINAL;
+            v->t = v->weight < 0;
+        }
+        else
+            v->parent = 0;
+    }
+    first = first->next;
+    last->next = nilNode;
+    nilNode->next = 0;
+
+    // run the search-path -> augment-graph -> restore-trees loop
+    for(;;)
+    {
+        Vtx* v, *u;
+        int e0 = -1, ei = 0, ej = 0;
+        TWeight minWeight, weight;
+        uchar vt;
+
+        // grow S & T search trees, find an edge connecting them
+        while( first != nilNode )
+        {
+            v = first;
+            if( v->parent )
+            {
+                vt = v->t;
+                for( ei = v->first; ei != 0; ei = edgePtr[ei].next )
+                {
+                    if( edgePtr[ei^vt].weight == 0 )
+                        continue;
+                    u = vtxPtr+edgePtr[ei].dst;
+                    if( !u->parent )
+                    {
+                        u->t = vt;
+                        u->parent = ei ^ 1;
+                        u->ts = v->ts;
+                        u->dist = v->dist + 1;
+                        if( !u->next )
+                        {
+                            u->next = nilNode;
+                            last = last->next = u;
+                        }
+                        continue;
+                    }
+
+                    if( u->t != vt )
+                    {
+                        e0 = ei ^ vt;
+                        break;
+                    }
+
+                    if( u->dist > v->dist+1 && u->ts <= v->ts )
+                    {
+                        // reassign the parent
+                        u->parent = ei ^ 1;
+                        u->ts = v->ts;
+                        u->dist = v->dist + 1;
+                    }
+                }
+                if( e0 > 0 )
+                    break;
+            }
+            // exclude the vertex from the active list
+            first = first->next;
+            v->next = 0;
+        }
+
+        if( e0 <= 0 )
+            break;
+
+        // find the minimum edge weight along the path
+        minWeight = edgePtr[e0].weight;
+        CV_Assert( minWeight > 0 );
+        // k = 1: source tree, k = 0: destination tree
+        for( int k = 1; k >= 0; k-- )
+        {
+            for( v = vtxPtr+edgePtr[e0^k].dst;; v = vtxPtr+edgePtr[ei].dst )
+            {
+                if( (ei = v->parent) < 0 )
+                    break;
+                weight = edgePtr[ei^k].weight;
+                minWeight = MIN(minWeight, weight);
+                CV_Assert( minWeight > 0 );
+            }
+            weight = fabs(v->weight);
+            minWeight = MIN(minWeight, weight);
+            CV_Assert( minWeight > 0 );
+        }
+
+        // modify weights of the edges along the path and collect orphans
+        edgePtr[e0].weight -= minWeight;
+        edgePtr[e0^1].weight += minWeight;
+        flow += minWeight;
+
+        // k = 1: source tree, k = 0: destination tree
+        for( int k = 1; k >= 0; k-- )
+        {
+            for( v = vtxPtr+edgePtr[e0^k].dst;; v = vtxPtr+edgePtr[ei].dst )
+            {
+                if( (ei = v->parent) < 0 )
+                    break;
+                edgePtr[ei^(k^1)].weight += minWeight;
+                if( (edgePtr[ei^k].weight -= minWeight) == 0 )
+                {
+                    orphans.push_back(v);
+                    v->parent = ORPHAN;
+                }
+            }
+
+            v->weight = v->weight + minWeight*(1-k*2);
+            if( v->weight == 0 )
+            {
+               orphans.push_back(v);
+               v->parent = ORPHAN;
+            }
+        }
+
+        // restore the search trees by finding new parents for the orphans
+        curr_ts++;
+        while( !orphans.empty() )
+        {
+            Vtx* v2 = orphans.back();
+            orphans.pop_back();
+
+            int d, minDist = INT_MAX;
+            e0 = 0;
+            vt = v2->t;
+
+            for( ei = v2->first; ei != 0; ei = edgePtr[ei].next )
+            {
+                if( edgePtr[ei^(vt^1)].weight == 0 )
+                    continue;
+                u = vtxPtr+edgePtr[ei].dst;
+                if( u->t != vt || u->parent == 0 )
+                    continue;
+                // compute the distance to the tree root
+                for( d = 0;; )
+                {
+                    if( u->ts == curr_ts )
+                    {
+                        d += u->dist;
+                        break;
+                    }
+                    ej = u->parent;
+                    d++;
+                    if( ej < 0 )
+                    {
+                        if( ej == ORPHAN )
+                            d = INT_MAX-1;
+                        else
+                        {
+                            u->ts = curr_ts;
+                            u->dist = 1;
+                        }
+                        break;
+                    }
+                    u = vtxPtr+edgePtr[ej].dst;
+                }
+
+                // update the distance
+                if( ++d < INT_MAX )
+                {
+                    if( d < minDist )
+                    {
+                        minDist = d;
+                        e0 = ei;
+                    }
+                    for( u = vtxPtr+edgePtr[ei].dst; u->ts != curr_ts; u = vtxPtr+edgePtr[u->parent].dst )
+                    {
+                        u->ts = curr_ts;
+                        u->dist = --d;
+                    }
+                }
+            }
+
+            if( (v2->parent = e0) > 0 )
+            {
+                v2->ts = curr_ts;
+                v2->dist = minDist;
+                continue;
+            }
+
+            /* no parent is found */
+            v2->ts = 0;
+            for( ei = v2->first; ei != 0; ei = edgePtr[ei].next )
+            {
+                u = vtxPtr+edgePtr[ei].dst;
+                ej = u->parent;
+                if( u->t != vt || !ej )
+                    continue;
+                if( edgePtr[ei^(vt^1)].weight && !u->next )
+                {
+                    u->next = nilNode;
+                    last = last->next = u;
+                }
+                if( ej > 0 && vtxPtr+edgePtr[ej].dst == v2 )
+                {
+                    orphans.push_back(u);
+                    u->parent = ORPHAN;
+                }
+            }
+        }
+    }
+    return flow;
+}
+
+template <class TWeight>
+bool GCGraph<TWeight>::inSourceSegment( int i )
+{
+    CV_Assert( i>=0 && i<(int)vtcs.size() );
+    return vtcs[i].t == 0;
+}
+
+}} // namespace detail, cv
+
+
+//! @endcond
+
+#endif  // OPENCV_IMGPROC_DETAIL_GCGRAPH_HPP
diff --git a/3rdParty/include/opencv2/imgproc/hal/hal.hpp b/3rdParty/include/opencv2/imgproc/hal/hal.hpp
new file mode 100644
index 0000000..f129012
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/hal/hal.hpp
@@ -0,0 +1,246 @@
+#ifndef CV_IMGPROC_HAL_HPP
+#define CV_IMGPROC_HAL_HPP
+
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/cvstd.hpp"
+#include "opencv2/core/hal/interface.h"
+
+namespace cv { namespace hal {
+
+//! @addtogroup imgproc_hal_functions
+//! @{
+
+//---------------------------
+//! @cond IGNORED
+
+struct CV_EXPORTS Filter2D
+{
+    CV_DEPRECATED static Ptr<hal::Filter2D> create(uchar * , size_t , int ,
+                                     int , int ,
+                                     int , int ,
+                                     int , int ,
+                                     int , double ,
+                                     int , int ,
+                                     bool , bool );
+    virtual void apply(uchar * , size_t ,
+                       uchar * , size_t ,
+                       int , int ,
+                       int , int ,
+                       int , int ) = 0;
+    virtual ~Filter2D() {}
+};
+
+struct CV_EXPORTS SepFilter2D
+{
+    CV_DEPRECATED static Ptr<hal::SepFilter2D> create(int , int , int ,
+                                        uchar * , int ,
+                                        uchar * , int ,
+                                        int , int ,
+                                        double , int );
+    virtual void apply(uchar * , size_t ,
+                       uchar * , size_t ,
+                       int , int ,
+                       int , int ,
+                       int , int ) = 0;
+    virtual ~SepFilter2D() {}
+};
+
+
+struct CV_EXPORTS Morph
+{
+    CV_DEPRECATED static Ptr<hal::Morph> create(int , int , int , int , int ,
+                                    int , uchar * , size_t ,
+                                    int , int ,
+                                    int , int ,
+                                    int , const double *,
+                                    int , bool , bool );
+    virtual void apply(uchar * , size_t , uchar * , size_t , int , int ,
+                       int , int , int , int ,
+                       int , int , int , int ) = 0;
+    virtual ~Morph() {}
+};
+
+//! @endcond
+//---------------------------
+
+CV_EXPORTS void filter2D(int stype, int dtype, int kernel_type,
+                         uchar * src_data, size_t src_step,
+                         uchar * dst_data, size_t dst_step,
+                         int width, int height,
+                         int full_width, int full_height,
+                         int offset_x, int offset_y,
+                         uchar * kernel_data, size_t kernel_step,
+                         int kernel_width, int kernel_height,
+                         int anchor_x, int anchor_y,
+                         double delta, int borderType,
+                         bool isSubmatrix);
+
+CV_EXPORTS void sepFilter2D(int stype, int dtype, int ktype,
+                            uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int full_width, int full_height,
+                            int offset_x, int offset_y,
+                            uchar * kernelx_data, int kernelx_len,
+                            uchar * kernely_data, int kernely_len,
+                            int anchor_x, int anchor_y,
+                            double delta, int borderType);
+
+CV_EXPORTS void morph(int op, int src_type, int dst_type,
+                      uchar * src_data, size_t src_step,
+                      uchar * dst_data, size_t dst_step,
+                      int width, int height,
+                      int roi_width, int roi_height, int roi_x, int roi_y,
+                      int roi_width2, int roi_height2, int roi_x2, int roi_y2,
+                      int kernel_type, uchar * kernel_data, size_t kernel_step,
+                      int kernel_width, int kernel_height, int anchor_x, int anchor_y,
+                      int borderType, const double borderValue[4],
+                      int iterations, bool isSubmatrix);
+
+
+CV_EXPORTS void resize(int src_type,
+                       const uchar * src_data, size_t src_step, int src_width, int src_height,
+                       uchar * dst_data, size_t dst_step, int dst_width, int dst_height,
+                       double inv_scale_x, double inv_scale_y, int interpolation);
+
+CV_EXPORTS void warpAffine(int src_type,
+                           const uchar * src_data, size_t src_step, int src_width, int src_height,
+                           uchar * dst_data, size_t dst_step, int dst_width, int dst_height,
+                           const double M[6], int interpolation, int borderType, const double borderValue[4]);
+
+CV_EXPORTS void warpPerspective(int src_type,
+                               const uchar * src_data, size_t src_step, int src_width, int src_height,
+                               uchar * dst_data, size_t dst_step, int dst_width, int dst_height,
+                               const double M[9], int interpolation, int borderType, const double borderValue[4]);
+
+CV_EXPORTS void cvtBGRtoBGR(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int scn, int dcn, bool swapBlue);
+
+CV_EXPORTS void cvtBGRtoBGR5x5(const uchar * src_data, size_t src_step,
+                               uchar * dst_data, size_t dst_step,
+                               int width, int height,
+                               int scn, bool swapBlue, int greenBits);
+
+CV_EXPORTS void cvtBGR5x5toBGR(const uchar * src_data, size_t src_step,
+                               uchar * dst_data, size_t dst_step,
+                               int width, int height,
+                               int dcn, bool swapBlue, int greenBits);
+
+CV_EXPORTS void cvtBGRtoGray(const uchar * src_data, size_t src_step,
+                             uchar * dst_data, size_t dst_step,
+                             int width, int height,
+                             int depth, int scn, bool swapBlue);
+
+CV_EXPORTS void cvtGraytoBGR(const uchar * src_data, size_t src_step,
+                             uchar * dst_data, size_t dst_step,
+                             int width, int height,
+                             int depth, int dcn);
+
+CV_EXPORTS void cvtBGR5x5toGray(const uchar * src_data, size_t src_step,
+                                uchar * dst_data, size_t dst_step,
+                                int width, int height,
+                                int greenBits);
+
+CV_EXPORTS void cvtGraytoBGR5x5(const uchar * src_data, size_t src_step,
+                                uchar * dst_data, size_t dst_step,
+                                int width, int height,
+                                int greenBits);
+CV_EXPORTS void cvtBGRtoYUV(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int scn, bool swapBlue, bool isCbCr);
+
+CV_EXPORTS void cvtYUVtoBGR(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int dcn, bool swapBlue, bool isCbCr);
+
+CV_EXPORTS void cvtBGRtoXYZ(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int scn, bool swapBlue);
+
+CV_EXPORTS void cvtXYZtoBGR(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int dcn, bool swapBlue);
+
+CV_EXPORTS void cvtBGRtoHSV(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int scn, bool swapBlue, bool isFullRange, bool isHSV);
+
+CV_EXPORTS void cvtHSVtoBGR(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int dcn, bool swapBlue, bool isFullRange, bool isHSV);
+
+CV_EXPORTS void cvtBGRtoLab(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int scn, bool swapBlue, bool isLab, bool srgb);
+
+CV_EXPORTS void cvtLabtoBGR(const uchar * src_data, size_t src_step,
+                            uchar * dst_data, size_t dst_step,
+                            int width, int height,
+                            int depth, int dcn, bool swapBlue, bool isLab, bool srgb);
+
+CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * src_data, size_t src_step,
+                                    uchar * dst_data, size_t dst_step,
+                                    int dst_width, int dst_height,
+                                    int dcn, bool swapBlue, int uIdx);
+
+//! Separate Y and UV planes
+CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * y_data, const uchar * uv_data, size_t src_step,
+                                    uchar * dst_data, size_t dst_step,
+                                    int dst_width, int dst_height,
+                                    int dcn, bool swapBlue, int uIdx);
+
+CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * y_data, size_t y_step, const uchar * uv_data, size_t uv_step,
+                                    uchar * dst_data, size_t dst_step,
+                                    int dst_width, int dst_height,
+                                    int dcn, bool swapBlue, int uIdx);
+
+CV_EXPORTS void cvtThreePlaneYUVtoBGR(const uchar * src_data, size_t src_step,
+                                      uchar * dst_data, size_t dst_step,
+                                      int dst_width, int dst_height,
+                                      int dcn, bool swapBlue, int uIdx);
+
+CV_EXPORTS void cvtBGRtoThreePlaneYUV(const uchar * src_data, size_t src_step,
+                                      uchar * dst_data, size_t dst_step,
+                                      int width, int height,
+                                      int scn, bool swapBlue, int uIdx);
+
+//! Separate Y and UV planes
+CV_EXPORTS void cvtBGRtoTwoPlaneYUV(const uchar * src_data, size_t src_step,
+                                    uchar * y_data, uchar * uv_data, size_t dst_step,
+                                    int width, int height,
+                                    int scn, bool swapBlue, int uIdx);
+
+CV_EXPORTS void cvtOnePlaneYUVtoBGR(const uchar * src_data, size_t src_step,
+                                    uchar * dst_data, size_t dst_step,
+                                    int width, int height,
+                                    int dcn, bool swapBlue, int uIdx, int ycn);
+
+CV_EXPORTS void cvtRGBAtoMultipliedRGBA(const uchar * src_data, size_t src_step,
+                                        uchar * dst_data, size_t dst_step,
+                                        int width, int height);
+
+CV_EXPORTS void cvtMultipliedRGBAtoRGBA(const uchar * src_data, size_t src_step,
+                                        uchar * dst_data, size_t dst_step,
+                                        int width, int height);
+
+CV_EXPORTS void integral(int depth, int sdepth, int sqdepth,
+                         const uchar* src, size_t srcstep,
+                         uchar* sum, size_t sumstep,
+                         uchar* sqsum, size_t sqsumstep,
+                         uchar* tilted, size_t tstep,
+                         int width, int height, int cn);
+
+//! @}
+
+}}
+
+#endif // CV_IMGPROC_HAL_HPP
diff --git a/3rdParty/include/opencv2/imgproc/hal/interface.h b/3rdParty/include/opencv2/imgproc/hal/interface.h
new file mode 100644
index 0000000..f8dbcfe
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/hal/interface.h
@@ -0,0 +1,46 @@
+#ifndef OPENCV_IMGPROC_HAL_INTERFACE_H
+#define OPENCV_IMGPROC_HAL_INTERFACE_H
+
+//! @addtogroup imgproc_hal_interface
+//! @{
+
+//! @name Interpolation modes
+//! @sa cv::InterpolationFlags
+//! @{
+#define CV_HAL_INTER_NEAREST 0
+#define CV_HAL_INTER_LINEAR 1
+#define CV_HAL_INTER_CUBIC 2
+#define CV_HAL_INTER_AREA 3
+#define CV_HAL_INTER_LANCZOS4 4
+//! @}
+
+//! @name Morphology operations
+//! @sa cv::MorphTypes
+//! @{
+#define CV_HAL_MORPH_ERODE 0
+#define CV_HAL_MORPH_DILATE 1
+//! @}
+
+//! @name Threshold types
+//! @sa cv::ThresholdTypes
+//! @{
+#define CV_HAL_THRESH_BINARY      0
+#define CV_HAL_THRESH_BINARY_INV  1
+#define CV_HAL_THRESH_TRUNC       2
+#define CV_HAL_THRESH_TOZERO      3
+#define CV_HAL_THRESH_TOZERO_INV  4
+#define CV_HAL_THRESH_MASK        7
+#define CV_HAL_THRESH_OTSU        8
+#define CV_HAL_THRESH_TRIANGLE    16
+//! @}
+
+//! @name Adaptive threshold algorithm
+//! @sa cv::AdaptiveThresholdTypes
+//! @{
+#define CV_HAL_ADAPTIVE_THRESH_MEAN_C     0
+#define CV_HAL_ADAPTIVE_THRESH_GAUSSIAN_C 1
+//! @}
+
+//! @}
+
+#endif
diff --git a/3rdParty/include/opencv2/imgproc/imgproc.hpp b/3rdParty/include/opencv2/imgproc/imgproc.hpp
new file mode 100644
index 0000000..4175bd0
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/imgproc.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/imgproc.hpp"
diff --git a/3rdParty/include/opencv2/imgproc/imgproc_c.h b/3rdParty/include/opencv2/imgproc/imgproc_c.h
new file mode 100644
index 0000000..86dc119
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/imgproc_c.h
@@ -0,0 +1,1177 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_IMGPROC_IMGPROC_C_H
+#define OPENCV_IMGPROC_IMGPROC_C_H
+
+#include "opencv2/imgproc/types_c.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/** @addtogroup imgproc_c
+@{
+*/
+
+/*********************** Background statistics accumulation *****************************/
+
+/** @brief Adds image to accumulator
+@see cv::accumulate
+*/
+CVAPI(void)  cvAcc( const CvArr* image, CvArr* sum,
+                   const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @brief Adds squared image to accumulator
+@see cv::accumulateSquare
+*/
+CVAPI(void)  cvSquareAcc( const CvArr* image, CvArr* sqsum,
+                         const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @brief Adds a product of two images to accumulator
+@see cv::accumulateProduct
+*/
+CVAPI(void)  cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc,
+                           const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha
+@see cv::accumulateWeighted
+*/
+CVAPI(void)  cvRunningAvg( const CvArr* image, CvArr* acc, double alpha,
+                          const CvArr* mask CV_DEFAULT(NULL) );
+
+/****************************************************************************************\
+*                                    Image Processing                                    *
+\****************************************************************************************/
+
+/** Copies source 2D array inside of the larger destination array and
+   makes a border of the specified type (IPL_BORDER_*) around the copied area. */
+CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset,
+                              int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0)));
+
+/** @brief Smooths the image in one of several ways.
+
+@param src The source image
+@param dst The destination image
+@param smoothtype Type of the smoothing, see SmoothMethod_c
+@param size1 The first parameter of the smoothing operation, the aperture width. Must be a
+positive odd number (1, 3, 5, ...)
+@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by
+CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if
+size2 is zero, it is set to size1. Otherwise it must be a positive odd number.
+@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$
+(standard deviation). If it is zero, it is calculated from the kernel size:
+\f[\sigma  = 0.3 (n/2 - 1) + 0.8  \quad   \text{where}   \quad  n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f]
+Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If
+sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the
+sigma (to provide accurate enough operation).
+@param sigma2 additional parameter for bilateral filtering
+
+@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter.
+ */
+CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst,
+                      int smoothtype CV_DEFAULT(CV_GAUSSIAN),
+                      int size1 CV_DEFAULT(3),
+                      int size2 CV_DEFAULT(0),
+                      double sigma1 CV_DEFAULT(0),
+                      double sigma2 CV_DEFAULT(0));
+
+/** @brief Convolves an image with the kernel.
+
+@param src input image.
+@param dst output image of the same size and the same number of channels as src.
+@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
+matrix; if you want to apply different kernels to different channels, split the image into
+separate color planes using split and process them individually.
+@param anchor anchor of the kernel that indicates the relative position of a filtered point within
+the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
+is at the kernel center.
+
+@see cv::filter2D
+ */
+CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel,
+                        CvPoint anchor CV_DEFAULT(cvPoint(-1,-1)));
+
+/** @brief Finds integral image: SUM(X,Y) = sum(x<X,y<Y)I(x,y)
+@see cv::integral
+*/
+CVAPI(void) cvIntegral( const CvArr* image, CvArr* sum,
+                       CvArr* sqsum CV_DEFAULT(NULL),
+                       CvArr* tilted_sum CV_DEFAULT(NULL));
+
+/** @brief Smoothes the input image with gaussian kernel and then down-samples it.
+
+   dst_width = floor(src_width/2)[+1],
+   dst_height = floor(src_height/2)[+1]
+   @see cv::pyrDown
+*/
+CVAPI(void)  cvPyrDown( const CvArr* src, CvArr* dst,
+                        int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
+
+/** @brief Up-samples image and smoothes the result with gaussian kernel.
+
+   dst_width = src_width*2,
+   dst_height = src_height*2
+   @see cv::pyrUp
+*/
+CVAPI(void)  cvPyrUp( const CvArr* src, CvArr* dst,
+                      int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
+
+/** @brief Builds pyramid for an image
+@see buildPyramid
+*/
+CVAPI(CvMat**) cvCreatePyramid( const CvArr* img, int extra_layers, double rate,
+                                const CvSize* layer_sizes CV_DEFAULT(0),
+                                CvArr* bufarr CV_DEFAULT(0),
+                                int calc CV_DEFAULT(1),
+                                int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
+
+/** @brief Releases pyramid */
+CVAPI(void)  cvReleasePyramid( CvMat*** pyramid, int extra_layers );
+
+
+/** @brief Filters image using meanshift algorithm
+@see cv::pyrMeanShiftFiltering
+*/
+CVAPI(void) cvPyrMeanShiftFiltering( const CvArr* src, CvArr* dst,
+    double sp, double sr, int max_level CV_DEFAULT(1),
+    CvTermCriteria termcrit CV_DEFAULT(cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,5,1)));
+
+/** @brief Segments image using seed "markers"
+@see cv::watershed
+*/
+CVAPI(void) cvWatershed( const CvArr* image, CvArr* markers );
+
+/** @brief Calculates an image derivative using generalized Sobel
+
+   (aperture_size = 1,3,5,7) or Scharr (aperture_size = -1) operator.
+   Scharr can be used only for the first dx or dy derivative
+@see cv::Sobel
+*/
+CVAPI(void) cvSobel( const CvArr* src, CvArr* dst,
+                    int xorder, int yorder,
+                    int aperture_size CV_DEFAULT(3));
+
+/** @brief Calculates the image Laplacian: (d2/dx + d2/dy)I
+@see cv::Laplacian
+*/
+CVAPI(void) cvLaplace( const CvArr* src, CvArr* dst,
+                      int aperture_size CV_DEFAULT(3) );
+
+/** @brief Converts input array pixels from one color space to another
+@see cv::cvtColor
+*/
+CVAPI(void)  cvCvtColor( const CvArr* src, CvArr* dst, int code );
+
+
+/** @brief Resizes image (input array is resized to fit the destination array)
+@see cv::resize
+*/
+CVAPI(void)  cvResize( const CvArr* src, CvArr* dst,
+                       int interpolation CV_DEFAULT( CV_INTER_LINEAR ));
+
+/** @brief Warps image with affine transform
+@note ::cvGetQuadrangleSubPix is similar to ::cvWarpAffine, but the outliers are extrapolated using
+replication border mode.
+@see cv::warpAffine
+*/
+CVAPI(void)  cvWarpAffine( const CvArr* src, CvArr* dst, const CvMat* map_matrix,
+                           int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
+                           CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
+
+/** @brief Computes affine transform matrix for mapping src[i] to dst[i] (i=0,1,2)
+@see cv::getAffineTransform
+*/
+CVAPI(CvMat*) cvGetAffineTransform( const CvPoint2D32f * src,
+                                    const CvPoint2D32f * dst,
+                                    CvMat * map_matrix );
+
+/** @brief Computes rotation_matrix matrix
+@see cv::getRotationMatrix2D
+*/
+CVAPI(CvMat*)  cv2DRotationMatrix( CvPoint2D32f center, double angle,
+                                   double scale, CvMat* map_matrix );
+
+/** @brief Warps image with perspective (projective) transform
+@see cv::warpPerspective
+*/
+CVAPI(void)  cvWarpPerspective( const CvArr* src, CvArr* dst, const CvMat* map_matrix,
+                                int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
+                                CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
+
+/** @brief Computes perspective transform matrix for mapping src[i] to dst[i] (i=0,1,2,3)
+@see cv::getPerspectiveTransform
+*/
+CVAPI(CvMat*) cvGetPerspectiveTransform( const CvPoint2D32f* src,
+                                         const CvPoint2D32f* dst,
+                                         CvMat* map_matrix );
+
+/** @brief Performs generic geometric transformation using the specified coordinate maps
+@see cv::remap
+*/
+CVAPI(void)  cvRemap( const CvArr* src, CvArr* dst,
+                      const CvArr* mapx, const CvArr* mapy,
+                      int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
+                      CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
+
+/** @brief Converts mapx & mapy from floating-point to integer formats for cvRemap
+@see cv::convertMaps
+*/
+CVAPI(void)  cvConvertMaps( const CvArr* mapx, const CvArr* mapy,
+                            CvArr* mapxy, CvArr* mapalpha );
+
+/** @brief Performs forward or inverse log-polar image transform
+@see cv::warpPolar
+*/
+CVAPI(void)  cvLogPolar( const CvArr* src, CvArr* dst,
+                         CvPoint2D32f center, double M,
+                         int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS));
+
+/** Performs forward or inverse linear-polar image transform
+@see cv::warpPolar
+*/
+CVAPI(void)  cvLinearPolar( const CvArr* src, CvArr* dst,
+                         CvPoint2D32f center, double maxRadius,
+                         int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS));
+
+/** @brief Returns a structuring element of the specified size and shape for morphological operations.
+
+@note the created structuring element IplConvKernel\* element must be released in the end using
+`cvReleaseStructuringElement(&element)`.
+
+@param cols Width of the structuring element
+@param rows Height of the structuring element
+@param anchor_x x-coordinate of the anchor
+@param anchor_y y-coordinate of the anchor
+@param shape element shape that could be one of the cv::MorphShapes_c
+@param values integer array of cols*rows elements that specifies the custom shape of the
+structuring element, when shape=CV_SHAPE_CUSTOM.
+
+@see cv::getStructuringElement
+ */
+ CVAPI(IplConvKernel*)  cvCreateStructuringElementEx(
+            int cols, int  rows, int  anchor_x, int  anchor_y,
+            int shape, int* values CV_DEFAULT(NULL) );
+
+/** @brief releases structuring element
+@see cvCreateStructuringElementEx
+*/
+CVAPI(void)  cvReleaseStructuringElement( IplConvKernel** element );
+
+/** @brief erodes input image (applies minimum filter) one or more times.
+   If element pointer is NULL, 3x3 rectangular element is used
+@see cv::erode
+*/
+CVAPI(void)  cvErode( const CvArr* src, CvArr* dst,
+                      IplConvKernel* element CV_DEFAULT(NULL),
+                      int iterations CV_DEFAULT(1) );
+
+/** @brief dilates input image (applies maximum filter) one or more times.
+
+   If element pointer is NULL, 3x3 rectangular element is used
+@see cv::dilate
+*/
+CVAPI(void)  cvDilate( const CvArr* src, CvArr* dst,
+                       IplConvKernel* element CV_DEFAULT(NULL),
+                       int iterations CV_DEFAULT(1) );
+
+/** @brief Performs complex morphological transformation
+@see cv::morphologyEx
+*/
+CVAPI(void)  cvMorphologyEx( const CvArr* src, CvArr* dst,
+                             CvArr* temp, IplConvKernel* element,
+                             int operation, int iterations CV_DEFAULT(1) );
+
+/** @brief Calculates all spatial and central moments up to the 3rd order
+@see cv::moments
+*/
+CVAPI(void) cvMoments( const CvArr* arr, CvMoments* moments, int binary CV_DEFAULT(0));
+
+/** @brief Retrieve spatial moments */
+CVAPI(double)  cvGetSpatialMoment( CvMoments* moments, int x_order, int y_order );
+/** @brief Retrieve central moments */
+CVAPI(double)  cvGetCentralMoment( CvMoments* moments, int x_order, int y_order );
+/** @brief Retrieve normalized central moments */
+CVAPI(double)  cvGetNormalizedCentralMoment( CvMoments* moments,
+                                             int x_order, int y_order );
+
+/** @brief Calculates 7 Hu's invariants from precalculated spatial and central moments
+@see cv::HuMoments
+*/
+CVAPI(void) cvGetHuMoments( CvMoments*  moments, CvHuMoments*  hu_moments );
+
+/*********************************** data sampling **************************************/
+
+/** @brief Fetches pixels that belong to the specified line segment and stores them to the buffer.
+
+   Returns the number of retrieved points.
+@see cv::LineSegmentDetector
+*/
+CVAPI(int)  cvSampleLine( const CvArr* image, CvPoint pt1, CvPoint pt2, void* buffer,
+                          int connectivity CV_DEFAULT(8));
+
+/** @brief Retrieves the rectangular image region with specified center from the input array.
+
+ dst(x,y) <- src(x + center.x - dst_width/2, y + center.y - dst_height/2).
+ Values of pixels with fractional coordinates are retrieved using bilinear interpolation
+@see cv::getRectSubPix
+*/
+CVAPI(void)  cvGetRectSubPix( const CvArr* src, CvArr* dst, CvPoint2D32f center );
+
+
+/** @brief Retrieves quadrangle from the input array.
+
+    matrixarr = ( a11  a12 | b1 )   dst(x,y) <- src(A[x y]' + b)
+                ( a21  a22 | b2 )   (bilinear interpolation is used to retrieve pixels
+                                     with fractional coordinates)
+@see cvWarpAffine
+*/
+CVAPI(void)  cvGetQuadrangleSubPix( const CvArr* src, CvArr* dst,
+                                    const CvMat* map_matrix );
+
+/** @brief Measures similarity between template and overlapped windows in the source image
+   and fills the resultant image with the measurements
+@see cv::matchTemplate
+*/
+CVAPI(void)  cvMatchTemplate( const CvArr* image, const CvArr* templ,
+                              CvArr* result, int method );
+
+/** @brief Computes earth mover distance between
+   two weighted point sets (called signatures)
+@see cv::EMD
+*/
+CVAPI(float)  cvCalcEMD2( const CvArr* signature1,
+                          const CvArr* signature2,
+                          int distance_type,
+                          CvDistanceFunction distance_func CV_DEFAULT(NULL),
+                          const CvArr* cost_matrix CV_DEFAULT(NULL),
+                          CvArr* flow CV_DEFAULT(NULL),
+                          float* lower_bound CV_DEFAULT(NULL),
+                          void* userdata CV_DEFAULT(NULL));
+
+/****************************************************************************************\
+*                              Contours retrieving                                       *
+\****************************************************************************************/
+
+/** @brief Retrieves outer and optionally inner boundaries of white (non-zero) connected
+   components in the black (zero) background
+@see cv::findContours, cvStartFindContours, cvFindNextContour, cvSubstituteContour, cvEndFindContours
+*/
+CVAPI(int)  cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** first_contour,
+                            int header_size CV_DEFAULT(sizeof(CvContour)),
+                            int mode CV_DEFAULT(CV_RETR_LIST),
+                            int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
+                            CvPoint offset CV_DEFAULT(cvPoint(0,0)));
+
+/** @brief Initializes contour retrieving process.
+
+   Calls cvStartFindContours.
+   Calls cvFindNextContour until null pointer is returned
+   or some other condition becomes true.
+   Calls cvEndFindContours at the end.
+@see cvFindContours
+*/
+CVAPI(CvContourScanner)  cvStartFindContours( CvArr* image, CvMemStorage* storage,
+                            int header_size CV_DEFAULT(sizeof(CvContour)),
+                            int mode CV_DEFAULT(CV_RETR_LIST),
+                            int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
+                            CvPoint offset CV_DEFAULT(cvPoint(0,0)));
+
+/** @brief Retrieves next contour
+@see cvFindContours
+*/
+CVAPI(CvSeq*)  cvFindNextContour( CvContourScanner scanner );
+
+
+/** @brief Substitutes the last retrieved contour with the new one
+
+   (if the substitutor is null, the last retrieved contour is removed from the tree)
+@see cvFindContours
+*/
+CVAPI(void)   cvSubstituteContour( CvContourScanner scanner, CvSeq* new_contour );
+
+
+/** @brief Releases contour scanner and returns pointer to the first outer contour
+@see cvFindContours
+*/
+CVAPI(CvSeq*)  cvEndFindContours( CvContourScanner* scanner );
+
+/** @brief Approximates Freeman chain(s) with a polygonal curve.
+
+This is a standalone contour approximation routine, not represented in the new interface. When
+cvFindContours retrieves contours as Freeman chains, it calls the function to get approximated
+contours, represented as polygons.
+
+@param src_seq Pointer to the approximated Freeman chain that can refer to other chains.
+@param storage Storage location for the resulting polylines.
+@param method Approximation method (see the description of the function :ocvFindContours ).
+@param parameter Method parameter (not used now).
+@param minimal_perimeter Approximates only those contours whose perimeters are not less than
+minimal_perimeter . Other chains are removed from the resulting structure.
+@param recursive Recursion flag. If it is non-zero, the function approximates all chains that can
+be obtained from chain by using the h_next or v_next links. Otherwise, the single input chain is
+approximated.
+@see cvStartReadChainPoints, cvReadChainPoint
+ */
+CVAPI(CvSeq*) cvApproxChains( CvSeq* src_seq, CvMemStorage* storage,
+                            int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
+                            double parameter CV_DEFAULT(0),
+                            int  minimal_perimeter CV_DEFAULT(0),
+                            int  recursive CV_DEFAULT(0));
+
+/** @brief Initializes Freeman chain reader.
+
+   The reader is used to iteratively get coordinates of all the chain points.
+   If the Freeman codes should be read as is, a simple sequence reader should be used
+@see cvApproxChains
+*/
+CVAPI(void) cvStartReadChainPoints( CvChain* chain, CvChainPtReader* reader );
+
+/** @brief Retrieves the next chain point
+@see cvApproxChains
+*/
+CVAPI(CvPoint) cvReadChainPoint( CvChainPtReader* reader );
+
+
+/****************************************************************************************\
+*                            Contour Processing and Shape Analysis                       *
+\****************************************************************************************/
+
+/** @brief Approximates a single polygonal curve (contour) or
+   a tree of polygonal curves (contours)
+@see cv::approxPolyDP
+*/
+CVAPI(CvSeq*)  cvApproxPoly( const void* src_seq,
+                             int header_size, CvMemStorage* storage,
+                             int method, double eps,
+                             int recursive CV_DEFAULT(0));
+
+/** @brief Calculates perimeter of a contour or length of a part of contour
+@see cv::arcLength
+*/
+CVAPI(double)  cvArcLength( const void* curve,
+                            CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ),
+                            int is_closed CV_DEFAULT(-1));
+
+/** same as cvArcLength for closed contour
+*/
+CV_INLINE double cvContourPerimeter( const void* contour )
+{
+    return cvArcLength( contour, CV_WHOLE_SEQ, 1 );
+}
+
+
+/** @brief Calculates contour bounding rectangle (update=1) or
+   just retrieves pre-calculated rectangle (update=0)
+@see cv::boundingRect
+*/
+CVAPI(CvRect)  cvBoundingRect( CvArr* points, int update CV_DEFAULT(0) );
+
+/** @brief Calculates area of a contour or contour segment
+@see cv::contourArea
+*/
+CVAPI(double)  cvContourArea( const CvArr* contour,
+                              CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ),
+                              int oriented CV_DEFAULT(0));
+
+/** @brief Finds minimum area rotated rectangle bounding a set of points
+@see cv::minAreaRect
+*/
+CVAPI(CvBox2D)  cvMinAreaRect2( const CvArr* points,
+                                CvMemStorage* storage CV_DEFAULT(NULL));
+
+/** @brief Finds minimum enclosing circle for a set of points
+@see cv::minEnclosingCircle
+*/
+CVAPI(int)  cvMinEnclosingCircle( const CvArr* points,
+                                  CvPoint2D32f* center, float* radius );
+
+/** @brief Compares two contours by matching their moments
+@see cv::matchShapes
+*/
+CVAPI(double)  cvMatchShapes( const void* object1, const void* object2,
+                              int method, double parameter CV_DEFAULT(0));
+
+/** @brief Calculates exact convex hull of 2d point set
+@see cv::convexHull
+*/
+CVAPI(CvSeq*) cvConvexHull2( const CvArr* input,
+                             void* hull_storage CV_DEFAULT(NULL),
+                             int orientation CV_DEFAULT(CV_CLOCKWISE),
+                             int return_points CV_DEFAULT(0));
+
+/** @brief Checks whether the contour is convex or not (returns 1 if convex, 0 if not)
+@see cv::isContourConvex
+*/
+CVAPI(int)  cvCheckContourConvexity( const CvArr* contour );
+
+
+/** @brief Finds convexity defects for the contour
+@see cv::convexityDefects
+*/
+CVAPI(CvSeq*)  cvConvexityDefects( const CvArr* contour, const CvArr* convexhull,
+                                   CvMemStorage* storage CV_DEFAULT(NULL));
+
+/** @brief Fits ellipse into a set of 2d points
+@see cv::fitEllipse
+*/
+CVAPI(CvBox2D) cvFitEllipse2( const CvArr* points );
+
+/** @brief Finds minimum rectangle containing two given rectangles */
+CVAPI(CvRect)  cvMaxRect( const CvRect* rect1, const CvRect* rect2 );
+
+/** @brief Finds coordinates of the box vertices */
+CVAPI(void) cvBoxPoints( CvBox2D box, CvPoint2D32f pt[4] );
+
+/** @brief Initializes sequence header for a matrix (column or row vector) of points
+
+   a wrapper for cvMakeSeqHeaderForArray (it does not initialize bounding rectangle!!!) */
+CVAPI(CvSeq*) cvPointSeqFromMat( int seq_kind, const CvArr* mat,
+                                 CvContour* contour_header,
+                                 CvSeqBlock* block );
+
+/** @brief Checks whether the point is inside polygon, outside, on an edge (at a vertex).
+
+   Returns positive, negative or zero value, correspondingly.
+   Optionally, measures a signed distance between
+   the point and the nearest polygon edge (measure_dist=1)
+@see cv::pointPolygonTest
+*/
+CVAPI(double) cvPointPolygonTest( const CvArr* contour,
+                                  CvPoint2D32f pt, int measure_dist );
+
+/****************************************************************************************\
+*                                  Histogram functions                                   *
+\****************************************************************************************/
+
+/** @brief Creates a histogram.
+
+The function creates a histogram of the specified size and returns a pointer to the created
+histogram. If the array ranges is 0, the histogram bin ranges must be specified later via the
+function cvSetHistBinRanges. Though cvCalcHist and cvCalcBackProject may process 8-bit images
+without setting bin ranges, they assume they are equally spaced in 0 to 255 bins.
+
+@param dims Number of histogram dimensions.
+@param sizes Array of the histogram dimension sizes.
+@param type Histogram representation format. CV_HIST_ARRAY means that the histogram data is
+represented as a multi-dimensional dense array CvMatND. CV_HIST_SPARSE means that histogram data
+is represented as a multi-dimensional sparse array CvSparseMat.
+@param ranges Array of ranges for the histogram bins. Its meaning depends on the uniform parameter
+value. The ranges are used when the histogram is calculated or backprojected to determine which
+histogram bin corresponds to which value/tuple of values from the input image(s).
+@param uniform Uniformity flag. If not zero, the histogram has evenly spaced bins and for every
+\f$0<=i<cDims\f$ ranges[i] is an array of two numbers: lower and upper boundaries for the i-th
+histogram dimension. The whole range [lower,upper] is then split into dims[i] equal parts to
+determine the i-th input tuple value ranges for every histogram bin. And if uniform=0 , then the
+i-th element of the ranges array contains dims[i]+1 elements: \f$\texttt{lower}_0,
+\texttt{upper}_0, \texttt{lower}_1, \texttt{upper}_1 = \texttt{lower}_2,
+...
+\texttt{upper}_{dims[i]-1}\f$ where \f$\texttt{lower}_j\f$ and \f$\texttt{upper}_j\f$ are lower
+and upper boundaries of the i-th input tuple value for the j-th bin, respectively. In either
+case, the input values that are beyond the specified range for a histogram bin are not counted
+by cvCalcHist and filled with 0 by cvCalcBackProject.
+ */
+CVAPI(CvHistogram*)  cvCreateHist( int dims, int* sizes, int type,
+                                   float** ranges CV_DEFAULT(NULL),
+                                   int uniform CV_DEFAULT(1));
+
+/** @brief Sets the bounds of the histogram bins.
+
+This is a standalone function for setting bin ranges in the histogram. For a more detailed
+description of the parameters ranges and uniform, see the :ocvCalcHist function that can initialize
+the ranges as well. Ranges for the histogram bins must be set before the histogram is calculated or
+the backproject of the histogram is calculated.
+
+@param hist Histogram.
+@param ranges Array of bin ranges arrays. See :ocvCreateHist for details.
+@param uniform Uniformity flag. See :ocvCreateHist for details.
+ */
+CVAPI(void)  cvSetHistBinRanges( CvHistogram* hist, float** ranges,
+                                int uniform CV_DEFAULT(1));
+
+/** @brief Makes a histogram out of an array.
+
+The function initializes the histogram, whose header and bins are allocated by the user.
+cvReleaseHist does not need to be called afterwards. Only dense histograms can be initialized this
+way. The function returns hist.
+
+@param dims Number of the histogram dimensions.
+@param sizes Array of the histogram dimension sizes.
+@param hist Histogram header initialized by the function.
+@param data Array used to store histogram bins.
+@param ranges Histogram bin ranges. See cvCreateHist for details.
+@param uniform Uniformity flag. See cvCreateHist for details.
+ */
+CVAPI(CvHistogram*)  cvMakeHistHeaderForArray(
+                            int  dims, int* sizes, CvHistogram* hist,
+                            float* data, float** ranges CV_DEFAULT(NULL),
+                            int uniform CV_DEFAULT(1));
+
+/** @brief Releases the histogram.
+
+The function releases the histogram (header and the data). The pointer to the histogram is cleared
+by the function. If \*hist pointer is already NULL, the function does nothing.
+
+@param hist Double pointer to the released histogram.
+ */
+CVAPI(void)  cvReleaseHist( CvHistogram** hist );
+
+/** @brief Clears the histogram.
+
+The function sets all of the histogram bins to 0 in case of a dense histogram and removes all
+histogram bins in case of a sparse array.
+
+@param hist Histogram.
+ */
+CVAPI(void)  cvClearHist( CvHistogram* hist );
+
+/** @brief Finds the minimum and maximum histogram bins.
+
+The function finds the minimum and maximum histogram bins and their positions. All of output
+arguments are optional. Among several extremas with the same value the ones with the minimum index
+(in the lexicographical order) are returned. In case of several maximums or minimums, the earliest
+in the lexicographical order (extrema locations) is returned.
+
+@param hist Histogram.
+@param min_value Pointer to the minimum value of the histogram.
+@param max_value Pointer to the maximum value of the histogram.
+@param min_idx Pointer to the array of coordinates for the minimum.
+@param max_idx Pointer to the array of coordinates for the maximum.
+ */
+CVAPI(void)  cvGetMinMaxHistValue( const CvHistogram* hist,
+                                   float* min_value, float* max_value,
+                                   int* min_idx CV_DEFAULT(NULL),
+                                   int* max_idx CV_DEFAULT(NULL));
+
+
+/** @brief Normalizes the histogram.
+
+The function normalizes the histogram bins by scaling them so that the sum of the bins becomes equal
+to factor.
+
+@param hist Pointer to the histogram.
+@param factor Normalization factor.
+ */
+CVAPI(void)  cvNormalizeHist( CvHistogram* hist, double factor );
+
+
+/** @brief Thresholds the histogram.
+
+The function clears histogram bins that are below the specified threshold.
+
+@param hist Pointer to the histogram.
+@param threshold Threshold level.
+ */
+CVAPI(void)  cvThreshHist( CvHistogram* hist, double threshold );
+
+
+/** Compares two histogram */
+CVAPI(double)  cvCompareHist( const CvHistogram* hist1,
+                              const CvHistogram* hist2,
+                              int method);
+
+/** @brief Copies a histogram.
+
+The function makes a copy of the histogram. If the second histogram pointer \*dst is NULL, a new
+histogram of the same size as src is created. Otherwise, both histograms must have equal types and
+sizes. Then the function copies the bin values of the source histogram to the destination histogram
+and sets the same bin value ranges as in src.
+
+@param src Source histogram.
+@param dst Pointer to the destination histogram.
+ */
+CVAPI(void)  cvCopyHist( const CvHistogram* src, CvHistogram** dst );
+
+
+/** @brief Calculates bayesian probabilistic histograms
+   (each or src and dst is an array of _number_ histograms */
+CVAPI(void)  cvCalcBayesianProb( CvHistogram** src, int number,
+                                CvHistogram** dst);
+
+/** @brief Calculates array histogram
+@see cv::calcHist
+*/
+CVAPI(void)  cvCalcArrHist( CvArr** arr, CvHistogram* hist,
+                            int accumulate CV_DEFAULT(0),
+                            const CvArr* mask CV_DEFAULT(NULL) );
+
+/** @overload */
+CV_INLINE  void  cvCalcHist( IplImage** image, CvHistogram* hist,
+                             int accumulate CV_DEFAULT(0),
+                             const CvArr* mask CV_DEFAULT(NULL) )
+{
+    cvCalcArrHist( (CvArr**)image, hist, accumulate, mask );
+}
+
+/** @brief Calculates back project
+@see cvCalcBackProject, cv::calcBackProject
+*/
+CVAPI(void)  cvCalcArrBackProject( CvArr** image, CvArr* dst,
+                                   const CvHistogram* hist );
+
+#define  cvCalcBackProject(image, dst, hist) cvCalcArrBackProject((CvArr**)image, dst, hist)
+
+
+/** @brief Locates a template within an image by using a histogram comparison.
+
+The function calculates the back projection by comparing histograms of the source image patches with
+the given histogram. The function is similar to matchTemplate, but instead of comparing the raster
+patch with all its possible positions within the search window, the function CalcBackProjectPatch
+compares histograms. See the algorithm diagram below:
+
+![image](pics/backprojectpatch.png)
+
+@param image Source images (though, you may pass CvMat\*\* as well).
+@param dst Destination image.
+@param range
+@param hist Histogram.
+@param method Comparison method passed to cvCompareHist (see the function description).
+@param factor Normalization factor for histograms that affects the normalization scale of the
+destination image. Pass 1 if not sure.
+
+@see cvCalcBackProjectPatch
+ */
+CVAPI(void)  cvCalcArrBackProjectPatch( CvArr** image, CvArr* dst, CvSize range,
+                                        CvHistogram* hist, int method,
+                                        double factor );
+
+#define  cvCalcBackProjectPatch( image, dst, range, hist, method, factor ) \
+     cvCalcArrBackProjectPatch( (CvArr**)image, dst, range, hist, method, factor )
+
+
+/** @brief Divides one histogram by another.
+
+The function calculates the object probability density from two histograms as:
+
+\f[\texttt{disthist} (I)= \forkthree{0}{if \(\texttt{hist1}(I)=0\)}{\texttt{scale}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) > \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f]
+
+@param hist1 First histogram (the divisor).
+@param hist2 Second histogram.
+@param dst_hist Destination histogram.
+@param scale Scale factor for the destination histogram.
+ */
+CVAPI(void)  cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2,
+                                CvHistogram* dst_hist, double scale CV_DEFAULT(255) );
+
+/** @brief equalizes histogram of 8-bit single-channel image
+@see cv::equalizeHist
+*/
+CVAPI(void)  cvEqualizeHist( const CvArr* src, CvArr* dst );
+
+
+/** @brief Applies distance transform to binary image
+@see cv::distanceTransform
+*/
+CVAPI(void)  cvDistTransform( const CvArr* src, CvArr* dst,
+                              int distance_type CV_DEFAULT(CV_DIST_L2),
+                              int mask_size CV_DEFAULT(3),
+                              const float* mask CV_DEFAULT(NULL),
+                              CvArr* labels CV_DEFAULT(NULL),
+                              int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP));
+
+
+/** @brief Applies fixed-level threshold to grayscale image.
+
+   This is a basic operation applied before retrieving contours
+@see cv::threshold
+*/
+CVAPI(double)  cvThreshold( const CvArr*  src, CvArr*  dst,
+                            double  threshold, double  max_value,
+                            int threshold_type );
+
+/** @brief Applies adaptive threshold to grayscale image.
+
+   The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and
+   CV_ADAPTIVE_THRESH_GAUSSIAN_C are:
+   neighborhood size (3, 5, 7 etc.),
+   and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...)
+@see cv::adaptiveThreshold
+*/
+CVAPI(void)  cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value,
+                                  int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C),
+                                  int threshold_type CV_DEFAULT(CV_THRESH_BINARY),
+                                  int block_size CV_DEFAULT(3),
+                                  double param1 CV_DEFAULT(5));
+
+/** @brief Fills the connected component until the color difference gets large enough
+@see cv::floodFill
+*/
+CVAPI(void)  cvFloodFill( CvArr* image, CvPoint seed_point,
+                          CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)),
+                          CvScalar up_diff CV_DEFAULT(cvScalarAll(0)),
+                          CvConnectedComp* comp CV_DEFAULT(NULL),
+                          int flags CV_DEFAULT(4),
+                          CvArr* mask CV_DEFAULT(NULL));
+
+/****************************************************************************************\
+*                                  Feature detection                                     *
+\****************************************************************************************/
+
+/** @brief Runs canny edge detector
+@see cv::Canny
+*/
+CVAPI(void)  cvCanny( const CvArr* image, CvArr* edges, double threshold1,
+                      double threshold2, int  aperture_size CV_DEFAULT(3) );
+
+/** @brief Calculates constraint image for corner detection
+
+   Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy.
+   Applying threshold to the result gives coordinates of corners
+@see cv::preCornerDetect
+*/
+CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners,
+                               int aperture_size CV_DEFAULT(3) );
+
+/** @brief Calculates eigen values and vectors of 2x2
+   gradient covariation matrix at every image pixel
+@see cv::cornerEigenValsAndVecs
+*/
+CVAPI(void)  cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv,
+                                       int block_size, int aperture_size CV_DEFAULT(3) );
+
+/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at
+   every image pixel
+@see cv::cornerMinEigenVal
+*/
+CVAPI(void)  cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval,
+                                  int block_size, int aperture_size CV_DEFAULT(3) );
+
+/** @brief Harris corner detector:
+
+   Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel
+@see cv::cornerHarris
+*/
+CVAPI(void)  cvCornerHarris( const CvArr* image, CvArr* harris_response,
+                             int block_size, int aperture_size CV_DEFAULT(3),
+                             double k CV_DEFAULT(0.04) );
+
+/** @brief Adjust corner position using some sort of gradient search
+@see cv::cornerSubPix
+*/
+CVAPI(void)  cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners,
+                                 int count, CvSize win, CvSize zero_zone,
+                                 CvTermCriteria  criteria );
+
+/** @brief Finds a sparse set of points within the selected region
+   that seem to be easy to track
+@see cv::goodFeaturesToTrack
+*/
+CVAPI(void)  cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image,
+                                    CvArr* temp_image, CvPoint2D32f* corners,
+                                    int* corner_count, double  quality_level,
+                                    double  min_distance,
+                                    const CvArr* mask CV_DEFAULT(NULL),
+                                    int block_size CV_DEFAULT(3),
+                                    int use_harris CV_DEFAULT(0),
+                                    double k CV_DEFAULT(0.04) );
+
+/** @brief Finds lines on binary image using one of several methods.
+
+   line_storage is either memory storage or 1 x _max number of lines_ CvMat, its
+   number of columns is changed by the function.
+   method is one of CV_HOUGH_*;
+   rho, theta and threshold are used for each of those methods;
+   param1 ~ line length, param2 ~ line gap - for probabilistic,
+   param1 ~ srn, param2 ~ stn - for multi-scale
+@see cv::HoughLines
+*/
+CVAPI(CvSeq*)  cvHoughLines2( CvArr* image, void* line_storage, int method,
+                              double rho, double theta, int threshold,
+                              double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0),
+                              double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI));
+
+/** @brief Finds circles in the image
+@see cv::HoughCircles
+*/
+CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage,
+                              int method, double dp, double min_dist,
+                              double param1 CV_DEFAULT(100),
+                              double param2 CV_DEFAULT(100),
+                              int min_radius CV_DEFAULT(0),
+                              int max_radius CV_DEFAULT(0));
+
+/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique)
+@see cv::fitLine
+*/
+CVAPI(void)  cvFitLine( const CvArr* points, int dist_type, double param,
+                        double reps, double aeps, float* line );
+
+/****************************************************************************************\
+*                                     Drawing                                            *
+\****************************************************************************************/
+
+/****************************************************************************************\
+*       Drawing functions work with images/matrices of arbitrary type.                   *
+*       For color images the channel order is BGR[A]                                     *
+*       Antialiasing is supported only for 8-bit image now.                              *
+*       All the functions include parameter color that means rgb value (that may be      *
+*       constructed with CV_RGB macro) for color images and brightness                   *
+*       for grayscale images.                                                            *
+*       If a drawn figure is partially or completely outside of the image, it is clipped.*
+\****************************************************************************************/
+
+#define CV_FILLED -1
+
+#define CV_AA 16
+
+/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points
+@see cv::line
+*/
+CVAPI(void)  cvLine( CvArr* img, CvPoint pt1, CvPoint pt2,
+                     CvScalar color, int thickness CV_DEFAULT(1),
+                     int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
+
+/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2)
+
+   if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn
+@see cv::rectangle
+*/
+CVAPI(void)  cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2,
+                          CvScalar color, int thickness CV_DEFAULT(1),
+                          int line_type CV_DEFAULT(8),
+                          int shift CV_DEFAULT(0));
+
+/** @brief Draws a rectangle specified by a CvRect structure
+@see cv::rectangle
+*/
+CVAPI(void)  cvRectangleR( CvArr* img, CvRect r,
+                           CvScalar color, int thickness CV_DEFAULT(1),
+                           int line_type CV_DEFAULT(8),
+                           int shift CV_DEFAULT(0));
+
+
+/** @brief Draws a circle with specified center and radius.
+
+   Thickness works in the same way as with cvRectangle
+@see cv::circle
+*/
+CVAPI(void)  cvCircle( CvArr* img, CvPoint center, int radius,
+                       CvScalar color, int thickness CV_DEFAULT(1),
+                       int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
+
+/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector
+
+   depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure
+   is rotated by _angle_. All the angles are in degrees
+@see cv::ellipse
+*/
+CVAPI(void)  cvEllipse( CvArr* img, CvPoint center, CvSize axes,
+                        double angle, double start_angle, double end_angle,
+                        CvScalar color, int thickness CV_DEFAULT(1),
+                        int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
+
+CV_INLINE  void  cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color,
+                               int thickness CV_DEFAULT(1),
+                               int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) )
+{
+    CvSize axes = cvSize(
+        cvRound(box.size.width*0.5),
+        cvRound(box.size.height*0.5)
+    );
+
+    cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle,
+               0, 360, color, thickness, line_type, shift );
+}
+
+/** @brief Fills convex or monotonous polygon.
+@see cv::fillConvexPoly
+*/
+CVAPI(void)  cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color,
+                               int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
+
+/** @brief Fills an area bounded by one or more arbitrary polygons
+@see cv::fillPoly
+*/
+CVAPI(void)  cvFillPoly( CvArr* img, CvPoint** pts, const int* npts,
+                         int contours, CvScalar color,
+                         int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
+
+/** @brief Draws one or more polygonal curves
+@see cv::polylines
+*/
+CVAPI(void)  cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours,
+                         int is_closed, CvScalar color, int thickness CV_DEFAULT(1),
+                         int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
+
+#define cvDrawRect cvRectangle
+#define cvDrawLine cvLine
+#define cvDrawCircle cvCircle
+#define cvDrawEllipse cvEllipse
+#define cvDrawPolyLine cvPolyLine
+
+/** @brief Clips the line segment connecting *pt1 and *pt2
+   by the rectangular window
+
+   (0<=x<img_size.width, 0<=y<img_size.height).
+@see cv::clipLine
+*/
+CVAPI(int) cvClipLine( CvSize img_size, CvPoint* pt1, CvPoint* pt2 );
+
+/** @brief Initializes line iterator.
+
+Initially, line_iterator->ptr will point to pt1 (or pt2, see left_to_right description) location in
+the image. Returns the number of pixels on the line between the ending points.
+@see cv::LineIterator
+*/
+CVAPI(int)  cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2,
+                                CvLineIterator* line_iterator,
+                                int connectivity CV_DEFAULT(8),
+                                int left_to_right CV_DEFAULT(0));
+
+#define CV_NEXT_LINE_POINT( line_iterator )                     \
+{                                                               \
+    int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \
+    (line_iterator).err += (line_iterator).minus_delta +        \
+        ((line_iterator).plus_delta & _line_iterator_mask);     \
+    (line_iterator).ptr += (line_iterator).minus_step +         \
+        ((line_iterator).plus_step & _line_iterator_mask);      \
+}
+
+
+#define CV_FONT_HERSHEY_SIMPLEX         0
+#define CV_FONT_HERSHEY_PLAIN           1
+#define CV_FONT_HERSHEY_DUPLEX          2
+#define CV_FONT_HERSHEY_COMPLEX         3
+#define CV_FONT_HERSHEY_TRIPLEX         4
+#define CV_FONT_HERSHEY_COMPLEX_SMALL   5
+#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX  6
+#define CV_FONT_HERSHEY_SCRIPT_COMPLEX  7
+
+#define CV_FONT_ITALIC                 16
+
+#define CV_FONT_VECTOR0    CV_FONT_HERSHEY_SIMPLEX
+
+
+/** Font structure */
+typedef struct CvFont
+{
+  const char* nameFont;   //Qt:nameFont
+  CvScalar color;       //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component])
+    int         font_face;    //Qt: bool italic         /** =CV_FONT_* */
+    const int*  ascii;      //!< font data and metrics
+    const int*  greek;
+    const int*  cyrillic;
+    float       hscale, vscale;
+    float       shear;      //!< slope coefficient: 0 - normal, >0 - italic
+    int         thickness;    //!< Qt: weight               /** letters thickness */
+    float       dx;       //!< horizontal interval between letters
+    int         line_type;    //!< Qt: PointSize
+}
+CvFont;
+
+/** @brief Initializes font structure (OpenCV 1.x API).
+
+The function initializes the font structure that can be passed to text rendering functions.
+
+@param font Pointer to the font structure initialized by the function
+@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers.
+@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width
+depending on the font type. If equal to 0.5f , the characters are of half the original width.
+@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending
+on the font type. If equal to 0.5f , the characters are of half the original height.
+@param shear Approximate tangent of the character slope relative to the vertical line. A zero
+value means a non-italic font, 1.0f means about a 45 degree slope, etc.
+@param thickness Thickness of the text strokes
+@param line_type Type of the strokes, see line description
+
+@sa cvPutText
+ */
+CVAPI(void)  cvInitFont( CvFont* font, int font_face,
+                         double hscale, double vscale,
+                         double shear CV_DEFAULT(0),
+                         int thickness CV_DEFAULT(1),
+                         int line_type CV_DEFAULT(8));
+
+CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) )
+{
+    CvFont font;
+    cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA );
+    return font;
+}
+
+/** @brief Renders text stroke with specified font and color at specified location.
+   CvFont should be initialized with cvInitFont
+@see cvInitFont, cvGetTextSize, cvFont, cv::putText
+*/
+CVAPI(void)  cvPutText( CvArr* img, const char* text, CvPoint org,
+                        const CvFont* font, CvScalar color );
+
+/** @brief Calculates bounding box of text stroke (useful for alignment)
+@see cv::getTextSize
+*/
+CVAPI(void)  cvGetTextSize( const char* text_string, const CvFont* font,
+                            CvSize* text_size, int* baseline );
+
+/** @brief Unpacks color value
+
+if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels
+(depending on arrtype) of destination scalar are set to the same value = _color_
+*/
+CVAPI(CvScalar)  cvColorToScalar( double packed_color, int arrtype );
+
+/** @brief Returns the polygon points which make up the given ellipse.
+
+The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial
+sweep of the ellipse arc can be done by specifying arc_start and arc_end to be something other than
+0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total
+number of points stored into 'pts' is returned by this function.
+@see cv::ellipse2Poly
+*/
+CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes,
+                 int angle, int arc_start, int arc_end, CvPoint * pts, int delta );
+
+/** @brief Draws contour outlines or filled interiors on the image
+@see cv::drawContours
+*/
+CVAPI(void)  cvDrawContours( CvArr *img, CvSeq* contour,
+                             CvScalar external_color, CvScalar hole_color,
+                             int max_level, int thickness CV_DEFAULT(1),
+                             int line_type CV_DEFAULT(8),
+                             CvPoint offset CV_DEFAULT(cvPoint(0,0)));
+
+/** @} */
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/imgproc/segmentation.hpp b/3rdParty/include/opencv2/imgproc/segmentation.hpp
new file mode 100644
index 0000000..26882f4
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/segmentation.hpp
@@ -0,0 +1,141 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_IMGPROC_SEGMENTATION_HPP
+#define OPENCV_IMGPROC_SEGMENTATION_HPP
+
+#include "opencv2/imgproc.hpp"
+
+namespace cv {
+
+namespace segmentation {
+
+//! @addtogroup imgproc_segmentation
+//! @{
+
+
+/** @brief Intelligent Scissors image segmentation
+ *
+ * This class is used to find the path (contour) between two points
+ * which can be used for image segmentation.
+ *
+ * Usage example:
+ * @snippet snippets/imgproc_segmentation.cpp usage_example_intelligent_scissors
+ *
+ * Reference: <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.3811&rep=rep1&type=pdf">"Intelligent Scissors for Image Composition"</a>
+ * algorithm designed by Eric N. Mortensen and William A. Barrett, Brigham Young University
+ * @cite Mortensen95intelligentscissors
+ */
+class CV_EXPORTS_W_SIMPLE IntelligentScissorsMB
+{
+public:
+    CV_WRAP
+    IntelligentScissorsMB();
+
+    /** @brief Specify weights of feature functions
+     *
+     * Consider keeping weights normalized (sum of weights equals to 1.0)
+     * Discrete dynamic programming (DP) goal is minimization of costs between pixels.
+     *
+     * @param weight_non_edge Specify cost of non-edge pixels (default: 0.43f)
+     * @param weight_gradient_direction Specify cost of gradient direction function (default: 0.43f)
+     * @param weight_gradient_magnitude Specify cost of gradient magnitude function (default: 0.14f)
+     */
+    CV_WRAP
+    IntelligentScissorsMB& setWeights(float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude);
+
+    /** @brief Specify gradient magnitude max value threshold
+     *
+     * Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article).
+     * Otherwize pixels with `gradient magnitude >= threshold` have zero cost.
+     *
+     * @note Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos).
+     *
+     * @param gradient_magnitude_threshold_max Specify gradient magnitude max value threshold (default: 0, disabled)
+     */
+    CV_WRAP
+    IntelligentScissorsMB& setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max = 0.0f);
+
+    /** @brief Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters
+     *
+     * This feature extractor is used by default according to article.
+     *
+     * Implementation has additional filtering for regions with low-amplitude noise.
+     * This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16).
+     *
+     * @note Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first).
+     *
+     * @note Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters().
+     *
+     * @param gradient_magnitude_min_value Minimal gradient magnitude value for edge pixels (default: 0, check is disabled)
+     */
+    CV_WRAP
+    IntelligentScissorsMB& setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value = 0.0f);
+
+    /** @brief Switch edge feature extractor to use Canny edge detector
+     *
+     * @note "Laplacian Zero-Crossing" feature extractor is used by default (following to original article)
+     *
+     * @sa Canny
+     */
+    CV_WRAP
+    IntelligentScissorsMB& setEdgeFeatureCannyParameters(
+            double threshold1, double threshold2,
+            int apertureSize = 3, bool L2gradient = false
+    );
+
+    /** @brief Specify input image and extract image features
+     *
+     * @param image input image. Type is #CV_8UC1 / #CV_8UC3
+     */
+    CV_WRAP
+    IntelligentScissorsMB& applyImage(InputArray image);
+
+    /** @brief Specify custom features of imput image
+     *
+     * Customized advanced variant of applyImage() call.
+     *
+     * @param non_edge Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are `{0, 1}`.
+     * @param gradient_direction Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: `x^2 + y^2 == 1`
+     * @param gradient_magnitude Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range `[0, 1]`.
+     * @param image **Optional parameter**. Must be specified if subset of features is specified (non-specified features are calculated internally)
+     */
+    CV_WRAP
+    IntelligentScissorsMB& applyImageFeatures(
+            InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude,
+            InputArray image = noArray()
+    );
+
+    /** @brief Prepares a map of optimal paths for the given source point on the image
+     *
+     * @note applyImage() / applyImageFeatures() must be called before this call
+     *
+     * @param sourcePt The source point used to find the paths
+     */
+    CV_WRAP void buildMap(const Point& sourcePt);
+
+    /** @brief Extracts optimal contour for the given target point on the image
+     *
+     * @note buildMap() must be called before this call
+     *
+     * @param targetPt The target point
+     * @param[out] contour The list of pixels which contains optimal path between the source and the target points of the image. Type is CV_32SC2 (compatible with `std::vector<Point>`)
+     * @param backward Flag to indicate reverse order of retrived pixels (use "true" value to fetch points from the target to the source point)
+     */
+    CV_WRAP void getContour(const Point& targetPt, OutputArray contour, bool backward = false) const;
+
+#ifndef CV_DOXYGEN
+    struct Impl;
+    inline Impl* getImpl() const { return impl.get(); }
+protected:
+    std::shared_ptr<Impl> impl;
+#endif
+};
+
+//! @}
+
+}  // namespace segmentation
+}  // namespace cv
+
+#endif // OPENCV_IMGPROC_SEGMENTATION_HPP
diff --git a/3rdParty/include/opencv2/imgproc/types_c.h b/3rdParty/include/opencv2/imgproc/types_c.h
new file mode 100644
index 0000000..d3e55f5
--- /dev/null
+++ b/3rdParty/include/opencv2/imgproc/types_c.h
@@ -0,0 +1,659 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_IMGPROC_TYPES_C_H
+#define OPENCV_IMGPROC_TYPES_C_H
+
+#include "opencv2/core/core_c.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/** @addtogroup imgproc_c
+  @{
+*/
+
+/** Connected component structure */
+typedef struct CvConnectedComp
+{
+    double area;    /**<area of the connected component  */
+    CvScalar value; /**<average color of the connected component */
+    CvRect rect;    /**<ROI of the component  */
+    CvSeq* contour; /**<optional component boundary
+                      (the contour might have child contours corresponding to the holes)*/
+}
+CvConnectedComp;
+
+/** Image smooth methods */
+enum SmoothMethod_c
+{
+    /** linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all 1's). If
+    you want to smooth different pixels with different-size box kernels, you can use the integral
+    image that is computed using integral */
+    CV_BLUR_NO_SCALE =0,
+    /** linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all
+    1's) with subsequent scaling by \f$1/(\texttt{size1}\cdot\texttt{size2})\f$ */
+    CV_BLUR  =1,
+    /** linear convolution with a \f$\texttt{size1}\times\texttt{size2}\f$ Gaussian kernel */
+    CV_GAUSSIAN  =2,
+    /** median filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture */
+    CV_MEDIAN =3,
+    /** bilateral filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture, color
+    sigma= sigma1 and spatial sigma= sigma2. If size1=0, the aperture square side is set to
+    cvRound(sigma2\*1.5)\*2+1. See cv::bilateralFilter */
+    CV_BILATERAL =4
+};
+
+/** Filters used in pyramid decomposition */
+enum
+{
+    CV_GAUSSIAN_5x5 = 7
+};
+
+/** Special filters */
+enum
+{
+    CV_SCHARR =-1,
+    CV_MAX_SOBEL_KSIZE =7
+};
+
+/** Constants for color conversion */
+enum
+{
+    CV_BGR2BGRA    =0,
+    CV_RGB2RGBA    =CV_BGR2BGRA,
+
+    CV_BGRA2BGR    =1,
+    CV_RGBA2RGB    =CV_BGRA2BGR,
+
+    CV_BGR2RGBA    =2,
+    CV_RGB2BGRA    =CV_BGR2RGBA,
+
+    CV_RGBA2BGR    =3,
+    CV_BGRA2RGB    =CV_RGBA2BGR,
+
+    CV_BGR2RGB     =4,
+    CV_RGB2BGR     =CV_BGR2RGB,
+
+    CV_BGRA2RGBA   =5,
+    CV_RGBA2BGRA   =CV_BGRA2RGBA,
+
+    CV_BGR2GRAY    =6,
+    CV_RGB2GRAY    =7,
+    CV_GRAY2BGR    =8,
+    CV_GRAY2RGB    =CV_GRAY2BGR,
+    CV_GRAY2BGRA   =9,
+    CV_GRAY2RGBA   =CV_GRAY2BGRA,
+    CV_BGRA2GRAY   =10,
+    CV_RGBA2GRAY   =11,
+
+    CV_BGR2BGR565  =12,
+    CV_RGB2BGR565  =13,
+    CV_BGR5652BGR  =14,
+    CV_BGR5652RGB  =15,
+    CV_BGRA2BGR565 =16,
+    CV_RGBA2BGR565 =17,
+    CV_BGR5652BGRA =18,
+    CV_BGR5652RGBA =19,
+
+    CV_GRAY2BGR565 =20,
+    CV_BGR5652GRAY =21,
+
+    CV_BGR2BGR555  =22,
+    CV_RGB2BGR555  =23,
+    CV_BGR5552BGR  =24,
+    CV_BGR5552RGB  =25,
+    CV_BGRA2BGR555 =26,
+    CV_RGBA2BGR555 =27,
+    CV_BGR5552BGRA =28,
+    CV_BGR5552RGBA =29,
+
+    CV_GRAY2BGR555 =30,
+    CV_BGR5552GRAY =31,
+
+    CV_BGR2XYZ     =32,
+    CV_RGB2XYZ     =33,
+    CV_XYZ2BGR     =34,
+    CV_XYZ2RGB     =35,
+
+    CV_BGR2YCrCb   =36,
+    CV_RGB2YCrCb   =37,
+    CV_YCrCb2BGR   =38,
+    CV_YCrCb2RGB   =39,
+
+    CV_BGR2HSV     =40,
+    CV_RGB2HSV     =41,
+
+    CV_BGR2Lab     =44,
+    CV_RGB2Lab     =45,
+
+    CV_BayerBG2BGR =46,
+    CV_BayerGB2BGR =47,
+    CV_BayerRG2BGR =48,
+    CV_BayerGR2BGR =49,
+
+    CV_BayerBG2RGB =CV_BayerRG2BGR,
+    CV_BayerGB2RGB =CV_BayerGR2BGR,
+    CV_BayerRG2RGB =CV_BayerBG2BGR,
+    CV_BayerGR2RGB =CV_BayerGB2BGR,
+
+    CV_BGR2Luv     =50,
+    CV_RGB2Luv     =51,
+    CV_BGR2HLS     =52,
+    CV_RGB2HLS     =53,
+
+    CV_HSV2BGR     =54,
+    CV_HSV2RGB     =55,
+
+    CV_Lab2BGR     =56,
+    CV_Lab2RGB     =57,
+    CV_Luv2BGR     =58,
+    CV_Luv2RGB     =59,
+    CV_HLS2BGR     =60,
+    CV_HLS2RGB     =61,
+
+    CV_BayerBG2BGR_VNG =62,
+    CV_BayerGB2BGR_VNG =63,
+    CV_BayerRG2BGR_VNG =64,
+    CV_BayerGR2BGR_VNG =65,
+
+    CV_BayerBG2RGB_VNG =CV_BayerRG2BGR_VNG,
+    CV_BayerGB2RGB_VNG =CV_BayerGR2BGR_VNG,
+    CV_BayerRG2RGB_VNG =CV_BayerBG2BGR_VNG,
+    CV_BayerGR2RGB_VNG =CV_BayerGB2BGR_VNG,
+
+    CV_BGR2HSV_FULL = 66,
+    CV_RGB2HSV_FULL = 67,
+    CV_BGR2HLS_FULL = 68,
+    CV_RGB2HLS_FULL = 69,
+
+    CV_HSV2BGR_FULL = 70,
+    CV_HSV2RGB_FULL = 71,
+    CV_HLS2BGR_FULL = 72,
+    CV_HLS2RGB_FULL = 73,
+
+    CV_LBGR2Lab     = 74,
+    CV_LRGB2Lab     = 75,
+    CV_LBGR2Luv     = 76,
+    CV_LRGB2Luv     = 77,
+
+    CV_Lab2LBGR     = 78,
+    CV_Lab2LRGB     = 79,
+    CV_Luv2LBGR     = 80,
+    CV_Luv2LRGB     = 81,
+
+    CV_BGR2YUV      = 82,
+    CV_RGB2YUV      = 83,
+    CV_YUV2BGR      = 84,
+    CV_YUV2RGB      = 85,
+
+    CV_BayerBG2GRAY = 86,
+    CV_BayerGB2GRAY = 87,
+    CV_BayerRG2GRAY = 88,
+    CV_BayerGR2GRAY = 89,
+
+    //YUV 4:2:0 formats family
+    CV_YUV2RGB_NV12 = 90,
+    CV_YUV2BGR_NV12 = 91,
+    CV_YUV2RGB_NV21 = 92,
+    CV_YUV2BGR_NV21 = 93,
+    CV_YUV420sp2RGB = CV_YUV2RGB_NV21,
+    CV_YUV420sp2BGR = CV_YUV2BGR_NV21,
+
+    CV_YUV2RGBA_NV12 = 94,
+    CV_YUV2BGRA_NV12 = 95,
+    CV_YUV2RGBA_NV21 = 96,
+    CV_YUV2BGRA_NV21 = 97,
+    CV_YUV420sp2RGBA = CV_YUV2RGBA_NV21,
+    CV_YUV420sp2BGRA = CV_YUV2BGRA_NV21,
+
+    CV_YUV2RGB_YV12 = 98,
+    CV_YUV2BGR_YV12 = 99,
+    CV_YUV2RGB_IYUV = 100,
+    CV_YUV2BGR_IYUV = 101,
+    CV_YUV2RGB_I420 = CV_YUV2RGB_IYUV,
+    CV_YUV2BGR_I420 = CV_YUV2BGR_IYUV,
+    CV_YUV420p2RGB = CV_YUV2RGB_YV12,
+    CV_YUV420p2BGR = CV_YUV2BGR_YV12,
+
+    CV_YUV2RGBA_YV12 = 102,
+    CV_YUV2BGRA_YV12 = 103,
+    CV_YUV2RGBA_IYUV = 104,
+    CV_YUV2BGRA_IYUV = 105,
+    CV_YUV2RGBA_I420 = CV_YUV2RGBA_IYUV,
+    CV_YUV2BGRA_I420 = CV_YUV2BGRA_IYUV,
+    CV_YUV420p2RGBA = CV_YUV2RGBA_YV12,
+    CV_YUV420p2BGRA = CV_YUV2BGRA_YV12,
+
+    CV_YUV2GRAY_420 = 106,
+    CV_YUV2GRAY_NV21 = CV_YUV2GRAY_420,
+    CV_YUV2GRAY_NV12 = CV_YUV2GRAY_420,
+    CV_YUV2GRAY_YV12 = CV_YUV2GRAY_420,
+    CV_YUV2GRAY_IYUV = CV_YUV2GRAY_420,
+    CV_YUV2GRAY_I420 = CV_YUV2GRAY_420,
+    CV_YUV420sp2GRAY = CV_YUV2GRAY_420,
+    CV_YUV420p2GRAY = CV_YUV2GRAY_420,
+
+    //YUV 4:2:2 formats family
+    CV_YUV2RGB_UYVY = 107,
+    CV_YUV2BGR_UYVY = 108,
+    //CV_YUV2RGB_VYUY = 109,
+    //CV_YUV2BGR_VYUY = 110,
+    CV_YUV2RGB_Y422 = CV_YUV2RGB_UYVY,
+    CV_YUV2BGR_Y422 = CV_YUV2BGR_UYVY,
+    CV_YUV2RGB_UYNV = CV_YUV2RGB_UYVY,
+    CV_YUV2BGR_UYNV = CV_YUV2BGR_UYVY,
+
+    CV_YUV2RGBA_UYVY = 111,
+    CV_YUV2BGRA_UYVY = 112,
+    //CV_YUV2RGBA_VYUY = 113,
+    //CV_YUV2BGRA_VYUY = 114,
+    CV_YUV2RGBA_Y422 = CV_YUV2RGBA_UYVY,
+    CV_YUV2BGRA_Y422 = CV_YUV2BGRA_UYVY,
+    CV_YUV2RGBA_UYNV = CV_YUV2RGBA_UYVY,
+    CV_YUV2BGRA_UYNV = CV_YUV2BGRA_UYVY,
+
+    CV_YUV2RGB_YUY2 = 115,
+    CV_YUV2BGR_YUY2 = 116,
+    CV_YUV2RGB_YVYU = 117,
+    CV_YUV2BGR_YVYU = 118,
+    CV_YUV2RGB_YUYV = CV_YUV2RGB_YUY2,
+    CV_YUV2BGR_YUYV = CV_YUV2BGR_YUY2,
+    CV_YUV2RGB_YUNV = CV_YUV2RGB_YUY2,
+    CV_YUV2BGR_YUNV = CV_YUV2BGR_YUY2,
+
+    CV_YUV2RGBA_YUY2 = 119,
+    CV_YUV2BGRA_YUY2 = 120,
+    CV_YUV2RGBA_YVYU = 121,
+    CV_YUV2BGRA_YVYU = 122,
+    CV_YUV2RGBA_YUYV = CV_YUV2RGBA_YUY2,
+    CV_YUV2BGRA_YUYV = CV_YUV2BGRA_YUY2,
+    CV_YUV2RGBA_YUNV = CV_YUV2RGBA_YUY2,
+    CV_YUV2BGRA_YUNV = CV_YUV2BGRA_YUY2,
+
+    CV_YUV2GRAY_UYVY = 123,
+    CV_YUV2GRAY_YUY2 = 124,
+    //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
+    CV_YUV2GRAY_Y422 = CV_YUV2GRAY_UYVY,
+    CV_YUV2GRAY_UYNV = CV_YUV2GRAY_UYVY,
+    CV_YUV2GRAY_YVYU = CV_YUV2GRAY_YUY2,
+    CV_YUV2GRAY_YUYV = CV_YUV2GRAY_YUY2,
+    CV_YUV2GRAY_YUNV = CV_YUV2GRAY_YUY2,
+
+    // alpha premultiplication
+    CV_RGBA2mRGBA = 125,
+    CV_mRGBA2RGBA = 126,
+
+    CV_RGB2YUV_I420 = 127,
+    CV_BGR2YUV_I420 = 128,
+    CV_RGB2YUV_IYUV = CV_RGB2YUV_I420,
+    CV_BGR2YUV_IYUV = CV_BGR2YUV_I420,
+
+    CV_RGBA2YUV_I420 = 129,
+    CV_BGRA2YUV_I420 = 130,
+    CV_RGBA2YUV_IYUV = CV_RGBA2YUV_I420,
+    CV_BGRA2YUV_IYUV = CV_BGRA2YUV_I420,
+    CV_RGB2YUV_YV12  = 131,
+    CV_BGR2YUV_YV12  = 132,
+    CV_RGBA2YUV_YV12 = 133,
+    CV_BGRA2YUV_YV12 = 134,
+
+    // Edge-Aware Demosaicing
+    CV_BayerBG2BGR_EA = 135,
+    CV_BayerGB2BGR_EA = 136,
+    CV_BayerRG2BGR_EA = 137,
+    CV_BayerGR2BGR_EA = 138,
+
+    CV_BayerBG2RGB_EA = CV_BayerRG2BGR_EA,
+    CV_BayerGB2RGB_EA = CV_BayerGR2BGR_EA,
+    CV_BayerRG2RGB_EA = CV_BayerBG2BGR_EA,
+    CV_BayerGR2RGB_EA = CV_BayerGB2BGR_EA,
+
+    CV_BayerBG2BGRA =139,
+    CV_BayerGB2BGRA =140,
+    CV_BayerRG2BGRA =141,
+    CV_BayerGR2BGRA =142,
+
+    CV_BayerBG2RGBA =CV_BayerRG2BGRA,
+    CV_BayerGB2RGBA =CV_BayerGR2BGRA,
+    CV_BayerRG2RGBA =CV_BayerBG2BGRA,
+    CV_BayerGR2RGBA =CV_BayerGB2BGRA,
+
+    CV_COLORCVT_MAX  = 143
+};
+
+
+/** Sub-pixel interpolation methods */
+enum
+{
+    CV_INTER_NN        =0,
+    CV_INTER_LINEAR    =1,
+    CV_INTER_CUBIC     =2,
+    CV_INTER_AREA      =3,
+    CV_INTER_LANCZOS4  =4
+};
+
+/** ... and other image warping flags */
+enum
+{
+    CV_WARP_FILL_OUTLIERS =8,
+    CV_WARP_INVERSE_MAP  =16
+};
+
+/** Shapes of a structuring element for morphological operations
+@see cv::MorphShapes, cv::getStructuringElement
+*/
+enum MorphShapes_c
+{
+    CV_SHAPE_RECT      =0,
+    CV_SHAPE_CROSS     =1,
+    CV_SHAPE_ELLIPSE   =2,
+    CV_SHAPE_CUSTOM    =100 //!< custom structuring element
+};
+
+/** Morphological operations */
+enum
+{
+    CV_MOP_ERODE        =0,
+    CV_MOP_DILATE       =1,
+    CV_MOP_OPEN         =2,
+    CV_MOP_CLOSE        =3,
+    CV_MOP_GRADIENT     =4,
+    CV_MOP_TOPHAT       =5,
+    CV_MOP_BLACKHAT     =6
+};
+
+/** Spatial and central moments */
+typedef struct CvMoments
+{
+    double  m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; /**< spatial moments */
+    double  mu20, mu11, mu02, mu30, mu21, mu12, mu03; /**< central moments */
+    double  inv_sqrt_m00; /**< m00 != 0 ? 1/sqrt(m00) : 0 */
+
+#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)
+    CvMoments(){}
+    CvMoments(const cv::Moments& m)
+    {
+        m00 = m.m00; m10 = m.m10; m01 = m.m01;
+        m20 = m.m20; m11 = m.m11; m02 = m.m02;
+        m30 = m.m30; m21 = m.m21; m12 = m.m12; m03 = m.m03;
+        mu20 = m.mu20; mu11 = m.mu11; mu02 = m.mu02;
+        mu30 = m.mu30; mu21 = m.mu21; mu12 = m.mu12; mu03 = m.mu03;
+        double am00 = std::abs(m.m00);
+        inv_sqrt_m00 = am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0;
+    }
+    operator cv::Moments() const
+    {
+        return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03);
+    }
+#endif
+}
+CvMoments;
+
+#ifdef __cplusplus
+} // extern "C"
+
+CV_INLINE CvMoments cvMoments()
+{
+#if !defined(CV__ENABLE_C_API_CTORS)
+    CvMoments self = CV_STRUCT_INITIALIZER; return self;
+#else
+    return CvMoments();
+#endif
+}
+
+CV_INLINE CvMoments cvMoments(const cv::Moments& m)
+{
+#if !defined(CV__ENABLE_C_API_CTORS)
+    double am00 = std::abs(m.m00);
+    CvMoments self = {
+        m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03,
+        m.mu20, m.mu11, m.mu02, m.mu30, m.mu21, m.mu12, m.mu03,
+        am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0
+    };
+    return self;
+#else
+    return CvMoments(m);
+#endif
+}
+
+extern "C" {
+#endif // __cplusplus
+
+/** Hu invariants */
+typedef struct CvHuMoments
+{
+    double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */
+}
+CvHuMoments;
+
+/** Template matching methods */
+enum
+{
+    CV_TM_SQDIFF        =0,
+    CV_TM_SQDIFF_NORMED =1,
+    CV_TM_CCORR         =2,
+    CV_TM_CCORR_NORMED  =3,
+    CV_TM_CCOEFF        =4,
+    CV_TM_CCOEFF_NORMED =5
+};
+
+typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param );
+
+/** Contour retrieval modes */
+enum
+{
+    CV_RETR_EXTERNAL=0,
+    CV_RETR_LIST=1,
+    CV_RETR_CCOMP=2,
+    CV_RETR_TREE=3,
+    CV_RETR_FLOODFILL=4
+};
+
+/** Contour approximation methods */
+enum
+{
+    CV_CHAIN_CODE=0,
+    CV_CHAIN_APPROX_NONE=1,
+    CV_CHAIN_APPROX_SIMPLE=2,
+    CV_CHAIN_APPROX_TC89_L1=3,
+    CV_CHAIN_APPROX_TC89_KCOS=4,
+    CV_LINK_RUNS=5
+};
+
+/*
+Internal structure that is used for sequential retrieving contours from the image.
+It supports both hierarchical and plane variants of Suzuki algorithm.
+*/
+typedef struct _CvContourScanner* CvContourScanner;
+
+/** Freeman chain reader state */
+typedef struct CvChainPtReader
+{
+    CV_SEQ_READER_FIELDS()
+    char      code;
+    CvPoint   pt;
+    schar     deltas[8][2];
+}
+CvChainPtReader;
+
+/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */
+#define  CV_INIT_3X3_DELTAS( deltas, step, nch )            \
+    ((deltas)[0] =  (nch),  (deltas)[1] = -(step) + (nch),  \
+     (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch),  \
+     (deltas)[4] = -(nch),  (deltas)[5] =  (step) - (nch),  \
+     (deltas)[6] =  (step), (deltas)[7] =  (step) + (nch))
+
+
+/** Contour approximation algorithms */
+enum
+{
+    CV_POLY_APPROX_DP = 0
+};
+
+/** Shape matching methods */
+enum
+{
+    CV_CONTOURS_MATCH_I1  =1, //!< \f[I_1(A,B) =  \sum _{i=1...7}  \left |  \frac{1}{m^A_i} -  \frac{1}{m^B_i} \right |\f]
+    CV_CONTOURS_MATCH_I2  =2, //!< \f[I_2(A,B) =  \sum _{i=1...7}  \left | m^A_i - m^B_i  \right |\f]
+    CV_CONTOURS_MATCH_I3  =3  //!< \f[I_3(A,B) =  \max _{i=1...7}  \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f]
+};
+
+/** Shape orientation */
+enum
+{
+    CV_CLOCKWISE         =1,
+    CV_COUNTER_CLOCKWISE =2
+};
+
+
+/** Convexity defect */
+typedef struct CvConvexityDefect
+{
+    CvPoint* start; /**< point of the contour where the defect begins */
+    CvPoint* end; /**< point of the contour where the defect ends */
+    CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */
+    float depth; /**< distance between the farthest point and the convex hull */
+} CvConvexityDefect;
+
+
+/** Histogram comparison methods */
+enum
+{
+    CV_COMP_CORREL        =0,
+    CV_COMP_CHISQR        =1,
+    CV_COMP_INTERSECT     =2,
+    CV_COMP_BHATTACHARYYA =3,
+    CV_COMP_HELLINGER     =CV_COMP_BHATTACHARYYA,
+    CV_COMP_CHISQR_ALT    =4,
+    CV_COMP_KL_DIV        =5
+};
+
+/** Mask size for distance transform */
+enum
+{
+    CV_DIST_MASK_3   =3,
+    CV_DIST_MASK_5   =5,
+    CV_DIST_MASK_PRECISE =0
+};
+
+/** Content of output label array: connected components or pixels */
+enum
+{
+  CV_DIST_LABEL_CCOMP = 0,
+  CV_DIST_LABEL_PIXEL = 1
+};
+
+/** Distance types for Distance Transform and M-estimators */
+enum
+{
+    CV_DIST_USER    =-1,  /**< User defined distance */
+    CV_DIST_L1      =1,   /**< distance = |x1-x2| + |y1-y2| */
+    CV_DIST_L2      =2,   /**< the simple euclidean distance */
+    CV_DIST_C       =3,   /**< distance = max(|x1-x2|,|y1-y2|) */
+    CV_DIST_L12     =4,   /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */
+    CV_DIST_FAIR    =5,   /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */
+    CV_DIST_WELSCH  =6,   /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */
+    CV_DIST_HUBER   =7    /**< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345 */
+};
+
+
+/** Threshold types */
+enum
+{
+    CV_THRESH_BINARY      =0,  /**< value = value > threshold ? max_value : 0       */
+    CV_THRESH_BINARY_INV  =1,  /**< value = value > threshold ? 0 : max_value       */
+    CV_THRESH_TRUNC       =2,  /**< value = value > threshold ? threshold : value   */
+    CV_THRESH_TOZERO      =3,  /**< value = value > threshold ? value : 0           */
+    CV_THRESH_TOZERO_INV  =4,  /**< value = value > threshold ? 0 : value           */
+    CV_THRESH_MASK        =7,
+    CV_THRESH_OTSU        =8, /**< use Otsu algorithm to choose the optimal threshold value;
+                                 combine the flag with one of the above CV_THRESH_* values */
+    CV_THRESH_TRIANGLE    =16  /**< use Triangle algorithm to choose the optimal threshold value;
+                                 combine the flag with one of the above CV_THRESH_* values, but not
+                                 with CV_THRESH_OTSU */
+};
+
+/** Adaptive threshold methods */
+enum
+{
+    CV_ADAPTIVE_THRESH_MEAN_C  =0,
+    CV_ADAPTIVE_THRESH_GAUSSIAN_C  =1
+};
+
+/** FloodFill flags */
+enum
+{
+    CV_FLOODFILL_FIXED_RANGE =(1 << 16),
+    CV_FLOODFILL_MASK_ONLY   =(1 << 17)
+};
+
+
+/** Canny edge detector flags */
+enum
+{
+    CV_CANNY_L2_GRADIENT  =(1 << 31)
+};
+
+/** Variants of a Hough transform */
+enum
+{
+    CV_HOUGH_STANDARD =0,
+    CV_HOUGH_PROBABILISTIC =1,
+    CV_HOUGH_MULTI_SCALE =2,
+    CV_HOUGH_GRADIENT =3
+};
+
+
+/* Fast search data structures  */
+struct CvFeatureTree;
+struct CvLSH;
+struct CvLSHOperations;
+
+/** @} */
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/ml.hpp b/3rdParty/include/opencv2/ml.hpp
new file mode 100644
index 0000000..d537ab7
--- /dev/null
+++ b/3rdParty/include/opencv2/ml.hpp
@@ -0,0 +1,1956 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2014, Itseez Inc, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_ML_HPP
+#define OPENCV_ML_HPP
+
+#ifdef __cplusplus
+#  include "opencv2/core.hpp"
+#endif
+
+#ifdef __cplusplus
+
+#include <float.h>
+#include <map>
+#include <iostream>
+
+/**
+  @defgroup ml Machine Learning
+
+  The Machine Learning Library (MLL) is a set of classes and functions for statistical
+  classification, regression, and clustering of data.
+
+  Most of the classification and regression algorithms are implemented as C++ classes. As the
+  algorithms have different sets of features (like an ability to handle missing measurements or
+  categorical input variables), there is a little common ground between the classes. This common
+  ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from.
+
+  See detailed overview here: @ref ml_intro.
+ */
+
+namespace cv
+{
+
+namespace ml
+{
+
+//! @addtogroup ml
+//! @{
+
+/** @brief Variable types */
+enum VariableTypes
+{
+    VAR_NUMERICAL    =0, //!< same as VAR_ORDERED
+    VAR_ORDERED      =0, //!< ordered variables
+    VAR_CATEGORICAL  =1  //!< categorical variables
+};
+
+/** @brief %Error types */
+enum ErrorTypes
+{
+    TEST_ERROR = 0,
+    TRAIN_ERROR = 1
+};
+
+/** @brief Sample types */
+enum SampleTypes
+{
+    ROW_SAMPLE = 0, //!< each training sample is a row of samples
+    COL_SAMPLE = 1  //!< each training sample occupies a column of samples
+};
+
+/** @brief The structure represents the logarithmic grid range of statmodel parameters.
+
+It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
+being computed by cross-validation.
+ */
+class CV_EXPORTS_W ParamGrid
+{
+public:
+    /** @brief Default constructor */
+    ParamGrid();
+    /** @brief Constructor with parameters */
+    ParamGrid(double _minVal, double _maxVal, double _logStep);
+
+    CV_PROP_RW double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
+    CV_PROP_RW double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
+    /** @brief Logarithmic step for iterating the statmodel parameter.
+
+    The grid determines the following iteration sequence of the statmodel parameter values:
+    \f[(minVal, minVal*step, minVal*{step}^2, \dots,  minVal*{logStep}^n),\f]
+    where \f$n\f$ is the maximal index satisfying
+    \f[\texttt{minVal} * \texttt{logStep} ^n <  \texttt{maxVal}\f]
+    The grid is logarithmic, so logStep must always be greater than 1. Default value is 1.
+    */
+    CV_PROP_RW double logStep;
+
+    /** @brief Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method
+
+    @param minVal minimum value of the parameter grid
+    @param maxVal maximum value of the parameter grid
+    @param logstep Logarithmic step for iterating the statmodel parameter
+    */
+    CV_WRAP static Ptr<ParamGrid> create(double minVal=0., double maxVal=0., double logstep=1.);
+};
+
+/** @brief Class encapsulating training data.
+
+Please note that the class only specifies the interface of training data, but not implementation.
+All the statistical model classes in _ml_ module accepts Ptr\<TrainData\> as parameter. In other
+words, you can create your own class derived from TrainData and pass smart pointer to the instance
+of this class into StatModel::train.
+
+@sa @ref ml_intro_data
+ */
+class CV_EXPORTS_W TrainData
+{
+public:
+    static inline float missingValue() { return FLT_MAX; }
+    virtual ~TrainData();
+
+    CV_WRAP virtual int getLayout() const = 0;
+    CV_WRAP virtual int getNTrainSamples() const = 0;
+    CV_WRAP virtual int getNTestSamples() const = 0;
+    CV_WRAP virtual int getNSamples() const = 0;
+    CV_WRAP virtual int getNVars() const = 0;
+    CV_WRAP virtual int getNAllVars() const = 0;
+
+    CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
+    CV_WRAP virtual Mat getSamples() const = 0;
+    CV_WRAP virtual Mat getMissing() const = 0;
+
+    /** @brief Returns matrix of train samples
+
+    @param layout The requested layout. If it's different from the initial one, the matrix is
+        transposed. See ml::SampleTypes.
+    @param compressSamples if true, the function returns only the training samples (specified by
+        sampleIdx)
+    @param compressVars if true, the function returns the shorter training samples, containing only
+        the active variables.
+
+    In current implementation the function tries to avoid physical data copying and returns the
+    matrix stored inside TrainData (unless the transposition or compression is needed).
+     */
+    CV_WRAP virtual Mat getTrainSamples(int layout=ROW_SAMPLE,
+                                bool compressSamples=true,
+                                bool compressVars=true) const = 0;
+
+    /** @brief Returns the vector of responses
+
+    The function returns ordered or the original categorical responses. Usually it's used in
+    regression algorithms.
+     */
+    CV_WRAP virtual Mat getTrainResponses() const = 0;
+
+    /** @brief Returns the vector of normalized categorical responses
+
+    The function returns vector of responses. Each response is integer from `0` to `<number of
+    classes>-1`. The actual label value can be retrieved then from the class label vector, see
+    TrainData::getClassLabels.
+     */
+    CV_WRAP virtual Mat getTrainNormCatResponses() const = 0;
+    CV_WRAP virtual Mat getTestResponses() const = 0;
+    CV_WRAP virtual Mat getTestNormCatResponses() const = 0;
+    CV_WRAP virtual Mat getResponses() const = 0;
+    CV_WRAP virtual Mat getNormCatResponses() const = 0;
+    CV_WRAP virtual Mat getSampleWeights() const = 0;
+    CV_WRAP virtual Mat getTrainSampleWeights() const = 0;
+    CV_WRAP virtual Mat getTestSampleWeights() const = 0;
+    CV_WRAP virtual Mat getVarIdx() const = 0;
+    CV_WRAP virtual Mat getVarType() const = 0;
+    CV_WRAP virtual Mat getVarSymbolFlags() const = 0;
+    CV_WRAP virtual int getResponseType() const = 0;
+    CV_WRAP virtual Mat getTrainSampleIdx() const = 0;
+    CV_WRAP virtual Mat getTestSampleIdx() const = 0;
+    CV_WRAP virtual void getValues(int vi, InputArray sidx, float* values) const = 0;
+    virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0;
+    CV_WRAP virtual Mat getDefaultSubstValues() const = 0;
+
+    CV_WRAP virtual int getCatCount(int vi) const = 0;
+
+    /** @brief Returns the vector of class labels
+
+    The function returns vector of unique labels occurred in the responses.
+     */
+    CV_WRAP virtual Mat getClassLabels() const = 0;
+
+    CV_WRAP virtual Mat getCatOfs() const = 0;
+    CV_WRAP virtual Mat getCatMap() const = 0;
+
+    /** @brief Splits the training data into the training and test parts
+    @sa TrainData::setTrainTestSplitRatio
+     */
+    CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
+
+    /** @brief Splits the training data into the training and test parts
+
+    The function selects a subset of specified relative size and then returns it as the training
+    set. If the function is not called, all the data is used for training. Please, note that for
+    each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test
+    subset can be retrieved and processed as well.
+    @sa TrainData::setTrainTestSplit
+     */
+    CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
+    CV_WRAP virtual void shuffleTrainTest() = 0;
+
+    /** @brief Returns matrix of test samples */
+    CV_WRAP virtual Mat getTestSamples() const = 0;
+
+    /** @brief Returns vector of symbolic names captured in loadFromCSV() */
+    CV_WRAP virtual void getNames(std::vector<String>& names) const = 0;
+
+    /** @brief Extract from 1D vector elements specified by passed indexes.
+    @param vec input vector (supported types: CV_32S, CV_32F, CV_64F)
+    @param idx 1D index vector
+     */
+    static CV_WRAP Mat getSubVector(const Mat& vec, const Mat& idx);
+
+    /** @brief Extract from matrix rows/cols specified by passed indexes.
+    @param matrix input matrix (supported types: CV_32S, CV_32F, CV_64F)
+    @param idx 1D index vector
+    @param layout specifies to extract rows (cv::ml::ROW_SAMPLES) or to extract columns (cv::ml::COL_SAMPLES)
+     */
+    static CV_WRAP Mat getSubMatrix(const Mat& matrix, const Mat& idx, int layout);
+
+    /** @brief Reads the dataset from a .csv file and returns the ready-to-use training data.
+
+    @param filename The input file name
+    @param headerLineCount The number of lines in the beginning to skip; besides the header, the
+        function also skips empty lines and lines staring with `#`
+    @param responseStartIdx Index of the first output variable. If -1, the function considers the
+        last variable as the response
+    @param responseEndIdx Index of the last output variable + 1. If -1, then there is single
+        response variable at responseStartIdx.
+    @param varTypeSpec The optional text string that specifies the variables' types. It has the
+        format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2`
+        (inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are
+        considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]`
+        should cover all the variables. If varTypeSpec is not specified, then algorithm uses the
+        following rules:
+        - all input variables are considered ordered by default. If some column contains has non-
+          numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding
+          variable is considered categorical.
+        - if there are several output variables, they are all considered as ordered. Error is
+          reported when non-numerical values are used.
+        - if there is a single output variable, then if its values are non-numerical or are all
+          integers, then it's considered categorical. Otherwise, it's considered ordered.
+    @param delimiter The character used to separate values in each line.
+    @param missch The character used to specify missing measurements. It should not be a digit.
+        Although it's a non-numerical value, it surely does not affect the decision of whether the
+        variable ordered or categorical.
+    @note If the dataset only contains input variables and no responses, use responseStartIdx = -2
+        and responseEndIdx = 0. The output variables vector will just contain zeros.
+     */
+    static Ptr<TrainData> loadFromCSV(const String& filename,
+                                      int headerLineCount,
+                                      int responseStartIdx=-1,
+                                      int responseEndIdx=-1,
+                                      const String& varTypeSpec=String(),
+                                      char delimiter=',',
+                                      char missch='?');
+
+    /** @brief Creates training data from in-memory arrays.
+
+    @param samples matrix of samples. It should have CV_32F type.
+    @param layout see ml::SampleTypes.
+    @param responses matrix of responses. If the responses are scalar, they should be stored as a
+        single row or as a single column. The matrix should have type CV_32F or CV_32S (in the
+        former case the responses are considered as ordered by default; in the latter case - as
+        categorical)
+    @param varIdx vector specifying which variables to use for training. It can be an integer vector
+        (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of
+        active variables.
+    @param sampleIdx vector specifying which samples to use for training. It can be an integer
+        vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask
+        of training samples.
+    @param sampleWeights optional vector with weights for each sample. It should have CV_32F type.
+    @param varType optional vector of type CV_8U and size `<number_of_variables_in_samples> +
+        <number_of_variables_in_responses>`, containing types of each input and output variable. See
+        ml::VariableTypes.
+     */
+    CV_WRAP static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
+                                 InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
+                                 InputArray sampleWeights=noArray(), InputArray varType=noArray());
+};
+
+/** @brief Base class for statistical models in OpenCV ML.
+ */
+class CV_EXPORTS_W StatModel : public Algorithm
+{
+public:
+    /** Predict options */
+    enum Flags {
+        UPDATE_MODEL = 1,
+        RAW_OUTPUT=1, //!< makes the method return the raw results (the sum), not the class label
+        COMPRESSED_INPUT=2,
+        PREPROCESSED_INPUT=4
+    };
+
+    /** @brief Returns the number of variables in training samples */
+    CV_WRAP virtual int getVarCount() const = 0;
+
+    CV_WRAP virtual bool empty() const CV_OVERRIDE;
+
+    /** @brief Returns true if the model is trained */
+    CV_WRAP virtual bool isTrained() const = 0;
+    /** @brief Returns true if the model is classifier */
+    CV_WRAP virtual bool isClassifier() const = 0;
+
+    /** @brief Trains the statistical model
+
+    @param trainData training data that can be loaded from file using TrainData::loadFromCSV or
+        created with TrainData::create.
+    @param flags optional flags, depending on the model. Some of the models can be updated with the
+        new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
+     */
+    CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
+
+    /** @brief Trains the statistical model
+
+    @param samples training samples
+    @param layout See ml::SampleTypes.
+    @param responses vector of responses associated with the training samples.
+    */
+    CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses );
+
+    /** @brief Computes error on the training or test dataset
+
+    @param data the training data
+    @param test if true, the error is computed over the test subset of the data, otherwise it's
+        computed over the training subset of the data. Please note that if you loaded a completely
+        different dataset to evaluate already trained classifier, you will probably want not to set
+        the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so
+        that the error is computed for the whole new set. Yes, this sounds a bit confusing.
+    @param resp the optional output responses.
+
+    The method uses StatModel::predict to compute the error. For regression models the error is
+    computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
+     */
+    CV_WRAP virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
+
+    /** @brief Predicts response(s) for the provided sample(s)
+
+    @param samples The input samples, floating-point matrix
+    @param results The optional output matrix of results.
+    @param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
+     */
+    CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
+
+    /** @brief Create and train model with default parameters
+
+    The class must implement static `create()` method with no parameters or with all default parameter values
+    */
+    template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, int flags=0)
+    {
+        Ptr<_Tp> model = _Tp::create();
+        return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
+    }
+};
+
+/****************************************************************************************\
+*                                 Normal Bayes Classifier                                *
+\****************************************************************************************/
+
+/** @brief Bayes classifier for normally distributed data.
+
+@sa @ref ml_intro_bayes
+ */
+class CV_EXPORTS_W NormalBayesClassifier : public StatModel
+{
+public:
+    /** @brief Predicts the response for sample(s).
+
+    The method estimates the most probable classes for input vectors. Input vectors (one or more)
+    are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one
+    output vector outputs. The predicted class for a single input vector is returned by the method.
+    The vector outputProbs contains the output probabilities corresponding to each element of
+    result.
+     */
+    CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs,
+                               OutputArray outputProbs, int flags=0 ) const = 0;
+
+    /** Creates empty model
+    Use StatModel::train to train the model after creation. */
+    CV_WRAP static Ptr<NormalBayesClassifier> create();
+
+    /** @brief Loads and creates a serialized NormalBayesClassifier from a file
+     *
+     * Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk.
+     * Load the NormalBayesClassifier from this file again, by calling this function with the path to the file.
+     * Optionally specify the node for the file containing the classifier
+     *
+     * @param filepath path to serialized NormalBayesClassifier
+     * @param nodeName name of node containing the classifier
+     */
+    CV_WRAP static Ptr<NormalBayesClassifier> load(const String& filepath , const String& nodeName = String());
+};
+
+/****************************************************************************************\
+*                          K-Nearest Neighbour Classifier                                *
+\****************************************************************************************/
+
+/** @brief The class implements K-Nearest Neighbors model
+
+@sa @ref ml_intro_knn
+ */
+class CV_EXPORTS_W KNearest : public StatModel
+{
+public:
+
+    /** Default number of neighbors to use in predict method. */
+    /** @see setDefaultK */
+    CV_WRAP virtual int getDefaultK() const = 0;
+    /** @copybrief getDefaultK @see getDefaultK */
+    CV_WRAP virtual void setDefaultK(int val) = 0;
+
+    /** Whether classification or regression model should be trained. */
+    /** @see setIsClassifier */
+    CV_WRAP virtual bool getIsClassifier() const = 0;
+    /** @copybrief getIsClassifier @see getIsClassifier */
+    CV_WRAP virtual void setIsClassifier(bool val) = 0;
+
+    /** Parameter for KDTree implementation. */
+    /** @see setEmax */
+    CV_WRAP virtual int getEmax() const = 0;
+    /** @copybrief getEmax @see getEmax */
+    CV_WRAP virtual void setEmax(int val) = 0;
+
+    /** %Algorithm type, one of KNearest::Types. */
+    /** @see setAlgorithmType */
+    CV_WRAP virtual int getAlgorithmType() const = 0;
+    /** @copybrief getAlgorithmType @see getAlgorithmType */
+    CV_WRAP virtual void setAlgorithmType(int val) = 0;
+
+    /** @brief Finds the neighbors and predicts responses for input vectors.
+
+    @param samples Input samples stored by rows. It is a single-precision floating-point matrix of
+        `<number_of_samples> * k` size.
+    @param k Number of used nearest neighbors. Should be greater than 1.
+    @param results Vector with results of prediction (regression or classification) for each input
+        sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
+    @param neighborResponses Optional output values for corresponding neighbors. It is a single-
+        precision floating-point matrix of `<number_of_samples> * k` size.
+    @param dist Optional output distances from the input vectors to the corresponding neighbors. It
+        is a single-precision floating-point matrix of `<number_of_samples> * k` size.
+
+    For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
+    In case of regression, the predicted result is a mean value of the particular vector's neighbor
+    responses. In case of classification, the class is determined by voting.
+
+    For each input vector, the neighbors are sorted by their distances to the vector.
+
+    In case of C++ interface you can use output pointers to empty matrices and the function will
+    allocate memory itself.
+
+    If only a single input vector is passed, all output matrices are optional and the predicted
+    value is returned by the method.
+
+    The function is parallelized with the TBB library.
+     */
+    CV_WRAP virtual float findNearest( InputArray samples, int k,
+                               OutputArray results,
+                               OutputArray neighborResponses=noArray(),
+                               OutputArray dist=noArray() ) const = 0;
+
+    /** @brief Implementations of KNearest algorithm
+       */
+    enum Types
+    {
+        BRUTE_FORCE=1,
+        KDTREE=2
+    };
+
+    /** @brief Creates the empty model
+
+    The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
+     */
+    CV_WRAP static Ptr<KNearest> create();
+    /** @brief Loads and creates a serialized knearest from a file
+     *
+     * Use KNearest::save to serialize and store an KNearest to disk.
+     * Load the KNearest from this file again, by calling this function with the path to the file.
+     *
+     * @param filepath path to serialized KNearest
+     */
+    CV_WRAP static Ptr<KNearest> load(const String& filepath);
+};
+
+/****************************************************************************************\
+*                                   Support Vector Machines                              *
+\****************************************************************************************/
+
+/** @brief Support Vector Machines.
+
+@sa @ref ml_intro_svm
+ */
+class CV_EXPORTS_W SVM : public StatModel
+{
+public:
+
+    class CV_EXPORTS Kernel : public Algorithm
+    {
+    public:
+        virtual int getType() const = 0;
+        virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
+    };
+
+    /** Type of a %SVM formulation.
+    See SVM::Types. Default value is SVM::C_SVC. */
+    /** @see setType */
+    CV_WRAP virtual int getType() const = 0;
+    /** @copybrief getType @see getType */
+    CV_WRAP virtual void setType(int val) = 0;
+
+    /** Parameter \f$\gamma\f$ of a kernel function.
+    For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
+    /** @see setGamma */
+    CV_WRAP virtual double getGamma() const = 0;
+    /** @copybrief getGamma @see getGamma */
+    CV_WRAP virtual void setGamma(double val) = 0;
+
+    /** Parameter _coef0_ of a kernel function.
+    For SVM::POLY or SVM::SIGMOID. Default value is 0.*/
+    /** @see setCoef0 */
+    CV_WRAP virtual double getCoef0() const = 0;
+    /** @copybrief getCoef0 @see getCoef0 */
+    CV_WRAP virtual void setCoef0(double val) = 0;
+
+    /** Parameter _degree_ of a kernel function.
+    For SVM::POLY. Default value is 0. */
+    /** @see setDegree */
+    CV_WRAP virtual double getDegree() const = 0;
+    /** @copybrief getDegree @see getDegree */
+    CV_WRAP virtual void setDegree(double val) = 0;
+
+    /** Parameter _C_ of a %SVM optimization problem.
+    For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */
+    /** @see setC */
+    CV_WRAP virtual double getC() const = 0;
+    /** @copybrief getC @see getC */
+    CV_WRAP virtual void setC(double val) = 0;
+
+    /** Parameter \f$\nu\f$ of a %SVM optimization problem.
+    For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */
+    /** @see setNu */
+    CV_WRAP virtual double getNu() const = 0;
+    /** @copybrief getNu @see getNu */
+    CV_WRAP virtual void setNu(double val) = 0;
+
+    /** Parameter \f$\epsilon\f$ of a %SVM optimization problem.
+    For SVM::EPS_SVR. Default value is 0. */
+    /** @see setP */
+    CV_WRAP virtual double getP() const = 0;
+    /** @copybrief getP @see getP */
+    CV_WRAP virtual void setP(double val) = 0;
+
+    /** Optional weights in the SVM::C_SVC problem, assigned to particular classes.
+    They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus
+    these weights affect the misclassification penalty for different classes. The larger weight,
+    the larger penalty on misclassification of data from the corresponding class. Default value is
+    empty Mat. */
+    /** @see setClassWeights */
+    CV_WRAP virtual cv::Mat getClassWeights() const = 0;
+    /** @copybrief getClassWeights @see getClassWeights */
+    CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0;
+
+    /** Termination criteria of the iterative %SVM training procedure which solves a partial
+    case of constrained quadratic optimization problem.
+    You can specify tolerance and/or the maximum number of iterations. Default value is
+    `TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */
+    /** @see setTermCriteria */
+    CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0;
+    /** @copybrief getTermCriteria @see getTermCriteria */
+    CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
+
+    /** Type of a %SVM kernel.
+    See SVM::KernelTypes. Default value is SVM::RBF. */
+    CV_WRAP virtual int getKernelType() const = 0;
+
+    /** Initialize with one of predefined kernels.
+    See SVM::KernelTypes. */
+    CV_WRAP virtual void setKernel(int kernelType) = 0;
+
+    /** Initialize with custom kernel.
+    See SVM::Kernel class for implementation details */
+    virtual void setCustomKernel(const Ptr<Kernel> &_kernel) = 0;
+
+    //! %SVM type
+    enum Types {
+        /** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows
+        imperfect separation of classes with penalty multiplier C for outliers. */
+        C_SVC=100,
+        /** \f$\nu\f$-Support Vector Classification. n-class classification with possible
+        imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother
+        the decision boundary) is used instead of C. */
+        NU_SVC=101,
+        /** Distribution Estimation (One-class %SVM). All the training data are from
+        the same class, %SVM builds a boundary that separates the class from the rest of the feature
+        space. */
+        ONE_CLASS=102,
+        /** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors
+        from the training set and the fitting hyper-plane must be less than p. For outliers the
+        penalty multiplier C is used. */
+        EPS_SVR=103,
+        /** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p.
+        See @cite LibSVM for details. */
+        NU_SVR=104
+    };
+
+    /** @brief %SVM kernel type
+
+    A comparison of different kernels on the following 2D test case with four classes. Four
+    SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three
+    different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score.
+    Bright means max-score \> 0, dark means max-score \< 0.
+    ![image](pics/SVM_Comparison.png)
+    */
+    enum KernelTypes {
+        /** Returned by SVM::getKernelType in case when custom kernel has been set */
+        CUSTOM=-1,
+        /** Linear kernel. No mapping is done, linear discrimination (or regression) is
+        done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */
+        LINEAR=0,
+        /** Polynomial kernel:
+        \f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */
+        POLY=1,
+        /** Radial basis function (RBF), a good choice in most cases.
+        \f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */
+        RBF=2,
+        /** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */
+        SIGMOID=3,
+        /** Exponential Chi2 kernel, similar to the RBF kernel:
+        \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */
+        CHI2=4,
+        /** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */
+        INTER=5
+    };
+
+    //! %SVM params type
+    enum ParamTypes {
+        C=0,
+        GAMMA=1,
+        P=2,
+        NU=3,
+        COEF=4,
+        DEGREE=5
+    };
+
+    /** @brief Trains an %SVM with optimal parameters.
+
+    @param data the training data that can be constructed using TrainData::create or
+        TrainData::loadFromCSV.
+    @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
+        subset is used to test the model, the others form the train set. So, the %SVM algorithm is
+        executed kFold times.
+    @param Cgrid grid for C
+    @param gammaGrid grid for gamma
+    @param pGrid grid for p
+    @param nuGrid grid for nu
+    @param coeffGrid grid for coeff
+    @param degreeGrid grid for degree
+    @param balanced If true and the problem is 2-class classification then the method creates more
+        balanced cross-validation subsets that is proportions between classes in subsets are close
+        to such proportion in the whole train dataset.
+
+    The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
+    nu, coef0, degree. Parameters are considered optimal when the cross-validation
+    estimate of the test set error is minimal.
+
+    If there is no need to optimize a parameter, the corresponding grid step should be set to any
+    value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step
+    = 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value
+    `Gamma` is taken for gamma.
+
+    And, finally, if the optimization in a parameter is required but the corresponding grid is
+    unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for
+    gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`.
+
+    This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
+    regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
+    the usual %SVM with parameters specified in params is executed.
+     */
+    virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
+                    ParamGrid Cgrid = getDefaultGrid(C),
+                    ParamGrid gammaGrid  = getDefaultGrid(GAMMA),
+                    ParamGrid pGrid      = getDefaultGrid(P),
+                    ParamGrid nuGrid     = getDefaultGrid(NU),
+                    ParamGrid coeffGrid  = getDefaultGrid(COEF),
+                    ParamGrid degreeGrid = getDefaultGrid(DEGREE),
+                    bool balanced=false) = 0;
+
+    /** @brief Trains an %SVM with optimal parameters
+
+    @param samples training samples
+    @param layout See ml::SampleTypes.
+    @param responses vector of responses associated with the training samples.
+    @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
+        subset is used to test the model, the others form the train set. So, the %SVM algorithm is
+    @param Cgrid grid for C
+    @param gammaGrid grid for gamma
+    @param pGrid grid for p
+    @param nuGrid grid for nu
+    @param coeffGrid grid for coeff
+    @param degreeGrid grid for degree
+    @param balanced If true and the problem is 2-class classification then the method creates more
+        balanced cross-validation subsets that is proportions between classes in subsets are close
+        to such proportion in the whole train dataset.
+
+    The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
+    nu, coef0, degree. Parameters are considered optimal when the cross-validation
+    estimate of the test set error is minimal.
+
+    This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
+    offers rudimentary parameter options.
+
+    This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
+    regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
+    the usual %SVM with parameters specified in params is executed.
+    */
+    CV_WRAP virtual bool trainAuto(InputArray samples,
+            int layout,
+            InputArray responses,
+            int kFold = 10,
+            Ptr<ParamGrid> Cgrid = SVM::getDefaultGridPtr(SVM::C),
+            Ptr<ParamGrid> gammaGrid  = SVM::getDefaultGridPtr(SVM::GAMMA),
+            Ptr<ParamGrid> pGrid      = SVM::getDefaultGridPtr(SVM::P),
+            Ptr<ParamGrid> nuGrid     = SVM::getDefaultGridPtr(SVM::NU),
+            Ptr<ParamGrid> coeffGrid  = SVM::getDefaultGridPtr(SVM::COEF),
+            Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE),
+            bool balanced=false) = 0;
+
+    /** @brief Retrieves all the support vectors
+
+    The method returns all the support vectors as a floating-point matrix, where support vectors are
+    stored as matrix rows.
+     */
+    CV_WRAP virtual Mat getSupportVectors() const = 0;
+
+    /** @brief Retrieves all the uncompressed support vectors of a linear %SVM
+
+    The method returns all the uncompressed support vectors of a linear %SVM that the compressed
+    support vector, used for prediction, was derived from. They are returned in a floating-point
+    matrix, where the support vectors are stored as matrix rows.
+     */
+    CV_WRAP virtual Mat getUncompressedSupportVectors() const = 0;
+
+    /** @brief Retrieves the decision function
+
+    @param i the index of the decision function. If the problem solved is regression, 1-class or
+        2-class classification, then there will be just one decision function and the index should
+        always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$
+        decision functions.
+    @param alpha the optional output vector for weights, corresponding to different support vectors.
+        In the case of linear %SVM all the alpha's will be 1's.
+    @param svidx the optional output vector of indices of support vectors within the matrix of
+        support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
+        %SVM each decision function consists of a single "compressed" support vector.
+
+    The method returns rho parameter of the decision function, a scalar subtracted from the weighted
+    sum of kernel responses.
+     */
+    CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
+
+    /** @brief Generates a grid for %SVM parameters.
+
+    @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
+    generated for the parameter with this ID.
+
+    The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be
+    passed to the function SVM::trainAuto.
+     */
+    static ParamGrid getDefaultGrid( int param_id );
+
+    /** @brief Generates a grid for %SVM parameters.
+
+    @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
+    generated for the parameter with this ID.
+
+    The function generates a grid pointer for the specified parameter of the %SVM algorithm.
+    The grid may be passed to the function SVM::trainAuto.
+     */
+    CV_WRAP static Ptr<ParamGrid> getDefaultGridPtr( int param_id );
+
+    /** Creates empty model.
+    Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
+    find the best parameters for your problem, it can be done with SVM::trainAuto. */
+    CV_WRAP static Ptr<SVM> create();
+
+    /** @brief Loads and creates a serialized svm from a file
+     *
+     * Use SVM::save to serialize and store an SVM to disk.
+     * Load the SVM from this file again, by calling this function with the path to the file.
+     *
+     * @param filepath path to serialized svm
+     */
+    CV_WRAP static Ptr<SVM> load(const String& filepath);
+};
+
+/****************************************************************************************\
+*                              Expectation - Maximization                                *
+\****************************************************************************************/
+
+/** @brief The class implements the Expectation Maximization algorithm.
+
+@sa @ref ml_intro_em
+ */
+class CV_EXPORTS_W EM : public StatModel
+{
+public:
+    //! Type of covariation matrices
+    enum Types {
+        /** A scaled identity matrix \f$\mu_k * I\f$. There is the only
+        parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases,
+        when the constraint is relevant, or as a first step in the optimization (for example in case
+        when the data is preprocessed with PCA). The results of such preliminary estimation may be
+        passed again to the optimization procedure, this time with
+        covMatType=EM::COV_MAT_DIAGONAL. */
+        COV_MAT_SPHERICAL=0,
+        /** A diagonal matrix with positive diagonal elements. The number of
+        free parameters is d for each matrix. This is most commonly used option yielding good
+        estimation results. */
+        COV_MAT_DIAGONAL=1,
+        /** A symmetric positively defined matrix. The number of free
+        parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless
+        there is pretty accurate initial estimation of the parameters and/or a huge number of
+        training samples. */
+        COV_MAT_GENERIC=2,
+        COV_MAT_DEFAULT=COV_MAT_DIAGONAL
+    };
+
+    //! Default parameters
+    enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
+
+    //! The initial step
+    enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
+
+    /** The number of mixture components in the Gaussian mixture model.
+    Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could
+    determine the optimal number of mixtures within a specified value range, but that is not the
+    case in ML yet. */
+    /** @see setClustersNumber */
+    CV_WRAP virtual int getClustersNumber() const = 0;
+    /** @copybrief getClustersNumber @see getClustersNumber */
+    CV_WRAP virtual void setClustersNumber(int val) = 0;
+
+    /** Constraint on covariance matrices which defines type of matrices.
+    See EM::Types. */
+    /** @see setCovarianceMatrixType */
+    CV_WRAP virtual int getCovarianceMatrixType() const = 0;
+    /** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */
+    CV_WRAP virtual void setCovarianceMatrixType(int val) = 0;
+
+    /** The termination criteria of the %EM algorithm.
+    The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of
+    M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default
+    maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */
+    /** @see setTermCriteria */
+    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+    /** @copybrief getTermCriteria @see getTermCriteria */
+    CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
+
+    /** @brief Returns weights of the mixtures
+
+    Returns vector with the number of elements equal to the number of mixtures.
+     */
+    CV_WRAP virtual Mat getWeights() const = 0;
+    /** @brief Returns the cluster centers (means of the Gaussian mixture)
+
+    Returns matrix with the number of rows equal to the number of mixtures and number of columns
+    equal to the space dimensionality.
+     */
+    CV_WRAP virtual Mat getMeans() const = 0;
+    /** @brief Returns covariation matrices
+
+    Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
+    each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
+     */
+    CV_WRAP virtual void getCovs(CV_OUT std::vector<Mat>& covs) const = 0;
+
+    /** @brief Returns posterior probabilities for the provided samples
+
+    @param samples The input samples, floating-point matrix
+    @param results The optional output \f$ nSamples \times nClusters\f$ matrix of results. It contains
+    posterior probabilities for each sample from the input
+    @param flags This parameter will be ignored
+     */
+    CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0;
+
+    /** @brief Returns a likelihood logarithm value and an index of the most probable mixture component
+    for the given sample.
+
+    @param sample A sample for classification. It should be a one-channel matrix of
+        \f$1 \times dims\f$ or \f$dims \times 1\f$ size.
+    @param probs Optional output matrix that contains posterior probabilities of each component
+        given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.
+
+    The method returns a two-element double vector. Zero element is a likelihood logarithm value for
+    the sample. First element is an index of the most probable mixture component for the given
+    sample.
+     */
+    CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
+
+    /** @brief Estimate the Gaussian mixture parameters from a samples set.
+
+    This variation starts with Expectation step. Initial values of the model parameters will be
+    estimated by the k-means algorithm.
+
+    Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
+    responses (class labels or function values) as input. Instead, it computes the *Maximum
+    Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
+    parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in
+    covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each
+    sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most
+    probable mixture component for each sample).
+
+    The trained model can be used further for prediction, just like any other classifier. The
+    trained model is similar to the NormalBayesClassifier.
+
+    @param samples Samples from which the Gaussian mixture model will be estimated. It should be a
+        one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
+        it will be converted to the inner matrix of such type for the further computing.
+    @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
+        each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
+    @param labels The optional output "class label" for each sample:
+        \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
+        mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
+    @param probs The optional output matrix that contains posterior probabilities of each Gaussian
+        mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
+        CV_64FC1 type.
+     */
+    CV_WRAP virtual bool trainEM(InputArray samples,
+                         OutputArray logLikelihoods=noArray(),
+                         OutputArray labels=noArray(),
+                         OutputArray probs=noArray()) = 0;
+
+    /** @brief Estimate the Gaussian mixture parameters from a samples set.
+
+    This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of
+    mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices
+    \f$S_k\f$ of mixture components.
+
+    @param samples Samples from which the Gaussian mixture model will be estimated. It should be a
+        one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
+        it will be converted to the inner matrix of such type for the further computing.
+    @param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
+        \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be
+        converted to the inner matrix of such type for the further computing.
+    @param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
+        covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices
+        do not have CV_64F type they will be converted to the inner matrices of such type for the
+        further computing.
+    @param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
+        floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
+    @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
+        each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
+    @param labels The optional output "class label" for each sample:
+        \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
+        mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
+    @param probs The optional output matrix that contains posterior probabilities of each Gaussian
+        mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
+        CV_64FC1 type.
+    */
+    CV_WRAP virtual bool trainE(InputArray samples, InputArray means0,
+                        InputArray covs0=noArray(),
+                        InputArray weights0=noArray(),
+                        OutputArray logLikelihoods=noArray(),
+                        OutputArray labels=noArray(),
+                        OutputArray probs=noArray()) = 0;
+
+    /** @brief Estimate the Gaussian mixture parameters from a samples set.
+
+    This variation starts with Maximization step. You need to provide initial probabilities
+    \f$p_{i,k}\f$ to use this option.
+
+    @param samples Samples from which the Gaussian mixture model will be estimated. It should be a
+        one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
+        it will be converted to the inner matrix of such type for the further computing.
+    @param probs0 the probabilities
+    @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
+        each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
+    @param labels The optional output "class label" for each sample:
+        \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
+        mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
+    @param probs The optional output matrix that contains posterior probabilities of each Gaussian
+        mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
+        CV_64FC1 type.
+    */
+    CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0,
+                        OutputArray logLikelihoods=noArray(),
+                        OutputArray labels=noArray(),
+                        OutputArray probs=noArray()) = 0;
+
+    /** Creates empty %EM model.
+    The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
+    can use one of the EM::train\* methods or load it from file using Algorithm::load\<EM\>(filename).
+     */
+    CV_WRAP static Ptr<EM> create();
+
+    /** @brief Loads and creates a serialized EM from a file
+     *
+     * Use EM::save to serialize and store an EM to disk.
+     * Load the EM from this file again, by calling this function with the path to the file.
+     * Optionally specify the node for the file containing the classifier
+     *
+     * @param filepath path to serialized EM
+     * @param nodeName name of node containing the classifier
+     */
+    CV_WRAP static Ptr<EM> load(const String& filepath , const String& nodeName = String());
+};
+
+/****************************************************************************************\
+*                                      Decision Tree                                     *
+\****************************************************************************************/
+
+/** @brief The class represents a single decision tree or a collection of decision trees.
+
+The current public interface of the class allows user to train only a single decision tree, however
+the class is capable of storing multiple decision trees and using them for prediction (by summing
+responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost)
+use this capability to implement decision tree ensembles.
+
+@sa @ref ml_intro_trees
+*/
+class CV_EXPORTS_W DTrees : public StatModel
+{
+public:
+    /** Predict options */
+    enum Flags { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) };
+
+    /** Cluster possible values of a categorical variable into K\<=maxCategories clusters to
+    find a suboptimal split.
+    If a discrete variable, on which the training procedure tries to make a split, takes more than
+    maxCategories values, the precise best subset estimation may take a very long time because the
+    algorithm is exponential. Instead, many decision trees engines (including our implementation)
+    try to find sub-optimal split in this case by clustering all the samples into maxCategories
+    clusters that is some categories are merged together. The clustering is applied only in n \>
+    2-class classification problems for categorical variables with N \> max_categories possible
+    values. In case of regression and 2-class classification the optimal split can be found
+    efficiently without employing clustering, thus the parameter is not used in these cases.
+    Default value is 10.*/
+    /** @see setMaxCategories */
+    CV_WRAP virtual int getMaxCategories() const = 0;
+    /** @copybrief getMaxCategories @see getMaxCategories */
+    CV_WRAP virtual void setMaxCategories(int val) = 0;
+
+    /** The maximum possible depth of the tree.
+    That is the training algorithms attempts to split a node while its depth is less than maxDepth.
+    The root node has zero depth. The actual depth may be smaller if the other termination criteria
+    are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the
+    tree is pruned. Default value is INT_MAX.*/
+    /** @see setMaxDepth */
+    CV_WRAP virtual int getMaxDepth() const = 0;
+    /** @copybrief getMaxDepth @see getMaxDepth */
+    CV_WRAP virtual void setMaxDepth(int val) = 0;
+
+    /** If the number of samples in a node is less than this parameter then the node will not be split.
+
+    Default value is 10.*/
+    /** @see setMinSampleCount */
+    CV_WRAP virtual int getMinSampleCount() const = 0;
+    /** @copybrief getMinSampleCount @see getMinSampleCount */
+    CV_WRAP virtual void setMinSampleCount(int val) = 0;
+
+    /** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
+    cross-validation procedure where K is equal to CVFolds.
+    Default value is 10.*/
+    /** @see setCVFolds */
+    CV_WRAP virtual int getCVFolds() const = 0;
+    /** @copybrief getCVFolds @see getCVFolds */
+    CV_WRAP virtual void setCVFolds(int val) = 0;
+
+    /** If true then surrogate splits will be built.
+    These splits allow to work with missing data and compute variable importance correctly.
+    Default value is false.
+    @note currently it's not implemented.*/
+    /** @see setUseSurrogates */
+    CV_WRAP virtual bool getUseSurrogates() const = 0;
+    /** @copybrief getUseSurrogates @see getUseSurrogates */
+    CV_WRAP virtual void setUseSurrogates(bool val) = 0;
+
+    /** If true then a pruning will be harsher.
+    This will make a tree more compact and more resistant to the training data noise but a bit less
+    accurate. Default value is true.*/
+    /** @see setUse1SERule */
+    CV_WRAP virtual bool getUse1SERule() const = 0;
+    /** @copybrief getUse1SERule @see getUse1SERule */
+    CV_WRAP virtual void setUse1SERule(bool val) = 0;
+
+    /** If true then pruned branches are physically removed from the tree.
+    Otherwise they are retained and it is possible to get results from the original unpruned (or
+    pruned less aggressively) tree. Default value is true.*/
+    /** @see setTruncatePrunedTree */
+    CV_WRAP virtual bool getTruncatePrunedTree() const = 0;
+    /** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */
+    CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0;
+
+    /** Termination criteria for regression trees.
+    If all absolute differences between an estimated value in a node and values of train samples
+    in this node are less than this parameter then the node will not be split further. Default
+    value is 0.01f*/
+    /** @see setRegressionAccuracy */
+    CV_WRAP virtual float getRegressionAccuracy() const = 0;
+    /** @copybrief getRegressionAccuracy @see getRegressionAccuracy */
+    CV_WRAP virtual void setRegressionAccuracy(float val) = 0;
+
+    /** @brief The array of a priori class probabilities, sorted by the class label value.
+
+    The parameter can be used to tune the decision tree preferences toward a certain class. For
+    example, if you want to detect some rare anomaly occurrence, the training base will likely
+    contain much more normal cases than anomalies, so a very good classification performance
+    will be achieved just by considering every case as normal. To avoid this, the priors can be
+    specified, where the anomaly probability is artificially increased (up to 0.5 or even
+    greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is
+    adjusted properly.
+
+    You can also think about this parameter as weights of prediction categories which determine
+    relative weights that you give to misclassification. That is, if the weight of the first
+    category is 1 and the weight of the second category is 10, then each mistake in predicting
+    the second category is equivalent to making 10 mistakes in predicting the first category.
+    Default value is empty Mat.*/
+    /** @see setPriors */
+    CV_WRAP virtual cv::Mat getPriors() const = 0;
+    /** @copybrief getPriors @see getPriors */
+    CV_WRAP virtual void setPriors(const cv::Mat &val) = 0;
+
+    /** @brief The class represents a decision tree node.
+     */
+    class CV_EXPORTS Node
+    {
+    public:
+        Node();
+        double value; //!< Value at the node: a class label in case of classification or estimated
+                      //!< function value in case of regression.
+        int classIdx; //!< Class index normalized to 0..class_count-1 range and assigned to the
+                      //!< node. It is used internally in classification trees and tree ensembles.
+        int parent; //!< Index of the parent node
+        int left; //!< Index of the left child node
+        int right; //!< Index of right child node
+        int defaultDir; //!< Default direction where to go (-1: left or +1: right). It helps in the
+                        //!< case of missing values.
+        int split; //!< Index of the first split
+    };
+
+    /** @brief The class represents split in a decision tree.
+     */
+    class CV_EXPORTS Split
+    {
+    public:
+        Split();
+        int varIdx; //!< Index of variable on which the split is created.
+        bool inversed; //!< If true, then the inverse split rule is used (i.e. left and right
+                       //!< branches are exchanged in the rule expressions below).
+        float quality; //!< The split quality, a positive number. It is used to choose the best split.
+        int next; //!< Index of the next split in the list of splits for the node
+        float c; /**< The threshold value in case of split on an ordered variable.
+                      The rule is:
+                      @code{.none}
+                      if var_value < c
+                        then next_node <- left
+                        else next_node <- right
+                      @endcode */
+        int subsetOfs; /**< Offset of the bitset used by the split on a categorical variable.
+                            The rule is:
+                            @code{.none}
+                            if bitset[var_value] == 1
+                                then next_node <- left
+                                else next_node <- right
+                            @endcode */
+    };
+
+    /** @brief Returns indices of root nodes
+    */
+    virtual const std::vector<int>& getRoots() const = 0;
+    /** @brief Returns all the nodes
+
+    all the node indices are indices in the returned vector
+     */
+    virtual const std::vector<Node>& getNodes() const = 0;
+    /** @brief Returns all the splits
+
+    all the split indices are indices in the returned vector
+     */
+    virtual const std::vector<Split>& getSplits() const = 0;
+    /** @brief Returns all the bitsets for categorical splits
+
+    Split::subsetOfs is an offset in the returned vector
+     */
+    virtual const std::vector<int>& getSubsets() const = 0;
+
+    /** @brief Creates the empty model
+
+    The static method creates empty decision tree with the specified parameters. It should be then
+    trained using train method (see StatModel::train). Alternatively, you can load the model from
+    file using Algorithm::load\<DTrees\>(filename).
+     */
+    CV_WRAP static Ptr<DTrees> create();
+
+    /** @brief Loads and creates a serialized DTrees from a file
+     *
+     * Use DTree::save to serialize and store an DTree to disk.
+     * Load the DTree from this file again, by calling this function with the path to the file.
+     * Optionally specify the node for the file containing the classifier
+     *
+     * @param filepath path to serialized DTree
+     * @param nodeName name of node containing the classifier
+     */
+    CV_WRAP static Ptr<DTrees> load(const String& filepath , const String& nodeName = String());
+};
+
+/****************************************************************************************\
+*                                   Random Trees Classifier                              *
+\****************************************************************************************/
+
+/** @brief The class implements the random forest predictor.
+
+@sa @ref ml_intro_rtrees
+ */
+class CV_EXPORTS_W RTrees : public DTrees
+{
+public:
+
+    /** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.
+    Default value is false.*/
+    /** @see setCalculateVarImportance */
+    CV_WRAP virtual bool getCalculateVarImportance() const = 0;
+    /** @copybrief getCalculateVarImportance @see getCalculateVarImportance */
+    CV_WRAP virtual void setCalculateVarImportance(bool val) = 0;
+
+    /** The size of the randomly selected subset of features at each tree node and that are used
+    to find the best split(s).
+    If you set it to 0 then the size will be set to the square root of the total number of
+    features. Default value is 0.*/
+    /** @see setActiveVarCount */
+    CV_WRAP virtual int getActiveVarCount() const = 0;
+    /** @copybrief getActiveVarCount @see getActiveVarCount */
+    CV_WRAP virtual void setActiveVarCount(int val) = 0;
+
+    /** The termination criteria that specifies when the training algorithm stops.
+    Either when the specified number of trees is trained and added to the ensemble or when
+    sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the
+    better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes
+    pass a certain number of trees. Also to keep in mind, the number of tree increases the
+    prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS +
+    TermCriteria::EPS, 50, 0.1)*/
+    /** @see setTermCriteria */
+    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+    /** @copybrief getTermCriteria @see getTermCriteria */
+    CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
+
+    /** Returns the variable importance array.
+    The method returns the variable importance vector, computed at the training stage when
+    CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is
+    returned.
+     */
+    CV_WRAP virtual Mat getVarImportance() const = 0;
+
+    /** Returns the result of each individual tree in the forest.
+    In case the model is a regression problem, the method will return each of the trees'
+    results for each of the sample cases. If the model is a classifier, it will return
+    a Mat with samples + 1 rows, where the first row gives the class number and the
+    following rows return the votes each class had for each sample.
+        @param samples Array containing the samples for which votes will be calculated.
+        @param results Array where the result of the calculation will be written.
+        @param flags Flags for defining the type of RTrees.
+    */
+    CV_WRAP virtual void getVotes(InputArray samples, OutputArray results, int flags) const = 0;
+
+    /** Returns the OOB error value, computed at the training stage when calcOOBError is set to true.
+     * If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.
+     */
+#if CV_VERSION_MAJOR == 4
+    CV_WRAP virtual double getOOBError() const { return 0; }
+#else
+    /*CV_WRAP*/ virtual double getOOBError() const = 0;
+#endif
+
+    /** Creates the empty model.
+    Use StatModel::train to train the model, StatModel::train to create and train the model,
+    Algorithm::load to load the pre-trained model.
+     */
+    CV_WRAP static Ptr<RTrees> create();
+
+    /** @brief Loads and creates a serialized RTree from a file
+     *
+     * Use RTree::save to serialize and store an RTree to disk.
+     * Load the RTree from this file again, by calling this function with the path to the file.
+     * Optionally specify the node for the file containing the classifier
+     *
+     * @param filepath path to serialized RTree
+     * @param nodeName name of node containing the classifier
+     */
+    CV_WRAP static Ptr<RTrees> load(const String& filepath , const String& nodeName = String());
+};
+
+/****************************************************************************************\
+*                                   Boosted tree classifier                              *
+\****************************************************************************************/
+
+/** @brief Boosted tree classifier derived from DTrees
+
+@sa @ref ml_intro_boost
+ */
+class CV_EXPORTS_W Boost : public DTrees
+{
+public:
+    /** Type of the boosting algorithm.
+    See Boost::Types. Default value is Boost::REAL. */
+    /** @see setBoostType */
+    CV_WRAP virtual int getBoostType() const = 0;
+    /** @copybrief getBoostType @see getBoostType */
+    CV_WRAP virtual void setBoostType(int val) = 0;
+
+    /** The number of weak classifiers.
+    Default value is 100. */
+    /** @see setWeakCount */
+    CV_WRAP virtual int getWeakCount() const = 0;
+    /** @copybrief getWeakCount @see getWeakCount */
+    CV_WRAP virtual void setWeakCount(int val) = 0;
+
+    /** A threshold between 0 and 1 used to save computational time.
+    Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next*
+    iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/
+    /** @see setWeightTrimRate */
+    CV_WRAP virtual double getWeightTrimRate() const = 0;
+    /** @copybrief getWeightTrimRate @see getWeightTrimRate */
+    CV_WRAP virtual void setWeightTrimRate(double val) = 0;
+
+    /** Boosting type.
+    Gentle AdaBoost and Real AdaBoost are often the preferable choices. */
+    enum Types {
+        DISCRETE=0, //!< Discrete AdaBoost.
+        REAL=1, //!< Real AdaBoost. It is a technique that utilizes confidence-rated predictions
+                //!< and works well with categorical data.
+        LOGIT=2, //!< LogitBoost. It can produce good regression fits.
+        GENTLE=3 //!< Gentle AdaBoost. It puts less weight on outlier data points and for that
+                 //!<reason is often good with regression data.
+    };
+
+    /** Creates the empty model.
+    Use StatModel::train to train the model, Algorithm::load\<Boost\>(filename) to load the pre-trained model. */
+    CV_WRAP static Ptr<Boost> create();
+
+    /** @brief Loads and creates a serialized Boost from a file
+     *
+     * Use Boost::save to serialize and store an RTree to disk.
+     * Load the Boost from this file again, by calling this function with the path to the file.
+     * Optionally specify the node for the file containing the classifier
+     *
+     * @param filepath path to serialized Boost
+     * @param nodeName name of node containing the classifier
+     */
+    CV_WRAP static Ptr<Boost> load(const String& filepath , const String& nodeName = String());
+};
+
+/****************************************************************************************\
+*                                   Gradient Boosted Trees                               *
+\****************************************************************************************/
+
+/*class CV_EXPORTS_W GBTrees : public DTrees
+{
+public:
+    struct CV_EXPORTS_W_MAP Params : public DTrees::Params
+    {
+        CV_PROP_RW int weakCount;
+        CV_PROP_RW int lossFunctionType;
+        CV_PROP_RW float subsamplePortion;
+        CV_PROP_RW float shrinkage;
+
+        Params();
+        Params( int lossFunctionType, int weakCount, float shrinkage,
+                float subsamplePortion, int maxDepth, bool useSurrogates );
+    };
+
+    enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
+
+    virtual void setK(int k) = 0;
+
+    virtual float predictSerial( InputArray samples,
+                                 OutputArray weakResponses, int flags) const = 0;
+
+    static Ptr<GBTrees> create(const Params& p);
+};*/
+
+/****************************************************************************************\
+*                              Artificial Neural Networks (ANN)                          *
+\****************************************************************************************/
+
+/////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
+
+/** @brief Artificial Neural Networks - Multi-Layer Perceptrons.
+
+Unlike many other models in ML that are constructed and trained at once, in the MLP model these
+steps are separated. First, a network with the specified topology is created using the non-default
+constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is
+trained using a set of input and output vectors. The training procedure can be repeated more than
+once, that is, the weights can be adjusted based on the new training data.
+
+Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
+
+@sa @ref ml_intro_ann
+ */
+class CV_EXPORTS_W ANN_MLP : public StatModel
+{
+public:
+    /** Available training methods */
+    enum TrainingMethods {
+        BACKPROP=0, //!< The back-propagation algorithm.
+        RPROP = 1, //!< The RPROP algorithm. See @cite RPROP93 for details.
+        ANNEAL = 2 //!< The simulated annealing algorithm. See @cite Kirkpatrick83 for details.
+    };
+
+    /** Sets training method and common parameters.
+    @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
+    @param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
+    @param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.
+    */
+    CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0;
+
+    /** Returns current training method */
+    CV_WRAP virtual int getTrainMethod() const = 0;
+
+    /** Initialize the activation function for each neuron.
+    Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
+    @param type The type of activation function. See ANN_MLP::ActivationFunctions.
+    @param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0.
+    @param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0.
+    */
+    CV_WRAP virtual void setActivationFunction(int type, double param1 = 0, double param2 = 0) = 0;
+
+    /**  Integer vector specifying the number of neurons in each layer including the input and output layers.
+    The very first element specifies the number of elements in the input layer.
+    The last element - number of elements in the output layer. Default value is empty Mat.
+    @sa getLayerSizes */
+    CV_WRAP virtual void setLayerSizes(InputArray _layer_sizes) = 0;
+
+    /**  Integer vector specifying the number of neurons in each layer including the input and output layers.
+    The very first element specifies the number of elements in the input layer.
+    The last element - number of elements in the output layer.
+    @sa setLayerSizes */
+    CV_WRAP virtual cv::Mat getLayerSizes() const = 0;
+
+    /** Termination criteria of the training algorithm.
+    You can specify the maximum number of iterations (maxCount) and/or how much the error could
+    change between the iterations to make the algorithm continue (epsilon). Default value is
+    TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).*/
+    /** @see setTermCriteria */
+    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+    /** @copybrief getTermCriteria @see getTermCriteria */
+    CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
+
+    /** BPROP: Strength of the weight gradient term.
+    The recommended value is about 0.1. Default value is 0.1.*/
+    /** @see setBackpropWeightScale */
+    CV_WRAP virtual double getBackpropWeightScale() const = 0;
+    /** @copybrief getBackpropWeightScale @see getBackpropWeightScale */
+    CV_WRAP virtual void setBackpropWeightScale(double val) = 0;
+
+    /** BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations).
+    This parameter provides some inertia to smooth the random fluctuations of the weights. It can
+    vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.
+    Default value is 0.1.*/
+    /** @see setBackpropMomentumScale */
+    CV_WRAP virtual double getBackpropMomentumScale() const = 0;
+    /** @copybrief getBackpropMomentumScale @see getBackpropMomentumScale */
+    CV_WRAP virtual void setBackpropMomentumScale(double val) = 0;
+
+    /** RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$.
+    Default value is 0.1.*/
+    /** @see setRpropDW0 */
+    CV_WRAP virtual double getRpropDW0() const = 0;
+    /** @copybrief getRpropDW0 @see getRpropDW0 */
+    CV_WRAP virtual void setRpropDW0(double val) = 0;
+
+    /** RPROP: Increase factor \f$\eta^+\f$.
+    It must be \>1. Default value is 1.2.*/
+    /** @see setRpropDWPlus */
+    CV_WRAP virtual double getRpropDWPlus() const = 0;
+    /** @copybrief getRpropDWPlus @see getRpropDWPlus */
+    CV_WRAP virtual void setRpropDWPlus(double val) = 0;
+
+    /** RPROP: Decrease factor \f$\eta^-\f$.
+    It must be \<1. Default value is 0.5.*/
+    /** @see setRpropDWMinus */
+    CV_WRAP virtual double getRpropDWMinus() const = 0;
+    /** @copybrief getRpropDWMinus @see getRpropDWMinus */
+    CV_WRAP virtual void setRpropDWMinus(double val) = 0;
+
+    /** RPROP: Update-values lower limit \f$\Delta_{min}\f$.
+    It must be positive. Default value is FLT_EPSILON.*/
+    /** @see setRpropDWMin */
+    CV_WRAP virtual double getRpropDWMin() const = 0;
+    /** @copybrief getRpropDWMin @see getRpropDWMin */
+    CV_WRAP virtual void setRpropDWMin(double val) = 0;
+
+    /** RPROP: Update-values upper limit \f$\Delta_{max}\f$.
+    It must be \>1. Default value is 50.*/
+    /** @see setRpropDWMax */
+    CV_WRAP virtual double getRpropDWMax() const = 0;
+    /** @copybrief getRpropDWMax @see getRpropDWMax */
+    CV_WRAP virtual void setRpropDWMax(double val) = 0;
+
+    /** ANNEAL: Update initial temperature.
+    It must be \>=0. Default value is 10.*/
+    /** @see setAnnealInitialT */
+    CV_WRAP virtual double getAnnealInitialT() const = 0;
+    /** @copybrief getAnnealInitialT @see getAnnealInitialT */
+    CV_WRAP virtual void setAnnealInitialT(double val) = 0;
+
+    /** ANNEAL: Update final temperature.
+    It must be \>=0 and less than initialT. Default value is 0.1.*/
+    /** @see setAnnealFinalT */
+    CV_WRAP virtual double getAnnealFinalT() const = 0;
+    /** @copybrief getAnnealFinalT @see getAnnealFinalT */
+    CV_WRAP virtual void setAnnealFinalT(double val) = 0;
+
+    /** ANNEAL: Update cooling ratio.
+    It must be \>0 and less than 1. Default value is 0.95.*/
+    /** @see setAnnealCoolingRatio */
+    CV_WRAP virtual double getAnnealCoolingRatio() const = 0;
+    /** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */
+    CV_WRAP virtual void setAnnealCoolingRatio(double val) = 0;
+
+    /** ANNEAL: Update iteration per step.
+    It must be \>0 . Default value is 10.*/
+    /** @see setAnnealItePerStep */
+    CV_WRAP virtual int getAnnealItePerStep() const = 0;
+    /** @copybrief getAnnealItePerStep @see getAnnealItePerStep */
+    CV_WRAP virtual void setAnnealItePerStep(int val) = 0;
+
+    /** @brief Set/initialize anneal RNG */
+    virtual void setAnnealEnergyRNG(const RNG& rng) = 0;
+
+    /** possible activation functions */
+    enum ActivationFunctions {
+        /** Identity function: \f$f(x)=x\f$ */
+        IDENTITY = 0,
+        /** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x})\f$
+        @note
+        If you are using the default sigmoid activation function with the default parameter values
+        fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output
+        will range from [-1.7159, 1.7159], instead of [0,1].*/
+        SIGMOID_SYM = 1,
+        /** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */
+        GAUSSIAN = 2,
+        /** ReLU function: \f$f(x)=max(0,x)\f$ */
+        RELU = 3,
+        /** Leaky ReLU function: for x>0 \f$f(x)=x \f$ and x<=0 \f$f(x)=\alpha x \f$*/
+        LEAKYRELU= 4
+    };
+
+    /** Train options */
+    enum TrainFlags {
+        /** Update the network weights, rather than compute them from scratch. In the latter case
+        the weights are initialized using the Nguyen-Widrow algorithm. */
+        UPDATE_WEIGHTS = 1,
+        /** Do not normalize the input vectors. If this flag is not set, the training algorithm
+        normalizes each input feature independently, shifting its mean value to 0 and making the
+        standard deviation equal to 1. If the network is assumed to be updated frequently, the new
+        training data could be much different from original one. In this case, you should take care
+        of proper normalization. */
+        NO_INPUT_SCALE = 2,
+        /** Do not normalize the output vectors. If the flag is not set, the training algorithm
+        normalizes each output feature independently, by transforming it to the certain range
+        depending on the used activation function. */
+        NO_OUTPUT_SCALE = 4
+    };
+
+    CV_WRAP virtual Mat getWeights(int layerIdx) const = 0;
+
+    /** @brief Creates empty model
+
+    Use StatModel::train to train the model, Algorithm::load\<ANN_MLP\>(filename) to load the pre-trained model.
+    Note that the train method has optional flags: ANN_MLP::TrainFlags.
+     */
+    CV_WRAP static Ptr<ANN_MLP> create();
+
+    /** @brief Loads and creates a serialized ANN from a file
+     *
+     * Use ANN::save to serialize and store an ANN to disk.
+     * Load the ANN from this file again, by calling this function with the path to the file.
+     *
+     * @param filepath path to serialized ANN
+     */
+    CV_WRAP static Ptr<ANN_MLP> load(const String& filepath);
+
+};
+
+#ifndef DISABLE_OPENCV_3_COMPATIBILITY
+typedef ANN_MLP ANN_MLP_ANNEAL;
+#endif
+
+/****************************************************************************************\
+*                           Logistic Regression                                          *
+\****************************************************************************************/
+
+/** @brief Implements Logistic Regression classifier.
+
+@sa @ref ml_intro_lr
+ */
+class CV_EXPORTS_W LogisticRegression : public StatModel
+{
+public:
+
+    /** Learning rate. */
+    /** @see setLearningRate */
+    CV_WRAP virtual double getLearningRate() const = 0;
+    /** @copybrief getLearningRate @see getLearningRate */
+    CV_WRAP virtual void setLearningRate(double val) = 0;
+
+    /** Number of iterations. */
+    /** @see setIterations */
+    CV_WRAP virtual int getIterations() const = 0;
+    /** @copybrief getIterations @see getIterations */
+    CV_WRAP virtual void setIterations(int val) = 0;
+
+    /** Kind of regularization to be applied. See LogisticRegression::RegKinds. */
+    /** @see setRegularization */
+    CV_WRAP virtual int getRegularization() const = 0;
+    /** @copybrief getRegularization @see getRegularization */
+    CV_WRAP virtual void setRegularization(int val) = 0;
+
+    /** Kind of training method used. See LogisticRegression::Methods. */
+    /** @see setTrainMethod */
+    CV_WRAP virtual int getTrainMethod() const = 0;
+    /** @copybrief getTrainMethod @see getTrainMethod */
+    CV_WRAP virtual void setTrainMethod(int val) = 0;
+
+    /** Specifies the number of training samples taken in each step of Mini-Batch Gradient
+    Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It
+    has to take values less than the total number of training samples. */
+    /** @see setMiniBatchSize */
+    CV_WRAP virtual int getMiniBatchSize() const = 0;
+    /** @copybrief getMiniBatchSize @see getMiniBatchSize */
+    CV_WRAP virtual void setMiniBatchSize(int val) = 0;
+
+    /** Termination criteria of the algorithm. */
+    /** @see setTermCriteria */
+    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+    /** @copybrief getTermCriteria @see getTermCriteria */
+    CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
+
+    //! Regularization kinds
+    enum RegKinds {
+        REG_DISABLE = -1, //!< Regularization disabled
+        REG_L1 = 0, //!< %L1 norm
+        REG_L2 = 1 //!< %L2 norm
+    };
+
+    //! Training methods
+    enum Methods {
+        BATCH = 0,
+        MINI_BATCH = 1 //!< Set MiniBatchSize to a positive integer when using this method.
+    };
+
+    /** @brief Predicts responses for input samples and returns a float type.
+
+    @param samples The input data for the prediction algorithm. Matrix [m x n], where each row
+        contains variables (features) of one object being classified. Should have data type CV_32F.
+    @param results Predicted labels as a column matrix of type CV_32S.
+    @param flags Not used.
+     */
+    CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0;
+
+    /** @brief This function returns the trained parameters arranged across rows.
+
+    For a two class classification problem, it returns a row matrix. It returns learnt parameters of
+    the Logistic Regression as a matrix of type CV_32F.
+     */
+    CV_WRAP virtual Mat get_learnt_thetas() const = 0;
+
+    /** @brief Creates empty model.
+
+    Creates Logistic Regression model with parameters given.
+     */
+    CV_WRAP static Ptr<LogisticRegression> create();
+
+    /** @brief Loads and creates a serialized LogisticRegression from a file
+     *
+     * Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
+     * Load the LogisticRegression from this file again, by calling this function with the path to the file.
+     * Optionally specify the node for the file containing the classifier
+     *
+     * @param filepath path to serialized LogisticRegression
+     * @param nodeName name of node containing the classifier
+     */
+    CV_WRAP static Ptr<LogisticRegression> load(const String& filepath , const String& nodeName = String());
+};
+
+
+/****************************************************************************************\
+*                        Stochastic Gradient Descent SVM Classifier                      *
+\****************************************************************************************/
+
+/*!
+@brief Stochastic Gradient Descent SVM classifier
+
+SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach,
+as presented in @cite bottou2010large.
+
+The classifier has following parameters:
+- model type,
+- margin type,
+- margin regularization (\f$\lambda\f$),
+- initial step size (\f$\gamma_0\f$),
+- step decreasing power (\f$c\f$),
+- and termination criteria.
+
+The model type may have one of the following values: \ref SGD and \ref ASGD.
+
+- \ref SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula
+  \f[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\f]
+  where
+  - \f$w_t\f$ is the weights vector for decision function at step \f$t\f$,
+  - \f$\gamma(t)\f$ is the step size of model parameters at the iteration \f$t\f$, it is decreased on each step by the formula
+    \f$\gamma(t) = \gamma_0  (1 + \lambda  \gamma_0 t) ^ {-c}\f$
+  - \f$Q_i\f$ is the target functional from SVM task for sample with number \f$i\f$, this sample is chosen stochastically on each step of the algorithm.
+
+- \ref ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula
+\f$\widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}\f$
+
+The recommended model type is ASGD (following @cite bottou2010large).
+
+The margin type may have one of the following values: \ref SOFT_MARGIN or \ref HARD_MARGIN.
+
+- You should use \ref HARD_MARGIN type, if you have linearly separable sets.
+- You should use \ref SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers.
+- In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN.
+
+The other parameters may be described as follows:
+- Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers
+  (the less the parameter, the less probability that an outlier will be ignored).
+  Recommended value for SGD model is 0.0001, for ASGD model is 0.00001.
+
+- Initial step size parameter is the initial value for the step size \f$\gamma(t)\f$.
+  You will have to find the best initial step for your problem.
+
+- Step decreasing power is the power parameter for \f$\gamma(t)\f$ decreasing by the formula, mentioned above.
+  Recommended value for SGD model is 1, for ASGD model is 0.75.
+
+- Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS.
+  You will have to find the best termination criteria for your problem.
+
+Note that the parameters margin regularization, initial step size, and step decreasing power should be positive.
+
+To use SVMSGD algorithm do as follows:
+
+- first, create the SVMSGD object. The algorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
+  setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
+
+- then the SVM model can be trained using the train features and the correspondent labels by the method train().
+
+- after that, the label of a new feature vector can be predicted using the method predict().
+
+@code
+// Create empty object
+cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
+
+// Train the Stochastic Gradient Descent SVM
+svmsgd->train(trainData);
+
+// Predict labels for the new samples
+svmsgd->predict(samples, responses);
+@endcode
+
+*/
+
+class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel
+{
+public:
+
+    /** SVMSGD type.
+    ASGD is often the preferable choice. */
+    enum SvmsgdType
+    {
+        SGD, //!< Stochastic Gradient Descent
+        ASGD //!< Average Stochastic Gradient Descent
+    };
+
+    /** Margin type.*/
+    enum MarginType
+    {
+        SOFT_MARGIN, //!< General case, suits to the case of non-linearly separable sets, allows outliers.
+        HARD_MARGIN  //!< More accurate for the case of linearly separable sets.
+    };
+
+    /**
+     * @return the weights of the trained model (decision function f(x) = weights * x + shift).
+    */
+    CV_WRAP virtual Mat getWeights() = 0;
+
+    /**
+     * @return the shift of the trained model (decision function f(x) = weights * x + shift).
+    */
+    CV_WRAP virtual float getShift() = 0;
+
+    /** @brief Creates empty model.
+     * Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
+     * find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
+    */
+    CV_WRAP static Ptr<SVMSGD> create();
+
+    /** @brief Loads and creates a serialized SVMSGD from a file
+     *
+     * Use SVMSGD::save to serialize and store an SVMSGD to disk.
+     * Load the SVMSGD from this file again, by calling this function with the path to the file.
+     * Optionally specify the node for the file containing the classifier
+     *
+     * @param filepath path to serialized SVMSGD
+     * @param nodeName name of node containing the classifier
+     */
+    CV_WRAP static Ptr<SVMSGD> load(const String& filepath , const String& nodeName = String());
+
+    /** @brief Function sets optimal parameters values for chosen SVM SGD model.
+     * @param svmsgdType is the type of SVMSGD classifier.
+     * @param marginType is the type of margin constraint.
+    */
+    CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0;
+
+    /** @brief %Algorithm type, one of SVMSGD::SvmsgdType. */
+    /** @see setSvmsgdType */
+    CV_WRAP virtual int getSvmsgdType() const = 0;
+    /** @copybrief getSvmsgdType @see getSvmsgdType */
+    CV_WRAP virtual void setSvmsgdType(int svmsgdType) = 0;
+
+    /** @brief %Margin type, one of SVMSGD::MarginType. */
+    /** @see setMarginType */
+    CV_WRAP virtual int getMarginType() const = 0;
+    /** @copybrief getMarginType @see getMarginType */
+    CV_WRAP virtual void setMarginType(int marginType) = 0;
+
+    /** @brief Parameter marginRegularization of a %SVMSGD optimization problem. */
+    /** @see setMarginRegularization */
+    CV_WRAP virtual float getMarginRegularization() const = 0;
+    /** @copybrief getMarginRegularization @see getMarginRegularization */
+    CV_WRAP virtual void setMarginRegularization(float marginRegularization) = 0;
+
+    /** @brief Parameter initialStepSize of a %SVMSGD optimization problem. */
+    /** @see setInitialStepSize */
+    CV_WRAP virtual float getInitialStepSize() const = 0;
+    /** @copybrief getInitialStepSize @see getInitialStepSize */
+    CV_WRAP virtual void setInitialStepSize(float InitialStepSize) = 0;
+
+    /** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. */
+    /** @see setStepDecreasingPower */
+    CV_WRAP virtual float getStepDecreasingPower() const = 0;
+    /** @copybrief getStepDecreasingPower @see getStepDecreasingPower */
+    CV_WRAP virtual void setStepDecreasingPower(float stepDecreasingPower) = 0;
+
+    /** @brief Termination criteria of the training algorithm.
+    You can specify the maximum number of iterations (maxCount) and/or how much the error could
+    change between the iterations to make the algorithm continue (epsilon).*/
+    /** @see setTermCriteria */
+    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+    /** @copybrief getTermCriteria @see getTermCriteria */
+    CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
+};
+
+
+/****************************************************************************************\
+*                           Auxiliary functions declarations                              *
+\****************************************************************************************/
+
+/** @brief Generates _sample_ from multivariate normal distribution
+
+@param mean an average row vector
+@param cov symmetric covariation matrix
+@param nsamples returned samples count
+@param samples returned samples array
+*/
+CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
+
+/** @brief Creates test set */
+CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
+                                                OutputArray samples, OutputArray responses);
+
+
+/****************************************************************************************\
+*                                   Simulated annealing solver                             *
+\****************************************************************************************/
+
+#ifdef CV_DOXYGEN
+/** @brief This class declares example interface for system state used in simulated annealing optimization algorithm.
+
+@note This class is not defined in C++ code and can't be use directly - you need your own implementation with the same methods.
+*/
+struct SimulatedAnnealingSolverSystem
+{
+    /** Give energy value for a state of system.*/
+    double energy() const;
+    /** Function which change the state of system (random perturbation).*/
+    void changeState();
+    /** Function to reverse to the previous state. Can be called once only after changeState(). */
+    void reverseState();
+};
+#endif // CV_DOXYGEN
+
+/** @brief The class implements simulated annealing for optimization.
+
+@cite Kirkpatrick83 for details
+
+@param solverSystem optimization system (see SimulatedAnnealingSolverSystem)
+@param initialTemperature initial temperature
+@param finalTemperature final temperature
+@param coolingRatio temperature step multiplies
+@param iterationsPerStep number of iterations per temperature changing step
+@param lastTemperature optional output for last used temperature
+@param rngEnergy specify custom random numbers generator (cv::theRNG() by default)
+*/
+template<class SimulatedAnnealingSolverSystem>
+int simulatedAnnealingSolver(SimulatedAnnealingSolverSystem& solverSystem,
+     double initialTemperature, double finalTemperature, double coolingRatio,
+     size_t iterationsPerStep,
+     CV_OUT double* lastTemperature = NULL,
+     cv::RNG& rngEnergy = cv::theRNG()
+);
+
+//! @} ml
+
+}
+}
+
+#include <opencv2/ml/ml.inl.hpp>
+
+#endif // __cplusplus
+#endif // OPENCV_ML_HPP
+
+/* End of file. */
diff --git a/3rdParty/include/opencv2/ml/ml.hpp b/3rdParty/include/opencv2/ml/ml.hpp
new file mode 100644
index 0000000..f6f9cd8
--- /dev/null
+++ b/3rdParty/include/opencv2/ml/ml.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/ml.hpp"
diff --git a/3rdParty/include/opencv2/ml/ml.inl.hpp b/3rdParty/include/opencv2/ml/ml.inl.hpp
new file mode 100644
index 0000000..dc9c783
--- /dev/null
+++ b/3rdParty/include/opencv2/ml/ml.inl.hpp
@@ -0,0 +1,60 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_ML_INL_HPP
+#define OPENCV_ML_INL_HPP
+
+namespace cv { namespace ml {
+
+// declared in ml.hpp
+template<class SimulatedAnnealingSolverSystem>
+int simulatedAnnealingSolver(SimulatedAnnealingSolverSystem& solverSystem,
+     double initialTemperature, double finalTemperature, double coolingRatio,
+     size_t iterationsPerStep,
+     CV_OUT double* lastTemperature,
+     cv::RNG& rngEnergy
+)
+{
+    CV_Assert(finalTemperature > 0);
+    CV_Assert(initialTemperature > finalTemperature);
+    CV_Assert(iterationsPerStep > 0);
+    CV_Assert(coolingRatio < 1.0f);
+    double Ti = initialTemperature;
+    double previousEnergy = solverSystem.energy();
+    int exchange = 0;
+    while (Ti > finalTemperature)
+    {
+        for (size_t i = 0; i < iterationsPerStep; i++)
+        {
+            solverSystem.changeState();
+            double newEnergy = solverSystem.energy();
+            if (newEnergy < previousEnergy)
+            {
+                previousEnergy = newEnergy;
+                exchange++;
+            }
+            else
+            {
+                double r = rngEnergy.uniform(0.0, 1.0);
+                if (r < std::exp(-(newEnergy - previousEnergy) / Ti))
+                {
+                    previousEnergy = newEnergy;
+                    exchange++;
+                }
+                else
+                {
+                    solverSystem.reverseState();
+                }
+            }
+        }
+        Ti *= coolingRatio;
+    }
+    if (lastTemperature)
+        *lastTemperature = Ti;
+    return exchange;
+}
+
+}} //namespace
+
+#endif // OPENCV_ML_INL_HPP
diff --git a/3rdParty/include/opencv2/objdetect.hpp b/3rdParty/include/opencv2/objdetect.hpp
new file mode 100644
index 0000000..3592255
--- /dev/null
+++ b/3rdParty/include/opencv2/objdetect.hpp
@@ -0,0 +1,836 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_OBJDETECT_HPP
+#define OPENCV_OBJDETECT_HPP
+
+#include "opencv2/core.hpp"
+
+/**
+@defgroup objdetect Object Detection
+
+Haar Feature-based Cascade Classifier for Object Detection
+----------------------------------------------------------
+
+The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
+improved by Rainer Lienhart @cite Lienhart02 .
+
+First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
+trained with a few hundred sample views of a particular object (i.e., a face or a car), called
+positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
+images of the same size.
+
+After a classifier is trained, it can be applied to a region of interest (of the same size as used
+during the training) in an input image. The classifier outputs a "1" if the region is likely to show
+the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can
+move the search window across the image and check every location using the classifier. The
+classifier is designed so that it can be easily "resized" in order to be able to find the objects of
+interest at different sizes, which is more efficient than resizing the image itself. So, to find an
+object of an unknown size in the image the scan procedure should be done several times at different
+scales.
+
+The word "cascade" in the classifier name means that the resultant classifier consists of several
+simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some
+stage the candidate is rejected or all the stages are passed. The word "boosted" means that the
+classifiers at every stage of the cascade are complex themselves and they are built out of basic
+classifiers using one of four different boosting techniques (weighted voting). Currently Discrete
+Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are
+decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
+classifiers, and are calculated as described below. The current algorithm uses the following
+Haar-like features:
+
+![image](pics/haarfeatures.png)
+
+The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within
+the region of interest and the scale (this scale is not the same as the scale used at the detection
+stage, though these two scales are multiplied). For example, in the case of the third line feature
+(2c) the response is calculated as the difference between the sum of image pixels under the
+rectangle covering the whole feature (including the two white stripes and the black stripe in the
+middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to
+compensate for the differences in the size of areas. The sums of pixel values over a rectangular
+regions are calculated rapidly using integral images (see below and the integral description).
+
+To see the object detector at work, have a look at the facedetect demo:
+<https://github.com/opencv/opencv/tree/4.x/samples/cpp/dbt_face_detection.cpp>
+
+The following reference is for the detection part only. There is a separate application called
+opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.
+
+@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
+addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
+using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
+<http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_CVPR2001.pdf>
+
+@{
+    @defgroup objdetect_c C API
+@}
+ */
+
+typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
+
+namespace cv
+{
+
+//! @addtogroup objdetect
+//! @{
+
+///////////////////////////// Object Detection ////////////////////////////
+
+//! class for grouping object candidates, detected by Cascade Classifier, HOG etc.
+//! instance of the class is to be passed to cv::partition (see cxoperations.hpp)
+class CV_EXPORTS SimilarRects
+{
+public:
+    SimilarRects(double _eps) : eps(_eps) {}
+    inline bool operator()(const Rect& r1, const Rect& r2) const
+    {
+        double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5;
+        return std::abs(r1.x - r2.x) <= delta &&
+            std::abs(r1.y - r2.y) <= delta &&
+            std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
+            std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
+    }
+    double eps;
+};
+
+/** @brief Groups the object candidate rectangles.
+
+@param rectList Input/output vector of rectangles. Output vector includes retained and grouped
+rectangles. (The Python list is not modified in place.)
+@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a
+group of rectangles to retain it.
+@param eps Relative difference between sides of the rectangles to merge them into a group.
+
+The function is a wrapper for the generic function partition . It clusters all the input rectangles
+using the rectangle equivalence criteria that combines rectangles with similar sizes and similar
+locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If
+\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small
+clusters containing less than or equal to groupThreshold rectangles are rejected. In each other
+cluster, the average rectangle is computed and put into the output rectangle list.
+ */
+CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
+/** @overload */
+CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights,
+                                  int groupThreshold, double eps = 0.2);
+/** @overload */
+CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold,
+                                  double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
+/** @overload */
+CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
+                                  std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
+/** @overload */
+CV_EXPORTS   void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
+                                            std::vector<double>& foundScales,
+                                            double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
+
+template<> struct DefaultDeleter<CvHaarClassifierCascade>{ CV_EXPORTS void operator ()(CvHaarClassifierCascade* obj) const; };
+
+enum { CASCADE_DO_CANNY_PRUNING    = 1,
+       CASCADE_SCALE_IMAGE         = 2,
+       CASCADE_FIND_BIGGEST_OBJECT = 4,
+       CASCADE_DO_ROUGH_SEARCH     = 8
+     };
+
+class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm
+{
+public:
+    virtual ~BaseCascadeClassifier();
+    virtual bool empty() const CV_OVERRIDE = 0;
+    virtual bool load( const String& filename ) = 0;
+    virtual void detectMultiScale( InputArray image,
+                           CV_OUT std::vector<Rect>& objects,
+                           double scaleFactor,
+                           int minNeighbors, int flags,
+                           Size minSize, Size maxSize ) = 0;
+
+    virtual void detectMultiScale( InputArray image,
+                           CV_OUT std::vector<Rect>& objects,
+                           CV_OUT std::vector<int>& numDetections,
+                           double scaleFactor,
+                           int minNeighbors, int flags,
+                           Size minSize, Size maxSize ) = 0;
+
+    virtual void detectMultiScale( InputArray image,
+                                   CV_OUT std::vector<Rect>& objects,
+                                   CV_OUT std::vector<int>& rejectLevels,
+                                   CV_OUT std::vector<double>& levelWeights,
+                                   double scaleFactor,
+                                   int minNeighbors, int flags,
+                                   Size minSize, Size maxSize,
+                                   bool outputRejectLevels ) = 0;
+
+    virtual bool isOldFormatCascade() const = 0;
+    virtual Size getOriginalWindowSize() const = 0;
+    virtual int getFeatureType() const = 0;
+    virtual void* getOldCascade() = 0;
+
+    class CV_EXPORTS MaskGenerator
+    {
+    public:
+        virtual ~MaskGenerator() {}
+        virtual Mat generateMask(const Mat& src)=0;
+        virtual void initializeMask(const Mat& /*src*/) { }
+    };
+    virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) = 0;
+    virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
+};
+
+/** @example samples/cpp/facedetect.cpp
+This program demonstrates usage of the Cascade classifier class
+\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254
+*/
+/** @brief Cascade classifier class for object detection.
+ */
+class CV_EXPORTS_W CascadeClassifier
+{
+public:
+    CV_WRAP CascadeClassifier();
+    /** @brief Loads a classifier from a file.
+
+    @param filename Name of the file from which the classifier is loaded.
+     */
+    CV_WRAP CascadeClassifier(const String& filename);
+    ~CascadeClassifier();
+    /** @brief Checks whether the classifier has been loaded.
+    */
+    CV_WRAP bool empty() const;
+    /** @brief Loads a classifier from a file.
+
+    @param filename Name of the file from which the classifier is loaded. The file may contain an old
+    HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
+    traincascade application.
+     */
+    CV_WRAP bool load( const String& filename );
+    /** @brief Reads a classifier from a FileStorage node.
+
+    @note The file may contain a new cascade classifier (trained traincascade application) only.
+     */
+    CV_WRAP bool read( const FileNode& node );
+
+    /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
+    of rectangles.
+
+    @param image Matrix of the type CV_8U containing an image where objects are detected.
+    @param objects Vector of rectangles where each rectangle contains the detected object, the
+    rectangles may be partially outside the original image.
+    @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
+    @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
+    to retain it.
+    @param flags Parameter with the same meaning for an old cascade as in the function
+    cvHaarDetectObjects. It is not used for a new cascade.
+    @param minSize Minimum possible object size. Objects smaller than that are ignored.
+    @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
+
+    The function is parallelized with the TBB library.
+
+    @note
+       -   (Python) A face detection example using cascade classifiers can be found at
+            opencv_source_code/samples/python/facedetect.py
+    */
+    CV_WRAP void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          double scaleFactor = 1.1,
+                          int minNeighbors = 3, int flags = 0,
+                          Size minSize = Size(),
+                          Size maxSize = Size() );
+
+    /** @overload
+    @param image Matrix of the type CV_8U containing an image where objects are detected.
+    @param objects Vector of rectangles where each rectangle contains the detected object, the
+    rectangles may be partially outside the original image.
+    @param numDetections Vector of detection numbers for the corresponding objects. An object's number
+    of detections is the number of neighboring positively classified rectangles that were joined
+    together to form the object.
+    @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
+    @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
+    to retain it.
+    @param flags Parameter with the same meaning for an old cascade as in the function
+    cvHaarDetectObjects. It is not used for a new cascade.
+    @param minSize Minimum possible object size. Objects smaller than that are ignored.
+    @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
+    */
+    CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          CV_OUT std::vector<int>& numDetections,
+                          double scaleFactor=1.1,
+                          int minNeighbors=3, int flags=0,
+                          Size minSize=Size(),
+                          Size maxSize=Size() );
+
+    /** @overload
+    This function allows you to retrieve the final stage decision certainty of classification.
+    For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
+    For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
+    This value can then be used to separate strong from weaker classifications.
+
+    A code sample on how to use it efficiently can be found below:
+    @code
+    Mat img;
+    vector<double> weights;
+    vector<int> levels;
+    vector<Rect> detections;
+    CascadeClassifier model("/path/to/your/model.xml");
+    model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
+    cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
+    @endcode
+    */
+    CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image,
+                                  CV_OUT std::vector<Rect>& objects,
+                                  CV_OUT std::vector<int>& rejectLevels,
+                                  CV_OUT std::vector<double>& levelWeights,
+                                  double scaleFactor = 1.1,
+                                  int minNeighbors = 3, int flags = 0,
+                                  Size minSize = Size(),
+                                  Size maxSize = Size(),
+                                  bool outputRejectLevels = false );
+
+    CV_WRAP bool isOldFormatCascade() const;
+    CV_WRAP Size getOriginalWindowSize() const;
+    CV_WRAP int getFeatureType() const;
+    void* getOldCascade();
+
+    CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
+
+    void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
+    Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
+
+    Ptr<BaseCascadeClassifier> cc;
+};
+
+CV_EXPORTS Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
+
+//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
+
+//! struct for detection region of interest (ROI)
+struct DetectionROI
+{
+   //! scale(size) of the bounding box
+   double scale;
+   //! set of requested locations to be evaluated
+   std::vector<cv::Point> locations;
+   //! vector that will contain confidence values for each location
+   std::vector<double> confidences;
+};
+
+/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
+
+the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 .
+
+useful links:
+
+https://hal.inria.fr/inria-00548512/document/
+
+https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
+
+https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor
+
+http://www.learnopencv.com/histogram-of-oriented-gradients
+
+http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial
+
+ */
+struct CV_EXPORTS_W HOGDescriptor
+{
+public:
+    enum HistogramNormType { L2Hys = 0 //!< Default histogramNormType
+         };
+    enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
+         };
+    enum DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW };
+
+    /**@brief Creates the HOG descriptor and detector with default params.
+
+    aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
+    */
+    CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
+        cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
+        histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
+        free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
+    {}
+
+    /** @overload
+    @param _winSize sets winSize with given value.
+    @param _blockSize sets blockSize with given value.
+    @param _blockStride sets blockStride with given value.
+    @param _cellSize sets cellSize with given value.
+    @param _nbins sets nbins with given value.
+    @param _derivAperture sets derivAperture with given value.
+    @param _winSigma sets winSigma with given value.
+    @param _histogramNormType sets histogramNormType with given value.
+    @param _L2HysThreshold sets L2HysThreshold with given value.
+    @param _gammaCorrection sets gammaCorrection with given value.
+    @param _nlevels sets nlevels with given value.
+    @param _signedGradient sets signedGradient with given value.
+    */
+    CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
+                  Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
+                  HOGDescriptor::HistogramNormType _histogramNormType=HOGDescriptor::L2Hys,
+                  double _L2HysThreshold=0.2, bool _gammaCorrection=false,
+                  int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
+    : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
+    nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
+    histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
+    gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
+    {}
+
+    /** @overload
+    @param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.
+    */
+    CV_WRAP HOGDescriptor(const String& filename)
+    {
+        load(filename);
+    }
+
+    /** @overload
+    @param d the HOGDescriptor which cloned to create a new one.
+    */
+    HOGDescriptor(const HOGDescriptor& d)
+    {
+        d.copyTo(*this);
+    }
+
+    /**@brief Default destructor.
+    */
+    virtual ~HOGDescriptor() {}
+
+    /**@brief Returns the number of coefficients required for the classification.
+    */
+    CV_WRAP size_t getDescriptorSize() const;
+
+    /** @brief Checks if detector size equal to descriptor size.
+    */
+    CV_WRAP bool checkDetectorSize() const;
+
+    /** @brief Returns winSigma value
+    */
+    CV_WRAP double getWinSigma() const;
+
+    /**@example samples/cpp/peopledetect.cpp
+    */
+    /**@brief Sets coefficients for the linear SVM classifier.
+    @param svmdetector coefficients for the linear SVM classifier.
+    */
+    CV_WRAP virtual void setSVMDetector(InputArray svmdetector);
+
+    /** @brief Reads HOGDescriptor parameters from a cv::FileNode.
+    @param fn File node
+    */
+    virtual bool read(FileNode& fn);
+
+    /** @brief Stores HOGDescriptor parameters in a cv::FileStorage.
+    @param fs File storage
+    @param objname Object name
+    */
+    virtual void write(FileStorage& fs, const String& objname) const;
+
+    /** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
+    @param filename Path of the file to read.
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used).
+    */
+    CV_WRAP virtual bool load(const String& filename, const String& objname = String());
+
+    /** @brief saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
+    @param filename File name
+    @param objname Object name
+    */
+    CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
+
+    /** @brief clones the HOGDescriptor
+    @param c cloned HOGDescriptor
+    */
+    virtual void copyTo(HOGDescriptor& c) const;
+
+    /**@example samples/cpp/train_HOG.cpp
+    */
+    /** @brief Computes HOG descriptors of given image.
+    @param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
+    @param descriptors Matrix of the type CV_32F
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param locations Vector of Point
+    */
+    CV_WRAP virtual void compute(InputArray img,
+                         CV_OUT std::vector<float>& descriptors,
+                         Size winStride = Size(), Size padding = Size(),
+                         const std::vector<Point>& locations = std::vector<Point>()) const;
+
+    /** @brief Performs object detection without a multi-scale window.
+    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
+    @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
+    @param weights Vector that will contain confidence values for each detected object.
+    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
+    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
+    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param searchLocations Vector of Point includes set of requested locations to be evaluated.
+    */
+    CV_WRAP virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
+                        CV_OUT std::vector<double>& weights,
+                        double hitThreshold = 0, Size winStride = Size(),
+                        Size padding = Size(),
+                        const std::vector<Point>& searchLocations = std::vector<Point>()) const;
+
+    /** @brief Performs object detection without a multi-scale window.
+    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
+    @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
+    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
+    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
+    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param searchLocations Vector of Point includes locations to search.
+    */
+    virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
+                        double hitThreshold = 0, Size winStride = Size(),
+                        Size padding = Size(),
+                        const std::vector<Point>& searchLocations=std::vector<Point>()) const;
+
+    /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
+    of rectangles.
+    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
+    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
+    @param foundWeights Vector that will contain confidence values for each detected object.
+    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
+    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
+    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param scale Coefficient of the detection window increase.
+    @param finalThreshold Final threshold
+    @param useMeanshiftGrouping indicates grouping algorithm
+    */
+    CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
+                                  CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
+                                  Size winStride = Size(), Size padding = Size(), double scale = 1.05,
+                                  double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
+
+    /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
+    of rectangles.
+    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
+    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
+    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
+    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
+    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param winStride Window stride. It must be a multiple of block stride.
+    @param padding Padding
+    @param scale Coefficient of the detection window increase.
+    @param finalThreshold Final threshold
+    @param useMeanshiftGrouping indicates grouping algorithm
+    */
+    virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
+                                  double hitThreshold = 0, Size winStride = Size(),
+                                  Size padding = Size(), double scale = 1.05,
+                                  double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
+
+    /** @brief  Computes gradients and quantized gradient orientations.
+    @param img Matrix contains the image to be computed
+    @param grad Matrix of type CV_32FC2 contains computed gradients
+    @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations
+    @param paddingTL Padding from top-left
+    @param paddingBR Padding from bottom-right
+    */
+    CV_WRAP virtual void computeGradient(InputArray img, InputOutputArray grad, InputOutputArray angleOfs,
+                                 Size paddingTL = Size(), Size paddingBR = Size()) const;
+
+    /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).
+    */
+    CV_WRAP static std::vector<float> getDefaultPeopleDetector();
+
+    /**@example samples/tapi/hog.cpp
+    */
+    /** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).
+    */
+    CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
+
+    //! Detection window size. Align to block size and block stride. Default value is Size(64,128).
+    CV_PROP Size winSize;
+
+    //! Block size in pixels. Align to cell size. Default value is Size(16,16).
+    CV_PROP Size blockSize;
+
+    //! Block stride. It must be a multiple of cell size. Default value is Size(8,8).
+    CV_PROP Size blockStride;
+
+    //! Cell size. Default value is Size(8,8).
+    CV_PROP Size cellSize;
+
+    //! Number of bins used in the calculation of histogram of gradients. Default value is 9.
+    CV_PROP int nbins;
+
+    //! not documented
+    CV_PROP int derivAperture;
+
+    //! Gaussian smoothing window parameter.
+    CV_PROP double winSigma;
+
+    //! histogramNormType
+    CV_PROP HOGDescriptor::HistogramNormType histogramNormType;
+
+    //! L2-Hys normalization method shrinkage.
+    CV_PROP double L2HysThreshold;
+
+    //! Flag to specify whether the gamma correction preprocessing is required or not.
+    CV_PROP bool gammaCorrection;
+
+    //! coefficients for the linear SVM classifier.
+    CV_PROP std::vector<float> svmDetector;
+
+    //! coefficients for the linear SVM classifier used when OpenCL is enabled
+    UMat oclSvmDetector;
+
+    //! not documented
+    float free_coef;
+
+    //! Maximum number of detection window increases. Default value is 64
+    CV_PROP int nlevels;
+
+    //! Indicates signed gradient will be used or not
+    CV_PROP bool signedGradient;
+
+    /** @brief evaluate specified ROI and return confidence value for each location
+    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
+    @param locations Vector of Point
+    @param foundLocations Vector of Point where each Point is detected object's top-left point.
+    @param confidences confidences
+    @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually
+    it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if
+    the free coefficient is omitted (which is allowed), you can specify it manually here
+    @param winStride winStride
+    @param padding padding
+    */
+    virtual void detectROI(InputArray img, const std::vector<cv::Point> &locations,
+                                   CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
+                                   double hitThreshold = 0, cv::Size winStride = Size(),
+                                   cv::Size padding = Size()) const;
+
+    /** @brief evaluate specified ROI and return confidence value for each location in multiple scales
+    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
+    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
+    @param locations Vector of DetectionROI
+    @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified
+    in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
+    @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
+    */
+    virtual void detectMultiScaleROI(InputArray img,
+                                     CV_OUT std::vector<cv::Rect>& foundLocations,
+                                     std::vector<DetectionROI>& locations,
+                                     double hitThreshold = 0,
+                                     int groupThreshold = 0) const;
+
+    /** @brief Groups the object candidate rectangles.
+    @param rectList  Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
+    @param weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.)
+    @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
+    @param eps Relative difference between sides of the rectangles to merge them into a group.
+    */
+    void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
+};
+
+class CV_EXPORTS_W QRCodeEncoder {
+protected:
+    QRCodeEncoder();  // use ::create()
+public:
+    virtual ~QRCodeEncoder();
+
+    enum EncodeMode {
+        MODE_AUTO              = -1,
+        MODE_NUMERIC           = 1, // 0b0001
+        MODE_ALPHANUMERIC      = 2, // 0b0010
+        MODE_BYTE              = 4, // 0b0100
+        MODE_ECI               = 7, // 0b0111
+        MODE_KANJI             = 8, // 0b1000
+        MODE_STRUCTURED_APPEND = 3  // 0b0011
+    };
+
+    enum CorrectionLevel {
+        CORRECT_LEVEL_L = 0,
+        CORRECT_LEVEL_M = 1,
+        CORRECT_LEVEL_Q = 2,
+        CORRECT_LEVEL_H = 3
+    };
+
+    enum ECIEncodings {
+        ECI_UTF8 = 26
+    };
+
+    /** @brief QR code encoder parameters.
+     @param version The optional version of QR code (by default - maximum possible depending on
+                    the length of the string).
+     @param correction_level The optional level of error correction (by default - the lowest).
+     @param mode The optional encoding mode - Numeric, Alphanumeric, Byte, Kanji, ECI or Structured Append.
+     @param structure_number The optional number of QR codes to generate in Structured Append mode.
+    */
+    struct CV_EXPORTS_W_SIMPLE Params
+    {
+        CV_WRAP Params();
+        CV_PROP_RW int version;
+        CV_PROP_RW CorrectionLevel correction_level;
+        CV_PROP_RW EncodeMode mode;
+        CV_PROP_RW int structure_number;
+    };
+
+    /** @brief Constructor
+    @param parameters QR code encoder parameters QRCodeEncoder::Params
+    */
+    static CV_WRAP
+    Ptr<QRCodeEncoder> create(const QRCodeEncoder::Params& parameters = QRCodeEncoder::Params());
+
+    /** @brief Generates QR code from input string.
+     @param encoded_info Input string to encode.
+     @param qrcode Generated QR code.
+    */
+    CV_WRAP virtual void encode(const String& encoded_info, OutputArray qrcode) = 0;
+
+    /** @brief Generates QR code from input string in Structured Append mode. The encoded message is splitting over a number of QR codes.
+     @param encoded_info Input string to encode.
+     @param qrcodes Vector of generated QR codes.
+    */
+    CV_WRAP virtual void encodeStructuredAppend(const String& encoded_info, OutputArrayOfArrays qrcodes) = 0;
+
+};
+
+class CV_EXPORTS_W QRCodeDetector
+{
+public:
+    CV_WRAP QRCodeDetector();
+    ~QRCodeDetector();
+
+    /** @brief sets the epsilon used during the horizontal scan of QR code stop marker detection.
+     @param epsX Epsilon neighborhood, which allows you to determine the horizontal pattern
+     of the scheme 1:1:3:1:1 according to QR code standard.
+    */
+    CV_WRAP void setEpsX(double epsX);
+    /** @brief sets the epsilon used during the vertical scan of QR code stop marker detection.
+     @param epsY Epsilon neighborhood, which allows you to determine the vertical pattern
+     of the scheme 1:1:3:1:1 according to QR code standard.
+     */
+    CV_WRAP void setEpsY(double epsY);
+
+    /** @brief Detects QR code in image and returns the quadrangle containing the code.
+     @param img grayscale or color (BGR) image containing (or not) QR code.
+     @param points Output vector of vertices of the minimum-area quadrangle containing the code.
+     */
+    CV_WRAP bool detect(InputArray img, OutputArray points) const;
+
+    /** @brief Decodes QR code in image once it's found by the detect() method.
+
+     Returns UTF8-encoded output string or empty string if the code cannot be decoded.
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points Quadrangle vertices found by detect() method (or some other algorithm).
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP std::string decode(InputArray img, InputArray points, OutputArray straight_qrcode = noArray());
+
+    /** @brief Decodes QR code on a curved surface in image once it's found by the detect() method.
+
+     Returns UTF8-encoded output string or empty string if the code cannot be decoded.
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points Quadrangle vertices found by detect() method (or some other algorithm).
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP cv::String decodeCurved(InputArray img, InputArray points, OutputArray straight_qrcode = noArray());
+
+    /** @brief Both detects and decodes QR code
+
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP std::string detectAndDecode(InputArray img, OutputArray points=noArray(),
+                                        OutputArray straight_qrcode = noArray());
+
+    /** @brief Both detects and decodes QR code on a curved surface
+
+     @param img grayscale or color (BGR) image containing QR code.
+     @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
+     @param straight_qrcode The optional output image containing rectified and binarized QR code
+     */
+    CV_WRAP std::string detectAndDecodeCurved(InputArray img, OutputArray points=noArray(),
+                                              OutputArray straight_qrcode = noArray());
+
+    /** @brief Detects QR codes in image and returns the vector of the quadrangles containing the codes.
+     @param img grayscale or color (BGR) image containing (or not) QR codes.
+     @param points Output vector of vector of vertices of the minimum-area quadrangle containing the codes.
+     */
+    CV_WRAP
+    bool detectMulti(InputArray img, OutputArray points) const;
+
+    /** @brief Decodes QR codes in image once it's found by the detect() method.
+     @param img grayscale or color (BGR) image containing QR codes.
+     @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded.
+     @param points vector of Quadrangle vertices found by detect() method (or some other algorithm).
+     @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes
+     */
+    CV_WRAP
+    bool decodeMulti(
+            InputArray img, InputArray points,
+            CV_OUT std::vector<std::string>& decoded_info,
+            OutputArrayOfArrays straight_qrcode = noArray()
+    ) const;
+
+    /** @brief Both detects and decodes QR codes
+    @param img grayscale or color (BGR) image containing QR codes.
+    @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded.
+    @param points optional output vector of vertices of the found QR code quadrangles. Will be empty if not found.
+    @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes
+    */
+    CV_WRAP
+    bool detectAndDecodeMulti(
+            InputArray img, CV_OUT std::vector<std::string>& decoded_info,
+            OutputArray points = noArray(),
+            OutputArrayOfArrays straight_qrcode = noArray()
+    ) const;
+
+protected:
+    struct Impl;
+    Ptr<Impl> p;
+};
+
+//! @} objdetect
+}
+
+#include "opencv2/objdetect/detection_based_tracker.hpp"
+#include "opencv2/objdetect/face.hpp"
+
+#endif
diff --git a/3rdParty/include/opencv2/objdetect/detection_based_tracker.hpp b/3rdParty/include/opencv2/objdetect/detection_based_tracker.hpp
new file mode 100644
index 0000000..18cde13
--- /dev/null
+++ b/3rdParty/include/opencv2/objdetect/detection_based_tracker.hpp
@@ -0,0 +1,222 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_OBJDETECT_DBT_HPP
+#define OPENCV_OBJDETECT_DBT_HPP
+
+#include <opencv2/core.hpp>
+
+#include <vector>
+
+namespace cv
+{
+
+//! @addtogroup objdetect
+//! @{
+
+class CV_EXPORTS DetectionBasedTracker
+{
+    public:
+        struct CV_EXPORTS Parameters
+        {
+            int maxTrackLifetime;
+            int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0
+
+            Parameters();
+        };
+
+        class IDetector
+        {
+            public:
+                IDetector():
+                    minObjSize(96, 96),
+                    maxObjSize(INT_MAX, INT_MAX),
+                    minNeighbours(2),
+                    scaleFactor(1.1f)
+                {}
+
+                virtual void detect(const cv::Mat& image, std::vector<cv::Rect>& objects) = 0;
+
+                void setMinObjectSize(const cv::Size& min)
+                {
+                    minObjSize = min;
+                }
+                void setMaxObjectSize(const cv::Size& max)
+                {
+                    maxObjSize = max;
+                }
+                cv::Size getMinObjectSize() const
+                {
+                    return minObjSize;
+                }
+                cv::Size getMaxObjectSize() const
+                {
+                    return maxObjSize;
+                }
+                float getScaleFactor()
+                {
+                    return scaleFactor;
+                }
+                void setScaleFactor(float value)
+                {
+                    scaleFactor = value;
+                }
+                int getMinNeighbours()
+                {
+                    return minNeighbours;
+                }
+                void setMinNeighbours(int value)
+                {
+                    minNeighbours = value;
+                }
+                virtual ~IDetector() {}
+
+            protected:
+                cv::Size minObjSize;
+                cv::Size maxObjSize;
+                int minNeighbours;
+                float scaleFactor;
+        };
+
+        DetectionBasedTracker(cv::Ptr<IDetector> mainDetector, cv::Ptr<IDetector> trackingDetector, const Parameters& params);
+        virtual ~DetectionBasedTracker();
+
+        virtual bool run();
+        virtual void stop();
+        virtual void resetTracking();
+
+        virtual void process(const cv::Mat& imageGray);
+
+        bool setParameters(const Parameters& params);
+        const Parameters& getParameters() const;
+
+
+        typedef std::pair<cv::Rect, int> Object;
+        virtual void getObjects(std::vector<cv::Rect>& result) const;
+        virtual void getObjects(std::vector<Object>& result) const;
+
+        enum ObjectStatus
+        {
+            DETECTED_NOT_SHOWN_YET,
+            DETECTED,
+            DETECTED_TEMPORARY_LOST,
+            WRONG_OBJECT
+        };
+        struct ExtObject
+        {
+            int id;
+            cv::Rect location;
+            ObjectStatus status;
+            ExtObject(int _id, cv::Rect _location, ObjectStatus _status)
+                :id(_id), location(_location), status(_status)
+            {
+            }
+        };
+        virtual void getObjects(std::vector<ExtObject>& result) const;
+
+
+        virtual int addObject(const cv::Rect& location); //returns id of the new object
+
+    protected:
+        class SeparateDetectionWork;
+        cv::Ptr<SeparateDetectionWork> separateDetectionWork;
+        friend void* workcycleObjectDetectorFunction(void* p);
+
+        struct InnerParameters
+        {
+            int numLastPositionsToTrack;
+            int numStepsToWaitBeforeFirstShow;
+            int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown;
+            int numStepsToShowWithoutDetecting;
+
+            float coeffTrackingWindowSize;
+            float coeffObjectSizeToTrack;
+            float coeffObjectSpeedUsingInPrediction;
+
+            InnerParameters();
+        };
+        Parameters parameters;
+        InnerParameters innerParameters;
+
+        struct TrackedObject
+        {
+            typedef std::vector<cv::Rect> PositionsVector;
+
+            PositionsVector lastPositions;
+
+            int numDetectedFrames;
+            int numFramesNotDetected;
+            int id;
+
+            TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0)
+            {
+                lastPositions.push_back(rect);
+                id=getNextId();
+            };
+
+            static int getNextId()
+            {
+                static int _id=0;
+                return _id++;
+            }
+        };
+
+        int numTrackedSteps;
+        std::vector<TrackedObject> trackedObjects;
+
+        std::vector<float> weightsPositionsSmoothing;
+        std::vector<float> weightsSizesSmoothing;
+
+        cv::Ptr<IDetector> cascadeForTracking;
+
+        void updateTrackedObjects(const std::vector<cv::Rect>& detectedObjects);
+        cv::Rect calcTrackedObjectPositionToShow(int i) const;
+        cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const;
+        void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector<cv::Rect>& detectedObjectsInRegions);
+};
+
+//! @} objdetect
+
+} //end of cv namespace
+
+#endif
diff --git a/3rdParty/include/opencv2/objdetect/face.hpp b/3rdParty/include/opencv2/objdetect/face.hpp
new file mode 100644
index 0000000..f2429c5
--- /dev/null
+++ b/3rdParty/include/opencv2/objdetect/face.hpp
@@ -0,0 +1,125 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_OBJDETECT_FACE_HPP
+#define OPENCV_OBJDETECT_FACE_HPP
+
+#include <opencv2/core.hpp>
+
+/** @defgroup dnn_face DNN-based face detection and recognition
+ */
+
+namespace cv
+{
+
+/** @brief DNN-based face detector, model download link: https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.
+ */
+class CV_EXPORTS_W FaceDetectorYN
+{
+public:
+    virtual ~FaceDetectorYN() {};
+
+    /** @brief Set the size for the network input, which overwrites the input size of creating model. Call this method when the size of input image does not match the input size when creating model
+     *
+     * @param input_size the size of the input image
+     */
+    CV_WRAP virtual void setInputSize(const Size& input_size) = 0;
+
+    CV_WRAP virtual Size getInputSize() = 0;
+
+    /** @brief Set the score threshold to filter out bounding boxes of score less than the given value
+     *
+     * @param score_threshold threshold for filtering out bounding boxes
+     */
+    CV_WRAP virtual void setScoreThreshold(float score_threshold) = 0;
+
+    CV_WRAP virtual float getScoreThreshold() = 0;
+
+    /** @brief Set the Non-maximum-suppression threshold to suppress bounding boxes that have IoU greater than the given value
+     *
+     * @param nms_threshold threshold for NMS operation
+     */
+    CV_WRAP virtual void setNMSThreshold(float nms_threshold) = 0;
+
+    CV_WRAP virtual float getNMSThreshold() = 0;
+
+    /** @brief Set the number of bounding boxes preserved before NMS
+     *
+     * @param top_k the number of bounding boxes to preserve from top rank based on score
+     */
+    CV_WRAP virtual void setTopK(int top_k) = 0;
+
+    CV_WRAP virtual int getTopK() = 0;
+
+    /** @brief A simple interface to detect face from given image
+     *
+     *  @param image an image to detect
+     *  @param faces detection results stored in a cv::Mat
+     */
+    CV_WRAP virtual int detect(InputArray image, OutputArray faces) = 0;
+
+    /** @brief Creates an instance of this class with given parameters
+     *
+     *  @param model the path to the requested model
+     *  @param config the path to the config file for compability, which is not requested for ONNX models
+     *  @param input_size the size of the input image
+     *  @param score_threshold the threshold to filter out bounding boxes of score smaller than the given value
+     *  @param nms_threshold the threshold to suppress bounding boxes of IoU bigger than the given value
+     *  @param top_k keep top K bboxes before NMS
+     *  @param backend_id the id of backend
+     *  @param target_id the id of target device
+     */
+    CV_WRAP static Ptr<FaceDetectorYN> create(const String& model,
+                                              const String& config,
+                                              const Size& input_size,
+                                              float score_threshold = 0.9f,
+                                              float nms_threshold = 0.3f,
+                                              int top_k = 5000,
+                                              int backend_id = 0,
+                                              int target_id = 0);
+};
+
+/** @brief DNN-based face recognizer, model download link: https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view.
+ */
+class CV_EXPORTS_W FaceRecognizerSF
+{
+public:
+    virtual ~FaceRecognizerSF() {};
+
+    /** @brief Definition of distance used for calculating the distance between two face features
+     */
+    enum DisType { FR_COSINE=0, FR_NORM_L2=1 };
+
+    /** @brief Aligning image to put face on the standard position
+     *  @param src_img input image
+     *  @param face_box the detection result used for indicate face in input image
+     *  @param aligned_img output aligned image
+     */
+    CV_WRAP virtual void alignCrop(InputArray src_img, InputArray face_box, OutputArray aligned_img) const = 0;
+
+    /** @brief Extracting face feature from aligned image
+     *  @param aligned_img input aligned image
+     *  @param face_feature output face feature
+     */
+    CV_WRAP virtual void feature(InputArray aligned_img, OutputArray face_feature) = 0;
+
+    /** @brief Calculating the distance between two face features
+     *  @param _face_feature1 the first input feature
+     *  @param _face_feature2 the second input feature of the same size and the same type as _face_feature1
+     *  @param dis_type defining the similarity with optional values "FR_OSINE" or "FR_NORM_L2"
+     */
+    CV_WRAP virtual double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type = FaceRecognizerSF::FR_COSINE) const = 0;
+
+    /** @brief Creates an instance of this class with given parameters
+     *  @param model the path of the onnx model used for face recognition
+     *  @param config the path to the config file for compability, which is not requested for ONNX models
+     *  @param backend_id the id of backend
+     *  @param target_id the id of target device
+     */
+    CV_WRAP static Ptr<FaceRecognizerSF> create(const String& model, const String& config, int backend_id = 0, int target_id = 0);
+};
+
+} // namespace cv
+
+#endif
diff --git a/3rdParty/include/opencv2/objdetect/objdetect.hpp b/3rdParty/include/opencv2/objdetect/objdetect.hpp
new file mode 100644
index 0000000..3ee284f
--- /dev/null
+++ b/3rdParty/include/opencv2/objdetect/objdetect.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/objdetect.hpp"
diff --git a/3rdParty/include/opencv2/opencv.hpp b/3rdParty/include/opencv2/opencv.hpp
new file mode 100644
index 0000000..d17b94a
--- /dev/null
+++ b/3rdParty/include/opencv2/opencv.hpp
@@ -0,0 +1,95 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_ALL_HPP
+#define OPENCV_ALL_HPP
+
+// File that defines what modules where included during the build of OpenCV
+// These are purely the defines of the correct HAVE_OPENCV_modulename values
+#include "opencv2/opencv_modules.hpp"
+
+// Then the list of defines is checked to include the correct headers
+// Core library is always included --> without no OpenCV functionality available
+#include "opencv2/core.hpp"
+
+// Then the optional modules are checked
+#ifdef HAVE_OPENCV_CALIB3D
+#include "opencv2/calib3d.hpp"
+#endif
+#ifdef HAVE_OPENCV_FEATURES2D
+#include "opencv2/features2d.hpp"
+#endif
+#ifdef HAVE_OPENCV_DNN
+#include "opencv2/dnn.hpp"
+#endif
+#ifdef HAVE_OPENCV_FLANN
+#include "opencv2/flann.hpp"
+#endif
+#ifdef HAVE_OPENCV_HIGHGUI
+#include "opencv2/highgui.hpp"
+#endif
+#ifdef HAVE_OPENCV_IMGCODECS
+#include "opencv2/imgcodecs.hpp"
+#endif
+#ifdef HAVE_OPENCV_IMGPROC
+#include "opencv2/imgproc.hpp"
+#endif
+#ifdef HAVE_OPENCV_ML
+#include "opencv2/ml.hpp"
+#endif
+#ifdef HAVE_OPENCV_OBJDETECT
+#include "opencv2/objdetect.hpp"
+#endif
+#ifdef HAVE_OPENCV_PHOTO
+#include "opencv2/photo.hpp"
+#endif
+#ifdef HAVE_OPENCV_STITCHING
+#include "opencv2/stitching.hpp"
+#endif
+#ifdef HAVE_OPENCV_VIDEO
+#include "opencv2/video.hpp"
+#endif
+#ifdef HAVE_OPENCV_VIDEOIO
+#include "opencv2/videoio.hpp"
+#endif
+
+#endif
diff --git a/3rdParty/include/opencv2/opencv_modules.hpp b/3rdParty/include/opencv2/opencv_modules.hpp
new file mode 100644
index 0000000..c9e24d8
--- /dev/null
+++ b/3rdParty/include/opencv2/opencv_modules.hpp
@@ -0,0 +1,30 @@
+/*
+ *      ** File generated automatically, do not modify **
+ *
+ * This file defines the list of modules available in current build configuration
+ *
+ *
+*/
+
+// This definition means that OpenCV is built with enabled non-free code.
+// For example, patented algorithms for non-profit/non-commercial use only.
+/* #undef OPENCV_ENABLE_NONFREE */
+
+#define HAVE_OPENCV_CALIB3D
+#define HAVE_OPENCV_CORE
+#define HAVE_OPENCV_DNN
+#define HAVE_OPENCV_FEATURES2D
+#define HAVE_OPENCV_FLANN
+#define HAVE_OPENCV_GAPI
+#define HAVE_OPENCV_HIGHGUI
+#define HAVE_OPENCV_IMGCODECS
+#define HAVE_OPENCV_IMGPROC
+#define HAVE_OPENCV_ML
+#define HAVE_OPENCV_OBJDETECT
+#define HAVE_OPENCV_PHOTO
+#define HAVE_OPENCV_STITCHING
+#define HAVE_OPENCV_VIDEO
+#define HAVE_OPENCV_VIDEOIO
+#define HAVE_OPENCV_WORLD
+
+
diff --git a/3rdParty/include/opencv2/photo.hpp b/3rdParty/include/opencv2/photo.hpp
new file mode 100644
index 0000000..c2e89a3
--- /dev/null
+++ b/3rdParty/include/opencv2/photo.hpp
@@ -0,0 +1,858 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_PHOTO_HPP
+#define OPENCV_PHOTO_HPP
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+
+/**
+@defgroup photo Computational Photography
+
+This module includes photo processing algorithms
+@{
+    @defgroup photo_inpaint Inpainting
+    @defgroup photo_denoise Denoising
+    @defgroup photo_hdr HDR imaging
+
+This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment,
+camera calibration with multiple exposures and exposure fusion.
+
+    @defgroup photo_decolor Contrast Preserving Decolorization
+
+Useful links:
+
+http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html
+
+    @defgroup photo_clone Seamless Cloning
+
+Useful links:
+
+https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp
+
+    @defgroup photo_render Non-Photorealistic Rendering
+
+Useful links:
+
+http://www.inf.ufrgs.br/~eslgastal/DomainTransform
+
+https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/
+
+    @defgroup photo_c C API
+@}
+  */
+
+namespace cv
+{
+
+//! @addtogroup photo
+//! @{
+
+//! @addtogroup photo_inpaint
+//! @{
+//! the inpainting algorithm
+enum
+{
+    INPAINT_NS    = 0, //!< Use Navier-Stokes based method
+    INPAINT_TELEA = 1 //!< Use the algorithm proposed by Alexandru Telea @cite Telea04
+};
+
+/** @brief Restores the selected region in an image using the region neighborhood.
+
+@param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image.
+@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that
+needs to be inpainted.
+@param dst Output image with the same size and type as src .
+@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered
+by the algorithm.
+@param flags Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA
+
+The function reconstructs the selected image area from the pixel near the area boundary. The
+function may be used to remove dust and scratches from a scanned photo, or to remove undesirable
+objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details.
+
+@note
+   -   An example using the inpainting technique can be found at
+        opencv_source_code/samples/cpp/inpaint.cpp
+   -   (Python) An example using the inpainting technique can be found at
+        opencv_source_code/samples/python/inpaint.py
+ */
+CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
+        OutputArray dst, double inpaintRadius, int flags );
+
+//! @} photo_inpaint
+
+//! @addtogroup photo_denoise
+//! @{
+
+/** @brief Perform image denoising using Non-local Means Denoising algorithm
+<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
+optimizations. Noise expected to be a gaussian white noise
+
+@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
+@param dst Output image with the same size and type as src .
+@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
+Should be odd. Recommended value 7 pixels
+@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
+denoising time. Recommended value 21 pixels
+@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
+removes image details, smaller h value preserves details but also preserves some noise
+
+This function expected to be applied to grayscale images. For colored images look at
+fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
+image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
+image to CIELAB colorspace and then separately denoise L and AB components with different h
+parameter.
+ */
+CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3,
+        int templateWindowSize = 7, int searchWindowSize = 21);
+
+/** @brief Perform image denoising using Non-local Means Denoising algorithm
+<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
+optimizations. Noise expected to be a gaussian white noise
+
+@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
+2-channel, 3-channel or 4-channel image.
+@param dst Output image with the same size and type as src .
+@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
+Should be odd. Recommended value 7 pixels
+@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
+denoising time. Recommended value 21 pixels
+@param h Array of parameters regulating filter strength, either one
+parameter applied to all channels or one per channel in dst. Big h value
+perfectly removes noise but also removes image details, smaller h
+value preserves details but also preserves some noise
+@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
+
+This function expected to be applied to grayscale images. For colored images look at
+fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
+image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
+image to CIELAB colorspace and then separately denoise L and AB components with different h
+parameter.
+ */
+CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
+                                        const std::vector<float>& h,
+                                        int templateWindowSize = 7, int searchWindowSize = 21,
+                                        int normType = NORM_L2);
+
+/** @brief Modification of fastNlMeansDenoising function for colored images
+
+@param src Input 8-bit 3-channel image.
+@param dst Output image with the same size and type as src .
+@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
+Should be odd. Recommended value 7 pixels
+@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
+denoising time. Recommended value 21 pixels
+@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
+removes noise but also removes image details, smaller h value preserves details but also preserves
+some noise
+@param hColor The same as h but for color components. For most images value equals 10
+will be enough to remove colored noise and do not distort colors
+
+The function converts image to CIELAB colorspace and then separately denoise L and AB components
+with given h parameters using fastNlMeansDenoising function.
+ */
+CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst,
+        float h = 3, float hColor = 3,
+        int templateWindowSize = 7, int searchWindowSize = 21);
+
+/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
+captured in small period of time. For example video. This version of the function is for grayscale
+images or for manual manipulation with colorspaces. For more details see
+<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
+
+@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or
+4-channel images sequence. All images should have the same type and
+size.
+@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
+@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
+be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
+imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
+srcImgs[imgToDenoiseIndex] image.
+@param dst Output image with the same size and type as srcImgs images.
+@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
+Should be odd. Recommended value 7 pixels
+@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
+denoising time. Recommended value 21 pixels
+@param h Parameter regulating filter strength. Bigger h value
+perfectly removes noise but also removes image details, smaller h
+value preserves details but also preserves some noise
+ */
+CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
+        int imgToDenoiseIndex, int temporalWindowSize,
+        float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
+
+/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
+captured in small period of time. For example video. This version of the function is for grayscale
+images or for manual manipulation with colorspaces. For more details see
+<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
+
+@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
+2-channel, 3-channel or 4-channel images sequence. All images should
+have the same type and size.
+@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
+@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
+be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
+imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
+srcImgs[imgToDenoiseIndex] image.
+@param dst Output image with the same size and type as srcImgs images.
+@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
+Should be odd. Recommended value 7 pixels
+@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
+denoising time. Recommended value 21 pixels
+@param h Array of parameters regulating filter strength, either one
+parameter applied to all channels or one per channel in dst. Big h value
+perfectly removes noise but also removes image details, smaller h
+value preserves details but also preserves some noise
+@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
+ */
+CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
+                                             int imgToDenoiseIndex, int temporalWindowSize,
+                                             const std::vector<float>& h,
+                                             int templateWindowSize = 7, int searchWindowSize = 21,
+                                             int normType = NORM_L2);
+
+/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
+
+@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
+size.
+@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
+@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
+be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
+imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
+srcImgs[imgToDenoiseIndex] image.
+@param dst Output image with the same size and type as srcImgs images.
+@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
+Should be odd. Recommended value 7 pixels
+@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
+denoising time. Recommended value 21 pixels
+@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
+removes noise but also removes image details, smaller h value preserves details but also preserves
+some noise.
+@param hColor The same as h but for color components.
+
+The function converts images to CIELAB colorspace and then separately denoise L and AB components
+with given h parameters using fastNlMeansDenoisingMulti function.
+ */
+CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst,
+        int imgToDenoiseIndex, int temporalWindowSize,
+        float h = 3, float hColor = 3,
+        int templateWindowSize = 7, int searchWindowSize = 21);
+
+/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,
+finding a function to minimize some functional). As the image denoising, in particular, may be seen
+as the variational problem, primal-dual algorithm then can be used to perform denoising and this is
+exactly what is implemented.
+
+It should be noted, that this implementation was taken from the July 2013 blog entry
+@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python.
+Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end
+of July 2013 and finally it was slightly adapted by later authors.
+
+Although the thorough discussion and justification of the algorithm involved may be found in
+@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin
+with, we consider the 1-byte gray-level images as the functions from the rectangular domain of
+pixels (it may be seen as set
+\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some
+\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with
+this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula
+
+\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f]
+
+\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our
+image to be smooth (ideally, having zero gradient, thus being constant) and the second states that
+we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
+exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
+
+@param observations This array should contain one or more noised versions of the image that is to
+be restored.
+@param result Here the denoised image will be stored. There is no need to do pre-allocation of
+storage space, as it will be automatically allocated, if necessary.
+@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth
+(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly
+speaking, as it becomes smaller, the result will be more blur but more sever outliers will be
+removed.
+@param niters Number of iterations that the algorithm will run. Of course, as more iterations as
+better, but it is hard to quantitatively refine this statement, so just use the default and
+increase it if the results are poor.
+ */
+CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
+
+//! @} photo_denoise
+
+//! @addtogroup photo_hdr
+//! @{
+
+enum { LDR_SIZE = 256 };
+
+/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
+ */
+class CV_EXPORTS_W Tonemap : public Algorithm
+{
+public:
+    /** @brief Tonemaps image
+
+    @param src source image - CV_32FC3 Mat (float 32 bits 3 channels)
+    @param dst destination image - CV_32FC3 Mat with values in [0, 1] range
+     */
+    CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0;
+
+    CV_WRAP virtual float getGamma() const = 0;
+    CV_WRAP virtual void setGamma(float gamma) = 0;
+};
+
+/** @brief Creates simple linear mapper with gamma correction
+
+@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma
+equal to 2.2f is suitable for most displays.
+Generally gamma \> 1 brightens the image and gamma \< 1 darkens it.
+ */
+CV_EXPORTS_W Ptr<Tonemap> createTonemap(float gamma = 1.0f);
+
+/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in
+logarithmic domain.
+
+Since it's a global operator the same function is applied to all the pixels, it is controlled by the
+bias parameter.
+
+Optional saturation enhancement is possible as described in @cite FL02 .
+
+For more information see @cite DM03 .
+ */
+class CV_EXPORTS_W TonemapDrago : public Tonemap
+{
+public:
+
+    CV_WRAP virtual float getSaturation() const = 0;
+    CV_WRAP virtual void setSaturation(float saturation) = 0;
+
+    CV_WRAP virtual float getBias() const = 0;
+    CV_WRAP virtual void setBias(float bias) = 0;
+};
+
+/** @brief Creates TonemapDrago object
+
+@param gamma gamma value for gamma correction. See createTonemap
+@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater
+than 1 increase saturation and values less than 1 decrease it.
+@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best
+results, default value is 0.85.
+ */
+CV_EXPORTS_W Ptr<TonemapDrago> createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f);
+
+
+/** @brief This is a global tonemapping operator that models human visual system.
+
+Mapping function is controlled by adaptation parameter, that is computed using light adaptation and
+color adaptation.
+
+For more information see @cite RD05 .
+ */
+class CV_EXPORTS_W TonemapReinhard : public Tonemap
+{
+public:
+    CV_WRAP virtual float getIntensity() const = 0;
+    CV_WRAP virtual void setIntensity(float intensity) = 0;
+
+    CV_WRAP virtual float getLightAdaptation() const = 0;
+    CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0;
+
+    CV_WRAP virtual float getColorAdaptation() const = 0;
+    CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0;
+};
+
+/** @brief Creates TonemapReinhard object
+
+@param gamma gamma value for gamma correction. See createTonemap
+@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results.
+@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel
+value, if 0 it's global, otherwise it's a weighted mean of this two cases.
+@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently,
+if 0 adaptation level is the same for each channel.
+ */
+CV_EXPORTS_W Ptr<TonemapReinhard>
+createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f);
+
+/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid,
+transforms contrast values to HVS response and scales the response. After this the image is
+reconstructed from new contrast values.
+
+For more information see @cite MM06 .
+ */
+class CV_EXPORTS_W TonemapMantiuk : public Tonemap
+{
+public:
+    CV_WRAP virtual float getScale() const = 0;
+    CV_WRAP virtual void setScale(float scale) = 0;
+
+    CV_WRAP virtual float getSaturation() const = 0;
+    CV_WRAP virtual void setSaturation(float saturation) = 0;
+};
+
+/** @brief Creates TonemapMantiuk object
+
+@param gamma gamma value for gamma correction. See createTonemap
+@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing
+dynamic range. Values from 0.6 to 0.9 produce best results.
+@param saturation saturation enhancement value. See createTonemapDrago
+ */
+CV_EXPORTS_W Ptr<TonemapMantiuk>
+createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f);
+
+/** @brief The base class for algorithms that align images of the same scene with different exposures
+ */
+class CV_EXPORTS_W AlignExposures : public Algorithm
+{
+public:
+    /** @brief Aligns images
+
+    @param src vector of input images
+    @param dst vector of aligned images
+    @param times vector of exposure time values for each image
+    @param response 256x1 matrix with inverse camera response function for each pixel value, it should
+    have the same number of channels as images.
+     */
+    CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
+                                 InputArray times, InputArray response) = 0;
+};
+
+/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median
+luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
+
+It is invariant to exposure, so exposure values and camera response are not necessary.
+
+In this implementation new image regions are filled with zeros.
+
+For more information see @cite GW03 .
+ */
+class CV_EXPORTS_W AlignMTB : public AlignExposures
+{
+public:
+    CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
+                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
+
+    /** @brief Short version of process, that doesn't take extra arguments.
+
+    @param src vector of input images
+    @param dst vector of aligned images
+     */
+    CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst) = 0;
+
+    /** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the
+    first.
+
+    @param img0 first image
+    @param img1 second image
+     */
+    CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0;
+    /** @brief Helper function, that shift Mat filling new regions with zeros.
+
+    @param src input image
+    @param dst result image
+    @param shift shift value
+     */
+    CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0;
+    /** @brief Computes median threshold and exclude bitmaps of given image.
+
+    @param img input image
+    @param tb median threshold bitmap
+    @param eb exclude bitmap
+     */
+    CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0;
+
+    CV_WRAP virtual int getMaxBits() const = 0;
+    CV_WRAP virtual void setMaxBits(int max_bits) = 0;
+
+    CV_WRAP virtual int getExcludeRange() const = 0;
+    CV_WRAP virtual void setExcludeRange(int exclude_range) = 0;
+
+    CV_WRAP virtual bool getCut() const = 0;
+    CV_WRAP virtual void setCut(bool value) = 0;
+};
+
+/** @brief Creates AlignMTB object
+
+@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are
+usually good enough (31 and 63 pixels shift respectively).
+@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the
+median value.
+@param cut if true cuts images, otherwise fills the new regions with zeros.
+ */
+CV_EXPORTS_W Ptr<AlignMTB> createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true);
+
+/** @brief The base class for camera response calibration algorithms.
+ */
+class CV_EXPORTS_W CalibrateCRF : public Algorithm
+{
+public:
+    /** @brief Recovers inverse camera response.
+
+    @param src vector of input images
+    @param dst 256x1 matrix with inverse camera response function
+    @param times vector of exposure time values for each image
+     */
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
+};
+
+/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
+function as linear system. Objective function is constructed using pixel values on the same position
+in all images, extra term is added to make the result smoother.
+
+For more information see @cite DM97 .
+ */
+class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF
+{
+public:
+    CV_WRAP virtual float getLambda() const = 0;
+    CV_WRAP virtual void setLambda(float lambda) = 0;
+
+    CV_WRAP virtual int getSamples() const = 0;
+    CV_WRAP virtual void setSamples(int samples) = 0;
+
+    CV_WRAP virtual bool getRandom() const = 0;
+    CV_WRAP virtual void setRandom(bool random) = 0;
+};
+
+/** @brief Creates CalibrateDebevec object
+
+@param samples number of pixel locations to use
+@param lambda smoothness term weight. Greater values produce smoother results, but can alter the
+response.
+@param random if true sample pixel locations are chosen at random, otherwise they form a
+rectangular grid.
+ */
+CV_EXPORTS_W Ptr<CalibrateDebevec> createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false);
+
+/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
+function as linear system. This algorithm uses all image pixels.
+
+For more information see @cite RB99 .
+ */
+class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF
+{
+public:
+    CV_WRAP virtual int getMaxIter() const = 0;
+    CV_WRAP virtual void setMaxIter(int max_iter) = 0;
+
+    CV_WRAP virtual float getThreshold() const = 0;
+    CV_WRAP virtual void setThreshold(float threshold) = 0;
+
+    CV_WRAP virtual Mat getRadiance() const = 0;
+};
+
+/** @brief Creates CalibrateRobertson object
+
+@param max_iter maximal number of Gauss-Seidel solver iterations.
+@param threshold target difference between results of two successive steps of the minimization.
+ */
+CV_EXPORTS_W Ptr<CalibrateRobertson> createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f);
+
+/** @brief The base class algorithms that can merge exposure sequence to a single image.
+ */
+class CV_EXPORTS_W MergeExposures : public Algorithm
+{
+public:
+    /** @brief Merges images.
+
+    @param src vector of input images
+    @param dst result image
+    @param times vector of exposure time values for each image
+    @param response 256x1 matrix with inverse camera response function for each pixel value, it should
+    have the same number of channels as images.
+     */
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
+                                 InputArray times, InputArray response) = 0;
+};
+
+/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
+values and camera response.
+
+For more information see @cite DM97 .
+ */
+class CV_EXPORTS_W MergeDebevec : public MergeExposures
+{
+public:
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
+                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
+};
+
+/** @brief Creates MergeDebevec object
+ */
+CV_EXPORTS_W Ptr<MergeDebevec> createMergeDebevec();
+
+/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are
+combined using laplacian pyramids.
+
+The resulting image weight is constructed as weighted average of contrast, saturation and
+well-exposedness measures.
+
+The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying
+by 255, but it's recommended to apply gamma correction and/or linear tonemapping.
+
+For more information see @cite MK07 .
+ */
+class CV_EXPORTS_W MergeMertens : public MergeExposures
+{
+public:
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
+                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
+    /** @brief Short version of process, that doesn't take extra arguments.
+
+    @param src vector of input images
+    @param dst result image
+     */
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0;
+
+    CV_WRAP virtual float getContrastWeight() const = 0;
+    CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0;
+
+    CV_WRAP virtual float getSaturationWeight() const = 0;
+    CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0;
+
+    CV_WRAP virtual float getExposureWeight() const = 0;
+    CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0;
+};
+
+/** @brief Creates MergeMertens object
+
+@param contrast_weight contrast measure weight. See MergeMertens.
+@param saturation_weight saturation measure weight
+@param exposure_weight well-exposedness measure weight
+ */
+CV_EXPORTS_W Ptr<MergeMertens>
+createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f);
+
+/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
+values and camera response.
+
+For more information see @cite RB99 .
+ */
+class CV_EXPORTS_W MergeRobertson : public MergeExposures
+{
+public:
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
+                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
+    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
+};
+
+/** @brief Creates MergeRobertson object
+ */
+CV_EXPORTS_W Ptr<MergeRobertson> createMergeRobertson();
+
+//! @} photo_hdr
+
+//! @addtogroup photo_decolor
+//! @{
+
+/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized
+black-and-white photograph rendering, and in many single channel image processing applications
+@cite CL12 .
+
+@param src Input 8-bit 3-channel image.
+@param grayscale Output 8-bit 1-channel image.
+@param color_boost Output 8-bit 3-channel image.
+
+This function is to be applied on color images.
+ */
+CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost);
+
+//! @} photo_decolor
+
+//! @addtogroup photo_clone
+//! @{
+
+
+//! seamlessClone algorithm flags
+enum
+{
+    /** The power of the method is fully expressed when inserting objects with complex outlines into a new background*/
+    NORMAL_CLONE = 1,
+    /** The classic method, color-based selection and alpha masking might be time consuming and often leaves an undesirable
+    halo. Seamless cloning, even averaged with the original image, is not effective. Mixed seamless cloning based on a loose selection proves effective.*/
+    MIXED_CLONE  = 2,
+    /** Monochrome transfer allows the user to easily replace certain features of one object by alternative features.*/
+    MONOCHROME_TRANSFER = 3};
+
+
+/** @example samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp
+An example using seamlessClone function
+*/
+/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters,
+deformations) or local changes concerned to a selection. Here we are interested in achieving local
+changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless
+manner. The extent of the changes ranges from slight distortions to complete replacement by novel
+content @cite PM03 .
+
+@param src Input 8-bit 3-channel image.
+@param dst Input 8-bit 3-channel image.
+@param mask Input 8-bit 1 or 3-channel image.
+@param p Point in dst image where object is placed.
+@param blend Output image with the same size and type as dst.
+@param flags Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER
+ */
+CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p,
+        OutputArray blend, int flags);
+
+/** @brief Given an original color image, two differently colored versions of this image can be mixed
+seamlessly.
+
+@param src Input 8-bit 3-channel image.
+@param mask Input 8-bit 1 or 3-channel image.
+@param dst Output image with the same size and type as src .
+@param red_mul R-channel multiply factor.
+@param green_mul G-channel multiply factor.
+@param blue_mul B-channel multiply factor.
+
+Multiplication factor is between .5 to 2.5.
+ */
+CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f,
+        float green_mul = 1.0f, float blue_mul = 1.0f);
+
+/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and
+then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
+
+@param src Input 8-bit 3-channel image.
+@param mask Input 8-bit 1 or 3-channel image.
+@param dst Output image with the same size and type as src.
+@param alpha Value ranges between 0-2.
+@param beta Value ranges between 0-2.
+
+This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
+ */
+CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst,
+        float alpha = 0.2f, float beta = 0.4f);
+
+/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one
+washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.
+
+@param src Input 8-bit 3-channel image.
+@param mask Input 8-bit 1 or 3-channel image.
+@param dst Output image with the same size and type as src.
+@param low_threshold %Range from 0 to 100.
+@param high_threshold Value \> 100.
+@param kernel_size The size of the Sobel kernel to be used.
+
+@note
+The algorithm assumes that the color of the source image is close to that of the destination. This
+assumption means that when the colors don't match, the source image color gets tinted toward the
+color of the destination image.
+ */
+CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst,
+        float low_threshold = 30, float high_threshold = 45,
+        int kernel_size = 3);
+
+//! @} photo_clone
+
+//! @addtogroup photo_render
+//! @{
+
+//! Edge preserving filters
+enum
+{
+    RECURS_FILTER = 1, //!< Recursive Filtering
+    NORMCONV_FILTER = 2 //!< Normalized Convolution Filtering
+};
+
+/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing
+filters are used in many different applications @cite EM11 .
+
+@param src Input 8-bit 3-channel image.
+@param dst Output 8-bit 3-channel image.
+@param flags Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
+@param sigma_s %Range between 0 to 200.
+@param sigma_r %Range between 0 to 1.
+ */
+CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1,
+        float sigma_s = 60, float sigma_r = 0.4f);
+
+/** @brief This filter enhances the details of a particular image.
+
+@param src Input 8-bit 3-channel image.
+@param dst Output image with the same size and type as src.
+@param sigma_s %Range between 0 to 200.
+@param sigma_r %Range between 0 to 1.
+ */
+CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10,
+        float sigma_r = 0.15f);
+
+/** @example samples/cpp/tutorial_code/photo/non_photorealistic_rendering/npr_demo.cpp
+An example using non-photorealistic line drawing functions
+*/
+/** @brief Pencil-like non-photorealistic line drawing
+
+@param src Input 8-bit 3-channel image.
+@param dst1 Output 8-bit 1-channel image.
+@param dst2 Output image with the same size and type as src.
+@param sigma_s %Range between 0 to 200.
+@param sigma_r %Range between 0 to 1.
+@param shade_factor %Range between 0 to 0.1.
+ */
+CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2,
+        float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f);
+
+/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on
+photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low
+contrast while preserving, or enhancing, high-contrast features.
+
+@param src Input 8-bit 3-channel image.
+@param dst Output image with the same size and type as src.
+@param sigma_s %Range between 0 to 200.
+@param sigma_r %Range between 0 to 1.
+ */
+CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60,
+        float sigma_r = 0.45f);
+
+//! @} photo_render
+
+//! @} photo
+
+} // cv
+
+#endif
diff --git a/3rdParty/include/opencv2/photo/cuda.hpp b/3rdParty/include/opencv2/photo/cuda.hpp
new file mode 100644
index 0000000..a2f3816
--- /dev/null
+++ b/3rdParty/include/opencv2/photo/cuda.hpp
@@ -0,0 +1,132 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_PHOTO_CUDA_HPP
+#define OPENCV_PHOTO_CUDA_HPP
+
+#include "opencv2/core/cuda.hpp"
+
+namespace cv { namespace cuda {
+
+//! @addtogroup photo_denoise
+//! @{
+
+/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast.
+
+@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
+@param dst Destination image.
+@param h Filter sigma regulating filter strength for color.
+@param search_window Size of search window.
+@param block_size Size of block used for computing weights.
+@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 ,
+BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
+@param stream Stream for the asynchronous version.
+
+@sa
+   fastNlMeansDenoising
+ */
+CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst,
+                              float h,
+                              int search_window = 21,
+                              int block_size = 7,
+                              int borderMode = BORDER_DEFAULT,
+                              Stream& stream = Stream::Null());
+
+/** @brief Perform image denoising using Non-local Means Denoising algorithm
+<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising> with several computational
+optimizations. Noise expected to be a gaussian white noise
+
+@param src Input 8-bit 1-channel, 2-channel or 3-channel image.
+@param dst Output image with the same size and type as src .
+@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
+removes image details, smaller h value preserves details but also preserves some noise
+@param search_window Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater search_window - greater
+denoising time. Recommended value 21 pixels
+@param block_size Size in pixels of the template patch that is used to compute weights. Should be
+odd. Recommended value 7 pixels
+@param stream Stream for the asynchronous invocations.
+
+This function expected to be applied to grayscale images. For colored images look at
+FastNonLocalMeansDenoising::labMethod.
+
+@sa
+   fastNlMeansDenoising
+ */
+CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst,
+                                     float h,
+                                     int search_window = 21,
+                                     int block_size = 7,
+                                     Stream& stream = Stream::Null());
+
+/** @brief Modification of fastNlMeansDenoising function for colored images
+
+@param src Input 8-bit 3-channel image.
+@param dst Output image with the same size and type as src .
+@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but
+also removes image details, smaller h value preserves details but also preserves some noise
+@param photo_render float The same as h but for color components. For most images value equals 10 will be
+enough to remove colored noise and do not distort colors
+@param search_window Size in pixels of the window that is used to compute weighted average for
+given pixel. Should be odd. Affect performance linearly: greater search_window - greater
+denoising time. Recommended value 21 pixels
+@param block_size Size in pixels of the template patch that is used to compute weights. Should be
+odd. Recommended value 7 pixels
+@param stream Stream for the asynchronous invocations.
+
+The function converts image to CIELAB colorspace and then separately denoise L and AB components
+with given h parameters using FastNonLocalMeansDenoising::simpleMethod function.
+
+@sa
+   fastNlMeansDenoisingColored
+ */
+CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst,
+                                            float h_luminance, float photo_render,
+                                            int search_window = 21,
+                                            int block_size = 7,
+                                            Stream& stream = Stream::Null());
+
+//! @} photo
+
+}} // namespace cv { namespace cuda {
+
+#endif /* OPENCV_PHOTO_CUDA_HPP */
diff --git a/3rdParty/include/opencv2/photo/legacy/constants_c.h b/3rdParty/include/opencv2/photo/legacy/constants_c.h
new file mode 100644
index 0000000..ec1d440
--- /dev/null
+++ b/3rdParty/include/opencv2/photo/legacy/constants_c.h
@@ -0,0 +1,14 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_PHOTO_LEGACY_CONSTANTS_H
+#define OPENCV_PHOTO_LEGACY_CONSTANTS_H
+
+enum InpaintingModes
+{
+    CV_INPAINT_NS      =0,
+    CV_INPAINT_TELEA   =1
+};
+
+#endif // OPENCV_PHOTO_LEGACY_CONSTANTS_H
diff --git a/3rdParty/include/opencv2/photo/photo.hpp b/3rdParty/include/opencv2/photo/photo.hpp
new file mode 100644
index 0000000..8af5e9f
--- /dev/null
+++ b/3rdParty/include/opencv2/photo/photo.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/photo.hpp"
diff --git a/3rdParty/include/opencv2/stitching.hpp b/3rdParty/include/opencv2/stitching.hpp
new file mode 100644
index 0000000..fb0ebe9
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching.hpp
@@ -0,0 +1,357 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_STITCHER_HPP
+#define OPENCV_STITCHING_STITCHER_HPP
+
+#include "opencv2/core.hpp"
+#include "opencv2/features2d.hpp"
+#include "opencv2/stitching/warpers.hpp"
+#include "opencv2/stitching/detail/matchers.hpp"
+#include "opencv2/stitching/detail/motion_estimators.hpp"
+#include "opencv2/stitching/detail/exposure_compensate.hpp"
+#include "opencv2/stitching/detail/seam_finders.hpp"
+#include "opencv2/stitching/detail/blenders.hpp"
+#include "opencv2/stitching/detail/camera.hpp"
+
+
+#if defined(Status)
+#  warning Detected X11 'Status' macro definition, it can cause build conflicts. Please, include this header before any X11 headers.
+#endif
+
+
+/**
+@defgroup stitching Images stitching
+
+This figure illustrates the stitching module pipeline implemented in the Stitcher class. Using that
+class it's possible to configure/remove some steps, i.e. adjust the stitching pipeline according to
+the particular needs. All building blocks from the pipeline are available in the detail namespace,
+one can combine and use them separately.
+
+The implemented stitching pipeline is very similar to the one proposed in @cite BL07 .
+
+![stitching pipeline](StitchingPipeline.jpg)
+
+Camera models
+-------------
+
+There are currently 2 camera models implemented in stitching pipeline.
+
+- _Homography model_ expecting perspective transformations between images
+  implemented in @ref cv::detail::BestOf2NearestMatcher cv::detail::HomographyBasedEstimator
+  cv::detail::BundleAdjusterReproj cv::detail::BundleAdjusterRay
+- _Affine model_ expecting affine transformation with 6 DOF or 4 DOF implemented in
+  @ref cv::detail::AffineBestOf2NearestMatcher cv::detail::AffineBasedEstimator
+  cv::detail::BundleAdjusterAffine cv::detail::BundleAdjusterAffinePartial cv::AffineWarper
+
+Homography model is useful for creating photo panoramas captured by camera,
+while affine-based model can be used to stitch scans and object captured by
+specialized devices. Use @ref cv::Stitcher::create to get preconfigured pipeline for one
+of those models.
+
+@note
+Certain detailed settings of @ref cv::Stitcher might not make sense. Especially
+you should not mix classes implementing affine model and classes implementing
+Homography model, as they work with different transformations.
+
+@{
+    @defgroup stitching_match Features Finding and Images Matching
+    @defgroup stitching_rotation Rotation Estimation
+    @defgroup stitching_autocalib Autocalibration
+    @defgroup stitching_warp Images Warping
+    @defgroup stitching_seam Seam Estimation
+    @defgroup stitching_exposure Exposure Compensation
+    @defgroup stitching_blend Image Blenders
+@}
+  */
+
+namespace cv {
+
+//! @addtogroup stitching
+//! @{
+
+/** @example samples/cpp/stitching.cpp
+A basic example on image stitching
+*/
+
+/** @example samples/python/stitching.py
+A basic example on image stitching in Python.
+*/
+
+/** @example samples/cpp/stitching_detailed.cpp
+A detailed example on image stitching
+*/
+
+/** @brief High level image stitcher.
+
+It's possible to use this class without being aware of the entire stitching pipeline. However, to
+be able to achieve higher stitching stability and quality of the final images at least being
+familiar with the theory is recommended.
+
+@note
+-   A basic example on image stitching can be found at
+    opencv_source_code/samples/cpp/stitching.cpp
+-   A basic example on image stitching in Python can be found at
+    opencv_source_code/samples/python/stitching.py
+-   A detailed example on image stitching can be found at
+    opencv_source_code/samples/cpp/stitching_detailed.cpp
+ */
+class CV_EXPORTS_W Stitcher
+{
+public:
+    /**
+     * When setting a resolution for stitching, this values is a placeholder
+     * for preserving the original resolution.
+     */
+#if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
+    static constexpr double ORIG_RESOL = -1.0;
+#else
+    // support MSVS 2013
+    static const double ORIG_RESOL; // Initialized in stitcher.cpp
+#endif
+
+    enum Status
+    {
+        OK = 0,
+        ERR_NEED_MORE_IMGS = 1,
+        ERR_HOMOGRAPHY_EST_FAIL = 2,
+        ERR_CAMERA_PARAMS_ADJUST_FAIL = 3
+    };
+
+    enum Mode
+    {
+        /** Mode for creating photo panoramas. Expects images under perspective
+        transformation and projects resulting pano to sphere.
+
+        @sa detail::BestOf2NearestMatcher SphericalWarper
+        */
+        PANORAMA = 0,
+        /** Mode for composing scans. Expects images under affine transformation does
+        not compensate exposure by default.
+
+        @sa detail::AffineBestOf2NearestMatcher AffineWarper
+        */
+        SCANS = 1,
+
+    };
+
+    /** @brief Creates a Stitcher configured in one of the stitching modes.
+
+    @param mode Scenario for stitcher operation. This is usually determined by source of images
+    to stitch and their transformation. Default parameters will be chosen for operation in given
+    scenario.
+    @return Stitcher class instance.
+     */
+    CV_WRAP static Ptr<Stitcher> create(Mode mode = Stitcher::PANORAMA);
+
+    CV_WRAP double registrationResol() const { return registr_resol_; }
+    CV_WRAP void setRegistrationResol(double resol_mpx) { registr_resol_ = resol_mpx; }
+
+    CV_WRAP double seamEstimationResol() const { return seam_est_resol_; }
+    CV_WRAP void setSeamEstimationResol(double resol_mpx) { seam_est_resol_ = resol_mpx; }
+
+    CV_WRAP double compositingResol() const { return compose_resol_; }
+    CV_WRAP void setCompositingResol(double resol_mpx) { compose_resol_ = resol_mpx; }
+
+    CV_WRAP double panoConfidenceThresh() const { return conf_thresh_; }
+    CV_WRAP void setPanoConfidenceThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
+
+    CV_WRAP bool waveCorrection() const { return do_wave_correct_; }
+    CV_WRAP void setWaveCorrection(bool flag) { do_wave_correct_ = flag; }
+
+    CV_WRAP InterpolationFlags interpolationFlags() const { return interp_flags_; }
+    CV_WRAP void setInterpolationFlags(InterpolationFlags interp_flags) { interp_flags_ = interp_flags; }
+
+    detail::WaveCorrectKind waveCorrectKind() const { return wave_correct_kind_; }
+    void setWaveCorrectKind(detail::WaveCorrectKind kind) { wave_correct_kind_ = kind; }
+
+    Ptr<Feature2D> featuresFinder() { return features_finder_; }
+    const Ptr<Feature2D> featuresFinder() const { return features_finder_; }
+    void setFeaturesFinder(Ptr<Feature2D> features_finder)
+        { features_finder_ = features_finder; }
+
+    Ptr<detail::FeaturesMatcher> featuresMatcher() { return features_matcher_; }
+    const Ptr<detail::FeaturesMatcher> featuresMatcher() const { return features_matcher_; }
+    void setFeaturesMatcher(Ptr<detail::FeaturesMatcher> features_matcher)
+        { features_matcher_ = features_matcher; }
+
+    const cv::UMat& matchingMask() const { return matching_mask_; }
+    void setMatchingMask(const cv::UMat &mask)
+    {
+        CV_Assert(mask.type() == CV_8U && mask.cols == mask.rows);
+        matching_mask_ = mask.clone();
+    }
+
+    Ptr<detail::BundleAdjusterBase> bundleAdjuster() { return bundle_adjuster_; }
+    const Ptr<detail::BundleAdjusterBase> bundleAdjuster() const { return bundle_adjuster_; }
+    void setBundleAdjuster(Ptr<detail::BundleAdjusterBase> bundle_adjuster)
+        { bundle_adjuster_ = bundle_adjuster; }
+
+    Ptr<detail::Estimator> estimator() { return estimator_; }
+    const Ptr<detail::Estimator> estimator() const { return estimator_; }
+    void setEstimator(Ptr<detail::Estimator> estimator)
+        { estimator_ = estimator; }
+
+    Ptr<WarperCreator> warper() { return warper_; }
+    const Ptr<WarperCreator> warper() const { return warper_; }
+    void setWarper(Ptr<WarperCreator> creator) { warper_ = creator; }
+
+    Ptr<detail::ExposureCompensator> exposureCompensator() { return exposure_comp_; }
+    const Ptr<detail::ExposureCompensator> exposureCompensator() const { return exposure_comp_; }
+    void setExposureCompensator(Ptr<detail::ExposureCompensator> exposure_comp)
+        { exposure_comp_ = exposure_comp; }
+
+    Ptr<detail::SeamFinder> seamFinder() { return seam_finder_; }
+    const Ptr<detail::SeamFinder> seamFinder() const { return seam_finder_; }
+    void setSeamFinder(Ptr<detail::SeamFinder> seam_finder) { seam_finder_ = seam_finder; }
+
+    Ptr<detail::Blender> blender() { return blender_; }
+    const Ptr<detail::Blender> blender() const { return blender_; }
+    void setBlender(Ptr<detail::Blender> b) { blender_ = b; }
+
+    /** @brief These functions try to match the given images and to estimate rotations of each camera.
+
+    @note Use the functions only if you're aware of the stitching pipeline, otherwise use
+    Stitcher::stitch.
+
+    @param images Input images.
+    @param masks Masks for each input image specifying where to look for keypoints (optional).
+    @return Status code.
+     */
+    CV_WRAP Status estimateTransform(InputArrayOfArrays images, InputArrayOfArrays masks = noArray());
+
+    /** @brief These function restors camera rotation and camera intrinsics of each camera
+     *  that can be got with @ref Stitcher::cameras call
+
+    @param images Input images.
+    @param cameras Estimated rotation of cameras for each of the input images.
+    @param component Indices (0-based) of images constituting the final panorama (optional).
+    @return Status code.
+     */
+    Status setTransform(InputArrayOfArrays images,
+                        const std::vector<detail::CameraParams> &cameras,
+                        const std::vector<int> &component);
+    /** @overload */
+    Status setTransform(InputArrayOfArrays images, const std::vector<detail::CameraParams> &cameras);
+
+    /** @overload */
+    CV_WRAP Status composePanorama(OutputArray pano);
+    /** @brief These functions try to compose the given images (or images stored internally from the other function
+    calls) into the final pano under the assumption that the image transformations were estimated
+    before.
+
+    @note Use the functions only if you're aware of the stitching pipeline, otherwise use
+    Stitcher::stitch.
+
+    @param images Input images.
+    @param pano Final pano.
+    @return Status code.
+     */
+    CV_WRAP Status composePanorama(InputArrayOfArrays images, OutputArray pano);
+
+    /** @overload */
+    CV_WRAP Status stitch(InputArrayOfArrays images, OutputArray pano);
+    /** @brief These functions try to stitch the given images.
+
+    @param images Input images.
+    @param masks Masks for each input image specifying where to look for keypoints (optional).
+    @param pano Final pano.
+    @return Status code.
+     */
+    CV_WRAP Status stitch(InputArrayOfArrays images, InputArrayOfArrays masks, OutputArray pano);
+
+    std::vector<int> component() const { return indices_; }
+    std::vector<detail::CameraParams> cameras() const { return cameras_; }
+    CV_WRAP double workScale() const { return work_scale_; }
+    UMat resultMask() const { return result_mask_; }
+
+private:
+    Status matchImages();
+    Status estimateCameraParams();
+
+    double registr_resol_;
+    double seam_est_resol_;
+    double compose_resol_;
+    double conf_thresh_;
+    InterpolationFlags interp_flags_;
+    Ptr<Feature2D> features_finder_;
+    Ptr<detail::FeaturesMatcher> features_matcher_;
+    cv::UMat matching_mask_;
+    Ptr<detail::BundleAdjusterBase> bundle_adjuster_;
+    Ptr<detail::Estimator> estimator_;
+    bool do_wave_correct_;
+    detail::WaveCorrectKind wave_correct_kind_;
+    Ptr<WarperCreator> warper_;
+    Ptr<detail::ExposureCompensator> exposure_comp_;
+    Ptr<detail::SeamFinder> seam_finder_;
+    Ptr<detail::Blender> blender_;
+
+    std::vector<cv::UMat> imgs_;
+    std::vector<cv::UMat> masks_;
+    std::vector<cv::Size> full_img_sizes_;
+    std::vector<detail::ImageFeatures> features_;
+    std::vector<detail::MatchesInfo> pairwise_matches_;
+    std::vector<cv::UMat> seam_est_imgs_;
+    std::vector<int> indices_;
+    std::vector<detail::CameraParams> cameras_;
+    UMat result_mask_;
+    double work_scale_;
+    double seam_scale_;
+    double seam_work_aspect_;
+    double warped_image_scale_;
+};
+
+/**
+ * @deprecated use Stitcher::create
+ */
+CV_DEPRECATED Ptr<Stitcher> createStitcher(bool try_use_gpu = false);
+
+/**
+ * @deprecated use Stitcher::create
+ */
+CV_DEPRECATED Ptr<Stitcher> createStitcherScans(bool try_use_gpu = false);
+
+//! @} stitching
+
+} // namespace cv
+
+#endif // OPENCV_STITCHING_STITCHER_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/autocalib.hpp b/3rdParty/include/opencv2/stitching/detail/autocalib.hpp
new file mode 100644
index 0000000..8eb6212
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/autocalib.hpp
@@ -0,0 +1,86 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_AUTOCALIB_HPP
+#define OPENCV_STITCHING_AUTOCALIB_HPP
+
+#include "opencv2/core.hpp"
+#include "matchers.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching_autocalib
+//! @{
+
+/** @brief Tries to estimate focal lengths from the given homography under the assumption that the camera
+undergoes rotations around its centre only.
+
+@param H Homography.
+@param f0 Estimated focal length along X axis.
+@param f1 Estimated focal length along Y axis.
+@param f0_ok True, if f0 was estimated successfully, false otherwise.
+@param f1_ok True, if f1 was estimated successfully, false otherwise.
+
+See "Construction of Panoramic Image Mosaics with Global and Local Alignment"
+by Heung-Yeung Shum and Richard Szeliski.
+ */
+void CV_EXPORTS_W focalsFromHomography(const Mat &H, double &f0, double &f1, bool &f0_ok, bool &f1_ok);
+
+/** @brief Estimates focal lengths for each given camera.
+
+@param features Features of images.
+@param pairwise_matches Matches between all image pairs.
+@param focals Estimated focal lengths for each camera.
+ */
+void CV_EXPORTS estimateFocal(const std::vector<ImageFeatures> &features,
+                              const std::vector<MatchesInfo> &pairwise_matches,
+                              std::vector<double> &focals);
+
+bool CV_EXPORTS_W calibrateRotatingCamera(const std::vector<Mat> &Hs,CV_OUT Mat &K);
+
+//! @} stitching_autocalib
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_STITCHING_AUTOCALIB_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/blenders.hpp b/3rdParty/include/opencv2/stitching/detail/blenders.hpp
new file mode 100644
index 0000000..ec35aa7
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/blenders.hpp
@@ -0,0 +1,184 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_BLENDERS_HPP
+#define OPENCV_STITCHING_BLENDERS_HPP
+
+#if defined(NO)
+#  warning Detected Apple 'NO' macro definition, it can cause build conflicts. Please, include this header before any Apple headers.
+#endif
+
+#include "opencv2/core.hpp"
+#include "opencv2/core/cuda.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching_blend
+//! @{
+
+/** @brief Base class for all blenders.
+
+Simple blender which puts one image over another
+*/
+class CV_EXPORTS_W Blender
+{
+public:
+    virtual ~Blender() {}
+
+    enum { NO, FEATHER, MULTI_BAND };
+    CV_WRAP static Ptr<Blender> createDefault(int type, bool try_gpu = false);
+
+    /** @brief Prepares the blender for blending.
+
+    @param corners Source images top-left corners
+    @param sizes Source image sizes
+     */
+    CV_WRAP virtual void prepare(const std::vector<Point> &corners, const std::vector<Size> &sizes);
+    /** @overload */
+    CV_WRAP virtual void prepare(Rect dst_roi);
+    /** @brief Processes the image.
+
+    @param img Source image
+    @param mask Source image mask
+    @param tl Source image top-left corners
+     */
+    CV_WRAP virtual void feed(InputArray img, InputArray mask, Point tl);
+    /** @brief Blends and returns the final pano.
+
+    @param dst Final pano
+    @param dst_mask Final pano mask
+     */
+    CV_WRAP virtual void blend(CV_IN_OUT InputOutputArray dst,CV_IN_OUT  InputOutputArray dst_mask);
+
+protected:
+    UMat dst_, dst_mask_;
+    Rect dst_roi_;
+};
+
+/** @brief Simple blender which mixes images at its borders.
+ */
+class CV_EXPORTS_W FeatherBlender : public Blender
+{
+public:
+    CV_WRAP FeatherBlender(float sharpness = 0.02f);
+
+    CV_WRAP float sharpness() const { return sharpness_; }
+    CV_WRAP void setSharpness(float val) { sharpness_ = val; }
+
+    CV_WRAP void prepare(Rect dst_roi) CV_OVERRIDE;
+    CV_WRAP void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE;
+    CV_WRAP void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE;
+
+    //! Creates weight maps for fixed set of source images by their masks and top-left corners.
+    //! Final image can be obtained by simple weighting of the source images.
+    CV_WRAP Rect createWeightMaps(const std::vector<UMat> &masks, const std::vector<Point> &corners,
+        CV_IN_OUT std::vector<UMat> &weight_maps);
+
+private:
+    float sharpness_;
+    UMat weight_map_;
+    UMat dst_weight_map_;
+};
+
+inline FeatherBlender::FeatherBlender(float _sharpness) { setSharpness(_sharpness); }
+
+/** @brief Blender which uses multi-band blending algorithm (see @cite BA83).
+ */
+class CV_EXPORTS_W MultiBandBlender : public Blender
+{
+public:
+    CV_WRAP MultiBandBlender(int try_gpu = false, int num_bands = 5, int weight_type = CV_32F);
+
+    CV_WRAP int numBands() const { return actual_num_bands_; }
+    CV_WRAP void setNumBands(int val) { actual_num_bands_ = val; }
+
+    CV_WRAP void prepare(Rect dst_roi) CV_OVERRIDE;
+    CV_WRAP void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE;
+    CV_WRAP void blend(CV_IN_OUT InputOutputArray dst, CV_IN_OUT InputOutputArray dst_mask) CV_OVERRIDE;
+
+private:
+    int actual_num_bands_, num_bands_;
+    std::vector<UMat> dst_pyr_laplace_;
+    std::vector<UMat> dst_band_weights_;
+    Rect dst_roi_final_;
+    bool can_use_gpu_;
+    int weight_type_; //CV_32F or CV_16S
+#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
+    std::vector<cuda::GpuMat> gpu_dst_pyr_laplace_;
+    std::vector<cuda::GpuMat> gpu_dst_band_weights_;
+    std::vector<Point> gpu_tl_points_;
+    std::vector<cuda::GpuMat> gpu_imgs_with_border_;
+    std::vector<std::vector<cuda::GpuMat> > gpu_weight_pyr_gauss_vec_;
+    std::vector<std::vector<cuda::GpuMat> > gpu_src_pyr_laplace_vec_;
+    std::vector<std::vector<cuda::GpuMat> > gpu_ups_;
+    cuda::GpuMat gpu_dst_mask_;
+    cuda::GpuMat gpu_mask_;
+    cuda::GpuMat gpu_img_;
+    cuda::GpuMat gpu_weight_map_;
+    cuda::GpuMat gpu_add_mask_;
+    int gpu_feed_idx_;
+    bool gpu_initialized_;
+#endif
+};
+
+
+//////////////////////////////////////////////////////////////////////////////
+// Auxiliary functions
+
+void CV_EXPORTS_W normalizeUsingWeightMap(InputArray weight, CV_IN_OUT InputOutputArray src);
+
+void CV_EXPORTS_W createWeightMap(InputArray mask, float sharpness, CV_IN_OUT InputOutputArray weight);
+
+void CV_EXPORTS_W createLaplacePyr(InputArray img, int num_levels, CV_IN_OUT std::vector<UMat>& pyr);
+void CV_EXPORTS_W createLaplacePyrGpu(InputArray img, int num_levels, CV_IN_OUT std::vector<UMat>& pyr);
+
+// Restores source image
+void CV_EXPORTS_W restoreImageFromLaplacePyr(CV_IN_OUT std::vector<UMat>& pyr);
+void CV_EXPORTS_W restoreImageFromLaplacePyrGpu(CV_IN_OUT std::vector<UMat>& pyr);
+
+//! @}
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_STITCHING_BLENDERS_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/camera.hpp b/3rdParty/include/opencv2/stitching/detail/camera.hpp
new file mode 100644
index 0000000..14ecf60
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/camera.hpp
@@ -0,0 +1,78 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_CAMERA_HPP
+#define OPENCV_STITCHING_CAMERA_HPP
+
+#include "opencv2/core.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching
+//! @{
+
+/** @brief Describes camera parameters.
+
+@note Translation is assumed to be zero during the whole stitching pipeline. :
+ */
+struct CV_EXPORTS_W_SIMPLE CameraParams
+{
+    CameraParams();
+    CameraParams(const CameraParams& other);
+    CameraParams& operator =(const CameraParams& other);
+    CV_WRAP Mat K() const;
+
+    CV_PROP_RW double focal; // Focal length
+    CV_PROP_RW double aspect; // Aspect ratio
+    CV_PROP_RW double ppx; // Principal point X
+    CV_PROP_RW double ppy; // Principal point Y
+    CV_PROP_RW Mat R; // Rotation
+    CV_PROP_RW Mat t; // Translation
+};
+
+//! @}
+
+} // namespace detail
+} // namespace cv
+
+#endif // #ifndef OPENCV_STITCHING_CAMERA_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/exposure_compensate.hpp b/3rdParty/include/opencv2/stitching/detail/exposure_compensate.hpp
new file mode 100644
index 0000000..074c9b6
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/exposure_compensate.hpp
@@ -0,0 +1,245 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP
+#define OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP
+
+#if defined(NO)
+#  warning Detected Apple 'NO' macro definition, it can cause build conflicts. Please, include this header before any Apple headers.
+#endif
+
+#include "opencv2/core.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching_exposure
+//! @{
+
+/** @brief Base class for all exposure compensators.
+ */
+class CV_EXPORTS_W ExposureCompensator
+{
+public:
+    ExposureCompensator(): updateGain(true) {}
+    virtual ~ExposureCompensator() {}
+
+    enum { NO, GAIN, GAIN_BLOCKS, CHANNELS, CHANNELS_BLOCKS };
+    CV_WRAP static Ptr<ExposureCompensator> createDefault(int type);
+
+    /**
+    @param corners Source image top-left corners
+    @param images Source images
+    @param masks Image masks to update (second value in pair specifies the value which should be used
+    to detect where image is)
+        */
+    CV_WRAP void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+        const std::vector<UMat> &masks);
+    /** @overload */
+    virtual void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+        const std::vector<std::pair<UMat, uchar> > &masks) = 0;
+    /** @brief Compensate exposure in the specified image.
+
+    @param index Image index
+    @param corner Image top-left corner
+    @param image Image to process
+    @param mask Image mask
+        */
+    CV_WRAP virtual void apply(int index, Point corner, InputOutputArray image, InputArray mask) = 0;
+    CV_WRAP virtual void getMatGains(CV_OUT std::vector<Mat>& ) {CV_Error(Error::StsInternal, "");};
+    CV_WRAP virtual void setMatGains(std::vector<Mat>& ) { CV_Error(Error::StsInternal, ""); };
+    CV_WRAP void setUpdateGain(bool b) { updateGain = b; };
+    CV_WRAP bool getUpdateGain() { return updateGain; };
+protected :
+    bool updateGain;
+};
+
+/** @brief Stub exposure compensator which does nothing.
+ */
+class CV_EXPORTS_W NoExposureCompensator : public ExposureCompensator
+{
+public:
+    void feed(const std::vector<Point> &/*corners*/, const std::vector<UMat> &/*images*/,
+              const std::vector<std::pair<UMat,uchar> > &/*masks*/) CV_OVERRIDE { }
+    CV_WRAP void apply(int /*index*/, Point /*corner*/, InputOutputArray /*image*/, InputArray /*mask*/) CV_OVERRIDE { }
+    CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE { umv.clear(); return; };
+    CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE { umv.clear(); return; };
+};
+
+/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image
+intensities, see @cite BL07 and @cite WJ10 for details.
+ */
+class CV_EXPORTS_W GainCompensator : public ExposureCompensator
+{
+public:
+    // This Constructor only exists to make source level compatibility detector happy
+    CV_WRAP GainCompensator()
+            : GainCompensator(1) {}
+    CV_WRAP GainCompensator(int nr_feeds)
+            : nr_feeds_(nr_feeds), similarity_threshold_(1) {}
+    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+              const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
+    void singleFeed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+                    const std::vector<std::pair<UMat,uchar> > &masks);
+    CV_WRAP void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE;
+    CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE ;
+    CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE ;
+    CV_WRAP void setNrFeeds(int nr_feeds) { nr_feeds_ = nr_feeds; }
+    CV_WRAP int getNrFeeds() { return nr_feeds_; }
+    CV_WRAP void setSimilarityThreshold(double similarity_threshold) { similarity_threshold_ = similarity_threshold; }
+    CV_WRAP double getSimilarityThreshold() const { return similarity_threshold_; }
+    void prepareSimilarityMask(const std::vector<Point> &corners, const std::vector<UMat> &images);
+    std::vector<double> gains() const;
+
+private:
+    UMat buildSimilarityMask(InputArray src_array1, InputArray src_array2);
+
+    Mat_<double> gains_;
+    int nr_feeds_;
+    double similarity_threshold_;
+    std::vector<UMat> similarities_;
+};
+
+/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image
+intensities on each channel independently.
+ */
+class CV_EXPORTS_W ChannelsCompensator : public ExposureCompensator
+{
+public:
+    CV_WRAP ChannelsCompensator(int nr_feeds=1)
+        : nr_feeds_(nr_feeds), similarity_threshold_(1) {}
+    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+              const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
+    CV_WRAP void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE;
+    CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE;
+    CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE;
+    CV_WRAP void setNrFeeds(int nr_feeds) { nr_feeds_ = nr_feeds; }
+    CV_WRAP int getNrFeeds() { return nr_feeds_; }
+    CV_WRAP void setSimilarityThreshold(double similarity_threshold) { similarity_threshold_ = similarity_threshold; }
+    CV_WRAP double getSimilarityThreshold() const { return similarity_threshold_; }
+    std::vector<Scalar> gains() const { return gains_; }
+
+private:
+    std::vector<Scalar> gains_;
+    int nr_feeds_;
+    double similarity_threshold_;
+};
+
+/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image blocks.
+ */
+class CV_EXPORTS_W BlocksCompensator : public ExposureCompensator
+{
+public:
+    BlocksCompensator(int bl_width=32, int bl_height=32, int nr_feeds=1)
+            : bl_width_(bl_width), bl_height_(bl_height), nr_feeds_(nr_feeds), nr_gain_filtering_iterations_(2),
+              similarity_threshold_(1) {}
+    CV_WRAP void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE;
+    CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE;
+    CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE;
+    CV_WRAP void setNrFeeds(int nr_feeds) { nr_feeds_ = nr_feeds; }
+    CV_WRAP int getNrFeeds() { return nr_feeds_; }
+    CV_WRAP void setSimilarityThreshold(double similarity_threshold) { similarity_threshold_ = similarity_threshold; }
+    CV_WRAP double getSimilarityThreshold() const { return similarity_threshold_; }
+    CV_WRAP void setBlockSize(int width, int height) { bl_width_ = width; bl_height_ = height; }
+    CV_WRAP void setBlockSize(Size size) { setBlockSize(size.width, size.height); }
+    CV_WRAP Size getBlockSize() const { return Size(bl_width_, bl_height_); }
+    CV_WRAP void setNrGainsFilteringIterations(int nr_iterations) { nr_gain_filtering_iterations_ = nr_iterations; }
+    CV_WRAP int getNrGainsFilteringIterations() const { return nr_gain_filtering_iterations_; }
+
+protected:
+    template<class Compensator>
+    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+              const std::vector<std::pair<UMat,uchar> > &masks);
+
+private:
+    UMat getGainMap(const GainCompensator& compensator, int bl_idx, Size bl_per_img);
+    UMat getGainMap(const ChannelsCompensator& compensator, int bl_idx, Size bl_per_img);
+
+    int bl_width_, bl_height_;
+    std::vector<UMat> gain_maps_;
+    int nr_feeds_;
+    int nr_gain_filtering_iterations_;
+    double similarity_threshold_;
+};
+
+/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image block
+intensities, see @cite UES01 for details.
+ */
+class CV_EXPORTS_W BlocksGainCompensator : public BlocksCompensator
+{
+public:
+    // This Constructor only exists to make source level compatibility detector happy
+    CV_WRAP BlocksGainCompensator(int bl_width = 32, int bl_height = 32)
+            : BlocksGainCompensator(bl_width, bl_height, 1) {}
+    CV_WRAP BlocksGainCompensator(int bl_width, int bl_height, int nr_feeds)
+            : BlocksCompensator(bl_width, bl_height, nr_feeds) {}
+
+    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+              const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
+
+    // This function only exists to make source level compatibility detector happy
+    CV_WRAP void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE {
+        BlocksCompensator::apply(index, corner, image, mask); }
+    // This function only exists to make source level compatibility detector happy
+    CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE { BlocksCompensator::getMatGains(umv); }
+    // This function only exists to make source level compatibility detector happy
+    CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE { BlocksCompensator::setMatGains(umv); }
+};
+
+/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image block
+on each channel.
+ */
+class CV_EXPORTS_W BlocksChannelsCompensator : public BlocksCompensator
+{
+public:
+    CV_WRAP BlocksChannelsCompensator(int bl_width=32, int bl_height=32, int nr_feeds=1)
+            : BlocksCompensator(bl_width, bl_height, nr_feeds) {}
+
+    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
+              const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
+};
+//! @}
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/matchers.hpp b/3rdParty/include/opencv2/stitching/detail/matchers.hpp
new file mode 100644
index 0000000..cd9749c
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/matchers.hpp
@@ -0,0 +1,253 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_MATCHERS_HPP
+#define OPENCV_STITCHING_MATCHERS_HPP
+
+#include "opencv2/core.hpp"
+#include "opencv2/features2d.hpp"
+
+#include "opencv2/opencv_modules.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching_match
+//! @{
+
+/** @brief Structure containing image keypoints and descriptors. */
+struct CV_EXPORTS_W_SIMPLE ImageFeatures
+{
+    CV_PROP_RW int img_idx;
+    CV_PROP_RW Size img_size;
+    CV_PROP_RW std::vector<KeyPoint> keypoints;
+    CV_PROP_RW UMat descriptors;
+    CV_WRAP std::vector<KeyPoint> getKeypoints() { return keypoints; };
+};
+/** @brief
+
+@param featuresFinder
+@param images
+@param features
+@param masks
+*/
+CV_EXPORTS_W void computeImageFeatures(
+    const Ptr<Feature2D> &featuresFinder,
+    InputArrayOfArrays  images,
+    CV_OUT std::vector<ImageFeatures> &features,
+    InputArrayOfArrays masks = noArray());
+
+/** @brief
+
+@param featuresFinder
+@param image
+@param features
+@param mask
+*/
+CV_EXPORTS_AS(computeImageFeatures2) void computeImageFeatures(
+    const Ptr<Feature2D> &featuresFinder,
+    InputArray image,
+    CV_OUT ImageFeatures &features,
+    InputArray mask = noArray());
+
+/** @brief Structure containing information about matches between two images.
+
+It's assumed that there is a transformation between those images. Transformation may be
+homography or affine transformation based on selected matcher.
+
+@sa detail::FeaturesMatcher
+*/
+struct CV_EXPORTS_W_SIMPLE MatchesInfo
+{
+    MatchesInfo();
+    MatchesInfo(const MatchesInfo &other);
+    MatchesInfo& operator =(const MatchesInfo &other);
+
+    CV_PROP_RW int src_img_idx;
+    CV_PROP_RW int dst_img_idx;       //!< Images indices (optional)
+    std::vector<DMatch> matches;
+    std::vector<uchar> inliers_mask;    //!< Geometrically consistent matches mask
+    CV_PROP_RW int num_inliers;                    //!< Number of geometrically consistent matches
+    CV_PROP_RW Mat H;                              //!< Estimated transformation
+    CV_PROP_RW double confidence;                  //!< Confidence two images are from the same panorama
+    CV_WRAP std::vector<DMatch> getMatches() { return matches; };
+    CV_WRAP std::vector<uchar> getInliers() { return inliers_mask; };
+};
+
+/** @brief Feature matchers base class. */
+class CV_EXPORTS_W FeaturesMatcher
+{
+public:
+    CV_WRAP virtual ~FeaturesMatcher() {}
+
+    /** @overload
+    @param features1 First image features
+    @param features2 Second image features
+    @param matches_info Found matches
+    */
+    CV_WRAP_AS(apply) void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
+                     CV_OUT MatchesInfo& matches_info) { match(features1, features2, matches_info); }
+
+    /** @brief Performs images matching.
+
+    @param features Features of the source images
+    @param pairwise_matches Found pairwise matches
+    @param mask Mask indicating which image pairs must be matched
+
+    The function is parallelized with the TBB library.
+
+    @sa detail::MatchesInfo
+    */
+    CV_WRAP_AS(apply2) void operator ()(const std::vector<ImageFeatures> &features, CV_OUT std::vector<MatchesInfo> &pairwise_matches,
+                     const cv::UMat &mask = cv::UMat());
+
+    /** @return True, if it's possible to use the same matcher instance in parallel, false otherwise
+    */
+   CV_WRAP bool isThreadSafe() const { return is_thread_safe_; }
+
+    /** @brief Frees unused memory allocated before if there is any.
+    */
+   CV_WRAP virtual void collectGarbage() {}
+
+protected:
+    FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
+
+    /** @brief This method must implement matching logic in order to make the wrappers
+    detail::FeaturesMatcher::operator()_ work.
+
+    @param features1 first image features
+    @param features2 second image features
+    @param matches_info found matches
+     */
+    virtual void match(const ImageFeatures &features1, const ImageFeatures &features2,
+                       MatchesInfo& matches_info) = 0;
+
+    bool is_thread_safe_;
+};
+
+/** @brief Features matcher which finds two best matches for each feature and leaves the best one only if the
+ratio between descriptor distances is greater than the threshold match_conf
+
+@sa detail::FeaturesMatcher
+ */
+class CV_EXPORTS_W BestOf2NearestMatcher : public FeaturesMatcher
+{
+public:
+    /** @brief Constructs a "best of 2 nearest" matcher.
+
+    @param try_use_gpu Should try to use GPU or not
+    @param match_conf Match distances ration threshold
+    @param num_matches_thresh1 Minimum number of matches required for the 2D projective transform
+    estimation used in the inliers classification step
+    @param num_matches_thresh2 Minimum number of matches required for the 2D projective transform
+    re-estimation on inliers
+     */
+    CV_WRAP BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
+                          int num_matches_thresh2 = 6);
+
+    CV_WRAP void collectGarbage() CV_OVERRIDE;
+    CV_WRAP static Ptr<BestOf2NearestMatcher> create(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
+        int num_matches_thresh2 = 6);
+
+protected:
+
+    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
+    int num_matches_thresh1_;
+    int num_matches_thresh2_;
+    Ptr<FeaturesMatcher> impl_;
+};
+
+class CV_EXPORTS_W BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
+{
+public:
+    CV_WRAP BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f,
+                            int num_matches_thresh1 = 6, int num_matches_thresh2 = 6);
+
+    void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
+                     const cv::UMat &mask = cv::UMat());
+
+
+protected:
+    int range_width_;
+};
+
+/** @brief Features matcher similar to cv::detail::BestOf2NearestMatcher which
+finds two best matches for each feature and leaves the best one only if the
+ratio between descriptor distances is greater than the threshold match_conf.
+
+Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine
+transformation (affine transformation estimate will be placed in matches_info).
+
+@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
+ */
+class CV_EXPORTS_W AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
+{
+public:
+    /** @brief Constructs a "best of 2 nearest" matcher that expects affine transformation
+    between images
+
+    @param full_affine whether to use full affine transformation with 6 degress of freedom or reduced
+    transformation with 4 degrees of freedom using only rotation, translation and uniform scaling
+    @param try_use_gpu Should try to use GPU or not
+    @param match_conf Match distances ration threshold
+    @param num_matches_thresh1 Minimum number of matches required for the 2D affine transform
+    estimation used in the inliers classification step
+
+    @sa cv::estimateAffine2D cv::estimateAffinePartial2D
+     */
+    CV_WRAP AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false,
+                                float match_conf = 0.3f, int num_matches_thresh1 = 6) :
+        BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1),
+        full_affine_(full_affine) {}
+
+protected:
+    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
+
+    bool full_affine_;
+};
+
+//! @} stitching_match
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_STITCHING_MATCHERS_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/motion_estimators.hpp b/3rdParty/include/opencv2/stitching/detail/motion_estimators.hpp
new file mode 100644
index 0000000..ad21ee1
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/motion_estimators.hpp
@@ -0,0 +1,373 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
+#define OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
+
+#include "opencv2/core.hpp"
+#include "matchers.hpp"
+#include "util.hpp"
+#include "camera.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching_rotation
+//! @{
+
+/** @brief Rotation estimator base class.
+
+It takes features of all images, pairwise matches between all images and estimates rotations of all
+cameras.
+
+@note The coordinate system origin is implementation-dependent, but you can always normalize the
+rotations in respect to the first camera, for instance. :
+ */
+class CV_EXPORTS_W Estimator
+{
+public:
+    virtual ~Estimator() {}
+
+    /** @brief Estimates camera parameters.
+
+    @param features Features of images
+    @param pairwise_matches Pairwise matches of images
+    @param cameras Estimated camera parameters
+    @return True in case of success, false otherwise
+     */
+    CV_WRAP_AS(apply) bool operator ()(const std::vector<ImageFeatures> &features,
+        const std::vector<MatchesInfo> &pairwise_matches,
+        CV_OUT CV_IN_OUT std::vector<CameraParams> &cameras)
+    {
+        return estimate(features, pairwise_matches, cameras);
+    }
+
+protected:
+    /** @brief This method must implement camera parameters estimation logic in order to make the wrapper
+    detail::Estimator::operator()_ work.
+
+    @param features Features of images
+    @param pairwise_matches Pairwise matches of images
+    @param cameras Estimated camera parameters
+    @return True in case of success, false otherwise
+     */
+    virtual bool estimate(const std::vector<ImageFeatures> &features,
+                          const std::vector<MatchesInfo> &pairwise_matches,
+                          CV_OUT std::vector<CameraParams> &cameras) = 0;
+};
+
+/** @brief Homography based rotation estimator.
+ */
+class CV_EXPORTS_W HomographyBasedEstimator : public Estimator
+{
+public:
+    CV_WRAP HomographyBasedEstimator(bool is_focals_estimated = false)
+        : is_focals_estimated_(is_focals_estimated) {}
+
+private:
+    virtual bool estimate(const std::vector<ImageFeatures> &features,
+                          const std::vector<MatchesInfo> &pairwise_matches,
+                          std::vector<CameraParams> &cameras) CV_OVERRIDE;
+
+    bool is_focals_estimated_;
+};
+
+/** @brief Affine transformation based estimator.
+
+This estimator uses pairwise transformations estimated by matcher to estimate
+final transformation for each camera.
+
+@sa cv::detail::HomographyBasedEstimator
+ */
+class CV_EXPORTS_W AffineBasedEstimator : public Estimator
+{
+public:
+    CV_WRAP AffineBasedEstimator(){}
+private:
+    virtual bool estimate(const std::vector<ImageFeatures> &features,
+                          const std::vector<MatchesInfo> &pairwise_matches,
+                          std::vector<CameraParams> &cameras) CV_OVERRIDE;
+};
+
+/** @brief Base class for all camera parameters refinement methods.
+ */
+class CV_EXPORTS_W BundleAdjusterBase : public Estimator
+{
+public:
+    CV_WRAP const Mat refinementMask() const { return refinement_mask_.clone(); }
+    CV_WRAP void setRefinementMask(const Mat &mask)
+    {
+        CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3));
+        refinement_mask_ = mask.clone();
+    }
+
+    CV_WRAP double confThresh() const { return conf_thresh_; }
+    CV_WRAP void setConfThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
+
+    CV_WRAP TermCriteria termCriteria() { return term_criteria_; }
+    CV_WRAP void setTermCriteria(const TermCriteria& term_criteria) { term_criteria_ = term_criteria; }
+
+protected:
+    /** @brief Construct a bundle adjuster base instance.
+
+    @param num_params_per_cam Number of parameters per camera
+    @param num_errs_per_measurement Number of error terms (components) per match
+     */
+    BundleAdjusterBase(int num_params_per_cam, int num_errs_per_measurement)
+        : num_images_(0), total_num_matches_(0),
+          num_params_per_cam_(num_params_per_cam),
+          num_errs_per_measurement_(num_errs_per_measurement),
+          features_(0), pairwise_matches_(0), conf_thresh_(0)
+    {
+        setRefinementMask(Mat::ones(3, 3, CV_8U));
+        setConfThresh(1.);
+        setTermCriteria(TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 1000, DBL_EPSILON));
+    }
+
+    // Runs bundle adjustment
+    virtual bool estimate(const std::vector<ImageFeatures> &features,
+                          const std::vector<MatchesInfo> &pairwise_matches,
+                          std::vector<CameraParams> &cameras) CV_OVERRIDE;
+
+    /** @brief Sets initial camera parameter to refine.
+
+    @param cameras Camera parameters
+     */
+    virtual void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) = 0;
+    /** @brief Gets the refined camera parameters.
+
+    @param cameras Refined camera parameters
+     */
+    virtual void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const = 0;
+    /** @brief Calculates error vector.
+
+    @param err Error column-vector of length total_num_matches \* num_errs_per_measurement
+     */
+    virtual void calcError(Mat &err) = 0;
+    /** @brief Calculates the cost function jacobian.
+
+    @param jac Jacobian matrix of dimensions
+    (total_num_matches \* num_errs_per_measurement) x (num_images \* num_params_per_cam)
+     */
+    virtual void calcJacobian(Mat &jac) = 0;
+
+    // 3x3 8U mask, where 0 means don't refine respective parameter, != 0 means refine
+    Mat refinement_mask_;
+
+    int num_images_;
+    int total_num_matches_;
+
+    int num_params_per_cam_;
+    int num_errs_per_measurement_;
+
+    const ImageFeatures *features_;
+    const MatchesInfo *pairwise_matches_;
+
+    // Threshold to filter out poorly matched image pairs
+    double conf_thresh_;
+
+    //Levenberg-Marquardt algorithm termination criteria
+    TermCriteria term_criteria_;
+
+    // Camera parameters matrix (CV_64F)
+    Mat cam_params_;
+
+    // Connected images pairs
+    std::vector<std::pair<int,int> > edges_;
+};
+
+
+/** @brief Stub bundle adjuster that does nothing.
+ */
+class CV_EXPORTS_W NoBundleAdjuster : public BundleAdjusterBase
+{
+public:
+    CV_WRAP NoBundleAdjuster() : BundleAdjusterBase(0, 0) {}
+
+private:
+    bool estimate(const std::vector<ImageFeatures> &, const std::vector<MatchesInfo> &,
+                  std::vector<CameraParams> &) CV_OVERRIDE
+    {
+        return true;
+    }
+    void setUpInitialCameraParams(const std::vector<CameraParams> &) CV_OVERRIDE {}
+    void obtainRefinedCameraParams(std::vector<CameraParams> &) const CV_OVERRIDE {}
+    void calcError(Mat &) CV_OVERRIDE {}
+    void calcJacobian(Mat &) CV_OVERRIDE {}
+};
+
+
+/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection
+error squares
+
+It can estimate focal length, aspect ratio, principal point.
+You can affect only on them via the refinement mask.
+ */
+class CV_EXPORTS_W BundleAdjusterReproj : public BundleAdjusterBase
+{
+public:
+    CV_WRAP BundleAdjusterReproj() : BundleAdjusterBase(7, 2) {}
+
+private:
+    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
+    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
+    void calcError(Mat &err) CV_OVERRIDE;
+    void calcJacobian(Mat &jac) CV_OVERRIDE;
+
+    Mat err1_, err2_;
+};
+
+
+/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the distances
+between the rays passing through the camera center and a feature. :
+
+It can estimate focal length. It ignores the refinement mask for now.
+ */
+class CV_EXPORTS_W BundleAdjusterRay : public BundleAdjusterBase
+{
+public:
+    CV_WRAP BundleAdjusterRay() : BundleAdjusterBase(4, 3) {}
+
+private:
+    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
+    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
+    void calcError(Mat &err) CV_OVERRIDE;
+    void calcJacobian(Mat &jac) CV_OVERRIDE;
+
+    Mat err1_, err2_;
+};
+
+
+/** @brief Bundle adjuster that expects affine transformation
+represented in homogeneous coordinates in R for each camera param. Implements
+camera parameters refinement algorithm which minimizes sum of the reprojection
+error squares
+
+It estimates all transformation parameters. Refinement mask is ignored.
+
+@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffinePartial
+ */
+class CV_EXPORTS_W BundleAdjusterAffine : public BundleAdjusterBase
+{
+public:
+    CV_WRAP BundleAdjusterAffine() : BundleAdjusterBase(6, 2) {}
+
+private:
+    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
+    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
+    void calcError(Mat &err) CV_OVERRIDE;
+    void calcJacobian(Mat &jac) CV_OVERRIDE;
+
+    Mat err1_, err2_;
+};
+
+
+/** @brief Bundle adjuster that expects affine transformation with 4 DOF
+represented in homogeneous coordinates in R for each camera param. Implements
+camera parameters refinement algorithm which minimizes sum of the reprojection
+error squares
+
+It estimates all transformation parameters. Refinement mask is ignored.
+
+@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffine
+ */
+class CV_EXPORTS_W BundleAdjusterAffinePartial : public BundleAdjusterBase
+{
+public:
+    CV_WRAP BundleAdjusterAffinePartial() : BundleAdjusterBase(4, 2) {}
+
+private:
+    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
+    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
+    void calcError(Mat &err) CV_OVERRIDE;
+    void calcJacobian(Mat &jac) CV_OVERRIDE;
+
+    Mat err1_, err2_;
+};
+
+
+enum WaveCorrectKind
+{
+    WAVE_CORRECT_HORIZ,
+    WAVE_CORRECT_VERT,
+    WAVE_CORRECT_AUTO
+};
+
+/** @brief Tries to detect the wave correction kind depending
+on whether a panorama spans horizontally or vertically
+
+@param rmats Camera rotation matrices.
+@return The correction kind to use for this panorama
+ */
+CV_EXPORTS
+WaveCorrectKind autoDetectWaveCorrectKind(const std::vector<Mat> &rmats);
+
+/** @brief Tries to make panorama more horizontal (or vertical).
+
+@param rmats Camera rotation matrices.
+@param kind Correction kind, see detail::WaveCorrectKind.
+ */
+void CV_EXPORTS_W waveCorrect(CV_IN_OUT std::vector<Mat> &rmats, WaveCorrectKind kind);
+
+
+//////////////////////////////////////////////////////////////////////////////
+// Auxiliary functions
+
+// Returns matches graph representation in DOT language
+String CV_EXPORTS_W matchesGraphAsString(std::vector<String> &pathes, std::vector<MatchesInfo> &pairwise_matches,
+                                            float conf_threshold);
+
+CV_EXPORTS_W std::vector<int>  leaveBiggestComponent(
+        std::vector<ImageFeatures> &features,
+        std::vector<MatchesInfo> &pairwise_matches,
+        float conf_threshold);
+
+void CV_EXPORTS findMaxSpanningTree(
+        int num_images, const std::vector<MatchesInfo> &pairwise_matches,
+        Graph &span_tree, std::vector<int> &centers);
+
+//! @} stitching_rotation
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/seam_finders.hpp b/3rdParty/include/opencv2/stitching/detail/seam_finders.hpp
new file mode 100644
index 0000000..71dae7f
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/seam_finders.hpp
@@ -0,0 +1,291 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_SEAM_FINDERS_HPP
+#define OPENCV_STITCHING_SEAM_FINDERS_HPP
+
+#include <set>
+#include "opencv2/core.hpp"
+#include "opencv2/opencv_modules.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching_seam
+//! @{
+
+/** @brief Base class for a seam estimator.
+ */
+class CV_EXPORTS_W SeamFinder
+{
+public:
+    CV_WRAP  virtual ~SeamFinder() {}
+    enum { NO, VORONOI_SEAM, DP_SEAM };
+    /** @brief Estimates seams.
+
+    @param src Source images
+    @param corners Source image top-left corners
+    @param masks Source image masks to update
+     */
+    CV_WRAP virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
+                      CV_IN_OUT std::vector<UMat> &masks) = 0;
+    CV_WRAP static Ptr<SeamFinder> createDefault(int type);
+};
+
+/** @brief Stub seam estimator which does nothing.
+ */
+class CV_EXPORTS_W NoSeamFinder : public SeamFinder
+{
+public:
+    CV_WRAP void find(const std::vector<UMat>&, const std::vector<Point>&, CV_IN_OUT std::vector<UMat>&) CV_OVERRIDE {}
+};
+
+/** @brief Base class for all pairwise seam estimators.
+ */
+class CV_EXPORTS_W PairwiseSeamFinder : public SeamFinder
+{
+public:
+    CV_WRAP virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
+                      CV_IN_OUT std::vector<UMat> &masks) CV_OVERRIDE;
+
+protected:
+    void run();
+    /** @brief Resolves masks intersection of two specified images in the given ROI.
+
+    @param first First image index
+    @param second Second image index
+    @param roi Region of interest
+     */
+    virtual void findInPair(size_t first, size_t second, Rect roi) = 0;
+
+    std::vector<UMat> images_;
+    std::vector<Size> sizes_;
+    std::vector<Point> corners_;
+    std::vector<UMat> masks_;
+};
+
+/** @brief Voronoi diagram-based seam estimator.
+ */
+class CV_EXPORTS_W VoronoiSeamFinder : public PairwiseSeamFinder
+{
+public:
+    CV_WRAP virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
+                      CV_IN_OUT std::vector<UMat> &masks) CV_OVERRIDE;
+    virtual void find(const std::vector<Size> &size, const std::vector<Point> &corners,
+                      std::vector<UMat> &masks);
+private:
+    void findInPair(size_t first, size_t second, Rect roi) CV_OVERRIDE;
+};
+
+
+class CV_EXPORTS_W DpSeamFinder : public SeamFinder
+{
+public:
+    enum CostFunction { COLOR, COLOR_GRAD };
+
+    DpSeamFinder(CostFunction costFunc = COLOR);
+    CV_WRAP DpSeamFinder(String costFunc );
+
+    CostFunction costFunction() const { return costFunc_; }
+    void setCostFunction(CostFunction val) { costFunc_ = val; }
+    CV_WRAP void setCostFunction(String val);
+
+    virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
+                      std::vector<UMat> &masks) CV_OVERRIDE;
+
+private:
+    enum ComponentState
+    {
+        FIRST = 1, SECOND = 2, INTERS = 4,
+        INTERS_FIRST = INTERS | FIRST,
+        INTERS_SECOND = INTERS | SECOND
+    };
+
+    class ImagePairLess
+    {
+    public:
+        ImagePairLess(const std::vector<Mat> &images, const std::vector<Point> &corners)
+            : src_(&images[0]), corners_(&corners[0]) {}
+
+        bool operator() (const std::pair<size_t, size_t> &l, const std::pair<size_t, size_t> &r) const
+        {
+            Point c1 = corners_[l.first] + Point(src_[l.first].cols / 2, src_[l.first].rows / 2);
+            Point c2 = corners_[l.second] + Point(src_[l.second].cols / 2, src_[l.second].rows / 2);
+            int d1 = (c1 - c2).dot(c1 - c2);
+
+            c1 = corners_[r.first] + Point(src_[r.first].cols / 2, src_[r.first].rows / 2);
+            c2 = corners_[r.second] + Point(src_[r.second].cols / 2, src_[r.second].rows / 2);
+            int d2 = (c1 - c2).dot(c1 - c2);
+
+            return d1 < d2;
+        }
+
+    private:
+        const Mat *src_;
+        const Point *corners_;
+    };
+
+    class ClosePoints
+    {
+    public:
+        ClosePoints(int minDist) : minDist_(minDist) {}
+
+        bool operator() (const Point &p1, const Point &p2) const
+        {
+            int dist2 = (p1.x-p2.x) * (p1.x-p2.x) + (p1.y-p2.y) * (p1.y-p2.y);
+            return dist2 < minDist_ * minDist_;
+        }
+
+    private:
+        int minDist_;
+    };
+
+    void process(
+            const Mat &image1, const Mat &image2, Point tl1, Point tl2,  Mat &mask1, Mat &mask2);
+
+    void findComponents();
+
+    void findEdges();
+
+    void resolveConflicts(
+            const Mat &image1, const Mat &image2, Point tl1, Point tl2, Mat &mask1, Mat &mask2);
+
+    void computeGradients(const Mat &image1, const Mat &image2);
+
+    bool hasOnlyOneNeighbor(int comp);
+
+    bool closeToContour(int y, int x, const Mat_<uchar> &contourMask);
+
+    bool getSeamTips(int comp1, int comp2, Point &p1, Point &p2);
+
+    void computeCosts(
+            const Mat &image1, const Mat &image2, Point tl1, Point tl2,
+            int comp, Mat_<float> &costV, Mat_<float> &costH);
+
+    bool estimateSeam(
+            const Mat &image1, const Mat &image2, Point tl1, Point tl2, int comp,
+            Point p1, Point p2, std::vector<Point> &seam, bool &isHorizontal);
+
+    void updateLabelsUsingSeam(
+            int comp1, int comp2, const std::vector<Point> &seam, bool isHorizontalSeam);
+
+    CostFunction costFunc_;
+
+    // processing images pair data
+    Point unionTl_, unionBr_;
+    Size unionSize_;
+    Mat_<uchar> mask1_, mask2_;
+    Mat_<uchar> contour1mask_, contour2mask_;
+    Mat_<float> gradx1_, grady1_;
+    Mat_<float> gradx2_, grady2_;
+
+    // components data
+    int ncomps_;
+    Mat_<int> labels_;
+    std::vector<ComponentState> states_;
+    std::vector<Point> tls_, brs_;
+    std::vector<std::vector<Point> > contours_;
+    std::set<std::pair<int, int> > edges_;
+};
+
+/** @brief Base class for all minimum graph-cut-based seam estimators.
+ */
+class CV_EXPORTS GraphCutSeamFinderBase
+{
+public:
+    enum CostType { COST_COLOR, COST_COLOR_GRAD };
+};
+
+/** @brief Minimum graph cut-based seam estimator. See details in @cite V03 .
+ */
+class CV_EXPORTS_W GraphCutSeamFinder : public GraphCutSeamFinderBase, public SeamFinder
+{
+public:
+    GraphCutSeamFinder(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f,
+                       float bad_region_penalty = 1000.f);
+    CV_WRAP GraphCutSeamFinder(String cost_type,float terminal_cost = 10000.f,
+        float bad_region_penalty = 1000.f);
+
+    ~GraphCutSeamFinder();
+
+    CV_WRAP void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
+              std::vector<UMat> &masks) CV_OVERRIDE;
+
+private:
+    // To avoid GCGraph dependency
+    class Impl;
+    Ptr<PairwiseSeamFinder> impl_;
+};
+
+
+#ifdef HAVE_OPENCV_CUDALEGACY
+class CV_EXPORTS GraphCutSeamFinderGpu : public GraphCutSeamFinderBase, public PairwiseSeamFinder
+{
+public:
+    GraphCutSeamFinderGpu(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f,
+                          float bad_region_penalty = 1000.f)
+                          : cost_type_(cost_type), terminal_cost_(terminal_cost),
+                            bad_region_penalty_(bad_region_penalty) {}
+
+    void find(const std::vector<cv::UMat> &src, const std::vector<cv::Point> &corners,
+              std::vector<cv::UMat> &masks) CV_OVERRIDE;
+    void findInPair(size_t first, size_t second, Rect roi) CV_OVERRIDE;
+
+private:
+    void setGraphWeightsColor(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &mask1, const cv::Mat &mask2,
+                              cv::Mat &terminals, cv::Mat &leftT, cv::Mat &rightT, cv::Mat &top, cv::Mat &bottom);
+    void setGraphWeightsColorGrad(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &dx1, const cv::Mat &dx2,
+                                  const cv::Mat &dy1, const cv::Mat &dy2, const cv::Mat &mask1, const cv::Mat &mask2,
+                                  cv::Mat &terminals, cv::Mat &leftT, cv::Mat &rightT, cv::Mat &top, cv::Mat &bottom);
+    std::vector<Mat> dx_, dy_;
+    int cost_type_;
+    float terminal_cost_;
+    float bad_region_penalty_;
+};
+#endif
+
+//! @}
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_STITCHING_SEAM_FINDERS_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/timelapsers.hpp b/3rdParty/include/opencv2/stitching/detail/timelapsers.hpp
new file mode 100644
index 0000000..f6f3da8
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/timelapsers.hpp
@@ -0,0 +1,91 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+
+#ifndef OPENCV_STITCHING_TIMELAPSERS_HPP
+#define OPENCV_STITCHING_TIMELAPSERS_HPP
+
+#include "opencv2/core.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching
+//! @{
+
+//  Base Timelapser class, takes a sequence of images, applies appropriate shift, stores result in dst_.
+
+class CV_EXPORTS_W Timelapser
+{
+public:
+
+    enum {AS_IS, CROP};
+
+    virtual ~Timelapser() {}
+
+    CV_WRAP static Ptr<Timelapser> createDefault(int type);
+
+    CV_WRAP virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes);
+    CV_WRAP virtual void process(InputArray img, InputArray mask, Point tl);
+    CV_WRAP virtual const UMat& getDst() {return dst_;}
+
+protected:
+
+    virtual bool test_point(Point pt);
+
+    UMat dst_;
+    Rect dst_roi_;
+};
+
+
+class CV_EXPORTS_W TimelapserCrop : public Timelapser
+{
+public:
+    virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes) CV_OVERRIDE;
+};
+
+//! @}
+
+} // namespace detail
+} // namespace cv
+
+#endif // OPENCV_STITCHING_TIMELAPSERS_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/util.hpp b/3rdParty/include/opencv2/stitching/detail/util.hpp
new file mode 100644
index 0000000..bf7a390
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/util.hpp
@@ -0,0 +1,121 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_UTIL_HPP
+#define OPENCV_STITCHING_UTIL_HPP
+
+#include <list>
+#include "opencv2/core.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching
+//! @{
+
+class CV_EXPORTS DisjointSets
+{
+public:
+    DisjointSets(int elem_count = 0) { createOneElemSets(elem_count); }
+
+    void createOneElemSets(int elem_count);
+    int findSetByElem(int elem);
+    int mergeSets(int set1, int set2);
+
+    std::vector<int> parent;
+    std::vector<int> size;
+
+private:
+    std::vector<int> rank_;
+};
+
+
+struct CV_EXPORTS GraphEdge
+{
+    GraphEdge(int from, int to, float weight);
+    bool operator <(const GraphEdge& other) const { return weight < other.weight; }
+    bool operator >(const GraphEdge& other) const { return weight > other.weight; }
+
+    int from, to;
+    float weight;
+};
+
+inline GraphEdge::GraphEdge(int _from, int _to, float _weight) : from(_from), to(_to), weight(_weight) {}
+
+
+class CV_EXPORTS Graph
+{
+public:
+    Graph(int num_vertices = 0) { create(num_vertices); }
+    void create(int num_vertices) { edges_.assign(num_vertices, std::list<GraphEdge>()); }
+    int numVertices() const { return static_cast<int>(edges_.size()); }
+    void addEdge(int from, int to, float weight);
+    template <typename B> B forEach(B body) const;
+    template <typename B> B walkBreadthFirst(int from, B body) const;
+
+private:
+    std::vector< std::list<GraphEdge> > edges_;
+};
+
+
+//////////////////////////////////////////////////////////////////////////////
+// Auxiliary functions
+
+CV_EXPORTS_W bool overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi);
+CV_EXPORTS_W Rect resultRoi(const std::vector<Point> &corners, const std::vector<UMat> &images);
+CV_EXPORTS_W Rect resultRoi(const std::vector<Point> &corners, const std::vector<Size> &sizes);
+CV_EXPORTS_W Rect resultRoiIntersection(const std::vector<Point> &corners, const std::vector<Size> &sizes);
+CV_EXPORTS_W Point resultTl(const std::vector<Point> &corners);
+
+// Returns random 'count' element subset of the {0,1,...,size-1} set
+CV_EXPORTS_W void selectRandomSubset(int count, int size, std::vector<int> &subset);
+
+CV_EXPORTS_W int& stitchingLogLevel();
+
+//! @}
+
+} // namespace detail
+} // namespace cv
+
+#include "util_inl.hpp"
+
+#endif // OPENCV_STITCHING_UTIL_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/util_inl.hpp b/3rdParty/include/opencv2/stitching/detail/util_inl.hpp
new file mode 100644
index 0000000..dafab8b
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/util_inl.hpp
@@ -0,0 +1,131 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_UTIL_INL_HPP
+#define OPENCV_STITCHING_UTIL_INL_HPP
+
+#include <queue>
+#include "opencv2/core.hpp"
+#include "util.hpp" // Make your IDE see declarations
+
+//! @cond IGNORED
+
+namespace cv {
+namespace detail {
+
+template <typename B>
+B Graph::forEach(B body) const
+{
+    for (int i = 0; i < numVertices(); ++i)
+    {
+        std::list<GraphEdge>::const_iterator edge = edges_[i].begin();
+        for (; edge != edges_[i].end(); ++edge)
+            body(*edge);
+    }
+    return body;
+}
+
+
+template <typename B>
+B Graph::walkBreadthFirst(int from, B body) const
+{
+    std::vector<bool> was(numVertices(), false);
+    std::queue<int> vertices;
+
+    was[from] = true;
+    vertices.push(from);
+
+    while (!vertices.empty())
+    {
+        int vertex = vertices.front();
+        vertices.pop();
+
+        std::list<GraphEdge>::const_iterator edge = edges_[vertex].begin();
+        for (; edge != edges_[vertex].end(); ++edge)
+        {
+            if (!was[edge->to])
+            {
+                body(*edge);
+                was[edge->to] = true;
+                vertices.push(edge->to);
+            }
+        }
+    }
+
+    return body;
+}
+
+
+//////////////////////////////////////////////////////////////////////////////
+// Some auxiliary math functions
+
+static inline
+float normL2(const Point3f& a)
+{
+    return a.x * a.x + a.y * a.y + a.z * a.z;
+}
+
+
+static inline
+float normL2(const Point3f& a, const Point3f& b)
+{
+    return normL2(a - b);
+}
+
+
+static inline
+double normL2sq(const Mat &r)
+{
+    return r.dot(r);
+}
+
+
+static inline int sqr(int x) { return x * x; }
+static inline float sqr(float x) { return x * x; }
+static inline double sqr(double x) { return x * x; }
+
+} // namespace detail
+} // namespace cv
+
+//! @endcond
+
+#endif // OPENCV_STITCHING_UTIL_INL_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/warpers.hpp b/3rdParty/include/opencv2/stitching/detail/warpers.hpp
new file mode 100644
index 0000000..ff005e8
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/warpers.hpp
@@ -0,0 +1,682 @@
+ /*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_WARPERS_HPP
+#define OPENCV_STITCHING_WARPERS_HPP
+
+#include "opencv2/core.hpp"
+#include "opencv2/core/cuda.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/opencv_modules.hpp"
+
+namespace cv {
+namespace detail {
+
+//! @addtogroup stitching_warp
+//! @{
+
+/** @brief Rotation-only model image warper interface.
+ */
+class CV_EXPORTS RotationWarper
+{
+public:
+    virtual ~RotationWarper() {}
+
+    /** @brief Projects the image point.
+
+    @param pt Source point
+    @param K Camera intrinsic parameters
+    @param R Camera rotation matrix
+    @return Projected point
+     */
+    virtual Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) = 0;
+
+    /** @brief Projects the image point backward.
+
+    @param pt Projected point
+    @param K Camera intrinsic parameters
+    @param R Camera rotation matrix
+    @return Backward-projected point
+    */
+#if CV_VERSION_MAJOR == 4
+    virtual Point2f warpPointBackward(const Point2f& pt, InputArray K, InputArray R)
+    {
+        CV_UNUSED(pt); CV_UNUSED(K); CV_UNUSED(R);
+        CV_Error(Error::StsNotImplemented, "");
+    }
+#else
+    virtual Point2f warpPointBackward(const Point2f& pt, InputArray K, InputArray R) = 0;
+#endif
+
+    /** @brief Builds the projection maps according to the given camera data.
+
+    @param src_size Source image size
+    @param K Camera intrinsic parameters
+    @param R Camera rotation matrix
+    @param xmap Projection map for the x axis
+    @param ymap Projection map for the y axis
+    @return Projected image minimum bounding box
+     */
+    virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) = 0;
+
+    /** @brief Projects the image.
+
+    @param src Source image
+    @param K Camera intrinsic parameters
+    @param R Camera rotation matrix
+    @param interp_mode Interpolation mode
+    @param border_mode Border extrapolation mode
+    @param dst Projected image
+    @return Project image top-left corner
+     */
+    virtual Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+                       CV_OUT OutputArray dst) = 0;
+
+    /** @brief Projects the image backward.
+
+    @param src Projected image
+    @param K Camera intrinsic parameters
+    @param R Camera rotation matrix
+    @param interp_mode Interpolation mode
+    @param border_mode Border extrapolation mode
+    @param dst_size Backward-projected image size
+    @param dst Backward-projected image
+     */
+    virtual void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+                              Size dst_size, CV_OUT OutputArray dst) = 0;
+
+    /**
+    @param src_size Source image bounding box
+    @param K Camera intrinsic parameters
+    @param R Camera rotation matrix
+    @return Projected image minimum bounding box
+     */
+    virtual Rect warpRoi(Size src_size, InputArray K, InputArray R) = 0;
+
+    virtual float getScale() const { return 1.f; }
+    virtual void setScale(float) {}
+};
+
+/** @brief Base class for warping logic implementation.
+ */
+struct CV_EXPORTS_W_SIMPLE ProjectorBase
+{
+    void setCameraParams(InputArray K = Mat::eye(3, 3, CV_32F),
+                         InputArray R = Mat::eye(3, 3, CV_32F),
+                         InputArray T = Mat::zeros(3, 1, CV_32F));
+
+    float scale;
+    float k[9];
+    float rinv[9];
+    float r_kinv[9];
+    float k_rinv[9];
+    float t[3];
+};
+
+/** @brief Base class for rotation-based warper using a detail::ProjectorBase_ derived class.
+ */
+template <class P>
+class CV_EXPORTS_TEMPLATE RotationWarperBase : public RotationWarper
+{
+public:
+    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
+
+    Point2f warpPointBackward(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
+
+    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+               OutputArray dst) CV_OVERRIDE;
+
+    void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+                      Size dst_size, OutputArray dst) CV_OVERRIDE;
+
+    Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE;
+
+    float getScale() const  CV_OVERRIDE{ return projector_.scale; }
+    void setScale(float val) CV_OVERRIDE { projector_.scale = val; }
+
+protected:
+
+    // Detects ROI of the destination image. It's correct for any projection.
+    virtual void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br);
+
+    // Detects ROI of the destination image by walking over image border.
+    // Correctness for any projection isn't guaranteed.
+    void detectResultRoiByBorder(Size src_size, Point &dst_tl, Point &dst_br);
+
+    P projector_;
+};
+
+
+struct CV_EXPORTS PlaneProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+/** @brief Warper that maps an image onto the z = 1 plane.
+ */
+class CV_EXPORTS PlaneWarper : public RotationWarperBase<PlaneProjector>
+{
+public:
+    /** @brief Construct an instance of the plane warper class.
+
+    @param scale Projected image scale multiplier
+     */
+    PlaneWarper(float scale = 1.f) { projector_.scale = scale; }
+
+    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
+    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R, InputArray T);
+
+    Point2f warpPointBackward(const Point2f& pt, InputArray K, InputArray R) CV_OVERRIDE;
+    Point2f warpPointBackward(const Point2f& pt, InputArray K, InputArray R, InputArray T);
+
+    virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, CV_OUT OutputArray xmap, CV_OUT OutputArray ymap);
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, CV_OUT OutputArray xmap, CV_OUT OutputArray ymap) CV_OVERRIDE;
+
+    Point warp(InputArray src, InputArray K, InputArray R,
+               int interp_mode, int border_mode, CV_OUT OutputArray dst) CV_OVERRIDE;
+    virtual Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
+        CV_OUT OutputArray dst);
+
+    Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE;
+    Rect warpRoi(Size src_size, InputArray K, InputArray R, InputArray T);
+
+protected:
+    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE;
+};
+
+
+/** @brief Affine warper that uses rotations and translations
+
+ Uses affine transformation in homogeneous coordinates to represent both rotation and
+ translation in camera rotation matrix.
+ */
+class CV_EXPORTS AffineWarper : public PlaneWarper
+{
+public:
+    /** @brief Construct an instance of the affine warper class.
+
+    @param scale Projected image scale multiplier
+     */
+    AffineWarper(float scale = 1.f) : PlaneWarper(scale) {}
+
+    /** @brief Projects the image point.
+
+    @param pt Source point
+    @param K Camera intrinsic parameters
+    @param H Camera extrinsic parameters
+    @return Projected point
+     */
+    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray H) CV_OVERRIDE;
+
+    /** @brief Projects the image point backward.
+
+    @param pt Projected point
+    @param K Camera intrinsic parameters
+    @param H Camera extrinsic parameters
+    @return Backward-projected point
+    */
+    Point2f warpPointBackward(const Point2f &pt, InputArray K, InputArray H) CV_OVERRIDE;
+
+    /** @brief Builds the projection maps according to the given camera data.
+
+    @param src_size Source image size
+    @param K Camera intrinsic parameters
+    @param H Camera extrinsic parameters
+    @param xmap Projection map for the x axis
+    @param ymap Projection map for the y axis
+    @return Projected image minimum bounding box
+     */
+    Rect buildMaps(Size src_size, InputArray K, InputArray H, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
+
+    /** @brief Projects the image.
+
+    @param src Source image
+    @param K Camera intrinsic parameters
+    @param H Camera extrinsic parameters
+    @param interp_mode Interpolation mode
+    @param border_mode Border extrapolation mode
+    @param dst Projected image
+    @return Project image top-left corner
+     */
+    Point warp(InputArray src, InputArray K, InputArray H,
+               int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
+
+    /**
+    @param src_size Source image bounding box
+    @param K Camera intrinsic parameters
+    @param H Camera extrinsic parameters
+    @return Projected image minimum bounding box
+     */
+    Rect warpRoi(Size src_size, InputArray K, InputArray H) CV_OVERRIDE;
+
+protected:
+    /** @brief Extracts rotation and translation matrices from matrix H representing
+        affine transformation in homogeneous coordinates
+     */
+    void getRTfromHomogeneous(InputArray H, Mat &R, Mat &T);
+};
+
+
+struct CV_EXPORTS_W_SIMPLE SphericalProjector : ProjectorBase
+{
+    CV_WRAP void mapForward(float x, float y, float &u, float &v);
+    CV_WRAP void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+/** @brief Warper that maps an image onto the unit sphere located at the origin.
+
+ Projects image onto unit sphere with origin at (0, 0, 0) and radius scale, measured in pixels.
+ A 360 panorama would therefore have a resulting width of 2 * scale * PI pixels.
+ Poles are located at (0, -1, 0) and (0, 1, 0) points.
+*/
+class CV_EXPORTS SphericalWarper : public RotationWarperBase<SphericalProjector>
+{
+public:
+    /** @brief Construct an instance of the spherical warper class.
+
+    @param scale Radius of the projected sphere, in pixels. An image spanning the
+                 whole sphere will have a width of 2 * scale * PI pixels.
+     */
+    SphericalWarper(float scale) { projector_.scale = scale; }
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
+    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
+protected:
+    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE;
+};
+
+
+struct CV_EXPORTS CylindricalProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+/** @brief Warper that maps an image onto the x\*x + z\*z = 1 cylinder.
+ */
+class CV_EXPORTS CylindricalWarper : public RotationWarperBase<CylindricalProjector>
+{
+public:
+    /** @brief Construct an instance of the cylindrical warper class.
+
+    @param scale Projected image scale multiplier
+     */
+    CylindricalWarper(float scale) { projector_.scale = scale; }
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
+    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
+protected:
+    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE
+    {
+        RotationWarperBase<CylindricalProjector>::detectResultRoiByBorder(src_size, dst_tl, dst_br);
+    }
+};
+
+
+struct CV_EXPORTS FisheyeProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS FisheyeWarper : public RotationWarperBase<FisheyeProjector>
+{
+public:
+    FisheyeWarper(float scale) { projector_.scale = scale; }
+};
+
+
+struct CV_EXPORTS StereographicProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS StereographicWarper : public RotationWarperBase<StereographicProjector>
+{
+public:
+    StereographicWarper(float scale) { projector_.scale = scale; }
+};
+
+
+struct CV_EXPORTS CompressedRectilinearProjector : ProjectorBase
+{
+    float a, b;
+
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS CompressedRectilinearWarper : public RotationWarperBase<CompressedRectilinearProjector>
+{
+public:
+    CompressedRectilinearWarper(float scale, float A = 1, float B = 1)
+    {
+        projector_.a = A;
+        projector_.b = B;
+        projector_.scale = scale;
+    }
+};
+
+
+struct CV_EXPORTS CompressedRectilinearPortraitProjector : ProjectorBase
+{
+    float a, b;
+
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS CompressedRectilinearPortraitWarper : public RotationWarperBase<CompressedRectilinearPortraitProjector>
+{
+public:
+   CompressedRectilinearPortraitWarper(float scale, float A = 1, float B = 1)
+   {
+       projector_.a = A;
+       projector_.b = B;
+       projector_.scale = scale;
+   }
+};
+
+
+struct CV_EXPORTS PaniniProjector : ProjectorBase
+{
+    float a, b;
+
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS PaniniWarper : public RotationWarperBase<PaniniProjector>
+{
+public:
+   PaniniWarper(float scale, float A = 1, float B = 1)
+   {
+       projector_.a = A;
+       projector_.b = B;
+       projector_.scale = scale;
+   }
+};
+
+
+struct CV_EXPORTS PaniniPortraitProjector : ProjectorBase
+{
+    float a, b;
+
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS PaniniPortraitWarper : public RotationWarperBase<PaniniPortraitProjector>
+{
+public:
+   PaniniPortraitWarper(float scale, float A = 1, float B = 1)
+   {
+       projector_.a = A;
+       projector_.b = B;
+       projector_.scale = scale;
+   }
+
+};
+
+
+struct CV_EXPORTS MercatorProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS MercatorWarper : public RotationWarperBase<MercatorProjector>
+{
+public:
+    MercatorWarper(float scale) { projector_.scale = scale; }
+};
+
+
+struct CV_EXPORTS TransverseMercatorProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS TransverseMercatorWarper : public RotationWarperBase<TransverseMercatorProjector>
+{
+public:
+    TransverseMercatorWarper(float scale) { projector_.scale = scale; }
+};
+
+
+class CV_EXPORTS PlaneWarperGpu : public PlaneWarper
+{
+public:
+    PlaneWarperGpu(float scale = 1.f) : PlaneWarper(scale) {}
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
+    {
+        Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_);
+        d_xmap_.download(xmap);
+        d_ymap_.download(ymap);
+        return result;
+    }
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
+    {
+        Rect result = buildMaps(src_size, K, R, T, d_xmap_, d_ymap_);
+        d_xmap_.download(xmap);
+        d_ymap_.download(ymap);
+        return result;
+    }
+
+    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+               OutputArray dst) CV_OVERRIDE
+    {
+        d_src_.upload(src);
+        Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_);
+        d_dst_.download(dst);
+        return result;
+    }
+
+    Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
+               OutputArray dst) CV_OVERRIDE
+    {
+        d_src_.upload(src);
+        Point result = warp(d_src_, K, R, T, interp_mode, border_mode, d_dst_);
+        d_dst_.download(dst);
+        return result;
+    }
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
+
+    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode,
+               cuda::GpuMat & dst);
+
+    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
+               cuda::GpuMat & dst);
+
+private:
+    cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_;
+};
+
+
+class CV_EXPORTS SphericalWarperGpu : public SphericalWarper
+{
+public:
+    SphericalWarperGpu(float scale) : SphericalWarper(scale) {}
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
+    {
+        Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_);
+        d_xmap_.download(xmap);
+        d_ymap_.download(ymap);
+        return result;
+    }
+
+    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+               OutputArray dst) CV_OVERRIDE
+    {
+        d_src_.upload(src);
+        Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_);
+        d_dst_.download(dst);
+        return result;
+    }
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
+
+    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode,
+               cuda::GpuMat & dst);
+
+private:
+    cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_;
+};
+
+
+class CV_EXPORTS CylindricalWarperGpu : public CylindricalWarper
+{
+public:
+    CylindricalWarperGpu(float scale) : CylindricalWarper(scale) {}
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
+    {
+        Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_);
+        d_xmap_.download(xmap);
+        d_ymap_.download(ymap);
+        return result;
+    }
+
+    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+               OutputArray dst) CV_OVERRIDE
+    {
+        d_src_.upload(src);
+        Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_);
+        d_dst_.download(dst);
+        return result;
+    }
+
+    Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
+
+    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode,
+               cuda::GpuMat & dst);
+
+private:
+    cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_;
+};
+
+
+struct CV_EXPORTS SphericalPortraitProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+// Projects image onto unit sphere with origin at (0, 0, 0).
+// Poles are located NOT at (0, -1, 0) and (0, 1, 0) points, BUT at (1, 0, 0) and (-1, 0, 0) points.
+class CV_EXPORTS SphericalPortraitWarper : public RotationWarperBase<SphericalPortraitProjector>
+{
+public:
+    SphericalPortraitWarper(float scale) { projector_.scale = scale; }
+
+protected:
+    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE;
+};
+
+struct CV_EXPORTS CylindricalPortraitProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS CylindricalPortraitWarper : public RotationWarperBase<CylindricalPortraitProjector>
+{
+public:
+    CylindricalPortraitWarper(float scale) { projector_.scale = scale; }
+
+protected:
+    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE
+    {
+        RotationWarperBase<CylindricalPortraitProjector>::detectResultRoiByBorder(src_size, dst_tl, dst_br);
+    }
+};
+
+struct CV_EXPORTS PlanePortraitProjector : ProjectorBase
+{
+    void mapForward(float x, float y, float &u, float &v);
+    void mapBackward(float u, float v, float &x, float &y);
+};
+
+
+class CV_EXPORTS PlanePortraitWarper : public RotationWarperBase<PlanePortraitProjector>
+{
+public:
+    PlanePortraitWarper(float scale) { projector_.scale = scale; }
+
+protected:
+    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE
+    {
+        RotationWarperBase<PlanePortraitProjector>::detectResultRoiByBorder(src_size, dst_tl, dst_br);
+    }
+};
+
+//! @} stitching_warp
+
+} // namespace detail
+} // namespace cv
+
+#include "warpers_inl.hpp"
+
+#endif // OPENCV_STITCHING_WARPERS_HPP
diff --git a/3rdParty/include/opencv2/stitching/detail/warpers_inl.hpp b/3rdParty/include/opencv2/stitching/detail/warpers_inl.hpp
new file mode 100644
index 0000000..72b5c08
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/detail/warpers_inl.hpp
@@ -0,0 +1,782 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_WARPERS_INL_HPP
+#define OPENCV_STITCHING_WARPERS_INL_HPP
+
+#include "opencv2/core.hpp"
+#include "warpers.hpp" // Make your IDE see declarations
+#include <limits>
+
+//! @cond IGNORED
+
+namespace cv {
+namespace detail {
+
+template <class P>
+Point2f RotationWarperBase<P>::warpPoint(const Point2f &pt, InputArray K, InputArray R)
+{
+    projector_.setCameraParams(K, R);
+    Point2f uv;
+    projector_.mapForward(pt.x, pt.y, uv.x, uv.y);
+    return uv;
+}
+
+template <class P>
+Point2f RotationWarperBase<P>::warpPointBackward(const Point2f& pt, InputArray K, InputArray R)
+{
+    projector_.setCameraParams(K, R);
+    Point2f xy;
+    projector_.mapBackward(pt.x, pt.y, xy.x, xy.y);
+    return xy;
+}
+
+template <class P>
+Rect RotationWarperBase<P>::buildMaps(Size src_size, InputArray K, InputArray R, OutputArray _xmap, OutputArray _ymap)
+{
+    projector_.setCameraParams(K, R);
+
+    Point dst_tl, dst_br;
+    detectResultRoi(src_size, dst_tl, dst_br);
+
+    _xmap.create(dst_br.y - dst_tl.y + 1, dst_br.x - dst_tl.x + 1, CV_32F);
+    _ymap.create(dst_br.y - dst_tl.y + 1, dst_br.x - dst_tl.x + 1, CV_32F);
+
+    Mat xmap = _xmap.getMat(), ymap = _ymap.getMat();
+
+    float x, y;
+    for (int v = dst_tl.y; v <= dst_br.y; ++v)
+    {
+        for (int u = dst_tl.x; u <= dst_br.x; ++u)
+        {
+            projector_.mapBackward(static_cast<float>(u), static_cast<float>(v), x, y);
+            xmap.at<float>(v - dst_tl.y, u - dst_tl.x) = x;
+            ymap.at<float>(v - dst_tl.y, u - dst_tl.x) = y;
+        }
+    }
+
+    return Rect(dst_tl, dst_br);
+}
+
+
+template <class P>
+Point RotationWarperBase<P>::warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+                                  OutputArray dst)
+{
+    UMat xmap, ymap;
+    Rect dst_roi = buildMaps(src.size(), K, R, xmap, ymap);
+
+    dst.create(dst_roi.height + 1, dst_roi.width + 1, src.type());
+    remap(src, dst, xmap, ymap, interp_mode, border_mode);
+
+    return dst_roi.tl();
+}
+
+
+template <class P>
+void RotationWarperBase<P>::warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+                                         Size dst_size, OutputArray dst)
+{
+    projector_.setCameraParams(K, R);
+
+    Point src_tl, src_br;
+    detectResultRoi(dst_size, src_tl, src_br);
+
+    Size size = src.size();
+    CV_Assert(src_br.x - src_tl.x + 1 == size.width && src_br.y - src_tl.y + 1 == size.height);
+
+    Mat xmap(dst_size, CV_32F);
+    Mat ymap(dst_size, CV_32F);
+
+    float u, v;
+    for (int y = 0; y < dst_size.height; ++y)
+    {
+        for (int x = 0; x < dst_size.width; ++x)
+        {
+            projector_.mapForward(static_cast<float>(x), static_cast<float>(y), u, v);
+            xmap.at<float>(y, x) = u - src_tl.x;
+            ymap.at<float>(y, x) = v - src_tl.y;
+        }
+    }
+
+    dst.create(dst_size, src.type());
+    remap(src, dst, xmap, ymap, interp_mode, border_mode);
+}
+
+
+template <class P>
+Rect RotationWarperBase<P>::warpRoi(Size src_size, InputArray K, InputArray R)
+{
+    projector_.setCameraParams(K, R);
+
+    Point dst_tl, dst_br;
+    detectResultRoi(src_size, dst_tl, dst_br);
+
+    return Rect(dst_tl, Point(dst_br.x + 1, dst_br.y + 1));
+}
+
+
+template <class P>
+void RotationWarperBase<P>::detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br)
+{
+    float tl_uf = (std::numeric_limits<float>::max)();
+    float tl_vf = (std::numeric_limits<float>::max)();
+    float br_uf = -(std::numeric_limits<float>::max)();
+    float br_vf = -(std::numeric_limits<float>::max)();
+
+    float u, v;
+    for (int y = 0; y < src_size.height; ++y)
+    {
+        for (int x = 0; x < src_size.width; ++x)
+        {
+            projector_.mapForward(static_cast<float>(x), static_cast<float>(y), u, v);
+            tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
+            br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
+        }
+    }
+
+    dst_tl.x = static_cast<int>(tl_uf);
+    dst_tl.y = static_cast<int>(tl_vf);
+    dst_br.x = static_cast<int>(br_uf);
+    dst_br.y = static_cast<int>(br_vf);
+}
+
+
+template <class P>
+void RotationWarperBase<P>::detectResultRoiByBorder(Size src_size, Point &dst_tl, Point &dst_br)
+{
+    float tl_uf = (std::numeric_limits<float>::max)();
+    float tl_vf = (std::numeric_limits<float>::max)();
+    float br_uf = -(std::numeric_limits<float>::max)();
+    float br_vf = -(std::numeric_limits<float>::max)();
+
+    float u, v;
+    for (float x = 0; x < src_size.width; ++x)
+    {
+        projector_.mapForward(static_cast<float>(x), 0, u, v);
+        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
+        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
+
+        projector_.mapForward(static_cast<float>(x), static_cast<float>(src_size.height - 1), u, v);
+        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
+        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
+    }
+    for (int y = 0; y < src_size.height; ++y)
+    {
+        projector_.mapForward(0, static_cast<float>(y), u, v);
+        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
+        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
+
+        projector_.mapForward(static_cast<float>(src_size.width - 1), static_cast<float>(y), u, v);
+        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
+        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
+    }
+
+    dst_tl.x = static_cast<int>(tl_uf);
+    dst_tl.y = static_cast<int>(tl_vf);
+    dst_br.x = static_cast<int>(br_uf);
+    dst_br.y = static_cast<int>(br_vf);
+}
+
+
+inline
+void PlaneProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    x_ = t[0] + x_ / z_ * (1 - t[2]);
+    y_ = t[1] + y_ / z_ * (1 - t[2]);
+
+    u = scale * x_;
+    v = scale * y_;
+}
+
+
+inline
+void PlaneProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u = u / scale - t[0];
+    v = v / scale - t[1];
+
+    float z;
+    x = k_rinv[0] * u + k_rinv[1] * v + k_rinv[2] * (1 - t[2]);
+    y = k_rinv[3] * u + k_rinv[4] * v + k_rinv[5] * (1 - t[2]);
+    z = k_rinv[6] * u + k_rinv[7] * v + k_rinv[8] * (1 - t[2]);
+
+    x /= z;
+    y /= z;
+}
+
+
+inline
+void SphericalProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    u = scale * atan2f(x_, z_);
+    float w = y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_);
+    v = scale * (static_cast<float>(CV_PI) - acosf(w == w ? w : 0));
+}
+
+
+inline
+void SphericalProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float sinv = sinf(static_cast<float>(CV_PI) - v);
+    float x_ = sinv * sinf(u);
+    float y_ = cosf(static_cast<float>(CV_PI) - v);
+    float z_ = sinv * cosf(u);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+
+inline
+void CylindricalProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    u = scale * atan2f(x_, z_);
+    v = scale * y_ / sqrtf(x_ * x_ + z_ * z_);
+}
+
+
+inline
+void CylindricalProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float x_ = sinf(u);
+    float y_ = v;
+    float z_ = cosf(u);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void FisheyeProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = (float)CV_PI - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    u = scale * v_ * cosf(u_);
+    v = scale * v_ * sinf(u_);
+}
+
+inline
+void FisheyeProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float u_ = atan2f(v, u);
+    float v_ = sqrtf(u*u + v*v);
+
+    float sinv = sinf((float)CV_PI - v_);
+    float x_ = sinv * sinf(u_);
+    float y_ = cosf((float)CV_PI - v_);
+    float z_ = sinv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void StereographicProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = (float)CV_PI - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    float r = sinf(v_) / (1 - cosf(v_));
+
+    u = scale * r * std::cos(u_);
+    v = scale * r * std::sin(u_);
+}
+
+inline
+void StereographicProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float u_ = atan2f(v, u);
+    float r = sqrtf(u*u + v*v);
+    float v_ = 2 * atanf(1.f / r);
+
+    float sinv = sinf((float)CV_PI - v_);
+    float x_ = sinv * sinf(u_);
+    float y_ = cosf((float)CV_PI - v_);
+    float z_ = sinv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void CompressedRectilinearProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    u = scale * a * tanf(u_ / a);
+    v = scale * b * tanf(v_) / cosf(u_);
+}
+
+inline
+void CompressedRectilinearProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float aatg = a * atanf(u / a);
+    float u_ = aatg;
+    float v_ = atanf(v * cosf(aatg) / b);
+
+    float cosv = cosf(v_);
+    float x_ = cosv * sinf(u_);
+    float y_ = sinf(v_);
+    float z_ = cosv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void CompressedRectilinearPortraitProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float y_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float x_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    u = - scale * a * tanf(u_ / a);
+    v = scale * b * tanf(v_) / cosf(u_);
+}
+
+inline
+void CompressedRectilinearPortraitProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= - scale;
+    v /= scale;
+
+    float aatg = a * atanf(u / a);
+    float u_ = aatg;
+    float v_ = atanf(v * cosf( aatg ) / b);
+
+    float cosv = cosf(v_);
+    float y_ = cosv * sinf(u_);
+    float x_ = sinf(v_);
+    float z_ = cosv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void PaniniProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    float tg = a * tanf(u_ / a);
+    u = scale * tg;
+
+    float sinu = sinf(u_);
+    if ( fabs(sinu) < 1E-7 )
+        v = scale * b * tanf(v_);
+    else
+        v = scale * b * tg * tanf(v_) / sinu;
+}
+
+inline
+void PaniniProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float lamda = a * atanf(u / a);
+    float u_ = lamda;
+
+    float v_;
+    if ( fabs(lamda) > 1E-7)
+        v_ = atanf(v * sinf(lamda) / (b * a * tanf(lamda / a)));
+    else
+        v_ = atanf(v / b);
+
+    float cosv = cosf(v_);
+    float x_ = cosv * sinf(u_);
+    float y_ = sinf(v_);
+    float z_ = cosv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void PaniniPortraitProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float y_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float x_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    float tg = a * tanf(u_ / a);
+    u = - scale * tg;
+
+    float sinu = sinf( u_ );
+    if ( fabs(sinu) < 1E-7 )
+        v = scale * b * tanf(v_);
+    else
+        v = scale * b * tg * tanf(v_) / sinu;
+}
+
+inline
+void PaniniPortraitProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= - scale;
+    v /= scale;
+
+    float lamda = a * atanf(u / a);
+    float u_ = lamda;
+
+    float v_;
+    if ( fabs(lamda) > 1E-7)
+        v_ = atanf(v * sinf(lamda) / (b * a * tanf(lamda/a)));
+    else
+        v_ = atanf(v / b);
+
+    float cosv = cosf(v_);
+    float y_ = cosv * sinf(u_);
+    float x_ = sinf(v_);
+    float z_ = cosv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void MercatorProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    u = scale * u_;
+    v = scale * logf( tanf( (float)(CV_PI/4) + v_/2 ) );
+}
+
+inline
+void MercatorProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float v_ = atanf( sinhf(v) );
+    float u_ = u;
+
+    float cosv = cosf(v_);
+    float x_ = cosv * sinf(u_);
+    float y_ = sinf(v_);
+    float z_ = cosv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void TransverseMercatorProjector::mapForward(float x, float y, float &u, float &v)
+{
+    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float u_ = atan2f(x_, z_);
+    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
+
+    float B = cosf(v_) * sinf(u_);
+
+    u = scale / 2 * logf( (1+B) / (1-B) );
+    v = scale * atan2f(tanf(v_), cosf(u_));
+}
+
+inline
+void TransverseMercatorProjector::mapBackward(float u, float v, float &x, float &y)
+{
+    u /= scale;
+    v /= scale;
+
+    float v_ = asinf( sinf(v) / coshf(u) );
+    float u_ = atan2f( sinhf(u), std::cos(v) );
+
+    float cosv = cosf(v_);
+    float x_ = cosv * sinf(u_);
+    float y_ = sinf(v_);
+    float z_ = cosv * cosf(u_);
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void SphericalPortraitProjector::mapForward(float x, float y, float &u0, float &v0)
+{
+    float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float x_ = y0_;
+    float y_ = x0_;
+    float u, v;
+
+    u = scale * atan2f(x_, z_);
+    v = scale * (static_cast<float>(CV_PI) - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)));
+
+    u0 = -u;//v;
+    v0 = v;//u;
+}
+
+
+inline
+void SphericalPortraitProjector::mapBackward(float u0, float v0, float &x, float &y)
+{
+    float u, v;
+    u = -u0;//v0;
+    v = v0;//u0;
+
+    u /= scale;
+    v /= scale;
+
+    float sinv = sinf(static_cast<float>(CV_PI) - v);
+    float x0_ = sinv * sinf(u);
+    float y0_ = cosf(static_cast<float>(CV_PI) - v);
+    float z_ = sinv * cosf(u);
+
+    float x_ = y0_;
+    float y_ = x0_;
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void CylindricalPortraitProjector::mapForward(float x, float y, float &u0, float &v0)
+{
+    float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_  = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float x_ = y0_;
+    float y_ = x0_;
+    float u, v;
+
+    u = scale * atan2f(x_, z_);
+    v = scale * y_ / sqrtf(x_ * x_ + z_ * z_);
+
+    u0 = -u;//v;
+    v0 = v;//u;
+}
+
+
+inline
+void CylindricalPortraitProjector::mapBackward(float u0, float v0, float &x, float &y)
+{
+    float u, v;
+    u = -u0;//v0;
+    v = v0;//u0;
+
+    u /= scale;
+    v /= scale;
+
+    float x0_ = sinf(u);
+    float y0_ = v;
+    float z_  = cosf(u);
+
+    float x_ = y0_;
+    float y_ = x0_;
+
+    float z;
+    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
+    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
+    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
+
+    if (z > 0) { x /= z; y /= z; }
+    else x = y = -1;
+}
+
+inline
+void PlanePortraitProjector::mapForward(float x, float y, float &u0, float &v0)
+{
+    float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
+    float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
+    float z_  = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
+
+    float x_ = y0_;
+    float y_ = x0_;
+
+    x_ = t[0] + x_ / z_ * (1 - t[2]);
+    y_ = t[1] + y_ / z_ * (1 - t[2]);
+
+    float u,v;
+    u = scale * x_;
+    v = scale * y_;
+
+    u0 = -u;
+    v0 = v;
+}
+
+
+inline
+void PlanePortraitProjector::mapBackward(float u0, float v0, float &x, float &y)
+{
+    float u, v;
+    u = -u0;
+    v = v0;
+
+    u = u / scale - t[0];
+    v = v / scale - t[1];
+
+    float z;
+    x = k_rinv[0] * v + k_rinv[1] * u + k_rinv[2] * (1 - t[2]);
+    y = k_rinv[3] * v + k_rinv[4] * u + k_rinv[5] * (1 - t[2]);
+    z = k_rinv[6] * v + k_rinv[7] * u + k_rinv[8] * (1 - t[2]);
+
+    x /= z;
+    y /= z;
+}
+
+
+} // namespace detail
+} // namespace cv
+
+//! @endcond
+
+#endif // OPENCV_STITCHING_WARPERS_INL_HPP
diff --git a/3rdParty/include/opencv2/stitching/warpers.hpp b/3rdParty/include/opencv2/stitching/warpers.hpp
new file mode 100644
index 0000000..aa1ce5a
--- /dev/null
+++ b/3rdParty/include/opencv2/stitching/warpers.hpp
@@ -0,0 +1,277 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_STITCHING_WARPER_CREATORS_HPP
+#define OPENCV_STITCHING_WARPER_CREATORS_HPP
+
+#include "opencv2/stitching/detail/warpers.hpp"
+#include <string>
+
+namespace cv {
+    class CV_EXPORTS_W PyRotationWarper
+    {
+        Ptr<detail::RotationWarper> rw;
+
+    public:
+        CV_WRAP PyRotationWarper(String type, float scale);
+        CV_WRAP PyRotationWarper() {};
+        ~PyRotationWarper() {}
+
+        /** @brief Projects the image point.
+
+        @param pt Source point
+        @param K Camera intrinsic parameters
+        @param R Camera rotation matrix
+        @return Projected point
+        */
+        CV_WRAP Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R);
+
+        /** @brief Projects the image point backward.
+
+        @param pt Projected point
+        @param K Camera intrinsic parameters
+        @param R Camera rotation matrix
+        @return Backward-projected point
+        */
+#if CV_VERSION_MAJOR == 4
+        CV_WRAP Point2f warpPointBackward(const Point2f& pt, InputArray K, InputArray R)
+        {
+            CV_UNUSED(pt); CV_UNUSED(K); CV_UNUSED(R);
+            CV_Error(Error::StsNotImplemented, "");
+        }
+#else
+        CV_WRAP Point2f warpPointBackward(const Point2f &pt, InputArray K, InputArray R);
+#endif
+        /** @brief Builds the projection maps according to the given camera data.
+
+        @param src_size Source image size
+        @param K Camera intrinsic parameters
+        @param R Camera rotation matrix
+        @param xmap Projection map for the x axis
+        @param ymap Projection map for the y axis
+        @return Projected image minimum bounding box
+        */
+        CV_WRAP Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap);
+
+        /** @brief Projects the image.
+
+        @param src Source image
+        @param K Camera intrinsic parameters
+        @param R Camera rotation matrix
+        @param interp_mode Interpolation mode
+        @param border_mode Border extrapolation mode
+        @param dst Projected image
+        @return Project image top-left corner
+        */
+        CV_WRAP Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+            CV_OUT OutputArray dst);
+
+        /** @brief Projects the image backward.
+
+        @param src Projected image
+        @param K Camera intrinsic parameters
+        @param R Camera rotation matrix
+        @param interp_mode Interpolation mode
+        @param border_mode Border extrapolation mode
+        @param dst_size Backward-projected image size
+        @param dst Backward-projected image
+        */
+        CV_WRAP void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
+            Size dst_size, CV_OUT OutputArray dst);
+
+        /**
+        @param src_size Source image bounding box
+        @param K Camera intrinsic parameters
+        @param R Camera rotation matrix
+        @return Projected image minimum bounding box
+        */
+        CV_WRAP Rect warpRoi(Size src_size, InputArray K, InputArray R);
+
+        CV_WRAP float getScale() const { return 1.f; }
+        CV_WRAP void setScale(float) {}
+    };
+
+//! @addtogroup stitching_warp
+//! @{
+
+/** @brief Image warper factories base class.
+ */
+
+class CV_EXPORTS_W WarperCreator
+{
+public:
+    CV_WRAP virtual ~WarperCreator() {}
+    virtual Ptr<detail::RotationWarper> create(float scale) const = 0;
+};
+
+
+/** @brief Plane warper factory class.
+  @sa detail::PlaneWarper
+ */
+class CV_EXPORTS  PlaneWarper : public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PlaneWarper>(scale); }
+};
+
+/** @brief Affine warper factory class.
+  @sa detail::AffineWarper
+ */
+class CV_EXPORTS  AffineWarper : public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::AffineWarper>(scale); }
+};
+
+/** @brief Cylindrical warper factory class.
+@sa detail::CylindricalWarper
+*/
+class CV_EXPORTS CylindricalWarper: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CylindricalWarper>(scale); }
+};
+
+/** @brief Spherical warper factory class */
+class CV_EXPORTS SphericalWarper: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::SphericalWarper>(scale); }
+};
+
+class CV_EXPORTS FisheyeWarper : public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::FisheyeWarper>(scale); }
+};
+
+class CV_EXPORTS StereographicWarper: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::StereographicWarper>(scale); }
+};
+
+class CV_EXPORTS CompressedRectilinearWarper: public WarperCreator
+{
+    float a, b;
+public:
+    CompressedRectilinearWarper(float A = 1, float B = 1)
+    {
+        a = A; b = B;
+    }
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearWarper>(scale, a, b); }
+};
+
+class CV_EXPORTS CompressedRectilinearPortraitWarper: public WarperCreator
+{
+    float a, b;
+public:
+    CompressedRectilinearPortraitWarper(float A = 1, float B = 1)
+    {
+        a = A; b = B;
+    }
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearPortraitWarper>(scale, a, b); }
+};
+
+class CV_EXPORTS PaniniWarper: public WarperCreator
+{
+    float a, b;
+public:
+    PaniniWarper(float A = 1, float B = 1)
+    {
+        a = A; b = B;
+    }
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniWarper>(scale, a, b); }
+};
+
+class CV_EXPORTS PaniniPortraitWarper: public WarperCreator
+{
+    float a, b;
+public:
+    PaniniPortraitWarper(float A = 1, float B = 1)
+    {
+        a = A; b = B;
+    }
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniPortraitWarper>(scale, a, b); }
+};
+
+class CV_EXPORTS MercatorWarper: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::MercatorWarper>(scale); }
+};
+
+class CV_EXPORTS TransverseMercatorWarper: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::TransverseMercatorWarper>(scale); }
+};
+
+
+
+#ifdef HAVE_OPENCV_CUDAWARPING
+class PlaneWarperGpu: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PlaneWarperGpu>(scale); }
+};
+
+
+class CylindricalWarperGpu: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CylindricalWarperGpu>(scale); }
+};
+
+
+class SphericalWarperGpu: public WarperCreator
+{
+public:
+    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::SphericalWarperGpu>(scale); }
+};
+#endif
+
+//! @} stitching_warp
+
+} // namespace cv
+
+#endif // OPENCV_STITCHING_WARPER_CREATORS_HPP
diff --git a/3rdParty/include/opencv2/video.hpp b/3rdParty/include/opencv2/video.hpp
new file mode 100644
index 0000000..a3dde60
--- /dev/null
+++ b/3rdParty/include/opencv2/video.hpp
@@ -0,0 +1,59 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_VIDEO_HPP
+#define OPENCV_VIDEO_HPP
+
+/**
+  @defgroup video Video Analysis
+  @{
+    @defgroup video_motion Motion Analysis
+    @defgroup video_track Object Tracking
+    @defgroup video_c C API
+  @}
+*/
+
+#include "opencv2/video/tracking.hpp"
+#include "opencv2/video/background_segm.hpp"
+
+#endif //OPENCV_VIDEO_HPP
diff --git a/3rdParty/include/opencv2/video/background_segm.hpp b/3rdParty/include/opencv2/video/background_segm.hpp
new file mode 100644
index 0000000..e1dfa15
--- /dev/null
+++ b/3rdParty/include/opencv2/video/background_segm.hpp
@@ -0,0 +1,317 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_BACKGROUND_SEGM_HPP
+#define OPENCV_BACKGROUND_SEGM_HPP
+
+#include "opencv2/core.hpp"
+
+namespace cv
+{
+
+//! @addtogroup video_motion
+//! @{
+
+/** @brief Base class for background/foreground segmentation. :
+
+The class is only used to define the common interface for the whole family of background/foreground
+segmentation algorithms.
+ */
+class CV_EXPORTS_W BackgroundSubtractor : public Algorithm
+{
+public:
+    /** @brief Computes a foreground mask.
+
+    @param image Next video frame.
+    @param fgmask The output foreground mask as an 8-bit binary image.
+    @param learningRate The value between 0 and 1 that indicates how fast the background model is
+    learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
+    rate. 0 means that the background model is not updated at all, 1 means that the background model
+    is completely reinitialized from the last frame.
+     */
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
+
+    /** @brief Computes a background image.
+
+    @param backgroundImage The output background image.
+
+    @note Sometimes the background image can be very blurry, as it contain the average background
+    statistics.
+     */
+    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0;
+};
+
+
+/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
+
+The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004
+and @cite Zivkovic2006 .
+ */
+class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor
+{
+public:
+    /** @brief Returns the number of last frames that affect the background model
+    */
+    CV_WRAP virtual int getHistory() const = 0;
+    /** @brief Sets the number of last frames that affect the background model
+    */
+    CV_WRAP virtual void setHistory(int history) = 0;
+
+    /** @brief Returns the number of gaussian components in the background model
+    */
+    CV_WRAP virtual int getNMixtures() const = 0;
+    /** @brief Sets the number of gaussian components in the background model.
+
+    The model needs to be reinitalized to reserve memory.
+    */
+    CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization!
+
+    /** @brief Returns the "background ratio" parameter of the algorithm
+
+    If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's
+    considered background and added to the model as a center of a new component. It corresponds to TB
+    parameter in the paper.
+     */
+    CV_WRAP virtual double getBackgroundRatio() const = 0;
+    /** @brief Sets the "background ratio" parameter of the algorithm
+    */
+    CV_WRAP virtual void setBackgroundRatio(double ratio) = 0;
+
+    /** @brief Returns the variance threshold for the pixel-model match
+
+    The main threshold on the squared Mahalanobis distance to decide if the sample is well described by
+    the background model or not. Related to Cthr from the paper.
+     */
+    CV_WRAP virtual double getVarThreshold() const = 0;
+    /** @brief Sets the variance threshold for the pixel-model match
+    */
+    CV_WRAP virtual void setVarThreshold(double varThreshold) = 0;
+
+    /** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation
+
+    Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the
+    existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it
+    is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg
+    value generates more components. A higher Tg value may result in a small number of components but
+    they can grow too large.
+     */
+    CV_WRAP virtual double getVarThresholdGen() const = 0;
+    /** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation
+    */
+    CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0;
+
+    /** @brief Returns the initial variance of each gaussian component
+    */
+    CV_WRAP virtual double getVarInit() const = 0;
+    /** @brief Sets the initial variance of each gaussian component
+    */
+    CV_WRAP virtual void setVarInit(double varInit) = 0;
+
+    CV_WRAP virtual double getVarMin() const = 0;
+    CV_WRAP virtual void setVarMin(double varMin) = 0;
+
+    CV_WRAP virtual double getVarMax() const = 0;
+    CV_WRAP virtual void setVarMax(double varMax) = 0;
+
+    /** @brief Returns the complexity reduction threshold
+
+    This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05
+    is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the
+    standard Stauffer&Grimson algorithm.
+     */
+    CV_WRAP virtual double getComplexityReductionThreshold() const = 0;
+    /** @brief Sets the complexity reduction threshold
+    */
+    CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0;
+
+    /** @brief Returns the shadow detection flag
+
+    If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for
+    details.
+     */
+    CV_WRAP virtual bool getDetectShadows() const = 0;
+    /** @brief Enables or disables shadow detection
+    */
+    CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
+
+    /** @brief Returns the shadow value
+
+    Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
+    in the mask always means background, 255 means foreground.
+     */
+    CV_WRAP virtual int getShadowValue() const = 0;
+    /** @brief Sets the shadow value
+    */
+    CV_WRAP virtual void setShadowValue(int value) = 0;
+
+    /** @brief Returns the shadow threshold
+
+    A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
+    the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
+    is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
+    *Detecting Moving Shadows...*, IEEE PAMI,2003.
+     */
+    CV_WRAP virtual double getShadowThreshold() const = 0;
+    /** @brief Sets the shadow threshold
+    */
+    CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
+
+    /** @brief Computes a foreground mask.
+
+    @param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$.
+    @param fgmask The output foreground mask as an 8-bit binary image.
+    @param learningRate The value between 0 and 1 that indicates how fast the background model is
+    learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
+    rate. 0 means that the background model is not updated at all, 1 means that the background model
+    is completely reinitialized from the last frame.
+     */
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
+};
+
+/** @brief Creates MOG2 Background Subtractor
+
+@param history Length of the history.
+@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
+to decide whether a pixel is well described by the background model. This parameter does not
+affect the background update.
+@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
+speed a bit, so if you do not need this feature, set the parameter to false.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorMOG2>
+    createBackgroundSubtractorMOG2(int history=500, double varThreshold=16,
+                                   bool detectShadows=true);
+
+/** @brief K-nearest neighbours - based Background/Foreground Segmentation Algorithm.
+
+The class implements the K-nearest neighbours background subtraction described in @cite Zivkovic2006 .
+Very efficient if number of foreground pixels is low.
+ */
+class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor
+{
+public:
+    /** @brief Returns the number of last frames that affect the background model
+    */
+    CV_WRAP virtual int getHistory() const = 0;
+    /** @brief Sets the number of last frames that affect the background model
+    */
+    CV_WRAP virtual void setHistory(int history) = 0;
+
+    /** @brief Returns the number of data samples in the background model
+    */
+    CV_WRAP virtual int getNSamples() const = 0;
+    /** @brief Sets the number of data samples in the background model.
+
+    The model needs to be reinitalized to reserve memory.
+    */
+    CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!
+
+    /** @brief Returns the threshold on the squared distance between the pixel and the sample
+
+    The threshold on the squared distance between the pixel and the sample to decide whether a pixel is
+    close to a data sample.
+     */
+    CV_WRAP virtual double getDist2Threshold() const = 0;
+    /** @brief Sets the threshold on the squared distance
+    */
+    CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0;
+
+    /** @brief Returns the number of neighbours, the k in the kNN.
+
+    K is the number of samples that need to be within dist2Threshold in order to decide that that
+    pixel is matching the kNN background model.
+     */
+    CV_WRAP virtual int getkNNSamples() const = 0;
+    /** @brief Sets the k in the kNN. How many nearest neighbours need to match.
+    */
+    CV_WRAP virtual void setkNNSamples(int _nkNN) = 0;
+
+    /** @brief Returns the shadow detection flag
+
+    If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for
+    details.
+     */
+    CV_WRAP virtual bool getDetectShadows() const = 0;
+    /** @brief Enables or disables shadow detection
+    */
+    CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
+
+    /** @brief Returns the shadow value
+
+    Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
+    in the mask always means background, 255 means foreground.
+     */
+    CV_WRAP virtual int getShadowValue() const = 0;
+    /** @brief Sets the shadow value
+    */
+    CV_WRAP virtual void setShadowValue(int value) = 0;
+
+    /** @brief Returns the shadow threshold
+
+    A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
+    the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
+    is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
+    *Detecting Moving Shadows...*, IEEE PAMI,2003.
+     */
+    CV_WRAP virtual double getShadowThreshold() const = 0;
+    /** @brief Sets the shadow threshold
+     */
+    CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
+};
+
+/** @brief Creates KNN Background Subtractor
+
+@param history Length of the history.
+@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
+whether a pixel is close to that sample. This parameter does not affect the background update.
+@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
+speed a bit, so if you do not need this feature, set the parameter to false.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorKNN>
+    createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0,
+                                   bool detectShadows=true);
+
+//! @} video_motion
+
+} // cv
+
+#endif
diff --git a/3rdParty/include/opencv2/video/detail/tracking.detail.hpp b/3rdParty/include/opencv2/video/detail/tracking.detail.hpp
new file mode 100644
index 0000000..1e61079
--- /dev/null
+++ b/3rdParty/include/opencv2/video/detail/tracking.detail.hpp
@@ -0,0 +1,406 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_VIDEO_DETAIL_TRACKING_HPP
+#define OPENCV_VIDEO_DETAIL_TRACKING_HPP
+
+/*
+ * Partially based on:
+ * ====================================================================================================================
+ *  - [AAM] S. Salti, A. Cavallaro, L. Di Stefano, Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation
+ *  - [AMVOT] X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, A. van den Hengel, A Survey of Appearance Models in Visual Object Tracking
+ *
+ * This Tracking API has been designed with PlantUML. If you modify this API please change UML files under modules/tracking/doc/uml
+ *
+ */
+
+#include "opencv2/core.hpp"
+
+namespace cv {
+namespace detail {
+inline namespace tracking {
+
+/** @addtogroup tracking_detail
+@{
+*/
+
+/************************************ TrackerFeature Base Classes ************************************/
+
+/** @brief Abstract base class for TrackerFeature that represents the feature.
+*/
+class CV_EXPORTS TrackerFeature
+{
+public:
+    virtual ~TrackerFeature();
+
+    /** @brief Compute the features in the images collection
+    @param images The images
+    @param response The output response
+    */
+    void compute(const std::vector<Mat>& images, Mat& response);
+
+protected:
+    virtual bool computeImpl(const std::vector<Mat>& images, Mat& response) = 0;
+};
+
+/** @brief Class that manages the extraction and selection of features
+
+@cite AAM Feature Extraction and Feature Set Refinement (Feature Processing and Feature Selection).
+See table I and section III C @cite AMVOT Appearance modelling -\> Visual representation (Table II,
+section 3.1 - 3.2)
+
+TrackerFeatureSet is an aggregation of TrackerFeature
+
+@sa
+   TrackerFeature
+
+*/
+class CV_EXPORTS TrackerFeatureSet
+{
+public:
+    TrackerFeatureSet();
+
+    ~TrackerFeatureSet();
+
+    /** @brief Extract features from the images collection
+    @param images The input images
+    */
+    void extraction(const std::vector<Mat>& images);
+
+    /** @brief Add TrackerFeature in the collection. Return true if TrackerFeature is added, false otherwise
+    @param feature The TrackerFeature class
+    */
+    bool addTrackerFeature(const Ptr<TrackerFeature>& feature);
+
+    /** @brief Get the TrackerFeature collection (TrackerFeature name, TrackerFeature pointer)
+    */
+    const std::vector<Ptr<TrackerFeature>>& getTrackerFeatures() const;
+
+    /** @brief Get the responses
+    @note Be sure to call extraction before getResponses Example TrackerFeatureSet::getResponses
+    */
+    const std::vector<Mat>& getResponses() const;
+
+private:
+    void clearResponses();
+    bool blockAddTrackerFeature;
+
+    std::vector<Ptr<TrackerFeature>> features;  // list of features
+    std::vector<Mat> responses;  // list of response after compute
+};
+
+/************************************ TrackerSampler Base Classes ************************************/
+
+/** @brief Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific
+sampler.
+*/
+class CV_EXPORTS TrackerSamplerAlgorithm
+{
+public:
+    virtual ~TrackerSamplerAlgorithm();
+
+    /** @brief Computes the regions starting from a position in an image.
+
+    Return true if samples are computed, false otherwise
+
+    @param image The current frame
+    @param boundingBox The bounding box from which regions can be calculated
+
+    @param sample The computed samples @cite AAM Fig. 1 variable Sk
+    */
+    virtual bool sampling(const Mat& image, const Rect& boundingBox, std::vector<Mat>& sample) = 0;
+};
+
+/**
+ * \brief Class that manages the sampler in order to select regions for the update the model of the tracker
+ * [AAM] Sampling e Labeling. See table I and section III B
+ */
+
+/** @brief Class that manages the sampler in order to select regions for the update the model of the tracker
+
+@cite AAM Sampling e Labeling. See table I and section III B
+
+TrackerSampler is an aggregation of TrackerSamplerAlgorithm
+@sa
+   TrackerSamplerAlgorithm
+ */
+class CV_EXPORTS TrackerSampler
+{
+public:
+    TrackerSampler();
+
+    ~TrackerSampler();
+
+    /** @brief Computes the regions starting from a position in an image
+    @param image The current frame
+    @param boundingBox The bounding box from which regions can be calculated
+    */
+    void sampling(const Mat& image, Rect boundingBox);
+
+    /** @brief Return the collection of the TrackerSamplerAlgorithm
+    */
+    const std::vector<Ptr<TrackerSamplerAlgorithm>>& getSamplers() const;
+
+    /** @brief Return the samples from all TrackerSamplerAlgorithm, @cite AAM Fig. 1 variable Sk
+    */
+    const std::vector<Mat>& getSamples() const;
+
+    /** @brief Add TrackerSamplerAlgorithm in the collection. Return true if sampler is added, false otherwise
+    @param sampler The TrackerSamplerAlgorithm
+    */
+    bool addTrackerSamplerAlgorithm(const Ptr<TrackerSamplerAlgorithm>& sampler);
+
+private:
+    std::vector<Ptr<TrackerSamplerAlgorithm>> samplers;
+    std::vector<Mat> samples;
+    bool blockAddTrackerSampler;
+
+    void clearSamples();
+};
+
+/************************************ TrackerModel Base Classes ************************************/
+
+/** @brief Abstract base class for TrackerTargetState that represents a possible state of the target.
+
+See @cite AAM \f$\hat{x}^{i}_{k}\f$ all the states candidates.
+
+Inherits this class with your Target state, In own implementation you can add scale variation,
+width, height, orientation, etc.
+*/
+class CV_EXPORTS TrackerTargetState
+{
+public:
+    virtual ~TrackerTargetState() {};
+    /** @brief Get the position
+    * @return The position
+    */
+    Point2f getTargetPosition() const;
+
+    /** @brief Set the position
+    * @param position The position
+    */
+    void setTargetPosition(const Point2f& position);
+    /** @brief Get the width of the target
+    * @return The width of the target
+    */
+    int getTargetWidth() const;
+
+    /** @brief Set the width of the target
+    * @param width The width of the target
+    */
+    void setTargetWidth(int width);
+    /** @brief Get the height of the target
+    * @return The height of the target
+    */
+    int getTargetHeight() const;
+
+    /** @brief Set the height of the target
+    * @param height The height of the target
+    */
+    void setTargetHeight(int height);
+
+protected:
+    Point2f targetPosition;
+    int targetWidth;
+    int targetHeight;
+};
+
+/** @brief Represents the model of the target at frame \f$k\f$ (all states and scores)
+
+See @cite AAM The set of the pair \f$\langle \hat{x}^{i}_{k}, C^{i}_{k} \rangle\f$
+@sa TrackerTargetState
+*/
+typedef std::vector<std::pair<Ptr<TrackerTargetState>, float>> ConfidenceMap;
+
+/** @brief Represents the estimate states for all frames
+
+@cite AAM \f$x_{k}\f$ is the trajectory of the target up to time \f$k\f$
+
+@sa TrackerTargetState
+*/
+typedef std::vector<Ptr<TrackerTargetState>> Trajectory;
+
+/** @brief Abstract base class for TrackerStateEstimator that estimates the most likely target state.
+
+See @cite AAM State estimator
+
+See @cite AMVOT Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid)
+*/
+class CV_EXPORTS TrackerStateEstimator
+{
+public:
+    virtual ~TrackerStateEstimator();
+
+    /** @brief Estimate the most likely target state, return the estimated state
+    @param confidenceMaps The overall appearance model as a list of :cConfidenceMap
+    */
+    Ptr<TrackerTargetState> estimate(const std::vector<ConfidenceMap>& confidenceMaps);
+
+    /** @brief Update the ConfidenceMap with the scores
+    @param confidenceMaps The overall appearance model as a list of :cConfidenceMap
+    */
+    void update(std::vector<ConfidenceMap>& confidenceMaps);
+
+    /** @brief Create TrackerStateEstimator by tracker state estimator type
+    @param trackeStateEstimatorType The TrackerStateEstimator name
+
+    The modes available now:
+
+    -   "BOOSTING" -- Boosting-based discriminative appearance models. See @cite AMVOT section 4.4
+
+    The modes available soon:
+
+    -   "SVM" -- SVM-based discriminative appearance models. See @cite AMVOT section 4.5
+    */
+    static Ptr<TrackerStateEstimator> create(const String& trackeStateEstimatorType);
+
+    /** @brief Get the name of the specific TrackerStateEstimator
+    */
+    String getClassName() const;
+
+protected:
+    virtual Ptr<TrackerTargetState> estimateImpl(const std::vector<ConfidenceMap>& confidenceMaps) = 0;
+    virtual void updateImpl(std::vector<ConfidenceMap>& confidenceMaps) = 0;
+    String className;
+};
+
+/** @brief Abstract class that represents the model of the target.
+
+It must be instantiated by specialized tracker
+
+See @cite AAM Ak
+
+Inherits this with your TrackerModel
+*/
+class CV_EXPORTS TrackerModel
+{
+public:
+    TrackerModel();
+
+    virtual ~TrackerModel();
+
+    /** @brief Set TrackerEstimator, return true if the tracker state estimator is added, false otherwise
+    @param trackerStateEstimator The TrackerStateEstimator
+    @note You can add only one TrackerStateEstimator
+    */
+    bool setTrackerStateEstimator(Ptr<TrackerStateEstimator> trackerStateEstimator);
+
+    /** @brief Estimate the most likely target location
+
+    @cite AAM ME, Model Estimation table I
+    @param responses Features extracted from TrackerFeatureSet
+    */
+    void modelEstimation(const std::vector<Mat>& responses);
+
+    /** @brief Update the model
+
+    @cite AAM MU, Model Update table I
+    */
+    void modelUpdate();
+
+    /** @brief Run the TrackerStateEstimator, return true if is possible to estimate a new state, false otherwise
+    */
+    bool runStateEstimator();
+
+    /** @brief Set the current TrackerTargetState in the Trajectory
+    @param lastTargetState The current TrackerTargetState
+    */
+    void setLastTargetState(const Ptr<TrackerTargetState>& lastTargetState);
+
+    /** @brief Get the last TrackerTargetState from Trajectory
+    */
+    Ptr<TrackerTargetState> getLastTargetState() const;
+
+    /** @brief Get the list of the ConfidenceMap
+    */
+    const std::vector<ConfidenceMap>& getConfidenceMaps() const;
+
+    /** @brief Get the last ConfidenceMap for the current frame
+    */
+    const ConfidenceMap& getLastConfidenceMap() const;
+
+    /** @brief Get the TrackerStateEstimator
+    */
+    Ptr<TrackerStateEstimator> getTrackerStateEstimator() const;
+
+private:
+    void clearCurrentConfidenceMap();
+
+protected:
+    std::vector<ConfidenceMap> confidenceMaps;
+    Ptr<TrackerStateEstimator> stateEstimator;
+    ConfidenceMap currentConfidenceMap;
+    Trajectory trajectory;
+    int maxCMLength;
+
+    virtual void modelEstimationImpl(const std::vector<Mat>& responses) = 0;
+    virtual void modelUpdateImpl() = 0;
+};
+
+/************************************ Specific TrackerStateEstimator Classes ************************************/
+
+// None
+
+/************************************ Specific TrackerSamplerAlgorithm Classes ************************************/
+
+/** @brief TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL
+ */
+class CV_EXPORTS TrackerSamplerCSC : public TrackerSamplerAlgorithm
+{
+public:
+    ~TrackerSamplerCSC();
+
+    enum MODE
+    {
+        MODE_INIT_POS = 1,  //!< mode for init positive samples
+        MODE_INIT_NEG = 2,  //!< mode for init negative samples
+        MODE_TRACK_POS = 3,  //!< mode for update positive samples
+        MODE_TRACK_NEG = 4,  //!< mode for update negative samples
+        MODE_DETECT = 5  //!< mode for detect samples
+    };
+
+    struct CV_EXPORTS Params
+    {
+        Params();
+        float initInRad;  //!< radius for gathering positive instances during init
+        float trackInPosRad;  //!< radius for gathering positive instances during tracking
+        float searchWinSize;  //!< size of search window
+        int initMaxNegNum;  //!< # negative samples to use during init
+        int trackMaxPosNum;  //!< # positive samples to use during training
+        int trackMaxNegNum;  //!< # negative samples to use during training
+    };
+
+    /** @brief Constructor
+    @param parameters TrackerSamplerCSC parameters TrackerSamplerCSC::Params
+    */
+    TrackerSamplerCSC(const TrackerSamplerCSC::Params& parameters = TrackerSamplerCSC::Params());
+
+    /** @brief Set the sampling mode of TrackerSamplerCSC
+    @param samplingMode The sampling mode
+
+    The modes are:
+
+    -   "MODE_INIT_POS = 1" -- for the positive sampling in initialization step
+    -   "MODE_INIT_NEG = 2" -- for the negative sampling in initialization step
+    -   "MODE_TRACK_POS = 3" -- for the positive sampling in update step
+    -   "MODE_TRACK_NEG = 4" -- for the negative sampling in update step
+    -   "MODE_DETECT = 5" -- for the sampling in detection step
+    */
+    void setMode(int samplingMode);
+
+    bool sampling(const Mat& image, const Rect& boundingBox, std::vector<Mat>& sample) CV_OVERRIDE;
+
+private:
+    Params params;
+    int mode;
+    RNG rng;
+
+    std::vector<Mat> sampleImage(const Mat& img, int x, int y, int w, int h, float inrad, float outrad = 0, int maxnum = 1000000);
+};
+
+//! @}
+
+}}}  // namespace cv::detail::tracking
+
+#endif  // OPENCV_VIDEO_DETAIL_TRACKING_HPP
diff --git a/3rdParty/include/opencv2/video/legacy/constants_c.h b/3rdParty/include/opencv2/video/legacy/constants_c.h
new file mode 100644
index 0000000..1a98f52
--- /dev/null
+++ b/3rdParty/include/opencv2/video/legacy/constants_c.h
@@ -0,0 +1,16 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_VIDEO_LEGACY_CONSTANTS_H
+#define OPENCV_VIDEO_LEGACY_CONSTANTS_H
+
+enum
+{
+    CV_LKFLOW_PYR_A_READY = 1,
+    CV_LKFLOW_PYR_B_READY = 2,
+    CV_LKFLOW_INITIAL_GUESSES = 4,
+    CV_LKFLOW_GET_MIN_EIGENVALS = 8
+};
+
+#endif // OPENCV_VIDEO_LEGACY_CONSTANTS_H
diff --git a/3rdParty/include/opencv2/video/tracking.hpp b/3rdParty/include/opencv2/video/tracking.hpp
new file mode 100644
index 0000000..7ec6bc5
--- /dev/null
+++ b/3rdParty/include/opencv2/video/tracking.hpp
@@ -0,0 +1,857 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_TRACKING_HPP
+#define OPENCV_TRACKING_HPP
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+
+namespace cv
+{
+
+//! @addtogroup video_track
+//! @{
+
+enum { OPTFLOW_USE_INITIAL_FLOW     = 4,
+       OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
+       OPTFLOW_FARNEBACK_GAUSSIAN   = 256
+     };
+
+/** @brief Finds an object center, size, and orientation.
+
+@param probImage Back projection of the object histogram. See calcBackProject.
+@param window Initial search window.
+@param criteria Stop criteria for the underlying meanShift.
+returns
+(in old interfaces) Number of iterations CAMSHIFT took to converge
+The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an
+object center using meanShift and then adjusts the window size and finds the optimal rotation. The
+function returns the rotated rectangle structure that includes the object position, size, and
+orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()
+
+See the OpenCV sample camshiftdemo.c that tracks colored objects.
+
+@note
+-   (Python) A sample explaining the camshift tracking algorithm can be found at
+    opencv_source_code/samples/python/camshift.py
+ */
+CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window,
+                                   TermCriteria criteria );
+/** @example samples/cpp/camshiftdemo.cpp
+An example using the mean-shift tracking algorithm
+*/
+
+/** @brief Finds an object on a back projection image.
+
+@param probImage Back projection of the object histogram. See calcBackProject for details.
+@param window Initial search window.
+@param criteria Stop criteria for the iterative search algorithm.
+returns
+:   Number of iterations CAMSHIFT took to converge.
+The function implements the iterative object search algorithm. It takes the input back projection of
+an object and the initial position. The mass center in window of the back projection image is
+computed and the search window center shifts to the mass center. The procedure is repeated until the
+specified number of iterations criteria.maxCount is done or until the window center shifts by less
+than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search
+window size or orientation do not change during the search. You can simply pass the output of
+calcBackProject to this function. But better results can be obtained if you pre-filter the back
+projection and remove the noise. For example, you can do this by retrieving connected components
+with findContours , throwing away contours with small area ( contourArea ), and rendering the
+remaining contours with drawContours.
+
+ */
+CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria );
+
+/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
+
+@param img 8-bit input image.
+@param pyramid output pyramid.
+@param winSize window size of optical flow algorithm. Must be not less than winSize argument of
+calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
+@param maxLevel 0-based maximal pyramid level number.
+@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
+constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
+@param pyrBorder the border mode for pyramid layers.
+@param derivBorder the border mode for gradients.
+@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
+to force data copying.
+@return number of levels in constructed pyramid. Can be less than maxLevel.
+ */
+CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays pyramid,
+                                          Size winSize, int maxLevel, bool withDerivatives = true,
+                                          int pyrBorder = BORDER_REFLECT_101,
+                                          int derivBorder = BORDER_CONSTANT,
+                                          bool tryReuseInputImage = true );
+
+/** @example samples/cpp/lkdemo.cpp
+An example using the Lucas-Kanade optical flow algorithm
+*/
+
+/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
+pyramids.
+
+@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
+@param nextImg second input image or pyramid of the same size and the same type as prevImg.
+@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
+single-precision floating-point numbers.
+@param nextPts output vector of 2D points (with single-precision floating-point coordinates)
+containing the calculated new positions of input features in the second image; when
+OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
+@param status output status vector (of unsigned chars); each element of the vector is set to 1 if
+the flow for the corresponding features has been found, otherwise, it is set to 0.
+@param err output vector of errors; each element of the vector is set to an error for the
+corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
+found then the error is not defined (use the status parameter to find such cases).
+@param winSize size of the search window at each pyramid level.
+@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
+level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
+algorithm will use as many levels as pyramids have but no more than maxLevel.
+@param criteria parameter, specifying the termination criteria of the iterative search algorithm
+(after the specified maximum number of iterations criteria.maxCount or when the search window
+moves by less than criteria.epsilon.
+@param flags operation flags:
+ -   **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
+     not set, then prevPts is copied to nextPts and is considered the initial estimate.
+ -   **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see
+     minEigThreshold description); if the flag is not set, then L1 distance between patches
+     around the original and a moved point, divided by number of pixels in a window, is used as a
+     error measure.
+@param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
+optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided
+by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
+feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
+performance boost.
+
+The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
+@cite Bouguet00 . The function is parallelized with the TBB library.
+
+@note
+
+-   An example using the Lucas-Kanade optical flow algorithm can be found at
+    opencv_source_code/samples/cpp/lkdemo.cpp
+-   (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
+    opencv_source_code/samples/python/lk_track.py
+-   (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
+    opencv_source_code/samples/python/lk_homography.py
+ */
+CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,
+                                        InputArray prevPts, InputOutputArray nextPts,
+                                        OutputArray status, OutputArray err,
+                                        Size winSize = Size(21,21), int maxLevel = 3,
+                                        TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
+                                        int flags = 0, double minEigThreshold = 1e-4 );
+
+/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm.
+
+@param prev first 8-bit single-channel input image.
+@param next second input image of the same size and the same type as prev.
+@param flow computed flow image that has the same size as prev and type CV_32FC2.
+@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image;
+pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous
+one.
+@param levels number of pyramid layers including the initial image; levels=1 means that no extra
+layers are created and only the original images are used.
+@param winsize averaging window size; larger values increase the algorithm robustness to image
+noise and give more chances for fast motion detection, but yield more blurred motion field.
+@param iterations number of iterations the algorithm does at each pyramid level.
+@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel;
+larger values mean that the image will be approximated with smoother surfaces, yielding more
+robust algorithm and more blurred motion field, typically poly_n =5 or 7.
+@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a
+basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a
+good value would be poly_sigma=1.5.
+@param flags operation flags that can be a combination of the following:
+ -   **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation.
+ -   **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$
+     filter instead of a box filter of the same size for optical flow estimation; usually, this
+     option gives z more accurate flow than with a box filter, at the cost of lower speed;
+     normally, winsize for a Gaussian window should be set to a larger value to achieve the same
+     level of robustness.
+
+The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that
+
+\f[\texttt{prev} (y,x)  \sim \texttt{next} ( y + \texttt{flow} (y,x)[1],  x + \texttt{flow} (y,x)[0])\f]
+
+@note
+
+-   An example using the optical flow algorithm described by Gunnar Farneback can be found at
+    opencv_source_code/samples/cpp/fback.cpp
+-   (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
+    found at opencv_source_code/samples/python/opt_flow.py
+ */
+CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow,
+                                            double pyr_scale, int levels, int winsize,
+                                            int iterations, int poly_n, double poly_sigma,
+                                            int flags );
+
+/** @brief Computes an optimal affine transformation between two 2D point sets.
+
+@param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat.
+@param dst Second input 2D point set of the same size and the same type as A, or another image.
+@param fullAffine If true, the function finds an optimal affine transformation with no additional
+restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is
+limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom).
+
+The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that
+approximates best the affine transformation between:
+
+*   Two point sets
+*   Two raster images. In this case, the function first finds some features in the src image and
+    finds the corresponding features in dst image. After that, the problem is reduced to the first
+    case.
+In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and
+2x1 vector *b* so that:
+
+\f[[A^*|b^*] = arg  \min _{[A|b]}  \sum _i  \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b  \| ^2\f]
+where src[i] and dst[i] are the i-th points in src and dst, respectively
+\f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of
+\f[\begin{bmatrix} a_{11} & a_{12} & b_1  \\ -a_{12} & a_{11} & b_2  \end{bmatrix}\f]
+when fullAffine=false.
+
+@deprecated Use cv::estimateAffine2D, cv::estimateAffinePartial2D instead. If you are using this function
+with images, extract points using cv::calcOpticalFlowPyrLK and then use the estimation functions.
+
+@sa
+estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography
+ */
+CV_DEPRECATED CV_EXPORTS Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine );
+
+enum
+{
+    MOTION_TRANSLATION = 0,
+    MOTION_EUCLIDEAN   = 1,
+    MOTION_AFFINE      = 2,
+    MOTION_HOMOGRAPHY  = 3
+};
+
+/** @brief Computes the Enhanced Correlation Coefficient value between two images @cite EP08 .
+
+@param templateImage single-channel template image; CV_8U or CV_32F array.
+@param inputImage single-channel input image to be warped to provide an image similar to
+ templateImage, same type as templateImage.
+@param inputMask An optional mask to indicate valid values of inputImage.
+
+@sa
+findTransformECC
+ */
+
+CV_EXPORTS_W double computeECC(InputArray templateImage, InputArray inputImage, InputArray inputMask = noArray());
+
+/** @example samples/cpp/image_alignment.cpp
+An example using the image alignment ECC algorithm
+*/
+
+/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .
+
+@param templateImage single-channel template image; CV_8U or CV_32F array.
+@param inputImage single-channel input image which should be warped with the final warpMatrix in
+order to provide an image similar to templateImage, same type as templateImage.
+@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp).
+@param motionType parameter, specifying the type of motion:
+ -   **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with
+     the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being
+     estimated.
+ -   **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
+     parameters are estimated; warpMatrix is \f$2\times 3\f$.
+ -   **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
+     warpMatrix is \f$2\times 3\f$.
+ -   **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
+     estimated;\`warpMatrix\` is \f$3\times 3\f$.
+@param criteria parameter, specifying the termination criteria of the ECC algorithm;
+criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
+iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
+Default values are shown in the declaration above.
+@param inputMask An optional mask to indicate valid values of inputImage.
+@param gaussFiltSize An optional value indicating size of gaussian blur filter; (DEFAULT: 5)
+
+The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
+(@cite EP08), that is
+
+\f[\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f]
+
+where
+
+\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f]
+
+(the equation holds with homogeneous coordinates for homography). It returns the final enhanced
+correlation coefficient, that is the correlation coefficient between the template image and the
+final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third
+row is ignored.
+
+Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
+area-based alignment that builds on intensity similarities. In essence, the function updates the
+initial transformation that roughly aligns the images. If this information is missing, the identity
+warp (unity matrix) is used as an initialization. Note that if images undergo strong
+displacements/rotations, an initial transformation that roughly aligns the images is necessary
+(e.g., a simple euclidean/similarity transform that allows for the images showing the same image
+content approximately). Use inverse warping in the second image to take an image close to the first
+one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV
+sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws
+an exception if algorithm does not converges.
+
+@sa
+computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography
+ */
+CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage,
+                                      InputOutputArray warpMatrix, int motionType,
+                                      TermCriteria criteria,
+                                      InputArray inputMask, int gaussFiltSize);
+
+/** @overload */
+CV_EXPORTS_W
+double findTransformECC(InputArray templateImage, InputArray inputImage,
+    InputOutputArray warpMatrix, int motionType = MOTION_AFFINE,
+    TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001),
+    InputArray inputMask = noArray());
+
+/** @example samples/cpp/kalman.cpp
+An example using the standard Kalman filter
+*/
+
+/** @brief Kalman filter class.
+
+The class implements a standard Kalman filter <http://en.wikipedia.org/wiki/Kalman_filter>,
+@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get
+an extended Kalman filter functionality.
+@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released
+with cvReleaseKalman(&kalmanFilter)
+ */
+class CV_EXPORTS_W KalmanFilter
+{
+public:
+    CV_WRAP KalmanFilter();
+    /** @overload
+    @param dynamParams Dimensionality of the state.
+    @param measureParams Dimensionality of the measurement.
+    @param controlParams Dimensionality of the control vector.
+    @param type Type of the created matrices that should be CV_32F or CV_64F.
+    */
+    CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );
+
+    /** @brief Re-initializes Kalman filter. The previous content is destroyed.
+
+    @param dynamParams Dimensionality of the state.
+    @param measureParams Dimensionality of the measurement.
+    @param controlParams Dimensionality of the control vector.
+    @param type Type of the created matrices that should be CV_32F or CV_64F.
+     */
+    void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );
+
+    /** @brief Computes a predicted state.
+
+    @param control The optional input control
+     */
+    CV_WRAP const Mat& predict( const Mat& control = Mat() );
+
+    /** @brief Updates the predicted state from the measurement.
+
+    @param measurement The measured system parameters
+     */
+    CV_WRAP const Mat& correct( const Mat& measurement );
+
+    CV_PROP_RW Mat statePre;           //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
+    CV_PROP_RW Mat statePost;          //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
+    CV_PROP_RW Mat transitionMatrix;   //!< state transition matrix (A)
+    CV_PROP_RW Mat controlMatrix;      //!< control matrix (B) (not used if there is no control)
+    CV_PROP_RW Mat measurementMatrix;  //!< measurement matrix (H)
+    CV_PROP_RW Mat processNoiseCov;    //!< process noise covariance matrix (Q)
+    CV_PROP_RW Mat measurementNoiseCov;//!< measurement noise covariance matrix (R)
+    CV_PROP_RW Mat errorCovPre;        //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
+    CV_PROP_RW Mat gain;               //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
+    CV_PROP_RW Mat errorCovPost;       //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
+
+    // temporary matrices
+    Mat temp1;
+    Mat temp2;
+    Mat temp3;
+    Mat temp4;
+    Mat temp5;
+};
+
+
+/** @brief Read a .flo file
+
+ @param path Path to the file to be loaded
+
+ The function readOpticalFlow loads a flow field from a file and returns it as a single matrix.
+ Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the
+ flow in the horizontal direction (u), second - vertical (v).
+ */
+CV_EXPORTS_W Mat readOpticalFlow( const String& path );
+/** @brief Write a .flo to disk
+
+ @param path Path to the file to be written
+ @param flow Flow field to be stored
+
+ The function stores a flow field in a file, returns true on success, false otherwise.
+ The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds
+ to the flow in the horizontal direction (u), second - vertical (v).
+ */
+CV_EXPORTS_W bool writeOpticalFlow( const String& path, InputArray flow );
+
+/**
+   Base class for dense optical flow algorithms
+*/
+class CV_EXPORTS_W DenseOpticalFlow : public Algorithm
+{
+public:
+    /** @brief Calculates an optical flow.
+
+    @param I0 first 8-bit single-channel input image.
+    @param I1 second input image of the same size and the same type as prev.
+    @param flow computed flow image that has the same size as prev and type CV_32FC2.
+     */
+    CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0;
+    /** @brief Releases all inner buffers.
+    */
+    CV_WRAP virtual void collectGarbage() = 0;
+};
+
+/** @brief Base interface for sparse optical flow algorithms.
+ */
+class CV_EXPORTS_W SparseOpticalFlow : public Algorithm
+{
+public:
+    /** @brief Calculates a sparse optical flow.
+
+    @param prevImg First input image.
+    @param nextImg Second input image of the same size and the same type as prevImg.
+    @param prevPts Vector of 2D points for which the flow needs to be found.
+    @param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image.
+    @param status Output status vector. Each element of the vector is set to 1 if the
+                  flow for the corresponding features has been found. Otherwise, it is set to 0.
+    @param err Optional output vector that contains error response for each point (inverse confidence).
+     */
+    CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg,
+                      InputArray prevPts, InputOutputArray nextPts,
+                      OutputArray status,
+                      OutputArray err = cv::noArray()) = 0;
+};
+
+
+/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm.
+ */
+class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow
+{
+public:
+    CV_WRAP virtual int getNumLevels() const = 0;
+    CV_WRAP virtual void setNumLevels(int numLevels) = 0;
+
+    CV_WRAP virtual double getPyrScale() const = 0;
+    CV_WRAP virtual void setPyrScale(double pyrScale) = 0;
+
+    CV_WRAP virtual bool getFastPyramids() const = 0;
+    CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0;
+
+    CV_WRAP virtual int getWinSize() const = 0;
+    CV_WRAP virtual void setWinSize(int winSize) = 0;
+
+    CV_WRAP virtual int getNumIters() const = 0;
+    CV_WRAP virtual void setNumIters(int numIters) = 0;
+
+    CV_WRAP virtual int getPolyN() const = 0;
+    CV_WRAP virtual void setPolyN(int polyN) = 0;
+
+    CV_WRAP virtual double getPolySigma() const = 0;
+    CV_WRAP virtual void setPolySigma(double polySigma) = 0;
+
+    CV_WRAP virtual int getFlags() const = 0;
+    CV_WRAP virtual void setFlags(int flags) = 0;
+
+    CV_WRAP static Ptr<FarnebackOpticalFlow> create(
+            int numLevels = 5,
+            double pyrScale = 0.5,
+            bool fastPyramids = false,
+            int winSize = 13,
+            int numIters = 10,
+            int polyN = 5,
+            double polySigma = 1.1,
+            int flags = 0);
+};
+
+/** @brief Variational optical flow refinement
+
+This class implements variational refinement of the input flow field, i.e.
+it uses input flow to initialize the minimization of the following functional:
+\f$E(U) = \int_{\Omega} \delta \Psi(E_I) + \gamma \Psi(E_G) + \alpha \Psi(E_S) \f$,
+where \f$E_I,E_G,E_S\f$ are color constancy, gradient constancy and smoothness terms
+respectively. \f$\Psi(s^2)=\sqrt{s^2+\epsilon^2}\f$ is a robust penalizer to limit the
+influence of outliers. A complete formulation and a description of the minimization
+procedure can be found in @cite Brox2004
+*/
+class CV_EXPORTS_W VariationalRefinement : public DenseOpticalFlow
+{
+public:
+    /** @brief @ref calc function overload to handle separate horizontal (u) and vertical (v) flow components
+    (to avoid extra splits/merges) */
+    CV_WRAP virtual void calcUV(InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v) = 0;
+
+    /** @brief Number of outer (fixed-point) iterations in the minimization procedure.
+    @see setFixedPointIterations */
+    CV_WRAP virtual int getFixedPointIterations() const = 0;
+    /** @copybrief getFixedPointIterations @see getFixedPointIterations */
+    CV_WRAP virtual void setFixedPointIterations(int val) = 0;
+
+    /** @brief Number of inner successive over-relaxation (SOR) iterations
+        in the minimization procedure to solve the respective linear system.
+    @see setSorIterations */
+    CV_WRAP virtual int getSorIterations() const = 0;
+    /** @copybrief getSorIterations @see getSorIterations */
+    CV_WRAP virtual void setSorIterations(int val) = 0;
+
+    /** @brief Relaxation factor in SOR
+    @see setOmega */
+    CV_WRAP virtual float getOmega() const = 0;
+    /** @copybrief getOmega @see getOmega */
+    CV_WRAP virtual void setOmega(float val) = 0;
+
+    /** @brief Weight of the smoothness term
+    @see setAlpha */
+    CV_WRAP virtual float getAlpha() const = 0;
+    /** @copybrief getAlpha @see getAlpha */
+    CV_WRAP virtual void setAlpha(float val) = 0;
+
+    /** @brief Weight of the color constancy term
+    @see setDelta */
+    CV_WRAP virtual float getDelta() const = 0;
+    /** @copybrief getDelta @see getDelta */
+    CV_WRAP virtual void setDelta(float val) = 0;
+
+    /** @brief Weight of the gradient constancy term
+    @see setGamma */
+    CV_WRAP virtual float getGamma() const = 0;
+    /** @copybrief getGamma @see getGamma */
+    CV_WRAP virtual void setGamma(float val) = 0;
+
+    /** @brief Creates an instance of VariationalRefinement
+    */
+    CV_WRAP static Ptr<VariationalRefinement> create();
+};
+
+/** @brief DIS optical flow algorithm.
+
+This class implements the Dense Inverse Search (DIS) optical flow algorithm. More
+details about the algorithm can be found at @cite Kroeger2016 . Includes three presets with preselected
+parameters to provide reasonable trade-off between speed and quality. However, even the slowest preset is
+still relatively fast, use DeepFlow if you need better quality and don't care about speed.
+
+This implementation includes several additional features compared to the algorithm described in the paper,
+including spatial propagation of flow vectors (@ref getUseSpatialPropagation), as well as an option to
+utilize an initial flow approximation passed to @ref calc (which is, essentially, temporal propagation,
+if the previous frame's flow field is passed).
+*/
+class CV_EXPORTS_W DISOpticalFlow : public DenseOpticalFlow
+{
+public:
+    enum
+    {
+        PRESET_ULTRAFAST = 0,
+        PRESET_FAST = 1,
+        PRESET_MEDIUM = 2
+    };
+
+    /** @brief Finest level of the Gaussian pyramid on which the flow is computed (zero level
+        corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.
+        @see setFinestScale */
+    CV_WRAP virtual int getFinestScale() const = 0;
+    /** @copybrief getFinestScale @see getFinestScale */
+    CV_WRAP virtual void setFinestScale(int val) = 0;
+
+    /** @brief Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well
+        enough in most cases.
+        @see setPatchSize */
+    CV_WRAP virtual int getPatchSize() const = 0;
+    /** @copybrief getPatchSize @see getPatchSize */
+    CV_WRAP virtual void setPatchSize(int val) = 0;
+
+    /** @brief Stride between neighbor patches. Must be less than patch size. Lower values correspond
+        to higher flow quality.
+        @see setPatchStride */
+    CV_WRAP virtual int getPatchStride() const = 0;
+    /** @copybrief getPatchStride @see getPatchStride */
+    CV_WRAP virtual void setPatchStride(int val) = 0;
+
+    /** @brief Maximum number of gradient descent iterations in the patch inverse search stage. Higher values
+        may improve quality in some cases.
+        @see setGradientDescentIterations */
+    CV_WRAP virtual int getGradientDescentIterations() const = 0;
+    /** @copybrief getGradientDescentIterations @see getGradientDescentIterations */
+    CV_WRAP virtual void setGradientDescentIterations(int val) = 0;
+
+    /** @brief Number of fixed point iterations of variational refinement per scale. Set to zero to
+        disable variational refinement completely. Higher values will typically result in more smooth and
+        high-quality flow.
+    @see setGradientDescentIterations */
+    CV_WRAP virtual int getVariationalRefinementIterations() const = 0;
+    /** @copybrief getGradientDescentIterations @see getGradientDescentIterations */
+    CV_WRAP virtual void setVariationalRefinementIterations(int val) = 0;
+
+    /** @brief Weight of the smoothness term
+    @see setVariationalRefinementAlpha */
+    CV_WRAP virtual float getVariationalRefinementAlpha() const = 0;
+    /** @copybrief getVariationalRefinementAlpha @see getVariationalRefinementAlpha */
+    CV_WRAP virtual void setVariationalRefinementAlpha(float val) = 0;
+
+    /** @brief Weight of the color constancy term
+    @see setVariationalRefinementDelta */
+    CV_WRAP virtual float getVariationalRefinementDelta() const = 0;
+    /** @copybrief getVariationalRefinementDelta @see getVariationalRefinementDelta */
+    CV_WRAP virtual void setVariationalRefinementDelta(float val) = 0;
+
+    /** @brief Weight of the gradient constancy term
+    @see setVariationalRefinementGamma */
+    CV_WRAP virtual float getVariationalRefinementGamma() const = 0;
+    /** @copybrief getVariationalRefinementGamma @see getVariationalRefinementGamma */
+    CV_WRAP virtual void setVariationalRefinementGamma(float val) = 0;
+
+
+    /** @brief Whether to use mean-normalization of patches when computing patch distance. It is turned on
+        by default as it typically provides a noticeable quality boost because of increased robustness to
+        illumination variations. Turn it off if you are certain that your sequence doesn't contain any changes
+        in illumination.
+    @see setUseMeanNormalization */
+    CV_WRAP virtual bool getUseMeanNormalization() const = 0;
+    /** @copybrief getUseMeanNormalization @see getUseMeanNormalization */
+    CV_WRAP virtual void setUseMeanNormalization(bool val) = 0;
+
+    /** @brief Whether to use spatial propagation of good optical flow vectors. This option is turned on by
+        default, as it tends to work better on average and can sometimes help recover from major errors
+        introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this
+        option off can make the output flow field a bit smoother, however.
+    @see setUseSpatialPropagation */
+    CV_WRAP virtual bool getUseSpatialPropagation() const = 0;
+    /** @copybrief getUseSpatialPropagation @see getUseSpatialPropagation */
+    CV_WRAP virtual void setUseSpatialPropagation(bool val) = 0;
+
+    /** @brief Creates an instance of DISOpticalFlow
+
+    @param preset one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM
+    */
+    CV_WRAP static Ptr<DISOpticalFlow> create(int preset = DISOpticalFlow::PRESET_FAST);
+};
+
+/** @brief Class used for calculating a sparse optical flow.
+
+The class can calculate an optical flow for a sparse feature set using the
+iterative Lucas-Kanade method with pyramids.
+
+@sa calcOpticalFlowPyrLK
+
+*/
+class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow
+{
+public:
+    CV_WRAP virtual Size getWinSize() const = 0;
+    CV_WRAP virtual void setWinSize(Size winSize) = 0;
+
+    CV_WRAP virtual int getMaxLevel() const = 0;
+    CV_WRAP virtual void setMaxLevel(int maxLevel) = 0;
+
+    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+    CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0;
+
+    CV_WRAP virtual int getFlags() const = 0;
+    CV_WRAP virtual void setFlags(int flags) = 0;
+
+    CV_WRAP virtual double getMinEigThreshold() const = 0;
+    CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0;
+
+    CV_WRAP static Ptr<SparsePyrLKOpticalFlow> create(
+            Size winSize = Size(21, 21),
+            int maxLevel = 3, TermCriteria crit =
+            TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
+            int flags = 0,
+            double minEigThreshold = 1e-4);
+};
+
+
+
+
+/** @brief Base abstract class for the long-term tracker
+ */
+class CV_EXPORTS_W Tracker
+{
+protected:
+    Tracker();
+public:
+    virtual ~Tracker();
+
+    /** @brief Initialize the tracker with a known bounding box that surrounded the target
+    @param image The initial frame
+    @param boundingBox The initial bounding box
+    */
+    CV_WRAP virtual
+    void init(InputArray image, const Rect& boundingBox) = 0;
+
+    /** @brief Update the tracker, find the new most likely bounding box for the target
+    @param image The current frame
+    @param boundingBox The bounding box that represent the new target location, if true was returned, not
+    modified otherwise
+
+    @return True means that target was located and false means that tracker cannot locate target in
+    current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed
+    missing from the frame (say, out of sight)
+    */
+    CV_WRAP virtual
+    bool update(InputArray image, CV_OUT Rect& boundingBox) = 0;
+};
+
+
+
+/** @brief The MIL algorithm trains a classifier in an online manner to separate the object from the
+background.
+
+Multiple Instance Learning avoids the drift problem for a robust tracking. The implementation is
+based on @cite MIL .
+
+Original code can be found here <http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml>
+ */
+class CV_EXPORTS_W TrackerMIL : public Tracker
+{
+protected:
+    TrackerMIL();  // use ::create()
+public:
+    virtual ~TrackerMIL() CV_OVERRIDE;
+
+    struct CV_EXPORTS_W_SIMPLE Params
+    {
+        CV_WRAP Params();
+        //parameters for sampler
+        CV_PROP_RW float samplerInitInRadius;  //!< radius for gathering positive instances during init
+        CV_PROP_RW int samplerInitMaxNegNum;  //!< # negative samples to use during init
+        CV_PROP_RW float samplerSearchWinSize;  //!< size of search window
+        CV_PROP_RW float samplerTrackInRadius;  //!< radius for gathering positive instances during tracking
+        CV_PROP_RW int samplerTrackMaxPosNum;  //!< # positive samples to use during tracking
+        CV_PROP_RW int samplerTrackMaxNegNum;  //!< # negative samples to use during tracking
+        CV_PROP_RW int featureSetNumFeatures;  //!< # features
+    };
+
+    /** @brief Create MIL tracker instance
+     *  @param parameters MIL parameters TrackerMIL::Params
+     */
+    static CV_WRAP
+    Ptr<TrackerMIL> create(const TrackerMIL::Params &parameters = TrackerMIL::Params());
+
+    //void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
+    //bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
+};
+
+
+
+/** @brief the GOTURN (Generic Object Tracking Using Regression Networks) tracker
+ *
+ *  GOTURN (@cite GOTURN) is kind of trackers based on Convolutional Neural Networks (CNN). While taking all advantages of CNN trackers,
+ *  GOTURN is much faster due to offline training without online fine-tuning nature.
+ *  GOTURN tracker addresses the problem of single target tracking: given a bounding box label of an object in the first frame of the video,
+ *  we track that object through the rest of the video. NOTE: Current method of GOTURN does not handle occlusions; however, it is fairly
+ *  robust to viewpoint changes, lighting changes, and deformations.
+ *  Inputs of GOTURN are two RGB patches representing Target and Search patches resized to 227x227.
+ *  Outputs of GOTURN are predicted bounding box coordinates, relative to Search patch coordinate system, in format X1,Y1,X2,Y2.
+ *  Original paper is here: <http://davheld.github.io/GOTURN/GOTURN.pdf>
+ *  As long as original authors implementation: <https://github.com/davheld/GOTURN#train-the-tracker>
+ *  Implementation of training algorithm is placed in separately here due to 3d-party dependencies:
+ *  <https://github.com/Auron-X/GOTURN_Training_Toolkit>
+ *  GOTURN architecture goturn.prototxt and trained model goturn.caffemodel are accessible on opencv_extra GitHub repository.
+ */
+class CV_EXPORTS_W TrackerGOTURN : public Tracker
+{
+protected:
+    TrackerGOTURN();  // use ::create()
+public:
+    virtual ~TrackerGOTURN() CV_OVERRIDE;
+
+    struct CV_EXPORTS_W_SIMPLE Params
+    {
+        CV_WRAP Params();
+        CV_PROP_RW std::string modelTxt;
+        CV_PROP_RW std::string modelBin;
+    };
+
+    /** @brief Constructor
+    @param parameters GOTURN parameters TrackerGOTURN::Params
+    */
+    static CV_WRAP
+    Ptr<TrackerGOTURN> create(const TrackerGOTURN::Params& parameters = TrackerGOTURN::Params());
+
+    //void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
+    //bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
+};
+
+class CV_EXPORTS_W TrackerDaSiamRPN : public Tracker
+{
+protected:
+    TrackerDaSiamRPN();  // use ::create()
+public:
+    virtual ~TrackerDaSiamRPN() CV_OVERRIDE;
+
+    struct CV_EXPORTS_W_SIMPLE Params
+    {
+        CV_WRAP Params();
+        CV_PROP_RW std::string model;
+        CV_PROP_RW std::string kernel_cls1;
+        CV_PROP_RW std::string kernel_r1;
+        CV_PROP_RW int backend;
+        CV_PROP_RW int target;
+    };
+
+    /** @brief Constructor
+    @param parameters DaSiamRPN parameters TrackerDaSiamRPN::Params
+    */
+    static CV_WRAP
+    Ptr<TrackerDaSiamRPN> create(const TrackerDaSiamRPN::Params& parameters = TrackerDaSiamRPN::Params());
+
+    /** @brief Return tracking score
+    */
+    CV_WRAP virtual float getTrackingScore() = 0;
+
+    //void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
+    //bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
+};
+
+
+//! @} video_track
+
+} // cv
+
+#endif
diff --git a/3rdParty/include/opencv2/video/video.hpp b/3rdParty/include/opencv2/video/video.hpp
new file mode 100644
index 0000000..8267b85
--- /dev/null
+++ b/3rdParty/include/opencv2/video/video.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/video.hpp"
diff --git a/3rdParty/include/opencv2/videoio.hpp b/3rdParty/include/opencv2/videoio.hpp
new file mode 100644
index 0000000..93ea8cd
--- /dev/null
+++ b/3rdParty/include/opencv2/videoio.hpp
@@ -0,0 +1,1129 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_VIDEOIO_HPP
+#define OPENCV_VIDEOIO_HPP
+
+#include "opencv2/core.hpp"
+
+/**
+  @defgroup videoio Video I/O
+
+  @brief Read and write video or images sequence with OpenCV
+
+  ### See also:
+  - @ref videoio_overview
+  - Tutorials: @ref tutorial_table_of_content_app
+  @{
+    @defgroup videoio_flags_base Flags for video I/O
+    @defgroup videoio_flags_others Additional flags for video I/O API backends
+    @defgroup videoio_hwaccel Hardware-accelerated video decoding and encoding
+    @defgroup videoio_c C API for video I/O
+    @defgroup videoio_ios iOS glue for video I/O
+    @defgroup videoio_winrt WinRT glue for video I/O
+    @defgroup videoio_registry Query I/O API backends registry
+  @}
+*/
+
+////////////////////////////////// video io /////////////////////////////////
+
+typedef struct CvCapture CvCapture;
+typedef struct CvVideoWriter CvVideoWriter;
+
+namespace cv
+{
+
+//! @addtogroup videoio
+//! @{
+
+//! @addtogroup videoio_flags_base
+//! @{
+
+
+/** @brief cv::VideoCapture API backends identifier.
+
+Select preferred API for a capture object.
+To be used in the VideoCapture::VideoCapture() constructor or VideoCapture::open()
+
+@note Backends are available only if they have been built with your OpenCV binaries.
+See @ref videoio_overview for more information.
+*/
+enum VideoCaptureAPIs {
+       CAP_ANY          = 0,            //!< Auto detect == 0
+       CAP_VFW          = 200,          //!< Video For Windows (obsolete, removed)
+       CAP_V4L          = 200,          //!< V4L/V4L2 capturing support
+       CAP_V4L2         = CAP_V4L,      //!< Same as CAP_V4L
+       CAP_FIREWIRE     = 300,          //!< IEEE 1394 drivers
+       CAP_FIREWARE     = CAP_FIREWIRE, //!< Same value as CAP_FIREWIRE
+       CAP_IEEE1394     = CAP_FIREWIRE, //!< Same value as CAP_FIREWIRE
+       CAP_DC1394       = CAP_FIREWIRE, //!< Same value as CAP_FIREWIRE
+       CAP_CMU1394      = CAP_FIREWIRE, //!< Same value as CAP_FIREWIRE
+       CAP_QT           = 500,          //!< QuickTime (obsolete, removed)
+       CAP_UNICAP       = 600,          //!< Unicap drivers (obsolete, removed)
+       CAP_DSHOW        = 700,          //!< DirectShow (via videoInput)
+       CAP_PVAPI        = 800,          //!< PvAPI, Prosilica GigE SDK
+       CAP_OPENNI       = 900,          //!< OpenNI (for Kinect)
+       CAP_OPENNI_ASUS  = 910,          //!< OpenNI (for Asus Xtion)
+       CAP_ANDROID      = 1000,         //!< Android - not used
+       CAP_XIAPI        = 1100,         //!< XIMEA Camera API
+       CAP_AVFOUNDATION = 1200,         //!< AVFoundation framework for iOS (OS X Lion will have the same API)
+       CAP_GIGANETIX    = 1300,         //!< Smartek Giganetix GigEVisionSDK
+       CAP_MSMF         = 1400,         //!< Microsoft Media Foundation (via videoInput)
+       CAP_WINRT        = 1410,         //!< Microsoft Windows Runtime using Media Foundation
+       CAP_INTELPERC    = 1500,         //!< RealSense (former Intel Perceptual Computing SDK)
+       CAP_REALSENSE    = 1500,         //!< Synonym for CAP_INTELPERC
+       CAP_OPENNI2      = 1600,         //!< OpenNI2 (for Kinect)
+       CAP_OPENNI2_ASUS = 1610,         //!< OpenNI2 (for Asus Xtion and Occipital Structure sensors)
+       CAP_OPENNI2_ASTRA= 1620,         //!< OpenNI2 (for Orbbec Astra)
+       CAP_GPHOTO2      = 1700,         //!< gPhoto2 connection
+       CAP_GSTREAMER    = 1800,         //!< GStreamer
+       CAP_FFMPEG       = 1900,         //!< Open and record video file or stream using the FFMPEG library
+       CAP_IMAGES       = 2000,         //!< OpenCV Image Sequence (e.g. img_%02d.jpg)
+       CAP_ARAVIS       = 2100,         //!< Aravis SDK
+       CAP_OPENCV_MJPEG = 2200,         //!< Built-in OpenCV MotionJPEG codec
+       CAP_INTEL_MFX    = 2300,         //!< Intel MediaSDK
+       CAP_XINE         = 2400,         //!< XINE engine (Linux)
+       CAP_UEYE         = 2500,         //!< uEye Camera API
+     };
+
+/** @brief cv::VideoCapture generic properties identifier.
+
+ Reading / writing properties involves many layers. Some unexpected result might happens along this chain.
+ Effective behaviour depends from device hardware, driver and API Backend.
+ @sa videoio_flags_others, VideoCapture::get(), VideoCapture::set()
+*/
+enum VideoCaptureProperties {
+       CAP_PROP_POS_MSEC       =0, //!< Current position of the video file in milliseconds.
+       CAP_PROP_POS_FRAMES     =1, //!< 0-based index of the frame to be decoded/captured next.
+       CAP_PROP_POS_AVI_RATIO  =2, //!< Relative position of the video file: 0=start of the film, 1=end of the film.
+       CAP_PROP_FRAME_WIDTH    =3, //!< Width of the frames in the video stream.
+       CAP_PROP_FRAME_HEIGHT   =4, //!< Height of the frames in the video stream.
+       CAP_PROP_FPS            =5, //!< Frame rate.
+       CAP_PROP_FOURCC         =6, //!< 4-character code of codec. see VideoWriter::fourcc .
+       CAP_PROP_FRAME_COUNT    =7, //!< Number of frames in the video file.
+       CAP_PROP_FORMAT         =8, //!< Format of the %Mat objects (see Mat::type()) returned by VideoCapture::retrieve().
+                                   //!< Set value -1 to fetch undecoded RAW video streams (as Mat 8UC1).
+       CAP_PROP_MODE           =9, //!< Backend-specific value indicating the current capture mode.
+       CAP_PROP_BRIGHTNESS    =10, //!< Brightness of the image (only for those cameras that support).
+       CAP_PROP_CONTRAST      =11, //!< Contrast of the image (only for cameras).
+       CAP_PROP_SATURATION    =12, //!< Saturation of the image (only for cameras).
+       CAP_PROP_HUE           =13, //!< Hue of the image (only for cameras).
+       CAP_PROP_GAIN          =14, //!< Gain of the image (only for those cameras that support).
+       CAP_PROP_EXPOSURE      =15, //!< Exposure (only for those cameras that support).
+       CAP_PROP_CONVERT_RGB   =16, //!< Boolean flags indicating whether images should be converted to RGB. <br/>
+                                   //!< *GStreamer note*: The flag is ignored in case if custom pipeline is used. It's user responsibility to interpret pipeline output.
+       CAP_PROP_WHITE_BALANCE_BLUE_U =17, //!< Currently unsupported.
+       CAP_PROP_RECTIFICATION =18, //!< Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently).
+       CAP_PROP_MONOCHROME    =19,
+       CAP_PROP_SHARPNESS     =20,
+       CAP_PROP_AUTO_EXPOSURE =21, //!< DC1394: exposure control done by camera, user can adjust reference level using this feature.
+       CAP_PROP_GAMMA         =22,
+       CAP_PROP_TEMPERATURE   =23,
+       CAP_PROP_TRIGGER       =24,
+       CAP_PROP_TRIGGER_DELAY =25,
+       CAP_PROP_WHITE_BALANCE_RED_V =26,
+       CAP_PROP_ZOOM          =27,
+       CAP_PROP_FOCUS         =28,
+       CAP_PROP_GUID          =29,
+       CAP_PROP_ISO_SPEED     =30,
+       CAP_PROP_BACKLIGHT     =32,
+       CAP_PROP_PAN           =33,
+       CAP_PROP_TILT          =34,
+       CAP_PROP_ROLL          =35,
+       CAP_PROP_IRIS          =36,
+       CAP_PROP_SETTINGS      =37, //!< Pop up video/camera filter dialog (note: only supported by DSHOW backend currently. The property value is ignored)
+       CAP_PROP_BUFFERSIZE    =38,
+       CAP_PROP_AUTOFOCUS     =39,
+       CAP_PROP_SAR_NUM       =40, //!< Sample aspect ratio: num/den (num)
+       CAP_PROP_SAR_DEN       =41, //!< Sample aspect ratio: num/den (den)
+       CAP_PROP_BACKEND       =42, //!< Current backend (enum VideoCaptureAPIs). Read-only property
+       CAP_PROP_CHANNEL       =43, //!< Video input or Channel Number (only for those cameras that support)
+       CAP_PROP_AUTO_WB       =44, //!< enable/ disable auto white-balance
+       CAP_PROP_WB_TEMPERATURE=45, //!< white-balance color temperature
+       CAP_PROP_CODEC_PIXEL_FORMAT =46,    //!< (read-only) codec's pixel format. 4-character code - see VideoWriter::fourcc . Subset of [AV_PIX_FMT_*](https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/raw.c) or -1 if unknown
+       CAP_PROP_BITRATE       =47, //!< (read-only) Video bitrate in kbits/s
+       CAP_PROP_ORIENTATION_META=48, //!< (read-only) Frame rotation defined by stream meta (applicable for FFmpeg back-end only)
+       CAP_PROP_ORIENTATION_AUTO=49, //!< if true - rotates output frames of CvCapture considering video file's metadata  (applicable for FFmpeg back-end only) (https://github.com/opencv/opencv/issues/15499)
+       CAP_PROP_HW_ACCELERATION=50, //!< (**open-only**) Hardware acceleration type (see #VideoAccelerationType). Setting supported only via `params` parameter in cv::VideoCapture constructor / .open() method. Default value is backend-specific.
+       CAP_PROP_HW_DEVICE      =51, //!< (**open-only**) Hardware device index (select GPU if multiple available). Device enumeration is acceleration type specific.
+       CAP_PROP_HW_ACCELERATION_USE_OPENCL=52, //!< (**open-only**) If non-zero, create new OpenCL context and bind it to current thread. The OpenCL context created with Video Acceleration context attached it (if not attached yet) for optimized GPU data copy between HW accelerated decoder and cv::UMat.
+       CAP_PROP_OPEN_TIMEOUT_MSEC=53, //!< (**open-only**) timeout in milliseconds for opening a video capture (applicable for FFmpeg back-end only)
+       CAP_PROP_READ_TIMEOUT_MSEC=54, //!< (**open-only**) timeout in milliseconds for reading from a video capture (applicable for FFmpeg back-end only)
+       CAP_PROP_STREAM_OPEN_TIME_USEC =55, //<! (read-only) time in microseconds since Jan 1 1970 when stream was opened. Applicable for FFmpeg backend only. Useful for RTSP and other live streams
+       CAP_PROP_VIDEO_TOTAL_CHANNELS = 56, //!< (read-only) Number of video channels
+       CAP_PROP_VIDEO_STREAM = 57, //!< (**open-only**) Specify video stream, 0-based index. Use -1 to disable video stream from file or IP cameras. Default value is 0.
+       CAP_PROP_AUDIO_STREAM = 58, //!< (**open-only**) Specify stream in multi-language media files, -1 - disable audio processing or microphone. Default value is -1.
+       CAP_PROP_AUDIO_POS = 59, //!< (read-only) Audio position is measured in samples. Accurate audio sample timestamp of previous grabbed fragment. See CAP_PROP_AUDIO_SAMPLES_PER_SECOND and CAP_PROP_AUDIO_SHIFT_NSEC.
+       CAP_PROP_AUDIO_SHIFT_NSEC = 60, //!< (read only) Contains the time difference between the start of the audio stream and the video stream in nanoseconds. Positive value means that audio is started after the first video frame. Negative value means that audio is started before the first video frame.
+       CAP_PROP_AUDIO_DATA_DEPTH = 61, //!< (open, read) Alternative definition to bits-per-sample, but with clear handling of 32F / 32S
+       CAP_PROP_AUDIO_SAMPLES_PER_SECOND = 62, //!< (open, read) determined from file/codec input. If not specified, then selected audio sample rate is 44100
+       CAP_PROP_AUDIO_BASE_INDEX = 63, //!< (read-only) Index of the first audio channel for .retrieve() calls. That audio channel number continues enumeration after video channels.
+       CAP_PROP_AUDIO_TOTAL_CHANNELS = 64, //!< (read-only) Number of audio channels in the selected audio stream (mono, stereo, etc)
+       CAP_PROP_AUDIO_TOTAL_STREAMS = 65, //!< (read-only) Number of audio streams.
+       CAP_PROP_AUDIO_SYNCHRONIZE = 66, //!< (open, read) Enables audio synchronization.
+       CAP_PROP_LRF_HAS_KEY_FRAME = 67, //!< FFmpeg back-end only - Indicates whether the Last Raw Frame (LRF), output from VideoCapture::read() when VideoCapture is initialized with VideoCapture::open(CAP_FFMPEG, {CAP_PROP_FORMAT, -1}) or VideoCapture::set(CAP_PROP_FORMAT,-1) is called before the first call to VideoCapture::read(), contains encoded data for a key frame.
+       CAP_PROP_CODEC_EXTRADATA_INDEX = 68, //!< Positive index indicates that returning extra data is supported by the video back end.  This can be retrieved as cap.retrieve(data, <returned index>).  E.g. When reading from a h264 encoded RTSP stream, the FFmpeg backend could return the SPS and/or PPS if available (if sent in reply to a DESCRIBE request), from calls to cap.retrieve(data, <returned index>).
+#ifndef CV_DOXYGEN
+       CV__CAP_PROP_LATEST
+#endif
+     };
+
+/** @brief cv::VideoWriter generic properties identifier.
+ @sa VideoWriter::get(), VideoWriter::set()
+*/
+enum VideoWriterProperties {
+  VIDEOWRITER_PROP_QUALITY = 1,    //!< Current quality (0..100%) of the encoded videostream. Can be adjusted dynamically in some codecs.
+  VIDEOWRITER_PROP_FRAMEBYTES = 2, //!< (Read-only): Size of just encoded video frame. Note that the encoding order may be different from representation order.
+  VIDEOWRITER_PROP_NSTRIPES = 3,   //!< Number of stripes for parallel encoding. -1 for auto detection.
+  VIDEOWRITER_PROP_IS_COLOR = 4,   //!< If it is not zero, the encoder will expect and encode color frames, otherwise it
+                                   //!< will work with grayscale frames.
+  VIDEOWRITER_PROP_DEPTH = 5,      //!< Defaults to CV_8U.
+  VIDEOWRITER_PROP_HW_ACCELERATION = 6, //!< (**open-only**) Hardware acceleration type (see #VideoAccelerationType). Setting supported only via `params` parameter in VideoWriter constructor / .open() method. Default value is backend-specific.
+  VIDEOWRITER_PROP_HW_DEVICE       = 7, //!< (**open-only**) Hardware device index (select GPU if multiple available). Device enumeration is acceleration type specific.
+  VIDEOWRITER_PROP_HW_ACCELERATION_USE_OPENCL= 8, //!< (**open-only**) If non-zero, create new OpenCL context and bind it to current thread. The OpenCL context created with Video Acceleration context attached it (if not attached yet) for optimized GPU data copy between cv::UMat and HW accelerated encoder.
+#ifndef CV_DOXYGEN
+  CV__VIDEOWRITER_PROP_LATEST
+#endif
+};
+
+//! @} videoio_flags_base
+
+//! @addtogroup videoio_flags_others
+//! @{
+
+/** @name Hardware acceleration support
+    @{
+*/
+
+/** @brief Video Acceleration type
+ *
+ * Used as value in #CAP_PROP_HW_ACCELERATION and #VIDEOWRITER_PROP_HW_ACCELERATION
+ *
+ * @note In case of FFmpeg backend, it translated to enum AVHWDeviceType (https://github.com/FFmpeg/FFmpeg/blob/master/libavutil/hwcontext.h)
+ */
+enum VideoAccelerationType
+{
+    VIDEO_ACCELERATION_NONE     =  0,  //!< Do not require any specific H/W acceleration, prefer software processing.
+                                       //!< Reading of this value means that special H/W accelerated handling is not added or not detected by OpenCV.
+
+    VIDEO_ACCELERATION_ANY      =  1,  //!< Prefer to use H/W acceleration. If no one supported, then fallback to software processing.
+                                       //!< @note H/W acceleration may require special configuration of used environment.
+                                       //!< @note Results in encoding scenario may differ between software and hardware accelerated encoders.
+
+    VIDEO_ACCELERATION_D3D11    =  2,  //!< DirectX 11
+    VIDEO_ACCELERATION_VAAPI    =  3,  //!< VAAPI
+    VIDEO_ACCELERATION_MFX      =  4,  //!< libmfx (Intel MediaSDK/oneVPL)
+};
+
+//! @} Hardware acceleration support
+
+/** @name IEEE 1394 drivers
+    @{
+*/
+
+/** @brief Modes of the IEEE 1394 controlling registers
+(can be: auto, manual, auto single push, absolute Latter allowed with any other mode)
+every feature can have only one mode turned on at a time
+*/
+enum { CAP_PROP_DC1394_OFF                = -4, //!< turn the feature off (not controlled manually nor automatically).
+       CAP_PROP_DC1394_MODE_MANUAL        = -3, //!< set automatically when a value of the feature is set by the user.
+       CAP_PROP_DC1394_MODE_AUTO          = -2,
+       CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1,
+       CAP_PROP_DC1394_MAX                = 31
+     };
+
+//! @} IEEE 1394 drivers
+
+/** @name OpenNI (for Kinect)
+    @{
+*/
+
+//! OpenNI map generators
+enum { CAP_OPENNI_DEPTH_GENERATOR = 1 << 31,
+       CAP_OPENNI_IMAGE_GENERATOR = 1 << 30,
+       CAP_OPENNI_IR_GENERATOR    = 1 << 29,
+       CAP_OPENNI_GENERATORS_MASK = CAP_OPENNI_DEPTH_GENERATOR + CAP_OPENNI_IMAGE_GENERATOR + CAP_OPENNI_IR_GENERATOR
+     };
+
+//! Properties of cameras available through OpenNI backend
+enum { CAP_PROP_OPENNI_OUTPUT_MODE       = 100,
+       CAP_PROP_OPENNI_FRAME_MAX_DEPTH   = 101, //!< In mm
+       CAP_PROP_OPENNI_BASELINE          = 102, //!< In mm
+       CAP_PROP_OPENNI_FOCAL_LENGTH      = 103, //!< In pixels
+       CAP_PROP_OPENNI_REGISTRATION      = 104, //!< Flag that synchronizes the remapping depth map to image map
+                                                //!< by changing depth generator's view point (if the flag is "on") or
+                                                //!< sets this view point to its normal one (if the flag is "off").
+       CAP_PROP_OPENNI_REGISTRATION_ON   = CAP_PROP_OPENNI_REGISTRATION,
+       CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105,
+       CAP_PROP_OPENNI_MAX_BUFFER_SIZE   = 106,
+       CAP_PROP_OPENNI_CIRCLE_BUFFER     = 107,
+       CAP_PROP_OPENNI_MAX_TIME_DURATION = 108,
+       CAP_PROP_OPENNI_GENERATOR_PRESENT = 109,
+       CAP_PROP_OPENNI2_SYNC             = 110,
+       CAP_PROP_OPENNI2_MIRROR           = 111
+     };
+
+//! OpenNI shortcuts
+enum { CAP_OPENNI_IMAGE_GENERATOR_PRESENT         = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
+       CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE     = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_OUTPUT_MODE,
+       CAP_OPENNI_DEPTH_GENERATOR_PRESENT         = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
+       CAP_OPENNI_DEPTH_GENERATOR_BASELINE        = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_BASELINE,
+       CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH    = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_FOCAL_LENGTH,
+       CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION    = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION,
+       CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION,
+       CAP_OPENNI_IR_GENERATOR_PRESENT            = CAP_OPENNI_IR_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
+     };
+
+//! OpenNI data given from depth generator
+enum { CAP_OPENNI_DEPTH_MAP         = 0, //!< Depth values in mm (CV_16UC1)
+       CAP_OPENNI_POINT_CLOUD_MAP   = 1, //!< XYZ in meters (CV_32FC3)
+       CAP_OPENNI_DISPARITY_MAP     = 2, //!< Disparity in pixels (CV_8UC1)
+       CAP_OPENNI_DISPARITY_MAP_32F = 3, //!< Disparity in pixels (CV_32FC1)
+       CAP_OPENNI_VALID_DEPTH_MASK  = 4, //!< CV_8UC1
+
+       CAP_OPENNI_BGR_IMAGE         = 5, //!< Data given from RGB image generator
+       CAP_OPENNI_GRAY_IMAGE        = 6, //!< Data given from RGB image generator
+
+       CAP_OPENNI_IR_IMAGE          = 7  //!< Data given from IR image generator
+     };
+
+//! Supported output modes of OpenNI image generator
+enum { CAP_OPENNI_VGA_30HZ  = 0,
+       CAP_OPENNI_SXGA_15HZ = 1,
+       CAP_OPENNI_SXGA_30HZ = 2,
+       CAP_OPENNI_QVGA_30HZ = 3,
+       CAP_OPENNI_QVGA_60HZ = 4
+     };
+
+//! @} OpenNI
+
+/** @name GStreamer
+    @{
+*/
+
+enum { CAP_PROP_GSTREAMER_QUEUE_LENGTH = 200 //!< Default is 1
+     };
+
+//! @} GStreamer
+
+/** @name PvAPI, Prosilica GigE SDK
+    @{
+*/
+
+//! PVAPI
+enum { CAP_PROP_PVAPI_MULTICASTIP           = 300, //!< IP for enable multicast master mode. 0 for disable multicast.
+       CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301, //!< FrameStartTriggerMode: Determines how a frame is initiated.
+       CAP_PROP_PVAPI_DECIMATIONHORIZONTAL  = 302, //!< Horizontal sub-sampling of the image.
+       CAP_PROP_PVAPI_DECIMATIONVERTICAL    = 303, //!< Vertical sub-sampling of the image.
+       CAP_PROP_PVAPI_BINNINGX              = 304, //!< Horizontal binning factor.
+       CAP_PROP_PVAPI_BINNINGY              = 305, //!< Vertical binning factor.
+       CAP_PROP_PVAPI_PIXELFORMAT           = 306  //!< Pixel format.
+     };
+
+//! PVAPI: FrameStartTriggerMode
+enum { CAP_PVAPI_FSTRIGMODE_FREERUN     = 0,    //!< Freerun
+       CAP_PVAPI_FSTRIGMODE_SYNCIN1     = 1,    //!< SyncIn1
+       CAP_PVAPI_FSTRIGMODE_SYNCIN2     = 2,    //!< SyncIn2
+       CAP_PVAPI_FSTRIGMODE_FIXEDRATE   = 3,    //!< FixedRate
+       CAP_PVAPI_FSTRIGMODE_SOFTWARE    = 4     //!< Software
+     };
+
+//! PVAPI: DecimationHorizontal, DecimationVertical
+enum { CAP_PVAPI_DECIMATION_OFF       = 1,    //!< Off
+       CAP_PVAPI_DECIMATION_2OUTOF4   = 2,    //!< 2 out of 4 decimation
+       CAP_PVAPI_DECIMATION_2OUTOF8   = 4,    //!< 2 out of 8 decimation
+       CAP_PVAPI_DECIMATION_2OUTOF16  = 8     //!< 2 out of 16 decimation
+     };
+
+//! PVAPI: PixelFormat
+enum { CAP_PVAPI_PIXELFORMAT_MONO8    = 1,    //!< Mono8
+       CAP_PVAPI_PIXELFORMAT_MONO16   = 2,    //!< Mono16
+       CAP_PVAPI_PIXELFORMAT_BAYER8   = 3,    //!< Bayer8
+       CAP_PVAPI_PIXELFORMAT_BAYER16  = 4,    //!< Bayer16
+       CAP_PVAPI_PIXELFORMAT_RGB24    = 5,    //!< Rgb24
+       CAP_PVAPI_PIXELFORMAT_BGR24    = 6,    //!< Bgr24
+       CAP_PVAPI_PIXELFORMAT_RGBA32   = 7,    //!< Rgba32
+       CAP_PVAPI_PIXELFORMAT_BGRA32   = 8,    //!< Bgra32
+     };
+
+//! @} PvAPI
+
+/** @name XIMEA Camera API
+    @{
+*/
+
+//! Properties of cameras available through XIMEA SDK backend
+enum { CAP_PROP_XI_DOWNSAMPLING                                 = 400, //!< Change image resolution by binning or skipping.
+       CAP_PROP_XI_DATA_FORMAT                                  = 401, //!< Output data format.
+       CAP_PROP_XI_OFFSET_X                                     = 402, //!< Horizontal offset from the origin to the area of interest (in pixels).
+       CAP_PROP_XI_OFFSET_Y                                     = 403, //!< Vertical offset from the origin to the area of interest (in pixels).
+       CAP_PROP_XI_TRG_SOURCE                                   = 404, //!< Defines source of trigger.
+       CAP_PROP_XI_TRG_SOFTWARE                                 = 405, //!< Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE.
+       CAP_PROP_XI_GPI_SELECTOR                                 = 406, //!< Selects general purpose input.
+       CAP_PROP_XI_GPI_MODE                                     = 407, //!< Set general purpose input mode.
+       CAP_PROP_XI_GPI_LEVEL                                    = 408, //!< Get general purpose level.
+       CAP_PROP_XI_GPO_SELECTOR                                 = 409, //!< Selects general purpose output.
+       CAP_PROP_XI_GPO_MODE                                     = 410, //!< Set general purpose output mode.
+       CAP_PROP_XI_LED_SELECTOR                                 = 411, //!< Selects camera signalling LED.
+       CAP_PROP_XI_LED_MODE                                     = 412, //!< Define camera signalling LED functionality.
+       CAP_PROP_XI_MANUAL_WB                                    = 413, //!< Calculates White Balance(must be called during acquisition).
+       CAP_PROP_XI_AUTO_WB                                      = 414, //!< Automatic white balance.
+       CAP_PROP_XI_AEAG                                         = 415, //!< Automatic exposure/gain.
+       CAP_PROP_XI_EXP_PRIORITY                                 = 416, //!< Exposure priority (0.5 - exposure 50%, gain 50%).
+       CAP_PROP_XI_AE_MAX_LIMIT                                 = 417, //!< Maximum limit of exposure in AEAG procedure.
+       CAP_PROP_XI_AG_MAX_LIMIT                                 = 418, //!< Maximum limit of gain in AEAG procedure.
+       CAP_PROP_XI_AEAG_LEVEL                                   = 419, //!< Average intensity of output signal AEAG should achieve(in %).
+       CAP_PROP_XI_TIMEOUT                                      = 420, //!< Image capture timeout in milliseconds.
+       CAP_PROP_XI_EXPOSURE                                     = 421, //!< Exposure time in microseconds.
+       CAP_PROP_XI_EXPOSURE_BURST_COUNT                         = 422, //!< Sets the number of times of exposure in one frame.
+       CAP_PROP_XI_GAIN_SELECTOR                                = 423, //!< Gain selector for parameter Gain allows to select different type of gains.
+       CAP_PROP_XI_GAIN                                         = 424, //!< Gain in dB.
+       CAP_PROP_XI_DOWNSAMPLING_TYPE                            = 426, //!< Change image downsampling type.
+       CAP_PROP_XI_BINNING_SELECTOR                             = 427, //!< Binning engine selector.
+       CAP_PROP_XI_BINNING_VERTICAL                             = 428, //!< Vertical Binning - number of vertical photo-sensitive cells to combine together.
+       CAP_PROP_XI_BINNING_HORIZONTAL                           = 429, //!< Horizontal Binning - number of horizontal photo-sensitive cells to combine together.
+       CAP_PROP_XI_BINNING_PATTERN                              = 430, //!< Binning pattern type.
+       CAP_PROP_XI_DECIMATION_SELECTOR                          = 431, //!< Decimation engine selector.
+       CAP_PROP_XI_DECIMATION_VERTICAL                          = 432, //!< Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor.
+       CAP_PROP_XI_DECIMATION_HORIZONTAL                        = 433, //!< Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor.
+       CAP_PROP_XI_DECIMATION_PATTERN                           = 434, //!< Decimation pattern type.
+       CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR              = 587, //!< Selects which test pattern generator is controlled by the TestPattern feature.
+       CAP_PROP_XI_TEST_PATTERN                                 = 588, //!< Selects which test pattern type is generated by the selected generator.
+       CAP_PROP_XI_IMAGE_DATA_FORMAT                            = 435, //!< Output data format.
+       CAP_PROP_XI_SHUTTER_TYPE                                 = 436, //!< Change sensor shutter type(CMOS sensor).
+       CAP_PROP_XI_SENSOR_TAPS                                  = 437, //!< Number of taps.
+       CAP_PROP_XI_AEAG_ROI_OFFSET_X                            = 439, //!< Automatic exposure/gain ROI offset X.
+       CAP_PROP_XI_AEAG_ROI_OFFSET_Y                            = 440, //!< Automatic exposure/gain ROI offset Y.
+       CAP_PROP_XI_AEAG_ROI_WIDTH                               = 441, //!< Automatic exposure/gain ROI Width.
+       CAP_PROP_XI_AEAG_ROI_HEIGHT                              = 442, //!< Automatic exposure/gain ROI Height.
+       CAP_PROP_XI_BPC                                          = 445, //!< Correction of bad pixels.
+       CAP_PROP_XI_WB_KR                                        = 448, //!< White balance red coefficient.
+       CAP_PROP_XI_WB_KG                                        = 449, //!< White balance green coefficient.
+       CAP_PROP_XI_WB_KB                                        = 450, //!< White balance blue coefficient.
+       CAP_PROP_XI_WIDTH                                        = 451, //!< Width of the Image provided by the device (in pixels).
+       CAP_PROP_XI_HEIGHT                                       = 452, //!< Height of the Image provided by the device (in pixels).
+       CAP_PROP_XI_REGION_SELECTOR                              = 589, //!< Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode.
+       CAP_PROP_XI_REGION_MODE                                  = 595, //!< Activates/deactivates Region selected by Region Selector.
+       CAP_PROP_XI_LIMIT_BANDWIDTH                              = 459, //!< Set/get bandwidth(datarate)(in Megabits).
+       CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH                        = 460, //!< Sensor output data bit depth.
+       CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH                        = 461, //!< Device output data bit depth.
+       CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH                         = 462, //!< bitdepth of data returned by function xiGetImage.
+       CAP_PROP_XI_OUTPUT_DATA_PACKING                          = 463, //!< Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available.
+       CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE                     = 464, //!< Data packing type. Some cameras supports only specific packing type.
+       CAP_PROP_XI_IS_COOLED                                    = 465, //!< Returns 1 for cameras that support cooling.
+       CAP_PROP_XI_COOLING                                      = 466, //!< Start camera cooling.
+       CAP_PROP_XI_TARGET_TEMP                                  = 467, //!< Set sensor target temperature for cooling.
+       CAP_PROP_XI_CHIP_TEMP                                    = 468, //!< Camera sensor temperature.
+       CAP_PROP_XI_HOUS_TEMP                                    = 469, //!< Camera housing temperature.
+       CAP_PROP_XI_HOUS_BACK_SIDE_TEMP                          = 590, //!< Camera housing back side temperature.
+       CAP_PROP_XI_SENSOR_BOARD_TEMP                            = 596, //!< Camera sensor board temperature.
+       CAP_PROP_XI_CMS                                          = 470, //!< Mode of color management system.
+       CAP_PROP_XI_APPLY_CMS                                    = 471, //!< Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE).
+       CAP_PROP_XI_IMAGE_IS_COLOR                               = 474, //!< Returns 1 for color cameras.
+       CAP_PROP_XI_COLOR_FILTER_ARRAY                           = 475, //!< Returns color filter array type of RAW data.
+       CAP_PROP_XI_GAMMAY                                       = 476, //!< Luminosity gamma.
+       CAP_PROP_XI_GAMMAC                                       = 477, //!< Chromaticity gamma.
+       CAP_PROP_XI_SHARPNESS                                    = 478, //!< Sharpness Strength.
+       CAP_PROP_XI_CC_MATRIX_00                                 = 479, //!< Color Correction Matrix element [0][0].
+       CAP_PROP_XI_CC_MATRIX_01                                 = 480, //!< Color Correction Matrix element [0][1].
+       CAP_PROP_XI_CC_MATRIX_02                                 = 481, //!< Color Correction Matrix element [0][2].
+       CAP_PROP_XI_CC_MATRIX_03                                 = 482, //!< Color Correction Matrix element [0][3].
+       CAP_PROP_XI_CC_MATRIX_10                                 = 483, //!< Color Correction Matrix element [1][0].
+       CAP_PROP_XI_CC_MATRIX_11                                 = 484, //!< Color Correction Matrix element [1][1].
+       CAP_PROP_XI_CC_MATRIX_12                                 = 485, //!< Color Correction Matrix element [1][2].
+       CAP_PROP_XI_CC_MATRIX_13                                 = 486, //!< Color Correction Matrix element [1][3].
+       CAP_PROP_XI_CC_MATRIX_20                                 = 487, //!< Color Correction Matrix element [2][0].
+       CAP_PROP_XI_CC_MATRIX_21                                 = 488, //!< Color Correction Matrix element [2][1].
+       CAP_PROP_XI_CC_MATRIX_22                                 = 489, //!< Color Correction Matrix element [2][2].
+       CAP_PROP_XI_CC_MATRIX_23                                 = 490, //!< Color Correction Matrix element [2][3].
+       CAP_PROP_XI_CC_MATRIX_30                                 = 491, //!< Color Correction Matrix element [3][0].
+       CAP_PROP_XI_CC_MATRIX_31                                 = 492, //!< Color Correction Matrix element [3][1].
+       CAP_PROP_XI_CC_MATRIX_32                                 = 493, //!< Color Correction Matrix element [3][2].
+       CAP_PROP_XI_CC_MATRIX_33                                 = 494, //!< Color Correction Matrix element [3][3].
+       CAP_PROP_XI_DEFAULT_CC_MATRIX                            = 495, //!< Set default Color Correction Matrix.
+       CAP_PROP_XI_TRG_SELECTOR                                 = 498, //!< Selects the type of trigger.
+       CAP_PROP_XI_ACQ_FRAME_BURST_COUNT                        = 499, //!< Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart.
+       CAP_PROP_XI_DEBOUNCE_EN                                  = 507, //!< Enable/Disable debounce to selected GPI.
+       CAP_PROP_XI_DEBOUNCE_T0                                  = 508, //!< Debounce time (x * 10us).
+       CAP_PROP_XI_DEBOUNCE_T1                                  = 509, //!< Debounce time (x * 10us).
+       CAP_PROP_XI_DEBOUNCE_POL                                 = 510, //!< Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge).
+       CAP_PROP_XI_LENS_MODE                                    = 511, //!< Status of lens control interface. This shall be set to XI_ON before any Lens operations.
+       CAP_PROP_XI_LENS_APERTURE_VALUE                          = 512, //!< Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11.
+       CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE                    = 513, //!< Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps.
+       CAP_PROP_XI_LENS_FOCUS_MOVE                              = 514, //!< Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE.
+       CAP_PROP_XI_LENS_FOCUS_DISTANCE                          = 515, //!< Lens focus distance in cm.
+       CAP_PROP_XI_LENS_FOCAL_LENGTH                            = 516, //!< Lens focal distance in mm.
+       CAP_PROP_XI_LENS_FEATURE_SELECTOR                        = 517, //!< Selects the current feature which is accessible by XI_PRM_LENS_FEATURE.
+       CAP_PROP_XI_LENS_FEATURE                                 = 518, //!< Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR.
+       CAP_PROP_XI_DEVICE_MODEL_ID                              = 521, //!< Returns device model id.
+       CAP_PROP_XI_DEVICE_SN                                    = 522, //!< Returns device serial number.
+       CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA                = 529, //!< The alpha channel of RGB32 output image format.
+       CAP_PROP_XI_IMAGE_PAYLOAD_SIZE                           = 530, //!< Buffer size in bytes sufficient for output image returned by xiGetImage.
+       CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT                       = 531, //!< Current format of pixels on transport layer.
+       CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ                         = 532, //!< Sensor clock frequency in Hz.
+       CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX                      = 533, //!< Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index.
+       CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT                  = 534, //!< Number of output channels from sensor used for data transfer.
+       CAP_PROP_XI_FRAMERATE                                    = 535, //!< Define framerate in Hz.
+       CAP_PROP_XI_COUNTER_SELECTOR                             = 536, //!< Select counter.
+       CAP_PROP_XI_COUNTER_VALUE                                = 537, //!< Counter status.
+       CAP_PROP_XI_ACQ_TIMING_MODE                              = 538, //!< Type of sensor frames timing.
+       CAP_PROP_XI_AVAILABLE_BANDWIDTH                          = 539, //!< Calculate and returns available interface bandwidth(int Megabits).
+       CAP_PROP_XI_BUFFER_POLICY                                = 540, //!< Data move policy.
+       CAP_PROP_XI_LUT_EN                                       = 541, //!< Activates LUT.
+       CAP_PROP_XI_LUT_INDEX                                    = 542, //!< Control the index (offset) of the coefficient to access in the LUT.
+       CAP_PROP_XI_LUT_VALUE                                    = 543, //!< Value at entry LUTIndex of the LUT.
+       CAP_PROP_XI_TRG_DELAY                                    = 544, //!< Specifies the delay in microseconds (us) to apply after the trigger reception before activating it.
+       CAP_PROP_XI_TS_RST_MODE                                  = 545, //!< Defines how time stamp reset engine will be armed.
+       CAP_PROP_XI_TS_RST_SOURCE                                = 546, //!< Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming).
+       CAP_PROP_XI_IS_DEVICE_EXIST                              = 547, //!< Returns 1 if camera connected and works properly.
+       CAP_PROP_XI_ACQ_BUFFER_SIZE                              = 548, //!< Acquisition buffer size in buffer_size_unit. Default bytes.
+       CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT                         = 549, //!< Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes.
+       CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE                    = 550, //!< Acquisition transport buffer size in bytes.
+       CAP_PROP_XI_BUFFERS_QUEUE_SIZE                           = 551, //!< Queue of field/frame buffers.
+       CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT                  = 552, //!< Number of buffers to commit to low level.
+       CAP_PROP_XI_RECENT_FRAME                                 = 553, //!< GetImage returns most recent frame.
+       CAP_PROP_XI_DEVICE_RESET                                 = 554, //!< Resets the camera to default state.
+       CAP_PROP_XI_COLUMN_FPN_CORRECTION                        = 555, //!< Correction of column FPN.
+       CAP_PROP_XI_ROW_FPN_CORRECTION                           = 591, //!< Correction of row FPN.
+       CAP_PROP_XI_SENSOR_MODE                                  = 558, //!< Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling.
+       CAP_PROP_XI_HDR                                          = 559, //!< Enable High Dynamic Range feature.
+       CAP_PROP_XI_HDR_KNEEPOINT_COUNT                          = 560, //!< The number of kneepoints in the PWLR.
+       CAP_PROP_XI_HDR_T1                                       = 561, //!< Position of first kneepoint(in % of XI_PRM_EXPOSURE).
+       CAP_PROP_XI_HDR_T2                                       = 562, //!< Position of second kneepoint (in % of XI_PRM_EXPOSURE).
+       CAP_PROP_XI_KNEEPOINT1                                   = 563, //!< Value of first kneepoint (% of sensor saturation).
+       CAP_PROP_XI_KNEEPOINT2                                   = 564, //!< Value of second kneepoint (% of sensor saturation).
+       CAP_PROP_XI_IMAGE_BLACK_LEVEL                            = 565, //!< Last image black level counts. Can be used for Offline processing to recall it.
+       CAP_PROP_XI_HW_REVISION                                  = 571, //!< Returns hardware revision number.
+       CAP_PROP_XI_DEBUG_LEVEL                                  = 572, //!< Set debug level.
+       CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION                   = 573, //!< Automatic bandwidth calculation.
+       CAP_PROP_XI_FFS_FILE_ID                                  = 594, //!< File number.
+       CAP_PROP_XI_FFS_FILE_SIZE                                = 580, //!< Size of file.
+       CAP_PROP_XI_FREE_FFS_SIZE                                = 581, //!< Size of free camera FFS.
+       CAP_PROP_XI_USED_FFS_SIZE                                = 582, //!< Size of used camera FFS.
+       CAP_PROP_XI_FFS_ACCESS_KEY                               = 583, //!< Setting of key enables file operations on some cameras.
+       CAP_PROP_XI_SENSOR_FEATURE_SELECTOR                      = 585, //!< Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE.
+       CAP_PROP_XI_SENSOR_FEATURE_VALUE                         = 586, //!< Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR.
+     };
+
+//! @} XIMEA
+
+
+/** @name ARAVIS Camera API
+    @{
+*/
+
+//! Properties of cameras available through ARAVIS backend
+enum { CAP_PROP_ARAVIS_AUTOTRIGGER                              = 600 //!< Automatically trigger frame capture if camera is configured with software trigger
+};
+
+//! @} ARAVIS
+
+/** @name AVFoundation framework for iOS
+    @{
+*/
+
+//! Properties of cameras available through AVFOUNDATION backend
+enum { CAP_PROP_IOS_DEVICE_FOCUS        = 9001,
+       CAP_PROP_IOS_DEVICE_EXPOSURE     = 9002,
+       CAP_PROP_IOS_DEVICE_FLASH        = 9003,
+       CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004,
+       CAP_PROP_IOS_DEVICE_TORCH        = 9005
+     };
+
+//! @} AVFoundation framework for iOS
+
+
+/** @name Smartek Giganetix GigEVisionSDK
+    @{
+*/
+
+//! Properties of cameras available through Smartek Giganetix Ethernet Vision backend
+/* --- Vladimir Litvinenko (litvinenko.vladimir@gmail.com) --- */
+enum { CAP_PROP_GIGA_FRAME_OFFSET_X   = 10001,
+       CAP_PROP_GIGA_FRAME_OFFSET_Y   = 10002,
+       CAP_PROP_GIGA_FRAME_WIDTH_MAX  = 10003,
+       CAP_PROP_GIGA_FRAME_HEIGH_MAX  = 10004,
+       CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005,
+       CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006
+     };
+
+//! @} Smartek
+
+/** @name Intel Perceptual Computing SDK
+    @{
+*/
+enum { CAP_PROP_INTELPERC_PROFILE_COUNT               = 11001,
+       CAP_PROP_INTELPERC_PROFILE_IDX                 = 11002,
+       CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE  = 11003,
+       CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE      = 11004,
+       CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD  = 11005,
+       CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ     = 11006,
+       CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT     = 11007
+     };
+
+//! Intel Perceptual Streams
+enum { CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29,
+       CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28,
+       CAP_INTELPERC_IR_GENERATOR    = 1 << 27,
+       CAP_INTELPERC_GENERATORS_MASK = CAP_INTELPERC_DEPTH_GENERATOR + CAP_INTELPERC_IMAGE_GENERATOR + CAP_INTELPERC_IR_GENERATOR
+     };
+
+enum { CAP_INTELPERC_DEPTH_MAP              = 0, //!< Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth.
+       CAP_INTELPERC_UVDEPTH_MAP            = 1, //!< Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates.
+       CAP_INTELPERC_IR_MAP                 = 2, //!< Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam.
+       CAP_INTELPERC_IMAGE                  = 3
+     };
+
+//! @} Intel Perceptual
+
+/** @name gPhoto2 connection
+    @{
+*/
+
+/** @brief gPhoto2 properties
+
+If `propertyId` is less than 0 then work on widget with that __additive inversed__ camera setting ID
+Get IDs by using CAP_PROP_GPHOTO2_WIDGET_ENUMERATE.
+@see CvCaptureCAM_GPHOTO2 for more info
+*/
+enum { CAP_PROP_GPHOTO2_PREVIEW           = 17001, //!< Capture only preview from liveview mode.
+       CAP_PROP_GPHOTO2_WIDGET_ENUMERATE  = 17002, //!< Readonly, returns (const char *).
+       CAP_PROP_GPHOTO2_RELOAD_CONFIG     = 17003, //!< Trigger, only by set. Reload camera settings.
+       CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE  = 17004, //!< Reload all settings on set.
+       CAP_PROP_GPHOTO2_COLLECT_MSGS      = 17005, //!< Collect messages with details.
+       CAP_PROP_GPHOTO2_FLUSH_MSGS        = 17006, //!< Readonly, returns (const char *).
+       CAP_PROP_SPEED                     = 17007, //!< Exposure speed. Can be readonly, depends on camera program.
+       CAP_PROP_APERTURE                  = 17008, //!< Aperture. Can be readonly, depends on camera program.
+       CAP_PROP_EXPOSUREPROGRAM           = 17009, //!< Camera exposure program.
+       CAP_PROP_VIEWFINDER                = 17010  //!< Enter liveview mode.
+     };
+
+//! @} gPhoto2
+
+
+/** @name Images backend
+    @{
+*/
+
+/** @brief Images backend properties
+
+*/
+enum { CAP_PROP_IMAGES_BASE = 18000,
+       CAP_PROP_IMAGES_LAST = 19000 // excluding
+     };
+
+//! @} Images
+
+//! @} videoio_flags_others
+
+
+class IVideoCapture;
+//! @cond IGNORED
+namespace internal { class VideoCapturePrivateAccessor; }
+//! @endcond IGNORED
+
+/** @brief Class for video capturing from video files, image sequences or cameras.
+
+The class provides C++ API for capturing video from cameras or for reading video files and image sequences.
+
+Here is how the class can be used:
+@include samples/cpp/videocapture_basic.cpp
+
+@note In @ref videoio_c "C API" the black-box structure `CvCapture` is used instead of %VideoCapture.
+@note
+-   (C++) A basic sample on using the %VideoCapture interface can be found at
+    `OPENCV_SOURCE_CODE/samples/cpp/videocapture_starter.cpp`
+-   (Python) A basic sample on using the %VideoCapture interface can be found at
+    `OPENCV_SOURCE_CODE/samples/python/video.py`
+-   (Python) A multi threaded video processing sample can be found at
+    `OPENCV_SOURCE_CODE/samples/python/video_threaded.py`
+-   (Python) %VideoCapture sample showcasing some features of the Video4Linux2 backend
+    `OPENCV_SOURCE_CODE/samples/python/video_v4l2.py`
+ */
+class CV_EXPORTS_W VideoCapture
+{
+public:
+    /** @brief Default constructor
+    @note In @ref videoio_c "C API", when you finished working with video, release CvCapture structure with
+    cvReleaseCapture(), or use Ptr\<CvCapture\> that calls cvReleaseCapture() automatically in the
+    destructor.
+     */
+    CV_WRAP VideoCapture();
+
+    /** @overload
+    @brief  Opens a video file or a capturing device or an IP video stream for video capturing with API Preference
+
+    @param filename it can be:
+    - name of video file (eg. `video.avi`)
+    - or image sequence (eg. `img_%02d.jpg`, which will read samples like `img_00.jpg, img_01.jpg, img_02.jpg, ...`)
+    - or URL of video stream (eg. `protocol://host:port/script_name?script_params|auth`)
+    - or GStreamer pipeline string in gst-launch tool format in case if GStreamer is used as backend
+      Note that each video stream or IP camera feed has its own URL scheme. Please refer to the
+      documentation of source stream to know the right URL.
+    @param apiPreference preferred Capture API backends to use. Can be used to enforce a specific reader
+    implementation if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW.
+
+    @sa cv::VideoCaptureAPIs
+    */
+    CV_WRAP explicit VideoCapture(const String& filename, int apiPreference = CAP_ANY);
+
+    /** @overload
+    @brief Opens a video file or a capturing device or an IP video stream for video capturing with API Preference and parameters
+
+    The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
+    See cv::VideoCaptureProperties
+    */
+    CV_WRAP explicit VideoCapture(const String& filename, int apiPreference, const std::vector<int>& params);
+
+    /** @overload
+    @brief  Opens a camera for video capturing
+
+    @param index id of the video capturing device to open. To open default camera using default backend just pass 0.
+    (to backward compatibility usage of camera_id + domain_offset (CAP_*) is valid when apiPreference is CAP_ANY)
+    @param apiPreference preferred Capture API backends to use. Can be used to enforce a specific reader
+    implementation if multiple are available: e.g. cv::CAP_DSHOW or cv::CAP_MSMF or cv::CAP_V4L.
+
+    @sa cv::VideoCaptureAPIs
+    */
+    CV_WRAP explicit VideoCapture(int index, int apiPreference = CAP_ANY);
+
+    /** @overload
+    @brief Opens a camera for video capturing with API Preference and parameters
+
+    The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
+    See cv::VideoCaptureProperties
+    */
+    CV_WRAP explicit VideoCapture(int index, int apiPreference, const std::vector<int>& params);
+
+    /** @brief Default destructor
+
+    The method first calls VideoCapture::release to close the already opened file or camera.
+    */
+    virtual ~VideoCapture();
+
+    /** @brief  Opens a video file or a capturing device or an IP video stream for video capturing.
+
+    @overload
+
+    Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference = CAP_ANY)
+    @return `true` if the file has been successfully opened
+
+    The method first calls VideoCapture::release to close the already opened file or camera.
+     */
+    CV_WRAP virtual bool open(const String& filename, int apiPreference = CAP_ANY);
+
+    /** @brief  Opens a camera for video capturing
+
+    @overload
+
+    The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
+    See cv::VideoCaptureProperties
+
+    @return `true` if the file has been successfully opened
+
+    The method first calls VideoCapture::release to close the already opened file or camera.
+     */
+    CV_WRAP virtual bool open(const String& filename, int apiPreference, const std::vector<int>& params);
+
+    /** @brief  Opens a camera for video capturing
+
+    @overload
+
+    Parameters are same as the constructor VideoCapture(int index, int apiPreference = CAP_ANY)
+    @return `true` if the camera has been successfully opened.
+
+    The method first calls VideoCapture::release to close the already opened file or camera.
+    */
+    CV_WRAP virtual bool open(int index, int apiPreference = CAP_ANY);
+
+    /** @brief Returns true if video capturing has been initialized already.
+
+    @overload
+
+    The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
+    See cv::VideoCaptureProperties
+
+    @return `true` if the camera has been successfully opened.
+
+    The method first calls VideoCapture::release to close the already opened file or camera.
+    */
+    CV_WRAP virtual bool open(int index, int apiPreference, const std::vector<int>& params);
+
+    /** @brief Returns true if video capturing has been initialized already.
+
+    If the previous call to VideoCapture constructor or VideoCapture::open() succeeded, the method returns
+    true.
+     */
+    CV_WRAP virtual bool isOpened() const;
+
+    /** @brief Closes video file or capturing device.
+
+    The method is automatically called by subsequent VideoCapture::open and by VideoCapture
+    destructor.
+
+    The C function also deallocates memory and clears \*capture pointer.
+     */
+    CV_WRAP virtual void release();
+
+    /** @brief Grabs the next frame from video file or capturing device.
+
+    @return `true` (non-zero) in the case of success.
+
+    The method/function grabs the next frame from video file or camera and returns true (non-zero) in
+    the case of success.
+
+    The primary use of the function is in multi-camera environments, especially when the cameras do not
+    have hardware synchronization. That is, you call VideoCapture::grab() for each camera and after that
+    call the slower method VideoCapture::retrieve() to decode and get frame from each camera. This way
+    the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames
+    from different cameras will be closer in time.
+
+    Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the
+    correct way of retrieving data from it is to call VideoCapture::grab() first and then call
+    VideoCapture::retrieve() one or more times with different values of the channel parameter.
+
+    @ref tutorial_kinect_openni
+     */
+    CV_WRAP virtual bool grab();
+
+    /** @brief Decodes and returns the grabbed video frame.
+
+    @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
+    @param flag it could be a frame index or a driver specific flag
+    @return `false` if no frames has been grabbed
+
+    The method decodes and returns the just grabbed frame. If no frames has been grabbed
+    (camera has been disconnected, or there are no more frames in video file), the method returns false
+    and the function returns an empty image (with %cv::Mat, test it with Mat::empty()).
+
+    @sa read()
+
+    @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
+    capturing structure. It is not allowed to modify or release the image! You can copy the frame using
+    cvCloneImage and then do whatever you want with the copy.
+     */
+    CV_WRAP virtual bool retrieve(OutputArray image, int flag = 0);
+
+    /** @brief Stream operator to read the next video frame.
+    @sa read()
+    */
+    virtual VideoCapture& operator >> (CV_OUT Mat& image);
+
+    /** @overload
+    @sa read()
+    */
+    virtual VideoCapture& operator >> (CV_OUT UMat& image);
+
+    /** @brief Grabs, decodes and returns the next video frame.
+
+    @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
+    @return `false` if no frames has been grabbed
+
+    The method/function combines VideoCapture::grab() and VideoCapture::retrieve() in one call. This is the
+    most convenient method for reading video files or capturing data from decode and returns the just
+    grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more
+    frames in video file), the method returns false and the function returns empty image (with %cv::Mat, test it with Mat::empty()).
+
+    @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
+    capturing structure. It is not allowed to modify or release the image! You can copy the frame using
+    cvCloneImage and then do whatever you want with the copy.
+     */
+    CV_WRAP virtual bool read(OutputArray image);
+
+    /** @brief Sets a property in the VideoCapture.
+
+    @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...)
+    or one from @ref videoio_flags_others
+    @param value Value of the property.
+    @return `true` if the property is supported by backend used by the VideoCapture instance.
+    @note Even if it returns `true` this doesn't ensure that the property
+    value has been accepted by the capture device. See note in VideoCapture::get()
+     */
+    CV_WRAP virtual bool set(int propId, double value);
+
+    /** @brief Returns the specified VideoCapture property
+
+    @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...)
+    or one from @ref videoio_flags_others
+    @return Value for the specified property. Value 0 is returned when querying a property that is
+    not supported by the backend used by the VideoCapture instance.
+
+    @note Reading / writing properties involves many layers. Some unexpected result might happens
+    along this chain.
+    @code{.txt}
+    VideoCapture -> API Backend -> Operating System -> Device Driver -> Device Hardware
+    @endcode
+    The returned value might be different from what really used by the device or it could be encoded
+    using device dependent rules (eg. steps or percentage). Effective behaviour depends from device
+    driver and API Backend
+
+    */
+    CV_WRAP virtual double get(int propId) const;
+
+    /** @brief Returns used backend API name
+
+     @note Stream should be opened.
+     */
+    CV_WRAP String getBackendName() const;
+
+    /** Switches exceptions mode
+     *
+     * methods raise exceptions if not successful instead of returning an error code
+     */
+    CV_WRAP void setExceptionMode(bool enable) { throwOnFail = enable; }
+
+    /// query if exception mode is active
+    CV_WRAP bool getExceptionMode() { return throwOnFail; }
+
+
+    /** @brief Wait for ready frames from VideoCapture.
+
+    @param streams input video streams
+    @param readyIndex stream indexes with grabbed frames (ready to use .retrieve() to fetch actual frame)
+    @param timeoutNs number of nanoseconds (0 - infinite)
+    @return `true` if streamReady is not empty
+
+    @throws Exception %Exception on stream errors (check .isOpened() to filter out malformed streams) or VideoCapture type is not supported
+
+    The primary use of the function is in multi-camera environments.
+    The method fills the ready state vector, grabs video frame, if camera is ready.
+
+    After this call use VideoCapture::retrieve() to decode and fetch frame data.
+    */
+    static /*CV_WRAP*/
+    bool waitAny(
+            const std::vector<VideoCapture>& streams,
+            CV_OUT std::vector<int>& readyIndex,
+            int64 timeoutNs = 0);
+
+protected:
+    Ptr<CvCapture> cap;
+    Ptr<IVideoCapture> icap;
+    bool throwOnFail;
+
+    friend class internal::VideoCapturePrivateAccessor;
+};
+
+class IVideoWriter;
+
+/** @example samples/cpp/tutorial_code/videoio/video-write/video-write.cpp
+Check @ref tutorial_video_write "the corresponding tutorial" for more details
+*/
+
+/** @example samples/cpp/videowriter_basic.cpp
+An example using VideoCapture and VideoWriter class
+*/
+
+/** @brief Video writer class.
+
+The class provides C++ API for writing video files or image sequences.
+*/
+class CV_EXPORTS_W VideoWriter
+{
+public:
+    /** @brief Default constructors
+
+    The constructors/functions initialize video writers.
+    -   On Linux FFMPEG is used to write videos;
+    -   On Windows FFMPEG or MSWF or DSHOW is used;
+    -   On MacOSX AVFoundation is used.
+     */
+    CV_WRAP VideoWriter();
+
+    /** @overload
+    @param filename Name of the output video file.
+    @param fourcc 4-character code of codec used to compress the frames. For example,
+    VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') is a
+    motion-jpeg codec etc. List of codes can be obtained at [Video Codecs by
+    FOURCC](http://www.fourcc.org/codecs.php) page. FFMPEG backend with MP4 container natively uses
+    other values as fourcc code: see [ObjectType](http://mp4ra.org/#/codecs),
+    so you may receive a warning message from OpenCV about fourcc code conversion.
+    @param fps Framerate of the created video stream.
+    @param frameSize Size of the video frames.
+    @param isColor If it is not zero, the encoder will expect and encode color frames, otherwise it
+    will work with grayscale frames.
+
+    @b Tips:
+    - With some backends `fourcc=-1` pops up the codec selection dialog from the system.
+    - To save image sequence use a proper filename (eg. `img_%02d.jpg`) and `fourcc=0`
+      OR `fps=0`. Use uncompressed image format (eg. `img_%02d.BMP`) to save raw frames.
+    - Most codecs are lossy. If you want lossless video file you need to use a lossless codecs
+      (eg. FFMPEG FFV1, Huffman HFYU, Lagarith LAGS, etc...)
+    - If FFMPEG is enabled, using `codec=0; fps=0;` you can create an uncompressed (raw) video file.
+    */
+    CV_WRAP VideoWriter(const String& filename, int fourcc, double fps,
+                Size frameSize, bool isColor = true);
+
+    /** @overload
+    The `apiPreference` parameter allows to specify API backends to use. Can be used to enforce a specific reader implementation
+    if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_GSTREAMER.
+     */
+    CV_WRAP VideoWriter(const String& filename, int apiPreference, int fourcc, double fps,
+                Size frameSize, bool isColor = true);
+
+    /** @overload
+     * The `params` parameter allows to specify extra encoder parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .)
+     * see cv::VideoWriterProperties
+     */
+    CV_WRAP VideoWriter(const String& filename, int fourcc, double fps, const Size& frameSize,
+                        const std::vector<int>& params);
+
+    /** @overload
+     */
+    CV_WRAP VideoWriter(const String& filename, int apiPreference, int fourcc, double fps,
+                        const Size& frameSize, const std::vector<int>& params);
+
+    /** @brief Default destructor
+
+    The method first calls VideoWriter::release to close the already opened file.
+    */
+    virtual ~VideoWriter();
+
+    /** @brief Initializes or reinitializes video writer.
+
+    The method opens video writer. Parameters are the same as in the constructor
+    VideoWriter::VideoWriter.
+    @return `true` if video writer has been successfully initialized
+
+    The method first calls VideoWriter::release to close the already opened file.
+     */
+    CV_WRAP virtual bool open(const String& filename, int fourcc, double fps,
+                      Size frameSize, bool isColor = true);
+
+    /** @overload
+     */
+    CV_WRAP bool open(const String& filename, int apiPreference, int fourcc, double fps,
+                      Size frameSize, bool isColor = true);
+
+    /** @overload
+     */
+    CV_WRAP bool open(const String& filename, int fourcc, double fps, const Size& frameSize,
+                      const std::vector<int>& params);
+
+    /** @overload
+     */
+    CV_WRAP bool open(const String& filename, int apiPreference, int fourcc, double fps,
+                      const Size& frameSize, const std::vector<int>& params);
+
+    /** @brief Returns true if video writer has been successfully initialized.
+    */
+    CV_WRAP virtual bool isOpened() const;
+
+    /** @brief Closes the video writer.
+
+    The method is automatically called by subsequent VideoWriter::open and by the VideoWriter
+    destructor.
+     */
+    CV_WRAP virtual void release();
+
+    /** @brief Stream operator to write the next video frame.
+    @sa write
+    */
+    virtual VideoWriter& operator << (const Mat& image);
+
+    /** @overload
+    @sa write
+    */
+    virtual VideoWriter& operator << (const UMat& image);
+
+    /** @brief Writes the next video frame
+
+    @param image The written frame. In general, color images are expected in BGR format.
+
+    The function/method writes the specified image to video file. It must have the same size as has
+    been specified when opening the video writer.
+     */
+    CV_WRAP virtual void write(InputArray image);
+
+    /** @brief Sets a property in the VideoWriter.
+
+     @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY)
+     or one of @ref videoio_flags_others
+
+     @param value Value of the property.
+     @return  `true` if the property is supported by the backend used by the VideoWriter instance.
+     */
+    CV_WRAP virtual bool set(int propId, double value);
+
+    /** @brief Returns the specified VideoWriter property
+
+     @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY)
+     or one of @ref videoio_flags_others
+
+     @return Value for the specified property. Value 0 is returned when querying a property that is
+     not supported by the backend used by the VideoWriter instance.
+     */
+    CV_WRAP virtual double get(int propId) const;
+
+    /** @brief Concatenates 4 chars to a fourcc code
+
+    @return a fourcc code
+
+    This static method constructs the fourcc code of the codec to be used in the constructor
+    VideoWriter::VideoWriter or VideoWriter::open.
+     */
+    CV_WRAP static int fourcc(char c1, char c2, char c3, char c4);
+
+    /** @brief Returns used backend API name
+
+     @note Stream should be opened.
+     */
+    CV_WRAP String getBackendName() const;
+
+protected:
+    Ptr<CvVideoWriter> writer;
+    Ptr<IVideoWriter> iwriter;
+
+    static Ptr<IVideoWriter> create(const String& filename, int fourcc, double fps,
+                                    Size frameSize, bool isColor = true);
+};
+
+//! @cond IGNORED
+template<> struct DefaultDeleter<CvCapture>{ CV_EXPORTS void operator ()(CvCapture* obj) const; };
+template<> struct DefaultDeleter<CvVideoWriter>{ CV_EXPORTS void operator ()(CvVideoWriter* obj) const; };
+//! @endcond IGNORED
+
+//! @} videoio
+
+} // cv
+
+#endif //OPENCV_VIDEOIO_HPP
diff --git a/3rdParty/include/opencv2/videoio/cap_ios.h b/3rdParty/include/opencv2/videoio/cap_ios.h
new file mode 100644
index 0000000..207ad46
--- /dev/null
+++ b/3rdParty/include/opencv2/videoio/cap_ios.h
@@ -0,0 +1,150 @@
+/*  For iOS video I/O
+ *  by Eduard Feicho on 29/07/12
+ *  Copyright 2012. All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice,
+ *    this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ *    this list of conditions and the following disclaimer in the documentation
+ *    and/or other materials provided with the distribution.
+ * 3. The name of the author may not be used to endorse or promote products
+ *    derived from this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED
+ * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+ * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
+ * EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
+ * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+ * WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+ * OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ * ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ */
+
+#import <UIKit/UIKit.h>
+#import <Accelerate/Accelerate.h>
+#import <AVFoundation/AVFoundation.h>
+#import <ImageIO/ImageIO.h>
+#include "opencv2/core.hpp"
+
+//! @addtogroup videoio_ios
+//! @{
+
+/////////////////////////////////////// CvAbstractCamera /////////////////////////////////////
+
+@class CvAbstractCamera;
+
+CV_EXPORTS @interface CvAbstractCamera : NSObject
+{
+    UIDeviceOrientation currentDeviceOrientation;
+
+    BOOL cameraAvailable;
+}
+
+@property (nonatomic, strong) AVCaptureSession* captureSession;
+@property (nonatomic, strong) AVCaptureConnection* videoCaptureConnection;
+
+@property (nonatomic, readonly) BOOL running;
+@property (nonatomic, readonly) BOOL captureSessionLoaded;
+
+@property (nonatomic, assign) int defaultFPS;
+@property (nonatomic, readonly) AVCaptureVideoPreviewLayer *captureVideoPreviewLayer;
+@property (nonatomic, assign) AVCaptureDevicePosition defaultAVCaptureDevicePosition;
+@property (nonatomic, assign) AVCaptureVideoOrientation defaultAVCaptureVideoOrientation;
+@property (nonatomic, assign) BOOL useAVCaptureVideoPreviewLayer;
+@property (nonatomic, strong) NSString *const defaultAVCaptureSessionPreset;
+
+@property (nonatomic, assign) int imageWidth;
+@property (nonatomic, assign) int imageHeight;
+
+@property (nonatomic, strong) UIView* parentView;
+
+- CV_UNUSED(start);
+- CV_UNUSED(stop);
+- CV_UNUSED(switchCameras);
+
+- (id)initWithParentView:(UIView*)parent;
+
+- CV_UNUSED(createCaptureOutput);
+- CV_UNUSED(createVideoPreviewLayer);
+- CV_UNUSED(updateOrientation);
+
+- CV_UNUSED(lockFocus);
+- CV_UNUSED(unlockFocus);
+- CV_UNUSED(lockExposure);
+- CV_UNUSED(unlockExposure);
+- CV_UNUSED(lockBalance);
+- CV_UNUSED(unlockBalance);
+
+@end
+
+///////////////////////////////// CvVideoCamera ///////////////////////////////////////////
+
+@class CvVideoCamera;
+
+CV_EXPORTS @protocol CvVideoCameraDelegate <NSObject>
+
+#ifdef __cplusplus
+// delegate method for processing image frames
+- (void)processImage:(cv::Mat&)image;
+#endif
+
+@end
+
+CV_EXPORTS @interface CvVideoCamera : CvAbstractCamera<AVCaptureVideoDataOutputSampleBufferDelegate>
+{
+    AVCaptureVideoDataOutput *videoDataOutput;
+
+    dispatch_queue_t videoDataOutputQueue;
+    CALayer *customPreviewLayer;
+
+    CMTime lastSampleTime;
+
+}
+
+@property (nonatomic, weak) id<CvVideoCameraDelegate> delegate;
+@property (nonatomic, assign) BOOL grayscaleMode;
+
+@property (nonatomic, assign) BOOL recordVideo;
+@property (nonatomic, assign) BOOL rotateVideo;
+@property (nonatomic, strong) AVAssetWriterInput* recordAssetWriterInput;
+@property (nonatomic, strong) AVAssetWriterInputPixelBufferAdaptor* recordPixelBufferAdaptor;
+@property (nonatomic, strong) AVAssetWriter* recordAssetWriter;
+
+- (void)adjustLayoutToInterfaceOrientation:(UIInterfaceOrientation)interfaceOrientation;
+- CV_UNUSED(layoutPreviewLayer);
+- CV_UNUSED(saveVideo);
+- (NSURL *)videoFileURL;
+- (NSString *)videoFileString;
+
+
+@end
+
+///////////////////////////////// CvPhotoCamera ///////////////////////////////////////////
+
+@class CvPhotoCamera;
+
+CV_EXPORTS @protocol CvPhotoCameraDelegate <NSObject>
+
+- (void)photoCamera:(CvPhotoCamera*)photoCamera capturedImage:(UIImage *)image;
+- (void)photoCameraCancel:(CvPhotoCamera*)photoCamera;
+
+@end
+
+CV_EXPORTS @interface CvPhotoCamera : CvAbstractCamera
+{
+    AVCaptureStillImageOutput *stillImageOutput;
+}
+
+@property (nonatomic, weak) id<CvPhotoCameraDelegate> delegate;
+
+- CV_UNUSED(takePicture);
+
+@end
+
+//! @} videoio_ios
diff --git a/3rdParty/include/opencv2/videoio/legacy/constants_c.h b/3rdParty/include/opencv2/videoio/legacy/constants_c.h
new file mode 100644
index 0000000..91f85f8
--- /dev/null
+++ b/3rdParty/include/opencv2/videoio/legacy/constants_c.h
@@ -0,0 +1,434 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_VIDEOIO_LEGACY_CONSTANTS_H
+#define OPENCV_VIDEOIO_LEGACY_CONSTANTS_H
+
+enum
+{
+    CV_CAP_ANY      =0,     // autodetect
+
+    CV_CAP_MIL      =100,   // MIL proprietary drivers
+
+    CV_CAP_VFW      =200,   // platform native
+    CV_CAP_V4L      =200,
+    CV_CAP_V4L2     =200,
+
+    CV_CAP_FIREWARE =300,   // IEEE 1394 drivers
+    CV_CAP_FIREWIRE =300,
+    CV_CAP_IEEE1394 =300,
+    CV_CAP_DC1394   =300,
+    CV_CAP_CMU1394  =300,
+
+    CV_CAP_STEREO   =400,   // TYZX proprietary drivers
+    CV_CAP_TYZX     =400,
+    CV_TYZX_LEFT    =400,
+    CV_TYZX_RIGHT   =401,
+    CV_TYZX_COLOR   =402,
+    CV_TYZX_Z       =403,
+
+    CV_CAP_QT       =500,   // QuickTime
+
+    CV_CAP_UNICAP   =600,   // Unicap drivers
+
+    CV_CAP_DSHOW    =700,   // DirectShow (via videoInput)
+    CV_CAP_MSMF     =1400,  // Microsoft Media Foundation (via videoInput)
+
+    CV_CAP_PVAPI    =800,   // PvAPI, Prosilica GigE SDK
+
+    CV_CAP_OPENNI   =900,   // OpenNI (for Kinect)
+    CV_CAP_OPENNI_ASUS =910,   // OpenNI (for Asus Xtion)
+
+    CV_CAP_ANDROID  =1000,  // Android - not used
+    CV_CAP_ANDROID_BACK =CV_CAP_ANDROID+99, // Android back camera - not used
+    CV_CAP_ANDROID_FRONT =CV_CAP_ANDROID+98, // Android front camera - not used
+
+    CV_CAP_XIAPI    =1100,   // XIMEA Camera API
+
+    CV_CAP_AVFOUNDATION = 1200,  // AVFoundation framework for iOS (OS X Lion will have the same API)
+
+    CV_CAP_GIGANETIX = 1300,  // Smartek Giganetix GigEVisionSDK
+
+    CV_CAP_INTELPERC = 1500, // Intel Perceptual Computing
+
+    CV_CAP_OPENNI2 = 1600,   // OpenNI2 (for Kinect)
+    CV_CAP_GPHOTO2 = 1700,
+    CV_CAP_GSTREAMER = 1800, // GStreamer
+    CV_CAP_FFMPEG = 1900,    // FFMPEG
+    CV_CAP_IMAGES = 2000,    // OpenCV Image Sequence (e.g. img_%02d.jpg)
+
+    CV_CAP_ARAVIS = 2100     // Aravis GigE SDK
+};
+
+enum
+{
+    // modes of the controlling registers (can be: auto, manual, auto single push, absolute Latter allowed with any other mode)
+    // every feature can have only one mode turned on at a time
+    CV_CAP_PROP_DC1394_OFF         = -4,  //turn the feature off (not controlled manually nor automatically)
+    CV_CAP_PROP_DC1394_MODE_MANUAL = -3, //set automatically when a value of the feature is set by the user
+    CV_CAP_PROP_DC1394_MODE_AUTO = -2,
+    CV_CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1,
+    CV_CAP_PROP_POS_MSEC       =0,
+    CV_CAP_PROP_POS_FRAMES     =1,
+    CV_CAP_PROP_POS_AVI_RATIO  =2,
+    CV_CAP_PROP_FRAME_WIDTH    =3,
+    CV_CAP_PROP_FRAME_HEIGHT   =4,
+    CV_CAP_PROP_FPS            =5,
+    CV_CAP_PROP_FOURCC         =6,
+    CV_CAP_PROP_FRAME_COUNT    =7,
+    CV_CAP_PROP_FORMAT         =8,
+    CV_CAP_PROP_MODE           =9,
+    CV_CAP_PROP_BRIGHTNESS    =10,
+    CV_CAP_PROP_CONTRAST      =11,
+    CV_CAP_PROP_SATURATION    =12,
+    CV_CAP_PROP_HUE           =13,
+    CV_CAP_PROP_GAIN          =14,
+    CV_CAP_PROP_EXPOSURE      =15,
+    CV_CAP_PROP_CONVERT_RGB   =16,
+    CV_CAP_PROP_WHITE_BALANCE_BLUE_U =17,
+    CV_CAP_PROP_RECTIFICATION =18,
+    CV_CAP_PROP_MONOCHROME    =19,
+    CV_CAP_PROP_SHARPNESS     =20,
+    CV_CAP_PROP_AUTO_EXPOSURE =21, // exposure control done by camera,
+                                   // user can adjust reference level
+                                   // using this feature
+    CV_CAP_PROP_GAMMA         =22,
+    CV_CAP_PROP_TEMPERATURE   =23,
+    CV_CAP_PROP_TRIGGER       =24,
+    CV_CAP_PROP_TRIGGER_DELAY =25,
+    CV_CAP_PROP_WHITE_BALANCE_RED_V =26,
+    CV_CAP_PROP_ZOOM          =27,
+    CV_CAP_PROP_FOCUS         =28,
+    CV_CAP_PROP_GUID          =29,
+    CV_CAP_PROP_ISO_SPEED     =30,
+    CV_CAP_PROP_MAX_DC1394    =31,
+    CV_CAP_PROP_BACKLIGHT     =32,
+    CV_CAP_PROP_PAN           =33,
+    CV_CAP_PROP_TILT          =34,
+    CV_CAP_PROP_ROLL          =35,
+    CV_CAP_PROP_IRIS          =36,
+    CV_CAP_PROP_SETTINGS      =37,
+    CV_CAP_PROP_BUFFERSIZE    =38,
+    CV_CAP_PROP_AUTOFOCUS     =39,
+    CV_CAP_PROP_SAR_NUM       =40,
+    CV_CAP_PROP_SAR_DEN       =41,
+
+    CV_CAP_PROP_AUTOGRAB      =1024, // property for videoio class CvCapture_Android only
+    CV_CAP_PROP_SUPPORTED_PREVIEW_SIZES_STRING=1025, // readonly, tricky property, returns cpnst char* indeed
+    CV_CAP_PROP_PREVIEW_FORMAT=1026, // readonly, tricky property, returns cpnst char* indeed
+
+    // OpenNI map generators
+    CV_CAP_OPENNI_DEPTH_GENERATOR = 1 << 31,
+    CV_CAP_OPENNI_IMAGE_GENERATOR = 1 << 30,
+    CV_CAP_OPENNI_IR_GENERATOR    = 1 << 29,
+    CV_CAP_OPENNI_GENERATORS_MASK = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_OPENNI_IR_GENERATOR,
+
+    // Properties of cameras available through OpenNI interfaces
+    CV_CAP_PROP_OPENNI_OUTPUT_MODE     = 100,
+    CV_CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101, // in mm
+    CV_CAP_PROP_OPENNI_BASELINE        = 102, // in mm
+    CV_CAP_PROP_OPENNI_FOCAL_LENGTH    = 103, // in pixels
+    CV_CAP_PROP_OPENNI_REGISTRATION    = 104, // flag
+    CV_CAP_PROP_OPENNI_REGISTRATION_ON = CV_CAP_PROP_OPENNI_REGISTRATION, // flag that synchronizes the remapping depth map to image map
+                                                                          // by changing depth generator's view point (if the flag is "on") or
+                                                                          // sets this view point to its normal one (if the flag is "off").
+    CV_CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105,
+    CV_CAP_PROP_OPENNI_MAX_BUFFER_SIZE   = 106,
+    CV_CAP_PROP_OPENNI_CIRCLE_BUFFER     = 107,
+    CV_CAP_PROP_OPENNI_MAX_TIME_DURATION = 108,
+
+    CV_CAP_PROP_OPENNI_GENERATOR_PRESENT = 109,
+    CV_CAP_PROP_OPENNI2_SYNC = 110,
+    CV_CAP_PROP_OPENNI2_MIRROR = 111,
+
+    CV_CAP_OPENNI_IMAGE_GENERATOR_PRESENT         = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
+    CV_CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE     = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_OUTPUT_MODE,
+    CV_CAP_OPENNI_DEPTH_GENERATOR_PRESENT         = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
+    CV_CAP_OPENNI_DEPTH_GENERATOR_BASELINE        = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_BASELINE,
+    CV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH    = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_FOCAL_LENGTH,
+    CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION    = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_REGISTRATION,
+    CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION,
+    CV_CAP_OPENNI_IR_GENERATOR_PRESENT            = CV_CAP_OPENNI_IR_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
+
+    // Properties of cameras available through GStreamer interface
+    CV_CAP_GSTREAMER_QUEUE_LENGTH           = 200, // default is 1
+
+    // PVAPI
+    CV_CAP_PROP_PVAPI_MULTICASTIP           = 300, // ip for anable multicast master mode. 0 for disable multicast
+    CV_CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301, // FrameStartTriggerMode: Determines how a frame is initiated
+    CV_CAP_PROP_PVAPI_DECIMATIONHORIZONTAL  = 302, // Horizontal sub-sampling of the image
+    CV_CAP_PROP_PVAPI_DECIMATIONVERTICAL    = 303, // Vertical sub-sampling of the image
+    CV_CAP_PROP_PVAPI_BINNINGX              = 304, // Horizontal binning factor
+    CV_CAP_PROP_PVAPI_BINNINGY              = 305, // Vertical binning factor
+    CV_CAP_PROP_PVAPI_PIXELFORMAT           = 306, // Pixel format
+
+    // Properties of cameras available through XIMEA SDK interface
+    CV_CAP_PROP_XI_DOWNSAMPLING                                 = 400, // Change image resolution by binning or skipping.
+    CV_CAP_PROP_XI_DATA_FORMAT                                  = 401, // Output data format.
+    CV_CAP_PROP_XI_OFFSET_X                                     = 402, // Horizontal offset from the origin to the area of interest (in pixels).
+    CV_CAP_PROP_XI_OFFSET_Y                                     = 403, // Vertical offset from the origin to the area of interest (in pixels).
+    CV_CAP_PROP_XI_TRG_SOURCE                                   = 404, // Defines source of trigger.
+    CV_CAP_PROP_XI_TRG_SOFTWARE                                 = 405, // Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE.
+    CV_CAP_PROP_XI_GPI_SELECTOR                                 = 406, // Selects general purpose input
+    CV_CAP_PROP_XI_GPI_MODE                                     = 407, // Set general purpose input mode
+    CV_CAP_PROP_XI_GPI_LEVEL                                    = 408, // Get general purpose level
+    CV_CAP_PROP_XI_GPO_SELECTOR                                 = 409, // Selects general purpose output
+    CV_CAP_PROP_XI_GPO_MODE                                     = 410, // Set general purpose output mode
+    CV_CAP_PROP_XI_LED_SELECTOR                                 = 411, // Selects camera signalling LED
+    CV_CAP_PROP_XI_LED_MODE                                     = 412, // Define camera signalling LED functionality
+    CV_CAP_PROP_XI_MANUAL_WB                                    = 413, // Calculates White Balance(must be called during acquisition)
+    CV_CAP_PROP_XI_AUTO_WB                                      = 414, // Automatic white balance
+    CV_CAP_PROP_XI_AEAG                                         = 415, // Automatic exposure/gain
+    CV_CAP_PROP_XI_EXP_PRIORITY                                 = 416, // Exposure priority (0.5 - exposure 50%, gain 50%).
+    CV_CAP_PROP_XI_AE_MAX_LIMIT                                 = 417, // Maximum limit of exposure in AEAG procedure
+    CV_CAP_PROP_XI_AG_MAX_LIMIT                                 = 418,  // Maximum limit of gain in AEAG procedure
+    CV_CAP_PROP_XI_AEAG_LEVEL                                   = 419, // Average intensity of output signal AEAG should achieve(in %)
+    CV_CAP_PROP_XI_TIMEOUT                                      = 420, // Image capture timeout in milliseconds
+    CV_CAP_PROP_XI_EXPOSURE                                     = 421, // Exposure time in microseconds
+    CV_CAP_PROP_XI_EXPOSURE_BURST_COUNT                         = 422, // Sets the number of times of exposure in one frame.
+    CV_CAP_PROP_XI_GAIN_SELECTOR                                = 423, // Gain selector for parameter Gain allows to select different type of gains.
+    CV_CAP_PROP_XI_GAIN                                         = 424, // Gain in dB
+    CV_CAP_PROP_XI_DOWNSAMPLING_TYPE                            = 426, // Change image downsampling type.
+    CV_CAP_PROP_XI_BINNING_SELECTOR                             = 427, // Binning engine selector.
+    CV_CAP_PROP_XI_BINNING_VERTICAL                             = 428, // Vertical Binning - number of vertical photo-sensitive cells to combine together.
+    CV_CAP_PROP_XI_BINNING_HORIZONTAL                           = 429, // Horizontal Binning - number of horizontal photo-sensitive cells to combine together.
+    CV_CAP_PROP_XI_BINNING_PATTERN                              = 430, // Binning pattern type.
+    CV_CAP_PROP_XI_DECIMATION_SELECTOR                          = 431, // Decimation engine selector.
+    CV_CAP_PROP_XI_DECIMATION_VERTICAL                          = 432, // Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor.
+    CV_CAP_PROP_XI_DECIMATION_HORIZONTAL                        = 433, // Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor.
+    CV_CAP_PROP_XI_DECIMATION_PATTERN                           = 434, // Decimation pattern type.
+    CV_CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR              = 587, // Selects which test pattern generator is controlled by the TestPattern feature.
+    CV_CAP_PROP_XI_TEST_PATTERN                                 = 588, // Selects which test pattern type is generated by the selected generator.
+    CV_CAP_PROP_XI_IMAGE_DATA_FORMAT                            = 435, // Output data format.
+    CV_CAP_PROP_XI_SHUTTER_TYPE                                 = 436, // Change sensor shutter type(CMOS sensor).
+    CV_CAP_PROP_XI_SENSOR_TAPS                                  = 437, // Number of taps
+    CV_CAP_PROP_XI_AEAG_ROI_OFFSET_X                            = 439, // Automatic exposure/gain ROI offset X
+    CV_CAP_PROP_XI_AEAG_ROI_OFFSET_Y                            = 440, // Automatic exposure/gain ROI offset Y
+    CV_CAP_PROP_XI_AEAG_ROI_WIDTH                               = 441, // Automatic exposure/gain ROI Width
+    CV_CAP_PROP_XI_AEAG_ROI_HEIGHT                              = 442, // Automatic exposure/gain ROI Height
+    CV_CAP_PROP_XI_BPC                                          = 445, // Correction of bad pixels
+    CV_CAP_PROP_XI_WB_KR                                        = 448, // White balance red coefficient
+    CV_CAP_PROP_XI_WB_KG                                        = 449, // White balance green coefficient
+    CV_CAP_PROP_XI_WB_KB                                        = 450, // White balance blue coefficient
+    CV_CAP_PROP_XI_WIDTH                                        = 451, // Width of the Image provided by the device (in pixels).
+    CV_CAP_PROP_XI_HEIGHT                                       = 452, // Height of the Image provided by the device (in pixels).
+    CV_CAP_PROP_XI_REGION_SELECTOR                              = 589, // Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode
+    CV_CAP_PROP_XI_REGION_MODE                                  = 595, // Activates/deactivates Region selected by Region Selector
+    CV_CAP_PROP_XI_LIMIT_BANDWIDTH                              = 459, // Set/get bandwidth(datarate)(in Megabits)
+    CV_CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH                        = 460, // Sensor output data bit depth.
+    CV_CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH                        = 461, // Device output data bit depth.
+    CV_CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH                         = 462, // bitdepth of data returned by function xiGetImage
+    CV_CAP_PROP_XI_OUTPUT_DATA_PACKING                          = 463, // Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available.
+    CV_CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE                     = 464, // Data packing type. Some cameras supports only specific packing type.
+    CV_CAP_PROP_XI_IS_COOLED                                    = 465, // Returns 1 for cameras that support cooling.
+    CV_CAP_PROP_XI_COOLING                                      = 466, // Start camera cooling.
+    CV_CAP_PROP_XI_TARGET_TEMP                                  = 467, // Set sensor target temperature for cooling.
+    CV_CAP_PROP_XI_CHIP_TEMP                                    = 468, // Camera sensor temperature
+    CV_CAP_PROP_XI_HOUS_TEMP                                    = 469, // Camera housing temperature
+    CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP                          = 590, // Camera housing back side temperature
+    CV_CAP_PROP_XI_SENSOR_BOARD_TEMP                            = 596, // Camera sensor board temperature
+    CV_CAP_PROP_XI_CMS                                          = 470, // Mode of color management system.
+    CV_CAP_PROP_XI_APPLY_CMS                                    = 471, // Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE).
+    CV_CAP_PROP_XI_IMAGE_IS_COLOR                               = 474, // Returns 1 for color cameras.
+    CV_CAP_PROP_XI_COLOR_FILTER_ARRAY                           = 475, // Returns color filter array type of RAW data.
+    CV_CAP_PROP_XI_GAMMAY                                       = 476, // Luminosity gamma
+    CV_CAP_PROP_XI_GAMMAC                                       = 477, // Chromaticity gamma
+    CV_CAP_PROP_XI_SHARPNESS                                    = 478, // Sharpness Strength
+    CV_CAP_PROP_XI_CC_MATRIX_00                                 = 479, // Color Correction Matrix element [0][0]
+    CV_CAP_PROP_XI_CC_MATRIX_01                                 = 480, // Color Correction Matrix element [0][1]
+    CV_CAP_PROP_XI_CC_MATRIX_02                                 = 481, // Color Correction Matrix element [0][2]
+    CV_CAP_PROP_XI_CC_MATRIX_03                                 = 482, // Color Correction Matrix element [0][3]
+    CV_CAP_PROP_XI_CC_MATRIX_10                                 = 483, // Color Correction Matrix element [1][0]
+    CV_CAP_PROP_XI_CC_MATRIX_11                                 = 484, // Color Correction Matrix element [1][1]
+    CV_CAP_PROP_XI_CC_MATRIX_12                                 = 485, // Color Correction Matrix element [1][2]
+    CV_CAP_PROP_XI_CC_MATRIX_13                                 = 486, // Color Correction Matrix element [1][3]
+    CV_CAP_PROP_XI_CC_MATRIX_20                                 = 487, // Color Correction Matrix element [2][0]
+    CV_CAP_PROP_XI_CC_MATRIX_21                                 = 488, // Color Correction Matrix element [2][1]
+    CV_CAP_PROP_XI_CC_MATRIX_22                                 = 489, // Color Correction Matrix element [2][2]
+    CV_CAP_PROP_XI_CC_MATRIX_23                                 = 490, // Color Correction Matrix element [2][3]
+    CV_CAP_PROP_XI_CC_MATRIX_30                                 = 491, // Color Correction Matrix element [3][0]
+    CV_CAP_PROP_XI_CC_MATRIX_31                                 = 492, // Color Correction Matrix element [3][1]
+    CV_CAP_PROP_XI_CC_MATRIX_32                                 = 493, // Color Correction Matrix element [3][2]
+    CV_CAP_PROP_XI_CC_MATRIX_33                                 = 494, // Color Correction Matrix element [3][3]
+    CV_CAP_PROP_XI_DEFAULT_CC_MATRIX                            = 495, // Set default Color Correction Matrix
+    CV_CAP_PROP_XI_TRG_SELECTOR                                 = 498, // Selects the type of trigger.
+    CV_CAP_PROP_XI_ACQ_FRAME_BURST_COUNT                        = 499, // Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart
+    CV_CAP_PROP_XI_DEBOUNCE_EN                                  = 507, // Enable/Disable debounce to selected GPI
+    CV_CAP_PROP_XI_DEBOUNCE_T0                                  = 508, // Debounce time (x * 10us)
+    CV_CAP_PROP_XI_DEBOUNCE_T1                                  = 509, // Debounce time (x * 10us)
+    CV_CAP_PROP_XI_DEBOUNCE_POL                                 = 510, // Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge)
+    CV_CAP_PROP_XI_LENS_MODE                                    = 511, // Status of lens control interface. This shall be set to XI_ON before any Lens operations.
+    CV_CAP_PROP_XI_LENS_APERTURE_VALUE                          = 512, // Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11
+    CV_CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE                    = 513, // Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps.
+    CV_CAP_PROP_XI_LENS_FOCUS_MOVE                              = 514, // Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE.
+    CV_CAP_PROP_XI_LENS_FOCUS_DISTANCE                          = 515, // Lens focus distance in cm.
+    CV_CAP_PROP_XI_LENS_FOCAL_LENGTH                            = 516, // Lens focal distance in mm.
+    CV_CAP_PROP_XI_LENS_FEATURE_SELECTOR                        = 517, // Selects the current feature which is accessible by XI_PRM_LENS_FEATURE.
+    CV_CAP_PROP_XI_LENS_FEATURE                                 = 518, // Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR.
+    CV_CAP_PROP_XI_DEVICE_MODEL_ID                              = 521, // Return device model id
+    CV_CAP_PROP_XI_DEVICE_SN                                    = 522, // Return device serial number
+    CV_CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA                = 529, // The alpha channel of RGB32 output image format.
+    CV_CAP_PROP_XI_IMAGE_PAYLOAD_SIZE                           = 530, // Buffer size in bytes sufficient for output image returned by xiGetImage
+    CV_CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT                       = 531, // Current format of pixels on transport layer.
+    CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ                         = 532, // Sensor clock frequency in Hz.
+    CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX                      = 533, // Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index.
+    CV_CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT                  = 534, // Number of output channels from sensor used for data transfer.
+    CV_CAP_PROP_XI_FRAMERATE                                    = 535, // Define framerate in Hz
+    CV_CAP_PROP_XI_COUNTER_SELECTOR                             = 536, // Select counter
+    CV_CAP_PROP_XI_COUNTER_VALUE                                = 537, // Counter status
+    CV_CAP_PROP_XI_ACQ_TIMING_MODE                              = 538, // Type of sensor frames timing.
+    CV_CAP_PROP_XI_AVAILABLE_BANDWIDTH                          = 539, // Calculate and return available interface bandwidth(int Megabits)
+    CV_CAP_PROP_XI_BUFFER_POLICY                                = 540, // Data move policy
+    CV_CAP_PROP_XI_LUT_EN                                       = 541, // Activates LUT.
+    CV_CAP_PROP_XI_LUT_INDEX                                    = 542, // Control the index (offset) of the coefficient to access in the LUT.
+    CV_CAP_PROP_XI_LUT_VALUE                                    = 543, // Value at entry LUTIndex of the LUT
+    CV_CAP_PROP_XI_TRG_DELAY                                    = 544, // Specifies the delay in microseconds (us) to apply after the trigger reception before activating it.
+    CV_CAP_PROP_XI_TS_RST_MODE                                  = 545, // Defines how time stamp reset engine will be armed
+    CV_CAP_PROP_XI_TS_RST_SOURCE                                = 546, // Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming)
+    CV_CAP_PROP_XI_IS_DEVICE_EXIST                              = 547, // Returns 1 if camera connected and works properly.
+    CV_CAP_PROP_XI_ACQ_BUFFER_SIZE                              = 548, // Acquisition buffer size in buffer_size_unit. Default bytes.
+    CV_CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT                         = 549, // Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes
+    CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE                    = 550, // Acquisition transport buffer size in bytes
+    CV_CAP_PROP_XI_BUFFERS_QUEUE_SIZE                           = 551, // Queue of field/frame buffers
+    CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT                  = 552, // Number of buffers to commit to low level
+    CV_CAP_PROP_XI_RECENT_FRAME                                 = 553, // GetImage returns most recent frame
+    CV_CAP_PROP_XI_DEVICE_RESET                                 = 554, // Resets the camera to default state.
+    CV_CAP_PROP_XI_COLUMN_FPN_CORRECTION                        = 555, // Correction of column FPN
+    CV_CAP_PROP_XI_ROW_FPN_CORRECTION                           = 591, // Correction of row FPN
+    CV_CAP_PROP_XI_SENSOR_MODE                                  = 558, // Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling.
+    CV_CAP_PROP_XI_HDR                                          = 559, // Enable High Dynamic Range feature.
+    CV_CAP_PROP_XI_HDR_KNEEPOINT_COUNT                          = 560, // The number of kneepoints in the PWLR.
+    CV_CAP_PROP_XI_HDR_T1                                       = 561, // position of first kneepoint(in % of XI_PRM_EXPOSURE)
+    CV_CAP_PROP_XI_HDR_T2                                       = 562, // position of second kneepoint (in % of XI_PRM_EXPOSURE)
+    CV_CAP_PROP_XI_KNEEPOINT1                                   = 563, // value of first kneepoint (% of sensor saturation)
+    CV_CAP_PROP_XI_KNEEPOINT2                                   = 564, // value of second kneepoint (% of sensor saturation)
+    CV_CAP_PROP_XI_IMAGE_BLACK_LEVEL                            = 565, // Last image black level counts. Can be used for Offline processing to recall it.
+    CV_CAP_PROP_XI_HW_REVISION                                  = 571, // Returns hardware revision number.
+    CV_CAP_PROP_XI_DEBUG_LEVEL                                  = 572, // Set debug level
+    CV_CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION                   = 573, // Automatic bandwidth calculation,
+    CV_CAP_PROP_XI_FFS_FILE_ID                                  = 594, // File number.
+    CV_CAP_PROP_XI_FFS_FILE_SIZE                                = 580, // Size of file.
+    CV_CAP_PROP_XI_FREE_FFS_SIZE                                = 581, // Size of free camera FFS.
+    CV_CAP_PROP_XI_USED_FFS_SIZE                                = 582, // Size of used camera FFS.
+    CV_CAP_PROP_XI_FFS_ACCESS_KEY                               = 583, // Setting of key enables file operations on some cameras.
+    CV_CAP_PROP_XI_SENSOR_FEATURE_SELECTOR                      = 585, // Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE.
+    CV_CAP_PROP_XI_SENSOR_FEATURE_VALUE                         = 586, // Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR.
+
+
+    // Properties for Android cameras
+    CV_CAP_PROP_ANDROID_FLASH_MODE = 8001,
+    CV_CAP_PROP_ANDROID_FOCUS_MODE = 8002,
+    CV_CAP_PROP_ANDROID_WHITE_BALANCE = 8003,
+    CV_CAP_PROP_ANDROID_ANTIBANDING = 8004,
+    CV_CAP_PROP_ANDROID_FOCAL_LENGTH = 8005,
+    CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_NEAR = 8006,
+    CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_OPTIMAL = 8007,
+    CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_FAR = 8008,
+    CV_CAP_PROP_ANDROID_EXPOSE_LOCK = 8009,
+    CV_CAP_PROP_ANDROID_WHITEBALANCE_LOCK = 8010,
+
+    // Properties of cameras available through AVFOUNDATION interface
+    CV_CAP_PROP_IOS_DEVICE_FOCUS = 9001,
+    CV_CAP_PROP_IOS_DEVICE_EXPOSURE = 9002,
+    CV_CAP_PROP_IOS_DEVICE_FLASH = 9003,
+    CV_CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004,
+    CV_CAP_PROP_IOS_DEVICE_TORCH = 9005,
+
+    // Properties of cameras available through Smartek Giganetix Ethernet Vision interface
+    /* --- Vladimir Litvinenko (litvinenko.vladimir@gmail.com) --- */
+    CV_CAP_PROP_GIGA_FRAME_OFFSET_X = 10001,
+    CV_CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002,
+    CV_CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003,
+    CV_CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004,
+    CV_CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005,
+    CV_CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006,
+
+    CV_CAP_PROP_INTELPERC_PROFILE_COUNT               = 11001,
+    CV_CAP_PROP_INTELPERC_PROFILE_IDX                 = 11002,
+    CV_CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE  = 11003,
+    CV_CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE      = 11004,
+    CV_CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD  = 11005,
+    CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ     = 11006,
+    CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT     = 11007,
+
+    // Intel PerC streams
+    CV_CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29,
+    CV_CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28,
+    CV_CAP_INTELPERC_GENERATORS_MASK = CV_CAP_INTELPERC_DEPTH_GENERATOR + CV_CAP_INTELPERC_IMAGE_GENERATOR
+};
+
+enum
+{
+    // Data given from depth generator.
+    CV_CAP_OPENNI_DEPTH_MAP                 = 0, // Depth values in mm (CV_16UC1)
+    CV_CAP_OPENNI_POINT_CLOUD_MAP           = 1, // XYZ in meters (CV_32FC3)
+    CV_CAP_OPENNI_DISPARITY_MAP             = 2, // Disparity in pixels (CV_8UC1)
+    CV_CAP_OPENNI_DISPARITY_MAP_32F         = 3, // Disparity in pixels (CV_32FC1)
+    CV_CAP_OPENNI_VALID_DEPTH_MASK          = 4, // CV_8UC1
+
+    // Data given from RGB image generator.
+    CV_CAP_OPENNI_BGR_IMAGE                 = 5,
+    CV_CAP_OPENNI_GRAY_IMAGE                = 6,
+
+    // Data given from IR image generator.
+    CV_CAP_OPENNI_IR_IMAGE                  = 7
+};
+
+// Supported output modes of OpenNI image generator
+enum
+{
+    CV_CAP_OPENNI_VGA_30HZ     = 0,
+    CV_CAP_OPENNI_SXGA_15HZ    = 1,
+    CV_CAP_OPENNI_SXGA_30HZ    = 2,
+    CV_CAP_OPENNI_QVGA_30HZ    = 3,
+    CV_CAP_OPENNI_QVGA_60HZ    = 4
+};
+
+enum
+{
+    CV_CAP_INTELPERC_DEPTH_MAP              = 0, // Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth.
+    CV_CAP_INTELPERC_UVDEPTH_MAP            = 1, // Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates.
+    CV_CAP_INTELPERC_IR_MAP                 = 2, // Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam.
+    CV_CAP_INTELPERC_IMAGE                  = 3
+};
+
+// gPhoto2 properties, if propertyId is less than 0 then work on widget with that __additive inversed__ camera setting ID
+// Get IDs by using CAP_PROP_GPHOTO2_WIDGET_ENUMERATE.
+// @see CvCaptureCAM_GPHOTO2 for more info
+enum
+{
+    CV_CAP_PROP_GPHOTO2_PREVIEW           = 17001, // Capture only preview from liveview mode.
+    CV_CAP_PROP_GPHOTO2_WIDGET_ENUMERATE  = 17002, // Readonly, returns (const char *).
+    CV_CAP_PROP_GPHOTO2_RELOAD_CONFIG     = 17003, // Trigger, only by set. Reload camera settings.
+    CV_CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE  = 17004, // Reload all settings on set.
+    CV_CAP_PROP_GPHOTO2_COLLECT_MSGS      = 17005, // Collect messages with details.
+    CV_CAP_PROP_GPHOTO2_FLUSH_MSGS        = 17006, // Readonly, returns (const char *).
+    CV_CAP_PROP_SPEED                     = 17007, // Exposure speed. Can be readonly, depends on camera program.
+    CV_CAP_PROP_APERTURE                  = 17008, // Aperture. Can be readonly, depends on camera program.
+    CV_CAP_PROP_EXPOSUREPROGRAM           = 17009, // Camera exposure program.
+    CV_CAP_PROP_VIEWFINDER                = 17010  // Enter liveview mode.
+};
+
+//! Macro to construct the fourcc code of the codec. Same as CV_FOURCC()
+#define CV_FOURCC_MACRO(c1, c2, c3, c4) (((c1) & 255) + (((c2) & 255) << 8) + (((c3) & 255) << 16) + (((c4) & 255) << 24))
+
+/** @brief Constructs the fourcc code of the codec function
+
+Simply call it with 4 chars fourcc code like `CV_FOURCC('I', 'Y', 'U', 'V')`
+
+List of codes can be obtained at [Video Codecs by FOURCC](http://www.fourcc.org/codecs.php) page.
+FFMPEG backend with MP4 container natively uses other values as fourcc code:
+see [ObjectType](http://mp4ra.org/#/codecs).
+*/
+CV_INLINE int CV_FOURCC(char c1, char c2, char c3, char c4)
+{
+    return CV_FOURCC_MACRO(c1, c2, c3, c4);
+}
+
+//! (Windows only) Open Codec Selection Dialog
+#define CV_FOURCC_PROMPT -1
+//! (Linux only) Use default codec for specified filename
+#define CV_FOURCC_DEFAULT CV_FOURCC('I', 'Y', 'U', 'V')
+
+#endif // OPENCV_VIDEOIO_LEGACY_CONSTANTS_H
diff --git a/3rdParty/include/opencv2/videoio/registry.hpp b/3rdParty/include/opencv2/videoio/registry.hpp
new file mode 100644
index 0000000..cf72247
--- /dev/null
+++ b/3rdParty/include/opencv2/videoio/registry.hpp
@@ -0,0 +1,72 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_VIDEOIO_REGISTRY_HPP
+#define OPENCV_VIDEOIO_REGISTRY_HPP
+
+#include <opencv2/videoio.hpp>
+
+namespace cv { namespace videoio_registry {
+/** @addtogroup videoio_registry
+This section contains API description how to query/configure available Video I/O backends.
+
+Runtime configuration options:
+- enable debug mode: `OPENCV_VIDEOIO_DEBUG=1`
+- change backend priority: `OPENCV_VIDEOIO_PRIORITY_<backend>=9999`
+- disable backend: `OPENCV_VIDEOIO_PRIORITY_<backend>=0`
+- specify list of backends with high priority (>100000): `OPENCV_VIDEOIO_PRIORITY_LIST=FFMPEG,GSTREAMER`
+
+@{
+ */
+
+
+/** @brief Returns backend API name or "UnknownVideoAPI(xxx)"
+@param api backend ID (#VideoCaptureAPIs)
+*/
+CV_EXPORTS_W cv::String getBackendName(VideoCaptureAPIs api);
+
+/** @brief Returns list of all available backends */
+CV_EXPORTS_W std::vector<VideoCaptureAPIs> getBackends();
+
+/** @brief Returns list of available backends which works via `cv::VideoCapture(int index)` */
+CV_EXPORTS_W std::vector<VideoCaptureAPIs> getCameraBackends();
+
+/** @brief Returns list of available backends which works via `cv::VideoCapture(filename)` */
+CV_EXPORTS_W std::vector<VideoCaptureAPIs> getStreamBackends();
+
+/** @brief Returns list of available backends which works via `cv::VideoWriter()` */
+CV_EXPORTS_W std::vector<VideoCaptureAPIs> getWriterBackends();
+
+/** @brief Returns true if backend is available */
+CV_EXPORTS_W bool hasBackend(VideoCaptureAPIs api);
+
+/** @brief Returns true if backend is built in (false if backend is used as plugin) */
+CV_EXPORTS_W bool isBackendBuiltIn(VideoCaptureAPIs api);
+
+/** @brief Returns description and ABI/API version of videoio plugin's camera interface */
+CV_EXPORTS_W std::string getCameraBackendPluginVersion(
+    VideoCaptureAPIs api,
+    CV_OUT int& version_ABI,
+    CV_OUT int& version_API
+);
+
+/** @brief Returns description and ABI/API version of videoio plugin's stream capture interface */
+CV_EXPORTS_W std::string getStreamBackendPluginVersion(
+    VideoCaptureAPIs api,
+    CV_OUT int& version_ABI,
+    CV_OUT int& version_API
+);
+
+/** @brief Returns description and ABI/API version of videoio plugin's writer interface */
+CV_EXPORTS_W std::string getWriterBackendPluginVersion(
+    VideoCaptureAPIs api,
+    CV_OUT int& version_ABI,
+    CV_OUT int& version_API
+);
+
+
+//! @}
+}} // namespace
+
+#endif // OPENCV_VIDEOIO_REGISTRY_HPP
diff --git a/3rdParty/include/opencv2/videoio/videoio.hpp b/3rdParty/include/opencv2/videoio/videoio.hpp
new file mode 100644
index 0000000..ec84cf7
--- /dev/null
+++ b/3rdParty/include/opencv2/videoio/videoio.hpp
@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/videoio.hpp"
diff --git a/3rdParty/include/opencv2/videoio/videoio_c.h b/3rdParty/include/opencv2/videoio/videoio_c.h
new file mode 100644
index 0000000..cf1a6d0
--- /dev/null
+++ b/3rdParty/include/opencv2/videoio/videoio_c.h
@@ -0,0 +1,153 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_VIDEOIO_H
+#define OPENCV_VIDEOIO_H
+
+#include "opencv2/core/core_c.h"
+
+#include "opencv2/videoio/legacy/constants_c.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+  @addtogroup videoio_c
+  @{
+*/
+
+/****************************************************************************************\
+*                         Working with Video Files and Cameras                           *
+\****************************************************************************************/
+
+/** @brief "black box" capture structure
+
+In C++ use cv::VideoCapture
+*/
+typedef struct CvCapture CvCapture;
+
+/** @brief start capturing frames from video file
+*/
+CVAPI(CvCapture*) cvCreateFileCapture( const char* filename );
+
+/** @brief start capturing frames from video file. allows specifying a preferred API to use
+*/
+CVAPI(CvCapture*) cvCreateFileCaptureWithPreference( const char* filename , int apiPreference);
+
+/** @brief start capturing frames from camera: index = camera_index + domain_offset (CV_CAP_*)
+*/
+CVAPI(CvCapture*) cvCreateCameraCapture( int index );
+
+/** @brief grab a frame, return 1 on success, 0 on fail.
+
+  this function is thought to be fast
+*/
+CVAPI(int) cvGrabFrame( CvCapture* capture );
+
+/** @brief get the frame grabbed with cvGrabFrame(..)
+
+  This function may apply some frame processing like
+  frame decompression, flipping etc.
+  @warning !!!DO NOT RELEASE or MODIFY the retrieved frame!!!
+*/
+CVAPI(IplImage*) cvRetrieveFrame( CvCapture* capture, int streamIdx CV_DEFAULT(0) );
+
+/** @brief Just a combination of cvGrabFrame and cvRetrieveFrame
+
+  @warning !!!DO NOT RELEASE or MODIFY the retrieved frame!!!
+*/
+CVAPI(IplImage*) cvQueryFrame( CvCapture* capture );
+
+/** @brief stop capturing/reading and free resources
+*/
+CVAPI(void) cvReleaseCapture( CvCapture** capture );
+
+/** @brief retrieve capture properties
+*/
+CVAPI(double) cvGetCaptureProperty( CvCapture* capture, int property_id );
+/** @brief set capture properties
+*/
+CVAPI(int)    cvSetCaptureProperty( CvCapture* capture, int property_id, double value );
+
+/** @brief Return the type of the capturer (eg, ::CV_CAP_VFW, ::CV_CAP_UNICAP)
+
+It is unknown if created with ::CV_CAP_ANY
+*/
+CVAPI(int)    cvGetCaptureDomain( CvCapture* capture);
+
+/** @brief "black box" video file writer structure
+
+In C++ use cv::VideoWriter
+*/
+typedef struct CvVideoWriter CvVideoWriter;
+
+/** @brief initialize video file writer
+*/
+CVAPI(CvVideoWriter*) cvCreateVideoWriter( const char* filename, int fourcc,
+                                           double fps, CvSize frame_size,
+                                           int is_color CV_DEFAULT(1));
+
+/** @brief write frame to video file
+*/
+CVAPI(int) cvWriteFrame( CvVideoWriter* writer, const IplImage* image );
+
+/** @brief close video file writer
+*/
+CVAPI(void) cvReleaseVideoWriter( CvVideoWriter** writer );
+
+// ***************************************************************************************
+//! @name Obsolete functions/synonyms
+//! @{
+#define cvCaptureFromCAM cvCreateCameraCapture //!< @deprecated use cvCreateCameraCapture() instead
+#define cvCaptureFromFile cvCreateFileCapture  //!< @deprecated use cvCreateFileCapture() instead
+#define cvCaptureFromAVI cvCaptureFromFile     //!< @deprecated use cvCreateFileCapture() instead
+#define cvCreateAVIWriter cvCreateVideoWriter  //!< @deprecated use cvCreateVideoWriter() instead
+#define cvWriteToAVI cvWriteFrame              //!< @deprecated use cvWriteFrame() instead
+//!  @} Obsolete...
+
+//! @} videoio_c
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif //OPENCV_VIDEOIO_H
diff --git a/3rdParty/include/opencv2/world.hpp b/3rdParty/include/opencv2/world.hpp
new file mode 100644
index 0000000..4902c2f
--- /dev/null
+++ b/3rdParty/include/opencv2/world.hpp
@@ -0,0 +1,58 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_WORLD_HPP
+#define OPENCV_WORLD_HPP
+
+#include "opencv2/core.hpp"
+
+#ifdef __cplusplus
+namespace cv
+{
+
+CV_EXPORTS_W bool initAll();
+
+}
+
+#endif
+
+#endif
diff --git a/3rdParty/my_ceres_vc14/lib/ceres.lib b/3rdParty/lib/ceres.lib
similarity index 100%
rename from 3rdParty/my_ceres_vc14/lib/ceres.lib
rename to 3rdParty/lib/ceres.lib
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similarity index 100%
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rename from 3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-config.cmake
rename to 3rdParty/lib/cmake/gflags/gflags-config.cmake
diff --git a/3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake b/3rdParty/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
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rename from 3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
rename to 3rdParty/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
diff --git a/3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake b/3rdParty/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake
similarity index 100%
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diff --git a/3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-nonamespace-targets.cmake b/3rdParty/lib/cmake/gflags/gflags-nonamespace-targets.cmake
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rename from 3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-nonamespace-targets.cmake
rename to 3rdParty/lib/cmake/gflags/gflags-nonamespace-targets.cmake
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similarity index 100%
rename from 3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-targets-debug.cmake
rename to 3rdParty/lib/cmake/gflags/gflags-targets-debug.cmake
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rename from 3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-targets-release.cmake
rename to 3rdParty/lib/cmake/gflags/gflags-targets-release.cmake
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rename from 3rdParty/my_ceres_vc14/lib/cmake/gflags/gflags-targets.cmake
rename to 3rdParty/lib/cmake/gflags/gflags-targets.cmake
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similarity index 100%
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rename to 3rdParty/lib/cmake/glog/glog-config-version.cmake
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similarity index 100%
rename from 3rdParty/my_ceres_vc14/lib/cmake/glog/glog-config.cmake
rename to 3rdParty/lib/cmake/glog/glog-config.cmake
diff --git a/3rdParty/my_ceres_vc14/lib/cmake/glog/glog-targets-debug.cmake b/3rdParty/lib/cmake/glog/glog-targets-debug.cmake
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rename from 3rdParty/my_ceres_vc14/lib/cmake/glog/glog-targets-debug.cmake
rename to 3rdParty/lib/cmake/glog/glog-targets-debug.cmake
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rename from 3rdParty/my_ceres_vc14/lib/gflags.lib
rename to 3rdParty/lib/gflags.lib
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index 0000000..c35f130
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diff --git a/3rdParty/my_ceres_vc14/lib/glogd.lib b/3rdParty/my_ceres_vc14/lib/glogd.lib
deleted file mode 100644
index 47d41c6..0000000
Binary files a/3rdParty/my_ceres_vc14/lib/glogd.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Cholesky b/3rdParty/my_ceres_vc17/include/Eigen/Cholesky
deleted file mode 100644
index a318ceb..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Cholesky
+++ /dev/null
@@ -1,45 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLESKY_MODULE_H
-#define EIGEN_CHOLESKY_MODULE_H
-
-#include "Core"
-#include "Jacobi"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Cholesky_Module Cholesky module
-  *
-  *
-  *
-  * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
-  * Those decompositions are also accessible via the following methods:
-  *  - MatrixBase::llt()
-  *  - MatrixBase::ldlt()
-  *  - SelfAdjointView::llt()
-  *  - SelfAdjointView::ldlt()
-  *
-  * \code
-  * #include <Eigen/Cholesky>
-  * \endcode
-  */
-
-#include "src/Cholesky/LLT.h"
-#include "src/Cholesky/LDLT.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/Cholesky/LLT_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CHOLESKY_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/CholmodSupport b/3rdParty/my_ceres_vc17/include/Eigen/CholmodSupport
deleted file mode 100644
index bed8924..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/CholmodSupport
+++ /dev/null
@@ -1,48 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
-#define EIGEN_CHOLMODSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-  #include <cholmod.h>
-}
-
-/** \ingroup Support_modules
-  * \defgroup CholmodSupport_Module CholmodSupport module
-  *
-  * This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  * It provides the two following main factorization classes:
-  * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
-  * - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
-  *
-  * For the sake of completeness, this module also propose the two following classes:
-  * - class CholmodSimplicialLLT
-  * - class CholmodSimplicialLDLT
-  * Note that these classes does not bring any particular advantage compared to the built-in
-  * SimplicialLLT and SimplicialLDLT factorization classes.
-  *
-  * \code
-  * #include <Eigen/CholmodSupport>
-  * \endcode
-  *
-  * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be linked to the cholmod library and its dependencies.
-  * The dependencies depend on how cholmod has been compiled.
-  * For a cmake based project, you can use our FindCholmod.cmake module to help you in this task.
-  *
-  */
-
-#include "src/CholmodSupport/CholmodSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CHOLMODSUPPORT_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Core b/3rdParty/my_ceres_vc17/include/Eigen/Core
deleted file mode 100644
index 5921e15..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Core
+++ /dev/null
@@ -1,384 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CORE_H
-#define EIGEN_CORE_H
-
-// first thing Eigen does: stop the compiler from reporting useless warnings.
-#include "src/Core/util/DisableStupidWarnings.h"
-
-// then include this file where all our macros are defined. It's really important to do it first because
-// it's where we do all the compiler/OS/arch detections and define most defaults.
-#include "src/Core/util/Macros.h"
-
-// This detects SSE/AVX/NEON/etc. and configure alignment settings
-#include "src/Core/util/ConfigureVectorization.h"
-
-// We need cuda_runtime.h/hip_runtime.h to ensure that
-// the EIGEN_USING_STD macro works properly on the device side
-#if defined(EIGEN_CUDACC)
-  #include <cuda_runtime.h>
-#elif defined(EIGEN_HIPCC)
-  #include <hip/hip_runtime.h>
-#endif
-
-
-#ifdef EIGEN_EXCEPTIONS
-  #include <new>
-#endif
-
-// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
-// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
-#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6) && EIGEN_GNUC_AT_MOST(5,5)
-  #pragma GCC optimize ("-fno-ipa-cp-clone")
-#endif
-
-// Prevent ICC from specializing std::complex operators that silently fail
-// on device. This allows us to use our own device-compatible specializations
-// instead.
-#if defined(EIGEN_COMP_ICC) && defined(EIGEN_GPU_COMPILE_PHASE) \
-    && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_)
-#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1
-#endif
-#include <complex>
-
-// this include file manages BLAS and MKL related macros
-// and inclusion of their respective header files
-#include "src/Core/util/MKL_support.h"
-
-
-#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
-  #define EIGEN_HAS_GPU_FP16
-#endif
-
-#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16)
-  #define EIGEN_HAS_GPU_BF16
-#endif
-
-#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
-  #define EIGEN_HAS_OPENMP
-#endif
-
-#ifdef EIGEN_HAS_OPENMP
-#include <omp.h>
-#endif
-
-// MSVC for windows mobile does not have the errno.h file
-#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
-#define EIGEN_HAS_ERRNO
-#endif
-
-#ifdef EIGEN_HAS_ERRNO
-#include <cerrno>
-#endif
-#include <cstddef>
-#include <cstdlib>
-#include <cmath>
-#include <cassert>
-#include <functional>
-#include <sstream>
-#ifndef EIGEN_NO_IO
-  #include <iosfwd>
-#endif
-#include <cstring>
-#include <string>
-#include <limits>
-#include <climits> // for CHAR_BIT
-// for min/max:
-#include <algorithm>
-
-#if EIGEN_HAS_CXX11
-#include <array>
-#endif
-
-// for std::is_nothrow_move_assignable
-#ifdef EIGEN_INCLUDE_TYPE_TRAITS
-#include <type_traits>
-#endif
-
-// for outputting debug info
-#ifdef EIGEN_DEBUG_ASSIGN
-#include <iostream>
-#endif
-
-// required for __cpuid, needs to be included after cmath
-#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
-  #include <intrin.h>
-#endif
-
-#if defined(EIGEN_USE_SYCL)
-  #undef min
-  #undef max
-  #undef isnan
-  #undef isinf
-  #undef isfinite
-  #include <CL/sycl.hpp>
-  #include <map>
-  #include <memory>
-  #include <utility>
-  #include <thread>
-  #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0
-  #define EIGEN_SYCL_LOCAL_THREAD_DIM0 16
-  #endif
-  #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1
-  #define EIGEN_SYCL_LOCAL_THREAD_DIM1 16
-  #endif
-#endif
-
-
-#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT
-// This will generate an error message:
-#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
-#endif
-
-namespace Eigen {
-
-// we use size_t frequently and we'll never remember to prepend it with std:: every time just to
-// ensure QNX/QCC support
-using std::size_t;
-// gcc 4.6.0 wants std:: for ptrdiff_t
-using std::ptrdiff_t;
-
-}
-
-/** \defgroup Core_Module Core module
-  * This is the main module of Eigen providing dense matrix and vector support
-  * (both fixed and dynamic size) with all the features corresponding to a BLAS library
-  * and much more...
-  *
-  * \code
-  * #include <Eigen/Core>
-  * \endcode
-  */
-
-#include "src/Core/util/Constants.h"
-#include "src/Core/util/Meta.h"
-#include "src/Core/util/ForwardDeclarations.h"
-#include "src/Core/util/StaticAssert.h"
-#include "src/Core/util/XprHelper.h"
-#include "src/Core/util/Memory.h"
-#include "src/Core/util/IntegralConstant.h"
-#include "src/Core/util/SymbolicIndex.h"
-
-#include "src/Core/NumTraits.h"
-#include "src/Core/MathFunctions.h"
-#include "src/Core/GenericPacketMath.h"
-#include "src/Core/MathFunctionsImpl.h"
-#include "src/Core/arch/Default/ConjHelper.h"
-// Generic half float support
-#include "src/Core/arch/Default/Half.h"
-#include "src/Core/arch/Default/BFloat16.h"
-#include "src/Core/arch/Default/TypeCasting.h"
-#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
-
-#if defined EIGEN_VECTORIZE_AVX512
-  #include "src/Core/arch/SSE/PacketMath.h"
-  #include "src/Core/arch/SSE/TypeCasting.h"
-  #include "src/Core/arch/SSE/Complex.h"
-  #include "src/Core/arch/AVX/PacketMath.h"
-  #include "src/Core/arch/AVX/TypeCasting.h"
-  #include "src/Core/arch/AVX/Complex.h"
-  #include "src/Core/arch/AVX512/PacketMath.h"
-  #include "src/Core/arch/AVX512/TypeCasting.h"
-  #include "src/Core/arch/AVX512/Complex.h"
-  #include "src/Core/arch/SSE/MathFunctions.h"
-  #include "src/Core/arch/AVX/MathFunctions.h"
-  #include "src/Core/arch/AVX512/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_AVX
-  // Use AVX for floats and doubles, SSE for integers
-  #include "src/Core/arch/SSE/PacketMath.h"
-  #include "src/Core/arch/SSE/TypeCasting.h"
-  #include "src/Core/arch/SSE/Complex.h"
-  #include "src/Core/arch/AVX/PacketMath.h"
-  #include "src/Core/arch/AVX/TypeCasting.h"
-  #include "src/Core/arch/AVX/Complex.h"
-  #include "src/Core/arch/SSE/MathFunctions.h"
-  #include "src/Core/arch/AVX/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_SSE
-  #include "src/Core/arch/SSE/PacketMath.h"
-  #include "src/Core/arch/SSE/TypeCasting.h"
-  #include "src/Core/arch/SSE/MathFunctions.h"
-  #include "src/Core/arch/SSE/Complex.h"
-#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
-  #include "src/Core/arch/AltiVec/PacketMath.h"
-  #include "src/Core/arch/AltiVec/MathFunctions.h"
-  #include "src/Core/arch/AltiVec/Complex.h"
-#elif defined EIGEN_VECTORIZE_NEON
-  #include "src/Core/arch/NEON/PacketMath.h"
-  #include "src/Core/arch/NEON/TypeCasting.h"
-  #include "src/Core/arch/NEON/MathFunctions.h"
-  #include "src/Core/arch/NEON/Complex.h"
-#elif defined EIGEN_VECTORIZE_SVE
-  #include "src/Core/arch/SVE/PacketMath.h"
-  #include "src/Core/arch/SVE/TypeCasting.h"
-  #include "src/Core/arch/SVE/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_ZVECTOR
-  #include "src/Core/arch/ZVector/PacketMath.h"
-  #include "src/Core/arch/ZVector/MathFunctions.h"
-  #include "src/Core/arch/ZVector/Complex.h"
-#elif defined EIGEN_VECTORIZE_MSA
-  #include "src/Core/arch/MSA/PacketMath.h"
-  #include "src/Core/arch/MSA/MathFunctions.h"
-  #include "src/Core/arch/MSA/Complex.h"
-#endif
-
-#if defined EIGEN_VECTORIZE_GPU
-  #include "src/Core/arch/GPU/PacketMath.h"
-  #include "src/Core/arch/GPU/MathFunctions.h"
-  #include "src/Core/arch/GPU/TypeCasting.h"
-#endif
-
-#if defined(EIGEN_USE_SYCL)
-  #include "src/Core/arch/SYCL/SyclMemoryModel.h"
-  #include "src/Core/arch/SYCL/InteropHeaders.h"
-#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
-  #include "src/Core/arch/SYCL/PacketMath.h"
-  #include "src/Core/arch/SYCL/MathFunctions.h"
-  #include "src/Core/arch/SYCL/TypeCasting.h"
-#endif
-#endif
-
-#include "src/Core/arch/Default/Settings.h"
-// This file provides generic implementations valid for scalar as well
-#include "src/Core/arch/Default/GenericPacketMathFunctions.h"
-
-#include "src/Core/functors/TernaryFunctors.h"
-#include "src/Core/functors/BinaryFunctors.h"
-#include "src/Core/functors/UnaryFunctors.h"
-#include "src/Core/functors/NullaryFunctors.h"
-#include "src/Core/functors/StlFunctors.h"
-#include "src/Core/functors/AssignmentFunctors.h"
-
-// Specialized functors to enable the processing of complex numbers
-// on CUDA devices
-#ifdef EIGEN_CUDACC
-#include "src/Core/arch/CUDA/Complex.h"
-#endif
-
-#include "src/Core/util/IndexedViewHelper.h"
-#include "src/Core/util/ReshapedHelper.h"
-#include "src/Core/ArithmeticSequence.h"
-#ifndef EIGEN_NO_IO
-  #include "src/Core/IO.h"
-#endif
-#include "src/Core/DenseCoeffsBase.h"
-#include "src/Core/DenseBase.h"
-#include "src/Core/MatrixBase.h"
-#include "src/Core/EigenBase.h"
-
-#include "src/Core/Product.h"
-#include "src/Core/CoreEvaluators.h"
-#include "src/Core/AssignEvaluator.h"
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
-                                // at least confirmed with Doxygen 1.5.5 and 1.5.6
-  #include "src/Core/Assign.h"
-#endif
-
-#include "src/Core/ArrayBase.h"
-#include "src/Core/util/BlasUtil.h"
-#include "src/Core/DenseStorage.h"
-#include "src/Core/NestByValue.h"
-
-// #include "src/Core/ForceAlignedAccess.h"
-
-#include "src/Core/ReturnByValue.h"
-#include "src/Core/NoAlias.h"
-#include "src/Core/PlainObjectBase.h"
-#include "src/Core/Matrix.h"
-#include "src/Core/Array.h"
-#include "src/Core/CwiseTernaryOp.h"
-#include "src/Core/CwiseBinaryOp.h"
-#include "src/Core/CwiseUnaryOp.h"
-#include "src/Core/CwiseNullaryOp.h"
-#include "src/Core/CwiseUnaryView.h"
-#include "src/Core/SelfCwiseBinaryOp.h"
-#include "src/Core/Dot.h"
-#include "src/Core/StableNorm.h"
-#include "src/Core/Stride.h"
-#include "src/Core/MapBase.h"
-#include "src/Core/Map.h"
-#include "src/Core/Ref.h"
-#include "src/Core/Block.h"
-#include "src/Core/VectorBlock.h"
-#include "src/Core/IndexedView.h"
-#include "src/Core/Reshaped.h"
-#include "src/Core/Transpose.h"
-#include "src/Core/DiagonalMatrix.h"
-#include "src/Core/Diagonal.h"
-#include "src/Core/DiagonalProduct.h"
-#include "src/Core/Redux.h"
-#include "src/Core/Visitor.h"
-#include "src/Core/Fuzzy.h"
-#include "src/Core/Swap.h"
-#include "src/Core/CommaInitializer.h"
-#include "src/Core/GeneralProduct.h"
-#include "src/Core/Solve.h"
-#include "src/Core/Inverse.h"
-#include "src/Core/SolverBase.h"
-#include "src/Core/PermutationMatrix.h"
-#include "src/Core/Transpositions.h"
-#include "src/Core/TriangularMatrix.h"
-#include "src/Core/SelfAdjointView.h"
-#include "src/Core/products/GeneralBlockPanelKernel.h"
-#include "src/Core/products/Parallelizer.h"
-#include "src/Core/ProductEvaluators.h"
-#include "src/Core/products/GeneralMatrixVector.h"
-#include "src/Core/products/GeneralMatrixMatrix.h"
-#include "src/Core/SolveTriangular.h"
-#include "src/Core/products/GeneralMatrixMatrixTriangular.h"
-#include "src/Core/products/SelfadjointMatrixVector.h"
-#include "src/Core/products/SelfadjointMatrixMatrix.h"
-#include "src/Core/products/SelfadjointProduct.h"
-#include "src/Core/products/SelfadjointRank2Update.h"
-#include "src/Core/products/TriangularMatrixVector.h"
-#include "src/Core/products/TriangularMatrixMatrix.h"
-#include "src/Core/products/TriangularSolverMatrix.h"
-#include "src/Core/products/TriangularSolverVector.h"
-#include "src/Core/BandMatrix.h"
-#include "src/Core/CoreIterators.h"
-#include "src/Core/ConditionEstimator.h"
-
-#if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
-  #include "src/Core/arch/AltiVec/MatrixProduct.h"
-#elif defined EIGEN_VECTORIZE_NEON
-  #include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
-#endif
-
-#include "src/Core/BooleanRedux.h"
-#include "src/Core/Select.h"
-#include "src/Core/VectorwiseOp.h"
-#include "src/Core/PartialReduxEvaluator.h"
-#include "src/Core/Random.h"
-#include "src/Core/Replicate.h"
-#include "src/Core/Reverse.h"
-#include "src/Core/ArrayWrapper.h"
-#include "src/Core/StlIterators.h"
-
-#ifdef EIGEN_USE_BLAS
-#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
-#include "src/Core/products/GeneralMatrixVector_BLAS.h"
-#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
-#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
-#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
-#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
-#include "src/Core/products/TriangularMatrixVector_BLAS.h"
-#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
-#endif // EIGEN_USE_BLAS
-
-#ifdef EIGEN_USE_MKL_VML
-#include "src/Core/Assign_MKL.h"
-#endif
-
-#include "src/Core/GlobalFunctions.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CORE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Dense b/3rdParty/my_ceres_vc17/include/Eigen/Dense
deleted file mode 100644
index 5768910..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Dense
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "Core"
-#include "LU"
-#include "Cholesky"
-#include "QR"
-#include "SVD"
-#include "Geometry"
-#include "Eigenvalues"
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Eigen b/3rdParty/my_ceres_vc17/include/Eigen/Eigen
deleted file mode 100644
index 654c8dc..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Eigen
+++ /dev/null
@@ -1,2 +0,0 @@
-#include "Dense"
-#include "Sparse"
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Eigenvalues b/3rdParty/my_ceres_vc17/include/Eigen/Eigenvalues
deleted file mode 100644
index 5467a2e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Eigenvalues
+++ /dev/null
@@ -1,60 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EIGENVALUES_MODULE_H
-#define EIGEN_EIGENVALUES_MODULE_H
-
-#include "Core"
-
-#include "Cholesky"
-#include "Jacobi"
-#include "Householder"
-#include "LU"
-#include "Geometry"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Eigenvalues_Module Eigenvalues module
-  *
-  *
-  *
-  * This module mainly provides various eigenvalue solvers.
-  * This module also provides some MatrixBase methods, including:
-  *  - MatrixBase::eigenvalues(),
-  *  - MatrixBase::operatorNorm()
-  *
-  * \code
-  * #include <Eigen/Eigenvalues>
-  * \endcode
-  */
-
-#include "src/misc/RealSvd2x2.h"
-#include "src/Eigenvalues/Tridiagonalization.h"
-#include "src/Eigenvalues/RealSchur.h"
-#include "src/Eigenvalues/EigenSolver.h"
-#include "src/Eigenvalues/SelfAdjointEigenSolver.h"
-#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h"
-#include "src/Eigenvalues/HessenbergDecomposition.h"
-#include "src/Eigenvalues/ComplexSchur.h"
-#include "src/Eigenvalues/ComplexEigenSolver.h"
-#include "src/Eigenvalues/RealQZ.h"
-#include "src/Eigenvalues/GeneralizedEigenSolver.h"
-#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/Eigenvalues/RealSchur_LAPACKE.h"
-#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
-#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_EIGENVALUES_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Geometry b/3rdParty/my_ceres_vc17/include/Eigen/Geometry
deleted file mode 100644
index bc78110..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Geometry
+++ /dev/null
@@ -1,59 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GEOMETRY_MODULE_H
-#define EIGEN_GEOMETRY_MODULE_H
-
-#include "Core"
-
-#include "SVD"
-#include "LU"
-#include <limits>
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Geometry_Module Geometry module
-  *
-  * This module provides support for:
-  *  - fixed-size homogeneous transformations
-  *  - translation, scaling, 2D and 3D rotations
-  *  - \link Quaternion quaternions \endlink
-  *  - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3)
-  *  - orthognal vector generation (\ref MatrixBase::unitOrthogonal)
-  *  - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
-  *  - \link AlignedBox axis aligned bounding boxes \endlink
-  *  - \link umeyama least-square transformation fitting \endlink
-  *
-  * \code
-  * #include <Eigen/Geometry>
-  * \endcode
-  */
-
-#include "src/Geometry/OrthoMethods.h"
-#include "src/Geometry/EulerAngles.h"
-
-#include "src/Geometry/Homogeneous.h"
-#include "src/Geometry/RotationBase.h"
-#include "src/Geometry/Rotation2D.h"
-#include "src/Geometry/Quaternion.h"
-#include "src/Geometry/AngleAxis.h"
-#include "src/Geometry/Transform.h"
-#include "src/Geometry/Translation.h"
-#include "src/Geometry/Scaling.h"
-#include "src/Geometry/Hyperplane.h"
-#include "src/Geometry/ParametrizedLine.h"
-#include "src/Geometry/AlignedBox.h"
-#include "src/Geometry/Umeyama.h"
-
-// Use the SSE optimized version whenever possible.
-#if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON)
-#include "src/Geometry/arch/Geometry_SIMD.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_GEOMETRY_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Householder b/3rdParty/my_ceres_vc17/include/Eigen/Householder
deleted file mode 100644
index f2fa799..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Householder
+++ /dev/null
@@ -1,29 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOUSEHOLDER_MODULE_H
-#define EIGEN_HOUSEHOLDER_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Householder_Module Householder module
-  * This module provides Householder transformations.
-  *
-  * \code
-  * #include <Eigen/Householder>
-  * \endcode
-  */
-
-#include "src/Householder/Householder.h"
-#include "src/Householder/HouseholderSequence.h"
-#include "src/Householder/BlockHouseholder.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_HOUSEHOLDER_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/IterativeLinearSolvers b/3rdParty/my_ceres_vc17/include/Eigen/IterativeLinearSolvers
deleted file mode 100644
index 957d575..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/IterativeLinearSolvers
+++ /dev/null
@@ -1,48 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
-#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** 
-  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
-  *
-  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
-  * Those solvers are accessible via the following classes:
-  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
-  *  - LeastSquaresConjugateGradient for rectangular least-square problems,
-  *  - BiCGSTAB for general square matrices.
-  *
-  * These iterative solvers are associated with some preconditioners:
-  *  - IdentityPreconditioner - not really useful
-  *  - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
-  *  - IncompleteLUT - incomplete LU factorization with dual thresholding
-  *
-  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
-  *
-    \code
-    #include <Eigen/IterativeLinearSolvers>
-    \endcode
-  */
-
-#include "src/IterativeLinearSolvers/SolveWithGuess.h"
-#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
-#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
-#include "src/IterativeLinearSolvers/ConjugateGradient.h"
-#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
-#include "src/IterativeLinearSolvers/BiCGSTAB.h"
-#include "src/IterativeLinearSolvers/IncompleteLUT.h"
-#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Jacobi b/3rdParty/my_ceres_vc17/include/Eigen/Jacobi
deleted file mode 100644
index 43edc7a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Jacobi
+++ /dev/null
@@ -1,32 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_JACOBI_MODULE_H
-#define EIGEN_JACOBI_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Jacobi_Module Jacobi module
-  * This module provides Jacobi and Givens rotations.
-  *
-  * \code
-  * #include <Eigen/Jacobi>
-  * \endcode
-  *
-  * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
-  *  - MatrixBase::applyOnTheLeft()
-  *  - MatrixBase::applyOnTheRight().
-  */
-
-#include "src/Jacobi/Jacobi.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_JACOBI_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/KLUSupport b/3rdParty/my_ceres_vc17/include/Eigen/KLUSupport
deleted file mode 100644
index b23d905..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/KLUSupport
+++ /dev/null
@@ -1,41 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_KLUSUPPORT_MODULE_H
-#define EIGEN_KLUSUPPORT_MODULE_H
-
-#include <Eigen/SparseCore>
-
-#include <Eigen/src/Core/util/DisableStupidWarnings.h>
-
-extern "C" {
-#include <btf.h>
-#include <klu.h>
-   }
-
-/** \ingroup Support_modules
-  * \defgroup KLUSupport_Module KLUSupport module
-  *
-  * This module provides an interface to the KLU library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  * It provides the following factorization class:
-  * - class KLU: a sparse LU factorization, well-suited for circuit simulation.
-  *
-  * \code
-  * #include <Eigen/KLUSupport>
-  * \endcode
-  *
-  * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must be linked to the klu library and its dependencies.
-  * The dependencies depend on how umfpack has been compiled.
-  * For a cmake based project, you can use our FindKLU.cmake module to help you in this task.
-  *
-  */
-
-#include "src/KLUSupport/KLUSupport.h"
-
-#include <Eigen/src/Core/util/ReenableStupidWarnings.h>
-
-#endif // EIGEN_KLUSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/LU b/3rdParty/my_ceres_vc17/include/Eigen/LU
deleted file mode 100644
index 1236ceb..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/LU
+++ /dev/null
@@ -1,47 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LU_MODULE_H
-#define EIGEN_LU_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup LU_Module LU module
-  * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
-  * This module defines the following MatrixBase methods:
-  *  - MatrixBase::inverse()
-  *  - MatrixBase::determinant()
-  *
-  * \code
-  * #include <Eigen/LU>
-  * \endcode
-  */
-
-#include "src/misc/Kernel.h"
-#include "src/misc/Image.h"
-#include "src/LU/FullPivLU.h"
-#include "src/LU/PartialPivLU.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/LU/PartialPivLU_LAPACKE.h"
-#endif
-#include "src/LU/Determinant.h"
-#include "src/LU/InverseImpl.h"
-
-#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON
-  #include "src/LU/arch/InverseSize4.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_LU_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/MetisSupport b/3rdParty/my_ceres_vc17/include/Eigen/MetisSupport
deleted file mode 100644
index 85c41bf..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/MetisSupport
+++ /dev/null
@@ -1,35 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_METISSUPPORT_MODULE_H
-#define EIGEN_METISSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <metis.h>
-}
-
-
-/** \ingroup Support_modules
-  * \defgroup MetisSupport_Module MetisSupport module
-  *
-  * \code
-  * #include <Eigen/MetisSupport>
-  * \endcode
-  * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis). 
-  * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
-  */
-
-
-#include "src/MetisSupport/MetisSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_METISSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/OrderingMethods b/3rdParty/my_ceres_vc17/include/Eigen/OrderingMethods
deleted file mode 100644
index 29691a6..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/OrderingMethods
+++ /dev/null
@@ -1,70 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
-#define EIGEN_ORDERINGMETHODS_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** 
-  * \defgroup OrderingMethods_Module OrderingMethods module
-  *
-  * This module is currently for internal use only
-  * 
-  * It defines various built-in and external ordering methods for sparse matrices. 
-  * They are typically used to reduce the number of elements during 
-  * the sparse matrix decomposition (LLT, LU, QR).
-  * Precisely, in a preprocessing step, a permutation matrix P is computed using 
-  * those ordering methods and applied to the columns of the matrix. 
-  * Using for instance the sparse Cholesky decomposition, it is expected that 
-  * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
-  * 
-  * 
-  * Usage : 
-  * \code
-  * #include <Eigen/OrderingMethods>
-  * \endcode
-  * 
-  * A simple usage is as a template parameter in the sparse decomposition classes : 
-  * 
-  * \code 
-  * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
-  * \endcode 
-  * 
-  * \code 
-  * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
-  * \endcode
-  * 
-  * It is possible as well to call directly a particular ordering method for your own purpose, 
-  * \code 
-  * AMDOrdering<int> ordering;
-  * PermutationMatrix<Dynamic, Dynamic, int> perm;
-  * SparseMatrix<double> A; 
-  * //Fill the matrix ...
-  * 
-  * ordering(A, perm); // Call AMD
-  * \endcode
-  * 
-  * \note Some of these methods (like AMD or METIS), need the sparsity pattern 
-  * of the input matrix to be symmetric. When the matrix is structurally unsymmetric, 
-  * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
-  * If your matrix is already symmetric (at leat in structure), you can avoid that
-  * by calling the method with a SelfAdjointView type.
-  * 
-  * \code
-  *  // Call the ordering on the pattern of the lower triangular matrix A
-  * ordering(A.selfadjointView<Lower>(), perm);
-  * \endcode
-  */
-
-#include "src/OrderingMethods/Amd.h"
-#include "src/OrderingMethods/Ordering.h"
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_ORDERINGMETHODS_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/PaStiXSupport b/3rdParty/my_ceres_vc17/include/Eigen/PaStiXSupport
deleted file mode 100644
index 234619a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/PaStiXSupport
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
-#define EIGEN_PASTIXSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <pastix_nompi.h>
-#include <pastix.h>
-}
-
-#ifdef complex
-#undef complex
-#endif
-
-/** \ingroup Support_modules
-  * \defgroup PaStiXSupport_Module PaStiXSupport module
-  * 
-  * This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
-  * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
-  * It provides the two following main factorization classes:
-  * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
-  * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
-  * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
-  * 
-  * \code
-  * #include <Eigen/PaStiXSupport>
-  * \endcode
-  *
-  * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be linked to the PaSTiX library and its dependencies.
-  * This wrapper resuires PaStiX version 5.x compiled without MPI support.
-  * The dependencies depend on how PaSTiX has been compiled.
-  * For a cmake based project, you can use our FindPaSTiX.cmake module to help you in this task.
-  *
-  */
-
-#include "src/PaStiXSupport/PaStiXSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_PASTIXSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/PardisoSupport b/3rdParty/my_ceres_vc17/include/Eigen/PardisoSupport
deleted file mode 100644
index 340edf5..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/PardisoSupport
+++ /dev/null
@@ -1,35 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
-#define EIGEN_PARDISOSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include <mkl_pardiso.h>
-
-/** \ingroup Support_modules
-  * \defgroup PardisoSupport_Module PardisoSupport module
-  *
-  * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
-  *
-  * \code
-  * #include <Eigen/PardisoSupport>
-  * \endcode
-  *
-  * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies.
-  * See this \ref TopicUsingIntelMKL "page" for more information on MKL-Eigen integration.
-  * 
-  */
-
-#include "src/PardisoSupport/PardisoSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_PARDISOSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/QR b/3rdParty/my_ceres_vc17/include/Eigen/QR
deleted file mode 100644
index 8465b62..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/QR
+++ /dev/null
@@ -1,50 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QR_MODULE_H
-#define EIGEN_QR_MODULE_H
-
-#include "Core"
-
-#include "Cholesky"
-#include "Jacobi"
-#include "Householder"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup QR_Module QR module
-  *
-  *
-  *
-  * This module provides various QR decompositions
-  * This module also provides some MatrixBase methods, including:
-  *  - MatrixBase::householderQr()
-  *  - MatrixBase::colPivHouseholderQr()
-  *  - MatrixBase::fullPivHouseholderQr()
-  *
-  * \code
-  * #include <Eigen/QR>
-  * \endcode
-  */
-
-#include "src/QR/HouseholderQR.h"
-#include "src/QR/FullPivHouseholderQR.h"
-#include "src/QR/ColPivHouseholderQR.h"
-#include "src/QR/CompleteOrthogonalDecomposition.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/QR/HouseholderQR_LAPACKE.h"
-#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_QR_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/QtAlignedMalloc b/3rdParty/my_ceres_vc17/include/Eigen/QtAlignedMalloc
deleted file mode 100644
index 6fe8237..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/QtAlignedMalloc
+++ /dev/null
@@ -1,39 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QTMALLOC_MODULE_H
-#define EIGEN_QTMALLOC_MODULE_H
-
-#include "Core"
-
-#if (!EIGEN_MALLOC_ALREADY_ALIGNED)
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-void *qMalloc(std::size_t size)
-{
-  return Eigen::internal::aligned_malloc(size);
-}
-
-void qFree(void *ptr)
-{
-  Eigen::internal::aligned_free(ptr);
-}
-
-void *qRealloc(void *ptr, std::size_t size)
-{
-  void* newPtr = Eigen::internal::aligned_malloc(size);
-  std::memcpy(newPtr, ptr, size);
-  Eigen::internal::aligned_free(ptr);
-  return newPtr;
-}
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif
-
-#endif // EIGEN_QTMALLOC_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/SPQRSupport b/3rdParty/my_ceres_vc17/include/Eigen/SPQRSupport
deleted file mode 100644
index f70390c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/SPQRSupport
+++ /dev/null
@@ -1,34 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPQRSUPPORT_MODULE_H
-#define EIGEN_SPQRSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include "SuiteSparseQR.hpp"
-
-/** \ingroup Support_modules
-  * \defgroup SPQRSupport_Module SuiteSparseQR module
-  * 
-  * This module provides an interface to the SPQR library, which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  *
-  * \code
-  * #include <Eigen/SPQRSupport>
-  * \endcode
-  *
-  * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).
-  * For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules
-  *
-  */
-
-#include "src/CholmodSupport/CholmodSupport.h"
-#include "src/SPQRSupport/SuiteSparseQRSupport.h"
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/SVD b/3rdParty/my_ceres_vc17/include/Eigen/SVD
deleted file mode 100644
index 3451794..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/SVD
+++ /dev/null
@@ -1,50 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SVD_MODULE_H
-#define EIGEN_SVD_MODULE_H
-
-#include "QR"
-#include "Householder"
-#include "Jacobi"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup SVD_Module SVD module
-  *
-  *
-  *
-  * This module provides SVD decomposition for matrices (both real and complex).
-  * Two decomposition algorithms are provided:
-  *  - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.
-  *  - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems.
-  * These decompositions are accessible via the respective classes and following MatrixBase methods:
-  *  - MatrixBase::jacobiSvd()
-  *  - MatrixBase::bdcSvd()
-  *
-  * \code
-  * #include <Eigen/SVD>
-  * \endcode
-  */
-
-#include "src/misc/RealSvd2x2.h"
-#include "src/SVD/UpperBidiagonalization.h"
-#include "src/SVD/SVDBase.h"
-#include "src/SVD/JacobiSVD.h"
-#include "src/SVD/BDCSVD.h"
-#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/SVD/JacobiSVD_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SVD_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/Sparse b/3rdParty/my_ceres_vc17/include/Eigen/Sparse
deleted file mode 100644
index a2ef7a6..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/Sparse
+++ /dev/null
@@ -1,34 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_MODULE_H
-#define EIGEN_SPARSE_MODULE_H
-
-/** \defgroup Sparse_Module Sparse meta-module
-  *
-  * Meta-module including all related modules:
-  * - \ref SparseCore_Module
-  * - \ref OrderingMethods_Module
-  * - \ref SparseCholesky_Module
-  * - \ref SparseLU_Module
-  * - \ref SparseQR_Module
-  * - \ref IterativeLinearSolvers_Module
-  *
-    \code
-    #include <Eigen/Sparse>
-    \endcode
-  */
-
-#include "SparseCore"
-#include "OrderingMethods"
-#include "SparseCholesky"
-#include "SparseLU"
-#include "SparseQR"
-#include "IterativeLinearSolvers"
-
-#endif // EIGEN_SPARSE_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/SparseCholesky b/3rdParty/my_ceres_vc17/include/Eigen/SparseCholesky
deleted file mode 100644
index d2b1f12..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/SparseCholesky
+++ /dev/null
@@ -1,37 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
-#define EIGEN_SPARSECHOLESKY_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** 
-  * \defgroup SparseCholesky_Module SparseCholesky module
-  *
-  * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.
-  * Those decompositions are accessible via the following classes:
-  *  - SimplicialLLt,
-  *  - SimplicialLDLt
-  *
-  * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
-  *
-  * \code
-  * #include <Eigen/SparseCholesky>
-  * \endcode
-  */
-
-#include "src/SparseCholesky/SimplicialCholesky.h"
-#include "src/SparseCholesky/SimplicialCholesky_impl.h"
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPARSECHOLESKY_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/SparseCore b/3rdParty/my_ceres_vc17/include/Eigen/SparseCore
deleted file mode 100644
index 76966c4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/SparseCore
+++ /dev/null
@@ -1,69 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSECORE_MODULE_H
-#define EIGEN_SPARSECORE_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include <vector>
-#include <map>
-#include <cstdlib>
-#include <cstring>
-#include <algorithm>
-
-/** 
-  * \defgroup SparseCore_Module SparseCore module
-  *
-  * This module provides a sparse matrix representation, and basic associated matrix manipulations
-  * and operations.
-  *
-  * See the \ref TutorialSparse "Sparse tutorial"
-  *
-  * \code
-  * #include <Eigen/SparseCore>
-  * \endcode
-  *
-  * This module depends on: Core.
-  */
-
-#include "src/SparseCore/SparseUtil.h"
-#include "src/SparseCore/SparseMatrixBase.h"
-#include "src/SparseCore/SparseAssign.h"
-#include "src/SparseCore/CompressedStorage.h"
-#include "src/SparseCore/AmbiVector.h"
-#include "src/SparseCore/SparseCompressedBase.h"
-#include "src/SparseCore/SparseMatrix.h"
-#include "src/SparseCore/SparseMap.h"
-#include "src/SparseCore/MappedSparseMatrix.h"
-#include "src/SparseCore/SparseVector.h"
-#include "src/SparseCore/SparseRef.h"
-#include "src/SparseCore/SparseCwiseUnaryOp.h"
-#include "src/SparseCore/SparseCwiseBinaryOp.h"
-#include "src/SparseCore/SparseTranspose.h"
-#include "src/SparseCore/SparseBlock.h"
-#include "src/SparseCore/SparseDot.h"
-#include "src/SparseCore/SparseRedux.h"
-#include "src/SparseCore/SparseView.h"
-#include "src/SparseCore/SparseDiagonalProduct.h"
-#include "src/SparseCore/ConservativeSparseSparseProduct.h"
-#include "src/SparseCore/SparseSparseProductWithPruning.h"
-#include "src/SparseCore/SparseProduct.h"
-#include "src/SparseCore/SparseDenseProduct.h"
-#include "src/SparseCore/SparseSelfAdjointView.h"
-#include "src/SparseCore/SparseTriangularView.h"
-#include "src/SparseCore/TriangularSolver.h"
-#include "src/SparseCore/SparsePermutation.h"
-#include "src/SparseCore/SparseFuzzy.h"
-#include "src/SparseCore/SparseSolverBase.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPARSECORE_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/SparseLU b/3rdParty/my_ceres_vc17/include/Eigen/SparseLU
deleted file mode 100644
index 37c4a5c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/SparseLU
+++ /dev/null
@@ -1,50 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSELU_MODULE_H
-#define EIGEN_SPARSELU_MODULE_H
-
-#include "SparseCore"
-
-/** 
-  * \defgroup SparseLU_Module SparseLU module
-  * This module defines a supernodal factorization of general sparse matrices.
-  * The code is fully optimized for supernode-panel updates with specialized kernels.
-  * Please, see the documentation of the SparseLU class for more details.
-  */
-
-// Ordering interface
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include "src/SparseLU/SparseLU_gemm_kernel.h"
-
-#include "src/SparseLU/SparseLU_Structs.h"
-#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
-#include "src/SparseLU/SparseLUImpl.h"
-#include "src/SparseCore/SparseColEtree.h"
-#include "src/SparseLU/SparseLU_Memory.h"
-#include "src/SparseLU/SparseLU_heap_relax_snode.h"
-#include "src/SparseLU/SparseLU_relax_snode.h"
-#include "src/SparseLU/SparseLU_pivotL.h"
-#include "src/SparseLU/SparseLU_panel_dfs.h"
-#include "src/SparseLU/SparseLU_kernel_bmod.h"
-#include "src/SparseLU/SparseLU_panel_bmod.h"
-#include "src/SparseLU/SparseLU_column_dfs.h"
-#include "src/SparseLU/SparseLU_column_bmod.h"
-#include "src/SparseLU/SparseLU_copy_to_ucol.h"
-#include "src/SparseLU/SparseLU_pruneL.h"
-#include "src/SparseLU/SparseLU_Utils.h"
-#include "src/SparseLU/SparseLU.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPARSELU_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/SparseQR b/3rdParty/my_ceres_vc17/include/Eigen/SparseQR
deleted file mode 100644
index f5fc5fa..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/SparseQR
+++ /dev/null
@@ -1,36 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEQR_MODULE_H
-#define EIGEN_SPARSEQR_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup SparseQR_Module SparseQR module
-  * \brief Provides QR decomposition for sparse matrices
-  * 
-  * This module provides a simplicial version of the left-looking Sparse QR decomposition. 
-  * The columns of the input matrix should be reordered to limit the fill-in during the 
-  * decomposition. Built-in methods (COLAMD, AMD) or external  methods (METIS) can be used to this end.
-  * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list 
-  * of built-in and external ordering methods.
-  * 
-  * \code
-  * #include <Eigen/SparseQR>
-  * \endcode
-  * 
-  * 
-  */
-
-#include "src/SparseCore/SparseColEtree.h"
-#include "src/SparseQR/SparseQR.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/StdDeque b/3rdParty/my_ceres_vc17/include/Eigen/StdDeque
deleted file mode 100644
index bc68397..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/StdDeque
+++ /dev/null
@@ -1,27 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDDEQUE_MODULE_H
-#define EIGEN_STDDEQUE_MODULE_H
-
-#include "Core"
-#include <deque>
-
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
-
-#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
-
-#else
-
-#include "src/StlSupport/StdDeque.h"
-
-#endif
-
-#endif // EIGEN_STDDEQUE_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/StdList b/3rdParty/my_ceres_vc17/include/Eigen/StdList
deleted file mode 100644
index 4c6262c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/StdList
+++ /dev/null
@@ -1,26 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDLIST_MODULE_H
-#define EIGEN_STDLIST_MODULE_H
-
-#include "Core"
-#include <list>
-
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
-
-#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
-
-#else
-
-#include "src/StlSupport/StdList.h"
-
-#endif
-
-#endif // EIGEN_STDLIST_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/StdVector b/3rdParty/my_ceres_vc17/include/Eigen/StdVector
deleted file mode 100644
index 0c4697a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/StdVector
+++ /dev/null
@@ -1,27 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDVECTOR_MODULE_H
-#define EIGEN_STDVECTOR_MODULE_H
-
-#include "Core"
-#include <vector>
-
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
-
-#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
-
-#else
-
-#include "src/StlSupport/StdVector.h"
-
-#endif
-
-#endif // EIGEN_STDVECTOR_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/SuperLUSupport b/3rdParty/my_ceres_vc17/include/Eigen/SuperLUSupport
deleted file mode 100644
index 59312a8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/SuperLUSupport
+++ /dev/null
@@ -1,64 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
-#define EIGEN_SUPERLUSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#ifdef EMPTY
-#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
-#endif
-
-typedef int int_t;
-#include <slu_Cnames.h>
-#include <supermatrix.h>
-#include <slu_util.h>
-
-// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
-// so we remove it in favor of a SUPERLU_EMPTY token.
-// If EMPTY was already defined then we don't undef it.
-
-#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
-# undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
-#elif defined(EMPTY)
-# undef EMPTY
-#endif
-
-#define SUPERLU_EMPTY (-1)
-
-namespace Eigen { struct SluMatrix; }
-
-/** \ingroup Support_modules
-  * \defgroup SuperLUSupport_Module SuperLUSupport module
-  *
-  * This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
-  * It provides the following factorization class:
-  * - class SuperLU: a supernodal sequential LU factorization.
-  * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods).
-  *
-  * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
-  *
-  * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
-  *
-  * \code
-  * #include <Eigen/SuperLUSupport>
-  * \endcode
-  *
-  * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be linked to the superlu library and its dependencies.
-  * The dependencies depend on how superlu has been compiled.
-  * For a cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
-  *
-  */
-
-#include "src/SuperLUSupport/SuperLUSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SUPERLUSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/UmfPackSupport b/3rdParty/my_ceres_vc17/include/Eigen/UmfPackSupport
deleted file mode 100644
index 00eec80..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/UmfPackSupport
+++ /dev/null
@@ -1,40 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
-#define EIGEN_UMFPACKSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <umfpack.h>
-}
-
-/** \ingroup Support_modules
-  * \defgroup UmfPackSupport_Module UmfPackSupport module
-  *
-  * This module provides an interface to the UmfPack library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  * It provides the following factorization class:
-  * - class UmfPackLU: a multifrontal sequential LU factorization.
-  *
-  * \code
-  * #include <Eigen/UmfPackSupport>
-  * \endcode
-  *
-  * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be linked to the umfpack library and its dependencies.
-  * The dependencies depend on how umfpack has been compiled.
-  * For a cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
-  *
-  */
-
-#include "src/UmfPackSupport/UmfPackSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_UMFPACKSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LDLT.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LDLT.h
deleted file mode 100644
index 1013ca0..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LDLT.h
+++ /dev/null
@@ -1,688 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LDLT_H
-#define EIGEN_LDLT_H
-
-namespace Eigen {
-
-namespace internal {
-  template<typename _MatrixType, int _UpLo> struct traits<LDLT<_MatrixType, _UpLo> >
-   : traits<_MatrixType>
-  {
-    typedef MatrixXpr XprKind;
-    typedef SolverStorage StorageKind;
-    typedef int StorageIndex;
-    enum { Flags = 0 };
-  };
-
-  template<typename MatrixType, int UpLo> struct LDLT_Traits;
-
-  // PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
-  enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
-}
-
-/** \ingroup Cholesky_Module
-  *
-  * \class LDLT
-  *
-  * \brief Robust Cholesky decomposition of a matrix with pivoting
-  *
-  * \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
-  * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
-  *             The other triangular part won't be read.
-  *
-  * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
-  * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
-  * is lower triangular with a unit diagonal and D is a diagonal matrix.
-  *
-  * The decomposition uses pivoting to ensure stability, so that D will have
-  * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
-  * on D also stabilizes the computation.
-  *
-  * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
-  * decomposition to determine whether a system of equations has a solution.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
-  */
-template<typename _MatrixType, int _UpLo> class LDLT
-        : public SolverBase<LDLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<LDLT> Base;
-    friend class SolverBase<LDLT>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-      UpLo = _UpLo
-    };
-    typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
-
-    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
-    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
-
-    typedef internal::LDLT_Traits<MatrixType,UpLo> Traits;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via LDLT::compute(const MatrixType&).
-      */
-    LDLT()
-      : m_matrix(),
-        m_transpositions(),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa LDLT()
-      */
-    explicit LDLT(Index size)
-      : m_matrix(size, size),
-        m_transpositions(size),
-        m_temporary(size),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {}
-
-    /** \brief Constructor with decomposition
-      *
-      * This calculates the decomposition for the input \a matrix.
-      *
-      * \sa LDLT(Index size)
-      */
-    template<typename InputType>
-    explicit LDLT(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.rows(), matrix.cols()),
-        m_transpositions(matrix.rows()),
-        m_temporary(matrix.rows()),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a LDLT factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa LDLT(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit LDLT(EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_transpositions(matrix.rows()),
-        m_temporary(matrix.rows()),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** Clear any existing decomposition
-     * \sa rankUpdate(w,sigma)
-     */
-    void setZero()
-    {
-      m_isInitialized = false;
-    }
-
-    /** \returns a view of the upper triangular matrix U */
-    inline typename Traits::MatrixU matrixU() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return Traits::getU(m_matrix);
-    }
-
-    /** \returns a view of the lower triangular matrix L */
-    inline typename Traits::MatrixL matrixL() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return Traits::getL(m_matrix);
-    }
-
-    /** \returns the permutation matrix P as a transposition sequence.
-      */
-    inline const TranspositionType& transpositionsP() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_transpositions;
-    }
-
-    /** \returns the coefficients of the diagonal matrix D */
-    inline Diagonal<const MatrixType> vectorD() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_matrix.diagonal();
-    }
-
-    /** \returns true if the matrix is positive (semidefinite) */
-    inline bool isPositive() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
-    }
-
-    /** \returns true if the matrix is negative (semidefinite) */
-    inline bool isNegative(void) const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
-      *
-      * \note_about_checking_solutions
-      *
-      * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
-      * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
-      * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
-      * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
-      * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
-      * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular.
-      *
-      * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
-      */
-    template<typename Rhs>
-    inline const Solve<LDLT, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    template<typename Derived>
-    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
-
-    template<typename InputType>
-    LDLT& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of
-     *  which \c *this is the LDLT decomposition.
-     */
-    RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    template <typename Derived>
-    LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
-
-    /** \returns the internal LDLT decomposition matrix
-      *
-      * TODO: document the storage layout
-      */
-    inline const MatrixType& matrixLDLT() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_matrix;
-    }
-
-    MatrixType reconstructedMatrix() const;
-
-    /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
-      *
-      * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
-      * \code x = decomposition.adjoint().solve(b) \endcode
-      */
-    const LDLT& adjoint() const { return *this; };
-
-    EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the factorization failed because of a zero pivot.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_info;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    /** \internal
-      * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
-      * The strict upper part is used during the decomposition, the strict lower
-      * part correspond to the coefficients of L (its diagonal is equal to 1 and
-      * is not stored), and the diagonal entries correspond to D.
-      */
-    MatrixType m_matrix;
-    RealScalar m_l1_norm;
-    TranspositionType m_transpositions;
-    TmpMatrixType m_temporary;
-    internal::SignMatrix m_sign;
-    bool m_isInitialized;
-    ComputationInfo m_info;
-};
-
-namespace internal {
-
-template<int UpLo> struct ldlt_inplace;
-
-template<> struct ldlt_inplace<Lower>
-{
-  template<typename MatrixType, typename TranspositionType, typename Workspace>
-  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
-  {
-    using std::abs;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename TranspositionType::StorageIndex IndexType;
-    eigen_assert(mat.rows()==mat.cols());
-    const Index size = mat.rows();
-    bool found_zero_pivot = false;
-    bool ret = true;
-
-    if (size <= 1)
-    {
-      transpositions.setIdentity();
-      if(size==0) sign = ZeroSign;
-      else if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
-      else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
-      else sign = ZeroSign;
-      return true;
-    }
-
-    for (Index k = 0; k < size; ++k)
-    {
-      // Find largest diagonal element
-      Index index_of_biggest_in_corner;
-      mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
-      index_of_biggest_in_corner += k;
-
-      transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
-      if(k != index_of_biggest_in_corner)
-      {
-        // apply the transposition while taking care to consider only
-        // the lower triangular part
-        Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element
-        mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
-        mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
-        std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
-        for(Index i=k+1;i<index_of_biggest_in_corner;++i)
-        {
-          Scalar tmp = mat.coeffRef(i,k);
-          mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
-          mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
-        }
-        if(NumTraits<Scalar>::IsComplex)
-          mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
-      }
-
-      // partition the matrix:
-      //       A00 |  -  |  -
-      // lu  = A10 | A11 |  -
-      //       A20 | A21 | A22
-      Index rs = size - k - 1;
-      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
-      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
-      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
-
-      if(k>0)
-      {
-        temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
-        mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
-        if(rs>0)
-          A21.noalias() -= A20 * temp.head(k);
-      }
-
-      // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
-      // was smaller than the cutoff value. However, since LDLT is not rank-revealing
-      // we should only make sure that we do not introduce INF or NaN values.
-      // Remark that LAPACK also uses 0 as the cutoff value.
-      RealScalar realAkk = numext::real(mat.coeffRef(k,k));
-      bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
-
-      if(k==0 && !pivot_is_valid)
-      {
-        // The entire diagonal is zero, there is nothing more to do
-        // except filling the transpositions, and checking whether the matrix is zero.
-        sign = ZeroSign;
-        for(Index j = 0; j<size; ++j)
-        {
-          transpositions.coeffRef(j) = IndexType(j);
-          ret = ret && (mat.col(j).tail(size-j-1).array()==Scalar(0)).all();
-        }
-        return ret;
-      }
-
-      if((rs>0) && pivot_is_valid)
-        A21 /= realAkk;
-      else if(rs>0)
-        ret = ret && (A21.array()==Scalar(0)).all();
-
-      if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
-      else if(!pivot_is_valid) found_zero_pivot = true;
-
-      if (sign == PositiveSemiDef) {
-        if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
-      } else if (sign == NegativeSemiDef) {
-        if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
-      } else if (sign == ZeroSign) {
-        if (realAkk > static_cast<RealScalar>(0)) sign = PositiveSemiDef;
-        else if (realAkk < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
-      }
-    }
-
-    return ret;
-  }
-
-  // Reference for the algorithm: Davis and Hager, "Multiple Rank
-  // Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
-  // Trivial rearrangements of their computations (Timothy E. Holy)
-  // allow their algorithm to work for rank-1 updates even if the
-  // original matrix is not of full rank.
-  // Here only rank-1 updates are implemented, to reduce the
-  // requirement for intermediate storage and improve accuracy
-  template<typename MatrixType, typename WDerived>
-  static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
-  {
-    using numext::isfinite;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-
-    const Index size = mat.rows();
-    eigen_assert(mat.cols() == size && w.size()==size);
-
-    RealScalar alpha = 1;
-
-    // Apply the update
-    for (Index j = 0; j < size; j++)
-    {
-      // Check for termination due to an original decomposition of low-rank
-      if (!(isfinite)(alpha))
-        break;
-
-      // Update the diagonal terms
-      RealScalar dj = numext::real(mat.coeff(j,j));
-      Scalar wj = w.coeff(j);
-      RealScalar swj2 = sigma*numext::abs2(wj);
-      RealScalar gamma = dj*alpha + swj2;
-
-      mat.coeffRef(j,j) += swj2/alpha;
-      alpha += swj2/dj;
-
-
-      // Update the terms of L
-      Index rs = size-j-1;
-      w.tail(rs) -= wj * mat.col(j).tail(rs);
-      if(gamma != 0)
-        mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
-    }
-    return true;
-  }
-
-  template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
-  static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
-  {
-    // Apply the permutation to the input w
-    tmp = transpositions * w;
-
-    return ldlt_inplace<Lower>::updateInPlace(mat,tmp,sigma);
-  }
-};
-
-template<> struct ldlt_inplace<Upper>
-{
-  template<typename MatrixType, typename TranspositionType, typename Workspace>
-  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
-  {
-    Transpose<MatrixType> matt(mat);
-    return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
-  }
-
-  template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
-  static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
-  {
-    Transpose<MatrixType> matt(mat);
-    return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
-  }
-};
-
-template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
-{
-  typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
-};
-
-template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
-{
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
-  typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
-};
-
-} // end namespace internal
-
-/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
-  */
-template<typename MatrixType, int _UpLo>
-template<typename InputType>
-LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
-{
-  check_template_parameters();
-
-  eigen_assert(a.rows()==a.cols());
-  const Index size = a.rows();
-
-  m_matrix = a.derived();
-
-  // Compute matrix L1 norm = max abs column sum.
-  m_l1_norm = RealScalar(0);
-  // TODO move this code to SelfAdjointView
-  for (Index col = 0; col < size; ++col) {
-    RealScalar abs_col_sum;
-    if (_UpLo == Lower)
-      abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
-    else
-      abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
-    if (abs_col_sum > m_l1_norm)
-      m_l1_norm = abs_col_sum;
-  }
-
-  m_transpositions.resize(size);
-  m_isInitialized = false;
-  m_temporary.resize(size);
-  m_sign = internal::ZeroSign;
-
-  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;
-
-  m_isInitialized = true;
-  return *this;
-}
-
-/** Update the LDLT decomposition:  given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
- * \param w a vector to be incorporated into the decomposition.
- * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1.
- * \sa setZero()
-  */
-template<typename MatrixType, int _UpLo>
-template<typename Derived>
-LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,_UpLo>::RealScalar& sigma)
-{
-  typedef typename TranspositionType::StorageIndex IndexType;
-  const Index size = w.rows();
-  if (m_isInitialized)
-  {
-    eigen_assert(m_matrix.rows()==size);
-  }
-  else
-  {
-    m_matrix.resize(size,size);
-    m_matrix.setZero();
-    m_transpositions.resize(size);
-    for (Index i = 0; i < size; i++)
-      m_transpositions.coeffRef(i) = IndexType(i);
-    m_temporary.resize(size);
-    m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
-    m_isInitialized = true;
-  }
-
-  internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
-
-  return *this;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType, int _UpLo>
-template<typename RhsType, typename DstType>
-void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  _solve_impl_transposed<true>(rhs, dst);
-}
-
-template<typename _MatrixType,int _UpLo>
-template<bool Conjugate, typename RhsType, typename DstType>
-void LDLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  // dst = P b
-  dst = m_transpositions * rhs;
-
-  // dst = L^-1 (P b)
-  // dst = L^-*T (P b)
-  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
-
-  // dst = D^-* (L^-1 P b)
-  // dst = D^-1 (L^-*T P b)
-  // more precisely, use pseudo-inverse of D (see bug 241)
-  using std::abs;
-  const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
-  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
-  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
-  // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
-  // However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
-  // diagonal element is not well justified and leads to numerical issues in some cases.
-  // Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
-  // Using numeric_limits::min() gives us more robustness to denormals.
-  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
-  for (Index i = 0; i < vecD.size(); ++i)
-  {
-    if(abs(vecD(i)) > tolerance)
-      dst.row(i) /= vecD(i);
-    else
-      dst.row(i).setZero();
-  }
-
-  // dst = L^-* (D^-* L^-1 P b)
-  // dst = L^-T (D^-1 L^-*T P b)
-  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
-
-  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
-  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
-  dst = m_transpositions.transpose() * dst;
-}
-#endif
-
-/** \internal use x = ldlt_object.solve(x);
-  *
-  * This is the \em in-place version of solve().
-  *
-  * \param bAndX represents both the right-hand side matrix b and result x.
-  *
-  * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
-  *
-  * This version avoids a copy when the right hand side matrix b is not
-  * needed anymore.
-  *
-  * \sa LDLT::solve(), MatrixBase::ldlt()
-  */
-template<typename MatrixType,int _UpLo>
-template<typename Derived>
-bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
-{
-  eigen_assert(m_isInitialized && "LDLT is not initialized.");
-  eigen_assert(m_matrix.rows() == bAndX.rows());
-
-  bAndX = this->solve(bAndX);
-
-  return true;
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: P^T L D L^* P.
- * This function is provided for debug purpose. */
-template<typename MatrixType, int _UpLo>
-MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LDLT is not initialized.");
-  const Index size = m_matrix.rows();
-  MatrixType res(size,size);
-
-  // P
-  res.setIdentity();
-  res = transpositionsP() * res;
-  // L^* P
-  res = matrixU() * res;
-  // D(L^*P)
-  res = vectorD().real().asDiagonal() * res;
-  // L(DL^*P)
-  res = matrixL() * res;
-  // P^T (LDL^*P)
-  res = transpositionsP().transpose() * res;
-
-  return res;
-}
-
-/** \cholesky_module
-  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
-  * \sa MatrixBase::ldlt()
-  */
-template<typename MatrixType, unsigned int UpLo>
-inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
-SelfAdjointView<MatrixType, UpLo>::ldlt() const
-{
-  return LDLT<PlainObject,UpLo>(m_matrix);
-}
-
-/** \cholesky_module
-  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
-  * \sa SelfAdjointView::ldlt()
-  */
-template<typename Derived>
-inline const LDLT<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::ldlt() const
-{
-  return LDLT<PlainObject>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LDLT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LLT.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LLT.h
deleted file mode 100644
index 8c9b2b3..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LLT.h
+++ /dev/null
@@ -1,558 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LLT_H
-#define EIGEN_LLT_H
-
-namespace Eigen {
-
-namespace internal{
-
-template<typename _MatrixType, int _UpLo> struct traits<LLT<_MatrixType, _UpLo> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-template<typename MatrixType, int UpLo> struct LLT_Traits;
-}
-
-/** \ingroup Cholesky_Module
-  *
-  * \class LLT
-  *
-  * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
-  * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
-  *               The other triangular part won't be read.
-  *
-  * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
-  * matrix A such that A = LL^* = U^*U, where L is lower triangular.
-  *
-  * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
-  * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
-  * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
-  * situations like generalised eigen problems with hermitian matrices.
-  *
-  * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices,
-  * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
-  * has a solution.
-  *
-  * Example: \include LLT_example.cpp
-  * Output: \verbinclude LLT_example.out
-  *
-  * \b Performance: for best performance, it is recommended to use a column-major storage format
-  * with the Lower triangular part (the default), or, equivalently, a row-major storage format
-  * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
-  * step, and rank-updates can be up to 3 times slower.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
-  * Therefore, the strict lower part does not have to store correct values.
-  *
-  * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
-  */
-template<typename _MatrixType, int _UpLo> class LLT
-        : public SolverBase<LLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<LLT> Base;
-    friend class SolverBase<LLT>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
-    enum {
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    enum {
-      PacketSize = internal::packet_traits<Scalar>::size,
-      AlignmentMask = int(PacketSize)-1,
-      UpLo = _UpLo
-    };
-
-    typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via LLT::compute(const MatrixType&).
-      */
-    LLT() : m_matrix(), m_isInitialized(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa LLT()
-      */
-    explicit LLT(Index size) : m_matrix(size, size),
-                    m_isInitialized(false) {}
-
-    template<typename InputType>
-    explicit LLT(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.rows(), matrix.cols()),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a LLT factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
-      * \c MatrixType is a Eigen::Ref.
-      *
-      * \sa LLT(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit LLT(EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \returns a view of the upper triangular matrix U */
-    inline typename Traits::MatrixU matrixU() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return Traits::getU(m_matrix);
-    }
-
-    /** \returns a view of the lower triangular matrix L */
-    inline typename Traits::MatrixL matrixL() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return Traits::getL(m_matrix);
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * Since this LLT class assumes anyway that the matrix A is invertible, the solution
-      * theoretically exists and is unique regardless of b.
-      *
-      * Example: \include LLT_solve.cpp
-      * Output: \verbinclude LLT_solve.out
-      *
-      * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
-      */
-    template<typename Rhs>
-    inline const Solve<LLT, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    template<typename Derived>
-    void solveInPlace(const MatrixBase<Derived> &bAndX) const;
-
-    template<typename InputType>
-    LLT& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of
-      *  which \c *this is the Cholesky decomposition.
-      */
-    RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    /** \returns the LLT decomposition matrix
-      *
-      * TODO: document the storage layout
-      */
-    inline const MatrixType& matrixLLT() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return m_matrix;
-    }
-
-    MatrixType reconstructedMatrix() const;
-
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears not to be positive definite.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return m_info;
-    }
-
-    /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
-      *
-      * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
-      * \code x = decomposition.adjoint().solve(b) \endcode
-      */
-    const LLT& adjoint() const EIGEN_NOEXCEPT { return *this; };
-
-    inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    template<typename VectorType>
-    LLT & rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    /** \internal
-      * Used to compute and store L
-      * The strict upper part is not used and even not initialized.
-      */
-    MatrixType m_matrix;
-    RealScalar m_l1_norm;
-    bool m_isInitialized;
-    ComputationInfo m_info;
-};
-
-namespace internal {
-
-template<typename Scalar, int UpLo> struct llt_inplace;
-
-template<typename MatrixType, typename VectorType>
-static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
-{
-  using std::sqrt;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::ColXpr ColXpr;
-  typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
-  typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
-  typedef Matrix<Scalar,Dynamic,1> TempVectorType;
-  typedef typename TempVectorType::SegmentReturnType TempVecSegment;
-
-  Index n = mat.cols();
-  eigen_assert(mat.rows()==n && vec.size()==n);
-
-  TempVectorType temp;
-
-  if(sigma>0)
-  {
-    // This version is based on Givens rotations.
-    // It is faster than the other one below, but only works for updates,
-    // i.e., for sigma > 0
-    temp = sqrt(sigma) * vec;
-
-    for(Index i=0; i<n; ++i)
-    {
-      JacobiRotation<Scalar> g;
-      g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
-
-      Index rs = n-i-1;
-      if(rs>0)
-      {
-        ColXprSegment x(mat.col(i).tail(rs));
-        TempVecSegment y(temp.tail(rs));
-        apply_rotation_in_the_plane(x, y, g);
-      }
-    }
-  }
-  else
-  {
-    temp = vec;
-    RealScalar beta = 1;
-    for(Index j=0; j<n; ++j)
-    {
-      RealScalar Ljj = numext::real(mat.coeff(j,j));
-      RealScalar dj = numext::abs2(Ljj);
-      Scalar wj = temp.coeff(j);
-      RealScalar swj2 = sigma*numext::abs2(wj);
-      RealScalar gamma = dj*beta + swj2;
-
-      RealScalar x = dj + swj2/beta;
-      if (x<=RealScalar(0))
-        return j;
-      RealScalar nLjj = sqrt(x);
-      mat.coeffRef(j,j) = nLjj;
-      beta += swj2/dj;
-
-      // Update the terms of L
-      Index rs = n-j-1;
-      if(rs)
-      {
-        temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
-        if(gamma != 0)
-          mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
-      }
-    }
-  }
-  return -1;
-}
-
-template<typename Scalar> struct llt_inplace<Scalar, Lower>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename MatrixType>
-  static Index unblocked(MatrixType& mat)
-  {
-    using std::sqrt;
-
-    eigen_assert(mat.rows()==mat.cols());
-    const Index size = mat.rows();
-    for(Index k = 0; k < size; ++k)
-    {
-      Index rs = size-k-1; // remaining size
-
-      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
-      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
-      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
-
-      RealScalar x = numext::real(mat.coeff(k,k));
-      if (k>0) x -= A10.squaredNorm();
-      if (x<=RealScalar(0))
-        return k;
-      mat.coeffRef(k,k) = x = sqrt(x);
-      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
-      if (rs>0) A21 /= x;
-    }
-    return -1;
-  }
-
-  template<typename MatrixType>
-  static Index blocked(MatrixType& m)
-  {
-    eigen_assert(m.rows()==m.cols());
-    Index size = m.rows();
-    if(size<32)
-      return unblocked(m);
-
-    Index blockSize = size/8;
-    blockSize = (blockSize/16)*16;
-    blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
-
-    for (Index k=0; k<size; k+=blockSize)
-    {
-      // partition the matrix:
-      //       A00 |  -  |  -
-      // lu  = A10 | A11 |  -
-      //       A20 | A21 | A22
-      Index bs = (std::min)(blockSize, size-k);
-      Index rs = size - k - bs;
-      Block<MatrixType,Dynamic,Dynamic> A11(m,k,   k,   bs,bs);
-      Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k,   rs,bs);
-      Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs);
-
-      Index ret;
-      if((ret=unblocked(A11))>=0) return k+ret;
-      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
-      if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck
-    }
-    return -1;
-  }
-
-  template<typename MatrixType, typename VectorType>
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
-  {
-    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
-  }
-};
-
-template<typename Scalar> struct llt_inplace<Scalar, Upper>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  template<typename MatrixType>
-  static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat)
-  {
-    Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::unblocked(matt);
-  }
-  template<typename MatrixType>
-  static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat)
-  {
-    Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::blocked(matt);
-  }
-  template<typename MatrixType, typename VectorType>
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
-  {
-    Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
-  }
-};
-
-template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
-{
-  typedef const TriangularView<const MatrixType, Lower> MatrixL;
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
-  static bool inplace_decomposition(MatrixType& m)
-  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }
-};
-
-template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
-{
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
-  typedef const TriangularView<const MatrixType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
-  static bool inplace_decomposition(MatrixType& m)
-  { return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; }
-};
-
-} // end namespace internal
-
-/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
-  *
-  * \returns a reference to *this
-  *
-  * Example: \include TutorialLinAlgComputeTwice.cpp
-  * Output: \verbinclude TutorialLinAlgComputeTwice.out
-  */
-template<typename MatrixType, int _UpLo>
-template<typename InputType>
-LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
-{
-  check_template_parameters();
-
-  eigen_assert(a.rows()==a.cols());
-  const Index size = a.rows();
-  m_matrix.resize(size, size);
-  if (!internal::is_same_dense(m_matrix, a.derived()))
-    m_matrix = a.derived();
-
-  // Compute matrix L1 norm = max abs column sum.
-  m_l1_norm = RealScalar(0);
-  // TODO move this code to SelfAdjointView
-  for (Index col = 0; col < size; ++col) {
-    RealScalar abs_col_sum;
-    if (_UpLo == Lower)
-      abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
-    else
-      abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
-    if (abs_col_sum > m_l1_norm)
-      m_l1_norm = abs_col_sum;
-  }
-
-  m_isInitialized = true;
-  bool ok = Traits::inplace_decomposition(m_matrix);
-  m_info = ok ? Success : NumericalIssue;
-
-  return *this;
-}
-
-/** Performs a rank one update (or dowdate) of the current decomposition.
-  * If A = LL^* before the rank one update,
-  * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
-  * of same dimension.
-  */
-template<typename _MatrixType, int _UpLo>
-template<typename VectorType>
-LLT<_MatrixType,_UpLo> & LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
-  eigen_assert(v.size()==m_matrix.cols());
-  eigen_assert(m_isInitialized);
-  if(internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix,v,sigma)>=0)
-    m_info = NumericalIssue;
-  else
-    m_info = Success;
-
-  return *this;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType,int _UpLo>
-template<typename RhsType, typename DstType>
-void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  _solve_impl_transposed<true>(rhs, dst);
-}
-
-template<typename _MatrixType,int _UpLo>
-template<bool Conjugate, typename RhsType, typename DstType>
-void LLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-    dst = rhs;
-
-    matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
-    matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
-}
-#endif
-
-/** \internal use x = llt_object.solve(x);
-  *
-  * This is the \em in-place version of solve().
-  *
-  * \param bAndX represents both the right-hand side matrix b and result x.
-  *
-  * This version avoids a copy when the right hand side matrix b is not needed anymore.
-  *
-  * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
-  * This function will const_cast it, so constness isn't honored here.
-  *
-  * \sa LLT::solve(), MatrixBase::llt()
-  */
-template<typename MatrixType, int _UpLo>
-template<typename Derived>
-void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
-{
-  eigen_assert(m_isInitialized && "LLT is not initialized.");
-  eigen_assert(m_matrix.rows()==bAndX.rows());
-  matrixL().solveInPlace(bAndX);
-  matrixU().solveInPlace(bAndX);
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: L L^*.
- * This function is provided for debug purpose. */
-template<typename MatrixType, int _UpLo>
-MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LLT is not initialized.");
-  return matrixL() * matrixL().adjoint().toDenseMatrix();
-}
-
-/** \cholesky_module
-  * \returns the LLT decomposition of \c *this
-  * \sa SelfAdjointView::llt()
-  */
-template<typename Derived>
-inline const LLT<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::llt() const
-{
-  return LLT<PlainObject>(derived());
-}
-
-/** \cholesky_module
-  * \returns the LLT decomposition of \c *this
-  * \sa SelfAdjointView::llt()
-  */
-template<typename MatrixType, unsigned int UpLo>
-inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
-SelfAdjointView<MatrixType, UpLo>::llt() const
-{
-  return LLT<PlainObject,UpLo>(m_matrix);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LLT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LLT_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LLT_LAPACKE.h
deleted file mode 100644
index bc6489e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Cholesky/LLT_LAPACKE.h
+++ /dev/null
@@ -1,99 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *     LLt decomposition based on LAPACKE_?potrf function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_LLT_LAPACKE_H
-#define EIGEN_LLT_LAPACKE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Scalar> struct lapacke_llt;
-
-#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \
-template<> struct lapacke_llt<EIGTYPE> \
-{ \
-  template<typename MatrixType> \
-  static inline Index potrf(MatrixType& m, char uplo) \
-  { \
-    lapack_int matrix_order; \
-    lapack_int size, lda, info, StorageOrder; \
-    EIGTYPE* a; \
-    eigen_assert(m.rows()==m.cols()); \
-    /* Set up parameters for ?potrf */ \
-    size = convert_index<lapack_int>(m.rows()); \
-    StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
-    matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
-    a = &(m.coeffRef(0,0)); \
-    lda = convert_index<lapack_int>(m.outerStride()); \
-\
-    info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
-    info = (info==0) ? -1 : info>0 ? info-1 : size; \
-    return info; \
-  } \
-}; \
-template<> struct llt_inplace<EIGTYPE, Lower> \
-{ \
-  template<typename MatrixType> \
-  static Index blocked(MatrixType& m) \
-  { \
-    return lapacke_llt<EIGTYPE>::potrf(m, 'L'); \
-  } \
-  template<typename MatrixType, typename VectorType> \
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
-  { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \
-}; \
-template<> struct llt_inplace<EIGTYPE, Upper> \
-{ \
-  template<typename MatrixType> \
-  static Index blocked(MatrixType& m) \
-  { \
-    return lapacke_llt<EIGTYPE>::potrf(m, 'U'); \
-  } \
-  template<typename MatrixType, typename VectorType> \
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
-  { \
-    Transpose<MatrixType> matt(mat); \
-    return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
-  } \
-};
-
-EIGEN_LAPACKE_LLT(double, double, d)
-EIGEN_LAPACKE_LLT(float, float, s)
-EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z)
-EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_LLT_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/CholmodSupport/CholmodSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/CholmodSupport/CholmodSupport.h
deleted file mode 100644
index adaf528..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/CholmodSupport/CholmodSupport.h
+++ /dev/null
@@ -1,682 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLMODSUPPORT_H
-#define EIGEN_CHOLMODSUPPORT_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar> struct cholmod_configure_matrix;
-
-template<> struct cholmod_configure_matrix<double> {
-  template<typename CholmodType>
-  static void run(CholmodType& mat) {
-    mat.xtype = CHOLMOD_REAL;
-    mat.dtype = CHOLMOD_DOUBLE;
-  }
-};
-
-template<> struct cholmod_configure_matrix<std::complex<double> > {
-  template<typename CholmodType>
-  static void run(CholmodType& mat) {
-    mat.xtype = CHOLMOD_COMPLEX;
-    mat.dtype = CHOLMOD_DOUBLE;
-  }
-};
-
-// Other scalar types are not yet supported by Cholmod
-// template<> struct cholmod_configure_matrix<float> {
-//   template<typename CholmodType>
-//   static void run(CholmodType& mat) {
-//     mat.xtype = CHOLMOD_REAL;
-//     mat.dtype = CHOLMOD_SINGLE;
-//   }
-// };
-//
-// template<> struct cholmod_configure_matrix<std::complex<float> > {
-//   template<typename CholmodType>
-//   static void run(CholmodType& mat) {
-//     mat.xtype = CHOLMOD_COMPLEX;
-//     mat.dtype = CHOLMOD_SINGLE;
-//   }
-// };
-
-} // namespace internal
-
-/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
-  * Note that the data are shared.
-  */
-template<typename _Scalar, int _Options, typename _StorageIndex>
-cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> > mat)
-{
-  cholmod_sparse res;
-  res.nzmax   = mat.nonZeros();
-  res.nrow    = mat.rows();
-  res.ncol    = mat.cols();
-  res.p       = mat.outerIndexPtr();
-  res.i       = mat.innerIndexPtr();
-  res.x       = mat.valuePtr();
-  res.z       = 0;
-  res.sorted  = 1;
-  if(mat.isCompressed())
-  {
-    res.packed  = 1;
-    res.nz = 0;
-  }
-  else
-  {
-    res.packed  = 0;
-    res.nz = mat.innerNonZeroPtr();
-  }
-
-  res.dtype   = 0;
-  res.stype   = -1;
-
-  if (internal::is_same<_StorageIndex,int>::value)
-  {
-    res.itype = CHOLMOD_INT;
-  }
-  else if (internal::is_same<_StorageIndex,SuiteSparse_long>::value)
-  {
-    res.itype = CHOLMOD_LONG;
-  }
-  else
-  {
-    eigen_assert(false && "Index type not supported yet");
-  }
-
-  // setup res.xtype
-  internal::cholmod_configure_matrix<_Scalar>::run(res);
-
-  res.stype = 0;
-
-  return res;
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
-{
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
-  return res;
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar,_Options,_Index>& mat)
-{
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
-  return res;
-}
-
-/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
-  * The data are not copied but shared. */
-template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
-cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
-{
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.matrix().const_cast_derived()));
-
-  if(UpLo==Upper) res.stype =  1;
-  if(UpLo==Lower) res.stype = -1;
-  // swap stype for rowmajor matrices (only works for real matrices)
-  EIGEN_STATIC_ASSERT((_Options & RowMajorBit) == 0 || NumTraits<_Scalar>::IsComplex == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  if(_Options & RowMajorBit) res.stype *=-1;
-
-  return res;
-}
-
-/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
-  * The data are not copied but shared. */
-template<typename Derived>
-cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
-{
-  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  typedef typename Derived::Scalar Scalar;
-
-  cholmod_dense res;
-  res.nrow   = mat.rows();
-  res.ncol   = mat.cols();
-  res.nzmax  = res.nrow * res.ncol;
-  res.d      = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
-  res.x      = (void*)(mat.derived().data());
-  res.z      = 0;
-
-  internal::cholmod_configure_matrix<Scalar>::run(res);
-
-  return res;
-}
-
-/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
-  * The data are not copied but shared. */
-template<typename Scalar, int Flags, typename StorageIndex>
-MappedSparseMatrix<Scalar,Flags,StorageIndex> viewAsEigen(cholmod_sparse& cm)
-{
-  return MappedSparseMatrix<Scalar,Flags,StorageIndex>
-         (cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
-          static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
-}
-
-namespace internal {
-
-// template specializations for int and long that call the correct cholmod method
-
-#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name) \
-    template<typename _StorageIndex> inline ret cm_ ## name       (cholmod_common &Common) { return cholmod_ ## name   (&Common); } \
-    template<>                       inline ret cm_ ## name<SuiteSparse_long> (cholmod_common &Common) { return cholmod_l_ ## name (&Common); }
-
-#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1) \
-    template<typename _StorageIndex> inline ret cm_ ## name       (t1& a1, cholmod_common &Common) { return cholmod_ ## name   (&a1, &Common); } \
-    template<>                       inline ret cm_ ## name<SuiteSparse_long> (t1& a1, cholmod_common &Common) { return cholmod_l_ ## name (&a1, &Common); }
-
-EIGEN_CHOLMOD_SPECIALIZE0(int, start)
-EIGEN_CHOLMOD_SPECIALIZE0(int, finish)
-
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense,  cholmod_dense*,  X)
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
-
-EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
-
-template<typename _StorageIndex> inline cholmod_dense*  cm_solve         (int sys, cholmod_factor& L, cholmod_dense&  B, cholmod_common &Common) { return cholmod_solve     (sys, &L, &B, &Common); }
-template<>                       inline cholmod_dense*  cm_solve<SuiteSparse_long>   (int sys, cholmod_factor& L, cholmod_dense&  B, cholmod_common &Common) { return cholmod_l_solve   (sys, &L, &B, &Common); }
-
-template<typename _StorageIndex> inline cholmod_sparse* cm_spsolve       (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_spsolve   (sys, &L, &B, &Common); }
-template<>                       inline cholmod_sparse* cm_spsolve<SuiteSparse_long> (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_l_spsolve (sys, &L, &B, &Common); }
-
-template<typename _StorageIndex>
-inline int  cm_factorize_p       (cholmod_sparse*  A, double beta[2], _StorageIndex* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_factorize_p   (A, beta, fset, fsize, L, &Common); }
-template<>
-inline int  cm_factorize_p<SuiteSparse_long> (cholmod_sparse*  A, double beta[2], SuiteSparse_long* fset,          std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_l_factorize_p (A, beta, fset, fsize, L, &Common); }
-
-#undef EIGEN_CHOLMOD_SPECIALIZE0
-#undef EIGEN_CHOLMOD_SPECIALIZE1
-
-}  // namespace internal
-
-
-enum CholmodMode {
-  CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt
-};
-
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodBase
-  * \brief The base class for the direct Cholesky factorization of Cholmod
-  * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo, typename Derived>
-class CholmodBase : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-  public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef MatrixType CholMatrixType;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    CholmodBase()
-      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
-      m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
-      internal::cm_start<StorageIndex>(m_cholmod);
-    }
-
-    explicit CholmodBase(const MatrixType& matrix)
-      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
-      m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
-      internal::cm_start<StorageIndex>(m_cholmod);
-      compute(matrix);
-    }
-
-    ~CholmodBase()
-    {
-      if(m_cholmodFactor)
-        internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
-      internal::cm_finish<StorageIndex>(m_cholmod);
-    }
-
-    inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
-    inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    Derived& compute(const MatrixType& matrix)
-    {
-      analyzePattern(matrix);
-      factorize(matrix);
-      return derived();
-    }
-
-    /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      if(m_cholmodFactor)
-      {
-        internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
-        m_cholmodFactor = 0;
-      }
-      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
-      m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);
-
-      this->m_isInitialized = true;
-      this->m_info = Success;
-      m_analysisIsOk = true;
-      m_factorizationIsOk = false;
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& matrix)
-    {
-      eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
-      internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);
-
-      // If the factorization failed, minor is the column at which it did. On success minor == n.
-      this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
-      m_factorizationIsOk = true;
-    }
-
-    /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
-     *  See the Cholmod user guide for details. */
-    cholmod_common& cholmod() { return m_cholmod; }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      const Index size = m_cholmodFactor->n;
-      EIGEN_UNUSED_VARIABLE(size);
-      eigen_assert(size==b.rows());
-
-      // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
-      Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b.derived());
-
-      cholmod_dense b_cd = viewAsCholmod(b_ref);
-      cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
-      if(!x_cd)
-      {
-        this->m_info = NumericalIssue;
-        return;
-      }
-      // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
-      // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
-      dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
-      internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
-    }
-
-    /** \internal */
-    template<typename RhsDerived, typename DestDerived>
-    void _solve_impl(const SparseMatrixBase<RhsDerived> &b, SparseMatrixBase<DestDerived> &dest) const
-    {
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      const Index size = m_cholmodFactor->n;
-      EIGEN_UNUSED_VARIABLE(size);
-      eigen_assert(size==b.rows());
-
-      // note: cs stands for Cholmod Sparse
-      Ref<SparseMatrix<typename RhsDerived::Scalar,ColMajor,typename RhsDerived::StorageIndex> > b_ref(b.const_cast_derived());
-      cholmod_sparse b_cs = viewAsCholmod(b_ref);
-      cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
-      if(!x_cs)
-      {
-        this->m_info = NumericalIssue;
-        return;
-      }
-      // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
-      // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's sparse solver)
-      dest.derived() = viewAsEigen<typename DestDerived::Scalar,ColMajor,typename DestDerived::StorageIndex>(*x_cs);
-      internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
-    }
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-
-
-    /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
-      *
-      * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
-      * \c d_ii = \a offset + \c d_ii
-      *
-      * The default is \a offset=0.
-      *
-      * \returns a reference to \c *this.
-      */
-    Derived& setShift(const RealScalar& offset)
-    {
-      m_shiftOffset[0] = double(offset);
-      return derived();
-    }
-
-    /** \returns the determinant of the underlying matrix from the current factorization */
-    Scalar determinant() const
-    {
-      using std::exp;
-      return exp(logDeterminant());
-    }
-
-    /** \returns the log determinant of the underlying matrix from the current factorization */
-    Scalar logDeterminant() const
-    {
-      using std::log;
-      using numext::real;
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-
-      RealScalar logDet = 0;
-      Scalar *x = static_cast<Scalar*>(m_cholmodFactor->x);
-      if (m_cholmodFactor->is_super)
-      {
-        // Supernodal factorization stored as a packed list of dense column-major blocs,
-        // as described by the following structure:
-
-        // super[k] == index of the first column of the j-th super node
-        StorageIndex *super = static_cast<StorageIndex*>(m_cholmodFactor->super);
-        // pi[k] == offset to the description of row indices
-        StorageIndex *pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
-        // px[k] == offset to the respective dense block
-        StorageIndex *px = static_cast<StorageIndex*>(m_cholmodFactor->px);
-
-        Index nb_super_nodes = m_cholmodFactor->nsuper;
-        for (Index k=0; k < nb_super_nodes; ++k)
-        {
-          StorageIndex ncols = super[k + 1] - super[k];
-          StorageIndex nrows = pi[k + 1] - pi[k];
-
-          Map<const Array<Scalar,1,Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows+1));
-          logDet += sk.real().log().sum();
-        }
-      }
-      else
-      {
-        // Simplicial factorization stored as standard CSC matrix.
-        StorageIndex *p = static_cast<StorageIndex*>(m_cholmodFactor->p);
-        Index size = m_cholmodFactor->n;
-        for (Index k=0; k<size; ++k)
-          logDet += log(real( x[p[k]] ));
-      }
-      if (m_cholmodFactor->is_ll)
-        logDet *= 2.0;
-      return logDet;
-    };
-
-    template<typename Stream>
-    void dumpMemory(Stream& /*s*/)
-    {}
-
-  protected:
-    mutable cholmod_common m_cholmod;
-    cholmod_factor* m_cholmodFactor;
-    double m_shiftOffset[2];
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-};
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodSimplicialLLT
-  * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
-  * using the Cholmod library.
-  * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
-  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodSimplicialLLT() : Base() { init(); }
-
-    CholmodSimplicialLLT(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodSimplicialLLT() {}
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 0;
-      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-      m_cholmod.final_ll = 1;
-    }
-};
-
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodSimplicialLDLT
-  * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
-  * using the Cholmod library.
-  * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
-  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodSimplicialLDLT() : Base() { init(); }
-
-    CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodSimplicialLDLT() {}
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 1;
-      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-    }
-};
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodSupernodalLLT
-  * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
-  * using the Cholmod library.
-  * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
-  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodSupernodalLLT() : Base() { init(); }
-
-    CholmodSupernodalLLT(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodSupernodalLLT() {}
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 1;
-      m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
-    }
-};
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodDecomposition
-  * \brief A general Cholesky factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
-  * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * This variant permits to change the underlying Cholesky method at runtime.
-  * On the other hand, it does not provide access to the result of the factorization.
-  * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodDecomposition() : Base() { init(); }
-
-    CholmodDecomposition(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodDecomposition() {}
-
-    void setMode(CholmodMode mode)
-    {
-      switch(mode)
-      {
-        case CholmodAuto:
-          m_cholmod.final_asis = 1;
-          m_cholmod.supernodal = CHOLMOD_AUTO;
-          break;
-        case CholmodSimplicialLLt:
-          m_cholmod.final_asis = 0;
-          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-          m_cholmod.final_ll = 1;
-          break;
-        case CholmodSupernodalLLt:
-          m_cholmod.final_asis = 1;
-          m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
-          break;
-        case CholmodLDLt:
-          m_cholmod.final_asis = 1;
-          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-          break;
-        default:
-          break;
-      }
-    }
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 1;
-      m_cholmod.supernodal = CHOLMOD_AUTO;
-    }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CHOLMODSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArithmeticSequence.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArithmeticSequence.h
deleted file mode 100644
index b6200fa..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArithmeticSequence.h
+++ /dev/null
@@ -1,413 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARITHMETIC_SEQUENCE_H
-#define EIGEN_ARITHMETIC_SEQUENCE_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if (!EIGEN_HAS_CXX11) || !((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48)
-template<typename T> struct aseq_negate {};
-
-template<> struct aseq_negate<Index> {
-  typedef Index type;
-};
-
-template<int N> struct aseq_negate<FixedInt<N> > {
-  typedef FixedInt<-N> type;
-};
-
-// Compilation error in the following case:
-template<> struct aseq_negate<FixedInt<DynamicIndex> > {};
-
-template<typename FirstType,typename SizeType,typename IncrType,
-         bool FirstIsSymbolic=symbolic::is_symbolic<FirstType>::value,
-         bool SizeIsSymbolic =symbolic::is_symbolic<SizeType>::value>
-struct aseq_reverse_first_type {
-  typedef Index type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct aseq_reverse_first_type<FirstType,SizeType,IncrType,true,true> {
-  typedef symbolic::AddExpr<FirstType,
-                            symbolic::ProductExpr<symbolic::AddExpr<SizeType,symbolic::ValueExpr<FixedInt<-1> > >,
-                                                  symbolic::ValueExpr<IncrType> >
-                           > type;
-};
-
-template<typename SizeType,typename IncrType,typename EnableIf = void>
-struct aseq_reverse_first_type_aux {
-  typedef Index type;
-};
-
-template<typename SizeType,typename IncrType>
-struct aseq_reverse_first_type_aux<SizeType,IncrType,typename internal::enable_if<bool((SizeType::value+IncrType::value)|0x1)>::type> {
-  typedef FixedInt<(SizeType::value-1)*IncrType::value> type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct aseq_reverse_first_type<FirstType,SizeType,IncrType,true,false> {
-  typedef typename aseq_reverse_first_type_aux<SizeType,IncrType>::type Aux;
-  typedef symbolic::AddExpr<FirstType,symbolic::ValueExpr<Aux> > type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct aseq_reverse_first_type<FirstType,SizeType,IncrType,false,true> {
-  typedef symbolic::AddExpr<symbolic::ProductExpr<symbolic::AddExpr<SizeType,symbolic::ValueExpr<FixedInt<-1> > >,
-                                                  symbolic::ValueExpr<IncrType> >,
-                            symbolic::ValueExpr<> > type;
-};
-#endif
-
-// Helper to cleanup the type of the increment:
-template<typename T> struct cleanup_seq_incr {
-  typedef typename cleanup_index_type<T,DynamicIndex>::type type;
-};
-
-}
-
-//--------------------------------------------------------------------------------
-// seq(first,last,incr) and seqN(first,size,incr)
-//--------------------------------------------------------------------------------
-
-template<typename FirstType=Index,typename SizeType=Index,typename IncrType=internal::FixedInt<1> >
-class ArithmeticSequence;
-
-template<typename FirstType,typename SizeType,typename IncrType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                   typename internal::cleanup_index_type<SizeType>::type,
-                   typename internal::cleanup_seq_incr<IncrType>::type >
-seqN(FirstType first, SizeType size, IncrType incr);
-
-/** \class ArithmeticSequence
-  * \ingroup Core_Module
-  *
-  * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by
-  * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride)
-  * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i.
-  *
-  * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments
-  * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the
-  * only way it is used.
-  *
-  * \tparam FirstType type of the first element, usually an Index,
-  *                   but internally it can be a symbolic expression
-  * \tparam SizeType type representing the size of the sequence, usually an Index
-  *                  or a compile time integral constant. Internally, it can also be a symbolic expression
-  * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is compile-time 1)
-  *
-  * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView
-  */
-template<typename FirstType,typename SizeType,typename IncrType>
-class ArithmeticSequence
-{
-public:
-  ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {}
-  ArithmeticSequence(FirstType first, SizeType size, IncrType incr) : m_first(first), m_size(size), m_incr(incr) {}
-
-  enum {
-    SizeAtCompileTime = internal::get_fixed_value<SizeType>::value,
-    IncrAtCompileTime = internal::get_fixed_value<IncrType,DynamicIndex>::value
-  };
-
-  /** \returns the size, i.e., number of elements, of the sequence */
-  Index size()  const { return m_size; }
-
-  /** \returns the first element \f$ a_0 \f$ in the sequence */
-  Index first()  const { return m_first; }
-
-  /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */
-  Index operator[](Index i) const { return m_first + i * m_incr; }
-
-  const FirstType& firstObject() const { return m_first; }
-  const SizeType&  sizeObject()  const { return m_size; }
-  const IncrType&  incrObject()  const { return m_incr; }
-
-protected:
-  FirstType m_first;
-  SizeType  m_size;
-  IncrType  m_incr;
-
-public:
-
-#if EIGEN_HAS_CXX11 && ((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48)
-  auto reverse() const -> decltype(Eigen::seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr)) {
-    return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr);
-  }
-#else
-protected:
-  typedef typename internal::aseq_negate<IncrType>::type ReverseIncrType;
-  typedef typename internal::aseq_reverse_first_type<FirstType,SizeType,IncrType>::type ReverseFirstType;
-public:
-  ArithmeticSequence<ReverseFirstType,SizeType,ReverseIncrType>
-  reverse() const {
-    return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr);
-  }
-#endif
-};
-
-/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
-  *
-  * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
-template<typename FirstType,typename SizeType,typename IncrType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type,typename internal::cleanup_seq_incr<IncrType>::type >
-seqN(FirstType first, SizeType size, IncrType incr)  {
-  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type,typename internal::cleanup_seq_incr<IncrType>::type>(first,size,incr);
-}
-
-/** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment
-  *
-  * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */
-template<typename FirstType,typename SizeType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type >
-seqN(FirstType first, SizeType size)  {
-  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type>(first,size);
-}
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a incr
-  *
-  * It is essentially an alias to:
-  * \code
-  * seqN(f, (l-f+incr)/incr, incr);
-  * \endcode
-  *
-  * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
-  */
-template<typename FirstType,typename LastType, typename IncrType>
-auto seq(FirstType f, LastType l, IncrType incr);
-
-/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
-  *
-  * It is essentially an alias to:
-  * \code
-  * seqN(f,l-f+1);
-  * \endcode
-  *
-  * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
-  */
-template<typename FirstType,typename LastType>
-auto seq(FirstType f, LastType l);
-
-#else // EIGEN_PARSED_BY_DOXYGEN
-
-#if EIGEN_HAS_CXX11
-template<typename FirstType,typename LastType>
-auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-                                                   (  typename internal::cleanup_index_type<LastType>::type(l)
-                                                    - typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())))
-{
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              (typename internal::cleanup_index_type<LastType>::type(l)
-               -typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
-}
-
-template<typename FirstType,typename LastType, typename IncrType>
-auto seq(FirstType f, LastType l, IncrType incr)
-  -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-                   (   typename internal::cleanup_index_type<LastType>::type(l)
-                     - typename internal::cleanup_index_type<FirstType>::type(f)+typename internal::cleanup_seq_incr<IncrType>::type(incr)
-                   ) / typename internal::cleanup_seq_incr<IncrType>::type(incr),
-                   typename internal::cleanup_seq_incr<IncrType>::type(incr)))
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              ( typename internal::cleanup_index_type<LastType>::type(l)
-               -typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr)) / CleanedIncrType(incr),
-              CleanedIncrType(incr));
-}
-
-#else // EIGEN_HAS_CXX11
-
-template<typename FirstType,typename LastType>
-typename internal::enable_if<!(symbolic::is_symbolic<FirstType>::value || symbolic::is_symbolic<LastType>::value),
-                             ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,Index> >::type
-seq(FirstType f, LastType l)
-{
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              Index((typename internal::cleanup_index_type<LastType>::type(l)-typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())));
-}
-
-template<typename FirstTypeDerived,typename LastType>
-typename internal::enable_if<!symbolic::is_symbolic<LastType>::value,
-    ArithmeticSequence<FirstTypeDerived, symbolic::AddExpr<symbolic::AddExpr<symbolic::NegateExpr<FirstTypeDerived>,symbolic::ValueExpr<> >,
-                                                            symbolic::ValueExpr<internal::FixedInt<1> > > > >::type
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, LastType l)
-{
-  return seqN(f.derived(),(typename internal::cleanup_index_type<LastType>::type(l)-f.derived()+fix<1>()));
-}
-
-template<typename FirstType,typename LastTypeDerived>
-typename internal::enable_if<!symbolic::is_symbolic<FirstType>::value,
-    ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                        symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::ValueExpr<> >,
-                                          symbolic::ValueExpr<internal::FixedInt<1> > > > >::type
-seq(FirstType f, const symbolic::BaseExpr<LastTypeDerived> &l)
-{
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),(l.derived()-typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
-}
-
-template<typename FirstTypeDerived,typename LastTypeDerived>
-ArithmeticSequence<FirstTypeDerived,
-                    symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::NegateExpr<FirstTypeDerived> >,symbolic::ValueExpr<internal::FixedInt<1> > > >
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<LastTypeDerived> &l)
-{
-  return seqN(f.derived(),(l.derived()-f.derived()+fix<1>()));
-}
-
-
-template<typename FirstType,typename LastType, typename IncrType>
-typename internal::enable_if<!(symbolic::is_symbolic<FirstType>::value || symbolic::is_symbolic<LastType>::value),
-    ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,Index,typename internal::cleanup_seq_incr<IncrType>::type> >::type
-seq(FirstType f, LastType l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              Index((typename internal::cleanup_index_type<LastType>::type(l)-typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr)), incr);
-}
-
-template<typename FirstTypeDerived,typename LastType, typename IncrType>
-typename internal::enable_if<!symbolic::is_symbolic<LastType>::value,
-    ArithmeticSequence<FirstTypeDerived,
-                        symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<symbolic::NegateExpr<FirstTypeDerived>,
-                                                                                   symbolic::ValueExpr<> >,
-                                                                 symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                                              symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                        typename internal::cleanup_seq_incr<IncrType>::type> >::type
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, LastType l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(f.derived(),(typename internal::cleanup_index_type<LastType>::type(l)-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
-}
-
-template<typename FirstType,typename LastTypeDerived, typename IncrType>
-typename internal::enable_if<!symbolic::is_symbolic<FirstType>::value,
-    ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                        symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::ValueExpr<> >,
-                                                                 symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                                               symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                        typename internal::cleanup_seq_incr<IncrType>::type> >::type
-seq(FirstType f, const symbolic::BaseExpr<LastTypeDerived> &l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              (l.derived()-typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
-}
-
-template<typename FirstTypeDerived,typename LastTypeDerived, typename IncrType>
-ArithmeticSequence<FirstTypeDerived,
-                    symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,
-                                                                               symbolic::NegateExpr<FirstTypeDerived> >,
-                                                             symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                                          symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                    typename internal::cleanup_seq_incr<IncrType>::type>
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<LastTypeDerived> &l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(f.derived(),(l.derived()-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
-}
-#endif // EIGEN_HAS_CXX11
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-
-#if EIGEN_HAS_CXX11 || defined(EIGEN_PARSED_BY_DOXYGEN)
-/** \cpp11
-  * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
-  *
-  * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
-  * 
-  * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
-template<typename SizeType,typename IncrType>
-auto lastN(SizeType size, IncrType incr)
--> decltype(seqN(Eigen::last-(size-fix<1>())*incr, size, incr))
-{
-  return seqN(Eigen::last-(size-fix<1>())*incr, size, incr);
-}
-
-/** \cpp11
-  * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
-  *
-  *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
-  * 
-  * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
-template<typename SizeType>
-auto lastN(SizeType size)
--> decltype(seqN(Eigen::last+fix<1>()-size, size))
-{
-  return seqN(Eigen::last+fix<1>()-size, size);
-}
-#endif
-
-namespace internal {
-
-// Convert a symbolic span into a usable one (i.e., remove last/end "keywords")
-template<typename T>
-struct make_size_type {
-  typedef typename internal::conditional<symbolic::is_symbolic<T>::value, Index, T>::type type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType,int XprSize>
-struct IndexedViewCompatibleType<ArithmeticSequence<FirstType,SizeType,IncrType>, XprSize> {
-  typedef ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType> type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType>
-makeIndexedViewCompatible(const ArithmeticSequence<FirstType,SizeType,IncrType>& ids, Index size,SpecializedType) {
-  return ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType>(
-            eval_expr_given_size(ids.firstObject(),size),eval_expr_given_size(ids.sizeObject(),size),ids.incrObject());
-}
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct get_compile_time_incr<ArithmeticSequence<FirstType,SizeType,IncrType> > {
-  enum { value = get_fixed_value<IncrType,DynamicIndex>::value };
-};
-
-} // end namespace internal
-
-/** \namespace Eigen::indexing
-  * \ingroup Core_Module
-  * 
-  * The sole purpose of this namespace is to be able to import all functions
-  * and symbols that are expected to be used within operator() for indexing
-  * and slicing. If you already imported the whole Eigen namespace:
-  * \code using namespace Eigen; \endcode
-  * then you are already all set. Otherwise, if you don't want/cannot import
-  * the whole Eigen namespace, the following line:
-  * \code using namespace Eigen::indexing; \endcode
-  * is equivalent to:
-  * \code
-  using Eigen::all;
-  using Eigen::seq;
-  using Eigen::seqN;
-  using Eigen::lastN; // c++11 only
-  using Eigen::last;
-  using Eigen::lastp1;
-  using Eigen::fix;
-  \endcode
-  */
-namespace indexing {
-  using Eigen::all;
-  using Eigen::seq;
-  using Eigen::seqN;
-  #if EIGEN_HAS_CXX11
-  using Eigen::lastN;
-  #endif
-  using Eigen::last;
-  using Eigen::lastp1;
-  using Eigen::fix;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARITHMETIC_SEQUENCE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Array.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Array.h
deleted file mode 100644
index 20c789b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Array.h
+++ /dev/null
@@ -1,417 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARRAY_H
-#define EIGEN_ARRAY_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-  typedef ArrayXpr XprKind;
-  typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
-};
-}
-
-/** \class Array
-  * \ingroup Core_Module
-  *
-  * \brief General-purpose arrays with easy API for coefficient-wise operations
-  *
-  * The %Array class is very similar to the Matrix class. It provides
-  * general-purpose one- and two-dimensional arrays. The difference between the
-  * %Array and the %Matrix class is primarily in the API: the API for the
-  * %Array class provides easy access to coefficient-wise operations, while the
-  * API for the %Matrix class provides easy access to linear-algebra
-  * operations.
-  *
-  * See documentation of class Matrix for detailed information on the template parameters
-  * storage layout.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
-  *
-  * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
-  */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-class Array
-  : public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-  public:
-
-    typedef PlainObjectBase<Array> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Array)
-
-    enum { Options = _Options };
-    typedef typename Base::PlainObject PlainObject;
-
-  protected:
-    template <typename Derived, typename OtherDerived, bool IsVector>
-    friend struct internal::conservative_resize_like_impl;
-
-    using Base::m_storage;
-
-  public:
-
-    using Base::base;
-    using Base::coeff;
-    using Base::coeffRef;
-
-    /**
-      * The usage of
-      *   using Base::operator=;
-      * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
-      * the usage of 'using'. This should be done only for operator=.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
-    {
-      return Base::operator=(other);
-    }
-
-    /** Set all the entries to \a value.
-      * \sa DenseBase::setConstant(), DenseBase::fill()
-      */
-    /* This overload is needed because the usage of
-      *   using Base::operator=;
-      * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
-      * the usage of 'using'. This should be done only for operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const Scalar &value)
-    {
-      Base::setConstant(value);
-      return *this;
-    }
-
-    /** Copies the value of the expression \a other into \c *this with automatic resizing.
-      *
-      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
-      * it will be initialized.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other)
-    {
-      return Base::_set(other);
-    }
-
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const Array& other)
-    {
-      return Base::_set(other);
-    }
-
-    /** Default constructor.
-      *
-      * For fixed-size matrices, does nothing.
-      *
-      * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
-      * is called a null matrix. This constructor is the unique way to create null matrices: resizing
-      * a matrix to 0 is not supported.
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array() : Base()
-    {
-      Base::_check_template_params();
-      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    // FIXME is it still needed ??
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    Array(internal::constructor_without_unaligned_array_assert)
-      : Base(internal::constructor_without_unaligned_array_assert())
-    {
-      Base::_check_template_params();
-      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-#endif
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
-      : Base(std::move(other))
-    {
-      Base::_check_template_params();
-    }
-    EIGEN_DEVICE_FUNC
-    Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
-    {
-      Base::operator=(std::move(other));
-      return *this;
-    }
-#endif
-
-    #if EIGEN_HAS_CXX11
-    /** \copydoc PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-     *
-     * Example: \include Array_variadic_ctor_cxx11.cpp
-     * Output: \verbinclude Array_variadic_ctor_cxx11.out
-     *
-     * \sa Array(const std::initializer_list<std::initializer_list<Scalar>>&)
-     * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&)
-     */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      : Base(a0, a1, a2, a3, args...) {}
-
-    /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11
-      *
-      * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
-      *
-      * Example: \include Array_initializer_list_23_cxx11.cpp
-      * Output: \verbinclude Array_initializer_list_23_cxx11.out
-      *
-      * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered.
-      *
-      * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed.
-      * Therefore <code> Array<int,Dynamic,1>{{1,2,3,4,5}}</code> is legal and the more verbose syntax
-      * <code>Array<int,Dynamic,1>{{1},{2},{3},{4},{5}}</code> can be avoided:
-      *
-      * Example: \include Array_initializer_list_vector_cxx11.cpp
-      * Output: \verbinclude Array_initializer_list_vector_cxx11.out
-      *
-      * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes,
-      * and implicit transposition is allowed for compile-time 1D arrays only.
-      *
-      * \sa  Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
-    #endif // end EIGEN_HAS_CXX11
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE explicit Array(const T& x)
-    {
-      Base::_check_template_params();
-      Base::template _init1<T>(x);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
-    {
-      Base::_check_template_params();
-      this->template _init2<T0,T1>(val0, val1);
-    }
-
-    #else
-    /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
-    EIGEN_DEVICE_FUNC explicit Array(const Scalar *data);
-    /** Constructs a vector or row-vector with given dimension. \only_for_vectors
-      *
-      * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
-      * it is redundant to pass the dimension here, so it makes more sense to use the default
-      * constructor Array() instead.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE explicit Array(Index dim);
-    /** constructs an initialized 1x1 Array with the given coefficient
-      * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */
-    Array(const Scalar& value);
-    /** constructs an uninitialized array with \a rows rows and \a cols columns.
-      *
-      * This is useful for dynamic-size arrays. For fixed-size arrays,
-      * it is redundant to pass these parameters, so one should use the default constructor
-      * Array() instead. */
-    Array(Index rows, Index cols);
-    /** constructs an initialized 2D vector with given coefficients
-      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */
-    Array(const Scalar& val0, const Scalar& val1);
-    #endif  // end EIGEN_PARSED_BY_DOXYGEN
-
-    /** constructs an initialized 3D vector with given coefficients
-      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
-      m_storage.data()[0] = val0;
-      m_storage.data()[1] = val1;
-      m_storage.data()[2] = val2;
-    }
-    /** constructs an initialized 4D vector with given coefficients
-      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
-      m_storage.data()[0] = val0;
-      m_storage.data()[1] = val1;
-      m_storage.data()[2] = val2;
-      m_storage.data()[3] = val3;
-    }
-
-    /** Copy constructor */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const Array& other)
-            : Base(other)
-    { }
-
-  private:
-    struct PrivateType {};
-  public:
-
-    /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
-                              typename internal::enable_if<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
-                                                           PrivateType>::type = PrivateType())
-      : Base(other.derived())
-    { }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT{ return 1; }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); }
-
-    #ifdef EIGEN_ARRAY_PLUGIN
-    #include EIGEN_ARRAY_PLUGIN
-    #endif
-
-  private:
-
-    template<typename MatrixType, typename OtherDerived, bool SwapPointers>
-    friend struct internal::matrix_swap_impl;
-};
-
-/** \defgroup arraytypedefs Global array typedefs
-  * \ingroup Core_Module
-  *
-  * %Eigen defines several typedef shortcuts for most common 1D and 2D array types.
-  *
-  * The general patterns are the following:
-  *
-  * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
-  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
-  * for complex double.
-  *
-  * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats.
-  *
-  * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
-  * a fixed-size 1D array of 4 complex floats.
-  *
-  * With \cpp11, template alias are also defined for common sizes.
-  * They follow the same pattern as above except that the scalar type suffix is replaced by a
-  * template parameter, i.e.:
-  *   - `ArrayRowsCols<Type>` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size.
-  *   - `ArraySize<Type>` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays.
-  *
-  * \sa class Array
-  */
-
-#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix;  \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Size, 1>    Array##SizeSuffix##TypeSuffix;
-
-#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix;  \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
-
-#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
-
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int,                  i)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float,                f)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double,               d)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>,  cf)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
-
-#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
-#undef EIGEN_MAKE_ARRAY_TYPEDEFS
-#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
-
-#if EIGEN_HAS_CXX11
-
-#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix)               \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##SizeSuffix##SizeSuffix = Array<Type, Size, Size>;    \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##SizeSuffix = Array<Type, Size, 1>;
-
-#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size)                     \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##Size##X = Array<Type, Size, Dynamic>;                \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##X##Size = Array<Type, Dynamic, Size>;
-
-EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2)
-EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3)
-EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4)
-EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
-
-#undef EIGEN_MAKE_ARRAY_TYPEDEFS
-#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
-
-#endif // EIGEN_HAS_CXX11
-
-#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
-using Eigen::Matrix##SizeSuffix##TypeSuffix; \
-using Eigen::Vector##SizeSuffix##TypeSuffix; \
-using Eigen::RowVector##SizeSuffix##TypeSuffix;
-
-#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
-
-#define EIGEN_USING_ARRAY_TYPEDEFS \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARRAY_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArrayBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArrayBase.h
deleted file mode 100644
index ea3dd1c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArrayBase.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARRAYBASE_H
-#define EIGEN_ARRAYBASE_H
-
-namespace Eigen { 
-
-template<typename ExpressionType> class MatrixWrapper;
-
-/** \class ArrayBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for all 1D and 2D array, and related expressions
-  *
-  * An array is similar to a dense vector or matrix. While matrices are mathematical
-  * objects with well defined linear algebra operators, an array is just a collection
-  * of scalar values arranged in a one or two dimensionnal fashion. As the main consequence,
-  * all operations applied to an array are performed coefficient wise. Furthermore,
-  * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
-  * constructors allowing to easily write generic code working for both scalar values
-  * and arrays.
-  *
-  * This class is the base that is inherited by all array expression types.
-  *
-  * \tparam Derived is the derived type, e.g., an array or an expression type.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
-  *
-  * \sa class MatrixBase, \ref TopicClassHierarchy
-  */
-template<typename Derived> class ArrayBase
-  : public DenseBase<Derived>
-{
-  public:
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** The base class for a given storage type. */
-    typedef ArrayBase StorageBaseType;
-
-    typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    typedef DenseBase<Derived> Base;
-    using Base::RowsAtCompileTime;
-    using Base::ColsAtCompileTime;
-    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-    
-    using Base::derived;
-    using Base::const_cast_derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-    using Base::lazyAssign;
-    using Base::operator-;
-    using Base::operator=;
-    using Base::operator+=;
-    using Base::operator-=;
-    using Base::operator*=;
-    using Base::operator/=;
-
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Base::PlainObject PlainObject;
-
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#   include "../plugins/MatrixCwiseUnaryOps.h"
-#   include "../plugins/ArrayCwiseUnaryOps.h"
-#   include "../plugins/CommonCwiseBinaryOps.h"
-#   include "../plugins/MatrixCwiseBinaryOps.h"
-#   include "../plugins/ArrayCwiseBinaryOps.h"
-#   ifdef EIGEN_ARRAYBASE_PLUGIN
-#     include EIGEN_ARRAYBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_UNARY_ADDONS
-
-    /** Special case of the template operator=, in order to prevent the compiler
-      * from generating a default operator= (issue hit with g++ 4.1)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const ArrayBase& other)
-    {
-      internal::call_assignment(derived(), other.derived());
-      return derived();
-    }
-    
-    /** Set all the entries to \a value.
-      * \sa DenseBase::setConstant(), DenseBase::fill() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const Scalar &value)
-    { Base::setConstant(value); return derived(); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator+=(const Scalar& scalar);
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator-=(const Scalar& scalar);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator+=(const ArrayBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator-=(const ArrayBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator*=(const ArrayBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator/=(const ArrayBase<OtherDerived>& other);
-
-  public:
-    EIGEN_DEVICE_FUNC
-    ArrayBase<Derived>& array() { return *this; }
-    EIGEN_DEVICE_FUNC
-    const ArrayBase<Derived>& array() const { return *this; }
-
-    /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
-      * \sa MatrixBase::array() */
-    EIGEN_DEVICE_FUNC
-    MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
-    EIGEN_DEVICE_FUNC
-    const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
-
-//     template<typename Dest>
-//     inline void evalTo(Dest& dst) const { dst = matrix(); }
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase)
-
-  private:
-    explicit ArrayBase(Index);
-    ArrayBase(Index,Index);
-    template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&);
-  protected:
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-};
-
-/** replaces \c *this by \c *this - \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this + \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this * \a other coefficient wise.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this / \a other coefficient wise.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARRAYBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArrayWrapper.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArrayWrapper.h
deleted file mode 100644
index 2e9555b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ArrayWrapper.h
+++ /dev/null
@@ -1,209 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARRAYWRAPPER_H
-#define EIGEN_ARRAYWRAPPER_H
-
-namespace Eigen {
-
-/** \class ArrayWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a mathematical vector or matrix as an array object
-  *
-  * This class is the return type of MatrixBase::array(), and most of the time
-  * this is the only way it is use.
-  *
-  * \sa MatrixBase::array(), class MatrixWrapper
-  */
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<ArrayWrapper<ExpressionType> >
-  : public traits<typename remove_all<typename ExpressionType::Nested>::type >
-{
-  typedef ArrayXpr XprKind;
-  // Let's remove NestByRefBit
-  enum {
-    Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
-    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
-    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
-  };
-};
-}
-
-template<typename ExpressionType>
-class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
-{
-  public:
-    typedef ArrayBase<ArrayWrapper> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
-    typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<ExpressionType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
-
-    using Base::coeffRef;
-
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return m_expression.data(); }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return m_expression.coeffRef(rowId, colId);
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      return m_expression.coeffRef(index);
-    }
-
-    template<typename Dest>
-    EIGEN_DEVICE_FUNC
-    inline void evalTo(Dest& dst) const { dst = m_expression; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<NestedExpressionType>::type&
-    nestedExpression() const
-    {
-      return m_expression;
-    }
-
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index)  */
-    EIGEN_DEVICE_FUNC
-    void resize(Index newSize) { m_expression.resize(newSize); }
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index,Index)*/
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
-
-  protected:
-    NestedExpressionType m_expression;
-};
-
-/** \class MatrixWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Expression of an array as a mathematical vector or matrix
-  *
-  * This class is the return type of ArrayBase::matrix(), and most of the time
-  * this is the only way it is use.
-  *
-  * \sa MatrixBase::matrix(), class ArrayWrapper
-  */
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<MatrixWrapper<ExpressionType> >
- : public traits<typename remove_all<typename ExpressionType::Nested>::type >
-{
-  typedef MatrixXpr XprKind;
-  // Let's remove NestByRefBit
-  enum {
-    Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
-    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
-    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
-  };
-};
-}
-
-template<typename ExpressionType>
-class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
-{
-  public:
-    typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
-    typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<ExpressionType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
-
-    using Base::coeffRef;
-
-    EIGEN_DEVICE_FUNC
-    explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return m_expression.data(); }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return m_expression.derived().coeffRef(rowId, colId);
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      return m_expression.coeffRef(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<NestedExpressionType>::type&
-    nestedExpression() const
-    {
-      return m_expression;
-    }
-
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index)  */
-    EIGEN_DEVICE_FUNC
-    void resize(Index newSize) { m_expression.resize(newSize); }
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index,Index)*/
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
-
-  protected:
-    NestedExpressionType m_expression;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARRAYWRAPPER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Assign.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Assign.h
deleted file mode 100644
index 655412e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Assign.h
+++ /dev/null
@@ -1,90 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ASSIGN_H
-#define EIGEN_ASSIGN_H
-
-namespace Eigen {
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
-  ::lazyAssign(const DenseBase<OtherDerived>& other)
-{
-  enum{
-    SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value
-  };
-
-  EIGEN_STATIC_ASSERT_LVALUE(Derived)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
-  EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  eigen_assert(rows() == other.rows() && cols() == other.cols());
-  internal::call_assignment_no_alias(derived(),other.derived());
-  
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
-{
-  other.derived().evalTo(derived());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ASSIGN_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/AssignEvaluator.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/AssignEvaluator.h
deleted file mode 100644
index 7d76f0c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/AssignEvaluator.h
+++ /dev/null
@@ -1,1010 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ASSIGN_EVALUATOR_H
-#define EIGEN_ASSIGN_EVALUATOR_H
-
-namespace Eigen {
-
-// This implementation is based on Assign.h
-
-namespace internal {
-
-/***************************************************************************
-* Part 1 : the logic deciding a strategy for traversal and unrolling       *
-***************************************************************************/
-
-// copy_using_evaluator_traits is based on assign_traits
-
-template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc, int MaxPacketSize = -1>
-struct copy_using_evaluator_traits
-{
-  typedef typename DstEvaluator::XprType Dst;
-  typedef typename Dst::Scalar DstScalar;
-
-  enum {
-    DstFlags = DstEvaluator::Flags,
-    SrcFlags = SrcEvaluator::Flags
-  };
-
-public:
-  enum {
-    DstAlignment = DstEvaluator::Alignment,
-    SrcAlignment = SrcEvaluator::Alignment,
-    DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
-    JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
-  };
-
-private:
-  enum {
-    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
-              : int(DstFlags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
-              : int(Dst::RowsAtCompileTime),
-    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
-              : int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
-              : int(Dst::MaxRowsAtCompileTime),
-    RestrictedInnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(InnerSize,MaxPacketSize),
-    RestrictedLinearSize = EIGEN_SIZE_MIN_PREFER_FIXED(Dst::SizeAtCompileTime,MaxPacketSize),
-    OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
-    MaxSizeAtCompileTime = Dst::SizeAtCompileTime
-  };
-
-  // TODO distinguish between linear traversal and inner-traversals
-  typedef typename find_best_packet<DstScalar,RestrictedLinearSize>::type LinearPacketType;
-  typedef typename find_best_packet<DstScalar,RestrictedInnerSize>::type InnerPacketType;
-
-  enum {
-    LinearPacketSize = unpacket_traits<LinearPacketType>::size,
-    InnerPacketSize = unpacket_traits<InnerPacketType>::size
-  };
-
-public:
-  enum {
-    LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
-    InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
-  };
-
-private:
-  enum {
-    DstIsRowMajor = DstFlags&RowMajorBit,
-    SrcIsRowMajor = SrcFlags&RowMajorBit,
-    StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
-    MightVectorize = bool(StorageOrdersAgree)
-                  && (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
-                  && bool(functor_traits<AssignFunc>::PacketAccess),
-    MayInnerVectorize  = MightVectorize
-                       && int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
-                       && int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
-                       && (EIGEN_UNALIGNED_VECTORIZE  || int(JointAlignment)>=int(InnerRequiredAlignment)),
-    MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
-    MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess)
-                       && (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
-      /* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
-         so it's only good for large enough sizes. */
-    MaySliceVectorize  = bool(MightVectorize) && bool(DstHasDirectAccess)
-                       && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize)))
-      /* slice vectorization can be slow, so we only want it if the slices are big, which is
-         indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
-         in a fixed-size matrix
-         However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
-  };
-
-public:
-  enum {
-    Traversal =  int(Dst::SizeAtCompileTime) == 0 ? int(AllAtOnceTraversal) // If compile-size is zero, traversing will fail at compile-time.
-              : (int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize)) ? int(LinearVectorizedTraversal)
-              : int(MayInnerVectorize)   ? int(InnerVectorizedTraversal)
-              : int(MayLinearVectorize)  ? int(LinearVectorizedTraversal)
-              : int(MaySliceVectorize)   ? int(SliceVectorizedTraversal)
-              : int(MayLinearize)        ? int(LinearTraversal)
-                                         : int(DefaultTraversal),
-    Vectorized = int(Traversal) == InnerVectorizedTraversal
-              || int(Traversal) == LinearVectorizedTraversal
-              || int(Traversal) == SliceVectorizedTraversal
-  };
-
-  typedef typename conditional<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType>::type PacketType;
-
-private:
-  enum {
-    ActualPacketSize    = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize
-                        : Vectorized ? InnerPacketSize
-                        : 1,
-    UnrollingLimit      = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
-    MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
-                       && int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
-    MayUnrollInner      = int(InnerSize) != Dynamic
-                       && int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
-  };
-
-public:
-  enum {
-    Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
-                ? (
-                    int(MayUnrollCompletely) ? int(CompleteUnrolling)
-                  : int(MayUnrollInner)      ? int(InnerUnrolling)
-                                             : int(NoUnrolling)
-                  )
-              : int(Traversal) == int(LinearVectorizedTraversal)
-                ? ( bool(MayUnrollCompletely) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)))
-                          ? int(CompleteUnrolling)
-                          : int(NoUnrolling) )
-              : int(Traversal) == int(LinearTraversal)
-                ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
-                                              : int(NoUnrolling) )
-#if EIGEN_UNALIGNED_VECTORIZE
-              : int(Traversal) == int(SliceVectorizedTraversal)
-                ? ( bool(MayUnrollInner) ? int(InnerUnrolling)
-                                         : int(NoUnrolling) )
-#endif
-              : int(NoUnrolling)
-  };
-
-#ifdef EIGEN_DEBUG_ASSIGN
-  static void debug()
-  {
-    std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
-    std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
-    std::cerr.setf(std::ios::hex, std::ios::basefield);
-    std::cerr << "DstFlags" << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
-    std::cerr << "SrcFlags" << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
-    std::cerr.unsetf(std::ios::hex);
-    EIGEN_DEBUG_VAR(DstAlignment)
-    EIGEN_DEBUG_VAR(SrcAlignment)
-    EIGEN_DEBUG_VAR(LinearRequiredAlignment)
-    EIGEN_DEBUG_VAR(InnerRequiredAlignment)
-    EIGEN_DEBUG_VAR(JointAlignment)
-    EIGEN_DEBUG_VAR(InnerSize)
-    EIGEN_DEBUG_VAR(InnerMaxSize)
-    EIGEN_DEBUG_VAR(LinearPacketSize)
-    EIGEN_DEBUG_VAR(InnerPacketSize)
-    EIGEN_DEBUG_VAR(ActualPacketSize)
-    EIGEN_DEBUG_VAR(StorageOrdersAgree)
-    EIGEN_DEBUG_VAR(MightVectorize)
-    EIGEN_DEBUG_VAR(MayLinearize)
-    EIGEN_DEBUG_VAR(MayInnerVectorize)
-    EIGEN_DEBUG_VAR(MayLinearVectorize)
-    EIGEN_DEBUG_VAR(MaySliceVectorize)
-    std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
-    EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
-    EIGEN_DEBUG_VAR(DstEvaluator::CoeffReadCost)
-    EIGEN_DEBUG_VAR(Dst::SizeAtCompileTime)
-    EIGEN_DEBUG_VAR(UnrollingLimit)
-    EIGEN_DEBUG_VAR(MayUnrollCompletely)
-    EIGEN_DEBUG_VAR(MayUnrollInner)
-    std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
-    std::cerr << std::endl;
-  }
-#endif
-};
-
-/***************************************************************************
-* Part 2 : meta-unrollers
-***************************************************************************/
-
-/************************
-*** Default traversal ***
-************************/
-
-template<typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
-{
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-
-  enum {
-    outer = Index / DstXprType::InnerSizeAtCompileTime,
-    inner = Index % DstXprType::InnerSizeAtCompileTime
-  };
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    kernel.assignCoeffByOuterInner(outer, inner);
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
-};
-
-template<typename Kernel, int Index_, int Stop>
-struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
-  {
-    kernel.assignCoeffByOuterInner(outer, Index_);
-    copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_+1, Stop>::run(kernel, outer);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { }
-};
-
-/***********************
-*** Linear traversal ***
-***********************/
-
-template<typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel)
-  {
-    kernel.assignCoeff(Index);
-    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
-};
-
-/**************************
-*** Inner vectorization ***
-**************************/
-
-template<typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_innervec_CompleteUnrolling
-{
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-  typedef typename Kernel::PacketType PacketType;
-
-  enum {
-    outer = Index / DstXprType::InnerSizeAtCompileTime,
-    inner = Index % DstXprType::InnerSizeAtCompileTime,
-    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
-    DstAlignment = Kernel::AssignmentTraits::DstAlignment
-  };
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
-    enum { NextIndex = Index + unpacket_traits<PacketType>::size };
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
-};
-
-template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
-struct copy_using_evaluator_innervec_InnerUnrolling
-{
-  typedef typename Kernel::PacketType PacketType;
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
-  {
-    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
-    enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
-    copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel, outer);
-  }
-};
-
-template<typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
-struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
-};
-
-/***************************************************************************
-* Part 3 : implementation of all cases
-***************************************************************************/
-
-// dense_assignment_loop is based on assign_impl
-
-template<typename Kernel,
-         int Traversal = Kernel::AssignmentTraits::Traversal,
-         int Unrolling = Kernel::AssignmentTraits::Unrolling>
-struct dense_assignment_loop;
-
-/************************
-***** Special Cases *****
-************************/
-
-// Zero-sized assignment is a no-op.
-template<typename Kernel, int Unrolling>
-struct dense_assignment_loop<Kernel, AllAtOnceTraversal, Unrolling>
-{
-  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel& /*kernel*/)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    EIGEN_STATIC_ASSERT(int(DstXprType::SizeAtCompileTime) == 0,
-      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT)
-  }
-};
-
-/************************
-*** Default traversal ***
-************************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel &kernel)
-  {
-    for(Index outer = 0; outer < kernel.outerSize(); ++outer) {
-      for(Index inner = 0; inner < kernel.innerSize(); ++inner) {
-        kernel.assignCoeffByOuterInner(outer, inner);
-      }
-    }
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-
-    const Index outerSize = kernel.outerSize();
-    for(Index outer = 0; outer < outerSize; ++outer)
-      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
-  }
-};
-
-/***************************
-*** Linear vectorization ***
-***************************/
-
-
-// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
-// of the non vectorizable beginning and ending parts
-
-template <bool IsAligned = false>
-struct unaligned_dense_assignment_loop
-{
-  // if IsAligned = true, then do nothing
-  template <typename Kernel>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {}
-};
-
-template <>
-struct unaligned_dense_assignment_loop<false>
-{
-  // MSVC must not inline this functions. If it does, it fails to optimize the
-  // packet access path.
-  // FIXME check which version exhibits this issue
-#if EIGEN_COMP_MSVC
-  template <typename Kernel>
-  static EIGEN_DONT_INLINE void run(Kernel &kernel,
-                                    Index start,
-                                    Index end)
-#else
-  template <typename Kernel>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
-                                      Index start,
-                                      Index end)
-#endif
-  {
-    for (Index index = start; index < end; ++index)
-      kernel.assignCoeff(index);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    const Index size = kernel.size();
-    typedef typename Kernel::Scalar Scalar;
-    typedef typename Kernel::PacketType PacketType;
-    enum {
-      requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
-      packetSize = unpacket_traits<PacketType>::size,
-      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
-      dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
-                                                            : int(Kernel::AssignmentTraits::DstAlignment),
-      srcAlignment = Kernel::AssignmentTraits::JointAlignment
-    };
-    const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned<requestedAlignment>(kernel.dstDataPtr(), size);
-    const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
-
-    unaligned_dense_assignment_loop<dstIsAligned!=0>::run(kernel, 0, alignedStart);
-
-    for(Index index = alignedStart; index < alignedEnd; index += packetSize)
-      kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
-
-    unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::PacketType PacketType;
-
-    enum { size = DstXprType::SizeAtCompileTime,
-           packetSize =unpacket_traits<PacketType>::size,
-           alignedSize = (int(size)/packetSize)*packetSize };
-
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel);
-  }
-};
-
-/**************************
-*** Inner vectorization ***
-**************************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
-{
-  typedef typename Kernel::PacketType PacketType;
-  enum {
-    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
-    DstAlignment = Kernel::AssignmentTraits::DstAlignment
-  };
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    const Index innerSize = kernel.innerSize();
-    const Index outerSize = kernel.outerSize();
-    const Index packetSize = unpacket_traits<PacketType>::size;
-    for(Index outer = 0; outer < outerSize; ++outer)
-      for(Index inner = 0; inner < innerSize; inner+=packetSize)
-        kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::AssignmentTraits Traits;
-    const Index outerSize = kernel.outerSize();
-    for(Index outer = 0; outer < outerSize; ++outer)
-      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime,
-                                                   Traits::SrcAlignment, Traits::DstAlignment>::run(kernel, outer);
-  }
-};
-
-/***********************
-*** Linear traversal ***
-***********************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    const Index size = kernel.size();
-    for(Index i = 0; i < size; ++i)
-      kernel.assignCoeff(i);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-/**************************
-*** Slice vectorization ***
-***************************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::Scalar Scalar;
-    typedef typename Kernel::PacketType PacketType;
-    enum {
-      packetSize = unpacket_traits<PacketType>::size,
-      requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
-      alignable = packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar),
-      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
-      dstAlignment = alignable ? int(requestedAlignment)
-                               : int(Kernel::AssignmentTraits::DstAlignment)
-    };
-    const Scalar *dst_ptr = kernel.dstDataPtr();
-    if((!bool(dstIsAligned)) && (UIntPtr(dst_ptr) % sizeof(Scalar))>0)
-    {
-      // the pointer is not aligned-on scalar, so alignment is not possible
-      return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);
-    }
-    const Index packetAlignedMask = packetSize - 1;
-    const Index innerSize = kernel.innerSize();
-    const Index outerSize = kernel.outerSize();
-    const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0;
-    Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, innerSize);
-
-    for(Index outer = 0; outer < outerSize; ++outer)
-    {
-      const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
-      // do the non-vectorizable part of the assignment
-      for(Index inner = 0; inner<alignedStart ; ++inner)
-        kernel.assignCoeffByOuterInner(outer, inner);
-
-      // do the vectorizable part of the assignment
-      for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
-        kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
-
-      // do the non-vectorizable part of the assignment
-      for(Index inner = alignedEnd; inner<innerSize ; ++inner)
-        kernel.assignCoeffByOuterInner(outer, inner);
-
-      alignedStart = numext::mini((alignedStart+alignedStep)%packetSize, innerSize);
-    }
-  }
-};
-
-#if EIGEN_UNALIGNED_VECTORIZE
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::PacketType PacketType;
-
-    enum { innerSize = DstXprType::InnerSizeAtCompileTime,
-           packetSize =unpacket_traits<PacketType>::size,
-           vectorizableSize = (int(innerSize) / int(packetSize)) * int(packetSize),
-           size = DstXprType::SizeAtCompileTime };
-
-    for(Index outer = 0; outer < kernel.outerSize(); ++outer)
-    {
-      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
-      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, innerSize>::run(kernel, outer);
-    }
-  }
-};
-#endif
-
-
-/***************************************************************************
-* Part 4 : Generic dense assignment kernel
-***************************************************************************/
-
-// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
-// to another dense writable evaluator.
-// It is parametrized by the two evaluators, and the actual assignment functor.
-// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
-// One can customize the assignment using this generic dense_assignment_kernel with different
-// functors, or by completely overloading it, by-passing a functor.
-template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
-class generic_dense_assignment_kernel
-{
-protected:
-  typedef typename DstEvaluatorTypeT::XprType DstXprType;
-  typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
-public:
-
-  typedef DstEvaluatorTypeT DstEvaluatorType;
-  typedef SrcEvaluatorTypeT SrcEvaluatorType;
-  typedef typename DstEvaluatorType::Scalar Scalar;
-  typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
-  typedef typename AssignmentTraits::PacketType PacketType;
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
-    : m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr)
-  {
-    #ifdef EIGEN_DEBUG_ASSIGN
-    AssignmentTraits::debug();
-    #endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_dstExpr.size(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index innerSize() const EIGEN_NOEXCEPT { return m_dstExpr.innerSize(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerSize() const EIGEN_NOEXCEPT { return m_dstExpr.outerSize(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dstExpr.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_dstExpr.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerStride() const EIGEN_NOEXCEPT { return m_dstExpr.outerStride(); }
-
-  EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() EIGEN_NOEXCEPT { return m_dst; }
-  EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const EIGEN_NOEXCEPT { return m_src; }
-
-  /// Assign src(row,col) to dst(row,col) through the assignment functor.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col)
-  {
-    m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col));
-  }
-
-  /// \sa assignCoeff(Index,Index)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index)
-  {
-    m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
-  }
-
-  /// \sa assignCoeff(Index,Index)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner)
-  {
-    Index row = rowIndexByOuterInner(outer, inner);
-    Index col = colIndexByOuterInner(outer, inner);
-    assignCoeff(row, col);
-  }
-
-
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
-  {
-    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode,PacketType>(row,col));
-  }
-
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index)
-  {
-    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,PacketType>(index));
-  }
-
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
-  {
-    Index row = rowIndexByOuterInner(outer, inner);
-    Index col = colIndexByOuterInner(outer, inner);
-    assignPacket<StoreMode,LoadMode,PacketType>(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner)
-  {
-    typedef typename DstEvaluatorType::ExpressionTraits Traits;
-    return int(Traits::RowsAtCompileTime) == 1 ? 0
-      : int(Traits::ColsAtCompileTime) == 1 ? inner
-      : int(DstEvaluatorType::Flags)&RowMajorBit ? outer
-      : inner;
-  }
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner)
-  {
-    typedef typename DstEvaluatorType::ExpressionTraits Traits;
-    return int(Traits::ColsAtCompileTime) == 1 ? 0
-      : int(Traits::RowsAtCompileTime) == 1 ? inner
-      : int(DstEvaluatorType::Flags)&RowMajorBit ? inner
-      : outer;
-  }
-
-  EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const
-  {
-    return m_dstExpr.data();
-  }
-
-protected:
-  DstEvaluatorType& m_dst;
-  const SrcEvaluatorType& m_src;
-  const Functor &m_functor;
-  // TODO find a way to avoid the needs of the original expression
-  DstXprType& m_dstExpr;
-};
-
-// Special kernel used when computing small products whose operands have dynamic dimensions.  It ensures that the
-// PacketSize used is no larger than 4, thereby increasing the chance that vectorized instructions will be used
-// when computing the product.
-
-template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor>
-class restricted_packet_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn> Base;
- public:
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::DstXprType DstXprType;
-    typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, 4> AssignmentTraits;
-    typedef typename AssignmentTraits::PacketType PacketType;
-
-    EIGEN_DEVICE_FUNC restricted_packet_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {
-  }
- };
-
-/***************************************************************************
-* Part 5 : Entry point for dense rectangular assignment
-***************************************************************************/
-
-template<typename DstXprType,typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const Functor &/*func*/)
-{
-  EIGEN_ONLY_USED_FOR_DEBUG(dst);
-  EIGEN_ONLY_USED_FOR_DEBUG(src);
-  eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-}
-
-template<typename DstXprType,typename SrcXprType, typename T1, typename T2>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::assign_op<T1,T2> &/*func*/)
-{
-  Index dstRows = src.rows();
-  Index dstCols = src.cols();
-  if(((dst.rows()!=dstRows) || (dst.cols()!=dstCols)))
-    dst.resize(dstRows, dstCols);
-  eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
-}
-
-template<typename DstXprType, typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
-{
-  typedef evaluator<DstXprType> DstEvaluatorType;
-  typedef evaluator<SrcXprType> SrcEvaluatorType;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
-  // we need to resize the destination after the source evaluator has been created.
-  resize_if_allowed(dst, src, func);
-
-  DstEvaluatorType dstEvaluator(dst);
-
-  typedef generic_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
-  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-  dense_assignment_loop<Kernel>::run(kernel);
-}
-
-// Specialization for filling the destination with a constant value.
-#ifndef EIGEN_GPU_COMPILE_PHASE
-template<typename DstXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<typename DstXprType::Scalar>, DstXprType>& src, const internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>& func)
-{
-  resize_if_allowed(dst, src, func);
-  std::fill_n(dst.data(), dst.size(), src.functor()());
-}
-#endif
-
-template<typename DstXprType, typename SrcXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src)
-{
-  call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
-}
-
-/***************************************************************************
-* Part 6 : Generic assignment
-***************************************************************************/
-
-// Based on the respective shapes of the destination and source,
-// the class AssignmentKind determine the kind of assignment mechanism.
-// AssignmentKind must define a Kind typedef.
-template<typename DstShape, typename SrcShape> struct AssignmentKind;
-
-// Assignment kind defined in this file:
-struct Dense2Dense {};
-struct EigenBase2EigenBase {};
-
-template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
-template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
-
-// This is the main assignment class
-template< typename DstXprType, typename SrcXprType, typename Functor,
-          typename Kind = typename AssignmentKind< typename evaluator_traits<DstXprType>::Shape , typename evaluator_traits<SrcXprType>::Shape >::Kind,
-          typename EnableIf = void>
-struct Assignment;
-
-
-// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic transposition.
-// Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite complicated.
-// So this intermediate function removes everything related to "assume-aliasing" such that Assignment
-// does not has to bother about these annoying details.
-
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(Dst& dst, const Src& src)
-{
-  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(const Dst& dst, const Src& src)
-{
-  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-
-// Deal with "assume-aliasing"
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
-{
-  typename plain_matrix_type<Src>::type tmp(src);
-  call_assignment_no_alias(dst, tmp, func);
-}
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
-{
-  call_assignment_no_alias(dst, src, func);
-}
-
-// by-pass "assume-aliasing"
-// When there is no aliasing, we require that 'dst' has been properly resized
-template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
-{
-  call_assignment_no_alias(dst.expression(), src, func);
-}
-
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
-{
-  enum {
-    NeedToTranspose = (    (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1)
-                        || (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)
-                      ) && int(Dst::SizeAtCompileTime) != 1
-  };
-
-  typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst>::type ActualDstTypeCleaned;
-  typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst&>::type ActualDstType;
-  ActualDstType actualDst(dst);
-
-  // TODO check whether this is the right place to perform these checks:
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned,Src)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar);
-
-  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
-}
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_restricted_packet_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
-{
-    typedef evaluator<Dst> DstEvaluatorType;
-    typedef evaluator<Src> SrcEvaluatorType;
-    typedef restricted_packet_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Func> Kernel;
-
-    EIGEN_STATIC_ASSERT_LVALUE(Dst)
-    EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
-
-    SrcEvaluatorType srcEvaluator(src);
-    resize_if_allowed(dst, src, func);
-
-    DstEvaluatorType dstEvaluator(dst);
-    Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-    dense_assignment_loop<Kernel>::run(kernel);
-}
-
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias(Dst& dst, const Src& src)
-{
-  call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
-{
-  // TODO check whether this is the right place to perform these checks:
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst,Src)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
-
-  Assignment<Dst,Src,Func>::run(dst, src, func);
-}
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
-{
-  call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-
-// forward declaration
-template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
-
-// Generic Dense to Dense assignment
-// Note that the last template argument "Weak" is needed to make it possible to perform
-// both partial specialization+SFINAE without ambiguous specialization
-template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak>
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-#ifndef EIGEN_NO_DEBUG
-    internal::check_for_aliasing(dst, src);
-#endif
-
-    call_dense_assignment_loop(dst, src, func);
-  }
-};
-
-// Generic assignment through evalTo.
-// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
-// Note that the last template argument "Weak" is needed to make it possible to perform
-// both partial specialization+SFINAE without ambiguous specialization
-template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.evalTo(dst);
-  }
-
-  // NOTE The following two functions are templated to avoid their instantiation if not needed
-  //      This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
-  template<typename SrcScalarType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.addTo(dst);
-  }
-
-  template<typename SrcScalarType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.subTo(dst);
-  }
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_ASSIGN_EVALUATOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Assign_MKL.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Assign_MKL.h
deleted file mode 100644
index c6140d1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Assign_MKL.h
+++ /dev/null
@@ -1,178 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
- Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
- 
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to Intel(R) MKL
- *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
- ********************************************************************************
-*/
-
-#ifndef EIGEN_ASSIGN_VML_H
-#define EIGEN_ASSIGN_VML_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Dst, typename Src>
-class vml_assign_traits
-{
-  private:
-    enum {
-      DstHasDirectAccess = Dst::Flags & DirectAccessBit,
-      SrcHasDirectAccess = Src::Flags & DirectAccessBit,
-      StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
-      InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
-                : int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
-                : int(Dst::RowsAtCompileTime),
-      InnerMaxSize  = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
-                    : int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
-                    : int(Dst::MaxRowsAtCompileTime),
-      MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
-
-      MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
-      MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
-      VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
-      LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD
-    };
-  public:
-    enum {
-      EnableVml = MightEnableVml && LargeEnough,
-      Traversal = MightLinearize ? LinearTraversal : DefaultTraversal
-    };
-};
-
-#define EIGEN_PP_EXPAND(ARG) ARG
-#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
-#define EIGEN_VMLMODE_EXPAND_xLA , VML_HA
-#else
-#define EIGEN_VMLMODE_EXPAND_xLA , VML_LA
-#endif
-
-#define EIGEN_VMLMODE_EXPAND_x_
-
-#define EIGEN_VMLMODE_PREFIX_xLA vm
-#define EIGEN_VMLMODE_PREFIX_x_  v
-#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x,VMLMODE)
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                                           \
-  template< typename DstXprType, typename SrcXprNested>                                                                         \
-  struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>,   \
-                   Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> {              \
-    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType;                                            \
-    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) {                       \
-      resize_if_allowed(dst, src, func);                                                                                        \
-      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                                       \
-      if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) {                                              \
-        VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(),                                                        \
-              (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) );                                           \
-      } else {                                                                                                                  \
-        const Index outerSize = dst.outerSize();                                                                                \
-        for(Index outer = 0; outer < outerSize; ++outer) {                                                                      \
-          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) :                             \
-                                                      &(src.nestedExpression().coeffRef(0, outer));                             \
-          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));                           \
-          VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr,                                                                      \
-                (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                                             \
-        }                                                                                                                       \
-      }                                                                                                                         \
-    }                                                                                                                           \
-  };                                                                                                                            \
-
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                         \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),s##VMLOP), float, float, VMLMODE)           \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),d##VMLOP), double, double, VMLMODE)
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)                                                         \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),c##VMLOP), scomplex, MKL_Complex8, VMLMODE) \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),z##VMLOP), dcomplex, MKL_Complex16, VMLMODE)
-  
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE)                                                              \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                               \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
-
-  
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin,   Sin,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin,  Asin,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh,  Sinh,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos,   Cos,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos,  Acos,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh,  Cosh,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan,   Tan,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan,  Atan,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh,  Tanh,  LA)
-// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,   Abs,    _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp,   Exp,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log,   Ln,    LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt,  Sqrt,  _)
-
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr,   _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg,      _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round,  _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor,  _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil,  Ceil,   _)
-
-#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                                           \
-  template< typename DstXprType, typename SrcXprNested, typename Plain>                                                       \
-  struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested,                       \
-                    const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>,    \
-                   Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> {            \
-    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested,                                           \
-                    const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType;                         \
-    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) {                     \
-      resize_if_allowed(dst, src, func);                                                                                      \
-      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                                     \
-      VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other);                                       \
-      if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal)                                              \
-      {                                                                                                                       \
-        VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent,                                                        \
-              (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) );                                         \
-      } else {                                                                                                                \
-        const Index outerSize = dst.outerSize();                                                                              \
-        for(Index outer = 0; outer < outerSize; ++outer) {                                                                    \
-          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) :                                        \
-                                                      &(src.lhs().coeffRef(0, outer));                                        \
-          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));                         \
-          VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent,                                                          \
-                 (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                                          \
-        }                                                                                                                     \
-      }                                                                                                                       \
-    }                                                                                                                         \
-  };
-  
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float,    float,         LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double,   double,        LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8,  LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_ASSIGN_VML_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/BandMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/BandMatrix.h
deleted file mode 100644
index 878c024..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/BandMatrix.h
+++ /dev/null
@@ -1,353 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BANDMATRIX_H
-#define EIGEN_BANDMATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Derived>
-class BandMatrixBase : public EigenBase<Derived>
-{
-  public:
-
-    enum {
-      Flags = internal::traits<Derived>::Flags,
-      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-      Supers = internal::traits<Derived>::Supers,
-      Subs   = internal::traits<Derived>::Subs,
-      Options = internal::traits<Derived>::Options
-    };
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
-    typedef typename DenseMatrixType::StorageIndex StorageIndex;
-    typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
-    typedef EigenBase<Derived> Base;
-
-  protected:
-    enum {
-      DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
-                            ? 1 + Supers + Subs
-                            : Dynamic,
-      SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
-    };
-
-  public:
-
-    using Base::derived;
-    using Base::rows;
-    using Base::cols;
-
-    /** \returns the number of super diagonals */
-    inline Index supers() const { return derived().supers(); }
-
-    /** \returns the number of sub diagonals */
-    inline Index subs() const { return derived().subs(); }
-
-    /** \returns an expression of the underlying coefficient matrix */
-    inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
-
-    /** \returns an expression of the underlying coefficient matrix */
-    inline CoefficientsType& coeffs() { return derived().coeffs(); }
-
-    /** \returns a vector expression of the \a i -th column,
-      * only the meaningful part is returned.
-      * \warning the internal storage must be column major. */
-    inline Block<CoefficientsType,Dynamic,1> col(Index i)
-    {
-      EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-      Index start = 0;
-      Index len = coeffs().rows();
-      if (i<=supers())
-      {
-        start = supers()-i;
-        len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
-      }
-      else if (i>=rows()-subs())
-        len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
-      return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1);
-    }
-
-    /** \returns a vector expression of the main diagonal */
-    inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
-    { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
-
-    /** \returns a vector expression of the main diagonal (const version) */
-    inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
-    { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
-
-    template<int Index> struct DiagonalIntReturnType {
-      enum {
-        ReturnOpposite = (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)),
-        Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
-        ActualIndex = ReturnOpposite ? -Index : Index,
-        DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
-                     ? Dynamic
-                     : (ActualIndex<0
-                     ? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
-                     : EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
-      };
-      typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
-      typedef typename internal::conditional<Conjugate,
-                 CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
-                 BuildType>::type Type;
-    };
-
-    /** \returns a vector expression of the \a N -th sub or super diagonal */
-    template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
-    {
-      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
-    }
-
-    /** \returns a vector expression of the \a N -th sub or super diagonal */
-    template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
-    {
-      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
-    }
-
-    /** \returns a vector expression of the \a i -th sub or super diagonal */
-    inline Block<CoefficientsType,1,Dynamic> diagonal(Index i)
-    {
-      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
-      return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
-    }
-
-    /** \returns a vector expression of the \a i -th sub or super diagonal */
-    inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const
-    {
-      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
-      return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
-    }
-
-    template<typename Dest> inline void evalTo(Dest& dst) const
-    {
-      dst.resize(rows(),cols());
-      dst.setZero();
-      dst.diagonal() = diagonal();
-      for (Index i=1; i<=supers();++i)
-        dst.diagonal(i) = diagonal(i);
-      for (Index i=1; i<=subs();++i)
-        dst.diagonal(-i) = diagonal(-i);
-    }
-
-    DenseMatrixType toDenseMatrix() const
-    {
-      DenseMatrixType res(rows(),cols());
-      evalTo(res);
-      return res;
-    }
-
-  protected:
-
-    inline Index diagonalLength(Index i) const
-    { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
-};
-
-/**
-  * \class BandMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Represents a rectangular matrix with a banded storage
-  *
-  * \tparam _Scalar Numeric type, i.e. float, double, int
-  * \tparam _Rows Number of rows, or \b Dynamic
-  * \tparam _Cols Number of columns, or \b Dynamic
-  * \tparam _Supers Number of super diagonal
-  * \tparam _Subs Number of sub diagonal
-  * \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
-  *                  The former controls \ref TopicStorageOrders "storage order", and defaults to
-  *                  column-major. The latter controls whether the matrix represents a selfadjoint
-  *                  matrix in which case either Supers of Subs have to be null.
-  *
-  * \sa class TridiagonalMatrix
-  */
-
-template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
-struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef _Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef Eigen::Index StorageIndex;
-  enum {
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = _Rows,
-    MaxColsAtCompileTime = _Cols,
-    Flags = LvalueBit,
-    Supers = _Supers,
-    Subs = _Subs,
-    Options = _Options,
-    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
-  };
-  typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor> CoefficientsType;
-};
-
-template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options>
-class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> >
-{
-  public:
-
-    typedef typename internal::traits<BandMatrix>::Scalar Scalar;
-    typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
-    typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
-
-    explicit inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
-      : m_coeffs(1+supers+subs,cols),
-        m_rows(rows), m_supers(supers), m_subs(subs)
-    {
-    }
-
-    /** \returns the number of columns */
-    inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
-
-    /** \returns the number of rows */
-    inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
-
-    /** \returns the number of super diagonals */
-    inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
-
-    /** \returns the number of sub diagonals */
-    inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
-
-    inline const CoefficientsType& coeffs() const { return m_coeffs; }
-    inline CoefficientsType& coeffs() { return m_coeffs; }
-
-  protected:
-
-    CoefficientsType m_coeffs;
-    internal::variable_if_dynamic<Index, Rows>   m_rows;
-    internal::variable_if_dynamic<Index, Supers> m_supers;
-    internal::variable_if_dynamic<Index, Subs>   m_subs;
-};
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-class BandMatrixWrapper;
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef typename _CoefficientsType::Scalar Scalar;
-  typedef typename _CoefficientsType::StorageKind StorageKind;
-  typedef typename _CoefficientsType::StorageIndex StorageIndex;
-  enum {
-    CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
-    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = _Rows,
-    MaxColsAtCompileTime = _Cols,
-    Flags = LvalueBit,
-    Supers = _Supers,
-    Subs = _Subs,
-    Options = _Options,
-    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
-  };
-  typedef _CoefficientsType CoefficientsType;
-};
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  public:
-
-    typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
-    typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
-    typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
-
-    explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
-      : m_coeffs(coeffs),
-        m_rows(rows), m_supers(supers), m_subs(subs)
-    {
-      EIGEN_UNUSED_VARIABLE(cols);
-      //internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
-    }
-
-    /** \returns the number of columns */
-    inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
-
-    /** \returns the number of rows */
-    inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
-
-    /** \returns the number of super diagonals */
-    inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
-
-    /** \returns the number of sub diagonals */
-    inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
-
-    inline const CoefficientsType& coeffs() const { return m_coeffs; }
-
-  protected:
-
-    const CoefficientsType& m_coeffs;
-    internal::variable_if_dynamic<Index, _Rows>   m_rows;
-    internal::variable_if_dynamic<Index, _Supers> m_supers;
-    internal::variable_if_dynamic<Index, _Subs>   m_subs;
-};
-
-/**
-  * \class TridiagonalMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Represents a tridiagonal matrix with a compact banded storage
-  *
-  * \tparam Scalar Numeric type, i.e. float, double, int
-  * \tparam Size Number of rows and cols, or \b Dynamic
-  * \tparam Options Can be 0 or \b SelfAdjoint
-  *
-  * \sa class BandMatrix
-  */
-template<typename Scalar, int Size, int Options>
-class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
-{
-    typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
-    typedef typename Base::StorageIndex StorageIndex;
-  public:
-    explicit TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
-
-    inline typename Base::template DiagonalIntReturnType<1>::Type super()
-    { return Base::template diagonal<1>(); }
-    inline const typename Base::template DiagonalIntReturnType<1>::Type super() const
-    { return Base::template diagonal<1>(); }
-    inline typename Base::template DiagonalIntReturnType<-1>::Type sub()
-    { return Base::template diagonal<-1>(); }
-    inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const
-    { return Base::template diagonal<-1>(); }
-  protected:
-};
-
-
-struct BandShape {};
-
-template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
-struct evaluator_traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
-  : public evaluator_traits_base<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef BandShape Shape;
-};
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-struct evaluator_traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-  : public evaluator_traits_base<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef BandShape Shape;
-};
-
-template<> struct AssignmentKind<DenseShape,BandShape> { typedef EigenBase2EigenBase Kind; };
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BANDMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Block.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Block.h
deleted file mode 100644
index 3206d66..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Block.h
+++ /dev/null
@@ -1,448 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BLOCK_H
-#define EIGEN_BLOCK_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType>
-{
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename traits<XprType>::StorageKind StorageKind;
-  typedef typename traits<XprType>::XprKind XprKind;
-  typedef typename ref_selector<XprType>::type XprTypeNested;
-  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
-  enum{
-    MatrixRows = traits<XprType>::RowsAtCompileTime,
-    MatrixCols = traits<XprType>::ColsAtCompileTime,
-    RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
-    ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
-    MaxRowsAtCompileTime = BlockRows==0 ? 0
-                         : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
-                         : int(traits<XprType>::MaxRowsAtCompileTime),
-    MaxColsAtCompileTime = BlockCols==0 ? 0
-                         : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
-                         : int(traits<XprType>::MaxColsAtCompileTime),
-
-    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
-    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
-               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
-               : XprTypeIsRowMajor,
-    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
-    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
-                             ? int(inner_stride_at_compile_time<XprType>::ret)
-                             : int(outer_stride_at_compile_time<XprType>::ret),
-    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
-                             ? int(outer_stride_at_compile_time<XprType>::ret)
-                             : int(inner_stride_at_compile_time<XprType>::ret),
-
-    // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
-    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
-    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
-    Flags = (traits<XprType>::Flags & (DirectAccessBit | (InnerPanel?CompressedAccessBit:0))) | FlagsLvalueBit | FlagsRowMajorBit,
-    // FIXME DirectAccessBit should not be handled by expressions
-    //
-    // Alignment is needed by MapBase's assertions
-    // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the respective evaluator
-    Alignment = 0
-  };
-};
-
-template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
-         bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense;
-
-} // end namespace internal
-
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl;
-
-/** \class Block
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a fixed-size or dynamic-size block
-  *
-  * \tparam XprType the type of the expression in which we are taking a block
-  * \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
-  * \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
-  * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
-  *         to set of columns of a column major matrix (optional). The parameter allows to determine
-  *         at compile time whether aligned access is possible on the block expression.
-  *
-  * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
-  * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
-  * most of the time this is the only way it is used.
-  *
-  * However, if you want to directly maniputate block expressions,
-  * for instance if you want to write a function returning such an expression, you
-  * will need to use this class.
-  *
-  * Here is an example illustrating the dynamic case:
-  * \include class_Block.cpp
-  * Output: \verbinclude class_Block.out
-  *
-  * \note Even though this expression has dynamic size, in the case where \a XprType
-  * has fixed size, this expression inherits a fixed maximal size which means that evaluating
-  * it does not cause a dynamic memory allocation.
-  *
-  * Here is an example illustrating the fixed-size case:
-  * \include class_FixedBlock.cpp
-  * Output: \verbinclude class_FixedBlock.out
-  *
-  * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
-  */
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block
-  : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind>
-{
-    typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
-  public:
-    //typedef typename Impl::Base Base;
-    typedef Impl Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
-
-    typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-    /** Column or Row constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Block(XprType& xpr, Index i) : Impl(xpr,i)
-    {
-      eigen_assert( (i>=0) && (
-          ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
-        ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
-    }
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Block(XprType& xpr, Index startRow, Index startCol)
-      : Impl(xpr, startRow, startCol)
-    {
-      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
-      eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows()
-             && startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
-    }
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Block(XprType& xpr,
-          Index startRow, Index startCol,
-          Index blockRows, Index blockCols)
-      : Impl(xpr, startRow, startCol, blockRows, blockCols)
-    {
-      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
-          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
-      eigen_assert(startRow >= 0 && blockRows >= 0 && startRow  <= xpr.rows() - blockRows
-          && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
-    }
-};
-
-// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense
-// that must be specialized for direct and non-direct access...
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
-  : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
-{
-    typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
-    typedef typename XprType::StorageIndex StorageIndex;
-  public:
-    typedef Impl Base;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : Impl(xpr, startRow, startCol, blockRows, blockCols) {}
-};
-
-namespace internal {
-
-/** \internal Internal implementation of dense Blocks in the general case. */
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense
-  : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
-{
-    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-  public:
-
-    typedef typename internal::dense_xpr_base<BlockType>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
-
-    // class InnerIterator; // FIXME apparently never used
-
-    /** Column or Row constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline BlockImpl_dense(XprType& xpr, Index i)
-      : m_xpr(xpr),
-        // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
-        // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
-        // all other cases are invalid.
-        // The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
-        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
-        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
-        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
-        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
-    {}
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
-                    m_blockRows(BlockRows), m_blockCols(BlockCols)
-    {}
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline BlockImpl_dense(XprType& xpr,
-          Index startRow, Index startCol,
-          Index blockRows, Index blockCols)
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
-                    m_blockRows(blockRows), m_blockCols(blockCols)
-    {}
-
-    EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
-    EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index rowId, Index colId)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(XprType)
-      return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
-    {
-      return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index index)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(XprType)
-      return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const CoeffReturnType coeff(Index index) const
-    {
-      return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                         m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    template<int LoadMode>
-    inline PacketScalar packet(Index rowId, Index colId) const
-    {
-      return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
-    {
-      m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
-    }
-
-    template<int LoadMode>
-    inline PacketScalar packet(Index index) const
-    {
-      return m_xpr.template packet<Unaligned>
-              (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index index, const PacketScalar& val)
-    {
-      m_xpr.template writePacket<Unaligned>
-         (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \sa MapBase::data() */
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const;
-    EIGEN_DEVICE_FUNC inline Index innerStride() const;
-    EIGEN_DEVICE_FUNC inline Index outerStride() const;
-    #endif
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
-    {
-      return m_xpr;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    XprType& nestedExpression() { return m_xpr; }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startRow() const EIGEN_NOEXCEPT
-    {
-      return m_startRow.value();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startCol() const EIGEN_NOEXCEPT
-    {
-      return m_startCol.value();
-    }
-
-  protected:
-
-    XprTypeNested m_xpr;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
-    const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
-    const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
-};
-
-/** \internal Internal implementation of dense Blocks in the direct access case.*/
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
-  : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
-{
-    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-    enum {
-      XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
-    };
-  public:
-
-    typedef MapBase<BlockType> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
-
-    /** Column or Row constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr, Index i)
-      : Base(xpr.data() + i * (    ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
-                                || ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
-             BlockRows==1 ? 1 : xpr.rows(),
-             BlockCols==1 ? 1 : xpr.cols()),
-        m_xpr(xpr),
-        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
-        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)
-    {
-      init();
-    }
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
-      : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
-        m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
-    {
-      init();
-    }
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr,
-          Index startRow, Index startCol,
-          Index blockRows, Index blockCols)
-      : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
-        m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
-    {
-      init();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const EIGEN_NOEXCEPT
-    {
-      return m_xpr;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    XprType& nestedExpression() { return m_xpr; }
-
-    /** \sa MapBase::innerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index innerStride() const EIGEN_NOEXCEPT
-    {
-      return internal::traits<BlockType>::HasSameStorageOrderAsXprType
-             ? m_xpr.innerStride()
-             : m_xpr.outerStride();
-    }
-
-    /** \sa MapBase::outerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index outerStride() const EIGEN_NOEXCEPT
-    {
-      return internal::traits<BlockType>::HasSameStorageOrderAsXprType
-                    ? m_xpr.outerStride()
-                    : m_xpr.innerStride();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startRow() const EIGEN_NOEXCEPT { return m_startRow.value(); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startCol() const EIGEN_NOEXCEPT { return m_startCol.value(); }
-
-  #ifndef __SUNPRO_CC
-  // FIXME sunstudio is not friendly with the above friend...
-  // META-FIXME there is no 'friend' keyword around here. Is this obsolete?
-  protected:
-  #endif
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal used by allowAligned() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
-      : Base(data, blockRows, blockCols), m_xpr(xpr)
-    {
-      init();
-    }
-    #endif
-
-  protected:
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void init()
-    {
-      m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
-                    ? m_xpr.outerStride()
-                    : m_xpr.innerStride();
-    }
-
-    XprTypeNested m_xpr;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
-    Index m_outerStride;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BLOCK_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/BooleanRedux.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/BooleanRedux.h
deleted file mode 100644
index 852de8b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/BooleanRedux.h
+++ /dev/null
@@ -1,162 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ALLANDANY_H
-#define EIGEN_ALLANDANY_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Derived, int UnrollCount, int Rows>
-struct all_unroller
-{
-  enum {
-    col = (UnrollCount-1) / Rows,
-    row = (UnrollCount-1) % Rows
-  };
-
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
-  {
-    return all_unroller<Derived, UnrollCount-1, Rows>::run(mat) && mat.coeff(row, col);
-  }
-};
-
-template<typename Derived, int Rows>
-struct all_unroller<Derived, 0, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &/*mat*/) { return true; }
-};
-
-template<typename Derived, int Rows>
-struct all_unroller<Derived, Dynamic, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
-};
-
-template<typename Derived, int UnrollCount, int Rows>
-struct any_unroller
-{
-  enum {
-    col = (UnrollCount-1) / Rows,
-    row = (UnrollCount-1) % Rows
-  };
-  
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
-  {
-    return any_unroller<Derived, UnrollCount-1, Rows>::run(mat) || mat.coeff(row, col);
-  }
-};
-
-template<typename Derived, int Rows>
-struct any_unroller<Derived, 0, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived & /*mat*/) { return false; }
-};
-
-template<typename Derived, int Rows>
-struct any_unroller<Derived, Dynamic, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
-};
-
-} // end namespace internal
-
-/** \returns true if all coefficients are true
-  *
-  * Example: \include MatrixBase_all.cpp
-  * Output: \verbinclude MatrixBase_all.out
-  *
-  * \sa any(), Cwise::operator<()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::all() const
-{
-  typedef internal::evaluator<Derived> Evaluator;
-  enum {
-    unroll = SizeAtCompileTime != Dynamic
-          && SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT
-  };
-  Evaluator evaluator(derived());
-  if(unroll)
-    return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
-  else
-  {
-    for(Index j = 0; j < cols(); ++j)
-      for(Index i = 0; i < rows(); ++i)
-        if (!evaluator.coeff(i, j)) return false;
-    return true;
-  }
-}
-
-/** \returns true if at least one coefficient is true
-  *
-  * \sa all()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::any() const
-{
-  typedef internal::evaluator<Derived> Evaluator;
-  enum {
-    unroll = SizeAtCompileTime != Dynamic
-          && SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT
-  };
-  Evaluator evaluator(derived());
-  if(unroll)
-    return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
-  else
-  {
-    for(Index j = 0; j < cols(); ++j)
-      for(Index i = 0; i < rows(); ++i)
-        if (evaluator.coeff(i, j)) return true;
-    return false;
-  }
-}
-
-/** \returns the number of coefficients which evaluate to true
-  *
-  * \sa all(), any()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Eigen::Index DenseBase<Derived>::count() const
-{
-  return derived().template cast<bool>().template cast<Index>().sum();
-}
-
-/** \returns true is \c *this contains at least one Not A Number (NaN).
-  *
-  * \sa allFinite()
-  */
-template<typename Derived>
-inline bool DenseBase<Derived>::hasNaN() const
-{
-#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
-  return derived().array().isNaN().any();
-#else
-  return !((derived().array()==derived().array()).all());
-#endif
-}
-
-/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
-  *
-  * \sa hasNaN()
-  */
-template<typename Derived>
-inline bool DenseBase<Derived>::allFinite() const
-{
-#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
-  return derived().array().isFinite().all();
-#else
-  return !((derived()-derived()).hasNaN());
-#endif
-}
-    
-} // end namespace Eigen
-
-#endif // EIGEN_ALLANDANY_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CommaInitializer.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CommaInitializer.h
deleted file mode 100644
index c0e29c7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CommaInitializer.h
+++ /dev/null
@@ -1,164 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMMAINITIALIZER_H
-#define EIGEN_COMMAINITIALIZER_H
-
-namespace Eigen { 
-
-/** \class CommaInitializer
-  * \ingroup Core_Module
-  *
-  * \brief Helper class used by the comma initializer operator
-  *
-  * This class is internally used to implement the comma initializer feature. It is
-  * the return type of MatrixBase::operator<<, and most of the time this is the only
-  * way it is used.
-  *
-  * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
-  */
-template<typename XprType>
-struct CommaInitializer
-{
-  typedef typename XprType::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  inline CommaInitializer(XprType& xpr, const Scalar& s)
-    : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
-  {
-    eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0
-      && "Cannot comma-initialize a 0x0 matrix (operator<<)");
-    m_xpr.coeffRef(0,0) = s;
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
-    : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
-  {
-    eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols()
-      && "Cannot comma-initialize a 0x0 matrix (operator<<)");
-    m_xpr.block(0, 0, other.rows(), other.cols()) = other;
-  }
-
-  /* Copy/Move constructor which transfers ownership. This is crucial in 
-   * absence of return value optimization to avoid assertions during destruction. */
-  // FIXME in C++11 mode this could be replaced by a proper RValue constructor
-  EIGEN_DEVICE_FUNC
-  inline CommaInitializer(const CommaInitializer& o)
-  : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
-    // Mark original object as finished. In absence of R-value references we need to const_cast:
-    const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
-    const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
-    const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
-  }
-
-  /* inserts a scalar value in the target matrix */
-  EIGEN_DEVICE_FUNC
-  CommaInitializer& operator,(const Scalar& s)
-  {
-    if (m_col==m_xpr.cols())
-    {
-      m_row+=m_currentBlockRows;
-      m_col = 0;
-      m_currentBlockRows = 1;
-      eigen_assert(m_row<m_xpr.rows()
-        && "Too many rows passed to comma initializer (operator<<)");
-    }
-    eigen_assert(m_col<m_xpr.cols()
-      && "Too many coefficients passed to comma initializer (operator<<)");
-    eigen_assert(m_currentBlockRows==1);
-    m_xpr.coeffRef(m_row, m_col++) = s;
-    return *this;
-  }
-
-  /* inserts a matrix expression in the target matrix */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
-  {
-    if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
-    {
-      m_row+=m_currentBlockRows;
-      m_col = 0;
-      m_currentBlockRows = other.rows();
-      eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
-        && "Too many rows passed to comma initializer (operator<<)");
-    }
-    eigen_assert((m_col + other.cols() <= m_xpr.cols())
-      && "Too many coefficients passed to comma initializer (operator<<)");
-    eigen_assert(m_currentBlockRows==other.rows());
-    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>
-                    (m_row, m_col, other.rows(), other.cols()) = other;
-    m_col += other.cols();
-    return *this;
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline ~CommaInitializer()
-#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
-  EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
-#endif
-  {
-    finished();
-  }
-
-  /** \returns the built matrix once all its coefficients have been set.
-    * Calling finished is 100% optional. Its purpose is to write expressions
-    * like this:
-    * \code
-    * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
-    * \endcode
-    */
-  EIGEN_DEVICE_FUNC
-  inline XprType& finished() {
-      eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
-           && m_col == m_xpr.cols()
-           && "Too few coefficients passed to comma initializer (operator<<)");
-      return m_xpr;
-  }
-
-  XprType& m_xpr;           // target expression
-  Index m_row;              // current row id
-  Index m_col;              // current col id
-  Index m_currentBlockRows; // current block height
-};
-
-/** \anchor MatrixBaseCommaInitRef
-  * Convenient operator to set the coefficients of a matrix.
-  *
-  * The coefficients must be provided in a row major order and exactly match
-  * the size of the matrix. Otherwise an assertion is raised.
-  *
-  * Example: \include MatrixBase_set.cpp
-  * Output: \verbinclude MatrixBase_set.out
-  * 
-  * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
-  *
-  * \sa CommaInitializer::finished(), class CommaInitializer
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s)
-{
-  return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
-}
-
-/** \sa operator<<(const Scalar&) */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived>
-DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other)
-{
-  return CommaInitializer<Derived>(*static_cast<Derived *>(this), other);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMMAINITIALIZER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ConditionEstimator.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ConditionEstimator.h
deleted file mode 100644
index 51a2e5f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ConditionEstimator.h
+++ /dev/null
@@ -1,175 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONDITIONESTIMATOR_H
-#define EIGEN_CONDITIONESTIMATOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Vector, typename RealVector, bool IsComplex>
-struct rcond_compute_sign {
-  static inline Vector run(const Vector& v) {
-    const RealVector v_abs = v.cwiseAbs();
-    return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
-            .select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
-  }
-};
-
-// Partial specialization to avoid elementwise division for real vectors.
-template <typename Vector>
-struct rcond_compute_sign<Vector, Vector, false> {
-  static inline Vector run(const Vector& v) {
-    return (v.array() < static_cast<typename Vector::RealScalar>(0))
-           .select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
-  }
-};
-
-/**
-  * \returns an estimate of ||inv(matrix)||_1 given a decomposition of
-  * \a matrix that implements .solve() and .adjoint().solve() methods.
-  *
-  * This function implements Algorithms 4.1 and 5.1 from
-  *   http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
-  * which also forms the basis for the condition number estimators in
-  * LAPACK. Since at most 10 calls to the solve method of dec are
-  * performed, the total cost is O(dims^2), as opposed to O(dims^3)
-  * needed to compute the inverse matrix explicitly.
-  *
-  * The most common usage is in estimating the condition number
-  * ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
-  * computed directly in O(n^2) operations.
-  *
-  * Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
-  * LLT.
-  *
-  * \sa FullPivLU, PartialPivLU, LDLT, LLT.
-  */
-template <typename Decomposition>
-typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec)
-{
-  typedef typename Decomposition::MatrixType MatrixType;
-  typedef typename Decomposition::Scalar Scalar;
-  typedef typename Decomposition::RealScalar RealScalar;
-  typedef typename internal::plain_col_type<MatrixType>::type Vector;
-  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
-  const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
-
-  eigen_assert(dec.rows() == dec.cols());
-  const Index n = dec.rows();
-  if (n == 0)
-    return 0;
-
-  // Disable Index to float conversion warning
-#ifdef __INTEL_COMPILER
-  #pragma warning push
-  #pragma warning ( disable : 2259 )
-#endif
-  Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
-#ifdef __INTEL_COMPILER
-  #pragma warning pop
-#endif
-
-  // lower_bound is a lower bound on
-  //   ||inv(matrix)||_1  = sup_v ||inv(matrix) v||_1 / ||v||_1
-  // and is the objective maximized by the ("super-") gradient ascent
-  // algorithm below.
-  RealScalar lower_bound = v.template lpNorm<1>();
-  if (n == 1)
-    return lower_bound;
-
-  // Gradient ascent algorithm follows: We know that the optimum is achieved at
-  // one of the simplices v = e_i, so in each iteration we follow a
-  // super-gradient to move towards the optimal one.
-  RealScalar old_lower_bound = lower_bound;
-  Vector sign_vector(n);
-  Vector old_sign_vector;
-  Index v_max_abs_index = -1;
-  Index old_v_max_abs_index = v_max_abs_index;
-  for (int k = 0; k < 4; ++k)
-  {
-    sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
-    if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
-      // Break if the solution stagnated.
-      break;
-    }
-    // v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
-    v = dec.adjoint().solve(sign_vector);
-    v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
-    if (v_max_abs_index == old_v_max_abs_index) {
-      // Break if the solution stagnated.
-      break;
-    }
-    // Move to the new simplex e_j, where j = v_max_abs_index.
-    v = dec.solve(Vector::Unit(n, v_max_abs_index));  // v = inv(matrix) * e_j.
-    lower_bound = v.template lpNorm<1>();
-    if (lower_bound <= old_lower_bound) {
-      // Break if the gradient step did not increase the lower_bound.
-      break;
-    }
-    if (!is_complex) {
-      old_sign_vector = sign_vector;
-    }
-    old_v_max_abs_index = v_max_abs_index;
-    old_lower_bound = lower_bound;
-  }
-  // The following calculates an independent estimate of ||matrix||_1 by
-  // multiplying matrix by a vector with entries of slowly increasing
-  // magnitude and alternating sign:
-  //   v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
-  // This improvement to Hager's algorithm above is due to Higham. It was
-  // added to make the algorithm more robust in certain corner cases where
-  // large elements in the matrix might otherwise escape detection due to
-  // exact cancellation (especially when op and op_adjoint correspond to a
-  // sequence of backsubstitutions and permutations), which could cause
-  // Hager's algorithm to vastly underestimate ||matrix||_1.
-  Scalar alternating_sign(RealScalar(1));
-  for (Index i = 0; i < n; ++i) {
-    // The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
-    v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
-    alternating_sign = -alternating_sign;
-  }
-  v = dec.solve(v);
-  const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
-  return numext::maxi(lower_bound, alternate_lower_bound);
-}
-
-/** \brief Reciprocal condition number estimator.
-  *
-  * Computing a decomposition of a dense matrix takes O(n^3) operations, while
-  * this method estimates the condition number quickly and reliably in O(n^2)
-  * operations.
-  *
-  * \returns an estimate of the reciprocal condition number
-  * (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
-  * its decomposition. Supports the following decompositions: FullPivLU,
-  * PartialPivLU, LDLT, and LLT.
-  *
-  * \sa FullPivLU, PartialPivLU, LDLT, LLT.
-  */
-template <typename Decomposition>
-typename Decomposition::RealScalar
-rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
-{
-  typedef typename Decomposition::RealScalar RealScalar;
-  eigen_assert(dec.rows() == dec.cols());
-  if (dec.rows() == 0)              return NumTraits<RealScalar>::infinity();
-  if (matrix_norm == RealScalar(0)) return RealScalar(0);
-  if (dec.rows() == 1)              return RealScalar(1);
-  const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
-  return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
-                                               : (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
-}
-
-}  // namespace internal
-
-}  // namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CoreEvaluators.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CoreEvaluators.h
deleted file mode 100644
index 0ff8c8d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CoreEvaluators.h
+++ /dev/null
@@ -1,1741 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_COREEVALUATORS_H
-#define EIGEN_COREEVALUATORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// This class returns the evaluator kind from the expression storage kind.
-// Default assumes index based accessors
-template<typename StorageKind>
-struct storage_kind_to_evaluator_kind {
-  typedef IndexBased Kind;
-};
-
-// This class returns the evaluator shape from the expression storage kind.
-// It can be Dense, Sparse, Triangular, Diagonal, SelfAdjoint, Band, etc.
-template<typename StorageKind> struct storage_kind_to_shape;
-
-template<> struct storage_kind_to_shape<Dense>                  { typedef DenseShape Shape;           };
-template<> struct storage_kind_to_shape<SolverStorage>          { typedef SolverShape Shape;           };
-template<> struct storage_kind_to_shape<PermutationStorage>     { typedef PermutationShape Shape;     };
-template<> struct storage_kind_to_shape<TranspositionsStorage>  { typedef TranspositionsShape Shape;  };
-
-// Evaluators have to be specialized with respect to various criteria such as:
-//  - storage/structure/shape
-//  - scalar type
-//  - etc.
-// Therefore, we need specialization of evaluator providing additional template arguments for each kind of evaluators.
-// We currently distinguish the following kind of evaluators:
-// - unary_evaluator    for expressions taking only one arguments (CwiseUnaryOp, CwiseUnaryView, Transpose, MatrixWrapper, ArrayWrapper, Reverse, Replicate)
-// - binary_evaluator   for expression taking two arguments (CwiseBinaryOp)
-// - ternary_evaluator   for expression taking three arguments (CwiseTernaryOp)
-// - product_evaluator  for linear algebra products (Product); special case of binary_evaluator because it requires additional tags for dispatching.
-// - mapbase_evaluator  for Map, Block, Ref
-// - block_evaluator    for Block (special dispatching to a mapbase_evaluator or unary_evaluator)
-
-template< typename T,
-          typename Arg1Kind   = typename evaluator_traits<typename T::Arg1>::Kind,
-          typename Arg2Kind   = typename evaluator_traits<typename T::Arg2>::Kind,
-          typename Arg3Kind   = typename evaluator_traits<typename T::Arg3>::Kind,
-          typename Arg1Scalar = typename traits<typename T::Arg1>::Scalar,
-          typename Arg2Scalar = typename traits<typename T::Arg2>::Scalar,
-          typename Arg3Scalar = typename traits<typename T::Arg3>::Scalar> struct ternary_evaluator;
-
-template< typename T,
-          typename LhsKind   = typename evaluator_traits<typename T::Lhs>::Kind,
-          typename RhsKind   = typename evaluator_traits<typename T::Rhs>::Kind,
-          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
-          typename RhsScalar = typename traits<typename T::Rhs>::Scalar> struct binary_evaluator;
-
-template< typename T,
-          typename Kind   = typename evaluator_traits<typename T::NestedExpression>::Kind,
-          typename Scalar = typename T::Scalar> struct unary_evaluator;
-
-// evaluator_traits<T> contains traits for evaluator<T>
-
-template<typename T>
-struct evaluator_traits_base
-{
-  // by default, get evaluator kind and shape from storage
-  typedef typename storage_kind_to_evaluator_kind<typename traits<T>::StorageKind>::Kind Kind;
-  typedef typename storage_kind_to_shape<typename traits<T>::StorageKind>::Shape Shape;
-};
-
-// Default evaluator traits
-template<typename T>
-struct evaluator_traits : public evaluator_traits_base<T>
-{
-};
-
-template<typename T, typename Shape = typename evaluator_traits<T>::Shape >
-struct evaluator_assume_aliasing {
-  static const bool value = false;
-};
-
-// By default, we assume a unary expression:
-template<typename T>
-struct evaluator : public unary_evaluator<T>
-{
-  typedef unary_evaluator<T> Base;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const T& xpr) : Base(xpr) {}
-};
-
-
-// TODO: Think about const-correctness
-template<typename T>
-struct evaluator<const T>
-  : evaluator<T>
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const T& xpr) : evaluator<T>(xpr) {}
-};
-
-// ---------- base class for all evaluators ----------
-
-template<typename ExpressionType>
-struct evaluator_base
-{
-  // TODO that's not very nice to have to propagate all these traits. They are currently only needed to handle outer,inner indices.
-  typedef traits<ExpressionType> ExpressionTraits;
-
-  enum {
-    Alignment = 0
-  };
-  // noncopyable:
-  // Don't make this class inherit noncopyable as this kills EBO (Empty Base Optimization)
-  // and make complex evaluator much larger than then should do.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE evaluator_base() {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~evaluator_base() {}
-private:
-  EIGEN_DEVICE_FUNC evaluator_base(const evaluator_base&);
-  EIGEN_DEVICE_FUNC const evaluator_base& operator=(const evaluator_base&);
-};
-
-// -------------------- Matrix and Array --------------------
-//
-// evaluator<PlainObjectBase> is a common base class for the
-// Matrix and Array evaluators.
-// Here we directly specialize evaluator. This is not really a unary expression, and it is, by definition, dense,
-// so no need for more sophisticated dispatching.
-
-// this helper permits to completely eliminate m_outerStride if it is known at compiletime.
-template<typename Scalar,int OuterStride> class plainobjectbase_evaluator_data {
-public:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr)
-  {
-#ifndef EIGEN_INTERNAL_DEBUGGING
-    EIGEN_UNUSED_VARIABLE(outerStride);
-#endif
-    eigen_internal_assert(outerStride==OuterStride);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index outerStride() const EIGEN_NOEXCEPT { return OuterStride; }
-  const Scalar *data;
-};
-
-template<typename Scalar> class plainobjectbase_evaluator_data<Scalar,Dynamic> {
-public:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr), m_outerStride(outerStride) {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Index outerStride() const { return m_outerStride; }
-  const Scalar *data;
-protected:
-  Index m_outerStride;
-};
-
-template<typename Derived>
-struct evaluator<PlainObjectBase<Derived> >
-  : evaluator_base<Derived>
-{
-  typedef PlainObjectBase<Derived> PlainObjectType;
-  typedef typename PlainObjectType::Scalar Scalar;
-  typedef typename PlainObjectType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    IsRowMajor = PlainObjectType::IsRowMajor,
-    IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime,
-    RowsAtCompileTime = PlainObjectType::RowsAtCompileTime,
-    ColsAtCompileTime = PlainObjectType::ColsAtCompileTime,
-
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    Flags = traits<Derived>::EvaluatorFlags,
-    Alignment = traits<Derived>::Alignment
-  };
-  enum {
-    // We do not need to know the outer stride for vectors
-    OuterStrideAtCompileTime = IsVectorAtCompileTime  ? 0
-                                                      : int(IsRowMajor) ? ColsAtCompileTime
-                                                                        : RowsAtCompileTime
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  evaluator()
-    : m_d(0,OuterStrideAtCompileTime)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const PlainObjectType& m)
-    : m_d(m.data(),IsVectorAtCompileTime ? 0 : m.outerStride())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    if (IsRowMajor)
-      return m_d.data[row * m_d.outerStride() + col];
-    else
-      return m_d.data[row + col * m_d.outerStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.data[index];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    if (IsRowMajor)
-      return const_cast<Scalar*>(m_d.data)[row * m_d.outerStride() + col];
-    else
-      return const_cast<Scalar*>(m_d.data)[row + col * m_d.outerStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return const_cast<Scalar*>(m_d.data)[index];
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    if (IsRowMajor)
-      return ploadt<PacketType, LoadMode>(m_d.data + row * m_d.outerStride() + col);
-    else
-      return ploadt<PacketType, LoadMode>(m_d.data + row + col * m_d.outerStride());
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return ploadt<PacketType, LoadMode>(m_d.data + index);
-  }
-
-  template<int StoreMode,typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    if (IsRowMajor)
-      return pstoret<Scalar, PacketType, StoreMode>
-	            (const_cast<Scalar*>(m_d.data) + row * m_d.outerStride() + col, x);
-    else
-      return pstoret<Scalar, PacketType, StoreMode>
-                    (const_cast<Scalar*>(m_d.data) + row + col * m_d.outerStride(), x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    return pstoret<Scalar, PacketType, StoreMode>(const_cast<Scalar*>(m_d.data) + index, x);
-  }
-
-protected:
-
-  plainobjectbase_evaluator_data<Scalar,OuterStrideAtCompileTime> m_d;
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-struct evaluator<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
-  : evaluator<PlainObjectBase<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > >
-{
-  typedef Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  evaluator() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& m)
-    : evaluator<PlainObjectBase<XprType> >(m)
-  { }
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-struct evaluator<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
-  : evaluator<PlainObjectBase<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > >
-{
-  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  evaluator() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& m)
-    : evaluator<PlainObjectBase<XprType> >(m)
-  { }
-};
-
-// -------------------- Transpose --------------------
-
-template<typename ArgType>
-struct unary_evaluator<Transpose<ArgType>, IndexBased>
-  : evaluator_base<Transpose<ArgType> >
-{
-  typedef Transpose<ArgType> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    Flags = evaluator<ArgType>::Flags ^ RowMajorBit,
-    Alignment = evaluator<ArgType>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& t) : m_argImpl(t.nestedExpression()) {}
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(col, row);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(col, row);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename XprType::Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(index);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(col, row);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(index);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode,PacketType>(col, row, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode,PacketType>(index, x);
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-};
-
-// -------------------- CwiseNullaryOp --------------------
-// Like Matrix and Array, this is not really a unary expression, so we directly specialize evaluator.
-// Likewise, there is not need to more sophisticated dispatching here.
-
-template<typename Scalar,typename NullaryOp,
-         bool has_nullary = has_nullary_operator<NullaryOp>::value,
-         bool has_unary   = has_unary_operator<NullaryOp>::value,
-         bool has_binary  = has_binary_operator<NullaryOp>::value>
-struct nullary_wrapper
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const { return op(i,j); }
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
-
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const { return op.template packetOp<T>(i,j); }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,true,false,false>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType=0, IndexType=0) const { return op(); }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType=0, IndexType=0) const { return op.template packetOp<T>(); }
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,false,false,true>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j=0) const { return op(i,j); }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j=0) const { return op.template packetOp<T>(i,j); }
-};
-
-// We need the following specialization for vector-only functors assigned to a runtime vector,
-// for instance, using linspace and assigning a RowVectorXd to a MatrixXd or even a row of a MatrixXd.
-// In this case, i==0 and j is used for the actual iteration.
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,false,true,false>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
-    eigen_assert(i==0 || j==0);
-    return op(i+j);
-  }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
-    eigen_assert(i==0 || j==0);
-    return op.template packetOp<T>(i+j);
-  }
-
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
-  template <typename T, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,false,false,false> {};
-
-#if 0 && EIGEN_COMP_MSVC>0
-// Disable this ugly workaround. This is now handled in traits<Ref>::match,
-// but this piece of code might still become handly if some other weird compilation
-// erros pop up again.
-
-// MSVC exhibits a weird compilation error when
-// compiling:
-//    Eigen::MatrixXf A = MatrixXf::Random(3,3);
-//    Ref<const MatrixXf> R = 2.f*A;
-// and that has_*ary_operator<scalar_constant_op<float>> have not been instantiated yet.
-// The "problem" is that evaluator<2.f*A> is instantiated by traits<Ref>::match<2.f*A>
-// and at that time has_*ary_operator<T> returns true regardless of T.
-// Then nullary_wrapper is badly instantiated as nullary_wrapper<.,.,true,true,true>.
-// The trick is thus to defer the proper instantiation of nullary_wrapper when coeff(),
-// and packet() are really instantiated as implemented below:
-
-// This is a simple wrapper around Index to enforce the re-instantiation of
-// has_*ary_operator when needed.
-template<typename T> struct nullary_wrapper_workaround_msvc {
-  nullary_wrapper_workaround_msvc(const T&);
-  operator T()const;
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,true,true,true>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i,j);
-  }
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i);
-  }
-
-  template <typename T, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i,j);
-  }
-  template <typename T, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i);
-  }
-};
-#endif // MSVC workaround
-
-template<typename NullaryOp, typename PlainObjectType>
-struct evaluator<CwiseNullaryOp<NullaryOp,PlainObjectType> >
-  : evaluator_base<CwiseNullaryOp<NullaryOp,PlainObjectType> >
-{
-  typedef CwiseNullaryOp<NullaryOp,PlainObjectType> XprType;
-  typedef typename internal::remove_all<PlainObjectType>::type PlainObjectTypeCleaned;
-
-  enum {
-    CoeffReadCost = internal::functor_traits<NullaryOp>::Cost,
-
-    Flags = (evaluator<PlainObjectTypeCleaned>::Flags
-          &  (  HereditaryBits
-              | (functor_has_linear_access<NullaryOp>::ret  ? LinearAccessBit : 0)
-              | (functor_traits<NullaryOp>::PacketAccess    ? PacketAccessBit : 0)))
-          | (functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit),
-    Alignment = AlignedMax
-  };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& n)
-    : m_functor(n.functor()), m_wrapper()
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(IndexType row, IndexType col) const
-  {
-    return m_wrapper(m_functor, row, col);
-  }
-
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(IndexType index) const
-  {
-    return m_wrapper(m_functor,index);
-  }
-
-  template<int LoadMode, typename PacketType, typename IndexType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(IndexType row, IndexType col) const
-  {
-    return m_wrapper.template packetOp<PacketType>(m_functor, row, col);
-  }
-
-  template<int LoadMode, typename PacketType, typename IndexType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(IndexType index) const
-  {
-    return m_wrapper.template packetOp<PacketType>(m_functor, index);
-  }
-
-protected:
-  const NullaryOp m_functor;
-  const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper;
-};
-
-// -------------------- CwiseUnaryOp --------------------
-
-template<typename UnaryOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IndexBased >
-  : evaluator_base<CwiseUnaryOp<UnaryOp, ArgType> >
-{
-  typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
-
-    Flags = evaluator<ArgType>::Flags
-          & (HereditaryBits | LinearAccessBit | (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
-    Alignment = evaluator<ArgType>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& op) : m_d(op)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(index));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(row, col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(index));
-  }
-
-protected:
-
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const UnaryOp& func() const { return op; }
-    UnaryOp op;
-    evaluator<ArgType> argImpl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- CwiseTernaryOp --------------------
-
-// this is a ternary expression
-template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
-struct evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
-  : public ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
-{
-  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
-  typedef ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
-struct ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>, IndexBased, IndexBased>
-  : evaluator_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
-{
-  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<Arg1>::CoeffReadCost) + int(evaluator<Arg2>::CoeffReadCost) + int(evaluator<Arg3>::CoeffReadCost) + int(functor_traits<TernaryOp>::Cost),
-
-    Arg1Flags = evaluator<Arg1>::Flags,
-    Arg2Flags = evaluator<Arg2>::Flags,
-    Arg3Flags = evaluator<Arg3>::Flags,
-    SameType = is_same<typename Arg1::Scalar,typename Arg2::Scalar>::value && is_same<typename Arg1::Scalar,typename Arg3::Scalar>::value,
-    StorageOrdersAgree = (int(Arg1Flags)&RowMajorBit)==(int(Arg2Flags)&RowMajorBit) && (int(Arg1Flags)&RowMajorBit)==(int(Arg3Flags)&RowMajorBit),
-    Flags0 = (int(Arg1Flags) | int(Arg2Flags) | int(Arg3Flags)) & (
-        HereditaryBits
-        | (int(Arg1Flags) & int(Arg2Flags) & int(Arg3Flags) &
-           ( (StorageOrdersAgree ? LinearAccessBit : 0)
-           | (functor_traits<TernaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
-           )
-        )
-     ),
-    Flags = (Flags0 & ~RowMajorBit) | (Arg1Flags & RowMajorBit),
-    Alignment = EIGEN_PLAIN_ENUM_MIN(
-        EIGEN_PLAIN_ENUM_MIN(evaluator<Arg1>::Alignment, evaluator<Arg2>::Alignment),
-        evaluator<Arg3>::Alignment)
-  };
-
-  EIGEN_DEVICE_FUNC explicit ternary_evaluator(const XprType& xpr) : m_d(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<TernaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.arg1Impl.coeff(row, col), m_d.arg2Impl.coeff(row, col), m_d.arg3Impl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.arg1Impl.coeff(index), m_d.arg2Impl.coeff(index), m_d.arg3Impl.coeff(index));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode,PacketType>(row, col),
-                               m_d.arg2Impl.template packet<LoadMode,PacketType>(row, col),
-                               m_d.arg3Impl.template packet<LoadMode,PacketType>(row, col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode,PacketType>(index),
-                               m_d.arg2Impl.template packet<LoadMode,PacketType>(index),
-                               m_d.arg3Impl.template packet<LoadMode,PacketType>(index));
-  }
-
-protected:
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), arg1Impl(xpr.arg1()), arg2Impl(xpr.arg2()), arg3Impl(xpr.arg3()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TernaryOp& func() const { return op; }
-    TernaryOp op;
-    evaluator<Arg1> arg1Impl;
-    evaluator<Arg2> arg2Impl;
-    evaluator<Arg3> arg3Impl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- CwiseBinaryOp --------------------
-
-// this is a binary expression
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-  : public binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IndexBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-
-    LhsFlags = evaluator<Lhs>::Flags,
-    RhsFlags = evaluator<Rhs>::Flags,
-    SameType = is_same<typename Lhs::Scalar,typename Rhs::Scalar>::value,
-    StorageOrdersAgree = (int(LhsFlags)&RowMajorBit)==(int(RhsFlags)&RowMajorBit),
-    Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
-        HereditaryBits
-      | (int(LhsFlags) & int(RhsFlags) &
-           ( (StorageOrdersAgree ? LinearAccessBit : 0)
-           | (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
-           )
-        )
-     ),
-    Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
-    Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<Lhs>::Alignment,evaluator<Rhs>::Alignment)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit binary_evaluator(const XprType& xpr) : m_d(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.lhsImpl.coeff(row, col), m_d.rhsImpl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.lhsImpl.coeff(index), m_d.rhsImpl.coeff(index));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode,PacketType>(row, col),
-                               m_d.rhsImpl.template packet<LoadMode,PacketType>(row, col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode,PacketType>(index),
-                               m_d.rhsImpl.template packet<LoadMode,PacketType>(index));
-  }
-
-protected:
-
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), lhsImpl(xpr.lhs()), rhsImpl(xpr.rhs()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const BinaryOp& func() const { return op; }
-    BinaryOp op;
-    evaluator<Lhs> lhsImpl;
-    evaluator<Rhs> rhsImpl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- CwiseUnaryView --------------------
-
-template<typename UnaryOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryView<UnaryOp, ArgType>, IndexBased>
-  : evaluator_base<CwiseUnaryView<UnaryOp, ArgType> >
-{
-  typedef CwiseUnaryView<UnaryOp, ArgType> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
-
-    Flags = (evaluator<ArgType>::Flags & (HereditaryBits | LinearAccessBit | DirectAccessBit)),
-
-    Alignment = 0 // FIXME it is not very clear why alignment is necessarily lost...
-  };
-
-  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op) : m_d(op)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(index));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_d.func()(m_d.argImpl.coeffRef(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_d.func()(m_d.argImpl.coeffRef(index));
-  }
-
-protected:
-
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const UnaryOp& func() const { return op; }
-    UnaryOp op;
-    evaluator<ArgType> argImpl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- Map --------------------
-
-// FIXME perhaps the PlainObjectType could be provided by Derived::PlainObject ?
-// but that might complicate template specialization
-template<typename Derived, typename PlainObjectType>
-struct mapbase_evaluator;
-
-template<typename Derived, typename PlainObjectType>
-struct mapbase_evaluator : evaluator_base<Derived>
-{
-  typedef Derived  XprType;
-  typedef typename XprType::PointerType PointerType;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    IsRowMajor = XprType::RowsAtCompileTime,
-    ColsAtCompileTime = XprType::ColsAtCompileTime,
-    CoeffReadCost = NumTraits<Scalar>::ReadCost
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit mapbase_evaluator(const XprType& map)
-    : m_data(const_cast<PointerType>(map.data())),
-      m_innerStride(map.innerStride()),
-      m_outerStride(map.outerStride())
-  {
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(evaluator<Derived>::Flags&PacketAccessBit, internal::inner_stride_at_compile_time<Derived>::ret==1),
-                        PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_data[col * colStride() + row * rowStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_data[index * m_innerStride.value()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_data[col * colStride() + row * rowStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_data[index * m_innerStride.value()];
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    PointerType ptr = m_data + row * rowStride() + col * colStride();
-    return internal::ploadt<PacketType, LoadMode>(ptr);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return internal::ploadt<PacketType, LoadMode>(m_data + index * m_innerStride.value());
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    PointerType ptr = m_data + row * rowStride() + col * colStride();
-    return internal::pstoret<Scalar, PacketType, StoreMode>(ptr, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    internal::pstoret<Scalar, PacketType, StoreMode>(m_data + index * m_innerStride.value(), x);
-  }
-protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index rowStride() const EIGEN_NOEXCEPT {
-    return XprType::IsRowMajor ? m_outerStride.value() : m_innerStride.value();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index colStride() const EIGEN_NOEXCEPT {
-     return XprType::IsRowMajor ? m_innerStride.value() : m_outerStride.value();
-  }
-
-  PointerType m_data;
-  const internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_innerStride;
-  const internal::variable_if_dynamic<Index, XprType::OuterStrideAtCompileTime> m_outerStride;
-};
-
-template<typename PlainObjectType, int MapOptions, typename StrideType>
-struct evaluator<Map<PlainObjectType, MapOptions, StrideType> >
-  : public mapbase_evaluator<Map<PlainObjectType, MapOptions, StrideType>, PlainObjectType>
-{
-  typedef Map<PlainObjectType, MapOptions, StrideType> XprType;
-  typedef typename XprType::Scalar Scalar;
-  // TODO: should check for smaller packet types once we can handle multi-sized packet types
-  typedef typename packet_traits<Scalar>::type PacketScalar;
-
-  enum {
-    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
-                             ? int(PlainObjectType::InnerStrideAtCompileTime)
-                             : int(StrideType::InnerStrideAtCompileTime),
-    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
-                             ? int(PlainObjectType::OuterStrideAtCompileTime)
-                             : int(StrideType::OuterStrideAtCompileTime),
-    HasNoInnerStride = InnerStrideAtCompileTime == 1,
-    HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0,
-    HasNoStride = HasNoInnerStride && HasNoOuterStride,
-    IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic,
-
-    PacketAccessMask = bool(HasNoInnerStride) ? ~int(0) : ~int(PacketAccessBit),
-    LinearAccessMask = bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime) ? ~int(0) : ~int(LinearAccessBit),
-    Flags = int( evaluator<PlainObjectType>::Flags) & (LinearAccessMask&PacketAccessMask),
-
-    Alignment = int(MapOptions)&int(AlignedMask)
-  };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& map)
-    : mapbase_evaluator<XprType, PlainObjectType>(map)
-  { }
-};
-
-// -------------------- Ref --------------------
-
-template<typename PlainObjectType, int RefOptions, typename StrideType>
-struct evaluator<Ref<PlainObjectType, RefOptions, StrideType> >
-  : public mapbase_evaluator<Ref<PlainObjectType, RefOptions, StrideType>, PlainObjectType>
-{
-  typedef Ref<PlainObjectType, RefOptions, StrideType> XprType;
-
-  enum {
-    Flags = evaluator<Map<PlainObjectType, RefOptions, StrideType> >::Flags,
-    Alignment = evaluator<Map<PlainObjectType, RefOptions, StrideType> >::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& ref)
-    : mapbase_evaluator<XprType, PlainObjectType>(ref)
-  { }
-};
-
-// -------------------- Block --------------------
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel,
-         bool HasDirectAccess = internal::has_direct_access<ArgType>::ret> struct block_evaluator;
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
-  : block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel>
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-  typedef typename XprType::Scalar Scalar;
-  // TODO: should check for smaller packet types once we can handle multi-sized packet types
-  typedef typename packet_traits<Scalar>::type PacketScalar;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-
-    RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
-    ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
-
-    ArgTypeIsRowMajor = (int(evaluator<ArgType>::Flags)&RowMajorBit) != 0,
-    IsRowMajor = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? 1
-               : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
-               : ArgTypeIsRowMajor,
-    HasSameStorageOrderAsArgType = (IsRowMajor == ArgTypeIsRowMajor),
-    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-    InnerStrideAtCompileTime = HasSameStorageOrderAsArgType
-                             ? int(inner_stride_at_compile_time<ArgType>::ret)
-                             : int(outer_stride_at_compile_time<ArgType>::ret),
-    OuterStrideAtCompileTime = HasSameStorageOrderAsArgType
-                             ? int(outer_stride_at_compile_time<ArgType>::ret)
-                             : int(inner_stride_at_compile_time<ArgType>::ret),
-    MaskPacketAccessBit = (InnerStrideAtCompileTime == 1 || HasSameStorageOrderAsArgType) ? PacketAccessBit : 0,
-
-    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (evaluator<ArgType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0,
-    FlagsRowMajorBit = XprType::Flags&RowMajorBit,
-    Flags0 = evaluator<ArgType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
-                                           DirectAccessBit |
-                                           MaskPacketAccessBit),
-    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit,
-
-    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
-    Alignment0 = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic)
-                             && (OuterStrideAtCompileTime!=0)
-                             && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0)) ? int(PacketAlignment) : 0,
-    Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ArgType>::Alignment, Alignment0)
-  };
-  typedef block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> block_evaluator_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& block) : block_evaluator_type(block)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-};
-
-// no direct-access => dispatch to a unary evaluator
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /*HasDirectAccess*/ false>
-  : unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit block_evaluator(const XprType& block)
-    : unary_evaluator<XprType>(block)
-  {}
-};
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBased>
-  : evaluator_base<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& block)
-    : m_argImpl(block.nestedExpression()),
-      m_startRow(block.startRow()),
-      m_startCol(block.startCol()),
-      m_linear_offset(ForwardLinearAccess?(ArgType::IsRowMajor ? block.startRow()*block.nestedExpression().cols() + block.startCol() : block.startCol()*block.nestedExpression().rows() + block.startRow()):0)
-  { }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    RowsAtCompileTime = XprType::RowsAtCompileTime,
-    ForwardLinearAccess = (InnerPanel || int(XprType::IsRowMajor)==int(ArgType::IsRowMajor)) && bool(evaluator<ArgType>::Flags&LinearAccessBit)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(m_startRow.value() + row, m_startCol.value() + col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return linear_coeff_impl(index, bool_constant<ForwardLinearAccess>());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(m_startRow.value() + row, m_startCol.value() + col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return linear_coeffRef_impl(index, bool_constant<ForwardLinearAccess>());
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    if (ForwardLinearAccess)
-      return m_argImpl.template packet<LoadMode,PacketType>(m_linear_offset.value() + index);
-    else
-      return packet<LoadMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
-                                         RowsAtCompileTime == 1 ? index : 0);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    return m_argImpl.template writePacket<StoreMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    if (ForwardLinearAccess)
-      return m_argImpl.template writePacket<StoreMode,PacketType>(m_linear_offset.value() + index, x);
-    else
-      return writePacket<StoreMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
-                                              RowsAtCompileTime == 1 ? index : 0,
-                                              x);
-  }
-
-protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType linear_coeff_impl(Index index, internal::true_type /* ForwardLinearAccess */) const
-  {
-    return m_argImpl.coeff(m_linear_offset.value() + index);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType linear_coeff_impl(Index index, internal::false_type /* not ForwardLinearAccess */) const
-  {
-    return coeff(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& linear_coeffRef_impl(Index index, internal::true_type /* ForwardLinearAccess */)
-  {
-    return m_argImpl.coeffRef(m_linear_offset.value() + index);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& linear_coeffRef_impl(Index index, internal::false_type /* not ForwardLinearAccess */)
-  {
-    return coeffRef(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
-  }
-
-  evaluator<ArgType> m_argImpl;
-  const variable_if_dynamic<Index, (ArgType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
-  const variable_if_dynamic<Index, (ArgType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
-  const variable_if_dynamic<Index, ForwardLinearAccess ? Dynamic : 0> m_linear_offset;
-};
-
-// TODO: This evaluator does not actually use the child evaluator;
-// all action is via the data() as returned by the Block expression.
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /* HasDirectAccess */ true>
-  : mapbase_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>,
-                      typename Block<ArgType, BlockRows, BlockCols, InnerPanel>::PlainObject>
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-  typedef typename XprType::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit block_evaluator(const XprType& block)
-    : mapbase_evaluator<XprType, typename XprType::PlainObject>(block)
-  {
-    // TODO: for the 3.3 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
-    eigen_assert(((internal::UIntPtr(block.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
-  }
-};
-
-
-// -------------------- Select --------------------
-// NOTE shall we introduce a ternary_evaluator?
-
-// TODO enable vectorization for Select
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
-struct evaluator<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
-  : evaluator_base<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
-{
-  typedef Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> XprType;
-  enum {
-    CoeffReadCost = evaluator<ConditionMatrixType>::CoeffReadCost
-                  + EIGEN_PLAIN_ENUM_MAX(evaluator<ThenMatrixType>::CoeffReadCost,
-                                         evaluator<ElseMatrixType>::CoeffReadCost),
-
-    Flags = (unsigned int)evaluator<ThenMatrixType>::Flags & evaluator<ElseMatrixType>::Flags & HereditaryBits,
-
-    Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ThenMatrixType>::Alignment, evaluator<ElseMatrixType>::Alignment)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& select)
-    : m_conditionImpl(select.conditionMatrix()),
-      m_thenImpl(select.thenMatrix()),
-      m_elseImpl(select.elseMatrix())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    if (m_conditionImpl.coeff(row, col))
-      return m_thenImpl.coeff(row, col);
-    else
-      return m_elseImpl.coeff(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    if (m_conditionImpl.coeff(index))
-      return m_thenImpl.coeff(index);
-    else
-      return m_elseImpl.coeff(index);
-  }
-
-protected:
-  evaluator<ConditionMatrixType> m_conditionImpl;
-  evaluator<ThenMatrixType> m_thenImpl;
-  evaluator<ElseMatrixType> m_elseImpl;
-};
-
-
-// -------------------- Replicate --------------------
-
-template<typename ArgType, int RowFactor, int ColFactor>
-struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
-  : evaluator_base<Replicate<ArgType, RowFactor, ColFactor> >
-{
-  typedef Replicate<ArgType, RowFactor, ColFactor> XprType;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  enum {
-    Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor
-  };
-  typedef typename internal::nested_eval<ArgType,Factor>::type ArgTypeNested;
-  typedef typename internal::remove_all<ArgTypeNested>::type ArgTypeNestedCleaned;
-
-  enum {
-    CoeffReadCost = evaluator<ArgTypeNestedCleaned>::CoeffReadCost,
-    LinearAccessMask = XprType::IsVectorAtCompileTime ? LinearAccessBit : 0,
-    Flags = (evaluator<ArgTypeNestedCleaned>::Flags & (HereditaryBits|LinearAccessMask) & ~RowMajorBit) | (traits<XprType>::Flags & RowMajorBit),
-
-    Alignment = evaluator<ArgTypeNestedCleaned>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& replicate)
-    : m_arg(replicate.nestedExpression()),
-      m_argImpl(m_arg),
-      m_rows(replicate.nestedExpression().rows()),
-      m_cols(replicate.nestedExpression().cols())
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    // try to avoid using modulo; this is a pure optimization strategy
-    const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
-                           : RowFactor==1 ? row
-                           : row % m_rows.value();
-    const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0
-                           : ColFactor==1 ? col
-                           : col % m_cols.value();
-
-    return m_argImpl.coeff(actual_row, actual_col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    // try to avoid using modulo; this is a pure optimization strategy
-    const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
-                                  ? (ColFactor==1 ?  index : index%m_cols.value())
-                                  : (RowFactor==1 ?  index : index%m_rows.value());
-
-    return m_argImpl.coeff(actual_index);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
-                           : RowFactor==1 ? row
-                           : row % m_rows.value();
-    const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0
-                           : ColFactor==1 ? col
-                           : col % m_cols.value();
-
-    return m_argImpl.template packet<LoadMode,PacketType>(actual_row, actual_col);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
-                                  ? (ColFactor==1 ?  index : index%m_cols.value())
-                                  : (RowFactor==1 ?  index : index%m_rows.value());
-
-    return m_argImpl.template packet<LoadMode,PacketType>(actual_index);
-  }
-
-protected:
-  const ArgTypeNested m_arg;
-  evaluator<ArgTypeNestedCleaned> m_argImpl;
-  const variable_if_dynamic<Index, ArgType::RowsAtCompileTime> m_rows;
-  const variable_if_dynamic<Index, ArgType::ColsAtCompileTime> m_cols;
-};
-
-// -------------------- MatrixWrapper and ArrayWrapper --------------------
-//
-// evaluator_wrapper_base<T> is a common base class for the
-// MatrixWrapper and ArrayWrapper evaluators.
-
-template<typename XprType>
-struct evaluator_wrapper_base
-  : evaluator_base<XprType>
-{
-  typedef typename remove_all<typename XprType::NestedExpressionType>::type ArgType;
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    Flags = evaluator<ArgType>::Flags,
-    Alignment = evaluator<ArgType>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator_wrapper_base(const ArgType& arg) : m_argImpl(arg) {}
-
-  typedef typename ArgType::Scalar Scalar;
-  typedef typename ArgType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(index);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(row, col);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(index);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode>(row, col, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode>(index, x);
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-};
-
-template<typename TArgType>
-struct unary_evaluator<MatrixWrapper<TArgType> >
-  : evaluator_wrapper_base<MatrixWrapper<TArgType> >
-{
-  typedef MatrixWrapper<TArgType> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& wrapper)
-    : evaluator_wrapper_base<MatrixWrapper<TArgType> >(wrapper.nestedExpression())
-  { }
-};
-
-template<typename TArgType>
-struct unary_evaluator<ArrayWrapper<TArgType> >
-  : evaluator_wrapper_base<ArrayWrapper<TArgType> >
-{
-  typedef ArrayWrapper<TArgType> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& wrapper)
-    : evaluator_wrapper_base<ArrayWrapper<TArgType> >(wrapper.nestedExpression())
-  { }
-};
-
-
-// -------------------- Reverse --------------------
-
-// defined in Reverse.h:
-template<typename PacketType, bool ReversePacket> struct reverse_packet_cond;
-
-template<typename ArgType, int Direction>
-struct unary_evaluator<Reverse<ArgType, Direction> >
-  : evaluator_base<Reverse<ArgType, Direction> >
-{
-  typedef Reverse<ArgType, Direction> XprType;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    IsRowMajor = XprType::IsRowMajor,
-    IsColMajor = !IsRowMajor,
-    ReverseRow = (Direction == Vertical)   || (Direction == BothDirections),
-    ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
-    ReversePacket = (Direction == BothDirections)
-                    || ((Direction == Vertical)   && IsColMajor)
-                    || ((Direction == Horizontal) && IsRowMajor),
-
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-
-    // let's enable LinearAccess only with vectorization because of the product overhead
-    // FIXME enable DirectAccess with negative strides?
-    Flags0 = evaluator<ArgType>::Flags,
-    LinearAccess = ( (Direction==BothDirections) && (int(Flags0)&PacketAccessBit) )
-                  || ((ReverseRow && XprType::ColsAtCompileTime==1) || (ReverseCol && XprType::RowsAtCompileTime==1))
-                 ? LinearAccessBit : 0,
-
-    Flags = int(Flags0) & (HereditaryBits | PacketAccessBit | LinearAccess),
-
-    Alignment = 0 // FIXME in some rare cases, Alignment could be preserved, like a Vector4f.
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& reverse)
-    : m_argImpl(reverse.nestedExpression()),
-      m_rows(ReverseRow ? reverse.nestedExpression().rows() : 1),
-      m_cols(ReverseCol ? reverse.nestedExpression().cols() : 1)
-  { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(ReverseRow ? m_rows.value() - row - 1 : row,
-                           ReverseCol ? m_cols.value() - col - 1 : col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(m_rows.value() * m_cols.value() - index - 1);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(ReverseRow ? m_rows.value() - row - 1 : row,
-                              ReverseCol ? m_cols.value() - col - 1 : col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(m_rows.value() * m_cols.value() - index - 1);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
-      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1
-    };
-    typedef internal::reverse_packet_cond<PacketType,ReversePacket> reverse_packet;
-    return reverse_packet::run(m_argImpl.template packet<LoadMode,PacketType>(
-                                  ReverseRow ? m_rows.value() - row - OffsetRow : row,
-                                  ReverseCol ? m_cols.value() - col - OffsetCol : col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    enum { PacketSize = unpacket_traits<PacketType>::size };
-    return preverse(m_argImpl.template packet<LoadMode,PacketType>(m_rows.value() * m_cols.value() - index - PacketSize));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    // FIXME we could factorize some code with packet(i,j)
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
-      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1
-    };
-    typedef internal::reverse_packet_cond<PacketType,ReversePacket> reverse_packet;
-    m_argImpl.template writePacket<LoadMode>(
-                                  ReverseRow ? m_rows.value() - row - OffsetRow : row,
-                                  ReverseCol ? m_cols.value() - col - OffsetCol : col,
-                                  reverse_packet::run(x));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    enum { PacketSize = unpacket_traits<PacketType>::size };
-    m_argImpl.template writePacket<LoadMode>
-      (m_rows.value() * m_cols.value() - index - PacketSize, preverse(x));
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-
-  // If we do not reverse rows, then we do not need to know the number of rows; same for columns
-  // Nonetheless, in this case it is important to set to 1 such that the coeff(index) method works fine for vectors.
-  const variable_if_dynamic<Index, ReverseRow ? ArgType::RowsAtCompileTime : 1> m_rows;
-  const variable_if_dynamic<Index, ReverseCol ? ArgType::ColsAtCompileTime : 1> m_cols;
-};
-
-
-// -------------------- Diagonal --------------------
-
-template<typename ArgType, int DiagIndex>
-struct evaluator<Diagonal<ArgType, DiagIndex> >
-  : evaluator_base<Diagonal<ArgType, DiagIndex> >
-{
-  typedef Diagonal<ArgType, DiagIndex> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-
-    Flags = (unsigned int)(evaluator<ArgType>::Flags & (HereditaryBits | DirectAccessBit) & ~RowMajorBit) | LinearAccessBit,
-
-    Alignment = 0
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& diagonal)
-    : m_argImpl(diagonal.nestedExpression()),
-      m_index(diagonal.index())
-  { }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index) const
-  {
-    return m_argImpl.coeff(row + rowOffset(), row + colOffset());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(index + rowOffset(), index + colOffset());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index)
-  {
-    return m_argImpl.coeffRef(row + rowOffset(), row + colOffset());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(index + rowOffset(), index + colOffset());
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-  const internal::variable_if_dynamicindex<Index, XprType::DiagIndex> m_index;
-
-private:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index rowOffset() const { return m_index.value() > 0 ? 0 : -m_index.value(); }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index colOffset() const { return m_index.value() > 0 ? m_index.value() : 0; }
-};
-
-
-//----------------------------------------------------------------------
-// deprecated code
-//----------------------------------------------------------------------
-
-// -------------------- EvalToTemp --------------------
-
-// expression class for evaluating nested expression to a temporary
-
-template<typename ArgType> class EvalToTemp;
-
-template<typename ArgType>
-struct traits<EvalToTemp<ArgType> >
-  : public traits<ArgType>
-{ };
-
-template<typename ArgType>
-class EvalToTemp
-  : public dense_xpr_base<EvalToTemp<ArgType> >::type
-{
- public:
-
-  typedef typename dense_xpr_base<EvalToTemp>::type Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(EvalToTemp)
-
-  explicit EvalToTemp(const ArgType& arg)
-    : m_arg(arg)
-  { }
-
-  const ArgType& arg() const
-  {
-    return m_arg;
-  }
-
-  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT
-  {
-    return m_arg.rows();
-  }
-
-  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT
-  {
-    return m_arg.cols();
-  }
-
- private:
-  const ArgType& m_arg;
-};
-
-template<typename ArgType>
-struct evaluator<EvalToTemp<ArgType> >
-  : public evaluator<typename ArgType::PlainObject>
-{
-  typedef EvalToTemp<ArgType>                   XprType;
-  typedef typename ArgType::PlainObject         PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
-    : m_result(xpr.arg())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-  }
-
-  // This constructor is used when nesting an EvalTo evaluator in another evaluator
-  EIGEN_DEVICE_FUNC evaluator(const ArgType& arg)
-    : m_result(arg)
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COREEVALUATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CoreIterators.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CoreIterators.h
deleted file mode 100644
index b967196..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CoreIterators.h
+++ /dev/null
@@ -1,132 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COREITERATORS_H
-#define EIGEN_COREITERATORS_H
-
-namespace Eigen { 
-
-/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
- */
-
-namespace internal {
-
-template<typename XprType, typename EvaluatorKind>
-class inner_iterator_selector;
-
-}
-
-/** \class InnerIterator
-  * \brief An InnerIterator allows to loop over the element of any matrix expression.
-  * 
-  * \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed.
-  * 
-  * TODO: add a usage example
-  */
-template<typename XprType>
-class InnerIterator
-{
-protected:
-  typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
-  typedef internal::evaluator<XprType> EvaluatorType;
-  typedef typename internal::traits<XprType>::Scalar Scalar;
-public:
-  /** Construct an iterator over the \a outerId -th row or column of \a xpr */
-  InnerIterator(const XprType &xpr, const Index &outerId)
-    : m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize())
-  {}
-  
-  /// \returns the value of the current coefficient.
-  EIGEN_STRONG_INLINE Scalar value() const          { return m_iter.value(); }
-  /** Increment the iterator \c *this to the next non-zero coefficient.
-    * Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
-    */
-  EIGEN_STRONG_INLINE InnerIterator& operator++()   { m_iter.operator++(); return *this; }
-  EIGEN_STRONG_INLINE InnerIterator& operator+=(Index i) { m_iter.operator+=(i); return *this; }
-  EIGEN_STRONG_INLINE InnerIterator operator+(Index i) 
-  { InnerIterator result(*this); result+=i; return result; }
-    
-
-  /// \returns the column or row index of the current coefficient.
-  EIGEN_STRONG_INLINE Index index() const           { return m_iter.index(); }
-  /// \returns the row index of the current coefficient.
-  EIGEN_STRONG_INLINE Index row() const             { return m_iter.row(); }
-  /// \returns the column index of the current coefficient.
-  EIGEN_STRONG_INLINE Index col() const             { return m_iter.col(); }
-  /// \returns \c true if the iterator \c *this still references a valid coefficient.
-  EIGEN_STRONG_INLINE operator bool() const         { return m_iter; }
-  
-protected:
-  EvaluatorType m_eval;
-  IteratorType m_iter;
-private:
-  // If you get here, then you're not using the right InnerIterator type, e.g.:
-  //   SparseMatrix<double,RowMajor> A;
-  //   SparseMatrix<double>::InnerIterator it(A,0);
-  template<typename T> InnerIterator(const EigenBase<T>&,Index outer);
-};
-
-namespace internal {
-
-// Generic inner iterator implementation for dense objects
-template<typename XprType>
-class inner_iterator_selector<XprType, IndexBased>
-{
-protected:
-  typedef evaluator<XprType> EvaluatorType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
-  
-public:
-  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
-    : m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize)
-  {}
-
-  EIGEN_STRONG_INLINE Scalar value() const
-  {
-    return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner)
-                        : m_eval.coeff(m_inner, m_outer);
-  }
-
-  EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; }
-
-  EIGEN_STRONG_INLINE Index index() const { return m_inner; }
-  inline Index row() const { return IsRowMajor ? m_outer : index(); }
-  inline Index col() const { return IsRowMajor ? index() : m_outer; }
-
-  EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
-
-protected:
-  const EvaluatorType& m_eval;
-  Index m_inner;
-  const Index m_outer;
-  const Index m_end;
-};
-
-// For iterator-based evaluator, inner-iterator is already implemented as
-// evaluator<>::InnerIterator
-template<typename XprType>
-class inner_iterator_selector<XprType, IteratorBased>
- : public evaluator<XprType>::InnerIterator
-{
-protected:
-  typedef typename evaluator<XprType>::InnerIterator Base;
-  typedef evaluator<XprType> EvaluatorType;
-  
-public:
-  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/)
-    : Base(eval, outerId)
-  {}  
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COREITERATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseBinaryOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseBinaryOp.h
deleted file mode 100644
index 2202b1c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseBinaryOp.h
+++ /dev/null
@@ -1,183 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_BINARY_OP_H
-#define EIGEN_CWISE_BINARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  // we must not inherit from traits<Lhs> since it has
-  // the potential to cause problems with MSVC
-  typedef typename remove_all<Lhs>::type Ancestor;
-  typedef typename traits<Ancestor>::XprKind XprKind;
-  enum {
-    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
-    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
-  };
-
-  // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
-  // we still want to handle the case when the result type is different.
-  typedef typename result_of<
-                     BinaryOp(
-                       const typename Lhs::Scalar&,
-                       const typename Rhs::Scalar&
-                     )
-                   >::type Scalar;
-  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,
-                                              typename traits<Rhs>::StorageKind,
-                                              BinaryOp>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
-                                      typename traits<Rhs>::StorageIndex>::type StorageIndex;
-  typedef typename Lhs::Nested LhsNested;
-  typedef typename Rhs::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  enum {
-    Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value
-  };
-};
-} // end namespace internal
-
-template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
-class CwiseBinaryOpImpl;
-
-/** \class CwiseBinaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
-  *
-  * \tparam BinaryOp template functor implementing the operator
-  * \tparam LhsType the type of the left-hand side
-  * \tparam RhsType the type of the right-hand side
-  *
-  * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
-  * It is the return type of binary operators, by which we mean only those binary operators where
-  * both the left-hand side and the right-hand side are Eigen expressions.
-  * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
-  *
-  * Most of the time, this is the only way that it is used, so you typically don't have to name
-  * CwiseBinaryOp types explicitly.
-  *
-  * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
-  */
-template<typename BinaryOp, typename LhsType, typename RhsType>
-class CwiseBinaryOp :
-  public CwiseBinaryOpImpl<
-          BinaryOp, LhsType, RhsType,
-          typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
-                                                        typename internal::traits<RhsType>::StorageKind,
-                                                        BinaryOp>::ret>,
-  internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::remove_all<BinaryOp>::type Functor;
-    typedef typename internal::remove_all<LhsType>::type Lhs;
-    typedef typename internal::remove_all<RhsType>::type Rhs;
-
-    typedef typename CwiseBinaryOpImpl<
-        BinaryOp, LhsType, RhsType,
-        typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
-                                                      typename internal::traits<Rhs>::StorageKind,
-                                                      BinaryOp>::ret>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
-
-    typedef typename internal::ref_selector<LhsType>::type LhsNested;
-    typedef typename internal::ref_selector<RhsType>::type RhsNested;
-    typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
-    typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
-
-#if EIGEN_COMP_MSVC && EIGEN_HAS_CXX11
-    //Required for Visual Studio or the Copy constructor will probably not get inlined!
-    EIGEN_STRONG_INLINE
-    CwiseBinaryOp(const CwiseBinaryOp<BinaryOp,LhsType,RhsType>&) = default;
-#endif
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
-      : m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
-    {
-      EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
-      // require the sizes to match
-      EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
-      eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT {
-      // return the fixed size type if available to enable compile time optimizations
-      return internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic ? m_rhs.rows() : m_lhs.rows();
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT {
-      // return the fixed size type if available to enable compile time optimizations
-      return internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic ? m_rhs.cols() : m_lhs.cols();
-    }
-
-    /** \returns the left hand side nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const _LhsNested& lhs() const { return m_lhs; }
-    /** \returns the right hand side nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const _RhsNested& rhs() const { return m_rhs; }
-    /** \returns the functor representing the binary operation */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const BinaryOp& functor() const { return m_functor; }
-
-  protected:
-    LhsNested m_lhs;
-    RhsNested m_rhs;
-    const BinaryOp m_functor;
-};
-
-// Generic API dispatcher
-template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
-class CwiseBinaryOpImpl
-  : public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
-};
-
-/** replaces \c *this by \c *this - \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this + \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_BINARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseNullaryOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseNullaryOp.h
deleted file mode 100644
index 289ec51..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseNullaryOp.h
+++ /dev/null
@@ -1,1001 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_NULLARY_OP_H
-#define EIGEN_CWISE_NULLARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename NullaryOp, typename PlainObjectType>
-struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
-{
-  enum {
-    Flags = traits<PlainObjectType>::Flags & RowMajorBit
-  };
-};
-
-} // namespace internal
-
-/** \class CwiseNullaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression of a matrix where all coefficients are defined by a functor
-  *
-  * \tparam NullaryOp template functor implementing the operator
-  * \tparam PlainObjectType the underlying plain matrix/array type
-  *
-  * This class represents an expression of a generic nullary operator.
-  * It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
-  * and most of the time this is the only way it is used.
-  *
-  * However, if you want to write a function returning such an expression, you
-  * will need to use this class.
-  *
-  * The functor NullaryOp must expose one of the following method:
-    <table class="manual">
-    <tr            ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries (e.g., random numbers)</td></tr>
-    <tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr>
-    <tr            ><td>\c operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)</td></tr>
-    </table>
-  * It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors.
-  *
-  * See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
-  * C++11 random number generators.
-  *
-  * A nullary expression can also be used to implement custom sophisticated matrix manipulations
-  * that cannot be covered by the existing set of natively supported matrix manipulations.
-  * See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
-  * on the behavior of CwiseNullaryOp.
-  *
-  * \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
-  */
-template<typename NullaryOp, typename PlainObjectType>
-class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
-
-    EIGEN_DEVICE_FUNC
-    CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
-      : m_rows(rows), m_cols(cols), m_functor(func)
-    {
-      eigen_assert(rows >= 0
-            && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
-            &&  cols >= 0
-            && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const { return m_rows.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const { return m_cols.value(); }
-
-    /** \returns the functor representing the nullary operation */
-    EIGEN_DEVICE_FUNC
-    const NullaryOp& functor() const { return m_functor; }
-
-  protected:
-    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
-    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
-    const NullaryOp m_functor;
-};
-
-
-/** \returns an expression of a matrix defined by a custom functor \a func
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-template<typename CustomNullaryOp>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const CwiseNullaryOp<CustomNullaryOp,typename DenseBase<Derived>::PlainObject>
-#else
-const CwiseNullaryOp<CustomNullaryOp,PlainObject>
-#endif
-DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
-{
-  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
-}
-
-/** \returns an expression of a matrix defined by a custom functor \a func
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * Here is an example with C++11 random generators: \include random_cpp11.cpp
-  * Output: \verbinclude random_cpp11.out
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-template<typename CustomNullaryOp>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
-#else
-const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-#endif
-DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, PlainObject>(1, size, func);
-  else return CwiseNullaryOp<CustomNullaryOp, PlainObject>(size, 1, func);
-}
-
-/** \returns an expression of a matrix defined by a custom functor \a func
-  *
-  * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-template<typename CustomNullaryOp>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
-#else
-const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-#endif
-DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
-{
-  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
-}
-
-/** \returns an expression of a constant matrix of value \a value
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this DenseBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
-{
-  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
-}
-
-/** \returns an expression of a constant matrix of value \a value
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this DenseBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Constant(Index size, const Scalar& value)
-{
-  return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
-}
-
-/** \returns an expression of a constant matrix of value \a value
-  *
-  * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Constant(const Scalar& value)
-{
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value));
-}
-
-/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&)
-  *
-  * \only_for_vectors
-  *
-  * Example: \include DenseBase_LinSpaced_seq_deprecated.cpp
-  * Output: \verbinclude DenseBase_LinSpaced_seq_deprecated.out
-  *
-  * \sa LinSpaced(Index,const Scalar&, const Scalar&), setLinSpaced(Index,const Scalar&,const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low,high,size));
-}
-
-/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&)
-  *
-  * \sa LinSpaced(const Scalar&, const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar>(low,high,Derived::SizeAtCompileTime));
-}
-
-/**
-  * \brief Sets a linearly spaced vector.
-  *
-  * The function generates 'size' equally spaced values in the closed interval [low,high].
-  * When size is set to 1, a vector of length 1 containing 'high' is returned.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include DenseBase_LinSpaced.cpp
-  * Output: \verbinclude DenseBase_LinSpaced.out
-  *
-  * For integer scalar types, an even spacing is possible if and only if the length of the range,
-  * i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the
-  * number of values \c high-low+1 (meaning each value can be repeated the same number of time).
-  * If one of these two considions is not satisfied, then \c high is lowered to the largest value
-  * satisfying one of this constraint.
-  * Here are some examples:
-  *
-  * Example: \include DenseBase_LinSpacedInt.cpp
-  * Output: \verbinclude DenseBase_LinSpacedInt.out
-  *
-  * \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low,high,size));
-}
-
-/**
-  * \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&)
-  * Special version for fixed size types which does not require the size parameter.
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar>(low,high,Derived::SizeAtCompileTime));
-}
-
-/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
-(const Scalar& val, const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = 0; i < rows(); ++i)
-      if(!internal::isApprox(self.coeff(i, j), val, prec))
-        return false;
-  return true;
-}
-
-/** This is just an alias for isApproxToConstant().
-  *
-  * \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant
-(const Scalar& val, const RealScalar& prec) const
-{
-  return isApproxToConstant(val, prec);
-}
-
-/** Alias for setConstant(): sets all coefficients in this expression to \a val.
-  *
-  * \sa setConstant(), Constant(), class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
-{
-  setConstant(val);
-}
-
-/** Sets all coefficients in this expression to value \a val.
-  *
-  * \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
-{
-  return derived() = Constant(rows(), cols(), val);
-}
-
-/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include Matrix_setConstant_int.cpp
-  * Output: \verbinclude Matrix_setConstant_int.out
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
-{
-  resize(size);
-  return setConstant(val);
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  * \param val the value to which all coefficients are set
-  *
-  * Example: \include Matrix_setConstant_int_int.cpp
-  * Output: \verbinclude Matrix_setConstant_int_int.out
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
-{
-  resize(rows, cols);
-  return setConstant(val);
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to the given value \a val. For the parameter
-  * of type NoChange_t, just pass the special value \c NoChange.
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(NoChange_t, Index cols, const Scalar& val)
-{
-  return setConstant(rows(), cols, val);
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to the given value \a val. For the parameter
-  * of type NoChange_t, just pass the special value \c NoChange.
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(Index rows, NoChange_t, const Scalar& val)
-{
-  return setConstant(rows, cols(), val);
-}
-
-
-/**
-  * \brief Sets a linearly spaced vector.
-  *
-  * The function generates 'size' equally spaced values in the closed interval [low,high].
-  * When size is set to 1, a vector of length 1 containing 'high' is returned.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include DenseBase_setLinSpaced.cpp
-  * Output: \verbinclude DenseBase_setLinSpaced.out
-  *
-  * For integer scalar types, do not miss the explanations on the definition
-  * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
-  *
-  * \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar>(low,high,newSize));
-}
-
-/**
-  * \brief Sets a linearly spaced vector.
-  *
-  * The function fills \c *this with equally spaced values in the closed interval [low,high].
-  * When size is set to 1, a vector of length 1 containing 'high' is returned.
-  *
-  * \only_for_vectors
-  *
-  * For integer scalar types, do not miss the explanations on the definition
-  * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
-  *
-  * \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return setLinSpaced(size(), low, high);
-}
-
-// zero:
-
-/** \returns an expression of a zero matrix.
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_zero_int_int.cpp
-  * Output: \verbinclude MatrixBase_zero_int_int.out
-  *
-  * \sa Zero(), Zero(Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Zero(Index rows, Index cols)
-{
-  return Constant(rows, cols, Scalar(0));
-}
-
-/** \returns an expression of a zero vector.
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Zero() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_zero_int.cpp
-  * Output: \verbinclude MatrixBase_zero_int.out
-  *
-  * \sa Zero(), Zero(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Zero(Index size)
-{
-  return Constant(size, Scalar(0));
-}
-
-/** \returns an expression of a fixed-size zero matrix or vector.
-  *
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * Example: \include MatrixBase_zero.cpp
-  * Output: \verbinclude MatrixBase_zero.out
-  *
-  * \sa Zero(Index), Zero(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Zero()
-{
-  return Constant(Scalar(0));
-}
-
-/** \returns true if *this is approximately equal to the zero matrix,
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isZero.cpp
-  * Output: \verbinclude MatrixBase_isZero.out
-  *
-  * \sa class CwiseNullaryOp, Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = 0; i < rows(); ++i)
-      if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec))
-        return false;
-  return true;
-}
-
-/** Sets all coefficients in this expression to zero.
-  *
-  * Example: \include MatrixBase_setZero.cpp
-  * Output: \verbinclude MatrixBase_setZero.out
-  *
-  * \sa class CwiseNullaryOp, Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
-{
-  return setConstant(Scalar(0));
-}
-
-/** Resizes to the given \a size, and sets all coefficients in this expression to zero.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include Matrix_setZero_int.cpp
-  * Output: \verbinclude Matrix_setZero_int.out
-  *
-  * \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(Index newSize)
-{
-  resize(newSize);
-  return setConstant(Scalar(0));
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to zero.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setZero_int_int.cpp
-  * Output: \verbinclude Matrix_setZero_int_int.out
-  *
-  * \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(Index rows, Index cols)
-{
-  resize(rows, cols);
-  return setConstant(Scalar(0));
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to zero. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
-  * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(NoChange_t, Index cols)
-{
-  return setZero(rows(), cols);
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to zero. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
-  * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(Index rows, NoChange_t)
-{
-  return setZero(rows, cols());
-}
-
-// ones:
-
-/** \returns an expression of a matrix where all coefficients equal one.
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_ones_int_int.cpp
-  * Output: \verbinclude MatrixBase_ones_int_int.out
-  *
-  * \sa Ones(), Ones(Index), isOnes(), class Ones
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Ones(Index rows, Index cols)
-{
-  return Constant(rows, cols, Scalar(1));
-}
-
-/** \returns an expression of a vector where all coefficients equal one.
-  *
-  * The parameter \a newSize is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Ones() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_ones_int.cpp
-  * Output: \verbinclude MatrixBase_ones_int.out
-  *
-  * \sa Ones(), Ones(Index,Index), isOnes(), class Ones
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Ones(Index newSize)
-{
-  return Constant(newSize, Scalar(1));
-}
-
-/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
-  *
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * Example: \include MatrixBase_ones.cpp
-  * Output: \verbinclude MatrixBase_ones.out
-  *
-  * \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Ones()
-{
-  return Constant(Scalar(1));
-}
-
-/** \returns true if *this is approximately equal to the matrix where all coefficients
-  *          are equal to 1, within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isOnes.cpp
-  * Output: \verbinclude MatrixBase_isOnes.out
-  *
-  * \sa class CwiseNullaryOp, Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes
-(const RealScalar& prec) const
-{
-  return isApproxToConstant(Scalar(1), prec);
-}
-
-/** Sets all coefficients in this expression to one.
-  *
-  * Example: \include MatrixBase_setOnes.cpp
-  * Output: \verbinclude MatrixBase_setOnes.out
-  *
-  * \sa class CwiseNullaryOp, Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
-{
-  return setConstant(Scalar(1));
-}
-
-/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include Matrix_setOnes_int.cpp
-  * Output: \verbinclude Matrix_setOnes_int.out
-  *
-  * \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(Index newSize)
-{
-  resize(newSize);
-  return setConstant(Scalar(1));
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to one.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setOnes_int_int.cpp
-  * Output: \verbinclude Matrix_setOnes_int_int.out
-  *
-  * \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
-{
-  resize(rows, cols);
-  return setConstant(Scalar(1));
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to one. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
- * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(NoChange_t, Index), class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(Index rows, NoChange_t)
-{
-  return setOnes(rows, cols());
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to one. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
- * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(Index, NoChange_t) class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(NoChange_t, Index cols)
-{
-  return setOnes(rows(), cols);
-}
-
-// Identity:
-
-/** \returns an expression of the identity matrix (not necessarily square).
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_identity_int_int.cpp
-  * Output: \verbinclude MatrixBase_identity_int_int.out
-  *
-  * \sa Identity(), setIdentity(), isIdentity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
-MatrixBase<Derived>::Identity(Index rows, Index cols)
-{
-  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
-}
-
-/** \returns an expression of the identity matrix (not necessarily square).
-  *
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variant taking size arguments.
-  *
-  * Example: \include MatrixBase_identity.cpp
-  * Output: \verbinclude MatrixBase_identity.out
-  *
-  * \sa Identity(Index,Index), setIdentity(), isIdentity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
-MatrixBase<Derived>::Identity()
-{
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return MatrixBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op<Scalar>());
-}
-
-/** \returns true if *this is approximately equal to the identity matrix
-  *          (not necessarily square),
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isIdentity.cpp
-  * Output: \verbinclude MatrixBase_isIdentity.out
-  *
-  * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isIdentity
-(const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index j = 0; j < cols(); ++j)
-  {
-    for(Index i = 0; i < rows(); ++i)
-    {
-      if(i == j)
-      {
-        if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
-          return false;
-      }
-      else
-      {
-        if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
-          return false;
-      }
-    }
-  }
-  return true;
-}
-
-namespace internal {
-
-template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
-struct setIdentity_impl
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
-  {
-    return m = Derived::Identity(m.rows(), m.cols());
-  }
-};
-
-template<typename Derived>
-struct setIdentity_impl<Derived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
-  {
-    m.setZero();
-    const Index size = numext::mini(m.rows(), m.cols());
-    for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
-    return m;
-  }
-};
-
-} // end namespace internal
-
-/** Writes the identity expression (not necessarily square) into *this.
-  *
-  * Example: \include MatrixBase_setIdentity.cpp
-  * Output: \verbinclude MatrixBase_setIdentity.out
-  *
-  * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
-{
-  return internal::setIdentity_impl<Derived>::run(derived());
-}
-
-/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setIdentity_int_int.cpp
-  * Output: \verbinclude Matrix_setIdentity_int_int.out
-  *
-  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
-{
-  derived().resize(rows, cols);
-  return setIdentity();
-}
-
-/** \returns an expression of the i-th unit (basis) vector.
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
-}
-
-/** \returns an expression of the i-th unit (basis) vector.
-  *
-  * \only_for_vectors
-  *
-  * This variant is for fixed-size vector only.
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return BasisReturnType(SquareMatrixType::Identity(),i);
-}
-
-/** \returns an expression of the X axis unit vector (1{,0}^*)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
-{ return Derived::Unit(0); }
-
-/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
-{ return Derived::Unit(1); }
-
-/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
-{ return Derived::Unit(2); }
-
-/** \returns an expression of the W axis unit vector (0,0,0,1)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
-{ return Derived::Unit(3); }
-
-/** \brief Set the coefficients of \c *this to the i-th unit (basis) vector
-  *
-  * \param i index of the unique coefficient to be set to 1
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  eigen_assert(i<size());
-  derived().setZero();
-  derived().coeffRef(i) = Scalar(1);
-  return derived();
-}
-
-/** \brief Resizes to the given \a newSize, and writes the i-th unit (basis) vector into *this.
-  *
-  * \param newSize the new size of the vector
-  * \param i index of the unique coefficient to be set to 1
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index newSize, Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  eigen_assert(i<newSize);
-  derived().resize(newSize);
-  return setUnit(i);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_NULLARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseTernaryOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseTernaryOp.h
deleted file mode 100644
index 9f3576f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseTernaryOp.h
+++ /dev/null
@@ -1,197 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_TERNARY_OP_H
-#define EIGEN_CWISE_TERNARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
-struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
-  // we must not inherit from traits<Arg1> since it has
-  // the potential to cause problems with MSVC
-  typedef typename remove_all<Arg1>::type Ancestor;
-  typedef typename traits<Ancestor>::XprKind XprKind;
-  enum {
-    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
-    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
-  };
-
-  // even though we require Arg1, Arg2, and Arg3 to have the same scalar type
-  // (see CwiseTernaryOp constructor),
-  // we still want to handle the case when the result type is different.
-  typedef typename result_of<TernaryOp(
-      const typename Arg1::Scalar&, const typename Arg2::Scalar&,
-      const typename Arg3::Scalar&)>::type Scalar;
-
-  typedef typename internal::traits<Arg1>::StorageKind StorageKind;
-  typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
-
-  typedef typename Arg1::Nested Arg1Nested;
-  typedef typename Arg2::Nested Arg2Nested;
-  typedef typename Arg3::Nested Arg3Nested;
-  typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
-  typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
-  typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
-  enum { Flags = _Arg1Nested::Flags & RowMajorBit };
-};
-}  // end namespace internal
-
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
-          typename StorageKind>
-class CwiseTernaryOpImpl;
-
-/** \class CwiseTernaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression where a coefficient-wise ternary operator is
- * applied to two expressions
-  *
-  * \tparam TernaryOp template functor implementing the operator
-  * \tparam Arg1Type the type of the first argument
-  * \tparam Arg2Type the type of the second argument
-  * \tparam Arg3Type the type of the third argument
-  *
-  * This class represents an expression where a coefficient-wise ternary
- * operator is applied to three expressions.
-  * It is the return type of ternary operators, by which we mean only those
- * ternary operators where
-  * all three arguments are Eigen expressions.
-  * For example, the return type of betainc(matrix1, matrix2, matrix3) is a
- * CwiseTernaryOp.
-  *
-  * Most of the time, this is the only way that it is used, so you typically
- * don't have to name
-  * CwiseTernaryOp types explicitly.
-  *
-  * \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
- * MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
- * class CwiseUnaryOp, class CwiseNullaryOp
-  */
-template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
-          typename Arg3Type>
-class CwiseTernaryOp : public CwiseTernaryOpImpl<
-                           TernaryOp, Arg1Type, Arg2Type, Arg3Type,
-                           typename internal::traits<Arg1Type>::StorageKind>,
-                       internal::no_assignment_operator
-{
- public:
-  typedef typename internal::remove_all<Arg1Type>::type Arg1;
-  typedef typename internal::remove_all<Arg2Type>::type Arg2;
-  typedef typename internal::remove_all<Arg3Type>::type Arg3;
-
-  typedef typename CwiseTernaryOpImpl<
-      TernaryOp, Arg1Type, Arg2Type, Arg3Type,
-      typename internal::traits<Arg1Type>::StorageKind>::Base Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
-
-  typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
-  typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
-  typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
-  typedef typename internal::remove_reference<Arg1Nested>::type _Arg1Nested;
-  typedef typename internal::remove_reference<Arg2Nested>::type _Arg2Nested;
-  typedef typename internal::remove_reference<Arg3Nested>::type _Arg3Nested;
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
-                                     const Arg3& a3,
-                                     const TernaryOp& func = TernaryOp())
-      : m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
-    // require the sizes to match
-    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
-    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
-
-    // The index types should match
-    EIGEN_STATIC_ASSERT((internal::is_same<
-                         typename internal::traits<Arg1Type>::StorageKind,
-                         typename internal::traits<Arg2Type>::StorageKind>::value),
-                        STORAGE_KIND_MUST_MATCH)
-    EIGEN_STATIC_ASSERT((internal::is_same<
-                         typename internal::traits<Arg1Type>::StorageKind,
-                         typename internal::traits<Arg3Type>::StorageKind>::value),
-                        STORAGE_KIND_MUST_MATCH)
-
-    eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
-                 a1.rows() == a3.rows() && a1.cols() == a3.cols());
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Index rows() const {
-    // return the fixed size type if available to enable compile time
-    // optimizations
-    if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                RowsAtCompileTime == Dynamic &&
-        internal::traits<typename internal::remove_all<Arg2Nested>::type>::
-                RowsAtCompileTime == Dynamic)
-      return m_arg3.rows();
-    else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                     RowsAtCompileTime == Dynamic &&
-             internal::traits<typename internal::remove_all<Arg3Nested>::type>::
-                     RowsAtCompileTime == Dynamic)
-      return m_arg2.rows();
-    else
-      return m_arg1.rows();
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Index cols() const {
-    // return the fixed size type if available to enable compile time
-    // optimizations
-    if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                ColsAtCompileTime == Dynamic &&
-        internal::traits<typename internal::remove_all<Arg2Nested>::type>::
-                ColsAtCompileTime == Dynamic)
-      return m_arg3.cols();
-    else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                     ColsAtCompileTime == Dynamic &&
-             internal::traits<typename internal::remove_all<Arg3Nested>::type>::
-                     ColsAtCompileTime == Dynamic)
-      return m_arg2.cols();
-    else
-      return m_arg1.cols();
-  }
-
-  /** \returns the first argument nested expression */
-  EIGEN_DEVICE_FUNC
-  const _Arg1Nested& arg1() const { return m_arg1; }
-  /** \returns the first argument nested expression */
-  EIGEN_DEVICE_FUNC
-  const _Arg2Nested& arg2() const { return m_arg2; }
-  /** \returns the third argument nested expression */
-  EIGEN_DEVICE_FUNC
-  const _Arg3Nested& arg3() const { return m_arg3; }
-  /** \returns the functor representing the ternary operation */
-  EIGEN_DEVICE_FUNC
-  const TernaryOp& functor() const { return m_functor; }
-
- protected:
-  Arg1Nested m_arg1;
-  Arg2Nested m_arg2;
-  Arg3Nested m_arg3;
-  const TernaryOp m_functor;
-};
-
-// Generic API dispatcher
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
-          typename StorageKind>
-class CwiseTernaryOpImpl
-    : public internal::generic_xpr_base<
-          CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type {
- public:
-  typedef typename internal::generic_xpr_base<
-      CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type Base;
-};
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CWISE_TERNARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseUnaryOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseUnaryOp.h
deleted file mode 100644
index e68c4f7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseUnaryOp.h
+++ /dev/null
@@ -1,103 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_UNARY_OP_H
-#define EIGEN_CWISE_UNARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename UnaryOp, typename XprType>
-struct traits<CwiseUnaryOp<UnaryOp, XprType> >
- : traits<XprType>
-{
-  typedef typename result_of<
-                     UnaryOp(const typename XprType::Scalar&)
-                   >::type Scalar;
-  typedef typename XprType::Nested XprTypeNested;
-  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
-  enum {
-    Flags = _XprTypeNested::Flags & RowMajorBit
-  };
-};
-}
-
-template<typename UnaryOp, typename XprType, typename StorageKind>
-class CwiseUnaryOpImpl;
-
-/** \class CwiseUnaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression where a coefficient-wise unary operator is applied to an expression
-  *
-  * \tparam UnaryOp template functor implementing the operator
-  * \tparam XprType the type of the expression to which we are applying the unary operator
-  *
-  * This class represents an expression where a unary operator is applied to an expression.
-  * It is the return type of all operations taking exactly 1 input expression, regardless of the
-  * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
-  * is considered unary, because only the right-hand side is an expression, and its
-  * return type is a specialization of CwiseUnaryOp.
-  *
-  * Most of the time, this is the only way that it is used, so you typically don't have to name
-  * CwiseUnaryOp types explicitly.
-  *
-  * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
-  */
-template<typename UnaryOp, typename XprType>
-class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>, internal::no_assignment_operator
-{
-  public:
-
-    typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
-    typedef typename internal::ref_selector<XprType>::type XprTypeNested;
-    typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
-      : m_xpr(xpr), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
-
-    /** \returns the functor representing the unary operation */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const UnaryOp& functor() const { return m_functor; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<XprTypeNested>::type&
-    nestedExpression() const { return m_xpr; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    typename internal::remove_all<XprTypeNested>::type&
-    nestedExpression() { return m_xpr; }
-
-  protected:
-    XprTypeNested m_xpr;
-    const UnaryOp m_functor;
-};
-
-// Generic API dispatcher
-template<typename UnaryOp, typename XprType, typename StorageKind>
-class CwiseUnaryOpImpl
-  : public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_UNARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseUnaryView.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseUnaryView.h
deleted file mode 100644
index a06d762..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/CwiseUnaryView.h
+++ /dev/null
@@ -1,132 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_UNARY_VIEW_H
-#define EIGEN_CWISE_UNARY_VIEW_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename ViewOp, typename MatrixType>
-struct traits<CwiseUnaryView<ViewOp, MatrixType> >
- : traits<MatrixType>
-{
-  typedef typename result_of<
-                     ViewOp(const typename traits<MatrixType>::Scalar&)
-                   >::type Scalar;
-  typedef typename MatrixType::Nested MatrixTypeNested;
-  typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = traits<_MatrixTypeNested>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
-    MatrixTypeInnerStride =  inner_stride_at_compile_time<MatrixType>::ret,
-    // need to cast the sizeof's from size_t to int explicitly, otherwise:
-    // "error: no integral type can represent all of the enumerator values
-    InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
-                             ? int(Dynamic)
-                             : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
-    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
-                             ? int(Dynamic)
-                             : outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
-  };
-};
-}
-
-template<typename ViewOp, typename MatrixType, typename StorageKind>
-class CwiseUnaryViewImpl;
-
-/** \class CwiseUnaryView
-  * \ingroup Core_Module
-  *
-  * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
-  *
-  * \tparam ViewOp template functor implementing the view
-  * \tparam MatrixType the type of the matrix we are applying the unary operator
-  *
-  * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
-  * It is the return type of real() and imag(), and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
-  */
-template<typename ViewOp, typename MatrixType>
-class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
-{
-  public:
-
-    typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
-    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-    explicit EIGEN_DEVICE_FUNC inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
-      : m_matrix(mat), m_functor(func) {}
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    /** \returns the functor representing unary operation */
-    EIGEN_DEVICE_FUNC const ViewOp& functor() const { return m_functor; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC const typename internal::remove_all<MatrixTypeNested>::type&
-    nestedExpression() const { return m_matrix; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC typename internal::remove_reference<MatrixTypeNested>::type&
-    nestedExpression() { return m_matrix; }
-
-  protected:
-    MatrixTypeNested m_matrix;
-    ViewOp m_functor;
-};
-
-// Generic API dispatcher
-template<typename ViewOp, typename XprType, typename StorageKind>
-class CwiseUnaryViewImpl
-  : public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type Base;
-};
-
-template<typename ViewOp, typename MatrixType>
-class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
-  : public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
-{
-  public:
-
-    typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
-    typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
-
-    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
-
-    EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const
-    {
-      return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const
-    {
-      return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
-    }
-  protected:
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_UNARY_VIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseBase.h
deleted file mode 100644
index 9b16db6..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseBase.h
+++ /dev/null
@@ -1,701 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DENSEBASE_H
-#define EIGEN_DENSEBASE_H
-
-namespace Eigen {
-
-namespace internal {
-
-// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
-// This dummy function simply aims at checking that at compile time.
-static inline void check_DenseIndex_is_signed() {
-  EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
-}
-
-} // end namespace internal
-
-/** \class DenseBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for all dense matrices, vectors, and arrays
-  *
-  * This class is the base that is inherited by all dense objects (matrix, vector, arrays,
-  * and related expression types). The common Eigen API for dense objects is contained in this class.
-  *
-  * \tparam Derived is the derived type, e.g., a matrix type or an expression.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived> class DenseBase
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  : public DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value>
-#else
-  : public DenseCoeffsBase<Derived,DirectWriteAccessors>
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-{
-  public:
-
-    /** Inner iterator type to iterate over the coefficients of a row or column.
-      * \sa class InnerIterator
-      */
-    typedef Eigen::InnerIterator<Derived> InnerIterator;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-
-    /**
-      * \brief The type used to store indices
-      * \details This typedef is relevant for types that store multiple indices such as
-      *          PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
-      * \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
-     */
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-
-    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-
-    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
-      *
-      * It is an alias for the Scalar type */
-    typedef Scalar value_type;
-
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value> Base;
-
-    using Base::derived;
-    using Base::const_cast_derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::rowIndexByOuterInner;
-    using Base::colIndexByOuterInner;
-    using Base::coeff;
-    using Base::coeffByOuterInner;
-    using Base::operator();
-    using Base::operator[];
-    using Base::x;
-    using Base::y;
-    using Base::z;
-    using Base::w;
-    using Base::stride;
-    using Base::innerStride;
-    using Base::outerStride;
-    using Base::rowStride;
-    using Base::colStride;
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-    enum {
-
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-        /**< The number of rows at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
-
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-        /**< The number of columns at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
-
-
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
-        /**< This is equal to the number of coefficients, i.e. the number of
-          * rows times the number of columns, or to \a Dynamic if this is not
-          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
-
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-        /**< This value is equal to the maximum possible number of rows that this expression
-          * might have. If this expression might have an arbitrarily high number of rows,
-          * this value is set to \a Dynamic.
-          *
-          * This value is useful to know when evaluating an expression, in order to determine
-          * whether it is possible to avoid doing a dynamic memory allocation.
-          *
-          * \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime
-          */
-
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-        /**< This value is equal to the maximum possible number of columns that this expression
-          * might have. If this expression might have an arbitrarily high number of columns,
-          * this value is set to \a Dynamic.
-          *
-          * This value is useful to know when evaluating an expression, in order to determine
-          * whether it is possible to avoid doing a dynamic memory allocation.
-          *
-          * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
-          */
-
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
-                                                      internal::traits<Derived>::MaxColsAtCompileTime>::ret),
-        /**< This value is equal to the maximum possible number of coefficients that this expression
-          * might have. If this expression might have an arbitrarily high number of coefficients,
-          * this value is set to \a Dynamic.
-          *
-          * This value is useful to know when evaluating an expression, in order to determine
-          * whether it is possible to avoid doing a dynamic memory allocation.
-          *
-          * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
-          */
-
-      IsVectorAtCompileTime = internal::traits<Derived>::RowsAtCompileTime == 1
-                           || internal::traits<Derived>::ColsAtCompileTime == 1,
-        /**< This is set to true if either the number of rows or the number of
-          * columns is known at compile-time to be equal to 1. Indeed, in that case,
-          * we are dealing with a column-vector (if there is only one column) or with
-          * a row-vector (if there is only one row). */
-
-      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2,
-        /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
-         * and 2 for matrices.
-         */
-
-      Flags = internal::traits<Derived>::Flags,
-        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
-          * constructed from this one. See the \ref flags "list of flags".
-          */
-
-      IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */
-
-      InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
-                             : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-
-      InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
-      OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
-    };
-
-    typedef typename internal::find_best_packet<Scalar,SizeAtCompileTime>::type PacketScalar;
-
-    enum { IsPlainObjectBase = 0 };
-
-    /** The plain matrix type corresponding to this expression.
-      * \sa PlainObject */
-    typedef Matrix<typename internal::traits<Derived>::Scalar,
-                internal::traits<Derived>::RowsAtCompileTime,
-                internal::traits<Derived>::ColsAtCompileTime,
-                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
-                internal::traits<Derived>::MaxRowsAtCompileTime,
-                internal::traits<Derived>::MaxColsAtCompileTime
-          > PlainMatrix;
-
-    /** The plain array type corresponding to this expression.
-      * \sa PlainObject */
-    typedef Array<typename internal::traits<Derived>::Scalar,
-                internal::traits<Derived>::RowsAtCompileTime,
-                internal::traits<Derived>::ColsAtCompileTime,
-                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
-                internal::traits<Derived>::MaxRowsAtCompileTime,
-                internal::traits<Derived>::MaxColsAtCompileTime
-          > PlainArray;
-
-    /** \brief The plain matrix or array type corresponding to this expression.
-      *
-      * This is not necessarily exactly the return type of eval(). In the case of plain matrices,
-      * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
-      * that the return type of eval() is either PlainObject or const PlainObject&.
-      */
-    typedef typename internal::conditional<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
-                                 PlainMatrix, PlainArray>::type PlainObject;
-
-    /** \returns the number of nonzero coefficients which is in practice the number
-      * of stored coefficients. */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index nonZeros() const { return size(); }
-
-    /** \returns the outer size.
-      *
-      * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
-      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
-      * column-major matrix, and the number of rows for a row-major matrix. */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index outerSize() const
-    {
-      return IsVectorAtCompileTime ? 1
-           : int(IsRowMajor) ? this->rows() : this->cols();
-    }
-
-    /** \returns the inner size.
-      *
-      * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
-      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
-      * column-major matrix, and the number of columns for a row-major matrix. */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index innerSize() const
-    {
-      return IsVectorAtCompileTime ? this->size()
-           : int(IsRowMajor) ? this->cols() : this->rows();
-    }
-
-    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
-      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
-      * nothing else.
-      */
-    EIGEN_DEVICE_FUNC
-    void resize(Index newSize)
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(newSize);
-      eigen_assert(newSize == this->size()
-                && "DenseBase::resize() does not actually allow to resize.");
-    }
-    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
-      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
-      * nothing else.
-      */
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols)
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(rows);
-      EIGEN_ONLY_USED_FOR_DEBUG(cols);
-      eigen_assert(rows == this->rows() && cols == this->cols()
-                && "DenseBase::resize() does not actually allow to resize.");
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
-    /** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
-    EIGEN_DEPRECATED typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> SequentialLinSpacedReturnType;
-    /** \internal Represents a vector with linearly spaced coefficients that allows random access. */
-    typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> RandomAccessLinSpacedReturnType;
-    /** \internal the return type of MatrixBase::eigenvalues() */
-    typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-    /** Copies \a other into *this. \returns a reference to *this. */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const DenseBase<OtherDerived>& other);
-
-    /** Special case of the template operator=, in order to prevent the compiler
-      * from generating a default operator= (issue hit with g++ 4.1)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const DenseBase& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const EigenBase<OtherDerived> &other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator+=(const EigenBase<OtherDerived> &other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator-=(const EigenBase<OtherDerived> &other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const ReturnByValue<OtherDerived>& func);
-
-    /** \internal
-      * Copies \a other into *this without evaluating other. \returns a reference to *this. */
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    Derived& lazyAssign(const DenseBase<OtherDerived>& other);
-
-    EIGEN_DEVICE_FUNC
-    CommaInitializer<Derived> operator<< (const Scalar& s);
-
-    template<unsigned int Added,unsigned int Removed>
-    /** \deprecated it now returns \c *this */
-    EIGEN_DEPRECATED
-    const Derived& flagged() const
-    { return derived(); }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
-
-    typedef Transpose<Derived> TransposeReturnType;
-    EIGEN_DEVICE_FUNC
-    TransposeReturnType transpose();
-    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
-    EIGEN_DEVICE_FUNC
-    ConstTransposeReturnType transpose() const;
-    EIGEN_DEVICE_FUNC
-    void transposeInPlace();
-
-    EIGEN_DEVICE_FUNC static const ConstantReturnType
-    Constant(Index rows, Index cols, const Scalar& value);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType
-    Constant(Index size, const Scalar& value);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType
-    Constant(const Scalar& value);
-
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
-
-    EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(Index size, const Scalar& low, const Scalar& high);
-    EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(const Scalar& low, const Scalar& high);
-
-    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
-    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-    NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
-    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
-    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-    NullaryExpr(Index size, const CustomNullaryOp& func);
-    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
-    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-    NullaryExpr(const CustomNullaryOp& func);
-
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero();
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
-
-    EIGEN_DEVICE_FUNC void fill(const Scalar& value);
-    EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
-    EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
-    EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
-    EIGEN_DEVICE_FUNC Derived& setZero();
-    EIGEN_DEVICE_FUNC Derived& setOnes();
-    EIGEN_DEVICE_FUNC Derived& setRandom();
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC
-    bool isApprox(const DenseBase<OtherDerived>& other,
-                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC
-    bool isMuchSmallerThan(const RealScalar& other,
-                           const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC
-    bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
-                           const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    inline bool hasNaN() const;
-    inline bool allFinite() const;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator*=(const Scalar& other);
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator/=(const Scalar& other);
-
-    typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
-    /** \returns the matrix or vector obtained by evaluating this expression.
-      *
-      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
-      * a const reference, in order to avoid a useless copy.
-      *
-      * \warning Be careful with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE EvalReturnType eval() const
-    {
-      // Even though MSVC does not honor strong inlining when the return type
-      // is a dynamic matrix, we desperately need strong inlining for fixed
-      // size types on MSVC.
-      return typename internal::eval<Derived>::type(derived());
-    }
-
-    /** swaps *this with the expression \a other.
-      *
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      eigen_assert(rows()==other.rows() && cols()==other.cols());
-      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    /** swaps *this with the matrix or array \a other.
-      *
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(PlainObjectBase<OtherDerived>& other)
-    {
-      eigen_assert(rows()==other.rows() && cols()==other.cols());
-      call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC inline const NestByValue<Derived> nestByValue() const;
-    EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
-    EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
-    template<bool Enable> EIGEN_DEVICE_FUNC
-    inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
-    template<bool Enable> EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
-
-    EIGEN_DEVICE_FUNC Scalar sum() const;
-    EIGEN_DEVICE_FUNC Scalar mean() const;
-    EIGEN_DEVICE_FUNC Scalar trace() const;
-
-    EIGEN_DEVICE_FUNC Scalar prod() const;
-
-    template<int NaNPropagation>
-    EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
-    template<int NaNPropagation>
-    EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
-
-
-    // By default, the fastest version with undefined NaN propagation semantics is
-    // used.
-    // TODO(rmlarsen): Replace with default template argument when we move to
-    // c++11 or beyond.
-    EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff() const {
-      return minCoeff<PropagateFast>();
-    }
-    EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff() const {
-      return maxCoeff<PropagateFast>();
-    }
-
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
-
-    // TODO(rmlarsen): Replace these methods with a default template argument.
-    template<typename IndexType>
-    EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const {
-      return minCoeff<PropagateFast>(row, col);
-    }
-    template<typename IndexType>
-    EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const {
-      return maxCoeff<PropagateFast>(row, col);
-    }
-    template<typename IndexType>
-     EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const {
-      return minCoeff<PropagateFast>(index);
-    }
-    template<typename IndexType>
-    EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const {
-      return maxCoeff<PropagateFast>(index);
-    }
-  
-    template<typename BinaryOp>
-    EIGEN_DEVICE_FUNC
-    Scalar redux(const BinaryOp& func) const;
-
-    template<typename Visitor>
-    EIGEN_DEVICE_FUNC
-    void visit(Visitor& func) const;
-
-    /** \returns a WithFormat proxy object allowing to print a matrix the with given
-      * format \a fmt.
-      *
-      * See class IOFormat for some examples.
-      *
-      * \sa class IOFormat, class WithFormat
-      */
-    inline const WithFormat<Derived> format(const IOFormat& fmt) const
-    {
-      return WithFormat<Derived>(derived(), fmt);
-    }
-
-    /** \returns the unique coefficient of a 1x1 expression */
-    EIGEN_DEVICE_FUNC
-    CoeffReturnType value() const
-    {
-      EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
-      eigen_assert(this->rows() == 1 && this->cols() == 1);
-      return derived().coeff(0,0);
-    }
-
-    EIGEN_DEVICE_FUNC bool all() const;
-    EIGEN_DEVICE_FUNC bool any() const;
-    EIGEN_DEVICE_FUNC Index count() const;
-
-    typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
-    typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
-    typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
-    typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
-
-    /** \returns a VectorwiseOp wrapper of *this for broadcasting and partial reductions
-    *
-    * Example: \include MatrixBase_rowwise.cpp
-    * Output: \verbinclude MatrixBase_rowwise.out
-    *
-    * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-    */
-    //Code moved here due to a CUDA compiler bug
-    EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const {
-      return ConstRowwiseReturnType(derived());
-    }
-    EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
-
-    /** \returns a VectorwiseOp wrapper of *this broadcasting and partial reductions
-    *
-    * Example: \include MatrixBase_colwise.cpp
-    * Output: \verbinclude MatrixBase_colwise.out
-    *
-    * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-    */
-    EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const {
-      return ConstColwiseReturnType(derived());
-    }
-    EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
-
-    typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>,PlainObject> RandomReturnType;
-    static const RandomReturnType Random(Index rows, Index cols);
-    static const RandomReturnType Random(Index size);
-    static const RandomReturnType Random();
-
-    template<typename ThenDerived,typename ElseDerived>
-    inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived,ElseDerived>
-    select(const DenseBase<ThenDerived>& thenMatrix,
-           const DenseBase<ElseDerived>& elseMatrix) const;
-
-    template<typename ThenDerived>
-    inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
-    select(const DenseBase<ThenDerived>& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const;
-
-    template<typename ElseDerived>
-    inline EIGEN_DEVICE_FUNC const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
-    select(const typename ElseDerived::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
-
-    template<int p> RealScalar lpNorm() const;
-
-    template<int RowFactor, int ColFactor>
-    EIGEN_DEVICE_FUNC
-    const Replicate<Derived,RowFactor,ColFactor> replicate() const;
-    /**
-    * \return an expression of the replication of \c *this
-    *
-    * Example: \include MatrixBase_replicate_int_int.cpp
-    * Output: \verbinclude MatrixBase_replicate_int_int.out
-    *
-    * \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
-    */
-    //Code moved here due to a CUDA compiler bug
-    EIGEN_DEVICE_FUNC
-    const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const
-    {
-      return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
-    }
-
-    typedef Reverse<Derived, BothDirections> ReverseReturnType;
-    typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
-    EIGEN_DEVICE_FUNC ReverseReturnType reverse();
-    /** This is the const version of reverse(). */
-    //Code moved here due to a CUDA compiler bug
-    EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const
-    {
-      return ConstReverseReturnType(derived());
-    }
-    EIGEN_DEVICE_FUNC void reverseInPlace();
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
-      * iterator type as returned by the begin() and end() methods.
-      */
-    typedef random_access_iterator_type iterator;
-    /** This is the const version of iterator (aka read-only) */
-    typedef random_access_iterator_type const_iterator;
-    #else
-    typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit,
-                                            internal::pointer_based_stl_iterator<Derived>,
-                                            internal::generic_randaccess_stl_iterator<Derived>
-                                          >::type iterator_type;
-
-    typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit,
-                                            internal::pointer_based_stl_iterator<const Derived>,
-                                            internal::generic_randaccess_stl_iterator<const Derived>
-                                          >::type const_iterator_type;
-
-    // Stl-style iterators are supported only for vectors.
-
-    typedef typename internal::conditional< IsVectorAtCompileTime,
-                                            iterator_type,
-                                            void
-                                          >::type iterator;
-
-    typedef typename internal::conditional< IsVectorAtCompileTime,
-                                            const_iterator_type,
-                                            void
-                                          >::type const_iterator;
-    #endif
-
-    inline iterator begin();
-    inline const_iterator begin() const;
-    inline const_iterator cbegin() const;
-    inline iterator end();
-    inline const_iterator end() const;
-    inline const_iterator cend() const;
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
-#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#   include "../plugins/CommonCwiseUnaryOps.h"
-#   include "../plugins/BlockMethods.h"
-#   include "../plugins/IndexedViewMethods.h"
-#   include "../plugins/ReshapedMethods.h"
-#   ifdef EIGEN_DENSEBASE_PLUGIN
-#     include EIGEN_DENSEBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
-#undef EIGEN_DOC_UNARY_ADDONS
-
-    // disable the use of evalTo for dense objects with a nice compilation error
-    template<typename Dest>
-    EIGEN_DEVICE_FUNC
-    inline void evalTo(Dest& ) const
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
-    }
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(DenseBase)
-    /** Default constructor. Do nothing. */
-    EIGEN_DEVICE_FUNC DenseBase()
-    {
-      /* Just checks for self-consistency of the flags.
-       * Only do it when debugging Eigen, as this borders on paranoia and could slow compilation down
-       */
-#ifdef EIGEN_INTERNAL_DEBUGGING
-      EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
-                        && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
-                          INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
-#endif
-    }
-
-  private:
-    EIGEN_DEVICE_FUNC explicit DenseBase(int);
-    EIGEN_DEVICE_FUNC DenseBase(int,int);
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_DENSEBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseCoeffsBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseCoeffsBase.h
deleted file mode 100644
index 37fcdb5..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseCoeffsBase.h
+++ /dev/null
@@ -1,685 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DENSECOEFFSBASE_H
-#define EIGEN_DENSECOEFFSBASE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename T> struct add_const_on_value_type_if_arithmetic
-{
-  typedef typename conditional<is_arithmetic<T>::value, T, typename add_const_on_value_type<T>::type>::type type;
-};
-}
-
-/** \brief Base class providing read-only coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #ReadOnlyAccessors Constant indicating read-only access
-  *
-  * This class defines the \c operator() \c const function and friends, which can be used to read specific
-  * entries of a matrix or array.
-  *
-  * \sa DenseCoeffsBase<Derived, WriteAccessors>, DenseCoeffsBase<Derived, DirectAccessors>,
-  *     \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
-{
-  public:
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-
-    // Explanation for this CoeffReturnType typedef.
-    // - This is the return type of the coeff() method.
-    // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
-    // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
-    // - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems
-    // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
-    // not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
-    typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
-                         const Scalar&,
-                         typename internal::conditional<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>::type
-                     >::type CoeffReturnType;
-
-    typedef typename internal::add_const_on_value_type_if_arithmetic<
-                         typename internal::packet_traits<Scalar>::type
-                     >::type PacketReturnType;
-
-    typedef EigenBase<Derived> Base;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
-    {
-      return int(Derived::RowsAtCompileTime) == 1 ? 0
-          : int(Derived::ColsAtCompileTime) == 1 ? inner
-          : int(Derived::Flags)&RowMajorBit ? outer
-          : inner;
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
-    {
-      return int(Derived::ColsAtCompileTime) == 1 ? 0
-          : int(Derived::RowsAtCompileTime) == 1 ? inner
-          : int(Derived::Flags)&RowMajorBit ? inner
-          : outer;
-    }
-
-    /** Short version: don't use this function, use
-      * \link operator()(Index,Index) const \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator()(Index,Index) const \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameters \a row and \a col are in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator()(Index,Index) const \endlink.
-      *
-      * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
-    {
-      eigen_internal_assert(row >= 0 && row < rows()
-                         && col >= 0 && col < cols());
-      return internal::evaluator<Derived>(derived()).coeff(row,col);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
-    {
-      return coeff(rowIndexByOuterInner(outer, inner),
-                   colIndexByOuterInner(outer, inner));
-    }
-
-    /** \returns the coefficient at given the given row and column.
-      *
-      * \sa operator()(Index,Index), operator[](Index)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
-    {
-      eigen_assert(row >= 0 && row < rows()
-          && col >= 0 && col < cols());
-      return coeff(row, col);
-    }
-
-    /** Short version: don't use this function, use
-      * \link operator[](Index) const \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator[](Index) const \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameter \a index is in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator[](Index) const \endlink.
-      *
-      * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    coeff(Index index) const
-    {
-      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
-                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
-      eigen_internal_assert(index >= 0 && index < size());
-      return internal::evaluator<Derived>(derived()).coeff(index);
-    }
-
-
-    /** \returns the coefficient at given index.
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
-      * z() const, w() const
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    operator[](Index index) const
-    {
-      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
-                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
-      eigen_assert(index >= 0 && index < size());
-      return coeff(index);
-    }
-
-    /** \returns the coefficient at given index.
-      *
-      * This is synonymous to operator[](Index) const.
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
-      * z() const, w() const
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    operator()(Index index) const
-    {
-      eigen_assert(index >= 0 && index < size());
-      return coeff(index);
-    }
-
-    /** equivalent to operator[](0).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    x() const { return (*this)[0]; }
-
-    /** equivalent to operator[](1).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    y() const
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
-      return (*this)[1];
-    }
-
-    /** equivalent to operator[](2).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    z() const
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
-      return (*this)[2];
-    }
-
-    /** equivalent to operator[](3).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    w() const
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
-      return (*this)[3];
-    }
-
-    /** \internal
-      * \returns the packet of coefficients starting at the given row and column. It is your responsibility
-      * to ensure that a packet really starts there. This method is only available on expressions having the
-      * PacketAccessBit.
-      *
-      * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
-      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
-      * starting at an address which is a multiple of the packet size.
-      */
-
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const
-    {
-      typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
-      eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
-      return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(row,col);
-    }
-
-
-    /** \internal */
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const
-    {
-      return packet<LoadMode>(rowIndexByOuterInner(outer, inner),
-                              colIndexByOuterInner(outer, inner));
-    }
-
-    /** \internal
-      * \returns the packet of coefficients starting at the given index. It is your responsibility
-      * to ensure that a packet really starts there. This method is only available on expressions having the
-      * PacketAccessBit and the LinearAccessBit.
-      *
-      * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
-      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
-      * starting at an address which is a multiple of the packet size.
-      */
-
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-    {
-      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
-                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
-      typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
-      eigen_internal_assert(index >= 0 && index < size());
-      return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(index);
-    }
-
-  protected:
-    // explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase.
-    // But some methods are only available in the DirectAccess case.
-    // So we add dummy methods here with these names, so that "using... " doesn't fail.
-    // It's not private so that the child class DenseBase can access them, and it's not public
-    // either since it's an implementation detail, so has to be protected.
-    void coeffRef();
-    void coeffRefByOuterInner();
-    void writePacket();
-    void writePacketByOuterInner();
-    void copyCoeff();
-    void copyCoeffByOuterInner();
-    void copyPacket();
-    void copyPacketByOuterInner();
-    void stride();
-    void innerStride();
-    void outerStride();
-    void rowStride();
-    void colStride();
-};
-
-/** \brief Base class providing read/write coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #WriteAccessors Constant indicating read/write access
-  *
-  * This class defines the non-const \c operator() function and friends, which can be used to write specific
-  * entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which
-  * defines the const variant for reading specific entries.
-  *
-  * \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
-{
-  public:
-
-    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    using Base::coeff;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-    using Base::rowIndexByOuterInner;
-    using Base::colIndexByOuterInner;
-    using Base::operator[];
-    using Base::operator();
-    using Base::x;
-    using Base::y;
-    using Base::z;
-    using Base::w;
-
-    /** Short version: don't use this function, use
-      * \link operator()(Index,Index) \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator()(Index,Index) \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameters \a row and \a col are in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator()(Index,Index) \endlink.
-      *
-      * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
-    {
-      eigen_internal_assert(row >= 0 && row < rows()
-                         && col >= 0 && col < cols());
-      return internal::evaluator<Derived>(derived()).coeffRef(row,col);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    coeffRefByOuterInner(Index outer, Index inner)
-    {
-      return coeffRef(rowIndexByOuterInner(outer, inner),
-                      colIndexByOuterInner(outer, inner));
-    }
-
-    /** \returns a reference to the coefficient at given the given row and column.
-      *
-      * \sa operator[](Index)
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    operator()(Index row, Index col)
-    {
-      eigen_assert(row >= 0 && row < rows()
-          && col >= 0 && col < cols());
-      return coeffRef(row, col);
-    }
-
-
-    /** Short version: don't use this function, use
-      * \link operator[](Index) \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator[](Index) \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameters \a row and \a col are in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator[](Index) \endlink.
-      *
-      * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    coeffRef(Index index)
-    {
-      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
-                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
-      eigen_internal_assert(index >= 0 && index < size());
-      return internal::evaluator<Derived>(derived()).coeffRef(index);
-    }
-
-    /** \returns a reference to the coefficient at given index.
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    operator[](Index index)
-    {
-      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
-                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
-      eigen_assert(index >= 0 && index < size());
-      return coeffRef(index);
-    }
-
-    /** \returns a reference to the coefficient at given index.
-      *
-      * This is synonymous to operator[](Index).
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    operator()(Index index)
-    {
-      eigen_assert(index >= 0 && index < size());
-      return coeffRef(index);
-    }
-
-    /** equivalent to operator[](0).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    x() { return (*this)[0]; }
-
-    /** equivalent to operator[](1).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    y()
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
-      return (*this)[1];
-    }
-
-    /** equivalent to operator[](2).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    z()
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
-      return (*this)[2];
-    }
-
-    /** equivalent to operator[](3).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    w()
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
-      return (*this)[3];
-    }
-};
-
-/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #DirectAccessors Constant indicating direct access
-  *
-  * This class defines functions to work with strides which can be used to access entries directly. This class
-  * inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
-  * \c operator() .
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
-{
-  public:
-
-    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-
-    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
-      *
-      * \sa outerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const
-    {
-      return derived().innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
-      *          in a column-major matrix).
-      *
-      * \sa innerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const
-    {
-      return derived().outerStride();
-    }
-
-    // FIXME shall we remove it ?
-    EIGEN_CONSTEXPR inline Index stride() const
-    {
-      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive rows.
-      *
-      * \sa innerStride(), outerStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rowStride() const
-    {
-      return Derived::IsRowMajor ? outerStride() : innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive columns.
-      *
-      * \sa innerStride(), outerStride(), rowStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index colStride() const
-    {
-      return Derived::IsRowMajor ? innerStride() : outerStride();
-    }
-};
-
-/** \brief Base class providing direct read/write coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #DirectWriteAccessors Constant indicating direct access
-  *
-  * This class defines functions to work with strides which can be used to access entries directly. This class
-  * inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
-  * \c operator().
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived, DirectWriteAccessors>
-  : public DenseCoeffsBase<Derived, WriteAccessors>
-{
-  public:
-
-    typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-
-    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
-      *
-      * \sa outerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT
-    {
-      return derived().innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
-      *          in a column-major matrix).
-      *
-      * \sa innerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT
-    {
-      return derived().outerStride();
-    }
-
-    // FIXME shall we remove it ?
-    EIGEN_CONSTEXPR inline Index stride() const EIGEN_NOEXCEPT
-    {
-      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive rows.
-      *
-      * \sa innerStride(), outerStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rowStride() const EIGEN_NOEXCEPT
-    {
-      return Derived::IsRowMajor ? outerStride() : innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive columns.
-      *
-      * \sa innerStride(), outerStride(), rowStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index colStride() const EIGEN_NOEXCEPT
-    {
-      return Derived::IsRowMajor ? innerStride() : outerStride();
-    }
-};
-
-namespace internal {
-
-template<int Alignment, typename Derived, bool JustReturnZero>
-struct first_aligned_impl
-{
-  static EIGEN_CONSTEXPR inline Index run(const Derived&) EIGEN_NOEXCEPT
-  { return 0; }
-};
-
-template<int Alignment, typename Derived>
-struct first_aligned_impl<Alignment, Derived, false>
-{
-  static inline Index run(const Derived& m)
-  {
-    return internal::first_aligned<Alignment>(m.data(), m.size());
-  }
-};
-
-/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect to \a Alignment for vectorization.
-  *
-  * \tparam Alignment requested alignment in Bytes.
-  *
-  * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
-  * documentation.
-  */
-template<int Alignment, typename Derived>
-static inline Index first_aligned(const DenseBase<Derived>& m)
-{
-  enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
-  return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
-}
-
-template<typename Derived>
-static inline Index first_default_aligned(const DenseBase<Derived>& m)
-{
-  typedef typename Derived::Scalar Scalar;
-  typedef typename packet_traits<Scalar>::type DefaultPacketType;
-  return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment),Derived>(m);
-}
-
-template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
-struct inner_stride_at_compile_time
-{
-  enum { ret = traits<Derived>::InnerStrideAtCompileTime };
-};
-
-template<typename Derived>
-struct inner_stride_at_compile_time<Derived, false>
-{
-  enum { ret = 0 };
-};
-
-template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
-struct outer_stride_at_compile_time
-{
-  enum { ret = traits<Derived>::OuterStrideAtCompileTime };
-};
-
-template<typename Derived>
-struct outer_stride_at_compile_time<Derived, false>
-{
-  enum { ret = 0 };
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_DENSECOEFFSBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseStorage.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseStorage.h
deleted file mode 100644
index 08ef6c5..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DenseStorage.h
+++ /dev/null
@@ -1,652 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIXSTORAGE_H
-#define EIGEN_MATRIXSTORAGE_H
-
-#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) X; EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
-#else
-  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-struct constructor_without_unaligned_array_assert {};
-
-template<typename T, int Size>
-EIGEN_DEVICE_FUNC
-void check_static_allocation_size()
-{
-  // if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
-  #if EIGEN_STACK_ALLOCATION_LIMIT
-  EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
-  #endif
-}
-
-/** \internal
-  * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
-  * to 16 bytes boundary if the total size is a multiple of 16 bytes.
-  */
-template <typename T, int Size, int MatrixOrArrayOptions,
-          int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
-                        : compute_default_alignment<T,Size>::value >
-struct plain_array
-{
-  T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
-  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
-#elif EIGEN_GNUC_AT_LEAST(4,7)
-  // GCC 4.7 is too aggressive in its optimizations and remove the alignment test based on the fact the array is declared to be aligned.
-  // See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
-  // Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
-  template<typename PtrType>
-  EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
-  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
-    eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
-              && "this assertion is explained here: " \
-              "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
-              " **** READ THIS WEB PAGE !!! ****");
-#else
-  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
-    eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \
-              && "this assertion is explained here: " \
-              "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
-              " **** READ THIS WEB PAGE !!! ****");
-#endif
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 8>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 16>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 32>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 64>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int MatrixOrArrayOptions, int Alignment>
-struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
-{
-  T array[1];
-  EIGEN_DEVICE_FUNC plain_array() {}
-  EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
-};
-
-struct plain_array_helper {
-  template<typename T, int Size, int MatrixOrArrayOptions, int Alignment>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  static void copy(const plain_array<T, Size, MatrixOrArrayOptions, Alignment>& src, const Eigen::Index size,
-                         plain_array<T, Size, MatrixOrArrayOptions, Alignment>& dst) {
-    smart_copy(src.array, src.array + size, dst.array);
-  }
-  
-  template<typename T, int Size, int MatrixOrArrayOptions, int Alignment>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  static void swap(plain_array<T, Size, MatrixOrArrayOptions, Alignment>& a, const Eigen::Index a_size,
-                   plain_array<T, Size, MatrixOrArrayOptions, Alignment>& b, const Eigen::Index b_size) {
-    if (a_size < b_size) {
-      std::swap_ranges(b.array, b.array + a_size, a.array);
-      smart_move(b.array + a_size, b.array + b_size, a.array + a_size);
-    } else if (a_size > b_size) {
-      std::swap_ranges(a.array, a.array + b_size, b.array);
-      smart_move(a.array + b_size, a.array + a_size, b.array + b_size);
-    } else {
-      std::swap_ranges(a.array, a.array + a_size, b.array);
-    }
-  }
-};
-
-} // end namespace internal
-
-/** \internal
-  *
-  * \class DenseStorage
-  * \ingroup Core_Module
-  *
-  * \brief Stores the data of a matrix
-  *
-  * This class stores the data of fixed-size, dynamic-size or mixed matrices
-  * in a way as compact as possible.
-  *
-  * \sa Matrix
-  */
-template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage;
-
-// purely fixed-size matrix
-template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage
-{
-    internal::plain_array<T,Size,_Options> m_data;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
-    }
-    EIGEN_DEVICE_FUNC
-    explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()) {}
-#if !EIGEN_HAS_CXX11 || defined(EIGEN_DENSE_STORAGE_CTOR_PLUGIN)
-    EIGEN_DEVICE_FUNC
-    DenseStorage(const DenseStorage& other) : m_data(other.m_data) {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
-    }
-#else
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) = default;
-#endif
-#if !EIGEN_HAS_CXX11
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other) m_data = other.m_data;
-      return *this;
-    }
-#else
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) = default;
-#endif
-#if EIGEN_HAS_RVALUE_REFERENCES
-#if !EIGEN_HAS_CXX11
-    EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-    {
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      if (this != &other)
-        m_data = std::move(other.m_data);
-      return *this;
-    }
-#else
-    EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&&) = default;
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&&) = default;
-#endif
-#endif
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
-      EIGEN_UNUSED_VARIABLE(size);
-      EIGEN_UNUSED_VARIABLE(rows);
-      EIGEN_UNUSED_VARIABLE(cols);
-    }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      numext::swap(m_data, other.m_data);
-    }
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// null matrix
-template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
-{
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {}
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; }
-    EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC const T *data() const { return 0; }
-    EIGEN_DEVICE_FUNC T *data() { return 0; }
-};
-
-// more specializations for null matrices; these are necessary to resolve ambiguities
-template<typename T, int _Options> class DenseStorage<T, 0, Dynamic, Dynamic, _Options>
-: public DenseStorage<T, 0, 0, 0, _Options> { };
-
-template<typename T, int _Rows, int _Options> class DenseStorage<T, 0, _Rows, Dynamic, _Options>
-: public DenseStorage<T, 0, 0, 0, _Options> { };
-
-template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic, _Cols, _Options>
-: public DenseStorage<T, 0, 0, 0, _Options> { };
-
-// dynamic-size matrix with fixed-size storage
-template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
-{
-    internal::plain_array<T,Size,_Options> m_data;
-    Index m_rows;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows), m_cols(other.m_cols)
-    {
-      internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        m_rows = other.m_rows;
-        m_cols = other.m_cols;
-        internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
-      }
-      return *this;
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
-    {
-      internal::plain_array_helper::swap(m_data, m_rows * m_cols, other.m_data, other.m_rows * other.m_cols);
-      numext::swap(m_rows,other.m_rows);
-      numext::swap(m_cols,other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC Index rows() const {return m_rows;}
-    EIGEN_DEVICE_FUNC Index cols() const {return m_cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
-    EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// dynamic-size matrix with fixed-size storage and fixed width
-template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
-{
-    internal::plain_array<T,Size,_Options> m_data;
-    Index m_rows;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows)
-    {
-      internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data);
-    }
-    
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        m_rows = other.m_rows;
-        internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data);
-      }
-      return *this;
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
-    { 
-      internal::plain_array_helper::swap(m_data, m_rows * _Cols, other.m_data, other.m_rows * _Cols);
-      numext::swap(m_rows, other.m_rows);
-    }
-    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols(void) const EIGEN_NOEXCEPT {return _Cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
-    EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// dynamic-size matrix with fixed-size storage and fixed height
-template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
-{
-    internal::plain_array<T,Size,_Options> m_data;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) 
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(other.m_cols)
-    {
-      internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        m_cols = other.m_cols;
-        internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data);
-      }
-      return *this;
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      internal::plain_array_helper::swap(m_data, _Rows * m_cols, other.m_data, _Rows * other.m_cols);
-      numext::swap(m_cols, other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows(void) const EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
-    EIGEN_DEVICE_FUNC void resize(Index, Index, Index cols) { m_cols = cols; }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// purely dynamic matrix.
-template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
-{
-    T *m_data;
-    Index m_rows;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-       : m_data(0), m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols))
-      , m_rows(other.m_rows)
-      , m_cols(other.m_cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*m_cols)
-      internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        DenseStorage tmp(other);
-        this->swap(tmp);
-      }
-      return *this;
-    }
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-      , m_rows(std::move(other.m_rows))
-      , m_cols(std::move(other.m_cols))
-    {
-      other.m_data = nullptr;
-      other.m_rows = 0;
-      other.m_cols = 0;
-    }
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      numext::swap(m_data, other.m_data);
-      numext::swap(m_rows, other.m_rows);
-      numext::swap(m_cols, other.m_cols);
-      return *this;
-    }
-#endif
-    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
-    {
-      numext::swap(m_data,other.m_data);
-      numext::swap(m_rows,other.m_rows);
-      numext::swap(m_cols,other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
-    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
-    void conservativeResize(Index size, Index rows, Index cols)
-    {
-      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
-      m_rows = rows;
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
-    {
-      if(size != m_rows*m_cols)
-      {
-        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols);
-        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
-        else
-          m_data = 0;
-        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      }
-      m_rows = rows;
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data; }
-};
-
-// matrix with dynamic width and fixed height (so that matrix has dynamic size).
-template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
-{
-    T *m_data;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
-    explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0);
-      EIGEN_UNUSED_VARIABLE(rows);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols))
-      , m_cols(other.m_cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*_Rows)
-      internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        DenseStorage tmp(other);
-        this->swap(tmp);
-      }
-      return *this;
-    }
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-      , m_cols(std::move(other.m_cols))
-    {
-      other.m_data = nullptr;
-      other.m_cols = 0;
-    }
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      numext::swap(m_data, other.m_data);
-      numext::swap(m_cols, other.m_cols);
-      return *this;
-    }
-#endif
-    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      numext::swap(m_data,other.m_data);
-      numext::swap(m_cols,other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
-    {
-      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
-    {
-      if(size != _Rows*m_cols)
-      {
-        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols);
-        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
-        else
-          m_data = 0;
-        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      }
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data; }
-};
-
-// matrix with dynamic height and fixed width (so that matrix has dynamic size).
-template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
-{
-    T *m_data;
-    Index m_rows;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
-    explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols);
-      EIGEN_UNUSED_VARIABLE(cols);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols))
-      , m_rows(other.m_rows)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*_Cols)
-      internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        DenseStorage tmp(other);
-        this->swap(tmp);
-      }
-      return *this;
-    }
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-      , m_rows(std::move(other.m_rows))
-    {
-      other.m_data = nullptr;
-      other.m_rows = 0;
-    }
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      numext::swap(m_data, other.m_data);
-      numext::swap(m_rows, other.m_rows);
-      return *this;
-    }
-#endif
-    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      numext::swap(m_data,other.m_data);
-      numext::swap(m_rows,other.m_rows);
-    }
-    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) {return _Cols;}
-    void conservativeResize(Index size, Index rows, Index)
-    {
-      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
-      m_rows = rows;
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
-    {
-      if(size != m_rows*_Cols)
-      {
-        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows);
-        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
-        else
-          m_data = 0;
-        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      }
-      m_rows = rows;
-    }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data; }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Diagonal.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Diagonal.h
deleted file mode 100644
index 3112d2c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Diagonal.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DIAGONAL_H
-#define EIGEN_DIAGONAL_H
-
-namespace Eigen {
-
-/** \class Diagonal
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
-  *
-  * \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
-  * \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
-  *              A positive value means a superdiagonal, a negative value means a subdiagonal.
-  *              You can also use DynamicIndex so the index can be set at runtime.
-  *
-  * The matrix is not required to be square.
-  *
-  * This class represents an expression of the main diagonal, or any sub/super diagonal
-  * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
-  * time this is the only way it is used.
-  *
-  * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
-  */
-
-namespace internal {
-template<typename MatrixType, int DiagIndex>
-struct traits<Diagonal<MatrixType,DiagIndex> >
- : traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  typedef typename MatrixType::StorageKind StorageKind;
-  enum {
-    RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
-                      : (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
-                                              MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
-    ColsAtCompileTime = 1,
-    MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
-                         : DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
-                                                                              MatrixType::MaxColsAtCompileTime)
-                         : (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
-                                                 MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
-    MaxColsAtCompileTime = 1,
-    MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
-    MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
-    InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
-    OuterStrideAtCompileTime = 0
-  };
-};
-}
-
-template<typename MatrixType, int _DiagIndex> class Diagonal
-   : public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type
-{
-  public:
-
-    enum { DiagIndex = _DiagIndex };
-    typedef typename internal::dense_xpr_base<Diagonal>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
-
-    EIGEN_DEVICE_FUNC
-    explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index)
-    {
-      eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() );
-    }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
-
-    EIGEN_DEVICE_FUNC
-    inline Index rows() const
-    {
-      return m_index.value()<0 ? numext::mini<Index>(m_matrix.cols(),m_matrix.rows()+m_index.value())
-                               : numext::mini<Index>(m_matrix.rows(),m_matrix.cols()-m_index.value());
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return 1; }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT {
-      return m_matrix.outerStride() + 1;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return 0; }
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<MatrixType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index row, Index) const
-    {
-      return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline CoeffReturnType coeff(Index row, Index) const
-    {
-      return m_matrix.coeff(row+rowOffset(), row+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index idx)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index idx) const
-    {
-      return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline CoeffReturnType coeff(Index idx) const
-    {
-      return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const typename internal::remove_all<typename MatrixType::Nested>::type&
-    nestedExpression() const
-    {
-      return m_matrix;
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Index index() const
-    {
-      return m_index.value();
-    }
-
-  protected:
-    typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
-    const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
-
-  private:
-    // some compilers may fail to optimize std::max etc in case of compile-time constants...
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index absDiagIndex() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rowOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? 0 : -m_index.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index colOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : 0; }
-    // trigger a compile-time error if someone try to call packet
-    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
-    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
-};
-
-/** \returns an expression of the main diagonal of the matrix \c *this
-  *
-  * \c *this is not required to be square.
-  *
-  * Example: \include MatrixBase_diagonal.cpp
-  * Output: \verbinclude MatrixBase_diagonal.out
-  *
-  * \sa class Diagonal */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalReturnType
-MatrixBase<Derived>::diagonal()
-{
-  return DiagonalReturnType(derived());
-}
-
-/** This is the const version of diagonal(). */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalReturnType
-MatrixBase<Derived>::diagonal() const
-{
-  return ConstDiagonalReturnType(derived());
-}
-
-/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
-  *
-  * \c *this is not required to be square.
-  *
-  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
-  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
-  *
-  * Example: \include MatrixBase_diagonal_int.cpp
-  * Output: \verbinclude MatrixBase_diagonal_int.out
-  *
-  * \sa MatrixBase::diagonal(), class Diagonal */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
-MatrixBase<Derived>::diagonal(Index index)
-{
-  return DiagonalDynamicIndexReturnType(derived(), index);
-}
-
-/** This is the const version of diagonal(Index). */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
-MatrixBase<Derived>::diagonal(Index index) const
-{
-  return ConstDiagonalDynamicIndexReturnType(derived(), index);
-}
-
-/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
-  *
-  * \c *this is not required to be square.
-  *
-  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
-  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
-  *
-  * Example: \include MatrixBase_diagonal_template_int.cpp
-  * Output: \verbinclude MatrixBase_diagonal_template_int.out
-  *
-  * \sa MatrixBase::diagonal(), class Diagonal */
-template<typename Derived>
-template<int Index_>
-EIGEN_DEVICE_FUNC
-inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
-MatrixBase<Derived>::diagonal()
-{
-  return typename DiagonalIndexReturnType<Index_>::Type(derived());
-}
-
-/** This is the const version of diagonal<int>(). */
-template<typename Derived>
-template<int Index_>
-EIGEN_DEVICE_FUNC
-inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
-MatrixBase<Derived>::diagonal() const
-{
-  return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DIAGONAL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DiagonalMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DiagonalMatrix.h
deleted file mode 100644
index 542685c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DiagonalMatrix.h
+++ /dev/null
@@ -1,391 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DIAGONALMATRIX_H
-#define EIGEN_DIAGONALMATRIX_H
-
-namespace Eigen { 
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename Derived>
-class DiagonalBase : public EigenBase<Derived>
-{
-  public:
-    typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
-    typedef typename DiagonalVectorType::Scalar Scalar;
-    typedef typename DiagonalVectorType::RealScalar RealScalar;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-
-    enum {
-      RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-      ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-      MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-      MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-      IsVectorAtCompileTime = 0,
-      Flags = NoPreferredStorageOrderBit
-    };
-
-    typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
-    typedef DenseMatrixType DenseType;
-    typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
-
-    EIGEN_DEVICE_FUNC
-    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    EIGEN_DEVICE_FUNC
-    inline Derived& derived() { return *static_cast<Derived*>(this); }
-
-    EIGEN_DEVICE_FUNC
-    DenseMatrixType toDenseMatrix() const { return derived(); }
-
-    EIGEN_DEVICE_FUNC
-    inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
-    EIGEN_DEVICE_FUNC
-    inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
-
-    EIGEN_DEVICE_FUNC
-    inline Index rows() const { return diagonal().size(); }
-    EIGEN_DEVICE_FUNC
-    inline Index cols() const { return diagonal().size(); }
-
-    template<typename MatrixDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived,MatrixDerived,LazyProduct>
-    operator*(const MatrixBase<MatrixDerived> &matrix) const
-    {
-      return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
-    }
-
-    typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
-    EIGEN_DEVICE_FUNC
-    inline const InverseReturnType
-    inverse() const
-    {
-      return InverseReturnType(diagonal().cwiseInverse());
-    }
-    
-    EIGEN_DEVICE_FUNC
-    inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
-    operator*(const Scalar& scalar) const
-    {
-      return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
-    }
-    EIGEN_DEVICE_FUNC
-    friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
-    operator*(const Scalar& scalar, const DiagonalBase& other)
-    {
-      return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    inline unspecified_expression_type
-    #else
-    inline const DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(DiagonalVectorType,typename OtherDerived::DiagonalVectorType,sum) >
-    #endif
-    operator+(const DiagonalBase<OtherDerived>& other) const
-    {
-      return (diagonal() + other.diagonal()).asDiagonal();
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    inline unspecified_expression_type
-    #else
-    inline const DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(DiagonalVectorType,typename OtherDerived::DiagonalVectorType,difference) >
-    #endif
-    operator-(const DiagonalBase<OtherDerived>& other) const
-    {
-      return (diagonal() - other.diagonal()).asDiagonal();
-    }
-};
-
-#endif
-
-/** \class DiagonalMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Represents a diagonal matrix with its storage
-  *
-  * \param _Scalar the type of coefficients
-  * \param SizeAtCompileTime the dimension of the matrix, or Dynamic
-  * \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
-  *        to SizeAtCompileTime. Most of the time, you do not need to specify it.
-  *
-  * \sa class DiagonalWrapper
-  */
-
-namespace internal {
-template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
-struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
- : traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
-  typedef DiagonalShape StorageKind;
-  enum {
-    Flags = LvalueBit | NoPreferredStorageOrderBit
-  };
-};
-}
-template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
-class DiagonalMatrix
-  : public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  public:
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
-    typedef const DiagonalMatrix& Nested;
-    typedef _Scalar Scalar;
-    typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
-    typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
-    #endif
-
-  protected:
-
-    DiagonalVectorType m_diagonal;
-
-  public:
-
-    /** const version of diagonal(). */
-    EIGEN_DEVICE_FUNC
-    inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
-    /** \returns a reference to the stored vector of diagonal coefficients. */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalVectorType& diagonal() { return m_diagonal; }
-
-    /** Default constructor without initialization */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix() {}
-
-    /** Constructs a diagonal matrix with given dimension  */
-    EIGEN_DEVICE_FUNC
-    explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
-
-    /** 2D constructor. */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
-
-    /** 3D constructor. */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
-
-    #if EIGEN_HAS_CXX11
-    /** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients. \cpp11
-      * 
-      * There exists C++98 anologue constructors for fixed-size diagonal matrices having 2 or 3 coefficients.
-      * 
-      * \warning To construct a diagonal matrix of fixed size, the number of values passed to this 
-      * constructor must match the fixed dimension of \c *this.
-      * 
-      * \sa DiagonalMatrix(const Scalar&, const Scalar&)
-      * \sa DiagonalMatrix(const Scalar&, const Scalar&, const Scalar&)
-      */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    DiagonalMatrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const ArgTypes&... args)
-      : m_diagonal(a0, a1, a2, args...) {}
-
-    /** \brief Constructs a DiagonalMatrix and initializes it by elements given by an initializer list of initializer
-      * lists \cpp11
-      */
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE DiagonalMatrix(const std::initializer_list<std::initializer_list<Scalar>>& list)
-      : m_diagonal(list) {}
-    #endif  // EIGEN_HAS_CXX11
-
-    /** Copy constructor. */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
-    inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
-    #endif
-
-    /** generic constructor from expression of the diagonal coefficients */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
-    {}
-
-    /** Copy operator. */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
-    {
-      m_diagonal = other.diagonal();
-      return *this;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    DiagonalMatrix& operator=(const DiagonalMatrix& other)
-    {
-      m_diagonal = other.diagonal();
-      return *this;
-    }
-    #endif
-
-    /** Resizes to given size. */
-    EIGEN_DEVICE_FUNC
-    inline void resize(Index size) { m_diagonal.resize(size); }
-    /** Sets all coefficients to zero. */
-    EIGEN_DEVICE_FUNC
-    inline void setZero() { m_diagonal.setZero(); }
-    /** Resizes and sets all coefficients to zero. */
-    EIGEN_DEVICE_FUNC
-    inline void setZero(Index size) { m_diagonal.setZero(size); }
-    /** Sets this matrix to be the identity matrix of the current size. */
-    EIGEN_DEVICE_FUNC
-    inline void setIdentity() { m_diagonal.setOnes(); }
-    /** Sets this matrix to be the identity matrix of the given size. */
-    EIGEN_DEVICE_FUNC
-    inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
-};
-
-/** \class DiagonalWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a diagonal matrix
-  *
-  * \param _DiagonalVectorType the type of the vector of diagonal coefficients
-  *
-  * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
-  * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
-  * and most of the time this is the only way that it is used.
-  *
-  * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
-  */
-
-namespace internal {
-template<typename _DiagonalVectorType>
-struct traits<DiagonalWrapper<_DiagonalVectorType> >
-{
-  typedef _DiagonalVectorType DiagonalVectorType;
-  typedef typename DiagonalVectorType::Scalar Scalar;
-  typedef typename DiagonalVectorType::StorageIndex StorageIndex;
-  typedef DiagonalShape StorageKind;
-  typedef typename traits<DiagonalVectorType>::XprKind XprKind;
-  enum {
-    RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    Flags =  (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
-  };
-};
-}
-
-template<typename _DiagonalVectorType>
-class DiagonalWrapper
-  : public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator
-{
-  public:
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef _DiagonalVectorType DiagonalVectorType;
-    typedef DiagonalWrapper Nested;
-    #endif
-
-    /** Constructor from expression of diagonal coefficients to wrap. */
-    EIGEN_DEVICE_FUNC
-    explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
-
-    /** \returns a const reference to the wrapped expression of diagonal coefficients. */
-    EIGEN_DEVICE_FUNC
-    const DiagonalVectorType& diagonal() const { return m_diagonal; }
-
-  protected:
-    typename DiagonalVectorType::Nested m_diagonal;
-};
-
-/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
-  *
-  * \only_for_vectors
-  *
-  * Example: \include MatrixBase_asDiagonal.cpp
-  * Output: \verbinclude MatrixBase_asDiagonal.out
-  *
-  * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
-  **/
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const DiagonalWrapper<const Derived>
-MatrixBase<Derived>::asDiagonal() const
-{
-  return DiagonalWrapper<const Derived>(derived());
-}
-
-/** \returns true if *this is approximately equal to a diagonal matrix,
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isDiagonal.cpp
-  * Output: \verbinclude MatrixBase_isDiagonal.out
-  *
-  * \sa asDiagonal()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
-{
-  if(cols() != rows()) return false;
-  RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
-  for(Index j = 0; j < cols(); ++j)
-  {
-    RealScalar absOnDiagonal = numext::abs(coeff(j,j));
-    if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
-  }
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = 0; i < j; ++i)
-    {
-      if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
-      if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
-    }
-  return true;
-}
-
-namespace internal {
-
-template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
-
-struct Diagonal2Dense {};
-
-template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
-
-// Diagonal matrix to Dense assignment
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
-{
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    
-    dst.setZero();
-    dst.diagonal() = src.diagonal();
-  }
-  
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.diagonal() += src.diagonal(); }
-  
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.diagonal() -= src.diagonal(); }
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_DIAGONALMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DiagonalProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DiagonalProduct.h
deleted file mode 100644
index 7911d1c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/DiagonalProduct.h
+++ /dev/null
@@ -1,28 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DIAGONALPRODUCT_H
-#define EIGEN_DIAGONALPRODUCT_H
-
-namespace Eigen { 
-
-/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
-  */
-template<typename Derived>
-template<typename DiagonalDerived>
-EIGEN_DEVICE_FUNC inline const Product<Derived, DiagonalDerived, LazyProduct>
-MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
-{
-  return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DIAGONALPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Dot.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Dot.h
deleted file mode 100644
index 5c3441b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Dot.h
+++ /dev/null
@@ -1,318 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DOT_H
-#define EIGEN_DOT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot
-// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
-// looking at the static assertions. Thus this is a trick to get better compile errors.
-template<typename T, typename U,
-// the NeedToTranspose condition here is taken straight from Assign.h
-         bool NeedToTranspose = T::IsVectorAtCompileTime
-                && U::IsVectorAtCompileTime
-                && ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1)
-                      |  // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
-                         // revert to || as soon as not needed anymore.
-                    (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
->
-struct dot_nocheck
-{
-  typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
-  typedef typename conj_prod::result_type ResScalar;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE
-  static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
-  {
-    return a.template binaryExpr<conj_prod>(b).sum();
-  }
-};
-
-template<typename T, typename U>
-struct dot_nocheck<T, U, true>
-{
-  typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
-  typedef typename conj_prod::result_type ResScalar;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE
-  static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
-  {
-    return a.transpose().template binaryExpr<conj_prod>(b).sum();
-  }
-};
-
-} // end namespace internal
-
-/** \fn MatrixBase::dot
-  * \returns the dot product of *this with other.
-  *
-  * \only_for_vectors
-  *
-  * \note If the scalar type is complex numbers, then this function returns the hermitian
-  * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
-  * second variable.
-  *
-  * \sa squaredNorm(), norm()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE
-typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
-MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
-#if !(defined(EIGEN_NO_STATIC_ASSERT) && defined(EIGEN_NO_DEBUG))
-  typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
-  EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
-#endif
-  
-  eigen_assert(size() == other.size());
-
-  return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
-}
-
-//---------- implementation of L2 norm and related functions ----------
-
-/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm.
-  * In both cases, it consists in the sum of the square of all the matrix entries.
-  * For vectors, this is also equals to the dot product of \c *this with itself.
-  *
-  * \sa dot(), norm(), lpNorm()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
-{
-  return numext::real((*this).cwiseAbs2().sum());
-}
-
-/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
-  * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
-  * For vectors, this is also equals to the square root of the dot product of \c *this with itself.
-  *
-  * \sa lpNorm(), dot(), squaredNorm()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
-{
-  return numext::sqrt(squaredNorm());
-}
-
-/** \returns an expression of the quotient of \c *this by its own norm.
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0),
-  *          then this function returns a copy of the input.
-  *
-  * \only_for_vectors
-  *
-  * \sa norm(), normalize()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::normalized() const
-{
-  typedef typename internal::nested_eval<Derived,2>::type _Nested;
-  _Nested n(derived());
-  RealScalar z = n.squaredNorm();
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
-  if(z>RealScalar(0))
-    return n / numext::sqrt(z);
-  else
-    return n;
-}
-
-/** Normalizes the vector, i.e. divides it by its own norm.
-  *
-  * \only_for_vectors
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
-  *
-  * \sa norm(), normalized()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize()
-{
-  RealScalar z = squaredNorm();
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
-  if(z>RealScalar(0))
-    derived() /= numext::sqrt(z);
-}
-
-/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
-  *
-  * \only_for_vectors
-  *
-  * This method is analogue to the normalized() method, but it reduces the risk of
-  * underflow and overflow when computing the norm.
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0),
-  *          then this function returns a copy of the input.
-  *
-  * \sa stableNorm(), stableNormalize(), normalized()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::stableNormalized() const
-{
-  typedef typename internal::nested_eval<Derived,3>::type _Nested;
-  _Nested n(derived());
-  RealScalar w = n.cwiseAbs().maxCoeff();
-  RealScalar z = (n/w).squaredNorm();
-  if(z>RealScalar(0))
-    return n / (numext::sqrt(z)*w);
-  else
-    return n;
-}
-
-/** Normalizes the vector while avoid underflow and overflow
-  *
-  * \only_for_vectors
-  *
-  * This method is analogue to the normalize() method, but it reduces the risk of
-  * underflow and overflow when computing the norm.
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
-  *
-  * \sa stableNorm(), stableNormalized(), normalize()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize()
-{
-  RealScalar w = cwiseAbs().maxCoeff();
-  RealScalar z = (derived()/w).squaredNorm();
-  if(z>RealScalar(0))
-    derived() /= numext::sqrt(z)*w;
-}
-
-//---------- implementation of other norms ----------
-
-namespace internal {
-
-template<typename Derived, int p>
-struct lpNorm_selector
-{
-  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const MatrixBase<Derived>& m)
-  {
-    EIGEN_USING_STD(pow)
-    return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
-  }
-};
-
-template<typename Derived>
-struct lpNorm_selector<Derived, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
-  {
-    return m.cwiseAbs().sum();
-  }
-};
-
-template<typename Derived>
-struct lpNorm_selector<Derived, 2>
-{
-  EIGEN_DEVICE_FUNC
-  static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
-  {
-    return m.norm();
-  }
-};
-
-template<typename Derived>
-struct lpNorm_selector<Derived, Infinity>
-{
-  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const MatrixBase<Derived>& m)
-  {
-    if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
-      return RealScalar(0);
-    return m.cwiseAbs().maxCoeff();
-  }
-};
-
-} // end namespace internal
-
-/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
-  *          of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
-  *          norm, that is the maximum of the absolute values of the coefficients of \c *this.
-  *
-  * In all cases, if \c *this is empty, then the value 0 is returned.
-  *
-  * \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
-  *
-  * \sa norm()
-  */
-template<typename Derived>
-template<int p>
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_DEVICE_FUNC inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-#else
-EIGEN_DEVICE_FUNC MatrixBase<Derived>::RealScalar
-#endif
-MatrixBase<Derived>::lpNorm() const
-{
-  return internal::lpNorm_selector<Derived, p>::run(*this);
-}
-
-//---------- implementation of isOrthogonal / isUnitary ----------
-
-/** \returns true if *this is approximately orthogonal to \a other,
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isOrthogonal.cpp
-  * Output: \verbinclude MatrixBase_isOrthogonal.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-bool MatrixBase<Derived>::isOrthogonal
-(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,2>::type nested(derived());
-  typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
-  return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
-}
-
-/** \returns true if *this is approximately an unitary matrix,
-  *          within the precision given by \a prec. In the case where the \a Scalar
-  *          type is real numbers, a unitary matrix is an orthogonal matrix, whence the name.
-  *
-  * \note This can be used to check whether a family of vectors forms an orthonormal basis.
-  *       Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an
-  *       orthonormal basis.
-  *
-  * Example: \include MatrixBase_isUnitary.cpp
-  * Output: \verbinclude MatrixBase_isUnitary.out
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index i = 0; i < cols(); ++i)
-  {
-    if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
-      return false;
-    for(Index j = 0; j < i; ++j)
-      if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
-        return false;
-  }
-  return true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DOT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/EigenBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/EigenBase.h
deleted file mode 100644
index 6b3c7d3..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/EigenBase.h
+++ /dev/null
@@ -1,160 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EIGENBASE_H
-#define EIGEN_EIGENBASE_H
-
-namespace Eigen {
-
-/** \class EigenBase
-  * \ingroup Core_Module
-  *
-  * Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
-  *
-  * In other words, an EigenBase object is an object that can be copied into a MatrixBase.
-  *
-  * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc.
-  *
-  * Notice that this class is trivial, it is only used to disambiguate overloaded functions.
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived> struct EigenBase
-{
-//   typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
-
-  /** \brief The interface type of indices
-    * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
-    * \sa StorageIndex, \ref TopicPreprocessorDirectives.
-    * DEPRECATED: Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
-    * Deprecation is not marked with a doxygen comment because there are too many existing usages to add the deprecation attribute.
-    */
-  typedef Eigen::Index Index;
-
-  // FIXME is it needed?
-  typedef typename internal::traits<Derived>::StorageKind StorageKind;
-
-  /** \returns a reference to the derived object */
-  EIGEN_DEVICE_FUNC
-  Derived& derived() { return *static_cast<Derived*>(this); }
-  /** \returns a const reference to the derived object */
-  EIGEN_DEVICE_FUNC
-  const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-  EIGEN_DEVICE_FUNC
-  inline Derived& const_cast_derived() const
-  { return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
-  EIGEN_DEVICE_FUNC
-  inline const Derived& const_derived() const
-  { return *static_cast<const Derived*>(this); }
-
-  /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
-  /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
-  /** \returns the number of coefficients, which is rows()*cols().
-    * \sa rows(), cols(), SizeAtCompileTime. */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index size() const EIGEN_NOEXCEPT { return rows() * cols(); }
-
-  /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC
-  inline void evalTo(Dest& dst) const
-  { derived().evalTo(dst); }
-
-  /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC
-  inline void addTo(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    typename Dest::PlainObject res(rows(),cols());
-    evalTo(res);
-    dst += res;
-  }
-
-  /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC
-  inline void subTo(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    typename Dest::PlainObject res(rows(),cols());
-    evalTo(res);
-    dst -= res;
-  }
-
-  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    dst = dst * this->derived();
-  }
-
-  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    dst = this->derived() * dst;
-  }
-
-};
-
-/***************************************************************************
-* Implementation of matrix base methods
-***************************************************************************/
-
-/** \brief Copies the generic expression \a other into *this.
-  *
-  * \details The expression must provide a (templated) evalTo(Derived& dst) const
-  * function which does the actual job. In practice, this allows any user to write
-  * its own special matrix without having to modify MatrixBase
-  *
-  * \returns a reference to *this.
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_EIGENBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ForceAlignedAccess.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ForceAlignedAccess.h
deleted file mode 100644
index 817a43a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ForceAlignedAccess.h
+++ /dev/null
@@ -1,150 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FORCEALIGNEDACCESS_H
-#define EIGEN_FORCEALIGNEDACCESS_H
-
-namespace Eigen {
-
-/** \class ForceAlignedAccess
-  * \ingroup Core_Module
-  *
-  * \brief Enforce aligned packet loads and stores regardless of what is requested
-  *
-  * \param ExpressionType the type of the object of which we are forcing aligned packet access
-  *
-  * This class is the return type of MatrixBase::forceAlignedAccess()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::forceAlignedAccess()
-  */
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<ForceAlignedAccess<ExpressionType> > : public traits<ExpressionType>
-{};
-}
-
-template<typename ExpressionType> class ForceAlignedAccess
-  : public internal::dense_xpr_base< ForceAlignedAccess<ExpressionType> >::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
-
-    EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
-
-    EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
-    {
-      return m_expression.coeff(row, col);
-    }
-
-    EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
-    {
-      return m_expression.const_cast_derived().coeffRef(row, col);
-    }
-
-    EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
-    {
-      return m_expression.coeff(index);
-    }
-
-    EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
-    {
-      return m_expression.const_cast_derived().coeffRef(index);
-    }
-
-    template<int LoadMode>
-    inline const PacketScalar packet(Index row, Index col) const
-    {
-      return m_expression.template packet<Aligned>(row, col);
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index row, Index col, const PacketScalar& x)
-    {
-      m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x);
-    }
-
-    template<int LoadMode>
-    inline const PacketScalar packet(Index index) const
-    {
-      return m_expression.template packet<Aligned>(index);
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index index, const PacketScalar& x)
-    {
-      m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
-    }
-
-    EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
-
-  protected:
-    const ExpressionType& m_expression;
-
-  private:
-    ForceAlignedAccess& operator=(const ForceAlignedAccess&);
-};
-
-/** \returns an expression of *this with forced aligned access
-  * \sa forceAlignedAccessIf(),class ForceAlignedAccess
-  */
-template<typename Derived>
-inline const ForceAlignedAccess<Derived>
-MatrixBase<Derived>::forceAlignedAccess() const
-{
-  return ForceAlignedAccess<Derived>(derived());
-}
-
-/** \returns an expression of *this with forced aligned access
-  * \sa forceAlignedAccessIf(), class ForceAlignedAccess
-  */
-template<typename Derived>
-inline ForceAlignedAccess<Derived>
-MatrixBase<Derived>::forceAlignedAccess()
-{
-  return ForceAlignedAccess<Derived>(derived());
-}
-
-/** \returns an expression of *this with forced aligned access if \a Enable is true.
-  * \sa forceAlignedAccess(), class ForceAlignedAccess
-  */
-template<typename Derived>
-template<bool Enable>
-inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type
-MatrixBase<Derived>::forceAlignedAccessIf() const
-{
-  return derived();  // FIXME This should not work but apparently is never used
-}
-
-/** \returns an expression of *this with forced aligned access if \a Enable is true.
-  * \sa forceAlignedAccess(), class ForceAlignedAccess
-  */
-template<typename Derived>
-template<bool Enable>
-inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
-MatrixBase<Derived>::forceAlignedAccessIf()
-{
-  return derived();  // FIXME This should not work but apparently is never used
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FORCEALIGNEDACCESS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Fuzzy.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Fuzzy.h
deleted file mode 100644
index 43aa49b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Fuzzy.h
+++ /dev/null
@@ -1,155 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FUZZY_H
-#define EIGEN_FUZZY_H
-
-namespace Eigen { 
-
-namespace internal
-{
-
-template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isApprox_selector
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
-  {
-    typename internal::nested_eval<Derived,2>::type nested(x);
-    typename internal::nested_eval<OtherDerived,2>::type otherNested(y);
-    return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
-  }
-};
-
-template<typename Derived, typename OtherDerived>
-struct isApprox_selector<Derived, OtherDerived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
-  {
-    return x.matrix() == y.matrix();
-  }
-};
-
-template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isMuchSmallerThan_object_selector
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
-  {
-    return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
-  }
-};
-
-template<typename Derived, typename OtherDerived>
-struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&)
-  {
-    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
-  }
-};
-
-template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isMuchSmallerThan_scalar_selector
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
-  {
-    return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
-  }
-};
-
-template<typename Derived>
-struct isMuchSmallerThan_scalar_selector<Derived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&)
-  {
-    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
-  }
-};
-
-} // end namespace internal
-
-
-/** \returns \c true if \c *this is approximately equal to \a other, within the precision
-  * determined by \a prec.
-  *
-  * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
-  * are considered to be approximately equal within precision \f$ p \f$ if
-  * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
-  * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
-  * L2 norm).
-  *
-  * \note Because of the multiplicativeness of this comparison, one can't use this function
-  * to check whether \c *this is approximately equal to the zero matrix or vector.
-  * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
-  * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const
-  * RealScalar&, RealScalar) instead.
-  *
-  * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApprox(
-  const DenseBase<OtherDerived>& other,
-  const RealScalar& prec
-) const
-{
-  return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
-}
-
-/** \returns \c true if the norm of \c *this is much smaller than \a other,
-  * within the precision determined by \a prec.
-  *
-  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
-  * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
-  * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
-  *
-  * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason,
-  * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm
-  * of a reference matrix of same dimensions.
-  *
-  * \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(
-  const typename NumTraits<Scalar>::Real& other,
-  const RealScalar& prec
-) const
-{
-  return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
-}
-
-/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
-  * within the precision determined by \a prec.
-  *
-  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
-  * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
-  * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
-  * For matrices, the comparison is done using the Hilbert-Schmidt norm.
-  *
-  * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(
-  const DenseBase<OtherDerived>& other,
-  const RealScalar& prec
-) const
-{
-  return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FUZZY_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GeneralProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GeneralProduct.h
deleted file mode 100644
index 6906aa7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GeneralProduct.h
+++ /dev/null
@@ -1,465 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_PRODUCT_H
-#define EIGEN_GENERAL_PRODUCT_H
-
-namespace Eigen {
-
-enum {
-  Large = 2,
-  Small = 3
-};
-
-// Define the threshold value to fallback from the generic matrix-matrix product
-// implementation (heavy) to the lightweight coeff-based product one.
-// See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
-// in products/GeneralMatrixMatrix.h for more details.
-// TODO This threshold should also be used in the compile-time selector below.
-#ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD
-// This default value has been obtained on a Haswell architecture.
-#define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20
-#endif
-
-namespace internal {
-
-template<int Rows, int Cols, int Depth> struct product_type_selector;
-
-template<int Size, int MaxSize> struct product_size_category
-{
-  enum {
-    #ifndef EIGEN_GPU_COMPILE_PHASE
-    is_large = MaxSize == Dynamic ||
-               Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
-               (Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
-    #else
-    is_large = 0,
-    #endif
-    value = is_large  ? Large
-          : Size == 1 ? 1
-                      : Small
-  };
-};
-
-template<typename Lhs, typename Rhs> struct product_type
-{
-  typedef typename remove_all<Lhs>::type _Lhs;
-  typedef typename remove_all<Rhs>::type _Rhs;
-  enum {
-    MaxRows = traits<_Lhs>::MaxRowsAtCompileTime,
-    Rows    = traits<_Lhs>::RowsAtCompileTime,
-    MaxCols = traits<_Rhs>::MaxColsAtCompileTime,
-    Cols    = traits<_Rhs>::ColsAtCompileTime,
-    MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::MaxColsAtCompileTime,
-                                           traits<_Rhs>::MaxRowsAtCompileTime),
-    Depth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::ColsAtCompileTime,
-                                        traits<_Rhs>::RowsAtCompileTime)
-  };
-
-  // the splitting into different lines of code here, introducing the _select enums and the typedef below,
-  // is to work around an internal compiler error with gcc 4.1 and 4.2.
-private:
-  enum {
-    rows_select = product_size_category<Rows,MaxRows>::value,
-    cols_select = product_size_category<Cols,MaxCols>::value,
-    depth_select = product_size_category<Depth,MaxDepth>::value
-  };
-  typedef product_type_selector<rows_select, cols_select, depth_select> selector;
-
-public:
-  enum {
-    value = selector::ret,
-    ret = selector::ret
-  };
-#ifdef EIGEN_DEBUG_PRODUCT
-  static void debug()
-  {
-      EIGEN_DEBUG_VAR(Rows);
-      EIGEN_DEBUG_VAR(Cols);
-      EIGEN_DEBUG_VAR(Depth);
-      EIGEN_DEBUG_VAR(rows_select);
-      EIGEN_DEBUG_VAR(cols_select);
-      EIGEN_DEBUG_VAR(depth_select);
-      EIGEN_DEBUG_VAR(value);
-  }
-#endif
-};
-
-/* The following allows to select the kind of product at compile time
- * based on the three dimensions of the product.
- * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
-// FIXME I'm not sure the current mapping is the ideal one.
-template<int M, int N>  struct product_type_selector<M,N,1>              { enum { ret = OuterProduct }; };
-template<int M>         struct product_type_selector<M, 1, 1>            { enum { ret = LazyCoeffBasedProductMode }; };
-template<int N>         struct product_type_selector<1, N, 1>            { enum { ret = LazyCoeffBasedProductMode }; };
-template<int Depth>     struct product_type_selector<1,    1,    Depth>  { enum { ret = InnerProduct }; };
-template<>              struct product_type_selector<1,    1,    1>      { enum { ret = InnerProduct }; };
-template<>              struct product_type_selector<Small,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<1,    Small,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small,Small,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small, Large, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
-template<>              struct product_type_selector<1,    Large,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<1,    Large,Large>  { enum { ret = GemvProduct }; };
-template<>              struct product_type_selector<1,    Small,Large>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large,1,    Large>  { enum { ret = GemvProduct }; };
-template<>              struct product_type_selector<Small,1,    Large>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small,Small,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Large,Small,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Small,Large,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Large,Large,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Large,Small,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small,Large,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large,Large,Small>  { enum { ret = GemmProduct }; };
-
-} // end namespace internal
-
-/***********************************************************************
-*  Implementation of Inner Vector Vector Product
-***********************************************************************/
-
-// FIXME : maybe the "inner product" could return a Scalar
-// instead of a 1x1 matrix ??
-// Pro: more natural for the user
-// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix
-// product ends up to a row-vector times col-vector product... To tackle this use
-// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
-
-/***********************************************************************
-*  Implementation of Outer Vector Vector Product
-***********************************************************************/
-
-/***********************************************************************
-*  Implementation of General Matrix Vector Product
-***********************************************************************/
-
-/*  According to the shape/flags of the matrix we have to distinghish 3 different cases:
- *   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
- *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
- *   3 - all other cases are handled using a simple loop along the outer-storage direction.
- *  Therefore we need a lower level meta selector.
- *  Furthermore, if the matrix is the rhs, then the product has to be transposed.
- */
-namespace internal {
-
-template<int Side, int StorageOrder, bool BlasCompatible>
-struct gemv_dense_selector;
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
-
-template<typename Scalar,int Size,int MaxSize>
-struct gemv_static_vector_if<Scalar,Size,MaxSize,false>
-{
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; }
-};
-
-template<typename Scalar,int Size>
-struct gemv_static_vector_if<Scalar,Size,Dynamic,true>
-{
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { return 0; }
-};
-
-template<typename Scalar,int Size,int MaxSize>
-struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
-{
-  enum {
-    ForceAlignment  = internal::packet_traits<Scalar>::Vectorizable,
-    PacketSize      = internal::packet_traits<Scalar>::size
-  };
-  #if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
-  internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0,EIGEN_PLAIN_ENUM_MIN(AlignedMax,PacketSize)> m_data;
-  EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
-  #else
-  // Some architectures cannot align on the stack,
-  // => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
-  internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
-  EIGEN_STRONG_INLINE Scalar* data() {
-    return ForceAlignment
-            ? reinterpret_cast<Scalar*>((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
-            : m_data.array;
-  }
-  #endif
-};
-
-// The vector is on the left => transposition
-template<int StorageOrder, bool BlasCompatible>
-struct gemv_dense_selector<OnTheLeft,StorageOrder,BlasCompatible>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    Transpose<Dest> destT(dest);
-    enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
-    gemv_dense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
-      ::run(rhs.transpose(), lhs.transpose(), destT, alpha);
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar   LhsScalar;
-    typedef typename Rhs::Scalar   RhsScalar;
-    typedef typename Dest::Scalar  ResScalar;
-    typedef typename Dest::RealScalar  RealScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  
-    typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
-
-    ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
-    ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
-
-    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
-
-    // make sure Dest is a compile-time vector type (bug 1166)
-    typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest;
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1),
-      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
-      MightCannotUseDest = ((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime!=0)
-    };
-
-    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
-    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
-
-    if(!MightCannotUseDest)
-    {
-      // shortcut if we are sure to be able to use dest directly,
-      // this ease the compiler to generate cleaner and more optimzized code for most common cases
-      general_matrix_vector_product
-          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
-          actualLhs.rows(), actualLhs.cols(),
-          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
-          dest.data(), 1,
-          compatibleAlpha);
-    }
-    else
-    {
-      gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
-
-      const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
-      const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
-
-      ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
-                                                    evalToDest ? dest.data() : static_dest.data());
-
-      if(!evalToDest)
-      {
-        #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-        Index size = dest.size();
-        EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-        #endif
-        if(!alphaIsCompatible)
-        {
-          MappedDest(actualDestPtr, dest.size()).setZero();
-          compatibleAlpha = RhsScalar(1);
-        }
-        else
-          MappedDest(actualDestPtr, dest.size()) = dest;
-      }
-
-      general_matrix_vector_product
-          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
-          actualLhs.rows(), actualLhs.cols(),
-          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
-          actualDestPtr, 1,
-          compatibleAlpha);
-
-      if (!evalToDest)
-      {
-        if(!alphaIsCompatible)
-          dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
-        else
-          dest = MappedDest(actualDestPtr, dest.size());
-      }
-    }
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,RowMajor,true>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar   LhsScalar;
-    typedef typename Rhs::Scalar   RhsScalar;
-    typedef typename Dest::Scalar  ResScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-    typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
-    typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
-
-    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime==0
-    };
-
-    gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
-
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
-        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
-
-    if(!DirectlyUseRhs)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = actualRhs.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
-    }
-
-    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
-    general_matrix_vector_product
-        <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
-        actualLhs.rows(), actualLhs.cols(),
-        LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-        RhsMapper(actualRhsPtr, 1),
-        dest.data(), dest.col(0).innerStride(), //NOTE  if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166)
-        actualAlpha);
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,ColMajor,false>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
-    // TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp
-    typename nested_eval<Rhs,1>::type actual_rhs(rhs);
-    const Index size = rhs.rows();
-    for(Index k=0; k<size; ++k)
-      dest += (alpha*actual_rhs.coeff(k)) * lhs.col(k);
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,RowMajor,false>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
-    typename nested_eval<Rhs,Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
-    const Index rows = dest.rows();
-    for(Index i=0; i<rows; ++i)
-      dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_rhs.transpose())).sum();
-  }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Implementation of matrix base methods
-***************************************************************************/
-
-/** \returns the matrix product of \c *this and \a other.
-  *
-  * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
-  *
-  * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const Product<Derived, OtherDerived>
-MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
-{
-  // A note regarding the function declaration: In MSVC, this function will sometimes
-  // not be inlined since DenseStorage is an unwindable object for dynamic
-  // matrices and product types are holding a member to store the result.
-  // Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
-  enum {
-    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
-                   || OtherDerived::RowsAtCompileTime==Dynamic
-                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
-    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
-    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
-  };
-  // note to the lost user:
-  //    * for a dot product use: v1.dot(v2)
-  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
-    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
-    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
-  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
-#ifdef EIGEN_DEBUG_PRODUCT
-  internal::product_type<Derived,OtherDerived>::debug();
-#endif
-
-  return Product<Derived, OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
-  *
-  * The returned product will behave like any other expressions: the coefficients of the product will be
-  * computed once at a time as requested. This might be useful in some extremely rare cases when only
-  * a small and no coherent fraction of the result's coefficients have to be computed.
-  *
-  * \warning This version of the matrix product can be much much slower. So use it only if you know
-  * what you are doing and that you measured a true speed improvement.
-  *
-  * \sa operator*(const MatrixBase&)
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const Product<Derived,OtherDerived,LazyProduct>
-MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
-{
-  enum {
-    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
-                   || OtherDerived::RowsAtCompileTime==Dynamic
-                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
-    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
-    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
-  };
-  // note to the lost user:
-  //    * for a dot product use: v1.dot(v2)
-  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
-    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
-    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
-  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
-
-  return Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GenericPacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GenericPacketMath.h
deleted file mode 100644
index cf677a1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GenericPacketMath.h
+++ /dev/null
@@ -1,1040 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERIC_PACKET_MATH_H
-#define EIGEN_GENERIC_PACKET_MATH_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * \file GenericPacketMath.h
-  *
-  * Default implementation for types not supported by the vectorization.
-  * In practice these functions are provided to make easier the writing
-  * of generic vectorized code.
-  */
-
-#ifndef EIGEN_DEBUG_ALIGNED_LOAD
-#define EIGEN_DEBUG_ALIGNED_LOAD
-#endif
-
-#ifndef EIGEN_DEBUG_UNALIGNED_LOAD
-#define EIGEN_DEBUG_UNALIGNED_LOAD
-#endif
-
-#ifndef EIGEN_DEBUG_ALIGNED_STORE
-#define EIGEN_DEBUG_ALIGNED_STORE
-#endif
-
-#ifndef EIGEN_DEBUG_UNALIGNED_STORE
-#define EIGEN_DEBUG_UNALIGNED_STORE
-#endif
-
-struct default_packet_traits
-{
-  enum {
-    HasHalfPacket = 0,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 0,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 1,
-    HasBlend     = 0,
-    // This flag is used to indicate whether packet comparison is supported.
-    // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
-    HasCmp       = 0,
-
-    HasDiv    = 0,
-    HasSqrt   = 0,
-    HasRsqrt  = 0,
-    HasExp    = 0,
-    HasExpm1  = 0,
-    HasLog    = 0,
-    HasLog1p  = 0,
-    HasLog10  = 0,
-    HasPow    = 0,
-
-    HasSin    = 0,
-    HasCos    = 0,
-    HasTan    = 0,
-    HasASin   = 0,
-    HasACos   = 0,
-    HasATan   = 0,
-    HasSinh   = 0,
-    HasCosh   = 0,
-    HasTanh   = 0,
-    HasLGamma = 0,
-    HasDiGamma = 0,
-    HasZeta = 0,
-    HasPolygamma = 0,
-    HasErf = 0,
-    HasErfc = 0,
-    HasNdtri = 0,
-    HasBessel = 0,
-    HasIGamma = 0,
-    HasIGammaDerA = 0,
-    HasGammaSampleDerAlpha = 0,
-    HasIGammac = 0,
-    HasBetaInc = 0,
-
-    HasRound  = 0,
-    HasRint   = 0,
-    HasFloor  = 0,
-    HasCeil   = 0,
-    HasSign   = 0
-  };
-};
-
-template<typename T> struct packet_traits : default_packet_traits
-{
-  typedef T type;
-  typedef T half;
-  enum {
-    Vectorizable = 0,
-    size = 1,
-    AlignedOnScalar = 0,
-    HasHalfPacket = 0
-  };
-  enum {
-    HasAdd    = 0,
-    HasSub    = 0,
-    HasMul    = 0,
-    HasNegate = 0,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasConj   = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<typename T> struct packet_traits<const T> : packet_traits<T> { };
-
-template<typename T> struct unpacket_traits
-{
-  typedef T type;
-  typedef T half;
-  enum
-  {
-    size = 1,
-    alignment = 1,
-    vectorizable = false,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<typename T> struct unpacket_traits<const T> : unpacket_traits<T> { };
-
-template <typename Src, typename Tgt> struct type_casting_traits {
-  enum {
-    VectorizedCast = 0,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-/** \internal Wrapper to ensure that multiple packet types can map to the same
-    same underlying vector type. */
-template<typename T, int unique_id = 0>
-struct eigen_packet_wrapper
-{
-  EIGEN_ALWAYS_INLINE operator T&() { return m_val; }
-  EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; }
-  EIGEN_ALWAYS_INLINE eigen_packet_wrapper() {}
-  EIGEN_ALWAYS_INLINE eigen_packet_wrapper(const T &v) : m_val(v) {}
-  EIGEN_ALWAYS_INLINE eigen_packet_wrapper& operator=(const T &v) {
-    m_val = v;
-    return *this;
-  }
-
-  T m_val;
-};
-
-
-/** \internal A convenience utility for determining if the type is a scalar.
- * This is used to enable some generic packet implementations.
- */
-template<typename Packet>
-struct is_scalar {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  enum {
-    value = internal::is_same<Packet, Scalar>::value
-  };
-};
-
-/** \internal \returns static_cast<TgtType>(a) (coeff-wise) */
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a) {
-  return static_cast<TgtPacket>(a);
-}
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a, const SrcPacket& /*b*/) {
-  return static_cast<TgtPacket>(a);
-}
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/) {
-  return static_cast<TgtPacket>(a);
-}
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/,
-      const SrcPacket& /*e*/, const SrcPacket& /*f*/, const SrcPacket& /*g*/, const SrcPacket& /*h*/) {
-  return static_cast<TgtPacket>(a);
-}
-
-/** \internal \returns reinterpret_cast<Target>(a) */
-template <typename Target, typename Packet>
-EIGEN_DEVICE_FUNC inline Target
-preinterpret(const Packet& a); /* { return reinterpret_cast<const Target&>(a); } */
-
-/** \internal \returns a + b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-padd(const Packet& a, const Packet& b) { return a+b; }
-// Avoid compiler warning for boolean algebra.
-template<> EIGEN_DEVICE_FUNC inline bool
-padd(const bool& a, const bool& b) { return a || b; }
-
-/** \internal \returns a - b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-psub(const Packet& a, const Packet& b) { return a-b; }
-
-/** \internal \returns -a (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pnegate(const Packet& a) { return -a; }
-
-template<> EIGEN_DEVICE_FUNC inline bool
-pnegate(const bool& a) { return !a; }
-
-/** \internal \returns conj(a) (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pconj(const Packet& a) { return numext::conj(a); }
-
-/** \internal \returns a * b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmul(const Packet& a, const Packet& b) { return a*b; }
-// Avoid compiler warning for boolean algebra.
-template<> EIGEN_DEVICE_FUNC inline bool
-pmul(const bool& a, const bool& b) { return a && b; }
-
-/** \internal \returns a / b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pdiv(const Packet& a, const Packet& b) { return a/b; }
-
-// In the generic case, memset to all one bits.
-template<typename Packet, typename EnableIf = void>
-struct ptrue_impl {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/){
-    Packet b;
-    memset(static_cast<void*>(&b), 0xff, sizeof(Packet));
-    return b;
-  }
-};
-
-// For non-trivial scalars, set to Scalar(1) (i.e. a non-zero value).
-// Although this is technically not a valid bitmask, the scalar path for pselect
-// uses a comparison to zero, so this should still work in most cases. We don't
-// have another option, since the scalar type requires initialization.
-template<typename T>
-struct ptrue_impl<T, 
-    typename internal::enable_if<is_scalar<T>::value && NumTraits<T>::RequireInitialization>::type > {
-  static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/){
-    return T(1);
-  }
-};
-
-/** \internal \returns one bits. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-ptrue(const Packet& a) {
-  return ptrue_impl<Packet>::run(a);
-}
-
-// In the general case, memset to zero.
-template<typename Packet, typename EnableIf = void>
-struct pzero_impl {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/) {
-    Packet b;
-    memset(static_cast<void*>(&b), 0x00, sizeof(Packet));
-    return b;
-  }
-};
-
-// For scalars, explicitly set to Scalar(0), since the underlying representation
-// for zero may not consist of all-zero bits.
-template<typename T>
-struct pzero_impl<T,
-    typename internal::enable_if<is_scalar<T>::value>::type> {
-  static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/) {
-    return T(0);
-  }
-};
-
-/** \internal \returns packet of zeros */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pzero(const Packet& a) {
-  return pzero_impl<Packet>::run(a);
-}
-
-/** \internal \returns a <= b as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_le(const Packet& a, const Packet& b)  { return a<=b ? ptrue(a) : pzero(a); }
-
-/** \internal \returns a < b as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_lt(const Packet& a, const Packet& b)  { return a<b ? ptrue(a) : pzero(a); }
-
-/** \internal \returns a == b as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_eq(const Packet& a, const Packet& b) { return a==b ? ptrue(a) : pzero(a); }
-
-/** \internal \returns a < b or a==NaN or b==NaN as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_lt_or_nan(const Packet& a, const Packet& b) { return a>=b ? pzero(a) : ptrue(a); }
-
-template<typename T>
-struct bit_and {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
-    return a & b;
-  }
-};
-
-template<typename T>
-struct bit_or {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
-    return a | b;
-  }
-};
-
-template<typename T>
-struct bit_xor {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
-    return a ^ b;
-  }
-};
-
-template<typename T>
-struct bit_not {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a) const {
-    return ~a;
-  }
-};
-
-// Use operators &, |, ^, ~.
-template<typename T>
-struct operator_bitwise_helper {
-  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) { return bit_and<T>()(a, b); }
-  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { return bit_or<T>()(a, b); }
-  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) { return bit_xor<T>()(a, b); }
-  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { return bit_not<T>()(a); }
-};
-
-// Apply binary operations byte-by-byte
-template<typename T>
-struct bytewise_bitwise_helper {
-  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) {
-    return binary(a, b, bit_and<unsigned char>());
-  }
-  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { 
-    return binary(a, b, bit_or<unsigned char>());
-   }
-  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) {
-    return binary(a, b, bit_xor<unsigned char>());
-  }
-  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { 
-    return unary(a,bit_not<unsigned char>());
-   }
-  
- private:
-  template<typename Op>
-  EIGEN_DEVICE_FUNC static inline T unary(const T& a, Op op) {
-    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
-    T c;
-    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
-    for (size_t i = 0; i < sizeof(T); ++i) {
-      *c_ptr++ = op(*a_ptr++);
-    }
-    return c;
-  }
-
-  template<typename Op>
-  EIGEN_DEVICE_FUNC static inline T binary(const T& a, const T& b, Op op) {
-    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
-    const unsigned char* b_ptr = reinterpret_cast<const unsigned char*>(&b);
-    T c;
-    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
-    for (size_t i = 0; i < sizeof(T); ++i) {
-      *c_ptr++ = op(*a_ptr++, *b_ptr++);
-    }
-    return c;
-  }
-};
-
-// In the general case, use byte-by-byte manipulation.
-template<typename T, typename EnableIf = void>
-struct bitwise_helper : public bytewise_bitwise_helper<T> {};
-
-// For integers or non-trivial scalars, use binary operators.
-template<typename T>
-struct bitwise_helper<T,
-  typename internal::enable_if<
-    is_scalar<T>::value && (NumTraits<T>::IsInteger || NumTraits<T>::RequireInitialization)>::type
-  > : public operator_bitwise_helper<T> {};
-
-/** \internal \returns the bitwise and of \a a and \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pand(const Packet& a, const Packet& b) {
-  return bitwise_helper<Packet>::bitwise_and(a, b);
-}
-
-/** \internal \returns the bitwise or of \a a and \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-por(const Packet& a, const Packet& b) {
-  return bitwise_helper<Packet>::bitwise_or(a, b);
-}
-
-/** \internal \returns the bitwise xor of \a a and \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pxor(const Packet& a, const Packet& b) {
-  return bitwise_helper<Packet>::bitwise_xor(a, b);
-}
-
-/** \internal \returns the bitwise not of \a a */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pnot(const Packet& a) {
-  return bitwise_helper<Packet>::bitwise_not(a);
-}
-
-/** \internal \returns the bitwise and of \a a and not \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pandnot(const Packet& a, const Packet& b) { return pand(a, pnot(b)); }
-
-// In the general case, use bitwise select.
-template<typename Packet, typename EnableIf = void>
-struct pselect_impl {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
-    return por(pand(a,mask),pandnot(b,mask));
-  }
-};
-
-// For scalars, use ternary select.
-template<typename Packet>
-struct pselect_impl<Packet, 
-    typename internal::enable_if<is_scalar<Packet>::value>::type > {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
-    return numext::equal_strict(mask, Packet(0)) ? b : a;
-  }
-};
-
-/** \internal \returns \a or \b for each field in packet according to \mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pselect(const Packet& mask, const Packet& a, const Packet& b) {
-  return pselect_impl<Packet>::run(mask, a, b);
-}
-
-template<> EIGEN_DEVICE_FUNC inline bool pselect<bool>(
-    const bool& cond, const bool& a, const bool& b) {
-  return cond ? a : b;
-}
-
-/** \internal \returns the min or of \a a and \a b (coeff-wise)
-    If either \a a or \a b are NaN, the result is implementation defined. */
-template<int NaNPropagation>
-struct pminmax_impl {
-  template <typename Packet, typename Op>
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
-    return op(a,b);
-  }
-};
-
-/** \internal \returns the min or max of \a a and \a b (coeff-wise)
-    If either \a a or \a b are NaN, NaN is returned. */
-template<>
-struct pminmax_impl<PropagateNaN> {
-  template <typename Packet, typename Op>
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet not_nan_mask_b = pcmp_eq(b, b);
-  return pselect(not_nan_mask_a,
-                 pselect(not_nan_mask_b, op(a, b), b),
-                 a);
-  }
-};
-
-/** \internal \returns the min or max of \a a and \a b (coeff-wise)
-    If both \a a and \a b are NaN, NaN is returned.
-    Equivalent to std::fmin(a, b).  */
-template<>
-struct pminmax_impl<PropagateNumbers> {
-  template <typename Packet, typename Op>
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet not_nan_mask_b = pcmp_eq(b, b);
-  return pselect(not_nan_mask_a,
-                 pselect(not_nan_mask_b, op(a, b), a),
-                 b);
-  }
-};
-
-
-#ifndef SYCL_DEVICE_ONLY
-#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) Func
-#else
-#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) \
-[](const Type& a, const Type& b) { \
-        return Func(a, b);}
-#endif
-
-/** \internal \returns the min of \a a and \a b  (coeff-wise).
-    If \a a or \b b is NaN, the return value is implementation defined. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmin(const Packet& a, const Packet& b) { return numext::mini(a,b); }
-
-/** \internal \returns the min of \a a and \a b  (coeff-wise).
-    NaNPropagation determines the NaN propagation semantics. */
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) {
-  return pminmax_impl<NaNPropagation>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet, (pmin<Packet>)));
-}
-
-/** \internal \returns the max of \a a and \a b  (coeff-wise)
-    If \a a or \b b is NaN, the return value is implementation defined. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmax(const Packet& a, const Packet& b) { return numext::maxi(a, b); }
-
-/** \internal \returns the max of \a a and \a b  (coeff-wise).
-    NaNPropagation determines the NaN propagation semantics. */
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline Packet pmax(const Packet& a, const Packet& b) {
-  return pminmax_impl<NaNPropagation>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet,(pmax<Packet>)));
-}
-
-/** \internal \returns the absolute value of \a a */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pabs(const Packet& a) { return numext::abs(a); }
-template<> EIGEN_DEVICE_FUNC inline unsigned int
-pabs(const unsigned int& a) { return a; }
-template<> EIGEN_DEVICE_FUNC inline unsigned long
-pabs(const unsigned long& a) { return a; }
-template<> EIGEN_DEVICE_FUNC inline unsigned long long
-pabs(const unsigned long long& a) { return a; }
-
-/** \internal \returns the addsub value of \a a,b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-paddsub(const Packet& a, const Packet& b) {
-  return pselect(peven_mask(a), padd(a, b), psub(a, b));
- }
-
-/** \internal \returns the phase angle of \a a */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-parg(const Packet& a) { using numext::arg; return arg(a); }
-
-
-/** \internal \returns \a a logically shifted by N bits to the right */
-template<int N> EIGEN_DEVICE_FUNC inline int
-parithmetic_shift_right(const int& a) { return a >> N; }
-template<int N> EIGEN_DEVICE_FUNC inline long int
-parithmetic_shift_right(const long int& a) { return a >> N; }
-
-/** \internal \returns \a a arithmetically shifted by N bits to the right */
-template<int N> EIGEN_DEVICE_FUNC inline int
-plogical_shift_right(const int& a) { return static_cast<int>(static_cast<unsigned int>(a) >> N); }
-template<int N> EIGEN_DEVICE_FUNC inline long int
-plogical_shift_right(const long int& a) { return static_cast<long>(static_cast<unsigned long>(a) >> N); }
-
-/** \internal \returns \a a shifted by N bits to the left */
-template<int N> EIGEN_DEVICE_FUNC inline int
-plogical_shift_left(const int& a) { return a << N; }
-template<int N> EIGEN_DEVICE_FUNC inline long int
-plogical_shift_left(const long int& a) { return a << N; }
-
-/** \internal \returns the significant and exponent of the underlying floating point numbers
-  * See https://en.cppreference.com/w/cpp/numeric/math/frexp
-  */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline Packet pfrexp(const Packet& a, Packet& exponent) {
-  int exp;
-  EIGEN_USING_STD(frexp);
-  Packet result = static_cast<Packet>(frexp(a, &exp));
-  exponent = static_cast<Packet>(exp);
-  return result;
-}
-
-/** \internal \returns a * 2^((int)exponent)
-  * See https://en.cppreference.com/w/cpp/numeric/math/ldexp
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pldexp(const Packet &a, const Packet &exponent) {
-  EIGEN_USING_STD(ldexp)
-  return static_cast<Packet>(ldexp(a, static_cast<int>(exponent)));
-}
-
-/** \internal \returns the min of \a a and \a b  (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pabsdiff(const Packet& a, const Packet& b) { return pselect(pcmp_lt(a, b), psub(b, a), psub(a, b)); }
-
-/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
-
-/** \internal \returns a packet version of \a *from, (un-aligned load) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
-
-/** \internal \returns a packet version of \a *from, (un-aligned masked load)
- * There is no generic implementation. We only have implementations for specialized
- * cases. Generic case should not be called.
- */
-template<typename Packet> EIGEN_DEVICE_FUNC inline
-typename enable_if<unpacket_traits<Packet>::masked_load_available, Packet>::type
-ploadu(const typename unpacket_traits<Packet>::type* from, typename unpacket_traits<Packet>::mask_t umask);
-
-/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pset1(const typename unpacket_traits<Packet>::type& a) { return a; }
-
-/** \internal \returns a packet with constant coefficients set from bits */
-template<typename Packet,typename BitsType> EIGEN_DEVICE_FUNC inline Packet
-pset1frombits(BitsType a);
-
-/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pload1(const typename unpacket_traits<Packet>::type  *a) { return pset1<Packet>(*a); }
-
-/** \internal \returns a packet with elements of \a *from duplicated.
-  * For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and
-  * duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
-  * Currently, this function is only used for scalar * complex products.
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
-ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
-
-/** \internal \returns a packet with elements of \a *from quadrupled.
-  * For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and
-  * replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]}
-  * Currently, this function is only used in matrix products.
-  * For packet-size smaller or equal to 4, this function is equivalent to pload1
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-ploadquad(const typename unpacket_traits<Packet>::type* from)
-{ return pload1<Packet>(from); }
-
-/** \internal equivalent to
-  * \code
-  * a0 = pload1(a+0);
-  * a1 = pload1(a+1);
-  * a2 = pload1(a+2);
-  * a3 = pload1(a+3);
-  * \endcode
-  * \sa pset1, pload1, ploaddup, pbroadcast2
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC
-inline void pbroadcast4(const typename unpacket_traits<Packet>::type *a,
-                        Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  a0 = pload1<Packet>(a+0);
-  a1 = pload1<Packet>(a+1);
-  a2 = pload1<Packet>(a+2);
-  a3 = pload1<Packet>(a+3);
-}
-
-/** \internal equivalent to
-  * \code
-  * a0 = pload1(a+0);
-  * a1 = pload1(a+1);
-  * \endcode
-  * \sa pset1, pload1, ploaddup, pbroadcast4
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC
-inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a,
-                        Packet& a0, Packet& a1)
-{
-  a0 = pload1<Packet>(a+0);
-  a1 = pload1<Packet>(a+1);
-}
-
-/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */
-template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
-plset(const typename unpacket_traits<Packet>::type& a) { return a; }
-
-/** \internal \returns a packet with constant coefficients \a a, e.g.: (x, 0, x, 0),
-     where x is the value of all 1-bits. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-peven_mask(const Packet& /*a*/) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  const size_t n = unpacket_traits<Packet>::size;
-  EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
-  for(size_t i = 0; i < n; ++i) {
-    memset(elements+i, ((i & 1) == 0 ? 0xff : 0), sizeof(Scalar));
-  }
-  return ploadu<Packet>(elements);
-}
-
-
-/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
-template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from)
-{ (*to) = from; }
-
-/** \internal copy the packet \a from to \a *to, (un-aligned store) */
-template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from)
-{  (*to) = from; }
-
-/** \internal copy the packet \a from to \a *to, (un-aligned store with a mask)
- * There is no generic implementation. We only have implementations for specialized
- * cases. Generic case should not be called.
- */
-template<typename Scalar, typename Packet>
-EIGEN_DEVICE_FUNC inline
-typename enable_if<unpacket_traits<Packet>::masked_store_available, void>::type
-pstoreu(Scalar* to, const Packet& from, typename unpacket_traits<Packet>::mask_t umask);
-
- template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/)
- { return ploadu<Packet>(from); }
-
- template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/)
- { pstore(to, from); }
-
-/** \internal tries to do cache prefetching of \a addr */
-template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr)
-{
-#if defined(EIGEN_HIP_DEVICE_COMPILE)
-  // do nothing
-#elif defined(EIGEN_CUDA_ARCH)
-#if defined(__LP64__) || EIGEN_OS_WIN64
-  // 64-bit pointer operand constraint for inlined asm
-  asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr));
-#else
-  // 32-bit pointer operand constraint for inlined asm
-  asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
-#endif
-#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
-  __builtin_prefetch(addr);
-#endif
-}
-
-/** \internal \returns the reversed elements of \a a*/
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a)
-{ return a; }
-
-/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
-{
-  return Packet(numext::imag(a),numext::real(a));
-}
-
-/**************************
-* Special math functions
-***************************/
-
-/** \internal \returns the sine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet psin(const Packet& a) { EIGEN_USING_STD(sin); return sin(a); }
-
-/** \internal \returns the cosine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pcos(const Packet& a) { EIGEN_USING_STD(cos); return cos(a); }
-
-/** \internal \returns the tan of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet ptan(const Packet& a) { EIGEN_USING_STD(tan); return tan(a); }
-
-/** \internal \returns the arc sine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pasin(const Packet& a) { EIGEN_USING_STD(asin); return asin(a); }
-
-/** \internal \returns the arc cosine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pacos(const Packet& a) { EIGEN_USING_STD(acos); return acos(a); }
-
-/** \internal \returns the arc tangent of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet patan(const Packet& a) { EIGEN_USING_STD(atan); return atan(a); }
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet psinh(const Packet& a) { EIGEN_USING_STD(sinh); return sinh(a); }
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pcosh(const Packet& a) { EIGEN_USING_STD(cosh); return cosh(a); }
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet ptanh(const Packet& a) { EIGEN_USING_STD(tanh); return tanh(a); }
-
-/** \internal \returns the exp of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pexp(const Packet& a) { EIGEN_USING_STD(exp); return exp(a); }
-
-/** \internal \returns the expm1 of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pexpm1(const Packet& a) { return numext::expm1(a); }
-
-/** \internal \returns the log of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog(const Packet& a) { EIGEN_USING_STD(log); return log(a); }
-
-/** \internal \returns the log1p of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog1p(const Packet& a) { return numext::log1p(a); }
-
-/** \internal \returns the log10 of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog10(const Packet& a) { EIGEN_USING_STD(log10); return log10(a); }
-
-/** \internal \returns the log10 of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog2(const Packet& a) {
-  typedef typename internal::unpacket_traits<Packet>::type Scalar;
-  return pmul(pset1<Packet>(Scalar(EIGEN_LOG2E)), plog(a)); 
-}
-
-/** \internal \returns the square-root of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet psqrt(const Packet& a) { return numext::sqrt(a); }
-
-/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet prsqrt(const Packet& a) {
-  typedef typename internal::unpacket_traits<Packet>::type Scalar;
-  return pdiv(pset1<Packet>(Scalar(1)), psqrt(a));
-}
-
-/** \internal \returns the rounded value of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pround(const Packet& a) { using numext::round; return round(a); }
-
-/** \internal \returns the floor of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
-
-/** \internal \returns the rounded value of \a a (coeff-wise) with current
- * rounding mode */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet print(const Packet& a) { using numext::rint; return rint(a); }
-
-/** \internal \returns the ceil of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
-
-/** \internal \returns the first element of a packet */
-template<typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
-pfirst(const Packet& a)
-{ return a; }
-
-/** \internal \returns the sum of the elements of upper and lower half of \a a if \a a is larger than 4.
-  * For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7}
-  * For packet-size smaller or equal to 4, this boils down to a noop.
-  */
-template<typename Packet>
-EIGEN_DEVICE_FUNC inline typename conditional<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>::type
-predux_half_dowto4(const Packet& a)
-{ return a; }
-
-// Slow generic implementation of Packet reduction.
-template <typename Packet, typename Op>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
-predux_helper(const Packet& a, Op op) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  const size_t n = unpacket_traits<Packet>::size;
-  EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
-  pstoreu<Scalar>(elements, a);
-  for(size_t k = n / 2; k > 0; k /= 2)  {
-    for(size_t i = 0; i < k; ++i) {
-      elements[i] = op(elements[i], elements[i + k]);
-    }
-  }
-  return elements[0];
-}
-
-/** \internal \returns the sum of the elements of \a a*/
-template<typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
-predux(const Packet& a)
-{
-  return a;
-}
-
-/** \internal \returns the product of the elements of \a a */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(
-    const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmul<Scalar>)));
-}
-
-/** \internal \returns the min of the elements of \a a */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
-    const Packet &a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<PropagateFast, Scalar>)));
-}
-
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
-    const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<NaNPropagation, Scalar>)));
-}
-
-/** \internal \returns the min of the elements of \a a */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
-    const Packet &a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<PropagateFast, Scalar>)));
-}
-
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
-    const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<NaNPropagation, Scalar>)));
-}
-
-#undef EIGEN_BINARY_OP_NAN_PROPAGATION
-
-/** \internal \returns true if all coeffs of \a a means "true"
-  * It is supposed to be called on values returned by pcmp_*.
-  */
-// not needed yet
-// template<typename Packet> EIGEN_DEVICE_FUNC inline bool predux_all(const Packet& a)
-// { return bool(a); }
-
-/** \internal \returns true if any coeffs of \a a means "true"
-  * It is supposed to be called on values returned by pcmp_*.
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC inline bool predux_any(const Packet& a)
-{
-  // Dirty but generic implementation where "true" is assumed to be non 0 and all the sames.
-  // It is expected that "true" is either:
-  //  - Scalar(1)
-  //  - bits full of ones (NaN for floats),
-  //  - or first bit equals to 1 (1 for ints, smallest denormal for floats).
-  // For all these cases, taking the sum is just fine, and this boils down to a no-op for scalars.
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  return numext::not_equal_strict(predux(a), Scalar(0));
-}
-
-/***************************************************************************
-* The following functions might not have to be overwritten for vectorized types
-***************************************************************************/
-
-/** \internal copy a packet with constant coefficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */
-// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type)
-template<typename Packet>
-inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename unpacket_traits<Packet>::type& a)
-{
-  pstore(to, pset1<Packet>(a));
-}
-
-/** \internal \returns a * b + c (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmadd(const Packet&  a,
-         const Packet&  b,
-         const Packet&  c)
-{ return padd(pmul(a, b),c); }
-
-/** \internal \returns a packet version of \a *from.
-  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
-template<typename Packet, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits<Packet>::type* from)
-{
-  if(Alignment >= unpacket_traits<Packet>::alignment)
-    return pload<Packet>(from);
-  else
-    return ploadu<Packet>(from);
-}
-
-/** \internal copy the packet \a from to \a *to.
-  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
-template<typename Scalar, typename Packet, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from)
-{
-  if(Alignment >= unpacket_traits<Packet>::alignment)
-    pstore(to, from);
-  else
-    pstoreu(to, from);
-}
-
-/** \internal \returns a packet version of \a *from.
-  * Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the
-  * hardware if available to speedup the loading of data that won't be modified
-  * by the current computation.
-  */
-template<typename Packet, int LoadMode>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from)
-{
-  return ploadt<Packet, LoadMode>(from);
-}
-
-/***************************************************************************
-* Fast complex products (GCC generates a function call which is very slow)
-***************************************************************************/
-
-// Eigen+CUDA does not support complexes.
-#if !defined(EIGEN_GPUCC)
-
-template<> inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b)
-{ return std::complex<float>(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); }
-
-template<> inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b)
-{ return std::complex<double>(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); }
-
-#endif
-
-
-/***************************************************************************
- * PacketBlock, that is a collection of N packets where the number of words
- * in the packet is a multiple of N.
-***************************************************************************/
-template <typename Packet,int N=unpacket_traits<Packet>::size> struct PacketBlock {
-  Packet packet[N];
-};
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet,1>& /*kernel*/) {
-  // Nothing to do in the scalar case, i.e. a 1x1 matrix.
-}
-
-/***************************************************************************
- * Selector, i.e. vector of N boolean values used to select (i.e. blend)
- * words from 2 packets.
-***************************************************************************/
-template <size_t N> struct Selector {
-  bool select[N];
-};
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pblend(const Selector<unpacket_traits<Packet>::size>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
-  return ifPacket.select[0] ? thenPacket : elsePacket;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERIC_PACKET_MATH_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GlobalFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GlobalFunctions.h
deleted file mode 100644
index 629af94..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/GlobalFunctions.h
+++ /dev/null
@@ -1,194 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GLOBAL_FUNCTIONS_H
-#define EIGEN_GLOBAL_FUNCTIONS_H
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
-  /** \returns an expression of the coefficient-wise DOC_OP of \a x
-
-    DOC_DETAILS
-
-    \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp
-    */ \
-  template<typename Derived> \
-  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
-  NAME(const Eigen::ArrayBase<Derived>& x);
-
-#else
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
-  template<typename Derived> \
-  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
-  (NAME)(const Eigen::ArrayBase<Derived>& x) { \
-    return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
-  }
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
-  \
-  template<typename Derived> \
-  struct NAME##_retval<ArrayBase<Derived> > \
-  { \
-    typedef const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> type; \
-  }; \
-  template<typename Derived> \
-  struct NAME##_impl<ArrayBase<Derived> > \
-  { \
-    static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
-    { \
-      return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived()); \
-    } \
-  };
-
-namespace Eigen
-{
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op,imaginary part,\sa ArrayBase::imag)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op,inverse,\sa ArrayBase::inverse)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op,sine,\sa ArrayBase::sin)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op,cosine,\sa ArrayBase::cos)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op,tangent,\sa ArrayBase::tan)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op,arc-tangent,\sa ArrayBase::atan)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op,arc-sine,\sa ArrayBase::asin)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op,arc-consine,\sa ArrayBase::acos)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
-#if EIGEN_HAS_CXX11_MATH
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asinh,scalar_asinh_op,inverse hyperbolic sine,\sa ArrayBase::asinh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acosh,scalar_acosh_op,inverse hyperbolic cosine,\sa ArrayBase::acosh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atanh,scalar_atanh_op,inverse hyperbolic tangent,\sa ArrayBase::atanh)
-#endif
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic,scalar_logistic_op,logistic function,\sa ArrayBase::logistic)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op,complement error function,\sa ArrayBase::erfc)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ndtri,scalar_ndtri_op,inverse normal distribution function,\sa ArrayBase::ndtri)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op,exponential,\sa ArrayBase::exp)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(expm1,scalar_expm1_op,exponential of a value minus 1,\sa ArrayBase::expm1)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op,natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p,scalar_log1p_op,natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op,base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log10)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log2,scalar_log2_op,base 2 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log2)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op,absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op,squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op,complex argument,\sa ArrayBase::arg DOXCOMMA MatrixBase::cwiseArg)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op,square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt,scalar_rsqrt_op,reciprocal square root,\sa ArrayBase::rsqrt)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op,square (power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op,cube (power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rint,scalar_rint_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op,nearest integer not greater than the giben value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op,nearest integer not less than the giben value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op,not-a-number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
-
-  /** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
-    *
-    * \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar).
-    *
-    * \sa ArrayBase::pow()
-    *
-    * \relates ArrayBase
-    */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-  template<typename Derived,typename ScalarExponent>
-  inline const CwiseBinaryOp<internal::scalar_pow_op<Derived::Scalar,ScalarExponent>,Derived,Constant<ScalarExponent> >
-  pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
-#else
-  template <typename Derived,typename ScalarExponent>
-  EIGEN_DEVICE_FUNC inline
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(
-    const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<typename Derived::Scalar
-                                                 EIGEN_COMMA ScalarExponent EIGEN_COMMA
-                                                 EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent)>::type,pow))
-  pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent)
-  {
-    typedef typename internal::promote_scalar_arg<typename Derived::Scalar,ScalarExponent,
-                                                  EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent)>::type PromotedExponent;
-    return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedExponent,pow)(x.derived(),
-           typename internal::plain_constant_type<Derived,PromotedExponent>::type(x.derived().rows(), x.derived().cols(), internal::scalar_constant_op<PromotedExponent>(exponent)));
-  }
-#endif
-
-  /** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
-    *
-    * This function computes the coefficient-wise power.
-    *
-    * Example: \include Cwise_array_power_array.cpp
-    * Output: \verbinclude Cwise_array_power_array.out
-    *
-    * \sa ArrayBase::pow()
-    *
-    * \relates ArrayBase
-    */
-  template<typename Derived,typename ExponentDerived>
-  inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
-  pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
-  {
-    return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
-      x.derived(),
-      exponents.derived()
-    );
-  }
-
-  /** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
-    *
-    * This function computes the coefficient-wise power between a scalar and an array of exponents.
-    *
-    * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
-    *
-    * Example: \include Cwise_scalar_power_array.cpp
-    * Output: \verbinclude Cwise_scalar_power_array.out
-    *
-    * \sa ArrayBase::pow()
-    *
-    * \relates ArrayBase
-    */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-  template<typename Scalar,typename Derived>
-  inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar,Derived::Scalar>,Constant<Scalar>,Derived>
-  pow(const Scalar& x,const Eigen::ArrayBase<Derived>& x);
-#else
-  template <typename Scalar, typename Derived>
-  EIGEN_DEVICE_FUNC inline
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(
-    const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<typename Derived::Scalar
-                                                 EIGEN_COMMA Scalar EIGEN_COMMA
-                                                 EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type,Derived,pow))
-  pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents) {
-    typedef typename internal::promote_scalar_arg<typename Derived::Scalar,Scalar,
-                                                  EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type PromotedScalar;
-    return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedScalar,Derived,pow)(
-           typename internal::plain_constant_type<Derived,PromotedScalar>::type(exponents.derived().rows(), exponents.derived().cols(), internal::scalar_constant_op<PromotedScalar>(x)), exponents.derived());
-  }
-#endif
-
-
-  namespace internal
-  {
-    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
-    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
-    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
-  }
-}
-
-// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...)
-
-#endif // EIGEN_GLOBAL_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/IO.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/IO.h
deleted file mode 100644
index e81c315..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/IO.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_IO_H
-#define EIGEN_IO_H
-
-namespace Eigen { 
-
-enum { DontAlignCols = 1 };
-enum { StreamPrecision = -1,
-       FullPrecision = -2 };
-
-namespace internal {
-template<typename Derived>
-std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt);
-}
-
-/** \class IOFormat
-  * \ingroup Core_Module
-  *
-  * \brief Stores a set of parameters controlling the way matrices are printed
-  *
-  * List of available parameters:
-  *  - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c FullPrecision.
-  *                 The default is the special value \c StreamPrecision which means to use the
-  *                 stream's own precision setting, as set for instance using \c cout.precision(3). The other special value
-  *                 \c FullPrecision means that the number of digits will be computed to match the full precision of each floating-point
-  *                 type.
-  *  - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c DontAlignCols which
-  *             allows to disable the alignment of columns, resulting in faster code.
-  *  - \b coeffSeparator string printed between two coefficients of the same row
-  *  - \b rowSeparator string printed between two rows
-  *  - \b rowPrefix string printed at the beginning of each row
-  *  - \b rowSuffix string printed at the end of each row
-  *  - \b matPrefix string printed at the beginning of the matrix
-  *  - \b matSuffix string printed at the end of the matrix
-  *  - \b fill character printed to fill the empty space in aligned columns
-  *
-  * Example: \include IOFormat.cpp
-  * Output: \verbinclude IOFormat.out
-  *
-  * \sa DenseBase::format(), class WithFormat
-  */
-struct IOFormat
-{
-  /** Default constructor, see class IOFormat for the meaning of the parameters */
-  IOFormat(int _precision = StreamPrecision, int _flags = 0,
-    const std::string& _coeffSeparator = " ",
-    const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
-    const std::string& _matPrefix="", const std::string& _matSuffix="", const char _fill=' ')
-  : matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
-    rowSpacer(""), coeffSeparator(_coeffSeparator), fill(_fill), precision(_precision), flags(_flags)
-  {
-    // TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline
-    // don't add rowSpacer if columns are not to be aligned
-    if((flags & DontAlignCols))
-      return;
-    int i = int(matSuffix.length())-1;
-    while (i>=0 && matSuffix[i]!='\n')
-    {
-      rowSpacer += ' ';
-      i--;
-    }
-  }
-  std::string matPrefix, matSuffix;
-  std::string rowPrefix, rowSuffix, rowSeparator, rowSpacer;
-  std::string coeffSeparator;
-  char fill;
-  int precision;
-  int flags;
-};
-
-/** \class WithFormat
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression providing matrix output with given format
-  *
-  * \tparam ExpressionType the type of the object on which IO stream operations are performed
-  *
-  * This class represents an expression with stream operators controlled by a given IOFormat.
-  * It is the return type of DenseBase::format()
-  * and most of the time this is the only way it is used.
-  *
-  * See class IOFormat for some examples.
-  *
-  * \sa DenseBase::format(), class IOFormat
-  */
-template<typename ExpressionType>
-class WithFormat
-{
-  public:
-
-    WithFormat(const ExpressionType& matrix, const IOFormat& format)
-      : m_matrix(matrix), m_format(format)
-    {}
-
-    friend std::ostream & operator << (std::ostream & s, const WithFormat& wf)
-    {
-      return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format);
-    }
-
-  protected:
-    typename ExpressionType::Nested m_matrix;
-    IOFormat m_format;
-};
-
-namespace internal {
-
-// NOTE: This helper is kept for backward compatibility with previous code specializing
-//       this internal::significant_decimals_impl structure. In the future we should directly
-//       call digits10() which has been introduced in July 2016 in 3.3.
-template<typename Scalar>
-struct significant_decimals_impl
-{
-  static inline int run()
-  {
-    return NumTraits<Scalar>::digits10();
-  }
-};
-
-/** \internal
-  * print the matrix \a _m to the output stream \a s using the output format \a fmt */
-template<typename Derived>
-std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt)
-{
-  using internal::is_same;
-  using internal::conditional;
-
-  if(_m.size() == 0)
-  {
-    s << fmt.matPrefix << fmt.matSuffix;
-    return s;
-  }
-  
-  typename Derived::Nested m = _m;
-  typedef typename Derived::Scalar Scalar;
-  typedef typename
-      conditional<
-          is_same<Scalar, char>::value ||
-            is_same<Scalar, unsigned char>::value ||
-            is_same<Scalar, numext::int8_t>::value ||
-            is_same<Scalar, numext::uint8_t>::value,
-          int,
-          typename conditional<
-              is_same<Scalar, std::complex<char> >::value ||
-                is_same<Scalar, std::complex<unsigned char> >::value ||
-                is_same<Scalar, std::complex<numext::int8_t> >::value ||
-                is_same<Scalar, std::complex<numext::uint8_t> >::value,
-              std::complex<int>,
-              const Scalar&
-            >::type
-        >::type PrintType;
-
-  Index width = 0;
-
-  std::streamsize explicit_precision;
-  if(fmt.precision == StreamPrecision)
-  {
-    explicit_precision = 0;
-  }
-  else if(fmt.precision == FullPrecision)
-  {
-    if (NumTraits<Scalar>::IsInteger)
-    {
-      explicit_precision = 0;
-    }
-    else
-    {
-      explicit_precision = significant_decimals_impl<Scalar>::run();
-    }
-  }
-  else
-  {
-    explicit_precision = fmt.precision;
-  }
-
-  std::streamsize old_precision = 0;
-  if(explicit_precision) old_precision = s.precision(explicit_precision);
-
-  bool align_cols = !(fmt.flags & DontAlignCols);
-  if(align_cols)
-  {
-    // compute the largest width
-    for(Index j = 0; j < m.cols(); ++j)
-      for(Index i = 0; i < m.rows(); ++i)
-      {
-        std::stringstream sstr;
-        sstr.copyfmt(s);
-        sstr << static_cast<PrintType>(m.coeff(i,j));
-        width = std::max<Index>(width, Index(sstr.str().length()));
-      }
-  }
-  std::streamsize old_width = s.width();
-  char old_fill_character = s.fill();
-  s << fmt.matPrefix;
-  for(Index i = 0; i < m.rows(); ++i)
-  {
-    if (i)
-      s << fmt.rowSpacer;
-    s << fmt.rowPrefix;
-    if(width) {
-      s.fill(fmt.fill);
-      s.width(width);
-    }
-    s << static_cast<PrintType>(m.coeff(i, 0));
-    for(Index j = 1; j < m.cols(); ++j)
-    {
-      s << fmt.coeffSeparator;
-      if(width) {
-        s.fill(fmt.fill);
-        s.width(width);
-      }
-      s << static_cast<PrintType>(m.coeff(i, j));
-    }
-    s << fmt.rowSuffix;
-    if( i < m.rows() - 1)
-      s << fmt.rowSeparator;
-  }
-  s << fmt.matSuffix;
-  if(explicit_precision) s.precision(old_precision);
-  if(width) {
-    s.fill(old_fill_character);
-    s.width(old_width);
-  }
-  return s;
-}
-
-} // end namespace internal
-
-/** \relates DenseBase
-  *
-  * Outputs the matrix, to the given stream.
-  *
-  * If you wish to print the matrix with a format different than the default, use DenseBase::format().
-  *
-  * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers.
-  * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters.
-  *
-  * \sa DenseBase::format()
-  */
-template<typename Derived>
-std::ostream & operator <<
-(std::ostream & s,
- const DenseBase<Derived> & m)
-{
-  return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_IO_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/IndexedView.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/IndexedView.h
deleted file mode 100644
index 0847625..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/IndexedView.h
+++ /dev/null
@@ -1,237 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INDEXED_VIEW_H
-#define EIGEN_INDEXED_VIEW_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename XprType, typename RowIndices, typename ColIndices>
-struct traits<IndexedView<XprType, RowIndices, ColIndices> >
- : traits<XprType>
-{
-  enum {
-    RowsAtCompileTime = int(array_size<RowIndices>::value),
-    ColsAtCompileTime = int(array_size<ColIndices>::value),
-    MaxRowsAtCompileTime = RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime) : Dynamic,
-    MaxColsAtCompileTime = ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime) : Dynamic,
-
-    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
-    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
-               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
-               : XprTypeIsRowMajor,
-
-    RowIncr = int(get_compile_time_incr<RowIndices>::value),
-    ColIncr = int(get_compile_time_incr<ColIndices>::value),
-    InnerIncr = IsRowMajor ? ColIncr : RowIncr,
-    OuterIncr = IsRowMajor ? RowIncr : ColIncr,
-
-    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
-    XprInnerStride = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType>::ret) : int(outer_stride_at_compile_time<XprType>::ret),
-    XprOuterstride = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType>::ret) : int(inner_stride_at_compile_time<XprType>::ret),
-
-    InnerSize = XprTypeIsRowMajor ? ColsAtCompileTime : RowsAtCompileTime,
-    IsBlockAlike = InnerIncr==1 && OuterIncr==1,
-    IsInnerPannel = HasSameStorageOrderAsXprType && is_same<AllRange<InnerSize>,typename conditional<XprTypeIsRowMajor,ColIndices,RowIndices>::type>::value,
-
-    InnerStrideAtCompileTime = InnerIncr<0 || InnerIncr==DynamicIndex || XprInnerStride==Dynamic ? Dynamic : XprInnerStride * InnerIncr,
-    OuterStrideAtCompileTime = OuterIncr<0 || OuterIncr==DynamicIndex || XprOuterstride==Dynamic ? Dynamic : XprOuterstride * OuterIncr,
-
-    ReturnAsScalar = is_same<RowIndices,SingleRange>::value && is_same<ColIndices,SingleRange>::value,
-    ReturnAsBlock = (!ReturnAsScalar) && IsBlockAlike,
-    ReturnAsIndexedView = (!ReturnAsScalar) && (!ReturnAsBlock),
-
-    // FIXME we deal with compile-time strides if and only if we have DirectAccessBit flag,
-    // but this is too strict regarding negative strides...
-    DirectAccessMask = (int(InnerIncr)!=UndefinedIncr && int(OuterIncr)!=UndefinedIncr && InnerIncr>=0 && OuterIncr>=0) ? DirectAccessBit : 0,
-    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
-    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
-    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
-    Flags = (traits<XprType>::Flags & (HereditaryBits | DirectAccessMask )) | FlagsLvalueBit | FlagsRowMajorBit | FlagsLinearAccessBit
-  };
-
-  typedef Block<XprType,RowsAtCompileTime,ColsAtCompileTime,IsInnerPannel> BlockType;
-};
-
-}
-
-template<typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
-class IndexedViewImpl;
-
-
-/** \class IndexedView
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a non-sequential sub-matrix defined by arbitrary sequences of row and column indices
-  *
-  * \tparam XprType the type of the expression in which we are taking the intersections of sub-rows and sub-columns
-  * \tparam RowIndices the type of the object defining the sequence of row indices
-  * \tparam ColIndices the type of the object defining the sequence of column indices
-  *
-  * This class represents an expression of a sub-matrix (or sub-vector) defined as the intersection
-  * of sub-sets of rows and columns, that are themself defined by generic sequences of row indices \f$ \{r_0,r_1,..r_{m-1}\} \f$
-  * and column indices \f$ \{c_0,c_1,..c_{n-1} \}\f$. Let \f$ A \f$  be the nested matrix, then the resulting matrix \f$ B \f$ has \c m
-  * rows and \c n columns, and its entries are given by: \f$ B(i,j) = A(r_i,c_j) \f$.
-  *
-  * The \c RowIndices and \c ColIndices types must be compatible with the following API:
-  * \code
-  * <integral type> operator[](Index) const;
-  * Index size() const;
-  * \endcode
-  *
-  * Typical supported types thus include:
-  *  - std::vector<int>
-  *  - std::valarray<int>
-  *  - std::array<int>
-  *  - Plain C arrays: int[N]
-  *  - Eigen::ArrayXi
-  *  - decltype(ArrayXi::LinSpaced(...))
-  *  - Any view/expressions of the previous types
-  *  - Eigen::ArithmeticSequence
-  *  - Eigen::internal::AllRange      (helper for Eigen::all)
-  *  - Eigen::internal::SingleRange  (helper for single index)
-  *  - etc.
-  *
-  * In typical usages of %Eigen, this class should never be used directly. It is the return type of
-  * DenseBase::operator()(const RowIndices&, const ColIndices&).
-  *
-  * \sa class Block
-  */
-template<typename XprType, typename RowIndices, typename ColIndices>
-class IndexedView : public IndexedViewImpl<XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind>
-{
-public:
-  typedef typename IndexedViewImpl<XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind>::Base Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(IndexedView)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedView)
-
-  typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
-  typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-  template<typename T0, typename T1>
-  IndexedView(XprType& xpr, const T0& rowIndices, const T1& colIndices)
-    : m_xpr(xpr), m_rowIndices(rowIndices), m_colIndices(colIndices)
-  {}
-
-  /** \returns number of rows */
-  Index rows() const { return internal::size(m_rowIndices); }
-
-  /** \returns number of columns */
-  Index cols() const { return internal::size(m_colIndices); }
-
-  /** \returns the nested expression */
-  const typename internal::remove_all<XprType>::type&
-  nestedExpression() const { return m_xpr; }
-
-  /** \returns the nested expression */
-  typename internal::remove_reference<XprType>::type&
-  nestedExpression() { return m_xpr; }
-
-  /** \returns a const reference to the object storing/generating the row indices */
-  const RowIndices& rowIndices() const { return m_rowIndices; }
-
-  /** \returns a const reference to the object storing/generating the column indices */
-  const ColIndices& colIndices() const { return m_colIndices; }
-
-protected:
-  MatrixTypeNested m_xpr;
-  RowIndices m_rowIndices;
-  ColIndices m_colIndices;
-};
-
-
-// Generic API dispatcher
-template<typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
-class IndexedViewImpl
-  : public internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices> >::type Base;
-};
-
-namespace internal {
-
-
-template<typename ArgType, typename RowIndices, typename ColIndices>
-struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
-  : evaluator_base<IndexedView<ArgType, RowIndices, ColIndices> >
-{
-  typedef IndexedView<ArgType, RowIndices, ColIndices> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of row/col index */,
-
-    FlagsLinearAccessBit = (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1) ? LinearAccessBit : 0,
-
-    FlagsRowMajorBit = traits<XprType>::FlagsRowMajorBit, 
-
-    Flags = (evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit /*| LinearAccessBit | DirectAccessBit*/)) | FlagsLinearAccessBit | FlagsRowMajorBit,
-
-    Alignment = 0
-  };
-
-  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
-    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar& coeffRef(Index index) const
-  {
-    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
-    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const CoeffReturnType coeff(Index index) const
-  {
-    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
-    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    return m_argImpl.coeff( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-protected:
-
-  evaluator<ArgType> m_argImpl;
-  const XprType& m_xpr;
-
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_INDEXED_VIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Inverse.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Inverse.h
deleted file mode 100644
index c514438..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Inverse.h
+++ /dev/null
@@ -1,117 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INVERSE_H
-#define EIGEN_INVERSE_H
-
-namespace Eigen {
-
-template<typename XprType,typename StorageKind> class InverseImpl;
-
-namespace internal {
-
-template<typename XprType>
-struct traits<Inverse<XprType> >
-  : traits<typename XprType::PlainObject>
-{
-  typedef typename XprType::PlainObject PlainObject;
-  typedef traits<PlainObject> BaseTraits;
-  enum {
-    Flags = BaseTraits::Flags & RowMajorBit
-  };
-};
-
-} // end namespace internal
-
-/** \class Inverse
-  *
-  * \brief Expression of the inverse of another expression
-  *
-  * \tparam XprType the type of the expression we are taking the inverse
-  *
-  * This class represents an abstract expression of A.inverse()
-  * and most of the time this is the only way it is used.
-  *
-  */
-template<typename XprType>
-class Inverse : public InverseImpl<XprType,typename internal::traits<XprType>::StorageKind>
-{
-public:
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename XprType::Scalar                            Scalar;
-  typedef typename internal::ref_selector<XprType>::type      XprTypeNested;
-  typedef typename internal::remove_all<XprTypeNested>::type  XprTypeNestedCleaned;
-  typedef typename internal::ref_selector<Inverse>::type Nested;
-  typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-  explicit EIGEN_DEVICE_FUNC Inverse(const XprType &xpr)
-    : m_xpr(xpr)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR  Index rows() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR  Index cols() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
-
-  EIGEN_DEVICE_FUNC const XprTypeNestedCleaned& nestedExpression() const { return m_xpr; }
-
-protected:
-  XprTypeNested m_xpr;
-};
-
-// Generic API dispatcher
-template<typename XprType, typename StorageKind>
-class InverseImpl
-  : public internal::generic_xpr_base<Inverse<XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<Inverse<XprType> >::type Base;
-  typedef typename XprType::Scalar Scalar;
-private:
-
-  Scalar coeff(Index row, Index col) const;
-  Scalar coeff(Index i) const;
-};
-
-namespace internal {
-
-/** \internal
-  * \brief Default evaluator for Inverse expression.
-  *
-  * This default evaluator for Inverse expression simply evaluate the inverse into a temporary
-  * by a call to internal::call_assignment_no_alias.
-  * Therefore, inverse implementers only have to specialize Assignment<Dst,Inverse<...>, ...> for
-  * there own nested expression.
-  *
-  * \sa class Inverse
-  */
-template<typename ArgType>
-struct unary_evaluator<Inverse<ArgType> >
-  : public evaluator<typename Inverse<ArgType>::PlainObject>
-{
-  typedef Inverse<ArgType> InverseType;
-  typedef typename InverseType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum { Flags = Base::Flags | EvalBeforeNestingBit };
-
-  unary_evaluator(const InverseType& inv_xpr)
-    : m_result(inv_xpr.rows(), inv_xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    internal::call_assignment_no_alias(m_result, inv_xpr);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_INVERSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Map.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Map.h
deleted file mode 100644
index 218cc15..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Map.h
+++ /dev/null
@@ -1,171 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MAP_H
-#define EIGEN_MAP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename PlainObjectType, int MapOptions, typename StrideType>
-struct traits<Map<PlainObjectType, MapOptions, StrideType> >
-  : public traits<PlainObjectType>
-{
-  typedef traits<PlainObjectType> TraitsBase;
-  enum {
-    PlainObjectTypeInnerSize = ((traits<PlainObjectType>::Flags&RowMajorBit)==RowMajorBit)
-                             ? PlainObjectType::ColsAtCompileTime
-                             : PlainObjectType::RowsAtCompileTime,
-
-    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
-                             ? int(PlainObjectType::InnerStrideAtCompileTime)
-                             : int(StrideType::InnerStrideAtCompileTime),
-    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
-                             ? (InnerStrideAtCompileTime==Dynamic || PlainObjectTypeInnerSize==Dynamic
-                                ? Dynamic
-                                : int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize))
-                             : int(StrideType::OuterStrideAtCompileTime),
-    Alignment = int(MapOptions)&int(AlignedMask),
-    Flags0 = TraitsBase::Flags & (~NestByRefBit),
-    Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
-  };
-private:
-  enum { Options }; // Expressions don't have Options
-};
-}
-
-/** \class Map
-  * \ingroup Core_Module
-  *
-  * \brief A matrix or vector expression mapping an existing array of data.
-  *
-  * \tparam PlainObjectType the equivalent matrix type of the mapped data
-  * \tparam MapOptions specifies the pointer alignment in bytes. It can be: \c #Aligned128, \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
-  *                The default is \c #Unaligned.
-  * \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout
-  *                   of an ordinary, contiguous array. This can be overridden by specifying strides.
-  *                   The type passed here must be a specialization of the Stride template, see examples below.
-  *
-  * This class represents a matrix or vector expression mapping an existing array of data.
-  * It can be used to let Eigen interface without any overhead with non-Eigen data structures,
-  * such as plain C arrays or structures from other libraries. By default, it assumes that the
-  * data is laid out contiguously in memory. You can however override this by explicitly specifying
-  * inner and outer strides.
-  *
-  * Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix:
-  * \include Map_simple.cpp
-  * Output: \verbinclude Map_simple.out
-  *
-  * If you need to map non-contiguous arrays, you can do so by specifying strides:
-  *
-  * Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer
-  * increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time
-  * fixed value.
-  * \include Map_inner_stride.cpp
-  * Output: \verbinclude Map_inner_stride.out
-  *
-  * Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping
-  * as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns.
-  * Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is
-  * a short version of \c OuterStride<Dynamic> because the default template parameter of OuterStride
-  * is  \c Dynamic
-  * \include Map_outer_stride.cpp
-  * Output: \verbinclude Map_outer_stride.out
-  *
-  * For more details and for an example of specifying both an inner and an outer stride, see class Stride.
-  *
-  * \b Tip: to change the array of data mapped by a Map object, you can use the C++
-  * placement new syntax:
-  *
-  * Example: \include Map_placement_new.cpp
-  * Output: \verbinclude Map_placement_new.out
-  *
-  * This class is the return type of PlainObjectBase::Map() but can also be used directly.
-  *
-  * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
-  */
-template<typename PlainObjectType, int MapOptions, typename StrideType> class Map
-  : public MapBase<Map<PlainObjectType, MapOptions, StrideType> >
-{
-  public:
-
-    typedef MapBase<Map> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Map)
-
-    typedef typename Base::PointerType PointerType;
-    typedef PointerType PointerArgType;
-    EIGEN_DEVICE_FUNC
-    inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const
-    {
-      return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const
-    {
-      return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
-           : internal::traits<Map>::OuterStrideAtCompileTime != Dynamic ? Index(internal::traits<Map>::OuterStrideAtCompileTime)
-           : IsVectorAtCompileTime ? (this->size() * innerStride())
-           : int(Flags)&RowMajorBit ? (this->cols() * innerStride())
-           : (this->rows() * innerStride());
-    }
-
-    /** Constructor in the fixed-size case.
-      *
-      * \param dataPtr pointer to the array to map
-      * \param stride optional Stride object, passing the strides.
-      */
-    EIGEN_DEVICE_FUNC
-    explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
-      : Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
-    {
-      PlainObjectType::Base::_check_template_params();
-    }
-
-    /** Constructor in the dynamic-size vector case.
-      *
-      * \param dataPtr pointer to the array to map
-      * \param size the size of the vector expression
-      * \param stride optional Stride object, passing the strides.
-      */
-    EIGEN_DEVICE_FUNC
-    inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
-      : Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
-    {
-      PlainObjectType::Base::_check_template_params();
-    }
-
-    /** Constructor in the dynamic-size matrix case.
-      *
-      * \param dataPtr pointer to the array to map
-      * \param rows the number of rows of the matrix expression
-      * \param cols the number of columns of the matrix expression
-      * \param stride optional Stride object, passing the strides.
-      */
-    EIGEN_DEVICE_FUNC
-    inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType())
-      : Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride)
-    {
-      PlainObjectType::Base::_check_template_params();
-    }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
-
-  protected:
-    StrideType m_stride;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_MAP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MapBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MapBase.h
deleted file mode 100644
index d856447..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MapBase.h
+++ /dev/null
@@ -1,310 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MAPBASE_H
-#define EIGEN_MAPBASE_H
-
-#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \
-      EIGEN_STATIC_ASSERT((int(internal::evaluator<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
-                          YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT)
-
-namespace Eigen {
-
-/** \ingroup Core_Module
-  *
-  * \brief Base class for dense Map and Block expression with direct access
-  *
-  * This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense
-  * Map and Block objects with direct access.
-  * Typical users do not have to directly deal with this class.
-  *
-  * This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN.
-  * See \link TopicCustomizing_Plugins customizing Eigen \endlink for details.
-  *
-  * The \c Derived class has to provide the following two methods describing the memory layout:
-  *  \code Index innerStride() const; \endcode
-  *  \code Index outerStride() const; \endcode
-  *
-  * \sa class Map, class Block
-  */
-template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
-  : public internal::dense_xpr_base<Derived>::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<Derived>::type Base;
-    enum {
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      InnerStrideAtCompileTime = internal::traits<Derived>::InnerStrideAtCompileTime,
-      SizeAtCompileTime = Base::SizeAtCompileTime
-    };
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef typename internal::conditional<
-                         bool(internal::is_lvalue<Derived>::value),
-                         Scalar *,
-                         const Scalar *>::type
-                     PointerType;
-
-    using Base::derived;
-//    using Base::RowsAtCompileTime;
-//    using Base::ColsAtCompileTime;
-//    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-    using Base::IsRowMajor;
-
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-    using Base::lazyAssign;
-    using Base::eval;
-
-    using Base::innerStride;
-    using Base::outerStride;
-    using Base::rowStride;
-    using Base::colStride;
-
-    // bug 217 - compile error on ICC 11.1
-    using Base::operator=;
-
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-    /** \copydoc DenseBase::rows() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_rows.value(); }
-    /** \copydoc DenseBase::cols() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_cols.value(); }
-
-    /** Returns a pointer to the first coefficient of the matrix or vector.
-      *
-      * \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride().
-      *
-      * \sa innerStride(), outerStride()
-      */
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; }
-
-    /** \copydoc PlainObjectBase::coeff(Index,Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeff(Index rowId, Index colId) const
-    {
-      return m_data[colId * colStride() + rowId * rowStride()];
-    }
-
-    /** \copydoc PlainObjectBase::coeff(Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeff(Index index) const
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return m_data[index * innerStride()];
-    }
-
-    /** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return this->m_data[colId * colStride() + rowId * rowStride()];
-    }
-
-    /** \copydoc PlainObjectBase::coeffRef(Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return this->m_data[index * innerStride()];
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    inline PacketScalar packet(Index rowId, Index colId) const
-    {
-      return internal::ploadt<PacketScalar, LoadMode>
-               (m_data + (colId * colStride() + rowId * rowStride()));
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    inline PacketScalar packet(Index index) const
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
-    }
-
-    /** \internal Constructor for fixed size matrices or vectors */
-    EIGEN_DEVICE_FUNC
-    explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
-    {
-      EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-      checkSanity<Derived>();
-    }
-
-    /** \internal Constructor for dynamically sized vectors */
-    EIGEN_DEVICE_FUNC
-    inline MapBase(PointerType dataPtr, Index vecSize)
-            : m_data(dataPtr),
-              m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
-              m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime))
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-      eigen_assert(vecSize >= 0);
-      eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
-      checkSanity<Derived>();
-    }
-
-    /** \internal Constructor for dynamically sized matrices */
-    EIGEN_DEVICE_FUNC
-    inline MapBase(PointerType dataPtr, Index rows, Index cols)
-            : m_data(dataPtr), m_rows(rows), m_cols(cols)
-    {
-      eigen_assert( (dataPtr == 0)
-              || (   rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
-                  && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
-      checkSanity<Derived>();
-    }
-
-    #ifdef EIGEN_MAPBASE_PLUGIN
-    #include EIGEN_MAPBASE_PLUGIN
-    #endif
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
-
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) const
-    {
-#if EIGEN_MAX_ALIGN_BYTES>0
-      // innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value:
-      const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime);
-      EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride);
-      eigen_assert((   ((internal::UIntPtr(m_data) % internal::traits<Derived>::Alignment) == 0)
-                    || (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
-#endif
-    }
-
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    void checkSanity(typename internal::enable_if<internal::traits<T>::Alignment==0,void*>::type = 0) const
-    {}
-
-    PointerType m_data;
-    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
-    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
-};
-
-/** \ingroup Core_Module
-  *
-  * \brief Base class for non-const dense Map and Block expression with direct access
-  *
-  * This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of
-  * dense Map and Block objects with direct access.
-  * It inherits MapBase<Derived, ReadOnlyAccessors> which defines the const variant for reading specific entries.
-  *
-  * \sa class Map, class Block
-  */
-template<typename Derived> class MapBase<Derived, WriteAccessors>
-  : public MapBase<Derived, ReadOnlyAccessors>
-{
-    typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
-  public:
-
-    typedef MapBase<Derived, ReadOnlyAccessors> Base;
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::PacketScalar PacketScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    typedef typename Base::PointerType PointerType;
-
-    using Base::derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-
-    using Base::innerStride;
-    using Base::outerStride;
-    using Base::rowStride;
-    using Base::colStride;
-
-    typedef typename internal::conditional<
-                    internal::is_lvalue<Derived>::value,
-                    Scalar,
-                    const Scalar
-                  >::type ScalarWithConstIfNotLvalue;
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return this->m_data; }
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col)
-    {
-      return this->m_data[col * colStride() + row * rowStride()];
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return this->m_data[index * innerStride()];
-    }
-
-    template<int StoreMode>
-    inline void writePacket(Index row, Index col, const PacketScalar& val)
-    {
-      internal::pstoret<Scalar, PacketScalar, StoreMode>
-               (this->m_data + (col * colStride() + row * rowStride()), val);
-    }
-
-    template<int StoreMode>
-    inline void writePacket(Index index, const PacketScalar& val)
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      internal::pstoret<Scalar, PacketScalar, StoreMode>
-                (this->m_data + index * innerStride(), val);
-    }
-
-    EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
-    EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
-    EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {}
-
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const MapBase& other)
-    {
-      ReadOnlyMapBase::Base::operator=(other);
-      return derived();
-    }
-
-    // In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
-    // see bugs 821 and 920.
-    using ReadOnlyMapBase::Base::operator=;
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
-};
-
-#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS
-
-} // end namespace Eigen
-
-#endif // EIGEN_MAPBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MathFunctions.h
deleted file mode 100644
index 61b78f4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MathFunctions.h
+++ /dev/null
@@ -1,2057 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATHFUNCTIONS_H
-#define EIGEN_MATHFUNCTIONS_H
-
-// TODO this should better be moved to NumTraits
-// Source: WolframAlpha
-#define EIGEN_PI    3.141592653589793238462643383279502884197169399375105820974944592307816406L
-#define EIGEN_LOG2E 1.442695040888963407359924681001892137426645954152985934135449406931109219L
-#define EIGEN_LN2   0.693147180559945309417232121458176568075500134360255254120680009493393621L
-
-namespace Eigen {
-
-// On WINCE, std::abs is defined for int only, so let's defined our own overloads:
-// This issue has been confirmed with MSVC 2008 only, but the issue might exist for more recent versions too.
-#if EIGEN_OS_WINCE && EIGEN_COMP_MSVC && EIGEN_COMP_MSVC<=1500
-long        abs(long        x) { return (labs(x));  }
-double      abs(double      x) { return (fabs(x));  }
-float       abs(float       x) { return (fabsf(x)); }
-long double abs(long double x) { return (fabsl(x)); }
-#endif
-
-namespace internal {
-
-/** \internal \class global_math_functions_filtering_base
-  *
-  * What it does:
-  * Defines a typedef 'type' as follows:
-  * - if type T has a member typedef Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl, then
-  *   global_math_functions_filtering_base<T>::type is a typedef for it.
-  * - otherwise, global_math_functions_filtering_base<T>::type is a typedef for T.
-  *
-  * How it's used:
-  * To allow to defined the global math functions (like sin...) in certain cases, like the Array expressions.
-  * When you do sin(array1+array2), the object array1+array2 has a complicated expression type, all what you want to know
-  * is that it inherits ArrayBase. So we implement a partial specialization of sin_impl for ArrayBase<Derived>.
-  * So we must make sure to use sin_impl<ArrayBase<Derived> > and not sin_impl<Derived>, otherwise our partial specialization
-  * won't be used. How does sin know that? That's exactly what global_math_functions_filtering_base tells it.
-  *
-  * How it's implemented:
-  * SFINAE in the style of enable_if. Highly susceptible of breaking compilers. With GCC, it sure does work, but if you replace
-  * the typename dummy by an integer template parameter, it doesn't work anymore!
-  */
-
-template<typename T, typename dummy = void>
-struct global_math_functions_filtering_base
-{
-  typedef T type;
-};
-
-template<typename T> struct always_void { typedef void type; };
-
-template<typename T>
-struct global_math_functions_filtering_base
-  <T,
-   typename always_void<typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl>::type
-  >
-{
-  typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type;
-};
-
-#define EIGEN_MATHFUNC_IMPL(func, scalar) Eigen::internal::func##_impl<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>
-#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename Eigen::internal::func##_retval<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>::type
-
-/****************************************************************************
-* Implementation of real                                                 *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct real_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return x;
-  }
-};
-
-template<typename Scalar>
-struct real_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    using std::real;
-    return real(x);
-  }
-};
-
-template<typename Scalar> struct real_impl : real_default_impl<Scalar> {};
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T>
-struct real_impl<std::complex<T> >
-{
-  typedef T RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline T run(const std::complex<T>& x)
-  {
-    return x.real();
-  }
-};
-#endif
-
-template<typename Scalar>
-struct real_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of imag                                                 *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct imag_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar&)
-  {
-    return RealScalar(0);
-  }
-};
-
-template<typename Scalar>
-struct imag_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    using std::imag;
-    return imag(x);
-  }
-};
-
-template<typename Scalar> struct imag_impl : imag_default_impl<Scalar> {};
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T>
-struct imag_impl<std::complex<T> >
-{
-  typedef T RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline T run(const std::complex<T>& x)
-  {
-    return x.imag();
-  }
-};
-#endif
-
-template<typename Scalar>
-struct imag_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of real_ref                                             *
-****************************************************************************/
-
-template<typename Scalar>
-struct real_ref_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar& run(Scalar& x)
-  {
-    return reinterpret_cast<RealScalar*>(&x)[0];
-  }
-  EIGEN_DEVICE_FUNC
-  static inline const RealScalar& run(const Scalar& x)
-  {
-    return reinterpret_cast<const RealScalar*>(&x)[0];
-  }
-};
-
-template<typename Scalar>
-struct real_ref_retval
-{
-  typedef typename NumTraits<Scalar>::Real & type;
-};
-
-/****************************************************************************
-* Implementation of imag_ref                                             *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex>
-struct imag_ref_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar& run(Scalar& x)
-  {
-    return reinterpret_cast<RealScalar*>(&x)[1];
-  }
-  EIGEN_DEVICE_FUNC
-  static inline const RealScalar& run(const Scalar& x)
-  {
-    return reinterpret_cast<RealScalar*>(&x)[1];
-  }
-};
-
-template<typename Scalar>
-struct imag_ref_default_impl<Scalar, false>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Scalar run(Scalar&)
-  {
-    return Scalar(0);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline const Scalar run(const Scalar&)
-  {
-    return Scalar(0);
-  }
-};
-
-template<typename Scalar>
-struct imag_ref_impl : imag_ref_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
-
-template<typename Scalar>
-struct imag_ref_retval
-{
-  typedef typename NumTraits<Scalar>::Real & type;
-};
-
-/****************************************************************************
-* Implementation of conj                                                 *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct conj_default_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    return x;
-  }
-};
-
-template<typename Scalar>
-struct conj_default_impl<Scalar,true>
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    using std::conj;
-    return conj(x);
-  }
-};
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct conj_impl : conj_default_impl<Scalar, IsComplex> {};
-
-template<typename Scalar>
-struct conj_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of abs2                                                 *
-****************************************************************************/
-
-template<typename Scalar,bool IsComplex>
-struct abs2_impl_default
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return x*x;
-  }
-};
-
-template<typename Scalar>
-struct abs2_impl_default<Scalar, true> // IsComplex
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return x.real()*x.real() + x.imag()*x.imag();
-  }
-};
-
-template<typename Scalar>
-struct abs2_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return abs2_impl_default<Scalar,NumTraits<Scalar>::IsComplex>::run(x);
-  }
-};
-
-template<typename Scalar>
-struct abs2_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of sqrt/rsqrt                                             *
-****************************************************************************/
-
-template<typename Scalar>
-struct sqrt_impl
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE Scalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(sqrt);
-    return sqrt(x);
-  }
-};
-
-// Complex sqrt defined in MathFunctionsImpl.h.
-template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& a_x);
-
-// Custom implementation is faster than `std::sqrt`, works on
-// GPU, and correctly handles special cases (unlike MSVC).
-template<typename T>
-struct sqrt_impl<std::complex<T> >
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x)
-  {
-    return complex_sqrt<T>(x);
-  }
-};
-
-template<typename Scalar>
-struct sqrt_retval
-{
-  typedef Scalar type;
-};
-
-// Default implementation relies on numext::sqrt, at bottom of file.
-template<typename T>
-struct rsqrt_impl;
-
-// Complex rsqrt defined in MathFunctionsImpl.h.
-template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_rsqrt(const std::complex<T>& a_x);
-
-template<typename T>
-struct rsqrt_impl<std::complex<T> >
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x)
-  {
-    return complex_rsqrt<T>(x);
-  }
-};
-
-template<typename Scalar>
-struct rsqrt_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of norm1                                                *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex>
-struct norm1_default_impl;
-
-template<typename Scalar>
-struct norm1_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(abs);
-    return abs(x.real()) + abs(x.imag());
-  }
-};
-
-template<typename Scalar>
-struct norm1_default_impl<Scalar, false>
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(abs);
-    return abs(x);
-  }
-};
-
-template<typename Scalar>
-struct norm1_impl : norm1_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
-
-template<typename Scalar>
-struct norm1_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of hypot                                                *
-****************************************************************************/
-
-template<typename Scalar> struct hypot_impl;
-
-template<typename Scalar>
-struct hypot_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of cast                                                 *
-****************************************************************************/
-
-template<typename OldType, typename NewType, typename EnableIf = void>
-struct cast_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline NewType run(const OldType& x)
-  {
-    return static_cast<NewType>(x);
-  }
-};
-
-// Casting from S -> Complex<T> leads to an implicit conversion from S to T,
-// generating warnings on clang.  Here we explicitly cast the real component.
-template<typename OldType, typename NewType>
-struct cast_impl<OldType, NewType,
-  typename internal::enable_if<
-    !NumTraits<OldType>::IsComplex && NumTraits<NewType>::IsComplex
-  >::type>
-{
-  EIGEN_DEVICE_FUNC
-  static inline NewType run(const OldType& x)
-  {
-    typedef typename NumTraits<NewType>::Real NewReal;
-    return static_cast<NewType>(static_cast<NewReal>(x));
-  }
-};
-
-// here, for once, we're plainly returning NewType: we don't want cast to do weird things.
-
-template<typename OldType, typename NewType>
-EIGEN_DEVICE_FUNC
-inline NewType cast(const OldType& x)
-{
-  return cast_impl<OldType, NewType>::run(x);
-}
-
-/****************************************************************************
-* Implementation of round                                                   *
-****************************************************************************/
-
-template<typename Scalar>
-struct round_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
-#if EIGEN_HAS_CXX11_MATH
-    EIGEN_USING_STD(round);
-#endif
-    return Scalar(round(x));
-  }
-};
-
-#if !EIGEN_HAS_CXX11_MATH
-#if EIGEN_HAS_C99_MATH
-// Use ::roundf for float.
-template<>
-struct round_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static inline float run(const float& x)
-  {
-    return ::roundf(x);
-  }
-};
-#else
-template<typename Scalar>
-struct round_using_floor_ceil_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
-    // Without C99 round/roundf, resort to floor/ceil.
-    EIGEN_USING_STD(floor);
-    EIGEN_USING_STD(ceil);
-    // If not enough precision to resolve a decimal at all, return the input.
-    // Otherwise, adding 0.5 can trigger an increment by 1.
-    const Scalar limit = Scalar(1ull << (NumTraits<Scalar>::digits() - 1));
-    if (x >= limit || x <= -limit) {
-      return x;
-    }
-    return (x > Scalar(0)) ? Scalar(floor(x + Scalar(0.5))) : Scalar(ceil(x - Scalar(0.5)));
-  }
-};
-
-template<>
-struct round_impl<float> : round_using_floor_ceil_impl<float> {};
-
-template<>
-struct round_impl<double> : round_using_floor_ceil_impl<double> {};
-#endif // EIGEN_HAS_C99_MATH
-#endif // !EIGEN_HAS_CXX11_MATH
-
-template<typename Scalar>
-struct round_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of rint                                                    *
-****************************************************************************/
-
-template<typename Scalar>
-struct rint_impl {
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
-#if EIGEN_HAS_CXX11_MATH
-      EIGEN_USING_STD(rint);
-#endif
-    return rint(x);
-  }
-};
-
-#if !EIGEN_HAS_CXX11_MATH
-template<>
-struct rint_impl<double> {
-  EIGEN_DEVICE_FUNC
-  static inline double run(const double& x)
-  {
-    return ::rint(x);
-  }
-};
-template<>
-struct rint_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static inline float run(const float& x)
-  {
-    return ::rintf(x);
-  }
-};
-#endif
-
-template<typename Scalar>
-struct rint_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of arg                                                     *
-****************************************************************************/
-
-// Visual Studio 2017 has a bug where arg(float) returns 0 for negative inputs.
-// This seems to be fixed in VS 2019.
-#if EIGEN_HAS_CXX11_MATH && (!EIGEN_COMP_MSVC || EIGEN_COMP_MSVC >= 1920)
-// std::arg is only defined for types of std::complex, or integer types or float/double/long double
-template<typename Scalar,
-          bool HasStdImpl = NumTraits<Scalar>::IsComplex || is_integral<Scalar>::value
-                            || is_same<Scalar, float>::value || is_same<Scalar, double>::value
-                            || is_same<Scalar, long double>::value >
-struct arg_default_impl;
-
-template<typename Scalar>
-struct arg_default_impl<Scalar, true> {
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    #if defined(EIGEN_HIP_DEVICE_COMPILE)
-    // HIP does not seem to have a native device side implementation for the math routine "arg"
-    using std::arg;
-    #else
-    EIGEN_USING_STD(arg);
-    #endif
-    return static_cast<RealScalar>(arg(x));
-  }
-};
-
-// Must be non-complex floating-point type (e.g. half/bfloat16).
-template<typename Scalar>
-struct arg_default_impl<Scalar, false> {
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return (x < Scalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
-  }
-};
-#else
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct arg_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return (x < RealScalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
-  }
-};
-
-template<typename Scalar>
-struct arg_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(arg);
-    return arg(x);
-  }
-};
-#endif
-template<typename Scalar> struct arg_impl : arg_default_impl<Scalar> {};
-
-template<typename Scalar>
-struct arg_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of expm1                                                   *
-****************************************************************************/
-
-// This implementation is based on GSL Math's expm1.
-namespace std_fallback {
-  // fallback expm1 implementation in case there is no expm1(Scalar) function in namespace of Scalar,
-  // or that there is no suitable std::expm1 function available. Implementation
-  // attributed to Kahan. See: http://www.plunk.org/~hatch/rightway.php.
-  template<typename Scalar>
-  EIGEN_DEVICE_FUNC inline Scalar expm1(const Scalar& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    EIGEN_USING_STD(exp);
-    Scalar u = exp(x);
-    if (numext::equal_strict(u, Scalar(1))) {
-      return x;
-    }
-    Scalar um1 = u - RealScalar(1);
-    if (numext::equal_strict(um1, Scalar(-1))) {
-      return RealScalar(-1);
-    }
-
-    EIGEN_USING_STD(log);
-    Scalar logu = log(u);
-    return numext::equal_strict(u, logu) ? u : (u - RealScalar(1)) * x / logu;
-  }
-}
-
-template<typename Scalar>
-struct expm1_impl {
-  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    #if EIGEN_HAS_CXX11_MATH
-    using std::expm1;
-    #else
-    using std_fallback::expm1;
-    #endif
-    return expm1(x);
-  }
-};
-
-template<typename Scalar>
-struct expm1_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of log                                                     *
-****************************************************************************/
-
-// Complex log defined in MathFunctionsImpl.h.
-template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_log(const std::complex<T>& z);
-
-template<typename Scalar>
-struct log_impl {
-  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(log);
-    return static_cast<Scalar>(log(x));
-  }
-};
-
-template<typename Scalar>
-struct log_impl<std::complex<Scalar> > {
-  EIGEN_DEVICE_FUNC static inline std::complex<Scalar> run(const std::complex<Scalar>& z)
-  {
-    return complex_log(z);
-  }
-};
-
-/****************************************************************************
-* Implementation of log1p                                                   *
-****************************************************************************/
-
-namespace std_fallback {
-  // fallback log1p implementation in case there is no log1p(Scalar) function in namespace of Scalar,
-  // or that there is no suitable std::log1p function available
-  template<typename Scalar>
-  EIGEN_DEVICE_FUNC inline Scalar log1p(const Scalar& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    EIGEN_USING_STD(log);
-    Scalar x1p = RealScalar(1) + x;
-    Scalar log_1p = log_impl<Scalar>::run(x1p);
-    const bool is_small = numext::equal_strict(x1p, Scalar(1));
-    const bool is_inf = numext::equal_strict(x1p, log_1p);
-    return (is_small || is_inf) ? x : x * (log_1p / (x1p - RealScalar(1)));
-  }
-}
-
-template<typename Scalar>
-struct log1p_impl {
-  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    #if EIGEN_HAS_CXX11_MATH
-    using std::log1p;
-    #else
-    using std_fallback::log1p;
-    #endif
-    return log1p(x);
-  }
-};
-
-// Specialization for complex types that are not supported by std::log1p.
-template <typename RealScalar>
-struct log1p_impl<std::complex<RealScalar> > {
-  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
-      const std::complex<RealScalar>& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
-    return std_fallback::log1p(x);
-  }
-};
-
-template<typename Scalar>
-struct log1p_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of pow                                                  *
-****************************************************************************/
-
-template<typename ScalarX,typename ScalarY, bool IsInteger = NumTraits<ScalarX>::IsInteger&&NumTraits<ScalarY>::IsInteger>
-struct pow_impl
-{
-  //typedef Scalar retval;
-  typedef typename ScalarBinaryOpTraits<ScalarX,ScalarY,internal::scalar_pow_op<ScalarX,ScalarY> >::ReturnType result_type;
-  static EIGEN_DEVICE_FUNC inline result_type run(const ScalarX& x, const ScalarY& y)
-  {
-    EIGEN_USING_STD(pow);
-    return pow(x, y);
-  }
-};
-
-template<typename ScalarX,typename ScalarY>
-struct pow_impl<ScalarX,ScalarY, true>
-{
-  typedef ScalarX result_type;
-  static EIGEN_DEVICE_FUNC inline ScalarX run(ScalarX x, ScalarY y)
-  {
-    ScalarX res(1);
-    eigen_assert(!NumTraits<ScalarY>::IsSigned || y >= 0);
-    if(y & 1) res *= x;
-    y >>= 1;
-    while(y)
-    {
-      x *= x;
-      if(y&1) res *= x;
-      y >>= 1;
-    }
-    return res;
-  }
-};
-
-/****************************************************************************
-* Implementation of random                                               *
-****************************************************************************/
-
-template<typename Scalar,
-         bool IsComplex,
-         bool IsInteger>
-struct random_default_impl {};
-
-template<typename Scalar>
-struct random_impl : random_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
-
-template<typename Scalar>
-struct random_retval
-{
-  typedef Scalar type;
-};
-
-template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y);
-template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random();
-
-template<typename Scalar>
-struct random_default_impl<Scalar, false, false>
-{
-  static inline Scalar run(const Scalar& x, const Scalar& y)
-  {
-    return x + (y-x) * Scalar(std::rand()) / Scalar(RAND_MAX);
-  }
-  static inline Scalar run()
-  {
-    return run(Scalar(NumTraits<Scalar>::IsSigned ? -1 : 0), Scalar(1));
-  }
-};
-
-enum {
-  meta_floor_log2_terminate,
-  meta_floor_log2_move_up,
-  meta_floor_log2_move_down,
-  meta_floor_log2_bogus
-};
-
-template<unsigned int n, int lower, int upper> struct meta_floor_log2_selector
-{
-  enum { middle = (lower + upper) / 2,
-         value = (upper <= lower + 1) ? int(meta_floor_log2_terminate)
-               : (n < (1 << middle)) ? int(meta_floor_log2_move_down)
-               : (n==0) ? int(meta_floor_log2_bogus)
-               : int(meta_floor_log2_move_up)
-  };
-};
-
-template<unsigned int n,
-         int lower = 0,
-         int upper = sizeof(unsigned int) * CHAR_BIT - 1,
-         int selector = meta_floor_log2_selector<n, lower, upper>::value>
-struct meta_floor_log2 {};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_down>
-{
-  enum { value = meta_floor_log2<n, lower, meta_floor_log2_selector<n, lower, upper>::middle>::value };
-};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_up>
-{
-  enum { value = meta_floor_log2<n, meta_floor_log2_selector<n, lower, upper>::middle, upper>::value };
-};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_terminate>
-{
-  enum { value = (n >= ((unsigned int)(1) << (lower+1))) ? lower+1 : lower };
-};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_bogus>
-{
-  // no value, error at compile time
-};
-
-template<typename Scalar>
-struct random_default_impl<Scalar, false, true>
-{
-  static inline Scalar run(const Scalar& x, const Scalar& y)
-  {
-    if (y <= x)
-      return x;
-    // ScalarU is the unsigned counterpart of Scalar, possibly Scalar itself.
-    typedef typename make_unsigned<Scalar>::type ScalarU;
-    // ScalarX is the widest of ScalarU and unsigned int.
-    // We'll deal only with ScalarX and unsigned int below thus avoiding signed
-    // types and arithmetic and signed overflows (which are undefined behavior).
-    typedef typename conditional<(ScalarU(-1) > unsigned(-1)), ScalarU, unsigned>::type ScalarX;
-    // The following difference doesn't overflow, provided our integer types are two's
-    // complement and have the same number of padding bits in signed and unsigned variants.
-    // This is the case in most modern implementations of C++.
-    ScalarX range = ScalarX(y) - ScalarX(x);
-    ScalarX offset = 0;
-    ScalarX divisor = 1;
-    ScalarX multiplier = 1;
-    const unsigned rand_max = RAND_MAX;
-    if (range <= rand_max) divisor = (rand_max + 1) / (range + 1);
-    else                   multiplier = 1 + range / (rand_max + 1);
-    // Rejection sampling.
-    do {
-      offset = (unsigned(std::rand()) * multiplier) / divisor;
-    } while (offset > range);
-    return Scalar(ScalarX(x) + offset);
-  }
-
-  static inline Scalar run()
-  {
-#ifdef EIGEN_MAKING_DOCS
-    return run(Scalar(NumTraits<Scalar>::IsSigned ? -10 : 0), Scalar(10));
-#else
-    enum { rand_bits = meta_floor_log2<(unsigned int)(RAND_MAX)+1>::value,
-           scalar_bits = sizeof(Scalar) * CHAR_BIT,
-           shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)),
-           offset = NumTraits<Scalar>::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0
-    };
-    return Scalar((std::rand() >> shift) - offset);
-#endif
-  }
-};
-
-template<typename Scalar>
-struct random_default_impl<Scalar, true, false>
-{
-  static inline Scalar run(const Scalar& x, const Scalar& y)
-  {
-    return Scalar(random(x.real(), y.real()),
-                  random(x.imag(), y.imag()));
-  }
-  static inline Scalar run()
-  {
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    return Scalar(random<RealScalar>(), random<RealScalar>());
-  }
-};
-
-template<typename Scalar>
-inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y)
-{
-  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(x, y);
-}
-
-template<typename Scalar>
-inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
-{
-  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run();
-}
-
-// Implementation of is* functions
-
-// std::is* do not work with fast-math and gcc, std::is* are available on MSVC 2013 and newer, as well as in clang.
-#if (EIGEN_HAS_CXX11_MATH && !(EIGEN_COMP_GNUC_STRICT && __FINITE_MATH_ONLY__)) || (EIGEN_COMP_MSVC>=1800) || (EIGEN_COMP_CLANG)
-#define EIGEN_USE_STD_FPCLASSIFY 1
-#else
-#define EIGEN_USE_STD_FPCLASSIFY 0
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<internal::is_integral<T>::value,bool>::type
-isnan_impl(const T&) { return false; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<internal::is_integral<T>::value,bool>::type
-isinf_impl(const T&) { return false; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<internal::is_integral<T>::value,bool>::type
-isfinite_impl(const T&) { return true; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
-isfinite_impl(const T& x)
-{
-  #if defined(EIGEN_GPU_COMPILE_PHASE)
-    return (::isfinite)(x);
-  #elif EIGEN_USE_STD_FPCLASSIFY
-    using std::isfinite;
-    return isfinite EIGEN_NOT_A_MACRO (x);
-  #else
-    return x<=NumTraits<T>::highest() && x>=NumTraits<T>::lowest();
-  #endif
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
-isinf_impl(const T& x)
-{
-  #if defined(EIGEN_GPU_COMPILE_PHASE)
-    return (::isinf)(x);
-  #elif EIGEN_USE_STD_FPCLASSIFY
-    using std::isinf;
-    return isinf EIGEN_NOT_A_MACRO (x);
-  #else
-    return x>NumTraits<T>::highest() || x<NumTraits<T>::lowest();
-  #endif
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
-isnan_impl(const T& x)
-{
-  #if defined(EIGEN_GPU_COMPILE_PHASE)
-    return (::isnan)(x);
-  #elif EIGEN_USE_STD_FPCLASSIFY
-    using std::isnan;
-    return isnan EIGEN_NOT_A_MACRO (x);
-  #else
-    return x != x;
-  #endif
-}
-
-#if (!EIGEN_USE_STD_FPCLASSIFY)
-
-#if EIGEN_COMP_MSVC
-
-template<typename T> EIGEN_DEVICE_FUNC bool isinf_msvc_helper(T x)
-{
-  return _fpclass(x)==_FPCLASS_NINF || _fpclass(x)==_FPCLASS_PINF;
-}
-
-//MSVC defines a _isnan builtin function, but for double only
-EIGEN_DEVICE_FUNC inline bool isnan_impl(const long double& x) { return _isnan(x)!=0; }
-EIGEN_DEVICE_FUNC inline bool isnan_impl(const double& x)      { return _isnan(x)!=0; }
-EIGEN_DEVICE_FUNC inline bool isnan_impl(const float& x)       { return _isnan(x)!=0; }
-
-EIGEN_DEVICE_FUNC inline bool isinf_impl(const long double& x) { return isinf_msvc_helper(x); }
-EIGEN_DEVICE_FUNC inline bool isinf_impl(const double& x)      { return isinf_msvc_helper(x); }
-EIGEN_DEVICE_FUNC inline bool isinf_impl(const float& x)       { return isinf_msvc_helper(x); }
-
-#elif (defined __FINITE_MATH_ONLY__ && __FINITE_MATH_ONLY__ && EIGEN_COMP_GNUC)
-
-#if EIGEN_GNUC_AT_LEAST(5,0)
-  #define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((optimize("no-finite-math-only")))
-#else
-  // NOTE the inline qualifier and noinline attribute are both needed: the former is to avoid linking issue (duplicate symbol),
-  //      while the second prevent too aggressive optimizations in fast-math mode:
-  #define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((noinline,optimize("no-finite-math-only")))
-#endif
-
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const long double& x) { return __builtin_isnan(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const double& x)      { return __builtin_isnan(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const float& x)       { return __builtin_isnan(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const double& x)      { return __builtin_isinf(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const float& x)       { return __builtin_isinf(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const long double& x) { return __builtin_isinf(x); }
-
-#undef EIGEN_TMP_NOOPT_ATTRIB
-
-#endif
-
-#endif
-
-// The following overload are defined at the end of this file
-template<typename T> EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x);
-template<typename T> EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x);
-template<typename T> EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x);
-
-template<typename T> T generic_fast_tanh_float(const T& a_x);
-} // end namespace internal
-
-/****************************************************************************
-* Generic math functions                                                    *
-****************************************************************************/
-
-namespace numext {
-
-#if (!defined(EIGEN_GPUCC) || defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
-{
-  EIGEN_USING_STD(min)
-  return min EIGEN_NOT_A_MACRO (x,y);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
-{
-  EIGEN_USING_STD(max)
-  return max EIGEN_NOT_A_MACRO (x,y);
-}
-#else
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
-{
-  return y < x ? y : x;
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y)
-{
-  return fminf(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE double mini(const double& x, const double& y)
-{
-  return fmin(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE long double mini(const long double& x, const long double& y)
-{
-#if defined(EIGEN_HIPCC)
-  // no "fminl" on HIP yet
-  return (x < y) ? x : y;
-#else
-  return fminl(x, y);
-#endif
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
-{
-  return x < y ? y : x;
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y)
-{
-  return fmaxf(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE double maxi(const double& x, const double& y)
-{
-  return fmax(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE long double maxi(const long double& x, const long double& y)
-{
-#if defined(EIGEN_HIPCC)
-  // no "fmaxl" on HIP yet
-  return (x > y) ? x : y;
-#else
-  return fmaxl(x, y);
-#endif
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-
-
-#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_char)   \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_short)  \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_int)    \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
-#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_char)   \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_short)  \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_int)    \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
-#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)  \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)   \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
-#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)  \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)   \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
-#define SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC)
-#define SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC)
-#define SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC,cl::sycl::cl_double)
-#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC,cl::sycl::cl_double)
-#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(NAME, FUNC, RET_TYPE) \
-  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_float) \
-  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_double)
-
-#define SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \
-template<>                                               \
-  EIGEN_DEVICE_FUNC                                      \
-  EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE& x) { \
-    return cl::sycl::FUNC(x);                            \
-  }
-
-#define SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, TYPE) \
-  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, TYPE, TYPE)
-
-#define SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE1, ARG_TYPE2) \
-  template<>                                                                  \
-  EIGEN_DEVICE_FUNC                                                           \
-  EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE1& x, const ARG_TYPE2& y) { \
-    return cl::sycl::FUNC(x, y);                                              \
-  }
-
-#define SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \
-  SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE, ARG_TYPE)
-
-#define SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, TYPE) \
-  SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, TYPE, TYPE)
-
-SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(mini, min)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(mini, fmin)
-SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(maxi, max)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(maxi, fmax)
-
-#endif
-
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x)
-{
-  return internal::real_ref_impl<Scalar>::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(arg, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
-{
-  return internal::imag_ref_impl<Scalar>::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
-}
-
-EIGEN_DEVICE_FUNC
-inline bool abs2(bool x) { return x; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T absdiff(const T& x, const T& y)
-{
-  return x > y ? x - y : y - x;
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE float absdiff(const float& x, const float& y)
-{
-  return fabsf(x - y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE double absdiff(const double& x, const double& y)
-{
-  return fabs(x - y);
-}
-
-#if !defined(EIGEN_GPUCC)
-// HIP and CUDA do not support long double.
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE long double absdiff(const long double& x, const long double& y) {
-  return fabsl(x - y);
-}
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y)
-{
-  return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-  SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(hypot, hypot)
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log1p, log1p)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float log1p(const float &x) { return ::log1pf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double log1p(const double &x) { return ::log1p(x); }
-#endif
-
-template<typename ScalarX,typename ScalarY>
-EIGEN_DEVICE_FUNC
-inline typename internal::pow_impl<ScalarX,ScalarY>::result_type pow(const ScalarX& x, const ScalarY& y)
-{
-  return internal::pow_impl<ScalarX,ScalarY>::run(x, y);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(pow, pow)
-#endif
-
-template<typename T> EIGEN_DEVICE_FUNC bool (isnan)   (const T &x) { return internal::isnan_impl(x); }
-template<typename T> EIGEN_DEVICE_FUNC bool (isinf)   (const T &x) { return internal::isinf_impl(x); }
-template<typename T> EIGEN_DEVICE_FUNC bool (isfinite)(const T &x) { return internal::isfinite_impl(x); }
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isnan, isnan, bool)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isinf, isinf, bool)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isfinite, isfinite, bool)
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(rint, Scalar) rint(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(rint, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(round, Scalar) round(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(round, Scalar)::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(round, round)
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-T (floor)(const T& x)
-{
-  EIGEN_USING_STD(floor)
-  return floor(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(floor, floor)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float floor(const float &x) { return ::floorf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double floor(const double &x) { return ::floor(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-T (ceil)(const T& x)
-{
-  EIGEN_USING_STD(ceil);
-  return ceil(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(ceil, ceil)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float ceil(const float &x) { return ::ceilf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double ceil(const double &x) { return ::ceil(x); }
-#endif
-
-
-/** Log base 2 for 32 bits positive integers.
-  * Conveniently returns 0 for x==0. */
-inline int log2(int x)
-{
-  eigen_assert(x>=0);
-  unsigned int v(x);
-  static const int table[32] = { 0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30, 8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31 };
-  v |= v >> 1;
-  v |= v >> 2;
-  v |= v >> 4;
-  v |= v >> 8;
-  v |= v >> 16;
-  return table[(v * 0x07C4ACDDU) >> 27];
-}
-
-/** \returns the square root of \a x.
-  *
-  * It is essentially equivalent to
-  * \code using std::sqrt; return sqrt(x); \endcode
-  * but slightly faster for float/double and some compilers (e.g., gcc), thanks to
-  * specializations when SSE is enabled.
-  *
-  * It's usage is justified in performance critical functions, like norm/normalize.
-  */
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE EIGEN_MATHFUNC_RETVAL(sqrt, Scalar) sqrt(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(sqrt, Scalar)::run(x);
-}
-
-// Boolean specialization, avoids implicit float to bool conversion (-Wimplicit-conversion-floating-point-to-bool).
-template<>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
-bool sqrt<bool>(const bool &x) { return x; }
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sqrt, sqrt)
-#endif
-
-/** \returns the reciprocal square root of \a x. **/
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T rsqrt(const T& x)
-{
-  return internal::rsqrt_impl<T>::run(x);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T log(const T &x) {
-  return internal::log_impl<T>::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log, log)
-#endif
-
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float log(const float &x) { return ::logf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double log(const double &x) { return ::log(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-typename internal::enable_if<NumTraits<T>::IsSigned || NumTraits<T>::IsComplex,typename NumTraits<T>::Real>::type
-abs(const T &x) {
-  EIGEN_USING_STD(abs);
-  return abs(x);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-typename internal::enable_if<!(NumTraits<T>::IsSigned || NumTraits<T>::IsComplex),typename NumTraits<T>::Real>::type
-abs(const T &x) {
-  return x;
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(abs, abs)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(abs, fabs)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float abs(const float &x) { return ::fabsf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double abs(const double &x) { return ::fabs(x); }
-
-template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float abs(const std::complex<float>& x) {
-  return ::hypotf(x.real(), x.imag());
-}
-
-template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double abs(const std::complex<double>& x) {
-  return ::hypot(x.real(), x.imag());
-}
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T exp(const T &x) {
-  EIGEN_USING_STD(exp);
-  return exp(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(exp, exp)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float exp(const float &x) { return ::expf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double exp(const double &x) { return ::exp(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-std::complex<float> exp(const std::complex<float>& x) {
-  float com = ::expf(x.real());
-  float res_real = com * ::cosf(x.imag());
-  float res_imag = com * ::sinf(x.imag());
-  return std::complex<float>(res_real, res_imag);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-std::complex<double> exp(const std::complex<double>& x) {
-  double com = ::exp(x.real());
-  double res_real = com * ::cos(x.imag());
-  double res_imag = com * ::sin(x.imag());
-  return std::complex<double>(res_real, res_imag);
-}
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(expm1, Scalar) expm1(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(expm1, Scalar)::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(expm1, expm1)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float expm1(const float &x) { return ::expm1f(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double expm1(const double &x) { return ::expm1(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T cos(const T &x) {
-  EIGEN_USING_STD(cos);
-  return cos(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cos,cos)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float cos(const float &x) { return ::cosf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double cos(const double &x) { return ::cos(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T sin(const T &x) {
-  EIGEN_USING_STD(sin);
-  return sin(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sin, sin)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float sin(const float &x) { return ::sinf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double sin(const double &x) { return ::sin(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T tan(const T &x) {
-  EIGEN_USING_STD(tan);
-  return tan(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tan, tan)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float tan(const float &x) { return ::tanf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double tan(const double &x) { return ::tan(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T acos(const T &x) {
-  EIGEN_USING_STD(acos);
-  return acos(x);
-}
-
-#if EIGEN_HAS_CXX11_MATH
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T acosh(const T &x) {
-  EIGEN_USING_STD(acosh);
-  return static_cast<T>(acosh(x));
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acos, acos)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acosh, acosh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float acos(const float &x) { return ::acosf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double acos(const double &x) { return ::acos(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T asin(const T &x) {
-  EIGEN_USING_STD(asin);
-  return asin(x);
-}
-
-#if EIGEN_HAS_CXX11_MATH
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T asinh(const T &x) {
-  EIGEN_USING_STD(asinh);
-  return static_cast<T>(asinh(x));
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asin, asin)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asinh, asinh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float asin(const float &x) { return ::asinf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double asin(const double &x) { return ::asin(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T atan(const T &x) {
-  EIGEN_USING_STD(atan);
-  return static_cast<T>(atan(x));
-}
-
-#if EIGEN_HAS_CXX11_MATH
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T atanh(const T &x) {
-  EIGEN_USING_STD(atanh);
-  return static_cast<T>(atanh(x));
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atan, atan)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atanh, atanh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float atan(const float &x) { return ::atanf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double atan(const double &x) { return ::atan(x); }
-#endif
-
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T cosh(const T &x) {
-  EIGEN_USING_STD(cosh);
-  return static_cast<T>(cosh(x));
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cosh, cosh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float cosh(const float &x) { return ::coshf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double cosh(const double &x) { return ::cosh(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T sinh(const T &x) {
-  EIGEN_USING_STD(sinh);
-  return static_cast<T>(sinh(x));
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sinh, sinh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float sinh(const float &x) { return ::sinhf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double sinh(const double &x) { return ::sinh(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T tanh(const T &x) {
-  EIGEN_USING_STD(tanh);
-  return tanh(x);
-}
-
-#if (!defined(EIGEN_GPUCC)) && EIGEN_FAST_MATH && !defined(SYCL_DEVICE_ONLY)
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float tanh(float x) { return internal::generic_fast_tanh_float(x); }
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tanh, tanh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float tanh(const float &x) { return ::tanhf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double tanh(const double &x) { return ::tanh(x); }
-#endif
-
-template <typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T fmod(const T& a, const T& b) {
-  EIGEN_USING_STD(fmod);
-  return fmod(a, b);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(fmod, fmod)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float fmod(const float& a, const float& b) {
-  return ::fmodf(a, b);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double fmod(const double& a, const double& b) {
-  return ::fmod(a, b);
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY
-#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY
-#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY
-#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
-#undef SYCL_SPECIALIZE_INTEGER_TYPES_BINARY
-#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
-#undef SYCL_SPECIALIZE_FLOATING_TYPES_BINARY
-#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY
-#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE
-#undef SYCL_SPECIALIZE_GEN_UNARY_FUNC
-#undef SYCL_SPECIALIZE_UNARY_FUNC
-#undef SYCL_SPECIALIZE_GEN1_BINARY_FUNC
-#undef SYCL_SPECIALIZE_GEN2_BINARY_FUNC
-#undef SYCL_SPECIALIZE_BINARY_FUNC
-#endif
-
-} // end namespace numext
-
-namespace internal {
-
-template<typename T>
-EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x)
-{
-  return (numext::isfinite)(numext::real(x)) && (numext::isfinite)(numext::imag(x));
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x)
-{
-  return (numext::isnan)(numext::real(x)) || (numext::isnan)(numext::imag(x));
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x)
-{
-  return ((numext::isinf)(numext::real(x)) || (numext::isinf)(numext::imag(x))) && (!(numext::isnan)(x));
-}
-
-/****************************************************************************
-* Implementation of fuzzy comparisons                                       *
-****************************************************************************/
-
-template<typename Scalar,
-         bool IsComplex,
-         bool IsInteger>
-struct scalar_fuzzy_default_impl {};
-
-template<typename Scalar>
-struct scalar_fuzzy_default_impl<Scalar, false, false>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
-  {
-    return numext::abs(x) <= numext::abs(y) * prec;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
-  {
-    return numext::abs(x - y) <= numext::mini(numext::abs(x), numext::abs(y)) * prec;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec)
-  {
-    return x <= y || isApprox(x, y, prec);
-  }
-};
-
-template<typename Scalar>
-struct scalar_fuzzy_default_impl<Scalar, false, true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const Scalar& x, const Scalar&, const RealScalar&)
-  {
-    return x == Scalar(0);
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar&)
-  {
-    return x == y;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar&)
-  {
-    return x <= y;
-  }
-};
-
-template<typename Scalar>
-struct scalar_fuzzy_default_impl<Scalar, true, false>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
-  {
-    return numext::abs2(x) <= numext::abs2(y) * prec * prec;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
-  {
-    return numext::abs2(x - y) <= numext::mini(numext::abs2(x), numext::abs2(y)) * prec * prec;
-  }
-};
-
-template<typename Scalar>
-struct scalar_fuzzy_impl : scalar_fuzzy_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
-
-template<typename Scalar, typename OtherScalar> EIGEN_DEVICE_FUNC
-inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y,
-                              const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
-{
-  return scalar_fuzzy_impl<Scalar>::template isMuchSmallerThan<OtherScalar>(x, y, precision);
-}
-
-template<typename Scalar> EIGEN_DEVICE_FUNC
-inline bool isApprox(const Scalar& x, const Scalar& y,
-                     const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
-{
-  return scalar_fuzzy_impl<Scalar>::isApprox(x, y, precision);
-}
-
-template<typename Scalar> EIGEN_DEVICE_FUNC
-inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y,
-                               const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
-{
-  return scalar_fuzzy_impl<Scalar>::isApproxOrLessThan(x, y, precision);
-}
-
-/******************************************
-***  The special case of the  bool type ***
-******************************************/
-
-template<> struct random_impl<bool>
-{
-  static inline bool run()
-  {
-    return random<int>(0,1)==0 ? false : true;
-  }
-
-  static inline bool run(const bool& a, const bool& b)
-  {
-    return random<int>(a, b)==0 ? false : true;
-  }
-};
-
-template<> struct scalar_fuzzy_impl<bool>
-{
-  typedef bool RealScalar;
-
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const bool& x, const bool&, const bool&)
-  {
-    return !x;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(bool x, bool y, bool)
-  {
-    return x == y;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline bool isApproxOrLessThan(const bool& x, const bool& y, const bool&)
-  {
-    return (!x) || y;
-  }
-
-};
-
-} // end namespace internal
-
-// Default implementations that rely on other numext implementations
-namespace internal {
-
-// Specialization for complex types that are not supported by std::expm1.
-template <typename RealScalar>
-struct expm1_impl<std::complex<RealScalar> > {
-  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
-      const std::complex<RealScalar>& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
-    RealScalar xr = x.real();
-    RealScalar xi = x.imag();
-    // expm1(z) = exp(z) - 1
-    //          = exp(x +  i * y) - 1
-    //          = exp(x) * (cos(y) + i * sin(y)) - 1
-    //          = exp(x) * cos(y) - 1 + i * exp(x) * sin(y)
-    // Imag(expm1(z)) = exp(x) * sin(y)
-    // Real(expm1(z)) = exp(x) * cos(y) - 1
-    //          = exp(x) * cos(y) - 1.
-    //          = expm1(x) + exp(x) * (cos(y) - 1)
-    //          = expm1(x) + exp(x) * (2 * sin(y / 2) ** 2)
-    RealScalar erm1 = numext::expm1<RealScalar>(xr);
-    RealScalar er = erm1 + RealScalar(1.);
-    RealScalar sin2 = numext::sin(xi / RealScalar(2.));
-    sin2 = sin2 * sin2;
-    RealScalar s = numext::sin(xi);
-    RealScalar real_part = erm1 - RealScalar(2.) * er * sin2;
-    return std::complex<RealScalar>(real_part, er * s);
-  }
-};
-
-template<typename T>
-struct rsqrt_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE T run(const T& x) {
-    return T(1)/numext::sqrt(x);
-  }
-};
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T>
-struct conj_impl<std::complex<T>, true>
-{
-  EIGEN_DEVICE_FUNC
-  static inline std::complex<T> run(const std::complex<T>& x)
-  {
-    return std::complex<T>(numext::real(x), -numext::imag(x));
-  }
-};
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATHFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MathFunctionsImpl.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MathFunctionsImpl.h
deleted file mode 100644
index 4eaaaa7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MathFunctionsImpl.h
+++ /dev/null
@@ -1,200 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATHFUNCTIONSIMPL_H
-#define EIGEN_MATHFUNCTIONSIMPL_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
-    Doesn't do anything fancy, just a 13/6-degree rational interpolant which
-    is accurate up to a couple of ulps in the (approximate) range [-8, 8],
-    outside of which tanh(x) = +/-1 in single precision. The input is clamped
-    to the range [-c, c]. The value c is chosen as the smallest value where
-    the approximation evaluates to exactly 1. In the reange [-0.0004, 0.0004]
-    the approxmation tanh(x) ~= x is used for better accuracy as x tends to zero.
-
-    This implementation works on both scalars and packets.
-*/
-template<typename T>
-T generic_fast_tanh_float(const T& a_x)
-{
-  // Clamp the inputs to the range [-c, c]
-#ifdef EIGEN_VECTORIZE_FMA
-  const T plus_clamp = pset1<T>(7.99881172180175781f);
-  const T minus_clamp = pset1<T>(-7.99881172180175781f);
-#else
-  const T plus_clamp = pset1<T>(7.90531110763549805f);
-  const T minus_clamp = pset1<T>(-7.90531110763549805f);
-#endif
-  const T tiny = pset1<T>(0.0004f);
-  const T x = pmax(pmin(a_x, plus_clamp), minus_clamp);
-  const T tiny_mask = pcmp_lt(pabs(a_x), tiny);
-  // The monomial coefficients of the numerator polynomial (odd).
-  const T alpha_1 = pset1<T>(4.89352455891786e-03f);
-  const T alpha_3 = pset1<T>(6.37261928875436e-04f);
-  const T alpha_5 = pset1<T>(1.48572235717979e-05f);
-  const T alpha_7 = pset1<T>(5.12229709037114e-08f);
-  const T alpha_9 = pset1<T>(-8.60467152213735e-11f);
-  const T alpha_11 = pset1<T>(2.00018790482477e-13f);
-  const T alpha_13 = pset1<T>(-2.76076847742355e-16f);
-
-  // The monomial coefficients of the denominator polynomial (even).
-  const T beta_0 = pset1<T>(4.89352518554385e-03f);
-  const T beta_2 = pset1<T>(2.26843463243900e-03f);
-  const T beta_4 = pset1<T>(1.18534705686654e-04f);
-  const T beta_6 = pset1<T>(1.19825839466702e-06f);
-
-  // Since the polynomials are odd/even, we need x^2.
-  const T x2 = pmul(x, x);
-
-  // Evaluate the numerator polynomial p.
-  T p = pmadd(x2, alpha_13, alpha_11);
-  p = pmadd(x2, p, alpha_9);
-  p = pmadd(x2, p, alpha_7);
-  p = pmadd(x2, p, alpha_5);
-  p = pmadd(x2, p, alpha_3);
-  p = pmadd(x2, p, alpha_1);
-  p = pmul(x, p);
-
-  // Evaluate the denominator polynomial q.
-  T q = pmadd(x2, beta_6, beta_4);
-  q = pmadd(x2, q, beta_2);
-  q = pmadd(x2, q, beta_0);
-
-  // Divide the numerator by the denominator.
-  return pselect(tiny_mask, x, pdiv(p, q));
-}
-
-template<typename RealScalar>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y)
-{
-  // IEEE IEC 6059 special cases.
-  if ((numext::isinf)(x) || (numext::isinf)(y))
-    return NumTraits<RealScalar>::infinity();
-  if ((numext::isnan)(x) || (numext::isnan)(y))
-    return NumTraits<RealScalar>::quiet_NaN();
-    
-  EIGEN_USING_STD(sqrt);
-  RealScalar p, qp;
-  p = numext::maxi(x,y);
-  if(p==RealScalar(0)) return RealScalar(0);
-  qp = numext::mini(y,x) / p;
-  return p * sqrt(RealScalar(1) + qp*qp);
-}
-
-template<typename Scalar>
-struct hypot_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  static EIGEN_DEVICE_FUNC
-  inline RealScalar run(const Scalar& x, const Scalar& y)
-  {
-    EIGEN_USING_STD(abs);
-    return positive_real_hypot<RealScalar>(abs(x), abs(y));
-  }
-};
-
-// Generic complex sqrt implementation that correctly handles corner cases
-// according to https://en.cppreference.com/w/cpp/numeric/complex/sqrt
-template<typename T>
-EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& z) {
-  // Computes the principal sqrt of the input.
-  //
-  // For a complex square root of the number x + i*y. We want to find real
-  // numbers u and v such that
-  //    (u + i*v)^2 = x + i*y  <=>
-  //    u^2 - v^2 + i*2*u*v = x + i*v.
-  // By equating the real and imaginary parts we get:
-  //    u^2 - v^2 = x
-  //    2*u*v = y.
-  //
-  // For x >= 0, this has the numerically stable solution
-  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
-  //    v = y / (2 * u)
-  // and for x < 0,
-  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
-  //    u = y / (2 * v)
-  //
-  // Letting w = sqrt(0.5 * (|x| + |z|)),
-  //   if x == 0: u = w, v = sign(y) * w
-  //   if x > 0:  u = w, v = y / (2 * w)
-  //   if x < 0:  u = |y| / (2 * w), v = sign(y) * w
-
-  const T x = numext::real(z);
-  const T y = numext::imag(z);
-  const T zero = T(0);
-  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + numext::hypot(x, y)));
-
-  return
-    (numext::isinf)(y) ? std::complex<T>(NumTraits<T>::infinity(), y)
-      : x == zero ? std::complex<T>(w, y < zero ? -w : w)
-      : x > zero ? std::complex<T>(w, y / (2 * w))
-      : std::complex<T>(numext::abs(y) / (2 * w), y < zero ? -w : w );
-}
-
-// Generic complex rsqrt implementation.
-template<typename T>
-EIGEN_DEVICE_FUNC std::complex<T> complex_rsqrt(const std::complex<T>& z) {
-  // Computes the principal reciprocal sqrt of the input.
-  //
-  // For a complex reciprocal square root of the number z = x + i*y. We want to
-  // find real numbers u and v such that
-  //    (u + i*v)^2 = 1 / (x + i*y)  <=>
-  //    u^2 - v^2 + i*2*u*v = x/|z|^2 - i*v/|z|^2.
-  // By equating the real and imaginary parts we get:
-  //    u^2 - v^2 = x/|z|^2
-  //    2*u*v = y/|z|^2.
-  //
-  // For x >= 0, this has the numerically stable solution
-  //    u = sqrt(0.5 * (x + |z|)) / |z|
-  //    v = -y / (2 * u * |z|)
-  // and for x < 0,
-  //    v = -sign(y) * sqrt(0.5 * (-x + |z|)) / |z|
-  //    u = -y / (2 * v * |z|)
-  //
-  // Letting w = sqrt(0.5 * (|x| + |z|)),
-  //   if x == 0: u = w / |z|, v = -sign(y) * w / |z|
-  //   if x > 0:  u = w / |z|, v = -y / (2 * w * |z|)
-  //   if x < 0:  u = |y| / (2 * w * |z|), v = -sign(y) * w / |z|
-
-  const T x = numext::real(z);
-  const T y = numext::imag(z);
-  const T zero = T(0);
-
-  const T abs_z = numext::hypot(x, y);
-  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + abs_z));
-  const T woz = w / abs_z;
-  // Corner cases consistent with 1/sqrt(z) on gcc/clang.
-  return
-    abs_z == zero ? std::complex<T>(NumTraits<T>::infinity(), NumTraits<T>::quiet_NaN())
-      : ((numext::isinf)(x) || (numext::isinf)(y)) ? std::complex<T>(zero, zero)
-      : x == zero ? std::complex<T>(woz, y < zero ? woz : -woz)
-      : x > zero ? std::complex<T>(woz, -y / (2 * w * abs_z))
-      : std::complex<T>(numext::abs(y) / (2 * w * abs_z), y < zero ? woz : -woz );
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC std::complex<T> complex_log(const std::complex<T>& z) {
-  // Computes complex log.
-  T a = numext::abs(z);
-  EIGEN_USING_STD(atan2);
-  T b = atan2(z.imag(), z.real());
-  return std::complex<T>(numext::log(a), b);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATHFUNCTIONSIMPL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Matrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Matrix.h
deleted file mode 100644
index f0e59a9..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Matrix.h
+++ /dev/null
@@ -1,565 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_H
-#define EIGEN_MATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-private:
-  enum { size = internal::size_at_compile_time<_Rows,_Cols>::ret };
-  typedef typename find_best_packet<_Scalar,size>::type PacketScalar;
-  enum {
-      row_major_bit = _Options&RowMajor ? RowMajorBit : 0,
-      is_dynamic_size_storage = _MaxRows==Dynamic || _MaxCols==Dynamic,
-      max_size = is_dynamic_size_storage ? Dynamic : _MaxRows*_MaxCols,
-      default_alignment = compute_default_alignment<_Scalar,max_size>::value,
-      actual_alignment = ((_Options&DontAlign)==0) ? default_alignment : 0,
-      required_alignment = unpacket_traits<PacketScalar>::alignment,
-      packet_access_bit = (packet_traits<_Scalar>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
-    };
-
-public:
-  typedef _Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef Eigen::Index StorageIndex;
-  typedef MatrixXpr XprKind;
-  enum {
-    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = _MaxRows,
-    MaxColsAtCompileTime = _MaxCols,
-    Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
-    Options = _Options,
-    InnerStrideAtCompileTime = 1,
-    OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime,
-
-    // FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase
-    EvaluatorFlags = LinearAccessBit | DirectAccessBit | packet_access_bit | row_major_bit,
-    Alignment = actual_alignment
-  };
-};
-}
-
-/** \class Matrix
-  * \ingroup Core_Module
-  *
-  * \brief The matrix class, also used for vectors and row-vectors
-  *
-  * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen.
-  * Vectors are matrices with one column, and row-vectors are matrices with one row.
-  *
-  * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
-  *
-  * The first three template parameters are required:
-  * \tparam _Scalar Numeric type, e.g. float, double, int or std::complex<float>.
-  *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
-  * \tparam _Rows Number of rows, or \b Dynamic
-  * \tparam _Cols Number of columns, or \b Dynamic
-  *
-  * The remaining template parameters are optional -- in most cases you don't have to worry about them.
-  * \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of either
-  *                 \b #AutoAlign or \b #DontAlign.
-  *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
-  *                 for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size.
-  * \tparam _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note").
-  * \tparam _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note").
-  *
-  * Eigen provides a number of typedefs covering the usual cases. Here are some examples:
-  *
-  * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix<double, 2, 2>)
-  * \li \c Vector4f is a vector of 4 floats (\c Matrix<float, 4, 1>)
-  * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix<int, 1, 3>)
-  *
-  * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>)
-  * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>)
-  *
-  * \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix<float, 2, Dynamic>)
-  * \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix<double, Dynamic, 3>)
-  *
-  * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs.
-  *
-  * You can access elements of vectors and matrices using normal subscripting:
-  *
-  * \code
-  * Eigen::VectorXd v(10);
-  * v[0] = 0.1;
-  * v[1] = 0.2;
-  * v(0) = 0.3;
-  * v(1) = 0.4;
-  *
-  * Eigen::MatrixXi m(10, 10);
-  * m(0, 1) = 1;
-  * m(0, 2) = 2;
-  * m(0, 3) = 3;
-  * \endcode
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
-  *
-  * <i><b>Some notes:</b></i>
-  *
-  * <dl>
-  * <dt><b>\anchor dense Dense versus sparse:</b></dt>
-  * <dd>This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the Sparse module.
-  *
-  * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary contiguous array.
-  * This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero coefficients.</dd>
-  *
-  * <dt><b>\anchor fixedsize Fixed-size versus dynamic-size:</b></dt>
-  * <dd>Fixed-size means that the numbers of rows and columns are known are compile-time. In this case, Eigen allocates the array
-  * of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices, typically up to 4x4, sometimes up
-  * to 16x16. Larger matrices should be declared as dynamic-size even if one happens to know their size at compile-time.
-  *
-  * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they are runtime
-  * variables, and the array of coefficients is allocated dynamically on the heap.
-  *
-  * Note that \em dense matrices, be they Fixed-size or Dynamic-size, <em>do not</em> expand dynamically in the sense of a std::map.
-  * If you want this behavior, see the Sparse module.</dd>
-  *
-  * <dt><b>\anchor maxrows _MaxRows and _MaxCols:</b></dt>
-  * <dd>In most cases, one just leaves these parameters to the default values.
-  * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases
-  * when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot
-  * exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case _MaxRows and _MaxCols
-  * are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
-  * </dl>
-  *
-  * <i><b>ABI and storage layout</b></i>
-  *
-  * The table below summarizes the ABI of some possible Matrix instances which is fixed thorough the lifetime of Eigen 3.
-  * <table  class="manual">
-  * <tr><th>Matrix type</th><th>Equivalent C structure</th></tr>
-  * <tr><td>\code Matrix<T,Dynamic,Dynamic> \endcode</td><td>\code
-  * struct {
-  *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
-  *   Eigen::Index rows, cols;
-  *  };
-  * \endcode</td></tr>
-  * <tr class="alt"><td>\code
-  * Matrix<T,Dynamic,1>
-  * Matrix<T,1,Dynamic> \endcode</td><td>\code
-  * struct {
-  *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
-  *   Eigen::Index size;
-  *  };
-  * \endcode</td></tr>
-  * <tr><td>\code Matrix<T,Rows,Cols> \endcode</td><td>\code
-  * struct {
-  *   T data[Rows*Cols];        // with (size_t(data)%A(Rows*Cols*sizeof(T)))==0
-  *  };
-  * \endcode</td></tr>
-  * <tr class="alt"><td>\code Matrix<T,Dynamic,Dynamic,0,MaxRows,MaxCols> \endcode</td><td>\code
-  * struct {
-  *   T data[MaxRows*MaxCols];  // with (size_t(data)%A(MaxRows*MaxCols*sizeof(T)))==0
-  *   Eigen::Index rows, cols;
-  *  };
-  * \endcode</td></tr>
-  * </table>
-  * Note that in this table Rows, Cols, MaxRows and MaxCols are all positive integers. A(S) is defined to the largest possible power-of-two
-  * smaller to EIGEN_MAX_STATIC_ALIGN_BYTES.
-  *
-  * \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
-  * \ref TopicStorageOrders
-  */
-
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-class Matrix
-  : public PlainObjectBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-  public:
-
-    /** \brief Base class typedef.
-      * \sa PlainObjectBase
-      */
-    typedef PlainObjectBase<Matrix> Base;
-
-    enum { Options = _Options };
-
-    EIGEN_DENSE_PUBLIC_INTERFACE(Matrix)
-
-    typedef typename Base::PlainObject PlainObject;
-
-    using Base::base;
-    using Base::coeffRef;
-
-    /**
-      * \brief Assigns matrices to each other.
-      *
-      * \note This is a special case of the templated operator=. Its purpose is
-      * to prevent a default operator= from hiding the templated operator=.
-      *
-      * \callgraph
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
-    {
-      return Base::_set(other);
-    }
-
-    /** \internal
-      * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
-      *
-      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
-      * it will be initialized.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase<OtherDerived>& other)
-    {
-      return Base::_set(other);
-    }
-
-    /* Here, doxygen failed to copy the brief information when using \copydoc */
-
-    /**
-      * \brief Copies the generic expression \a other into *this.
-      * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived> &other)
-    {
-      return Base::operator=(other);
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func)
-    {
-      return Base::operator=(func);
-    }
-
-    /** \brief Default constructor.
-      *
-      * For fixed-size matrices, does nothing.
-      *
-      * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
-      * is called a null matrix. This constructor is the unique way to create null matrices: resizing
-      * a matrix to 0 is not supported.
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix() : Base()
-    {
-      Base::_check_template_params();
-      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-    // FIXME is it still needed
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    explicit Matrix(internal::constructor_without_unaligned_array_assert)
-      : Base(internal::constructor_without_unaligned_array_assert())
-    { Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
-      : Base(std::move(other))
-    {
-      Base::_check_template_params();
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
-    {
-      Base::operator=(std::move(other));
-      return *this;
-    }
-#endif
-
-#if EIGEN_HAS_CXX11
-    /** \copydoc PlainObjectBase(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&... args)
-     *
-     * Example: \include Matrix_variadic_ctor_cxx11.cpp
-     * Output: \verbinclude Matrix_variadic_ctor_cxx11.out
-     *
-     * \sa Matrix(const std::initializer_list<std::initializer_list<Scalar>>&)
-     */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
-      : Base(a0, a1, a2, a3, args...) {}
-
-    /** \brief Constructs a Matrix and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11
-      *
-      * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
-      *
-      * Example: \include Matrix_initializer_list_23_cxx11.cpp
-      * Output: \verbinclude Matrix_initializer_list_23_cxx11.out
-      *
-      * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered.
-      *
-      * In the case of a compile-time column vector, implicit transposition from a single row is allowed.
-      * Therefore <code>VectorXd{{1,2,3,4,5}}</code> is legal and the more verbose syntax
-      * <code>RowVectorXd{{1},{2},{3},{4},{5}}</code> can be avoided:
-      *
-      * Example: \include Matrix_initializer_list_vector_cxx11.cpp
-      * Output: \verbinclude Matrix_initializer_list_vector_cxx11.out
-      *
-      * In the case of fixed-sized matrices, the initializer list sizes must exactly match the matrix sizes,
-      * and implicit transposition is allowed for compile-time vectors only.
-      *
-      * \sa Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE Matrix(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
-#endif // end EIGEN_HAS_CXX11
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-    // This constructor is for both 1x1 matrices and dynamic vectors
-    template<typename T>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    explicit Matrix(const T& x)
-    {
-      Base::_check_template_params();
-      Base::template _init1<T>(x);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix(const T0& x, const T1& y)
-    {
-      Base::_check_template_params();
-      Base::template _init2<T0,T1>(x, y);
-    }
-
-
-#else
-    /** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */
-    EIGEN_DEVICE_FUNC
-    explicit Matrix(const Scalar *data);
-
-    /** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
-      *
-      * This is useful for dynamic-size vectors. For fixed-size vectors,
-      * it is redundant to pass these parameters, so one should use the default constructor
-      * Matrix() instead.
-      *
-      * \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance,
-      * calling Matrix<double,1,1>(1) will call the initialization constructor: Matrix(const Scalar&).
-      * For fixed-size \c 1x1 matrices it is therefore recommended to use the default
-      * constructor Matrix() instead, especially when using one of the non standard
-      * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
-      */
-    EIGEN_STRONG_INLINE explicit Matrix(Index dim);
-    /** \brief Constructs an initialized 1x1 matrix with the given coefficient
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
-    Matrix(const Scalar& x);
-    /** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
-      *
-      * This is useful for dynamic-size matrices. For fixed-size matrices,
-      * it is redundant to pass these parameters, so one should use the default constructor
-      * Matrix() instead.
-      *
-      * \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance,
-      * calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y).
-      * For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default
-      * constructor Matrix() instead, especially when using one of the non standard
-      * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
-      */
-    EIGEN_DEVICE_FUNC
-    Matrix(Index rows, Index cols);
-
-    /** \brief Constructs an initialized 2D vector with given coefficients
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
-    Matrix(const Scalar& x, const Scalar& y);
-    #endif  // end EIGEN_PARSED_BY_DOXYGEN
-
-    /** \brief Constructs an initialized 3D vector with given coefficients
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3)
-      m_storage.data()[0] = x;
-      m_storage.data()[1] = y;
-      m_storage.data()[2] = z;
-    }
-    /** \brief Constructs an initialized 4D vector with given coefficients
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4)
-      m_storage.data()[0] = x;
-      m_storage.data()[1] = y;
-      m_storage.data()[2] = z;
-      m_storage.data()[3] = w;
-    }
-
-
-    /** \brief Copy constructor */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other)
-    { }
-
-    /** \brief Copy constructor for generic expressions.
-      * \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
-      : Base(other.derived())
-    { }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return 1; }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); }
-
-    /////////// Geometry module ///////////
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
-
-    // allow to extend Matrix outside Eigen
-    #ifdef EIGEN_MATRIX_PLUGIN
-    #include EIGEN_MATRIX_PLUGIN
-    #endif
-
-  protected:
-    template <typename Derived, typename OtherDerived, bool IsVector>
-    friend struct internal::conservative_resize_like_impl;
-
-    using Base::m_storage;
-};
-
-/** \defgroup matrixtypedefs Global matrix typedefs
-  *
-  * \ingroup Core_Module
-  *
-  * %Eigen defines several typedef shortcuts for most common matrix and vector types.
-  *
-  * The general patterns are the following:
-  *
-  * \c MatrixSizeType where \c Size can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
-  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
-  * for complex double.
-  *
-  * For example, \c Matrix3d is a fixed-size 3x3 matrix type of doubles, and \c MatrixXf is a dynamic-size matrix of floats.
-  *
-  * There are also \c VectorSizeType and \c RowVectorSizeType which are self-explanatory. For example, \c Vector4cf is
-  * a fixed-size vector of 4 complex floats.
-  *
-  * With \cpp11, template alias are also defined for common sizes.
-  * They follow the same pattern as above except that the scalar type suffix is replaced by a
-  * template parameter, i.e.:
-  *   - `MatrixSize<Type>` where `Size` can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size.
-  *   - `MatrixXSize<Type>` and `MatrixSizeX<Type>` where `Size` can be \c 2,\c 3,\c 4 for hybrid dynamic/fixed matrices.
-  *   - `VectorSize<Type>` and `RowVectorSize<Type>` for column and row vectors.
-  *
-  * With \cpp11, you can also use fully generic column and row vector types: `Vector<Type,Size>` and `RowVector<Type,Size>`.
-  *
-  * \sa class Matrix
-  */
-
-#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Size, Size> Matrix##SizeSuffix##TypeSuffix;  \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Size, 1>    Vector##SizeSuffix##TypeSuffix;  \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, 1, Size>    RowVector##SizeSuffix##TypeSuffix;
-
-#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix;  \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix;
-
-#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
-EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
-EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
-EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
-
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int,                  i)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float,                f)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double,               d)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<float>,  cf)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
-
-#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
-#undef EIGEN_MAKE_TYPEDEFS
-#undef EIGEN_MAKE_FIXED_TYPEDEFS
-
-#if EIGEN_HAS_CXX11
-
-#define EIGEN_MAKE_TYPEDEFS(Size, SizeSuffix)                     \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Matrix##SizeSuffix = Matrix<Type, Size, Size>;              \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Vector##SizeSuffix = Matrix<Type, Size, 1>;                 \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using RowVector##SizeSuffix = Matrix<Type, 1, Size>;
-
-#define EIGEN_MAKE_FIXED_TYPEDEFS(Size)                           \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Matrix##Size##X = Matrix<Type, Size, Dynamic>;              \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Matrix##X##Size = Matrix<Type, Dynamic, Size>;
-
-EIGEN_MAKE_TYPEDEFS(2, 2)
-EIGEN_MAKE_TYPEDEFS(3, 3)
-EIGEN_MAKE_TYPEDEFS(4, 4)
-EIGEN_MAKE_TYPEDEFS(Dynamic, X)
-EIGEN_MAKE_FIXED_TYPEDEFS(2)
-EIGEN_MAKE_FIXED_TYPEDEFS(3)
-EIGEN_MAKE_FIXED_TYPEDEFS(4)
-
-/** \ingroup matrixtypedefs
-  * \brief \cpp11 */
-template <typename Type, int Size>
-using Vector = Matrix<Type, Size, 1>;
-
-/** \ingroup matrixtypedefs
-  * \brief \cpp11 */
-template <typename Type, int Size>
-using RowVector = Matrix<Type, 1, Size>;
-
-#undef EIGEN_MAKE_TYPEDEFS
-#undef EIGEN_MAKE_FIXED_TYPEDEFS
-
-#endif // EIGEN_HAS_CXX11
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MatrixBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MatrixBase.h
deleted file mode 100644
index 45c3a59..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/MatrixBase.h
+++ /dev/null
@@ -1,547 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIXBASE_H
-#define EIGEN_MATRIXBASE_H
-
-namespace Eigen {
-
-/** \class MatrixBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for all dense matrices, vectors, and expressions
-  *
-  * This class is the base that is inherited by all matrix, vector, and related expression
-  * types. Most of the Eigen API is contained in this class, and its base classes. Other important
-  * classes for the Eigen API are Matrix, and VectorwiseOp.
-  *
-  * Note that some methods are defined in other modules such as the \ref LU_Module LU module
-  * for all functions related to matrix inversions.
-  *
-  * \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc.
-  *
-  * When writing a function taking Eigen objects as argument, if you want your function
-  * to take as argument any matrix, vector, or expression, just let it take a
-  * MatrixBase argument. As an example, here is a function printFirstRow which, given
-  * a matrix, vector, or expression \a x, prints the first row of \a x.
-  *
-  * \code
-    template<typename Derived>
-    void printFirstRow(const Eigen::MatrixBase<Derived>& x)
-    {
-      cout << x.row(0) << endl;
-    }
-  * \endcode
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived> class MatrixBase
-  : public DenseBase<Derived>
-{
-  public:
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef MatrixBase StorageBaseType;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    typedef DenseBase<Derived> Base;
-    using Base::RowsAtCompileTime;
-    using Base::ColsAtCompileTime;
-    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-
-    using Base::derived;
-    using Base::const_cast_derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-    using Base::lazyAssign;
-    using Base::eval;
-    using Base::operator-;
-    using Base::operator+=;
-    using Base::operator-=;
-    using Base::operator*=;
-    using Base::operator/=;
-
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-    typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
-    typedef typename Base::RowXpr RowXpr;
-    typedef typename Base::ColXpr ColXpr;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** type of the equivalent square matrix */
-    typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
-                          EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-    /** \returns the size of the main diagonal, which is min(rows(),cols()).
-      * \sa rows(), cols(), SizeAtCompileTime. */
-    EIGEN_DEVICE_FUNC
-    inline Index diagonalSize() const { return (numext::mini)(rows(),cols()); }
-
-    typedef typename Base::PlainObject PlainObject;
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
-    /** \internal the return type of MatrixBase::adjoint() */
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
-                        ConstTransposeReturnType
-                     >::type AdjointReturnType;
-    /** \internal Return type of eigenvalues() */
-    typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType;
-    /** \internal the return type of identity */
-    typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,PlainObject> IdentityReturnType;
-    /** \internal the return type of unit vectors */
-    typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>,
-                  internal::traits<Derived>::RowsAtCompileTime,
-                  internal::traits<Derived>::ColsAtCompileTime> BasisReturnType;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#   include "../plugins/CommonCwiseBinaryOps.h"
-#   include "../plugins/MatrixCwiseUnaryOps.h"
-#   include "../plugins/MatrixCwiseBinaryOps.h"
-#   ifdef EIGEN_MATRIXBASE_PLUGIN
-#     include EIGEN_MATRIXBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_UNARY_ADDONS
-
-    /** Special case of the template operator=, in order to prevent the compiler
-      * from generating a default operator= (issue hit with g++ 4.1)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const MatrixBase& other);
-
-    // We cannot inherit here via Base::operator= since it is causing
-    // trouble with MSVC.
-
-    template <typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const DenseBase<OtherDerived>& other);
-
-    template <typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const EigenBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const ReturnByValue<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator+=(const MatrixBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator-=(const MatrixBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived,OtherDerived>
-    operator*(const MatrixBase<OtherDerived> &other) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived,OtherDerived,LazyProduct>
-    lazyProduct(const MatrixBase<OtherDerived> &other) const;
-
-    template<typename OtherDerived>
-    Derived& operator*=(const EigenBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    void applyOnTheLeft(const EigenBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    void applyOnTheRight(const EigenBase<OtherDerived>& other);
-
-    template<typename DiagonalDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived, DiagonalDerived, LazyProduct>
-    operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
-    dot(const MatrixBase<OtherDerived>& other) const;
-
-    EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
-    EIGEN_DEVICE_FUNC RealScalar norm() const;
-    RealScalar stableNorm() const;
-    RealScalar blueNorm() const;
-    RealScalar hypotNorm() const;
-    EIGEN_DEVICE_FUNC const PlainObject normalized() const;
-    EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const;
-    EIGEN_DEVICE_FUNC void normalize();
-    EIGEN_DEVICE_FUNC void stableNormalize();
-
-    EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const;
-    EIGEN_DEVICE_FUNC void adjointInPlace();
-
-    typedef Diagonal<Derived> DiagonalReturnType;
-    EIGEN_DEVICE_FUNC
-    DiagonalReturnType diagonal();
-
-    typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
-    EIGEN_DEVICE_FUNC
-    ConstDiagonalReturnType diagonal() const;
-
-    template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
-    template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
-
-    template<int Index>
-    EIGEN_DEVICE_FUNC
-    typename DiagonalIndexReturnType<Index>::Type diagonal();
-
-    template<int Index>
-    EIGEN_DEVICE_FUNC
-    typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
-
-    typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
-    typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
-
-    EIGEN_DEVICE_FUNC
-    DiagonalDynamicIndexReturnType diagonal(Index index);
-    EIGEN_DEVICE_FUNC
-    ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
-
-    template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
-    template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
-
-    template<unsigned int Mode>
-    EIGEN_DEVICE_FUNC
-    typename TriangularViewReturnType<Mode>::Type triangularView();
-    template<unsigned int Mode>
-    EIGEN_DEVICE_FUNC
-    typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
-
-    template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; };
-    template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; };
-
-    template<unsigned int UpLo>
-    EIGEN_DEVICE_FUNC
-    typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
-    template<unsigned int UpLo>
-    EIGEN_DEVICE_FUNC
-    typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
-
-    const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
-                                         const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC static const IdentityReturnType Identity();
-    EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i);
-    EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i);
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitX();
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitY();
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ();
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitW();
-
-    EIGEN_DEVICE_FUNC
-    const DiagonalWrapper<const Derived> asDiagonal() const;
-    const PermutationWrapper<const Derived> asPermutation() const;
-
-    EIGEN_DEVICE_FUNC
-    Derived& setIdentity();
-    EIGEN_DEVICE_FUNC
-    Derived& setIdentity(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setUnit(Index i);
-    EIGEN_DEVICE_FUNC Derived& setUnit(Index newSize, Index i);
-
-    bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    template<typename OtherDerived>
-    bool isOrthogonal(const MatrixBase<OtherDerived>& other,
-                      const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
-      * \warning When using floating point scalar values you probably should rather use a
-      *          fuzzy comparison such as isApprox()
-      * \sa isApprox(), operator!= */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase<OtherDerived>& other) const
-    { return cwiseEqual(other).all(); }
-
-    /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
-      * \warning When using floating point scalar values you probably should rather use a
-      *          fuzzy comparison such as isApprox()
-      * \sa isApprox(), operator== */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase<OtherDerived>& other) const
-    { return cwiseNotEqual(other).any(); }
-
-    NoAlias<Derived,Eigen::MatrixBase > EIGEN_DEVICE_FUNC noalias();
-
-    // TODO forceAlignedAccess is temporarily disabled
-    // Need to find a nicer workaround.
-    inline const Derived& forceAlignedAccess() const { return derived(); }
-    inline Derived& forceAlignedAccess() { return derived(); }
-    template<bool Enable> inline const Derived& forceAlignedAccessIf() const { return derived(); }
-    template<bool Enable> inline Derived& forceAlignedAccessIf() { return derived(); }
-
-    EIGEN_DEVICE_FUNC Scalar trace() const;
-
-    template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
-
-    EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
-    EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; }
-
-    /** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
-      * \sa ArrayBase::matrix() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return ArrayWrapper<Derived>(derived()); }
-    /** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
-      * \sa ArrayBase::matrix() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return ArrayWrapper<const Derived>(derived()); }
-
-/////////// LU module ///////////
-
-    inline const FullPivLU<PlainObject> fullPivLu() const;
-    inline const PartialPivLU<PlainObject> partialPivLu() const;
-
-    inline const PartialPivLU<PlainObject> lu() const;
-
-    EIGEN_DEVICE_FUNC
-    inline const Inverse<Derived> inverse() const;
-
-    template<typename ResultType>
-    inline void computeInverseAndDetWithCheck(
-      ResultType& inverse,
-      typename ResultType::Scalar& determinant,
-      bool& invertible,
-      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
-    ) const;
-
-    template<typename ResultType>
-    inline void computeInverseWithCheck(
-      ResultType& inverse,
-      bool& invertible,
-      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
-    ) const;
-
-    EIGEN_DEVICE_FUNC
-    Scalar determinant() const;
-
-/////////// Cholesky module ///////////
-
-    inline const LLT<PlainObject>  llt() const;
-    inline const LDLT<PlainObject> ldlt() const;
-
-/////////// QR module ///////////
-
-    inline const HouseholderQR<PlainObject> householderQr() const;
-    inline const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
-    inline const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
-    inline const CompleteOrthogonalDecomposition<PlainObject> completeOrthogonalDecomposition() const;
-
-/////////// Eigenvalues module ///////////
-
-    inline EigenvaluesReturnType eigenvalues() const;
-    inline RealScalar operatorNorm() const;
-
-/////////// SVD module ///////////
-
-    inline JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
-    inline BDCSVD<PlainObject>    bdcSvd(unsigned int computationOptions = 0) const;
-
-/////////// Geometry module ///////////
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /// \internal helper struct to form the return type of the cross product
-    template<typename OtherDerived> struct cross_product_return_type {
-      typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
-      typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type;
-    };
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    inline typename cross_product_return_type<OtherDerived>::type
-#else
-    inline PlainObject
-#endif
-    cross(const MatrixBase<OtherDerived>& other) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    inline PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
-
-    EIGEN_DEVICE_FUNC
-    inline PlainObject unitOrthogonal(void) const;
-
-    EIGEN_DEVICE_FUNC
-    inline Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
-
-    // put this as separate enum value to work around possible GCC 4.3 bug (?)
-    enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits<Derived>::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical)
-                                          : ColsAtCompileTime==1 ? Vertical : Horizontal };
-    typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
-    EIGEN_DEVICE_FUNC
-    inline HomogeneousReturnType homogeneous() const;
-
-    enum {
-      SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
-    };
-    typedef Block<const Derived,
-                  internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
-                  internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
-    typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType;
-    EIGEN_DEVICE_FUNC
-    inline const HNormalizedReturnType hnormalized() const;
-
-////////// Householder module ///////////
-
-    EIGEN_DEVICE_FUNC
-    void makeHouseholderInPlace(Scalar& tau, RealScalar& beta);
-    template<typename EssentialPart>
-    EIGEN_DEVICE_FUNC
-    void makeHouseholder(EssentialPart& essential,
-                         Scalar& tau, RealScalar& beta) const;
-    template<typename EssentialPart>
-    EIGEN_DEVICE_FUNC
-    void applyHouseholderOnTheLeft(const EssentialPart& essential,
-                                   const Scalar& tau,
-                                   Scalar* workspace);
-    template<typename EssentialPart>
-    EIGEN_DEVICE_FUNC
-    void applyHouseholderOnTheRight(const EssentialPart& essential,
-                                    const Scalar& tau,
-                                    Scalar* workspace);
-
-///////// Jacobi module /////////
-
-    template<typename OtherScalar>
-    EIGEN_DEVICE_FUNC
-    void applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j);
-    template<typename OtherScalar>
-    EIGEN_DEVICE_FUNC
-    void applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j);
-
-///////// SparseCore module /////////
-
-    template<typename OtherDerived>
-    EIGEN_STRONG_INLINE const typename SparseMatrixBase<OtherDerived>::template CwiseProductDenseReturnType<Derived>::Type
-    cwiseProduct(const SparseMatrixBase<OtherDerived> &other) const
-    {
-      return other.cwiseProduct(derived());
-    }
-
-///////// MatrixFunctions module /////////
-
-    typedef typename internal::stem_function<Scalar>::type StemFunction;
-#define EIGEN_MATRIX_FUNCTION(ReturnType, Name, Description) \
-    /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
-    const ReturnType<Derived> Name() const;
-#define EIGEN_MATRIX_FUNCTION_1(ReturnType, Name, Description, Argument) \
-    /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
-    const ReturnType<Derived> Name(Argument) const;
-
-    EIGEN_MATRIX_FUNCTION(MatrixExponentialReturnValue, exp, exponential)
-    /** \brief Helper function for the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>.*/
-    const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine)
-#if EIGEN_HAS_CXX11_MATH
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, atanh, inverse hyperbolic cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, acosh, inverse hyperbolic cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, asinh, inverse hyperbolic sine)
-#endif
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine)
-    EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root)
-    EIGEN_MATRIX_FUNCTION(MatrixLogarithmReturnValue, log, logarithm)
-    EIGEN_MATRIX_FUNCTION_1(MatrixPowerReturnValue,        pow, power to \c p, const RealScalar& p)
-    EIGEN_MATRIX_FUNCTION_1(MatrixComplexPowerReturnValue, pow, power to \c p, const std::complex<RealScalar>& p)
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MatrixBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MatrixBase)
-
-  private:
-    EIGEN_DEVICE_FUNC explicit MatrixBase(int);
-    EIGEN_DEVICE_FUNC MatrixBase(int,int);
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&);
-  protected:
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator-=(const ArrayBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-};
-
-
-/***************************************************************************
-* Implementation of matrix base methods
-***************************************************************************/
-
-/** replaces \c *this by \c *this * \a other.
-  *
-  * \returns a reference to \c *this
-  *
-  * Example: \include MatrixBase_applyOnTheRight.cpp
-  * Output: \verbinclude MatrixBase_applyOnTheRight.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-inline Derived&
-MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
-{
-  other.derived().applyThisOnTheRight(derived());
-  return derived();
-}
-
-/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
-  *
-  * Example: \include MatrixBase_applyOnTheRight.cpp
-  * Output: \verbinclude MatrixBase_applyOnTheRight.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
-{
-  other.derived().applyThisOnTheRight(derived());
-}
-
-/** replaces \c *this by \a other * \c *this.
-  *
-  * Example: \include MatrixBase_applyOnTheLeft.cpp
-  * Output: \verbinclude MatrixBase_applyOnTheLeft.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
-{
-  other.derived().applyThisOnTheLeft(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIXBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NestByValue.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NestByValue.h
deleted file mode 100644
index b427576..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NestByValue.h
+++ /dev/null
@@ -1,85 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NESTBYVALUE_H
-#define EIGEN_NESTBYVALUE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType>
-{
-  enum {
-    Flags = traits<ExpressionType>::Flags & ~NestByRefBit
-  };
-};
-}
-
-/** \class NestByValue
-  * \ingroup Core_Module
-  *
-  * \brief Expression which must be nested by value
-  *
-  * \tparam ExpressionType the type of the object of which we are requiring nesting-by-value
-  *
-  * This class is the return type of MatrixBase::nestByValue()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::nestByValue()
-  */
-template<typename ExpressionType> class NestByValue
-  : public internal::dense_xpr_base< NestByValue<ExpressionType> >::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<NestByValue>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
-
-    EIGEN_DEVICE_FUNC explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-
-    EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
-
-    EIGEN_DEVICE_FUNC const ExpressionType& nestedExpression() const { return m_expression; }
-
-  protected:
-    const ExpressionType m_expression;
-};
-
-/** \returns an expression of the temporary version of *this.
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const NestByValue<Derived>
-DenseBase<Derived>::nestByValue() const
-{
-  return NestByValue<Derived>(derived());
-}
-
-namespace internal {
-
-// Evaluator of Solve -> eval into a temporary
-template<typename ArgType>
-struct evaluator<NestByValue<ArgType> >
-  : public evaluator<ArgType>
-{
-  typedef evaluator<ArgType> Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const NestByValue<ArgType>& xpr)
-    : Base(xpr.nestedExpression())
-  {}
-};
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_NESTBYVALUE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NoAlias.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NoAlias.h
deleted file mode 100644
index 570283d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NoAlias.h
+++ /dev/null
@@ -1,109 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NOALIAS_H
-#define EIGEN_NOALIAS_H
-
-namespace Eigen {
-
-/** \class NoAlias
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression providing an operator = assuming no aliasing
-  *
-  * \tparam ExpressionType the type of the object on which to do the lazy assignment
-  *
-  * This class represents an expression with special assignment operators
-  * assuming no aliasing between the target expression and the source expression.
-  * More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression.
-  * It is the return type of MatrixBase::noalias()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::noalias()
-  */
-template<typename ExpressionType, template <typename> class StorageBase>
-class NoAlias
-{
-  public:
-    typedef typename ExpressionType::Scalar Scalar;
-    
-    EIGEN_DEVICE_FUNC
-    explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
-    
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
-    {
-      call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-      return m_expression;
-    }
-    
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
-    {
-      call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-      return m_expression;
-    }
-    
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
-    {
-      call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-      return m_expression;
-    }
-
-    EIGEN_DEVICE_FUNC
-    ExpressionType& expression() const
-    {
-      return m_expression;
-    }
-
-  protected:
-    ExpressionType& m_expression;
-};
-
-/** \returns a pseudo expression of \c *this with an operator= assuming
-  * no aliasing between \c *this and the source expression.
-  *
-  * More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag.
-  * Currently, even though several expressions may alias, only product
-  * expressions have this flag. Therefore, noalias() is only useful when
-  * the source expression contains a matrix product.
-  *
-  * Here are some examples where noalias is useful:
-  * \code
-  * D.noalias()  = A * B;
-  * D.noalias() += A.transpose() * B;
-  * D.noalias() -= 2 * A * B.adjoint();
-  * \endcode
-  *
-  * On the other hand the following example will lead to a \b wrong result:
-  * \code
-  * A.noalias() = A * B;
-  * \endcode
-  * because the result matrix A is also an operand of the matrix product. Therefore,
-  * there is no alternative than evaluating A * B in a temporary, that is the default
-  * behavior when you write:
-  * \code
-  * A = A * B;
-  * \endcode
-  *
-  * \sa class NoAlias
-  */
-template<typename Derived>
-NoAlias<Derived,MatrixBase> EIGEN_DEVICE_FUNC MatrixBase<Derived>::noalias()
-{
-  return NoAlias<Derived, Eigen::MatrixBase >(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_NOALIAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NumTraits.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NumTraits.h
deleted file mode 100644
index 72eac5a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/NumTraits.h
+++ /dev/null
@@ -1,335 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NUMTRAITS_H
-#define EIGEN_NUMTRAITS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// default implementation of digits10(), based on numeric_limits if specialized,
-// 0 for integer types, and log10(epsilon()) otherwise.
-template< typename T,
-          bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
-          bool is_integer = NumTraits<T>::IsInteger>
-struct default_digits10_impl
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return std::numeric_limits<T>::digits10; }
-};
-
-template<typename T>
-struct default_digits10_impl<T,false,false> // Floating point
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() {
-    using std::log10;
-    using std::ceil;
-    typedef typename NumTraits<T>::Real Real;
-    return int(ceil(-log10(NumTraits<Real>::epsilon())));
-  }
-};
-
-template<typename T>
-struct default_digits10_impl<T,false,true> // Integer
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return 0; }
-};
-
-
-// default implementation of digits(), based on numeric_limits if specialized,
-// 0 for integer types, and log2(epsilon()) otherwise.
-template< typename T,
-          bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
-          bool is_integer = NumTraits<T>::IsInteger>
-struct default_digits_impl
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return std::numeric_limits<T>::digits; }
-};
-
-template<typename T>
-struct default_digits_impl<T,false,false> // Floating point
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() {
-    using std::log;
-    using std::ceil;
-    typedef typename NumTraits<T>::Real Real;
-    return int(ceil(-log(NumTraits<Real>::epsilon())/log(static_cast<Real>(2))));
-  }
-};
-
-template<typename T>
-struct default_digits_impl<T,false,true> // Integer
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return 0; }
-};
-
-} // end namespace internal
-
-namespace numext {
-/** \internal bit-wise cast without changing the underlying bit representation. */
-
-// TODO: Replace by std::bit_cast (available in C++20)
-template <typename Tgt, typename Src>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) {
-#if EIGEN_HAS_TYPE_TRAITS
-  // The behaviour of memcpy is not specified for non-trivially copyable types
-  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Src>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Tgt>::value && std::is_default_constructible<Tgt>::value,
-                      THIS_TYPE_IS_NOT_SUPPORTED);
-#endif
-
-  EIGEN_STATIC_ASSERT(sizeof(Src) == sizeof(Tgt), THIS_TYPE_IS_NOT_SUPPORTED);
-  Tgt tgt;
-  EIGEN_USING_STD(memcpy)
-  memcpy(&tgt, &src, sizeof(Tgt));
-  return tgt;
-}
-}  // namespace numext
-
-/** \class NumTraits
-  * \ingroup Core_Module
-  *
-  * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
-  *
-  * \tparam T the numeric type at hand
-  *
-  * This class stores enums, typedefs and static methods giving information about a numeric type.
-  *
-  * The provided data consists of:
-  * \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
-  *     then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
-  *     is a typedef to \a U.
-  * \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
-  *     such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
-  *     \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
-  *     take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
-  *     only intended as a helper for code that needs to explicitly promote types.
-  * \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
-  *     Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
-  * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
-  *     this means, just use \a T here.
-  * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
-  *     type, and to 0 otherwise.
-  * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int,
-  *     and to \c 0 otherwise.
-  * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed
-  *     to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers.
-  *     Stay vague here. No need to do architecture-specific stuff. If you don't know what this means, just use \c Eigen::HugeCost.
-  * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
-  * \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
-  *     be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
-  * \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">std::numeric_limits::epsilon()</a>,
-  *     it returns a \a Real instead of a \a T.
-  * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
-  *     value by the fuzzy comparison operators.
-  * \li highest() and lowest() functions returning the highest and lowest possible values respectively.
-  * \li digits() function returning the number of radix digits (non-sign digits for integers, mantissa for floating-point). This is
-  *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits">std::numeric_limits<T>::digits</a>
-  *     which is used as the default implementation if specialized.
-  * \li digits10() function returning the number of decimal digits that can be represented without change. This is
-  *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">std::numeric_limits<T>::digits10</a>
-  *     which is used as the default implementation if specialized.
-  * \li min_exponent() and max_exponent() functions returning the highest and lowest possible values, respectively,
-  *     such that the radix raised to the power exponent-1 is a normalized floating-point number.  These are equivalent to
-  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/min_exponent">std::numeric_limits<T>::min_exponent</a>/
-  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_exponent">std::numeric_limits<T>::max_exponent</a>.
-  * \li infinity() function returning a representation of positive infinity, if available.
-  * \li quiet_NaN function returning a non-signaling "not-a-number", if available.
-  */
-
-template<typename T> struct GenericNumTraits
-{
-  enum {
-    IsInteger = std::numeric_limits<T>::is_integer,
-    IsSigned = std::numeric_limits<T>::is_signed,
-    IsComplex = 0,
-    RequireInitialization = internal::is_arithmetic<T>::value ? 0 : 1,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-
-  typedef T Real;
-  typedef typename internal::conditional<
-                     IsInteger,
-                     typename internal::conditional<sizeof(T)<=2, float, double>::type,
-                     T
-                   >::type NonInteger;
-  typedef T Nested;
-  typedef T Literal;
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real epsilon()
-  {
-    return numext::numeric_limits<T>::epsilon();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int digits10()
-  {
-    return internal::default_digits10_impl<T>::run();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int digits()
-  {
-    return internal::default_digits_impl<T>::run();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int min_exponent()
-  {
-    return numext::numeric_limits<T>::min_exponent;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int max_exponent()
-  {
-    return numext::numeric_limits<T>::max_exponent;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real dummy_precision()
-  {
-    // make sure to override this for floating-point types
-    return Real(0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T highest() {
-    return (numext::numeric_limits<T>::max)();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T lowest()  {
-    return IsInteger ? (numext::numeric_limits<T>::min)()
-                     : static_cast<T>(-(numext::numeric_limits<T>::max)());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T infinity() {
-    return numext::numeric_limits<T>::infinity();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T quiet_NaN() {
-    return numext::numeric_limits<T>::quiet_NaN();
-  }
-};
-
-template<typename T> struct NumTraits : GenericNumTraits<T>
-{};
-
-template<> struct NumTraits<float>
-  : GenericNumTraits<float>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline float dummy_precision() { return 1e-5f; }
-};
-
-template<> struct NumTraits<double> : GenericNumTraits<double>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline double dummy_precision() { return 1e-12; }
-};
-
-template<> struct NumTraits<long double>
-  : GenericNumTraits<long double>
-{
-  EIGEN_CONSTEXPR
-  static inline long double dummy_precision() { return 1e-15l; }
-};
-
-template<typename _Real> struct NumTraits<std::complex<_Real> >
-  : GenericNumTraits<std::complex<_Real> >
-{
-  typedef _Real Real;
-  typedef typename NumTraits<_Real>::Literal Literal;
-  enum {
-    IsComplex = 1,
-    RequireInitialization = NumTraits<_Real>::RequireInitialization,
-    ReadCost = 2 * NumTraits<_Real>::ReadCost,
-    AddCost = 2 * NumTraits<Real>::AddCost,
-    MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real epsilon() { return NumTraits<Real>::epsilon(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int digits10() { return NumTraits<Real>::digits10(); }
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
-{
-  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> ArrayType;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Array<RealScalar, Rows, Cols, Options, MaxRows, MaxCols> Real;
-  typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
-  typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
-  typedef ArrayType & Nested;
-  typedef typename NumTraits<Scalar>::Literal Literal;
-
-  enum {
-    IsComplex = NumTraits<Scalar>::IsComplex,
-    IsInteger = NumTraits<Scalar>::IsInteger,
-    IsSigned  = NumTraits<Scalar>::IsSigned,
-    RequireInitialization = 1,
-    ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::ReadCost),
-    AddCost  = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::AddCost),
-    MulCost  = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::MulCost)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
-
-  EIGEN_CONSTEXPR
-  static inline int digits10() { return NumTraits<Scalar>::digits10(); }
-};
-
-template<> struct NumTraits<std::string>
-  : GenericNumTraits<std::string>
-{
-  enum {
-    RequireInitialization = 1,
-    ReadCost = HugeCost,
-    AddCost  = HugeCost,
-    MulCost  = HugeCost
-  };
-
-  EIGEN_CONSTEXPR
-  static inline int digits10() { return 0; }
-
-private:
-  static inline std::string epsilon();
-  static inline std::string dummy_precision();
-  static inline std::string lowest();
-  static inline std::string highest();
-  static inline std::string infinity();
-  static inline std::string quiet_NaN();
-};
-
-// Empty specialization for void to allow template specialization based on NumTraits<T>::Real with T==void and SFINAE.
-template<> struct NumTraits<void> {};
-
-template<> struct NumTraits<bool> : GenericNumTraits<bool> {};
-
-} // end namespace Eigen
-
-#endif // EIGEN_NUMTRAITS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PartialReduxEvaluator.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PartialReduxEvaluator.h
deleted file mode 100644
index 29abf35..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PartialReduxEvaluator.h
+++ /dev/null
@@ -1,232 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARTIALREDUX_H
-#define EIGEN_PARTIALREDUX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-/***************************************************************************
-*
-* This file provides evaluators for partial reductions.
-* There are two modes:
-*
-*  - scalar path: simply calls the respective function on the column or row.
-*    -> nothing special here, all the tricky part is handled by the return
-*       types of VectorwiseOp's members. They embed the functor calling the
-*       respective DenseBase's member function.
-*
-*  - vectorized path: implements a packet-wise reductions followed by
-*    some (optional) processing of the outcome, e.g., division by n for mean.
-*
-* For the vectorized path let's observe that the packet-size and outer-unrolling
-* are both decided by the assignement logic. So all we have to do is to decide
-* on the inner unrolling.
-*
-* For the unrolling, we can reuse "internal::redux_vec_unroller" from Redux.h,
-* but be need to be careful to specify correct increment.
-*
-***************************************************************************/
-
-
-/* logic deciding a strategy for unrolling of vectorized paths */
-template<typename Func, typename Evaluator>
-struct packetwise_redux_traits
-{
-  enum {
-    OuterSize = int(Evaluator::IsRowMajor) ? Evaluator::RowsAtCompileTime : Evaluator::ColsAtCompileTime,
-    Cost = OuterSize == Dynamic ? HugeCost
-         : OuterSize * Evaluator::CoeffReadCost + (OuterSize-1) * functor_traits<Func>::Cost,
-    Unrolling = Cost <= EIGEN_UNROLLING_LIMIT ? CompleteUnrolling : NoUnrolling
-  };
-
-};
-
-/* Value to be returned when size==0 , by default let's return 0 */
-template<typename PacketType,typename Func>
-EIGEN_DEVICE_FUNC
-PacketType packetwise_redux_empty_value(const Func& ) { return pset1<PacketType>(0); }
-
-/* For products the default is 1 */
-template<typename PacketType,typename Scalar>
-EIGEN_DEVICE_FUNC
-PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar,Scalar>& ) { return pset1<PacketType>(1); }
-
-/* Perform the actual reduction */
-template<typename Func, typename Evaluator,
-         int Unrolling = packetwise_redux_traits<Func, Evaluator>::Unrolling
->
-struct packetwise_redux_impl;
-
-/* Perform the actual reduction with unrolling */
-template<typename Func, typename Evaluator>
-struct packetwise_redux_impl<Func, Evaluator, CompleteUnrolling>
-{
-  typedef redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
-  typedef typename Evaluator::Scalar Scalar;
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  PacketType run(const Evaluator &eval, const Func& func, Index /*size*/)
-  {
-    return redux_vec_unroller<Func, Evaluator, 0, packetwise_redux_traits<Func, Evaluator>::OuterSize>::template run<PacketType>(eval,func);
-  }
-};
-
-/* Add a specialization of redux_vec_unroller for size==0 at compiletime.
- * This specialization is not required for general reductions, which is
- * why it is defined here.
- */
-template<typename Func, typename Evaluator, int Start>
-struct redux_vec_unroller<Func, Evaluator, Start, 0>
-{
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &, const Func& f)
-  {
-    return packetwise_redux_empty_value<PacketType>(f);
-  }
-};
-
-/* Perform the actual reduction for dynamic sizes */
-template<typename Func, typename Evaluator>
-struct packetwise_redux_impl<Func, Evaluator, NoUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static PacketType run(const Evaluator &eval, const Func& func, Index size)
-  {
-    if(size==0)
-      return packetwise_redux_empty_value<PacketType>(func);
-    
-    const Index size4 = (size-1)&(~3);
-    PacketType p = eval.template packetByOuterInner<Unaligned,PacketType>(0,0);
-    Index i = 1;
-    // This loop is optimized for instruction pipelining:
-    // - each iteration generates two independent instructions
-    // - thanks to branch prediction and out-of-order execution we have independent instructions across loops
-    for(; i<size4; i+=4)
-      p = func.packetOp(p,
-            func.packetOp(
-              func.packetOp(eval.template packetByOuterInner<Unaligned,PacketType>(i+0,0),eval.template packetByOuterInner<Unaligned,PacketType>(i+1,0)),
-              func.packetOp(eval.template packetByOuterInner<Unaligned,PacketType>(i+2,0),eval.template packetByOuterInner<Unaligned,PacketType>(i+3,0))));
-    for(; i<size; ++i)
-      p = func.packetOp(p, eval.template packetByOuterInner<Unaligned,PacketType>(i,0));
-    return p;
-  }
-};
-
-template< typename ArgType, typename MemberOp, int Direction>
-struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
-  : evaluator_base<PartialReduxExpr<ArgType, MemberOp, Direction> >
-{
-  typedef PartialReduxExpr<ArgType, MemberOp, Direction> XprType;
-  typedef typename internal::nested_eval<ArgType,1>::type ArgTypeNested;
-  typedef typename internal::add_const_on_value_type<ArgTypeNested>::type ConstArgTypeNested;
-  typedef typename internal::remove_all<ArgTypeNested>::type ArgTypeNestedCleaned;
-  typedef typename ArgType::Scalar InputScalar;
-  typedef typename XprType::Scalar Scalar;
-  enum {
-    TraversalSize = Direction==int(Vertical) ? int(ArgType::RowsAtCompileTime) :  int(ArgType::ColsAtCompileTime)
-  };
-  typedef typename MemberOp::template Cost<int(TraversalSize)> CostOpType;
-  enum {
-    CoeffReadCost = TraversalSize==Dynamic ? HugeCost
-                  : TraversalSize==0 ? 1
-                  : int(TraversalSize) * int(evaluator<ArgType>::CoeffReadCost) + int(CostOpType::value),
-    
-    _ArgFlags = evaluator<ArgType>::Flags,
-
-    _Vectorizable =  bool(int(_ArgFlags)&PacketAccessBit)
-                  && bool(MemberOp::Vectorizable)
-                  && (Direction==int(Vertical) ? bool(_ArgFlags&RowMajorBit) : (_ArgFlags&RowMajorBit)==0)
-                  && (TraversalSize!=0),
-                  
-    Flags = (traits<XprType>::Flags&RowMajorBit)
-          | (evaluator<ArgType>::Flags&(HereditaryBits&(~RowMajorBit)))
-          | (_Vectorizable ? PacketAccessBit : 0)
-          | LinearAccessBit,
-    
-    Alignment = 0 // FIXME this will need to be improved once PartialReduxExpr is vectorized
-  };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType xpr)
-    : m_arg(xpr.nestedExpression()), m_functor(xpr.functor())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(TraversalSize==Dynamic ? HugeCost : (TraversalSize==0 ? 1 : int(CostOpType::value)));
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar coeff(Index i, Index j) const
-  {
-    return coeff(Direction==Vertical ? j : i);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar coeff(Index index) const
-  {
-    return m_functor(m_arg.template subVector<DirectionType(Direction)>(index));
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketType packet(Index i, Index j) const
-  {
-    return packet<LoadMode,PacketType>(Direction==Vertical ? j : i);
-  }
-  
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-  PacketType packet(Index idx) const
-  {
-    enum { PacketSize = internal::unpacket_traits<PacketType>::size };
-    typedef Block<const ArgTypeNestedCleaned,
-                  Direction==Vertical ? int(ArgType::RowsAtCompileTime) : int(PacketSize),
-                  Direction==Vertical ? int(PacketSize) : int(ArgType::ColsAtCompileTime),
-                  true /* InnerPanel */> PanelType;
-    
-    PanelType panel(m_arg,
-                    Direction==Vertical ? 0 : idx,
-                    Direction==Vertical ? idx : 0,
-                    Direction==Vertical ? m_arg.rows() : Index(PacketSize),
-                    Direction==Vertical ? Index(PacketSize) : m_arg.cols());
-
-    // FIXME
-    // See bug 1612, currently if PacketSize==1 (i.e. complex<double> with 128bits registers) then the storage-order of panel get reversed
-    // and methods like packetByOuterInner do not make sense anymore in this context.
-    // So let's just by pass "vectorization" in this case:
-    if(PacketSize==1)
-      return internal::pset1<PacketType>(coeff(idx));
-    
-    typedef typename internal::redux_evaluator<PanelType> PanelEvaluator;
-    PanelEvaluator panel_eval(panel);
-    typedef typename MemberOp::BinaryOp BinaryOp;
-    PacketType p = internal::packetwise_redux_impl<BinaryOp,PanelEvaluator>::template run<PacketType>(panel_eval,m_functor.binaryFunc(),m_arg.outerSize());
-    return p;
-  }
-
-protected:
-  ConstArgTypeNested m_arg;
-  const MemberOp m_functor;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIALREDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PermutationMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PermutationMatrix.h
deleted file mode 100644
index 69401bf..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PermutationMatrix.h
+++ /dev/null
@@ -1,605 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PERMUTATIONMATRIX_H
-#define EIGEN_PERMUTATIONMATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-enum PermPermProduct_t {PermPermProduct};
-
-} // end namespace internal
-
-/** \class PermutationBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for permutations
-  *
-  * \tparam Derived the derived class
-  *
-  * This class is the base class for all expressions representing a permutation matrix,
-  * internally stored as a vector of integers.
-  * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix
-  * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have:
-  *  \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f]
-  * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have:
-  *  \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f]
-  *
-  * Permutation matrices are square and invertible.
-  *
-  * Notice that in addition to the member functions and operators listed here, there also are non-member
-  * operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase)
-  * on either side.
-  *
-  * \sa class PermutationMatrix, class PermutationWrapper
-  */
-template<typename Derived>
-class PermutationBase : public EigenBase<Derived>
-{
-    typedef internal::traits<Derived> Traits;
-    typedef EigenBase<Derived> Base;
-  public:
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    enum {
-      Flags = Traits::Flags,
-      RowsAtCompileTime = Traits::RowsAtCompileTime,
-      ColsAtCompileTime = Traits::ColsAtCompileTime,
-      MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
-    };
-    typedef typename Traits::StorageIndex StorageIndex;
-    typedef Matrix<StorageIndex,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
-            DenseMatrixType;
-    typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,StorageIndex>
-            PlainPermutationType;
-    typedef PlainPermutationType PlainObject;
-    using Base::derived;
-    typedef Inverse<Derived> InverseReturnType;
-    typedef void Scalar;
-    #endif
-
-    /** Copies the other permutation into *this */
-    template<typename OtherDerived>
-    Derived& operator=(const PermutationBase<OtherDerived>& other)
-    {
-      indices() = other.indices();
-      return derived();
-    }
-
-    /** Assignment from the Transpositions \a tr */
-    template<typename OtherDerived>
-    Derived& operator=(const TranspositionsBase<OtherDerived>& tr)
-    {
-      setIdentity(tr.size());
-      for(Index k=size()-1; k>=0; --k)
-        applyTranspositionOnTheRight(k,tr.coeff(k));
-      return derived();
-    }
-
-    /** \returns the number of rows */
-    inline EIGEN_DEVICE_FUNC Index rows() const { return Index(indices().size()); }
-
-    /** \returns the number of columns */
-    inline EIGEN_DEVICE_FUNC Index cols() const { return Index(indices().size()); }
-
-    /** \returns the size of a side of the respective square matrix, i.e., the number of indices */
-    inline EIGEN_DEVICE_FUNC Index size() const { return Index(indices().size()); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename DenseDerived>
-    void evalTo(MatrixBase<DenseDerived>& other) const
-    {
-      other.setZero();
-      for (Index i=0; i<rows(); ++i)
-        other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
-    }
-    #endif
-
-    /** \returns a Matrix object initialized from this permutation matrix. Notice that it
-      * is inefficient to return this Matrix object by value. For efficiency, favor using
-      * the Matrix constructor taking EigenBase objects.
-      */
-    DenseMatrixType toDenseMatrix() const
-    {
-      return derived();
-    }
-
-    /** const version of indices(). */
-    const IndicesType& indices() const { return derived().indices(); }
-    /** \returns a reference to the stored array representing the permutation. */
-    IndicesType& indices() { return derived().indices(); }
-
-    /** Resizes to given size.
-      */
-    inline void resize(Index newSize)
-    {
-      indices().resize(newSize);
-    }
-
-    /** Sets *this to be the identity permutation matrix */
-    void setIdentity()
-    {
-      StorageIndex n = StorageIndex(size());
-      for(StorageIndex i = 0; i < n; ++i)
-        indices().coeffRef(i) = i;
-    }
-
-    /** Sets *this to be the identity permutation matrix of given size.
-      */
-    void setIdentity(Index newSize)
-    {
-      resize(newSize);
-      setIdentity();
-    }
-
-    /** Multiplies *this by the transposition \f$(ij)\f$ on the left.
-      *
-      * \returns a reference to *this.
-      *
-      * \warning This is much slower than applyTranspositionOnTheRight(Index,Index):
-      * this has linear complexity and requires a lot of branching.
-      *
-      * \sa applyTranspositionOnTheRight(Index,Index)
-      */
-    Derived& applyTranspositionOnTheLeft(Index i, Index j)
-    {
-      eigen_assert(i>=0 && j>=0 && i<size() && j<size());
-      for(Index k = 0; k < size(); ++k)
-      {
-        if(indices().coeff(k) == i) indices().coeffRef(k) = StorageIndex(j);
-        else if(indices().coeff(k) == j) indices().coeffRef(k) = StorageIndex(i);
-      }
-      return derived();
-    }
-
-    /** Multiplies *this by the transposition \f$(ij)\f$ on the right.
-      *
-      * \returns a reference to *this.
-      *
-      * This is a fast operation, it only consists in swapping two indices.
-      *
-      * \sa applyTranspositionOnTheLeft(Index,Index)
-      */
-    Derived& applyTranspositionOnTheRight(Index i, Index j)
-    {
-      eigen_assert(i>=0 && j>=0 && i<size() && j<size());
-      std::swap(indices().coeffRef(i), indices().coeffRef(j));
-      return derived();
-    }
-
-    /** \returns the inverse permutation matrix.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    inline InverseReturnType inverse() const
-    { return InverseReturnType(derived()); }
-    /** \returns the tranpose permutation matrix.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    inline InverseReturnType transpose() const
-    { return InverseReturnType(derived()); }
-
-    /**** multiplication helpers to hopefully get RVO ****/
-
-  
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  protected:
-    template<typename OtherDerived>
-    void assignTranspose(const PermutationBase<OtherDerived>& other)
-    {
-      for (Index i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i;
-    }
-    template<typename Lhs,typename Rhs>
-    void assignProduct(const Lhs& lhs, const Rhs& rhs)
-    {
-      eigen_assert(lhs.cols() == rhs.rows());
-      for (Index i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
-    }
-#endif
-
-  public:
-
-    /** \returns the product permutation matrix.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    template<typename Other>
-    inline PlainPermutationType operator*(const PermutationBase<Other>& other) const
-    { return PlainPermutationType(internal::PermPermProduct, derived(), other.derived()); }
-
-    /** \returns the product of a permutation with another inverse permutation.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    template<typename Other>
-    inline PlainPermutationType operator*(const InverseImpl<Other,PermutationStorage>& other) const
-    { return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); }
-
-    /** \returns the product of an inverse permutation with another permutation.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    template<typename Other> friend
-    inline PlainPermutationType operator*(const InverseImpl<Other, PermutationStorage>& other, const PermutationBase& perm)
-    { return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); }
-    
-    /** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the permutation.
-      *
-      * This function is O(\c n) procedure allocating a buffer of \c n booleans.
-      */
-    Index determinant() const
-    {
-      Index res = 1;
-      Index n = size();
-      Matrix<bool,RowsAtCompileTime,1,0,MaxRowsAtCompileTime> mask(n);
-      mask.fill(false);
-      Index r = 0;
-      while(r < n)
-      {
-        // search for the next seed
-        while(r<n && mask[r]) r++;
-        if(r>=n)
-          break;
-        // we got one, let's follow it until we are back to the seed
-        Index k0 = r++;
-        mask.coeffRef(k0) = true;
-        for(Index k=indices().coeff(k0); k!=k0; k=indices().coeff(k))
-        {
-          mask.coeffRef(k) = true;
-          res = -res;
-        }
-      }
-      return res;
-    }
-
-  protected:
-
-};
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
- : traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  typedef PermutationStorage StorageKind;
-  typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
-  typedef _StorageIndex StorageIndex;
-  typedef void Scalar;
-};
-}
-
-/** \class PermutationMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Permutation matrix
-  *
-  * \tparam SizeAtCompileTime the number of rows/cols, or Dynamic
-  * \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
-  * \tparam _StorageIndex the integer type of the indices
-  *
-  * This class represents a permutation matrix, internally stored as a vector of integers.
-  *
-  * \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
-  */
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
-{
-    typedef PermutationBase<PermutationMatrix> Base;
-    typedef internal::traits<PermutationMatrix> Traits;
-  public:
-
-    typedef const PermutationMatrix& Nested;
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename Traits::StorageIndex StorageIndex;
-    #endif
-
-    inline PermutationMatrix()
-    {}
-
-    /** Constructs an uninitialized permutation matrix of given size.
-      */
-    explicit inline PermutationMatrix(Index size) : m_indices(size)
-    {
-      eigen_internal_assert(size <= NumTraits<StorageIndex>::highest());
-    }
-
-    /** Copy constructor. */
-    template<typename OtherDerived>
-    inline PermutationMatrix(const PermutationBase<OtherDerived>& other)
-      : m_indices(other.indices()) {}
-
-    /** Generic constructor from expression of the indices. The indices
-      * array has the meaning that the permutations sends each integer i to indices[i].
-      *
-      * \warning It is your responsibility to check that the indices array that you passes actually
-      * describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the
-      * array's size.
-      */
-    template<typename Other>
-    explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
-    {}
-
-    /** Convert the Transpositions \a tr to a permutation matrix */
-    template<typename Other>
-    explicit PermutationMatrix(const TranspositionsBase<Other>& tr)
-      : m_indices(tr.size())
-    {
-      *this = tr;
-    }
-
-    /** Copies the other permutation into *this */
-    template<typename Other>
-    PermutationMatrix& operator=(const PermutationBase<Other>& other)
-    {
-      m_indices = other.indices();
-      return *this;
-    }
-
-    /** Assignment from the Transpositions \a tr */
-    template<typename Other>
-    PermutationMatrix& operator=(const TranspositionsBase<Other>& tr)
-    {
-      return Base::operator=(tr.derived());
-    }
-
-    /** const version of indices(). */
-    const IndicesType& indices() const { return m_indices; }
-    /** \returns a reference to the stored array representing the permutation. */
-    IndicesType& indices() { return m_indices; }
-
-
-    /**** multiplication helpers to hopefully get RVO ****/
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename Other>
-    PermutationMatrix(const InverseImpl<Other,PermutationStorage>& other)
-      : m_indices(other.derived().nestedExpression().size())
-    {
-      eigen_internal_assert(m_indices.size() <= NumTraits<StorageIndex>::highest());
-      StorageIndex end = StorageIndex(m_indices.size());
-      for (StorageIndex i=0; i<end;++i)
-        m_indices.coeffRef(other.derived().nestedExpression().indices().coeff(i)) = i;
-    }
-    template<typename Lhs,typename Rhs>
-    PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
-      : m_indices(lhs.indices().size())
-    {
-      Base::assignProduct(lhs,rhs);
-    }
-#endif
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
-struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
- : traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  typedef PermutationStorage StorageKind;
-  typedef Map<const Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
-  typedef _StorageIndex StorageIndex;
-  typedef void Scalar;
-};
-}
-
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
-class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess>
-  : public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
-{
-    typedef PermutationBase<Map> Base;
-    typedef internal::traits<Map> Traits;
-  public:
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-    #endif
-
-    inline Map(const StorageIndex* indicesPtr)
-      : m_indices(indicesPtr)
-    {}
-
-    inline Map(const StorageIndex* indicesPtr, Index size)
-      : m_indices(indicesPtr,size)
-    {}
-
-    /** Copies the other permutation into *this */
-    template<typename Other>
-    Map& operator=(const PermutationBase<Other>& other)
-    { return Base::operator=(other.derived()); }
-
-    /** Assignment from the Transpositions \a tr */
-    template<typename Other>
-    Map& operator=(const TranspositionsBase<Other>& tr)
-    { return Base::operator=(tr.derived()); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    Map& operator=(const Map& other)
-    {
-      m_indices = other.m_indices;
-      return *this;
-    }
-    #endif
-
-    /** const version of indices(). */
-    const IndicesType& indices() const { return m_indices; }
-    /** \returns a reference to the stored array representing the permutation. */
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-template<typename _IndicesType> class TranspositionsWrapper;
-namespace internal {
-template<typename _IndicesType>
-struct traits<PermutationWrapper<_IndicesType> >
-{
-  typedef PermutationStorage StorageKind;
-  typedef void Scalar;
-  typedef typename _IndicesType::Scalar StorageIndex;
-  typedef _IndicesType IndicesType;
-  enum {
-    RowsAtCompileTime = _IndicesType::SizeAtCompileTime,
-    ColsAtCompileTime = _IndicesType::SizeAtCompileTime,
-    MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
-    MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
-    Flags = 0
-  };
-};
-}
-
-/** \class PermutationWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Class to view a vector of integers as a permutation matrix
-  *
-  * \tparam _IndicesType the type of the vector of integer (can be any compatible expression)
-  *
-  * This class allows to view any vector expression of integers as a permutation matrix.
-  *
-  * \sa class PermutationBase, class PermutationMatrix
-  */
-template<typename _IndicesType>
-class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesType> >
-{
-    typedef PermutationBase<PermutationWrapper> Base;
-    typedef internal::traits<PermutationWrapper> Traits;
-  public:
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    #endif
-
-    inline PermutationWrapper(const IndicesType& indices)
-      : m_indices(indices)
-    {}
-
-    /** const version of indices(). */
-    const typename internal::remove_all<typename IndicesType::Nested>::type&
-    indices() const { return m_indices; }
-
-  protected:
-
-    typename IndicesType::Nested m_indices;
-};
-
-
-/** \returns the matrix with the permutation applied to the columns.
-  */
-template<typename MatrixDerived, typename PermutationDerived>
-EIGEN_DEVICE_FUNC
-const Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
-operator*(const MatrixBase<MatrixDerived> &matrix,
-          const PermutationBase<PermutationDerived>& permutation)
-{
-  return Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
-            (matrix.derived(), permutation.derived());
-}
-
-/** \returns the matrix with the permutation applied to the rows.
-  */
-template<typename PermutationDerived, typename MatrixDerived>
-EIGEN_DEVICE_FUNC
-const Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
-operator*(const PermutationBase<PermutationDerived> &permutation,
-          const MatrixBase<MatrixDerived>& matrix)
-{
-  return Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
-            (permutation.derived(), matrix.derived());
-}
-
-
-template<typename PermutationType>
-class InverseImpl<PermutationType, PermutationStorage>
-  : public EigenBase<Inverse<PermutationType> >
-{
-    typedef typename PermutationType::PlainPermutationType PlainPermutationType;
-    typedef internal::traits<PermutationType> PermTraits;
-  protected:
-    InverseImpl() {}
-  public:
-    typedef Inverse<PermutationType> InverseType;
-    using EigenBase<Inverse<PermutationType> >::derived;
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename PermutationType::DenseMatrixType DenseMatrixType;
-    enum {
-      RowsAtCompileTime = PermTraits::RowsAtCompileTime,
-      ColsAtCompileTime = PermTraits::ColsAtCompileTime,
-      MaxRowsAtCompileTime = PermTraits::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = PermTraits::MaxColsAtCompileTime
-    };
-    #endif
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename DenseDerived>
-    void evalTo(MatrixBase<DenseDerived>& other) const
-    {
-      other.setZero();
-      for (Index i=0; i<derived().rows();++i)
-        other.coeffRef(i, derived().nestedExpression().indices().coeff(i)) = typename DenseDerived::Scalar(1);
-    }
-    #endif
-
-    /** \return the equivalent permutation matrix */
-    PlainPermutationType eval() const { return derived(); }
-
-    DenseMatrixType toDenseMatrix() const { return derived(); }
-
-    /** \returns the matrix with the inverse permutation applied to the columns.
-      */
-    template<typename OtherDerived> friend
-    const Product<OtherDerived, InverseType, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix, const InverseType& trPerm)
-    {
-      return Product<OtherDerived, InverseType, AliasFreeProduct>(matrix.derived(), trPerm.derived());
-    }
-
-    /** \returns the matrix with the inverse permutation applied to the rows.
-      */
-    template<typename OtherDerived>
-    const Product<InverseType, OtherDerived, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix) const
-    {
-      return Product<InverseType, OtherDerived, AliasFreeProduct>(derived(), matrix.derived());
-    }
-};
-
-template<typename Derived>
-const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() const
-{
-  return derived();
-}
-
-namespace internal {
-
-template<> struct AssignmentKind<DenseShape,PermutationShape> { typedef EigenBase2EigenBase Kind; };
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PERMUTATIONMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PlainObjectBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PlainObjectBase.h
deleted file mode 100644
index e2ddbd1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/PlainObjectBase.h
+++ /dev/null
@@ -1,1128 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DENSESTORAGEBASE_H
-#define EIGEN_DENSESTORAGEBASE_H
-
-#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO)
-# define EIGEN_INITIALIZE_COEFFS
-# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(Index i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
-#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN)
-# define EIGEN_INITIALIZE_COEFFS
-# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(Index i=0;i<base().size();++i) coeffRef(i)=std::numeric_limits<Scalar>::quiet_NaN();
-#else
-# undef EIGEN_INITIALIZE_COEFFS
-# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
-  template<typename Index>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE void run(Index, Index)
-  {
-  }
-};
-
-template<> struct check_rows_cols_for_overflow<Dynamic> {
-  template<typename Index>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols)
-  {
-    // http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
-    // we assume Index is signed
-    Index max_index = (std::size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
-    bool error = (rows == 0 || cols == 0) ? false
-               : (rows > max_index / cols);
-    if (error)
-      throw_std_bad_alloc();
-  }
-};
-
-template <typename Derived,
-          typename OtherDerived = Derived,
-          bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)>
-struct conservative_resize_like_impl;
-
-template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
-
-} // end namespace internal
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-namespace doxygen {
-
-// This is a workaround to doxygen not being able to understand the inheritance logic
-// when it is hidden by the dense_xpr_base helper struct.
-// Moreover, doxygen fails to include members that are not documented in the declaration body of
-// MatrixBase if we inherits MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >,
-// this is why we simply inherits MatrixBase, though this does not make sense.
-
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename Derived> struct dense_xpr_base_dispatcher;
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-    : public MatrixBase {};
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-    : public ArrayBase {};
-
-} // namespace doxygen
-
-/** \class PlainObjectBase
-  * \ingroup Core_Module
-  * \brief %Dense storage base class for matrices and arrays.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
-  *
-  * \tparam Derived is the derived type, e.g., a Matrix or Array
-  *
-  * \sa \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher<Derived>
-#else
-template<typename Derived>
-class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
-#endif
-{
-  public:
-    enum { Options = internal::traits<Derived>::Options };
-    typedef typename internal::dense_xpr_base<Derived>::type Base;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Derived DenseType;
-
-    using Base::RowsAtCompileTime;
-    using Base::ColsAtCompileTime;
-    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-
-    typedef Eigen::Map<Derived, Unaligned>  MapType;
-    typedef const Eigen::Map<const Derived, Unaligned> ConstMapType;
-    typedef Eigen::Map<Derived, AlignedMax> AlignedMapType;
-    typedef const Eigen::Map<const Derived, AlignedMax> ConstAlignedMapType;
-    template<typename StrideType> struct StridedMapType { typedef Eigen::Map<Derived, Unaligned, StrideType> type; };
-    template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; };
-    template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, AlignedMax, StrideType> type; };
-    template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, AlignedMax, StrideType> type; };
-
-  protected:
-    DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
-
-  public:
-    enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits<Derived>::Alignment>0) };
-    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
-
-    EIGEN_DEVICE_FUNC
-    Base& base() { return *static_cast<Base*>(this); }
-    EIGEN_DEVICE_FUNC
-    const Base& base() const { return *static_cast<const Base*>(this); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_storage.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_storage.cols(); }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index,Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
-    {
-      if(Flags & RowMajorBit)
-        return m_storage.data()[colId + rowId * m_storage.cols()];
-      else // column-major
-        return m_storage.data()[rowId + colId * m_storage.rows()];
-    }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
-    {
-      return m_storage.data()[index];
-    }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
-    {
-      if(Flags & RowMajorBit)
-        return m_storage.data()[colId + rowId * m_storage.cols()];
-      else // column-major
-        return m_storage.data()[rowId + colId * m_storage.rows()];
-    }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
-    {
-      return m_storage.data()[index];
-    }
-
-    /** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
-      * It is provided for convenience. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      if(Flags & RowMajorBit)
-        return m_storage.data()[colId + rowId * m_storage.cols()];
-      else // column-major
-        return m_storage.data()[rowId + colId * m_storage.rows()];
-    }
-
-    /** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
-      * It is provided for convenience. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
-    {
-      return m_storage.data()[index];
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
-    {
-      return internal::ploadt<PacketScalar, LoadMode>
-               (m_storage.data() + (Flags & RowMajorBit
-                                   ? colId + rowId * m_storage.cols()
-                                   : rowId + colId * m_storage.rows()));
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
-    {
-      return internal::ploadt<PacketScalar, LoadMode>(m_storage.data() + index);
-    }
-
-    /** \internal */
-    template<int StoreMode>
-    EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val)
-    {
-      internal::pstoret<Scalar, PacketScalar, StoreMode>
-              (m_storage.data() + (Flags & RowMajorBit
-                                   ? colId + rowId * m_storage.cols()
-                                   : rowId + colId * m_storage.rows()), val);
-    }
-
-    /** \internal */
-    template<int StoreMode>
-    EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val)
-    {
-      internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val);
-    }
-
-    /** \returns a const pointer to the data array of this matrix */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const
-    { return m_storage.data(); }
-
-    /** \returns a pointer to the data array of this matrix */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data()
-    { return m_storage.data(); }
-
-    /** Resizes \c *this to a \a rows x \a cols matrix.
-      *
-      * This method is intended for dynamic-size matrices, although it is legal to call it on any
-      * matrix as long as fixed dimensions are left unchanged. If you only want to change the number
-      * of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t).
-      *
-      * If the current number of coefficients of \c *this exactly matches the
-      * product \a rows * \a cols, then no memory allocation is performed and
-      * the current values are left unchanged. In all other cases, including
-      * shrinking, the data is reallocated and all previous values are lost.
-      *
-      * Example: \include Matrix_resize_int_int.cpp
-      * Output: \verbinclude Matrix_resize_int_int.out
-      *
-      * \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
-    {
-      eigen_assert(   EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime)
-                   && EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime)
-                   && EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime)
-                   && EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime)
-                   && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
-      internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
-      #ifdef EIGEN_INITIALIZE_COEFFS
-        Index size = rows*cols;
-        bool size_changed = size != this->size();
-        m_storage.resize(size, rows, cols);
-        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-      #else
-        m_storage.resize(rows*cols, rows, cols);
-      #endif
-    }
-
-    /** Resizes \c *this to a vector of length \a size
-      *
-      * \only_for_vectors. This method does not work for
-      * partially dynamic matrices when the static dimension is anything other
-      * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
-      *
-      * Example: \include Matrix_resize_int.cpp
-      * Output: \verbinclude Matrix_resize_int.out
-      *
-      * \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
-      */
-    EIGEN_DEVICE_FUNC
-    inline void resize(Index size)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
-      eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0);
-      #ifdef EIGEN_INITIALIZE_COEFFS
-        bool size_changed = size != this->size();
-      #endif
-      if(RowsAtCompileTime == 1)
-        m_storage.resize(size, 1, size);
-      else
-        m_storage.resize(size, size, 1);
-      #ifdef EIGEN_INITIALIZE_COEFFS
-        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-      #endif
-    }
-
-    /** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the special value \c NoChange
-      * as in the example below.
-      *
-      * Example: \include Matrix_resize_NoChange_int.cpp
-      * Output: \verbinclude Matrix_resize_NoChange_int.out
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC
-    inline void resize(NoChange_t, Index cols)
-    {
-      resize(rows(), cols);
-    }
-
-    /** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
-      * as in the example below.
-      *
-      * Example: \include Matrix_resize_int_NoChange.cpp
-      * Output: \verbinclude Matrix_resize_int_NoChange.out
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC
-    inline void resize(Index rows, NoChange_t)
-    {
-      resize(rows, cols());
-    }
-
-    /** Resizes \c *this to have the same dimensions as \a other.
-      * Takes care of doing all the checking that's needed.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
-    {
-      const OtherDerived& other = _other.derived();
-      internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.rows(), other.cols());
-      const Index othersize = other.rows()*other.cols();
-      if(RowsAtCompileTime == 1)
-      {
-        eigen_assert(other.rows() == 1 || other.cols() == 1);
-        resize(1, othersize);
-      }
-      else if(ColsAtCompileTime == 1)
-      {
-        eigen_assert(other.rows() == 1 || other.cols() == 1);
-        resize(othersize, 1);
-      }
-      else resize(other.rows(), other.cols());
-    }
-
-    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
-      *
-      * The method is intended for matrices of dynamic size. If you only want to change the number
-      * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
-      * conservativeResize(Index, NoChange_t).
-      *
-      * Matrices are resized relative to the top-left element. In case values need to be
-      * appended to the matrix they will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
-    {
-      internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
-    }
-
-    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
-      *
-      * As opposed to conservativeResize(Index rows, Index cols), this version leaves
-      * the number of columns unchanged.
-      *
-      * In case the matrix is growing, new rows will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
-    {
-      // Note: see the comment in conservativeResize(Index,Index)
-      conservativeResize(rows, cols());
-    }
-
-    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
-      *
-      * As opposed to conservativeResize(Index rows, Index cols), this version leaves
-      * the number of rows unchanged.
-      *
-      * In case the matrix is growing, new columns will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
-    {
-      // Note: see the comment in conservativeResize(Index,Index)
-      conservativeResize(rows(), cols);
-    }
-
-    /** Resizes the vector to \a size while retaining old values.
-      *
-      * \only_for_vectors. This method does not work for
-      * partially dynamic matrices when the static dimension is anything other
-      * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
-      *
-      * When values are appended, they will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(Index size)
-    {
-      internal::conservative_resize_like_impl<Derived>::run(*this, size);
-    }
-
-    /** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched.
-      *
-      * The method is intended for matrices of dynamic size. If you only want to change the number
-      * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
-      * conservativeResize(Index, NoChange_t).
-      *
-      * Matrices are resized relative to the top-left element. In case values need to be
-      * appended to the matrix they will copied from \c other.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other)
-    {
-      internal::conservative_resize_like_impl<Derived,OtherDerived>::run(*this, other);
-    }
-
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other)
-    {
-      return _set(other);
-    }
-
-    /** \sa MatrixBase::lazyAssign() */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other)
-    {
-      _resize_to_match(other);
-      return Base::lazyAssign(other.derived());
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func)
-    {
-      resize(func.rows(), func.cols());
-      return Base::operator=(func);
-    }
-
-    // Prevent user from trying to instantiate PlainObjectBase objects
-    // by making all its constructor protected. See bug 1074.
-  protected:
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
-    {
-//       _check_template_params();
-//       EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    // FIXME is it still needed ?
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert)
-      : m_storage(internal::constructor_without_unaligned_array_assert())
-    {
-//       _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-#endif
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
-      : m_storage( std::move(other.m_storage) )
-    {
-    }
-
-    EIGEN_DEVICE_FUNC
-    PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
-    {
-      _check_template_params();
-      m_storage = std::move(other.m_storage);
-      return *this;
-    }
-#endif
-
-    /** Copy constructor */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other)
-      : Base(), m_storage(other.m_storage) { }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
-      : m_storage(size, rows, cols)
-    {
-//       _check_template_params();
-//       EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-    #if EIGEN_HAS_CXX11
-    /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients. \cpp11
-      *
-      * \only_for_vectors
-      *
-      * This constructor is for 1D array or vectors with more than 4 coefficients.
-      * There exists C++98 analogue constructors for fixed-size array/vector having 1, 2, 3, or 4 coefficients.
-      *
-      * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
-      * constructor must match the the fixed number of rows (resp. columns) of \c *this.
-      */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
-      : m_storage()
-    {
-      _check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, sizeof...(args) + 4);
-      m_storage.data()[0] = a0;
-      m_storage.data()[1] = a1;
-      m_storage.data()[2] = a2;
-      m_storage.data()[3] = a3;
-      Index i = 4;
-      auto x = {(m_storage.data()[i++] = args, 0)...};
-      static_cast<void>(x);
-    }
-
-    /** \brief Constructs a Matrix or Array and initializes it by elements given by an initializer list of initializer
-      * lists \cpp11
-      */
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE PlainObjectBase(const std::initializer_list<std::initializer_list<Scalar>>& list)
-      : m_storage()
-    {
-      _check_template_params();
-
-      size_t list_size = 0;
-      if (list.begin() != list.end()) {
-        list_size = list.begin()->size();
-      }
-
-      // This is to allow syntax like VectorXi {{1, 2, 3, 4}}
-      if (ColsAtCompileTime == 1 && list.size() == 1) {
-        eigen_assert(list_size == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
-        resize(list_size, ColsAtCompileTime);
-        std::copy(list.begin()->begin(), list.begin()->end(), m_storage.data());
-      } else {
-        eigen_assert(list.size() == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
-        eigen_assert(list_size == static_cast<size_t>(ColsAtCompileTime) || ColsAtCompileTime == Dynamic);
-        resize(list.size(), list_size);
-
-        Index row_index = 0;
-        for (const std::initializer_list<Scalar>& row : list) {
-          eigen_assert(list_size == row.size());
-          Index col_index = 0;
-          for (const Scalar& e : row) {
-            coeffRef(row_index, col_index) = e;
-            ++col_index;
-          }
-          ++row_index;
-        }
-      }
-    }
-    #endif  // end EIGEN_HAS_CXX11
-
-    /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
-      : m_storage()
-    {
-      _check_template_params();
-      resizeLike(other);
-      _set_noalias(other);
-    }
-
-    /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
-      : m_storage()
-    {
-      _check_template_params();
-      resizeLike(other);
-      *this = other.derived();
-    }
-    /** \brief Copy constructor with in-place evaluation */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue<OtherDerived>& other)
-    {
-      _check_template_params();
-      // FIXME this does not automatically transpose vectors if necessary
-      resize(other.rows(), other.cols());
-      other.evalTo(this->derived());
-    }
-
-  public:
-
-    /** \brief Copies the generic expression \a other into *this.
-      * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
-    {
-      _resize_to_match(other);
-      Base::operator=(other.derived());
-      return this->derived();
-    }
-
-    /** \name Map
-      * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
-      * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
-      * \a data pointers.
-      *
-      * Here is an example using strides:
-      * \include Matrix_Map_stride.cpp
-      * Output: \verbinclude Matrix_Map_stride.out
-      *
-      * \see class Map
-      */
-    //@{
-    static inline ConstMapType Map(const Scalar* data)
-    { return ConstMapType(data); }
-    static inline MapType Map(Scalar* data)
-    { return MapType(data); }
-    static inline ConstMapType Map(const Scalar* data, Index size)
-    { return ConstMapType(data, size); }
-    static inline MapType Map(Scalar* data, Index size)
-    { return MapType(data, size); }
-    static inline ConstMapType Map(const Scalar* data, Index rows, Index cols)
-    { return ConstMapType(data, rows, cols); }
-    static inline MapType Map(Scalar* data, Index rows, Index cols)
-    { return MapType(data, rows, cols); }
-
-    static inline ConstAlignedMapType MapAligned(const Scalar* data)
-    { return ConstAlignedMapType(data); }
-    static inline AlignedMapType MapAligned(Scalar* data)
-    { return AlignedMapType(data); }
-    static inline ConstAlignedMapType MapAligned(const Scalar* data, Index size)
-    { return ConstAlignedMapType(data, size); }
-    static inline AlignedMapType MapAligned(Scalar* data, Index size)
-    { return AlignedMapType(data, size); }
-    static inline ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols)
-    { return ConstAlignedMapType(data, rows, cols); }
-    static inline AlignedMapType MapAligned(Scalar* data, Index rows, Index cols)
-    { return AlignedMapType(data, rows, cols); }
-
-    template<int Outer, int Inner>
-    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-
-    template<int Outer, int Inner>
-    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-    //@}
-
-    using Base::setConstant;
-    EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val);
-    EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& val);
-    EIGEN_DEVICE_FUNC Derived& setConstant(NoChange_t, Index cols, const Scalar& val);
-    EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, NoChange_t, const Scalar& val);
-
-    using Base::setZero;
-    EIGEN_DEVICE_FUNC Derived& setZero(Index size);
-    EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setZero(NoChange_t, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setZero(Index rows, NoChange_t);
-
-    using Base::setOnes;
-    EIGEN_DEVICE_FUNC Derived& setOnes(Index size);
-    EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setOnes(NoChange_t, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, NoChange_t);
-
-    using Base::setRandom;
-    Derived& setRandom(Index size);
-    Derived& setRandom(Index rows, Index cols);
-    Derived& setRandom(NoChange_t, Index cols);
-    Derived& setRandom(Index rows, NoChange_t);
-
-    #ifdef EIGEN_PLAINOBJECTBASE_PLUGIN
-    #include EIGEN_PLAINOBJECTBASE_PLUGIN
-    #endif
-
-  protected:
-    /** \internal Resizes *this in preparation for assigning \a other to it.
-      * Takes care of doing all the checking that's needed.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
-    {
-      #ifdef EIGEN_NO_AUTOMATIC_RESIZING
-      eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
-                 : (rows() == other.rows() && cols() == other.cols())))
-        && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
-      EIGEN_ONLY_USED_FOR_DEBUG(other);
-      #else
-      resizeLike(other);
-      #endif
-    }
-
-    /**
-      * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
-      *
-      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
-      * it will be initialized.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      *
-      * \sa operator=(const MatrixBase<OtherDerived>&), _set_noalias()
-      *
-      * \internal
-      */
-    // aliasing is dealt once in internal::call_assignment
-    // so at this stage we have to assume aliasing... and resising has to be done later.
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& _set(const DenseBase<OtherDerived>& other)
-    {
-      internal::call_assignment(this->derived(), other.derived());
-      return this->derived();
-    }
-
-    /** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
-      * is the case when creating a new matrix) so one can enforce lazy evaluation.
-      *
-      * \sa operator=(const MatrixBase<OtherDerived>&), _set()
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other)
-    {
-      // I don't think we need this resize call since the lazyAssign will anyways resize
-      // and lazyAssign will be called by the assign selector.
-      //_resize_to_match(other);
-      // the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
-      // it wouldn't allow to copy a row-vector into a column-vector.
-      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-      return this->derived();
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
-    {
-      const bool t0_is_integer_alike = internal::is_valid_index_type<T0>::value;
-      const bool t1_is_integer_alike = internal::is_valid_index_type<T1>::value;
-      EIGEN_STATIC_ASSERT(t0_is_integer_alike &&
-                          t1_is_integer_alike,
-                          FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
-      resize(rows,cols);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
-      m_storage.data()[0] = Scalar(val0);
-      m_storage.data()[1] = Scalar(val1);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1,
-                                    typename internal::enable_if<    (!internal::is_same<Index,Scalar>::value)
-                                                                  && (internal::is_same<T0,Index>::value)
-                                                                  && (internal::is_same<T1,Index>::value)
-                                                                  && Base::SizeAtCompileTime==2,T1>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
-      m_storage.data()[0] = Scalar(val0);
-      m_storage.data()[1] = Scalar(val1);
-    }
-
-    // The argument is convertible to the Index type and we either have a non 1x1 Matrix, or a dynamic-sized Array,
-    // then the argument is meant to be the size of the object.
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(Index size, typename internal::enable_if<    (Base::SizeAtCompileTime!=1 || !internal::is_convertible<T, Scalar>::value)
-                                                                              && ((!internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value || Base::SizeAtCompileTime==Dynamic)),T>::type* = 0)
-    {
-      // NOTE MSVC 2008 complains if we directly put bool(NumTraits<T>::IsInteger) as the EIGEN_STATIC_ASSERT argument.
-      const bool is_integer_alike = internal::is_valid_index_type<T>::value;
-      EIGEN_UNUSED_VARIABLE(is_integer_alike);
-      EIGEN_STATIC_ASSERT(is_integer_alike,
-                          FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
-      resize(size);
-    }
-
-    // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitly converted)
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Scalar& val0, typename internal::enable_if<Base::SizeAtCompileTime==1 && internal::is_convertible<T, Scalar>::value,T>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
-      m_storage.data()[0] = val0;
-    }
-
-    // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type match the index type)
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Index& val0,
-                                    typename internal::enable_if<    (!internal::is_same<Index,Scalar>::value)
-                                                                  && (internal::is_same<Index,T>::value)
-                                                                  && Base::SizeAtCompileTime==1
-                                                                  && internal::is_convertible<T, Scalar>::value,T*>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
-      m_storage.data()[0] = Scalar(val0);
-    }
-
-    // Initialize a fixed size matrix from a pointer to raw data
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Scalar* data){
-      this->_set_noalias(ConstMapType(data));
-    }
-
-    // Initialize an arbitrary matrix from a dense expression
-    template<typename T, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const DenseBase<OtherDerived>& other){
-      this->_set_noalias(other);
-    }
-
-    // Initialize an arbitrary matrix from an object convertible to the Derived type.
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Derived& other){
-      this->_set_noalias(other);
-    }
-
-    // Initialize an arbitrary matrix from a generic Eigen expression
-    template<typename T, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const EigenBase<OtherDerived>& other){
-      this->derived() = other;
-    }
-
-    template<typename T, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const ReturnByValue<OtherDerived>& other)
-    {
-      resize(other.rows(), other.cols());
-      other.evalTo(this->derived());
-    }
-
-    template<typename T, typename OtherDerived, int ColsAtCompileTime>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
-    {
-      this->derived() = r;
-    }
-
-    // For fixed-size Array<Scalar,...>
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Scalar& val0,
-                                    typename internal::enable_if<    Base::SizeAtCompileTime!=Dynamic
-                                                                  && Base::SizeAtCompileTime!=1
-                                                                  && internal::is_convertible<T, Scalar>::value
-                                                                  && internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T>::type* = 0)
-    {
-      Base::setConstant(val0);
-    }
-
-    // For fixed-size Array<Index,...>
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Index& val0,
-                                    typename internal::enable_if<    (!internal::is_same<Index,Scalar>::value)
-                                                                  && (internal::is_same<Index,T>::value)
-                                                                  && Base::SizeAtCompileTime!=Dynamic
-                                                                  && Base::SizeAtCompileTime!=1
-                                                                  && internal::is_convertible<T, Scalar>::value
-                                                                  && internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T*>::type* = 0)
-    {
-      Base::setConstant(val0);
-    }
-
-    template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
-    friend struct internal::matrix_swap_impl;
-
-  public:
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal
-      * \brief Override DenseBase::swap() since for dynamic-sized matrices
-      * of same type it is enough to swap the data pointers.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(DenseBase<OtherDerived> & other)
-    {
-      enum { SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime==Dynamic };
-      internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.derived());
-    }
-
-    /** \internal
-      * \brief const version forwarded to DenseBase::swap
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(DenseBase<OtherDerived> const & other)
-    { Base::swap(other.derived()); }
-
-    EIGEN_DEVICE_FUNC
-    static EIGEN_STRONG_INLINE void _check_template_params()
-    {
-      EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (int(Options)&RowMajor)==RowMajor)
-                        && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (int(Options)&RowMajor)==0)
-                        && ((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0))
-                        && ((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0))
-                        && ((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0))
-                        && ((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0))
-                        && (MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic)
-                        && (MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic)
-                        && (Options & (DontAlign|RowMajor)) == Options),
-        INVALID_MATRIX_TEMPLATE_PARAMETERS)
-    }
-
-    enum { IsPlainObjectBase = 1 };
-#endif
-  public:
-    // These apparently need to be down here for nvcc+icc to prevent duplicate
-    // Map symbol.
-    template<typename PlainObjectType, int MapOptions, typename StrideType> friend class Eigen::Map;
-    friend class Eigen::Map<Derived, Unaligned>;
-    friend class Eigen::Map<const Derived, Unaligned>;
-#if EIGEN_MAX_ALIGN_BYTES>0
-    // for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class twice.
-    friend class Eigen::Map<Derived, AlignedMax>;
-    friend class Eigen::Map<const Derived, AlignedMax>;
-#endif
-};
-
-namespace internal {
-
-template <typename Derived, typename OtherDerived, bool IsVector>
-struct conservative_resize_like_impl
-{
-  #if EIGEN_HAS_TYPE_TRAITS
-  static const bool IsRelocatable = std::is_trivially_copyable<typename Derived::Scalar>::value;
-  #else
-  static const bool IsRelocatable = !NumTraits<typename Derived::Scalar>::RequireInitialization;
-  #endif
-  static void run(DenseBase<Derived>& _this, Index rows, Index cols)
-  {
-    if (_this.rows() == rows && _this.cols() == cols) return;
-    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
-
-    if ( IsRelocatable
-          && (( Derived::IsRowMajor && _this.cols() == cols) ||  // row-major and we change only the number of rows
-              (!Derived::IsRowMajor && _this.rows() == rows) ))  // column-major and we change only the number of columns
-    {
-      internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime>::run(rows, cols);
-      _this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
-    }
-    else
-    {
-      // The storage order does not allow us to use reallocation.
-      Derived tmp(rows,cols);
-      const Index common_rows = numext::mini(rows, _this.rows());
-      const Index common_cols = numext::mini(cols, _this.cols());
-      tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
-      _this.derived().swap(tmp);
-    }
-  }
-
-  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
-  {
-    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
-
-    // Note: Here is space for improvement. Basically, for conservativeResize(Index,Index),
-    // neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the
-    // dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or
-    // conservativeResize(NoChange_t, Index cols). For these methods new static asserts like
-    // EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good.
-    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
-    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived)
-
-    if ( IsRelocatable &&
-          (( Derived::IsRowMajor && _this.cols() == other.cols()) ||  // row-major and we change only the number of rows
-           (!Derived::IsRowMajor && _this.rows() == other.rows()) ))  // column-major and we change only the number of columns
-    {
-      const Index new_rows = other.rows() - _this.rows();
-      const Index new_cols = other.cols() - _this.cols();
-      _this.derived().m_storage.conservativeResize(other.size(),other.rows(),other.cols());
-      if (new_rows>0)
-        _this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows);
-      else if (new_cols>0)
-        _this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols);
-    }
-    else
-    {
-      // The storage order does not allow us to use reallocation.
-      Derived tmp(other);
-      const Index common_rows = numext::mini(tmp.rows(), _this.rows());
-      const Index common_cols = numext::mini(tmp.cols(), _this.cols());
-      tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
-      _this.derived().swap(tmp);
-    }
-  }
-};
-
-// Here, the specialization for vectors inherits from the general matrix case
-// to allow calling .conservativeResize(rows,cols) on vectors.
-template <typename Derived, typename OtherDerived>
-struct conservative_resize_like_impl<Derived,OtherDerived,true>
-  : conservative_resize_like_impl<Derived,OtherDerived,false>
-{
-  typedef conservative_resize_like_impl<Derived,OtherDerived,false> Base;
-  using Base::run;
-  using Base::IsRelocatable;
-
-  static void run(DenseBase<Derived>& _this, Index size)
-  {
-    const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
-    const Index new_cols = Derived::RowsAtCompileTime==1 ? size : 1;
-    if(IsRelocatable)
-      _this.derived().m_storage.conservativeResize(size,new_rows,new_cols);
-    else
-      Base::run(_this.derived(), new_rows, new_cols);
-  }
-
-  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
-  {
-    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
-
-    const Index num_new_elements = other.size() - _this.size();
-
-    const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : other.rows();
-    const Index new_cols = Derived::RowsAtCompileTime==1 ? other.cols() : 1;
-    if(IsRelocatable)
-      _this.derived().m_storage.conservativeResize(other.size(),new_rows,new_cols);
-    else
-      Base::run(_this.derived(), new_rows, new_cols);
-
-    if (num_new_elements > 0)
-      _this.tail(num_new_elements) = other.tail(num_new_elements);
-  }
-};
-
-template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
-struct matrix_swap_impl
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(MatrixTypeA& a, MatrixTypeB& b)
-  {
-    a.base().swap(b);
-  }
-};
-
-template<typename MatrixTypeA, typename MatrixTypeB>
-struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(MatrixTypeA& a, MatrixTypeB& b)
-  {
-    static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_DENSESTORAGEBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Product.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Product.h
deleted file mode 100644
index 70a6c10..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Product.h
+++ /dev/null
@@ -1,191 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PRODUCT_H
-#define EIGEN_PRODUCT_H
-
-namespace Eigen {
-
-template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Option>
-struct traits<Product<Lhs, Rhs, Option> >
-{
-  typedef typename remove_all<Lhs>::type LhsCleaned;
-  typedef typename remove_all<Rhs>::type RhsCleaned;
-  typedef traits<LhsCleaned> LhsTraits;
-  typedef traits<RhsCleaned> RhsTraits;
-
-  typedef MatrixXpr XprKind;
-
-  typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
-  typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
-                                                typename RhsTraits::StorageKind,
-                                                internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;
-  typedef typename promote_index_type<typename LhsTraits::StorageIndex,
-                                      typename RhsTraits::StorageIndex>::type StorageIndex;
-
-  enum {
-    RowsAtCompileTime    = LhsTraits::RowsAtCompileTime,
-    ColsAtCompileTime    = RhsTraits::ColsAtCompileTime,
-    MaxRowsAtCompileTime = LhsTraits::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime,
-
-    // FIXME: only needed by GeneralMatrixMatrixTriangular
-    InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime),
-
-    // The storage order is somewhat arbitrary here. The correct one will be determined through the evaluator.
-    Flags = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? RowMajorBit
-          : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
-          : (   ((LhsTraits::Flags&NoPreferredStorageOrderBit) && (RhsTraits::Flags&RowMajorBit))
-             || ((RhsTraits::Flags&NoPreferredStorageOrderBit) && (LhsTraits::Flags&RowMajorBit)) ) ? RowMajorBit
-          : NoPreferredStorageOrderBit
-  };
-};
-
-} // end namespace internal
-
-/** \class Product
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the product of two arbitrary matrices or vectors
-  *
-  * \tparam _Lhs the type of the left-hand side expression
-  * \tparam _Rhs the type of the right-hand side expression
-  *
-  * This class represents an expression of the product of two arbitrary matrices.
-  *
-  * The other template parameters are:
-  * \tparam Option     can be DefaultProduct, AliasFreeProduct, or LazyProduct
-  *
-  */
-template<typename _Lhs, typename _Rhs, int Option>
-class Product : public ProductImpl<_Lhs,_Rhs,Option,
-                                   typename internal::product_promote_storage_type<typename internal::traits<_Lhs>::StorageKind,
-                                                                                   typename internal::traits<_Rhs>::StorageKind,
-                                                                                   internal::product_type<_Lhs,_Rhs>::ret>::ret>
-{
-  public:
-
-    typedef _Lhs Lhs;
-    typedef _Rhs Rhs;
-
-    typedef typename ProductImpl<
-        Lhs, Rhs, Option,
-        typename internal::product_promote_storage_type<typename internal::traits<Lhs>::StorageKind,
-                                                        typename internal::traits<Rhs>::StorageKind,
-                                                        internal::product_type<Lhs,Rhs>::ret>::ret>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
-
-    typedef typename internal::ref_selector<Lhs>::type LhsNested;
-    typedef typename internal::ref_selector<Rhs>::type RhsNested;
-    typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
-    typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
-    {
-      eigen_assert(lhs.cols() == rhs.rows()
-        && "invalid matrix product"
-        && "if you wanted a coeff-wise or a dot product use the respective explicit functions");
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const LhsNestedCleaned& lhs() const { return m_lhs; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const RhsNestedCleaned& rhs() const { return m_rhs; }
-
-  protected:
-
-    LhsNested m_lhs;
-    RhsNested m_rhs;
-};
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Option, int ProductTag = internal::product_type<Lhs,Rhs>::ret>
-class dense_product_base
- : public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
-{};
-
-/** Conversion to scalar for inner-products */
-template<typename Lhs, typename Rhs, int Option>
-class dense_product_base<Lhs, Rhs, Option, InnerProduct>
- : public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
-{
-  typedef Product<Lhs,Rhs,Option> ProductXpr;
-  typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
-public:
-  using Base::derived;
-  typedef typename Base::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator const Scalar() const
-  {
-    return internal::evaluator<ProductXpr>(derived()).coeff(0,0);
-  }
-};
-
-} // namespace internal
-
-// Generic API dispatcher
-template<typename Lhs, typename Rhs, int Option, typename StorageKind>
-class ProductImpl : public internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type
-{
-  public:
-    typedef typename internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type Base;
-};
-
-template<typename Lhs, typename Rhs, int Option>
-class ProductImpl<Lhs,Rhs,Option,Dense>
-  : public internal::dense_product_base<Lhs,Rhs,Option>
-{
-    typedef Product<Lhs, Rhs, Option> Derived;
-
-  public:
-
-    typedef typename internal::dense_product_base<Lhs, Rhs, Option> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-  protected:
-    enum {
-      IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) &&
-                   (ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic),
-      EnableCoeff = IsOneByOne || Option==LazyProduct
-    };
-
-  public:
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const
-    {
-      EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
-      eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
-
-      return internal::evaluator<Derived>(derived()).coeff(row,col);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index i) const
-    {
-      EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
-      eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
-
-      return internal::evaluator<Derived>(derived()).coeff(i);
-    }
-
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ProductEvaluators.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ProductEvaluators.h
deleted file mode 100644
index 8cf294b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ProductEvaluators.h
+++ /dev/null
@@ -1,1179 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_PRODUCTEVALUATORS_H
-#define EIGEN_PRODUCTEVALUATORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * Evaluator of a product expression.
-  * Since products require special treatments to handle all possible cases,
-  * we simply defer the evaluation logic to a product_evaluator class
-  * which offers more partial specialization possibilities.
-  *
-  * \sa class product_evaluator
-  */
-template<typename Lhs, typename Rhs, int Options>
-struct evaluator<Product<Lhs, Rhs, Options> >
- : public product_evaluator<Product<Lhs, Rhs, Options> >
-{
-  typedef Product<Lhs, Rhs, Options> XprType;
-  typedef product_evaluator<XprType> Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
-// TODO we should apply that rule only if that's really helpful
-template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
-struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
-                                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
-                                               const Product<Lhs, Rhs, DefaultProduct> > >
-{
-  static const bool value = true;
-};
-template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
-struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
-                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
-                               const Product<Lhs, Rhs, DefaultProduct> > >
- : public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> >
-{
-  typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
-                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
-                               const Product<Lhs, Rhs, DefaultProduct> > XprType;
-  typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
-    : Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
-  {}
-};
-
-
-template<typename Lhs, typename Rhs, int DiagIndex>
-struct evaluator<Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> >
- : public evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> >
-{
-  typedef Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> XprType;
-  typedef evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> > Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
-    : Base(Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>(
-        Product<Lhs, Rhs, LazyProduct>(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()),
-        xpr.index() ))
-  {}
-};
-
-
-// Helper class to perform a matrix product with the destination at hand.
-// Depending on the sizes of the factors, there are different evaluation strategies
-// as controlled by internal::product_type.
-template< typename Lhs, typename Rhs,
-          typename LhsShape = typename evaluator_traits<Lhs>::Shape,
-          typename RhsShape = typename evaluator_traits<Rhs>::Shape,
-          int ProductType = internal::product_type<Lhs,Rhs>::value>
-struct generic_product_impl;
-
-template<typename Lhs, typename Rhs>
-struct evaluator_assume_aliasing<Product<Lhs, Rhs, DefaultProduct> > {
-  static const bool value = true;
-};
-
-// This is the default evaluator implementation for products:
-// It creates a temporary and call generic_product_impl
-template<typename Lhs, typename Rhs, int Options, int ProductTag, typename LhsShape, typename RhsShape>
-struct product_evaluator<Product<Lhs, Rhs, Options>, ProductTag, LhsShape, RhsShape>
-  : public evaluator<typename Product<Lhs, Rhs, Options>::PlainObject>
-{
-  typedef Product<Lhs, Rhs, Options> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit product_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-
-// FIXME shall we handle nested_eval here?,
-// if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in permutation_matrix_product, transposition_matrix_product, etc.)
-//     typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
-//     typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
-//     typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
-//     typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
-//
-//     const LhsNested lhs(xpr.lhs());
-//     const RhsNested rhs(xpr.rhs());
-//
-//     generic_product_impl<LhsNestedCleaned, RhsNestedCleaned>::evalTo(m_result, lhs, rhs);
-
-    generic_product_impl<Lhs, Rhs, LhsShape, RhsShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-// The following three shortcuts are enabled only if the scalar types match exactly.
-// TODO: we could enable them for different scalar types when the product is not vectorized.
-
-// Dense = Product
-template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
-  typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
-{
-  typedef Product<Lhs,Rhs,Options> SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    // FIXME shall we handle nested_eval here?
-    generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
-  }
-};
-
-// Dense += Product
-template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar,Scalar>, Dense2Dense,
-  typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
-{
-  typedef Product<Lhs,Rhs,Options> SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
-  {
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    // FIXME shall we handle nested_eval here?
-    generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
-  }
-};
-
-// Dense -= Product
-template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar,Scalar>, Dense2Dense,
-  typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
-{
-  typedef Product<Lhs,Rhs,Options> SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
-  {
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    // FIXME shall we handle nested_eval here?
-    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
-  }
-};
-
-
-// Dense ?= scalar * Product
-// TODO we should apply that rule if that's really helpful
-// for instance, this is not good for inner products
-template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain>
-struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>, const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
-                                           const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense>
-{
-  typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>,
-                        const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
-                        const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
-  {
-    call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func);
-  }
-};
-
-//----------------------------------------
-// Catch "Dense ?= xpr + Product<>" expression to save one temporary
-// FIXME we could probably enable these rules for any product, i.e., not only Dense and DefaultProduct
-
-template<typename OtherXpr, typename Lhs, typename Rhs>
-struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
-                                               const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
-  static const bool value = true;
-};
-
-template<typename OtherXpr, typename Lhs, typename Rhs>
-struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_difference_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
-                                               const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
-  static const bool value = true;
-};
-
-template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
-struct assignment_from_xpr_op_product
-{
-  template<typename SrcXprType, typename InitialFunc>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
-  {
-    call_assignment_no_alias(dst, src.lhs(), Func1());
-    call_assignment_no_alias(dst, src.rhs(), Func2());
-  }
-};
-
-#define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP,BINOP,ASSIGN_OP2) \
-  template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar> \
-  struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<OtherScalar,ProdScalar>, const OtherXpr, \
-                                            const Product<Lhs,Rhs,DefaultProduct> >, internal::ASSIGN_OP<DstScalar,SrcScalar>, Dense2Dense> \
-    : assignment_from_xpr_op_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::ASSIGN_OP<DstScalar,OtherScalar>, internal::ASSIGN_OP2<DstScalar,ProdScalar> > \
-  {}
-
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_sum_op,sub_assign_op);
-
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_difference_op,add_assign_op);
-
-//----------------------------------------
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,InnerProduct>
-{
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    dst.coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    dst.coeffRef(0,0) += (lhs.transpose().cwiseProduct(rhs)).sum();
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { dst.coeffRef(0,0) -= (lhs.transpose().cwiseProduct(rhs)).sum(); }
-};
-
-
-/***********************************************************************
-*  Implementation of outer dense * dense vector product
-***********************************************************************/
-
-// Column major result
-template<typename Dst, typename Lhs, typename Rhs, typename Func>
-void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&)
-{
-  evaluator<Rhs> rhsEval(rhs);
-  ei_declare_local_nested_eval(Lhs,lhs,Rhs::SizeAtCompileTime,actual_lhs);
-  // FIXME if cols is large enough, then it might be useful to make sure that lhs is sequentially stored
-  // FIXME not very good if rhs is real and lhs complex while alpha is real too
-  const Index cols = dst.cols();
-  for (Index j=0; j<cols; ++j)
-    func(dst.col(j), rhsEval.coeff(Index(0),j) * actual_lhs);
-}
-
-// Row major result
-template<typename Dst, typename Lhs, typename Rhs, typename Func>
-void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&)
-{
-  evaluator<Lhs> lhsEval(lhs);
-  ei_declare_local_nested_eval(Rhs,rhs,Lhs::SizeAtCompileTime,actual_rhs);
-  // FIXME if rows is large enough, then it might be useful to make sure that rhs is sequentially stored
-  // FIXME not very good if lhs is real and rhs complex while alpha is real too
-  const Index rows = dst.rows();
-  for (Index i=0; i<rows; ++i)
-    func(dst.row(i), lhsEval.coeff(i,Index(0)) * actual_rhs);
-}
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,OuterProduct>
-{
-  template<typename T> struct is_row_major : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  // TODO it would be nice to be able to exploit our *_assign_op functors for that purpose
-  struct set  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived()  = src; } };
-  struct add  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
-  struct sub  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
-  struct adds {
-    Scalar m_scale;
-    explicit adds(const Scalar& s) : m_scale(s) {}
-    template<typename Dst, typename Src> void EIGEN_DEVICE_FUNC operator()(const Dst& dst, const Src& src) const {
-      dst.const_cast_derived() += m_scale * src;
-    }
-  };
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major<Dst>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major<Dst>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major<Dst>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major<Dst>());
-  }
-
-};
-
-
-// This base class provides default implementations for evalTo, addTo, subTo, in terms of scaleAndAddTo
-template<typename Lhs, typename Rhs, typename Derived>
-struct generic_product_impl_base
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { scaleAndAddTo(dst,lhs, rhs, Scalar(1)); }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }
-
-};
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct> >
-{
-  typedef typename nested_eval<Lhs,1>::type LhsNested;
-  typedef typename nested_eval<Rhs,1>::type RhsNested;
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
-  typedef typename internal::remove_all<typename internal::conditional<int(Side)==OnTheRight,LhsNested,RhsNested>::type>::type MatrixType;
-
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    // Fallback to inner product if both the lhs and rhs is a runtime vector.
-    if (lhs.rows() == 1 && rhs.cols() == 1) {
-      dst.coeffRef(0,0) += alpha * lhs.row(0).conjugate().dot(rhs.col(0));
-      return;
-    }
-    LhsNested actual_lhs(lhs);
-    RhsNested actual_rhs(rhs);
-    internal::gemv_dense_selector<Side,
-                            (int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
-                            bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)
-                           >::run(actual_lhs, actual_rhs, dst, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // Same as: dst.noalias() = lhs.lazyProduct(rhs);
-    // but easier on the compiler side
-    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // dst.noalias() += lhs.lazyProduct(rhs);
-    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // dst.noalias() -= lhs.lazyProduct(rhs);
-    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
-  }
-
-  // This is a special evaluation path called from generic_product_impl<...,GemmProduct> in file GeneralMatrixMatrix.h
-  // This variant tries to extract scalar multiples from both the LHS and RHS and factor them out. For instance:
-  //   dst {,+,-}= (s1*A)*(B*s2)
-  // will be rewritten as:
-  //   dst {,+,-}= (s1*s2) * (A.lazyProduct(B))
-  // There are at least four benefits of doing so:
-  //  1 - huge performance gain for heap-allocated matrix types as it save costly allocations.
-  //  2 - it is faster than simply by-passing the heap allocation through stack allocation.
-  //  3 - it makes this fallback consistent with the heavy GEMM routine.
-  //  4 - it fully by-passes huge stack allocation attempts when multiplying huge fixed-size matrices.
-  //      (see https://stackoverflow.com/questions/54738495)
-  // For small fixed sizes matrices, howver, the gains are less obvious, it is sometimes x2 faster, but sometimes x3 slower,
-  // and the behavior depends also a lot on the compiler... This is why this re-writting strategy is currently
-  // enabled only when falling back from the main GEMM.
-  template<typename Dst, typename Func>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void eval_dynamic(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Func &func)
-  {
-    enum {
-      HasScalarFactor = blas_traits<Lhs>::HasScalarFactor || blas_traits<Rhs>::HasScalarFactor,
-      ConjLhs = blas_traits<Lhs>::NeedToConjugate,
-      ConjRhs = blas_traits<Rhs>::NeedToConjugate
-    };
-    // FIXME: in c++11 this should be auto, and extractScalarFactor should also return auto
-    //        this is important for real*complex_mat
-    Scalar actualAlpha = combine_scalar_factors<Scalar>(lhs, rhs);
-
-    eval_dynamic_impl(dst,
-                      blas_traits<Lhs>::extract(lhs).template conjugateIf<ConjLhs>(),
-                      blas_traits<Rhs>::extract(rhs).template conjugateIf<ConjRhs>(),
-                      func,
-                      actualAlpha,
-                      typename conditional<HasScalarFactor,true_type,false_type>::type());
-  }
-
-protected:
-
-  template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar&  s /* == 1 */, false_type)
-  {
-    EIGEN_UNUSED_VARIABLE(s);
-    eigen_internal_assert(s==Scalar(1));
-    call_restricted_packet_assignment_no_alias(dst, lhs.lazyProduct(rhs), func);
-  }
-
-  template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar& s, true_type)
-  {
-    call_restricted_packet_assignment_no_alias(dst, s * lhs.lazyProduct(rhs), func);
-  }
-};
-
-// This specialization enforces the use of a coefficient-based evaluation strategy
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,LazyCoeffBasedProductMode>
-  : generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> {};
-
-// Case 2: Evaluate coeff by coeff
-//
-// This is mostly taken from CoeffBasedProduct.h
-// The main difference is that we add an extra argument to the etor_product_*_impl::run() function
-// for the inner dimension of the product, because evaluator object do not know their size.
-
-template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
-struct etor_product_coeff_impl;
-
-template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, DenseShape>
-    : evaluator_base<Product<Lhs, Rhs, LazyProduct> >
-{
-  typedef Product<Lhs, Rhs, LazyProduct> XprType;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit product_evaluator(const XprType& xpr)
-    : m_lhs(xpr.lhs()),
-      m_rhs(xpr.rhs()),
-      m_lhsImpl(m_lhs),     // FIXME the creation of the evaluator objects should result in a no-op, but check that!
-      m_rhsImpl(m_rhs),     //       Moreover, they are only useful for the packet path, so we could completely disable them when not needed,
-                            //       or perhaps declare them on the fly on the packet method... We have experiment to check what's best.
-      m_innerDim(xpr.lhs().cols())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::AddCost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-#if 0
-    std::cerr << "LhsOuterStrideBytes=  " << LhsOuterStrideBytes << "\n";
-    std::cerr << "RhsOuterStrideBytes=  " << RhsOuterStrideBytes << "\n";
-    std::cerr << "LhsAlignment=         " << LhsAlignment << "\n";
-    std::cerr << "RhsAlignment=         " << RhsAlignment << "\n";
-    std::cerr << "CanVectorizeLhs=      " << CanVectorizeLhs << "\n";
-    std::cerr << "CanVectorizeRhs=      " << CanVectorizeRhs << "\n";
-    std::cerr << "CanVectorizeInner=    " << CanVectorizeInner << "\n";
-    std::cerr << "EvalToRowMajor=       " << EvalToRowMajor << "\n";
-    std::cerr << "Alignment=            " << Alignment << "\n";
-    std::cerr << "Flags=                " << Flags << "\n";
-#endif
-  }
-
-  // Everything below here is taken from CoeffBasedProduct.h
-
-  typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
-  typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
-
-  typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
-  typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
-
-  typedef evaluator<LhsNestedCleaned> LhsEtorType;
-  typedef evaluator<RhsNestedCleaned> RhsEtorType;
-
-  enum {
-    RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
-    ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
-    InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
-    MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
-  };
-
-  typedef typename find_best_packet<Scalar,RowsAtCompileTime>::type LhsVecPacketType;
-  typedef typename find_best_packet<Scalar,ColsAtCompileTime>::type RhsVecPacketType;
-
-  enum {
-
-    LhsCoeffReadCost = LhsEtorType::CoeffReadCost,
-    RhsCoeffReadCost = RhsEtorType::CoeffReadCost,
-    CoeffReadCost = InnerSize==0 ? NumTraits<Scalar>::ReadCost
-                  : InnerSize == Dynamic ? HugeCost
-                    : InnerSize * (NumTraits<Scalar>::MulCost + int(LhsCoeffReadCost) + int(RhsCoeffReadCost))
-                    + (InnerSize - 1) * NumTraits<Scalar>::AddCost,
-
-    Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
-
-    LhsFlags = LhsEtorType::Flags,
-    RhsFlags = RhsEtorType::Flags,
-
-    LhsRowMajor = LhsFlags & RowMajorBit,
-    RhsRowMajor = RhsFlags & RowMajorBit,
-
-    LhsVecPacketSize = unpacket_traits<LhsVecPacketType>::size,
-    RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
-
-    // Here, we don't care about alignment larger than the usable packet size.
-    LhsAlignment = EIGEN_PLAIN_ENUM_MIN(LhsEtorType::Alignment,LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
-    RhsAlignment = EIGEN_PLAIN_ENUM_MIN(RhsEtorType::Alignment,RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
-
-    SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
-
-    CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime!=1),
-    CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime!=1),
-
-    EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
-                    : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
-                    : (bool(RhsRowMajor) && !CanVectorizeLhs),
-
-    Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & ~RowMajorBit)
-          | (EvalToRowMajor ? RowMajorBit : 0)
-          // TODO enable vectorization for mixed types
-          | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0)
-          | (XprType::IsVectorAtCompileTime ? LinearAccessBit : 0),
-
-    LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
-    RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
-
-    Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment)
-              : bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment)
-              : 0,
-
-    /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
-     * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
-     * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
-     * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
-     */
-    CanVectorizeInner =    SameType
-                        && LhsRowMajor
-                        && (!RhsRowMajor)
-                        && (int(LhsFlags) & int(RhsFlags) & ActualPacketAccessBit)
-                        && (int(InnerSize) % packet_traits<Scalar>::size == 0)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
-  {
-    return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
-  }
-
-  /* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
-   * which is why we don't set the LinearAccessBit.
-   * TODO: this seems possible when the result is a vector
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const CoeffReturnType coeff(Index index) const
-  {
-    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
-    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
-    return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const PacketType packet(Index row, Index col) const
-  {
-    PacketType res;
-    typedef etor_product_packet_impl<bool(int(Flags)&RowMajorBit) ? RowMajor : ColMajor,
-                                     Unroll ? int(InnerSize) : Dynamic,
-                                     LhsEtorType, RhsEtorType, PacketType, LoadMode> PacketImpl;
-    PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res);
-    return res;
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const PacketType packet(Index index) const
-  {
-    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
-    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
-    return packet<LoadMode,PacketType>(row,col);
-  }
-
-protected:
-  typename internal::add_const_on_value_type<LhsNested>::type m_lhs;
-  typename internal::add_const_on_value_type<RhsNested>::type m_rhs;
-
-  LhsEtorType m_lhsImpl;
-  RhsEtorType m_rhsImpl;
-
-  // TODO: Get rid of m_innerDim if known at compile time
-  Index m_innerDim;
-};
-
-template<typename Lhs, typename Rhs>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, LazyCoeffBasedProductMode, DenseShape, DenseShape>
-  : product_evaluator<Product<Lhs, Rhs, LazyProduct>, CoeffBasedProductMode, DenseShape, DenseShape>
-{
-  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
-  typedef Product<Lhs, Rhs, LazyProduct> BaseProduct;
-  typedef product_evaluator<BaseProduct, CoeffBasedProductMode, DenseShape, DenseShape> Base;
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit product_evaluator(const XprType& xpr)
-    : Base(BaseProduct(xpr.lhs(),xpr.rhs()))
-  {}
-};
-
-/****************************************
-*** Coeff based product, Packet path  ***
-****************************************/
-
-template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
-  {
-    etor_product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
-    res =  pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex-1))), rhs.template packet<LoadMode,Packet>(Index(UnrollingIndex-1), col), res);
-  }
-};
-
-template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
-  {
-    etor_product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
-    res =  pmadd(lhs.template packet<LoadMode,Packet>(row, Index(UnrollingIndex-1)), pset1<Packet>(rhs.coeff(Index(UnrollingIndex-1), col)), res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, 1, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
-  {
-    res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))),rhs.template packet<LoadMode,Packet>(Index(0), col));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, 1, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
-  {
-    res = pmul(lhs.template packet<LoadMode,Packet>(row, Index(0)), pset1<Packet>(rhs.coeff(Index(0), col)));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-    for(Index i = 0; i < innerDim; ++i)
-      res =  pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode,Packet>(i, col), res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-    for(Index i = 0; i < innerDim; ++i)
-      res =  pmadd(lhs.template packet<LoadMode,Packet>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
-  }
-};
-
-
-/***************************************************************************
-* Triangular products
-***************************************************************************/
-template<int Mode, bool LhsIsTriangular,
-         typename Lhs, bool LhsIsVector,
-         typename Rhs, bool RhsIsVector>
-struct triangular_product_impl;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    triangular_product_impl<Lhs::Mode,true,typename Lhs::MatrixType,false,Rhs, Rhs::ColsAtCompileTime==1>
-        ::run(dst, lhs.nestedExpression(), rhs, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag>
-: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    triangular_product_impl<Rhs::Mode,false,Lhs,Lhs::RowsAtCompileTime==1, typename Rhs::MatrixType, false>::run(dst, lhs, rhs.nestedExpression(), alpha);
-  }
-};
-
-
-/***************************************************************************
-* SelfAdjoint products
-***************************************************************************/
-template <typename Lhs, int LhsMode, bool LhsIsVector,
-          typename Rhs, int RhsMode, bool RhsIsVector>
-struct selfadjoint_product_impl;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC
-  void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    selfadjoint_product_impl<typename Lhs::MatrixType,Lhs::Mode,false,Rhs,0,Rhs::IsVectorAtCompileTime>::run(dst, lhs.nestedExpression(), rhs, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag>
-: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    selfadjoint_product_impl<Lhs,0,Lhs::IsVectorAtCompileTime,typename Rhs::MatrixType,Rhs::Mode,false>::run(dst, lhs, rhs.nestedExpression(), alpha);
-  }
-};
-
-
-/***************************************************************************
-* Diagonal products
-***************************************************************************/
-
-template<typename MatrixType, typename DiagonalType, typename Derived, int ProductOrder>
-struct diagonal_product_evaluator_base
-  : evaluator_base<Derived>
-{
-   typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
-public:
-  enum {
-    CoeffReadCost = int(NumTraits<Scalar>::MulCost) + int(evaluator<MatrixType>::CoeffReadCost) + int(evaluator<DiagonalType>::CoeffReadCost),
-
-    MatrixFlags = evaluator<MatrixType>::Flags,
-    DiagFlags = evaluator<DiagonalType>::Flags,
-
-    _StorageOrder = (Derived::MaxRowsAtCompileTime==1 && Derived::MaxColsAtCompileTime!=1) ? RowMajor
-                  : (Derived::MaxColsAtCompileTime==1 && Derived::MaxRowsAtCompileTime!=1) ? ColMajor
-                  : MatrixFlags & RowMajorBit ? RowMajor : ColMajor,
-    _SameStorageOrder = _StorageOrder == (MatrixFlags & RowMajorBit ? RowMajor : ColMajor),
-
-    _ScalarAccessOnDiag =  !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
-                           ||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)),
-    _SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
-    // FIXME currently we need same types, but in the future the next rule should be the one
-    //_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagFlags)&PacketAccessBit))),
-    _Vectorizable =   bool(int(MatrixFlags)&PacketAccessBit)
-                  &&  _SameTypes
-                  && (_SameStorageOrder || (MatrixFlags&LinearAccessBit)==LinearAccessBit)
-                  && (_ScalarAccessOnDiag || (bool(int(DiagFlags)&PacketAccessBit))),
-    _LinearAccessMask = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
-    Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixFlags)) | (_Vectorizable ? PacketAccessBit : 0),
-    Alignment = evaluator<MatrixType>::Alignment,
-
-    AsScalarProduct =     (DiagonalType::SizeAtCompileTime==1)
-                      ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::RowsAtCompileTime==1 && ProductOrder==OnTheLeft)
-                      ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime==1 && ProductOrder==OnTheRight)
-  };
-
-  EIGEN_DEVICE_FUNC diagonal_product_evaluator_base(const MatrixType &mat, const DiagonalType &diag)
-    : m_diagImpl(diag), m_matImpl(mat)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
-  {
-    if(AsScalarProduct)
-      return m_diagImpl.coeff(0) * m_matImpl.coeff(idx);
-    else
-      return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx);
-  }
-
-protected:
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::true_type) const
-  {
-    return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
-                          internal::pset1<PacketType>(m_diagImpl.coeff(id)));
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::false_type) const
-  {
-    enum {
-      InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
-      DiagonalPacketLoadMode = EIGEN_PLAIN_ENUM_MIN(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment)) // FIXME hardcoded 16!!
-    };
-    return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
-                          m_diagImpl.template packet<DiagonalPacketLoadMode,PacketType>(id));
-  }
-
-  evaluator<DiagonalType> m_diagImpl;
-  evaluator<MatrixType>   m_matImpl;
-};
-
-// diagonal * dense
-template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalShape, DenseShape>
-  : diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft>
-{
-  typedef diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft> Base;
-  using Base::m_diagImpl;
-  using Base::m_matImpl;
-  using Base::coeff;
-  typedef typename Base::Scalar Scalar;
-
-  typedef Product<Lhs, Rhs, ProductKind> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef typename Lhs::DiagonalVectorType DiagonalType;
-
-
-  enum { StorageOrder = Base::_StorageOrder };
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.rhs(), xpr.lhs().diagonal())
-  {
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
-  {
-    return m_diagImpl.coeff(row) * m_matImpl.coeff(row, col);
-  }
-
-#ifndef EIGEN_GPUCC
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
-  {
-    // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
-    // See also similar calls below.
-    return this->template packet_impl<LoadMode,PacketType>(row,col, row,
-                                 typename internal::conditional<int(StorageOrder)==RowMajor, internal::true_type, internal::false_type>::type());
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index idx) const
-  {
-    return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
-  }
-#endif
-};
-
-// dense * diagonal
-template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape, DiagonalShape>
-  : diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight>
-{
-  typedef diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight> Base;
-  using Base::m_diagImpl;
-  using Base::m_matImpl;
-  using Base::coeff;
-  typedef typename Base::Scalar Scalar;
-
-  typedef Product<Lhs, Rhs, ProductKind> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-
-  enum { StorageOrder = Base::_StorageOrder };
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.lhs(), xpr.rhs().diagonal())
-  {
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
-  {
-    return m_matImpl.coeff(row, col) * m_diagImpl.coeff(col);
-  }
-
-#ifndef EIGEN_GPUCC
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
-  {
-    return this->template packet_impl<LoadMode,PacketType>(row,col, col,
-                                 typename internal::conditional<int(StorageOrder)==ColMajor, internal::true_type, internal::false_type>::type());
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index idx) const
-  {
-    return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
-  }
-#endif
-};
-
-/***************************************************************************
-* Products with permutation matrices
-***************************************************************************/
-
-/** \internal
-  * \class permutation_matrix_product
-  * Internal helper class implementing the product between a permutation matrix and a matrix.
-  * This class is specialized for DenseShape below and for SparseShape in SparseCore/SparsePermutation.h
-  */
-template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
-struct permutation_matrix_product;
-
-template<typename ExpressionType, int Side, bool Transposed>
-struct permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
-{
-    typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
-    typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-
-    template<typename Dest, typename PermutationType>
-    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
-    {
-      MatrixType mat(xpr);
-      const Index n = Side==OnTheLeft ? mat.rows() : mat.cols();
-      // FIXME we need an is_same for expression that is not sensitive to constness. For instance
-      // is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
-      //if(is_same<MatrixTypeCleaned,Dest>::value && extract_data(dst) == extract_data(mat))
-      if(is_same_dense(dst, mat))
-      {
-        // apply the permutation inplace
-        Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(perm.size());
-        mask.fill(false);
-        Index r = 0;
-        while(r < perm.size())
-        {
-          // search for the next seed
-          while(r<perm.size() && mask[r]) r++;
-          if(r>=perm.size())
-            break;
-          // we got one, let's follow it until we are back to the seed
-          Index k0 = r++;
-          Index kPrev = k0;
-          mask.coeffRef(k0) = true;
-          for(Index k=perm.indices().coeff(k0); k!=k0; k=perm.indices().coeff(k))
-          {
-                  Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
-            .swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
-                       (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
-
-            mask.coeffRef(k) = true;
-            kPrev = k;
-          }
-        }
-      }
-      else
-      {
-        for(Index i = 0; i < n; ++i)
-        {
-          Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
-               (dst, ((Side==OnTheLeft) ^ Transposed) ? perm.indices().coeff(i) : i)
-
-          =
-
-          Block<const MatrixTypeCleaned,Side==OnTheLeft ? 1 : MatrixTypeCleaned::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixTypeCleaned::ColsAtCompileTime>
-               (mat, ((Side==OnTheRight) ^ Transposed) ? perm.indices().coeff(i) : i);
-        }
-      }
-    }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, PermutationShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    permutation_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, MatrixShape, PermutationShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    permutation_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Inverse<Lhs>, Rhs, PermutationShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Inverse<Lhs>& lhs, const Rhs& rhs)
-  {
-    permutation_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Inverse<Rhs>, MatrixShape, PermutationShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Inverse<Rhs>& rhs)
-  {
-    permutation_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
-  }
-};
-
-
-/***************************************************************************
-* Products with transpositions matrices
-***************************************************************************/
-
-// FIXME could we unify Transpositions and Permutation into a single "shape"??
-
-/** \internal
-  * \class transposition_matrix_product
-  * Internal helper class implementing the product between a permutation matrix and a matrix.
-  */
-template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
-struct transposition_matrix_product
-{
-  typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
-  typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-
-  template<typename Dest, typename TranspositionType>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const TranspositionType& tr, const ExpressionType& xpr)
-  {
-    MatrixType mat(xpr);
-    typedef typename TranspositionType::StorageIndex StorageIndex;
-    const Index size = tr.size();
-    StorageIndex j = 0;
-
-    if(!is_same_dense(dst,mat))
-      dst = mat;
-
-    for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)
-      if(Index(j=tr.coeff(k))!=k)
-      {
-        if(Side==OnTheLeft)        dst.row(k).swap(dst.row(j));
-        else if(Side==OnTheRight)  dst.col(k).swap(dst.col(j));
-      }
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, TranspositionsShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    transposition_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, MatrixShape, TranspositionsShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    transposition_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
-  }
-};
-
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Transpose<Lhs>, Rhs, TranspositionsShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Transpose<Lhs>& lhs, const Rhs& rhs)
-  {
-    transposition_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Transpose<Rhs>, MatrixShape, TranspositionsShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Transpose<Rhs>& rhs)
-  {
-    transposition_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PRODUCT_EVALUATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Random.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Random.h
deleted file mode 100644
index dab2ac8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Random.h
+++ /dev/null
@@ -1,218 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_RANDOM_H
-#define EIGEN_RANDOM_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Scalar> struct scalar_random_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op)
-  inline const Scalar operator() () const { return random<Scalar>(); }
-};
-
-template<typename Scalar>
-struct functor_traits<scalar_random_op<Scalar> >
-{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false }; };
-
-} // end namespace internal
-
-/** \returns a random matrix expression
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * \not_reentrant
-  * 
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Random() should be used
-  * instead.
-  * 
-  *
-  * Example: \include MatrixBase_random_int_int.cpp
-  * Output: \verbinclude MatrixBase_random_int_int.out
-  *
-  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
-  * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
-  * behavior with expressions involving random matrices.
-  * 
-  * See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators.
-  *
-  * \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random()
-  */
-template<typename Derived>
-inline const typename DenseBase<Derived>::RandomReturnType
-DenseBase<Derived>::Random(Index rows, Index cols)
-{
-  return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>());
-}
-
-/** \returns a random vector expression
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  * \not_reentrant
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Random() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_random_int.cpp
-  * Output: \verbinclude MatrixBase_random_int.out
-  *
-  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
-  * a temporary vector whenever it is nested in a larger expression. This prevents unexpected
-  * behavior with expressions involving random matrices.
-  *
-  * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random()
-  */
-template<typename Derived>
-inline const typename DenseBase<Derived>::RandomReturnType
-DenseBase<Derived>::Random(Index size)
-{
-  return NullaryExpr(size, internal::scalar_random_op<Scalar>());
-}
-
-/** \returns a fixed-size random matrix or vector expression
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * Example: \include MatrixBase_random.cpp
-  * Output: \verbinclude MatrixBase_random.out
-  *
-  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
-  * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
-  * behavior with expressions involving random matrices.
-  * 
-  * \not_reentrant
-  *
-  * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index)
-  */
-template<typename Derived>
-inline const typename DenseBase<Derived>::RandomReturnType
-DenseBase<Derived>::Random()
-{
-  return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>());
-}
-
-/** Sets all coefficients in this expression to random values.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * \not_reentrant
-  * 
-  * Example: \include MatrixBase_setRandom.cpp
-  * Output: \verbinclude MatrixBase_setRandom.out
-  *
-  * \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Derived& DenseBase<Derived>::setRandom()
-{
-  return *this = Random(rows(), cols());
-}
-
-/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * \only_for_vectors
-  * \not_reentrant
-  *
-  * Example: \include Matrix_setRandom_int.cpp
-  * Output: \verbinclude Matrix_setRandom_int.out
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(Index newSize)
-{
-  resize(newSize);
-  return setRandom();
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to random values.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * \not_reentrant
-  * 
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setRandom_int_int.cpp
-  * Output: \verbinclude Matrix_setRandom_int_int.out
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
-{
-  resize(rows, cols);
-  return setRandom();
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to random values. For the parameter of type
-  * NoChange_t, just pass the special value \c NoChange.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * \not_reentrant
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index), setRandom(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(NoChange_t, Index cols)
-{
-  return setRandom(rows(), cols);
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to random values. For the parameter of type
-  * NoChange_t, just pass the special value \c NoChange.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * \not_reentrant
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index), setRandom(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(Index rows, NoChange_t)
-{
-  return setRandom(rows, cols());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_RANDOM_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Redux.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Redux.h
deleted file mode 100644
index b6790d1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Redux.h
+++ /dev/null
@@ -1,515 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REDUX_H
-#define EIGEN_REDUX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// TODO
-//  * implement other kind of vectorization
-//  * factorize code
-
-/***************************************************************************
-* Part 1 : the logic deciding a strategy for vectorization and unrolling
-***************************************************************************/
-
-template<typename Func, typename Evaluator>
-struct redux_traits
-{
-public:
-    typedef typename find_best_packet<typename Evaluator::Scalar,Evaluator::SizeAtCompileTime>::type PacketType;
-  enum {
-    PacketSize = unpacket_traits<PacketType>::size,
-    InnerMaxSize = int(Evaluator::IsRowMajor)
-                 ? Evaluator::MaxColsAtCompileTime
-                 : Evaluator::MaxRowsAtCompileTime,
-    OuterMaxSize = int(Evaluator::IsRowMajor)
-                 ? Evaluator::MaxRowsAtCompileTime
-                 : Evaluator::MaxColsAtCompileTime,
-    SliceVectorizedWork = int(InnerMaxSize)==Dynamic ? Dynamic
-                        : int(OuterMaxSize)==Dynamic ? (int(InnerMaxSize)>=int(PacketSize) ? Dynamic : 0)
-                        : (int(InnerMaxSize)/int(PacketSize)) * int(OuterMaxSize)
-  };
-
-  enum {
-    MightVectorize = (int(Evaluator::Flags)&ActualPacketAccessBit)
-                  && (functor_traits<Func>::PacketAccess),
-    MayLinearVectorize = bool(MightVectorize) && (int(Evaluator::Flags)&LinearAccessBit),
-    MaySliceVectorize  = bool(MightVectorize) && (int(SliceVectorizedWork)==Dynamic || int(SliceVectorizedWork)>=3)
-  };
-
-public:
-  enum {
-    Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
-              : int(MaySliceVectorize)  ? int(SliceVectorizedTraversal)
-                                        : int(DefaultTraversal)
-  };
-
-public:
-  enum {
-    Cost = Evaluator::SizeAtCompileTime == Dynamic ? HugeCost
-         : int(Evaluator::SizeAtCompileTime) * int(Evaluator::CoeffReadCost) + (Evaluator::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
-    UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize))
-  };
-
-public:
-  enum {
-    Unrolling = Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling
-  };
-  
-#ifdef EIGEN_DEBUG_ASSIGN
-  static void debug()
-  {
-    std::cerr << "Xpr: " << typeid(typename Evaluator::XprType).name() << std::endl;
-    std::cerr.setf(std::ios::hex, std::ios::basefield);
-    EIGEN_DEBUG_VAR(Evaluator::Flags)
-    std::cerr.unsetf(std::ios::hex);
-    EIGEN_DEBUG_VAR(InnerMaxSize)
-    EIGEN_DEBUG_VAR(OuterMaxSize)
-    EIGEN_DEBUG_VAR(SliceVectorizedWork)
-    EIGEN_DEBUG_VAR(PacketSize)
-    EIGEN_DEBUG_VAR(MightVectorize)
-    EIGEN_DEBUG_VAR(MayLinearVectorize)
-    EIGEN_DEBUG_VAR(MaySliceVectorize)
-    std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
-    EIGEN_DEBUG_VAR(UnrollingLimit)
-    std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
-    std::cerr << std::endl;
-  }
-#endif
-};
-
-/***************************************************************************
-* Part 2 : unrollers
-***************************************************************************/
-
-/*** no vectorization ***/
-
-template<typename Func, typename Evaluator, int Start, int Length>
-struct redux_novec_unroller
-{
-  enum {
-    HalfLength = Length/2
-  };
-
-  typedef typename Evaluator::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func& func)
-  {
-    return func(redux_novec_unroller<Func, Evaluator, Start, HalfLength>::run(eval,func),
-                redux_novec_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::run(eval,func));
-  }
-};
-
-template<typename Func, typename Evaluator, int Start>
-struct redux_novec_unroller<Func, Evaluator, Start, 1>
-{
-  enum {
-    outer = Start / Evaluator::InnerSizeAtCompileTime,
-    inner = Start % Evaluator::InnerSizeAtCompileTime
-  };
-
-  typedef typename Evaluator::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func&)
-  {
-    return eval.coeffByOuterInner(outer, inner);
-  }
-};
-
-// This is actually dead code and will never be called. It is required
-// to prevent false warnings regarding failed inlining though
-// for 0 length run() will never be called at all.
-template<typename Func, typename Evaluator, int Start>
-struct redux_novec_unroller<Func, Evaluator, Start, 0>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  EIGEN_DEVICE_FUNC 
-  static EIGEN_STRONG_INLINE Scalar run(const Evaluator&, const Func&) { return Scalar(); }
-};
-
-/*** vectorization ***/
-
-template<typename Func, typename Evaluator, int Start, int Length>
-struct redux_vec_unroller
-{
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func& func)
-  {
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      HalfLength = Length/2
-    };
-
-    return func.packetOp(
-            redux_vec_unroller<Func, Evaluator, Start, HalfLength>::template run<PacketType>(eval,func),
-            redux_vec_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::template run<PacketType>(eval,func) );
-  }
-};
-
-template<typename Func, typename Evaluator, int Start>
-struct redux_vec_unroller<Func, Evaluator, Start, 1>
-{
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func&)
-  {
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      index = Start * PacketSize,
-      outer = index / int(Evaluator::InnerSizeAtCompileTime),
-      inner = index % int(Evaluator::InnerSizeAtCompileTime),
-      alignment = Evaluator::Alignment
-    };
-    return eval.template packetByOuterInner<alignment,PacketType>(outer, inner);
-  }
-};
-
-/***************************************************************************
-* Part 3 : implementation of all cases
-***************************************************************************/
-
-template<typename Func, typename Evaluator,
-         int Traversal = redux_traits<Func, Evaluator>::Traversal,
-         int Unrolling = redux_traits<Func, Evaluator>::Unrolling
->
-struct redux_impl;
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
-  {
-    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
-    Scalar res;
-    res = eval.coeffByOuterInner(0, 0);
-    for(Index i = 1; i < xpr.innerSize(); ++i)
-      res = func(res, eval.coeffByOuterInner(0, i));
-    for(Index i = 1; i < xpr.outerSize(); ++i)
-      for(Index j = 0; j < xpr.innerSize(); ++j)
-        res = func(res, eval.coeffByOuterInner(i, j));
-    return res;
-  }
-};
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func,Evaluator, DefaultTraversal, CompleteUnrolling>
-  : redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime>
-{
-  typedef redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
-  typedef typename Evaluator::Scalar Scalar;
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  Scalar run(const Evaluator &eval, const Func& func, const XprType& /*xpr*/)
-  {
-    return Base::run(eval,func);
-  }
-};
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
-
-  template<typename XprType>
-  static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
-  {
-    const Index size = xpr.size();
-    
-    const Index packetSize = redux_traits<Func, Evaluator>::PacketSize;
-    const int packetAlignment = unpacket_traits<PacketScalar>::alignment;
-    enum {
-      alignment0 = (bool(Evaluator::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned),
-      alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Evaluator::Alignment)
-    };
-    const Index alignedStart = internal::first_default_aligned(xpr);
-    const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize);
-    const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize);
-    const Index alignedEnd2 = alignedStart + alignedSize2;
-    const Index alignedEnd  = alignedStart + alignedSize;
-    Scalar res;
-    if(alignedSize)
-    {
-      PacketScalar packet_res0 = eval.template packet<alignment,PacketScalar>(alignedStart);
-      if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop
-      {
-        PacketScalar packet_res1 = eval.template packet<alignment,PacketScalar>(alignedStart+packetSize);
-        for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize)
-        {
-          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment,PacketScalar>(index));
-          packet_res1 = func.packetOp(packet_res1, eval.template packet<alignment,PacketScalar>(index+packetSize));
-        }
-
-        packet_res0 = func.packetOp(packet_res0,packet_res1);
-        if(alignedEnd>alignedEnd2)
-          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment,PacketScalar>(alignedEnd2));
-      }
-      res = func.predux(packet_res0);
-
-      for(Index index = 0; index < alignedStart; ++index)
-        res = func(res,eval.coeff(index));
-
-      for(Index index = alignedEnd; index < size; ++index)
-        res = func(res,eval.coeff(index));
-    }
-    else // too small to vectorize anything.
-         // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
-    {
-      res = eval.coeff(0);
-      for(Index index = 1; index < size; ++index)
-        res = func(res,eval.coeff(index));
-    }
-
-    return res;
-  }
-};
-
-// NOTE: for SliceVectorizedTraversal we simply bypass unrolling
-template<typename Func, typename Evaluator, int Unrolling>
-struct redux_impl<Func, Evaluator, SliceVectorizedTraversal, Unrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
-
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
-  {
-    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
-    const Index innerSize = xpr.innerSize();
-    const Index outerSize = xpr.outerSize();
-    enum {
-      packetSize = redux_traits<Func, Evaluator>::PacketSize
-    };
-    const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
-    Scalar res;
-    if(packetedInnerSize)
-    {
-      PacketType packet_res = eval.template packet<Unaligned,PacketType>(0,0);
-      for(Index j=0; j<outerSize; ++j)
-        for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize))
-          packet_res = func.packetOp(packet_res, eval.template packetByOuterInner<Unaligned,PacketType>(j,i));
-
-      res = func.predux(packet_res);
-      for(Index j=0; j<outerSize; ++j)
-        for(Index i=packetedInnerSize; i<innerSize; ++i)
-          res = func(res, eval.coeffByOuterInner(j,i));
-    }
-    else // too small to vectorize anything.
-         // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
-    {
-      res = redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>::run(eval, func, xpr);
-    }
-
-    return res;
-  }
-};
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, CompleteUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
-  enum {
-    PacketSize = redux_traits<Func, Evaluator>::PacketSize,
-    Size = Evaluator::SizeAtCompileTime,
-    VectorizedSize = (int(Size) / int(PacketSize)) * int(PacketSize)
-  };
-
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  Scalar run(const Evaluator &eval, const Func& func, const XprType &xpr)
-  {
-    EIGEN_ONLY_USED_FOR_DEBUG(xpr)
-    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
-    if (VectorizedSize > 0) {
-      Scalar res = func.predux(redux_vec_unroller<Func, Evaluator, 0, Size / PacketSize>::template run<PacketType>(eval,func));
-      if (VectorizedSize != Size)
-        res = func(res,redux_novec_unroller<Func, Evaluator, VectorizedSize, Size-VectorizedSize>::run(eval,func));
-      return res;
-    }
-    else {
-      return redux_novec_unroller<Func, Evaluator, 0, Size>::run(eval,func);
-    }
-  }
-};
-
-// evaluator adaptor
-template<typename _XprType>
-class redux_evaluator : public internal::evaluator<_XprType>
-{
-  typedef internal::evaluator<_XprType> Base;
-public:
-  typedef _XprType XprType;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit redux_evaluator(const XprType &xpr) : Base(xpr) {}
-  
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename XprType::PacketScalar PacketScalar;
-  
-  enum {
-    MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = XprType::MaxColsAtCompileTime,
-    // TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator
-    Flags = Base::Flags & ~DirectAccessBit,
-    IsRowMajor = XprType::IsRowMajor,
-    SizeAtCompileTime = XprType::SizeAtCompileTime,
-    InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime
-  };
-  
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
-  { return Base::coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
-  
-  template<int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketType packetByOuterInner(Index outer, Index inner) const
-  { return Base::template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
-  
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Part 4 : public API
-***************************************************************************/
-
-
-/** \returns the result of a full redux operation on the whole matrix or vector using \a func
-  *
-  * The template parameter \a BinaryOp is the type of the functor \a func which must be
-  * an associative operator. Both current C++98 and C++11 functor styles are handled.
-  *
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
-  */
-template<typename Derived>
-template<typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::redux(const Func& func) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
-  ThisEvaluator thisEval(derived());
-
-  // The initial expression is passed to the reducer as an additional argument instead of
-  // passing it as a member of redux_evaluator to help  
-  return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func, derived());
-}
-
-/** \returns the minimum of all coefficients of \c *this.
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is minimum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  */
-template<typename Derived>
-template<int NaNPropagation>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::minCoeff() const
-{
-  return derived().redux(Eigen::internal::scalar_min_op<Scalar,Scalar, NaNPropagation>());
-}
-
-/** \returns the maximum of all coefficients of \c *this. 
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  */
-template<typename Derived>
-template<int NaNPropagation>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::maxCoeff() const
-{
-  return derived().redux(Eigen::internal::scalar_max_op<Scalar,Scalar, NaNPropagation>());
-}
-
-/** \returns the sum of all coefficients of \c *this
-  *
-  * If \c *this is empty, then the value 0 is returned.
-  *
-  * \sa trace(), prod(), mean()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::sum() const
-{
-  if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
-    return Scalar(0);
-  return derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>());
-}
-
-/** \returns the mean of all coefficients of *this
-*
-* \sa trace(), prod(), sum()
-*/
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::mean() const
-{
-#ifdef __INTEL_COMPILER
-  #pragma warning push
-  #pragma warning ( disable : 2259 )
-#endif
-  return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size());
-#ifdef __INTEL_COMPILER
-  #pragma warning pop
-#endif
-}
-
-/** \returns the product of all coefficients of *this
-  *
-  * Example: \include MatrixBase_prod.cpp
-  * Output: \verbinclude MatrixBase_prod.out
-  *
-  * \sa sum(), mean(), trace()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::prod() const
-{
-  if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
-    return Scalar(1);
-  return derived().redux(Eigen::internal::scalar_product_op<Scalar>());
-}
-
-/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.
-  *
-  * \c *this can be any matrix, not necessarily square.
-  *
-  * \sa diagonal(), sum()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-MatrixBase<Derived>::trace() const
-{
-  return derived().diagonal().sum();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Ref.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Ref.h
deleted file mode 100644
index c2a37ea..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Ref.h
+++ /dev/null
@@ -1,381 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REF_H
-#define EIGEN_REF_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename _PlainObjectType, int _Options, typename _StrideType>
-struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
-  : public traits<Map<_PlainObjectType, _Options, _StrideType> >
-{
-  typedef _PlainObjectType PlainObjectType;
-  typedef _StrideType StrideType;
-  enum {
-    Options = _Options,
-    Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit,
-    Alignment = traits<Map<_PlainObjectType, _Options, _StrideType> >::Alignment
-  };
-
-  template<typename Derived> struct match {
-    enum {
-      IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime,
-      HasDirectAccess = internal::has_direct_access<Derived>::ret,
-      StorageOrderMatch = IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
-      InnerStrideMatch = int(StrideType::InnerStrideAtCompileTime)==int(Dynamic)
-                      || int(StrideType::InnerStrideAtCompileTime)==int(Derived::InnerStrideAtCompileTime)
-                      || (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
-      OuterStrideMatch = IsVectorAtCompileTime
-                      || int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
-      // NOTE, this indirection of evaluator<Derived>::Alignment is needed
-      // to workaround a very strange bug in MSVC related to the instantiation
-      // of has_*ary_operator in evaluator<CwiseNullaryOp>.
-      // This line is surprisingly very sensitive. For instance, simply adding parenthesis
-      // as "DerivedAlignment = (int(evaluator<Derived>::Alignment))," will make MSVC fail...
-      DerivedAlignment = int(evaluator<Derived>::Alignment),
-      AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (DerivedAlignment >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment
-      ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
-      MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch
-    };
-    typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
-  };
-
-};
-
-template<typename Derived>
-struct traits<RefBase<Derived> > : public traits<Derived> {};
-
-}
-
-template<typename Derived> class RefBase
- : public MapBase<Derived>
-{
-  typedef typename internal::traits<Derived>::PlainObjectType PlainObjectType;
-  typedef typename internal::traits<Derived>::StrideType StrideType;
-
-public:
-
-  typedef MapBase<Derived> Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const
-  {
-    return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const
-  {
-    return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
-         : IsVectorAtCompileTime ? this->size()
-         : int(Flags)&RowMajorBit ? this->cols()
-         : this->rows();
-  }
-
-  EIGEN_DEVICE_FUNC RefBase()
-    : Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime),
-      // Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values:
-      m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime,
-               StrideType::InnerStrideAtCompileTime==Dynamic?0:StrideType::InnerStrideAtCompileTime)
-  {}
-
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)
-
-protected:
-
-  typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
-
-  // Resolves inner stride if default 0.
-  static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveInnerStride(Index inner) {
-    return inner == 0 ? 1 : inner;
-  }
-
-  // Resolves outer stride if default 0.
-  static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveOuterStride(Index inner, Index outer, Index rows, Index cols, bool isVectorAtCompileTime, bool isRowMajor) {
-    return outer == 0 ? isVectorAtCompileTime ? inner * rows * cols : isRowMajor ? inner * cols : inner * rows : outer;
-  }
-
-  // Returns true if construction is valid, false if there is a stride mismatch,
-  // and fails if there is a size mismatch.
-  template<typename Expression>
-  EIGEN_DEVICE_FUNC bool construct(Expression& expr)
-  {
-    // Check matrix sizes.  If this is a compile-time vector, we do allow
-    // implicitly transposing.
-    EIGEN_STATIC_ASSERT(
-      EIGEN_PREDICATE_SAME_MATRIX_SIZE(PlainObjectType, Expression)
-      // If it is a vector, the transpose sizes might match.
-      || ( PlainObjectType::IsVectorAtCompileTime
-            && ((int(PlainObjectType::RowsAtCompileTime)==Eigen::Dynamic
-              || int(Expression::ColsAtCompileTime)==Eigen::Dynamic
-              || int(PlainObjectType::RowsAtCompileTime)==int(Expression::ColsAtCompileTime))
-            &&  (int(PlainObjectType::ColsAtCompileTime)==Eigen::Dynamic
-              || int(Expression::RowsAtCompileTime)==Eigen::Dynamic
-              || int(PlainObjectType::ColsAtCompileTime)==int(Expression::RowsAtCompileTime)))),
-      YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES
-    )
-
-    // Determine runtime rows and columns.
-    Index rows = expr.rows();
-    Index cols = expr.cols();
-    if(PlainObjectType::RowsAtCompileTime==1)
-    {
-      eigen_assert(expr.rows()==1 || expr.cols()==1);
-      rows = 1;
-      cols = expr.size();
-    }
-    else if(PlainObjectType::ColsAtCompileTime==1)
-    {
-      eigen_assert(expr.rows()==1 || expr.cols()==1);
-      rows = expr.size();
-      cols = 1;
-    }
-    // Verify that the sizes are valid.
-    eigen_assert(
-      (PlainObjectType::RowsAtCompileTime == Dynamic) || (PlainObjectType::RowsAtCompileTime == rows));
-    eigen_assert(
-      (PlainObjectType::ColsAtCompileTime == Dynamic) || (PlainObjectType::ColsAtCompileTime == cols));
-
-
-    // If this is a vector, we might be transposing, which means that stride should swap.
-    const bool transpose = PlainObjectType::IsVectorAtCompileTime && (rows != expr.rows());
-    // If the storage format differs, we also need to swap the stride.
-    const bool row_major = ((PlainObjectType::Flags)&RowMajorBit) != 0;
-    const bool expr_row_major = (Expression::Flags&RowMajorBit) != 0;
-    const bool storage_differs =  (row_major != expr_row_major);
-
-    const bool swap_stride = (transpose != storage_differs);
-
-    // Determine expr's actual strides, resolving any defaults if zero.
-    const Index expr_inner_actual = resolveInnerStride(expr.innerStride());
-    const Index expr_outer_actual = resolveOuterStride(expr_inner_actual,
-                                                       expr.outerStride(),
-                                                       expr.rows(),
-                                                       expr.cols(),
-                                                       Expression::IsVectorAtCompileTime != 0,
-                                                       expr_row_major);
-
-    // If this is a column-major row vector or row-major column vector, the inner-stride
-    // is arbitrary, so set it to either the compile-time inner stride or 1.
-    const bool row_vector = (rows == 1);
-    const bool col_vector = (cols == 1);
-    const Index inner_stride =
-        ( (!row_major && row_vector) || (row_major && col_vector) ) ?
-            ( StrideType::InnerStrideAtCompileTime > 0 ? Index(StrideType::InnerStrideAtCompileTime) : 1)
-            : swap_stride ? expr_outer_actual : expr_inner_actual;
-
-    // If this is a column-major column vector or row-major row vector, the outer-stride
-    // is arbitrary, so set it to either the compile-time outer stride or vector size.
-    const Index outer_stride =
-      ( (!row_major && col_vector) || (row_major && row_vector) ) ?
-          ( StrideType::OuterStrideAtCompileTime > 0 ? Index(StrideType::OuterStrideAtCompileTime) : rows * cols * inner_stride)
-          : swap_stride ? expr_inner_actual : expr_outer_actual;
-
-    // Check if given inner/outer strides are compatible with compile-time strides.
-    const bool inner_valid = (StrideType::InnerStrideAtCompileTime == Dynamic)
-        || (resolveInnerStride(Index(StrideType::InnerStrideAtCompileTime)) == inner_stride);
-    if (!inner_valid) {
-      return false;
-    }
-
-    const bool outer_valid = (StrideType::OuterStrideAtCompileTime == Dynamic)
-        || (resolveOuterStride(
-              inner_stride,
-              Index(StrideType::OuterStrideAtCompileTime),
-              rows, cols, PlainObjectType::IsVectorAtCompileTime != 0,
-              row_major)
-            == outer_stride);
-    if (!outer_valid) {
-      return false;
-    }
-
-    ::new (static_cast<Base*>(this)) Base(expr.data(), rows, cols);
-    ::new (&m_stride) StrideBase(
-      (StrideType::OuterStrideAtCompileTime == 0) ? 0 : outer_stride,
-      (StrideType::InnerStrideAtCompileTime == 0) ? 0 : inner_stride );
-    return true;
-  }
-
-  StrideBase m_stride;
-};
-
-/** \class Ref
-  * \ingroup Core_Module
-  *
-  * \brief A matrix or vector expression mapping an existing expression
-  *
-  * \tparam PlainObjectType the equivalent matrix type of the mapped data
-  * \tparam Options specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
-  *                 The default is \c #Unaligned.
-  * \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1),
-  *                   but accepts a variable outer stride (leading dimension).
-  *                   This can be overridden by specifying strides.
-  *                   The type passed here must be a specialization of the Stride template, see examples below.
-  *
-  * This class provides a way to write non-template functions taking Eigen objects as parameters while limiting the number of copies.
-  * A Ref<> object can represent either a const expression or a l-value:
-  * \code
-  * // in-out argument:
-  * void foo1(Ref<VectorXf> x);
-  *
-  * // read-only const argument:
-  * void foo2(const Ref<const VectorXf>& x);
-  * \endcode
-  *
-  * In the in-out case, the input argument must satisfy the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered.
-  * By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout.
-  * Likewise, a Ref<MatrixXf> can reference any column-major dense matrix expression of float whose column's elements are contiguously stored with
-  * the possibility to have a constant space in-between each column, i.e. the inner stride must be equal to 1, but the outer stride (or leading dimension)
-  * can be greater than the number of rows.
-  *
-  * In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function.
-  * Here are some examples:
-  * \code
-  * MatrixXf A;
-  * VectorXf a;
-  * foo1(a.head());             // OK
-  * foo1(A.col());              // OK
-  * foo1(A.row());              // Compilation error because here innerstride!=1
-  * foo2(A.row());              // Compilation error because A.row() is a 1xN object while foo2 is expecting a Nx1 object
-  * foo2(A.row().transpose());  // The row is copied into a contiguous temporary
-  * foo2(2*a);                  // The expression is evaluated into a temporary
-  * foo2(A.col().segment(2,4)); // No temporary
-  * \endcode
-  *
-  * The range of inputs that can be referenced without temporary can be enlarged using the last two template parameters.
-  * Here is an example accepting an innerstride!=1:
-  * \code
-  * // in-out argument:
-  * void foo3(Ref<VectorXf,0,InnerStride<> > x);
-  * foo3(A.row());              // OK
-  * \endcode
-  * The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involve more
-  * expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overload internally calling a
-  * template function, e.g.:
-  * \code
-  * // in the .h:
-  * void foo(const Ref<MatrixXf>& A);
-  * void foo(const Ref<MatrixXf,0,Stride<> >& A);
-  *
-  * // in the .cpp:
-  * template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
-  *     ... // crazy code goes here
-  * }
-  * void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
-  * void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
-  * \endcode
-  *
-  * See also the following stackoverflow questions for further references:
-  *  - <a href="http://stackoverflow.com/questions/21132538/correct-usage-of-the-eigenref-class">Correct usage of the Eigen::Ref<> class</a>
-  *
-  * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
-  */
-template<typename PlainObjectType, int Options, typename StrideType> class Ref
-  : public RefBase<Ref<PlainObjectType, Options, StrideType> >
-{
-  private:
-    typedef internal::traits<Ref> Traits;
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0);
-  public:
-
-    typedef RefBase<Ref> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
-
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      // Construction must pass since we will not create temprary storage in the non-const case.
-      const bool success = Base::construct(expr.derived());
-      EIGEN_UNUSED_VARIABLE(success)
-      eigen_assert(success);
-    }
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
-    #else
-    /** Implicit constructor from any dense expression */
-    template<typename Derived>
-    inline Ref(DenseBase<Derived>& expr)
-    #endif
-    {
-      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      // Construction must pass since we will not create temporary storage in the non-const case.
-      const bool success = Base::construct(expr.const_cast_derived());
-      EIGEN_UNUSED_VARIABLE(success)
-      eigen_assert(success);
-    }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)
-
-};
-
-// this is the const ref version
-template<typename TPlainObjectType, int Options, typename StrideType> class Ref<const TPlainObjectType, Options, StrideType>
-  : public RefBase<Ref<const TPlainObjectType, Options, StrideType> >
-{
-    typedef internal::traits<Ref> Traits;
-  public:
-
-    typedef RefBase<Ref> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
-
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>::type* = 0)
-    {
-//      std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
-//      std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";
-//      std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n";
-      construct(expr.derived(), typename Traits::template match<Derived>::type());
-    }
-
-    EIGEN_DEVICE_FUNC inline Ref(const Ref& other) : Base(other) {
-      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
-    }
-
-    template<typename OtherRef>
-    EIGEN_DEVICE_FUNC inline Ref(const RefBase<OtherRef>& other) {
-      construct(other.derived(), typename Traits::template match<OtherRef>::type());
-    }
-
-  protected:
-
-    template<typename Expression>
-    EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type)
-    {
-      // Check if we can use the underlying expr's storage directly, otherwise call the copy version.
-      if (!Base::construct(expr)) {
-        construct(expr, internal::false_type());
-      }
-    }
-
-    template<typename Expression>
-    EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
-    {
-      internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar,Scalar>());
-      Base::construct(m_object);
-    }
-
-  protected:
-    TPlainObjectType m_object;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_REF_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Replicate.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Replicate.h
deleted file mode 100644
index ab5be7e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Replicate.h
+++ /dev/null
@@ -1,142 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REPLICATE_H
-#define EIGEN_REPLICATE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename MatrixType,int RowFactor,int ColFactor>
-struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
- : traits<MatrixType>
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename traits<MatrixType>::XprKind XprKind;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic
-                      ? Dynamic
-                      : RowFactor * MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = ColFactor==Dynamic || int(MatrixType::ColsAtCompileTime)==Dynamic
-                      ? Dynamic
-                      : ColFactor * MatrixType::ColsAtCompileTime,
-   //FIXME we don't propagate the max sizes !!!
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    IsRowMajor = MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1 ? 1
-               : MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1 ? 0
-               : (MatrixType::Flags & RowMajorBit) ? 1 : 0,
-
-    // FIXME enable DirectAccess with negative strides?
-    Flags = IsRowMajor ? RowMajorBit : 0
-  };
-};
-}
-
-/**
-  * \class Replicate
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the multiple replication of a matrix or vector
-  *
-  * \tparam MatrixType the type of the object we are replicating
-  * \tparam RowFactor number of repetitions at compile time along the vertical direction, can be Dynamic.
-  * \tparam ColFactor number of repetitions at compile time along the horizontal direction, can be Dynamic.
-  *
-  * This class represents an expression of the multiple replication of a matrix or vector.
-  * It is the return type of DenseBase::replicate() and most of the time
-  * this is the only way it is used.
-  *
-  * \sa DenseBase::replicate()
-  */
-template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
-  : public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type
-{
-    typedef typename internal::traits<Replicate>::MatrixTypeNested MatrixTypeNested;
-    typedef typename internal::traits<Replicate>::_MatrixTypeNested _MatrixTypeNested;
-  public:
-
-    typedef typename internal::dense_xpr_base<Replicate>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-    template<typename OriginalMatrixType>
-    EIGEN_DEVICE_FUNC
-    inline explicit Replicate(const OriginalMatrixType& matrix)
-      : m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
-                          THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
-      eigen_assert(RowFactor!=Dynamic && ColFactor!=Dynamic);
-    }
-
-    template<typename OriginalMatrixType>
-    EIGEN_DEVICE_FUNC
-    inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
-      : m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
-                          THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
-
-    EIGEN_DEVICE_FUNC
-    const _MatrixTypeNested& nestedExpression() const
-    {
-      return m_matrix;
-    }
-
-  protected:
-    MatrixTypeNested m_matrix;
-    const internal::variable_if_dynamic<Index, RowFactor> m_rowFactor;
-    const internal::variable_if_dynamic<Index, ColFactor> m_colFactor;
-};
-
-/**
-  * \return an expression of the replication of \c *this
-  *
-  * Example: \include MatrixBase_replicate.cpp
-  * Output: \verbinclude MatrixBase_replicate.out
-  *
-  * \sa VectorwiseOp::replicate(), DenseBase::replicate(Index,Index), class Replicate
-  */
-template<typename Derived>
-template<int RowFactor, int ColFactor>
-EIGEN_DEVICE_FUNC const Replicate<Derived,RowFactor,ColFactor>
-DenseBase<Derived>::replicate() const
-{
-  return Replicate<Derived,RowFactor,ColFactor>(derived());
-}
-
-/**
-  * \return an expression of the replication of each column (or row) of \c *this
-  *
-  * Example: \include DirectionWise_replicate_int.cpp
-  * Output: \verbinclude DirectionWise_replicate_int.out
-  *
-  * \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate
-  */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC const typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
-VectorwiseOp<ExpressionType,Direction>::replicate(Index factor) const
-{
-  return typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
-          (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REPLICATE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Reshaped.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Reshaped.h
deleted file mode 100644
index 52de73b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Reshaped.h
+++ /dev/null
@@ -1,454 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2014 yoco <peter.xiau@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_RESHAPED_H
-#define EIGEN_RESHAPED_H
-
-namespace Eigen {
-
-/** \class Reshaped
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a fixed-size or dynamic-size reshape
-  *
-  * \tparam XprType the type of the expression in which we are taking a reshape
-  * \tparam Rows the number of rows of the reshape we are taking at compile time (optional)
-  * \tparam Cols the number of columns of the reshape we are taking at compile time (optional)
-  * \tparam Order can be ColMajor or RowMajor, default is ColMajor.
-  *
-  * This class represents an expression of either a fixed-size or dynamic-size reshape.
-  * It is the return type of DenseBase::reshaped(NRowsType,NColsType) and
-  * most of the time this is the only way it is used.
-  *
-  * However, in C++98, if you want to directly maniputate reshaped expressions,
-  * for instance if you want to write a function returning such an expression, you
-  * will need to use this class. In C++11, it is advised to use the \em auto
-  * keyword for such use cases.
-  *
-  * Here is an example illustrating the dynamic case:
-  * \include class_Reshaped.cpp
-  * Output: \verbinclude class_Reshaped.out
-  *
-  * Here is an example illustrating the fixed-size case:
-  * \include class_FixedReshaped.cpp
-  * Output: \verbinclude class_FixedReshaped.out
-  *
-  * \sa DenseBase::reshaped(NRowsType,NColsType)
-  */
-
-namespace internal {
-
-template<typename XprType, int Rows, int Cols, int Order>
-struct traits<Reshaped<XprType, Rows, Cols, Order> > : traits<XprType>
-{
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename traits<XprType>::StorageKind StorageKind;
-  typedef typename traits<XprType>::XprKind XprKind;
-  enum{
-    MatrixRows = traits<XprType>::RowsAtCompileTime,
-    MatrixCols = traits<XprType>::ColsAtCompileTime,
-    RowsAtCompileTime = Rows,
-    ColsAtCompileTime = Cols,
-    MaxRowsAtCompileTime = Rows,
-    MaxColsAtCompileTime = Cols,
-    XpxStorageOrder = ((int(traits<XprType>::Flags) & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor,
-    ReshapedStorageOrder = (RowsAtCompileTime == 1 && ColsAtCompileTime != 1) ? RowMajor
-                         : (ColsAtCompileTime == 1 && RowsAtCompileTime != 1) ? ColMajor
-                         : XpxStorageOrder,
-    HasSameStorageOrderAsXprType = (ReshapedStorageOrder == XpxStorageOrder),
-    InnerSize = (ReshapedStorageOrder==int(RowMajor)) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
-                             ? int(inner_stride_at_compile_time<XprType>::ret)
-                             : Dynamic,
-    OuterStrideAtCompileTime = Dynamic,
-
-    HasDirectAccess = internal::has_direct_access<XprType>::ret
-                    && (Order==int(XpxStorageOrder))
-                    && ((evaluator<XprType>::Flags&LinearAccessBit)==LinearAccessBit),
-
-    MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
-                       && (InnerStrideAtCompileTime == 1)
-                        ? PacketAccessBit : 0,
-    //MaskAlignedBit = ((OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
-    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
-    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
-    FlagsRowMajorBit = (ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0,
-    FlagsDirectAccessBit = HasDirectAccess ? DirectAccessBit : 0,
-    Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) | MaskPacketAccessBit),
-
-    Flags = (Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit | FlagsDirectAccessBit)
-  };
-};
-
-template<typename XprType, int Rows, int Cols, int Order, bool HasDirectAccess> class ReshapedImpl_dense;
-
-} // end namespace internal
-
-template<typename XprType, int Rows, int Cols, int Order, typename StorageKind> class ReshapedImpl;
-
-template<typename XprType, int Rows, int Cols, int Order> class Reshaped
-  : public ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind>
-{
-    typedef ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind> Impl;
-  public:
-    //typedef typename Impl::Base Base;
-    typedef Impl Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Reshaped)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reshaped)
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline Reshaped(XprType& xpr)
-      : Impl(xpr)
-    {
-      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
-      eigen_assert(Rows * Cols == xpr.rows() * xpr.cols());
-    }
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline Reshaped(XprType& xpr,
-          Index reshapeRows, Index reshapeCols)
-      : Impl(xpr, reshapeRows, reshapeCols)
-    {
-      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==reshapeRows)
-          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==reshapeCols));
-      eigen_assert(reshapeRows * reshapeCols == xpr.rows() * xpr.cols());
-    }
-};
-
-// The generic default implementation for dense reshape simply forward to the internal::ReshapedImpl_dense
-// that must be specialized for direct and non-direct access...
-template<typename XprType, int Rows, int Cols, int Order>
-class ReshapedImpl<XprType, Rows, Cols, Order, Dense>
-  : public internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,internal::traits<Reshaped<XprType,Rows,Cols,Order> >::HasDirectAccess>
-{
-    typedef internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,internal::traits<Reshaped<XprType,Rows,Cols,Order> >::HasDirectAccess> Impl;
-  public:
-    typedef Impl Base;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl)
-    EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr) : Impl(xpr) {}
-    EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr, Index reshapeRows, Index reshapeCols)
-      : Impl(xpr, reshapeRows, reshapeCols) {}
-};
-
-namespace internal {
-
-/** \internal Internal implementation of dense Reshaped in the general case. */
-template<typename XprType, int Rows, int Cols, int Order>
-class ReshapedImpl_dense<XprType,Rows,Cols,Order,false>
-  : public internal::dense_xpr_base<Reshaped<XprType, Rows, Cols, Order> >::type
-{
-    typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
-  public:
-
-    typedef typename internal::dense_xpr_base<ReshapedType>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
-
-    typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
-    typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-    class InnerIterator;
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr)
-      : m_xpr(xpr), m_rows(Rows), m_cols(Cols)
-    {}
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
-      : m_xpr(xpr), m_rows(nRows), m_cols(nCols)
-    {}
-
-    EIGEN_DEVICE_FUNC Index rows() const { return m_rows; }
-    EIGEN_DEVICE_FUNC Index cols() const { return m_cols; }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \sa MapBase::data() */
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const;
-    EIGEN_DEVICE_FUNC inline Index innerStride() const;
-    EIGEN_DEVICE_FUNC inline Index outerStride() const;
-    #endif
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<XprType>::type&
-    nestedExpression() const { return m_xpr; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC
-    typename internal::remove_reference<XprType>::type&
-    nestedExpression() { return m_xpr; }
-
-  protected:
-
-    MatrixTypeNested m_xpr;
-    const internal::variable_if_dynamic<Index, Rows> m_rows;
-    const internal::variable_if_dynamic<Index, Cols> m_cols;
-};
-
-
-/** \internal Internal implementation of dense Reshaped in the direct access case. */
-template<typename XprType, int Rows, int Cols, int Order>
-class ReshapedImpl_dense<XprType, Rows, Cols, Order, true>
-  : public MapBase<Reshaped<XprType, Rows, Cols, Order> >
-{
-    typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
-    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-  public:
-
-    typedef MapBase<ReshapedType> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr)
-      : Base(xpr.data()), m_xpr(xpr)
-    {}
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
-      : Base(xpr.data(), nRows, nCols),
-        m_xpr(xpr)
-    {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
-    {
-      return m_xpr;
-    }
-
-    EIGEN_DEVICE_FUNC
-    XprType& nestedExpression() { return m_xpr; }
-
-    /** \sa MapBase::innerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const
-    {
-      return m_xpr.innerStride();
-    }
-
-    /** \sa MapBase::outerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const
-    {
-      return ((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows();
-    }
-
-  protected:
-
-    XprTypeNested m_xpr;
-};
-
-// Evaluators
-template<typename ArgType, int Rows, int Cols, int Order, bool HasDirectAccess> struct reshaped_evaluator;
-
-template<typename ArgType, int Rows, int Cols, int Order>
-struct evaluator<Reshaped<ArgType, Rows, Cols, Order> >
-  : reshaped_evaluator<ArgType, Rows, Cols, Order, traits<Reshaped<ArgType,Rows,Cols,Order> >::HasDirectAccess>
-{
-  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
-  typedef typename XprType::Scalar Scalar;
-  // TODO: should check for smaller packet types
-  typedef typename packet_traits<Scalar>::type PacketScalar;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    HasDirectAccess = traits<XprType>::HasDirectAccess,
-
-//     RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
-//     ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
-//     MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
-//     MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
-//
-//     InnerStrideAtCompileTime = traits<XprType>::HasSameStorageOrderAsXprType
-//                              ? int(inner_stride_at_compile_time<ArgType>::ret)
-//                              : Dynamic,
-//     OuterStrideAtCompileTime = Dynamic,
-
-    FlagsLinearAccessBit = (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1 || HasDirectAccess) ? LinearAccessBit : 0,
-    FlagsRowMajorBit = (traits<XprType>::ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0,
-    FlagsDirectAccessBit =  HasDirectAccess ? DirectAccessBit : 0,
-    Flags0 = evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit),
-    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit | FlagsDirectAccessBit,
-
-    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
-    Alignment = evaluator<ArgType>::Alignment
-  };
-  typedef reshaped_evaluator<ArgType, Rows, Cols, Order, HasDirectAccess> reshaped_evaluator_type;
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : reshaped_evaluator_type(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-};
-
-template<typename ArgType, int Rows, int Cols, int Order>
-struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ false>
-  : evaluator_base<Reshaped<ArgType, Rows, Cols, Order> >
-{
-  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of index computations */,
-
-    Flags = (evaluator<ArgType>::Flags & (HereditaryBits /*| LinearAccessBit | DirectAccessBit*/)),
-
-    Alignment = 0
-  };
-
-  EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  typedef std::pair<Index, Index> RowCol;
-
-  inline RowCol index_remap(Index rowId, Index colId) const
-  {
-    if(Order==ColMajor)
-    {
-      const Index nth_elem_idx = colId * m_xpr.rows() + rowId;
-      return RowCol(nth_elem_idx % m_xpr.nestedExpression().rows(),
-                    nth_elem_idx / m_xpr.nestedExpression().rows());
-    }
-    else
-    {
-      const Index nth_elem_idx = colId + rowId * m_xpr.cols();
-      return RowCol(nth_elem_idx / m_xpr.nestedExpression().cols(),
-                    nth_elem_idx % m_xpr.nestedExpression().cols());
-    }
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline Scalar& coeffRef(Index rowId, Index colId)
-  {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline const Scalar& coeffRef(Index rowId, Index colId) const
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.coeff(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline Scalar& coeffRef(Index index)
-  {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
-                                       Rows == 1 ? index : 0);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline const Scalar& coeffRef(Index index) const
-  {
-    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
-                                       Rows == 1 ? index : 0);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline const CoeffReturnType coeff(Index index) const
-  {
-    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
-                                       Rows == 1 ? index : 0);
-    return m_argImpl.coeff(row_col.first, row_col.second);
-  }
-#if 0
-  EIGEN_DEVICE_FUNC
-  template<int LoadMode>
-  inline PacketScalar packet(Index rowId, Index colId) const
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second);
-
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC
-  inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    m_argImpl.const_cast_derived().template writePacket<Unaligned>
-            (row_col.first, row_col.second, val);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC
-  inline PacketScalar packet(Index index) const
-  {
-    const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index,
-                                        RowsAtCompileTime == 1 ? index : 0);
-    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC
-  inline void writePacket(Index index, const PacketScalar& val)
-  {
-    const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index,
-                                        RowsAtCompileTime == 1 ? index : 0);
-    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second, val);
-  }
-#endif
-protected:
-
-  evaluator<ArgType> m_argImpl;
-  const XprType& m_xpr;
-
-};
-
-template<typename ArgType, int Rows, int Cols, int Order>
-struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ true>
-: mapbase_evaluator<Reshaped<ArgType, Rows, Cols, Order>,
-                      typename Reshaped<ArgType, Rows, Cols, Order>::PlainObject>
-{
-  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
-  typedef typename XprType::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr)
-    : mapbase_evaluator<XprType, typename XprType::PlainObject>(xpr)
-  {
-    // TODO: for the 3.4 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
-    eigen_assert(((internal::UIntPtr(xpr.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_RESHAPED_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ReturnByValue.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ReturnByValue.h
deleted file mode 100644
index 4dad13e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/ReturnByValue.h
+++ /dev/null
@@ -1,119 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_RETURNBYVALUE_H
-#define EIGEN_RETURNBYVALUE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Derived>
-struct traits<ReturnByValue<Derived> >
-  : public traits<typename traits<Derived>::ReturnType>
-{
-  enum {
-    // We're disabling the DirectAccess because e.g. the constructor of
-    // the Block-with-DirectAccess expression requires to have a coeffRef method.
-    // Also, we don't want to have to implement the stride stuff.
-    Flags = (traits<typename traits<Derived>::ReturnType>::Flags
-             | EvalBeforeNestingBit) & ~DirectAccessBit
-  };
-};
-
-/* The ReturnByValue object doesn't even have a coeff() method.
- * So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix.
- * So internal::nested always gives the plain return matrix type.
- *
- * FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ??
- * Answer: EvalBeforeNestingBit should be deprecated since we have the evaluators
- */
-template<typename Derived,int n,typename PlainObject>
-struct nested_eval<ReturnByValue<Derived>, n, PlainObject>
-{
-  typedef typename traits<Derived>::ReturnType type;
-};
-
-} // end namespace internal
-
-/** \class ReturnByValue
-  * \ingroup Core_Module
-  *
-  */
-template<typename Derived> class ReturnByValue
-  : public internal::dense_xpr_base< ReturnByValue<Derived> >::type, internal::no_assignment_operator
-{
-  public:
-    typedef typename internal::traits<Derived>::ReturnType ReturnType;
-
-    typedef typename internal::dense_xpr_base<ReturnByValue>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue)
-
-    template<typename Dest>
-    EIGEN_DEVICE_FUNC
-    inline void evalTo(Dest& dst) const
-    { static_cast<const Derived*>(this)->evalTo(dst); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return static_cast<const Derived*>(this)->rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return static_cast<const Derived*>(this)->cols(); }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT
-    class Unusable{
-      Unusable(const Unusable&) {}
-      Unusable& operator=(const Unusable&) {return *this;}
-    };
-    const Unusable& coeff(Index) const { return *reinterpret_cast<const Unusable*>(this); }
-    const Unusable& coeff(Index,Index) const { return *reinterpret_cast<const Unusable*>(this); }
-    Unusable& coeffRef(Index) { return *reinterpret_cast<Unusable*>(this); }
-    Unusable& coeffRef(Index,Index) { return *reinterpret_cast<Unusable*>(this); }
-#undef Unusable
-#endif
-};
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
-{
-  other.evalTo(derived());
-  return derived();
-}
-
-namespace internal {
-
-// Expression is evaluated in a temporary; default implementation of Assignment is bypassed so that
-// when a ReturnByValue expression is assigned, the evaluator is not constructed.
-// TODO: Finalize port to new regime; ReturnByValue should not exist in the expression world
-
-template<typename Derived>
-struct evaluator<ReturnByValue<Derived> >
-  : public evaluator<typename internal::traits<Derived>::ReturnType>
-{
-  typedef ReturnByValue<Derived> XprType;
-  typedef typename internal::traits<Derived>::ReturnType PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    xpr.evalTo(m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_RETURNBYVALUE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Reverse.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Reverse.h
deleted file mode 100644
index 28cdd76..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Reverse.h
+++ /dev/null
@@ -1,217 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com>
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REVERSE_H
-#define EIGEN_REVERSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType, int Direction>
-struct traits<Reverse<MatrixType, Direction> >
- : traits<MatrixType>
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename traits<MatrixType>::XprKind XprKind;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Flags = _MatrixTypeNested::Flags & (RowMajorBit | LvalueBit)
-  };
-};
-
-template<typename PacketType, bool ReversePacket> struct reverse_packet_cond
-{
-  static inline PacketType run(const PacketType& x) { return preverse(x); }
-};
-
-template<typename PacketType> struct reverse_packet_cond<PacketType,false>
-{
-  static inline PacketType run(const PacketType& x) { return x; }
-};
-
-} // end namespace internal
-
-/** \class Reverse
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the reverse of a vector or matrix
-  *
-  * \tparam MatrixType the type of the object of which we are taking the reverse
-  * \tparam Direction defines the direction of the reverse operation, can be Vertical, Horizontal, or BothDirections
-  *
-  * This class represents an expression of the reverse of a vector.
-  * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::reverse(), VectorwiseOp::reverse()
-  */
-template<typename MatrixType, int Direction> class Reverse
-  : public internal::dense_xpr_base< Reverse<MatrixType, Direction> >::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<Reverse>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-    using Base::IsRowMajor;
-
-  protected:
-    enum {
-      PacketSize = internal::packet_traits<Scalar>::size,
-      IsColMajor = !IsRowMajor,
-      ReverseRow = (Direction == Vertical)   || (Direction == BothDirections),
-      ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
-      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
-      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1,
-      ReversePacket = (Direction == BothDirections)
-                    || ((Direction == Vertical)   && IsColMajor)
-                    || ((Direction == Horizontal) && IsRowMajor)
-    };
-    typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet;
-  public:
-
-    EIGEN_DEVICE_FUNC explicit inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse)
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    EIGEN_DEVICE_FUNC inline Index innerStride() const
-    {
-      return -m_matrix.innerStride();
-    }
-
-    EIGEN_DEVICE_FUNC const typename internal::remove_all<typename MatrixType::Nested>::type&
-    nestedExpression() const
-    {
-      return m_matrix;
-    }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-};
-
-/** \returns an expression of the reverse of *this.
-  *
-  * Example: \include MatrixBase_reverse.cpp
-  * Output: \verbinclude MatrixBase_reverse.out
-  *
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ReverseReturnType
-DenseBase<Derived>::reverse()
-{
-  return ReverseReturnType(derived());
-}
-
-
-//reverse const overload moved DenseBase.h due to a CUDA compiler bug
-
-/** This is the "in place" version of reverse: it reverses \c *this.
-  *
-  * In most cases it is probably better to simply use the reversed expression
-  * of a matrix. However, when reversing the matrix data itself is really needed,
-  * then this "in-place" version is probably the right choice because it provides
-  * the following additional benefits:
-  *  - less error prone: doing the same operation with .reverse() requires special care:
-  *    \code m = m.reverse().eval(); \endcode
-  *  - this API enables reverse operations without the need for a temporary
-  *  - it allows future optimizations (cache friendliness, etc.)
-  *
-  * \sa VectorwiseOp::reverseInPlace(), reverse() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::reverseInPlace()
-{
-  if(cols()>rows())
-  {
-    Index half = cols()/2;
-    leftCols(half).swap(rightCols(half).reverse());
-    if((cols()%2)==1)
-    {
-      Index half2 = rows()/2;
-      col(half).head(half2).swap(col(half).tail(half2).reverse());
-    }
-  }
-  else
-  {
-    Index half = rows()/2;
-    topRows(half).swap(bottomRows(half).reverse());
-    if((rows()%2)==1)
-    {
-      Index half2 = cols()/2;
-      row(half).head(half2).swap(row(half).tail(half2).reverse());
-    }
-  }
-}
-
-namespace internal {
-
-template<int Direction>
-struct vectorwise_reverse_inplace_impl;
-
-template<>
-struct vectorwise_reverse_inplace_impl<Vertical>
-{
-  template<typename ExpressionType>
-  static void run(ExpressionType &xpr)
-  {
-    const int HalfAtCompileTime = ExpressionType::RowsAtCompileTime==Dynamic?Dynamic:ExpressionType::RowsAtCompileTime/2;
-    Index half = xpr.rows()/2;
-    xpr.topRows(fix<HalfAtCompileTime>(half))
-       .swap(xpr.bottomRows(fix<HalfAtCompileTime>(half)).colwise().reverse());
-  }
-};
-
-template<>
-struct vectorwise_reverse_inplace_impl<Horizontal>
-{
-  template<typename ExpressionType>
-  static void run(ExpressionType &xpr)
-  {
-    const int HalfAtCompileTime = ExpressionType::ColsAtCompileTime==Dynamic?Dynamic:ExpressionType::ColsAtCompileTime/2;
-    Index half = xpr.cols()/2;
-    xpr.leftCols(fix<HalfAtCompileTime>(half))
-       .swap(xpr.rightCols(fix<HalfAtCompileTime>(half)).rowwise().reverse());
-  }
-};
-
-} // end namespace internal
-
-/** This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of \c *this.
-  *
-  * In most cases it is probably better to simply use the reversed expression
-  * of a matrix. However, when reversing the matrix data itself is really needed,
-  * then this "in-place" version is probably the right choice because it provides
-  * the following additional benefits:
-  *  - less error prone: doing the same operation with .reverse() requires special care:
-  *    \code m = m.reverse().eval(); \endcode
-  *  - this API enables reverse operations without the need for a temporary
-  *
-  * \sa DenseBase::reverseInPlace(), reverse() */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC void VectorwiseOp<ExpressionType,Direction>::reverseInPlace()
-{
-  internal::vectorwise_reverse_inplace_impl<Direction>::run(m_matrix);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REVERSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Select.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Select.h
deleted file mode 100644
index 7c86bf8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Select.h
+++ /dev/null
@@ -1,164 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELECT_H
-#define EIGEN_SELECT_H
-
-namespace Eigen {
-
-/** \class Select
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a coefficient wise version of the C++ ternary operator ?:
-  *
-  * \param ConditionMatrixType the type of the \em condition expression which must be a boolean matrix
-  * \param ThenMatrixType the type of the \em then expression
-  * \param ElseMatrixType the type of the \em else expression
-  *
-  * This class represents an expression of a coefficient wise version of the C++ ternary operator ?:.
-  * It is the return type of DenseBase::select() and most of the time this is the only way it is used.
-  *
-  * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const
-  */
-
-namespace internal {
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
-struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
- : traits<ThenMatrixType>
-{
-  typedef typename traits<ThenMatrixType>::Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef typename traits<ThenMatrixType>::XprKind XprKind;
-  typedef typename ConditionMatrixType::Nested ConditionMatrixNested;
-  typedef typename ThenMatrixType::Nested ThenMatrixNested;
-  typedef typename ElseMatrixType::Nested ElseMatrixNested;
-  enum {
-    RowsAtCompileTime = ConditionMatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime,
-    Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & RowMajorBit
-  };
-};
-}
-
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
-class Select : public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type,
-               internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<Select>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Select)
-
-    inline EIGEN_DEVICE_FUNC
-    Select(const ConditionMatrixType& a_conditionMatrix,
-           const ThenMatrixType& a_thenMatrix,
-           const ElseMatrixType& a_elseMatrix)
-      : m_condition(a_conditionMatrix), m_then(a_thenMatrix), m_else(a_elseMatrix)
-    {
-      eigen_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows());
-      eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols());
-    }
-
-    inline EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_condition.rows(); }
-    inline EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_condition.cols(); }
-
-    inline EIGEN_DEVICE_FUNC
-    const Scalar coeff(Index i, Index j) const
-    {
-      if (m_condition.coeff(i,j))
-        return m_then.coeff(i,j);
-      else
-        return m_else.coeff(i,j);
-    }
-
-    inline EIGEN_DEVICE_FUNC
-    const Scalar coeff(Index i) const
-    {
-      if (m_condition.coeff(i))
-        return m_then.coeff(i);
-      else
-        return m_else.coeff(i);
-    }
-
-    inline EIGEN_DEVICE_FUNC const ConditionMatrixType& conditionMatrix() const
-    {
-      return m_condition;
-    }
-
-    inline EIGEN_DEVICE_FUNC const ThenMatrixType& thenMatrix() const
-    {
-      return m_then;
-    }
-
-    inline EIGEN_DEVICE_FUNC const ElseMatrixType& elseMatrix() const
-    {
-      return m_else;
-    }
-
-  protected:
-    typename ConditionMatrixType::Nested m_condition;
-    typename ThenMatrixType::Nested m_then;
-    typename ElseMatrixType::Nested m_else;
-};
-
-
-/** \returns a matrix where each coefficient (i,j) is equal to \a thenMatrix(i,j)
-  * if \c *this(i,j), and \a elseMatrix(i,j) otherwise.
-  *
-  * Example: \include MatrixBase_select.cpp
-  * Output: \verbinclude MatrixBase_select.out
-  *
-  * \sa class Select
-  */
-template<typename Derived>
-template<typename ThenDerived,typename ElseDerived>
-inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived,ElseDerived>
-DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
-                            const DenseBase<ElseDerived>& elseMatrix) const
-{
-  return Select<Derived,ThenDerived,ElseDerived>(derived(), thenMatrix.derived(), elseMatrix.derived());
-}
-
-/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
-  * the \em else expression being a scalar value.
-  *
-  * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
-  */
-template<typename Derived>
-template<typename ThenDerived>
-inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
-DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
-                           const typename ThenDerived::Scalar& elseScalar) const
-{
-  return Select<Derived,ThenDerived,typename ThenDerived::ConstantReturnType>(
-    derived(), thenMatrix.derived(), ThenDerived::Constant(rows(),cols(),elseScalar));
-}
-
-/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
-  * the \em then expression being a scalar value.
-  *
-  * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
-  */
-template<typename Derived>
-template<typename ElseDerived>
-inline EIGEN_DEVICE_FUNC const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
-DenseBase<Derived>::select(const typename ElseDerived::Scalar& thenScalar,
-                           const DenseBase<ElseDerived>& elseMatrix) const
-{
-  return Select<Derived,typename ElseDerived::ConstantReturnType,ElseDerived>(
-    derived(), ElseDerived::Constant(rows(),cols(),thenScalar), elseMatrix.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELECT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SelfAdjointView.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SelfAdjointView.h
deleted file mode 100644
index 8ce3b37..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SelfAdjointView.h
+++ /dev/null
@@ -1,365 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINTMATRIX_H
-#define EIGEN_SELFADJOINTMATRIX_H
-
-namespace Eigen {
-
-/** \class SelfAdjointView
-  * \ingroup Core_Module
-  *
-  *
-  * \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix
-  *
-  * \param MatrixType the type of the dense matrix storing the coefficients
-  * \param TriangularPart can be either \c #Lower or \c #Upper
-  *
-  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
-  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
-  * and most of the time this is the only way that it is used.
-  *
-  * \sa class TriangularBase, MatrixBase::selfadjointView()
-  */
-
-namespace internal {
-template<typename MatrixType, unsigned int UpLo>
-struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-  typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
-  typedef MatrixType ExpressionType;
-  typedef typename MatrixType::PlainObject FullMatrixType;
-  enum {
-    Mode = UpLo | SelfAdjoint,
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags =  MatrixTypeNestedCleaned::Flags & (HereditaryBits|FlagsLvalueBit)
-           & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)) // FIXME these flags should be preserved
-  };
-};
-}
-
-
-template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
-  : public TriangularBase<SelfAdjointView<_MatrixType, UpLo> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef TriangularBase<SelfAdjointView> Base;
-    typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
-    typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
-    typedef MatrixTypeNestedCleaned NestedExpression;
-
-    /** \brief The type of coefficients in this matrix */
-    typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
-    typedef SelfAdjointView<typename internal::add_const<MatrixType>::type, UpLo> ConstSelfAdjointView;
-
-    enum {
-      Mode = internal::traits<SelfAdjointView>::Mode,
-      Flags = internal::traits<SelfAdjointView>::Flags,
-      TransposeMode = ((int(Mode) & int(Upper)) ? Lower : 0) | ((int(Mode) & int(Lower)) ? Upper : 0)
-    };
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    EIGEN_DEVICE_FUNC
-    explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
-    {
-      EIGEN_STATIC_ASSERT(UpLo==Lower || UpLo==Upper,SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_matrix.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_matrix.innerStride(); }
-
-    /** \sa MatrixBase::coeff()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar coeff(Index row, Index col) const
-    {
-      Base::check_coordinates_internal(row, col);
-      return m_matrix.coeff(row, col);
-    }
-
-    /** \sa MatrixBase::coeffRef()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(SelfAdjointView);
-      Base::check_coordinates_internal(row, col);
-      return m_matrix.coeffRef(row, col);
-    }
-
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    const MatrixTypeNestedCleaned& _expression() const { return m_matrix; }
-
-    EIGEN_DEVICE_FUNC
-    const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
-    EIGEN_DEVICE_FUNC
-    MatrixTypeNestedCleaned& nestedExpression() { return m_matrix; }
-
-    /** Efficient triangular matrix times vector/matrix product */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<SelfAdjointView,OtherDerived>
-    operator*(const MatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<SelfAdjointView,OtherDerived>(*this, rhs.derived());
-    }
-
-    /** Efficient vector/matrix times triangular matrix product */
-    template<typename OtherDerived> friend
-    EIGEN_DEVICE_FUNC
-    const Product<OtherDerived,SelfAdjointView>
-    operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView& rhs)
-    {
-      return Product<OtherDerived,SelfAdjointView>(lhs.derived(),rhs);
-    }
-
-    friend EIGEN_DEVICE_FUNC
-    const SelfAdjointView<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,MatrixType,product),UpLo>
-    operator*(const Scalar& s, const SelfAdjointView& mat)
-    {
-      return (s*mat.nestedExpression()).template selfadjointView<UpLo>();
-    }
-
-    /** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this:
-      * \f$ this = this + \alpha u v^* + conj(\alpha) v u^* \f$
-      * \returns a reference to \c *this
-      *
-      * The vectors \a u and \c v \b must be column vectors, however they can be
-      * a adjoint expression without any overhead. Only the meaningful triangular
-      * part of the matrix is updated, the rest is left unchanged.
-      *
-      * \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
-      */
-    template<typename DerivedU, typename DerivedV>
-    EIGEN_DEVICE_FUNC
-    SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha = Scalar(1));
-
-    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
-      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
-      *
-      * \returns a reference to \c *this
-      *
-      * Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
-      * call this function with u.adjoint().
-      *
-      * \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
-      */
-    template<typename DerivedU>
-    EIGEN_DEVICE_FUNC
-    SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
-
-    /** \returns an expression of a triangular view extracted from the current selfadjoint view of a given triangular part
-      *
-      * The parameter \a TriMode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
-      * \c #Lower, \c #StrictlyLower, \c #UnitLower.
-      *
-      * If \c TriMode references the same triangular part than \c *this, then this method simply return a \c TriangularView of the nested expression,
-      * otherwise, the nested expression is first transposed, thus returning a \c TriangularView<Transpose<MatrixType>> object.
-      *
-      * \sa MatrixBase::triangularView(), class TriangularView
-      */
-    template<unsigned int TriMode>
-    EIGEN_DEVICE_FUNC
-    typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
-                                   TriangularView<MatrixType,TriMode>,
-                                   TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type
-    triangularView() const
-    {
-      typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::ConstTransposeReturnType>::type tmp1(m_matrix);
-      typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::AdjointReturnType>::type tmp2(tmp1);
-      return typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
-                                   TriangularView<MatrixType,TriMode>,
-                                   TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type(tmp2);
-    }
-
-    typedef SelfAdjointView<const MatrixConjugateReturnType,UpLo> ConjugateReturnType;
-    /** \sa MatrixBase::conjugate() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConjugateReturnType conjugate() const
-    { return ConjugateReturnType(m_matrix.conjugate()); }
-
-    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
-     *           returns \c *this otherwise.
-     */
-    template<bool Cond>
-    EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Cond,ConjugateReturnType,ConstSelfAdjointView>::type
-    conjugateIf() const
-    {
-      typedef typename internal::conditional<Cond,ConjugateReturnType,ConstSelfAdjointView>::type ReturnType;
-      return ReturnType(m_matrix.template conjugateIf<Cond>());
-    }
-
-    typedef SelfAdjointView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
-    /** \sa MatrixBase::adjoint() const */
-    EIGEN_DEVICE_FUNC
-    inline const AdjointReturnType adjoint() const
-    { return AdjointReturnType(m_matrix.adjoint()); }
-
-    typedef SelfAdjointView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
-     /** \sa MatrixBase::transpose() */
-    EIGEN_DEVICE_FUNC
-    inline TransposeReturnType transpose()
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      typename MatrixType::TransposeReturnType tmp(m_matrix);
-      return TransposeReturnType(tmp);
-    }
-
-    typedef SelfAdjointView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
-    /** \sa MatrixBase::transpose() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConstTransposeReturnType transpose() const
-    {
-      return ConstTransposeReturnType(m_matrix.transpose());
-    }
-
-    /** \returns a const expression of the main diagonal of the matrix \c *this
-      *
-      * This method simply returns the diagonal of the nested expression, thus by-passing the SelfAdjointView decorator.
-      *
-      * \sa MatrixBase::diagonal(), class Diagonal */
-    EIGEN_DEVICE_FUNC
-    typename MatrixType::ConstDiagonalReturnType diagonal() const
-    {
-      return typename MatrixType::ConstDiagonalReturnType(m_matrix);
-    }
-
-/////////// Cholesky module ///////////
-
-    const LLT<PlainObject, UpLo> llt() const;
-    const LDLT<PlainObject, UpLo> ldlt() const;
-
-/////////// Eigenvalue module ///////////
-
-    /** Real part of #Scalar */
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    /** Return type of eigenvalues() */
-    typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType;
-
-    EIGEN_DEVICE_FUNC
-    EigenvaluesReturnType eigenvalues() const;
-    EIGEN_DEVICE_FUNC
-    RealScalar operatorNorm() const;
-
-  protected:
-    MatrixTypeNested m_matrix;
-};
-
-
-// template<typename OtherDerived, typename MatrixType, unsigned int UpLo>
-// internal::selfadjoint_matrix_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >
-// operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView<MatrixType,UpLo>& rhs)
-// {
-//   return internal::matrix_selfadjoint_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >(lhs.derived(),rhs);
-// }
-
-// selfadjoint to dense matrix
-
-namespace internal {
-
-// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
-//      in the future selfadjoint-ness should be defined by the expression traits
-//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
-template<typename MatrixType, unsigned int Mode>
-struct evaluator_traits<SelfAdjointView<MatrixType,Mode> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef SelfAdjointShape Shape;
-};
-
-template<int UpLo, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version>
-class triangular_dense_assignment_kernel<UpLo,SelfAdjoint,SetOpposite,DstEvaluatorTypeT,SrcEvaluatorTypeT,Functor,Version>
-  : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
-  typedef typename Base::DstXprType DstXprType;
-  typedef typename Base::SrcXprType SrcXprType;
-  using Base::m_dst;
-  using Base::m_src;
-  using Base::m_functor;
-public:
-
-  typedef typename Base::DstEvaluatorType DstEvaluatorType;
-  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
-  typedef typename Base::Scalar Scalar;
-  typedef typename Base::AssignmentTraits AssignmentTraits;
-
-
-  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {}
-
-  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
-  {
-    eigen_internal_assert(row!=col);
-    Scalar tmp = m_src.coeff(row,col);
-    m_functor.assignCoeff(m_dst.coeffRef(row,col), tmp);
-    m_functor.assignCoeff(m_dst.coeffRef(col,row), numext::conj(tmp));
-  }
-
-  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
-  {
-    Base::assignCoeff(id,id);
-  }
-
-  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index, Index)
-  { eigen_internal_assert(false && "should never be called"); }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Implementation of MatrixBase methods
-***************************************************************************/
-
-/** This is the const version of MatrixBase::selfadjointView() */
-template<typename Derived>
-template<unsigned int UpLo>
-EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
-MatrixBase<Derived>::selfadjointView() const
-{
-  return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
-}
-
-/** \returns an expression of a symmetric/self-adjoint view extracted from the upper or lower triangular part of the current matrix
-  *
-  * The parameter \a UpLo can be either \c #Upper or \c #Lower
-  *
-  * Example: \include MatrixBase_selfadjointView.cpp
-  * Output: \verbinclude MatrixBase_selfadjointView.out
-  *
-  * \sa class SelfAdjointView
-  */
-template<typename Derived>
-template<unsigned int UpLo>
-EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
-MatrixBase<Derived>::selfadjointView()
-{
-  return typename SelfAdjointViewReturnType<UpLo>::Type(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINTMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SelfCwiseBinaryOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SelfCwiseBinaryOp.h
deleted file mode 100644
index 7c89c2e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SelfCwiseBinaryOp.h
+++ /dev/null
@@ -1,47 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFCWISEBINARYOP_H
-#define EIGEN_SELFCWISEBINARYOP_H
-
-namespace Eigen { 
-
-// TODO generalize the scalar type of 'other'
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFCWISEBINARYOP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Solve.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Solve.h
deleted file mode 100644
index 23d5cb7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Solve.h
+++ /dev/null
@@ -1,188 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVE_H
-#define EIGEN_SOLVE_H
-
-namespace Eigen {
-
-template<typename Decomposition, typename RhsType, typename StorageKind> class SolveImpl;
-
-/** \class Solve
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression representing a solving operation
-  *
-  * \tparam Decomposition the type of the matrix or decomposition object
-  * \tparam Rhstype the type of the right-hand side
-  *
-  * This class represents an expression of A.solve(B)
-  * and most of the time this is the only way it is used.
-  *
-  */
-namespace internal {
-
-// this solve_traits class permits to determine the evaluation type with respect to storage kind (Dense vs Sparse)
-template<typename Decomposition, typename RhsType,typename StorageKind> struct solve_traits;
-
-template<typename Decomposition, typename RhsType>
-struct solve_traits<Decomposition,RhsType,Dense>
-{
-  typedef typename make_proper_matrix_type<typename RhsType::Scalar,
-                 Decomposition::ColsAtCompileTime,
-                 RhsType::ColsAtCompileTime,
-                 RhsType::PlainObject::Options,
-                 Decomposition::MaxColsAtCompileTime,
-                 RhsType::MaxColsAtCompileTime>::type PlainObject;
-};
-
-template<typename Decomposition, typename RhsType>
-struct traits<Solve<Decomposition, RhsType> >
-  : traits<typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject>
-{
-  typedef typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject PlainObject;
-  typedef typename promote_index_type<typename Decomposition::StorageIndex, typename RhsType::StorageIndex>::type StorageIndex;
-  typedef traits<PlainObject> BaseTraits;
-  enum {
-    Flags = BaseTraits::Flags & RowMajorBit,
-    CoeffReadCost = HugeCost
-  };
-};
-
-}
-
-
-template<typename Decomposition, typename RhsType>
-class Solve : public SolveImpl<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>
-{
-public:
-  typedef typename internal::traits<Solve>::PlainObject PlainObject;
-  typedef typename internal::traits<Solve>::StorageIndex StorageIndex;
-
-  Solve(const Decomposition &dec, const RhsType &rhs)
-    : m_dec(dec), m_rhs(rhs)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dec.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  EIGEN_DEVICE_FUNC const Decomposition& dec() const { return m_dec; }
-  EIGEN_DEVICE_FUNC const RhsType&       rhs() const { return m_rhs; }
-
-protected:
-  const Decomposition &m_dec;
-  const RhsType       &m_rhs;
-};
-
-
-// Specialization of the Solve expression for dense results
-template<typename Decomposition, typename RhsType>
-class SolveImpl<Decomposition,RhsType,Dense>
-  : public MatrixBase<Solve<Decomposition,RhsType> >
-{
-  typedef Solve<Decomposition,RhsType> Derived;
-
-public:
-
-  typedef MatrixBase<Solve<Decomposition,RhsType> > Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-
-private:
-
-  Scalar coeff(Index row, Index col) const;
-  Scalar coeff(Index i) const;
-};
-
-// Generic API dispatcher
-template<typename Decomposition, typename RhsType, typename StorageKind>
-class SolveImpl : public internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type
-{
-  public:
-    typedef typename internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type Base;
-};
-
-namespace internal {
-
-// Evaluator of Solve -> eval into a temporary
-template<typename Decomposition, typename RhsType>
-struct evaluator<Solve<Decomposition,RhsType> >
-  : public evaluator<typename Solve<Decomposition,RhsType>::PlainObject>
-{
-  typedef Solve<Decomposition,RhsType> SolveType;
-  typedef typename SolveType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum { Flags = Base::Flags | EvalBeforeNestingBit };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const SolveType& solve)
-    : m_result(solve.rows(), solve.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    solve.dec()._solve_impl(solve.rhs(), m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-// Specialization for "dst = dec.solve(rhs)"
-// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef Solve<DecType,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec()._solve_impl(src.rhs(), dst);
-  }
-};
-
-// Specialization for "dst = dec.transpose().solve(rhs)"
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef Solve<Transpose<const DecType>,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec().nestedExpression().template _solve_impl_transposed<false>(src.rhs(), dst);
-  }
-};
-
-// Specialization for "dst = dec.adjoint().solve(rhs)"
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>,
-                  internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec().nestedExpression().nestedExpression().template _solve_impl_transposed<true>(src.rhs(), dst);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SolveTriangular.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SolveTriangular.h
deleted file mode 100644
index dfbf995..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SolveTriangular.h
+++ /dev/null
@@ -1,235 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVETRIANGULAR_H
-#define EIGEN_SOLVETRIANGULAR_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Forward declarations:
-// The following two routines are implemented in the products/TriangularSolver*.h files
-template<typename LhsScalar, typename RhsScalar, typename Index, int Side, int Mode, bool Conjugate, int StorageOrder>
-struct triangular_solve_vector;
-
-template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherStorageOrder, int OtherInnerStride>
-struct triangular_solve_matrix;
-
-// small helper struct extracting some traits on the underlying solver operation
-template<typename Lhs, typename Rhs, int Side>
-class trsolve_traits
-{
-  private:
-    enum {
-      RhsIsVectorAtCompileTime = (Side==OnTheLeft ? Rhs::ColsAtCompileTime : Rhs::RowsAtCompileTime)==1
-    };
-  public:
-    enum {
-      Unrolling   = (RhsIsVectorAtCompileTime && Rhs::SizeAtCompileTime != Dynamic && Rhs::SizeAtCompileTime <= 8)
-                  ? CompleteUnrolling : NoUnrolling,
-      RhsVectors  = RhsIsVectorAtCompileTime ? 1 : Dynamic
-    };
-};
-
-template<typename Lhs, typename Rhs,
-  int Side, // can be OnTheLeft/OnTheRight
-  int Mode, // can be Upper/Lower | UnitDiag
-  int Unrolling = trsolve_traits<Lhs,Rhs,Side>::Unrolling,
-  int RhsVectors = trsolve_traits<Lhs,Rhs,Side>::RhsVectors
-  >
-struct triangular_solver_selector;
-
-template<typename Lhs, typename Rhs, int Side, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,1>
-{
-  typedef typename Lhs::Scalar LhsScalar;
-  typedef typename Rhs::Scalar RhsScalar;
-  typedef blas_traits<Lhs> LhsProductTraits;
-  typedef typename LhsProductTraits::ExtractType ActualLhsType;
-  typedef Map<Matrix<RhsScalar,Dynamic,1>, Aligned> MappedRhs;
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    ActualLhsType actualLhs = LhsProductTraits::extract(lhs);
-
-    // FIXME find a way to allow an inner stride if packet_traits<Scalar>::size==1
-
-    bool useRhsDirectly = Rhs::InnerStrideAtCompileTime==1 || rhs.innerStride()==1;
-
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhs,rhs.size(),
-                                                  (useRhsDirectly ? rhs.data() : 0));
-
-    if(!useRhsDirectly)
-      MappedRhs(actualRhs,rhs.size()) = rhs;
-
-    triangular_solve_vector<LhsScalar, RhsScalar, Index, Side, Mode, LhsProductTraits::NeedToConjugate,
-                            (int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor>
-      ::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs);
-
-    if(!useRhsDirectly)
-      rhs = MappedRhs(actualRhs, rhs.size());
-  }
-};
-
-// the rhs is a matrix
-template<typename Lhs, typename Rhs, int Side, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef blas_traits<Lhs> LhsProductTraits;
-  typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType;
-
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsProductTraits::extract(lhs);
-
-    const Index size = lhs.rows();
-    const Index othersize = Side==OnTheLeft? rhs.cols() : rhs.rows();
-
-    typedef internal::gemm_blocking_space<(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
-              Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxRowsAtCompileTime,4> BlockingType;
-
-    BlockingType blocking(rhs.rows(), rhs.cols(), size, 1, false);
-
-    triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor,
-                               (Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor, Rhs::InnerStrideAtCompileTime>
-      ::run(size, othersize, &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.innerStride(), rhs.outerStride(), blocking);
-  }
-};
-
-/***************************************************************************
-* meta-unrolling implementation
-***************************************************************************/
-
-template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size,
-         bool Stop = LoopIndex==Size>
-struct triangular_solver_unroller;
-
-template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
-struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,false> {
-  enum {
-    IsLower = ((Mode&Lower)==Lower),
-    DiagIndex  = IsLower ? LoopIndex : Size - LoopIndex - 1,
-    StartIndex = IsLower ? 0         : DiagIndex+1
-  };
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    if (LoopIndex>0)
-      rhs.coeffRef(DiagIndex) -= lhs.row(DiagIndex).template segment<LoopIndex>(StartIndex).transpose()
-                                .cwiseProduct(rhs.template segment<LoopIndex>(StartIndex)).sum();
-
-    if(!(Mode & UnitDiag))
-      rhs.coeffRef(DiagIndex) /= lhs.coeff(DiagIndex,DiagIndex);
-
-    triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex+1,Size>::run(lhs,rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
-struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,true> {
-  static EIGEN_DEVICE_FUNC void run(const Lhs&, Rhs&) {}
-};
-
-template<typename Lhs, typename Rhs, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,OnTheLeft,Mode,CompleteUnrolling,1> {
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  { triangular_solver_unroller<Lhs,Rhs,Mode,0,Rhs::SizeAtCompileTime>::run(lhs,rhs); }
-};
-
-template<typename Lhs, typename Rhs, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    Transpose<const Lhs> trLhs(lhs);
-    Transpose<Rhs> trRhs(rhs);
-
-    triangular_solver_unroller<Transpose<const Lhs>,Transpose<Rhs>,
-                              ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag),
-                              0,Rhs::SizeAtCompileTime>::run(trLhs,trRhs);
-  }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* TriangularView methods
-***************************************************************************/
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatrixType, unsigned int Mode>
-template<int Side, typename OtherDerived>
-EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
-{
-  OtherDerived& other = _other.const_cast_derived();
-  eigen_assert( derived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols())) );
-  eigen_assert((!(int(Mode) & int(ZeroDiag))) && bool(int(Mode) & (int(Upper) | int(Lower))));
-  // If solving for a 0x0 matrix, nothing to do, simply return.
-  if (derived().cols() == 0)
-    return;
-
-  enum { copy = (internal::traits<OtherDerived>::Flags & RowMajorBit)  && OtherDerived::IsVectorAtCompileTime && OtherDerived::SizeAtCompileTime!=1};
-  typedef typename internal::conditional<copy,
-    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
-  OtherCopy otherCopy(other);
-
-  internal::triangular_solver_selector<MatrixType, typename internal::remove_reference<OtherCopy>::type,
-    Side, Mode>::run(derived().nestedExpression(), otherCopy);
-
-  if (copy)
-    other = otherCopy;
-}
-
-template<typename Derived, unsigned int Mode>
-template<int Side, typename Other>
-const internal::triangular_solve_retval<Side,TriangularView<Derived,Mode>,Other>
-TriangularViewImpl<Derived,Mode,Dense>::solve(const MatrixBase<Other>& other) const
-{
-  return internal::triangular_solve_retval<Side,TriangularViewType,Other>(derived(), other.derived());
-}
-#endif
-
-namespace internal {
-
-
-template<int Side, typename TriangularType, typename Rhs>
-struct traits<triangular_solve_retval<Side, TriangularType, Rhs> >
-{
-  typedef typename internal::plain_matrix_type_column_major<Rhs>::type ReturnType;
-};
-
-template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval
- : public ReturnByValue<triangular_solve_retval<Side, TriangularType, Rhs> >
-{
-  typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned;
-  typedef ReturnByValue<triangular_solve_retval> Base;
-
-  triangular_solve_retval(const TriangularType& tri, const Rhs& rhs)
-    : m_triangularMatrix(tri), m_rhs(rhs)
-  {}
-
-  inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_rhs.rows(); }
-  inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  template<typename Dest> inline void evalTo(Dest& dst) const
-  {
-    if(!is_same_dense(dst,m_rhs))
-      dst = m_rhs;
-    m_triangularMatrix.template solveInPlace<Side>(dst);
-  }
-
-  protected:
-    const TriangularType& m_triangularMatrix;
-    typename Rhs::Nested m_rhs;
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVETRIANGULAR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SolverBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SolverBase.h
deleted file mode 100644
index 5014610..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/SolverBase.h
+++ /dev/null
@@ -1,168 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVERBASE_H
-#define EIGEN_SOLVERBASE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Derived>
-struct solve_assertion {
-    template<bool Transpose_, typename Rhs>
-    static void run(const Derived& solver, const Rhs& b) { solver.template _check_solve_assertion<Transpose_>(b); }
-};
-
-template<typename Derived>
-struct solve_assertion<Transpose<Derived> >
-{
-    typedef Transpose<Derived> type;
-
-    template<bool Transpose_, typename Rhs>
-    static void run(const type& transpose, const Rhs& b)
-    {
-        internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<true>(transpose.nestedExpression(), b);
-    }
-};
-
-template<typename Scalar, typename Derived>
-struct solve_assertion<CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > >
-{
-    typedef CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > type;
-
-    template<bool Transpose_, typename Rhs>
-    static void run(const type& adjoint, const Rhs& b)
-    {
-        internal::solve_assertion<typename internal::remove_all<Transpose<Derived> >::type>::template run<true>(adjoint.nestedExpression(), b);
-    }
-};
-} // end namespace internal
-
-/** \class SolverBase
-  * \brief A base class for matrix decomposition and solvers
-  *
-  * \tparam Derived the actual type of the decomposition/solver.
-  *
-  * Any matrix decomposition inheriting this base class provide the following API:
-  *
-  * \code
-  * MatrixType A, b, x;
-  * DecompositionType dec(A);
-  * x = dec.solve(b);             // solve A   * x = b
-  * x = dec.transpose().solve(b); // solve A^T * x = b
-  * x = dec.adjoint().solve(b);   // solve A'  * x = b
-  * \endcode
-  *
-  * \warning Currently, any other usage of transpose() and adjoint() are not supported and will produce compilation errors.
-  *
-  * \sa class PartialPivLU, class FullPivLU, class HouseholderQR, class ColPivHouseholderQR, class FullPivHouseholderQR, class CompleteOrthogonalDecomposition, class LLT, class LDLT, class SVDBase
-  */
-template<typename Derived>
-class SolverBase : public EigenBase<Derived>
-{
-  public:
-
-    typedef EigenBase<Derived> Base;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef Scalar CoeffReturnType;
-
-    template<typename Derived_>
-    friend struct internal::solve_assertion;
-
-    enum {
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                          internal::traits<Derived>::ColsAtCompileTime>::ret),
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
-                                                             internal::traits<Derived>::MaxColsAtCompileTime>::ret),
-      IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
-                           || internal::traits<Derived>::MaxColsAtCompileTime == 1,
-      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2
-    };
-
-    /** Default constructor */
-    SolverBase()
-    {}
-
-    ~SolverBase()
-    {}
-
-    using Base::derived;
-
-    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      */
-    template<typename Rhs>
-    inline const Solve<Derived, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<false>(derived(), b);
-      return Solve<Derived, Rhs>(derived(), b.derived());
-    }
-
-    /** \internal the return type of transpose() */
-    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
-    /** \returns an expression of the transposed of the factored matrix.
-      *
-      * A typical usage is to solve for the transposed problem A^T x = b:
-      * \code x = dec.transpose().solve(b); \endcode
-      *
-      * \sa adjoint(), solve()
-      */
-    inline ConstTransposeReturnType transpose() const
-    {
-      return ConstTransposeReturnType(derived());
-    }
-
-    /** \internal the return type of adjoint() */
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
-                        ConstTransposeReturnType
-                     >::type AdjointReturnType;
-    /** \returns an expression of the adjoint of the factored matrix
-      *
-      * A typical usage is to solve for the adjoint problem A' x = b:
-      * \code x = dec.adjoint().solve(b); \endcode
-      *
-      * For real scalar types, this function is equivalent to transpose().
-      *
-      * \sa transpose(), solve()
-      */
-    inline AdjointReturnType adjoint() const
-    {
-      return AdjointReturnType(derived().transpose());
-    }
-
-  protected:
-
-    template<bool Transpose_, typename Rhs>
-    void _check_solve_assertion(const Rhs& b) const {
-        EIGEN_ONLY_USED_FOR_DEBUG(b);
-        eigen_assert(derived().m_isInitialized && "Solver is not initialized.");
-        eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b");
-    }
-};
-
-namespace internal {
-
-template<typename Derived>
-struct generic_xpr_base<Derived, MatrixXpr, SolverStorage>
-{
-  typedef SolverBase<Derived> type;
-
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVERBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/StableNorm.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/StableNorm.h
deleted file mode 100644
index 4a3f0cc..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/StableNorm.h
+++ /dev/null
@@ -1,251 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STABLENORM_H
-#define EIGEN_STABLENORM_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename ExpressionType, typename Scalar>
-inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
-{
-  Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
-  
-  if(maxCoeff>scale)
-  {
-    ssq = ssq * numext::abs2(scale/maxCoeff);
-    Scalar tmp = Scalar(1)/maxCoeff;
-    if(tmp > NumTraits<Scalar>::highest())
-    {
-      invScale = NumTraits<Scalar>::highest();
-      scale = Scalar(1)/invScale;
-    }
-    else if(maxCoeff>NumTraits<Scalar>::highest()) // we got a INF
-    {
-      invScale = Scalar(1);
-      scale = maxCoeff;
-    }
-    else
-    {
-      scale = maxCoeff;
-      invScale = tmp;
-    }
-  }
-  else if(maxCoeff!=maxCoeff) // we got a NaN
-  {
-    scale = maxCoeff;
-  }
-  
-  // TODO if the maxCoeff is much much smaller than the current scale,
-  // then we can neglect this sub vector
-  if(scale>Scalar(0)) // if scale==0, then bl is 0 
-    ssq += (bl*invScale).squaredNorm();
-}
-
-template<typename VectorType, typename RealScalar>
-void stable_norm_impl_inner_step(const VectorType &vec, RealScalar& ssq, RealScalar& scale, RealScalar& invScale)
-{
-  typedef typename VectorType::Scalar Scalar;
-  const Index blockSize = 4096;
-  
-  typedef typename internal::nested_eval<VectorType,2>::type VectorTypeCopy;
-  typedef typename internal::remove_all<VectorTypeCopy>::type VectorTypeCopyClean;
-  const VectorTypeCopy copy(vec);
-  
-  enum {
-    CanAlign = (   (int(VectorTypeCopyClean::Flags)&DirectAccessBit)
-                || (int(internal::evaluator<VectorTypeCopyClean>::Alignment)>0) // FIXME Alignment)>0 might not be enough
-               ) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT)
-                 && (EIGEN_MAX_STATIC_ALIGN_BYTES>0) // if we cannot allocate on the stack, then let's not bother about this optimization
-  };
-  typedef typename internal::conditional<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<VectorTypeCopyClean>::Alignment>,
-                                                   typename VectorTypeCopyClean::ConstSegmentReturnType>::type SegmentWrapper;
-  Index n = vec.size();
-  
-  Index bi = internal::first_default_aligned(copy);
-  if (bi>0)
-    internal::stable_norm_kernel(copy.head(bi), ssq, scale, invScale);
-  for (; bi<n; bi+=blockSize)
-    internal::stable_norm_kernel(SegmentWrapper(copy.segment(bi,numext::mini(blockSize, n - bi))), ssq, scale, invScale);
-}
-
-template<typename VectorType>
-typename VectorType::RealScalar
-stable_norm_impl(const VectorType &vec, typename enable_if<VectorType::IsVectorAtCompileTime>::type* = 0 )
-{
-  using std::sqrt;
-  using std::abs;
-
-  Index n = vec.size();
-
-  if(n==1)
-    return abs(vec.coeff(0));
-
-  typedef typename VectorType::RealScalar RealScalar;
-  RealScalar scale(0);
-  RealScalar invScale(1);
-  RealScalar ssq(0); // sum of squares
-
-  stable_norm_impl_inner_step(vec, ssq, scale, invScale);
-  
-  return scale * sqrt(ssq);
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar
-stable_norm_impl(const MatrixType &mat, typename enable_if<!MatrixType::IsVectorAtCompileTime>::type* = 0 )
-{
-  using std::sqrt;
-
-  typedef typename MatrixType::RealScalar RealScalar;
-  RealScalar scale(0);
-  RealScalar invScale(1);
-  RealScalar ssq(0); // sum of squares
-
-  for(Index j=0; j<mat.outerSize(); ++j)
-    stable_norm_impl_inner_step(mat.innerVector(j), ssq, scale, invScale);
-  return scale * sqrt(ssq);
-}
-
-template<typename Derived>
-inline typename NumTraits<typename traits<Derived>::Scalar>::Real
-blueNorm_impl(const EigenBase<Derived>& _vec)
-{
-  typedef typename Derived::RealScalar RealScalar;  
-  using std::pow;
-  using std::sqrt;
-  using std::abs;
-
-  // This program calculates the machine-dependent constants
-  // bl, b2, slm, s2m, relerr overfl
-  // from the "basic" machine-dependent numbers
-  // nbig, ibeta, it, iemin, iemax, rbig.
-  // The following define the basic machine-dependent constants.
-  // For portability, the PORT subprograms "ilmaeh" and "rlmach"
-  // are used. For any specific computer, each of the assignment
-  // statements can be replaced
-  static const int ibeta = std::numeric_limits<RealScalar>::radix;  // base for floating-point numbers
-  static const int it    = NumTraits<RealScalar>::digits();  // number of base-beta digits in mantissa
-  static const int iemin = NumTraits<RealScalar>::min_exponent();  // minimum exponent
-  static const int iemax = NumTraits<RealScalar>::max_exponent();  // maximum exponent
-  static const RealScalar rbig   = NumTraits<RealScalar>::highest();  // largest floating-point number
-  static const RealScalar b1     = RealScalar(pow(RealScalar(ibeta),RealScalar(-((1-iemin)/2))));  // lower boundary of midrange
-  static const RealScalar b2     = RealScalar(pow(RealScalar(ibeta),RealScalar((iemax + 1 - it)/2)));  // upper boundary of midrange
-  static const RealScalar s1m    = RealScalar(pow(RealScalar(ibeta),RealScalar((2-iemin)/2)));  // scaling factor for lower range
-  static const RealScalar s2m    = RealScalar(pow(RealScalar(ibeta),RealScalar(- ((iemax+it)/2))));  // scaling factor for upper range
-  static const RealScalar eps    = RealScalar(pow(double(ibeta), 1-it));
-  static const RealScalar relerr = sqrt(eps);  // tolerance for neglecting asml
-
-  const Derived& vec(_vec.derived());
-  Index n = vec.size();
-  RealScalar ab2 = b2 / RealScalar(n);
-  RealScalar asml = RealScalar(0);
-  RealScalar amed = RealScalar(0);
-  RealScalar abig = RealScalar(0);
-
-  for(Index j=0; j<vec.outerSize(); ++j)
-  {
-    for(typename Derived::InnerIterator iter(vec, j); iter; ++iter)
-    {
-      RealScalar ax = abs(iter.value());
-      if(ax > ab2)     abig += numext::abs2(ax*s2m);
-      else if(ax < b1) asml += numext::abs2(ax*s1m);
-      else             amed += numext::abs2(ax);
-    }
-  }
-  if(amed!=amed)
-    return amed;  // we got a NaN
-  if(abig > RealScalar(0))
-  {
-    abig = sqrt(abig);
-    if(abig > rbig) // overflow, or *this contains INF values
-      return abig;  // return INF
-    if(amed > RealScalar(0))
-    {
-      abig = abig/s2m;
-      amed = sqrt(amed);
-    }
-    else
-      return abig/s2m;
-  }
-  else if(asml > RealScalar(0))
-  {
-    if (amed > RealScalar(0))
-    {
-      abig = sqrt(amed);
-      amed = sqrt(asml) / s1m;
-    }
-    else
-      return sqrt(asml)/s1m;
-  }
-  else
-    return sqrt(amed);
-  asml = numext::mini(abig, amed);
-  abig = numext::maxi(abig, amed);
-  if(asml <= abig*relerr)
-    return abig;
-  else
-    return abig * sqrt(RealScalar(1) + numext::abs2(asml/abig));
-}
-
-} // end namespace internal
-
-/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
-  * This version use a blockwise two passes algorithm:
-  *  1 - find the absolute largest coefficient \c s
-  *  2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way
-  *
-  * For architecture/scalar types supporting vectorization, this version
-  * is faster than blueNorm(). Otherwise the blueNorm() is much faster.
-  *
-  * \sa norm(), blueNorm(), hypotNorm()
-  */
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::stableNorm() const
-{
-  return internal::stable_norm_impl(derived());
-}
-
-/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
-  * A Portable Fortran Program to Find the Euclidean Norm of a Vector,
-  * ACM TOMS, Vol 4, Issue 1, 1978.
-  *
-  * For architecture/scalar types without vectorization, this version
-  * is much faster than stableNorm(). Otherwise the stableNorm() is faster.
-  *
-  * \sa norm(), stableNorm(), hypotNorm()
-  */
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::blueNorm() const
-{
-  return internal::blueNorm_impl(*this);
-}
-
-/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow.
-  * This version use a concatenation of hypot() calls, and it is very slow.
-  *
-  * \sa norm(), stableNorm()
-  */
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::hypotNorm() const
-{
-  if(size()==1)
-    return numext::abs(coeff(0,0));
-  else
-    return this->cwiseAbs().redux(internal::scalar_hypot_op<RealScalar>());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_STABLENORM_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/StlIterators.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/StlIterators.h
deleted file mode 100644
index 09041db..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/StlIterators.h
+++ /dev/null
@@ -1,463 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STLITERATORS_H
-#define EIGEN_STLITERATORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename IteratorType>
-struct indexed_based_stl_iterator_traits;
-
-template<typename  Derived>
-class indexed_based_stl_iterator_base
-{
-protected:
-  typedef indexed_based_stl_iterator_traits<Derived> traits;
-  typedef typename traits::XprType XprType;
-  typedef indexed_based_stl_iterator_base<typename traits::non_const_iterator> non_const_iterator;
-  typedef indexed_based_stl_iterator_base<typename traits::const_iterator> const_iterator;
-  typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
-  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
-  friend class indexed_based_stl_iterator_base<typename traits::const_iterator>;
-  friend class indexed_based_stl_iterator_base<typename traits::non_const_iterator>;
-public:
-  typedef Index difference_type;
-  typedef std::random_access_iterator_tag iterator_category;
-
-  indexed_based_stl_iterator_base() EIGEN_NO_THROW : mp_xpr(0), m_index(0) {}
-  indexed_based_stl_iterator_base(XprType& xpr, Index index) EIGEN_NO_THROW : mp_xpr(&xpr), m_index(index) {}
-
-  indexed_based_stl_iterator_base(const non_const_iterator& other) EIGEN_NO_THROW
-    : mp_xpr(other.mp_xpr), m_index(other.m_index)
-  {}
-
-  indexed_based_stl_iterator_base& operator=(const non_const_iterator& other)
-  {
-    mp_xpr = other.mp_xpr;
-    m_index = other.m_index;
-    return *this;
-  }
-
-  Derived& operator++() { ++m_index; return derived(); }
-  Derived& operator--() { --m_index; return derived(); }
-
-  Derived operator++(int) { Derived prev(derived()); operator++(); return prev;}
-  Derived operator--(int) { Derived prev(derived()); operator--(); return prev;}
-
-  friend Derived operator+(const indexed_based_stl_iterator_base& a, Index b) { Derived ret(a.derived()); ret += b; return ret; }
-  friend Derived operator-(const indexed_based_stl_iterator_base& a, Index b) { Derived ret(a.derived()); ret -= b; return ret; }
-  friend Derived operator+(Index a, const indexed_based_stl_iterator_base& b) { Derived ret(b.derived()); ret += a; return ret; }
-  friend Derived operator-(Index a, const indexed_based_stl_iterator_base& b) { Derived ret(b.derived()); ret -= a; return ret; }
-  
-  Derived& operator+=(Index b) { m_index += b; return derived(); }
-  Derived& operator-=(Index b) { m_index -= b; return derived(); }
-
-  difference_type operator-(const indexed_based_stl_iterator_base& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return m_index - other.m_index;
-  }
-
-  difference_type operator-(const other_iterator& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return m_index - other.m_index;
-  }
-
-  bool operator==(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator<=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-  bool operator> (const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator>=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-
-  bool operator==(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator<=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-  bool operator> (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator>=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-
-protected:
-
-  Derived& derived() { return static_cast<Derived&>(*this); }
-  const Derived& derived() const { return static_cast<const Derived&>(*this); }
-
-  XprType *mp_xpr;
-  Index m_index;
-};
-
-template<typename  Derived>
-class indexed_based_stl_reverse_iterator_base
-{
-protected:
-  typedef indexed_based_stl_iterator_traits<Derived> traits;
-  typedef typename traits::XprType XprType;
-  typedef indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator> non_const_iterator;
-  typedef indexed_based_stl_reverse_iterator_base<typename traits::const_iterator> const_iterator;
-  typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
-  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
-  friend class indexed_based_stl_reverse_iterator_base<typename traits::const_iterator>;
-  friend class indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator>;
-public:
-  typedef Index difference_type;
-  typedef std::random_access_iterator_tag iterator_category;
-
-  indexed_based_stl_reverse_iterator_base() : mp_xpr(0), m_index(0) {}
-  indexed_based_stl_reverse_iterator_base(XprType& xpr, Index index) : mp_xpr(&xpr), m_index(index) {}
-
-  indexed_based_stl_reverse_iterator_base(const non_const_iterator& other)
-    : mp_xpr(other.mp_xpr), m_index(other.m_index)
-  {}
-
-  indexed_based_stl_reverse_iterator_base& operator=(const non_const_iterator& other)
-  {
-    mp_xpr = other.mp_xpr;
-    m_index = other.m_index;
-    return *this;
-  }
-
-  Derived& operator++() { --m_index; return derived(); }
-  Derived& operator--() { ++m_index; return derived(); }
-
-  Derived operator++(int) { Derived prev(derived()); operator++(); return prev;}
-  Derived operator--(int) { Derived prev(derived()); operator--(); return prev;}
-
-  friend Derived operator+(const indexed_based_stl_reverse_iterator_base& a, Index b) { Derived ret(a.derived()); ret += b; return ret; }
-  friend Derived operator-(const indexed_based_stl_reverse_iterator_base& a, Index b) { Derived ret(a.derived()); ret -= b; return ret; }
-  friend Derived operator+(Index a, const indexed_based_stl_reverse_iterator_base& b) { Derived ret(b.derived()); ret += a; return ret; }
-  friend Derived operator-(Index a, const indexed_based_stl_reverse_iterator_base& b) { Derived ret(b.derived()); ret -= a; return ret; }
-  
-  Derived& operator+=(Index b) { m_index -= b; return derived(); }
-  Derived& operator-=(Index b) { m_index += b; return derived(); }
-
-  difference_type operator-(const indexed_based_stl_reverse_iterator_base& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return other.m_index - m_index;
-  }
-
-  difference_type operator-(const other_iterator& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return other.m_index - m_index;
-  }
-
-  bool operator==(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator<=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-  bool operator> (const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator>=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-
-  bool operator==(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator<=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-  bool operator> (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator>=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-
-protected:
-
-  Derived& derived() { return static_cast<Derived&>(*this); }
-  const Derived& derived() const { return static_cast<const Derived&>(*this); }
-
-  XprType *mp_xpr;
-  Index m_index;
-};
-
-template<typename XprType>
-class pointer_based_stl_iterator
-{
-  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
-  typedef pointer_based_stl_iterator<typename internal::remove_const<XprType>::type> non_const_iterator;
-  typedef pointer_based_stl_iterator<typename internal::add_const<XprType>::type> const_iterator;
-  typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
-  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
-  friend class pointer_based_stl_iterator<typename internal::add_const<XprType>::type>;
-  friend class pointer_based_stl_iterator<typename internal::remove_const<XprType>::type>;
-public:
-  typedef Index difference_type;
-  typedef typename XprType::Scalar value_type;
-  typedef std::random_access_iterator_tag iterator_category;
-  typedef typename internal::conditional<bool(is_lvalue), value_type*, const value_type*>::type pointer;
-  typedef typename internal::conditional<bool(is_lvalue), value_type&, const value_type&>::type reference;
-
-
-  pointer_based_stl_iterator() EIGEN_NO_THROW : m_ptr(0) {}
-  pointer_based_stl_iterator(XprType& xpr, Index index) EIGEN_NO_THROW : m_incr(xpr.innerStride())
-  {
-    m_ptr = xpr.data() + index * m_incr.value();
-  }
-
-  pointer_based_stl_iterator(const non_const_iterator& other) EIGEN_NO_THROW
-    : m_ptr(other.m_ptr), m_incr(other.m_incr)
-  {}
-
-  pointer_based_stl_iterator& operator=(const non_const_iterator& other) EIGEN_NO_THROW
-  {
-    m_ptr = other.m_ptr;
-    m_incr.setValue(other.m_incr);
-    return *this;
-  }
-
-  reference operator*()         const { return *m_ptr;   }
-  reference operator[](Index i) const { return *(m_ptr+i*m_incr.value()); }
-  pointer   operator->()        const { return m_ptr;    }
-
-  pointer_based_stl_iterator& operator++() { m_ptr += m_incr.value(); return *this; }
-  pointer_based_stl_iterator& operator--() { m_ptr -= m_incr.value(); return *this; }
-
-  pointer_based_stl_iterator operator++(int) { pointer_based_stl_iterator prev(*this); operator++(); return prev;}
-  pointer_based_stl_iterator operator--(int) { pointer_based_stl_iterator prev(*this); operator--(); return prev;}
-
-  friend pointer_based_stl_iterator operator+(const pointer_based_stl_iterator& a, Index b) { pointer_based_stl_iterator ret(a); ret += b; return ret; }
-  friend pointer_based_stl_iterator operator-(const pointer_based_stl_iterator& a, Index b) { pointer_based_stl_iterator ret(a); ret -= b; return ret; }
-  friend pointer_based_stl_iterator operator+(Index a, const pointer_based_stl_iterator& b) { pointer_based_stl_iterator ret(b); ret += a; return ret; }
-  friend pointer_based_stl_iterator operator-(Index a, const pointer_based_stl_iterator& b) { pointer_based_stl_iterator ret(b); ret -= a; return ret; }
-  
-  pointer_based_stl_iterator& operator+=(Index b) { m_ptr += b*m_incr.value(); return *this; }
-  pointer_based_stl_iterator& operator-=(Index b) { m_ptr -= b*m_incr.value(); return *this; }
-
-  difference_type operator-(const pointer_based_stl_iterator& other) const {
-    return (m_ptr - other.m_ptr)/m_incr.value();
-  }
-
-  difference_type operator-(const other_iterator& other) const {
-    return (m_ptr - other.m_ptr)/m_incr.value();
-  }
-
-  bool operator==(const pointer_based_stl_iterator& other) const { return m_ptr == other.m_ptr; }
-  bool operator!=(const pointer_based_stl_iterator& other) const { return m_ptr != other.m_ptr; }
-  bool operator< (const pointer_based_stl_iterator& other) const { return m_ptr <  other.m_ptr; }
-  bool operator<=(const pointer_based_stl_iterator& other) const { return m_ptr <= other.m_ptr; }
-  bool operator> (const pointer_based_stl_iterator& other) const { return m_ptr >  other.m_ptr; }
-  bool operator>=(const pointer_based_stl_iterator& other) const { return m_ptr >= other.m_ptr; }
-
-  bool operator==(const other_iterator& other) const { return m_ptr == other.m_ptr; }
-  bool operator!=(const other_iterator& other) const { return m_ptr != other.m_ptr; }
-  bool operator< (const other_iterator& other) const { return m_ptr <  other.m_ptr; }
-  bool operator<=(const other_iterator& other) const { return m_ptr <= other.m_ptr; }
-  bool operator> (const other_iterator& other) const { return m_ptr >  other.m_ptr; }
-  bool operator>=(const other_iterator& other) const { return m_ptr >= other.m_ptr; }
-
-protected:
-
-  pointer m_ptr;
-  internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_incr;
-};
-
-template<typename _XprType>
-struct indexed_based_stl_iterator_traits<generic_randaccess_stl_iterator<_XprType> >
-{
-  typedef _XprType XprType;
-  typedef generic_randaccess_stl_iterator<typename internal::remove_const<XprType>::type> non_const_iterator;
-  typedef generic_randaccess_stl_iterator<typename internal::add_const<XprType>::type> const_iterator;
-};
-
-template<typename XprType>
-class generic_randaccess_stl_iterator : public indexed_based_stl_iterator_base<generic_randaccess_stl_iterator<XprType> >
-{
-public:
-  typedef typename XprType::Scalar value_type;
-
-protected:
-
-  enum {
-    has_direct_access = (internal::traits<XprType>::Flags & DirectAccessBit) ? 1 : 0,
-    is_lvalue  = internal::is_lvalue<XprType>::value
-  };
-
-  typedef indexed_based_stl_iterator_base<generic_randaccess_stl_iterator> Base;
-  using Base::m_index;
-  using Base::mp_xpr;
-
-  // TODO currently const Transpose/Reshape expressions never returns const references,
-  // so lets return by value too.
-  //typedef typename internal::conditional<bool(has_direct_access), const value_type&, const value_type>::type read_only_ref_t;
-  typedef const value_type read_only_ref_t;
-
-public:
-  
-  typedef typename internal::conditional<bool(is_lvalue), value_type *, const value_type *>::type pointer;
-  typedef typename internal::conditional<bool(is_lvalue), value_type&, read_only_ref_t>::type reference;
-  
-  generic_randaccess_stl_iterator() : Base() {}
-  generic_randaccess_stl_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
-  generic_randaccess_stl_iterator(const typename Base::non_const_iterator& other) : Base(other) {}
-  using Base::operator=;
-
-  reference operator*()         const { return   (*mp_xpr)(m_index);   }
-  reference operator[](Index i) const { return   (*mp_xpr)(m_index+i); }
-  pointer   operator->()        const { return &((*mp_xpr)(m_index)); }
-};
-
-template<typename _XprType, DirectionType Direction>
-struct indexed_based_stl_iterator_traits<subvector_stl_iterator<_XprType,Direction> >
-{
-  typedef _XprType XprType;
-  typedef subvector_stl_iterator<typename internal::remove_const<XprType>::type, Direction> non_const_iterator;
-  typedef subvector_stl_iterator<typename internal::add_const<XprType>::type, Direction> const_iterator;
-};
-
-template<typename XprType, DirectionType Direction>
-class subvector_stl_iterator : public indexed_based_stl_iterator_base<subvector_stl_iterator<XprType,Direction> >
-{
-protected:
-
-  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
-
-  typedef indexed_based_stl_iterator_base<subvector_stl_iterator> Base;
-  using Base::m_index;
-  using Base::mp_xpr;
-
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr>::type SubVectorType;
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr>::type ConstSubVectorType;
-
-
-public:
-  typedef typename internal::conditional<bool(is_lvalue), SubVectorType, ConstSubVectorType>::type reference;
-  typedef typename reference::PlainObject value_type;
-
-private:
-  class subvector_stl_iterator_ptr
-  {
-  public:
-      subvector_stl_iterator_ptr(const reference &subvector) : m_subvector(subvector) {}
-      reference* operator->() { return &m_subvector; }
-  private:
-      reference m_subvector;
-  };
-public:
-
-  typedef subvector_stl_iterator_ptr pointer;
-  
-  subvector_stl_iterator() : Base() {}
-  subvector_stl_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
-
-  reference operator*()         const { return (*mp_xpr).template subVector<Direction>(m_index); }
-  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index+i); }
-  pointer   operator->()        const { return (*mp_xpr).template subVector<Direction>(m_index); }
-};
-
-template<typename _XprType, DirectionType Direction>
-struct indexed_based_stl_iterator_traits<subvector_stl_reverse_iterator<_XprType,Direction> >
-{
-  typedef _XprType XprType;
-  typedef subvector_stl_reverse_iterator<typename internal::remove_const<XprType>::type, Direction> non_const_iterator;
-  typedef subvector_stl_reverse_iterator<typename internal::add_const<XprType>::type, Direction> const_iterator;
-};
-
-template<typename XprType, DirectionType Direction>
-class subvector_stl_reverse_iterator : public indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator<XprType,Direction> >
-{
-protected:
-
-  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
-
-  typedef indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator> Base;
-  using Base::m_index;
-  using Base::mp_xpr;
-
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr>::type SubVectorType;
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr>::type ConstSubVectorType;
-
-
-public:
-  typedef typename internal::conditional<bool(is_lvalue), SubVectorType, ConstSubVectorType>::type reference;
-  typedef typename reference::PlainObject value_type;
-
-private:
-  class subvector_stl_reverse_iterator_ptr
-  {
-  public:
-      subvector_stl_reverse_iterator_ptr(const reference &subvector) : m_subvector(subvector) {}
-      reference* operator->() { return &m_subvector; }
-  private:
-      reference m_subvector;
-  };
-public:
-
-  typedef subvector_stl_reverse_iterator_ptr pointer;
-  
-  subvector_stl_reverse_iterator() : Base() {}
-  subvector_stl_reverse_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
-
-  reference operator*()         const { return (*mp_xpr).template subVector<Direction>(m_index); }
-  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index+i); }
-  pointer   operator->()        const { return (*mp_xpr).template subVector<Direction>(m_index); }
-};
-
-} // namespace internal
-
-
-/** returns an iterator to the first element of the 1D vector or array
-  * \only_for_vectors
-  * \sa end(), cbegin()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::iterator DenseBase<Derived>::begin()
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return iterator(derived(), 0);
-}
-
-/** const version of begin() */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::begin() const
-{
-  return cbegin();
-}
-
-/** returns a read-only const_iterator to the first element of the 1D vector or array
-  * \only_for_vectors
-  * \sa cend(), begin()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cbegin() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return const_iterator(derived(), 0);
-}
-
-/** returns an iterator to the element following the last element of the 1D vector or array
-  * \only_for_vectors
-  * \sa begin(), cend()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::iterator DenseBase<Derived>::end()
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return iterator(derived(), size());
-}
-
-/** const version of end() */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::end() const
-{
-  return cend();
-}
-
-/** returns a read-only const_iterator to the element following the last element of the 1D vector or array
-  * \only_for_vectors
-  * \sa begin(), cend()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cend() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return const_iterator(derived(), size());
-}
-
-} // namespace Eigen
-
-#endif // EIGEN_STLITERATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Stride.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Stride.h
deleted file mode 100644
index 6494d51..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Stride.h
+++ /dev/null
@@ -1,116 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STRIDE_H
-#define EIGEN_STRIDE_H
-
-namespace Eigen {
-
-/** \class Stride
-  * \ingroup Core_Module
-  *
-  * \brief Holds strides information for Map
-  *
-  * This class holds the strides information for mapping arrays with strides with class Map.
-  *
-  * It holds two values: the inner stride and the outer stride.
-  *
-  * The inner stride is the pointer increment between two consecutive entries within a given row of a
-  * row-major matrix or within a given column of a column-major matrix.
-  *
-  * The outer stride is the pointer increment between two consecutive rows of a row-major matrix or
-  * between two consecutive columns of a column-major matrix.
-  *
-  * These two values can be passed either at compile-time as template parameters, or at runtime as
-  * arguments to the constructor.
-  *
-  * Indeed, this class takes two template parameters:
-  *  \tparam _OuterStrideAtCompileTime the outer stride, or Dynamic if you want to specify it at runtime.
-  *  \tparam _InnerStrideAtCompileTime the inner stride, or Dynamic if you want to specify it at runtime.
-  *
-  * Here is an example:
-  * \include Map_general_stride.cpp
-  * Output: \verbinclude Map_general_stride.out
-  *
-  * Both strides can be negative, however, a negative stride of -1 cannot be specified at compiletime
-  * because of the ambiguity with Dynamic which is defined to -1 (historically, negative strides were
-  * not allowed).
-  *
-  * \sa class InnerStride, class OuterStride, \ref TopicStorageOrders
-  */
-template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime>
-class Stride
-{
-  public:
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    enum {
-      InnerStrideAtCompileTime = _InnerStrideAtCompileTime,
-      OuterStrideAtCompileTime = _OuterStrideAtCompileTime
-    };
-
-    /** Default constructor, for use when strides are fixed at compile time */
-    EIGEN_DEVICE_FUNC
-    Stride()
-      : m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime)
-    {
-      // FIXME: for Eigen 4 we should use DynamicIndex instead of Dynamic.
-      // FIXME: for Eigen 4 we should also unify this API with fix<>
-      eigen_assert(InnerStrideAtCompileTime != Dynamic && OuterStrideAtCompileTime != Dynamic);
-    }
-
-    /** Constructor allowing to pass the strides at runtime */
-    EIGEN_DEVICE_FUNC
-    Stride(Index outerStride, Index innerStride)
-      : m_outer(outerStride), m_inner(innerStride)
-    {
-    }
-
-    /** Copy constructor */
-    EIGEN_DEVICE_FUNC
-    Stride(const Stride& other)
-      : m_outer(other.outer()), m_inner(other.inner())
-    {}
-
-    /** \returns the outer stride */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outer() const { return m_outer.value(); }
-    /** \returns the inner stride */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index inner() const { return m_inner.value(); }
-
-  protected:
-    internal::variable_if_dynamic<Index, OuterStrideAtCompileTime> m_outer;
-    internal::variable_if_dynamic<Index, InnerStrideAtCompileTime> m_inner;
-};
-
-/** \brief Convenience specialization of Stride to specify only an inner stride
-  * See class Map for some examples */
-template<int Value>
-class InnerStride : public Stride<0, Value>
-{
-    typedef Stride<0, Value> Base;
-  public:
-    EIGEN_DEVICE_FUNC InnerStride() : Base() {}
-    EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {} // FIXME making this explicit could break valid code
-};
-
-/** \brief Convenience specialization of Stride to specify only an outer stride
-  * See class Map for some examples */
-template<int Value>
-class OuterStride : public Stride<Value, 0>
-{
-    typedef Stride<Value, 0> Base;
-  public:
-    EIGEN_DEVICE_FUNC OuterStride() : Base() {}
-    EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} // FIXME making this explicit could break valid code
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_STRIDE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Swap.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Swap.h
deleted file mode 100644
index 180a4e5..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Swap.h
+++ /dev/null
@@ -1,68 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SWAP_H
-#define EIGEN_SWAP_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// Overload default assignPacket behavior for swapping them
-template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
-class generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, Specialized>
- : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn> Base;
-  using Base::m_dst;
-  using Base::m_src;
-  using Base::m_functor;
-  
-public:
-  typedef typename Base::Scalar Scalar;
-  typedef typename Base::DstXprType DstXprType;
-  typedef swap_assign_op<Scalar> Functor;
-  
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  generic_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {}
-  
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
-  {
-    PacketType tmp = m_src.template packet<LoadMode,PacketType>(row,col);
-    const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(row,col, m_dst.template packet<StoreMode,PacketType>(row,col));
-    m_dst.template writePacket<StoreMode>(row,col,tmp);
-  }
-  
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE void assignPacket(Index index)
-  {
-    PacketType tmp = m_src.template packet<LoadMode,PacketType>(index);
-    const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(index, m_dst.template packet<StoreMode,PacketType>(index));
-    m_dst.template writePacket<StoreMode>(index,tmp);
-  }
-  
-  // TODO find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I mean no CRTP (Gael)
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
-  {
-    Index row = Base::rowIndexByOuterInner(outer, inner); 
-    Index col = Base::colIndexByOuterInner(outer, inner);
-    assignPacket<StoreMode,LoadMode,PacketType>(row, col);
-  }
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SWAP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Transpose.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Transpose.h
deleted file mode 100644
index 2bc658f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Transpose.h
+++ /dev/null
@@ -1,464 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSPOSE_H
-#define EIGEN_TRANSPOSE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename MatrixType>
-struct traits<Transpose<MatrixType> > : public traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedPlain;
-  enum {
-    RowsAtCompileTime = MatrixType::ColsAtCompileTime,
-    ColsAtCompileTime = MatrixType::RowsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags0 = traits<MatrixTypeNestedPlain>::Flags & ~(LvalueBit | NestByRefBit),
-    Flags1 = Flags0 | FlagsLvalueBit,
-    Flags = Flags1 ^ RowMajorBit,
-    InnerStrideAtCompileTime = inner_stride_at_compile_time<MatrixType>::ret,
-    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
-  };
-};
-}
-
-template<typename MatrixType, typename StorageKind> class TransposeImpl;
-
-/** \class Transpose
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the transpose of a matrix
-  *
-  * \tparam MatrixType the type of the object of which we are taking the transpose
-  *
-  * This class represents an expression of the transpose of a matrix.
-  * It is the return type of MatrixBase::transpose() and MatrixBase::adjoint()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::transpose(), MatrixBase::adjoint()
-  */
-template<typename MatrixType> class Transpose
-  : public TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>
-{
-  public:
-
-    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-
-    typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE Transpose(MatrixType& matrix) : m_matrix(matrix) {}
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<MatrixTypeNested>::type&
-    nestedExpression() const { return m_matrix; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    typename internal::remove_reference<MatrixTypeNested>::type&
-    nestedExpression() { return m_matrix; }
-
-    /** \internal */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void resize(Index nrows, Index ncols) {
-      m_matrix.resize(ncols,nrows);
-    }
-
-  protected:
-    typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
-};
-
-namespace internal {
-
-template<typename MatrixType, bool HasDirectAccess = has_direct_access<MatrixType>::ret>
-struct TransposeImpl_base
-{
-  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
-};
-
-template<typename MatrixType>
-struct TransposeImpl_base<MatrixType, false>
-{
-  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
-};
-
-} // end namespace internal
-
-// Generic API dispatcher
-template<typename XprType, typename StorageKind>
-class TransposeImpl
-  : public internal::generic_xpr_base<Transpose<XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<Transpose<XprType> >::type Base;
-};
-
-template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
-  : public internal::TransposeImpl_base<MatrixType>::type
-{
-  public:
-
-    typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
-    using Base::coeffRef;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Index innerStride() const { return derived().nestedExpression().innerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Index outerStride() const { return derived().nestedExpression().outerStride(); }
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<MatrixType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Scalar* data() const { return derived().nestedExpression().data(); }
-
-    // FIXME: shall we keep the const version of coeffRef?
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return derived().nestedExpression().coeffRef(colId, rowId);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Scalar& coeffRef(Index index) const
-    {
-      return derived().nestedExpression().coeffRef(index);
-    }
-  protected:
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TransposeImpl)
-};
-
-/** \returns an expression of the transpose of *this.
-  *
-  * Example: \include MatrixBase_transpose.cpp
-  * Output: \verbinclude MatrixBase_transpose.out
-  *
-  * \warning If you want to replace a matrix by its own transpose, do \b NOT do this:
-  * \code
-  * m = m.transpose(); // bug!!! caused by aliasing effect
-  * \endcode
-  * Instead, use the transposeInPlace() method:
-  * \code
-  * m.transposeInPlace();
-  * \endcode
-  * which gives Eigen good opportunities for optimization, or alternatively you can also do:
-  * \code
-  * m = m.transpose().eval();
-  * \endcode
-  *
-  * \sa transposeInPlace(), adjoint() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-Transpose<Derived>
-DenseBase<Derived>::transpose()
-{
-  return TransposeReturnType(derived());
-}
-
-/** This is the const version of transpose().
-  *
-  * Make sure you read the warning for transpose() !
-  *
-  * \sa transposeInPlace(), adjoint() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename DenseBase<Derived>::ConstTransposeReturnType
-DenseBase<Derived>::transpose() const
-{
-  return ConstTransposeReturnType(derived());
-}
-
-/** \returns an expression of the adjoint (i.e. conjugate transpose) of *this.
-  *
-  * Example: \include MatrixBase_adjoint.cpp
-  * Output: \verbinclude MatrixBase_adjoint.out
-  *
-  * \warning If you want to replace a matrix by its own adjoint, do \b NOT do this:
-  * \code
-  * m = m.adjoint(); // bug!!! caused by aliasing effect
-  * \endcode
-  * Instead, use the adjointInPlace() method:
-  * \code
-  * m.adjointInPlace();
-  * \endcode
-  * which gives Eigen good opportunities for optimization, or alternatively you can also do:
-  * \code
-  * m = m.adjoint().eval();
-  * \endcode
-  *
-  * \sa adjointInPlace(), transpose(), conjugate(), class Transpose, class internal::scalar_conjugate_op */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::AdjointReturnType
-MatrixBase<Derived>::adjoint() const
-{
-  return AdjointReturnType(this->transpose());
-}
-
-/***************************************************************************
-* "in place" transpose implementation
-***************************************************************************/
-
-namespace internal {
-
-template<typename MatrixType,
-  bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic,
-  bool MatchPacketSize =
-        (int(MatrixType::RowsAtCompileTime) == int(internal::packet_traits<typename MatrixType::Scalar>::size))
-    &&  (internal::evaluator<MatrixType>::Flags&PacketAccessBit) >
-struct inplace_transpose_selector;
-
-template<typename MatrixType>
-struct inplace_transpose_selector<MatrixType,true,false> { // square matrix
-  static void run(MatrixType& m) {
-    m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose().template triangularView<StrictlyUpper>());
-  }
-};
-
-template<typename MatrixType>
-struct inplace_transpose_selector<MatrixType,true,true> { // PacketSize x PacketSize
-  static void run(MatrixType& m) {
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
-    const Index PacketSize = internal::packet_traits<Scalar>::size;
-    const Index Alignment = internal::evaluator<MatrixType>::Alignment;
-    PacketBlock<Packet> A;
-    for (Index i=0; i<PacketSize; ++i)
-      A.packet[i] = m.template packetByOuterInner<Alignment>(i,0);
-    internal::ptranspose(A);
-    for (Index i=0; i<PacketSize; ++i)
-      m.template writePacket<Alignment>(m.rowIndexByOuterInner(i,0), m.colIndexByOuterInner(i,0), A.packet[i]);
-  }
-};
-
-
-template <typename MatrixType, Index Alignment>
-void BlockedInPlaceTranspose(MatrixType& m) {
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
-  const Index PacketSize = internal::packet_traits<Scalar>::size;
-  eigen_assert(m.rows() == m.cols());
-  int row_start = 0;
-  for (; row_start + PacketSize <= m.rows(); row_start += PacketSize) {
-    for (int col_start = row_start; col_start + PacketSize <= m.cols(); col_start += PacketSize) {
-      PacketBlock<Packet> A;
-      if (row_start == col_start) {
-        for (Index i=0; i<PacketSize; ++i)
-          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i,col_start);
-        internal::ptranspose(A);
-        for (Index i=0; i<PacketSize; ++i)
-          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start), m.colIndexByOuterInner(row_start + i,col_start), A.packet[i]);
-      } else {
-        PacketBlock<Packet> B;
-        for (Index i=0; i<PacketSize; ++i) {
-          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i,col_start);
-          B.packet[i] = m.template packetByOuterInner<Alignment>(col_start + i, row_start);
-        }
-        internal::ptranspose(A);
-        internal::ptranspose(B);
-        for (Index i=0; i<PacketSize; ++i) {
-          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start), m.colIndexByOuterInner(row_start + i,col_start), B.packet[i]);
-          m.template writePacket<Alignment>(m.rowIndexByOuterInner(col_start + i, row_start), m.colIndexByOuterInner(col_start + i,row_start), A.packet[i]);
-        }
-      }
-    }
-  }
-  for (Index row = row_start; row < m.rows(); ++row) {
-    m.matrix().row(row).head(row).swap(
-        m.matrix().col(row).head(row).transpose());
-  }
-}
-
-template<typename MatrixType,bool MatchPacketSize>
-struct inplace_transpose_selector<MatrixType,false,MatchPacketSize> { // non square or dynamic matrix
-  static void run(MatrixType& m) {
-    typedef typename MatrixType::Scalar Scalar;
-    if (m.rows() == m.cols()) {
-      const Index PacketSize = internal::packet_traits<Scalar>::size;
-      if (!NumTraits<Scalar>::IsComplex && m.rows() >= PacketSize) {
-        if ((m.rows() % PacketSize) == 0)
-          BlockedInPlaceTranspose<MatrixType,internal::evaluator<MatrixType>::Alignment>(m);
-        else
-          BlockedInPlaceTranspose<MatrixType,Unaligned>(m);
-      }
-      else {
-        m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose().template triangularView<StrictlyUpper>());
-      }
-    } else {
-      m = m.transpose().eval();
-    }
-  }
-};
-
-
-} // end namespace internal
-
-/** This is the "in place" version of transpose(): it replaces \c *this by its own transpose.
-  * Thus, doing
-  * \code
-  * m.transposeInPlace();
-  * \endcode
-  * has the same effect on m as doing
-  * \code
-  * m = m.transpose().eval();
-  * \endcode
-  * and is faster and also safer because in the latter line of code, forgetting the eval() results
-  * in a bug caused by \ref TopicAliasing "aliasing".
-  *
-  * Notice however that this method is only useful if you want to replace a matrix by its own transpose.
-  * If you just need the transpose of a matrix, use transpose().
-  *
-  * \note if the matrix is not square, then \c *this must be a resizable matrix.
-  * This excludes (non-square) fixed-size matrices, block-expressions and maps.
-  *
-  * \sa transpose(), adjoint(), adjointInPlace() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::transposeInPlace()
-{
-  eigen_assert((rows() == cols() || (RowsAtCompileTime == Dynamic && ColsAtCompileTime == Dynamic))
-               && "transposeInPlace() called on a non-square non-resizable matrix");
-  internal::inplace_transpose_selector<Derived>::run(derived());
-}
-
-/***************************************************************************
-* "in place" adjoint implementation
-***************************************************************************/
-
-/** This is the "in place" version of adjoint(): it replaces \c *this by its own transpose.
-  * Thus, doing
-  * \code
-  * m.adjointInPlace();
-  * \endcode
-  * has the same effect on m as doing
-  * \code
-  * m = m.adjoint().eval();
-  * \endcode
-  * and is faster and also safer because in the latter line of code, forgetting the eval() results
-  * in a bug caused by aliasing.
-  *
-  * Notice however that this method is only useful if you want to replace a matrix by its own adjoint.
-  * If you just need the adjoint of a matrix, use adjoint().
-  *
-  * \note if the matrix is not square, then \c *this must be a resizable matrix.
-  * This excludes (non-square) fixed-size matrices, block-expressions and maps.
-  *
-  * \sa transpose(), adjoint(), transposeInPlace() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline void MatrixBase<Derived>::adjointInPlace()
-{
-  derived() = adjoint().eval();
-}
-
-#ifndef EIGEN_NO_DEBUG
-
-// The following is to detect aliasing problems in most common cases.
-
-namespace internal {
-
-template<bool DestIsTransposed, typename OtherDerived>
-struct check_transpose_aliasing_compile_time_selector
-{
-  enum { ret = bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed };
-};
-
-template<bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
-struct check_transpose_aliasing_compile_time_selector<DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> >
-{
-  enum { ret =    bool(blas_traits<DerivedA>::IsTransposed) != DestIsTransposed
-               || bool(blas_traits<DerivedB>::IsTransposed) != DestIsTransposed
-  };
-};
-
-template<typename Scalar, bool DestIsTransposed, typename OtherDerived>
-struct check_transpose_aliasing_run_time_selector
-{
-  static bool run(const Scalar* dest, const OtherDerived& src)
-  {
-    return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src));
-  }
-};
-
-template<typename Scalar, bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
-struct check_transpose_aliasing_run_time_selector<Scalar,DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> >
-{
-  static bool run(const Scalar* dest, const CwiseBinaryOp<BinOp,DerivedA,DerivedB>& src)
-  {
-    return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.lhs())))
-        || ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.rhs())));
-  }
-};
-
-// the following selector, checkTransposeAliasing_impl, based on MightHaveTransposeAliasing,
-// is because when the condition controlling the assert is known at compile time, ICC emits a warning.
-// This is actually a good warning: in expressions that don't have any transposing, the condition is
-// known at compile time to be false, and using that, we can avoid generating the code of the assert again
-// and again for all these expressions that don't need it.
-
-template<typename Derived, typename OtherDerived,
-         bool MightHaveTransposeAliasing
-                 = check_transpose_aliasing_compile_time_selector
-                     <blas_traits<Derived>::IsTransposed,OtherDerived>::ret
-        >
-struct checkTransposeAliasing_impl
-{
-    static void run(const Derived& dst, const OtherDerived& other)
-    {
-        eigen_assert((!check_transpose_aliasing_run_time_selector
-                      <typename Derived::Scalar,blas_traits<Derived>::IsTransposed,OtherDerived>
-                      ::run(extract_data(dst), other))
-          && "aliasing detected during transposition, use transposeInPlace() "
-             "or evaluate the rhs into a temporary using .eval()");
-
-    }
-};
-
-template<typename Derived, typename OtherDerived>
-struct checkTransposeAliasing_impl<Derived, OtherDerived, false>
-{
-    static void run(const Derived&, const OtherDerived&)
-    {
-    }
-};
-
-template<typename Dst, typename Src>
-void check_for_aliasing(const Dst &dst, const Src &src)
-{
-  if((!Dst::IsVectorAtCompileTime) && dst.rows()>1 && dst.cols()>1)
-    internal::checkTransposeAliasing_impl<Dst, Src>::run(dst, src);
-}
-
-} // end namespace internal
-
-#endif // EIGEN_NO_DEBUG
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSPOSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Transpositions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Transpositions.h
deleted file mode 100644
index 38a7b01..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Transpositions.h
+++ /dev/null
@@ -1,386 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSPOSITIONS_H
-#define EIGEN_TRANSPOSITIONS_H
-
-namespace Eigen {
-
-template<typename Derived>
-class TranspositionsBase
-{
-    typedef internal::traits<Derived> Traits;
-
-  public:
-
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    EIGEN_DEVICE_FUNC
-    Derived& derived() { return *static_cast<Derived*>(this); }
-    EIGEN_DEVICE_FUNC
-    const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    Derived& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      indices() = other.indices();
-      return derived();
-    }
-
-    /** \returns the number of transpositions */
-    EIGEN_DEVICE_FUNC
-    Index size() const { return indices().size(); }
-    /** \returns the number of rows of the equivalent permutation matrix */
-    EIGEN_DEVICE_FUNC
-    Index rows() const { return indices().size(); }
-    /** \returns the number of columns of the equivalent permutation matrix */
-    EIGEN_DEVICE_FUNC
-    Index cols() const { return indices().size(); }
-
-    /** Direct access to the underlying index vector */
-    EIGEN_DEVICE_FUNC
-    inline const StorageIndex& coeff(Index i) const { return indices().coeff(i); }
-    /** Direct access to the underlying index vector */
-    inline StorageIndex& coeffRef(Index i) { return indices().coeffRef(i); }
-    /** Direct access to the underlying index vector */
-    inline const StorageIndex& operator()(Index i) const { return indices()(i); }
-    /** Direct access to the underlying index vector */
-    inline StorageIndex& operator()(Index i) { return indices()(i); }
-    /** Direct access to the underlying index vector */
-    inline const StorageIndex& operator[](Index i) const { return indices()(i); }
-    /** Direct access to the underlying index vector */
-    inline StorageIndex& operator[](Index i) { return indices()(i); }
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return derived().indices(); }
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return derived().indices(); }
-
-    /** Resizes to given size. */
-    inline void resize(Index newSize)
-    {
-      indices().resize(newSize);
-    }
-
-    /** Sets \c *this to represents an identity transformation */
-    void setIdentity()
-    {
-      for(StorageIndex i = 0; i < indices().size(); ++i)
-        coeffRef(i) = i;
-    }
-
-    // FIXME: do we want such methods ?
-    // might be useful when the target matrix expression is complex, e.g.:
-    // object.matrix().block(..,..,..,..) = trans * object.matrix().block(..,..,..,..);
-    /*
-    template<typename MatrixType>
-    void applyForwardToRows(MatrixType& mat) const
-    {
-      for(Index k=0 ; k<size() ; ++k)
-        if(m_indices(k)!=k)
-          mat.row(k).swap(mat.row(m_indices(k)));
-    }
-
-    template<typename MatrixType>
-    void applyBackwardToRows(MatrixType& mat) const
-    {
-      for(Index k=size()-1 ; k>=0 ; --k)
-        if(m_indices(k)!=k)
-          mat.row(k).swap(mat.row(m_indices(k)));
-    }
-    */
-
-    /** \returns the inverse transformation */
-    inline Transpose<TranspositionsBase> inverse() const
-    { return Transpose<TranspositionsBase>(derived()); }
-
-    /** \returns the tranpose transformation */
-    inline Transpose<TranspositionsBase> transpose() const
-    { return Transpose<TranspositionsBase>(derived()); }
-
-  protected:
-};
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
- : traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
-{
-  typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
-  typedef TranspositionsStorage StorageKind;
-};
-}
-
-/** \class Transpositions
-  * \ingroup Core_Module
-  *
-  * \brief Represents a sequence of transpositions (row/column interchange)
-  *
-  * \tparam SizeAtCompileTime the number of transpositions, or Dynamic
-  * \tparam MaxSizeAtCompileTime the maximum number of transpositions, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
-  *
-  * This class represents a permutation transformation as a sequence of \em n transpositions
-  * \f$[T_{n-1} \ldots T_{i} \ldots T_{0}]\f$. It is internally stored as a vector of integers \c indices.
-  * Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges
-  * the rows \c i and \c indices[i] of the matrix \c M.
-  * A transposition applied on the right (e.g., \f$ M T_{i}\f$) yields a column interchange.
-  *
-  * Compared to the class PermutationMatrix, such a sequence of transpositions is what is
-  * computed during a decomposition with pivoting, and it is faster when applying the permutation in-place.
-  *
-  * To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example:
-  * \code
-  * Transpositions tr;
-  * MatrixXf mat;
-  * mat = tr * mat;
-  * \endcode
-  * In this example, we detect that the matrix appears on both side, and so the transpositions
-  * are applied in-place without any temporary or extra copy.
-  *
-  * \sa class PermutationMatrix
-  */
-
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
-{
-    typedef internal::traits<Transpositions> Traits;
-  public:
-
-    typedef TranspositionsBase<Transpositions> Base;
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-
-    inline Transpositions() {}
-
-    /** Copy constructor. */
-    template<typename OtherDerived>
-    inline Transpositions(const TranspositionsBase<OtherDerived>& other)
-      : m_indices(other.indices()) {}
-
-    /** Generic constructor from expression of the transposition indices. */
-    template<typename Other>
-    explicit inline Transpositions(const MatrixBase<Other>& indices) : m_indices(indices)
-    {}
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    Transpositions& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      return Base::operator=(other);
-    }
-
-    /** Constructs an uninitialized permutation matrix of given size.
-      */
-    inline Transpositions(Index size) : m_indices(size)
-    {}
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return m_indices; }
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
-struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,_PacketAccess> >
- : traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
-{
-  typedef Map<const Matrix<_StorageIndex,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType;
-  typedef _StorageIndex StorageIndex;
-  typedef TranspositionsStorage StorageKind;
-};
-}
-
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int PacketAccess>
-class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess>
- : public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess> >
-{
-    typedef internal::traits<Map> Traits;
-  public:
-
-    typedef TranspositionsBase<Map> Base;
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-
-    explicit inline Map(const StorageIndex* indicesPtr)
-      : m_indices(indicesPtr)
-    {}
-
-    inline Map(const StorageIndex* indicesPtr, Index size)
-      : m_indices(indicesPtr,size)
-    {}
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    Map& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      return Base::operator=(other);
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    Map& operator=(const Map& other)
-    {
-      m_indices = other.m_indices;
-      return *this;
-    }
-    #endif
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return m_indices; }
-
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-namespace internal {
-template<typename _IndicesType>
-struct traits<TranspositionsWrapper<_IndicesType> >
- : traits<PermutationWrapper<_IndicesType> >
-{
-  typedef TranspositionsStorage StorageKind;
-};
-}
-
-template<typename _IndicesType>
-class TranspositionsWrapper
- : public TranspositionsBase<TranspositionsWrapper<_IndicesType> >
-{
-    typedef internal::traits<TranspositionsWrapper> Traits;
-  public:
-
-    typedef TranspositionsBase<TranspositionsWrapper> Base;
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-
-    explicit inline TranspositionsWrapper(IndicesType& indices)
-      : m_indices(indices)
-    {}
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    TranspositionsWrapper& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      return Base::operator=(other);
-    }
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return m_indices; }
-
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    typename IndicesType::Nested m_indices;
-};
-
-
-
-/** \returns the \a matrix with the \a transpositions applied to the columns.
-  */
-template<typename MatrixDerived, typename TranspositionsDerived>
-EIGEN_DEVICE_FUNC
-const Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
-operator*(const MatrixBase<MatrixDerived> &matrix,
-          const TranspositionsBase<TranspositionsDerived>& transpositions)
-{
-  return Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
-            (matrix.derived(), transpositions.derived());
-}
-
-/** \returns the \a matrix with the \a transpositions applied to the rows.
-  */
-template<typename TranspositionsDerived, typename MatrixDerived>
-EIGEN_DEVICE_FUNC
-const Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
-operator*(const TranspositionsBase<TranspositionsDerived> &transpositions,
-          const MatrixBase<MatrixDerived>& matrix)
-{
-  return Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
-            (transpositions.derived(), matrix.derived());
-}
-
-// Template partial specialization for transposed/inverse transpositions
-
-namespace internal {
-
-template<typename Derived>
-struct traits<Transpose<TranspositionsBase<Derived> > >
- : traits<Derived>
-{};
-
-} // end namespace internal
-
-template<typename TranspositionsDerived>
-class Transpose<TranspositionsBase<TranspositionsDerived> >
-{
-    typedef TranspositionsDerived TranspositionType;
-    typedef typename TranspositionType::IndicesType IndicesType;
-  public:
-
-    explicit Transpose(const TranspositionType& t) : m_transpositions(t) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index size() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
-
-    /** \returns the \a matrix with the inverse transpositions applied to the columns.
-      */
-    template<typename OtherDerived> friend
-    const Product<OtherDerived, Transpose, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trt)
-    {
-      return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt);
-    }
-
-    /** \returns the \a matrix with the inverse transpositions applied to the rows.
-      */
-    template<typename OtherDerived>
-    const Product<Transpose, OtherDerived, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix) const
-    {
-      return Product<Transpose, OtherDerived, AliasFreeProduct>(*this, matrix.derived());
-    }
-
-    EIGEN_DEVICE_FUNC
-    const TranspositionType& nestedExpression() const { return m_transpositions; }
-
-  protected:
-    const TranspositionType& m_transpositions;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSPOSITIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/TriangularMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/TriangularMatrix.h
deleted file mode 100644
index fdb8bc1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/TriangularMatrix.h
+++ /dev/null
@@ -1,1001 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULARMATRIX_H
-#define EIGEN_TRIANGULARMATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval;
-
-}
-
-/** \class TriangularBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for triangular part in a matrix
-  */
-template<typename Derived> class TriangularBase : public EigenBase<Derived>
-{
-  public:
-
-    enum {
-      Mode = internal::traits<Derived>::Mode,
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
-      /**< This is equal to the number of coefficients, i.e. the number of
-          * rows times the number of columns, or to \a Dynamic if this is not
-          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
-
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
-                                                   internal::traits<Derived>::MaxColsAtCompileTime>::ret)
-
-    };
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-    typedef typename internal::traits<Derived>::FullMatrixType DenseMatrixType;
-    typedef DenseMatrixType DenseType;
-    typedef Derived const& Nested;
-
-    EIGEN_DEVICE_FUNC
-    inline TriangularBase() { eigen_assert(!((int(Mode) & int(UnitDiag)) && (int(Mode) & int(ZeroDiag)))); }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return derived().outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return derived().innerStride(); }
-
-    // dummy resize function
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols)
-    {
-      EIGEN_UNUSED_VARIABLE(rows);
-      EIGEN_UNUSED_VARIABLE(cols);
-      eigen_assert(rows==this->rows() && cols==this->cols());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar coeff(Index row, Index col) const  { return derived().coeff(row,col); }
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index col) { return derived().coeffRef(row,col); }
-
-    /** \see MatrixBase::copyCoeff(row,col)
-      */
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, Other& other)
-    {
-      derived().coeffRef(row, col) = other.coeff(row, col);
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar operator()(Index row, Index col) const
-    {
-      check_coordinates(row, col);
-      return coeff(row,col);
-    }
-    EIGEN_DEVICE_FUNC
-    inline Scalar& operator()(Index row, Index col)
-    {
-      check_coordinates(row, col);
-      return coeffRef(row,col);
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    EIGEN_DEVICE_FUNC
-    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    EIGEN_DEVICE_FUNC
-    inline Derived& derived() { return *static_cast<Derived*>(this); }
-    #endif // not EIGEN_PARSED_BY_DOXYGEN
-
-    template<typename DenseDerived>
-    EIGEN_DEVICE_FUNC
-    void evalTo(MatrixBase<DenseDerived> &other) const;
-    template<typename DenseDerived>
-    EIGEN_DEVICE_FUNC
-    void evalToLazy(MatrixBase<DenseDerived> &other) const;
-
-    EIGEN_DEVICE_FUNC
-    DenseMatrixType toDenseMatrix() const
-    {
-      DenseMatrixType res(rows(), cols());
-      evalToLazy(res);
-      return res;
-    }
-
-  protected:
-
-    void check_coordinates(Index row, Index col) const
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(row);
-      EIGEN_ONLY_USED_FOR_DEBUG(col);
-      eigen_assert(col>=0 && col<cols() && row>=0 && row<rows());
-      const int mode = int(Mode) & ~SelfAdjoint;
-      EIGEN_ONLY_USED_FOR_DEBUG(mode);
-      eigen_assert((mode==Upper && col>=row)
-                || (mode==Lower && col<=row)
-                || ((mode==StrictlyUpper || mode==UnitUpper) && col>row)
-                || ((mode==StrictlyLower || mode==UnitLower) && col<row));
-    }
-
-    #ifdef EIGEN_INTERNAL_DEBUGGING
-    void check_coordinates_internal(Index row, Index col) const
-    {
-      check_coordinates(row, col);
-    }
-    #else
-    void check_coordinates_internal(Index , Index ) const {}
-    #endif
-
-};
-
-/** \class TriangularView
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a triangular part in a matrix
-  *
-  * \param MatrixType the type of the object in which we are taking the triangular part
-  * \param Mode the kind of triangular matrix expression to construct. Can be #Upper,
-  *             #Lower, #UnitUpper, #UnitLower, #StrictlyUpper, or #StrictlyLower.
-  *             This is in fact a bit field; it must have either #Upper or #Lower,
-  *             and additionally it may have #UnitDiag or #ZeroDiag or neither.
-  *
-  * This class represents a triangular part of a matrix, not necessarily square. Strictly speaking, for rectangular
-  * matrices one should speak of "trapezoid" parts. This class is the return type
-  * of MatrixBase::triangularView() and SparseMatrixBase::triangularView(), and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::triangularView()
-  */
-namespace internal {
-template<typename MatrixType, unsigned int _Mode>
-struct traits<TriangularView<MatrixType, _Mode> > : traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef;
-  typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
-  typedef typename MatrixType::PlainObject FullMatrixType;
-  typedef MatrixType ExpressionType;
-  enum {
-    Mode = _Mode,
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = (MatrixTypeNestedCleaned::Flags & (HereditaryBits | FlagsLvalueBit) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)))
-  };
-};
-}
-
-template<typename _MatrixType, unsigned int _Mode, typename StorageKind> class TriangularViewImpl;
-
-template<typename _MatrixType, unsigned int _Mode> class TriangularView
-  : public TriangularViewImpl<_MatrixType, _Mode, typename internal::traits<_MatrixType>::StorageKind >
-{
-  public:
-
-    typedef TriangularViewImpl<_MatrixType, _Mode, typename internal::traits<_MatrixType>::StorageKind > Base;
-    typedef typename internal::traits<TriangularView>::Scalar Scalar;
-    typedef _MatrixType MatrixType;
-
-  protected:
-    typedef typename internal::traits<TriangularView>::MatrixTypeNested MatrixTypeNested;
-    typedef typename internal::traits<TriangularView>::MatrixTypeNestedNonRef MatrixTypeNestedNonRef;
-
-    typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
-    typedef TriangularView<typename internal::add_const<MatrixType>::type, _Mode> ConstTriangularView;
-
-  public:
-
-    typedef typename internal::traits<TriangularView>::StorageKind StorageKind;
-    typedef typename internal::traits<TriangularView>::MatrixTypeNestedCleaned NestedExpression;
-
-    enum {
-      Mode = _Mode,
-      Flags = internal::traits<TriangularView>::Flags,
-      TransposeMode = (Mode & Upper ? Lower : 0)
-                    | (Mode & Lower ? Upper : 0)
-                    | (Mode & (UnitDiag))
-                    | (Mode & (ZeroDiag)),
-      IsVectorAtCompileTime = false
-    };
-
-    EIGEN_DEVICE_FUNC
-    explicit inline TriangularView(MatrixType& matrix) : m_matrix(matrix)
-    {}
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TriangularView)
-
-    /** \copydoc EigenBase::rows() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    /** \copydoc EigenBase::cols() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    /** \returns a const reference to the nested expression */
-    EIGEN_DEVICE_FUNC
-    const NestedExpression& nestedExpression() const { return m_matrix; }
-
-    /** \returns a reference to the nested expression */
-    EIGEN_DEVICE_FUNC
-    NestedExpression& nestedExpression() { return m_matrix; }
-
-    typedef TriangularView<const MatrixConjugateReturnType,Mode> ConjugateReturnType;
-    /** \sa MatrixBase::conjugate() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConjugateReturnType conjugate() const
-    { return ConjugateReturnType(m_matrix.conjugate()); }
-
-    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
-     *           returns \c *this otherwise.
-     */
-    template<bool Cond>
-    EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Cond,ConjugateReturnType,ConstTriangularView>::type
-    conjugateIf() const
-    {
-      typedef typename internal::conditional<Cond,ConjugateReturnType,ConstTriangularView>::type ReturnType;
-      return ReturnType(m_matrix.template conjugateIf<Cond>());
-    }
-
-    typedef TriangularView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
-    /** \sa MatrixBase::adjoint() const */
-    EIGEN_DEVICE_FUNC
-    inline const AdjointReturnType adjoint() const
-    { return AdjointReturnType(m_matrix.adjoint()); }
-
-    typedef TriangularView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
-     /** \sa MatrixBase::transpose() */
-    EIGEN_DEVICE_FUNC
-    inline TransposeReturnType transpose()
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      typename MatrixType::TransposeReturnType tmp(m_matrix);
-      return TransposeReturnType(tmp);
-    }
-
-    typedef TriangularView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
-    /** \sa MatrixBase::transpose() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConstTransposeReturnType transpose() const
-    {
-      return ConstTransposeReturnType(m_matrix.transpose());
-    }
-
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    inline const Solve<TriangularView, Other>
-    solve(const MatrixBase<Other>& other) const
-    { return Solve<TriangularView, Other>(*this, other.derived()); }
-
-  // workaround MSVC ICE
-  #if EIGEN_COMP_MSVC
-    template<int Side, typename Other>
-    EIGEN_DEVICE_FUNC
-    inline const internal::triangular_solve_retval<Side,TriangularView, Other>
-    solve(const MatrixBase<Other>& other) const
-    { return Base::template solve<Side>(other); }
-  #else
-    using Base::solve;
-  #endif
-
-    /** \returns a selfadjoint view of the referenced triangular part which must be either \c #Upper or \c #Lower.
-      *
-      * This is a shortcut for \code this->nestedExpression().selfadjointView<(*this)::Mode>() \endcode
-      * \sa MatrixBase::selfadjointView() */
-    EIGEN_DEVICE_FUNC
-    SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView()
-    {
-      EIGEN_STATIC_ASSERT((Mode&(UnitDiag|ZeroDiag))==0,PROGRAMMING_ERROR);
-      return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix);
-    }
-
-    /** This is the const version of selfadjointView() */
-    EIGEN_DEVICE_FUNC
-    const SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView() const
-    {
-      EIGEN_STATIC_ASSERT((Mode&(UnitDiag|ZeroDiag))==0,PROGRAMMING_ERROR);
-      return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix);
-    }
-
-
-    /** \returns the determinant of the triangular matrix
-      * \sa MatrixBase::determinant() */
-    EIGEN_DEVICE_FUNC
-    Scalar determinant() const
-    {
-      if (Mode & UnitDiag)
-        return 1;
-      else if (Mode & ZeroDiag)
-        return 0;
-      else
-        return m_matrix.diagonal().prod();
-    }
-
-  protected:
-
-    MatrixTypeNested m_matrix;
-};
-
-/** \ingroup Core_Module
-  *
-  * \brief Base class for a triangular part in a \b dense matrix
-  *
-  * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be instantiated.
-  * It extends class TriangularView with additional methods which available for dense expressions only.
-  *
-  * \sa class TriangularView, MatrixBase::triangularView()
-  */
-template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_MatrixType,_Mode,Dense>
-  : public TriangularBase<TriangularView<_MatrixType, _Mode> >
-{
-  public:
-
-    typedef TriangularView<_MatrixType, _Mode> TriangularViewType;
-    typedef TriangularBase<TriangularViewType> Base;
-    typedef typename internal::traits<TriangularViewType>::Scalar Scalar;
-
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::PlainObject DenseMatrixType;
-    typedef DenseMatrixType PlainObject;
-
-  public:
-    using Base::evalToLazy;
-    using Base::derived;
-
-    typedef typename internal::traits<TriangularViewType>::StorageKind StorageKind;
-
-    enum {
-      Mode = _Mode,
-      Flags = internal::traits<TriangularViewType>::Flags
-    };
-
-    /** \returns the outer-stride of the underlying dense matrix
-      * \sa DenseCoeffsBase::outerStride() */
-    EIGEN_DEVICE_FUNC
-    inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
-    /** \returns the inner-stride of the underlying dense matrix
-      * \sa DenseCoeffsBase::innerStride() */
-    EIGEN_DEVICE_FUNC
-    inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
-
-    /** \sa MatrixBase::operator+=() */
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator+=(const DenseBase<Other>& other) {
-      internal::call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename Other::Scalar>());
-      return derived();
-    }
-    /** \sa MatrixBase::operator-=() */
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator-=(const DenseBase<Other>& other) {
-      internal::call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename Other::Scalar>());
-      return derived();
-    }
-
-    /** \sa MatrixBase::operator*=() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator*=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() * other; }
-    /** \sa DenseBase::operator/=() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator/=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() / other; }
-
-    /** \sa MatrixBase::fill() */
-    EIGEN_DEVICE_FUNC
-    void fill(const Scalar& value) { setConstant(value); }
-    /** \sa MatrixBase::setConstant() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& setConstant(const Scalar& value)
-    { return *this = MatrixType::Constant(derived().rows(), derived().cols(), value); }
-    /** \sa MatrixBase::setZero() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& setZero() { return setConstant(Scalar(0)); }
-    /** \sa MatrixBase::setOnes() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& setOnes() { return setConstant(Scalar(1)); }
-
-    /** \sa MatrixBase::coeff()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar coeff(Index row, Index col) const
-    {
-      Base::check_coordinates_internal(row, col);
-      return derived().nestedExpression().coeff(row, col);
-    }
-
-    /** \sa MatrixBase::coeffRef()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(TriangularViewType);
-      Base::check_coordinates_internal(row, col);
-      return derived().nestedExpression().coeffRef(row, col);
-    }
-
-    /** Assigns a triangular matrix to a triangular part of a dense matrix */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& operator=(const TriangularBase<OtherDerived>& other);
-
-    /** Shortcut for\code *this = other.other.triangularView<(*this)::Mode>() \endcode */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& operator=(const MatrixBase<OtherDerived>& other);
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& operator=(const TriangularViewImpl& other)
-    { return *this = other.derived().nestedExpression(); }
-
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    void lazyAssign(const TriangularBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    void lazyAssign(const MatrixBase<OtherDerived>& other);
-#endif
-
-    /** Efficient triangular matrix times vector/matrix product */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<TriangularViewType,OtherDerived>
-    operator*(const MatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<TriangularViewType,OtherDerived>(derived(), rhs.derived());
-    }
-
-    /** Efficient vector/matrix times triangular matrix product */
-    template<typename OtherDerived> friend
-    EIGEN_DEVICE_FUNC
-    const Product<OtherDerived,TriangularViewType>
-    operator*(const MatrixBase<OtherDerived>& lhs, const TriangularViewImpl& rhs)
-    {
-      return Product<OtherDerived,TriangularViewType>(lhs.derived(),rhs.derived());
-    }
-
-    /** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
-      *
-      * This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if
-      * \a Side==OnTheLeft (the default), or the right-inverse-multiply  \a other * inverse(\c *this) if
-      * \a Side==OnTheRight.
-      *
-      * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
-      *
-      * The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
-      * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
-      * is an upper (resp. lower) triangular matrix.
-      *
-      * Example: \include Triangular_solve.cpp
-      * Output: \verbinclude Triangular_solve.out
-      *
-      * This function returns an expression of the inverse-multiply and can works in-place if it is assigned
-      * to the same matrix or vector \a other.
-      *
-      * For users coming from BLAS, this function (and more specifically solveInPlace()) offer
-      * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
-      *
-      * \sa TriangularView::solveInPlace()
-      */
-    template<int Side, typename Other>
-    inline const internal::triangular_solve_retval<Side,TriangularViewType, Other>
-    solve(const MatrixBase<Other>& other) const;
-
-    /** "in-place" version of TriangularView::solve() where the result is written in \a other
-      *
-      * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
-      * This function will const_cast it, so constness isn't honored here.
-      *
-      * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
-      *
-      * See TriangularView:solve() for the details.
-      */
-    template<int Side, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    void solveInPlace(const MatrixBase<OtherDerived>& other) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    void solveInPlace(const MatrixBase<OtherDerived>& other) const
-    { return solveInPlace<OnTheLeft>(other); }
-
-    /** Swaps the coefficients of the common triangular parts of two matrices */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-    void swap(TriangularBase<OtherDerived> &other)
-#else
-    void swap(TriangularBase<OtherDerived> const & other)
-#endif
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
-      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    /** Shortcut for \code (*this).swap(other.triangularView<(*this)::Mode>()) \endcode */
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    void swap(MatrixBase<OtherDerived> const & other)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
-      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    template<typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _solve_impl(const RhsType &rhs, DstType &dst) const {
-      if(!internal::is_same_dense(dst,rhs))
-        dst = rhs;
-      this->solveInPlace(dst);
-    }
-
-    template<typename ProductType>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha, bool beta);
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TriangularViewImpl)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TriangularViewImpl)
-
-};
-
-/***************************************************************************
-* Implementation of triangular evaluation/assignment
-***************************************************************************/
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-// FIXME should we keep that possibility
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>&
-TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const MatrixBase<OtherDerived>& other)
-{
-  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-// FIXME should we keep that possibility
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const MatrixBase<OtherDerived>& other)
-{
-  internal::call_assignment_no_alias(derived(), other.template triangularView<Mode>());
-}
-
-
-
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>&
-TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const TriangularBase<OtherDerived>& other)
-{
-  eigen_assert(Mode == int(OtherDerived::Mode));
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const TriangularBase<OtherDerived>& other)
-{
-  eigen_assert(Mode == int(OtherDerived::Mode));
-  internal::call_assignment_no_alias(derived(), other.derived());
-}
-#endif
-
-/***************************************************************************
-* Implementation of TriangularBase methods
-***************************************************************************/
-
-/** Assigns a triangular or selfadjoint matrix to a dense matrix.
-  * If the matrix is triangular, the opposite part is set to zero. */
-template<typename Derived>
-template<typename DenseDerived>
-EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
-{
-  evalToLazy(other.derived());
-}
-
-/***************************************************************************
-* Implementation of TriangularView methods
-***************************************************************************/
-
-/***************************************************************************
-* Implementation of MatrixBase methods
-***************************************************************************/
-
-/**
-  * \returns an expression of a triangular view extracted from the current matrix
-  *
-  * The parameter \a Mode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
-  * \c #Lower, \c #StrictlyLower, \c #UnitLower.
-  *
-  * Example: \include MatrixBase_triangularView.cpp
-  * Output: \verbinclude MatrixBase_triangularView.out
-  *
-  * \sa class TriangularView
-  */
-template<typename Derived>
-template<unsigned int Mode>
-EIGEN_DEVICE_FUNC
-typename MatrixBase<Derived>::template TriangularViewReturnType<Mode>::Type
-MatrixBase<Derived>::triangularView()
-{
-  return typename TriangularViewReturnType<Mode>::Type(derived());
-}
-
-/** This is the const version of MatrixBase::triangularView() */
-template<typename Derived>
-template<unsigned int Mode>
-EIGEN_DEVICE_FUNC
-typename MatrixBase<Derived>::template ConstTriangularViewReturnType<Mode>::Type
-MatrixBase<Derived>::triangularView() const
-{
-  return typename ConstTriangularViewReturnType<Mode>::Type(derived());
-}
-
-/** \returns true if *this is approximately equal to an upper triangular matrix,
-  *          within the precision given by \a prec.
-  *
-  * \sa isLowerTriangular()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isUpperTriangular(const RealScalar& prec) const
-{
-  RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
-  for(Index j = 0; j < cols(); ++j)
-  {
-    Index maxi = numext::mini(j, rows()-1);
-    for(Index i = 0; i <= maxi; ++i)
-    {
-      RealScalar absValue = numext::abs(coeff(i,j));
-      if(absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue;
-    }
-  }
-  RealScalar threshold = maxAbsOnUpperPart * prec;
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = j+1; i < rows(); ++i)
-      if(numext::abs(coeff(i, j)) > threshold) return false;
-  return true;
-}
-
-/** \returns true if *this is approximately equal to a lower triangular matrix,
-  *          within the precision given by \a prec.
-  *
-  * \sa isUpperTriangular()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isLowerTriangular(const RealScalar& prec) const
-{
-  RealScalar maxAbsOnLowerPart = static_cast<RealScalar>(-1);
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = j; i < rows(); ++i)
-    {
-      RealScalar absValue = numext::abs(coeff(i,j));
-      if(absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue;
-    }
-  RealScalar threshold = maxAbsOnLowerPart * prec;
-  for(Index j = 1; j < cols(); ++j)
-  {
-    Index maxi = numext::mini(j, rows()-1);
-    for(Index i = 0; i < maxi; ++i)
-      if(numext::abs(coeff(i, j)) > threshold) return false;
-  }
-  return true;
-}
-
-
-/***************************************************************************
-****************************************************************************
-* Evaluators and Assignment of triangular expressions
-***************************************************************************
-***************************************************************************/
-
-namespace internal {
-
-
-// TODO currently a triangular expression has the form TriangularView<.,.>
-//      in the future triangular-ness should be defined by the expression traits
-//      such that Transpose<TriangularView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
-template<typename MatrixType, unsigned int Mode>
-struct evaluator_traits<TriangularView<MatrixType,Mode> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef typename glue_shapes<typename evaluator_traits<MatrixType>::Shape, TriangularShape>::type Shape;
-};
-
-template<typename MatrixType, unsigned int Mode>
-struct unary_evaluator<TriangularView<MatrixType,Mode>, IndexBased>
- : evaluator<typename internal::remove_all<MatrixType>::type>
-{
-  typedef TriangularView<MatrixType,Mode> XprType;
-  typedef evaluator<typename internal::remove_all<MatrixType>::type> Base;
-  EIGEN_DEVICE_FUNC
-  unary_evaluator(const XprType &xpr) : Base(xpr.nestedExpression()) {}
-};
-
-// Additional assignment kinds:
-struct Triangular2Triangular    {};
-struct Triangular2Dense         {};
-struct Dense2Triangular         {};
-
-
-template<typename Kernel, unsigned int Mode, int UnrollCount, bool ClearOpposite> struct triangular_assignment_loop;
-
-
-/** \internal Specialization of the dense assignment kernel for triangular matrices.
-  * The main difference is that the triangular, diagonal, and opposite parts are processed through three different functions.
-  * \tparam UpLo must be either Lower or Upper
-  * \tparam Mode must be either 0, UnitDiag, ZeroDiag, or SelfAdjoint
-  */
-template<int UpLo, int Mode, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
-class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
-  typedef typename Base::DstXprType DstXprType;
-  typedef typename Base::SrcXprType SrcXprType;
-  using Base::m_dst;
-  using Base::m_src;
-  using Base::m_functor;
-public:
-
-  typedef typename Base::DstEvaluatorType DstEvaluatorType;
-  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
-  typedef typename Base::Scalar Scalar;
-  typedef typename Base::AssignmentTraits AssignmentTraits;
-
-
-  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {}
-
-#ifdef EIGEN_INTERNAL_DEBUGGING
-  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
-  {
-    eigen_internal_assert(row!=col);
-    Base::assignCoeff(row,col);
-  }
-#else
-  using Base::assignCoeff;
-#endif
-
-  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
-  {
-         if(Mode==UnitDiag && SetOpposite) m_functor.assignCoeff(m_dst.coeffRef(id,id), Scalar(1));
-    else if(Mode==ZeroDiag && SetOpposite) m_functor.assignCoeff(m_dst.coeffRef(id,id), Scalar(0));
-    else if(Mode==0)                       Base::assignCoeff(id,id);
-  }
-
-  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index row, Index col)
-  {
-    eigen_internal_assert(row!=col);
-    if(SetOpposite)
-      m_functor.assignCoeff(m_dst.coeffRef(row,col), Scalar(0));
-  }
-};
-
-template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
-{
-  typedef evaluator<DstXprType> DstEvaluatorType;
-  typedef evaluator<SrcXprType> SrcEvaluatorType;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  Index dstRows = src.rows();
-  Index dstCols = src.cols();
-  if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-    dst.resize(dstRows, dstCols);
-  DstEvaluatorType dstEvaluator(dst);
-
-  typedef triangular_dense_assignment_kernel< Mode&(Lower|Upper),Mode&(UnitDiag|ZeroDiag|SelfAdjoint),SetOpposite,
-                                              DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
-  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-  enum {
-      unroll = DstXprType::SizeAtCompileTime != Dynamic
-            && SrcEvaluatorType::CoeffReadCost < HugeCost
-            && DstXprType::SizeAtCompileTime * (int(DstEvaluatorType::CoeffReadCost) + int(SrcEvaluatorType::CoeffReadCost)) / 2 <= EIGEN_UNROLLING_LIMIT
-    };
-
-  triangular_assignment_loop<Kernel, Mode, unroll ? int(DstXprType::SizeAtCompileTime) : Dynamic, SetOpposite>::run(kernel);
-}
-
-template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src)
-{
-  call_triangular_assignment_loop<Mode,SetOpposite>(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
-}
-
-template<> struct AssignmentKind<TriangularShape,TriangularShape> { typedef Triangular2Triangular Kind; };
-template<> struct AssignmentKind<DenseShape,TriangularShape>      { typedef Triangular2Dense      Kind; };
-template<> struct AssignmentKind<TriangularShape,DenseShape>      { typedef Dense2Triangular      Kind; };
-
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Triangular>
-{
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    eigen_assert(int(DstXprType::Mode) == int(SrcXprType::Mode));
-
-    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
-  }
-};
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Dense>
-{
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    call_triangular_assignment_loop<SrcXprType::Mode, (int(SrcXprType::Mode) & int(SelfAdjoint)) == 0>(dst, src, func);
-  }
-};
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Dense2Triangular>
-{
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
-  }
-};
-
-
-template<typename Kernel, unsigned int Mode, int UnrollCount, bool SetOpposite>
-struct triangular_assignment_loop
-{
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-
-  enum {
-    col = (UnrollCount-1) / DstXprType::RowsAtCompileTime,
-    row = (UnrollCount-1) % DstXprType::RowsAtCompileTime
-  };
-
-  typedef typename Kernel::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  static inline void run(Kernel &kernel)
-  {
-    triangular_assignment_loop<Kernel, Mode, UnrollCount-1, SetOpposite>::run(kernel);
-
-    if(row==col)
-      kernel.assignDiagonalCoeff(row);
-    else if( ((Mode&Lower) && row>col) || ((Mode&Upper) && row<col) )
-      kernel.assignCoeff(row,col);
-    else if(SetOpposite)
-      kernel.assignOppositeCoeff(row,col);
-  }
-};
-
-// prevent buggy user code from causing an infinite recursion
-template<typename Kernel, unsigned int Mode, bool SetOpposite>
-struct triangular_assignment_loop<Kernel, Mode, 0, SetOpposite>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(Kernel &) {}
-};
-
-
-
-// TODO: experiment with a recursive assignment procedure splitting the current
-//       triangular part into one rectangular and two triangular parts.
-
-
-template<typename Kernel, unsigned int Mode, bool SetOpposite>
-struct triangular_assignment_loop<Kernel, Mode, Dynamic, SetOpposite>
-{
-  typedef typename Kernel::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  static inline void run(Kernel &kernel)
-  {
-    for(Index j = 0; j < kernel.cols(); ++j)
-    {
-      Index maxi = numext::mini(j, kernel.rows());
-      Index i = 0;
-      if (((Mode&Lower) && SetOpposite) || (Mode&Upper))
-      {
-        for(; i < maxi; ++i)
-          if(Mode&Upper) kernel.assignCoeff(i, j);
-          else           kernel.assignOppositeCoeff(i, j);
-      }
-      else
-        i = maxi;
-
-      if(i<kernel.rows()) // then i==j
-        kernel.assignDiagonalCoeff(i++);
-
-      if (((Mode&Upper) && SetOpposite) || (Mode&Lower))
-      {
-        for(; i < kernel.rows(); ++i)
-          if(Mode&Lower) kernel.assignCoeff(i, j);
-          else           kernel.assignOppositeCoeff(i, j);
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-/** Assigns a triangular or selfadjoint matrix to a dense matrix.
-  * If the matrix is triangular, the opposite part is set to zero. */
-template<typename Derived>
-template<typename DenseDerived>
-EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalToLazy(MatrixBase<DenseDerived> &other) const
-{
-  other.derived().resize(this->rows(), this->cols());
-  internal::call_triangular_assignment_loop<Derived::Mode, (int(Derived::Mode) & int(SelfAdjoint)) == 0 /* SetOpposite */>(other.derived(), derived().nestedExpression());
-}
-
-namespace internal {
-
-// Triangular = Product
-template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
-{
-  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename SrcXprType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst._assignProduct(src, Scalar(1), false);
-  }
-};
-
-// Triangular += Product
-template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
-{
-  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,typename SrcXprType::Scalar> &)
-  {
-    dst._assignProduct(src, Scalar(1), true);
-  }
-};
-
-// Triangular -= Product
-template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
-{
-  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,typename SrcXprType::Scalar> &)
-  {
-    dst._assignProduct(src, Scalar(-1), true);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULARMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/VectorBlock.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/VectorBlock.h
deleted file mode 100644
index 71c5b95..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/VectorBlock.h
+++ /dev/null
@@ -1,96 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_VECTORBLOCK_H
-#define EIGEN_VECTORBLOCK_H
-
-namespace Eigen { 
-
-namespace internal {
-template<typename VectorType, int Size>
-struct traits<VectorBlock<VectorType, Size> >
-  : public traits<Block<VectorType,
-                     traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
-                     traits<VectorType>::Flags & RowMajorBit ? Size : 1> >
-{
-};
-}
-
-/** \class VectorBlock
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a fixed-size or dynamic-size sub-vector
-  *
-  * \tparam VectorType the type of the object in which we are taking a sub-vector
-  * \tparam Size size of the sub-vector we are taking at compile time (optional)
-  *
-  * This class represents an expression of either a fixed-size or dynamic-size sub-vector.
-  * It is the return type of DenseBase::segment(Index,Index) and DenseBase::segment<int>(Index) and
-  * most of the time this is the only way it is used.
-  *
-  * However, if you want to directly manipulate sub-vector expressions,
-  * for instance if you want to write a function returning such an expression, you
-  * will need to use this class.
-  *
-  * Here is an example illustrating the dynamic case:
-  * \include class_VectorBlock.cpp
-  * Output: \verbinclude class_VectorBlock.out
-  *
-  * \note Even though this expression has dynamic size, in the case where \a VectorType
-  * has fixed size, this expression inherits a fixed maximal size which means that evaluating
-  * it does not cause a dynamic memory allocation.
-  *
-  * Here is an example illustrating the fixed-size case:
-  * \include class_FixedVectorBlock.cpp
-  * Output: \verbinclude class_FixedVectorBlock.out
-  *
-  * \sa class Block, DenseBase::segment(Index,Index,Index,Index), DenseBase::segment(Index,Index)
-  */
-template<typename VectorType, int Size> class VectorBlock
-  : public Block<VectorType,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1>
-{
-    typedef Block<VectorType,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1> Base;
-    enum {
-      IsColVector = !(internal::traits<VectorType>::Flags & RowMajorBit)
-    };
-  public:
-    EIGEN_DENSE_PUBLIC_INTERFACE(VectorBlock)
-
-    using Base::operator=;
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    VectorBlock(VectorType& vector, Index start, Index size)
-      : Base(vector,
-             IsColVector ? start : 0, IsColVector ? 0 : start,
-             IsColVector ? size  : 1, IsColVector ? 1 : size)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock);
-    }
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    VectorBlock(VectorType& vector, Index start)
-      : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock);
-    }
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_VECTORBLOCK_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/VectorwiseOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/VectorwiseOp.h
deleted file mode 100644
index 870f4f1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/VectorwiseOp.h
+++ /dev/null
@@ -1,784 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARTIAL_REDUX_H
-#define EIGEN_PARTIAL_REDUX_H
-
-namespace Eigen {
-
-/** \class PartialReduxExpr
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression of a partially reduxed matrix
-  *
-  * \tparam MatrixType the type of the matrix we are applying the redux operation
-  * \tparam MemberOp type of the member functor
-  * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal)
-  *
-  * This class represents an expression of a partial redux operator of a matrix.
-  * It is the return type of some VectorwiseOp functions,
-  * and most of the time this is the only way it is used.
-  *
-  * \sa class VectorwiseOp
-  */
-
-template< typename MatrixType, typename MemberOp, int Direction>
-class PartialReduxExpr;
-
-namespace internal {
-template<typename MatrixType, typename MemberOp, int Direction>
-struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
- : traits<MatrixType>
-{
-  typedef typename MemberOp::result_type Scalar;
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename traits<MatrixType>::XprKind XprKind;
-  typedef typename MatrixType::Scalar InputScalar;
-  enum {
-    RowsAtCompileTime = Direction==Vertical   ? 1 : MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = Direction==Vertical   ? 1 : MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
-    Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0,
-    TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime :  MatrixType::ColsAtCompileTime
-  };
-};
-}
-
-template< typename MatrixType, typename MemberOp, int Direction>
-class PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type,
-                         internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr)
-
-    EIGEN_DEVICE_FUNC
-    explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
-      : m_matrix(mat), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return (Direction==Vertical   ? 1 : m_matrix.rows()); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return (Direction==Horizontal ? 1 : m_matrix.cols()); }
-
-    EIGEN_DEVICE_FUNC
-    typename MatrixType::Nested nestedExpression() const { return m_matrix; }
-
-    EIGEN_DEVICE_FUNC
-    const MemberOp& functor() const { return m_functor; }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-    const MemberOp m_functor;
-};
-
-template<typename A,typename B> struct partial_redux_dummy_func;
-
-#define EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,VECTORIZABLE,BINARYOP)                \
-  template <typename ResultType,typename Scalar>                                                            \
-  struct member_##MEMBER {                                                                  \
-    EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER)                                                \
-    typedef ResultType result_type;                                                         \
-    typedef BINARYOP<Scalar,Scalar> BinaryOp;   \
-    template<int Size> struct Cost { enum { value = COST }; };             \
-    enum { Vectorizable = VECTORIZABLE };                                                   \
-    template<typename XprType>                                                              \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                   \
-    ResultType operator()(const XprType& mat) const                                         \
-    { return mat.MEMBER(); }                                                                \
-    BinaryOp binaryFunc() const { return BinaryOp(); }                                      \
-  }
-
-#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \
-  EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,0,partial_redux_dummy_func)
-
-namespace internal {
-
-EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost );
-EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
-
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_sum_op);
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_min_op);
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_max_op);
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost, 1, internal::scalar_product_op);
-
-template <int p, typename ResultType,typename Scalar>
-struct member_lpnorm {
-  typedef ResultType result_type;
-  enum { Vectorizable = 0 };
-  template<int Size> struct Cost
-  { enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; };
-  EIGEN_DEVICE_FUNC member_lpnorm() {}
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const
-  { return mat.template lpNorm<p>(); }
-};
-
-template <typename BinaryOpT, typename Scalar>
-struct member_redux {
-  typedef BinaryOpT BinaryOp;
-  typedef typename result_of<
-                     BinaryOp(const Scalar&,const Scalar&)
-                   >::type  result_type;
-
-  enum { Vectorizable = functor_traits<BinaryOp>::PacketAccess };
-  template<int Size> struct Cost { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; };
-  EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {}
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const
-  { return mat.redux(m_functor); }
-  const BinaryOp& binaryFunc() const { return m_functor; }
-  const BinaryOp m_functor;
-};
-}
-
-/** \class VectorwiseOp
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression providing broadcasting and partial reduction operations
-  *
-  * \tparam ExpressionType the type of the object on which to do partial reductions
-  * \tparam Direction indicates whether to operate on columns (#Vertical) or rows (#Horizontal)
-  *
-  * This class represents a pseudo expression with broadcasting and partial reduction features.
-  * It is the return type of DenseBase::colwise() and DenseBase::rowwise()
-  * and most of the time this is the only way it is explicitly used.
-  *
-  * To understand the logic of rowwise/colwise expression, let's consider a generic case `A.colwise().foo()`
-  * where `foo` is any method of `VectorwiseOp`. This expression is equivalent to applying `foo()` to each
-  * column of `A` and then re-assemble the outputs in a matrix expression:
-  * \code [A.col(0).foo(), A.col(1).foo(), ..., A.col(A.cols()-1).foo()] \endcode
-  *
-  * Example: \include MatrixBase_colwise.cpp
-  * Output: \verbinclude MatrixBase_colwise.out
-  *
-  * The begin() and end() methods are obviously exceptions to the previous rule as they
-  * return STL-compatible begin/end iterators to the rows or columns of the nested expression.
-  * Typical use cases include for-range-loop and calls to STL algorithms:
-  *
-  * Example: \include MatrixBase_colwise_iterator_cxx11.cpp
-  * Output: \verbinclude MatrixBase_colwise_iterator_cxx11.out
-  *
-  * For a partial reduction on an empty input, some rules apply.
-  * For the sake of clarity, let's consider a vertical reduction:
-  *   - If the number of columns is zero, then a 1x0 row-major vector expression is returned.
-  *   - Otherwise, if the number of rows is zero, then
-  *       - a row vector of zeros is returned for sum-like reductions (sum, squaredNorm, norm, etc.)
-  *       - a row vector of ones is returned for a product reduction (e.g., <code>MatrixXd(n,0).colwise().prod()</code>)
-  *       - an assert is triggered for all other reductions (minCoeff,maxCoeff,redux(bin_op))
-  *
-  * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr
-  */
-template<typename ExpressionType, int Direction> class VectorwiseOp
-{
-  public:
-
-    typedef typename ExpressionType::Scalar Scalar;
-    typedef typename ExpressionType::RealScalar RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
-    typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned;
-
-    template<template<typename OutScalar,typename InputScalar> class Functor,
-                      typename ReturnScalar=Scalar> struct ReturnType
-    {
-      typedef PartialReduxExpr<ExpressionType,
-                               Functor<ReturnScalar,Scalar>,
-                               Direction
-                              > Type;
-    };
-
-    template<typename BinaryOp> struct ReduxReturnType
-    {
-      typedef PartialReduxExpr<ExpressionType,
-                               internal::member_redux<BinaryOp,Scalar>,
-                               Direction
-                              > Type;
-    };
-
-    enum {
-      isVertical   = (Direction==Vertical) ? 1 : 0,
-      isHorizontal = (Direction==Horizontal) ? 1 : 0
-    };
-
-  protected:
-
-    template<typename OtherDerived> struct ExtendedType {
-      typedef Replicate<OtherDerived,
-                        isVertical   ? 1 : ExpressionType::RowsAtCompileTime,
-                        isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
-    };
-
-    /** \internal
-      * Replicates a vector to match the size of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    typename ExtendedType<OtherDerived>::Type
-    extendedTo(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxColsAtCompileTime==1),
-                          YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
-                          YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
-      return typename ExtendedType<OtherDerived>::Type
-                      (other.derived(),
-                       isVertical   ? 1 : m_matrix.rows(),
-                       isHorizontal ? 1 : m_matrix.cols());
-    }
-
-    template<typename OtherDerived> struct OppositeExtendedType {
-      typedef Replicate<OtherDerived,
-                        isHorizontal ? 1 : ExpressionType::RowsAtCompileTime,
-                        isVertical   ? 1 : ExpressionType::ColsAtCompileTime> Type;
-    };
-
-    /** \internal
-      * Replicates a vector in the opposite direction to match the size of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    typename OppositeExtendedType<OtherDerived>::Type
-    extendedToOpposite(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
-                          YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
-                          YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
-      return typename OppositeExtendedType<OtherDerived>::Type
-                      (other.derived(),
-                       isHorizontal  ? 1 : m_matrix.rows(),
-                       isVertical    ? 1 : m_matrix.cols());
-    }
-
-  public:
-    EIGEN_DEVICE_FUNC
-    explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
-
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    inline const ExpressionType& _expression() const { return m_matrix; }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
-      * iterator type over the columns or rows as returned by the begin() and end() methods.
-      */
-    random_access_iterator_type iterator;
-    /** This is the const version of iterator (aka read-only) */
-    random_access_iterator_type const_iterator;
-    #else
-    typedef internal::subvector_stl_iterator<ExpressionType,               DirectionType(Direction)> iterator;
-    typedef internal::subvector_stl_iterator<const ExpressionType,         DirectionType(Direction)> const_iterator;
-    typedef internal::subvector_stl_reverse_iterator<ExpressionType,       DirectionType(Direction)> reverse_iterator;
-    typedef internal::subvector_stl_reverse_iterator<const ExpressionType, DirectionType(Direction)> const_reverse_iterator;
-    #endif
-
-    /** returns an iterator to the first row (rowwise) or column (colwise) of the nested expression.
-      * \sa end(), cbegin()
-      */
-    iterator                 begin()       { return iterator      (m_matrix, 0); }
-    /** const version of begin() */
-    const_iterator           begin() const { return const_iterator(m_matrix, 0); }
-    /** const version of begin() */
-    const_iterator          cbegin() const { return const_iterator(m_matrix, 0); }
-
-    /** returns a reverse iterator to the last row (rowwise) or column (colwise) of the nested expression.
-      * \sa rend(), crbegin()
-      */
-    reverse_iterator        rbegin()       { return reverse_iterator       (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
-	/** const version of rbegin() */
-    const_reverse_iterator  rbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
-	/** const version of rbegin() */
-	const_reverse_iterator crbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
-
-    /** returns an iterator to the row (resp. column) following the last row (resp. column) of the nested expression
-      * \sa begin(), cend()
-      */
-    iterator                 end()         { return iterator      (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
-    /** const version of end() */
-    const_iterator           end()  const  { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
-    /** const version of end() */
-    const_iterator          cend()  const  { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
-
-    /** returns a reverse iterator to the row (resp. column) before the first row (resp. column) of the nested expression
-      * \sa begin(), cend()
-      */
-    reverse_iterator        rend()         { return reverse_iterator       (m_matrix, -1); }
-    /** const version of rend() */
-    const_reverse_iterator  rend()  const  { return const_reverse_iterator (m_matrix, -1); }
-    /** const version of rend() */
-    const_reverse_iterator crend()  const  { return const_reverse_iterator (m_matrix, -1); }
-
-    /** \returns a row or column vector expression of \c *this reduxed by \a func
-      *
-      * The template parameter \a BinaryOp is the type of the functor
-      * of the custom redux operator. Note that func must be an associative operator.
-      *
-      * \warning the size along the reduction direction must be strictly positive,
-      *          otherwise an assertion is triggered.
-      *
-      * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise()
-      */
-    template<typename BinaryOp>
-    EIGEN_DEVICE_FUNC
-    const typename ReduxReturnType<BinaryOp>::Type
-    redux(const BinaryOp& func = BinaryOp()) const
-    {
-      eigen_assert(redux_length()>0 && "you are using an empty matrix");
-      return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func));
-    }
-
-    typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType;
-    typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType;
-    typedef PartialReduxExpr<const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const ExpressionTypeNestedCleaned>,internal::member_sum<RealScalar,RealScalar>,Direction> SquaredNormReturnType;
-    typedef CwiseUnaryOp<internal::scalar_sqrt_op<RealScalar>, const SquaredNormReturnType> NormReturnType;
-    typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType;
-    typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType;
-    typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType;
-    typedef typename ReturnType<internal::member_sum>::Type SumReturnType;
-    typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SumReturnType,Scalar,quotient) MeanReturnType;
-    typedef typename ReturnType<internal::member_all>::Type AllReturnType;
-    typedef typename ReturnType<internal::member_any>::Type AnyReturnType;
-    typedef PartialReduxExpr<ExpressionType, internal::member_count<Index,Scalar>, Direction> CountReturnType;
-    typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
-    typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType;
-    typedef Reverse<ExpressionType, Direction> ReverseReturnType;
-
-    template<int p> struct LpNormReturnType {
-      typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar,Scalar>,Direction> Type;
-    };
-
-    /** \returns a row (or column) vector expression of the smallest coefficient
-      * of each column (or row) of the referenced expression.
-      *
-      * \warning the size along the reduction direction must be strictly positive,
-      *          otherwise an assertion is triggered.
-      *
-      * \warning the result is undefined if \c *this contains NaN.
-      *
-      * Example: \include PartialRedux_minCoeff.cpp
-      * Output: \verbinclude PartialRedux_minCoeff.out
-      *
-      * \sa DenseBase::minCoeff() */
-    EIGEN_DEVICE_FUNC
-    const MinCoeffReturnType minCoeff() const
-    {
-      eigen_assert(redux_length()>0 && "you are using an empty matrix");
-      return MinCoeffReturnType(_expression());
-    }
-
-    /** \returns a row (or column) vector expression of the largest coefficient
-      * of each column (or row) of the referenced expression.
-      *
-      * \warning the size along the reduction direction must be strictly positive,
-      *          otherwise an assertion is triggered.
-      *
-      * \warning the result is undefined if \c *this contains NaN.
-      *
-      * Example: \include PartialRedux_maxCoeff.cpp
-      * Output: \verbinclude PartialRedux_maxCoeff.out
-      *
-      * \sa DenseBase::maxCoeff() */
-    EIGEN_DEVICE_FUNC
-    const MaxCoeffReturnType maxCoeff() const
-    {
-      eigen_assert(redux_length()>0 && "you are using an empty matrix");
-      return MaxCoeffReturnType(_expression());
-    }
-
-    /** \returns a row (or column) vector expression of the squared norm
-      * of each column (or row) of the referenced expression.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * Example: \include PartialRedux_squaredNorm.cpp
-      * Output: \verbinclude PartialRedux_squaredNorm.out
-      *
-      * \sa DenseBase::squaredNorm() */
-    EIGEN_DEVICE_FUNC
-    const SquaredNormReturnType squaredNorm() const
-    { return SquaredNormReturnType(m_matrix.cwiseAbs2()); }
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * Example: \include PartialRedux_norm.cpp
-      * Output: \verbinclude PartialRedux_norm.out
-      *
-      * \sa DenseBase::norm() */
-    EIGEN_DEVICE_FUNC
-    const NormReturnType norm() const
-    { return NormReturnType(squaredNorm()); }
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * Example: \include PartialRedux_norm.cpp
-      * Output: \verbinclude PartialRedux_norm.out
-      *
-      * \sa DenseBase::norm() */
-    template<int p>
-    EIGEN_DEVICE_FUNC
-    const typename LpNormReturnType<p>::Type lpNorm() const
-    { return typename LpNormReturnType<p>::Type(_expression()); }
-
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression, using
-      * Blue's algorithm.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * \sa DenseBase::blueNorm() */
-    EIGEN_DEVICE_FUNC
-    const BlueNormReturnType blueNorm() const
-    { return BlueNormReturnType(_expression()); }
-
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression, avoiding
-      * underflow and overflow.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * \sa DenseBase::stableNorm() */
-    EIGEN_DEVICE_FUNC
-    const StableNormReturnType stableNorm() const
-    { return StableNormReturnType(_expression()); }
-
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression, avoiding
-      * underflow and overflow using a concatenation of hypot() calls.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * \sa DenseBase::hypotNorm() */
-    EIGEN_DEVICE_FUNC
-    const HypotNormReturnType hypotNorm() const
-    { return HypotNormReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression of the sum
-      * of each column (or row) of the referenced expression.
-      *
-      * Example: \include PartialRedux_sum.cpp
-      * Output: \verbinclude PartialRedux_sum.out
-      *
-      * \sa DenseBase::sum() */
-    EIGEN_DEVICE_FUNC
-    const SumReturnType sum() const
-    { return SumReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression of the mean
-    * of each column (or row) of the referenced expression.
-    *
-    * \sa DenseBase::mean() */
-    EIGEN_DEVICE_FUNC
-    const MeanReturnType mean() const
-    { return sum() / Scalar(Direction==Vertical?m_matrix.rows():m_matrix.cols()); }
-
-    /** \returns a row (or column) vector expression representing
-      * whether \b all coefficients of each respective column (or row) are \c true.
-      * This expression can be assigned to a vector with entries of type \c bool.
-      *
-      * \sa DenseBase::all() */
-    EIGEN_DEVICE_FUNC
-    const AllReturnType all() const
-    { return AllReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression representing
-      * whether \b at \b least one coefficient of each respective column (or row) is \c true.
-      * This expression can be assigned to a vector with entries of type \c bool.
-      *
-      * \sa DenseBase::any() */
-    EIGEN_DEVICE_FUNC
-    const AnyReturnType any() const
-    { return AnyReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression representing
-      * the number of \c true coefficients of each respective column (or row).
-      * This expression can be assigned to a vector whose entries have the same type as is used to
-      * index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t .
-      *
-      * Example: \include PartialRedux_count.cpp
-      * Output: \verbinclude PartialRedux_count.out
-      *
-      * \sa DenseBase::count() */
-    EIGEN_DEVICE_FUNC
-    const CountReturnType count() const
-    { return CountReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression of the product
-      * of each column (or row) of the referenced expression.
-      *
-      * Example: \include PartialRedux_prod.cpp
-      * Output: \verbinclude PartialRedux_prod.out
-      *
-      * \sa DenseBase::prod() */
-    EIGEN_DEVICE_FUNC
-    const ProdReturnType prod() const
-    { return ProdReturnType(_expression()); }
-
-
-    /** \returns a matrix expression
-      * where each column (or row) are reversed.
-      *
-      * Example: \include Vectorwise_reverse.cpp
-      * Output: \verbinclude Vectorwise_reverse.out
-      *
-      * \sa DenseBase::reverse() */
-    EIGEN_DEVICE_FUNC
-    const ConstReverseReturnType reverse() const
-    { return ConstReverseReturnType( _expression() ); }
-
-    /** \returns a writable matrix expression
-      * where each column (or row) are reversed.
-      *
-      * \sa reverse() const */
-    EIGEN_DEVICE_FUNC
-    ReverseReturnType reverse()
-    { return ReverseReturnType( _expression() ); }
-
-    typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType;
-    EIGEN_DEVICE_FUNC
-    const ReplicateReturnType replicate(Index factor) const;
-
-    /**
-      * \return an expression of the replication of each column (or row) of \c *this
-      *
-      * Example: \include DirectionWise_replicate.cpp
-      * Output: \verbinclude DirectionWise_replicate.out
-      *
-      * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate
-      */
-    // NOTE implemented here because of sunstudio's compilation errors
-    // isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator
-    template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical>
-    EIGEN_DEVICE_FUNC
-    replicate(Index factor = Factor) const
-    {
-      return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)>
-          (_expression(),isVertical?factor:1,isHorizontal?factor:1);
-    }
-
-/////////// Artithmetic operators ///////////
-
-    /** Copies the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME
-      return m_matrix = extendedTo(other.derived());
-    }
-
-    /** Adds the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator+=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix += extendedTo(other.derived());
-    }
-
-    /** Substracts the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator-=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix -= extendedTo(other.derived());
-    }
-
-    /** Multiples each subvector of \c *this by the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator*=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      m_matrix *= extendedTo(other.derived());
-      return m_matrix;
-    }
-
-    /** Divides each subvector of \c *this by the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator/=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      m_matrix /= extendedTo(other.derived());
-      return m_matrix;
-    }
-
-    /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    operator+(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix + extendedTo(other.derived());
-    }
-
-    /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    operator-(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix - extendedTo(other.derived());
-    }
-
-    /** Returns the expression where each subvector is the product of the vector \a other
-      * by the corresponding subvector of \c *this */
-    template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_product_op<Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    EIGEN_DEVICE_FUNC
-    operator*(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix * extendedTo(other.derived());
-    }
-
-    /** Returns the expression where each subvector is the quotient of the corresponding
-      * subvector of \c *this by the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    operator/(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix / extendedTo(other.derived());
-    }
-
-    /** \returns an expression where each column (or row) of the referenced matrix are normalized.
-      * The referenced matrix is \b not modified.
-      * \sa MatrixBase::normalized(), normalize()
-      */
-    EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename OppositeExtendedType<NormReturnType>::Type>
-    normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); }
-
-
-    /** Normalize in-place each row or columns of the referenced matrix.
-      * \sa MatrixBase::normalize(), normalized()
-      */
-    EIGEN_DEVICE_FUNC void normalize() {
-      m_matrix = this->normalized();
-    }
-
-    EIGEN_DEVICE_FUNC inline void reverseInPlace();
-
-/////////// Geometry module ///////////
-
-    typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType;
-    EIGEN_DEVICE_FUNC
-    HomogeneousReturnType homogeneous() const;
-
-    typedef typename ExpressionType::PlainObject CrossReturnType;
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const;
-
-    enum {
-      HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime
-                                             : internal::traits<ExpressionType>::ColsAtCompileTime,
-      HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1
-    };
-    typedef Block<const ExpressionType,
-                  Direction==Vertical   ? int(HNormalized_SizeMinusOne)
-                                        : int(internal::traits<ExpressionType>::RowsAtCompileTime),
-                  Direction==Horizontal ? int(HNormalized_SizeMinusOne)
-                                        : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
-            HNormalized_Block;
-    typedef Block<const ExpressionType,
-                  Direction==Vertical   ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime),
-                  Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
-            HNormalized_Factors;
-    typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>,
-                const HNormalized_Block,
-                const Replicate<HNormalized_Factors,
-                  Direction==Vertical   ? HNormalized_SizeMinusOne : 1,
-                  Direction==Horizontal ? HNormalized_SizeMinusOne : 1> >
-            HNormalizedReturnType;
-
-    EIGEN_DEVICE_FUNC
-    const HNormalizedReturnType hnormalized() const;
-
-#   ifdef EIGEN_VECTORWISEOP_PLUGIN
-#     include EIGEN_VECTORWISEOP_PLUGIN
-#   endif
-
-  protected:
-    Index redux_length() const
-    {
-      return Direction==Vertical ? m_matrix.rows() : m_matrix.cols();
-    }
-    ExpressionTypeNested m_matrix;
-};
-
-//const colwise moved to DenseBase.h due to CUDA compiler bug
-
-
-/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
-  *
-  * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ColwiseReturnType
-DenseBase<Derived>::colwise()
-{
-  return ColwiseReturnType(derived());
-}
-
-//const rowwise moved to DenseBase.h due to CUDA compiler bug
-
-
-/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
-  *
-  * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::RowwiseReturnType
-DenseBase<Derived>::rowwise()
-{
-  return RowwiseReturnType(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIAL_REDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Visitor.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Visitor.h
deleted file mode 100644
index 00bcca8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/Visitor.h
+++ /dev/null
@@ -1,381 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_VISITOR_H
-#define EIGEN_VISITOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Visitor, typename Derived, int UnrollCount>
-struct visitor_impl
-{
-  enum {
-    col = (UnrollCount-1) / Derived::RowsAtCompileTime,
-    row = (UnrollCount-1) % Derived::RowsAtCompileTime
-  };
-
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived &mat, Visitor& visitor)
-  {
-    visitor_impl<Visitor, Derived, UnrollCount-1>::run(mat, visitor);
-    visitor(mat.coeff(row, col), row, col);
-  }
-};
-
-template<typename Visitor, typename Derived>
-struct visitor_impl<Visitor, Derived, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived &mat, Visitor& visitor)
-  {
-    return visitor.init(mat.coeff(0, 0), 0, 0);
-  }
-};
-
-// This specialization enables visitors on empty matrices at compile-time
-template<typename Visitor, typename Derived>
-struct visitor_impl<Visitor, Derived, 0> {
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived &/*mat*/, Visitor& /*visitor*/)
-  {}
-};
-
-template<typename Visitor, typename Derived>
-struct visitor_impl<Visitor, Derived, Dynamic>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived& mat, Visitor& visitor)
-  {
-    visitor.init(mat.coeff(0,0), 0, 0);
-    for(Index i = 1; i < mat.rows(); ++i)
-      visitor(mat.coeff(i, 0), i, 0);
-    for(Index j = 1; j < mat.cols(); ++j)
-      for(Index i = 0; i < mat.rows(); ++i)
-        visitor(mat.coeff(i, j), i, j);
-  }
-};
-
-// evaluator adaptor
-template<typename XprType>
-class visitor_evaluator
-{
-public:
-  EIGEN_DEVICE_FUNC
-  explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    RowsAtCompileTime = XprType::RowsAtCompileTime,
-    CoeffReadCost = internal::evaluator<XprType>::CoeffReadCost
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_xpr.size(); }
-
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
-  { return m_evaluator.coeff(row, col); }
-
-protected:
-  internal::evaluator<XprType> m_evaluator;
-  const XprType &m_xpr;
-};
-} // end namespace internal
-
-/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
-  *
-  * The template parameter \a Visitor is the type of the visitor and provides the following interface:
-  * \code
-  * struct MyVisitor {
-  *   // called for the first coefficient
-  *   void init(const Scalar& value, Index i, Index j);
-  *   // called for all other coefficients
-  *   void operator() (const Scalar& value, Index i, Index j);
-  * };
-  * \endcode
-  *
-  * \note compared to one or two \em for \em loops, visitors offer automatic
-  * unrolling for small fixed size matrix.
-  *
-  * \note if the matrix is empty, then the visitor is left unchanged.
-  *
-  * \sa minCoeff(Index*,Index*), maxCoeff(Index*,Index*), DenseBase::redux()
-  */
-template<typename Derived>
-template<typename Visitor>
-EIGEN_DEVICE_FUNC
-void DenseBase<Derived>::visit(Visitor& visitor) const
-{
-  if(size()==0)
-    return;
-
-  typedef typename internal::visitor_evaluator<Derived> ThisEvaluator;
-  ThisEvaluator thisEval(derived());
-
-  enum {
-    unroll =  SizeAtCompileTime != Dynamic
-           && SizeAtCompileTime * int(ThisEvaluator::CoeffReadCost) + (SizeAtCompileTime-1) * int(internal::functor_traits<Visitor>::Cost) <= EIGEN_UNROLLING_LIMIT
-  };
-  return internal::visitor_impl<Visitor, ThisEvaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(thisEval, visitor);
-}
-
-namespace internal {
-
-/** \internal
-  * \brief Base class to implement min and max visitors
-  */
-template <typename Derived>
-struct coeff_visitor
-{
-  // default initialization to avoid countless invalid maybe-uninitialized warnings by gcc
-  EIGEN_DEVICE_FUNC
-  coeff_visitor() : row(-1), col(-1), res(0) {}
-  typedef typename Derived::Scalar Scalar;
-  Index row, col;
-  Scalar res;
-  EIGEN_DEVICE_FUNC
-  inline void init(const Scalar& value, Index i, Index j)
-  {
-    res = value;
-    row = i;
-    col = j;
-  }
-};
-
-/** \internal
-  * \brief Visitor computing the min coefficient with its value and coordinates
-  *
-  * \sa DenseBase::minCoeff(Index*, Index*)
-  */
-template <typename Derived, int NaNPropagation>
-struct min_coeff_visitor : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if(value < this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct min_coeff_visitor<Derived, PropagateNumbers> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(this->res) || (!(numext::isnan)(value) && value < this->res))
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct min_coeff_visitor<Derived, PropagateNaN> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(value) || value < this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template<typename Scalar, int NaNPropagation>
-    struct functor_traits<min_coeff_visitor<Scalar, NaNPropagation> > {
-  enum {
-    Cost = NumTraits<Scalar>::AddCost
-  };
-};
-
-/** \internal
-  * \brief Visitor computing the max coefficient with its value and coordinates
-  *
-  * \sa DenseBase::maxCoeff(Index*, Index*)
-  */
-template <typename Derived, int NaNPropagation>
-struct max_coeff_visitor : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if(value > this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct max_coeff_visitor<Derived, PropagateNumbers> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(this->res) || (!(numext::isnan)(value) && value > this->res))
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct max_coeff_visitor<Derived, PropagateNaN> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(value) || value > this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template<typename Scalar, int NaNPropagation>
-struct functor_traits<max_coeff_visitor<Scalar, NaNPropagation> > {
-  enum {
-    Cost = NumTraits<Scalar>::AddCost
-  };
-};
-
-} // end namespace internal
-
-/** \fn DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
-  * \returns the minimum of all coefficients of *this and puts in *row and *col its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visit(), DenseBase::minCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  internal::min_coeff_visitor<Derived, NaNPropagation> minVisitor;
-  this->visit(minVisitor);
-  *rowId = minVisitor.row;
-  if (colId) *colId = minVisitor.col;
-  return minVisitor.res;
-}
-
-/** \returns the minimum of all coefficients of *this and puts in *index its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::minCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::minCoeff(IndexType* index) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-      internal::min_coeff_visitor<Derived, NaNPropagation> minVisitor;
-  this->visit(minVisitor);
-  *index = IndexType((RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row);
-  return minVisitor.res;
-}
-
-/** \fn DenseBase<Derived>::maxCoeff(IndexType* rowId, IndexType* colId) const
-  * \returns the maximum of all coefficients of *this and puts in *row and *col its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::maxCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  internal::max_coeff_visitor<Derived, NaNPropagation> maxVisitor;
-  this->visit(maxVisitor);
-  *rowPtr = maxVisitor.row;
-  if (colPtr) *colPtr = maxVisitor.col;
-  return maxVisitor.res;
-}
-
-/** \returns the maximum of all coefficients of *this and puts in *index its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::maxCoeff(IndexType* index) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-      internal::max_coeff_visitor<Derived, NaNPropagation> maxVisitor;
-  this->visit(maxVisitor);
-  *index = (RowsAtCompileTime==1) ? maxVisitor.col : maxVisitor.row;
-  return maxVisitor.res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_VISITOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/Complex.h
deleted file mode 100644
index ab7bd6c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/Complex.h
+++ /dev/null
@@ -1,372 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_AVX_H
-#define EIGEN_COMPLEX_AVX_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet4cf
-{
-  EIGEN_STRONG_INLINE Packet4cf() {}
-  EIGEN_STRONG_INLINE explicit Packet4cf(const __m256& a) : v(a) {}
-  __m256  v;
-};
-
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet4cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet4cf> {
-  typedef std::complex<float> type;
-  typedef Packet2cf half;
-  typedef Packet8f as_real;
-  enum {
-    size=4,
-    alignment=Aligned32,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4cf padd<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_add_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf psub<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_sub_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf pnegate(const Packet4cf& a)
-{
-  return Packet4cf(pnegate(a.v));
-}
-template<> EIGEN_STRONG_INLINE Packet4cf pconj(const Packet4cf& a)
-{
-  const __m256 mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000));
-  return Packet4cf(_mm256_xor_ps(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf pmul<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
-{
-  __m256 tmp1 = _mm256_mul_ps(_mm256_moveldup_ps(a.v), b.v);
-  __m256 tmp2 = _mm256_mul_ps(_mm256_movehdup_ps(a.v), _mm256_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1)));
-  __m256 result = _mm256_addsub_ps(tmp1, tmp2);
-  return Packet4cf(result);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4cf pcmp_eq(const Packet4cf& a, const Packet4cf& b) {
-  __m256 eq = _mm256_cmp_ps(a.v, b.v, _CMP_EQ_OQ);
-  return Packet4cf(_mm256_and_ps(eq, _mm256_permute_ps(eq, 0xb1)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf ptrue<Packet4cf>(const Packet4cf& a) { return Packet4cf(ptrue(Packet8f(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet4cf pand   <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_and_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf por    <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_or_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf pxor   <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_xor_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf pandnot<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_andnot_ps(b.v,a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet4cf pload <Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet4cf(pload<Packet8f>(&numext::real_ref(*from))); }
-template<> EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cf(ploadu<Packet8f>(&numext::real_ref(*from))); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from)
-{
-  return Packet4cf(_mm256_castpd_ps(_mm256_broadcast_sd((const double*)(const void*)&from)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from)
-{
-  // FIXME The following might be optimized using _mm256_movedup_pd
-  Packet2cf a = ploaddup<Packet2cf>(from);
-  Packet2cf b = ploaddup<Packet2cf>(from+1);
-  return  Packet4cf(_mm256_insertf128_ps(_mm256_castps128_ps256(a.v), b.v, 1));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from, Index stride)
-{
-  return Packet4cf(_mm256_set_ps(std::imag(from[3*stride]), std::real(from[3*stride]),
-                                 std::imag(from[2*stride]), std::real(from[2*stride]),
-                                 std::imag(from[1*stride]), std::real(from[1*stride]),
-                                 std::imag(from[0*stride]), std::real(from[0*stride])));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, Index stride)
-{
-  __m128 low = _mm256_extractf128_ps(from.v, 0);
-  to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 0)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1)));
-  to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 2)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3)));
-
-  __m128 high = _mm256_extractf128_ps(from.v, 1);
-  to[stride*2] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 0)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1)));
-  to[stride*3] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 2)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3)));
-
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet4cf>(const Packet4cf& a)
-{
-  return pfirst(Packet2cf(_mm256_castps256_ps128(a.v)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf preverse(const Packet4cf& a) {
-  __m128 low  = _mm256_extractf128_ps(a.v, 0);
-  __m128 high = _mm256_extractf128_ps(a.v, 1);
-  __m128d lowd  = _mm_castps_pd(low);
-  __m128d highd = _mm_castps_pd(high);
-  low  = _mm_castpd_ps(_mm_shuffle_pd(lowd,lowd,0x1));
-  high = _mm_castpd_ps(_mm_shuffle_pd(highd,highd,0x1));
-  __m256 result = _mm256_setzero_ps();
-  result = _mm256_insertf128_ps(result, low, 1);
-  result = _mm256_insertf128_ps(result, high, 0);
-  return Packet4cf(result);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet4cf>(const Packet4cf& a)
-{
-  return predux(padd(Packet2cf(_mm256_extractf128_ps(a.v,0)),
-                     Packet2cf(_mm256_extractf128_ps(a.v,1))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const Packet4cf& a)
-{
-  return predux_mul(pmul(Packet2cf(_mm256_extractf128_ps(a.v, 0)),
-                         Packet2cf(_mm256_extractf128_ps(a.v, 1))));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf,Packet8f)
-
-template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
-{
-  Packet4cf num = pmul(a, pconj(b));
-  __m256 tmp = _mm256_mul_ps(b.v, b.v);
-  __m256 tmp2    = _mm256_shuffle_ps(tmp,tmp,0xB1);
-  __m256 denom = _mm256_add_ps(tmp, tmp2);
-  return Packet4cf(_mm256_div_ps(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x)
-{
-  return Packet4cf(_mm256_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1)));
-}
-
-//---------- double ----------
-struct Packet2cd
-{
-  EIGEN_STRONG_INLINE Packet2cd() {}
-  EIGEN_STRONG_INLINE explicit Packet2cd(const __m256d& a) : v(a) {}
-  __m256d  v;
-};
-
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet2cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 2,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet2cd> {
-  typedef std::complex<double> type;
-  typedef Packet1cd half;
-  typedef Packet4d as_real;
-  enum {
-    size=2,
-    alignment=Aligned32,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2cd padd<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_add_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd psub<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_sub_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pnegate(const Packet2cd& a) { return Packet2cd(pnegate(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pconj(const Packet2cd& a)
-{
-  const __m256d mask = _mm256_castsi256_pd(_mm256_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0));
-  return Packet2cd(_mm256_xor_pd(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd pmul<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
-{
-  __m256d tmp1 = _mm256_shuffle_pd(a.v,a.v,0x0);
-  __m256d even = _mm256_mul_pd(tmp1, b.v);
-  __m256d tmp2 = _mm256_shuffle_pd(a.v,a.v,0xF);
-  __m256d tmp3 = _mm256_shuffle_pd(b.v,b.v,0x5);
-  __m256d odd  = _mm256_mul_pd(tmp2, tmp3);
-  return Packet2cd(_mm256_addsub_pd(even, odd));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cd pcmp_eq(const Packet2cd& a, const Packet2cd& b) {
-  __m256d eq = _mm256_cmp_pd(a.v, b.v, _CMP_EQ_OQ);
-  return Packet2cd(pand(eq, _mm256_permute_pd(eq, 0x5)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd ptrue<Packet2cd>(const Packet2cd& a) { return Packet2cd(ptrue(Packet4d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet2cd pand   <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_and_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd por    <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_or_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pxor   <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_xor_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pandnot<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_andnot_pd(b.v,a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cd pload <Packet2cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cd(pload<Packet4d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cd ploadu<Packet2cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cd(ploadu<Packet4d>((const double*)from)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cd pset1<Packet2cd>(const std::complex<double>& from)
-{
-  // in case casting to a __m128d* is really not safe, then we can still fallback to this version: (much slower though)
-//   return Packet2cd(_mm256_loadu2_m128d((const double*)&from,(const double*)&from));
-    return Packet2cd(_mm256_broadcast_pd((const __m128d*)(const void*)&from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd ploaddup<Packet2cd>(const std::complex<double>* from) { return pset1<Packet2cd>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet2cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet2cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from, Index stride)
-{
-  return Packet2cd(_mm256_set_pd(std::imag(from[1*stride]), std::real(from[1*stride]),
-				 std::imag(from[0*stride]), std::real(from[0*stride])));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from, Index stride)
-{
-  __m128d low = _mm256_extractf128_pd(from.v, 0);
-  to[stride*0] = std::complex<double>(_mm_cvtsd_f64(low), _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1)));
-  __m128d high = _mm256_extractf128_pd(from.v, 1);
-  to[stride*1] = std::complex<double>(_mm_cvtsd_f64(high), _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet2cd>(const Packet2cd& a)
-{
-  __m128d low = _mm256_extractf128_pd(a.v, 0);
-  EIGEN_ALIGN16 double res[2];
-  _mm_store_pd(res, low);
-  return std::complex<double>(res[0],res[1]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd preverse(const Packet2cd& a) {
-  __m256d result = _mm256_permute2f128_pd(a.v, a.v, 1);
-  return Packet2cd(result);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet2cd>(const Packet2cd& a)
-{
-  return predux(padd(Packet1cd(_mm256_extractf128_pd(a.v,0)),
-                     Packet1cd(_mm256_extractf128_pd(a.v,1))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet2cd>(const Packet2cd& a)
-{
-  return predux(pmul(Packet1cd(_mm256_extractf128_pd(a.v,0)),
-                     Packet1cd(_mm256_extractf128_pd(a.v,1))));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd,Packet4d)
-
-template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
-{
-  Packet2cd num = pmul(a, pconj(b));
-  __m256d tmp = _mm256_mul_pd(b.v, b.v);
-  __m256d denom = _mm256_hadd_pd(tmp, tmp);
-  return Packet2cd(_mm256_div_pd(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x)
-{
-  return Packet2cd(_mm256_shuffle_pd(x.v, x.v, 0x5));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4cf,4>& kernel) {
-  __m256d P0 = _mm256_castps_pd(kernel.packet[0].v);
-  __m256d P1 = _mm256_castps_pd(kernel.packet[1].v);
-  __m256d P2 = _mm256_castps_pd(kernel.packet[2].v);
-  __m256d P3 = _mm256_castps_pd(kernel.packet[3].v);
-
-  __m256d T0 = _mm256_shuffle_pd(P0, P1, 15);
-  __m256d T1 = _mm256_shuffle_pd(P0, P1, 0);
-  __m256d T2 = _mm256_shuffle_pd(P2, P3, 15);
-  __m256d T3 = _mm256_shuffle_pd(P2, P3, 0);
-
-  kernel.packet[1].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 32));
-  kernel.packet[3].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 49));
-  kernel.packet[0].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 32));
-  kernel.packet[2].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 49));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2cd,2>& kernel) {
-  __m256d tmp = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 0+(2<<4));
-  kernel.packet[1].v = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 1+(3<<4));
- kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd psqrt<Packet2cd>(const Packet2cd& a) {
-  return psqrt_complex<Packet2cd>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf psqrt<Packet4cf>(const Packet4cf& a) {
-  return psqrt_complex<Packet4cf>(a);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/MathFunctions.h
deleted file mode 100644
index 67041c8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/MathFunctions.h
+++ /dev/null
@@ -1,228 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_AVX_H
-#define EIGEN_MATH_FUNCTIONS_AVX_H
-
-/* The sin and cos functions of this file are loosely derived from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-namespace Eigen {
-
-namespace internal {
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-psin<Packet8f>(const Packet8f& _x) {
-  return psin_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-pcos<Packet8f>(const Packet8f& _x) {
-  return pcos_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-plog<Packet8f>(const Packet8f& _x) {
-  return plog_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
-plog<Packet4d>(const Packet4d& _x) {
-  return plog_double(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-plog2<Packet8f>(const Packet8f& _x) {
-  return plog2_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
-plog2<Packet4d>(const Packet4d& _x) {
-  return plog2_double(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f plog1p<Packet8f>(const Packet8f& _x) {
-  return generic_plog1p(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f pexpm1<Packet8f>(const Packet8f& _x) {
-  return generic_expm1(_x);
-}
-
-// Exponential function. Works by writing "x = m*log(2) + r" where
-// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
-// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-pexp<Packet8f>(const Packet8f& _x) {
-  return pexp_float(_x);
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-ptanh<Packet8f>(const Packet8f& _x) {
-  return internal::generic_fast_tanh_float(_x);
-}
-
-// Exponential function for doubles.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
-pexp<Packet4d>(const Packet4d& _x) {
-  return pexp_double(_x);
-}
-
-// Functions for sqrt.
-// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
-// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
-// exact solution. It does not handle +inf, or denormalized numbers correctly.
-// The main advantage of this approach is not just speed, but also the fact that
-// it can be inlined and pipelined with other computations, further reducing its
-// effective latency. This is similar to Quake3's fast inverse square root.
-// For detail see here: http://www.beyond3d.com/content/articles/8/
-#if EIGEN_FAST_MATH
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f psqrt<Packet8f>(const Packet8f& _x) {
-  Packet8f minus_half_x = pmul(_x, pset1<Packet8f>(-0.5f));
-  Packet8f denormal_mask = pandnot(
-      pcmp_lt(_x, pset1<Packet8f>((std::numeric_limits<float>::min)())),
-      pcmp_lt(_x, pzero(_x)));
-
-  // Compute approximate reciprocal sqrt.
-  Packet8f x = _mm256_rsqrt_ps(_x);
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(minus_half_x, pmul(x,x), pset1<Packet8f>(1.5f)));
-  // Flush results for denormals to zero.
-  return pandnot(pmul(_x,x), denormal_mask);
-}
-
-#else
-
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f psqrt<Packet8f>(const Packet8f& _x) {
-  return _mm256_sqrt_ps(_x);
-}
-
-#endif
-
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4d psqrt<Packet4d>(const Packet4d& _x) {
-  return _mm256_sqrt_pd(_x);
-}
-
-#if EIGEN_FAST_MATH
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f prsqrt<Packet8f>(const Packet8f& _x) {
-  _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inf, 0x7f800000);
-  _EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f);
-  _EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f);
-  _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000);
-
-  Packet8f neg_half = pmul(_x, p8f_minus_half);
-
-  // select only the inverse sqrt of positive normal inputs (denormals are
-  // flushed to zero and cause infs as well).
-  Packet8f lt_min_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_LT_OQ);
-  Packet8f inf_mask =  _mm256_cmp_ps(_x, p8f_inf, _CMP_EQ_OQ);
-  Packet8f not_normal_finite_mask = _mm256_or_ps(lt_min_mask, inf_mask);
-
-  // Compute an approximate result using the rsqrt intrinsic.
-  Packet8f y_approx = _mm256_rsqrt_ps(_x);
-
-  // Do a single step of Newton-Raphson iteration to improve the approximation.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet8f y_newton = pmul(y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p8f_one_point_five));
-
-  // Select the result of the Newton-Raphson step for positive normal arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(x) = +inf if
-  // x is zero or a positive denormalized float (equivalent to flushing positive
-  // denormalized inputs to zero).
-  return pselect<Packet8f>(not_normal_finite_mask, y_approx, y_newton);
-}
-
-#else
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f prsqrt<Packet8f>(const Packet8f& _x) {
-  _EIGEN_DECLARE_CONST_Packet8f(one, 1.0f);
-  return _mm256_div_ps(p8f_one, _mm256_sqrt_ps(_x));
-}
-#endif
-
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4d prsqrt<Packet4d>(const Packet4d& _x) {
-  _EIGEN_DECLARE_CONST_Packet4d(one, 1.0);
-  return _mm256_div_pd(p4d_one, _mm256_sqrt_pd(_x));
-}
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, psin)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pcos)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, plog)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, plog2)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, plog1p)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pexpm1)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pexp)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, ptanh)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, psqrt)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, prsqrt)
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pfrexp(const Packet8h& a, Packet8h& exponent) {
-  Packet8f fexponent;
-  const Packet8h out = float2half(pfrexp<Packet8f>(half2float(a), fexponent));
-  exponent = float2half(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pldexp(const Packet8h& a, const Packet8h& exponent) {
-  return float2half(pldexp<Packet8f>(half2float(a), half2float(exponent)));
-}
-
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, psin)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pcos)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog2)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog1p)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pexpm1)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pexp)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, ptanh)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, psqrt)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, prsqrt)
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pfrexp(const Packet8bf& a, Packet8bf& exponent) {
-  Packet8f fexponent;
-  const Packet8bf out = F32ToBf16(pfrexp<Packet8f>(Bf16ToF32(a), fexponent));
-  exponent = F32ToBf16(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pldexp(const Packet8bf& a, const Packet8bf& exponent) {
-  return F32ToBf16(pldexp<Packet8f>(Bf16ToF32(a), Bf16ToF32(exponent)));
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/PacketMath.h
deleted file mode 100644
index 7fc32fd..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/PacketMath.h
+++ /dev/null
@@ -1,1574 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_AVX_H
-#define EIGEN_PACKET_MATH_AVX_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#if !defined(EIGEN_VECTORIZE_AVX512) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
-#endif
-
-#ifdef EIGEN_VECTORIZE_FMA
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-#endif
-
-typedef __m256  Packet8f;
-typedef __m256i Packet8i;
-typedef __m256d Packet4d;
-typedef eigen_packet_wrapper<__m128i, 2> Packet8h;
-typedef eigen_packet_wrapper<__m128i, 3> Packet8bf;
-
-template<> struct is_arithmetic<__m256>  { enum { value = true }; };
-template<> struct is_arithmetic<__m256i> { enum { value = true }; };
-template<> struct is_arithmetic<__m256d> { enum { value = true }; };
-template<> struct is_arithmetic<Packet8h> { enum { value = true }; };
-template<> struct is_arithmetic<Packet8bf> { enum { value = true }; };
-
-#define _EIGEN_DECLARE_CONST_Packet8f(NAME,X) \
-  const Packet8f p8f_##NAME = pset1<Packet8f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4d(NAME,X) \
-  const Packet4d p4d_##NAME = pset1<Packet4d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(NAME,X) \
-  const Packet8f p8f_##NAME = _mm256_castsi256_ps(pset1<Packet8i>(X))
-
-#define _EIGEN_DECLARE_CONST_Packet8i(NAME,X) \
-  const Packet8i p8i_##NAME = pset1<Packet8i>(X)
-
-// Use the packet_traits defined in AVX512/PacketMath.h instead if we're going
-// to leverage AVX512 instructions.
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> struct packet_traits<float>  : default_packet_traits
-{
-  typedef Packet8f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasLog1p = 1,
-    HasExpm1 = 1,
-    HasExp = 1,
-    HasNdtri = 1,
-    HasBessel = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasBlend = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
-};
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet4d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=4,
-    HasHalfPacket = 1,
-
-    HasCmp  = 1,
-    HasDiv  = 1,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasBlend = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
-};
-
-template <>
-struct packet_traits<Eigen::half> : default_packet_traits {
-  typedef Packet8h type;
-  // There is no half-size packet for Packet8h.
-  typedef Packet8h half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasCmp    = 1,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasSin    = EIGEN_FAST_MATH,
-    HasCos    = EIGEN_FAST_MATH,
-    HasNegate = 1,
-    HasAbs    = 1,
-    HasAbs2   = 0,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasSetLinear = 0,
-    HasLog    = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasExp    = 1,
-    HasSqrt   = 1,
-    HasRsqrt  = 1,
-    HasTanh   = EIGEN_FAST_MATH,
-    HasErf    = EIGEN_FAST_MATH,
-    HasBlend  = 0,
-    HasRound  = 1,
-    HasFloor  = 1,
-    HasCeil   = 1,
-    HasRint   = 1,
-    HasBessel = 1,
-    HasNdtri  = 1
-  };
-};
-
-template <>
-struct packet_traits<bfloat16> : default_packet_traits {
-  typedef Packet8bf type;
-  // There is no half-size packet for current Packet8bf.
-  // TODO: support as SSE path.
-  typedef Packet8bf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasCmp = 1,
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasNegate = 1,
-    HasAbs    = 1,
-    HasAbs2   = 0,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasSetLinear = 0,
-    HasLog = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasBlend = 0,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasBessel = 1,
-    HasNdtri  = 1
-  };
-};
-#endif
-
-template<> struct scalar_div_cost<float,true> { enum { value = 14 }; };
-template<> struct scalar_div_cost<double,true> { enum { value = 16 }; };
-
-/* Proper support for integers is only provided by AVX2. In the meantime, we'll
-   use SSE instructions and packets to deal with integers.
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet8i type;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=8
-  };
-};
-*/
-
-template<> struct unpacket_traits<Packet8f> {
-  typedef float     type;
-  typedef Packet4f  half;
-  typedef Packet8i  integer_packet;
-  typedef uint8_t   mask_t;
-  enum {size=8, alignment=Aligned32, vectorizable=true, masked_load_available=true, masked_store_available=true};
-};
-template<> struct unpacket_traits<Packet4d> {
-  typedef double type;
-  typedef Packet2d half;
-  enum {size=4, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet8i> { typedef int    type; typedef Packet4i half; enum {size=8, alignment=Aligned32, vectorizable=false, masked_load_available=false, masked_store_available=false}; };
-template<> struct unpacket_traits<Packet8bf> { typedef bfloat16 type; typedef Packet8bf half; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; };
-
-// Helper function for bit packing snippet of low precision comparison.
-// It packs the flags from 16x16 to 8x16.
-EIGEN_STRONG_INLINE __m128i Pack16To8(Packet8f rf) {
-  return _mm_packs_epi32(_mm256_extractf128_si256(_mm256_castps_si256(rf), 0),
-                         _mm256_extractf128_si256(_mm256_castps_si256(rf), 1));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float&  from) { return _mm256_set1_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet4d pset1<Packet4d>(const double& from) { return _mm256_set1_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet8i pset1<Packet8i>(const int&    from) { return _mm256_set1_epi32(from); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pset1frombits<Packet8f>(unsigned int from) { return _mm256_castsi256_ps(pset1<Packet8i>(from)); }
-template<> EIGEN_STRONG_INLINE Packet4d pset1frombits<Packet4d>(uint64_t from) { return _mm256_castsi256_pd(_mm256_set1_epi64x(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pzero(const Packet8f& /*a*/) { return _mm256_setzero_ps(); }
-template<> EIGEN_STRONG_INLINE Packet4d pzero(const Packet4d& /*a*/) { return _mm256_setzero_pd(); }
-template<> EIGEN_STRONG_INLINE Packet8i pzero(const Packet8i& /*a*/) { return _mm256_setzero_si256(); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8f peven_mask(const Packet8f& /*a*/) { return _mm256_castsi256_ps(_mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1)); }
-template<> EIGEN_STRONG_INLINE Packet8i peven_mask(const Packet8i& /*a*/) { return _mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1); }
-template<> EIGEN_STRONG_INLINE Packet4d peven_mask(const Packet4d& /*a*/) { return _mm256_castsi256_pd(_mm256_set_epi32(0, 0, -1, -1, 0, 0, -1, -1)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pload1<Packet8f>(const float*  from) { return _mm256_broadcast_ss(from); }
-template<> EIGEN_STRONG_INLINE Packet4d pload1<Packet4d>(const double* from) { return _mm256_broadcast_sd(from); }
-
-template<> EIGEN_STRONG_INLINE Packet8f plset<Packet8f>(const float& a) { return _mm256_add_ps(_mm256_set1_ps(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); }
-template<> EIGEN_STRONG_INLINE Packet4d plset<Packet4d>(const double& a) { return _mm256_add_pd(_mm256_set1_pd(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_add_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d padd<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_add_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i padd<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_add_epi32(a,b);
-#else
-  __m128i lo = _mm_add_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  __m128i hi = _mm_add_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f psub<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_sub_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d psub<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_sub_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i psub<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_sub_epi32(a,b);
-#else
-  __m128i lo = _mm_sub_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  __m128i hi = _mm_sub_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a)
-{
-  return _mm256_sub_ps(_mm256_set1_ps(0.0),a);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pnegate(const Packet4d& a)
-{
-  return _mm256_sub_pd(_mm256_set1_pd(0.0),a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pconj(const Packet8f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4d pconj(const Packet4d& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8i pconj(const Packet8i& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet8f pmul<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_mul_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pmul<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_mul_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pmul<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_mullo_epi32(a,b);
-#else
-  const __m128i lo = _mm_mullo_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  const __m128i hi = _mm_mullo_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pdiv<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_div_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pdiv<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_div_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& /*a*/, const Packet8i& /*b*/)
-{ eigen_assert(false && "packet integer division are not supported by AVX");
-  return pset1<Packet8i>(0);
-}
-
-#ifdef EIGEN_VECTORIZE_FMA
-template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
-#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
-  // Clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
-  //  and even register spilling with clang>=6.0 (bug 1637).
-  // Gcc stupidly generates a vfmadd132ps instruction.
-  // So let's enforce it to generate a vfmadd231ps instruction since the most common use
-  //  case is to accumulate the result of the product.
-  Packet8f res = c;
-  __asm__("vfmadd231ps %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  return _mm256_fmadd_ps(a,b,c);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
-#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
-  // see above
-  Packet4d res = c;
-  __asm__("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  return _mm256_fmadd_pd(a,b,c);
-#endif
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_le(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_LE_OQ); }
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_lt(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_LT_OQ); }
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_lt_or_nan(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a, b, _CMP_NGE_UQ); }
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_eq(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_EQ_OQ); }
-
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_le(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_LE_OQ); }
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_LT_OQ); }
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt_or_nan(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a, b, _CMP_NGE_UQ); }
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_eq(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_EQ_OQ); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8i pcmp_eq(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_cmpeq_epi32(a,b);
-#else
-  __m128i lo = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  __m128i hi = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pmin<Packet8f>(const Packet8f& a, const Packet8f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may flip
-  // the argument order in calls to _mm_min_ps/_mm_max_ps, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  Packet8f res;
-  asm("vminps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::min.
-  return _mm256_min_ps(b,a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4d pmin<Packet4d>(const Packet4d& a, const Packet4d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // See pmin above
-  Packet4d res;
-  asm("vminpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::min.
-  return _mm256_min_pd(b,a);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pmax<Packet8f>(const Packet8f& a, const Packet8f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // See pmin above
-  Packet8f res;
-  asm("vmaxps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::max.
-  return _mm256_max_ps(b,a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4d pmax<Packet4d>(const Packet4d& a, const Packet4d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // See pmin above
-  Packet4d res;
-  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::max.
-  return _mm256_max_pd(b,a);
-#endif
-}
-
-// Add specializations for min/max with prescribed NaN progation.
-template<>
-EIGEN_STRONG_INLINE Packet8f pmin<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmin<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet4d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8f pmax<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmax<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet4d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8f pmin<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmin<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet4d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8f pmax<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmax<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet4d>);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f print<Packet8f>(const Packet8f& a) { return _mm256_round_ps(a, _MM_FROUND_CUR_DIRECTION); }
-template<> EIGEN_STRONG_INLINE Packet4d print<Packet4d>(const Packet4d& a) { return _mm256_round_pd(a, _MM_FROUND_CUR_DIRECTION); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pceil<Packet8f>(const Packet8f& a) { return _mm256_ceil_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet4d pceil<Packet4d>(const Packet4d& a) { return _mm256_ceil_pd(a); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pfloor<Packet8f>(const Packet8f& a) { return _mm256_floor_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet4d pfloor<Packet4d>(const Packet4d& a) { return _mm256_floor_pd(a); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8i ptrue<Packet8i>(const Packet8i& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  // vpcmpeqd has lower latency than the more general vcmpps
-  return _mm256_cmpeq_epi32(a,a);
-#else
-  const __m256 b = _mm256_castsi256_ps(a);
-  return _mm256_castps_si256(_mm256_cmp_ps(b,b,_CMP_TRUE_UQ));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f ptrue<Packet8f>(const Packet8f& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  // vpcmpeqd has lower latency than the more general vcmpps
-  const __m256i b = _mm256_castps_si256(a);
-  return _mm256_castsi256_ps(_mm256_cmpeq_epi32(b,b));
-#else
-  return _mm256_cmp_ps(a,a,_CMP_TRUE_UQ);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4d ptrue<Packet4d>(const Packet4d& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  // vpcmpeqq has lower latency than the more general vcmppd
-  const __m256i b = _mm256_castpd_si256(a);
-  return _mm256_castsi256_pd(_mm256_cmpeq_epi64(b,b));
-#else
-  return _mm256_cmp_pd(a,a,_CMP_TRUE_UQ);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pand<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_and_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pand<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_and_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pand<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_and_si256(a,b);
-#else
-  return _mm256_castps_si256(_mm256_and_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f por<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_or_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d por<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_or_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i por<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_or_si256(a,b);
-#else
-  return _mm256_castps_si256(_mm256_or_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pxor<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_xor_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pxor<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_xor_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pxor<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_xor_si256(a,b);
-#else
-  return _mm256_castps_si256(_mm256_xor_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pandnot<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_andnot_ps(b,a); }
-template<> EIGEN_STRONG_INLINE Packet4d pandnot<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_andnot_pd(b,a); }
-template<> EIGEN_STRONG_INLINE Packet8i pandnot<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_andnot_si256(b,a);
-#else
-  return _mm256_castps_si256(_mm256_andnot_ps(_mm256_castsi256_ps(b),_mm256_castsi256_ps(a)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pround<Packet8f>(const Packet8f& a)
-{
-  const Packet8f mask = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x80000000u));
-  const Packet8f prev0dot5 = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
-  return _mm256_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pround<Packet4d>(const Packet4d& a)
-{
-  const Packet4d mask = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
-  const Packet4d prev0dot5 = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
-  return _mm256_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pselect<Packet8f>(const Packet8f& mask, const Packet8f& a, const Packet8f& b)
-{ return _mm256_blendv_ps(b,a,mask); }
-template<> EIGEN_STRONG_INLINE Packet4d pselect<Packet4d>(const Packet4d& mask, const Packet4d& a, const Packet4d& b)
-{ return _mm256_blendv_pd(b,a,mask); }
-
-template<int N> EIGEN_STRONG_INLINE Packet8i parithmetic_shift_right(Packet8i a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_srai_epi32(a, N);
-#else
-  __m128i lo = _mm_srai_epi32(_mm256_extractf128_si256(a, 0), N);
-  __m128i hi = _mm_srai_epi32(_mm256_extractf128_si256(a, 1), N);
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet8i plogical_shift_right(Packet8i a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_srli_epi32(a, N);
-#else
-  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(a, 0), N);
-  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(a, 1), N);
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet8i plogical_shift_left(Packet8i a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_slli_epi32(a, N);
-#else
-  __m128i lo = _mm_slli_epi32(_mm256_extractf128_si256(a, 0), N);
-  __m128i hi = _mm_slli_epi32(_mm256_extractf128_si256(a, 1), N);
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pload<Packet8f>(const float*   from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet4d pload<Packet4d>(const double*  from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet8i pload<Packet8i>(const int*     from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet4d ploadu<Packet4d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet8i ploadu<Packet8i>(const int* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from, uint8_t umask) {
-  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
-  const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
-  mask = por<Packet8i>(mask, bit_mask);
-  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_maskload_ps(from, mask);
-}
-
-// Loads 4 floats from memory a returns the packet {a0, a0  a1, a1, a2, a2, a3, a3}
-template<> EIGEN_STRONG_INLINE Packet8f ploaddup<Packet8f>(const float* from)
-{
-  // TODO try to find a way to avoid the need of a temporary register
-//   Packet8f tmp  = _mm256_castps128_ps256(_mm_loadu_ps(from));
-//   tmp = _mm256_insertf128_ps(tmp, _mm_movehl_ps(_mm256_castps256_ps128(tmp),_mm256_castps256_ps128(tmp)), 1);
-//   return _mm256_unpacklo_ps(tmp,tmp);
-
-  // _mm256_insertf128_ps is very slow on Haswell, thus:
-  Packet8f tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
-  // mimic an "inplace" permutation of the lower 128bits using a blend
-  tmp = _mm256_blend_ps(tmp,_mm256_castps128_ps256(_mm_permute_ps( _mm256_castps256_ps128(tmp), _MM_SHUFFLE(1,0,1,0))), 15);
-  // then we can perform a consistent permutation on the global register to get everything in shape:
-  return  _mm256_permute_ps(tmp, _MM_SHUFFLE(3,3,2,2));
-}
-// Loads 2 doubles from memory a returns the packet {a0, a0  a1, a1}
-template<> EIGEN_STRONG_INLINE Packet4d ploaddup<Packet4d>(const double* from)
-{
-  Packet4d tmp = _mm256_broadcast_pd((const __m128d*)(const void*)from);
-  return  _mm256_permute_pd(tmp, 3<<2);
-}
-
-// Loads 2 floats from memory a returns the packet {a0, a0  a0, a0, a1, a1, a1, a1}
-template<> EIGEN_STRONG_INLINE Packet8f ploadquad<Packet8f>(const float* from)
-{
-  Packet8f tmp = _mm256_castps128_ps256(_mm_broadcast_ss(from));
-  return _mm256_insertf128_ps(tmp, _mm_broadcast_ss(from+1), 1);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet8f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet8i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet8f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*       to, const Packet8i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet8f& from, uint8_t umask) {
-  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
-  const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
-  mask = por<Packet8i>(mask, bit_mask);
-  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
-  EIGEN_DEBUG_UNALIGNED_STORE return _mm256_maskstore_ps(to, mask, from);
-}
-
-// NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available
-// NOTE: for the record the following seems to be slower: return _mm256_i32gather_ps(from, _mm256_set1_epi32(stride), 4);
-template<> EIGEN_DEVICE_FUNC inline Packet8f pgather<float, Packet8f>(const float* from, Index stride)
-{
-  return _mm256_set_ps(from[7*stride], from[6*stride], from[5*stride], from[4*stride],
-                       from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-template<> EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const double* from, Index stride)
-{
-  return _mm256_set_pd(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, Index stride)
-{
-  __m128 low = _mm256_extractf128_ps(from, 0);
-  to[stride*0] = _mm_cvtss_f32(low);
-  to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1));
-  to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 2));
-  to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3));
-
-  __m128 high = _mm256_extractf128_ps(from, 1);
-  to[stride*4] = _mm_cvtss_f32(high);
-  to[stride*5] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1));
-  to[stride*6] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 2));
-  to[stride*7] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to, const Packet4d& from, Index stride)
-{
-  __m128d low = _mm256_extractf128_pd(from, 0);
-  to[stride*0] = _mm_cvtsd_f64(low);
-  to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1));
-  __m128d high = _mm256_extractf128_pd(from, 1);
-  to[stride*2] = _mm_cvtsd_f64(high);
-  to[stride*3] = _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore1<Packet8f>(float* to, const float& a)
-{
-  Packet8f pa = pset1<Packet8f>(a);
-  pstore(to, pa);
-}
-template<> EIGEN_STRONG_INLINE void pstore1<Packet4d>(double* to, const double& a)
-{
-  Packet4d pa = pset1<Packet4d>(a);
-  pstore(to, pa);
-}
-template<> EIGEN_STRONG_INLINE void pstore1<Packet8i>(int* to, const int& a)
-{
-  Packet8i pa = pset1<Packet8i>(a);
-  pstore(to, pa);
-}
-
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-#endif
-
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet8f>(const Packet8f& a) {
-  return _mm_cvtss_f32(_mm256_castps256_ps128(a));
-}
-template<> EIGEN_STRONG_INLINE double pfirst<Packet4d>(const Packet4d& a) {
-  return _mm_cvtsd_f64(_mm256_castpd256_pd128(a));
-}
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet8i>(const Packet8i& a) {
-  return _mm_cvtsi128_si32(_mm256_castsi256_si128(a));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet8f preverse(const Packet8f& a)
-{
-  __m256 tmp = _mm256_shuffle_ps(a,a,0x1b);
-  return _mm256_permute2f128_ps(tmp, tmp, 1);
-}
-template<> EIGEN_STRONG_INLINE Packet4d preverse(const Packet4d& a)
-{
-   __m256d tmp = _mm256_shuffle_pd(a,a,5);
-  return _mm256_permute2f128_pd(tmp, tmp, 1);
-  #if 0
-  // This version is unlikely to be faster as _mm256_shuffle_ps and _mm256_permute_pd
-  // exhibit the same latency/throughput, but it is here for future reference/benchmarking...
-  __m256d swap_halves = _mm256_permute2f128_pd(a,a,1);
-    return _mm256_permute_pd(swap_halves,5);
-  #endif
-}
-
-// pabs should be ok
-template<> EIGEN_STRONG_INLINE Packet8f pabs(const Packet8f& a)
-{
-  const Packet8f mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF));
-  return _mm256_and_ps(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pabs(const Packet4d& a)
-{
-  const Packet4d mask = _mm256_castsi256_pd(_mm256_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF));
-  return _mm256_and_pd(a,mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pfrexp<Packet8f>(const Packet8f& a, Packet8f& exponent) {
-  return pfrexp_generic(a,exponent);
-}
-
-// Extract exponent without existence of Packet4l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet4d pfrexp_generic_get_biased_exponent(const Packet4d& a) {
-  const Packet4d cst_exp_mask  = pset1frombits<Packet4d>(static_cast<uint64_t>(0x7ff0000000000000ull));
-  __m256i a_expo = _mm256_castpd_si256(pand(a, cst_exp_mask));
-#ifdef EIGEN_VECTORIZE_AVX2
-  a_expo = _mm256_srli_epi64(a_expo, 52);
-  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
-  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
-#else
-  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
-  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
-  lo = _mm_srli_epi64(lo, 52);
-  hi = _mm_srli_epi64(hi, 52);
-#endif
-  Packet2d exponent_lo = _mm_cvtepi32_pd(vec4i_swizzle1(lo, 0, 2, 1, 3));
-  Packet2d exponent_hi = _mm_cvtepi32_pd(vec4i_swizzle1(hi, 0, 2, 1, 3));
-  Packet4d exponent = _mm256_insertf128_pd(_mm256_setzero_pd(), exponent_lo, 0);
-  exponent = _mm256_insertf128_pd(exponent, exponent_hi, 1);
-  return exponent;
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4d pfrexp<Packet4d>(const Packet4d& a, Packet4d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pldexp<Packet8f>(const Packet8f& a, const Packet8f& exponent) {
-  return pldexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4d pldexp<Packet4d>(const Packet4d& a, const Packet4d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet4d max_exponent = pset1<Packet4d>(2099.0);
-  const Packet4i e = _mm256_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-  
-  // Split 2^e into four factors and multiply.
-  const Packet4i bias = pset1<Packet4i>(1023);
-  Packet4i b = parithmetic_shift_right<2>(e);  // floor(e/4)
-  
-  // 2^b
-  Packet4i hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
-  Packet4i lo = _mm_slli_epi64(hi, 52);
-  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
-  Packet4d c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
-  Packet4d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
-  
-  // 2^(e - 3b)
-  b = psub(psub(psub(e, b), b), b);  // e - 3b
-  hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
-  lo = _mm_slli_epi64(hi, 52);
-  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
-  c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
-  out = pmul(out, c); // a * 2^e
-  return out;
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet8f>(const Packet8f& a)
-{
-  return predux(Packet4f(_mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1))));
-}
-template<> EIGEN_STRONG_INLINE double predux<Packet4d>(const Packet4d& a)
-{
-  return predux(Packet2d(_mm_add_pd(_mm256_castpd256_pd128(a),_mm256_extractf128_pd(a,1))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f predux_half_dowto4<Packet8f>(const Packet8f& a)
-{
-  return _mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet8f>(const Packet8f& a)
-{
-  Packet8f tmp;
-  tmp = _mm256_mul_ps(a, _mm256_permute2f128_ps(a,a,1));
-  tmp = _mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
-  return pfirst(_mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet4d>(const Packet4d& a)
-{
-  Packet4d tmp;
-  tmp = _mm256_mul_pd(a, _mm256_permute2f128_pd(a,a,1));
-  return pfirst(_mm256_mul_pd(tmp, _mm256_shuffle_pd(tmp,tmp,1)));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_min<Packet8f>(const Packet8f& a)
-{
-  Packet8f tmp = _mm256_min_ps(a, _mm256_permute2f128_ps(a,a,1));
-  tmp = _mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
-  return pfirst(_mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_min<Packet4d>(const Packet4d& a)
-{
-  Packet4d tmp = _mm256_min_pd(a, _mm256_permute2f128_pd(a,a,1));
-  return pfirst(_mm256_min_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_max<Packet8f>(const Packet8f& a)
-{
-  Packet8f tmp = _mm256_max_ps(a, _mm256_permute2f128_ps(a,a,1));
-  tmp = _mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
-  return pfirst(_mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
-}
-
-template<> EIGEN_STRONG_INLINE double predux_max<Packet4d>(const Packet4d& a)
-{
-  Packet4d tmp = _mm256_max_pd(a, _mm256_permute2f128_pd(a,a,1));
-  return pfirst(_mm256_max_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1)));
-}
-
-// not needed yet
-// template<> EIGEN_STRONG_INLINE bool predux_all(const Packet8f& x)
-// {
-//   return _mm256_movemask_ps(x)==0xFF;
-// }
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet8f& x)
-{
-  return _mm256_movemask_ps(x)!=0;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8f,8>& kernel) {
-  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-  __m256 T4 = _mm256_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
-  __m256 T5 = _mm256_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
-  __m256 T6 = _mm256_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
-  __m256 T7 = _mm256_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
-  __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0));
-  __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2));
-  __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0));
-  __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2));
-  __m256 S4 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(1,0,1,0));
-  __m256 S5 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(3,2,3,2));
-  __m256 S6 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(1,0,1,0));
-  __m256 S7 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(3,2,3,2));
-  kernel.packet[0] = _mm256_permute2f128_ps(S0, S4, 0x20);
-  kernel.packet[1] = _mm256_permute2f128_ps(S1, S5, 0x20);
-  kernel.packet[2] = _mm256_permute2f128_ps(S2, S6, 0x20);
-  kernel.packet[3] = _mm256_permute2f128_ps(S3, S7, 0x20);
-  kernel.packet[4] = _mm256_permute2f128_ps(S0, S4, 0x31);
-  kernel.packet[5] = _mm256_permute2f128_ps(S1, S5, 0x31);
-  kernel.packet[6] = _mm256_permute2f128_ps(S2, S6, 0x31);
-  kernel.packet[7] = _mm256_permute2f128_ps(S3, S7, 0x31);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8f,4>& kernel) {
-  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-
-  __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0));
-  __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2));
-  __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0));
-  __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2));
-
-  kernel.packet[0] = _mm256_permute2f128_ps(S0, S1, 0x20);
-  kernel.packet[1] = _mm256_permute2f128_ps(S2, S3, 0x20);
-  kernel.packet[2] = _mm256_permute2f128_ps(S0, S1, 0x31);
-  kernel.packet[3] = _mm256_permute2f128_ps(S2, S3, 0x31);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4d,4>& kernel) {
-  __m256d T0 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 15);
-  __m256d T1 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
-  __m256d T2 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 15);
-  __m256d T3 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
-
-  kernel.packet[1] = _mm256_permute2f128_pd(T0, T2, 32);
-  kernel.packet[3] = _mm256_permute2f128_pd(T0, T2, 49);
-  kernel.packet[0] = _mm256_permute2f128_pd(T1, T3, 32);
-  kernel.packet[2] = _mm256_permute2f128_pd(T1, T3, 49);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pblend(const Selector<8>& ifPacket, const Packet8f& thenPacket, const Packet8f& elsePacket) {
-  const __m256 zero = _mm256_setzero_ps();
-  const __m256 select = _mm256_set_ps(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m256 false_mask = _mm256_cmp_ps(select, zero, _CMP_EQ_UQ);
-  return _mm256_blendv_ps(thenPacket, elsePacket, false_mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pblend(const Selector<4>& ifPacket, const Packet4d& thenPacket, const Packet4d& elsePacket) {
-  const __m256d zero = _mm256_setzero_pd();
-  const __m256d select = _mm256_set_pd(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m256d false_mask = _mm256_cmp_pd(select, zero, _CMP_EQ_UQ);
-  return _mm256_blendv_pd(thenPacket, elsePacket, false_mask);
-}
-
-// Packet math for Eigen::half
-
-template<> struct unpacket_traits<Packet8h> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet8h half; };
-
-template<> EIGEN_STRONG_INLINE Packet8h pset1<Packet8h>(const Eigen::half& from) {
-  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8h>(const Packet8h& from) {
-  return numext::bit_cast<Eigen::half>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pload<Packet8h>(const Eigen::half* from) {
-  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h ploadu<Packet8h>(const Eigen::half* from) {
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8h& from) {
-  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8h& from) {
-  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h
-ploaddup<Packet8h>(const Eigen::half*  from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
-  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
-  return _mm_set_epi16(d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h
-ploadquad<Packet8h>(const Eigen::half* from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  return _mm_set_epi16(b, b, b, b, a, a, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h ptrue(const Packet8h& a) {
- return _mm_cmpeq_epi32(a, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pabs(const Packet8h& a) {
-  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_andnot_si128(sign_mask, a);
-}
-
-EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm256_cvtph_ps(a);
-#else
-  EIGEN_ALIGN32 Eigen::half aux[8];
-  pstore(aux, a);
-  float f0(aux[0]);
-  float f1(aux[1]);
-  float f2(aux[2]);
-  float f3(aux[3]);
-  float f4(aux[4]);
-  float f5(aux[5]);
-  float f6(aux[6]);
-  float f7(aux[7]);
-
-  return _mm256_set_ps(f7, f6, f5, f4, f3, f2, f1, f0);
-#endif
-}
-
-EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
-#else
-  EIGEN_ALIGN32 float aux[8];
-  pstore(aux, a);
-  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[0]));
-  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[1]));
-  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[2]));
-  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[3]));
-  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[4]));
-  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[5]));
-  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[6]));
-  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[7]));
-  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pmin<Packet8h>(const Packet8h& a,
-                                            const Packet8h& b) {
-  return float2half(pmin<Packet8f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pmax<Packet8h>(const Packet8h& a,
-                                            const Packet8h& b) {
-  return float2half(pmax<Packet8f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h plset<Packet8h>(const half& a) {
-  return float2half(plset<Packet8f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h por(const Packet8h& a,const Packet8h& b) {
-  // in some cases Packet4i is a wrapper around __m128i, so we either need to
-  // cast to Packet4i to directly call the intrinsics as below:
-  return _mm_or_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8h pxor(const Packet8h& a,const Packet8h& b) {
-  return _mm_xor_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8h pand(const Packet8h& a,const Packet8h& b) {
-  return _mm_and_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8h pandnot(const Packet8h& a,const Packet8h& b) {
-  return _mm_andnot_si128(b,a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pselect(const Packet8h& mask, const Packet8h& a, const Packet8h& b) {
-  return _mm_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pround<Packet8h>(const Packet8h& a) {
-  return float2half(pround<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h print<Packet8h>(const Packet8h& a) {
-  return float2half(print<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pceil<Packet8h>(const Packet8h& a) {
-  return float2half(pceil<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pfloor<Packet8h>(const Packet8h& a) {
-  return float2half(pfloor<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_eq(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_eq(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_le(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_le(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_lt(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_lt(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_lt_or_nan(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_lt_or_nan(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pconj(const Packet8h& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet8h pnegate(const Packet8h& a) {
-  Packet8h sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_xor_si128(a, sign_mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = padd(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h psub<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = psub(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pmul<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = pmul(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pdiv<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = pdiv(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pgather<Eigen::half, Packet8h>(const Eigen::half* from, Index stride)
-{
-  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0*stride]);
-  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1*stride]);
-  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2*stride]);
-  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3*stride]);
-  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4*stride]);
-  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5*stride]);
-  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6*stride]);
-  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7*stride]);
-  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8h>(Eigen::half* to, const Packet8h& from, Index stride)
-{
-  EIGEN_ALIGN32 Eigen::half aux[8];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux_max<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_min<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux_min<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux_mul<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h preverse(const Packet8h& a)
-{
-  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-  return _mm_shuffle_epi8(a,m);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8h,8>& kernel) {
-  __m128i a = kernel.packet[0];
-  __m128i b = kernel.packet[1];
-  __m128i c = kernel.packet[2];
-  __m128i d = kernel.packet[3];
-  __m128i e = kernel.packet[4];
-  __m128i f = kernel.packet[5];
-  __m128i g = kernel.packet[6];
-  __m128i h = kernel.packet[7];
-
-  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
-  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
-  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
-  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
-  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
-  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
-  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
-  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
-
-  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
-  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
-  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
-  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
-  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
-  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
-  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
-  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
-
-  __m128i a0b0c0d0e0f0g0h0 = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
-  __m128i a1b1c1d1e1f1g1h1 = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
-  __m128i a2b2c2d2e2f2g2h2 = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
-  __m128i a3b3c3d3e3f3g3h3 = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
-  __m128i a4b4c4d4e4f4g4h4 = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
-  __m128i a5b5c5d5e5f5g5h5 = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
-  __m128i a6b6c6d6e6f6g6h6 = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
-  __m128i a7b7c7d7e7f7g7h7 = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
-
-  kernel.packet[0] = a0b0c0d0e0f0g0h0;
-  kernel.packet[1] = a1b1c1d1e1f1g1h1;
-  kernel.packet[2] = a2b2c2d2e2f2g2h2;
-  kernel.packet[3] = a3b3c3d3e3f3g3h3;
-  kernel.packet[4] = a4b4c4d4e4f4g4h4;
-  kernel.packet[5] = a5b5c5d5e5f5g5h5;
-  kernel.packet[6] = a6b6c6d6e6f6g6h6;
-  kernel.packet[7] = a7b7c7d7e7f7g7h7;
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8h,4>& kernel) {
-  EIGEN_ALIGN32 Eigen::half in[4][8];
-  pstore<Eigen::half>(in[0], kernel.packet[0]);
-  pstore<Eigen::half>(in[1], kernel.packet[1]);
-  pstore<Eigen::half>(in[2], kernel.packet[2]);
-  pstore<Eigen::half>(in[3], kernel.packet[3]);
-
-  EIGEN_ALIGN32 Eigen::half out[4][8];
-
-  for (int i = 0; i < 4; ++i) {
-    for (int j = 0; j < 4; ++j) {
-      out[i][j] = in[j][2*i];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+4] = in[j][2*i+1];
-    }
-  }
-
-  kernel.packet[0] = pload<Packet8h>(out[0]);
-  kernel.packet[1] = pload<Packet8h>(out[1]);
-  kernel.packet[2] = pload<Packet8h>(out[2]);
-  kernel.packet[3] = pload<Packet8h>(out[3]);
-}
-
-// BFloat16 implementation.
-
-EIGEN_STRONG_INLINE Packet8f Bf16ToF32(const Packet8bf& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  __m256i extend = _mm256_cvtepu16_epi32(a);
-  return _mm256_castsi256_ps(_mm256_slli_epi32(extend, 16));
-#else
-  __m128i lo = _mm_cvtepu16_epi32(a);
-  __m128i hi = _mm_cvtepu16_epi32(_mm_srli_si128(a, 8));
-  __m128i lo_shift = _mm_slli_epi32(lo, 16);
-  __m128i hi_shift = _mm_slli_epi32(hi, 16);
-  return _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(lo_shift), hi_shift, 1));
-#endif
-}
-
-// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16(const Packet8f& a) {
-  Packet8bf r;
-
-  __m256i input = _mm256_castps_si256(a);
-
-#ifdef EIGEN_VECTORIZE_AVX2
-  // uint32_t lsb = (input >> 16);
-  __m256i t = _mm256_srli_epi32(input, 16);
-  // uint32_t lsb = lsb & 1;
-  t = _mm256_and_si256(t, _mm256_set1_epi32(1));
-  // uint32_t rounding_bias = 0x7fff + lsb;
-  t = _mm256_add_epi32(t, _mm256_set1_epi32(0x7fff));
-  // input += rounding_bias;
-  t = _mm256_add_epi32(t, input);
-  // input = input >> 16;
-  t = _mm256_srli_epi32(t, 16);
-  // Check NaN before converting back to bf16
-  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
-  __m256i nan = _mm256_set1_epi32(0x7fc0);
-  t = _mm256_blendv_epi8(nan, t, _mm256_castps_si256(mask));
-  // output = numext::bit_cast<uint16_t>(input);
-  return _mm_packus_epi32(_mm256_extractf128_si256(t, 0),
-                         _mm256_extractf128_si256(t, 1));
-#else
-  // uint32_t lsb = (input >> 16);
-  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(input, 0), 16);
-  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(input, 1), 16);
-  // uint32_t lsb = lsb & 1;
-  lo = _mm_and_si128(lo, _mm_set1_epi32(1));
-  hi = _mm_and_si128(hi, _mm_set1_epi32(1));
-  // uint32_t rounding_bias = 0x7fff + lsb;
-  lo = _mm_add_epi32(lo, _mm_set1_epi32(0x7fff));
-  hi = _mm_add_epi32(hi, _mm_set1_epi32(0x7fff));
-  // input += rounding_bias;
-  lo = _mm_add_epi32(lo, _mm256_extractf128_si256(input, 0));
-  hi = _mm_add_epi32(hi, _mm256_extractf128_si256(input, 1));
-  // input = input >> 16;
-  lo = _mm_srli_epi32(lo, 16);
-  hi = _mm_srli_epi32(hi, 16);
-  // Check NaN before converting back to bf16
-  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
-  __m128i nan = _mm_set1_epi32(0x7fc0);
-  lo = _mm_blendv_epi8(nan, lo, _mm_castps_si128(_mm256_castps256_ps128(mask)));
-  hi = _mm_blendv_epi8(nan, hi, _mm_castps_si128(_mm256_extractf128_ps(mask, 1)));
-  // output = numext::bit_cast<uint16_t>(input);
-  return _mm_packus_epi32(lo, hi);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16& from) {
-  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 pfirst<Packet8bf>(const Packet8bf& from) {
-  return numext::bit_cast<bfloat16>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16* from) {
-  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from) {
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet8bf& from) {
-  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet8bf& from) {
-  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf
-ploaddup<Packet8bf>(const bfloat16* from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
-  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
-  return _mm_set_epi16(d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf
-ploadquad<Packet8bf>(const bfloat16* from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  return _mm_set_epi16(b, b, b, b, a, a, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ptrue(const Packet8bf& a) {
- return _mm_cmpeq_epi32(a, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pabs(const Packet8bf& a) {
-  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_andnot_si128(sign_mask, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a,
-                                                const Packet8bf& b) {
-  return F32ToBf16(pmin<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a,
-                                                const Packet8bf& b) {
-  return F32ToBf16(pmax<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
-  return F32ToBf16(plset<Packet8f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf por(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_or_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pxor(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_xor_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pand(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_and_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pandnot(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_andnot_si128(b,a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pselect(const Packet8bf& mask, const Packet8bf& a, const Packet8bf& b) {
-  return _mm_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf>(const Packet8bf& a)
-{
-  return F32ToBf16(pround<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf print<Packet8bf>(const Packet8bf& a) {
-  return F32ToBf16(print<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf>(const Packet8bf& a) {
-  return F32ToBf16(pceil<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf>(const Packet8bf& a) {
-  return F32ToBf16(pfloor<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pconj(const Packet8bf& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet8bf pnegate(const Packet8bf& a) {
-  Packet8bf sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_xor_si128(a, sign_mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(padd<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(psub<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(pmul<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(pdiv<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride)
-{
-  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0*stride]);
-  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1*stride]);
-  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2*stride]);
-  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3*stride]);
-  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4*stride]);
-  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5*stride]);
-  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6*stride]);
-  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7*stride]);
-  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride)
-{
-  EIGEN_ALIGN32 bfloat16 aux[8];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux_max<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux_min<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux_mul<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a)
-{
-  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-  return _mm_shuffle_epi8(a,m);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8bf,8>& kernel) {
-  __m128i a = kernel.packet[0];
-  __m128i b = kernel.packet[1];
-  __m128i c = kernel.packet[2];
-  __m128i d = kernel.packet[3];
-  __m128i e = kernel.packet[4];
-  __m128i f = kernel.packet[5];
-  __m128i g = kernel.packet[6];
-  __m128i h = kernel.packet[7];
-
-  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
-  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
-  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
-  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
-  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
-  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
-  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
-  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
-
-  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
-  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
-  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
-  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
-  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
-  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
-  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
-  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
-
-  kernel.packet[0] = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
-  kernel.packet[1] = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
-  kernel.packet[2] = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
-  kernel.packet[3] = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
-  kernel.packet[4] = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
-  kernel.packet[5] = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
-  kernel.packet[6] = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
-  kernel.packet[7] = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8bf,4>& kernel) {
-  __m128i a = kernel.packet[0];
-  __m128i b = kernel.packet[1];
-  __m128i c = kernel.packet[2];
-  __m128i d = kernel.packet[3];
-
-  __m128i ab_03 = _mm_unpacklo_epi16(a, b);
-  __m128i cd_03 = _mm_unpacklo_epi16(c, d);
-  __m128i ab_47 = _mm_unpackhi_epi16(a, b);
-  __m128i cd_47 = _mm_unpackhi_epi16(c, d);
-
-  kernel.packet[0] = _mm_unpacklo_epi32(ab_03, cd_03);
-  kernel.packet[1] = _mm_unpackhi_epi32(ab_03, cd_03);
-  kernel.packet[2] = _mm_unpacklo_epi32(ab_47, cd_47);
-  kernel.packet[3] = _mm_unpackhi_epi32(ab_47, cd_47);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/TypeCasting.h
deleted file mode 100644
index d507fb6..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX/TypeCasting.h
+++ /dev/null
@@ -1,115 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_AVX_H
-#define EIGEN_TYPE_CASTING_AVX_H
-
-namespace Eigen {
-
-namespace internal {
-
-// For now we use SSE to handle integers, so we can't use AVX instructions to cast
-// from int to float
-template <>
-struct type_casting_traits<float, int> {
-  enum {
-    VectorizedCast = 0,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<int, float> {
-  enum {
-    VectorizedCast = 0,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-
-#ifndef EIGEN_VECTORIZE_AVX512
-
-template <>
-struct type_casting_traits<Eigen::half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-
-template <>
-struct type_casting_traits<float, Eigen::half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<bfloat16, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<float, bfloat16> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-#endif  // EIGEN_VECTORIZE_AVX512
-
-template<> EIGEN_STRONG_INLINE Packet8i pcast<Packet8f, Packet8i>(const Packet8f& a) {
-  return _mm256_cvttps_epi32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8i, Packet8f>(const Packet8i& a) {
-  return _mm256_cvtepi32_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i,Packet8f>(const Packet8f& a) {
-  return _mm256_castps_si256(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f preinterpret<Packet8f,Packet8i>(const Packet8i& a) {
-  return _mm256_castsi256_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8h, Packet8f>(const Packet8h& a) {
-  return half2float(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8bf, Packet8f>(const Packet8bf& a) {
-  return Bf16ToF32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcast<Packet8f, Packet8h>(const Packet8f& a) {
-  return float2half(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcast<Packet8f, Packet8bf>(const Packet8f& a) {
-  return F32ToBf16(a);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/Complex.h
deleted file mode 100644
index 49c72b3..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/Complex.h
+++ /dev/null
@@ -1,422 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_AVX512_H
-#define EIGEN_COMPLEX_AVX512_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet8cf
-{
-  EIGEN_STRONG_INLINE Packet8cf() {}
-  EIGEN_STRONG_INLINE explicit Packet8cf(const __m512& a) : v(a) {}
-  __m512  v;
-};
-
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet8cf type;
-  typedef Packet4cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet8cf> {
-  typedef std::complex<float> type;
-  typedef Packet4cf half;
-  typedef Packet16f as_real;
-  enum {
-    size = 8,
-    alignment=unpacket_traits<Packet16f>::alignment,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet8cf ptrue<Packet8cf>(const Packet8cf& a) { return Packet8cf(ptrue(Packet16f(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet8cf padd<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(_mm512_add_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf psub<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(_mm512_sub_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf pnegate(const Packet8cf& a)
-{
-  return Packet8cf(pnegate(a.v));
-}
-template<> EIGEN_STRONG_INLINE Packet8cf pconj(const Packet8cf& a)
-{
-  const __m512 mask = _mm512_castsi512_ps(_mm512_setr_epi32(
-    0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,
-    0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000));
-  return Packet8cf(pxor(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pmul<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
-{
-  __m512 tmp2 = _mm512_mul_ps(_mm512_movehdup_ps(a.v), _mm512_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1)));
-  return Packet8cf(_mm512_fmaddsub_ps(_mm512_moveldup_ps(a.v), b.v, tmp2));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pand   <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pand(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf por    <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(por(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf pxor   <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pxor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf pandnot<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pandnot(a.v,b.v)); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8cf pcmp_eq(const Packet8cf& a, const Packet8cf& b) {
-  __m512 eq = pcmp_eq<Packet16f>(a.v, b.v);
-  return Packet8cf(pand(eq, _mm512_permute_ps(eq, 0xB1)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pload <Packet8cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet8cf(pload<Packet16f>(&numext::real_ref(*from))); }
-template<> EIGEN_STRONG_INLINE Packet8cf ploadu<Packet8cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet8cf(ploadu<Packet16f>(&numext::real_ref(*from))); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8cf pset1<Packet8cf>(const std::complex<float>& from)
-{
-  return Packet8cf(_mm512_castpd_ps(pload1<Packet8d>((const double*)(const void*)&from)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf ploaddup<Packet8cf>(const std::complex<float>* from)
-{
-  return Packet8cf( _mm512_castpd_ps( ploaddup<Packet8d>((const double*)(const void*)from )) );
-}
-template<> EIGEN_STRONG_INLINE Packet8cf ploadquad<Packet8cf>(const std::complex<float>* from)
-{
-  return Packet8cf( _mm512_castpd_ps( ploadquad<Packet8d>((const double*)(const void*)from )) );
-}
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet8cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet8cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet8cf pgather<std::complex<float>, Packet8cf>(const std::complex<float>* from, Index stride)
-{
-  return Packet8cf(_mm512_castpd_ps(pgather<double,Packet8d>((const double*)(const void*)from, stride)));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet8cf>(std::complex<float>* to, const Packet8cf& from, Index stride)
-{
-  pscatter((double*)(void*)to, _mm512_castps_pd(from.v), stride);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet8cf>(const Packet8cf& a)
-{
-  return pfirst(Packet2cf(_mm512_castps512_ps128(a.v)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf preverse(const Packet8cf& a) {
-  return Packet8cf(_mm512_castsi512_ps(
-            _mm512_permutexvar_epi64( _mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7),
-                                      _mm512_castps_si512(a.v))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet8cf>(const Packet8cf& a)
-{
-  return predux(padd(Packet4cf(extract256<0>(a.v)),
-                     Packet4cf(extract256<1>(a.v))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet8cf>(const Packet8cf& a)
-{
-  return predux_mul(pmul(Packet4cf(extract256<0>(a.v)),
-                         Packet4cf(extract256<1>(a.v))));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4cf predux_half_dowto4<Packet8cf>(const Packet8cf& a) {
-  __m256 lane0 = extract256<0>(a.v);
-  __m256 lane1 = extract256<1>(a.v);
-  __m256 res = _mm256_add_ps(lane0, lane1);
-  return Packet4cf(res);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet8cf,Packet16f)
-
-template<> EIGEN_STRONG_INLINE Packet8cf pdiv<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
-{
-  Packet8cf num = pmul(a, pconj(b));
-  __m512 tmp = _mm512_mul_ps(b.v, b.v);
-  __m512 tmp2    = _mm512_shuffle_ps(tmp,tmp,0xB1);
-  __m512 denom = _mm512_add_ps(tmp, tmp2);
-  return Packet8cf(_mm512_div_ps(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pcplxflip<Packet8cf>(const Packet8cf& x)
-{
-  return Packet8cf(_mm512_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1)));
-}
-
-//---------- double ----------
-struct Packet4cd
-{
-  EIGEN_STRONG_INLINE Packet4cd() {}
-  EIGEN_STRONG_INLINE explicit Packet4cd(const __m512d& a) : v(a) {}
-  __m512d  v;
-};
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet4cd type;
-  typedef Packet2cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet4cd> {
-  typedef std::complex<double> type;
-  typedef Packet2cd half;
-  typedef Packet8d as_real;
-  enum {
-    size = 4,
-    alignment = unpacket_traits<Packet8d>::alignment,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4cd padd<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(_mm512_add_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd psub<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(_mm512_sub_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pnegate(const Packet4cd& a) { return Packet4cd(pnegate(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pconj(const Packet4cd& a)
-{
-  const __m512d mask = _mm512_castsi512_pd(
-          _mm512_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0,
-                           0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0));
-  return Packet4cd(pxor(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd pmul<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
-{
-  __m512d tmp1 = _mm512_shuffle_pd(a.v,a.v,0x0);
-  __m512d tmp2 = _mm512_shuffle_pd(a.v,a.v,0xFF);
-  __m512d tmp3 = _mm512_shuffle_pd(b.v,b.v,0x55);
-  __m512d odd  = _mm512_mul_pd(tmp2, tmp3);
-  return Packet4cd(_mm512_fmaddsub_pd(tmp1, b.v, odd));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd ptrue<Packet4cd>(const Packet4cd& a) { return Packet4cd(ptrue(Packet8d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet4cd pand   <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pand(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd por    <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(por(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pxor   <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pxor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pandnot<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pandnot(a.v,b.v)); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4cd pcmp_eq(const Packet4cd& a, const Packet4cd& b) {
-  __m512d eq = pcmp_eq<Packet8d>(a.v, b.v);
-  return Packet4cd(pand(eq, _mm512_permute_pd(eq, 0x55)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd pload <Packet4cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet4cd(pload<Packet8d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet4cd ploadu<Packet4cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cd(ploadu<Packet8d>((const double*)from)); }
-
-template<> EIGEN_STRONG_INLINE Packet4cd pset1<Packet4cd>(const std::complex<double>& from)
-{
-  #ifdef EIGEN_VECTORIZE_AVX512DQ
-  return Packet4cd(_mm512_broadcast_f64x2(pset1<Packet1cd>(from).v));
-  #else
-  return Packet4cd(_mm512_castps_pd(_mm512_broadcast_f32x4( _mm_castpd_ps(pset1<Packet1cd>(from).v))));
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd ploaddup<Packet4cd>(const std::complex<double>* from) {
-  return Packet4cd(_mm512_insertf64x4(
-          _mm512_castpd256_pd512(ploaddup<Packet2cd>(from).v), ploaddup<Packet2cd>(from+1).v, 1));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet4cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet4cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet4cd pgather<std::complex<double>, Packet4cd>(const std::complex<double>* from, Index stride)
-{
-  return Packet4cd(_mm512_insertf64x4(_mm512_castpd256_pd512(
-            _mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+0*stride).v), ploadu<Packet1cd>(from+1*stride).v,1)),
-            _mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+2*stride).v), ploadu<Packet1cd>(from+3*stride).v,1), 1));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet4cd>(std::complex<double>* to, const Packet4cd& from, Index stride)
-{
-  __m512i fromi = _mm512_castpd_si512(from.v);
-  double* tod = (double*)(void*)to;
-  _mm_storeu_pd(tod+0*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,0)) );
-  _mm_storeu_pd(tod+2*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,1)) );
-  _mm_storeu_pd(tod+4*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,2)) );
-  _mm_storeu_pd(tod+6*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,3)) );
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet4cd>(const Packet4cd& a)
-{
-  __m128d low = extract128<0>(a.v);
-  EIGEN_ALIGN16 double res[2];
-  _mm_store_pd(res, low);
-  return std::complex<double>(res[0],res[1]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd preverse(const Packet4cd& a) {
-  return Packet4cd(_mm512_shuffle_f64x2(a.v, a.v, (shuffle_mask<3,2,1,0>::mask)));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet4cd>(const Packet4cd& a)
-{
-  return predux(padd(Packet2cd(_mm512_extractf64x4_pd(a.v,0)),
-                     Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet4cd>(const Packet4cd& a)
-{
-  return predux_mul(pmul(Packet2cd(_mm512_extractf64x4_pd(a.v,0)),
-                         Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
-}
-
-template<> struct conj_helper<Packet4cd, Packet4cd, false,true>
-{
-  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
-  { return padd(pmul(x,y),c); }
-
-  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
-  {
-    return internal::pmul(a, pconj(b));
-  }
-};
-
-template<> struct conj_helper<Packet4cd, Packet4cd, true,false>
-{
-  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
-  { return padd(pmul(x,y),c); }
-
-  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
-  {
-    return internal::pmul(pconj(a), b);
-  }
-};
-
-template<> struct conj_helper<Packet4cd, Packet4cd, true,true>
-{
-  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
-  { return padd(pmul(x,y),c); }
-
-  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
-  {
-    return pconj(internal::pmul(a, b));
-  }
-};
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cd,Packet8d)
-
-template<> EIGEN_STRONG_INLINE Packet4cd pdiv<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
-{
-  Packet4cd num = pmul(a, pconj(b));
-  __m512d tmp = _mm512_mul_pd(b.v, b.v);
-  __m512d denom =  padd(_mm512_permute_pd(tmp,0x55), tmp);
-  return Packet4cd(_mm512_div_pd(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd pcplxflip<Packet4cd>(const Packet4cd& x)
-{
-  return Packet4cd(_mm512_permute_pd(x.v,0x55));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8cf,4>& kernel) {
-  PacketBlock<Packet8d,4> pb;
-  
-  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
-  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
-  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
-  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
-  ptranspose(pb);
-  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
-  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
-  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
-  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8cf,8>& kernel) {
-  PacketBlock<Packet8d,8> pb;
-  
-  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
-  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
-  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
-  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
-  pb.packet[4] = _mm512_castps_pd(kernel.packet[4].v);
-  pb.packet[5] = _mm512_castps_pd(kernel.packet[5].v);
-  pb.packet[6] = _mm512_castps_pd(kernel.packet[6].v);
-  pb.packet[7] = _mm512_castps_pd(kernel.packet[7].v);
-  ptranspose(pb);
-  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
-  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
-  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
-  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
-  kernel.packet[4].v = _mm512_castpd_ps(pb.packet[4]);
-  kernel.packet[5].v = _mm512_castpd_ps(pb.packet[5]);
-  kernel.packet[6].v = _mm512_castpd_ps(pb.packet[6]);
-  kernel.packet[7].v = _mm512_castpd_ps(pb.packet[7]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4cd,4>& kernel) {
-  __m512d T0 = _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<0,1,0,1>::mask)); // [a0 a1 b0 b1]
-  __m512d T1 = _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<2,3,2,3>::mask)); // [a2 a3 b2 b3]
-  __m512d T2 = _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<0,1,0,1>::mask)); // [c0 c1 d0 d1]
-  __m512d T3 = _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<2,3,2,3>::mask)); // [c2 c3 d2 d3]
-
-  kernel.packet[3] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<1,3,1,3>::mask))); // [a3 b3 c3 d3]
-  kernel.packet[2] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<0,2,0,2>::mask))); // [a2 b2 c2 d2]
-  kernel.packet[1] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<1,3,1,3>::mask))); // [a1 b1 c1 d1]
-  kernel.packet[0] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<0,2,0,2>::mask))); // [a0 b0 c0 d0]
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd psqrt<Packet4cd>(const Packet4cd& a) {
-  return psqrt_complex<Packet4cd>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf psqrt<Packet8cf>(const Packet8cf& a) {
-  return psqrt_complex<Packet8cf>(a);
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_AVX512_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
deleted file mode 100644
index 6fd726d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
+++ /dev/null
@@ -1,362 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Pedro Gonnet (pedro.gonnet@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
-#define THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
-
-namespace Eigen {
-
-namespace internal {
-
-// Disable the code for older versions of gcc that don't support many of the required avx512 instrinsics.
-#if EIGEN_GNUC_AT_LEAST(5, 3) || EIGEN_COMP_CLANG  || EIGEN_COMP_MSVC >= 1923
-
-#define _EIGEN_DECLARE_CONST_Packet16f(NAME, X) \
-  const Packet16f p16f_##NAME = pset1<Packet16f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet16f_FROM_INT(NAME, X) \
-  const Packet16f p16f_##NAME =  preinterpret<Packet16f,Packet16i>(pset1<Packet16i>(X))
-
-#define _EIGEN_DECLARE_CONST_Packet8d(NAME, X) \
-  const Packet8d p8d_##NAME = pset1<Packet8d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet8d_FROM_INT64(NAME, X) \
-  const Packet8d p8d_##NAME = _mm512_castsi512_pd(_mm512_set1_epi64(X))
-
-#define _EIGEN_DECLARE_CONST_Packet16bf(NAME, X) \
-  const Packet16bf p16bf_##NAME = pset1<Packet16bf>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet16bf_FROM_INT(NAME, X) \
-  const Packet16bf p16bf_##NAME =  preinterpret<Packet16bf,Packet16i>(pset1<Packet16i>(X))
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-plog<Packet16f>(const Packet16f& _x) {
-  return plog_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-plog<Packet8d>(const Packet8d& _x) {
-  return plog_double(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, plog)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog)
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-plog2<Packet16f>(const Packet16f& _x) {
-  return plog2_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-plog2<Packet8d>(const Packet8d& _x) {
-  return plog2_double(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, plog2)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog2)
-
-// Exponential function. Works by writing "x = m*log(2) + r" where
-// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
-// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-pexp<Packet16f>(const Packet16f& _x) {
-  _EIGEN_DECLARE_CONST_Packet16f(1, 1.0f);
-  _EIGEN_DECLARE_CONST_Packet16f(half, 0.5f);
-  _EIGEN_DECLARE_CONST_Packet16f(127, 127.0f);
-
-  _EIGEN_DECLARE_CONST_Packet16f(exp_hi, 88.3762626647950f);
-  _EIGEN_DECLARE_CONST_Packet16f(exp_lo, -88.3762626647949f);
-
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_LOG2EF, 1.44269504088896341f);
-
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p0, 1.9875691500E-4f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p1, 1.3981999507E-3f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p2, 8.3334519073E-3f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p3, 4.1665795894E-2f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p4, 1.6666665459E-1f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p5, 5.0000001201E-1f);
-
-  // Clamp x.
-  Packet16f x = pmax(pmin(_x, p16f_exp_hi), p16f_exp_lo);
-
-  // Express exp(x) as exp(m*ln(2) + r), start by extracting
-  // m = floor(x/ln(2) + 0.5).
-  Packet16f m = _mm512_floor_ps(pmadd(x, p16f_cephes_LOG2EF, p16f_half));
-
-  // Get r = x - m*ln(2). Note that we can do this without losing more than one
-  // ulp precision due to the FMA instruction.
-  _EIGEN_DECLARE_CONST_Packet16f(nln2, -0.6931471805599453f);
-  Packet16f r = _mm512_fmadd_ps(m, p16f_nln2, x);
-  Packet16f r2 = pmul(r, r);
-  Packet16f r3 = pmul(r2, r);
-
-  // Evaluate the polynomial approximant,improved by instruction-level parallelism.
-  Packet16f y, y1, y2;
-  y  = pmadd(p16f_cephes_exp_p0, r, p16f_cephes_exp_p1);
-  y1 = pmadd(p16f_cephes_exp_p3, r, p16f_cephes_exp_p4);
-  y2 = padd(r, p16f_1);
-  y  = pmadd(y, r, p16f_cephes_exp_p2);
-  y1 = pmadd(y1, r, p16f_cephes_exp_p5);
-  y  = pmadd(y, r3, y1);
-  y  = pmadd(y, r2, y2);
-
-  // Build emm0 = 2^m.
-  Packet16i emm0 = _mm512_cvttps_epi32(padd(m, p16f_127));
-  emm0 = _mm512_slli_epi32(emm0, 23);
-
-  // Return 2^m * exp(r).
-  return pmax(pmul(y, _mm512_castsi512_ps(emm0)), _x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-pexp<Packet8d>(const Packet8d& _x) {
-  return pexp_double(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pexp)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexp)
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pfrexp(const Packet16h& a, Packet16h& exponent) {
-  Packet16f fexponent;
-  const Packet16h out = float2half(pfrexp<Packet16f>(half2float(a), fexponent));
-  exponent = float2half(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pldexp(const Packet16h& a, const Packet16h& exponent) {
-  return float2half(pldexp<Packet16f>(half2float(a), half2float(exponent)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pfrexp(const Packet16bf& a, Packet16bf& exponent) {
-  Packet16f fexponent;
-  const Packet16bf out = F32ToBf16(pfrexp<Packet16f>(Bf16ToF32(a), fexponent));
-  exponent = F32ToBf16(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pldexp(const Packet16bf& a, const Packet16bf& exponent) {
-  return F32ToBf16(pldexp<Packet16f>(Bf16ToF32(a), Bf16ToF32(exponent)));
-}
-
-// Functions for sqrt.
-// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
-// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
-// exact solution. The main advantage of this approach is not just speed, but
-// also the fact that it can be inlined and pipelined with other computations,
-// further reducing its effective latency.
-#if EIGEN_FAST_MATH
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-psqrt<Packet16f>(const Packet16f& _x) {
-  Packet16f neg_half = pmul(_x, pset1<Packet16f>(-.5f));
-  __mmask16 denormal_mask = _mm512_kand(
-      _mm512_cmp_ps_mask(_x, pset1<Packet16f>((std::numeric_limits<float>::min)()),
-                        _CMP_LT_OQ),
-      _mm512_cmp_ps_mask(_x, _mm512_setzero_ps(), _CMP_GE_OQ));
-
-  Packet16f x = _mm512_rsqrt14_ps(_x);
-
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet16f>(1.5f)));
-
-  // Flush results for denormals to zero.
-  return _mm512_mask_blend_ps(denormal_mask, pmul(_x,x), _mm512_setzero_ps());
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-psqrt<Packet8d>(const Packet8d& _x) {
-  Packet8d neg_half = pmul(_x, pset1<Packet8d>(-.5));
-  __mmask16 denormal_mask = _mm512_kand(
-      _mm512_cmp_pd_mask(_x, pset1<Packet8d>((std::numeric_limits<double>::min)()),
-                        _CMP_LT_OQ),
-      _mm512_cmp_pd_mask(_x, _mm512_setzero_pd(), _CMP_GE_OQ));
-
-  Packet8d x = _mm512_rsqrt14_pd(_x);
-
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet8d>(1.5)));
-
-  // Do a second step of Newton's iteration.
-  x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet8d>(1.5)));
-
-  return _mm512_mask_blend_pd(denormal_mask, pmul(_x,x), _mm512_setzero_pd());
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet16f psqrt<Packet16f>(const Packet16f& x) {
-  return _mm512_sqrt_ps(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d psqrt<Packet8d>(const Packet8d& x) {
-  return _mm512_sqrt_pd(x);
-}
-#endif
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, psqrt)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, psqrt)
-
-// prsqrt for float.
-#if defined(EIGEN_VECTORIZE_AVX512ER)
-
-template <>
-EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
-  return _mm512_rsqrt28_ps(x);
-}
-#elif EIGEN_FAST_MATH
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-prsqrt<Packet16f>(const Packet16f& _x) {
-  _EIGEN_DECLARE_CONST_Packet16f_FROM_INT(inf, 0x7f800000);
-  _EIGEN_DECLARE_CONST_Packet16f(one_point_five, 1.5f);
-  _EIGEN_DECLARE_CONST_Packet16f(minus_half, -0.5f);
-
-  Packet16f neg_half = pmul(_x, p16f_minus_half);
-
-  // Identity infinite, negative and denormal arguments.
-  __mmask16 inf_mask = _mm512_cmp_ps_mask(_x, p16f_inf, _CMP_EQ_OQ);
-  __mmask16 not_pos_mask = _mm512_cmp_ps_mask(_x, _mm512_setzero_ps(), _CMP_LE_OQ);
-  __mmask16 not_finite_pos_mask = not_pos_mask | inf_mask;
-
-  // Compute an approximate result using the rsqrt intrinsic, forcing +inf
-  // for denormals for consistency with AVX and SSE implementations.
-  Packet16f y_approx = _mm512_rsqrt14_ps(_x);
-
-  // Do a single step of Newton-Raphson iteration to improve the approximation.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet16f y_newton = pmul(y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p16f_one_point_five));
-
-  // Select the result of the Newton-Raphson step for positive finite arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(0) = +inf.
-  return _mm512_mask_blend_ps(not_finite_pos_mask, y_newton, y_approx);
-}
-#else
-
-template <>
-EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
-  _EIGEN_DECLARE_CONST_Packet16f(one, 1.0f);
-  return _mm512_div_ps(p16f_one, _mm512_sqrt_ps(x));
-}
-#endif
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, prsqrt)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, prsqrt)
-
-// prsqrt for double.
-#if EIGEN_FAST_MATH
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-prsqrt<Packet8d>(const Packet8d& _x) {
-  _EIGEN_DECLARE_CONST_Packet8d(one_point_five, 1.5);
-  _EIGEN_DECLARE_CONST_Packet8d(minus_half, -0.5);
-  _EIGEN_DECLARE_CONST_Packet8d_FROM_INT64(inf, 0x7ff0000000000000LL);
-
-  Packet8d neg_half = pmul(_x, p8d_minus_half);
-
-  // Identity infinite, negative and denormal arguments.
-  __mmask8 inf_mask = _mm512_cmp_pd_mask(_x, p8d_inf, _CMP_EQ_OQ);
-  __mmask8 not_pos_mask = _mm512_cmp_pd_mask(_x, _mm512_setzero_pd(), _CMP_LE_OQ);
-  __mmask8 not_finite_pos_mask = not_pos_mask | inf_mask;
-
-  // Compute an approximate result using the rsqrt intrinsic, forcing +inf
-  // for denormals for consistency with AVX and SSE implementations.
-#if defined(EIGEN_VECTORIZE_AVX512ER)
-  Packet8d y_approx = _mm512_rsqrt28_pd(_x);
-#else
-  Packet8d y_approx = _mm512_rsqrt14_pd(_x);
-#endif
-  // Do one or two steps of Newton-Raphson's to improve the approximation, depending on the
-  // starting accuracy (either 2^-14 or 2^-28, depending on whether AVX512ER is available).
-  // The Newton-Raphson algorithm has quadratic convergence and roughly doubles the number
-  // of correct digits for each step.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet8d y_newton = pmul(y_approx, pmadd(neg_half, pmul(y_approx, y_approx), p8d_one_point_five));
-#if !defined(EIGEN_VECTORIZE_AVX512ER)
-  y_newton = pmul(y_newton, pmadd(y_newton, pmul(neg_half, y_newton), p8d_one_point_five));
-#endif
-  // Select the result of the Newton-Raphson step for positive finite arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(0) = +inf.
-  return _mm512_mask_blend_pd(not_finite_pos_mask, y_newton, y_approx);
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet8d prsqrt<Packet8d>(const Packet8d& x) {
-  _EIGEN_DECLARE_CONST_Packet8d(one, 1.0f);
-  return _mm512_div_pd(p8d_one, _mm512_sqrt_pd(x));
-}
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet16f plog1p<Packet16f>(const Packet16f& _x) {
-  return generic_plog1p(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, plog1p)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog1p)
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet16f pexpm1<Packet16f>(const Packet16f& _x) {
-  return generic_expm1(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pexpm1)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexpm1)
-
-#endif
-
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-psin<Packet16f>(const Packet16f& _x) {
-  return psin_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-pcos<Packet16f>(const Packet16f& _x) {
-  return pcos_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-ptanh<Packet16f>(const Packet16f& _x) {
-  return internal::generic_fast_tanh_float(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, psin)
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pcos)
-F16_PACKET_FUNCTION(Packet16f, Packet16h, ptanh)
-
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, psin)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pcos)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, ptanh)
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/PacketMath.h
deleted file mode 100644
index 34d49ab..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/PacketMath.h
+++ /dev/null
@@ -1,2303 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner (benoit.steiner.goog@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_AVX512_H
-#define EIGEN_PACKET_MATH_AVX512_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-#endif
-
-#ifdef EIGEN_VECTORIZE_FMA
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-#endif
-
-typedef __m512 Packet16f;
-typedef __m512i Packet16i;
-typedef __m512d Packet8d;
-typedef eigen_packet_wrapper<__m256i, 1> Packet16h;
-typedef eigen_packet_wrapper<__m256i, 2> Packet16bf;
-
-template <>
-struct is_arithmetic<__m512> {
-  enum { value = true };
-};
-template <>
-struct is_arithmetic<__m512i> {
-  enum { value = true };
-};
-template <>
-struct is_arithmetic<__m512d> {
-  enum { value = true };
-};
-
-template<> struct is_arithmetic<Packet16h> { enum { value = true }; };
-
-template <>
-struct packet_traits<half> : default_packet_traits {
-  typedef Packet16h type;
-  // There is no half-size packet for Packet16h.
-  typedef Packet16h half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasCmp    = 1,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 1,
-    HasAbs2   = 0,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasSetLinear = 0,
-    HasLog    = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasExp    = 1,
-    HasSqrt   = 1,
-    HasRsqrt  = 1,
-    HasSin    = EIGEN_FAST_MATH,
-    HasCos    = EIGEN_FAST_MATH,
-    HasTanh   = EIGEN_FAST_MATH,
-    HasErf    = EIGEN_FAST_MATH,
-    HasBlend = 0,
-    HasRound  = 1,
-    HasFloor  = 1,
-    HasCeil   = 1,
-    HasRint   = 1,
-    HasBessel = 1,
-    HasNdtri  = 1
-  };
-};
-
-template<> struct packet_traits<float>  : default_packet_traits
-{
-  typedef Packet16f type;
-  typedef Packet8f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasAbs = 1,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasBlend = 0,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
-    HasLog = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasNdtri = 1,
-    HasBessel  = 1,
-    HasExp = 1,
-    HasSqrt = EIGEN_FAST_MATH,
-    HasRsqrt = EIGEN_FAST_MATH,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
- };
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet8d type;
-  typedef Packet4d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
-    HasLog  = 1,
-    HasExp = 1,
-    HasSqrt = EIGEN_FAST_MATH,
-    HasRsqrt = EIGEN_FAST_MATH,
-#endif
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
-};
-
-/* TODO Implement AVX512 for integers
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet16i type;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=8
-  };
-};
-*/
-
-template <>
-struct unpacket_traits<Packet16f> {
-  typedef float type;
-  typedef Packet8f half;
-  typedef Packet16i integer_packet;
-  typedef uint16_t mask_t;
-  enum { size = 16, alignment=Aligned64, vectorizable=true, masked_load_available=true, masked_store_available=true };
-};
-template <>
-struct unpacket_traits<Packet8d> {
-  typedef double type;
-  typedef Packet4d half;
-  enum { size = 8, alignment=Aligned64, vectorizable=true, masked_load_available=false, masked_store_available=false };
-};
-template <>
-struct unpacket_traits<Packet16i> {
-  typedef int type;
-  typedef Packet8i half;
-  enum { size = 16, alignment=Aligned64, vectorizable=false, masked_load_available=false, masked_store_available=false };
-};
-
-template<>
-struct unpacket_traits<Packet16h> {
-  typedef Eigen::half type;
-  typedef Packet8h half;
-  enum {size=16, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pset1<Packet16f>(const float& from) {
-  return _mm512_set1_ps(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pset1<Packet8d>(const double& from) {
-  return _mm512_set1_pd(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pset1<Packet16i>(const int& from) {
-  return _mm512_set1_epi32(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pset1frombits<Packet16f>(unsigned int from) {
-  return _mm512_castsi512_ps(_mm512_set1_epi32(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d pset1frombits<Packet8d>(const numext::uint64_t from) {
-  return _mm512_castsi512_pd(_mm512_set1_epi64(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pzero(const Packet16f& /*a*/) { return _mm512_setzero_ps(); }
-template<> EIGEN_STRONG_INLINE Packet8d pzero(const Packet8d& /*a*/) { return _mm512_setzero_pd(); }
-template<> EIGEN_STRONG_INLINE Packet16i pzero(const Packet16i& /*a*/) { return _mm512_setzero_si512(); }
-
-template<> EIGEN_STRONG_INLINE Packet16f peven_mask(const Packet16f& /*a*/) {
-  return _mm512_castsi512_ps(_mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1,
-                                              0, -1, 0, -1, 0, -1, 0, -1));
-}
-template<> EIGEN_STRONG_INLINE Packet16i peven_mask(const Packet16i& /*a*/) {
-  return _mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1,
-                          0, -1, 0, -1, 0, -1, 0, -1);
-}
-template<> EIGEN_STRONG_INLINE Packet8d peven_mask(const Packet8d& /*a*/) {
-  return _mm512_castsi512_pd(_mm512_set_epi32(0, 0, -1, -1, 0, 0, -1, -1,
-                                              0, 0, -1, -1, 0, 0, -1, -1));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pload1<Packet16f>(const float* from) {
-  return _mm512_broadcastss_ps(_mm_load_ps1(from));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pload1<Packet8d>(const double* from) {
-  return _mm512_set1_pd(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f plset<Packet16f>(const float& a) {
-  return _mm512_add_ps(
-      _mm512_set1_ps(a),
-      _mm512_set_ps(15.0f, 14.0f, 13.0f, 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f,
-                    4.0f, 3.0f, 2.0f, 1.0f, 0.0f));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d plset<Packet8d>(const double& a) {
-  return _mm512_add_pd(_mm512_set1_pd(a),
-                       _mm512_set_pd(7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f padd<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_add_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d padd<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_add_pd(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i padd<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_add_epi32(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f psub<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_sub_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d psub<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_sub_pd(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i psub<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_sub_epi32(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pnegate(const Packet16f& a) {
-  return _mm512_sub_ps(_mm512_set1_ps(0.0), a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pnegate(const Packet8d& a) {
-  return _mm512_sub_pd(_mm512_set1_pd(0.0), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pconj(const Packet16f& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pconj(const Packet8d& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pconj(const Packet16i& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pmul<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_mul_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmul<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_mul_pd(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pmul<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_mullo_epi32(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pdiv<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_div_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pdiv<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_div_pd(a, b);
-}
-
-#ifdef EIGEN_VECTORIZE_FMA
-template <>
-EIGEN_STRONG_INLINE Packet16f pmadd(const Packet16f& a, const Packet16f& b,
-                                    const Packet16f& c) {
-  return _mm512_fmadd_ps(a, b, c);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmadd(const Packet8d& a, const Packet8d& b,
-                                   const Packet8d& c) {
-  return _mm512_fmadd_pd(a, b, c);
-}
-#endif
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet16f pselect(const Packet16f& mask,
-                                           const Packet16f& a,
-                                           const Packet16f& b) {
-  __mmask16 mask16 = _mm512_cmp_epi32_mask(
-      _mm512_castps_si512(mask), _mm512_setzero_epi32(), _MM_CMPINT_EQ);
-  return _mm512_mask_blend_ps(mask16, a, b);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet8d pselect(const Packet8d& mask,
-                                          const Packet8d& a,
-                                          const Packet8d& b) {
-  __mmask8 mask8 = _mm512_cmp_epi64_mask(_mm512_castpd_si512(mask),
-                                         _mm512_setzero_epi32(), _MM_CMPINT_EQ);
-  return _mm512_mask_blend_pd(mask8, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pmin<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm512_min_ps(b, a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmin<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm512_min_pd(b, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pmax<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm512_max_ps(b, a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmax<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm512_max_pd(b, a);
-}
-
-// Add specializations for min/max with prescribed NaN progation.
-template<>
-EIGEN_STRONG_INLINE Packet16f pmin<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmin<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet8d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet16f pmax<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmax<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet8d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet16f pmin<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmin<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet8d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet16f pmax<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmax<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet8d>);
-}
-
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-template<int I_> EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) { return _mm512_extractf32x8_ps(x,I_); }
-template<int I_> EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) { return _mm512_extractf64x2_pd(x,I_); }
-EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) { return _mm512_insertf32x8(_mm512_castps256_ps512(a),b,1); }
-#else
-// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
-template<int I_> EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) {
-  return  _mm256_castsi256_ps(_mm512_extracti64x4_epi64( _mm512_castps_si512(x),I_));
-}
-
-// AVX512F does not define _mm512_extractf64x2_pd to extract _m128 from _m512
-template<int I_> EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) {
-  return _mm_castsi128_pd(_mm512_extracti32x4_epi32( _mm512_castpd_si512(x),I_));
-}
-
-EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) {
-  return _mm512_castsi512_ps(_mm512_inserti64x4(_mm512_castsi256_si512(_mm256_castps_si256(a)),
-                                                _mm256_castps_si256(b),1));
-}
-#endif
-
-// Helper function for bit packing snippet of low precision comparison.
-// It packs the flags from 32x16 to 16x16.
-EIGEN_STRONG_INLINE __m256i Pack32To16(Packet16f rf) {
-  // Split data into small pieces and handle with AVX instructions
-  // to guarantee internal order of vector.
-  // Operation:
-  //   dst[15:0]    := Saturate16(rf[31:0])
-  //   dst[31:16]   := Saturate16(rf[63:32])
-  //   ...
-  //   dst[255:240] := Saturate16(rf[255:224])
-  __m256i lo = _mm256_castps_si256(extract256<0>(rf));
-  __m256i hi = _mm256_castps_si256(extract256<1>(rf));
-  __m128i result_lo = _mm_packs_epi32(_mm256_extractf128_si256(lo, 0),
-                                      _mm256_extractf128_si256(lo, 1));
-  __m128i result_hi = _mm_packs_epi32(_mm256_extractf128_si256(hi, 0),
-                                      _mm256_extractf128_si256(hi, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(result_lo), result_hi, 1);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pcmp_eq(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_EQ_OQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-template<> EIGEN_STRONG_INLINE Packet16f pcmp_le(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LE_OQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pcmp_lt(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LT_OQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pcmp_lt_or_nan(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_NGE_UQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16i pcmp_eq(const Packet16i& a, const Packet16i& b) {
-  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _CMP_EQ_OQ);
-  return _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu);
-}
-
-
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_eq(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_EQ_OQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_le(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LE_OQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_lt(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LT_OQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_lt_or_nan(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_NGE_UQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f print<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_CUR_DIRECTION); }
-template<> EIGEN_STRONG_INLINE Packet8d print<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_CUR_DIRECTION); }
-
-template<> EIGEN_STRONG_INLINE Packet16f pceil<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_TO_POS_INF); }
-template<> EIGEN_STRONG_INLINE Packet8d pceil<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_TO_POS_INF); }
-
-template<> EIGEN_STRONG_INLINE Packet16f pfloor<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_TO_NEG_INF); }
-template<> EIGEN_STRONG_INLINE Packet8d pfloor<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_TO_NEG_INF); }
-
-template <>
-EIGEN_STRONG_INLINE Packet16i ptrue<Packet16i>(const Packet16i& /*a*/) {
-  return _mm512_set1_epi32(0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f ptrue<Packet16f>(const Packet16f& a) {
-  return _mm512_castsi512_ps(ptrue<Packet16i>(_mm512_castps_si512(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d ptrue<Packet8d>(const Packet8d& a) {
-  return _mm512_castsi512_pd(ptrue<Packet16i>(_mm512_castpd_si512(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i pand<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_and_si512(a,b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pand<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_and_ps(a, b);
-#else
-  return _mm512_castsi512_ps(pand(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pand<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_and_pd(a, b);
-#else
-  Packet8d res = _mm512_undefined_pd();
-  Packet4d lane0_a = _mm512_extractf64x4_pd(a, 0);
-  Packet4d lane0_b = _mm512_extractf64x4_pd(b, 0);
-  res = _mm512_insertf64x4(res, _mm256_and_pd(lane0_a, lane0_b), 0);
-
-  Packet4d lane1_a = _mm512_extractf64x4_pd(a, 1);
-  Packet4d lane1_b = _mm512_extractf64x4_pd(b, 1);
-  return _mm512_insertf64x4(res, _mm256_and_pd(lane1_a, lane1_b), 1);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i por<Packet16i>(const Packet16i& a, const Packet16i& b) {
-  return _mm512_or_si512(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f por<Packet16f>(const Packet16f& a, const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_or_ps(a, b);
-#else
-  return _mm512_castsi512_ps(por(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d por<Packet8d>(const Packet8d& a,
-                                           const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_or_pd(a, b);
-#else
-  return _mm512_castsi512_pd(por(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i pxor<Packet16i>(const Packet16i& a, const Packet16i& b) {
-  return _mm512_xor_si512(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pxor<Packet16f>(const Packet16f& a, const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_xor_ps(a, b);
-#else
-  return _mm512_castsi512_ps(pxor(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d pxor<Packet8d>(const Packet8d& a, const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_xor_pd(a, b);
-#else
-  return _mm512_castsi512_pd(pxor(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i pandnot<Packet16i>(const Packet16i& a, const Packet16i& b) {
-  return _mm512_andnot_si512(b, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pandnot<Packet16f>(const Packet16f& a, const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_andnot_ps(b, a);
-#else
-  return _mm512_castsi512_ps(pandnot(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pandnot<Packet8d>(const Packet8d& a,const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_andnot_pd(b, a);
-#else
-  return _mm512_castsi512_pd(pandnot(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pround<Packet16f>(const Packet16f& a)
-{
-  // Work-around for default std::round rounding mode.
-  const Packet16f mask = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x80000000u));
-  const Packet16f prev0dot5 = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
-  return _mm512_roundscale_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-template<> EIGEN_STRONG_INLINE Packet8d pround<Packet8d>(const Packet8d& a)
-{
-  // Work-around for default std::round rounding mode.
-  const Packet8d mask = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
-  const Packet8d prev0dot5 = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
-  return _mm512_roundscale_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet16i parithmetic_shift_right(Packet16i a) {
-  return _mm512_srai_epi32(a, N);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet16i plogical_shift_right(Packet16i a) {
-  return _mm512_srli_epi32(a, N);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet16i plogical_shift_left(Packet16i a) {
-  return _mm512_slli_epi32(a, N);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pload<Packet16f>(const float* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_ps(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pload<Packet8d>(const double* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_pd(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pload<Packet16i>(const int* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_si512(
-      reinterpret_cast<const __m512i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_ps(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d ploadu<Packet8d>(const double* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_pd(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i ploadu<Packet16i>(const int* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_si512(
-      reinterpret_cast<const __m512i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from, uint16_t umask) {
-  __mmask16 mask = static_cast<__mmask16>(umask);
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_maskz_loadu_ps(mask, from);
-}
-
-// Loads 8 floats from memory a returns the packet
-// {a0, a0  a1, a1, a2, a2, a3, a3, a4, a4, a5, a5, a6, a6, a7, a7}
-template <>
-EIGEN_STRONG_INLINE Packet16f ploaddup<Packet16f>(const float* from) {
-  // an unaligned load is required here as there is no requirement
-  // on the alignment of input pointer 'from'
-  __m256i low_half = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
-  __m512 even_elements = _mm512_castsi512_ps(_mm512_cvtepu32_epi64(low_half));
-  __m512 pairs = _mm512_permute_ps(even_elements, _MM_SHUFFLE(2, 2, 0, 0));
-  return pairs;
-}
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-// FIXME: this does not look optimal, better load a Packet4d and shuffle...
-// Loads 4 doubles from memory a returns the packet {a0, a0  a1, a1, a2, a2, a3,
-// a3}
-template <>
-EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
- __m512d x = _mm512_setzero_pd();
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[0]), 0);
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[1]), 1);
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[2]), 2);
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[3]), 3);
-  return x;
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
-  __m512d x = _mm512_setzero_pd();
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<0, _mm_load_sd(from+0));
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<2, _mm_load_sd(from+1));
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<4, _mm_load_sd(from+2));
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<6, _mm_load_sd(from+3));
-  return x;
-}
-#endif
-
-// Loads 4 floats from memory a returns the packet
-// {a0, a0  a0, a0, a1, a1, a1, a1, a2, a2, a2, a2, a3, a3, a3, a3}
-template <>
-EIGEN_STRONG_INLINE Packet16f ploadquad<Packet16f>(const float* from) {
-  Packet16f tmp = _mm512_castps128_ps512(ploadu<Packet4f>(from));
-  const Packet16i scatter_mask = _mm512_set_epi32(3,3,3,3, 2,2,2,2, 1,1,1,1, 0,0,0,0);
-  return _mm512_permutexvar_ps(scatter_mask, tmp);
-}
-
-// Loads 2 doubles from memory a returns the packet
-// {a0, a0  a0, a0, a1, a1, a1, a1}
-template <>
-EIGEN_STRONG_INLINE Packet8d ploadquad<Packet8d>(const double* from) {
-  __m256d lane0 = _mm256_set1_pd(*from);
-  __m256d lane1 = _mm256_set1_pd(*(from+1));
-  __m512d tmp = _mm512_undefined_pd();
-  tmp = _mm512_insertf64x4(tmp, lane0, 0);
-  return _mm512_insertf64x4(tmp, lane1, 1);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet16f& from) {
-  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_ps(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet8d& from) {
-  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_pd(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet16i& from) {
-  EIGEN_DEBUG_ALIGNED_STORE _mm512_storeu_si512(reinterpret_cast<__m512i*>(to),
-                                                from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_ps(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet8d& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_pd(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet16i& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_si512(
-      reinterpret_cast<__m512i*>(to), from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from, uint16_t umask) {
-  __mmask16 mask = static_cast<__mmask16>(umask);
-  EIGEN_DEBUG_UNALIGNED_STORE return _mm512_mask_storeu_ps(to, mask, from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet16f pgather<float, Packet16f>(const float* from,
-                                                             Index stride) {
-  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
-  Packet16i stride_multiplier =
-      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
-  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
-
-  return _mm512_i32gather_ps(indices, from, 4);
-}
-template <>
-EIGEN_DEVICE_FUNC inline Packet8d pgather<double, Packet8d>(const double* from,
-                                                            Index stride) {
-  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
-  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
-  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
-
-  return _mm512_i32gather_pd(indices, from, 8);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<float, Packet16f>(float* to,
-                                                         const Packet16f& from,
-                                                         Index stride) {
-  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
-  Packet16i stride_multiplier =
-      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
-  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
-  _mm512_i32scatter_ps(to, indices, from, 4);
-}
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<double, Packet8d>(double* to,
-                                                         const Packet8d& from,
-                                                         Index stride) {
-  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
-  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
-  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
-  _mm512_i32scatter_pd(to, indices, from, 8);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore1<Packet16f>(float* to, const float& a) {
-  Packet16f pa = pset1<Packet16f>(a);
-  pstore(to, pa);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore1<Packet8d>(double* to, const double& a) {
-  Packet8d pa = pset1<Packet8d>(a);
-  pstore(to, pa);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore1<Packet16i>(int* to, const int& a) {
-  Packet16i pa = pset1<Packet16i>(a);
-  pstore(to, pa);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-
-template <>
-EIGEN_STRONG_INLINE float pfirst<Packet16f>(const Packet16f& a) {
-  return _mm_cvtss_f32(_mm512_extractf32x4_ps(a, 0));
-}
-template <>
-EIGEN_STRONG_INLINE double pfirst<Packet8d>(const Packet8d& a) {
-  return _mm_cvtsd_f64(_mm256_extractf128_pd(_mm512_extractf64x4_pd(a, 0), 0));
-}
-template <>
-EIGEN_STRONG_INLINE int pfirst<Packet16i>(const Packet16i& a) {
-  return _mm_extract_epi32(_mm512_extracti32x4_epi32(a, 0), 0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f preverse(const Packet16f& a)
-{
-  return _mm512_permutexvar_ps(_mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8d preverse(const Packet8d& a)
-{
-  return _mm512_permutexvar_pd(_mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pabs(const Packet16f& a)
-{
-  // _mm512_abs_ps intrinsic not found, so hack around it
-  return _mm512_castsi512_ps(_mm512_and_si512(_mm512_castps_si512(a), _mm512_set1_epi32(0x7fffffff)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pabs(const Packet8d& a) {
-  // _mm512_abs_ps intrinsic not found, so hack around it
-  return _mm512_castsi512_pd(_mm512_and_si512(_mm512_castpd_si512(a),
-                                   _mm512_set1_epi64(0x7fffffffffffffff)));
-}
-
-template<>
-EIGEN_STRONG_INLINE Packet16f pfrexp<Packet16f>(const Packet16f& a, Packet16f& exponent){
-  return pfrexp_generic(a, exponent);
-}
-
-// Extract exponent without existence of Packet8l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet8d pfrexp_generic_get_biased_exponent(const Packet8d& a) {
-  const Packet8d cst_exp_mask  = pset1frombits<Packet8d>(static_cast<uint64_t>(0x7ff0000000000000ull));
-  #ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_cvtepi64_pd(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52));
-  #else
-  return _mm512_cvtepi32_pd(_mm512_cvtepi64_epi32(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52)));
-  #endif
-}
-
-template<>
-EIGEN_STRONG_INLINE Packet8d pfrexp<Packet8d>(const Packet8d& a, Packet8d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pldexp<Packet16f>(const Packet16f& a, const Packet16f& exponent) {
-  return pldexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8d pldexp<Packet8d>(const Packet8d& a, const Packet8d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet8d max_exponent = pset1<Packet8d>(2099.0);
-  const Packet8i e = _mm512_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-  
-  // Split 2^e into four factors and multiply.
-  const Packet8i bias = pset1<Packet8i>(1023);
-  Packet8i b = parithmetic_shift_right<2>(e);  // floor(e/4)
-  
-  // 2^b
-  const Packet8i permute_idx = _mm256_setr_epi32(0, 4, 1, 5, 2, 6, 3, 7);
-  Packet8i hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
-  Packet8i lo = _mm256_slli_epi64(hi, 52);
-  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
-  Packet8d c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
-  Packet8d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
-  
-  // 2^(e - 3b)
-  b = psub(psub(psub(e, b), b), b);  // e - 3b
-  hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
-  lo = _mm256_slli_epi64(hi, 52);
-  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
-  c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
-  out = pmul(out, c);  // a * 2^e
-  return out;
-}
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
-#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)                           \
-  __m256 OUTPUT##_0 = _mm512_extractf32x8_ps(INPUT, 0);                    \
-  __m256 OUTPUT##_1 = _mm512_extractf32x8_ps(INPUT, 1)
-#else
-#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)                \
-  __m256 OUTPUT##_0 = _mm256_insertf128_ps(                     \
-      _mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 0)), \
-      _mm512_extractf32x4_ps(INPUT, 1), 1);                     \
-  __m256 OUTPUT##_1 = _mm256_insertf128_ps(                     \
-      _mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 2)), \
-      _mm512_extractf32x4_ps(INPUT, 3), 1);
-#endif
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB) \
-  OUTPUT = _mm512_insertf32x8(_mm512_castps256_ps512(INPUTA), INPUTB, 1);
-#else
-#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB)                    \
-  OUTPUT = _mm512_undefined_ps();                                           \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 0), 0); \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 1), 1); \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 0), 2); \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 1), 3);
-#endif
-
-template <>
-EIGEN_STRONG_INLINE float predux<Packet16f>(const Packet16f& a) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  __m256 lane0 = _mm512_extractf32x8_ps(a, 0);
-  __m256 lane1 = _mm512_extractf32x8_ps(a, 1);
-  Packet8f x = _mm256_add_ps(lane0, lane1);
-  return predux<Packet8f>(x);
-#else
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 sum = _mm_add_ps(_mm_add_ps(lane0, lane1), _mm_add_ps(lane2, lane3));
-  sum = _mm_hadd_ps(sum, sum);
-  sum = _mm_hadd_ps(sum, _mm_permute_ps(sum, 1));
-  return _mm_cvtss_f32(sum);
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE double predux<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d sum = _mm256_add_pd(lane0, lane1);
-  __m256d tmp0 = _mm256_hadd_pd(sum, _mm256_permute2f128_pd(sum, sum, 1));
-  return _mm_cvtsd_f64(_mm256_castpd256_pd128(_mm256_hadd_pd(tmp0, tmp0)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8f predux_half_dowto4<Packet16f>(const Packet16f& a) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  __m256 lane0 = _mm512_extractf32x8_ps(a, 0);
-  __m256 lane1 = _mm512_extractf32x8_ps(a, 1);
-  return _mm256_add_ps(lane0, lane1);
-#else
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 sum0 = _mm_add_ps(lane0, lane2);
-  __m128 sum1 = _mm_add_ps(lane1, lane3);
-  return _mm256_insertf128_ps(_mm256_castps128_ps256(sum0), sum1, 1);
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE Packet4d predux_half_dowto4<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  return _mm256_add_pd(lane0, lane1);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_mul<Packet16f>(const Packet16f& a) {
-//#ifdef EIGEN_VECTORIZE_AVX512DQ
-#if 0
-  Packet8f lane0 = _mm512_extractf32x8_ps(a, 0);
-  Packet8f lane1 = _mm512_extractf32x8_ps(a, 1);
-  Packet8f res = pmul(lane0, lane1);
-  res = pmul(res, _mm256_permute2f128_ps(res, res, 1));
-  res = pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-#else
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 res = pmul(pmul(lane0, lane1), pmul(lane2, lane3));
-  res = pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE double predux_mul<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d res = pmul(lane0, lane1);
-  res = pmul(res, _mm256_permute2f128_pd(res, res, 1));
-  return pfirst(pmul(res, _mm256_shuffle_pd(res, res, 1)));
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_min<Packet16f>(const Packet16f& a) {
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 res = _mm_min_ps(_mm_min_ps(lane0, lane1), _mm_min_ps(lane2, lane3));
-  res = _mm_min_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(_mm_min_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-}
-template <>
-EIGEN_STRONG_INLINE double predux_min<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d res = _mm256_min_pd(lane0, lane1);
-  res = _mm256_min_pd(res, _mm256_permute2f128_pd(res, res, 1));
-  return pfirst(_mm256_min_pd(res, _mm256_shuffle_pd(res, res, 1)));
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_max<Packet16f>(const Packet16f& a) {
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 res = _mm_max_ps(_mm_max_ps(lane0, lane1), _mm_max_ps(lane2, lane3));
-  res = _mm_max_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(_mm_max_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-}
-
-template <>
-EIGEN_STRONG_INLINE double predux_max<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d res = _mm256_max_pd(lane0, lane1);
-  res = _mm256_max_pd(res, _mm256_permute2f128_pd(res, res, 1));
-  return pfirst(_mm256_max_pd(res, _mm256_shuffle_pd(res, res, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet16f& x)
-{
-  Packet16i xi = _mm512_castps_si512(x);
-  __mmask16 tmp = _mm512_test_epi32_mask(xi,xi);
-  return !_mm512_kortestz(tmp,tmp);
-}
-
-
-
-#define PACK_OUTPUT(OUTPUT, INPUT, INDEX, STRIDE) \
-  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[INDEX], INPUT[INDEX + STRIDE]);
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 16>& kernel) {
-  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
-  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
-  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
-  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
-  __m512 T8 = _mm512_unpacklo_ps(kernel.packet[8], kernel.packet[9]);
-  __m512 T9 = _mm512_unpackhi_ps(kernel.packet[8], kernel.packet[9]);
-  __m512 T10 = _mm512_unpacklo_ps(kernel.packet[10], kernel.packet[11]);
-  __m512 T11 = _mm512_unpackhi_ps(kernel.packet[10], kernel.packet[11]);
-  __m512 T12 = _mm512_unpacklo_ps(kernel.packet[12], kernel.packet[13]);
-  __m512 T13 = _mm512_unpackhi_ps(kernel.packet[12], kernel.packet[13]);
-  __m512 T14 = _mm512_unpacklo_ps(kernel.packet[14], kernel.packet[15]);
-  __m512 T15 = _mm512_unpackhi_ps(kernel.packet[14], kernel.packet[15]);
-  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S4 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S5 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S6 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S7 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S8 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S9 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S10 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S11 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S12 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S13 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S14 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S15 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(3, 2, 3, 2));
-
-  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
-  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
-  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
-  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
-  EIGEN_EXTRACT_8f_FROM_16f(S4, S4);
-  EIGEN_EXTRACT_8f_FROM_16f(S5, S5);
-  EIGEN_EXTRACT_8f_FROM_16f(S6, S6);
-  EIGEN_EXTRACT_8f_FROM_16f(S7, S7);
-  EIGEN_EXTRACT_8f_FROM_16f(S8, S8);
-  EIGEN_EXTRACT_8f_FROM_16f(S9, S9);
-  EIGEN_EXTRACT_8f_FROM_16f(S10, S10);
-  EIGEN_EXTRACT_8f_FROM_16f(S11, S11);
-  EIGEN_EXTRACT_8f_FROM_16f(S12, S12);
-  EIGEN_EXTRACT_8f_FROM_16f(S13, S13);
-  EIGEN_EXTRACT_8f_FROM_16f(S14, S14);
-  EIGEN_EXTRACT_8f_FROM_16f(S15, S15);
-
-  PacketBlock<Packet8f, 32> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S4_0, 0x20);
-  tmp.packet[1] = _mm256_permute2f128_ps(S1_0, S5_0, 0x20);
-  tmp.packet[2] = _mm256_permute2f128_ps(S2_0, S6_0, 0x20);
-  tmp.packet[3] = _mm256_permute2f128_ps(S3_0, S7_0, 0x20);
-  tmp.packet[4] = _mm256_permute2f128_ps(S0_0, S4_0, 0x31);
-  tmp.packet[5] = _mm256_permute2f128_ps(S1_0, S5_0, 0x31);
-  tmp.packet[6] = _mm256_permute2f128_ps(S2_0, S6_0, 0x31);
-  tmp.packet[7] = _mm256_permute2f128_ps(S3_0, S7_0, 0x31);
-
-  tmp.packet[8] = _mm256_permute2f128_ps(S0_1, S4_1, 0x20);
-  tmp.packet[9] = _mm256_permute2f128_ps(S1_1, S5_1, 0x20);
-  tmp.packet[10] = _mm256_permute2f128_ps(S2_1, S6_1, 0x20);
-  tmp.packet[11] = _mm256_permute2f128_ps(S3_1, S7_1, 0x20);
-  tmp.packet[12] = _mm256_permute2f128_ps(S0_1, S4_1, 0x31);
-  tmp.packet[13] = _mm256_permute2f128_ps(S1_1, S5_1, 0x31);
-  tmp.packet[14] = _mm256_permute2f128_ps(S2_1, S6_1, 0x31);
-  tmp.packet[15] = _mm256_permute2f128_ps(S3_1, S7_1, 0x31);
-
-  // Second set of _m256 outputs
-  tmp.packet[16] = _mm256_permute2f128_ps(S8_0, S12_0, 0x20);
-  tmp.packet[17] = _mm256_permute2f128_ps(S9_0, S13_0, 0x20);
-  tmp.packet[18] = _mm256_permute2f128_ps(S10_0, S14_0, 0x20);
-  tmp.packet[19] = _mm256_permute2f128_ps(S11_0, S15_0, 0x20);
-  tmp.packet[20] = _mm256_permute2f128_ps(S8_0, S12_0, 0x31);
-  tmp.packet[21] = _mm256_permute2f128_ps(S9_0, S13_0, 0x31);
-  tmp.packet[22] = _mm256_permute2f128_ps(S10_0, S14_0, 0x31);
-  tmp.packet[23] = _mm256_permute2f128_ps(S11_0, S15_0, 0x31);
-
-  tmp.packet[24] = _mm256_permute2f128_ps(S8_1, S12_1, 0x20);
-  tmp.packet[25] = _mm256_permute2f128_ps(S9_1, S13_1, 0x20);
-  tmp.packet[26] = _mm256_permute2f128_ps(S10_1, S14_1, 0x20);
-  tmp.packet[27] = _mm256_permute2f128_ps(S11_1, S15_1, 0x20);
-  tmp.packet[28] = _mm256_permute2f128_ps(S8_1, S12_1, 0x31);
-  tmp.packet[29] = _mm256_permute2f128_ps(S9_1, S13_1, 0x31);
-  tmp.packet[30] = _mm256_permute2f128_ps(S10_1, S14_1, 0x31);
-  tmp.packet[31] = _mm256_permute2f128_ps(S11_1, S15_1, 0x31);
-
-  // Pack them into the output
-  PACK_OUTPUT(kernel.packet, tmp.packet, 0, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 1, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 2, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 3, 16);
-
-  PACK_OUTPUT(kernel.packet, tmp.packet, 4, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 5, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 6, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 7, 16);
-
-  PACK_OUTPUT(kernel.packet, tmp.packet, 8, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 9, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 10, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 11, 16);
-
-  PACK_OUTPUT(kernel.packet, tmp.packet, 12, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 13, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 14, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 15, 16);
-}
-#define PACK_OUTPUT_2(OUTPUT, INPUT, INDEX, STRIDE)         \
-  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[2 * INDEX], \
-                           INPUT[2 * INDEX + STRIDE]);
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 4>& kernel) {
-  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-
-  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
-
-  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
-  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
-  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
-  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
-
-  PacketBlock<Packet8f, 8> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S1_0, 0x20);
-  tmp.packet[1] = _mm256_permute2f128_ps(S2_0, S3_0, 0x20);
-  tmp.packet[2] = _mm256_permute2f128_ps(S0_0, S1_0, 0x31);
-  tmp.packet[3] = _mm256_permute2f128_ps(S2_0, S3_0, 0x31);
-
-  tmp.packet[4] = _mm256_permute2f128_ps(S0_1, S1_1, 0x20);
-  tmp.packet[5] = _mm256_permute2f128_ps(S2_1, S3_1, 0x20);
-  tmp.packet[6] = _mm256_permute2f128_ps(S0_1, S1_1, 0x31);
-  tmp.packet[7] = _mm256_permute2f128_ps(S2_1, S3_1, 0x31);
-
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 0, 1);
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 1, 1);
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 2, 1);
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 3, 1);
-}
-
-#define PACK_OUTPUT_SQ_D(OUTPUT, INPUT, INDEX, STRIDE)                \
-  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX], 0); \
-  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX + STRIDE], 1);
-
-#define PACK_OUTPUT_D(OUTPUT, INPUT, INDEX, STRIDE)                         \
-  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX)], 0); \
-  OUTPUT[INDEX] =                                                           \
-      _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX) + STRIDE], 1);
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 4>& kernel) {
-  __m512d T0 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
-  __m512d T1 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0xff);
-  __m512d T2 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
-  __m512d T3 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0xff);
-
-  PacketBlock<Packet4d, 8> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x20);
-  tmp.packet[1] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x20);
-  tmp.packet[2] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x31);
-  tmp.packet[3] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x31);
-
-  tmp.packet[4] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x20);
-  tmp.packet[5] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x20);
-  tmp.packet[6] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x31);
-  tmp.packet[7] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x31);
-
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 0, 1);
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 1, 1);
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 2, 1);
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 3, 1);
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 8>& kernel) {
-  __m512d T0 = _mm512_unpacklo_pd(kernel.packet[0], kernel.packet[1]);
-  __m512d T1 = _mm512_unpackhi_pd(kernel.packet[0], kernel.packet[1]);
-  __m512d T2 = _mm512_unpacklo_pd(kernel.packet[2], kernel.packet[3]);
-  __m512d T3 = _mm512_unpackhi_pd(kernel.packet[2], kernel.packet[3]);
-  __m512d T4 = _mm512_unpacklo_pd(kernel.packet[4], kernel.packet[5]);
-  __m512d T5 = _mm512_unpackhi_pd(kernel.packet[4], kernel.packet[5]);
-  __m512d T6 = _mm512_unpacklo_pd(kernel.packet[6], kernel.packet[7]);
-  __m512d T7 = _mm512_unpackhi_pd(kernel.packet[6], kernel.packet[7]);
-
-  PacketBlock<Packet4d, 16> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x20);
-  tmp.packet[1] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x20);
-  tmp.packet[2] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x31);
-  tmp.packet[3] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x31);
-
-  tmp.packet[4] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x20);
-  tmp.packet[5] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x20);
-  tmp.packet[6] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x31);
-  tmp.packet[7] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x31);
-
-  tmp.packet[8] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 0),
-                                         _mm512_extractf64x4_pd(T6, 0), 0x20);
-  tmp.packet[9] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 0),
-                                         _mm512_extractf64x4_pd(T7, 0), 0x20);
-  tmp.packet[10] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 0),
-                                          _mm512_extractf64x4_pd(T6, 0), 0x31);
-  tmp.packet[11] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 0),
-                                          _mm512_extractf64x4_pd(T7, 0), 0x31);
-
-  tmp.packet[12] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 1),
-                                          _mm512_extractf64x4_pd(T6, 1), 0x20);
-  tmp.packet[13] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 1),
-                                          _mm512_extractf64x4_pd(T7, 1), 0x20);
-  tmp.packet[14] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 1),
-                                          _mm512_extractf64x4_pd(T6, 1), 0x31);
-  tmp.packet[15] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 1),
-                                          _mm512_extractf64x4_pd(T7, 1), 0x31);
-
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 0, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 1, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 2, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 3, 8);
-
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 4, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 5, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 6, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 7, 8);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16f pblend(const Selector<16>& /*ifPacket*/,
-                                     const Packet16f& /*thenPacket*/,
-                                     const Packet16f& /*elsePacket*/) {
-  assert(false && "To be implemented");
-  return Packet16f();
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pblend(const Selector<8>& ifPacket,
-                                    const Packet8d& thenPacket,
-                                    const Packet8d& elsePacket) {
-  __mmask8 m = (ifPacket.select[0]   )
-             | (ifPacket.select[1]<<1)
-             | (ifPacket.select[2]<<2)
-             | (ifPacket.select[3]<<3)
-             | (ifPacket.select[4]<<4)
-             | (ifPacket.select[5]<<5)
-             | (ifPacket.select[6]<<6)
-             | (ifPacket.select[7]<<7);
-  return _mm512_mask_blend_pd(m, elsePacket, thenPacket);
-}
-
-// Packet math for Eigen::half
-template<> EIGEN_STRONG_INLINE Packet16h pset1<Packet16h>(const Eigen::half& from) {
-  return _mm256_set1_epi16(from.x);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet16h>(const Packet16h& from) {
-  return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm256_extract_epi16(from, 0)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pload<Packet16h>(const Eigen::half* from) {
-  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h ploadu<Packet16h>(const Eigen::half* from) {
-  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet16h& from) {
-  // (void*) -> workaround clang warning:
-  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
-  _mm256_store_si256((__m256i*)(void*)to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet16h& from) {
-  // (void*) -> workaround clang warning:
-  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
-  _mm256_storeu_si256((__m256i*)(void*)to, from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h
-ploaddup<Packet16h>(const Eigen::half*  from) {
-  unsigned short a = from[0].x;
-  unsigned short b = from[1].x;
-  unsigned short c = from[2].x;
-  unsigned short d = from[3].x;
-  unsigned short e = from[4].x;
-  unsigned short f = from[5].x;
-  unsigned short g = from[6].x;
-  unsigned short h = from[7].x;
-  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h
-ploadquad(const Eigen::half* from) {
-  unsigned short a = from[0].x;
-  unsigned short b = from[1].x;
-  unsigned short c = from[2].x;
-  unsigned short d = from[3].x;
-  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
-}
-
-EIGEN_STRONG_INLINE Packet16f half2float(const Packet16h& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm512_cvtph_ps(a);
-#else
-  EIGEN_ALIGN64 half aux[16];
-  pstore(aux, a);
-  float f0(aux[0]);
-  float f1(aux[1]);
-  float f2(aux[2]);
-  float f3(aux[3]);
-  float f4(aux[4]);
-  float f5(aux[5]);
-  float f6(aux[6]);
-  float f7(aux[7]);
-  float f8(aux[8]);
-  float f9(aux[9]);
-  float fa(aux[10]);
-  float fb(aux[11]);
-  float fc(aux[12]);
-  float fd(aux[13]);
-  float fe(aux[14]);
-  float ff(aux[15]);
-
-  return _mm512_set_ps(
-      ff, fe, fd, fc, fb, fa, f9, f8, f7, f6, f5, f4, f3, f2, f1, f0);
-#endif
-}
-
-EIGEN_STRONG_INLINE Packet16h float2half(const Packet16f& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm512_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
-#else
-  EIGEN_ALIGN64 float aux[16];
-  pstore(aux, a);
-  half h0(aux[0]);
-  half h1(aux[1]);
-  half h2(aux[2]);
-  half h3(aux[3]);
-  half h4(aux[4]);
-  half h5(aux[5]);
-  half h6(aux[6]);
-  half h7(aux[7]);
-  half h8(aux[8]);
-  half h9(aux[9]);
-  half ha(aux[10]);
-  half hb(aux[11]);
-  half hc(aux[12]);
-  half hd(aux[13]);
-  half he(aux[14]);
-  half hf(aux[15]);
-
-  return _mm256_set_epi16(
-      hf.x, he.x, hd.x, hc.x, hb.x, ha.x, h9.x, h8.x,
-      h7.x, h6.x, h5.x, h4.x, h3.x, h2.x, h1.x, h0.x);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h ptrue(const Packet16h& a) {
-  return ptrue(Packet8i(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pabs(const Packet16h& a) {
-  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm256_andnot_si256(sign_mask, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pmin<Packet16h>(const Packet16h& a,
-                                              const Packet16h& b) {
-  return float2half(pmin<Packet16f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pmax<Packet16h>(const Packet16h& a,
-                                              const Packet16h& b) {
-  return float2half(pmax<Packet16f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h plset<Packet16h>(const half& a) {
-  return float2half(plset<Packet16f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h por(const Packet16h& a,const Packet16h& b) {
-  // in some cases Packet8i is a wrapper around __m256i, so we need to
-  // cast to Packet8i to call the correct overload.
-  return por(Packet8i(a),Packet8i(b));
-}
-template<> EIGEN_STRONG_INLINE Packet16h pxor(const Packet16h& a,const Packet16h& b) {
-  return pxor(Packet8i(a),Packet8i(b));
-}
-template<> EIGEN_STRONG_INLINE Packet16h pand(const Packet16h& a,const Packet16h& b) {
-  return pand(Packet8i(a),Packet8i(b));
-}
-template<> EIGEN_STRONG_INLINE Packet16h pandnot(const Packet16h& a,const Packet16h& b) {
-  return pandnot(Packet8i(a),Packet8i(b));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pselect(const Packet16h& mask, const Packet16h& a, const Packet16h& b) {
-  return _mm256_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pround<Packet16h>(const Packet16h& a) {
-  return float2half(pround<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h print<Packet16h>(const Packet16h& a) {
-  return float2half(print<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pceil<Packet16h>(const Packet16h& a) {
-  return float2half(pceil<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pfloor<Packet16h>(const Packet16h& a) {
-  return float2half(pfloor<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_eq(const Packet16h& a,const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  return Pack32To16(pcmp_eq(af, bf));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_le(const Packet16h& a,const Packet16h& b) {
-  return Pack32To16(pcmp_le(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_lt(const Packet16h& a,const Packet16h& b) {
-  return Pack32To16(pcmp_lt(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_lt_or_nan(const Packet16h& a,const Packet16h& b) {
-  return Pack32To16(pcmp_lt_or_nan(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pconj(const Packet16h& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet16h pnegate(const Packet16h& a) {
-  Packet16h sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
-  return _mm256_xor_si256(a, sign_mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h padd<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = padd(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h psub<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = psub(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pmul<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = pmul(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pdiv<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = pdiv(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE half predux<Packet16h>(const Packet16h& from) {
-  Packet16f from_float = half2float(from);
-  return half(predux(from_float));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h predux_half_dowto4<Packet16h>(const Packet16h& a) {
-  Packet8h lane0 = _mm256_extractf128_si256(a, 0);
-  Packet8h lane1 = _mm256_extractf128_si256(a, 1);
-  return padd<Packet8h>(lane0, lane1);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_max<Packet16h>(const Packet16h& a) {
-  Packet16f af = half2float(a);
-  float reduced = predux_max<Packet16f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_min<Packet16h>(const Packet16h& a) {
-  Packet16f af = half2float(a);
-  float reduced = predux_min<Packet16f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE half predux_mul<Packet16h>(const Packet16h& from) {
-  Packet16f from_float = half2float(from);
-  return half(predux_mul(from_float));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h preverse(const Packet16h& a)
-{
-  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-  return _mm256_insertf128_si256(
-                    _mm256_castsi128_si256(_mm_shuffle_epi8(_mm256_extractf128_si256(a,1),m)),
-                                           _mm_shuffle_epi8(_mm256_extractf128_si256(a,0),m), 1);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pgather<Eigen::half, Packet16h>(const Eigen::half* from, Index stride)
-{
-  return _mm256_set_epi16(
-      from[15*stride].x, from[14*stride].x, from[13*stride].x, from[12*stride].x,
-      from[11*stride].x, from[10*stride].x, from[9*stride].x, from[8*stride].x,
-      from[7*stride].x, from[6*stride].x, from[5*stride].x, from[4*stride].x,
-      from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<half, Packet16h>(half* to, const Packet16h& from, Index stride)
-{
-  EIGEN_ALIGN64 half aux[16];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-  to[stride*8] = aux[8];
-  to[stride*9] = aux[9];
-  to[stride*10] = aux[10];
-  to[stride*11] = aux[11];
-  to[stride*12] = aux[12];
-  to[stride*13] = aux[13];
-  to[stride*14] = aux[14];
-  to[stride*15] = aux[15];
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet16h,16>& kernel) {
-  __m256i a = kernel.packet[0];
-  __m256i b = kernel.packet[1];
-  __m256i c = kernel.packet[2];
-  __m256i d = kernel.packet[3];
-  __m256i e = kernel.packet[4];
-  __m256i f = kernel.packet[5];
-  __m256i g = kernel.packet[6];
-  __m256i h = kernel.packet[7];
-  __m256i i = kernel.packet[8];
-  __m256i j = kernel.packet[9];
-  __m256i k = kernel.packet[10];
-  __m256i l = kernel.packet[11];
-  __m256i m = kernel.packet[12];
-  __m256i n = kernel.packet[13];
-  __m256i o = kernel.packet[14];
-  __m256i p = kernel.packet[15];
-
-  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
-  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
-  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
-  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
-  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
-  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
-  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
-  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
-
-  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
-  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
-  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
-  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
-  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
-  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
-  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
-  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
-
-  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
-  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
-  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
-  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
-  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
-  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
-  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
-  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
-
-  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
-  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
-  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
-  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
-  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
-  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
-  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
-  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
-
-  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
-  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
-  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
-  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
-  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
-  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
-  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
-  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
-  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
-  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
-  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
-  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
-  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
-  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
-  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
-  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
-
-  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
-  __m256i a_p_0 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
-  __m256i a_p_1 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
-  __m256i a_p_2 = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
-  __m256i a_p_3 = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
-  __m256i a_p_4 = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
-  __m256i a_p_5 = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
-  __m256i a_p_6 = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
-  __m256i a_p_7 = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
-  __m256i a_p_8 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
-  __m256i a_p_9 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
-  __m256i a_p_a = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
-  __m256i a_p_b = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
-  __m256i a_p_c = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
-  __m256i a_p_d = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
-  __m256i a_p_e = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
-  __m256i a_p_f = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
-
-  kernel.packet[0] = a_p_0;
-  kernel.packet[1] = a_p_1;
-  kernel.packet[2] = a_p_2;
-  kernel.packet[3] = a_p_3;
-  kernel.packet[4] = a_p_4;
-  kernel.packet[5] = a_p_5;
-  kernel.packet[6] = a_p_6;
-  kernel.packet[7] = a_p_7;
-  kernel.packet[8] = a_p_8;
-  kernel.packet[9] = a_p_9;
-  kernel.packet[10] = a_p_a;
-  kernel.packet[11] = a_p_b;
-  kernel.packet[12] = a_p_c;
-  kernel.packet[13] = a_p_d;
-  kernel.packet[14] = a_p_e;
-  kernel.packet[15] = a_p_f;
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet16h,8>& kernel) {
-  EIGEN_ALIGN64 half in[8][16];
-  pstore<half>(in[0], kernel.packet[0]);
-  pstore<half>(in[1], kernel.packet[1]);
-  pstore<half>(in[2], kernel.packet[2]);
-  pstore<half>(in[3], kernel.packet[3]);
-  pstore<half>(in[4], kernel.packet[4]);
-  pstore<half>(in[5], kernel.packet[5]);
-  pstore<half>(in[6], kernel.packet[6]);
-  pstore<half>(in[7], kernel.packet[7]);
-
-  EIGEN_ALIGN64 half out[8][16];
-
-  for (int i = 0; i < 8; ++i) {
-    for (int j = 0; j < 8; ++j) {
-      out[i][j] = in[j][2*i];
-    }
-    for (int j = 0; j < 8; ++j) {
-      out[i][j+8] = in[j][2*i+1];
-    }
-  }
-
-  kernel.packet[0] = pload<Packet16h>(out[0]);
-  kernel.packet[1] = pload<Packet16h>(out[1]);
-  kernel.packet[2] = pload<Packet16h>(out[2]);
-  kernel.packet[3] = pload<Packet16h>(out[3]);
-  kernel.packet[4] = pload<Packet16h>(out[4]);
-  kernel.packet[5] = pload<Packet16h>(out[5]);
-  kernel.packet[6] = pload<Packet16h>(out[6]);
-  kernel.packet[7] = pload<Packet16h>(out[7]);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet16h,4>& kernel) {
-  EIGEN_ALIGN64 half in[4][16];
-  pstore<half>(in[0], kernel.packet[0]);
-  pstore<half>(in[1], kernel.packet[1]);
-  pstore<half>(in[2], kernel.packet[2]);
-  pstore<half>(in[3], kernel.packet[3]);
-
-  EIGEN_ALIGN64 half out[4][16];
-
-  for (int i = 0; i < 4; ++i) {
-    for (int j = 0; j < 4; ++j) {
-      out[i][j] = in[j][4*i];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+4] = in[j][4*i+1];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+8] = in[j][4*i+2];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+12] = in[j][4*i+3];
-    }
-  }
-
-  kernel.packet[0] = pload<Packet16h>(out[0]);
-  kernel.packet[1] = pload<Packet16h>(out[1]);
-  kernel.packet[2] = pload<Packet16h>(out[2]);
-  kernel.packet[3] = pload<Packet16h>(out[3]);
-}
-
-template <> struct is_arithmetic<Packet16bf> { enum { value = true }; };
-
-template <>
-struct packet_traits<bfloat16> : default_packet_traits {
-  typedef Packet16bf type;
-  typedef Packet8bf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-    HasBlend = 0,
-    HasInsert = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-    HasLog = 1,  // Currently fails test with bad accuracy.
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasNdtri = 1,
-    HasBessel  = 1,
-#endif
-    HasExp = 1,
-    HasSqrt = EIGEN_FAST_MATH,
-    HasRsqrt = EIGEN_FAST_MATH,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasCmp  = 1,
-    HasDiv = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet16bf>
-{
-  typedef bfloat16 type;
-  enum {size=16, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
-  typedef Packet8bf half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pset1<Packet16bf>(const bfloat16& from) {
-  return _mm256_set1_epi16(from.value);
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 pfirst<Packet16bf>(const Packet16bf& from) {
-  bfloat16 t;
-  t.value = static_cast<unsigned short>(_mm256_extract_epi16(from, 0));
-  return t;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pload<Packet16bf>(const bfloat16* from) {
-  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf ploadu<Packet16bf>(const bfloat16* from) {
-  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to,
-                                          const Packet16bf& from) {
-  _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to,
-                                           const Packet16bf& from) {
-  _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf
-ploaddup<Packet16bf>(const bfloat16* from) {
-  Packet16bf r;
-  unsigned short a = from[0].value;
-  unsigned short b = from[1].value;
-  unsigned short c = from[2].value;
-  unsigned short d = from[3].value;
-  unsigned short e = from[4].value;
-  unsigned short f = from[5].value;
-  unsigned short g = from[6].value;
-  unsigned short h = from[7].value;
-  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf
-ploadquad(const bfloat16* from) {
-  Packet16bf r;
-  unsigned short a = from[0].value;
-  unsigned short b = from[1].value;
-  unsigned short c = from[2].value;
-  unsigned short d = from[3].value;
-  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
-}
-
-EIGEN_STRONG_INLINE Packet16f Bf16ToF32(const Packet16bf& a) {
-  return _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16));
-}
-
-// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
-EIGEN_STRONG_INLINE Packet16bf F32ToBf16(const Packet16f& a) {
-  Packet16bf r;
-
-#if defined(EIGEN_VECTORIZE_AVX512BF16) && EIGEN_GNUC_AT_LEAST(10, 1)
-  // Since GCC 10.1 supports avx512bf16 and C style explicit cast
-  // (C++ static_cast is not supported yet), do converion via intrinsic
-  // and register path for performance.
-  r = (__m256i)(_mm512_cvtneps_pbh(a));
-
-#else
-  __m512i t;
-  __m512i input = _mm512_castps_si512(a);
-  __m512i nan = _mm512_set1_epi32(0x7fc0);
-
-  // uint32_t lsb = (input >> 16) & 1;
-  t = _mm512_and_si512(_mm512_srli_epi32(input, 16), _mm512_set1_epi32(1));
-  // uint32_t rounding_bias = 0x7fff + lsb;
-  t = _mm512_add_epi32(t, _mm512_set1_epi32(0x7fff));
-  // input += rounding_bias;
-  t = _mm512_add_epi32(t, input);
-  // input = input >> 16;
-  t = _mm512_srli_epi32(t, 16);
-
-  // Check NaN before converting back to bf16
-  __mmask16 mask = _mm512_cmp_ps_mask(a, a, _CMP_ORD_Q);
-
-  t = _mm512_mask_blend_epi32(mask, nan, t);
-  // output.value = static_cast<uint16_t>(input);
-  r = _mm512_cvtepi32_epi16(t);
-#endif // EIGEN_VECTORIZE_AVX512BF16
-
-  return r;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf ptrue(const Packet16bf& a) {
-  return ptrue<Packet8i>(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf por(const Packet16bf& a, const Packet16bf& b) {
-  return por<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pxor(const Packet16bf& a, const Packet16bf& b) {
-  return pxor<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pand(const Packet16bf& a, const Packet16bf& b) {
-  return pand<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pandnot(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return pandnot<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pselect(const Packet16bf& mask,
-                                       const Packet16bf& a,
-                                       const Packet16bf& b) {
-  // Input mask is expected to be all 0/1, handle it with 8-bit
-  // intrinsic for performance.
-  return _mm256_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf pround<Packet16bf>(const Packet16bf& a)
-{
-  return F32ToBf16(pround<Packet16f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf print<Packet16bf>(const Packet16bf& a) {
-  return F32ToBf16(print<Packet16f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf pceil<Packet16bf>(const Packet16bf& a) {
-  return F32ToBf16(pceil<Packet16f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf pfloor<Packet16bf>(const Packet16bf& a) {
-  return F32ToBf16(pfloor<Packet16f>(Bf16ToF32(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_eq(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return Pack32To16(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_le(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return Pack32To16(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_lt(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return Pack32To16(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_lt_or_nan(const Packet16bf& a,
-                                              const Packet16bf& b) {
-  return Pack32To16(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pnegate(const Packet16bf& a) {
-  Packet16bf sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
-  return _mm256_xor_si256(a, sign_mask);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pconj(const Packet16bf& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pabs(const Packet16bf& a) {
-  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm256_andnot_si256(sign_mask, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf padd<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(padd<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf psub<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(psub<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pmul<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pmul<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pdiv<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pdiv<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pmin<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pmin<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pmax<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pmax<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf plset<Packet16bf>(const bfloat16& a) {
-  return F32ToBf16(plset<Packet16f>(static_cast<float>(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf predux_half_dowto4<Packet16bf>(const Packet16bf& a) {
-  Packet8bf lane0 = _mm256_extractf128_si256(a, 0);
-  Packet8bf lane1 = _mm256_extractf128_si256(a, 1);
-  return padd<Packet8bf>(lane0, lane1);
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux<Packet16bf>(const Packet16bf& p) {
-  return static_cast<bfloat16>(predux<Packet16f>(Bf16ToF32(p)));
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet16bf>(const Packet16bf& from) {
-  return static_cast<bfloat16>(predux_mul<Packet16f>(Bf16ToF32(from)));
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux_min<Packet16bf>(const Packet16bf& from) {
-  return static_cast<bfloat16>(predux_min<Packet16f>(Bf16ToF32(from)));
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux_max<Packet16bf>(const Packet16bf& from) {
-  return static_cast<bfloat16>(predux_max<Packet16f>(Bf16ToF32(from)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf preverse(const Packet16bf& a) {
-  __m256i m = _mm256_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1,
-                               14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-
-  Packet16bf res;
-  // Swap hi and lo first because shuffle is in 128-bit lanes.
-  res = _mm256_permute2x128_si256(a, a, 1);
-  // Shuffle 8-bit values in src within 2*128-bit lanes.
-  return _mm256_shuffle_epi8(res, m);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pgather<bfloat16, Packet16bf>(const bfloat16* from,
-                                                             Index stride) {
-  return _mm256_set_epi16(
-      from[15*stride].value, from[14*stride].value, from[13*stride].value, from[12*stride].value,
-      from[11*stride].value, from[10*stride].value, from[9*stride].value, from[8*stride].value,
-      from[7*stride].value, from[6*stride].value, from[5*stride].value, from[4*stride].value,
-      from[3*stride].value, from[2*stride].value, from[1*stride].value, from[0*stride].value);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet16bf>(bfloat16* to,
-                                                        const Packet16bf& from,
-                                                        Index stride) {
-  EIGEN_ALIGN64 bfloat16 aux[16];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-  to[stride*8] = aux[8];
-  to[stride*9] = aux[9];
-  to[stride*10] = aux[10];
-  to[stride*11] = aux[11];
-  to[stride*12] = aux[12];
-  to[stride*13] = aux[13];
-  to[stride*14] = aux[14];
-  to[stride*15] = aux[15];
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf,16>& kernel) {
-  __m256i a = kernel.packet[0];
-  __m256i b = kernel.packet[1];
-  __m256i c = kernel.packet[2];
-  __m256i d = kernel.packet[3];
-  __m256i e = kernel.packet[4];
-  __m256i f = kernel.packet[5];
-  __m256i g = kernel.packet[6];
-  __m256i h = kernel.packet[7];
-  __m256i i = kernel.packet[8];
-  __m256i j = kernel.packet[9];
-  __m256i k = kernel.packet[10];
-  __m256i l = kernel.packet[11];
-  __m256i m = kernel.packet[12];
-  __m256i n = kernel.packet[13];
-  __m256i o = kernel.packet[14];
-  __m256i p = kernel.packet[15];
-
-  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
-  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
-  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
-  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
-  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
-  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
-  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
-  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
-
-  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
-  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
-  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
-  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
-  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
-  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
-  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
-  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
-
-  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
-  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
-  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
-  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
-  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
-  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
-  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
-  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
-
-  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
-  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
-  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
-  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
-  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
-  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
-  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
-  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
-
-  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
-  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
-  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
-  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
-  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
-  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
-  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
-  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
-  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
-  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
-  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
-  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
-  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
-  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
-  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
-  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
-
-  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
-  kernel.packet[0] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
-  kernel.packet[1] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
-  kernel.packet[2] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
-  kernel.packet[3] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
-  kernel.packet[4] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
-  kernel.packet[5] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
-  kernel.packet[6] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
-  kernel.packet[7] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
-  kernel.packet[8] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
-  kernel.packet[9] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
-  kernel.packet[10] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
-  kernel.packet[11] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
-  kernel.packet[12] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
-  kernel.packet[13] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
-  kernel.packet[14] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
-  kernel.packet[15] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf,4>& kernel) {
-  __m256i a = kernel.packet[0];
-  __m256i b = kernel.packet[1];
-  __m256i c = kernel.packet[2];
-  __m256i d = kernel.packet[3];
-
-  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
-  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
-  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
-  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
-
-  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
-  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
-  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
-  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
-
-  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
-  kernel.packet[0] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x20);
-  kernel.packet[1] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x20);
-  kernel.packet[2] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x31);
-  kernel.packet[3] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x31);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_AVX512_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/TypeCasting.h
deleted file mode 100644
index 3304127..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AVX512/TypeCasting.h
+++ /dev/null
@@ -1,89 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_AVX512_H
-#define EIGEN_TYPE_CASTING_AVX512_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_STRONG_INLINE Packet16i pcast<Packet16f, Packet16i>(const Packet16f& a) {
-  return _mm512_cvttps_epi32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16i, Packet16f>(const Packet16i& a) {
-  return _mm512_cvtepi32_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16i preinterpret<Packet16i, Packet16f>(const Packet16f& a) {
-  return _mm512_castps_si512(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet16i>(const Packet16i& a) {
-  return _mm512_castsi512_ps(a);
-}
-
-template <>
-struct type_casting_traits<half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16h, Packet16f>(const Packet16h& a) {
-  return half2float(a);
-}
-
-template <>
-struct type_casting_traits<float, half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16h pcast<Packet16f, Packet16h>(const Packet16f& a) {
-  return float2half(a);
-}
-
-template <>
-struct type_casting_traits<bfloat16, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16bf, Packet16f>(const Packet16bf& a) {
-  return Bf16ToF32(a);
-}
-
-template <>
-struct type_casting_traits<float, bfloat16> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16bf pcast<Packet16f, Packet16bf>(const Packet16f& a) {
-  return F32ToBf16(a);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_AVX512_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/Complex.h
deleted file mode 100644
index f424f11..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/Complex.h
+++ /dev/null
@@ -1,417 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010-2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX32_ALTIVEC_H
-#define EIGEN_COMPLEX32_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-static Packet4ui  p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
-#ifdef __VSX__
-#if defined(_BIG_ENDIAN)
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-#else
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-#endif
-#endif
-
-//---------- float ----------
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE explicit Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
-
-  EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b)
-  {
-    Packet4f v1, v2;
-
-    // Permute and multiply the real parts of a and b
-    v1 = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
-    // Get the imaginary parts of a
-    v2 = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
-    // multiply a_re * b
-    v1 = vec_madd(v1, b.v, p4f_ZERO);
-    // multiply a_im * b and get the conjugate result
-    v2 = vec_madd(v2, b.v, p4f_ZERO);
-    v2 = reinterpret_cast<Packet4f>(pxor(v2, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR)));
-    // permute back to a proper order
-    v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
-
-    return Packet2cf(padd<Packet4f>(v1, v2));
-  }
-
-  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
-    v = pmul(Packet2cf(*this), b).v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const {
-    return Packet2cf(*this) *= b;
-  }
-
-  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const {
-    return Packet2cf(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const {
-    return Packet2cf(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(void) const {
-    return Packet2cf(-v);
-  }
-
-  Packet4f  v;
-};
-
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  typedef Packet4f as_real;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-#ifdef __VSX__
-    HasBlend  = 1,
-#endif
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2cf half; typedef Packet4f as_real; };
-
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-  if((std::ptrdiff_t(&from) % 16) == 0)
-    res.v = pload<Packet4f>((const float *)&from);
-  else
-    res.v = ploadu<Packet4f>((const float *)&from);
-  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>*        from) { return Packet2cf(pload<Packet4f>((const float *) from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>*       from) { return Packet2cf(ploadu<Packet4f>((const float*) from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>*     from) { return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstore((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstoreu((float*)to, from.v); }
-
-EIGEN_STRONG_INLINE Packet2cf pload2(const std::complex<float>* from0, const std::complex<float>* from1)
-{
-  Packet4f res0, res1;
-#ifdef __VSX__
-  __asm__ ("lxsdx %x0,%y1" : "=wa" (res0) : "Z" (*from0));
-  __asm__ ("lxsdx %x0,%y1" : "=wa" (res1) : "Z" (*from1));
-#ifdef _BIG_ENDIAN
-  __asm__ ("xxpermdi %x0, %x1, %x2, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
-#else
-  __asm__ ("xxpermdi %x0, %x2, %x1, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
-#endif
-#else
-  *reinterpret_cast<std::complex<float> *>(&res0) = *from0;
-  *reinterpret_cast<std::complex<float> *>(&res1) = *from1;
-  res0 = vec_perm(res0, res1, p16uc_TRANSPOSE64_HI);
-#endif
-  return Packet2cf(res0);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
-{
-  EIGEN_ALIGN16 std::complex<float> af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
-  return pload<Packet2cf>(af);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  EIGEN_ALIGN16 std::complex<float> af[2];
-  pstore<std::complex<float> >((std::complex<float> *) af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v + b.v); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v - b.v); }
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pxor<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pandnot<Packet4f>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr)    { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
-{
-  EIGEN_ALIGN16 std::complex<float> res[2];
-  pstore((float *)&res, a.v);
-
-  return res[0];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{
-  Packet4f rev_a;
-  rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
-  return Packet2cf(rev_a);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  b = vec_sld(a.v, a.v, 8);
-  b = padd<Packet4f>(a.v, b);
-  return pfirst<Packet2cf>(Packet2cf(b));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  Packet2cf prod;
-  b = vec_sld(a.v, a.v, 8);
-  prod = pmul<Packet2cf>(a, Packet2cf(b));
-
-  return pfirst<Packet2cf>(prod);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for AltiVec
-  Packet2cf res = pmul(a, pconj(b));
-  Packet4f s = pmul<Packet4f>(b.v, b.v);
-  return Packet2cf(pdiv(res.v, padd<Packet4f>(s, vec_perm(s, s, p16uc_COMPLEX32_REV))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
-{
-  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
-{
-  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
-  Packet4f eq = reinterpret_cast<Packet4f>(vec_cmpeq(a.v,b.v));
-  return Packet2cf(vec_and(eq, vec_perm(eq, eq, p16uc_COMPLEX32_REV)));
-}
-
-#ifdef __VSX__
-template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  Packet2cf result;
-  result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
-  return result;
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a)
-{
-  return psqrt_complex<Packet2cf>(a);
-}
-
-//---------- double ----------
-#ifdef __VSX__
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
-
-  EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b)
-  {
-    Packet2d a_re, a_im, v1, v2;
-
-    // Permute and multiply the real parts of a and b
-    a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
-    // Get the imaginary parts of a
-    a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
-    // multiply a_re * b
-    v1 = vec_madd(a_re, b.v, p2d_ZERO);
-    // multiply a_im * b and get the conjugate result
-    v2 = vec_madd(a_im, b.v, p2d_ZERO);
-    v2 = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(v2), reinterpret_cast<Packet4ui>(v2), 8));
-    v2 = pxor(v2, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR1));
-
-    return Packet1cd(padd<Packet2d>(v1, v2));
-  }
-
-  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
-    v = pmul(Packet1cd(*this), b).v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const {
-    return Packet1cd(*this) *= b;
-  }
-
-  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const {
-    return Packet1cd(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const {
-    return Packet1cd(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(void) const {
-    return Packet1cd(-v);
-  }
-
-  Packet2d v;
-};
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  typedef Packet2d as_real;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet1cd half; typedef Packet2d as_real; };
-
-template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { return Packet1cd(pload<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { return Packet1cd(ploadu<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { pstoreu((double*)to, from.v); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
-{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index)
-{
-  return pload<Packet1cd>(from);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index)
-{
-  pstore<std::complex<double> >(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR2))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pand   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pand(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd por    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(por(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pxor   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pxor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pandnot(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>*     from)  { return pset1<Packet1cd>(*from); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr)    { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
-{
-  EIGEN_ALIGN16 std::complex<double> res[2];
-  pstore<std::complex<double> >(res, a);
-
-  return res[0];
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for AltiVec
-  Packet1cd res = pmul(a,pconj(b));
-  Packet2d s = pmul<Packet2d>(b.v, b.v);
-  return Packet1cd(pdiv(res.v, padd<Packet2d>(s, vec_perm(s, s, p16uc_REVERSE64))));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
-{
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
-{
-  Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a)==re(b), im(a)==im(b)]
-  Packet2d eq = reinterpret_cast<Packet2d>(vec_cmpeq(a.v,b.v));
-  // Swap real/imag elements in the mask in to get:
-  // [im(a)==im(b), re(a)==re(b)]
-  Packet2d eq_swapped = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(eq), reinterpret_cast<Packet4ui>(eq), 8));
-  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet1cd(vec_and(eq, eq_swapped));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a)
-{
-  return psqrt_complex<Packet1cd>(a);
-}
-
-#endif // __VSX__
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX32_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
deleted file mode 100644
index 3a7a329..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
+++ /dev/null
@@ -1,90 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog<Packet4f>(const Packet4f& _x)
-{
-  return plog_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexp<Packet4f>(const Packet4f& _x)
-{
-  return pexp_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psin<Packet4f>(const Packet4f& _x)
-{
-  return psin_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pcos<Packet4f>(const Packet4f& _x)
-{
-  return pcos_float(_x);
-}
-
-#ifndef EIGEN_COMP_CLANG
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& x)
-{
-  return  vec_rsqrt(x);
-}
-#endif
-
-#ifdef __VSX__
-#ifndef EIGEN_COMP_CLANG
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d prsqrt<Packet2d>(const Packet2d& x)
-{
-  return  vec_rsqrt(x);
-}
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& x)
-{
-  return  vec_sqrt(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d psqrt<Packet2d>(const Packet2d& x)
-{
-  return  vec_sqrt(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d pexp<Packet2d>(const Packet2d& _x)
-{
-  return pexp_double(_x);
-}
-#endif
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
deleted file mode 100644
index 3f79b97..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
+++ /dev/null
@@ -1,2937 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
-// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_PRODUCT_ALTIVEC_H
-#define EIGEN_MATRIX_PRODUCT_ALTIVEC_H
-
-#ifndef EIGEN_ALTIVEC_USE_CUSTOM_PACK
-#define EIGEN_ALTIVEC_USE_CUSTOM_PACK    1
-#endif
-
-#include "MatrixProductCommon.h"
-
-// Since LLVM doesn't support dynamic dispatching, force either always MMA or VSX
-#if EIGEN_COMP_LLVM
-#if !defined(EIGEN_ALTIVEC_DISABLE_MMA) && !defined(EIGEN_ALTIVEC_MMA_ONLY)
-#ifdef __MMA__
-#define EIGEN_ALTIVEC_MMA_ONLY
-#else
-#define EIGEN_ALTIVEC_DISABLE_MMA
-#endif
-#endif
-#endif
-
-#ifdef __has_builtin
-#if __has_builtin(__builtin_mma_assemble_acc)
-  #define ALTIVEC_MMA_SUPPORT
-#endif
-#endif
-
-#if defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-  #include "MatrixProductMMA.h"
-#endif
-
-/**************************************************************************************************
- * TODO                                                                                           *
- * - Check StorageOrder on dhs_pack (the innermost second loop seems unvectorized when it could). *
- * - Check the possibility of transposing as GETREAL and GETIMAG when needed.                     *
- **************************************************************************************************/
-namespace Eigen {
-
-namespace internal {
-
-/**************************
- * Constants and typedefs *
- **************************/
-template<typename Scalar>
-struct quad_traits
-{
-  typedef typename packet_traits<Scalar>::type    vectortype;
-  typedef PacketBlock<vectortype,4>                     type;
-  typedef vectortype                                 rhstype;
-  enum
-  {
-    vectorsize = packet_traits<Scalar>::size,
-    size = 4,
-    rows = 4
-  };
-};
-
-template<>
-struct quad_traits<double>
-{
-  typedef Packet2d                        vectortype;
-  typedef PacketBlock<vectortype,4>             type;
-  typedef PacketBlock<Packet2d,2>            rhstype;
-  enum
-  {
-    vectorsize = packet_traits<double>::size,
-    size = 2,
-    rows = 4
-  };
-};
-
-// MatrixProduct decomposes real/imaginary vectors into a real vector and an imaginary vector, this turned out
-// to be faster than Eigen's usual approach of having real/imaginary pairs on a single vector. This constants then
-// are responsible to extract from convert between Eigen's and MatrixProduct approach.
-
-const static Packet16uc p16uc_GETREAL32 = {  0,  1,  2,  3,
-                                             8,  9, 10, 11,
-                                            16, 17, 18, 19,
-                                            24, 25, 26, 27};
-
-const static Packet16uc p16uc_GETIMAG32 = {  4,  5,  6,  7,
-                                            12, 13, 14, 15,
-                                            20, 21, 22, 23,
-                                            28, 29, 30, 31};
-const static Packet16uc p16uc_GETREAL64 = {  0,  1,  2,  3,  4,  5,  6,  7,
-                                            16, 17, 18, 19, 20, 21, 22, 23};
-
-//[a,ai],[b,bi] = [ai,bi]
-const static Packet16uc p16uc_GETIMAG64 = {  8,  9, 10, 11, 12, 13, 14, 15,
-                                            24, 25, 26, 27, 28, 29, 30, 31};
-
-/*********************************************
- * Single precision real and complex packing *
- * *******************************************/
-
-/**
- * Symm packing is related to packing of symmetric adjoint blocks, as expected the packing leaves
- * the diagonal real, whatever is below it is copied from the respective upper diagonal element and 
- * conjugated. There's no PanelMode available for symm packing.
- *
- * Packing in general is supposed to leave the lhs block and the rhs block easy to be read by gemm using 
- * its respective rank-update instructions. The float32/64 versions are different because at this moment
- * the size of the accumulator is fixed at 512-bits so you can't have a 4x4 accumulator of 64-bit elements.
- * 
- * As mentioned earlier MatrixProduct breaks complex numbers into a real vector and a complex vector so packing has
- * to take that into account, at the moment, we run pack the real part and then the imaginary part, this is the main
- * reason why packing for complex is broken down into several different parts, also the reason why we endup having a
- * float32/64 and complex float32/64 version.
- **/
-template<typename Scalar, typename Index, int StorageOrder>
-EIGEN_ALWAYS_INLINE std::complex<Scalar> getAdjointVal(Index i, Index j, const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder>& dt)
-{
-  std::complex<Scalar> v;
-  if(i < j)
-  {
-    v.real( dt(j,i).real());
-    v.imag(-dt(j,i).imag());
-  } else if(i > j)
-  {
-    v.real( dt(i,j).real());
-    v.imag( dt(i,j).imag());
-  } else {
-    v.real( dt(i,j).real());
-    v.imag((Scalar)0.0);
-  }
-  return v;
-}
-
-template<typename Scalar, typename Index, int StorageOrder, int N>
-EIGEN_STRONG_INLINE void symm_pack_complex_rhs_helper(std::complex<Scalar>* blockB, const std::complex<Scalar>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-{
-  const Index depth = k2 + rows;
-  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> rhs(_rhs, rhsStride);
-  const Index vectorSize = N*quad_traits<Scalar>::vectorsize;
-  const Index vectorDelta = vectorSize * rows;
-  Scalar* blockBf = reinterpret_cast<Scalar *>(blockB);
-
-  Index rir = 0, rii, j = 0;
-  for(; j + vectorSize <= cols; j+=vectorSize)
-  {
-    rii = rir + vectorDelta;
-
-    for(Index i = k2; i < depth; i++)
-    {
-      for(Index k = 0; k < vectorSize; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(i, j + k, rhs);
-
-        blockBf[rir + k] = v.real();
-        blockBf[rii + k] = v.imag();
-      }
-      rir += vectorSize;
-      rii += vectorSize;
-    }
-
-    rir += vectorDelta;
-  }
-  if (j < cols)
-  {
-    rii = rir + ((cols - j) * rows);
-
-    for(Index i = k2; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < cols; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(i, k, rhs);
-
-        blockBf[rir] = v.real();
-        blockBf[rii] = v.imag();
-
-        rir += 1;
-        rii += 1;
-      }
-    }
-  }
-}
-
-template<typename Scalar, typename Index, int StorageOrder>
-EIGEN_STRONG_INLINE void symm_pack_complex_lhs_helper(std::complex<Scalar>* blockA, const std::complex<Scalar>* _lhs, Index lhsStride, Index cols, Index rows)
-{
-  const Index depth = cols;
-  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> lhs(_lhs, lhsStride);
-  const Index vectorSize = quad_traits<Scalar>::vectorsize;
-  const Index vectorDelta = vectorSize * depth;
-  Scalar* blockAf = (Scalar *)(blockA);
-
-  Index rir = 0, rii, j = 0;
-  for(; j + vectorSize <= rows; j+=vectorSize)
-  {
-    rii = rir + vectorDelta;
-
-    for(Index i = 0; i < depth; i++)
-    {
-      for(Index k = 0; k < vectorSize; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(j+k, i, lhs);
-
-        blockAf[rir + k] = v.real();
-        blockAf[rii + k] = v.imag();
-      }
-      rir += vectorSize;
-      rii += vectorSize;
-    }
-
-    rir += vectorDelta;
-  }
-
-  if (j < rows)
-  {
-    rii = rir + ((rows - j) * depth);
-
-    for(Index i = 0; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < rows; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(k, i, lhs);
-
-        blockAf[rir] = v.real();
-        blockAf[rii] = v.imag();
-
-        rir += 1;
-        rii += 1;
-      }
-    }
-  }
-}
-
-template<typename Scalar, typename Index, int StorageOrder, int N>
-EIGEN_STRONG_INLINE void symm_pack_rhs_helper(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-{
-  const Index depth = k2 + rows;
-  const_blas_data_mapper<Scalar, Index, StorageOrder> rhs(_rhs, rhsStride);
-  const Index vectorSize = quad_traits<Scalar>::vectorsize;
-
-  Index ri = 0, j = 0;
-  for(; j + N*vectorSize <= cols; j+=N*vectorSize)
-  {
-    Index i = k2;
-    for(; i < depth; i++)
-    {
-      for(Index k = 0; k < N*vectorSize; k++)
-      {
-        if(i <= j+k)
-          blockB[ri + k] = rhs(j+k, i);
-        else
-          blockB[ri + k] = rhs(i, j+k);
-      }
-      ri += N*vectorSize;
-    }
-  }
-
-  if (j < cols)
-  {
-    for(Index i = k2; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < cols; k++)
-      {
-        if(k <= i)
-          blockB[ri] = rhs(i, k);
-        else
-          blockB[ri] = rhs(k, i);
-        ri += 1;
-      }
-    }
-  }
-}
-
-template<typename Scalar, typename Index, int StorageOrder>
-EIGEN_STRONG_INLINE void symm_pack_lhs_helper(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
-{
-  const Index depth = cols;
-  const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs, lhsStride);
-  const Index vectorSize = quad_traits<Scalar>::vectorsize;
-
-  Index ri = 0, j = 0;
-  for(; j + vectorSize <= rows; j+=vectorSize)
-  {
-    Index i = 0;
-
-    for(; i < depth; i++)
-    {
-      for(Index k = 0; k < vectorSize; k++)
-      {
-        if(i <= j+k)
-          blockA[ri + k] = lhs(j+k, i);
-        else
-          blockA[ri + k] = lhs(i, j+k);
-      }
-      ri += vectorSize;
-    }
-  }
-
-  if (j < rows)
-  {
-    for(Index i = 0; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < rows; k++)
-      {
-        if(i <= k)
-          blockA[ri] = lhs(k, i);
-        else
-          blockA[ri] = lhs(i, k);
-        ri += 1;
-      }
-    }
-  }
-}
-
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<std::complex<float>, Index, nr, StorageOrder>
-{
-  void operator()(std::complex<float>* blockB, const std::complex<float>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_complex_rhs_helper<float, Index, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<std::complex<float>, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(std::complex<float>* blockA, const std::complex<float>* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_complex_lhs_helper<float, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-// *********** symm_pack std::complex<float64> ***********
-
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<std::complex<double>, Index, nr, StorageOrder>
-{
-  void operator()(std::complex<double>* blockB, const std::complex<double>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_complex_rhs_helper<double, Index, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<std::complex<double>, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(std::complex<double>* blockA, const std::complex<double>* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_complex_lhs_helper<double, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-// *********** symm_pack float32 ***********
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<float, Index, nr, StorageOrder>
-{
-  void operator()(float* blockB, const float* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_rhs_helper<float, Index, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<float, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(float* blockA, const float* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_lhs_helper<float, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-// *********** symm_pack float64 ***********
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<double, Index, nr, StorageOrder>
-{
-  void operator()(double* blockB, const double* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_rhs_helper<double, Index, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<double, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(double* blockA, const double* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_lhs_helper<double, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-/**
- * PanelMode
- * Packing might be called several times before being multiplied by gebp_kernel, this happens because 
- * on special occasions it fills part of block with other parts of the matrix. Two variables control
- * how PanelMode should behave: offset and stride. The idea is that those variables represent whatever
- * is going to be the real offset and stride in the future and this is what you should obey. The process
- * is to behave as you would with normal packing but leave the start of each part with the correct offset
- * and the end as well respecting the real stride the block will have. Gebp is aware of both blocks stride
- * and offset and behaves accordingly.
- **/
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,4>& block)
-{
-  const Index size = 16 / sizeof(Scalar);
-  pstore<Scalar>(to + (0 * size), block.packet[0]);
-  pstore<Scalar>(to + (1 * size), block.packet[1]);
-  pstore<Scalar>(to + (2 * size), block.packet[2]);
-  pstore<Scalar>(to + (3 * size), block.packet[3]);
-}
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,2>& block)
-{
-  const Index size = 16 / sizeof(Scalar);
-  pstore<Scalar>(to + (0 * size), block.packet[0]);
-  pstore<Scalar>(to + (1 * size), block.packet[1]);
-}
-
-// General template for lhs & rhs complex packing.
-template<typename Scalar, typename Index, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode, bool UseLhs>
-struct dhs_cpack {
-  EIGEN_STRONG_INLINE void operator()(std::complex<Scalar>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<Scalar>::vectorsize;
-    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
-    Index rir = ((PanelMode) ? (vectorSize*offset) : 0), rii;
-    Scalar* blockAt = reinterpret_cast<Scalar *>(blockA);
-    Index j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      rii = rir + vectorDelta;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet,4> blockr, blocki;
-        PacketBlock<PacketC,8> cblock;
-
-        if (UseLhs) {
-          bload<DataMapper, PacketC, Index, 2, 0, StorageOrder>(cblock, lhs, j, i);
-        } else {
-          bload<DataMapper, PacketC, Index, 2, 0, StorageOrder>(cblock, lhs, i, j);
-        }
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, p16uc_GETREAL32);
-        blockr.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[5].v, p16uc_GETREAL32);
-        blockr.packet[2] = vec_perm(cblock.packet[2].v, cblock.packet[6].v, p16uc_GETREAL32);
-        blockr.packet[3] = vec_perm(cblock.packet[3].v, cblock.packet[7].v, p16uc_GETREAL32);
-
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, p16uc_GETIMAG32);
-        blocki.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[5].v, p16uc_GETIMAG32);
-        blocki.packet[2] = vec_perm(cblock.packet[2].v, cblock.packet[6].v, p16uc_GETIMAG32);
-        blocki.packet[3] = vec_perm(cblock.packet[3].v, cblock.packet[7].v, p16uc_GETIMAG32);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-          blocki.packet[1] = -blocki.packet[1];
-          blocki.packet[2] = -blocki.packet[2];
-          blocki.packet[3] = -blocki.packet[3];
-        }
-
-        if(((StorageOrder == RowMajor) && UseLhs) || (((StorageOrder == ColMajor) && !UseLhs)))
-        {
-          ptranspose(blockr);
-          ptranspose(blocki);
-        }
-
-        storeBlock<Scalar, Packet, Index>(blockAt + rir, blockr);
-        storeBlock<Scalar, Packet, Index>(blockAt + rii, blocki);
-
-        rir += 4*vectorSize;
-        rii += 4*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        PacketBlock<Packet,1> blockr, blocki;
-        PacketBlock<PacketC,2> cblock;
-
-        if(((StorageOrder == ColMajor) && UseLhs) || (((StorageOrder == RowMajor) && !UseLhs)))
-        {
-          if (UseLhs) {
-            cblock.packet[0] = lhs.template loadPacket<PacketC>(j + 0, i);
-            cblock.packet[1] = lhs.template loadPacket<PacketC>(j + 2, i);
-          } else {
-            cblock.packet[0] = lhs.template loadPacket<PacketC>(i, j + 0);
-            cblock.packet[1] = lhs.template loadPacket<PacketC>(i, j + 2);
-          }
-        } else {
-          std::complex<Scalar> lhs0, lhs1;
-          if (UseLhs) {
-            lhs0 = lhs(j + 0, i);
-            lhs1 = lhs(j + 1, i);
-            cblock.packet[0] = pload2(&lhs0, &lhs1);
-            lhs0 = lhs(j + 2, i);
-            lhs1 = lhs(j + 3, i);
-            cblock.packet[1] = pload2(&lhs0, &lhs1);
-          } else {
-            lhs0 = lhs(i, j + 0);
-            lhs1 = lhs(i, j + 1);
-            cblock.packet[0] = pload2(&lhs0, &lhs1);
-            lhs0 = lhs(i, j + 2);
-            lhs1 = lhs(i, j + 3);
-            cblock.packet[1] = pload2(&lhs0, &lhs1);
-          }
-        }
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL32);
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG32);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-        }
-
-        pstore<Scalar>(blockAt + rir, blockr.packet[0]);
-        pstore<Scalar>(blockAt + rii, blocki.packet[0]);
-
-        rir += vectorSize;
-        rii += vectorSize;
-      }
-
-      rir += ((PanelMode) ? (vectorSize*(2*stride - depth)) : vectorDelta);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) rir += (offset*(rows - j - vectorSize));
-      rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          if (UseLhs) {
-            blockAt[rir] = lhs(k, i).real();
-
-            if(Conjugate)
-              blockAt[rii] = -lhs(k, i).imag();
-            else
-              blockAt[rii] =  lhs(k, i).imag();
-          } else {
-            blockAt[rir] = lhs(i, k).real();
-
-            if(Conjugate)
-              blockAt[rii] = -lhs(i, k).imag();
-            else
-              blockAt[rii] =  lhs(i, k).imag();
-          }
-
-          rir += 1;
-          rii += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for lhs & rhs packing.
-template<typename Scalar, typename Index, typename DataMapper, typename Packet, int StorageOrder, bool PanelMode, bool UseLhs>
-struct dhs_pack{
-  EIGEN_STRONG_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<Scalar>::vectorsize;
-    Index ri = 0, j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      if(PanelMode) ri += vectorSize*offset;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet,4> block;
-
-        if (UseLhs) {
-          bload<DataMapper, Packet, Index, 4, 0, StorageOrder>(block, lhs, j, i);
-        } else {
-          bload<DataMapper, Packet, Index, 4, 0, StorageOrder>(block, lhs, i, j);
-        }
-        if(((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs))
-        {
-          ptranspose(block);
-        }
-
-        storeBlock<Scalar, Packet, Index>(blockA + ri, block);
-
-        ri += 4*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        if(((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs))
-        {
-          if (UseLhs) {
-            blockA[ri+0] = lhs(j+0, i);
-            blockA[ri+1] = lhs(j+1, i);
-            blockA[ri+2] = lhs(j+2, i);
-            blockA[ri+3] = lhs(j+3, i);
-          } else {
-            blockA[ri+0] = lhs(i, j+0);
-            blockA[ri+1] = lhs(i, j+1);
-            blockA[ri+2] = lhs(i, j+2);
-            blockA[ri+3] = lhs(i, j+3);
-          }
-        } else {
-          Packet lhsV;
-          if (UseLhs) {
-            lhsV = lhs.template loadPacket<Packet>(j, i);
-          } else {
-            lhsV = lhs.template loadPacket<Packet>(i, j);
-          }
-          pstore<Scalar>(blockA + ri, lhsV);
-        }
-
-        ri += vectorSize;
-      }
-
-      if(PanelMode) ri += vectorSize*(stride - offset - depth);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) ri += offset*(rows - j);
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          if (UseLhs) {
-            blockA[ri] = lhs(k, i);
-          } else {
-            blockA[ri] = lhs(i, k);
-          }
-          ri += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for lhs packing, float64 specialization.
-template<typename Index, typename DataMapper, int StorageOrder, bool PanelMode>
-struct dhs_pack<double, Index, DataMapper, Packet2d, StorageOrder, PanelMode, true>
-{
-  EIGEN_STRONG_INLINE void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    Index ri = 0, j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      if(PanelMode) ri += vectorSize*offset;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet2d,2> block;
-        if(StorageOrder == RowMajor)
-        {
-          block.packet[0] = lhs.template loadPacket<Packet2d>(j + 0, i);
-          block.packet[1] = lhs.template loadPacket<Packet2d>(j + 1, i);
-
-          ptranspose(block);
-        } else {
-          block.packet[0] = lhs.template loadPacket<Packet2d>(j, i + 0);
-          block.packet[1] = lhs.template loadPacket<Packet2d>(j, i + 1);
-        }
-
-        storeBlock<double, Packet2d, Index>(blockA + ri, block);
-
-        ri += 2*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        if(StorageOrder == RowMajor)
-        {
-          blockA[ri+0] = lhs(j+0, i);
-          blockA[ri+1] = lhs(j+1, i);
-        } else {
-          Packet2d lhsV = lhs.template loadPacket<Packet2d>(j, i);
-          pstore<double>(blockA + ri, lhsV);
-        }
-
-        ri += vectorSize;
-      }
-
-      if(PanelMode) ri += vectorSize*(stride - offset - depth);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) ri += offset*(rows - j);
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          blockA[ri] = lhs(k, i);
-          ri += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for rhs packing, float64 specialization.
-template<typename Index, typename DataMapper, int StorageOrder, bool PanelMode>
-struct dhs_pack<double, Index, DataMapper, Packet2d, StorageOrder, PanelMode, false>
-{
-  EIGEN_STRONG_INLINE void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    Index ri = 0, j = 0;
-
-    for(; j + 2*vectorSize <= cols; j+=2*vectorSize)
-    {
-      Index i = 0;
-
-      if(PanelMode) ri += offset*(2*vectorSize);
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet2d,4> block;
-        if(StorageOrder == ColMajor)
-        {
-          PacketBlock<Packet2d,2> block1, block2;
-          block1.packet[0] = rhs.template loadPacket<Packet2d>(i, j + 0);
-          block1.packet[1] = rhs.template loadPacket<Packet2d>(i, j + 1);
-          block2.packet[0] = rhs.template loadPacket<Packet2d>(i, j + 2);
-          block2.packet[1] = rhs.template loadPacket<Packet2d>(i, j + 3);
-
-          ptranspose(block1);
-          ptranspose(block2);
-
-          pstore<double>(blockB + ri    , block1.packet[0]);
-          pstore<double>(blockB + ri + 2, block2.packet[0]);
-          pstore<double>(blockB + ri + 4, block1.packet[1]);
-          pstore<double>(blockB + ri + 6, block2.packet[1]);
-        } else {
-          block.packet[0] = rhs.template loadPacket<Packet2d>(i + 0, j + 0); //[a1 a2]
-          block.packet[1] = rhs.template loadPacket<Packet2d>(i + 0, j + 2); //[a3 a4]
-          block.packet[2] = rhs.template loadPacket<Packet2d>(i + 1, j + 0); //[b1 b2]
-          block.packet[3] = rhs.template loadPacket<Packet2d>(i + 1, j + 2); //[b3 b4]
-
-          storeBlock<double, Packet2d, Index>(blockB + ri, block);
-        }
-
-        ri += 4*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        if(StorageOrder == ColMajor)
-        {
-          blockB[ri+0] = rhs(i, j+0);
-          blockB[ri+1] = rhs(i, j+1);
-
-          ri += vectorSize;
-
-          blockB[ri+0] = rhs(i, j+2);
-          blockB[ri+1] = rhs(i, j+3);
-        } else {
-          Packet2d rhsV = rhs.template loadPacket<Packet2d>(i, j);
-          pstore<double>(blockB + ri, rhsV);
-
-          ri += vectorSize;
-
-          rhsV = rhs.template loadPacket<Packet2d>(i, j + 2);
-          pstore<double>(blockB + ri, rhsV);
-        }
-        ri += vectorSize;
-      }
-
-      if(PanelMode) ri += (2*vectorSize)*(stride - offset - depth);
-    }
-
-    if (j < cols)
-    {
-      if(PanelMode) ri += offset*(cols - j);
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < cols; k++)
-        {
-          blockB[ri] = rhs(i, k);
-          ri += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for lhs complex packing, float64 specialization.
-template<typename Index, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
-struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, true>
-{
-  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
-    Index rir = ((PanelMode) ? (vectorSize*offset) : 0), rii;
-    double* blockAt = reinterpret_cast<double *>(blockA);
-    Index j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      rii = rir + vectorDelta;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet,2> blockr, blocki;
-        PacketBlock<PacketC,4> cblock;
-
-        if(StorageOrder == ColMajor)
-        {
-          cblock.packet[0] = lhs.template loadPacket<PacketC>(j, i + 0); //[a1 a1i]
-          cblock.packet[1] = lhs.template loadPacket<PacketC>(j, i + 1); //[b1 b1i]
-
-          cblock.packet[2] = lhs.template loadPacket<PacketC>(j + 1, i + 0); //[a2 a2i]
-          cblock.packet[3] = lhs.template loadPacket<PacketC>(j + 1, i + 1); //[b2 b2i]
-
-          blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[2].v, p16uc_GETREAL64); //[a1 a2]
-          blockr.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[3].v, p16uc_GETREAL64); //[b1 b2]
-
-          blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[2].v, p16uc_GETIMAG64);
-          blocki.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[3].v, p16uc_GETIMAG64);
-        } else {
-          cblock.packet[0] = lhs.template loadPacket<PacketC>(j + 0, i); //[a1 a1i]
-          cblock.packet[1] = lhs.template loadPacket<PacketC>(j + 1, i); //[a2 a2i]
-
-          cblock.packet[2] = lhs.template loadPacket<PacketC>(j + 0, i + 1); //[b1 b1i]
-          cblock.packet[3] = lhs.template loadPacket<PacketC>(j + 1, i + 1); //[b2 b2i
-
-          blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL64); //[a1 a2]
-          blockr.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETREAL64); //[b1 b2]
-
-          blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG64);
-          blocki.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETIMAG64);
-        }
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-          blocki.packet[1] = -blocki.packet[1];
-        }
-
-        storeBlock<double, Packet, Index>(blockAt + rir, blockr);
-        storeBlock<double, Packet, Index>(blockAt + rii, blocki);
-
-        rir += 2*vectorSize;
-        rii += 2*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        PacketBlock<Packet,1> blockr, blocki;
-        PacketBlock<PacketC,2> cblock;
-
-        cblock.packet[0] = lhs.template loadPacket<PacketC>(j + 0, i);
-        cblock.packet[1] = lhs.template loadPacket<PacketC>(j + 1, i);
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL64);
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG64);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-        }
-
-        pstore<double>(blockAt + rir, blockr.packet[0]);
-        pstore<double>(blockAt + rii, blocki.packet[0]);
-
-        rir += vectorSize;
-        rii += vectorSize;
-      }
-
-      rir += ((PanelMode) ? (vectorSize*(2*stride - depth)) : vectorDelta);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) rir += (offset*(rows - j - vectorSize));
-      rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          blockAt[rir] = lhs(k, i).real();
-
-          if(Conjugate)
-            blockAt[rii] = -lhs(k, i).imag();
-          else
-            blockAt[rii] =  lhs(k, i).imag();
-
-          rir += 1;
-          rii += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for rhs complex packing, float64 specialization.
-template<typename Index, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
-struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, false>
-{
-  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    const Index vectorDelta = 2*vectorSize * ((PanelMode) ? stride : depth);
-    Index rir = ((PanelMode) ? (2*vectorSize*offset) : 0), rii;
-    double* blockBt = reinterpret_cast<double *>(blockB);
-    Index j = 0;
-
-    for(; j + 2*vectorSize <= cols; j+=2*vectorSize)
-    {
-      Index i = 0;
-
-      rii = rir + vectorDelta;
-
-      for(; i < depth; i++)
-      {
-        PacketBlock<PacketC,4> cblock;
-        PacketBlock<Packet,2> blockr, blocki;
-
-        bload<DataMapper, PacketC, Index, 2, 0, ColMajor>(cblock, rhs, i, j);
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL64);
-        blockr.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETREAL64);
-
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG64);
-        blocki.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETIMAG64);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-          blocki.packet[1] = -blocki.packet[1];
-        }
-
-        storeBlock<double, Packet, Index>(blockBt + rir, blockr);
-        storeBlock<double, Packet, Index>(blockBt + rii, blocki);
-
-        rir += 2*vectorSize;
-        rii += 2*vectorSize;
-      }
-
-      rir += ((PanelMode) ? (2*vectorSize*(2*stride - depth)) : vectorDelta);
-    }
-
-    if (j < cols)
-    {
-      if(PanelMode) rir += (offset*(cols - j - 2*vectorSize));
-      rii = rir + (((PanelMode) ? stride : depth) * (cols - j));
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < cols; k++)
-        {
-          blockBt[rir] = rhs(i, k).real();
-
-          if(Conjugate)
-            blockBt[rii] = -rhs(i, k).imag();
-          else
-            blockBt[rii] =  rhs(i, k).imag();
-
-          rir += 1;
-          rii += 1;
-        }
-      }
-    }
-  }
-};
-
-/**************
- * GEMM utils *
- **************/
-
-// 512-bits rank1-update of acc. It can either positive or negative accumulate (useful for complex gemm).
-template<typename Packet, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,4>* acc, const Packet& lhsV, const Packet* rhsV)
-{
-  if(NegativeAccumulate)
-  {
-    acc->packet[0] = vec_nmsub(lhsV, rhsV[0], acc->packet[0]);
-    acc->packet[1] = vec_nmsub(lhsV, rhsV[1], acc->packet[1]);
-    acc->packet[2] = vec_nmsub(lhsV, rhsV[2], acc->packet[2]);
-    acc->packet[3] = vec_nmsub(lhsV, rhsV[3], acc->packet[3]);
-  } else {
-    acc->packet[0] = vec_madd(lhsV, rhsV[0], acc->packet[0]);
-    acc->packet[1] = vec_madd(lhsV, rhsV[1], acc->packet[1]);
-    acc->packet[2] = vec_madd(lhsV, rhsV[2], acc->packet[2]);
-    acc->packet[3] = vec_madd(lhsV, rhsV[3], acc->packet[3]);
-  }
-}
-
-template<typename Packet, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,1>* acc, const Packet& lhsV, const Packet* rhsV)
-{
-  if(NegativeAccumulate)
-  {
-    acc->packet[0] = vec_nmsub(lhsV, rhsV[0], acc->packet[0]);
-  } else {
-    acc->packet[0] = vec_madd(lhsV, rhsV[0], acc->packet[0]);
-  }
-}
-
-template<int N, typename Scalar, typename Packet, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, const Packet* rhsV)
-{
-  Packet lhsV = pload<Packet>(lhs);
-
-  pger_common<Packet, NegativeAccumulate>(acc, lhsV, rhsV);
-}
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs, Packet &lhsV, Index remaining_rows)
-{
-#ifdef _ARCH_PWR9
-  lhsV = vec_xl_len((Scalar *)lhs, remaining_rows * sizeof(Scalar));
-#else
-  Index i = 0;
-  do {
-    lhsV[i] = lhs[i];
-  } while (++i < remaining_rows);
-#endif
-}
-
-template<int N, typename Scalar, typename Packet, typename Index, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, const Packet* rhsV, Index remaining_rows)
-{
-  Packet lhsV;
-  loadPacketRemaining<Scalar, Packet, Index>(lhs, lhsV, remaining_rows);
-
-  pger_common<Packet, NegativeAccumulate>(acc, lhsV, rhsV);
-}
-
-// 512-bits rank1-update of complex acc. It takes decoupled accumulators as entries. It also takes cares of mixed types real * complex and complex * real.
-template<int N, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgerc_common(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Packet &lhsV, const Packet &lhsVi, const Packet* rhsV, const Packet* rhsVi)
-{
-  pger_common<Packet, false>(accReal, lhsV, rhsV);
-  if(LhsIsReal)
-  {
-    pger_common<Packet, ConjugateRhs>(accImag, lhsV, rhsVi);
-    EIGEN_UNUSED_VARIABLE(lhsVi);
-  } else {
-    if (!RhsIsReal) {
-      pger_common<Packet, ConjugateLhs == ConjugateRhs>(accReal, lhsVi, rhsVi);
-      pger_common<Packet, ConjugateRhs>(accImag, lhsV, rhsVi);
-    } else {
-      EIGEN_UNUSED_VARIABLE(rhsVi);
-    }
-    pger_common<Packet, ConjugateLhs>(accImag, lhsVi, rhsV);
-  }
-}
-
-template<int N, typename Scalar, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi)
-{
-  Packet lhsV = ploadLhs<Scalar, Packet>(lhs_ptr);
-  Packet lhsVi;
-  if(!LhsIsReal) lhsVi = ploadLhs<Scalar, Packet>(lhs_ptr_imag);
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-
-  pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
-}
-
-template<typename Scalar, typename Packet, typename Index, bool LhsIsReal>
-EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, Packet &lhsV, Packet &lhsVi, Index remaining_rows)
-{
-#ifdef _ARCH_PWR9
-  lhsV = vec_xl_len((Scalar *)lhs_ptr, remaining_rows * sizeof(Scalar));
-  if(!LhsIsReal) lhsVi = vec_xl_len((Scalar *)lhs_ptr_imag, remaining_rows * sizeof(Scalar));
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-#else
-  Index i = 0;
-  do {
-    lhsV[i] = lhs_ptr[i];
-    if(!LhsIsReal) lhsVi[i] = lhs_ptr_imag[i];
-  } while (++i < remaining_rows);
-  if(LhsIsReal) EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-#endif
-}
-
-template<int N, typename Scalar, typename Packet, typename Index, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi, Index remaining_rows)
-{
-  Packet lhsV, lhsVi;
-  loadPacketRemaining<Scalar, Packet, Index, LhsIsReal>(lhs_ptr, lhs_ptr_imag, lhsV, lhsVi, remaining_rows);
-
-  pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
-}
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadLhs(const Scalar* lhs)
-{
-  return ploadu<Packet>(lhs);
-}
-
-// Zero the accumulator on PacketBlock.
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,4>& acc)
-{
-  acc.packet[0] = pset1<Packet>((Scalar)0);
-  acc.packet[1] = pset1<Packet>((Scalar)0);
-  acc.packet[2] = pset1<Packet>((Scalar)0);
-  acc.packet[3] = pset1<Packet>((Scalar)0);
-}
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,1>& acc)
-{
-  acc.packet[0] = pset1<Packet>((Scalar)0);
-}
-
-// Scale the PacketBlock vectors by alpha.
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmadd(pAlpha, accZ.packet[0], acc.packet[0]);
-  acc.packet[1] = pmadd(pAlpha, accZ.packet[1], acc.packet[1]);
-  acc.packet[2] = pmadd(pAlpha, accZ.packet[2], acc.packet[2]);
-  acc.packet[3] = pmadd(pAlpha, accZ.packet[3], acc.packet[3]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,1>& acc, PacketBlock<Packet,1>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmadd(pAlpha, accZ.packet[0], acc.packet[0]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmul<Packet>(accZ.packet[0], pAlpha);
-  acc.packet[1] = pmul<Packet>(accZ.packet[1], pAlpha);
-  acc.packet[2] = pmul<Packet>(accZ.packet[2], pAlpha);
-  acc.packet[3] = pmul<Packet>(accZ.packet[3], pAlpha);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,1>& acc, PacketBlock<Packet,1>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmul<Packet>(accZ.packet[0], pAlpha);
-}
-
-// Complex version of PacketBlock scaling.
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag)
-{
-  bscalec_common<Packet>(cReal, aReal, bReal);
-
-  bscalec_common<Packet>(cImag, aImag, bReal);
-
-  pger_common<Packet, true>(&cReal, bImag, aImag.packet);
-
-  pger_common<Packet, false>(&cImag, bImag, aReal.packet);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void band(PacketBlock<Packet,4>& acc, const Packet& pMask)
-{
-  acc.packet[0] = pand(acc.packet[0], pMask);
-  acc.packet[1] = pand(acc.packet[1], pMask);
-  acc.packet[2] = pand(acc.packet[2], pMask);
-  acc.packet[3] = pand(acc.packet[3], pMask);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,4>& aReal, PacketBlock<Packet,4>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,4>& cReal, PacketBlock<Packet,4>& cImag, const Packet& pMask)
-{
-  band<Packet>(aReal, pMask);
-  band<Packet>(aImag, pMask);
-
-  bscalec<Packet,4>(aReal, aImag, bReal, bImag, cReal, cImag);
-}
-
-// Load a PacketBlock, the N parameters make tunning gemm easier so we can add more accumulators as needed.
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,4>& acc, const DataMapper& res, Index row, Index col)
-{
-  if (StorageOrder == RowMajor) {
-    acc.packet[0] = res.template loadPacket<Packet>(row + 0, col + N*accCols);
-    acc.packet[1] = res.template loadPacket<Packet>(row + 1, col + N*accCols);
-    acc.packet[2] = res.template loadPacket<Packet>(row + 2, col + N*accCols);
-    acc.packet[3] = res.template loadPacket<Packet>(row + 3, col + N*accCols);
-  } else {
-    acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
-    acc.packet[1] = res.template loadPacket<Packet>(row + N*accCols, col + 1);
-    acc.packet[2] = res.template loadPacket<Packet>(row + N*accCols, col + 2);
-    acc.packet[3] = res.template loadPacket<Packet>(row + N*accCols, col + 3);
-  }
-}
-
-// An overload of bload when you have a PacketBLock with 8 vectors.
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,8>& acc, const DataMapper& res, Index row, Index col)
-{
-  if (StorageOrder == RowMajor) {
-    acc.packet[0] = res.template loadPacket<Packet>(row + 0, col + N*accCols);
-    acc.packet[1] = res.template loadPacket<Packet>(row + 1, col + N*accCols);
-    acc.packet[2] = res.template loadPacket<Packet>(row + 2, col + N*accCols);
-    acc.packet[3] = res.template loadPacket<Packet>(row + 3, col + N*accCols);
-    acc.packet[4] = res.template loadPacket<Packet>(row + 0, col + (N+1)*accCols);
-    acc.packet[5] = res.template loadPacket<Packet>(row + 1, col + (N+1)*accCols);
-    acc.packet[6] = res.template loadPacket<Packet>(row + 2, col + (N+1)*accCols);
-    acc.packet[7] = res.template loadPacket<Packet>(row + 3, col + (N+1)*accCols);
-  } else {
-    acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
-    acc.packet[1] = res.template loadPacket<Packet>(row + N*accCols, col + 1);
-    acc.packet[2] = res.template loadPacket<Packet>(row + N*accCols, col + 2);
-    acc.packet[3] = res.template loadPacket<Packet>(row + N*accCols, col + 3);
-    acc.packet[4] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 0);
-    acc.packet[5] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 1);
-    acc.packet[6] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 2);
-    acc.packet[7] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 3);
-  }
-}
-
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,2>& acc, const DataMapper& res, Index row, Index col)
-{
-  acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
-  acc.packet[1] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 0);
-}
-
-const static Packet4i mask41 = { -1,  0,  0,  0 };
-const static Packet4i mask42 = { -1, -1,  0,  0 };
-const static Packet4i mask43 = { -1, -1, -1,  0 };
-
-const static Packet2l mask21 = { -1, 0 };
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE Packet bmask(const int remaining_rows)
-{
-  if (remaining_rows == 0) {
-    return pset1<Packet>(float(0.0));  // Not used
-  } else {
-    switch (remaining_rows) {
-      case 1:  return Packet(mask41);
-      case 2:  return Packet(mask42);
-      default: return Packet(mask43);
-    }
-  }
-}
-
-template<>
-EIGEN_ALWAYS_INLINE Packet2d bmask<Packet2d>(const int remaining_rows)
-{
-  if (remaining_rows == 0) {
-    return pset1<Packet2d>(double(0.0));  // Not used
-  } else {
-    return Packet2d(mask21);
-  }
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha, const Packet& pMask)
-{
-  band<Packet>(accZ, pMask);
-
-  bscale<Packet>(acc, accZ, pAlpha);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void pbroadcast4_old(const __UNPACK_TYPE__(Packet)* a, Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  pbroadcast4<Packet>(a, a0, a1, a2, a3);
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void pbroadcast4_old<Packet2d>(const double* a, Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-  a1 = pload<Packet2d>(a);
-  a3 = pload<Packet2d>(a + 2);
-  a0 = vec_splat(a1, 0);
-  a1 = vec_splat(a1, 1);
-  a2 = vec_splat(a3, 0);
-  a3 = vec_splat(a3, 1);
-}
-
-// PEEL loop factor.
-#define PEEL 7
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void MICRO_EXTRA_COL(
-  const Scalar* &lhs_ptr,
-  const Scalar* &rhs_ptr,
-  PacketBlock<Packet,1> &accZero,
-  Index remaining_rows,
-  Index remaining_cols)
-{
-  Packet rhsV[1];
-  rhsV[0] = pset1<Packet>(rhs_ptr[0]);
-  pger<1,Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
-  lhs_ptr += remaining_rows;
-  rhs_ptr += remaining_cols;
-}
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows>
-EIGEN_STRONG_INLINE void gemm_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr = lhs_base + row*strideA + remaining_rows*offsetA;
-  PacketBlock<Packet,1> accZero;
-
-  bsetzero<Scalar, Packet>(accZero);
-
-  Index remaining_depth = (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL <= remaining_depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    EIGEN_POWER_PREFETCH(lhs_ptr);
-    for (int l = 0; l < PEEL; l++) {
-      MICRO_EXTRA_COL<Scalar, Packet, Index>(lhs_ptr, rhs_ptr, accZero, remaining_rows, remaining_cols);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_EXTRA_COL<Scalar, Packet, Index>(lhs_ptr, rhs_ptr, accZero, remaining_rows, remaining_cols);
-  }
-  for(; k < depth; k++)
-  {
-    Packet rhsV[1];
-    rhsV[0] = pset1<Packet>(rhs_ptr[0]);
-    pger<1, Scalar, Packet, Index, false>(&accZero, lhs_ptr, rhsV, remaining_rows);
-    lhs_ptr += remaining_rows;
-    rhs_ptr += remaining_cols;
-  }
-
-  accZero.packet[0] = vec_mul(pAlpha, accZero.packet[0]);
-  for(Index i = 0; i < remaining_rows; i++) {
-    res(row + i, col) += accZero.packet[0][i];
-  }
-}
-
-template<typename Scalar, typename Packet, typename Index, const Index accRows>
-EIGEN_ALWAYS_INLINE void MICRO_EXTRA_ROW(
-  const Scalar* &lhs_ptr,
-  const Scalar* &rhs_ptr,
-  PacketBlock<Packet,4> &accZero,
-  Index remaining_rows)
-{
-  Packet rhsV[4];
-  pbroadcast4<Packet>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-  pger<4, Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
-  lhs_ptr += remaining_rows;
-  rhs_ptr += accRows;
-}
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr = lhs_base + row*strideA + remaining_rows*offsetA;
-  PacketBlock<Packet,4> accZero, acc;
-
-  bsetzero<Scalar, Packet>(accZero);
-
-  Index remaining_depth = (col + accRows < cols) ? depth : (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL <= remaining_depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    EIGEN_POWER_PREFETCH(lhs_ptr);
-    for (int l = 0; l < PEEL; l++) {
-      MICRO_EXTRA_ROW<Scalar, Packet, Index, accRows>(lhs_ptr, rhs_ptr, accZero, remaining_rows);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_EXTRA_ROW<Scalar, Packet, Index, accRows>(lhs_ptr, rhs_ptr, accZero, remaining_rows);
-  }
-
-  if ((remaining_depth == depth) && (rows >= accCols))
-  {
-    for(Index j = 0; j < 4; j++) {
-      acc.packet[j] = res.template loadPacket<Packet>(row, col + j);
-    }
-    bscale<Packet>(acc, accZero, pAlpha, pMask);
-    res.template storePacketBlock<Packet,4>(row, col, acc);
-  } else {
-    for(; k < depth; k++)
-    {
-      Packet rhsV[4];
-      pbroadcast4<Packet>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-      pger<4, Scalar, Packet, Index, false>(&accZero, lhs_ptr, rhsV, remaining_rows);
-      lhs_ptr += remaining_rows;
-      rhs_ptr += accRows;
-    }
-
-    for(Index j = 0; j < 4; j++) {
-      accZero.packet[j] = vec_mul(pAlpha, accZero.packet[j]);
-    }
-    for(Index j = 0; j < 4; j++) {
-      for(Index i = 0; i < remaining_rows; i++) {
-        res(row + i, col + j) += accZero.packet[j][i];
-      }
-    }
-  }
-}
-
-#define MICRO_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
-
-#define MICRO_UNROLL_WORK(func, func2, peel) \
-    MICRO_UNROLL(func2); \
-    func(0,peel) func(1,peel) func(2,peel) func(3,peel) \
-    func(4,peel) func(5,peel) func(6,peel) func(7,peel)
-
-#define MICRO_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr##iter); \
-    lhs_ptr##iter += accCols; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-  }
-
-#define MICRO_WORK_ONE(iter, peel) \
-  if (unroll_factor > iter) { \
-    pger_common<Packet, false>(&accZero##iter, lhsV##iter, rhsV##peel); \
-  }
-
-#define MICRO_TYPE_PEEL4(func, func2, peel) \
-  if (PEEL > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
-    pbroadcast4<Packet>(rhs_ptr + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
-    MICRO_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-  }
-
-#define MICRO_TYPE_PEEL1(func, func2, peel) \
-  if (PEEL > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
-    rhsV##peel[0] = pset1<Packet>(rhs_ptr[remaining_cols * peel]); \
-    MICRO_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-  }
-
-#define MICRO_UNROLL_TYPE_PEEL(M, func, func1, func2) \
-  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M], rhsV8[M], rhsV9[M]; \
-  func(func1,func2,0); func(func1,func2,1); \
-  func(func1,func2,2); func(func1,func2,3); \
-  func(func1,func2,4); func(func1,func2,5); \
-  func(func1,func2,6); func(func1,func2,7); \
-  func(func1,func2,8); func(func1,func2,9);
-
-#define MICRO_UNROLL_TYPE_ONE(M, func, func1, func2) \
-  Packet rhsV0[M]; \
-  func(func1,func2,0);
-
-#define MICRO_ONE_PEEL4 \
-  MICRO_UNROLL_TYPE_PEEL(4, MICRO_TYPE_PEEL4, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += (accRows * PEEL);
-
-#define MICRO_ONE4 \
-  MICRO_UNROLL_TYPE_ONE(4, MICRO_TYPE_PEEL4, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += accRows;
-
-#define MICRO_ONE_PEEL1 \
-  MICRO_UNROLL_TYPE_PEEL(1, MICRO_TYPE_PEEL1, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += (remaining_cols * PEEL);
-
-#define MICRO_ONE1 \
-  MICRO_UNROLL_TYPE_ONE(1, MICRO_TYPE_PEEL1, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += remaining_cols;
-
-#define MICRO_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzero<Scalar, Packet>(accZero##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accZero##iter); \
-  }
-
-#define MICRO_DST_PTR MICRO_UNROLL(MICRO_DST_PTR_ONE)
-
-#define MICRO_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols + accCols*offsetA; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr##iter); \
-  }
-
-#define MICRO_SRC_PTR MICRO_UNROLL(MICRO_SRC_PTR_ONE)
-
-#define MICRO_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr##iter); \
-  }
-
-#define MICRO_PREFETCH MICRO_UNROLL(MICRO_PREFETCH_ONE)
-
-#define MICRO_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    acc.packet[0] = res.template loadPacket<Packet>(row + iter*accCols, col + 0); \
-    acc.packet[1] = res.template loadPacket<Packet>(row + iter*accCols, col + 1); \
-    acc.packet[2] = res.template loadPacket<Packet>(row + iter*accCols, col + 2); \
-    acc.packet[3] = res.template loadPacket<Packet>(row + iter*accCols, col + 3); \
-    bscale<Packet>(acc, accZero##iter, pAlpha); \
-    res.template storePacketBlock<Packet,4>(row + iter*accCols, col, acc); \
-  }
-
-#define MICRO_STORE MICRO_UNROLL(MICRO_STORE_ONE)
-
-#define MICRO_COL_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    acc.packet[0] = res.template loadPacket<Packet>(row + iter*accCols, col + 0); \
-    bscale<Packet>(acc, accZero##iter, pAlpha); \
-    res.template storePacketBlock<Packet,1>(row + iter*accCols, col, acc); \
-  }
-
-#define MICRO_COL_STORE MICRO_UNROLL(MICRO_COL_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index col,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr0 = NULL, *  lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, * lhs_ptr7 = NULL;
-  PacketBlock<Packet,4> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
-  PacketBlock<Packet,4> acc;
-
-  MICRO_SRC_PTR
-  MICRO_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL <= depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    MICRO_PREFETCH
-    MICRO_ONE_PEEL4
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_ONE4
-  }
-  MICRO_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_col_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr0 = NULL, * lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, *lhs_ptr7 = NULL;
-  PacketBlock<Packet,1> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
-  PacketBlock<Packet,1> acc;
-
-  MICRO_SRC_PTR
-  MICRO_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL <= depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    MICRO_PREFETCH
-    MICRO_ONE_PEEL1
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_ONE1
-  }
-  MICRO_COL_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlpha)
-{
-#define MAX_UNROLL 6
-  while(row + MAX_UNROLL*accCols <= rows) {
-    gemm_unrolled_col_iteration<MAX_UNROLL, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-  }
-  switch( (rows-row)/accCols ) {
-#if MAX_UNROLL > 7
-    case 7:
-      gemm_unrolled_col_iteration<7, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 6
-    case 6:
-      gemm_unrolled_col_iteration<6, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 5
-   case 5:
-      gemm_unrolled_col_iteration<5, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 4
-   case 4:
-      gemm_unrolled_col_iteration<4, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 3
-   case 3:
-     gemm_unrolled_col_iteration<3, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-     break;
-#endif
-#if MAX_UNROLL > 2
-   case 2:
-     gemm_unrolled_col_iteration<2, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-     break;
-#endif
-#if MAX_UNROLL > 1
-   case 1:
-     gemm_unrolled_col_iteration<1, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-     break;
-#endif
-   default:
-     break;
-  }
-#undef MAX_UNROLL
-}
-
-/****************
- * GEMM kernels *
- * **************/
-template<typename Scalar, typename Index, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlpha = pset1<Packet>(alpha);
-      const Packet pMask  = bmask<Packet>((const int)(remaining_rows));
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-        Index row = 0;
-
-#define MAX_UNROLL 6
-        while(row + MAX_UNROLL*accCols <= rows) {
-          gemm_unrolled_iteration<MAX_UNROLL, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_UNROLL > 7
-          case 7:
-            gemm_unrolled_iteration<7, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 6
-          case 6:
-            gemm_unrolled_iteration<6, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 5
-          case 5:
-            gemm_unrolled_iteration<5, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 4
-          case 4:
-            gemm_unrolled_iteration<4, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 3
-          case 3:
-            gemm_unrolled_iteration<3, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 2
-          case 2:
-            gemm_unrolled_iteration<2, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 1
-          case 1:
-            gemm_unrolled_iteration<1, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
-        }
-    }
-
-    if(remaining_cols > 0)
-    {
-      const Scalar* rhs_base = blockB + col*strideB + remaining_cols*offsetB;
-      const Scalar* lhs_base = blockA;
-
-      for(; col < cols; col++)
-      {
-        Index row = 0;
-
-        gemm_unrolled_col<Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, rows, col, remaining_cols, pAlpha);
-
-        if (remaining_rows > 0)
-        {
-          gemm_extra_col<Scalar, Packet, DataMapper, Index, accRows>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_rows, remaining_cols, pAlpha);
-        }
-        rhs_base++;
-      }
-    }
-}
-
-#define accColsC (accCols / 2)
-#define advanceRows ((LhsIsReal) ? 1 : 2)
-#define advanceCols ((RhsIsReal) ? 1 : 2)
-
-// PEEL_COMPLEX loop factor.
-#define PEEL_COMPLEX 3
-
-template<typename Scalar, typename Packet, typename Index, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_COL(
-  const Scalar* &lhs_ptr_real, const Scalar* &lhs_ptr_imag,
-  const Scalar* &rhs_ptr_real, const Scalar* &rhs_ptr_imag,
-  PacketBlock<Packet,1> &accReal, PacketBlock<Packet,1> &accImag,
-  Index remaining_rows,
-  Index remaining_cols)
-{
-  Packet rhsV[1], rhsVi[1];
-  rhsV[0] = pset1<Packet>(rhs_ptr_real[0]);
-  if(!RhsIsReal) rhsVi[0] = pset1<Packet>(rhs_ptr_imag[0]);
-  pgerc<1, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
-  lhs_ptr_real += remaining_rows;
-  if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  rhs_ptr_real += remaining_cols;
-  if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-}
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) rhs_ptr_imag = rhs_base + remaining_cols*strideB;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  const Scalar* lhs_ptr_real = lhs_base + advanceRows*row*strideA + remaining_rows*offsetA;
-  const Scalar* lhs_ptr_imag;
-  if(!LhsIsReal) lhs_ptr_imag = lhs_ptr_real + remaining_rows*strideA;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  PacketBlock<Packet,1> accReal, accImag;
-  PacketBlock<Packet,1> taccReal, taccImag;
-  PacketBlock<Packetc,1> acc0, acc1;
-
-  bsetzero<Scalar, Packet>(accReal);
-  bsetzero<Scalar, Packet>(accImag);
-
-  Index remaining_depth = (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= remaining_depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    EIGEN_POWER_PREFETCH(lhs_ptr_real);
-    if(!LhsIsReal) {
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag);
-    }
-    for (int l = 0; l < PEEL_COMPLEX; l++) {
-      MICRO_COMPLEX_EXTRA_COL<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows, remaining_cols);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_COMPLEX_EXTRA_COL<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows, remaining_cols);
-  }
-
-  for(; k < depth; k++)
-  {
-    Packet rhsV[1], rhsVi[1];
-    rhsV[0] = pset1<Packet>(rhs_ptr_real[0]);
-    if(!RhsIsReal) rhsVi[0] = pset1<Packet>(rhs_ptr_imag[0]);
-    pgerc<1, Scalar, Packet, Index, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi, remaining_rows);
-    lhs_ptr_real += remaining_rows;
-    if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-    rhs_ptr_real += remaining_cols;
-    if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
-  }
-
-  bscalec<Packet,1>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag);
-  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc0, acc1);
-
-  if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1))
-  {
-    res(row + 0, col + 0) += pfirst<Packetc>(acc0.packet[0]);
-  } else {
-    acc0.packet[0] += res.template loadPacket<Packetc>(row + 0, col + 0);
-    res.template storePacketBlock<Packetc,1>(row + 0, col + 0, acc0);
-    if(remaining_rows > accColsC) {
-      res(row + accColsC, col + 0) += pfirst<Packetc>(acc1.packet[0]);
-    }
-  }
-}
-
-template<typename Scalar, typename Packet, typename Index, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_ROW(
-  const Scalar* &lhs_ptr_real, const Scalar* &lhs_ptr_imag,
-  const Scalar* &rhs_ptr_real, const Scalar* &rhs_ptr_imag,
-  PacketBlock<Packet,4> &accReal, PacketBlock<Packet,4> &accImag,
-  Index remaining_rows)
-{
-  Packet rhsV[4], rhsVi[4];
-  pbroadcast4_old<Packet>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-  if(!RhsIsReal) pbroadcast4_old<Packet>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
-  pgerc<4, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
-  lhs_ptr_real += remaining_rows;
-  if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  rhs_ptr_real += accRows;
-  if(!RhsIsReal) rhs_ptr_imag += accRows;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-}
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) rhs_ptr_imag = rhs_base + accRows*strideB;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  const Scalar* lhs_ptr_real = lhs_base + advanceRows*row*strideA + remaining_rows*offsetA;
-  const Scalar* lhs_ptr_imag;
-  if(!LhsIsReal) lhs_ptr_imag = lhs_ptr_real + remaining_rows*strideA;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  PacketBlock<Packet,4> accReal, accImag;
-  PacketBlock<Packet,4> taccReal, taccImag;
-  PacketBlock<Packetc,4> acc0, acc1;
-  PacketBlock<Packetc,8> tRes;
-
-  bsetzero<Scalar, Packet>(accReal);
-  bsetzero<Scalar, Packet>(accImag);
-
-  Index remaining_depth = (col + accRows < cols) ? depth : (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= remaining_depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    EIGEN_POWER_PREFETCH(lhs_ptr_real);
-    if(!LhsIsReal) {
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag);
-    }
-    for (int l = 0; l < PEEL_COMPLEX; l++) {
-      MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows);
-  }
-
-  if ((remaining_depth == depth) && (rows >= accCols))
-  {
-    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row, col);
-    bscalec<Packet>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
-    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1);
-    res.template storePacketBlock<Packetc,4>(row + 0, col, acc0);
-    res.template storePacketBlock<Packetc,4>(row + accColsC, col, acc1);
-  } else {
-    for(; k < depth; k++)
-    {
-      Packet rhsV[4], rhsVi[4];
-      pbroadcast4_old<Packet>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-      if(!RhsIsReal) pbroadcast4_old<Packet>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
-      pgerc<4, Scalar, Packet, Index, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi, remaining_rows);
-      lhs_ptr_real += remaining_rows;
-      if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-      rhs_ptr_real += accRows;
-      if(!RhsIsReal) rhs_ptr_imag += accRows;
-    }
-
-    bscalec<Packet,4>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag);
-    bcouple_common<Packet, Packetc>(taccReal, taccImag, acc0, acc1);
-
-    if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1))
-    {
-      for(Index j = 0; j < 4; j++) {
-        res(row + 0, col + j) += pfirst<Packetc>(acc0.packet[j]);
-      }
-    } else {
-      for(Index j = 0; j < 4; j++) {
-        PacketBlock<Packetc,1> acc2;
-        acc2.packet[0] = res.template loadPacket<Packetc>(row + 0, col + j) + acc0.packet[j];
-        res.template storePacketBlock<Packetc,1>(row + 0, col + j, acc2);
-        if(remaining_rows > accColsC) {
-          res(row + accColsC, col + j) += pfirst<Packetc>(acc1.packet[j]);
-        }
-      }
-    }
-  }
-}
-
-#define MICRO_COMPLEX_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4)
-
-#define MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
-    MICRO_COMPLEX_UNROLL(func2); \
-    func(0,peel) func(1,peel) func(2,peel) func(3,peel) func(4,peel)
-
-#define MICRO_COMPLEX_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter); \
-    lhs_ptr_real##iter += accCols; \
-    if(!LhsIsReal) { \
-      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_imag##iter); \
-      lhs_ptr_imag##iter += accCols; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-    EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-  }
-
-#define MICRO_COMPLEX_WORK_ONE4(iter, peel) \
-  if (unroll_factor > iter) { \
-    pgerc_common<4, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_WORK_ONE1(iter, peel) \
-  if (unroll_factor > iter) { \
-    pgerc_common<1, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_TYPE_PEEL4(func, func2, peel) \
-  if (PEEL_COMPLEX > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
-    pbroadcast4_old<Packet>(rhs_ptr_real + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
-    if(!RhsIsReal) { \
-      pbroadcast4_old<Packet>(rhs_ptr_imag + (accRows * peel), rhsVi##peel[0], rhsVi##peel[1], rhsVi##peel[2], rhsVi##peel[3]); \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-    } \
-    MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_TYPE_PEEL1(func, func2, peel) \
-  if (PEEL_COMPLEX > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
-    rhsV##peel[0] = pset1<Packet>(rhs_ptr_real[remaining_cols * peel]); \
-    if(!RhsIsReal) { \
-      rhsVi##peel[0] = pset1<Packet>(rhs_ptr_imag[remaining_cols * peel]); \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-    } \
-    MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_UNROLL_TYPE_PEEL(M, func, func1, func2) \
-  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M], rhsV8[M], rhsV9[M]; \
-  Packet rhsVi0[M], rhsVi1[M], rhsVi2[M], rhsVi3[M], rhsVi4[M], rhsVi5[M], rhsVi6[M], rhsVi7[M], rhsVi8[M], rhsVi9[M]; \
-  func(func1,func2,0); func(func1,func2,1); \
-  func(func1,func2,2); func(func1,func2,3); \
-  func(func1,func2,4); func(func1,func2,5); \
-  func(func1,func2,6); func(func1,func2,7); \
-  func(func1,func2,8); func(func1,func2,9);
-
-#define MICRO_COMPLEX_UNROLL_TYPE_ONE(M, func, func1, func2) \
-  Packet rhsV0[M], rhsVi0[M];\
-  func(func1,func2,0);
-
-#define MICRO_COMPLEX_ONE_PEEL4 \
-  MICRO_COMPLEX_UNROLL_TYPE_PEEL(4, MICRO_COMPLEX_TYPE_PEEL4, MICRO_COMPLEX_WORK_ONE4, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += (accRows * PEEL_COMPLEX); \
-  if(!RhsIsReal) rhs_ptr_imag += (accRows * PEEL_COMPLEX);
-
-#define MICRO_COMPLEX_ONE4 \
-  MICRO_COMPLEX_UNROLL_TYPE_ONE(4, MICRO_COMPLEX_TYPE_PEEL4, MICRO_COMPLEX_WORK_ONE4, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += accRows; \
-  if(!RhsIsReal) rhs_ptr_imag += accRows;
-
-#define MICRO_COMPLEX_ONE_PEEL1 \
-  MICRO_COMPLEX_UNROLL_TYPE_PEEL(1, MICRO_COMPLEX_TYPE_PEEL1, MICRO_COMPLEX_WORK_ONE1, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += (remaining_cols * PEEL_COMPLEX); \
-  if(!RhsIsReal) rhs_ptr_imag += (remaining_cols * PEEL_COMPLEX);
-
-#define MICRO_COMPLEX_ONE1 \
-  MICRO_COMPLEX_UNROLL_TYPE_ONE(1, MICRO_COMPLEX_TYPE_PEEL1, MICRO_COMPLEX_WORK_ONE1, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += remaining_cols; \
-  if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
-
-#define MICRO_COMPLEX_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzero<Scalar, Packet>(accReal##iter); \
-    bsetzero<Scalar, Packet>(accImag##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accReal##iter); \
-    EIGEN_UNUSED_VARIABLE(accImag##iter); \
-  }
-
-#define MICRO_COMPLEX_DST_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_DST_PTR_ONE)
-
-#define MICRO_COMPLEX_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols + accCols*offsetA; \
-    if(!LhsIsReal) { \
-      lhs_ptr_imag##iter = lhs_ptr_real##iter + accCols*strideA; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_real##iter); \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-  }
-
-#define MICRO_COMPLEX_SRC_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_SRC_PTR_ONE)
-
-#define MICRO_COMPLEX_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr_real##iter); \
-    if(!LhsIsReal) { \
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag##iter); \
-    } \
-  }
-
-#define MICRO_COMPLEX_PREFETCH MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_PREFETCH_ONE)
-
-#define MICRO_COMPLEX_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row + iter*accCols, col); \
-    bscalec<Packet,4>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag); \
-    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1); \
-    res.template storePacketBlock<Packetc,4>(row + iter*accCols + 0, col, acc0); \
-    res.template storePacketBlock<Packetc,4>(row + iter*accCols + accColsC, col, acc1); \
-  }
-
-#define MICRO_COMPLEX_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_STORE_ONE)
-
-#define MICRO_COMPLEX_COL_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row + iter*accCols, col); \
-    bscalec<Packet,1>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag); \
-    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1); \
-    res.template storePacketBlock<Packetc,1>(row + iter*accCols + 0, col, acc0); \
-    res.template storePacketBlock<Packetc,1>(row + iter*accCols + accColsC, col, acc1); \
-  }
-
-#define MICRO_COMPLEX_COL_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_COL_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index col,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) {
-    rhs_ptr_imag = rhs_base + accRows*strideB;
-  } else {
-    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
-  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
-  PacketBlock<Packet,4> accReal0, accImag0, accReal1, accImag1;
-  PacketBlock<Packet,4> accReal2, accImag2, accReal3, accImag3;
-  PacketBlock<Packet,4> accReal4, accImag4;
-  PacketBlock<Packet,4> taccReal, taccImag;
-  PacketBlock<Packetc,4> acc0, acc1;
-  PacketBlock<Packetc,8> tRes;
-
-  MICRO_COMPLEX_SRC_PTR
-  MICRO_COMPLEX_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    MICRO_COMPLEX_PREFETCH
-    MICRO_COMPLEX_ONE_PEEL4
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_COMPLEX_ONE4
-  }
-  MICRO_COMPLEX_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_col_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) {
-    rhs_ptr_imag = rhs_base + remaining_cols*strideB;
-  } else {
-    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
-  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
-  PacketBlock<Packet,1> accReal0, accImag0, accReal1, accImag1;
-  PacketBlock<Packet,1> accReal2, accImag2, accReal3, accImag3;
-  PacketBlock<Packet,1> accReal4, accImag4;
-  PacketBlock<Packet,1> taccReal, taccImag;
-  PacketBlock<Packetc,1> acc0, acc1;
-  PacketBlock<Packetc,2> tRes;
-
-  MICRO_COMPLEX_SRC_PTR
-  MICRO_COMPLEX_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    MICRO_COMPLEX_PREFETCH
-    MICRO_COMPLEX_ONE_PEEL1
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_COMPLEX_ONE1
-  }
-  MICRO_COMPLEX_COL_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-#define MAX_COMPLEX_UNROLL 3
-  while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
-    gemm_complex_unrolled_col_iteration<MAX_COMPLEX_UNROLL, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-  }
-  switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_UNROLL > 4
-   case 4:
-     gemm_complex_unrolled_col_iteration<4, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-#if MAX_COMPLEX_UNROLL > 3
-   case 3:
-     gemm_complex_unrolled_col_iteration<3, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-#if MAX_COMPLEX_UNROLL > 2
-   case 2:
-     gemm_complex_unrolled_col_iteration<2, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-#if MAX_COMPLEX_UNROLL > 1
-   case 1:
-     gemm_complex_unrolled_col_iteration<1, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-   default:
-     break;
-  }
-#undef MAX_COMPLEX_UNROLL
-}
-
-template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Index, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlphaReal = pset1<Packet>(alpha.real());
-      const Packet pAlphaImag = pset1<Packet>(alpha.imag());
-      const Packet pMask = bmask<Packet>((const int)(remaining_rows));
-
-      const Scalar* blockA = (Scalar *) blockAc;
-      const Scalar* blockB = (Scalar *) blockBc;
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-        Index row = 0;
-
-#define MAX_COMPLEX_UNROLL 3
-        while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
-          gemm_complex_unrolled_iteration<MAX_COMPLEX_UNROLL, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_UNROLL > 4
-          case 4:
-            gemm_complex_unrolled_iteration<4, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_UNROLL > 3
-          case 3:
-            gemm_complex_unrolled_iteration<3, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_UNROLL > 2
-          case 2:
-            gemm_complex_unrolled_iteration<2, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_UNROLL > 1
-          case 1:
-            gemm_complex_unrolled_iteration<1, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_COMPLEX_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
-        }
-      }
-
-      if(remaining_cols > 0)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + remaining_cols*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        for(; col < cols; col++)
-        {
-          Index row = 0;
-
-          gemm_complex_unrolled_col<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, col, remaining_cols, pAlphaReal, pAlphaImag);
-
-          if (remaining_rows > 0)
-          {
-            gemm_complex_extra_col<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_rows, remaining_cols, pAlphaReal, pAlphaImag);
-          }
-          rhs_base++;
-        }
-      }
-}
-
-#undef accColsC
-#undef advanceCols
-#undef advanceRows
-
-/************************************
- * ppc64le template specializations *
- * **********************************/
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-    dhs_pack<double, Index, DataMapper, Packet2d, ColMajor, PanelMode, true> pack;
-    pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-    dhs_pack<double, Index, DataMapper, Packet2d, RowMajor, PanelMode, true> pack;
-    pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<double, Index, DataMapper, Packet2d, ColMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<double, Index, DataMapper, Packet2d, RowMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-#endif
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, RowMajor, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, ColMajor, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, ColMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, RowMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-#endif
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-// ********* gebp specializations *********
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef typename quad_traits<float>::vectortype   Packet;
-  typedef typename quad_traits<float>::rhstype      RhsPacket;
-
-  void operator()(const DataMapper& res, const float* blockA, const float* blockB,
-                  Index rows, Index depth, Index cols, float alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const float* blockA, const float* blockB,
-               Index rows, Index depth, Index cols, float alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const float*, const float*, Index, Index, Index, float, Index, Index, Index, Index);
-
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemmMMA<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemmMMA<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #endif
-      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef Packet4f   Packet;
-  typedef Packet2cf  Packetc;
-  typedef Packet4f   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<float>* blockA, const std::complex<float>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<float> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<float>* blockA, const std::complex<float>* blockB,
-               Index rows, Index depth, Index cols, std::complex<float> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<float>*, const std::complex<float>*,
-          Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #endif
-      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef Packet4f   Packet;
-  typedef Packet2cf  Packetc;
-  typedef Packet4f   RhsPacket;
-
-  void operator()(const DataMapper& res, const float* blockA, const std::complex<float>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<float> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const float* blockA, const std::complex<float>* blockB,
-               Index rows, Index depth, Index cols, std::complex<float> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const float*, const std::complex<float>*,
-          Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef Packet4f   Packet;
-  typedef Packet2cf  Packetc;
-  typedef Packet4f   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<float>* blockA, const float* blockB,
-                  Index rows, Index depth, Index cols, std::complex<float> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<float>* blockA, const float* blockB,
-               Index rows, Index depth, Index cols, std::complex<float> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<float>*, const float*,
-          Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef typename quad_traits<double>::vectortype  Packet;
-  typedef typename quad_traits<double>::rhstype     RhsPacket;
-
-  void operator()(const DataMapper& res, const double* blockA, const double* blockB,
-                  Index rows, Index depth, Index cols, double alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const double* blockA, const double* blockB,
-               Index rows, Index depth, Index cols, double alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const double*, const double*, Index, Index, Index, double, Index, Index, Index, Index);
-
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemmMMA<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemmMMA<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #endif
-      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef quad_traits<double>::vectortype   Packet;
-  typedef Packet1cd  Packetc;
-  typedef quad_traits<double>::rhstype   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<double>* blockA, const std::complex<double>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<double> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<double>* blockA, const std::complex<double>* blockB,
-               Index rows, Index depth, Index cols, std::complex<double> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<double>*, const std::complex<double>*,
-          Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef quad_traits<double>::vectortype   Packet;
-  typedef Packet1cd  Packetc;
-  typedef quad_traits<double>::rhstype   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<double>* blockA, const double* blockB,
-                  Index rows, Index depth, Index cols, std::complex<double> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<double>* blockA, const double* blockB,
-               Index rows, Index depth, Index cols, std::complex<double> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<double>*, const double*,
-          Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef quad_traits<double>::vectortype   Packet;
-  typedef Packet1cd  Packetc;
-  typedef quad_traits<double>::rhstype   RhsPacket;
-
-  void operator()(const DataMapper& res, const double* blockA, const std::complex<double>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<double> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const double* blockA, const std::complex<double>* blockB,
-               Index rows, Index depth, Index cols, std::complex<double> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const double*, const std::complex<double>*,
-          Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_PRODUCT_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
deleted file mode 100644
index 33d5434..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
+++ /dev/null
@@ -1,221 +0,0 @@
-//#define EIGEN_POWER_USE_PREFETCH  // Use prefetching in gemm routines
-#ifdef EIGEN_POWER_USE_PREFETCH
-#define EIGEN_POWER_PREFETCH(p)  prefetch(p)
-#else
-#define EIGEN_POWER_PREFETCH(p)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows>
-EIGEN_STRONG_INLINE void gemm_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlpha);
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask);
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlpha);
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE Packet bmask(const int remaining_rows);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag);
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadLhs(const Scalar* lhs);
-
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,4>& acc, const DataMapper& res, Index row, Index col);
-
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,8>& acc, const DataMapper& res, Index row, Index col);
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha);
-
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag);
-
-const static Packet16uc p16uc_SETCOMPLEX32_FIRST = {  0,  1,  2,  3,
-                                                     16, 17, 18, 19,
-                                                      4,  5,  6,  7,
-                                                     20, 21, 22, 23};
-
-const static Packet16uc p16uc_SETCOMPLEX32_SECOND = {  8,  9, 10, 11,
-                                                      24, 25, 26, 27,
-                                                      12, 13, 14, 15,
-                                                      28, 29, 30, 31};
-//[a,b],[ai,bi] = [a,ai] - This is equivalent to p16uc_GETREAL64
-const static Packet16uc p16uc_SETCOMPLEX64_FIRST = {  0,  1,  2,  3,  4,  5,  6,  7,
-                                                     16, 17, 18, 19, 20, 21, 22, 23};
-
-//[a,b],[ai,bi] = [b,bi] - This is equivalent to p16uc_GETIMAG64
-const static Packet16uc p16uc_SETCOMPLEX64_SECOND = {  8,  9, 10, 11, 12, 13, 14, 15,
-                                                      24, 25, 26, 27, 28, 29, 30, 31};
-
-
-// Grab two decouples real/imaginary PacketBlocks and return two coupled (real/imaginary pairs) PacketBlocks.
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,4>& taccReal, PacketBlock<Packet,4>& taccImag, PacketBlock<Packetc, 4>& acc1, PacketBlock<Packetc, 4>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_FIRST);
-  acc1.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX32_FIRST);
-  acc1.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX32_FIRST);
-  acc1.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX32_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_SECOND);
-  acc2.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX32_SECOND);
-  acc2.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX32_SECOND);
-  acc2.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX32_SECOND);
-}
-
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,4>& taccReal, PacketBlock<Packet,4>& taccImag, PacketBlock<Packetc,8>& tRes, PacketBlock<Packetc, 4>& acc1, PacketBlock<Packetc, 4>& acc2)
-{
-  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc1, acc2);
-
-  acc1.packet[0] = padd<Packetc>(tRes.packet[0], acc1.packet[0]);
-  acc1.packet[1] = padd<Packetc>(tRes.packet[1], acc1.packet[1]);
-  acc1.packet[2] = padd<Packetc>(tRes.packet[2], acc1.packet[2]);
-  acc1.packet[3] = padd<Packetc>(tRes.packet[3], acc1.packet[3]);
-
-  acc2.packet[0] = padd<Packetc>(tRes.packet[4], acc2.packet[0]);
-  acc2.packet[1] = padd<Packetc>(tRes.packet[5], acc2.packet[1]);
-  acc2.packet[2] = padd<Packetc>(tRes.packet[6], acc2.packet[2]);
-  acc2.packet[3] = padd<Packetc>(tRes.packet[7], acc2.packet[3]);
-}
-
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,1>& taccReal, PacketBlock<Packet,1>& taccImag, PacketBlock<Packetc, 1>& acc1, PacketBlock<Packetc, 1>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_SECOND);
-}
-
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,1>& taccReal, PacketBlock<Packet,1>& taccImag, PacketBlock<Packetc,2>& tRes, PacketBlock<Packetc, 1>& acc1, PacketBlock<Packetc, 1>& acc2)
-{
-  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc1, acc2);
-
-  acc1.packet[0] = padd<Packetc>(tRes.packet[0], acc1.packet[0]);
-
-  acc2.packet[0] = padd<Packetc>(tRes.packet[1], acc2.packet[0]);
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void bcouple_common<Packet2d, Packet1cd>(PacketBlock<Packet2d,4>& taccReal, PacketBlock<Packet2d,4>& taccImag, PacketBlock<Packet1cd, 4>& acc1, PacketBlock<Packet1cd, 4>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_FIRST);
-  acc1.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX64_FIRST);
-  acc1.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX64_FIRST);
-  acc1.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX64_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_SECOND);
-  acc2.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX64_SECOND);
-  acc2.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX64_SECOND);
-  acc2.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX64_SECOND);
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void bcouple_common<Packet2d, Packet1cd>(PacketBlock<Packet2d,1>& taccReal, PacketBlock<Packet2d,1>& taccImag, PacketBlock<Packet1cd, 1>& acc1, PacketBlock<Packet1cd, 1>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_SECOND);
-}
-
-// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadRhs(const Scalar* rhs)
-{
-  return ploadu<Packet>(rhs);
-}
-
-} // end namespace internal
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
deleted file mode 100644
index 6540c6f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
+++ /dev/null
@@ -1,629 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
-// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
-#define EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
-
-#pragma GCC target("cpu=power10")
-
-#ifdef __has_builtin
-#if !__has_builtin(__builtin_vsx_assemble_pair)
-#define __builtin_vsx_assemble_pair __builtin_mma_assemble_pair
-#endif
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void bsetzeroMMA(__vector_quad* acc)
-{
-  __builtin_mma_xxsetaccz(acc);
-}
-
-template<typename DataMapper, typename Index, typename Packet, const Index accCols>
-EIGEN_ALWAYS_INLINE void storeAccumulator(Index i, Index j, const DataMapper& data, const Packet& alpha, __vector_quad* acc)
-{
-  PacketBlock<Packet, 4> result;
-  __builtin_mma_disassemble_acc(&result.packet, acc);
-
-  PacketBlock<Packet, 4> tRes;
-  bload<DataMapper, Packet, Index, accCols, 0, ColMajor>(tRes, data, i, j);
-
-  bscale<Packet>(tRes, result, alpha);
-
-  data.template storePacketBlock<Packet, 4>(i, j, tRes);
-}
-
-template<typename DataMapper, typename Index, typename Packet, typename Packetc, const Index accColsC, int N>
-EIGEN_ALWAYS_INLINE void storeComplexAccumulator(Index i, Index j, const DataMapper& data, const Packet& alphaReal, const Packet& alphaImag, __vector_quad* accReal, __vector_quad* accImag)
-{
-  PacketBlock<Packet, 4> resultReal, resultImag;
-  __builtin_mma_disassemble_acc(&resultReal.packet, accReal);
-  __builtin_mma_disassemble_acc(&resultImag.packet, accImag);
-
-  PacketBlock<Packetc, 8> tRes;
-  bload<DataMapper, Packetc, Index, accColsC, N, ColMajor>(tRes, data, i, j);
-
-  PacketBlock<Packet,4> taccReal, taccImag;
-  bscalec<Packet,4>(resultReal, resultImag, alphaReal, alphaImag, taccReal, taccImag);
-
-  PacketBlock<Packetc, 4> acc1, acc2;
-  bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc1, acc2);
-
-  data.template storePacketBlock<Packetc, 4>(i + N*accColsC, j, acc1);
-  data.template storePacketBlock<Packetc, 4>(i + (N+1)*accColsC, j, acc2);
-}
-
-// Defaults to float32, since Eigen still supports C++03 we can't use default template arguments
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const RhsPacket& a, const LhsPacket& b)
-{
-  if(NegativeAccumulate)
-  {
-    __builtin_mma_xvf32gernp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-  } else {
-    __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-  }
-}
-
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const PacketBlock<Packet2d,2>& a, const Packet2d& b)
-{
-  __vector_pair* a0 = (__vector_pair *)(&a.packet[0]);
-  if(NegativeAccumulate)
-  {
-    __builtin_mma_xvf64gernp(acc, *a0, (__vector unsigned char)b);
-  } else {
-    __builtin_mma_xvf64gerpp(acc, *a0, (__vector unsigned char)b);
-  }
-}
-
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const __vector_pair& a, const Packet2d& b)
-{
-  if(NegativeAccumulate)
-  {
-    __builtin_mma_xvf64gernp(acc, (__vector_pair)a, (__vector unsigned char)b);
-  } else {
-    __builtin_mma_xvf64gerpp(acc, (__vector_pair)a, (__vector unsigned char)b);
-  }
-}
-
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad*, const __vector_pair&, const Packet4f&)
-{
-  // Just for compilation
-}
-
-template<typename Scalar, typename Packet, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgercMMA(__vector_quad* accReal, __vector_quad* accImag, const Packet& lhsV, const Packet& lhsVi, const RhsPacket& rhsV, const RhsPacket& rhsVi)
-{
-  pgerMMA<Packet, RhsPacket, false>(accReal,  rhsV,  lhsV);
-  if(LhsIsReal) {
-    pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi,  lhsV);
-  } else {
-    if(!RhsIsReal) {
-      pgerMMA<Packet, RhsPacket, ConjugateLhs == ConjugateRhs>(accReal, rhsVi, lhsVi);
-      pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi,  lhsV);
-    } else {
-      EIGEN_UNUSED_VARIABLE(rhsVi);
-    }
-    pgerMMA<Packet, RhsPacket, ConjugateLhs>(accImag,  rhsV, lhsVi);
-  }
-}
-
-// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA(const Scalar* rhs, Packet& rhsV)
-{
-  rhsV = ploadRhs<Scalar, Packet>((const Scalar*)(rhs));
-} 
-
-template<>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA<double, PacketBlock<Packet2d, 2> >(const double* rhs, PacketBlock<Packet2d, 2>& rhsV)
-{
-  rhsV.packet[0] = ploadRhs<double, Packet2d>((const double *)((Packet2d *)rhs      ));
-  rhsV.packet[1] = ploadRhs<double, Packet2d>((const double *)(((Packet2d *)rhs) + 1));
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA<double, __vector_pair>(const double* rhs, __vector_pair& rhsV)
-{
-#if EIGEN_COMP_LLVM
-  __builtin_vsx_assemble_pair(&rhsV,
-    (__vector unsigned char)(ploadRhs<double, Packet2d>((const double *)(((Packet2d *)rhs) + 1))),
-    (__vector unsigned char)(ploadRhs<double, Packet2d>((const double *)((Packet2d *)rhs      ))));
-#else
-  __asm__ ("lxvp %x0,%1" : "=wa" (rhsV) : "Y" (*rhs));
-#endif
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA(const float*, __vector_pair&)
-{
-  // Just for compilation
-}
-
-// PEEL_MMA loop factor.
-#define PEEL_MMA 7
-
-#define MICRO_MMA_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
-
-#define MICRO_MMA_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr##iter); \
-    lhs_ptr##iter += accCols; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-  }
-
-#define MICRO_MMA_WORK_ONE(iter, type, peel) \
-  if (unroll_factor > iter) { \
-    pgerMMA<Packet, type, false>(&accZero##iter, rhsV##peel, lhsV##iter); \
-  }
-
-#define MICRO_MMA_TYPE_PEEL(func, func2, type, peel) \
-  if (PEEL_MMA > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
-    ploadRhsMMA<Scalar, type>(rhs_ptr + (accRows * peel), rhsV##peel); \
-    MICRO_MMA_UNROLL(func2); \
-    func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel) \
-    func(4,type,peel) func(5,type,peel) func(6,type,peel) func(7,type,peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-  }
-
-#define MICRO_MMA_UNROLL_TYPE_PEEL(func, func2, type) \
-  type rhsV0, rhsV1, rhsV2, rhsV3, rhsV4, rhsV5, rhsV6, rhsV7, rhsV8, rhsV9; \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,0); MICRO_MMA_TYPE_PEEL(func,func2,type,1); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,2); MICRO_MMA_TYPE_PEEL(func,func2,type,3); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,4); MICRO_MMA_TYPE_PEEL(func,func2,type,5); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,6); MICRO_MMA_TYPE_PEEL(func,func2,type,7); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,8); MICRO_MMA_TYPE_PEEL(func,func2,type,9);
-
-#define MICRO_MMA_UNROLL_TYPE_ONE(func, func2, type) \
-  type rhsV0; \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,0);
-
-#define MICRO_MMA_ONE_PEEL \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_MMA_UNROLL_TYPE_PEEL(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_MMA_UNROLL_TYPE_PEEL(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr += (accRows * PEEL_MMA);
-
-#define MICRO_MMA_ONE \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_MMA_UNROLL_TYPE_ONE(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_MMA_UNROLL_TYPE_ONE(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr += accRows;
-
-#define MICRO_MMA_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzeroMMA<Scalar, Packet>(&accZero##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accZero##iter); \
-  }
-
-#define MICRO_MMA_DST_PTR MICRO_MMA_UNROLL(MICRO_MMA_DST_PTR_ONE)
-
-#define MICRO_MMA_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols + accCols*offsetA; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr##iter); \
-  }
-
-#define MICRO_MMA_SRC_PTR MICRO_MMA_UNROLL(MICRO_MMA_SRC_PTR_ONE)
-
-#define MICRO_MMA_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr##iter); \
-  }
-
-#define MICRO_MMA_PREFETCH MICRO_MMA_UNROLL(MICRO_MMA_PREFETCH_ONE)
-
-#define MICRO_MMA_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    storeAccumulator<DataMapper, Index, Packet, accCols>(row + iter*accCols, col, res, pAlpha, &accZero##iter); \
-  }
-
-#define MICRO_MMA_STORE MICRO_MMA_UNROLL(MICRO_MMA_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_MMA_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index col,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr0 = NULL, * lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, * lhs_ptr7 = NULL;
-  __vector_quad accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
-
-  MICRO_MMA_SRC_PTR
-  MICRO_MMA_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_MMA <= depth; k+= PEEL_MMA)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    MICRO_MMA_PREFETCH
-    MICRO_MMA_ONE_PEEL
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_MMA_ONE
-  }
-  MICRO_MMA_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename Scalar, typename Index, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
-void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlpha = pset1<Packet>(alpha);
-      const Packet pMask  = bmask<Packet>((const int)(remaining_rows));
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        Index row = 0;
-#define MAX_MMA_UNROLL 7
-        while(row + MAX_MMA_UNROLL*accCols <= rows) {
-          gemm_unrolled_MMA_iteration<MAX_MMA_UNROLL, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_MMA_UNROLL > 7
-          case 7:
-            gemm_unrolled_MMA_iteration<7, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 6
-          case 6:
-            gemm_unrolled_MMA_iteration<6, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 5
-          case 5:
-            gemm_unrolled_MMA_iteration<5, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 4
-          case 4:
-            gemm_unrolled_MMA_iteration<4, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 3
-          case 3:
-            gemm_unrolled_MMA_iteration<3, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 2
-          case 2:
-            gemm_unrolled_MMA_iteration<2, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 1
-          case 1:
-            gemm_unrolled_MMA_iteration<1, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_MMA_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
-        }
-      }
-
-      if(remaining_cols > 0)
-      {
-        const Scalar* rhs_base = blockB + col*strideB + remaining_cols*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        for(; col < cols; col++)
-        {
-          Index row = 0;
-
-          gemm_unrolled_col<Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, rows, col, remaining_cols, pAlpha);
-
-          if (remaining_rows > 0)
-          {
-            gemm_extra_col<Scalar, Packet, DataMapper, Index, accRows>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_rows, remaining_cols, pAlpha);
-          }
-          rhs_base++;
-        }
-      }
-}
-
-#define accColsC (accCols / 2)
-#define advanceRows ((LhsIsReal) ? 1 : 2)
-#define advanceCols ((RhsIsReal) ? 1 : 2)
-
-// PEEL_COMPLEX_MMA loop factor.
-#define PEEL_COMPLEX_MMA 7
-
-#define MICRO_COMPLEX_MMA_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4)
-
-#define MICRO_COMPLEX_MMA_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter); \
-    lhs_ptr_real##iter += accCols; \
-    if(!LhsIsReal) { \
-      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_imag##iter); \
-      lhs_ptr_imag##iter += accCols; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-    EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_WORK_ONE(iter, type, peel) \
-  if (unroll_factor > iter) { \
-    pgercMMA<Scalar, Packet, type, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_MMA_TYPE_PEEL(func, func2, type, peel) \
-  if (PEEL_COMPLEX_MMA > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
-    ploadRhsMMA<Scalar, type>(rhs_ptr_real + (accRows * peel), rhsV##peel); \
-    if(!RhsIsReal) { \
-      ploadRhsMMA<Scalar, type>(rhs_ptr_imag + (accRows * peel), rhsVi##peel); \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-    } \
-    MICRO_COMPLEX_MMA_UNROLL(func2); \
-    func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel) func(4,type,peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(func, func2, type) \
-  type rhsV0, rhsV1, rhsV2, rhsV3, rhsV4, rhsV5, rhsV6, rhsV7, rhsV8, rhsV9; \
-  type rhsVi0, rhsVi1, rhsVi2, rhsVi3, rhsVi4, rhsVi5, rhsVi6, rhsVi7, rhsVi8, rhsVi9; \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,0); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,1); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,2); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,3); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,4); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,5); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,6); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,7); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,8); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,9);
-
-#define MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(func, func2, type) \
-  type rhsV0, rhsVi0; \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,0);
-
-#define MICRO_COMPLEX_MMA_ONE_PEEL \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr_real += (accRows * PEEL_COMPLEX_MMA); \
-  if(!RhsIsReal) rhs_ptr_imag += (accRows * PEEL_COMPLEX_MMA);
-
-#define MICRO_COMPLEX_MMA_ONE \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr_real += accRows; \
-  if(!RhsIsReal) rhs_ptr_imag += accRows;
-
-#define MICRO_COMPLEX_MMA_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzeroMMA<Scalar, Packet>(&accReal##iter); \
-    bsetzeroMMA<Scalar, Packet>(&accImag##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accReal##iter); \
-    EIGEN_UNUSED_VARIABLE(accImag##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_DST_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_DST_PTR_ONE)
-
-#define MICRO_COMPLEX_MMA_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols + accCols*offsetA; \
-    if(!LhsIsReal) { \
-      lhs_ptr_imag##iter = lhs_ptr_real##iter + accCols*strideA; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_real##iter); \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_SRC_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_SRC_PTR_ONE)
-
-#define MICRO_COMPLEX_MMA_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr_real##iter); \
-    if(!LhsIsReal) { \
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag##iter); \
-    } \
-  }
-
-#define MICRO_COMPLEX_MMA_PREFETCH MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_PREFETCH_ONE)
-
-#define MICRO_COMPLEX_MMA_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    storeComplexAccumulator<DataMapper, Index, Packet, Packetc, accColsC, 0>(row + iter*accCols, col, res, pAlphaReal, pAlphaImag, &accReal##iter, &accImag##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_STORE MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_MMA_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index col,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) {
-    rhs_ptr_imag = rhs_base + accRows*strideB;
-  } else {
-    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
-  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
-  __vector_quad accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3, accReal4, accImag4;
-
-  MICRO_COMPLEX_MMA_SRC_PTR
-  MICRO_COMPLEX_MMA_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_COMPLEX_MMA <= depth; k+= PEEL_COMPLEX_MMA)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    MICRO_COMPLEX_MMA_PREFETCH
-    MICRO_COMPLEX_MMA_ONE_PEEL
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_COMPLEX_MMA_ONE
-  }
-  MICRO_COMPLEX_MMA_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Index, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-void gemm_complexMMA(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlphaReal = pset1<Packet>(alpha.real());
-      const Packet pAlphaImag = pset1<Packet>(alpha.imag());
-      const Packet pMask = bmask<Packet>((const int)(remaining_rows));
-
-      const Scalar* blockA = (Scalar *) blockAc;
-      const Scalar* blockB = (Scalar *) blockBc;
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-        Index row = 0;
-
-#define MAX_COMPLEX_MMA_UNROLL 4
-        while(row + MAX_COMPLEX_MMA_UNROLL*accCols <= rows) {
-          gemm_complex_unrolled_MMA_iteration<MAX_COMPLEX_MMA_UNROLL, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_MMA_UNROLL > 4
-          case 4:
-            gemm_complex_unrolled_MMA_iteration<4, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 3
-          case 3:
-            gemm_complex_unrolled_MMA_iteration<3, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 2
-          case 2:
-            gemm_complex_unrolled_MMA_iteration<2, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 1
-          case 1:
-            gemm_complex_unrolled_MMA_iteration<1, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_COMPLEX_MMA_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
-        }
-      }
-
-      if(remaining_cols > 0)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + remaining_cols*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        for(; col < cols; col++)
-        {
-          Index row = 0;
-
-          gemm_complex_unrolled_col<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, col, remaining_cols, pAlphaReal, pAlphaImag);
-
-          if (remaining_rows > 0)
-          {
-            gemm_complex_extra_col<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_rows, remaining_cols, pAlphaReal, pAlphaImag);
-          }
-          rhs_base++;
-        }
-      }
-}
-
-#undef accColsC
-#undef advanceRows
-#undef advanceCols
-
-#pragma GCC reset_options
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
-
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
deleted file mode 100644
index 2a44054..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
+++ /dev/null
@@ -1,2711 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_ALTIVEC_H
-#define EIGEN_PACKET_MATH_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-// NOTE Altivec has 32 registers, but Eigen only accepts a value of 8 or 16
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS  32
-#endif
-
-typedef __vector float                   Packet4f;
-typedef __vector int                     Packet4i;
-typedef __vector unsigned int            Packet4ui;
-typedef __vector __bool int              Packet4bi;
-typedef __vector short int               Packet8s;
-typedef __vector unsigned short int      Packet8us;
-typedef __vector signed char             Packet16c;
-typedef __vector unsigned char           Packet16uc;
-typedef eigen_packet_wrapper<__vector unsigned short int,0> Packet8bf;
-
-// We don't want to write the same code all the time, but we need to reuse the constants
-// and it doesn't really work to declare them global, so we define macros instead
-#define _EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = {X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = vec_splat_s32(X)
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet4ui(NAME,X) \
-  Packet4ui p4ui_##NAME = {X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet8us(NAME,X) \
-  Packet8us p8us_##NAME = {X, X, X, X, X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet16uc(NAME,X) \
-  Packet16uc p16uc_##NAME = {X, X, X, X, X, X, X, X, X, X, X, X, X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
-  Packet2d p2d_##NAME = pset1<Packet2d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
-  Packet2l p2l_##NAME = pset1<Packet2l>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
-
-#define DST_CHAN 1
-#define DST_CTRL(size, count, stride) (((size) << 24) | ((count) << 16) | (stride))
-#define __UNPACK_TYPE__(PACKETNAME) typename unpacket_traits<PACKETNAME>::type 
-
-// These constants are endian-agnostic
-static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); //{ 1, 1, 1, 1}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16,-16); //{ -16, -16, -16, -16}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
-static _EIGEN_DECLARE_CONST_FAST_Packet4ui(SIGN, 0x80000000u);
-static _EIGEN_DECLARE_CONST_FAST_Packet4ui(PREV0DOT5, 0x3EFFFFFFu);
-static _EIGEN_DECLARE_CONST_FAST_Packet8us(ONE,1); //{ 1, 1, 1, 1, 1, 1, 1, 1}
-static _EIGEN_DECLARE_CONST_FAST_Packet16uc(ONE,1);
-static Packet4f p4f_MZERO = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
-#ifndef __VSX__
-static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
-#endif
-
-static Packet4f  p4f_COUNTDOWN  = { 0.0, 1.0, 2.0, 3.0 };
-static Packet4i  p4i_COUNTDOWN  = { 0, 1, 2, 3 };
-static Packet8s  p8s_COUNTDOWN  = { 0, 1, 2, 3, 4, 5, 6, 7 };
-static Packet8us p8us_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7 };
-
-static Packet16c  p16c_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7,
-                                    8, 9, 10, 11, 12, 13, 14, 15};
-static Packet16uc p16uc_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7, 
-                                    8, 9, 10, 11, 12, 13, 14, 15};
-
-static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
-static Packet16uc p16uc_REVERSE16 = { 14,15, 12,13, 10,11, 8,9, 6,7, 4,5, 2,3, 0,1 };
-static Packet16uc p16uc_REVERSE8 = { 15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,0 };
-
-static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
-static Packet16uc p16uc_DUPLICATE16_HI = { 0,1,0,1, 2,3,2,3, 4,5,4,5, 6,7,6,7 };
-static Packet16uc p16uc_DUPLICATE8_HI = { 0,0, 1,1, 2,2, 3,3, 4,4, 5,5, 6,6, 7,7 };
-static const Packet16uc p16uc_DUPLICATE16_EVEN= { 0,1 ,0,1, 4,5, 4,5, 8,9, 8,9, 12,13, 12,13 };
-static const Packet16uc p16uc_DUPLICATE16_ODD = { 2,3 ,2,3, 6,7, 6,7, 10,11, 10,11, 14,15, 14,15 };
-
-static Packet16uc p16uc_QUADRUPLICATE16_HI = { 0,1,0,1,0,1,0,1, 2,3,2,3,2,3,2,3 };
-
-// Handle endianness properly while loading constants
-// Define global static constants:
-#ifdef _BIG_ENDIAN
-static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
-#ifdef __VSX__
-static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-#endif
-static Packet16uc p16uc_PSET32_WODD   = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
-static Packet16uc p16uc_PSET32_WEVEN  = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
-static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
-#else
-static Packet16uc p16uc_FORWARD = p16uc_REVERSE32;
-static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 1), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
-static Packet16uc p16uc_PSET32_WEVEN = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
-static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 0), (Packet16uc)p4i_ZERO, 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
-#endif // _BIG_ENDIAN
-
-static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
-static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
-static Packet16uc p16uc_TRANSPOSE64_HI = p16uc_PSET64_HI + p16uc_HALF64_0_16;                                         //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
-static Packet16uc p16uc_TRANSPOSE64_LO = p16uc_PSET64_LO + p16uc_HALF64_0_16;                                         //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
-
-static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);                                         //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
-
-#ifdef _BIG_ENDIAN
-static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);                                            //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-#else
-static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_PSET64_HI, p16uc_PSET64_LO, 8);                                            //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-#endif // _BIG_ENDIAN
-
-#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
-  #define EIGEN_PPC_PREFETCH(ADDR) __builtin_prefetch(ADDR);
-#else
-  #define EIGEN_PPC_PREFETCH(ADDR) asm( "   dcbt [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
-#endif
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasAbs = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-#ifdef __VSX__
-    HasSqrt = 1,
-#if !EIGEN_COMP_CLANG
-    HasRsqrt = 1,
-#else
-    HasRsqrt = 0,
-#endif
-#else
-    HasSqrt = 0,
-    HasRsqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-template <>
-struct packet_traits<bfloat16> : default_packet_traits {
-  typedef Packet8bf type;
-  typedef Packet8bf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasAbs = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-#ifdef __VSX__
-    HasSqrt = 1,
-#if !EIGEN_COMP_CLANG
-    HasRsqrt = 1,
-#else
-    HasRsqrt = 0,
-#endif
-#else
-    HasSqrt = 0,
-    HasRsqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<int> : default_packet_traits {
-  typedef Packet4i type;
-  typedef Packet4i half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasAdd   = 1,
-    HasSub   = 1,
-    HasShift = 1,
-    HasMul   = 1,
-    HasDiv   = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<short int> : default_packet_traits {
-  typedef Packet8s type;
-  typedef Packet8s half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<unsigned short int> : default_packet_traits {
-  typedef Packet8us type;
-  typedef Packet8us half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<signed char> : default_packet_traits {
-  typedef Packet16c type;
-  typedef Packet16c half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<unsigned char> : default_packet_traits {
-  typedef Packet16uc type;
-  typedef Packet16uc half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet4f>
-{
-  typedef float     type;
-  typedef Packet4f  half;
-  typedef Packet4i  integer_packet;
-  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet4i>
-{
-  typedef int       type;
-  typedef Packet4i  half;
-  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet8s>
-{
-  typedef short int type;
-  typedef Packet8s  half;
-  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet8us>
-{
-  typedef unsigned short int type;
-  typedef Packet8us          half;
-  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-template<> struct unpacket_traits<Packet16c>
-{
-  typedef signed char type;
-  typedef Packet16c  half;
-  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet16uc>
-{
-  typedef unsigned char type;
-  typedef Packet16uc  half;
-  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-template<> struct unpacket_traits<Packet8bf>
-{
-  typedef bfloat16 type;
-  typedef Packet8bf          half;
-  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-inline std::ostream & operator <<(std::ostream & s, const Packet16c & v)
-{
-  union {
-    Packet16c   v;
-    signed char n[16];
-  } vt;
-  vt.v = v;
-  for (int i=0; i< 16; i++)
-    s << vt.n[i] << ", ";
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet16uc & v)
-{
-  union {
-    Packet16uc   v;
-    unsigned char n[16];
-  } vt;
-  vt.v = v;
-  for (int i=0; i< 16; i++)
-    s << vt.n[i] << ", ";
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
-{
-  union {
-    Packet4f   v;
-    float n[4];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4i & v)
-{
-  union {
-    Packet4i   v;
-    int n[4];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
-{
-  union {
-    Packet4ui   v;
-    unsigned int n[4];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
-  return s;
-}
-
-template <typename Packet>
-EIGEN_STRONG_INLINE Packet pload_common(const __UNPACK_TYPE__(Packet)* from)
-{
-  // some versions of GCC throw "unused-but-set-parameter".
-  // ignoring these warnings for now.
-  EIGEN_UNUSED_VARIABLE(from);
-  EIGEN_DEBUG_ALIGNED_LOAD
-#ifdef __VSX__
-  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
-#else
-  return vec_ld(0, from);
-#endif
-}
-
-// Need to define them first or we get specialization after instantiation errors
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
-{
-  return pload_common<Packet4f>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from)
-{
-  return pload_common<Packet4i>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const short int* from)
-{
-  return pload_common<Packet8s>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const unsigned short int* from)
-{
-  return pload_common<Packet8us>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const signed char*     from)
-{
-  return pload_common<Packet16c>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const unsigned char*     from)
-{
-  return pload_common<Packet16uc>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16*     from)
-{
-  return pload_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-
-template <typename Packet>
-EIGEN_STRONG_INLINE void pstore_common(__UNPACK_TYPE__(Packet)* to, const Packet& from){
-  // some versions of GCC throw "unused-but-set-parameter" (float *to).
-  // ignoring these warnings for now.
-  EIGEN_UNUSED_VARIABLE(to);
-  EIGEN_DEBUG_ALIGNED_STORE
-#ifdef __VSX__
-  vec_xst(from, 0, to);
-#else
-  vec_st(from, 0, to);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from)
-{
-  pstore_common<Packet4f>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from)
-{
-  pstore_common<Packet4i>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<short int>(short int*       to, const Packet8s& from)
-{
-  pstore_common<Packet8s>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<unsigned short int>(unsigned short int*       to, const Packet8us& from)
-{
-  pstore_common<Packet8us>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16*       to, const Packet8bf& from)
-{
-  pstore_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<signed char>(signed char*       to, const Packet16c& from)
-{
-  pstore_common<Packet16c>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<unsigned char>(unsigned char*       to, const Packet16uc& from)
-{
-  pstore_common<Packet16uc>(to, from);
-}
-
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet pset1_size4(const __UNPACK_TYPE__(Packet)& from)
-{
-  Packet v = {from, from, from, from};
-  return v;
-}
-
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet pset1_size8(const __UNPACK_TYPE__(Packet)& from)
-{
-  Packet v = {from, from, from, from, from, from, from, from};
-  return v;
-}
-
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet pset1_size16(const __UNPACK_TYPE__(Packet)& from)
-{
-  Packet v = {from, from, from, from, from, from, from, from, from, from, from, from, from, from, from, from};
-  return v;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) {
-  return pset1_size4<Packet4f>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from)   {
-  return pset1_size4<Packet4i>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const short int&    from)   {
-  return pset1_size8<Packet8s>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const unsigned short int&    from)   {
-  return pset1_size8<Packet8us>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const signed char&    from)   {
-  return pset1_size16<Packet16c>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const unsigned char&    from)   {
-  return pset1_size16<Packet16uc>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) {
-  return reinterpret_cast<Packet4f>(pset1<Packet4i>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16&    from)   {
-  return pset1_size8<Packet8us>(reinterpret_cast<const unsigned short int&>(from));
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE void
-pbroadcast4_common(const __UNPACK_TYPE__(Packet) *a,
-                      Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  a3 = pload<Packet>(a);
-  a0 = vec_splat(a3, 0);
-  a1 = vec_splat(a3, 1);
-  a2 = vec_splat(a3, 2);
-  a3 = vec_splat(a3, 3);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  pbroadcast4_common<Packet4f>(a, a0, a1, a2, a3);
-}
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4i>(const int *a,
-                      Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
-{
-  pbroadcast4_common<Packet4i>(a, a0, a1, a2, a3);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_common(const __UNPACK_TYPE__(Packet)* from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[4];
-  a[0] = from[0*stride];
-  a[1] = from[1*stride];
-  a[2] = from[2*stride];
-  a[3] = from[3*stride];
-  return pload<Packet>(a);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  return pgather_common<Packet4f>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
-{
-  return pgather_common<Packet4i>(from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_size8(const __UNPACK_TYPE__(Packet)* from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[8];
-  a[0] = from[0*stride];
-  a[1] = from[1*stride];
-  a[2] = from[2*stride];
-  a[3] = from[3*stride];
-  a[4] = from[4*stride];
-  a[5] = from[5*stride];
-  a[6] = from[6*stride];
-  a[7] = from[7*stride];
-  return pload<Packet>(a);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet8s pgather<short int, Packet8s>(const short int* from, Index stride)
-{
-  return pgather_size8<Packet8s>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet8us pgather<unsigned short int, Packet8us>(const unsigned short int* from, Index stride)
-{
-  return pgather_size8<Packet8us>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride)
-{
-  return pgather_size8<Packet8bf>(from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_size16(const __UNPACK_TYPE__(Packet)* from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[16];
-  a[0] = from[0*stride];
-  a[1] = from[1*stride];
-  a[2] = from[2*stride];
-  a[3] = from[3*stride];
-  a[4] = from[4*stride];
-  a[5] = from[5*stride];
-  a[6] = from[6*stride];
-  a[7] = from[7*stride];
-  a[8] = from[8*stride];
-  a[9] = from[9*stride];
-  a[10] = from[10*stride];
-  a[11] = from[11*stride];
-  a[12] = from[12*stride];
-  a[13] = from[13*stride];
-  a[14] = from[14*stride];
-  a[15] = from[15*stride];
-  return pload<Packet>(a);
-}
-
-
-template<> EIGEN_DEVICE_FUNC inline Packet16c pgather<signed char, Packet16c>(const signed char* from, Index stride)
-{
-  return pgather_size16<Packet16c>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet16uc pgather<unsigned char, Packet16uc>(const unsigned char* from, Index stride)
-{
-  return pgather_size16<Packet16uc>(from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_size4(__UNPACK_TYPE__(Packet)* to, const Packet& from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[4];
-  pstore<__UNPACK_TYPE__(Packet)>(a, from);
-  to[0*stride] = a[0];
-  to[1*stride] = a[1];
-  to[2*stride] = a[2];
-  to[3*stride] = a[3];
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  pscatter_size4<Packet4f>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
-{
-  pscatter_size4<Packet4i>(to, from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_size8(__UNPACK_TYPE__(Packet)* to, const Packet& from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[8];
-  pstore<__UNPACK_TYPE__(Packet)>(a, from);
-  to[0*stride] = a[0];
-  to[1*stride] = a[1];
-  to[2*stride] = a[2];
-  to[3*stride] = a[3];
-  to[4*stride] = a[4];
-  to[5*stride] = a[5];
-  to[6*stride] = a[6];
-  to[7*stride] = a[7];
-}
-
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<short int, Packet8s>(short int* to, const Packet8s& from, Index stride)
-{
-  pscatter_size8<Packet8s>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<unsigned short int, Packet8us>(unsigned short int* to, const Packet8us& from, Index stride)
-{
-  pscatter_size8<Packet8us>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride)
-{
-  pscatter_size8<Packet8bf>(to, from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_size16(__UNPACK_TYPE__(Packet)* to, const Packet& from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[16];
-  pstore<__UNPACK_TYPE__(Packet)>(a, from);
-  to[0*stride] = a[0];
-  to[1*stride] = a[1];
-  to[2*stride] = a[2];
-  to[3*stride] = a[3];
-  to[4*stride] = a[4];
-  to[5*stride] = a[5];
-  to[6*stride] = a[6];
-  to[7*stride] = a[7];
-  to[8*stride] = a[8];
-  to[9*stride] = a[9];
-  to[10*stride] = a[10];
-  to[11*stride] = a[11];
-  to[12*stride] = a[12];
-  to[13*stride] = a[13];
-  to[14*stride] = a[14];
-  to[15*stride] = a[15];
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<signed char, Packet16c>(signed char* to, const Packet16c& from, Index stride)
-{
-  pscatter_size16<Packet16c>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<unsigned char, Packet16uc>(unsigned char* to, const Packet16uc& from, Index stride)
-{
-  pscatter_size16<Packet16uc>(to, from, stride);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f   plset<Packet4f>(const float&     a) { return pset1<Packet4f>(a) + p4f_COUNTDOWN;  }
-template<> EIGEN_STRONG_INLINE Packet4i   plset<Packet4i>(const int&       a) { return pset1<Packet4i>(a) + p4i_COUNTDOWN;  }
-template<> EIGEN_STRONG_INLINE Packet8s   plset<Packet8s>(const short int& a) { return pset1<Packet8s>(a) + p8s_COUNTDOWN; }
-template<> EIGEN_STRONG_INLINE Packet8us  plset<Packet8us>(const unsigned short int& a) { return pset1<Packet8us>(a) + p8us_COUNTDOWN; }
-template<> EIGEN_STRONG_INLINE Packet16c  plset<Packet16c>(const signed char& a)   { return pset1<Packet16c>(a) + p16c_COUNTDOWN; }
-template<> EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const unsigned char& a)   { return pset1<Packet16uc>(a) + p16uc_COUNTDOWN; }
-
-template<> EIGEN_STRONG_INLINE Packet4f   padd<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet4i   padd<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet4ui   padd<Packet4ui>  (const Packet4ui&   a, const Packet4ui&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet8s   padd<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet8us  padd<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet16c  padd<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return a + b; }
-
-template<> EIGEN_STRONG_INLINE Packet4f   psub<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet4i   psub<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet8s   psub<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet8us  psub<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet16c  psub<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return a - b; }
-
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return p4f_ZERO - a; }
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return p4i_ZERO - a; }
-
-template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4f   pmul<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return vec_madd(a,b, p4f_MZERO); }
-template<> EIGEN_STRONG_INLINE Packet4i   pmul<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a * b; }
-template<> EIGEN_STRONG_INLINE Packet8s   pmul<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return vec_mul(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us  pmul<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return vec_mul(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c  pmul<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return vec_mul(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_mul(a,b); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-#ifndef __VSX__  // VSX actually provides a div instruction
-  Packet4f t, y_0, y_1;
-
-  // Altivec does not offer a divide instruction, we have to do a reciprocal approximation
-  y_0 = vec_re(b);
-
-  // Do one Newton-Raphson iteration to get the needed accuracy
-  t   = vec_nmsub(y_0, b, p4f_ONE);
-  y_1 = vec_madd(y_0, t, y_0);
-
-  return vec_madd(a, y_1, p4f_MZERO);
-#else
-  return vec_div(a, b);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
-{ eigen_assert(false && "packet integer division are not supported by AltiVec");
-  return pset1<Packet4i>(0);
-}
-
-// for some weird raisons, it has to be overloaded for packet of integers
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a,b,c); }
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return a*b + c; }
-template<> EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c) { return vec_madd(a,b,c); }
-template<> EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c) { return vec_madd(a,b,c); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  #ifdef __VSX__
-  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
-  Packet4f ret;
-  __asm__ ("xvcmpgesp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
-  #else
-  return vec_min(a, b);
-  #endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_min(a, b); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  #ifdef __VSX__
-  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
-  Packet4f ret;
-  __asm__ ("xvcmpgtsp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
-  #else
-  return vec_max(a, b);
-  #endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_max(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) {
-  Packet4f c = reinterpret_cast<Packet4f>(vec_cmpge(a,b));
-  return vec_nor(c,c);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_le(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_lt(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_eq(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_le(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_lt(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_eq(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_le(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_lt(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_eq(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_le(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_lt(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_eq(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmpeq(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8bf pand<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return pand<Packet8us>(a, b);
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8bf por<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return por<Packet8us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8bf pxor<Packet8bf>(const Packet8bf& a, const Packet8bf& b) { 
-  return pxor<Packet8us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_andc(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_andc(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
-  return vec_sel(b, a, reinterpret_cast<Packet4ui>(mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
-{
-    Packet4f t = vec_add(reinterpret_cast<Packet4f>(vec_or(vec_and(reinterpret_cast<Packet4ui>(a), p4ui_SIGN), p4ui_PREV0DOT5)), a);
-    Packet4f res;
-
-#ifdef __VSX__
-    __asm__("xvrspiz %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (t));
-#else
-    __asm__("vrfiz %0, %1\n\t"
-        : "=v" (res)
-        : "v" (t));
-#endif
-
-    return res;
-}
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const  Packet4f& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return vec_floor(a); }
-template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
-{
-    Packet4f res;
-
-    __asm__("xvrspic %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (a));
-
-    return res;
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE Packet ploadu_common(const __UNPACK_TYPE__(Packet)* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD
-#ifdef _BIG_ENDIAN
-  Packet16uc MSQ, LSQ;
-  Packet16uc mask;
-  MSQ = vec_ld(0, (unsigned char *)from);          // most significant quadword
-  LSQ = vec_ld(15, (unsigned char *)from);         // least significant quadword
-  mask = vec_lvsl(0, from);                        // create the permute mask
-  //TODO: Add static_cast here
-  return (Packet) vec_perm(MSQ, LSQ, mask);           // align the data
-#else
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
-{
-  return ploadu_common<Packet4f>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
-{
-  return ploadu_common<Packet4i>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const short int* from)
-{
-  return ploadu_common<Packet8s>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const unsigned short int* from)
-{
-  return ploadu_common<Packet8us>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from)
-{
-  return ploadu_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-template<> EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const signed char* from)
-{
-  return ploadu_common<Packet16c>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const unsigned char* from)
-{
-  return ploadu_common<Packet16uc>(from);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE Packet ploaddup_common(const __UNPACK_TYPE__(Packet)*   from)
-{
-  Packet p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet>(from);
-  else                                  p = ploadu<Packet>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
-}
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*   from)
-{
-  return ploaddup_common<Packet4f>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
-{
-  return ploaddup_common<Packet4i>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const short int*     from)
-{
-  Packet8s p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8s>(from);
-  else                                  p = ploadu<Packet8s>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const unsigned short int*     from)
-{
-  Packet8us p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8us>(from);
-  else                                  p = ploadu<Packet8us>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const short int*     from)
-{
-  Packet8s p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8s>(from);
-  else                                  p = ploadu<Packet8s>(from);
-  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const unsigned short int*     from)
-{
-  Packet8us p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8us>(from);
-  else                                  p = ploadu<Packet8us>(from);
-  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ploadquad<Packet8bf>(const bfloat16*     from)
-{
-  return ploadquad<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const signed char*     from)
-{
-  Packet16c p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet16c>(from);
-  else                                  p = ploadu<Packet16c>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE8_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const unsigned char*     from)
-{
-  Packet16uc p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet16uc>(from);
-  else                                  p = ploadu<Packet16uc>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE8_HI);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE void pstoreu_common(__UNPACK_TYPE__(Packet)*  to, const Packet& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE
-#ifdef _BIG_ENDIAN
-  // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
-  // Warning: not thread safe!
-  Packet16uc MSQ, LSQ, edges;
-  Packet16uc edgeAlign, align;
-
-  MSQ = vec_ld(0, (unsigned char *)to);                     // most significant quadword
-  LSQ = vec_ld(15, (unsigned char *)to);                    // least significant quadword
-  edgeAlign = vec_lvsl(0, to);                              // permute map to extract edges
-  edges=vec_perm(LSQ,MSQ,edgeAlign);                        // extract the edges
-  align = vec_lvsr( 0, to );                                // permute map to misalign data
-  MSQ = vec_perm(edges,(Packet16uc)from,align);             // misalign the data (MSQ)
-  LSQ = vec_perm((Packet16uc)from,edges,align);             // misalign the data (LSQ)
-  vec_st( LSQ, 15, (unsigned char *)to );                   // Store the LSQ part first
-  vec_st( MSQ, 0, (unsigned char *)to );                   // Store the MSQ part second
-#else
-  vec_xst(from, 0, to);
-#endif
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*  to, const Packet4f& from)
-{
-  pstoreu_common<Packet4f>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*      to, const Packet4i& from)
-{
-  pstoreu_common<Packet4i>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<short int>(short int*      to, const Packet8s& from)
-{
-  pstoreu_common<Packet8s>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<unsigned short int>(unsigned short int*      to, const Packet8us& from)
-{
-  pstoreu_common<Packet8us>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16*      to, const Packet8bf& from)
-{
-  pstoreu_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<signed char>(signed char*      to, const Packet16c& from)
-{
-  pstoreu_common<Packet16c>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<unsigned char>(unsigned char*      to, const Packet16uc& from)
-{
-  pstoreu_common<Packet16uc>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr)    { EIGEN_PPC_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*     addr)    { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { EIGEN_ALIGN16 float x; vec_ste(a, 0, &x); return x; }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { EIGEN_ALIGN16 int   x; vec_ste(a, 0, &x); return x; }
-
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) pfirst_common(const Packet& a) {
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) x;
-  vec_ste(a, 0, &x);
-  return x;
-}
-
-template<> EIGEN_STRONG_INLINE short int pfirst<Packet8s>(const Packet8s& a) {
-  return pfirst_common<Packet8s>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int pfirst<Packet8us>(const Packet8us& a) {
-  return pfirst_common<Packet8us>(a);
-}
-
-template<> EIGEN_STRONG_INLINE signed char pfirst<Packet16c>(const Packet16c& a)
-{
-  return pfirst_common<Packet16c>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char pfirst<Packet16uc>(const Packet16uc& a)
-{
-  return pfirst_common<Packet16uc>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  return reinterpret_cast<Packet4f>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
-{
-  return reinterpret_cast<Packet4i>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-template<> EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a)
-{
-  return reinterpret_cast<Packet8s>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
-}
-template<> EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a)
-{
-  return reinterpret_cast<Packet8us>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
-}
-template<> EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a)
-{
-  return vec_perm(a, a, p16uc_REVERSE8);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a)
-{
-  return vec_perm(a, a, p16uc_REVERSE8);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a)
-{
-  return preverse<Packet8us>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8bf  pabs(const Packet8bf& a) {
-  _EIGEN_DECLARE_CONST_FAST_Packet8us(abs_mask,0x7FFF);
-  return pand<Packet8us>(p8us_abs_mask, a);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a)
-{ return vec_sra(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right(const Packet4i& a)
-{ return vec_sr(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left(const Packet4i& a)
-{ return vec_sl(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
-template<int N> EIGEN_STRONG_INLINE Packet4f plogical_shift_left(const Packet4f& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  Packet4ui r = vec_sl(reinterpret_cast<Packet4ui>(a), p4ui_mask);
-  return reinterpret_cast<Packet4f>(r);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4f plogical_shift_right(const Packet4f& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  Packet4ui r = vec_sr(reinterpret_cast<Packet4ui>(a), p4ui_mask);
-  return reinterpret_cast<Packet4f>(r);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(const Packet4ui& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  return vec_sr(a, p4ui_mask);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(const Packet4ui& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  return vec_sl(a, p4ui_mask);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_left(const Packet8us& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
-  return vec_sl(a, p8us_mask);
-}
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_right(const Packet8us& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
-  return vec_sr(a, p8us_mask);
-}
-
-EIGEN_STRONG_INLINE Packet4f Bf16ToF32Even(const Packet8bf& bf){
-  return plogical_shift_left<16>(reinterpret_cast<Packet4f>(bf.m_val));
-}
-
-EIGEN_STRONG_INLINE Packet4f Bf16ToF32Odd(const Packet8bf& bf){
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(high_mask, 0xFFFF0000);
-  return pand<Packet4f>(
-    reinterpret_cast<Packet4f>(bf.m_val),
-    reinterpret_cast<Packet4f>(p4ui_high_mask)
-  );
-}
-
-// Simple interleaving of bool masks, prevents true values from being
-// converted to NaNs.
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16Bool(Packet4f even, Packet4f odd) {
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(high_mask, 0xFFFF0000);
-  Packet4f bf_odd, bf_even;
-  bf_odd = pand(reinterpret_cast<Packet4f>(p4ui_high_mask), odd);
-  bf_even = plogical_shift_right<16>(even);
-  return reinterpret_cast<Packet8us>(por<Packet4f>(bf_even, bf_odd));
-}
-
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f p4f){
-  Packet4ui input = reinterpret_cast<Packet4ui>(p4f);
-  Packet4ui lsb = plogical_shift_right<16>(input);
-  lsb = pand<Packet4ui>(lsb, reinterpret_cast<Packet4ui>(p4i_ONE));
-
-  _EIGEN_DECLARE_CONST_FAST_Packet4ui(BIAS,0x7FFFu);
-  Packet4ui rounding_bias = padd<Packet4ui>(lsb, p4ui_BIAS);
-  input = padd<Packet4ui>(input, rounding_bias);
-
-  //Test NaN and Subnormal - Begin
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(exp_mask, 0x7F800000);
-  Packet4ui exp = pand<Packet4ui>(p4ui_exp_mask, reinterpret_cast<Packet4ui>(p4f));
-
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mantissa_mask, 0x7FFFFF);
-  Packet4ui mantissa = pand<Packet4ui>(p4ui_mantissa_mask, reinterpret_cast<Packet4ui>(p4f));
-
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(max_exp, 0x7F800000);
-  Packet4bi is_max_exp = vec_cmpeq(exp, p4ui_max_exp);
-  Packet4bi is_zero_exp = vec_cmpeq(exp, reinterpret_cast<Packet4ui>(p4i_ZERO));
-
-  Packet4bi is_mant_zero = vec_cmpeq(mantissa, reinterpret_cast<Packet4ui>(p4i_ZERO));
-  Packet4ui nan_selector = pandnot<Packet4ui>(
-      reinterpret_cast<Packet4ui>(is_max_exp),
-      reinterpret_cast<Packet4ui>(is_mant_zero)
-  );
-
-  Packet4ui subnormal_selector = pandnot<Packet4ui>(
-      reinterpret_cast<Packet4ui>(is_zero_exp),
-      reinterpret_cast<Packet4ui>(is_mant_zero)
-  );
-
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(nan, 0x7FC00000);
-  input = vec_sel(input, p4ui_nan, nan_selector);
-  input = vec_sel(input, reinterpret_cast<Packet4ui>(p4f), subnormal_selector);
-  //Test NaN and Subnormal - End
-
-  input = plogical_shift_right<16>(input);
-  return reinterpret_cast<Packet8us>(input);
-}
-
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f even, Packet4f odd){
-  Packet4f bf_odd, bf_even;
-  bf_odd = reinterpret_cast<Packet4f>(F32ToBf16(odd).m_val);
-  bf_odd = plogical_shift_left<16>(bf_odd);
-  bf_even = reinterpret_cast<Packet4f>(F32ToBf16(even).m_val);
-  return reinterpret_cast<Packet8us>(por<Packet4f>(bf_even, bf_odd));
-}
-#define BF16_TO_F32_UNARY_OP_WRAPPER(OP, A) \
-  Packet4f a_even = Bf16ToF32Even(A);\
-  Packet4f a_odd = Bf16ToF32Odd(A);\
-  Packet4f op_even = OP(a_even);\
-  Packet4f op_odd = OP(a_odd);\
-  return F32ToBf16(op_even, op_odd);\
-
-#define BF16_TO_F32_BINARY_OP_WRAPPER(OP, A, B) \
-  Packet4f a_even = Bf16ToF32Even(A);\
-  Packet4f a_odd = Bf16ToF32Odd(A);\
-  Packet4f b_even = Bf16ToF32Even(B);\
-  Packet4f b_odd = Bf16ToF32Odd(B);\
-  Packet4f op_even = OP(a_even, b_even);\
-  Packet4f op_odd = OP(a_odd, b_odd);\
-  return F32ToBf16(op_even, op_odd);\
-
-#define BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(OP, A, B) \
-  Packet4f a_even = Bf16ToF32Even(A);\
-  Packet4f a_odd = Bf16ToF32Odd(A);\
-  Packet4f b_even = Bf16ToF32Even(B);\
-  Packet4f b_odd = Bf16ToF32Odd(B);\
-  Packet4f op_even = OP(a_even, b_even);\
-  Packet4f op_odd = OP(a_odd, b_odd);\
-  return F32ToBf16Bool(op_even, op_odd);\
-
-template<> EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(padd<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pmul<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pdiv<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pnegate<Packet8bf>(const Packet8bf& a) {
-  BF16_TO_F32_UNARY_OP_WRAPPER(pnegate<Packet4f>, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(psub<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psqrt<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(vec_sqrt, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf prsqrt<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(prsqrt<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pexp<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pexp_float, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
-  return pldexp_generic(a,exponent);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pldexp<Packet8bf> (const Packet8bf& a, const Packet8bf& exponent){
-  BF16_TO_F32_BINARY_OP_WRAPPER(pldexp<Packet4f>, a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
-  return pfrexp_generic(a,exponent);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pfrexp<Packet8bf> (const Packet8bf& a, Packet8bf& e){
-  Packet4f a_even = Bf16ToF32Even(a);
-  Packet4f a_odd = Bf16ToF32Odd(a);
-  Packet4f e_even;
-  Packet4f e_odd;
-  Packet4f op_even = pfrexp<Packet4f>(a_even, e_even);
-  Packet4f op_odd = pfrexp<Packet4f>(a_odd, e_odd);
-  e = F32ToBf16(e_even, e_odd);
-  return F32ToBf16(op_even, op_odd);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psin<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(psin_float, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcos<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pcos_float, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf plog<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(plog_float, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pfloor<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pceil<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pround<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf print<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(print<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pmadd(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
-  Packet4f a_even = Bf16ToF32Even(a);
-  Packet4f a_odd = Bf16ToF32Odd(a);
-  Packet4f b_even = Bf16ToF32Even(b);
-  Packet4f b_odd = Bf16ToF32Odd(b);
-  Packet4f c_even = Bf16ToF32Even(c);
-  Packet4f c_odd = Bf16ToF32Odd(c);
-  Packet4f pmadd_even = pmadd<Packet4f>(a_even, b_even, c_even);
-  Packet4f pmadd_odd = pmadd<Packet4f>(a_odd, b_odd, c_odd);
-  return F32ToBf16(pmadd_even, pmadd_odd);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pmin<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pmax<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt<Packet4f>, a, b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt_or_nan<Packet4f>, a, b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_le<Packet4f>, a, b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_eq<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 pfirst(const Packet8bf& a) {
-  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16((pfirst<Packet8us>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ploaddup<Packet8bf>(const  bfloat16*     from)
-{
-  return ploaddup<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
-  bfloat16 countdown[8] = { bfloat16(0), bfloat16(1), bfloat16(2), bfloat16(3),
-                            bfloat16(4), bfloat16(5), bfloat16(6), bfloat16(7) };
-  return padd<Packet8bf>(pset1<Packet8bf>(a), pload<Packet8bf>(countdown));
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, sum;
-  b   = vec_sld(a, a, 8);
-  sum = a + b;
-  b   = vec_sld(sum, sum, 4);
-  sum += b;
-  return pfirst(sum);
-}
-
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i sum;
-  sum = vec_sums(a, p4i_ZERO);
-#ifdef _BIG_ENDIAN
-  sum = vec_sld(sum, p4i_ZERO, 12);
-#else
-  sum = vec_sld(p4i_ZERO, sum, 4);
-#endif
-  return pfirst(sum);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = redux_even + redux_odd;
-  return bfloat16(f32_result);
-}
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size8(const Packet& a)
-{
-  union{
-    Packet v;
-    __UNPACK_TYPE__(Packet) n[8];
-  } vt;
-  vt.v = a;
-
-  EIGEN_ALIGN16 int first_loader[4] = { vt.n[0], vt.n[1], vt.n[2], vt.n[3] };
-  EIGEN_ALIGN16 int second_loader[4] = { vt.n[4], vt.n[5], vt.n[6], vt.n[7] };
-  Packet4i first_half  = pload<Packet4i>(first_loader);
-  Packet4i second_half = pload<Packet4i>(second_loader);
-
-  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_half) + predux(second_half));
-}
-
-template<> EIGEN_STRONG_INLINE short int predux<Packet8s>(const Packet8s& a)
-{
-  return predux_size8<Packet8s>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux<Packet8us>(const Packet8us& a)
-{
-  return predux_size8<Packet8us>(a);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size16(const Packet& a)
-{
-  union{
-    Packet v;
-    __UNPACK_TYPE__(Packet) n[16];
-  } vt;
-  vt.v = a;
-
-  EIGEN_ALIGN16 int first_loader[4] = { vt.n[0], vt.n[1], vt.n[2], vt.n[3] };
-  EIGEN_ALIGN16 int second_loader[4] = { vt.n[4], vt.n[5], vt.n[6], vt.n[7] };
-  EIGEN_ALIGN16 int third_loader[4] = { vt.n[8], vt.n[9], vt.n[10], vt.n[11] };
-  EIGEN_ALIGN16 int fourth_loader[4] = { vt.n[12], vt.n[13], vt.n[14], vt.n[15] };
-
-  Packet4i first_quarter = pload<Packet4i>(first_loader);
-  Packet4i second_quarter = pload<Packet4i>(second_loader);
-  Packet4i third_quarter = pload<Packet4i>(third_loader);
-  Packet4i fourth_quarter = pload<Packet4i>(fourth_loader);
-
-  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_quarter) + predux(second_quarter)
-		                  + predux(third_quarter) + predux(fourth_quarter));
-}
-
-template<> EIGEN_STRONG_INLINE signed char predux<Packet16c>(const Packet16c& a)
-{
-  return predux_size16<Packet16c>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux<Packet16uc>(const Packet16uc& a)
-{
-  return predux_size16<Packet16uc>(a);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  Packet4f prod;
-  prod = pmul(a, vec_sld(a, a, 8));
-  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
-}
-
-template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
-{
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  return aux[0] * aux[1] * aux[2] * aux[3];
-}
-
-template<> EIGEN_STRONG_INLINE short int predux_mul<Packet8s>(const Packet8s& a)
-{
-  Packet8s pair, quad, octo;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux_mul<Packet8us>(const Packet8us& a)
-{
-  Packet8us pair, quad, octo;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux_mul<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux_mul<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = redux_even * redux_odd;
-  return bfloat16(f32_result);
-}
-
-
-template<> EIGEN_STRONG_INLINE signed char predux_mul<Packet16c>(const Packet16c& a)
-{
-  Packet16c pair, quad, octo, result;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-  result = vec_mul(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux_mul<Packet16uc>(const Packet16uc& a)
-{
-  Packet16uc pair, quad, octo, result;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-  result = vec_mul(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-// min
-template<typename Packet> EIGEN_STRONG_INLINE
-__UNPACK_TYPE__(Packet) predux_min4(const Packet& a)
-{
-  Packet b, res;
-  b = vec_min(a, vec_sld(a, a, 8));
-  res = vec_min(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  return predux_min4<Packet4f>(a);
-}
-
-template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
-{
-  return predux_min4<Packet4i>(a);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux_min<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux_min<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = (std::min)(redux_even, redux_odd);
-  return bfloat16(f32_result);
-}
-
-template<> EIGEN_STRONG_INLINE short int predux_min<Packet8s>(const Packet8s& a)
-{
-  Packet8s pair, quad, octo;
-  
-  //pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
-  pair = vec_min(a, vec_sld(a, a, 8)); 
-
-  //quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux_min<Packet8us>(const Packet8us& a)
-{
-  Packet8us pair, quad, octo;
-  
-  //pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
-  pair = vec_min(a, vec_sld(a, a, 8)); 
-
-  //quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE signed char predux_min<Packet16c>(const Packet16c& a)
-{
-  Packet16c pair, quad, octo, result;
-
-  pair = vec_min(a, vec_sld(a, a, 8));
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  result = vec_min(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux_min<Packet16uc>(const Packet16uc& a)
-{
-  Packet16uc pair, quad, octo, result;
-
-  pair = vec_min(a, vec_sld(a, a, 8));
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  result = vec_min(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-// max
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_max4(const Packet& a)
-{
-  Packet b, res;
-  b = vec_max(a, vec_sld(a, a, 8));
-  res = vec_max(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  return predux_max4<Packet4f>(a);
-}
-
-template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
-{
-  return predux_max4<Packet4i>(a);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux_max<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux_max<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = (std::max)(redux_even, redux_odd);
-  return bfloat16(f32_result);
-}
-
-template<> EIGEN_STRONG_INLINE short int predux_max<Packet8s>(const Packet8s& a)
-{
-  Packet8s pair, quad, octo;
-  
-  //pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
-  pair = vec_max(a, vec_sld(a, a, 8)); 
-
-  //quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux_max<Packet8us>(const Packet8us& a)
-{
-  Packet8us pair, quad, octo;
-  
-  //pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
-  pair = vec_max(a, vec_sld(a, a, 8)); 
-
-  //quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE signed char predux_max<Packet16c>(const Packet16c& a)
-{
-  Packet16c pair, quad, octo, result;
-
-  pair = vec_max(a, vec_sld(a, a, 8));
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  result = vec_max(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux_max<Packet16uc>(const Packet16uc& a)
-{
-  Packet16uc pair, quad, octo, result;
-
-  pair = vec_max(a, vec_sld(a, a, 8));
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  result = vec_max(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
-{
-  return vec_any_ne(x, pzero(x));
-}
-
-template <typename T> EIGEN_DEVICE_FUNC inline void
-ptranpose_common(PacketBlock<T,4>& kernel){
-  T t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  ptranpose_common<Packet4f>(kernel);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4i,4>& kernel) {
-  ptranpose_common<Packet4i>(kernel);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8s,4>& kernel) {
-  Packet8s t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8us,4>& kernel) {
-  Packet8us t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8bf,4>& kernel) {
-  Packet8us t0, t1, t2, t3;
-
-  t0 = vec_mergeh(kernel.packet[0].m_val, kernel.packet[2].m_val);
-  t1 = vec_mergel(kernel.packet[0].m_val, kernel.packet[2].m_val);
-  t2 = vec_mergeh(kernel.packet[1].m_val, kernel.packet[3].m_val);
-  t3 = vec_mergel(kernel.packet[1].m_val, kernel.packet[3].m_val);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16c,4>& kernel) {
-  Packet16c t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16uc,4>& kernel) {
-  Packet16uc t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8s,8>& kernel) {
-  Packet8s v[8], sum[8];
-
-  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
-  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
-  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
-  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
-  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
-  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
-  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
-  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
-  sum[0] = vec_mergeh(v[0], v[4]);
-  sum[1] = vec_mergel(v[0], v[4]);
-  sum[2] = vec_mergeh(v[1], v[5]);
-  sum[3] = vec_mergel(v[1], v[5]);
-  sum[4] = vec_mergeh(v[2], v[6]);
-  sum[5] = vec_mergel(v[2], v[6]);
-  sum[6] = vec_mergeh(v[3], v[7]);
-  sum[7] = vec_mergel(v[3], v[7]);
-
-  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
-  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
-  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
-  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
-  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
-  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
-  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
-  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8us,8>& kernel) {
-  Packet8us v[8], sum[8];
-
-  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
-  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
-  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
-  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
-  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
-  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
-  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
-  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
-  sum[0] = vec_mergeh(v[0], v[4]);
-  sum[1] = vec_mergel(v[0], v[4]);
-  sum[2] = vec_mergeh(v[1], v[5]);
-  sum[3] = vec_mergel(v[1], v[5]);
-  sum[4] = vec_mergeh(v[2], v[6]);
-  sum[5] = vec_mergel(v[2], v[6]);
-  sum[6] = vec_mergeh(v[3], v[7]);
-  sum[7] = vec_mergel(v[3], v[7]);
-
-  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
-  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
-  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
-  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
-  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
-  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
-  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
-  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8bf,8>& kernel) {
-  Packet8bf v[8], sum[8];
-
-  v[0] = vec_mergeh(kernel.packet[0].m_val, kernel.packet[4].m_val);
-  v[1] = vec_mergel(kernel.packet[0].m_val, kernel.packet[4].m_val);
-  v[2] = vec_mergeh(kernel.packet[1].m_val, kernel.packet[5].m_val);
-  v[3] = vec_mergel(kernel.packet[1].m_val, kernel.packet[5].m_val);
-  v[4] = vec_mergeh(kernel.packet[2].m_val, kernel.packet[6].m_val);
-  v[5] = vec_mergel(kernel.packet[2].m_val, kernel.packet[6].m_val);
-  v[6] = vec_mergeh(kernel.packet[3].m_val, kernel.packet[7].m_val);
-  v[7] = vec_mergel(kernel.packet[3].m_val, kernel.packet[7].m_val);
-  sum[0] = vec_mergeh(v[0].m_val, v[4].m_val);
-  sum[1] = vec_mergel(v[0].m_val, v[4].m_val);
-  sum[2] = vec_mergeh(v[1].m_val, v[5].m_val);
-  sum[3] = vec_mergel(v[1].m_val, v[5].m_val);
-  sum[4] = vec_mergeh(v[2].m_val, v[6].m_val);
-  sum[5] = vec_mergel(v[2].m_val, v[6].m_val);
-  sum[6] = vec_mergeh(v[3].m_val, v[7].m_val);
-  sum[7] = vec_mergel(v[3].m_val, v[7].m_val);
-
-  kernel.packet[0] = vec_mergeh(sum[0].m_val, sum[4].m_val);
-  kernel.packet[1] = vec_mergel(sum[0].m_val, sum[4].m_val);
-  kernel.packet[2] = vec_mergeh(sum[1].m_val, sum[5].m_val);
-  kernel.packet[3] = vec_mergel(sum[1].m_val, sum[5].m_val);
-  kernel.packet[4] = vec_mergeh(sum[2].m_val, sum[6].m_val);
-  kernel.packet[5] = vec_mergel(sum[2].m_val, sum[6].m_val);
-  kernel.packet[6] = vec_mergeh(sum[3].m_val, sum[7].m_val);
-  kernel.packet[7] = vec_mergel(sum[3].m_val, sum[7].m_val);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16c,16>& kernel) {
-  Packet16c step1[16], step2[16], step3[16];
-
-  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
-  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
-  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
-  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
-  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
-  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
-  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
-  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
-  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
-  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
-  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
-  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
-  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
-  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
-  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
-  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
-
-  step2[0]  = vec_mergeh(step1[0], step1[8]);
-  step2[1]  = vec_mergel(step1[0], step1[8]);
-  step2[2]  = vec_mergeh(step1[1], step1[9]);
-  step2[3]  = vec_mergel(step1[1], step1[9]);
-  step2[4]  = vec_mergeh(step1[2], step1[10]);
-  step2[5]  = vec_mergel(step1[2], step1[10]);
-  step2[6]  = vec_mergeh(step1[3], step1[11]);
-  step2[7]  = vec_mergel(step1[3], step1[11]);
-  step2[8]  = vec_mergeh(step1[4], step1[12]);
-  step2[9]  = vec_mergel(step1[4], step1[12]);
-  step2[10] = vec_mergeh(step1[5], step1[13]);
-  step2[11] = vec_mergel(step1[5], step1[13]);
-  step2[12] = vec_mergeh(step1[6], step1[14]);
-  step2[13] = vec_mergel(step1[6], step1[14]);
-  step2[14] = vec_mergeh(step1[7], step1[15]);
-  step2[15] = vec_mergel(step1[7], step1[15]);
-
-  step3[0]  = vec_mergeh(step2[0], step2[8]);
-  step3[1]  = vec_mergel(step2[0], step2[8]);
-  step3[2]  = vec_mergeh(step2[1], step2[9]);
-  step3[3]  = vec_mergel(step2[1], step2[9]);
-  step3[4]  = vec_mergeh(step2[2], step2[10]);
-  step3[5]  = vec_mergel(step2[2], step2[10]);
-  step3[6]  = vec_mergeh(step2[3], step2[11]);
-  step3[7]  = vec_mergel(step2[3], step2[11]);
-  step3[8]  = vec_mergeh(step2[4], step2[12]);
-  step3[9]  = vec_mergel(step2[4], step2[12]);
-  step3[10] = vec_mergeh(step2[5], step2[13]);
-  step3[11] = vec_mergel(step2[5], step2[13]);
-  step3[12] = vec_mergeh(step2[6], step2[14]);
-  step3[13] = vec_mergel(step2[6], step2[14]);
-  step3[14] = vec_mergeh(step2[7], step2[15]);
-  step3[15] = vec_mergel(step2[7], step2[15]);
-
-  kernel.packet[0]  = vec_mergeh(step3[0], step3[8]);
-  kernel.packet[1]  = vec_mergel(step3[0], step3[8]);
-  kernel.packet[2]  = vec_mergeh(step3[1], step3[9]);
-  kernel.packet[3]  = vec_mergel(step3[1], step3[9]);
-  kernel.packet[4]  = vec_mergeh(step3[2], step3[10]);
-  kernel.packet[5]  = vec_mergel(step3[2], step3[10]);
-  kernel.packet[6]  = vec_mergeh(step3[3], step3[11]);
-  kernel.packet[7]  = vec_mergel(step3[3], step3[11]);
-  kernel.packet[8]  = vec_mergeh(step3[4], step3[12]);
-  kernel.packet[9]  = vec_mergel(step3[4], step3[12]);
-  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
-  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
-  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
-  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
-  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
-  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16uc,16>& kernel) {
-  Packet16uc step1[16], step2[16], step3[16];
-
-  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
-  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
-  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
-  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
-  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
-  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
-  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
-  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
-  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
-  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
-  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
-  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
-  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
-  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
-  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
-  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
-
-  step2[0]  = vec_mergeh(step1[0], step1[8]);
-  step2[1]  = vec_mergel(step1[0], step1[8]);
-  step2[2]  = vec_mergeh(step1[1], step1[9]);
-  step2[3]  = vec_mergel(step1[1], step1[9]);
-  step2[4]  = vec_mergeh(step1[2], step1[10]);
-  step2[5]  = vec_mergel(step1[2], step1[10]);
-  step2[6]  = vec_mergeh(step1[3], step1[11]);
-  step2[7]  = vec_mergel(step1[3], step1[11]);
-  step2[8]  = vec_mergeh(step1[4], step1[12]);
-  step2[9]  = vec_mergel(step1[4], step1[12]);
-  step2[10] = vec_mergeh(step1[5], step1[13]);
-  step2[11] = vec_mergel(step1[5], step1[13]);
-  step2[12] = vec_mergeh(step1[6], step1[14]);
-  step2[13] = vec_mergel(step1[6], step1[14]);
-  step2[14] = vec_mergeh(step1[7], step1[15]);
-  step2[15] = vec_mergel(step1[7], step1[15]);
-
-  step3[0]  = vec_mergeh(step2[0], step2[8]);
-  step3[1]  = vec_mergel(step2[0], step2[8]);
-  step3[2]  = vec_mergeh(step2[1], step2[9]);
-  step3[3]  = vec_mergel(step2[1], step2[9]);
-  step3[4]  = vec_mergeh(step2[2], step2[10]);
-  step3[5]  = vec_mergel(step2[2], step2[10]);
-  step3[6]  = vec_mergeh(step2[3], step2[11]);
-  step3[7]  = vec_mergel(step2[3], step2[11]);
-  step3[8]  = vec_mergeh(step2[4], step2[12]);
-  step3[9]  = vec_mergel(step2[4], step2[12]);
-  step3[10] = vec_mergeh(step2[5], step2[13]);
-  step3[11] = vec_mergel(step2[5], step2[13]);
-  step3[12] = vec_mergeh(step2[6], step2[14]);
-  step3[13] = vec_mergel(step2[6], step2[14]);
-  step3[14] = vec_mergeh(step2[7], step2[15]);
-  step3[15] = vec_mergel(step2[7], step2[15]);
-
-  kernel.packet[0]  = vec_mergeh(step3[0], step3[8]);
-  kernel.packet[1]  = vec_mergel(step3[0], step3[8]);
-  kernel.packet[2]  = vec_mergeh(step3[1], step3[9]);
-  kernel.packet[3]  = vec_mergel(step3[1], step3[9]);
-  kernel.packet[4]  = vec_mergeh(step3[2], step3[10]);
-  kernel.packet[5]  = vec_mergel(step3[2], step3[10]);
-  kernel.packet[6]  = vec_mergeh(step3[3], step3[11]);
-  kernel.packet[7]  = vec_mergel(step3[3], step3[11]);
-  kernel.packet[8]  = vec_mergeh(step3[4], step3[12]);
-  kernel.packet[9]  = vec_mergel(step3[4], step3[12]);
-  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
-  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
-  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
-  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
-  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
-  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE
-Packet pblend4(const Selector<4>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
-  Packet4ui mask = reinterpret_cast<Packet4ui>(vec_cmpeq(reinterpret_cast<Packet4ui>(select), reinterpret_cast<Packet4ui>(p4i_ONE)));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
-  return pblend4<Packet4i>(ifPacket, thenPacket, elsePacket);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  return pblend4<Packet4f>(ifPacket, thenPacket, elsePacket);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s pblend(const Selector<8>& ifPacket, const Packet8s& thenPacket, const Packet8s& elsePacket) {
-  Packet8us select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7] };
-  Packet8us mask = reinterpret_cast<Packet8us>(vec_cmpeq(select, p8us_ONE));
-  Packet8s result = vec_sel(elsePacket, thenPacket, mask);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pblend(const Selector<8>& ifPacket, const Packet8us& thenPacket, const Packet8us& elsePacket) {
-  Packet8us select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7] };
-  Packet8us mask = reinterpret_cast<Packet8us>(vec_cmpeq(reinterpret_cast<Packet8us>(select), p8us_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pblend(const Selector<8>& ifPacket, const Packet8bf& thenPacket, const Packet8bf& elsePacket) {
-  return pblend<Packet8us>(ifPacket, thenPacket, elsePacket);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c pblend(const Selector<16>& ifPacket, const Packet16c& thenPacket, const Packet16c& elsePacket) {
-  Packet16uc select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7],
-                       ifPacket.select[8], ifPacket.select[9], ifPacket.select[10], ifPacket.select[11],
-                       ifPacket.select[12], ifPacket.select[13], ifPacket.select[14], ifPacket.select[15] };
-
-  Packet16uc mask = reinterpret_cast<Packet16uc>(vec_cmpeq(reinterpret_cast<Packet16uc>(select), p16uc_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc pblend(const Selector<16>& ifPacket, const Packet16uc& thenPacket, const Packet16uc& elsePacket) {
-  Packet16uc select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7],
-                       ifPacket.select[8], ifPacket.select[9], ifPacket.select[10], ifPacket.select[11],
-                       ifPacket.select[12], ifPacket.select[13], ifPacket.select[14], ifPacket.select[15] };
-
-  Packet16uc mask = reinterpret_cast<Packet16uc>(vec_cmpeq(reinterpret_cast<Packet16uc>(select), p16uc_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template <>
-struct type_casting_traits<float, int> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<int, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<bfloat16, unsigned short int> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<unsigned short int, bfloat16> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
-  return vec_cts(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
-  return vec_ctu(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
-  return vec_ctf(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
-  return vec_ctf(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pcast<Packet8bf, Packet8us>(const Packet8bf& a) {
-  Packet4f float_even = Bf16ToF32Even(a);
-  Packet4f float_odd = Bf16ToF32Odd(a);
-  Packet4ui int_even = pcast<Packet4f, Packet4ui>(float_even);
-  Packet4ui int_odd = pcast<Packet4f, Packet4ui>(float_odd);
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
-  Packet4ui low_even = pand<Packet4ui>(int_even, p4ui_low_mask);
-  Packet4ui low_odd = pand<Packet4ui>(int_odd, p4ui_low_mask);
-
-  //Check values that are bigger than USHRT_MAX (0xFFFF)
-  Packet4bi overflow_selector;
-  if(vec_any_gt(int_even, p4ui_low_mask)){
-    overflow_selector = vec_cmpgt(int_even, p4ui_low_mask);
-    low_even = vec_sel(low_even, p4ui_low_mask, overflow_selector);
-  }
-  if(vec_any_gt(int_odd, p4ui_low_mask)){
-    overflow_selector = vec_cmpgt(int_odd, p4ui_low_mask);
-    low_odd = vec_sel(low_even, p4ui_low_mask, overflow_selector);
-  }
-
-  low_odd = plogical_shift_left<16>(low_odd);
-
-  Packet4ui int_final = por<Packet4ui>(low_even, low_odd);
-  return reinterpret_cast<Packet8us>(int_final);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcast<Packet8us, Packet8bf>(const Packet8us& a) {
-  //short -> int -> float -> bfloat16
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
-  Packet4ui int_cast = reinterpret_cast<Packet4ui>(a);
-  Packet4ui int_even = pand<Packet4ui>(int_cast, p4ui_low_mask);
-  Packet4ui int_odd = plogical_shift_right<16>(int_cast);
-  Packet4f float_even = pcast<Packet4ui, Packet4f>(int_even);
-  Packet4f float_odd = pcast<Packet4ui, Packet4f>(int_odd);
-  return F32ToBf16(float_even, float_odd);
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet4f>(const Packet4f& a) {
-  return reinterpret_cast<Packet4i>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f,Packet4i>(const Packet4i& a) {
-  return reinterpret_cast<Packet4f>(a);
-}
-
-
-
-//---------- double ----------
-#ifdef __VSX__
-typedef __vector double              Packet2d;
-typedef __vector unsigned long long  Packet2ul;
-typedef __vector long long           Packet2l;
-#if EIGEN_COMP_CLANG
-typedef Packet2ul                    Packet2bl;
-#else
-typedef __vector __bool long         Packet2bl;
-#endif
-
-static Packet2l  p2l_ONE  = { 1, 1 };
-static Packet2l  p2l_ZERO = reinterpret_cast<Packet2l>(p4i_ZERO);
-static Packet2ul p2ul_SIGN = { 0x8000000000000000ull, 0x8000000000000000ull };
-static Packet2ul p2ul_PREV0DOT5 = { 0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull };
-static Packet2d  p2d_ONE  = { 1.0, 1.0 };
-static Packet2d  p2d_ZERO = reinterpret_cast<Packet2d>(p4f_ZERO);
-static Packet2d  p2d_MZERO = { numext::bit_cast<double>(0x8000000000000000ull),
-                               numext::bit_cast<double>(0x8000000000000000ull) };
-
-#ifdef _BIG_ENDIAN
-static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ZERO), reinterpret_cast<Packet4f>(p2d_ONE), 8));
-#else
-static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ONE), reinterpret_cast<Packet4f>(p2d_ZERO), 8));
-#endif
-
-template<int index> Packet2d vec_splat_dbl(Packet2d& a)
-{
-  return vec_splat(a, index);
-}
-
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 1,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 1,
-    HasMin  = 1,
-    HasMax  = 1,
-    HasAbs  = 1,
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 0,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2d half; };
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2l & v)
-{
-  union {
-    Packet2l   v;
-    int64_t n[2];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
-{
-  union {
-    Packet2d   v;
-    double n[2];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1];
-  return s;
-}
-
-// Need to define them first or we get specialization after instantiation errors
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD
-  return vec_xl(0, const_cast<double *>(from)); // cast needed by Clang
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<double>(double*   to, const Packet2d& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE
-  vec_xst(from, 0, to);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double&  from) {
-  Packet2d v = {from, from};
-  return v;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(unsigned long from) {
-  Packet2l v = {static_cast<long long>(from), static_cast<long long>(from)};
-  return reinterpret_cast<Packet2d>(v);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet2d>(const double *a,
-                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-  //This way is faster than vec_splat (at least for doubles in Power 9)
-  a0 = pset1<Packet2d>(a[0]);
-  a1 = pset1<Packet2d>(a[1]);
-  a2 = pset1<Packet2d>(a[2]);
-  a3 = pset1<Packet2d>(a[3]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
-  EIGEN_ALIGN16 double af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
- return pload<Packet2d>(af);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  EIGEN_ALIGN16 double af[2];
-  pstore<double>(af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return pset1<Packet2d>(a) + p2d_COUNTDOWN; }
-
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return a + b; }
-
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return a - b; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return p2d_ZERO - a; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_MZERO); }
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_div(a,b); }
-
-// for some weird raisons, it has to be overloaded for packet of integers
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b)
-{
-  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
-  Packet2d ret;
-  __asm__ ("xvcmpgedp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
- }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b)
-{
-  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
-  Packet2d ret;
-  __asm__ ("xvcmpgtdp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) {
-  Packet2d c = reinterpret_cast<Packet2d>(vec_cmpge(a,b));
-  return vec_nor(c,c);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a)
-{
-    Packet2d t = vec_add(reinterpret_cast<Packet2d>(vec_or(vec_and(reinterpret_cast<Packet2ul>(a), p2ul_SIGN), p2ul_PREV0DOT5)), a);
-    Packet2d res;
-
-    __asm__("xvrdpiz %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (t));
-
-    return res;
-}
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const  Packet2d& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return vec_floor(a); }
-template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a)
-{
-    Packet2d res;
-
-    __asm__("xvrdpic %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (a));
-
-    return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return vec_xl(0, const_cast<double*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*   from)
-{
-  Packet2d p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet2d>(from);
-  else                                  p = ploadu<Packet2d>(from);
-  return vec_splat_dbl<0>(p);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double*  to, const Packet2d& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE
-  vec_xst(from, 0, to);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE double  pfirst<Packet2d>(const Packet2d& a) { EIGEN_ALIGN16 double x[2]; pstore<double>(x, a); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
-{
-  return reinterpret_cast<Packet2d>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE64));
-}
-template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vec_abs(a); }
-
-// VSX support varies between different compilers and even different
-// versions of the same compiler.  For gcc version >= 4.9.3, we can use
-// vec_cts to efficiently convert Packet2d to Packet2l.  Otherwise, use
-// a slow version that works with older compilers. 
-// Update: apparently vec_cts/vec_ctf intrinsics for 64-bit doubles
-// are buggy, https://gcc.gnu.org/bugzilla/show_bug.cgi?id=70963
-template<>
-inline Packet2l pcast<Packet2d, Packet2l>(const Packet2d& x) {
-#if EIGEN_GNUC_AT_LEAST(5, 4) || \
-    (EIGEN_GNUC_AT(6, 1) && __GNUC_PATCHLEVEL__ >= 1)
-  return vec_cts(x, 0);    // TODO: check clang version.
-#else
-  double tmp[2];
-  memcpy(tmp, &x, sizeof(tmp));
-  Packet2l l = { static_cast<long long>(tmp[0]),
-                 static_cast<long long>(tmp[1]) };
-  return l;
-#endif
-}
-
-template<>
-inline Packet2d pcast<Packet2l, Packet2d>(const Packet2l& x) {
-  unsigned long long tmp[2];
-  memcpy(tmp, &x, sizeof(tmp));
-  Packet2d d = { static_cast<double>(tmp[0]),
-                 static_cast<double>(tmp[1]) };
-  return d;
-}
-
-
-// Packet2l shifts.
-// For POWER8 we simply use vec_sr/l. 
-//
-// Things are more complicated for POWER7. There is actually a
-// vec_xxsxdi intrinsic but it is not supported by some gcc versions.
-// So we need to shift by N % 32 and rearrage bytes.
-#ifdef __POWER8_VECTOR__
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
-  const Packet2ul shift = { N, N };
-  return vec_sl(a, shift); 
-}
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
-  const Packet2ul shift = { N, N };
-  return vec_sr(a, shift); 
-}
-
-#else
-
-// Shifts [A, B, C, D] to [B, 0, D, 0].
-// Used to implement left shifts for Packet2l.
-EIGEN_ALWAYS_INLINE Packet4i shift_even_left(const Packet4i& a) {
-  static const Packet16uc perm = {
-      0x14, 0x15, 0x16, 0x17, 0x00, 0x01, 0x02, 0x03, 
-      0x1c, 0x1d, 0x1e, 0x1f, 0x08, 0x09, 0x0a, 0x0b };
-  #ifdef  _BIG_ENDIAN
-    return vec_perm(p4i_ZERO, a, perm);
-  #else
-    return vec_perm(a, p4i_ZERO, perm);
-  #endif
-}
-
-// Shifts [A, B, C, D] to [0, A, 0, C].
-// Used to implement right shifts for Packet2l.
-EIGEN_ALWAYS_INLINE Packet4i shift_odd_right(const Packet4i& a) {
-  static const Packet16uc perm = {
-      0x04, 0x05, 0x06, 0x07, 0x10, 0x11, 0x12, 0x13, 
-      0x0c, 0x0d, 0x0e, 0x0f, 0x18, 0x19, 0x1a, 0x1b };
-  #ifdef  _BIG_ENDIAN
-    return vec_perm(p4i_ZERO, a, perm);
-  #else
-    return vec_perm(a, p4i_ZERO, perm);
-  #endif
-}
-
-template<int N, typename EnableIf = void>
-struct plogical_shift_left_impl;
-
-template<int N>
-struct plogical_shift_left_impl<N, typename enable_if<(N < 32) && (N >= 0)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned n = static_cast<unsigned>(N);
-    const Packet4ui shift = {n, n, n, n};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    static const unsigned m = static_cast<unsigned>(32 - N);
-    const Packet4ui shift_right = {m, m, m, m};
-    const Packet4i out_hi = vec_sl(ai, shift);
-    const Packet4i out_lo = shift_even_left(vec_sr(ai, shift_right));
-    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
-  }
-};
-
-template<int N>
-struct plogical_shift_left_impl<N, typename enable_if<(N >= 32)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned m = static_cast<unsigned>(N - 32);
-    const Packet4ui shift = {m, m, m, m};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    return reinterpret_cast<Packet2l>(shift_even_left(vec_sl(ai, shift)));
-  }
-};
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
-  return plogical_shift_left_impl<N>::run(a); 
-}
-
-template<int N, typename EnableIf = void>
-struct plogical_shift_right_impl;
-
-template<int N>
-struct plogical_shift_right_impl<N, typename enable_if<(N < 32) && (N >= 0)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned n = static_cast<unsigned>(N);
-    const Packet4ui shift = {n, n, n, n};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    static const unsigned m = static_cast<unsigned>(32 - N);
-    const Packet4ui shift_left = {m, m, m, m};
-    const Packet4i out_lo = vec_sr(ai, shift);
-    const Packet4i out_hi = shift_odd_right(vec_sl(ai, shift_left));
-    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
-  }
-};
-
-template<int N>
-struct plogical_shift_right_impl<N, typename enable_if<(N >= 32)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned m = static_cast<unsigned>(N - 32);
-    const Packet4ui shift = {m, m, m, m};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    return reinterpret_cast<Packet2l>(shift_odd_right(vec_sr(ai, shift)));
-  }
-};
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
-  return plogical_shift_right_impl<N>::run(a); 
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
-  const Packet2l e = pcast<Packet2d, Packet2l>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-
-  // Split 2^e into four factors and multiply:
-  const Packet2l  bias = { 1023, 1023 };
-  Packet2l b = plogical_shift_right<2>(e);  // floor(e/4)
-  Packet2d c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias));
-  Packet2d out = pmul(pmul(pmul(a, c), c), c); // a * 2^(3b)
-  b = psub(psub(psub(e, b), b), b);  // e - 3b
-  c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias)); // 2^(e - 3b)
-  out = pmul(out, c); // a * 2^e
-  return out;
-}
-
-
-// Extract exponent without existence of Packet2l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
-  return pcast<Packet2l, Packet2d>(plogical_shift_right<52>(reinterpret_cast<Packet2l>(pabs(a))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d> (const Packet2d& a, Packet2d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{
-  Packet2d b, sum;
-  b   = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(a), reinterpret_cast<Packet4f>(a), 8));
-  sum = a + b;
-  return pfirst<Packet2d>(sum);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmin(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
-}
-
-// max
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmax(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2d,2>& kernel) {
-  Packet2d t0, t1;
-  t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
-  t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
-  kernel.packet[0] = t0;
-  kernel.packet[1] = t1;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
-  Packet2l select = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2bl mask = reinterpret_cast<Packet2bl>( vec_cmpeq(reinterpret_cast<Packet2d>(select), reinterpret_cast<Packet2d>(p2l_ONE)) );
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-
-#endif // __VSX__
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/CUDA/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/CUDA/Complex.h
deleted file mode 100644
index deb4c86..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/CUDA/Complex.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2021 C. Antonio Sanchez <cantonios@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_CUDA_H
-#define EIGEN_COMPLEX_CUDA_H
-
-// clang-format off
-// Many std::complex methods such as operator+, operator-, operator* and
-// operator/ are not constexpr. Due to this, GCC and older versions of clang do
-// not treat them as device functions and thus Eigen functors making use of
-// these operators fail to compile. Here, we manually specialize these
-// operators and functors for complex types when building for CUDA to enable
-// their use on-device.
-
-#if defined(EIGEN_CUDACC) && defined(EIGEN_GPU_COMPILE_PHASE)
-    
-// ICC already specializes std::complex<float> and std::complex<double>
-// operators, preventing us from making them device functions here.
-// This will lead to silent runtime errors if the operators are used on device.
-//
-// To allow std::complex operator use on device, define _OVERRIDE_COMPLEX_SPECIALIZATION_
-// prior to first inclusion of <complex>.  This prevents ICC from adding
-// its own specializations, so our custom ones below can be used instead.
-#if !(defined(EIGEN_COMP_ICC) && defined(_USE_COMPLEX_SPECIALIZATION_))
-
-// Import Eigen's internal operator specializations.
-#define EIGEN_USING_STD_COMPLEX_OPERATORS           \
-  using Eigen::complex_operator_detail::operator+;  \
-  using Eigen::complex_operator_detail::operator-;  \
-  using Eigen::complex_operator_detail::operator*;  \
-  using Eigen::complex_operator_detail::operator/;  \
-  using Eigen::complex_operator_detail::operator+=; \
-  using Eigen::complex_operator_detail::operator-=; \
-  using Eigen::complex_operator_detail::operator*=; \
-  using Eigen::complex_operator_detail::operator/=; \
-  using Eigen::complex_operator_detail::operator==; \
-  using Eigen::complex_operator_detail::operator!=;
-
-namespace Eigen {
-
-// Specialized std::complex overloads.
-namespace complex_operator_detail {
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_multiply(const std::complex<T>& a, const std::complex<T>& b) {
-  const T a_real = numext::real(a);
-  const T a_imag = numext::imag(a);
-  const T b_real = numext::real(b);
-  const T b_imag = numext::imag(b);
-  return std::complex<T>(
-      a_real * b_real - a_imag * b_imag,
-      a_imag * b_real + a_real * b_imag);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_divide_fast(const std::complex<T>& a, const std::complex<T>& b) {
-  const T a_real = numext::real(a);
-  const T a_imag = numext::imag(a);
-  const T b_real = numext::real(b);
-  const T b_imag = numext::imag(b);
-  const T norm = (b_real * b_real + b_imag * b_imag);
-  return std::complex<T>((a_real * b_real + a_imag * b_imag) / norm,
-                          (a_imag * b_real - a_real * b_imag) / norm);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_divide_stable(const std::complex<T>& a, const std::complex<T>& b) {
-  const T a_real = numext::real(a);
-  const T a_imag = numext::imag(a);
-  const T b_real = numext::real(b);
-  const T b_imag = numext::imag(b);
-  // Smith's complex division (https://arxiv.org/pdf/1210.4539.pdf),
-  // guards against over/under-flow.
-  const bool scale_imag = numext::abs(b_imag) <= numext::abs(b_real);
-  const T rscale = scale_imag ? T(1) : b_real / b_imag;
-  const T iscale = scale_imag ? b_imag / b_real : T(1);
-  const T denominator = b_real * rscale + b_imag * iscale;
-  return std::complex<T>((a_real * rscale + a_imag * iscale) / denominator, 
-                         (a_imag * rscale - a_real * iscale) / denominator);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_divide(const std::complex<T>& a, const std::complex<T>& b) {
-#if EIGEN_FAST_MATH
-  return complex_divide_fast(a, b);
-#else
-  return complex_divide_stable(a, b);
-#endif
-}
-
-// NOTE: We cannot specialize compound assignment operators with Scalar T,
-//         (i.e.  operator@=(const T&), for @=+,-,*,/)
-//       since they are already specialized for float/double/long double within
-//       the standard <complex> header. We also do not specialize the stream
-//       operators.
-#define EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(T)                                    \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const std::complex<T>& a) { return a; }                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const std::complex<T>& a) {                                           \
-  return std::complex<T>(-numext::real(a), -numext::imag(a));                                   \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return std::complex<T>(numext::real(a) + numext::real(b), numext::imag(a) + numext::imag(b)); \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) + b, numext::imag(a));                                 \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const T& a, const std::complex<T>& b) {                               \
-  return std::complex<T>(a + numext::real(b), numext::imag(b));                                 \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return std::complex<T>(numext::real(a) - numext::real(b), numext::imag(a) - numext::imag(b)); \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) - b, numext::imag(a));                                 \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const T& a, const std::complex<T>& b) {                               \
-  return std::complex<T>(a - numext::real(b), -numext::imag(b));                                \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator*(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return complex_multiply(a, b);                                                                \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator*(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) * b, numext::imag(a) * b);                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator*(const T& a, const std::complex<T>& b) {                               \
-  return std::complex<T>(a * numext::real(b), a * numext::imag(b));                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator/(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return complex_divide(a, b);                                                                  \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator/(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) / b, numext::imag(a) / b);                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator/(const T& a, const std::complex<T>& b) {                               \
-  return complex_divide(std::complex<T>(a, 0), b);                                              \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator+=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  numext::real_ref(a) += numext::real(b);                                                       \
-  numext::imag_ref(a) += numext::imag(b);                                                       \
-  return a;                                                                                     \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator-=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  numext::real_ref(a) -= numext::real(b);                                                       \
-  numext::imag_ref(a) -= numext::imag(b);                                                       \
-  return a;                                                                                     \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator*=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  a = complex_multiply(a, b);                                                                   \
-  return a;                                                                                     \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator/=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  a = complex_divide(a, b);                                                                     \
-  return  a;                                                                                    \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator==(const std::complex<T>& a, const std::complex<T>& b) {                           \
-  return numext::real(a) == numext::real(b) && numext::imag(a) == numext::imag(b);              \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator==(const std::complex<T>& a, const T& b) {                                         \
-  return numext::real(a) == b && numext::imag(a) == 0;                                          \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator==(const T& a, const std::complex<T>& b) {                                         \
-  return a  == numext::real(b) && 0 == numext::imag(b);                                         \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator!=(const std::complex<T>& a, const std::complex<T>& b) {                           \
-  return !(a == b);                                                                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator!=(const std::complex<T>& a, const T& b) {                                         \
-  return !(a == b);                                                                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator!=(const T& a, const std::complex<T>& b) {                                         \
-  return !(a == b);                                                                             \
-}
-
-// Do not specialize for long double, since that reduces to double on device.
-EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(float)
-EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(double)
-
-#undef EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS
-
-  
-}  // namespace complex_operator_detail
-
-EIGEN_USING_STD_COMPLEX_OPERATORS
-
-namespace numext {
-EIGEN_USING_STD_COMPLEX_OPERATORS
-}  // namespace numext
-
-namespace internal {
-EIGEN_USING_STD_COMPLEX_OPERATORS
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // !(EIGEN_COMP_ICC && _USE_COMPLEX_SPECIALIZATION_)
-
-#endif  // EIGEN_CUDACC && EIGEN_GPU_COMPILE_PHASE
-
-#endif  // EIGEN_COMPLEX_CUDA_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/BFloat16.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/BFloat16.h
deleted file mode 100644
index 1c28f4f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/BFloat16.h
+++ /dev/null
@@ -1,700 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef EIGEN_BFLOAT16_H
-#define EIGEN_BFLOAT16_H
-
-#define BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, METHOD)         \
-  template <>                                                       \
-  EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED  \
-  PACKET_BF16 METHOD<PACKET_BF16>(const PACKET_BF16& _x) {          \
-    return F32ToBf16(METHOD<PACKET_F>(Bf16ToF32(_x)));              \
-  }
-
-namespace Eigen {
-
-struct bfloat16;
-
-namespace bfloat16_impl {
-
-// Make our own __bfloat16_raw definition.
-struct __bfloat16_raw {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() : value(0) {}
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(unsigned short raw) : value(raw) {}
-  unsigned short value;
-};
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(unsigned short value);
-template <bool AssumeArgumentIsNormalOrInfinityOrZero>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne(float ff);
-// Forward declarations of template specializations, to avoid Visual C++ 2019 errors, saying:
-// > error C2908: explicit specialization; 'float_to_bfloat16_rtne' has already been instantiated
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff);
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff);
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h);
-
-struct bfloat16_base : public __bfloat16_raw {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base() {}
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base(const __bfloat16_raw& h) : __bfloat16_raw(h) {}
-};
-
-} // namespace bfloat16_impl
-
-// Class definition.
-struct bfloat16 : public bfloat16_impl::bfloat16_base {
-
-  typedef bfloat16_impl::__bfloat16_raw __bfloat16_raw;
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const __bfloat16_raw& h) : bfloat16_impl::bfloat16_base(h) {}
-
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(bool b)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::raw_uint16_to_bfloat16(b ? 0x3f80 : 0)) {}
-
-  template<class T>
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(T val)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<internal::is_integral<T>::value>(static_cast<float>(val))) {}
-
-  explicit EIGEN_DEVICE_FUNC bfloat16(float f)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(f)) {}
-
-  // Following the convention of numpy, converting between complex and
-  // float will lead to loss of imag value.
-  template<typename RealScalar>
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const std::complex<RealScalar>& val)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(static_cast<float>(val.real()))) {}
-
-  EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
-    return bfloat16_impl::bfloat16_to_float(*this);
-  }
-};
-} // namespace Eigen
-
-namespace std {
-template<>
-struct numeric_limits<Eigen::bfloat16> {
-  static const bool is_specialized = true;
-  static const bool is_signed = true;
-  static const bool is_integer = false;
-  static const bool is_exact = false;
-  static const bool has_infinity = true;
-  static const bool has_quiet_NaN = true;
-  static const bool has_signaling_NaN = true;
-  static const float_denorm_style has_denorm = std::denorm_absent;
-  static const bool has_denorm_loss = false;
-  static const std::float_round_style round_style = numeric_limits<float>::round_style;
-  static const bool is_iec559 = false;
-  static const bool is_bounded = true;
-  static const bool is_modulo = false;
-  static const int digits = 8;
-  static const int digits10 = 2;
-  static const int max_digits10 = 4;
-  static const int radix = 2;
-  static const int min_exponent = numeric_limits<float>::min_exponent;
-  static const int min_exponent10 = numeric_limits<float>::min_exponent10;
-  static const int max_exponent = numeric_limits<float>::max_exponent;
-  static const int max_exponent10 = numeric_limits<float>::max_exponent10;
-  static const bool traps = numeric_limits<float>::traps;
-  static const bool tinyness_before = numeric_limits<float>::tinyness_before;
-
-  static Eigen::bfloat16 (min)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0080); }
-  static Eigen::bfloat16 lowest() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0xff7f); }
-  static Eigen::bfloat16 (max)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f7f); }
-  static Eigen::bfloat16 epsilon() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x3c00); }
-  static Eigen::bfloat16 round_error() { return Eigen::bfloat16(0x3f00); }
-  static Eigen::bfloat16 infinity() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f80); }
-  static Eigen::bfloat16 quiet_NaN() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0); }
-  static Eigen::bfloat16 signaling_NaN() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f81); }
-  static Eigen::bfloat16 denorm_min() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0001); }
-};
-
-// If std::numeric_limits<T> is specialized, should also specialize
-// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
-// std::numeric_limits<const volatile T>
-// https://stackoverflow.com/a/16519653/
-template<>
-struct numeric_limits<const Eigen::bfloat16> : numeric_limits<Eigen::bfloat16> {};
-template<>
-struct numeric_limits<volatile Eigen::bfloat16> : numeric_limits<Eigen::bfloat16> {};
-template<>
-struct numeric_limits<const volatile Eigen::bfloat16> : numeric_limits<Eigen::bfloat16> {};
-} // namespace std
-
-namespace Eigen {
-
-namespace bfloat16_impl {
-
-// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
-// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
-// of the functions, while the latter can only deal with one of them.
-#if !defined(EIGEN_HAS_NATIVE_BF16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC) // Emulate support for bfloat16 floats
-
-#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
-// We need to provide emulated *host-side* BF16 operators for clang.
-#pragma push_macro("EIGEN_DEVICE_FUNC")
-#undef EIGEN_DEVICE_FUNC
-#if defined(EIGEN_HAS_CUDA_BF16) && defined(EIGEN_HAS_NATIVE_BF16)
-#define EIGEN_DEVICE_FUNC __host__
-#else // both host and device need emulated ops.
-#define EIGEN_DEVICE_FUNC __host__ __device__
-#endif
-#endif
-
-// Definitions for CPUs, mostly working through conversion
-// to/from fp32.
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) + float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const bfloat16& a, const int& b) {
-  return bfloat16(float(a) + static_cast<float>(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const int& a, const bfloat16& b) {
-  return bfloat16(static_cast<float>(a) + float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator * (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) * float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator - (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) - float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator / (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) / float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator - (const bfloat16& a) {
-  bfloat16 result;
-  result.value = a.value ^ 0x8000;
-  return result;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator += (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) + float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator *= (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) * float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator -= (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) - float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator /= (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) / float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a) {
-  a += bfloat16(1);
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a) {
-  a -= bfloat16(1);
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a, int) {
-  bfloat16 original_value = a;
-  ++a;
-  return original_value;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a, int) {
-  bfloat16 original_value = a;
-  --a;
-  return original_value;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const bfloat16& a, const bfloat16& b) {
-  return numext::equal_strict(float(a),float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const bfloat16& a, const bfloat16& b) {
-  return numext::not_equal_strict(float(a), float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const bfloat16& a, const bfloat16& b) {
-  return float(a) < float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const bfloat16& a, const bfloat16& b) {
-  return float(a) <= float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const bfloat16& a, const bfloat16& b) {
-  return float(a) > float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const bfloat16& a, const bfloat16& b) {
-  return float(a) >= float(b);
-}
-
-#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
-#pragma pop_macro("EIGEN_DEVICE_FUNC")
-#endif
-#endif  // Emulate support for bfloat16 floats
-
-// Division by an index. Do it in full float precision to avoid accuracy
-// issues in converting the denominator to bfloat16.
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator / (const bfloat16& a, Index b) {
-  return bfloat16(static_cast<float>(a) / static_cast<float>(b));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw truncate_to_bfloat16(const float v) {
-  __bfloat16_raw output;
-  if (Eigen::numext::isnan EIGEN_NOT_A_MACRO(v)) {
-    output.value = std::signbit(v) ? 0xFFC0: 0x7FC0;
-    return output;
-  }
-  const uint16_t* p = reinterpret_cast<const uint16_t*>(&v);
-#if defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
-  output.value = p[0];
-#else
-  output.value = p[1];
-#endif
-  return output;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(numext::uint16_t value) {
-  return __bfloat16_raw(value);
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR numext::uint16_t raw_bfloat16_as_uint16(const __bfloat16_raw& bf) {
-  return bf.value;
-}
-
-// float_to_bfloat16_rtne template specialization that does not make any
-// assumption about the value of its function argument (ff).
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff) {
-#if (defined(EIGEN_HAS_CUDA_BF16) && defined(EIGEN_HAS_HIP_BF16))
-  // Nothing to do here
-#else
-  __bfloat16_raw output;
-
-  if (Eigen::numext::isnan EIGEN_NOT_A_MACRO(ff)) {
-    // If the value is a NaN, squash it to a qNaN with msb of fraction set,
-    // this makes sure after truncation we don't end up with an inf.
-    //
-    // qNaN magic: All exponent bits set + most significant bit of fraction
-    // set.
-    output.value = std::signbit(ff) ? 0xFFC0: 0x7FC0;
-  } else {
-    // Fast rounding algorithm that rounds a half value to nearest even. This
-    // reduces expected error when we convert a large number of floats. Here
-    // is how it works:
-    //
-    // Definitions:
-    // To convert a float 32 to bfloat16, a float 32 can be viewed as 32 bits
-    // with the following tags:
-    //
-    // Sign |  Exp (8 bits) | Frac (23 bits)
-    //  S     EEEEEEEE         FFFFFFLRTTTTTTTTTTTTTTT
-    //
-    //  S: Sign bit.
-    //  E: Exponent bits.
-    //  F: First 6 bits of fraction.
-    //  L: Least significant bit of resulting bfloat16 if we truncate away the
-    //  rest of the float32. This is also the 7th bit of fraction
-    //  R: Rounding bit, 8th bit of fraction.
-    //  T: Sticky bits, rest of fraction, 15 bits.
-    //
-    // To round half to nearest even, there are 3 cases where we want to round
-    // down (simply truncate the result of the bits away, which consists of
-    // rounding bit and sticky bits) and two cases where we want to round up
-    // (truncate then add one to the result).
-    //
-    // The fast converting algorithm simply adds lsb (L) to 0x7fff (15 bits of
-    // 1s) as the rounding bias, adds the rounding bias to the input, then
-    // truncates the last 16 bits away.
-    //
-    // To understand how it works, we can analyze this algorithm case by case:
-    //
-    // 1. L = 0, R = 0:
-    //   Expect: round down, this is less than half value.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 0 = 0x7fff
-    //   - Adding rounding bias to input may create any carry, depending on
-    //   whether there is any value set to 1 in T bits.
-    //   - R may be set to 1 if there is a carry.
-    //   - L remains 0.
-    //   - Note that this case also handles Inf and -Inf, where all fraction
-    //   bits, including L, R and Ts are all 0. The output remains Inf after
-    //   this algorithm.
-    //
-    // 2. L = 1, R = 0:
-    //   Expect: round down, this is less than half value.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 1 = 0x8000
-    //   - Adding rounding bias to input doesn't change sticky bits but
-    //   adds 1 to rounding bit.
-    //   - L remains 1.
-    //
-    // 3. L = 0, R = 1, all of T are 0:
-    //   Expect: round down, this is exactly at half, the result is already
-    //   even (L=0).
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 0 = 0x7fff
-    //   - Adding rounding bias to input sets all sticky bits to 1, but
-    //   doesn't create a carry.
-    //   - R remains 1.
-    //   - L remains 0.
-    //
-    // 4. L = 1, R = 1:
-    //   Expect: round up, this is exactly at half, the result needs to be
-    //   round to the next even number.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 1 = 0x8000
-    //   - Adding rounding bias to input doesn't change sticky bits, but
-    //   creates a carry from rounding bit.
-    //   - The carry sets L to 0, creates another carry bit and propagate
-    //   forward to F bits.
-    //   - If all the F bits are 1, a carry then propagates to the exponent
-    //   bits, which then creates the minimum value with the next exponent
-    //   value. Note that we won't have the case where exponents are all 1,
-    //   since that's either a NaN (handled in the other if condition) or inf
-    //   (handled in case 1).
-    //
-    // 5. L = 0, R = 1, any of T is 1:
-    //   Expect: round up, this is greater than half.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 0 = 0x7fff
-    //   - Adding rounding bias to input creates a carry from sticky bits,
-    //   sets rounding bit to 0, then create another carry.
-    //   - The second carry sets L to 1.
-    //
-    // Examples:
-    //
-    //  Exact half value that is already even:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0     1000000000000000
-    //
-    //     This falls into case 3. We truncate the rest of 16 bits and no
-    //     carry is created into F and L:
-    //
-    //    Output:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
-    //
-    //  Exact half value, round to next even number:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 0 1     1000000000000000
-    //
-    //     This falls into case 4. We create a carry from R and T,
-    //     which then propagates into L and F:
-    //
-    //    Output:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
-    //
-    //
-    //  Max denormal value round to min normal value:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     0 0 0 0 0 0 0 0      1 1 1 1 1 1 1     1111111111111111
-    //
-    //     This falls into case 4. We create a carry from R and T,
-    //     propagate into L and F, which then propagates into exponent
-    //     bits:
-    //
-    //    Output:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     0 0 0 0 0 0 0 1      0 0 0 0 0 0 0
-    //
-    //  Max normal value round to Inf:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     1 1 1 1 1 1 1 0      1 1 1 1 1 1 1     1111111111111111
-    //
-    //     This falls into case 4. We create a carry from R and T,
-    //     propagate into L and F, which then propagates into exponent
-    //     bits:
-    //
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     1 1 1 1 1 1 1 1      0 0 0 0 0 0 0
-
-    // At this point, ff must be either a normal float, or +/-infinity.
-    output = float_to_bfloat16_rtne<true>(ff);
-  }
-  return output;
-#endif
-}
-
-// float_to_bfloat16_rtne template specialization that assumes that its function
-// argument (ff) is either a normal floating point number, or +/-infinity, or
-// zero. Used to improve the runtime performance of conversion from an integer
-// type to bfloat16.
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff) {
-#if (defined(EIGEN_HAS_CUDA_BF16) && defined(EIGEN_HAS_HIP_BF16))
-    // Nothing to do here
-#else
-    numext::uint32_t input = numext::bit_cast<numext::uint32_t>(ff);
-    __bfloat16_raw output;
-
-    // Least significant bit of resulting bfloat.
-    numext::uint32_t lsb = (input >> 16) & 1;
-    numext::uint32_t rounding_bias = 0x7fff + lsb;
-    input += rounding_bias;
-    output.value = static_cast<numext::uint16_t>(input >> 16);
-    return output;
-#endif
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h) {
-    float result = 0;
-    unsigned short* q = reinterpret_cast<unsigned short*>(&result);
-#if defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
-    q[0] = h.value;
-#else
-    q[1] = h.value;
-#endif
-    return result;
-}
-// --- standard functions ---
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const bfloat16& a) {
-  EIGEN_USING_STD(isinf);
-  return (isinf)(float(a));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const bfloat16& a) {
-  EIGEN_USING_STD(isnan);
-  return (isnan)(float(a));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const bfloat16& a) {
-  return !(isinf EIGEN_NOT_A_MACRO (a)) && !(isnan EIGEN_NOT_A_MACRO (a));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 abs(const bfloat16& a) {
-  bfloat16 result;
-  result.value = a.value & 0x7FFF;
-  return result;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 exp(const bfloat16& a) {
-   return bfloat16(::expf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 expm1(const bfloat16& a) {
-  return bfloat16(numext::expm1(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log(const bfloat16& a) {
-  return bfloat16(::logf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log1p(const bfloat16& a) {
-  return bfloat16(numext::log1p(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log10(const bfloat16& a) {
-  return bfloat16(::log10f(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log2(const bfloat16& a) {
-  return bfloat16(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sqrt(const bfloat16& a) {
-    return bfloat16(::sqrtf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 pow(const bfloat16& a, const bfloat16& b) {
-  return bfloat16(::powf(float(a), float(b)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sin(const bfloat16& a) {
-  return bfloat16(::sinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cos(const bfloat16& a) {
-  return bfloat16(::cosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tan(const bfloat16& a) {
-  return bfloat16(::tanf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asin(const bfloat16& a) {
-  return bfloat16(::asinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acos(const bfloat16& a) {
-  return bfloat16(::acosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atan(const bfloat16& a) {
-  return bfloat16(::atanf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sinh(const bfloat16& a) {
-  return bfloat16(::sinhf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cosh(const bfloat16& a) {
-  return bfloat16(::coshf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tanh(const bfloat16& a) {
-  return bfloat16(::tanhf(float(a)));
-}
-#if EIGEN_HAS_CXX11_MATH
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asinh(const bfloat16& a) {
-  return bfloat16(::asinhf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acosh(const bfloat16& a) {
-  return bfloat16(::acoshf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atanh(const bfloat16& a) {
-  return bfloat16(::atanhf(float(a)));
-}
-#endif
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 floor(const bfloat16& a) {
-  return bfloat16(::floorf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 ceil(const bfloat16& a) {
-  return bfloat16(::ceilf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 rint(const bfloat16& a) {
-  return bfloat16(::rintf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 round(const bfloat16& a) {
-  return bfloat16(::roundf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmod(const bfloat16& a, const bfloat16& b) {
-  return bfloat16(::fmodf(float(a), float(b)));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 (min)(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f2 < f1 ? b : a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 (max)(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f1 < f2 ? b : a;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmin(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return bfloat16(::fminf(f1, f2));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmax(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return bfloat16(::fmaxf(f1, f2));
-}
-
-#ifndef EIGEN_NO_IO
-EIGEN_ALWAYS_INLINE std::ostream& operator << (std::ostream& os, const bfloat16& v) {
-  os << static_cast<float>(v);
-  return os;
-}
-#endif
-
-} // namespace bfloat16_impl
-
-namespace internal {
-
-template<>
-struct random_default_impl<bfloat16, false, false>
-{
-  static inline bfloat16 run(const bfloat16& x, const bfloat16& y)
-  {
-    return x + (y-x) * bfloat16(float(std::rand()) / float(RAND_MAX));
-  }
-  static inline bfloat16 run()
-  {
-    return run(bfloat16(-1.f), bfloat16(1.f));
-  }
-};
-
-template<> struct is_arithmetic<bfloat16> { enum { value = true }; };
-
-} // namespace internal
-
-template<> struct NumTraits<Eigen::bfloat16>
-    : GenericNumTraits<Eigen::bfloat16>
-{
-  enum {
-    IsSigned = true,
-    IsInteger = false,
-    IsComplex = false,
-    RequireInitialization = false
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 epsilon() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x3c00);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 dummy_precision() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x3D4D);  // bfloat16(5e-2f);
-
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 highest() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x7F7F);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 lowest() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0xFF7F);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 infinity() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x7f80);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 quiet_NaN() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0);
-  }
-};
-
-} // namespace Eigen
-
-namespace Eigen {
-namespace numext {
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool (isnan)(const Eigen::bfloat16& h) {
-  return (bfloat16_impl::isnan)(h);
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool (isinf)(const Eigen::bfloat16& h) {
-  return (bfloat16_impl::isinf)(h);
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool (isfinite)(const Eigen::bfloat16& h) {
-  return (bfloat16_impl::isfinite)(h);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bit_cast<Eigen::bfloat16, uint16_t>(const uint16_t& src) {
-  return Eigen::bfloat16(Eigen::bfloat16_impl::raw_uint16_to_bfloat16(src));
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::bfloat16>(const Eigen::bfloat16& src) {
-  return Eigen::bfloat16_impl::raw_bfloat16_as_uint16(src);
-}
-
-}  // namespace numext
-}  // namespace Eigen
-
-#if EIGEN_HAS_STD_HASH
-namespace std {
-template <>
-struct hash<Eigen::bfloat16> {
-  EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::bfloat16& a) const {
-    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
-  }
-};
-} // namespace std
-#endif
-
-
-#endif // EIGEN_BFLOAT16_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/ConjHelper.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/ConjHelper.h
deleted file mode 100644
index 53830b5..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/ConjHelper.h
+++ /dev/null
@@ -1,117 +0,0 @@
-
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARCH_CONJ_HELPER_H
-#define EIGEN_ARCH_CONJ_HELPER_H
-
-#define EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(PACKET_CPLX, PACKET_REAL)      \
-  template <>                                                           \
-  struct conj_helper<PACKET_REAL, PACKET_CPLX, false, false> {          \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_REAL& x,         \
-                                          const PACKET_CPLX& y,         \
-                                          const PACKET_CPLX& c) const { \
-      return padd(c, this->pmul(x, y));                                 \
-    }                                                                   \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_REAL& x,          \
-                                         const PACKET_CPLX& y) const {  \
-      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x, y.v));   \
-    }                                                                   \
-  };                                                                    \
-                                                                        \
-  template <>                                                           \
-  struct conj_helper<PACKET_CPLX, PACKET_REAL, false, false> {          \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_CPLX& x,         \
-                                          const PACKET_REAL& y,         \
-                                          const PACKET_CPLX& c) const { \
-      return padd(c, this->pmul(x, y));                                 \
-    }                                                                   \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_CPLX& x,          \
-                                         const PACKET_REAL& y) const {  \
-      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x.v, y));   \
-    }                                                                   \
-  };
-
-namespace Eigen {
-namespace internal {
-
-template<bool Conjugate> struct conj_if;
-
-template<> struct conj_if<true> {
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const { return numext::conj(x); }
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T pconj(const T& x) const { return internal::pconj(x); }
-};
-
-template<> struct conj_if<false> {
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator()(const T& x) const { return x; }
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& pconj(const T& x) const { return x; }
-};
-
-// Generic Implementation, assume scalars since the packet-version is
-// specialized below.
-template<typename LhsType, typename RhsType, bool ConjLhs, bool ConjRhs>
-struct conj_helper {
-  typedef typename ScalarBinaryOpTraits<LhsType, RhsType>::ReturnType ResultType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmadd(const LhsType& x, const RhsType& y, const ResultType& c) const
-  { return this->pmul(x, y) + c; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmul(const LhsType& x, const RhsType& y) const
-  { return conj_if<ConjLhs>()(x) * conj_if<ConjRhs>()(y); }
-};
-
-template<typename LhsScalar, typename RhsScalar>
-struct conj_helper<LhsScalar, RhsScalar, true, true> {
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar>::ReturnType ResultType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmadd(const LhsScalar& x, const RhsScalar& y, const ResultType& c) const
-  { return this->pmul(x, y) + c; }
-
-  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmul(const LhsScalar& x, const RhsScalar& y) const
-  { return numext::conj(x * y); }
-};
-
-// Implementation with equal type, use packet operations.
-template<typename Packet, bool ConjLhs, bool ConjRhs>
-struct conj_helper<Packet, Packet, ConjLhs, ConjRhs>
-{
-  typedef Packet ResultType;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const
-  { return Eigen::internal::pmadd(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y), c); }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const
-  { return Eigen::internal::pmul(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y)); }
-};
-
-template<typename Packet>
-struct conj_helper<Packet, Packet, true, true>
-{
-  typedef Packet ResultType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const
-  { return Eigen::internal::pmadd(pconj(x), pconj(y), c); }
-  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const
-  { return pconj(Eigen::internal::pmul(x, y)); }
-};
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_ARCH_CONJ_HELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
deleted file mode 100644
index c9fbaf6..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
+++ /dev/null
@@ -1,1649 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The exp and log functions of this file initially come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
-#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-// Creates a Scalar integer type with same bit-width.
-template<typename T> struct make_integer;
-template<> struct make_integer<float>    { typedef numext::int32_t type; };
-template<> struct make_integer<double>   { typedef numext::int64_t type; };
-template<> struct make_integer<half>     { typedef numext::int16_t type; };
-template<> struct make_integer<bfloat16> { typedef numext::int16_t type; };
-
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC  
-Packet pfrexp_generic_get_biased_exponent(const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-  enum { mantissa_bits = numext::numeric_limits<Scalar>::digits - 1};
-  return pcast<PacketI, Packet>(plogical_shift_right<mantissa_bits>(preinterpret<PacketI>(pabs(a))));
-}
-
-// Safely applies frexp, correctly handles denormals.
-// Assumes IEEE floating point format.
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pfrexp_generic(const Packet& a, Packet& exponent) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename make_unsigned<typename make_integer<Scalar>::type>::type ScalarUI;
-  enum {
-    TotalBits = sizeof(Scalar) * CHAR_BIT,
-    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
-    ExponentBits = int(TotalBits) - int(MantissaBits) - 1
-  };
-
-  EIGEN_CONSTEXPR ScalarUI scalar_sign_mantissa_mask = 
-      ~(((ScalarUI(1) << int(ExponentBits)) - ScalarUI(1)) << int(MantissaBits)); // ~0x7f800000
-  const Packet sign_mantissa_mask = pset1frombits<Packet>(static_cast<ScalarUI>(scalar_sign_mantissa_mask)); 
-  const Packet half = pset1<Packet>(Scalar(0.5));
-  const Packet zero = pzero(a);
-  const Packet normal_min = pset1<Packet>((numext::numeric_limits<Scalar>::min)()); // Minimum normal value, 2^-126
-  
-  // To handle denormals, normalize by multiplying by 2^(int(MantissaBits)+1).
-  const Packet is_denormal = pcmp_lt(pabs(a), normal_min);
-  EIGEN_CONSTEXPR ScalarUI scalar_normalization_offset = ScalarUI(int(MantissaBits) + 1); // 24
-  // The following cannot be constexpr because bfloat16(uint16_t) is not constexpr.
-  const Scalar scalar_normalization_factor = Scalar(ScalarUI(1) << int(scalar_normalization_offset)); // 2^24
-  const Packet normalization_factor = pset1<Packet>(scalar_normalization_factor);  
-  const Packet normalized_a = pselect(is_denormal, pmul(a, normalization_factor), a);
-  
-  // Determine exponent offset: -126 if normal, -126-24 if denormal
-  const Scalar scalar_exponent_offset = -Scalar((ScalarUI(1)<<(int(ExponentBits)-1)) - ScalarUI(2)); // -126
-  Packet exponent_offset = pset1<Packet>(scalar_exponent_offset);
-  const Packet normalization_offset = pset1<Packet>(-Scalar(scalar_normalization_offset)); // -24
-  exponent_offset = pselect(is_denormal, padd(exponent_offset, normalization_offset), exponent_offset);
-  
-  // Determine exponent and mantissa from normalized_a.
-  exponent = pfrexp_generic_get_biased_exponent(normalized_a);
-  // Zero, Inf and NaN return 'a' unmodified, exponent is zero
-  // (technically the exponent is unspecified for inf/NaN, but GCC/Clang set it to zero)
-  const Scalar scalar_non_finite_exponent = Scalar((ScalarUI(1) << int(ExponentBits)) - ScalarUI(1));  // 255
-  const Packet non_finite_exponent = pset1<Packet>(scalar_non_finite_exponent);
-  const Packet is_zero_or_not_finite = por(pcmp_eq(a, zero), pcmp_eq(exponent, non_finite_exponent));
-  const Packet m = pselect(is_zero_or_not_finite, a, por(pand(normalized_a, sign_mantissa_mask), half));
-  exponent = pselect(is_zero_or_not_finite, zero, padd(exponent, exponent_offset));  
-  return m;
-}
-
-// Safely applies ldexp, correctly handles overflows, underflows and denormals.
-// Assumes IEEE floating point format.
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pldexp_generic(const Packet& a, const Packet& exponent) {
-  // We want to return a * 2^exponent, allowing for all possible integer
-  // exponents without overflowing or underflowing in intermediate
-  // computations.
-  //
-  // Since 'a' and the output can be denormal, the maximum range of 'exponent'
-  // to consider for a float is:
-  //   -255-23 -> 255+23
-  // Below -278 any finite float 'a' will become zero, and above +278 any
-  // finite float will become inf, including when 'a' is the smallest possible 
-  // denormal.
-  //
-  // Unfortunately, 2^(278) cannot be represented using either one or two
-  // finite normal floats, so we must split the scale factor into at least
-  // three parts. It turns out to be faster to split 'exponent' into four
-  // factors, since [exponent>>2] is much faster to compute that [exponent/3].
-  //
-  // Set e = min(max(exponent, -278), 278);
-  //     b = floor(e/4);
-  //   out = ((((a * 2^(b)) * 2^(b)) * 2^(b)) * 2^(e-3*b))
-  //
-  // This will avoid any intermediate overflows and correctly handle 0, inf,
-  // NaN cases.
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename unpacket_traits<PacketI>::type ScalarI;
-  enum {
-    TotalBits = sizeof(Scalar) * CHAR_BIT,
-    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
-    ExponentBits = int(TotalBits) - int(MantissaBits) - 1
-  };
-
-  const Packet max_exponent = pset1<Packet>(Scalar((ScalarI(1)<<int(ExponentBits)) + ScalarI(int(MantissaBits) - 1)));  // 278
-  const PacketI bias = pset1<PacketI>((ScalarI(1)<<(int(ExponentBits)-1)) - ScalarI(1));  // 127
-  const PacketI e = pcast<Packet, PacketI>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-  PacketI b = parithmetic_shift_right<2>(e); // floor(e/4);
-  Packet c = preinterpret<Packet>(plogical_shift_left<int(MantissaBits)>(padd(b, bias)));  // 2^b
-  Packet out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
-  b = psub(psub(psub(e, b), b), b); // e - 3b
-  c = preinterpret<Packet>(plogical_shift_left<int(MantissaBits)>(padd(b, bias)));  // 2^(e-3*b)
-  out = pmul(out, c);
-  return out;
-}
-
-// Explicitly multiplies 
-//    a * (2^e)
-// clamping e to the range
-// [NumTraits<Scalar>::min_exponent()-2, NumTraits<Scalar>::max_exponent()]
-//
-// This is approx 7x faster than pldexp_impl, but will prematurely over/underflow
-// if 2^e doesn't fit into a normal floating-point Scalar.
-//
-// Assumes IEEE floating point format
-template<typename Packet>
-struct pldexp_fast_impl {
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename unpacket_traits<PacketI>::type ScalarI;
-  enum {
-    TotalBits = sizeof(Scalar) * CHAR_BIT,
-    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
-    ExponentBits = int(TotalBits) - int(MantissaBits) - 1
-  };
-  
-  static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-  Packet run(const Packet& a, const Packet& exponent) {
-    const Packet bias = pset1<Packet>(Scalar((ScalarI(1)<<(int(ExponentBits)-1)) - ScalarI(1)));  // 127
-    const Packet limit = pset1<Packet>(Scalar((ScalarI(1)<<int(ExponentBits)) - ScalarI(1)));     // 255
-    // restrict biased exponent between 0 and 255 for float.
-    const PacketI e = pcast<Packet, PacketI>(pmin(pmax(padd(exponent, bias), pzero(limit)), limit)); // exponent + 127
-    // return a * (2^e)
-    return pmul(a, preinterpret<Packet>(plogical_shift_left<int(MantissaBits)>(e)));
-  }
-};
-
-// Natural or base 2 logarithm.
-// Computes log(x) as log(2^e * m) = C*e + log(m), where the constant C =log(2)
-// and m is in the range [sqrt(1/2),sqrt(2)). In this range, the logarithm can
-// be easily approximated by a polynomial centered on m=1 for stability.
-// TODO(gonnet): Further reduce the interval allowing for lower-degree
-//               polynomial interpolants -> ... -> profit!
-template <typename Packet, bool base2>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_impl_float(const Packet _x)
-{
-  Packet x = _x;
-
-  const Packet cst_1              = pset1<Packet>(1.0f);
-  const Packet cst_neg_half       = pset1<Packet>(-0.5f);
-  // The smallest non denormalized float number.
-  const Packet cst_min_norm_pos   = pset1frombits<Packet>( 0x00800000u);
-  const Packet cst_minus_inf      = pset1frombits<Packet>( 0xff800000u);
-  const Packet cst_pos_inf        = pset1frombits<Packet>( 0x7f800000u);
-
-  // Polynomial coefficients.
-  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.707106781186547524f);
-  const Packet cst_cephes_log_p0 = pset1<Packet>(7.0376836292E-2f);
-  const Packet cst_cephes_log_p1 = pset1<Packet>(-1.1514610310E-1f);
-  const Packet cst_cephes_log_p2 = pset1<Packet>(1.1676998740E-1f);
-  const Packet cst_cephes_log_p3 = pset1<Packet>(-1.2420140846E-1f);
-  const Packet cst_cephes_log_p4 = pset1<Packet>(+1.4249322787E-1f);
-  const Packet cst_cephes_log_p5 = pset1<Packet>(-1.6668057665E-1f);
-  const Packet cst_cephes_log_p6 = pset1<Packet>(+2.0000714765E-1f);
-  const Packet cst_cephes_log_p7 = pset1<Packet>(-2.4999993993E-1f);
-  const Packet cst_cephes_log_p8 = pset1<Packet>(+3.3333331174E-1f);
-
-  // Truncate input values to the minimum positive normal.
-  x = pmax(x, cst_min_norm_pos);
-
-  Packet e;
-  // extract significant in the range [0.5,1) and exponent
-  x = pfrexp(x,e);
-
-  // part2: Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
-  // and shift by -1. The values are then centered around 0, which improves
-  // the stability of the polynomial evaluation.
-  //   if( x < SQRTHF ) {
-  //     e -= 1;
-  //     x = x + x - 1.0;
-  //   } else { x = x - 1.0; }
-  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
-  Packet tmp = pand(x, mask);
-  x = psub(x, cst_1);
-  e = psub(e, pand(cst_1, mask));
-  x = padd(x, tmp);
-
-  Packet x2 = pmul(x, x);
-  Packet x3 = pmul(x2, x);
-
-  // Evaluate the polynomial approximant of degree 8 in three parts, probably
-  // to improve instruction-level parallelism.
-  Packet y, y1, y2;
-  y  = pmadd(cst_cephes_log_p0, x, cst_cephes_log_p1);
-  y1 = pmadd(cst_cephes_log_p3, x, cst_cephes_log_p4);
-  y2 = pmadd(cst_cephes_log_p6, x, cst_cephes_log_p7);
-  y  = pmadd(y, x, cst_cephes_log_p2);
-  y1 = pmadd(y1, x, cst_cephes_log_p5);
-  y2 = pmadd(y2, x, cst_cephes_log_p8);
-  y  = pmadd(y, x3, y1);
-  y  = pmadd(y, x3, y2);
-  y  = pmul(y, x3);
-
-  y = pmadd(cst_neg_half, x2, y);
-  x = padd(x, y);
-
-  // Add the logarithm of the exponent back to the result of the interpolation.
-  if (base2) {
-    const Packet cst_log2e = pset1<Packet>(static_cast<float>(EIGEN_LOG2E));
-    x = pmadd(x, cst_log2e, e);
-  } else {
-    const Packet cst_ln2 = pset1<Packet>(static_cast<float>(EIGEN_LN2));
-    x = pmadd(e, cst_ln2, x);
-  }
-
-  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
-  Packet iszero_mask  = pcmp_eq(_x,pzero(_x));
-  Packet pos_inf_mask = pcmp_eq(_x,cst_pos_inf);
-  // Filter out invalid inputs, i.e.:
-  //  - negative arg will be NAN
-  //  - 0 will be -INF
-  //  - +INF will be +INF
-  return pselect(iszero_mask, cst_minus_inf,
-                              por(pselect(pos_inf_mask,cst_pos_inf,x), invalid_mask));
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_float(const Packet _x)
-{
-  return plog_impl_float<Packet, /* base2 */ false>(_x);
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_float(const Packet _x)
-{
-  return plog_impl_float<Packet, /* base2 */ true>(_x);
-}
-
-/* Returns the base e (2.718...) or base 2 logarithm of x.
- * The argument is separated into its exponent and fractional parts.
- * The logarithm of the fraction in the interval [sqrt(1/2), sqrt(2)],
- * is approximated by
- *
- *     log(1+x) = x - 0.5 x**2 + x**3 P(x)/Q(x).
- *
- * for more detail see: http://www.netlib.org/cephes/
- */
-template <typename Packet, bool base2>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_impl_double(const Packet _x)
-{
-  Packet x = _x;
-
-  const Packet cst_1              = pset1<Packet>(1.0);
-  const Packet cst_neg_half       = pset1<Packet>(-0.5);
-  // The smallest non denormalized double.
-  const Packet cst_min_norm_pos   = pset1frombits<Packet>( static_cast<uint64_t>(0x0010000000000000ull));
-  const Packet cst_minus_inf      = pset1frombits<Packet>( static_cast<uint64_t>(0xfff0000000000000ull));
-  const Packet cst_pos_inf        = pset1frombits<Packet>( static_cast<uint64_t>(0x7ff0000000000000ull));
-
-
- // Polynomial Coefficients for log(1+x) = x - x**2/2 + x**3 P(x)/Q(x)
- //                             1/sqrt(2) <= x < sqrt(2)
-  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.70710678118654752440E0);
-  const Packet cst_cephes_log_p0 = pset1<Packet>(1.01875663804580931796E-4);
-  const Packet cst_cephes_log_p1 = pset1<Packet>(4.97494994976747001425E-1);
-  const Packet cst_cephes_log_p2 = pset1<Packet>(4.70579119878881725854E0);
-  const Packet cst_cephes_log_p3 = pset1<Packet>(1.44989225341610930846E1);
-  const Packet cst_cephes_log_p4 = pset1<Packet>(1.79368678507819816313E1);
-  const Packet cst_cephes_log_p5 = pset1<Packet>(7.70838733755885391666E0);
-
-  const Packet cst_cephes_log_q0 = pset1<Packet>(1.0);
-  const Packet cst_cephes_log_q1 = pset1<Packet>(1.12873587189167450590E1);
-  const Packet cst_cephes_log_q2 = pset1<Packet>(4.52279145837532221105E1);
-  const Packet cst_cephes_log_q3 = pset1<Packet>(8.29875266912776603211E1);
-  const Packet cst_cephes_log_q4 = pset1<Packet>(7.11544750618563894466E1);
-  const Packet cst_cephes_log_q5 = pset1<Packet>(2.31251620126765340583E1);
-
-  // Truncate input values to the minimum positive normal.
-  x = pmax(x, cst_min_norm_pos);
-
-  Packet e;
-  // extract significant in the range [0.5,1) and exponent
-  x = pfrexp(x,e);
-  
-  // Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
-  // and shift by -1. The values are then centered around 0, which improves
-  // the stability of the polynomial evaluation.
-  //   if( x < SQRTHF ) {
-  //     e -= 1;
-  //     x = x + x - 1.0;
-  //   } else { x = x - 1.0; }
-  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
-  Packet tmp = pand(x, mask);
-  x = psub(x, cst_1);
-  e = psub(e, pand(cst_1, mask));
-  x = padd(x, tmp);
-
-  Packet x2 = pmul(x, x);
-  Packet x3 = pmul(x2, x);
-
-  // Evaluate the polynomial approximant , probably to improve instruction-level parallelism.
-  // y = x - 0.5*x^2 + x^3 * polevl( x, P, 5 ) / p1evl( x, Q, 5 ) );
-  Packet y, y1, y_;
-  y  = pmadd(cst_cephes_log_p0, x, cst_cephes_log_p1);
-  y1 = pmadd(cst_cephes_log_p3, x, cst_cephes_log_p4);
-  y  = pmadd(y, x, cst_cephes_log_p2);
-  y1 = pmadd(y1, x, cst_cephes_log_p5);
-  y_ = pmadd(y, x3, y1);
-
-  y  = pmadd(cst_cephes_log_q0, x, cst_cephes_log_q1);
-  y1 = pmadd(cst_cephes_log_q3, x, cst_cephes_log_q4);
-  y  = pmadd(y, x, cst_cephes_log_q2);
-  y1 = pmadd(y1, x, cst_cephes_log_q5);
-  y  = pmadd(y, x3, y1);
-
-  y_ = pmul(y_, x3);
-  y  = pdiv(y_, y);
-
-  y = pmadd(cst_neg_half, x2, y);
-  x = padd(x, y);
-
-  // Add the logarithm of the exponent back to the result of the interpolation.
-  if (base2) {
-    const Packet cst_log2e = pset1<Packet>(static_cast<double>(EIGEN_LOG2E));
-    x = pmadd(x, cst_log2e, e);
-  } else {
-    const Packet cst_ln2 = pset1<Packet>(static_cast<double>(EIGEN_LN2));
-    x = pmadd(e, cst_ln2, x);
-  }
-
-  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
-  Packet iszero_mask  = pcmp_eq(_x,pzero(_x));
-  Packet pos_inf_mask = pcmp_eq(_x,cst_pos_inf);
-  // Filter out invalid inputs, i.e.:
-  //  - negative arg will be NAN
-  //  - 0 will be -INF
-  //  - +INF will be +INF
-  return pselect(iszero_mask, cst_minus_inf,
-                              por(pselect(pos_inf_mask,cst_pos_inf,x), invalid_mask));
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_double(const Packet _x)
-{
-  return plog_impl_double<Packet, /* base2 */ false>(_x);
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_double(const Packet _x)
-{
-  return plog_impl_double<Packet, /* base2 */ true>(_x);
-}
-
-/** \internal \returns log(1 + x) computed using W. Kahan's formula.
-    See: http://www.plunk.org/~hatch/rightway.php
- */
-template<typename Packet>
-Packet generic_plog1p(const Packet& x)
-{
-  typedef typename unpacket_traits<Packet>::type ScalarType;
-  const Packet one = pset1<Packet>(ScalarType(1));
-  Packet xp1 = padd(x, one);
-  Packet small_mask = pcmp_eq(xp1, one);
-  Packet log1 = plog(xp1);
-  Packet inf_mask = pcmp_eq(xp1, log1);
-  Packet log_large = pmul(x, pdiv(log1, psub(xp1, one)));
-  return pselect(por(small_mask, inf_mask), x, log_large);
-}
-
-/** \internal \returns exp(x)-1 computed using W. Kahan's formula.
-    See: http://www.plunk.org/~hatch/rightway.php
- */
-template<typename Packet>
-Packet generic_expm1(const Packet& x)
-{
-  typedef typename unpacket_traits<Packet>::type ScalarType;
-  const Packet one = pset1<Packet>(ScalarType(1));
-  const Packet neg_one = pset1<Packet>(ScalarType(-1));
-  Packet u = pexp(x);
-  Packet one_mask = pcmp_eq(u, one);
-  Packet u_minus_one = psub(u, one);
-  Packet neg_one_mask = pcmp_eq(u_minus_one, neg_one);
-  Packet logu = plog(u);
-  // The following comparison is to catch the case where
-  // exp(x) = +inf. It is written in this way to avoid having
-  // to form the constant +inf, which depends on the packet
-  // type.
-  Packet pos_inf_mask = pcmp_eq(logu, u);
-  Packet expm1 = pmul(u_minus_one, pdiv(x, logu));
-  expm1 = pselect(pos_inf_mask, u, expm1);
-  return pselect(one_mask,
-                 x,
-                 pselect(neg_one_mask,
-                         neg_one,
-                         expm1));
-}
-
-
-// Exponential function. Works by writing "x = m*log(2) + r" where
-// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
-// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_float(const Packet _x)
-{
-  const Packet cst_1      = pset1<Packet>(1.0f);
-  const Packet cst_half   = pset1<Packet>(0.5f);
-  const Packet cst_exp_hi = pset1<Packet>( 88.723f);
-  const Packet cst_exp_lo = pset1<Packet>(-88.723f);
-
-  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.44269504088896341f);
-  const Packet cst_cephes_exp_p0 = pset1<Packet>(1.9875691500E-4f);
-  const Packet cst_cephes_exp_p1 = pset1<Packet>(1.3981999507E-3f);
-  const Packet cst_cephes_exp_p2 = pset1<Packet>(8.3334519073E-3f);
-  const Packet cst_cephes_exp_p3 = pset1<Packet>(4.1665795894E-2f);
-  const Packet cst_cephes_exp_p4 = pset1<Packet>(1.6666665459E-1f);
-  const Packet cst_cephes_exp_p5 = pset1<Packet>(5.0000001201E-1f);
-
-  // Clamp x.
-  Packet x = pmax(pmin(_x, cst_exp_hi), cst_exp_lo);
-
-  // Express exp(x) as exp(m*ln(2) + r), start by extracting
-  // m = floor(x/ln(2) + 0.5).
-  Packet m = pfloor(pmadd(x, cst_cephes_LOG2EF, cst_half));
-
-  // Get r = x - m*ln(2). If no FMA instructions are available, m*ln(2) is
-  // subtracted out in two parts, m*C1+m*C2 = m*ln(2), to avoid accumulating
-  // truncation errors.
-  const Packet cst_cephes_exp_C1 = pset1<Packet>(-0.693359375f);
-  const Packet cst_cephes_exp_C2 = pset1<Packet>(2.12194440e-4f);
-  Packet r = pmadd(m, cst_cephes_exp_C1, x);
-  r = pmadd(m, cst_cephes_exp_C2, r);
-
-  Packet r2 = pmul(r, r);
-  Packet r3 = pmul(r2, r);
-
-  // Evaluate the polynomial approximant,improved by instruction-level parallelism.
-  Packet y, y1, y2;
-  y  = pmadd(cst_cephes_exp_p0, r, cst_cephes_exp_p1);
-  y1 = pmadd(cst_cephes_exp_p3, r, cst_cephes_exp_p4);
-  y2 = padd(r, cst_1);
-  y  = pmadd(y, r, cst_cephes_exp_p2);
-  y1 = pmadd(y1, r, cst_cephes_exp_p5);
-  y  = pmadd(y, r3, y1);
-  y  = pmadd(y, r2, y2);
-
-  // Return 2^m * exp(r).
-  // TODO: replace pldexp with faster implementation since y in [-1, 1).
-  return pmax(pldexp(y,m), _x);
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_double(const Packet _x)
-{
-  Packet x = _x;
-
-  const Packet cst_1 = pset1<Packet>(1.0);
-  const Packet cst_2 = pset1<Packet>(2.0);
-  const Packet cst_half = pset1<Packet>(0.5);
-
-  const Packet cst_exp_hi = pset1<Packet>(709.784);
-  const Packet cst_exp_lo = pset1<Packet>(-709.784);
-
-  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.4426950408889634073599);
-  const Packet cst_cephes_exp_p0 = pset1<Packet>(1.26177193074810590878e-4);
-  const Packet cst_cephes_exp_p1 = pset1<Packet>(3.02994407707441961300e-2);
-  const Packet cst_cephes_exp_p2 = pset1<Packet>(9.99999999999999999910e-1);
-  const Packet cst_cephes_exp_q0 = pset1<Packet>(3.00198505138664455042e-6);
-  const Packet cst_cephes_exp_q1 = pset1<Packet>(2.52448340349684104192e-3);
-  const Packet cst_cephes_exp_q2 = pset1<Packet>(2.27265548208155028766e-1);
-  const Packet cst_cephes_exp_q3 = pset1<Packet>(2.00000000000000000009e0);
-  const Packet cst_cephes_exp_C1 = pset1<Packet>(0.693145751953125);
-  const Packet cst_cephes_exp_C2 = pset1<Packet>(1.42860682030941723212e-6);
-
-  Packet tmp, fx;
-
-  // clamp x
-  x = pmax(pmin(x, cst_exp_hi), cst_exp_lo);
-  // Express exp(x) as exp(g + n*log(2)).
-  fx = pmadd(cst_cephes_LOG2EF, x, cst_half);
-
-  // Get the integer modulus of log(2), i.e. the "n" described above.
-  fx = pfloor(fx);
-
-  // Get the remainder modulo log(2), i.e. the "g" described above. Subtract
-  // n*log(2) out in two steps, i.e. n*C1 + n*C2, C1+C2=log2 to get the last
-  // digits right.
-  tmp = pmul(fx, cst_cephes_exp_C1);
-  Packet z = pmul(fx, cst_cephes_exp_C2);
-  x = psub(x, tmp);
-  x = psub(x, z);
-
-  Packet x2 = pmul(x, x);
-
-  // Evaluate the numerator polynomial of the rational interpolant.
-  Packet px = cst_cephes_exp_p0;
-  px = pmadd(px, x2, cst_cephes_exp_p1);
-  px = pmadd(px, x2, cst_cephes_exp_p2);
-  px = pmul(px, x);
-
-  // Evaluate the denominator polynomial of the rational interpolant.
-  Packet qx = cst_cephes_exp_q0;
-  qx = pmadd(qx, x2, cst_cephes_exp_q1);
-  qx = pmadd(qx, x2, cst_cephes_exp_q2);
-  qx = pmadd(qx, x2, cst_cephes_exp_q3);
-
-  // I don't really get this bit, copied from the SSE2 routines, so...
-  // TODO(gonnet): Figure out what is going on here, perhaps find a better
-  // rational interpolant?
-  x = pdiv(px, psub(qx, px));
-  x = pmadd(cst_2, x, cst_1);
-
-  // Construct the result 2^n * exp(g) = e * x. The max is used to catch
-  // non-finite values in the input.
-  // TODO: replace pldexp with faster implementation since x in [-1, 1).
-  return pmax(pldexp(x,fx), _x);
-}
-
-// The following code is inspired by the following stack-overflow answer:
-//   https://stackoverflow.com/questions/30463616/payne-hanek-algorithm-implementation-in-c/30465751#30465751
-// It has been largely optimized:
-//  - By-pass calls to frexp.
-//  - Aligned loads of required 96 bits of 2/pi. This is accomplished by
-//    (1) balancing the mantissa and exponent to the required bits of 2/pi are
-//    aligned on 8-bits, and (2) replicating the storage of the bits of 2/pi.
-//  - Avoid a branch in rounding and extraction of the remaining fractional part.
-// Overall, I measured a speed up higher than x2 on x86-64.
-inline float trig_reduce_huge (float xf, int *quadrant)
-{
-  using Eigen::numext::int32_t;
-  using Eigen::numext::uint32_t;
-  using Eigen::numext::int64_t;
-  using Eigen::numext::uint64_t;
-
-  const double pio2_62 = 3.4061215800865545e-19;    // pi/2 * 2^-62
-  const uint64_t zero_dot_five = uint64_t(1) << 61; // 0.5 in 2.62-bit fixed-point foramt
-
-  // 192 bits of 2/pi for Payne-Hanek reduction
-  // Bits are introduced by packet of 8 to enable aligned reads.
-  static const uint32_t two_over_pi [] = 
-  {
-    0x00000028, 0x000028be, 0x0028be60, 0x28be60db,
-    0xbe60db93, 0x60db9391, 0xdb939105, 0x9391054a,
-    0x91054a7f, 0x054a7f09, 0x4a7f09d5, 0x7f09d5f4,
-    0x09d5f47d, 0xd5f47d4d, 0xf47d4d37, 0x7d4d3770,
-    0x4d377036, 0x377036d8, 0x7036d8a5, 0x36d8a566,
-    0xd8a5664f, 0xa5664f10, 0x664f10e4, 0x4f10e410,
-    0x10e41000, 0xe4100000
-  };
-  
-  uint32_t xi = numext::bit_cast<uint32_t>(xf);
-  // Below, -118 = -126 + 8.
-  //   -126 is to get the exponent,
-  //   +8 is to enable alignment of 2/pi's bits on 8 bits.
-  // This is possible because the fractional part of x as only 24 meaningful bits.
-  uint32_t e = (xi >> 23) - 118;
-  // Extract the mantissa and shift it to align it wrt the exponent
-  xi = ((xi & 0x007fffffu)| 0x00800000u) << (e & 0x7);
-
-  uint32_t i = e >> 3;
-  uint32_t twoopi_1  = two_over_pi[i-1];
-  uint32_t twoopi_2  = two_over_pi[i+3];
-  uint32_t twoopi_3  = two_over_pi[i+7];
-
-  // Compute x * 2/pi in 2.62-bit fixed-point format.
-  uint64_t p;
-  p = uint64_t(xi) * twoopi_3;
-  p = uint64_t(xi) * twoopi_2 + (p >> 32);
-  p = (uint64_t(xi * twoopi_1) << 32) + p;
-
-  // Round to nearest: add 0.5 and extract integral part.
-  uint64_t q = (p + zero_dot_five) >> 62;
-  *quadrant = int(q);
-  // Now it remains to compute "r = x - q*pi/2" with high accuracy,
-  // since we have p=x/(pi/2) with high accuracy, we can more efficiently compute r as:
-  //   r = (p-q)*pi/2,
-  // where the product can be be carried out with sufficient accuracy using double precision.
-  p -= q<<62;
-  return float(double(int64_t(p)) * pio2_62);
-}
-
-template<bool ComputeSine,typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-#if EIGEN_GNUC_AT_LEAST(4,4) && EIGEN_COMP_GNUC_STRICT
-__attribute__((optimize("-fno-unsafe-math-optimizations")))
-#endif
-Packet psincos_float(const Packet& _x)
-{
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-
-  const Packet  cst_2oPI            = pset1<Packet>(0.636619746685028076171875f); // 2/PI
-  const Packet  cst_rounding_magic  = pset1<Packet>(12582912); // 2^23 for rounding
-  const PacketI csti_1              = pset1<PacketI>(1);
-  const Packet  cst_sign_mask       = pset1frombits<Packet>(0x80000000u);
-
-  Packet x = pabs(_x);
-
-  // Scale x by 2/Pi to find x's octant.
-  Packet y = pmul(x, cst_2oPI);
-
-  // Rounding trick:
-  Packet y_round = padd(y, cst_rounding_magic);
-  EIGEN_OPTIMIZATION_BARRIER(y_round)
-  PacketI y_int = preinterpret<PacketI>(y_round); // last 23 digits represent integer (if abs(x)<2^24)
-  y = psub(y_round, cst_rounding_magic); // nearest integer to x*4/pi
-
-  // Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4
-  // using "Extended precision modular arithmetic"
-  #if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD)
-  // This version requires true FMA for high accuracy
-  // It provides a max error of 1ULP up to (with absolute_error < 5.9605e-08):
-  const float huge_th = ComputeSine ? 117435.992f : 71476.0625f;
-  x = pmadd(y, pset1<Packet>(-1.57079601287841796875f), x);
-  x = pmadd(y, pset1<Packet>(-3.1391647326017846353352069854736328125e-07f), x);
-  x = pmadd(y, pset1<Packet>(-5.390302529957764765544681040410068817436695098876953125e-15f), x);
-  #else
-  // Without true FMA, the previous set of coefficients maintain 1ULP accuracy
-  // up to x<15.7 (for sin), but accuracy is immediately lost for x>15.7.
-  // We thus use one more iteration to maintain 2ULPs up to reasonably large inputs.
-
-  // The following set of coefficients maintain 1ULP up to 9.43 and 14.16 for sin and cos respectively.
-  // and 2 ULP up to:
-  const float huge_th = ComputeSine ? 25966.f : 18838.f;
-  x = pmadd(y, pset1<Packet>(-1.5703125), x); // = 0xbfc90000
-  EIGEN_OPTIMIZATION_BARRIER(x)
-  x = pmadd(y, pset1<Packet>(-0.000483989715576171875), x); // = 0xb9fdc000
-  EIGEN_OPTIMIZATION_BARRIER(x)
-  x = pmadd(y, pset1<Packet>(1.62865035235881805419921875e-07), x); // = 0x342ee000
-  x = pmadd(y, pset1<Packet>(5.5644315544167710640977020375430583953857421875e-11), x); // = 0x2e74b9ee
-
-  // For the record, the following set of coefficients maintain 2ULP up
-  // to a slightly larger range:
-  // const float huge_th = ComputeSine ? 51981.f : 39086.125f;
-  // but it slightly fails to maintain 1ULP for two values of sin below pi.
-  // x = pmadd(y, pset1<Packet>(-3.140625/2.), x);
-  // x = pmadd(y, pset1<Packet>(-0.00048351287841796875), x);
-  // x = pmadd(y, pset1<Packet>(-3.13855707645416259765625e-07), x);
-  // x = pmadd(y, pset1<Packet>(-6.0771006282767103812147979624569416046142578125e-11), x);
-
-  // For the record, with only 3 iterations it is possible to maintain
-  // 1 ULP up to 3PI (maybe more) and 2ULP up to 255.
-  // The coefficients are: 0xbfc90f80, 0xb7354480, 0x2e74b9ee
-  #endif
-
-  if(predux_any(pcmp_le(pset1<Packet>(huge_th),pabs(_x))))
-  {
-    const int PacketSize = unpacket_traits<Packet>::size;
-    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float vals[PacketSize];
-    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float x_cpy[PacketSize];
-    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) int y_int2[PacketSize];
-    pstoreu(vals, pabs(_x));
-    pstoreu(x_cpy, x);
-    pstoreu(y_int2, y_int);
-    for(int k=0; k<PacketSize;++k)
-    {
-      float val = vals[k];
-      if(val>=huge_th && (numext::isfinite)(val))
-        x_cpy[k] = trig_reduce_huge(val,&y_int2[k]);
-    }
-    x = ploadu<Packet>(x_cpy);
-    y_int = ploadu<PacketI>(y_int2);
-  }
-
-  // Compute the sign to apply to the polynomial.
-  // sin: sign = second_bit(y_int) xor signbit(_x)
-  // cos: sign = second_bit(y_int+1)
-  Packet sign_bit = ComputeSine ? pxor(_x, preinterpret<Packet>(plogical_shift_left<30>(y_int)))
-                                : preinterpret<Packet>(plogical_shift_left<30>(padd(y_int,csti_1)));
-  sign_bit = pand(sign_bit, cst_sign_mask); // clear all but left most bit
-
-  // Get the polynomial selection mask from the second bit of y_int
-  // We'll calculate both (sin and cos) polynomials and then select from the two.
-  Packet poly_mask = preinterpret<Packet>(pcmp_eq(pand(y_int, csti_1), pzero(y_int)));
-
-  Packet x2 = pmul(x,x);
-
-  // Evaluate the cos(x) polynomial. (-Pi/4 <= x <= Pi/4)
-  Packet y1 =        pset1<Packet>(2.4372266125283204019069671630859375e-05f);
-  y1 = pmadd(y1, x2, pset1<Packet>(-0.00138865201734006404876708984375f     ));
-  y1 = pmadd(y1, x2, pset1<Packet>(0.041666619479656219482421875f           ));
-  y1 = pmadd(y1, x2, pset1<Packet>(-0.5f));
-  y1 = pmadd(y1, x2, pset1<Packet>(1.f));
-
-  // Evaluate the sin(x) polynomial. (Pi/4 <= x <= Pi/4)
-  // octave/matlab code to compute those coefficients:
-  //    x = (0:0.0001:pi/4)';
-  //    A = [x.^3 x.^5 x.^7];
-  //    w = ((1.-(x/(pi/4)).^2).^5)*2000+1;         # weights trading relative accuracy
-  //    c = (A'*diag(w)*A)\(A'*diag(w)*(sin(x)-x)); # weighted LS, linear coeff forced to 1
-  //    printf('%.64f\n %.64f\n%.64f\n', c(3), c(2), c(1))
-  //
-  Packet y2 =        pset1<Packet>(-0.0001959234114083702898469196984621021329076029360294342041015625f);
-  y2 = pmadd(y2, x2, pset1<Packet>( 0.0083326873655616851693794799871284340042620897293090820312500000f));
-  y2 = pmadd(y2, x2, pset1<Packet>(-0.1666666203982298255503735617821803316473960876464843750000000000f));
-  y2 = pmul(y2, x2);
-  y2 = pmadd(y2, x, x);
-
-  // Select the correct result from the two polynomials.
-  y = ComputeSine ? pselect(poly_mask,y2,y1)
-                  : pselect(poly_mask,y1,y2);
-
-  // Update the sign and filter huge inputs
-  return pxor(y, sign_bit);
-}
-
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psin_float(const Packet& x)
-{
-  return psincos_float<true>(x);
-}
-
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pcos_float(const Packet& x)
-{
-  return psincos_float<false>(x);
-}
-
-
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psqrt_complex(const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename Scalar::value_type RealScalar;
-  typedef typename unpacket_traits<Packet>::as_real RealPacket;
-
-  // Computes the principal sqrt of the complex numbers in the input.
-  //
-  // For example, for packets containing 2 complex numbers stored in interleaved format
-  //    a = [a0, a1] = [x0, y0, x1, y1],
-  // where x0 = real(a0), y0 = imag(a0) etc., this function returns
-  //    b = [b0, b1] = [u0, v0, u1, v1],
-  // such that b0^2 = a0, b1^2 = a1.
-  //
-  // To derive the formula for the complex square roots, let's consider the equation for
-  // a single complex square root of the number x + i*y. We want to find real numbers
-  // u and v such that
-  //    (u + i*v)^2 = x + i*y  <=>
-  //    u^2 - v^2 + i*2*u*v = x + i*v.
-  // By equating the real and imaginary parts we get:
-  //    u^2 - v^2 = x
-  //    2*u*v = y.
-  //
-  // For x >= 0, this has the numerically stable solution
-  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
-  //    v = 0.5 * (y / u)
-  // and for x < 0,
-  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
-  //    u = 0.5 * (y / v)
-  //
-  //  To avoid unnecessary over- and underflow, we compute sqrt(x^2 + y^2) as
-  //     l = max(|x|, |y|) * sqrt(1 + (min(|x|, |y|) / max(|x|, |y|))^2) ,
-
-  // In the following, without lack of generality, we have annotated the code, assuming
-  // that the input is a packet of 2 complex numbers.
-  //
-  // Step 1. Compute l = [l0, l0, l1, l1], where
-  //    l0 = sqrt(x0^2 + y0^2),  l1 = sqrt(x1^2 + y1^2)
-  // To avoid over- and underflow, we use the stable formula for each hypotenuse
-  //    l0 = (min0 == 0 ? max0 : max0 * sqrt(1 + (min0/max0)**2)),
-  // where max0 = max(|x0|, |y0|), min0 = min(|x0|, |y0|), and similarly for l1.
-
-  RealPacket a_abs = pabs(a.v);           // [|x0|, |y0|, |x1|, |y1|]
-  RealPacket a_abs_flip = pcplxflip(Packet(a_abs)).v; // [|y0|, |x0|, |y1|, |x1|]
-  RealPacket a_max = pmax(a_abs, a_abs_flip);
-  RealPacket a_min = pmin(a_abs, a_abs_flip);
-  RealPacket a_min_zero_mask = pcmp_eq(a_min, pzero(a_min));
-  RealPacket a_max_zero_mask = pcmp_eq(a_max, pzero(a_max));
-  RealPacket r = pdiv(a_min, a_max);
-  const RealPacket cst_one  = pset1<RealPacket>(RealScalar(1));
-  RealPacket l = pmul(a_max, psqrt(padd(cst_one, pmul(r, r))));  // [l0, l0, l1, l1]
-  // Set l to a_max if a_min is zero.
-  l = pselect(a_min_zero_mask, a_max, l);
-
-  // Step 2. Compute [rho0, *, rho1, *], where
-  // rho0 = sqrt(0.5 * (l0 + |x0|)), rho1 =  sqrt(0.5 * (l1 + |x1|))
-  // We don't care about the imaginary parts computed here. They will be overwritten later.
-  const RealPacket cst_half = pset1<RealPacket>(RealScalar(0.5));
-  Packet rho;
-  rho.v = psqrt(pmul(cst_half, padd(a_abs, l)));
-
-  // Step 3. Compute [rho0, eta0, rho1, eta1], where
-  // eta0 = (y0 / l0) / 2, and eta1 = (y1 / l1) / 2.
-  // set eta = 0 of input is 0 + i0.
-  RealPacket eta = pandnot(pmul(cst_half, pdiv(a.v, pcplxflip(rho).v)), a_max_zero_mask);
-  RealPacket real_mask = peven_mask(a.v);
-  Packet positive_real_result;
-  // Compute result for inputs with positive real part.
-  positive_real_result.v = pselect(real_mask, rho.v, eta);
-
-  // Step 4. Compute solution for inputs with negative real part:
-  //         [|eta0|, sign(y0)*rho0, |eta1|, sign(y1)*rho1]
-  const RealScalar neg_zero = RealScalar(numext::bit_cast<float>(0x80000000u));
-  const RealPacket cst_imag_sign_mask = pset1<Packet>(Scalar(RealScalar(0.0), neg_zero)).v;
-  RealPacket imag_signs = pand(a.v, cst_imag_sign_mask);
-  Packet negative_real_result;
-  // Notice that rho is positive, so taking it's absolute value is a noop.
-  negative_real_result.v = por(pabs(pcplxflip(positive_real_result).v), imag_signs);
-
-  // Step 5. Select solution branch based on the sign of the real parts.
-  Packet negative_real_mask;
-  negative_real_mask.v = pcmp_lt(pand(real_mask, a.v), pzero(a.v));
-  negative_real_mask.v = por(negative_real_mask.v, pcplxflip(negative_real_mask).v);
-  Packet result = pselect(negative_real_mask, negative_real_result, positive_real_result);
-
-  // Step 6. Handle special cases for infinities:
-  // * If z is (x,+∞), the result is (+∞,+∞) even if x is NaN
-  // * If z is (x,-∞), the result is (+∞,-∞) even if x is NaN
-  // * If z is (-∞,y), the result is (0*|y|,+∞) for finite or NaN y
-  // * If z is (+∞,y), the result is (+∞,0*|y|) for finite or NaN y
-  const RealPacket cst_pos_inf = pset1<RealPacket>(NumTraits<RealScalar>::infinity());
-  Packet is_inf;
-  is_inf.v = pcmp_eq(a_abs, cst_pos_inf);
-  Packet is_real_inf;
-  is_real_inf.v = pand(is_inf.v, real_mask);
-  is_real_inf = por(is_real_inf, pcplxflip(is_real_inf));
-  // prepare packet of (+∞,0*|y|) or (0*|y|,+∞), depending on the sign of the infinite real part.
-  Packet real_inf_result;
-  real_inf_result.v = pmul(a_abs, pset1<Packet>(Scalar(RealScalar(1.0), RealScalar(0.0))).v);
-  real_inf_result.v = pselect(negative_real_mask.v, pcplxflip(real_inf_result).v, real_inf_result.v);
-  // prepare packet of (+∞,+∞) or (+∞,-∞), depending on the sign of the infinite imaginary part.
-  Packet is_imag_inf;
-  is_imag_inf.v = pandnot(is_inf.v, real_mask);
-  is_imag_inf = por(is_imag_inf, pcplxflip(is_imag_inf));
-  Packet imag_inf_result;
-  imag_inf_result.v = por(pand(cst_pos_inf, real_mask), pandnot(a.v, real_mask));
-
-  return  pselect(is_imag_inf, imag_inf_result,
-                  pselect(is_real_inf, real_inf_result,result));
-}
-
-// TODO(rmlarsen): The following set of utilities for double word arithmetic
-// should perhaps be refactored as a separate file, since it would be generally
-// useful for special function implementation etc. Writing the algorithms in
-// terms if a double word type would also make the code more readable.
-
-// This function splits x into the nearest integer n and fractional part r,
-// such that x = n + r holds exactly.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void absolute_split(const Packet& x, Packet& n, Packet& r) {
-  n = pround(x);
-  r = psub(x, n);
-}
-
-// This function computes the sum {s, r}, such that x + y = s_hi + s_lo
-// holds exactly, and s_hi = fl(x+y), if |x| >= |y|.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void fast_twosum(const Packet& x, const Packet& y, Packet& s_hi, Packet& s_lo) {
-  s_hi = padd(x, y);
-  const Packet t = psub(s_hi, x);
-  s_lo = psub(y, t);
-}
-
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-// This function implements the extended precision product of
-// a pair of floating point numbers. Given {x, y}, it computes the pair
-// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
-// p_hi = fl(x * y).
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x, const Packet& y,
-             Packet& p_hi, Packet& p_lo) {
-  p_hi = pmul(x, y);
-  p_lo = pmadd(x, y, pnegate(p_hi));
-}
-
-#else
-
-// This function implements the Veltkamp splitting. Given a floating point
-// number x it returns the pair {x_hi, x_lo} such that x_hi + x_lo = x holds
-// exactly and that half of the significant of x fits in x_hi.
-// This is Algorithm 3 from Jean-Michel Muller, "Elementary Functions",
-// 3rd edition, Birkh\"auser, 2016.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void veltkamp_splitting(const Packet& x, Packet& x_hi, Packet& x_lo) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  EIGEN_CONSTEXPR int shift = (NumTraits<Scalar>::digits() + 1) / 2;
-  const Scalar shift_scale = Scalar(uint64_t(1) << shift);  // Scalar constructor not necessarily constexpr.
-  const Packet gamma = pmul(pset1<Packet>(shift_scale + Scalar(1)), x);
-  Packet rho = psub(x, gamma);
-  x_hi = padd(rho, gamma);
-  x_lo = psub(x, x_hi);
-}
-
-// This function implements Dekker's algorithm for products x * y.
-// Given floating point numbers {x, y} computes the pair
-// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
-// p_hi = fl(x * y).
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x, const Packet& y,
-             Packet& p_hi, Packet& p_lo) {
-  Packet x_hi, x_lo, y_hi, y_lo;
-  veltkamp_splitting(x, x_hi, x_lo);
-  veltkamp_splitting(y, y_hi, y_lo);
-
-  p_hi = pmul(x, y);
-  p_lo = pmadd(x_hi, y_hi, pnegate(p_hi));
-  p_lo = pmadd(x_hi, y_lo, p_lo);
-  p_lo = pmadd(x_lo, y_hi, p_lo);
-  p_lo = pmadd(x_lo, y_lo, p_lo);
-}
-
-#endif  // EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-
-
-// This function implements Dekker's algorithm for the addition
-// of two double word numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
-// It returns the result as a pair {s_hi, s_lo} such that
-// x_hi + x_lo + y_hi + y_lo = s_hi + s_lo holds exactly.
-// This is Algorithm 5 from Jean-Michel Muller, "Elementary Functions",
-// 3rd edition, Birkh\"auser, 2016.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-  void twosum(const Packet& x_hi, const Packet& x_lo,
-              const Packet& y_hi, const Packet& y_lo,
-              Packet& s_hi, Packet& s_lo) {
-  const Packet x_greater_mask = pcmp_lt(pabs(y_hi), pabs(x_hi));
-  Packet r_hi_1, r_lo_1;
-  fast_twosum(x_hi, y_hi,r_hi_1, r_lo_1);
-  Packet r_hi_2, r_lo_2;
-  fast_twosum(y_hi, x_hi,r_hi_2, r_lo_2);
-  const Packet r_hi = pselect(x_greater_mask, r_hi_1, r_hi_2);
-
-  const Packet s1 = padd(padd(y_lo, r_lo_1), x_lo);
-  const Packet s2 = padd(padd(x_lo, r_lo_2), y_lo);
-  const Packet s = pselect(x_greater_mask, s1, s2);
-
-  fast_twosum(r_hi, s, s_hi, s_lo);
-}
-
-// This is a version of twosum for double word numbers,
-// which assumes that |x_hi| >= |y_hi|.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-  void fast_twosum(const Packet& x_hi, const Packet& x_lo,
-              const Packet& y_hi, const Packet& y_lo,
-              Packet& s_hi, Packet& s_lo) {
-  Packet r_hi, r_lo;
-  fast_twosum(x_hi, y_hi, r_hi, r_lo);
-  const Packet s = padd(padd(y_lo, r_lo), x_lo);
-  fast_twosum(r_hi, s, s_hi, s_lo);
-}
-
-// This is a version of twosum for adding a floating point number x to
-// double word number {y_hi, y_lo} number, with the assumption
-// that |x| >= |y_hi|.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void fast_twosum(const Packet& x,
-                 const Packet& y_hi, const Packet& y_lo,
-                 Packet& s_hi, Packet& s_lo) {
-  Packet r_hi, r_lo;
-  fast_twosum(x, y_hi, r_hi, r_lo);
-  const Packet s = padd(y_lo, r_lo);
-  fast_twosum(r_hi, s, s_hi, s_lo);
-}
-
-// This function implements the multiplication of a double word
-// number represented by {x_hi, x_lo} by a floating point number y.
-// It returns the result as a pair {p_hi, p_lo} such that
-// (x_hi + x_lo) * y = p_hi + p_lo hold with a relative error
-// of less than 2*2^{-2p}, where p is the number of significand bit
-// in the floating point type.
-// This is Algorithm 7 from Jean-Michel Muller, "Elementary Functions",
-// 3rd edition, Birkh\"auser, 2016.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x_hi, const Packet& x_lo, const Packet& y,
-             Packet& p_hi, Packet& p_lo) {
-  Packet c_hi, c_lo1;
-  twoprod(x_hi, y, c_hi, c_lo1);
-  const Packet c_lo2 = pmul(x_lo, y);
-  Packet t_hi, t_lo1;
-  fast_twosum(c_hi, c_lo2, t_hi, t_lo1);
-  const Packet t_lo2 = padd(t_lo1, c_lo1);
-  fast_twosum(t_hi, t_lo2, p_hi, p_lo);
-}
-
-// This function implements the multiplication of two double word
-// numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
-// It returns the result as a pair {p_hi, p_lo} such that
-// (x_hi + x_lo) * (y_hi + y_lo) = p_hi + p_lo holds with a relative error
-// of less than 2*2^{-2p}, where p is the number of significand bit
-// in the floating point type.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x_hi, const Packet& x_lo,
-             const Packet& y_hi, const Packet& y_lo,
-             Packet& p_hi, Packet& p_lo) {
-  Packet p_hi_hi, p_hi_lo;
-  twoprod(x_hi, x_lo, y_hi, p_hi_hi, p_hi_lo);
-  Packet p_lo_hi, p_lo_lo;
-  twoprod(x_hi, x_lo, y_lo, p_lo_hi, p_lo_lo);
-  fast_twosum(p_hi_hi, p_hi_lo, p_lo_hi, p_lo_lo, p_hi, p_lo);
-}
-
-// This function computes the reciprocal of a floating point number
-// with extra precision and returns the result as a double word.
-template <typename Packet>
-void doubleword_reciprocal(const Packet& x, Packet& recip_hi, Packet& recip_lo) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  // 1. Approximate the reciprocal as the reciprocal of the high order element.
-  Packet approx_recip = prsqrt(x);
-  approx_recip = pmul(approx_recip, approx_recip);
-
-  // 2. Run one step of Newton-Raphson iteration in double word arithmetic
-  // to get the bottom half. The NR iteration for reciprocal of 'a' is
-  //    x_{i+1} = x_i * (2 - a * x_i)
-
-  // -a*x_i
-  Packet t1_hi, t1_lo;
-  twoprod(pnegate(x), approx_recip, t1_hi, t1_lo);
-  // 2 - a*x_i
-  Packet t2_hi, t2_lo;
-  fast_twosum(pset1<Packet>(Scalar(2)), t1_hi, t2_hi, t2_lo);
-  Packet t3_hi, t3_lo;
-  fast_twosum(t2_hi, padd(t2_lo, t1_lo), t3_hi, t3_lo);
-  // x_i * (2 - a * x_i)
-  twoprod(t3_hi, t3_lo, approx_recip, recip_hi, recip_lo);
-}
-
-
-// This function computes log2(x) and returns the result as a double word.
-template <typename Scalar>
-struct accurate_log2 {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
-    log2_x_hi = plog2(x);
-    log2_x_lo = pzero(x);
-  }
-};
-
-// This specialization uses a more accurate algorithm to compute log2(x) for
-// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~6.42e-10.
-// This additional accuracy is needed to counter the error-magnification
-// inherent in multiplying by a potentially large exponent in pow(x,y).
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-template <>
-struct accurate_log2<float> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  void operator()(const Packet& z, Packet& log2_x_hi, Packet& log2_x_lo) {
-    // The function log(1+x)/x is approximated in the interval
-    // [1/sqrt(2)-1;sqrt(2)-1] by a degree 10 polynomial of the form
-    //  Q(x) = (C0 + x * (C1 + x * (C2 + x * (C3 + x * P(x))))),
-    // where the degree 6 polynomial P(x) is evaluated in single precision,
-    // while the remaining 4 terms of Q(x), as well as the final multiplication by x
-    // to reconstruct log(1+x) are evaluated in extra precision using
-    // double word arithmetic. C0 through C3 are extra precise constants
-    // stored as double words.
-    //
-    // The polynomial coefficients were calculated using Sollya commands:
-    // > n = 10;
-    // > f = log2(1+x)/x;
-    // > interval = [sqrt(0.5)-1;sqrt(2)-1];
-    // > p = fpminimax(f,n,[|double,double,double,double,single...|],interval,relative,floating);
-    
-    const Packet p6 = pset1<Packet>( 9.703654795885e-2f);
-    const Packet p5 = pset1<Packet>(-0.1690667718648f);
-    const Packet p4 = pset1<Packet>( 0.1720575392246f);
-    const Packet p3 = pset1<Packet>(-0.1789081543684f);
-    const Packet p2 = pset1<Packet>( 0.2050433009862f);
-    const Packet p1 = pset1<Packet>(-0.2404672354459f);
-    const Packet p0 = pset1<Packet>( 0.2885761857032f);
-
-    const Packet C3_hi = pset1<Packet>(-0.360674142838f);
-    const Packet C3_lo = pset1<Packet>(-6.13283912543e-09f);
-    const Packet C2_hi = pset1<Packet>(0.480897903442f);
-    const Packet C2_lo = pset1<Packet>(-1.44861207474e-08f);
-    const Packet C1_hi = pset1<Packet>(-0.721347510815f);
-    const Packet C1_lo = pset1<Packet>(-4.84483164698e-09f);
-    const Packet C0_hi = pset1<Packet>(1.44269502163f);
-    const Packet C0_lo = pset1<Packet>(2.01711713999e-08f);
-    const Packet one = pset1<Packet>(1.0f);
-
-    const Packet x = psub(z, one);
-    // Evaluate P(x) in working precision.
-    // We evaluate it in multiple parts to improve instruction level
-    // parallelism.
-    Packet x2 = pmul(x,x);
-    Packet p_even = pmadd(p6, x2, p4);
-    p_even = pmadd(p_even, x2, p2);
-    p_even = pmadd(p_even, x2, p0);
-    Packet p_odd = pmadd(p5, x2, p3);
-    p_odd = pmadd(p_odd, x2, p1);
-    Packet p = pmadd(p_odd, x, p_even);
-
-    // Now evaluate the low-order tems of Q(x) in double word precision.
-    // In the following, due to the alternating signs and the fact that
-    // |x| < sqrt(2)-1, we can assume that |C*_hi| >= q_i, and use
-    // fast_twosum instead of the slower twosum.
-    Packet q_hi, q_lo;
-    Packet t_hi, t_lo;
-    // C3 + x * p(x)
-    twoprod(p, x, t_hi, t_lo);
-    fast_twosum(C3_hi, C3_lo, t_hi, t_lo, q_hi, q_lo);
-    // C2 + x * p(x)
-    twoprod(q_hi, q_lo, x, t_hi, t_lo);
-    fast_twosum(C2_hi, C2_lo, t_hi, t_lo, q_hi, q_lo);
-    // C1 + x * p(x)
-    twoprod(q_hi, q_lo, x, t_hi, t_lo);
-    fast_twosum(C1_hi, C1_lo, t_hi, t_lo, q_hi, q_lo);
-    // C0 + x * p(x)
-    twoprod(q_hi, q_lo, x, t_hi, t_lo);
-    fast_twosum(C0_hi, C0_lo, t_hi, t_lo, q_hi, q_lo);
-
-    // log(z) ~= x * Q(x)
-    twoprod(q_hi, q_lo, x, log2_x_hi, log2_x_lo);
-  }
-};
-
-// This specialization uses a more accurate algorithm to compute log2(x) for
-// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~1.27e-18.
-// This additional accuracy is needed to counter the error-magnification
-// inherent in multiplying by a potentially large exponent in pow(x,y).
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-
-template <>
-struct accurate_log2<double> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
-    // We use a transformation of variables:
-    //    r = c * (x-1) / (x+1),
-    // such that
-    //    log2(x) = log2((1 + r/c) / (1 - r/c)) = f(r).
-    // The function f(r) can be approximated well using an odd polynomial
-    // of the form
-    //   P(r) = ((Q(r^2) * r^2 + C) * r^2 + 1) * r,
-    // For the implementation of log2<double> here, Q is of degree 6 with
-    // coefficient represented in working precision (double), while C is a
-    // constant represented in extra precision as a double word to achieve
-    // full accuracy.
-    //
-    // The polynomial coefficients were computed by the Sollya script:
-    //
-    // c = 2 / log(2);
-    // trans = c * (x-1)/(x+1);
-    // itrans = (1+x/c)/(1-x/c);
-    // interval=[trans(sqrt(0.5)); trans(sqrt(2))];
-    // print(interval);
-    // f = log2(itrans(x));
-    // p=fpminimax(f,[|1,3,5,7,9,11,13,15,17|],[|1,DD,double...|],interval,relative,floating);
-    const Packet q12 = pset1<Packet>(2.87074255468000586e-9);
-    const Packet q10 = pset1<Packet>(2.38957980901884082e-8);
-    const Packet q8 = pset1<Packet>(2.31032094540014656e-7);
-    const Packet q6 = pset1<Packet>(2.27279857398537278e-6);
-    const Packet q4 = pset1<Packet>(2.31271023278625638e-5);
-    const Packet q2 = pset1<Packet>(2.47556738444535513e-4);
-    const Packet q0 = pset1<Packet>(2.88543873228900172e-3);
-    const Packet C_hi = pset1<Packet>(0.0400377511598501157);
-    const Packet C_lo = pset1<Packet>(-4.77726582251425391e-19);
-    const Packet one = pset1<Packet>(1.0);
-
-    const Packet cst_2_log2e_hi = pset1<Packet>(2.88539008177792677);
-    const Packet cst_2_log2e_lo = pset1<Packet>(4.07660016854549667e-17);
-    // c * (x - 1)
-    Packet num_hi, num_lo;
-    twoprod(cst_2_log2e_hi, cst_2_log2e_lo, psub(x, one), num_hi, num_lo);
-    // TODO(rmlarsen): Investigate if using the division algorithm by
-    // Muller et al. is faster/more accurate.
-    // 1 / (x + 1)
-    Packet denom_hi, denom_lo;
-    doubleword_reciprocal(padd(x, one), denom_hi, denom_lo);
-    // r =  c * (x-1) / (x+1),
-    Packet r_hi, r_lo;
-    twoprod(num_hi, num_lo, denom_hi, denom_lo, r_hi, r_lo);
-    // r2 = r * r
-    Packet r2_hi, r2_lo;
-    twoprod(r_hi, r_lo, r_hi, r_lo, r2_hi, r2_lo);
-    // r4 = r2 * r2
-    Packet r4_hi, r4_lo;
-    twoprod(r2_hi, r2_lo, r2_hi, r2_lo, r4_hi, r4_lo);
-
-    // Evaluate Q(r^2) in working precision. We evaluate it in two parts
-    // (even and odd in r^2) to improve instruction level parallelism.
-    Packet q_even = pmadd(q12, r4_hi, q8);
-    Packet q_odd = pmadd(q10, r4_hi, q6);
-    q_even = pmadd(q_even, r4_hi, q4);
-    q_odd = pmadd(q_odd, r4_hi, q2);
-    q_even = pmadd(q_even, r4_hi, q0);
-    Packet q = pmadd(q_odd, r2_hi, q_even);
-
-    // Now evaluate the low order terms of P(x) in double word precision.
-    // In the following, due to the increasing magnitude of the coefficients
-    // and r being constrained to [-0.5, 0.5] we can use fast_twosum instead
-    // of the slower twosum.
-    // Q(r^2) * r^2
-    Packet p_hi, p_lo;
-    twoprod(r2_hi, r2_lo, q, p_hi, p_lo);
-    // Q(r^2) * r^2 + C
-    Packet p1_hi, p1_lo;
-    fast_twosum(C_hi, C_lo, p_hi, p_lo, p1_hi, p1_lo);
-    // (Q(r^2) * r^2 + C) * r^2
-    Packet p2_hi, p2_lo;
-    twoprod(r2_hi, r2_lo, p1_hi, p1_lo, p2_hi, p2_lo);
-    // ((Q(r^2) * r^2 + C) * r^2 + 1)
-    Packet p3_hi, p3_lo;
-    fast_twosum(one, p2_hi, p2_lo, p3_hi, p3_lo);
-
-    // log(z) ~= ((Q(r^2) * r^2 + C) * r^2 + 1) * r
-    twoprod(p3_hi, p3_lo, r_hi, r_lo, log2_x_hi, log2_x_lo);
-  }
-};
-
-// This function computes exp2(x) (i.e. 2**x).
-template <typename Scalar>
-struct fast_accurate_exp2 {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  Packet operator()(const Packet& x) {
-    // TODO(rmlarsen): Add a pexp2 packetop.
-    return pexp(pmul(pset1<Packet>(Scalar(EIGEN_LN2)), x));
-  }
-};
-
-// This specialization uses a faster algorithm to compute exp2(x) for floats
-// in [-0.5;0.5] with a relative accuracy of 1 ulp.
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-template <>
-struct fast_accurate_exp2<float> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  Packet operator()(const Packet& x) {
-    // This function approximates exp2(x) by a degree 6 polynomial of the form
-    // Q(x) = 1 + x * (C + x * P(x)), where the degree 4 polynomial P(x) is evaluated in
-    // single precision, and the remaining steps are evaluated with extra precision using
-    // double word arithmetic. C is an extra precise constant stored as a double word.
-    //
-    // The polynomial coefficients were calculated using Sollya commands:
-    // > n = 6;
-    // > f = 2^x;
-    // > interval = [-0.5;0.5];
-    // > p = fpminimax(f,n,[|1,double,single...|],interval,relative,floating);
-
-    const Packet p4 = pset1<Packet>(1.539513905e-4f);
-    const Packet p3 = pset1<Packet>(1.340007293e-3f);
-    const Packet p2 = pset1<Packet>(9.618283249e-3f);
-    const Packet p1 = pset1<Packet>(5.550328270e-2f);
-    const Packet p0 = pset1<Packet>(0.2402264923f);
-
-    const Packet C_hi = pset1<Packet>(0.6931471825f);
-    const Packet C_lo = pset1<Packet>(2.36836577e-08f);
-    const Packet one = pset1<Packet>(1.0f);
-
-    // Evaluate P(x) in working precision.
-    // We evaluate even and odd parts of the polynomial separately
-    // to gain some instruction level parallelism.
-    Packet x2 = pmul(x,x);
-    Packet p_even = pmadd(p4, x2, p2);
-    Packet p_odd = pmadd(p3, x2, p1);
-    p_even = pmadd(p_even, x2, p0);
-    Packet p = pmadd(p_odd, x, p_even);
-
-    // Evaluate the remaining terms of Q(x) with extra precision using
-    // double word arithmetic.
-    Packet p_hi, p_lo;
-    // x * p(x)
-    twoprod(p, x, p_hi, p_lo);
-    // C + x * p(x)
-    Packet q1_hi, q1_lo;
-    twosum(p_hi, p_lo, C_hi, C_lo, q1_hi, q1_lo);
-    // x * (C + x * p(x))
-    Packet q2_hi, q2_lo;
-    twoprod(q1_hi, q1_lo, x, q2_hi, q2_lo);
-    // 1 + x * (C + x * p(x))
-    Packet q3_hi, q3_lo;
-    // Since |q2_hi| <= sqrt(2)-1 < 1, we can use fast_twosum
-    // for adding it to unity here.
-    fast_twosum(one, q2_hi, q3_hi, q3_lo);
-    return padd(q3_hi, padd(q2_lo, q3_lo));
-  }
-};
-
-// in [-0.5;0.5] with a relative accuracy of 1 ulp.
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-template <>
-struct fast_accurate_exp2<double> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  Packet operator()(const Packet& x) {
-    // This function approximates exp2(x) by a degree 10 polynomial of the form
-    // Q(x) = 1 + x * (C + x * P(x)), where the degree 8 polynomial P(x) is evaluated in
-    // single precision, and the remaining steps are evaluated with extra precision using
-    // double word arithmetic. C is an extra precise constant stored as a double word.
-    //
-    // The polynomial coefficients were calculated using Sollya commands:
-    // > n = 11;
-    // > f = 2^x;
-    // > interval = [-0.5;0.5];
-    // > p = fpminimax(f,n,[|1,DD,double...|],interval,relative,floating);
-
-    const Packet p9 = pset1<Packet>(4.431642109085495276e-10);
-    const Packet p8 = pset1<Packet>(7.073829923303358410e-9);
-    const Packet p7 = pset1<Packet>(1.017822306737031311e-7);
-    const Packet p6 = pset1<Packet>(1.321543498017646657e-6);
-    const Packet p5 = pset1<Packet>(1.525273342728892877e-5);
-    const Packet p4 = pset1<Packet>(1.540353045780084423e-4);
-    const Packet p3 = pset1<Packet>(1.333355814685869807e-3);
-    const Packet p2 = pset1<Packet>(9.618129107593478832e-3);
-    const Packet p1 = pset1<Packet>(5.550410866481961247e-2);
-    const Packet p0 = pset1<Packet>(0.240226506959101332);
-    const Packet C_hi = pset1<Packet>(0.693147180559945286); 
-    const Packet C_lo = pset1<Packet>(4.81927865669806721e-17);
-    const Packet one = pset1<Packet>(1.0);
-
-    // Evaluate P(x) in working precision.
-    // We evaluate even and odd parts of the polynomial separately
-    // to gain some instruction level parallelism.
-    Packet x2 = pmul(x,x);
-    Packet p_even = pmadd(p8, x2, p6);
-    Packet p_odd = pmadd(p9, x2, p7);
-    p_even = pmadd(p_even, x2, p4);
-    p_odd = pmadd(p_odd, x2, p5);
-    p_even = pmadd(p_even, x2, p2);
-    p_odd = pmadd(p_odd, x2, p3);
-    p_even = pmadd(p_even, x2, p0);
-    p_odd = pmadd(p_odd, x2, p1);
-    Packet p = pmadd(p_odd, x, p_even);
-
-    // Evaluate the remaining terms of Q(x) with extra precision using
-    // double word arithmetic.
-    Packet p_hi, p_lo;
-    // x * p(x)
-    twoprod(p, x, p_hi, p_lo);
-    // C + x * p(x)
-    Packet q1_hi, q1_lo;
-    twosum(p_hi, p_lo, C_hi, C_lo, q1_hi, q1_lo);
-    // x * (C + x * p(x))
-    Packet q2_hi, q2_lo;
-    twoprod(q1_hi, q1_lo, x, q2_hi, q2_lo);
-    // 1 + x * (C + x * p(x))
-    Packet q3_hi, q3_lo;
-    // Since |q2_hi| <= sqrt(2)-1 < 1, we can use fast_twosum
-    // for adding it to unity here.
-    fast_twosum(one, q2_hi, q3_hi, q3_lo);
-    return padd(q3_hi, padd(q2_lo, q3_lo));
-  }
-};
-
-// This function implements the non-trivial case of pow(x,y) where x is
-// positive and y is (possibly) non-integer.
-// Formally, pow(x,y) = exp2(y * log2(x)), where exp2(x) is shorthand for 2^x.
-// TODO(rmlarsen): We should probably add this as a packet up 'ppow', to make it
-// easier to specialize or turn off for specific types and/or backends.x
-template <typename Packet>
-EIGEN_STRONG_INLINE Packet generic_pow_impl(const Packet& x, const Packet& y) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  // Split x into exponent e_x and mantissa m_x.
-  Packet e_x;
-  Packet m_x = pfrexp(x, e_x);
-
-  // Adjust m_x to lie in [1/sqrt(2):sqrt(2)] to minimize absolute error in log2(m_x).
-  EIGEN_CONSTEXPR Scalar sqrt_half = Scalar(0.70710678118654752440);
-  const Packet m_x_scale_mask = pcmp_lt(m_x, pset1<Packet>(sqrt_half));
-  m_x = pselect(m_x_scale_mask, pmul(pset1<Packet>(Scalar(2)), m_x), m_x);
-  e_x = pselect(m_x_scale_mask, psub(e_x, pset1<Packet>(Scalar(1))), e_x);
-
-  // Compute log2(m_x) with 6 extra bits of accuracy.
-  Packet rx_hi, rx_lo;
-  accurate_log2<Scalar>()(m_x, rx_hi, rx_lo);
-
-  // Compute the two terms {y * e_x, y * r_x} in f = y * log2(x) with doubled
-  // precision using double word arithmetic.
-  Packet f1_hi, f1_lo, f2_hi, f2_lo;
-  twoprod(e_x, y, f1_hi, f1_lo);
-  twoprod(rx_hi, rx_lo, y, f2_hi, f2_lo);
-  // Sum the two terms in f using double word arithmetic. We know
-  // that |e_x| > |log2(m_x)|, except for the case where e_x==0.
-  // This means that we can use fast_twosum(f1,f2).
-  // In the case e_x == 0, e_x * y = f1 = 0, so we don't lose any
-  // accuracy by violating the assumption of fast_twosum, because
-  // it's a no-op.
-  Packet f_hi, f_lo;
-  fast_twosum(f1_hi, f1_lo, f2_hi, f2_lo, f_hi, f_lo);
-
-  // Split f into integer and fractional parts.
-  Packet n_z, r_z;
-  absolute_split(f_hi, n_z, r_z);
-  r_z = padd(r_z, f_lo);
-  Packet n_r;
-  absolute_split(r_z, n_r, r_z);
-  n_z = padd(n_z, n_r);
-
-  // We now have an accurate split of f = n_z + r_z and can compute
-  //   x^y = 2**{n_z + r_z) = exp2(r_z) * 2**{n_z}.
-  // Since r_z is in [-0.5;0.5], we compute the first factor to high accuracy
-  // using a specialized algorithm. Multiplication by the second factor can
-  // be done exactly using pldexp(), since it is an integer power of 2.
-  const Packet e_r = fast_accurate_exp2<Scalar>()(r_z);
-  return pldexp(e_r, n_z);
-}
-
-// Generic implementation of pow(x,y).
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet generic_pow(const Packet& x, const Packet& y) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-
-  const Packet cst_pos_inf = pset1<Packet>(NumTraits<Scalar>::infinity());
-  const Packet cst_zero = pset1<Packet>(Scalar(0));
-  const Packet cst_one = pset1<Packet>(Scalar(1));
-  const Packet cst_nan = pset1<Packet>(NumTraits<Scalar>::quiet_NaN());
-
-  const Packet abs_x = pabs(x);
-  // Predicates for sign and magnitude of x.
-  const Packet x_is_zero = pcmp_eq(x, cst_zero);
-  const Packet x_is_neg = pcmp_lt(x, cst_zero);
-  const Packet abs_x_is_inf = pcmp_eq(abs_x, cst_pos_inf);
-  const Packet abs_x_is_one =  pcmp_eq(abs_x, cst_one);
-  const Packet abs_x_is_gt_one = pcmp_lt(cst_one, abs_x);
-  const Packet abs_x_is_lt_one = pcmp_lt(abs_x, cst_one);
-  const Packet x_is_one =  pandnot(abs_x_is_one, x_is_neg);
-  const Packet x_is_neg_one =  pand(abs_x_is_one, x_is_neg);
-  const Packet x_is_nan = pandnot(ptrue(x), pcmp_eq(x, x));
-
-  // Predicates for sign and magnitude of y.
-  const Packet y_is_one = pcmp_eq(y, cst_one);
-  const Packet y_is_zero = pcmp_eq(y, cst_zero);
-  const Packet y_is_neg = pcmp_lt(y, cst_zero);
-  const Packet y_is_pos = pandnot(ptrue(y), por(y_is_zero, y_is_neg));
-  const Packet y_is_nan = pandnot(ptrue(y), pcmp_eq(y, y));
-  const Packet abs_y_is_inf = pcmp_eq(pabs(y), cst_pos_inf);
-  EIGEN_CONSTEXPR Scalar huge_exponent =
-      (NumTraits<Scalar>::max_exponent() * Scalar(EIGEN_LN2)) /
-       NumTraits<Scalar>::epsilon();
-  const Packet abs_y_is_huge = pcmp_le(pset1<Packet>(huge_exponent), pabs(y));
-
-  // Predicates for whether y is integer and/or even.
-  const Packet y_is_int = pcmp_eq(pfloor(y), y);
-  const Packet y_div_2 = pmul(y, pset1<Packet>(Scalar(0.5)));
-  const Packet y_is_even = pcmp_eq(pround(y_div_2), y_div_2);
-
-  // Predicates encoding special cases for the value of pow(x,y)
-  const Packet invalid_negative_x = pandnot(pandnot(pandnot(x_is_neg, abs_x_is_inf),
-                                                    y_is_int),
-                                            abs_y_is_inf);
-  const Packet pow_is_one = por(por(x_is_one, y_is_zero),
-                                pand(x_is_neg_one,
-                                     por(abs_y_is_inf, pandnot(y_is_even, invalid_negative_x))));
-  const Packet pow_is_nan = por(invalid_negative_x, por(x_is_nan, y_is_nan));
-  const Packet pow_is_zero = por(por(por(pand(x_is_zero, y_is_pos),
-                                         pand(abs_x_is_inf, y_is_neg)),
-                                     pand(pand(abs_x_is_lt_one, abs_y_is_huge),
-                                          y_is_pos)),
-                                 pand(pand(abs_x_is_gt_one, abs_y_is_huge),
-                                      y_is_neg));
-  const Packet pow_is_inf = por(por(por(pand(x_is_zero, y_is_neg),
-                                        pand(abs_x_is_inf, y_is_pos)),
-                                    pand(pand(abs_x_is_lt_one, abs_y_is_huge),
-                                         y_is_neg)),
-                                pand(pand(abs_x_is_gt_one, abs_y_is_huge),
-                                     y_is_pos));
-
-  // General computation of pow(x,y) for positive x or negative x and integer y.
-  const Packet negate_pow_abs = pandnot(x_is_neg, y_is_even);
-  const Packet pow_abs = generic_pow_impl(abs_x, y);
-  return pselect(y_is_one, x,
-                 pselect(pow_is_one, cst_one,
-                         pselect(pow_is_nan, cst_nan,
-                                 pselect(pow_is_inf, cst_pos_inf,
-                                         pselect(pow_is_zero, cst_zero,
-                                                 pselect(negate_pow_abs, pnegate(pow_abs), pow_abs))))));
-}
-
-
-
-/* polevl (modified for Eigen)
- *
- *      Evaluate polynomial
- *
- *
- *
- * SYNOPSIS:
- *
- * int N;
- * Scalar x, y, coef[N+1];
- *
- * y = polevl<decltype(x), N>( x, coef);
- *
- *
- *
- * DESCRIPTION:
- *
- * Evaluates polynomial of degree N:
- *
- *                     2          N
- * y  =  C  + C x + C x  +...+ C x
- *        0    1     2          N
- *
- * Coefficients are stored in reverse order:
- *
- * coef[0] = C  , ..., coef[N] = C  .
- *            N                   0
- *
- *  The function p1evl() assumes that coef[N] = 1.0 and is
- * omitted from the array.  Its calling arguments are
- * otherwise the same as polevl().
- *
- *
- * The Eigen implementation is templatized.  For best speed, store
- * coef as a const array (constexpr), e.g.
- *
- * const double coef[] = {1.0, 2.0, 3.0, ...};
- *
- */
-template <typename Packet, int N>
-struct ppolevl {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const typename unpacket_traits<Packet>::type coeff[]) {
-    EIGEN_STATIC_ASSERT((N > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    return pmadd(ppolevl<Packet, N-1>::run(x, coeff), x, pset1<Packet>(coeff[N]));
-  }
-};
-
-template <typename Packet>
-struct ppolevl<Packet, 0> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const typename unpacket_traits<Packet>::type coeff[]) {
-    EIGEN_UNUSED_VARIABLE(x);
-    return pset1<Packet>(coeff[0]);
-  }
-};
-
-/* chbevl (modified for Eigen)
- *
- *     Evaluate Chebyshev series
- *
- *
- *
- * SYNOPSIS:
- *
- * int N;
- * Scalar x, y, coef[N], chebevl();
- *
- * y = chbevl( x, coef, N );
- *
- *
- *
- * DESCRIPTION:
- *
- * Evaluates the series
- *
- *        N-1
- *         - '
- *  y  =   >   coef[i] T (x/2)
- *         -            i
- *        i=0
- *
- * of Chebyshev polynomials Ti at argument x/2.
- *
- * Coefficients are stored in reverse order, i.e. the zero
- * order term is last in the array.  Note N is the number of
- * coefficients, not the order.
- *
- * If coefficients are for the interval a to b, x must
- * have been transformed to x -> 2(2x - b - a)/(b-a) before
- * entering the routine.  This maps x from (a, b) to (-1, 1),
- * over which the Chebyshev polynomials are defined.
- *
- * If the coefficients are for the inverted interval, in
- * which (a, b) is mapped to (1/b, 1/a), the transformation
- * required is x -> 2(2ab/x - b - a)/(b-a).  If b is infinity,
- * this becomes x -> 4a/x - 1.
- *
- *
- *
- * SPEED:
- *
- * Taking advantage of the recurrence properties of the
- * Chebyshev polynomials, the routine requires one more
- * addition per loop than evaluating a nested polynomial of
- * the same degree.
- *
- */
-
-template <typename Packet, int N>
-struct pchebevl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Packet run(Packet x, const typename unpacket_traits<Packet>::type coef[]) {
-    typedef typename unpacket_traits<Packet>::type Scalar;
-    Packet b0 = pset1<Packet>(coef[0]);
-    Packet b1 = pset1<Packet>(static_cast<Scalar>(0.f));
-    Packet b2;
-
-    for (int i = 1; i < N; i++) {
-      b2 = b1;
-      b1 = b0;
-      b0 = psub(pmadd(x, b1, pset1<Packet>(coef[i])), b2);
-    }
-
-    return pmul(pset1<Packet>(static_cast<Scalar>(0.5f)), psub(b0, b2));
-  }
-};
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
deleted file mode 100644
index 177a04e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
+++ /dev/null
@@ -1,110 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
-#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
-
-namespace Eigen {
-namespace internal {
-
-// Forward declarations of the generic math functions
-// implemented in GenericPacketMathFunctions.h
-// This is needed to workaround a circular dependency.
-
-/***************************************************************************
- * Some generic implementations to be used by implementors
-***************************************************************************/
-
-/** Default implementation of pfrexp.
-  * It is expected to be called by implementers of template<> pfrexp.
-  */
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pfrexp_generic(const Packet& a, Packet& exponent);
-
-// Extracts the biased exponent value from Packet p, and casts the results to
-// a floating-point Packet type. Used by pfrexp_generic. Override this if
-// there is no unpacket_traits<Packet>::integer_packet.
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pfrexp_generic_get_biased_exponent(const Packet& p);
-
-/** Default implementation of pldexp.
-  * It is expected to be called by implementers of template<> pldexp.
-  */
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pldexp_generic(const Packet& a, const Packet& exponent);
-
-/** \internal \returns log(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_float(const Packet _x);
-
-/** \internal \returns log2(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_float(const Packet _x);
-
-/** \internal \returns log(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_double(const Packet _x);
-
-/** \internal \returns log2(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_double(const Packet _x);
-
-/** \internal \returns log(1 + x) */
-template<typename Packet>
-Packet generic_plog1p(const Packet& x);
-
-/** \internal \returns exp(x)-1 */
-template<typename Packet>
-Packet generic_expm1(const Packet& x);
-
-/** \internal \returns exp(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_float(const Packet _x);
-
-/** \internal \returns exp(x) for double precision real numbers */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_double(const Packet _x);
-
-/** \internal \returns sin(x) for single precision float */
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psin_float(const Packet& x);
-
-/** \internal \returns cos(x) for single precision float */
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pcos_float(const Packet& x);
-
-/** \internal \returns sqrt(x) for complex types */
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psqrt_complex(const Packet& a);
-
-template <typename Packet, int N> struct ppolevl;
-
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/Half.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/Half.h
deleted file mode 100644
index 9f8e8cc..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/Half.h
+++ /dev/null
@@ -1,942 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-//
-// The conversion routines are Copyright (c) Fabian Giesen, 2016.
-// The original license follows:
-//
-// Copyright (c) Fabian Giesen, 2016
-// All rights reserved.
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted.
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-
-// Standard 16-bit float type, mostly useful for GPUs. Defines a new
-// type Eigen::half (inheriting either from CUDA's or HIP's __half struct) with
-// operator overloads such that it behaves basically as an arithmetic
-// type. It will be quite slow on CPUs (so it is recommended to stay
-// in fp32 for CPUs, except for simple parameter conversions, I/O
-// to disk and the likes), but fast on GPUs.
-
-
-#ifndef EIGEN_HALF_H
-#define EIGEN_HALF_H
-
-#include <sstream>
-
-#if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-// When compiling with GPU support, the "__half_raw" base class as well as
-// some other routines are defined in the GPU compiler header files
-// (cuda_fp16.h, hip_fp16.h), and they are not tagged constexpr
-// As a consequence, we get compile failures when compiling Eigen with
-// GPU support. Hence the need to disable EIGEN_CONSTEXPR when building
-// Eigen with GPU support
-  #pragma push_macro("EIGEN_CONSTEXPR")
-  #undef EIGEN_CONSTEXPR
-  #define EIGEN_CONSTEXPR
-#endif
-
-#define F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, METHOD)           \
-  template <>                                                       \
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED                \
-  PACKET_F16 METHOD<PACKET_F16>(const PACKET_F16& _x) {             \
-    return float2half(METHOD<PACKET_F>(half2float(_x)));            \
-  }
-
-namespace Eigen {
-
-struct half;
-
-namespace half_impl {
-
-// We want to use the __half_raw struct from the HIP header file only during the device compile phase.
-// This is required because of a quirk in the way TensorFlow GPU builds are done.
-// When compiling TensorFlow source code with GPU support, files that
-//  * contain GPU kernels (i.e. *.cu.cc files) are compiled via hipcc
-//  * do not contain GPU kernels ( i.e. *.cc files) are compiled via gcc (typically)
-//
-// Tensorflow uses the Eigen::half type as its FP16 type, and there are functions that
-//  * are defined in a file that gets compiled via hipcc AND
-//  * have Eigen::half as a pass-by-value argument AND
-//  * are called in a file that gets compiled via gcc
-//
-// In the scenario described above the caller and callee will see different versions
-// of the Eigen::half base class __half_raw, and they will be compiled by different compilers
-//
-// There appears to be an ABI mismatch between gcc and clang (which is called by hipcc) that results in
-// the callee getting corrupted values for the Eigen::half argument.
-//
-// Making the host side compile phase of hipcc use the same Eigen::half impl, as the gcc compile, resolves
-// this error, and hence the following convoluted #if condition
-#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
-// Make our own __half_raw definition that is similar to CUDA's.
-struct __half_raw {
-#if (defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE))
-  // Eigen::half can be used as the datatype for shared memory declarations (in Eigen and TF)
-  // The element type for shared memory cannot have non-trivial constructors
-  // and hence the following special casing (which skips the zero-initilization).
-  // Note that this check gets done even in the host compilation phase, and
-  // hence the need for this
-  EIGEN_DEVICE_FUNC __half_raw() {}
-#else
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw() : x(0) {}
-#endif
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw(numext::uint16_t raw) : x(numext::bit_cast<__fp16>(raw)) {
-  }
-  __fp16 x;
-#else
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw(numext::uint16_t raw) : x(raw) {}
-  numext::uint16_t x;
-#endif
-};
-
-#elif defined(EIGEN_HAS_HIP_FP16)
-  // Nothing to do here
-  // HIP fp16 header file has a definition for __half_raw
-#elif defined(EIGEN_HAS_CUDA_FP16)
-  #if EIGEN_CUDA_SDK_VER < 90000
-    // In CUDA < 9.0, __half is the equivalent of CUDA 9's __half_raw
-    typedef __half __half_raw;
-  #endif // defined(EIGEN_HAS_CUDA_FP16)
-#elif defined(SYCL_DEVICE_ONLY)
-  typedef cl::sycl::half __half_raw;
-#endif
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x);
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff);
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h);
-
-struct half_base : public __half_raw {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base() {}
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half_raw& h) : __half_raw(h) {}
-
-#if defined(EIGEN_HAS_GPU_FP16)
- #if defined(EIGEN_HAS_HIP_FP16)
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half& h) { x = __half_as_ushort(h); }
- #elif defined(EIGEN_HAS_CUDA_FP16)
-  #if EIGEN_CUDA_SDK_VER >= 90000
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half& h) : __half_raw(*(__half_raw*)&h) {}
-  #endif
- #endif
-#endif
-};
-
-} // namespace half_impl
-
-// Class definition.
-struct half : public half_impl::half_base {
-
-  // Writing this out as separate #if-else blocks to make the code easier to follow
-  // The same applies to most #if-else blocks in this file
-#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
-  // Use the same base class for the following two scenarios
-  // * when compiling without GPU support enabled
-  // * during host compile phase when compiling with GPU support enabled
-  typedef half_impl::__half_raw __half_raw;
-#elif defined(EIGEN_HAS_HIP_FP16)
-  // Nothing to do here
-  // HIP fp16 header file has a definition for __half_raw
-#elif defined(EIGEN_HAS_CUDA_FP16)
-  // Note that EIGEN_CUDA_SDK_VER is set to 0 even when compiling with HIP, so
-  // (EIGEN_CUDA_SDK_VER < 90000) is true even for HIP!  So keeping this within
-  // #if defined(EIGEN_HAS_CUDA_FP16) is needed
-  #if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
-    typedef half_impl::__half_raw __half_raw;
-  #endif
-#endif
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half_raw& h) : half_impl::half_base(h) {}
-
-#if defined(EIGEN_HAS_GPU_FP16)
- #if defined(EIGEN_HAS_HIP_FP16)
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
- #elif defined(EIGEN_HAS_CUDA_FP16)
-  #if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
-  #endif
- #endif
-#endif
-
-
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(bool b)
-      : half_impl::half_base(half_impl::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
-  template<class T>
-  explicit EIGEN_DEVICE_FUNC half(T val)
-      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(val))) {}
-  explicit EIGEN_DEVICE_FUNC half(float f)
-      : half_impl::half_base(half_impl::float_to_half_rtne(f)) {}
-
-  // Following the convention of numpy, converting between complex and
-  // float will lead to loss of imag value.
-  template<typename RealScalar>
-  explicit EIGEN_DEVICE_FUNC half(std::complex<RealScalar> c)
-      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(c.real()))) {}
-
-   EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
-    return half_impl::half_to_float(*this);
-  }
-
-#if defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE)
-  EIGEN_DEVICE_FUNC operator __half() const {
-    ::__half_raw hr;
-    hr.x = x;
-    return __half(hr);
-  }
-#endif
-};
-
-} // end namespace Eigen
-
-namespace std {
-template<>
-struct numeric_limits<Eigen::half> {
-  static const bool is_specialized = true;
-  static const bool is_signed = true;
-  static const bool is_integer = false;
-  static const bool is_exact = false;
-  static const bool has_infinity = true;
-  static const bool has_quiet_NaN = true;
-  static const bool has_signaling_NaN = true;
-  static const float_denorm_style has_denorm = denorm_present;
-  static const bool has_denorm_loss = false;
-  static const std::float_round_style round_style = std::round_to_nearest;
-  static const bool is_iec559 = false;
-  static const bool is_bounded = false;
-  static const bool is_modulo = false;
-  static const int digits = 11;
-  static const int digits10 = 3;      // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
-  static const int max_digits10 = 5;  // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
-  static const int radix = 2;
-  static const int min_exponent = -13;
-  static const int min_exponent10 = -4;
-  static const int max_exponent = 16;
-  static const int max_exponent10 = 4;
-  static const bool traps = true;
-  static const bool tinyness_before = false;
-
-  static Eigen::half (min)() { return Eigen::half_impl::raw_uint16_to_half(0x400); }
-  static Eigen::half lowest() { return Eigen::half_impl::raw_uint16_to_half(0xfbff); }
-  static Eigen::half (max)() { return Eigen::half_impl::raw_uint16_to_half(0x7bff); }
-  static Eigen::half epsilon() { return Eigen::half_impl::raw_uint16_to_half(0x0800); }
-  static Eigen::half round_error() { return Eigen::half(0.5); }
-  static Eigen::half infinity() { return Eigen::half_impl::raw_uint16_to_half(0x7c00); }
-  static Eigen::half quiet_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
-  static Eigen::half signaling_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7d00); }
-  static Eigen::half denorm_min() { return Eigen::half_impl::raw_uint16_to_half(0x1); }
-};
-
-// If std::numeric_limits<T> is specialized, should also specialize
-// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
-// std::numeric_limits<const volatile T>
-// https://stackoverflow.com/a/16519653/
-template<>
-struct numeric_limits<const Eigen::half> : numeric_limits<Eigen::half> {};
-template<>
-struct numeric_limits<volatile Eigen::half> : numeric_limits<Eigen::half> {};
-template<>
-struct numeric_limits<const volatile Eigen::half> : numeric_limits<Eigen::half> {};
-} // end namespace std
-
-namespace Eigen {
-
-namespace half_impl {
-
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && \
-     EIGEN_CUDA_ARCH >= 530) ||                                  \
-    (defined(EIGEN_HAS_HIP_FP16) && defined(HIP_DEVICE_COMPILE))
-// Note: We deliberatly do *not* define this to 1 even if we have Arm's native
-// fp16 type since GPU halfs are rather different from native CPU halfs.
-// TODO: Rename to something like EIGEN_HAS_NATIVE_GPU_FP16
-#define EIGEN_HAS_NATIVE_FP16
-#endif
-
-// Intrinsics for native fp16 support. Note that on current hardware,
-// these are no faster than fp32 arithmetic (you need to use the half2
-// versions to get the ALU speed increased), but you do save the
-// conversion steps back and forth.
-
-#if defined(EIGEN_HAS_NATIVE_FP16)
-EIGEN_STRONG_INLINE __device__ half operator + (const half& a, const half& b) {
-#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
-  return __hadd(::__half(a), ::__half(b));
-#else
-  return __hadd(a, b);
-#endif
-}
-EIGEN_STRONG_INLINE __device__ half operator * (const half& a, const half& b) {
-  return __hmul(a, b);
-}
-EIGEN_STRONG_INLINE __device__ half operator - (const half& a, const half& b) {
-  return __hsub(a, b);
-}
-EIGEN_STRONG_INLINE __device__ half operator / (const half& a, const half& b) {
-#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
-  return __hdiv(a, b);
-#else
-  float num = __half2float(a);
-  float denom = __half2float(b);
-  return __float2half(num / denom);
-#endif
-}
-EIGEN_STRONG_INLINE __device__ half operator - (const half& a) {
-  return __hneg(a);
-}
-EIGEN_STRONG_INLINE __device__ half& operator += (half& a, const half& b) {
-  a = a + b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ half& operator *= (half& a, const half& b) {
-  a = a * b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ half& operator -= (half& a, const half& b) {
-  a = a - b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ half& operator /= (half& a, const half& b) {
-  a = a / b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ bool operator == (const half& a, const half& b) {
-  return __heq(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator != (const half& a, const half& b) {
-  return __hne(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator < (const half& a, const half& b) {
-  return __hlt(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator <= (const half& a, const half& b) {
-  return __hle(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator > (const half& a, const half& b) {
-  return __hgt(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator >= (const half& a, const half& b) {
-  return __hge(a, b);
-}
-#endif
-
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
-  return half(vaddh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
-  return half(vmulh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
-  return half(vsubh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
-  return half(vdivh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
-  return half(vnegh_f16(a.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
-  a = half(vaddh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
-  a = half(vmulh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
-  a = half(vsubh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
-  a = half(vdivh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
-  return vceqh_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
-  return !vceqh_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
-  return vclth_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
-  return vcleh_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
-  return vcgth_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
-  return vcgeh_f16(a.x, b.x);
-}
-// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
-// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
-// of the functions, while the latter can only deal with one of them.
-#elif !defined(EIGEN_HAS_NATIVE_FP16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC) // Emulate support for half floats
-
-#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
-// We need to provide emulated *host-side* FP16 operators for clang.
-#pragma push_macro("EIGEN_DEVICE_FUNC")
-#undef EIGEN_DEVICE_FUNC
-#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_HAS_NATIVE_FP16)
-#define EIGEN_DEVICE_FUNC __host__
-#else // both host and device need emulated ops.
-#define EIGEN_DEVICE_FUNC __host__ __device__
-#endif
-#endif
-
-// Definitions for CPUs and older HIP+CUDA, mostly working through conversion
-// to/from fp32.
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
-  return half(float(a) + float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
-  return half(float(a) * float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
-  return half(float(a) - float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
-  return half(float(a) / float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
-  half result;
-  result.x = a.x ^ 0x8000;
-  return result;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
-  a = half(float(a) + float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
-  a = half(float(a) * float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
-  a = half(float(a) - float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
-  a = half(float(a) / float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
-  return numext::equal_strict(float(a),float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
-  return numext::not_equal_strict(float(a), float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
-  return float(a) < float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
-  return float(a) <= float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
-  return float(a) > float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
-  return float(a) >= float(b);
-}
-
-#if defined(__clang__) && defined(__CUDA__)
-#pragma pop_macro("EIGEN_DEVICE_FUNC")
-#endif
-#endif  // Emulate support for half floats
-
-// Division by an index. Do it in full float precision to avoid accuracy
-// issues in converting the denominator to half.
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, Index b) {
-  return half(static_cast<float>(a) / static_cast<float>(b));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a) {
-  a += half(1);
-  return a;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a) {
-  a -= half(1);
-  return a;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a, int) {
-  half original_value = a;
-  ++a;
-  return original_value;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a, int) {
-  half original_value = a;
-  --a;
-  return original_value;
-}
-
-// Conversion routines, including fallbacks for the host or older CUDA.
-// Note that newer Intel CPUs (Haswell or newer) have vectorized versions of
-// these in hardware. If we need more performance on older/other CPUs, they are
-// also possible to vectorize directly.
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x) {
-  // We cannot simply do a "return __half_raw(x)" here, because __half_raw is union type
-  // in the hip_fp16 header file, and that will trigger a compile error
-  // On the other hand, having anything but a return statement also triggers a compile error
-  // because this is constexpr function.
-  // Fortunately, since we need to disable EIGEN_CONSTEXPR for GPU anyway, we can get out
-  // of this catch22 by having separate bodies for GPU / non GPU
-#if defined(EIGEN_HAS_GPU_FP16)
-   __half_raw h;
-   h.x = x;
-  return h;
-#else
-  return __half_raw(x);
-#endif
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC numext::uint16_t raw_half_as_uint16(const __half_raw& h) {
-  // HIP/CUDA/Default have a member 'x' of type uint16_t.
-  // For ARM64 native half, the member 'x' is of type __fp16, so we need to bit-cast.
-  // For SYCL, cl::sycl::half is _Float16, so cast directly.
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return numext::bit_cast<numext::uint16_t>(h.x);
-#elif defined(SYCL_DEVICE_ONLY)
-  return numext::bit_cast<numext::uint16_t>(h);
-#else
-  return h.x;
-#endif
-}
-
-union float32_bits {
-  unsigned int u;
-  float f;
-};
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  __half tmp_ff = __float2half(ff);
-  return *(__half_raw*)&tmp_ff;
-
-#elif defined(EIGEN_HAS_FP16_C)
-  __half_raw h;
-  h.x = _cvtss_sh(ff, 0);
-  return h;
-
-#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  __half_raw h;
-  h.x = static_cast<__fp16>(ff);
-  return h;
-
-#else
-  float32_bits f; f.f = ff;
-
-  const float32_bits f32infty = { 255 << 23 };
-  const float32_bits f16max = { (127 + 16) << 23 };
-  const float32_bits denorm_magic = { ((127 - 15) + (23 - 10) + 1) << 23 };
-  unsigned int sign_mask = 0x80000000u;
-  __half_raw o;
-  o.x = static_cast<numext::uint16_t>(0x0u);
-
-  unsigned int sign = f.u & sign_mask;
-  f.u ^= sign;
-
-  // NOTE all the integer compares in this function can be safely
-  // compiled into signed compares since all operands are below
-  // 0x80000000. Important if you want fast straight SSE2 code
-  // (since there's no unsigned PCMPGTD).
-
-  if (f.u >= f16max.u) {  // result is Inf or NaN (all exponent bits set)
-    o.x = (f.u > f32infty.u) ? 0x7e00 : 0x7c00; // NaN->qNaN and Inf->Inf
-  } else {  // (De)normalized number or zero
-    if (f.u < (113 << 23)) {  // resulting FP16 is subnormal or zero
-      // use a magic value to align our 10 mantissa bits at the bottom of
-      // the float. as long as FP addition is round-to-nearest-even this
-      // just works.
-      f.f += denorm_magic.f;
-
-      // and one integer subtract of the bias later, we have our final float!
-      o.x = static_cast<numext::uint16_t>(f.u - denorm_magic.u);
-    } else {
-      unsigned int mant_odd = (f.u >> 13) & 1; // resulting mantissa is odd
-
-      // update exponent, rounding bias part 1
-      // Equivalent to `f.u += ((unsigned int)(15 - 127) << 23) + 0xfff`, but
-      // without arithmetic overflow.
-      f.u += 0xc8000fffU;
-      // rounding bias part 2
-      f.u += mant_odd;
-      // take the bits!
-      o.x = static_cast<numext::uint16_t>(f.u >> 13);
-    }
-  }
-
-  o.x |= static_cast<numext::uint16_t>(sign >> 16);
-  return o;
-#endif
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __half2float(h);
-#elif defined(EIGEN_HAS_FP16_C)
-  return _cvtsh_ss(h.x);
-#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return static_cast<float>(h.x);
-#else
-  const float32_bits magic = { 113 << 23 };
-  const unsigned int shifted_exp = 0x7c00 << 13; // exponent mask after shift
-  float32_bits o;
-
-  o.u = (h.x & 0x7fff) << 13;             // exponent/mantissa bits
-  unsigned int exp = shifted_exp & o.u;   // just the exponent
-  o.u += (127 - 15) << 23;                // exponent adjust
-
-  // handle exponent special cases
-  if (exp == shifted_exp) {     // Inf/NaN?
-    o.u += (128 - 16) << 23;    // extra exp adjust
-  } else if (exp == 0) {        // Zero/Denormal?
-    o.u += 1 << 23;             // extra exp adjust
-    o.f -= magic.f;             // renormalize
-  }
-
-  o.u |= (h.x & 0x8000) << 16;    // sign bit
-  return o.f;
-#endif
-}
-
-// --- standard functions ---
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const half& a) {
-#ifdef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
-  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) == 0x7c00;
-#else
-  return (a.x & 0x7fff) == 0x7c00;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const half& a) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __hisnan(a);
-#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) > 0x7c00;
-#else
-  return (a.x & 0x7fff) > 0x7c00;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const half& a) {
-  return !(isinf EIGEN_NOT_A_MACRO (a)) && !(isnan EIGEN_NOT_A_MACRO (a));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half abs(const half& a) {
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return half(vabsh_f16(a.x));
-#else
-  half result;
-  result.x = a.x & 0x7FFF;
-  return result;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half exp(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hexp(a));
-#else
-   return half(::expf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half expm1(const half& a) {
-  return half(numext::expm1(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log(const half& a) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return half(::hlog(a));
-#else
-  return half(::logf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log1p(const half& a) {
-  return half(numext::log1p(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log10(const half& a) {
-  return half(::log10f(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log2(const half& a) {
-  return half(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sqrt(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hsqrt(a));
-#else
-    return half(::sqrtf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half pow(const half& a, const half& b) {
-  return half(::powf(float(a), float(b)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sin(const half& a) {
-  return half(::sinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half cos(const half& a) {
-  return half(::cosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tan(const half& a) {
-  return half(::tanf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tanh(const half& a) {
-  return half(::tanhf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half asin(const half& a) {
-  return half(::asinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half acos(const half& a) {
-  return half(::acosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half floor(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hfloor(a));
-#else
-  return half(::floorf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half ceil(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hceil(a));
-#else
-  return half(::ceilf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half rint(const half& a) {
-  return half(::rintf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half round(const half& a) {
-  return half(::roundf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half fmod(const half& a, const half& b) {
-  return half(::fmodf(float(a), float(b)));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (min)(const half& a, const half& b) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __hlt(b, a) ? b : a;
-#else
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f2 < f1 ? b : a;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (max)(const half& a, const half& b) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __hlt(a, b) ? b : a;
-#else
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f1 < f2 ? b : a;
-#endif
-}
-
-#ifndef EIGEN_NO_IO
-EIGEN_ALWAYS_INLINE std::ostream& operator << (std::ostream& os, const half& v) {
-  os << static_cast<float>(v);
-  return os;
-}
-#endif
-
-} // end namespace half_impl
-
-// import Eigen::half_impl::half into Eigen namespace
-// using half_impl::half;
-
-namespace internal {
-
-template<>
-struct random_default_impl<half, false, false>
-{
-  static inline half run(const half& x, const half& y)
-  {
-    return x + (y-x) * half(float(std::rand()) / float(RAND_MAX));
-  }
-  static inline half run()
-  {
-    return run(half(-1.f), half(1.f));
-  }
-};
-
-template<> struct is_arithmetic<half> { enum { value = true }; };
-
-} // end namespace internal
-
-template<> struct NumTraits<Eigen::half>
-    : GenericNumTraits<Eigen::half>
-{
-  enum {
-    IsSigned = true,
-    IsInteger = false,
-    IsComplex = false,
-    RequireInitialization = false
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half epsilon() {
-    return half_impl::raw_uint16_to_half(0x0800);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half dummy_precision() {
-    return half_impl::raw_uint16_to_half(0x211f); //  Eigen::half(1e-2f);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half highest() {
-    return half_impl::raw_uint16_to_half(0x7bff);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half lowest() {
-    return half_impl::raw_uint16_to_half(0xfbff);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half infinity() {
-    return half_impl::raw_uint16_to_half(0x7c00);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half quiet_NaN() {
-    return half_impl::raw_uint16_to_half(0x7e00);
-  }
-};
-
-} // end namespace Eigen
-
-#if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  #pragma pop_macro("EIGEN_CONSTEXPR")
-#endif
-
-namespace Eigen {
-namespace numext {
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isnan)(const Eigen::half& h) {
-  return (half_impl::isnan)(h);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isinf)(const Eigen::half& h) {
-  return (half_impl::isinf)(h);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isfinite)(const Eigen::half& h) {
-  return (half_impl::isfinite)(h);
-}
-
-#endif
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bit_cast<Eigen::half, uint16_t>(const uint16_t& src) {
-  return Eigen::half(Eigen::half_impl::raw_uint16_to_half(src));
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::half>(const Eigen::half& src) {
-  return Eigen::half_impl::raw_half_as_uint16(src);
-}
-
-}  // namespace numext
-}  // namespace Eigen
-
-// Add the missing shfl* intrinsics.
-// The __shfl* functions are only valid on HIP or _CUDA_ARCH_ >= 300.
-//   CUDA defines them for (__CUDA_ARCH__ >= 300 || !defined(__CUDA_ARCH__))
-//
-// HIP and CUDA prior to SDK 9.0 define
-//    __shfl, __shfl_up, __shfl_down, __shfl_xor for int and float
-// CUDA since 9.0 deprecates those and instead defines
-//    __shfl_sync, __shfl_up_sync, __shfl_down_sync, __shfl_xor_sync,
-//    with native support for __half and __nv_bfloat16
-//
-// Note that the following are __device__ - only functions.
-#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 300)) \
-    || defined(EIGEN_HIPCC)
-
-#if defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 90000
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_sync(unsigned mask, Eigen::half var, int srcLane, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_sync(mask, h, srcLane, width));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up_sync(unsigned mask, Eigen::half var, unsigned int delta, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_up_sync(mask, h, delta, width));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down_sync(unsigned mask, Eigen::half var, unsigned int delta, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_down_sync(mask, h, delta, width));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor_sync(unsigned mask, Eigen::half var, int laneMask, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_xor_sync(mask, h, laneMask, width));
-}
-
-#else // HIP or CUDA SDK < 9.0
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl(Eigen::half var, int srcLane, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl(ivar, srcLane, width)));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up(Eigen::half var, unsigned int delta, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_up(ivar, delta, width)));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down(Eigen::half var, unsigned int delta, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_down(ivar, delta, width)));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor(Eigen::half var, int laneMask, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_xor(ivar, laneMask, width)));
-}
-
-#endif // HIP vs CUDA
-#endif // __shfl*
-
-// ldg() has an overload for __half_raw, but we also need one for Eigen::half.
-#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 350)) \
-    || defined(EIGEN_HIPCC)
-EIGEN_STRONG_INLINE __device__ Eigen::half __ldg(const Eigen::half* ptr) {
-  return Eigen::half_impl::raw_uint16_to_half(__ldg(reinterpret_cast<const Eigen::numext::uint16_t*>(ptr)));
-}
-#endif // __ldg
-
-#if EIGEN_HAS_STD_HASH
-namespace std {
-template <>
-struct hash<Eigen::half> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::half& a) const {
-    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
-  }
-};
-} // end namespace std
-#endif
-
-#endif // EIGEN_HALF_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/Settings.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/Settings.h
deleted file mode 100644
index a5c3ada..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/Settings.h
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-/* All the parameters defined in this file can be specialized in the
- * architecture specific files, and/or by the user.
- * More to come... */
-
-#ifndef EIGEN_DEFAULT_SETTINGS_H
-#define EIGEN_DEFAULT_SETTINGS_H
-
-/** Defines the maximal loop size to enable meta unrolling of loops.
-  * Note that the value here is expressed in Eigen's own notion of "number of FLOPS",
-  * it does not correspond to the number of iterations or the number of instructions
-  */
-#ifndef EIGEN_UNROLLING_LIMIT
-#define EIGEN_UNROLLING_LIMIT 110
-#endif
-
-/** Defines the threshold between a "small" and a "large" matrix.
-  * This threshold is mainly used to select the proper product implementation.
-  */
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-/** Defines the maximal width of the blocks used in the triangular product and solver
-  * for vectors (level 2 blas xTRMV and xTRSV). The default is 8.
-  */
-#ifndef EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH
-#define EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH 8
-#endif
-
-
-/** Defines the default number of registers available for that architecture.
-  * Currently it must be 8 or 16. Other values will fail.
-  */
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 8
-#endif
-
-#endif // EIGEN_DEFAULT_SETTINGS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/TypeCasting.h
deleted file mode 100644
index fb8183b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/Default/TypeCasting.h
+++ /dev/null
@@ -1,120 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERIC_TYPE_CASTING_H
-#define EIGEN_GENERIC_TYPE_CASTING_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<>
-struct scalar_cast_op<float, Eigen::half> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::half result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half operator() (const float& a) const {
-    #if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-      (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-      return __float2half(a);
-    #else
-      return Eigen::half(a);
-    #endif
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<float, Eigen::half> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<int, Eigen::half> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::half result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half operator() (const int& a) const {
-    #if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-      (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-      return __float2half(static_cast<float>(a));
-    #else
-      return Eigen::half(static_cast<float>(a));
-    #endif
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<int, Eigen::half> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<Eigen::half, float> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef float result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator() (const Eigen::half& a) const {
-    #if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-      (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-      return __half2float(a);
-    #else
-      return static_cast<float>(a);
-    #endif
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<Eigen::half, float> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<float, Eigen::bfloat16> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::bfloat16 result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::bfloat16 operator() (const float& a) const {
-    return Eigen::bfloat16(a);
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<float, Eigen::bfloat16> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<int, Eigen::bfloat16> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::bfloat16 result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::bfloat16 operator() (const int& a) const {
-    return Eigen::bfloat16(static_cast<float>(a));
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<int, Eigen::bfloat16> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<Eigen::bfloat16, float> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef float result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator() (const Eigen::bfloat16& a) const {
-    return static_cast<float>(a);
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<Eigen::bfloat16, float> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-}
-}
-
-#endif  // EIGEN_GENERIC_TYPE_CASTING_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/MathFunctions.h
deleted file mode 100644
index d2b3a25..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/MathFunctions.h
+++ /dev/null
@@ -1,103 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_GPU_H
-#define EIGEN_MATH_FUNCTIONS_GPU_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 plog<float4>(const float4& a)
-{
-  return make_float4(logf(a.x), logf(a.y), logf(a.z), logf(a.w));
-}
-
-template<>  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 plog<double2>(const double2& a)
-{
-  using ::log;
-  return make_double2(log(a.x), log(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 plog1p<float4>(const float4& a)
-{
-  return make_float4(log1pf(a.x), log1pf(a.y), log1pf(a.z), log1pf(a.w));
-}
-
-template<>  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 plog1p<double2>(const double2& a)
-{
-  return make_double2(log1p(a.x), log1p(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pexp<float4>(const float4& a)
-{
-  return make_float4(expf(a.x), expf(a.y), expf(a.z), expf(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pexp<double2>(const double2& a)
-{
-  using ::exp;
-  return make_double2(exp(a.x), exp(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pexpm1<float4>(const float4& a)
-{
-  return make_float4(expm1f(a.x), expm1f(a.y), expm1f(a.z), expm1f(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pexpm1<double2>(const double2& a)
-{
-  return make_double2(expm1(a.x), expm1(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 psqrt<float4>(const float4& a)
-{
-  return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 psqrt<double2>(const double2& a)
-{
-  using ::sqrt;
-  return make_double2(sqrt(a.x), sqrt(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 prsqrt<float4>(const float4& a)
-{
-  return make_float4(rsqrtf(a.x), rsqrtf(a.y), rsqrtf(a.z), rsqrtf(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 prsqrt<double2>(const double2& a)
-{
-  return make_double2(rsqrt(a.x), rsqrt(a.y));
-}
-
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATH_FUNCTIONS_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/PacketMath.h
deleted file mode 100644
index 689110d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/PacketMath.h
+++ /dev/null
@@ -1,1685 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_GPU_H
-#define EIGEN_PACKET_MATH_GPU_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Read-only data cached load available.
-#if defined(EIGEN_HIP_DEVICE_COMPILE) || (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350)
-#define EIGEN_GPU_HAS_LDG 1
-#endif
-
-// FP16 math available.
-#if (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530)
-#define EIGEN_CUDA_HAS_FP16_ARITHMETIC 1
-#endif
-
-#if defined(EIGEN_HIP_DEVICE_COMPILE) || defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-#define EIGEN_GPU_HAS_FP16_ARITHMETIC 1
-#endif
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
-
-template<> struct is_arithmetic<float4>  { enum { value = true }; };
-template<> struct is_arithmetic<double2> { enum { value = true }; };
-
-template<> struct packet_traits<float> : default_packet_traits
-{
-  typedef float4 type;
-  typedef float4 half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=4,
-    HasHalfPacket = 0,
-
-    HasDiv  = 1,
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasLGamma = 1,
-    HasDiGamma = 1,
-    HasZeta = 1,
-    HasPolygamma = 1,
-    HasErf = 1,
-    HasErfc = 1,
-    HasNdtri = 1,
-    HasBessel = 1,
-    HasIGamma = 1,
-    HasIGammaDerA = 1,
-    HasGammaSampleDerAlpha = 1,
-    HasIGammac = 1,
-    HasBetaInc = 1,
-
-    HasBlend = 0,
-    HasFloor = 1,
-  };
-};
-
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef double2 type;
-  typedef double2 half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 0,
-
-    HasDiv  = 1,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasLGamma = 1,
-    HasDiGamma = 1,
-    HasZeta = 1,
-    HasPolygamma = 1,
-    HasErf = 1,
-    HasErfc = 1,
-    HasNdtri = 1,
-    HasBessel = 1,
-    HasIGamma = 1,
-    HasIGammaDerA = 1,
-    HasGammaSampleDerAlpha = 1,
-    HasIGammac = 1,
-    HasBetaInc = 1,
-
-    HasBlend = 0,
-    HasFloor = 1,
-  };
-};
-
-
-template<> struct unpacket_traits<float4>  { typedef float  type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef float4 half; };
-template<> struct unpacket_traits<double2> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef double2 half; };
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pset1<float4>(const float&  from) {
-  return make_float4(from, from, from, from);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pset1<double2>(const double& from) {
-  return make_double2(from, from);
-}
-
-// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
-// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
-// of the functions, while the latter can only deal with one of them.
-#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-namespace {
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_and(const float& a,
-                                                        const float& b) {
-  return __int_as_float(__float_as_int(a) & __float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_and(const double& a,
-                                                         const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) &
-                              __double_as_longlong(b));
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_or(const float& a,
-                                                       const float& b) {
-  return __int_as_float(__float_as_int(a) | __float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_or(const double& a,
-                                                        const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) |
-                              __double_as_longlong(b));
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_xor(const float& a,
-                                                        const float& b) {
-  return __int_as_float(__float_as_int(a) ^ __float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_xor(const double& a,
-                                                         const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) ^
-                              __double_as_longlong(b));
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_andnot(const float& a,
-                                                           const float& b) {
-  return __int_as_float(__float_as_int(a) & ~__float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_andnot(const double& a,
-                                                            const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) &
-                              ~__double_as_longlong(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float eq_mask(const float& a,
-                                                    const float& b) {
-  return __int_as_float(a == b ? 0xffffffffu : 0u);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double eq_mask(const double& a,
-                                                     const double& b) {
-  return __longlong_as_double(a == b ? 0xffffffffffffffffull : 0ull);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float lt_mask(const float& a,
-                                                    const float& b) {
-  return __int_as_float(a < b ? 0xffffffffu : 0u);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double lt_mask(const double& a,
-                                                     const double& b) {
-  return __longlong_as_double(a < b ? 0xffffffffffffffffull : 0ull);
-}
-
-}  // namespace
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pand<float4>(const float4& a,
-                                                          const float4& b) {
-  return make_float4(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y),
-                     bitwise_and(a.z, b.z), bitwise_and(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pand<double2>(const double2& a,
-                                                            const double2& b) {
-  return make_double2(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 por<float4>(const float4& a,
-                                                         const float4& b) {
-  return make_float4(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y),
-                     bitwise_or(a.z, b.z), bitwise_or(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 por<double2>(const double2& a,
-                                                           const double2& b) {
-  return make_double2(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pxor<float4>(const float4& a,
-                                                          const float4& b) {
-  return make_float4(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y),
-                     bitwise_xor(a.z, b.z), bitwise_xor(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pxor<double2>(const double2& a,
-                                                            const double2& b) {
-  return make_double2(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pandnot<float4>(const float4& a,
-                                                             const float4& b) {
-  return make_float4(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y),
-                     bitwise_andnot(a.z, b.z), bitwise_andnot(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pandnot<double2>(const double2& a, const double2& b) {
-  return make_double2(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_eq<float4>(const float4& a,
-                                                             const float4& b) {
-  return make_float4(eq_mask(a.x, b.x), eq_mask(a.y, b.y), eq_mask(a.z, b.z),
-                     eq_mask(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_lt<float4>(const float4& a,
-                                                             const float4& b) {
-  return make_float4(lt_mask(a.x, b.x), lt_mask(a.y, b.y), lt_mask(a.z, b.z),
-                     lt_mask(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pcmp_eq<double2>(const double2& a, const double2& b) {
-  return make_double2(eq_mask(a.x, b.x), eq_mask(a.y, b.y));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pcmp_lt<double2>(const double2& a, const double2& b) {
-  return make_double2(lt_mask(a.x, b.x), lt_mask(a.y, b.y));
-}
-#endif // defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plset<float4>(const float& a) {
-  return make_float4(a, a+1, a+2, a+3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plset<double2>(const double& a) {
-  return make_double2(a, a+1);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 padd<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x+b.x, a.y+b.y, a.z+b.z, a.w+b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 padd<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x+b.x, a.y+b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 psub<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x-b.x, a.y-b.y, a.z-b.z, a.w-b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 psub<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x-b.x, a.y-b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pnegate(const float4& a) {
-  return make_float4(-a.x, -a.y, -a.z, -a.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pnegate(const double2& a) {
-  return make_double2(-a.x, -a.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pconj(const float4& a) { return a; }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pconj(const double2& a) { return a; }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmul<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x*b.x, a.y*b.y, a.z*b.z, a.w*b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmul<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x*b.x, a.y*b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pdiv<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x/b.x, a.y/b.y, a.z/b.z, a.w/b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pdiv<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x/b.x, a.y/b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmin<float4>(const float4& a, const float4& b) {
-  return make_float4(fminf(a.x, b.x), fminf(a.y, b.y), fminf(a.z, b.z), fminf(a.w, b.w));
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmin<double2>(const double2& a, const double2& b) {
-  return make_double2(fmin(a.x, b.x), fmin(a.y, b.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmax<float4>(const float4& a, const float4& b) {
-  return make_float4(fmaxf(a.x, b.x), fmaxf(a.y, b.y), fmaxf(a.z, b.z), fmaxf(a.w, b.w));
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmax<double2>(const double2& a, const double2& b) {
-  return make_double2(fmax(a.x, b.x), fmax(a.y, b.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pload<float4>(const float* from) {
-  return *reinterpret_cast<const float4*>(from);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pload<double2>(const double* from) {
-  return *reinterpret_cast<const double2*>(from);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploadu<float4>(const float* from) {
-  return make_float4(from[0], from[1], from[2], from[3]);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploadu<double2>(const double* from) {
-  return make_double2(from[0], from[1]);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploaddup<float4>(const float*   from) {
-  return make_float4(from[0], from[0], from[1], from[1]);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploaddup<double2>(const double*  from) {
-  return make_double2(from[0], from[0]);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<float>(float*   to, const float4& from) {
-  *reinterpret_cast<float4*>(to) = from;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<double>(double* to, const double2& from) {
-  *reinterpret_cast<double2*>(to) = from;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<float>(float*  to, const float4& from) {
-  to[0] = from.x;
-  to[1] = from.y;
-  to[2] = from.z;
-  to[3] = from.w;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const double2& from) {
-  to[0] = from.x;
-  to[1] = from.y;
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Aligned>(const float* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return __ldg((const float4*)from);
-#else
-  return make_float4(from[0], from[1], from[2], from[3]);
-#endif
-}
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Aligned>(const double* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return __ldg((const double2*)from);
-#else
-  return make_double2(from[0], from[1]);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Unaligned>(const float* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return make_float4(__ldg(from+0), __ldg(from+1), __ldg(from+2), __ldg(from+3));
-#else
-  return make_float4(from[0], from[1], from[2], from[3]);
-#endif
-}
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Unaligned>(const double* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return make_double2(__ldg(from+0), __ldg(from+1));
-#else
-  return make_double2(from[0], from[1]);
-#endif
-}
-
-template<> EIGEN_DEVICE_FUNC inline float4 pgather<float, float4>(const float* from, Index stride) {
-  return make_float4(from[0*stride], from[1*stride], from[2*stride], from[3*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline double2 pgather<double, double2>(const double* from, Index stride) {
-  return make_double2(from[0*stride], from[1*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, float4>(float* to, const float4& from, Index stride) {
-  to[stride*0] = from.x;
-  to[stride*1] = from.y;
-  to[stride*2] = from.z;
-  to[stride*3] = from.w;
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, double2>(double* to, const double2& from, Index stride) {
-  to[stride*0] = from.x;
-  to[stride*1] = from.y;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  pfirst<float4>(const float4& a) {
-  return a.x;
-}
-template<> EIGEN_DEVICE_FUNC inline double pfirst<double2>(const double2& a) {
-  return a.x;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux<float4>(const float4& a) {
-  return a.x + a.y + a.z + a.w;
-}
-template<> EIGEN_DEVICE_FUNC inline double predux<double2>(const double2& a) {
-  return a.x + a.y;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux_max<float4>(const float4& a) {
-  return fmaxf(fmaxf(a.x, a.y), fmaxf(a.z, a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double predux_max<double2>(const double2& a) {
-  return fmax(a.x, a.y);
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux_min<float4>(const float4& a) {
-  return fminf(fminf(a.x, a.y), fminf(a.z, a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double predux_min<double2>(const double2& a) {
-  return fmin(a.x, a.y);
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux_mul<float4>(const float4& a) {
-  return a.x * a.y * a.z * a.w;
-}
-template<> EIGEN_DEVICE_FUNC inline double predux_mul<double2>(const double2& a) {
-  return a.x * a.y;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float4  pabs<float4>(const float4& a) {
-  return make_float4(fabsf(a.x), fabsf(a.y), fabsf(a.z), fabsf(a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double2 pabs<double2>(const double2& a) {
-  return make_double2(fabs(a.x), fabs(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC inline float4  pfloor<float4>(const float4& a) {
-  return make_float4(floorf(a.x), floorf(a.y), floorf(a.z), floorf(a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double2 pfloor<double2>(const double2& a) {
-  return make_double2(floor(a.x), floor(a.y));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<float4,4>& kernel) {
-  float tmp = kernel.packet[0].y;
-  kernel.packet[0].y = kernel.packet[1].x;
-  kernel.packet[1].x = tmp;
-
-  tmp = kernel.packet[0].z;
-  kernel.packet[0].z = kernel.packet[2].x;
-  kernel.packet[2].x = tmp;
-
-  tmp = kernel.packet[0].w;
-  kernel.packet[0].w = kernel.packet[3].x;
-  kernel.packet[3].x = tmp;
-
-  tmp = kernel.packet[1].z;
-  kernel.packet[1].z = kernel.packet[2].y;
-  kernel.packet[2].y = tmp;
-
-  tmp = kernel.packet[1].w;
-  kernel.packet[1].w = kernel.packet[3].y;
-  kernel.packet[3].y = tmp;
-
-  tmp = kernel.packet[2].w;
-  kernel.packet[2].w = kernel.packet[3].z;
-  kernel.packet[3].z = tmp;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<double2,2>& kernel) {
-  double tmp = kernel.packet[0].y;
-  kernel.packet[0].y = kernel.packet[1].x;
-  kernel.packet[1].x = tmp;
-}
-
-#endif // defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
-
-// Packet4h2 must be defined in the macro without EIGEN_CUDA_ARCH, meaning
-// its corresponding packet_traits<Eigen::half> must be visible on host.
-#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
-
-typedef ulonglong2 Packet4h2;
-template<> struct unpacket_traits<Packet4h2> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4h2 half; };
-template<> struct is_arithmetic<Packet4h2> { enum { value = true }; };
-
-template<> struct unpacket_traits<half2> { typedef Eigen::half type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef half2 half; };
-template<> struct is_arithmetic<half2> { enum { value = true }; };
-
-template<> struct packet_traits<Eigen::half> : default_packet_traits
-{
-  typedef Packet4h2 type;
-  typedef Packet4h2 half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=8,
-    HasHalfPacket = 0,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasSqrt   = 1,
-    HasRsqrt  = 1,
-    HasExp    = 1,
-    HasExpm1  = 1,
-    HasLog    = 1,
-    HasLog1p  = 1
-  };
-};
-
-namespace {
-// This is equivalent to make_half2, which is undocumented and doesn't seem to always exist.
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 combine_half(const __half& a, const __half& b) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __halves2half2(a, b);
-#else
-  // Round-about way since __halves2half2 is a __device__ function.
-  return __floats2half2_rn(__half2float(a), __half2float(b));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE __half get_half2_low(const half2& a) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __low2half(a);
-#else
-  return __float2half(__low2float(a));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE __half get_half2_high(const half2& a) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __high2half(a);
-#else
-  return __float2half(__high2float(a));
-#endif
-}
-} // namespace
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pset1<half2>(const Eigen::half& from) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __half2half2(from);
-#else
-  const float f = __half2float(from);
-  return __floats2half2_rn(f, f);
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pset1<Packet4h2>(const Eigen::half& from) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = pset1<half2>(from);
-  p_alias[1] = pset1<half2>(from);
-  p_alias[2] = pset1<half2>(from);
-  p_alias[3] = pset1<half2>(from);
-  return r;
-}
-
-// We now need this visible on both host and device.
-// #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-namespace {
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pload(const Eigen::half* from) {
-  return *reinterpret_cast<const half2*>(from);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploadu(const Eigen::half* from) {
-  return combine_half(from[0], from[1]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploaddup(const Eigen::half*  from) {
-  return combine_half(from[0], from[0]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore(Eigen::half* to,
-                                                  const half2& from) {
-  *reinterpret_cast<half2*>(to) = from;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu(Eigen::half* to,
-                                                   const half2& from) {
-  to[0] = get_half2_low(from);
-  to[1] = get_half2_high(from);
-}
-
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_aligned(
-    const Eigen::half* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  // Input is guaranteed to be properly aligned.
-  return __ldg(reinterpret_cast<const half2*>(from));
-#else
-  return combine_half(*(from+0), *(from+1));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_unaligned(
-    const Eigen::half* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return __halves2half2(__ldg(from+0), __ldg(from+1));
-#else
-  return combine_half(*(from+0), *(from+1));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pgather(const Eigen::half* from,
-                                                    Index stride) {
-  return combine_half(from[0*stride], from[1*stride]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter(
-    Eigen::half* to, const half2& from, Index stride) {
-  to[stride*0] = get_half2_low(from);
-  to[stride*1] = get_half2_high(from);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst(const half2& a) {
-  return get_half2_low(a);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pabs(const half2& a) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half result1 = half_impl::raw_uint16_to_half(a1.x & 0x7FFF);
-  half result2 = half_impl::raw_uint16_to_half(a2.x & 0x7FFF);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ptrue(const half2& /*a*/) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  return pset1<half2>(true_half);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pzero(const half2& /*a*/) {
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  return pset1<half2>(false_half);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<half2,2>& kernel) {
-  __half a1 = get_half2_low(kernel.packet[0]);
-  __half a2 = get_half2_high(kernel.packet[0]);
-  __half b1 = get_half2_low(kernel.packet[1]);
-  __half b2 = get_half2_high(kernel.packet[1]);
-  kernel.packet[0] = combine_half(a1, b1);
-  kernel.packet[1] = combine_half(a2, b2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plset(const Eigen::half& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __halves2half2(a, __hadd(a, __float2half(1.0f)));
-#else
-  float f = __half2float(a) + 1.0f;
-  return combine_half(a, __float2half(f));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pselect(const half2& mask,
-                                                    const half2& a,
-                                                    const half2& b) {
-  half mask_low = get_half2_low(mask);
-  half mask_high = get_half2_high(mask);
-  half result_low = mask_low == half(0) ? get_half2_low(b) : get_half2_low(a);
-  half result_high = mask_high == half(0) ? get_half2_high(b) : get_half2_high(a);
-  return combine_half(result_low, result_high);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_eq(const half2& a,
-                                                    const half2& b) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half eq1 = __half2float(a1) == __half2float(b1) ? true_half : false_half;
-  half eq2 = __half2float(a2) == __half2float(b2) ? true_half : false_half;
-  return combine_half(eq1, eq2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_lt(const half2& a,
-                                                    const half2& b) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half eq1 = __half2float(a1) < __half2float(b1) ? true_half : false_half;
-  half eq2 = __half2float(a2) < __half2float(b2) ? true_half : false_half;
-  return combine_half(eq1, eq2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pand(const half2& a,
-                                                 const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x & b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x & b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 por(const half2& a,
-                                                const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x | b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x | b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pxor(const half2& a,
-                                                 const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x ^ b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x ^ b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pandnot(const half2& a,
-                                                    const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x & ~b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x & ~b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hadd2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 + b1;
-  float r2 = a2 + b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psub(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hsub2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 - b1;
-  float r2 = a2 - b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pnegate(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hneg2(a);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return __floats2half2_rn(-a1, -a2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pconj(const half2& a) { return a; }
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hmul2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 * b1;
-  float r2 = a2 * b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmadd(const half2& a,
-                                                  const half2& b,
-                                                  const half2& c) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-   return __hfma2(a, b, c);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float c1 = __low2float(c);
-  float c2 = __high2float(c);
-  float r1 = a1 * b1 + c1;
-  float r2 = a2 * b2 + c2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __h2div(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 / b1;
-  float r2 = a2 / b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin(const half2& a,
-                                                 const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 < b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 < b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax(const half2& a,
-                                                 const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 > b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 > b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hadd(__low2half(a), __high2half(a));
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return Eigen::half(__float2half(a1 + a2));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  __half first = __low2half(a);
-  __half second = __high2half(a);
-  return __hgt(first, second) ? first : second;
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return a1 > a2 ? get_half2_low(a) : get_half2_high(a);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  __half first = __low2half(a);
-  __half second = __high2half(a);
-  return __hlt(first, second) ? first : second;
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return a1 < a2 ? get_half2_low(a) : get_half2_high(a);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hmul(__low2half(a), __high2half(a));
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return Eigen::half(__float2half(a1 * a2));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog1p(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = log1pf(a1);
-  float r2 = log1pf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexpm1(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = expm1f(a1);
-  float r2 = expm1f(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 plog(const half2& a) {
-  return h2log(a);
-}
-
- EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 pexp(const half2& a) {
-  return h2exp(a);
-}
-
- EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 psqrt(const half2& a) {
-  return h2sqrt(a);
-}
-
- EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 prsqrt(const half2& a) {
-  return h2rsqrt(a);
-}
-
-#else
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = logf(a1);
-  float r2 = logf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexp(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = expf(a1);
-  float r2 = expf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psqrt(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = sqrtf(a1);
-  float r2 = sqrtf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 prsqrt(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = rsqrtf(a1);
-  float r2 = rsqrtf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-#endif
-} // namespace
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pload<Packet4h2>(const Eigen::half* from) {
-  return *reinterpret_cast<const Packet4h2*>(from);
-}
-
-// unaligned load;
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-ploadu<Packet4h2>(const Eigen::half* from) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = ploadu(from + 0);
-  p_alias[1] = ploadu(from + 2);
-  p_alias[2] = ploadu(from + 4);
-  p_alias[3] = ploadu(from + 6);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-ploaddup<Packet4h2>(const Eigen::half* from) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = ploaddup(from + 0);
-  p_alias[1] = ploaddup(from + 1);
-  p_alias[2] = ploaddup(from + 2);
-  p_alias[3] = ploaddup(from + 3);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<Eigen::half>(
-    Eigen::half* to, const Packet4h2& from) {
-  *reinterpret_cast<Packet4h2*>(to) = from;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(
-    Eigen::half* to, const Packet4h2& from) {
-  const half2* from_alias = reinterpret_cast<const half2*>(&from);
-  pstoreu(to + 0,from_alias[0]);
-  pstoreu(to + 2,from_alias[1]);
-  pstoreu(to + 4,from_alias[2]);
-  pstoreu(to + 6,from_alias[3]);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2
-ploadt_ro<Packet4h2, Aligned>(const Eigen::half* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  Packet4h2 r;
-  r = __ldg(reinterpret_cast<const Packet4h2*>(from));
-  return r;
-#else
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  r_alias[0] = ploadt_ro_aligned(from + 0);
-  r_alias[1] = ploadt_ro_aligned(from + 2);
-  r_alias[2] = ploadt_ro_aligned(from + 4);
-  r_alias[3] = ploadt_ro_aligned(from + 6);
-  return r;
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2
-ploadt_ro<Packet4h2, Unaligned>(const Eigen::half* from) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  r_alias[0] = ploadt_ro_unaligned(from + 0);
-  r_alias[1] = ploadt_ro_unaligned(from + 2);
-  r_alias[2] = ploadt_ro_unaligned(from + 4);
-  r_alias[3] = ploadt_ro_unaligned(from + 6);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pgather<Eigen::half, Packet4h2>(const Eigen::half* from, Index stride) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = combine_half(from[0 * stride], from[1 * stride]);
-  p_alias[1] = combine_half(from[2 * stride], from[3 * stride]);
-  p_alias[2] = combine_half(from[4 * stride], from[5 * stride]);
-  p_alias[3] = combine_half(from[6 * stride], from[7 * stride]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h2>(
-    Eigen::half* to, const Packet4h2& from, Index stride) {
-  const half2* from_alias = reinterpret_cast<const half2*>(&from);
-  pscatter(to + stride * 0, from_alias[0], stride);
-  pscatter(to + stride * 2, from_alias[1], stride);
-  pscatter(to + stride * 4, from_alias[2], stride);
-  pscatter(to + stride * 6, from_alias[3], stride);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h2>(
-    const Packet4h2& a) {
-  return pfirst(*(reinterpret_cast<const half2*>(&a)));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pabs<Packet4h2>(
-    const Packet4h2& a) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  p_alias[0] = pabs(a_alias[0]);
-  p_alias[1] = pabs(a_alias[1]);
-  p_alias[2] = pabs(a_alias[2]);
-  p_alias[3] = pabs(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 ptrue<Packet4h2>(
-    const Packet4h2& /*a*/) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  return pset1<Packet4h2>(true_half);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pzero<Packet4h2>(const Packet4h2& /*a*/) {
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  return pset1<Packet4h2>(false_half);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_double(
-    double* d_row0, double* d_row1, double* d_row2, double* d_row3,
-    double* d_row4, double* d_row5, double* d_row6, double* d_row7) {
-  double d_tmp;
-  d_tmp = d_row0[1];
-  d_row0[1] = d_row4[0];
-  d_row4[0] = d_tmp;
-
-  d_tmp = d_row1[1];
-  d_row1[1] = d_row5[0];
-  d_row5[0] = d_tmp;
-
-  d_tmp = d_row2[1];
-  d_row2[1] = d_row6[0];
-  d_row6[0] = d_tmp;
-
-  d_tmp = d_row3[1];
-  d_row3[1] = d_row7[0];
-  d_row7[0] = d_tmp;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_half2(
-    half2* f_row0, half2* f_row1, half2* f_row2, half2* f_row3) {
-  half2 f_tmp;
-  f_tmp = f_row0[1];
-  f_row0[1] = f_row2[0];
-  f_row2[0] = f_tmp;
-
-  f_tmp = f_row1[1];
-  f_row1[1] = f_row3[0];
-  f_row3[0] = f_tmp;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose_half(half2& f0, half2& f1) {
-  __half a1 = get_half2_low(f0);
-  __half a2 = get_half2_high(f0);
-  __half b1 = get_half2_low(f1);
-  __half b2 = get_half2_high(f1);
-  f0 = combine_half(a1, b1);
-  f1 = combine_half(a2, b2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet4h2,8>& kernel) {
-  double* d_row0 = reinterpret_cast<double*>(&kernel.packet[0]);
-  double* d_row1 = reinterpret_cast<double*>(&kernel.packet[1]);
-  double* d_row2 = reinterpret_cast<double*>(&kernel.packet[2]);
-  double* d_row3 = reinterpret_cast<double*>(&kernel.packet[3]);
-  double* d_row4 = reinterpret_cast<double*>(&kernel.packet[4]);
-  double* d_row5 = reinterpret_cast<double*>(&kernel.packet[5]);
-  double* d_row6 = reinterpret_cast<double*>(&kernel.packet[6]);
-  double* d_row7 = reinterpret_cast<double*>(&kernel.packet[7]);
-  ptranspose_double(d_row0, d_row1, d_row2, d_row3,
-                    d_row4, d_row5, d_row6, d_row7);
-
-
-  half2* f_row0 = reinterpret_cast<half2*>(d_row0);
-  half2* f_row1 = reinterpret_cast<half2*>(d_row1);
-  half2* f_row2 = reinterpret_cast<half2*>(d_row2);
-  half2* f_row3 = reinterpret_cast<half2*>(d_row3);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-  f_row0 = reinterpret_cast<half2*>(d_row0 + 1);
-  f_row1 = reinterpret_cast<half2*>(d_row1 + 1);
-  f_row2 = reinterpret_cast<half2*>(d_row2 + 1);
-  f_row3 = reinterpret_cast<half2*>(d_row3 + 1);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-  f_row0 = reinterpret_cast<half2*>(d_row4);
-  f_row1 = reinterpret_cast<half2*>(d_row5);
-  f_row2 = reinterpret_cast<half2*>(d_row6);
-  f_row3 = reinterpret_cast<half2*>(d_row7);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-  f_row0 = reinterpret_cast<half2*>(d_row4 + 1);
-  f_row1 = reinterpret_cast<half2*>(d_row5 + 1);
-  f_row2 = reinterpret_cast<half2*>(d_row6 + 1);
-  f_row3 = reinterpret_cast<half2*>(d_row7 + 1);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-plset<Packet4h2>(const Eigen::half& a) {
-#if defined(EIGEN_HIP_DEVICE_COMPILE)
-
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = __halves2half2(a, __hadd(a, __float2half(1.0f)));
-  p_alias[1] = __halves2half2(__hadd(a, __float2half(2.0f)),
-                              __hadd(a, __float2half(3.0f)));
-  p_alias[2] = __halves2half2(__hadd(a, __float2half(4.0f)),
-                              __hadd(a, __float2half(5.0f)));
-  p_alias[3] = __halves2half2(__hadd(a, __float2half(6.0f)),
-                              __hadd(a, __float2half(7.0f)));
-  return r;
-#elif defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-
-  half2 b = pset1<half2>(a);
-  half2 c;
-  half2 half_offset0 = __halves2half2(__float2half(0.0f),__float2half(2.0f));
-  half2 half_offset1 = __halves2half2(__float2half(4.0f),__float2half(6.0f));
-
-  c = __hadd2(b, half_offset0);
-  r_alias[0] = plset(__low2half(c));
-  r_alias[1] = plset(__high2half(c));
-
-  c = __hadd2(b, half_offset1);
-  r_alias[2] = plset(__low2half(c));
-  r_alias[3] = plset(__high2half(c));
-
-  return r;
-
-#else
-  float f = __half2float(a);
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = combine_half(a, __float2half(f + 1.0f));
-  p_alias[1] = combine_half(__float2half(f + 2.0f), __float2half(f + 3.0f));
-  p_alias[2] = combine_half(__float2half(f + 4.0f), __float2half(f + 5.0f));
-  p_alias[3] = combine_half(__float2half(f + 6.0f), __float2half(f + 7.0f));
-  return r;
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pselect<Packet4h2>(const Packet4h2& mask, const Packet4h2& a,
-                   const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* mask_alias = reinterpret_cast<const half2*>(&mask);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pselect(mask_alias[0], a_alias[0], b_alias[0]);
-  r_alias[1] = pselect(mask_alias[1], a_alias[1], b_alias[1]);
-  r_alias[2] = pselect(mask_alias[2], a_alias[2], b_alias[2]);
-  r_alias[3] = pselect(mask_alias[3], a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pcmp_eq<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pcmp_eq(a_alias[0], b_alias[0]);
-  r_alias[1] = pcmp_eq(a_alias[1], b_alias[1]);
-  r_alias[2] = pcmp_eq(a_alias[2], b_alias[2]);
-  r_alias[3] = pcmp_eq(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pand<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pand(a_alias[0], b_alias[0]);
-  r_alias[1] = pand(a_alias[1], b_alias[1]);
-  r_alias[2] = pand(a_alias[2], b_alias[2]);
-  r_alias[3] = pand(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 por<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = por(a_alias[0], b_alias[0]);
-  r_alias[1] = por(a_alias[1], b_alias[1]);
-  r_alias[2] = por(a_alias[2], b_alias[2]);
-  r_alias[3] = por(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pxor<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pxor(a_alias[0], b_alias[0]);
-  r_alias[1] = pxor(a_alias[1], b_alias[1]);
-  r_alias[2] = pxor(a_alias[2], b_alias[2]);
-  r_alias[3] = pxor(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pandnot<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pandnot(a_alias[0], b_alias[0]);
-  r_alias[1] = pandnot(a_alias[1], b_alias[1]);
-  r_alias[2] = pandnot(a_alias[2], b_alias[2]);
-  r_alias[3] = pandnot(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 padd<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = padd(a_alias[0], b_alias[0]);
-  r_alias[1] = padd(a_alias[1], b_alias[1]);
-  r_alias[2] = padd(a_alias[2], b_alias[2]);
-  r_alias[3] = padd(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psub<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = psub(a_alias[0], b_alias[0]);
-  r_alias[1] = psub(a_alias[1], b_alias[1]);
-  r_alias[2] = psub(a_alias[2], b_alias[2]);
-  r_alias[3] = psub(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pnegate(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = pnegate(a_alias[0]);
-  r_alias[1] = pnegate(a_alias[1]);
-  r_alias[2] = pnegate(a_alias[2]);
-  r_alias[3] = pnegate(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pconj(const Packet4h2& a) {
-  return a;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmul<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pmul(a_alias[0], b_alias[0]);
-  r_alias[1] = pmul(a_alias[1], b_alias[1]);
-  r_alias[2] = pmul(a_alias[2], b_alias[2]);
-  r_alias[3] = pmul(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmadd<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b, const Packet4h2& c) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  const half2* c_alias = reinterpret_cast<const half2*>(&c);
-  r_alias[0] = pmadd(a_alias[0], b_alias[0], c_alias[0]);
-  r_alias[1] = pmadd(a_alias[1], b_alias[1], c_alias[1]);
-  r_alias[2] = pmadd(a_alias[2], b_alias[2], c_alias[2]);
-  r_alias[3] = pmadd(a_alias[3], b_alias[3], c_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pdiv<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pdiv(a_alias[0], b_alias[0]);
-  r_alias[1] = pdiv(a_alias[1], b_alias[1]);
-  r_alias[2] = pdiv(a_alias[2], b_alias[2]);
-  r_alias[3] = pdiv(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmin<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pmin(a_alias[0], b_alias[0]);
-  r_alias[1] = pmin(a_alias[1], b_alias[1]);
-  r_alias[2] = pmin(a_alias[2], b_alias[2]);
-  r_alias[3] = pmin(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmax<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pmax(a_alias[0], b_alias[0]);
-  r_alias[1] = pmax(a_alias[1], b_alias[1]);
-  r_alias[2] = pmax(a_alias[2], b_alias[2]);
-  r_alias[3] = pmax(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-
-  return predux(a_alias[0]) + predux(a_alias[1]) +
-         predux(a_alias[2]) + predux(a_alias[3]);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  half2 m0 = combine_half(predux_max(a_alias[0]),
-                            predux_max(a_alias[1]));
-  half2 m1 = combine_half(predux_max(a_alias[2]),
-                            predux_max(a_alias[3]));
-  __half first  = predux_max(m0);
-  __half second = predux_max(m1);
-#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-  return (__hgt(first, second) ? first : second);
-#else
-  float ffirst  = __half2float(first);
-  float fsecond = __half2float(second);
-  return (ffirst > fsecond)? first: second;
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  half2 m0 = combine_half(predux_min(a_alias[0]),
-                            predux_min(a_alias[1]));
-  half2 m1 = combine_half(predux_min(a_alias[2]),
-                            predux_min(a_alias[3]));
-  __half first  = predux_min(m0);
-  __half second = predux_min(m1);
-#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-  return (__hlt(first, second) ? first : second);
-#else
-  float ffirst  = __half2float(first);
-  float fsecond = __half2float(second);
-  return (ffirst < fsecond)? first: second;
-#endif
-}
-
-// likely overflow/underflow
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  return predux_mul(pmul(pmul(a_alias[0], a_alias[1]),
-                                       pmul(a_alias[2], a_alias[3])));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-plog1p<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = plog1p(a_alias[0]);
-  r_alias[1] = plog1p(a_alias[1]);
-  r_alias[2] = plog1p(a_alias[2]);
-  r_alias[3] = plog1p(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pexpm1<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = pexpm1(a_alias[0]);
-  r_alias[1] = pexpm1(a_alias[1]);
-  r_alias[2] = pexpm1(a_alias[2]);
-  r_alias[3] = pexpm1(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 plog<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = plog(a_alias[0]);
-  r_alias[1] = plog(a_alias[1]);
-  r_alias[2] = plog(a_alias[2]);
-  r_alias[3] = plog(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pexp<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = pexp(a_alias[0]);
-  r_alias[1] = pexp(a_alias[1]);
-  r_alias[2] = pexp(a_alias[2]);
-  r_alias[3] = pexp(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psqrt<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = psqrt(a_alias[0]);
-  r_alias[1] = psqrt(a_alias[1]);
-  r_alias[2] = psqrt(a_alias[2]);
-  r_alias[3] = psqrt(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-prsqrt<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = prsqrt(a_alias[0]);
-  r_alias[1] = prsqrt(a_alias[1]);
-  r_alias[2] = prsqrt(a_alias[2]);
-  r_alias[3] = prsqrt(a_alias[3]);
-  return r;
-}
-
-// The following specialized padd, pmul, pdiv, pmin, pmax, pset1 are needed for
-// the implementation of GPU half reduction.
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd<half2>(const half2& a,
-                                                        const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hadd2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 + b1;
-  float r2 = a2 + b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul<half2>(const half2& a,
-                                                        const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hmul2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 * b1;
-  float r2 = a2 * b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv<half2>(const half2& a,
-                                                        const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __h2div(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 / b1;
-  float r2 = a2 / b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin<half2>(const half2& a,
-                                                        const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 < b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 < b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax<half2>(const half2& a,
-                                                        const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 > b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 > b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-// #endif // defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-
-#endif // defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
-
-#undef EIGEN_GPU_HAS_LDG
-#undef EIGEN_CUDA_HAS_FP16_ARITHMETIC
-#undef EIGEN_GPU_HAS_FP16_ARITHMETIC
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-
-#endif // EIGEN_PACKET_MATH_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/TypeCasting.h
deleted file mode 100644
index 7545462..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/GPU/TypeCasting.h
+++ /dev/null
@@ -1,80 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_GPU_H
-#define EIGEN_TYPE_CASTING_GPU_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-
-
-template <>
-struct type_casting_traits<Eigen::half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 2
-  };
-};
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<half2, float4>(const half2& a, const half2& b) {
-  float2 r1 = __half22float2(a);
-  float2 r2 = __half22float2(b);
-  return make_float4(r1.x, r1.y, r2.x, r2.y);
-}
-
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pcast<float4, Packet4h2>(const float4& a, const float4& b) {
-  Packet4h2 r;
-  half2* r_alias=reinterpret_cast<half2*>(&r);
-  r_alias[0]=__floats2half2_rn(a.x,a.y);
-  r_alias[1]=__floats2half2_rn(a.z,a.w);
-  r_alias[2]=__floats2half2_rn(b.x,b.y);
-  r_alias[3]=__floats2half2_rn(b.z,b.w);
-  return r;
-}
-
-template <>
-struct type_casting_traits<float, Eigen::half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 2,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<Packet4h2, float4>(const Packet4h2& a) {
-  // Simply discard the second half of the input
-  float4 r;
-  const half2* a_alias=reinterpret_cast<const half2*>(&a);
-  float2 r1 = __half22float2(a_alias[0]);
-  float2 r2 = __half22float2(a_alias[1]);
-  r.x=static_cast<float>(r1.x);
-  r.y=static_cast<float>(r1.y);
-  r.z=static_cast<float>(r2.x);
-  r.w=static_cast<float>(r2.y);
-  return r;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcast<float4, half2>(const float4& a) {
-  // Simply discard the second half of the input
-  return __floats2half2_rn(a.x, a.y);
-}
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h
deleted file mode 100644
index 25375a0..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * math_constants.h - 
- *  HIP equivalent of the CUDA header of the same name
- */
-
-#ifndef __MATH_CONSTANTS_H__
-#define __MATH_CONSTANTS_H__
-
-/* single precision constants */
-
-#define HIPRT_INF_F        __int_as_float(0x7f800000)
-#define HIPRT_NAN_F        __int_as_float(0x7fffffff)
-#define HIPRT_MIN_DENORM_F __int_as_float(0x00000001)
-#define HIPRT_MAX_NORMAL_F __int_as_float(0x7f7fffff)
-#define HIPRT_NEG_ZERO_F   __int_as_float(0x80000000)
-#define HIPRT_ZERO_F       0.0f
-#define HIPRT_ONE_F        1.0f
-
-/* double precision constants */
-#define HIPRT_INF          __hiloint2double(0x7ff00000, 0x00000000)
-#define HIPRT_NAN          __hiloint2double(0xfff80000, 0x00000000)
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/Complex.h
deleted file mode 100644
index 53dacfa..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/Complex.h
+++ /dev/null
@@ -1,648 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Wave Computing, Inc.
-// Written by:
-//   Chris Larsen
-//   Alexey Frunze (afrunze@wavecomp.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_MSA_H
-#define EIGEN_COMPLEX_MSA_H
-
-#include <iostream>
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet2cf {
-  EIGEN_STRONG_INLINE Packet2cf() {
-  }
-  EIGEN_STRONG_INLINE explicit Packet2cf(const std::complex<float>& a,
-                                         const std::complex<float>& b) {
-    Packet4f t = { std::real(a), std::imag(a), std::real(b), std::imag(b) };
-    v = t;
-  }
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {
-  }
-  EIGEN_STRONG_INLINE Packet2cf(const Packet2cf& a) : v(a.v) {
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator=(const Packet2cf& b) {
-    v = b.v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf conjugate(void) const {
-    return Packet2cf((Packet4f)__builtin_msa_bnegi_d((v2u64)v, 63));
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
-    Packet4f v1, v2;
-
-    // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
-    v1 = (Packet4f)__builtin_msa_ilvev_w((v4i32)v, (v4i32)v);
-    // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
-    v2 = (Packet4f)__builtin_msa_ilvod_w((v4i32)v, (v4i32)v);
-    // Multiply the real a with b
-    v1 = pmul(v1, b.v);
-    // Multiply the imag a with b
-    v2 = pmul(v2, b.v);
-    // Conjugate v2
-    v2 = Packet2cf(v2).conjugate().v;
-    // Swap real/imag elements in v2.
-    v2 = (Packet4f)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(1, 0, 3, 2));
-    // Add and return the result
-    v = padd(v1, v2);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const {
-    return Packet2cf(*this) *= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const {
-    return Packet2cf(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const {
-    return Packet2cf(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator/=(const Packet2cf& b) {
-    *this *= b.conjugate();
-    Packet4f s = pmul<Packet4f>(b.v, b.v);
-    s = padd(s, (Packet4f)__builtin_msa_shf_w((v4i32)s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-    v = pdiv(v, s);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator/(const Packet2cf& b) const {
-    return Packet2cf(*this) /= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(void) const {
-    return Packet2cf(pnegate(v));
-  }
-
-  Packet4f v;
-};
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2cf& value) {
-  os << "[ (" << value.v[0] << ", " << value.v[1]
-     << "i),"
-        "  ("
-     << value.v[2] << ", " << value.v[3] << "i) ]";
-  return os;
-}
-
-template <>
-struct packet_traits<std::complex<float> > : default_packet_traits {
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasNegate = 1,
-    HasAbs = 0,
-    HasAbs2 = 0,
-    HasMin = 0,
-    HasMax = 0,
-    HasSetLinear = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet2cf> {
-  typedef std::complex<float> type;
-  enum { size = 2, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet2cf half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
-  EIGEN_MSA_DEBUG;
-
-  float f0 = from.real(), f1 = from.imag();
-  Packet4f v0 = { f0, f0, f0, f0 };
-  Packet4f v1 = { f1, f1, f1, f1 };
-  return Packet2cf((Packet4f)__builtin_msa_ilvr_w((Packet4i)v1, (Packet4i)v0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a + b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a - b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return -a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a.conjugate();
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a * b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(pand(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(por(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(pxor(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(pandnot(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
-  EIGEN_MSA_DEBUG;
-
-  return pset1<Packet2cf>(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to,
-                                                      const Packet2cf& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE pstore<float>((float*)to, from.v);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to,
-                                                       const Packet2cf& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<float>((float*)to, from.v);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(
-    const std::complex<float>* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(from[0 * stride], from[1 * stride]);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to,
-                                                                       const Packet2cf& from,
-                                                                       Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = std::complex<float>(from.v[0], from.v[1]);
-  to += stride;
-  *to = std::complex<float>(from.v[2], from.v[3]);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
-  EIGEN_MSA_DEBUG;
-
-  prefetch(reinterpret_cast<const float*>(addr));
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return std::complex<float>(a.v[0], a.v[1]);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f value = (Packet4f)preverse((Packet2d)a.v);
-  value += a.v;
-  return std::complex<float>(value[0], value[1]);
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return std::complex<float>((a.v[0] * a.v[2]) - (a.v[1] * a.v[3]),
-                             (a.v[0] * a.v[3]) + (a.v[1] * a.v[2]));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a / b;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2cf, 2>& value) {
-  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f tmp =
-      (Packet4f)__builtin_msa_ilvl_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
-  kernel.packet[0].v =
-      (Packet4f)__builtin_msa_ilvr_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
-  kernel.packet[1].v = tmp;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket,
-                                     const Packet2cf& elsePacket) {
-  return (Packet2cf)(Packet4f)pblend<Packet2d>(ifPacket, (Packet2d)thenPacket.v,
-                                               (Packet2d)elsePacket.v);
-}
-
-//---------- double ----------
-
-struct Packet1cd {
-  EIGEN_STRONG_INLINE Packet1cd() {
-  }
-  EIGEN_STRONG_INLINE explicit Packet1cd(const std::complex<double>& a) {
-    v[0] = std::real(a);
-    v[1] = std::imag(a);
-  }
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {
-  }
-  EIGEN_STRONG_INLINE Packet1cd(const Packet1cd& a) : v(a.v) {
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator=(const Packet1cd& b) {
-    v = b.v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd conjugate(void) const {
-    static const v2u64 p2ul_CONJ_XOR = { 0x0, 0x8000000000000000 };
-    return (Packet1cd)pxor(v, (Packet2d)p2ul_CONJ_XOR);
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
-    Packet2d v1, v2;
-
-    // Get the real values of a | a1_re | a1_re
-    v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)v, (v2i64)v);
-    // Get the imag values of a | a1_im | a1_im
-    v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)v, (v2i64)v);
-    // Multiply the real a with b
-    v1 = pmul(v1, b.v);
-    // Multiply the imag a with b
-    v2 = pmul(v2, b.v);
-    // Conjugate v2
-    v2 = Packet1cd(v2).conjugate().v;
-    // Swap real/imag elements in v2.
-    v2 = (Packet2d)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-    // Add and return the result
-    v = padd(v1, v2);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const {
-    return Packet1cd(*this) *= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const {
-    return Packet1cd(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const {
-    return Packet1cd(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator/=(const Packet1cd& b) {
-    *this *= b.conjugate();
-    Packet2d s = pmul<Packet2d>(b.v, b.v);
-    s = padd(s, preverse<Packet2d>(s));
-    v = pdiv(v, s);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator/(const Packet1cd& b) const {
-    return Packet1cd(*this) /= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(void) const {
-    return Packet1cd(pnegate(v));
-  }
-
-  Packet2d v;
-};
-
-inline std::ostream& operator<<(std::ostream& os, const Packet1cd& value) {
-  os << "[ (" << value.v[0] << ", " << value.v[1] << "i) ]";
-  return os;
-}
-
-template <>
-struct packet_traits<std::complex<double> > : default_packet_traits {
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasNegate = 1,
-    HasAbs = 0,
-    HasAbs2 = 0,
-    HasMin = 0,
-    HasMax = 0,
-    HasSetLinear = 0
-  };
-};
-
-template <>
-struct unpacket_traits<Packet1cd> {
-  typedef std::complex<double> type;
-  enum { size = 1, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet1cd half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a + b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a - b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return -a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a.conjugate();
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a * b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(pand(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(por(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(pxor(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(pandnot(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
-  EIGEN_MSA_DEBUG;
-
-  return pset1<Packet1cd>(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to,
-                                                       const Packet1cd& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE pstore<double>((double*)to, from.v);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to,
-                                                        const Packet1cd& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<double>((double*)to, from.v);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
-  EIGEN_MSA_DEBUG;
-
-  prefetch(reinterpret_cast<const double*>(addr));
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(
-    const std::complex<double>* from, Index stride __attribute__((unused))) {
-  EIGEN_MSA_DEBUG;
-
-  Packet1cd res;
-  res.v[0] = std::real(from[0]);
-  res.v[1] = std::imag(from[0]);
-  return res;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to,
-                                                                        const Packet1cd& from,
-                                                                        Index stride
-                                                                        __attribute__((unused))) {
-  EIGEN_MSA_DEBUG;
-
-  pstore(to, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return std::complex<double>(a.v[0], a.v[1]);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return pfirst(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return pfirst(a);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a / b;
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip /*<Packet1cd>*/ (const Packet1cd& x) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet1cd, 2>& value) {
-  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
-  return os;
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d v1, v2;
-
-  v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
-  // Get the imag values of a
-  v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
-
-  kernel.packet[0].v = v1;
-  kernel.packet[1].v = v2;
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_COMPLEX_MSA_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/MathFunctions.h
deleted file mode 100644
index f5181b9..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/MathFunctions.h
+++ /dev/null
@@ -1,387 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Copyright (C) 2018 Wave Computing, Inc.
-// Written by:
-//   Chris Larsen
-//   Alexey Frunze (afrunze@wavecomp.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The sin, cos, exp, and log functions of this file come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-/* The tanh function of this file is an adaptation of
- * template<typename T> T generic_fast_tanh_float(const T&)
- * from MathFunctionsImpl.h.
- */
-
-#ifndef EIGEN_MATH_FUNCTIONS_MSA_H
-#define EIGEN_MATH_FUNCTIONS_MSA_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-plog<Packet4f>(const Packet4f& _x) {
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292e-2f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, -1.1514610310e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, -1.2420140846e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, +1.4249322787e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, -1.6668057665e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, +2.0000714765e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, -2.4999993993e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, +3.3333331174e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
-  static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
-
-  // Convert negative argument into NAN (quiet negative, to be specific).
-  Packet4f zero = (Packet4f)__builtin_msa_ldi_w(0);
-  Packet4i neg_mask = __builtin_msa_fclt_w(_x, zero);
-  Packet4i zero_mask = __builtin_msa_fceq_w(_x, zero);
-  Packet4f non_neg_x_or_nan = padd(_x, (Packet4f)neg_mask);  // Add 0.0 or NAN.
-  Packet4f x = non_neg_x_or_nan;
-
-  // Extract exponent from x = mantissa * 2**exponent, where 1.0 <= mantissa < 2.0.
-  // N.B. the exponent is one less of what frexpf() would return.
-  Packet4i e_int = __builtin_msa_ftint_s_w(__builtin_msa_flog2_w(x));
-  // Multiply x by 2**(-exponent-1) to get 0.5 <= x < 1.0 as from frexpf().
-  x = __builtin_msa_fexp2_w(x, (Packet4i)__builtin_msa_nori_b((v16u8)e_int, 0));
-
-  /*
-     if (x < SQRTHF) {
-       x = x + x - 1.0;
-     } else {
-       e += 1;
-       x = x - 1.0;
-     }
-  */
-  Packet4f xx = padd(x, x);
-  Packet4i ge_mask = __builtin_msa_fcle_w(p4f_cephes_SQRTHF, x);
-  e_int = psub(e_int, ge_mask);
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)ge_mask, (v16u8)xx, (v16u8)x);
-  x = psub(x, p4f_1);
-  Packet4f e = __builtin_msa_ffint_s_w(e_int);
-
-  Packet4f x2 = pmul(x, x);
-  Packet4f x3 = pmul(x2, x);
-
-  Packet4f y, y1, y2;
-  y = pmadd(p4f_cephes_log_p0, x, p4f_cephes_log_p1);
-  y1 = pmadd(p4f_cephes_log_p3, x, p4f_cephes_log_p4);
-  y2 = pmadd(p4f_cephes_log_p6, x, p4f_cephes_log_p7);
-  y = pmadd(y, x, p4f_cephes_log_p2);
-  y1 = pmadd(y1, x, p4f_cephes_log_p5);
-  y2 = pmadd(y2, x, p4f_cephes_log_p8);
-  y = pmadd(y, x3, y1);
-  y = pmadd(y, x3, y2);
-  y = pmul(y, x3);
-
-  y = pmadd(e, p4f_cephes_log_q1, y);
-  x = __builtin_msa_fmsub_w(x, x2, p4f_half);
-  x = padd(x, y);
-  x = pmadd(e, p4f_cephes_log_q2, x);
-
-  // x is now the logarithm result candidate. We still need to handle the
-  // extreme arguments of zero and positive infinity, though.
-  // N.B. if the argument is +INFINITY, x is NAN because the polynomial terms
-  // contain infinities of both signs (see the coefficients and code above).
-  // INFINITY - INFINITY is NAN.
-
-  // If the argument is +INFINITY, make it the new result candidate.
-  // To achieve that we choose the smaller of the result candidate and the
-  // argument.
-  // This is correct for all finite pairs of values (the logarithm is smaller
-  // than the argument).
-  // This is also correct in the special case when the argument is +INFINITY
-  // and the result candidate is NAN. This is because the fmin.df instruction
-  // prefers non-NANs to NANs.
-  x = __builtin_msa_fmin_w(x, non_neg_x_or_nan);
-
-  // If the argument is zero (including -0.0), the result becomes -INFINITY.
-  Packet4i neg_infs = __builtin_msa_slli_w(zero_mask, 23);
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)zero_mask, (v16u8)x, (v16u8)neg_infs);
-
-  return x;
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-pexp<Packet4f>(const Packet4f& _x) {
-  // Limiting single-precision pexp's argument to [-128, +128] lets pexp
-  // reach 0 and INFINITY naturally.
-  static _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -128.0f);
-  static _EIGEN_DECLARE_CONST_Packet4f(exp_hi, +128.0f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894e-2f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
-
-  Packet4f x = _x;
-
-  // Clamp x.
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(x, p4f_exp_lo), (v16u8)x,
-                                     (v16u8)p4f_exp_lo);
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_exp_hi, x), (v16u8)x,
-                                     (v16u8)p4f_exp_hi);
-
-  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
-  Packet4f x2_add = (Packet4f)__builtin_msa_binsli_w((v4u32)p4f_half, (v4u32)x, 0);
-  Packet4f x2 = pmadd(x, p4f_cephes_LOG2EF, x2_add);
-  Packet4i x2_int = __builtin_msa_ftrunc_s_w(x2);
-  Packet4f x2_int_f = __builtin_msa_ffint_s_w(x2_int);
-
-  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C1);
-  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C2);
-
-  Packet4f z = pmul(x, x);
-
-  Packet4f y = p4f_cephes_exp_p0;
-  y = pmadd(y, x, p4f_cephes_exp_p1);
-  y = pmadd(y, x, p4f_cephes_exp_p2);
-  y = pmadd(y, x, p4f_cephes_exp_p3);
-  y = pmadd(y, x, p4f_cephes_exp_p4);
-  y = pmadd(y, x, p4f_cephes_exp_p5);
-  y = pmadd(y, z, x);
-  y = padd(y, p4f_1);
-
-  // y *= 2**exponent.
-  y = __builtin_msa_fexp2_w(y, x2_int);
-
-  return y;
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& _x) {
-  static _EIGEN_DECLARE_CONST_Packet4f(tanh_tiny, 1e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(tanh_hi, 9.0f);
-  // The monomial coefficients of the numerator polynomial (odd).
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_1, 4.89352455891786e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_3, 6.37261928875436e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_5, 1.48572235717979e-5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_7, 5.12229709037114e-8f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_9, -8.60467152213735e-11f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_11, 2.00018790482477e-13f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_13, -2.76076847742355e-16f);
-  // The monomial coefficients of the denominator polynomial (even).
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_0, 4.89352518554385e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_2, 2.26843463243900e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_4, 1.18534705686654e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_6, 1.19825839466702e-6f);
-
-  Packet4f x = pabs(_x);
-  Packet4i tiny_mask = __builtin_msa_fclt_w(x, p4f_tanh_tiny);
-
-  // Clamp the inputs to the range [-9, 9] since anything outside
-  // this range is -/+1.0f in single-precision.
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_tanh_hi, x), (v16u8)x,
-                                     (v16u8)p4f_tanh_hi);
-
-  // Since the polynomials are odd/even, we need x**2.
-  Packet4f x2 = pmul(x, x);
-
-  // Evaluate the numerator polynomial p.
-  Packet4f p = pmadd(x2, p4f_alpha_13, p4f_alpha_11);
-  p = pmadd(x2, p, p4f_alpha_9);
-  p = pmadd(x2, p, p4f_alpha_7);
-  p = pmadd(x2, p, p4f_alpha_5);
-  p = pmadd(x2, p, p4f_alpha_3);
-  p = pmadd(x2, p, p4f_alpha_1);
-  p = pmul(x, p);
-
-  // Evaluate the denominator polynomial q.
-  Packet4f q = pmadd(x2, p4f_beta_6, p4f_beta_4);
-  q = pmadd(x2, q, p4f_beta_2);
-  q = pmadd(x2, q, p4f_beta_0);
-
-  // Divide the numerator by the denominator.
-  p = pdiv(p, q);
-
-  // Reinstate the sign.
-  p = (Packet4f)__builtin_msa_binsli_w((v4u32)p, (v4u32)_x, 0);
-
-  // When the argument is very small in magnitude it's more accurate to just return it.
-  p = (Packet4f)__builtin_msa_bsel_v((v16u8)tiny_mask, (v16u8)p, (v16u8)_x);
-
-  return p;
-}
-
-template <bool sine>
-Packet4f psincos_inner_msa_float(const Packet4f& _x) {
-  static _EIGEN_DECLARE_CONST_Packet4f(sincos_max_arg, 13176795.0f);  // Approx. (2**24) / (4/Pi).
-  static _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP1, -0.78515625f);
-  static _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP2, -2.4187564849853515625e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP3, -3.77489497744594108e-8f);
-  static _EIGEN_DECLARE_CONST_Packet4f(sincof_p0, -1.9515295891e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(sincof_p1, 8.3321608736e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(sincof_p2, -1.6666654611e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(coscof_p0, 2.443315711809948e-5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(coscof_p1, -1.388731625493765e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(coscof_p2, 4.166664568298827e-2f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_FOPI, 1.27323954473516f);  // 4/Pi.
-  static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
-
-  Packet4f x = pabs(_x);
-
-  // Translate infinite arguments into NANs.
-  Packet4f zero_or_nan_if_inf = psub(_x, _x);
-  x = padd(x, zero_or_nan_if_inf);
-  // Prevent sin/cos from generating values larger than 1.0 in magnitude
-  // for very large arguments by setting x to 0.0.
-  Packet4i small_or_nan_mask = __builtin_msa_fcult_w(x, p4f_sincos_max_arg);
-  x = pand(x, (Packet4f)small_or_nan_mask);
-
-  // Scale x by 4/Pi to find x's octant.
-  Packet4f y = pmul(x, p4f_cephes_FOPI);
-  // Get the octant. We'll reduce x by this number of octants or by one more than it.
-  Packet4i y_int = __builtin_msa_ftrunc_s_w(y);
-  // x's from even-numbered octants will translate to octant 0: [0, +Pi/4].
-  // x's from odd-numbered octants will translate to octant -1: [-Pi/4, 0].
-  // Adjustment for odd-numbered octants: octant = (octant + 1) & (~1).
-  Packet4i y_int1 = __builtin_msa_addvi_w(y_int, 1);
-  Packet4i y_int2 = (Packet4i)__builtin_msa_bclri_w((Packet4ui)y_int1, 0); // bclri = bit-clear
-  y = __builtin_msa_ffint_s_w(y_int2);
-
-  // Compute the sign to apply to the polynomial.
-  Packet4i sign_mask = sine ? pxor(__builtin_msa_slli_w(y_int1, 29), (Packet4i)_x)
-                            : __builtin_msa_slli_w(__builtin_msa_addvi_w(y_int, 3), 29);
-
-  // Get the polynomial selection mask.
-  // We'll calculate both (sin and cos) polynomials and then select from the two.
-  Packet4i poly_mask = __builtin_msa_ceqi_w(__builtin_msa_slli_w(y_int2, 30), 0);
-
-  // Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4.
-  // The magic pass: "Extended precision modular arithmetic"
-  // x = ((x - y * DP1) - y * DP2) - y * DP3
-  Packet4f tmp1 = pmul(y, p4f_minus_cephes_DP1);
-  Packet4f tmp2 = pmul(y, p4f_minus_cephes_DP2);
-  Packet4f tmp3 = pmul(y, p4f_minus_cephes_DP3);
-  x = padd(x, tmp1);
-  x = padd(x, tmp2);
-  x = padd(x, tmp3);
-
-  // Evaluate the cos(x) polynomial.
-  y = p4f_coscof_p0;
-  Packet4f z = pmul(x, x);
-  y = pmadd(y, z, p4f_coscof_p1);
-  y = pmadd(y, z, p4f_coscof_p2);
-  y = pmul(y, z);
-  y = pmul(y, z);
-  y = __builtin_msa_fmsub_w(y, z, p4f_half);
-  y = padd(y, p4f_1);
-
-  // Evaluate the sin(x) polynomial.
-  Packet4f y2 = p4f_sincof_p0;
-  y2 = pmadd(y2, z, p4f_sincof_p1);
-  y2 = pmadd(y2, z, p4f_sincof_p2);
-  y2 = pmul(y2, z);
-  y2 = pmadd(y2, x, x);
-
-  // Select the correct result from the two polynomials.
-  y = sine ? (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y, (v16u8)y2)
-           : (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y2, (v16u8)y);
-
-  // Update the sign.
-  sign_mask = pxor(sign_mask, (Packet4i)y);
-  y = (Packet4f)__builtin_msa_binsli_w((v4u32)y, (v4u32)sign_mask, 0); // binsli = bit-insert-left
-  return y;
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-psin<Packet4f>(const Packet4f& x) {
-  return psincos_inner_msa_float</* sine */ true>(x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-pcos<Packet4f>(const Packet4f& x) {
-  return psincos_inner_msa_float</* sine */ false>(x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2d
-pexp<Packet2d>(const Packet2d& _x) {
-  // Limiting double-precision pexp's argument to [-1024, +1024] lets pexp
-  // reach 0 and INFINITY naturally.
-  static _EIGEN_DECLARE_CONST_Packet2d(exp_lo, -1024.0);
-  static _EIGEN_DECLARE_CONST_Packet2d(exp_hi, +1024.0);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
-  static _EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
-  static _EIGEN_DECLARE_CONST_Packet2d(1, 1.0);
-  static _EIGEN_DECLARE_CONST_Packet2d(2, 2.0);
-
-  Packet2d x = _x;
-
-  // Clamp x.
-  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(x, p2d_exp_lo), (v16u8)x,
-                                     (v16u8)p2d_exp_lo);
-  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(p2d_exp_hi, x), (v16u8)x,
-                                     (v16u8)p2d_exp_hi);
-
-  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
-  Packet2d x2_add = (Packet2d)__builtin_msa_binsli_d((v2u64)p2d_half, (v2u64)x, 0);
-  Packet2d x2 = pmadd(x, p2d_cephes_LOG2EF, x2_add);
-  Packet2l x2_long = __builtin_msa_ftrunc_s_d(x2);
-  Packet2d x2_long_d = __builtin_msa_ffint_s_d(x2_long);
-
-  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C1);
-  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C2);
-
-  x2 = pmul(x, x);
-
-  Packet2d px = p2d_cephes_exp_p0;
-  px = pmadd(px, x2, p2d_cephes_exp_p1);
-  px = pmadd(px, x2, p2d_cephes_exp_p2);
-  px = pmul(px, x);
-
-  Packet2d qx = p2d_cephes_exp_q0;
-  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
-
-  x = pdiv(px, psub(qx, px));
-  x = pmadd(p2d_2, x, p2d_1);
-
-  // x *= 2**exponent.
-  x = __builtin_msa_fexp2_d(x, x2_long);
-
-  return x;
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_MSA_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/PacketMath.h
deleted file mode 100644
index afe8f33..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/MSA/PacketMath.h
+++ /dev/null
@@ -1,1233 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Wave Computing, Inc.
-// Written by:
-//   Chris Larsen
-//   Alexey Frunze (afrunze@wavecomp.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_MSA_H
-#define EIGEN_PACKET_MATH_MSA_H
-
-#include <iostream>
-#include <string>
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-#endif
-
-#if 0
-#define EIGEN_MSA_DEBUG                                                             \
-  static bool firstTime = true;                                                     \
-  do {                                                                              \
-    if (firstTime) {                                                                \
-      std::cout << __FILE__ << ':' << __LINE__ << ':' << __FUNCTION__ << std::endl; \
-      firstTime = false;                                                            \
-    }                                                                               \
-  } while (0)
-#else
-#define EIGEN_MSA_DEBUG
-#endif
-
-#define EIGEN_MSA_SHF_I8(a, b, c, d) (((d) << 6) | ((c) << 4) | ((b) << 2) | (a))
-
-typedef v4f32 Packet4f;
-typedef v4i32 Packet4i;
-typedef v4u32 Packet4ui;
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME, X) const Packet4f p4f_##NAME = { X, X, X, X }
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME, X) const Packet4i p4i_##NAME = { X, X, X, X }
-#define _EIGEN_DECLARE_CONST_Packet4ui(NAME, X) const Packet4ui p4ui_##NAME = { X, X, X, X }
-
-inline std::ostream& operator<<(std::ostream& os, const Packet4f& value) {
-  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet4i& value) {
-  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet4ui& value) {
-  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
-  return os;
-}
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;  // Packet2f intrinsics not implemented yet
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,  // Packet2f intrinsics not implemented yet
-    // FIXME check the Has*
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<int32_t> : default_packet_traits {
-  typedef Packet4i type;
-  typedef Packet4i half;  // Packet2i intrinsics not implemented yet
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,  // Packet2i intrinsics not implemented yet
-    // FIXME check the Has*
-    HasDiv = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet4f> {
-  typedef float type;
-  enum { size = 4, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet4f half;
-};
-
-template <>
-struct unpacket_traits<Packet4i> {
-  typedef int32_t type;
-  enum { size = 4, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet4i half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f v = { from, from, from, from };
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fill_w(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  float f = *from;
-  Packet4f v = { f, f, f, f };
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pload1<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fill_w(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fadd_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_addv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
-  EIGEN_MSA_DEBUG;
-
-  static const Packet4f countdown = { 0.0f, 1.0f, 2.0f, 3.0f };
-  return padd(pset1<Packet4f>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a) {
-  EIGEN_MSA_DEBUG;
-
-  static const Packet4i countdown = { 0, 1, 2, 3 };
-  return padd(pset1<Packet4i>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsub_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_subv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_bnegi_w((v4u32)a, 31);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_addvi_w((v4i32)__builtin_msa_nori_b((v16u8)a, 0), 1);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmul_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_mulv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fdiv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_div_s_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmadd_w(c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
-  EIGEN_MSA_DEBUG;
-
-  // Use "asm" construct to avoid __builtin_msa_maddv_w GNU C bug.
-  Packet4i value = c;
-  __asm__("maddv.w %w[value], %w[a], %w[b]\n"
-          // Outputs
-          : [value] "+f"(value)
-          // Inputs
-          : [a] "f"(a), [b] "f"(b));
-  return value;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_and_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4i)__builtin_msa_and_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_or_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4i)__builtin_msa_or_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4i)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return pand(a, (Packet4f)__builtin_msa_xori_b((v16u8)b, 255));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return pand(a, (Packet4i)__builtin_msa_xori_b((v16u8)b, 255));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmin_w(a, b);
-#else
-  // This prefers NaNs to numbers.
-  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
-  Packet4i aMinOrNaN = por(__builtin_msa_fclt_w(a, b), aNaN);
-  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_min_s_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmax_w(a, b);
-#else
-  // This prefers NaNs to numbers.
-  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
-  Packet4i aMaxOrNaN = por(__builtin_msa_fclt_w(b, a), aNaN);
-  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_max_s_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return __builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4i)__builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  float f0 = from[0], f1 = from[1];
-  Packet4f v0 = { f0, f0, f0, f0 };
-  Packet4f v1 = { f1, f1, f1, f1 };
-  return (Packet4f)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  int32_t i0 = from[0], i1 = from[1];
-  Packet4i v0 = { i0, i0, i0, i0 };
-  Packet4i v1 = { i1, i1, i1, i1 };
-  return (Packet4i)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w(from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w(from, to, 0);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  float f = *from;
-  Packet4f v = { f, f, f, f };
-  v[1] = from[stride];
-  v[2] = from[2 * stride];
-  v[3] = from[3 * stride];
-  return v;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  int32_t i = *from;
-  Packet4i v = { i, i, i, i };
-  v[1] = from[stride];
-  v[2] = from[2 * stride];
-  v[3] = from[3 * stride];
-  return v;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from,
-                                                        Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = from[0];
-  to += stride;
-  *to = from[1];
-  to += stride;
-  *to = from[2];
-  to += stride;
-  *to = from[3];
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from,
-                                                          Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = from[0];
-  to += stride;
-  *to = from[1];
-  to += stride;
-  *to = from[2];
-  to += stride;
-  *to = from[3];
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
-  EIGEN_MSA_DEBUG;
-
-  __builtin_prefetch(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) {
-  EIGEN_MSA_DEBUG;
-
-  __builtin_prefetch(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_bclri_w((v4u32)a, 31);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i zero = __builtin_msa_ldi_w(0);
-  return __builtin_msa_add_a_w(zero, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f s = padd(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  s = padd(s, (Packet4f)__builtin_msa_shf_w((v4i32)s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return s[0];
-}
-
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i s = padd(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  s = padd(s, __builtin_msa_shf_w(s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return s[0];
-}
-
-// Other reduction functions:
-// mul
-template <>
-EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f p = pmul(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  p = pmul(p, (Packet4f)__builtin_msa_shf_w((v4i32)p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return p[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i p = pmul(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  p = pmul(p, __builtin_msa_shf_w(p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return p[0];
-}
-
-// min
-template <>
-EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  // Swap 64-bit halves of a.
-  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-#if !EIGEN_FAST_MATH
-  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
-  // masks of all zeroes/ones in low 64 bits.
-  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
-  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
-  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
-#endif
-  // Continue with min computation.
-  Packet4f v = __builtin_msa_fmin_w(a, swapped);
-  v = __builtin_msa_fmin_w(
-      v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-#if !EIGEN_FAST_MATH
-  // Based on the mask select between v and 4 qNaNs.
-  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
-  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
-#endif
-  return v[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i m = pmin(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  m = pmin(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return m[0];
-}
-
-// max
-template <>
-EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  // Swap 64-bit halves of a.
-  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-#if !EIGEN_FAST_MATH
-  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
-  // masks of all zeroes/ones in low 64 bits.
-  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
-  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
-  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
-#endif
-  // Continue with max computation.
-  Packet4f v = __builtin_msa_fmax_w(a, swapped);
-  v = __builtin_msa_fmax_w(
-      v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-#if !EIGEN_FAST_MATH
-  // Based on the mask select between v and 4 qNaNs.
-  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
-  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
-#endif
-  return v[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i m = pmax(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  m = pmax(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return m[0];
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4f, 4>& value) {
-  os << "[ " << value.packet[0] << "," << std::endl
-     << "  " << value.packet[1] << "," << std::endl
-     << "  " << value.packet[2] << "," << std::endl
-     << "  " << value.packet[3] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  v4i32 tmp1, tmp2, tmp3, tmp4;
-
-  tmp1 = __builtin_msa_ilvr_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
-  tmp2 = __builtin_msa_ilvr_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
-  tmp3 = __builtin_msa_ilvl_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
-  tmp4 = __builtin_msa_ilvl_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
-
-  kernel.packet[0] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[1] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[2] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
-  kernel.packet[3] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4i, 4>& value) {
-  os << "[ " << value.packet[0] << "," << std::endl
-     << "  " << value.packet[1] << "," << std::endl
-     << "  " << value.packet[2] << "," << std::endl
-     << "  " << value.packet[3] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  v4i32 tmp1, tmp2, tmp3, tmp4;
-
-  tmp1 = __builtin_msa_ilvr_w(kernel.packet[1], kernel.packet[0]);
-  tmp2 = __builtin_msa_ilvr_w(kernel.packet[3], kernel.packet[2]);
-  tmp3 = __builtin_msa_ilvl_w(kernel.packet[1], kernel.packet[0]);
-  tmp4 = __builtin_msa_ilvl_w(kernel.packet[3], kernel.packet[2]);
-
-  kernel.packet[0] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[1] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[2] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
-  kernel.packet[3] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsqrt_w(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  return __builtin_msa_frsqrt_w(a);
-#else
-  Packet4f ones = __builtin_msa_ffint_s_w(__builtin_msa_ldi_w(1));
-  return pdiv(ones, psqrt(a));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
-  Packet4f v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.w %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
-  Packet4f v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.w %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
-  Packet4f v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.w %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket,
-                                    const Packet4f& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2],
-                       ifPacket.select[3] };
-  Packet4i mask = __builtin_msa_ceqi_w((Packet4i)select, 0);
-  return (Packet4f)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket,
-                                    const Packet4i& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2],
-                       ifPacket.select[3] };
-  Packet4i mask = __builtin_msa_ceqi_w((Packet4i)select, 0);
-  return (Packet4i)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
-}
-
-//---------- double ----------
-
-typedef v2f64 Packet2d;
-typedef v2i64 Packet2l;
-typedef v2u64 Packet2ul;
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME, X) const Packet2d p2d_##NAME = { X, X }
-#define _EIGEN_DECLARE_CONST_Packet2l(NAME, X) const Packet2l p2l_##NAME = { X, X }
-#define _EIGEN_DECLARE_CONST_Packet2ul(NAME, X) const Packet2ul p2ul_##NAME = { X, X }
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2d& value) {
-  os << "[ " << value[0] << ", " << value[1] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2l& value) {
-  os << "[ " << value[0] << ", " << value[1] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2ul& value) {
-  os << "[ " << value[0] << ", " << value[1] << " ]";
-  return os;
-}
-
-template <>
-struct packet_traits<double> : default_packet_traits {
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-    // FIXME check the Has*
-    HasDiv = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet2d> {
-  typedef double type;
-  enum { size = 2, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet2d half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d value = { from, from };
-  return value;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fadd_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
-  EIGEN_MSA_DEBUG;
-
-  static const Packet2d countdown = { 0.0, 1.0 };
-  return padd(pset1<Packet2d>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsub_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_bnegi_d((v2u64)a, 63);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmul_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fdiv_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmadd_d(c, a, b);
-}
-
-// Logical Operations are not supported for float, so we have to reinterpret casts using MSA
-// intrinsics
-template <>
-EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_and_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_or_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return pand(a, (Packet2d)__builtin_msa_xori_b((v16u8)b, 255));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmin_d(a, b);
-#else
-  // This prefers NaNs to numbers.
-  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
-  v2i64 aMinOrNaN = por(__builtin_msa_fclt_d(a, b), aNaN);
-  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmax_d(a, b);
-#else
-  // This prefers NaNs to numbers.
-  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
-  v2i64 aMaxOrNaN = por(__builtin_msa_fclt_d(b, a), aNaN);
-  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d value = { *from, *from };
-  return value;
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d value;
-  value[0] = *from;
-  from += stride;
-  value[1] = *from;
-  return value;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from,
-                                                         Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = from[0];
-  to += stride;
-  *to = from[1];
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
-  EIGEN_MSA_DEBUG;
-
-  __builtin_prefetch(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_bclri_d((v2u64)a, 63);
-}
-
-template <>
-EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d s = padd(a, preverse(a));
-  return s[0];
-}
-
-// Other reduction functions:
-// mul
-template <>
-EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d p = pmul(a, preverse(a));
-  return p[0];
-}
-
-// min
-template <>
-EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-  Packet2d v = __builtin_msa_fmin_d(a, swapped);
-  return v[0];
-#else
-  double a0 = a[0], a1 = a[1];
-  return ((numext::isnan)(a0) || a0 < a1) ? a0 : a1;
-#endif
-}
-
-// max
-template <>
-EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-  Packet2d v = __builtin_msa_fmax_d(a, swapped);
-  return v[0];
-#else
-  double a0 = a[0], a1 = a[1];
-  return ((numext::isnan)(a0) || a0 > a1) ? a0 : a1;
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsqrt_d(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  return __builtin_msa_frsqrt_d(a);
-#else
-  Packet2d ones = __builtin_msa_ffint_s_d(__builtin_msa_ldi_d(1));
-  return pdiv(ones, psqrt(a));
-#endif
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2d, 2>& value) {
-  os << "[ " << value.packet[0] << "," << std::endl << "  " << value.packet[1] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d trn1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
-  Packet2d trn2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
-  kernel.packet[0] = trn1;
-  kernel.packet[1] = trn2;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
-  Packet2d v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.d %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
-  Packet2d v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.d %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
-  Packet2d v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.d %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket,
-                                    const Packet2d& elsePacket) {
-  Packet2ul select = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2l mask = __builtin_msa_ceqi_d((Packet2l)select, 0);
-  return (Packet2d)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_PACKET_MATH_MSA_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/Complex.h
deleted file mode 100644
index f40af7f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/Complex.h
+++ /dev/null
@@ -1,584 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_NEON_H
-#define EIGEN_COMPLEX_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-inline uint32x4_t p4ui_CONJ_XOR()
-{
-// See bug 1325, clang fails to call vld1q_u64.
-#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML
-  uint32x4_t ret = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
-  return ret;
-#else
-  static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
-  return vld1q_u32( conj_XOR_DATA );
-#endif
-}
-
-inline uint32x2_t p2ui_CONJ_XOR()
-{
-  static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000 };
-  return vld1_u32( conj_XOR_DATA );
-}
-
-//---------- float ----------
-
-struct Packet1cf
-{
-  EIGEN_STRONG_INLINE Packet1cf() {}
-  EIGEN_STRONG_INLINE explicit Packet1cf(const Packet2f& a) : v(a) {}
-  Packet2f v;
-};
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
-  Packet4f v;
-};
-
-template<> struct packet_traits<std::complex<float> > : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet1cf half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasMul       = 1,
-    HasDiv       = 1,
-    HasNegate    = 1,
-    HasAbs       = 0,
-    HasAbs2      = 0,
-    HasMin       = 0,
-    HasMax       = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet1cf>
-{
-  typedef std::complex<float> type;
-  typedef Packet1cf half;
-  typedef Packet2f as_real;
-  enum
-  {
-    size = 1,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2cf>
-{
-  typedef std::complex<float> type;
-  typedef Packet1cf half;
-  typedef Packet4f as_real;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet1cf pcast<float,Packet1cf>(const float& a)
-{ return Packet1cf(vset_lane_f32(a, vdup_n_f32(0.f), 0)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pcast<Packet2f,Packet2cf>(const Packet2f& a)
-{ return Packet2cf(vreinterpretq_f32_u64(vmovl_u32(vreinterpret_u32_f32(a)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pset1<Packet1cf>(const std::complex<float>& from)
-{ return Packet1cf(vld1_f32(reinterpret_cast<const float*>(&from))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
-{
-  const float32x2_t r64 = vld1_f32(reinterpret_cast<const float*>(&from));
-  return Packet2cf(vcombine_f32(r64, r64));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf padd<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(padd<Packet2f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(padd<Packet4f>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf psub<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(psub<Packet2f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(psub<Packet4f>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pnegate(const Packet1cf& a) { return Packet1cf(pnegate<Packet2f>(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate<Packet4f>(a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pconj(const Packet1cf& a)
-{
-  const Packet2ui b = vreinterpret_u32_f32(a.v);
-  return Packet1cf(vreinterpret_f32_u32(veor_u32(b, p2ui_CONJ_XOR())));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
-{
-  const Packet4ui b = vreinterpretq_u32_f32(a.v);
-  return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR())));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf pmul<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{
-  Packet2f v1, v2;
-
-  // Get the real values of a | a1_re | a1_re |
-  v1 = vdup_lane_f32(a.v, 0);
-  // Get the imag values of a | a1_im | a1_im |
-  v2 = vdup_lane_f32(a.v, 1);
-  // Multiply the real a with b
-  v1 = vmul_f32(v1, b.v);
-  // Multiply the imag a with b
-  v2 = vmul_f32(v2, b.v);
-  // Conjugate v2
-  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
-  // Swap real/imag elements in v2.
-  v2 = vrev64_f32(v2);
-  // Add and return the result
-  return Packet1cf(vadd_f32(v1, v2));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  Packet4f v1, v2;
-
-  // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
-  v1 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 0), vdup_lane_f32(vget_high_f32(a.v), 0));
-  // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
-  v2 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 1), vdup_lane_f32(vget_high_f32(a.v), 1));
-  // Multiply the real a with b
-  v1 = vmulq_f32(v1, b.v);
-  // Multiply the imag a with b
-  v2 = vmulq_f32(v2, b.v);
-  // Conjugate v2
-  v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR()));
-  // Swap real/imag elements in v2.
-  v2 = vrev64q_f32(v2);
-  // Add and return the result
-  return Packet2cf(vaddq_f32(v1, v2));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf pcmp_eq(const Packet1cf& a, const Packet1cf& b)
-{
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a[0])==re(b[0]), im(a[0])==im(b[0])]
-  Packet2f eq = pcmp_eq<Packet2f>(a.v, b.v);
-  // Swap real/imag elements in the mask in to get:
-  // [im(a[0])==im(b[0]), re(a[0])==re(b[0])]
-  Packet2f eq_swapped = vrev64_f32(eq);
-  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet1cf(pand<Packet2f>(eq, eq_swapped));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b)
-{
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a[0])==re(b[0]), im(a[0])==im(b[0]), re(a[1])==re(b[1]), im(a[1])==im(b[1])]
-  Packet4f eq = pcmp_eq<Packet4f>(a.v, b.v);
-  // Swap real/imag elements in the mask in to get:
-  // [im(a[0])==im(b[0]), re(a[0])==re(b[0]), im(a[1])==im(b[1]), re(a[1])==re(b[1])]
-  Packet4f eq_swapped = vrev64q_f32(eq);
-  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet2cf(pand<Packet4f>(eq, eq_swapped));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf pand<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf por<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pxor<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pandnot<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pload<Packet1cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cf(pload<Packet2f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(reinterpret_cast<const float*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf ploadu<Packet1cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cf(ploadu<Packet2f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(reinterpret_cast<const float*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf ploaddup<Packet1cf>(const std::complex<float>* from)
-{ return pset1<Packet1cf>(*from); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from)
-{ return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *to, const Packet1cf& from)
-{ EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *to, const Packet2cf& from)
-{ EIGEN_DEBUG_ALIGNED_STORE pstore(reinterpret_cast<float*>(to), from.v); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *to, const Packet1cf& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *to, const Packet2cf& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<float*>(to), from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cf pgather<std::complex<float>, Packet1cf>(
-    const std::complex<float>* from, Index stride)
-{
-  const Packet2f tmp = vdup_n_f32(std::real(from[0*stride]));
-  return Packet1cf(vset_lane_f32(std::imag(from[0*stride]), tmp, 1));
-}
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(
-    const std::complex<float>* from, Index stride)
-{
-  Packet4f res = vdupq_n_f32(std::real(from[0*stride]));
-  res = vsetq_lane_f32(std::imag(from[0*stride]), res, 1);
-  res = vsetq_lane_f32(std::real(from[1*stride]), res, 2);
-  res = vsetq_lane_f32(std::imag(from[1*stride]), res, 3);
-  return Packet2cf(res);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet1cf>(
-    std::complex<float>* to, const Packet1cf& from, Index stride)
-{ to[stride*0] = std::complex<float>(vget_lane_f32(from.v, 0), vget_lane_f32(from.v, 1)); }
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(
-    std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  to[stride*0] = std::complex<float>(vgetq_lane_f32(from.v, 0), vgetq_lane_f32(from.v, 1));
-  to[stride*1] = std::complex<float>(vgetq_lane_f32(from.v, 2), vgetq_lane_f32(from.v, 3));
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *addr)
-{ EIGEN_ARM_PREFETCH(reinterpret_cast<const float*>(addr)); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet1cf>(const Packet1cf& a)
-{
-  EIGEN_ALIGN16 std::complex<float> x;
-  vst1_f32(reinterpret_cast<float*>(&x), a.v);
-  return x;
-}
-template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a)
-{
-  EIGEN_ALIGN16 std::complex<float> x[2];
-  vst1q_f32(reinterpret_cast<float*>(x), a.v);
-  return x[0];
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf preverse(const Packet1cf& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{ return Packet2cf(vcombine_f32(vget_high_f32(a.v), vget_low_f32(a.v))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pcplxflip<Packet1cf>(const Packet1cf& a)
-{ return Packet1cf(vrev64_f32(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a)
-{ return Packet2cf(vrev64q_f32(a.v)); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet1cf>(const Packet1cf& a)
-{
-  std::complex<float> s;
-  vst1_f32((float *)&s, a.v);
-  return s;
-}
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> s;
-  vst1_f32(reinterpret_cast<float*>(&s), vadd_f32(vget_low_f32(a.v), vget_high_f32(a.v)));
-  return s;
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet1cf>(const Packet1cf& a)
-{
-  std::complex<float> s;
-  vst1_f32((float *)&s, a.v);
-  return s;
-}
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  float32x2_t a1, a2, v1, v2, prod;
-  std::complex<float> s;
-
-  a1 = vget_low_f32(a.v);
-  a2 = vget_high_f32(a.v);
-   // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
-  v1 = vdup_lane_f32(a1, 0);
-  // Get the real values of a | a1_im | a1_im | a2_im | a2_im |
-  v2 = vdup_lane_f32(a1, 1);
-  // Multiply the real a with b
-  v1 = vmul_f32(v1, a2);
-  // Multiply the imag a with b
-  v2 = vmul_f32(v2, a2);
-  // Conjugate v2
-  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
-  // Swap real/imag elements in v2.
-  v2 = vrev64_f32(v2);
-  // Add v1, v2
-  prod = vadd_f32(v1, v2);
-
-  vst1_f32(reinterpret_cast<float*>(&s), prod);
-
-  return s;
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cf,Packet2f)
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet1cf pdiv<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{
-  // TODO optimize it for NEON
-  Packet1cf res = pmul(a, pconj(b));
-  Packet2f s, rev_s;
-
-  // this computes the norm
-  s = vmul_f32(b.v, b.v);
-  rev_s = vrev64_f32(s);
-
-  return Packet1cf(pdiv<Packet2f>(res.v, vadd_f32(s, rev_s)));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for NEON
-  Packet2cf res = pmul(a,pconj(b));
-  Packet4f s, rev_s;
-
-  // this computes the norm
-  s = vmulq_f32(b.v, b.v);
-  rev_s = vrev64q_f32(s);
-
-  return Packet2cf(pdiv<Packet4f>(res.v, vaddq_f32(s, rev_s)));
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1cf, 1>& /*kernel*/) {}
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel)
-{
-  Packet4f tmp = vcombine_f32(vget_high_f32(kernel.packet[0].v), vget_high_f32(kernel.packet[1].v));
-  kernel.packet[0].v = vcombine_f32(vget_low_f32(kernel.packet[0].v), vget_low_f32(kernel.packet[1].v));
-  kernel.packet[1].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf psqrt<Packet1cf>(const Packet1cf& a) {
-  return psqrt_complex<Packet1cf>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
-  return psqrt_complex<Packet2cf>(a);
-}
-
-//---------- double ----------
-#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
-
-// See bug 1325, clang fails to call vld1q_u64.
-#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML
-  static uint64x2_t p2ul_CONJ_XOR = {0x0, 0x8000000000000000};
-#else
-  const uint64_t  p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
-  static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
-#endif
-
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
-  Packet2d v;
-};
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet1cd>
-{
-  typedef std::complex<double> type;
-  typedef Packet1cd half;
-  typedef Packet2d as_real;
-  enum
-  {
-    size=1,
-    alignment=Aligned16,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>(reinterpret_cast<const double*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>(reinterpret_cast<const double*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
-{
-  /* here we really have to use unaligned loads :( */
-  return ploadu<Packet1cd>(&from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(padd<Packet2d>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(psub<Packet2d>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a)
-{ return Packet1cd(pnegate<Packet2d>(a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a)
-{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  Packet2d v1, v2;
-
-  // Get the real values of a
-  v1 = vdupq_lane_f64(vget_low_f64(a.v), 0);
-  // Get the imag values of a
-  v2 = vdupq_lane_f64(vget_high_f64(a.v), 0);
-  // Multiply the real a with b
-  v1 = vmulq_f64(v1, b.v);
-  // Multiply the imag a with b
-  v2 = vmulq_f64(v2, b.v);
-  // Conjugate v2
-  v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR));
-  // Swap real/imag elements in v2.
-  v2 = preverse<Packet2d>(v2);
-  // Add and return the result
-  return Packet1cd(vaddq_f64(v1, v2));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b)
-{
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a)==re(b), im(a)==im(b)]
-  Packet2d eq = pcmp_eq<Packet2d>(a.v, b.v);
-  // Swap real/imag elements in the mask in to get:
-  // [im(a)==im(b), re(a)==re(b)]
-  Packet2d eq_swapped = vreinterpretq_f64_u32(vrev64q_u32(vreinterpretq_u32_f64(eq)));
-  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet1cd(pand<Packet2d>(eq, eq_swapped));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from)
-{ return pset1<Packet1cd>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *to, const Packet1cd& from)
-{ EIGEN_DEBUG_ALIGNED_STORE pstore(reinterpret_cast<double*>(to), from.v); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *to, const Packet1cd& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<double*>(to), from.v); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *addr)
-{ EIGEN_ARM_PREFETCH(reinterpret_cast<const double*>(addr)); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(
-    const std::complex<double>* from, Index stride)
-{
-  Packet2d res = pset1<Packet2d>(0.0);
-  res = vsetq_lane_f64(std::real(from[0*stride]), res, 0);
-  res = vsetq_lane_f64(std::imag(from[0*stride]), res, 1);
-  return Packet1cd(res);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(
-    std::complex<double>* to, const Packet1cd& from, Index stride)
-{ to[stride*0] = std::complex<double>(vgetq_lane_f64(from.v, 0), vgetq_lane_f64(from.v, 1)); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
-{
-  EIGEN_ALIGN16 std::complex<double> res;
-  pstore<std::complex<double> >(&res, a);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for NEON
-  Packet1cd res = pmul(a,pconj(b));
-  Packet2d s = pmul<Packet2d>(b.v, b.v);
-  Packet2d rev_s = preverse<Packet2d>(s);
-
-  return Packet1cd(pdiv(res.v, padd<Packet2d>(s,rev_s)));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
-{ return Packet1cd(preverse(Packet2d(x.v))); }
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
-{
-  Packet2d tmp = vcombine_f64(vget_high_f64(kernel.packet[0].v), vget_high_f64(kernel.packet[1].v));
-  kernel.packet[0].v = vcombine_f64(vget_low_f64(kernel.packet[0].v), vget_low_f64(kernel.packet[1].v));
-  kernel.packet[1].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
-  return psqrt_complex<Packet1cd>(a);
-}
-
-#endif // EIGEN_ARCH_ARM64
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
deleted file mode 100644
index 3481f33..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
+++ /dev/null
@@ -1,183 +0,0 @@
-namespace Eigen {
-namespace internal {
-  
-#if EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
-
-// Clang seems to excessively spill registers in the GEBP kernel on 32-bit arm.
-// Here we specialize gebp_traits to eliminate these register spills.
-// See #2138.
-template<>
-struct gebp_traits <float,float,false,false,Architecture::NEON,GEBPPacketFull>
- : gebp_traits<float,float,false,false,Architecture::Generic,GEBPPacketFull>
-{
-  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
-  { 
-    // This volatile inline ASM both acts as a barrier to prevent reordering,
-    // as well as enforces strict register use.
-    asm volatile(
-      "vmla.f32 %q[r], %q[c], %q[alpha]"
-      : [r] "+w" (r)
-      : [c] "w" (c),
-        [alpha] "w" (alpha)
-      : );
-  }
-
-  template <typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const Packet4f& b,
-                                Packet4f& c, Packet4f& tmp,
-                                const LaneIdType&) const {
-    acc(a, b, c);
-  }
-  
-  template <typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const QuadPacket<Packet4f>& b,
-                                Packet4f& c, Packet4f& tmp,
-                                const LaneIdType& lane) const {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-};
-
-#endif // EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
-
-#if EIGEN_ARCH_ARM64
-
-template<>
-struct gebp_traits <float,float,false,false,Architecture::NEON,GEBPPacketFull>
- : gebp_traits<float,float,false,false,Architecture::Generic,GEBPPacketFull>
-{
-  typedef float RhsPacket;
-  typedef float32x4_t RhsPacketx4;
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = *b;
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    dest = vld1q_f32(b);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = *b;
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  {
-    c = vfmaq_n_f32(c, a, b);
-  }
-
-  // NOTE: Template parameter inference failed when compiled with Android NDK:
-  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  { madd_helper<0>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<1>&) const
-  { madd_helper<1>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<2>&) const
-  { madd_helper<2>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<3>&) const
-  { madd_helper<3>(a, b, c); }
-
- private:
-  template<int LaneID>
-  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const
-  {
-    #if EIGEN_COMP_GNUC_STRICT && !(EIGEN_GNUC_AT_LEAST(9,0))
-    // workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
-    // vfmaq_laneq_f32 is implemented through a costly dup
-         if(LaneID==0)  asm("fmla %0.4s, %1.4s, %2.s[0]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    else if(LaneID==1)  asm("fmla %0.4s, %1.4s, %2.s[1]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    else if(LaneID==2)  asm("fmla %0.4s, %1.4s, %2.s[2]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    else if(LaneID==3)  asm("fmla %0.4s, %1.4s, %2.s[3]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    #else
-    c = vfmaq_laneq_f32(c, a, b, LaneID);
-    #endif
-  }
-};
-
-
-template<>
-struct gebp_traits <double,double,false,false,Architecture::NEON>
- : gebp_traits<double,double,false,false,Architecture::Generic>
-{
-  typedef double RhsPacket;
-
-  struct RhsPacketx4 {
-    float64x2_t B_0, B_1;
-  };
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = *b;
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    dest.B_0 = vld1q_f64(b);
-    dest.B_1 = vld1q_f64(b+2);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  {
-    c = vfmaq_n_f64(c, a, b);
-  }
-
-  // NOTE: Template parameter inference failed when compiled with Android NDK:
-  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  { madd_helper<0>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<1>&) const
-  { madd_helper<1>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<2>&) const
-  { madd_helper<2>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<3>&) const
-  { madd_helper<3>(a, b, c); }
-
- private:
-  template <int LaneID>
-  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const
-  {
-    #if EIGEN_COMP_GNUC_STRICT && !(EIGEN_GNUC_AT_LEAST(9,0))
-    // workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
-    // vfmaq_laneq_f64 is implemented through a costly dup
-         if(LaneID==0)  asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w" (c) : "w" (a), "w" (b.B_0) :  );
-    else if(LaneID==1)  asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w" (c) : "w" (a), "w" (b.B_0) :  );
-    else if(LaneID==2)  asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w" (c) : "w" (a), "w" (b.B_1) :  );
-    else if(LaneID==3)  asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w" (c) : "w" (a), "w" (b.B_1) :  );
-    #else
-         if(LaneID==0) c = vfmaq_laneq_f64(c, a, b.B_0, 0);
-    else if(LaneID==1) c = vfmaq_laneq_f64(c, a, b.B_0, 1);
-    else if(LaneID==2) c = vfmaq_laneq_f64(c, a, b.B_1, 0);
-    else if(LaneID==3) c = vfmaq_laneq_f64(c, a, b.B_1, 1);
-    #endif
-  }
-};
-
-#endif // EIGEN_ARCH_ARM64
-
-}  // namespace internal
-}  // namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/MathFunctions.h
deleted file mode 100644
index fa6615a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/MathFunctions.h
+++ /dev/null
@@ -1,75 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_NEON_H
-#define EIGEN_MATH_FUNCTIONS_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f pexp<Packet2f>(const Packet2f& x)
-{ return pexp_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f pexp<Packet4f>(const Packet4f& x)
-{ return pexp_float(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f plog<Packet2f>(const Packet2f& x)
-{ return plog_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f plog<Packet4f>(const Packet4f& x)
-{ return plog_float(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f psin<Packet2f>(const Packet2f& x)
-{ return psin_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f psin<Packet4f>(const Packet4f& x)
-{ return psin_float(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f pcos<Packet2f>(const Packet2f& x)
-{ return pcos_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f pcos<Packet4f>(const Packet4f& x)
-{ return pcos_float(x); }
-
-// Hyperbolic Tangent function.
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f ptanh<Packet2f>(const Packet2f& x)
-{ return internal::generic_fast_tanh_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f ptanh<Packet4f>(const Packet4f& x)
-{ return internal::generic_fast_tanh_float(x); }
-
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, psin)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pcos)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, plog)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pexp)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, ptanh)
-
-template <>
-EIGEN_STRONG_INLINE Packet4bf pfrexp(const Packet4bf& a, Packet4bf& exponent) {
-  Packet4f fexponent;
-  const Packet4bf out = F32ToBf16(pfrexp<Packet4f>(Bf16ToF32(a), fexponent));
-  exponent = F32ToBf16(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4bf pldexp(const Packet4bf& a, const Packet4bf& exponent) {
-  return F32ToBf16(pldexp<Packet4f>(Bf16ToF32(a), Bf16ToF32(exponent)));
-}
-
-//---------- double ----------
-
-#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2d pexp<Packet2d>(const Packet2d& x)
-{ return pexp_double(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2d plog<Packet2d>(const Packet2d& x)
-{ return plog_double(x); }
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATH_FUNCTIONS_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/PacketMath.h
deleted file mode 100644
index d2aeef4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/PacketMath.h
+++ /dev/null
@@ -1,4587 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
-// Heavily based on Gael's SSE version.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_NEON_H
-#define EIGEN_PACKET_MATH_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#if EIGEN_ARCH_ARM64
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-#else
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
-#endif
-#endif
-
-#if EIGEN_COMP_MSVC_STRICT
-
-// In MSVC's arm_neon.h header file, all NEON vector types
-// are aliases to the same underlying type __n128.
-// We thus have to wrap them to make them different C++ types.
-// (See also bug 1428)
-typedef eigen_packet_wrapper<float32x2_t,0>  Packet2f;
-typedef eigen_packet_wrapper<float32x4_t,1>  Packet4f;
-typedef eigen_packet_wrapper<int32_t    ,2>  Packet4c;
-typedef eigen_packet_wrapper<int8x8_t   ,3>  Packet8c;
-typedef eigen_packet_wrapper<int8x16_t  ,4>  Packet16c;
-typedef eigen_packet_wrapper<uint32_t   ,5>  Packet4uc;
-typedef eigen_packet_wrapper<uint8x8_t  ,6>  Packet8uc;
-typedef eigen_packet_wrapper<uint8x16_t ,7>  Packet16uc;
-typedef eigen_packet_wrapper<int16x4_t  ,8>  Packet4s;
-typedef eigen_packet_wrapper<int16x8_t  ,9>  Packet8s;
-typedef eigen_packet_wrapper<uint16x4_t ,10> Packet4us;
-typedef eigen_packet_wrapper<uint16x8_t ,11> Packet8us;
-typedef eigen_packet_wrapper<int32x2_t  ,12> Packet2i;
-typedef eigen_packet_wrapper<int32x4_t  ,13> Packet4i;
-typedef eigen_packet_wrapper<uint32x2_t ,14> Packet2ui;
-typedef eigen_packet_wrapper<uint32x4_t ,15> Packet4ui;
-typedef eigen_packet_wrapper<int64x2_t  ,16> Packet2l;
-typedef eigen_packet_wrapper<uint64x2_t ,17> Packet2ul;
-
-#else
-
-typedef float32x2_t                          Packet2f;
-typedef float32x4_t                          Packet4f;
-typedef eigen_packet_wrapper<int32_t    ,2>  Packet4c;
-typedef int8x8_t                             Packet8c;
-typedef int8x16_t                            Packet16c;
-typedef eigen_packet_wrapper<uint32_t   ,5>  Packet4uc;
-typedef uint8x8_t                            Packet8uc;
-typedef uint8x16_t                           Packet16uc;
-typedef int16x4_t                            Packet4s;
-typedef int16x8_t                            Packet8s;
-typedef uint16x4_t                           Packet4us;
-typedef uint16x8_t                           Packet8us;
-typedef int32x2_t                            Packet2i;
-typedef int32x4_t                            Packet4i;
-typedef uint32x2_t                           Packet2ui;
-typedef uint32x4_t                           Packet4ui;
-typedef int64x2_t                            Packet2l;
-typedef uint64x2_t                           Packet2ul;
-
-#endif // EIGEN_COMP_MSVC_STRICT
-
-EIGEN_STRONG_INLINE Packet4f shuffle1(const Packet4f& m, int mask){
-  const float* a = reinterpret_cast<const float*>(&m);
-  Packet4f res = {*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3 )), *(a + ((mask >> 6) & 3))};
-  return res;
-}
-
-// fuctionally equivalent to _mm_shuffle_ps in SSE when interleave
-// == false (i.e. shuffle<false>(m, n, mask) equals _mm_shuffle_ps(m, n, mask)),
-// interleave m and n when interleave == true. Currently used in LU/arch/InverseSize4.h
-// to enable a shared implementation for fast inversion of matrices of size 4. 
-template<bool interleave> 
-EIGEN_STRONG_INLINE Packet4f shuffle2(const Packet4f &m, const Packet4f &n, int mask)
-{
-  const float* a = reinterpret_cast<const float*>(&m);
-  const float* b = reinterpret_cast<const float*>(&n);
-  Packet4f res = {*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(b + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3))};
-  return res;
-}
-
-template<> 
-EIGEN_STRONG_INLINE Packet4f shuffle2<true>(const Packet4f &m, const Packet4f &n, int mask) 
-{
-  const float* a = reinterpret_cast<const float*>(&m);
-  const float* b = reinterpret_cast<const float*>(&n);
-  Packet4f res = {*(a + (mask & 3)), *(b + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3))};
-  return res;
-}
-
-EIGEN_STRONG_INLINE static int eigen_neon_shuffle_mask(int p, int q, int r, int s) {return ((s)<<6|(r)<<4|(q)<<2|(p));}
-
-EIGEN_STRONG_INLINE Packet4f vec4f_swizzle1(const Packet4f& a, int p, int q, int r, int s)
-{ 
-  return shuffle1(a, eigen_neon_shuffle_mask(p, q, r, s));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_swizzle2(const Packet4f& a, const Packet4f& b, int p, int q, int r, int s)
-{ 
-  return shuffle2<false>(a,b,eigen_neon_shuffle_mask(p, q, r, s));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<false>(a,b,eigen_neon_shuffle_mask(0, 1, 0, 1));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<false>(b,a,eigen_neon_shuffle_mask(2, 3, 2, 3));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<true>(a,b,eigen_neon_shuffle_mask(0, 0, 1, 1));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<true>(a,b,eigen_neon_shuffle_mask(2, 2, 3, 3));
-}
-#define vec4f_duplane(a, p) \
-  vdupq_lane_f32(vget_low_f32(a), p)
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  const Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = vreinterpretq_f32_u32(pset1<int32_t>(X))
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  const Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-#if EIGEN_ARCH_ARM64
-  // __builtin_prefetch tends to do nothing on ARM64 compilers because the
-  // prefetch instructions there are too detailed for __builtin_prefetch to map
-  // meaningfully to them.
-  #define EIGEN_ARM_PREFETCH(ADDR)  __asm__ __volatile__("prfm pldl1keep, [%[addr]]\n" ::[addr] "r"(ADDR) : );
-#elif EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
-  #define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
-#elif defined __pld
-  #define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
-#elif EIGEN_ARCH_ARM32
-  #define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ("pld [%[addr]]\n" :: [addr] "r" (ADDR) : );
-#else
-  // by default no explicit prefetching
-  #define EIGEN_ARM_PREFETCH(ADDR)
-#endif
-
-template <>
-struct packet_traits<float> : default_packet_traits
-{
-  typedef Packet4f type;
-  typedef Packet2f half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasDiv   = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-
-    HasSin  = EIGEN_FAST_MATH,
-    HasCos  = EIGEN_FAST_MATH,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf  = EIGEN_FAST_MATH,
-    HasBessel = 0,  // Issues with accuracy.
-    HasNdtri = 0
-  };
-};
-
-template <>
-struct packet_traits<int8_t> : default_packet_traits
-{
-  typedef Packet16c type;
-  typedef Packet8c half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint8_t> : default_packet_traits
-{
-  typedef Packet16uc type;
-  typedef Packet8uc half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 1,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasSqrt = 1
-  };
-};
-
-template <>
-struct packet_traits<int16_t> : default_packet_traits
-{
-  typedef Packet8s type;
-  typedef Packet4s half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint16_t> : default_packet_traits
-{
-  typedef Packet8us type;
-  typedef Packet4us half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 0,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-    HasSqrt = 1
-  };
-};
-
-template <>
-struct packet_traits<int32_t> : default_packet_traits
-{
-  typedef Packet4i type;
-  typedef Packet2i half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint32_t> : default_packet_traits
-{
-  typedef Packet4ui type;
-  typedef Packet2ui half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 0,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasSqrt = 1
-  };
-};
-
-template <>
-struct packet_traits<int64_t> : default_packet_traits
-{
-  typedef Packet2l type;
-  typedef Packet2l half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint64_t> : default_packet_traits
-{
-  typedef Packet2ul type;
-  typedef Packet2ul half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 0,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-#if EIGEN_GNUC_AT_MOST(4, 4) && !EIGEN_COMP_LLVM
-// workaround gcc 4.2, 4.3 and 4.4 compilation issue
-EIGEN_STRONG_INLINE float32x4_t vld1q_f32(const float* x) { return ::vld1q_f32((const float32_t*)x); }
-EIGEN_STRONG_INLINE float32x2_t vld1_f32(const float* x) { return ::vld1_f32 ((const float32_t*)x); }
-EIGEN_STRONG_INLINE float32x2_t vld1_dup_f32(const float* x) { return ::vld1_dup_f32 ((const float32_t*)x); }
-EIGEN_STRONG_INLINE void vst1q_f32(float* to, float32x4_t from) { ::vst1q_f32((float32_t*)to,from); }
-EIGEN_STRONG_INLINE void vst1_f32 (float* to, float32x2_t from) { ::vst1_f32 ((float32_t*)to,from); }
-#endif
-
-template<> struct unpacket_traits<Packet2f>
-{
-  typedef float type;
-  typedef Packet2f half;
-  typedef Packet2i integer_packet;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4f>
-{
-  typedef float type;
-  typedef Packet2f half;
-  typedef Packet4i integer_packet;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4c>
-{
-  typedef int8_t type;
-  typedef Packet4c half;
-  enum
-  {
-    size = 4,
-    alignment = Unaligned,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8c>
-{
-  typedef int8_t type;
-  typedef Packet4c half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet16c>
-{
-  typedef int8_t type;
-  typedef Packet8c half;
-  enum
-  {
-    size = 16,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4uc>
-{
-  typedef uint8_t type;
-  typedef Packet4uc half;
-  enum
-  {
-    size = 4,
-    alignment = Unaligned,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8uc>
-{
-  typedef uint8_t type;
-  typedef Packet4uc half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet16uc>
-{
-  typedef uint8_t type;
-  typedef Packet8uc half;
-  enum
-  {
-    size = 16,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false};
-};
-template<> struct unpacket_traits<Packet4s>
-{
-  typedef int16_t type;
-  typedef Packet4s half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8s>
-{
-  typedef int16_t type;
-  typedef Packet4s half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4us>
-{
-  typedef uint16_t type;
-  typedef Packet4us half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8us>
-{
-  typedef uint16_t type;
-  typedef Packet4us half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2i>
-{
-  typedef int32_t type;
-  typedef Packet2i half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4i>
-{
-  typedef int32_t type;
-  typedef Packet2i half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2ui>
-{
-  typedef uint32_t type;
-  typedef Packet2ui half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4ui>
-{
-  typedef uint32_t type;
-  typedef Packet2ui half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2l>
-{
-  typedef int64_t type;
-  typedef Packet2l half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2ul>
-{
-  typedef uint64_t type;
-  typedef Packet2ul half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2f pset1<Packet2f>(const float& from) { return vdup_n_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) { return vdupq_n_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c pset1<Packet4c>(const int8_t& from)
-{ return vget_lane_s32(vreinterpret_s32_s8(vdup_n_s8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c pset1<Packet8c>(const int8_t& from) { return vdup_n_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const int8_t& from) { return vdupq_n_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet4uc pset1<Packet4uc>(const uint8_t& from)
-{ return vget_lane_u32(vreinterpret_u32_u8(vdup_n_u8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc pset1<Packet8uc>(const uint8_t& from) { return vdup_n_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const uint8_t& from) { return vdupq_n_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet4s pset1<Packet4s>(const int16_t& from) { return vdup_n_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const int16_t& from) { return vdupq_n_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet4us pset1<Packet4us>(const uint16_t& from) { return vdup_n_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const uint16_t& from) { return vdupq_n_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet2i pset1<Packet2i>(const int32_t& from) { return vdup_n_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) { return vdupq_n_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet2ui pset1<Packet2ui>(const uint32_t& from) { return vdup_n_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui pset1<Packet4ui>(const uint32_t& from) { return vdupq_n_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet2l pset1<Packet2l>(const int64_t& from) { return vdupq_n_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul pset1<Packet2ul>(const uint64_t& from) { return vdupq_n_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pset1frombits<Packet2f>(unsigned int from)
-{ return vreinterpret_f32_u32(vdup_n_u32(from)); }
-template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from)
-{ return vreinterpretq_f32_u32(vdupq_n_u32(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet2f plset<Packet2f>(const float& a)
-{
-  const float c[] = {0.0f,1.0f};
-  return vadd_f32(pset1<Packet2f>(a), vld1_f32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a)
-{
-  const float c[] = {0.0f,1.0f,2.0f,3.0f};
-  return vaddq_f32(pset1<Packet4f>(a), vld1q_f32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4c plset<Packet4c>(const int8_t& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(vreinterpret_s8_u32(vdup_n_u32(0x03020100)), vdup_n_s8(a))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c plset<Packet8c>(const int8_t& a)
-{
-  const int8_t c[] = {0,1,2,3,4,5,6,7};
-  return vadd_s8(pset1<Packet8c>(a), vld1_s8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet16c plset<Packet16c>(const int8_t& a)
-{
-  const int8_t c[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15};
-  return vaddq_s8(pset1<Packet16c>(a), vld1q_s8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4uc plset<Packet4uc>(const uint8_t& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(vreinterpret_u8_u32(vdup_n_u32(0x03020100)), vdup_n_u8(a))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc plset<Packet8uc>(const uint8_t& a)
-{
-  const uint8_t c[] = {0,1,2,3,4,5,6,7};
-  return vadd_u8(pset1<Packet8uc>(a), vld1_u8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const uint8_t& a)
-{
-  const uint8_t c[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15};
-  return vaddq_u8(pset1<Packet16uc>(a), vld1q_u8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4s plset<Packet4s>(const int16_t& a)
-{
-  const int16_t c[] = {0,1,2,3};
-  return vadd_s16(pset1<Packet4s>(a), vld1_s16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4us plset<Packet4us>(const uint16_t& a)
-{
-  const uint16_t c[] = {0,1,2,3};
-  return vadd_u16(pset1<Packet4us>(a), vld1_u16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet8s plset<Packet8s>(const int16_t& a)
-{
-  const int16_t c[] = {0,1,2,3,4,5,6,7};
-  return vaddq_s16(pset1<Packet8s>(a), vld1q_s16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet8us plset<Packet8us>(const uint16_t& a)
-{
-  const uint16_t c[] = {0,1,2,3,4,5,6,7};
-  return vaddq_u16(pset1<Packet8us>(a), vld1q_u16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2i plset<Packet2i>(const int32_t& a)
-{
-  const int32_t c[] = {0,1};
-  return vadd_s32(pset1<Packet2i>(a), vld1_s32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a)
-{
-  const int32_t c[] = {0,1,2,3};
-  return vaddq_s32(pset1<Packet4i>(a), vld1q_s32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2ui plset<Packet2ui>(const uint32_t& a)
-{
-  const uint32_t c[] = {0,1};
-  return vadd_u32(pset1<Packet2ui>(a), vld1_u32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4ui plset<Packet4ui>(const uint32_t& a)
-{
-  const uint32_t c[] = {0,1,2,3};
-  return vaddq_u32(pset1<Packet4ui>(a), vld1q_u32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2l plset<Packet2l>(const int64_t& a)
-{
-  const int64_t c[] = {0,1};
-  return vaddq_s64(pset1<Packet2l>(a), vld1q_s64(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul plset<Packet2ul>(const uint64_t& a)
-{
-  const uint64_t c[] = {0,1};
-  return vaddq_u64(pset1<Packet2ul>(a), vld1q_u64(c));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f padd<Packet2f>(const Packet2f& a, const Packet2f& b) { return vadd_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return vaddq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c padd<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c padd<Packet8c>(const Packet8c& a, const Packet8c& b) { return vadd_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c padd<Packet16c>(const Packet16c& a, const Packet16c& b) { return vaddq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc padd<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc padd<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vadd_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vaddq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s padd<Packet4s>(const Packet4s& a, const Packet4s& b) { return vadd_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s padd<Packet8s>(const Packet8s& a, const Packet8s& b) { return vaddq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us padd<Packet4us>(const Packet4us& a, const Packet4us& b) { return vadd_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us padd<Packet8us>(const Packet8us& a, const Packet8us& b) { return vaddq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i padd<Packet2i>(const Packet2i& a, const Packet2i& b) { return vadd_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return vaddq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui padd<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vadd_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vaddq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l padd<Packet2l>(const Packet2l& a, const Packet2l& b) { return vaddq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul padd<Packet2ul>(const Packet2ul& a, const Packet2ul& b) { return vaddq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f psub<Packet2f>(const Packet2f& a, const Packet2f& b) { return vsub_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return vsubq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c psub<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vsub_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c psub<Packet8c>(const Packet8c& a, const Packet8c& b) { return vsub_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c psub<Packet16c>(const Packet16c& a, const Packet16c& b) { return vsubq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc psub<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vsub_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc psub<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vsub_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vsubq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s psub<Packet4s>(const Packet4s& a, const Packet4s& b) { return vsub_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s psub<Packet8s>(const Packet8s& a, const Packet8s& b) { return vsubq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us psub<Packet4us>(const Packet4us& a, const Packet4us& b) { return vsub_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us psub<Packet8us>(const Packet8us& a, const Packet8us& b) { return vsubq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i psub<Packet2i>(const Packet2i& a, const Packet2i& b) { return vsub_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return vsubq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui psub<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vsub_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui psub<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vsubq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l psub<Packet2l>(const Packet2l& a, const Packet2l& b) { return vsubq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul psub<Packet2ul>(const Packet2ul& a, const Packet2ul& b) { return vsubq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b);
-template<> EIGEN_STRONG_INLINE Packet2f paddsub<Packet2f>(const Packet2f& a, const Packet2f & b) {
-  Packet2f mask = {numext::bit_cast<float>(0x80000000u), 0.0f};
-  return padd(a, pxor(mask, b));
-}
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
-template<> EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  Packet4f mask = {numext::bit_cast<float>(0x80000000u), 0.0f, numext::bit_cast<float>(0x80000000u), 0.0f};
-  return padd(a, pxor(mask, b));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pnegate(const Packet2f& a) { return vneg_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return vnegq_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4c pnegate(const Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vneg_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c pnegate(const Packet8c& a) { return vneg_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet16c pnegate(const Packet16c& a) { return vnegq_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet4s pnegate(const Packet4s& a) { return vneg_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a) { return vnegq_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet2i pnegate(const Packet2i& a) { return vneg_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return vnegq_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet2l pnegate(const Packet2l& a) {
-#if EIGEN_ARCH_ARM64
-  return vnegq_s64(a);
-#else
-  return vcombine_s64(
-      vdup_n_s64(-vgetq_lane_s64(a, 0)),
-      vdup_n_s64(-vgetq_lane_s64(a, 1)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pconj(const Packet2f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4c pconj(const Packet4c& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8c pconj(const Packet8c& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16c pconj(const Packet16c& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4uc pconj(const Packet4uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8uc pconj(const Packet8uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16uc pconj(const Packet16uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4s pconj(const Packet4s& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8s pconj(const Packet8s& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4us pconj(const Packet4us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8us pconj(const Packet8us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2i pconj(const Packet2i& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2ui pconj(const Packet2ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4ui pconj(const Packet4ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2l pconj(const Packet2l& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2ul pconj(const Packet2ul& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmul<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmul_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmulq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c pmul<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmul_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmul<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmul_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmul<Packet16c>(const Packet16c& a, const Packet16c& b) { return vmulq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmul<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmul_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmul<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmul_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vmulq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmul<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmul_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmul<Packet8s>(const Packet8s& a, const Packet8s& b) { return vmulq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmul<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmul_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmul<Packet8us>(const Packet8us& a, const Packet8us& b) { return vmulq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmul<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmul_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmulq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmul<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmul_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmul<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vmulq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pmul<Packet2l>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s64(
-    vdup_n_s64(vgetq_lane_s64(a, 0)*vgetq_lane_s64(b, 0)),
-    vdup_n_s64(vgetq_lane_s64(a, 1)*vgetq_lane_s64(b, 1)));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pmul<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u64(
-    vdup_n_u64(vgetq_lane_u64(a, 0)*vgetq_lane_u64(b, 0)),
-    vdup_n_u64(vgetq_lane_u64(a, 1)*vgetq_lane_u64(b, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pdiv<Packet2f>(const Packet2f& a, const Packet2f& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vdiv_f32(a,b);
-#else
-  Packet2f inv, restep, div;
-
-  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
-  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
-  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
-  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
-  // Newton-Raphson and vrecpsq_f32()
-  inv = vrecpe_f32(b);
-
-  // This returns a differential, by which we will have to multiply inv to get a better
-  // approximation of 1/b.
-  restep = vrecps_f32(b, inv);
-  inv = vmul_f32(restep, inv);
-
-  // Finally, multiply a by 1/b and get the wanted result of the division.
-  div = vmul_f32(a, inv);
-
-  return div;
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vdivq_f32(a,b);
-#else
-  Packet4f inv, restep, div;
-
-  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
-  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
-  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
-  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
-  // Newton-Raphson and vrecpsq_f32()
-  inv = vrecpeq_f32(b);
-
-  // This returns a differential, by which we will have to multiply inv to get a better
-  // approximation of 1/b.
-  restep = vrecpsq_f32(b, inv);
-  inv = vmulq_f32(restep, inv);
-
-  // Finally, multiply a by 1/b and get the wanted result of the division.
-  div = vmulq_f32(a, inv);
-
-  return div;
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4c pdiv<Packet4c>(const Packet4c& /*a*/, const Packet4c& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4c>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pdiv<Packet8c>(const Packet8c& /*a*/, const Packet8c& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8c>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet16c pdiv<Packet16c>(const Packet16c& /*a*/, const Packet16c& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet16c>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4uc pdiv<Packet4uc>(const Packet4uc& /*a*/, const Packet4uc& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4uc>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pdiv<Packet8uc>(const Packet8uc& /*a*/, const Packet8uc& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8uc>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc pdiv<Packet16uc>(const Packet16uc& /*a*/, const Packet16uc& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet16uc>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4s pdiv<Packet4s>(const Packet4s& /*a*/, const Packet4s& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4s>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8s pdiv<Packet8s>(const Packet8s& /*a*/, const Packet8s& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8s>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4us pdiv<Packet4us>(const Packet4us& /*a*/, const Packet4us& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4us>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8us pdiv<Packet8us>(const Packet8us& /*a*/, const Packet8us& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8us>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet2i pdiv<Packet2i>(const Packet2i& /*a*/, const Packet2i& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2i>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4i>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet2ui pdiv<Packet2ui>(const Packet2ui& /*a*/, const Packet2ui& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2ui>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4ui pdiv<Packet4ui>(const Packet4ui& /*a*/, const Packet4ui& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4ui>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet2l pdiv<Packet2l>(const Packet2l& /*a*/, const Packet2l& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2l>(0LL);
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pdiv<Packet2ul>(const Packet2ul& /*a*/, const Packet2ul& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2ul>(0ULL);
-}
-
-
-#ifdef __ARM_FEATURE_FMA
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
-{ return vfmaq_f32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c)
-{ return vfma_f32(c,a,b); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
-{
-  return vmlaq_f32(c,a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c)
-{
-  return vmla_f32(c,a,b);
-}
-#endif
-
-// No FMA instruction for int, so use MLA unconditionally.
-template<> EIGEN_STRONG_INLINE Packet4c pmadd(const Packet4c& a, const Packet4c& b, const Packet4c& c)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmla_s8(
-      vreinterpret_s8_s32(vdup_n_s32(c)),
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmadd(const Packet8c& a, const Packet8c& b, const Packet8c& c)
-{ return vmla_s8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmadd(const Packet16c& a, const Packet16c& b, const Packet16c& c)
-{ return vmlaq_s8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmadd(const Packet4uc& a, const Packet4uc& b, const Packet4uc& c)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmla_u8(
-      vreinterpret_u8_u32(vdup_n_u32(c)),
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmadd(const Packet8uc& a, const Packet8uc& b, const Packet8uc& c)
-{ return vmla_u8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmadd(const Packet16uc& a, const Packet16uc& b, const Packet16uc& c)
-{ return vmlaq_u8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmadd(const Packet4s& a, const Packet4s& b, const Packet4s& c)
-{ return vmla_s16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c)
-{ return vmlaq_s16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmadd(const Packet4us& a, const Packet4us& b, const Packet4us& c)
-{ return vmla_u16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c)
-{ return vmlaq_u16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmadd(const Packet2i& a, const Packet2i& b, const Packet2i& c)
-{ return vmla_s32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c)
-{ return vmlaq_s32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmadd(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c)
-{ return vmla_u32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmadd(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c)
-{ return vmlaq_u32(c,a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pabsdiff<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vabd_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pabsdiff<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vabdq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c pabsdiff<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vabd_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pabsdiff<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vabd_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pabsdiff<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vabdq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pabsdiff<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vabd_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pabsdiff<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vabd_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pabsdiff<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vabdq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pabsdiff<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vabd_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pabsdiff<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vabdq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pabsdiff<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vabd_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pabsdiff<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vabdq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pabsdiff<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vabd_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pabsdiff<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vabdq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pabsdiff<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vabd_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pabsdiff<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vabdq_u32(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmin<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmin_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vminq_f32(a,b); }
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) { return vminnmq_f32(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2f pmin<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) { return vminnm_f32(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) { return pmin<Packet4f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmin<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) { return pmin<Packet2f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4c pmin<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmin_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmin<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmin_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) { return vminq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmin<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmin_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmin<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmin_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vminq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmin<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmin_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) { return vminq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmin<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmin_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) { return vminq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmin<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmin_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vminq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmin<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmin_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmin<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vminq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pmin<Packet2l>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s64(
-      vdup_n_s64((std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
-      vdup_n_s64((std::min)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pmin<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u64(
-      vdup_n_u64((std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
-      vdup_n_u64((std::min)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pmax<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmax_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmaxq_f32(a,b); }
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) { return vmaxnmq_f32(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2f pmax<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) { return vmaxnm_f32(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) { return pmax<Packet4f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmax<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) { return pmax<Packet2f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4c pmax<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmax_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmax<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmax_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) { return vmaxq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmax<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmax_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmax<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmax_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vmaxq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmax<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmax_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) { return vmaxq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmax<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmax_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) { return vmaxq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmax<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmax_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmaxq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmax<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmax_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmax<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vmaxq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pmax<Packet2l>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s64(
-      vdup_n_s64((std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
-      vdup_n_s64((std::max)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pmax<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u64(
-      vdup_n_u64((std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
-      vdup_n_u64((std::max)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_le<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vcle_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vcleq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4c pcmp_le<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_u8(vcle_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pcmp_le<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vreinterpret_s8_u8(vcle_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_le<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vreinterpretq_s8_u8(vcleq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4uc pcmp_le<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vcle_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pcmp_le<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vcle_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_le<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vcleq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pcmp_le<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vreinterpret_s16_u16(vcle_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_le<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vreinterpretq_s16_u16(vcleq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4us pcmp_le<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vcle_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_le<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vcleq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pcmp_le<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vreinterpret_s32_u32(vcle_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_le<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vreinterpretq_s32_u32(vcleq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2ui pcmp_le<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vcle_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pcmp_le<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vcleq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pcmp_le<Packet2l>(const Packet2l& a, const Packet2l& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vreinterpretq_s64_u64(vcleq_s64(a,b));
-#else
-  return vcombine_s64(
-      vdup_n_s64(vgetq_lane_s64(a, 0) <= vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
-      vdup_n_s64(vgetq_lane_s64(a, 1) <= vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pcmp_le<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vcleq_u64(a,b);
-#else
-  return vcombine_u64(
-      vdup_n_u64(vgetq_lane_u64(a, 0) <= vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
-      vdup_n_u64(vgetq_lane_u64(a, 1) <= vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_lt<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vclt_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vcltq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4c pcmp_lt<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_u8(vclt_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pcmp_lt<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vreinterpret_s8_u8(vclt_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_lt<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vreinterpretq_s8_u8(vcltq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4uc pcmp_lt<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vclt_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pcmp_lt<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vclt_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_lt<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vcltq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pcmp_lt<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vreinterpret_s16_u16(vclt_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_lt<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vreinterpretq_s16_u16(vcltq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4us pcmp_lt<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vclt_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_lt<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vcltq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pcmp_lt<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vreinterpret_s32_u32(vclt_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vreinterpretq_s32_u32(vcltq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2ui pcmp_lt<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vclt_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pcmp_lt<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vcltq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pcmp_lt<Packet2l>(const Packet2l& a, const Packet2l& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vreinterpretq_s64_u64(vcltq_s64(a,b));
-#else
-  return vcombine_s64(
-      vdup_n_s64(vgetq_lane_s64(a, 0) < vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
-      vdup_n_s64(vgetq_lane_s64(a, 1) < vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pcmp_lt<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vcltq_u64(a,b);
-#else
-  return vcombine_u64(
-      vdup_n_u64(vgetq_lane_u64(a, 0) < vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
-      vdup_n_u64(vgetq_lane_u64(a, 1) < vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_eq<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vceq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vceqq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4c pcmp_eq<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_u8(vceq_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pcmp_eq<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vreinterpret_s8_u8(vceq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_eq<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vreinterpretq_s8_u8(vceqq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4uc pcmp_eq<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vceq_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pcmp_eq<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vceq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_eq<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vceqq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pcmp_eq<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vreinterpret_s16_u16(vceq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_eq<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vreinterpretq_s16_u16(vceqq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4us pcmp_eq<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vceq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_eq<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vceqq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pcmp_eq<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vreinterpret_s32_u32(vceq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vreinterpretq_s32_u32(vceqq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2ui pcmp_eq<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vceq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pcmp_eq<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vceqq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pcmp_eq<Packet2l>(const Packet2l& a, const Packet2l& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vreinterpretq_s64_u64(vceqq_s64(a,b));
-#else
-  return vcombine_s64(
-      vdup_n_s64(vgetq_lane_s64(a, 0) == vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
-      vdup_n_s64(vgetq_lane_s64(a, 1) == vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pcmp_eq<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vceqq_u64(a,b);
-#else
-  return vcombine_u64(
-      vdup_n_u64(vgetq_lane_u64(a, 0) == vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
-      vdup_n_u64(vgetq_lane_u64(a, 1) == vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_lt_or_nan<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vmvn_u32(vcge_f32(a,b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vmvnq_u32(vcgeq_f32(a,b))); }
-
-// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
-template<> EIGEN_STRONG_INLINE Packet2f pand<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c pand<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a & b; }
-template<> EIGEN_STRONG_INLINE Packet8c pand<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vand_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pand<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vandq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pand<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a & b; }
-template<> EIGEN_STRONG_INLINE Packet8uc pand<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vand_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pand<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vandq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pand<Packet4s>(const Packet4s& a, const Packet4s& b) { return vand_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pand<Packet8s>(const Packet8s& a, const Packet8s& b) { return vandq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pand<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vand_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vandq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pand<Packet2i>(const Packet2i& a, const Packet2i& b) { return vand_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vandq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pand<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vand_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vandq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pand<Packet2l>(const Packet2l& a, const Packet2l& b) { return vandq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul pand<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return vandq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f por<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c por<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a | b; }
-template<> EIGEN_STRONG_INLINE Packet8c por<Packet8c>(const Packet8c& a, const Packet8c& b) { return vorr_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c por<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vorrq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc por<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a | b; }
-template<> EIGEN_STRONG_INLINE Packet8uc por<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vorr_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc por<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vorrq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s por<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vorr_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vorrq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us por<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vorr_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vorrq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i por<Packet2i>(const Packet2i& a, const Packet2i& b) { return vorr_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vorrq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui por<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vorr_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui por<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vorrq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l por<Packet2l>(const Packet2l& a, const Packet2l& b)
-{ return vorrq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul por<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return vorrq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c pxor<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a ^ b; }
-template<> EIGEN_STRONG_INLINE Packet8c pxor<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return veor_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pxor<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return veorq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pxor<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a ^ b; }
-template<> EIGEN_STRONG_INLINE Packet8uc pxor<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return veor_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pxor<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return veorq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pxor<Packet4s>(const Packet4s& a, const Packet4s& b) { return veor_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pxor<Packet8s>(const Packet8s& a, const Packet8s& b) { return veorq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pxor<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return veor_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pxor<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return veorq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pxor<Packet2i>(const Packet2i& a, const Packet2i& b) { return veor_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return veorq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pxor<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return veor_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pxor<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return veorq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pxor<Packet2l>(const Packet2l& a, const Packet2l& b)
-{ return veorq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul pxor<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return veorq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pandnot<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c pandnot<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a & ~b; }
-template<> EIGEN_STRONG_INLINE Packet8c pandnot<Packet8c>(const Packet8c& a, const Packet8c& b) { return vbic_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pandnot<Packet16c>(const Packet16c& a, const Packet16c& b) { return vbicq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pandnot<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a & ~b; }
-template<> EIGEN_STRONG_INLINE Packet8uc pandnot<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vbic_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pandnot<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vbicq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pandnot<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vbic_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pandnot<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vbicq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pandnot<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vbic_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pandnot<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vbicq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pandnot<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vbic_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vbicq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pandnot<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vbic_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pandnot<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vbicq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pandnot<Packet2l>(const Packet2l& a, const Packet2l& b)
-{ return vbicq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul pandnot<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return vbicq_u64(a,b); }
-
-
-template<int N> EIGEN_STRONG_INLINE Packet4c parithmetic_shift_right(Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vshr_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8c parithmetic_shift_right(Packet8c a) { return vshr_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16c parithmetic_shift_right(Packet16c a) { return vshrq_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4uc parithmetic_shift_right(Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vshr_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8uc parithmetic_shift_right(Packet8uc a) { return vshr_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16uc parithmetic_shift_right(Packet16uc a) { return vshrq_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4s parithmetic_shift_right(Packet4s a) { return vshr_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8s parithmetic_shift_right(Packet8s a) { return vshrq_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4us parithmetic_shift_right(Packet4us a) { return vshr_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8us parithmetic_shift_right(Packet8us a) { return vshrq_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2i parithmetic_shift_right(Packet2i a) { return vshr_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(Packet4i a) { return vshrq_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ui parithmetic_shift_right(Packet2ui a) { return vshr_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4ui parithmetic_shift_right(Packet4ui a) { return vshrq_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2l parithmetic_shift_right(Packet2l a) { return vshrq_n_s64(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ul parithmetic_shift_right(Packet2ul a) { return vshrq_n_u64(a,N); }
-
-template<int N> EIGEN_STRONG_INLINE Packet4c plogical_shift_right(Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_u8(vshr_n_u8(vreinterpret_u8_s32(vdup_n_s32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8c plogical_shift_right(Packet8c a)
-{ return vreinterpret_s8_u8(vshr_n_u8(vreinterpret_u8_s8(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet16c plogical_shift_right(Packet16c a)
-{ return vreinterpretq_s8_u8(vshrq_n_u8(vreinterpretq_u8_s8(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet4uc plogical_shift_right(Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_s8(vshr_n_s8(vreinterpret_s8_u32(vdup_n_u32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8uc plogical_shift_right(Packet8uc a) { return vshr_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16uc plogical_shift_right(Packet16uc a) { return vshrq_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4s plogical_shift_right(Packet4s a)
-{ return vreinterpret_s16_u16(vshr_n_u16(vreinterpret_u16_s16(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet8s plogical_shift_right(Packet8s a)
-{ return vreinterpretq_s16_u16(vshrq_n_u16(vreinterpretq_u16_s16(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet4us plogical_shift_right(Packet4us a) { return vshr_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_right(Packet8us a) { return vshrq_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2i plogical_shift_right(Packet2i a)
-{ return vreinterpret_s32_u32(vshr_n_u32(vreinterpret_u32_s32(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right(Packet4i a)
-{ return vreinterpretq_s32_u32(vshrq_n_u32(vreinterpretq_u32_s32(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet2ui plogical_shift_right(Packet2ui a) { return vshr_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(Packet4ui a) { return vshrq_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2l plogical_shift_right(Packet2l a)
-{ return vreinterpretq_s64_u64(vshrq_n_u64(vreinterpretq_u64_s64(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet2ul plogical_shift_right(Packet2ul a) { return vshrq_n_u64(a,N); }
-
-template<int N> EIGEN_STRONG_INLINE Packet4c plogical_shift_left(Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vshl_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8c plogical_shift_left(Packet8c a) { return vshl_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16c plogical_shift_left(Packet16c a) { return vshlq_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4uc plogical_shift_left(Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vshl_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8uc plogical_shift_left(Packet8uc a) { return vshl_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16uc plogical_shift_left(Packet16uc a) { return vshlq_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4s plogical_shift_left(Packet4s a) { return vshl_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8s plogical_shift_left(Packet8s a) { return vshlq_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4us plogical_shift_left(Packet4us a) { return vshl_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_left(Packet8us a) { return vshlq_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2i plogical_shift_left(Packet2i a) { return vshl_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left(Packet4i a) { return vshlq_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ui plogical_shift_left(Packet2ui a) { return vshl_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(Packet4ui a) { return vshlq_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2l plogical_shift_left(Packet2l a) { return vshlq_n_s64(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ul plogical_shift_left(Packet2ul a) { return vshlq_n_u64(a,N); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pload<Packet2f>(const float* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c pload<Packet4c>(const int8_t* from)
-{
-  Packet4c res;
-  memcpy(&res, from, sizeof(Packet4c));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8c pload<Packet8c>(const int8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const int8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet4uc pload<Packet4uc>(const uint8_t* from)
-{
-  Packet4uc res;
-  memcpy(&res, from, sizeof(Packet4uc));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pload<Packet8uc>(const uint8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const uint8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet4s pload<Packet4s>(const int16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const int16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet4us pload<Packet4us>(const uint16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const uint16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet2i pload<Packet2i>(const int32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet2ui pload<Packet2ui>(const uint32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui pload<Packet4ui>(const uint32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet2l pload<Packet2l>(const int64_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul pload<Packet2ul>(const uint64_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2f ploadu<Packet2f>(const float* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c ploadu<Packet4c>(const int8_t* from)
-{
-  Packet4c res;
-  memcpy(&res, from, sizeof(Packet4c));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8c ploadu<Packet8c>(const int8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const int8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet4uc ploadu<Packet4uc>(const uint8_t* from)
-{
-  Packet4uc res;
-  memcpy(&res, from, sizeof(Packet4uc));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8uc ploadu<Packet8uc>(const uint8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const uint8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet4s ploadu<Packet4s>(const int16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const int16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet4us ploadu<Packet4us>(const uint16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const uint16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet2i ploadu<Packet2i>(const int32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet2ui ploadu<Packet2ui>(const uint32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui ploadu<Packet4ui>(const uint32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet2l ploadu<Packet2l>(const int64_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul ploadu<Packet2ul>(const uint64_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2f ploaddup<Packet2f>(const float* from)
-{ return vld1_dup_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
-{ return vcombine_f32(vld1_dup_f32(from), vld1_dup_f32(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet4c ploaddup<Packet4c>(const int8_t* from)
-{
-  const int8x8_t a = vreinterpret_s8_s32(vdup_n_s32(pload<Packet4c>(from)));
-  return vget_lane_s32(vreinterpret_s32_s8(vzip_s8(a,a).val[0]), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c ploaddup<Packet8c>(const int8_t* from)
-{
-  const int8x8_t a = vld1_s8(from);
-  return vzip_s8(a,a).val[0];
-}
-template<> EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const int8_t* from)
-{
-  const int8x8_t a = vld1_s8(from);
-  const int8x8x2_t b = vzip_s8(a,a);
-  return vcombine_s8(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet4uc ploaddup<Packet4uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vreinterpret_u8_u32(vdup_n_u32(pload<Packet4uc>(from)));
-  return vget_lane_u32(vreinterpret_u32_u8(vzip_u8(a,a).val[0]), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc ploaddup<Packet8uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vld1_u8(from);
-  return vzip_u8(a,a).val[0];
-}
-template<> EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vld1_u8(from);
-  const uint8x8x2_t b = vzip_u8(a,a);
-  return vcombine_u8(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet4s ploaddup<Packet4s>(const int16_t* from)
-{
-  return vreinterpret_s16_u32(vzip_u32(vreinterpret_u32_s16(vld1_dup_s16(from)),
-      vreinterpret_u32_s16(vld1_dup_s16(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const int16_t* from)
-{
-  const int16x4_t a = vld1_s16(from);
-  const int16x4x2_t b = vzip_s16(a,a);
-  return vcombine_s16(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet4us ploaddup<Packet4us>(const uint16_t* from)
-{
-  return vreinterpret_u16_u32(vzip_u32(vreinterpret_u32_u16(vld1_dup_u16(from)),
-      vreinterpret_u32_u16(vld1_dup_u16(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const uint16_t* from)
-{
-  const uint16x4_t a = vld1_u16(from);
-  const uint16x4x2_t b = vzip_u16(a,a);
-  return vcombine_u16(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet2i ploaddup<Packet2i>(const int32_t* from)
-{ return vld1_dup_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from)
-{ return vcombine_s32(vld1_dup_s32(from), vld1_dup_s32(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet2ui ploaddup<Packet2ui>(const uint32_t* from)
-{ return vld1_dup_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui ploaddup<Packet4ui>(const uint32_t* from)
-{ return vcombine_u32(vld1_dup_u32(from), vld1_dup_u32(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet2l ploaddup<Packet2l>(const int64_t* from)
-{ return vld1q_dup_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul ploaddup<Packet2ul>(const uint64_t* from)
-{ return vld1q_dup_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet4f ploadquad<Packet4f>(const float* from) { return vld1q_dup_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c ploadquad<Packet4c>(const int8_t* from)
-{ return vget_lane_s32(vreinterpret_s32_s8(vld1_dup_s8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c ploadquad<Packet8c>(const int8_t* from)
-{
-  return vreinterpret_s8_u32(vzip_u32(
-      vreinterpret_u32_s8(vld1_dup_s8(from)),
-      vreinterpret_u32_s8(vld1_dup_s8(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet16c ploadquad<Packet16c>(const int8_t* from)
-{
-  const int8x8_t a = vreinterpret_s8_u32(vzip_u32(
-      vreinterpret_u32_s8(vld1_dup_s8(from)),
-      vreinterpret_u32_s8(vld1_dup_s8(from+1))).val[0]);
-  const int8x8_t b = vreinterpret_s8_u32(vzip_u32(
-      vreinterpret_u32_s8(vld1_dup_s8(from+2)),
-      vreinterpret_u32_s8(vld1_dup_s8(from+3))).val[0]);
-  return vcombine_s8(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet4uc ploadquad<Packet4uc>(const uint8_t* from)
-{ return vget_lane_u32(vreinterpret_u32_u8(vld1_dup_u8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc ploadquad<Packet8uc>(const uint8_t* from)
-{
-  return vreinterpret_u8_u32(vzip_u32(
-      vreinterpret_u32_u8(vld1_dup_u8(from)),
-      vreinterpret_u32_u8(vld1_dup_u8(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc ploadquad<Packet16uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vreinterpret_u8_u32(vzip_u32(
-      vreinterpret_u32_u8(vld1_dup_u8(from)),
-      vreinterpret_u32_u8(vld1_dup_u8(from+1))).val[0]);
-  const uint8x8_t b = vreinterpret_u8_u32(vzip_u32(
-      vreinterpret_u32_u8(vld1_dup_u8(from+2)),
-      vreinterpret_u32_u8(vld1_dup_u8(from+3))).val[0]);
-  return vcombine_u8(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const int16_t* from)
-{ return vcombine_s16(vld1_dup_s16(from), vld1_dup_s16(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const uint16_t* from)
-{ return vcombine_u16(vld1_dup_u16(from), vld1_dup_u16(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet4i ploadquad<Packet4i>(const int32_t* from) { return vld1q_dup_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui ploadquad<Packet4ui>(const uint32_t* from) { return vld1q_dup_u32(from); }
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet2f& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet4c& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet8c& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet16c& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet4uc& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet8uc& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet16uc& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet4s& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet8s& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet4us& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet8us& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet2i& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet2ui& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet4ui& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet2l& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s64(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint64_t>(uint64_t* to, const Packet2ul& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u64(to,from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet2f& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet4c& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet8c& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet16c& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet4uc& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet8uc& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet16uc& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet4s& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet8s& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet4us& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet8us& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet2i& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet2ui& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet4ui& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet2l& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s64(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint64_t>(uint64_t* to, const Packet2ul& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u64(to,from); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pgather<float, Packet2f>(const float* from, Index stride)
-{
-  Packet2f res = vld1_dup_f32(from);
-  res = vld1_lane_f32(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  Packet4f res = vld1q_dup_f32(from);
-  res = vld1q_lane_f32(from + 1*stride, res, 1);
-  res = vld1q_lane_f32(from + 2*stride, res, 2);
-  res = vld1q_lane_f32(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c pgather<int8_t, Packet4c>(const int8_t* from, Index stride)
-{
-  Packet4c res;
-  for (int i = 0; i != 4; i++)
-    reinterpret_cast<int8_t*>(&res)[i] = *(from + i * stride);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pgather<int8_t, Packet8c>(const int8_t* from, Index stride)
-{
-  Packet8c res = vld1_dup_s8(from);
-  res = vld1_lane_s8(from + 1*stride, res, 1);
-  res = vld1_lane_s8(from + 2*stride, res, 2);
-  res = vld1_lane_s8(from + 3*stride, res, 3);
-  res = vld1_lane_s8(from + 4*stride, res, 4);
-  res = vld1_lane_s8(from + 5*stride, res, 5);
-  res = vld1_lane_s8(from + 6*stride, res, 6);
-  res = vld1_lane_s8(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pgather<int8_t, Packet16c>(const int8_t* from, Index stride)
-{
-  Packet16c res = vld1q_dup_s8(from);
-  res = vld1q_lane_s8(from + 1*stride, res, 1);
-  res = vld1q_lane_s8(from + 2*stride, res, 2);
-  res = vld1q_lane_s8(from + 3*stride, res, 3);
-  res = vld1q_lane_s8(from + 4*stride, res, 4);
-  res = vld1q_lane_s8(from + 5*stride, res, 5);
-  res = vld1q_lane_s8(from + 6*stride, res, 6);
-  res = vld1q_lane_s8(from + 7*stride, res, 7);
-  res = vld1q_lane_s8(from + 8*stride, res, 8);
-  res = vld1q_lane_s8(from + 9*stride, res, 9);
-  res = vld1q_lane_s8(from + 10*stride, res, 10);
-  res = vld1q_lane_s8(from + 11*stride, res, 11);
-  res = vld1q_lane_s8(from + 12*stride, res, 12);
-  res = vld1q_lane_s8(from + 13*stride, res, 13);
-  res = vld1q_lane_s8(from + 14*stride, res, 14);
-  res = vld1q_lane_s8(from + 15*stride, res, 15);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc pgather<uint8_t, Packet4uc>(const uint8_t* from, Index stride)
-{
-  Packet4uc res;
-  for (int i = 0; i != 4; i++)
-    reinterpret_cast<uint8_t*>(&res)[i] = *(from + i * stride);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pgather<uint8_t, Packet8uc>(const uint8_t* from, Index stride)
-{
-  Packet8uc res = vld1_dup_u8(from);
-  res = vld1_lane_u8(from + 1*stride, res, 1);
-  res = vld1_lane_u8(from + 2*stride, res, 2);
-  res = vld1_lane_u8(from + 3*stride, res, 3);
-  res = vld1_lane_u8(from + 4*stride, res, 4);
-  res = vld1_lane_u8(from + 5*stride, res, 5);
-  res = vld1_lane_u8(from + 6*stride, res, 6);
-  res = vld1_lane_u8(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pgather<uint8_t, Packet16uc>(const uint8_t* from, Index stride)
-{
-  Packet16uc res = vld1q_dup_u8(from);
-  res = vld1q_lane_u8(from + 1*stride, res, 1);
-  res = vld1q_lane_u8(from + 2*stride, res, 2);
-  res = vld1q_lane_u8(from + 3*stride, res, 3);
-  res = vld1q_lane_u8(from + 4*stride, res, 4);
-  res = vld1q_lane_u8(from + 5*stride, res, 5);
-  res = vld1q_lane_u8(from + 6*stride, res, 6);
-  res = vld1q_lane_u8(from + 7*stride, res, 7);
-  res = vld1q_lane_u8(from + 8*stride, res, 8);
-  res = vld1q_lane_u8(from + 9*stride, res, 9);
-  res = vld1q_lane_u8(from + 10*stride, res, 10);
-  res = vld1q_lane_u8(from + 11*stride, res, 11);
-  res = vld1q_lane_u8(from + 12*stride, res, 12);
-  res = vld1q_lane_u8(from + 13*stride, res, 13);
-  res = vld1q_lane_u8(from + 14*stride, res, 14);
-  res = vld1q_lane_u8(from + 15*stride, res, 15);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pgather<int16_t, Packet4s>(const int16_t* from, Index stride)
-{
-  Packet4s res = vld1_dup_s16(from);
-  res = vld1_lane_s16(from + 1*stride, res, 1);
-  res = vld1_lane_s16(from + 2*stride, res, 2);
-  res = vld1_lane_s16(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pgather<int16_t, Packet8s>(const int16_t* from, Index stride)
-{
-  Packet8s res = vld1q_dup_s16(from);
-  res = vld1q_lane_s16(from + 1*stride, res, 1);
-  res = vld1q_lane_s16(from + 2*stride, res, 2);
-  res = vld1q_lane_s16(from + 3*stride, res, 3);
-  res = vld1q_lane_s16(from + 4*stride, res, 4);
-  res = vld1q_lane_s16(from + 5*stride, res, 5);
-  res = vld1q_lane_s16(from + 6*stride, res, 6);
-  res = vld1q_lane_s16(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pgather<uint16_t, Packet4us>(const uint16_t* from, Index stride)
-{
-  Packet4us res = vld1_dup_u16(from);
-  res = vld1_lane_u16(from + 1*stride, res, 1);
-  res = vld1_lane_u16(from + 2*stride, res, 2);
-  res = vld1_lane_u16(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pgather<uint16_t, Packet8us>(const uint16_t* from, Index stride)
-{
-  Packet8us res = vld1q_dup_u16(from);
-  res = vld1q_lane_u16(from + 1*stride, res, 1);
-  res = vld1q_lane_u16(from + 2*stride, res, 2);
-  res = vld1q_lane_u16(from + 3*stride, res, 3);
-  res = vld1q_lane_u16(from + 4*stride, res, 4);
-  res = vld1q_lane_u16(from + 5*stride, res, 5);
-  res = vld1q_lane_u16(from + 6*stride, res, 6);
-  res = vld1q_lane_u16(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pgather<int32_t, Packet2i>(const int32_t* from, Index stride)
-{
-  Packet2i res = vld1_dup_s32(from);
-  res = vld1_lane_s32(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride)
-{
-  Packet4i res = vld1q_dup_s32(from);
-  res = vld1q_lane_s32(from + 1*stride, res, 1);
-  res = vld1q_lane_s32(from + 2*stride, res, 2);
-  res = vld1q_lane_s32(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pgather<uint32_t, Packet2ui>(const uint32_t* from, Index stride)
-{
-  Packet2ui res = vld1_dup_u32(from);
-  res = vld1_lane_u32(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pgather<uint32_t, Packet4ui>(const uint32_t* from, Index stride)
-{
-  Packet4ui res = vld1q_dup_u32(from);
-  res = vld1q_lane_u32(from + 1*stride, res, 1);
-  res = vld1q_lane_u32(from + 2*stride, res, 2);
-  res = vld1q_lane_u32(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pgather<int64_t, Packet2l>(const int64_t* from, Index stride)
-{
-  Packet2l res = vld1q_dup_s64(from);
-  res = vld1q_lane_s64(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pgather<uint64_t, Packet2ul>(const uint64_t* from, Index stride)
-{
-  Packet2ul res = vld1q_dup_u64(from);
-  res = vld1q_lane_u64(from + 1*stride, res, 1);
-  return res;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet2f>(float* to, const Packet2f& from, Index stride)
-{
-  vst1_lane_f32(to + stride*0, from, 0);
-  vst1_lane_f32(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  vst1q_lane_f32(to + stride*0, from, 0);
-  vst1q_lane_f32(to + stride*1, from, 1);
-  vst1q_lane_f32(to + stride*2, from, 2);
-  vst1q_lane_f32(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet4c>(int8_t* to, const Packet4c& from, Index stride)
-{
-  for (int i = 0; i != 4; i++)
-    *(to + i * stride) = reinterpret_cast<const int8_t*>(&from)[i];
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet8c>(int8_t* to, const Packet8c& from, Index stride)
-{
-  vst1_lane_s8(to + stride*0, from, 0);
-  vst1_lane_s8(to + stride*1, from, 1);
-  vst1_lane_s8(to + stride*2, from, 2);
-  vst1_lane_s8(to + stride*3, from, 3);
-  vst1_lane_s8(to + stride*4, from, 4);
-  vst1_lane_s8(to + stride*5, from, 5);
-  vst1_lane_s8(to + stride*6, from, 6);
-  vst1_lane_s8(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet16c>(int8_t* to, const Packet16c& from, Index stride)
-{
-  vst1q_lane_s8(to + stride*0, from, 0);
-  vst1q_lane_s8(to + stride*1, from, 1);
-  vst1q_lane_s8(to + stride*2, from, 2);
-  vst1q_lane_s8(to + stride*3, from, 3);
-  vst1q_lane_s8(to + stride*4, from, 4);
-  vst1q_lane_s8(to + stride*5, from, 5);
-  vst1q_lane_s8(to + stride*6, from, 6);
-  vst1q_lane_s8(to + stride*7, from, 7);
-  vst1q_lane_s8(to + stride*8, from, 8);
-  vst1q_lane_s8(to + stride*9, from, 9);
-  vst1q_lane_s8(to + stride*10, from, 10);
-  vst1q_lane_s8(to + stride*11, from, 11);
-  vst1q_lane_s8(to + stride*12, from, 12);
-  vst1q_lane_s8(to + stride*13, from, 13);
-  vst1q_lane_s8(to + stride*14, from, 14);
-  vst1q_lane_s8(to + stride*15, from, 15);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet4uc>(uint8_t* to, const Packet4uc& from, Index stride)
-{
-  for (int i = 0; i != 4; i++)
-    *(to + i * stride) = reinterpret_cast<const uint8_t*>(&from)[i];
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet8uc>(uint8_t* to, const Packet8uc& from, Index stride)
-{
-  vst1_lane_u8(to + stride*0, from, 0);
-  vst1_lane_u8(to + stride*1, from, 1);
-  vst1_lane_u8(to + stride*2, from, 2);
-  vst1_lane_u8(to + stride*3, from, 3);
-  vst1_lane_u8(to + stride*4, from, 4);
-  vst1_lane_u8(to + stride*5, from, 5);
-  vst1_lane_u8(to + stride*6, from, 6);
-  vst1_lane_u8(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet16uc>(uint8_t* to, const Packet16uc& from, Index stride)
-{
-  vst1q_lane_u8(to + stride*0, from, 0);
-  vst1q_lane_u8(to + stride*1, from, 1);
-  vst1q_lane_u8(to + stride*2, from, 2);
-  vst1q_lane_u8(to + stride*3, from, 3);
-  vst1q_lane_u8(to + stride*4, from, 4);
-  vst1q_lane_u8(to + stride*5, from, 5);
-  vst1q_lane_u8(to + stride*6, from, 6);
-  vst1q_lane_u8(to + stride*7, from, 7);
-  vst1q_lane_u8(to + stride*8, from, 8);
-  vst1q_lane_u8(to + stride*9, from, 9);
-  vst1q_lane_u8(to + stride*10, from, 10);
-  vst1q_lane_u8(to + stride*11, from, 11);
-  vst1q_lane_u8(to + stride*12, from, 12);
-  vst1q_lane_u8(to + stride*13, from, 13);
-  vst1q_lane_u8(to + stride*14, from, 14);
-  vst1q_lane_u8(to + stride*15, from, 15);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet4s>(int16_t* to, const Packet4s& from, Index stride)
-{
-  vst1_lane_s16(to + stride*0, from, 0);
-  vst1_lane_s16(to + stride*1, from, 1);
-  vst1_lane_s16(to + stride*2, from, 2);
-  vst1_lane_s16(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet8s>(int16_t* to, const Packet8s& from, Index stride)
-{
-  vst1q_lane_s16(to + stride*0, from, 0);
-  vst1q_lane_s16(to + stride*1, from, 1);
-  vst1q_lane_s16(to + stride*2, from, 2);
-  vst1q_lane_s16(to + stride*3, from, 3);
-  vst1q_lane_s16(to + stride*4, from, 4);
-  vst1q_lane_s16(to + stride*5, from, 5);
-  vst1q_lane_s16(to + stride*6, from, 6);
-  vst1q_lane_s16(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet4us>(uint16_t* to, const Packet4us& from, Index stride)
-{
-  vst1_lane_u16(to + stride*0, from, 0);
-  vst1_lane_u16(to + stride*1, from, 1);
-  vst1_lane_u16(to + stride*2, from, 2);
-  vst1_lane_u16(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet8us>(uint16_t* to, const Packet8us& from, Index stride)
-{
-  vst1q_lane_u16(to + stride*0, from, 0);
-  vst1q_lane_u16(to + stride*1, from, 1);
-  vst1q_lane_u16(to + stride*2, from, 2);
-  vst1q_lane_u16(to + stride*3, from, 3);
-  vst1q_lane_u16(to + stride*4, from, 4);
-  vst1q_lane_u16(to + stride*5, from, 5);
-  vst1q_lane_u16(to + stride*6, from, 6);
-  vst1q_lane_u16(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet2i>(int32_t* to, const Packet2i& from, Index stride)
-{
-  vst1_lane_s32(to + stride*0, from, 0);
-  vst1_lane_s32(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from, Index stride)
-{
-  vst1q_lane_s32(to + stride*0, from, 0);
-  vst1q_lane_s32(to + stride*1, from, 1);
-  vst1q_lane_s32(to + stride*2, from, 2);
-  vst1q_lane_s32(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet2ui>(uint32_t* to, const Packet2ui& from, Index stride)
-{
-  vst1_lane_u32(to + stride*0, from, 0);
-  vst1_lane_u32(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from, Index stride)
-{
-  vst1q_lane_u32(to + stride*0, from, 0);
-  vst1q_lane_u32(to + stride*1, from, 1);
-  vst1q_lane_u32(to + stride*2, from, 2);
-  vst1q_lane_u32(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int64_t, Packet2l>(int64_t* to, const Packet2l& from, Index stride)
-{
-  vst1q_lane_s64(to + stride*0, from, 0);
-  vst1q_lane_s64(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint64_t, Packet2ul>(uint64_t* to, const Packet2ul& from, Index stride)
-{
-  vst1q_lane_u64(to + stride*0, from, 0);
-  vst1q_lane_u64(to + stride*1, from, 1);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int8_t>(const int8_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint8_t>(const uint8_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int16_t>(const int16_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint16_t>(const uint16_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int64_t>(const int64_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint64_t>(const uint64_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE float pfirst<Packet2f>(const Packet2f& a) { return vget_lane_f32(a,0); }
-template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { return vgetq_lane_f32(a,0); }
-template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet4c>(const Packet4c& a) { return static_cast<int8_t>(a & 0xff); }
-template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet8c>(const Packet8c& a) { return vget_lane_s8(a,0); }
-template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet16c>(const Packet16c& a) { return vgetq_lane_s8(a,0); }
-template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet4uc>(const Packet4uc& a) { return static_cast<uint8_t>(a & 0xff); }
-template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet8uc>(const Packet8uc& a) { return vget_lane_u8(a,0); }
-template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet16uc>(const Packet16uc& a) { return vgetq_lane_u8(a,0); }
-template<> EIGEN_STRONG_INLINE int16_t pfirst<Packet4s>(const Packet4s& a) { return vget_lane_s16(a,0); }
-template<> EIGEN_STRONG_INLINE int16_t pfirst<Packet8s>(const Packet8s& a) { return vgetq_lane_s16(a,0); }
-template<> EIGEN_STRONG_INLINE uint16_t pfirst<Packet4us>(const Packet4us& a) { return vget_lane_u16(a,0); }
-template<> EIGEN_STRONG_INLINE uint16_t pfirst<Packet8us>(const Packet8us& a) { return vgetq_lane_u16(a,0); }
-template<> EIGEN_STRONG_INLINE int32_t pfirst<Packet2i>(const Packet2i& a) { return vget_lane_s32(a,0); }
-template<> EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) { return vgetq_lane_s32(a,0); }
-template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet2ui>(const Packet2ui& a) { return vget_lane_u32(a,0); }
-template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) { return vgetq_lane_u32(a,0); }
-template<> EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) { return vgetq_lane_s64(a,0); }
-template<> EIGEN_STRONG_INLINE uint64_t pfirst<Packet2ul>(const Packet2ul& a) { return vgetq_lane_u64(a,0); }
-
-template<> EIGEN_STRONG_INLINE Packet2f preverse(const Packet2f& a) { return vrev64_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  const float32x4_t a_r64 = vrev64q_f32(a);
-  return vcombine_f32(vget_high_f32(a_r64), vget_low_f32(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4c preverse(const Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vrev64_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c preverse(const Packet8c& a) { return vrev64_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a)
-{
-  const int8x16_t a_r64 = vrev64q_s8(a);
-  return vcombine_s8(vget_high_s8(a_r64), vget_low_s8(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4uc preverse(const Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vrev64_u8(vreinterpret_u8_u32(vdup_n_u32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc preverse(const Packet8uc& a) { return vrev64_u8(a); }
-template<> EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a)
-{
-  const uint8x16_t a_r64 = vrev64q_u8(a);
-  return vcombine_u8(vget_high_u8(a_r64), vget_low_u8(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4s preverse(const Packet4s& a) { return vrev64_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a)
-{
-  const int16x8_t a_r64 = vrev64q_s16(a);
-  return vcombine_s16(vget_high_s16(a_r64), vget_low_s16(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4us preverse(const Packet4us& a) { return vrev64_u16(a); }
-template<> EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a)
-{
-  const uint16x8_t a_r64 = vrev64q_u16(a);
-  return vcombine_u16(vget_high_u16(a_r64), vget_low_u16(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet2i preverse(const Packet2i& a) { return vrev64_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
-{
-  const int32x4_t a_r64 = vrev64q_s32(a);
-  return vcombine_s32(vget_high_s32(a_r64), vget_low_s32(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet2ui preverse(const Packet2ui& a) { return vrev64_u32(a); }
-template<> EIGEN_STRONG_INLINE Packet4ui preverse(const Packet4ui& a)
-{
-  const uint32x4_t a_r64 = vrev64q_u32(a);
-  return vcombine_u32(vget_high_u32(a_r64), vget_low_u32(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet2l preverse(const Packet2l& a)
-{ return vcombine_s64(vget_high_s64(a), vget_low_s64(a)); }
-template<> EIGEN_STRONG_INLINE Packet2ul preverse(const Packet2ul& a)
-{ return vcombine_u64(vget_high_u64(a), vget_low_u64(a)); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pabs(const Packet2f& a) { return vabs_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vabsq_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4c pabs<Packet4c>(const Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vabs_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c pabs(const Packet8c& a) { return vabs_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) { return vabsq_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet4uc pabs(const Packet4uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8uc pabs(const Packet8uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4s pabs(const Packet4s& a) { return vabs_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) { return vabsq_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet4us pabs(const Packet4us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2i pabs(const Packet2i& a) { return vabs_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vabsq_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet2ui pabs(const Packet2ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4ui pabs(const Packet4ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2l pabs(const Packet2l& a) {
-#if EIGEN_ARCH_ARM64
-  return vabsq_s64(a);
-#else
-  return vcombine_s64(
-      vdup_n_s64((std::abs)(vgetq_lane_s64(a, 0))),
-      vdup_n_s64((std::abs)(vgetq_lane_s64(a, 1))));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pabs(const Packet2ul& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2f pfrexp<Packet2f>(const Packet2f& a, Packet2f& exponent)
-{ return pfrexp_generic(a,exponent); }
-template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent)
-{ return pfrexp_generic(a,exponent); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pldexp<Packet2f>(const Packet2f& a, const Packet2f& exponent)
-{ return pldexp_generic(a,exponent); }
-template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent)
-{ return pldexp_generic(a,exponent); }
-
-template<> EIGEN_STRONG_INLINE float predux<Packet2f>(const Packet2f& a) { return vget_lane_f32(vpadd_f32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  const float32x2_t sum = vadd_f32(vget_low_f32(a), vget_high_f32(a));
-  return vget_lane_f32(vpadd_f32(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux<Packet4c>(const Packet4c& a)
-{
-  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
-  int8x8_t sum = vpadd_s8(a_dup, a_dup);
-  sum = vpadd_s8(sum, sum);
-  return vget_lane_s8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux<Packet8c>(const Packet8c& a)
-{
-  int8x8_t sum = vpadd_s8(a,a);
-  sum = vpadd_s8(sum, sum);
-  sum = vpadd_s8(sum, sum);
-  return vget_lane_s8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux<Packet16c>(const Packet16c& a)
-{
-  int8x8_t sum = vadd_s8(vget_low_s8(a), vget_high_s8(a));
-  sum = vpadd_s8(sum, sum);
-  sum = vpadd_s8(sum, sum);
-  sum = vpadd_s8(sum, sum);
-  return vget_lane_s8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux<Packet4uc>(const Packet4uc& a)
-{
-  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t sum = vpadd_u8(a_dup, a_dup);
-  sum = vpadd_u8(sum, sum);
-  return vget_lane_u8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t sum = vpadd_u8(a,a);
-  sum = vpadd_u8(sum, sum);
-  sum = vpadd_u8(sum, sum);
-  return vget_lane_u8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux<Packet16uc>(const Packet16uc& a)
-{
-  uint8x8_t sum = vadd_u8(vget_low_u8(a), vget_high_u8(a));
-  sum = vpadd_u8(sum, sum);
-  sum = vpadd_u8(sum, sum);
-  sum = vpadd_u8(sum, sum);
-  return vget_lane_u8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t sum = vpadd_s16(a,a);
-  return vget_lane_s16(vpadd_s16(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux<Packet8s>(const Packet8s& a)
-{
-  int16x4_t sum = vadd_s16(vget_low_s16(a), vget_high_s16(a));
-  sum = vpadd_s16(sum, sum);
-  sum = vpadd_s16(sum, sum);
-  return vget_lane_s16(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t sum = vpadd_u16(a,a);
-  return vget_lane_u16(vpadd_u16(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t sum = vadd_u16(vget_low_u16(a), vget_high_u16(a));
-  sum = vpadd_u16(sum, sum);
-  sum = vpadd_u16(sum, sum);
-  return vget_lane_u16(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux<Packet2i>(const Packet2i& a) { return vget_lane_s32(vpadd_s32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a)
-{
-  const int32x2_t sum = vadd_s32(vget_low_s32(a), vget_high_s32(a));
-  return vget_lane_s32(vpadd_s32(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE uint32_t predux<Packet2ui>(const Packet2ui& a) { return vget_lane_u32(vpadd_u32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a)
-{
-  const uint32x2_t sum = vadd_u32(vget_low_u32(a), vget_high_u32(a));
-  return vget_lane_u32(vpadd_u32(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE int64_t predux<Packet2l>(const Packet2l& a)
-{ return vgetq_lane_s64(a, 0) + vgetq_lane_s64(a, 1); }
-template<> EIGEN_STRONG_INLINE uint64_t predux<Packet2ul>(const Packet2ul& a)
-{ return vgetq_lane_u64(a, 0) + vgetq_lane_u64(a, 1); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c predux_half_dowto4(const Packet8c& a)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(a,
-      vreinterpret_s8_s32(vrev64_s32(vreinterpret_s32_s8(a))))), 0);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c predux_half_dowto4(const Packet16c& a)
-{ return vadd_s8(vget_high_s8(a), vget_low_s8(a)); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc predux_half_dowto4(const Packet8uc& a)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(a,
-      vreinterpret_u8_u32(vrev64_u32(vreinterpret_u32_u8(a))))), 0);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc predux_half_dowto4(const Packet16uc& a)
-{ return vadd_u8(vget_high_u8(a), vget_low_u8(a)); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s predux_half_dowto4(const Packet8s& a)
-{ return vadd_s16(vget_high_s16(a), vget_low_s16(a)); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us predux_half_dowto4(const Packet8us& a)
-{ return vadd_u16(vget_high_u16(a), vget_low_u16(a)); }
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet2f>(const Packet2f& a)
-{ return vget_lane_f32(a, 0) * vget_lane_f32(a, 1); }
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{ return predux_mul(vmul_f32(vget_low_f32(a), vget_high_f32(a))); }
-template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet4c>(const Packet4c& a)
-{
-  int8x8_t prod = vreinterpret_s8_s32(vdup_n_s32(a));
-  prod = vmul_s8(prod, vrev16_s8(prod));
-  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet8c>(const Packet8c& a)
-{
-  int8x8_t prod = vmul_s8(a, vrev16_s8(a));
-  prod = vmul_s8(prod, vrev32_s8(prod));
-  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 4);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet16c>(const Packet16c& a)
-{ return predux_mul(vmul_s8(vget_low_s8(a), vget_high_s8(a))); }
-template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet4uc>(const Packet4uc& a)
-{
-  uint8x8_t prod = vreinterpret_u8_u32(vdup_n_u32(a));
-  prod = vmul_u8(prod, vrev16_u8(prod));
-  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t prod = vmul_u8(a, vrev16_u8(a));
-  prod = vmul_u8(prod, vrev32_u8(prod));
-  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 4);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet16uc>(const Packet16uc& a)
-{ return predux_mul(vmul_u8(vget_low_u8(a), vget_high_u8(a))); }
-template<> EIGEN_STRONG_INLINE int16_t predux_mul<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t prod = vmul_s16(a, vrev32_s16(a));
-  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_mul<Packet8s>(const Packet8s& a)
-{
-  int16x4_t prod;
-
-  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
-  prod = vmul_s16(vget_low_s16(a), vget_high_s16(a));
-  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
-  prod = vmul_s16(prod, vrev32_s16(prod));
-  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
-  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_mul<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t prod = vmul_u16(a, vrev32_u16(a));
-  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_mul<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t prod;
-
-  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
-  prod = vmul_u16(vget_low_u16(a), vget_high_u16(a));
-  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
-  prod = vmul_u16(prod, vrev32_u16(prod));
-  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
-  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet2i>(const Packet2i& a)
-{ return vget_lane_s32(a, 0) * vget_lane_s32(a, 1); }
-template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a)
-{ return predux_mul(vmul_s32(vget_low_s32(a), vget_high_s32(a))); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet2ui>(const Packet2ui& a)
-{ return vget_lane_u32(a, 0) * vget_lane_u32(a, 1); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet4ui>(const Packet4ui& a)
-{ return predux_mul(vmul_u32(vget_low_u32(a), vget_high_u32(a))); }
-template<> EIGEN_STRONG_INLINE int64_t predux_mul<Packet2l>(const Packet2l& a)
-{ return vgetq_lane_s64(a, 0) * vgetq_lane_s64(a, 1); }
-template<> EIGEN_STRONG_INLINE uint64_t predux_mul<Packet2ul>(const Packet2ul& a)
-{ return vgetq_lane_u64(a, 0) * vgetq_lane_u64(a, 1); }
-
-// min
-template<> EIGEN_STRONG_INLINE float predux_min<Packet2f>(const Packet2f& a)
-{ return vget_lane_f32(vpmin_f32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  const float32x2_t min = vmin_f32(vget_low_f32(a), vget_high_f32(a));
-  return vget_lane_f32(vpmin_f32(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet4c>(const Packet4c& a)
-{
-  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
-  int8x8_t min = vpmin_s8(a_dup, a_dup);
-  min = vpmin_s8(min, min);
-  return vget_lane_s8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet8c>(const Packet8c& a)
-{
-  int8x8_t min = vpmin_s8(a,a);
-  min = vpmin_s8(min, min);
-  min = vpmin_s8(min, min);
-  return vget_lane_s8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet16c>(const Packet16c& a)
-{
-  int8x8_t min = vmin_s8(vget_low_s8(a), vget_high_s8(a));
-  min = vpmin_s8(min, min);
-  min = vpmin_s8(min, min);
-  min = vpmin_s8(min, min);
-  return vget_lane_s8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet4uc>(const Packet4uc& a)
-{
-  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t min = vpmin_u8(a_dup, a_dup);
-  min = vpmin_u8(min, min);
-  return vget_lane_u8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t min = vpmin_u8(a,a);
-  min = vpmin_u8(min, min);
-  min = vpmin_u8(min, min);
-  return vget_lane_u8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet16uc>(const Packet16uc& a)
-{
-  uint8x8_t min = vmin_u8(vget_low_u8(a), vget_high_u8(a));
-  min = vpmin_u8(min, min);
-  min = vpmin_u8(min, min);
-  min = vpmin_u8(min, min);
-  return vget_lane_u8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t min = vpmin_s16(a,a);
-  return vget_lane_s16(vpmin_s16(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet8s>(const Packet8s& a)
-{
-  int16x4_t min = vmin_s16(vget_low_s16(a), vget_high_s16(a));
-  min = vpmin_s16(min, min);
-  min = vpmin_s16(min, min);
-  return vget_lane_s16(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t min = vpmin_u16(a,a);
-  return vget_lane_u16(vpmin_u16(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t min = vmin_u16(vget_low_u16(a), vget_high_u16(a));
-  min = vpmin_u16(min, min);
-  min = vpmin_u16(min, min);
-  return vget_lane_u16(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet2i>(const Packet2i& a)
-{ return vget_lane_s32(vpmin_s32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a)
-{
-  const int32x2_t min = vmin_s32(vget_low_s32(a), vget_high_s32(a));
-  return vget_lane_s32(vpmin_s32(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet2ui>(const Packet2ui& a)
-{ return vget_lane_u32(vpmin_u32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a)
-{
-  const uint32x2_t min = vmin_u32(vget_low_u32(a), vget_high_u32(a));
-  return vget_lane_u32(vpmin_u32(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE int64_t predux_min<Packet2l>(const Packet2l& a)
-{ return (std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1)); }
-template<> EIGEN_STRONG_INLINE uint64_t predux_min<Packet2ul>(const Packet2ul& a)
-{ return (std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1)); }
-
-// max
-template<> EIGEN_STRONG_INLINE float predux_max<Packet2f>(const Packet2f& a)
-{ return vget_lane_f32(vpmax_f32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  const float32x2_t max = vmax_f32(vget_low_f32(a), vget_high_f32(a));
-  return vget_lane_f32(vpmax_f32(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet4c>(const Packet4c& a)
-{
-  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
-  int8x8_t max = vpmax_s8(a_dup, a_dup);
-  max = vpmax_s8(max, max);
-  return vget_lane_s8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet8c>(const Packet8c& a)
-{
-  int8x8_t max = vpmax_s8(a,a);
-  max = vpmax_s8(max, max);
-  max = vpmax_s8(max, max);
-  return vget_lane_s8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet16c>(const Packet16c& a)
-{
-  int8x8_t max = vmax_s8(vget_low_s8(a), vget_high_s8(a));
-  max = vpmax_s8(max, max);
-  max = vpmax_s8(max, max);
-  max = vpmax_s8(max, max);
-  return vget_lane_s8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet4uc>(const Packet4uc& a)
-{
-  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t max = vpmax_u8(a_dup, a_dup);
-  max = vpmax_u8(max, max);
-  return vget_lane_u8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t max = vpmax_u8(a,a);
-  max = vpmax_u8(max, max);
-  max = vpmax_u8(max, max);
-  return vget_lane_u8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet16uc>(const Packet16uc& a)
-{
-  uint8x8_t max = vmax_u8(vget_low_u8(a), vget_high_u8(a));
-  max = vpmax_u8(max, max);
-  max = vpmax_u8(max, max);
-  max = vpmax_u8(max, max);
-  return vget_lane_u8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t max = vpmax_s16(a,a);
-  return vget_lane_s16(vpmax_s16(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet8s>(const Packet8s& a)
-{
-  int16x4_t max = vmax_s16(vget_low_s16(a), vget_high_s16(a));
-  max = vpmax_s16(max, max);
-  max = vpmax_s16(max, max);
-  return vget_lane_s16(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t max = vpmax_u16(a,a);
-  return vget_lane_u16(vpmax_u16(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t max = vmax_u16(vget_low_u16(a), vget_high_u16(a));
-  max = vpmax_u16(max, max);
-  max = vpmax_u16(max, max);
-  return vget_lane_u16(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet2i>(const Packet2i& a)
-{ return vget_lane_s32(vpmax_s32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a)
-{
-  const int32x2_t max = vmax_s32(vget_low_s32(a), vget_high_s32(a));
-  return vget_lane_s32(vpmax_s32(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet2ui>(const Packet2ui& a)
-{ return vget_lane_u32(vpmax_u32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a)
-{
-  const uint32x2_t max = vmax_u32(vget_low_u32(a), vget_high_u32(a));
-  return vget_lane_u32(vpmax_u32(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE int64_t predux_max<Packet2l>(const Packet2l& a)
-{ return (std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1)); }
-template<> EIGEN_STRONG_INLINE uint64_t predux_max<Packet2ul>(const Packet2ul& a)
-{ return (std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1)); }
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
-{
-  uint32x2_t tmp = vorr_u32(vget_low_u32( vreinterpretq_u32_f32(x)),
-                            vget_high_u32(vreinterpretq_u32_f32(x)));
-  return vget_lane_u32(vpmax_u32(tmp, tmp), 0);
-}
-
-// Helpers for ptranspose.
-namespace detail {
-  
-template<typename Packet>
-void zip_in_place(Packet& p1, Packet& p2);
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet2f>(Packet2f& p1, Packet2f& p2) {
-  const float32x2x2_t tmp = vzip_f32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4f>(Packet4f& p1, Packet4f& p2) {
-  const float32x4x2_t tmp = vzipq_f32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8c>(Packet8c& p1, Packet8c& p2) {
-  const int8x8x2_t tmp = vzip_s8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet16c>(Packet16c& p1, Packet16c& p2) {
-  const int8x16x2_t tmp = vzipq_s8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8uc>(Packet8uc& p1, Packet8uc& p2) {
-  const uint8x8x2_t tmp = vzip_u8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet16uc>(Packet16uc& p1, Packet16uc& p2) {
-  const uint8x16x2_t tmp = vzipq_u8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet2i>(Packet2i& p1, Packet2i& p2) {
-  const int32x2x2_t tmp = vzip_s32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4i>(Packet4i& p1, Packet4i& p2) {
-  const int32x4x2_t tmp = vzipq_s32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet2ui>(Packet2ui& p1, Packet2ui& p2) {
-  const uint32x2x2_t tmp = vzip_u32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4ui>(Packet4ui& p1, Packet4ui& p2) {
-  const uint32x4x2_t tmp = vzipq_u32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4s>(Packet4s& p1, Packet4s& p2) {
-  const int16x4x2_t tmp = vzip_s16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8s>(Packet8s& p1, Packet8s& p2) {
-  const int16x8x2_t tmp = vzipq_s16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4us>(Packet4us& p1, Packet4us& p2) {
-  const uint16x4x2_t tmp = vzip_u16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8us>(Packet8us& p1, Packet8us& p2) {
-  const uint16x8x2_t tmp = vzipq_u16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 2>& kernel) {
-  zip_in_place(kernel.packet[0], kernel.packet[1]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 4>& kernel) {
-  zip_in_place(kernel.packet[0], kernel.packet[2]);
-  zip_in_place(kernel.packet[1], kernel.packet[3]);
-  zip_in_place(kernel.packet[0], kernel.packet[1]);
-  zip_in_place(kernel.packet[2], kernel.packet[3]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 8>& kernel) {
-  zip_in_place(kernel.packet[0], kernel.packet[4]);
-  zip_in_place(kernel.packet[1], kernel.packet[5]);
-  zip_in_place(kernel.packet[2], kernel.packet[6]);
-  zip_in_place(kernel.packet[3], kernel.packet[7]);
-
-  zip_in_place(kernel.packet[0], kernel.packet[2]);
-  zip_in_place(kernel.packet[1], kernel.packet[3]);
-  zip_in_place(kernel.packet[4], kernel.packet[6]);
-  zip_in_place(kernel.packet[5], kernel.packet[7]);
-  
-  zip_in_place(kernel.packet[0], kernel.packet[1]);
-  zip_in_place(kernel.packet[2], kernel.packet[3]);
-  zip_in_place(kernel.packet[4], kernel.packet[5]);
-  zip_in_place(kernel.packet[6], kernel.packet[7]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 16>& kernel) {
-  EIGEN_UNROLL_LOOP
-  for (int i=0; i<4; ++i) {
-    const int m = (1 << i);
-    EIGEN_UNROLL_LOOP
-    for (int j=0; j<m; ++j) {
-      const int n = (1 << (3-i));
-      EIGEN_UNROLL_LOOP
-      for (int k=0; k<n; ++k) {
-        const int idx = 2*j*n+k;
-        zip_in_place(kernel.packet[idx], kernel.packet[idx + n]);
-      }
-    }
-  }
-}
-
-} // namespace detail
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2f, 2>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4c, 4>& kernel)
-{
-  const int8x8_t a = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[2], vdup_n_s32(kernel.packet[0]), 1));
-  const int8x8_t b = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[3], vdup_n_s32(kernel.packet[1]), 1));
-
-  const int8x8x2_t zip8 = vzip_s8(a,b);
-  const int16x4x2_t zip16 = vzip_s16(vreinterpret_s16_s8(zip8.val[0]), vreinterpret_s16_s8(zip8.val[1]));
-
-  kernel.packet[0] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 0);
-  kernel.packet[1] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 1);
-  kernel.packet[2] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 0);
-  kernel.packet[3] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 1);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 16>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4uc, 4>& kernel)
-{
-  const uint8x8_t a = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[2], vdup_n_u32(kernel.packet[0]), 1));
-  const uint8x8_t b = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[3], vdup_n_u32(kernel.packet[1]), 1));
-
-  const uint8x8x2_t zip8 = vzip_u8(a,b);
-  const uint16x4x2_t zip16 = vzip_u16(vreinterpret_u16_u8(zip8.val[0]), vreinterpret_u16_u8(zip8.val[1]));
-
-  kernel.packet[0] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 0);
-  kernel.packet[1] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 1);
-  kernel.packet[2] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 0);
-  kernel.packet[3] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 1);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 16>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4s, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4us, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2i, 2>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
-    detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2ui, 2>& kernel) {
-  detail::zip_in_place(kernel.packet[0], kernel.packet[1]);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4ui, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet2l, 2>& kernel)
-{
-#if EIGEN_ARCH_ARM64
-  const int64x2_t tmp1 = vzip1q_s64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[1] = vzip2q_s64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[0] = tmp1;
-#else
-  const int64x1_t tmp[2][2] = {
-    { vget_low_s64(kernel.packet[0]), vget_high_s64(kernel.packet[0]) },
-    { vget_low_s64(kernel.packet[1]), vget_high_s64(kernel.packet[1]) }
-  };
-
-  kernel.packet[0] = vcombine_s64(tmp[0][0], tmp[1][0]);
-  kernel.packet[1] = vcombine_s64(tmp[0][1], tmp[1][1]);
-#endif
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet2ul, 2>& kernel)
-{
-#if EIGEN_ARCH_ARM64
-  const uint64x2_t tmp1 = vzip1q_u64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[1] = vzip2q_u64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[0] = tmp1;
-#else
-  const uint64x1_t tmp[2][2] = {
-    { vget_low_u64(kernel.packet[0]), vget_high_u64(kernel.packet[0]) },
-    { vget_low_u64(kernel.packet[1]), vget_high_u64(kernel.packet[1]) }
-  };
-
-  kernel.packet[0] = vcombine_u64(tmp[0][0], tmp[1][0]);
-  kernel.packet[1] = vcombine_u64(tmp[0][1], tmp[1][1]);
-#endif
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pselect( const Packet2f& mask, const Packet2f& a, const Packet2f& b)
-{ return vbsl_f32(vreinterpret_u32_f32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b)
-{ return vbslq_f32(vreinterpretq_u32_f32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pselect(const Packet8c& mask, const Packet8c& a, const Packet8c& b)
-{ return vbsl_s8(vreinterpret_u8_s8(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pselect(const Packet16c& mask, const Packet16c& a, const Packet16c& b)
-{ return vbslq_s8(vreinterpretq_u8_s8(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pselect(const Packet8uc& mask, const Packet8uc& a, const Packet8uc& b)
-{ return vbsl_u8(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pselect(const Packet16uc& mask, const Packet16uc& a, const Packet16uc& b)
-{ return vbslq_u8(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pselect(const Packet4s& mask, const Packet4s& a, const Packet4s& b)
-{ return vbsl_s16(vreinterpret_u16_s16(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pselect(const Packet8s& mask, const Packet8s& a, const Packet8s& b)
-{ return vbslq_s16(vreinterpretq_u16_s16(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pselect(const Packet4us& mask, const Packet4us& a, const Packet4us& b)
-{ return vbsl_u16(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pselect(const Packet8us& mask, const Packet8us& a, const Packet8us& b)
-{ return vbslq_u16(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pselect(const Packet2i& mask, const Packet2i& a, const Packet2i& b)
-{ return vbsl_s32(vreinterpret_u32_s32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b)
-{ return vbslq_s32(vreinterpretq_u32_s32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pselect(const Packet2ui& mask, const Packet2ui& a, const Packet2ui& b)
-{ return vbsl_u32(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pselect(const Packet4ui& mask, const Packet4ui& a, const Packet4ui& b)
-{ return vbslq_u32(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pselect(const Packet2l& mask, const Packet2l& a, const Packet2l& b)
-{ return vbslq_s64(vreinterpretq_u64_s64(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pselect(const Packet2ul& mask, const Packet2ul& a, const Packet2ul& b)
-{ return vbslq_u64(mask, a, b); }
-
-// Use armv8 rounding intinsics if available.
-#if EIGEN_ARCH_ARMV8
-template<> EIGEN_STRONG_INLINE Packet2f print<Packet2f>(const Packet2f& a)
-{ return vrndn_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
-{ return vrndnq_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pfloor<Packet2f>(const Packet2f& a)
-{ return vrndm_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{ return vrndmq_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a)
-{ return vrndp_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
-{ return vrndpq_f32(a); }
-
-#else
-
-template<> EIGEN_STRONG_INLINE Packet4f print(const Packet4f& a) {
-  // Adds and subtracts signum(a) * 2^23 to force rounding.
-  const Packet4f limit = pset1<Packet4f>(static_cast<float>(1<<23));
-  const Packet4f abs_a = pabs(a);
-  Packet4f r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f print(const Packet2f& a) {
-  // Adds and subtracts signum(a) * 2^23 to force rounding.
-  const Packet2f limit = pset1<Packet2f>(static_cast<float>(1<<23));
-  const Packet2f abs_a = pabs(a);
-  Packet2f r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If greater, subtract one.
-  Packet4f mask = pcmp_lt(a, tmp);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pfloor<Packet2f>(const Packet2f& a)
-{
-  const Packet2f cst_1 = pset1<Packet2f>(1.0f);
-  Packet2f tmp  = print<Packet2f>(a);
-  // If greater, subtract one.
-  Packet2f mask = pcmp_lt(a, tmp);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If smaller, add one.
-  Packet4f mask = pcmp_lt(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a)
-{
-  const Packet2f cst_1 = pset1<Packet2f>(1.0);
-  Packet2f tmp  = print<Packet2f>(a);
-  // If smaller, add one.
-  Packet2f mask = pcmp_lt(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-
-#endif
-
-/**
- * Computes the integer square root
- * @remarks The calculation is performed using an algorithm which iterates through each binary digit of the result
- *   and tests whether setting that digit to 1 would cause the square of the value to be greater than the argument
- *   value. The algorithm is described in detail here: http://ww1.microchip.com/downloads/en/AppNotes/91040a.pdf .
- */
-template<> EIGEN_STRONG_INLINE Packet4uc psqrt(const Packet4uc& a) {
-  uint8x8_t x = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t res = vdup_n_u8(0);
-  uint8x8_t add = vdup_n_u8(0x8);
-  for (int i = 0; i < 4; i++)
-  {
-    const uint8x8_t temp = vorr_u8(res, add);
-    res = vbsl_u8(vcge_u8(x, vmul_u8(temp, temp)), temp, res);
-    add = vshr_n_u8(add, 1);
-  }
-  return vget_lane_u32(vreinterpret_u32_u8(res), 0);
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet8uc psqrt(const Packet8uc& a) {
-  uint8x8_t res = vdup_n_u8(0);
-  uint8x8_t add = vdup_n_u8(0x8);
-  for (int i = 0; i < 4; i++)
-  {
-    const uint8x8_t temp = vorr_u8(res, add);
-    res = vbsl_u8(vcge_u8(a, vmul_u8(temp, temp)), temp, res);
-    add = vshr_n_u8(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet16uc psqrt(const Packet16uc& a) {
-  uint8x16_t res = vdupq_n_u8(0);
-  uint8x16_t add = vdupq_n_u8(0x8);
-  for (int i = 0; i < 4; i++)
-  {
-    const uint8x16_t temp = vorrq_u8(res, add);
-    res = vbslq_u8(vcgeq_u8(a, vmulq_u8(temp, temp)), temp, res);
-    add = vshrq_n_u8(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet4us psqrt(const Packet4us& a) {
-  uint16x4_t res = vdup_n_u16(0);
-  uint16x4_t add = vdup_n_u16(0x80);
-  for (int i = 0; i < 8; i++)
-  {
-    const uint16x4_t temp = vorr_u16(res, add);
-    res = vbsl_u16(vcge_u16(a, vmul_u16(temp, temp)), temp, res);
-    add = vshr_n_u16(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet8us psqrt(const Packet8us& a) {
-  uint16x8_t res = vdupq_n_u16(0);
-  uint16x8_t add = vdupq_n_u16(0x80);
-  for (int i = 0; i < 8; i++)
-  {
-    const uint16x8_t temp = vorrq_u16(res, add);
-    res = vbslq_u16(vcgeq_u16(a, vmulq_u16(temp, temp)), temp, res);
-    add = vshrq_n_u16(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet2ui psqrt(const Packet2ui& a) {
-  uint32x2_t res = vdup_n_u32(0);
-  uint32x2_t add = vdup_n_u32(0x8000);
-  for (int i = 0; i < 16; i++)
-  {
-    const uint32x2_t temp = vorr_u32(res, add);
-    res = vbsl_u32(vcge_u32(a, vmul_u32(temp, temp)), temp, res);
-    add = vshr_n_u32(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet4ui psqrt(const Packet4ui& a) {
-  uint32x4_t res = vdupq_n_u32(0);
-  uint32x4_t add = vdupq_n_u32(0x8000);
-  for (int i = 0; i < 16; i++)
-  {
-    const uint32x4_t temp = vorrq_u32(res, add);
-    res = vbslq_u32(vcgeq_u32(a, vmulq_u32(temp, temp)), temp, res);
-    add = vshrq_n_u32(add, 1);
-  }
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
-  // Compute approximate reciprocal sqrt.
-  Packet4f x = vrsqrteq_f32(a);
-  // Do Newton iterations for 1/sqrt(x).
-  x = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, x), x), x);
-  x = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, x), x), x);
-  const Packet4f infinity = pset1<Packet4f>(NumTraits<float>::infinity());
-  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f prsqrt(const Packet2f& a) {
-  // Compute approximate reciprocal sqrt.
-  Packet2f x = vrsqrte_f32(a);
-  // Do Newton iterations for 1/sqrt(x).
-  x = vmul_f32(vrsqrts_f32(vmul_f32(a, x), x), x);
-  x = vmul_f32(vrsqrts_f32(vmul_f32(a, x), x), x);
-  const Packet2f infinity = pset1<Packet2f>(NumTraits<float>::infinity());
-  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
-}
-
-// Unfortunately vsqrt_f32 is only available for A64.
-#if EIGEN_ARCH_ARM64
-template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& _x){return vsqrtq_f32(_x);}
-template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& _x){return vsqrt_f32(_x); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
-  const Packet4f infinity = pset1<Packet4f>(NumTraits<float>::infinity());
-  const Packet4f is_zero_or_inf = por(pcmp_eq(a, pzero(a)), pcmp_eq(a, infinity));
-  return pselect(is_zero_or_inf, a, pmul(a, prsqrt(a)));
-}
-template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& a) {
-  const Packet2f infinity = pset1<Packet2f>(NumTraits<float>::infinity());
-  const Packet2f is_zero_or_inf = por(pcmp_eq(a, pzero(a)), pcmp_eq(a, infinity));
-  return pselect(is_zero_or_inf, a, pmul(a, prsqrt(a)));
-}
-#endif
-
-//---------- bfloat16 ----------
-// TODO: Add support for native armv8.6-a bfloat16_t
-
-// TODO: Guard if we have native bfloat16 support
-typedef eigen_packet_wrapper<uint16x4_t, 19> Packet4bf;
-
-template<> struct is_arithmetic<Packet4bf> { enum { value = true }; };
-
-template<> struct packet_traits<bfloat16> : default_packet_traits
-{
-  typedef Packet4bf type;
-  typedef Packet4bf half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-    HasDiv       = 1,
-    HasFloor     = 1,
-    HasCeil      = 1,
-    HasRint      = 1,
-
-    HasSin  = EIGEN_FAST_MATH,
-    HasCos  = EIGEN_FAST_MATH,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf  = EIGEN_FAST_MATH,
-    HasBessel = 0,  // Issues with accuracy.
-    HasNdtri = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet4bf>
-{
-  typedef bfloat16 type;
-  typedef Packet4bf half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-namespace detail {  
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4bf>(Packet4bf& p1, Packet4bf& p2) {
-  const uint16x4x2_t tmp = vzip_u16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-} // namespace detail
-
-EIGEN_STRONG_INLINE Packet4bf F32ToBf16(const Packet4f& p)
-{
-  // See the scalar implemention in BFloat16.h for a comprehensible explanation
-  // of this fast rounding algorithm
-  Packet4ui input = reinterpret_cast<Packet4ui>(p);
-
-  // lsb = (input >> 16) & 1
-  Packet4ui lsb =  vandq_u32(vshrq_n_u32(input, 16), vdupq_n_u32(1));
-
-  // rounding_bias = 0x7fff + lsb
-  Packet4ui rounding_bias = vaddq_u32(lsb, vdupq_n_u32(0x7fff));
-
-  // input += rounding_bias
-  input = vaddq_u32(input, rounding_bias);
-
-  // input = input >> 16
-  input = vshrq_n_u32(input, 16);
-
-  // Replace float-nans by bfloat16-nans, that is 0x7fc0
-  const Packet4ui bf16_nan = vdupq_n_u32(0x7fc0);
-  const Packet4ui mask = vceqq_f32(p, p);
-  input = vbslq_u32(mask, input, bf16_nan);
-
-  // output = static_cast<uint16_t>(input)
-  return vmovn_u32(input);
-}
-
-EIGEN_STRONG_INLINE Packet4f Bf16ToF32(const Packet4bf& p)
-{
-  return reinterpret_cast<Packet4f>(vshlq_n_u32(vmovl_u16(p), 16));
-}
-
-EIGEN_STRONG_INLINE Packet4bf F32MaskToBf16Mask(const Packet4f& p) {
-  return vmovn_u32(vreinterpretq_u32_f32(p));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pset1<Packet4bf>(const bfloat16& from) {
-  return pset1<Packet4us>(from.value);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 pfirst<Packet4bf>(const Packet4bf& from) {
-  return bfloat16_impl::raw_uint16_to_bfloat16(static_cast<uint16_t>(pfirst<Packet4us>(from)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pload<Packet4bf>(const bfloat16* from)
-{
-  return pload<Packet4us>(reinterpret_cast<const uint16_t*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf ploadu<Packet4bf>(const bfloat16* from)
-{
-  return ploadu<Packet4us>(reinterpret_cast<const uint16_t*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet4bf& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE vst1_u16(reinterpret_cast<uint16_t*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet4bf& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(reinterpret_cast<uint16_t*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf ploaddup<Packet4bf>(const bfloat16* from)
-{
-  return ploaddup<Packet4us>(reinterpret_cast<const uint16_t*>(from));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pabs(const Packet4bf& a) {
-  return F32ToBf16(pabs<Packet4f>(Bf16ToF32(a)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNumbers, Packet4bf>(const Packet4bf &a,
-                                                                            const Packet4bf &b)
-{
-  return F32ToBf16(pmin<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-template <> EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNaN, Packet4bf>(const Packet4bf &a,
-                                                                        const Packet4bf &b)
-{
-  return F32ToBf16(pmin<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmin<Packet4bf>(const Packet4bf &a,
-                                                          const Packet4bf &b)
-{
-  return F32ToBf16(pmin<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNumbers, Packet4bf>(const Packet4bf &a,
-                                                                            const Packet4bf &b)
-{
-  return F32ToBf16(pmax<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-template <> EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNaN, Packet4bf>(const Packet4bf &a,
-                                                                        const Packet4bf &b)
-{
-  return F32ToBf16(pmax<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmax<Packet4bf>(const Packet4bf &a,
-                                                          const Packet4bf &b)
-{
-  return F32ToBf16(pmax<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf plset<Packet4bf>(const bfloat16& a)
-{
-  return F32ToBf16(plset<Packet4f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf por(const Packet4bf& a,const Packet4bf& b) {
-  return por<Packet4us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pxor(const Packet4bf& a,const Packet4bf& b) {
-  return pxor<Packet4us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pand(const Packet4bf& a,const Packet4bf& b) {
-  return pand<Packet4us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pandnot(const Packet4bf& a,const Packet4bf& b) {
-  return pandnot<Packet4us>(a, b);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4bf pselect(const Packet4bf& mask, const Packet4bf& a,
-                                                      const Packet4bf& b)
-{
-  return pselect<Packet4us>(mask, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf print<Packet4bf>(const Packet4bf& a)
-{
-  return F32ToBf16(print<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pfloor<Packet4bf>(const Packet4bf& a)
-{
-  return F32ToBf16(pfloor<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pceil<Packet4bf>(const Packet4bf& a)
-{
-  return F32ToBf16(pceil<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pconj(const Packet4bf& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4bf padd<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(padd<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf psub<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(psub<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pmul<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(pmul<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pdiv<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(pdiv<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<>
-EIGEN_STRONG_INLINE Packet4bf pgather<bfloat16, Packet4bf>(const bfloat16* from, Index stride)
-{
-  return pgather<uint16_t, Packet4us>(reinterpret_cast<const uint16_t*>(from), stride);
-}
-
-template<>
-EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet4bf>(bfloat16* to, const Packet4bf& from, Index stride)
-{
-  pscatter<uint16_t, Packet4us>(reinterpret_cast<uint16_t*>(to), from, stride);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux_max<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux_min<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux_mul<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf preverse<Packet4bf>(const Packet4bf& a)
-{
-  return preverse<Packet4us>(a);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4bf, 4>& kernel)
-{
-  detail::ptranspose_impl(kernel);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pabsdiff<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32ToBf16(pabsdiff<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_eq<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_eq<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_lt<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_lt<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_lt_or_nan<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_lt_or_nan<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_le<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_le<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pnegate<Packet4bf>(const Packet4bf& a)
-{
-  return pxor<Packet4us>(a, pset1<Packet4us>(static_cast<uint16_t>(0x8000)));
-}
-
-//---------- double ----------
-
-// Clang 3.5 in the iOS toolchain has an ICE triggered by NEON intrisics for double.
-// Confirmed at least with __apple_build_version__ = 6000054.
-#ifdef __apple_build_version__
-// Let's hope that by the time __apple_build_version__ hits the 601* range, the bug will be fixed.
-// https://gist.github.com/yamaya/2924292 suggests that the 3 first digits are only updated with
-// major toolchain updates.
-#define EIGEN_APPLE_DOUBLE_NEON_BUG (__apple_build_version__ < 6010000)
-#else
-#define EIGEN_APPLE_DOUBLE_NEON_BUG 0
-#endif
-
-#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
-
-// Bug 907: workaround missing declarations of the following two functions in the ADK
-// Defining these functions as templates ensures that if these intrinsics are
-// already defined in arm_neon.h, then our workaround doesn't cause a conflict
-// and has lower priority in overload resolution.
-template <typename T> uint64x2_t vreinterpretq_u64_f64(T a) { return (uint64x2_t) a; }
-
-template <typename T> float64x2_t vreinterpretq_f64_u64(T a) { return (float64x2_t) a; }
-
-typedef float64x2_t Packet2d;
-typedef float64x1_t Packet1d;
-
-// fuctionally equivalent to _mm_shuffle_pd in SSE (i.e. shuffle(m, n, mask) equals _mm_shuffle_pd(m,n,mask))
-// Currently used in LU/arch/InverseSize4.h to enable a shared implementation
-// for fast inversion of matrices of size 4.
-EIGEN_STRONG_INLINE Packet2d shuffle(const Packet2d& m, const Packet2d& n, int mask)
-{
-  const double* a = reinterpret_cast<const double*>(&m);
-  const double* b = reinterpret_cast<const double*>(&n);
-  Packet2d res = {*(a + (mask & 1)), *(b + ((mask >> 1) & 1))};
-  return res;
-}
-
-EIGEN_STRONG_INLINE Packet2d vec2d_swizzle2(const Packet2d& a, const Packet2d& b, int mask)
-{
-  return shuffle(a, b, mask);
-}
-EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a,const Packet2d& b)
-{
-  return shuffle(a, b, 0);
-}
-EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a,const Packet2d& b)
-{
-  return shuffle(a, b, 3);
-}
-#define vec2d_duplane(a, p) \
-  vdupq_laneq_f64(a, p)
-
-template<> struct packet_traits<double>  : default_packet_traits
-{
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasDiv   = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = 0,
-    HasErf  = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet2d>
-{
-  typedef double type;
-  typedef Packet2d half;
-  typedef Packet2l integer_packet;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double&  from) { return vdupq_n_f64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a)
-{
-  const double c[] = {0.0,1.0};
-  return vaddq_f64(pset1<Packet2d>(a), vld1q_f64(c));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vaddq_f64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vsubq_f64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& , const Packet2d& );
-template<> EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b){
-  const Packet2d mask = {numext::bit_cast<double>(0x8000000000000000ull),0.0};
-  return padd(a, pxor(mask, b));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return vnegq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmulq_f64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vdivq_f64(a,b); }
-
-#ifdef __ARM_FEATURE_FMA
-// See bug 936. See above comment about FMA for float.
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c)
-{ return vfmaq_f64(c,a,b); }
-#else
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c)
-{ return vmlaq_f64(c,a,b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vminq_f64(a,b); }
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) { return vminnmq_f64(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxnmq_f64(a, b); }
-
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) { return pmin<Packet2d>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxq_f64(a,b); }
-
-
-template<> EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) { return pmax<Packet2d>(a, b); }
-
-// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vcleq_f64(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vcltq_f64(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u32(vmvnq_u32(vreinterpretq_u32_u64(vcgeq_f64(a,b)))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vceqq_f64(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) { return vld1q_dup_f64(from); }
-template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_f64(to,from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_f64(to,from); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
-  Packet2d res = pset1<Packet2d>(0.0);
-  res = vld1q_lane_f64(from + 0*stride, res, 0);
-  res = vld1q_lane_f64(from + 1*stride, res, 1);
-  return res;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  vst1q_lane_f64(to + stride*0, from, 0);
-  vst1q_lane_f64(to + stride*1, from, 1);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ARM_PREFETCH(addr); }
-
-// FIXME only store the 2 first elements ?
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(a,0); }
-
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
-{ return vcombine_f64(vget_high_f64(a), vget_low_f64(a)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vabsq_f64(a); }
-
-#if EIGEN_COMP_CLANG && defined(__apple_build_version__)
-// workaround ICE, see bug 907
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{ return (vget_low_f64(a) + vget_high_f64(a))[0]; }
-#else
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{ return vget_lane_f64(vget_low_f64(a) + vget_high_f64(a), 0); }
-#endif
-
-// Other reduction functions:
-// mul
-#if EIGEN_COMP_CLANG && defined(__apple_build_version__)
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{ return (vget_low_f64(a) * vget_high_f64(a))[0]; }
-#else
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{ return vget_lane_f64(vget_low_f64(a) * vget_high_f64(a), 0); }
-#endif
-
-// min
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{ return vgetq_lane_f64(vpminq_f64(a,a), 0); }
-
-// max
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{ return vgetq_lane_f64(vpmaxq_f64(a,a), 0); }
-
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet2d, 2>& kernel)
-{
-  const float64x2_t tmp1 = vzip1q_f64(kernel.packet[0], kernel.packet[1]);
-  const float64x2_t tmp2 = vzip2q_f64(kernel.packet[0], kernel.packet[1]);
-
-  kernel.packet[0] = tmp1;
-  kernel.packet[1] = tmp2;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pselect( const Packet2d& mask, const Packet2d& a, const Packet2d& b)
-{ return vbslq_f64(vreinterpretq_u64_f64(mask), a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a)
-{ return vrndnq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a)
-{ return vrndmq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a)
-{ return vrndpq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent)
-{ return pldexp_generic(a, exponent); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent)
-{ return pfrexp_generic(a,exponent); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from)
-{ return vreinterpretq_f64_u64(vdupq_n_u64(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
-  // Compute approximate reciprocal sqrt.
-  Packet2d x = vrsqrteq_f64(a);
-  // Do Newton iterations for 1/sqrt(x).
-  x = vmulq_f64(vrsqrtsq_f64(vmulq_f64(a, x), x), x);
-  x = vmulq_f64(vrsqrtsq_f64(vmulq_f64(a, x), x), x);
-  x = vmulq_f64(vrsqrtsq_f64(vmulq_f64(a, x), x), x);
-  const Packet2d infinity = pset1<Packet2d>(NumTraits<double>::infinity());
-  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& _x){ return vsqrtq_f64(_x); }
-
-#endif // EIGEN_ARCH_ARM64
-
-// Do we have an fp16 types and supporting Neon intrinsics?
-#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-typedef float16x4_t Packet4hf;
-typedef float16x8_t Packet8hf;
-
-template <>
-struct packet_traits<Eigen::half> : default_packet_traits {
-  typedef Packet8hf type;
-  typedef Packet4hf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasCmp = 1,
-    HasCast = 1,
-    HasAdd = 1,
-    HasSub = 1,
-    HasShift = 1,
-    HasMul = 1,
-    HasNegate = 1,
-    HasAbs = 1,
-    HasArg = 0,
-    HasAbs2 = 1,
-    HasAbsDiff = 0,
-    HasMin = 1,
-    HasMax = 1,
-    HasConj = 1,
-    HasSetLinear = 0,
-    HasBlend = 0,
-    HasInsert = 1,
-    HasReduxp = 1,
-    HasDiv = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasSin = 0,
-    HasCos = 0,
-    HasLog = 0,
-    HasExp = 0,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasErf = EIGEN_FAST_MATH,
-    HasBessel = 0,  // Issues with accuracy.
-    HasNdtri = 0
-  };
-};
-
-template <>
-struct unpacket_traits<Packet4hf> {
-  typedef Eigen::half type;
-  typedef Packet4hf half;
-  enum {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template <>
-struct unpacket_traits<Packet8hf> {
-  typedef Eigen::half type;
-  typedef Packet4hf half;
-  enum {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf predux_half_dowto4<Packet8hf>(const Packet8hf& a) {
-  return vadd_f16(vget_low_f16(a), vget_high_f16(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pset1<Packet8hf>(const Eigen::half& from) {
-  return vdupq_n_f16(from.x);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pset1<Packet4hf>(const Eigen::half& from) {
-  return vdup_n_f16(from.x);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf plset<Packet8hf>(const Eigen::half& a) {
-  const float16_t f[] = {0, 1, 2, 3, 4, 5, 6, 7};
-  Packet8hf countdown = vld1q_f16(f);
-  return vaddq_f16(pset1<Packet8hf>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf plset<Packet4hf>(const Eigen::half& a) {
-  const float16_t f[] = {0, 1, 2, 3};
-  Packet4hf countdown = vld1_f16(f);
-  return vadd_f16(pset1<Packet4hf>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf padd<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vaddq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf padd<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vadd_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf psub<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vsubq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf psub<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vsub_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pnegate(const Packet8hf& a) {
-  return vnegq_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pnegate(const Packet4hf& a) {
-  return vneg_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pconj(const Packet8hf& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pconj(const Packet4hf& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmul<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vmulq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmul<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vmul_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pdiv<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vdivq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pdiv<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vdiv_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmadd(const Packet8hf& a, const Packet8hf& b, const Packet8hf& c) {
-  return vfmaq_f16(c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmadd(const Packet4hf& a, const Packet4hf& b, const Packet4hf& c) {
-  return vfma_f16(c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmin<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vminq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmin<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vmin_f16(a, b);
-}
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return vminnm_f16(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return vminnmq_f16(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return pmin<Packet4hf>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return pmin<Packet8hf>(a, b); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmax<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vmaxq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmax<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vmax_f16(a, b);
-}
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return vmaxnm_f16(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return vmaxnmq_f16(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return pmax<Packet4hf>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return pmax<Packet8hf>(a, b); }
-
-#define EIGEN_MAKE_ARM_FP16_CMP_8(name)                                               \
-  template <>                                                                         \
-  EIGEN_STRONG_INLINE Packet8hf pcmp_##name(const Packet8hf& a, const Packet8hf& b) { \
-    return vreinterpretq_f16_u16(vc##name##q_f16(a, b));                              \
-  }
-
-#define EIGEN_MAKE_ARM_FP16_CMP_4(name)                                               \
-  template <>                                                                         \
-  EIGEN_STRONG_INLINE Packet4hf pcmp_##name(const Packet4hf& a, const Packet4hf& b) { \
-    return vreinterpret_f16_u16(vc##name##_f16(a, b));                                \
-  }
-
-EIGEN_MAKE_ARM_FP16_CMP_8(eq)
-EIGEN_MAKE_ARM_FP16_CMP_8(lt)
-EIGEN_MAKE_ARM_FP16_CMP_8(le)
-
-EIGEN_MAKE_ARM_FP16_CMP_4(eq)
-EIGEN_MAKE_ARM_FP16_CMP_4(lt)
-EIGEN_MAKE_ARM_FP16_CMP_4(le)
-
-#undef EIGEN_MAKE_ARM_FP16_CMP_8
-#undef EIGEN_MAKE_ARM_FP16_CMP_4
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pcmp_lt_or_nan<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vmvnq_u16(vcgeq_f16(a, b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pcmp_lt_or_nan<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vmvn_u16(vcge_f16(a, b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf print<Packet8hf>(const Packet8hf& a)
-{ return vrndnq_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf print<Packet4hf>(const Packet4hf& a)
-{ return vrndn_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pfloor<Packet8hf>(const Packet8hf& a)
-{ return vrndmq_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pfloor<Packet4hf>(const Packet4hf& a)
-{ return vrndm_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pceil<Packet8hf>(const Packet8hf& a)
-{ return vrndpq_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pceil<Packet4hf>(const Packet4hf& a)
-{ return vrndp_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf psqrt<Packet8hf>(const Packet8hf& a) {
-  return vsqrtq_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf psqrt<Packet4hf>(const Packet4hf& a) {
-  return vsqrt_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pand<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vandq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pand<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vand_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf por<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vorrq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf por<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vorr_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pxor<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(veorq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pxor<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(veor_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pandnot<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vbicq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pandnot<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vbic_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pload<Packet8hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pload<Packet4hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return vld1_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf ploadu<Packet8hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf ploadu<Packet4hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf ploaddup<Packet8hf>(const Eigen::half* from) {
-  Packet8hf packet;
-  packet[0] = from[0].x;
-  packet[1] = from[0].x;
-  packet[2] = from[1].x;
-  packet[3] = from[1].x;
-  packet[4] = from[2].x;
-  packet[5] = from[2].x;
-  packet[6] = from[3].x;
-  packet[7] = from[3].x;
-  return packet;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf ploaddup<Packet4hf>(const Eigen::half* from) {
-  float16x4_t packet;
-  float16_t* tmp;
-  tmp = (float16_t*)&packet;
-  tmp[0] = from[0].x;
-  tmp[1] = from[0].x;
-  tmp[2] = from[1].x;
-  tmp[3] = from[1].x;
-  return packet;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf ploadquad<Packet8hf>(const Eigen::half* from) {
-  Packet4hf lo, hi;
-  lo = vld1_dup_f16(reinterpret_cast<const float16_t*>(from));
-  hi = vld1_dup_f16(reinterpret_cast<const float16_t*>(from+1));
-  return vcombine_f16(lo, hi);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertfirst(const Packet8hf& a, Eigen::half b) { return vsetq_lane_f16(b.x, a, 0); }
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertfirst(const Packet4hf& a, Eigen::half b) { return vset_lane_f16(b.x, a, 0); }
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pselect(const Packet8hf& mask, const Packet8hf& a, const Packet8hf& b) {
-  return vbslq_f16(vreinterpretq_u16_f16(mask), a, b);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pselect(const Packet4hf& mask, const Packet4hf& a, const Packet4hf& b) {
-  return vbsl_f16(vreinterpret_u16_f16(mask), a, b);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertlast(const Packet8hf& a, Eigen::half b) { return vsetq_lane_f16(b.x, a, 7); }
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertlast(const Packet4hf& a, Eigen::half b) { return vset_lane_f16(b.x, a, 3); }
-
-template <>
-EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
-  EIGEN_DEBUG_ALIGNED_STORE vst1q_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
-  EIGEN_DEBUG_ALIGNED_STORE vst1_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE vst1q_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE vst1_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pgather<Eigen::half, Packet8hf>(const Eigen::half* from, Index stride) {
-  Packet8hf res = pset1<Packet8hf>(Eigen::half(0.f));
-  res = vsetq_lane_f16(from[0 * stride].x, res, 0);
-  res = vsetq_lane_f16(from[1 * stride].x, res, 1);
-  res = vsetq_lane_f16(from[2 * stride].x, res, 2);
-  res = vsetq_lane_f16(from[3 * stride].x, res, 3);
-  res = vsetq_lane_f16(from[4 * stride].x, res, 4);
-  res = vsetq_lane_f16(from[5 * stride].x, res, 5);
-  res = vsetq_lane_f16(from[6 * stride].x, res, 6);
-  res = vsetq_lane_f16(from[7 * stride].x, res, 7);
-  return res;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pgather<Eigen::half, Packet4hf>(const Eigen::half* from, Index stride) {
-  Packet4hf res = pset1<Packet4hf>(Eigen::half(0.f));
-  res = vset_lane_f16(from[0 * stride].x, res, 0);
-  res = vset_lane_f16(from[1 * stride].x, res, 1);
-  res = vset_lane_f16(from[2 * stride].x, res, 2);
-  res = vset_lane_f16(from[3 * stride].x, res, 3);
-  return res;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8hf>(Eigen::half* to, const Packet8hf& from, Index stride) {
-  to[stride * 0].x = vgetq_lane_f16(from, 0);
-  to[stride * 1].x = vgetq_lane_f16(from, 1);
-  to[stride * 2].x = vgetq_lane_f16(from, 2);
-  to[stride * 3].x = vgetq_lane_f16(from, 3);
-  to[stride * 4].x = vgetq_lane_f16(from, 4);
-  to[stride * 5].x = vgetq_lane_f16(from, 5);
-  to[stride * 6].x = vgetq_lane_f16(from, 6);
-  to[stride * 7].x = vgetq_lane_f16(from, 7);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4hf>(Eigen::half* to, const Packet4hf& from, Index stride) {
-  to[stride * 0].x = vget_lane_f16(from, 0);
-  to[stride * 1].x = vget_lane_f16(from, 1);
-  to[stride * 2].x = vget_lane_f16(from, 2);
-  to[stride * 3].x = vget_lane_f16(from, 3);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<Eigen::half>(const Eigen::half* addr) {
-  EIGEN_ARM_PREFETCH(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8hf>(const Packet8hf& a) {
-  float16_t x[8];
-  vst1q_f16(x, a);
-  Eigen::half h;
-  h.x = x[0];
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4hf>(const Packet4hf& a) {
-  float16_t x[4];
-  vst1_f16(x, a);
-  Eigen::half h;
-  h.x = x[0];
-  return h;
-}
-
-template<> EIGEN_STRONG_INLINE Packet8hf preverse(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi;
-  Packet8hf a_r64;
-
-  a_r64 = vrev64q_f16(a);
-  a_lo = vget_low_f16(a_r64);
-  a_hi = vget_high_f16(a_r64);
-  return vcombine_f16(a_hi, a_lo);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf preverse<Packet4hf>(const Packet4hf& a) {
-  return vrev64_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pabs<Packet8hf>(const Packet8hf& a) {
-  return vabsq_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pabs<Packet4hf>(const Packet4hf& a) {
-  return vabs_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, sum;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  sum = vpadd_f16(a_lo, a_hi);
-  sum = vpadd_f16(sum, sum);
-  sum = vpadd_f16(sum, sum);
-
-  Eigen::half h;
-  h.x = vget_lane_f16(sum, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux<Packet4hf>(const Packet4hf& a) {
-  float16x4_t sum;
-
-  sum = vpadd_f16(a, a);
-  sum = vpadd_f16(sum, sum);
-  Eigen::half h;
-  h.x = vget_lane_f16(sum, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, prod;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  prod = vmul_f16(a_lo, a_hi);
-  prod = vmul_f16(prod, vrev64_f16(prod));
-
-  Eigen::half h;
-  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4hf>(const Packet4hf& a) {
-  float16x4_t prod;
-  prod = vmul_f16(a, vrev64_f16(a));
-  Eigen::half h;
-  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_min<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, min;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  min = vpmin_f16(a_lo, a_hi);
-  min = vpmin_f16(min, min);
-  min = vpmin_f16(min, min);
-
-  Eigen::half h;
-  h.x = vget_lane_f16(min, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4hf>(const Packet4hf& a) {
-  Packet4hf tmp;
-  tmp = vpmin_f16(a, a);
-  tmp = vpmin_f16(tmp, tmp);
-  Eigen::half h;
-  h.x = vget_lane_f16(tmp, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, max;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  max = vpmax_f16(a_lo, a_hi);
-  max = vpmax_f16(max, max);
-  max = vpmax_f16(max, max);
-
-  Eigen::half h;
-  h.x = vget_lane_f16(max, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4hf>(const Packet4hf& a) {
-  Packet4hf tmp;
-  tmp = vpmax_f16(a, a);
-  tmp = vpmax_f16(tmp, tmp);
-  Eigen::half h;
-  h.x = vget_lane_f16(tmp, 0);
-  return h;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 4>& kernel)
-{
-  const float16x8x2_t zip16_1 = vzipq_f16(kernel.packet[0], kernel.packet[1]);
-  const float16x8x2_t zip16_2 = vzipq_f16(kernel.packet[2], kernel.packet[3]);
-
-  const float32x4x2_t zip32_1 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[0]), vreinterpretq_f32_f16(zip16_2.val[0]));
-  const float32x4x2_t zip32_2 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[1]), vreinterpretq_f32_f16(zip16_2.val[1]));
-
-  kernel.packet[0] = vreinterpretq_f16_f32(zip32_1.val[0]);
-  kernel.packet[1] = vreinterpretq_f16_f32(zip32_1.val[1]);
-  kernel.packet[2] = vreinterpretq_f16_f32(zip32_2.val[0]);
-  kernel.packet[3] = vreinterpretq_f16_f32(zip32_2.val[1]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4hf, 4>& kernel) {
-  EIGEN_ALIGN16 float16x4x4_t tmp_x4;
-  float16_t* tmp = (float16_t*)&kernel;
-  tmp_x4 = vld4_f16(tmp);
-
-  kernel.packet[0] = tmp_x4.val[0];
-  kernel.packet[1] = tmp_x4.val[1];
-  kernel.packet[2] = tmp_x4.val[2];
-  kernel.packet[3] = tmp_x4.val[3];
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 8>& kernel) {
-  float16x8x2_t T_1[4];
-
-  T_1[0] = vuzpq_f16(kernel.packet[0], kernel.packet[1]);
-  T_1[1] = vuzpq_f16(kernel.packet[2], kernel.packet[3]);
-  T_1[2] = vuzpq_f16(kernel.packet[4], kernel.packet[5]);
-  T_1[3] = vuzpq_f16(kernel.packet[6], kernel.packet[7]);
-
-  float16x8x2_t T_2[4];
-  T_2[0] = vuzpq_f16(T_1[0].val[0], T_1[1].val[0]);
-  T_2[1] = vuzpq_f16(T_1[0].val[1], T_1[1].val[1]);
-  T_2[2] = vuzpq_f16(T_1[2].val[0], T_1[3].val[0]);
-  T_2[3] = vuzpq_f16(T_1[2].val[1], T_1[3].val[1]);
-
-  float16x8x2_t T_3[4];
-  T_3[0] = vuzpq_f16(T_2[0].val[0], T_2[2].val[0]);
-  T_3[1] = vuzpq_f16(T_2[0].val[1], T_2[2].val[1]);
-  T_3[2] = vuzpq_f16(T_2[1].val[0], T_2[3].val[0]);
-  T_3[3] = vuzpq_f16(T_2[1].val[1], T_2[3].val[1]);
-
-  kernel.packet[0] = T_3[0].val[0];
-  kernel.packet[1] = T_3[2].val[0];
-  kernel.packet[2] = T_3[1].val[0];
-  kernel.packet[3] = T_3[3].val[0];
-  kernel.packet[4] = T_3[0].val[1];
-  kernel.packet[5] = T_3[2].val[1];
-  kernel.packet[6] = T_3[1].val[1];
-  kernel.packet[7] = T_3[3].val[1];
-}
-#endif // end EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/TypeCasting.h
deleted file mode 100644
index 54f9733..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/NEON/TypeCasting.h
+++ /dev/null
@@ -1,1419 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
-// Copyright (C) 2020 Antonio Sanchez <cantonios@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_NEON_H
-#define EIGEN_TYPE_CASTING_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-//==============================================================================
-// pcast, SrcType = float
-//==============================================================================
-template <>
-struct type_casting_traits<float, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet4f, Packet4f>(const Packet4f& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet2f, Packet2f>(const Packet2f& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<float, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-struct type_casting_traits<float, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-// If float64 exists, first convert to that to keep as much precision as possible.
-#if EIGEN_ARCH_ARM64
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
-  // Discard second half of input.
-  return vcvtq_s64_f64(vcvt_f64_f32(vget_low_f32(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
-  // Discard second half of input.
-  return vcvtq_u64_f64(vcvt_f64_f32(vget_low_f32(a)));
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
-  // Discard second half of input.
-  return vmovl_s32(vget_low_s32(vcvtq_s32_f32(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
-  // Discard second half of input.
-  return vmovl_u32(vget_low_u32(vcvtq_u32_f32(a)));
-}
-#endif  // EIGEN_ARCH_ARM64
-
-template <>
-struct type_casting_traits<float, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
-  return vcvtq_s32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet2f, Packet2i>(const Packet2f& a) {
-  return vcvt_s32_f32(a);
-}
-
-template <>
-struct type_casting_traits<float, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
-  return vcvtq_u32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet2f, Packet2ui>(const Packet2f& a) {
-  return vcvt_u32_f32(a);
-}
-
-template <>
-struct type_casting_traits<float, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet4f, Packet8s>(const Packet4f& a, const Packet4f& b) {
-  return vcombine_s16(vmovn_s32(vcvtq_s32_f32(a)), vmovn_s32(vcvtq_s32_f32(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet2f, Packet4s>(const Packet2f& a, const Packet2f& b) {
-  return vmovn_s32(vcombine_s32(vcvt_s32_f32(a), vcvt_s32_f32(b)));
-}
-
-template <>
-struct type_casting_traits<float, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet4f, Packet8us>(const Packet4f& a, const Packet4f& b) {
-  return vcombine_u16(vmovn_u32(vcvtq_u32_f32(a)), vmovn_u32(vcvtq_u32_f32(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet2f, Packet4us>(const Packet2f& a, const Packet2f& b) {
-  return vmovn_u32(vcombine_u32(vcvt_u32_f32(a), vcvt_u32_f32(b)));
-}
-
-template <>
-struct type_casting_traits<float, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet4f, Packet16c>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
-                                                         const Packet4f& d) {
-  const int16x8_t ab_s16 = pcast<Packet4f, Packet8s>(a, b);
-  const int16x8_t cd_s16 = pcast<Packet4f, Packet8s>(c, d);
-  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet2f, Packet8c>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
-                                                       const Packet2f& d) {
-  const int16x4_t ab_s16 = pcast<Packet2f, Packet4s>(a, b);
-  const int16x4_t cd_s16 = pcast<Packet2f, Packet4s>(c, d);
-  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
-}
-
-template <>
-struct type_casting_traits<float, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet4f, Packet16uc>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
-                                                           const Packet4f& d) {
-  const uint16x8_t ab_u16 = pcast<Packet4f, Packet8us>(a, b);
-  const uint16x8_t cd_u16 = pcast<Packet4f, Packet8us>(c, d);
-  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet2f, Packet8uc>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
-                                                         const Packet2f& d) {
-  const uint16x4_t ab_u16 = pcast<Packet2f, Packet4us>(a, b);
-  const uint16x4_t cd_u16 = pcast<Packet2f, Packet4us>(c, d);
-  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
-}
-
-//==============================================================================
-// pcast, SrcType = int8_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int8_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet16c, Packet4f>(const Packet16c& a) {
-  // Discard all but first 4 bytes.
-  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a)))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet8c, Packet2f>(const Packet8c& a) {
-  // Discard all but first 2 bytes.
-  return vcvt_f32_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a)))));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet16c, Packet2l>(const Packet16c& a) {
-  // Discard all but first two bytes.
-  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))))));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet16c, Packet2ul>(const Packet16c& a) {
-  return vreinterpretq_u64_s64(pcast<Packet16c, Packet2l>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet16c, Packet4i>(const Packet16c& a) {
-  // Discard all but first 4 bytes.
-  return vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet8c, Packet2i>(const Packet8c& a) {
-  // Discard all but first 2 bytes.
-  return vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a))));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet16c, Packet4ui>(const Packet16c& a) {
-  return vreinterpretq_u32_s32(pcast<Packet16c, Packet4i>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet8c, Packet2ui>(const Packet8c& a) {
-  return vreinterpret_u32_s32(pcast<Packet8c, Packet2i>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet16c, Packet8s>(const Packet16c& a) {
-  // Discard second half of input.
-  return vmovl_s8(vget_low_s8(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet8c, Packet4s>(const Packet8c& a) {
-  // Discard second half of input.
-  return vget_low_s16(vmovl_s8(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet16c, Packet8us>(const Packet16c& a) {
-  return vreinterpretq_u16_s16(pcast<Packet16c, Packet8s>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet8c, Packet4us>(const Packet8c& a) {
-  return vreinterpret_u16_s16(pcast<Packet8c, Packet4s>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet16c, Packet16c>(const Packet16c& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet8c, Packet8c>(const Packet8c& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4c pcast<Packet4c, Packet4c>(const Packet4c& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet16c, Packet16uc>(const Packet16c& a) {
-  return vreinterpretq_u8_s8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet8c, Packet8uc>(const Packet8c& a) {
-  return vreinterpret_u8_s8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4uc pcast<Packet4c, Packet4uc>(const Packet4c& a) {
-  return static_cast<Packet4uc>(a);
-}
-
-//==============================================================================
-// pcast, SrcType = uint8_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint8_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet16uc, Packet4f>(const Packet16uc& a) {
-  // Discard all but first 4 bytes.
-  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a)))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet8uc, Packet2f>(const Packet8uc& a) {
-  // Discard all but first 2 bytes.
-  return vcvt_f32_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a)))));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet16uc, Packet2ul>(const Packet16uc& a) {
-  // Discard all but first two bytes.
-  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet16uc, Packet2l>(const Packet16uc& a) {
-  return vreinterpretq_s64_u64(pcast<Packet16uc, Packet2ul>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet16uc, Packet4ui>(const Packet16uc& a) {
-  // Discard all but first 4 bytes.
-  return vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet8uc, Packet2ui>(const Packet8uc& a) {
-  // Discard all but first 2 bytes.
-  return vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a))));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet16uc, Packet4i>(const Packet16uc& a) {
-  return vreinterpretq_s32_u32(pcast<Packet16uc, Packet4ui>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet8uc, Packet2i>(const Packet8uc& a) {
-  return vreinterpret_s32_u32(pcast<Packet8uc, Packet2ui>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet16uc, Packet8us>(const Packet16uc& a) {
-  // Discard second half of input.
-  return vmovl_u8(vget_low_u8(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet8uc, Packet4us>(const Packet8uc& a) {
-  // Discard second half of input.
-  return vget_low_u16(vmovl_u8(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet16uc, Packet8s>(const Packet16uc& a) {
-  return vreinterpretq_s16_u16(pcast<Packet16uc, Packet8us>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet8uc, Packet4s>(const Packet8uc& a) {
-  return vreinterpret_s16_u16(pcast<Packet8uc, Packet4us>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet16uc, Packet16uc>(const Packet16uc& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet8uc, Packet8uc>(const Packet8uc& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4uc pcast<Packet4uc, Packet4uc>(const Packet4uc& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet16uc, Packet16c>(const Packet16uc& a) {
-  return vreinterpretq_s8_u8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet8uc, Packet8c>(const Packet8uc& a) {
-  return vreinterpret_s8_u8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4c pcast<Packet4uc, Packet4c>(const Packet4uc& a) {
-  return static_cast<Packet4c>(a);
-}
-
-//==============================================================================
-// pcast, SrcType = int16_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int16_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet8s, Packet4f>(const Packet8s& a) {
-  // Discard second half of input.
-  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet4s, Packet2f>(const Packet4s& a) {
-  // Discard second half of input.
-  return vcvt_f32_s32(vget_low_s32(vmovl_s16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet8s, Packet2l>(const Packet8s& a) {
-  // Discard all but first two values.
-  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(a))));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet8s, Packet2ul>(const Packet8s& a) {
-  return vreinterpretq_u64_s64(pcast<Packet8s, Packet2l>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet8s, Packet4i>(const Packet8s& a) {
-  // Discard second half of input.
-  return vmovl_s16(vget_low_s16(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet4s, Packet2i>(const Packet4s& a) {
-  // Discard second half of input.
-  return vget_low_s32(vmovl_s16(a));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet8s, Packet4ui>(const Packet8s& a) {
-  return vreinterpretq_u32_s32(pcast<Packet8s, Packet4i>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet4s, Packet2ui>(const Packet4s& a) {
-  return vreinterpret_u32_s32(pcast<Packet4s, Packet2i>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet8s, Packet8s>(const Packet8s& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet4s, Packet4s>(const Packet4s& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet8s, Packet8us>(const Packet8s& a) {
-  return vreinterpretq_u16_s16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet4s, Packet4us>(const Packet4s& a) {
-  return vreinterpret_u16_s16(a);
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet8s, Packet16c>(const Packet8s& a, const Packet8s& b) {
-  return vcombine_s8(vmovn_s16(a), vmovn_s16(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet4s, Packet8c>(const Packet4s& a, const Packet4s& b) {
-  return vmovn_s16(vcombine_s16(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet8s, Packet16uc>(const Packet8s& a, const Packet8s& b) {
-  return vcombine_u8(vmovn_u16(vreinterpretq_u16_s16(a)), vmovn_u16(vreinterpretq_u16_s16(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet4s, Packet8uc>(const Packet4s& a, const Packet4s& b) {
-  return vmovn_u16(vcombine_u16(vreinterpret_u16_s16(a), vreinterpret_u16_s16(b)));
-}
-
-//==============================================================================
-// pcast, SrcType = uint16_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint16_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet8us, Packet4f>(const Packet8us& a) {
-  // Discard second half of input.
-  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet4us, Packet2f>(const Packet4us& a) {
-  // Discard second half of input.
-  return vcvt_f32_u32(vget_low_u32(vmovl_u16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet8us, Packet2ul>(const Packet8us& a) {
-  // Discard all but first two values.
-  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(a))));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet8us, Packet2l>(const Packet8us& a) {
-  return vreinterpretq_s64_u64(pcast<Packet8us, Packet2ul>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet8us, Packet4ui>(const Packet8us& a) {
-  // Discard second half of input.
-  return vmovl_u16(vget_low_u16(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet4us, Packet2ui>(const Packet4us& a) {
-  // Discard second half of input.
-  return vget_low_u32(vmovl_u16(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet8us, Packet4i>(const Packet8us& a) {
-  return vreinterpretq_s32_u32(pcast<Packet8us, Packet4ui>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet4us, Packet2i>(const Packet4us& a) {
-  return vreinterpret_s32_u32(pcast<Packet4us, Packet2ui>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet8us, Packet8us>(const Packet8us& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet4us, Packet4us>(const Packet4us& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet8us, Packet8s>(const Packet8us& a) {
-  return vreinterpretq_s16_u16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet4us, Packet4s>(const Packet4us& a) {
-  return vreinterpret_s16_u16(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet8us, Packet16uc>(const Packet8us& a, const Packet8us& b) {
-  return vcombine_u8(vmovn_u16(a), vmovn_u16(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet4us, Packet8uc>(const Packet4us& a, const Packet4us& b) {
-  return vmovn_u16(vcombine_u16(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet8us, Packet16c>(const Packet8us& a, const Packet8us& b) {
-  return vreinterpretq_s8_u8(pcast<Packet8us, Packet16uc>(a, b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet4us, Packet8c>(const Packet4us& a, const Packet4us& b) {
-  return vreinterpret_s8_u8(pcast<Packet4us, Packet8uc>(a, b));
-}
-
-//==============================================================================
-// pcast, SrcType = int32_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int32_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
-  return vcvtq_f32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet2i, Packet2f>(const Packet2i& a) {
-  return vcvt_f32_s32(a);
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4i, Packet2l>(const Packet4i& a) {
-  // Discard second half of input.
-  return vmovl_s32(vget_low_s32(a));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4i, Packet2ul>(const Packet4i& a) {
-  return vreinterpretq_u64_s64(pcast<Packet4i, Packet2l>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet4i, Packet4i>(const Packet4i& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet2i, Packet2i>(const Packet2i& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet4i, Packet4ui>(const Packet4i& a) {
-  return vreinterpretq_u32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet2i, Packet2ui>(const Packet2i& a) {
-  return vreinterpret_u32_s32(a);
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet4i, Packet8s>(const Packet4i& a, const Packet4i& b) {
-  return vcombine_s16(vmovn_s32(a), vmovn_s32(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet2i, Packet4s>(const Packet2i& a, const Packet2i& b) {
-  return vmovn_s32(vcombine_s32(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet4i, Packet8us>(const Packet4i& a, const Packet4i& b) {
-  return vcombine_u16(vmovn_u32(vreinterpretq_u32_s32(a)), vmovn_u32(vreinterpretq_u32_s32(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet2i, Packet4us>(const Packet2i& a, const Packet2i& b) {
-  return vmovn_u32(vreinterpretq_u32_s32(vcombine_s32(a, b)));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet4i, Packet16c>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
-                                                         const Packet4i& d) {
-  const int16x8_t ab_s16 = pcast<Packet4i, Packet8s>(a, b);
-  const int16x8_t cd_s16 = pcast<Packet4i, Packet8s>(c, d);
-  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet2i, Packet8c>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
-                                                       const Packet2i& d) {
-  const int16x4_t ab_s16 = vmovn_s32(vcombine_s32(a, b));
-  const int16x4_t cd_s16 = vmovn_s32(vcombine_s32(c, d));
-  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet4i, Packet16uc>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
-                                                           const Packet4i& d) {
-  const uint16x8_t ab_u16 = pcast<Packet4i, Packet8us>(a, b);
-  const uint16x8_t cd_u16 = pcast<Packet4i, Packet8us>(c, d);
-  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet2i, Packet8uc>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
-                                                         const Packet2i& d) {
-  const uint16x4_t ab_u16 = pcast<Packet2i, Packet4us>(a, b);
-  const uint16x4_t cd_u16 = pcast<Packet2i, Packet4us>(c, d);
-  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
-}
-
-//==============================================================================
-// pcast, SrcType = uint32_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint32_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
-  return vcvtq_f32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet2ui, Packet2f>(const Packet2ui& a) {
-  return vcvt_f32_u32(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4ui, Packet2ul>(const Packet4ui& a) {
-  // Discard second half of input.
-  return vmovl_u32(vget_low_u32(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4ui, Packet2l>(const Packet4ui& a) {
-  return vreinterpretq_s64_u64(pcast<Packet4ui, Packet2ul>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet4ui, Packet4ui>(const Packet4ui& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet2ui, Packet2ui>(const Packet2ui& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet4ui, Packet4i>(const Packet4ui& a) {
-  return vreinterpretq_s32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet2ui, Packet2i>(const Packet2ui& a) {
-  return vreinterpret_s32_u32(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet4ui, Packet8us>(const Packet4ui& a, const Packet4ui& b) {
-  return vcombine_u16(vmovn_u32(a), vmovn_u32(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet2ui, Packet4us>(const Packet2ui& a, const Packet2ui& b) {
-  return vmovn_u32(vcombine_u32(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet4ui, Packet8s>(const Packet4ui& a, const Packet4ui& b) {
-  return vreinterpretq_s16_u16(pcast<Packet4ui, Packet8us>(a, b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet2ui, Packet4s>(const Packet2ui& a, const Packet2ui& b) {
-  return vreinterpret_s16_u16(pcast<Packet2ui, Packet4us>(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet4ui, Packet16uc>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
-                                                            const Packet4ui& d) {
-  const uint16x8_t ab_u16 = vcombine_u16(vmovn_u32(a), vmovn_u32(b));
-  const uint16x8_t cd_u16 = vcombine_u16(vmovn_u32(c), vmovn_u32(d));
-  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet2ui, Packet8uc>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
-                                                          const Packet2ui& d) {
-  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(a, b));
-  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(c, d));
-  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet4ui, Packet16c>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
-                                                          const Packet4ui& d) {
-  return vreinterpretq_s8_u8(pcast<Packet4ui, Packet16uc>(a, b, c, d));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet2ui, Packet8c>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
-                                                        const Packet2ui& d) {
-  return vreinterpret_s8_u8(pcast<Packet2ui, Packet8uc>(a, b, c, d));
-}
-
-//==============================================================================
-// pcast, SrcType = int64_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int64_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet2l, Packet4f>(const Packet2l& a, const Packet2l& b) {
-  return vcvtq_f32_s32(vcombine_s32(vmovn_s64(a), vmovn_s64(b)));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet2l, Packet2l>(const Packet2l& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet2l, Packet2ul>(const Packet2l& a) {
-  return vreinterpretq_u64_s64(a);
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet2l, Packet4i>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s32(vmovn_s64(a), vmovn_s64(b));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet2l, Packet4ui>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_u32(vmovn_u64(vreinterpretq_u64_s64(a)), vmovn_u64(vreinterpretq_u64_s64(b)));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet2l, Packet8s>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                       const Packet2l& d) {
-  const int32x4_t ab_s32 = pcast<Packet2l, Packet4i>(a, b);
-  const int32x4_t cd_s32 = pcast<Packet2l, Packet4i>(c, d);
-  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet2l, Packet8us>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                         const Packet2l& d) {
-  const uint32x4_t ab_u32 = pcast<Packet2l, Packet4ui>(a, b);
-  const uint32x4_t cd_u32 = pcast<Packet2l, Packet4ui>(c, d);
-  return vcombine_u16(vmovn_u32(ab_u32), vmovn_u32(cd_u32));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet2l, Packet16c>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                         const Packet2l& d, const Packet2l& e, const Packet2l& f,
-                                                         const Packet2l& g, const Packet2l& h) {
-  const int16x8_t abcd_s16 = pcast<Packet2l, Packet8s>(a, b, c, d);
-  const int16x8_t efgh_s16 = pcast<Packet2l, Packet8s>(e, f, g, h);
-  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet2l, Packet16uc>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                           const Packet2l& d, const Packet2l& e, const Packet2l& f,
-                                                           const Packet2l& g, const Packet2l& h) {
-  const uint16x8_t abcd_u16 = pcast<Packet2l, Packet8us>(a, b, c, d);
-  const uint16x8_t efgh_u16 = pcast<Packet2l, Packet8us>(e, f, g, h);
-  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
-}
-
-//==============================================================================
-// pcast, SrcType = uint64_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint64_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet2ul, Packet4f>(const Packet2ul& a, const Packet2ul& b) {
-  return vcvtq_f32_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet2ul, Packet2ul>(const Packet2ul& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet2ul, Packet2l>(const Packet2ul& a) {
-  return vreinterpretq_s64_u64(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet2ul, Packet4ui>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u32(vmovn_u64(a), vmovn_u64(b));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet2ul, Packet4i>(const Packet2ul& a, const Packet2ul& b) {
-  return vreinterpretq_s32_u32(pcast<Packet2ul, Packet4ui>(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet2ul, Packet8us>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                          const Packet2ul& d) {
-  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
-  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(vmovn_u64(c), vmovn_u64(d)));
-  return vcombine_u16(ab_u16, cd_u16);
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet2ul, Packet8s>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                        const Packet2ul& d) {
-  return vreinterpretq_s16_u16(pcast<Packet2ul, Packet8us>(a, b, c, d));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet2ul, Packet16uc>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                            const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
-                                                            const Packet2ul& g, const Packet2ul& h) {
-  const uint16x8_t abcd_u16 = pcast<Packet2ul, Packet8us>(a, b, c, d);
-  const uint16x8_t efgh_u16 = pcast<Packet2ul, Packet8us>(e, f, g, h);
-  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet2ul, Packet16c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                          const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
-                                                          const Packet2ul& g, const Packet2ul& h) {
-  return vreinterpretq_s8_u8(pcast<Packet2ul, Packet16uc>(a, b, c, d, e, f, g, h));
-}
-
-//==============================================================================
-// preinterpret
-//==============================================================================
-template <>
-EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2i>(const Packet2i& a) {
-  return vreinterpret_f32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2ui>(const Packet2ui& a) {
-  return vreinterpret_f32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
-  return vreinterpretq_f32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4ui>(const Packet4ui& a) {
-  return vreinterpretq_f32_u32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet4uc>(const Packet4uc& a) {
-  return static_cast<Packet4c>(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet8uc>(const Packet8uc& a) {
-  return vreinterpret_s8_u8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16c preinterpret<Packet16c, Packet16uc>(const Packet16uc& a) {
-  return vreinterpretq_s8_u8(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet4c>(const Packet4c& a) {
-  return static_cast<Packet4uc>(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet8c>(const Packet8c& a) {
-  return vreinterpret_u8_s8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16uc preinterpret<Packet16uc, Packet16c>(const Packet16c& a) {
-  return vreinterpretq_u8_s8(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet4us>(const Packet4us& a) {
-  return vreinterpret_s16_u16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8us>(const Packet8us& a) {
-  return vreinterpretq_s16_u16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet4s>(const Packet4s& a) {
-  return vreinterpret_u16_s16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8us preinterpret<Packet8us, Packet8s>(const Packet8s& a) {
-  return vreinterpretq_u16_s16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2f>(const Packet2f& a) {
-  return vreinterpret_s32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2ui>(const Packet2ui& a) {
-  return vreinterpret_s32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
-  return vreinterpretq_s32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
-  return vreinterpretq_s32_u32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2f>(const Packet2f& a) {
-  return vreinterpret_u32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2i>(const Packet2i& a) {
-  return vreinterpret_u32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4f>(const Packet4f& a) {
-  return vreinterpretq_u32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
-  return vreinterpretq_u32_s32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2ul>(const Packet2ul& a) {
-  return vreinterpretq_s64_u64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2l>(const Packet2l& a) {
-  return vreinterpretq_u64_s64(a);
-}
-
-#if EIGEN_ARCH_ARM64
-
-//==============================================================================
-// pcast/preinterpret, Double
-//==============================================================================
-
-template <>
-struct type_casting_traits<double, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet2d, Packet2d>(const Packet2d& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<double, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
-  return vcombine_f32(vcvt_f32_f64(a), vcvt_f32_f64(b));
-}
-
-template <>
-struct type_casting_traits<double, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet2d, Packet2l>(const Packet2d& a) {
-  return vcvtq_s64_f64(a);
-}
-
-template <>
-struct type_casting_traits<double, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet2d, Packet2ul>(const Packet2d& a) {
-  return vcvtq_u64_f64(a);
-}
-
-template <>
-struct type_casting_traits<double, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet2d, Packet4i>(const Packet2d& a, const Packet2d& b) {
-  return vcombine_s32(vmovn_s64(vcvtq_s64_f64(a)), vmovn_s64(vcvtq_s64_f64(b)));
-}
-
-template <>
-struct type_casting_traits<double, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet2d, Packet4ui>(const Packet2d& a, const Packet2d& b) {
-  return vcombine_u32(vmovn_u64(vcvtq_u64_f64(a)), vmovn_u64(vcvtq_u64_f64(b)));
-}
-
-template <>
-struct type_casting_traits<double, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet2d, Packet8s>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                       const Packet2d& d) {
-  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
-  const int32x4_t cd_s32 = pcast<Packet2d, Packet4i>(c, d);
-  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
-}
-
-template <>
-struct type_casting_traits<double, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet2d, Packet8us>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                         const Packet2d& d) {
-  const uint32x4_t ab_u32 = pcast<Packet2d, Packet4ui>(a, b);
-  const uint32x4_t cd_u32 = pcast<Packet2d, Packet4ui>(c, d);
-  return vcombine_u16(vmovn_u32(ab_u32), vmovn_u32(cd_u32));
-}
-
-template <>
-struct type_casting_traits<double, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet2d, Packet16c>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                         const Packet2d& d, const Packet2d& e, const Packet2d& f,
-                                                         const Packet2d& g, const Packet2d& h) {
-  const int16x8_t abcd_s16 = pcast<Packet2d, Packet8s>(a, b, c, d);
-  const int16x8_t efgh_s16 = pcast<Packet2d, Packet8s>(e, f, g, h);
-  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
-}
-
-template <>
-struct type_casting_traits<double, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet2d, Packet16uc>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                           const Packet2d& d, const Packet2d& e, const Packet2d& f,
-                                                           const Packet2d& g, const Packet2d& h) {
-  const uint16x8_t abcd_u16 = pcast<Packet2d, Packet8us>(a, b, c, d);
-  const uint16x8_t efgh_u16 = pcast<Packet2d, Packet8us>(e, f, g, h);
-  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
-}
-
-template <>
-struct type_casting_traits<float, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
-  // Discard second-half of input.
-  return vcvt_f64_f32(vget_low_f32(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet16c, Packet2d>(const Packet16c& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet8c, Packet2f>(vget_low_s8(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet16uc, Packet2d>(const Packet16uc& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet8uc, Packet2f>(vget_low_u8(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet8s, Packet2d>(const Packet8s& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet4s, Packet2f>(vget_low_s16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet8us, Packet2d>(const Packet8us& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet4us, Packet2f>(vget_low_u16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet4i, Packet2d>(const Packet4i& a) {
-  // Discard second half of input.
-  return vcvtq_f64_s64(vmovl_s32(vget_low_s32(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet4ui, Packet2d>(const Packet4ui& a) {
-  // Discard second half of input.
-  return vcvtq_f64_u64(vmovl_u32(vget_low_u32(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet2l, Packet2d>(const Packet2l& a) {
-  return vcvtq_f64_s64(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet2ul, Packet2d>(const Packet2ul& a) {
-  return vcvtq_f64_u64(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
-  return vreinterpretq_f64_s64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2ul>(const Packet2ul& a) {
-  return vreinterpretq_f64_u64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
-  return vreinterpretq_s64_f64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2d>(const Packet2d& a) {
-  return vreinterpretq_u64_f64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
-  return vreinterpretq_f64_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
-  return vreinterpretq_s32_f64(a);
-}
-
-#endif  // EIGEN_ARCH_ARM64
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_TYPE_CASTING_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/Complex.h
deleted file mode 100644
index 8fe22da..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/Complex.h
+++ /dev/null
@@ -1,351 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_SSE_H
-#define EIGEN_COMPLEX_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const __m128& a) : v(a) {}
-  Packet4f v;
-};
-
-// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
-// to leverage AVX instructions.
-#ifndef EIGEN_VECTORIZE_AVX
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0,
-    HasBlend  = 1
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet2cf> {
-  typedef std::complex<float> type;
-  typedef Packet2cf half;
-  typedef Packet4f as_real;
-  enum {
-    size=2,
-    alignment=Aligned16,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_add_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_sub_ps(a.v,b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a)
-{
-  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000));
-  return Packet2cf(_mm_xor_ps(a.v,mask));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
-{
-  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000));
-  return Packet2cf(_mm_xor_ps(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  #ifdef EIGEN_VECTORIZE_SSE3
-  return Packet2cf(_mm_addsub_ps(_mm_mul_ps(_mm_moveldup_ps(a.v), b.v),
-                                 _mm_mul_ps(_mm_movehdup_ps(a.v),
-                                            vec4f_swizzle1(b.v, 1, 0, 3, 2))));
-//   return Packet2cf(_mm_addsub_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v),
-//                                  _mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3),
-//                                             vec4f_swizzle1(b.v, 1, 0, 3, 2))));
-  #else
-  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x00000000,0x80000000,0x00000000));
-  return Packet2cf(_mm_add_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v),
-                              _mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3),
-                                                    vec4f_swizzle1(b.v, 1, 0, 3, 2)), mask)));
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf ptrue  <Packet2cf>(const Packet2cf& a) { return Packet2cf(ptrue(Packet4f(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_and_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_or_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_xor_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_andnot_ps(b.v,a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&numext::real_ref(*from))); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&numext::real_ref(*from))); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-#ifdef EIGEN_VECTORIZE_SSE3
-  res.v = _mm_castpd_ps(_mm_loaddup_pd(reinterpret_cast<double const*>(&from)));
-#else
-  res.v = _mm_castpd_ps(_mm_load_sd(reinterpret_cast<double const*>(&from)));
-  res.v = _mm_movelh_ps(res.v, res.v);
-#endif
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), Packet4f(from.v)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), Packet4f(from.v)); }
-
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
-{
-  return Packet2cf(_mm_set_ps(std::imag(from[1*stride]), std::real(from[1*stride]),
-                              std::imag(from[0*stride]), std::real(from[0*stride])));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 0)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 1)));
-  to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 2)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 3)));
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
-{
-  #if EIGEN_GNUC_AT_MOST(4,3)
-  // Workaround gcc 4.2 ICE - this is not performance wise ideal, but who cares...
-  // This workaround also fix invalid code generation with gcc 4.3
-  EIGEN_ALIGN16 std::complex<float> res[2];
-  _mm_store_ps((float*)res, a.v);
-  return res[0];
-  #else
-  std::complex<float> res;
-  _mm_storel_pi((__m64*)&res, a.v);
-  return res;
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) { return Packet2cf(_mm_castpd_ps(preverse(Packet2d(_mm_castps_pd(a.v))))); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  return pfirst(Packet2cf(_mm_add_ps(a.v, _mm_movehl_ps(a.v,a.v))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  return pfirst(pmul(a, Packet2cf(_mm_movehl_ps(a.v,a.v))));
-}
-
-EIGEN_STRONG_INLINE Packet2cf pcplxflip/* <Packet2cf> */(const Packet2cf& x)
-{
-  return Packet2cf(vec4f_swizzle1(x.v, 1, 0, 3, 2));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for SSE3 and 4
-  Packet2cf res = pmul(a, pconj(b));
-  __m128 s = _mm_mul_ps(b.v,b.v);
-  return Packet2cf(_mm_div_ps(res.v,_mm_add_ps(s,vec4f_swizzle1(s, 1, 0, 3, 2))));
-}
-
-
-
-//---------- double ----------
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const __m128d& a) : v(a) {}
-  Packet2d v;
-};
-
-// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
-// to leverage AVX instructions.
-#ifndef EIGEN_VECTORIZE_AVX
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet1cd> {
-  typedef std::complex<double> type;
-  typedef Packet1cd half;
-  typedef Packet2d as_real;
-  enum {
-    size=1,
-    alignment=Aligned16,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_add_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_sub_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a)
-{
-  const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0));
-  return Packet1cd(_mm_xor_pd(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  #ifdef EIGEN_VECTORIZE_SSE3
-  return Packet1cd(_mm_addsub_pd(_mm_mul_pd(_mm_movedup_pd(a.v), b.v),
-                                 _mm_mul_pd(vec2d_swizzle1(a.v, 1, 1),
-                                            vec2d_swizzle1(b.v, 1, 0))));
-  #else
-  const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0));
-  return Packet1cd(_mm_add_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v),
-                              _mm_xor_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 1, 1),
-                                                    vec2d_swizzle1(b.v, 1, 0)), mask)));
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd ptrue  <Packet1cd>(const Packet1cd& a) { return Packet1cd(ptrue(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pand   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_and_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd por    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_or_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pxor   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_xor_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_andnot_pd(b.v,a.v)); }
-
-// FIXME force unaligned load, this is a temporary fix
-template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
-{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) { return pset1<Packet1cd>(*from); }
-
-// FIXME force unaligned store, this is a temporary fix
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, Packet2d(from.v)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, Packet2d(from.v)); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
-{
-  EIGEN_ALIGN16 double res[2];
-  _mm_store_pd(res, a.v);
-  return std::complex<double>(res[0],res[1]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for SSE3 and 4
-  Packet1cd res = pmul(a,pconj(b));
-  __m128d s = _mm_mul_pd(b.v,b.v);
-  return Packet1cd(_mm_div_pd(res.v, _mm_add_pd(s,_mm_shuffle_pd(s, s, 0x1))));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/* <Packet1cd> */(const Packet1cd& x)
-{
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2cf,2>& kernel) {
-  __m128d w1 = _mm_castps_pd(kernel.packet[0].v);
-  __m128d w2 = _mm_castps_pd(kernel.packet[1].v);
-
-  __m128 tmp = _mm_castpd_ps(_mm_unpackhi_pd(w1, w2));
-  kernel.packet[0].v = _mm_castpd_ps(_mm_unpacklo_pd(w1, w2));
-  kernel.packet[1].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b)
-{
-  __m128 eq = _mm_cmpeq_ps(a.v, b.v);
-  return Packet2cf(pand<Packet4f>(eq, vec4f_swizzle1(eq, 1, 0, 3, 2)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b)
-{
-  __m128d eq = _mm_cmpeq_pd(a.v, b.v);
-  return Packet1cd(pand<Packet2d>(eq, vec2d_swizzle1(eq, 1, 0)));
-}
-
-template<>  EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  __m128d result = pblend<Packet2d>(ifPacket, _mm_castps_pd(thenPacket.v), _mm_castps_pd(elsePacket.v));
-  return Packet2cf(_mm_castpd_ps(result));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
-  return psqrt_complex<Packet1cd>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
-  return psqrt_complex<Packet2cf>(a);
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/MathFunctions.h
deleted file mode 100644
index 8736d0d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/MathFunctions.h
+++ /dev/null
@@ -1,199 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The sin and cos and functions of this file come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-#ifndef EIGEN_MATH_FUNCTIONS_SSE_H
-#define EIGEN_MATH_FUNCTIONS_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog<Packet4f>(const Packet4f& _x) {
-  return plog_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d plog<Packet2d>(const Packet2d& _x) {
-  return plog_double(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog2<Packet4f>(const Packet4f& _x) {
-  return plog2_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d plog2<Packet2d>(const Packet2d& _x) {
-  return plog2_double(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog1p<Packet4f>(const Packet4f& _x) {
-  return generic_plog1p(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexpm1<Packet4f>(const Packet4f& _x) {
-  return generic_expm1(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexp<Packet4f>(const Packet4f& _x)
-{
-  return pexp_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d pexp<Packet2d>(const Packet2d& x)
-{
-  return pexp_double(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psin<Packet4f>(const Packet4f& _x)
-{
-  return psin_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pcos<Packet4f>(const Packet4f& _x)
-{
-  return pcos_float(_x);
-}
-
-#if EIGEN_FAST_MATH
-
-// Functions for sqrt.
-// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
-// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
-// exact solution. It does not handle +inf, or denormalized numbers correctly.
-// The main advantage of this approach is not just speed, but also the fact that
-// it can be inlined and pipelined with other computations, further reducing its
-// effective latency. This is similar to Quake3's fast inverse square root.
-// For detail see here: http://www.beyond3d.com/content/articles/8/
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& _x)
-{
-  Packet4f minus_half_x = pmul(_x, pset1<Packet4f>(-0.5f));
-  Packet4f denormal_mask = pandnot(
-      pcmp_lt(_x, pset1<Packet4f>((std::numeric_limits<float>::min)())),
-      pcmp_lt(_x, pzero(_x)));
-
-  // Compute approximate reciprocal sqrt.
-  Packet4f x = _mm_rsqrt_ps(_x);
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(minus_half_x, pmul(x,x), pset1<Packet4f>(1.5f)));
-  // Flush results for denormals to zero.
-  return pandnot(pmul(_x,x), denormal_mask);
-}
-
-#else
-
-template<>EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& x) { return _mm_sqrt_ps(x); }
-
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d psqrt<Packet2d>(const Packet2d& x) { return _mm_sqrt_pd(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet16b psqrt<Packet16b>(const Packet16b& x) { return x; }
-
-#if EIGEN_FAST_MATH
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& _x) {
-  _EIGEN_DECLARE_CONST_Packet4f(one_point_five, 1.5f);
-  _EIGEN_DECLARE_CONST_Packet4f(minus_half, -0.5f);
-  _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inf, 0x7f800000u);
-  _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(flt_min, 0x00800000u);
-
-  Packet4f neg_half = pmul(_x, p4f_minus_half);
-
-  // Identity infinite, zero, negative and denormal arguments.
-  Packet4f lt_min_mask = _mm_cmplt_ps(_x, p4f_flt_min);
-  Packet4f inf_mask = _mm_cmpeq_ps(_x, p4f_inf);
-  Packet4f not_normal_finite_mask = _mm_or_ps(lt_min_mask, inf_mask);
-
-  // Compute an approximate result using the rsqrt intrinsic.
-  Packet4f y_approx = _mm_rsqrt_ps(_x);
-
-  // Do a single step of Newton-Raphson iteration to improve the approximation.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet4f y_newton = pmul(
-      y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p4f_one_point_five));
-
-  // Select the result of the Newton-Raphson step for positive normal arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(x) = +inf if
-  // x is zero or a positive denormalized float (equivalent to flushing positive
-  // denormalized inputs to zero).
-  return pselect<Packet4f>(not_normal_finite_mask, y_approx, y_newton);
-}
-
-#else
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& x) {
-  // Unfortunately we can't use the much faster mm_rsqrt_ps since it only provides an approximation.
-  return _mm_div_ps(pset1<Packet4f>(1.0f), _mm_sqrt_ps(x));
-}
-
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d prsqrt<Packet2d>(const Packet2d& x) {
-  return _mm_div_pd(pset1<Packet2d>(1.0), _mm_sqrt_pd(x));
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-
-} // end namespace internal
-
-namespace numext {
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float sqrt(const float &x)
-{
-  return internal::pfirst(internal::Packet4f(_mm_sqrt_ss(_mm_set_ss(x))));
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double sqrt(const double &x)
-{
-#if EIGEN_COMP_GNUC_STRICT
-  // This works around a GCC bug generating poor code for _mm_sqrt_pd
-  // See https://gitlab.com/libeigen/eigen/commit/8dca9f97e38970
-  return internal::pfirst(internal::Packet2d(__builtin_ia32_sqrtsd(_mm_set_sd(x))));
-#else
-  return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x))));
-#endif
-}
-
-} // end namespace numex
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATH_FUNCTIONS_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/PacketMath.h
deleted file mode 100644
index db102c7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/PacketMath.h
+++ /dev/null
@@ -1,1505 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_SSE_H
-#define EIGEN_PACKET_MATH_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#if !defined(EIGEN_VECTORIZE_AVX) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
-// 32 bits =>  8 registers
-// 64 bits => 16 registers
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*))
-#endif
-
-#ifdef EIGEN_VECTORIZE_FMA
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-#endif
-
-#if ((defined EIGEN_VECTORIZE_AVX) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_MINGW) && (__GXX_ABI_VERSION < 1004)) || EIGEN_OS_QNX
-// With GCC's default ABI version, a __m128 or __m256 are the same types and therefore we cannot
-// have overloads for both types without linking error.
-// One solution is to increase ABI version using -fabi-version=4 (or greater).
-// Otherwise, we workaround this inconvenience by wrapping 128bit types into the following helper
-// structure:
-typedef eigen_packet_wrapper<__m128>  Packet4f;
-typedef eigen_packet_wrapper<__m128d> Packet2d;
-#else
-typedef __m128  Packet4f;
-typedef __m128d Packet2d;
-#endif
-
-typedef eigen_packet_wrapper<__m128i, 0> Packet4i;
-typedef eigen_packet_wrapper<__m128i, 1> Packet16b;
-
-template<> struct is_arithmetic<__m128>  { enum { value = true }; };
-template<> struct is_arithmetic<__m128i> { enum { value = true }; };
-template<> struct is_arithmetic<__m128d> { enum { value = true }; };
-template<> struct is_arithmetic<Packet4i>  { enum { value = true }; };
-template<> struct is_arithmetic<Packet16b>  { enum { value = true }; };
-
-template<int p, int q, int r, int s>
-struct shuffle_mask{
- enum { mask = (s)<<6|(r)<<4|(q)<<2|(p) };
-};
-
-// TODO: change the implementation of all swizzle* ops from macro to template,
-#define vec4f_swizzle1(v,p,q,r,s) \
-  Packet4f(_mm_castsi128_ps(_mm_shuffle_epi32( _mm_castps_si128(v), (shuffle_mask<p,q,r,s>::mask))))
-
-#define vec4i_swizzle1(v,p,q,r,s) \
-  Packet4i(_mm_shuffle_epi32( v, (shuffle_mask<p,q,r,s>::mask)))
-
-#define vec2d_swizzle1(v,p,q) \
-  Packet2d(_mm_castsi128_pd(_mm_shuffle_epi32( _mm_castpd_si128(v), (shuffle_mask<2*p,2*p+1,2*q,2*q+1>::mask))))
-
-#define vec4f_swizzle2(a,b,p,q,r,s) \
-  Packet4f(_mm_shuffle_ps( (a), (b), (shuffle_mask<p,q,r,s>::mask)))
-
-#define vec4i_swizzle2(a,b,p,q,r,s) \
-  Packet4i(_mm_castps_si128( (_mm_shuffle_ps( _mm_castsi128_ps(a), _mm_castsi128_ps(b), (shuffle_mask<p,q,r,s>::mask)))))
-
-EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_movelh_ps(a,b));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_movehl_ps(a,b));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_unpacklo_ps(a,b));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_unpackhi_ps(a,b));
-}
-#define vec4f_duplane(a,p) \
-  vec4f_swizzle2(a,a,p,p,p,p)
-
-#define vec2d_swizzle2(a,b,mask) \
-  Packet2d(_mm_shuffle_pd(a,b,mask))
-
-EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a, const Packet2d& b)
-{
-  return Packet2d(_mm_unpacklo_pd(a,b));
-}
-EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a, const Packet2d& b)
-{
-  return Packet2d(_mm_unpackhi_pd(a,b));
-}
-#define vec2d_duplane(a,p) \
-  vec2d_swizzle2(a,a,(p<<1)|p)
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  const Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
-  const Packet2d p2d_##NAME = pset1<Packet2d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = pset1frombits<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  const Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-
-// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
-// to leverage AVX instructions.
-#ifndef EIGEN_VECTORIZE_AVX
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasLog1p = 1,
-    HasExpm1 = 1,
-    HasNdtri = 1,
-    HasExp = 1,
-    HasBessel = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasBlend = 1,
-    HasCeil = 1,
-    HasFloor = 1,
-#ifdef EIGEN_VECTORIZE_SSE4_1
-    HasRound = 1,
-#endif
-    HasRint = 1
-  };
-};
-template <>
-struct packet_traits<double> : default_packet_traits {
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 0,
-
-    HasCmp  = 1,
-    HasDiv  = 1,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasBlend = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-#ifdef EIGEN_VECTORIZE_SSE4_1
-    HasRound = 1,
-#endif
-    HasRint = 1
-  };
-};
-#endif
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet4i type;
-  typedef Packet4i half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=4,
-
-    HasShift = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct packet_traits<bool> : default_packet_traits
-{
-  typedef Packet16b type;
-  typedef Packet16b half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    HasHalfPacket = 0,
-    size=16,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 0,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 0,
-    HasAbs2      = 0,
-    HasMin       = 0,
-    HasMax       = 0,
-    HasConj      = 0,
-    HasSqrt      = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet4f> {
-  typedef float     type;
-  typedef Packet4f  half;
-  typedef Packet4i  integer_packet;
-  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet2d> {
-  typedef double    type;
-  typedef Packet2d  half;
-  enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet4i> {
-  typedef int       type;
-  typedef Packet4i  half;
-  enum {size=4, alignment=Aligned16, vectorizable=false, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet16b> {
-  typedef bool       type;
-  typedef Packet16b  half;
-  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-#ifndef EIGEN_VECTORIZE_AVX
-template<> struct scalar_div_cost<float,true> { enum { value = 7 }; };
-template<> struct scalar_div_cost<double,true> { enum { value = 8 }; };
-#endif
-
-#if EIGEN_COMP_MSVC==1500
-// Workaround MSVC 9 internal compiler error.
-// TODO: It has been detected with win64 builds (amd64), so let's check whether it also happens in 32bits+SSE mode
-// TODO: let's check whether there does not exist a better fix, like adding a pset0() function. (it crashed on pset1(0)).
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) { return _mm_set_ps(from,from,from,from); }
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return _mm_set_pd(from,from); }
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from) { return _mm_set_epi32(from,from,from,from); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) { return _mm_set_ps1(from); }
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return _mm_set1_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from) { return _mm_set1_epi32(from); }
-#endif
-template<> EIGEN_STRONG_INLINE Packet16b pset1<Packet16b>(const bool&    from) { return _mm_set1_epi8(static_cast<char>(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) { return _mm_castsi128_ps(pset1<Packet4i>(from)); }
-template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from) { return _mm_castsi128_pd(_mm_set1_epi64x(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f peven_mask(const Packet4f& /*a*/) { return _mm_castsi128_ps(_mm_set_epi32(0, -1, 0, -1)); }
-template<> EIGEN_STRONG_INLINE Packet4i peven_mask(const Packet4i& /*a*/) { return _mm_set_epi32(0, -1, 0, -1); }
-template<> EIGEN_STRONG_INLINE Packet2d peven_mask(const Packet2d& /*a*/) { return _mm_castsi128_pd(_mm_set_epi32(0, 0, -1, -1)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pzero(const Packet4f& /*a*/) { return _mm_setzero_ps(); }
-template<> EIGEN_STRONG_INLINE Packet2d pzero(const Packet2d& /*a*/) { return _mm_setzero_pd(); }
-template<> EIGEN_STRONG_INLINE Packet4i pzero(const Packet4i& /*a*/) { return _mm_setzero_si128(); }
-
-// GCC generates a shufps instruction for _mm_set1_ps/_mm_load1_ps instead of the more efficient pshufd instruction.
-// However, using inrinsics for pset1 makes gcc to generate crappy code in some cases (see bug 203)
-// Using inline assembly is also not an option because then gcc fails to reorder properly the instructions.
-// Therefore, we introduced the pload1 functions to be used in product kernels for which bug 203 does not apply.
-// Also note that with AVX, we want it to generate a vbroadcastss.
-#if EIGEN_COMP_GNUC_STRICT && (!defined __AVX__)
-template<> EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float *from) {
-  return vec4f_swizzle1(_mm_load_ss(from),0,0,0,0);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) { return _mm_add_ps(pset1<Packet4f>(a), _mm_set_ps(3,2,1,0)); }
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return _mm_add_pd(pset1<Packet2d>(a),_mm_set_pd(1,0)); }
-template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) { return _mm_add_epi32(pset1<Packet4i>(a),_mm_set_epi32(3,2,1,0)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_add_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_add_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_add_epi32(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet16b padd<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_or_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_sub_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_sub_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_sub_epi32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b psub<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_xor_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
-template<> EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE3
-  return _mm_addsub_ps(a,b);
-#else
-  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x0,0x80000000,0x0));
-  return padd(a, pxor(mask, b));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& , const Packet2d& );
-template<> EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b) 
-{
-#ifdef EIGEN_VECTORIZE_SSE3  
-  return _mm_addsub_pd(a,b); 
-#else
-  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0,0x80000000,0x0,0x0)); 
-  return padd(a, pxor(mask, b));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
-{
-  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000));
-  return _mm_xor_ps(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a)
-{
-  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0,0x80000000,0x0,0x80000000));
-  return _mm_xor_pd(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a)
-{
-  return psub(Packet4i(_mm_setr_epi32(0,0,0,0)), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16b pnegate(const Packet16b& a)
-{
-  return psub(pset1<Packet16b>(false), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_mul_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_mul_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_mullo_epi32(a,b);
-#else
-  // this version is slightly faster than 4 scalar products
-  return vec4i_swizzle1(
-            vec4i_swizzle2(
-              _mm_mul_epu32(a,b),
-              _mm_mul_epu32(vec4i_swizzle1(a,1,0,3,2),
-                            vec4i_swizzle1(b,1,0,3,2)),
-              0,2,0,2),
-            0,2,1,3);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet16b pmul<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_and_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_div_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_div_pd(a,b); }
-
-// for some weird raisons, it has to be overloaded for packet of integers
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd(pmul(a,b), c); }
-#ifdef EIGEN_VECTORIZE_FMA
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fmadd_ps(a,b,c); }
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fmadd_pd(a,b,c); }
-#endif
-
-#ifdef EIGEN_VECTORIZE_SSE4_1
-template<> EIGEN_DEVICE_FUNC inline Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
-  return _mm_blendv_ps(b,a,mask);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b) {
-  return _mm_castps_si128(_mm_blendv_ps(_mm_castsi128_ps(b),_mm_castsi128_ps(a),_mm_castsi128_ps(mask)));
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2d pselect(const Packet2d& mask, const Packet2d& a, const Packet2d& b) {  return _mm_blendv_pd(b,a,mask); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet16b pselect(const Packet16b& mask, const Packet16b& a, const Packet16b& b) {
-  return _mm_blendv_epi8(b,a,mask);
-}
-#else
-template<> EIGEN_DEVICE_FUNC inline Packet16b pselect(const Packet16b& mask, const Packet16b& a, const Packet16b& b) {
-  Packet16b a_part = _mm_and_si128(mask, a);
-  Packet16b b_part = _mm_andnot_si128(mask, b);
-  return _mm_or_si128(a_part, b_part);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4i ptrue<Packet4i>(const Packet4i& a) { return _mm_cmpeq_epi32(a, a); }
-template<> EIGEN_STRONG_INLINE Packet16b ptrue<Packet16b>(const Packet16b& a) { return _mm_cmpeq_epi8(a, a); }
-template<> EIGEN_STRONG_INLINE Packet4f
-ptrue<Packet4f>(const Packet4f& a) {
-  Packet4i b = _mm_castps_si128(a);
-  return _mm_castsi128_ps(_mm_cmpeq_epi32(b, b));
-}
-template<> EIGEN_STRONG_INLINE Packet2d
-ptrue<Packet2d>(const Packet2d& a) {
-  Packet4i b = _mm_castpd_si128(a);
-  return _mm_castsi128_pd(_mm_cmpeq_epi32(b, b));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_and_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_and_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_and_si128(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b pand<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_and_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_or_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_or_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_or_si128(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b por<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_or_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_xor_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_xor_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_xor_si128(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b pxor<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_xor_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_andnot_ps(b,a); }
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_andnot_pd(b,a); }
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_andnot_si128(b,a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) { return _mm_cmple_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) { return _mm_cmplt_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) { return _mm_cmpnge_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) { return _mm_cmpeq_ps(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) { return _mm_cmple_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) { return _mm_cmplt_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) { return _mm_cmpnge_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) { return _mm_cmpeq_pd(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) { return _mm_cmplt_epi32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) { return _mm_cmpeq_epi32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b pcmp_eq(const Packet16b& a, const Packet16b& b) { return _mm_cmpeq_epi8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return por(pcmp_lt(a,b), pcmp_eq(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_min_ps, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet4f res;
-  asm("vminps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet4f res = b;
-  asm("minps %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm_min_ps(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_min_pd, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet2d res;
-  asm("vminpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet2d res = b;
-  asm("minpd %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm_min_pd(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_min_epi32(a,b);
-#else
-  // after some bench, this version *is* faster than a scalar implementation
-  Packet4i mask = _mm_cmplt_epi32(a,b);
-  return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
-#endif
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_max_ps, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet4f res;
-  asm("vmaxps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet4f res = b;
-  asm("maxps %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm_max_ps(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_max_pd, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet2d res;
-  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet2d res = b;
-  asm("maxpd %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm_max_pd(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_max_epi32(a,b);
-#else
-  // after some bench, this version *is* faster than a scalar implementation
-  Packet4i mask = _mm_cmpgt_epi32(a,b);
-  return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
-#endif
-}
-
-template <typename Packet, typename Op>
-EIGEN_STRONG_INLINE Packet pminmax_propagate_numbers(const Packet& a, const Packet& b, Op op) {
-  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
-  // always return a if either a or b is NaN.
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet m = op(a, b);
-  return pselect<Packet>(not_nan_mask_a, m, b);
-}
-
-template <typename Packet, typename Op>
-EIGEN_STRONG_INLINE Packet pminmax_propagate_nan(const Packet& a, const Packet& b, Op op) {
-  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
-  // always return a if either a or b is NaN.
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet m = op(b, a);
-  return pselect<Packet>(not_nan_mask_a, m, a);
-}
-
-// Add specializations for min/max with prescribed NaN progation.
-template<>
-EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet2d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet2d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet2d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet2d>);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a) { return _mm_srai_epi32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right   (const Packet4i& a) { return _mm_srli_epi32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left    (const Packet4i& a) { return _mm_slli_epi32(a,N); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a)
-{
-  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF));
-  return _mm_and_ps(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a)
-{
-  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF));
-  return _mm_and_pd(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a)
-{
-  #ifdef EIGEN_VECTORIZE_SSSE3
-  return _mm_abs_epi32(a);
-  #else
-  Packet4i aux = _mm_srai_epi32(a,31);
-  return _mm_sub_epi32(_mm_xor_si128(a,aux),aux);
-  #endif
-}
-
-#ifdef EIGEN_VECTORIZE_SSE4_1
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
-{
-  // Unfortunatly _mm_round_ps doesn't have a rounding mode to implement numext::round.
-  const Packet4f mask = pset1frombits<Packet4f>(0x80000000u);
-  const Packet4f prev0dot5 = pset1frombits<Packet4f>(0x3EFFFFFFu);
-  return _mm_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a)
-{
-  const Packet2d mask = _mm_castsi128_pd(_mm_set_epi64x(0x8000000000000000ull, 0x8000000000000000ull));
-  const Packet2d prev0dot5 = _mm_castsi128_pd(_mm_set_epi64x(0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull));
-  return _mm_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a) { return _mm_round_ps(a, _MM_FROUND_CUR_DIRECTION); }
-template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a) { return _mm_round_pd(a, _MM_FROUND_CUR_DIRECTION); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) { return _mm_ceil_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) { return _mm_ceil_pd(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return _mm_floor_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return _mm_floor_pd(a); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f print(const Packet4f& a) {
-  // Adds and subtracts signum(a) * 2^23 to force rounding.
-  const Packet4f limit = pset1<Packet4f>(static_cast<float>(1<<23));
-  const Packet4f abs_a = pabs(a);
-  Packet4f r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d print(const Packet2d& a) {
-  // Adds and subtracts signum(a) * 2^52 to force rounding.
-  const Packet2d limit = pset1<Packet2d>(static_cast<double>(1ull<<52));
-  const Packet2d abs_a = pabs(a);
-  Packet2d r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If greater, subtract one.
-  Packet4f mask = _mm_cmpgt_ps(tmp, a);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a)
-{
-  const Packet2d cst_1 = pset1<Packet2d>(1.0);
-  Packet2d tmp  = print<Packet2d>(a);
-  // If greater, subtract one.
-  Packet2d mask = _mm_cmpgt_pd(tmp, a);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If smaller, add one.
-  Packet4f mask = _mm_cmplt_ps(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a)
-{
-  const Packet2d cst_1 = pset1<Packet2d>(1.0);
-  Packet2d tmp  = print<Packet2d>(a);
-  // If smaller, add one.
-  Packet2d mask = _mm_cmplt_pd(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float*   from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double*  from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
-template<> EIGEN_STRONG_INLINE Packet16b pload<Packet16b>(const bool*     from) { EIGEN_DEBUG_ALIGNED_LOAD return  _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
-
-#if EIGEN_COMP_MSVC
-  template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float*  from) {
-    EIGEN_DEBUG_UNALIGNED_LOAD
-    #if (EIGEN_COMP_MSVC==1600)
-    // NOTE Some version of MSVC10 generates bad code when using _mm_loadu_ps
-    // (i.e., it does not generate an unaligned load!!
-    __m128 res = _mm_loadl_pi(_mm_set1_ps(0.0f), (const __m64*)(from));
-    res = _mm_loadh_pi(res, (const __m64*)(from+2));
-    return res;
-    #else
-    return _mm_loadu_ps(from);
-    #endif
-  }
-#else
-// NOTE: with the code below, MSVC's compiler crashes!
-
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_ps(from);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_pd(from);
-}
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-template<> EIGEN_STRONG_INLINE Packet16b ploadu<Packet16b>(const bool*     from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*   from)
-{
-  return vec4f_swizzle1(_mm_castpd_ps(_mm_load_sd(reinterpret_cast<const double*>(from))), 0, 0, 1, 1);
-}
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*  from)
-{ return pset1<Packet2d>(from[0]); }
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
-{
-  Packet4i tmp;
-  tmp = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(from));
-  return vec4i_swizzle1(tmp, 0, 0, 1, 1);
-}
-
-// Loads 8 bools from memory and returns the packet
-// {b0, b0, b1, b1, b2, b2, b3, b3, b4, b4, b5, b5, b6, b6, b7, b7}
-template<> EIGEN_STRONG_INLINE Packet16b ploaddup<Packet16b>(const bool*     from)
-{
-  __m128i tmp = _mm_castpd_si128(pload1<Packet2d>(reinterpret_cast<const double*>(from)));
-  return  _mm_unpacklo_epi8(tmp, tmp);
-}
-
-// Loads 4 bools from memory and returns the packet
-// {b0, b0  b0, b0, b1, b1, b1, b1, b2, b2, b2, b2, b3, b3, b3, b3}
-template<> EIGEN_STRONG_INLINE Packet16b
-ploadquad<Packet16b>(const bool* from) {
-  __m128i tmp = _mm_castps_si128(pload1<Packet4f>(reinterpret_cast<const float*>(from)));
-  tmp = _mm_unpacklo_epi8(tmp, tmp);
-  return  _mm_unpacklo_epi16(tmp, tmp);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
-template<> EIGEN_STRONG_INLINE void pstore<bool>(bool*     to, const Packet16b& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*       to, const Packet4i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<bool>(bool*     to, const Packet16b& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
- return _mm_set_ps(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
- return _mm_set_pd(from[1*stride], from[0*stride]);
-}
-template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
-{
- return _mm_set_epi32(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet16b pgather<bool, Packet16b>(const bool* from, Index stride)
-{
-  return _mm_set_epi8(from[15*stride], from[14*stride], from[13*stride], from[12*stride],
-                      from[11*stride], from[10*stride], from[9*stride], from[8*stride],
-                      from[7*stride], from[6*stride], from[5*stride], from[4*stride],
-                      from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  to[stride*0] = _mm_cvtss_f32(from);
-  to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 1));
-  to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 2));
-  to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 3));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  to[stride*0] = _mm_cvtsd_f64(from);
-  to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(from, from, 1));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
-{
-  to[stride*0] = _mm_cvtsi128_si32(from);
-  to[stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1));
-  to[stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2));
-  to[stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<bool, Packet16b>(bool* to, const Packet16b& from, Index stride)
-{
-  to[4*stride*0] = _mm_cvtsi128_si32(from);
-  to[4*stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1));
-  to[4*stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2));
-  to[4*stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3));
-}
-
-
-// some compilers might be tempted to perform multiple moves instead of using a vector path.
-template<> EIGEN_STRONG_INLINE void pstore1<Packet4f>(float* to, const float& a)
-{
-  Packet4f pa = _mm_set_ss(a);
-  pstore(to, Packet4f(vec4f_swizzle1(pa,0,0,0,0)));
-}
-// some compilers might be tempted to perform multiple moves instead of using a vector path.
-template<> EIGEN_STRONG_INLINE void pstore1<Packet2d>(double* to, const double& a)
-{
-  Packet2d pa = _mm_set_sd(a);
-  pstore(to, Packet2d(vec2d_swizzle1(pa,0,0)));
-}
-
-#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
-typedef const void * SsePrefetchPtrType;
-#else
-typedef const char * SsePrefetchPtrType;
-#endif
-
-#ifndef EIGEN_VECTORIZE_AVX
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-#endif
-
-#if EIGEN_COMP_MSVC_STRICT && EIGEN_OS_WIN64
-// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
-// Direct of the struct members fixed bug #62.
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { return a.m128_f32[0]; }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return a.m128d_f64[0]; }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; }
-#elif EIGEN_COMP_MSVC_STRICT
-// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { float x = _mm_cvtss_f32(a); return x; }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double x = _mm_cvtsd_f64(a); return x; }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; }
-#else
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { return _mm_cvtss_f32(a); }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return _mm_cvtsd_f64(a); }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { return _mm_cvtsi128_si32(a); }
-#endif
-template<> EIGEN_STRONG_INLINE bool   pfirst<Packet16b>(const Packet16b& a) { int x = _mm_cvtsi128_si32(a); return static_cast<bool>(x & 1); }
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return _mm_shuffle_ps(a,a,0x1B); }
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return _mm_shuffle_pd(a,a,0x1); }
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return _mm_shuffle_epi32(a,0x1B); }
-template<> EIGEN_STRONG_INLINE Packet16b preverse(const Packet16b& a) {
-#ifdef EIGEN_VECTORIZE_SSSE3
-  __m128i mask = _mm_set_epi8(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
-  return _mm_shuffle_epi8(a, mask);
-#else
-  Packet16b tmp = _mm_shuffle_epi32(a, _MM_SHUFFLE(0, 1, 2, 3));
-  tmp = _mm_shufflehi_epi16(_mm_shufflelo_epi16(tmp, _MM_SHUFFLE(2, 3, 0, 1)), _MM_SHUFFLE(2, 3, 0, 1));
-  return _mm_or_si128(_mm_slli_epi16(tmp, 8), _mm_srli_epi16(tmp, 8));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
-  return pfrexp_generic(a,exponent);
-}
-
-// Extract exponent without existence of Packet2l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
-  const Packet2d cst_exp_mask  = pset1frombits<Packet2d>(static_cast<uint64_t>(0x7ff0000000000000ull));
-  __m128i a_expo = _mm_srli_epi64(_mm_castpd_si128(pand(a, cst_exp_mask)), 52);
-  return _mm_cvtepi32_pd(vec4i_swizzle1(a_expo, 0, 2, 1, 3));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
-  return pldexp_generic(a,exponent);
-}
-
-// We specialize pldexp here, since the generic implementation uses Packet2l, which is not well
-// supported by SSE, and has more range than is needed for exponents.
-template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
-  const Packet2d e = pmin(pmax(exponent, pnegate(max_exponent)), max_exponent);
-  
-  // Convert e to integer and swizzle to low-order bits.
-  const Packet4i ei = vec4i_swizzle1(_mm_cvtpd_epi32(e), 0, 3, 1, 3);
-  
-  // Split 2^e into four factors and multiply:
-  const Packet4i bias = _mm_set_epi32(0, 1023, 0, 1023);
-  Packet4i b = parithmetic_shift_right<2>(ei);  // floor(e/4)
-  Packet2d c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));  // 2^b
-  Packet2d out = pmul(pmul(pmul(a, c), c), c); // a * 2^(3b)
-  b = psub(psub(psub(ei, b), b), b);  // e - 3b
-  c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));  // 2^(e - 3b)
-  out = pmul(out, c);  // a * 2^e
-  return out;
-}
-
-// with AVX, the default implementations based on pload1 are faster
-#ifndef __AVX__
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  a3 = pload<Packet4f>(a);
-  a0 = vec4f_swizzle1(a3, 0,0,0,0);
-  a1 = vec4f_swizzle1(a3, 1,1,1,1);
-  a2 = vec4f_swizzle1(a3, 2,2,2,2);
-  a3 = vec4f_swizzle1(a3, 3,3,3,3);
-}
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet2d>(const double *a,
-                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-#ifdef EIGEN_VECTORIZE_SSE3
-  a0 = _mm_loaddup_pd(a+0);
-  a1 = _mm_loaddup_pd(a+1);
-  a2 = _mm_loaddup_pd(a+2);
-  a3 = _mm_loaddup_pd(a+3);
-#else
-  a1 = pload<Packet2d>(a);
-  a0 = vec2d_swizzle1(a1, 0,0);
-  a1 = vec2d_swizzle1(a1, 1,1);
-  a3 = pload<Packet2d>(a+2);
-  a2 = vec2d_swizzle1(a3, 0,0);
-  a3 = vec2d_swizzle1(a3, 1,1);
-#endif
-}
-#endif
-
-EIGEN_STRONG_INLINE void punpackp(Packet4f* vecs)
-{
-  vecs[1] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x55));
-  vecs[2] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xAA));
-  vecs[3] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xFF));
-  vecs[0] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x00));
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
-  // (from Nehalem to Haswell)
-// #ifdef EIGEN_VECTORIZE_SSE3
-//   Packet4f tmp = _mm_add_ps(a, vec4f_swizzle1(a,2,3,2,3));
-//   return pfirst<Packet4f>(_mm_hadd_ps(tmp, tmp));
-// #else
-  Packet4f tmp = _mm_add_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_add_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-// #endif
-}
-
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{
-  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
-  // (from Nehalem to Haswell)
-// #ifdef EIGEN_VECTORIZE_SSE3
-//   return pfirst<Packet2d>(_mm_hadd_pd(a, a));
-// #else
-  return pfirst<Packet2d>(_mm_add_sd(a, _mm_unpackhi_pd(a,a)));
-// #endif
-}
-
-#ifdef EIGEN_VECTORIZE_SSSE3
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i tmp0 = _mm_hadd_epi32(a,a);
-  return pfirst<Packet4i>(_mm_hadd_epi32(tmp0,tmp0));
-}
-
-#else
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i tmp = _mm_add_epi32(a, _mm_unpackhi_epi64(a,a));
-  return pfirst(tmp) + pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1));
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE bool predux<Packet16b>(const Packet16b& a) {
-  Packet4i tmp = _mm_or_si128(a, _mm_unpackhi_epi64(a,a));
-  return (pfirst(tmp) != 0) || (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) != 0);
-}
-
-// Other reduction functions:
-
-
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp = _mm_mul_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_mul_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{
-  return pfirst<Packet2d>(_mm_mul_sd(a, _mm_unpackhi_pd(a,a)));
-}
-template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
-{
-  // after some experiments, it is seems this is the fastest way to implement it
-  // for GCC (eg., reusing pmul is very slow !)
-  // TODO try to call _mm_mul_epu32 directly
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  return  (aux[0] * aux[1]) * (aux[2] * aux[3]);
-}
-
-template<> EIGEN_STRONG_INLINE bool predux_mul<Packet16b>(const Packet16b& a) {
-  Packet4i tmp = _mm_and_si128(a, _mm_unpackhi_epi64(a,a));
-  return ((pfirst<Packet4i>(tmp) == 0x01010101) &&
-          (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) == 0x01010101));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp = _mm_min_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_min_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{
-  return pfirst<Packet2d>(_mm_min_sd(a, _mm_unpackhi_pd(a,a)));
-}
-template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  Packet4i tmp = _mm_min_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
-  return pfirst<Packet4i>(_mm_min_epi32(tmp,_mm_shuffle_epi32(tmp, 1)));
-#else
-  // after some experiments, it is seems this is the fastest way to implement it
-  // for GCC (eg., it does not like using std::min after the pstore !!)
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  int aux0 = aux[0]<aux[1] ? aux[0] : aux[1];
-  int aux2 = aux[2]<aux[3] ? aux[2] : aux[3];
-  return aux0<aux2 ? aux0 : aux2;
-#endif // EIGEN_VECTORIZE_SSE4_1
-}
-
-// max
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp = _mm_max_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_max_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{
-  return pfirst<Packet2d>(_mm_max_sd(a, _mm_unpackhi_pd(a,a)));
-}
-template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  Packet4i tmp = _mm_max_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
-  return pfirst<Packet4i>(_mm_max_epi32(tmp,_mm_shuffle_epi32(tmp, 1)));
-#else
-  // after some experiments, it is seems this is the fastest way to implement it
-  // for GCC (eg., it does not like using std::min after the pstore !!)
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  int aux0 = aux[0]>aux[1] ? aux[0] : aux[1];
-  int aux2 = aux[2]>aux[3] ? aux[2] : aux[3];
-  return aux0>aux2 ? aux0 : aux2;
-#endif // EIGEN_VECTORIZE_SSE4_1
-}
-
-// not needed yet
-// template<> EIGEN_STRONG_INLINE bool predux_all(const Packet4f& x)
-// {
-//   return _mm_movemask_ps(x) == 0xF;
-// }
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
-{
-  return _mm_movemask_ps(x) != 0x0;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  _MM_TRANSPOSE4_PS(kernel.packet[0], kernel.packet[1], kernel.packet[2], kernel.packet[3]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2d,2>& kernel) {
-  __m128d tmp = _mm_unpackhi_pd(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[0] = _mm_unpacklo_pd(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[1] = tmp;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4i,4>& kernel) {
-  __m128i T0 = _mm_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
-  __m128i T1 = _mm_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
-  __m128i T2 = _mm_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
-  __m128i T3 = _mm_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
-
-  kernel.packet[0] = _mm_unpacklo_epi64(T0, T1);
-  kernel.packet[1] = _mm_unpackhi_epi64(T0, T1);
-  kernel.packet[2] = _mm_unpacklo_epi64(T2, T3);
-  kernel.packet[3] = _mm_unpackhi_epi64(T2, T3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16b,4>& kernel) {
-  __m128i T0 =  _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]);
-  __m128i T1 =  _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]);
-  __m128i T2 =  _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]);
-  __m128i T3 =  _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]);
-  kernel.packet[0] = _mm_unpacklo_epi16(T0, T2);
-  kernel.packet[1] = _mm_unpackhi_epi16(T0, T2);
-  kernel.packet[2] = _mm_unpacklo_epi16(T1, T3);
-  kernel.packet[3] = _mm_unpackhi_epi16(T1, T3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16b,16>& kernel) {
-  // If we number the elements in the input thus:
-  // kernel.packet[ 0] = {00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0a, 0b, 0c, 0d, 0e, 0f}
-  // kernel.packet[ 1] = {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1a, 1b, 1c, 1d, 1e, 1f}
-  // ...
-  // kernel.packet[15] = {f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, fa, fb, fc, fd, fe, ff},
-  //
-  // the desired output is:
-  // kernel.packet[ 0] = {00, 10, 20, 30, 40, 50, 60, 70, 80, 90, a0, b0, c0, d0, e0, f0}
-  // kernel.packet[ 1] = {01, 11, 21, 31, 41, 51, 61, 71, 81, 91, a1, b1, c1, d1, e1, f1}
-  // ...
-  // kernel.packet[15] = {0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, af, bf, cf, df, ef, ff},
-  __m128i t0 =  _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]); // 00 10 01 11 02 12 03 13 04 14 05 15 06 16 07 17
-  __m128i t1 =  _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]); // 08 18 09 19 0a 1a 0b 1b 0c 1c 0d 1d 0e 1e 0f 1f
-  __m128i t2 =  _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]); // 20 30 21 31 22 32 ...                     27 37
-  __m128i t3 =  _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]); // 28 38 29 39 2a 3a ...                     2f 3f
-  __m128i t4 =  _mm_unpacklo_epi8(kernel.packet[4], kernel.packet[5]); // 40 50 41 51 42 52                         47 57
-  __m128i t5 =  _mm_unpackhi_epi8(kernel.packet[4], kernel.packet[5]); // 48 58 49 59 4a 5a
-  __m128i t6 =  _mm_unpacklo_epi8(kernel.packet[6], kernel.packet[7]);
-  __m128i t7 =  _mm_unpackhi_epi8(kernel.packet[6], kernel.packet[7]);
-  __m128i t8 =  _mm_unpacklo_epi8(kernel.packet[8], kernel.packet[9]);
-  __m128i t9 =  _mm_unpackhi_epi8(kernel.packet[8], kernel.packet[9]);
-  __m128i ta =  _mm_unpacklo_epi8(kernel.packet[10], kernel.packet[11]);
-  __m128i tb =  _mm_unpackhi_epi8(kernel.packet[10], kernel.packet[11]);
-  __m128i tc =  _mm_unpacklo_epi8(kernel.packet[12], kernel.packet[13]);
-  __m128i td =  _mm_unpackhi_epi8(kernel.packet[12], kernel.packet[13]);
-  __m128i te =  _mm_unpacklo_epi8(kernel.packet[14], kernel.packet[15]);
-  __m128i tf =  _mm_unpackhi_epi8(kernel.packet[14], kernel.packet[15]);
-
-  __m128i s0 =  _mm_unpacklo_epi16(t0, t2); // 00 10 20 30 01 11 21 31 02 12 22 32 03 13 23 33
-  __m128i s1 =  _mm_unpackhi_epi16(t0, t2); // 04 14 24 34
-  __m128i s2 =  _mm_unpacklo_epi16(t1, t3); // 08 18 28 38 ...
-  __m128i s3 =  _mm_unpackhi_epi16(t1, t3); // 0c 1c 2c 3c ...
-  __m128i s4 =  _mm_unpacklo_epi16(t4, t6); // 40 50 60 70 41 51 61 71 42 52 62 72 43 53 63 73
-  __m128i s5 =  _mm_unpackhi_epi16(t4, t6); // 44 54 64 74 ...
-  __m128i s6 =  _mm_unpacklo_epi16(t5, t7);
-  __m128i s7 =  _mm_unpackhi_epi16(t5, t7);
-  __m128i s8 =  _mm_unpacklo_epi16(t8, ta);
-  __m128i s9 =  _mm_unpackhi_epi16(t8, ta);
-  __m128i sa =  _mm_unpacklo_epi16(t9, tb);
-  __m128i sb =  _mm_unpackhi_epi16(t9, tb);
-  __m128i sc =  _mm_unpacklo_epi16(tc, te);
-  __m128i sd =  _mm_unpackhi_epi16(tc, te);
-  __m128i se =  _mm_unpacklo_epi16(td, tf);
-  __m128i sf =  _mm_unpackhi_epi16(td, tf);
-
-  __m128i u0 =  _mm_unpacklo_epi32(s0, s4); // 00 10 20 30 40 50 60 70 01 11 21 31 41 51 61 71
-  __m128i u1 =  _mm_unpackhi_epi32(s0, s4); // 02 12 22 32 42 52 62 72 03 13 23 33 43 53 63 73
-  __m128i u2 =  _mm_unpacklo_epi32(s1, s5);
-  __m128i u3 =  _mm_unpackhi_epi32(s1, s5);
-  __m128i u4 =  _mm_unpacklo_epi32(s2, s6);
-  __m128i u5 =  _mm_unpackhi_epi32(s2, s6);
-  __m128i u6 =  _mm_unpacklo_epi32(s3, s7);
-  __m128i u7 =  _mm_unpackhi_epi32(s3, s7);
-  __m128i u8 =  _mm_unpacklo_epi32(s8, sc);
-  __m128i u9 =  _mm_unpackhi_epi32(s8, sc);
-  __m128i ua =  _mm_unpacklo_epi32(s9, sd);
-  __m128i ub =  _mm_unpackhi_epi32(s9, sd);
-  __m128i uc =  _mm_unpacklo_epi32(sa, se);
-  __m128i ud =  _mm_unpackhi_epi32(sa, se);
-  __m128i ue =  _mm_unpacklo_epi32(sb, sf);
-  __m128i uf =  _mm_unpackhi_epi32(sb, sf);
-
-  kernel.packet[0]  = _mm_unpacklo_epi64(u0, u8);
-  kernel.packet[1]  = _mm_unpackhi_epi64(u0, u8);
-  kernel.packet[2]  = _mm_unpacklo_epi64(u1, u9);
-  kernel.packet[3]  = _mm_unpackhi_epi64(u1, u9);
-  kernel.packet[4]  = _mm_unpacklo_epi64(u2, ua);
-  kernel.packet[5]  = _mm_unpackhi_epi64(u2, ua);
-  kernel.packet[6]  = _mm_unpacklo_epi64(u3, ub);
-  kernel.packet[7]  = _mm_unpackhi_epi64(u3, ub);
-  kernel.packet[8]  = _mm_unpacklo_epi64(u4, uc);
-  kernel.packet[9]  = _mm_unpackhi_epi64(u4, uc);
-  kernel.packet[10] = _mm_unpacklo_epi64(u5, ud);
-  kernel.packet[11] = _mm_unpackhi_epi64(u5, ud);
-  kernel.packet[12] = _mm_unpacklo_epi64(u6, ue);
-  kernel.packet[13] = _mm_unpackhi_epi64(u6, ue);
-  kernel.packet[14] = _mm_unpacklo_epi64(u7, uf);
-  kernel.packet[15] = _mm_unpackhi_epi64(u7, uf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
-  const __m128i zero = _mm_setzero_si128();
-  const __m128i select = _mm_set_epi32(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m128i false_mask = _mm_cmpeq_epi32(select, zero);
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_blendv_epi8(thenPacket, elsePacket, false_mask);
-#else
-  return _mm_or_si128(_mm_andnot_si128(false_mask, thenPacket), _mm_and_si128(false_mask, elsePacket));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  const __m128 zero = _mm_setzero_ps();
-  const __m128 select = _mm_set_ps(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m128 false_mask = _mm_cmpeq_ps(select, zero);
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_blendv_ps(thenPacket, elsePacket, false_mask);
-#else
-  return _mm_or_ps(_mm_andnot_ps(false_mask, thenPacket), _mm_and_ps(false_mask, elsePacket));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
-  const __m128d zero = _mm_setzero_pd();
-  const __m128d select = _mm_set_pd(ifPacket.select[1], ifPacket.select[0]);
-  __m128d false_mask = _mm_cmpeq_pd(select, zero);
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_blendv_pd(thenPacket, elsePacket, false_mask);
-#else
-  return _mm_or_pd(_mm_andnot_pd(false_mask, thenPacket), _mm_and_pd(false_mask, elsePacket));
-#endif
-}
-
-// Scalar path for pmadd with FMA to ensure consistency with vectorized path.
-#ifdef EIGEN_VECTORIZE_FMA
-template<> EIGEN_STRONG_INLINE float pmadd(const float& a, const float& b, const float& c) {
-  return ::fmaf(a,b,c);
-}
-template<> EIGEN_STRONG_INLINE double pmadd(const double& a, const double& b, const double& c) {
-  return ::fma(a,b,c);
-}
-#endif
-
-
-// Packet math for Eigen::half
-// Disable the following code since it's broken on too many platforms / compilers.
-//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
-#if 0
-
-typedef struct {
-  __m64 x;
-} Packet4h;
-
-
-template<> struct is_arithmetic<Packet4h> { enum { value = true }; };
-
-template <>
-struct packet_traits<Eigen::half> : default_packet_traits {
-  typedef Packet4h type;
-  // There is no half-size packet for Packet4h.
-  typedef Packet4h half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 0,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasConj   = 0,
-    HasSetLinear = 0,
-    HasSqrt = 0,
-    HasRsqrt = 0,
-    HasExp = 0,
-    HasLog = 0,
-    HasBlend = 0
-  };
-};
-
-
-template<> struct unpacket_traits<Packet4h> { typedef Eigen::half type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4h half; };
-
-template<> EIGEN_STRONG_INLINE Packet4h pset1<Packet4h>(const Eigen::half& from) {
-  Packet4h result;
-  result.x = _mm_set1_pi16(from.x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h>(const Packet4h& from) {
-  return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm_cvtsi64_si32(from.x)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pconj(const Packet4h& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4h padd<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha + hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha + hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha + hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha + hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h psub<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha - hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha - hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha - hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha - hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pmul<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha * hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha * hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha * hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha * hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pdiv<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha / hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha / hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha / hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha / hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pload<Packet4h>(const Eigen::half* from) {
-  Packet4h result;
-  result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h ploadu<Packet4h>(const Eigen::half* from) {
-  Packet4h result;
-  result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4h& from) {
-  __int64_t r = _mm_cvtm64_si64(from.x);
-  *(reinterpret_cast<__int64_t*>(to)) = r;
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4h& from) {
-  __int64_t r = _mm_cvtm64_si64(from.x);
-  *(reinterpret_cast<__int64_t*>(to)) = r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h
-ploadquad<Packet4h>(const Eigen::half* from) {
-  return pset1<Packet4h>(*from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pgather<Eigen::half, Packet4h>(const Eigen::half* from, Index stride)
-{
-  Packet4h result;
-  result.x = _mm_set_pi16(from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h>(Eigen::half* to, const Packet4h& from, Index stride)
-{
-  __int64_t a = _mm_cvtm64_si64(from.x);
-  to[stride*0].x = static_cast<unsigned short>(a);
-  to[stride*1].x = static_cast<unsigned short>(a >> 16);
-  to[stride*2].x = static_cast<unsigned short>(a >> 32);
-  to[stride*3].x = static_cast<unsigned short>(a >> 48);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet4h,4>& kernel) {
-  __m64 T0 = _mm_unpacklo_pi16(kernel.packet[0].x, kernel.packet[1].x);
-  __m64 T1 = _mm_unpacklo_pi16(kernel.packet[2].x, kernel.packet[3].x);
-  __m64 T2 = _mm_unpackhi_pi16(kernel.packet[0].x, kernel.packet[1].x);
-  __m64 T3 = _mm_unpackhi_pi16(kernel.packet[2].x, kernel.packet[3].x);
-
-  kernel.packet[0].x = _mm_unpacklo_pi32(T0, T1);
-  kernel.packet[1].x = _mm_unpackhi_pi32(T0, T1);
-  kernel.packet[2].x = _mm_unpacklo_pi32(T2, T3);
-  kernel.packet[3].x = _mm_unpackhi_pi32(T2, T3);
-}
-
-#endif
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
-// PGI++ does not define the following intrinsics in C++ mode.
-static inline __m128  _mm_castpd_ps   (__m128d x) { return reinterpret_cast<__m128&>(x);  }
-static inline __m128i _mm_castpd_si128(__m128d x) { return reinterpret_cast<__m128i&>(x); }
-static inline __m128d _mm_castps_pd   (__m128  x) { return reinterpret_cast<__m128d&>(x); }
-static inline __m128i _mm_castps_si128(__m128  x) { return reinterpret_cast<__m128i&>(x); }
-static inline __m128  _mm_castsi128_ps(__m128i x) { return reinterpret_cast<__m128&>(x);  }
-static inline __m128d _mm_castsi128_pd(__m128i x) { return reinterpret_cast<__m128d&>(x); }
-#endif
-
-#endif // EIGEN_PACKET_MATH_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/TypeCasting.h
deleted file mode 100644
index d2a0037..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SSE/TypeCasting.h
+++ /dev/null
@@ -1,142 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_SSE_H
-#define EIGEN_TYPE_CASTING_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_VECTORIZE_AVX
-template <>
-struct type_casting_traits<float, int> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<int, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<double, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 2,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<float, double> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 2
-  };
-};
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
-  return _mm_cvttps_epi32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
-  return _mm_cvtepi32_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
-  return _mm_shuffle_ps(_mm_cvtpd_ps(a), _mm_cvtpd_ps(b), (1 << 2) | (1 << 6));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
-  // Simply discard the second half of the input
-  return _mm_cvtps_pd(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet4f>(const Packet4f& a) {
-  return _mm_castps_si128(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f,Packet4i>(const Packet4i& a) {
-  return _mm_castsi128_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d,Packet4i>(const Packet4i& a) {
-  return _mm_castsi128_pd(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet2d>(const Packet2d& a) {
-  return _mm_castpd_si128(a);
-}
-
-// Disable the following code since it's broken on too many platforms / compilers.
-//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
-#if 0
-
-template <>
-struct type_casting_traits<Eigen::half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4h, Packet4f>(const Packet4h& a) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  Eigen::half h = raw_uint16_to_half(static_cast<unsigned short>(a64));
-  float f1 = static_cast<float>(h);
-  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  float f2 = static_cast<float>(h);
-  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  float f3 = static_cast<float>(h);
-  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  float f4 = static_cast<float>(h);
-  return _mm_set_ps(f4, f3, f2, f1);
-}
-
-template <>
-struct type_casting_traits<float, Eigen::half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4h pcast<Packet4f, Packet4h>(const Packet4f& a) {
-  EIGEN_ALIGN16 float aux[4];
-  pstore(aux, a);
-  Eigen::half h0(aux[0]);
-  Eigen::half h1(aux[1]);
-  Eigen::half h2(aux[2]);
-  Eigen::half h3(aux[3]);
-
-  Packet4h result;
-  result.x = _mm_set_pi16(h3.x, h2.x, h1.x, h0.x);
-  return result;
-}
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/MathFunctions.h
deleted file mode 100644
index b139ea2..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/MathFunctions.h
+++ /dev/null
@@ -1,44 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_SVE_H
-#define EIGEN_MATH_FUNCTIONS_SVE_H
-
-namespace Eigen {
-namespace internal {
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf pexp<PacketXf>(const PacketXf& x) {
-  return pexp_float(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf plog<PacketXf>(const PacketXf& x) {
-  return plog_float(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf psin<PacketXf>(const PacketXf& x) {
-  return psin_float(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf pcos<PacketXf>(const PacketXf& x) {
-  return pcos_float(x);
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf ptanh<PacketXf>(const PacketXf& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-}  // end namespace internal
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_SVE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/PacketMath.h
deleted file mode 100644
index 9060b37..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/PacketMath.h
+++ /dev/null
@@ -1,752 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_SVE_H
-#define EIGEN_PACKET_MATH_SVE_H
-
-namespace Eigen
-{
-namespace internal
-{
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-
-template <typename Scalar, int SVEVectorLength>
-struct sve_packet_size_selector {
-  enum { size = SVEVectorLength / (sizeof(Scalar) * CHAR_BIT) };
-};
-
-/********************************* int32 **************************************/
-typedef svint32_t PacketXi __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
-
-template <>
-struct packet_traits<numext::int32_t> : default_packet_traits {
-  typedef PacketXi type;
-  typedef PacketXi half;  // Half not implemented yet
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasShift = 1,
-    HasMul = 1,
-    HasNegate = 1,
-    HasAbs = 1,
-    HasArg = 0,
-    HasAbs2 = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasConj = 1,
-    HasSetLinear = 0,
-    HasBlend = 0,
-    HasReduxp = 0  // Not implemented in SVE
-  };
-};
-
-template <>
-struct unpacket_traits<PacketXi> {
-  typedef numext::int32_t type;
-  typedef PacketXi half;  // Half not yet implemented
-  enum {
-    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
-    alignment = Aligned64,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<numext::int32_t>(const numext::int32_t* addr)
-{
-  svprfw(svptrue_b32(), addr, SV_PLDL1KEEP);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pset1<PacketXi>(const numext::int32_t& from)
-{
-  return svdup_n_s32(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi plset<PacketXi>(const numext::int32_t& a)
-{
-  numext::int32_t c[packet_traits<numext::int32_t>::size];
-  for (int i = 0; i < packet_traits<numext::int32_t>::size; i++) c[i] = i;
-  return svadd_s32_z(svptrue_b32(), pset1<PacketXi>(a), svld1_s32(svptrue_b32(), c));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi padd<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svadd_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi psub<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svsub_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pnegate(const PacketXi& a)
-{
-  return svneg_s32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pconj(const PacketXi& a)
-{
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmul<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svmul_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pdiv<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdiv_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmadd(const PacketXi& a, const PacketXi& b, const PacketXi& c)
-{
-  return svmla_s32_z(svptrue_b32(), c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmin<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svmin_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmax<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svmax_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcmp_le<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdup_n_s32_z(svcmplt_s32(svptrue_b32(), a, b), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcmp_lt<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdup_n_s32_z(svcmplt_s32(svptrue_b32(), a, b), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcmp_eq<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdup_n_s32_z(svcmpeq_s32(svptrue_b32(), a, b), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ptrue<PacketXi>(const PacketXi& /*a*/)
-{
-  return svdup_n_s32_z(svptrue_b32(), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pzero<PacketXi>(const PacketXi& /*a*/)
-{
-  return svdup_n_s32_z(svptrue_b32(), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pand<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svand_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi por<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svorr_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pxor<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return sveor_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pandnot<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svbic_s32_z(svptrue_b32(), a, b);
-}
-
-template <int N>
-EIGEN_STRONG_INLINE PacketXi parithmetic_shift_right(PacketXi a)
-{
-  return svasrd_n_s32_z(svptrue_b32(), a, N);
-}
-
-template <int N>
-EIGEN_STRONG_INLINE PacketXi plogical_shift_right(PacketXi a)
-{
-  return svreinterpret_s32_u32(svlsr_u32_z(svptrue_b32(), svreinterpret_u32_s32(a), svdup_n_u32_z(svptrue_b32(), N)));
-}
-
-template <int N>
-EIGEN_STRONG_INLINE PacketXi plogical_shift_left(PacketXi a)
-{
-  return svlsl_s32_z(svptrue_b32(), a, svdup_n_u32_z(svptrue_b32(), N));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pload<PacketXi>(const numext::int32_t* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ploadu<PacketXi>(const numext::int32_t* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ploaddup<PacketXi>(const numext::int32_t* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ploadquad<PacketXi>(const numext::int32_t* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
-  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<numext::int32_t>(numext::int32_t* to, const PacketXi& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<numext::int32_t>(numext::int32_t* to, const PacketXi& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline PacketXi pgather<numext::int32_t, PacketXi>(const numext::int32_t* from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  return svld1_gather_s32index_s32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<numext::int32_t, PacketXi>(numext::int32_t* to, const PacketXi& from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  svst1_scatter_s32index_s32(svptrue_b32(), to, indices, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t pfirst<PacketXi>(const PacketXi& a)
-{
-  // svlasta returns the first element if all predicate bits are 0
-  return svlasta_s32(svpfalse_b(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi preverse(const PacketXi& a)
-{
-  return svrev_s32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pabs(const PacketXi& a)
-{
-  return svabs_s32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux<PacketXi>(const PacketXi& a)
-{
-  return static_cast<numext::int32_t>(svaddv_s32(svptrue_b32(), a));
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux_mul<PacketXi>(const PacketXi& a)
-{
-  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0),
-                      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
-
-  // Multiply the vector by its reverse
-  svint32_t prod = svmul_s32_z(svptrue_b32(), a, svrev_s32(a));
-  svint32_t half_prod;
-
-  // Extract the high half of the vector. Depending on the VL more reductions need to be done
-  if (EIGEN_ARM64_SVE_VL >= 2048) {
-    half_prod = svtbl_s32(prod, svindex_u32(32, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 1024) {
-    half_prod = svtbl_s32(prod, svindex_u32(16, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 512) {
-    half_prod = svtbl_s32(prod, svindex_u32(8, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 256) {
-    half_prod = svtbl_s32(prod, svindex_u32(4, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  // Last reduction
-  half_prod = svtbl_s32(prod, svindex_u32(2, 1));
-  prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-
-  // The reduction is done to the first element.
-  return pfirst<PacketXi>(prod);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux_min<PacketXi>(const PacketXi& a)
-{
-  return svminv_s32(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux_max<PacketXi>(const PacketXi& a)
-{
-  return svmaxv_s32(svptrue_b32(), a);
-}
-
-template <int N>
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXi, N>& kernel) {
-  int buffer[packet_traits<numext::int32_t>::size * N] = {0};
-  int i = 0;
-
-  PacketXi stride_index = svindex_s32(0, N);
-
-  for (i = 0; i < N; i++) {
-    svst1_scatter_s32index_s32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
-  }
-  for (i = 0; i < N; i++) {
-    kernel.packet[i] = svld1_s32(svptrue_b32(), buffer + i * packet_traits<numext::int32_t>::size);
-  }
-}
-
-/********************************* float32 ************************************/
-
-typedef svfloat32_t PacketXf __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef PacketXf type;
-  typedef PacketXf half;
-
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasShift = 1,
-    HasMul = 1,
-    HasNegate = 1,
-    HasAbs = 1,
-    HasArg = 0,
-    HasAbs2 = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasConj = 1,
-    HasSetLinear = 0,
-    HasBlend = 0,
-    HasReduxp = 0,  // Not implemented in SVE
-
-    HasDiv = 1,
-    HasFloor = 1,
-
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-    HasSqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH
-  };
-};
-
-template <>
-struct unpacket_traits<PacketXf> {
-  typedef float type;
-  typedef PacketXf half;  // Half not yet implemented
-  typedef PacketXi integer_packet;
-
-  enum {
-    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
-    alignment = Aligned64,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pset1<PacketXf>(const float& from)
-{
-  return svdup_n_f32(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pset1frombits<PacketXf>(numext::uint32_t from)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svptrue_b32(), from));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf plset<PacketXf>(const float& a)
-{
-  float c[packet_traits<float>::size];
-  for (int i = 0; i < packet_traits<float>::size; i++) c[i] = i;
-  return svadd_f32_z(svptrue_b32(), pset1<PacketXf>(a), svld1_f32(svptrue_b32(), c));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf padd<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svadd_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf psub<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svsub_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pnegate(const PacketXf& a)
-{
-  return svneg_f32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pconj(const PacketXf& a)
-{
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmul<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmul_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pdiv<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svdiv_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmadd(const PacketXf& a, const PacketXf& b, const PacketXf& c)
-{
-  return svmla_f32_z(svptrue_b32(), c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmin<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmin_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmin<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return pmin<PacketXf>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmin<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svminnm_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmax<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmax_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmax<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return pmax<PacketXf>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmax<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmaxnm_f32_z(svptrue_b32(), a, b);
-}
-
-// Float comparisons in SVE return svbool (predicate). Use svdup to set active
-// lanes to 1 (0xffffffffu) and inactive lanes to 0.
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_le<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svcmplt_f32(svptrue_b32(), a, b), 0xffffffffu));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_lt<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svcmplt_f32(svptrue_b32(), a, b), 0xffffffffu));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_eq<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svcmpeq_f32(svptrue_b32(), a, b), 0xffffffffu));
-}
-
-// Do a predicate inverse (svnot_b_z) on the predicate resulted from the
-// greater/equal comparison (svcmpge_f32). Then fill a float vector with the
-// active elements.
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_lt_or_nan<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svnot_b_z(svptrue_b32(), svcmpge_f32(svptrue_b32(), a, b)), 0xffffffffu));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pfloor<PacketXf>(const PacketXf& a)
-{
-  return svrintm_f32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ptrue<PacketXf>(const PacketXf& /*a*/)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svptrue_b32(), 0xffffffffu));
-}
-
-// Logical Operations are not supported for float, so reinterpret casts
-template <>
-EIGEN_STRONG_INLINE PacketXf pand<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svand_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf por<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svorr_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pxor<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(sveor_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pandnot<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svbic_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pload<PacketXf>(const float* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ploadu<PacketXf>(const float* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ploaddup<PacketXf>(const float* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ploadquad<PacketXf>(const float* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
-  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<float>(float* to, const PacketXf& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const PacketXf& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline PacketXf pgather<float, PacketXf>(const float* from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  return svld1_gather_s32index_f32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<float, PacketXf>(float* to, const PacketXf& from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  svst1_scatter_s32index_f32(svptrue_b32(), to, indices, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE float pfirst<PacketXf>(const PacketXf& a)
-{
-  // svlasta returns the first element if all predicate bits are 0
-  return svlasta_f32(svpfalse_b(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf preverse(const PacketXf& a)
-{
-  return svrev_f32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pabs(const PacketXf& a)
-{
-  return svabs_f32_z(svptrue_b32(), a);
-}
-
-// TODO(tellenbach): Should this go into MathFunctions.h? If so, change for 
-// all vector extensions and the generic version.
-template <>
-EIGEN_STRONG_INLINE PacketXf pfrexp<PacketXf>(const PacketXf& a, PacketXf& exponent)
-{
-  return pfrexp_generic(a, exponent);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux<PacketXf>(const PacketXf& a)
-{
-  return svaddv_f32(svptrue_b32(), a);
-}
-
-// Other reduction functions:
-// mul
-// Only works for SVE Vls multiple of 128
-template <>
-EIGEN_STRONG_INLINE float predux_mul<PacketXf>(const PacketXf& a)
-{
-  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0),
-                      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
-  // Multiply the vector by its reverse
-  svfloat32_t prod = svmul_f32_z(svptrue_b32(), a, svrev_f32(a));
-  svfloat32_t half_prod;
-
-  // Extract the high half of the vector. Depending on the VL more reductions need to be done
-  if (EIGEN_ARM64_SVE_VL >= 2048) {
-    half_prod = svtbl_f32(prod, svindex_u32(32, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 1024) {
-    half_prod = svtbl_f32(prod, svindex_u32(16, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 512) {
-    half_prod = svtbl_f32(prod, svindex_u32(8, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 256) {
-    half_prod = svtbl_f32(prod, svindex_u32(4, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  // Last reduction
-  half_prod = svtbl_f32(prod, svindex_u32(2, 1));
-  prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-
-  // The reduction is done to the first element.
-  return pfirst<PacketXf>(prod);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_min<PacketXf>(const PacketXf& a)
-{
-  return svminv_f32(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_max<PacketXf>(const PacketXf& a)
-{
-  return svmaxv_f32(svptrue_b32(), a);
-}
-
-template<int N>
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXf, N>& kernel)
-{
-  float buffer[packet_traits<float>::size * N] = {0};
-  int i = 0;
-
-  PacketXi stride_index = svindex_s32(0, N);
-
-  for (i = 0; i < N; i++) {
-    svst1_scatter_s32index_f32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
-  }
-
-  for (i = 0; i < N; i++) {
-    kernel.packet[i] = svld1_f32(svptrue_b32(), buffer + i * packet_traits<float>::size);
-  }
-}
-
-template<>
-EIGEN_STRONG_INLINE PacketXf pldexp<PacketXf>(const PacketXf& a, const PacketXf& exponent)
-{
-  return pldexp_generic(a, exponent);
-}
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_PACKET_MATH_SVE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/TypeCasting.h
deleted file mode 100644
index 7ba5d9c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SVE/TypeCasting.h
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_SVE_H
-#define EIGEN_TYPE_CASTING_SVE_H
-
-namespace Eigen {
-namespace internal {
-
-template <>
-struct type_casting_traits<float, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-struct type_casting_traits<numext::int32_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pcast<PacketXi, PacketXf>(const PacketXi& a) {
-  return svcvt_f32_s32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcast<PacketXf, PacketXi>(const PacketXf& a) {
-  return svcvt_s32_f32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf preinterpret<PacketXf, PacketXi>(const PacketXi& a) {
-  return svreinterpret_f32_s32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi preinterpret<PacketXi, PacketXf>(const PacketXf& a) {
-  return svreinterpret_s32_f32(a);
-}
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_SVE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h
deleted file mode 100644
index 10856ff..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h
+++ /dev/null
@@ -1,232 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * InteropHeaders.h
- *
- * \brief:
- *  InteropHeaders
- *
- *****************************************************************/
-
-#ifndef EIGEN_INTEROP_HEADERS_SYCL_H
-#define EIGEN_INTEROP_HEADERS_SYCL_H
-
-namespace Eigen {
-
-#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
-
-namespace internal {
-
-template <int has_blend, int lengths>
-struct sycl_packet_traits : default_packet_traits {
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = lengths,
-    HasHalfPacket = 0,
-    HasDiv = 1,
-    HasLog = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasSin = 1,
-    HasCos = 1,
-    HasTan = 1,
-    HasASin = 1,
-    HasACos = 1,
-    HasATan = 1,
-    HasSinh = 1,
-    HasCosh = 1,
-    HasTanh = 1,
-    HasLGamma = 0,
-    HasDiGamma = 0,
-    HasZeta = 0,
-    HasPolygamma = 0,
-    HasErf = 0,
-    HasErfc = 0,
-    HasNdtri = 0,
-    HasIGamma = 0,
-    HasIGammac = 0,
-    HasBetaInc = 0,
-    HasBlend = has_blend,
-    // This flag is used to indicate whether packet comparison is supported.
-    // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
-    HasCmp = 1,
-    HasMax = 1,
-    HasMin = 1,
-    HasMul = 1,
-    HasAdd = 1,
-    HasFloor = 1,
-    HasRound = 1,
-    HasRint = 1,
-    HasLog1p = 1,
-    HasExpm1 = 1,
-    HasCeil = 1,
-  };
-};
-
-#ifdef SYCL_DEVICE_ONLY
-#define SYCL_PACKET_TRAITS(packet_type, has_blend, unpacket_type, lengths) \
-  template <>                                                              \
-  struct packet_traits<unpacket_type>                                      \
-      : sycl_packet_traits<has_blend, lengths> {                           \
-    typedef packet_type type;                                              \
-    typedef packet_type half;                                              \
-  };
-
-SYCL_PACKET_TRAITS(cl::sycl::cl_float4, 1, float, 4)
-SYCL_PACKET_TRAITS(cl::sycl::cl_float4, 1, const float, 4)
-SYCL_PACKET_TRAITS(cl::sycl::cl_double2, 0, double, 2)
-SYCL_PACKET_TRAITS(cl::sycl::cl_double2, 0, const double, 2)
-#undef SYCL_PACKET_TRAITS
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#define SYCL_ARITHMETIC(packet_type)  \
-  template <>                         \
-  struct is_arithmetic<packet_type> { \
-    enum { value = true };            \
-  };
-SYCL_ARITHMETIC(cl::sycl::cl_float4)
-SYCL_ARITHMETIC(cl::sycl::cl_double2)
-#undef SYCL_ARITHMETIC
-
-#define SYCL_UNPACKET_TRAITS(packet_type, unpacket_type, lengths)        \
-  template <>                                                            \
-  struct unpacket_traits<packet_type> {                                  \
-    typedef unpacket_type type;                                          \
-    enum { size = lengths, vectorizable = true, alignment = Aligned16 }; \
-    typedef packet_type half;                                            \
-  };
-SYCL_UNPACKET_TRAITS(cl::sycl::cl_float4, float, 4)
-SYCL_UNPACKET_TRAITS(cl::sycl::cl_double2, double, 2)
-
-#undef SYCL_UNPACKET_TRAITS
-#endif
-
-}  // end namespace internal
-
-#endif
-
-namespace TensorSycl {
-namespace internal {
-
-template <typename PacketReturnType, int PacketSize>
-struct PacketWrapper;
-// This function should never get called on the device
-#ifndef SYCL_DEVICE_ONLY
-template <typename PacketReturnType, int PacketSize>
-struct PacketWrapper {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC static Scalar scalarize(Index, PacketReturnType &) {
-    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
-    abort();
-  }
-  EIGEN_DEVICE_FUNC static PacketReturnType convert_to_packet_type(Scalar in,
-                                                                   Scalar) {
-    return ::Eigen::internal::template plset<PacketReturnType>(in);
-  }
-  EIGEN_DEVICE_FUNC static void set_packet(PacketReturnType, Scalar *) {
-    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
-    abort();
-  }
-};
-
-#elif defined(SYCL_DEVICE_ONLY)
-template <typename PacketReturnType>
-struct PacketWrapper<PacketReturnType, 4> {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType &in) {
-    switch (index) {
-      case 0:
-        return in.x();
-      case 1:
-        return in.y();
-      case 2:
-        return in.z();
-      case 3:
-        return in.w();
-      default:
-      //INDEX MUST BE BETWEEN 0 and 3.There is no abort function in SYCL kernel. so we cannot use abort here. 
-      // The code will never reach here
-      __builtin_unreachable();
-    }
-    __builtin_unreachable();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(
-      Scalar in, Scalar other) {
-    return PacketReturnType(in, other, other, other);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
-    lhs = PacketReturnType(rhs[0], rhs[1], rhs[2], rhs[3]);
-  }
-};
-
-template <typename PacketReturnType>
-struct PacketWrapper<PacketReturnType, 1> {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index, PacketReturnType &in) {
-    return in;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(Scalar in,
-                                                                   Scalar) {
-    return PacketReturnType(in);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
-    lhs = rhs[0];
-  }
-};
-
-template <typename PacketReturnType>
-struct PacketWrapper<PacketReturnType, 2> {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType &in) {
-    switch (index) {
-      case 0:
-        return in.x();
-      case 1:
-        return in.y();
-      default:
-        //INDEX MUST BE BETWEEN 0 and 1.There is no abort function in SYCL kernel. so we cannot use abort here. 
-      // The code will never reach here
-        __builtin_unreachable();
-    }
-    __builtin_unreachable();
-  }
-  
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(
-      Scalar in, Scalar other) {
-    return PacketReturnType(in, other);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
-    lhs = PacketReturnType(rhs[0], rhs[1]);
-  }
-};
-
-#endif
-
-}  // end namespace internal
-}  // end namespace TensorSycl
-}  // end namespace Eigen
-
-#endif  // EIGEN_INTEROP_HEADERS_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/MathFunctions.h
deleted file mode 100644
index 2ab0f2a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/MathFunctions.h
+++ /dev/null
@@ -1,301 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * MathFunctions.h
- *
- * \brief:
- *  MathFunctions
- *
- *****************************************************************/
-
-#ifndef EIGEN_MATH_FUNCTIONS_SYCL_H
-#define EIGEN_MATH_FUNCTIONS_SYCL_H
-namespace Eigen {
-
-namespace internal {
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#if defined(SYCL_DEVICE_ONLY)
-#define SYCL_PLOG(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::log(a);                                           \
-  }
-
-SYCL_PLOG(cl::sycl::cl_float4)
-SYCL_PLOG(cl::sycl::cl_double2)
-#undef SYCL_PLOG
-
-#define SYCL_PLOG1P(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog1p<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::log1p(a);                                           \
-  }
-
-SYCL_PLOG1P(cl::sycl::cl_float4)
-SYCL_PLOG1P(cl::sycl::cl_double2)
-#undef SYCL_PLOG1P
-
-#define SYCL_PLOG10(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog10<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::log10(a);                                           \
-  }
-
-SYCL_PLOG10(cl::sycl::cl_float4)
-SYCL_PLOG10(cl::sycl::cl_double2)
-#undef SYCL_PLOG10
-
-#define SYCL_PEXP(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pexp<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::exp(a);                                           \
-  }
-
-SYCL_PEXP(cl::sycl::cl_float4)
-SYCL_PEXP(cl::sycl::cl_float)
-SYCL_PEXP(cl::sycl::cl_double2)
-#undef SYCL_PEXP
-
-#define SYCL_PEXPM1(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pexpm1<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::expm1(a);                                           \
-  }
-
-SYCL_PEXPM1(cl::sycl::cl_float4)
-SYCL_PEXPM1(cl::sycl::cl_double2)
-#undef SYCL_PEXPM1
-
-#define SYCL_PSQRT(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psqrt<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::sqrt(a);                                           \
-  }
-
-SYCL_PSQRT(cl::sycl::cl_float4)
-SYCL_PSQRT(cl::sycl::cl_double2)
-#undef SYCL_PSQRT
-
-#define SYCL_PRSQRT(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type prsqrt<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::rsqrt(a);                                           \
-  }
-
-SYCL_PRSQRT(cl::sycl::cl_float4)
-SYCL_PRSQRT(cl::sycl::cl_double2)
-#undef SYCL_PRSQRT
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-#define SYCL_PSIN(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psin<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::sin(a);                                           \
-  }
-
-SYCL_PSIN(cl::sycl::cl_float4)
-SYCL_PSIN(cl::sycl::cl_double2)
-#undef SYCL_PSIN
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-#define SYCL_PCOS(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pcos<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::cos(a);                                           \
-  }
-
-SYCL_PCOS(cl::sycl::cl_float4)
-SYCL_PCOS(cl::sycl::cl_double2)
-#undef SYCL_PCOS
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-#define SYCL_PTAN(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ptan<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::tan(a);                                           \
-  }
-
-SYCL_PTAN(cl::sycl::cl_float4)
-SYCL_PTAN(cl::sycl::cl_double2)
-#undef SYCL_PTAN
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-#define SYCL_PASIN(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pasin<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::asin(a);                                           \
-  }
-
-SYCL_PASIN(cl::sycl::cl_float4)
-SYCL_PASIN(cl::sycl::cl_double2)
-#undef SYCL_PASIN
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-#define SYCL_PACOS(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pacos<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::acos(a);                                           \
-  }
-
-SYCL_PACOS(cl::sycl::cl_float4)
-SYCL_PACOS(cl::sycl::cl_double2)
-#undef SYCL_PACOS
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-#define SYCL_PATAN(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type patan<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::atan(a);                                           \
-  }
-
-SYCL_PATAN(cl::sycl::cl_float4)
-SYCL_PATAN(cl::sycl::cl_double2)
-#undef SYCL_PATAN
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-#define SYCL_PSINH(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psinh<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::sinh(a);                                           \
-  }
-
-SYCL_PSINH(cl::sycl::cl_float4)
-SYCL_PSINH(cl::sycl::cl_double2)
-#undef SYCL_PSINH
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-#define SYCL_PCOSH(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pcosh<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::cosh(a);                                           \
-  }
-
-SYCL_PCOSH(cl::sycl::cl_float4)
-SYCL_PCOSH(cl::sycl::cl_double2)
-#undef SYCL_PCOSH
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-#define SYCL_PTANH(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ptanh<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::tanh(a);                                           \
-  }
-
-SYCL_PTANH(cl::sycl::cl_float4)
-SYCL_PTANH(cl::sycl::cl_double2)
-#undef SYCL_PTANH
-
-#define SYCL_PCEIL(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pceil<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::ceil(a);                                           \
-  }
-
-SYCL_PCEIL(cl::sycl::cl_float4)
-SYCL_PCEIL(cl::sycl::cl_double2)
-#undef SYCL_PCEIL
-
-#define SYCL_PROUND(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pround<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::round(a);                                           \
-  }
-
-SYCL_PROUND(cl::sycl::cl_float4)
-SYCL_PROUND(cl::sycl::cl_double2)
-#undef SYCL_PROUND
-
-#define SYCL_PRINT(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type print<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::rint(a);                                           \
-  }
-
-SYCL_PRINT(cl::sycl::cl_float4)
-SYCL_PRINT(cl::sycl::cl_double2)
-#undef SYCL_PRINT
-
-#define SYCL_FLOOR(packet_type)                                          \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pfloor<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::floor(a);                                           \
-  }
-
-SYCL_FLOOR(cl::sycl::cl_float4)
-SYCL_FLOOR(cl::sycl::cl_double2)
-#undef SYCL_FLOOR
-
-#define SYCL_PMIN(packet_type, expr)                                   \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pmin<packet_type>( \
-      const packet_type& a, const packet_type& b) {                    \
-    return expr;                                                       \
-  }
-
-SYCL_PMIN(cl::sycl::cl_float4, cl::sycl::fmin(a, b))
-SYCL_PMIN(cl::sycl::cl_double2, cl::sycl::fmin(a, b))
-#undef SYCL_PMIN
-
-#define SYCL_PMAX(packet_type, expr)                                   \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pmax<packet_type>( \
-      const packet_type& a, const packet_type& b) {                    \
-    return expr;                                                       \
-  }
-
-SYCL_PMAX(cl::sycl::cl_float4, cl::sycl::fmax(a, b))
-SYCL_PMAX(cl::sycl::cl_double2, cl::sycl::fmax(a, b))
-#undef SYCL_PMAX
-
-#define SYCL_PLDEXP(packet_type)                                             \
-  template <>                                                                \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pldexp(                  \
-      const packet_type& a, const packet_type& exponent) {                   \
-    return cl::sycl::ldexp(                                                  \
-        a, exponent.template convert<cl::sycl::cl_int,                       \
-                                     cl::sycl::rounding_mode::automatic>()); \
-  }
-
-SYCL_PLDEXP(cl::sycl::cl_float4)
-SYCL_PLDEXP(cl::sycl::cl_double2)
-#undef SYCL_PLDEXP
-
-#endif
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/PacketMath.h
deleted file mode 100644
index 87badc0..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/PacketMath.h
+++ /dev/null
@@ -1,670 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * PacketMath.h
- *
- * \brief:
- *  PacketMath
- *
- *****************************************************************/
-
-#ifndef EIGEN_PACKET_MATH_SYCL_H
-#define EIGEN_PACKET_MATH_SYCL_H
-#include <type_traits>
-namespace Eigen {
-
-namespace internal {
-#ifdef SYCL_DEVICE_ONLY
-
-#define SYCL_PLOADT_RO(address_space_target)                                 \
-  template <typename packet_type, int Alignment>                             \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type ploadt_ro(               \
-      typename cl::sycl::multi_ptr<                                          \
-          const typename unpacket_traits<packet_type>::type,                 \
-          cl::sycl::access::address_space::address_space_target>::pointer_t  \
-          from) {                                                            \
-    typedef typename unpacket_traits<packet_type>::type scalar;              \
-    typedef cl::sycl::multi_ptr<                                             \
-        scalar, cl::sycl::access::address_space::address_space_target>       \
-        multi_ptr;                                                           \
-    auto res = packet_type(                                                  \
-        static_cast<typename unpacket_traits<packet_type>::type>(0));        \
-    res.load(0, multi_ptr(const_cast<typename multi_ptr::pointer_t>(from))); \
-    return res;                                                              \
-  }
-
-SYCL_PLOADT_RO(global_space)
-SYCL_PLOADT_RO(local_space)
-#undef SYCL_PLOADT_RO
-#endif
-
-template <typename packet_type, int Alignment, typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type
-ploadt_ro(const Eigen::TensorSycl::internal::RangeAccess<
-          cl::sycl::access::mode::read_write, T>& from) {
-  return ploadt_ro<packet_type, Alignment>(from.get_pointer());
-}
-
-#ifdef SYCL_DEVICE_ONLY
-#define SYCL_PLOAD(address_space_target, Alignment, AlignedType)            \
-  template <typename packet_type>                                           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pload##AlignedType(     \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from) {                                                           \
-    return ploadt_ro<packet_type, Alignment>(from);                         \
-  }
-
-// global space
-SYCL_PLOAD(global_space, Unaligned, u)
-SYCL_PLOAD(global_space, Aligned, )
-// local space
-SYCL_PLOAD(local_space, Unaligned, u)
-SYCL_PLOAD(local_space, Aligned, )
-
-#undef SYCL_PLOAD
-#endif
-
-#define SYCL_PLOAD(Alignment, AlignedType)                              \
-  template <typename packet_type>                                       \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pload##AlignedType( \
-      const Eigen::TensorSycl::internal::RangeAccess<                   \
-          cl::sycl::access::mode::read_write,                           \
-          typename unpacket_traits<packet_type>::type>                  \
-          from) {                                                       \
-    return ploadt_ro<packet_type, Alignment>(from);                     \
-  }
-SYCL_PLOAD(Unaligned, u)
-SYCL_PLOAD(Aligned, )
-#undef SYCL_PLOAD
-
-#ifdef SYCL_DEVICE_ONLY
-/** \internal \returns a packet version of \a *from.
- * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
-#define SYCL_PLOADT(address_space_target)                                   \
-  template <typename packet_type, int Alignment>                            \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type ploadt(                 \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from) {                                                           \
-    if (Alignment >= unpacket_traits<packet_type>::alignment)               \
-      return pload<packet_type>(from);                                      \
-    else                                                                    \
-      return ploadu<packet_type>(from);                                     \
-  }
-
-// global space
-SYCL_PLOADT(global_space)
-// local space
-SYCL_PLOADT(local_space)
-#undef SYCL_PLOADT
-#endif
-
-template <typename packet_type, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type
-ploadt(const Eigen::TensorSycl::internal::RangeAccess<
-       cl::sycl::access::mode::read_write,
-       typename unpacket_traits<packet_type>::type>& from) {
-  return ploadt<packet_type, Alignment>(from.get_pointer());
-}
-#ifdef SYCL_DEVICE_ONLY
-
-// private_space
-#define SYCL_PLOADT_RO_SPECIAL(packet_type, Alignment)                 \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type                    \
-  ploadt_ro<packet_type, Alignment>(                                   \
-      const typename unpacket_traits<packet_type>::type* from) {       \
-    typedef typename unpacket_traits<packet_type>::type scalar;        \
-    auto res = packet_type(static_cast<scalar>(0));                    \
-    res.template load<cl::sycl::access::address_space::private_space>( \
-        0, const_cast<scalar*>(from));                                 \
-    return res;                                                        \
-  }
-
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_float4, Aligned)
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_double2, Aligned)
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_float4, Unaligned)
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_double2, Unaligned)
-
-#define SYCL_PLOAD_SPECIAL(packet_type, alignment_type)                    \
-  template <>                                                              \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pload##alignment_type( \
-      const typename unpacket_traits<packet_type>::type* from) {           \
-    typedef typename unpacket_traits<packet_type>::type scalar;            \
-    auto res = packet_type(static_cast<scalar>(0));                        \
-    res.template load<cl::sycl::access::address_space::private_space>(     \
-        0, const_cast<scalar*>(from));                                     \
-    return res;                                                            \
-  }
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_float4, )
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_double2, )
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_float4, u)
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_double2, u)
-
-#undef SYCL_PLOAD_SPECIAL
-
-#define SYCL_PSTORE(scalar, packet_type, address_space_target, alignment)   \
-  template <>                                                               \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstore##alignment(             \
-      typename cl::sycl::multi_ptr<                                         \
-          scalar,                                                           \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          to,                                                               \
-      const packet_type& from) {                                            \
-    typedef cl::sycl::multi_ptr<                                            \
-        scalar, cl::sycl::access::address_space::address_space_target>      \
-        multi_ptr;                                                          \
-    from.store(0, multi_ptr(to));                                           \
-  }
-
-// global space
-SYCL_PSTORE(float, cl::sycl::cl_float4, global_space, )
-SYCL_PSTORE(float, cl::sycl::cl_float4, global_space, u)
-SYCL_PSTORE(double, cl::sycl::cl_double2, global_space, )
-SYCL_PSTORE(double, cl::sycl::cl_double2, global_space, u)
-SYCL_PSTORE(float, cl::sycl::cl_float4, local_space, )
-SYCL_PSTORE(float, cl::sycl::cl_float4, local_space, u)
-SYCL_PSTORE(double, cl::sycl::cl_double2, local_space, )
-SYCL_PSTORE(double, cl::sycl::cl_double2, local_space, u)
-
-SYCL_PSTORE(float, cl::sycl::cl_float4, private_space, )
-SYCL_PSTORE(float, cl::sycl::cl_float4, private_space, u)
-SYCL_PSTORE(double, cl::sycl::cl_double2, private_space, )
-SYCL_PSTORE(double, cl::sycl::cl_double2, private_space, u)
-#undef SYCL_PSTORE
-
-#define SYCL_PSTORE_T(address_space_target)                                 \
-  template <typename scalar, typename packet_type, int Alignment>           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(                       \
-      typename cl::sycl::multi_ptr<                                         \
-          scalar,                                                           \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          to,                                                               \
-      const packet_type& from) {                                            \
-    if (Alignment)                                                          \
-      pstore(to, from);                                                     \
-    else                                                                    \
-      pstoreu(to, from);                                                    \
-  }
-
-SYCL_PSTORE_T(global_space)
-
-SYCL_PSTORE_T(local_space)
-
-#undef SYCL_PSTORE_T
-
-#define SYCL_PSET1(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pset1<packet_type>( \
-      const typename unpacket_traits<packet_type>::type& from) {        \
-    return packet_type(from);                                           \
-  }
-
-// global space
-SYCL_PSET1(cl::sycl::cl_float4)
-SYCL_PSET1(cl::sycl::cl_double2)
-
-#undef SYCL_PSET1
-
-template <typename packet_type>
-struct get_base_packet {
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type
-  get_ploaddup(sycl_multi_pointer) {}
-
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type
-  get_pgather(sycl_multi_pointer, Index) {}
-};
-
-template <>
-struct get_base_packet<cl::sycl::cl_float4> {
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_ploaddup(
-      sycl_multi_pointer from) {
-    return cl::sycl::cl_float4(from[0], from[0], from[1], from[1]);
-  }
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_pgather(
-      sycl_multi_pointer from, Index stride) {
-    return cl::sycl::cl_float4(from[0 * stride], from[1 * stride],
-                               from[2 * stride], from[3 * stride]);
-  }
-
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(
-      sycl_multi_pointer to, const cl::sycl::cl_float4& from, Index stride) {
-    auto tmp = stride;
-    to[0] = from.x();
-    to[tmp] = from.y();
-    to[tmp += stride] = from.z();
-    to[tmp += stride] = from.w();
-  }
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 set_plset(
-      const float& a) {
-    return cl::sycl::cl_float4(static_cast<float>(a), static_cast<float>(a + 1),
-                               static_cast<float>(a + 2),
-                               static_cast<float>(a + 3));
-  }
-};
-
-template <>
-struct get_base_packet<cl::sycl::cl_double2> {
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2
-  get_ploaddup(const sycl_multi_pointer from) {
-    return cl::sycl::cl_double2(from[0], from[0]);
-  }
-
-  template <typename sycl_multi_pointer, typename Index>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 get_pgather(
-      const sycl_multi_pointer from, Index stride) {
-    return cl::sycl::cl_double2(from[0 * stride], from[1 * stride]);
-  }
-
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(
-      sycl_multi_pointer to, const cl::sycl::cl_double2& from, Index stride) {
-    to[0] = from.x();
-    to[stride] = from.y();
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 set_plset(
-      const double& a) {
-    return cl::sycl::cl_double2(static_cast<double>(a),
-                                static_cast<double>(a + 1));
-  }
-};
-
-#define SYCL_PLOAD_DUP(address_space_target)                                \
-  template <typename packet_type>                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ploaddup(               \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from) {                                                           \
-    return get_base_packet<packet_type>::get_ploaddup(from);                \
-  }
-
-// global space
-SYCL_PLOAD_DUP(global_space)
-// local_space
-SYCL_PLOAD_DUP(local_space)
-#undef SYCL_PLOAD_DUP
-
-#define SYCL_PLOAD_DUP_SPECILIZE(packet_type)                              \
-  template <>                                                              \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ploaddup<packet_type>( \
-      const typename unpacket_traits<packet_type>::type* from) {           \
-    return get_base_packet<packet_type>::get_ploaddup(from);               \
-  }
-
-SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_float4)
-SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_double2)
-
-#undef SYCL_PLOAD_DUP_SPECILIZE
-
-#define SYCL_PLSET(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type plset<packet_type>( \
-      const typename unpacket_traits<packet_type>::type& a) {           \
-    return get_base_packet<packet_type>::set_plset(a);                  \
-  }
-
-SYCL_PLSET(cl::sycl::cl_float4)
-SYCL_PLSET(cl::sycl::cl_double2)
-
-#undef SYCL_PLSET
-
-#define SYCL_PGATHER(address_space_target)                                  \
-  template <typename Scalar, typename packet_type>                          \
-  EIGEN_DEVICE_FUNC inline packet_type pgather(                             \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from,                                                             \
-      Index stride) {                                                       \
-    return get_base_packet<packet_type>::get_pgather(from, stride);         \
-  }
-
-// global space
-SYCL_PGATHER(global_space)
-// local space
-SYCL_PGATHER(local_space)
-
-#undef SYCL_PGATHER
-
-#define SYCL_PGATHER_SPECILIZE(scalar, packet_type)                            \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type                            \
-  pgather<scalar, packet_type>(                                                \
-      const typename unpacket_traits<packet_type>::type* from, Index stride) { \
-    return get_base_packet<packet_type>::get_pgather(from, stride);            \
-  }
-
-SYCL_PGATHER_SPECILIZE(float, cl::sycl::cl_float4)
-SYCL_PGATHER_SPECILIZE(double, cl::sycl::cl_double2)
-
-#undef SYCL_PGATHER_SPECILIZE
-
-#define SYCL_PSCATTER(address_space_target)                                 \
-  template <typename Scalar, typename packet_type>                          \
-  EIGEN_DEVICE_FUNC inline void pscatter(                                   \
-      typename cl::sycl::multi_ptr<                                         \
-          typename unpacket_traits<packet_type>::type,                      \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          to,                                                               \
-      const packet_type& from, Index stride) {                              \
-    get_base_packet<packet_type>::set_pscatter(to, from, stride);           \
-  }
-
-// global space
-SYCL_PSCATTER(global_space)
-// local space
-SYCL_PSCATTER(local_space)
-
-#undef SYCL_PSCATTER
-
-#define SYCL_PSCATTER_SPECILIZE(scalar, packet_type)                        \
-  template <>                                                               \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<scalar, packet_type>( \
-      typename unpacket_traits<packet_type>::type * to,                     \
-      const packet_type& from, Index stride) {                              \
-    get_base_packet<packet_type>::set_pscatter(to, from, stride);           \
-  }
-
-SYCL_PSCATTER_SPECILIZE(float, cl::sycl::cl_float4)
-SYCL_PSCATTER_SPECILIZE(double, cl::sycl::cl_double2)
-
-#undef SYCL_PSCATTER_SPECILIZE
-
-#define SYCL_PMAD(packet_type)                                            \
-  template <>                                                             \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pmadd(                \
-      const packet_type& a, const packet_type& b, const packet_type& c) { \
-    return cl::sycl::mad(a, b, c);                                        \
-  }
-
-SYCL_PMAD(cl::sycl::cl_float4)
-SYCL_PMAD(cl::sycl::cl_double2)
-#undef SYCL_PMAD
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float pfirst<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return a.x();
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double pfirst<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return a.x();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return a.x() + a.y() + a.z() + a.w();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return a.x() + a.y();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_max<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return cl::sycl::fmax(cl::sycl::fmax(a.x(), a.y()),
-                        cl::sycl::fmax(a.z(), a.w()));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_max<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return cl::sycl::fmax(a.x(), a.y());
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_min<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return cl::sycl::fmin(cl::sycl::fmin(a.x(), a.y()),
-                        cl::sycl::fmin(a.z(), a.w()));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_min<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return cl::sycl::fmin(a.x(), a.y());
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_mul<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return a.x() * a.y() * a.z() * a.w();
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_mul<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return a.x() * a.y();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
-pabs<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
-  return cl::sycl::cl_float4(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()),
-                             cl::sycl::fabs(a.z()), cl::sycl::fabs(a.w()));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2
-pabs<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
-  return cl::sycl::cl_double2(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()));
-}
-
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_le(const Packet &a,
-                                                          const Packet &b) {
-  return ((a <= b)
-              .template convert<typename unpacket_traits<Packet>::type,
-                                cl::sycl::rounding_mode::automatic>());
-}
-
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_lt(const Packet &a,
-                                                          const Packet &b) {
-  return ((a < b)
-              .template convert<typename unpacket_traits<Packet>::type,
-                                cl::sycl::rounding_mode::automatic>());
-}
-
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_eq(const Packet &a,
-                                                          const Packet &b) {
-  return ((a == b)
-              .template convert<typename unpacket_traits<Packet>::type,
-                                cl::sycl::rounding_mode::automatic>());
-}
-
-#define SYCL_PCMP(OP, TYPE)                                                    \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TYPE pcmp_##OP<TYPE>(const TYPE &a,    \
-                                                             const TYPE &b) {  \
-    return sycl_pcmp_##OP<TYPE>(a, b);                                         \
-  }
-
-SYCL_PCMP(le, cl::sycl::cl_float4)
-SYCL_PCMP(lt, cl::sycl::cl_float4)
-SYCL_PCMP(eq, cl::sycl::cl_float4)
-SYCL_PCMP(le, cl::sycl::cl_double2)
-SYCL_PCMP(lt, cl::sycl::cl_double2)
-SYCL_PCMP(eq, cl::sycl::cl_double2)
-#undef SYCL_PCMP
-
-template <typename T> struct convert_to_integer;
-
-template <> struct convert_to_integer<float> {
-  using type = std::int32_t;
-  using packet_type = cl::sycl::cl_int4;
-};
-template <> struct convert_to_integer<double> {
-  using type = std::int64_t;
-  using packet_type = cl::sycl::cl_long2;
-};
-
-template <typename PacketIn>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename convert_to_integer<
-    typename unpacket_traits<PacketIn>::type>::packet_type
-vector_as_int(const PacketIn &p) {
-  return (
-      p.template convert<typename convert_to_integer<
-                             typename unpacket_traits<PacketIn>::type>::type,
-                         cl::sycl::rounding_mode::automatic>());
-}
-
-template <typename packetOut, typename PacketIn>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packetOut
-convert_vector(const PacketIn &p) {
-  return (p.template convert<typename unpacket_traits<packetOut>::type,
-                             cl::sycl::rounding_mode::automatic>());
-}
-
-#define SYCL_PAND(TYPE)                                                        \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE pand<TYPE>(const TYPE &a,         \
-                                                        const TYPE &b) {       \
-    return convert_vector<TYPE>(vector_as_int(a) & vector_as_int(b));          \
-  }
-SYCL_PAND(cl::sycl::cl_float4)
-SYCL_PAND(cl::sycl::cl_double2)
-#undef SYCL_PAND
-
-#define SYCL_POR(TYPE)                                                         \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE por<TYPE>(const TYPE &a,          \
-                                                       const TYPE &b) {        \
-    return convert_vector<TYPE>(vector_as_int(a) | vector_as_int(b));          \
-  }
-
-SYCL_POR(cl::sycl::cl_float4)
-SYCL_POR(cl::sycl::cl_double2)
-#undef SYCL_POR
-
-#define SYCL_PXOR(TYPE)                                                        \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE pxor<TYPE>(const TYPE &a,         \
-                                                        const TYPE &b) {       \
-    return convert_vector<TYPE>(vector_as_int(a) ^ vector_as_int(b));          \
-  }
-
-SYCL_PXOR(cl::sycl::cl_float4)
-SYCL_PXOR(cl::sycl::cl_double2)
-#undef SYCL_PXOR
-
-#define SYCL_PANDNOT(TYPE)                                                     \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE pandnot<TYPE>(const TYPE &a,      \
-                                                           const TYPE &b) {    \
-    return convert_vector<TYPE>(vector_as_int(a) & (~vector_as_int(b)));       \
-  }
-SYCL_PANDNOT(cl::sycl::cl_float4)
-SYCL_PANDNOT(cl::sycl::cl_double2)
-#undef SYCL_PANDNOT
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(
-    PacketBlock<cl::sycl::cl_float4, 4>& kernel) {
-  float tmp = kernel.packet[0].y();
-  kernel.packet[0].y() = kernel.packet[1].x();
-  kernel.packet[1].x() = tmp;
-
-  tmp = kernel.packet[0].z();
-  kernel.packet[0].z() = kernel.packet[2].x();
-  kernel.packet[2].x() = tmp;
-
-  tmp = kernel.packet[0].w();
-  kernel.packet[0].w() = kernel.packet[3].x();
-  kernel.packet[3].x() = tmp;
-
-  tmp = kernel.packet[1].z();
-  kernel.packet[1].z() = kernel.packet[2].y();
-  kernel.packet[2].y() = tmp;
-
-  tmp = kernel.packet[1].w();
-  kernel.packet[1].w() = kernel.packet[3].y();
-  kernel.packet[3].y() = tmp;
-
-  tmp = kernel.packet[2].w();
-  kernel.packet[2].w() = kernel.packet[3].z();
-  kernel.packet[3].z() = tmp;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(
-    PacketBlock<cl::sycl::cl_double2, 2>& kernel) {
-  double tmp = kernel.packet[0].y();
-  kernel.packet[0].y() = kernel.packet[1].x();
-  kernel.packet[1].x() = tmp;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4 pblend(
-    const Selector<unpacket_traits<cl::sycl::cl_float4>::size>& ifPacket,
-    const cl::sycl::cl_float4& thenPacket,
-    const cl::sycl::cl_float4& elsePacket) {
-  cl::sycl::cl_int4 condition(
-      ifPacket.select[0] ? 0 : -1, ifPacket.select[1] ? 0 : -1,
-      ifPacket.select[2] ? 0 : -1, ifPacket.select[3] ? 0 : -1);
-  return cl::sycl::select(thenPacket, elsePacket, condition);
-}
-
-template <>
-inline cl::sycl::cl_double2 pblend(
-    const Selector<unpacket_traits<cl::sycl::cl_double2>::size>& ifPacket,
-    const cl::sycl::cl_double2& thenPacket,
-    const cl::sycl::cl_double2& elsePacket) {
-  cl::sycl::cl_long2 condition(ifPacket.select[0] ? 0 : -1,
-                               ifPacket.select[1] ? 0 : -1);
-  return cl::sycl::select(thenPacket, elsePacket, condition);
-}
-#endif  // SYCL_DEVICE_ONLY
-
-#define SYCL_PSTORE(alignment)                                  \
-  template <typename packet_type>                               \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstore##alignment( \
-      const Eigen::TensorSycl::internal::RangeAccess<           \
-          cl::sycl::access::mode::read_write,                   \
-          typename unpacket_traits<packet_type>::type>& to,     \
-      const packet_type& from) {                                \
-    pstore##alignment(to.get_pointer(), from);                  \
-  }
-
-// global space
-SYCL_PSTORE()
-SYCL_PSTORE(u)
-
-#undef SYCL_PSTORE
-
-template <typename scalar, typename packet_type, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(
-    Eigen::TensorSycl::internal::RangeAccess<
-        cl::sycl::access::mode::read_write,
-        typename unpacket_traits<packet_type>::type>
-        to,
-    const packet_type& from) {
-  pstoret<scalar, packet_type, Alignment>(to.get_pointer(), from);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_PACKET_MATH_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
deleted file mode 100644
index f81e59d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
+++ /dev/null
@@ -1,694 +0,0 @@
-/***************************************************************************
- *  Copyright (C) 2017 Codeplay Software Limited
- *  This Source Code Form is subject to the terms of the Mozilla
- *  Public License v. 2.0. If a copy of the MPL was not distributed
- *  with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
- *
- *
- *  SyclMemoryModel.h
- *
- *  Description:
- *    Interface for SYCL buffers to behave as a non-dereferenceable pointer
- *    Interface for Placeholder accessor to behave as a pointer on both host
- *    and device
- *
- * Authors:
- *
- *    Ruyman Reyes   Codeplay Software Ltd.
- *    Mehdi Goli     Codeplay Software Ltd.
- *    Vanya Yaneva   Codeplay Software Ltd.
- *
- **************************************************************************/
-
-#if defined(EIGEN_USE_SYCL) && \
-    !defined(EIGEN_CXX11_TENSOR_TENSOR_SYCL_STORAGE_MEMORY_H)
-#define EIGEN_CXX11_TENSOR_TENSOR_SYCL_STORAGE_MEMORY_H
-
-#include <CL/sycl.hpp>
-#ifdef EIGEN_EXCEPTIONS
-#include <stdexcept>
-#endif
-#include <cstddef>
-#include <queue>
-#include <set>
-#include <unordered_map>
-
-namespace Eigen {
-namespace TensorSycl {
-namespace internal {
-
-using sycl_acc_target = cl::sycl::access::target;
-using sycl_acc_mode = cl::sycl::access::mode;
-
-/**
- * Default values for template arguments
- */
-using buffer_data_type_t = uint8_t;
-const sycl_acc_target default_acc_target = sycl_acc_target::global_buffer;
-const sycl_acc_mode default_acc_mode = sycl_acc_mode::read_write;
-
-/**
- * PointerMapper
- *  Associates fake pointers with buffers.
- *
- */
-class PointerMapper {
- public:
-  using base_ptr_t = std::intptr_t;
-
-  /* Structure of a virtual pointer
-   *
-   * |================================================|
-   * |               POINTER ADDRESS                  |
-   * |================================================|
-   */
-  struct virtual_pointer_t {
-    /* Type for the pointers
-     */
-    base_ptr_t m_contents;
-
-    /** Conversions from virtual_pointer_t to
-     * void * should just reinterpret_cast the integer number
-     */
-    operator void *() const { return reinterpret_cast<void *>(m_contents); }
-
-    /**
-     * Convert back to the integer number.
-     */
-    operator base_ptr_t() const { return m_contents; }
-
-    /**
-     * Add a certain value to the pointer to create a
-     * new pointer to that offset
-     */
-    virtual_pointer_t operator+(size_t off) { return m_contents + off; }
-
-    /* Numerical order for sorting pointers in containers. */
-    bool operator<(virtual_pointer_t rhs) const {
-      return (static_cast<base_ptr_t>(m_contents) <
-              static_cast<base_ptr_t>(rhs.m_contents));
-    }
-
-    bool operator>(virtual_pointer_t rhs) const {
-      return (static_cast<base_ptr_t>(m_contents) >
-              static_cast<base_ptr_t>(rhs.m_contents));
-    }
-
-    /**
-     * Numerical order for sorting pointers in containers
-     */
-    bool operator==(virtual_pointer_t rhs) const {
-      return (static_cast<base_ptr_t>(m_contents) ==
-              static_cast<base_ptr_t>(rhs.m_contents));
-    }
-
-    /**
-     * Simple forward to the equality overload.
-     */
-    bool operator!=(virtual_pointer_t rhs) const {
-      return !(this->operator==(rhs));
-    }
-
-    /**
-     * Converts a void * into a virtual pointer structure.
-     * Note that this will only work if the void * was
-     * already a virtual_pointer_t, but we have no way of
-     * checking
-     */
-    virtual_pointer_t(const void *ptr)
-        : m_contents(reinterpret_cast<base_ptr_t>(ptr)){};
-
-    /**
-     * Creates a virtual_pointer_t from the given integer
-     * number
-     */
-    virtual_pointer_t(base_ptr_t u) : m_contents(u){};
-  };
-
-  /* Definition of a null pointer
-   */
-  const virtual_pointer_t null_virtual_ptr = nullptr;
-
-  /**
-   * Whether if a pointer is null or not.
-   * A pointer is nullptr if the value is of null_virtual_ptr
-   */
-  static inline bool is_nullptr(virtual_pointer_t ptr) {
-    return (static_cast<void *>(ptr) == nullptr);
-  }
-
-  /* basic type for all buffers
-   */
-  using buffer_t = cl::sycl::buffer_mem;
-
-  /**
-   * Node that stores information about a device allocation.
-   * Nodes are sorted by size to organise a free list of nodes
-   * that can be recovered.
-   */
-  struct pMapNode_t {
-    buffer_t m_buffer;
-    size_t m_size;
-    bool m_free;
-
-    pMapNode_t(buffer_t b, size_t size, bool f)
-        : m_buffer{b}, m_size{size}, m_free{f} {
-      m_buffer.set_final_data(nullptr);
-    }
-
-    bool operator<=(const pMapNode_t &rhs) { return (m_size <= rhs.m_size); }
-  };
-
-  /** Storage of the pointer / buffer tree
-   */
-  using pointerMap_t = std::map<virtual_pointer_t, pMapNode_t>;
-
-  /**
-   * Obtain the insertion point in the pointer map for
-   * a pointer of the given size.
-   * \param requiredSize Size attemted to reclaim
-   */
-  typename pointerMap_t::iterator get_insertion_point(size_t requiredSize) {
-    typename pointerMap_t::iterator retVal;
-    bool reuse = false;
-    if (!m_freeList.empty()) {
-      // try to re-use an existing block
-      for (auto freeElem : m_freeList) {
-        if (freeElem->second.m_size >= requiredSize) {
-          retVal = freeElem;
-          reuse = true;
-          // Element is not going to be free anymore
-          m_freeList.erase(freeElem);
-          break;
-        }
-      }
-    }
-    if (!reuse) {
-      retVal = std::prev(m_pointerMap.end());
-    }
-    return retVal;
-  }
-
-  /**
-   * Returns an iterator to the node that stores the information
-   * of the given virtual pointer from the given pointer map structure.
-   * If pointer is not found, throws std::out_of_range.
-   * If the pointer map structure is empty, throws std::out_of_range
-   *
-   * \param pMap the pointerMap_t structure storing all the pointers
-   * \param virtual_pointer_ptr The virtual pointer to obtain the node of
-   * \throws std::out:of_range if the pointer is not found or pMap is empty
-   */
-  typename pointerMap_t::iterator get_node(const virtual_pointer_t ptr) {
-    if (this->count() == 0) {
-      m_pointerMap.clear();
-      EIGEN_THROW_X(std::out_of_range("There are no pointers allocated\n"));
-
-    }
-    if (is_nullptr(ptr)) {
-      m_pointerMap.clear();
-      EIGEN_THROW_X(std::out_of_range("Cannot access null pointer\n"));
-    }
-    // The previous element to the lower bound is the node that
-    // holds this memory address
-    auto node = m_pointerMap.lower_bound(ptr);
-    // If the value of the pointer is not the one of the node
-    // then we return the previous one
-    if (node == std::end(m_pointerMap)) {
-      --node;
-    } else if (node->first != ptr) {
-      if (node == std::begin(m_pointerMap)) {
-        m_pointerMap.clear();
-        EIGEN_THROW_X(
-            std::out_of_range("The pointer is not registered in the map\n"));
-
-      }
-      --node;
-    }
-
-    return node;
-  }
-
-  /* get_buffer.
-   * Returns a buffer from the map using the pointer address
-   */
-  template <typename buffer_data_type = buffer_data_type_t>
-  cl::sycl::buffer<buffer_data_type, 1> get_buffer(
-      const virtual_pointer_t ptr) {
-    using sycl_buffer_t = cl::sycl::buffer<buffer_data_type, 1>;
-
-    // get_node() returns a `buffer_mem`, so we need to cast it to a `buffer<>`.
-    // We can do this without the `buffer_mem` being a pointer, as we
-    // only declare member variables in the base class (`buffer_mem`) and not in
-    // the child class (`buffer<>).
-    auto node = get_node(ptr);
-    eigen_assert(node->first == ptr || node->first < ptr);
-    eigen_assert(ptr < static_cast<virtual_pointer_t>(node->second.m_size +
-                                                      node->first));
-    return *(static_cast<sycl_buffer_t *>(&node->second.m_buffer));
-  }
-
-  /**
-   * @brief Returns an accessor to the buffer of the given virtual pointer
-   * @param accessMode
-   * @param accessTarget
-   * @param ptr The virtual pointer
-   */
-  template <sycl_acc_mode access_mode = default_acc_mode,
-            sycl_acc_target access_target = default_acc_target,
-            typename buffer_data_type = buffer_data_type_t>
-  cl::sycl::accessor<buffer_data_type, 1, access_mode, access_target>
-  get_access(const virtual_pointer_t ptr) {
-    auto buf = get_buffer<buffer_data_type>(ptr);
-    return buf.template get_access<access_mode, access_target>();
-  }
-
-  /**
-   * @brief Returns an accessor to the buffer of the given virtual pointer
-   *        in the given command group scope
-   * @param accessMode
-   * @param accessTarget
-   * @param ptr The virtual pointer
-   * @param cgh Reference to the command group scope
-   */
-  template <sycl_acc_mode access_mode = default_acc_mode,
-            sycl_acc_target access_target = default_acc_target,
-            typename buffer_data_type = buffer_data_type_t>
-  cl::sycl::accessor<buffer_data_type, 1, access_mode, access_target>
-  get_access(const virtual_pointer_t ptr, cl::sycl::handler &cgh) {
-    auto buf = get_buffer<buffer_data_type>(ptr);
-    return buf.template get_access<access_mode, access_target>(cgh);
-  }
-
-  /*
-   * Returns the offset from the base address of this pointer.
-   */
-  inline std::ptrdiff_t get_offset(const virtual_pointer_t ptr) {
-    // The previous element to the lower bound is the node that
-    // holds this memory address
-    auto node = get_node(ptr);
-    auto start = node->first;
-    eigen_assert(start == ptr || start < ptr);
-    eigen_assert(ptr < start + node->second.m_size);
-    return (ptr - start);
-  }
-
-  /*
-   * Returns the number of elements by which the given pointer is offset from
-   * the base address.
-   */
-  template <typename buffer_data_type>
-  inline size_t get_element_offset(const virtual_pointer_t ptr) {
-    return get_offset(ptr) / sizeof(buffer_data_type);
-  }
-
-  /**
-   * Constructs the PointerMapper structure.
-   */
-  PointerMapper(base_ptr_t baseAddress = 4096)
-      : m_pointerMap{}, m_freeList{}, m_baseAddress{baseAddress} {
-    if (m_baseAddress == 0) {
-      EIGEN_THROW_X(std::invalid_argument("Base address cannot be zero\n"));
-    }
-  };
-
-  /**
-   * PointerMapper cannot be copied or moved
-   */
-  PointerMapper(const PointerMapper &) = delete;
-
-  /**
-   * Empty the pointer list
-   */
-  inline void clear() {
-    m_freeList.clear();
-    m_pointerMap.clear();
-  }
-
-  /* add_pointer.
-   * Adds an existing pointer to the map and returns the virtual pointer id.
-   */
-  inline virtual_pointer_t add_pointer(const buffer_t &b) {
-    return add_pointer_impl(b);
-  }
-
-  /* add_pointer.
-   * Adds a pointer to the map and returns the virtual pointer id.
-   */
-  inline virtual_pointer_t add_pointer(buffer_t &&b) {
-    return add_pointer_impl(b);
-  }
-
-  /**
-   * @brief Fuses the given node with the previous nodes in the
-   *        pointer map if they are free
-   *
-   * @param node A reference to the free node to be fused
-   */
-  void fuse_forward(typename pointerMap_t::iterator &node) {
-    while (node != std::prev(m_pointerMap.end())) {
-      // if following node is free
-      // remove it and extend the current node with its size
-      auto fwd_node = std::next(node);
-      if (!fwd_node->second.m_free) {
-        break;
-      }
-      auto fwd_size = fwd_node->second.m_size;
-      m_freeList.erase(fwd_node);
-      m_pointerMap.erase(fwd_node);
-
-      node->second.m_size += fwd_size;
-    }
-  }
-
-  /**
-   * @brief Fuses the given node with the following nodes in the
-   *        pointer map if they are free
-   *
-   * @param node A reference to the free node to be fused
-   */
-  void fuse_backward(typename pointerMap_t::iterator &node) {
-    while (node != m_pointerMap.begin()) {
-      // if previous node is free, extend it
-      // with the size of the current one
-      auto prev_node = std::prev(node);
-      if (!prev_node->second.m_free) {
-        break;
-      }
-      prev_node->second.m_size += node->second.m_size;
-
-      // remove the current node
-      m_freeList.erase(node);
-      m_pointerMap.erase(node);
-
-      // point to the previous node
-      node = prev_node;
-    }
-  }
-
-  /* remove_pointer.
-   * Removes the given pointer from the map.
-   * The pointer is allowed to be reused only if ReUse if true.
-   */
-  template <bool ReUse = true>
-  void remove_pointer(const virtual_pointer_t ptr) {
-    if (is_nullptr(ptr)) {
-      return;
-    }
-    auto node = this->get_node(ptr);
-
-    node->second.m_free = true;
-    m_freeList.emplace(node);
-
-    // Fuse the node
-    // with free nodes before and after it
-    fuse_forward(node);
-    fuse_backward(node);
-
-    // If after fusing the node is the last one
-    // simply remove it (since it is free)
-    if (node == std::prev(m_pointerMap.end())) {
-      m_freeList.erase(node);
-      m_pointerMap.erase(node);
-    }
-  }
-
-  /* count.
-   * Return the number of active pointers (i.e, pointers that
-   * have been malloc but not freed).
-   */
-  size_t count() const { return (m_pointerMap.size() - m_freeList.size()); }
-
- private:
-  /* add_pointer_impl.
-   * Adds a pointer to the map and returns the virtual pointer id.
-   * BufferT is either a const buffer_t& or a buffer_t&&.
-   */
-  template <class BufferT>
-  virtual_pointer_t add_pointer_impl(BufferT b) {
-    virtual_pointer_t retVal = nullptr;
-    size_t bufSize = b.get_count();
-    pMapNode_t p{b, bufSize, false};
-    // If this is the first pointer:
-    if (m_pointerMap.empty()) {
-      virtual_pointer_t initialVal{m_baseAddress};
-      m_pointerMap.emplace(initialVal, p);
-      return initialVal;
-    }
-
-    auto lastElemIter = get_insertion_point(bufSize);
-    // We are recovering an existing free node
-    if (lastElemIter->second.m_free) {
-      lastElemIter->second.m_buffer = b;
-      lastElemIter->second.m_free = false;
-
-      // If the recovered node is bigger than the inserted one
-      // add a new free node with the remaining space
-      if (lastElemIter->second.m_size > bufSize) {
-        // create a new node with the remaining space
-        auto remainingSize = lastElemIter->second.m_size - bufSize;
-        pMapNode_t p2{b, remainingSize, true};
-
-        // update size of the current node
-        lastElemIter->second.m_size = bufSize;
-
-        // add the new free node
-        auto newFreePtr = lastElemIter->first + bufSize;
-        auto freeNode = m_pointerMap.emplace(newFreePtr, p2).first;
-        m_freeList.emplace(freeNode);
-      }
-
-      retVal = lastElemIter->first;
-    } else {
-      size_t lastSize = lastElemIter->second.m_size;
-      retVal = lastElemIter->first + lastSize;
-      m_pointerMap.emplace(retVal, p);
-    }
-    return retVal;
-  }
-
-  /**
-   * Compare two iterators to pointer map entries according to
-   * the size of the allocation on the device.
-   */
-  struct SortBySize {
-    bool operator()(typename pointerMap_t::iterator a,
-                    typename pointerMap_t::iterator b) const {
-      return ((a->first < b->first) && (a->second <= b->second)) ||
-             ((a->first < b->first) && (b->second <= a->second));
-    }
-  };
-
-  /* Maps the pointer addresses to buffer and size pairs.
-   */
-  pointerMap_t m_pointerMap;
-
-  /* List of free nodes available for re-using
-   */
-  std::set<typename pointerMap_t::iterator, SortBySize> m_freeList;
-
-  /* Base address used when issuing the first virtual pointer, allows users
-   * to specify alignment. Cannot be zero. */
-  std::intptr_t m_baseAddress;
-};
-
-/* remove_pointer.
- * Removes the given pointer from the map.
- * The pointer is allowed to be reused only if ReUse if true.
- */
-template <>
-inline void PointerMapper::remove_pointer<false>(const virtual_pointer_t ptr) {
-  if (is_nullptr(ptr)) {
-    return;
-  }
-  m_pointerMap.erase(this->get_node(ptr));
-}
-
-/**
- * Malloc-like interface to the pointer-mapper.
- * Given a size, creates a byte-typed buffer and returns a
- * fake pointer to keep track of it.
- * \param size Size in bytes of the desired allocation
- * \throw cl::sycl::exception if error while creating the buffer
- */
-inline void *SYCLmalloc(size_t size, PointerMapper &pMap) {
-  if (size == 0) {
-    return nullptr;
-  }
-  // Create a generic buffer of the given size
-  using buffer_t = cl::sycl::buffer<buffer_data_type_t, 1>;
-  auto thePointer = pMap.add_pointer(buffer_t(cl::sycl::range<1>{size}));
-  // Store the buffer on the global list
-  return static_cast<void *>(thePointer);
-}
-
-/**
- * Free-like interface to the pointer mapper.
- * Given a fake-pointer created with the virtual-pointer malloc,
- * destroys the buffer and remove it from the list.
- * If ReUse is false, the pointer is not added to the freeList,
- * it should be false only for sub-buffers.
- */
-template <bool ReUse = true, typename PointerMapper>
-inline void SYCLfree(void *ptr, PointerMapper &pMap) {
-  pMap.template remove_pointer<ReUse>(ptr);
-}
-
-/**
- * Clear all the memory allocated by SYCL.
- */
-template <typename PointerMapper>
-inline void SYCLfreeAll(PointerMapper &pMap) {
-  pMap.clear();
-}
-
-template <cl::sycl::access::mode AcMd, typename T>
-struct RangeAccess {
-  static const auto global_access = cl::sycl::access::target::global_buffer;
-  static const auto is_place_holder = cl::sycl::access::placeholder::true_t;
-  typedef T scalar_t;
-  typedef scalar_t &ref_t;
-  typedef typename cl::sycl::global_ptr<scalar_t>::pointer_t ptr_t;
-
-  // the accessor type does not necessarily the same as T
-  typedef cl::sycl::accessor<scalar_t, 1, AcMd, global_access, is_place_holder>
-      accessor;
-
-  typedef RangeAccess<AcMd, T> self_t;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE RangeAccess(accessor access,
-                                                    size_t offset,
-                                                    std::intptr_t virtual_ptr)
-      : access_(access), offset_(offset), virtual_ptr_(virtual_ptr) {}
-
-  RangeAccess(cl::sycl::buffer<scalar_t, 1> buff =
-                  cl::sycl::buffer<scalar_t, 1>(cl::sycl::range<1>(1)))
-      : access_{accessor{buff}}, offset_(0), virtual_ptr_(-1) {}
-
-  // This should be only used for null constructor on the host side
-  RangeAccess(std::nullptr_t) : RangeAccess() {}
-  // This template parameter must be removed and scalar_t should be replaced
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptr_t get_pointer() const {
-    return (access_.get_pointer().get() + offset_);
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t &operator+=(Index offset) {
-    offset_ += (offset);
-    return *this;
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t operator+(Index offset) const {
-    return self_t(access_, offset_ + offset, virtual_ptr_);
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t operator-(Index offset) const {
-    return self_t(access_, offset_ - offset, virtual_ptr_);
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t &operator-=(Index offset) {
-    offset_ -= offset;
-    return *this;
-  }
-
-  // THIS IS FOR NULL COMPARISON ONLY
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator==(
-      const RangeAccess &lhs, std::nullptr_t) {
-    return ((lhs.virtual_ptr_ == -1));
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator!=(
-      const RangeAccess &lhs, std::nullptr_t i) {
-    return !(lhs == i);
-  }
-
-  // THIS IS FOR NULL COMPARISON ONLY
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator==(
-      std::nullptr_t, const RangeAccess &rhs) {
-    return ((rhs.virtual_ptr_ == -1));
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator!=(
-      std::nullptr_t i, const RangeAccess &rhs) {
-    return !(i == rhs);
-  }
-  // Prefix operator (Increment and return value)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t &operator++() {
-    offset_++;
-    return (*this);
-  }
-
-  // Postfix operator (Return value and increment)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t operator++(int i) {
-    EIGEN_UNUSED_VARIABLE(i);
-    self_t temp_iterator(*this);
-    offset_++;
-    return temp_iterator;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t get_size() const {
-    return (access_.get_count() - offset_);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t get_offset() const {
-    return offset_;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_offset(std::ptrdiff_t offset) {
-    offset_ = offset;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator*() const {
-    return *get_pointer();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator*() {
-    return *get_pointer();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptr_t operator->() = delete;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator[](int x) {
-    return *(get_pointer() + x);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator[](int x) const {
-    return *(get_pointer() + x);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_t *get_virtual_pointer() const {
-    return reinterpret_cast<scalar_t *>(virtual_ptr_ +
-                                        (offset_ * sizeof(scalar_t)));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit operator bool() const {
-    return (virtual_ptr_ != -1);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator RangeAccess<AcMd, const T>() {
-    return RangeAccess<AcMd, const T>(access_, offset_, virtual_ptr_);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  operator RangeAccess<AcMd, const T>() const {
-    return RangeAccess<AcMd, const T>(access_, offset_, virtual_ptr_);
-  }
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(
-      cl::sycl::handler &cgh) const {
-    cgh.require(access_);
-  }
-
- private:
-  accessor access_;
-  size_t offset_;
-  std::intptr_t virtual_ptr_;  // the location of the buffer in the map
-};
-
-template <cl::sycl::access::mode AcMd, typename T>
-struct RangeAccess<AcMd, const T> : RangeAccess<AcMd, T> {
-  typedef RangeAccess<AcMd, T> Base;
-  using Base::Base;
-};
-
-}  // namespace internal
-}  // namespace TensorSycl
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_SYCL_STORAGE_MEMORY_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/TypeCasting.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/TypeCasting.h
deleted file mode 100644
index 9208ab2..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/SYCL/TypeCasting.h
+++ /dev/null
@@ -1,85 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * TypeCasting.h
- *
- * \brief:
- *  TypeCasting
- *
- *****************************************************************/
-
-#ifndef EIGEN_TYPE_CASTING_SYCL_H
-#define EIGEN_TYPE_CASTING_SYCL_H
-
-namespace Eigen {
-
-namespace internal {
-#ifdef SYCL_DEVICE_ONLY
-template <>
-struct type_casting_traits<float, int> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_int4
-pcast<cl::sycl::cl_float4, cl::sycl::cl_int4>(const cl::sycl::cl_float4& a) {
-  return a
-      .template convert<cl::sycl::cl_int, cl::sycl::rounding_mode::automatic>();
-}
-
-template <>
-struct type_casting_traits<int, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
-pcast<cl::sycl::cl_int4, cl::sycl::cl_float4>(const cl::sycl::cl_int4& a) {
-  return a.template convert<cl::sycl::cl_float,
-                            cl::sycl::rounding_mode::automatic>();
-}
-
-template <>
-struct type_casting_traits<double, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
-pcast<cl::sycl::cl_double2, cl::sycl::cl_float4>(
-    const cl::sycl::cl_double2& a, const cl::sycl::cl_double2& b) {
-  auto a1 = a.template convert<cl::sycl::cl_float,
-                               cl::sycl::rounding_mode::automatic>();
-  auto b1 = b.template convert<cl::sycl::cl_float,
-                               cl::sycl::rounding_mode::automatic>();
-  return cl::sycl::float4(a1.x(), a1.y(), b1.x(), b1.y());
-}
-
-template <>
-struct type_casting_traits<float, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2
-pcast<cl::sycl::cl_float4, cl::sycl::cl_double2>(const cl::sycl::cl_float4& a) {
-  // Simply discard the second half of the input
-  return cl::sycl::cl_double2(a.x(), a.y());
-}
-
-#endif
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_TYPE_CASTING_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/Complex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/Complex.h
deleted file mode 100644
index 0b9b33d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/Complex.h
+++ /dev/null
@@ -1,426 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX32_ALTIVEC_H
-#define EIGEN_COMPLEX32_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-static Packet4ui  p4ui_CONJ_XOR = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 }; //vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);
-#endif
-
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
-  Packet2d v;
-};
-
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-  union {
-    Packet4f v;
-    Packet1cd cd[2];
-  };
-#else
-  Packet4f v;
-#endif
-};
-
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasBlend  = 1,
-    HasSetLinear = 0
-  };
-};
-
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float>  type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2cf half; };
-template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet1cd half; };
-
-/* Forward declaration */
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel);
-
-/* complex<double> first */
-template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
-{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index stride EIGEN_UNUSED)
-{
-  return pload<Packet1cd>(from);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index stride EIGEN_UNUSED)
-{
-  pstore<std::complex<double> >(to, from);
-}
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  Packet2d a_re, a_im, v1, v2;
-
-  // Permute and multiply the real parts of a and b
-  a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
-  // Get the imaginary parts of a
-  a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
-  // multiply a_re * b
-  v1 = vec_madd(a_re, b.v, p2d_ZERO);
-  // multiply a_im * b and get the conjugate result
-  v2 = vec_madd(a_im, b.v, p2d_ZERO);
-  v2 = (Packet2d) vec_sld((Packet4ui)v2, (Packet4ui)v2, 8);
-  v2 = (Packet2d) vec_xor((Packet2d)v2, (Packet2d) p2ul_CONJ_XOR1);
-
-  return Packet1cd(v1 + v2);
-}
-template<> EIGEN_STRONG_INLINE Packet1cd pand    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd por     <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_or(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pxor    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_xor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v, vec_nor(b.v,b.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>*     from) {  return pset1<Packet1cd>(*from); }
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
-  Packet2d eq = vec_cmpeq (a.v, b.v);
-  Packet2d tmp = { eq[1], eq[0] };
-  return (Packet1cd)pand<Packet2d>(eq, tmp);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *   addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
-{
-  std::complex<double> EIGEN_ALIGN16 res;
-  pstore<std::complex<double> >(&res, a);
-
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for AltiVec
-  Packet1cd res = pmul(a,pconj(b));
-  Packet2d s = vec_madd(b.v, b.v, p2d_ZERO_);
-  return Packet1cd(pdiv(res.v, s + vec_perm(s, s, p16uc_REVERSE64)));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
-{
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
-{
-  Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-/* complex<float> follows */
-template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from)  { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from)  { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *     to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *     to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> EIGEN_ALIGN16 res[2];
-  pstore<std::complex<float> >(res, a);
-
-  return res[0];
-}
-
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-  res.cd[0] = Packet1cd(vec_ld2f((const float *)&from));
-  res.cd[1] = res.cd[0];
-  return res;
-}
-#else
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-  if((std::ptrdiff_t(&from) % 16) == 0)
-    res.v = pload<Packet4f>((const float *)&from);
-  else
-    res.v = ploadu<Packet4f>((const float *)&from);
-  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
-  return res;
-}
-#endif
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
-{
-  std::complex<float> EIGEN_ALIGN16 af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
-  return pload<Packet2cf>(af);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  std::complex<float> EIGEN_ALIGN16 af[2];
-  pstore<std::complex<float> >((std::complex<float> *) af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(padd<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(psub<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(Packet4f(a.v))); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pxor<Packet4f>(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pandnot<Packet4f>(a.v,b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>*      from) {  return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *     addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
-  Packet4f eq = pcmp_eq<Packet4f> (a.v, b.v);
-  Packet2cf res;
-  Packet2d tmp1 = { eq.v4f[0][1], eq.v4f[0][0] };
-  Packet2d tmp2 = { eq.v4f[1][1], eq.v4f[1][0] };
-  res.v.v4f[0] = pand<Packet2d>(eq.v4f[0], tmp1);
-  res.v.v4f[1] = pand<Packet2d>(eq.v4f[1], tmp2);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
-{
-  Packet2cf res;
-  res.v.v4f[0] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0]))).v;
-  res.v.v4f[1] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1]))).v;
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  Packet2cf res;
-  res.v.v4f[0] = pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[0]))).v;
-  res.v.v4f[1] = pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[1]))).v;
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{
-  Packet2cf res;
-  res.cd[0] = a.cd[1];
-  res.cd[1] = a.cd[0];
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> res;
-  Packet1cd b = padd<Packet1cd>(a.cd[0], a.cd[1]);
-  vec_st2f(b.v, (float*)&res);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> res;
-  Packet1cd b = pmul<Packet1cd>(a.cd[0], a.cd[1]);
-  vec_st2f(b.v, (float*)&res);
-  return res;
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for AltiVec
-  Packet2cf res;
-  res.cd[0] = pdiv<Packet1cd>(a.cd[0], b.cd[0]);
-  res.cd[1] = pdiv<Packet1cd>(a.cd[1], b.cd[1]);
-  return res;
-}
-
-EIGEN_STRONG_INLINE Packet2cf pcplxflip/*<Packet2cf>*/(const Packet2cf& x)
-{
-  Packet2cf res;
-  res.cd[0] = pcplxflip(x.cd[0]);
-  res.cd[1] = pcplxflip(x.cd[1]);
-  return res;
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
-{
-  Packet1cd tmp = kernel.packet[0].cd[1];
-  kernel.packet[0].cd[1] = kernel.packet[1].cd[0];
-  kernel.packet[1].cd[0] = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  Packet2cf result;
-  const Selector<4> ifPacket4 = { ifPacket.select[0], ifPacket.select[0], ifPacket.select[1], ifPacket.select[1] };
-  result.v = pblend<Packet4f>(ifPacket4, thenPacket.v, elsePacket.v);
-  return result;
-}
-#else
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
-  Packet4f eq = vec_cmpeq (a.v, b.v);
-  Packet4f tmp = { eq[1], eq[0], eq[3], eq[2] };
-  return (Packet2cf)pand<Packet4f>(eq, tmp);
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  Packet4f a_re, a_im, prod, prod_im;
-
-  // Permute and multiply the real parts of a and b
-  a_re = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
-  
-  // Get the imaginary parts of a
-  a_im = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
-
-  // multiply a_im * b and get the conjugate result
-  prod_im = a_im * b.v;
-  prod_im = pxor<Packet4f>(prod_im, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR));
-  // permute back to a proper order
-  prod_im = vec_perm(prod_im, prod_im, p16uc_COMPLEX32_REV);
-
-  // multiply a_re * b, add prod_im
-  prod = pmadd<Packet4f>(a_re, b.v, prod_im);
- 
-  return Packet2cf(prod);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{
-  Packet4f rev_a;
-  rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
-  return Packet2cf(rev_a);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  b = vec_sld(a.v, a.v, 8);
-  b = padd<Packet4f>(a.v, b);
-  return pfirst<Packet2cf>(Packet2cf(b));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  Packet2cf prod;
-  b = vec_sld(a.v, a.v, 8);
-  prod = pmul<Packet2cf>(a, Packet2cf(b));
-
-  return pfirst<Packet2cf>(prod);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for AltiVec
-  Packet2cf res = pmul(a, pconj(b));
-  Packet4f s = pmul<Packet4f>(b.v, b.v);
-  return Packet2cf(pdiv(res.v, padd<Packet4f>(s, vec_perm(s, s, p16uc_COMPLEX32_REV))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
-{
-  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
-{
-  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  Packet2cf result;
-  result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
-  return result;
-}
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX32_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/MathFunctions.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
deleted file mode 100644
index 1635e12..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
+++ /dev/null
@@ -1,233 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The sin, cos, exp, and log functions of this file come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-static _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
-static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-static _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
-static _EIGEN_DECLARE_CONST_Packet4i(23, 23);
-
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000);
-
-/* the smallest non denormalized float number */
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos,  0x00800000);
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf,     0xff800000); // -1.f/0.f
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan,     0xffffffff);
-  
-/* natural logarithm computed for 4 simultaneous float
-  return NaN for x <= 0
-*/
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
-
-static _EIGEN_DECLARE_CONST_Packet4f(exp_hi,  88.3762626647950f);
-static _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f);
-
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
-
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f);
-#endif
-
-static _EIGEN_DECLARE_CONST_Packet2d(1 , 1.0);
-static _EIGEN_DECLARE_CONST_Packet2d(2 , 2.0);
-static _EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
-
-static _EIGEN_DECLARE_CONST_Packet2d(exp_hi,  709.437);
-static _EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d pexp<Packet2d>(const Packet2d& _x)
-{
-  Packet2d x = _x;
-
-  Packet2d tmp, fx;
-  Packet2l emm0;
-
-  // clamp x
-  x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo);
-  /* express exp(x) as exp(g + n*log(2)) */
-  fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half);
-
-  fx = vec_floor(fx);
-
-  tmp = pmul(fx, p2d_cephes_exp_C1);
-  Packet2d z = pmul(fx, p2d_cephes_exp_C2);
-  x = psub(x, tmp);
-  x = psub(x, z);
-
-  Packet2d x2 = pmul(x,x);
-
-  Packet2d px = p2d_cephes_exp_p0;
-  px = pmadd(px, x2, p2d_cephes_exp_p1);
-  px = pmadd(px, x2, p2d_cephes_exp_p2);
-  px = pmul (px, x);
-
-  Packet2d qx = p2d_cephes_exp_q0;
-  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
-
-  x = pdiv(px,psub(qx,px));
-  x = pmadd(p2d_2,x,p2d_1);
-
-  // build 2^n
-  emm0 = vec_ctsl(fx, 0);
-
-  static const Packet2l p2l_1023 = { 1023, 1023 };
-  static const Packet2ul p2ul_52 = { 52, 52 };
-
-  emm0 = emm0 + p2l_1023;
-  emm0 = emm0 << reinterpret_cast<Packet2l>(p2ul_52);
-
-  // Altivec's max & min operators just drop silent NaNs. Check NaNs in 
-  // inputs and return them unmodified.
-  Packet2ul isnumber_mask = reinterpret_cast<Packet2ul>(vec_cmpeq(_x, _x));
-  return vec_sel(_x, pmax(pmul(x, reinterpret_cast<Packet2d>(emm0)), _x),
-                 isnumber_mask);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexp<Packet4f>(const Packet4f& _x)
-{
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  Packet4f x = _x;
-
-  Packet4f tmp, fx;
-  Packet4i emm0;
-
-  // clamp x
-  x = pmax(pmin(x, p4f_exp_hi), p4f_exp_lo);
-
-  // express exp(x) as exp(g + n*log(2))
-  fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half);
-
-  fx = pfloor(fx);
-
-  tmp = pmul(fx, p4f_cephes_exp_C1);
-  Packet4f z = pmul(fx, p4f_cephes_exp_C2);
-  x = psub(x, tmp);
-  x = psub(x, z);
-
-  z = pmul(x,x);
-
-  Packet4f y = p4f_cephes_exp_p0;
-  y = pmadd(y, x, p4f_cephes_exp_p1);
-  y = pmadd(y, x, p4f_cephes_exp_p2);
-  y = pmadd(y, x, p4f_cephes_exp_p3);
-  y = pmadd(y, x, p4f_cephes_exp_p4);
-  y = pmadd(y, x, p4f_cephes_exp_p5);
-  y = pmadd(y, z, x);
-  y = padd(y, p4f_1);
-
-  // build 2^n
-  emm0 = (Packet4i){ (int)fx[0], (int)fx[1], (int)fx[2], (int)fx[3] };
-  emm0 = emm0 + p4i_0x7f;
-  emm0 = emm0 << reinterpret_cast<Packet4i>(p4i_23);
-
-  return pmax(pmul(y, reinterpret_cast<Packet4f>(emm0)), _x);
-#else
-  Packet4f res;
-  res.v4f[0] = pexp<Packet2d>(_x.v4f[0]);
-  res.v4f[1] = pexp<Packet2d>(_x.v4f[1]);
-  return res;
-#endif
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d psqrt<Packet2d>(const Packet2d& x)
-{
-  return vec_sqrt(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& x)
-{
-  Packet4f res;
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  res = vec_sqrt(x);
-#else
-  res.v4f[0] = psqrt<Packet2d>(x.v4f[0]);
-  res.v4f[1] = psqrt<Packet2d>(x.v4f[1]);
-#endif
-  return res;
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d prsqrt<Packet2d>(const Packet2d& x) {
-  return pset1<Packet2d>(1.0) / psqrt<Packet2d>(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& x) {
-  Packet4f res;
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  res = pset1<Packet4f>(1.0) / psqrt<Packet4f>(x);
-#else
-  res.v4f[0] = prsqrt<Packet2d>(x.v4f[0]);
-  res.v4f[1] = prsqrt<Packet2d>(x.v4f[1]);
-#endif
-  return res;
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/PacketMath.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/PacketMath.h
deleted file mode 100644
index 1f55a90..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/arch/ZVector/PacketMath.h
+++ /dev/null
@@ -1,1060 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_ZVECTOR_H
-#define EIGEN_PACKET_MATH_ZVECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS  32
-#endif
-
-typedef __vector int                 Packet4i;
-typedef __vector unsigned int        Packet4ui;
-typedef __vector __bool int          Packet4bi;
-typedef __vector short int           Packet8i;
-typedef __vector unsigned char       Packet16uc;
-typedef __vector double              Packet2d;
-typedef __vector unsigned long long  Packet2ul;
-typedef __vector long long           Packet2l;
-
-// Z14 has builtin support for float vectors
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-typedef __vector float               Packet4f;
-#else
-typedef struct {
-	Packet2d  v4f[2];
-} Packet4f;
-#endif
-
-typedef union {
-  numext::int32_t   i[4];
-  numext::uint32_t ui[4];
-  numext::int64_t   l[2];
-  numext::uint64_t ul[2];
-  double    d[2];
-  float     f[4];
-  Packet4i  v4i;
-  Packet4ui v4ui;
-  Packet2l  v2l;
-  Packet2ul v2ul;
-  Packet2d  v2d;
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  Packet4f  v4f;
-#endif
-} Packet;
-
-// We don't want to write the same code all the time, but we need to reuse the constants
-// and it doesn't really work to declare them global, so we define macros instead
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = reinterpret_cast<Packet4i>(vec_splat_s32(X))
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet2d(NAME,X) \
-  Packet2d p2d_##NAME = reinterpret_cast<Packet2d>(vec_splat_s64(X))
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet2l(NAME,X) \
-  Packet2l p2l_##NAME = reinterpret_cast<Packet2l>(vec_splat_s64(X))
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
-  Packet2d p2d_##NAME = pset1<Packet2d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
-  Packet2l p2l_##NAME = pset1<Packet2l>(X)
-
-// These constants are endian-agnostic
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE, 1); //{ 1, 1, 1, 1}
-
-static _EIGEN_DECLARE_CONST_FAST_Packet2d(ZERO, 0);
-static _EIGEN_DECLARE_CONST_FAST_Packet2l(ZERO, 0);
-static _EIGEN_DECLARE_CONST_FAST_Packet2l(ONE, 1);
-
-static Packet2d p2d_ONE = { 1.0, 1.0 };
-static Packet2d p2d_ZERO_ = { numext::bit_cast<double>0x8000000000000000ull),
-                              numext::bit_cast<double>0x8000000000000000ull) };
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-#define _EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(vec_splat_s32(X))
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
-
-static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
-static Packet4f p4f_MZERO = { 0x80000000, 0x80000000, 0x80000000, 0x80000000};
-#endif
-
-static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
-static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
-static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet16uc>(p2d_ZERO), reinterpret_cast<Packet16uc>(p2d_ONE), 8));
-
-static Packet16uc p16uc_PSET64_HI = { 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
-static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
-
-// Mask alignment
-#define _EIGEN_MASK_ALIGNMENT	0xfffffffffffffff0
-
-#define _EIGEN_ALIGNED_PTR(x)	((std::ptrdiff_t)(x) & _EIGEN_MASK_ALIGNMENT)
-
-// Handle endianness properly while loading constants
-// Define global static constants:
-
-static Packet16uc p16uc_FORWARD =   { 0,1,2,3, 4,5,6,7, 8,9,10,11, 12,13,14,15 };
-static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
-static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-
-static Packet16uc p16uc_PSET32_WODD   = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
-static Packet16uc p16uc_PSET32_WEVEN  = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
-/*static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
-
-static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };*/
-static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
-/*static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16);                                         //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
-static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16);                                         //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};*/
-static Packet16uc p16uc_TRANSPOSE64_HI = { 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
-static Packet16uc p16uc_TRANSPOSE64_LO = { 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
-
-static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);                                         //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
-
-static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);                                            //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-
-
-#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
-  #define EIGEN_ZVECTOR_PREFETCH(ADDR) __builtin_prefetch(ADDR);
-#else
-  #define EIGEN_ZVECTOR_PREFETCH(ADDR) asm( "   pfd [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
-#endif
-
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet4i type;
-  typedef Packet4i half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasAbs = 1,
-    HasSin = 0,
-    HasCos = 0,
-    HasLog = 0,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = 1,
-    HasErf = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 1,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 1,
-    HasMin  = 1,
-    HasMax  = 1,
-    HasAbs  = 1,
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 0,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet4i> { typedef int    type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4i half; };
-template<> struct unpacket_traits<Packet4f> { typedef float  type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4f half; };
-template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2d half; };
-
-/* Forward declaration */
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f,4>& kernel);
- 
-inline std::ostream & operator <<(std::ostream & s, const Packet4i & v)
-{
-  Packet vt;
-  vt.v4i = v;
-  s << vt.i[0] << ", " << vt.i[1] << ", " << vt.i[2] << ", " << vt.i[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
-{
-  Packet vt;
-  vt.v4ui = v;
-  s << vt.ui[0] << ", " << vt.ui[1] << ", " << vt.ui[2] << ", " << vt.ui[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2l & v)
-{
-  Packet vt;
-  vt.v2l = v;
-  s << vt.l[0] << ", " << vt.l[1];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2ul & v)
-{
-  Packet vt;
-  vt.v2ul = v;
-  s << vt.ul[0] << ", " << vt.ul[1] ;
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
-{
-  Packet vt;
-  vt.v2d = v;
-  s << vt.d[0] << ", " << vt.d[1];
-  return s;
-}
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
-{
-  Packet vt;
-  vt.v4f = v;
-  s << vt.f[0] << ", " << vt.f[1] << ", " << vt.f[2] << ", " << vt.f[3];
-  return s;
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet *vfrom;
-  vfrom = (Packet *) from;
-  return vfrom->v4i;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet *vfrom;
-  vfrom = (Packet *) from;
-  return vfrom->v2d;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  Packet *vto;
-  vto = (Packet *) to;
-  vto->v4i = from;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<double>(double*   to, const Packet2d& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  Packet *vto;
-  vto = (Packet *) to;
-  vto->v2d = from;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from)
-{
-  return vec_splats(from);
-}
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
-  return vec_splats(from);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4i>(const int *a,
-                      Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
-{
-  a3 = pload<Packet4i>(a);
-  a0 = vec_splat(a3, 0);
-  a1 = vec_splat(a3, 1);
-  a2 = vec_splat(a3, 2);
-  a3 = vec_splat(a3, 3);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet2d>(const double *a,
-                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-  a1 = pload<Packet2d>(a);
-  a0 = vec_splat(a1, 0);
-  a1 = vec_splat(a1, 1);
-  a3 = pload<Packet2d>(a+2);
-  a2 = vec_splat(a3, 0);
-  a3 = vec_splat(a3, 1);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
-{
-  int EIGEN_ALIGN16 ai[4];
-  ai[0] = from[0*stride];
-  ai[1] = from[1*stride];
-  ai[2] = from[2*stride];
-  ai[3] = from[3*stride];
- return pload<Packet4i>(ai);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
-  double EIGEN_ALIGN16 af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
- return pload<Packet2d>(af);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
-{
-  int EIGEN_ALIGN16 ai[4];
-  pstore<int>((int *)ai, from);
-  to[0*stride] = ai[0];
-  to[1*stride] = ai[1];
-  to[2*stride] = ai[2];
-  to[3*stride] = ai[3];
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  double EIGEN_ALIGN16 af[2];
-  pstore<double>(af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a + b); }
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a + b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a - b); }
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a - b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a * b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a * b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a / b); }
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a / b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return (-a); }
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return (-a); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd<Packet4i>(pmul<Packet4i>(a, b), c); }
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
-
-template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a)    { return padd<Packet4i>(pset1<Packet4i>(a), p4i_COUNTDOWN); }
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return padd<Packet2d>(pset1<Packet2d>(a), p2d_COUNTDOWN); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_min(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_max(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return pand<Packet4i>(a, vec_nor(b, b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) { return vec_round(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const  Packet2d& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return vec_floor(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int*       from) { return pload<Packet4i>(from); }
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double*    from) { return pload<Packet2d>(from); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
-{
-  Packet4i p = pload<Packet4i>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*   from)
-{
-  Packet2d p = pload<Packet2d>(from);
-  return vec_perm(p, p, p16uc_PSET64_HI);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*        to, const Packet4i& from) { pstore<int>(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double*  to, const Packet2d& from) { pstore<double>(to, from); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int    EIGEN_ALIGN16 x[4]; pstore(x, a); return x[0]; }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore(x, a); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
-{
-  return reinterpret_cast<Packet4i>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
-{
-  return reinterpret_cast<Packet2d>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE64));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pabs<Packet4i>(const Packet4i& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pabs<Packet2d>(const Packet2d& a) { return vec_abs(a); }
-
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i b, sum;
-  b   = vec_sld(a, a, 8);
-  sum = padd<Packet4i>(a, b);
-  b   = vec_sld(sum, sum, 4);
-  sum = padd<Packet4i>(sum, b);
-  return pfirst(sum);
-}
-
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{
-  Packet2d b, sum;
-  b   = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8));
-  sum = padd<Packet2d>(a, b);
-  return pfirst(sum);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
-{
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  return aux[0] * aux[1] * aux[2] * aux[3];
-}
-
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
-{
-  Packet4i b, res;
-  b   = pmin<Packet4i>(a, vec_sld(a, a, 8));
-  res = pmin<Packet4i>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmin<Packet2d>(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
-}
-
-// max
-template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
-{
-  Packet4i b, res;
-  b = pmax<Packet4i>(a, vec_sld(a, a, 8));
-  res = pmax<Packet4i>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-// max
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmax<Packet2d>(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4i,4>& kernel) {
-  Packet4i t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  Packet4i t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  Packet4i t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  Packet4i t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2d,2>& kernel) {
-  Packet2d t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
-  Packet2d t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
-  kernel.packet[0] = t0;
-  kernel.packet[1] = t1;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
-  Packet4ui mask = vec_cmpeq(select, reinterpret_cast<Packet4ui>(p4i_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
-  Packet2ul select = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2ul mask = vec_cmpeq(select, reinterpret_cast<Packet2ul>(p2l_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-/* z13 has no vector float support so we emulate that with double
-   z14 has proper vector float support.
-*/
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-/* Helper function to simulate a vec_splat_packet4f
- */
-template<int element> EIGEN_STRONG_INLINE Packet4f vec_splat_packet4f(const Packet4f&   from)
-{
-  Packet4f splat;
-  switch (element) {
-  case 0:
-    splat.v4f[0] = vec_splat(from.v4f[0], 0);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  case 1:
-    splat.v4f[0] = vec_splat(from.v4f[0], 1);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  case 2:
-    splat.v4f[0] = vec_splat(from.v4f[1], 0);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  case 3:
-    splat.v4f[0] = vec_splat(from.v4f[1], 1);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  }
-  return splat;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float*   from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet4f vfrom;
-  vfrom.v4f[0] = vec_ld2f(&from[0]);
-  vfrom.v4f[1] = vec_ld2f(&from[2]);
-  return vfrom;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  vec_st2f(from.v4f[0], &to[0]);
-  vec_st2f(from.v4f[1], &to[2]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&    from)
-{
-  Packet4f to;
-  to.v4f[0] = pset1<Packet2d>(static_cast<const double&>(from));
-  to.v4f[1] = to.v4f[0];
-  return to;
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  a3 = pload<Packet4f>(a);
-  a0 = vec_splat_packet4f<0>(a3);
-  a1 = vec_splat_packet4f<1>(a3);
-  a2 = vec_splat_packet4f<2>(a3);
-  a3 = vec_splat_packet4f<3>(a3);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  float EIGEN_ALIGN16 ai[4];
-  ai[0] = from[0*stride];
-  ai[1] = from[1*stride];
-  ai[2] = from[2*stride];
-  ai[3] = from[3*stride];
- return pload<Packet4f>(ai);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  float EIGEN_ALIGN16 ai[4];
-  pstore<float>((float *)ai, from);
-  to[0*stride] = ai[0];
-  to[1*stride] = ai[1];
-  to[2*stride] = ai[2];
-  to[3*stride] = ai[3];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] + b.v4f[0];
-  c.v4f[1] = a.v4f[1] + b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] - b.v4f[0];
-  c.v4f[1] = a.v4f[1] - b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] * b.v4f[0];
-  c.v4f[1] = a.v4f[1] * b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] / b.v4f[0];
-  c.v4f[1] = a.v4f[1] / b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
-{
-  Packet4f c;
-  c.v4f[0] = -a.v4f[0];
-  c.v4f[1] = -a.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
-{
-  Packet4f res;
-  res.v4f[0] = vec_madd(a.v4f[0], b.v4f[0], c.v4f[0]);
-  res.v4f[1] = vec_madd(a.v4f[1], b.v4f[1], c.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pmin(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pmin(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pmax(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pmax(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pand(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pand(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = por(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = por(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pxor(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pxor(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pandnot(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pandnot(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = vec_round(a.v4f[0]);
-  res.v4f[1] = vec_round(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const  Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = vec_ceil(a.v4f[0]);
-  res.v4f[1] = vec_ceil(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = vec_floor(a.v4f[0]);
-  res.v4f[1] = vec_floor(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*    from)
-{
-  Packet4f p = pload<Packet4f>(from);
-  p.v4f[1] = vec_splat(p.v4f[0], 1);
-  p.v4f[0] = vec_splat(p.v4f[0], 0);
-  return p;
-}
-
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { float  EIGEN_ALIGN16 x[2]; vec_st2f(a.v4f[0], &x[0]); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  Packet4f rev;
-  rev.v4f[0] = preverse<Packet2d>(a.v4f[1]);
-  rev.v4f[1] = preverse<Packet2d>(a.v4f[0]);
-  return rev;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>(const Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = pabs(a.v4f[0]);
-  res.v4f[1] = pabs(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet2d sum;
-  sum = padd<Packet2d>(a.v4f[0], a.v4f[1]);
-  double first = predux<Packet2d>(sum);
-  return static_cast<float>(first);
-}
-
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  // Return predux_mul<Packet2d> of the subvectors product
-  return static_cast<float>(pfirst(predux_mul(pmul(a.v4f[0], a.v4f[1]))));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  Packet2d b, res;
-  b   = pmin<Packet2d>(a.v4f[0], a.v4f[1]);
-  res = pmin<Packet2d>(b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
-  return static_cast<float>(pfirst(res));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  Packet2d b, res;
-  b   = pmax<Packet2d>(a.v4f[0], a.v4f[1]);
-  res = pmax<Packet2d>(b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
-  return static_cast<float>(pfirst(res));
-}
-
-/* Split the Packet4f PacketBlock into 4 Packet2d PacketBlocks and transpose each one
- */
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  PacketBlock<Packet2d,2> t0,t1,t2,t3;
-  // copy top-left 2x2 Packet2d block
-  t0.packet[0] = kernel.packet[0].v4f[0];
-  t0.packet[1] = kernel.packet[1].v4f[0];
-
-  // copy top-right 2x2 Packet2d block
-  t1.packet[0] = kernel.packet[0].v4f[1];
-  t1.packet[1] = kernel.packet[1].v4f[1];
-
-  // copy bottom-left 2x2 Packet2d block
-  t2.packet[0] = kernel.packet[2].v4f[0];
-  t2.packet[1] = kernel.packet[3].v4f[0];
-
-  // copy bottom-right 2x2 Packet2d block
-  t3.packet[0] = kernel.packet[2].v4f[1];
-  t3.packet[1] = kernel.packet[3].v4f[1];
-
-  // Transpose all 2x2 blocks
-  ptranspose(t0);
-  ptranspose(t1);
-  ptranspose(t2);
-  ptranspose(t3);
-
-  // Copy back transposed blocks, but exchange t1 and t2 due to transposition
-  kernel.packet[0].v4f[0] = t0.packet[0];
-  kernel.packet[0].v4f[1] = t2.packet[0];
-  kernel.packet[1].v4f[0] = t0.packet[1];
-  kernel.packet[1].v4f[1] = t2.packet[1];
-  kernel.packet[2].v4f[0] = t1.packet[0];
-  kernel.packet[2].v4f[1] = t3.packet[0];
-  kernel.packet[3].v4f[0] = t1.packet[1];
-  kernel.packet[3].v4f[1] = t3.packet[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  Packet2ul select_hi = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2ul select_lo = { ifPacket.select[2], ifPacket.select[3] };
-  Packet2ul mask_hi = vec_cmpeq(select_hi, reinterpret_cast<Packet2ul>(p2l_ONE));
-  Packet2ul mask_lo = vec_cmpeq(select_lo, reinterpret_cast<Packet2ul>(p2l_ONE));
-  Packet4f result;
-  result.v4f[0] = vec_sel(elsePacket.v4f[0], thenPacket.v4f[0], mask_hi);
-  result.v4f[1] = vec_sel(elsePacket.v4f[1], thenPacket.v4f[1], mask_lo);
-  return result;
-}
-
-template<> Packet4f EIGEN_STRONG_INLINE pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pcmp_le(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pcmp_le(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> Packet4f EIGEN_STRONG_INLINE pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pcmp_lt(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pcmp_lt(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> Packet4f EIGEN_STRONG_INLINE pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pcmp_eq(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pcmp_eq(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-#else
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet *vfrom;
-  vfrom = (Packet *) from;
-  return vfrom->v4f;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  Packet *vto;
-  vto = (Packet *) to;
-  vto->v4f = from;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from)
-{
-  return vec_splats(from);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  a3 = pload<Packet4f>(a);
-  a0 = vec_splat(a3, 0);
-  a1 = vec_splat(a3, 1);
-  a2 = vec_splat(a3, 2);
-  a3 = vec_splat(a3, 3);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  float EIGEN_ALIGN16 af[4];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
-  af[2] = from[2*stride];
-  af[3] = from[3*stride];
- return pload<Packet4f>(af);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  float EIGEN_ALIGN16 af[4];
-  pstore<float>((float*)af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-  to[2*stride] = af[2];
-  to[3*stride] = af[3];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a + b); }
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a - b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a * b); }
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a / b); }
-template<> EIGEN_STRONG_INLINE Packet4f pnegate<Packet4f>(const Packet4f& a) { return (-a); }
-template<> EIGEN_STRONG_INLINE Packet4f pconj<Packet4f>  (const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4f pmadd<Packet4f>  (const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a, b, c); }
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>    (const Packet4f& a, const Packet4f& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, vec_nor(b, b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f> (const Packet4f& a) { return vec_round(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>  (const Packet4f& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f> (const Packet4f& a) { return vec_floor(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>   (const Packet4f& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; pstore(x, a); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
-{
-  Packet4f p = pload<Packet4f>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  return reinterpret_cast<Packet4f>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, sum;
-  b   = vec_sld(a, a, 8);
-  sum = padd<Packet4f>(a, b);
-  b   = vec_sld(sum, sum, 4);
-  sum = padd<Packet4f>(sum, b);
-  return pfirst(sum);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  Packet4f prod;
-  prod = pmul(a, vec_sld(a, a, 8));
-  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, res;
-  b   = pmin<Packet4f>(a, vec_sld(a, a, 8));
-  res = pmin<Packet4f>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-// max
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, res;
-  b = pmax<Packet4f>(a, vec_sld(a, a, 8));
-  res = pmax<Packet4f>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  Packet4f t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  Packet4f t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  Packet4f t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  Packet4f t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
-  Packet4ui mask = vec_cmpeq(select, reinterpret_cast<Packet4ui>(p4i_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-#endif
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f> (const float* from) { return pload<Packet4f>(from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) { pstore<float>(to, from); }
-template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>  (const float& a)  { return padd<Packet4f>(pset1<Packet4f>(a), p4f_COUNTDOWN); }
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_ZVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/AssignmentFunctors.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/AssignmentFunctors.h
deleted file mode 100644
index bf64ef4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/AssignmentFunctors.h
+++ /dev/null
@@ -1,177 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ASSIGNMENT_FUNCTORS_H
-#define EIGEN_ASSIGNMENT_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-  
-/** \internal
-  * \brief Template functor for scalar/packet assignment
-  *
-  */
-template<typename DstScalar,typename SrcScalar> struct assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a = b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,b); }
-};
-
-// Empty overload for void type (used by PermutationMatrix)
-template<typename DstScalar> struct assign_op<DstScalar,void> {};
-
-template<typename DstScalar,typename SrcScalar>
-struct functor_traits<assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::Vectorizable && packet_traits<SrcScalar>::Vectorizable
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with addition
-  *
-  */
-template<typename DstScalar,typename SrcScalar> struct add_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(add_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a += b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar,typename SrcScalar>
-struct functor_traits<add_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasAdd
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with subtraction
-  *
-  */
-template<typename DstScalar,typename SrcScalar> struct sub_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(sub_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a -= b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar,typename SrcScalar>
-struct functor_traits<sub_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasSub
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with multiplication
-  *
-  */
-template<typename DstScalar, typename SrcScalar=DstScalar>
-struct mul_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(mul_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a *= b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pmul(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar, typename SrcScalar>
-struct functor_traits<mul_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasMul
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with diviving
-  *
-  */
-template<typename DstScalar, typename SrcScalar=DstScalar> struct div_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(div_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a /= b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pdiv(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar, typename SrcScalar>
-struct functor_traits<div_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasDiv
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with swapping
-  *
-  * It works as follow. For a non-vectorized evaluation loop, we have:
-  *   for(i) func(A.coeffRef(i), B.coeff(i));
-  * where B is a SwapWrapper expression. The trick is to make SwapWrapper::coeff behaves like a non-const coeffRef.
-  * Actually, SwapWrapper might not even be needed since even if B is a plain expression, since it has to be writable
-  * B.coeff already returns a const reference to the underlying scalar value.
-  * 
-  * The case of a vectorized loop is more tricky:
-  *   for(i,j) func.assignPacket<A_Align>(&A.coeffRef(i,j), B.packet<B_Align>(i,j));
-  * Here, B must be a SwapWrapper whose packet function actually returns a proxy object holding a Scalar*,
-  * the actual alignment and Packet type.
-  *
-  */
-template<typename Scalar> struct swap_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(swap_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const
-  {
-#ifdef EIGEN_GPUCC
-    // FIXME is there some kind of cuda::swap?
-    Scalar t=b; const_cast<Scalar&>(b)=a; a=t;
-#else
-    using std::swap;
-    swap(a,const_cast<Scalar&>(b));
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<swap_assign_op<Scalar> > {
-  enum {
-    Cost = 3 * NumTraits<Scalar>::ReadCost,
-    PacketAccess = 
-    #if defined(EIGEN_VECTORIZE_AVX) && EIGEN_COMP_CLANG && (EIGEN_COMP_CLANG<800 || defined(__apple_build_version__))
-    // This is a partial workaround for a bug in clang generating bad code
-    // when mixing 256/512 bits loads and 128 bits moves.
-    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1684
-    //     https://bugs.llvm.org/show_bug.cgi?id=40815
-    0
-    #else
-    packet_traits<Scalar>::Vectorizable
-    #endif
-  };
-};
-
-} // namespace internal
-
-} // namespace Eigen
-
-#endif // EIGEN_ASSIGNMENT_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/BinaryFunctors.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/BinaryFunctors.h
deleted file mode 100644
index 63f09ab..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/BinaryFunctors.h
+++ /dev/null
@@ -1,541 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BINARY_FUNCTORS_H
-#define EIGEN_BINARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- associative binary functors ----------
-
-template<typename Arg1, typename Arg2>
-struct binary_op_base
-{
-  typedef Arg1 first_argument_type;
-  typedef Arg2 second_argument_type;
-};
-
-/** \internal
-  * \brief Template functor to compute the sum of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum()
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_sum_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_sum_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
-#else
-  scalar_sum_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a + b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::padd(a,b); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  { return internal::predux(a); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_sum_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2, // rough estimate!
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd
-    // TODO vectorize mixed sum
-  };
-};
-
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_sum_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a || b; }
-
-
-/** \internal
-  * \brief Template functor to compute the product of two scalars
-  *
-  * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_product_op  : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_product_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
-#else
-  scalar_product_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pmul(a,b); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  { return internal::predux_mul(a); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (int(NumTraits<LhsScalar>::MulCost) + int(NumTraits<RhsScalar>::MulCost))/2, // rough estimate!
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
-    // TODO vectorize mixed product
-  };
-};
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_product_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a && b; }
-
-
-/** \internal
-  * \brief Template functor to compute the conjugate product of two scalars
-  *
-  * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_conj_product_op  : binary_op_base<LhsScalar,RhsScalar>
-{
-
-  enum {
-    Conj = NumTraits<LhsScalar>::IsComplex
-  };
-  
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_conj_product_op>::ReturnType result_type;
-  
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const
-  { return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); }
-  
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  { return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = NumTraits<LhsScalar>::MulCost,
-    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the min of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
-  */
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct scalar_min_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_min_op>::ReturnType result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const {
-    return internal::pmin<NaNPropagation>(a, b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  {
-    return internal::pmin<NaNPropagation>(a,b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  {
-    return internal::predux_min<NaNPropagation>(a);
-  }
-};
-
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct functor_traits<scalar_min_op<LhsScalar,RhsScalar, NaNPropagation> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the max of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
-  */
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct scalar_max_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_max_op>::ReturnType result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const {
-    return internal::pmax<NaNPropagation>(a,b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  {
-    return internal::pmax<NaNPropagation>(a,b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  {
-    return internal::predux_max<NaNPropagation>(a);
-  }
-};
-
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct functor_traits<scalar_max_op<LhsScalar,RhsScalar, NaNPropagation> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax
-  };
-};
-
-/** \internal
-  * \brief Template functors for comparison of two scalars
-  * \todo Implement packet-comparisons
-  */
-template<typename LhsScalar, typename RhsScalar, ComparisonName cmp> struct scalar_cmp_op;
-
-template<typename LhsScalar, typename RhsScalar, ComparisonName cmp>
-struct functor_traits<scalar_cmp_op<LhsScalar,RhsScalar, cmp> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = false
-  };
-};
-
-template<ComparisonName Cmp, typename LhsScalar, typename RhsScalar>
-struct result_of<scalar_cmp_op<LhsScalar, RhsScalar, Cmp>(LhsScalar,RhsScalar)> {
-  typedef bool type;
-};
-
-
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_EQ> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a==b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LT> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LE> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<=b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GT> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GE> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>=b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_UNORD> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return !(a<=b || b<=a);}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_NEQ> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a!=b;}
-};
-
-/** \internal
-  * \brief Template functor to compute the hypot of two \b positive \b and \b real scalars
-  *
-  * \sa MatrixBase::stableNorm(), class Redux
-  */
-template<typename Scalar>
-struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar>
-{
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar &x, const Scalar &y) const
-  {
-    // This functor is used by hypotNorm only for which it is faster to first apply abs
-    // on all coefficients prior to reduction through hypot.
-    // This way we avoid calling abs on positive and real entries, and this also permits
-    // to seamlessly handle complexes. Otherwise we would have to handle both real and complexes
-    // through the same functor...
-    return internal::positive_real_hypot(x,y);
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_hypot_op<Scalar,Scalar> > {
-  enum
-  {
-    Cost = 3 * NumTraits<Scalar>::AddCost +
-           2 * NumTraits<Scalar>::MulCost +
-           2 * scalar_div_cost<Scalar,false>::value,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the pow of two scalars
-  * See the specification of pow in https://en.cppreference.com/w/cpp/numeric/math/pow
-  */
-template<typename Scalar, typename Exponent>
-struct scalar_pow_op  : binary_op_base<Scalar,Exponent>
-{
-  typedef typename ScalarBinaryOpTraits<Scalar,Exponent,scalar_pow_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_pow_op)
-#else
-  scalar_pow_op() {
-    typedef Scalar LhsScalar;
-    typedef Exponent RhsScalar;
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC
-  inline result_type operator() (const Scalar& a, const Exponent& b) const { return numext::pow(a, b); }
-
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  {
-    return generic_pow(a,b);
-  }
-};
-
-template<typename Scalar, typename Exponent>
-struct functor_traits<scalar_pow_op<Scalar,Exponent> > {
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = (!NumTraits<Scalar>::IsComplex && !NumTraits<Scalar>::IsInteger &&
-                    packet_traits<Scalar>::HasExp && packet_traits<Scalar>::HasLog &&
-                    packet_traits<Scalar>::HasRound && packet_traits<Scalar>::HasCmp &&
-                    // Temporarly disable packet access for half/bfloat16 until
-                    // accuracy is improved.
-                    !is_same<Scalar, half>::value && !is_same<Scalar, bfloat16>::value
-                    )
-  };
-};
-
-//---------- non associative binary functors ----------
-
-/** \internal
-  * \brief Template functor to compute the difference of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::operator-
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_difference_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_difference_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
-#else
-  scalar_difference_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a - b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::psub(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_difference_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2,
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the quotient of two scalars
-  *
-  * \sa class CwiseBinaryOp, Cwise::operator/()
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_quotient_op  : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_quotient_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
-#else
-  scalar_quotient_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pdiv(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > {
-  typedef typename scalar_quotient_op<LhsScalar,RhsScalar>::result_type result_type;
-  enum {
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv,
-    Cost = scalar_div_cost<result_type,PacketAccess>::value
-  };
-};
-
-
-
-/** \internal
-  * \brief Template functor to compute the and of two booleans
-  *
-  * \sa class CwiseBinaryOp, ArrayBase::operator&&
-  */
-struct scalar_boolean_and_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a && b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pand(a,b); }
-};
-template<> struct functor_traits<scalar_boolean_and_op> {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = true
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the or of two booleans
-  *
-  * \sa class CwiseBinaryOp, ArrayBase::operator||
-  */
-struct scalar_boolean_or_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a || b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::por(a,b); }
-};
-template<> struct functor_traits<scalar_boolean_or_op> {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = true
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the xor of two booleans
- *
- * \sa class CwiseBinaryOp, ArrayBase::operator^
- */
-struct scalar_boolean_xor_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_xor_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a ^ b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pxor(a,b); }
-};
-template<> struct functor_traits<scalar_boolean_xor_op> {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = true
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the absolute difference of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::absolute_difference
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_absolute_difference_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_absolute_difference_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_absolute_difference_op)
-#else
-  scalar_absolute_difference_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
-  { return numext::absdiff(a,b); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pabsdiff(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_absolute_difference_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAbsDiff
-  };
-};
-
-
-
-//---------- binary functors bound to a constant, thus appearing as a unary functor ----------
-
-// The following two classes permits to turn any binary functor into a unary one with one argument bound to a constant value.
-// They are analogues to std::binder1st/binder2nd but with the following differences:
-//  - they are compatible with packetOp
-//  - they are portable across C++ versions (the std::binder* are deprecated in C++11)
-template<typename BinaryOp> struct bind1st_op : BinaryOp {
-
-  typedef typename BinaryOp::first_argument_type  first_argument_type;
-  typedef typename BinaryOp::second_argument_type second_argument_type;
-  typedef typename BinaryOp::result_type          result_type;
-
-  EIGEN_DEVICE_FUNC explicit bind1st_op(const first_argument_type &val) : m_value(val) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const second_argument_type& b) const { return BinaryOp::operator()(m_value,b); }
-
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& b) const
-  { return BinaryOp::packetOp(internal::pset1<Packet>(m_value), b); }
-
-  first_argument_type m_value;
-};
-template<typename BinaryOp> struct functor_traits<bind1st_op<BinaryOp> > : functor_traits<BinaryOp> {};
-
-
-template<typename BinaryOp> struct bind2nd_op : BinaryOp {
-
-  typedef typename BinaryOp::first_argument_type  first_argument_type;
-  typedef typename BinaryOp::second_argument_type second_argument_type;
-  typedef typename BinaryOp::result_type          result_type;
-
-  EIGEN_DEVICE_FUNC explicit bind2nd_op(const second_argument_type &val) : m_value(val) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const first_argument_type& a) const { return BinaryOp::operator()(a,m_value); }
-
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return BinaryOp::packetOp(a,internal::pset1<Packet>(m_value)); }
-
-  second_argument_type m_value;
-};
-template<typename BinaryOp> struct functor_traits<bind2nd_op<BinaryOp> > : functor_traits<BinaryOp> {};
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BINARY_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/NullaryFunctors.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/NullaryFunctors.h
deleted file mode 100644
index 192f225..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/NullaryFunctors.h
+++ /dev/null
@@ -1,189 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NULLARY_FUNCTORS_H
-#define EIGEN_NULLARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar>
-struct scalar_constant_op {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() () const { return m_other; }
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetOp() const { return internal::pset1<PacketType>(m_other); }
-  const Scalar m_other;
-};
-template<typename Scalar>
-struct functor_traits<scalar_constant_op<Scalar> >
-{ enum { Cost = 0 /* as the constant value should be loaded in register only once for the whole expression */,
-         PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
-
-template<typename Scalar> struct scalar_identity_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op)
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType row, IndexType col) const { return row==col ? Scalar(1) : Scalar(0); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_identity_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; };
-
-template <typename Scalar, bool IsInteger> struct linspaced_op_impl;
-
-template <typename Scalar>
-struct linspaced_op_impl<Scalar,/*IsInteger*/false>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
-    m_low(low), m_high(high), m_size1(num_steps==1 ? 1 : num_steps-1), m_step(num_steps==1 ? Scalar() : Scalar((high-low)/RealScalar(num_steps-1))),
-    m_flip(numext::abs(high)<numext::abs(low))
-  {}
-
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const {
-    if(m_flip)
-      return (i==0)? m_low : Scalar(m_high - RealScalar(m_size1-i)*m_step);
-    else
-      return (i==m_size1)? m_high : Scalar(m_low + RealScalar(i)*m_step);
-  }
-
-  template<typename Packet, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const
-  {
-    // Principle:
-    // [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
-    if(m_flip)
-    {
-      Packet pi = plset<Packet>(Scalar(i-m_size1));
-      Packet res = padd(pset1<Packet>(m_high), pmul(pset1<Packet>(m_step), pi));
-      if (EIGEN_PREDICT_TRUE(i != 0)) return res;
-      Packet mask = pcmp_lt(pset1<Packet>(0), plset<Packet>(0));
-      return pselect<Packet>(mask, res, pset1<Packet>(m_low));
-    }
-    else
-    {
-      Packet pi = plset<Packet>(Scalar(i));
-      Packet res = padd(pset1<Packet>(m_low), pmul(pset1<Packet>(m_step), pi));
-      if(EIGEN_PREDICT_TRUE(i != m_size1-unpacket_traits<Packet>::size+1)) return res;
-      Packet mask = pcmp_lt(plset<Packet>(0), pset1<Packet>(unpacket_traits<Packet>::size-1));
-      return pselect<Packet>(mask, res, pset1<Packet>(m_high));
-    }
-  }
-
-  const Scalar m_low;
-  const Scalar m_high;
-  const Index m_size1;
-  const Scalar m_step;
-  const bool m_flip;
-};
-
-template <typename Scalar>
-struct linspaced_op_impl<Scalar,/*IsInteger*/true>
-{
-  EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
-    m_low(low),
-    m_multiplier((high-low)/convert_index<Scalar>(num_steps<=1 ? 1 : num_steps-1)),
-    m_divisor(convert_index<Scalar>((high>=low?num_steps:-num_steps)+(high-low))/((numext::abs(high-low)+1)==0?1:(numext::abs(high-low)+1))),
-    m_use_divisor(num_steps>1 && (numext::abs(high-low)+1)<num_steps)
-  {}
-
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar operator() (IndexType i) const
-  {
-    if(m_use_divisor) return m_low + convert_index<Scalar>(i)/m_divisor;
-    else              return m_low + convert_index<Scalar>(i)*m_multiplier;
-  }
-
-  const Scalar m_low;
-  const Scalar m_multiplier;
-  const Scalar m_divisor;
-  const bool m_use_divisor;
-};
-
-// ----- Linspace functor ----------------------------------------------------------------
-
-// Forward declaration (we default to random access which does not really give
-// us a speed gain when using packet access but it allows to use the functor in
-// nested expressions).
-template <typename Scalar> struct linspaced_op;
-template <typename Scalar> struct functor_traits< linspaced_op<Scalar> >
-{
-  enum
-  {
-    Cost = 1,
-    PacketAccess =   (!NumTraits<Scalar>::IsInteger) && packet_traits<Scalar>::HasSetLinear && packet_traits<Scalar>::HasBlend,
-                  /*&& ((!NumTraits<Scalar>::IsInteger) || packet_traits<Scalar>::HasDiv),*/ // <- vectorization for integer is currently disabled
-    IsRepeatable = true
-  };
-};
-template <typename Scalar> struct linspaced_op
-{
-  EIGEN_DEVICE_FUNC linspaced_op(const Scalar& low, const Scalar& high, Index num_steps)
-    : impl((num_steps==1 ? high : low),high,num_steps)
-  {}
-
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const { return impl(i); }
-
-  template<typename Packet,typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const { return impl.template packetOp<Packet>(i); }
-
-  // This proxy object handles the actual required temporaries and the different
-  // implementations (integer vs. floating point).
-  const linspaced_op_impl<Scalar,NumTraits<Scalar>::IsInteger> impl;
-};
-
-// Linear access is automatically determined from the operator() prototypes available for the given functor.
-// If it exposes an operator()(i,j), then we assume the i and j coefficients are required independently
-// and linear access is not possible. In all other cases, linear access is enabled.
-// Users should not have to deal with this structure.
-template<typename Functor> struct functor_has_linear_access { enum { ret = !has_binary_operator<Functor>::value }; };
-
-// For unreliable compilers, let's specialize the has_*ary_operator
-// helpers so that at least built-in nullary functors work fine.
-#if !( (EIGEN_COMP_MSVC>1600) || (EIGEN_GNUC_AT_LEAST(4,8)) || (EIGEN_COMP_ICC>=1600))
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 1}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; };
-
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 1}; };
-
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 1}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; };
-
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 1}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; };
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_NULLARY_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/StlFunctors.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/StlFunctors.h
deleted file mode 100644
index 4570c9b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/StlFunctors.h
+++ /dev/null
@@ -1,166 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STL_FUNCTORS_H
-#define EIGEN_STL_FUNCTORS_H
-
-namespace Eigen {
-
-// Portable replacements for certain functors.
-namespace numext {
-
-template<typename T = void>
-struct equal_to {
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC bool operator()(const T& lhs, const T& rhs) const {
-    return lhs == rhs;
-  }
-};
-
-template<typename T = void>
-struct not_equal_to {
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC bool operator()(const T& lhs, const T& rhs) const {
-    return lhs != rhs;
-  }
-};
-
-}
-
-
-namespace internal {
-
-// default functor traits for STL functors:
-
-template<typename T>
-struct functor_traits<std::multiplies<T> >
-{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::divides<T> >
-{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::plus<T> >
-{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::minus<T> >
-{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::negate<T> >
-{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::logical_or<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::logical_and<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::logical_not<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::greater<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::less<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::greater_equal<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::less_equal<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::equal_to<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<numext::equal_to<T> >
-  : functor_traits<std::equal_to<T> > {};
-
-template<typename T>
-struct functor_traits<std::not_equal_to<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<numext::not_equal_to<T> >
-  : functor_traits<std::not_equal_to<T> > {};
-
-#if (EIGEN_COMP_CXXVER < 11)
-// std::binder* are deprecated since c++11 and will be removed in c++17
-template<typename T>
-struct functor_traits<std::binder2nd<T> >
-{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::binder1st<T> >
-{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
-#endif
-
-#if (EIGEN_COMP_CXXVER < 17)
-// std::unary_negate is deprecated since c++17 and will be removed in c++20
-template<typename T>
-struct functor_traits<std::unary_negate<T> >
-{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
-
-// std::binary_negate is deprecated since c++17 and will be removed in c++20
-template<typename T>
-struct functor_traits<std::binary_negate<T> >
-{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
-#endif
-
-#ifdef EIGEN_STDEXT_SUPPORT
-
-template<typename T0,typename T1>
-struct functor_traits<std::project1st<T0,T1> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::project2nd<T0,T1> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::select2nd<std::pair<T0,T1> > >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::select1st<std::pair<T0,T1> > >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::unary_compose<T0,T1> >
-{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; };
-
-template<typename T0,typename T1,typename T2>
-struct functor_traits<std::binary_compose<T0,T1,T2> >
-{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; };
-
-#endif // EIGEN_STDEXT_SUPPORT
-
-// allow to add new functors and specializations of functor_traits from outside Eigen.
-// this macro is really needed because functor_traits must be specialized after it is declared but before it is used...
-#ifdef EIGEN_FUNCTORS_PLUGIN
-#include EIGEN_FUNCTORS_PLUGIN
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_STL_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/TernaryFunctors.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/TernaryFunctors.h
deleted file mode 100644
index b254e96..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/TernaryFunctors.h
+++ /dev/null
@@ -1,25 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TERNARY_FUNCTORS_H
-#define EIGEN_TERNARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- associative ternary functors ----------
-
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TERNARY_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/UnaryFunctors.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/UnaryFunctors.h
deleted file mode 100644
index 16136d1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/functors/UnaryFunctors.h
+++ /dev/null
@@ -1,1131 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UNARY_FUNCTORS_H
-#define EIGEN_UNARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * \brief Template functor to compute the opposite of a scalar
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::operator-
-  */
-template<typename Scalar> struct scalar_opposite_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::pnegate(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_opposite_op<Scalar> >
-{ enum {
-    Cost = NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasNegate };
-};
-
-/** \internal
-  * \brief Template functor to compute the absolute value of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::abs
-  */
-template<typename Scalar> struct scalar_abs_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::pabs(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_abs_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasAbs
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the score of a scalar, to chose a pivot
-  *
-  * \sa class CwiseUnaryOp
-  */
-template<typename Scalar> struct scalar_score_coeff_op : scalar_abs_op<Scalar>
-{
-  typedef void Score_is_abs;
-};
-template<typename Scalar>
-struct functor_traits<scalar_score_coeff_op<Scalar> > : functor_traits<scalar_abs_op<Scalar> > {};
-
-/* Avoid recomputing abs when we know the score and they are the same. Not a true Eigen functor.  */
-template<typename Scalar, typename=void> struct abs_knowing_score
-{
-  EIGEN_EMPTY_STRUCT_CTOR(abs_knowing_score)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  template<typename Score>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a, const Score&) const { return numext::abs(a); }
-};
-template<typename Scalar> struct abs_knowing_score<Scalar, typename scalar_score_coeff_op<Scalar>::Score_is_abs>
-{
-  EIGEN_EMPTY_STRUCT_CTOR(abs_knowing_score)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  template<typename Scal>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scal&, const result_type& a) const { return a; }
-};
-
-/** \internal
-  * \brief Template functor to compute the squared absolute value of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::abs2
-  */
-template<typename Scalar> struct scalar_abs2_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::pmul(a,a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_abs2_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; };
-
-/** \internal
-  * \brief Template functor to compute the conjugate of a complex value
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::conjugate()
-  */
-template<typename Scalar> struct scalar_conjugate_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op)
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::conj(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_conjugate_op<Scalar> >
-{
-  enum {
-    Cost = 0,
-    // Yes the cost is zero even for complexes because in most cases for which
-    // the cost is used, conjugation turns to be a no-op. Some examples:
-    //   cost(a*conj(b)) == cost(a*b)
-    //   cost(a+conj(b)) == cost(a+b)
-    //   <etc.
-    // If we don't set it to zero, then:
-    //   A.conjugate().lazyProduct(B.conjugate())
-    // will bake its operands. We definitely don't want that!
-    PacketAccess = packet_traits<Scalar>::HasConj
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the phase angle of a complex
-  *
-  * \sa class CwiseUnaryOp, Cwise::arg
-  */
-template<typename Scalar> struct scalar_arg_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_arg_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::arg(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::parg(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_arg_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::IsComplex ? 5 * NumTraits<Scalar>::MulCost : NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasArg
-  };
-};
-/** \internal
-  * \brief Template functor to cast a scalar to another type
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::cast()
-  */
-template<typename Scalar, typename NewType>
-struct scalar_cast_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef NewType result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); }
-};
-template<typename Scalar, typename NewType>
-struct functor_traits<scalar_cast_op<Scalar,NewType> >
-{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to arithmetically shift a scalar right by a number of bits
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::shift_right()
-  */
-template<typename Scalar, int N>
-struct scalar_shift_right_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_shift_right_op)
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const
-  { return a >> N; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::parithmetic_shift_right<N>(a); }
-};
-template<typename Scalar, int N>
-struct functor_traits<scalar_shift_right_op<Scalar,N> >
-{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; };
-
-/** \internal
-  * \brief Template functor to logically shift a scalar left by a number of bits
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::shift_left()
-  */
-template<typename Scalar, int N>
-struct scalar_shift_left_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_shift_left_op)
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const
-  { return a << N; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::plogical_shift_left<N>(a); }
-};
-template<typename Scalar, int N>
-struct functor_traits<scalar_shift_left_op<Scalar,N> >
-{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; };
-
-/** \internal
-  * \brief Template functor to extract the real part of a complex
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::real()
-  */
-template<typename Scalar>
-struct scalar_real_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_real_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to extract the imaginary part of a complex
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::imag()
-  */
-template<typename Scalar>
-struct scalar_imag_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_imag_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to extract the real part of a complex as a reference
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::real()
-  */
-template<typename Scalar>
-struct scalar_real_ref_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_real_ref_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to extract the imaginary part of a complex as a reference
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::imag()
-  */
-template<typename Scalar>
-struct scalar_imag_ref_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_imag_ref_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  *
-  * \brief Template functor to compute the exponential of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::exp()
-  */
-template<typename Scalar> struct scalar_exp_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::exp(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexp(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_exp_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasExp,
-    // The following numbers are based on the AVX implementation.
-#ifdef EIGEN_VECTORIZE_FMA
-    // Haswell can issue 2 add/mul/madd per cycle.
-    Cost =
-    (sizeof(Scalar) == 4
-     // float: 8 pmadd, 4 pmul, 2 padd/psub, 6 other
-     ? (8 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost)
-     // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
-     : (14 * NumTraits<Scalar>::AddCost +
-        6 * NumTraits<Scalar>::MulCost +
-        scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
-#else
-    Cost =
-    (sizeof(Scalar) == 4
-     // float: 7 pmadd, 6 pmul, 4 padd/psub, 10 other
-     ? (21 * NumTraits<Scalar>::AddCost + 13 * NumTraits<Scalar>::MulCost)
-     // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
-     : (23 * NumTraits<Scalar>::AddCost +
-        12 * NumTraits<Scalar>::MulCost +
-        scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
-#endif
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the exponential of a scalar - 1.
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::expm1()
-  */
-template<typename Scalar> struct scalar_expm1_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_expm1_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::expm1(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexpm1(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_expm1_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasExpm1,
-    Cost = functor_traits<scalar_exp_op<Scalar> >::Cost // TODO measure cost of expm1
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the logarithm of a scalar
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::log()
-  */
-template<typename Scalar> struct scalar_log_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_log_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasLog,
-    Cost =
-    (PacketAccess
-     // The following numbers are based on the AVX implementation.
-#ifdef EIGEN_VECTORIZE_FMA
-     // 8 pmadd, 6 pmul, 8 padd/psub, 16 other, can issue 2 add/mul/madd per cycle.
-     ? (20 * NumTraits<Scalar>::AddCost + 7 * NumTraits<Scalar>::MulCost)
-#else
-     // 8 pmadd, 6 pmul, 8 padd/psub, 20 other
-     ? (36 * NumTraits<Scalar>::AddCost + 14 * NumTraits<Scalar>::MulCost)
-#endif
-     // Measured cost of std::log.
-     : sizeof(Scalar)==4 ? 40 : 85)
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the logarithm of 1 plus a scalar value
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::log1p()
-  */
-template<typename Scalar> struct scalar_log1p_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log1p_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log1p(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog1p(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_log1p_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasLog1p,
-    Cost = functor_traits<scalar_log_op<Scalar> >::Cost // TODO measure cost of log1p
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the base-10 logarithm of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::log10()
-  */
-template<typename Scalar> struct scalar_log10_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log10_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { EIGEN_USING_STD(log10) return log10(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog10(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_log10_op<Scalar> >
-{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog10 }; };
-
-/** \internal
-  *
-  * \brief Template functor to compute the base-2 logarithm of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::log2()
-  */
-template<typename Scalar> struct scalar_log2_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log2_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return Scalar(EIGEN_LOG2E) * numext::log(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog2(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_log2_op<Scalar> >
-{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; };
-
-/** \internal
-  * \brief Template functor to compute the square root of a scalar
-  * \sa class CwiseUnaryOp, Cwise::sqrt()
-  */
-template<typename Scalar> struct scalar_sqrt_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sqrt(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_sqrt_op<Scalar> > {
-  enum {
-#if EIGEN_FAST_MATH
-    // The following numbers are based on the AVX implementation.
-    Cost = (sizeof(Scalar) == 8 ? 28
-                                // 4 pmul, 1 pmadd, 3 other
-                                : (3 * NumTraits<Scalar>::AddCost +
-                                   5 * NumTraits<Scalar>::MulCost)),
-#else
-    // The following numbers are based on min VSQRT throughput on Haswell.
-    Cost = (sizeof(Scalar) == 8 ? 28 : 14),
-#endif
-    PacketAccess = packet_traits<Scalar>::HasSqrt
-  };
-};
-
-// Boolean specialization to eliminate -Wimplicit-conversion-floating-point-to-bool warnings.
-template<> struct scalar_sqrt_op<bool> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
-  template <typename Packet>
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return a; }
-};
-template <>
-struct functor_traits<scalar_sqrt_op<bool> > {
-  enum { Cost = 1, PacketAccess = packet_traits<bool>::Vectorizable };
-};
-
-/** \internal
-  * \brief Template functor to compute the reciprocal square root of a scalar
-  * \sa class CwiseUnaryOp, Cwise::rsqrt()
-  */
-template<typename Scalar> struct scalar_rsqrt_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_rsqrt_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::rsqrt(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::prsqrt(a); }
-};
-
-template<typename Scalar>
-struct functor_traits<scalar_rsqrt_op<Scalar> >
-{ enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasRsqrt
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the cosine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::cos()
-  */
-template<typename Scalar> struct scalar_cos_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return numext::cos(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcos(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_cos_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasCos
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the sine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::sin()
-  */
-template<typename Scalar> struct scalar_sin_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sin(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psin(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_sin_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasSin
-  };
-};
-
-
-/** \internal
-  * \brief Template functor to compute the tan of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::tan()
-  */
-template<typename Scalar> struct scalar_tan_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::tan(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::ptan(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_tan_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasTan
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the arc cosine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::acos()
-  */
-template<typename Scalar> struct scalar_acos_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::acos(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pacos(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_acos_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasACos
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the arc sine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::asin()
-  */
-template<typename Scalar> struct scalar_asin_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::asin(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pasin(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_asin_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasASin
-  };
-};
-
-
-/** \internal
-  * \brief Template functor to compute the atan of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::atan()
-  */
-template<typename Scalar> struct scalar_atan_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_atan_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::atan(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::patan(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_atan_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasATan
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the tanh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::tanh()
-  */
-template <typename Scalar>
-struct scalar_tanh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::tanh(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const { return ptanh(x); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_tanh_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasTanh,
-    Cost = ( (EIGEN_FAST_MATH && is_same<Scalar,float>::value)
-// The following numbers are based on the AVX implementation,
-#ifdef EIGEN_VECTORIZE_FMA
-                // Haswell can issue 2 add/mul/madd per cycle.
-                // 9 pmadd, 2 pmul, 1 div, 2 other
-                ? (2 * NumTraits<Scalar>::AddCost +
-                   6 * NumTraits<Scalar>::MulCost +
-                   scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
-#else
-                ? (11 * NumTraits<Scalar>::AddCost +
-                   11 * NumTraits<Scalar>::MulCost +
-                   scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
-#endif
-                // This number assumes a naive implementation of tanh
-                : (6 * NumTraits<Scalar>::AddCost +
-                   3 * NumTraits<Scalar>::MulCost +
-                   2 * scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value +
-                   functor_traits<scalar_exp_op<Scalar> >::Cost))
-  };
-};
-
-#if EIGEN_HAS_CXX11_MATH
-/** \internal
-  * \brief Template functor to compute the atanh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::atanh()
-  */
-template <typename Scalar>
-struct scalar_atanh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_atanh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::atanh(a); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_atanh_op<Scalar> > {
-  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
-};
-#endif
-
-/** \internal
-  * \brief Template functor to compute the sinh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::sinh()
-  */
-template<typename Scalar> struct scalar_sinh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sinh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sinh(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psinh(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_sinh_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasSinh
-  };
-};
-
-#if EIGEN_HAS_CXX11_MATH
-/** \internal
-  * \brief Template functor to compute the asinh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::asinh()
-  */
-template <typename Scalar>
-struct scalar_asinh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::asinh(a); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_asinh_op<Scalar> > {
-  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
-};
-#endif
-
-/** \internal
-  * \brief Template functor to compute the cosh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::cosh()
-  */
-template<typename Scalar> struct scalar_cosh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cosh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::cosh(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcosh(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_cosh_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasCosh
-  };
-};
-
-#if EIGEN_HAS_CXX11_MATH
-/** \internal
-  * \brief Template functor to compute the acosh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::acosh()
-  */
-template <typename Scalar>
-struct scalar_acosh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_acosh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::acosh(a); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_acosh_op<Scalar> > {
-  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
-};
-#endif
-
-/** \internal
-  * \brief Template functor to compute the inverse of a scalar
-  * \sa class CwiseUnaryOp, Cwise::inverse()
-  */
-template<typename Scalar>
-struct scalar_inverse_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return internal::pdiv(pset1<Packet>(Scalar(1)),a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_inverse_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasDiv,
-    Cost = scalar_div_cost<Scalar, PacketAccess>::value
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the square of a scalar
-  * \sa class CwiseUnaryOp, Cwise::square()
-  */
-template<typename Scalar>
-struct scalar_square_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return internal::pmul(a,a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_square_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
-
-// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
-template<>
-struct scalar_square_op<bool> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op)
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
-  template<typename Packet>
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return a; }
-};
-template<>
-struct functor_traits<scalar_square_op<bool> >
-{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
-
-/** \internal
-  * \brief Template functor to compute the cube of a scalar
-  * \sa class CwiseUnaryOp, Cwise::cube()
-  */
-template<typename Scalar>
-struct scalar_cube_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a*a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return internal::pmul(a,pmul(a,a)); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_cube_op<Scalar> >
-{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
-
-// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
-template<>
-struct scalar_cube_op<bool> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op)
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
-  template<typename Packet>
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return a; }
-};
-template<>
-struct functor_traits<scalar_cube_op<bool> >
-{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
-
-/** \internal
-  * \brief Template functor to compute the rounded value of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::round()
-  */
-template<typename Scalar> struct scalar_round_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_round_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::round(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pround(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_round_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasRound
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the floor of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::floor()
-  */
-template<typename Scalar> struct scalar_floor_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_floor_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::floor(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pfloor(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_floor_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasFloor
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the rounded (with current rounding mode)  value of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::rint()
-  */
-template<typename Scalar> struct scalar_rint_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_rint_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::rint(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::print(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_rint_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasRint
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the ceil of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::ceil()
-  */
-template<typename Scalar> struct scalar_ceil_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_ceil_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::ceil(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pceil(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_ceil_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasCeil
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute whether a scalar is NaN
-  * \sa class CwiseUnaryOp, ArrayBase::isnan()
-  */
-template<typename Scalar> struct scalar_isnan_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_isnan_op)
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
-#if defined(SYCL_DEVICE_ONLY)
-    return numext::isnan(a);
-#else
-    return (numext::isnan)(a);
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_isnan_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to check whether a scalar is +/-inf
-  * \sa class CwiseUnaryOp, ArrayBase::isinf()
-  */
-template<typename Scalar> struct scalar_isinf_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_isinf_op)
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
-#if defined(SYCL_DEVICE_ONLY)
-    return numext::isinf(a);
-#else
-    return (numext::isinf)(a);
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_isinf_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to check whether a scalar has a finite value
-  * \sa class CwiseUnaryOp, ArrayBase::isfinite()
-  */
-template<typename Scalar> struct scalar_isfinite_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_isfinite_op)
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
-#if defined(SYCL_DEVICE_ONLY)
-    return numext::isfinite(a);
-#else
-    return (numext::isfinite)(a);
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_isfinite_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the logical not of a boolean
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::operator!
-  */
-template<typename Scalar> struct scalar_boolean_not_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_not_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a) const { return !a; }
-};
-template<typename Scalar>
-struct functor_traits<scalar_boolean_not_op<Scalar> > {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the signum of a scalar
-  * \sa class CwiseUnaryOp, Cwise::sign()
-  */
-template<typename Scalar,bool is_complex=(NumTraits<Scalar>::IsComplex!=0), bool is_integer=(NumTraits<Scalar>::IsInteger!=0) > struct scalar_sign_op;
-template<typename Scalar>
-struct scalar_sign_op<Scalar, false, true> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const
-  {
-      return Scalar( (a>Scalar(0)) - (a<Scalar(0)) );
-  }
-  //TODO
-  //template <typename Packet>
-  //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); }
-};
-
-template<typename Scalar>
-struct scalar_sign_op<Scalar, false, false> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const
-  {
-    return (numext::isnan)(a) ? a : Scalar( (a>Scalar(0)) - (a<Scalar(0)) );
-  }
-  //TODO
-  //template <typename Packet>
-  //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); }
-};
-
-template<typename Scalar, bool is_integer>
-struct scalar_sign_op<Scalar,true, is_integer> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const
-  {
-    typedef typename NumTraits<Scalar>::Real real_type;
-    real_type aa = numext::abs(a);
-    if (aa==real_type(0))
-      return Scalar(0);
-    aa = real_type(1)/aa;
-    return Scalar(a.real()*aa, a.imag()*aa );
-  }
-  //TODO
-  //template <typename Packet>
-  //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_sign_op<Scalar> >
-{ enum {
-    Cost =
-        NumTraits<Scalar>::IsComplex
-        ? ( 8*NumTraits<Scalar>::MulCost  ) // roughly
-        : ( 3*NumTraits<Scalar>::AddCost),
-    PacketAccess = packet_traits<Scalar>::HasSign
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the logistic function of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::logistic()
-  */
-template <typename T>
-struct scalar_logistic_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_logistic_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
-    return packetOp(x);
-  }
-
-  template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Packet packetOp(const Packet& x) const {
-    const Packet one = pset1<Packet>(T(1));
-    return pdiv(one, padd(one, pexp(pnegate(x))));
-  }
-};
-
-#ifndef EIGEN_GPU_COMPILE_PHASE
-/** \internal
-  * \brief Template specialization of the logistic function for float.
-  *
-  *  Uses just a 9/10-degree rational interpolant which
-  *  interpolates 1/(1+exp(-x)) - 0.5 up to a couple of ulps in the range
-  *  [-9, 18]. Below -9 we use the more accurate approximation
-  *  1/(1+exp(-x)) ~= exp(x), and above 18 the logistic function is 1 withing
-  *  one ulp. The shifted logistic is interpolated because it was easier to
-  *  make the fit converge.
-  *
-  */
-template <>
-struct scalar_logistic_op<float> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_logistic_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator()(const float& x) const {
-    return packetOp(x);
-  }
-
-  template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Packet packetOp(const Packet& _x) const {
-    const Packet cutoff_lower = pset1<Packet>(-9.f);
-    const Packet lt_mask = pcmp_lt<Packet>(_x, cutoff_lower);
-    const bool any_small = predux_any(lt_mask);
-
-    // The upper cut-off is the smallest x for which the rational approximation evaluates to 1.
-    // Choosing this value saves us a few instructions clamping the results at the end.
-#ifdef EIGEN_VECTORIZE_FMA
-    const Packet cutoff_upper = pset1<Packet>(15.7243833541870117f);
-#else
-    const Packet cutoff_upper = pset1<Packet>(15.6437711715698242f);
-#endif
-    const Packet x = pmin(_x, cutoff_upper);
-
-    // The monomial coefficients of the numerator polynomial (odd).
-    const Packet alpha_1 = pset1<Packet>(2.48287947061529e-01f);
-    const Packet alpha_3 = pset1<Packet>(8.51377133304701e-03f);
-    const Packet alpha_5 = pset1<Packet>(6.08574864600143e-05f);
-    const Packet alpha_7 = pset1<Packet>(1.15627324459942e-07f);
-    const Packet alpha_9 = pset1<Packet>(4.37031012579801e-11f);
-
-    // The monomial coefficients of the denominator polynomial (even).
-    const Packet beta_0 = pset1<Packet>(9.93151921023180e-01f);
-    const Packet beta_2 = pset1<Packet>(1.16817656904453e-01f);
-    const Packet beta_4 = pset1<Packet>(1.70198817374094e-03f);
-    const Packet beta_6 = pset1<Packet>(6.29106785017040e-06f);
-    const Packet beta_8 = pset1<Packet>(5.76102136993427e-09f);
-    const Packet beta_10 = pset1<Packet>(6.10247389755681e-13f);
-
-    // Since the polynomials are odd/even, we need x^2.
-    const Packet x2 = pmul(x, x);
-
-    // Evaluate the numerator polynomial p.
-    Packet p = pmadd(x2, alpha_9, alpha_7);
-    p = pmadd(x2, p, alpha_5);
-    p = pmadd(x2, p, alpha_3);
-    p = pmadd(x2, p, alpha_1);
-    p = pmul(x, p);
-
-    // Evaluate the denominator polynomial q.
-    Packet q = pmadd(x2, beta_10, beta_8);
-    q = pmadd(x2, q, beta_6);
-    q = pmadd(x2, q, beta_4);
-    q = pmadd(x2, q, beta_2);
-    q = pmadd(x2, q, beta_0);
-    // Divide the numerator by the denominator and shift it up.
-    const Packet logistic = padd(pdiv(p, q), pset1<Packet>(0.5f));
-    if (EIGEN_PREDICT_FALSE(any_small)) {
-      const Packet exponential = pexp(_x);
-      return pselect(lt_mask, exponential, logistic);
-    } else {
-      return logistic;
-    }
-  }
-};
-#endif  // #ifndef EIGEN_GPU_COMPILE_PHASE
-
-template <typename T>
-struct functor_traits<scalar_logistic_op<T> > {
-  enum {
-    // The cost estimate for float here here is for the common(?) case where
-    // all arguments are greater than -9.
-    Cost = scalar_div_cost<T, packet_traits<T>::HasDiv>::value +
-           (internal::is_same<T, float>::value
-                ? NumTraits<T>::AddCost * 15 + NumTraits<T>::MulCost * 11
-                : NumTraits<T>::AddCost * 2 +
-                      functor_traits<scalar_exp_op<T> >::Cost),
-    PacketAccess =
-        packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
-        (internal::is_same<T, float>::value
-             ? packet_traits<T>::HasMul && packet_traits<T>::HasMax &&
-                   packet_traits<T>::HasMin
-             : packet_traits<T>::HasNegate && packet_traits<T>::HasExp)
-  };
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
deleted file mode 100644
index f35b760..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
+++ /dev/null
@@ -1,2645 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_BLOCK_PANEL_H
-#define EIGEN_GENERAL_BLOCK_PANEL_H
-
-
-namespace Eigen {
-
-namespace internal {
-
-enum GEBPPacketSizeType {
-  GEBPPacketFull = 0,
-  GEBPPacketHalf,
-  GEBPPacketQuarter
-};
-
-template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false, int Arch=Architecture::Target, int _PacketSize=GEBPPacketFull>
-class gebp_traits;
-
-
-/** \internal \returns b if a<=0, and returns a otherwise. */
-inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b)
-{
-  return a<=0 ? b : a;
-}
-
-#if defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
-#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) EIGEN_DEFAULT_L1_CACHE_SIZE
-#else
-#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) val
-#endif // defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
-
-#if defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
-#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) EIGEN_DEFAULT_L2_CACHE_SIZE
-#else
-#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) val
-#endif // defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
-
-#if defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
-#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) EIGEN_DEFAULT_L3_CACHE_SIZE
-#else
-#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) val
-#endif // defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
-  
-#if EIGEN_ARCH_i386_OR_x86_64
-const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(32*1024);
-const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(256*1024);
-const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(2*1024*1024);
-#elif EIGEN_ARCH_PPC
-const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(64*1024);
-const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512*1024);
-const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(4*1024*1024);
-#else
-const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(16*1024);
-const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512*1024);
-const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(512*1024);
-#endif
-
-#undef EIGEN_SET_DEFAULT_L1_CACHE_SIZE
-#undef EIGEN_SET_DEFAULT_L2_CACHE_SIZE
-#undef EIGEN_SET_DEFAULT_L3_CACHE_SIZE
-
-/** \internal */
-struct CacheSizes {
-  CacheSizes(): m_l1(-1),m_l2(-1),m_l3(-1) {
-    int l1CacheSize, l2CacheSize, l3CacheSize;
-    queryCacheSizes(l1CacheSize, l2CacheSize, l3CacheSize);
-    m_l1 = manage_caching_sizes_helper(l1CacheSize, defaultL1CacheSize);
-    m_l2 = manage_caching_sizes_helper(l2CacheSize, defaultL2CacheSize);
-    m_l3 = manage_caching_sizes_helper(l3CacheSize, defaultL3CacheSize);
-  }
-
-  std::ptrdiff_t m_l1;
-  std::ptrdiff_t m_l2;
-  std::ptrdiff_t m_l3;
-};
-
-/** \internal */
-inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1, std::ptrdiff_t* l2, std::ptrdiff_t* l3)
-{
-  static CacheSizes m_cacheSizes;
-
-  if(action==SetAction)
-  {
-    // set the cpu cache size and cache all block sizes from a global cache size in byte
-    eigen_internal_assert(l1!=0 && l2!=0);
-    m_cacheSizes.m_l1 = *l1;
-    m_cacheSizes.m_l2 = *l2;
-    m_cacheSizes.m_l3 = *l3;
-  }
-  else if(action==GetAction)
-  {
-    eigen_internal_assert(l1!=0 && l2!=0);
-    *l1 = m_cacheSizes.m_l1;
-    *l2 = m_cacheSizes.m_l2;
-    *l3 = m_cacheSizes.m_l3;
-  }
-  else
-  {
-    eigen_internal_assert(false);
-  }
-}
-
-/* Helper for computeProductBlockingSizes.
- *
- * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
- * this function computes the blocking size parameters along the respective dimensions
- * for matrix products and related algorithms. The blocking sizes depends on various
- * parameters:
- * - the L1 and L2 cache sizes,
- * - the register level blocking sizes defined by gebp_traits,
- * - the number of scalars that fit into a packet (when vectorization is enabled).
- *
- * \sa setCpuCacheSizes */
-
-template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
-void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index num_threads = 1)
-{
-  typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-  // Explanations:
-  // Let's recall that the product algorithms form mc x kc vertical panels A' on the lhs and
-  // kc x nc blocks B' on the rhs. B' has to fit into L2/L3 cache. Moreover, A' is processed
-  // per mr x kc horizontal small panels where mr is the blocking size along the m dimension
-  // at the register level. This small horizontal panel has to stay within L1 cache.
-  std::ptrdiff_t l1, l2, l3;
-  manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  #ifdef EIGEN_VECTORIZE_AVX512
-  // We need to find a rationale for that, but without this adjustment,
-  // performance with AVX512 is pretty bad, like -20% slower.
-  // One reason is that with increasing packet-size, the blocking size k
-  // has to become pretty small if we want that 1 lhs panel fit within L1.
-  // For instance, with the 3pX4 kernel and double, the size of the lhs+rhs panels are:
-  //   k*(3*64 + 4*8) Bytes, with l1=32kBytes, and k%8=0, we have k=144.
-  // This is quite small for a good reuse of the accumulation registers.
-  l1 *= 4;
-  #endif
-
-  if (num_threads > 1) {
-    typedef typename Traits::ResScalar ResScalar;
-    enum {
-      kdiv = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
-      ksub = Traits::mr * Traits::nr * sizeof(ResScalar),
-      kr = 8,
-      mr = Traits::mr,
-      nr = Traits::nr
-    };
-    // Increasing k gives us more time to prefetch the content of the "C"
-    // registers. However once the latency is hidden there is no point in
-    // increasing the value of k, so we'll cap it at 320 (value determined
-    // experimentally).
-    // To avoid that k vanishes, we make k_cache at least as big as kr
-    const Index k_cache = numext::maxi<Index>(kr, (numext::mini<Index>)((l1-ksub)/kdiv, 320));
-    if (k_cache < k) {
-      k = k_cache - (k_cache % kr);
-      eigen_internal_assert(k > 0);
-    }
-
-    const Index n_cache = (l2-l1) / (nr * sizeof(RhsScalar) * k);
-    const Index n_per_thread = numext::div_ceil(n, num_threads);
-    if (n_cache <= n_per_thread) {
-      // Don't exceed the capacity of the l2 cache.
-      eigen_internal_assert(n_cache >= static_cast<Index>(nr));
-      n = n_cache - (n_cache % nr);
-      eigen_internal_assert(n > 0);
-    } else {
-      n = (numext::mini<Index>)(n, (n_per_thread + nr - 1) - ((n_per_thread + nr - 1) % nr));
-    }
-
-    if (l3 > l2) {
-      // l3 is shared between all cores, so we'll give each thread its own chunk of l3.
-      const Index m_cache = (l3-l2) / (sizeof(LhsScalar) * k * num_threads);
-      const Index m_per_thread = numext::div_ceil(m, num_threads);
-      if(m_cache < m_per_thread && m_cache >= static_cast<Index>(mr)) {
-        m = m_cache - (m_cache % mr);
-        eigen_internal_assert(m > 0);
-      } else {
-        m = (numext::mini<Index>)(m, (m_per_thread + mr - 1) - ((m_per_thread + mr - 1) % mr));
-      }
-    }
-  }
-  else {
-    // In unit tests we do not want to use extra large matrices,
-    // so we reduce the cache size to check the blocking strategy is not flawed
-#ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
-    l1 = 9*1024;
-    l2 = 32*1024;
-    l3 = 512*1024;
-#endif
-
-    // Early return for small problems because the computation below are time consuming for small problems.
-    // Perhaps it would make more sense to consider k*n*m??
-    // Note that for very tiny problem, this function should be bypassed anyway
-    // because we use the coefficient-based implementation for them.
-    if((numext::maxi)(k,(numext::maxi)(m,n))<48)
-      return;
-
-    typedef typename Traits::ResScalar ResScalar;
-    enum {
-      k_peeling = 8,
-      k_div = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
-      k_sub = Traits::mr * Traits::nr * sizeof(ResScalar)
-    };
-
-    // ---- 1st level of blocking on L1, yields kc ----
-
-    // Blocking on the third dimension (i.e., k) is chosen so that an horizontal panel
-    // of size mr x kc of the lhs plus a vertical panel of kc x nr of the rhs both fits within L1 cache.
-    // We also include a register-level block of the result (mx x nr).
-    // (In an ideal world only the lhs panel would stay in L1)
-    // Moreover, kc has to be a multiple of 8 to be compatible with loop peeling, leading to a maximum blocking size of:
-    const Index max_kc = numext::maxi<Index>(((l1-k_sub)/k_div) & (~(k_peeling-1)),1);
-    const Index old_k = k;
-    if(k>max_kc)
-    {
-      // We are really blocking on the third dimension:
-      // -> reduce blocking size to make sure the last block is as large as possible
-      //    while keeping the same number of sweeps over the result.
-      k = (k%max_kc)==0 ? max_kc
-                        : max_kc - k_peeling * ((max_kc-1-(k%max_kc))/(k_peeling*(k/max_kc+1)));
-
-      eigen_internal_assert(((old_k/k) == (old_k/max_kc)) && "the number of sweeps has to remain the same");
-    }
-
-    // ---- 2nd level of blocking on max(L2,L3), yields nc ----
-
-    // TODO find a reliable way to get the actual amount of cache per core to use for 2nd level blocking, that is:
-    //      actual_l2 = max(l2, l3/nb_core_sharing_l3)
-    // The number below is quite conservative: it is better to underestimate the cache size rather than overestimating it)
-    // For instance, it corresponds to 6MB of L3 shared among 4 cores.
-    #ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
-    const Index actual_l2 = l3;
-    #else
-    const Index actual_l2 = 1572864; // == 1.5 MB
-    #endif
-
-    // Here, nc is chosen such that a block of kc x nc of the rhs fit within half of L2.
-    // The second half is implicitly reserved to access the result and lhs coefficients.
-    // When k<max_kc, then nc can arbitrarily growth. In practice, it seems to be fruitful
-    // to limit this growth: we bound nc to growth by a factor x1.5.
-    // However, if the entire lhs block fit within L1, then we are not going to block on the rows at all,
-    // and it becomes fruitful to keep the packed rhs blocks in L1 if there is enough remaining space.
-    Index max_nc;
-    const Index lhs_bytes = m * k * sizeof(LhsScalar);
-    const Index remaining_l1 = l1- k_sub - lhs_bytes;
-    if(remaining_l1 >= Index(Traits::nr*sizeof(RhsScalar))*k)
-    {
-      // L1 blocking
-      max_nc = remaining_l1 / (k*sizeof(RhsScalar));
-    }
-    else
-    {
-      // L2 blocking
-      max_nc = (3*actual_l2)/(2*2*max_kc*sizeof(RhsScalar));
-    }
-    // WARNING Below, we assume that Traits::nr is a power of two.
-    Index nc = numext::mini<Index>(actual_l2/(2*k*sizeof(RhsScalar)), max_nc) & (~(Traits::nr-1));
-    if(n>nc)
-    {
-      // We are really blocking over the columns:
-      // -> reduce blocking size to make sure the last block is as large as possible
-      //    while keeping the same number of sweeps over the packed lhs.
-      //    Here we allow one more sweep if this gives us a perfect match, thus the commented "-1"
-      n = (n%nc)==0 ? nc
-                    : (nc - Traits::nr * ((nc/*-1*/-(n%nc))/(Traits::nr*(n/nc+1))));
-    }
-    else if(old_k==k)
-    {
-      // So far, no blocking at all, i.e., kc==k, and nc==n.
-      // In this case, let's perform a blocking over the rows such that the packed lhs data is kept in cache L1/L2
-      // TODO: part of this blocking strategy is now implemented within the kernel itself, so the L1-based heuristic here should be obsolete.
-      Index problem_size = k*n*sizeof(LhsScalar);
-      Index actual_lm = actual_l2;
-      Index max_mc = m;
-      if(problem_size<=1024)
-      {
-        // problem is small enough to keep in L1
-        // Let's choose m such that lhs's block fit in 1/3 of L1
-        actual_lm = l1;
-      }
-      else if(l3!=0 && problem_size<=32768)
-      {
-        // we have both L2 and L3, and problem is small enough to be kept in L2
-        // Let's choose m such that lhs's block fit in 1/3 of L2
-        actual_lm = l2;
-        max_mc = (numext::mini<Index>)(576,max_mc);
-      }
-      Index mc = (numext::mini<Index>)(actual_lm/(3*k*sizeof(LhsScalar)), max_mc);
-      if (mc > Traits::mr) mc -= mc % Traits::mr;
-      else if (mc==0) return;
-      m = (m%mc)==0 ? mc
-                    : (mc - Traits::mr * ((mc/*-1*/-(m%mc))/(Traits::mr*(m/mc+1))));
-    }
-  }
-}
-
-template <typename Index>
-inline bool useSpecificBlockingSizes(Index& k, Index& m, Index& n)
-{
-#ifdef EIGEN_TEST_SPECIFIC_BLOCKING_SIZES
-  if (EIGEN_TEST_SPECIFIC_BLOCKING_SIZES) {
-    k = numext::mini<Index>(k, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K);
-    m = numext::mini<Index>(m, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M);
-    n = numext::mini<Index>(n, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N);
-    return true;
-  }
-#else
-  EIGEN_UNUSED_VARIABLE(k)
-  EIGEN_UNUSED_VARIABLE(m)
-  EIGEN_UNUSED_VARIABLE(n)
-#endif
-  return false;
-}
-
-/** \brief Computes the blocking parameters for a m x k times k x n matrix product
-  *
-  * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension.
-  * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension.
-  * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension.
-  *
-  * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
-  * this function computes the blocking size parameters along the respective dimensions
-  * for matrix products and related algorithms.
-  *
-  * The blocking size parameters may be evaluated:
-  *   - either by a heuristic based on cache sizes;
-  *   - or using fixed prescribed values (for testing purposes).
-  *
-  * \sa setCpuCacheSizes */
-
-template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
-void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
-{
-  if (!useSpecificBlockingSizes(k, m, n)) {
-    evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor, Index>(k, m, n, num_threads);
-  }
-}
-
-template<typename LhsScalar, typename RhsScalar, typename Index>
-inline void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
-{
-  computeProductBlockingSizes<LhsScalar,RhsScalar,1,Index>(k, m, n, num_threads);
-}
-
-template <typename RhsPacket, typename RhsPacketx4, int registers_taken>
-struct RhsPanelHelper {
- private:
-  static const int remaining_registers = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS - registers_taken;
- public:
-  typedef typename conditional<remaining_registers>=4, RhsPacketx4, RhsPacket>::type type;
-};
-
-template <typename Packet>
-struct QuadPacket
-{
-  Packet B_0, B1, B2, B3;
-  const Packet& get(const FixedInt<0>&) const { return B_0; }
-  const Packet& get(const FixedInt<1>&) const { return B1; }
-  const Packet& get(const FixedInt<2>&) const { return B2; }
-  const Packet& get(const FixedInt<3>&) const { return B3; }
-};
-
-template <int N, typename T1, typename T2, typename T3>
-struct packet_conditional { typedef T3 type; };
-
-template <typename T1, typename T2, typename T3>
-struct packet_conditional<GEBPPacketFull, T1, T2, T3> { typedef T1 type; };
-
-template <typename T1, typename T2, typename T3>
-struct packet_conditional<GEBPPacketHalf, T1, T2, T3> { typedef T2 type; };
-
-#define PACKET_DECL_COND_PREFIX(prefix, name, packet_size)         \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<name ## Scalar>::type, \
-                                      typename packet_traits<name ## Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
-  prefix ## name ## Packet
-
-#define PACKET_DECL_COND(name, packet_size)                        \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<name ## Scalar>::type, \
-                                      typename packet_traits<name ## Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
-  name ## Packet
-
-#define PACKET_DECL_COND_SCALAR_PREFIX(prefix, packet_size)        \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<Scalar>::type, \
-                                      typename packet_traits<Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type \
-  prefix ## ScalarPacket
-
-#define PACKET_DECL_COND_SCALAR(packet_size)                       \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<Scalar>::type, \
-                                      typename packet_traits<Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type \
-  ScalarPacket
-
-/* Vectorization logic
- *  real*real: unpack rhs to constant packets, ...
- * 
- *  cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i),
- *          storing each res packet into two packets (2x2),
- *          at the end combine them: swap the second and addsub them 
- *  cf*cf : same but with 2x4 blocks
- *  cplx*real : unpack rhs to constant packets, ...
- *  real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual
- */
-template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs, int Arch, int _PacketSize>
-class gebp_traits
-{
-public:
-  typedef _LhsScalar LhsScalar;
-  typedef _RhsScalar RhsScalar;
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-
-  enum {
-    ConjLhs = _ConjLhs,
-    ConjRhs = _ConjRhs,
-    Vectorizable = unpacket_traits<_LhsPacket>::vectorizable && unpacket_traits<_RhsPacket>::vectorizable,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-    ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-
-    // register block size along the N direction must be 1 or 4
-    nr = 4,
-
-    // register block size along the M direction (currently, this one cannot be modified)
-    default_mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*LhsPacketSize,
-#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX) \
-    && ((!EIGEN_COMP_MSVC) || (EIGEN_COMP_MSVC>=1914))
-    // we assume 16 registers or more
-    // See bug 992, if the scalar type is not vectorizable but that EIGEN_HAS_SINGLE_INSTRUCTION_MADD is defined,
-    // then using 3*LhsPacketSize triggers non-implemented paths in syrk.
-    // Bug 1515: MSVC prior to v19.14 yields to register spilling.
-    mr = Vectorizable ? 3*LhsPacketSize : default_mr,
-#else
-    mr = default_mr,
-#endif
-    
-    LhsProgress = LhsPacketSize,
-    RhsProgress = 1
-  };
-
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-  typedef LhsPacket LhsPacket4Packing;
-
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-  typedef ResPacket AccPacket;
-  
-  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
-  {
-    p = pset1<ResPacket>(ResScalar(0));
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    dest = pset1<RhsPacketType>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {
-  }
-
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = ploadquad<RhsPacket>(b);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = pload<LhsPacketType>(a);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploadu<LhsPacketType>(a);
-  }
-
-  template<typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
-  {
-    conj_helper<LhsPacketType,RhsPacketType,ConjLhs,ConjRhs> cj;
-    // It would be a lot cleaner to call pmadd all the time. Unfortunately if we
-    // let gcc allocate the register in which to store the result of the pmul
-    // (in the case where there is no FMA) gcc fails to figure out how to avoid
-    // spilling register.
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-    EIGEN_UNUSED_VARIABLE(tmp);
-    c = cj.pmadd(a,b,c);
-#else
-    tmp = b; tmp = cj.pmul(a,tmp); c = padd(c,tmp);
-#endif
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-
-  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
-  {
-    r = pmadd(c,alpha,r);
-  }
-  
-  template<typename ResPacketHalf>
-  EIGEN_STRONG_INLINE void acc(const ResPacketHalf& c, const ResPacketHalf& alpha, ResPacketHalf& r) const
-  {
-    r = pmadd(c,alpha,r);
-  }
-
-};
-
-template<typename RealScalar, bool _ConjLhs, int Arch, int _PacketSize>
-class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false, Arch, _PacketSize>
-{
-public:
-  typedef std::complex<RealScalar> LhsScalar;
-  typedef RealScalar RhsScalar;
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-
-  enum {
-    ConjLhs = _ConjLhs,
-    ConjRhs = false,
-    Vectorizable = unpacket_traits<_LhsPacket>::vectorizable && unpacket_traits<_RhsPacket>::vectorizable,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-    ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-    nr = 4,
-#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
-    // we assume 16 registers
-    mr = 3*LhsPacketSize,
-#else
-    mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*LhsPacketSize,
-#endif
-
-    LhsProgress = LhsPacketSize,
-    RhsProgress = 1
-  };
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-  typedef LhsPacket LhsPacket4Packing;
-
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-
-  typedef ResPacket AccPacket;
-
-  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
-  {
-    p = pset1<ResPacket>(ResScalar(0));
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    dest = pset1<RhsPacketType>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-  
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhsQuad_impl(b,dest, typename conditional<RhsPacketSize==16,true_type,false_type>::type());
-  }
-
-  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const true_type&) const
-  {
-    // FIXME we can do better!
-    // what we want here is a ploadheight
-    RhsScalar tmp[4] = {b[0],b[0],b[1],b[1]};
-    dest = ploadquad<RhsPacket>(tmp);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const false_type&) const
-  {
-    eigen_internal_assert(RhsPacketSize<=8);
-    dest = pset1<RhsPacket>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
-  {
-    dest = pload<LhsPacket>(a);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploadu<LhsPacketType>(a);
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
-  {
-    madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const true_type&) const
-  {
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-    EIGEN_UNUSED_VARIABLE(tmp);
-    c.v = pmadd(a.v,b,c.v);
-#else
-    tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp);
-#endif
-  }
-
-  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
-  {
-    c += a * b;
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-
-  template <typename ResPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const
-  {
-    conj_helper<ResPacketType,ResPacketType,ConjLhs,false> cj;
-    r = cj.pmadd(c,alpha,r);
-  }
-
-protected:
-};
-
-template<typename Packet>
-struct DoublePacket
-{
-  Packet first;
-  Packet second;
-};
-
-template<typename Packet>
-DoublePacket<Packet> padd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b)
-{
-  DoublePacket<Packet> res;
-  res.first  = padd(a.first, b.first);
-  res.second = padd(a.second,b.second);
-  return res;
-}
-
-// note that for DoublePacket<RealPacket> the "4" in "downto4"
-// corresponds to the number of complexes, so it means "8"
-// it terms of real coefficients.
-
-template<typename Packet>
-const DoublePacket<Packet>&
-predux_half_dowto4(const DoublePacket<Packet> &a,
-                   typename enable_if<unpacket_traits<Packet>::size<=8>::type* = 0)
-{
-  return a;
-}
-
-template<typename Packet>
-DoublePacket<typename unpacket_traits<Packet>::half>
-predux_half_dowto4(const DoublePacket<Packet> &a,
-                   typename enable_if<unpacket_traits<Packet>::size==16>::type* = 0)
-{
-  // yes, that's pretty hackish :(
-  DoublePacket<typename unpacket_traits<Packet>::half> res;
-  typedef std::complex<typename unpacket_traits<Packet>::type> Cplx;
-  typedef typename packet_traits<Cplx>::type CplxPacket;
-  res.first  = predux_half_dowto4(CplxPacket(a.first)).v;
-  res.second = predux_half_dowto4(CplxPacket(a.second)).v;
-  return res;
-}
-
-// same here, "quad" actually means "8" in terms of real coefficients
-template<typename Scalar, typename RealPacket>
-void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
-                            typename enable_if<unpacket_traits<RealPacket>::size<=8>::type* = 0)
-{
-  dest.first  = pset1<RealPacket>(numext::real(*b));
-  dest.second = pset1<RealPacket>(numext::imag(*b));
-}
-
-template<typename Scalar, typename RealPacket>
-void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
-                            typename enable_if<unpacket_traits<RealPacket>::size==16>::type* = 0)
-{
-  // yes, that's pretty hackish too :(
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  RealScalar r[4] = {numext::real(b[0]), numext::real(b[0]), numext::real(b[1]), numext::real(b[1])};
-  RealScalar i[4] = {numext::imag(b[0]), numext::imag(b[0]), numext::imag(b[1]), numext::imag(b[1])};
-  dest.first  = ploadquad<RealPacket>(r);
-  dest.second = ploadquad<RealPacket>(i);
-}
-
-
-template<typename Packet> struct unpacket_traits<DoublePacket<Packet> > {
-  typedef DoublePacket<typename unpacket_traits<Packet>::half> half;
-};
-// template<typename Packet>
-// DoublePacket<Packet> pmadd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b)
-// {
-//   DoublePacket<Packet> res;
-//   res.first  = padd(a.first, b.first);
-//   res.second = padd(a.second,b.second);
-//   return res;
-// }
-
-template<typename RealScalar, bool _ConjLhs, bool _ConjRhs, int Arch, int _PacketSize>
-class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs, Arch, _PacketSize >
-{
-public:
-  typedef std::complex<RealScalar>  Scalar;
-  typedef std::complex<RealScalar>  LhsScalar;
-  typedef std::complex<RealScalar>  RhsScalar;
-  typedef std::complex<RealScalar>  ResScalar;
-  
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-  PACKET_DECL_COND(Real, _PacketSize);
-  PACKET_DECL_COND_SCALAR(_PacketSize);
-
-  enum {
-    ConjLhs = _ConjLhs,
-    ConjRhs = _ConjRhs,
-    Vectorizable = unpacket_traits<RealPacket>::vectorizable
-                && unpacket_traits<ScalarPacket>::vectorizable,
-    ResPacketSize   = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<RhsScalar>::size : 1,
-    RealPacketSize  = Vectorizable ? unpacket_traits<RealPacket>::size : 1,
-
-    // FIXME: should depend on NumberOfRegisters
-    nr = 4,
-    mr = ResPacketSize,
-
-    LhsProgress = ResPacketSize,
-    RhsProgress = 1
-  };
-  
-  typedef DoublePacket<RealPacket>                 DoublePacketType;
-
-  typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type LhsPacket4Packing;
-  typedef typename conditional<Vectorizable,RealPacket,  Scalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,DoublePacketType,Scalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket;
-  typedef typename conditional<Vectorizable,DoublePacketType,Scalar>::type AccPacket;
-
-  // this actualy holds 8 packets!
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-  
-  EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); }
-
-  EIGEN_STRONG_INLINE void initAcc(DoublePacketType& p)
-  {
-    p.first   = pset1<RealPacket>(RealScalar(0));
-    p.second  = pset1<RealPacket>(RealScalar(0));
-  }
-
-  // Scalar path
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ScalarPacket& dest) const
-  {
-    dest = pset1<ScalarPacket>(*b);
-  }
-
-  // Vectorized path
-  template<typename RealPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const
-  {
-    dest.first  = pset1<RealPacketType>(numext::real(*b));
-    dest.second = pset1<RealPacketType>(numext::imag(*b));
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    loadRhs(b, dest.B_0);
-    loadRhs(b + 1, dest.B1);
-    loadRhs(b + 2, dest.B2);
-    loadRhs(b + 3, dest.B3);
-  }
-
-  // Scalar path
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, ScalarPacket& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  // Vectorized path
-  template<typename RealPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
-  
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, ResPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, DoublePacketType& dest) const
-  {
-    loadQuadToDoublePacket(b,dest);
-  }
-
-  // nothing special here
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
-  {
-    dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a));
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploadu<LhsPacketType>((const typename unpacket_traits<LhsPacketType>::type*)(a));
-  }
-
-  template<typename LhsPacketType, typename RhsPacketType, typename ResPacketType, typename TmpType, typename LaneIdType>
-  EIGEN_STRONG_INLINE
-  typename enable_if<!is_same<RhsPacketType,RhsPacketx4>::value>::type
-  madd(const LhsPacketType& a, const RhsPacketType& b, DoublePacket<ResPacketType>& c, TmpType& /*tmp*/, const LaneIdType&) const
-  {
-    c.first   = padd(pmul(a,b.first), c.first);
-    c.second  = padd(pmul(a,b.second),c.second);
-  }
-
-  template<typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/, const LaneIdType&) const
-  {
-    c = cj.pmadd(a,b,c);
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-  
-  EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
-  
-  template<typename RealPacketType, typename ResPacketType>
-  EIGEN_STRONG_INLINE void acc(const DoublePacket<RealPacketType>& c, const ResPacketType& alpha, ResPacketType& r) const
-  {
-    // assemble c
-    ResPacketType tmp;
-    if((!ConjLhs)&&(!ConjRhs))
-    {
-      tmp = pcplxflip(pconj(ResPacketType(c.second)));
-      tmp = padd(ResPacketType(c.first),tmp);
-    }
-    else if((!ConjLhs)&&(ConjRhs))
-    {
-      tmp = pconj(pcplxflip(ResPacketType(c.second)));
-      tmp = padd(ResPacketType(c.first),tmp);
-    }
-    else if((ConjLhs)&&(!ConjRhs))
-    {
-      tmp = pcplxflip(ResPacketType(c.second));
-      tmp = padd(pconj(ResPacketType(c.first)),tmp);
-    }
-    else if((ConjLhs)&&(ConjRhs))
-    {
-      tmp = pcplxflip(ResPacketType(c.second));
-      tmp = psub(pconj(ResPacketType(c.first)),tmp);
-    }
-    
-    r = pmadd(tmp,alpha,r);
-  }
-
-protected:
-  conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
-};
-
-template<typename RealScalar, bool _ConjRhs, int Arch, int _PacketSize>
-class gebp_traits<RealScalar, std::complex<RealScalar>, false, _ConjRhs, Arch, _PacketSize >
-{
-public:
-  typedef std::complex<RealScalar>  Scalar;
-  typedef RealScalar  LhsScalar;
-  typedef Scalar      RhsScalar;
-  typedef Scalar      ResScalar;
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Real, _PacketSize);
-  PACKET_DECL_COND_SCALAR_PREFIX(_, _PacketSize);
-
-#undef PACKET_DECL_COND_SCALAR_PREFIX
-#undef PACKET_DECL_COND_PREFIX
-#undef PACKET_DECL_COND_SCALAR
-#undef PACKET_DECL_COND
-
-  enum {
-    ConjLhs = false,
-    ConjRhs = _ConjRhs,
-    Vectorizable = unpacket_traits<_RealPacket>::vectorizable
-                && unpacket_traits<_ScalarPacket>::vectorizable,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-    ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-    // FIXME: should depend on NumberOfRegisters
-    nr = 4,
-    mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*ResPacketSize,
-
-    LhsProgress = ResPacketSize,
-    RhsProgress = 1
-  };
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-  typedef LhsPacket LhsPacket4Packing;
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-  typedef ResPacket AccPacket;
-
-  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
-  {
-    p = pset1<ResPacket>(ResScalar(0));
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    dest = pset1<RhsPacketType>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
-  {
-    dest = ploaddup<LhsPacket>(a);
-  }
-  
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = ploadquad<RhsPacket>(b);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploaddup<LhsPacketType>(a);
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
-  {
-    madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const true_type&) const
-  {
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-    EIGEN_UNUSED_VARIABLE(tmp);
-    c.v = pmadd(a,b.v,c.v);
-#else
-    tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp);
-#endif
-    
-  }
-
-  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
-  {
-    c += a * b;
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-
-  template <typename ResPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const
-  {
-    conj_helper<ResPacketType,ResPacketType,false,ConjRhs> cj;
-    r = cj.pmadd(alpha,c,r);
-  }
-
-protected:
-
-};
-
-/* optimized General packed Block * packed Panel product kernel
- *
- * Mixing type logic: C += A * B
- *  |  A  |  B  | comments
- *  |real |cplx | no vectorization yet, would require to pack A with duplication
- *  |cplx |real | easy vectorization
- */
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel
-{
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target,GEBPPacketHalf> HalfTraits;
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target,GEBPPacketQuarter> QuarterTraits;
-  
-  typedef typename Traits::ResScalar ResScalar;
-  typedef typename Traits::LhsPacket LhsPacket;
-  typedef typename Traits::RhsPacket RhsPacket;
-  typedef typename Traits::ResPacket ResPacket;
-  typedef typename Traits::AccPacket AccPacket;
-  typedef typename Traits::RhsPacketx4 RhsPacketx4;
-
-  typedef typename RhsPanelHelper<RhsPacket, RhsPacketx4, 15>::type RhsPanel15;
-
-  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
-
-  typedef typename SwappedTraits::ResScalar SResScalar;
-  typedef typename SwappedTraits::LhsPacket SLhsPacket;
-  typedef typename SwappedTraits::RhsPacket SRhsPacket;
-  typedef typename SwappedTraits::ResPacket SResPacket;
-  typedef typename SwappedTraits::AccPacket SAccPacket;
-
-  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
-  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
-  typedef typename HalfTraits::ResPacket ResPacketHalf;
-  typedef typename HalfTraits::AccPacket AccPacketHalf;
-
-  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
-  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
-  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
-  typedef typename QuarterTraits::AccPacket AccPacketQuarter;
-
-  typedef typename DataMapper::LinearMapper LinearMapper;
-
-  enum {
-    Vectorizable  = Traits::Vectorizable,
-    LhsProgress   = Traits::LhsProgress,
-    LhsProgressHalf      = HalfTraits::LhsProgress,
-    LhsProgressQuarter   = QuarterTraits::LhsProgress,
-    RhsProgress   = Traits::RhsProgress,
-    RhsProgressHalf      = HalfTraits::RhsProgress,
-    RhsProgressQuarter   = QuarterTraits::RhsProgress,
-    ResPacketSize = Traits::ResPacketSize
-  };
-
-  EIGEN_DONT_INLINE
-  void operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
-                  Index rows, Index depth, Index cols, ResScalar alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs,
-int SwappedLhsProgress = gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target>::LhsProgress>
-struct last_row_process_16_packets
-{
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
-  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
-
-  typedef typename Traits::ResScalar ResScalar;
-  typedef typename SwappedTraits::LhsPacket SLhsPacket;
-  typedef typename SwappedTraits::RhsPacket SRhsPacket;
-  typedef typename SwappedTraits::ResPacket SResPacket;
-  typedef typename SwappedTraits::AccPacket SAccPacket;
-
-  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits &straits, const LhsScalar* blA,
-                  const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
-                  ResScalar alpha, SAccPacket &C0)
-    {
-      EIGEN_UNUSED_VARIABLE(res);
-      EIGEN_UNUSED_VARIABLE(straits);
-      EIGEN_UNUSED_VARIABLE(blA);
-      EIGEN_UNUSED_VARIABLE(blB);
-      EIGEN_UNUSED_VARIABLE(depth);
-      EIGEN_UNUSED_VARIABLE(endk);
-      EIGEN_UNUSED_VARIABLE(i);
-      EIGEN_UNUSED_VARIABLE(j2);
-      EIGEN_UNUSED_VARIABLE(alpha);
-      EIGEN_UNUSED_VARIABLE(C0);
-    }
-};
-
-
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper,  mr,  nr, ConjugateLhs,  ConjugateRhs, 16> {
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
-  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
-
-  typedef typename Traits::ResScalar ResScalar;
-  typedef typename SwappedTraits::LhsPacket SLhsPacket;
-  typedef typename SwappedTraits::RhsPacket SRhsPacket;
-  typedef typename SwappedTraits::ResPacket SResPacket;
-  typedef typename SwappedTraits::AccPacket SAccPacket;
-
-  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits &straits, const LhsScalar* blA,
-                  const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
-                  ResScalar alpha, SAccPacket &C0)
-  {
-    typedef typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half SResPacketQuarter;
-    typedef typename unpacket_traits<typename unpacket_traits<SLhsPacket>::half>::half SLhsPacketQuarter;
-    typedef typename unpacket_traits<typename unpacket_traits<SRhsPacket>::half>::half SRhsPacketQuarter;
-    typedef typename unpacket_traits<typename unpacket_traits<SAccPacket>::half>::half SAccPacketQuarter;
-
-    SResPacketQuarter R = res.template gatherPacket<SResPacketQuarter>(i, j2);
-    SResPacketQuarter alphav = pset1<SResPacketQuarter>(alpha);
-
-    if (depth - endk > 0)
-      {
-	// We have to handle the last row(s) of the rhs, which
-	// correspond to a half-packet
-	SAccPacketQuarter c0 = predux_half_dowto4(predux_half_dowto4(C0));
-
-	for (Index kk = endk; kk < depth; kk++)
-	  {
-	    SLhsPacketQuarter a0;
-	    SRhsPacketQuarter b0;
-	    straits.loadLhsUnaligned(blB, a0);
-	    straits.loadRhs(blA, b0);
-	    straits.madd(a0,b0,c0,b0, fix<0>);
-	    blB += SwappedTraits::LhsProgress/4;
-	    blA += 1;
-	  }
-	straits.acc(c0, alphav, R);
-      }
-    else
-      {
-	straits.acc(predux_half_dowto4(predux_half_dowto4(C0)), alphav, R);
-      }
-    res.scatterPacket(i, j2, R);
-  }
-};
-
-template<int nr, Index LhsProgress, Index RhsProgress, typename LhsScalar, typename RhsScalar, typename ResScalar, typename AccPacket, typename LhsPacket, typename RhsPacket, typename ResPacket, typename GEBPTraits, typename LinearMapper, typename DataMapper>
-struct lhs_process_one_packet
-{
-  typedef typename GEBPTraits::RhsPacketx4 RhsPacketx4;
-
-  EIGEN_STRONG_INLINE void peeled_kc_onestep(Index K, const LhsScalar* blA, const RhsScalar* blB, GEBPTraits traits, LhsPacket *A0, RhsPacketx4 *rhs_panel, RhsPacket *T0, AccPacket *C0, AccPacket *C1, AccPacket *C2, AccPacket *C3)
-  {
-    EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1X4");
-    EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!");
-    traits.loadLhs(&blA[(0+1*K)*LhsProgress], *A0);
-    traits.loadRhs(&blB[(0+4*K)*RhsProgress], *rhs_panel);
-    traits.madd(*A0, *rhs_panel, *C0, *T0, fix<0>);
-    traits.madd(*A0, *rhs_panel, *C1, *T0, fix<1>);
-    traits.madd(*A0, *rhs_panel, *C2, *T0, fix<2>);
-    traits.madd(*A0, *rhs_panel, *C3, *T0, fix<3>);
-    #if EIGEN_GNUC_AT_LEAST(6,0) && defined(EIGEN_VECTORIZE_SSE)
-    __asm__  ("" : "+x,m" (*A0));
-    #endif
-    EIGEN_ASM_COMMENT("end step of gebp micro kernel 1X4");
-  }
-
-  EIGEN_STRONG_INLINE void operator()(
-    const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB, ResScalar alpha,
-    Index peelStart, Index peelEnd, Index strideA, Index strideB, Index offsetA, Index offsetB,
-    int prefetch_res_offset, Index peeled_kc, Index pk, Index cols, Index depth, Index packet_cols4)
-  {
-    GEBPTraits traits;
-
-    // loops on each largest micro horizontal panel of lhs
-    // (LhsProgress x depth)
-    for(Index i=peelStart; i<peelEnd; i+=LhsProgress)
-    {
-      // loops on each largest micro vertical panel of rhs (depth * nr)
-      for(Index j2=0; j2<packet_cols4; j2+=nr)
-      {
-        // We select a LhsProgress x nr micro block of res
-        // which is entirely stored into 1 x nr registers.
-
-        const LhsScalar* blA = &blockA[i*strideA+offsetA*(LhsProgress)];
-        prefetch(&blA[0]);
-
-        // gets res block as register
-        AccPacket C0, C1, C2, C3;
-        traits.initAcc(C0);
-        traits.initAcc(C1);
-        traits.initAcc(C2);
-        traits.initAcc(C3);
-        // To improve instruction pipelining, let's double the accumulation registers:
-        //  even k will accumulate in C*, while odd k will accumulate in D*.
-        // This trick is crutial to get good performance with FMA, otherwise it is 
-        // actually faster to perform separated MUL+ADD because of a naturally
-        // better instruction-level parallelism.
-        AccPacket D0, D1, D2, D3;
-        traits.initAcc(D0);
-        traits.initAcc(D1);
-        traits.initAcc(D2);
-        traits.initAcc(D3);
-
-        LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
-        LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
-        LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
-        LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
-
-        r0.prefetch(prefetch_res_offset);
-        r1.prefetch(prefetch_res_offset);
-        r2.prefetch(prefetch_res_offset);
-        r3.prefetch(prefetch_res_offset);
-
-        // performs "inner" products
-        const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-        prefetch(&blB[0]);
-        LhsPacket A0, A1;
-
-        for(Index k=0; k<peeled_kc; k+=pk)
-        {
-          EIGEN_ASM_COMMENT("begin gebp micro kernel 1/half/quarterX4");
-          RhsPacketx4 rhs_panel;
-          RhsPacket T0;
-
-          internal::prefetch(blB+(48+0));
-          peeled_kc_onestep(0, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(1, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-          peeled_kc_onestep(2, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(3, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-          internal::prefetch(blB+(48+16));
-          peeled_kc_onestep(4, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(5, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-          peeled_kc_onestep(6, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(7, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-
-          blB += pk*4*RhsProgress;
-          blA += pk*LhsProgress;
-
-          EIGEN_ASM_COMMENT("end gebp micro kernel 1/half/quarterX4");
-        }
-        C0 = padd(C0,D0);
-        C1 = padd(C1,D1);
-        C2 = padd(C2,D2);
-        C3 = padd(C3,D3);
-
-        // process remaining peeled loop
-        for(Index k=peeled_kc; k<depth; k++)
-        {
-          RhsPacketx4 rhs_panel;
-          RhsPacket T0;
-          peeled_kc_onestep(0, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          blB += 4*RhsProgress;
-          blA += LhsProgress;
-        }
-
-        ResPacket R0, R1;
-        ResPacket alphav = pset1<ResPacket>(alpha);
-
-        R0 = r0.template loadPacket<ResPacket>(0);
-        R1 = r1.template loadPacket<ResPacket>(0);
-        traits.acc(C0, alphav, R0);
-        traits.acc(C1,  alphav, R1);
-        r0.storePacket(0, R0);
-        r1.storePacket(0, R1);
-
-        R0 = r2.template loadPacket<ResPacket>(0);
-        R1 = r3.template loadPacket<ResPacket>(0);
-        traits.acc(C2,  alphav, R0);
-        traits.acc(C3,  alphav, R1);
-        r2.storePacket(0, R0);
-        r3.storePacket(0, R1);
-      }
-
-      // Deal with remaining columns of the rhs
-      for(Index j2=packet_cols4; j2<cols; j2++)
-      {
-        // One column at a time
-        const LhsScalar* blA = &blockA[i*strideA+offsetA*(LhsProgress)];
-        prefetch(&blA[0]);
-
-        // gets res block as register
-        AccPacket C0;
-        traits.initAcc(C0);
-
-        LinearMapper r0 = res.getLinearMapper(i, j2);
-
-        // performs "inner" products
-        const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-        LhsPacket A0;
-
-        for(Index k= 0; k<peeled_kc; k+=pk)
-        {
-          EIGEN_ASM_COMMENT("begin gebp micro kernel 1/half/quarterX1");
-          RhsPacket B_0;
-
-#define EIGEN_GEBGP_ONESTEP(K)                                          \
-	      do {                                                      \
-		EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1/half/quarterX1"); \
-		EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-    /* FIXME: why unaligned???? */ \
-		traits.loadLhsUnaligned(&blA[(0+1*K)*LhsProgress], A0); \
-		traits.loadRhs(&blB[(0+K)*RhsProgress], B_0);		\
-		traits.madd(A0, B_0, C0, B_0, fix<0>);				\
-		EIGEN_ASM_COMMENT("end step of gebp micro kernel 1/half/quarterX1"); \
-	      } while(false);
-
-          EIGEN_GEBGP_ONESTEP(0);
-          EIGEN_GEBGP_ONESTEP(1);
-          EIGEN_GEBGP_ONESTEP(2);
-          EIGEN_GEBGP_ONESTEP(3);
-          EIGEN_GEBGP_ONESTEP(4);
-          EIGEN_GEBGP_ONESTEP(5);
-          EIGEN_GEBGP_ONESTEP(6);
-          EIGEN_GEBGP_ONESTEP(7);
-
-          blB += pk*RhsProgress;
-          blA += pk*LhsProgress;
-
-          EIGEN_ASM_COMMENT("end gebp micro kernel 1/half/quarterX1");
-        }
-
-        // process remaining peeled loop
-        for(Index k=peeled_kc; k<depth; k++)
-        {
-          RhsPacket B_0;
-          EIGEN_GEBGP_ONESTEP(0);
-          blB += RhsProgress;
-          blA += LhsProgress;
-        }
-#undef EIGEN_GEBGP_ONESTEP
-        ResPacket R0;
-        ResPacket alphav = pset1<ResPacket>(alpha);
-        R0 = r0.template loadPacket<ResPacket>(0);
-        traits.acc(C0, alphav, R0);
-        r0.storePacket(0, R0);
-      }
-    }
-  }
-};
-
-template<int nr, Index LhsProgress, Index RhsProgress, typename LhsScalar, typename RhsScalar, typename ResScalar, typename AccPacket, typename LhsPacket, typename RhsPacket, typename ResPacket, typename GEBPTraits, typename LinearMapper, typename DataMapper>
-struct lhs_process_fraction_of_packet : lhs_process_one_packet<nr, LhsProgress, RhsProgress, LhsScalar, RhsScalar, ResScalar, AccPacket, LhsPacket, RhsPacket, ResPacket, GEBPTraits, LinearMapper, DataMapper>
-{
-
-EIGEN_STRONG_INLINE void peeled_kc_onestep(Index K, const LhsScalar* blA, const RhsScalar* blB, GEBPTraits traits, LhsPacket *A0, RhsPacket *B_0, RhsPacket *B1, RhsPacket *B2, RhsPacket *B3, AccPacket *C0, AccPacket *C1, AccPacket *C2, AccPacket *C3)
-  {
-        EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1X4");
-        EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!");
-        traits.loadLhsUnaligned(&blA[(0+1*K)*(LhsProgress)], *A0);
-        traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], *B_0, *B1, *B2, *B3);
-        traits.madd(*A0, *B_0, *C0, *B_0);
-        traits.madd(*A0, *B1,  *C1, *B1);
-        traits.madd(*A0, *B2,  *C2, *B2);
-        traits.madd(*A0, *B3,  *C3, *B3);
-        EIGEN_ASM_COMMENT("end step of gebp micro kernel 1X4");
-  }
-};
-
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_DONT_INLINE
-void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,ConjugateRhs>
-  ::operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
-               Index rows, Index depth, Index cols, ResScalar alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    Traits traits;
-    SwappedTraits straits;
-    
-    if(strideA==-1) strideA = depth;
-    if(strideB==-1) strideB = depth;
-    conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
-    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-    const Index peeled_mc3 = mr>=3*Traits::LhsProgress ? (rows/(3*LhsProgress))*(3*LhsProgress) : 0;
-    const Index peeled_mc2 = mr>=2*Traits::LhsProgress ? peeled_mc3+((rows-peeled_mc3)/(2*LhsProgress))*(2*LhsProgress) : 0;
-    const Index peeled_mc1 = mr>=1*Traits::LhsProgress ? peeled_mc2+((rows-peeled_mc2)/(1*LhsProgress))*(1*LhsProgress) : 0;
-    const Index peeled_mc_half = mr>=LhsProgressHalf ? peeled_mc1+((rows-peeled_mc1)/(LhsProgressHalf))*(LhsProgressHalf) : 0;
-    const Index peeled_mc_quarter = mr>=LhsProgressQuarter ? peeled_mc_half+((rows-peeled_mc_half)/(LhsProgressQuarter))*(LhsProgressQuarter) : 0;
-    enum { pk = 8 }; // NOTE Such a large peeling factor is important for large matrices (~ +5% when >1000 on Haswell)
-    const Index peeled_kc  = depth & ~(pk-1);
-    const int prefetch_res_offset = 32/sizeof(ResScalar);    
-//     const Index depth2     = depth & ~1;
-
-    //---------- Process 3 * LhsProgress rows at once ----------
-    // This corresponds to 3*LhsProgress x nr register blocks.
-    // Usually, make sense only with FMA
-    if(mr>=3*Traits::LhsProgress)
-    {
-      // Here, the general idea is to loop on each largest micro horizontal panel of the lhs (3*Traits::LhsProgress x depth)
-      // and on each largest micro vertical panel of the rhs (depth * nr).
-      // Blocking sizes, i.e., 'depth' has been computed so that the micro horizontal panel of the lhs fit in L1.
-      // However, if depth is too small, we can extend the number of rows of these horizontal panels.
-      // This actual number of rows is computed as follow:
-      const Index l1 = defaultL1CacheSize; // in Bytes, TODO, l1 should be passed to this function.
-      // The max(1, ...) here is needed because we may be using blocking params larger than what our known l1 cache size
-      // suggests we should be using: either because our known l1 cache size is inaccurate (e.g. on Android, we can only guess),
-      // or because we are testing specific blocking sizes.
-      const Index actual_panel_rows = (3*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 3*LhsProgress) ));
-      for(Index i1=0; i1<peeled_mc3; i1+=actual_panel_rows)
-      {
-        const Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc3);
-        for(Index j2=0; j2<packet_cols4; j2+=nr)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
-          {
-          
-          // We selected a 3*Traits::LhsProgress x nr micro block of res which is entirely
-          // stored into 3 x nr registers.
-          
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C1, C2,  C3,
-                    C4, C5, C6,  C7,
-                    C8, C9, C10, C11;
-          traits.initAcc(C0);  traits.initAcc(C1);  traits.initAcc(C2);  traits.initAcc(C3);
-          traits.initAcc(C4);  traits.initAcc(C5);  traits.initAcc(C6);  traits.initAcc(C7);
-          traits.initAcc(C8);  traits.initAcc(C9);  traits.initAcc(C10); traits.initAcc(C11);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
-          LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
-          LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
-          LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
-
-          r0.prefetch(0);
-          r1.prefetch(0);
-          r2.prefetch(0);
-          r3.prefetch(0);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-          prefetch(&blB[0]);
-          LhsPacket A0, A1;
-
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX4");
-            // 15 registers are taken (12 for acc, 2 for lhs).
-            RhsPanel15 rhs_panel;
-            RhsPacket T0;
-            LhsPacket A2;
-            #if EIGEN_COMP_GNUC_STRICT && EIGEN_ARCH_ARM64 && defined(EIGEN_VECTORIZE_NEON) && !(EIGEN_GNUC_AT_LEAST(9,0))
-            // see http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1633
-            // without this workaround A0, A1, and A2 are loaded in the same register,
-            // which is not good for pipelining
-            #define EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND __asm__  ("" : "+w,m" (A0), "+w,m" (A1), "+w,m" (A2));
-            #else
-            #define EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND
-            #endif
-#define EIGEN_GEBP_ONESTEP(K)                                                     \
-            do {                                                                  \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX4");          \
-              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-              internal::prefetch(blA + (3 * K + 16) * LhsProgress);               \
-              if (EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) {                            \
-                internal::prefetch(blB + (4 * K + 16) * RhsProgress);             \
-              } /* Bug 953 */                                                     \
-              traits.loadLhs(&blA[(0 + 3 * K) * LhsProgress], A0);                \
-              traits.loadLhs(&blA[(1 + 3 * K) * LhsProgress], A1);                \
-              traits.loadLhs(&blA[(2 + 3 * K) * LhsProgress], A2);                \
-              EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND \
-              traits.loadRhs(blB + (0+4*K) * Traits::RhsProgress, rhs_panel);     \
-              traits.madd(A0, rhs_panel, C0, T0, fix<0>);                         \
-              traits.madd(A1, rhs_panel, C4, T0, fix<0>);                         \
-              traits.madd(A2, rhs_panel, C8, T0, fix<0>);                         \
-              traits.updateRhs(blB + (1+4*K) * Traits::RhsProgress, rhs_panel);   \
-              traits.madd(A0, rhs_panel, C1, T0, fix<1>);                         \
-              traits.madd(A1, rhs_panel, C5, T0, fix<1>);                         \
-              traits.madd(A2, rhs_panel, C9, T0, fix<1>);                         \
-              traits.updateRhs(blB + (2+4*K) * Traits::RhsProgress, rhs_panel);   \
-              traits.madd(A0, rhs_panel, C2, T0, fix<2>);                         \
-              traits.madd(A1, rhs_panel, C6, T0, fix<2>);                         \
-              traits.madd(A2, rhs_panel, C10, T0, fix<2>);                        \
-              traits.updateRhs(blB + (3+4*K) * Traits::RhsProgress, rhs_panel);   \
-              traits.madd(A0, rhs_panel, C3, T0, fix<3>);                         \
-              traits.madd(A1, rhs_panel, C7, T0, fix<3>);                         \
-              traits.madd(A2, rhs_panel, C11, T0, fix<3>);                        \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX4");            \
-            } while (false)
-
-            internal::prefetch(blB);
-            EIGEN_GEBP_ONESTEP(0);
-            EIGEN_GEBP_ONESTEP(1);
-            EIGEN_GEBP_ONESTEP(2);
-            EIGEN_GEBP_ONESTEP(3);
-            EIGEN_GEBP_ONESTEP(4);
-            EIGEN_GEBP_ONESTEP(5);
-            EIGEN_GEBP_ONESTEP(6);
-            EIGEN_GEBP_ONESTEP(7);
-
-            blB += pk*4*RhsProgress;
-            blA += pk*3*Traits::LhsProgress;
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 3pX4");
-          }
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPanel15 rhs_panel;
-            RhsPacket T0;
-            LhsPacket A2;
-            EIGEN_GEBP_ONESTEP(0);
-            blB += 4*RhsProgress;
-            blA += 3*Traits::LhsProgress;
-          }
-
-#undef EIGEN_GEBP_ONESTEP
-
-          ResPacket R0, R1, R2;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r0.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          traits.acc(C8, alphav, R2);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          r0.storePacket(2 * Traits::ResPacketSize, R2);
-
-          R0 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r1.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C1, alphav, R0);
-          traits.acc(C5, alphav, R1);
-          traits.acc(C9, alphav, R2);
-          r1.storePacket(0 * Traits::ResPacketSize, R0);
-          r1.storePacket(1 * Traits::ResPacketSize, R1);
-          r1.storePacket(2 * Traits::ResPacketSize, R2);
-
-          R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r2.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C2, alphav, R0);
-          traits.acc(C6, alphav, R1);
-          traits.acc(C10, alphav, R2);
-          r2.storePacket(0 * Traits::ResPacketSize, R0);
-          r2.storePacket(1 * Traits::ResPacketSize, R1);
-          r2.storePacket(2 * Traits::ResPacketSize, R2);
-
-          R0 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r3.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C3, alphav, R0);
-          traits.acc(C7, alphav, R1);
-          traits.acc(C11, alphav, R2);
-          r3.storePacket(0 * Traits::ResPacketSize, R0);
-          r3.storePacket(1 * Traits::ResPacketSize, R1);
-          r3.storePacket(2 * Traits::ResPacketSize, R2);          
-          }
-        }
-
-        // Deal with remaining columns of the rhs
-        for(Index j2=packet_cols4; j2<cols; j2++)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
-          {
-          // One column at a time
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*Traits::LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C4, C8;
-          traits.initAcc(C0);
-          traits.initAcc(C4);
-          traits.initAcc(C8);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2);
-          r0.prefetch(0);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-          LhsPacket A0, A1, A2;
-          
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX1");
-            RhsPacket B_0;
-#define EIGEN_GEBGP_ONESTEP(K)                                                    \
-            do {                                                                  \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX1");          \
-              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-              traits.loadLhs(&blA[(0 + 3 * K) * LhsProgress], A0);                \
-              traits.loadLhs(&blA[(1 + 3 * K) * LhsProgress], A1);                \
-              traits.loadLhs(&blA[(2 + 3 * K) * LhsProgress], A2);                \
-              traits.loadRhs(&blB[(0 + K) * RhsProgress], B_0);                   \
-              traits.madd(A0, B_0, C0, B_0, fix<0>);                              \
-              traits.madd(A1, B_0, C4, B_0, fix<0>);                              \
-              traits.madd(A2, B_0, C8, B_0, fix<0>);                              \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX1");            \
-            } while (false)
-
-            EIGEN_GEBGP_ONESTEP(0);
-            EIGEN_GEBGP_ONESTEP(1);
-            EIGEN_GEBGP_ONESTEP(2);
-            EIGEN_GEBGP_ONESTEP(3);
-            EIGEN_GEBGP_ONESTEP(4);
-            EIGEN_GEBGP_ONESTEP(5);
-            EIGEN_GEBGP_ONESTEP(6);
-            EIGEN_GEBGP_ONESTEP(7);
-
-            blB += int(pk) * int(RhsProgress);
-            blA += int(pk) * 3 * int(Traits::LhsProgress);
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 3pX1");
-          }
-
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPacket B_0;
-            EIGEN_GEBGP_ONESTEP(0);
-            blB += RhsProgress;
-            blA += 3*Traits::LhsProgress;
-          }
-#undef EIGEN_GEBGP_ONESTEP
-          ResPacket R0, R1, R2;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r0.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          traits.acc(C8, alphav, R2);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          r0.storePacket(2 * Traits::ResPacketSize, R2);          
-          }
-        }
-      }
-    }
-
-    //---------- Process 2 * LhsProgress rows at once ----------
-    if(mr>=2*Traits::LhsProgress)
-    {
-      const Index l1 = defaultL1CacheSize; // in Bytes, TODO, l1 should be passed to this function.
-      // The max(1, ...) here is needed because we may be using blocking params larger than what our known l1 cache size
-      // suggests we should be using: either because our known l1 cache size is inaccurate (e.g. on Android, we can only guess),
-      // or because we are testing specific blocking sizes.
-      Index actual_panel_rows = (2*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 2*LhsProgress) ));
-
-      for(Index i1=peeled_mc3; i1<peeled_mc2; i1+=actual_panel_rows)
-      {
-        Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc2);
-        for(Index j2=0; j2<packet_cols4; j2+=nr)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
-          {
-          
-          // We selected a 2*Traits::LhsProgress x nr micro block of res which is entirely
-          // stored into 2 x nr registers.
-          
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C1, C2, C3,
-                    C4, C5, C6, C7;
-          traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3);
-          traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
-          LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
-          LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
-          LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
-
-          r0.prefetch(prefetch_res_offset);
-          r1.prefetch(prefetch_res_offset);
-          r2.prefetch(prefetch_res_offset);
-          r3.prefetch(prefetch_res_offset);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-          prefetch(&blB[0]);
-          LhsPacket A0, A1;
-
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX4");
-            RhsPacketx4 rhs_panel;
-            RhsPacket T0;
-
-          // NOTE: the begin/end asm comments below work around bug 935!
-          // but they are not enough for gcc>=6 without FMA (bug 1637)
-          #if EIGEN_GNUC_AT_LEAST(6,0) && defined(EIGEN_VECTORIZE_SSE)
-            #define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND __asm__  ("" : [a0] "+x,m" (A0),[a1] "+x,m" (A1));
-          #else
-            #define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND
-          #endif
-#define EIGEN_GEBGP_ONESTEP(K)                                            \
-            do {                                                          \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX4");  \
-              traits.loadLhs(&blA[(0 + 2 * K) * LhsProgress], A0);        \
-              traits.loadLhs(&blA[(1 + 2 * K) * LhsProgress], A1);        \
-              traits.loadRhs(&blB[(0 + 4 * K) * RhsProgress], rhs_panel); \
-              traits.madd(A0, rhs_panel, C0, T0, fix<0>);                 \
-              traits.madd(A1, rhs_panel, C4, T0, fix<0>);                 \
-              traits.madd(A0, rhs_panel, C1, T0, fix<1>);                 \
-              traits.madd(A1, rhs_panel, C5, T0, fix<1>);                 \
-              traits.madd(A0, rhs_panel, C2, T0, fix<2>);                 \
-              traits.madd(A1, rhs_panel, C6, T0, fix<2>);                 \
-              traits.madd(A0, rhs_panel, C3, T0, fix<3>);                 \
-              traits.madd(A1, rhs_panel, C7, T0, fix<3>);                 \
-              EIGEN_GEBP_2PX4_SPILLING_WORKAROUND                         \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX4");    \
-            } while (false)
-
-            internal::prefetch(blB+(48+0));
-            EIGEN_GEBGP_ONESTEP(0);
-            EIGEN_GEBGP_ONESTEP(1);
-            EIGEN_GEBGP_ONESTEP(2);
-            EIGEN_GEBGP_ONESTEP(3);
-            internal::prefetch(blB+(48+16));
-            EIGEN_GEBGP_ONESTEP(4);
-            EIGEN_GEBGP_ONESTEP(5);
-            EIGEN_GEBGP_ONESTEP(6);
-            EIGEN_GEBGP_ONESTEP(7);
-
-            blB += pk*4*RhsProgress;
-            blA += pk*(2*Traits::LhsProgress);
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 2pX4");
-          }
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPacketx4 rhs_panel;
-            RhsPacket T0;
-            EIGEN_GEBGP_ONESTEP(0);
-            blB += 4*RhsProgress;
-            blA += 2*Traits::LhsProgress;
-          }
-#undef EIGEN_GEBGP_ONESTEP
-
-          ResPacket R0, R1, R2, R3;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R3 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          traits.acc(C1, alphav, R2);
-          traits.acc(C5, alphav, R3);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          r1.storePacket(0 * Traits::ResPacketSize, R2);
-          r1.storePacket(1 * Traits::ResPacketSize, R3);
-
-          R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R3 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          traits.acc(C2,  alphav, R0);
-          traits.acc(C6,  alphav, R1);
-          traits.acc(C3,  alphav, R2);
-          traits.acc(C7,  alphav, R3);
-          r2.storePacket(0 * Traits::ResPacketSize, R0);
-          r2.storePacket(1 * Traits::ResPacketSize, R1);
-          r3.storePacket(0 * Traits::ResPacketSize, R2);
-          r3.storePacket(1 * Traits::ResPacketSize, R3);
-          }
-        }
-      
-        // Deal with remaining columns of the rhs
-        for(Index j2=packet_cols4; j2<cols; j2++)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
-          {
-          // One column at a time
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C4;
-          traits.initAcc(C0);
-          traits.initAcc(C4);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2);
-          r0.prefetch(prefetch_res_offset);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-          LhsPacket A0, A1;
-
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1");
-            RhsPacket B_0, B1;
-        
-#define EIGEN_GEBGP_ONESTEP(K) \
-            do {                                                                  \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1");          \
-              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-              traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0);                      \
-              traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1);                      \
-              traits.loadRhs(&blB[(0+K)*RhsProgress], B_0);                       \
-              traits.madd(A0, B_0, C0, B1, fix<0>);                               \
-              traits.madd(A1, B_0, C4, B_0, fix<0>);                              \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX1");            \
-            } while(false)
-        
-            EIGEN_GEBGP_ONESTEP(0);
-            EIGEN_GEBGP_ONESTEP(1);
-            EIGEN_GEBGP_ONESTEP(2);
-            EIGEN_GEBGP_ONESTEP(3);
-            EIGEN_GEBGP_ONESTEP(4);
-            EIGEN_GEBGP_ONESTEP(5);
-            EIGEN_GEBGP_ONESTEP(6);
-            EIGEN_GEBGP_ONESTEP(7);
-
-            blB += int(pk) * int(RhsProgress);
-            blA += int(pk) * 2 * int(Traits::LhsProgress);
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 2pX1");
-          }
-
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPacket B_0, B1;
-            EIGEN_GEBGP_ONESTEP(0);
-            blB += RhsProgress;
-            blA += 2*Traits::LhsProgress;
-          }
-#undef EIGEN_GEBGP_ONESTEP
-          ResPacket R0, R1;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          }
-        }
-      }
-    }
-    //---------- Process 1 * LhsProgress rows at once ----------
-    if(mr>=1*Traits::LhsProgress)
-    {
-      lhs_process_one_packet<nr, LhsProgress, RhsProgress, LhsScalar, RhsScalar, ResScalar, AccPacket, LhsPacket, RhsPacket, ResPacket, Traits, LinearMapper, DataMapper> p;
-      p(res, blockA, blockB, alpha, peeled_mc2, peeled_mc1, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
-    }
-    //---------- Process LhsProgressHalf rows at once ----------
-    if((LhsProgressHalf < LhsProgress) && mr>=LhsProgressHalf)
-    {
-      lhs_process_fraction_of_packet<nr, LhsProgressHalf, RhsProgressHalf, LhsScalar, RhsScalar, ResScalar, AccPacketHalf, LhsPacketHalf, RhsPacketHalf, ResPacketHalf, HalfTraits, LinearMapper, DataMapper> p;
-      p(res, blockA, blockB, alpha, peeled_mc1, peeled_mc_half, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
-    }
-    //---------- Process LhsProgressQuarter rows at once ----------
-    if((LhsProgressQuarter < LhsProgressHalf) && mr>=LhsProgressQuarter)
-    {
-      lhs_process_fraction_of_packet<nr, LhsProgressQuarter, RhsProgressQuarter, LhsScalar, RhsScalar, ResScalar, AccPacketQuarter, LhsPacketQuarter, RhsPacketQuarter, ResPacketQuarter, QuarterTraits, LinearMapper, DataMapper> p;
-      p(res, blockA, blockB, alpha, peeled_mc_half, peeled_mc_quarter, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
-    }
-    //---------- Process remaining rows, 1 at once ----------
-    if(peeled_mc_quarter<rows)
-    {
-      // loop on each panel of the rhs
-      for(Index j2=0; j2<packet_cols4; j2+=nr)
-      {
-        // loop on each row of the lhs (1*LhsProgress x depth)
-        for(Index i=peeled_mc_quarter; i<rows; i+=1)
-        {
-          const LhsScalar* blA = &blockA[i*strideA+offsetA];
-          prefetch(&blA[0]);
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-
-          // If LhsProgress is 8 or 16, it assumes that there is a
-          // half or quarter packet, respectively, of the same size as
-          // nr (which is currently 4) for the return type.
-          const int SResPacketHalfSize = unpacket_traits<typename unpacket_traits<SResPacket>::half>::size;
-          const int SResPacketQuarterSize = unpacket_traits<typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half>::size;
-          if ((SwappedTraits::LhsProgress % 4) == 0 &&
-              (SwappedTraits::LhsProgress<=16) &&
-              (SwappedTraits::LhsProgress!=8  || SResPacketHalfSize==nr) &&
-              (SwappedTraits::LhsProgress!=16 || SResPacketQuarterSize==nr))
-          {
-            SAccPacket C0, C1, C2, C3;
-            straits.initAcc(C0);
-            straits.initAcc(C1);
-            straits.initAcc(C2);
-            straits.initAcc(C3);
-
-            const Index spk   = (std::max)(1,SwappedTraits::LhsProgress/4);
-            const Index endk  = (depth/spk)*spk;
-            const Index endk4 = (depth/(spk*4))*(spk*4);
-
-            Index k=0;
-            for(; k<endk4; k+=4*spk)
-            {
-              SLhsPacket A0,A1;
-              SRhsPacket B_0,B_1;
-
-              straits.loadLhsUnaligned(blB+0*SwappedTraits::LhsProgress, A0);
-              straits.loadLhsUnaligned(blB+1*SwappedTraits::LhsProgress, A1);
-
-              straits.loadRhsQuad(blA+0*spk, B_0);
-              straits.loadRhsQuad(blA+1*spk, B_1);
-              straits.madd(A0,B_0,C0,B_0, fix<0>);
-              straits.madd(A1,B_1,C1,B_1, fix<0>);
-
-              straits.loadLhsUnaligned(blB+2*SwappedTraits::LhsProgress, A0);
-              straits.loadLhsUnaligned(blB+3*SwappedTraits::LhsProgress, A1);
-              straits.loadRhsQuad(blA+2*spk, B_0);
-              straits.loadRhsQuad(blA+3*spk, B_1);
-              straits.madd(A0,B_0,C2,B_0, fix<0>);
-              straits.madd(A1,B_1,C3,B_1, fix<0>);
-
-              blB += 4*SwappedTraits::LhsProgress;
-              blA += 4*spk;
-            }
-            C0 = padd(padd(C0,C1),padd(C2,C3));
-            for(; k<endk; k+=spk)
-            {
-              SLhsPacket A0;
-              SRhsPacket B_0;
-
-              straits.loadLhsUnaligned(blB, A0);
-              straits.loadRhsQuad(blA, B_0);
-              straits.madd(A0,B_0,C0,B_0, fix<0>);
-
-              blB += SwappedTraits::LhsProgress;
-              blA += spk;
-            }
-            if(SwappedTraits::LhsProgress==8)
-            {
-              // Special case where we have to first reduce the accumulation register C0
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SResPacket>::half,SResPacket>::type SResPacketHalf;
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SLhsPacket>::half,SLhsPacket>::type SLhsPacketHalf;
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SRhsPacket>::half,SRhsPacket>::type SRhsPacketHalf;
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SAccPacket>::half,SAccPacket>::type SAccPacketHalf;
-
-              SResPacketHalf R = res.template gatherPacket<SResPacketHalf>(i, j2);
-              SResPacketHalf alphav = pset1<SResPacketHalf>(alpha);
-
-              if(depth-endk>0)
-              {
-                // We have to handle the last row of the rhs which corresponds to a half-packet
-                SLhsPacketHalf a0;
-                SRhsPacketHalf b0;
-                straits.loadLhsUnaligned(blB, a0);
-                straits.loadRhs(blA, b0);
-                SAccPacketHalf c0 = predux_half_dowto4(C0);
-                straits.madd(a0,b0,c0,b0, fix<0>);
-                straits.acc(c0, alphav, R);
-              }
-              else
-              {
-                straits.acc(predux_half_dowto4(C0), alphav, R);
-              }
-              res.scatterPacket(i, j2, R);
-            }
-            else if (SwappedTraits::LhsProgress==16)
-            {
-              // Special case where we have to first reduce the
-              // accumulation register C0. We specialize the block in
-              // template form, so that LhsProgress < 16 paths don't
-              // fail to compile
-              last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> p;
-	            p(res, straits, blA, blB, depth, endk, i, j2,alpha, C0);
-            }
-            else
-            {
-              SResPacket R = res.template gatherPacket<SResPacket>(i, j2);
-              SResPacket alphav = pset1<SResPacket>(alpha);
-              straits.acc(C0, alphav, R);
-              res.scatterPacket(i, j2, R);
-            }
-          }
-          else // scalar path
-          {
-            // get a 1 x 4 res block as registers
-            ResScalar C0(0), C1(0), C2(0), C3(0);
-
-            for(Index k=0; k<depth; k++)
-            {
-              LhsScalar A0;
-              RhsScalar B_0, B_1;
-
-              A0 = blA[k];
-
-              B_0 = blB[0];
-              B_1 = blB[1];
-              C0 = cj.pmadd(A0,B_0,C0);
-              C1 = cj.pmadd(A0,B_1,C1);
-
-              B_0 = blB[2];
-              B_1 = blB[3];
-              C2 = cj.pmadd(A0,B_0,C2);
-              C3 = cj.pmadd(A0,B_1,C3);
-
-              blB += 4;
-            }
-            res(i, j2 + 0) += alpha * C0;
-            res(i, j2 + 1) += alpha * C1;
-            res(i, j2 + 2) += alpha * C2;
-            res(i, j2 + 3) += alpha * C3;
-          }
-        }
-      }
-      // remaining columns
-      for(Index j2=packet_cols4; j2<cols; j2++)
-      {
-        // loop on each row of the lhs (1*LhsProgress x depth)
-        for(Index i=peeled_mc_quarter; i<rows; i+=1)
-        {
-          const LhsScalar* blA = &blockA[i*strideA+offsetA];
-          prefetch(&blA[0]);
-          // gets a 1 x 1 res block as registers
-          ResScalar C0(0);
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-          for(Index k=0; k<depth; k++)
-          {
-            LhsScalar A0 = blA[k];
-            RhsScalar B_0 = blB[k];
-            C0 = cj.pmadd(A0, B_0, C0);
-          }
-          res(i, j2) += alpha * C0;
-        }
-      }
-    }
-  }
-
-
-// pack a block of the lhs
-// The traversal is as follow (mr==4):
-//   0  4  8 12 ...
-//   1  5  9 13 ...
-//   2  6 10 14 ...
-//   3  7 11 15 ...
-//
-//  16 20 24 28 ...
-//  17 21 25 29 ...
-//  18 22 26 30 ...
-//  19 23 27 31 ...
-//
-//  32 33 34 35 ...
-//  36 36 38 39 ...
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  typedef typename unpacket_traits<Packet>::half HalfPacket;
-  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
-  enum { PacketSize = unpacket_traits<Packet>::size,
-         HalfPacketSize = unpacket_traits<HalfPacket>::size,
-         QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
-         HasHalf = (int)HalfPacketSize < (int)PacketSize,
-         HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
-
-  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
-  EIGEN_UNUSED_VARIABLE(stride);
-  EIGEN_UNUSED_VARIABLE(offset);
-  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-  eigen_assert( ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) || (Pack1<=4) );
-  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-  Index count = 0;
-
-  const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0;
-  const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0;
-  const Index peeled_mc1 = Pack1>=1*PacketSize ? peeled_mc2+((rows-peeled_mc2)/(1*PacketSize))*(1*PacketSize) : 0;
-  const Index peeled_mc_half = Pack1>=HalfPacketSize ? peeled_mc1+((rows-peeled_mc1)/(HalfPacketSize))*(HalfPacketSize) : 0;
-  const Index peeled_mc_quarter = Pack1>=QuarterPacketSize ? (rows/(QuarterPacketSize))*(QuarterPacketSize) : 0;
-  const Index last_lhs_progress = rows > peeled_mc_quarter ? (rows - peeled_mc_quarter) & ~1 : 0;
-  const Index peeled_mc0 = Pack2>=PacketSize ? peeled_mc_quarter
-                         : Pack2>1 && last_lhs_progress ? (rows/last_lhs_progress)*last_lhs_progress : 0;
-
-  Index i=0;
-
-  // Pack 3 packets
-  if(Pack1>=3*PacketSize)
-  {
-    for(; i<peeled_mc3; i+=3*PacketSize)
-    {
-      if(PanelMode) count += (3*PacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        Packet A, B, C;
-        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
-        B = lhs.template loadPacket<Packet>(i+1*PacketSize, k);
-        C = lhs.template loadPacket<Packet>(i+2*PacketSize, k);
-        pstore(blockA+count, cj.pconj(A)); count+=PacketSize;
-        pstore(blockA+count, cj.pconj(B)); count+=PacketSize;
-        pstore(blockA+count, cj.pconj(C)); count+=PacketSize;
-      }
-      if(PanelMode) count += (3*PacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack 2 packets
-  if(Pack1>=2*PacketSize)
-  {
-    for(; i<peeled_mc2; i+=2*PacketSize)
-    {
-      if(PanelMode) count += (2*PacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        Packet A, B;
-        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
-        B = lhs.template loadPacket<Packet>(i+1*PacketSize, k);
-        pstore(blockA+count, cj.pconj(A)); count+=PacketSize;
-        pstore(blockA+count, cj.pconj(B)); count+=PacketSize;
-      }
-      if(PanelMode) count += (2*PacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack 1 packets
-  if(Pack1>=1*PacketSize)
-  {
-    for(; i<peeled_mc1; i+=1*PacketSize)
-    {
-      if(PanelMode) count += (1*PacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        Packet A;
-        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
-        pstore(blockA+count, cj.pconj(A));
-        count+=PacketSize;
-      }
-      if(PanelMode) count += (1*PacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack half packets
-  if(HasHalf && Pack1>=HalfPacketSize)
-  {
-    for(; i<peeled_mc_half; i+=HalfPacketSize)
-    {
-      if(PanelMode) count += (HalfPacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        HalfPacket A;
-        A = lhs.template loadPacket<HalfPacket>(i+0*(HalfPacketSize), k);
-        pstoreu(blockA+count, cj.pconj(A));
-        count+=HalfPacketSize;
-      }
-      if(PanelMode) count += (HalfPacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack quarter packets
-  if(HasQuarter && Pack1>=QuarterPacketSize)
-  {
-    for(; i<peeled_mc_quarter; i+=QuarterPacketSize)
-    {
-      if(PanelMode) count += (QuarterPacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        QuarterPacket A;
-        A = lhs.template loadPacket<QuarterPacket>(i+0*(QuarterPacketSize), k);
-        pstoreu(blockA+count, cj.pconj(A));
-        count+=QuarterPacketSize;
-      }
-      if(PanelMode) count += (QuarterPacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack2 may be *smaller* than PacketSize—that happens for
-  // products like real * complex, where we have to go half the
-  // progress on the lhs in order to duplicate those operands to
-  // address both real & imaginary parts on the rhs. This portion will
-  // pack those half ones until they match the number expected on the
-  // last peeling loop at this point (for the rhs).
-  if(Pack2<PacketSize && Pack2>1)
-  {
-    for(; i<peeled_mc0; i+=last_lhs_progress)
-    {
-      if(PanelMode) count += last_lhs_progress * offset;
-
-      for(Index k=0; k<depth; k++)
-        for(Index w=0; w<last_lhs_progress; w++)
-          blockA[count++] = cj(lhs(i+w, k));
-
-      if(PanelMode) count += last_lhs_progress * (stride-offset-depth);
-    }
-  }
-  // Pack scalars
-  for(; i<rows; i++)
-  {
-    if(PanelMode) count += offset;
-    for(Index k=0; k<depth; k++)
-      blockA[count++] = cj(lhs(i, k));
-    if(PanelMode) count += (stride-offset-depth);
-  }
-}
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  typedef typename unpacket_traits<Packet>::half HalfPacket;
-  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
-  enum { PacketSize = unpacket_traits<Packet>::size,
-         HalfPacketSize = unpacket_traits<HalfPacket>::size,
-         QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
-         HasHalf = (int)HalfPacketSize < (int)PacketSize,
-         HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
-
-  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
-  EIGEN_UNUSED_VARIABLE(stride);
-  EIGEN_UNUSED_VARIABLE(offset);
-  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-  Index count = 0;
-  bool gone_half = false, gone_quarter = false, gone_last = false;
-
-  Index i = 0;
-  int pack = Pack1;
-  int psize = PacketSize;
-  while(pack>0)
-  {
-    Index remaining_rows = rows-i;
-    Index peeled_mc = gone_last ? Pack2>1 ? (rows/pack)*pack : 0 : i+(remaining_rows/pack)*pack;
-    Index starting_pos = i;
-    for(; i<peeled_mc; i+=pack)
-    {
-      if(PanelMode) count += pack * offset;
-
-      Index k=0;
-      if(pack>=psize && psize >= QuarterPacketSize)
-      {
-        const Index peeled_k = (depth/psize)*psize;
-        for(; k<peeled_k; k+=psize)
-        {
-          for (Index m = 0; m < pack; m += psize)
-          {
-            if (psize == PacketSize) {
-              PacketBlock<Packet> kernel;
-              for (int p = 0; p < psize; ++p) kernel.packet[p] = lhs.template loadPacket<Packet>(i+p+m, k);
-              ptranspose(kernel);
-              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel.packet[p]));
-            } else if (HasHalf && psize == HalfPacketSize) {
-              gone_half = true;
-              PacketBlock<HalfPacket> kernel_half;
-              for (int p = 0; p < psize; ++p) kernel_half.packet[p] = lhs.template loadPacket<HalfPacket>(i+p+m, k);
-              ptranspose(kernel_half);
-              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_half.packet[p]));
-            } else if (HasQuarter && psize == QuarterPacketSize) {
-              gone_quarter = true;
-              PacketBlock<QuarterPacket> kernel_quarter;
-              for (int p = 0; p < psize; ++p) kernel_quarter.packet[p] = lhs.template loadPacket<QuarterPacket>(i+p+m, k);
-              ptranspose(kernel_quarter);
-              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_quarter.packet[p]));
-	    }
-          }
-          count += psize*pack;
-        }
-      }
-
-      for(; k<depth; k++)
-      {
-        Index w=0;
-        for(; w<pack-3; w+=4)
-        {
-          Scalar a(cj(lhs(i+w+0, k))),
-                 b(cj(lhs(i+w+1, k))),
-                 c(cj(lhs(i+w+2, k))),
-                 d(cj(lhs(i+w+3, k)));
-          blockA[count++] = a;
-          blockA[count++] = b;
-          blockA[count++] = c;
-          blockA[count++] = d;
-        }
-        if(pack%4)
-          for(;w<pack;++w)
-            blockA[count++] = cj(lhs(i+w, k));
-      }
-
-      if(PanelMode) count += pack * (stride-offset-depth);
-    }
-
-    pack -= psize;
-    Index left = rows - i;
-    if (pack <= 0) {
-      if (!gone_last &&
-          (starting_pos == i || left >= psize/2 || left >= psize/4) &&
-          ((psize/2 == HalfPacketSize && HasHalf && !gone_half) ||
-           (psize/2 == QuarterPacketSize && HasQuarter && !gone_quarter))) {
-        psize /= 2;
-        pack = psize;
-        continue;
-      }
-      // Pack2 may be *smaller* than PacketSize—that happens for
-      // products like real * complex, where we have to go half the
-      // progress on the lhs in order to duplicate those operands to
-      // address both real & imaginary parts on the rhs. This portion will
-      // pack those half ones until they match the number expected on the
-      // last peeling loop at this point (for the rhs).
-      if (Pack2 < PacketSize && !gone_last) {
-        gone_last = true;
-        psize = pack = left & ~1;
-      }
-    }
-  }
-
-  for(; i<rows; i++)
-  {
-    if(PanelMode) count += offset;
-    for(Index k=0; k<depth; k++)
-      blockA[count++] = cj(lhs(i, k));
-    if(PanelMode) count += (stride-offset-depth);
-  }
-}
-
-// copy a complete panel of the rhs
-// this version is optimized for column major matrices
-// The traversal order is as follow: (nr==4):
-//  0  1  2  3   12 13 14 15   24 27
-//  4  5  6  7   16 17 18 19   25 28
-//  8  9 10 11   20 21 22 23   26 29
-//  .  .  .  .    .  .  .  .    .  .
-template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  typedef typename packet_traits<Scalar>::type Packet;
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  enum { PacketSize = packet_traits<Scalar>::size };
-  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
-  EIGEN_UNUSED_VARIABLE(stride);
-  EIGEN_UNUSED_VARIABLE(offset);
-  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-  Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
-  Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-  Index count = 0;
-  const Index peeled_k = (depth/PacketSize)*PacketSize;
-//   if(nr>=8)
-//   {
-//     for(Index j2=0; j2<packet_cols8; j2+=8)
-//     {
-//       // skip what we have before
-//       if(PanelMode) count += 8 * offset;
-//       const Scalar* b0 = &rhs[(j2+0)*rhsStride];
-//       const Scalar* b1 = &rhs[(j2+1)*rhsStride];
-//       const Scalar* b2 = &rhs[(j2+2)*rhsStride];
-//       const Scalar* b3 = &rhs[(j2+3)*rhsStride];
-//       const Scalar* b4 = &rhs[(j2+4)*rhsStride];
-//       const Scalar* b5 = &rhs[(j2+5)*rhsStride];
-//       const Scalar* b6 = &rhs[(j2+6)*rhsStride];
-//       const Scalar* b7 = &rhs[(j2+7)*rhsStride];
-//       Index k=0;
-//       if(PacketSize==8) // TODO enable vectorized transposition for PacketSize==4
-//       {
-//         for(; k<peeled_k; k+=PacketSize) {
-//           PacketBlock<Packet> kernel;
-//           for (int p = 0; p < PacketSize; ++p) {
-//             kernel.packet[p] = ploadu<Packet>(&rhs[(j2+p)*rhsStride+k]);
-//           }
-//           ptranspose(kernel);
-//           for (int p = 0; p < PacketSize; ++p) {
-//             pstoreu(blockB+count, cj.pconj(kernel.packet[p]));
-//             count+=PacketSize;
-//           }
-//         }
-//       }
-//       for(; k<depth; k++)
-//       {
-//         blockB[count+0] = cj(b0[k]);
-//         blockB[count+1] = cj(b1[k]);
-//         blockB[count+2] = cj(b2[k]);
-//         blockB[count+3] = cj(b3[k]);
-//         blockB[count+4] = cj(b4[k]);
-//         blockB[count+5] = cj(b5[k]);
-//         blockB[count+6] = cj(b6[k]);
-//         blockB[count+7] = cj(b7[k]);
-//         count += 8;
-//       }
-//       // skip what we have after
-//       if(PanelMode) count += 8 * (stride-offset-depth);
-//     }
-//   }
-
-  if(nr>=4)
-  {
-    for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
-    {
-      // skip what we have before
-      if(PanelMode) count += 4 * offset;
-      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
-      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
-      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
-      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
-
-      Index k=0;
-      if((PacketSize%4)==0) // TODO enable vectorized transposition for PacketSize==2 ??
-      {
-        for(; k<peeled_k; k+=PacketSize) {
-          PacketBlock<Packet,(PacketSize%4)==0?4:PacketSize> kernel;
-          kernel.packet[0           ] = dm0.template loadPacket<Packet>(k);
-          kernel.packet[1%PacketSize] = dm1.template loadPacket<Packet>(k);
-          kernel.packet[2%PacketSize] = dm2.template loadPacket<Packet>(k);
-          kernel.packet[3%PacketSize] = dm3.template loadPacket<Packet>(k);
-          ptranspose(kernel);
-          pstoreu(blockB+count+0*PacketSize, cj.pconj(kernel.packet[0]));
-          pstoreu(blockB+count+1*PacketSize, cj.pconj(kernel.packet[1%PacketSize]));
-          pstoreu(blockB+count+2*PacketSize, cj.pconj(kernel.packet[2%PacketSize]));
-          pstoreu(blockB+count+3*PacketSize, cj.pconj(kernel.packet[3%PacketSize]));
-          count+=4*PacketSize;
-        }
-      }
-      for(; k<depth; k++)
-      {
-        blockB[count+0] = cj(dm0(k));
-        blockB[count+1] = cj(dm1(k));
-        blockB[count+2] = cj(dm2(k));
-        blockB[count+3] = cj(dm3(k));
-        count += 4;
-      }
-      // skip what we have after
-      if(PanelMode) count += 4 * (stride-offset-depth);
-    }
-  }
-
-  // copy the remaining columns one at a time (nr==1)
-  for(Index j2=packet_cols4; j2<cols; ++j2)
-  {
-    if(PanelMode) count += offset;
-    const LinearMapper dm0 = rhs.getLinearMapper(0, j2);
-    for(Index k=0; k<depth; k++)
-    {
-      blockB[count] = cj(dm0(k));
-      count += 1;
-    }
-    if(PanelMode) count += (stride-offset-depth);
-  }
-}
-
-// this version is optimized for row major matrices
-template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  typedef typename packet_traits<Scalar>::type Packet;
-  typedef typename unpacket_traits<Packet>::half HalfPacket;
-  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  enum { PacketSize = packet_traits<Scalar>::size,
-         HalfPacketSize = unpacket_traits<HalfPacket>::size,
-		 QuarterPacketSize = unpacket_traits<QuarterPacket>::size};
-  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0)
-  {
-    EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
-    EIGEN_UNUSED_VARIABLE(stride);
-    EIGEN_UNUSED_VARIABLE(offset);
-    eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-    const bool HasHalf = (int)HalfPacketSize < (int)PacketSize;
-    const bool HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize;
-    conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
-    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-    Index count = 0;
-
-  //   if(nr>=8)
-  //   {
-  //     for(Index j2=0; j2<packet_cols8; j2+=8)
-  //     {
-  //       // skip what we have before
-  //       if(PanelMode) count += 8 * offset;
-  //       for(Index k=0; k<depth; k++)
-  //       {
-  //         if (PacketSize==8) {
-  //           Packet A = ploadu<Packet>(&rhs[k*rhsStride + j2]);
-  //           pstoreu(blockB+count, cj.pconj(A));
-  //         } else if (PacketSize==4) {
-  //           Packet A = ploadu<Packet>(&rhs[k*rhsStride + j2]);
-  //           Packet B = ploadu<Packet>(&rhs[k*rhsStride + j2 + PacketSize]);
-  //           pstoreu(blockB+count, cj.pconj(A));
-  //           pstoreu(blockB+count+PacketSize, cj.pconj(B));
-  //         } else {
-  //           const Scalar* b0 = &rhs[k*rhsStride + j2];
-  //           blockB[count+0] = cj(b0[0]);
-  //           blockB[count+1] = cj(b0[1]);
-  //           blockB[count+2] = cj(b0[2]);
-  //           blockB[count+3] = cj(b0[3]);
-  //           blockB[count+4] = cj(b0[4]);
-  //           blockB[count+5] = cj(b0[5]);
-  //           blockB[count+6] = cj(b0[6]);
-  //           blockB[count+7] = cj(b0[7]);
-  //         }
-  //         count += 8;
-  //       }
-  //       // skip what we have after
-  //       if(PanelMode) count += 8 * (stride-offset-depth);
-  //     }
-  //   }
-    if(nr>=4)
-    {
-      for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
-      {
-        // skip what we have before
-        if(PanelMode) count += 4 * offset;
-        for(Index k=0; k<depth; k++)
-        {
-          if (PacketSize==4) {
-            Packet A = rhs.template loadPacket<Packet>(k, j2);
-            pstoreu(blockB+count, cj.pconj(A));
-            count += PacketSize;
-          } else if (HasHalf && HalfPacketSize==4) {
-            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
-            pstoreu(blockB+count, cj.pconj(A));
-            count += HalfPacketSize;
-          } else if (HasQuarter && QuarterPacketSize==4) {
-            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
-            pstoreu(blockB+count, cj.pconj(A));
-            count += QuarterPacketSize;
-          } else {
-            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
-            blockB[count+0] = cj(dm0(0));
-            blockB[count+1] = cj(dm0(1));
-            blockB[count+2] = cj(dm0(2));
-            blockB[count+3] = cj(dm0(3));
-            count += 4;
-          }
-        }
-        // skip what we have after
-        if(PanelMode) count += 4 * (stride-offset-depth);
-      }
-    }
-    // copy the remaining columns one at a time (nr==1)
-    for(Index j2=packet_cols4; j2<cols; ++j2)
-    {
-      if(PanelMode) count += offset;
-      for(Index k=0; k<depth; k++)
-      {
-        blockB[count] = cj(rhs(k, j2));
-        count += 1;
-      }
-      if(PanelMode) count += stride-offset-depth;
-    }
-  }
-};
-
-} // end namespace internal
-
-/** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
-  * \sa setCpuCacheSize */
-inline std::ptrdiff_t l1CacheSize()
-{
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  return l1;
-}
-
-/** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
-  * \sa setCpuCacheSize */
-inline std::ptrdiff_t l2CacheSize()
-{
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  return l2;
-}
-
-/** \returns the currently set level 3 cpu cache size (in bytes) used to estimate the ideal blocking size paramete\
-rs.                                                                                                                
-* \sa setCpuCacheSize */
-inline std::ptrdiff_t l3CacheSize()
-{
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  return l3;
-}
-
-/** Set the cpu L1 and L2 cache sizes (in bytes).
-  * These values are use to adjust the size of the blocks
-  * for the algorithms working per blocks.
-  *
-  * \sa computeProductBlockingSizes */
-inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2, std::ptrdiff_t l3)
-{
-  internal::manage_caching_sizes(SetAction, &l1, &l2, &l3);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_BLOCK_PANEL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
deleted file mode 100644
index caa65fc..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
+++ /dev/null
@@ -1,517 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename _LhsScalar, typename _RhsScalar> class level3_blocking;
-
-/* Specialization for a row-major destination matrix => simple transposition of the product */
-template<
-  typename Index,
-  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-  int ResInnerStride>
-struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride>
-{
-  typedef gebp_traits<RhsScalar,LhsScalar> Traits;
-
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  static EIGEN_STRONG_INLINE void run(
-    Index rows, Index cols, Index depth,
-    const LhsScalar* lhs, Index lhsStride,
-    const RhsScalar* rhs, Index rhsStride,
-    ResScalar* res, Index resIncr, Index resStride,
-    ResScalar alpha,
-    level3_blocking<RhsScalar,LhsScalar>& blocking,
-    GemmParallelInfo<Index>* info = 0)
-  {
-    // transpose the product such that the result is column major
-    general_matrix_matrix_product<Index,
-      RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
-      LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
-      ColMajor,ResInnerStride>
-    ::run(cols,rows,depth,rhs,rhsStride,lhs,lhsStride,res,resIncr,resStride,alpha,blocking,info);
-  }
-};
-
-/*  Specialization for a col-major destination matrix
- *    => Blocking algorithm following Goto's paper */
-template<
-  typename Index,
-  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-  int ResInnerStride>
-struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride>
-{
-
-typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-static void run(Index rows, Index cols, Index depth,
-  const LhsScalar* _lhs, Index lhsStride,
-  const RhsScalar* _rhs, Index rhsStride,
-  ResScalar* _res, Index resIncr, Index resStride,
-  ResScalar alpha,
-  level3_blocking<LhsScalar,RhsScalar>& blocking,
-  GemmParallelInfo<Index>* info = 0)
-{
-  typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
-  typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
-  typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor,Unaligned,ResInnerStride> ResMapper;
-  LhsMapper lhs(_lhs, lhsStride);
-  RhsMapper rhs(_rhs, rhsStride);
-  ResMapper res(_res, resStride, resIncr);
-
-  Index kc = blocking.kc();                   // cache block size along the K direction
-  Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-  Index nc = (std::min)(cols,blocking.nc());  // cache block size along the N direction
-
-  gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-  gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
-  gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
-
-#ifdef EIGEN_HAS_OPENMP
-  if(info)
-  {
-    // this is the parallel version!
-    int tid = omp_get_thread_num();
-    int threads = omp_get_num_threads();
-
-    LhsScalar* blockA = blocking.blockA();
-    eigen_internal_assert(blockA!=0);
-
-    std::size_t sizeB = kc*nc;
-    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, 0);
-
-    // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs...
-    for(Index k=0; k<depth; k+=kc)
-    {
-      const Index actual_kc = (std::min)(k+kc,depth)-k; // => rows of B', and cols of the A'
-
-      // In order to reduce the chance that a thread has to wait for the other,
-      // let's start by packing B'.
-      pack_rhs(blockB, rhs.getSubMapper(k,0), actual_kc, nc);
-
-      // Pack A_k to A' in a parallel fashion:
-      // each thread packs the sub block A_k,i to A'_i where i is the thread id.
-
-      // However, before copying to A'_i, we have to make sure that no other thread is still using it,
-      // i.e., we test that info[tid].users equals 0.
-      // Then, we set info[tid].users to the number of threads to mark that all other threads are going to use it.
-      while(info[tid].users!=0) {}
-      info[tid].users = threads;
-
-      pack_lhs(blockA+info[tid].lhs_start*actual_kc, lhs.getSubMapper(info[tid].lhs_start,k), actual_kc, info[tid].lhs_length);
-
-      // Notify the other threads that the part A'_i is ready to go.
-      info[tid].sync = k;
-
-      // Computes C_i += A' * B' per A'_i
-      for(int shift=0; shift<threads; ++shift)
-      {
-        int i = (tid+shift)%threads;
-
-        // At this point we have to make sure that A'_i has been updated by the thread i,
-        // we use testAndSetOrdered to mimic a volatile access.
-        // However, no need to wait for the B' part which has been updated by the current thread!
-        if (shift>0) {
-          while(info[i].sync!=k) {
-          }
-        }
-
-        gebp(res.getSubMapper(info[i].lhs_start, 0), blockA+info[i].lhs_start*actual_kc, blockB, info[i].lhs_length, actual_kc, nc, alpha);
-      }
-
-      // Then keep going as usual with the remaining B'
-      for(Index j=nc; j<cols; j+=nc)
-      {
-        const Index actual_nc = (std::min)(j+nc,cols)-j;
-
-        // pack B_k,j to B'
-        pack_rhs(blockB, rhs.getSubMapper(k,j), actual_kc, actual_nc);
-
-        // C_j += A' * B'
-        gebp(res.getSubMapper(0, j), blockA, blockB, rows, actual_kc, actual_nc, alpha);
-      }
-
-      // Release all the sub blocks A'_i of A' for the current thread,
-      // i.e., we simply decrement the number of users by 1
-      for(Index i=0; i<threads; ++i)
-#if !EIGEN_HAS_CXX11_ATOMIC
-        #pragma omp atomic
-#endif
-        info[i].users -= 1;
-    }
-  }
-  else
-#endif // EIGEN_HAS_OPENMP
-  {
-    EIGEN_UNUSED_VARIABLE(info);
-
-    // this is the sequential version!
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*nc;
-
-    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
-
-    const bool pack_rhs_once = mc!=rows && kc==depth && nc==cols;
-
-    // For each horizontal panel of the rhs, and corresponding panel of the lhs...
-    for(Index i2=0; i2<rows; i2+=mc)
-    {
-      const Index actual_mc = (std::min)(i2+mc,rows)-i2;
-
-      for(Index k2=0; k2<depth; k2+=kc)
-      {
-        const Index actual_kc = (std::min)(k2+kc,depth)-k2;
-
-        // OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs.
-        // => Pack lhs's panel into a sequential chunk of memory (L2/L3 caching)
-        // Note that this panel will be read as many times as the number of blocks in the rhs's
-        // horizontal panel which is, in practice, a very low number.
-        pack_lhs(blockA, lhs.getSubMapper(i2,k2), actual_kc, actual_mc);
-
-        // For each kc x nc block of the rhs's horizontal panel...
-        for(Index j2=0; j2<cols; j2+=nc)
-        {
-          const Index actual_nc = (std::min)(j2+nc,cols)-j2;
-
-          // We pack the rhs's block into a sequential chunk of memory (L2 caching)
-          // Note that this block will be read a very high number of times, which is equal to the number of
-          // micro horizontal panel of the large rhs's panel (e.g., rows/12 times).
-          if((!pack_rhs_once) || i2==0)
-            pack_rhs(blockB, rhs.getSubMapper(k2,j2), actual_kc, actual_nc);
-
-          // Everything is packed, we can now call the panel * block kernel:
-          gebp(res.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, alpha);
-        }
-      }
-    }
-  }
-}
-
-};
-
-/*********************************************************************************
-*  Specialization of generic_product_impl for "large" GEMM, i.e.,
-*  implementation of the high level wrapper to general_matrix_matrix_product
-**********************************************************************************/
-
-template<typename Scalar, typename Index, typename Gemm, typename Lhs, typename Rhs, typename Dest, typename BlockingType>
-struct gemm_functor
-{
-  gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, const Scalar& actualAlpha, BlockingType& blocking)
-    : m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking)
-  {}
-
-  void initParallelSession(Index num_threads) const
-  {
-    m_blocking.initParallel(m_lhs.rows(), m_rhs.cols(), m_lhs.cols(), num_threads);
-    m_blocking.allocateA();
-  }
-
-  void operator() (Index row, Index rows, Index col=0, Index cols=-1, GemmParallelInfo<Index>* info=0) const
-  {
-    if(cols==-1)
-      cols = m_rhs.cols();
-
-    Gemm::run(rows, cols, m_lhs.cols(),
-              &m_lhs.coeffRef(row,0), m_lhs.outerStride(),
-              &m_rhs.coeffRef(0,col), m_rhs.outerStride(),
-              (Scalar*)&(m_dest.coeffRef(row,col)), m_dest.innerStride(), m_dest.outerStride(),
-              m_actualAlpha, m_blocking, info);
-  }
-
-  typedef typename Gemm::Traits Traits;
-
-  protected:
-    const Lhs& m_lhs;
-    const Rhs& m_rhs;
-    Dest& m_dest;
-    Scalar m_actualAlpha;
-    BlockingType& m_blocking;
-};
-
-template<int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor=1,
-bool FiniteAtCompileTime = MaxRows!=Dynamic && MaxCols!=Dynamic && MaxDepth != Dynamic> class gemm_blocking_space;
-
-template<typename _LhsScalar, typename _RhsScalar>
-class level3_blocking
-{
-    typedef _LhsScalar LhsScalar;
-    typedef _RhsScalar RhsScalar;
-
-  protected:
-    LhsScalar* m_blockA;
-    RhsScalar* m_blockB;
-
-    Index m_mc;
-    Index m_nc;
-    Index m_kc;
-
-  public:
-
-    level3_blocking()
-      : m_blockA(0), m_blockB(0), m_mc(0), m_nc(0), m_kc(0)
-    {}
-
-    inline Index mc() const { return m_mc; }
-    inline Index nc() const { return m_nc; }
-    inline Index kc() const { return m_kc; }
-
-    inline LhsScalar* blockA() { return m_blockA; }
-    inline RhsScalar* blockB() { return m_blockB; }
-};
-
-template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor>
-class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, true /* == FiniteAtCompileTime */>
-  : public level3_blocking<
-      typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type,
-      typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type>
-{
-    enum {
-      Transpose = StorageOrder==RowMajor,
-      ActualRows = Transpose ? MaxCols : MaxRows,
-      ActualCols = Transpose ? MaxRows : MaxCols
-    };
-    typedef typename conditional<Transpose,_RhsScalar,_LhsScalar>::type LhsScalar;
-    typedef typename conditional<Transpose,_LhsScalar,_RhsScalar>::type RhsScalar;
-    typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-    enum {
-      SizeA = ActualRows * MaxDepth,
-      SizeB = ActualCols * MaxDepth
-    };
-
-#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
-    EIGEN_ALIGN_MAX LhsScalar m_staticA[SizeA];
-    EIGEN_ALIGN_MAX RhsScalar m_staticB[SizeB];
-#else
-    EIGEN_ALIGN_MAX char m_staticA[SizeA * sizeof(LhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES-1];
-    EIGEN_ALIGN_MAX char m_staticB[SizeB * sizeof(RhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES-1];
-#endif
-
-  public:
-
-    gemm_blocking_space(Index /*rows*/, Index /*cols*/, Index /*depth*/, Index /*num_threads*/, bool /*full_rows = false*/)
-    {
-      this->m_mc = ActualRows;
-      this->m_nc = ActualCols;
-      this->m_kc = MaxDepth;
-#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
-      this->m_blockA = m_staticA;
-      this->m_blockB = m_staticB;
-#else
-      this->m_blockA = reinterpret_cast<LhsScalar*>((internal::UIntPtr(m_staticA) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
-      this->m_blockB = reinterpret_cast<RhsScalar*>((internal::UIntPtr(m_staticB) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
-#endif
-    }
-
-    void initParallel(Index, Index, Index, Index)
-    {}
-
-    inline void allocateA() {}
-    inline void allocateB() {}
-    inline void allocateAll() {}
-};
-
-template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor>
-class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, false>
-  : public level3_blocking<
-      typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type,
-      typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type>
-{
-    enum {
-      Transpose = StorageOrder==RowMajor
-    };
-    typedef typename conditional<Transpose,_RhsScalar,_LhsScalar>::type LhsScalar;
-    typedef typename conditional<Transpose,_LhsScalar,_RhsScalar>::type RhsScalar;
-    typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-    Index m_sizeA;
-    Index m_sizeB;
-
-  public:
-
-    gemm_blocking_space(Index rows, Index cols, Index depth, Index num_threads, bool l3_blocking)
-    {
-      this->m_mc = Transpose ? cols : rows;
-      this->m_nc = Transpose ? rows : cols;
-      this->m_kc = depth;
-
-      if(l3_blocking)
-      {
-        computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, this->m_nc, num_threads);
-      }
-      else  // no l3 blocking
-      {
-        Index n = this->m_nc;
-        computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, n, num_threads);
-      }
-
-      m_sizeA = this->m_mc * this->m_kc;
-      m_sizeB = this->m_kc * this->m_nc;
-    }
-
-    void initParallel(Index rows, Index cols, Index depth, Index num_threads)
-    {
-      this->m_mc = Transpose ? cols : rows;
-      this->m_nc = Transpose ? rows : cols;
-      this->m_kc = depth;
-
-      eigen_internal_assert(this->m_blockA==0 && this->m_blockB==0);
-      Index m = this->m_mc;
-      computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, m, this->m_nc, num_threads);
-      m_sizeA = this->m_mc * this->m_kc;
-      m_sizeB = this->m_kc * this->m_nc;
-    }
-
-    void allocateA()
-    {
-      if(this->m_blockA==0)
-        this->m_blockA = aligned_new<LhsScalar>(m_sizeA);
-    }
-
-    void allocateB()
-    {
-      if(this->m_blockB==0)
-        this->m_blockB = aligned_new<RhsScalar>(m_sizeB);
-    }
-
-    void allocateAll()
-    {
-      allocateA();
-      allocateB();
-    }
-
-    ~gemm_blocking_space()
-    {
-      aligned_delete(this->m_blockA, m_sizeA);
-      aligned_delete(this->m_blockB, m_sizeB);
-    }
-};
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  typedef typename Lhs::Scalar LhsScalar;
-  typedef typename Rhs::Scalar RhsScalar;
-
-  typedef internal::blas_traits<Lhs> LhsBlasTraits;
-  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-  typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
-
-  typedef internal::blas_traits<Rhs> RhsBlasTraits;
-  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-  enum {
-    MaxDepthAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(Lhs::MaxColsAtCompileTime,Rhs::MaxRowsAtCompileTime)
-  };
-
-  typedef generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> lazyproduct;
-
-  template<typename Dst>
-  static void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=404 for a discussion and helper program
-    // to determine the following heuristic.
-    // EIGEN_GEMM_TO_COEFFBASED_THRESHOLD is typically defined to 20 in GeneralProduct.h,
-    // unless it has been specialized by the user or for a given architecture.
-    // Note that the condition rhs.rows()>0 was required because lazy product is (was?) not happy with empty inputs.
-    // I'm not sure it is still required.
-    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
-      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::assign_op<typename Dst::Scalar,Scalar>());
-    else
-    {
-      dst.setZero();
-      scaleAndAddTo(dst, lhs, rhs, Scalar(1));
-    }
-  }
-
-  template<typename Dst>
-  static void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
-      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::add_assign_op<typename Dst::Scalar,Scalar>());
-    else
-      scaleAndAddTo(dst,lhs, rhs, Scalar(1));
-  }
-
-  template<typename Dst>
-  static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
-      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::sub_assign_op<typename Dst::Scalar,Scalar>());
-    else
-      scaleAndAddTo(dst, lhs, rhs, Scalar(-1));
-  }
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
-    if(a_lhs.cols()==0 || a_lhs.rows()==0 || a_rhs.cols()==0)
-      return;
-
-    if (dst.cols() == 1)
-    {
-      // Fallback to GEMV if either the lhs or rhs is a runtime vector
-      typename Dest::ColXpr dst_vec(dst.col(0));
-      return internal::generic_product_impl<Lhs,typename Rhs::ConstColXpr,DenseShape,DenseShape,GemvProduct>
-        ::scaleAndAddTo(dst_vec, a_lhs, a_rhs.col(0), alpha);
-    }
-    else if (dst.rows() == 1)
-    {
-      // Fallback to GEMV if either the lhs or rhs is a runtime vector
-      typename Dest::RowXpr dst_vec(dst.row(0));
-      return internal::generic_product_impl<typename Lhs::ConstRowXpr,Rhs,DenseShape,DenseShape,GemvProduct>
-        ::scaleAndAddTo(dst_vec, a_lhs.row(0), a_rhs, alpha);
-    }
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    Scalar actualAlpha = combine_scalar_factors(alpha, a_lhs, a_rhs);
-
-    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,LhsScalar,RhsScalar,
-            Dest::MaxRowsAtCompileTime,Dest::MaxColsAtCompileTime,MaxDepthAtCompileTime> BlockingType;
-
-    typedef internal::gemm_functor<
-      Scalar, Index,
-      internal::general_matrix_matrix_product<
-        Index,
-        LhsScalar, (ActualLhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(LhsBlasTraits::NeedToConjugate),
-        RhsScalar, (ActualRhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate),
-        (Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,
-        Dest::InnerStrideAtCompileTime>,
-      ActualLhsTypeCleaned, ActualRhsTypeCleaned, Dest, BlockingType> GemmFunctor;
-
-    BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true);
-    internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime>32 || Dest::MaxRowsAtCompileTime==Dynamic)>
-        (GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), a_lhs.rows(), a_rhs.cols(), a_lhs.cols(), Dest::Flags&RowMajorBit);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
deleted file mode 100644
index 6ba0d9b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
+++ /dev/null
@@ -1,317 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
-
-namespace Eigen { 
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs>
-struct selfadjoint_rank1_update;
-
-namespace internal {
-
-/**********************************************************************
-* This file implements a general A * B product while
-* evaluating only one triangular part of the product.
-* This is a more general version of self adjoint product (C += A A^T)
-* as the level 3 SYRK Blas routine.
-**********************************************************************/
-
-// forward declarations (defined at the end of this file)
-template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int ResInnerStride, int UpLo>
-struct tribb_kernel;
-  
-/* Optimized matrix-matrix product evaluating only one triangular half */
-template <typename Index,
-          typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-                              int ResStorageOrder, int ResInnerStride, int  UpLo, int Version = Specialized>
-struct general_matrix_matrix_triangular_product;
-
-// as usual if the result is row major => we transpose the product
-template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-                          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-                          int ResInnerStride, int  UpLo, int Version>
-struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride,UpLo,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride,
-                                      const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resIncr, Index resStride,
-                                      const ResScalar& alpha, level3_blocking<RhsScalar,LhsScalar>& blocking)
-  {
-    general_matrix_matrix_triangular_product<Index,
-        RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
-        LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
-        ColMajor, ResInnerStride, UpLo==Lower?Upper:Lower>
-      ::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resIncr,resStride,alpha,blocking);
-  }
-};
-
-template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-                          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-                          int ResInnerStride, int  UpLo, int Version>
-struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,UpLo,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
-                                      const RhsScalar* _rhs, Index rhsStride,
-                                      ResScalar* _res, Index resIncr, Index resStride,
-                                      const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)
-  {
-    typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-    typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();
-    Index mc = (std::min)(size,blocking.mc());
-
-    // !!! mc must be a multiple of nr:
-    if(mc > Traits::nr)
-      mc = (mc/Traits::nr)*Traits::nr;
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*size;
-
-    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
-
-    gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
-    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
-    tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, ResInnerStride, UpLo> sybb;
-
-    for(Index k2=0; k2<depth; k2+=kc)
-    {
-      const Index actual_kc = (std::min)(k2+kc,depth)-k2;
-
-      // note that the actual rhs is the transpose/adjoint of mat
-      pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, size);
-
-      for(Index i2=0; i2<size; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,size)-i2;
-
-        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        // the selected actual_mc * size panel of res is split into three different part:
-        //  1 - before the diagonal => processed with gebp or skipped
-        //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
-        //  3 - after the diagonal => processed with gebp or skipped
-        if (UpLo==Lower)
-          gebp(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc,
-               (std::min)(size,i2), alpha, -1, -1, 0, 0);
-
-        sybb(_res+resStride*i2 + resIncr*i2, resIncr, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha);
-
-        if (UpLo==Upper)
-        {
-          Index j2 = i2+actual_mc;
-          gebp(res.getSubMapper(i2, j2), blockA, blockB+actual_kc*j2, actual_mc,
-               actual_kc, (std::max)(Index(0), size-j2), alpha, -1, -1, 0, 0);
-        }
-      }
-    }
-  }
-};
-
-// Optimized packed Block * packed Block product kernel evaluating only one given triangular part
-// This kernel is built on top of the gebp kernel:
-// - the current destination block is processed per panel of actual_mc x BlockSize
-//   where BlockSize is set to the minimal value allowing gebp to be as fast as possible
-// - then, as usual, each panel is split into three parts along the diagonal,
-//   the sub blocks above and below the diagonal are processed as usual,
-//   while the triangular block overlapping the diagonal is evaluated into a
-//   small temporary buffer which is then accumulated into the result using a
-//   triangular traversal.
-template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int ResInnerStride, int UpLo>
-struct tribb_kernel
-{
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjLhs,ConjRhs> Traits;
-  typedef typename Traits::ResScalar ResScalar;
-
-  enum {
-    BlockSize  = meta_least_common_multiple<EIGEN_PLAIN_ENUM_MAX(mr,nr),EIGEN_PLAIN_ENUM_MIN(mr,nr)>::ret
-  };
-  void operator()(ResScalar* _res, Index resIncr, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha)
-  {
-    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned> BufferMapper;
-    ResMapper res(_res, resStride, resIncr);
-    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel1;
-    gebp_kernel<LhsScalar, RhsScalar, Index, BufferMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel2;
-
-    Matrix<ResScalar,BlockSize,BlockSize,ColMajor> buffer((internal::constructor_without_unaligned_array_assert()));
-
-    // let's process the block per panel of actual_mc x BlockSize,
-    // again, each is split into three parts, etc.
-    for (Index j=0; j<size; j+=BlockSize)
-    {
-      Index actualBlockSize = std::min<Index>(BlockSize,size - j);
-      const RhsScalar* actual_b = blockB+j*depth;
-
-      if(UpLo==Upper)
-        gebp_kernel1(res.getSubMapper(0, j), blockA, actual_b, j, depth, actualBlockSize, alpha,
-                     -1, -1, 0, 0);
-      
-      // selfadjoint micro block
-      {
-        Index i = j;
-        buffer.setZero();
-        // 1 - apply the kernel on the temporary buffer
-        gebp_kernel2(BufferMapper(buffer.data(), BlockSize), blockA+depth*i, actual_b, actualBlockSize, depth, actualBlockSize, alpha,
-                     -1, -1, 0, 0);
-
-        // 2 - triangular accumulation
-        for(Index j1=0; j1<actualBlockSize; ++j1)
-        {
-          typename ResMapper::LinearMapper r = res.getLinearMapper(i,j+j1);
-          for(Index i1=UpLo==Lower ? j1 : 0;
-              UpLo==Lower ? i1<actualBlockSize : i1<=j1; ++i1)
-            r(i1) += buffer(i1,j1);
-        }
-      }
-
-      if(UpLo==Lower)
-      {
-        Index i = j+actualBlockSize;
-        gebp_kernel1(res.getSubMapper(i, j), blockA+depth*i, actual_b, size-i, 
-                     depth, actualBlockSize, alpha, -1, -1, 0, 0);
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-// high level API
-
-template<typename MatrixType, typename ProductType, int UpLo, bool IsOuterProduct>
-struct general_product_to_triangular_selector;
-
-
-template<typename MatrixType, typename ProductType, int UpLo>
-struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true>
-{
-  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    
-    typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs;
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
-    typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
-    typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
-    
-    typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
-    typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
-    typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
-
-    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
-
-    if(!beta)
-      mat.template triangularView<UpLo>().setZero();
-
-    enum {
-      StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-      UseLhsDirectly = _ActualLhs::InnerStrideAtCompileTime==1,
-      UseRhsDirectly = _ActualRhs::InnerStrideAtCompileTime==1
-    };
-    
-    internal::gemv_static_vector_if<Scalar,Lhs::SizeAtCompileTime,Lhs::MaxSizeAtCompileTime,!UseLhsDirectly> static_lhs;
-    ei_declare_aligned_stack_constructed_variable(Scalar, actualLhsPtr, actualLhs.size(),
-      (UseLhsDirectly ? const_cast<Scalar*>(actualLhs.data()) : static_lhs.data()));
-    if(!UseLhsDirectly) Map<typename _ActualLhs::PlainObject>(actualLhsPtr, actualLhs.size()) = actualLhs;
-    
-    internal::gemv_static_vector_if<Scalar,Rhs::SizeAtCompileTime,Rhs::MaxSizeAtCompileTime,!UseRhsDirectly> static_rhs;
-    ei_declare_aligned_stack_constructed_variable(Scalar, actualRhsPtr, actualRhs.size(),
-      (UseRhsDirectly ? const_cast<Scalar*>(actualRhs.data()) : static_rhs.data()));
-    if(!UseRhsDirectly) Map<typename _ActualRhs::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
-    
-    
-    selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
-                              LhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
-                              RhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex>
-          ::run(actualLhs.size(), mat.data(), mat.outerStride(), actualLhsPtr, actualRhsPtr, actualAlpha);
-  }
-};
-
-template<typename MatrixType, typename ProductType, int UpLo>
-struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
-{
-  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
-  {
-    typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs;
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
-    typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
-    typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
-    
-    typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
-    typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
-    typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
-
-    typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
-
-    if(!beta)
-      mat.template triangularView<UpLo>().setZero();
-
-    enum {
-      IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
-      LhsIsRowMajor = _ActualLhs::Flags&RowMajorBit ? 1 : 0,
-      RhsIsRowMajor = _ActualRhs::Flags&RowMajorBit ? 1 : 0,
-      SkipDiag = (UpLo&(UnitDiag|ZeroDiag))!=0
-    };
-
-    Index size = mat.cols();
-    if(SkipDiag)
-      size--;
-    Index depth = actualLhs.cols();
-
-    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,typename Lhs::Scalar,typename Rhs::Scalar,
-          MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, _ActualRhs::MaxColsAtCompileTime> BlockingType;
-
-    BlockingType blocking(size, size, depth, 1, false);
-
-    internal::general_matrix_matrix_triangular_product<Index,
-      typename Lhs::Scalar, LhsIsRowMajor ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
-      typename Rhs::Scalar, RhsIsRowMajor ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
-      IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime, UpLo&(Lower|Upper)>
-      ::run(size, depth,
-            &actualLhs.coeffRef(SkipDiag&&(UpLo&Lower)==Lower ? 1 : 0,0), actualLhs.outerStride(),
-            &actualRhs.coeffRef(0,SkipDiag&&(UpLo&Upper)==Upper ? 1 : 0), actualRhs.outerStride(),
-            mat.data() + (SkipDiag ? (bool(IsRowMajor) != ((UpLo&Lower)==Lower) ? mat.innerStride() : mat.outerStride() ) : 0),
-            mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
-  }
-};
-
-template<typename MatrixType, unsigned int UpLo>
-template<typename ProductType>
-EIGEN_DEVICE_FUNC TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha, bool beta)
-{
-  EIGEN_STATIC_ASSERT((UpLo&UnitDiag)==0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
-  eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
-
-  general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha, beta);
-
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
deleted file mode 100644
index 9a650ec..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
+++ /dev/null
@@ -1,145 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Level 3 BLAS SYRK/HERK implementation.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Index, typename Scalar, int AStorageOrder, bool ConjugateA, int ResStorageOrder, int UpLo>
-struct general_matrix_matrix_rankupdate :
-       general_matrix_matrix_triangular_product<
-         Index,Scalar,AStorageOrder,ConjugateA,Scalar,AStorageOrder,ConjugateA,ResStorageOrder,1,UpLo,BuiltIn> {};
-
-
-// try to go to BLAS specialization
-#define EIGEN_BLAS_RANKUPDATE_SPECIALIZE(Scalar) \
-template <typename Index, int LhsStorageOrder, bool ConjugateLhs, \
-                          int RhsStorageOrder, bool ConjugateRhs, int  UpLo> \
-struct general_matrix_matrix_triangular_product<Index,Scalar,LhsStorageOrder,ConjugateLhs, \
-               Scalar,RhsStorageOrder,ConjugateRhs,ColMajor,1,UpLo,Specialized> { \
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \
-                          const Scalar* rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride, Scalar alpha, level3_blocking<Scalar, Scalar>& blocking) \
-  { \
-    if ( lhs==rhs && ((UpLo&(Lower|Upper))==UpLo) ) { \
-      general_matrix_matrix_rankupdate<Index,Scalar,LhsStorageOrder,ConjugateLhs,ColMajor,UpLo> \
-      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha,blocking); \
-    } else { \
-      general_matrix_matrix_triangular_product<Index, \
-        Scalar, LhsStorageOrder, ConjugateLhs, \
-        Scalar, RhsStorageOrder, ConjugateRhs, \
-        ColMajor, 1, UpLo, BuiltIn> \
-      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resIncr,resStride,alpha,blocking); \
-    } \
-  } \
-};
-
-EIGEN_BLAS_RANKUPDATE_SPECIALIZE(double)
-EIGEN_BLAS_RANKUPDATE_SPECIALIZE(float)
-// TODO handle complex cases
-// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(dcomplex)
-// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(scomplex)
-
-// SYRK for float/double
-#define EIGEN_BLAS_RANKUPDATE_R(EIGTYPE, BLASTYPE, BLASFUNC) \
-template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
-struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
-  enum { \
-    IsLower = (UpLo&Lower) == Lower, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = ((AStorageOrder==ColMajor) && ConjugateA) ? 1 : 0 \
-  }; \
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
-                          const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-  /* typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;*/ \
-\
-   BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
-   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'T':'N'); \
-   EIGTYPE beta(1); \
-   BLASFUNC(&uplo, &trans, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), lhs, &lda, (const BLASTYPE*)&numext::real_ref(beta), res, &ldc); \
-  } \
-};
-
-// HERK for complex data
-#define EIGEN_BLAS_RANKUPDATE_C(EIGTYPE, BLASTYPE, RTYPE, BLASFUNC) \
-template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
-struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
-  enum { \
-    IsLower = (UpLo&Lower) == Lower, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = (((AStorageOrder==ColMajor) && ConjugateA) || ((AStorageOrder==RowMajor) && !ConjugateA)) ? 1 : 0 \
-  }; \
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
-                          const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, AStorageOrder> MatrixType; \
-\
-   BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
-   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'C':'N'); \
-   RTYPE alpha_, beta_; \
-   const EIGTYPE* a_ptr; \
-\
-   alpha_ = alpha.real(); \
-   beta_ = 1.0; \
-/* Copy with conjugation in some cases*/ \
-   MatrixType a; \
-   if (conjA) { \
-     Map<const MatrixType, 0, OuterStride<> > mapA(lhs,n,k,OuterStride<>(lhsStride)); \
-     a = mapA.conjugate(); \
-     lda = a.outerStride(); \
-     a_ptr = a.data(); \
-   } else a_ptr=lhs; \
-   BLASFUNC(&uplo, &trans, &n, &k, &alpha_, (BLASTYPE*)a_ptr, &lda, &beta_, (BLASTYPE*)res, &ldc); \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk)
-EIGEN_BLAS_RANKUPDATE_R(float,  float,  ssyrk)
-#else
-EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk_)
-EIGEN_BLAS_RANKUPDATE_R(float,  float,  ssyrk_)
-#endif
-
-// TODO hanlde complex cases
-// EIGEN_BLAS_RANKUPDATE_C(dcomplex, double, double, zherk_)
-// EIGEN_BLAS_RANKUPDATE_C(scomplex, float,  float, cherk_)
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
deleted file mode 100644
index 71abf40..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   General matrix-matrix product functionality based on ?GEMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements general matrix-matrix multiplication using BLAS
-* gemm function via partial specialization of
-* general_matrix_matrix_product::run(..) method for float, double,
-* std::complex<float> and std::complex<double> types
-**********************************************************************/
-
-// gemm specialization
-
-#define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, BLASTYPE, BLASFUNC) \
-template< \
-  typename Index, \
-  int LhsStorageOrder, bool ConjugateLhs, \
-  int RhsStorageOrder, bool ConjugateRhs> \
-struct general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1> \
-{ \
-typedef gebp_traits<EIGTYPE,EIGTYPE> Traits; \
-\
-static void run(Index rows, Index cols, Index depth, \
-  const EIGTYPE* _lhs, Index lhsStride, \
-  const EIGTYPE* _rhs, Index rhsStride, \
-  EIGTYPE* res, Index resIncr, Index resStride, \
-  EIGTYPE alpha, \
-  level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/, \
-  GemmParallelInfo<Index>* /*info = 0*/) \
-{ \
-  using std::conj; \
-\
-  EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-  eigen_assert(resIncr == 1); \
-  char transa, transb; \
-  BlasIndex m, n, k, lda, ldb, ldc; \
-  const EIGTYPE *a, *b; \
-  EIGTYPE beta(1); \
-  MatrixX##EIGPREFIX a_tmp, b_tmp; \
-\
-/* Set transpose options */ \
-  transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
-  transb = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
-\
-/* Set m, n, k */ \
-  m = convert_index<BlasIndex>(rows);  \
-  n = convert_index<BlasIndex>(cols);  \
-  k = convert_index<BlasIndex>(depth); \
-\
-/* Set lda, ldb, ldc */ \
-  lda = convert_index<BlasIndex>(lhsStride); \
-  ldb = convert_index<BlasIndex>(rhsStride); \
-  ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-  if ((LhsStorageOrder==ColMajor) && (ConjugateLhs)) { \
-    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,k,OuterStride<>(lhsStride)); \
-    a_tmp = lhs.conjugate(); \
-    a = a_tmp.data(); \
-    lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-  } else a = _lhs; \
-\
-  if ((RhsStorageOrder==ColMajor) && (ConjugateRhs)) { \
-    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,k,n,OuterStride<>(rhsStride)); \
-    b_tmp = rhs.conjugate(); \
-    b = b_tmp.data(); \
-    ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-  } else b = _rhs; \
-\
-  BLASFUNC(&transa, &transb, &m, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-}};
-
-#ifdef EIGEN_USE_MKL
-GEMM_SPECIALIZATION(double,   d,  double, dgemm)
-GEMM_SPECIALIZATION(float,    f,  float,  sgemm)
-GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, zgemm)
-GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8,  cgemm)
-#else
-GEMM_SPECIALIZATION(double,   d,  double, dgemm_)
-GEMM_SPECIALIZATION(float,    f,  float,  sgemm_)
-GEMM_SPECIALIZATION(dcomplex, cd, double, zgemm_)
-GEMM_SPECIALIZATION(scomplex, cf, float,  cgemm_)
-#endif
-
-} // end namespase internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixVector.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixVector.h
deleted file mode 100644
index dfb6aeb..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixVector.h
+++ /dev/null
@@ -1,518 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_MATRIX_VECTOR_H
-#define EIGEN_GENERAL_MATRIX_VECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-enum GEMVPacketSizeType {
-  GEMVPacketFull = 0,
-  GEMVPacketHalf,
-  GEMVPacketQuarter
-};
-
-template <int N, typename T1, typename T2, typename T3>
-struct gemv_packet_cond { typedef T3 type; };
-
-template <typename T1, typename T2, typename T3>
-struct gemv_packet_cond<GEMVPacketFull, T1, T2, T3> { typedef T1 type; };
-
-template <typename T1, typename T2, typename T3>
-struct gemv_packet_cond<GEMVPacketHalf, T1, T2, T3> { typedef T2 type; };
-
-template<typename LhsScalar, typename RhsScalar, int _PacketSize=GEMVPacketFull>
-class gemv_traits
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-#define PACKET_DECL_COND_PREFIX(prefix, name, packet_size)                        \
-  typedef typename gemv_packet_cond<packet_size,                                  \
-                                    typename packet_traits<name ## Scalar>::type, \
-                                    typename packet_traits<name ## Scalar>::half, \
-                                    typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
-  prefix ## name ## Packet
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-#undef PACKET_DECL_COND_PREFIX
-
-public:
-  enum {
-        Vectorizable = unpacket_traits<_LhsPacket>::vectorizable &&
-        unpacket_traits<_RhsPacket>::vectorizable &&
-        int(unpacket_traits<_LhsPacket>::size)==int(unpacket_traits<_RhsPacket>::size),
-        LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-        RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-        ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1
-  };
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-};
-
-
-/* Optimized col-major matrix * vector product:
- * This algorithm processes the matrix per vertical panels,
- * which are then processed horizontaly per chunck of 8*PacketSize x 1 vertical segments.
- *
- * Mixing type logic: C += alpha * A * B
- *  |  A  |  B  |alpha| comments
- *  |real |cplx |cplx | no vectorization
- *  |real |cplx |real | alpha is converted to a cplx when calling the run function, no vectorization
- *  |cplx |real |cplx | invalid, the caller has to do tmp: = A * B; C += alpha*tmp
- *  |cplx |real |real | optimal case, vectorization possible via real-cplx mul
- *
- * The same reasoning apply for the transposed case.
- */
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
-{
-  typedef gemv_traits<LhsScalar,RhsScalar> Traits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketHalf> HalfTraits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketQuarter> QuarterTraits;
-
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  typedef typename Traits::LhsPacket LhsPacket;
-  typedef typename Traits::RhsPacket RhsPacket;
-  typedef typename Traits::ResPacket ResPacket;
-
-  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
-  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
-  typedef typename HalfTraits::ResPacket ResPacketHalf;
-
-  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
-  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
-  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
-
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(
-  Index rows, Index cols,
-  const LhsMapper& lhs,
-  const RhsMapper& rhs,
-        ResScalar* res, Index resIncr,
-  RhsScalar alpha);
-};
-
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run(
-  Index rows, Index cols,
-  const LhsMapper& alhs,
-  const RhsMapper& rhs,
-        ResScalar* res, Index resIncr,
-  RhsScalar alpha)
-{
-  EIGEN_UNUSED_VARIABLE(resIncr);
-  eigen_internal_assert(resIncr==1);
-
-  // The following copy tells the compiler that lhs's attributes are not modified outside this function
-  // This helps GCC to generate propoer code.
-  LhsMapper lhs(alhs);
-
-  conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
-  conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
-  conj_helper<LhsPacketHalf,RhsPacketHalf,ConjugateLhs,ConjugateRhs> pcj_half;
-  conj_helper<LhsPacketQuarter,RhsPacketQuarter,ConjugateLhs,ConjugateRhs> pcj_quarter;
-
-  const Index lhsStride = lhs.stride();
-  // TODO: for padded aligned inputs, we could enable aligned reads
-  enum { LhsAlignment = Unaligned,
-         ResPacketSize = Traits::ResPacketSize,
-         ResPacketSizeHalf = HalfTraits::ResPacketSize,
-         ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
-         LhsPacketSize = Traits::LhsPacketSize,
-         HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
-         HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
-  };
-
-  const Index n8 = rows-8*ResPacketSize+1;
-  const Index n4 = rows-4*ResPacketSize+1;
-  const Index n3 = rows-3*ResPacketSize+1;
-  const Index n2 = rows-2*ResPacketSize+1;
-  const Index n1 = rows-1*ResPacketSize+1;
-  const Index n_half = rows-1*ResPacketSizeHalf+1;
-  const Index n_quarter = rows-1*ResPacketSizeQuarter+1;
-
-  // TODO: improve the following heuristic:
-  const Index block_cols = cols<128 ? cols : (lhsStride*sizeof(LhsScalar)<32000?16:4);
-  ResPacket palpha = pset1<ResPacket>(alpha);
-  ResPacketHalf palpha_half = pset1<ResPacketHalf>(alpha);
-  ResPacketQuarter palpha_quarter = pset1<ResPacketQuarter>(alpha);
-
-  for(Index j2=0; j2<cols; j2+=block_cols)
-  {
-    Index jend = numext::mini(j2+block_cols,cols);
-    Index i=0;
-    for(; i<n8; i+=ResPacketSize*8)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0)),
-                c2 = pset1<ResPacket>(ResScalar(0)),
-                c3 = pset1<ResPacket>(ResScalar(0)),
-                c4 = pset1<ResPacket>(ResScalar(0)),
-                c5 = pset1<ResPacket>(ResScalar(0)),
-                c6 = pset1<ResPacket>(ResScalar(0)),
-                c7 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
-        c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*3,j),b0,c3);
-        c4 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*4,j),b0,c4);
-        c5 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*5,j),b0,c5);
-        c6 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*6,j),b0,c6);
-        c7 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*7,j),b0,c7);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
-      pstoreu(res+i+ResPacketSize*3, pmadd(c3,palpha,ploadu<ResPacket>(res+i+ResPacketSize*3)));
-      pstoreu(res+i+ResPacketSize*4, pmadd(c4,palpha,ploadu<ResPacket>(res+i+ResPacketSize*4)));
-      pstoreu(res+i+ResPacketSize*5, pmadd(c5,palpha,ploadu<ResPacket>(res+i+ResPacketSize*5)));
-      pstoreu(res+i+ResPacketSize*6, pmadd(c6,palpha,ploadu<ResPacket>(res+i+ResPacketSize*6)));
-      pstoreu(res+i+ResPacketSize*7, pmadd(c7,palpha,ploadu<ResPacket>(res+i+ResPacketSize*7)));
-    }
-    if(i<n4)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0)),
-                c2 = pset1<ResPacket>(ResScalar(0)),
-                c3 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
-        c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*3,j),b0,c3);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
-      pstoreu(res+i+ResPacketSize*3, pmadd(c3,palpha,ploadu<ResPacket>(res+i+ResPacketSize*3)));
-
-      i+=ResPacketSize*4;
-    }
-    if(i<n3)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0)),
-                c2 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
-
-      i+=ResPacketSize*3;
-    }
-    if(i<n2)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      i+=ResPacketSize*2;
-    }
-    if(i<n1)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0));
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      i+=ResPacketSize;
-    }
-    if(HasHalf && i<n_half)
-    {
-      ResPacketHalf c0 = pset1<ResPacketHalf>(ResScalar(0));
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacketHalf b0 = pset1<RhsPacketHalf>(rhs(j,0));
-        c0 = pcj_half.pmadd(lhs.template load<LhsPacketHalf,LhsAlignment>(i+0,j),b0,c0);
-      }
-      pstoreu(res+i+ResPacketSizeHalf*0, pmadd(c0,palpha_half,ploadu<ResPacketHalf>(res+i+ResPacketSizeHalf*0)));
-      i+=ResPacketSizeHalf;
-    }
-    if(HasQuarter && i<n_quarter)
-    {
-      ResPacketQuarter c0 = pset1<ResPacketQuarter>(ResScalar(0));
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacketQuarter b0 = pset1<RhsPacketQuarter>(rhs(j,0));
-        c0 = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter,LhsAlignment>(i+0,j),b0,c0);
-      }
-      pstoreu(res+i+ResPacketSizeQuarter*0, pmadd(c0,palpha_quarter,ploadu<ResPacketQuarter>(res+i+ResPacketSizeQuarter*0)));
-      i+=ResPacketSizeQuarter;
-    }
-    for(;i<rows;++i)
-    {
-      ResScalar c0(0);
-      for(Index j=j2; j<jend; j+=1)
-        c0 += cj.pmul(lhs(i,j), rhs(j,0));
-      res[i] += alpha*c0;
-    }
-  }
-}
-
-/* Optimized row-major matrix * vector product:
- * This algorithm processes 4 rows at once that allows to both reduce
- * the number of load/stores of the result by a factor 4 and to reduce
- * the instruction dependency. Moreover, we know that all bands have the
- * same alignment pattern.
- *
- * Mixing type logic:
- *  - alpha is always a complex (or converted to a complex)
- *  - no vectorization
- */
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
-{
-  typedef gemv_traits<LhsScalar,RhsScalar> Traits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketHalf> HalfTraits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketQuarter> QuarterTraits;
-
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  typedef typename Traits::LhsPacket LhsPacket;
-  typedef typename Traits::RhsPacket RhsPacket;
-  typedef typename Traits::ResPacket ResPacket;
-
-  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
-  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
-  typedef typename HalfTraits::ResPacket ResPacketHalf;
-
-  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
-  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
-  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
-
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(
-  Index rows, Index cols,
-  const LhsMapper& lhs,
-  const RhsMapper& rhs,
-        ResScalar* res, Index resIncr,
-  ResScalar alpha);
-};
-
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run(
-  Index rows, Index cols,
-  const LhsMapper& alhs,
-  const RhsMapper& rhs,
-  ResScalar* res, Index resIncr,
-  ResScalar alpha)
-{
-  // The following copy tells the compiler that lhs's attributes are not modified outside this function
-  // This helps GCC to generate propoer code.
-  LhsMapper lhs(alhs);
-
-  eigen_internal_assert(rhs.stride()==1);
-  conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
-  conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
-  conj_helper<LhsPacketHalf,RhsPacketHalf,ConjugateLhs,ConjugateRhs> pcj_half;
-  conj_helper<LhsPacketQuarter,RhsPacketQuarter,ConjugateLhs,ConjugateRhs> pcj_quarter;
-
-  // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
-  //       processing 8 rows at once might be counter productive wrt cache.
-  const Index n8 = lhs.stride()*sizeof(LhsScalar)>32000 ? 0 : rows-7;
-  const Index n4 = rows-3;
-  const Index n2 = rows-1;
-
-  // TODO: for padded aligned inputs, we could enable aligned reads
-  enum { LhsAlignment = Unaligned,
-         ResPacketSize = Traits::ResPacketSize,
-         ResPacketSizeHalf = HalfTraits::ResPacketSize,
-         ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
-         LhsPacketSize = Traits::LhsPacketSize,
-         LhsPacketSizeHalf = HalfTraits::LhsPacketSize,
-         LhsPacketSizeQuarter = QuarterTraits::LhsPacketSize,
-         HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
-         HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
-  };
-
-  Index i=0;
-  for(; i<n8; i+=8)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-              c1 = pset1<ResPacket>(ResScalar(0)),
-              c2 = pset1<ResPacket>(ResScalar(0)),
-              c3 = pset1<ResPacket>(ResScalar(0)),
-              c4 = pset1<ResPacket>(ResScalar(0)),
-              c5 = pset1<ResPacket>(ResScalar(0)),
-              c6 = pset1<ResPacket>(ResScalar(0)),
-              c7 = pset1<ResPacket>(ResScalar(0));
-
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
-
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
-      c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+2,j),b0,c2);
-      c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+3,j),b0,c3);
-      c4 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+4,j),b0,c4);
-      c5 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+5,j),b0,c5);
-      c6 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+6,j),b0,c6);
-      c7 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+7,j),b0,c7);
-    }
-    ResScalar cc0 = predux(c0);
-    ResScalar cc1 = predux(c1);
-    ResScalar cc2 = predux(c2);
-    ResScalar cc3 = predux(c3);
-    ResScalar cc4 = predux(c4);
-    ResScalar cc5 = predux(c5);
-    ResScalar cc6 = predux(c6);
-    ResScalar cc7 = predux(c7);
-    for(; j<cols; ++j)
-    {
-      RhsScalar b0 = rhs(j,0);
-
-      cc0 += cj.pmul(lhs(i+0,j), b0);
-      cc1 += cj.pmul(lhs(i+1,j), b0);
-      cc2 += cj.pmul(lhs(i+2,j), b0);
-      cc3 += cj.pmul(lhs(i+3,j), b0);
-      cc4 += cj.pmul(lhs(i+4,j), b0);
-      cc5 += cj.pmul(lhs(i+5,j), b0);
-      cc6 += cj.pmul(lhs(i+6,j), b0);
-      cc7 += cj.pmul(lhs(i+7,j), b0);
-    }
-    res[(i+0)*resIncr] += alpha*cc0;
-    res[(i+1)*resIncr] += alpha*cc1;
-    res[(i+2)*resIncr] += alpha*cc2;
-    res[(i+3)*resIncr] += alpha*cc3;
-    res[(i+4)*resIncr] += alpha*cc4;
-    res[(i+5)*resIncr] += alpha*cc5;
-    res[(i+6)*resIncr] += alpha*cc6;
-    res[(i+7)*resIncr] += alpha*cc7;
-  }
-  for(; i<n4; i+=4)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-              c1 = pset1<ResPacket>(ResScalar(0)),
-              c2 = pset1<ResPacket>(ResScalar(0)),
-              c3 = pset1<ResPacket>(ResScalar(0));
-
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
-
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
-      c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+2,j),b0,c2);
-      c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+3,j),b0,c3);
-    }
-    ResScalar cc0 = predux(c0);
-    ResScalar cc1 = predux(c1);
-    ResScalar cc2 = predux(c2);
-    ResScalar cc3 = predux(c3);
-    for(; j<cols; ++j)
-    {
-      RhsScalar b0 = rhs(j,0);
-
-      cc0 += cj.pmul(lhs(i+0,j), b0);
-      cc1 += cj.pmul(lhs(i+1,j), b0);
-      cc2 += cj.pmul(lhs(i+2,j), b0);
-      cc3 += cj.pmul(lhs(i+3,j), b0);
-    }
-    res[(i+0)*resIncr] += alpha*cc0;
-    res[(i+1)*resIncr] += alpha*cc1;
-    res[(i+2)*resIncr] += alpha*cc2;
-    res[(i+3)*resIncr] += alpha*cc3;
-  }
-  for(; i<n2; i+=2)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-              c1 = pset1<ResPacket>(ResScalar(0));
-
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
-
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
-    }
-    ResScalar cc0 = predux(c0);
-    ResScalar cc1 = predux(c1);
-    for(; j<cols; ++j)
-    {
-      RhsScalar b0 = rhs(j,0);
-
-      cc0 += cj.pmul(lhs(i+0,j), b0);
-      cc1 += cj.pmul(lhs(i+1,j), b0);
-    }
-    res[(i+0)*resIncr] += alpha*cc0;
-    res[(i+1)*resIncr] += alpha*cc1;
-  }
-  for(; i<rows; ++i)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0));
-    ResPacketHalf c0_h = pset1<ResPacketHalf>(ResScalar(0));
-    ResPacketQuarter c0_q = pset1<ResPacketQuarter>(ResScalar(0));
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket,Unaligned>(j,0);
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i,j),b0,c0);
-    }
-    ResScalar cc0 = predux(c0);
-    if (HasHalf) {
-      for(; j+LhsPacketSizeHalf<=cols; j+=LhsPacketSizeHalf)
-        {
-          RhsPacketHalf b0 = rhs.template load<RhsPacketHalf,Unaligned>(j,0);
-          c0_h = pcj_half.pmadd(lhs.template load<LhsPacketHalf,LhsAlignment>(i,j),b0,c0_h);
-        }
-      cc0 += predux(c0_h);
-    }
-    if (HasQuarter) {
-      for(; j+LhsPacketSizeQuarter<=cols; j+=LhsPacketSizeQuarter)
-        {
-          RhsPacketQuarter b0 = rhs.template load<RhsPacketQuarter,Unaligned>(j,0);
-          c0_q = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter,LhsAlignment>(i,j),b0,c0_q);
-        }
-      cc0 += predux(c0_q);
-    }
-    for(; j<cols; ++j)
-    {
-      cc0 += cj.pmul(lhs(i,j), rhs(j,0));
-    }
-    res[i*resIncr] += alpha*cc0;
-  }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_VECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
deleted file mode 100644
index 6e36c2b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
+++ /dev/null
@@ -1,136 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   General matrix-vector product functionality based on ?GEMV.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
-#define EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements general matrix-vector multiplication using BLAS
-* gemv function via partial specialization of
-* general_matrix_vector_product::run(..) method for float, double,
-* std::complex<float> and std::complex<double> types
-**********************************************************************/
-
-// gemv specialization
-
-template<typename Index, typename LhsScalar, int StorageOrder, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs>
-struct general_matrix_vector_product_gemv;
-
-#define EIGEN_BLAS_GEMV_SPECIALIZE(Scalar) \
-template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
-struct general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ColMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,ConjugateRhs,Specialized> { \
-static void run( \
-  Index rows, Index cols, \
-  const const_blas_data_mapper<Scalar,Index,ColMajor> &lhs, \
-  const const_blas_data_mapper<Scalar,Index,RowMajor> &rhs, \
-  Scalar* res, Index resIncr, Scalar alpha) \
-{ \
-  if (ConjugateLhs) { \
-    general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ColMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,ConjugateRhs,BuiltIn>::run( \
-      rows, cols, lhs, rhs, res, resIncr, alpha); \
-  } else { \
-    general_matrix_vector_product_gemv<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
-      rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha); \
-  } \
-} \
-}; \
-template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
-struct general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,RowMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ConjugateRhs,Specialized> { \
-static void run( \
-  Index rows, Index cols, \
-  const const_blas_data_mapper<Scalar,Index,RowMajor> &lhs, \
-  const const_blas_data_mapper<Scalar,Index,ColMajor> &rhs, \
-  Scalar* res, Index resIncr, Scalar alpha) \
-{ \
-    general_matrix_vector_product_gemv<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
-      rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha); \
-} \
-}; \
-
-EIGEN_BLAS_GEMV_SPECIALIZE(double)
-EIGEN_BLAS_GEMV_SPECIALIZE(float)
-EIGEN_BLAS_GEMV_SPECIALIZE(dcomplex)
-EIGEN_BLAS_GEMV_SPECIALIZE(scomplex)
-
-#define EIGEN_BLAS_GEMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASFUNC) \
-template<typename Index, int LhsStorageOrder, bool ConjugateLhs, bool ConjugateRhs> \
-struct general_matrix_vector_product_gemv<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,ConjugateRhs> \
-{ \
-typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> GEMVVector;\
-\
-static void run( \
-  Index rows, Index cols, \
-  const EIGTYPE* lhs, Index lhsStride, \
-  const EIGTYPE* rhs, Index rhsIncr, \
-  EIGTYPE* res, Index resIncr, EIGTYPE alpha) \
-{ \
-  BlasIndex m=convert_index<BlasIndex>(rows), n=convert_index<BlasIndex>(cols), \
-            lda=convert_index<BlasIndex>(lhsStride), incx=convert_index<BlasIndex>(rhsIncr), incy=convert_index<BlasIndex>(resIncr); \
-  const EIGTYPE beta(1); \
-  const EIGTYPE *x_ptr; \
-  char trans=(LhsStorageOrder==ColMajor) ? 'N' : (ConjugateLhs) ? 'C' : 'T'; \
-  if (LhsStorageOrder==RowMajor) { \
-    m = convert_index<BlasIndex>(cols); \
-    n = convert_index<BlasIndex>(rows); \
-  }\
-  GEMVVector x_tmp; \
-  if (ConjugateRhs) { \
-    Map<const GEMVVector, 0, InnerStride<> > map_x(rhs,cols,1,InnerStride<>(incx)); \
-    x_tmp=map_x.conjugate(); \
-    x_ptr=x_tmp.data(); \
-    incx=1; \
-  } else x_ptr=rhs; \
-  BLASFUNC(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
-}\
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_GEMV_SPECIALIZATION(double,   double, dgemv)
-EIGEN_BLAS_GEMV_SPECIALIZATION(float,    float,  sgemv)
-EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, zgemv)
-EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, MKL_Complex8 , cgemv)
-#else
-EIGEN_BLAS_GEMV_SPECIALIZATION(double,   double, dgemv_)
-EIGEN_BLAS_GEMV_SPECIALIZATION(float,    float,  sgemv_)
-EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, double, zgemv_)
-EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, float,  cgemv_)
-#endif
-
-} // end namespase internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/Parallelizer.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/Parallelizer.h
deleted file mode 100644
index 8f91879..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/Parallelizer.h
+++ /dev/null
@@ -1,180 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARALLELIZER_H
-#define EIGEN_PARALLELIZER_H
-
-#if EIGEN_HAS_CXX11_ATOMIC
-#include <atomic>
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal */
-inline void manage_multi_threading(Action action, int* v)
-{
-  static int m_maxThreads = -1;
-  EIGEN_UNUSED_VARIABLE(m_maxThreads)
-
-  if(action==SetAction)
-  {
-    eigen_internal_assert(v!=0);
-    m_maxThreads = *v;
-  }
-  else if(action==GetAction)
-  {
-    eigen_internal_assert(v!=0);
-    #ifdef EIGEN_HAS_OPENMP
-    if(m_maxThreads>0)
-      *v = m_maxThreads;
-    else
-      *v = omp_get_max_threads();
-    #else
-    *v = 1;
-    #endif
-  }
-  else
-  {
-    eigen_internal_assert(false);
-  }
-}
-
-}
-
-/** Must be call first when calling Eigen from multiple threads */
-inline void initParallel()
-{
-  int nbt;
-  internal::manage_multi_threading(GetAction, &nbt);
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-}
-
-/** \returns the max number of threads reserved for Eigen
-  * \sa setNbThreads */
-inline int nbThreads()
-{
-  int ret;
-  internal::manage_multi_threading(GetAction, &ret);
-  return ret;
-}
-
-/** Sets the max number of threads reserved for Eigen
-  * \sa nbThreads */
-inline void setNbThreads(int v)
-{
-  internal::manage_multi_threading(SetAction, &v);
-}
-
-namespace internal {
-
-template<typename Index> struct GemmParallelInfo
-{
-  GemmParallelInfo() : sync(-1), users(0), lhs_start(0), lhs_length(0) {}
-
-  // volatile is not enough on all architectures (see bug 1572)
-  // to guarantee that when thread A says to thread B that it is
-  // done with packing a block, then all writes have been really
-  // carried out... C++11 memory model+atomic guarantees this.
-#if EIGEN_HAS_CXX11_ATOMIC
-  std::atomic<Index> sync;
-  std::atomic<int> users;
-#else
-  Index volatile sync;
-  int volatile users;
-#endif
-
-  Index lhs_start;
-  Index lhs_length;
-};
-
-template<bool Condition, typename Functor, typename Index>
-void parallelize_gemm(const Functor& func, Index rows, Index cols, Index depth, bool transpose)
-{
-  // TODO when EIGEN_USE_BLAS is defined,
-  // we should still enable OMP for other scalar types
-  // Without C++11, we have to disable GEMM's parallelization on
-  // non x86 architectures because there volatile is not enough for our purpose.
-  // See bug 1572.
-#if (! defined(EIGEN_HAS_OPENMP)) || defined(EIGEN_USE_BLAS) || ((!EIGEN_HAS_CXX11_ATOMIC) && !(EIGEN_ARCH_i386_OR_x86_64))
-  // FIXME the transpose variable is only needed to properly split
-  // the matrix product when multithreading is enabled. This is a temporary
-  // fix to support row-major destination matrices. This whole
-  // parallelizer mechanism has to be redesigned anyway.
-  EIGEN_UNUSED_VARIABLE(depth);
-  EIGEN_UNUSED_VARIABLE(transpose);
-  func(0,rows, 0,cols);
-#else
-
-  // Dynamically check whether we should enable or disable OpenMP.
-  // The conditions are:
-  // - the max number of threads we can create is greater than 1
-  // - we are not already in a parallel code
-  // - the sizes are large enough
-
-  // compute the maximal number of threads from the size of the product:
-  // This first heuristic takes into account that the product kernel is fully optimized when working with nr columns at once.
-  Index size = transpose ? rows : cols;
-  Index pb_max_threads = std::max<Index>(1,size / Functor::Traits::nr);
-
-  // compute the maximal number of threads from the total amount of work:
-  double work = static_cast<double>(rows) * static_cast<double>(cols) *
-      static_cast<double>(depth);
-  double kMinTaskSize = 50000;  // FIXME improve this heuristic.
-  pb_max_threads = std::max<Index>(1, std::min<Index>(pb_max_threads, static_cast<Index>( work / kMinTaskSize ) ));
-
-  // compute the number of threads we are going to use
-  Index threads = std::min<Index>(nbThreads(), pb_max_threads);
-
-  // if multi-threading is explicitly disabled, not useful, or if we already are in a parallel session,
-  // then abort multi-threading
-  // FIXME omp_get_num_threads()>1 only works for openmp, what if the user does not use openmp?
-  if((!Condition) || (threads==1) || (omp_get_num_threads()>1))
-    return func(0,rows, 0,cols);
-
-  Eigen::initParallel();
-  func.initParallelSession(threads);
-
-  if(transpose)
-    std::swap(rows,cols);
-
-  ei_declare_aligned_stack_constructed_variable(GemmParallelInfo<Index>,info,threads,0);
-
-  #pragma omp parallel num_threads(threads)
-  {
-    Index i = omp_get_thread_num();
-    // Note that the actual number of threads might be lower than the number of request ones.
-    Index actual_threads = omp_get_num_threads();
-
-    Index blockCols = (cols / actual_threads) & ~Index(0x3);
-    Index blockRows = (rows / actual_threads);
-    blockRows = (blockRows/Functor::Traits::mr)*Functor::Traits::mr;
-
-    Index r0 = i*blockRows;
-    Index actualBlockRows = (i+1==actual_threads) ? rows-r0 : blockRows;
-
-    Index c0 = i*blockCols;
-    Index actualBlockCols = (i+1==actual_threads) ? cols-c0 : blockCols;
-
-    info[i].lhs_start = r0;
-    info[i].lhs_length = actualBlockRows;
-
-    if(transpose) func(c0, actualBlockCols, 0, rows, info);
-    else          func(0, rows, c0, actualBlockCols, info);
-  }
-#endif
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARALLELIZER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
deleted file mode 100644
index 33ecf10..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
+++ /dev/null
@@ -1,544 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_H
-#define EIGEN_SELFADJOINT_MATRIX_MATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// pack a selfadjoint block diagonal for use with the gebp_kernel
-template<typename Scalar, typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs
-{
-  template<int BlockRows> inline
-  void pack(Scalar* blockA, const const_blas_data_mapper<Scalar,Index,StorageOrder>& lhs, Index cols, Index i, Index& count)
-  {
-    // normal copy
-    for(Index k=0; k<i; k++)
-      for(Index w=0; w<BlockRows; w++)
-        blockA[count++] = lhs(i+w,k);           // normal
-    // symmetric copy
-    Index h = 0;
-    for(Index k=i; k<i+BlockRows; k++)
-    {
-      for(Index w=0; w<h; w++)
-        blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
-
-      blockA[count++] = numext::real(lhs(k,k));   // real (diagonal)
-
-      for(Index w=h+1; w<BlockRows; w++)
-        blockA[count++] = lhs(i+w, k);          // normal
-      ++h;
-    }
-    // transposed copy
-    for(Index k=i+BlockRows; k<cols; k++)
-      for(Index w=0; w<BlockRows; w++)
-        blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
-  }
-  void operator()(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    typedef typename unpacket_traits<typename packet_traits<Scalar>::type>::half HalfPacket;
-    typedef typename unpacket_traits<typename unpacket_traits<typename packet_traits<Scalar>::type>::half>::half QuarterPacket;
-    enum { PacketSize = packet_traits<Scalar>::size,
-           HalfPacketSize = unpacket_traits<HalfPacket>::size,
-           QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
-           HasHalf = (int)HalfPacketSize < (int)PacketSize,
-           HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
-
-    const_blas_data_mapper<Scalar,Index,StorageOrder> lhs(_lhs,lhsStride);
-    Index count = 0;
-    //Index peeled_mc3 = (rows/Pack1)*Pack1;
-    
-    const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0;
-    const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0;
-    const Index peeled_mc1 = Pack1>=1*PacketSize ? peeled_mc2+((rows-peeled_mc2)/(1*PacketSize))*(1*PacketSize) : 0;
-    const Index peeled_mc_half = Pack1>=HalfPacketSize ? peeled_mc1+((rows-peeled_mc1)/(HalfPacketSize))*(HalfPacketSize) : 0;
-    const Index peeled_mc_quarter = Pack1>=QuarterPacketSize ? peeled_mc_half+((rows-peeled_mc_half)/(QuarterPacketSize))*(QuarterPacketSize) : 0;
-    
-    if(Pack1>=3*PacketSize)
-      for(Index i=0; i<peeled_mc3; i+=3*PacketSize)
-        pack<3*PacketSize>(blockA, lhs, cols, i, count);
-    
-    if(Pack1>=2*PacketSize)
-      for(Index i=peeled_mc3; i<peeled_mc2; i+=2*PacketSize)
-        pack<2*PacketSize>(blockA, lhs, cols, i, count);
-    
-    if(Pack1>=1*PacketSize)
-      for(Index i=peeled_mc2; i<peeled_mc1; i+=1*PacketSize)
-        pack<1*PacketSize>(blockA, lhs, cols, i, count);
-
-    if(HasHalf && Pack1>=HalfPacketSize)
-      for(Index i=peeled_mc1; i<peeled_mc_half; i+=HalfPacketSize)
-        pack<HalfPacketSize>(blockA, lhs, cols, i, count);
-
-    if(HasQuarter && Pack1>=QuarterPacketSize)
-      for(Index i=peeled_mc_half; i<peeled_mc_quarter; i+=QuarterPacketSize)
-        pack<QuarterPacketSize>(blockA, lhs, cols, i, count);
-
-    // do the same with mr==1
-    for(Index i=peeled_mc_quarter; i<rows; i++)
-    {
-      for(Index k=0; k<i; k++)
-        blockA[count++] = lhs(i, k);                   // normal
-
-      blockA[count++] = numext::real(lhs(i, i));       // real (diagonal)
-
-      for(Index k=i+1; k<cols; k++)
-        blockA[count++] = numext::conj(lhs(k, i));     // transposed
-    }
-  }
-};
-
-template<typename Scalar, typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs
-{
-  enum { PacketSize = packet_traits<Scalar>::size };
-  void operator()(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    Index end_k = k2 + rows;
-    Index count = 0;
-    const_blas_data_mapper<Scalar,Index,StorageOrder> rhs(_rhs,rhsStride);
-    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
-    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-
-    // first part: normal case
-    for(Index j2=0; j2<k2; j2+=nr)
-    {
-      for(Index k=k2; k<end_k; k++)
-      {
-        blockB[count+0] = rhs(k,j2+0);
-        blockB[count+1] = rhs(k,j2+1);
-        if (nr>=4)
-        {
-          blockB[count+2] = rhs(k,j2+2);
-          blockB[count+3] = rhs(k,j2+3);
-        }
-        if (nr>=8)
-        {
-          blockB[count+4] = rhs(k,j2+4);
-          blockB[count+5] = rhs(k,j2+5);
-          blockB[count+6] = rhs(k,j2+6);
-          blockB[count+7] = rhs(k,j2+7);
-        }
-        count += nr;
-      }
-    }
-
-    // second part: diagonal block
-    Index end8 = nr>=8 ? (std::min)(k2+rows,packet_cols8) : k2;
-    if(nr>=8)
-    {
-      for(Index j2=k2; j2<end8; j2+=8)
-      {
-        // again we can split vertically in three different parts (transpose, symmetric, normal)
-        // transpose
-        for(Index k=k2; k<j2; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          blockB[count+4] = numext::conj(rhs(j2+4,k));
-          blockB[count+5] = numext::conj(rhs(j2+5,k));
-          blockB[count+6] = numext::conj(rhs(j2+6,k));
-          blockB[count+7] = numext::conj(rhs(j2+7,k));
-          count += 8;
-        }
-        // symmetric
-        Index h = 0;
-        for(Index k=j2; k<j2+8; k++)
-        {
-          // normal
-          for (Index w=0 ; w<h; ++w)
-            blockB[count+w] = rhs(k,j2+w);
-
-          blockB[count+h] = numext::real(rhs(k,k));
-
-          // transpose
-          for (Index w=h+1 ; w<8; ++w)
-            blockB[count+w] = numext::conj(rhs(j2+w,k));
-          count += 8;
-          ++h;
-        }
-        // normal
-        for(Index k=j2+8; k<end_k; k++)
-        {
-          blockB[count+0] = rhs(k,j2+0);
-          blockB[count+1] = rhs(k,j2+1);
-          blockB[count+2] = rhs(k,j2+2);
-          blockB[count+3] = rhs(k,j2+3);
-          blockB[count+4] = rhs(k,j2+4);
-          blockB[count+5] = rhs(k,j2+5);
-          blockB[count+6] = rhs(k,j2+6);
-          blockB[count+7] = rhs(k,j2+7);
-          count += 8;
-        }
-      }
-    }
-    if(nr>=4)
-    {
-      for(Index j2=end8; j2<(std::min)(k2+rows,packet_cols4); j2+=4)
-      {
-        // again we can split vertically in three different parts (transpose, symmetric, normal)
-        // transpose
-        for(Index k=k2; k<j2; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          count += 4;
-        }
-        // symmetric
-        Index h = 0;
-        for(Index k=j2; k<j2+4; k++)
-        {
-          // normal
-          for (Index w=0 ; w<h; ++w)
-            blockB[count+w] = rhs(k,j2+w);
-
-          blockB[count+h] = numext::real(rhs(k,k));
-
-          // transpose
-          for (Index w=h+1 ; w<4; ++w)
-            blockB[count+w] = numext::conj(rhs(j2+w,k));
-          count += 4;
-          ++h;
-        }
-        // normal
-        for(Index k=j2+4; k<end_k; k++)
-        {
-          blockB[count+0] = rhs(k,j2+0);
-          blockB[count+1] = rhs(k,j2+1);
-          blockB[count+2] = rhs(k,j2+2);
-          blockB[count+3] = rhs(k,j2+3);
-          count += 4;
-        }
-      }
-    }
-
-    // third part: transposed
-    if(nr>=8)
-    {
-      for(Index j2=k2+rows; j2<packet_cols8; j2+=8)
-      {
-        for(Index k=k2; k<end_k; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          blockB[count+4] = numext::conj(rhs(j2+4,k));
-          blockB[count+5] = numext::conj(rhs(j2+5,k));
-          blockB[count+6] = numext::conj(rhs(j2+6,k));
-          blockB[count+7] = numext::conj(rhs(j2+7,k));
-          count += 8;
-        }
-      }
-    }
-    if(nr>=4)
-    {
-      for(Index j2=(std::max)(packet_cols8,k2+rows); j2<packet_cols4; j2+=4)
-      {
-        for(Index k=k2; k<end_k; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          count += 4;
-        }
-      }
-    }
-
-    // copy the remaining columns one at a time (=> the same with nr==1)
-    for(Index j2=packet_cols4; j2<cols; ++j2)
-    {
-      // transpose
-      Index half = (std::min)(end_k,j2);
-      for(Index k=k2; k<half; k++)
-      {
-        blockB[count] = numext::conj(rhs(j2,k));
-        count += 1;
-      }
-
-      if(half==j2 && half<k2+rows)
-      {
-        blockB[count] = numext::real(rhs(j2,j2));
-        count += 1;
-      }
-      else
-        half--;
-
-      // normal
-      for(Index k=half+1; k<k2+rows; k++)
-      {
-        blockB[count] = rhs(k,j2);
-        count += 1;
-      }
-    }
-  }
-};
-
-/* Optimized selfadjoint matrix * matrix (_SYMM) product built on top of
- * the general matrix matrix product.
- */
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
-          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs,
-          int ResStorageOrder, int ResInnerStride>
-struct product_selfadjoint_matrix;
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
-          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs,
-          int ResInnerStride>
-struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,LhsSelfAdjoint,ConjugateLhs, RhsStorageOrder,RhsSelfAdjoint,ConjugateRhs,RowMajor,ResInnerStride>
-{
-
-  static EIGEN_STRONG_INLINE void run(
-    Index rows, Index cols,
-    const Scalar* lhs, Index lhsStride,
-    const Scalar* rhs, Index rhsStride,
-    Scalar* res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    product_selfadjoint_matrix<Scalar, Index,
-      EIGEN_LOGICAL_XOR(RhsSelfAdjoint,RhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
-      RhsSelfAdjoint, NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(RhsSelfAdjoint,ConjugateRhs),
-      EIGEN_LOGICAL_XOR(LhsSelfAdjoint,LhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
-      LhsSelfAdjoint, NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsSelfAdjoint,ConjugateLhs),
-      ColMajor,ResInnerStride>
-      ::run(cols, rows,  rhs, rhsStride,  lhs, lhsStride,  res, resIncr, resStride,  alpha, blocking);
-  }
-};
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor,ResInnerStride>
-{
-
-  static EIGEN_DONT_INLINE void run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor,ResInnerStride>::run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index size = rows;
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, (LhsStorageOrder == RowMajor) ? ColMajor : RowMajor> LhsTransposeMapper;
-    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    LhsTransposeMapper lhs_transpose(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-    // kc must be smaller than mc
-    kc = (std::min)(kc,mc);
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    symm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
-    gemm_pack_lhs<Scalar, Index, LhsTransposeMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder==RowMajor?ColMajor:RowMajor, true> pack_lhs_transposed;
-
-    for(Index k2=0; k2<size; k2+=kc)
-    {
-      const Index actual_kc = (std::min)(k2+kc,size)-k2;
-
-      // we have selected one row panel of rhs and one column panel of lhs
-      // pack rhs's panel into a sequential chunk of memory
-      // and expand each coeff to a constant packet for further reuse
-      pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, cols);
-
-      // the select lhs's panel has to be split in three different parts:
-      //  1 - the transposed panel above the diagonal block => transposed packed copy
-      //  2 - the diagonal block => special packed copy
-      //  3 - the panel below the diagonal block => generic packed copy
-      for(Index i2=0; i2<k2; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,k2)-i2;
-        // transposed packed copy
-        pack_lhs_transposed(blockA, lhs_transpose.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-      // the block diagonal
-      {
-        const Index actual_mc = (std::min)(k2+kc,size)-k2;
-        // symmetric packed copy
-        pack_lhs(blockA, &lhs(k2,k2), lhsStride, actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(k2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-
-      for(Index i2=k2+kc; i2<size; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,size)-i2;
-        gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder,false>()
-          (blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-    }
-  }
-
-// matrix * selfadjoint product
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor,ResInnerStride>
-{
-
-  static EIGEN_DONT_INLINE void run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor,ResInnerStride>::run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index size = cols;
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    ResMapper res(_res,resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    symm_pack_rhs<Scalar, Index, Traits::nr,RhsStorageOrder> pack_rhs;
-
-    for(Index k2=0; k2<size; k2+=kc)
-    {
-      const Index actual_kc = (std::min)(k2+kc,size)-k2;
-
-      pack_rhs(blockB, _rhs, rhsStride, actual_kc, cols, k2);
-
-      // => GEPP
-      for(Index i2=0; i2<rows; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,rows)-i2;
-        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-    }
-  }
-
-} // end namespace internal
-
-/***************************************************************************
-* Wrapper to product_selfadjoint_matrix
-***************************************************************************/
-
-namespace internal {
-  
-template<typename Lhs, int LhsMode, typename Rhs, int RhsMode>
-struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,RhsMode,false>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  typedef internal::blas_traits<Lhs> LhsBlasTraits;
-  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-  typedef internal::blas_traits<Rhs> RhsBlasTraits;
-  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  
-  enum {
-    LhsIsUpper = (LhsMode&(Upper|Lower))==Upper,
-    LhsIsSelfAdjoint = (LhsMode&SelfAdjoint)==SelfAdjoint,
-    RhsIsUpper = (RhsMode&(Upper|Lower))==Upper,
-    RhsIsSelfAdjoint = (RhsMode&SelfAdjoint)==SelfAdjoint
-  };
-  
-  template<typename Dest>
-  static void run(Dest &dst, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
-                               * RhsBlasTraits::extractScalarFactor(a_rhs);
-
-    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
-              Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,1> BlockingType;
-
-    BlockingType blocking(lhs.rows(), rhs.cols(), lhs.cols(), 1, false);
-
-    internal::product_selfadjoint_matrix<Scalar, Index,
-      EIGEN_LOGICAL_XOR(LhsIsUpper,internal::traits<Lhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint,
-      NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsIsUpper,bool(LhsBlasTraits::NeedToConjugate)),
-      EIGEN_LOGICAL_XOR(RhsIsUpper,internal::traits<Rhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, RhsIsSelfAdjoint,
-      NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(RhsIsUpper,bool(RhsBlasTraits::NeedToConjugate)),
-      internal::traits<Dest>::Flags&RowMajorBit  ? RowMajor : ColMajor,
-      Dest::InnerStrideAtCompileTime>
-      ::run(
-        lhs.rows(), rhs.cols(),                 // sizes
-        &lhs.coeffRef(0,0), lhs.outerStride(),  // lhs info
-        &rhs.coeffRef(0,0), rhs.outerStride(),  // rhs info
-        &dst.coeffRef(0,0), dst.innerStride(), dst.outerStride(),  // result info
-        actualAlpha, blocking                   // alpha
-      );
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
deleted file mode 100644
index 61396db..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
+++ /dev/null
@@ -1,295 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
-#define EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-/* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */
-
-#define EIGEN_BLAS_SYMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor,1> \
-{\
-\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='L', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-\
-/* Set transpose options */ \
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows);  \
-    n = convert_index<BlasIndex>(cols);  \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(lhsStride); \
-    ldb = convert_index<BlasIndex>(rhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (LhsStorageOrder==RowMajor) uplo='U'; \
-    a = _lhs; \
-\
-    if (RhsStorageOrder==RowMajor) { \
-      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
-      b_tmp = rhs.adjoint(); \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } else b = _rhs; \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-\
-  } \
-};
-
-
-#define EIGEN_BLAS_HEMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor,1> \
-{\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='L', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-    Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> a_tmp; \
-\
-/* Set transpose options */ \
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows); \
-    n = convert_index<BlasIndex>(cols); \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(lhsStride); \
-    ldb = convert_index<BlasIndex>(rhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (((LhsStorageOrder==ColMajor) && ConjugateLhs) || ((LhsStorageOrder==RowMajor) && (!ConjugateLhs))) { \
-      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder>, 0, OuterStride<> > lhs(_lhs,m,m,OuterStride<>(lhsStride)); \
-      a_tmp = lhs.conjugate(); \
-      a = a_tmp.data(); \
-      lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-    } else a = _lhs; \
-    if (LhsStorageOrder==RowMajor) uplo='U'; \
-\
-    if (RhsStorageOrder==ColMajor && (!ConjugateRhs)) { \
-       b = _rhs; } \
-    else { \
-      if (RhsStorageOrder==ColMajor && ConjugateRhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,m,n,OuterStride<>(rhsStride)); \
-        b_tmp = rhs.conjugate(); \
-      } else \
-      if (ConjugateRhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
-        b_tmp = rhs.adjoint(); \
-      } else { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
-        b_tmp = rhs.transpose(); \
-      } \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-\
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_SYMM_L(double, double, d, dsymm)
-EIGEN_BLAS_SYMM_L(float, float, f, ssymm)
-EIGEN_BLAS_HEMM_L(dcomplex, MKL_Complex16, cd, zhemm)
-EIGEN_BLAS_HEMM_L(scomplex, MKL_Complex8, cf, chemm)
-#else
-EIGEN_BLAS_SYMM_L(double, double, d, dsymm_)
-EIGEN_BLAS_SYMM_L(float, float, f, ssymm_)
-EIGEN_BLAS_HEMM_L(dcomplex, double, cd, zhemm_)
-EIGEN_BLAS_HEMM_L(scomplex, float, cf, chemm_)
-#endif
-
-/* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */
-
-#define EIGEN_BLAS_SYMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor,1> \
-{\
-\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='R', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-\
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows);  \
-    n = convert_index<BlasIndex>(cols);  \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(rhsStride); \
-    ldb = convert_index<BlasIndex>(lhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (RhsStorageOrder==RowMajor) uplo='U'; \
-    a = _rhs; \
-\
-    if (LhsStorageOrder==RowMajor) { \
-      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(rhsStride)); \
-      b_tmp = lhs.adjoint(); \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } else b = _lhs; \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-\
-  } \
-};
-
-
-#define EIGEN_BLAS_HEMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor,1> \
-{\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='R', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-    Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> a_tmp; \
-\
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows); \
-    n = convert_index<BlasIndex>(cols); \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(rhsStride); \
-    ldb = convert_index<BlasIndex>(lhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (((RhsStorageOrder==ColMajor) && ConjugateRhs) || ((RhsStorageOrder==RowMajor) && (!ConjugateRhs))) { \
-      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder>, 0, OuterStride<> > rhs(_rhs,n,n,OuterStride<>(rhsStride)); \
-      a_tmp = rhs.conjugate(); \
-      a = a_tmp.data(); \
-      lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-    } else a = _rhs; \
-    if (RhsStorageOrder==RowMajor) uplo='U'; \
-\
-    if (LhsStorageOrder==ColMajor && (!ConjugateLhs)) { \
-       b = _lhs; } \
-    else { \
-      if (LhsStorageOrder==ColMajor && ConjugateLhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,n,OuterStride<>(lhsStride)); \
-        b_tmp = lhs.conjugate(); \
-      } else \
-      if (ConjugateLhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
-        b_tmp = lhs.adjoint(); \
-      } else { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
-        b_tmp = lhs.transpose(); \
-      } \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_SYMM_R(double, double, d, dsymm)
-EIGEN_BLAS_SYMM_R(float, float, f, ssymm)
-EIGEN_BLAS_HEMM_R(dcomplex, MKL_Complex16, cd, zhemm)
-EIGEN_BLAS_HEMM_R(scomplex, MKL_Complex8, cf, chemm)
-#else
-EIGEN_BLAS_SYMM_R(double, double, d, dsymm_)
-EIGEN_BLAS_SYMM_R(float, float, f, ssymm_)
-EIGEN_BLAS_HEMM_R(dcomplex, double, cd, zhemm_)
-EIGEN_BLAS_HEMM_R(scomplex, float, cf, chemm_)
-#endif
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixVector.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
deleted file mode 100644
index d38fd72..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
+++ /dev/null
@@ -1,262 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H
-#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/* Optimized selfadjoint matrix * vector product:
- * This algorithm processes 2 columns at once that allows to both reduce
- * the number of load/stores of the result by a factor 2 and to reduce
- * the instruction dependency.
- */
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version=Specialized>
-struct selfadjoint_matrix_vector_product;
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version>
-struct selfadjoint_matrix_vector_product
-
-{
-static EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC
-void run(
-  Index size,
-  const Scalar*  lhs, Index lhsStride,
-  const Scalar*  rhs,
-  Scalar* res,
-  Scalar alpha);
-};
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version>
-EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC
-void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Version>::run(
-  Index size,
-  const Scalar*  lhs, Index lhsStride,
-  const Scalar*  rhs,
-  Scalar* res,
-  Scalar alpha)
-{
-  typedef typename packet_traits<Scalar>::type Packet;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  const Index PacketSize = sizeof(Packet)/sizeof(Scalar);
-
-  enum {
-    IsRowMajor = StorageOrder==RowMajor ? 1 : 0,
-    IsLower = UpLo == Lower ? 1 : 0,
-    FirstTriangular = IsRowMajor == IsLower
-  };
-
-  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs,  IsRowMajor), ConjugateRhs> cj0;
-  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1;
-  conj_helper<RealScalar,Scalar,false, ConjugateRhs> cjd;
-
-  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs,  IsRowMajor), ConjugateRhs> pcj0;
-  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1;
-
-  Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;
-
-  Index bound = numext::maxi(Index(0), size-8) & 0xfffffffe;
-  if (FirstTriangular)
-    bound = size - bound;
-
-  for (Index j=FirstTriangular ? bound : 0;
-       j<(FirstTriangular ? size : bound);j+=2)
-  {
-    const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
-    const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride;
-
-    Scalar t0 = cjAlpha * rhs[j];
-    Packet ptmp0 = pset1<Packet>(t0);
-    Scalar t1 = cjAlpha * rhs[j+1];
-    Packet ptmp1 = pset1<Packet>(t1);
-
-    Scalar t2(0);
-    Packet ptmp2 = pset1<Packet>(t2);
-    Scalar t3(0);
-    Packet ptmp3 = pset1<Packet>(t3);
-
-    Index starti = FirstTriangular ? 0 : j+2;
-    Index endi   = FirstTriangular ? j : size;
-    Index alignedStart = (starti) + internal::first_default_aligned(&res[starti], endi-starti);
-    Index alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize);
-
-    res[j]   += cjd.pmul(numext::real(A0[j]), t0);
-    res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1);
-    if(FirstTriangular)
-    {
-      res[j]   += cj0.pmul(A1[j],   t1);
-      t3       += cj1.pmul(A1[j],   rhs[j]);
-    }
-    else
-    {
-      res[j+1] += cj0.pmul(A0[j+1],t0);
-      t2 += cj1.pmul(A0[j+1], rhs[j+1]);
-    }
-
-    for (Index i=starti; i<alignedStart; ++i)
-    {
-      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);
-      t2 += cj1.pmul(A0[i], rhs[i]);
-      t3 += cj1.pmul(A1[i], rhs[i]);
-    }
-    // Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)
-    // gcc 4.2 does this optimization automatically.
-    const Scalar* EIGEN_RESTRICT a0It  = A0  + alignedStart;
-    const Scalar* EIGEN_RESTRICT a1It  = A1  + alignedStart;
-    const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart;
-          Scalar* EIGEN_RESTRICT resIt = res + alignedStart;
-    for (Index i=alignedStart; i<alignedEnd; i+=PacketSize)
-    {
-      Packet A0i = ploadu<Packet>(a0It);  a0It  += PacketSize;
-      Packet A1i = ploadu<Packet>(a1It);  a1It  += PacketSize;
-      Packet Bi  = ploadu<Packet>(rhsIt); rhsIt += PacketSize; // FIXME should be aligned in most cases
-      Packet Xi  = pload <Packet>(resIt);
-
-      Xi    = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi));
-      ptmp2 = pcj1.pmadd(A0i,  Bi, ptmp2);
-      ptmp3 = pcj1.pmadd(A1i,  Bi, ptmp3);
-      pstore(resIt,Xi); resIt += PacketSize;
-    }
-    for (Index i=alignedEnd; i<endi; i++)
-    {
-      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);
-      t2 += cj1.pmul(A0[i], rhs[i]);
-      t3 += cj1.pmul(A1[i], rhs[i]);
-    }
-
-    res[j]   += alpha * (t2 + predux(ptmp2));
-    res[j+1] += alpha * (t3 + predux(ptmp3));
-  }
-  for (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++)
-  {
-    const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
-
-    Scalar t1 = cjAlpha * rhs[j];
-    Scalar t2(0);
-    res[j] += cjd.pmul(numext::real(A0[j]), t1);
-    for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++)
-    {
-      res[i] += cj0.pmul(A0[i], t1);
-      t2 += cj1.pmul(A0[i], rhs[i]);
-    }
-    res[j] += alpha * t2;
-  }
-}
-
-} // end namespace internal 
-
-/***************************************************************************
-* Wrapper to product_selfadjoint_vector
-***************************************************************************/
-
-namespace internal {
-
-template<typename Lhs, int LhsMode, typename Rhs>
-struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,0,true>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  typedef internal::blas_traits<Lhs> LhsBlasTraits;
-  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-  typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
-  
-  typedef internal::blas_traits<Rhs> RhsBlasTraits;
-  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-  enum { LhsUpLo = LhsMode&(Upper|Lower) };
-
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC
-  void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
-  {
-    typedef typename Dest::Scalar ResScalar;
-    typedef typename Rhs::Scalar RhsScalar;
-    typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
-    
-    eigen_assert(dest.rows()==a_lhs.rows() && dest.cols()==a_rhs.cols());
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
-                               * RhsBlasTraits::extractScalarFactor(a_rhs);
-
-    enum {
-      EvalToDest = (Dest::InnerStrideAtCompileTime==1),
-      UseRhs = (ActualRhsTypeCleaned::InnerStrideAtCompileTime==1)
-    };
-    
-    internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest;
-    internal::gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!UseRhs> static_rhs;
-
-    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
-                                                  EvalToDest ? dest.data() : static_dest.data());
-                                                  
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(),
-        UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data());
-    
-    if(!EvalToDest)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = dest.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      MappedDest(actualDestPtr, dest.size()) = dest;
-    }
-      
-    if(!UseRhs)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = rhs.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, rhs.size()) = rhs;
-    }
-      
-      
-    internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-                                                int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run
-      (
-        lhs.rows(),                             // size
-        &lhs.coeffRef(0,0),  lhs.outerStride(), // lhs info
-        actualRhsPtr,                           // rhs info
-        actualDestPtr,                          // result info
-        actualAlpha                             // scale factor
-      );
-    
-    if(!EvalToDest)
-      dest = MappedDest(actualDestPtr, dest.size());
-  }
-};
-
-template<typename Lhs, typename Rhs, int RhsMode>
-struct selfadjoint_product_impl<Lhs,0,true,Rhs,RhsMode,false>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  enum { RhsUpLo = RhsMode&(Upper|Lower)  };
-
-  template<typename Dest>
-  static void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
-  {
-    // let's simply transpose the product
-    Transpose<Dest> destT(dest);
-    selfadjoint_product_impl<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false,
-                             Transpose<const Lhs>, 0, true>::run(destT, a_rhs.transpose(), a_lhs.transpose(), alpha);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
deleted file mode 100644
index 1238345..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
+++ /dev/null
@@ -1,118 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Selfadjoint matrix-vector product functionality based on ?SYMV/HEMV.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
-#define EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements selfadjoint matrix-vector multiplication using BLAS
-**********************************************************************/
-
-// symv/hemv specialization
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs>
-struct selfadjoint_matrix_vector_product_symv :
-  selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn> {};
-
-#define EIGEN_BLAS_SYMV_SPECIALIZE(Scalar) \
-template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
-struct selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Specialized> { \
-static void run( \
-  Index size, const Scalar*  lhs, Index lhsStride, \
-  const Scalar* _rhs, Scalar* res, Scalar alpha) { \
-    enum {\
-      IsColMajor = StorageOrder==ColMajor \
-    }; \
-    if (IsColMajor == ConjugateLhs) {\
-      selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn>::run( \
-        size, lhs, lhsStride, _rhs, res, alpha);  \
-    } else {\
-      selfadjoint_matrix_vector_product_symv<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs>::run( \
-        size, lhs, lhsStride, _rhs, res, alpha);  \
-    }\
-  } \
-}; \
-
-EIGEN_BLAS_SYMV_SPECIALIZE(double)
-EIGEN_BLAS_SYMV_SPECIALIZE(float)
-EIGEN_BLAS_SYMV_SPECIALIZE(dcomplex)
-EIGEN_BLAS_SYMV_SPECIALIZE(scomplex)
-
-#define EIGEN_BLAS_SYMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASFUNC) \
-template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
-struct selfadjoint_matrix_vector_product_symv<EIGTYPE,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs> \
-{ \
-typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> SYMVVector;\
-\
-static void run( \
-Index size, const EIGTYPE*  lhs, Index lhsStride, \
-const EIGTYPE* _rhs, EIGTYPE* res, EIGTYPE alpha) \
-{ \
-  enum {\
-    IsRowMajor = StorageOrder==RowMajor ? 1 : 0, \
-    IsLower = UpLo == Lower ? 1 : 0 \
-  }; \
-  BlasIndex n=convert_index<BlasIndex>(size), lda=convert_index<BlasIndex>(lhsStride), incx=1, incy=1; \
-  EIGTYPE beta(1); \
-  const EIGTYPE *x_ptr; \
-  char uplo=(IsRowMajor) ? (IsLower ? 'U' : 'L') : (IsLower ? 'L' : 'U'); \
-  SYMVVector x_tmp; \
-  if (ConjugateRhs) { \
-    Map<const SYMVVector, 0 > map_x(_rhs,size,1); \
-    x_tmp=map_x.conjugate(); \
-    x_ptr=x_tmp.data(); \
-  } else x_ptr=_rhs; \
-  BLASFUNC(&uplo, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
-}\
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_SYMV_SPECIALIZATION(double,   double, dsymv)
-EIGEN_BLAS_SYMV_SPECIALIZATION(float,    float,  ssymv)
-EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv)
-EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, MKL_Complex8,  chemv)
-#else
-EIGEN_BLAS_SYMV_SPECIALIZATION(double,   double, dsymv_)
-EIGEN_BLAS_SYMV_SPECIALIZATION(float,    float,  ssymv_)
-EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, double, zhemv_)
-EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, float,  chemv_)
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointProduct.h
deleted file mode 100644
index a21be80..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointProduct.h
+++ /dev/null
@@ -1,133 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINT_PRODUCT_H
-#define EIGEN_SELFADJOINT_PRODUCT_H
-
-/**********************************************************************
-* This file implements a self adjoint product: C += A A^T updating only
-* half of the selfadjoint matrix C.
-* It corresponds to the level 3 SYRK and level 2 SYR Blas routines.
-**********************************************************************/
-
-namespace Eigen { 
-
-
-template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
-struct selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo,ConjLhs,ConjRhs>
-{
-  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
-  {
-    internal::conj_if<ConjRhs> cj;
-    typedef Map<const Matrix<Scalar,Dynamic,1> > OtherMap;
-    typedef typename internal::conditional<ConjLhs,typename OtherMap::ConjugateReturnType,const OtherMap&>::type ConjLhsType;
-    for (Index i=0; i<size; ++i)
-    {
-      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+(UpLo==Lower ? i : 0), (UpLo==Lower ? size-i : (i+1)))
-          += (alpha * cj(vecY[i])) * ConjLhsType(OtherMap(vecX+(UpLo==Lower ? i : 0),UpLo==Lower ? size-i : (i+1)));
-    }
-  }
-};
-
-template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
-struct selfadjoint_rank1_update<Scalar,Index,RowMajor,UpLo,ConjLhs,ConjRhs>
-{
-  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
-  {
-    selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo==Lower?Upper:Lower,ConjRhs,ConjLhs>::run(size,mat,stride,vecY,vecX,alpha);
-  }
-};
-
-template<typename MatrixType, typename OtherType, int UpLo, bool OtherIsVector = OtherType::IsVectorAtCompileTime>
-struct selfadjoint_product_selector;
-
-template<typename MatrixType, typename OtherType, int UpLo>
-struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,true>
-{
-  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    typedef internal::blas_traits<OtherType> OtherBlasTraits;
-    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
-    typedef typename internal::remove_all<ActualOtherType>::type _ActualOtherType;
-    typename internal::add_const_on_value_type<ActualOtherType>::type actualOther = OtherBlasTraits::extract(other.derived());
-
-    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
-
-    enum {
-      StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-      UseOtherDirectly = _ActualOtherType::InnerStrideAtCompileTime==1
-    };
-    internal::gemv_static_vector_if<Scalar,OtherType::SizeAtCompileTime,OtherType::MaxSizeAtCompileTime,!UseOtherDirectly> static_other;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, actualOtherPtr, other.size(),
-      (UseOtherDirectly ? const_cast<Scalar*>(actualOther.data()) : static_other.data()));
-      
-    if(!UseOtherDirectly)
-      Map<typename _ActualOtherType::PlainObject>(actualOtherPtr, actualOther.size()) = actualOther;
-    
-    selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
-                              OtherBlasTraits::NeedToConjugate  && NumTraits<Scalar>::IsComplex,
-                            (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex>
-          ::run(other.size(), mat.data(), mat.outerStride(), actualOtherPtr, actualOtherPtr, actualAlpha);
-  }
-};
-
-template<typename MatrixType, typename OtherType, int UpLo>
-struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,false>
-{
-  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    typedef internal::blas_traits<OtherType> OtherBlasTraits;
-    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
-    typedef typename internal::remove_all<ActualOtherType>::type _ActualOtherType;
-    typename internal::add_const_on_value_type<ActualOtherType>::type actualOther = OtherBlasTraits::extract(other.derived());
-
-    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
-
-    enum {
-      IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
-      OtherIsRowMajor = _ActualOtherType::Flags&RowMajorBit ? 1 : 0
-    };
-
-    Index size = mat.cols();
-    Index depth = actualOther.cols();
-
-    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,Scalar,Scalar,
-              MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, _ActualOtherType::MaxColsAtCompileTime> BlockingType;
-
-    BlockingType blocking(size, size, depth, 1, false);
-
-
-    internal::general_matrix_matrix_triangular_product<Index,
-      Scalar, OtherIsRowMajor ? RowMajor : ColMajor,   OtherBlasTraits::NeedToConjugate  && NumTraits<Scalar>::IsComplex,
-      Scalar, OtherIsRowMajor ? ColMajor : RowMajor, (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex,
-      IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime, UpLo>
-      ::run(size, depth,
-            actualOther.data(), actualOther.outerStride(), actualOther.data(), actualOther.outerStride(),
-            mat.data(), mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
-  }
-};
-
-// high level API
-
-template<typename MatrixType, unsigned int UpLo>
-template<typename DerivedU>
-EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
-::rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha)
-{
-  selfadjoint_product_selector<MatrixType,DerivedU,UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha);
-
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointRank2Update.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointRank2Update.h
deleted file mode 100644
index f752a0b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/SelfadjointRank2Update.h
+++ /dev/null
@@ -1,94 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINTRANK2UPTADE_H
-#define EIGEN_SELFADJOINTRANK2UPTADE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/* Optimized selfadjoint matrix += alpha * uv' + conj(alpha)*vu'
- * It corresponds to the Level2 syr2 BLAS routine
- */
-
-template<typename Scalar, typename Index, typename UType, typename VType, int UpLo>
-struct selfadjoint_rank2_update_selector;
-
-template<typename Scalar, typename Index, typename UType, typename VType>
-struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Lower>
-{
-  static EIGEN_DEVICE_FUNC
-  void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
-  {
-    const Index size = u.size();
-    for (Index i=0; i<size; ++i)
-    {
-      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+i, size-i) +=
-                        (numext::conj(alpha) * numext::conj(u.coeff(i))) * v.tail(size-i)
-                      + (alpha * numext::conj(v.coeff(i))) * u.tail(size-i);
-    }
-  }
-};
-
-template<typename Scalar, typename Index, typename UType, typename VType>
-struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Upper>
-{
-  static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
-  {
-    const Index size = u.size();
-    for (Index i=0; i<size; ++i)
-      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i, i+1) +=
-                        (numext::conj(alpha)  * numext::conj(u.coeff(i))) * v.head(i+1)
-                      + (alpha * numext::conj(v.coeff(i))) * u.head(i+1);
-  }
-};
-
-template<bool Cond, typename T> struct conj_expr_if
-  : conditional<!Cond, const T&,
-      CwiseUnaryOp<scalar_conjugate_op<typename traits<T>::Scalar>,T> > {};
-
-} // end namespace internal
-
-template<typename MatrixType, unsigned int UpLo>
-template<typename DerivedU, typename DerivedV>
-EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
-::rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha)
-{
-  typedef internal::blas_traits<DerivedU> UBlasTraits;
-  typedef typename UBlasTraits::DirectLinearAccessType ActualUType;
-  typedef typename internal::remove_all<ActualUType>::type _ActualUType;
-  typename internal::add_const_on_value_type<ActualUType>::type actualU = UBlasTraits::extract(u.derived());
-
-  typedef internal::blas_traits<DerivedV> VBlasTraits;
-  typedef typename VBlasTraits::DirectLinearAccessType ActualVType;
-  typedef typename internal::remove_all<ActualVType>::type _ActualVType;
-  typename internal::add_const_on_value_type<ActualVType>::type actualV = VBlasTraits::extract(v.derived());
-
-  // If MatrixType is row major, then we use the routine for lower triangular in the upper triangular case and
-  // vice versa, and take the complex conjugate of all coefficients and vector entries.
-
-  enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 };
-  Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived())
-                             * numext::conj(VBlasTraits::extractScalarFactor(v.derived()));
-  if (IsRowMajor)
-    actualAlpha = numext::conj(actualAlpha);
-
-  typedef typename internal::remove_all<typename internal::conj_expr_if<int(IsRowMajor) ^ int(UBlasTraits::NeedToConjugate), _ActualUType>::type>::type UType;
-  typedef typename internal::remove_all<typename internal::conj_expr_if<int(IsRowMajor) ^ int(VBlasTraits::NeedToConjugate), _ActualVType>::type>::type VType;
-  internal::selfadjoint_rank2_update_selector<Scalar, Index, UType, VType,
-    (IsRowMajor ? int(UpLo==Upper ? Lower : Upper) : UpLo)>
-    ::run(_expression().const_cast_derived().data(),_expression().outerStride(),UType(actualU),VType(actualV),actualAlpha);
-
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINTRANK2UPTADE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
deleted file mode 100644
index f0c6050..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
+++ /dev/null
@@ -1,472 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_H
-#define EIGEN_TRIANGULAR_MATRIX_MATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// template<typename Scalar, int mr, int StorageOrder, bool Conjugate, int Mode>
-// struct gemm_pack_lhs_triangular
-// {
-//   Matrix<Scalar,mr,mr,
-//   void operator()(Scalar* blockA, const EIGEN_RESTRICT Scalar* _lhs, int lhsStride, int depth, int rows)
-//   {
-//     conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-//     const_blas_data_mapper<Scalar, StorageOrder> lhs(_lhs,lhsStride);
-//     int count = 0;
-//     const int peeled_mc = (rows/mr)*mr;
-//     for(int i=0; i<peeled_mc; i+=mr)
-//     {
-//       for(int k=0; k<depth; k++)
-//         for(int w=0; w<mr; w++)
-//           blockA[count++] = cj(lhs(i+w, k));
-//     }
-//     for(int i=peeled_mc; i<rows; i++)
-//     {
-//       for(int k=0; k<depth; k++)
-//         blockA[count++] = cj(lhs(i, k));
-//     }
-//   }
-// };
-
-/* Optimized triangular matrix * matrix (_TRMM++) product built on top of
- * the general matrix matrix product.
- */
-template <typename Scalar, typename Index,
-          int Mode, bool LhsIsTriangular,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResStorageOrder, int ResInnerStride,
-          int Version = Specialized>
-struct product_triangular_matrix_matrix;
-
-template <typename Scalar, typename Index,
-          int Mode, bool LhsIsTriangular,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-struct product_triangular_matrix_matrix<Scalar,Index,Mode,LhsIsTriangular,
-                                           LhsStorageOrder,ConjugateLhs,
-                                           RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride,Version>
-{
-  static EIGEN_STRONG_INLINE void run(
-    Index rows, Index cols, Index depth,
-    const Scalar* lhs, Index lhsStride,
-    const Scalar* rhs, Index rhsStride,
-    Scalar* res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    product_triangular_matrix_matrix<Scalar, Index,
-      (Mode&(UnitDiag|ZeroDiag)) | ((Mode&Upper) ? Lower : Upper),
-      (!LhsIsTriangular),
-      RhsStorageOrder==RowMajor ? ColMajor : RowMajor,
-      ConjugateRhs,
-      LhsStorageOrder==RowMajor ? ColMajor : RowMajor,
-      ConjugateLhs,
-      ColMajor, ResInnerStride>
-      ::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride, res, resIncr, resStride, alpha, blocking);
-  }
-};
-
-// implements col-major += alpha * op(triangular) * op(general)
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
-                                           LhsStorageOrder,ConjugateLhs,
-                                           RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>
-{
-  
-  typedef gebp_traits<Scalar,Scalar> Traits;
-  enum {
-    SmallPanelWidth   = 2 * EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-    IsLower = (Mode&Lower) == Lower,
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1
-  };
-
-  static EIGEN_DONT_INLINE void run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
-                                                        LhsStorageOrder,ConjugateLhs,
-                                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    // strip zeros
-    Index diagSize  = (std::min)(_rows,_depth);
-    Index rows      = IsLower ? _rows : diagSize;
-    Index depth     = IsLower ? diagSize : _depth;
-    Index cols      = _cols;
-    
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-    // The small panel size must not be larger than blocking size.
-    // Usually this should never be the case because SmallPanelWidth^2 is very small
-    // compared to L2 cache size, but let's be safe:
-    Index panelWidth = (std::min)(Index(SmallPanelWidth),(std::min)(kc,mc));
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    // To work around an "error: member reference base type 'Matrix<...>
-    // (Eigen::internal::constructor_without_unaligned_array_assert (*)())' is
-    // not a structure or union" compilation error in nvcc (tested V8.0.61),
-    // create a dummy internal::constructor_without_unaligned_array_assert
-    // object to pass to the Matrix constructor.
-    internal::constructor_without_unaligned_array_assert a;
-    Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,LhsStorageOrder> triangularBuffer(a);
-    triangularBuffer.setZero();
-    if((Mode&ZeroDiag)==ZeroDiag)
-      triangularBuffer.diagonal().setZero();
-    else
-      triangularBuffer.diagonal().setOnes();
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
-
-    for(Index k2=IsLower ? depth : 0;
-        IsLower ? k2>0 : k2<depth;
-        IsLower ? k2-=kc : k2+=kc)
-    {
-      Index actual_kc = (std::min)(IsLower ? k2 : depth-k2, kc);
-      Index actual_k2 = IsLower ? k2-actual_kc : k2;
-
-      // align blocks with the end of the triangular part for trapezoidal lhs
-      if((!IsLower)&&(k2<rows)&&(k2+actual_kc>rows))
-      {
-        actual_kc = rows-k2;
-        k2 = k2+actual_kc-kc;
-      }
-
-      pack_rhs(blockB, rhs.getSubMapper(actual_k2,0), actual_kc, cols);
-
-      // the selected lhs's panel has to be split in three different parts:
-      //  1 - the part which is zero => skip it
-      //  2 - the diagonal block => special kernel
-      //  3 - the dense panel below (lower case) or above (upper case) the diagonal block => GEPP
-
-      // the block diagonal, if any:
-      if(IsLower || actual_k2<rows)
-      {
-        // for each small vertical panels of lhs
-        for (Index k1=0; k1<actual_kc; k1+=panelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-k1, panelWidth);
-          Index lengthTarget = IsLower ? actual_kc-k1-actualPanelWidth : k1;
-          Index startBlock   = actual_k2+k1;
-          Index blockBOffset = k1;
-
-          // => GEBP with the micro triangular block
-          // The trick is to pack this micro block while filling the opposite triangular part with zeros.
-          // To this end we do an extra triangular copy to a small temporary buffer
-          for (Index k=0;k<actualPanelWidth;++k)
-          {
-            if (SetDiag)
-              triangularBuffer.coeffRef(k,k) = lhs(startBlock+k,startBlock+k);
-            for (Index i=IsLower ? k+1 : 0; IsLower ? i<actualPanelWidth : i<k; ++i)
-              triangularBuffer.coeffRef(i,k) = lhs(startBlock+i,startBlock+k);
-          }
-          pack_lhs(blockA, LhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()), actualPanelWidth, actualPanelWidth);
-
-          gebp_kernel(res.getSubMapper(startBlock, 0), blockA, blockB,
-                      actualPanelWidth, actualPanelWidth, cols, alpha,
-                      actualPanelWidth, actual_kc, 0, blockBOffset);
-
-          // GEBP with remaining micro panel
-          if (lengthTarget>0)
-          {
-            Index startTarget  = IsLower ? actual_k2+k1+actualPanelWidth : actual_k2;
-
-            pack_lhs(blockA, lhs.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget);
-
-            gebp_kernel(res.getSubMapper(startTarget, 0), blockA, blockB,
-                        lengthTarget, actualPanelWidth, cols, alpha,
-                        actualPanelWidth, actual_kc, 0, blockBOffset);
-          }
-        }
-      }
-      // the part below (lower case) or above (upper case) the diagonal => GEPP
-      {
-        Index start = IsLower ? k2 : 0;
-        Index end   = IsLower ? rows : (std::min)(actual_k2,rows);
-        for(Index i2=start; i2<end; i2+=mc)
-        {
-          const Index actual_mc = (std::min)(i2+mc,end)-i2;
-          gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr,Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder,false>()
-            (blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
-
-          gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc,
-                      actual_kc, cols, alpha, -1, -1, 0, 0);
-        }
-      }
-    }
-  }
-
-// implements col-major += alpha * op(general) * op(triangular)
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
-                                        LhsStorageOrder,ConjugateLhs,
-                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>
-{
-  typedef gebp_traits<Scalar,Scalar> Traits;
-  enum {
-    SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-    IsLower = (Mode&Lower) == Lower,
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1
-  };
-
-  static EIGEN_DONT_INLINE void run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
-                                                        LhsStorageOrder,ConjugateLhs,
-                                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    const Index PacketBytes = packet_traits<Scalar>::size*sizeof(Scalar);
-    // strip zeros
-    Index diagSize  = (std::min)(_cols,_depth);
-    Index rows      = _rows;
-    Index depth     = IsLower ? _depth : diagSize;
-    Index cols      = IsLower ? diagSize : _cols;
-    
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols+EIGEN_MAX_ALIGN_BYTES/sizeof(Scalar);
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    internal::constructor_without_unaligned_array_assert a;
-    Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,RhsStorageOrder> triangularBuffer(a);
-    triangularBuffer.setZero();
-    if((Mode&ZeroDiag)==ZeroDiag)
-      triangularBuffer.diagonal().setZero();
-    else
-      triangularBuffer.diagonal().setOnes();
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder,false,true> pack_rhs_panel;
-
-    for(Index k2=IsLower ? 0 : depth;
-        IsLower ? k2<depth  : k2>0;
-        IsLower ? k2+=kc   : k2-=kc)
-    {
-      Index actual_kc = (std::min)(IsLower ? depth-k2 : k2, kc);
-      Index actual_k2 = IsLower ? k2 : k2-actual_kc;
-
-      // align blocks with the end of the triangular part for trapezoidal rhs
-      if(IsLower && (k2<cols) && (actual_k2+actual_kc>cols))
-      {
-        actual_kc = cols-k2;
-        k2 = actual_k2 + actual_kc - kc;
-      }
-
-      // remaining size
-      Index rs = IsLower ? (std::min)(cols,actual_k2) : cols - k2;
-      // size of the triangular part
-      Index ts = (IsLower && actual_k2>=cols) ? 0 : actual_kc;
-
-      Scalar* geb = blockB+ts*ts;
-      geb = geb + internal::first_aligned<PacketBytes>(geb,PacketBytes/sizeof(Scalar));
-
-      pack_rhs(geb, rhs.getSubMapper(actual_k2,IsLower ? 0 : k2), actual_kc, rs);
-
-      // pack the triangular part of the rhs padding the unrolled blocks with zeros
-      if(ts>0)
-      {
-        for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-          Index actual_j2 = actual_k2 + j2;
-          Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
-          Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2;
-          // general part
-          pack_rhs_panel(blockB+j2*actual_kc,
-                         rhs.getSubMapper(actual_k2+panelOffset, actual_j2),
-                         panelLength, actualPanelWidth,
-                         actual_kc, panelOffset);
-
-          // append the triangular part via a temporary buffer
-          for (Index j=0;j<actualPanelWidth;++j)
-          {
-            if (SetDiag)
-              triangularBuffer.coeffRef(j,j) = rhs(actual_j2+j,actual_j2+j);
-            for (Index k=IsLower ? j+1 : 0; IsLower ? k<actualPanelWidth : k<j; ++k)
-              triangularBuffer.coeffRef(k,j) = rhs(actual_j2+k,actual_j2+j);
-          }
-
-          pack_rhs_panel(blockB+j2*actual_kc,
-                         RhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()),
-                         actualPanelWidth, actualPanelWidth,
-                         actual_kc, j2);
-        }
-      }
-
-      for (Index i2=0; i2<rows; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(mc,rows-i2);
-        pack_lhs(blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
-
-        // triangular kernel
-        if(ts>0)
-        {
-          for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
-          {
-            Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-            Index panelLength = IsLower ? actual_kc-j2 : j2+actualPanelWidth;
-            Index blockOffset = IsLower ? j2 : 0;
-
-            gebp_kernel(res.getSubMapper(i2, actual_k2 + j2),
-                        blockA, blockB+j2*actual_kc,
-                        actual_mc, panelLength, actualPanelWidth,
-                        alpha,
-                        actual_kc, actual_kc,  // strides
-                        blockOffset, blockOffset);// offsets
-          }
-        }
-        gebp_kernel(res.getSubMapper(i2, IsLower ? 0 : k2),
-                    blockA, geb, actual_mc, actual_kc, rs,
-                    alpha,
-                    -1, -1, 0, 0);
-      }
-    }
-  }
-
-/***************************************************************************
-* Wrapper to product_triangular_matrix_matrix
-***************************************************************************/
-
-} // end namespace internal
-
-namespace internal {
-template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs>
-struct triangular_product_impl<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
-{
-  template<typename Dest> static void run(Dest& dst, const Lhs &a_lhs, const Rhs &a_rhs, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar  LhsScalar;
-    typedef typename Rhs::Scalar  RhsScalar;
-    typedef typename Dest::Scalar Scalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-    
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(a_lhs);
-    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(a_rhs);
-    Scalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
-
-    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
-              Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,4> BlockingType;
-
-    enum { IsLower = (Mode&Lower) == Lower };
-    Index stripedRows  = ((!LhsIsTriangular) || (IsLower))  ? lhs.rows() : (std::min)(lhs.rows(),lhs.cols());
-    Index stripedCols  = ((LhsIsTriangular)  || (!IsLower)) ? rhs.cols() : (std::min)(rhs.cols(),rhs.rows());
-    Index stripedDepth = LhsIsTriangular ? ((!IsLower) ? lhs.cols() : (std::min)(lhs.cols(),lhs.rows()))
-                                         : ((IsLower)  ? rhs.rows() : (std::min)(rhs.rows(),rhs.cols()));
-
-    BlockingType blocking(stripedRows, stripedCols, stripedDepth, 1, false);
-
-    internal::product_triangular_matrix_matrix<Scalar, Index,
-      Mode, LhsIsTriangular,
-      (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
-      (internal::traits<ActualRhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
-      (internal::traits<Dest          >::Flags&RowMajorBit) ? RowMajor : ColMajor, Dest::InnerStrideAtCompileTime>
-      ::run(
-        stripedRows, stripedCols, stripedDepth,   // sizes
-        &lhs.coeffRef(0,0), lhs.outerStride(),    // lhs info
-        &rhs.coeffRef(0,0), rhs.outerStride(),    // rhs info
-        &dst.coeffRef(0,0), dst.innerStride(), dst.outerStride(),    // result info
-        actualAlpha, blocking
-      );
-
-    // Apply correction if the diagonal is unit and a scalar factor was nested:
-    if ((Mode&UnitDiag)==UnitDiag)
-    {
-      if (LhsIsTriangular && lhs_alpha!=LhsScalar(1))
-      {
-        Index diagSize = (std::min)(lhs.rows(),lhs.cols());
-        dst.topRows(diagSize) -= ((lhs_alpha-LhsScalar(1))*a_rhs).topRows(diagSize);
-      }
-      else if ((!LhsIsTriangular) && rhs_alpha!=RhsScalar(1))
-      {
-        Index diagSize = (std::min)(rhs.rows(),rhs.cols());
-        dst.leftCols(diagSize) -= (rhs_alpha-RhsScalar(1))*a_lhs.leftCols(diagSize);
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
deleted file mode 100644
index a98d12e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
+++ /dev/null
@@ -1,317 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Triangular matrix * matrix product functionality based on ?TRMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
-#define EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-template <typename Scalar, typename Index,
-          int Mode, bool LhsIsTriangular,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResStorageOrder>
-struct product_triangular_matrix_matrix_trmm :
-       product_triangular_matrix_matrix<Scalar,Index,Mode,
-          LhsIsTriangular,LhsStorageOrder,ConjugateLhs,
-          RhsStorageOrder, ConjugateRhs, ResStorageOrder, 1, BuiltIn> {};
-
-
-// try to go to BLAS specialization
-#define EIGEN_BLAS_TRMM_SPECIALIZE(Scalar, LhsIsTriangular) \
-template <typename Index, int Mode, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_triangular_matrix_matrix<Scalar,Index, Mode, LhsIsTriangular, \
-           LhsStorageOrder,ConjugateLhs, RhsStorageOrder,ConjugateRhs,ColMajor,1,Specialized> { \
-  static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,\
-    const Scalar* _rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride, Scalar alpha, level3_blocking<Scalar,Scalar>& blocking) { \
-      EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-      eigen_assert(resIncr == 1); \
-      product_triangular_matrix_matrix_trmm<Scalar,Index,Mode, \
-        LhsIsTriangular,LhsStorageOrder,ConjugateLhs, \
-        RhsStorageOrder, ConjugateRhs, ColMajor>::run( \
-          _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \
-  } \
-};
-
-EIGEN_BLAS_TRMM_SPECIALIZE(double, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(double, false)
-EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, false)
-EIGEN_BLAS_TRMM_SPECIALIZE(float, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(float, false)
-EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, false)
-
-// implements col-major += alpha * op(triangular) * op(general)
-#define EIGEN_BLAS_TRMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, int Mode, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
-         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = ((LhsStorageOrder==ColMajor) && ConjugateLhs) ? 1 : 0 \
-  }; \
-\
-  static void run( \
-    Index _rows, Index _cols, Index _depth, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
-  { \
-   Index diagSize  = (std::min)(_rows,_depth); \
-   Index rows      = IsLower ? _rows : diagSize; \
-   Index depth     = IsLower ? diagSize : _depth; \
-   Index cols      = _cols; \
-\
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
-\
-/* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/ \
-   if (rows != depth) { \
-\
-     /* FIXME handle mkl_domain_get_max_threads */ \
-     /*int nthr = mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS);*/ int nthr = 1;\
-\
-     if (((nthr==1) && (((std::max)(rows,depth)-diagSize)/(double)diagSize < 0.5))) { \
-     /* Most likely no benefit to call TRMM or GEMM from BLAS */ \
-       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,true, \
-       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, 1, BuiltIn>::run( \
-           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, 1, resStride, alpha, blocking); \
-     /*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/ \
-     } else { \
-     /* Make sense to call GEMM */ \
-       Map<const MatrixLhs, 0, OuterStride<> > lhsMap(_lhs,rows,depth,OuterStride<>(lhsStride)); \
-       MatrixLhs aa_tmp=lhsMap.template triangularView<Mode>(); \
-       BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride()); \
-       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth, 1, true); \
-       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1>::run( \
-       rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, 1, resStride, alpha, gemm_blocking, 0); \
-\
-     /*std::cout << "TRMM_L: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/ \
-     } \
-     return; \
-   } \
-   char side = 'L', transa, uplo, diag = 'N'; \
-   EIGTYPE *b; \
-   const EIGTYPE *a; \
-   BlasIndex m, n, lda, ldb; \
-\
-/* Set m, n */ \
-   m = convert_index<BlasIndex>(diagSize); \
-   n = convert_index<BlasIndex>(cols); \
-\
-/* Set trans */ \
-   transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
-\
-/* Set b, ldb */ \
-   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols,OuterStride<>(rhsStride)); \
-   MatrixX##EIGPREFIX b_tmp; \
-\
-   if (ConjugateRhs) b_tmp = rhs.conjugate(); else b_tmp = rhs; \
-   b = b_tmp.data(); \
-   ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-\
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (LhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
-   MatrixLhs a_tmp; \
-\
-   if ((conjA!=0) || (SetDiag==0)) { \
-     if (conjA) a_tmp = lhs.conjugate(); else a_tmp = lhs; \
-     if (IsZeroDiag) \
-       a_tmp.diagonal().setZero(); \
-     else if (IsUnitDiag) \
-       a_tmp.diagonal().setOnes();\
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _lhs; \
-     lda = convert_index<BlasIndex>(lhsStride); \
-   } \
-   /*std::cout << "TRMM_L: A is square! Go to BLAS TRMM implementation! \n";*/ \
-/* call ?trmm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
-\
-/* Add op(a_triangular)*b into res*/ \
-   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
-   res_tmp=res_tmp+b_tmp; \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMM_L(double, double, d, dtrmm)
-EIGEN_BLAS_TRMM_L(dcomplex, MKL_Complex16, cd, ztrmm)
-EIGEN_BLAS_TRMM_L(float, float, f, strmm)
-EIGEN_BLAS_TRMM_L(scomplex, MKL_Complex8, cf, ctrmm)
-#else
-EIGEN_BLAS_TRMM_L(double, double, d, dtrmm_)
-EIGEN_BLAS_TRMM_L(dcomplex, double, cd, ztrmm_)
-EIGEN_BLAS_TRMM_L(float, float, f, strmm_)
-EIGEN_BLAS_TRMM_L(scomplex, float, cf, ctrmm_)
-#endif
-
-// implements col-major += alpha * op(general) * op(triangular)
-#define EIGEN_BLAS_TRMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, int Mode, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
-         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = ((RhsStorageOrder==ColMajor) && ConjugateRhs) ? 1 : 0 \
-  }; \
-\
-  static void run( \
-    Index _rows, Index _cols, Index _depth, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
-  { \
-   Index diagSize  = (std::min)(_cols,_depth); \
-   Index rows      = _rows; \
-   Index depth     = IsLower ? _depth : diagSize; \
-   Index cols      = IsLower ? diagSize : _cols; \
-\
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
-\
-/* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/ \
-   if (cols != depth) { \
-\
-     int nthr = 1 /*mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS)*/; \
-\
-     if ((nthr==1) && (((std::max)(cols,depth)-diagSize)/(double)diagSize < 0.5)) { \
-     /* Most likely no benefit to call TRMM or GEMM from BLAS*/ \
-       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,false, \
-       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, 1, BuiltIn>::run( \
-           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, 1, resStride, alpha, blocking); \
-       /*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/ \
-     } else { \
-     /* Make sense to call GEMM */ \
-       Map<const MatrixRhs, 0, OuterStride<> > rhsMap(_rhs,depth,cols, OuterStride<>(rhsStride)); \
-       MatrixRhs aa_tmp=rhsMap.template triangularView<Mode>(); \
-       BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride()); \
-       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth, 1, true); \
-       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1>::run( \
-       rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, 1, resStride, alpha, gemm_blocking, 0); \
-\
-     /*std::cout << "TRMM_R: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/ \
-     } \
-     return; \
-   } \
-   char side = 'R', transa, uplo, diag = 'N'; \
-   EIGTYPE *b; \
-   const EIGTYPE *a; \
-   BlasIndex m, n, lda, ldb; \
-\
-/* Set m, n */ \
-   m = convert_index<BlasIndex>(rows); \
-   n = convert_index<BlasIndex>(diagSize); \
-\
-/* Set trans */ \
-   transa = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
-\
-/* Set b, ldb */ \
-   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
-   MatrixX##EIGPREFIX b_tmp; \
-\
-   if (ConjugateLhs) b_tmp = lhs.conjugate(); else b_tmp = lhs; \
-   b = b_tmp.data(); \
-   ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-\
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (RhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols, OuterStride<>(rhsStride)); \
-   MatrixRhs a_tmp; \
-\
-   if ((conjA!=0) || (SetDiag==0)) { \
-     if (conjA) a_tmp = rhs.conjugate(); else a_tmp = rhs; \
-     if (IsZeroDiag) \
-       a_tmp.diagonal().setZero(); \
-     else if (IsUnitDiag) \
-       a_tmp.diagonal().setOnes();\
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _rhs; \
-     lda = convert_index<BlasIndex>(rhsStride); \
-   } \
-   /*std::cout << "TRMM_R: A is square! Go to BLAS TRMM implementation! \n";*/ \
-/* call ?trmm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
-\
-/* Add op(a_triangular)*b into res*/ \
-   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
-   res_tmp=res_tmp+b_tmp; \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMM_R(double, double, d, dtrmm)
-EIGEN_BLAS_TRMM_R(dcomplex, MKL_Complex16, cd, ztrmm)
-EIGEN_BLAS_TRMM_R(float, float, f, strmm)
-EIGEN_BLAS_TRMM_R(scomplex, MKL_Complex8, cf, ctrmm)
-#else
-EIGEN_BLAS_TRMM_R(double, double, d, dtrmm_)
-EIGEN_BLAS_TRMM_R(dcomplex, double, cd, ztrmm_)
-EIGEN_BLAS_TRMM_R(float, float, f, strmm_)
-EIGEN_BLAS_TRMM_R(scomplex, float, cf, ctrmm_)
-#endif
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixVector.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixVector.h
deleted file mode 100644
index 76bfa15..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixVector.h
+++ /dev/null
@@ -1,350 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULARMATRIXVECTOR_H
-#define EIGEN_TRIANGULARMATRIXVECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder, int Version=Specialized>
-struct triangular_matrix_vector_product;
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
-struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  enum {
-    IsLower = ((Mode&Lower)==Lower),
-    HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
-    HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
-  };
-  static EIGEN_DONT_INLINE  void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-                                     const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha);
-};
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
-EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
-  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha)
-  {
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    Index size = (std::min)(_rows,_cols);
-    Index rows = IsLower ? _rows : (std::min)(_rows,_cols);
-    Index cols = IsLower ? (std::min)(_rows,_cols) : _cols;
-
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
-    typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
-
-    typedef Map<const Matrix<RhsScalar,Dynamic,1>, 0, InnerStride<> > RhsMap;
-    const RhsMap rhs(_rhs,cols,InnerStride<>(rhsIncr));
-    typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
-
-    typedef Map<Matrix<ResScalar,Dynamic,1> > ResMap;
-    ResMap res(_res,rows);
-
-    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
-
-    for (Index pi=0; pi<size; pi+=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(PanelWidth, size-pi);
-      for (Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = pi + k;
-        Index s = IsLower ? ((HasUnitDiag||HasZeroDiag) ? i+1 : i ) : pi;
-        Index r = IsLower ? actualPanelWidth-k : k+1;
-        if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0)
-          res.segment(s,r) += (alpha * cjRhs.coeff(i)) * cjLhs.col(i).segment(s,r);
-        if (HasUnitDiag)
-          res.coeffRef(i) += alpha * cjRhs.coeff(i);
-      }
-      Index r = IsLower ? rows - pi - actualPanelWidth : pi;
-      if (r>0)
-      {
-        Index s = IsLower ? pi+actualPanelWidth : 0;
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run(
-            r, actualPanelWidth,
-            LhsMapper(&lhs.coeffRef(s,pi), lhsStride),
-            RhsMapper(&rhs.coeffRef(pi), rhsIncr),
-            &res.coeffRef(s), resIncr, alpha);
-      }
-    }
-    if((!IsLower) && cols>size)
-    {
-      general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run(
-          rows, cols-size,
-          LhsMapper(&lhs.coeffRef(0,size), lhsStride),
-          RhsMapper(&rhs.coeffRef(size), rhsIncr),
-          _res, resIncr, alpha);
-    }
-  }
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
-struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  enum {
-    IsLower = ((Mode&Lower)==Lower),
-    HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
-    HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
-  };
-  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-                                    const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha);
-};
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
-EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
-  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha)
-  {
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    Index diagSize = (std::min)(_rows,_cols);
-    Index rows = IsLower ? _rows : diagSize;
-    Index cols = IsLower ? diagSize : _cols;
-
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
-    typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
-
-    typedef Map<const Matrix<RhsScalar,Dynamic,1> > RhsMap;
-    const RhsMap rhs(_rhs,cols);
-    typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
-
-    typedef Map<Matrix<ResScalar,Dynamic,1>, 0, InnerStride<> > ResMap;
-    ResMap res(_res,rows,InnerStride<>(resIncr));
-
-    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
-
-    for (Index pi=0; pi<diagSize; pi+=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(PanelWidth, diagSize-pi);
-      for (Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = pi + k;
-        Index s = IsLower ? pi  : ((HasUnitDiag||HasZeroDiag) ? i+1 : i);
-        Index r = IsLower ? k+1 : actualPanelWidth-k;
-        if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0)
-          res.coeffRef(i) += alpha * (cjLhs.row(i).segment(s,r).cwiseProduct(cjRhs.segment(s,r).transpose())).sum();
-        if (HasUnitDiag)
-          res.coeffRef(i) += alpha * cjRhs.coeff(i);
-      }
-      Index r = IsLower ? pi : cols - pi - actualPanelWidth;
-      if (r>0)
-      {
-        Index s = IsLower ? 0 : pi + actualPanelWidth;
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run(
-            actualPanelWidth, r,
-            LhsMapper(&lhs.coeffRef(pi,s), lhsStride),
-            RhsMapper(&rhs.coeffRef(s), rhsIncr),
-            &res.coeffRef(pi), resIncr, alpha);
-      }
-    }
-    if(IsLower && rows>diagSize)
-    {
-      general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run(
-            rows-diagSize, cols,
-            LhsMapper(&lhs.coeffRef(diagSize,0), lhsStride),
-            RhsMapper(&rhs.coeffRef(0), rhsIncr),
-            &res.coeffRef(diagSize), resIncr, alpha);
-    }
-  }
-
-/***************************************************************************
-* Wrapper to product_triangular_vector
-***************************************************************************/
-
-template<int Mode,int StorageOrder>
-struct trmv_selector;
-
-} // end namespace internal
-
-namespace internal {
-
-template<int Mode, typename Lhs, typename Rhs>
-struct triangular_product_impl<Mode,true,Lhs,false,Rhs,true>
-{
-  template<typename Dest> static void run(Dest& dst, const Lhs &lhs, const Rhs &rhs, const typename Dest::Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==lhs.rows() && dst.cols()==rhs.cols());
-  
-    internal::trmv_selector<Mode,(int(internal::traits<Lhs>::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(lhs, rhs, dst, alpha);
-  }
-};
-
-template<int Mode, typename Lhs, typename Rhs>
-struct triangular_product_impl<Mode,false,Lhs,true,Rhs,false>
-{
-  template<typename Dest> static void run(Dest& dst, const Lhs &lhs, const Rhs &rhs, const typename Dest::Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==lhs.rows() && dst.cols()==rhs.cols());
-
-    Transpose<Dest> dstT(dst);
-    internal::trmv_selector<(Mode & (UnitDiag|ZeroDiag)) | ((Mode & Lower) ? Upper : Lower),
-                            (int(internal::traits<Rhs>::Flags)&RowMajorBit) ? ColMajor : RowMajor>
-            ::run(rhs.transpose(),lhs.transpose(), dstT, alpha);
-  }
-};
-
-} // end namespace internal
-
-namespace internal {
-
-// TODO: find a way to factorize this piece of code with gemv_selector since the logic is exactly the same.
-  
-template<int Mode> struct trmv_selector<Mode,ColMajor>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar      LhsScalar;
-    typedef typename Rhs::Scalar      RhsScalar;
-    typedef typename Dest::Scalar     ResScalar;
-    typedef typename Dest::RealScalar RealScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    
-    typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
-
-    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
-    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
-    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1,
-      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
-      MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal
-    };
-
-    gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
-
-    bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
-    bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
-
-    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
-
-    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
-                                                  evalToDest ? dest.data() : static_dest.data());
-
-    if(!evalToDest)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = dest.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      if(!alphaIsCompatible)
-      {
-        MappedDest(actualDestPtr, dest.size()).setZero();
-        compatibleAlpha = RhsScalar(1);
-      }
-      else
-        MappedDest(actualDestPtr, dest.size()) = dest;
-    }
-
-    internal::triangular_matrix_vector_product
-      <Index,Mode,
-       LhsScalar, LhsBlasTraits::NeedToConjugate,
-       RhsScalar, RhsBlasTraits::NeedToConjugate,
-       ColMajor>
-      ::run(actualLhs.rows(),actualLhs.cols(),
-            actualLhs.data(),actualLhs.outerStride(),
-            actualRhs.data(),actualRhs.innerStride(),
-            actualDestPtr,1,compatibleAlpha);
-
-    if (!evalToDest)
-    {
-      if(!alphaIsCompatible)
-        dest += actualAlpha * MappedDest(actualDestPtr, dest.size());
-      else
-        dest = MappedDest(actualDestPtr, dest.size());
-    }
-
-    if ( ((Mode&UnitDiag)==UnitDiag) && (lhs_alpha!=LhsScalar(1)) )
-    {
-      Index diagSize = (std::min)(lhs.rows(),lhs.cols());
-      dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
-    }
-  }
-};
-
-template<int Mode> struct trmv_selector<Mode,RowMajor>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar      LhsScalar;
-    typedef typename Rhs::Scalar      RhsScalar;
-    typedef typename Dest::Scalar     ResScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-    typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
-    typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
-
-    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
-    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
-    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
-
-    enum {
-      DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1
-    };
-
-    gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
-
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
-        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
-
-    if(!DirectlyUseRhs)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = actualRhs.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
-    }
-
-    internal::triangular_matrix_vector_product
-      <Index,Mode,
-       LhsScalar, LhsBlasTraits::NeedToConjugate,
-       RhsScalar, RhsBlasTraits::NeedToConjugate,
-       RowMajor>
-      ::run(actualLhs.rows(),actualLhs.cols(),
-            actualLhs.data(),actualLhs.outerStride(),
-            actualRhsPtr,1,
-            dest.data(),dest.innerStride(),
-            actualAlpha);
-
-    if ( ((Mode&UnitDiag)==UnitDiag) && (lhs_alpha!=LhsScalar(1)) )
-    {
-      Index diagSize = (std::min)(lhs.rows(),lhs.cols());
-      dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
-    }
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULARMATRIXVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
deleted file mode 100644
index 3d47a2b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
+++ /dev/null
@@ -1,255 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Triangular matrix-vector product functionality based on ?TRMV.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
-#define EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements triangular matrix-vector multiplication using BLAS
-**********************************************************************/
-
-// trmv/hemv specialization
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder>
-struct triangular_matrix_vector_product_trmv :
-  triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,StorageOrder,BuiltIn> {};
-
-#define EIGEN_BLAS_TRMV_SPECIALIZE(Scalar) \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor,Specialized> { \
- static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
-                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
-      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor>::run( \
-        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-  } \
-}; \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor,Specialized> { \
- static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
-                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
-      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor>::run( \
-        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-  } \
-};
-
-EIGEN_BLAS_TRMV_SPECIALIZE(double)
-EIGEN_BLAS_TRMV_SPECIALIZE(float)
-EIGEN_BLAS_TRMV_SPECIALIZE(dcomplex)
-EIGEN_BLAS_TRMV_SPECIALIZE(scomplex)
-
-// implements col-major: res += alpha * op(triangular) * vector
-#define EIGEN_BLAS_TRMV_CM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor> { \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper \
-  }; \
- static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
-                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
- { \
-   if (ConjLhs || IsZeroDiag) { \
-     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \
-       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-     return; \
-   }\
-   Index size = (std::min)(_rows,_cols); \
-   Index rows = IsLower ? _rows : size; \
-   Index cols = IsLower ? size : _cols; \
-\
-   typedef VectorX##EIGPREFIX VectorRhs; \
-   EIGTYPE *x, *y;\
-\
-/* Set x*/ \
-   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
-   VectorRhs x_tmp; \
-   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-   x = x_tmp.data(); \
-\
-/* Square part handling */\
-\
-   char trans, uplo, diag; \
-   BlasIndex m, n, lda, incx, incy; \
-   EIGTYPE const *a; \
-   EIGTYPE beta(1); \
-\
-/* Set m, n */ \
-   n = convert_index<BlasIndex>(size); \
-   lda = convert_index<BlasIndex>(lhsStride); \
-   incx = 1; \
-   incy = convert_index<BlasIndex>(resIncr); \
-\
-/* Set uplo, trans and diag*/ \
-   trans = 'N'; \
-   uplo = IsLower ? 'L' : 'U'; \
-   diag = IsUnitDiag ? 'U' : 'N'; \
-\
-/* call ?TRMV*/ \
-   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
-\
-/* Add op(a_tr)rhs into res*/ \
-   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
-/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
-   if (size<(std::max)(rows,cols)) { \
-     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-     x = x_tmp.data(); \
-     if (size<rows) { \
-       y = _res + size*resIncr; \
-       a = _lhs + size; \
-       m = convert_index<BlasIndex>(rows-size); \
-       n = convert_index<BlasIndex>(size); \
-     } \
-     else { \
-       x += size; \
-       y = _res; \
-       a = _lhs + size*lda; \
-       m = convert_index<BlasIndex>(size); \
-       n = convert_index<BlasIndex>(cols-size); \
-     } \
-     BLASPREFIX##gemv##BLASPOSTFIX(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
-   } \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMV_CM(double,   double, d,  d,)
-EIGEN_BLAS_TRMV_CM(dcomplex, MKL_Complex16, cd, z,)
-EIGEN_BLAS_TRMV_CM(float,    float,  f,  s,)
-EIGEN_BLAS_TRMV_CM(scomplex, MKL_Complex8,  cf, c,)
-#else
-EIGEN_BLAS_TRMV_CM(double,   double, d,  d, _)
-EIGEN_BLAS_TRMV_CM(dcomplex, double, cd, z, _)
-EIGEN_BLAS_TRMV_CM(float,    float,  f,  s, _)
-EIGEN_BLAS_TRMV_CM(scomplex, float,  cf, c, _)
-#endif
-
-// implements row-major: res += alpha * op(triangular) * vector
-#define EIGEN_BLAS_TRMV_RM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor> { \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper \
-  }; \
- static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
-                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
- { \
-   if (IsZeroDiag) { \
-     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \
-       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-     return; \
-   }\
-   Index size = (std::min)(_rows,_cols); \
-   Index rows = IsLower ? _rows : size; \
-   Index cols = IsLower ? size : _cols; \
-\
-   typedef VectorX##EIGPREFIX VectorRhs; \
-   EIGTYPE *x, *y;\
-\
-/* Set x*/ \
-   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
-   VectorRhs x_tmp; \
-   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-   x = x_tmp.data(); \
-\
-/* Square part handling */\
-\
-   char trans, uplo, diag; \
-   BlasIndex m, n, lda, incx, incy; \
-   EIGTYPE const *a; \
-   EIGTYPE beta(1); \
-\
-/* Set m, n */ \
-   n = convert_index<BlasIndex>(size); \
-   lda = convert_index<BlasIndex>(lhsStride); \
-   incx = 1; \
-   incy = convert_index<BlasIndex>(resIncr); \
-\
-/* Set uplo, trans and diag*/ \
-   trans = ConjLhs ? 'C' : 'T'; \
-   uplo = IsLower ? 'U' : 'L'; \
-   diag = IsUnitDiag ? 'U' : 'N'; \
-\
-/* call ?TRMV*/ \
-   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
-\
-/* Add op(a_tr)rhs into res*/ \
-   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
-/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
-   if (size<(std::max)(rows,cols)) { \
-     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-     x = x_tmp.data(); \
-     if (size<rows) { \
-       y = _res + size*resIncr; \
-       a = _lhs + size*lda; \
-       m = convert_index<BlasIndex>(rows-size); \
-       n = convert_index<BlasIndex>(size); \
-     } \
-     else { \
-       x += size; \
-       y = _res; \
-       a = _lhs + size; \
-       m = convert_index<BlasIndex>(size); \
-       n = convert_index<BlasIndex>(cols-size); \
-     } \
-     BLASPREFIX##gemv##BLASPOSTFIX(&trans, &n, &m, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
-   } \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMV_RM(double,   double, d,  d,)
-EIGEN_BLAS_TRMV_RM(dcomplex, MKL_Complex16, cd, z,)
-EIGEN_BLAS_TRMV_RM(float,    float,  f,  s,)
-EIGEN_BLAS_TRMV_RM(scomplex, MKL_Complex8,  cf, c,)
-#else
-EIGEN_BLAS_TRMV_RM(double,   double, d,  d,_)
-EIGEN_BLAS_TRMV_RM(dcomplex, double, cd, z,_)
-EIGEN_BLAS_TRMV_RM(float,    float,  f,  s,_)
-EIGEN_BLAS_TRMV_RM(scomplex, float,  cf, c,_)
-#endif
-
-} // end namespase internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverMatrix.h
deleted file mode 100644
index 6d879ba..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverMatrix.h
+++ /dev/null
@@ -1,337 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_H
-#define EIGEN_TRIANGULAR_SOLVER_MATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// if the rhs is row major, let's transpose the product
-template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-struct triangular_solve_matrix<Scalar,Index,Side,Mode,Conjugate,TriStorageOrder,RowMajor,OtherInnerStride>
-{
-  static void run(
-    Index size, Index cols,
-    const Scalar*  tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking)
-  {
-    triangular_solve_matrix<
-      Scalar, Index, Side==OnTheLeft?OnTheRight:OnTheLeft,
-      (Mode&UnitDiag) | ((Mode&Upper) ? Lower : Upper),
-      NumTraits<Scalar>::IsComplex && Conjugate,
-      TriStorageOrder==RowMajor ? ColMajor : RowMajor, ColMajor, OtherInnerStride>
-      ::run(size, cols, tri, triStride, _other, otherIncr, otherStride, blocking);
-  }
-};
-
-/* Optimized triangular solver with multiple right hand side and the triangular matrix on the left
- */
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder,int OtherInnerStride>
-struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>
-{
-  static EIGEN_DONT_INLINE void run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking);
-};
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>::run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index cols = otherSize;
-
-    typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> TriMapper;
-    typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> OtherMapper;
-    TriMapper tri(_tri, triStride);
-    OtherMapper other(_other, otherStride, otherIncr);
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-
-    enum {
-      SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-      IsLower = (Mode&Lower) == Lower
-    };
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(size,blocking.mc());  // cache block size along the M direction
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    conj_if<Conjugate> conj;
-    gebp_kernel<Scalar, Scalar, Index, OtherMapper, Traits::mr, Traits::nr, Conjugate, false> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, TriMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, TriStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, OtherMapper, Traits::nr, ColMajor, false, true> pack_rhs;
-
-    // the goal here is to subdivise the Rhs panels such that we keep some cache
-    // coherence when accessing the rhs elements
-    std::ptrdiff_t l1, l2, l3;
-    manage_caching_sizes(GetAction, &l1, &l2, &l3);
-    Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * std::max<Index>(otherStride,size)) : 0;
-    subcols = std::max<Index>((subcols/Traits::nr)*Traits::nr, Traits::nr);
-
-    for(Index k2=IsLower ? 0 : size;
-        IsLower ? k2<size : k2>0;
-        IsLower ? k2+=kc : k2-=kc)
-    {
-      const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc);
-
-      // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel,
-      // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into
-      // A11 (the triangular part) and A21 the remaining rectangular part.
-      // Then the high level algorithm is:
-      //  - B = R1                    => general block copy (done during the next step)
-      //  - R1 = A11^-1 B             => tricky part
-      //  - update B from the new R1  => actually this has to be performed continuously during the above step
-      //  - R2 -= A21 * B             => GEPP
-
-      // The tricky part: compute R1 = A11^-1 B while updating B from R1
-      // The idea is to split A11 into multiple small vertical panels.
-      // Each panel can be split into a small triangular part T1k which is processed without optimization,
-      // and the remaining small part T2k which is processed using gebp with appropriate block strides
-      for(Index j2=0; j2<cols; j2+=subcols)
-      {
-        Index actual_cols = (std::min)(cols-j2,subcols);
-        // for each small vertical panels [T1k^T, T2k^T]^T of lhs
-        for (Index k1=0; k1<actual_kc; k1+=SmallPanelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-k1, SmallPanelWidth);
-          // tr solve
-          for (Index k=0; k<actualPanelWidth; ++k)
-          {
-            // TODO write a small kernel handling this (can be shared with trsv)
-            Index i  = IsLower ? k2+k1+k : k2-k1-k-1;
-            Index rs = actualPanelWidth - k - 1; // remaining size
-            Index s  = TriStorageOrder==RowMajor ? (IsLower ? k2+k1 : i+1)
-                                                 :  IsLower ? i+1 : i-rs;
-
-            Scalar a = (Mode & UnitDiag) ? Scalar(1) : Scalar(1)/conj(tri(i,i));
-            for (Index j=j2; j<j2+actual_cols; ++j)
-            {
-              if (TriStorageOrder==RowMajor)
-              {
-                Scalar b(0);
-                const Scalar* l = &tri(i,s);
-                typename OtherMapper::LinearMapper r = other.getLinearMapper(s,j);
-                for (Index i3=0; i3<k; ++i3)
-                  b += conj(l[i3]) * r(i3);
-
-                other(i,j) = (other(i,j) - b)*a;
-              }
-              else
-              {
-                Scalar& otherij = other(i,j);
-                otherij *= a;
-                Scalar b = otherij;
-                typename OtherMapper::LinearMapper r = other.getLinearMapper(s,j);
-                typename TriMapper::LinearMapper l = tri.getLinearMapper(s,i);
-                for (Index i3=0;i3<rs;++i3)
-                  r(i3) -= b * conj(l(i3));
-              }
-            }
-          }
-
-          Index lengthTarget = actual_kc-k1-actualPanelWidth;
-          Index startBlock   = IsLower ? k2+k1 : k2-k1-actualPanelWidth;
-          Index blockBOffset = IsLower ? k1 : lengthTarget;
-
-          // update the respective rows of B from other
-          pack_rhs(blockB+actual_kc*j2, other.getSubMapper(startBlock,j2), actualPanelWidth, actual_cols, actual_kc, blockBOffset);
-
-          // GEBP
-          if (lengthTarget>0)
-          {
-            Index startTarget  = IsLower ? k2+k1+actualPanelWidth : k2-actual_kc;
-
-            pack_lhs(blockA, tri.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget);
-
-            gebp_kernel(other.getSubMapper(startTarget,j2), blockA, blockB+actual_kc*j2, lengthTarget, actualPanelWidth, actual_cols, Scalar(-1),
-                        actualPanelWidth, actual_kc, 0, blockBOffset);
-          }
-        }
-      }
-      
-      // R2 -= A21 * B => GEPP
-      {
-        Index start = IsLower ? k2+kc : 0;
-        Index end   = IsLower ? size : k2-kc;
-        for(Index i2=start; i2<end; i2+=mc)
-        {
-          const Index actual_mc = (std::min)(mc,end-i2);
-          if (actual_mc>0)
-          {
-            pack_lhs(blockA, tri.getSubMapper(i2, IsLower ? k2 : k2-kc), actual_kc, actual_mc);
-
-            gebp_kernel(other.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1), -1, -1, 0, 0);
-          }
-        }
-      }
-    }
-  }
-
-/* Optimized triangular solver with multiple left hand sides and the triangular matrix on the right
- */
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>
-{
-  static EIGEN_DONT_INLINE void run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking);
-};
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>::run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index rows = otherSize;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper;
-    LhsMapper lhs(_other, otherStride, otherIncr);
-    RhsMapper rhs(_tri, triStride);
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-    enum {
-      RhsStorageOrder   = TriStorageOrder,
-      SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-      IsLower = (Mode&Lower) == Lower
-    };
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*size;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    conj_if<Conjugate> conj;
-    gebp_kernel<Scalar, Scalar, Index, LhsMapper, Traits::mr, Traits::nr, false, Conjugate> gebp_kernel;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder,false,true> pack_rhs_panel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor, false, true> pack_lhs_panel;
-
-    for(Index k2=IsLower ? size : 0;
-        IsLower ? k2>0 : k2<size;
-        IsLower ? k2-=kc : k2+=kc)
-    {
-      const Index actual_kc = (std::min)(IsLower ? k2 : size-k2, kc);
-      Index actual_k2 = IsLower ? k2-actual_kc : k2 ;
-
-      Index startPanel = IsLower ? 0 : k2+actual_kc;
-      Index rs = IsLower ? actual_k2 : size - actual_k2 - actual_kc;
-      Scalar* geb = blockB+actual_kc*actual_kc;
-
-      if (rs>0) pack_rhs(geb, rhs.getSubMapper(actual_k2,startPanel), actual_kc, rs);
-
-      // triangular packing (we only pack the panels off the diagonal,
-      // neglecting the blocks overlapping the diagonal
-      {
-        for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-          Index actual_j2 = actual_k2 + j2;
-          Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
-          Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2;
-
-          if (panelLength>0)
-          pack_rhs_panel(blockB+j2*actual_kc,
-                         rhs.getSubMapper(actual_k2+panelOffset, actual_j2),
-                         panelLength, actualPanelWidth,
-                         actual_kc, panelOffset);
-        }
-      }
-
-      for(Index i2=0; i2<rows; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(mc,rows-i2);
-
-        // triangular solver kernel
-        {
-          // for each small block of the diagonal (=> vertical panels of rhs)
-          for (Index j2 = IsLower
-                      ? (actual_kc - ((actual_kc%SmallPanelWidth) ? Index(actual_kc%SmallPanelWidth)
-                                                                  : Index(SmallPanelWidth)))
-                      : 0;
-               IsLower ? j2>=0 : j2<actual_kc;
-               IsLower ? j2-=SmallPanelWidth : j2+=SmallPanelWidth)
-          {
-            Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-            Index absolute_j2 = actual_k2 + j2;
-            Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
-            Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2;
-
-            // GEBP
-            if(panelLength>0)
-            {
-              gebp_kernel(lhs.getSubMapper(i2,absolute_j2),
-                          blockA, blockB+j2*actual_kc,
-                          actual_mc, panelLength, actualPanelWidth,
-                          Scalar(-1),
-                          actual_kc, actual_kc, // strides
-                          panelOffset, panelOffset); // offsets
-            }
-
-            // unblocked triangular solve
-            for (Index k=0; k<actualPanelWidth; ++k)
-            {
-              Index j = IsLower ? absolute_j2+actualPanelWidth-k-1 : absolute_j2+k;
-
-              typename LhsMapper::LinearMapper r = lhs.getLinearMapper(i2,j);
-              for (Index k3=0; k3<k; ++k3)
-              {
-                Scalar b = conj(rhs(IsLower ? j+1+k3 : absolute_j2+k3,j));
-                typename LhsMapper::LinearMapper a = lhs.getLinearMapper(i2,IsLower ? j+1+k3 : absolute_j2+k3);
-                for (Index i=0; i<actual_mc; ++i)
-                  r(i) -= a(i) * b;
-              }
-              if((Mode & UnitDiag)==0)
-              {
-                Scalar inv_rjj = RealScalar(1)/conj(rhs(j,j));
-                for (Index i=0; i<actual_mc; ++i)
-                  r(i) *= inv_rjj;
-              }
-            }
-
-            // pack the just computed part of lhs to A
-            pack_lhs_panel(blockA, lhs.getSubMapper(i2,absolute_j2),
-                           actualPanelWidth, actual_mc,
-                           actual_kc, j2);
-          }
-        }
-
-        if (rs>0)
-          gebp_kernel(lhs.getSubMapper(i2, startPanel), blockA, geb,
-                      actual_mc, actual_kc, rs, Scalar(-1),
-                      -1, -1, 0, 0);
-      }
-    }
-  }
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
deleted file mode 100644
index 621194c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
+++ /dev/null
@@ -1,167 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Triangular matrix * matrix product functionality based on ?TRMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
-#define EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// implements LeftSide op(triangular)^-1 * general
-#define EIGEN_BLAS_TRSM_L(EIGTYPE, BLASTYPE, BLASFUNC) \
-template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
-struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,1> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
-  }; \
-  static void run( \
-      Index size, Index otherSize, \
-      const EIGTYPE* _tri, Index triStride, \
-      EIGTYPE* _other, Index otherIncr, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \
-  { \
-   EIGEN_ONLY_USED_FOR_DEBUG(otherIncr); \
-   eigen_assert(otherIncr == 1); \
-   BlasIndex m = convert_index<BlasIndex>(size), n = convert_index<BlasIndex>(otherSize), lda, ldb; \
-   char side = 'L', uplo, diag='N', transa; \
-   /* Set alpha_ */ \
-   EIGTYPE alpha(1); \
-   ldb = convert_index<BlasIndex>(otherStride);\
-\
-   const EIGTYPE *a; \
-/* Set trans */ \
-   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
-   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
-   MatrixTri a_tmp; \
-\
-   if (conjA) { \
-     a_tmp = tri.conjugate(); \
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _tri; \
-     lda = convert_index<BlasIndex>(triStride); \
-   } \
-   if (IsUnitDiag) diag='U'; \
-/* call ?trsm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
- } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRSM_L(double,   double, dtrsm)
-EIGEN_BLAS_TRSM_L(dcomplex, MKL_Complex16, ztrsm)
-EIGEN_BLAS_TRSM_L(float,    float,  strsm)
-EIGEN_BLAS_TRSM_L(scomplex, MKL_Complex8, ctrsm)
-#else
-EIGEN_BLAS_TRSM_L(double,   double, dtrsm_)
-EIGEN_BLAS_TRSM_L(dcomplex, double, ztrsm_)
-EIGEN_BLAS_TRSM_L(float,    float,  strsm_)
-EIGEN_BLAS_TRSM_L(scomplex, float,  ctrsm_)
-#endif
-
-// implements RightSide general * op(triangular)^-1
-#define EIGEN_BLAS_TRSM_R(EIGTYPE, BLASTYPE, BLASFUNC) \
-template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
-struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,1> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
-  }; \
-  static void run( \
-      Index size, Index otherSize, \
-      const EIGTYPE* _tri, Index triStride, \
-      EIGTYPE* _other, Index otherIncr, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \
-  { \
-   EIGEN_ONLY_USED_FOR_DEBUG(otherIncr); \
-   eigen_assert(otherIncr == 1); \
-   BlasIndex m = convert_index<BlasIndex>(otherSize), n = convert_index<BlasIndex>(size), lda, ldb; \
-   char side = 'R', uplo, diag='N', transa; \
-   /* Set alpha_ */ \
-   EIGTYPE alpha(1); \
-   ldb = convert_index<BlasIndex>(otherStride);\
-\
-   const EIGTYPE *a; \
-/* Set trans */ \
-   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
-   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
-   MatrixTri a_tmp; \
-\
-   if (conjA) { \
-     a_tmp = tri.conjugate(); \
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _tri; \
-     lda = convert_index<BlasIndex>(triStride); \
-   } \
-   if (IsUnitDiag) diag='U'; \
-/* call ?trsm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
-   /*std::cout << "TRMS_L specialization!\n";*/ \
- } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRSM_R(double,   double, dtrsm)
-EIGEN_BLAS_TRSM_R(dcomplex, MKL_Complex16, ztrsm)
-EIGEN_BLAS_TRSM_R(float,    float,  strsm)
-EIGEN_BLAS_TRSM_R(scomplex, MKL_Complex8,  ctrsm)
-#else
-EIGEN_BLAS_TRSM_R(double,   double, dtrsm_)
-EIGEN_BLAS_TRSM_R(dcomplex, double, ztrsm_)
-EIGEN_BLAS_TRSM_R(float,    float,  strsm_)
-EIGEN_BLAS_TRSM_R(scomplex, float,  ctrsm_)
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverVector.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverVector.h
deleted file mode 100644
index 6473170..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/products/TriangularSolverVector.h
+++ /dev/null
@@ -1,148 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULAR_SOLVER_VECTOR_H
-#define EIGEN_TRIANGULAR_SOLVER_VECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate, int StorageOrder>
-struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheRight, Mode, Conjugate, StorageOrder>
-{
-  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
-  {
-    triangular_solve_vector<LhsScalar,RhsScalar,Index,OnTheLeft,
-        ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag),
-        Conjugate,StorageOrder==RowMajor?ColMajor:RowMajor
-      >::run(size, _lhs, lhsStride, rhs);
-  }
-};
-
-// forward and backward substitution, row-major, rhs is a vector
-template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
-struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, RowMajor>
-{
-  enum {
-    IsLower = ((Mode&Lower)==Lower)
-  };
-  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
-  {
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride));
-
-    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
-
-    typename internal::conditional<
-                          Conjugate,
-                          const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>,
-                          const LhsMap&>
-                        ::type cjLhs(lhs);
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    for(Index pi=IsLower ? 0 : size;
-        IsLower ? pi<size : pi>0;
-        IsLower ? pi+=PanelWidth : pi-=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
-
-      Index r = IsLower ? pi : size - pi; // remaining size
-      if (r > 0)
-      {
-        // let's directly call the low level product function because:
-        // 1 - it is faster to compile
-        // 2 - it is slightly faster at runtime
-        Index startRow = IsLower ? pi : pi-actualPanelWidth;
-        Index startCol = IsLower ? 0 : pi;
-
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,Conjugate,RhsScalar,RhsMapper,false>::run(
-          actualPanelWidth, r,
-          LhsMapper(&lhs.coeffRef(startRow,startCol), lhsStride),
-          RhsMapper(rhs + startCol, 1),
-          rhs + startRow, 1,
-          RhsScalar(-1));
-      }
-
-      for(Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = IsLower ? pi+k : pi-k-1;
-        Index s = IsLower ? pi   : i+1;
-        if (k>0)
-          rhs[i] -= (cjLhs.row(i).segment(s,k).transpose().cwiseProduct(Map<const Matrix<RhsScalar,Dynamic,1> >(rhs+s,k))).sum();
-
-        if((!(Mode & UnitDiag)) && numext::not_equal_strict(rhs[i],RhsScalar(0)))
-          rhs[i] /= cjLhs(i,i);
-      }
-    }
-  }
-};
-
-// forward and backward substitution, column-major, rhs is a vector
-template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
-struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, ColMajor>
-{
-  enum {
-    IsLower = ((Mode&Lower)==Lower)
-  };
-  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
-  {
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride));
-    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
-    typename internal::conditional<Conjugate,
-                                   const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>,
-                                   const LhsMap&
-                                  >::type cjLhs(lhs);
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-
-    for(Index pi=IsLower ? 0 : size;
-        IsLower ? pi<size : pi>0;
-        IsLower ? pi+=PanelWidth : pi-=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
-      Index startBlock = IsLower ? pi : pi-actualPanelWidth;
-      Index endBlock = IsLower ? pi + actualPanelWidth : 0;
-
-      for(Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = IsLower ? pi+k : pi-k-1;
-        if(numext::not_equal_strict(rhs[i],RhsScalar(0)))
-        {
-          if(!(Mode & UnitDiag))
-            rhs[i] /= cjLhs.coeff(i,i);
-
-          Index r = actualPanelWidth - k - 1; // remaining size
-          Index s = IsLower ? i+1 : i-r;
-          if (r>0)
-            Map<Matrix<RhsScalar,Dynamic,1> >(rhs+s,r) -= rhs[i] * cjLhs.col(i).segment(s,r);
-        }
-      }
-      Index r = IsLower ? size - endBlock : startBlock; // remaining size
-      if (r > 0)
-      {
-        // let's directly call the low level product function because:
-        // 1 - it is faster to compile
-        // 2 - it is slightly faster at runtime
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,Conjugate,RhsScalar,RhsMapper,false>::run(
-            r, actualPanelWidth,
-            LhsMapper(&lhs.coeffRef(endBlock,startBlock), lhsStride),
-            RhsMapper(rhs+startBlock, 1),
-            rhs+endBlock, 1, RhsScalar(-1));
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_SOLVER_VECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/BlasUtil.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/BlasUtil.h
deleted file mode 100644
index e16a564..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/BlasUtil.h
+++ /dev/null
@@ -1,583 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BLASUTIL_H
-#define EIGEN_BLASUTIL_H
-
-// This file contains many lightweight helper classes used to
-// implement and control fast level 2 and level 3 BLAS-like routines.
-
-namespace Eigen {
-
-namespace internal {
-
-// forward declarations
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs=false, bool ConjugateRhs=false>
-struct gebp_kernel;
-
-template<typename Scalar, typename Index, typename DataMapper, int nr, int StorageOrder, bool Conjugate = false, bool PanelMode=false>
-struct gemm_pack_rhs;
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, int StorageOrder, bool Conjugate = false, bool PanelMode = false>
-struct gemm_pack_lhs;
-
-template<
-  typename Index,
-  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-  int ResStorageOrder, int ResInnerStride>
-struct general_matrix_matrix_product;
-
-template<typename Index,
-         typename LhsScalar, typename LhsMapper, int LhsStorageOrder, bool ConjugateLhs,
-         typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version=Specialized>
-struct general_matrix_vector_product;
-
-template<typename From,typename To> struct get_factor {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE To run(const From& x) { return To(x); }
-};
-
-template<typename Scalar> struct get_factor<Scalar,typename NumTraits<Scalar>::Real> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return numext::real(x); }
-};
-
-
-template<typename Scalar, typename Index>
-class BlasVectorMapper {
-  public:
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasVectorMapper(Scalar *data) : m_data(data) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
-    return m_data[i];
-  }
-  template <typename Packet, int AlignmentType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet load(Index i) const {
-    return ploadt<Packet, AlignmentType>(m_data + i);
-  }
-
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC bool aligned(Index i) const {
-    return (UIntPtr(m_data+i)%sizeof(Packet))==0;
-  }
-
-  protected:
-  Scalar* m_data;
-};
-
-template<typename Scalar, typename Index, int AlignmentType, int Incr=1>
-class BlasLinearMapper;
-
-template<typename Scalar, typename Index, int AlignmentType>
-class BlasLinearMapper<Scalar,Index,AlignmentType>
-{
-public:
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar *data, Index incr=1)
-    : m_data(data)
-  {
-    EIGEN_ONLY_USED_FOR_DEBUG(incr);
-    eigen_assert(incr==1);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(int i) const {
-    internal::prefetch(&operator()(i));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const {
-    return m_data[i];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
-    return ploadt<PacketType, AlignmentType>(m_data + i);
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType &p) const {
-    pstoret<Scalar, PacketType, AlignmentType>(m_data + i, p);
-  }
-
-protected:
-  Scalar *m_data;
-};
-
-// Lightweight helper class to access matrix coefficients.
-template<typename Scalar, typename Index, int StorageOrder, int AlignmentType = Unaligned, int Incr = 1>
-class blas_data_mapper;
-
-// TMP to help PacketBlock store implementation.
-// There's currently no known use case for PacketBlock load.
-// The default implementation assumes ColMajor order.
-// It always store each packet sequentially one `stride` apart.
-template<typename Index, typename Scalar, typename Packet, int n, int idx, int StorageOrder>
-struct PacketBlockManagement
-{
-  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, StorageOrder> pbm;
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    pbm.store(to, stride, i, j, block);
-    pstoreu<Scalar>(to + i + (j + idx)*stride, block.packet[idx]);
-  }
-};
-
-// PacketBlockManagement specialization to take care of RowMajor order without ifs.
-template<typename Index, typename Scalar, typename Packet, int n, int idx>
-struct PacketBlockManagement<Index, Scalar, Packet, n, idx, RowMajor>
-{
-  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, RowMajor> pbm;
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    pbm.store(to, stride, i, j, block);
-    pstoreu<Scalar>(to + j + (i + idx)*stride, block.packet[idx]);
-  }
-};
-
-template<typename Index, typename Scalar, typename Packet, int n, int StorageOrder>
-struct PacketBlockManagement<Index, Scalar, Packet, n, -1, StorageOrder>
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    EIGEN_UNUSED_VARIABLE(to);
-    EIGEN_UNUSED_VARIABLE(stride);
-    EIGEN_UNUSED_VARIABLE(i);
-    EIGEN_UNUSED_VARIABLE(j);
-    EIGEN_UNUSED_VARIABLE(block);
-  }
-};
-
-template<typename Index, typename Scalar, typename Packet, int n>
-struct PacketBlockManagement<Index, Scalar, Packet, n, -1, RowMajor>
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    EIGEN_UNUSED_VARIABLE(to);
-    EIGEN_UNUSED_VARIABLE(stride);
-    EIGEN_UNUSED_VARIABLE(i);
-    EIGEN_UNUSED_VARIABLE(j);
-    EIGEN_UNUSED_VARIABLE(block);
-  }
-};
-
-template<typename Scalar, typename Index, int StorageOrder, int AlignmentType>
-class blas_data_mapper<Scalar,Index,StorageOrder,AlignmentType,1>
-{
-public:
-  typedef BlasLinearMapper<Scalar, Index, AlignmentType> LinearMapper;
-  typedef BlasVectorMapper<Scalar, Index> VectorMapper;
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr=1)
-   : m_data(data), m_stride(stride)
-  {
-    EIGEN_ONLY_USED_FOR_DEBUG(incr);
-    eigen_assert(incr==1);
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType>
-  getSubMapper(Index i, Index j) const {
-    return blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType>(&operator()(i, j), m_stride);
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
-    return LinearMapper(&operator()(i, j));
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
-    return VectorMapper(&operator()(i, j));
-  }
-
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
-    return m_data[StorageOrder==RowMajor ? j + i*m_stride : i + j*m_stride];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
-    return ploadt<PacketType, AlignmentType>(&operator()(i, j));
-  }
-
-  template <typename PacketT, int AlignmentT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
-    return ploadt<PacketT, AlignmentT>(&operator()(i, j));
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket &p) const {
-    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
-    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
-  }
-
-  EIGEN_DEVICE_FUNC const Index stride() const { return m_stride; }
-  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; }
-
-  EIGEN_DEVICE_FUNC Index firstAligned(Index size) const {
-    if (UIntPtr(m_data)%sizeof(Scalar)) {
-      return -1;
-    }
-    return internal::first_default_aligned(m_data, size);
-  }
-
-  template<typename SubPacket, int n>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j, const PacketBlock<SubPacket, n> &block) const {
-    PacketBlockManagement<Index, Scalar, SubPacket, n, n-1, StorageOrder> pbm;
-    pbm.store(m_data, m_stride, i, j, block);
-  }
-protected:
-  Scalar* EIGEN_RESTRICT m_data;
-  const Index m_stride;
-};
-
-// Implementation of non-natural increment (i.e. inner-stride != 1)
-// The exposed API is not complete yet compared to the Incr==1 case
-// because some features makes less sense in this case.
-template<typename Scalar, typename Index, int AlignmentType, int Incr>
-class BlasLinearMapper
-{
-public:
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar *data,Index incr) : m_data(data), m_incr(incr) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(int i) const {
-    internal::prefetch(&operator()(i));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const {
-    return m_data[i*m_incr.value()];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
-    return pgather<Scalar,PacketType>(m_data + i*m_incr.value(), m_incr.value());
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType &p) const {
-    pscatter<Scalar, PacketType>(m_data + i*m_incr.value(), p, m_incr.value());
-  }
-
-protected:
-  Scalar *m_data;
-  const internal::variable_if_dynamic<Index,Incr> m_incr;
-};
-
-template<typename Scalar, typename Index, int StorageOrder, int AlignmentType,int Incr>
-class blas_data_mapper
-{
-public:
-  typedef BlasLinearMapper<Scalar, Index, AlignmentType,Incr> LinearMapper;
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr) : m_data(data), m_stride(stride), m_incr(incr) {}
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE blas_data_mapper
-  getSubMapper(Index i, Index j) const {
-    return blas_data_mapper(&operator()(i, j), m_stride, m_incr.value());
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
-    return LinearMapper(&operator()(i, j), m_incr.value());
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
-    return m_data[StorageOrder==RowMajor ? j*m_incr.value() + i*m_stride : i*m_incr.value() + j*m_stride];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
-    return pgather<Scalar,PacketType>(&operator()(i, j),m_incr.value());
-  }
-
-  template <typename PacketT, int AlignmentT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
-    return pgather<Scalar,PacketT>(&operator()(i, j),m_incr.value());
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket &p) const {
-    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
-    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
-  }
-
-  // storePacketBlock_helper defines a way to access values inside the PacketBlock, this is essentially required by the Complex types.
-  template<typename SubPacket, typename ScalarT, int n, int idx>
-  struct storePacketBlock_helper
-  {
-    storePacketBlock_helper<SubPacket, ScalarT, n, idx-1> spbh;
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
-      spbh.store(sup, i,j,block);
-      for(int l = 0; l < unpacket_traits<SubPacket>::size; l++)
-      {
-        ScalarT *v = &sup->operator()(i+l, j+idx);
-        *v = block.packet[idx][l];
-      }
-    }
-  };
-
-  template<typename SubPacket, int n, int idx>
-  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, idx>
-  {
-    storePacketBlock_helper<SubPacket, std::complex<float>, n, idx-1> spbh;
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
-      spbh.store(sup,i,j,block);
-      for(int l = 0; l < unpacket_traits<SubPacket>::size; l++)
-      {
-        std::complex<float> *v = &sup->operator()(i+l, j+idx);
-        v->real(block.packet[idx].v[2*l+0]);
-        v->imag(block.packet[idx].v[2*l+1]);
-      }
-    }
-  };
-
-  template<typename SubPacket, int n, int idx>
-  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, idx>
-  {
-    storePacketBlock_helper<SubPacket, std::complex<double>, n, idx-1> spbh;
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
-      spbh.store(sup,i,j,block);
-      for(int l = 0; l < unpacket_traits<SubPacket>::size; l++)
-      {
-        std::complex<double> *v = &sup->operator()(i+l, j+idx);
-        v->real(block.packet[idx].v[2*l+0]);
-        v->imag(block.packet[idx].v[2*l+1]);
-      }
-    }
-  };
-
-  template<typename SubPacket, typename ScalarT, int n>
-  struct storePacketBlock_helper<SubPacket, ScalarT, n, -1>
-  {
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
-    }
-  };
-
-  template<typename SubPacket, int n>
-  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, -1>
-  {
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
-    }
-  };
-
-  template<typename SubPacket, int n>
-  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, -1>
-  {
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
-    }
-  };
-  // This function stores a PacketBlock on m_data, this approach is really quite slow compare to Incr=1 and should be avoided when possible.
-  template<typename SubPacket, int n>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j, const PacketBlock<SubPacket, n>&block) const {
-    storePacketBlock_helper<SubPacket, Scalar, n, n-1> spb;
-    spb.store(this, i,j,block);
-  }
-protected:
-  Scalar* EIGEN_RESTRICT m_data;
-  const Index m_stride;
-  const internal::variable_if_dynamic<Index,Incr> m_incr;
-};
-
-// lightweight helper class to access matrix coefficients (const version)
-template<typename Scalar, typename Index, int StorageOrder>
-class const_blas_data_mapper : public blas_data_mapper<const Scalar, Index, StorageOrder> {
-  public:
-  EIGEN_ALWAYS_INLINE const_blas_data_mapper(const Scalar *data, Index stride) : blas_data_mapper<const Scalar, Index, StorageOrder>(data, stride) {}
-
-  EIGEN_ALWAYS_INLINE const_blas_data_mapper<Scalar, Index, StorageOrder> getSubMapper(Index i, Index j) const {
-    return const_blas_data_mapper<Scalar, Index, StorageOrder>(&(this->operator()(i, j)), this->m_stride);
-  }
-};
-
-
-/* Helper class to analyze the factors of a Product expression.
- * In particular it allows to pop out operator-, scalar multiples,
- * and conjugate */
-template<typename XprType> struct blas_traits
-{
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef const XprType& ExtractType;
-  typedef XprType _ExtractType;
-  enum {
-    IsComplex = NumTraits<Scalar>::IsComplex,
-    IsTransposed = false,
-    NeedToConjugate = false,
-    HasUsableDirectAccess = (    (int(XprType::Flags)&DirectAccessBit)
-                              && (   bool(XprType::IsVectorAtCompileTime)
-                                  || int(inner_stride_at_compile_time<XprType>::ret) == 1)
-                             ) ?  1 : 0,
-    HasScalarFactor = false
-  };
-  typedef typename conditional<bool(HasUsableDirectAccess),
-    ExtractType,
-    typename _ExtractType::PlainObject
-    >::type DirectLinearAccessType;
-  static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) { return x; }
-  static inline EIGEN_DEVICE_FUNC const Scalar extractScalarFactor(const XprType&) { return Scalar(1); }
-};
-
-// pop conjugate
-template<typename Scalar, typename NestedXpr>
-struct blas_traits<CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> XprType;
-  typedef typename Base::ExtractType ExtractType;
-
-  enum {
-    IsComplex = NumTraits<Scalar>::IsComplex,
-    NeedToConjugate = Base::NeedToConjugate ? 0 : IsComplex
-  };
-  static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
-  static inline Scalar extractScalarFactor(const XprType& x) { return conj(Base::extractScalarFactor(x.nestedExpression())); }
-};
-
-// pop scalar multiple
-template<typename Scalar, typename NestedXpr, typename Plain>
-struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  enum {
-    HasScalarFactor = true
-  };
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> XprType;
-  typedef typename Base::ExtractType ExtractType;
-  static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) { return Base::extract(x.rhs()); }
-  static inline EIGEN_DEVICE_FUNC Scalar extractScalarFactor(const XprType& x)
-  { return x.lhs().functor().m_other * Base::extractScalarFactor(x.rhs()); }
-};
-template<typename Scalar, typename NestedXpr, typename Plain>
-struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > >
- : blas_traits<NestedXpr>
-{
-  enum {
-    HasScalarFactor = true
-  };
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > XprType;
-  typedef typename Base::ExtractType ExtractType;
-  static inline ExtractType extract(const XprType& x) { return Base::extract(x.lhs()); }
-  static inline Scalar extractScalarFactor(const XprType& x)
-  { return Base::extractScalarFactor(x.lhs()) * x.rhs().functor().m_other; }
-};
-template<typename Scalar, typename Plain1, typename Plain2>
-struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1>,
-                                                            const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain2> > >
- : blas_traits<CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1> >
-{};
-
-// pop opposite
-template<typename Scalar, typename NestedXpr>
-struct blas_traits<CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  enum {
-    HasScalarFactor = true
-  };
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> XprType;
-  typedef typename Base::ExtractType ExtractType;
-  static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
-  static inline Scalar extractScalarFactor(const XprType& x)
-  { return - Base::extractScalarFactor(x.nestedExpression()); }
-};
-
-// pop/push transpose
-template<typename NestedXpr>
-struct blas_traits<Transpose<NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  typedef typename NestedXpr::Scalar Scalar;
-  typedef blas_traits<NestedXpr> Base;
-  typedef Transpose<NestedXpr> XprType;
-  typedef Transpose<const typename Base::_ExtractType>  ExtractType; // const to get rid of a compile error; anyway blas traits are only used on the RHS
-  typedef Transpose<const typename Base::_ExtractType> _ExtractType;
-  typedef typename conditional<bool(Base::HasUsableDirectAccess),
-    ExtractType,
-    typename ExtractType::PlainObject
-    >::type DirectLinearAccessType;
-  enum {
-    IsTransposed = Base::IsTransposed ? 0 : 1
-  };
-  static inline ExtractType extract(const XprType& x) { return ExtractType(Base::extract(x.nestedExpression())); }
-  static inline Scalar extractScalarFactor(const XprType& x) { return Base::extractScalarFactor(x.nestedExpression()); }
-};
-
-template<typename T>
-struct blas_traits<const T>
-     : blas_traits<T>
-{};
-
-template<typename T, bool HasUsableDirectAccess=blas_traits<T>::HasUsableDirectAccess>
-struct extract_data_selector {
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static const typename T::Scalar* run(const T& m)
-  {
-    return blas_traits<T>::extract(m).data();
-  }
-};
-
-template<typename T>
-struct extract_data_selector<T,false> {
-  static typename T::Scalar* run(const T&) { return 0; }
-};
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename T::Scalar* extract_data(const T& m)
-{
-  return extract_data_selector<T>::run(m);
-}
-
-/**
- * \c combine_scalar_factors extracts and multiplies factors from GEMM and GEMV products.
- * There is a specialization for booleans
- */
-template<typename ResScalar, typename Lhs, typename Rhs>
-struct combine_scalar_factors_impl
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static ResScalar run(const Lhs& lhs, const Rhs& rhs)
-  {
-    return blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static ResScalar run(const ResScalar& alpha, const Lhs& lhs, const Rhs& rhs)
-  {
-    return alpha * blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-};
-template<typename Lhs, typename Rhs>
-struct combine_scalar_factors_impl<bool, Lhs, Rhs>
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static bool run(const Lhs& lhs, const Rhs& rhs)
-  {
-    return blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static bool run(const bool& alpha, const Lhs& lhs, const Rhs& rhs)
-  {
-    return alpha && blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-};
-
-template<typename ResScalar, typename Lhs, typename Rhs>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const ResScalar& alpha, const Lhs& lhs, const Rhs& rhs)
-{
-  return combine_scalar_factors_impl<ResScalar,Lhs,Rhs>::run(alpha, lhs, rhs);
-}
-template<typename ResScalar, typename Lhs, typename Rhs>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const Lhs& lhs, const Rhs& rhs)
-{
-  return combine_scalar_factors_impl<ResScalar,Lhs,Rhs>::run(lhs, rhs);
-}
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BLASUTIL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ConfigureVectorization.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ConfigureVectorization.h
deleted file mode 100644
index af4e696..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ConfigureVectorization.h
+++ /dev/null
@@ -1,512 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONFIGURE_VECTORIZATION_H
-#define EIGEN_CONFIGURE_VECTORIZATION_H
-
-//------------------------------------------------------------------------------------------
-// Static and dynamic alignment control
-//
-// The main purpose of this section is to define EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES
-// as the maximal boundary in bytes on which dynamically and statically allocated data may be alignment respectively.
-// The values of EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES can be specified by the user. If not,
-// a default value is automatically computed based on architecture, compiler, and OS.
-//
-// This section also defines macros EIGEN_ALIGN_TO_BOUNDARY(N) and the shortcuts EIGEN_ALIGN{8,16,32,_MAX}
-// to be used to declare statically aligned buffers.
-//------------------------------------------------------------------------------------------
-
-
-/* EIGEN_ALIGN_TO_BOUNDARY(n) forces data to be n-byte aligned. This is used to satisfy SIMD requirements.
- * However, we do that EVEN if vectorization (EIGEN_VECTORIZE) is disabled,
- * so that vectorization doesn't affect binary compatibility.
- *
- * If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link
- * vectorized and non-vectorized code.
- * 
- * FIXME: this code can be cleaned up once we switch to proper C++11 only.
- */
-#if (defined EIGEN_CUDACC)
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __align__(n)
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#elif EIGEN_HAS_ALIGNAS
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) alignas(n)
-  #define EIGEN_ALIGNOF(x) alignof(x)
-#elif EIGEN_COMP_GNUC || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n)))
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#elif EIGEN_COMP_MSVC
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __declspec(align(n))
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#elif EIGEN_COMP_SUNCC
-  // FIXME not sure about this one:
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n)))
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#else
-  #error Please tell me what is the equivalent of alignas(n) and alignof(x) for your compiler
-#endif
-
-// If the user explicitly disable vectorization, then we also disable alignment
-#if defined(EIGEN_DONT_VECTORIZE)
-  #if defined(EIGEN_GPUCC)
-    // GPU code is always vectorized and requires memory alignment for
-    // statically allocated buffers.
-    #define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
-  #else
-    #define EIGEN_IDEAL_MAX_ALIGN_BYTES 0
-  #endif
-#elif defined(__AVX512F__)
-  // 64 bytes static alignment is preferred only if really required
-  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 64
-#elif defined(__AVX__)
-  // 32 bytes static alignment is preferred only if really required
-  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 32
-#else
-  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
-#endif
-
-
-// EIGEN_MIN_ALIGN_BYTES defines the minimal value for which the notion of explicit alignment makes sense
-#define EIGEN_MIN_ALIGN_BYTES 16
-
-// Defined the boundary (in bytes) on which the data needs to be aligned. Note
-// that unless EIGEN_ALIGN is defined and not equal to 0, the data may not be
-// aligned at all regardless of the value of this #define.
-
-#if (defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN))  && defined(EIGEN_MAX_STATIC_ALIGN_BYTES) && EIGEN_MAX_STATIC_ALIGN_BYTES>0
-#error EIGEN_MAX_STATIC_ALIGN_BYTES and EIGEN_DONT_ALIGN[_STATICALLY] are both defined with EIGEN_MAX_STATIC_ALIGN_BYTES!=0. Use EIGEN_MAX_STATIC_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN_STATICALLY.
-#endif
-
-// EIGEN_DONT_ALIGN_STATICALLY and EIGEN_DONT_ALIGN are deprecated
-// They imply EIGEN_MAX_STATIC_ALIGN_BYTES=0
-#if defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN)
-  #ifdef EIGEN_MAX_STATIC_ALIGN_BYTES
-    #undef EIGEN_MAX_STATIC_ALIGN_BYTES
-  #endif
-  #define EIGEN_MAX_STATIC_ALIGN_BYTES 0
-#endif
-
-#ifndef EIGEN_MAX_STATIC_ALIGN_BYTES
-
-  // Try to automatically guess what is the best default value for EIGEN_MAX_STATIC_ALIGN_BYTES
-
-  // 16 byte alignment is only useful for vectorization. Since it affects the ABI, we need to enable
-  // 16 byte alignment on all platforms where vectorization might be enabled. In theory we could always
-  // enable alignment, but it can be a cause of problems on some platforms, so we just disable it in
-  // certain common platform (compiler+architecture combinations) to avoid these problems.
-  // Only static alignment is really problematic (relies on nonstandard compiler extensions),
-  // try to keep heap alignment even when we have to disable static alignment.
-  #if EIGEN_COMP_GNUC && !(EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64 || EIGEN_ARCH_PPC || EIGEN_ARCH_IA64 || EIGEN_ARCH_MIPS)
-  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1
-  #elif EIGEN_ARCH_ARM_OR_ARM64 && EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_MOST(4, 6)
-  // Old versions of GCC on ARM, at least 4.4, were once seen to have buggy static alignment support.
-  // Not sure which version fixed it, hopefully it doesn't affect 4.7, which is still somewhat in use.
-  // 4.8 and newer seem definitely unaffected.
-  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1
-  #else
-  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 0
-  #endif
-
-  // static alignment is completely disabled with GCC 3, Sun Studio, and QCC/QNX
-  #if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT \
-  && !EIGEN_GCC3_OR_OLDER \
-  && !EIGEN_COMP_SUNCC \
-  && !EIGEN_OS_QNX
-    #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 1
-  #else
-    #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 0
-  #endif
-
-  #if EIGEN_ARCH_WANTS_STACK_ALIGNMENT
-    #define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
-  #else
-    #define EIGEN_MAX_STATIC_ALIGN_BYTES 0
-  #endif
-
-#endif
-
-// If EIGEN_MAX_ALIGN_BYTES is defined, then it is considered as an upper bound for EIGEN_MAX_STATIC_ALIGN_BYTES
-#if defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES<EIGEN_MAX_STATIC_ALIGN_BYTES
-#undef EIGEN_MAX_STATIC_ALIGN_BYTES
-#define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
-#endif
-
-#if EIGEN_MAX_STATIC_ALIGN_BYTES==0 && !defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
-  #define EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
-#endif
-
-// At this stage, EIGEN_MAX_STATIC_ALIGN_BYTES>0 is the true test whether we want to align arrays on the stack or not.
-// It takes into account both the user choice to explicitly enable/disable alignment (by setting EIGEN_MAX_STATIC_ALIGN_BYTES)
-// and the architecture config (EIGEN_ARCH_WANTS_STACK_ALIGNMENT).
-// Henceforth, only EIGEN_MAX_STATIC_ALIGN_BYTES should be used.
-
-
-// Shortcuts to EIGEN_ALIGN_TO_BOUNDARY
-#define EIGEN_ALIGN8  EIGEN_ALIGN_TO_BOUNDARY(8)
-#define EIGEN_ALIGN16 EIGEN_ALIGN_TO_BOUNDARY(16)
-#define EIGEN_ALIGN32 EIGEN_ALIGN_TO_BOUNDARY(32)
-#define EIGEN_ALIGN64 EIGEN_ALIGN_TO_BOUNDARY(64)
-#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
-#define EIGEN_ALIGN_MAX EIGEN_ALIGN_TO_BOUNDARY(EIGEN_MAX_STATIC_ALIGN_BYTES)
-#else
-#define EIGEN_ALIGN_MAX
-#endif
-
-
-// Dynamic alignment control
-
-#if defined(EIGEN_DONT_ALIGN) && defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES>0
-#error EIGEN_MAX_ALIGN_BYTES and EIGEN_DONT_ALIGN are both defined with EIGEN_MAX_ALIGN_BYTES!=0. Use EIGEN_MAX_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN.
-#endif
-
-#ifdef EIGEN_DONT_ALIGN
-  #ifdef EIGEN_MAX_ALIGN_BYTES
-    #undef EIGEN_MAX_ALIGN_BYTES
-  #endif
-  #define EIGEN_MAX_ALIGN_BYTES 0
-#elif !defined(EIGEN_MAX_ALIGN_BYTES)
-  #define EIGEN_MAX_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
-#endif
-
-#if EIGEN_IDEAL_MAX_ALIGN_BYTES > EIGEN_MAX_ALIGN_BYTES
-#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
-#else
-#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
-#endif
-
-
-#ifndef EIGEN_UNALIGNED_VECTORIZE
-#define EIGEN_UNALIGNED_VECTORIZE 1
-#endif
-
-//----------------------------------------------------------------------
-
-// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
-// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
-#if EIGEN_MAX_ALIGN_BYTES==0
-  #ifndef EIGEN_DONT_VECTORIZE
-    #define EIGEN_DONT_VECTORIZE
-  #endif
-#endif
-
-
-// The following (except #include <malloc.h> and _M_IX86_FP ??) can likely be
-// removed as gcc 4.1 and msvc 2008 are not supported anyways.
-#if EIGEN_COMP_MSVC
-  #include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
-  #if (EIGEN_COMP_MSVC >= 1500) // 2008 or later
-    // a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
-    #if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
-      #define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
-    #endif
-  #endif
-#else
-  #if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )
-    #define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
-  #endif
-#endif
-
-#if !(defined(EIGEN_DONT_VECTORIZE) || defined(EIGEN_GPUCC))
-
-  #if defined (EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
-
-    // Defines symbols for compile-time detection of which instructions are
-    // used.
-    // EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_SSE
-    #define EIGEN_VECTORIZE_SSE2
-
-    // Detect sse3/ssse3/sse4:
-    // gcc and icc defines __SSE3__, ...
-    // there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
-    // want to force the use of those instructions with msvc.
-    #ifdef __SSE3__
-      #define EIGEN_VECTORIZE_SSE3
-    #endif
-    #ifdef __SSSE3__
-      #define EIGEN_VECTORIZE_SSSE3
-    #endif
-    #ifdef __SSE4_1__
-      #define EIGEN_VECTORIZE_SSE4_1
-    #endif
-    #ifdef __SSE4_2__
-      #define EIGEN_VECTORIZE_SSE4_2
-    #endif
-    #ifdef __AVX__
-      #ifndef EIGEN_USE_SYCL 
-        #define EIGEN_VECTORIZE_AVX
-      #endif
-      #define EIGEN_VECTORIZE_SSE3
-      #define EIGEN_VECTORIZE_SSSE3
-      #define EIGEN_VECTORIZE_SSE4_1
-      #define EIGEN_VECTORIZE_SSE4_2
-    #endif
-    #ifdef __AVX2__
-      #ifndef EIGEN_USE_SYCL 
-        #define EIGEN_VECTORIZE_AVX2
-        #define EIGEN_VECTORIZE_AVX
-      #endif
-      #define EIGEN_VECTORIZE_SSE3
-      #define EIGEN_VECTORIZE_SSSE3
-      #define EIGEN_VECTORIZE_SSE4_1
-      #define EIGEN_VECTORIZE_SSE4_2
-    #endif
-    #if defined(__FMA__) || (EIGEN_COMP_MSVC && defined(__AVX2__))
-      // MSVC does not expose a switch dedicated for FMA
-      // For MSVC, AVX2 => FMA
-      #define EIGEN_VECTORIZE_FMA
-    #endif
-    #if defined(__AVX512F__)
-      #ifndef EIGEN_VECTORIZE_FMA
-      #if EIGEN_COMP_GNUC
-      #error Please add -mfma to your compiler flags: compiling with -mavx512f alone without SSE/AVX FMA is not supported (bug 1638).
-      #else
-      #error Please enable FMA in your compiler flags (e.g. -mfma): compiling with AVX512 alone without SSE/AVX FMA is not supported (bug 1638).
-      #endif
-      #endif
-      #ifndef EIGEN_USE_SYCL
-        #define EIGEN_VECTORIZE_AVX512
-        #define EIGEN_VECTORIZE_AVX2
-        #define EIGEN_VECTORIZE_AVX
-      #endif
-      #define EIGEN_VECTORIZE_FMA
-      #define EIGEN_VECTORIZE_SSE3
-      #define EIGEN_VECTORIZE_SSSE3
-      #define EIGEN_VECTORIZE_SSE4_1
-      #define EIGEN_VECTORIZE_SSE4_2
-      #ifndef EIGEN_USE_SYCL
-        #ifdef __AVX512DQ__
-          #define EIGEN_VECTORIZE_AVX512DQ
-        #endif
-        #ifdef __AVX512ER__
-          #define EIGEN_VECTORIZE_AVX512ER
-        #endif
-        #ifdef __AVX512BF16__
-          #define EIGEN_VECTORIZE_AVX512BF16
-        #endif
-      #endif
-    #endif
-
-    // Disable AVX support on broken xcode versions
-    #if defined(__apple_build_version__) && (__apple_build_version__ == 11000033 ) && ( __MAC_OS_X_VERSION_MIN_REQUIRED == 101500 )
-      // A nasty bug in the clang compiler shipped with xcode in a common compilation situation
-      // when XCode 11.0 and Mac deployment target macOS 10.15 is https://trac.macports.org/ticket/58776#no1
-      #ifdef EIGEN_VECTORIZE_AVX
-        #undef EIGEN_VECTORIZE_AVX
-        #warning "Disabling AVX support: clang compiler shipped with XCode 11.[012] generates broken assembly with -macosx-version-min=10.15 and AVX enabled. "
-        #ifdef EIGEN_VECTORIZE_AVX2
-          #undef EIGEN_VECTORIZE_AVX2
-        #endif
-        #ifdef EIGEN_VECTORIZE_FMA
-          #undef EIGEN_VECTORIZE_FMA
-        #endif
-        #ifdef EIGEN_VECTORIZE_AVX512
-          #undef EIGEN_VECTORIZE_AVX512
-        #endif
-        #ifdef EIGEN_VECTORIZE_AVX512DQ
-          #undef EIGEN_VECTORIZE_AVX512DQ
-        #endif
-        #ifdef EIGEN_VECTORIZE_AVX512ER
-          #undef EIGEN_VECTORIZE_AVX512ER
-        #endif
-      #endif
-      // NOTE: Confirmed test failures in XCode 11.0, and XCode 11.2 with  -macosx-version-min=10.15 and AVX
-      // NOTE using -macosx-version-min=10.15 with Xcode 11.0 results in runtime segmentation faults in many tests, 11.2 produce core dumps in 3 tests
-      // NOTE using -macosx-version-min=10.14 produces functioning and passing tests in all cases
-      // NOTE __clang_version__ "11.0.0 (clang-1100.0.33.8)"  XCode 11.0 <- Produces many segfault and core dumping tests
-      //                                                                    with  -macosx-version-min=10.15 and AVX
-      // NOTE __clang_version__ "11.0.0 (clang-1100.0.33.12)" XCode 11.2 <- Produces 3 core dumping tests with  
-      //                                                                    -macosx-version-min=10.15 and AVX
-    #endif
-
-    // include files
-
-    // This extern "C" works around a MINGW-w64 compilation issue
-    // https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
-    // In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
-    // However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
-    // with conflicting linkage.  The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
-    // so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
-    // notice that since these are C headers, the extern "C" is theoretically needed anyways.
-    extern "C" {
-      // In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
-      // Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
-      #if EIGEN_COMP_ICC >= 1110
-        #include <immintrin.h>
-      #else
-        #include <mmintrin.h>
-        #include <emmintrin.h>
-        #include <xmmintrin.h>
-        #ifdef  EIGEN_VECTORIZE_SSE3
-        #include <pmmintrin.h>
-        #endif
-        #ifdef EIGEN_VECTORIZE_SSSE3
-        #include <tmmintrin.h>
-        #endif
-        #ifdef EIGEN_VECTORIZE_SSE4_1
-        #include <smmintrin.h>
-        #endif
-        #ifdef EIGEN_VECTORIZE_SSE4_2
-        #include <nmmintrin.h>
-        #endif
-        #if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)
-        #include <immintrin.h>
-        #endif
-      #endif
-    } // end extern "C"
-
-  #elif defined __VSX__
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_VSX
-    #include <altivec.h>
-    // We need to #undef all these ugly tokens defined in <altivec.h>
-    // => use __vector instead of vector
-    #undef bool
-    #undef vector
-    #undef pixel
-
-  #elif defined __ALTIVEC__
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_ALTIVEC
-    #include <altivec.h>
-    // We need to #undef all these ugly tokens defined in <altivec.h>
-    // => use __vector instead of vector
-    #undef bool
-    #undef vector
-    #undef pixel
-
-  #elif ((defined  __ARM_NEON) || (defined __ARM_NEON__)) && !(defined EIGEN_ARM64_USE_SVE)
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_NEON
-    #include <arm_neon.h>
-
-  // We currently require SVE to be enabled explicitly via EIGEN_ARM64_USE_SVE and
-  // will not select the backend automatically
-  #elif (defined __ARM_FEATURE_SVE) && (defined EIGEN_ARM64_USE_SVE)
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_SVE
-    #include <arm_sve.h>
-
-    // Since we depend on knowing SVE vector lengths at compile-time, we need
-    // to ensure a fixed lengths is set
-    #if defined __ARM_FEATURE_SVE_BITS
-      #define EIGEN_ARM64_SVE_VL __ARM_FEATURE_SVE_BITS
-    #else
-#error "Eigen requires a fixed SVE lector length but EIGEN_ARM64_SVE_VL is not set."
-#endif
-
-#elif (defined __s390x__ && defined __VEC__)
-
-#define EIGEN_VECTORIZE
-#define EIGEN_VECTORIZE_ZVECTOR
-#include <vecintrin.h>
-
-#elif defined __mips_msa
-
-// Limit MSA optimizations to little-endian CPUs for now.
-// TODO: Perhaps, eventually support MSA optimizations on big-endian CPUs?
-#if defined(__BYTE_ORDER__) && (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
-#if defined(__LP64__)
-#define EIGEN_MIPS_64
-#else
-#define EIGEN_MIPS_32
-#endif
-#define EIGEN_VECTORIZE
-#define EIGEN_VECTORIZE_MSA
-#include <msa.h>
-#endif
-
-#endif
-#endif
-
-// Following the Arm ACLE arm_neon.h should also include arm_fp16.h but not all
-// compilers seem to follow this. We therefore include it explicitly.
-// See also: https://bugs.llvm.org/show_bug.cgi?id=47955
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  #include <arm_fp16.h>
-#endif
-
-#if defined(__F16C__) && (!defined(EIGEN_GPUCC) && (!defined(EIGEN_COMP_CLANG) || EIGEN_COMP_CLANG>=380))
-  // We can use the optimized fp16 to float and float to fp16 conversion routines
-  #define EIGEN_HAS_FP16_C
-
-  #if defined(EIGEN_COMP_CLANG)
-    // Workaround for clang: The FP16C intrinsics for clang are included by
-    // immintrin.h, as opposed to emmintrin.h as suggested by Intel:
-    // https://software.intel.com/sites/landingpage/IntrinsicsGuide/#othertechs=FP16C&expand=1711
-    #include <immintrin.h>
-  #endif
-#endif
-
-#if defined EIGEN_CUDACC
-  #define EIGEN_VECTORIZE_GPU
-  #include <vector_types.h>
-  #if EIGEN_CUDA_SDK_VER >= 70500
-    #define EIGEN_HAS_CUDA_FP16
-  #endif
-#endif
-
-#if defined(EIGEN_HAS_CUDA_FP16)
-  #include <cuda_runtime_api.h>
-  #include <cuda_fp16.h>
-#endif
-
-#if defined(EIGEN_HIPCC)
-  #define EIGEN_VECTORIZE_GPU
-  #include <hip/hip_vector_types.h>
-  #define EIGEN_HAS_HIP_FP16
-  #include <hip/hip_fp16.h>
-#endif
-
-
-/** \brief Namespace containing all symbols from the %Eigen library. */
-namespace Eigen {
-
-inline static const char *SimdInstructionSetsInUse(void) {
-#if defined(EIGEN_VECTORIZE_AVX512)
-  return "AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
-#elif defined(EIGEN_VECTORIZE_AVX)
-  return "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
-#elif defined(EIGEN_VECTORIZE_SSE4_2)
-  return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
-#elif defined(EIGEN_VECTORIZE_SSE4_1)
-  return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
-#elif defined(EIGEN_VECTORIZE_SSSE3)
-  return "SSE, SSE2, SSE3, SSSE3";
-#elif defined(EIGEN_VECTORIZE_SSE3)
-  return "SSE, SSE2, SSE3";
-#elif defined(EIGEN_VECTORIZE_SSE2)
-  return "SSE, SSE2";
-#elif defined(EIGEN_VECTORIZE_ALTIVEC)
-  return "AltiVec";
-#elif defined(EIGEN_VECTORIZE_VSX)
-  return "VSX";
-#elif defined(EIGEN_VECTORIZE_NEON)
-  return "ARM NEON";
-#elif defined(EIGEN_VECTORIZE_SVE)
-  return "ARM SVE";
-#elif defined(EIGEN_VECTORIZE_ZVECTOR)
-  return "S390X ZVECTOR";
-#elif defined(EIGEN_VECTORIZE_MSA)
-  return "MIPS MSA";
-#else
-  return "None";
-#endif
-}
-
-} // end namespace Eigen
-
-
-#endif // EIGEN_CONFIGURE_VECTORIZATION_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Constants.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Constants.h
deleted file mode 100644
index 35dcaa7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Constants.h
+++ /dev/null
@@ -1,563 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONSTANTS_H
-#define EIGEN_CONSTANTS_H
-
-namespace Eigen {
-
-/** This value means that a positive quantity (e.g., a size) is not known at compile-time, and that instead the value is
-  * stored in some runtime variable.
-  *
-  * Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix.
-  */
-const int Dynamic = -1;
-
-/** This value means that a signed quantity (e.g., a signed index) is not known at compile-time, and that instead its value
-  * has to be specified at runtime.
-  */
-const int DynamicIndex = 0xffffff;
-
-/** This value means that the increment to go from one value to another in a sequence is not constant for each step.
-  */
-const int UndefinedIncr = 0xfffffe;
-
-/** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>().
-  * The value Infinity there means the L-infinity norm.
-  */
-const int Infinity = -1;
-
-/** This value means that the cost to evaluate an expression coefficient is either very expensive or
-  * cannot be known at compile time.
-  *
-  * This value has to be positive to (1) simplify cost computation, and (2) allow to distinguish between a very expensive and very very expensive expressions.
-  * It thus must also be large enough to make sure unrolling won't happen and that sub expressions will be evaluated, but not too large to avoid overflow.
-  */
-const int HugeCost = 10000;
-
-/** \defgroup flags Flags
-  * \ingroup Core_Module
-  *
-  * These are the possible bits which can be OR'ed to constitute the flags of a matrix or
-  * expression.
-  *
-  * It is important to note that these flags are a purely compile-time notion. They are a compile-time property of
-  * an expression type, implemented as enum's. They are not stored in memory at runtime, and they do not incur any
-  * runtime overhead.
-  *
-  * \sa MatrixBase::Flags
-  */
-
-/** \ingroup flags
-  *
-  * for a matrix, this means that the storage order is row-major.
-  * If this bit is not set, the storage order is column-major.
-  * For an expression, this determines the storage order of
-  * the matrix created by evaluation of that expression.
-  * \sa \blank  \ref TopicStorageOrders */
-const unsigned int RowMajorBit = 0x1;
-
-/** \ingroup flags
-  * means the expression should be evaluated by the calling expression */
-const unsigned int EvalBeforeNestingBit = 0x2;
-
-/** \ingroup flags
-  * \deprecated
-  * means the expression should be evaluated before any assignment */
-EIGEN_DEPRECATED
-const unsigned int EvalBeforeAssigningBit = 0x4; // FIXME deprecated
-
-/** \ingroup flags
-  *
-  * Short version: means the expression might be vectorized
-  *
-  * Long version: means that the coefficients can be handled by packets
-  * and start at a memory location whose alignment meets the requirements
-  * of the present CPU architecture for optimized packet access. In the fixed-size
-  * case, there is the additional condition that it be possible to access all the
-  * coefficients by packets (this implies the requirement that the size be a multiple of 16 bytes,
-  * and that any nontrivial strides don't break the alignment). In the dynamic-size case,
-  * there is no such condition on the total size and strides, so it might not be possible to access
-  * all coeffs by packets.
-  *
-  * \note This bit can be set regardless of whether vectorization is actually enabled.
-  *       To check for actual vectorizability, see \a ActualPacketAccessBit.
-  */
-const unsigned int PacketAccessBit = 0x8;
-
-#ifdef EIGEN_VECTORIZE
-/** \ingroup flags
-  *
-  * If vectorization is enabled (EIGEN_VECTORIZE is defined) this constant
-  * is set to the value \a PacketAccessBit.
-  *
-  * If vectorization is not enabled (EIGEN_VECTORIZE is not defined) this constant
-  * is set to the value 0.
-  */
-const unsigned int ActualPacketAccessBit = PacketAccessBit;
-#else
-const unsigned int ActualPacketAccessBit = 0x0;
-#endif
-
-/** \ingroup flags
-  *
-  * Short version: means the expression can be seen as 1D vector.
-  *
-  * Long version: means that one can access the coefficients
-  * of this expression by coeff(int), and coeffRef(int) in the case of a lvalue expression. These
-  * index-based access methods are guaranteed
-  * to not have to do any runtime computation of a (row, col)-pair from the index, so that it
-  * is guaranteed that whenever it is available, index-based access is at least as fast as
-  * (row,col)-based access. Expressions for which that isn't possible don't have the LinearAccessBit.
-  *
-  * If both PacketAccessBit and LinearAccessBit are set, then the
-  * packets of this expression can be accessed by packet(int), and writePacket(int) in the case of a
-  * lvalue expression.
-  *
-  * Typically, all vector expressions have the LinearAccessBit, but there is one exception:
-  * Product expressions don't have it, because it would be troublesome for vectorization, even when the
-  * Product is a vector expression. Thus, vector Product expressions allow index-based coefficient access but
-  * not index-based packet access, so they don't have the LinearAccessBit.
-  */
-const unsigned int LinearAccessBit = 0x10;
-
-/** \ingroup flags
-  *
-  * Means the expression has a coeffRef() method, i.e. is writable as its individual coefficients are directly addressable.
-  * This rules out read-only expressions.
-  *
-  * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but note
-  * the other:
-  *   \li writable expressions that don't have a very simple memory layout as a strided array, have LvalueBit but not DirectAccessBit
-  *   \li Map-to-const expressions, for example Map<const Matrix>, have DirectAccessBit but not LvalueBit
-  *
-  * Expressions having LvalueBit also have their coeff() method returning a const reference instead of returning a new value.
-  */
-const unsigned int LvalueBit = 0x20;
-
-/** \ingroup flags
-  *
-  * Means that the underlying array of coefficients can be directly accessed as a plain strided array. The memory layout
-  * of the array of coefficients must be exactly the natural one suggested by rows(), cols(),
-  * outerStride(), innerStride(), and the RowMajorBit. This rules out expressions such as Diagonal, whose coefficients,
-  * though referencable, do not have such a regular memory layout.
-  *
-  * See the comment on LvalueBit for an explanation of how LvalueBit and DirectAccessBit are mutually orthogonal.
-  */
-const unsigned int DirectAccessBit = 0x40;
-
-/** \deprecated \ingroup flags
-  *
-  * means the first coefficient packet is guaranteed to be aligned.
-  * An expression cannot have the AlignedBit without the PacketAccessBit flag.
-  * In other words, this means we are allow to perform an aligned packet access to the first element regardless
-  * of the expression kind:
-  * \code
-  * expression.packet<Aligned>(0);
-  * \endcode
-  */
-EIGEN_DEPRECATED const unsigned int AlignedBit = 0x80;
-
-const unsigned int NestByRefBit = 0x100;
-
-/** \ingroup flags
-  *
-  * for an expression, this means that the storage order
-  * can be either row-major or column-major.
-  * The precise choice will be decided at evaluation time or when
-  * combined with other expressions.
-  * \sa \blank  \ref RowMajorBit, \ref TopicStorageOrders */
-const unsigned int NoPreferredStorageOrderBit = 0x200;
-
-/** \ingroup flags
-  *
-  * Means that the underlying coefficients can be accessed through pointers to the sparse (un)compressed storage format,
-  * that is, the expression provides:
-  * \code
-    inline const Scalar* valuePtr() const;
-    inline const Index* innerIndexPtr() const;
-    inline const Index* outerIndexPtr() const;
-    inline const Index* innerNonZeroPtr() const;
-    \endcode
-  */
-const unsigned int CompressedAccessBit = 0x400;
-
-
-// list of flags that are inherited by default
-const unsigned int HereditaryBits = RowMajorBit
-                                  | EvalBeforeNestingBit;
-
-/** \defgroup enums Enumerations
-  * \ingroup Core_Module
-  *
-  * Various enumerations used in %Eigen. Many of these are used as template parameters.
-  */
-
-/** \ingroup enums
-  * Enum containing possible values for the \c Mode or \c UpLo parameter of
-  * MatrixBase::selfadjointView() and MatrixBase::triangularView(), and selfadjoint solvers. */
-enum UpLoType {
-  /** View matrix as a lower triangular matrix. */
-  Lower=0x1,                      
-  /** View matrix as an upper triangular matrix. */
-  Upper=0x2,                      
-  /** %Matrix has ones on the diagonal; to be used in combination with #Lower or #Upper. */
-  UnitDiag=0x4, 
-  /** %Matrix has zeros on the diagonal; to be used in combination with #Lower or #Upper. */
-  ZeroDiag=0x8,
-  /** View matrix as a lower triangular matrix with ones on the diagonal. */
-  UnitLower=UnitDiag|Lower, 
-  /** View matrix as an upper triangular matrix with ones on the diagonal. */
-  UnitUpper=UnitDiag|Upper,
-  /** View matrix as a lower triangular matrix with zeros on the diagonal. */
-  StrictlyLower=ZeroDiag|Lower, 
-  /** View matrix as an upper triangular matrix with zeros on the diagonal. */
-  StrictlyUpper=ZeroDiag|Upper,
-  /** Used in BandMatrix and SelfAdjointView to indicate that the matrix is self-adjoint. */
-  SelfAdjoint=0x10,
-  /** Used to support symmetric, non-selfadjoint, complex matrices. */
-  Symmetric=0x20
-};
-
-/** \ingroup enums
-  * Enum for indicating whether a buffer is aligned or not. */
-enum AlignmentType {
-  Unaligned=0,        /**< Data pointer has no specific alignment. */
-  Aligned8=8,         /**< Data pointer is aligned on a 8 bytes boundary. */
-  Aligned16=16,       /**< Data pointer is aligned on a 16 bytes boundary. */
-  Aligned32=32,       /**< Data pointer is aligned on a 32 bytes boundary. */
-  Aligned64=64,       /**< Data pointer is aligned on a 64 bytes boundary. */
-  Aligned128=128,     /**< Data pointer is aligned on a 128 bytes boundary. */
-  AlignedMask=255,
-  Aligned=16,         /**< \deprecated Synonym for Aligned16. */
-#if EIGEN_MAX_ALIGN_BYTES==128
-  AlignedMax = Aligned128
-#elif EIGEN_MAX_ALIGN_BYTES==64
-  AlignedMax = Aligned64
-#elif EIGEN_MAX_ALIGN_BYTES==32
-  AlignedMax = Aligned32
-#elif EIGEN_MAX_ALIGN_BYTES==16
-  AlignedMax = Aligned16
-#elif EIGEN_MAX_ALIGN_BYTES==8
-  AlignedMax = Aligned8
-#elif EIGEN_MAX_ALIGN_BYTES==0
-  AlignedMax = Unaligned
-#else
-#error Invalid value for EIGEN_MAX_ALIGN_BYTES
-#endif
-};
-
-/** \ingroup enums
-  * Enum containing possible values for the \p Direction parameter of
-  * Reverse, PartialReduxExpr and VectorwiseOp. */
-enum DirectionType { 
-  /** For Reverse, all columns are reversed; 
-    * for PartialReduxExpr and VectorwiseOp, act on columns. */
-  Vertical, 
-  /** For Reverse, all rows are reversed; 
-    * for PartialReduxExpr and VectorwiseOp, act on rows. */
-  Horizontal, 
-  /** For Reverse, both rows and columns are reversed; 
-    * not used for PartialReduxExpr and VectorwiseOp. */
-  BothDirections 
-};
-
-/** \internal \ingroup enums
-  * Enum to specify how to traverse the entries of a matrix. */
-enum TraversalType {
-  /** \internal Default traversal, no vectorization, no index-based access */
-  DefaultTraversal,
-  /** \internal No vectorization, use index-based access to have only one for loop instead of 2 nested loops */
-  LinearTraversal,
-  /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignment
-    * and good size */
-  InnerVectorizedTraversal,
-  /** \internal Vectorization path using a single loop plus scalar loops for the
-    * unaligned boundaries */
-  LinearVectorizedTraversal,
-  /** \internal Generic vectorization path using one vectorized loop per row/column with some
-    * scalar loops to handle the unaligned boundaries */
-  SliceVectorizedTraversal,
-  /** \internal Special case to properly handle incompatible scalar types or other defecting cases*/
-  InvalidTraversal,
-  /** \internal Evaluate all entries at once */
-  AllAtOnceTraversal
-};
-
-/** \internal \ingroup enums
-  * Enum to specify whether to unroll loops when traversing over the entries of a matrix. */
-enum UnrollingType {
-  /** \internal Do not unroll loops. */
-  NoUnrolling,
-  /** \internal Unroll only the inner loop, but not the outer loop. */
-  InnerUnrolling,
-  /** \internal Unroll both the inner and the outer loop. If there is only one loop, 
-    * because linear traversal is used, then unroll that loop. */
-  CompleteUnrolling
-};
-
-/** \internal \ingroup enums
-  * Enum to specify whether to use the default (built-in) implementation or the specialization. */
-enum SpecializedType {
-  Specialized,
-  BuiltIn
-};
-
-/** \ingroup enums
-  * Enum containing possible values for the \p _Options template parameter of
-  * Matrix, Array and BandMatrix. */
-enum StorageOptions {
-  /** Storage order is column major (see \ref TopicStorageOrders). */
-  ColMajor = 0,
-  /** Storage order is row major (see \ref TopicStorageOrders). */
-  RowMajor = 0x1,  // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that
-  /** Align the matrix itself if it is vectorizable fixed-size */
-  AutoAlign = 0,
-  /** Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
-  DontAlign = 0x2
-};
-
-/** \ingroup enums
-  * Enum for specifying whether to apply or solve on the left or right. */
-enum SideType {
-  /** Apply transformation on the left. */
-  OnTheLeft = 1,
-  /** Apply transformation on the right. */
-  OnTheRight = 2
-};
-
-/** \ingroup enums
- * Enum for specifying NaN-propagation behavior, e.g. for coeff-wise min/max. */
-enum NaNPropagationOptions {
-  /**  Implementation defined behavior if NaNs are present. */
-  PropagateFast = 0,
-  /**  Always propagate NaNs. */
-  PropagateNaN,
-  /**  Always propagate not-NaNs. */
-  PropagateNumbers
-};
-
-/* the following used to be written as:
- *
- *   struct NoChange_t {};
- *   namespace {
- *     EIGEN_UNUSED NoChange_t NoChange;
- *   }
- *
- * on the ground that it feels dangerous to disambiguate overloaded functions on enum/integer types.  
- * However, this leads to "variable declared but never referenced" warnings on Intel Composer XE,
- * and we do not know how to get rid of them (bug 450).
- */
-
-enum NoChange_t   { NoChange };
-enum Sequential_t { Sequential };
-enum Default_t    { Default };
-
-/** \internal \ingroup enums
-  * Used in AmbiVector. */
-enum AmbiVectorMode {
-  IsDense         = 0,
-  IsSparse
-};
-
-/** \ingroup enums
-  * Used as template parameter in DenseCoeffBase and MapBase to indicate 
-  * which accessors should be provided. */
-enum AccessorLevels {
-  /** Read-only access via a member function. */
-  ReadOnlyAccessors, 
-  /** Read/write access via member functions. */
-  WriteAccessors, 
-  /** Direct read-only access to the coefficients. */
-  DirectAccessors, 
-  /** Direct read/write access to the coefficients. */
-  DirectWriteAccessors
-};
-
-/** \ingroup enums
-  * Enum with options to give to various decompositions. */
-enum DecompositionOptions {
-  /** \internal Not used (meant for LDLT?). */
-  Pivoting            = 0x01, 
-  /** \internal Not used (meant for LDLT?). */
-  NoPivoting          = 0x02, 
-  /** Used in JacobiSVD to indicate that the square matrix U is to be computed. */
-  ComputeFullU        = 0x04,
-  /** Used in JacobiSVD to indicate that the thin matrix U is to be computed. */
-  ComputeThinU        = 0x08,
-  /** Used in JacobiSVD to indicate that the square matrix V is to be computed. */
-  ComputeFullV        = 0x10,
-  /** Used in JacobiSVD to indicate that the thin matrix V is to be computed. */
-  ComputeThinV        = 0x20,
-  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
-    * that only the eigenvalues are to be computed and not the eigenvectors. */
-  EigenvaluesOnly     = 0x40,
-  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
-    * that both the eigenvalues and the eigenvectors are to be computed. */
-  ComputeEigenvectors = 0x80,
-  /** \internal */
-  EigVecMask = EigenvaluesOnly | ComputeEigenvectors,
-  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
-    * solve the generalized eigenproblem \f$ Ax = \lambda B x \f$. */
-  Ax_lBx              = 0x100,
-  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
-    * solve the generalized eigenproblem \f$ ABx = \lambda x \f$. */
-  ABx_lx              = 0x200,
-  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
-    * solve the generalized eigenproblem \f$ BAx = \lambda x \f$. */
-  BAx_lx              = 0x400,
-  /** \internal */
-  GenEigMask = Ax_lBx | ABx_lx | BAx_lx
-};
-
-/** \ingroup enums
-  * Possible values for the \p QRPreconditioner template parameter of JacobiSVD. */
-enum QRPreconditioners {
-  /** Do not specify what is to be done if the SVD of a non-square matrix is asked for. */
-  NoQRPreconditioner,
-  /** Use a QR decomposition without pivoting as the first step. */
-  HouseholderQRPreconditioner,
-  /** Use a QR decomposition with column pivoting as the first step. */
-  ColPivHouseholderQRPreconditioner,
-  /** Use a QR decomposition with full pivoting as the first step. */
-  FullPivHouseholderQRPreconditioner
-};
-
-#ifdef Success
-#error The preprocessor symbol 'Success' is defined, possibly by the X11 header file X.h
-#endif
-
-/** \ingroup enums
-  * Enum for reporting the status of a computation. */
-enum ComputationInfo {
-  /** Computation was successful. */
-  Success = 0,        
-  /** The provided data did not satisfy the prerequisites. */
-  NumericalIssue = 1, 
-  /** Iterative procedure did not converge. */
-  NoConvergence = 2,
-  /** The inputs are invalid, or the algorithm has been improperly called.
-    * When assertions are enabled, such errors trigger an assert. */
-  InvalidInput = 3
-};
-
-/** \ingroup enums
-  * Enum used to specify how a particular transformation is stored in a matrix.
-  * \sa Transform, Hyperplane::transform(). */
-enum TransformTraits {
-  /** Transformation is an isometry. */
-  Isometry      = 0x1,
-  /** Transformation is an affine transformation stored as a (Dim+1)^2 matrix whose last row is 
-    * assumed to be [0 ... 0 1]. */
-  Affine        = 0x2,
-  /** Transformation is an affine transformation stored as a (Dim) x (Dim+1) matrix. */
-  AffineCompact = 0x10 | Affine,
-  /** Transformation is a general projective transformation stored as a (Dim+1)^2 matrix. */
-  Projective    = 0x20
-};
-
-/** \internal \ingroup enums
-  * Enum used to choose between implementation depending on the computer architecture. */
-namespace Architecture
-{
-  enum Type {
-    Generic = 0x0,
-    SSE = 0x1,
-    AltiVec = 0x2,
-    VSX = 0x3,
-    NEON = 0x4,
-    MSA = 0x5,
-    SVE = 0x6,
-#if defined EIGEN_VECTORIZE_SSE
-    Target = SSE
-#elif defined EIGEN_VECTORIZE_ALTIVEC
-    Target = AltiVec
-#elif defined EIGEN_VECTORIZE_VSX
-    Target = VSX
-#elif defined EIGEN_VECTORIZE_NEON
-    Target = NEON
-#elif defined EIGEN_VECTORIZE_SVE
-    Target = SVE
-#elif defined EIGEN_VECTORIZE_MSA
-    Target = MSA
-#else
-    Target = Generic
-#endif
-  };
-}
-
-/** \internal \ingroup enums
-  * Enum used as template parameter in Product and product evaluators. */
-enum ProductImplType
-{ DefaultProduct=0, LazyProduct, AliasFreeProduct, CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct };
-
-/** \internal \ingroup enums
-  * Enum used in experimental parallel implementation. */
-enum Action {GetAction, SetAction};
-
-/** The type used to identify a dense storage. */
-struct Dense {};
-
-/** The type used to identify a general sparse storage. */
-struct Sparse {};
-
-/** The type used to identify a general solver (factored) storage. */
-struct SolverStorage {};
-
-/** The type used to identify a permutation storage. */
-struct PermutationStorage {};
-
-/** The type used to identify a permutation storage. */
-struct TranspositionsStorage {};
-
-/** The type used to identify a matrix expression */
-struct MatrixXpr {};
-
-/** The type used to identify an array expression */
-struct ArrayXpr {};
-
-// An evaluator must define its shape. By default, it can be one of the following:
-struct DenseShape             { static std::string debugName() { return "DenseShape"; } };
-struct SolverShape            { static std::string debugName() { return "SolverShape"; } };
-struct HomogeneousShape       { static std::string debugName() { return "HomogeneousShape"; } };
-struct DiagonalShape          { static std::string debugName() { return "DiagonalShape"; } };
-struct BandShape              { static std::string debugName() { return "BandShape"; } };
-struct TriangularShape        { static std::string debugName() { return "TriangularShape"; } };
-struct SelfAdjointShape       { static std::string debugName() { return "SelfAdjointShape"; } };
-struct PermutationShape       { static std::string debugName() { return "PermutationShape"; } };
-struct TranspositionsShape    { static std::string debugName() { return "TranspositionsShape"; } };
-struct SparseShape            { static std::string debugName() { return "SparseShape"; } };
-
-namespace internal {
-
-  // random access iterators based on coeff*() accessors.
-struct IndexBased {};
-
-// evaluator based on iterators to access coefficients. 
-struct IteratorBased {};
-
-/** \internal
- * Constants for comparison functors
- */
-enum ComparisonName {
-  cmp_EQ = 0,
-  cmp_LT = 1,
-  cmp_LE = 2,
-  cmp_UNORD = 3,
-  cmp_NEQ = 4,
-  cmp_GT = 5,
-  cmp_GE = 6
-};
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONSTANTS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/DisableStupidWarnings.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/DisableStupidWarnings.h
deleted file mode 100644
index fe0cfec..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/DisableStupidWarnings.h
+++ /dev/null
@@ -1,106 +0,0 @@
-#ifndef EIGEN_WARNINGS_DISABLED
-#define EIGEN_WARNINGS_DISABLED
-
-#ifdef _MSC_VER
-  // 4100 - unreferenced formal parameter (occurred e.g. in aligned_allocator::destroy(pointer p))
-  // 4101 - unreferenced local variable
-  // 4181 - qualifier applied to reference type ignored
-  // 4211 - nonstandard extension used : redefined extern to static
-  // 4244 - 'argument' : conversion from 'type1' to 'type2', possible loss of data
-  // 4273 - QtAlignedMalloc, inconsistent DLL linkage
-  // 4324 - structure was padded due to declspec(align())
-  // 4503 - decorated name length exceeded, name was truncated
-  // 4512 - assignment operator could not be generated
-  // 4522 - 'class' : multiple assignment operators specified
-  // 4700 - uninitialized local variable 'xyz' used
-  // 4714 - function marked as __forceinline not inlined
-  // 4717 - 'function' : recursive on all control paths, function will cause runtime stack overflow
-  // 4800 - 'type' : forcing value to bool 'true' or 'false' (performance warning)
-  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-    #pragma warning( push )
-  #endif
-  #pragma warning( disable : 4100 4101 4181 4211 4244 4273 4324 4503 4512 4522 4700 4714 4717 4800)
-
-#elif defined __INTEL_COMPILER
-  // 2196 - routine is both "inline" and "noinline" ("noinline" assumed)
-  //        ICC 12 generates this warning even without any inline keyword, when defining class methods 'inline' i.e. inside of class body
-  //        typedef that may be a reference type.
-  // 279  - controlling expression is constant
-  //        ICC 12 generates this warning on assert(constant_expression_depending_on_template_params) and frankly this is a legitimate use case.
-  // 1684 - conversion from pointer to same-sized integral type (potential portability problem)
-  // 2259 - non-pointer conversion from "Eigen::Index={ptrdiff_t={long}}" to "int" may lose significant bits
-  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-    #pragma warning push
-  #endif
-  #pragma warning disable 2196 279 1684 2259
-
-#elif defined __clang__
-  // -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant
-  //     this is really a stupid warning as it warns on compile-time expressions involving enums
-  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-    #pragma clang diagnostic push
-  #endif
-  #pragma clang diagnostic ignored "-Wconstant-logical-operand"
-  #if __clang_major__ >= 3 && __clang_minor__ >= 5
-    #pragma clang diagnostic ignored "-Wabsolute-value"
-  #endif
-  #if __clang_major__ >= 10
-    #pragma clang diagnostic ignored "-Wimplicit-int-float-conversion"
-  #endif
-  #if ( defined(__ALTIVEC__) || defined(__VSX__) ) && __cplusplus < 201103L
-    // warning: generic selections are a C11-specific feature
-    // ignoring warnings thrown at vec_ctf in Altivec/PacketMath.h
-    #pragma clang diagnostic ignored "-Wc11-extensions"
-  #endif
-
-#elif defined __GNUC__
-
-  #if (!defined(EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS)) &&  (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-    #pragma GCC diagnostic push
-  #endif
-  // g++ warns about local variables shadowing member functions, which is too strict
-  #pragma GCC diagnostic ignored "-Wshadow"
-  #if __GNUC__ == 4 && __GNUC_MINOR__ < 8
-    // Until g++-4.7 there are warnings when comparing unsigned int vs 0, even in templated functions:
-    #pragma GCC diagnostic ignored "-Wtype-limits"
-  #endif
-  #if __GNUC__>=6
-    #pragma GCC diagnostic ignored "-Wignored-attributes"
-  #endif
-  #if __GNUC__==7
-    // See: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89325
-    #pragma GCC diagnostic ignored "-Wattributes"
-  #endif
-#endif
-
-#if defined __NVCC__
-  #pragma diag_suppress boolean_controlling_expr_is_constant
-  // Disable the "statement is unreachable" message
-  #pragma diag_suppress code_is_unreachable
-  // Disable the "dynamic initialization in unreachable code" message
-  #pragma diag_suppress initialization_not_reachable
-  // Disable the "invalid error number" message that we get with older versions of nvcc
-  #pragma diag_suppress 1222
-  // Disable the "calling a __host__ function from a __host__ __device__ function is not allowed" messages (yes, there are many of them and they seem to change with every version of the compiler)
-  #pragma diag_suppress 2527
-  #pragma diag_suppress 2529
-  #pragma diag_suppress 2651
-  #pragma diag_suppress 2653
-  #pragma diag_suppress 2668
-  #pragma diag_suppress 2669
-  #pragma diag_suppress 2670
-  #pragma diag_suppress 2671
-  #pragma diag_suppress 2735
-  #pragma diag_suppress 2737
-  #pragma diag_suppress 2739
-#endif
-
-#else
-// warnings already disabled:
-# ifndef EIGEN_WARNINGS_DISABLED_2
-#  define EIGEN_WARNINGS_DISABLED_2
-# elif defined(EIGEN_INTERNAL_DEBUGGING)
-#  error "Do not include \"DisableStupidWarnings.h\" recursively more than twice!"
-# endif
-
-#endif // not EIGEN_WARNINGS_DISABLED
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ForwardDeclarations.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ForwardDeclarations.h
deleted file mode 100644
index 2f9cc44..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ForwardDeclarations.h
+++ /dev/null
@@ -1,322 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FORWARDDECLARATIONS_H
-#define EIGEN_FORWARDDECLARATIONS_H
-
-namespace Eigen {
-namespace internal {
-
-template<typename T> struct traits;
-
-// here we say once and for all that traits<const T> == traits<T>
-// When constness must affect traits, it has to be constness on template parameters on which T itself depends.
-// For example, traits<Map<const T> > != traits<Map<T> >, but
-//              traits<const Map<T> > == traits<Map<T> >
-template<typename T> struct traits<const T> : traits<T> {};
-
-template<typename Derived> struct has_direct_access
-{
-  enum { ret = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0 };
-};
-
-template<typename Derived> struct accessors_level
-{
-  enum { has_direct_access = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0,
-         has_write_access = (traits<Derived>::Flags & LvalueBit) ? 1 : 0,
-         value = has_direct_access ? (has_write_access ? DirectWriteAccessors : DirectAccessors)
-                                   : (has_write_access ? WriteAccessors       : ReadOnlyAccessors)
-  };
-};
-
-template<typename T> struct evaluator_traits;
-
-template< typename T> struct evaluator;
-
-} // end namespace internal
-
-template<typename T> struct NumTraits;
-
-template<typename Derived> struct EigenBase;
-template<typename Derived> class DenseBase;
-template<typename Derived> class PlainObjectBase;
-template<typename Derived, int Level> class DenseCoeffsBase;
-
-template<typename _Scalar, int _Rows, int _Cols,
-         int _Options = AutoAlign |
-#if EIGEN_GNUC_AT(3,4)
-    // workaround a bug in at least gcc 3.4.6
-    // the innermost ?: ternary operator is misparsed. We write it slightly
-    // differently and this makes gcc 3.4.6 happy, but it's ugly.
-    // The error would only show up with EIGEN_DEFAULT_TO_ROW_MAJOR is defined
-    // (when EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION is RowMajor)
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : !(_Cols==1 && _Rows!=1) ?  EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION
-                          : Eigen::ColMajor ),
-#else
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : (_Cols==1 && _Rows!=1) ? Eigen::ColMajor
-                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
-#endif
-         int _MaxRows = _Rows,
-         int _MaxCols = _Cols
-> class Matrix;
-
-template<typename Derived> class MatrixBase;
-template<typename Derived> class ArrayBase;
-
-template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged;
-template<typename ExpressionType, template <typename> class StorageBase > class NoAlias;
-template<typename ExpressionType> class NestByValue;
-template<typename ExpressionType> class ForceAlignedAccess;
-template<typename ExpressionType> class SwapWrapper;
-
-template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false> class Block;
-template<typename XprType, typename RowIndices, typename ColIndices> class IndexedView;
-template<typename XprType, int Rows=Dynamic, int Cols=Dynamic, int Order=0> class Reshaped;
-
-template<typename MatrixType, int Size=Dynamic> class VectorBlock;
-template<typename MatrixType> class Transpose;
-template<typename MatrixType> class Conjugate;
-template<typename NullaryOp, typename MatrixType>         class CwiseNullaryOp;
-template<typename UnaryOp,   typename MatrixType>         class CwiseUnaryOp;
-template<typename ViewOp,    typename MatrixType>         class CwiseUnaryView;
-template<typename BinaryOp,  typename Lhs, typename Rhs>  class CwiseBinaryOp;
-template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>  class CwiseTernaryOp;
-template<typename Decomposition, typename Rhstype>        class Solve;
-template<typename XprType>                                class Inverse;
-
-template<typename Lhs, typename Rhs, int Option = DefaultProduct> class Product;
-
-template<typename Derived> class DiagonalBase;
-template<typename _DiagonalVectorType> class DiagonalWrapper;
-template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime=SizeAtCompileTime> class DiagonalMatrix;
-template<typename MatrixType, typename DiagonalType, int ProductOrder> class DiagonalProduct;
-template<typename MatrixType, int Index = 0> class Diagonal;
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class PermutationMatrix;
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class Transpositions;
-template<typename Derived> class PermutationBase;
-template<typename Derived> class TranspositionsBase;
-template<typename _IndicesType> class PermutationWrapper;
-template<typename _IndicesType> class TranspositionsWrapper;
-
-template<typename Derived,
-         int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors
-> class MapBase;
-template<int OuterStrideAtCompileTime, int InnerStrideAtCompileTime> class Stride;
-template<int Value = Dynamic> class InnerStride;
-template<int Value = Dynamic> class OuterStride;
-template<typename MatrixType, int MapOptions=Unaligned, typename StrideType = Stride<0,0> > class Map;
-template<typename Derived> class RefBase;
-template<typename PlainObjectType, int Options = 0,
-         typename StrideType = typename internal::conditional<PlainObjectType::IsVectorAtCompileTime,InnerStride<1>,OuterStride<> >::type > class Ref;
-
-template<typename Derived> class TriangularBase;
-template<typename MatrixType, unsigned int Mode> class TriangularView;
-template<typename MatrixType, unsigned int Mode> class SelfAdjointView;
-template<typename MatrixType> class SparseView;
-template<typename ExpressionType> class WithFormat;
-template<typename MatrixType> struct CommaInitializer;
-template<typename Derived> class ReturnByValue;
-template<typename ExpressionType> class ArrayWrapper;
-template<typename ExpressionType> class MatrixWrapper;
-template<typename Derived> class SolverBase;
-template<typename XprType> class InnerIterator;
-
-namespace internal {
-template<typename XprType> class generic_randaccess_stl_iterator;
-template<typename XprType> class pointer_based_stl_iterator;
-template<typename XprType, DirectionType Direction> class subvector_stl_iterator;
-template<typename XprType, DirectionType Direction> class subvector_stl_reverse_iterator;
-template<typename DecompositionType> struct kernel_retval_base;
-template<typename DecompositionType> struct kernel_retval;
-template<typename DecompositionType> struct image_retval_base;
-template<typename DecompositionType> struct image_retval;
-} // end namespace internal
-
-namespace internal {
-template<typename _Scalar, int Rows=Dynamic, int Cols=Dynamic, int Supers=Dynamic, int Subs=Dynamic, int Options=0> class BandMatrix;
-}
-
-namespace internal {
-template<typename Lhs, typename Rhs> struct product_type;
-
-template<bool> struct EnableIf;
-
-/** \internal
-  * \class product_evaluator
-  * Products need their own evaluator with more template arguments allowing for
-  * easier partial template specializations.
-  */
-template< typename T,
-          int ProductTag = internal::product_type<typename T::Lhs,typename T::Rhs>::ret,
-          typename LhsShape = typename evaluator_traits<typename T::Lhs>::Shape,
-          typename RhsShape = typename evaluator_traits<typename T::Rhs>::Shape,
-          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
-          typename RhsScalar = typename traits<typename T::Rhs>::Scalar
-        > struct product_evaluator;
-}
-
-template<typename Lhs, typename Rhs,
-         int ProductType = internal::product_type<Lhs,Rhs>::value>
-struct ProductReturnType;
-
-// this is a workaround for sun CC
-template<typename Lhs, typename Rhs> struct LazyProductReturnType;
-
-namespace internal {
-
-// Provides scalar/packet-wise product and product with accumulation
-// with optional conjugation of the arguments.
-template<typename LhsScalar, typename RhsScalar, bool ConjLhs=false, bool ConjRhs=false> struct conj_helper;
-
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_sum_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_difference_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_conj_product_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar, int NaNPropagation=PropagateFast> struct scalar_min_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar, int NaNPropagation=PropagateFast> struct scalar_max_op;
-template<typename Scalar> struct scalar_opposite_op;
-template<typename Scalar> struct scalar_conjugate_op;
-template<typename Scalar> struct scalar_real_op;
-template<typename Scalar> struct scalar_imag_op;
-template<typename Scalar> struct scalar_abs_op;
-template<typename Scalar> struct scalar_abs2_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_absolute_difference_op;
-template<typename Scalar> struct scalar_sqrt_op;
-template<typename Scalar> struct scalar_rsqrt_op;
-template<typename Scalar> struct scalar_exp_op;
-template<typename Scalar> struct scalar_log_op;
-template<typename Scalar> struct scalar_cos_op;
-template<typename Scalar> struct scalar_sin_op;
-template<typename Scalar> struct scalar_acos_op;
-template<typename Scalar> struct scalar_asin_op;
-template<typename Scalar> struct scalar_tan_op;
-template<typename Scalar> struct scalar_inverse_op;
-template<typename Scalar> struct scalar_square_op;
-template<typename Scalar> struct scalar_cube_op;
-template<typename Scalar, typename NewType> struct scalar_cast_op;
-template<typename Scalar> struct scalar_random_op;
-template<typename Scalar> struct scalar_constant_op;
-template<typename Scalar> struct scalar_identity_op;
-template<typename Scalar,bool is_complex, bool is_integer> struct scalar_sign_op;
-template<typename Scalar,typename ScalarExponent> struct scalar_pow_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_hypot_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op;
-
-// SpecialFunctions module
-template<typename Scalar> struct scalar_lgamma_op;
-template<typename Scalar> struct scalar_digamma_op;
-template<typename Scalar> struct scalar_erf_op;
-template<typename Scalar> struct scalar_erfc_op;
-template<typename Scalar> struct scalar_ndtri_op;
-template<typename Scalar> struct scalar_igamma_op;
-template<typename Scalar> struct scalar_igammac_op;
-template<typename Scalar> struct scalar_zeta_op;
-template<typename Scalar> struct scalar_betainc_op;
-
-// Bessel functions in SpecialFunctions module
-template<typename Scalar> struct scalar_bessel_i0_op;
-template<typename Scalar> struct scalar_bessel_i0e_op;
-template<typename Scalar> struct scalar_bessel_i1_op;
-template<typename Scalar> struct scalar_bessel_i1e_op;
-template<typename Scalar> struct scalar_bessel_j0_op;
-template<typename Scalar> struct scalar_bessel_y0_op;
-template<typename Scalar> struct scalar_bessel_j1_op;
-template<typename Scalar> struct scalar_bessel_y1_op;
-template<typename Scalar> struct scalar_bessel_k0_op;
-template<typename Scalar> struct scalar_bessel_k0e_op;
-template<typename Scalar> struct scalar_bessel_k1_op;
-template<typename Scalar> struct scalar_bessel_k1e_op;
-
-
-} // end namespace internal
-
-struct IOFormat;
-
-// Array module
-template<typename _Scalar, int _Rows, int _Cols,
-         int _Options = AutoAlign |
-#if EIGEN_GNUC_AT(3,4)
-    // workaround a bug in at least gcc 3.4.6
-    // the innermost ?: ternary operator is misparsed. We write it slightly
-    // differently and this makes gcc 3.4.6 happy, but it's ugly.
-    // The error would only show up with EIGEN_DEFAULT_TO_ROW_MAJOR is defined
-    // (when EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION is RowMajor)
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : !(_Cols==1 && _Rows!=1) ?  EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION
-                          : Eigen::ColMajor ),
-#else
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : (_Cols==1 && _Rows!=1) ? Eigen::ColMajor
-                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
-#endif
-         int _MaxRows = _Rows, int _MaxCols = _Cols> class Array;
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType> class Select;
-template<typename MatrixType, typename BinaryOp, int Direction> class PartialReduxExpr;
-template<typename ExpressionType, int Direction> class VectorwiseOp;
-template<typename MatrixType,int RowFactor,int ColFactor> class Replicate;
-template<typename MatrixType, int Direction = BothDirections> class Reverse;
-
-template<typename MatrixType> class FullPivLU;
-template<typename MatrixType> class PartialPivLU;
-namespace internal {
-template<typename MatrixType> struct inverse_impl;
-}
-template<typename MatrixType> class HouseholderQR;
-template<typename MatrixType> class ColPivHouseholderQR;
-template<typename MatrixType> class FullPivHouseholderQR;
-template<typename MatrixType> class CompleteOrthogonalDecomposition;
-template<typename MatrixType> class SVDBase;
-template<typename MatrixType, int QRPreconditioner = ColPivHouseholderQRPreconditioner> class JacobiSVD;
-template<typename MatrixType> class BDCSVD;
-template<typename MatrixType, int UpLo = Lower> class LLT;
-template<typename MatrixType, int UpLo = Lower> class LDLT;
-template<typename VectorsType, typename CoeffsType, int Side=OnTheLeft> class HouseholderSequence;
-template<typename Scalar>     class JacobiRotation;
-
-// Geometry module:
-template<typename Derived, int _Dim> class RotationBase;
-template<typename Lhs, typename Rhs> class Cross;
-template<typename Derived> class QuaternionBase;
-template<typename Scalar> class Rotation2D;
-template<typename Scalar> class AngleAxis;
-template<typename Scalar,int Dim> class Translation;
-template<typename Scalar,int Dim> class AlignedBox;
-template<typename Scalar, int Options = AutoAlign> class Quaternion;
-template<typename Scalar,int Dim,int Mode,int _Options=AutoAlign> class Transform;
-template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class ParametrizedLine;
-template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class Hyperplane;
-template<typename Scalar> class UniformScaling;
-template<typename MatrixType,int Direction> class Homogeneous;
-
-// Sparse module:
-template<typename Derived> class SparseMatrixBase;
-
-// MatrixFunctions module
-template<typename Derived> struct MatrixExponentialReturnValue;
-template<typename Derived> class MatrixFunctionReturnValue;
-template<typename Derived> class MatrixSquareRootReturnValue;
-template<typename Derived> class MatrixLogarithmReturnValue;
-template<typename Derived> class MatrixPowerReturnValue;
-template<typename Derived> class MatrixComplexPowerReturnValue;
-
-namespace internal {
-template <typename Scalar>
-struct stem_function
-{
-  typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-  typedef ComplexScalar type(ComplexScalar, int);
-};
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FORWARDDECLARATIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/IndexedViewHelper.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/IndexedViewHelper.h
deleted file mode 100644
index f85de30..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/IndexedViewHelper.h
+++ /dev/null
@@ -1,186 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_INDEXED_VIEW_HELPER_H
-#define EIGEN_INDEXED_VIEW_HELPER_H
-
-namespace Eigen {
-
-namespace internal {
-struct symbolic_last_tag {};
-}
-
-/** \var last
-  * \ingroup Core_Module
-  *
-  * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically reference the last element/row/columns
-  * of the underlying vector or matrix once passed to DenseBase::operator()(const RowIndices&, const ColIndices&).
-  *
-  * This symbolic placeholder supports standard arithmetic operations.
-  *
-  * A typical usage example would be:
-  * \code
-  * using namespace Eigen;
-  * using Eigen::last;
-  * VectorXd v(n);
-  * v(seq(2,last-2)).setOnes();
-  * \endcode
-  *
-  * \sa end
-  */
-static const symbolic::SymbolExpr<internal::symbolic_last_tag> last; // PLEASE use Eigen::last   instead of Eigen::placeholders::last
-
-/** \var lastp1
-  * \ingroup Core_Module
-  *
-  * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically
-  * reference the last+1 element/row/columns of the underlying vector or matrix once
-  * passed to DenseBase::operator()(const RowIndices&, const ColIndices&).
-  *
-  * This symbolic placeholder supports standard arithmetic operations.
-  * It is essentially an alias to last+fix<1>.
-  *
-  * \sa last
-  */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-static const auto lastp1 = last+fix<1>;
-#else
-// Using a FixedExpr<1> expression is important here to make sure the compiler
-// can fully optimize the computation starting indices with zero overhead.
-static const symbolic::AddExpr<symbolic::SymbolExpr<internal::symbolic_last_tag>,symbolic::ValueExpr<Eigen::internal::FixedInt<1> > > lastp1(last+fix<1>());
-#endif
-
-namespace internal {
-
- // Replace symbolic last/end "keywords" by their true runtime value
-inline Index eval_expr_given_size(Index x, Index /* size */)   { return x; }
-
-template<int N>
-FixedInt<N> eval_expr_given_size(FixedInt<N> x, Index /*size*/)   { return x; }
-
-template<typename Derived>
-Index eval_expr_given_size(const symbolic::BaseExpr<Derived> &x, Index size)
-{
-  return x.derived().eval(last=size-1);
-}
-
-// Extract increment/step at compile time
-template<typename T, typename EnableIf = void> struct get_compile_time_incr {
-  enum { value = UndefinedIncr };
-};
-
-// Analogue of std::get<0>(x), but tailored for our needs.
-template<typename T>
-EIGEN_CONSTEXPR Index first(const T& x) EIGEN_NOEXCEPT { return x.first(); }
-
-// IndexedViewCompatibleType/makeIndexedViewCompatible turn an arbitrary object of type T into something usable by MatrixSlice
-// The generic implementation is a no-op
-template<typename T,int XprSize,typename EnableIf=void>
-struct IndexedViewCompatibleType {
-  typedef T type;
-};
-
-template<typename T,typename Q>
-const T& makeIndexedViewCompatible(const T& x, Index /*size*/, Q) { return x; }
-
-//--------------------------------------------------------------------------------
-// Handling of a single Index
-//--------------------------------------------------------------------------------
-
-struct SingleRange {
-  enum {
-    SizeAtCompileTime = 1
-  };
-  SingleRange(Index val) : m_value(val) {}
-  Index operator[](Index) const { return m_value; }
-  static EIGEN_CONSTEXPR Index size() EIGEN_NOEXCEPT { return 1; }
-  Index first() const EIGEN_NOEXCEPT { return m_value; }
-  Index m_value;
-};
-
-template<> struct get_compile_time_incr<SingleRange> {
-  enum { value = 1 }; // 1 or 0 ??
-};
-
-// Turn a single index into something that looks like an array (i.e., that exposes a .size(), and operator[](int) methods)
-template<typename T, int XprSize>
-struct IndexedViewCompatibleType<T,XprSize,typename internal::enable_if<internal::is_integral<T>::value>::type> {
-  // Here we could simply use Array, but maybe it's less work for the compiler to use
-  // a simpler wrapper as SingleRange
-  //typedef Eigen::Array<Index,1,1> type;
-  typedef SingleRange type;
-};
-
-template<typename T, int XprSize>
-struct IndexedViewCompatibleType<T, XprSize, typename enable_if<symbolic::is_symbolic<T>::value>::type> {
-  typedef SingleRange type;
-};
-
-
-template<typename T>
-typename enable_if<symbolic::is_symbolic<T>::value,SingleRange>::type
-makeIndexedViewCompatible(const T& id, Index size, SpecializedType) {
-  return eval_expr_given_size(id,size);
-}
-
-//--------------------------------------------------------------------------------
-// Handling of all
-//--------------------------------------------------------------------------------
-
-struct all_t { all_t() {} };
-
-// Convert a symbolic 'all' into a usable range type
-template<int XprSize>
-struct AllRange {
-  enum { SizeAtCompileTime = XprSize };
-  AllRange(Index size = XprSize) : m_size(size) {}
-  EIGEN_CONSTEXPR Index operator[](Index i) const EIGEN_NOEXCEPT { return i; }
-  EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_size.value(); }
-  EIGEN_CONSTEXPR Index first() const EIGEN_NOEXCEPT { return 0; }
-  variable_if_dynamic<Index,XprSize> m_size;
-};
-
-template<int XprSize>
-struct IndexedViewCompatibleType<all_t,XprSize> {
-  typedef AllRange<XprSize> type;
-};
-
-template<typename XprSizeType>
-inline AllRange<get_fixed_value<XprSizeType>::value> makeIndexedViewCompatible(all_t , XprSizeType size, SpecializedType) {
-  return AllRange<get_fixed_value<XprSizeType>::value>(size);
-}
-
-template<int Size> struct get_compile_time_incr<AllRange<Size> > {
-  enum { value = 1 };
-};
-
-} // end namespace internal
-
-
-/** \var all
-  * \ingroup Core_Module
-  * Can be used as a parameter to DenseBase::operator()(const RowIndices&, const ColIndices&) to index all rows or columns
-  */
-static const Eigen::internal::all_t all; // PLEASE use Eigen::all instead of Eigen::placeholders::all
-
-
-namespace placeholders {
-  typedef symbolic::SymbolExpr<internal::symbolic_last_tag> last_t;
-  typedef symbolic::AddExpr<symbolic::SymbolExpr<internal::symbolic_last_tag>,symbolic::ValueExpr<Eigen::internal::FixedInt<1> > > end_t;
-  typedef Eigen::internal::all_t all_t;
-
-  EIGEN_DEPRECATED static const all_t  all  = Eigen::all;    // PLEASE use Eigen::all    instead of Eigen::placeholders::all
-  EIGEN_DEPRECATED static const last_t last = Eigen::last;   // PLEASE use Eigen::last   instead of Eigen::placeholders::last
-  EIGEN_DEPRECATED static const end_t  end  = Eigen::lastp1; // PLEASE use Eigen::lastp1 instead of Eigen::placeholders::end
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_INDEXED_VIEW_HELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/IntegralConstant.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/IntegralConstant.h
deleted file mode 100644
index 945d426..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/IntegralConstant.h
+++ /dev/null
@@ -1,272 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_INTEGRAL_CONSTANT_H
-#define EIGEN_INTEGRAL_CONSTANT_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<int N> class FixedInt;
-template<int N> class VariableAndFixedInt;
-
-/** \internal
-  * \class FixedInt
-  *
-  * This class embeds a compile-time integer \c N.
-  *
-  * It is similar to c++11 std::integral_constant<int,N> but with some additional features
-  * such as:
-  *  - implicit conversion to int
-  *  - arithmetic and some bitwise operators: -, +, *, /, %, &, |
-  *  - c++98/14 compatibility with fix<N> and fix<N>() syntax to define integral constants.
-  *
-  * It is strongly discouraged to directly deal with this class FixedInt. Instances are expcected to
-  * be created by the user using Eigen::fix<N> or Eigen::fix<N>(). In C++98-11, the former syntax does
-  * not create a FixedInt<N> instance but rather a point to function that needs to be \em cleaned-up
-  * using the generic helper:
-  * \code
-  * internal::cleanup_index_type<T>::type
-  * internal::cleanup_index_type<T,DynamicKey>::type
-  * \endcode
-  * where T can a FixedInt<N>, a pointer to function FixedInt<N> (*)(), or numerous other integer-like representations.
-  * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
-  *
-  * For convenience, you can extract the compile-time value \c N in a generic way using the following helper:
-  * \code
-  * internal::get_fixed_value<T,DefaultVal>::value
-  * \endcode
-  * that will give you \c N if T equals FixedInt<N> or FixedInt<N> (*)(), and \c DefaultVal if T does not embed any compile-time value (e.g., T==int).
-  *
-  * \sa fix<N>, class VariableAndFixedInt
-  */
-template<int N> class FixedInt
-{
-public:
-  static const int value = N;
-  EIGEN_CONSTEXPR operator int() const { return value; }
-  FixedInt() {}
-  FixedInt( VariableAndFixedInt<N> other) {
-    #ifndef EIGEN_INTERNAL_DEBUGGING
-    EIGEN_UNUSED_VARIABLE(other);
-    #endif
-    eigen_internal_assert(int(other)==N);
-  }
-
-  FixedInt<-N> operator-() const { return FixedInt<-N>(); }
-  template<int M>
-  FixedInt<N+M> operator+( FixedInt<M>) const { return FixedInt<N+M>(); }
-  template<int M>
-  FixedInt<N-M> operator-( FixedInt<M>) const { return FixedInt<N-M>(); }
-  template<int M>
-  FixedInt<N*M> operator*( FixedInt<M>) const { return FixedInt<N*M>(); }
-  template<int M>
-  FixedInt<N/M> operator/( FixedInt<M>) const { return FixedInt<N/M>(); }
-  template<int M>
-  FixedInt<N%M> operator%( FixedInt<M>) const { return FixedInt<N%M>(); }
-  template<int M>
-  FixedInt<N|M> operator|( FixedInt<M>) const { return FixedInt<N|M>(); }
-  template<int M>
-  FixedInt<N&M> operator&( FixedInt<M>) const { return FixedInt<N&M>(); }
-
-#if EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
-  // Needed in C++14 to allow fix<N>():
-  FixedInt operator() () const { return *this; }
-
-  VariableAndFixedInt<N> operator() (int val) const { return VariableAndFixedInt<N>(val); }
-#else
-  FixedInt ( FixedInt<N> (*)() ) {}
-#endif
-
-#if EIGEN_HAS_CXX11
-  FixedInt(std::integral_constant<int,N>) {}
-#endif
-};
-
-/** \internal
-  * \class VariableAndFixedInt
-  *
-  * This class embeds both a compile-time integer \c N and a runtime integer.
-  * Both values are supposed to be equal unless the compile-time value \c N has a special
-  * value meaning that the runtime-value should be used. Depending on the context, this special
-  * value can be either Eigen::Dynamic (for positive quantities) or Eigen::DynamicIndex (for
-  * quantities that can be negative).
-  *
-  * It is the return-type of the function Eigen::fix<N>(int), and most of the time this is the only
-  * way it is used. It is strongly discouraged to directly deal with instances of VariableAndFixedInt.
-  * Indeed, in order to write generic code, it is the responsibility of the callee to properly convert
-  * it to either a true compile-time quantity (i.e. a FixedInt<N>), or to a runtime quantity (e.g., an Index)
-  * using the following generic helper:
-  * \code
-  * internal::cleanup_index_type<T>::type
-  * internal::cleanup_index_type<T,DynamicKey>::type
-  * \endcode
-  * where T can be a template instantiation of VariableAndFixedInt or numerous other integer-like representations.
-  * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
-  *
-  * For convenience, you can also extract the compile-time value \c N using the following helper:
-  * \code
-  * internal::get_fixed_value<T,DefaultVal>::value
-  * \endcode
-  * that will give you \c N if T equals VariableAndFixedInt<N>, and \c DefaultVal if T does not embed any compile-time value (e.g., T==int).
-  *
-  * \sa fix<N>(int), class FixedInt
-  */
-template<int N> class VariableAndFixedInt
-{
-public:
-  static const int value = N;
-  operator int() const { return m_value; }
-  VariableAndFixedInt(int val) { m_value = val; }
-protected:
-  int m_value;
-};
-
-template<typename T, int Default=Dynamic> struct get_fixed_value {
-  static const int value = Default;
-};
-
-template<int N,int Default> struct get_fixed_value<FixedInt<N>,Default> {
-  static const int value = N;
-};
-
-#if !EIGEN_HAS_CXX14
-template<int N,int Default> struct get_fixed_value<FixedInt<N> (*)(),Default> {
-  static const int value = N;
-};
-#endif
-
-template<int N,int Default> struct get_fixed_value<VariableAndFixedInt<N>,Default> {
-  static const int value = N ;
-};
-
-template<typename T, int N, int Default>
-struct get_fixed_value<variable_if_dynamic<T,N>,Default> {
-  static const int value = N;
-};
-
-template<typename T> EIGEN_DEVICE_FUNC Index get_runtime_value(const T &x) { return x; }
-#if !EIGEN_HAS_CXX14
-template<int N> EIGEN_DEVICE_FUNC Index get_runtime_value(FixedInt<N> (*)()) { return N; }
-#endif
-
-// Cleanup integer/FixedInt/VariableAndFixedInt/etc types:
-
-// By default, no cleanup:
-template<typename T, int DynamicKey=Dynamic, typename EnableIf=void> struct cleanup_index_type { typedef T type; };
-
-// Convert any integral type (e.g., short, int, unsigned int, etc.) to Eigen::Index
-template<typename T, int DynamicKey> struct cleanup_index_type<T,DynamicKey,typename internal::enable_if<internal::is_integral<T>::value>::type> { typedef Index type; };
-
-#if !EIGEN_HAS_CXX14
-// In c++98/c++11, fix<N> is a pointer to function that we better cleanup to a true FixedInt<N>:
-template<int N, int DynamicKey> struct cleanup_index_type<FixedInt<N> (*)(), DynamicKey> { typedef FixedInt<N> type; };
-#endif
-
-// If VariableAndFixedInt does not match DynamicKey, then we turn it to a pure compile-time value:
-template<int N, int DynamicKey> struct cleanup_index_type<VariableAndFixedInt<N>, DynamicKey> { typedef FixedInt<N> type; };
-// If VariableAndFixedInt matches DynamicKey, then we turn it to a pure runtime-value (aka Index):
-template<int DynamicKey> struct cleanup_index_type<VariableAndFixedInt<DynamicKey>, DynamicKey> { typedef Index type; };
-
-#if EIGEN_HAS_CXX11
-template<int N, int DynamicKey> struct cleanup_index_type<std::integral_constant<int,N>, DynamicKey> { typedef FixedInt<N> type; };
-#endif
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-#if EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
-template<int N>
-static const internal::FixedInt<N> fix{};
-#else
-template<int N>
-inline internal::FixedInt<N> fix() { return internal::FixedInt<N>(); }
-
-// The generic typename T is mandatory. Otherwise, a code like fix<N> could refer to either the function above or this next overload.
-// This way a code like fix<N> can only refer to the previous function.
-template<int N,typename T>
-inline internal::VariableAndFixedInt<N> fix(T val) { return internal::VariableAndFixedInt<N>(internal::convert_index<int>(val)); }
-#endif
-
-#else // EIGEN_PARSED_BY_DOXYGEN
-
-/** \var fix<N>()
-  * \ingroup Core_Module
-  *
-  * This \em identifier permits to construct an object embedding a compile-time integer \c N.
-  *
-  * \tparam N the compile-time integer value
-  *
-  * It is typically used in conjunction with the Eigen::seq and Eigen::seqN functions to pass compile-time values to them:
-  * \code
-  * seqN(10,fix<4>,fix<-3>)   // <=> [10 7 4 1]
-  * \endcode
-  *
-  * See also the function fix(int) to pass both a compile-time and runtime value.
-  *
-  * In c++14, it is implemented as:
-  * \code
-  * template<int N> static const internal::FixedInt<N> fix{};
-  * \endcode
-  * where internal::FixedInt<N> is an internal template class similar to
-  * <a href="http://en.cppreference.com/w/cpp/types/integral_constant">\c std::integral_constant </a><tt> <int,N> </tt>
-  * Here, \c fix<N> is thus an object of type \c internal::FixedInt<N>.
-  *
-  * In c++98/11, it is implemented as a function:
-  * \code
-  * template<int N> inline internal::FixedInt<N> fix();
-  * \endcode
-  * Here internal::FixedInt<N> is thus a pointer to function.
-  *
-  * If for some reason you want a true object in c++98 then you can write: \code fix<N>() \endcode which is also valid in c++14.
-  *
-  * \sa fix<N>(int), seq, seqN
-  */
-template<int N>
-static const auto fix();
-
-/** \fn fix<N>(int)
-  * \ingroup Core_Module
-  *
-  * This function returns an object embedding both a compile-time integer \c N, and a fallback runtime value \a val.
-  *
-  * \tparam N the compile-time integer value
-  * \param  val the fallback runtime integer value
-  *
-  * This function is a more general version of the \ref fix identifier/function that can be used in template code
-  * where the compile-time value could turn out to actually mean "undefined at compile-time". For positive integers
-  * such as a size or a dimension, this case is identified by Eigen::Dynamic, whereas runtime signed integers
-  * (e.g., an increment/stride) are identified as Eigen::DynamicIndex. In such a case, the runtime value \a val
-  * will be used as a fallback.
-  *
-  * A typical use case would be:
-  * \code
-  * template<typename Derived> void foo(const MatrixBase<Derived> &mat) {
-  *   const int N = Derived::RowsAtCompileTime==Dynamic ? Dynamic : Derived::RowsAtCompileTime/2;
-  *   const int n = mat.rows()/2;
-  *   ... mat( seqN(0,fix<N>(n) ) ...;
-  * }
-  * \endcode
-  * In this example, the function Eigen::seqN knows that the second argument is expected to be a size.
-  * If the passed compile-time value N equals Eigen::Dynamic, then the proxy object returned by fix will be dissmissed, and converted to an Eigen::Index of value \c n.
-  * Otherwise, the runtime-value \c n will be dissmissed, and the returned ArithmeticSequence will be of the exact same type as <tt> seqN(0,fix<N>) </tt>.
-  *
-  * \sa fix, seqN, class ArithmeticSequence
-  */
-template<int N>
-static const auto fix(int val);
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-} // end namespace Eigen
-
-#endif // EIGEN_INTEGRAL_CONSTANT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/MKL_support.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/MKL_support.h
deleted file mode 100644
index 17963fa..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/MKL_support.h
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to Intel(R) MKL
- *   Include file with common MKL declarations
- ********************************************************************************
-*/
-
-#ifndef EIGEN_MKL_SUPPORT_H
-#define EIGEN_MKL_SUPPORT_H
-
-#ifdef EIGEN_USE_MKL_ALL
-  #ifndef EIGEN_USE_BLAS
-    #define EIGEN_USE_BLAS
-  #endif
-  #ifndef EIGEN_USE_LAPACKE
-    #define EIGEN_USE_LAPACKE
-  #endif
-  #ifndef EIGEN_USE_MKL_VML
-    #define EIGEN_USE_MKL_VML
-  #endif
-#endif
-
-#ifdef EIGEN_USE_LAPACKE_STRICT
-  #define EIGEN_USE_LAPACKE
-#endif
-
-#if defined(EIGEN_USE_MKL_VML) && !defined(EIGEN_USE_MKL)
-  #define EIGEN_USE_MKL
-#endif
-
-
-#if defined EIGEN_USE_MKL
-#   if (!defined MKL_DIRECT_CALL) && (!defined EIGEN_MKL_NO_DIRECT_CALL)
-#       define MKL_DIRECT_CALL
-#       define MKL_DIRECT_CALL_JUST_SET
-#   endif
-#   include <mkl.h>
-/*Check IMKL version for compatibility: < 10.3 is not usable with Eigen*/
-#   ifndef INTEL_MKL_VERSION
-#       undef EIGEN_USE_MKL /* INTEL_MKL_VERSION is not even defined on older versions */
-#   elif INTEL_MKL_VERSION < 100305    /* the intel-mkl-103-release-notes say this was when the lapacke.h interface was added*/
-#       undef EIGEN_USE_MKL
-#   endif
-#   ifndef EIGEN_USE_MKL
-    /*If the MKL version is too old, undef everything*/
-#       undef   EIGEN_USE_MKL_ALL
-#       undef   EIGEN_USE_LAPACKE
-#       undef   EIGEN_USE_MKL_VML
-#       undef   EIGEN_USE_LAPACKE_STRICT
-#       undef   EIGEN_USE_LAPACKE
-#       ifdef   MKL_DIRECT_CALL_JUST_SET
-#           undef MKL_DIRECT_CALL
-#       endif
-#   endif
-#endif
-
-#if defined EIGEN_USE_MKL
-
-#define EIGEN_MKL_VML_THRESHOLD 128
-
-/* MKL_DOMAIN_BLAS, etc are defined only in 10.3 update 7 */
-/* MKL_BLAS, etc are not defined in 11.2 */
-#ifdef MKL_DOMAIN_ALL
-#define EIGEN_MKL_DOMAIN_ALL MKL_DOMAIN_ALL
-#else
-#define EIGEN_MKL_DOMAIN_ALL MKL_ALL
-#endif
-
-#ifdef MKL_DOMAIN_BLAS
-#define EIGEN_MKL_DOMAIN_BLAS MKL_DOMAIN_BLAS
-#else
-#define EIGEN_MKL_DOMAIN_BLAS MKL_BLAS
-#endif
-
-#ifdef MKL_DOMAIN_FFT
-#define EIGEN_MKL_DOMAIN_FFT MKL_DOMAIN_FFT
-#else
-#define EIGEN_MKL_DOMAIN_FFT MKL_FFT
-#endif
-
-#ifdef MKL_DOMAIN_VML
-#define EIGEN_MKL_DOMAIN_VML MKL_DOMAIN_VML
-#else
-#define EIGEN_MKL_DOMAIN_VML MKL_VML
-#endif
-
-#ifdef MKL_DOMAIN_PARDISO
-#define EIGEN_MKL_DOMAIN_PARDISO MKL_DOMAIN_PARDISO
-#else
-#define EIGEN_MKL_DOMAIN_PARDISO MKL_PARDISO
-#endif
-#endif
-
-#if defined(EIGEN_USE_BLAS) && !defined(EIGEN_USE_MKL)
-#include "../../misc/blas.h"
-#endif
-
-namespace Eigen {
-
-typedef std::complex<double> dcomplex;
-typedef std::complex<float>  scomplex;
-
-#if defined(EIGEN_USE_MKL)
-typedef MKL_INT BlasIndex;
-#else
-typedef int BlasIndex;
-#endif
-
-} // end namespace Eigen
-
-
-#endif // EIGEN_MKL_SUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Macros.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Macros.h
deleted file mode 100644
index 986c3d4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Macros.h
+++ /dev/null
@@ -1,1464 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MACROS_H
-#define EIGEN_MACROS_H
-
-//------------------------------------------------------------------------------------------
-// Eigen version and basic defaults
-//------------------------------------------------------------------------------------------
-
-#define EIGEN_WORLD_VERSION 3
-#define EIGEN_MAJOR_VERSION 4
-#define EIGEN_MINOR_VERSION 0
-
-#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
-                                      (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
-                                                                 EIGEN_MINOR_VERSION>=z))))
-
-#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
-#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::RowMajor
-#else
-#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::ColMajor
-#endif
-
-#ifndef EIGEN_DEFAULT_DENSE_INDEX_TYPE
-#define EIGEN_DEFAULT_DENSE_INDEX_TYPE std::ptrdiff_t
-#endif
-
-// Upperbound on the C++ version to use.
-// Expected values are 03, 11, 14, 17, etc.
-// By default, let's use an arbitrarily large C++ version.
-#ifndef EIGEN_MAX_CPP_VER
-#define EIGEN_MAX_CPP_VER 99
-#endif
-
-/** Allows to disable some optimizations which might affect the accuracy of the result.
-  * Such optimization are enabled by default, and set EIGEN_FAST_MATH to 0 to disable them.
-  * They currently include:
-  *   - single precision ArrayBase::sin() and ArrayBase::cos() for SSE and AVX vectorization.
-  */
-#ifndef EIGEN_FAST_MATH
-#define EIGEN_FAST_MATH 1
-#endif
-
-#ifndef EIGEN_STACK_ALLOCATION_LIMIT
-// 131072 == 128 KB
-#define EIGEN_STACK_ALLOCATION_LIMIT 131072
-#endif
-
-//------------------------------------------------------------------------------------------
-// Compiler identification, EIGEN_COMP_*
-//------------------------------------------------------------------------------------------
-
-/// \internal EIGEN_COMP_GNUC set to 1 for all compilers compatible with GCC
-#ifdef __GNUC__
-  #define EIGEN_COMP_GNUC (__GNUC__*10+__GNUC_MINOR__)
-#else
-  #define EIGEN_COMP_GNUC 0
-#endif
-
-/// \internal EIGEN_COMP_CLANG set to major+minor version (e.g., 307 for clang 3.7) if the compiler is clang
-#if defined(__clang__)
-  #define EIGEN_COMP_CLANG (__clang_major__*100+__clang_minor__)
-#else
-  #define EIGEN_COMP_CLANG 0
-#endif
-
-/// \internal EIGEN_COMP_CASTXML set to 1 if being preprocessed by CastXML
-#if defined(__castxml__)
-  #define EIGEN_COMP_CASTXML 1
-#else
-  #define EIGEN_COMP_CASTXML 0
-#endif
-
-/// \internal EIGEN_COMP_LLVM set to 1 if the compiler backend is llvm
-#if defined(__llvm__)
-  #define EIGEN_COMP_LLVM 1
-#else
-  #define EIGEN_COMP_LLVM 0
-#endif
-
-/// \internal EIGEN_COMP_ICC set to __INTEL_COMPILER if the compiler is Intel compiler, 0 otherwise
-#if defined(__INTEL_COMPILER)
-  #define EIGEN_COMP_ICC __INTEL_COMPILER
-#else
-  #define EIGEN_COMP_ICC 0
-#endif
-
-/// \internal EIGEN_COMP_MINGW set to 1 if the compiler is mingw
-#if defined(__MINGW32__)
-  #define EIGEN_COMP_MINGW 1
-#else
-  #define EIGEN_COMP_MINGW 0
-#endif
-
-/// \internal EIGEN_COMP_SUNCC set to 1 if the compiler is Solaris Studio
-#if defined(__SUNPRO_CC)
-  #define EIGEN_COMP_SUNCC 1
-#else
-  #define EIGEN_COMP_SUNCC 0
-#endif
-
-/// \internal EIGEN_COMP_MSVC set to _MSC_VER if the compiler is Microsoft Visual C++, 0 otherwise.
-#if defined(_MSC_VER)
-  #define EIGEN_COMP_MSVC _MSC_VER
-#else
-  #define EIGEN_COMP_MSVC 0
-#endif
-
-#if defined(__NVCC__)
-#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
-  #define EIGEN_COMP_NVCC  ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
-#elif defined(__CUDACC_VER__)
-  #define EIGEN_COMP_NVCC __CUDACC_VER__
-#else
-  #error "NVCC did not define compiler version."
-#endif
-#else
-  #define EIGEN_COMP_NVCC 0
-#endif
-
-// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC:
-//  name        ver   MSC_VER
-//  2008         9      1500
-//  2010        10      1600
-//  2012        11      1700
-//  2013        12      1800
-//  2015        14      1900
-//  "15"        15      1900
-//  2017-14.1   15.0    1910
-//  2017-14.11  15.3    1911
-//  2017-14.12  15.5    1912
-//  2017-14.13  15.6    1913
-//  2017-14.14  15.7    1914
-
-/// \internal EIGEN_COMP_MSVC_LANG set to _MSVC_LANG if the compiler is Microsoft Visual C++, 0 otherwise.
-#if defined(_MSVC_LANG)
-  #define EIGEN_COMP_MSVC_LANG _MSVC_LANG
-#else
-  #define EIGEN_COMP_MSVC_LANG 0
-#endif
-
-// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC_LANG:
-// MSVC option                          Standard  MSVC_LANG
-// /std:c++14 (default as of VS 2019)   C++14     201402L
-// /std:c++17                           C++17     201703L
-// /std:c++latest                       >C++17    >201703L
-
-/// \internal EIGEN_COMP_MSVC_STRICT set to 1 if the compiler is really Microsoft Visual C++ and not ,e.g., ICC or clang-cl
-#if EIGEN_COMP_MSVC && !(EIGEN_COMP_ICC || EIGEN_COMP_LLVM || EIGEN_COMP_CLANG)
-  #define EIGEN_COMP_MSVC_STRICT _MSC_VER
-#else
-  #define EIGEN_COMP_MSVC_STRICT 0
-#endif
-
-/// \internal EIGEN_COMP_IBM set to xlc version if the compiler is IBM XL C++
-// XLC   version
-// 3.1   0x0301
-// 4.5   0x0405
-// 5.0   0x0500
-// 12.1  0x0C01
-#if defined(__IBMCPP__) || defined(__xlc__) || defined(__ibmxl__)
-  #define EIGEN_COMP_IBM __xlC__
-#else
-  #define EIGEN_COMP_IBM 0
-#endif
-
-/// \internal EIGEN_COMP_PGI set to PGI version if the compiler is Portland Group Compiler
-#if defined(__PGI)
-  #define EIGEN_COMP_PGI (__PGIC__*100+__PGIC_MINOR__)
-#else
-  #define EIGEN_COMP_PGI 0
-#endif
-
-/// \internal EIGEN_COMP_ARM set to 1 if the compiler is ARM Compiler
-#if defined(__CC_ARM) || defined(__ARMCC_VERSION)
-  #define EIGEN_COMP_ARM 1
-#else
-  #define EIGEN_COMP_ARM 0
-#endif
-
-/// \internal EIGEN_COMP_EMSCRIPTEN set to 1 if the compiler is Emscripten Compiler
-#if defined(__EMSCRIPTEN__)
-  #define EIGEN_COMP_EMSCRIPTEN 1
-#else
-  #define EIGEN_COMP_EMSCRIPTEN 0
-#endif
-
-
-/// \internal EIGEN_GNUC_STRICT set to 1 if the compiler is really GCC and not a compatible compiler (e.g., ICC, clang, mingw, etc.)
-#if EIGEN_COMP_GNUC && !(EIGEN_COMP_CLANG || EIGEN_COMP_ICC || EIGEN_COMP_MINGW || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM || EIGEN_COMP_EMSCRIPTEN)
-  #define EIGEN_COMP_GNUC_STRICT 1
-#else
-  #define EIGEN_COMP_GNUC_STRICT 0
-#endif
-
-
-#if EIGEN_COMP_GNUC
-  #define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__==x && __GNUC_MINOR__>=y) || __GNUC__>x)
-  #define EIGEN_GNUC_AT_MOST(x,y)  ((__GNUC__==x && __GNUC_MINOR__<=y) || __GNUC__<x)
-  #define EIGEN_GNUC_AT(x,y)       ( __GNUC__==x && __GNUC_MINOR__==y )
-#else
-  #define EIGEN_GNUC_AT_LEAST(x,y) 0
-  #define EIGEN_GNUC_AT_MOST(x,y)  0
-  #define EIGEN_GNUC_AT(x,y)       0
-#endif
-
-// FIXME: could probably be removed as we do not support gcc 3.x anymore
-#if EIGEN_COMP_GNUC && (__GNUC__ <= 3)
-#define EIGEN_GCC3_OR_OLDER 1
-#else
-#define EIGEN_GCC3_OR_OLDER 0
-#endif
-
-
-
-//------------------------------------------------------------------------------------------
-// Architecture identification, EIGEN_ARCH_*
-//------------------------------------------------------------------------------------------
-
-
-#if defined(__x86_64__) || (defined(_M_X64) && !defined(_M_ARM64EC)) || defined(__amd64)
-  #define EIGEN_ARCH_x86_64 1
-#else
-  #define EIGEN_ARCH_x86_64 0
-#endif
-
-#if defined(__i386__) || defined(_M_IX86) || defined(_X86_) || defined(__i386)
-  #define EIGEN_ARCH_i386 1
-#else
-  #define EIGEN_ARCH_i386 0
-#endif
-
-#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_i386
-  #define EIGEN_ARCH_i386_OR_x86_64 1
-#else
-  #define EIGEN_ARCH_i386_OR_x86_64 0
-#endif
-
-/// \internal EIGEN_ARCH_ARM set to 1 if the architecture is ARM
-#if defined(__arm__)
-  #define EIGEN_ARCH_ARM 1
-#else
-  #define EIGEN_ARCH_ARM 0
-#endif
-
-/// \internal EIGEN_ARCH_ARM64 set to 1 if the architecture is ARM64
-#if defined(__aarch64__) || defined(_M_ARM64) || defined(_M_ARM64EC)
-  #define EIGEN_ARCH_ARM64 1
-#else
-  #define EIGEN_ARCH_ARM64 0
-#endif
-
-/// \internal EIGEN_ARCH_ARM_OR_ARM64 set to 1 if the architecture is ARM or ARM64
-#if EIGEN_ARCH_ARM || EIGEN_ARCH_ARM64
-  #define EIGEN_ARCH_ARM_OR_ARM64 1
-#else
-  #define EIGEN_ARCH_ARM_OR_ARM64 0
-#endif
-
-/// \internal EIGEN_ARCH_ARMV8 set to 1 if the architecture is armv8 or greater.
-#if EIGEN_ARCH_ARM_OR_ARM64 && defined(__ARM_ARCH) && __ARM_ARCH >= 8
-#define EIGEN_ARCH_ARMV8 1
-#else
-#define EIGEN_ARCH_ARMV8 0
-#endif
-
-
-/// \internal EIGEN_HAS_ARM64_FP16 set to 1 if the architecture provides an IEEE
-/// compliant Arm fp16 type
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16
-    #if defined(__ARM_FP16_FORMAT_IEEE)
-      #define EIGEN_HAS_ARM64_FP16 1
-    #else
-      #define EIGEN_HAS_ARM64_FP16 0
-    #endif
-  #endif
-#endif
-
-/// \internal EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC set to 1 if the architecture
-/// supports Neon vector intrinsics for fp16.
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-    #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 1
-    #else
-      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 0
-    #endif
-  #endif
-#endif
-
-/// \internal EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC set to 1 if the architecture
-/// supports Neon scalar intrinsics for fp16.
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
-    #if defined(__ARM_FEATURE_FP16_SCALAR_ARITHMETIC)
-      #define EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC 1
-    #endif
-  #endif
-#endif
-
-/// \internal EIGEN_ARCH_MIPS set to 1 if the architecture is MIPS
-#if defined(__mips__) || defined(__mips)
-  #define EIGEN_ARCH_MIPS 1
-#else
-  #define EIGEN_ARCH_MIPS 0
-#endif
-
-/// \internal EIGEN_ARCH_SPARC set to 1 if the architecture is SPARC
-#if defined(__sparc__) || defined(__sparc)
-  #define EIGEN_ARCH_SPARC 1
-#else
-  #define EIGEN_ARCH_SPARC 0
-#endif
-
-/// \internal EIGEN_ARCH_IA64 set to 1 if the architecture is Intel Itanium
-#if defined(__ia64__)
-  #define EIGEN_ARCH_IA64 1
-#else
-  #define EIGEN_ARCH_IA64 0
-#endif
-
-/// \internal EIGEN_ARCH_PPC set to 1 if the architecture is PowerPC
-#if defined(__powerpc__) || defined(__ppc__) || defined(_M_PPC)
-  #define EIGEN_ARCH_PPC 1
-#else
-  #define EIGEN_ARCH_PPC 0
-#endif
-
-
-
-//------------------------------------------------------------------------------------------
-// Operating system identification, EIGEN_OS_*
-//------------------------------------------------------------------------------------------
-
-/// \internal EIGEN_OS_UNIX set to 1 if the OS is a unix variant
-#if defined(__unix__) || defined(__unix)
-  #define EIGEN_OS_UNIX 1
-#else
-  #define EIGEN_OS_UNIX 0
-#endif
-
-/// \internal EIGEN_OS_LINUX set to 1 if the OS is based on Linux kernel
-#if defined(__linux__)
-  #define EIGEN_OS_LINUX 1
-#else
-  #define EIGEN_OS_LINUX 0
-#endif
-
-/// \internal EIGEN_OS_ANDROID set to 1 if the OS is Android
-// note: ANDROID is defined when using ndk_build, __ANDROID__ is defined when using a standalone toolchain.
-#if defined(__ANDROID__) || defined(ANDROID)
-  #define EIGEN_OS_ANDROID 1
-#else
-  #define EIGEN_OS_ANDROID 0
-#endif
-
-/// \internal EIGEN_OS_GNULINUX set to 1 if the OS is GNU Linux and not Linux-based OS (e.g., not android)
-#if defined(__gnu_linux__) && !(EIGEN_OS_ANDROID)
-  #define EIGEN_OS_GNULINUX 1
-#else
-  #define EIGEN_OS_GNULINUX 0
-#endif
-
-/// \internal EIGEN_OS_BSD set to 1 if the OS is a BSD variant
-#if defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__) || defined(__bsdi__) || defined(__DragonFly__)
-  #define EIGEN_OS_BSD 1
-#else
-  #define EIGEN_OS_BSD 0
-#endif
-
-/// \internal EIGEN_OS_MAC set to 1 if the OS is MacOS
-#if defined(__APPLE__)
-  #define EIGEN_OS_MAC 1
-#else
-  #define EIGEN_OS_MAC 0
-#endif
-
-/// \internal EIGEN_OS_QNX set to 1 if the OS is QNX
-#if defined(__QNX__)
-  #define EIGEN_OS_QNX 1
-#else
-  #define EIGEN_OS_QNX 0
-#endif
-
-/// \internal EIGEN_OS_WIN set to 1 if the OS is Windows based
-#if defined(_WIN32)
-  #define EIGEN_OS_WIN 1
-#else
-  #define EIGEN_OS_WIN 0
-#endif
-
-/// \internal EIGEN_OS_WIN64 set to 1 if the OS is Windows 64bits
-#if defined(_WIN64)
-  #define EIGEN_OS_WIN64 1
-#else
-  #define EIGEN_OS_WIN64 0
-#endif
-
-/// \internal EIGEN_OS_WINCE set to 1 if the OS is Windows CE
-#if defined(_WIN32_WCE)
-  #define EIGEN_OS_WINCE 1
-#else
-  #define EIGEN_OS_WINCE 0
-#endif
-
-/// \internal EIGEN_OS_CYGWIN set to 1 if the OS is Windows/Cygwin
-#if defined(__CYGWIN__)
-  #define EIGEN_OS_CYGWIN 1
-#else
-  #define EIGEN_OS_CYGWIN 0
-#endif
-
-/// \internal EIGEN_OS_WIN_STRICT set to 1 if the OS is really Windows and not some variants
-#if EIGEN_OS_WIN && !( EIGEN_OS_WINCE || EIGEN_OS_CYGWIN )
-  #define EIGEN_OS_WIN_STRICT 1
-#else
-  #define EIGEN_OS_WIN_STRICT 0
-#endif
-
-/// \internal EIGEN_OS_SUN set to __SUNPRO_C if the OS is SUN
-// compiler  solaris   __SUNPRO_C
-// version   studio
-// 5.7       10        0x570
-// 5.8       11        0x580
-// 5.9       12        0x590
-// 5.10	     12.1      0x5100
-// 5.11	     12.2      0x5110
-// 5.12	     12.3      0x5120
-#if (defined(sun) || defined(__sun)) && !(defined(__SVR4) || defined(__svr4__))
-  #define EIGEN_OS_SUN __SUNPRO_C
-#else
-  #define EIGEN_OS_SUN 0
-#endif
-
-/// \internal EIGEN_OS_SOLARIS set to 1 if the OS is Solaris
-#if (defined(sun) || defined(__sun)) && (defined(__SVR4) || defined(__svr4__))
-  #define EIGEN_OS_SOLARIS 1
-#else
-  #define EIGEN_OS_SOLARIS 0
-#endif
-
-
-//------------------------------------------------------------------------------------------
-// Detect GPU compilers and architectures
-//------------------------------------------------------------------------------------------
-
-// NVCC is not supported as the target platform for HIPCC
-// Note that this also makes EIGEN_CUDACC and EIGEN_HIPCC mutually exclusive
-#if defined(__NVCC__) && defined(__HIPCC__)
-  #error "NVCC as the target platform for HIPCC is currently not supported."
-#endif
-
-#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA)
-  // Means the compiler is either nvcc or clang with CUDA enabled
-  #define EIGEN_CUDACC __CUDACC__
-#endif
-
-#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA)
-  // Means we are generating code for the device
-  #define EIGEN_CUDA_ARCH __CUDA_ARCH__
-#endif
-
-#if defined(EIGEN_CUDACC)
-#include <cuda.h>
-  #define EIGEN_CUDA_SDK_VER (CUDA_VERSION * 10)
-#else
-  #define EIGEN_CUDA_SDK_VER 0
-#endif
-
-#if defined(__HIPCC__) && !defined(EIGEN_NO_HIP)
-  // Means the compiler is HIPCC (analogous to EIGEN_CUDACC, but for HIP)
-  #define EIGEN_HIPCC __HIPCC__
-
-  // We need to include hip_runtime.h here because it pulls in
-  // ++ hip_common.h which contains the define for  __HIP_DEVICE_COMPILE__
-  // ++ host_defines.h which contains the defines for the __host__ and __device__ macros
-  #include <hip/hip_runtime.h>
-
-  #if defined(__HIP_DEVICE_COMPILE__)
-    // analogous to EIGEN_CUDA_ARCH, but for HIP
-    #define EIGEN_HIP_DEVICE_COMPILE __HIP_DEVICE_COMPILE__
-  #endif
-
-  // For HIP (ROCm 3.5 and higher), we need to explicitly set the launch_bounds attribute
-  // value to 1024. The compiler assigns a default value of 256 when the attribute is not
-  // specified. This results in failures on the HIP platform, for cases when a GPU kernel
-  // without an explicit launch_bounds attribute is called with a threads_per_block value
-  // greater than 256.
-  //
-  // This is a regression in functioanlity and is expected to be fixed within the next
-  // couple of ROCm releases (compiler will go back to using 1024 value as the default)
-  //
-  // In the meantime, we will use a "only enabled for HIP" macro to set the launch_bounds
-  // attribute.
-
-  #define EIGEN_HIP_LAUNCH_BOUNDS_1024 __launch_bounds__(1024)
-
-#endif
-
-#if !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
-#define EIGEN_HIP_LAUNCH_BOUNDS_1024
-#endif // !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
-
-// Unify CUDA/HIPCC
-
-#if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
-//
-// If either EIGEN_CUDACC or EIGEN_HIPCC is defined, then define EIGEN_GPUCC
-//
-#define EIGEN_GPUCC
-//
-// EIGEN_HIPCC implies the HIP compiler and is used to tweak Eigen code for use in HIP kernels
-// EIGEN_CUDACC implies the CUDA compiler and is used to tweak Eigen code for use in CUDA kernels
-//
-// In most cases the same tweaks are required to the Eigen code to enable in both the HIP and CUDA kernels.
-// For those cases, the corresponding code should be guarded with
-//      #if defined(EIGEN_GPUCC)
-// instead of
-//      #if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
-//
-// For cases where the tweak is specific to HIP, the code should be guarded with
-//      #if defined(EIGEN_HIPCC)
-//
-// For cases where the tweak is specific to CUDA, the code should be guarded with
-//      #if defined(EIGEN_CUDACC)
-//
-#endif
-
-#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
-//
-// If either EIGEN_CUDA_ARCH or EIGEN_HIP_DEVICE_COMPILE is defined, then define EIGEN_GPU_COMPILE_PHASE
-//
-#define EIGEN_GPU_COMPILE_PHASE
-//
-// GPU compilers (HIPCC, NVCC) typically do two passes over the source code,
-//   + one to compile the source for the "host" (ie CPU)
-//   + another to compile the source for the "device" (ie. GPU)
-//
-// Code that needs to enabled only during the either the "host" or "device" compilation phase
-// needs to be guarded with a macro that indicates the current compilation phase
-//
-// EIGEN_HIP_DEVICE_COMPILE implies the device compilation phase in HIP
-// EIGEN_CUDA_ARCH implies the device compilation phase in CUDA
-//
-// In most cases, the "host" / "device" specific code is the same for both HIP and CUDA
-// For those cases, the code should be guarded with
-//       #if defined(EIGEN_GPU_COMPILE_PHASE)
-// instead of
-//       #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
-//
-// For cases where the tweak is specific to HIP, the code should be guarded with
-//      #if defined(EIGEN_HIP_DEVICE_COMPILE)
-//
-// For cases where the tweak is specific to CUDA, the code should be guarded with
-//      #if defined(EIGEN_CUDA_ARCH)
-//
-#endif
-
-#if defined(EIGEN_USE_SYCL) && defined(__SYCL_DEVICE_ONLY__)
-// EIGEN_USE_SYCL is a user-defined macro while __SYCL_DEVICE_ONLY__ is a compiler-defined macro.
-// In most cases we want to check if both macros are defined which can be done using the define below.
-#define SYCL_DEVICE_ONLY
-#endif
-
-//------------------------------------------------------------------------------------------
-// Detect Compiler/Architecture/OS specific features
-//------------------------------------------------------------------------------------------
-
-#if EIGEN_GNUC_AT_MOST(4,3) && !EIGEN_COMP_CLANG
-  // see bug 89
-  #define EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO 0
-#else
-  #define EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO 1
-#endif
-
-// Cross compiler wrapper around LLVM's __has_builtin
-#ifdef __has_builtin
-#  define EIGEN_HAS_BUILTIN(x) __has_builtin(x)
-#else
-#  define EIGEN_HAS_BUILTIN(x) 0
-#endif
-
-// A Clang feature extension to determine compiler features.
-// We use it to determine 'cxx_rvalue_references'
-#ifndef __has_feature
-# define __has_feature(x) 0
-#endif
-
-// Some old compilers do not support template specializations like:
-// template<typename T,int N> void foo(const T x[N]);
-#if !(   EIGEN_COMP_CLANG && (   (EIGEN_COMP_CLANG<309)                                                       \
-                              || (defined(__apple_build_version__) && (__apple_build_version__ < 9000000)))  \
-      || EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<49)
-#define EIGEN_HAS_STATIC_ARRAY_TEMPLATE 1
-#else
-#define EIGEN_HAS_STATIC_ARRAY_TEMPLATE 0
-#endif
-
-// The macro EIGEN_CPLUSPLUS is a replacement for __cplusplus/_MSVC_LANG that
-// works for both platforms, indicating the C++ standard version number.
-//
-// With MSVC, without defining /Zc:__cplusplus, the __cplusplus macro will
-// report 199711L regardless of the language standard specified via /std.
-// We need to rely on _MSVC_LANG instead, which is only available after
-// VS2015.3.
-#if EIGEN_COMP_MSVC_LANG > 0
-#define EIGEN_CPLUSPLUS EIGEN_COMP_MSVC_LANG
-#elif EIGEN_COMP_MSVC >= 1900
-#define EIGEN_CPLUSPLUS 201103L
-#elif defined(__cplusplus)
-#define EIGEN_CPLUSPLUS __cplusplus
-#else
-#define EIGEN_CPLUSPLUS 0
-#endif
-
-// The macro EIGEN_COMP_CXXVER defines the c++ verson expected by the compiler.
-// For instance, if compiling with gcc and -std=c++17, then EIGEN_COMP_CXXVER
-// is defined to 17.
-#if EIGEN_CPLUSPLUS > 201703L
-  #define EIGEN_COMP_CXXVER 20
-#elif EIGEN_CPLUSPLUS > 201402L
-  #define EIGEN_COMP_CXXVER 17
-#elif EIGEN_CPLUSPLUS > 201103L
-  #define EIGEN_COMP_CXXVER 14
-#elif EIGEN_CPLUSPLUS >= 201103L
-  #define EIGEN_COMP_CXXVER 11
-#else
-  #define EIGEN_COMP_CXXVER 03
-#endif
-
-#ifndef EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
-  #if defined(__cpp_variable_templates) && __cpp_variable_templates >= 201304 && EIGEN_MAX_CPP_VER>=14
-    #define EIGEN_HAS_CXX14_VARIABLE_TEMPLATES 1
-  #else
-    #define EIGEN_HAS_CXX14_VARIABLE_TEMPLATES 0
-  #endif
-#endif
-
-
-// The macros EIGEN_HAS_CXX?? defines a rough estimate of available c++ features
-// but in practice we should not rely on them but rather on the availabilty of
-// individual features as defined later.
-// This is why there is no EIGEN_HAS_CXX17.
-// FIXME: get rid of EIGEN_HAS_CXX14 and maybe even EIGEN_HAS_CXX11.
-#if EIGEN_MAX_CPP_VER>=11 && EIGEN_COMP_CXXVER>=11
-#define EIGEN_HAS_CXX11 1
-#else
-#define EIGEN_HAS_CXX11 0
-#endif
-
-#if EIGEN_MAX_CPP_VER>=14 && EIGEN_COMP_CXXVER>=14
-#define EIGEN_HAS_CXX14 1
-#else
-#define EIGEN_HAS_CXX14 0
-#endif
-
-// Do we support r-value references?
-#ifndef EIGEN_HAS_RVALUE_REFERENCES
-#if EIGEN_MAX_CPP_VER>=11 && \
-    (__has_feature(cxx_rvalue_references) || \
-     (EIGEN_COMP_CXXVER >= 11) || (EIGEN_COMP_MSVC >= 1600))
-  #define EIGEN_HAS_RVALUE_REFERENCES 1
-#else
-  #define EIGEN_HAS_RVALUE_REFERENCES 0
-#endif
-#endif
-
-// Does the compiler support C99?
-// Need to include <cmath> to make sure _GLIBCXX_USE_C99 gets defined
-#include <cmath>
-#ifndef EIGEN_HAS_C99_MATH
-#if EIGEN_MAX_CPP_VER>=11 && \
-    ((defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901))       \
-  || (defined(__GNUC__) && defined(_GLIBCXX_USE_C99)) \
-  || (defined(_LIBCPP_VERSION) && !defined(_MSC_VER)) \
-  || (EIGEN_COMP_MSVC >= 1900) || defined(SYCL_DEVICE_ONLY))
-  #define EIGEN_HAS_C99_MATH 1
-#else
-  #define EIGEN_HAS_C99_MATH 0
-#endif
-#endif
-
-// Does the compiler support result_of?
-// result_of was deprecated in c++17 and removed in c++ 20
-#ifndef EIGEN_HAS_STD_RESULT_OF
-#if EIGEN_HAS_CXX11 && EIGEN_COMP_CXXVER < 17
-#define EIGEN_HAS_STD_RESULT_OF 1
-#else
-#define EIGEN_HAS_STD_RESULT_OF 0
-#endif
-#endif
-
-// Does the compiler support std::hash?
-#ifndef EIGEN_HAS_STD_HASH
-// The std::hash struct is defined in C++11 but is not labelled as a __device__
-// function and is not constexpr, so cannot be used on device.
-#if EIGEN_HAS_CXX11 && !defined(EIGEN_GPU_COMPILE_PHASE)
-#define EIGEN_HAS_STD_HASH 1
-#else
-#define EIGEN_HAS_STD_HASH 0
-#endif
-#endif  // EIGEN_HAS_STD_HASH
-
-#ifndef EIGEN_HAS_STD_INVOKE_RESULT
-#if EIGEN_MAX_CPP_VER >= 17 && EIGEN_COMP_CXXVER >= 17
-#define EIGEN_HAS_STD_INVOKE_RESULT 1
-#else
-#define EIGEN_HAS_STD_INVOKE_RESULT 0
-#endif
-#endif
-
-#ifndef EIGEN_HAS_ALIGNAS
-#if EIGEN_MAX_CPP_VER>=11 && EIGEN_HAS_CXX11 &&   \
-      (     __has_feature(cxx_alignas)            \
-        ||  EIGEN_HAS_CXX14                       \
-        || (EIGEN_COMP_MSVC >= 1800)              \
-        || (EIGEN_GNUC_AT_LEAST(4,8))             \
-        || (EIGEN_COMP_CLANG>=305)                \
-        || (EIGEN_COMP_ICC>=1500)                 \
-        || (EIGEN_COMP_PGI>=1500)                 \
-        || (EIGEN_COMP_SUNCC>=0x5130))
-#define EIGEN_HAS_ALIGNAS 1
-#else
-#define EIGEN_HAS_ALIGNAS 0
-#endif
-#endif
-
-// Does the compiler support type_traits?
-// - full support of type traits was added only to GCC 5.1.0.
-// - 20150626 corresponds to the last release of 4.x libstdc++
-#ifndef EIGEN_HAS_TYPE_TRAITS
-#if EIGEN_MAX_CPP_VER>=11 && (EIGEN_HAS_CXX11 || EIGEN_COMP_MSVC >= 1700) \
-  && ((!EIGEN_COMP_GNUC_STRICT) || EIGEN_GNUC_AT_LEAST(5, 1)) \
-  && ((!defined(__GLIBCXX__))   || __GLIBCXX__ > 20150626)
-#define EIGEN_HAS_TYPE_TRAITS 1
-#define EIGEN_INCLUDE_TYPE_TRAITS
-#else
-#define EIGEN_HAS_TYPE_TRAITS 0
-#endif
-#endif
-
-// Does the compiler support variadic templates?
-#ifndef EIGEN_HAS_VARIADIC_TEMPLATES
-#if EIGEN_MAX_CPP_VER>=11 && (EIGEN_COMP_CXXVER >= 11) \
-  && (!defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64 || (EIGEN_COMP_NVCC >= 80000) )
-    // ^^ Disable the use of variadic templates when compiling with versions of nvcc older than 8.0 on ARM devices:
-    //    this prevents nvcc from crashing when compiling Eigen on Tegra X1
-#define EIGEN_HAS_VARIADIC_TEMPLATES 1
-#elif  EIGEN_MAX_CPP_VER>=11 && (EIGEN_COMP_CXXVER >= 11) && defined(SYCL_DEVICE_ONLY)
-#define EIGEN_HAS_VARIADIC_TEMPLATES 1
-#else
-#define EIGEN_HAS_VARIADIC_TEMPLATES 0
-#endif
-#endif
-
-// Does the compiler fully support const expressions? (as in c++14)
-#ifndef EIGEN_HAS_CONSTEXPR
-  #if defined(EIGEN_CUDACC)
-  // Const expressions are supported provided that c++11 is enabled and we're using either clang or nvcc 7.5 or above
-    #if EIGEN_MAX_CPP_VER>=14 && (EIGEN_COMP_CXXVER >= 11 && (EIGEN_COMP_CLANG || EIGEN_COMP_NVCC >= 70500))
-      #define EIGEN_HAS_CONSTEXPR 1
-    #endif
-  #elif EIGEN_MAX_CPP_VER>=14 && (__has_feature(cxx_relaxed_constexpr) || (EIGEN_COMP_CXXVER >= 14) || \
-    (EIGEN_GNUC_AT_LEAST(4,8) && (EIGEN_COMP_CXXVER >= 11)) || \
-    (EIGEN_COMP_CLANG >= 306 && (EIGEN_COMP_CXXVER >= 11)))
-    #define EIGEN_HAS_CONSTEXPR 1
-  #endif
-
-  #ifndef EIGEN_HAS_CONSTEXPR
-    #define EIGEN_HAS_CONSTEXPR 0
-  #endif
-
-#endif // EIGEN_HAS_CONSTEXPR
-
-#if EIGEN_HAS_CONSTEXPR
-#define EIGEN_CONSTEXPR constexpr
-#else
-#define EIGEN_CONSTEXPR
-#endif
-
-// Does the compiler support C++11 math?
-// Let's be conservative and enable the default C++11 implementation only if we are sure it exists
-#ifndef EIGEN_HAS_CXX11_MATH
-  #if EIGEN_MAX_CPP_VER>=11 && ((EIGEN_COMP_CXXVER > 11) || (EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC)  \
-      && (EIGEN_ARCH_i386_OR_x86_64) && (EIGEN_OS_GNULINUX || EIGEN_OS_WIN_STRICT || EIGEN_OS_MAC))
-    #define EIGEN_HAS_CXX11_MATH 1
-  #else
-    #define EIGEN_HAS_CXX11_MATH 0
-  #endif
-#endif
-
-// Does the compiler support proper C++11 containers?
-#ifndef EIGEN_HAS_CXX11_CONTAINERS
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-         ((EIGEN_COMP_CXXVER > 11) \
-      || ((EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC>=1400)))
-    #define EIGEN_HAS_CXX11_CONTAINERS 1
-  #else
-    #define EIGEN_HAS_CXX11_CONTAINERS 0
-  #endif
-#endif
-
-// Does the compiler support C++11 noexcept?
-#ifndef EIGEN_HAS_CXX11_NOEXCEPT
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-         (__has_feature(cxx_noexcept) \
-      || (EIGEN_COMP_CXXVER > 11) \
-      || ((EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC>=1400)))
-    #define EIGEN_HAS_CXX11_NOEXCEPT 1
-  #else
-    #define EIGEN_HAS_CXX11_NOEXCEPT 0
-  #endif
-#endif
-
-#ifndef EIGEN_HAS_CXX11_ATOMIC
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-         (__has_feature(cxx_atomic) \
-      || (EIGEN_COMP_CXXVER > 11) \
-      || ((EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_MSVC==0 || EIGEN_COMP_MSVC >= 1700)))
-    #define EIGEN_HAS_CXX11_ATOMIC 1
-  #else
-    #define EIGEN_HAS_CXX11_ATOMIC 0
-  #endif
-#endif
-
-#ifndef EIGEN_HAS_CXX11_OVERRIDE_FINAL
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-       (EIGEN_COMP_CXXVER >= 11 || EIGEN_COMP_MSVC >= 1700)
-    #define EIGEN_HAS_CXX11_OVERRIDE_FINAL 1
-  #else
-    #define EIGEN_HAS_CXX11_OVERRIDE_FINAL 0
-  #endif
-#endif
-
-// NOTE: the required Apple's clang version is very conservative
-//       and it could be that XCode 9 works just fine.
-// NOTE: the MSVC version is based on https://en.cppreference.com/w/cpp/compiler_support
-//       and not tested.
-#ifndef EIGEN_HAS_CXX17_OVERALIGN
-#if EIGEN_MAX_CPP_VER>=17 && EIGEN_COMP_CXXVER>=17 && (                                 \
-           (EIGEN_COMP_MSVC >= 1912)                                                    \
-        || (EIGEN_GNUC_AT_LEAST(7,0))                                                   \
-        || ((!defined(__apple_build_version__)) && (EIGEN_COMP_CLANG>=500))             \
-        || (( defined(__apple_build_version__)) && (__apple_build_version__>=10000000)) \
-      )
-#define EIGEN_HAS_CXX17_OVERALIGN 1
-#else
-#define EIGEN_HAS_CXX17_OVERALIGN 0
-#endif
-#endif
-
-#if defined(EIGEN_CUDACC) && EIGEN_HAS_CONSTEXPR
-  // While available already with c++11, this is useful mostly starting with c++14 and relaxed constexpr rules
-  #if defined(__NVCC__)
-    // nvcc considers constexpr functions as __host__ __device__ with the option --expt-relaxed-constexpr
-    #ifdef __CUDACC_RELAXED_CONSTEXPR__
-      #define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
-    #endif
-  #elif defined(__clang__) && defined(__CUDA__) && __has_feature(cxx_relaxed_constexpr)
-    // clang++ always considers constexpr functions as implicitly __host__ __device__
-    #define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
-  #endif
-#endif
-
-// Does the compiler support the __int128 and __uint128_t extensions for 128-bit
-// integer arithmetic?
-//
-// Clang and GCC define __SIZEOF_INT128__ when these extensions are supported,
-// but we avoid using them in certain cases:
-//
-// * Building using Clang for Windows, where the Clang runtime library has
-//   128-bit support only on LP64 architectures, but Windows is LLP64.
-#ifndef EIGEN_HAS_BUILTIN_INT128
-#if defined(__SIZEOF_INT128__) && !(EIGEN_OS_WIN && EIGEN_COMP_CLANG)
-#define EIGEN_HAS_BUILTIN_INT128 1
-#else
-#define EIGEN_HAS_BUILTIN_INT128 0
-#endif
-#endif
-
-//------------------------------------------------------------------------------------------
-// Preprocessor programming helpers
-//------------------------------------------------------------------------------------------
-
-// This macro can be used to prevent from macro expansion, e.g.:
-//   std::max EIGEN_NOT_A_MACRO(a,b)
-#define EIGEN_NOT_A_MACRO
-
-#define EIGEN_DEBUG_VAR(x) std::cerr << #x << " = " << x << std::endl;
-
-// concatenate two tokens
-#define EIGEN_CAT2(a,b) a ## b
-#define EIGEN_CAT(a,b) EIGEN_CAT2(a,b)
-
-#define EIGEN_COMMA ,
-
-// convert a token to a string
-#define EIGEN_MAKESTRING2(a) #a
-#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
-
-// EIGEN_STRONG_INLINE is a stronger version of the inline, using __forceinline on MSVC,
-// but it still doesn't use GCC's always_inline. This is useful in (common) situations where MSVC needs forceinline
-// but GCC is still doing fine with just inline.
-#ifndef EIGEN_STRONG_INLINE
-#if (EIGEN_COMP_MSVC || EIGEN_COMP_ICC) && !defined(EIGEN_GPUCC)
-#define EIGEN_STRONG_INLINE __forceinline
-#else
-#define EIGEN_STRONG_INLINE inline
-#endif
-#endif
-
-// EIGEN_ALWAYS_INLINE is the stronget, it has the effect of making the function inline and adding every possible
-// attribute to maximize inlining. This should only be used when really necessary: in particular,
-// it uses __attribute__((always_inline)) on GCC, which most of the time is useless and can severely harm compile times.
-// FIXME with the always_inline attribute,
-// gcc 3.4.x and 4.1 reports the following compilation error:
-//   Eval.h:91: sorry, unimplemented: inlining failed in call to 'const Eigen::Eval<Derived> Eigen::MatrixBase<Scalar, Derived>::eval() const'
-//    : function body not available
-//   See also bug 1367
-#if EIGEN_GNUC_AT_LEAST(4,2) && !defined(SYCL_DEVICE_ONLY)
-#define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline
-#else
-#define EIGEN_ALWAYS_INLINE EIGEN_STRONG_INLINE
-#endif
-
-#if EIGEN_COMP_GNUC
-#define EIGEN_DONT_INLINE __attribute__((noinline))
-#elif EIGEN_COMP_MSVC
-#define EIGEN_DONT_INLINE __declspec(noinline)
-#else
-#define EIGEN_DONT_INLINE
-#endif
-
-#if EIGEN_COMP_GNUC
-#define EIGEN_PERMISSIVE_EXPR __extension__
-#else
-#define EIGEN_PERMISSIVE_EXPR
-#endif
-
-// GPU stuff
-
-// Disable some features when compiling with GPU compilers (NVCC/clang-cuda/SYCL/HIPCC)
-#if defined(EIGEN_CUDACC) || defined(SYCL_DEVICE_ONLY) || defined(EIGEN_HIPCC)
-  // Do not try asserts on device code
-  #ifndef EIGEN_NO_DEBUG
-  #define EIGEN_NO_DEBUG
-  #endif
-
-  #ifdef EIGEN_INTERNAL_DEBUGGING
-  #undef EIGEN_INTERNAL_DEBUGGING
-  #endif
-
-  #ifdef EIGEN_EXCEPTIONS
-  #undef EIGEN_EXCEPTIONS
-  #endif
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-  #ifndef EIGEN_DONT_VECTORIZE
-    #define EIGEN_DONT_VECTORIZE
-  #endif
-  #define EIGEN_DEVICE_FUNC __attribute__((flatten)) __attribute__((always_inline))
-// All functions callable from CUDA/HIP code must be qualified with __device__
-#elif defined(EIGEN_GPUCC)
-    #define EIGEN_DEVICE_FUNC __host__ __device__
-#else
-  #define EIGEN_DEVICE_FUNC
-#endif
-
-
-// this macro allows to get rid of linking errors about multiply defined functions.
-//  - static is not very good because it prevents definitions from different object files to be merged.
-//           So static causes the resulting linked executable to be bloated with multiple copies of the same function.
-//  - inline is not perfect either as it unwantedly hints the compiler toward inlining the function.
-#define EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
-#define EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC inline
-
-#ifdef NDEBUG
-# ifndef EIGEN_NO_DEBUG
-#  define EIGEN_NO_DEBUG
-# endif
-#endif
-
-// eigen_plain_assert is where we implement the workaround for the assert() bug in GCC <= 4.3, see bug 89
-#ifdef EIGEN_NO_DEBUG
-  #ifdef SYCL_DEVICE_ONLY // used to silence the warning on SYCL device
-    #define eigen_plain_assert(x) EIGEN_UNUSED_VARIABLE(x)
-  #else
-    #define eigen_plain_assert(x)
-  #endif
-#else
-  #if EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO
-    namespace Eigen {
-    namespace internal {
-    inline bool copy_bool(bool b) { return b; }
-    }
-    }
-    #define eigen_plain_assert(x) assert(x)
-  #else
-    // work around bug 89
-    #include <cstdlib>   // for abort
-    #include <iostream>  // for std::cerr
-
-    namespace Eigen {
-    namespace internal {
-    // trivial function copying a bool. Must be EIGEN_DONT_INLINE, so we implement it after including Eigen headers.
-    // see bug 89.
-    namespace {
-    EIGEN_DONT_INLINE bool copy_bool(bool b) { return b; }
-    }
-    inline void assert_fail(const char *condition, const char *function, const char *file, int line)
-    {
-      std::cerr << "assertion failed: " << condition << " in function " << function << " at " << file << ":" << line << std::endl;
-      abort();
-    }
-    }
-    }
-    #define eigen_plain_assert(x) \
-      do { \
-        if(!Eigen::internal::copy_bool(x)) \
-          Eigen::internal::assert_fail(EIGEN_MAKESTRING(x), __PRETTY_FUNCTION__, __FILE__, __LINE__); \
-      } while(false)
-  #endif
-#endif
-
-// eigen_assert can be overridden
-#ifndef eigen_assert
-#define eigen_assert(x) eigen_plain_assert(x)
-#endif
-
-#ifdef EIGEN_INTERNAL_DEBUGGING
-#define eigen_internal_assert(x) eigen_assert(x)
-#else
-#define eigen_internal_assert(x)
-#endif
-
-#ifdef EIGEN_NO_DEBUG
-#define EIGEN_ONLY_USED_FOR_DEBUG(x) EIGEN_UNUSED_VARIABLE(x)
-#else
-#define EIGEN_ONLY_USED_FOR_DEBUG(x)
-#endif
-
-#ifndef EIGEN_NO_DEPRECATED_WARNING
-  #if EIGEN_COMP_GNUC
-    #define EIGEN_DEPRECATED __attribute__((deprecated))
-  #elif EIGEN_COMP_MSVC
-    #define EIGEN_DEPRECATED __declspec(deprecated)
-  #else
-    #define EIGEN_DEPRECATED
-  #endif
-#else
-  #define EIGEN_DEPRECATED
-#endif
-
-#if EIGEN_COMP_GNUC
-#define EIGEN_UNUSED __attribute__((unused))
-#else
-#define EIGEN_UNUSED
-#endif
-
-// Suppresses 'unused variable' warnings.
-namespace Eigen {
-  namespace internal {
-    template<typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ignore_unused_variable(const T&) {}
-  }
-}
-#define EIGEN_UNUSED_VARIABLE(var) Eigen::internal::ignore_unused_variable(var);
-
-#if !defined(EIGEN_ASM_COMMENT)
-  #if EIGEN_COMP_GNUC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64)
-    #define EIGEN_ASM_COMMENT(X)  __asm__("#" X)
-  #else
-    #define EIGEN_ASM_COMMENT(X)
-  #endif
-#endif
-
-
-// Acts as a barrier preventing operations involving `X` from crossing. This
-// occurs, for example, in the fast rounding trick where a magic constant is
-// added then subtracted, which is otherwise compiled away with -ffast-math.
-//
-// See bug 1674
-#if !defined(EIGEN_OPTIMIZATION_BARRIER)
-  #if EIGEN_COMP_GNUC
-    // According to https://gcc.gnu.org/onlinedocs/gcc/Constraints.html:
-    //   X: Any operand whatsoever.
-    //   r: A register operand is allowed provided that it is in a general
-    //      register.
-    //   g: Any register, memory or immediate integer operand is allowed, except
-    //      for registers that are not general registers.
-    //   w: (AArch32/AArch64) Floating point register, Advanced SIMD vector
-    //      register or SVE vector register.
-    //   x: (SSE) Any SSE register.
-    //      (AArch64) Like w, but restricted to registers 0 to 15 inclusive.
-    //   v: (PowerPC) An Altivec vector register.
-    //   wa:(PowerPC) A VSX register.
-    //
-    // "X" (uppercase) should work for all cases, though this seems to fail for
-    // some versions of GCC for arm/aarch64 with
-    //   "error: inconsistent operand constraints in an 'asm'"
-    // Clang x86_64/arm/aarch64 seems to require "g" to support both scalars and
-    // vectors, otherwise
-    //   "error: non-trivial scalar-to-vector conversion, possible invalid
-    //    constraint for vector type"
-    //
-    // GCC for ppc64le generates an internal compiler error with x/X/g.
-    // GCC for AVX generates an internal compiler error with X.
-    //
-    // Tested on icc/gcc/clang for sse, avx, avx2, avx512dq
-    //           gcc for arm, aarch64,
-    //           gcc for ppc64le,
-    // both vectors and scalars.
-    //
-    // Note that this is restricted to plain types - this will not work
-    // directly for std::complex<T>, Eigen::half, Eigen::bfloat16. For these,
-    // you will need to apply to the underlying POD type.
-    #if EIGEN_ARCH_PPC && EIGEN_COMP_GNUC_STRICT
-      // This seems to be broken on clang.  Packet4f is loaded into a single
-      //   register rather than a vector, zeroing out some entries.  Integer
-      //   types also generate a compile error.
-      // General, Altivec, VSX.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,v,wa" (X));
-    #elif EIGEN_ARCH_ARM_OR_ARM64
-      // General, NEON.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,w" (X));
-    #elif EIGEN_ARCH_i386_OR_x86_64
-      // General, SSE.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,x" (X));
-    #else
-      // Not implemented for other architectures.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)
-    #endif
-  #else
-    // Not implemented for other compilers.
-    #define EIGEN_OPTIMIZATION_BARRIER(X)
-  #endif
-#endif
-
-#if EIGEN_COMP_MSVC
-  // NOTE MSVC often gives C4127 warnings with compiletime if statements. See bug 1362.
-  // This workaround is ugly, but it does the job.
-#  define EIGEN_CONST_CONDITIONAL(cond)  (void)0, cond
-#else
-#  define EIGEN_CONST_CONDITIONAL(cond)  cond
-#endif
-
-#ifdef EIGEN_DONT_USE_RESTRICT_KEYWORD
-  #define EIGEN_RESTRICT
-#endif
-#ifndef EIGEN_RESTRICT
-  #define EIGEN_RESTRICT __restrict
-#endif
-
-
-#ifndef EIGEN_DEFAULT_IO_FORMAT
-#ifdef EIGEN_MAKING_DOCS
-// format used in Eigen's documentation
-// needed to define it here as escaping characters in CMake add_definition's argument seems very problematic.
-#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat(3, 0, " ", "\n", "", "")
-#else
-#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat()
-#endif
-#endif
-
-// just an empty macro !
-#define EIGEN_EMPTY
-
-
-// When compiling CUDA/HIP device code with NVCC or HIPCC
-// pull in math functions from the global namespace.
-// In host mode, and when device code is compiled with clang,
-// use the std versions.
-#if (defined(EIGEN_CUDA_ARCH) && defined(__NVCC__)) || defined(EIGEN_HIP_DEVICE_COMPILE)
-  #define EIGEN_USING_STD(FUNC) using ::FUNC;
-#else
-  #define EIGEN_USING_STD(FUNC) using std::FUNC;
-#endif
-
-#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC < 1900 || (EIGEN_COMP_MSVC == 1900 && EIGEN_COMP_NVCC))
-  // For older MSVC versions, as well as 1900 && CUDA 8, using the base operator is necessary,
-  //   otherwise we get duplicate definition errors
-  // For later MSVC versions, we require explicit operator= definition, otherwise we get
-  //   use of implicitly deleted operator errors.
-  // (cf Bugs 920, 1000, 1324, 2291)
-  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    using Base::operator =;
-#elif EIGEN_COMP_CLANG // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
-  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    using Base::operator =; \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \
-    template <typename OtherDerived> \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }
-#else
-  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    using Base::operator =; \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) \
-    { \
-      Base::operator=(other); \
-      return *this; \
-    }
-#endif
-
-
-/**
- * \internal
- * \brief Macro to explicitly define the default copy constructor.
- * This is necessary, because the implicit definition is deprecated if the copy-assignment is overridden.
- */
-#if EIGEN_HAS_CXX11
-#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS) CLASS(const CLASS&) = default;
-#else
-#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS)
-#endif
-
-
-
-/** \internal
- * \brief Macro to manually inherit assignment operators.
- * This is necessary, because the implicitly defined assignment operator gets deleted when a custom operator= is defined.
- * With C++11 or later this also default-implements the copy-constructor
- */
-#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived)  \
-    EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(Derived)
-
-/** \internal
- * \brief Macro to manually define default constructors and destructors.
- * This is necessary when the copy constructor is re-defined.
- * For empty helper classes this should usually be protected, to avoid accidentally creating empty objects.
- *
- * Hiding the default destructor lead to problems in C++03 mode together with boost::multiprecision
- */
-#if EIGEN_HAS_CXX11
-#define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived)  \
-    Derived() = default; \
-    ~Derived() = default;
-#else
-#define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived)  \
-    Derived() {}; \
-    /* ~Derived() {}; */
-#endif
-
-
-
-
-
-/**
-* Just a side note. Commenting within defines works only by documenting
-* behind the object (via '!<'). Comments cannot be multi-line and thus
-* we have these extra long lines. What is confusing doxygen over here is
-* that we use '\' and basically have a bunch of typedefs with their
-* documentation in a single line.
-**/
-
-#define EIGEN_GENERIC_PUBLIC_INTERFACE(Derived) \
-  typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex<float>. */ \
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is e.g. std::complex<T>, T were corresponding to RealScalar. */ \
-  typedef typename Base::CoeffReturnType CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&' or simply 'Scalar' for objects that do not allow direct coefficient access. */ \
-  typedef typename Eigen::internal::ref_selector<Derived>::type Nested; \
-  typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \
-  typedef typename Eigen::internal::traits<Derived>::StorageIndex StorageIndex; \
-  enum CompileTimeTraits \
-      { RowsAtCompileTime = Eigen::internal::traits<Derived>::RowsAtCompileTime, \
-        ColsAtCompileTime = Eigen::internal::traits<Derived>::ColsAtCompileTime, \
-        Flags = Eigen::internal::traits<Derived>::Flags, \
-        SizeAtCompileTime = Base::SizeAtCompileTime, \
-        MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime, \
-        IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \
-  using Base::derived; \
-  using Base::const_cast_derived;
-
-
-// FIXME Maybe the EIGEN_DENSE_PUBLIC_INTERFACE could be removed as importing PacketScalar is rarely needed
-#define EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \
-  EIGEN_GENERIC_PUBLIC_INTERFACE(Derived) \
-  typedef typename Base::PacketScalar PacketScalar;
-
-
-#define EIGEN_PLAIN_ENUM_MIN(a,b) (((int)a <= (int)b) ? (int)a : (int)b)
-#define EIGEN_PLAIN_ENUM_MAX(a,b) (((int)a >= (int)b) ? (int)a : (int)b)
-
-// EIGEN_SIZE_MIN_PREFER_DYNAMIC gives the min between compile-time sizes. 0 has absolute priority, followed by 1,
-// followed by Dynamic, followed by other finite values. The reason for giving Dynamic the priority over
-// finite values is that min(3, Dynamic) should be Dynamic, since that could be anything between 0 and 3.
-#define EIGEN_SIZE_MIN_PREFER_DYNAMIC(a,b) (((int)a == 0 || (int)b == 0) ? 0 \
-                           : ((int)a == 1 || (int)b == 1) ? 1 \
-                           : ((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \
-                           : ((int)a <= (int)b) ? (int)a : (int)b)
-
-// EIGEN_SIZE_MIN_PREFER_FIXED is a variant of EIGEN_SIZE_MIN_PREFER_DYNAMIC comparing MaxSizes. The difference is that finite values
-// now have priority over Dynamic, so that min(3, Dynamic) gives 3. Indeed, whatever the actual value is
-// (between 0 and 3), it is not more than 3.
-#define EIGEN_SIZE_MIN_PREFER_FIXED(a,b)  (((int)a == 0 || (int)b == 0) ? 0 \
-                           : ((int)a == 1 || (int)b == 1) ? 1 \
-                           : ((int)a == Dynamic && (int)b == Dynamic) ? Dynamic \
-                           : ((int)a == Dynamic) ? (int)b \
-                           : ((int)b == Dynamic) ? (int)a \
-                           : ((int)a <= (int)b) ? (int)a : (int)b)
-
-// see EIGEN_SIZE_MIN_PREFER_DYNAMIC. No need for a separate variant for MaxSizes here.
-#define EIGEN_SIZE_MAX(a,b) (((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \
-                           : ((int)a >= (int)b) ? (int)a : (int)b)
-
-#define EIGEN_LOGICAL_XOR(a,b) (((a) || (b)) && !((a) && (b)))
-
-#define EIGEN_IMPLIES(a,b) (!(a) || (b))
-
-#if EIGEN_HAS_BUILTIN(__builtin_expect) || EIGEN_COMP_GNUC
-#define EIGEN_PREDICT_FALSE(x) (__builtin_expect(x, false))
-#define EIGEN_PREDICT_TRUE(x) (__builtin_expect(false || (x), true))
-#else
-#define EIGEN_PREDICT_FALSE(x) (x)
-#define EIGEN_PREDICT_TRUE(x) (x)
-#endif
-
-// the expression type of a standard coefficient wise binary operation
-#define EIGEN_CWISE_BINARY_RETURN_TYPE(LHS,RHS,OPNAME) \
-    CwiseBinaryOp< \
-      EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)< \
-          typename internal::traits<LHS>::Scalar, \
-          typename internal::traits<RHS>::Scalar \
-      >, \
-      const LHS, \
-      const RHS \
-    >
-
-#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,OPNAME) \
-  template<typename OtherDerived> \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME) \
-  (METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
-  { \
-    return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME)(derived(), other.derived()); \
-  }
-
-#define EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,TYPEA,TYPEB) \
-  (Eigen::internal::has_ReturnType<Eigen::ScalarBinaryOpTraits<TYPEA,TYPEB,EIGEN_CAT(EIGEN_CAT(Eigen::internal::scalar_,OPNAME),_op)<TYPEA,TYPEB>  > >::value)
-
-#define EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(EXPR,SCALAR,OPNAME) \
-  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<typename internal::traits<EXPR>::Scalar,SCALAR>, const EXPR, \
-                const typename internal::plain_constant_type<EXPR,SCALAR>::type>
-
-#define EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(SCALAR,EXPR,OPNAME) \
-  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<SCALAR,typename internal::traits<EXPR>::Scalar>, \
-                const typename internal::plain_constant_type<EXPR,SCALAR>::type, const EXPR>
-
-// Workaround for MSVC 2010 (see ML thread "patch with compile for for MSVC 2010")
-#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC_STRICT<=1600)
-#define EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(X) typename internal::enable_if<true,X>::type
-#else
-#define EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(X) X
-#endif
-
-#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME) \
-  template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type,OPNAME))\
-  (METHOD)(const T& scalar) const { \
-    typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type PromotedT; \
-    return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedT,OPNAME)(derived(), \
-           typename internal::plain_constant_type<Derived,PromotedT>::type(derived().rows(), derived().cols(), internal::scalar_constant_op<PromotedT>(scalar))); \
-  }
-
-#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
-  template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend \
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type,Derived,OPNAME)) \
-  (METHOD)(const T& scalar, const StorageBaseType& matrix) { \
-    typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type PromotedT; \
-    return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedT,Derived,OPNAME)( \
-           typename internal::plain_constant_type<Derived,PromotedT>::type(matrix.derived().rows(), matrix.derived().cols(), internal::scalar_constant_op<PromotedT>(scalar)), matrix.derived()); \
-  }
-
-#define EIGEN_MAKE_SCALAR_BINARY_OP(METHOD,OPNAME) \
-  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
-  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME)
-
-
-#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(EIGEN_CUDA_ARCH) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL) && !defined(EIGEN_HIP_DEVICE_COMPILE)
-  #define EIGEN_EXCEPTIONS
-#endif
-
-
-#ifdef EIGEN_EXCEPTIONS
-#  define EIGEN_THROW_X(X) throw X
-#  define EIGEN_THROW throw
-#  define EIGEN_TRY try
-#  define EIGEN_CATCH(X) catch (X)
-#else
-#  if defined(EIGEN_CUDA_ARCH)
-#    define EIGEN_THROW_X(X) asm("trap;")
-#    define EIGEN_THROW asm("trap;")
-#  elif defined(EIGEN_HIP_DEVICE_COMPILE)
-#    define EIGEN_THROW_X(X) asm("s_trap 0")
-#    define EIGEN_THROW asm("s_trap 0")
-#  else
-#    define EIGEN_THROW_X(X) std::abort()
-#    define EIGEN_THROW std::abort()
-#  endif
-#  define EIGEN_TRY if (true)
-#  define EIGEN_CATCH(X) else
-#endif
-
-
-#if EIGEN_HAS_CXX11_NOEXCEPT
-#   define EIGEN_INCLUDE_TYPE_TRAITS
-#   define EIGEN_NOEXCEPT noexcept
-#   define EIGEN_NOEXCEPT_IF(x) noexcept(x)
-#   define EIGEN_NO_THROW noexcept(true)
-#   define EIGEN_EXCEPTION_SPEC(X) noexcept(false)
-#else
-#   define EIGEN_NOEXCEPT
-#   define EIGEN_NOEXCEPT_IF(x)
-#   define EIGEN_NO_THROW throw()
-#   if EIGEN_COMP_MSVC || EIGEN_COMP_CXXVER>=17
-      // MSVC does not support exception specifications (warning C4290),
-      // and they are deprecated in c++11 anyway. This is even an error in c++17.
-#     define EIGEN_EXCEPTION_SPEC(X) throw()
-#   else
-#     define EIGEN_EXCEPTION_SPEC(X) throw(X)
-#   endif
-#endif
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-// The all function is used to enable a variadic version of eigen_assert which can take a parameter pack as its input.
-namespace Eigen {
-namespace internal {
-
-inline bool all(){ return true; }
-
-template<typename T, typename ...Ts>
-bool all(T t, Ts ... ts){ return t && all(ts...); }
-
-}
-}
-#endif
-
-#if EIGEN_HAS_CXX11_OVERRIDE_FINAL
-// provide override and final specifiers if they are available:
-#   define EIGEN_OVERRIDE override
-#   define EIGEN_FINAL final
-#else
-#   define EIGEN_OVERRIDE
-#   define EIGEN_FINAL
-#endif
-
-// Wrapping #pragma unroll in a macro since it is required for SYCL
-#if defined(SYCL_DEVICE_ONLY)
-  #if defined(_MSC_VER)
-    #define EIGEN_UNROLL_LOOP __pragma(unroll)
-  #else
-    #define EIGEN_UNROLL_LOOP _Pragma("unroll")
-  #endif
-#else
-  #define EIGEN_UNROLL_LOOP
-#endif
-
-#endif // EIGEN_MACROS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Memory.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Memory.h
deleted file mode 100644
index 875318c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Memory.h
+++ /dev/null
@@ -1,1163 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Kenneth Riddile <kfriddile@yahoo.com>
-// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
-// Copyright (C) 2010 Thomas Capricelli <orzel@freehackers.org>
-// Copyright (C) 2013 Pavel Holoborodko <pavel@holoborodko.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-/*****************************************************************************
-*** Platform checks for aligned malloc functions                           ***
-*****************************************************************************/
-
-#ifndef EIGEN_MEMORY_H
-#define EIGEN_MEMORY_H
-
-#ifndef EIGEN_MALLOC_ALREADY_ALIGNED
-
-// Try to determine automatically if malloc is already aligned.
-
-// On 64-bit systems, glibc's malloc returns 16-byte-aligned pointers, see:
-//   http://www.gnu.org/s/libc/manual/html_node/Aligned-Memory-Blocks.html
-// This is true at least since glibc 2.8.
-// This leaves the question how to detect 64-bit. According to this document,
-//   http://gcc.fyxm.net/summit/2003/Porting%20to%2064%20bit.pdf
-// page 114, "[The] LP64 model [...] is used by all 64-bit UNIX ports" so it's indeed
-// quite safe, at least within the context of glibc, to equate 64-bit with LP64.
-#if defined(__GLIBC__) && ((__GLIBC__>=2 && __GLIBC_MINOR__ >= 8) || __GLIBC__>2) \
- && defined(__LP64__) && ! defined( __SANITIZE_ADDRESS__ ) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
-  #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 1
-#else
-  #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 0
-#endif
-
-// FreeBSD 6 seems to have 16-byte aligned malloc
-//   See http://svn.freebsd.org/viewvc/base/stable/6/lib/libc/stdlib/malloc.c?view=markup
-// FreeBSD 7 seems to have 16-byte aligned malloc except on ARM and MIPS architectures
-//   See http://svn.freebsd.org/viewvc/base/stable/7/lib/libc/stdlib/malloc.c?view=markup
-#if defined(__FreeBSD__) && !(EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
-  #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 1
-#else
-  #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 0
-#endif
-
-#if (EIGEN_OS_MAC && (EIGEN_DEFAULT_ALIGN_BYTES == 16))     \
- || (EIGEN_OS_WIN64 && (EIGEN_DEFAULT_ALIGN_BYTES == 16))   \
- || EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED              \
- || EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED
-  #define EIGEN_MALLOC_ALREADY_ALIGNED 1
-#else
-  #define EIGEN_MALLOC_ALREADY_ALIGNED 0
-#endif
-
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-EIGEN_DEVICE_FUNC
-inline void throw_std_bad_alloc()
-{
-  #ifdef EIGEN_EXCEPTIONS
-    throw std::bad_alloc();
-  #else
-    std::size_t huge = static_cast<std::size_t>(-1);
-    #if defined(EIGEN_HIPCC)
-    //
-    // calls to "::operator new" are to be treated as opaque function calls (i.e no inlining),
-    // and as a consequence the code in the #else block triggers the hipcc warning :
-    // "no overloaded function has restriction specifiers that are compatible with the ambient context"
-    //
-    // "throw_std_bad_alloc" has the EIGEN_DEVICE_FUNC attribute, so it seems that hipcc expects
-    // the same on "operator new"
-    // Reverting code back to the old version in this #if block for the hipcc compiler
-    //
-    new int[huge];
-    #else
-    void* unused = ::operator new(huge);
-    EIGEN_UNUSED_VARIABLE(unused);
-    #endif
-  #endif
-}
-
-/*****************************************************************************
-*** Implementation of handmade aligned functions                           ***
-*****************************************************************************/
-
-/* ----- Hand made implementations of aligned malloc/free and realloc ----- */
-
-/** \internal Like malloc, but the returned pointer is guaranteed to be 16-byte aligned.
-  * Fast, but wastes 16 additional bytes of memory. Does not throw any exception.
-  */
-EIGEN_DEVICE_FUNC inline void* handmade_aligned_malloc(std::size_t size, std::size_t alignment = EIGEN_DEFAULT_ALIGN_BYTES)
-{
-  eigen_assert(alignment >= sizeof(void*) && (alignment & (alignment-1)) == 0 && "Alignment must be at least sizeof(void*) and a power of 2");
-
-  EIGEN_USING_STD(malloc)
-  void *original = malloc(size+alignment);
-  
-  if (original == 0) return 0;
-  void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(alignment-1))) + alignment);
-  *(reinterpret_cast<void**>(aligned) - 1) = original;
-  return aligned;
-}
-
-/** \internal Frees memory allocated with handmade_aligned_malloc */
-EIGEN_DEVICE_FUNC inline void handmade_aligned_free(void *ptr)
-{
-  if (ptr) {
-    EIGEN_USING_STD(free)
-    free(*(reinterpret_cast<void**>(ptr) - 1));
-  }
-}
-
-/** \internal
-  * \brief Reallocates aligned memory.
-  * Since we know that our handmade version is based on std::malloc
-  * we can use std::realloc to implement efficient reallocation.
-  */
-inline void* handmade_aligned_realloc(void* ptr, std::size_t size, std::size_t = 0)
-{
-  if (ptr == 0) return handmade_aligned_malloc(size);
-  void *original = *(reinterpret_cast<void**>(ptr) - 1);
-  std::ptrdiff_t previous_offset = static_cast<char *>(ptr)-static_cast<char *>(original);
-  original = std::realloc(original,size+EIGEN_DEFAULT_ALIGN_BYTES);
-  if (original == 0) return 0;
-  void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1))) + EIGEN_DEFAULT_ALIGN_BYTES);
-  void *previous_aligned = static_cast<char *>(original)+previous_offset;
-  if(aligned!=previous_aligned)
-    std::memmove(aligned, previous_aligned, size);
-
-  *(reinterpret_cast<void**>(aligned) - 1) = original;
-  return aligned;
-}
-
-/*****************************************************************************
-*** Implementation of portable aligned versions of malloc/free/realloc     ***
-*****************************************************************************/
-
-#ifdef EIGEN_NO_MALLOC
-EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
-{
-  eigen_assert(false && "heap allocation is forbidden (EIGEN_NO_MALLOC is defined)");
-}
-#elif defined EIGEN_RUNTIME_NO_MALLOC
-EIGEN_DEVICE_FUNC inline bool is_malloc_allowed_impl(bool update, bool new_value = false)
-{
-  static bool value = true;
-  if (update == 1)
-    value = new_value;
-  return value;
-}
-EIGEN_DEVICE_FUNC inline bool is_malloc_allowed() { return is_malloc_allowed_impl(false); }
-EIGEN_DEVICE_FUNC inline bool set_is_malloc_allowed(bool new_value) { return is_malloc_allowed_impl(true, new_value); }
-EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
-{
-  eigen_assert(is_malloc_allowed() && "heap allocation is forbidden (EIGEN_RUNTIME_NO_MALLOC is defined and g_is_malloc_allowed is false)");
-}
-#else
-EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
-{}
-#endif
-
-/** \internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 or 32 bytes alignment depending on the requirements.
-  * On allocation error, the returned pointer is null, and std::bad_alloc is thrown.
-  */
-EIGEN_DEVICE_FUNC inline void* aligned_malloc(std::size_t size)
-{
-  check_that_malloc_is_allowed();
-
-  void *result;
-  #if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
-
-    EIGEN_USING_STD(malloc)
-    result = malloc(size);
-
-    #if EIGEN_DEFAULT_ALIGN_BYTES==16
-    eigen_assert((size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator.");
-    #endif
-  #else
-    result = handmade_aligned_malloc(size);
-  #endif
-
-  if(!result && size)
-    throw_std_bad_alloc();
-
-  return result;
-}
-
-/** \internal Frees memory allocated with aligned_malloc. */
-EIGEN_DEVICE_FUNC inline void aligned_free(void *ptr)
-{
-  #if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
-
-    EIGEN_USING_STD(free)
-    free(ptr);
-
-  #else
-    handmade_aligned_free(ptr);
-  #endif
-}
-
-/**
-  * \internal
-  * \brief Reallocates an aligned block of memory.
-  * \throws std::bad_alloc on allocation failure
-  */
-inline void* aligned_realloc(void *ptr, std::size_t new_size, std::size_t old_size)
-{
-  EIGEN_UNUSED_VARIABLE(old_size)
-
-  void *result;
-#if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
-  result = std::realloc(ptr,new_size);
-#else
-  result = handmade_aligned_realloc(ptr,new_size,old_size);
-#endif
-
-  if (!result && new_size)
-    throw_std_bad_alloc();
-
-  return result;
-}
-
-/*****************************************************************************
-*** Implementation of conditionally aligned functions                      ***
-*****************************************************************************/
-
-/** \internal Allocates \a size bytes. If Align is true, then the returned ptr is 16-byte-aligned.
-  * On allocation error, the returned pointer is null, and a std::bad_alloc is thrown.
-  */
-template<bool Align> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc(std::size_t size)
-{
-  return aligned_malloc(size);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc<false>(std::size_t size)
-{
-  check_that_malloc_is_allowed();
-
-  EIGEN_USING_STD(malloc)
-  void *result = malloc(size);
-
-  if(!result && size)
-    throw_std_bad_alloc();
-  return result;
-}
-
-/** \internal Frees memory allocated with conditional_aligned_malloc */
-template<bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_free(void *ptr)
-{
-  aligned_free(ptr);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void conditional_aligned_free<false>(void *ptr)
-{
-  EIGEN_USING_STD(free)
-  free(ptr);
-}
-
-template<bool Align> inline void* conditional_aligned_realloc(void* ptr, std::size_t new_size, std::size_t old_size)
-{
-  return aligned_realloc(ptr, new_size, old_size);
-}
-
-template<> inline void* conditional_aligned_realloc<false>(void* ptr, std::size_t new_size, std::size_t)
-{
-  return std::realloc(ptr, new_size);
-}
-
-/*****************************************************************************
-*** Construction/destruction of array elements                             ***
-*****************************************************************************/
-
-/** \internal Destructs the elements of an array.
-  * The \a size parameters tells on how many objects to call the destructor of T.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline void destruct_elements_of_array(T *ptr, std::size_t size)
-{
-  // always destruct an array starting from the end.
-  if(ptr)
-    while(size) ptr[--size].~T();
-}
-
-/** \internal Constructs the elements of an array.
-  * The \a size parameter tells on how many objects to call the constructor of T.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline T* construct_elements_of_array(T *ptr, std::size_t size)
-{
-  std::size_t i;
-  EIGEN_TRY
-  {
-      for (i = 0; i < size; ++i) ::new (ptr + i) T;
-      return ptr;
-  }
-  EIGEN_CATCH(...)
-  {
-    destruct_elements_of_array(ptr, i);
-    EIGEN_THROW;
-  }
-  return NULL;
-}
-
-/*****************************************************************************
-*** Implementation of aligned new/delete-like functions                    ***
-*****************************************************************************/
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void check_size_for_overflow(std::size_t size)
-{
-  if(size > std::size_t(-1) / sizeof(T))
-    throw_std_bad_alloc();
-}
-
-/** \internal Allocates \a size objects of type T. The returned pointer is guaranteed to have 16 bytes alignment.
-  * On allocation error, the returned pointer is undefined, but a std::bad_alloc is thrown.
-  * The default constructor of T is called.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline T* aligned_new(std::size_t size)
-{
-  check_size_for_overflow<T>(size);
-  T *result = reinterpret_cast<T*>(aligned_malloc(sizeof(T)*size));
-  EIGEN_TRY
-  {
-    return construct_elements_of_array(result, size);
-  }
-  EIGEN_CATCH(...)
-  {
-    aligned_free(result);
-    EIGEN_THROW;
-  }
-  return result;
-}
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new(std::size_t size)
-{
-  check_size_for_overflow<T>(size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
-  EIGEN_TRY
-  {
-    return construct_elements_of_array(result, size);
-  }
-  EIGEN_CATCH(...)
-  {
-    conditional_aligned_free<Align>(result);
-    EIGEN_THROW;
-  }
-  return result;
-}
-
-/** \internal Deletes objects constructed with aligned_new
-  * The \a size parameters tells on how many objects to call the destructor of T.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline void aligned_delete(T *ptr, std::size_t size)
-{
-  destruct_elements_of_array<T>(ptr, size);
-  Eigen::internal::aligned_free(ptr);
-}
-
-/** \internal Deletes objects constructed with conditional_aligned_new
-  * The \a size parameters tells on how many objects to call the destructor of T.
-  */
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete(T *ptr, std::size_t size)
-{
-  destruct_elements_of_array<T>(ptr, size);
-  conditional_aligned_free<Align>(ptr);
-}
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_realloc_new(T* pts, std::size_t new_size, std::size_t old_size)
-{
-  check_size_for_overflow<T>(new_size);
-  check_size_for_overflow<T>(old_size);
-  if(new_size < old_size)
-    destruct_elements_of_array(pts+new_size, old_size-new_size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
-  if(new_size > old_size)
-  {
-    EIGEN_TRY
-    {
-      construct_elements_of_array(result+old_size, new_size-old_size);
-    }
-    EIGEN_CATCH(...)
-    {
-      conditional_aligned_free<Align>(result);
-      EIGEN_THROW;
-    }
-  }
-  return result;
-}
-
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new_auto(std::size_t size)
-{
-  if(size==0)
-    return 0; // short-cut. Also fixes Bug 884
-  check_size_for_overflow<T>(size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
-  if(NumTraits<T>::RequireInitialization)
-  {
-    EIGEN_TRY
-    {
-      construct_elements_of_array(result, size);
-    }
-    EIGEN_CATCH(...)
-    {
-      conditional_aligned_free<Align>(result);
-      EIGEN_THROW;
-    }
-  }
-  return result;
-}
-
-template<typename T, bool Align> inline T* conditional_aligned_realloc_new_auto(T* pts, std::size_t new_size, std::size_t old_size)
-{
-  check_size_for_overflow<T>(new_size);
-  check_size_for_overflow<T>(old_size);
-  if(NumTraits<T>::RequireInitialization && (new_size < old_size))
-    destruct_elements_of_array(pts+new_size, old_size-new_size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
-  if(NumTraits<T>::RequireInitialization && (new_size > old_size))
-  {
-    EIGEN_TRY
-    {
-      construct_elements_of_array(result+old_size, new_size-old_size);
-    }
-    EIGEN_CATCH(...)
-    {
-      conditional_aligned_free<Align>(result);
-      EIGEN_THROW;
-    }
-  }
-  return result;
-}
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete_auto(T *ptr, std::size_t size)
-{
-  if(NumTraits<T>::RequireInitialization)
-    destruct_elements_of_array<T>(ptr, size);
-  conditional_aligned_free<Align>(ptr);
-}
-
-/****************************************************************************/
-
-/** \internal Returns the index of the first element of the array that is well aligned with respect to the requested \a Alignment.
-  *
-  * \tparam Alignment requested alignment in Bytes.
-  * \param array the address of the start of the array
-  * \param size the size of the array
-  *
-  * \note If no element of the array is well aligned or the requested alignment is not a multiple of a scalar,
-  * the size of the array is returned. For example with SSE, the requested alignment is typically 16-bytes. If
-  * packet size for the given scalar type is 1, then everything is considered well-aligned.
-  *
-  * \note Otherwise, if the Alignment is larger that the scalar size, we rely on the assumptions that sizeof(Scalar) is a
-  * power of 2. On the other hand, we do not assume that the array address is a multiple of sizeof(Scalar), as that fails for
-  * example with Scalar=double on certain 32-bit platforms, see bug #79.
-  *
-  * There is also the variant first_aligned(const MatrixBase&) defined in DenseCoeffsBase.h.
-  * \sa first_default_aligned()
-  */
-template<int Alignment, typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC inline Index first_aligned(const Scalar* array, Index size)
-{
-  const Index ScalarSize = sizeof(Scalar);
-  const Index AlignmentSize = Alignment / ScalarSize;
-  const Index AlignmentMask = AlignmentSize-1;
-
-  if(AlignmentSize<=1)
-  {
-    // Either the requested alignment if smaller than a scalar, or it exactly match a 1 scalar
-    // so that all elements of the array have the same alignment.
-    return 0;
-  }
-  else if( (UIntPtr(array) & (sizeof(Scalar)-1)) || (Alignment%ScalarSize)!=0)
-  {
-    // The array is not aligned to the size of a single scalar, or the requested alignment is not a multiple of the scalar size.
-    // Consequently, no element of the array is well aligned.
-    return size;
-  }
-  else
-  {
-    Index first = (AlignmentSize - (Index((UIntPtr(array)/sizeof(Scalar))) & AlignmentMask)) & AlignmentMask;
-    return (first < size) ? first : size;
-  }
-}
-
-/** \internal Returns the index of the first element of the array that is well aligned with respect the largest packet requirement.
-   * \sa first_aligned(Scalar*,Index) and first_default_aligned(DenseBase<Derived>) */
-template<typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC inline Index first_default_aligned(const Scalar* array, Index size)
-{
-  typedef typename packet_traits<Scalar>::type DefaultPacketType;
-  return first_aligned<unpacket_traits<DefaultPacketType>::alignment>(array, size);
-}
-
-/** \internal Returns the smallest integer multiple of \a base and greater or equal to \a size
-  */
-template<typename Index>
-inline Index first_multiple(Index size, Index base)
-{
-  return ((size+base-1)/base)*base;
-}
-
-// std::copy is much slower than memcpy, so let's introduce a smart_copy which
-// use memcpy on trivial types, i.e., on types that does not require an initialization ctor.
-template<typename T, bool UseMemcpy> struct smart_copy_helper;
-
-template<typename T> EIGEN_DEVICE_FUNC void smart_copy(const T* start, const T* end, T* target)
-{
-  smart_copy_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target);
-}
-
-template<typename T> struct smart_copy_helper<T,true> {
-  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target)
-  {
-    IntPtr size = IntPtr(end)-IntPtr(start);
-    if(size==0) return;
-    eigen_internal_assert(start!=0 && end!=0 && target!=0);
-    EIGEN_USING_STD(memcpy)
-    memcpy(target, start, size);
-  }
-};
-
-template<typename T> struct smart_copy_helper<T,false> {
-  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target)
-  { std::copy(start, end, target); }
-};
-
-// intelligent memmove. falls back to std::memmove for POD types, uses std::copy otherwise.
-template<typename T, bool UseMemmove> struct smart_memmove_helper;
-
-template<typename T> void smart_memmove(const T* start, const T* end, T* target)
-{
-  smart_memmove_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target);
-}
-
-template<typename T> struct smart_memmove_helper<T,true> {
-  static inline void run(const T* start, const T* end, T* target)
-  {
-    IntPtr size = IntPtr(end)-IntPtr(start);
-    if(size==0) return;
-    eigen_internal_assert(start!=0 && end!=0 && target!=0);
-    std::memmove(target, start, size);
-  }
-};
-
-template<typename T> struct smart_memmove_helper<T,false> {
-  static inline void run(const T* start, const T* end, T* target)
-  {
-    if (UIntPtr(target) < UIntPtr(start))
-    {
-      std::copy(start, end, target);
-    }
-    else
-    {
-      std::ptrdiff_t count = (std::ptrdiff_t(end)-std::ptrdiff_t(start)) / sizeof(T);
-      std::copy_backward(start, end, target + count);
-    }
-  }
-};
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-template<typename T> EIGEN_DEVICE_FUNC T* smart_move(T* start, T* end, T* target)
-{
-  return std::move(start, end, target);
-}
-#else
-template<typename T> EIGEN_DEVICE_FUNC T* smart_move(T* start, T* end, T* target)
-{
-  return std::copy(start, end, target);
-}
-#endif
-
-/*****************************************************************************
-*** Implementation of runtime stack allocation (falling back to malloc)    ***
-*****************************************************************************/
-
-// you can overwrite Eigen's default behavior regarding alloca by defining EIGEN_ALLOCA
-// to the appropriate stack allocation function
-#if ! defined EIGEN_ALLOCA && ! defined EIGEN_GPU_COMPILE_PHASE
-  #if EIGEN_OS_LINUX || EIGEN_OS_MAC || (defined alloca)
-    #define EIGEN_ALLOCA alloca
-  #elif EIGEN_COMP_MSVC
-    #define EIGEN_ALLOCA _alloca
-  #endif
-#endif
-
-// With clang -Oz -mthumb, alloca changes the stack pointer in a way that is
-// not allowed in Thumb2. -DEIGEN_STACK_ALLOCATION_LIMIT=0 doesn't work because
-// the compiler still emits bad code because stack allocation checks use "<=".
-// TODO: Eliminate after https://bugs.llvm.org/show_bug.cgi?id=23772
-// is fixed.
-#if defined(__clang__) && defined(__thumb__)
-  #undef EIGEN_ALLOCA
-#endif
-
-// This helper class construct the allocated memory, and takes care of destructing and freeing the handled data
-// at destruction time. In practice this helper class is mainly useful to avoid memory leak in case of exceptions.
-template<typename T> class aligned_stack_memory_handler : noncopyable
-{
-  public:
-    /* Creates a stack_memory_handler responsible for the buffer \a ptr of size \a size.
-     * Note that \a ptr can be 0 regardless of the other parameters.
-     * This constructor takes care of constructing/initializing the elements of the buffer if required by the scalar type T (see NumTraits<T>::RequireInitialization).
-     * In this case, the buffer elements will also be destructed when this handler will be destructed.
-     * Finally, if \a dealloc is true, then the pointer \a ptr is freed.
-     **/
-    EIGEN_DEVICE_FUNC
-    aligned_stack_memory_handler(T* ptr, std::size_t size, bool dealloc)
-      : m_ptr(ptr), m_size(size), m_deallocate(dealloc)
-    {
-      if(NumTraits<T>::RequireInitialization && m_ptr)
-        Eigen::internal::construct_elements_of_array(m_ptr, size);
-    }
-    EIGEN_DEVICE_FUNC
-    ~aligned_stack_memory_handler()
-    {
-      if(NumTraits<T>::RequireInitialization && m_ptr)
-        Eigen::internal::destruct_elements_of_array<T>(m_ptr, m_size);
-      if(m_deallocate)
-        Eigen::internal::aligned_free(m_ptr);
-    }
-  protected:
-    T* m_ptr;
-    std::size_t m_size;
-    bool m_deallocate;
-};
-
-#ifdef EIGEN_ALLOCA
-
-template<typename Xpr, int NbEvaluations,
-         bool MapExternalBuffer = nested_eval<Xpr,NbEvaluations>::Evaluate && Xpr::MaxSizeAtCompileTime==Dynamic
-         >
-struct local_nested_eval_wrapper
-{
-  static const bool NeedExternalBuffer = false;
-  typedef typename Xpr::Scalar Scalar;
-  typedef typename nested_eval<Xpr,NbEvaluations>::type ObjectType;
-  ObjectType object;
-
-  EIGEN_DEVICE_FUNC
-  local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr) : object(xpr)
-  {
-    EIGEN_UNUSED_VARIABLE(ptr);
-    eigen_internal_assert(ptr==0);
-  }
-};
-
-template<typename Xpr, int NbEvaluations>
-struct local_nested_eval_wrapper<Xpr,NbEvaluations,true>
-{
-  static const bool NeedExternalBuffer = true;
-  typedef typename Xpr::Scalar Scalar;
-  typedef typename plain_object_eval<Xpr>::type PlainObject;
-  typedef Map<PlainObject,EIGEN_DEFAULT_ALIGN_BYTES> ObjectType;
-  ObjectType object;
-
-  EIGEN_DEVICE_FUNC
-  local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr)
-    : object(ptr==0 ? reinterpret_cast<Scalar*>(Eigen::internal::aligned_malloc(sizeof(Scalar)*xpr.size())) : ptr, xpr.rows(), xpr.cols()),
-      m_deallocate(ptr==0)
-  {
-    if(NumTraits<Scalar>::RequireInitialization && object.data())
-      Eigen::internal::construct_elements_of_array(object.data(), object.size());
-    object = xpr;
-  }
-
-  EIGEN_DEVICE_FUNC
-  ~local_nested_eval_wrapper()
-  {
-    if(NumTraits<Scalar>::RequireInitialization && object.data())
-      Eigen::internal::destruct_elements_of_array(object.data(), object.size());
-    if(m_deallocate)
-      Eigen::internal::aligned_free(object.data());
-  }
-
-private:
-  bool m_deallocate;
-};
-
-#endif // EIGEN_ALLOCA
-
-template<typename T> class scoped_array : noncopyable
-{
-  T* m_ptr;
-public:
-  explicit scoped_array(std::ptrdiff_t size)
-  {
-    m_ptr = new T[size];
-  }
-  ~scoped_array()
-  {
-    delete[] m_ptr;
-  }
-  T& operator[](std::ptrdiff_t i) { return m_ptr[i]; }
-  const T& operator[](std::ptrdiff_t i) const { return m_ptr[i]; }
-  T* &ptr() { return m_ptr; }
-  const T* ptr() const { return m_ptr; }
-  operator const T*() const { return m_ptr; }
-};
-
-template<typename T> void swap(scoped_array<T> &a,scoped_array<T> &b)
-{
-  std::swap(a.ptr(),b.ptr());
-}
-
-} // end namespace internal
-
-/** \internal
-  *
-  * The macro ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) declares, allocates,
-  * and construct an aligned buffer named NAME of SIZE elements of type TYPE on the stack
-  * if the size in bytes is smaller than EIGEN_STACK_ALLOCATION_LIMIT, and if stack allocation is supported by the platform
-  * (currently, this is Linux, OSX and Visual Studio only). Otherwise the memory is allocated on the heap.
-  * The allocated buffer is automatically deleted when exiting the scope of this declaration.
-  * If BUFFER is non null, then the declared variable is simply an alias for BUFFER, and no allocation/deletion occurs.
-  * Here is an example:
-  * \code
-  * {
-  *   ei_declare_aligned_stack_constructed_variable(float,data,size,0);
-  *   // use data[0] to data[size-1]
-  * }
-  * \endcode
-  * The underlying stack allocation function can controlled with the EIGEN_ALLOCA preprocessor token.
-  *
-  * The macro ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) is analogue to
-  * \code
-  *   typename internal::nested_eval<XPRT_T,N>::type NAME(XPR);
-  * \endcode
-  * with the advantage of using aligned stack allocation even if the maximal size of XPR at compile time is unknown.
-  * This is accomplished through alloca if this later is supported and if the required number of bytes
-  * is below EIGEN_STACK_ALLOCATION_LIMIT.
-  */
-#ifdef EIGEN_ALLOCA
-
-  #if EIGEN_DEFAULT_ALIGN_BYTES>0
-    // We always manually re-align the result of EIGEN_ALLOCA.
-    // If alloca is already aligned, the compiler should be smart enough to optimize away the re-alignment.
-    #define EIGEN_ALIGNED_ALLOCA(SIZE) reinterpret_cast<void*>((internal::UIntPtr(EIGEN_ALLOCA(SIZE+EIGEN_DEFAULT_ALIGN_BYTES-1)) + EIGEN_DEFAULT_ALIGN_BYTES-1) & ~(std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1)))
-  #else
-    #define EIGEN_ALIGNED_ALLOCA(SIZE) EIGEN_ALLOCA(SIZE)
-  #endif
-
-  #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
-    Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
-    TYPE* NAME = (BUFFER)!=0 ? (BUFFER) \
-               : reinterpret_cast<TYPE*>( \
-                      (sizeof(TYPE)*SIZE<=EIGEN_STACK_ALLOCATION_LIMIT) ? EIGEN_ALIGNED_ALLOCA(sizeof(TYPE)*SIZE) \
-                    : Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE) );  \
-    Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,sizeof(TYPE)*SIZE>EIGEN_STACK_ALLOCATION_LIMIT)
-
-
-  #define ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) \
-    Eigen::internal::local_nested_eval_wrapper<XPR_T,N> EIGEN_CAT(NAME,_wrapper)(XPR, reinterpret_cast<typename XPR_T::Scalar*>( \
-      ( (Eigen::internal::local_nested_eval_wrapper<XPR_T,N>::NeedExternalBuffer) && ((sizeof(typename XPR_T::Scalar)*XPR.size())<=EIGEN_STACK_ALLOCATION_LIMIT) ) \
-        ? EIGEN_ALIGNED_ALLOCA( sizeof(typename XPR_T::Scalar)*XPR.size() ) : 0 ) ) ; \
-    typename Eigen::internal::local_nested_eval_wrapper<XPR_T,N>::ObjectType NAME(EIGEN_CAT(NAME,_wrapper).object)
-
-#else
-
-  #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
-    Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
-    TYPE* NAME = (BUFFER)!=0 ? BUFFER : reinterpret_cast<TYPE*>(Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE));    \
-    Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,true)
-
-
-#define ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) typename Eigen::internal::nested_eval<XPR_T,N>::type NAME(XPR)
-
-#endif
-
-
-/*****************************************************************************
-*** Implementation of EIGEN_MAKE_ALIGNED_OPERATOR_NEW [_IF]                ***
-*****************************************************************************/
-
-#if EIGEN_HAS_CXX17_OVERALIGN
-
-// C++17 -> no need to bother about alignment anymore :)
-
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign)
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size)
-
-#else
-
-// HIP does not support new/delete on device.
-#if EIGEN_MAX_ALIGN_BYTES!=0 && !defined(EIGEN_HIP_DEVICE_COMPILE)
-  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
-      EIGEN_DEVICE_FUNC \
-      void* operator new(std::size_t size, const std::nothrow_t&) EIGEN_NO_THROW { \
-        EIGEN_TRY { return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); } \
-        EIGEN_CATCH (...) { return 0; } \
-      }
-  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) \
-      EIGEN_DEVICE_FUNC \
-      void *operator new(std::size_t size) { \
-        return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \
-      } \
-      EIGEN_DEVICE_FUNC \
-      void *operator new[](std::size_t size) { \
-        return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \
-      } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void * ptr) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete[](void * ptr) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void * ptr, std::size_t /* sz */) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete[](void * ptr, std::size_t /* sz */) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      /* in-place new and delete. since (at least afaik) there is no actual   */ \
-      /* memory allocated we can safely let the default implementation handle */ \
-      /* this particular case. */ \
-      EIGEN_DEVICE_FUNC \
-      static void *operator new(std::size_t size, void *ptr) { return ::operator new(size,ptr); } \
-      EIGEN_DEVICE_FUNC \
-      static void *operator new[](std::size_t size, void* ptr) { return ::operator new[](size,ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void * memory, void *ptr) EIGEN_NO_THROW { return ::operator delete(memory,ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete[](void * memory, void *ptr) EIGEN_NO_THROW { return ::operator delete[](memory,ptr); } \
-      /* nothrow-new (returns zero instead of std::bad_alloc) */ \
-      EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void *ptr, const std::nothrow_t&) EIGEN_NO_THROW { \
-        Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); \
-      } \
-      typedef void eigen_aligned_operator_new_marker_type;
-#else
-  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
-#endif
-
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(true)
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size)                        \
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(                                                             \
-        ((Size)!=Eigen::Dynamic) &&                                                                    \
-        (((EIGEN_MAX_ALIGN_BYTES>=16) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES  )==0)) ||    \
-         ((EIGEN_MAX_ALIGN_BYTES>=32) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES/2)==0)) ||    \
-         ((EIGEN_MAX_ALIGN_BYTES>=64) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES/4)==0))   )))
-
-#endif
-
-/****************************************************************************/
-
-/** \class aligned_allocator
-* \ingroup Core_Module
-*
-* \brief STL compatible allocator to use with types requiring a non standrad alignment.
-*
-* The memory is aligned as for dynamically aligned matrix/array types such as MatrixXd.
-* By default, it will thus provide at least 16 bytes alignment and more in following cases:
-*  - 32 bytes alignment if AVX is enabled.
-*  - 64 bytes alignment if AVX512 is enabled.
-*
-* This can be controlled using the \c EIGEN_MAX_ALIGN_BYTES macro as documented
-* \link TopicPreprocessorDirectivesPerformance there \endlink.
-*
-* Example:
-* \code
-* // Matrix4f requires 16 bytes alignment:
-* std::map< int, Matrix4f, std::less<int>,
-*           aligned_allocator<std::pair<const int, Matrix4f> > > my_map_mat4;
-* // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator:
-* std::map< int, Vector3f > my_map_vec3;
-* \endcode
-*
-* \sa \blank \ref TopicStlContainers.
-*/
-template<class T>
-class aligned_allocator : public std::allocator<T>
-{
-public:
-  typedef std::size_t     size_type;
-  typedef std::ptrdiff_t  difference_type;
-  typedef T*              pointer;
-  typedef const T*        const_pointer;
-  typedef T&              reference;
-  typedef const T&        const_reference;
-  typedef T               value_type;
-
-  template<class U>
-  struct rebind
-  {
-    typedef aligned_allocator<U> other;
-  };
-
-  aligned_allocator() : std::allocator<T>() {}
-
-  aligned_allocator(const aligned_allocator& other) : std::allocator<T>(other) {}
-
-  template<class U>
-  aligned_allocator(const aligned_allocator<U>& other) : std::allocator<T>(other) {}
-
-  ~aligned_allocator() {}
-
-  #if EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_LEAST(7,0)
-  // In gcc std::allocator::max_size() is bugged making gcc triggers a warning:
-  // eigen/Eigen/src/Core/util/Memory.h:189:12: warning: argument 1 value '18446744073709551612' exceeds maximum object size 9223372036854775807
-  // See https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87544
-  size_type max_size() const {
-    return (std::numeric_limits<std::ptrdiff_t>::max)()/sizeof(T);
-  }
-  #endif
-
-  pointer allocate(size_type num, const void* /*hint*/ = 0)
-  {
-    internal::check_size_for_overflow<T>(num);
-    return static_cast<pointer>( internal::aligned_malloc(num * sizeof(T)) );
-  }
-
-  void deallocate(pointer p, size_type /*num*/)
-  {
-    internal::aligned_free(p);
-  }
-};
-
-//---------- Cache sizes ----------
-
-#if !defined(EIGEN_NO_CPUID)
-#  if EIGEN_COMP_GNUC && EIGEN_ARCH_i386_OR_x86_64
-#    if defined(__PIC__) && EIGEN_ARCH_i386
-       // Case for x86 with PIC
-#      define EIGEN_CPUID(abcd,func,id) \
-         __asm__ __volatile__ ("xchgl %%ebx, %k1;cpuid; xchgl %%ebx,%k1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id));
-#    elif defined(__PIC__) && EIGEN_ARCH_x86_64
-       // Case for x64 with PIC. In theory this is only a problem with recent gcc and with medium or large code model, not with the default small code model.
-       // However, we cannot detect which code model is used, and the xchg overhead is negligible anyway.
-#      define EIGEN_CPUID(abcd,func,id) \
-        __asm__ __volatile__ ("xchg{q}\t{%%}rbx, %q1; cpuid; xchg{q}\t{%%}rbx, %q1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id));
-#    else
-       // Case for x86_64 or x86 w/o PIC
-#      define EIGEN_CPUID(abcd,func,id) \
-         __asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id) );
-#    endif
-#  elif EIGEN_COMP_MSVC
-#    if (EIGEN_COMP_MSVC > 1500) && EIGEN_ARCH_i386_OR_x86_64
-#      define EIGEN_CPUID(abcd,func,id) __cpuidex((int*)abcd,func,id)
-#    endif
-#  endif
-#endif
-
-namespace internal {
-
-#ifdef EIGEN_CPUID
-
-inline bool cpuid_is_vendor(int abcd[4], const int vendor[3])
-{
-  return abcd[1]==vendor[0] && abcd[3]==vendor[1] && abcd[2]==vendor[2];
-}
-
-inline void queryCacheSizes_intel_direct(int& l1, int& l2, int& l3)
-{
-  int abcd[4];
-  l1 = l2 = l3 = 0;
-  int cache_id = 0;
-  int cache_type = 0;
-  do {
-    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-    EIGEN_CPUID(abcd,0x4,cache_id);
-    cache_type  = (abcd[0] & 0x0F) >> 0;
-    if(cache_type==1||cache_type==3) // data or unified cache
-    {
-      int cache_level = (abcd[0] & 0xE0) >> 5;  // A[7:5]
-      int ways        = (abcd[1] & 0xFFC00000) >> 22; // B[31:22]
-      int partitions  = (abcd[1] & 0x003FF000) >> 12; // B[21:12]
-      int line_size   = (abcd[1] & 0x00000FFF) >>  0; // B[11:0]
-      int sets        = (abcd[2]);                    // C[31:0]
-
-      int cache_size = (ways+1) * (partitions+1) * (line_size+1) * (sets+1);
-
-      switch(cache_level)
-      {
-        case 1: l1 = cache_size; break;
-        case 2: l2 = cache_size; break;
-        case 3: l3 = cache_size; break;
-        default: break;
-      }
-    }
-    cache_id++;
-  } while(cache_type>0 && cache_id<16);
-}
-
-inline void queryCacheSizes_intel_codes(int& l1, int& l2, int& l3)
-{
-  int abcd[4];
-  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-  l1 = l2 = l3 = 0;
-  EIGEN_CPUID(abcd,0x00000002,0);
-  unsigned char * bytes = reinterpret_cast<unsigned char *>(abcd)+2;
-  bool check_for_p2_core2 = false;
-  for(int i=0; i<14; ++i)
-  {
-    switch(bytes[i])
-    {
-      case 0x0A: l1 = 8; break;   // 0Ah   data L1 cache, 8 KB, 2 ways, 32 byte lines
-      case 0x0C: l1 = 16; break;  // 0Ch   data L1 cache, 16 KB, 4 ways, 32 byte lines
-      case 0x0E: l1 = 24; break;  // 0Eh   data L1 cache, 24 KB, 6 ways, 64 byte lines
-      case 0x10: l1 = 16; break;  // 10h   data L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
-      case 0x15: l1 = 16; break;  // 15h   code L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
-      case 0x2C: l1 = 32; break;  // 2Ch   data L1 cache, 32 KB, 8 ways, 64 byte lines
-      case 0x30: l1 = 32; break;  // 30h   code L1 cache, 32 KB, 8 ways, 64 byte lines
-      case 0x60: l1 = 16; break;  // 60h   data L1 cache, 16 KB, 8 ways, 64 byte lines, sectored
-      case 0x66: l1 = 8; break;   // 66h   data L1 cache, 8 KB, 4 ways, 64 byte lines, sectored
-      case 0x67: l1 = 16; break;  // 67h   data L1 cache, 16 KB, 4 ways, 64 byte lines, sectored
-      case 0x68: l1 = 32; break;  // 68h   data L1 cache, 32 KB, 4 ways, 64 byte lines, sectored
-      case 0x1A: l2 = 96; break;   // code and data L2 cache, 96 KB, 6 ways, 64 byte lines (IA-64)
-      case 0x22: l3 = 512; break;   // code and data L3 cache, 512 KB, 4 ways (!), 64 byte lines, dual-sectored
-      case 0x23: l3 = 1024; break;   // code and data L3 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x25: l3 = 2048; break;   // code and data L3 cache, 2048 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x29: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x39: l2 = 128; break;   // code and data L2 cache, 128 KB, 4 ways, 64 byte lines, sectored
-      case 0x3A: l2 = 192; break;   // code and data L2 cache, 192 KB, 6 ways, 64 byte lines, sectored
-      case 0x3B: l2 = 128; break;   // code and data L2 cache, 128 KB, 2 ways, 64 byte lines, sectored
-      case 0x3C: l2 = 256; break;   // code and data L2 cache, 256 KB, 4 ways, 64 byte lines, sectored
-      case 0x3D: l2 = 384; break;   // code and data L2 cache, 384 KB, 6 ways, 64 byte lines, sectored
-      case 0x3E: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 64 byte lines, sectored
-      case 0x40: l2 = 0; break;   // no integrated L2 cache (P6 core) or L3 cache (P4 core)
-      case 0x41: l2 = 128; break;   // code and data L2 cache, 128 KB, 4 ways, 32 byte lines
-      case 0x42: l2 = 256; break;   // code and data L2 cache, 256 KB, 4 ways, 32 byte lines
-      case 0x43: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 32 byte lines
-      case 0x44: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 4 ways, 32 byte lines
-      case 0x45: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 4 ways, 32 byte lines
-      case 0x46: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines
-      case 0x47: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 8 ways, 64 byte lines
-      case 0x48: l2 = 3072; break;   // code and data L2 cache, 3072 KB, 12 ways, 64 byte lines
-      case 0x49: if(l2!=0) l3 = 4096; else {check_for_p2_core2=true; l3 = l2 = 4096;} break;// code and data L3 cache, 4096 KB, 16 ways, 64 byte lines (P4) or L2 for core2
-      case 0x4A: l3 = 6144; break;   // code and data L3 cache, 6144 KB, 12 ways, 64 byte lines
-      case 0x4B: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 16 ways, 64 byte lines
-      case 0x4C: l3 = 12288; break;   // code and data L3 cache, 12288 KB, 12 ways, 64 byte lines
-      case 0x4D: l3 = 16384; break;   // code and data L3 cache, 16384 KB, 16 ways, 64 byte lines
-      case 0x4E: l2 = 6144; break;   // code and data L2 cache, 6144 KB, 24 ways, 64 byte lines
-      case 0x78: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 4 ways, 64 byte lines
-      case 0x79: l2 = 128; break;   // code and data L2 cache, 128 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7A: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7B: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7C: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7D: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 8 ways, 64 byte lines
-      case 0x7E: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 128 byte lines, sect. (IA-64)
-      case 0x7F: l2 = 512; break;   // code and data L2 cache, 512 KB, 2 ways, 64 byte lines
-      case 0x80: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 64 byte lines
-      case 0x81: l2 = 128; break;   // code and data L2 cache, 128 KB, 8 ways, 32 byte lines
-      case 0x82: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 32 byte lines
-      case 0x83: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 32 byte lines
-      case 0x84: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 32 byte lines
-      case 0x85: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 8 ways, 32 byte lines
-      case 0x86: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 64 byte lines
-      case 0x87: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines
-      case 0x88: l3 = 2048; break;   // code and data L3 cache, 2048 KB, 4 ways, 64 byte lines (IA-64)
-      case 0x89: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines (IA-64)
-      case 0x8A: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 4 ways, 64 byte lines (IA-64)
-      case 0x8D: l3 = 3072; break;   // code and data L3 cache, 3072 KB, 12 ways, 128 byte lines (IA-64)
-
-      default: break;
-    }
-  }
-  if(check_for_p2_core2 && l2 == l3)
-    l3 = 0;
-  l1 *= 1024;
-  l2 *= 1024;
-  l3 *= 1024;
-}
-
-inline void queryCacheSizes_intel(int& l1, int& l2, int& l3, int max_std_funcs)
-{
-  if(max_std_funcs>=4)
-    queryCacheSizes_intel_direct(l1,l2,l3);
-  else if(max_std_funcs>=2)
-    queryCacheSizes_intel_codes(l1,l2,l3);
-  else
-    l1 = l2 = l3 = 0;
-}
-
-inline void queryCacheSizes_amd(int& l1, int& l2, int& l3)
-{
-  int abcd[4];
-  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-  
-  // First query the max supported function.
-  EIGEN_CPUID(abcd,0x80000000,0);
-  if(static_cast<numext::uint32_t>(abcd[0]) >= static_cast<numext::uint32_t>(0x80000006))
-  {
-    EIGEN_CPUID(abcd,0x80000005,0);
-    l1 = (abcd[2] >> 24) * 1024; // C[31:24] = L1 size in KB
-    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-    EIGEN_CPUID(abcd,0x80000006,0);
-    l2 = (abcd[2] >> 16) * 1024; // C[31;16] = l2 cache size in KB
-    l3 = ((abcd[3] & 0xFFFC000) >> 18) * 512 * 1024; // D[31;18] = l3 cache size in 512KB
-  }
-  else
-  {
-    l1 = l2 = l3 = 0;
-  }
-}
-#endif
-
-/** \internal
- * Queries and returns the cache sizes in Bytes of the L1, L2, and L3 data caches respectively */
-inline void queryCacheSizes(int& l1, int& l2, int& l3)
-{
-  #ifdef EIGEN_CPUID
-  int abcd[4];
-  const int GenuineIntel[] = {0x756e6547, 0x49656e69, 0x6c65746e};
-  const int AuthenticAMD[] = {0x68747541, 0x69746e65, 0x444d4163};
-  const int AMDisbetter_[] = {0x69444d41, 0x74656273, 0x21726574}; // "AMDisbetter!"
-
-  // identify the CPU vendor
-  EIGEN_CPUID(abcd,0x0,0);
-  int max_std_funcs = abcd[0];
-  if(cpuid_is_vendor(abcd,GenuineIntel))
-    queryCacheSizes_intel(l1,l2,l3,max_std_funcs);
-  else if(cpuid_is_vendor(abcd,AuthenticAMD) || cpuid_is_vendor(abcd,AMDisbetter_))
-    queryCacheSizes_amd(l1,l2,l3);
-  else
-    // by default let's use Intel's API
-    queryCacheSizes_intel(l1,l2,l3,max_std_funcs);
-
-  // here is the list of other vendors:
-//   ||cpuid_is_vendor(abcd,"VIA VIA VIA ")
-//   ||cpuid_is_vendor(abcd,"CyrixInstead")
-//   ||cpuid_is_vendor(abcd,"CentaurHauls")
-//   ||cpuid_is_vendor(abcd,"GenuineTMx86")
-//   ||cpuid_is_vendor(abcd,"TransmetaCPU")
-//   ||cpuid_is_vendor(abcd,"RiseRiseRise")
-//   ||cpuid_is_vendor(abcd,"Geode by NSC")
-//   ||cpuid_is_vendor(abcd,"SiS SiS SiS ")
-//   ||cpuid_is_vendor(abcd,"UMC UMC UMC ")
-//   ||cpuid_is_vendor(abcd,"NexGenDriven")
-  #else
-  l1 = l2 = l3 = -1;
-  #endif
-}
-
-/** \internal
- * \returns the size in Bytes of the L1 data cache */
-inline int queryL1CacheSize()
-{
-  int l1(-1), l2, l3;
-  queryCacheSizes(l1,l2,l3);
-  return l1;
-}
-
-/** \internal
- * \returns the size in Bytes of the L2 or L3 cache if this later is present */
-inline int queryTopLevelCacheSize()
-{
-  int l1, l2(-1), l3(-1);
-  queryCacheSizes(l1,l2,l3);
-  return (std::max)(l2,l3);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MEMORY_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Meta.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Meta.h
deleted file mode 100644
index 81ae2a3..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/Meta.h
+++ /dev/null
@@ -1,812 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_META_H
-#define EIGEN_META_H
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-
- #include <cfloat>
-
- #if defined(EIGEN_CUDA_ARCH)
-  #include <math_constants.h>
- #endif
-
- #if defined(EIGEN_HIP_DEVICE_COMPILE)
-  #include "Eigen/src/Core/arch/HIP/hcc/math_constants.h"
-  #endif
-
-#endif
-
-// Recent versions of ICC require <cstdint> for pointer types below.
-#define EIGEN_ICC_NEEDS_CSTDINT (EIGEN_COMP_ICC>=1600 && EIGEN_COMP_CXXVER >= 11)
-
-// Define portable (u)int{32,64} types
-#if EIGEN_HAS_CXX11 || EIGEN_ICC_NEEDS_CSTDINT
-#include <cstdint>
-namespace Eigen {
-namespace numext {
-typedef std::uint8_t  uint8_t;
-typedef std::int8_t   int8_t;
-typedef std::uint16_t uint16_t;
-typedef std::int16_t  int16_t;
-typedef std::uint32_t uint32_t;
-typedef std::int32_t  int32_t;
-typedef std::uint64_t uint64_t;
-typedef std::int64_t  int64_t;
-}
-}
-#else
-// Without c++11, all compilers able to compile Eigen also
-// provide the C99 stdint.h header file.
-#include <stdint.h>
-namespace Eigen {
-namespace numext {
-typedef ::uint8_t  uint8_t;
-typedef ::int8_t   int8_t;
-typedef ::uint16_t uint16_t;
-typedef ::int16_t  int16_t;
-typedef ::uint32_t uint32_t;
-typedef ::int32_t  int32_t;
-typedef ::uint64_t uint64_t;
-typedef ::int64_t  int64_t;
-}
-}
-#endif
-
-namespace Eigen {
-
-typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex;
-
-/**
- * \brief The Index type as used for the API.
- * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
- * \sa \blank \ref TopicPreprocessorDirectives, StorageIndex.
- */
-
-typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Index;
-
-namespace internal {
-
-/** \internal
-  * \file Meta.h
-  * This file contains generic metaprogramming classes which are not specifically related to Eigen.
-  * \note In case you wonder, yes we're aware that Boost already provides all these features,
-  * we however don't want to add a dependency to Boost.
-  */
-
-// Only recent versions of ICC complain about using ptrdiff_t to hold pointers,
-// and older versions do not provide *intptr_t types.
-#if EIGEN_ICC_NEEDS_CSTDINT
-typedef std::intptr_t  IntPtr;
-typedef std::uintptr_t UIntPtr;
-#else
-typedef std::ptrdiff_t IntPtr;
-typedef std::size_t UIntPtr;
-#endif
-#undef EIGEN_ICC_NEEDS_CSTDINT
-
-struct true_type {  enum { value = 1 }; };
-struct false_type { enum { value = 0 }; };
-
-template<bool Condition>
-struct bool_constant;
-
-template<>
-struct bool_constant<true> : true_type {};
-
-template<>
-struct bool_constant<false> : false_type {};
-
-template<bool Condition, typename Then, typename Else>
-struct conditional { typedef Then type; };
-
-template<typename Then, typename Else>
-struct conditional <false, Then, Else> { typedef Else type; };
-
-template<typename T> struct remove_reference { typedef T type; };
-template<typename T> struct remove_reference<T&> { typedef T type; };
-
-template<typename T> struct remove_pointer { typedef T type; };
-template<typename T> struct remove_pointer<T*> { typedef T type; };
-template<typename T> struct remove_pointer<T*const> { typedef T type; };
-
-template <class T> struct remove_const { typedef T type; };
-template <class T> struct remove_const<const T> { typedef T type; };
-template <class T> struct remove_const<const T[]> { typedef T type[]; };
-template <class T, unsigned int Size> struct remove_const<const T[Size]> { typedef T type[Size]; };
-
-template<typename T> struct remove_all { typedef T type; };
-template<typename T> struct remove_all<const T>   { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T const&>  { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T&>        { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T const*>  { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T*>        { typedef typename remove_all<T>::type type; };
-
-template<typename T> struct is_arithmetic      { enum { value = false }; };
-template<> struct is_arithmetic<float>         { enum { value = true }; };
-template<> struct is_arithmetic<double>        { enum { value = true }; };
-template<> struct is_arithmetic<long double>   { enum { value = true }; };
-template<> struct is_arithmetic<bool>          { enum { value = true }; };
-template<> struct is_arithmetic<char>          { enum { value = true }; };
-template<> struct is_arithmetic<signed char>   { enum { value = true }; };
-template<> struct is_arithmetic<unsigned char> { enum { value = true }; };
-template<> struct is_arithmetic<signed short>  { enum { value = true }; };
-template<> struct is_arithmetic<unsigned short>{ enum { value = true }; };
-template<> struct is_arithmetic<signed int>    { enum { value = true }; };
-template<> struct is_arithmetic<unsigned int>  { enum { value = true }; };
-template<> struct is_arithmetic<signed long>   { enum { value = true }; };
-template<> struct is_arithmetic<unsigned long> { enum { value = true }; };
-
-template<typename T, typename U> struct is_same { enum { value = 0 }; };
-template<typename T> struct is_same<T,T> { enum { value = 1 }; };
-
-template< class T >
-struct is_void : is_same<void, typename remove_const<T>::type> {};
-
-#if EIGEN_HAS_CXX11
-template<> struct is_arithmetic<signed long long>   { enum { value = true }; };
-template<> struct is_arithmetic<unsigned long long> { enum { value = true }; };
-using std::is_integral;
-#else
-template<typename T> struct is_integral               { enum { value = false }; };
-template<> struct is_integral<bool>                   { enum { value = true }; };
-template<> struct is_integral<char>                   { enum { value = true }; };
-template<> struct is_integral<signed char>            { enum { value = true }; };
-template<> struct is_integral<unsigned char>          { enum { value = true }; };
-template<> struct is_integral<signed short>           { enum { value = true }; };
-template<> struct is_integral<unsigned short>         { enum { value = true }; };
-template<> struct is_integral<signed int>             { enum { value = true }; };
-template<> struct is_integral<unsigned int>           { enum { value = true }; };
-template<> struct is_integral<signed long>            { enum { value = true }; };
-template<> struct is_integral<unsigned long>          { enum { value = true }; };
-#if EIGEN_COMP_MSVC
-template<> struct is_integral<signed __int64>         { enum { value = true }; };
-template<> struct is_integral<unsigned __int64>       { enum { value = true }; };
-#endif
-#endif
-
-#if EIGEN_HAS_CXX11
-using std::make_unsigned;
-#else
-// TODO: Possibly improve this implementation of make_unsigned.
-// It is currently used only by
-// template<typename Scalar> struct random_default_impl<Scalar, false, true>.
-template<typename> struct make_unsigned;
-template<> struct make_unsigned<char>             { typedef unsigned char type; };
-template<> struct make_unsigned<signed char>      { typedef unsigned char type; };
-template<> struct make_unsigned<unsigned char>    { typedef unsigned char type; };
-template<> struct make_unsigned<signed short>     { typedef unsigned short type; };
-template<> struct make_unsigned<unsigned short>   { typedef unsigned short type; };
-template<> struct make_unsigned<signed int>       { typedef unsigned int type; };
-template<> struct make_unsigned<unsigned int>     { typedef unsigned int type; };
-template<> struct make_unsigned<signed long>      { typedef unsigned long type; };
-template<> struct make_unsigned<unsigned long>    { typedef unsigned long type; };
-#if EIGEN_COMP_MSVC
-template<> struct make_unsigned<signed __int64>   { typedef unsigned __int64 type; };
-template<> struct make_unsigned<unsigned __int64> { typedef unsigned __int64 type; };
-#endif
-
-// Some platforms define int64_t as `long long` even for C++03, where
-// `long long` is not guaranteed by the standard. In this case we are missing
-// the definition for make_unsigned. If we just define it, we run into issues
-// where `long long` doesn't exist in some compilers for C++03. We therefore add
-// the specialization for these platforms only.
-#if EIGEN_OS_MAC || EIGEN_COMP_MINGW
-template<> struct make_unsigned<unsigned long long> { typedef unsigned long long type; };
-template<> struct make_unsigned<long long>          { typedef unsigned long long type; };
-#endif
-#endif
-
-template <typename T> struct add_const { typedef const T type; };
-template <typename T> struct add_const<T&> { typedef T& type; };
-
-template <typename T> struct is_const { enum { value = 0 }; };
-template <typename T> struct is_const<T const> { enum { value = 1 }; };
-
-template<typename T> struct add_const_on_value_type            { typedef const T type;  };
-template<typename T> struct add_const_on_value_type<T&>        { typedef T const& type; };
-template<typename T> struct add_const_on_value_type<T*>        { typedef T const* type; };
-template<typename T> struct add_const_on_value_type<T* const>  { typedef T const* const type; };
-template<typename T> struct add_const_on_value_type<T const* const>  { typedef T const* const type; };
-
-#if EIGEN_HAS_CXX11
-
-using std::is_convertible;
-
-#else
-
-template<typename From, typename To>
-struct is_convertible_impl
-{
-private:
-  struct any_conversion
-  {
-    template <typename T> any_conversion(const volatile T&);
-    template <typename T> any_conversion(T&);
-  };
-  struct yes {int a[1];};
-  struct no  {int a[2];};
-
-  template<typename T>
-  static yes test(T, int);
-
-  template<typename T>
-  static no  test(any_conversion, ...);
-
-public:
-  static typename internal::remove_reference<From>::type* ms_from;
-#ifdef __INTEL_COMPILER
-  #pragma warning push
-  #pragma warning ( disable : 2259 )
-#endif
-  enum { value = sizeof(test<To>(*ms_from, 0))==sizeof(yes) };
-#ifdef __INTEL_COMPILER
-  #pragma warning pop
-#endif
-};
-
-template<typename From, typename To>
-struct is_convertible
-{
-  enum { value = is_convertible_impl<From,To>::value };
-};
-
-template<typename T>
-struct is_convertible<T,T&> { enum { value = false }; };
-
-template<typename T>
-struct is_convertible<const T,const T&> { enum { value = true }; };
-
-#endif
-
-/** \internal Allows to enable/disable an overload
-  * according to a compile time condition.
-  */
-template<bool Condition, typename T=void> struct enable_if;
-
-template<typename T> struct enable_if<true,T>
-{ typedef T type; };
-
-#if defined(EIGEN_GPU_COMPILE_PHASE) && !EIGEN_HAS_CXX11
-#if !defined(__FLT_EPSILON__)
-#define __FLT_EPSILON__ FLT_EPSILON
-#define __DBL_EPSILON__ DBL_EPSILON
-#endif
-
-namespace device {
-
-template<typename T> struct numeric_limits
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_CONSTEXPR T epsilon() { return 0; }
-  static T (max)() { assert(false && "Highest not supported for this type"); }
-  static T (min)() { assert(false && "Lowest not supported for this type"); }
-  static T infinity() { assert(false && "Infinity not supported for this type"); }
-  static T quiet_NaN() { assert(false && "quiet_NaN not supported for this type"); }
-};
-template<> struct numeric_limits<float>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static float epsilon() { return __FLT_EPSILON__; }
-  EIGEN_DEVICE_FUNC
-  static float (max)() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_MAX_NORMAL_F;
-  #else
-    return HIPRT_MAX_NORMAL_F;
-  #endif
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static float (min)() { return FLT_MIN; }
-  EIGEN_DEVICE_FUNC
-  static float infinity() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_INF_F;
-  #else
-    return HIPRT_INF_F;
-  #endif
-  }
-  EIGEN_DEVICE_FUNC
-  static float quiet_NaN() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_NAN_F;
-  #else
-    return HIPRT_NAN_F;
-  #endif
-  }
-};
-template<> struct numeric_limits<double>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static double epsilon() { return __DBL_EPSILON__; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static double (max)() { return DBL_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static double (min)() { return DBL_MIN; }
-  EIGEN_DEVICE_FUNC
-  static double infinity() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_INF;
-  #else
-    return HIPRT_INF;
-  #endif
-  }
-  EIGEN_DEVICE_FUNC
-  static double quiet_NaN() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_NAN;
-  #else
-    return HIPRT_NAN;
-  #endif
-  }
-};
-template<> struct numeric_limits<int>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int (max)() { return INT_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int (min)() { return INT_MIN; }
-};
-template<> struct numeric_limits<unsigned int>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned int epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned int (max)() { return UINT_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned int (min)() { return 0; }
-};
-template<> struct numeric_limits<long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long (max)() { return LONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long (min)() { return LONG_MIN; }
-};
-template<> struct numeric_limits<unsigned long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long (max)() { return ULONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long (min)() { return 0; }
-};
-template<> struct numeric_limits<long long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long long (max)() { return LLONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long long (min)() { return LLONG_MIN; }
-};
-template<> struct numeric_limits<unsigned long long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long long (max)() { return ULLONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long long (min)() { return 0; }
-};
-template<> struct numeric_limits<bool>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static bool epsilon() { return false; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static bool (max)() { return true; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR 
-  static bool (min)() { return false; }
-};
-
-}
-
-#endif // defined(EIGEN_GPU_COMPILE_PHASE) && !EIGEN_HAS_CXX11
-
-/** \internal
-  * A base class do disable default copy ctor and copy assignment operator.
-  */
-class noncopyable
-{
-  EIGEN_DEVICE_FUNC noncopyable(const noncopyable&);
-  EIGEN_DEVICE_FUNC const noncopyable& operator=(const noncopyable&);
-protected:
-  EIGEN_DEVICE_FUNC noncopyable() {}
-  EIGEN_DEVICE_FUNC ~noncopyable() {}
-};
-
-/** \internal
-  * Provides access to the number of elements in the object of as a compile-time constant expression.
-  * It "returns" Eigen::Dynamic if the size cannot be resolved at compile-time (default).
-  *
-  * Similar to std::tuple_size, but more general.
-  *
-  * It currently supports:
-  *  - any types T defining T::SizeAtCompileTime
-  *  - plain C arrays as T[N]
-  *  - std::array (c++11)
-  *  - some internal types such as SingleRange and AllRange
-  *
-  * The second template parameter eases SFINAE-based specializations.
-  */
-template<typename T, typename EnableIf = void> struct array_size {
-  enum { value = Dynamic };
-};
-
-template<typename T> struct array_size<T,typename internal::enable_if<((T::SizeAtCompileTime&0)==0)>::type> {
-  enum { value = T::SizeAtCompileTime };
-};
-
-template<typename T, int N> struct array_size<const T (&)[N]> {
-  enum { value = N };
-};
-template<typename T, int N> struct array_size<T (&)[N]> {
-  enum { value = N };
-};
-
-#if EIGEN_HAS_CXX11
-template<typename T, std::size_t N> struct array_size<const std::array<T,N> > {
-  enum { value = N };
-};
-template<typename T, std::size_t N> struct array_size<std::array<T,N> > {
-  enum { value = N };
-};
-#endif
-
-/** \internal
-  * Analogue of the std::size free function.
-  * It returns the size of the container or view \a x of type \c T
-  *
-  * It currently supports:
-  *  - any types T defining a member T::size() const
-  *  - plain C arrays as T[N]
-  *
-  */
-template<typename T>
-EIGEN_CONSTEXPR Index size(const T& x) { return x.size(); }
-
-template<typename T,std::size_t N>
-EIGEN_CONSTEXPR Index size(const T (&) [N]) { return N; }
-
-/** \internal
-  * Convenient struct to get the result type of a nullary, unary, binary, or
-  * ternary functor.
-  * 
-  * Pre C++11:
-  * Supports both a Func::result_type member and templated
-  * Func::result<Func(ArgTypes...)>::type member.
-  * 
-  * If none of these members is provided, then the type of the first
-  * argument is returned.
-  * 
-  * Post C++11:
-  * This uses std::result_of. However, note the `type` member removes
-  * const and converts references/pointers to their corresponding value type.
-  */
-#if EIGEN_HAS_STD_INVOKE_RESULT
-template<typename T> struct result_of;
-
-template<typename F, typename... ArgTypes>
-struct result_of<F(ArgTypes...)> {
-  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#elif EIGEN_HAS_STD_RESULT_OF
-template<typename T> struct result_of {
-  typedef typename std::result_of<T>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#else
-template<typename T> struct result_of { };
-
-struct has_none {int a[1];};
-struct has_std_result_type {int a[2];};
-struct has_tr1_result {int a[3];};
-
-template<typename Func, int SizeOf>
-struct nullary_result_of_select {};
-
-template<typename Func>
-struct nullary_result_of_select<Func, sizeof(has_std_result_type)> {typedef typename Func::result_type type;};
-
-template<typename Func>
-struct nullary_result_of_select<Func, sizeof(has_tr1_result)> {typedef typename Func::template result<Func()>::type type;};
-
-template<typename Func>
-struct result_of<Func()> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T()>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename nullary_result_of_select<Func, FunctorType>::type type;
-};
-
-template<typename Func, typename ArgType, int SizeOf=sizeof(has_none)>
-struct unary_result_of_select {typedef typename internal::remove_all<ArgType>::type type;};
-
-template<typename Func, typename ArgType>
-struct unary_result_of_select<Func, ArgType, sizeof(has_std_result_type)> {typedef typename Func::result_type type;};
-
-template<typename Func, typename ArgType>
-struct unary_result_of_select<Func, ArgType, sizeof(has_tr1_result)> {typedef typename Func::template result<Func(ArgType)>::type type;};
-
-template<typename Func, typename ArgType>
-struct result_of<Func(ArgType)> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T(ArgType)>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename unary_result_of_select<Func, ArgType, FunctorType>::type type;
-};
-
-template<typename Func, typename ArgType0, typename ArgType1, int SizeOf=sizeof(has_none)>
-struct binary_result_of_select {typedef typename internal::remove_all<ArgType0>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1>
-struct binary_result_of_select<Func, ArgType0, ArgType1, sizeof(has_std_result_type)>
-{typedef typename Func::result_type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1>
-struct binary_result_of_select<Func, ArgType0, ArgType1, sizeof(has_tr1_result)>
-{typedef typename Func::template result<Func(ArgType0,ArgType1)>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1>
-struct result_of<Func(ArgType0,ArgType1)> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T(ArgType0,ArgType1)>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename binary_result_of_select<Func, ArgType0, ArgType1, FunctorType>::type type;
-};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2, int SizeOf=sizeof(has_none)>
-struct ternary_result_of_select {typedef typename internal::remove_all<ArgType0>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
-struct ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, sizeof(has_std_result_type)>
-{typedef typename Func::result_type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
-struct ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, sizeof(has_tr1_result)>
-{typedef typename Func::template result<Func(ArgType0,ArgType1,ArgType2)>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
-struct result_of<Func(ArgType0,ArgType1,ArgType2)> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T(ArgType0,ArgType1,ArgType2)>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, FunctorType>::type type;
-};
-
-#endif
-
-#if EIGEN_HAS_STD_INVOKE_RESULT
-template<typename F, typename... ArgTypes>
-struct invoke_result {
-  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#elif EIGEN_HAS_CXX11
-template<typename F, typename... ArgTypes>
-struct invoke_result {
-  typedef typename result_of<F(ArgTypes...)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#else
-template<typename F, typename ArgType0 = void, typename ArgType1 = void, typename ArgType2 = void>
-struct invoke_result {
-  typedef typename result_of<F(ArgType0, ArgType1, ArgType2)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-
-template<typename F>
-struct invoke_result<F, void, void, void> {
-  typedef typename result_of<F()>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-
-template<typename F, typename ArgType0>
-struct invoke_result<F, ArgType0, void, void> {
-  typedef typename result_of<F(ArgType0)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-
-template<typename F, typename ArgType0, typename ArgType1>
-struct invoke_result<F, ArgType0, ArgType1, void> {
-  typedef typename result_of<F(ArgType0, ArgType1)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#endif
-
-struct meta_yes { char a[1]; };
-struct meta_no  { char a[2]; };
-
-// Check whether T::ReturnType does exist
-template <typename T>
-struct has_ReturnType
-{
-  template <typename C> static meta_yes testFunctor(C const *, typename C::ReturnType const * = 0);
-  template <typename C> static meta_no  testFunctor(...);
-
-  enum { value = sizeof(testFunctor<T>(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-template<typename T> const T* return_ptr();
-
-template <typename T, typename IndexType=Index>
-struct has_nullary_operator
-{
-  template <typename C> static meta_yes testFunctor(C const *,typename enable_if<(sizeof(return_ptr<C>()->operator()())>0)>::type * = 0);
-  static meta_no testFunctor(...);
-
-  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-template <typename T, typename IndexType=Index>
-struct has_unary_operator
-{
-  template <typename C> static meta_yes testFunctor(C const *,typename enable_if<(sizeof(return_ptr<C>()->operator()(IndexType(0)))>0)>::type * = 0);
-  static meta_no testFunctor(...);
-
-  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-template <typename T, typename IndexType=Index>
-struct has_binary_operator
-{
-  template <typename C> static meta_yes testFunctor(C const *,typename enable_if<(sizeof(return_ptr<C>()->operator()(IndexType(0),IndexType(0)))>0)>::type * = 0);
-  static meta_no testFunctor(...);
-
-  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer.
-  * Usage example: \code meta_sqrt<1023>::ret \endcode
-  */
-template<int Y,
-         int InfX = 0,
-         int SupX = ((Y==1) ? 1 : Y/2),
-         bool Done = ((SupX-InfX)<=1 ? true : ((SupX*SupX <= Y) && ((SupX+1)*(SupX+1) > Y))) >
-                                // use ?: instead of || just to shut up a stupid gcc 4.3 warning
-class meta_sqrt
-{
-    enum {
-      MidX = (InfX+SupX)/2,
-      TakeInf = MidX*MidX > Y ? 1 : 0,
-      NewInf = int(TakeInf) ? InfX : int(MidX),
-      NewSup = int(TakeInf) ? int(MidX) : SupX
-    };
-  public:
-    enum { ret = meta_sqrt<Y,NewInf,NewSup>::ret };
-};
-
-template<int Y, int InfX, int SupX>
-class meta_sqrt<Y, InfX, SupX, true> { public:  enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; };
-
-
-/** \internal Computes the least common multiple of two positive integer A and B
-  * at compile-time. 
-  */
-template<int A, int B, int K=1, bool Done = ((A*K)%B)==0, bool Big=(A>=B)>
-struct meta_least_common_multiple
-{
-  enum { ret = meta_least_common_multiple<A,B,K+1>::ret };
-};
-template<int A, int B, int K, bool Done>
-struct meta_least_common_multiple<A,B,K,Done,false>
-{
-  enum { ret = meta_least_common_multiple<B,A,K>::ret };
-};
-template<int A, int B, int K>
-struct meta_least_common_multiple<A,B,K,true,true>
-{
-  enum { ret = A*K };
-};
-
-
-/** \internal determines whether the product of two numeric types is allowed and what the return type is */
-template<typename T, typename U> struct scalar_product_traits
-{
-  enum { Defined = 0 };
-};
-
-// FIXME quick workaround around current limitation of result_of
-// template<typename Scalar, typename ArgType0, typename ArgType1>
-// struct result_of<scalar_product_op<Scalar>(ArgType0,ArgType1)> {
-// typedef typename scalar_product_traits<typename remove_all<ArgType0>::type, typename remove_all<ArgType1>::type>::ReturnType type;
-// };
-
-/** \internal Obtains a POD type suitable to use as storage for an object of a size
-  * of at most Len bytes, aligned as specified by \c Align.
-  */
-template<unsigned Len, unsigned Align>
-struct aligned_storage {
-  struct type {
-    EIGEN_ALIGN_TO_BOUNDARY(Align) unsigned char data[Len];
-  };
-};
-
-} // end namespace internal
-
-namespace numext {
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T> EIGEN_DEVICE_FUNC   void swap(T &a, T &b) { T tmp = b; b = a; a = tmp; }
-#else
-template<typename T> EIGEN_STRONG_INLINE void swap(T &a, T &b) { std::swap(a,b); }
-#endif
-
-#if defined(EIGEN_GPU_COMPILE_PHASE) && !EIGEN_HAS_CXX11
-using internal::device::numeric_limits;
-#else
-using std::numeric_limits;
-#endif
-
-// Integer division with rounding up.
-// T is assumed to be an integer type with a>=0, and b>0
-template<typename T>
-EIGEN_DEVICE_FUNC
-T div_ceil(const T &a, const T &b)
-{
-  return (a+b-1) / b;
-}
-
-// The aim of the following functions is to bypass -Wfloat-equal warnings
-// when we really want a strict equality comparison on floating points.
-template<typename X, typename Y> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool equal_strict(const X& x,const Y& y) { return x == y; }
-
-#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool equal_strict(const float& x,const float& y) { return std::equal_to<float>()(x,y); }
-
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool equal_strict(const double& x,const double& y) { return std::equal_to<double>()(x,y); }
-#endif
-
-template<typename X, typename Y> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool not_equal_strict(const X& x,const Y& y) { return x != y; }
-
-#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool not_equal_strict(const float& x,const float& y) { return std::not_equal_to<float>()(x,y); }
-
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool not_equal_strict(const double& x,const double& y) { return std::not_equal_to<double>()(x,y); }
-#endif
-
-} // end namespace numext
-
-} // end namespace Eigen
-
-#endif // EIGEN_META_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/NonMPL2.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/NonMPL2.h
deleted file mode 100644
index 1af67cf..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/NonMPL2.h
+++ /dev/null
@@ -1,3 +0,0 @@
-#ifdef EIGEN_MPL2_ONLY
-#error Including non-MPL2 code in EIGEN_MPL2_ONLY mode
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ReenableStupidWarnings.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ReenableStupidWarnings.h
deleted file mode 100644
index 1ce6fd1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ReenableStupidWarnings.h
+++ /dev/null
@@ -1,31 +0,0 @@
-#ifdef EIGEN_WARNINGS_DISABLED_2
-// "DisableStupidWarnings.h" was included twice recursively: Do not reenable warnings yet!
-#  undef EIGEN_WARNINGS_DISABLED_2
-
-#elif defined(EIGEN_WARNINGS_DISABLED)
-#undef EIGEN_WARNINGS_DISABLED
-
-#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-  #ifdef _MSC_VER
-    #pragma warning( pop )
-  #elif defined __INTEL_COMPILER
-    #pragma warning pop
-  #elif defined __clang__
-    #pragma clang diagnostic pop
-  #elif defined __GNUC__  &&  (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-    #pragma GCC diagnostic pop
-  #endif
-
-  #if defined __NVCC__
-//    Don't reenable the diagnostic messages, as it turns out these messages need
-//    to be disabled at the point of the template instantiation (i.e the user code)
-//    otherwise they'll be triggered by nvcc.
-//    #pragma diag_default code_is_unreachable
-//    #pragma diag_default initialization_not_reachable
-//    #pragma diag_default 2651
-//    #pragma diag_default 2653
-  #endif
-
-#endif
-
-#endif // EIGEN_WARNINGS_DISABLED
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ReshapedHelper.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ReshapedHelper.h
deleted file mode 100644
index 4124321..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/ReshapedHelper.h
+++ /dev/null
@@ -1,51 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_RESHAPED_HELPER_H
-#define EIGEN_RESHAPED_HELPER_H
-
-namespace Eigen {
-
-enum AutoSize_t   { AutoSize };
-const int AutoOrder = 2;
-
-namespace internal {
-
-template<typename SizeType,typename OtherSize, int TotalSize>
-struct get_compiletime_reshape_size {
-  enum { value = get_fixed_value<SizeType>::value };
-};
-
-template<typename SizeType>
-Index get_runtime_reshape_size(SizeType size, Index /*other*/, Index /*total*/) {
-  return internal::get_runtime_value(size);
-}
-
-template<typename OtherSize, int TotalSize>
-struct get_compiletime_reshape_size<AutoSize_t,OtherSize,TotalSize> {
-  enum {
-    other_size = get_fixed_value<OtherSize>::value,
-    value = (TotalSize==Dynamic || other_size==Dynamic) ? Dynamic : TotalSize / other_size };
-};
-
-inline Index get_runtime_reshape_size(AutoSize_t /*size*/, Index other, Index total) {
-  return total/other;
-}
-
-template<int Flags, int Order>
-struct get_compiletime_reshape_order {
-  enum { value = Order == AutoOrder ? Flags & RowMajorBit : Order };
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_RESHAPED_HELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/StaticAssert.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/StaticAssert.h
deleted file mode 100644
index c45de59..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/StaticAssert.h
+++ /dev/null
@@ -1,221 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STATIC_ASSERT_H
-#define EIGEN_STATIC_ASSERT_H
-
-/* Some notes on Eigen's static assertion mechanism:
- *
- *  - in EIGEN_STATIC_ASSERT(CONDITION,MSG) the parameter CONDITION must be a compile time boolean
- *    expression, and MSG an enum listed in struct internal::static_assertion<true>
- *
- *  - define EIGEN_NO_STATIC_ASSERT to disable them (and save compilation time)
- *    in that case, the static assertion is converted to the following runtime assert:
- *      eigen_assert(CONDITION && "MSG")
- *
- *  - currently EIGEN_STATIC_ASSERT can only be used in function scope
- *
- */
-
-#ifndef EIGEN_STATIC_ASSERT
-#ifndef EIGEN_NO_STATIC_ASSERT
-
-  #if EIGEN_MAX_CPP_VER>=11 && (__has_feature(cxx_static_assert) || (EIGEN_COMP_CXXVER >= 11) || (EIGEN_COMP_MSVC >= 1600))
-
-    // if native static_assert is enabled, let's use it
-    #define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG);
-
-  #else // not CXX0X
-
-    namespace Eigen {
-
-    namespace internal {
-
-    template<bool condition>
-    struct static_assertion {};
-
-    template<>
-    struct static_assertion<true>
-    {
-      enum {
-        YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX=1,
-        YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES=1,
-        YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES=1,
-        THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE=1,
-        THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE=1,
-        THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE=1,
-        OUT_OF_RANGE_ACCESS=1,
-        YOU_MADE_A_PROGRAMMING_MISTAKE=1,
-        EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT=1,
-        EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE=1,
-        YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR=1,
-        YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR=1,
-        UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC=1,
-        THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES=1,
-        FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED=1,
-        NUMERIC_TYPE_MUST_BE_REAL=1,
-        COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED=1,
-        WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED=1,
-        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE=1,
-        INVALID_MATRIX_PRODUCT=1,
-        INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS=1,
-        INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION=1,
-        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY=1,
-        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES=1,
-        THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES=1,
-        INVALID_MATRIX_TEMPLATE_PARAMETERS=1,
-        INVALID_MATRIXBASE_TEMPLATE_PARAMETERS=1,
-        BOTH_MATRICES_MUST_HAVE_THE_SAME_STORAGE_ORDER=1,
-        THIS_METHOD_IS_ONLY_FOR_DIAGONAL_MATRIX=1,
-        THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE=1,
-        THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES=1,
-        YOU_ALREADY_SPECIFIED_THIS_STRIDE=1,
-        INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION=1,
-        THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD=1,
-        PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1=1,
-        THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS=1,
-        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES=1,
-        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION=1,
-        THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY=1,
-        YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT=1,
-        THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS=1,
-        THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS=1,
-        THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL=1,
-        THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES=1,
-        YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED=1,
-        YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED=1,
-        THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE=1,
-        THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH=1,
-        OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG=1,
-        IMPLICIT_CONVERSION_TO_SCALAR_IS_FOR_INNER_PRODUCT_ONLY=1,
-        STORAGE_LAYOUT_DOES_NOT_MATCH=1,
-        EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE=1,
-        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS=1,
-        MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY=1,
-        THIS_TYPE_IS_NOT_SUPPORTED=1,
-        STORAGE_KIND_MUST_MATCH=1,
-        STORAGE_INDEX_MUST_MATCH=1,
-        CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY=1,
-        SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY=1,
-        INVALID_TEMPLATE_PARAMETER=1,
-        GPU_TENSOR_CONTRACTION_DOES_NOT_SUPPORT_OUTPUT_KERNELS=1,
-        THE_ARRAY_SIZE_SHOULD_EQUAL_WITH_PACKET_SIZE=1
-      };
-    };
-
-    } // end namespace internal
-
-    } // end namespace Eigen
-
-    // Specialized implementation for MSVC to avoid "conditional
-    // expression is constant" warnings.  This implementation doesn't
-    // appear to work under GCC, hence the multiple implementations.
-    #if EIGEN_COMP_MSVC
-
-      #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \
-        {Eigen::internal::static_assertion<bool(CONDITION)>::MSG;}
-
-    #else
-      // In some cases clang interprets bool(CONDITION) as function declaration
-      #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \
-        if (Eigen::internal::static_assertion<static_cast<bool>(CONDITION)>::MSG) {}
-
-    #endif
-
-  #endif // not CXX0X
-
-#else // EIGEN_NO_STATIC_ASSERT
-
-  #define EIGEN_STATIC_ASSERT(CONDITION,MSG) eigen_assert((CONDITION) && #MSG);
-
-#endif // EIGEN_NO_STATIC_ASSERT
-#endif // EIGEN_STATIC_ASSERT
-
-// static assertion failing if the type \a TYPE is not a vector type
-#define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \
-  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime, \
-                      YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX)
-
-// static assertion failing if the type \a TYPE is not fixed-size
-#define EIGEN_STATIC_ASSERT_FIXED_SIZE(TYPE) \
-  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime!=Eigen::Dynamic, \
-                      YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR)
-
-// static assertion failing if the type \a TYPE is not dynamic-size
-#define EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(TYPE) \
-  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime==Eigen::Dynamic, \
-                      YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR)
-
-// static assertion failing if the type \a TYPE is not a vector type of the given size
-#define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE) \
-  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime && TYPE::SizeAtCompileTime==SIZE, \
-                      THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE)
-
-// static assertion failing if the type \a TYPE is not a vector type of the given size
-#define EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(TYPE, ROWS, COLS) \
-  EIGEN_STATIC_ASSERT(TYPE::RowsAtCompileTime==ROWS && TYPE::ColsAtCompileTime==COLS, \
-                      THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE)
-
-// static assertion failing if the two vector expression types are not compatible (same fixed-size or dynamic size)
-#define EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(TYPE0,TYPE1) \
-  EIGEN_STATIC_ASSERT( \
-      (int(TYPE0::SizeAtCompileTime)==Eigen::Dynamic \
-    || int(TYPE1::SizeAtCompileTime)==Eigen::Dynamic \
-    || int(TYPE0::SizeAtCompileTime)==int(TYPE1::SizeAtCompileTime)),\
-    YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES)
-
-#define EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
-     ( \
-        (int(Eigen::internal::size_of_xpr_at_compile_time<TYPE0>::ret)==0 && int(Eigen::internal::size_of_xpr_at_compile_time<TYPE1>::ret)==0) \
-    || (\
-          (int(TYPE0::RowsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE1::RowsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE0::RowsAtCompileTime)==int(TYPE1::RowsAtCompileTime)) \
-      &&  (int(TYPE0::ColsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE1::ColsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE0::ColsAtCompileTime)==int(TYPE1::ColsAtCompileTime))\
-       ) \
-     )
-
-#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
-    EIGEN_STATIC_ASSERT(!Eigen::NumTraits<TYPE>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
-
-
-// static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes
-#define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
-  EIGEN_STATIC_ASSERT( \
-     EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1),\
-    YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES)
-
-#define EIGEN_STATIC_ASSERT_SIZE_1x1(TYPE) \
-      EIGEN_STATIC_ASSERT((TYPE::RowsAtCompileTime == 1 || TYPE::RowsAtCompileTime == Eigen::Dynamic) && \
-                          (TYPE::ColsAtCompileTime == 1 || TYPE::ColsAtCompileTime == Eigen::Dynamic), \
-                          THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS)
-
-#define EIGEN_STATIC_ASSERT_LVALUE(Derived) \
-      EIGEN_STATIC_ASSERT(Eigen::internal::is_lvalue<Derived>::value, \
-                          THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY)
-
-#define EIGEN_STATIC_ASSERT_ARRAYXPR(Derived) \
-      EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived>::XprKind, ArrayXpr>::value), \
-                          THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES)
-
-#define EIGEN_STATIC_ASSERT_SAME_XPR_KIND(Derived1, Derived2) \
-      EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived1>::XprKind, \
-                                             typename Eigen::internal::traits<Derived2>::XprKind \
-                                            >::value), \
-                          YOU_CANNOT_MIX_ARRAYS_AND_MATRICES)
-
-// Check that a cost value is positive, and that is stay within a reasonable range
-// TODO this check could be enabled for internal debugging only
-#define EIGEN_INTERNAL_CHECK_COST_VALUE(C) \
-      EIGEN_STATIC_ASSERT((C)>=0 && (C)<=HugeCost*HugeCost, EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE);
-
-#endif // EIGEN_STATIC_ASSERT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/SymbolicIndex.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/SymbolicIndex.h
deleted file mode 100644
index 354dd9a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/SymbolicIndex.h
+++ /dev/null
@@ -1,293 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SYMBOLIC_INDEX_H
-#define EIGEN_SYMBOLIC_INDEX_H
-
-namespace Eigen {
-
-/** \namespace Eigen::symbolic
-  * \ingroup Core_Module
-  *
-  * This namespace defines a set of classes and functions to build and evaluate symbolic expressions of scalar type Index.
-  * Here is a simple example:
-  *
-  * \code
-  * // First step, defines symbols:
-  * struct x_tag {};  static const symbolic::SymbolExpr<x_tag> x;
-  * struct y_tag {};  static const symbolic::SymbolExpr<y_tag> y;
-  * struct z_tag {};  static const symbolic::SymbolExpr<z_tag> z;
-  *
-  * // Defines an expression:
-  * auto expr = (x+3)/y+z;
-  *
-  * // And evaluate it: (c++14)
-  * std::cout << expr.eval(x=6,y=3,z=-13) << "\n";
-  *
-  * // In c++98/11, only one symbol per expression is supported for now:
-  * auto expr98 = (3-x)/2;
-  * std::cout << expr98.eval(x=6) << "\n";
-  * \endcode
-  *
-  * It is currently only used internally to define and manipulate the Eigen::last and Eigen::lastp1 symbols in Eigen::seq and Eigen::seqN.
-  *
-  */
-namespace symbolic {
-
-template<typename Tag> class Symbol;
-template<typename Arg0> class NegateExpr;
-template<typename Arg1,typename Arg2> class AddExpr;
-template<typename Arg1,typename Arg2> class ProductExpr;
-template<typename Arg1,typename Arg2> class QuotientExpr;
-
-// A simple wrapper around an integral value to provide the eval method.
-// We could also use a free-function symbolic_eval...
-template<typename IndexType=Index>
-class ValueExpr {
-public:
-  ValueExpr(IndexType val) : m_value(val) {}
-  template<typename T>
-  IndexType eval_impl(const T&) const { return m_value; }
-protected:
-  IndexType m_value;
-};
-
-// Specialization for compile-time value,
-// It is similar to ValueExpr(N) but this version helps the compiler to generate better code.
-template<int N>
-class ValueExpr<internal::FixedInt<N> > {
-public:
-  ValueExpr() {}
-  template<typename T>
-  EIGEN_CONSTEXPR Index eval_impl(const T&) const { return N; }
-};
-
-
-/** \class BaseExpr
-  * \ingroup Core_Module
-  * Common base class of any symbolic expressions
-  */
-template<typename Derived>
-class BaseExpr
-{
-public:
-  const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-  /** Evaluate the expression given the \a values of the symbols.
-    *
-    * \param values defines the values of the symbols, it can either be a SymbolValue or a std::tuple of SymbolValue
-    *               as constructed by SymbolExpr::operator= operator.
-    *
-    */
-  template<typename T>
-  Index eval(const T& values) const { return derived().eval_impl(values); }
-
-#if EIGEN_HAS_CXX14
-  template<typename... Types>
-  Index eval(Types&&... values) const { return derived().eval_impl(std::make_tuple(values...)); }
-#endif
-
-  NegateExpr<Derived> operator-() const { return NegateExpr<Derived>(derived()); }
-
-  AddExpr<Derived,ValueExpr<> > operator+(Index b) const
-  { return AddExpr<Derived,ValueExpr<> >(derived(),  b); }
-  AddExpr<Derived,ValueExpr<> > operator-(Index a) const
-  { return AddExpr<Derived,ValueExpr<> >(derived(), -a); }
-  ProductExpr<Derived,ValueExpr<> > operator*(Index a) const
-  { return ProductExpr<Derived,ValueExpr<> >(derived(),a); }
-  QuotientExpr<Derived,ValueExpr<> > operator/(Index a) const
-  { return QuotientExpr<Derived,ValueExpr<> >(derived(),a); }
-
-  friend AddExpr<Derived,ValueExpr<> > operator+(Index a, const BaseExpr& b)
-  { return AddExpr<Derived,ValueExpr<> >(b.derived(), a); }
-  friend AddExpr<NegateExpr<Derived>,ValueExpr<> > operator-(Index a, const BaseExpr& b)
-  { return AddExpr<NegateExpr<Derived>,ValueExpr<> >(-b.derived(), a); }
-  friend ProductExpr<ValueExpr<>,Derived> operator*(Index a, const BaseExpr& b)
-  { return ProductExpr<ValueExpr<>,Derived>(a,b.derived()); }
-  friend QuotientExpr<ValueExpr<>,Derived> operator/(Index a, const BaseExpr& b)
-  { return QuotientExpr<ValueExpr<>,Derived>(a,b.derived()); }
-
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N>) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > > operator-(internal::FixedInt<N>) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > >(derived(), ValueExpr<internal::FixedInt<-N> >()); }
-  template<int N>
-  ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator*(internal::FixedInt<N>) const
-  { return ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator/(internal::FixedInt<N>) const
-  { return QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-
-  template<int N>
-  friend AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N>, const BaseExpr& b)
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > > operator-(internal::FixedInt<N>, const BaseExpr& b)
-  { return AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > >(-b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator*(internal::FixedInt<N>, const BaseExpr& b)
-  { return ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-  template<int N>
-  friend QuotientExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator/(internal::FixedInt<N>, const BaseExpr& b)
-  { return QuotientExpr<ValueExpr<internal::FixedInt<N> > ,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-
-#if (!EIGEN_HAS_CXX14)
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N> (*)()) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > > operator-(internal::FixedInt<N> (*)()) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > >(derived(), ValueExpr<internal::FixedInt<-N> >()); }
-  template<int N>
-  ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator*(internal::FixedInt<N> (*)()) const
-  { return ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator/(internal::FixedInt<N> (*)()) const
-  { return QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-
-  template<int N>
-  friend AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > > operator-(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > >(-b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator*(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-  template<int N>
-  friend QuotientExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator/(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return QuotientExpr<ValueExpr<internal::FixedInt<N> > ,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-#endif
-
-
-  template<typename OtherDerived>
-  AddExpr<Derived,OtherDerived> operator+(const BaseExpr<OtherDerived> &b) const
-  { return AddExpr<Derived,OtherDerived>(derived(),  b.derived()); }
-
-  template<typename OtherDerived>
-  AddExpr<Derived,NegateExpr<OtherDerived> > operator-(const BaseExpr<OtherDerived> &b) const
-  { return AddExpr<Derived,NegateExpr<OtherDerived> >(derived(), -b.derived()); }
-
-  template<typename OtherDerived>
-  ProductExpr<Derived,OtherDerived> operator*(const BaseExpr<OtherDerived> &b) const
-  { return ProductExpr<Derived,OtherDerived>(derived(), b.derived()); }
-
-  template<typename OtherDerived>
-  QuotientExpr<Derived,OtherDerived> operator/(const BaseExpr<OtherDerived> &b) const
-  { return QuotientExpr<Derived,OtherDerived>(derived(), b.derived()); }
-};
-
-template<typename T>
-struct is_symbolic {
-  // BaseExpr has no conversion ctor, so we only have to check whether T can be statically cast to its base class BaseExpr<T>.
-  enum { value = internal::is_convertible<T,BaseExpr<T> >::value };
-};
-
-/** Represents the actual value of a symbol identified by its tag
-  *
-  * It is the return type of SymbolValue::operator=, and most of the time this is only way it is used.
-  */
-template<typename Tag>
-class SymbolValue
-{
-public:
-  /** Default constructor from the value \a val */
-  SymbolValue(Index val) : m_value(val) {}
-
-  /** \returns the stored value of the symbol */
-  Index value() const { return m_value; }
-protected:
-  Index m_value;
-};
-
-/** Expression of a symbol uniquely identified by the template parameter type \c tag */
-template<typename tag>
-class SymbolExpr : public BaseExpr<SymbolExpr<tag> >
-{
-public:
-  /** Alias to the template parameter \c tag */
-  typedef tag Tag;
-
-  SymbolExpr() {}
-
-  /** Associate the value \a val to the given symbol \c *this, uniquely identified by its \c Tag.
-    *
-    * The returned object should be passed to ExprBase::eval() to evaluate a given expression with this specified runtime-time value.
-    */
-  SymbolValue<Tag> operator=(Index val) const {
-    return SymbolValue<Tag>(val);
-  }
-
-  Index eval_impl(const SymbolValue<Tag> &values) const { return values.value(); }
-
-#if EIGEN_HAS_CXX14
-  // C++14 versions suitable for multiple symbols
-  template<typename... Types>
-  Index eval_impl(const std::tuple<Types...>& values) const { return std::get<SymbolValue<Tag> >(values).value(); }
-#endif
-};
-
-template<typename Arg0>
-class NegateExpr : public BaseExpr<NegateExpr<Arg0> >
-{
-public:
-  NegateExpr(const Arg0& arg0) : m_arg0(arg0) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return -m_arg0.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-};
-
-template<typename Arg0, typename Arg1>
-class AddExpr : public BaseExpr<AddExpr<Arg0,Arg1> >
-{
-public:
-  AddExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) + m_arg1.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-  Arg1 m_arg1;
-};
-
-template<typename Arg0, typename Arg1>
-class ProductExpr : public BaseExpr<ProductExpr<Arg0,Arg1> >
-{
-public:
-  ProductExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) * m_arg1.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-  Arg1 m_arg1;
-};
-
-template<typename Arg0, typename Arg1>
-class QuotientExpr : public BaseExpr<QuotientExpr<Arg0,Arg1> >
-{
-public:
-  QuotientExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) / m_arg1.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-  Arg1 m_arg1;
-};
-
-} // end namespace symbolic
-
-} // end namespace Eigen
-
-#endif // EIGEN_SYMBOLIC_INDEX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/XprHelper.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/XprHelper.h
deleted file mode 100644
index 71c32b8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Core/util/XprHelper.h
+++ /dev/null
@@ -1,856 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_XPRHELPER_H
-#define EIGEN_XPRHELPER_H
-
-// just a workaround because GCC seems to not really like empty structs
-// FIXME: gcc 4.3 generates bad code when strict-aliasing is enabled
-// so currently we simply disable this optimization for gcc 4.3
-#if EIGEN_COMP_GNUC && !EIGEN_GNUC_AT(4,3)
-  #define EIGEN_EMPTY_STRUCT_CTOR(X) \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X() {} \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X(const X& ) {}
-#else
-  #define EIGEN_EMPTY_STRUCT_CTOR(X)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename IndexDest, typename IndexSrc>
-EIGEN_DEVICE_FUNC
-inline IndexDest convert_index(const IndexSrc& idx) {
-  // for sizeof(IndexDest)>=sizeof(IndexSrc) compilers should be able to optimize this away:
-  eigen_internal_assert(idx <= NumTraits<IndexDest>::highest() && "Index value to big for target type");
-  return IndexDest(idx);
-}
-
-// true if T can be considered as an integral index (i.e., and integral type or enum)
-template<typename T> struct is_valid_index_type
-{
-  enum { value =
-#if EIGEN_HAS_TYPE_TRAITS
-    internal::is_integral<T>::value || std::is_enum<T>::value
-#elif EIGEN_COMP_MSVC
-    internal::is_integral<T>::value || __is_enum(T)
-#else
-    // without C++11, we use is_convertible to Index instead of is_integral in order to treat enums as Index.
-    internal::is_convertible<T,Index>::value && !internal::is_same<T,float>::value && !is_same<T,double>::value
-#endif
-  };
-};
-
-// true if both types are not valid index types
-template<typename RowIndices, typename ColIndices>
-struct valid_indexed_view_overload {
-  enum { value = !(internal::is_valid_index_type<RowIndices>::value && internal::is_valid_index_type<ColIndices>::value) };
-};
-
-// promote_scalar_arg is an helper used in operation between an expression and a scalar, like:
-//    expression * scalar
-// Its role is to determine how the type T of the scalar operand should be promoted given the scalar type ExprScalar of the given expression.
-// The IsSupported template parameter must be provided by the caller as: internal::has_ReturnType<ScalarBinaryOpTraits<ExprScalar,T,op> >::value using the proper order for ExprScalar and T.
-// Then the logic is as follows:
-//  - if the operation is natively supported as defined by IsSupported, then the scalar type is not promoted, and T is returned.
-//  - otherwise, NumTraits<ExprScalar>::Literal is returned if T is implicitly convertible to NumTraits<ExprScalar>::Literal AND that this does not imply a float to integer conversion.
-//  - otherwise, ExprScalar is returned if T is implicitly convertible to ExprScalar AND that this does not imply a float to integer conversion.
-//  - In all other cases, the promoted type is not defined, and the respective operation is thus invalid and not available (SFINAE).
-template<typename ExprScalar,typename T, bool IsSupported>
-struct promote_scalar_arg;
-
-template<typename S,typename T>
-struct promote_scalar_arg<S,T,true>
-{
-  typedef T type;
-};
-
-// Recursively check safe conversion to PromotedType, and then ExprScalar if they are different.
-template<typename ExprScalar,typename T,typename PromotedType,
-  bool ConvertibleToLiteral = internal::is_convertible<T,PromotedType>::value,
-  bool IsSafe = NumTraits<T>::IsInteger || !NumTraits<PromotedType>::IsInteger>
-struct promote_scalar_arg_unsupported;
-
-// Start recursion with NumTraits<ExprScalar>::Literal
-template<typename S,typename T>
-struct promote_scalar_arg<S,T,false> : promote_scalar_arg_unsupported<S,T,typename NumTraits<S>::Literal> {};
-
-// We found a match!
-template<typename S,typename T, typename PromotedType>
-struct promote_scalar_arg_unsupported<S,T,PromotedType,true,true>
-{
-  typedef PromotedType type;
-};
-
-// No match, but no real-to-integer issues, and ExprScalar and current PromotedType are different,
-// so let's try to promote to ExprScalar
-template<typename ExprScalar,typename T, typename PromotedType>
-struct promote_scalar_arg_unsupported<ExprScalar,T,PromotedType,false,true>
-   : promote_scalar_arg_unsupported<ExprScalar,T,ExprScalar>
-{};
-
-// Unsafe real-to-integer, let's stop.
-template<typename S,typename T, typename PromotedType, bool ConvertibleToLiteral>
-struct promote_scalar_arg_unsupported<S,T,PromotedType,ConvertibleToLiteral,false> {};
-
-// T is not even convertible to ExprScalar, let's stop.
-template<typename S,typename T>
-struct promote_scalar_arg_unsupported<S,T,S,false,true> {};
-
-//classes inheriting no_assignment_operator don't generate a default operator=.
-class no_assignment_operator
-{
-  private:
-    no_assignment_operator& operator=(const no_assignment_operator&);
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(no_assignment_operator)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(no_assignment_operator)
-};
-
-/** \internal return the index type with the largest number of bits */
-template<typename I1, typename I2>
-struct promote_index_type
-{
-  typedef typename conditional<(sizeof(I1)<sizeof(I2)), I2, I1>::type type;
-};
-
-/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that
-  * can be accessed using value() and setValue().
-  * Otherwise, this class is an empty structure and value() just returns the template parameter Value.
-  */
-template<typename T, int Value> class variable_if_dynamic
-{
-  public:
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(variable_if_dynamic)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
-    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    T value() { return T(Value); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    operator T() const { return T(Value); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void setValue(T v) const { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
-};
-
-template<typename T> class variable_if_dynamic<T, Dynamic>
-{
-    T m_value;
-  public:
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T value = 0) EIGEN_NO_THROW : m_value(value) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T value() const { return m_value; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator T() const { return m_value; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
-};
-
-/** \internal like variable_if_dynamic but for DynamicIndex
-  */
-template<typename T, int Value> class variable_if_dynamicindex
-{
-  public:
-    EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamicindex)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
-    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    T value() { return T(Value); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void setValue(T) {}
-};
-
-template<typename T> class variable_if_dynamicindex<T, DynamicIndex>
-{
-    T m_value;
-    EIGEN_DEVICE_FUNC variable_if_dynamicindex() { eigen_assert(false); }
-  public:
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T value) : m_value(value) {}
-    EIGEN_DEVICE_FUNC T EIGEN_STRONG_INLINE value() const { return m_value; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
-};
-
-template<typename T> struct functor_traits
-{
-  enum
-  {
-    Cost = 10,
-    PacketAccess = false,
-    IsRepeatable = false
-  };
-};
-
-template<typename T> struct packet_traits;
-
-template<typename T> struct unpacket_traits;
-
-template<int Size, typename PacketType,
-         bool Stop = Size==Dynamic || (Size%unpacket_traits<PacketType>::size)==0 || is_same<PacketType,typename unpacket_traits<PacketType>::half>::value>
-struct find_best_packet_helper;
-
-template< int Size, typename PacketType>
-struct find_best_packet_helper<Size,PacketType,true>
-{
-  typedef PacketType type;
-};
-
-template<int Size, typename PacketType>
-struct find_best_packet_helper<Size,PacketType,false>
-{
-  typedef typename find_best_packet_helper<Size,typename unpacket_traits<PacketType>::half>::type type;
-};
-
-template<typename T, int Size>
-struct find_best_packet
-{
-  typedef typename find_best_packet_helper<Size,typename packet_traits<T>::type>::type type;
-};
-
-#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
-template<int ArrayBytes, int AlignmentBytes,
-         bool Match     =  bool((ArrayBytes%AlignmentBytes)==0),
-         bool TryHalf   =  bool(EIGEN_MIN_ALIGN_BYTES<AlignmentBytes) >
-struct compute_default_alignment_helper
-{
-  enum { value = 0 };
-};
-
-template<int ArrayBytes, int AlignmentBytes, bool TryHalf>
-struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, true, TryHalf> // Match
-{
-  enum { value = AlignmentBytes };
-};
-
-template<int ArrayBytes, int AlignmentBytes>
-struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, false, true> // Try-half
-{
-  // current packet too large, try with an half-packet
-  enum { value = compute_default_alignment_helper<ArrayBytes, AlignmentBytes/2>::value };
-};
-#else
-// If static alignment is disabled, no need to bother.
-// This also avoids a division by zero in "bool Match =  bool((ArrayBytes%AlignmentBytes)==0)"
-template<int ArrayBytes, int AlignmentBytes>
-struct compute_default_alignment_helper
-{
-  enum { value = 0 };
-};
-#endif
-
-template<typename T, int Size> struct compute_default_alignment {
-  enum { value = compute_default_alignment_helper<Size*sizeof(T),EIGEN_MAX_STATIC_ALIGN_BYTES>::value };
-};
-
-template<typename T> struct compute_default_alignment<T,Dynamic> {
-  enum { value = EIGEN_MAX_ALIGN_BYTES };
-};
-
-template<typename _Scalar, int _Rows, int _Cols,
-         int _Options = AutoAlign |
-                          ( (_Rows==1 && _Cols!=1) ? RowMajor
-                          : (_Cols==1 && _Rows!=1) ? ColMajor
-                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
-         int _MaxRows = _Rows,
-         int _MaxCols = _Cols
-> class make_proper_matrix_type
-{
-    enum {
-      IsColVector = _Cols==1 && _Rows!=1,
-      IsRowVector = _Rows==1 && _Cols!=1,
-      Options = IsColVector ? (_Options | ColMajor) & ~RowMajor
-              : IsRowVector ? (_Options | RowMajor) & ~ColMajor
-              : _Options
-    };
-  public:
-    typedef Matrix<_Scalar, _Rows, _Cols, Options, _MaxRows, _MaxCols> type;
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-class compute_matrix_flags
-{
-    enum { row_major_bit = Options&RowMajor ? RowMajorBit : 0 };
-  public:
-    // FIXME currently we still have to handle DirectAccessBit at the expression level to handle DenseCoeffsBase<>
-    // and then propagate this information to the evaluator's flags.
-    // However, I (Gael) think that DirectAccessBit should only matter at the evaluation stage.
-    enum { ret = DirectAccessBit | LvalueBit | NestByRefBit | row_major_bit };
-};
-
-template<int _Rows, int _Cols> struct size_at_compile_time
-{
-  enum { ret = (_Rows==Dynamic || _Cols==Dynamic) ? Dynamic : _Rows * _Cols };
-};
-
-template<typename XprType> struct size_of_xpr_at_compile_time
-{
-  enum { ret = size_at_compile_time<traits<XprType>::RowsAtCompileTime,traits<XprType>::ColsAtCompileTime>::ret };
-};
-
-/* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type,
- * whereas eval is a const reference in the case of a matrix
- */
-
-template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_matrix_type;
-template<typename T, typename BaseClassType, int Flags> struct plain_matrix_type_dense;
-template<typename T> struct plain_matrix_type<T,Dense>
-{
-  typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, traits<T>::Flags>::type type;
-};
-template<typename T> struct plain_matrix_type<T,DiagonalShape>
-{
-  typedef typename T::PlainObject type;
-};
-
-template<typename T, int Flags> struct plain_matrix_type_dense<T,MatrixXpr,Flags>
-{
-  typedef Matrix<typename traits<T>::Scalar,
-                traits<T>::RowsAtCompileTime,
-                traits<T>::ColsAtCompileTime,
-                AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
-                traits<T>::MaxRowsAtCompileTime,
-                traits<T>::MaxColsAtCompileTime
-          > type;
-};
-
-template<typename T, int Flags> struct plain_matrix_type_dense<T,ArrayXpr,Flags>
-{
-  typedef Array<typename traits<T>::Scalar,
-                traits<T>::RowsAtCompileTime,
-                traits<T>::ColsAtCompileTime,
-                AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
-                traits<T>::MaxRowsAtCompileTime,
-                traits<T>::MaxColsAtCompileTime
-          > type;
-};
-
-/* eval : the return type of eval(). For matrices, this is just a const reference
- * in order to avoid a useless copy
- */
-
-template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct eval;
-
-template<typename T> struct eval<T,Dense>
-{
-  typedef typename plain_matrix_type<T>::type type;
-//   typedef typename T::PlainObject type;
-//   typedef T::Matrix<typename traits<T>::Scalar,
-//                 traits<T>::RowsAtCompileTime,
-//                 traits<T>::ColsAtCompileTime,
-//                 AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
-//                 traits<T>::MaxRowsAtCompileTime,
-//                 traits<T>::MaxColsAtCompileTime
-//           > type;
-};
-
-template<typename T> struct eval<T,DiagonalShape>
-{
-  typedef typename plain_matrix_type<T>::type type;
-};
-
-// for matrices, no need to evaluate, just use a const reference to avoid a useless copy
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct eval<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
-{
-  typedef const Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
-};
-
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct eval<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
-{
-  typedef const Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
-};
-
-
-/* similar to plain_matrix_type, but using the evaluator's Flags */
-template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_object_eval;
-
-template<typename T>
-struct plain_object_eval<T,Dense>
-{
-  typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, evaluator<T>::Flags>::type type;
-};
-
-
-/* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major
- */
-template<typename T> struct plain_matrix_type_column_major
-{
-  enum { Rows = traits<T>::RowsAtCompileTime,
-         Cols = traits<T>::ColsAtCompileTime,
-         MaxRows = traits<T>::MaxRowsAtCompileTime,
-         MaxCols = traits<T>::MaxColsAtCompileTime
-  };
-  typedef Matrix<typename traits<T>::Scalar,
-                Rows,
-                Cols,
-                (MaxRows==1&&MaxCols!=1) ? RowMajor : ColMajor,
-                MaxRows,
-                MaxCols
-          > type;
-};
-
-/* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major
- */
-template<typename T> struct plain_matrix_type_row_major
-{
-  enum { Rows = traits<T>::RowsAtCompileTime,
-         Cols = traits<T>::ColsAtCompileTime,
-         MaxRows = traits<T>::MaxRowsAtCompileTime,
-         MaxCols = traits<T>::MaxColsAtCompileTime
-  };
-  typedef Matrix<typename traits<T>::Scalar,
-                Rows,
-                Cols,
-                (MaxCols==1&&MaxRows!=1) ? ColMajor : RowMajor,
-                MaxRows,
-                MaxCols
-          > type;
-};
-
-/** \internal The reference selector for template expressions. The idea is that we don't
-  * need to use references for expressions since they are light weight proxy
-  * objects which should generate no copying overhead. */
-template <typename T>
-struct ref_selector
-{
-  typedef typename conditional<
-    bool(traits<T>::Flags & NestByRefBit),
-    T const&,
-    const T
-  >::type type;
-
-  typedef typename conditional<
-    bool(traits<T>::Flags & NestByRefBit),
-    T &,
-    T
-  >::type non_const_type;
-};
-
-/** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */
-template<typename T1, typename T2>
-struct transfer_constness
-{
-  typedef typename conditional<
-    bool(internal::is_const<T1>::value),
-    typename internal::add_const_on_value_type<T2>::type,
-    T2
-  >::type type;
-};
-
-
-// However, we still need a mechanism to detect whether an expression which is evaluated multiple time
-// has to be evaluated into a temporary.
-// That's the purpose of this new nested_eval helper:
-/** \internal Determines how a given expression should be nested when evaluated multiple times.
-  * For example, when you do a * (b+c), Eigen will determine how the expression b+c should be
-  * evaluated into the bigger product expression. The choice is between nesting the expression b+c as-is, or
-  * evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is
-  * a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes
-  * many coefficient accesses in the nested expressions -- as is the case with matrix product for example.
-  *
-  * \tparam T the type of the expression being nested.
-  * \tparam n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression.
-  * \tparam PlainObject the type of the temporary if needed.
-  */
-template<typename T, int n, typename PlainObject = typename plain_object_eval<T>::type> struct nested_eval
-{
-  enum {
-    ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost,
-    CoeffReadCost = evaluator<T>::CoeffReadCost,  // NOTE What if an evaluator evaluate itself into a temporary?
-                                                  //      Then CoeffReadCost will be small (e.g., 1) but we still have to evaluate, especially if n>1.
-                                                  //      This situation is already taken care by the EvalBeforeNestingBit flag, which is turned ON
-                                                  //      for all evaluator creating a temporary. This flag is then propagated by the parent evaluators.
-                                                  //      Another solution could be to count the number of temps?
-    NAsInteger = n == Dynamic ? HugeCost : n,
-    CostEval   = (NAsInteger+1) * ScalarReadCost + CoeffReadCost,
-    CostNoEval = NAsInteger * CoeffReadCost,
-    Evaluate = (int(evaluator<T>::Flags) & EvalBeforeNestingBit) || (int(CostEval) < int(CostNoEval))
-  };
-
-  typedef typename conditional<Evaluate, PlainObject, typename ref_selector<T>::type>::type type;
-};
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-inline T* const_cast_ptr(const T* ptr)
-{
-  return const_cast<T*>(ptr);
-}
-
-template<typename Derived, typename XprKind = typename traits<Derived>::XprKind>
-struct dense_xpr_base
-{
-  /* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the ArrayXpr cases */
-};
-
-template<typename Derived>
-struct dense_xpr_base<Derived, MatrixXpr>
-{
-  typedef MatrixBase<Derived> type;
-};
-
-template<typename Derived>
-struct dense_xpr_base<Derived, ArrayXpr>
-{
-  typedef ArrayBase<Derived> type;
-};
-
-template<typename Derived, typename XprKind = typename traits<Derived>::XprKind, typename StorageKind = typename traits<Derived>::StorageKind>
-struct generic_xpr_base;
-
-template<typename Derived, typename XprKind>
-struct generic_xpr_base<Derived, XprKind, Dense>
-{
-  typedef typename dense_xpr_base<Derived,XprKind>::type type;
-};
-
-template<typename XprType, typename CastType> struct cast_return_type
-{
-  typedef typename XprType::Scalar CurrentScalarType;
-  typedef typename remove_all<CastType>::type _CastType;
-  typedef typename _CastType::Scalar NewScalarType;
-  typedef typename conditional<is_same<CurrentScalarType,NewScalarType>::value,
-                              const XprType&,CastType>::type type;
-};
-
-template <typename A, typename B> struct promote_storage_type;
-
-template <typename A> struct promote_storage_type<A,A>
-{
-  typedef A ret;
-};
-template <typename A> struct promote_storage_type<A, const A>
-{
-  typedef A ret;
-};
-template <typename A> struct promote_storage_type<const A, A>
-{
-  typedef A ret;
-};
-
-/** \internal Specify the "storage kind" of applying a coefficient-wise
-  * binary operations between two expressions of kinds A and B respectively.
-  * The template parameter Functor permits to specialize the resulting storage kind wrt to
-  * the functor.
-  * The default rules are as follows:
-  * \code
-  * A      op A      -> A
-  * A      op dense  -> dense
-  * dense  op B      -> dense
-  * sparse op dense  -> sparse
-  * dense  op sparse -> sparse
-  * \endcode
-  */
-template <typename A, typename B, typename Functor> struct cwise_promote_storage_type;
-
-template <typename A, typename Functor>                   struct cwise_promote_storage_type<A,A,Functor>                                      { typedef A      ret; };
-template <typename Functor>                               struct cwise_promote_storage_type<Dense,Dense,Functor>                              { typedef Dense  ret; };
-template <typename A, typename Functor>                   struct cwise_promote_storage_type<A,Dense,Functor>                                  { typedef Dense  ret; };
-template <typename B, typename Functor>                   struct cwise_promote_storage_type<Dense,B,Functor>                                  { typedef Dense  ret; };
-template <typename Functor>                               struct cwise_promote_storage_type<Sparse,Dense,Functor>                             { typedef Sparse ret; };
-template <typename Functor>                               struct cwise_promote_storage_type<Dense,Sparse,Functor>                             { typedef Sparse ret; };
-
-template <typename LhsKind, typename RhsKind, int LhsOrder, int RhsOrder> struct cwise_promote_storage_order {
-  enum { value = LhsOrder };
-};
-
-template <typename LhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<LhsKind,Sparse,LhsOrder,RhsOrder>                { enum { value = RhsOrder }; };
-template <typename RhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<Sparse,RhsKind,LhsOrder,RhsOrder>                { enum { value = LhsOrder }; };
-template <int Order>                                      struct cwise_promote_storage_order<Sparse,Sparse,Order,Order>                       { enum { value = Order }; };
-
-
-/** \internal Specify the "storage kind" of multiplying an expression of kind A with kind B.
-  * The template parameter ProductTag permits to specialize the resulting storage kind wrt to
-  * some compile-time properties of the product: GemmProduct, GemvProduct, OuterProduct, InnerProduct.
-  * The default rules are as follows:
-  * \code
-  *  K * K            -> K
-  *  dense * K        -> dense
-  *  K * dense        -> dense
-  *  diag * K         -> K
-  *  K * diag         -> K
-  *  Perm * K         -> K
-  * K * Perm          -> K
-  * \endcode
-  */
-template <typename A, typename B, int ProductTag> struct product_promote_storage_type;
-
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  A,                  ProductTag> { typedef A     ret;};
-template <int ProductTag>             struct product_promote_storage_type<Dense,              Dense,              ProductTag> { typedef Dense ret;};
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  Dense,              ProductTag> { typedef Dense ret; };
-template <typename B, int ProductTag> struct product_promote_storage_type<Dense,              B,                  ProductTag> { typedef Dense ret; };
-
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  DiagonalShape,      ProductTag> { typedef A ret; };
-template <typename B, int ProductTag> struct product_promote_storage_type<DiagonalShape,      B,                  ProductTag> { typedef B ret; };
-template <int ProductTag>             struct product_promote_storage_type<Dense,              DiagonalShape,      ProductTag> { typedef Dense ret; };
-template <int ProductTag>             struct product_promote_storage_type<DiagonalShape,      Dense,              ProductTag> { typedef Dense ret; };
-
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  PermutationStorage, ProductTag> { typedef A ret; };
-template <typename B, int ProductTag> struct product_promote_storage_type<PermutationStorage, B,                  ProductTag> { typedef B ret; };
-template <int ProductTag>             struct product_promote_storage_type<Dense,              PermutationStorage, ProductTag> { typedef Dense ret; };
-template <int ProductTag>             struct product_promote_storage_type<PermutationStorage, Dense,              ProductTag> { typedef Dense ret; };
-
-/** \internal gives the plain matrix or array type to store a row/column/diagonal of a matrix type.
-  * \tparam Scalar optional parameter allowing to pass a different scalar type than the one of the MatrixType.
-  */
-template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
-struct plain_row_type
-{
-  typedef Matrix<Scalar, 1, ExpressionType::ColsAtCompileTime,
-                 int(ExpressionType::PlainObject::Options) | int(RowMajor), 1, ExpressionType::MaxColsAtCompileTime> MatrixRowType;
-  typedef Array<Scalar, 1, ExpressionType::ColsAtCompileTime,
-                 int(ExpressionType::PlainObject::Options) | int(RowMajor), 1, ExpressionType::MaxColsAtCompileTime> ArrayRowType;
-
-  typedef typename conditional<
-    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
-    MatrixRowType,
-    ArrayRowType
-  >::type type;
-};
-
-template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
-struct plain_col_type
-{
-  typedef Matrix<Scalar, ExpressionType::RowsAtCompileTime, 1,
-                 ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> MatrixColType;
-  typedef Array<Scalar, ExpressionType::RowsAtCompileTime, 1,
-                 ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> ArrayColType;
-
-  typedef typename conditional<
-    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
-    MatrixColType,
-    ArrayColType
-  >::type type;
-};
-
-template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
-struct plain_diag_type
-{
-  enum { diag_size = EIGEN_SIZE_MIN_PREFER_DYNAMIC(ExpressionType::RowsAtCompileTime, ExpressionType::ColsAtCompileTime),
-         max_diag_size = EIGEN_SIZE_MIN_PREFER_FIXED(ExpressionType::MaxRowsAtCompileTime, ExpressionType::MaxColsAtCompileTime)
-  };
-  typedef Matrix<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> MatrixDiagType;
-  typedef Array<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> ArrayDiagType;
-
-  typedef typename conditional<
-    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
-    MatrixDiagType,
-    ArrayDiagType
-  >::type type;
-};
-
-template<typename Expr,typename Scalar = typename Expr::Scalar>
-struct plain_constant_type
-{
-  enum { Options = (traits<Expr>::Flags&RowMajorBit)?RowMajor:0 };
-
-  typedef Array<Scalar,  traits<Expr>::RowsAtCompileTime,   traits<Expr>::ColsAtCompileTime,
-                Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> array_type;
-
-  typedef Matrix<Scalar,  traits<Expr>::RowsAtCompileTime,   traits<Expr>::ColsAtCompileTime,
-                 Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> matrix_type;
-
-  typedef CwiseNullaryOp<scalar_constant_op<Scalar>, const typename conditional<is_same< typename traits<Expr>::XprKind, MatrixXpr >::value, matrix_type, array_type>::type > type;
-};
-
-template<typename ExpressionType>
-struct is_lvalue
-{
-  enum { value = (!bool(is_const<ExpressionType>::value)) &&
-                 bool(traits<ExpressionType>::Flags & LvalueBit) };
-};
-
-template<typename T> struct is_diagonal
-{ enum { ret = false }; };
-
-template<typename T> struct is_diagonal<DiagonalBase<T> >
-{ enum { ret = true }; };
-
-template<typename T> struct is_diagonal<DiagonalWrapper<T> >
-{ enum { ret = true }; };
-
-template<typename T, int S> struct is_diagonal<DiagonalMatrix<T,S> >
-{ enum { ret = true }; };
-
-
-template<typename T> struct is_identity
-{ enum { value = false }; };
-
-template<typename T> struct is_identity<CwiseNullaryOp<internal::scalar_identity_op<typename T::Scalar>, T> >
-{ enum { value = true }; };
-
-
-template<typename S1, typename S2> struct glue_shapes;
-template<> struct glue_shapes<DenseShape,TriangularShape> { typedef TriangularShape type;  };
-
-template<typename T1, typename T2>
-struct possibly_same_dense {
-  enum { value = has_direct_access<T1>::ret && has_direct_access<T2>::ret && is_same<typename T1::Scalar,typename T2::Scalar>::value };
-};
-
-template<typename T1, typename T2>
-EIGEN_DEVICE_FUNC
-bool is_same_dense(const T1 &mat1, const T2 &mat2, typename enable_if<possibly_same_dense<T1,T2>::value>::type * = 0)
-{
-  return (mat1.data()==mat2.data()) && (mat1.innerStride()==mat2.innerStride()) && (mat1.outerStride()==mat2.outerStride());
-}
-
-template<typename T1, typename T2>
-EIGEN_DEVICE_FUNC
-bool is_same_dense(const T1 &, const T2 &, typename enable_if<!possibly_same_dense<T1,T2>::value>::type * = 0)
-{
-  return false;
-}
-
-// Internal helper defining the cost of a scalar division for the type T.
-// The default heuristic can be specialized for each scalar type and architecture.
-template<typename T,bool Vectorized=false,typename EnableIf = void>
-struct scalar_div_cost {
-  enum { value = 8*NumTraits<T>::MulCost };
-};
-
-template<typename T,bool Vectorized>
-struct scalar_div_cost<std::complex<T>, Vectorized> {
-  enum { value = 2*scalar_div_cost<T>::value
-               + 6*NumTraits<T>::MulCost
-               + 3*NumTraits<T>::AddCost
-  };
-};
-
-
-template<bool Vectorized>
-struct scalar_div_cost<signed long,Vectorized,typename conditional<sizeof(long)==8,void,false_type>::type> { enum { value = 24 }; };
-template<bool Vectorized>
-struct scalar_div_cost<unsigned long,Vectorized,typename conditional<sizeof(long)==8,void,false_type>::type> { enum { value = 21 }; };
-
-
-#ifdef EIGEN_DEBUG_ASSIGN
-std::string demangle_traversal(int t)
-{
-  if(t==DefaultTraversal) return "DefaultTraversal";
-  if(t==LinearTraversal) return "LinearTraversal";
-  if(t==InnerVectorizedTraversal) return "InnerVectorizedTraversal";
-  if(t==LinearVectorizedTraversal) return "LinearVectorizedTraversal";
-  if(t==SliceVectorizedTraversal) return "SliceVectorizedTraversal";
-  return "?";
-}
-std::string demangle_unrolling(int t)
-{
-  if(t==NoUnrolling) return "NoUnrolling";
-  if(t==InnerUnrolling) return "InnerUnrolling";
-  if(t==CompleteUnrolling) return "CompleteUnrolling";
-  return "?";
-}
-std::string demangle_flags(int f)
-{
-  std::string res;
-  if(f&RowMajorBit)                 res += " | RowMajor";
-  if(f&PacketAccessBit)             res += " | Packet";
-  if(f&LinearAccessBit)             res += " | Linear";
-  if(f&LvalueBit)                   res += " | Lvalue";
-  if(f&DirectAccessBit)             res += " | Direct";
-  if(f&NestByRefBit)                res += " | NestByRef";
-  if(f&NoPreferredStorageOrderBit)  res += " | NoPreferredStorageOrderBit";
-
-  return res;
-}
-#endif
-
-} // end namespace internal
-
-
-/** \class ScalarBinaryOpTraits
-  * \ingroup Core_Module
-  *
-  * \brief Determines whether the given binary operation of two numeric types is allowed and what the scalar return type is.
-  *
-  * This class permits to control the scalar return type of any binary operation performed on two different scalar types through (partial) template specializations.
-  *
-  * For instance, let \c U1, \c U2 and \c U3 be three user defined scalar types for which most operations between instances of \c U1 and \c U2 returns an \c U3.
-  * You can let %Eigen knows that by defining:
-    \code
-    template<typename BinaryOp>
-    struct ScalarBinaryOpTraits<U1,U2,BinaryOp> { typedef U3 ReturnType;  };
-    template<typename BinaryOp>
-    struct ScalarBinaryOpTraits<U2,U1,BinaryOp> { typedef U3 ReturnType;  };
-    \endcode
-  * You can then explicitly disable some particular operations to get more explicit error messages:
-    \code
-    template<>
-    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_max_op<U1,U2> > {};
-    \endcode
-  * Or customize the return type for individual operation:
-    \code
-    template<>
-    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_sum_op<U1,U2> > { typedef U1 ReturnType; };
-    \endcode
-  *
-  * By default, the following generic combinations are supported:
-  <table class="manual">
-  <tr><th>ScalarA</th><th>ScalarB</th><th>BinaryOp</th><th>ReturnType</th><th>Note</th></tr>
-  <tr            ><td>\c T </td><td>\c T </td><td>\c * </td><td>\c T </td><td></td></tr>
-  <tr class="alt"><td>\c NumTraits<T>::Real </td><td>\c T </td><td>\c * </td><td>\c T </td><td>Only if \c NumTraits<T>::IsComplex </td></tr>
-  <tr            ><td>\c T </td><td>\c NumTraits<T>::Real </td><td>\c * </td><td>\c T </td><td>Only if \c NumTraits<T>::IsComplex </td></tr>
-  </table>
-  *
-  * \sa CwiseBinaryOp
-  */
-template<typename ScalarA, typename ScalarB, typename BinaryOp=internal::scalar_product_op<ScalarA,ScalarB> >
-struct ScalarBinaryOpTraits
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  // for backward compatibility, use the hints given by the (deprecated) internal::scalar_product_traits class.
-  : internal::scalar_product_traits<ScalarA,ScalarB>
-#endif // EIGEN_PARSED_BY_DOXYGEN
-{};
-
-template<typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<T,T,BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-template <typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<T, typename NumTraits<typename internal::enable_if<NumTraits<T>::IsComplex,T>::type>::Real, BinaryOp>
-{
-  typedef T ReturnType;
-};
-template <typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<typename NumTraits<typename internal::enable_if<NumTraits<T>::IsComplex,T>::type>::Real, T, BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-// For Matrix * Permutation
-template<typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<T,void,BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-// For Permutation * Matrix
-template<typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<void,T,BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-// for Permutation*Permutation
-template<typename BinaryOp>
-struct ScalarBinaryOpTraits<void,void,BinaryOp>
-{
-  typedef void ReturnType;
-};
-
-// We require Lhs and Rhs to have "compatible" scalar types.
-// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
-// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
-// add together a float matrix and a double matrix.
-#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
-  EIGEN_STATIC_ASSERT((Eigen::internal::has_ReturnType<ScalarBinaryOpTraits<LHS, RHS,BINOP> >::value), \
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-} // end namespace Eigen
-
-#endif // EIGEN_XPRHELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
deleted file mode 100644
index 081e918..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
+++ /dev/null
@@ -1,346 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Claire Maurice
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_EIGEN_SOLVER_H
-#define EIGEN_COMPLEX_EIGEN_SOLVER_H
-
-#include "./ComplexSchur.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class ComplexEigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of general complex matrices
-  *
-  * \tparam _MatrixType the type of the matrix of which we are
-  * computing the eigendecomposition; this is expected to be an
-  * instantiation of the Matrix class template.
-  *
-  * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
-  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v
-  * \f$.  If \f$ D \f$ is a diagonal matrix with the eigenvalues on
-  * the diagonal, and \f$ V \f$ is a matrix with the eigenvectors as
-  * its columns, then \f$ A V = V D \f$. The matrix \f$ V \f$ is
-  * almost always invertible, in which case we have \f$ A = V D V^{-1}
-  * \f$. This is called the eigendecomposition.
-  *
-  * The main function in this class is compute(), which computes the
-  * eigenvalues and eigenvectors of a given function. The
-  * documentation for that function contains an example showing the
-  * main features of the class.
-  *
-  * \sa class EigenSolver, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class ComplexEigenSolver
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for #MatrixType.
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for vector of eigenvalues as returned by eigenvalues().
-      *
-      * This is a column vector with entries of type #ComplexScalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options&(~RowMajor), MaxColsAtCompileTime, 1> EigenvalueType;
-
-    /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
-      *
-      * This is a square matrix with entries of type #ComplexScalar.
-      * The size is the same as the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorType;
-
-    /** \brief Default constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().
-      */
-    ComplexEigenSolver()
-            : m_eivec(),
-              m_eivalues(),
-              m_schur(),
-              m_isInitialized(false),
-              m_eigenvectorsOk(false),
-              m_matX()
-    {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa ComplexEigenSolver()
-      */
-    explicit ComplexEigenSolver(Index size)
-            : m_eivec(size, size),
-              m_eivalues(size),
-              m_schur(size),
-              m_isInitialized(false),
-              m_eigenvectorsOk(false),
-              m_matX(size, size)
-    {}
-
-    /** \brief Constructor; computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed.
-      *
-      * This constructor calls compute() to compute the eigendecomposition.
-      */
-    template<typename InputType>
-    explicit ComplexEigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
-            : m_eivec(matrix.rows(),matrix.cols()),
-              m_eivalues(matrix.cols()),
-              m_schur(matrix.rows()),
-              m_isInitialized(false),
-              m_eigenvectorsOk(false),
-              m_matX(matrix.rows(),matrix.cols())
-    {
-      compute(matrix.derived(), computeEigenvectors);
-    }
-
-    /** \brief Returns the eigenvectors of given matrix.
-      *
-      * \returns  A const reference to the matrix whose columns are the eigenvectors.
-      *
-      * \pre Either the constructor
-      * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
-      * function compute(const MatrixType& matrix, bool) has been called before
-      * to compute the eigendecomposition of a matrix, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * This function returns a matrix whose columns are the eigenvectors. Column
-      * \f$ k \f$ is an eigenvector corresponding to eigenvalue number \f$ k
-      * \f$ as returned by eigenvalues().  The eigenvectors are normalized to
-      * have (Euclidean) norm equal to one. The matrix returned by this
-      * function is the matrix \f$ V \f$ in the eigendecomposition \f$ A = V D
-      * V^{-1} \f$, if it exists.
-      *
-      * Example: \include ComplexEigenSolver_eigenvectors.cpp
-      * Output: \verbinclude ComplexEigenSolver_eigenvectors.out
-      */
-    const EigenvectorType& eigenvectors() const
-    {
-      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec;
-    }
-
-    /** \brief Returns the eigenvalues of given matrix.
-      *
-      * \returns A const reference to the column vector containing the eigenvalues.
-      *
-      * \pre Either the constructor
-      * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
-      * function compute(const MatrixType& matrix, bool) has been called before
-      * to compute the eigendecomposition of a matrix.
-      *
-      * This function returns a column vector containing the
-      * eigenvalues. Eigenvalues are repeated according to their
-      * algebraic multiplicity, so there are as many eigenvalues as
-      * rows in the matrix. The eigenvalues are not sorted in any particular
-      * order.
-      *
-      * Example: \include ComplexEigenSolver_eigenvalues.cpp
-      * Output: \verbinclude ComplexEigenSolver_eigenvalues.out
-      */
-    const EigenvalueType& eigenvalues() const
-    {
-      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
-      return m_eivalues;
-    }
-
-    /** \brief Computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed.
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of the complex matrix \p matrix.
-      * The eigenvalues() function can be used to retrieve them.  If
-      * \p computeEigenvectors is true, then the eigenvectors are also computed
-      * and can be retrieved by calling eigenvectors().
-      *
-      * The matrix is first reduced to Schur form using the
-      * ComplexSchur class. The Schur decomposition is then used to
-      * compute the eigenvalues and eigenvectors.
-      *
-      * The cost of the computation is dominated by the cost of the
-      * Schur decomposition, which is \f$ O(n^3) \f$ where \f$ n \f$
-      * is the size of the matrix.
-      *
-      * Example: \include ComplexEigenSolver_compute.cpp
-      * Output: \verbinclude ComplexEigenSolver_compute.out
-      */
-    template<typename InputType>
-    ComplexEigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
-      return m_schur.info();
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. */
-    ComplexEigenSolver& setMaxIterations(Index maxIters)
-    {
-      m_schur.setMaxIterations(maxIters);
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_schur.getMaxIterations();
-    }
-
-  protected:
-    
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-    
-    EigenvectorType m_eivec;
-    EigenvalueType m_eivalues;
-    ComplexSchur<MatrixType> m_schur;
-    bool m_isInitialized;
-    bool m_eigenvectorsOk;
-    EigenvectorType m_matX;
-
-  private:
-    void doComputeEigenvectors(RealScalar matrixnorm);
-    void sortEigenvalues(bool computeEigenvectors);
-};
-
-
-template<typename MatrixType>
-template<typename InputType>
-ComplexEigenSolver<MatrixType>& 
-ComplexEigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
-{
-  check_template_parameters();
-  
-  // this code is inspired from Jampack
-  eigen_assert(matrix.cols() == matrix.rows());
-
-  // Do a complex Schur decomposition, A = U T U^*
-  // The eigenvalues are on the diagonal of T.
-  m_schur.compute(matrix.derived(), computeEigenvectors);
-
-  if(m_schur.info() == Success)
-  {
-    m_eivalues = m_schur.matrixT().diagonal();
-    if(computeEigenvectors)
-      doComputeEigenvectors(m_schur.matrixT().norm());
-    sortEigenvalues(computeEigenvectors);
-  }
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-  return *this;
-}
-
-
-template<typename MatrixType>
-void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(RealScalar matrixnorm)
-{
-  const Index n = m_eivalues.size();
-
-  matrixnorm = numext::maxi(matrixnorm,(std::numeric_limits<RealScalar>::min)());
-
-  // Compute X such that T = X D X^(-1), where D is the diagonal of T.
-  // The matrix X is unit triangular.
-  m_matX = EigenvectorType::Zero(n, n);
-  for(Index k=n-1 ; k>=0 ; k--)
-  {
-    m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0);
-    // Compute X(i,k) using the (i,k) entry of the equation X T = D X
-    for(Index i=k-1 ; i>=0 ; i--)
-    {
-      m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
-      if(k-i-1>0)
-        m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.col(k).segment(i+1,k-i-1)).value();
-      ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k);
-      if(z==ComplexScalar(0))
-      {
-        // If the i-th and k-th eigenvalue are equal, then z equals 0.
-        // Use a small value instead, to prevent division by zero.
-        numext::real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
-      }
-      m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
-    }
-  }
-
-  // Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
-  m_eivec.noalias() = m_schur.matrixU() * m_matX;
-  // .. and normalize the eigenvectors
-  for(Index k=0 ; k<n ; k++)
-  {
-    m_eivec.col(k).normalize();
-  }
-}
-
-
-template<typename MatrixType>
-void ComplexEigenSolver<MatrixType>::sortEigenvalues(bool computeEigenvectors)
-{
-  const Index n =  m_eivalues.size();
-  for (Index i=0; i<n; i++)
-  {
-    Index k;
-    m_eivalues.cwiseAbs().tail(n-i).minCoeff(&k);
-    if (k != 0)
-    {
-      k += i;
-      std::swap(m_eivalues[k],m_eivalues[i]);
-      if(computeEigenvectors)
-	m_eivec.col(i).swap(m_eivec.col(k));
-    }
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_EIGEN_SOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexSchur.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexSchur.h
deleted file mode 100644
index fc71468..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexSchur.h
+++ /dev/null
@@ -1,462 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Claire Maurice
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_SCHUR_H
-#define EIGEN_COMPLEX_SCHUR_H
-
-#include "./HessenbergDecomposition.h"
-
-namespace Eigen { 
-
-namespace internal {
-template<typename MatrixType, bool IsComplex> struct complex_schur_reduce_to_hessenberg;
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class ComplexSchur
-  *
-  * \brief Performs a complex Schur decomposition of a real or complex square matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are
-  * computing the Schur decomposition; this is expected to be an
-  * instantiation of the Matrix class template.
-  *
-  * Given a real or complex square matrix A, this class computes the
-  * Schur decomposition: \f$ A = U T U^*\f$ where U is a unitary
-  * complex matrix, and T is a complex upper triangular matrix.  The
-  * diagonal of the matrix T corresponds to the eigenvalues of the
-  * matrix A.
-  *
-  * Call the function compute() to compute the Schur decomposition of
-  * a given matrix. Alternatively, you can use the 
-  * ComplexSchur(const MatrixType&, bool) constructor which computes
-  * the Schur decomposition at construction time. Once the
-  * decomposition is computed, you can use the matrixU() and matrixT()
-  * functions to retrieve the matrices U and V in the decomposition.
-  *
-  * \note This code is inspired from Jampack
-  *
-  * \sa class RealSchur, class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType> class ComplexSchur
-{
-  public:
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type \p _MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for \p _MatrixType. 
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for the matrices in the Schur decomposition.
-      *
-      * This is a square matrix with entries of type #ComplexScalar. 
-      * The size is the same as the size of \p _MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> ComplexMatrixType;
-
-    /** \brief Default constructor.
-      *
-      * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user
-      * intends to perform decompositions via compute().  The \p size
-      * parameter is only used as a hint. It is not an error to give a
-      * wrong \p size, but it may impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit ComplexSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime)
-      : m_matT(size,size),
-        m_matU(size,size),
-        m_hess(size),
-        m_isInitialized(false),
-        m_matUisUptodate(false),
-        m_maxIters(-1)
-    {}
-
-    /** \brief Constructor; computes Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-      *
-      * This constructor calls compute() to compute the Schur decomposition.
-      *
-      * \sa matrixT() and matrixU() for examples.
-      */
-    template<typename InputType>
-    explicit ComplexSchur(const EigenBase<InputType>& matrix, bool computeU = true)
-      : m_matT(matrix.rows(),matrix.cols()),
-        m_matU(matrix.rows(),matrix.cols()),
-        m_hess(matrix.rows()),
-        m_isInitialized(false),
-        m_matUisUptodate(false),
-        m_maxIters(-1)
-    {
-      compute(matrix.derived(), computeU);
-    }
-
-    /** \brief Returns the unitary matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix U.
-      *
-      * It is assumed that either the constructor
-      * ComplexSchur(const MatrixType& matrix, bool computeU) or the
-      * member function compute(const MatrixType& matrix, bool computeU)
-      * has been called before to compute the Schur decomposition of a
-      * matrix, and that \p computeU was set to true (the default
-      * value).
-      *
-      * Example: \include ComplexSchur_matrixU.cpp
-      * Output: \verbinclude ComplexSchur_matrixU.out
-      */
-    const ComplexMatrixType& matrixU() const
-    {
-      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
-      eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the ComplexSchur decomposition.");
-      return m_matU;
-    }
-
-    /** \brief Returns the triangular matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix T.
-      *
-      * It is assumed that either the constructor
-      * ComplexSchur(const MatrixType& matrix, bool computeU) or the
-      * member function compute(const MatrixType& matrix, bool computeU)
-      * has been called before to compute the Schur decomposition of a
-      * matrix.
-      *
-      * Note that this function returns a plain square matrix. If you want to reference
-      * only the upper triangular part, use:
-      * \code schur.matrixT().triangularView<Upper>() \endcode 
-      *
-      * Example: \include ComplexSchur_matrixT.cpp
-      * Output: \verbinclude ComplexSchur_matrixT.out
-      */
-    const ComplexMatrixType& matrixT() const
-    {
-      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
-      return m_matT;
-    }
-
-    /** \brief Computes Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix  Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-
-      * \returns    Reference to \c *this
-      *
-      * The Schur decomposition is computed by first reducing the
-      * matrix to Hessenberg form using the class
-      * HessenbergDecomposition. The Hessenberg matrix is then reduced
-      * to triangular form by performing QR iterations with a single
-      * shift. The cost of computing the Schur decomposition depends
-      * on the number of iterations; as a rough guide, it may be taken
-      * on the number of iterations; as a rough guide, it may be taken
-      * to be \f$25n^3\f$ complex flops, or \f$10n^3\f$ complex flops
-      * if \a computeU is false.
-      *
-      * Example: \include ComplexSchur_compute.cpp
-      * Output: \verbinclude ComplexSchur_compute.out
-      *
-      * \sa compute(const MatrixType&, bool, Index)
-      */
-    template<typename InputType>
-    ComplexSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
-    
-    /** \brief Compute Schur decomposition from a given Hessenberg matrix
-     *  \param[in] matrixH Matrix in Hessenberg form H
-     *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
-     *  \param computeU Computes the matriX U of the Schur vectors
-     * \return Reference to \c *this
-     * 
-     *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
-     *  using either the class HessenbergDecomposition or another mean. 
-     *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
-     *  When computeU is true, this routine computes the matrix U such that 
-     *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
-     * 
-     * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
-     * is not available, the user should give an identity matrix (Q.setIdentity())
-     * 
-     * \sa compute(const MatrixType&, bool)
-     */
-    template<typename HessMatrixType, typename OrthMatrixType>
-    ComplexSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU=true);
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. 
-      *
-      * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
-      * of the matrix.
-      */
-    ComplexSchur& setMaxIterations(Index maxIters)
-    {
-      m_maxIters = maxIters;
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_maxIters;
-    }
-
-    /** \brief Maximum number of iterations per row.
-      *
-      * If not otherwise specified, the maximum number of iterations is this number times the size of the
-      * matrix. It is currently set to 30.
-      */
-    static const int m_maxIterationsPerRow = 30;
-
-  protected:
-    ComplexMatrixType m_matT, m_matU;
-    HessenbergDecomposition<MatrixType> m_hess;
-    ComputationInfo m_info;
-    bool m_isInitialized;
-    bool m_matUisUptodate;
-    Index m_maxIters;
-
-  private:  
-    bool subdiagonalEntryIsNeglegible(Index i);
-    ComplexScalar computeShift(Index iu, Index iter);
-    void reduceToTriangularForm(bool computeU);
-    friend struct internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>;
-};
-
-/** If m_matT(i+1,i) is neglegible in floating point arithmetic
-  * compared to m_matT(i,i) and m_matT(j,j), then set it to zero and
-  * return true, else return false. */
-template<typename MatrixType>
-inline bool ComplexSchur<MatrixType>::subdiagonalEntryIsNeglegible(Index i)
-{
-  RealScalar d = numext::norm1(m_matT.coeff(i,i)) + numext::norm1(m_matT.coeff(i+1,i+1));
-  RealScalar sd = numext::norm1(m_matT.coeff(i+1,i));
-  if (internal::isMuchSmallerThan(sd, d, NumTraits<RealScalar>::epsilon()))
-  {
-    m_matT.coeffRef(i+1,i) = ComplexScalar(0);
-    return true;
-  }
-  return false;
-}
-
-
-/** Compute the shift in the current QR iteration. */
-template<typename MatrixType>
-typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::computeShift(Index iu, Index iter)
-{
-  using std::abs;
-  if (iter == 10 || iter == 20) 
-  {
-    // exceptional shift, taken from http://www.netlib.org/eispack/comqr.f
-    return abs(numext::real(m_matT.coeff(iu,iu-1))) + abs(numext::real(m_matT.coeff(iu-1,iu-2)));
-  }
-
-  // compute the shift as one of the eigenvalues of t, the 2x2
-  // diagonal block on the bottom of the active submatrix
-  Matrix<ComplexScalar,2,2> t = m_matT.template block<2,2>(iu-1,iu-1);
-  RealScalar normt = t.cwiseAbs().sum();
-  t /= normt;     // the normalization by sf is to avoid under/overflow
-
-  ComplexScalar b = t.coeff(0,1) * t.coeff(1,0);
-  ComplexScalar c = t.coeff(0,0) - t.coeff(1,1);
-  ComplexScalar disc = sqrt(c*c + RealScalar(4)*b);
-  ComplexScalar det = t.coeff(0,0) * t.coeff(1,1) - b;
-  ComplexScalar trace = t.coeff(0,0) + t.coeff(1,1);
-  ComplexScalar eival1 = (trace + disc) / RealScalar(2);
-  ComplexScalar eival2 = (trace - disc) / RealScalar(2);
-  RealScalar eival1_norm = numext::norm1(eival1);
-  RealScalar eival2_norm = numext::norm1(eival2);
-  // A division by zero can only occur if eival1==eival2==0.
-  // In this case, det==0, and all we have to do is checking that eival2_norm!=0
-  if(eival1_norm > eival2_norm)
-    eival2 = det / eival1;
-  else if(eival2_norm!=RealScalar(0))
-    eival1 = det / eival2;
-
-  // choose the eigenvalue closest to the bottom entry of the diagonal
-  if(numext::norm1(eival1-t.coeff(1,1)) < numext::norm1(eival2-t.coeff(1,1)))
-    return normt * eival1;
-  else
-    return normt * eival2;
-}
-
-
-template<typename MatrixType>
-template<typename InputType>
-ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
-{
-  m_matUisUptodate = false;
-  eigen_assert(matrix.cols() == matrix.rows());
-
-  if(matrix.cols() == 1)
-  {
-    m_matT = matrix.derived().template cast<ComplexScalar>();
-    if(computeU)  m_matU = ComplexMatrixType::Identity(1,1);
-    m_info = Success;
-    m_isInitialized = true;
-    m_matUisUptodate = computeU;
-    return *this;
-  }
-
-  internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>::run(*this, matrix.derived(), computeU);
-  computeFromHessenberg(m_matT, m_matU, computeU);
-  return *this;
-}
-
-template<typename MatrixType>
-template<typename HessMatrixType, typename OrthMatrixType>
-ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU)
-{
-  m_matT = matrixH;
-  if(computeU)
-    m_matU = matrixQ;
-  reduceToTriangularForm(computeU);
-  return *this;
-}
-namespace internal {
-
-/* Reduce given matrix to Hessenberg form */
-template<typename MatrixType, bool IsComplex>
-struct complex_schur_reduce_to_hessenberg
-{
-  // this is the implementation for the case IsComplex = true
-  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU)
-  {
-    _this.m_hess.compute(matrix);
-    _this.m_matT = _this.m_hess.matrixH();
-    if(computeU)  _this.m_matU = _this.m_hess.matrixQ();
-  }
-};
-
-template<typename MatrixType>
-struct complex_schur_reduce_to_hessenberg<MatrixType, false>
-{
-  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU)
-  {
-    typedef typename ComplexSchur<MatrixType>::ComplexScalar ComplexScalar;
-
-    // Note: m_hess is over RealScalar; m_matT and m_matU is over ComplexScalar
-    _this.m_hess.compute(matrix);
-    _this.m_matT = _this.m_hess.matrixH().template cast<ComplexScalar>();
-    if(computeU)  
-    {
-      // This may cause an allocation which seems to be avoidable
-      MatrixType Q = _this.m_hess.matrixQ(); 
-      _this.m_matU = Q.template cast<ComplexScalar>();
-    }
-  }
-};
-
-} // end namespace internal
-
-// Reduce the Hessenberg matrix m_matT to triangular form by QR iteration.
-template<typename MatrixType>
-void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
-{  
-  Index maxIters = m_maxIters;
-  if (maxIters == -1)
-    maxIters = m_maxIterationsPerRow * m_matT.rows();
-
-  // The matrix m_matT is divided in three parts. 
-  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. 
-  // Rows il,...,iu is the part we are working on (the active submatrix).
-  // Rows iu+1,...,end are already brought in triangular form.
-  Index iu = m_matT.cols() - 1;
-  Index il;
-  Index iter = 0; // number of iterations we are working on the (iu,iu) element
-  Index totalIter = 0; // number of iterations for whole matrix
-
-  while(true)
-  {
-    // find iu, the bottom row of the active submatrix
-    while(iu > 0)
-    {
-      if(!subdiagonalEntryIsNeglegible(iu-1)) break;
-      iter = 0;
-      --iu;
-    }
-
-    // if iu is zero then we are done; the whole matrix is triangularized
-    if(iu==0) break;
-
-    // if we spent too many iterations, we give up
-    iter++;
-    totalIter++;
-    if(totalIter > maxIters) break;
-
-    // find il, the top row of the active submatrix
-    il = iu-1;
-    while(il > 0 && !subdiagonalEntryIsNeglegible(il-1))
-    {
-      --il;
-    }
-
-    /* perform the QR step using Givens rotations. The first rotation
-       creates a bulge; the (il+2,il) element becomes nonzero. This
-       bulge is chased down to the bottom of the active submatrix. */
-
-    ComplexScalar shift = computeShift(iu, iter);
-    JacobiRotation<ComplexScalar> rot;
-    rot.makeGivens(m_matT.coeff(il,il) - shift, m_matT.coeff(il+1,il));
-    m_matT.rightCols(m_matT.cols()-il).applyOnTheLeft(il, il+1, rot.adjoint());
-    m_matT.topRows((std::min)(il+2,iu)+1).applyOnTheRight(il, il+1, rot);
-    if(computeU) m_matU.applyOnTheRight(il, il+1, rot);
-
-    for(Index i=il+1 ; i<iu ; i++)
-    {
-      rot.makeGivens(m_matT.coeffRef(i,i-1), m_matT.coeffRef(i+1,i-1), &m_matT.coeffRef(i,i-1));
-      m_matT.coeffRef(i+1,i-1) = ComplexScalar(0);
-      m_matT.rightCols(m_matT.cols()-i).applyOnTheLeft(i, i+1, rot.adjoint());
-      m_matT.topRows((std::min)(i+2,iu)+1).applyOnTheRight(i, i+1, rot);
-      if(computeU) m_matU.applyOnTheRight(i, i+1, rot);
-    }
-  }
-
-  if(totalIter <= maxIters)
-    m_info = Success;
-  else
-    m_info = NoConvergence;
-
-  m_isInitialized = true;
-  m_matUisUptodate = computeU;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_SCHUR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
deleted file mode 100644
index 4980a3e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Complex Schur needed to complex unsymmetrical eigenvalues/eigenvectors.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_COMPLEX_SCHUR_LAPACKE_H
-#define EIGEN_COMPLEX_SCHUR_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_SCHUR_COMPLEX(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW, LAPACKE_COLROW) \
-template<> template<typename InputType> inline \
-ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
-ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, bool computeU) \
-{ \
-  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \
-  typedef MatrixType::RealScalar RealScalar; \
-  typedef std::complex<RealScalar> ComplexScalar; \
-\
-  eigen_assert(matrix.cols() == matrix.rows()); \
-\
-  m_matUisUptodate = false; \
-  if(matrix.cols() == 1) \
-  { \
-    m_matT = matrix.derived().template cast<ComplexScalar>(); \
-    if(computeU)  m_matU = ComplexMatrixType::Identity(1,1); \
-      m_info = Success; \
-      m_isInitialized = true; \
-      m_matUisUptodate = computeU; \
-      return *this; \
-  } \
-  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info; \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  char jobvs, sort='N'; \
-  LAPACK_##LAPACKE_PREFIX_U##_SELECT1 select = 0; \
-  jobvs = (computeU) ? 'V' : 'N'; \
-  m_matU.resize(n, n); \
-  lapack_int ldvs  = internal::convert_index<lapack_int>(m_matU.outerStride()); \
-  m_matT = matrix; \
-  lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride()); \
-  Matrix<EIGTYPE, Dynamic, Dynamic> w; \
-  w.resize(n, 1);\
-  info = LAPACKE_##LAPACKE_PREFIX##gees( matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda, &sdim, (LAPACKE_TYPE*)w.data(), (LAPACKE_TYPE*)m_matU.data(), ldvs ); \
-  if(info == 0) \
-    m_info = Success; \
-  else \
-    m_info = NoConvergence; \
-\
-  m_isInitialized = true; \
-  m_matUisUptodate = computeU; \
-  return *this; \
-\
-}
-
-EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float,  c, C, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float,  c, C, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_SCHUR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/EigenSolver.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/EigenSolver.h
deleted file mode 100644
index 572b29e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/EigenSolver.h
+++ /dev/null
@@ -1,622 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EIGENSOLVER_H
-#define EIGEN_EIGENSOLVER_H
-
-#include "./RealSchur.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class EigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of general matrices
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * eigendecomposition; this is expected to be an instantiation of the Matrix
-  * class template. Currently, only real matrices are supported.
-  *
-  * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
-  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v \f$.  If
-  * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
-  * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
-  * V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
-  * have \f$ A = V D V^{-1} \f$. This is called the eigendecomposition.
-  *
-  * The eigenvalues and eigenvectors of a matrix may be complex, even when the
-  * matrix is real. However, we can choose real matrices \f$ V \f$ and \f$ D
-  * \f$ satisfying \f$ A V = V D \f$, just like the eigendecomposition, if the
-  * matrix \f$ D \f$ is not required to be diagonal, but if it is allowed to
-  * have blocks of the form
-  * \f[ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f]
-  * (where \f$ u \f$ and \f$ v \f$ are real numbers) on the diagonal.  These
-  * blocks correspond to complex eigenvalue pairs \f$ u \pm iv \f$. We call
-  * this variant of the eigendecomposition the pseudo-eigendecomposition.
-  *
-  * Call the function compute() to compute the eigenvalues and eigenvectors of
-  * a given matrix. Alternatively, you can use the 
-  * EigenSolver(const MatrixType&, bool) constructor which computes the
-  * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
-  * eigenvectors are computed, they can be retrieved with the eigenvalues() and
-  * eigenvectors() functions. The pseudoEigenvalueMatrix() and
-  * pseudoEigenvectors() methods allow the construction of the
-  * pseudo-eigendecomposition.
-  *
-  * The documentation for EigenSolver(const MatrixType&, bool) contains an
-  * example of the typical use of this class.
-  *
-  * \note The implementation is adapted from
-  * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
-  * Their code is based on EISPACK.
-  *
-  * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class EigenSolver
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for #MatrixType. 
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for vector of eigenvalues as returned by eigenvalues(). 
-      *
-      * This is a column vector with entries of type #ComplexScalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
-
-    /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). 
-      *
-      * This is a square matrix with entries of type #ComplexScalar. 
-      * The size is the same as the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
-
-    /** \brief Default constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
-      *
-      * \sa compute() for an example.
-      */
-    EigenSolver() : m_eivec(), m_eivalues(), m_isInitialized(false), m_eigenvectorsOk(false), m_realSchur(), m_matT(), m_tmp() {}
-
-    /** \brief Default constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa EigenSolver()
-      */
-    explicit EigenSolver(Index size)
-      : m_eivec(size, size),
-        m_eivalues(size),
-        m_isInitialized(false),
-        m_eigenvectorsOk(false),
-        m_realSchur(size),
-        m_matT(size, size), 
-        m_tmp(size)
-    {}
-
-    /** \brief Constructor; computes eigendecomposition of given matrix. 
-      * 
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed. 
-      *
-      * This constructor calls compute() to compute the eigenvalues
-      * and eigenvectors.
-      *
-      * Example: \include EigenSolver_EigenSolver_MatrixType.cpp
-      * Output: \verbinclude EigenSolver_EigenSolver_MatrixType.out
-      *
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit EigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
-      : m_eivec(matrix.rows(), matrix.cols()),
-        m_eivalues(matrix.cols()),
-        m_isInitialized(false),
-        m_eigenvectorsOk(false),
-        m_realSchur(matrix.cols()),
-        m_matT(matrix.rows(), matrix.cols()), 
-        m_tmp(matrix.cols())
-    {
-      compute(matrix.derived(), computeEigenvectors);
-    }
-
-    /** \brief Returns the eigenvectors of given matrix. 
-      *
-      * \returns  %Matrix whose columns are the (possibly complex) eigenvectors.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
-      * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
-      * eigenvectors are normalized to have (Euclidean) norm equal to one. The
-      * matrix returned by this function is the matrix \f$ V \f$ in the
-      * eigendecomposition \f$ A = V D V^{-1} \f$, if it exists.
-      *
-      * Example: \include EigenSolver_eigenvectors.cpp
-      * Output: \verbinclude EigenSolver_eigenvectors.out
-      *
-      * \sa eigenvalues(), pseudoEigenvectors()
-      */
-    EigenvectorsType eigenvectors() const;
-
-    /** \brief Returns the pseudo-eigenvectors of given matrix. 
-      *
-      * \returns  Const reference to matrix whose columns are the pseudo-eigenvectors.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * The real matrix \f$ V \f$ returned by this function and the
-      * block-diagonal matrix \f$ D \f$ returned by pseudoEigenvalueMatrix()
-      * satisfy \f$ AV = VD \f$.
-      *
-      * Example: \include EigenSolver_pseudoEigenvectors.cpp
-      * Output: \verbinclude EigenSolver_pseudoEigenvectors.out
-      *
-      * \sa pseudoEigenvalueMatrix(), eigenvectors()
-      */
-    const MatrixType& pseudoEigenvectors() const
-    {
-      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec;
-    }
-
-    /** \brief Returns the block-diagonal matrix in the pseudo-eigendecomposition.
-      *
-      * \returns  A block-diagonal matrix.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before.
-      *
-      * The matrix \f$ D \f$ returned by this function is real and
-      * block-diagonal. The blocks on the diagonal are either 1-by-1 or 2-by-2
-      * blocks of the form
-      * \f$ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f$.
-      * These blocks are not sorted in any particular order.
-      * The matrix \f$ D \f$ and the matrix \f$ V \f$ returned by
-      * pseudoEigenvectors() satisfy \f$ AV = VD \f$.
-      *
-      * \sa pseudoEigenvectors() for an example, eigenvalues()
-      */
-    MatrixType pseudoEigenvalueMatrix() const;
-
-    /** \brief Returns the eigenvalues of given matrix. 
-      *
-      * \returns A const reference to the column vector containing the eigenvalues.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before.
-      *
-      * The eigenvalues are repeated according to their algebraic multiplicity,
-      * so there are as many eigenvalues as rows in the matrix. The eigenvalues 
-      * are not sorted in any particular order.
-      *
-      * Example: \include EigenSolver_eigenvalues.cpp
-      * Output: \verbinclude EigenSolver_eigenvalues.out
-      *
-      * \sa eigenvectors(), pseudoEigenvalueMatrix(),
-      *     MatrixBase::eigenvalues()
-      */
-    const EigenvalueType& eigenvalues() const
-    {
-      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-      return m_eivalues;
-    }
-
-    /** \brief Computes eigendecomposition of given matrix. 
-      * 
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed. 
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of the real matrix \p matrix.
-      * The eigenvalues() function can be used to retrieve them.  If 
-      * \p computeEigenvectors is true, then the eigenvectors are also computed
-      * and can be retrieved by calling eigenvectors().
-      *
-      * The matrix is first reduced to real Schur form using the RealSchur
-      * class. The Schur decomposition is then used to compute the eigenvalues
-      * and eigenvectors.
-      *
-      * The cost of the computation is dominated by the cost of the
-      * Schur decomposition, which is very approximately \f$ 25n^3 \f$
-      * (where \f$ n \f$ is the size of the matrix) if \p computeEigenvectors 
-      * is true, and \f$ 10n^3 \f$ if \p computeEigenvectors is false.
-      *
-      * This method reuses of the allocated data in the EigenSolver object.
-      *
-      * Example: \include EigenSolver_compute.cpp
-      * Output: \verbinclude EigenSolver_compute.out
-      */
-    template<typename InputType>
-    EigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
-
-    /** \returns NumericalIssue if the input contains INF or NaN values or overflow occurred. Returns Success otherwise. */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. */
-    EigenSolver& setMaxIterations(Index maxIters)
-    {
-      m_realSchur.setMaxIterations(maxIters);
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_realSchur.getMaxIterations();
-    }
-
-  private:
-    void doComputeEigenvectors();
-
-  protected:
-    
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-      EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL);
-    }
-    
-    MatrixType m_eivec;
-    EigenvalueType m_eivalues;
-    bool m_isInitialized;
-    bool m_eigenvectorsOk;
-    ComputationInfo m_info;
-    RealSchur<MatrixType> m_realSchur;
-    MatrixType m_matT;
-
-    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
-    ColumnVectorType m_tmp;
-};
-
-template<typename MatrixType>
-MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
-{
-  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-  const RealScalar precision = RealScalar(2)*NumTraits<RealScalar>::epsilon();
-  Index n = m_eivalues.rows();
-  MatrixType matD = MatrixType::Zero(n,n);
-  for (Index i=0; i<n; ++i)
-  {
-    if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i)), precision))
-      matD.coeffRef(i,i) = numext::real(m_eivalues.coeff(i));
-    else
-    {
-      matD.template block<2,2>(i,i) <<  numext::real(m_eivalues.coeff(i)), numext::imag(m_eivalues.coeff(i)),
-                                       -numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i));
-      ++i;
-    }
-  }
-  return matD;
-}
-
-template<typename MatrixType>
-typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eigenvectors() const
-{
-  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-  eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-  const RealScalar precision = RealScalar(2)*NumTraits<RealScalar>::epsilon();
-  Index n = m_eivec.cols();
-  EigenvectorsType matV(n,n);
-  for (Index j=0; j<n; ++j)
-  {
-    if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(j)), numext::real(m_eivalues.coeff(j)), precision) || j+1==n)
-    {
-      // we have a real eigen value
-      matV.col(j) = m_eivec.col(j).template cast<ComplexScalar>();
-      matV.col(j).normalize();
-    }
-    else
-    {
-      // we have a pair of complex eigen values
-      for (Index i=0; i<n; ++i)
-      {
-        matV.coeffRef(i,j)   = ComplexScalar(m_eivec.coeff(i,j),  m_eivec.coeff(i,j+1));
-        matV.coeffRef(i,j+1) = ComplexScalar(m_eivec.coeff(i,j), -m_eivec.coeff(i,j+1));
-      }
-      matV.col(j).normalize();
-      matV.col(j+1).normalize();
-      ++j;
-    }
-  }
-  return matV;
-}
-
-template<typename MatrixType>
-template<typename InputType>
-EigenSolver<MatrixType>& 
-EigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
-{
-  check_template_parameters();
-  
-  using std::sqrt;
-  using std::abs;
-  using numext::isfinite;
-  eigen_assert(matrix.cols() == matrix.rows());
-
-  // Reduce to real Schur form.
-  m_realSchur.compute(matrix.derived(), computeEigenvectors);
-  
-  m_info = m_realSchur.info();
-
-  if (m_info == Success)
-  {
-    m_matT = m_realSchur.matrixT();
-    if (computeEigenvectors)
-      m_eivec = m_realSchur.matrixU();
-  
-    // Compute eigenvalues from matT
-    m_eivalues.resize(matrix.cols());
-    Index i = 0;
-    while (i < matrix.cols()) 
-    {
-      if (i == matrix.cols() - 1 || m_matT.coeff(i+1, i) == Scalar(0)) 
-      {
-        m_eivalues.coeffRef(i) = m_matT.coeff(i, i);
-        if(!(isfinite)(m_eivalues.coeffRef(i)))
-        {
-          m_isInitialized = true;
-          m_eigenvectorsOk = false;
-          m_info = NumericalIssue;
-          return *this;
-        }
-        ++i;
-      }
-      else
-      {
-        Scalar p = Scalar(0.5) * (m_matT.coeff(i, i) - m_matT.coeff(i+1, i+1));
-        Scalar z;
-        // Compute z = sqrt(abs(p * p + m_matT.coeff(i+1, i) * m_matT.coeff(i, i+1)));
-        // without overflow
-        {
-          Scalar t0 = m_matT.coeff(i+1, i);
-          Scalar t1 = m_matT.coeff(i, i+1);
-          Scalar maxval = numext::maxi<Scalar>(abs(p),numext::maxi<Scalar>(abs(t0),abs(t1)));
-          t0 /= maxval;
-          t1 /= maxval;
-          Scalar p0 = p/maxval;
-          z = maxval * sqrt(abs(p0 * p0 + t0 * t1));
-        }
-        
-        m_eivalues.coeffRef(i)   = ComplexScalar(m_matT.coeff(i+1, i+1) + p, z);
-        m_eivalues.coeffRef(i+1) = ComplexScalar(m_matT.coeff(i+1, i+1) + p, -z);
-        if(!((isfinite)(m_eivalues.coeffRef(i)) && (isfinite)(m_eivalues.coeffRef(i+1))))
-        {
-          m_isInitialized = true;
-          m_eigenvectorsOk = false;
-          m_info = NumericalIssue;
-          return *this;
-        }
-        i += 2;
-      }
-    }
-    
-    // Compute eigenvectors.
-    if (computeEigenvectors)
-      doComputeEigenvectors();
-  }
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-
-  return *this;
-}
-
-
-template<typename MatrixType>
-void EigenSolver<MatrixType>::doComputeEigenvectors()
-{
-  using std::abs;
-  const Index size = m_eivec.cols();
-  const Scalar eps = NumTraits<Scalar>::epsilon();
-
-  // inefficient! this is already computed in RealSchur
-  Scalar norm(0);
-  for (Index j = 0; j < size; ++j)
-  {
-    norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum();
-  }
-  
-  // Backsubstitute to find vectors of upper triangular form
-  if (norm == Scalar(0))
-  {
-    return;
-  }
-
-  for (Index n = size-1; n >= 0; n--)
-  {
-    Scalar p = m_eivalues.coeff(n).real();
-    Scalar q = m_eivalues.coeff(n).imag();
-
-    // Scalar vector
-    if (q == Scalar(0))
-    {
-      Scalar lastr(0), lastw(0);
-      Index l = n;
-
-      m_matT.coeffRef(n,n) = Scalar(1);
-      for (Index i = n-1; i >= 0; i--)
-      {
-        Scalar w = m_matT.coeff(i,i) - p;
-        Scalar r = m_matT.row(i).segment(l,n-l+1).dot(m_matT.col(n).segment(l, n-l+1));
-
-        if (m_eivalues.coeff(i).imag() < Scalar(0))
-        {
-          lastw = w;
-          lastr = r;
-        }
-        else
-        {
-          l = i;
-          if (m_eivalues.coeff(i).imag() == Scalar(0))
-          {
-            if (w != Scalar(0))
-              m_matT.coeffRef(i,n) = -r / w;
-            else
-              m_matT.coeffRef(i,n) = -r / (eps * norm);
-          }
-          else // Solve real equations
-          {
-            Scalar x = m_matT.coeff(i,i+1);
-            Scalar y = m_matT.coeff(i+1,i);
-            Scalar denom = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag();
-            Scalar t = (x * lastr - lastw * r) / denom;
-            m_matT.coeffRef(i,n) = t;
-            if (abs(x) > abs(lastw))
-              m_matT.coeffRef(i+1,n) = (-r - w * t) / x;
-            else
-              m_matT.coeffRef(i+1,n) = (-lastr - y * t) / lastw;
-          }
-
-          // Overflow control
-          Scalar t = abs(m_matT.coeff(i,n));
-          if ((eps * t) * t > Scalar(1))
-            m_matT.col(n).tail(size-i) /= t;
-        }
-      }
-    }
-    else if (q < Scalar(0) && n > 0) // Complex vector
-    {
-      Scalar lastra(0), lastsa(0), lastw(0);
-      Index l = n-1;
-
-      // Last vector component imaginary so matrix is triangular
-      if (abs(m_matT.coeff(n,n-1)) > abs(m_matT.coeff(n-1,n)))
-      {
-        m_matT.coeffRef(n-1,n-1) = q / m_matT.coeff(n,n-1);
-        m_matT.coeffRef(n-1,n) = -(m_matT.coeff(n,n) - p) / m_matT.coeff(n,n-1);
-      }
-      else
-      {
-        ComplexScalar cc = ComplexScalar(Scalar(0),-m_matT.coeff(n-1,n)) / ComplexScalar(m_matT.coeff(n-1,n-1)-p,q);
-        m_matT.coeffRef(n-1,n-1) = numext::real(cc);
-        m_matT.coeffRef(n-1,n) = numext::imag(cc);
-      }
-      m_matT.coeffRef(n,n-1) = Scalar(0);
-      m_matT.coeffRef(n,n) = Scalar(1);
-      for (Index i = n-2; i >= 0; i--)
-      {
-        Scalar ra = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n-1).segment(l, n-l+1));
-        Scalar sa = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n).segment(l, n-l+1));
-        Scalar w = m_matT.coeff(i,i) - p;
-
-        if (m_eivalues.coeff(i).imag() < Scalar(0))
-        {
-          lastw = w;
-          lastra = ra;
-          lastsa = sa;
-        }
-        else
-        {
-          l = i;
-          if (m_eivalues.coeff(i).imag() == RealScalar(0))
-          {
-            ComplexScalar cc = ComplexScalar(-ra,-sa) / ComplexScalar(w,q);
-            m_matT.coeffRef(i,n-1) = numext::real(cc);
-            m_matT.coeffRef(i,n) = numext::imag(cc);
-          }
-          else
-          {
-            // Solve complex equations
-            Scalar x = m_matT.coeff(i,i+1);
-            Scalar y = m_matT.coeff(i+1,i);
-            Scalar vr = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag() - q * q;
-            Scalar vi = (m_eivalues.coeff(i).real() - p) * Scalar(2) * q;
-            if ((vr == Scalar(0)) && (vi == Scalar(0)))
-              vr = eps * norm * (abs(w) + abs(q) + abs(x) + abs(y) + abs(lastw));
-
-            ComplexScalar cc = ComplexScalar(x*lastra-lastw*ra+q*sa,x*lastsa-lastw*sa-q*ra) / ComplexScalar(vr,vi);
-            m_matT.coeffRef(i,n-1) = numext::real(cc);
-            m_matT.coeffRef(i,n) = numext::imag(cc);
-            if (abs(x) > (abs(lastw) + abs(q)))
-            {
-              m_matT.coeffRef(i+1,n-1) = (-ra - w * m_matT.coeff(i,n-1) + q * m_matT.coeff(i,n)) / x;
-              m_matT.coeffRef(i+1,n) = (-sa - w * m_matT.coeff(i,n) - q * m_matT.coeff(i,n-1)) / x;
-            }
-            else
-            {
-              cc = ComplexScalar(-lastra-y*m_matT.coeff(i,n-1),-lastsa-y*m_matT.coeff(i,n)) / ComplexScalar(lastw,q);
-              m_matT.coeffRef(i+1,n-1) = numext::real(cc);
-              m_matT.coeffRef(i+1,n) = numext::imag(cc);
-            }
-          }
-
-          // Overflow control
-          Scalar t = numext::maxi<Scalar>(abs(m_matT.coeff(i,n-1)),abs(m_matT.coeff(i,n)));
-          if ((eps * t) * t > Scalar(1))
-            m_matT.block(i, n-1, size-i, 2) /= t;
-
-        }
-      }
-      
-      // We handled a pair of complex conjugate eigenvalues, so need to skip them both
-      n--;
-    }
-    else
-    {
-      eigen_assert(0 && "Internal bug in EigenSolver (INF or NaN has not been detected)"); // this should not happen
-    }
-  }
-
-  // Back transformation to get eigenvectors of original matrix
-  for (Index j = size-1; j >= 0; j--)
-  {
-    m_tmp.noalias() = m_eivec.leftCols(j+1) * m_matT.col(j).segment(0, j+1);
-    m_eivec.col(j) = m_tmp;
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_EIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
deleted file mode 100644
index 87d789b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
+++ /dev/null
@@ -1,418 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-// Copyright (C) 2016 Tobias Wood <tobias@spinicist.org.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERALIZEDEIGENSOLVER_H
-#define EIGEN_GENERALIZEDEIGENSOLVER_H
-
-#include "./RealQZ.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class GeneralizedEigenSolver
-  *
-  * \brief Computes the generalized eigenvalues and eigenvectors of a pair of general matrices
-  *
-  * \tparam _MatrixType the type of the matrices of which we are computing the
-  * eigen-decomposition; this is expected to be an instantiation of the Matrix
-  * class template. Currently, only real matrices are supported.
-  *
-  * The generalized eigenvalues and eigenvectors of a matrix pair \f$ A \f$ and \f$ B \f$ are scalars
-  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda Bv \f$.  If
-  * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
-  * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
-  * B V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
-  * have \f$ A = B V D V^{-1} \f$. This is called the generalized eigen-decomposition.
-  *
-  * The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the
-  * matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is
-  * singular. To workaround this difficulty, the eigenvalues are provided as a pair of complex \f$ \alpha \f$
-  * and real \f$ \beta \f$ such that: \f$ \lambda_i = \alpha_i / \beta_i \f$. If \f$ \beta_i \f$ is (nearly) zero,
-  * then one can consider the well defined left eigenvalue \f$ \mu = \beta_i / \alpha_i\f$ such that:
-  * \f$ \mu_i A v_i = B v_i \f$, or even \f$ \mu_i u_i^T A  = u_i^T B \f$ where \f$ u_i \f$ is
-  * called the left eigenvector.
-  *
-  * Call the function compute() to compute the generalized eigenvalues and eigenvectors of
-  * a given matrix pair. Alternatively, you can use the
-  * GeneralizedEigenSolver(const MatrixType&, const MatrixType&, bool) constructor which computes the
-  * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
-  * eigenvectors are computed, they can be retrieved with the eigenvalues() and
-  * eigenvectors() functions.
-  *
-  * Here is an usage example of this class:
-  * Example: \include GeneralizedEigenSolver.cpp
-  * Output: \verbinclude GeneralizedEigenSolver.out
-  *
-  * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class GeneralizedEigenSolver
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for #MatrixType. 
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for vector of real scalar values eigenvalues as returned by betas().
-      *
-      * This is a column vector with entries of type #Scalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType;
-
-    /** \brief Type for vector of complex scalar values eigenvalues as returned by alphas().
-      *
-      * This is a column vector with entries of type #ComplexScalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType;
-
-    /** \brief Expression type for the eigenvalues as returned by eigenvalues().
-      */
-    typedef CwiseBinaryOp<internal::scalar_quotient_op<ComplexScalar,Scalar>,ComplexVectorType,VectorType> EigenvalueType;
-
-    /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). 
-      *
-      * This is a square matrix with entries of type #ComplexScalar. 
-      * The size is the same as the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
-
-    /** \brief Default constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
-      *
-      * \sa compute() for an example.
-      */
-    GeneralizedEigenSolver()
-      : m_eivec(),
-        m_alphas(),
-        m_betas(),
-        m_valuesOkay(false),
-        m_vectorsOkay(false),
-        m_realQZ()
-    {}
-
-    /** \brief Default constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa GeneralizedEigenSolver()
-      */
-    explicit GeneralizedEigenSolver(Index size)
-      : m_eivec(size, size),
-        m_alphas(size),
-        m_betas(size),
-        m_valuesOkay(false),
-        m_vectorsOkay(false),
-        m_realQZ(size),
-        m_tmp(size)
-    {}
-
-    /** \brief Constructor; computes the generalized eigendecomposition of given matrix pair.
-      * 
-      * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are computed.
-      *
-      * This constructor calls compute() to compute the generalized eigenvalues
-      * and eigenvectors.
-      *
-      * \sa compute()
-      */
-    GeneralizedEigenSolver(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true)
-      : m_eivec(A.rows(), A.cols()),
-        m_alphas(A.cols()),
-        m_betas(A.cols()),
-        m_valuesOkay(false),
-        m_vectorsOkay(false),
-        m_realQZ(A.cols()),
-        m_tmp(A.cols())
-    {
-      compute(A, B, computeEigenvectors);
-    }
-
-    /* \brief Returns the computed generalized eigenvectors.
-      *
-      * \returns  %Matrix whose columns are the (possibly complex) right eigenvectors.
-      * i.e. the eigenvectors that solve (A - l*B)x = 0. The ordering matches the eigenvalues.
-      *
-      * \pre Either the constructor 
-      * GeneralizedEigenSolver(const MatrixType&,const MatrixType&, bool) or the member function
-      * compute(const MatrixType&, const MatrixType& bool) has been called before, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * \sa eigenvalues()
-      */
-    EigenvectorsType eigenvectors() const {
-      eigen_assert(m_vectorsOkay && "Eigenvectors for GeneralizedEigenSolver were not calculated.");
-      return m_eivec;
-    }
-
-    /** \brief Returns an expression of the computed generalized eigenvalues.
-      *
-      * \returns An expression of the column vector containing the eigenvalues.
-      *
-      * It is a shortcut for \code this->alphas().cwiseQuotient(this->betas()); \endcode
-      * Not that betas might contain zeros. It is therefore not recommended to use this function,
-      * but rather directly deal with the alphas and betas vectors.
-      *
-      * \pre Either the constructor 
-      * GeneralizedEigenSolver(const MatrixType&,const MatrixType&,bool) or the member function
-      * compute(const MatrixType&,const MatrixType&,bool) has been called before.
-      *
-      * The eigenvalues are repeated according to their algebraic multiplicity,
-      * so there are as many eigenvalues as rows in the matrix. The eigenvalues 
-      * are not sorted in any particular order.
-      *
-      * \sa alphas(), betas(), eigenvectors()
-      */
-    EigenvalueType eigenvalues() const
-    {
-      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
-      return EigenvalueType(m_alphas,m_betas);
-    }
-
-    /** \returns A const reference to the vectors containing the alpha values
-      *
-      * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
-      *
-      * \sa betas(), eigenvalues() */
-    ComplexVectorType alphas() const
-    {
-      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
-      return m_alphas;
-    }
-
-    /** \returns A const reference to the vectors containing the beta values
-      *
-      * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
-      *
-      * \sa alphas(), eigenvalues() */
-    VectorType betas() const
-    {
-      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
-      return m_betas;
-    }
-
-    /** \brief Computes generalized eigendecomposition of given matrix.
-      * 
-      * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed. 
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of the real matrix \p matrix.
-      * The eigenvalues() function can be used to retrieve them.  If 
-      * \p computeEigenvectors is true, then the eigenvectors are also computed
-      * and can be retrieved by calling eigenvectors().
-      *
-      * The matrix is first reduced to real generalized Schur form using the RealQZ
-      * class. The generalized Schur decomposition is then used to compute the eigenvalues
-      * and eigenvectors.
-      *
-      * The cost of the computation is dominated by the cost of the
-      * generalized Schur decomposition.
-      *
-      * This method reuses of the allocated data in the GeneralizedEigenSolver object.
-      */
-    GeneralizedEigenSolver& compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true);
-
-    ComputationInfo info() const
-    {
-      eigen_assert(m_valuesOkay && "EigenSolver is not initialized.");
-      return m_realQZ.info();
-    }
-
-    /** Sets the maximal number of iterations allowed.
-    */
-    GeneralizedEigenSolver& setMaxIterations(Index maxIters)
-    {
-      m_realQZ.setMaxIterations(maxIters);
-      return *this;
-    }
-
-  protected:
-    
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-      EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL);
-    }
-    
-    EigenvectorsType m_eivec;
-    ComplexVectorType m_alphas;
-    VectorType m_betas;
-    bool m_valuesOkay, m_vectorsOkay;
-    RealQZ<MatrixType> m_realQZ;
-    ComplexVectorType m_tmp;
-};
-
-template<typename MatrixType>
-GeneralizedEigenSolver<MatrixType>&
-GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors)
-{
-  check_template_parameters();
-  
-  using std::sqrt;
-  using std::abs;
-  eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows());
-  Index size = A.cols();
-  m_valuesOkay = false;
-  m_vectorsOkay = false;
-  // Reduce to generalized real Schur form:
-  // A = Q S Z and B = Q T Z
-  m_realQZ.compute(A, B, computeEigenvectors);
-  if (m_realQZ.info() == Success)
-  {
-    // Resize storage
-    m_alphas.resize(size);
-    m_betas.resize(size);
-    if (computeEigenvectors)
-    {
-      m_eivec.resize(size,size);
-      m_tmp.resize(size);
-    }
-
-    // Aliases:
-    Map<VectorType> v(reinterpret_cast<Scalar*>(m_tmp.data()), size);
-    ComplexVectorType &cv = m_tmp;
-    const MatrixType &mS = m_realQZ.matrixS();
-    const MatrixType &mT = m_realQZ.matrixT();
-
-    Index i = 0;
-    while (i < size)
-    {
-      if (i == size - 1 || mS.coeff(i+1, i) == Scalar(0))
-      {
-        // Real eigenvalue
-        m_alphas.coeffRef(i) = mS.diagonal().coeff(i);
-        m_betas.coeffRef(i)  = mT.diagonal().coeff(i);
-        if (computeEigenvectors)
-        {
-          v.setConstant(Scalar(0.0));
-          v.coeffRef(i) = Scalar(1.0);
-          // For singular eigenvalues do nothing more
-          if(abs(m_betas.coeffRef(i)) >= (std::numeric_limits<RealScalar>::min)())
-          {
-            // Non-singular eigenvalue
-            const Scalar alpha = real(m_alphas.coeffRef(i));
-            const Scalar beta = m_betas.coeffRef(i);
-            for (Index j = i-1; j >= 0; j--)
-            {
-              const Index st = j+1;
-              const Index sz = i-j;
-              if (j > 0 && mS.coeff(j, j-1) != Scalar(0))
-              {
-                // 2x2 block
-                Matrix<Scalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( v.segment(st,sz) );
-                Matrix<Scalar, 2, 2> lhs = beta * mS.template block<2,2>(j-1,j-1) - alpha * mT.template block<2,2>(j-1,j-1);
-                v.template segment<2>(j-1) = lhs.partialPivLu().solve(rhs);
-                j--;
-              }
-              else
-              {
-                v.coeffRef(j) = -v.segment(st,sz).transpose().cwiseProduct(beta*mS.block(j,st,1,sz) - alpha*mT.block(j,st,1,sz)).sum() / (beta*mS.coeffRef(j,j) - alpha*mT.coeffRef(j,j));
-              }
-            }
-          }
-          m_eivec.col(i).real().noalias() = m_realQZ.matrixZ().transpose() * v;
-          m_eivec.col(i).real().normalize();
-          m_eivec.col(i).imag().setConstant(0);
-        }
-        ++i;
-      }
-      else
-      {
-        // We need to extract the generalized eigenvalues of the pair of a general 2x2 block S and a positive diagonal 2x2 block T
-        // Then taking beta=T_00*T_11, we can avoid any division, and alpha is the eigenvalues of A = (U^-1 * S * U) * diag(T_11,T_00):
-
-        // T =  [a 0]
-        //      [0 b]
-        RealScalar a = mT.diagonal().coeff(i),
-                   b = mT.diagonal().coeff(i+1);
-        const RealScalar beta = m_betas.coeffRef(i) = m_betas.coeffRef(i+1) = a*b;
-
-        // ^^ NOTE: using diagonal()(i) instead of coeff(i,i) workarounds a MSVC bug.
-        Matrix<RealScalar,2,2> S2 = mS.template block<2,2>(i,i) * Matrix<Scalar,2,1>(b,a).asDiagonal();
-
-        Scalar p = Scalar(0.5) * (S2.coeff(0,0) - S2.coeff(1,1));
-        Scalar z = sqrt(abs(p * p + S2.coeff(1,0) * S2.coeff(0,1)));
-        const ComplexScalar alpha = ComplexScalar(S2.coeff(1,1) + p, (beta > 0) ? z : -z);
-        m_alphas.coeffRef(i)   = conj(alpha);
-        m_alphas.coeffRef(i+1) = alpha;
-
-        if (computeEigenvectors) {
-          // Compute eigenvector in position (i+1) and then position (i) is just the conjugate
-          cv.setZero();
-          cv.coeffRef(i+1) = Scalar(1.0);
-          // here, the "static_cast" workaound expression template issues.
-          cv.coeffRef(i) = -(static_cast<Scalar>(beta*mS.coeffRef(i,i+1)) - alpha*mT.coeffRef(i,i+1))
-                          / (static_cast<Scalar>(beta*mS.coeffRef(i,i))   - alpha*mT.coeffRef(i,i));
-          for (Index j = i-1; j >= 0; j--)
-          {
-            const Index st = j+1;
-            const Index sz = i+1-j;
-            if (j > 0 && mS.coeff(j, j-1) != Scalar(0))
-            {
-              // 2x2 block
-              Matrix<ComplexScalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( cv.segment(st,sz) );
-              Matrix<ComplexScalar, 2, 2> lhs = beta * mS.template block<2,2>(j-1,j-1) - alpha * mT.template block<2,2>(j-1,j-1);
-              cv.template segment<2>(j-1) = lhs.partialPivLu().solve(rhs);
-              j--;
-            } else {
-              cv.coeffRef(j) =  cv.segment(st,sz).transpose().cwiseProduct(beta*mS.block(j,st,1,sz) - alpha*mT.block(j,st,1,sz)).sum()
-                              / (alpha*mT.coeffRef(j,j) - static_cast<Scalar>(beta*mS.coeffRef(j,j)));
-            }
-          }
-          m_eivec.col(i+1).noalias() = (m_realQZ.matrixZ().transpose() * cv);
-          m_eivec.col(i+1).normalize();
-          m_eivec.col(i) = m_eivec.col(i+1).conjugate();
-        }
-        i += 2;
-      }
-    }
-
-    m_valuesOkay = true;
-    m_vectorsOkay = computeEigenvectors;
-  }
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERALIZEDEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
deleted file mode 100644
index d0f9091..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
-#define EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
-
-#include "./Tridiagonalization.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class GeneralizedSelfAdjointEigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of the generalized selfadjoint eigen problem
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * eigendecomposition; this is expected to be an instantiation of the Matrix
-  * class template.
-  *
-  * This class solves the generalized eigenvalue problem
-  * \f$ Av = \lambda Bv \f$. In this case, the matrix \f$ A \f$ should be
-  * selfadjoint and the matrix \f$ B \f$ should be positive definite.
-  *
-  * Only the \b lower \b triangular \b part of the input matrix is referenced.
-  *
-  * Call the function compute() to compute the eigenvalues and eigenvectors of
-  * a given matrix. Alternatively, you can use the
-  * GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
-  * constructor which computes the eigenvalues and eigenvectors at construction time.
-  * Once the eigenvalue and eigenvectors are computed, they can be retrieved with the eigenvalues()
-  * and eigenvectors() functions.
-  *
-  * The documentation for GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
-  * contains an example of the typical use of this class.
-  *
-  * \sa class SelfAdjointEigenSolver, class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType>
-class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<_MatrixType>
-{
-    typedef SelfAdjointEigenSolver<_MatrixType> Base;
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    /** \brief Default constructor for fixed-size matrices.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute(). This constructor
-      * can only be used if \p _MatrixType is a fixed-size matrix; use
-      * GeneralizedSelfAdjointEigenSolver(Index) for dynamic-size matrices.
-      */
-    GeneralizedSelfAdjointEigenSolver() : Base() {}
-
-    /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
-      *
-      * \param [in]  size  Positive integer, size of the matrix whose
-      * eigenvalues and eigenvectors will be computed.
-      *
-      * This constructor is useful for dynamic-size matrices, when the user
-      * intends to perform decompositions via compute(). The \p size
-      * parameter is only used as a hint. It is not an error to give a wrong
-      * \p size, but it may impair performance.
-      *
-      * \sa compute() for an example
-      */
-    explicit GeneralizedSelfAdjointEigenSolver(Index size)
-        : Base(size)
-    {}
-
-    /** \brief Constructor; computes generalized eigendecomposition of given matrix pencil.
-      *
-      * \param[in]  matA  Selfadjoint matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  matB  Positive-definite matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
-      *                     Default is #ComputeEigenvectors|#Ax_lBx.
-      *
-      * This constructor calls compute(const MatrixType&, const MatrixType&, int)
-      * to compute the eigenvalues and (if requested) the eigenvectors of the
-      * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the
-      * selfadjoint matrix \f$ A \f$ and \a matB the positive definite matrix
-      * \f$ B \f$. Each eigenvector \f$ x \f$ satisfies the property
-      * \f$ x^* B x = 1 \f$. The eigenvectors are computed if
-      * \a options contains ComputeEigenvectors.
-      *
-      * In addition, the two following variants can be solved via \p options:
-      * - \c ABx_lx: \f$ ABx = \lambda x \f$
-      * - \c BAx_lx: \f$ BAx = \lambda x \f$
-      *
-      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.out
-      *
-      * \sa compute(const MatrixType&, const MatrixType&, int)
-      */
-    GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB,
-                                      int options = ComputeEigenvectors|Ax_lBx)
-      : Base(matA.cols())
-    {
-      compute(matA, matB, options);
-    }
-
-    /** \brief Computes generalized eigendecomposition of given matrix pencil.
-      *
-      * \param[in]  matA  Selfadjoint matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  matB  Positive-definite matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
-      *                     Default is #ComputeEigenvectors|#Ax_lBx.
-      *
-      * \returns    Reference to \c *this
-      *
-      * According to \p options, this function computes eigenvalues and (if requested)
-      * the eigenvectors of one of the following three generalized eigenproblems:
-      * - \c Ax_lBx: \f$ Ax = \lambda B x \f$
-      * - \c ABx_lx: \f$ ABx = \lambda x \f$
-      * - \c BAx_lx: \f$ BAx = \lambda x \f$
-      * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite
-      * matrix \f$ B \f$.
-      * In addition, each eigenvector \f$ x \f$ satisfies the property \f$ x^* B x = 1 \f$.
-      *
-      * The eigenvalues() function can be used to retrieve
-      * the eigenvalues. If \p options contains ComputeEigenvectors, then the
-      * eigenvectors are also computed and can be retrieved by calling
-      * eigenvectors().
-      *
-      * The implementation uses LLT to compute the Cholesky decomposition
-      * \f$ B = LL^* \f$ and computes the classical eigendecomposition
-      * of the selfadjoint matrix \f$ L^{-1} A (L^*)^{-1} \f$ if \p options contains Ax_lBx
-      * and of \f$ L^{*} A L \f$ otherwise. This solves the
-      * generalized eigenproblem, because any solution of the generalized
-      * eigenproblem \f$ Ax = \lambda B x \f$ corresponds to a solution
-      * \f$ L^{-1} A (L^*)^{-1} (L^* x) = \lambda (L^* x) \f$ of the
-      * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$. Similar statements
-      * can be made for the two other variants.
-      *
-      * Example: \include SelfAdjointEigenSolver_compute_MatrixType2.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType2.out
-      *
-      * \sa GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
-      */
-    GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB,
-                                               int options = ComputeEigenvectors|Ax_lBx);
-
-  protected:
-
-};
-
-
-template<typename MatrixType>
-GeneralizedSelfAdjointEigenSolver<MatrixType>& GeneralizedSelfAdjointEigenSolver<MatrixType>::
-compute(const MatrixType& matA, const MatrixType& matB, int options)
-{
-  eigen_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows());
-  eigen_assert((options&~(EigVecMask|GenEigMask))==0
-          && (options&EigVecMask)!=EigVecMask
-          && ((options&GenEigMask)==0 || (options&GenEigMask)==Ax_lBx
-           || (options&GenEigMask)==ABx_lx || (options&GenEigMask)==BAx_lx)
-          && "invalid option parameter");
-
-  bool computeEigVecs = ((options&EigVecMask)==0) || ((options&EigVecMask)==ComputeEigenvectors);
-
-  // Compute the cholesky decomposition of matB = L L' = U'U
-  LLT<MatrixType> cholB(matB);
-
-  int type = (options&GenEigMask);
-  if(type==0)
-    type = Ax_lBx;
-
-  if(type==Ax_lBx)
-  {
-    // compute C = inv(L) A inv(L')
-    MatrixType matC = matA.template selfadjointView<Lower>();
-    cholB.matrixL().template solveInPlace<OnTheLeft>(matC);
-    cholB.matrixU().template solveInPlace<OnTheRight>(matC);
-
-    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly );
-
-    // transform back the eigen vectors: evecs = inv(U) * evecs
-    if(computeEigVecs)
-      cholB.matrixU().solveInPlace(Base::m_eivec);
-  }
-  else if(type==ABx_lx)
-  {
-    // compute C = L' A L
-    MatrixType matC = matA.template selfadjointView<Lower>();
-    matC = matC * cholB.matrixL();
-    matC = cholB.matrixU() * matC;
-
-    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
-
-    // transform back the eigen vectors: evecs = inv(U) * evecs
-    if(computeEigVecs)
-      cholB.matrixU().solveInPlace(Base::m_eivec);
-  }
-  else if(type==BAx_lx)
-  {
-    // compute C = L' A L
-    MatrixType matC = matA.template selfadjointView<Lower>();
-    matC = matC * cholB.matrixL();
-    matC = cholB.matrixU() * matC;
-
-    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
-
-    // transform back the eigen vectors: evecs = L * evecs
-    if(computeEigVecs)
-      Base::m_eivec = cholB.matrixL() * Base::m_eivec;
-  }
-
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
deleted file mode 100644
index 1f21139..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
+++ /dev/null
@@ -1,374 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HESSENBERGDECOMPOSITION_H
-#define EIGEN_HESSENBERGDECOMPOSITION_H
-
-namespace Eigen { 
-
-namespace internal {
-  
-template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType;
-template<typename MatrixType>
-struct traits<HessenbergDecompositionMatrixHReturnType<MatrixType> >
-{
-  typedef MatrixType ReturnType;
-};
-
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class HessenbergDecomposition
-  *
-  * \brief Reduces a square matrix to Hessenberg form by an orthogonal similarity transformation
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the Hessenberg decomposition
-  *
-  * This class performs an Hessenberg decomposition of a matrix \f$ A \f$. In
-  * the real case, the Hessenberg decomposition consists of an orthogonal
-  * matrix \f$ Q \f$ and a Hessenberg matrix \f$ H \f$ such that \f$ A = Q H
-  * Q^T \f$. An orthogonal matrix is a matrix whose inverse equals its
-  * transpose (\f$ Q^{-1} = Q^T \f$). A Hessenberg matrix has zeros below the
-  * subdiagonal, so it is almost upper triangular. The Hessenberg decomposition
-  * of a complex matrix is \f$ A = Q H Q^* \f$ with \f$ Q \f$ unitary (that is,
-  * \f$ Q^{-1} = Q^* \f$).
-  *
-  * Call the function compute() to compute the Hessenberg decomposition of a
-  * given matrix. Alternatively, you can use the
-  * HessenbergDecomposition(const MatrixType&) constructor which computes the
-  * Hessenberg decomposition at construction time. Once the decomposition is
-  * computed, you can use the matrixH() and matrixQ() functions to construct
-  * the matrices H and Q in the decomposition.
-  *
-  * The documentation for matrixH() contains an example of the typical use of
-  * this class.
-  *
-  * \sa class ComplexSchur, class Tridiagonalization, \ref QR_Module "QR Module"
-  */
-template<typename _MatrixType> class HessenbergDecomposition
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      Size = MatrixType::RowsAtCompileTime,
-      SizeMinusOne = Size == Dynamic ? Dynamic : Size - 1,
-      Options = MatrixType::Options,
-      MaxSize = MatrixType::MaxRowsAtCompileTime,
-      MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : MaxSize - 1
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Type for vector of Householder coefficients.
-      *
-      * This is column vector with entries of type #Scalar. The length of the
-      * vector is one less than the size of #MatrixType, if it is a fixed-side
-      * type.
-      */
-    typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
-
-    /** \brief Return type of matrixQ() */
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType;
-    
-    typedef internal::HessenbergDecompositionMatrixHReturnType<MatrixType> MatrixHReturnType;
-
-    /** \brief Default constructor; the decomposition will be computed later.
-      *
-      * \param [in] size  The size of the matrix whose Hessenberg decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().  The \p size parameter is only
-      * used as a hint. It is not an error to give a wrong \p size, but it may
-      * impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit HessenbergDecomposition(Index size = Size==Dynamic ? 2 : Size)
-      : m_matrix(size,size),
-        m_temp(size),
-        m_isInitialized(false)
-    {
-      if(size>1)
-        m_hCoeffs.resize(size-1);
-    }
-
-    /** \brief Constructor; computes Hessenberg decomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
-      *
-      * This constructor calls compute() to compute the Hessenberg
-      * decomposition.
-      *
-      * \sa matrixH() for an example.
-      */
-    template<typename InputType>
-    explicit HessenbergDecomposition(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_temp(matrix.rows()),
-        m_isInitialized(false)
-    {
-      if(matrix.rows()<2)
-      {
-        m_isInitialized = true;
-        return;
-      }
-      m_hCoeffs.resize(matrix.rows()-1,1);
-      _compute(m_matrix, m_hCoeffs, m_temp);
-      m_isInitialized = true;
-    }
-
-    /** \brief Computes Hessenberg decomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
-      * \returns    Reference to \c *this
-      *
-      * The Hessenberg decomposition is computed by bringing the columns of the
-      * matrix successively in the required form using Householder reflections
-      * (see, e.g., Algorithm 7.4.2 in Golub \& Van Loan, <i>%Matrix
-      * Computations</i>). The cost is \f$ 10n^3/3 \f$ flops, where \f$ n \f$
-      * denotes the size of the given matrix.
-      *
-      * This method reuses of the allocated data in the HessenbergDecomposition
-      * object.
-      *
-      * Example: \include HessenbergDecomposition_compute.cpp
-      * Output: \verbinclude HessenbergDecomposition_compute.out
-      */
-    template<typename InputType>
-    HessenbergDecomposition& compute(const EigenBase<InputType>& matrix)
-    {
-      m_matrix = matrix.derived();
-      if(matrix.rows()<2)
-      {
-        m_isInitialized = true;
-        return *this;
-      }
-      m_hCoeffs.resize(matrix.rows()-1,1);
-      _compute(m_matrix, m_hCoeffs, m_temp);
-      m_isInitialized = true;
-      return *this;
-    }
-
-    /** \brief Returns the Householder coefficients.
-      *
-      * \returns a const reference to the vector of Householder coefficients
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * The Householder coefficients allow the reconstruction of the matrix
-      * \f$ Q \f$ in the Hessenberg decomposition from the packed data.
-      *
-      * \sa packedMatrix(), \ref Householder_Module "Householder module"
-      */
-    const CoeffVectorType& householderCoefficients() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return m_hCoeffs;
-    }
-
-    /** \brief Returns the internal representation of the decomposition
-      *
-      *	\returns a const reference to a matrix with the internal representation
-      *	         of the decomposition.
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * The returned matrix contains the following information:
-      *  - the upper part and lower sub-diagonal represent the Hessenberg matrix H
-      *  - the rest of the lower part contains the Householder vectors that, combined with
-      *    Householder coefficients returned by householderCoefficients(),
-      *    allows to reconstruct the matrix Q as
-      *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
-      *    Here, the matrices \f$ H_i \f$ are the Householder transformations
-      *       \f$ H_i = (I - h_i v_i v_i^T) \f$
-      *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
-      *    \f$ v_i \f$ is the Householder vector defined by
-      *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
-      *    with M the matrix returned by this function.
-      *
-      * See LAPACK for further details on this packed storage.
-      *
-      * Example: \include HessenbergDecomposition_packedMatrix.cpp
-      * Output: \verbinclude HessenbergDecomposition_packedMatrix.out
-      *
-      * \sa householderCoefficients()
-      */
-    const MatrixType& packedMatrix() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return m_matrix;
-    }
-
-    /** \brief Reconstructs the orthogonal matrix Q in the decomposition
-      *
-      * \returns object representing the matrix Q
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * This function returns a light-weight object of template class
-      * HouseholderSequence. You can either apply it directly to a matrix or
-      * you can convert it to a matrix of type #MatrixType.
-      *
-      * \sa matrixH() for an example, class HouseholderSequence
-      */
-    HouseholderSequenceType matrixQ() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate())
-             .setLength(m_matrix.rows() - 1)
-             .setShift(1);
-    }
-
-    /** \brief Constructs the Hessenberg matrix H in the decomposition
-      *
-      * \returns expression object representing the matrix H
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * The object returned by this function constructs the Hessenberg matrix H
-      * when it is assigned to a matrix or otherwise evaluated. The matrix H is
-      * constructed from the packed matrix as returned by packedMatrix(): The
-      * upper part (including the subdiagonal) of the packed matrix contains
-      * the matrix H. It may sometimes be better to directly use the packed
-      * matrix instead of constructing the matrix H.
-      *
-      * Example: \include HessenbergDecomposition_matrixH.cpp
-      * Output: \verbinclude HessenbergDecomposition_matrixH.out
-      *
-      * \sa matrixQ(), packedMatrix()
-      */
-    MatrixHReturnType matrixH() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return MatrixHReturnType(*this);
-    }
-
-  private:
-
-    typedef Matrix<Scalar, 1, Size, int(Options) | int(RowMajor), 1, MaxSize> VectorType;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp);
-
-  protected:
-    MatrixType m_matrix;
-    CoeffVectorType m_hCoeffs;
-    VectorType m_temp;
-    bool m_isInitialized;
-};
-
-/** \internal
-  * Performs a tridiagonal decomposition of \a matA in place.
-  *
-  * \param matA the input selfadjoint matrix
-  * \param hCoeffs returned Householder coefficients
-  *
-  * The result is written in the lower triangular part of \a matA.
-  *
-  * Implemented from Golub's "%Matrix Computations", algorithm 8.3.1.
-  *
-  * \sa packedMatrix()
-  */
-template<typename MatrixType>
-void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp)
-{
-  eigen_assert(matA.rows()==matA.cols());
-  Index n = matA.rows();
-  temp.resize(n);
-  for (Index i = 0; i<n-1; ++i)
-  {
-    // let's consider the vector v = i-th column starting at position i+1
-    Index remainingSize = n-i-1;
-    RealScalar beta;
-    Scalar h;
-    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
-    matA.col(i).coeffRef(i+1) = beta;
-    hCoeffs.coeffRef(i) = h;
-
-    // Apply similarity transformation to remaining columns,
-    // i.e., compute A = H A H'
-
-    // A = H A
-    matA.bottomRightCorner(remainingSize, remainingSize)
-        .applyHouseholderOnTheLeft(matA.col(i).tail(remainingSize-1), h, &temp.coeffRef(0));
-
-    // A = A H'
-    matA.rightCols(remainingSize)
-        .applyHouseholderOnTheRight(matA.col(i).tail(remainingSize-1), numext::conj(h), &temp.coeffRef(0));
-  }
-}
-
-namespace internal {
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \brief Expression type for return value of HessenbergDecomposition::matrixH()
-  *
-  * \tparam MatrixType type of matrix in the Hessenberg decomposition
-  *
-  * Objects of this type represent the Hessenberg matrix in the Hessenberg
-  * decomposition of some matrix. The object holds a reference to the
-  * HessenbergDecomposition class until the it is assigned or evaluated for
-  * some other reason (the reference should remain valid during the life time
-  * of this object). This class is the return type of
-  * HessenbergDecomposition::matrixH(); there is probably no other use for this
-  * class.
-  */
-template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType
-: public ReturnByValue<HessenbergDecompositionMatrixHReturnType<MatrixType> >
-{
-  public:
-    /** \brief Constructor.
-      *
-      * \param[in] hess  Hessenberg decomposition
-      */
-    HessenbergDecompositionMatrixHReturnType(const HessenbergDecomposition<MatrixType>& hess) : m_hess(hess) { }
-
-    /** \brief Hessenberg matrix in decomposition.
-      *
-      * \param[out] result  Hessenberg matrix in decomposition \p hess which
-      *                     was passed to the constructor
-      */
-    template <typename ResultType>
-    inline void evalTo(ResultType& result) const
-    {
-      result = m_hess.packedMatrix();
-      Index n = result.rows();
-      if (n>2)
-        result.bottomLeftCorner(n-2, n-2).template triangularView<Lower>().setZero();
-    }
-
-    Index rows() const { return m_hess.packedMatrix().rows(); }
-    Index cols() const { return m_hess.packedMatrix().cols(); }
-
-  protected:
-    const HessenbergDecomposition<MatrixType>& m_hess;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_HESSENBERGDECOMPOSITION_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
deleted file mode 100644
index 66e5a3d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
+++ /dev/null
@@ -1,158 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIXBASEEIGENVALUES_H
-#define EIGEN_MATRIXBASEEIGENVALUES_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Derived, bool IsComplex>
-struct eigenvalues_selector
-{
-  // this is the implementation for the case IsComplex = true
-  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const
-  run(const MatrixBase<Derived>& m)
-  {
-    typedef typename Derived::PlainObject PlainObject;
-    PlainObject m_eval(m);
-    return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues();
-  }
-};
-
-template<typename Derived>
-struct eigenvalues_selector<Derived, false>
-{
-  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const
-  run(const MatrixBase<Derived>& m)
-  {
-    typedef typename Derived::PlainObject PlainObject;
-    PlainObject m_eval(m);
-    return EigenSolver<PlainObject>(m_eval, false).eigenvalues();
-  }
-};
-
-} // end namespace internal
-
-/** \brief Computes the eigenvalues of a matrix 
-  * \returns Column vector containing the eigenvalues.
-  *
-  * \eigenvalues_module
-  * This function computes the eigenvalues with the help of the EigenSolver
-  * class (for real matrices) or the ComplexEigenSolver class (for complex
-  * matrices). 
-  *
-  * The eigenvalues are repeated according to their algebraic multiplicity,
-  * so there are as many eigenvalues as rows in the matrix.
-  *
-  * The SelfAdjointView class provides a better algorithm for selfadjoint
-  * matrices.
-  *
-  * Example: \include MatrixBase_eigenvalues.cpp
-  * Output: \verbinclude MatrixBase_eigenvalues.out
-  *
-  * \sa EigenSolver::eigenvalues(), ComplexEigenSolver::eigenvalues(),
-  *     SelfAdjointView::eigenvalues()
-  */
-template<typename Derived>
-inline typename MatrixBase<Derived>::EigenvaluesReturnType
-MatrixBase<Derived>::eigenvalues() const
-{
-  return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived());
-}
-
-/** \brief Computes the eigenvalues of a matrix
-  * \returns Column vector containing the eigenvalues.
-  *
-  * \eigenvalues_module
-  * This function computes the eigenvalues with the help of the
-  * SelfAdjointEigenSolver class.  The eigenvalues are repeated according to
-  * their algebraic multiplicity, so there are as many eigenvalues as rows in
-  * the matrix.
-  *
-  * Example: \include SelfAdjointView_eigenvalues.cpp
-  * Output: \verbinclude SelfAdjointView_eigenvalues.out
-  *
-  * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues()
-  */
-template<typename MatrixType, unsigned int UpLo> 
-EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType
-SelfAdjointView<MatrixType, UpLo>::eigenvalues() const
-{
-  PlainObject thisAsMatrix(*this);
-  return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
-}
-
-
-
-/** \brief Computes the L2 operator norm
-  * \returns Operator norm of the matrix.
-  *
-  * \eigenvalues_module
-  * This function computes the L2 operator norm of a matrix, which is also
-  * known as the spectral norm. The norm of a matrix \f$ A \f$ is defined to be
-  * \f[ \|A\|_2 = \max_x \frac{\|Ax\|_2}{\|x\|_2} \f]
-  * where the maximum is over all vectors and the norm on the right is the
-  * Euclidean vector norm. The norm equals the largest singular value, which is
-  * the square root of the largest eigenvalue of the positive semi-definite
-  * matrix \f$ A^*A \f$.
-  *
-  * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed
-  * by SelfAdjointView::eigenvalues(), to compute the operator norm of a
-  * matrix.  The SelfAdjointView class provides a better algorithm for
-  * selfadjoint matrices.
-  *
-  * Example: \include MatrixBase_operatorNorm.cpp
-  * Output: \verbinclude MatrixBase_operatorNorm.out
-  *
-  * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm()
-  */
-template<typename Derived>
-inline typename MatrixBase<Derived>::RealScalar
-MatrixBase<Derived>::operatorNorm() const
-{
-  using std::sqrt;
-  typename Derived::PlainObject m_eval(derived());
-  // FIXME if it is really guaranteed that the eigenvalues are already sorted,
-  // then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
-  return sqrt((m_eval*m_eval.adjoint())
-                 .eval()
-		 .template selfadjointView<Lower>()
-		 .eigenvalues()
-		 .maxCoeff()
-		 );
-}
-
-/** \brief Computes the L2 operator norm
-  * \returns Operator norm of the matrix.
-  *
-  * \eigenvalues_module
-  * This function computes the L2 operator norm of a self-adjoint matrix. For a
-  * self-adjoint matrix, the operator norm is the largest eigenvalue.
-  *
-  * The current implementation uses the eigenvalues of the matrix, as computed
-  * by eigenvalues(), to compute the operator norm of the matrix.
-  *
-  * Example: \include SelfAdjointView_operatorNorm.cpp
-  * Output: \verbinclude SelfAdjointView_operatorNorm.out
-  *
-  * \sa eigenvalues(), MatrixBase::operatorNorm()
-  */
-template<typename MatrixType, unsigned int UpLo>
-EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::RealScalar
-SelfAdjointView<MatrixType, UpLo>::operatorNorm() const
-{
-  return eigenvalues().cwiseAbs().maxCoeff();
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealQZ.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealQZ.h
deleted file mode 100644
index 5091301..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealQZ.h
+++ /dev/null
@@ -1,657 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Alexey Korepanov <kaikaikai@yandex.ru>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REAL_QZ_H
-#define EIGEN_REAL_QZ_H
-
-namespace Eigen {
-
-  /** \eigenvalues_module \ingroup Eigenvalues_Module
-   *
-   *
-   * \class RealQZ
-   *
-   * \brief Performs a real QZ decomposition of a pair of square matrices
-   *
-   * \tparam _MatrixType the type of the matrix of which we are computing the
-   * real QZ decomposition; this is expected to be an instantiation of the
-   * Matrix class template.
-   *
-   * Given a real square matrices A and B, this class computes the real QZ
-   * decomposition: \f$ A = Q S Z \f$, \f$ B = Q T Z \f$ where Q and Z are
-   * real orthogonal matrixes, T is upper-triangular matrix, and S is upper
-   * quasi-triangular matrix. An orthogonal matrix is a matrix whose
-   * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
-   * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
-   * blocks and 2-by-2 blocks where further reduction is impossible due to
-   * complex eigenvalues. 
-   *
-   * The eigenvalues of the pencil \f$ A - z B \f$ can be obtained from
-   * 1x1 and 2x2 blocks on the diagonals of S and T.
-   *
-   * Call the function compute() to compute the real QZ decomposition of a
-   * given pair of matrices. Alternatively, you can use the 
-   * RealQZ(const MatrixType& B, const MatrixType& B, bool computeQZ)
-   * constructor which computes the real QZ decomposition at construction
-   * time. Once the decomposition is computed, you can use the matrixS(),
-   * matrixT(), matrixQ() and matrixZ() functions to retrieve the matrices
-   * S, T, Q and Z in the decomposition. If computeQZ==false, some time
-   * is saved by not computing matrices Q and Z.
-   *
-   * Example: \include RealQZ_compute.cpp
-   * Output: \include RealQZ_compute.out
-   *
-   * \note The implementation is based on the algorithm in "Matrix Computations"
-   * by Gene H. Golub and Charles F. Van Loan, and a paper "An algorithm for
-   * generalized eigenvalue problems" by C.B.Moler and G.W.Stewart.
-   *
-   * \sa class RealSchur, class ComplexSchur, class EigenSolver, class ComplexEigenSolver
-   */
-
-  template<typename _MatrixType> class RealQZ
-  {
-    public:
-      typedef _MatrixType MatrixType;
-      enum {
-        RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-        ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-        Options = MatrixType::Options,
-        MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-        MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-      };
-      typedef typename MatrixType::Scalar Scalar;
-      typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-      typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-      typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
-      typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
-
-      /** \brief Default constructor.
-       *
-       * \param [in] size  Positive integer, size of the matrix whose QZ decomposition will be computed.
-       *
-       * The default constructor is useful in cases in which the user intends to
-       * perform decompositions via compute().  The \p size parameter is only
-       * used as a hint. It is not an error to give a wrong \p size, but it may
-       * impair performance.
-       *
-       * \sa compute() for an example.
-       */
-      explicit RealQZ(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime) :
-        m_S(size, size),
-        m_T(size, size),
-        m_Q(size, size),
-        m_Z(size, size),
-        m_workspace(size*2),
-        m_maxIters(400),
-        m_isInitialized(false),
-        m_computeQZ(true)
-      {}
-
-      /** \brief Constructor; computes real QZ decomposition of given matrices
-       * 
-       * \param[in]  A          Matrix A.
-       * \param[in]  B          Matrix B.
-       * \param[in]  computeQZ  If false, A and Z are not computed.
-       *
-       * This constructor calls compute() to compute the QZ decomposition.
-       */
-      RealQZ(const MatrixType& A, const MatrixType& B, bool computeQZ = true) :
-        m_S(A.rows(),A.cols()),
-        m_T(A.rows(),A.cols()),
-        m_Q(A.rows(),A.cols()),
-        m_Z(A.rows(),A.cols()),
-        m_workspace(A.rows()*2),
-        m_maxIters(400),
-        m_isInitialized(false),
-        m_computeQZ(true)
-      {
-        compute(A, B, computeQZ);
-      }
-
-      /** \brief Returns matrix Q in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix Q.
-       */
-      const MatrixType& matrixQ() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
-        return m_Q;
-      }
-
-      /** \brief Returns matrix Z in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix Z.
-       */
-      const MatrixType& matrixZ() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
-        return m_Z;
-      }
-
-      /** \brief Returns matrix S in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix S.
-       */
-      const MatrixType& matrixS() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_S;
-      }
-
-      /** \brief Returns matrix S in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix S.
-       */
-      const MatrixType& matrixT() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_T;
-      }
-
-      /** \brief Computes QZ decomposition of given matrix. 
-       * 
-       * \param[in]  A          Matrix A.
-       * \param[in]  B          Matrix B.
-       * \param[in]  computeQZ  If false, A and Z are not computed.
-       * \returns    Reference to \c *this
-       */
-      RealQZ& compute(const MatrixType& A, const MatrixType& B, bool computeQZ = true);
-
-      /** \brief Reports whether previous computation was successful.
-       *
-       * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-       */
-      ComputationInfo info() const
-      {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_info;
-      }
-
-      /** \brief Returns number of performed QR-like iterations.
-      */
-      Index iterations() const
-      {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_global_iter;
-      }
-
-      /** Sets the maximal number of iterations allowed to converge to one eigenvalue
-       * or decouple the problem.
-      */
-      RealQZ& setMaxIterations(Index maxIters)
-      {
-        m_maxIters = maxIters;
-        return *this;
-      }
-
-    private:
-
-      MatrixType m_S, m_T, m_Q, m_Z;
-      Matrix<Scalar,Dynamic,1> m_workspace;
-      ComputationInfo m_info;
-      Index m_maxIters;
-      bool m_isInitialized;
-      bool m_computeQZ;
-      Scalar m_normOfT, m_normOfS;
-      Index m_global_iter;
-
-      typedef Matrix<Scalar,3,1> Vector3s;
-      typedef Matrix<Scalar,2,1> Vector2s;
-      typedef Matrix<Scalar,2,2> Matrix2s;
-      typedef JacobiRotation<Scalar> JRs;
-
-      void hessenbergTriangular();
-      void computeNorms();
-      Index findSmallSubdiagEntry(Index iu);
-      Index findSmallDiagEntry(Index f, Index l);
-      void splitOffTwoRows(Index i);
-      void pushDownZero(Index z, Index f, Index l);
-      void step(Index f, Index l, Index iter);
-
-  }; // RealQZ
-
-  /** \internal Reduces S and T to upper Hessenberg - triangular form */
-  template<typename MatrixType>
-    void RealQZ<MatrixType>::hessenbergTriangular()
-    {
-
-      const Index dim = m_S.cols();
-
-      // perform QR decomposition of T, overwrite T with R, save Q
-      HouseholderQR<MatrixType> qrT(m_T);
-      m_T = qrT.matrixQR();
-      m_T.template triangularView<StrictlyLower>().setZero();
-      m_Q = qrT.householderQ();
-      // overwrite S with Q* S
-      m_S.applyOnTheLeft(m_Q.adjoint());
-      // init Z as Identity
-      if (m_computeQZ)
-        m_Z = MatrixType::Identity(dim,dim);
-      // reduce S to upper Hessenberg with Givens rotations
-      for (Index j=0; j<=dim-3; j++) {
-        for (Index i=dim-1; i>=j+2; i--) {
-          JRs G;
-          // kill S(i,j)
-          if(m_S.coeff(i,j) != 0)
-          {
-            G.makeGivens(m_S.coeff(i-1,j), m_S.coeff(i,j), &m_S.coeffRef(i-1, j));
-            m_S.coeffRef(i,j) = Scalar(0.0);
-            m_S.rightCols(dim-j-1).applyOnTheLeft(i-1,i,G.adjoint());
-            m_T.rightCols(dim-i+1).applyOnTheLeft(i-1,i,G.adjoint());
-            // update Q
-            if (m_computeQZ)
-              m_Q.applyOnTheRight(i-1,i,G);
-          }
-          // kill T(i,i-1)
-          if(m_T.coeff(i,i-1)!=Scalar(0))
-          {
-            G.makeGivens(m_T.coeff(i,i), m_T.coeff(i,i-1), &m_T.coeffRef(i,i));
-            m_T.coeffRef(i,i-1) = Scalar(0.0);
-            m_S.applyOnTheRight(i,i-1,G);
-            m_T.topRows(i).applyOnTheRight(i,i-1,G);
-            // update Z
-            if (m_computeQZ)
-              m_Z.applyOnTheLeft(i,i-1,G.adjoint());
-          }
-        }
-      }
-    }
-
-  /** \internal Computes vector L1 norms of S and T when in Hessenberg-Triangular form already */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::computeNorms()
-    {
-      const Index size = m_S.cols();
-      m_normOfS = Scalar(0.0);
-      m_normOfT = Scalar(0.0);
-      for (Index j = 0; j < size; ++j)
-      {
-        m_normOfS += m_S.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum();
-        m_normOfT += m_T.row(j).segment(j, size - j).cwiseAbs().sum();
-      }
-    }
-
-
-  /** \internal Look for single small sub-diagonal element S(res, res-1) and return res (or 0) */
-  template<typename MatrixType>
-    inline Index RealQZ<MatrixType>::findSmallSubdiagEntry(Index iu)
-    {
-      using std::abs;
-      Index res = iu;
-      while (res > 0)
-      {
-        Scalar s = abs(m_S.coeff(res-1,res-1)) + abs(m_S.coeff(res,res));
-        if (s == Scalar(0.0))
-          s = m_normOfS;
-        if (abs(m_S.coeff(res,res-1)) < NumTraits<Scalar>::epsilon() * s)
-          break;
-        res--;
-      }
-      return res;
-    }
-
-  /** \internal Look for single small diagonal element T(res, res) for res between f and l, and return res (or f-1)  */
-  template<typename MatrixType>
-    inline Index RealQZ<MatrixType>::findSmallDiagEntry(Index f, Index l)
-    {
-      using std::abs;
-      Index res = l;
-      while (res >= f) {
-        if (abs(m_T.coeff(res,res)) <= NumTraits<Scalar>::epsilon() * m_normOfT)
-          break;
-        res--;
-      }
-      return res;
-    }
-
-  /** \internal decouple 2x2 diagonal block in rows i, i+1 if eigenvalues are real */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::splitOffTwoRows(Index i)
-    {
-      using std::abs;
-      using std::sqrt;
-      const Index dim=m_S.cols();
-      if (abs(m_S.coeff(i+1,i))==Scalar(0))
-        return;
-      Index j = findSmallDiagEntry(i,i+1);
-      if (j==i-1)
-      {
-        // block of (S T^{-1})
-        Matrix2s STi = m_T.template block<2,2>(i,i).template triangularView<Upper>().
-          template solve<OnTheRight>(m_S.template block<2,2>(i,i));
-        Scalar p = Scalar(0.5)*(STi(0,0)-STi(1,1));
-        Scalar q = p*p + STi(1,0)*STi(0,1);
-        if (q>=0) {
-          Scalar z = sqrt(q);
-          // one QR-like iteration for ABi - lambda I
-          // is enough - when we know exact eigenvalue in advance,
-          // convergence is immediate
-          JRs G;
-          if (p>=0)
-            G.makeGivens(p + z, STi(1,0));
-          else
-            G.makeGivens(p - z, STi(1,0));
-          m_S.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint());
-          m_T.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint());
-          // update Q
-          if (m_computeQZ)
-            m_Q.applyOnTheRight(i,i+1,G);
-
-          G.makeGivens(m_T.coeff(i+1,i+1), m_T.coeff(i+1,i));
-          m_S.topRows(i+2).applyOnTheRight(i+1,i,G);
-          m_T.topRows(i+2).applyOnTheRight(i+1,i,G);
-          // update Z
-          if (m_computeQZ)
-            m_Z.applyOnTheLeft(i+1,i,G.adjoint());
-
-          m_S.coeffRef(i+1,i) = Scalar(0.0);
-          m_T.coeffRef(i+1,i) = Scalar(0.0);
-        }
-      }
-      else
-      {
-        pushDownZero(j,i,i+1);
-      }
-    }
-
-  /** \internal use zero in T(z,z) to zero S(l,l-1), working in block f..l */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::pushDownZero(Index z, Index f, Index l)
-    {
-      JRs G;
-      const Index dim = m_S.cols();
-      for (Index zz=z; zz<l; zz++)
-      {
-        // push 0 down
-        Index firstColS = zz>f ? (zz-1) : zz;
-        G.makeGivens(m_T.coeff(zz, zz+1), m_T.coeff(zz+1, zz+1));
-        m_S.rightCols(dim-firstColS).applyOnTheLeft(zz,zz+1,G.adjoint());
-        m_T.rightCols(dim-zz).applyOnTheLeft(zz,zz+1,G.adjoint());
-        m_T.coeffRef(zz+1,zz+1) = Scalar(0.0);
-        // update Q
-        if (m_computeQZ)
-          m_Q.applyOnTheRight(zz,zz+1,G);
-        // kill S(zz+1, zz-1)
-        if (zz>f)
-        {
-          G.makeGivens(m_S.coeff(zz+1, zz), m_S.coeff(zz+1,zz-1));
-          m_S.topRows(zz+2).applyOnTheRight(zz, zz-1,G);
-          m_T.topRows(zz+1).applyOnTheRight(zz, zz-1,G);
-          m_S.coeffRef(zz+1,zz-1) = Scalar(0.0);
-          // update Z
-          if (m_computeQZ)
-            m_Z.applyOnTheLeft(zz,zz-1,G.adjoint());
-        }
-      }
-      // finally kill S(l,l-1)
-      G.makeGivens(m_S.coeff(l,l), m_S.coeff(l,l-1));
-      m_S.applyOnTheRight(l,l-1,G);
-      m_T.applyOnTheRight(l,l-1,G);
-      m_S.coeffRef(l,l-1)=Scalar(0.0);
-      // update Z
-      if (m_computeQZ)
-        m_Z.applyOnTheLeft(l,l-1,G.adjoint());
-    }
-
-  /** \internal QR-like iterative step for block f..l */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::step(Index f, Index l, Index iter)
-    {
-      using std::abs;
-      const Index dim = m_S.cols();
-
-      // x, y, z
-      Scalar x, y, z;
-      if (iter==10)
-      {
-        // Wilkinson ad hoc shift
-        const Scalar
-          a11=m_S.coeff(f+0,f+0), a12=m_S.coeff(f+0,f+1),
-          a21=m_S.coeff(f+1,f+0), a22=m_S.coeff(f+1,f+1), a32=m_S.coeff(f+2,f+1),
-          b12=m_T.coeff(f+0,f+1),
-          b11i=Scalar(1.0)/m_T.coeff(f+0,f+0),
-          b22i=Scalar(1.0)/m_T.coeff(f+1,f+1),
-          a87=m_S.coeff(l-1,l-2),
-          a98=m_S.coeff(l-0,l-1),
-          b77i=Scalar(1.0)/m_T.coeff(l-2,l-2),
-          b88i=Scalar(1.0)/m_T.coeff(l-1,l-1);
-        Scalar ss = abs(a87*b77i) + abs(a98*b88i),
-               lpl = Scalar(1.5)*ss,
-               ll = ss*ss;
-        x = ll + a11*a11*b11i*b11i - lpl*a11*b11i + a12*a21*b11i*b22i
-          - a11*a21*b12*b11i*b11i*b22i;
-        y = a11*a21*b11i*b11i - lpl*a21*b11i + a21*a22*b11i*b22i 
-          - a21*a21*b12*b11i*b11i*b22i;
-        z = a21*a32*b11i*b22i;
-      }
-      else if (iter==16)
-      {
-        // another exceptional shift
-        x = m_S.coeff(f,f)/m_T.coeff(f,f)-m_S.coeff(l,l)/m_T.coeff(l,l) + m_S.coeff(l,l-1)*m_T.coeff(l-1,l) /
-          (m_T.coeff(l-1,l-1)*m_T.coeff(l,l));
-        y = m_S.coeff(f+1,f)/m_T.coeff(f,f);
-        z = 0;
-      }
-      else if (iter>23 && !(iter%8))
-      {
-        // extremely exceptional shift
-        x = internal::random<Scalar>(-1.0,1.0);
-        y = internal::random<Scalar>(-1.0,1.0);
-        z = internal::random<Scalar>(-1.0,1.0);
-      }
-      else
-      {
-        // Compute the shifts: (x,y,z,0...) = (AB^-1 - l1 I) (AB^-1 - l2 I) e1
-        // where l1 and l2 are the eigenvalues of the 2x2 matrix C = U V^-1 where
-        // U and V are 2x2 bottom right sub matrices of A and B. Thus:
-        //  = AB^-1AB^-1 + l1 l2 I - (l1+l2)(AB^-1)
-        //  = AB^-1AB^-1 + det(M) - tr(M)(AB^-1)
-        // Since we are only interested in having x, y, z with a correct ratio, we have:
-        const Scalar
-          a11 = m_S.coeff(f,f),     a12 = m_S.coeff(f,f+1),
-          a21 = m_S.coeff(f+1,f),   a22 = m_S.coeff(f+1,f+1),
-                                    a32 = m_S.coeff(f+2,f+1),
-
-          a88 = m_S.coeff(l-1,l-1), a89 = m_S.coeff(l-1,l),
-          a98 = m_S.coeff(l,l-1),   a99 = m_S.coeff(l,l),
-
-          b11 = m_T.coeff(f,f),     b12 = m_T.coeff(f,f+1),
-                                    b22 = m_T.coeff(f+1,f+1),
-
-          b88 = m_T.coeff(l-1,l-1), b89 = m_T.coeff(l-1,l),
-                                    b99 = m_T.coeff(l,l);
-
-        x = ( (a88/b88 - a11/b11)*(a99/b99 - a11/b11) - (a89/b99)*(a98/b88) + (a98/b88)*(b89/b99)*(a11/b11) ) * (b11/a21)
-          + a12/b22 - (a11/b11)*(b12/b22);
-        y = (a22/b22-a11/b11) - (a21/b11)*(b12/b22) - (a88/b88-a11/b11) - (a99/b99-a11/b11) + (a98/b88)*(b89/b99);
-        z = a32/b22;
-      }
-
-      JRs G;
-
-      for (Index k=f; k<=l-2; k++)
-      {
-        // variables for Householder reflections
-        Vector2s essential2;
-        Scalar tau, beta;
-
-        Vector3s hr(x,y,z);
-
-        // Q_k to annihilate S(k+1,k-1) and S(k+2,k-1)
-        hr.makeHouseholderInPlace(tau, beta);
-        essential2 = hr.template bottomRows<2>();
-        Index fc=(std::max)(k-1,Index(0));  // first col to update
-        m_S.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
-        m_T.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
-        if (m_computeQZ)
-          m_Q.template middleCols<3>(k).applyHouseholderOnTheRight(essential2, tau, m_workspace.data());
-        if (k>f)
-          m_S.coeffRef(k+2,k-1) = m_S.coeffRef(k+1,k-1) = Scalar(0.0);
-
-        // Z_{k1} to annihilate T(k+2,k+1) and T(k+2,k)
-        hr << m_T.coeff(k+2,k+2),m_T.coeff(k+2,k),m_T.coeff(k+2,k+1);
-        hr.makeHouseholderInPlace(tau, beta);
-        essential2 = hr.template bottomRows<2>();
-        {
-          Index lr = (std::min)(k+4,dim); // last row to update
-          Map<Matrix<Scalar,Dynamic,1> > tmp(m_workspace.data(),lr);
-          // S
-          tmp = m_S.template middleCols<2>(k).topRows(lr) * essential2;
-          tmp += m_S.col(k+2).head(lr);
-          m_S.col(k+2).head(lr) -= tau*tmp;
-          m_S.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint();
-          // T
-          tmp = m_T.template middleCols<2>(k).topRows(lr) * essential2;
-          tmp += m_T.col(k+2).head(lr);
-          m_T.col(k+2).head(lr) -= tau*tmp;
-          m_T.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint();
-        }
-        if (m_computeQZ)
-        {
-          // Z
-          Map<Matrix<Scalar,1,Dynamic> > tmp(m_workspace.data(),dim);
-          tmp = essential2.adjoint()*(m_Z.template middleRows<2>(k));
-          tmp += m_Z.row(k+2);
-          m_Z.row(k+2) -= tau*tmp;
-          m_Z.template middleRows<2>(k) -= essential2 * (tau*tmp);
-        }
-        m_T.coeffRef(k+2,k) = m_T.coeffRef(k+2,k+1) = Scalar(0.0);
-
-        // Z_{k2} to annihilate T(k+1,k)
-        G.makeGivens(m_T.coeff(k+1,k+1), m_T.coeff(k+1,k));
-        m_S.applyOnTheRight(k+1,k,G);
-        m_T.applyOnTheRight(k+1,k,G);
-        // update Z
-        if (m_computeQZ)
-          m_Z.applyOnTheLeft(k+1,k,G.adjoint());
-        m_T.coeffRef(k+1,k) = Scalar(0.0);
-
-        // update x,y,z
-        x = m_S.coeff(k+1,k);
-        y = m_S.coeff(k+2,k);
-        if (k < l-2)
-          z = m_S.coeff(k+3,k);
-      } // loop over k
-
-      // Q_{n-1} to annihilate y = S(l,l-2)
-      G.makeGivens(x,y);
-      m_S.applyOnTheLeft(l-1,l,G.adjoint());
-      m_T.applyOnTheLeft(l-1,l,G.adjoint());
-      if (m_computeQZ)
-        m_Q.applyOnTheRight(l-1,l,G);
-      m_S.coeffRef(l,l-2) = Scalar(0.0);
-
-      // Z_{n-1} to annihilate T(l,l-1)
-      G.makeGivens(m_T.coeff(l,l),m_T.coeff(l,l-1));
-      m_S.applyOnTheRight(l,l-1,G);
-      m_T.applyOnTheRight(l,l-1,G);
-      if (m_computeQZ)
-        m_Z.applyOnTheLeft(l,l-1,G.adjoint());
-      m_T.coeffRef(l,l-1) = Scalar(0.0);
-    }
-
-  template<typename MatrixType>
-    RealQZ<MatrixType>& RealQZ<MatrixType>::compute(const MatrixType& A_in, const MatrixType& B_in, bool computeQZ)
-    {
-
-      const Index dim = A_in.cols();
-
-      eigen_assert (A_in.rows()==dim && A_in.cols()==dim 
-          && B_in.rows()==dim && B_in.cols()==dim 
-          && "Need square matrices of the same dimension");
-
-      m_isInitialized = true;
-      m_computeQZ = computeQZ;
-      m_S = A_in; m_T = B_in;
-      m_workspace.resize(dim*2);
-      m_global_iter = 0;
-
-      // entrance point: hessenberg triangular decomposition
-      hessenbergTriangular();
-      // compute L1 vector norms of T, S into m_normOfS, m_normOfT
-      computeNorms();
-
-      Index l = dim-1, 
-            f, 
-            local_iter = 0;
-
-      while (l>0 && local_iter<m_maxIters)
-      {
-        f = findSmallSubdiagEntry(l);
-        // now rows and columns f..l (including) decouple from the rest of the problem
-        if (f>0) m_S.coeffRef(f,f-1) = Scalar(0.0);
-        if (f == l) // One root found
-        {
-          l--;
-          local_iter = 0;
-        }
-        else if (f == l-1) // Two roots found
-        {
-          splitOffTwoRows(f);
-          l -= 2;
-          local_iter = 0;
-        }
-        else // No convergence yet
-        {
-          // if there's zero on diagonal of T, we can isolate an eigenvalue with Givens rotations
-          Index z = findSmallDiagEntry(f,l);
-          if (z>=f)
-          {
-            // zero found
-            pushDownZero(z,f,l);
-          }
-          else
-          {
-            // We are sure now that S.block(f,f, l-f+1,l-f+1) is underuced upper-Hessenberg 
-            // and T.block(f,f, l-f+1,l-f+1) is invertible uper-triangular, which allows to
-            // apply a QR-like iteration to rows and columns f..l.
-            step(f,l, local_iter);
-            local_iter++;
-            m_global_iter++;
-          }
-        }
-      }
-      // check if we converged before reaching iterations limit
-      m_info = (local_iter<m_maxIters) ? Success : NoConvergence;
-
-      // For each non triangular 2x2 diagonal block of S,
-      //    reduce the respective 2x2 diagonal block of T to positive diagonal form using 2x2 SVD.
-      // This step is not mandatory for QZ, but it does help further extraction of eigenvalues/eigenvectors,
-      // and is in par with Lapack/Matlab QZ.
-      if(m_info==Success)
-      {
-        for(Index i=0; i<dim-1; ++i)
-        {
-          if(m_S.coeff(i+1, i) != Scalar(0))
-          {
-            JacobiRotation<Scalar> j_left, j_right;
-            internal::real_2x2_jacobi_svd(m_T, i, i+1, &j_left, &j_right);
-
-            // Apply resulting Jacobi rotations
-            m_S.applyOnTheLeft(i,i+1,j_left);
-            m_S.applyOnTheRight(i,i+1,j_right);
-            m_T.applyOnTheLeft(i,i+1,j_left);
-            m_T.applyOnTheRight(i,i+1,j_right);
-            m_T(i+1,i) = m_T(i,i+1) = Scalar(0);
-
-            if(m_computeQZ) {
-              m_Q.applyOnTheRight(i,i+1,j_left.transpose());
-              m_Z.applyOnTheLeft(i,i+1,j_right.transpose());
-            }
-
-            i++;
-          }
-        }
-      }
-
-      return *this;
-    } // end compute
-
-} // end namespace Eigen
-
-#endif //EIGEN_REAL_QZ
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealSchur.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealSchur.h
deleted file mode 100644
index 7304ef3..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealSchur.h
+++ /dev/null
@@ -1,558 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REAL_SCHUR_H
-#define EIGEN_REAL_SCHUR_H
-
-#include "./HessenbergDecomposition.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class RealSchur
-  *
-  * \brief Performs a real Schur decomposition of a square matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * real Schur decomposition; this is expected to be an instantiation of the
-  * Matrix class template.
-  *
-  * Given a real square matrix A, this class computes the real Schur
-  * decomposition: \f$ A = U T U^T \f$ where U is a real orthogonal matrix and
-  * T is a real quasi-triangular matrix. An orthogonal matrix is a matrix whose
-  * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
-  * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
-  * blocks and 2-by-2 blocks with complex eigenvalues. The eigenvalues of the
-  * blocks on the diagonal of T are the same as the eigenvalues of the matrix
-  * A, and thus the real Schur decomposition is used in EigenSolver to compute
-  * the eigendecomposition of a matrix.
-  *
-  * Call the function compute() to compute the real Schur decomposition of a
-  * given matrix. Alternatively, you can use the RealSchur(const MatrixType&, bool)
-  * constructor which computes the real Schur decomposition at construction
-  * time. Once the decomposition is computed, you can use the matrixU() and
-  * matrixT() functions to retrieve the matrices U and T in the decomposition.
-  *
-  * The documentation of RealSchur(const MatrixType&, bool) contains an example
-  * of the typical use of this class.
-  *
-  * \note The implementation is adapted from
-  * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
-  * Their code is based on EISPACK.
-  *
-  * \sa class ComplexSchur, class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType> class RealSchur
-{
-  public:
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
-    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
-
-    /** \brief Default constructor.
-      *
-      * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().  The \p size parameter is only
-      * used as a hint. It is not an error to give a wrong \p size, but it may
-      * impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit RealSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime)
-            : m_matT(size, size),
-              m_matU(size, size),
-              m_workspaceVector(size),
-              m_hess(size),
-              m_isInitialized(false),
-              m_matUisUptodate(false),
-              m_maxIters(-1)
-    { }
-
-    /** \brief Constructor; computes real Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-      *
-      * This constructor calls compute() to compute the Schur decomposition.
-      *
-      * Example: \include RealSchur_RealSchur_MatrixType.cpp
-      * Output: \verbinclude RealSchur_RealSchur_MatrixType.out
-      */
-    template<typename InputType>
-    explicit RealSchur(const EigenBase<InputType>& matrix, bool computeU = true)
-            : m_matT(matrix.rows(),matrix.cols()),
-              m_matU(matrix.rows(),matrix.cols()),
-              m_workspaceVector(matrix.rows()),
-              m_hess(matrix.rows()),
-              m_isInitialized(false),
-              m_matUisUptodate(false),
-              m_maxIters(-1)
-    {
-      compute(matrix.derived(), computeU);
-    }
-
-    /** \brief Returns the orthogonal matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix U.
-      *
-      * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
-      * member function compute(const MatrixType&, bool) has been called before
-      * to compute the Schur decomposition of a matrix, and \p computeU was set
-      * to true (the default value).
-      *
-      * \sa RealSchur(const MatrixType&, bool) for an example
-      */
-    const MatrixType& matrixU() const
-    {
-      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
-      eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the RealSchur decomposition.");
-      return m_matU;
-    }
-
-    /** \brief Returns the quasi-triangular matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix T.
-      *
-      * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
-      * member function compute(const MatrixType&, bool) has been called before
-      * to compute the Schur decomposition of a matrix.
-      *
-      * \sa RealSchur(const MatrixType&, bool) for an example
-      */
-    const MatrixType& matrixT() const
-    {
-      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
-      return m_matT;
-    }
-  
-    /** \brief Computes Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-      * \returns    Reference to \c *this
-      *
-      * The Schur decomposition is computed by first reducing the matrix to
-      * Hessenberg form using the class HessenbergDecomposition. The Hessenberg
-      * matrix is then reduced to triangular form by performing Francis QR
-      * iterations with implicit double shift. The cost of computing the Schur
-      * decomposition depends on the number of iterations; as a rough guide, it
-      * may be taken to be \f$25n^3\f$ flops if \a computeU is true and
-      * \f$10n^3\f$ flops if \a computeU is false.
-      *
-      * Example: \include RealSchur_compute.cpp
-      * Output: \verbinclude RealSchur_compute.out
-      *
-      * \sa compute(const MatrixType&, bool, Index)
-      */
-    template<typename InputType>
-    RealSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
-
-    /** \brief Computes Schur decomposition of a Hessenberg matrix H = Z T Z^T
-     *  \param[in] matrixH Matrix in Hessenberg form H
-     *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
-     *  \param computeU Computes the matriX U of the Schur vectors
-     * \return Reference to \c *this
-     * 
-     *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
-     *  using either the class HessenbergDecomposition or another mean. 
-     *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
-     *  When computeU is true, this routine computes the matrix U such that 
-     *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
-     * 
-     * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
-     * is not available, the user should give an identity matrix (Q.setIdentity())
-     * 
-     * \sa compute(const MatrixType&, bool)
-     */
-    template<typename HessMatrixType, typename OrthMatrixType>
-    RealSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU);
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. 
-      *
-      * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
-      * of the matrix.
-      */
-    RealSchur& setMaxIterations(Index maxIters)
-    {
-      m_maxIters = maxIters;
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_maxIters;
-    }
-
-    /** \brief Maximum number of iterations per row.
-      *
-      * If not otherwise specified, the maximum number of iterations is this number times the size of the
-      * matrix. It is currently set to 40.
-      */
-    static const int m_maxIterationsPerRow = 40;
-
-  private:
-    
-    MatrixType m_matT;
-    MatrixType m_matU;
-    ColumnVectorType m_workspaceVector;
-    HessenbergDecomposition<MatrixType> m_hess;
-    ComputationInfo m_info;
-    bool m_isInitialized;
-    bool m_matUisUptodate;
-    Index m_maxIters;
-
-    typedef Matrix<Scalar,3,1> Vector3s;
-
-    Scalar computeNormOfT();
-    Index findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero);
-    void splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift);
-    void computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo);
-    void initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector);
-    void performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace);
-};
-
-
-template<typename MatrixType>
-template<typename InputType>
-RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
-{
-  const Scalar considerAsZero = (std::numeric_limits<Scalar>::min)();
-
-  eigen_assert(matrix.cols() == matrix.rows());
-  Index maxIters = m_maxIters;
-  if (maxIters == -1)
-    maxIters = m_maxIterationsPerRow * matrix.rows();
-
-  Scalar scale = matrix.derived().cwiseAbs().maxCoeff();
-  if(scale<considerAsZero)
-  {
-    m_matT.setZero(matrix.rows(),matrix.cols());
-    if(computeU)
-      m_matU.setIdentity(matrix.rows(),matrix.cols());
-    m_info = Success;
-    m_isInitialized = true;
-    m_matUisUptodate = computeU;
-    return *this;
-  }
-
-  // Step 1. Reduce to Hessenberg form
-  m_hess.compute(matrix.derived()/scale);
-
-  // Step 2. Reduce to real Schur form
-  // Note: we copy m_hess.matrixQ() into m_matU here and not in computeFromHessenberg
-  //       to be able to pass our working-space buffer for the Householder to Dense evaluation.
-  m_workspaceVector.resize(matrix.cols());
-  if(computeU)
-    m_hess.matrixQ().evalTo(m_matU, m_workspaceVector);
-  computeFromHessenberg(m_hess.matrixH(), m_matU, computeU);
-
-  m_matT *= scale;
-  
-  return *this;
-}
-template<typename MatrixType>
-template<typename HessMatrixType, typename OrthMatrixType>
-RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU)
-{
-  using std::abs;
-
-  m_matT = matrixH;
-  m_workspaceVector.resize(m_matT.cols());
-  if(computeU && !internal::is_same_dense(m_matU,matrixQ))
-    m_matU = matrixQ;
-  
-  Index maxIters = m_maxIters;
-  if (maxIters == -1)
-    maxIters = m_maxIterationsPerRow * matrixH.rows();
-  Scalar* workspace = &m_workspaceVector.coeffRef(0);
-
-  // The matrix m_matT is divided in three parts. 
-  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. 
-  // Rows il,...,iu is the part we are working on (the active window).
-  // Rows iu+1,...,end are already brought in triangular form.
-  Index iu = m_matT.cols() - 1;
-  Index iter = 0;      // iteration count for current eigenvalue
-  Index totalIter = 0; // iteration count for whole matrix
-  Scalar exshift(0);   // sum of exceptional shifts
-  Scalar norm = computeNormOfT();
-  // sub-diagonal entries smaller than considerAsZero will be treated as zero.
-  // We use eps^2 to enable more precision in small eigenvalues.
-  Scalar considerAsZero = numext::maxi<Scalar>( norm * numext::abs2(NumTraits<Scalar>::epsilon()),
-                                                (std::numeric_limits<Scalar>::min)() );
-
-  if(norm!=Scalar(0))
-  {
-    while (iu >= 0)
-    {
-      Index il = findSmallSubdiagEntry(iu,considerAsZero);
-
-      // Check for convergence
-      if (il == iu) // One root found
-      {
-        m_matT.coeffRef(iu,iu) = m_matT.coeff(iu,iu) + exshift;
-        if (iu > 0)
-          m_matT.coeffRef(iu, iu-1) = Scalar(0);
-        iu--;
-        iter = 0;
-      }
-      else if (il == iu-1) // Two roots found
-      {
-        splitOffTwoRows(iu, computeU, exshift);
-        iu -= 2;
-        iter = 0;
-      }
-      else // No convergence yet
-      {
-        // The firstHouseholderVector vector has to be initialized to something to get rid of a silly GCC warning (-O1 -Wall -DNDEBUG )
-        Vector3s firstHouseholderVector = Vector3s::Zero(), shiftInfo;
-        computeShift(iu, iter, exshift, shiftInfo);
-        iter = iter + 1;
-        totalIter = totalIter + 1;
-        if (totalIter > maxIters) break;
-        Index im;
-        initFrancisQRStep(il, iu, shiftInfo, im, firstHouseholderVector);
-        performFrancisQRStep(il, im, iu, computeU, firstHouseholderVector, workspace);
-      }
-    }
-  }
-  if(totalIter <= maxIters)
-    m_info = Success;
-  else
-    m_info = NoConvergence;
-
-  m_isInitialized = true;
-  m_matUisUptodate = computeU;
-  return *this;
-}
-
-/** \internal Computes and returns vector L1 norm of T */
-template<typename MatrixType>
-inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT()
-{
-  const Index size = m_matT.cols();
-  // FIXME to be efficient the following would requires a triangular reduxion code
-  // Scalar norm = m_matT.upper().cwiseAbs().sum() 
-  //               + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum();
-  Scalar norm(0);
-  for (Index j = 0; j < size; ++j)
-    norm += m_matT.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum();
-  return norm;
-}
-
-/** \internal Look for single small sub-diagonal element and returns its index */
-template<typename MatrixType>
-inline Index RealSchur<MatrixType>::findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero)
-{
-  using std::abs;
-  Index res = iu;
-  while (res > 0)
-  {
-    Scalar s = abs(m_matT.coeff(res-1,res-1)) + abs(m_matT.coeff(res,res));
-
-    s = numext::maxi<Scalar>(s * NumTraits<Scalar>::epsilon(), considerAsZero);
-    
-    if (abs(m_matT.coeff(res,res-1)) <= s)
-      break;
-    res--;
-  }
-  return res;
-}
-
-/** \internal Update T given that rows iu-1 and iu decouple from the rest. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift)
-{
-  using std::sqrt;
-  using std::abs;
-  const Index size = m_matT.cols();
-
-  // The eigenvalues of the 2x2 matrix [a b; c d] are 
-  // trace +/- sqrt(discr/4) where discr = tr^2 - 4*det, tr = a + d, det = ad - bc
-  Scalar p = Scalar(0.5) * (m_matT.coeff(iu-1,iu-1) - m_matT.coeff(iu,iu));
-  Scalar q = p * p + m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu);   // q = tr^2 / 4 - det = discr/4
-  m_matT.coeffRef(iu,iu) += exshift;
-  m_matT.coeffRef(iu-1,iu-1) += exshift;
-
-  if (q >= Scalar(0)) // Two real eigenvalues
-  {
-    Scalar z = sqrt(abs(q));
-    JacobiRotation<Scalar> rot;
-    if (p >= Scalar(0))
-      rot.makeGivens(p + z, m_matT.coeff(iu, iu-1));
-    else
-      rot.makeGivens(p - z, m_matT.coeff(iu, iu-1));
-
-    m_matT.rightCols(size-iu+1).applyOnTheLeft(iu-1, iu, rot.adjoint());
-    m_matT.topRows(iu+1).applyOnTheRight(iu-1, iu, rot);
-    m_matT.coeffRef(iu, iu-1) = Scalar(0); 
-    if (computeU)
-      m_matU.applyOnTheRight(iu-1, iu, rot);
-  }
-
-  if (iu > 1) 
-    m_matT.coeffRef(iu-1, iu-2) = Scalar(0);
-}
-
-/** \internal Form shift in shiftInfo, and update exshift if an exceptional shift is performed. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo)
-{
-  using std::sqrt;
-  using std::abs;
-  shiftInfo.coeffRef(0) = m_matT.coeff(iu,iu);
-  shiftInfo.coeffRef(1) = m_matT.coeff(iu-1,iu-1);
-  shiftInfo.coeffRef(2) = m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu);
-
-  // Wilkinson's original ad hoc shift
-  if (iter == 10)
-  {
-    exshift += shiftInfo.coeff(0);
-    for (Index i = 0; i <= iu; ++i)
-      m_matT.coeffRef(i,i) -= shiftInfo.coeff(0);
-    Scalar s = abs(m_matT.coeff(iu,iu-1)) + abs(m_matT.coeff(iu-1,iu-2));
-    shiftInfo.coeffRef(0) = Scalar(0.75) * s;
-    shiftInfo.coeffRef(1) = Scalar(0.75) * s;
-    shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
-  }
-
-  // MATLAB's new ad hoc shift
-  if (iter == 30)
-  {
-    Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
-    s = s * s + shiftInfo.coeff(2);
-    if (s > Scalar(0))
-    {
-      s = sqrt(s);
-      if (shiftInfo.coeff(1) < shiftInfo.coeff(0))
-        s = -s;
-      s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
-      s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
-      exshift += s;
-      for (Index i = 0; i <= iu; ++i)
-        m_matT.coeffRef(i,i) -= s;
-      shiftInfo.setConstant(Scalar(0.964));
-    }
-  }
-}
-
-/** \internal Compute index im at which Francis QR step starts and the first Householder vector. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector)
-{
-  using std::abs;
-  Vector3s& v = firstHouseholderVector; // alias to save typing
-
-  for (im = iu-2; im >= il; --im)
-  {
-    const Scalar Tmm = m_matT.coeff(im,im);
-    const Scalar r = shiftInfo.coeff(0) - Tmm;
-    const Scalar s = shiftInfo.coeff(1) - Tmm;
-    v.coeffRef(0) = (r * s - shiftInfo.coeff(2)) / m_matT.coeff(im+1,im) + m_matT.coeff(im,im+1);
-    v.coeffRef(1) = m_matT.coeff(im+1,im+1) - Tmm - r - s;
-    v.coeffRef(2) = m_matT.coeff(im+2,im+1);
-    if (im == il) {
-      break;
-    }
-    const Scalar lhs = m_matT.coeff(im,im-1) * (abs(v.coeff(1)) + abs(v.coeff(2)));
-    const Scalar rhs = v.coeff(0) * (abs(m_matT.coeff(im-1,im-1)) + abs(Tmm) + abs(m_matT.coeff(im+1,im+1)));
-    if (abs(lhs) < NumTraits<Scalar>::epsilon() * rhs)
-      break;
-  }
-}
-
-/** \internal Perform a Francis QR step involving rows il:iu and columns im:iu. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace)
-{
-  eigen_assert(im >= il);
-  eigen_assert(im <= iu-2);
-
-  const Index size = m_matT.cols();
-
-  for (Index k = im; k <= iu-2; ++k)
-  {
-    bool firstIteration = (k == im);
-
-    Vector3s v;
-    if (firstIteration)
-      v = firstHouseholderVector;
-    else
-      v = m_matT.template block<3,1>(k,k-1);
-
-    Scalar tau, beta;
-    Matrix<Scalar, 2, 1> ess;
-    v.makeHouseholder(ess, tau, beta);
-    
-    if (beta != Scalar(0)) // if v is not zero
-    {
-      if (firstIteration && k > il)
-        m_matT.coeffRef(k,k-1) = -m_matT.coeff(k,k-1);
-      else if (!firstIteration)
-        m_matT.coeffRef(k,k-1) = beta;
-
-      // These Householder transformations form the O(n^3) part of the algorithm
-      m_matT.block(k, k, 3, size-k).applyHouseholderOnTheLeft(ess, tau, workspace);
-      m_matT.block(0, k, (std::min)(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
-      if (computeU)
-        m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, tau, workspace);
-    }
-  }
-
-  Matrix<Scalar, 2, 1> v = m_matT.template block<2,1>(iu-1, iu-2);
-  Scalar tau, beta;
-  Matrix<Scalar, 1, 1> ess;
-  v.makeHouseholder(ess, tau, beta);
-
-  if (beta != Scalar(0)) // if v is not zero
-  {
-    m_matT.coeffRef(iu-1, iu-2) = beta;
-    m_matT.block(iu-1, iu-1, 2, size-iu+1).applyHouseholderOnTheLeft(ess, tau, workspace);
-    m_matT.block(0, iu-1, iu+1, 2).applyHouseholderOnTheRight(ess, tau, workspace);
-    if (computeU)
-      m_matU.block(0, iu-1, size, 2).applyHouseholderOnTheRight(ess, tau, workspace);
-  }
-
-  // clean up pollution due to round-off errors
-  for (Index i = im+2; i <= iu; ++i)
-  {
-    m_matT.coeffRef(i,i-2) = Scalar(0);
-    if (i > im+2)
-      m_matT.coeffRef(i,i-3) = Scalar(0);
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REAL_SCHUR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
deleted file mode 100644
index 2c22517..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Real Schur needed to real unsymmetrical eigenvalues/eigenvectors.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_REAL_SCHUR_LAPACKE_H
-#define EIGEN_REAL_SCHUR_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_SCHUR_REAL(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW, LAPACKE_COLROW) \
-template<> template<typename InputType> inline \
-RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
-RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, bool computeU) \
-{ \
-  eigen_assert(matrix.cols() == matrix.rows()); \
-\
-  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info; \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  char jobvs, sort='N'; \
-  LAPACK_##LAPACKE_PREFIX_U##_SELECT2 select = 0; \
-  jobvs = (computeU) ? 'V' : 'N'; \
-  m_matU.resize(n, n); \
-  lapack_int ldvs  = internal::convert_index<lapack_int>(m_matU.outerStride()); \
-  m_matT = matrix; \
-  lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride()); \
-  Matrix<EIGTYPE, Dynamic, Dynamic> wr, wi; \
-  wr.resize(n, 1); wi.resize(n, 1); \
-  info = LAPACKE_##LAPACKE_PREFIX##gees( matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda, &sdim, (LAPACKE_TYPE*)wr.data(), (LAPACKE_TYPE*)wi.data(), (LAPACKE_TYPE*)m_matU.data(), ldvs ); \
-  if(info == 0) \
-    m_info = Success; \
-  else \
-    m_info = NoConvergence; \
-\
-  m_isInitialized = true; \
-  m_matUisUptodate = computeU; \
-  return *this; \
-\
-}
-
-EIGEN_LAPACKE_SCHUR_REAL(double,   double, d, D, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_REAL(float,    float,  s, S, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_REAL(double,   double, d, D, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SCHUR_REAL(float,    float,  s, S, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_REAL_SCHUR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
deleted file mode 100644
index 1469236..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
+++ /dev/null
@@ -1,904 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINTEIGENSOLVER_H
-#define EIGEN_SELFADJOINTEIGENSOLVER_H
-
-#include "./Tridiagonalization.h"
-
-namespace Eigen { 
-
-template<typename _MatrixType>
-class GeneralizedSelfAdjointEigenSolver;
-
-namespace internal {
-template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues;
-
-template<typename MatrixType, typename DiagType, typename SubDiagType>
-EIGEN_DEVICE_FUNC
-ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const Index maxIterations, bool computeEigenvectors, MatrixType& eivec);
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class SelfAdjointEigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of selfadjoint matrices
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * eigendecomposition; this is expected to be an instantiation of the Matrix
-  * class template.
-  *
-  * A matrix \f$ A \f$ is selfadjoint if it equals its adjoint. For real
-  * matrices, this means that the matrix is symmetric: it equals its
-  * transpose. This class computes the eigenvalues and eigenvectors of a
-  * selfadjoint matrix. These are the scalars \f$ \lambda \f$ and vectors
-  * \f$ v \f$ such that \f$ Av = \lambda v \f$.  The eigenvalues of a
-  * selfadjoint matrix are always real. If \f$ D \f$ is a diagonal matrix with
-  * the eigenvalues on the diagonal, and \f$ V \f$ is a matrix with the
-  * eigenvectors as its columns, then \f$ A = V D V^{-1} \f$. This is called the
-  * eigendecomposition.
-  *
-  * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
-  * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
-  * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
-  * equal to its transpose, \f$ V^{-1} = V^T \f$.
-  *
-  * The algorithm exploits the fact that the matrix is selfadjoint, making it
-  * faster and more accurate than the general purpose eigenvalue algorithms
-  * implemented in EigenSolver and ComplexEigenSolver.
-  *
-  * Only the \b lower \b triangular \b part of the input matrix is referenced.
-  *
-  * Call the function compute() to compute the eigenvalues and eigenvectors of
-  * a given matrix. Alternatively, you can use the
-  * SelfAdjointEigenSolver(const MatrixType&, int) constructor which computes
-  * the eigenvalues and eigenvectors at construction time. Once the eigenvalue
-  * and eigenvectors are computed, they can be retrieved with the eigenvalues()
-  * and eigenvectors() functions.
-  *
-  * The documentation for SelfAdjointEigenSolver(const MatrixType&, int)
-  * contains an example of the typical use of this class.
-  *
-  * To solve the \em generalized eigenvalue problem \f$ Av = \lambda Bv \f$ and
-  * the likes, see the class GeneralizedSelfAdjointEigenSolver.
-  *
-  * \sa MatrixBase::eigenvalues(), class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType> class SelfAdjointEigenSolver
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    enum {
-      Size = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-    /** \brief Scalar type for matrices of type \p _MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    
-    typedef Matrix<Scalar,Size,Size,ColMajor,MaxColsAtCompileTime,MaxColsAtCompileTime> EigenvectorsType;
-
-    /** \brief Real scalar type for \p _MatrixType.
-      *
-      * This is just \c Scalar if #Scalar is real (e.g., \c float or
-      * \c double), and the type of the real part of \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    
-    friend struct internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>;
-
-    /** \brief Type for vector of eigenvalues as returned by eigenvalues().
-      *
-      * This is a column vector with entries of type #RealScalar.
-      * The length of the vector is the size of \p _MatrixType.
-      */
-    typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
-    typedef Tridiagonalization<MatrixType> TridiagonalizationType;
-    typedef typename TridiagonalizationType::SubDiagonalType SubDiagonalType;
-
-    /** \brief Default constructor for fixed-size matrices.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute(). This constructor
-      * can only be used if \p _MatrixType is a fixed-size matrix; use
-      * SelfAdjointEigenSolver(Index) for dynamic-size matrices.
-      *
-      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver.out
-      */
-    EIGEN_DEVICE_FUNC
-    SelfAdjointEigenSolver()
-        : m_eivec(),
-          m_eivalues(),
-          m_subdiag(),
-          m_hcoeffs(),
-          m_info(InvalidInput),
-          m_isInitialized(false),
-          m_eigenvectorsOk(false)
-    { }
-
-    /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
-      *
-      * \param [in]  size  Positive integer, size of the matrix whose
-      * eigenvalues and eigenvectors will be computed.
-      *
-      * This constructor is useful for dynamic-size matrices, when the user
-      * intends to perform decompositions via compute(). The \p size
-      * parameter is only used as a hint. It is not an error to give a wrong
-      * \p size, but it may impair performance.
-      *
-      * \sa compute() for an example
-      */
-    EIGEN_DEVICE_FUNC
-    explicit SelfAdjointEigenSolver(Index size)
-        : m_eivec(size, size),
-          m_eivalues(size),
-          m_subdiag(size > 1 ? size - 1 : 1),
-          m_hcoeffs(size > 1 ? size - 1 : 1),
-          m_isInitialized(false),
-          m_eigenvectorsOk(false)
-    {}
-
-    /** \brief Constructor; computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
-      *    be computed. Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-      *
-      * This constructor calls compute(const MatrixType&, int) to compute the
-      * eigenvalues of the matrix \p matrix. The eigenvectors are computed if
-      * \p options equals #ComputeEigenvectors.
-      *
-      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.out
-      *
-      * \sa compute(const MatrixType&, int)
-      */
-    template<typename InputType>
-    EIGEN_DEVICE_FUNC
-    explicit SelfAdjointEigenSolver(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors)
-      : m_eivec(matrix.rows(), matrix.cols()),
-        m_eivalues(matrix.cols()),
-        m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1),
-        m_hcoeffs(matrix.cols() > 1 ? matrix.cols() - 1 : 1),
-        m_isInitialized(false),
-        m_eigenvectorsOk(false)
-    {
-      compute(matrix.derived(), options);
-    }
-
-    /** \brief Computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
-      *    be computed. Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of \p matrix.  The eigenvalues()
-      * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
-      * then the eigenvectors are also computed and can be retrieved by
-      * calling eigenvectors().
-      *
-      * This implementation uses a symmetric QR algorithm. The matrix is first
-      * reduced to tridiagonal form using the Tridiagonalization class. The
-      * tridiagonal matrix is then brought to diagonal form with implicit
-      * symmetric QR steps with Wilkinson shift. Details can be found in
-      * Section 8.3 of Golub \& Van Loan, <i>%Matrix Computations</i>.
-      *
-      * The cost of the computation is about \f$ 9n^3 \f$ if the eigenvectors
-      * are required and \f$ 4n^3/3 \f$ if they are not required.
-      *
-      * This method reuses the memory in the SelfAdjointEigenSolver object that
-      * was allocated when the object was constructed, if the size of the
-      * matrix does not change.
-      *
-      * Example: \include SelfAdjointEigenSolver_compute_MatrixType.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType.out
-      *
-      * \sa SelfAdjointEigenSolver(const MatrixType&, int)
-      */
-    template<typename InputType>
-    EIGEN_DEVICE_FUNC
-    SelfAdjointEigenSolver& compute(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors);
-    
-    /** \brief Computes eigendecomposition of given matrix using a closed-form algorithm
-      *
-      * This is a variant of compute(const MatrixType&, int options) which
-      * directly solves the underlying polynomial equation.
-      * 
-      * Currently only 2x2 and 3x3 matrices for which the sizes are known at compile time are supported (e.g., Matrix3d).
-      * 
-      * This method is usually significantly faster than the QR iterative algorithm
-      * but it might also be less accurate. It is also worth noting that
-      * for 3x3 matrices it involves trigonometric operations which are
-      * not necessarily available for all scalar types.
-      * 
-      * For the 3x3 case, we observed the following worst case relative error regarding the eigenvalues:
-      *   - double: 1e-8
-      *   - float:  1e-3
-      *
-      * \sa compute(const MatrixType&, int options)
-      */
-    EIGEN_DEVICE_FUNC
-    SelfAdjointEigenSolver& computeDirect(const MatrixType& matrix, int options = ComputeEigenvectors);
-
-    /**
-      *\brief Computes the eigen decomposition from a tridiagonal symmetric matrix
-      *
-      * \param[in] diag The vector containing the diagonal of the matrix.
-      * \param[in] subdiag The subdiagonal of the matrix.
-      * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-      * \returns Reference to \c *this
-      *
-      * This function assumes that the matrix has been reduced to tridiagonal form.
-      *
-      * \sa compute(const MatrixType&, int) for more information
-      */
-    SelfAdjointEigenSolver& computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options=ComputeEigenvectors);
-
-    /** \brief Returns the eigenvectors of given matrix.
-      *
-      * \returns  A const reference to the matrix whose columns are the eigenvectors.
-      *
-      * \pre The eigenvectors have been computed before.
-      *
-      * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
-      * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
-      * eigenvectors are normalized to have (Euclidean) norm equal to one. If
-      * this object was used to solve the eigenproblem for the selfadjoint
-      * matrix \f$ A \f$, then the matrix returned by this function is the
-      * matrix \f$ V \f$ in the eigendecomposition \f$ A = V D V^{-1} \f$.
-      *
-      * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
-      * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
-      * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
-      * equal to its transpose, \f$ V^{-1} = V^T \f$.
-      *
-      * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out
-      *
-      * \sa eigenvalues()
-      */
-    EIGEN_DEVICE_FUNC
-    const EigenvectorsType& eigenvectors() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec;
-    }
-
-    /** \brief Returns the eigenvalues of given matrix.
-      *
-      * \returns A const reference to the column vector containing the eigenvalues.
-      *
-      * \pre The eigenvalues have been computed before.
-      *
-      * The eigenvalues are repeated according to their algebraic multiplicity,
-      * so there are as many eigenvalues as rows in the matrix. The eigenvalues
-      * are sorted in increasing order.
-      *
-      * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out
-      *
-      * \sa eigenvectors(), MatrixBase::eigenvalues()
-      */
-    EIGEN_DEVICE_FUNC
-    const RealVectorType& eigenvalues() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      return m_eivalues;
-    }
-
-    /** \brief Computes the positive-definite square root of the matrix.
-      *
-      * \returns the positive-definite square root of the matrix
-      *
-      * \pre The eigenvalues and eigenvectors of a positive-definite matrix
-      * have been computed before.
-      *
-      * The square root of a positive-definite matrix \f$ A \f$ is the
-      * positive-definite matrix whose square equals \f$ A \f$. This function
-      * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the
-      * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$.
-      *
-      * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out
-      *
-      * \sa operatorInverseSqrt(), <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
-      */
-    EIGEN_DEVICE_FUNC
-    MatrixType operatorSqrt() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
-    }
-
-    /** \brief Computes the inverse square root of the matrix.
-      *
-      * \returns the inverse positive-definite square root of the matrix
-      *
-      * \pre The eigenvalues and eigenvectors of a positive-definite matrix
-      * have been computed before.
-      *
-      * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to
-      * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is
-      * cheaper than first computing the square root with operatorSqrt() and
-      * then its inverse with MatrixBase::inverse().
-      *
-      * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out
-      *
-      * \sa operatorSqrt(), MatrixBase::inverse(), <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
-      */
-    EIGEN_DEVICE_FUNC
-    MatrixType operatorInverseSqrt() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
-    }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    EIGEN_DEVICE_FUNC
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Maximum number of iterations.
-      *
-      * The algorithm terminates if it does not converge within m_maxIterations * n iterations, where n
-      * denotes the size of the matrix. This value is currently set to 30 (copied from LAPACK).
-      */
-    static const int m_maxIterations = 30;
-
-  protected:
-    static EIGEN_DEVICE_FUNC
-    void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-    
-    EigenvectorsType m_eivec;
-    RealVectorType m_eivalues;
-    typename TridiagonalizationType::SubDiagonalType m_subdiag;
-    typename TridiagonalizationType::CoeffVectorType m_hcoeffs;
-    ComputationInfo m_info;
-    bool m_isInitialized;
-    bool m_eigenvectorsOk;
-};
-
-namespace internal {
-/** \internal
-  *
-  * \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  * Performs a QR step on a tridiagonal symmetric matrix represented as a
-  * pair of two vectors \a diag and \a subdiag.
-  *
-  * \param diag the diagonal part of the input selfadjoint tridiagonal matrix
-  * \param subdiag the sub-diagonal part of the input selfadjoint tridiagonal matrix
-  * \param start starting index of the submatrix to work on
-  * \param end last+1 index of the submatrix to work on
-  * \param matrixQ pointer to the column-major matrix holding the eigenvectors, can be 0
-  * \param n size of the input matrix
-  *
-  * For compilation efficiency reasons, this procedure does not use eigen expression
-  * for its arguments.
-  *
-  * Implemented from Golub's "Matrix Computations", algorithm 8.3.2:
-  * "implicit symmetric QR step with Wilkinson shift"
-  */
-template<int StorageOrder,typename RealScalar, typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC
-static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n);
-}
-
-template<typename MatrixType>
-template<typename InputType>
-EIGEN_DEVICE_FUNC
-SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
-::compute(const EigenBase<InputType>& a_matrix, int options)
-{
-  check_template_parameters();
-  
-  const InputType &matrix(a_matrix.derived());
-  
-  EIGEN_USING_STD(abs);
-  eigen_assert(matrix.cols() == matrix.rows());
-  eigen_assert((options&~(EigVecMask|GenEigMask))==0
-          && (options&EigVecMask)!=EigVecMask
-          && "invalid option parameter");
-  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-  Index n = matrix.cols();
-  m_eivalues.resize(n,1);
-
-  if(n==1)
-  {
-    m_eivec = matrix;
-    m_eivalues.coeffRef(0,0) = numext::real(m_eivec.coeff(0,0));
-    if(computeEigenvectors)
-      m_eivec.setOnes(n,n);
-    m_info = Success;
-    m_isInitialized = true;
-    m_eigenvectorsOk = computeEigenvectors;
-    return *this;
-  }
-
-  // declare some aliases
-  RealVectorType& diag = m_eivalues;
-  EigenvectorsType& mat = m_eivec;
-
-  // map the matrix coefficients to [-1:1] to avoid over- and underflow.
-  mat = matrix.template triangularView<Lower>();
-  RealScalar scale = mat.cwiseAbs().maxCoeff();
-  if(scale==RealScalar(0)) scale = RealScalar(1);
-  mat.template triangularView<Lower>() /= scale;
-  m_subdiag.resize(n-1);
-  m_hcoeffs.resize(n-1);
-  internal::tridiagonalization_inplace(mat, diag, m_subdiag, m_hcoeffs, computeEigenvectors);
-
-  m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
-  
-  // scale back the eigen values
-  m_eivalues *= scale;
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-  return *this;
-}
-
-template<typename MatrixType>
-SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
-::computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options)
-{
-  //TODO : Add an option to scale the values beforehand
-  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-
-  m_eivalues = diag;
-  m_subdiag = subdiag;
-  if (computeEigenvectors)
-  {
-    m_eivec.setIdentity(diag.size(), diag.size());
-  }
-  m_info = internal::computeFromTridiagonal_impl(m_eivalues, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-  return *this;
-}
-
-namespace internal {
-/**
-  * \internal
-  * \brief Compute the eigendecomposition from a tridiagonal matrix
-  *
-  * \param[in,out] diag : On input, the diagonal of the matrix, on output the eigenvalues
-  * \param[in,out] subdiag : The subdiagonal part of the matrix (entries are modified during the decomposition)
-  * \param[in] maxIterations : the maximum number of iterations
-  * \param[in] computeEigenvectors : whether the eigenvectors have to be computed or not
-  * \param[out] eivec : The matrix to store the eigenvectors if computeEigenvectors==true. Must be allocated on input.
-  * \returns \c Success or \c NoConvergence
-  */
-template<typename MatrixType, typename DiagType, typename SubDiagType>
-EIGEN_DEVICE_FUNC
-ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const Index maxIterations, bool computeEigenvectors, MatrixType& eivec)
-{
-  ComputationInfo info;
-  typedef typename MatrixType::Scalar Scalar;
-
-  Index n = diag.size();
-  Index end = n-1;
-  Index start = 0;
-  Index iter = 0; // total number of iterations
-  
-  typedef typename DiagType::RealScalar RealScalar;
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-  const RealScalar precision_inv = RealScalar(1)/NumTraits<RealScalar>::epsilon();
-  while (end>0)
-  {
-    for (Index i = start; i<end; ++i) {
-      if (numext::abs(subdiag[i]) < considerAsZero) {
-        subdiag[i] = RealScalar(0);
-      } else {
-        // abs(subdiag[i]) <= epsilon * sqrt(abs(diag[i]) + abs(diag[i+1]))
-        // Scaled to prevent underflows.
-        const RealScalar scaled_subdiag = precision_inv * subdiag[i];
-        if (scaled_subdiag * scaled_subdiag <= (numext::abs(diag[i])+numext::abs(diag[i+1]))) {
-          subdiag[i] = RealScalar(0);
-        }
-      }
-    }
-
-    // find the largest unreduced block at the end of the matrix.
-    while (end>0 && subdiag[end-1]==RealScalar(0))
-    {
-      end--;
-    }
-    if (end<=0)
-      break;
-
-    // if we spent too many iterations, we give up
-    iter++;
-    if(iter > maxIterations * n) break;
-
-    start = end - 1;
-    while (start>0 && subdiag[start-1]!=0)
-      start--;
-
-    internal::tridiagonal_qr_step<MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor>(diag.data(), subdiag.data(), start, end, computeEigenvectors ? eivec.data() : (Scalar*)0, n);
-  }
-  if (iter <= maxIterations * n)
-    info = Success;
-  else
-    info = NoConvergence;
-
-  // Sort eigenvalues and corresponding vectors.
-  // TODO make the sort optional ?
-  // TODO use a better sort algorithm !!
-  if (info == Success)
-  {
-    for (Index i = 0; i < n-1; ++i)
-    {
-      Index k;
-      diag.segment(i,n-i).minCoeff(&k);
-      if (k > 0)
-      {
-        numext::swap(diag[i], diag[k+i]);
-        if(computeEigenvectors)
-          eivec.col(i).swap(eivec.col(k+i));
-      }
-    }
-  }
-  return info;
-}
-  
-template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(SolverType& eig, const typename SolverType::MatrixType& A, int options)
-  { eig.compute(A,options); }
-};
-
-template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3,false>
-{
-  typedef typename SolverType::MatrixType MatrixType;
-  typedef typename SolverType::RealVectorType VectorType;
-  typedef typename SolverType::Scalar Scalar;
-  typedef typename SolverType::EigenvectorsType EigenvectorsType;
-  
-
-  /** \internal
-   * Computes the roots of the characteristic polynomial of \a m.
-   * For numerical stability m.trace() should be near zero and to avoid over- or underflow m should be normalized.
-   */
-  EIGEN_DEVICE_FUNC
-  static inline void computeRoots(const MatrixType& m, VectorType& roots)
-  {
-    EIGEN_USING_STD(sqrt)
-    EIGEN_USING_STD(atan2)
-    EIGEN_USING_STD(cos)
-    EIGEN_USING_STD(sin)
-    const Scalar s_inv3 = Scalar(1)/Scalar(3);
-    const Scalar s_sqrt3 = sqrt(Scalar(3));
-
-    // The characteristic equation is x^3 - c2*x^2 + c1*x - c0 = 0.  The
-    // eigenvalues are the roots to this equation, all guaranteed to be
-    // real-valued, because the matrix is symmetric.
-    Scalar c0 = m(0,0)*m(1,1)*m(2,2) + Scalar(2)*m(1,0)*m(2,0)*m(2,1) - m(0,0)*m(2,1)*m(2,1) - m(1,1)*m(2,0)*m(2,0) - m(2,2)*m(1,0)*m(1,0);
-    Scalar c1 = m(0,0)*m(1,1) - m(1,0)*m(1,0) + m(0,0)*m(2,2) - m(2,0)*m(2,0) + m(1,1)*m(2,2) - m(2,1)*m(2,1);
-    Scalar c2 = m(0,0) + m(1,1) + m(2,2);
-
-    // Construct the parameters used in classifying the roots of the equation
-    // and in solving the equation for the roots in closed form.
-    Scalar c2_over_3 = c2*s_inv3;
-    Scalar a_over_3 = (c2*c2_over_3 - c1)*s_inv3;
-    a_over_3 = numext::maxi(a_over_3, Scalar(0));
-
-    Scalar half_b = Scalar(0.5)*(c0 + c2_over_3*(Scalar(2)*c2_over_3*c2_over_3 - c1));
-
-    Scalar q = a_over_3*a_over_3*a_over_3 - half_b*half_b;
-    q = numext::maxi(q, Scalar(0));
-
-    // Compute the eigenvalues by solving for the roots of the polynomial.
-    Scalar rho = sqrt(a_over_3);
-    Scalar theta = atan2(sqrt(q),half_b)*s_inv3;  // since sqrt(q) > 0, atan2 is in [0, pi] and theta is in [0, pi/3]
-    Scalar cos_theta = cos(theta);
-    Scalar sin_theta = sin(theta);
-    // roots are already sorted, since cos is monotonically decreasing on [0, pi]
-    roots(0) = c2_over_3 - rho*(cos_theta + s_sqrt3*sin_theta); // == 2*rho*cos(theta+2pi/3)
-    roots(1) = c2_over_3 - rho*(cos_theta - s_sqrt3*sin_theta); // == 2*rho*cos(theta+ pi/3)
-    roots(2) = c2_over_3 + Scalar(2)*rho*cos_theta;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline bool extract_kernel(MatrixType& mat, Ref<VectorType> res, Ref<VectorType> representative)
-  {
-    EIGEN_USING_STD(abs);
-    EIGEN_USING_STD(sqrt);
-    Index i0;
-    // Find non-zero column i0 (by construction, there must exist a non zero coefficient on the diagonal):
-    mat.diagonal().cwiseAbs().maxCoeff(&i0);
-    // mat.col(i0) is a good candidate for an orthogonal vector to the current eigenvector,
-    // so let's save it:
-    representative = mat.col(i0);
-    Scalar n0, n1;
-    VectorType c0, c1;
-    n0 = (c0 = representative.cross(mat.col((i0+1)%3))).squaredNorm();
-    n1 = (c1 = representative.cross(mat.col((i0+2)%3))).squaredNorm();
-    if(n0>n1) res = c0/sqrt(n0);
-    else      res = c1/sqrt(n1);
-
-    return true;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline void run(SolverType& solver, const MatrixType& mat, int options)
-  {
-    eigen_assert(mat.cols() == 3 && mat.cols() == mat.rows());
-    eigen_assert((options&~(EigVecMask|GenEigMask))==0
-            && (options&EigVecMask)!=EigVecMask
-            && "invalid option parameter");
-    bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-    
-    EigenvectorsType& eivecs = solver.m_eivec;
-    VectorType& eivals = solver.m_eivalues;
-  
-    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
-    Scalar shift = mat.trace() / Scalar(3);
-    // TODO Avoid this copy. Currently it is necessary to suppress bogus values when determining maxCoeff and for computing the eigenvectors later
-    MatrixType scaledMat = mat.template selfadjointView<Lower>();
-    scaledMat.diagonal().array() -= shift;
-    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
-    if(scale > 0) scaledMat /= scale;   // TODO for scale==0 we could save the remaining operations
-
-    // compute the eigenvalues
-    computeRoots(scaledMat,eivals);
-
-    // compute the eigenvectors
-    if(computeEigenvectors)
-    {
-      if((eivals(2)-eivals(0))<=Eigen::NumTraits<Scalar>::epsilon())
-      {
-        // All three eigenvalues are numerically the same
-        eivecs.setIdentity();
-      }
-      else
-      {
-        MatrixType tmp;
-        tmp = scaledMat;
-
-        // Compute the eigenvector of the most distinct eigenvalue
-        Scalar d0 = eivals(2) - eivals(1);
-        Scalar d1 = eivals(1) - eivals(0);
-        Index k(0), l(2);
-        if(d0 > d1)
-        {
-          numext::swap(k,l);
-          d0 = d1;
-        }
-
-        // Compute the eigenvector of index k
-        {
-          tmp.diagonal().array () -= eivals(k);
-          // By construction, 'tmp' is of rank 2, and its kernel corresponds to the respective eigenvector.
-          extract_kernel(tmp, eivecs.col(k), eivecs.col(l));
-        }
-
-        // Compute eigenvector of index l
-        if(d0<=2*Eigen::NumTraits<Scalar>::epsilon()*d1)
-        {
-          // If d0 is too small, then the two other eigenvalues are numerically the same,
-          // and thus we only have to ortho-normalize the near orthogonal vector we saved above.
-          eivecs.col(l) -= eivecs.col(k).dot(eivecs.col(l))*eivecs.col(l);
-          eivecs.col(l).normalize();
-        }
-        else
-        {
-          tmp = scaledMat;
-          tmp.diagonal().array () -= eivals(l);
-
-          VectorType dummy;
-          extract_kernel(tmp, eivecs.col(l), dummy);
-        }
-
-        // Compute last eigenvector from the other two
-        eivecs.col(1) = eivecs.col(2).cross(eivecs.col(0)).normalized();
-      }
-    }
-
-    // Rescale back to the original size.
-    eivals *= scale;
-    eivals.array() += shift;
-    
-    solver.m_info = Success;
-    solver.m_isInitialized = true;
-    solver.m_eigenvectorsOk = computeEigenvectors;
-  }
-};
-
-// 2x2 direct eigenvalues decomposition, code from Hauke Heibel
-template<typename SolverType> 
-struct direct_selfadjoint_eigenvalues<SolverType,2,false>
-{
-  typedef typename SolverType::MatrixType MatrixType;
-  typedef typename SolverType::RealVectorType VectorType;
-  typedef typename SolverType::Scalar Scalar;
-  typedef typename SolverType::EigenvectorsType EigenvectorsType;
-  
-  EIGEN_DEVICE_FUNC
-  static inline void computeRoots(const MatrixType& m, VectorType& roots)
-  {
-    EIGEN_USING_STD(sqrt);
-    const Scalar t0 = Scalar(0.5) * sqrt( numext::abs2(m(0,0)-m(1,1)) + Scalar(4)*numext::abs2(m(1,0)));
-    const Scalar t1 = Scalar(0.5) * (m(0,0) + m(1,1));
-    roots(0) = t1 - t0;
-    roots(1) = t1 + t0;
-  }
-  
-  EIGEN_DEVICE_FUNC
-  static inline void run(SolverType& solver, const MatrixType& mat, int options)
-  {
-    EIGEN_USING_STD(sqrt);
-    EIGEN_USING_STD(abs);
-    
-    eigen_assert(mat.cols() == 2 && mat.cols() == mat.rows());
-    eigen_assert((options&~(EigVecMask|GenEigMask))==0
-            && (options&EigVecMask)!=EigVecMask
-            && "invalid option parameter");
-    bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-    
-    EigenvectorsType& eivecs = solver.m_eivec;
-    VectorType& eivals = solver.m_eivalues;
-  
-    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
-    Scalar shift = mat.trace() / Scalar(2);
-    MatrixType scaledMat = mat;
-    scaledMat.coeffRef(0,1) = mat.coeff(1,0);
-    scaledMat.diagonal().array() -= shift;
-    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
-    if(scale > Scalar(0))
-      scaledMat /= scale;
-
-    // Compute the eigenvalues
-    computeRoots(scaledMat,eivals);
-
-    // compute the eigen vectors
-    if(computeEigenvectors)
-    {
-      if((eivals(1)-eivals(0))<=abs(eivals(1))*Eigen::NumTraits<Scalar>::epsilon())
-      {
-        eivecs.setIdentity();
-      }
-      else
-      {
-        scaledMat.diagonal().array () -= eivals(1);
-        Scalar a2 = numext::abs2(scaledMat(0,0));
-        Scalar c2 = numext::abs2(scaledMat(1,1));
-        Scalar b2 = numext::abs2(scaledMat(1,0));
-        if(a2>c2)
-        {
-          eivecs.col(1) << -scaledMat(1,0), scaledMat(0,0);
-          eivecs.col(1) /= sqrt(a2+b2);
-        }
-        else
-        {
-          eivecs.col(1) << -scaledMat(1,1), scaledMat(1,0);
-          eivecs.col(1) /= sqrt(c2+b2);
-        }
-
-        eivecs.col(0) << eivecs.col(1).unitOrthogonal();
-      }
-    }
-
-    // Rescale back to the original size.
-    eivals *= scale;
-    eivals.array() += shift;
-
-    solver.m_info = Success;
-    solver.m_isInitialized = true;
-    solver.m_eigenvectorsOk = computeEigenvectors;
-  }
-};
-
-}
-
-template<typename MatrixType>
-EIGEN_DEVICE_FUNC
-SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
-::computeDirect(const MatrixType& matrix, int options)
-{
-  internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>::run(*this,matrix,options);
-  return *this;
-}
-
-namespace internal {
-
-// Francis implicit QR step.
-template<int StorageOrder,typename RealScalar, typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC
-static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n)
-{
-  // Wilkinson Shift.
-  RealScalar td = (diag[end-1] - diag[end])*RealScalar(0.5);
-  RealScalar e = subdiag[end-1];
-  // Note that thanks to scaling, e^2 or td^2 cannot overflow, however they can still
-  // underflow thus leading to inf/NaN values when using the following commented code:
-  //   RealScalar e2 = numext::abs2(subdiag[end-1]);
-  //   RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * sqrt(td*td + e2));
-  // This explain the following, somewhat more complicated, version:
-  RealScalar mu = diag[end];
-  if(td==RealScalar(0)) {
-    mu -= numext::abs(e);
-  } else if (e != RealScalar(0)) {
-    const RealScalar e2 = numext::abs2(e);
-    const RealScalar h = numext::hypot(td,e);
-    if(e2 == RealScalar(0)) {
-      mu -= e / ((td + (td>RealScalar(0) ? h : -h)) / e);
-    } else {
-      mu -= e2 / (td + (td>RealScalar(0) ? h : -h)); 
-    }
-  }
-
-  RealScalar x = diag[start] - mu;
-  RealScalar z = subdiag[start];
-  // If z ever becomes zero, the Givens rotation will be the identity and
-  // z will stay zero for all future iterations.
-  for (Index k = start; k < end && z != RealScalar(0); ++k)
-  {
-    JacobiRotation<RealScalar> rot;
-    rot.makeGivens(x, z);
-
-    // do T = G' T G
-    RealScalar sdk = rot.s() * diag[k] + rot.c() * subdiag[k];
-    RealScalar dkp1 = rot.s() * subdiag[k] + rot.c() * diag[k+1];
-
-    diag[k] = rot.c() * (rot.c() * diag[k] - rot.s() * subdiag[k]) - rot.s() * (rot.c() * subdiag[k] - rot.s() * diag[k+1]);
-    diag[k+1] = rot.s() * sdk + rot.c() * dkp1;
-    subdiag[k] = rot.c() * sdk - rot.s() * dkp1;
-    
-    if (k > start)
-      subdiag[k - 1] = rot.c() * subdiag[k-1] - rot.s() * z;
-
-    // "Chasing the bulge" to return to triangular form.
-    x = subdiag[k];
-    if (k < end - 1)
-    {
-      z = -rot.s() * subdiag[k+1];
-      subdiag[k + 1] = rot.c() * subdiag[k+1];
-    }
-    
-    // apply the givens rotation to the unit matrix Q = Q * G
-    if (matrixQ)
-    {
-      // FIXME if StorageOrder == RowMajor this operation is not very efficient
-      Map<Matrix<Scalar,Dynamic,Dynamic,StorageOrder> > q(matrixQ,n,n);
-      q.applyOnTheRight(k,k+1,rot);
-    }
-  }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINTEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
deleted file mode 100644
index b0c947d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Self-adjoint eigenvalues/eigenvectors.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_SAEIGENSOLVER_LAPACKE_H
-#define EIGEN_SAEIGENSOLVER_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, EIGCOLROW ) \
-template<> template<typename InputType> inline \
-SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
-SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, int options) \
-{ \
-  eigen_assert(matrix.cols() == matrix.rows()); \
-  eigen_assert((options&~(EigVecMask|GenEigMask))==0 \
-          && (options&EigVecMask)!=EigVecMask \
-          && "invalid option parameter"); \
-  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \
-  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), lda, info; \
-  m_eivalues.resize(n,1); \
-  m_subdiag.resize(n-1); \
-  m_eivec = matrix; \
-\
-  if(n==1) \
-  { \
-    m_eivalues.coeffRef(0,0) = numext::real(m_eivec.coeff(0,0)); \
-    if(computeEigenvectors) m_eivec.setOnes(n,n); \
-    m_info = Success; \
-    m_isInitialized = true; \
-    m_eigenvectorsOk = computeEigenvectors; \
-    return *this; \
-  } \
-\
-  lda = internal::convert_index<lapack_int>(m_eivec.outerStride()); \
-  char jobz, uplo='L'/*, range='A'*/; \
-  jobz = computeEigenvectors ? 'V' : 'N'; \
-\
-  info = LAPACKE_##LAPACKE_NAME( LAPACK_COL_MAJOR, jobz, uplo, n, (LAPACKE_TYPE*)m_eivec.data(), lda, (LAPACKE_RTYPE*)m_eivalues.data() ); \
-  m_info = (info==0) ? Success : NoConvergence; \
-  m_isInitialized = true; \
-  m_eigenvectorsOk = computeEigenvectors; \
-  return *this; \
-}
-
-#define EIGEN_LAPACKE_EIG_SELFADJ(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME )              \
-        EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, ColMajor )  \
-        EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, RowMajor ) 
-
-EIGEN_LAPACKE_EIG_SELFADJ(double,   double,                double, dsyev)
-EIGEN_LAPACKE_EIG_SELFADJ(float,    float,                 float,  ssyev)
-EIGEN_LAPACKE_EIG_SELFADJ(dcomplex, lapack_complex_double, double, zheev)
-EIGEN_LAPACKE_EIG_SELFADJ(scomplex, lapack_complex_float,  float,  cheev)
-
-} // end namespace Eigen
-
-#endif // EIGEN_SAEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/Tridiagonalization.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/Tridiagonalization.h
deleted file mode 100644
index 674c92a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Eigenvalues/Tridiagonalization.h
+++ /dev/null
@@ -1,561 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIDIAGONALIZATION_H
-#define EIGEN_TRIDIAGONALIZATION_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType> struct TridiagonalizationMatrixTReturnType;
-template<typename MatrixType>
-struct traits<TridiagonalizationMatrixTReturnType<MatrixType> >
-  : public traits<typename MatrixType::PlainObject>
-{
-  typedef typename MatrixType::PlainObject ReturnType; // FIXME shall it be a BandMatrix?
-  enum { Flags = 0 };
-};
-
-template<typename MatrixType, typename CoeffVectorType>
-EIGEN_DEVICE_FUNC
-void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class Tridiagonalization
-  *
-  * \brief Tridiagonal decomposition of a selfadjoint matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * tridiagonal decomposition; this is expected to be an instantiation of the
-  * Matrix class template.
-  *
-  * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that:
-  * \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix.
-  *
-  * A tridiagonal matrix is a matrix which has nonzero elements only on the
-  * main diagonal and the first diagonal below and above it. The Hessenberg
-  * decomposition of a selfadjoint matrix is in fact a tridiagonal
-  * decomposition. This class is used in SelfAdjointEigenSolver to compute the
-  * eigenvalues and eigenvectors of a selfadjoint matrix.
-  *
-  * Call the function compute() to compute the tridiagonal decomposition of a
-  * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&)
-  * constructor which computes the tridiagonal Schur decomposition at
-  * construction time. Once the decomposition is computed, you can use the
-  * matrixQ() and matrixT() functions to retrieve the matrices Q and T in the
-  * decomposition.
-  *
-  * The documentation of Tridiagonalization(const MatrixType&) contains an
-  * example of the typical use of this class.
-  *
-  * \sa class HessenbergDecomposition, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class Tridiagonalization
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    enum {
-      Size = MatrixType::RowsAtCompileTime,
-      SizeMinusOne = Size == Dynamic ? Dynamic : (Size > 1 ? Size - 1 : 1),
-      Options = MatrixType::Options,
-      MaxSize = MatrixType::MaxRowsAtCompileTime,
-      MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : (MaxSize > 1 ? MaxSize - 1 : 1)
-    };
-
-    typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
-    typedef typename internal::plain_col_type<MatrixType, RealScalar>::type DiagonalType;
-    typedef Matrix<RealScalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> SubDiagonalType;
-    typedef typename internal::remove_all<typename MatrixType::RealReturnType>::type MatrixTypeRealView;
-    typedef internal::TridiagonalizationMatrixTReturnType<MatrixTypeRealView> MatrixTReturnType;
-
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-              typename internal::add_const_on_value_type<typename Diagonal<const MatrixType>::RealReturnType>::type,
-              const Diagonal<const MatrixType>
-            >::type DiagonalReturnType;
-
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-              typename internal::add_const_on_value_type<typename Diagonal<const MatrixType, -1>::RealReturnType>::type,
-              const Diagonal<const MatrixType, -1>
-            >::type SubDiagonalReturnType;
-
-    /** \brief Return type of matrixQ() */
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType;
-
-    /** \brief Default constructor.
-      *
-      * \param [in]  size  Positive integer, size of the matrix whose tridiagonal
-      * decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().  The \p size parameter is only
-      * used as a hint. It is not an error to give a wrong \p size, but it may
-      * impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit Tridiagonalization(Index size = Size==Dynamic ? 2 : Size)
-      : m_matrix(size,size),
-        m_hCoeffs(size > 1 ? size-1 : 1),
-        m_isInitialized(false)
-    {}
-
-    /** \brief Constructor; computes tridiagonal decomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
-      * is to be computed.
-      *
-      * This constructor calls compute() to compute the tridiagonal decomposition.
-      *
-      * Example: \include Tridiagonalization_Tridiagonalization_MatrixType.cpp
-      * Output: \verbinclude Tridiagonalization_Tridiagonalization_MatrixType.out
-      */
-    template<typename InputType>
-    explicit Tridiagonalization(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_hCoeffs(matrix.cols() > 1 ? matrix.cols()-1 : 1),
-        m_isInitialized(false)
-    {
-      internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
-      m_isInitialized = true;
-    }
-
-    /** \brief Computes tridiagonal decomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
-      * is to be computed.
-      * \returns    Reference to \c *this
-      *
-      * The tridiagonal decomposition is computed by bringing the columns of
-      * the matrix successively in the required form using Householder
-      * reflections. The cost is \f$ 4n^3/3 \f$ flops, where \f$ n \f$ denotes
-      * the size of the given matrix.
-      *
-      * This method reuses of the allocated data in the Tridiagonalization
-      * object, if the size of the matrix does not change.
-      *
-      * Example: \include Tridiagonalization_compute.cpp
-      * Output: \verbinclude Tridiagonalization_compute.out
-      */
-    template<typename InputType>
-    Tridiagonalization& compute(const EigenBase<InputType>& matrix)
-    {
-      m_matrix = matrix.derived();
-      m_hCoeffs.resize(matrix.rows()-1, 1);
-      internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
-      m_isInitialized = true;
-      return *this;
-    }
-
-    /** \brief Returns the Householder coefficients.
-      *
-      * \returns a const reference to the vector of Householder coefficients
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * The Householder coefficients allow the reconstruction of the matrix
-      * \f$ Q \f$ in the tridiagonal decomposition from the packed data.
-      *
-      * Example: \include Tridiagonalization_householderCoefficients.cpp
-      * Output: \verbinclude Tridiagonalization_householderCoefficients.out
-      *
-      * \sa packedMatrix(), \ref Householder_Module "Householder module"
-      */
-    inline CoeffVectorType householderCoefficients() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return m_hCoeffs;
-    }
-
-    /** \brief Returns the internal representation of the decomposition
-      *
-      *	\returns a const reference to a matrix with the internal representation
-      *	         of the decomposition.
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * The returned matrix contains the following information:
-      *  - the strict upper triangular part is equal to the input matrix A.
-      *  - the diagonal and lower sub-diagonal represent the real tridiagonal
-      *    symmetric matrix T.
-      *  - the rest of the lower part contains the Householder vectors that,
-      *    combined with Householder coefficients returned by
-      *    householderCoefficients(), allows to reconstruct the matrix Q as
-      *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
-      *    Here, the matrices \f$ H_i \f$ are the Householder transformations
-      *       \f$ H_i = (I - h_i v_i v_i^T) \f$
-      *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
-      *    \f$ v_i \f$ is the Householder vector defined by
-      *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
-      *    with M the matrix returned by this function.
-      *
-      * See LAPACK for further details on this packed storage.
-      *
-      * Example: \include Tridiagonalization_packedMatrix.cpp
-      * Output: \verbinclude Tridiagonalization_packedMatrix.out
-      *
-      * \sa householderCoefficients()
-      */
-    inline const MatrixType& packedMatrix() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return m_matrix;
-    }
-
-    /** \brief Returns the unitary matrix Q in the decomposition
-      *
-      * \returns object representing the matrix Q
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * This function returns a light-weight object of template class
-      * HouseholderSequence. You can either apply it directly to a matrix or
-      * you can convert it to a matrix of type #MatrixType.
-      *
-      * \sa Tridiagonalization(const MatrixType&) for an example,
-      *     matrixT(), class HouseholderSequence
-      */
-    HouseholderSequenceType matrixQ() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate())
-             .setLength(m_matrix.rows() - 1)
-             .setShift(1);
-    }
-
-    /** \brief Returns an expression of the tridiagonal matrix T in the decomposition
-      *
-      * \returns expression object representing the matrix T
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * Currently, this function can be used to extract the matrix T from internal
-      * data and copy it to a dense matrix object. In most cases, it may be
-      * sufficient to directly use the packed matrix or the vector expressions
-      * returned by diagonal() and subDiagonal() instead of creating a new
-      * dense copy matrix with this function.
-      *
-      * \sa Tridiagonalization(const MatrixType&) for an example,
-      * matrixQ(), packedMatrix(), diagonal(), subDiagonal()
-      */
-    MatrixTReturnType matrixT() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return MatrixTReturnType(m_matrix.real());
-    }
-
-    /** \brief Returns the diagonal of the tridiagonal matrix T in the decomposition.
-      *
-      * \returns expression representing the diagonal of T
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * Example: \include Tridiagonalization_diagonal.cpp
-      * Output: \verbinclude Tridiagonalization_diagonal.out
-      *
-      * \sa matrixT(), subDiagonal()
-      */
-    DiagonalReturnType diagonal() const;
-
-    /** \brief Returns the subdiagonal of the tridiagonal matrix T in the decomposition.
-      *
-      * \returns expression representing the subdiagonal of T
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * \sa diagonal() for an example, matrixT()
-      */
-    SubDiagonalReturnType subDiagonal() const;
-
-  protected:
-
-    MatrixType m_matrix;
-    CoeffVectorType m_hCoeffs;
-    bool m_isInitialized;
-};
-
-template<typename MatrixType>
-typename Tridiagonalization<MatrixType>::DiagonalReturnType
-Tridiagonalization<MatrixType>::diagonal() const
-{
-  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-  return m_matrix.diagonal().real();
-}
-
-template<typename MatrixType>
-typename Tridiagonalization<MatrixType>::SubDiagonalReturnType
-Tridiagonalization<MatrixType>::subDiagonal() const
-{
-  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-  return m_matrix.template diagonal<-1>().real();
-}
-
-namespace internal {
-
-/** \internal
-  * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place.
-  *
-  * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced.
-  *                     On output, the strict upper part is left unchanged, and the lower triangular part
-  *                     represents the T and Q matrices in packed format has detailed below.
-  * \param[out]    hCoeffs returned Householder coefficients (see below)
-  *
-  * On output, the tridiagonal selfadjoint matrix T is stored in the diagonal
-  * and lower sub-diagonal of the matrix \a matA.
-  * The unitary matrix Q is represented in a compact way as a product of
-  * Householder reflectors \f$ H_i \f$ such that:
-  *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
-  * The Householder reflectors are defined as
-  *       \f$ H_i = (I - h_i v_i v_i^T) \f$
-  * where \f$ h_i = hCoeffs[i]\f$ is the \f$ i \f$th Householder coefficient and
-  * \f$ v_i \f$ is the Householder vector defined by
-  *       \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$.
-  *
-  * Implemented from Golub's "Matrix Computations", algorithm 8.3.1.
-  *
-  * \sa Tridiagonalization::packedMatrix()
-  */
-template<typename MatrixType, typename CoeffVectorType>
-EIGEN_DEVICE_FUNC
-void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
-{
-  using numext::conj;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  Index n = matA.rows();
-  eigen_assert(n==matA.cols());
-  eigen_assert(n==hCoeffs.size()+1 || n==1);
-
-  for (Index i = 0; i<n-1; ++i)
-  {
-    Index remainingSize = n-i-1;
-    RealScalar beta;
-    Scalar h;
-    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
-
-    // Apply similarity transformation to remaining columns,
-    // i.e., A = H A H' where H = I - h v v' and v = matA.col(i).tail(n-i-1)
-    matA.col(i).coeffRef(i+1) = 1;
-
-    hCoeffs.tail(n-i-1).noalias() = (matA.bottomRightCorner(remainingSize,remainingSize).template selfadjointView<Lower>()
-                                  * (conj(h) * matA.col(i).tail(remainingSize)));
-
-    hCoeffs.tail(n-i-1) += (conj(h)*RealScalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1);
-
-    matA.bottomRightCorner(remainingSize, remainingSize).template selfadjointView<Lower>()
-      .rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), Scalar(-1));
-
-    matA.col(i).coeffRef(i+1) = beta;
-    hCoeffs.coeffRef(i) = h;
-  }
-}
-
-// forward declaration, implementation at the end of this file
-template<typename MatrixType,
-         int Size=MatrixType::ColsAtCompileTime,
-         bool IsComplex=NumTraits<typename MatrixType::Scalar>::IsComplex>
-struct tridiagonalization_inplace_selector;
-
-/** \brief Performs a full tridiagonalization in place
-  *
-  * \param[in,out]  mat  On input, the selfadjoint matrix whose tridiagonal
-  *    decomposition is to be computed. Only the lower triangular part referenced.
-  *    The rest is left unchanged. On output, the orthogonal matrix Q
-  *    in the decomposition if \p extractQ is true.
-  * \param[out]  diag  The diagonal of the tridiagonal matrix T in the
-  *    decomposition.
-  * \param[out]  subdiag  The subdiagonal of the tridiagonal matrix T in
-  *    the decomposition.
-  * \param[in]  extractQ  If true, the orthogonal matrix Q in the
-  *    decomposition is computed and stored in \p mat.
-  *
-  * Computes the tridiagonal decomposition of the selfadjoint matrix \p mat in place
-  * such that \f$ mat = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real
-  * symmetric tridiagonal matrix.
-  *
-  * The tridiagonal matrix T is passed to the output parameters \p diag and \p subdiag. If
-  * \p extractQ is true, then the orthogonal matrix Q is passed to \p mat. Otherwise the lower
-  * part of the matrix \p mat is destroyed.
-  *
-  * The vectors \p diag and \p subdiag are not resized. The function
-  * assumes that they are already of the correct size. The length of the
-  * vector \p diag should equal the number of rows in \p mat, and the
-  * length of the vector \p subdiag should be one left.
-  *
-  * This implementation contains an optimized path for 3-by-3 matrices
-  * which is especially useful for plane fitting.
-  *
-  * \note Currently, it requires two temporary vectors to hold the intermediate
-  * Householder coefficients, and to reconstruct the matrix Q from the Householder
-  * reflectors.
-  *
-  * Example (this uses the same matrix as the example in
-  *    Tridiagonalization::Tridiagonalization(const MatrixType&)):
-  *    \include Tridiagonalization_decomposeInPlace.cpp
-  * Output: \verbinclude Tridiagonalization_decomposeInPlace.out
-  *
-  * \sa class Tridiagonalization
-  */
-template<typename MatrixType, typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType>
-EIGEN_DEVICE_FUNC
-void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag,
-                                CoeffVectorType& hcoeffs, bool extractQ)
-{
-  eigen_assert(mat.cols()==mat.rows() && diag.size()==mat.rows() && subdiag.size()==mat.rows()-1);
-  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, hcoeffs, extractQ);
-}
-
-/** \internal
-  * General full tridiagonalization
-  */
-template<typename MatrixType, int Size, bool IsComplex>
-struct tridiagonalization_inplace_selector
-{
-  typedef typename Tridiagonalization<MatrixType>::CoeffVectorType CoeffVectorType;
-  typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType;
-  template<typename DiagonalType, typename SubDiagonalType>
-  static EIGEN_DEVICE_FUNC
-      void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType& hCoeffs, bool extractQ)
-  {
-    tridiagonalization_inplace(mat, hCoeffs);
-    diag = mat.diagonal().real();
-    subdiag = mat.template diagonal<-1>().real();
-    if(extractQ)
-      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())
-            .setLength(mat.rows() - 1)
-            .setShift(1);
-  }
-};
-
-/** \internal
-  * Specialization for 3x3 real matrices.
-  * Especially useful for plane fitting.
-  */
-template<typename MatrixType>
-struct tridiagonalization_inplace_selector<MatrixType,3,false>
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-
-  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType>
-  static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType&, bool extractQ)
-  {
-    using std::sqrt;
-    const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
-    diag[0] = mat(0,0);
-    RealScalar v1norm2 = numext::abs2(mat(2,0));
-    if(v1norm2 <= tol)
-    {
-      diag[1] = mat(1,1);
-      diag[2] = mat(2,2);
-      subdiag[0] = mat(1,0);
-      subdiag[1] = mat(2,1);
-      if (extractQ)
-        mat.setIdentity();
-    }
-    else
-    {
-      RealScalar beta = sqrt(numext::abs2(mat(1,0)) + v1norm2);
-      RealScalar invBeta = RealScalar(1)/beta;
-      Scalar m01 = mat(1,0) * invBeta;
-      Scalar m02 = mat(2,0) * invBeta;
-      Scalar q = RealScalar(2)*m01*mat(2,1) + m02*(mat(2,2) - mat(1,1));
-      diag[1] = mat(1,1) + m02*q;
-      diag[2] = mat(2,2) - m02*q;
-      subdiag[0] = beta;
-      subdiag[1] = mat(2,1) - m01 * q;
-      if (extractQ)
-      {
-        mat << 1,   0,    0,
-               0, m01,  m02,
-               0, m02, -m01;
-      }
-    }
-  }
-};
-
-/** \internal
-  * Trivial specialization for 1x1 matrices
-  */
-template<typename MatrixType, bool IsComplex>
-struct tridiagonalization_inplace_selector<MatrixType,1,IsComplex>
-{
-  typedef typename MatrixType::Scalar Scalar;
-
-  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType>
-  static EIGEN_DEVICE_FUNC
-  void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, CoeffVectorType&, bool extractQ)
-  {
-    diag(0,0) = numext::real(mat(0,0));
-    if(extractQ)
-      mat(0,0) = Scalar(1);
-  }
-};
-
-/** \internal
-  * \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  * \brief Expression type for return value of Tridiagonalization::matrixT()
-  *
-  * \tparam MatrixType type of underlying dense matrix
-  */
-template<typename MatrixType> struct TridiagonalizationMatrixTReturnType
-: public ReturnByValue<TridiagonalizationMatrixTReturnType<MatrixType> >
-{
-  public:
-    /** \brief Constructor.
-      *
-      * \param[in] mat The underlying dense matrix
-      */
-    TridiagonalizationMatrixTReturnType(const MatrixType& mat) : m_matrix(mat) { }
-
-    template <typename ResultType>
-    inline void evalTo(ResultType& result) const
-    {
-      result.setZero();
-      result.template diagonal<1>() = m_matrix.template diagonal<-1>().conjugate();
-      result.diagonal() = m_matrix.diagonal();
-      result.template diagonal<-1>() = m_matrix.template diagonal<-1>();
-    }
-
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIDIAGONALIZATION_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/AlignedBox.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/AlignedBox.h
deleted file mode 100644
index 55a9d0a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/AlignedBox.h
+++ /dev/null
@@ -1,486 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// Function void Eigen::AlignedBox::transform(const Transform& transform)
-// is provided under the following license agreement:
-//
-// Software License Agreement (BSD License)
-//
-// Copyright (c) 2011-2014, Willow Garage, Inc.
-// Copyright (c) 2014-2015, Open Source Robotics Foundation
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions
-// are met:
-//
-//  * Redistributions of source code must retain the above copyright
-//    notice, this list of conditions and the following disclaimer.
-//  * Redistributions in binary form must reproduce the above
-//    copyright notice, this list of conditions and the following
-//    disclaimer in the documentation and/or other materials provided
-//    with the distribution.
-//  * Neither the name of Open Source Robotics Foundation nor the names of its
-//    contributors may be used to endorse or promote products derived
-//    from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-// FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-// COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-// BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-// LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-
-#ifndef EIGEN_ALIGNEDBOX_H
-#define EIGEN_ALIGNEDBOX_H
-
-namespace Eigen {
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  *
-  * \class AlignedBox
-  *
-  * \brief An axis aligned box
-  *
-  * \tparam _Scalar the type of the scalar coefficients
-  * \tparam _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
-  *
-  * This class represents an axis aligned box as a pair of the minimal and maximal corners.
-  * \warning The result of most methods is undefined when applied to an empty box. You can check for empty boxes using isEmpty().
-  * \sa alignedboxtypedefs
-  */
-template <typename _Scalar, int _AmbientDim>
-class AlignedBox
-{
-public:
-EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
-  enum { AmbientDimAtCompileTime = _AmbientDim };
-  typedef _Scalar                                   Scalar;
-  typedef NumTraits<Scalar>                         ScalarTraits;
-  typedef Eigen::Index                              Index; ///< \deprecated since Eigen 3.3
-  typedef typename ScalarTraits::Real               RealScalar;
-  typedef typename ScalarTraits::NonInteger         NonInteger;
-  typedef Matrix<Scalar,AmbientDimAtCompileTime,1>  VectorType;
-  typedef CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const VectorType, const VectorType> VectorTypeSum;
-
-  /** Define constants to name the corners of a 1D, 2D or 3D axis aligned bounding box */
-  enum CornerType
-  {
-    /** 1D names @{ */
-    Min=0, Max=1,
-    /** @} */
-
-    /** Identifier for 2D corner @{ */
-    BottomLeft=0, BottomRight=1,
-    TopLeft=2, TopRight=3,
-    /** @} */
-
-    /** Identifier for 3D corner  @{ */
-    BottomLeftFloor=0, BottomRightFloor=1,
-    TopLeftFloor=2, TopRightFloor=3,
-    BottomLeftCeil=4, BottomRightCeil=5,
-    TopLeftCeil=6, TopRightCeil=7
-    /** @} */
-  };
-
-
-  /** Default constructor initializing a null box. */
-  EIGEN_DEVICE_FUNC inline AlignedBox()
-  { if (EIGEN_CONST_CONDITIONAL(AmbientDimAtCompileTime!=Dynamic)) setEmpty(); }
-
-  /** Constructs a null box with \a _dim the dimension of the ambient space. */
-  EIGEN_DEVICE_FUNC inline explicit AlignedBox(Index _dim) : m_min(_dim), m_max(_dim)
-  { setEmpty(); }
-
-  /** Constructs a box with extremities \a _min and \a _max.
-   * \warning If either component of \a _min is larger than the same component of \a _max, the constructed box is empty. */
-  template<typename OtherVectorType1, typename OtherVectorType2>
-  EIGEN_DEVICE_FUNC inline AlignedBox(const OtherVectorType1& _min, const OtherVectorType2& _max) : m_min(_min), m_max(_max) {}
-
-  /** Constructs a box containing a single point \a p. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const MatrixBase<Derived>& p) : m_min(p), m_max(m_min)
-  { }
-
-  EIGEN_DEVICE_FUNC ~AlignedBox() {}
-
-  /** \returns the dimension in which the box holds */
-  EIGEN_DEVICE_FUNC inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size() : Index(AmbientDimAtCompileTime); }
-
-  /** \deprecated use isEmpty() */
-  EIGEN_DEVICE_FUNC inline bool isNull() const { return isEmpty(); }
-
-  /** \deprecated use setEmpty() */
-  EIGEN_DEVICE_FUNC inline void setNull() { setEmpty(); }
-
-  /** \returns true if the box is empty.
-   * \sa setEmpty */
-  EIGEN_DEVICE_FUNC inline bool isEmpty() const { return (m_min.array() > m_max.array()).any(); }
-
-  /** Makes \c *this an empty box.
-   * \sa isEmpty */
-  EIGEN_DEVICE_FUNC inline void setEmpty()
-  {
-    m_min.setConstant( ScalarTraits::highest() );
-    m_max.setConstant( ScalarTraits::lowest() );
-  }
-
-  /** \returns the minimal corner */
-  EIGEN_DEVICE_FUNC inline const VectorType& (min)() const { return m_min; }
-  /** \returns a non const reference to the minimal corner */
-  EIGEN_DEVICE_FUNC inline VectorType& (min)() { return m_min; }
-  /** \returns the maximal corner */
-  EIGEN_DEVICE_FUNC inline const VectorType& (max)() const { return m_max; }
-  /** \returns a non const reference to the maximal corner */
-  EIGEN_DEVICE_FUNC inline VectorType& (max)() { return m_max; }
-
-  /** \returns the center of the box */
-  EIGEN_DEVICE_FUNC inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(VectorTypeSum, RealScalar, quotient)
-  center() const
-  { return (m_min+m_max)/RealScalar(2); }
-
-  /** \returns the lengths of the sides of the bounding box.
-    * Note that this function does not get the same
-    * result for integral or floating scalar types: see
-    */
-  EIGEN_DEVICE_FUNC inline const CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> sizes() const
-  { return m_max - m_min; }
-
-  /** \returns the volume of the bounding box */
-  EIGEN_DEVICE_FUNC inline Scalar volume() const
-  { return sizes().prod(); }
-
-  /** \returns an expression for the bounding box diagonal vector
-    * if the length of the diagonal is needed: diagonal().norm()
-    * will provide it.
-    */
-  EIGEN_DEVICE_FUNC inline CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> diagonal() const
-  { return sizes(); }
-
-  /** \returns the vertex of the bounding box at the corner defined by
-    * the corner-id corner. It works only for a 1D, 2D or 3D bounding box.
-    * For 1D bounding boxes corners are named by 2 enum constants:
-    * BottomLeft and BottomRight.
-    * For 2D bounding boxes, corners are named by 4 enum constants:
-    * BottomLeft, BottomRight, TopLeft, TopRight.
-    * For 3D bounding boxes, the following names are added:
-    * BottomLeftCeil, BottomRightCeil, TopLeftCeil, TopRightCeil.
-    */
-  EIGEN_DEVICE_FUNC inline VectorType corner(CornerType corner) const
-  {
-    EIGEN_STATIC_ASSERT(_AmbientDim <= 3, THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE);
-
-    VectorType res;
-
-    Index mult = 1;
-    for(Index d=0; d<dim(); ++d)
-    {
-      if( mult & corner ) res[d] = m_max[d];
-      else                res[d] = m_min[d];
-      mult *= 2;
-    }
-    return res;
-  }
-
-  /** \returns a random point inside the bounding box sampled with
-   * a uniform distribution */
-  EIGEN_DEVICE_FUNC inline VectorType sample() const
-  {
-    VectorType r(dim());
-    for(Index d=0; d<dim(); ++d)
-    {
-      if(!ScalarTraits::IsInteger)
-      {
-        r[d] = m_min[d] + (m_max[d]-m_min[d])
-             * internal::random<Scalar>(Scalar(0), Scalar(1));
-      }
-      else
-        r[d] = internal::random(m_min[d], m_max[d]);
-    }
-    return r;
-  }
-
-  /** \returns true if the point \a p is inside the box \c *this. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline bool contains(const MatrixBase<Derived>& p) const
-  {
-    typename internal::nested_eval<Derived,2>::type p_n(p.derived());
-    return (m_min.array()<=p_n.array()).all() && (p_n.array()<=m_max.array()).all();
-  }
-
-  /** \returns true if the box \a b is entirely inside the box \c *this. */
-  EIGEN_DEVICE_FUNC inline bool contains(const AlignedBox& b) const
-  { return (m_min.array()<=(b.min)().array()).all() && ((b.max)().array()<=m_max.array()).all(); }
-
-  /** \returns true if the box \a b is intersecting the box \c *this.
-   * \sa intersection, clamp */
-  EIGEN_DEVICE_FUNC inline bool intersects(const AlignedBox& b) const
-  { return (m_min.array()<=(b.max)().array()).all() && ((b.min)().array()<=m_max.array()).all(); }
-
-  /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this.
-   * \sa extend(const AlignedBox&) */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const MatrixBase<Derived>& p)
-  {
-    typename internal::nested_eval<Derived,2>::type p_n(p.derived());
-    m_min = m_min.cwiseMin(p_n);
-    m_max = m_max.cwiseMax(p_n);
-    return *this;
-  }
-
-  /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this.
-   * \sa merged, extend(const MatrixBase&) */
-  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const AlignedBox& b)
-  {
-    m_min = m_min.cwiseMin(b.m_min);
-    m_max = m_max.cwiseMax(b.m_max);
-    return *this;
-  }
-
-  /** Clamps \c *this by the box \a b and returns a reference to \c *this.
-   * \note If the boxes don't intersect, the resulting box is empty.
-   * \sa intersection(), intersects() */
-  EIGEN_DEVICE_FUNC inline AlignedBox& clamp(const AlignedBox& b)
-  {
-    m_min = m_min.cwiseMax(b.m_min);
-    m_max = m_max.cwiseMin(b.m_max);
-    return *this;
-  }
-
-  /** Returns an AlignedBox that is the intersection of \a b and \c *this
-   * \note If the boxes don't intersect, the resulting box is empty.
-   * \sa intersects(), clamp, contains()  */
-  EIGEN_DEVICE_FUNC inline AlignedBox intersection(const AlignedBox& b) const
-  {return AlignedBox(m_min.cwiseMax(b.m_min), m_max.cwiseMin(b.m_max)); }
-
-  /** Returns an AlignedBox that is the union of \a b and \c *this.
-   * \note Merging with an empty box may result in a box bigger than \c *this.
-   * \sa extend(const AlignedBox&) */
-  EIGEN_DEVICE_FUNC inline AlignedBox merged(const AlignedBox& b) const
-  { return AlignedBox(m_min.cwiseMin(b.m_min), m_max.cwiseMax(b.m_max)); }
-
-  /** Translate \c *this by the vector \a t and returns a reference to \c *this. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline AlignedBox& translate(const MatrixBase<Derived>& a_t)
-  {
-    const typename internal::nested_eval<Derived,2>::type t(a_t.derived());
-    m_min += t;
-    m_max += t;
-    return *this;
-  }
-
-  /** \returns a copy of \c *this translated by the vector \a t. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline AlignedBox translated(const MatrixBase<Derived>& a_t) const
-  {
-    AlignedBox result(m_min, m_max);
-    result.translate(a_t);
-    return result;
-  }
-
-  /** \returns the squared distance between the point \a p and the box \c *this,
-    * and zero if \a p is inside the box.
-    * \sa exteriorDistance(const MatrixBase&), squaredExteriorDistance(const AlignedBox&)
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const MatrixBase<Derived>& p) const;
-
-  /** \returns the squared distance between the boxes \a b and \c *this,
-    * and zero if the boxes intersect.
-    * \sa exteriorDistance(const AlignedBox&), squaredExteriorDistance(const MatrixBase&)
-    */
-  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const AlignedBox& b) const;
-
-  /** \returns the distance between the point \a p and the box \c *this,
-    * and zero if \a p is inside the box.
-    * \sa squaredExteriorDistance(const MatrixBase&), exteriorDistance(const AlignedBox&)
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const MatrixBase<Derived>& p) const
-  { EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(p))); }
-
-  /** \returns the distance between the boxes \a b and \c *this,
-    * and zero if the boxes intersect.
-    * \sa squaredExteriorDistance(const AlignedBox&), exteriorDistance(const MatrixBase&)
-    */
-  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const AlignedBox& b) const
-  { EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(b))); }
-
-  /**
-   * Specialization of transform for pure translation.
-   */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC inline void transform(
-      const typename Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>::TranslationType& translation)
-  {
-    this->translate(translation);
-  }
-
-  /**
-   * Transforms this box by \a transform and recomputes it to
-   * still be an axis-aligned box.
-   *
-   * \note This method is provided under BSD license (see the top of this file).
-   */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC inline void transform(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform)
-  {
-    // Only Affine and Isometry transforms are currently supported.
-    EIGEN_STATIC_ASSERT(Mode == Affine || Mode == AffineCompact || Mode == Isometry, THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS);
-
-    // Method adapted from FCL src/shape/geometric_shapes_utility.cpp#computeBV<AABB, Box>(...)
-    // https://github.com/flexible-collision-library/fcl/blob/fcl-0.4/src/shape/geometric_shapes_utility.cpp#L292
-    //
-    // Here's a nice explanation why it works: https://zeuxcg.org/2010/10/17/aabb-from-obb-with-component-wise-abs/
-
-    // two times rotated extent
-    const VectorType rotated_extent_2 = transform.linear().cwiseAbs() * sizes();
-    // two times new center
-    const VectorType rotated_center_2 = transform.linear() * (this->m_max + this->m_min) +
-        Scalar(2) * transform.translation();
-
-    this->m_max = (rotated_center_2 + rotated_extent_2) / Scalar(2);
-    this->m_min = (rotated_center_2 - rotated_extent_2) / Scalar(2);
-  }
-
-  /**
-   * \returns a copy of \c *this transformed by \a transform and recomputed to
-   * still be an axis-aligned box.
-   */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC AlignedBox transformed(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform) const
-  {
-    AlignedBox result(m_min, m_max);
-    result.transform(transform);
-    return result;
-  }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<AlignedBox,
-           AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
-  {
-    return typename internal::cast_return_type<AlignedBox,
-                    AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
-  }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
-  {
-    m_min = (other.min)().template cast<Scalar>();
-    m_max = (other.max)().template cast<Scalar>();
-  }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const AlignedBox& other, const RealScalar& prec = ScalarTraits::dummy_precision()) const
-  { return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec); }
-
-protected:
-
-  VectorType m_min, m_max;
-};
-
-
-
-template<typename Scalar,int AmbientDim>
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const MatrixBase<Derived>& a_p) const
-{
-  typename internal::nested_eval<Derived,2*AmbientDim>::type p(a_p.derived());
-  Scalar dist2(0);
-  Scalar aux;
-  for (Index k=0; k<dim(); ++k)
-  {
-    if( m_min[k] > p[k] )
-    {
-      aux = m_min[k] - p[k];
-      dist2 += aux*aux;
-    }
-    else if( p[k] > m_max[k] )
-    {
-      aux = p[k] - m_max[k];
-      dist2 += aux*aux;
-    }
-  }
-  return dist2;
-}
-
-template<typename Scalar,int AmbientDim>
-EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const AlignedBox& b) const
-{
-  Scalar dist2(0);
-  Scalar aux;
-  for (Index k=0; k<dim(); ++k)
-  {
-    if( m_min[k] > b.m_max[k] )
-    {
-      aux = m_min[k] - b.m_max[k];
-      dist2 += aux*aux;
-    }
-    else if( b.m_min[k] > m_max[k] )
-    {
-      aux = b.m_min[k] - m_max[k];
-      dist2 += aux*aux;
-    }
-  }
-  return dist2;
-}
-
-/** \defgroup alignedboxtypedefs Global aligned box typedefs
-  *
-  * \ingroup Geometry_Module
-  *
-  * Eigen defines several typedef shortcuts for most common aligned box types.
-  *
-  * The general patterns are the following:
-  *
-  * \c AlignedBoxSizeType where \c Size can be \c 1, \c 2,\c 3,\c 4 for fixed size boxes or \c X for dynamic size,
-  * and where \c Type can be \c i for integer, \c f for float, \c d for double.
-  *
-  * For example, \c AlignedBox3d is a fixed-size 3x3 aligned box type of doubles, and \c AlignedBoxXf is a dynamic-size aligned box of floats.
-  *
-  * \sa class AlignedBox
-  */
-
-#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)    \
-/** \ingroup alignedboxtypedefs */                                 \
-typedef AlignedBox<Type, Size>   AlignedBox##SizeSuffix##TypeSuffix;
-
-#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 1, 1) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X)
-
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int,                  i)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float,                f)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double,               d)
-
-#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
-#undef EIGEN_MAKE_TYPEDEFS
-
-} // end namespace Eigen
-
-#endif // EIGEN_ALIGNEDBOX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/AngleAxis.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/AngleAxis.h
deleted file mode 100644
index 78328b6..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/AngleAxis.h
+++ /dev/null
@@ -1,247 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ANGLEAXIS_H
-#define EIGEN_ANGLEAXIS_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class AngleAxis
-  *
-  * \brief Represents a 3D rotation as a rotation angle around an arbitrary 3D axis
-  *
-  * \param _Scalar the scalar type, i.e., the type of the coefficients.
-  *
-  * \warning When setting up an AngleAxis object, the axis vector \b must \b be \b normalized.
-  *
-  * The following two typedefs are provided for convenience:
-  * \li \c AngleAxisf for \c float
-  * \li \c AngleAxisd for \c double
-  *
-  * Combined with MatrixBase::Unit{X,Y,Z}, AngleAxis can be used to easily
-  * mimic Euler-angles. Here is an example:
-  * \include AngleAxis_mimic_euler.cpp
-  * Output: \verbinclude AngleAxis_mimic_euler.out
-  *
-  * \note This class is not aimed to be used to store a rotation transformation,
-  * but rather to make easier the creation of other rotation (Quaternion, rotation Matrix)
-  * and transformation objects.
-  *
-  * \sa class Quaternion, class Transform, MatrixBase::UnitX()
-  */
-
-namespace internal {
-template<typename _Scalar> struct traits<AngleAxis<_Scalar> >
-{
-  typedef _Scalar Scalar;
-};
-}
-
-template<typename _Scalar>
-class AngleAxis : public RotationBase<AngleAxis<_Scalar>,3>
-{
-  typedef RotationBase<AngleAxis<_Scalar>,3> Base;
-
-public:
-
-  using Base::operator*;
-
-  enum { Dim = 3 };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  typedef Matrix<Scalar,3,3> Matrix3;
-  typedef Matrix<Scalar,3,1> Vector3;
-  typedef Quaternion<Scalar> QuaternionType;
-
-protected:
-
-  Vector3 m_axis;
-  Scalar m_angle;
-
-public:
-
-  /** Default constructor without initialization. */
-  EIGEN_DEVICE_FUNC AngleAxis() {}
-  /** Constructs and initialize the angle-axis rotation from an \a angle in radian
-    * and an \a axis which \b must \b be \b normalized.
-    *
-    * \warning If the \a axis vector is not normalized, then the angle-axis object
-    *          represents an invalid rotation. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC 
-  inline AngleAxis(const Scalar& angle, const MatrixBase<Derived>& axis) : m_axis(axis), m_angle(angle) {}
-  /** Constructs and initialize the angle-axis rotation from a quaternion \a q.
-    * This function implicitly normalizes the quaternion \a q.
-    */
-  template<typename QuatDerived> 
-  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const QuaternionBase<QuatDerived>& q) { *this = q; }
-  /** Constructs and initialize the angle-axis rotation from a 3x3 rotation matrix. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const MatrixBase<Derived>& m) { *this = m; }
-
-  /** \returns the value of the rotation angle in radian */
-  EIGEN_DEVICE_FUNC Scalar angle() const { return m_angle; }
-  /** \returns a read-write reference to the stored angle in radian */
-  EIGEN_DEVICE_FUNC Scalar& angle() { return m_angle; }
-
-  /** \returns the rotation axis */
-  EIGEN_DEVICE_FUNC const Vector3& axis() const { return m_axis; }
-  /** \returns a read-write reference to the stored rotation axis.
-    *
-    * \warning The rotation axis must remain a \b unit vector.
-    */
-  EIGEN_DEVICE_FUNC Vector3& axis() { return m_axis; }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline QuaternionType operator* (const AngleAxis& other) const
-  { return QuaternionType(*this) * QuaternionType(other); }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline QuaternionType operator* (const QuaternionType& other) const
-  { return QuaternionType(*this) * other; }
-
-  /** Concatenates two rotations */
-  friend EIGEN_DEVICE_FUNC inline QuaternionType operator* (const QuaternionType& a, const AngleAxis& b)
-  { return a * QuaternionType(b); }
-
-  /** \returns the inverse rotation, i.e., an angle-axis with opposite rotation angle */
-  EIGEN_DEVICE_FUNC AngleAxis inverse() const
-  { return AngleAxis(-m_angle, m_axis); }
-
-  template<class QuatDerived>
-  EIGEN_DEVICE_FUNC AngleAxis& operator=(const QuaternionBase<QuatDerived>& q);
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC AngleAxis& operator=(const MatrixBase<Derived>& m);
-
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m);
-  EIGEN_DEVICE_FUNC Matrix3 toRotationMatrix(void) const;
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type cast() const
-  { return typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other)
-  {
-    m_axis = other.axis().template cast<Scalar>();
-    m_angle = Scalar(other.angle());
-  }
-
-  EIGEN_DEVICE_FUNC static inline const AngleAxis Identity() { return AngleAxis(Scalar(0), Vector3::UnitX()); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const AngleAxis& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_axis.isApprox(other.m_axis, prec) && internal::isApprox(m_angle,other.m_angle, prec); }
-};
-
-/** \ingroup Geometry_Module
-  * single precision angle-axis type */
-typedef AngleAxis<float> AngleAxisf;
-/** \ingroup Geometry_Module
-  * double precision angle-axis type */
-typedef AngleAxis<double> AngleAxisd;
-
-/** Set \c *this from a \b unit quaternion.
-  *
-  * The resulting axis is normalized, and the computed angle is in the [0,pi] range.
-  * 
-  * This function implicitly normalizes the quaternion \a q.
-  */
-template<typename Scalar>
-template<typename QuatDerived>
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionBase<QuatDerived>& q)
-{
-  EIGEN_USING_STD(atan2)
-  EIGEN_USING_STD(abs)
-  Scalar n = q.vec().norm();
-  if(n<NumTraits<Scalar>::epsilon())
-    n = q.vec().stableNorm();
-
-  if (n != Scalar(0))
-  {
-    m_angle = Scalar(2)*atan2(n, abs(q.w()));
-    if(q.w() < Scalar(0))
-      n = -n;
-    m_axis  = q.vec() / n;
-  }
-  else
-  {
-    m_angle = Scalar(0);
-    m_axis << Scalar(1), Scalar(0), Scalar(0);
-  }
-  return *this;
-}
-
-/** Set \c *this from a 3x3 rotation matrix \a mat.
-  */
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const MatrixBase<Derived>& mat)
-{
-  // Since a direct conversion would not be really faster,
-  // let's use the robust Quaternion implementation:
-  return *this = QuaternionType(mat);
-}
-
-/**
-* \brief Sets \c *this from a 3x3 rotation matrix.
-**/
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
-{
-  return *this = QuaternionType(mat);
-}
-
-/** Constructs and \returns an equivalent 3x3 rotation matrix.
-  */
-template<typename Scalar>
-typename AngleAxis<Scalar>::Matrix3
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>::toRotationMatrix(void) const
-{
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  Matrix3 res;
-  Vector3 sin_axis  = sin(m_angle) * m_axis;
-  Scalar c = cos(m_angle);
-  Vector3 cos1_axis = (Scalar(1)-c) * m_axis;
-
-  Scalar tmp;
-  tmp = cos1_axis.x() * m_axis.y();
-  res.coeffRef(0,1) = tmp - sin_axis.z();
-  res.coeffRef(1,0) = tmp + sin_axis.z();
-
-  tmp = cos1_axis.x() * m_axis.z();
-  res.coeffRef(0,2) = tmp + sin_axis.y();
-  res.coeffRef(2,0) = tmp - sin_axis.y();
-
-  tmp = cos1_axis.y() * m_axis.z();
-  res.coeffRef(1,2) = tmp - sin_axis.x();
-  res.coeffRef(2,1) = tmp + sin_axis.x();
-
-  res.diagonal() = (cos1_axis.cwiseProduct(m_axis)).array() + c;
-
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ANGLEAXIS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/EulerAngles.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/EulerAngles.h
deleted file mode 100644
index 19b734c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/EulerAngles.h
+++ /dev/null
@@ -1,114 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EULERANGLES_H
-#define EIGEN_EULERANGLES_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  *
-  * \returns the Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a a0,\a a1,\a a2)
-  *
-  * Each of the three parameters \a a0,\a a1,\a a2 represents the respective rotation axis as an integer in {0,1,2}.
-  * For instance, in:
-  * \code Vector3f ea = mat.eulerAngles(2, 0, 2); \endcode
-  * "2" represents the z axis and "0" the x axis, etc. The returned angles are such that
-  * we have the following equality:
-  * \code
-  * mat == AngleAxisf(ea[0], Vector3f::UnitZ())
-  *      * AngleAxisf(ea[1], Vector3f::UnitX())
-  *      * AngleAxisf(ea[2], Vector3f::UnitZ()); \endcode
-  * This corresponds to the right-multiply conventions (with right hand side frames).
-  * 
-  * The returned angles are in the ranges [0:pi]x[-pi:pi]x[-pi:pi].
-  * 
-  * \sa class AngleAxis
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Matrix<typename MatrixBase<Derived>::Scalar,3,1>
-MatrixBase<Derived>::eulerAngles(Index a0, Index a1, Index a2) const
-{
-  EIGEN_USING_STD(atan2)
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  /* Implemented from Graphics Gems IV */
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived,3,3)
-
-  Matrix<Scalar,3,1> res;
-  typedef Matrix<typename Derived::Scalar,2,1> Vector2;
-
-  const Index odd = ((a0+1)%3 == a1) ? 0 : 1;
-  const Index i = a0;
-  const Index j = (a0 + 1 + odd)%3;
-  const Index k = (a0 + 2 - odd)%3;
-  
-  if (a0==a2)
-  {
-    res[0] = atan2(coeff(j,i), coeff(k,i));
-    if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0)))
-    {
-      if(res[0] > Scalar(0)) {
-        res[0] -= Scalar(EIGEN_PI);
-      }
-      else {
-        res[0] += Scalar(EIGEN_PI);
-      }
-      Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm();
-      res[1] = -atan2(s2, coeff(i,i));
-    }
-    else
-    {
-      Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm();
-      res[1] = atan2(s2, coeff(i,i));
-    }
-    
-    // With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles,
-    // we can compute their respective rotation, and apply its inverse to M. Since the result must
-    // be a rotation around x, we have:
-    //
-    //  c2  s1.s2 c1.s2                   1  0   0 
-    //  0   c1    -s1       *    M    =   0  c3  s3
-    //  -s2 s1.c2 c1.c2                   0 -s3  c3
-    //
-    //  Thus:  m11.c1 - m21.s1 = c3  &   m12.c1 - m22.s1 = s3
-    
-    Scalar s1 = sin(res[0]);
-    Scalar c1 = cos(res[0]);
-    res[2] = atan2(c1*coeff(j,k)-s1*coeff(k,k), c1*coeff(j,j) - s1 * coeff(k,j));
-  } 
-  else
-  {
-    res[0] = atan2(coeff(j,k), coeff(k,k));
-    Scalar c2 = Vector2(coeff(i,i), coeff(i,j)).norm();
-    if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0))) {
-      if(res[0] > Scalar(0)) {
-        res[0] -= Scalar(EIGEN_PI);
-      }
-      else {
-        res[0] += Scalar(EIGEN_PI);
-      }
-      res[1] = atan2(-coeff(i,k), -c2);
-    }
-    else
-      res[1] = atan2(-coeff(i,k), c2);
-    Scalar s1 = sin(res[0]);
-    Scalar c1 = cos(res[0]);
-    res[2] = atan2(s1*coeff(k,i)-c1*coeff(j,i), c1*coeff(j,j) - s1 * coeff(k,j));
-  }
-  if (!odd)
-    res = -res;
-  
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_EULERANGLES_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Homogeneous.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Homogeneous.h
deleted file mode 100644
index 94083ac..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Homogeneous.h
+++ /dev/null
@@ -1,501 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOMOGENEOUS_H
-#define EIGEN_HOMOGENEOUS_H
-
-namespace Eigen {
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Homogeneous
-  *
-  * \brief Expression of one (or a set of) homogeneous vector(s)
-  *
-  * \param MatrixType the type of the object in which we are making homogeneous
-  *
-  * This class represents an expression of one (or a set of) homogeneous vector(s).
-  * It is the return type of MatrixBase::homogeneous() and most of the time
-  * this is the only way it is used.
-  *
-  * \sa MatrixBase::homogeneous()
-  */
-
-namespace internal {
-
-template<typename MatrixType,int Direction>
-struct traits<Homogeneous<MatrixType,Direction> >
- : traits<MatrixType>
-{
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    RowsPlusOne = (MatrixType::RowsAtCompileTime != Dynamic) ?
-                  int(MatrixType::RowsAtCompileTime) + 1 : Dynamic,
-    ColsPlusOne = (MatrixType::ColsAtCompileTime != Dynamic) ?
-                  int(MatrixType::ColsAtCompileTime) + 1 : Dynamic,
-    RowsAtCompileTime = Direction==Vertical  ?  RowsPlusOne : MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = Direction==Horizontal ? ColsPlusOne : MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    TmpFlags = _MatrixTypeNested::Flags & HereditaryBits,
-    Flags = ColsAtCompileTime==1 ? (TmpFlags & ~RowMajorBit)
-          : RowsAtCompileTime==1 ? (TmpFlags | RowMajorBit)
-          : TmpFlags
-  };
-};
-
-template<typename MatrixType,typename Lhs> struct homogeneous_left_product_impl;
-template<typename MatrixType,typename Rhs> struct homogeneous_right_product_impl;
-
-} // end namespace internal
-
-template<typename MatrixType,int _Direction> class Homogeneous
-  : public MatrixBase<Homogeneous<MatrixType,_Direction> >, internal::no_assignment_operator
-{
-  public:
-
-    typedef MatrixType NestedExpression;
-    enum { Direction = _Direction };
-
-    typedef MatrixBase<Homogeneous> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Homogeneous)
-
-    EIGEN_DEVICE_FUNC explicit inline Homogeneous(const MatrixType& matrix)
-      : m_matrix(matrix)
-    {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows() + (int(Direction)==Vertical   ? 1 : 0); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols() + (int(Direction)==Horizontal ? 1 : 0); }
-
-    EIGEN_DEVICE_FUNC const NestedExpression& nestedExpression() const { return m_matrix; }
-
-    template<typename Rhs>
-    EIGEN_DEVICE_FUNC inline const Product<Homogeneous,Rhs>
-    operator* (const MatrixBase<Rhs>& rhs) const
-    {
-      eigen_assert(int(Direction)==Horizontal);
-      return Product<Homogeneous,Rhs>(*this,rhs.derived());
-    }
-
-    template<typename Lhs> friend
-    EIGEN_DEVICE_FUNC inline const Product<Lhs,Homogeneous>
-    operator* (const MatrixBase<Lhs>& lhs, const Homogeneous& rhs)
-    {
-      eigen_assert(int(Direction)==Vertical);
-      return Product<Lhs,Homogeneous>(lhs.derived(),rhs);
-    }
-
-    template<typename Scalar, int Dim, int Mode, int Options> friend
-    EIGEN_DEVICE_FUNC inline const Product<Transform<Scalar,Dim,Mode,Options>, Homogeneous >
-    operator* (const Transform<Scalar,Dim,Mode,Options>& lhs, const Homogeneous& rhs)
-    {
-      eigen_assert(int(Direction)==Vertical);
-      return Product<Transform<Scalar,Dim,Mode,Options>, Homogeneous>(lhs,rhs);
-    }
-
-    template<typename Func>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::result_of<Func(Scalar,Scalar)>::type
-    redux(const Func& func) const
-    {
-      return func(m_matrix.redux(func), Scalar(1));
-    }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-};
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns a vector expression that is one longer than the vector argument, with the value 1 symbolically appended as the last coefficient.
-  *
-  * This can be used to convert affine coordinates to homogeneous coordinates.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include MatrixBase_homogeneous.cpp
-  * Output: \verbinclude MatrixBase_homogeneous.out
-  *
-  * \sa VectorwiseOp::homogeneous(), class Homogeneous
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::HomogeneousReturnType
-MatrixBase<Derived>::homogeneous() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return HomogeneousReturnType(derived());
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns an expression where the value 1 is symbolically appended as the final coefficient to each column (or row) of the matrix.
-  *
-  * This can be used to convert affine coordinates to homogeneous coordinates.
-  *
-  * Example: \include VectorwiseOp_homogeneous.cpp
-  * Output: \verbinclude VectorwiseOp_homogeneous.out
-  *
-  * \sa MatrixBase::homogeneous(), class Homogeneous */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC inline Homogeneous<ExpressionType,Direction>
-VectorwiseOp<ExpressionType,Direction>::homogeneous() const
-{
-  return HomogeneousReturnType(_expression());
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \brief homogeneous normalization
-  *
-  * \returns a vector expression of the N-1 first coefficients of \c *this divided by that last coefficient.
-  *
-  * This can be used to convert homogeneous coordinates to affine coordinates.
-  *
-  * It is essentially a shortcut for:
-  * \code
-    this->head(this->size()-1)/this->coeff(this->size()-1);
-    \endcode
-  *
-  * Example: \include MatrixBase_hnormalized.cpp
-  * Output: \verbinclude MatrixBase_hnormalized.out
-  *
-  * \sa VectorwiseOp::hnormalized() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::HNormalizedReturnType
-MatrixBase<Derived>::hnormalized() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return ConstStartMinusOne(derived(),0,0,
-    ColsAtCompileTime==1?size()-1:1,
-    ColsAtCompileTime==1?1:size()-1) / coeff(size()-1);
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \brief column or row-wise homogeneous normalization
-  *
-  * \returns an expression of the first N-1 coefficients of each column (or row) of \c *this divided by the last coefficient of each column (or row).
-  *
-  * This can be used to convert homogeneous coordinates to affine coordinates.
-  *
-  * It is conceptually equivalent to calling MatrixBase::hnormalized() to each column (or row) of \c *this.
-  *
-  * Example: \include DirectionWise_hnormalized.cpp
-  * Output: \verbinclude DirectionWise_hnormalized.out
-  *
-  * \sa MatrixBase::hnormalized() */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC inline const typename VectorwiseOp<ExpressionType,Direction>::HNormalizedReturnType
-VectorwiseOp<ExpressionType,Direction>::hnormalized() const
-{
-  return HNormalized_Block(_expression(),0,0,
-      Direction==Vertical   ? _expression().rows()-1 : _expression().rows(),
-      Direction==Horizontal ? _expression().cols()-1 : _expression().cols()).cwiseQuotient(
-      Replicate<HNormalized_Factors,
-                Direction==Vertical   ? HNormalized_SizeMinusOne : 1,
-                Direction==Horizontal ? HNormalized_SizeMinusOne : 1>
-        (HNormalized_Factors(_expression(),
-          Direction==Vertical    ? _expression().rows()-1:0,
-          Direction==Horizontal  ? _expression().cols()-1:0,
-          Direction==Vertical    ? 1 : _expression().rows(),
-          Direction==Horizontal  ? 1 : _expression().cols()),
-         Direction==Vertical   ? _expression().rows()-1 : 1,
-         Direction==Horizontal ? _expression().cols()-1 : 1));
-}
-
-namespace internal {
-
-template<typename MatrixOrTransformType>
-struct take_matrix_for_product
-{
-  typedef MatrixOrTransformType type;
-  EIGEN_DEVICE_FUNC static const type& run(const type &x) { return x; }
-};
-
-template<typename Scalar, int Dim, int Mode,int Options>
-struct take_matrix_for_product<Transform<Scalar, Dim, Mode, Options> >
-{
-  typedef Transform<Scalar, Dim, Mode, Options> TransformType;
-  typedef typename internal::add_const<typename TransformType::ConstAffinePart>::type type;
-  EIGEN_DEVICE_FUNC static type run (const TransformType& x) { return x.affine(); }
-};
-
-template<typename Scalar, int Dim, int Options>
-struct take_matrix_for_product<Transform<Scalar, Dim, Projective, Options> >
-{
-  typedef Transform<Scalar, Dim, Projective, Options> TransformType;
-  typedef typename TransformType::MatrixType type;
-  EIGEN_DEVICE_FUNC static const type& run (const TransformType& x) { return x.matrix(); }
-};
-
-template<typename MatrixType,typename Lhs>
-struct traits<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> >
-{
-  typedef typename take_matrix_for_product<Lhs>::type LhsMatrixType;
-  typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-  typedef typename remove_all<LhsMatrixType>::type LhsMatrixTypeCleaned;
-  typedef typename make_proper_matrix_type<
-                 typename traits<MatrixTypeCleaned>::Scalar,
-                 LhsMatrixTypeCleaned::RowsAtCompileTime,
-                 MatrixTypeCleaned::ColsAtCompileTime,
-                 MatrixTypeCleaned::PlainObject::Options,
-                 LhsMatrixTypeCleaned::MaxRowsAtCompileTime,
-                 MatrixTypeCleaned::MaxColsAtCompileTime>::type ReturnType;
-};
-
-template<typename MatrixType,typename Lhs>
-struct homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs>
-  : public ReturnByValue<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> >
-{
-  typedef typename traits<homogeneous_left_product_impl>::LhsMatrixType LhsMatrixType;
-  typedef typename remove_all<LhsMatrixType>::type LhsMatrixTypeCleaned;
-  typedef typename remove_all<typename LhsMatrixTypeCleaned::Nested>::type LhsMatrixTypeNested;
-  EIGEN_DEVICE_FUNC homogeneous_left_product_impl(const Lhs& lhs, const MatrixType& rhs)
-    : m_lhs(take_matrix_for_product<Lhs>::run(lhs)),
-      m_rhs(rhs)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  template<typename Dest> EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const
-  {
-    // FIXME investigate how to allow lazy evaluation of this product when possible
-    dst = Block<const LhsMatrixTypeNested,
-              LhsMatrixTypeNested::RowsAtCompileTime,
-              LhsMatrixTypeNested::ColsAtCompileTime==Dynamic?Dynamic:LhsMatrixTypeNested::ColsAtCompileTime-1>
-            (m_lhs,0,0,m_lhs.rows(),m_lhs.cols()-1) * m_rhs;
-    dst += m_lhs.col(m_lhs.cols()-1).rowwise()
-            .template replicate<MatrixType::ColsAtCompileTime>(m_rhs.cols());
-  }
-
-  typename LhsMatrixTypeCleaned::Nested m_lhs;
-  typename MatrixType::Nested m_rhs;
-};
-
-template<typename MatrixType,typename Rhs>
-struct traits<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> >
-{
-  typedef typename make_proper_matrix_type<typename traits<MatrixType>::Scalar,
-                 MatrixType::RowsAtCompileTime,
-                 Rhs::ColsAtCompileTime,
-                 MatrixType::PlainObject::Options,
-                 MatrixType::MaxRowsAtCompileTime,
-                 Rhs::MaxColsAtCompileTime>::type ReturnType;
-};
-
-template<typename MatrixType,typename Rhs>
-struct homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs>
-  : public ReturnByValue<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> >
-{
-  typedef typename remove_all<typename Rhs::Nested>::type RhsNested;
-  EIGEN_DEVICE_FUNC homogeneous_right_product_impl(const MatrixType& lhs, const Rhs& rhs)
-    : m_lhs(lhs), m_rhs(rhs)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  template<typename Dest> EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const
-  {
-    // FIXME investigate how to allow lazy evaluation of this product when possible
-    dst = m_lhs * Block<const RhsNested,
-                        RhsNested::RowsAtCompileTime==Dynamic?Dynamic:RhsNested::RowsAtCompileTime-1,
-                        RhsNested::ColsAtCompileTime>
-            (m_rhs,0,0,m_rhs.rows()-1,m_rhs.cols());
-    dst += m_rhs.row(m_rhs.rows()-1).colwise()
-            .template replicate<MatrixType::RowsAtCompileTime>(m_lhs.rows());
-  }
-
-  typename MatrixType::Nested m_lhs;
-  typename Rhs::Nested m_rhs;
-};
-
-template<typename ArgType,int Direction>
-struct evaluator_traits<Homogeneous<ArgType,Direction> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename ArgType::StorageKind>::Kind Kind;
-  typedef HomogeneousShape Shape;
-};
-
-template<> struct AssignmentKind<DenseShape,HomogeneousShape> { typedef Dense2Dense Kind; };
-
-
-template<typename ArgType,int Direction>
-struct unary_evaluator<Homogeneous<ArgType,Direction>, IndexBased>
-  : evaluator<typename Homogeneous<ArgType,Direction>::PlainObject >
-{
-  typedef Homogeneous<ArgType,Direction> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op)
-    : Base(), m_temp(op)
-  {
-    ::new (static_cast<Base*>(this)) Base(m_temp);
-  }
-
-protected:
-  PlainObject m_temp;
-};
-
-// dense = homogeneous
-template< typename DstXprType, typename ArgType, typename Scalar>
-struct Assignment<DstXprType, Homogeneous<ArgType,Vertical>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense>
-{
-  typedef Homogeneous<ArgType,Vertical> SrcXprType;
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst.template topRows<ArgType::RowsAtCompileTime>(src.nestedExpression().rows()) = src.nestedExpression();
-    dst.row(dst.rows()-1).setOnes();
-  }
-};
-
-// dense = homogeneous
-template< typename DstXprType, typename ArgType, typename Scalar>
-struct Assignment<DstXprType, Homogeneous<ArgType,Horizontal>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense>
-{
-  typedef Homogeneous<ArgType,Horizontal> SrcXprType;
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst.template leftCols<ArgType::ColsAtCompileTime>(src.nestedExpression().cols()) = src.nestedExpression();
-    dst.col(dst.cols()-1).setOnes();
-  }
-};
-
-template<typename LhsArg, typename Rhs, int ProductTag>
-struct generic_product_impl<Homogeneous<LhsArg,Horizontal>, Rhs, HomogeneousShape, DenseShape, ProductTag>
-{
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Homogeneous<LhsArg,Horizontal>& lhs, const Rhs& rhs)
-  {
-    homogeneous_right_product_impl<Homogeneous<LhsArg,Horizontal>, Rhs>(lhs.nestedExpression(), rhs).evalTo(dst);
-  }
-};
-
-template<typename Lhs,typename Rhs>
-struct homogeneous_right_product_refactoring_helper
-{
-  enum {
-    Dim  = Lhs::ColsAtCompileTime,
-    Rows = Lhs::RowsAtCompileTime
-  };
-  typedef typename Rhs::template ConstNRowsBlockXpr<Dim>::Type          LinearBlockConst;
-  typedef typename remove_const<LinearBlockConst>::type                 LinearBlock;
-  typedef typename Rhs::ConstRowXpr                                     ConstantColumn;
-  typedef Replicate<const ConstantColumn,Rows,1>                        ConstantBlock;
-  typedef Product<Lhs,LinearBlock,LazyProduct>                          LinearProduct;
-  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, HomogeneousShape, DenseShape>
- : public evaluator<typename homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression,Rhs>::Xpr>
-{
-  typedef Product<Lhs, Rhs, LazyProduct> XprType;
-  typedef homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression,Rhs> helper;
-  typedef typename helper::ConstantBlock ConstantBlock;
-  typedef typename helper::Xpr RefactoredXpr;
-  typedef evaluator<RefactoredXpr> Base;
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(  xpr.lhs().nestedExpression() .lazyProduct(  xpr.rhs().template topRows<helper::Dim>(xpr.lhs().nestedExpression().cols()) )
-            + ConstantBlock(xpr.rhs().row(xpr.rhs().rows()-1),xpr.lhs().rows(), 1) )
-  {}
-};
-
-template<typename Lhs, typename RhsArg, int ProductTag>
-struct generic_product_impl<Lhs, Homogeneous<RhsArg,Vertical>, DenseShape, HomogeneousShape, ProductTag>
-{
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
-  {
-    homogeneous_left_product_impl<Homogeneous<RhsArg,Vertical>, Lhs>(lhs, rhs.nestedExpression()).evalTo(dst);
-  }
-};
-
-// TODO: the following specialization is to address a regression from 3.2 to 3.3
-// In the future, this path should be optimized.
-template<typename Lhs, typename RhsArg, int ProductTag>
-struct generic_product_impl<Lhs, Homogeneous<RhsArg,Vertical>, TriangularShape, HomogeneousShape, ProductTag>
-{
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
-  {
-    dst.noalias() = lhs * rhs.eval();
-  }
-};
-
-template<typename Lhs,typename Rhs>
-struct homogeneous_left_product_refactoring_helper
-{
-  enum {
-    Dim = Rhs::RowsAtCompileTime,
-    Cols = Rhs::ColsAtCompileTime
-  };
-  typedef typename Lhs::template ConstNColsBlockXpr<Dim>::Type          LinearBlockConst;
-  typedef typename remove_const<LinearBlockConst>::type                 LinearBlock;
-  typedef typename Lhs::ConstColXpr                                     ConstantColumn;
-  typedef Replicate<const ConstantColumn,1,Cols>                        ConstantBlock;
-  typedef Product<LinearBlock,Rhs,LazyProduct>                          LinearProduct;
-  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, HomogeneousShape>
- : public evaluator<typename homogeneous_left_product_refactoring_helper<Lhs,typename Rhs::NestedExpression>::Xpr>
-{
-  typedef Product<Lhs, Rhs, LazyProduct> XprType;
-  typedef homogeneous_left_product_refactoring_helper<Lhs,typename Rhs::NestedExpression> helper;
-  typedef typename helper::ConstantBlock ConstantBlock;
-  typedef typename helper::Xpr RefactoredXpr;
-  typedef evaluator<RefactoredXpr> Base;
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(   xpr.lhs().template leftCols<helper::Dim>(xpr.rhs().nestedExpression().rows()) .lazyProduct( xpr.rhs().nestedExpression() )
-            + ConstantBlock(xpr.lhs().col(xpr.lhs().cols()-1),1,xpr.rhs().cols()) )
-  {}
-};
-
-template<typename Scalar, int Dim, int Mode,int Options, typename RhsArg, int ProductTag>
-struct generic_product_impl<Transform<Scalar,Dim,Mode,Options>, Homogeneous<RhsArg,Vertical>, DenseShape, HomogeneousShape, ProductTag>
-{
-  typedef Transform<Scalar,Dim,Mode,Options> TransformType;
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const TransformType& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
-  {
-    homogeneous_left_product_impl<Homogeneous<RhsArg,Vertical>, TransformType>(lhs, rhs.nestedExpression()).evalTo(dst);
-  }
-};
-
-template<typename ExpressionType, int Side, bool Transposed>
-struct permutation_matrix_product<ExpressionType, Side, Transposed, HomogeneousShape>
-  : public permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
-{};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_HOMOGENEOUS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Hyperplane.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Hyperplane.h
deleted file mode 100644
index cebe035..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Hyperplane.h
+++ /dev/null
@@ -1,282 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HYPERPLANE_H
-#define EIGEN_HYPERPLANE_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Hyperplane
-  *
-  * \brief A hyperplane
-  *
-  * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n.
-  * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane.
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
-  *             Notice that the dimension of the hyperplane is _AmbientDim-1.
-  *
-  * This class represents an hyperplane as the zero set of the implicit equation
-  * \f$ n \cdot x + d = 0 \f$ where \f$ n \f$ is a unit normal vector of the plane (linear part)
-  * and \f$ d \f$ is the distance (offset) to the origin.
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-class Hyperplane
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1)
-  enum {
-    AmbientDimAtCompileTime = _AmbientDim,
-    Options = _Options
-  };
-  typedef _Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
-  typedef Matrix<Scalar,Index(AmbientDimAtCompileTime)==Dynamic
-                        ? Dynamic
-                        : Index(AmbientDimAtCompileTime)+1,1,Options> Coefficients;
-  typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType;
-  typedef const Block<const Coefficients,AmbientDimAtCompileTime,1> ConstNormalReturnType;
-
-  /** Default constructor without initialization */
-  EIGEN_DEVICE_FUNC inline Hyperplane() {}
-  
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC Hyperplane(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)
-   : m_coeffs(other.coeffs())
-  {}
-
-  /** Constructs a dynamic-size hyperplane with \a _dim the dimension
-    * of the ambient space */
-  EIGEN_DEVICE_FUNC inline explicit Hyperplane(Index _dim) : m_coeffs(_dim+1) {}
-
-  /** Construct a plane from its normal \a n and a point \a e onto the plane.
-    * \warning the vector normal is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const VectorType& e)
-    : m_coeffs(n.size()+1)
-  {
-    normal() = n;
-    offset() = -n.dot(e);
-  }
-
-  /** Constructs a plane from its normal \a n and distance to the origin \a d
-    * such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$.
-    * \warning the vector normal is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const Scalar& d)
-    : m_coeffs(n.size()+1)
-  {
-    normal() = n;
-    offset() = d;
-  }
-
-  /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space
-    * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made.
-    */
-  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1)
-  {
-    Hyperplane result(p0.size());
-    result.normal() = (p1 - p0).unitOrthogonal();
-    result.offset() = -p0.dot(result.normal());
-    return result;
-  }
-
-  /** Constructs a hyperplane passing through the three points. The dimension of the ambient space
-    * is required to be exactly 3.
-    */
-  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2)
-  {
-    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3)
-    Hyperplane result(p0.size());
-    VectorType v0(p2 - p0), v1(p1 - p0);
-    result.normal() = v0.cross(v1);
-    RealScalar norm = result.normal().norm();
-    if(norm <= v0.norm() * v1.norm() * NumTraits<RealScalar>::epsilon())
-    {
-      Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
-      JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
-      result.normal() = svd.matrixV().col(2);
-    }
-    else
-      result.normal() /= norm;
-    result.offset() = -p0.dot(result.normal());
-    return result;
-  }
-
-  /** Constructs a hyperplane passing through the parametrized line \a parametrized.
-    * If the dimension of the ambient space is greater than 2, then there isn't uniqueness,
-    * so an arbitrary choice is made.
-    */
-  // FIXME to be consistent with the rest this could be implemented as a static Through function ??
-  EIGEN_DEVICE_FUNC explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized)
-  {
-    normal() = parametrized.direction().unitOrthogonal();
-    offset() = -parametrized.origin().dot(normal());
-  }
-
-  EIGEN_DEVICE_FUNC ~Hyperplane() {}
-
-  /** \returns the dimension in which the plane holds */
-  EIGEN_DEVICE_FUNC inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_coeffs.size()-1 : Index(AmbientDimAtCompileTime); }
-
-  /** normalizes \c *this */
-  EIGEN_DEVICE_FUNC void normalize(void)
-  {
-    m_coeffs /= normal().norm();
-  }
-
-  /** \returns the signed distance between the plane \c *this and a point \a p.
-    * \sa absDistance()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar signedDistance(const VectorType& p) const { return normal().dot(p) + offset(); }
-
-  /** \returns the absolute distance between the plane \c *this and a point \a p.
-    * \sa signedDistance()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar absDistance(const VectorType& p) const { return numext::abs(signedDistance(p)); }
-
-  /** \returns the projection of a point \a p onto the plane \c *this.
-    */
-  EIGEN_DEVICE_FUNC inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); }
-
-  /** \returns a constant reference to the unit normal vector of the plane, which corresponds
-    * to the linear part of the implicit equation.
-    */
-  EIGEN_DEVICE_FUNC inline ConstNormalReturnType normal() const { return ConstNormalReturnType(m_coeffs,0,0,dim(),1); }
-
-  /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds
-    * to the linear part of the implicit equation.
-    */
-  EIGEN_DEVICE_FUNC inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); }
-
-  /** \returns the distance to the origin, which is also the "constant term" of the implicit equation
-    * \warning the vector normal is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC inline const Scalar& offset() const { return m_coeffs.coeff(dim()); }
-
-  /** \returns a non-constant reference to the distance to the origin, which is also the constant part
-    * of the implicit equation */
-  EIGEN_DEVICE_FUNC inline Scalar& offset() { return m_coeffs(dim()); }
-
-  /** \returns a constant reference to the coefficients c_i of the plane equation:
-    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
-    */
-  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
-
-  /** \returns a non-constant reference to the coefficients c_i of the plane equation:
-    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
-    */
-  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
-
-  /** \returns the intersection of *this with \a other.
-    *
-    * \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines.
-    *
-    * \note If \a other is approximately parallel to *this, this method will return any point on *this.
-    */
-  EIGEN_DEVICE_FUNC VectorType intersection(const Hyperplane& other) const
-  {
-    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
-    Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
-    // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests
-    // whether the two lines are approximately parallel.
-    if(internal::isMuchSmallerThan(det, Scalar(1)))
-    {   // special case where the two lines are approximately parallel. Pick any point on the first line.
-        if(numext::abs(coeffs().coeff(1))>numext::abs(coeffs().coeff(0)))
-            return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0));
-        else
-            return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0));
-    }
-    else
-    {   // general case
-        Scalar invdet = Scalar(1) / det;
-        return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)),
-                          invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2)));
-    }
-  }
-
-  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
-    *
-    * \param mat the Dim x Dim transformation matrix
-    * \param traits specifies whether the matrix \a mat represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    */
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
-  {
-    if (traits==Affine)
-    {
-      normal() = mat.inverse().transpose() * normal();
-      m_coeffs /= normal().norm();
-    }
-    else if (traits==Isometry)
-      normal() = mat * normal();
-    else
-    {
-      eigen_assert(0 && "invalid traits value in Hyperplane::transform()");
-    }
-    return *this;
-  }
-
-  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
-    *
-    * \param t the transformation of dimension Dim
-    * \param traits specifies whether the transformation \a t represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    *               Other kind of transformations are not supported.
-    */
-  template<int TrOptions>
-  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime,Affine,TrOptions>& t,
-                                TransformTraits traits = Affine)
-  {
-    transform(t.linear(), traits);
-    offset() -= normal().dot(t.translation());
-    return *this;
-  }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Hyperplane,
-           Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const
-  {
-    return typename internal::cast_return_type<Hyperplane,
-                    Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this);
-  }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline explicit Hyperplane(const Hyperplane<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other)
-  { m_coeffs = other.coeffs().template cast<Scalar>(); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC bool isApprox(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_coeffs.isApprox(other.m_coeffs, prec); }
-
-protected:
-
-  Coefficients m_coeffs;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_HYPERPLANE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/OrthoMethods.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/OrthoMethods.h
deleted file mode 100644
index 524aebe..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/OrthoMethods.h
+++ /dev/null
@@ -1,235 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORTHOMETHODS_H
-#define EIGEN_ORTHOMETHODS_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns the cross product of \c *this and \a other
-  *
-  * Here is a very good explanation of cross-product: http://xkcd.com/199/
-  * 
-  * With complex numbers, the cross product is implemented as
-  * \f$ (\mathbf{a}+i\mathbf{b}) \times (\mathbf{c}+i\mathbf{d}) = (\mathbf{a} \times \mathbf{c} - \mathbf{b} \times \mathbf{d}) - i(\mathbf{a} \times \mathbf{d} - \mathbf{b} \times \mathbf{c})\f$
-  * 
-  * \sa MatrixBase::cross3()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename MatrixBase<Derived>::template cross_product_return_type<OtherDerived>::type
-#else
-typename MatrixBase<Derived>::PlainObject
-#endif
-MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,3)
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
-
-  // Note that there is no need for an expression here since the compiler
-  // optimize such a small temporary very well (even within a complex expression)
-  typename internal::nested_eval<Derived,2>::type lhs(derived());
-  typename internal::nested_eval<OtherDerived,2>::type rhs(other.derived());
-  return typename cross_product_return_type<OtherDerived>::type(
-    numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
-    numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
-    numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0))
-  );
-}
-
-namespace internal {
-
-template< int Arch,typename VectorLhs,typename VectorRhs,
-          typename Scalar = typename VectorLhs::Scalar,
-          bool Vectorizable = bool((VectorLhs::Flags&VectorRhs::Flags)&PacketAccessBit)>
-struct cross3_impl {
-  EIGEN_DEVICE_FUNC static inline typename internal::plain_matrix_type<VectorLhs>::type
-  run(const VectorLhs& lhs, const VectorRhs& rhs)
-  {
-    return typename internal::plain_matrix_type<VectorLhs>::type(
-      numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
-      numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
-      numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)),
-      0
-    );
-  }
-};
-
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns the cross product of \c *this and \a other using only the x, y, and z coefficients
-  *
-  * The size of \c *this and \a other must be four. This function is especially useful
-  * when using 4D vectors instead of 3D ones to get advantage of SSE/AltiVec vectorization.
-  *
-  * \sa MatrixBase::cross()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::cross3(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,4)
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,4)
-
-  typedef typename internal::nested_eval<Derived,2>::type DerivedNested;
-  typedef typename internal::nested_eval<OtherDerived,2>::type OtherDerivedNested;
-  DerivedNested lhs(derived());
-  OtherDerivedNested rhs(other.derived());
-
-  return internal::cross3_impl<Architecture::Target,
-                        typename internal::remove_all<DerivedNested>::type,
-                        typename internal::remove_all<OtherDerivedNested>::type>::run(lhs,rhs);
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns a matrix expression of the cross product of each column or row
-  * of the referenced expression with the \a other vector.
-  *
-  * The referenced matrix must have one dimension equal to 3.
-  * The result matrix has the same dimensions than the referenced one.
-  *
-  * \sa MatrixBase::cross() */
-template<typename ExpressionType, int Direction>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC 
-const typename VectorwiseOp<ExpressionType,Direction>::CrossReturnType
-VectorwiseOp<ExpressionType,Direction>::cross(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-  
-  typename internal::nested_eval<ExpressionType,2>::type mat(_expression());
-  typename internal::nested_eval<OtherDerived,2>::type vec(other.derived());
-
-  CrossReturnType res(_expression().rows(),_expression().cols());
-  if(Direction==Vertical)
-  {
-    eigen_assert(CrossReturnType::RowsAtCompileTime==3 && "the matrix must have exactly 3 rows");
-    res.row(0) = (mat.row(1) * vec.coeff(2) - mat.row(2) * vec.coeff(1)).conjugate();
-    res.row(1) = (mat.row(2) * vec.coeff(0) - mat.row(0) * vec.coeff(2)).conjugate();
-    res.row(2) = (mat.row(0) * vec.coeff(1) - mat.row(1) * vec.coeff(0)).conjugate();
-  }
-  else
-  {
-    eigen_assert(CrossReturnType::ColsAtCompileTime==3 && "the matrix must have exactly 3 columns");
-    res.col(0) = (mat.col(1) * vec.coeff(2) - mat.col(2) * vec.coeff(1)).conjugate();
-    res.col(1) = (mat.col(2) * vec.coeff(0) - mat.col(0) * vec.coeff(2)).conjugate();
-    res.col(2) = (mat.col(0) * vec.coeff(1) - mat.col(1) * vec.coeff(0)).conjugate();
-  }
-  return res;
-}
-
-namespace internal {
-
-template<typename Derived, int Size = Derived::SizeAtCompileTime>
-struct unitOrthogonal_selector
-{
-  typedef typename plain_matrix_type<Derived>::type VectorType;
-  typedef typename traits<Derived>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Matrix<Scalar,2,1> Vector2;
-  EIGEN_DEVICE_FUNC
-  static inline VectorType run(const Derived& src)
-  {
-    VectorType perp = VectorType::Zero(src.size());
-    Index maxi = 0;
-    Index sndi = 0;
-    src.cwiseAbs().maxCoeff(&maxi);
-    if (maxi==0)
-      sndi = 1;
-    RealScalar invnm = RealScalar(1)/(Vector2() << src.coeff(sndi),src.coeff(maxi)).finished().norm();
-    perp.coeffRef(maxi) = -numext::conj(src.coeff(sndi)) * invnm;
-    perp.coeffRef(sndi) =  numext::conj(src.coeff(maxi)) * invnm;
-
-    return perp;
-   }
-};
-
-template<typename Derived>
-struct unitOrthogonal_selector<Derived,3>
-{
-  typedef typename plain_matrix_type<Derived>::type VectorType;
-  typedef typename traits<Derived>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline VectorType run(const Derived& src)
-  {
-    VectorType perp;
-    /* Let us compute the crossed product of *this with a vector
-     * that is not too close to being colinear to *this.
-     */
-
-    /* unless the x and y coords are both close to zero, we can
-     * simply take ( -y, x, 0 ) and normalize it.
-     */
-    if((!isMuchSmallerThan(src.x(), src.z()))
-    || (!isMuchSmallerThan(src.y(), src.z())))
-    {
-      RealScalar invnm = RealScalar(1)/src.template head<2>().norm();
-      perp.coeffRef(0) = -numext::conj(src.y())*invnm;
-      perp.coeffRef(1) = numext::conj(src.x())*invnm;
-      perp.coeffRef(2) = 0;
-    }
-    /* if both x and y are close to zero, then the vector is close
-     * to the z-axis, so it's far from colinear to the x-axis for instance.
-     * So we take the crossed product with (1,0,0) and normalize it.
-     */
-    else
-    {
-      RealScalar invnm = RealScalar(1)/src.template tail<2>().norm();
-      perp.coeffRef(0) = 0;
-      perp.coeffRef(1) = -numext::conj(src.z())*invnm;
-      perp.coeffRef(2) = numext::conj(src.y())*invnm;
-    }
-
-    return perp;
-   }
-};
-
-template<typename Derived>
-struct unitOrthogonal_selector<Derived,2>
-{
-  typedef typename plain_matrix_type<Derived>::type VectorType;
-  EIGEN_DEVICE_FUNC
-  static inline VectorType run(const Derived& src)
-  { return VectorType(-numext::conj(src.y()), numext::conj(src.x())).normalized(); }
-};
-
-} // end namespace internal
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns a unit vector which is orthogonal to \c *this
-  *
-  * The size of \c *this must be at least 2. If the size is exactly 2,
-  * then the returned vector is a counter clock wise rotation of \c *this, i.e., (-y,x).normalized().
-  *
-  * \sa cross()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::unitOrthogonal() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return internal::unitOrthogonal_selector<Derived>::run(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ORTHOMETHODS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/ParametrizedLine.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/ParametrizedLine.h
deleted file mode 100644
index 584f500..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/ParametrizedLine.h
+++ /dev/null
@@ -1,232 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARAMETRIZEDLINE_H
-#define EIGEN_PARAMETRIZEDLINE_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class ParametrizedLine
-  *
-  * \brief A parametrized line
-  *
-  * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit
-  * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to
-  * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ t \in \mathbf{R} \f$.
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-class ParametrizedLine
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
-  enum {
-    AmbientDimAtCompileTime = _AmbientDim,
-    Options = _Options
-  };
-  typedef _Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  typedef Matrix<Scalar,AmbientDimAtCompileTime,1,Options> VectorType;
-
-  /** Default constructor without initialization */
-  EIGEN_DEVICE_FUNC inline ParametrizedLine() {}
-  
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC ParametrizedLine(const ParametrizedLine<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)
-   : m_origin(other.origin()), m_direction(other.direction())
-  {}
-
-  /** Constructs a dynamic-size line with \a _dim the dimension
-    * of the ambient space */
-  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(Index _dim) : m_origin(_dim), m_direction(_dim) {}
-
-  /** Initializes a parametrized line of direction \a direction and origin \a origin.
-    * \warning the vector direction is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC ParametrizedLine(const VectorType& origin, const VectorType& direction)
-    : m_origin(origin), m_direction(direction) {}
-
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane);
-
-  /** Constructs a parametrized line going from \a p0 to \a p1. */
-  EIGEN_DEVICE_FUNC static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1)
-  { return ParametrizedLine(p0, (p1-p0).normalized()); }
-
-  EIGEN_DEVICE_FUNC ~ParametrizedLine() {}
-
-  /** \returns the dimension in which the line holds */
-  EIGEN_DEVICE_FUNC inline Index dim() const { return m_direction.size(); }
-
-  EIGEN_DEVICE_FUNC const VectorType& origin() const { return m_origin; }
-  EIGEN_DEVICE_FUNC VectorType& origin() { return m_origin; }
-
-  EIGEN_DEVICE_FUNC const VectorType& direction() const { return m_direction; }
-  EIGEN_DEVICE_FUNC VectorType& direction() { return m_direction; }
-
-  /** \returns the squared distance of a point \a p to its projection onto the line \c *this.
-    * \sa distance()
-    */
-  EIGEN_DEVICE_FUNC RealScalar squaredDistance(const VectorType& p) const
-  {
-    VectorType diff = p - origin();
-    return (diff - direction().dot(diff) * direction()).squaredNorm();
-  }
-  /** \returns the distance of a point \a p to its projection onto the line \c *this.
-    * \sa squaredDistance()
-    */
-  EIGEN_DEVICE_FUNC RealScalar distance(const VectorType& p) const { EIGEN_USING_STD(sqrt) return sqrt(squaredDistance(p)); }
-
-  /** \returns the projection of a point \a p onto the line \c *this. */
-  EIGEN_DEVICE_FUNC VectorType projection(const VectorType& p) const
-  { return origin() + direction().dot(p-origin()) * direction(); }
-
-  EIGEN_DEVICE_FUNC VectorType pointAt(const Scalar& t) const;
-  
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC Scalar intersectionParameter(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
- 
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC Scalar intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
-  
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC VectorType intersectionPoint(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
-
-  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
-    *
-    * \param mat the Dim x Dim transformation matrix
-    * \param traits specifies whether the matrix \a mat represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    */
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
-  {
-    if (traits==Affine)
-      direction() = (mat * direction()).normalized();
-    else if (traits==Isometry)
-      direction() = mat * direction();
-    else
-    {
-      eigen_assert(0 && "invalid traits value in ParametrizedLine::transform()");
-    }
-    origin() = mat * origin();
-    return *this;
-  }
-
-  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
-    *
-    * \param t the transformation of dimension Dim
-    * \param traits specifies whether the transformation \a t represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    *               Other kind of transformations are not supported.
-    */
-  template<int TrOptions>
-  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(const Transform<Scalar,AmbientDimAtCompileTime,Affine,TrOptions>& t,
-                                                       TransformTraits traits = Affine)
-  {
-    transform(t.linear(), traits);
-    origin() += t.translation();
-    return *this;
-  }
-
-/** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<ParametrizedLine,
-           ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const
-  {
-    return typename internal::cast_return_type<ParametrizedLine,
-                    ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this);
-  }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(const ParametrizedLine<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other)
-  {
-    m_origin = other.origin().template cast<Scalar>();
-    m_direction = other.direction().template cast<Scalar>();
-  }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const ParametrizedLine& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); }
-
-protected:
-
-  VectorType m_origin, m_direction;
-};
-
-/** Constructs a parametrized line from a 2D hyperplane
-  *
-  * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline ParametrizedLine<_Scalar, _AmbientDim,_Options>::ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim,OtherOptions>& hyperplane)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
-  direction() = hyperplane.normal().unitOrthogonal();
-  origin() = -hyperplane.normal()*hyperplane.offset();
-}
-
-/** \returns the point at \a t along this line
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-EIGEN_DEVICE_FUNC inline typename ParametrizedLine<_Scalar, _AmbientDim,_Options>::VectorType
-ParametrizedLine<_Scalar, _AmbientDim,_Options>::pointAt(const _Scalar& t) const
-{
-  return origin() + (direction()*t); 
-}
-
-/** \returns the parameter value of the intersection between \c *this and the given \a hyperplane
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersectionParameter(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const
-{
-  return -(hyperplane.offset()+hyperplane.normal().dot(origin()))
-          / hyperplane.normal().dot(direction());
-}
-
-
-/** \deprecated use intersectionParameter()
-  * \returns the parameter value of the intersection between \c *this and the given \a hyperplane
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const
-{
-  return intersectionParameter(hyperplane);
-}
-
-/** \returns the point of the intersection between \c *this and the given hyperplane
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline typename ParametrizedLine<_Scalar, _AmbientDim,_Options>::VectorType
-ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersectionPoint(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const
-{
-  return pointAt(intersectionParameter(hyperplane));
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARAMETRIZEDLINE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Quaternion.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Quaternion.h
deleted file mode 100644
index 3259e59..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Quaternion.h
+++ /dev/null
@@ -1,870 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Mathieu Gautier <mathieu.gautier@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QUATERNION_H
-#define EIGEN_QUATERNION_H
-namespace Eigen { 
-
-
-/***************************************************************************
-* Definition of QuaternionBase<Derived>
-* The implementation is at the end of the file
-***************************************************************************/
-
-namespace internal {
-template<typename Other,
-         int OtherRows=Other::RowsAtCompileTime,
-         int OtherCols=Other::ColsAtCompileTime>
-struct quaternionbase_assign_impl;
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  * \class QuaternionBase
-  * \brief Base class for quaternion expressions
-  * \tparam Derived derived type (CRTP)
-  * \sa class Quaternion
-  */
-template<class Derived>
-class QuaternionBase : public RotationBase<Derived, 3>
-{
- public:
-  typedef RotationBase<Derived, 3> Base;
-
-  using Base::operator*;
-  using Base::derived;
-
-  typedef typename internal::traits<Derived>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef typename internal::traits<Derived>::Coefficients Coefficients;
-  typedef typename Coefficients::CoeffReturnType CoeffReturnType;
-  typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
-                                        Scalar&, CoeffReturnType>::type NonConstCoeffReturnType;
-
-
-  enum {
-    Flags = Eigen::internal::traits<Derived>::Flags
-  };
-
- // typedef typename Matrix<Scalar,4,1> Coefficients;
-  /** the type of a 3D vector */
-  typedef Matrix<Scalar,3,1> Vector3;
-  /** the equivalent rotation matrix type */
-  typedef Matrix<Scalar,3,3> Matrix3;
-  /** the equivalent angle-axis type */
-  typedef AngleAxis<Scalar> AngleAxisType;
-
-
-
-  /** \returns the \c x coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType x() const { return this->derived().coeffs().coeff(0); }
-  /** \returns the \c y coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType y() const { return this->derived().coeffs().coeff(1); }
-  /** \returns the \c z coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType z() const { return this->derived().coeffs().coeff(2); }
-  /** \returns the \c w coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType w() const { return this->derived().coeffs().coeff(3); }
-
-  /** \returns a reference to the \c x coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType x() { return this->derived().coeffs().x(); }
-  /** \returns a reference to the \c y coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType y() { return this->derived().coeffs().y(); }
-  /** \returns a reference to the \c z coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType z() { return this->derived().coeffs().z(); }
-  /** \returns a reference to the \c w coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType w() { return this->derived().coeffs().w(); }
-
-  /** \returns a read-only vector expression of the imaginary part (x,y,z) */
-  EIGEN_DEVICE_FUNC inline const VectorBlock<const Coefficients,3> vec() const { return coeffs().template head<3>(); }
-
-  /** \returns a vector expression of the imaginary part (x,y,z) */
-  EIGEN_DEVICE_FUNC inline VectorBlock<Coefficients,3> vec() { return coeffs().template head<3>(); }
-
-  /** \returns a read-only vector expression of the coefficients (x,y,z,w) */
-  EIGEN_DEVICE_FUNC inline const typename internal::traits<Derived>::Coefficients& coeffs() const { return derived().coeffs(); }
-
-  /** \returns a vector expression of the coefficients (x,y,z,w) */
-  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Coefficients& coeffs() { return derived().coeffs(); }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& operator=(const QuaternionBase<Derived>& other);
-  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const QuaternionBase<OtherDerived>& other);
-
-// disabled this copy operator as it is giving very strange compilation errors when compiling
-// test_stdvector with GCC 4.4.2. This looks like a GCC bug though, so feel free to re-enable it if it's
-// useful; however notice that we already have the templated operator= above and e.g. in MatrixBase
-// we didn't have to add, in addition to templated operator=, such a non-templated copy operator.
-//  Derived& operator=(const QuaternionBase& other)
-//  { return operator=<Derived>(other); }
-
-  EIGEN_DEVICE_FUNC Derived& operator=(const AngleAxisType& aa);
-  template<class OtherDerived> EIGEN_DEVICE_FUNC Derived& operator=(const MatrixBase<OtherDerived>& m);
-
-  /** \returns a quaternion representing an identity rotation
-    * \sa MatrixBase::Identity()
-    */
-  EIGEN_DEVICE_FUNC static inline Quaternion<Scalar> Identity() { return Quaternion<Scalar>(Scalar(1), Scalar(0), Scalar(0), Scalar(0)); }
-
-  /** \sa QuaternionBase::Identity(), MatrixBase::setIdentity()
-    */
-  EIGEN_DEVICE_FUNC inline QuaternionBase& setIdentity() { coeffs() << Scalar(0), Scalar(0), Scalar(0), Scalar(1); return *this; }
-
-  /** \returns the squared norm of the quaternion's coefficients
-    * \sa QuaternionBase::norm(), MatrixBase::squaredNorm()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar squaredNorm() const { return coeffs().squaredNorm(); }
-
-  /** \returns the norm of the quaternion's coefficients
-    * \sa QuaternionBase::squaredNorm(), MatrixBase::norm()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar norm() const { return coeffs().norm(); }
-
-  /** Normalizes the quaternion \c *this
-    * \sa normalized(), MatrixBase::normalize() */
-  EIGEN_DEVICE_FUNC inline void normalize() { coeffs().normalize(); }
-  /** \returns a normalized copy of \c *this
-    * \sa normalize(), MatrixBase::normalized() */
-  EIGEN_DEVICE_FUNC inline Quaternion<Scalar> normalized() const { return Quaternion<Scalar>(coeffs().normalized()); }
-
-    /** \returns the dot product of \c *this and \a other
-    * Geometrically speaking, the dot product of two unit quaternions
-    * corresponds to the cosine of half the angle between the two rotations.
-    * \sa angularDistance()
-    */
-  template<class OtherDerived> EIGEN_DEVICE_FUNC inline Scalar dot(const QuaternionBase<OtherDerived>& other) const { return coeffs().dot(other.coeffs()); }
-
-  template<class OtherDerived> EIGEN_DEVICE_FUNC Scalar angularDistance(const QuaternionBase<OtherDerived>& other) const;
-
-  /** \returns an equivalent 3x3 rotation matrix */
-  EIGEN_DEVICE_FUNC inline Matrix3 toRotationMatrix() const;
-
-  /** \returns the quaternion which transform \a a into \a b through a rotation */
-  template<typename Derived1, typename Derived2>
-  EIGEN_DEVICE_FUNC Derived& setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
-
-  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<Scalar> operator* (const QuaternionBase<OtherDerived>& q) const;
-  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*= (const QuaternionBase<OtherDerived>& q);
-
-  /** \returns the quaternion describing the inverse rotation */
-  EIGEN_DEVICE_FUNC Quaternion<Scalar> inverse() const;
-
-  /** \returns the conjugated quaternion */
-  EIGEN_DEVICE_FUNC Quaternion<Scalar> conjugate() const;
-
-  template<class OtherDerived> EIGEN_DEVICE_FUNC Quaternion<Scalar> slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const;
-
-  /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
-    * \warning When using floating point scalar values you probably should rather use a
-    *          fuzzy comparison such as isApprox()
-    * \sa isApprox(), operator!= */
-  template<class OtherDerived>
-  EIGEN_DEVICE_FUNC inline bool operator==(const QuaternionBase<OtherDerived>& other) const
-  { return coeffs() == other.coeffs(); }
-
-  /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
-    * \warning When using floating point scalar values you probably should rather use a
-    *          fuzzy comparison such as isApprox()
-    * \sa isApprox(), operator== */
-  template<class OtherDerived>
-  EIGEN_DEVICE_FUNC inline bool operator!=(const QuaternionBase<OtherDerived>& other) const
-  { return coeffs() != other.coeffs(); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  template<class OtherDerived>
-  EIGEN_DEVICE_FUNC bool isApprox(const QuaternionBase<OtherDerived>& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return coeffs().isApprox(other.coeffs(), prec); }
-
-  /** return the result vector of \a v through the rotation*/
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Vector3 _transformVector(const Vector3& v) const;
-
-  #ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type cast() const;
-
-  #else
-
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline
-  typename internal::enable_if<internal::is_same<Scalar,NewScalarType>::value,const Derived&>::type cast() const
-  {
-    return derived();
-  }
-
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline
-  typename internal::enable_if<!internal::is_same<Scalar,NewScalarType>::value,Quaternion<NewScalarType> >::type cast() const
-  {
-    return Quaternion<NewScalarType>(coeffs().template cast<NewScalarType>());
-  }
-  #endif
-
-#ifndef EIGEN_NO_IO
-  friend std::ostream& operator<<(std::ostream& s, const QuaternionBase<Derived>& q) {
-    s << q.x() << "i + " << q.y() << "j + " << q.z() << "k" << " + " << q.w();
-    return s;
-  }
-#endif
-
-#ifdef EIGEN_QUATERNIONBASE_PLUGIN
-# include EIGEN_QUATERNIONBASE_PLUGIN
-#endif
-protected:
-  EIGEN_DEFAULT_COPY_CONSTRUCTOR(QuaternionBase)
-  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(QuaternionBase)
-};
-
-/***************************************************************************
-* Definition/implementation of Quaternion<Scalar>
-***************************************************************************/
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Quaternion
-  *
-  * \brief The quaternion class used to represent 3D orientations and rotations
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _Options controls the memory alignment of the coefficients. Can be \# AutoAlign or \# DontAlign. Default is AutoAlign.
-  *
-  * This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of
-  * orientations and rotations of objects in three dimensions. Compared to other representations
-  * like Euler angles or 3x3 matrices, quaternions offer the following advantages:
-  * \li \b compact storage (4 scalars)
-  * \li \b efficient to compose (28 flops),
-  * \li \b stable spherical interpolation
-  *
-  * The following two typedefs are provided for convenience:
-  * \li \c Quaternionf for \c float
-  * \li \c Quaterniond for \c double
-  *
-  * \warning Operations interpreting the quaternion as rotation have undefined behavior if the quaternion is not normalized.
-  *
-  * \sa  class AngleAxis, class Transform
-  */
-
-namespace internal {
-template<typename _Scalar,int _Options>
-struct traits<Quaternion<_Scalar,_Options> >
-{
-  typedef Quaternion<_Scalar,_Options> PlainObject;
-  typedef _Scalar Scalar;
-  typedef Matrix<_Scalar,4,1,_Options> Coefficients;
-  enum{
-    Alignment = internal::traits<Coefficients>::Alignment,
-    Flags = LvalueBit
-  };
-};
-}
-
-template<typename _Scalar, int _Options>
-class Quaternion : public QuaternionBase<Quaternion<_Scalar,_Options> >
-{
-public:
-  typedef QuaternionBase<Quaternion<_Scalar,_Options> > Base;
-  enum { NeedsAlignment = internal::traits<Quaternion>::Alignment>0 };
-
-  typedef _Scalar Scalar;
-
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Quaternion)
-  using Base::operator*=;
-
-  typedef typename internal::traits<Quaternion>::Coefficients Coefficients;
-  typedef typename Base::AngleAxisType AngleAxisType;
-
-  /** Default constructor leaving the quaternion uninitialized. */
-  EIGEN_DEVICE_FUNC inline Quaternion() {}
-
-  /** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from
-    * its four coefficients \a w, \a x, \a y and \a z.
-    *
-    * \warning Note the order of the arguments: the real \a w coefficient first,
-    * while internally the coefficients are stored in the following order:
-    * [\c x, \c y, \c z, \c w]
-    */
-  EIGEN_DEVICE_FUNC inline Quaternion(const Scalar& w, const Scalar& x, const Scalar& y, const Scalar& z) : m_coeffs(x, y, z, w){}
-
-  /** Constructs and initialize a quaternion from the array data */
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Scalar* data) : m_coeffs(data) {}
-
-  /** Copy constructor */
-  template<class Derived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion(const QuaternionBase<Derived>& other) { this->Base::operator=(other); }
-
-  /** Constructs and initializes a quaternion from the angle-axis \a aa */
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const AngleAxisType& aa) { *this = aa; }
-
-  /** Constructs and initializes a quaternion from either:
-    *  - a rotation matrix expression,
-    *  - a 4D vector expression representing quaternion coefficients.
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const MatrixBase<Derived>& other) { *this = other; }
-
-  /** Explicit copy constructor with scalar conversion */
-  template<typename OtherScalar, int OtherOptions>
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Quaternion<OtherScalar, OtherOptions>& other)
-  { m_coeffs = other.coeffs().template cast<Scalar>(); }
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-  // We define a copy constructor, which means we don't get an implicit move constructor or assignment operator.
-  /** Default move constructor */
-  EIGEN_DEVICE_FUNC inline Quaternion(Quaternion&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
-    : m_coeffs(std::move(other.coeffs()))
-  {}
-
-  /** Default move assignment operator */
-  EIGEN_DEVICE_FUNC Quaternion& operator=(Quaternion&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
-  {
-    m_coeffs = std::move(other.coeffs());
-    return *this;
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC static Quaternion UnitRandom();
-
-  template<typename Derived1, typename Derived2>
-  EIGEN_DEVICE_FUNC static Quaternion FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
-
-  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs;}
-  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs;}
-
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(NeedsAlignment))
-  
-#ifdef EIGEN_QUATERNION_PLUGIN
-# include EIGEN_QUATERNION_PLUGIN
-#endif
-
-protected:
-  Coefficients m_coeffs;
-  
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    static EIGEN_STRONG_INLINE void _check_template_params()
-    {
-      EIGEN_STATIC_ASSERT( (_Options & DontAlign) == _Options,
-        INVALID_MATRIX_TEMPLATE_PARAMETERS)
-    }
-#endif
-};
-
-/** \ingroup Geometry_Module
-  * single precision quaternion type */
-typedef Quaternion<float> Quaternionf;
-/** \ingroup Geometry_Module
-  * double precision quaternion type */
-typedef Quaternion<double> Quaterniond;
-
-/***************************************************************************
-* Specialization of Map<Quaternion<Scalar>>
-***************************************************************************/
-
-namespace internal {
-  template<typename _Scalar, int _Options>
-  struct traits<Map<Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> >
-  {
-    typedef Map<Matrix<_Scalar,4,1>, _Options> Coefficients;
-  };
-}
-
-namespace internal {
-  template<typename _Scalar, int _Options>
-  struct traits<Map<const Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> >
-  {
-    typedef Map<const Matrix<_Scalar,4,1>, _Options> Coefficients;
-    typedef traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> > TraitsBase;
-    enum {
-      Flags = TraitsBase::Flags & ~LvalueBit
-    };
-  };
-}
-
-/** \ingroup Geometry_Module
-  * \brief Quaternion expression mapping a constant memory buffer
-  *
-  * \tparam _Scalar the type of the Quaternion coefficients
-  * \tparam _Options see class Map
-  *
-  * This is a specialization of class Map for Quaternion. This class allows to view
-  * a 4 scalar memory buffer as an Eigen's Quaternion object.
-  *
-  * \sa class Map, class Quaternion, class QuaternionBase
-  */
-template<typename _Scalar, int _Options>
-class Map<const Quaternion<_Scalar>, _Options >
-  : public QuaternionBase<Map<const Quaternion<_Scalar>, _Options> >
-{
-  public:
-    typedef QuaternionBase<Map<const Quaternion<_Scalar>, _Options> > Base;
-
-    typedef _Scalar Scalar;
-    typedef typename internal::traits<Map>::Coefficients Coefficients;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
-    using Base::operator*=;
-
-    /** Constructs a Mapped Quaternion object from the pointer \a coeffs
-      *
-      * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
-      * \code *coeffs == {x, y, z, w} \endcode
-      *
-      * If the template parameter _Options is set to #Aligned, then the pointer coeffs must be aligned. */
-    EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(const Scalar* coeffs) : m_coeffs(coeffs) {}
-
-    EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs;}
-
-  protected:
-    const Coefficients m_coeffs;
-};
-
-/** \ingroup Geometry_Module
-  * \brief Expression of a quaternion from a memory buffer
-  *
-  * \tparam _Scalar the type of the Quaternion coefficients
-  * \tparam _Options see class Map
-  *
-  * This is a specialization of class Map for Quaternion. This class allows to view
-  * a 4 scalar memory buffer as an Eigen's  Quaternion object.
-  *
-  * \sa class Map, class Quaternion, class QuaternionBase
-  */
-template<typename _Scalar, int _Options>
-class Map<Quaternion<_Scalar>, _Options >
-  : public QuaternionBase<Map<Quaternion<_Scalar>, _Options> >
-{
-  public:
-    typedef QuaternionBase<Map<Quaternion<_Scalar>, _Options> > Base;
-
-    typedef _Scalar Scalar;
-    typedef typename internal::traits<Map>::Coefficients Coefficients;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
-    using Base::operator*=;
-
-    /** Constructs a Mapped Quaternion object from the pointer \a coeffs
-      *
-      * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
-      * \code *coeffs == {x, y, z, w} \endcode
-      *
-      * If the template parameter _Options is set to #Aligned, then the pointer coeffs must be aligned. */
-    EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(Scalar* coeffs) : m_coeffs(coeffs) {}
-
-    EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
-    EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
-
-  protected:
-    Coefficients m_coeffs;
-};
-
-/** \ingroup Geometry_Module
-  * Map an unaligned array of single precision scalars as a quaternion */
-typedef Map<Quaternion<float>, 0>         QuaternionMapf;
-/** \ingroup Geometry_Module
-  * Map an unaligned array of double precision scalars as a quaternion */
-typedef Map<Quaternion<double>, 0>        QuaternionMapd;
-/** \ingroup Geometry_Module
-  * Map a 16-byte aligned array of single precision scalars as a quaternion */
-typedef Map<Quaternion<float>, Aligned>   QuaternionMapAlignedf;
-/** \ingroup Geometry_Module
-  * Map a 16-byte aligned array of double precision scalars as a quaternion */
-typedef Map<Quaternion<double>, Aligned>  QuaternionMapAlignedd;
-
-/***************************************************************************
-* Implementation of QuaternionBase methods
-***************************************************************************/
-
-// Generic Quaternion * Quaternion product
-// This product can be specialized for a given architecture via the Arch template argument.
-namespace internal {
-template<int Arch, class Derived1, class Derived2, typename Scalar> struct quat_product
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived1>& a, const QuaternionBase<Derived2>& b){
-    return Quaternion<Scalar>
-    (
-      a.w() * b.w() - a.x() * b.x() - a.y() * b.y() - a.z() * b.z(),
-      a.w() * b.x() + a.x() * b.w() + a.y() * b.z() - a.z() * b.y(),
-      a.w() * b.y() + a.y() * b.w() + a.z() * b.x() - a.x() * b.z(),
-      a.w() * b.z() + a.z() * b.w() + a.x() * b.y() - a.y() * b.x()
-    );
-  }
-};
-}
-
-/** \returns the concatenation of two rotations as a quaternion-quaternion product */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<typename internal::traits<Derived>::Scalar>
-QuaternionBase<Derived>::operator* (const QuaternionBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value),
-   YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-  return internal::quat_product<Architecture::Target, Derived, OtherDerived,
-                         typename internal::traits<Derived>::Scalar>::run(*this, other);
-}
-
-/** \sa operator*(Quaternion) */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator*= (const QuaternionBase<OtherDerived>& other)
-{
-  derived() = derived() * other.derived();
-  return derived();
-}
-
-/** Rotation of a vector by a quaternion.
-  * \remarks If the quaternion is used to rotate several points (>1)
-  * then it is much more efficient to first convert it to a 3x3 Matrix.
-  * Comparison of the operation cost for n transformations:
-  *   - Quaternion2:    30n
-  *   - Via a Matrix3: 24 + 15n
-  */
-template <class Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename QuaternionBase<Derived>::Vector3
-QuaternionBase<Derived>::_transformVector(const Vector3& v) const
-{
-    // Note that this algorithm comes from the optimization by hand
-    // of the conversion to a Matrix followed by a Matrix/Vector product.
-    // It appears to be much faster than the common algorithm found
-    // in the literature (30 versus 39 flops). It also requires two
-    // Vector3 as temporaries.
-    Vector3 uv = this->vec().cross(v);
-    uv += uv;
-    return v + this->w() * uv + this->vec().cross(uv);
-}
-
-template<class Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& QuaternionBase<Derived>::operator=(const QuaternionBase<Derived>& other)
-{
-  coeffs() = other.coeffs();
-  return derived();
-}
-
-template<class Derived>
-template<class OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const QuaternionBase<OtherDerived>& other)
-{
-  coeffs() = other.coeffs();
-  return derived();
-}
-
-/** Set \c *this from an angle-axis \a aa and returns a reference to \c *this
-  */
-template<class Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const AngleAxisType& aa)
-{
-  EIGEN_USING_STD(cos)
-  EIGEN_USING_STD(sin)
-  Scalar ha = Scalar(0.5)*aa.angle(); // Scalar(0.5) to suppress precision loss warnings
-  this->w() = cos(ha);
-  this->vec() = sin(ha) * aa.axis();
-  return derived();
-}
-
-/** Set \c *this from the expression \a xpr:
-  *   - if \a xpr is a 4x1 vector, then \a xpr is assumed to be a quaternion
-  *   - if \a xpr is a 3x3 matrix, then \a xpr is assumed to be rotation matrix
-  *     and \a xpr is converted to a quaternion
-  */
-
-template<class Derived>
-template<class MatrixDerived>
-EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::operator=(const MatrixBase<MatrixDerived>& xpr)
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename MatrixDerived::Scalar>::value),
-   YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-  internal::quaternionbase_assign_impl<MatrixDerived>::run(*this, xpr.derived());
-  return derived();
-}
-
-/** Convert the quaternion to a 3x3 rotation matrix. The quaternion is required to
-  * be normalized, otherwise the result is undefined.
-  */
-template<class Derived>
-EIGEN_DEVICE_FUNC inline typename QuaternionBase<Derived>::Matrix3
-QuaternionBase<Derived>::toRotationMatrix(void) const
-{
-  // NOTE if inlined, then gcc 4.2 and 4.4 get rid of the temporary (not gcc 4.3 !!)
-  // if not inlined then the cost of the return by value is huge ~ +35%,
-  // however, not inlining this function is an order of magnitude slower, so
-  // it has to be inlined, and so the return by value is not an issue
-  Matrix3 res;
-
-  const Scalar tx  = Scalar(2)*this->x();
-  const Scalar ty  = Scalar(2)*this->y();
-  const Scalar tz  = Scalar(2)*this->z();
-  const Scalar twx = tx*this->w();
-  const Scalar twy = ty*this->w();
-  const Scalar twz = tz*this->w();
-  const Scalar txx = tx*this->x();
-  const Scalar txy = ty*this->x();
-  const Scalar txz = tz*this->x();
-  const Scalar tyy = ty*this->y();
-  const Scalar tyz = tz*this->y();
-  const Scalar tzz = tz*this->z();
-
-  res.coeffRef(0,0) = Scalar(1)-(tyy+tzz);
-  res.coeffRef(0,1) = txy-twz;
-  res.coeffRef(0,2) = txz+twy;
-  res.coeffRef(1,0) = txy+twz;
-  res.coeffRef(1,1) = Scalar(1)-(txx+tzz);
-  res.coeffRef(1,2) = tyz-twx;
-  res.coeffRef(2,0) = txz-twy;
-  res.coeffRef(2,1) = tyz+twx;
-  res.coeffRef(2,2) = Scalar(1)-(txx+tyy);
-
-  return res;
-}
-
-/** Sets \c *this to be a quaternion representing a rotation between
-  * the two arbitrary vectors \a a and \a b. In other words, the built
-  * rotation represent a rotation sending the line of direction \a a
-  * to the line of direction \a b, both lines passing through the origin.
-  *
-  * \returns a reference to \c *this.
-  *
-  * Note that the two input vectors do \b not have to be normalized, and
-  * do not need to have the same norm.
-  */
-template<class Derived>
-template<typename Derived1, typename Derived2>
-EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b)
-{
-  EIGEN_USING_STD(sqrt)
-  Vector3 v0 = a.normalized();
-  Vector3 v1 = b.normalized();
-  Scalar c = v1.dot(v0);
-
-  // if dot == -1, vectors are nearly opposites
-  // => accurately compute the rotation axis by computing the
-  //    intersection of the two planes. This is done by solving:
-  //       x^T v0 = 0
-  //       x^T v1 = 0
-  //    under the constraint:
-  //       ||x|| = 1
-  //    which yields a singular value problem
-  if (c < Scalar(-1)+NumTraits<Scalar>::dummy_precision())
-  {
-    c = numext::maxi(c,Scalar(-1));
-    Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
-    JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
-    Vector3 axis = svd.matrixV().col(2);
-
-    Scalar w2 = (Scalar(1)+c)*Scalar(0.5);
-    this->w() = sqrt(w2);
-    this->vec() = axis * sqrt(Scalar(1) - w2);
-    return derived();
-  }
-  Vector3 axis = v0.cross(v1);
-  Scalar s = sqrt((Scalar(1)+c)*Scalar(2));
-  Scalar invs = Scalar(1)/s;
-  this->vec() = axis * invs;
-  this->w() = s * Scalar(0.5);
-
-  return derived();
-}
-
-/** \returns a random unit quaternion following a uniform distribution law on SO(3)
-  *
-  * \note The implementation is based on http://planning.cs.uiuc.edu/node198.html
-  */
-template<typename Scalar, int Options>
-EIGEN_DEVICE_FUNC Quaternion<Scalar,Options> Quaternion<Scalar,Options>::UnitRandom()
-{
-  EIGEN_USING_STD(sqrt)
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  const Scalar u1 = internal::random<Scalar>(0, 1),
-               u2 = internal::random<Scalar>(0, 2*EIGEN_PI),
-               u3 = internal::random<Scalar>(0, 2*EIGEN_PI);
-  const Scalar a = sqrt(Scalar(1) - u1),
-               b = sqrt(u1);
-  return Quaternion (a * sin(u2), a * cos(u2), b * sin(u3), b * cos(u3));
-}
-
-
-/** Returns a quaternion representing a rotation between
-  * the two arbitrary vectors \a a and \a b. In other words, the built
-  * rotation represent a rotation sending the line of direction \a a
-  * to the line of direction \a b, both lines passing through the origin.
-  *
-  * \returns resulting quaternion
-  *
-  * Note that the two input vectors do \b not have to be normalized, and
-  * do not need to have the same norm.
-  */
-template<typename Scalar, int Options>
-template<typename Derived1, typename Derived2>
-EIGEN_DEVICE_FUNC Quaternion<Scalar,Options> Quaternion<Scalar,Options>::FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b)
-{
-    Quaternion quat;
-    quat.setFromTwoVectors(a, b);
-    return quat;
-}
-
-
-/** \returns the multiplicative inverse of \c *this
-  * Note that in most cases, i.e., if you simply want the opposite rotation,
-  * and/or the quaternion is normalized, then it is enough to use the conjugate.
-  *
-  * \sa QuaternionBase::conjugate()
-  */
-template <class Derived>
-EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar> QuaternionBase<Derived>::inverse() const
-{
-  // FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite()  ??
-  Scalar n2 = this->squaredNorm();
-  if (n2 > Scalar(0))
-    return Quaternion<Scalar>(conjugate().coeffs() / n2);
-  else
-  {
-    // return an invalid result to flag the error
-    return Quaternion<Scalar>(Coefficients::Zero());
-  }
-}
-
-// Generic conjugate of a Quaternion
-namespace internal {
-template<int Arch, class Derived, typename Scalar> struct quat_conj
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived>& q){
-    return Quaternion<Scalar>(q.w(),-q.x(),-q.y(),-q.z());
-  }
-};
-}
-                         
-/** \returns the conjugate of the \c *this which is equal to the multiplicative inverse
-  * if the quaternion is normalized.
-  * The conjugate of a quaternion represents the opposite rotation.
-  *
-  * \sa Quaternion2::inverse()
-  */
-template <class Derived>
-EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar>
-QuaternionBase<Derived>::conjugate() const
-{
-  return internal::quat_conj<Architecture::Target, Derived,
-                         typename internal::traits<Derived>::Scalar>::run(*this);
-                         
-}
-
-/** \returns the angle (in radian) between two rotations
-  * \sa dot()
-  */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar
-QuaternionBase<Derived>::angularDistance(const QuaternionBase<OtherDerived>& other) const
-{
-  EIGEN_USING_STD(atan2)
-  Quaternion<Scalar> d = (*this) * other.conjugate();
-  return Scalar(2) * atan2( d.vec().norm(), numext::abs(d.w()) );
-}
-
- 
-    
-/** \returns the spherical linear interpolation between the two quaternions
-  * \c *this and \a other at the parameter \a t in [0;1].
-  * 
-  * This represents an interpolation for a constant motion between \c *this and \a other,
-  * see also http://en.wikipedia.org/wiki/Slerp.
-  */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC Quaternion<typename internal::traits<Derived>::Scalar>
-QuaternionBase<Derived>::slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const
-{
-  EIGEN_USING_STD(acos)
-  EIGEN_USING_STD(sin)
-  const Scalar one = Scalar(1) - NumTraits<Scalar>::epsilon();
-  Scalar d = this->dot(other);
-  Scalar absD = numext::abs(d);
-
-  Scalar scale0;
-  Scalar scale1;
-
-  if(absD>=one)
-  {
-    scale0 = Scalar(1) - t;
-    scale1 = t;
-  }
-  else
-  {
-    // theta is the angle between the 2 quaternions
-    Scalar theta = acos(absD);
-    Scalar sinTheta = sin(theta);
-
-    scale0 = sin( ( Scalar(1) - t ) * theta) / sinTheta;
-    scale1 = sin( ( t * theta) ) / sinTheta;
-  }
-  if(d<Scalar(0)) scale1 = -scale1;
-
-  return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs());
-}
-
-namespace internal {
-
-// set from a rotation matrix
-template<typename Other>
-struct quaternionbase_assign_impl<Other,3,3>
-{
-  typedef typename Other::Scalar Scalar;
-  template<class Derived> EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& a_mat)
-  {
-    const typename internal::nested_eval<Other,2>::type mat(a_mat);
-    EIGEN_USING_STD(sqrt)
-    // This algorithm comes from  "Quaternion Calculus and Fast Animation",
-    // Ken Shoemake, 1987 SIGGRAPH course notes
-    Scalar t = mat.trace();
-    if (t > Scalar(0))
-    {
-      t = sqrt(t + Scalar(1.0));
-      q.w() = Scalar(0.5)*t;
-      t = Scalar(0.5)/t;
-      q.x() = (mat.coeff(2,1) - mat.coeff(1,2)) * t;
-      q.y() = (mat.coeff(0,2) - mat.coeff(2,0)) * t;
-      q.z() = (mat.coeff(1,0) - mat.coeff(0,1)) * t;
-    }
-    else
-    {
-      Index i = 0;
-      if (mat.coeff(1,1) > mat.coeff(0,0))
-        i = 1;
-      if (mat.coeff(2,2) > mat.coeff(i,i))
-        i = 2;
-      Index j = (i+1)%3;
-      Index k = (j+1)%3;
-
-      t = sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + Scalar(1.0));
-      q.coeffs().coeffRef(i) = Scalar(0.5) * t;
-      t = Scalar(0.5)/t;
-      q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t;
-      q.coeffs().coeffRef(j) = (mat.coeff(j,i)+mat.coeff(i,j))*t;
-      q.coeffs().coeffRef(k) = (mat.coeff(k,i)+mat.coeff(i,k))*t;
-    }
-  }
-};
-
-// set from a vector of coefficients assumed to be a quaternion
-template<typename Other>
-struct quaternionbase_assign_impl<Other,4,1>
-{
-  typedef typename Other::Scalar Scalar;
-  template<class Derived> EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& vec)
-  {
-    q.coeffs() = vec;
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_QUATERNION_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Rotation2D.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Rotation2D.h
deleted file mode 100644
index d0bd575..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Rotation2D.h
+++ /dev/null
@@ -1,199 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ROTATION2D_H
-#define EIGEN_ROTATION2D_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Rotation2D
-  *
-  * \brief Represents a rotation/orientation in a 2 dimensional space.
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  *
-  * This class is equivalent to a single scalar representing a counter clock wise rotation
-  * as a single angle in radian. It provides some additional features such as the automatic
-  * conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar
-  * interface to Quaternion in order to facilitate the writing of generic algorithms
-  * dealing with rotations.
-  *
-  * \sa class Quaternion, class Transform
-  */
-
-namespace internal {
-
-template<typename _Scalar> struct traits<Rotation2D<_Scalar> >
-{
-  typedef _Scalar Scalar;
-};
-} // end namespace internal
-
-template<typename _Scalar>
-class Rotation2D : public RotationBase<Rotation2D<_Scalar>,2>
-{
-  typedef RotationBase<Rotation2D<_Scalar>,2> Base;
-
-public:
-
-  using Base::operator*;
-
-  enum { Dim = 2 };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  typedef Matrix<Scalar,2,1> Vector2;
-  typedef Matrix<Scalar,2,2> Matrix2;
-
-protected:
-
-  Scalar m_angle;
-
-public:
-
-  /** Construct a 2D counter clock wise rotation from the angle \a a in radian. */
-  EIGEN_DEVICE_FUNC explicit inline Rotation2D(const Scalar& a) : m_angle(a) {}
-  
-  /** Default constructor wihtout initialization. The represented rotation is undefined. */
-  EIGEN_DEVICE_FUNC Rotation2D() {}
-
-  /** Construct a 2D rotation from a 2x2 rotation matrix \a mat.
-    *
-    * \sa fromRotationMatrix()
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC explicit Rotation2D(const MatrixBase<Derived>& m)
-  {
-    fromRotationMatrix(m.derived());
-  }
-
-  /** \returns the rotation angle */
-  EIGEN_DEVICE_FUNC inline Scalar angle() const { return m_angle; }
-
-  /** \returns a read-write reference to the rotation angle */
-  EIGEN_DEVICE_FUNC inline Scalar& angle() { return m_angle; }
-  
-  /** \returns the rotation angle in [0,2pi] */
-  EIGEN_DEVICE_FUNC inline Scalar smallestPositiveAngle() const {
-    Scalar tmp = numext::fmod(m_angle,Scalar(2*EIGEN_PI));
-    return tmp<Scalar(0) ? tmp + Scalar(2*EIGEN_PI) : tmp;
-  }
-  
-  /** \returns the rotation angle in [-pi,pi] */
-  EIGEN_DEVICE_FUNC inline Scalar smallestAngle() const {
-    Scalar tmp = numext::fmod(m_angle,Scalar(2*EIGEN_PI));
-    if(tmp>Scalar(EIGEN_PI))       tmp -= Scalar(2*EIGEN_PI);
-    else if(tmp<-Scalar(EIGEN_PI)) tmp += Scalar(2*EIGEN_PI);
-    return tmp;
-  }
-
-  /** \returns the inverse rotation */
-  EIGEN_DEVICE_FUNC inline Rotation2D inverse() const { return Rotation2D(-m_angle); }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline Rotation2D operator*(const Rotation2D& other) const
-  { return Rotation2D(m_angle + other.m_angle); }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline Rotation2D& operator*=(const Rotation2D& other)
-  { m_angle += other.m_angle; return *this; }
-
-  /** Applies the rotation to a 2D vector */
-  EIGEN_DEVICE_FUNC Vector2 operator* (const Vector2& vec) const
-  { return toRotationMatrix() * vec; }
-  
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m);
-  EIGEN_DEVICE_FUNC Matrix2 toRotationMatrix() const;
-
-  /** Set \c *this from a 2x2 rotation matrix \a mat.
-    * In other words, this function extract the rotation angle from the rotation matrix.
-    *
-    * This method is an alias for fromRotationMatrix()
-    *
-    * \sa fromRotationMatrix()
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC Rotation2D& operator=(const  MatrixBase<Derived>& m)
-  { return fromRotationMatrix(m.derived()); }
-
-  /** \returns the spherical interpolation between \c *this and \a other using
-    * parameter \a t. It is in fact equivalent to a linear interpolation.
-    */
-  EIGEN_DEVICE_FUNC inline Rotation2D slerp(const Scalar& t, const Rotation2D& other) const
-  {
-    Scalar dist = Rotation2D(other.m_angle-m_angle).smallestAngle();
-    return Rotation2D(m_angle + dist*t);
-  }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type cast() const
-  { return typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other)
-  {
-    m_angle = Scalar(other.angle());
-  }
-
-  EIGEN_DEVICE_FUNC static inline Rotation2D Identity() { return Rotation2D(0); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const Rotation2D& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return internal::isApprox(m_angle,other.m_angle, prec); }
-  
-};
-
-/** \ingroup Geometry_Module
-  * single precision 2D rotation type */
-typedef Rotation2D<float> Rotation2Df;
-/** \ingroup Geometry_Module
-  * double precision 2D rotation type */
-typedef Rotation2D<double> Rotation2Dd;
-
-/** Set \c *this from a 2x2 rotation matrix \a mat.
-  * In other words, this function extract the rotation angle
-  * from the rotation matrix.
-  */
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
-{
-  EIGEN_USING_STD(atan2)
-  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
-  m_angle = atan2(mat.coeff(1,0), mat.coeff(0,0));
-  return *this;
-}
-
-/** Constructs and \returns an equivalent 2x2 rotation matrix.
-  */
-template<typename Scalar>
-typename Rotation2D<Scalar>::Matrix2
-EIGEN_DEVICE_FUNC Rotation2D<Scalar>::toRotationMatrix(void) const
-{
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  Scalar sinA = sin(m_angle);
-  Scalar cosA = cos(m_angle);
-  return (Matrix2() << cosA, -sinA, sinA, cosA).finished();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ROTATION2D_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/RotationBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/RotationBase.h
deleted file mode 100644
index f0ee0bd..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/RotationBase.h
+++ /dev/null
@@ -1,206 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ROTATIONBASE_H
-#define EIGEN_ROTATIONBASE_H
-
-namespace Eigen { 
-
-// forward declaration
-namespace internal {
-template<typename RotationDerived, typename MatrixType, bool IsVector=MatrixType::IsVectorAtCompileTime>
-struct rotation_base_generic_product_selector;
-}
-
-/** \class RotationBase
-  *
-  * \brief Common base class for compact rotation representations
-  *
-  * \tparam Derived is the derived type, i.e., a rotation type
-  * \tparam _Dim the dimension of the space
-  */
-template<typename Derived, int _Dim>
-class RotationBase
-{
-  public:
-    enum { Dim = _Dim };
-    /** the scalar type of the coefficients */
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-
-    /** corresponding linear transformation matrix type */
-    typedef Matrix<Scalar,Dim,Dim> RotationMatrixType;
-    typedef Matrix<Scalar,Dim,1> VectorType;
-
-  public:
-    EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
-
-    /** \returns an equivalent rotation matrix */
-    EIGEN_DEVICE_FUNC inline RotationMatrixType toRotationMatrix() const { return derived().toRotationMatrix(); }
-
-    /** \returns an equivalent rotation matrix 
-      * This function is added to be conform with the Transform class' naming scheme.
-      */
-    EIGEN_DEVICE_FUNC inline RotationMatrixType matrix() const { return derived().toRotationMatrix(); }
-
-    /** \returns the inverse rotation */
-    EIGEN_DEVICE_FUNC inline Derived inverse() const { return derived().inverse(); }
-
-    /** \returns the concatenation of the rotation \c *this with a translation \a t */
-    EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Isometry> operator*(const Translation<Scalar,Dim>& t) const
-    { return Transform<Scalar,Dim,Isometry>(*this) * t; }
-
-    /** \returns the concatenation of the rotation \c *this with a uniform scaling \a s */
-    EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const UniformScaling<Scalar>& s) const
-    { return toRotationMatrix() * s.factor(); }
-
-    /** \returns the concatenation of the rotation \c *this with a generic expression \a e
-      * \a e can be:
-      *  - a DimxDim linear transformation matrix
-      *  - a DimxDim diagonal matrix (axis aligned scaling)
-      *  - a vector of size Dim
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::rotation_base_generic_product_selector<Derived,OtherDerived,OtherDerived::IsVectorAtCompileTime>::ReturnType
-    operator*(const EigenBase<OtherDerived>& e) const
-    { return internal::rotation_base_generic_product_selector<Derived,OtherDerived>::run(derived(), e.derived()); }
-
-    /** \returns the concatenation of a linear transformation \a l with the rotation \a r */
-    template<typename OtherDerived> friend
-    EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const EigenBase<OtherDerived>& l, const Derived& r)
-    { return l.derived() * r.toRotationMatrix(); }
-
-    /** \returns the concatenation of a scaling \a l with the rotation \a r */
-    EIGEN_DEVICE_FUNC friend inline Transform<Scalar,Dim,Affine> operator*(const DiagonalMatrix<Scalar,Dim>& l, const Derived& r)
-    { 
-      Transform<Scalar,Dim,Affine> res(r);
-      res.linear().applyOnTheLeft(l);
-      return res;
-    }
-
-    /** \returns the concatenation of the rotation \c *this with a transformation \a t */
-    template<int Mode, int Options>
-    EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode> operator*(const Transform<Scalar,Dim,Mode,Options>& t) const
-    { return toRotationMatrix() * t; }
-
-    template<typename OtherVectorType>
-    EIGEN_DEVICE_FUNC inline VectorType _transformVector(const OtherVectorType& v) const
-    { return toRotationMatrix() * v; }
-};
-
-namespace internal {
-
-// implementation of the generic product rotation * matrix
-template<typename RotationDerived, typename MatrixType>
-struct rotation_base_generic_product_selector<RotationDerived,MatrixType,false>
-{
-  enum { Dim = RotationDerived::Dim };
-  typedef Matrix<typename RotationDerived::Scalar,Dim,Dim> ReturnType;
-  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r, const MatrixType& m)
-  { return r.toRotationMatrix() * m; }
-};
-
-template<typename RotationDerived, typename Scalar, int Dim, int MaxDim>
-struct rotation_base_generic_product_selector< RotationDerived, DiagonalMatrix<Scalar,Dim,MaxDim>, false >
-{
-  typedef Transform<Scalar,Dim,Affine> ReturnType;
-  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r, const DiagonalMatrix<Scalar,Dim,MaxDim>& m)
-  {
-    ReturnType res(r);
-    res.linear() *= m;
-    return res;
-  }
-};
-
-template<typename RotationDerived,typename OtherVectorType>
-struct rotation_base_generic_product_selector<RotationDerived,OtherVectorType,true>
-{
-  enum { Dim = RotationDerived::Dim };
-  typedef Matrix<typename RotationDerived::Scalar,Dim,1> ReturnType;
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE ReturnType run(const RotationDerived& r, const OtherVectorType& v)
-  {
-    return r._transformVector(v);
-  }
-};
-
-} // end namespace internal
-
-/** \geometry_module
-  *
-  * \brief Constructs a Dim x Dim rotation matrix from the rotation \a r
-  */
-template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
-::Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
-{
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
-  *this = r.toRotationMatrix();
-}
-
-/** \geometry_module
-  *
-  * \brief Set a Dim x Dim rotation matrix from the rotation \a r
-  */
-template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>&
-Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
-::operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
-{
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
-  return *this = r.toRotationMatrix();
-}
-
-namespace internal {
-
-/** \internal
-  *
-  * Helper function to return an arbitrary rotation object to a rotation matrix.
-  *
-  * \tparam Scalar the numeric type of the matrix coefficients
-  * \tparam Dim the dimension of the current space
-  *
-  * It returns a Dim x Dim fixed size matrix.
-  *
-  * Default specializations are provided for:
-  *   - any scalar type (2D),
-  *   - any matrix expression,
-  *   - any type based on RotationBase (e.g., Quaternion, AngleAxis, Rotation2D)
-  *
-  * Currently toRotationMatrix is only used by Transform.
-  *
-  * \sa class Transform, class Rotation2D, class Quaternion, class AngleAxis
-  */
-template<typename Scalar, int Dim>
-EIGEN_DEVICE_FUNC static inline Matrix<Scalar,2,2> toRotationMatrix(const Scalar& s)
-{
-  EIGEN_STATIC_ASSERT(Dim==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
-  return Rotation2D<Scalar>(s).toRotationMatrix();
-}
-
-template<typename Scalar, int Dim, typename OtherDerived>
-EIGEN_DEVICE_FUNC static inline Matrix<Scalar,Dim,Dim> toRotationMatrix(const RotationBase<OtherDerived,Dim>& r)
-{
-  return r.toRotationMatrix();
-}
-
-template<typename Scalar, int Dim, typename OtherDerived>
-EIGEN_DEVICE_FUNC static inline const MatrixBase<OtherDerived>& toRotationMatrix(const MatrixBase<OtherDerived>& mat)
-{
-  EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime==Dim && OtherDerived::ColsAtCompileTime==Dim,
-    YOU_MADE_A_PROGRAMMING_MISTAKE)
-  return mat;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_ROTATIONBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Scaling.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Scaling.h
deleted file mode 100644
index d352f1f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Scaling.h
+++ /dev/null
@@ -1,188 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SCALING_H
-#define EIGEN_SCALING_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class UniformScaling
-  *
-  * \brief Represents a generic uniform scaling transformation
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients.
-  *
-  * This class represent a uniform scaling transformation. It is the return
-  * type of Scaling(Scalar), and most of the time this is the only way it
-  * is used. In particular, this class is not aimed to be used to store a scaling transformation,
-  * but rather to make easier the constructions and updates of Transform objects.
-  *
-  * To represent an axis aligned scaling, use the DiagonalMatrix class.
-  *
-  * \sa Scaling(), class DiagonalMatrix, MatrixBase::asDiagonal(), class Translation, class Transform
-  */
-
-namespace internal
-{
-  // This helper helps nvcc+MSVC to properly parse this file.
-  // See bug 1412.
-  template <typename Scalar, int Dim, int Mode>
-  struct uniformscaling_times_affine_returntype
-  {
-    enum
-    {
-      NewMode = int(Mode) == int(Isometry) ? Affine : Mode
-    };
-    typedef Transform <Scalar, Dim, NewMode> type;
-  };
-}
-
-template<typename _Scalar>
-class UniformScaling
-{
-public:
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-
-protected:
-
-  Scalar m_factor;
-
-public:
-
-  /** Default constructor without initialization. */
-  UniformScaling() {}
-  /** Constructs and initialize a uniform scaling transformation */
-  explicit inline UniformScaling(const Scalar& s) : m_factor(s) {}
-
-  inline const Scalar& factor() const { return m_factor; }
-  inline Scalar& factor() { return m_factor; }
-
-  /** Concatenates two uniform scaling */
-  inline UniformScaling operator* (const UniformScaling& other) const
-  { return UniformScaling(m_factor * other.factor()); }
-
-  /** Concatenates a uniform scaling and a translation */
-  template<int Dim>
-  inline Transform<Scalar,Dim,Affine> operator* (const Translation<Scalar,Dim>& t) const;
-
-  /** Concatenates a uniform scaling and an affine transformation */
-  template<int Dim, int Mode, int Options>
-  inline typename
-	internal::uniformscaling_times_affine_returntype<Scalar,Dim,Mode>::type
-	operator* (const Transform<Scalar, Dim, Mode, Options>& t) const
-  {
-    typename internal::uniformscaling_times_affine_returntype<Scalar,Dim,Mode>::type res = t;
-    res.prescale(factor());
-    return res;
-  }
-
-  /** Concatenates a uniform scaling and a linear transformation matrix */
-  // TODO returns an expression
-  template<typename Derived>
-  inline typename Eigen::internal::plain_matrix_type<Derived>::type operator* (const MatrixBase<Derived>& other) const
-  { return other * m_factor; }
-
-  template<typename Derived,int Dim>
-  inline Matrix<Scalar,Dim,Dim> operator*(const RotationBase<Derived,Dim>& r) const
-  { return r.toRotationMatrix() * m_factor; }
-
-  /** \returns the inverse scaling */
-  inline UniformScaling inverse() const
-  { return UniformScaling(Scalar(1)/m_factor); }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  inline UniformScaling<NewScalarType> cast() const
-  { return UniformScaling<NewScalarType>(NewScalarType(m_factor)); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  inline explicit UniformScaling(const UniformScaling<OtherScalarType>& other)
-  { m_factor = Scalar(other.factor()); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  bool isApprox(const UniformScaling& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return internal::isApprox(m_factor, other.factor(), prec); }
-
-};
-
-/** \addtogroup Geometry_Module */
-//@{
-
-/** Concatenates a linear transformation matrix and a uniform scaling
-  * \relates UniformScaling
-  */
-// NOTE this operator is defined in MatrixBase and not as a friend function
-// of UniformScaling to fix an internal crash of Intel's ICC
-template<typename Derived,typename Scalar>
-EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,Scalar,product)
-operator*(const MatrixBase<Derived>& matrix, const UniformScaling<Scalar>& s)
-{ return matrix.derived() * s.factor(); }
-
-/** Constructs a uniform scaling from scale factor \a s */
-inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
-/** Constructs a uniform scaling from scale factor \a s */
-inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
-/** Constructs a uniform scaling from scale factor \a s */
-template<typename RealScalar>
-inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s)
-{ return UniformScaling<std::complex<RealScalar> >(s); }
-
-/** Constructs a 2D axis aligned scaling */
-template<typename Scalar>
-inline DiagonalMatrix<Scalar,2> Scaling(const Scalar& sx, const Scalar& sy)
-{ return DiagonalMatrix<Scalar,2>(sx, sy); }
-/** Constructs a 3D axis aligned scaling */
-template<typename Scalar>
-inline DiagonalMatrix<Scalar,3> Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz)
-{ return DiagonalMatrix<Scalar,3>(sx, sy, sz); }
-
-/** Constructs an axis aligned scaling expression from vector expression \a coeffs
-  * This is an alias for coeffs.asDiagonal()
-  */
-template<typename Derived>
-inline const DiagonalWrapper<const Derived> Scaling(const MatrixBase<Derived>& coeffs)
-{ return coeffs.asDiagonal(); }
-
-/** \deprecated */
-typedef DiagonalMatrix<float, 2> AlignedScaling2f;
-/** \deprecated */
-typedef DiagonalMatrix<double,2> AlignedScaling2d;
-/** \deprecated */
-typedef DiagonalMatrix<float, 3> AlignedScaling3f;
-/** \deprecated */
-typedef DiagonalMatrix<double,3> AlignedScaling3d;
-//@}
-
-template<typename Scalar>
-template<int Dim>
-inline Transform<Scalar,Dim,Affine>
-UniformScaling<Scalar>::operator* (const Translation<Scalar,Dim>& t) const
-{
-  Transform<Scalar,Dim,Affine> res;
-  res.matrix().setZero();
-  res.linear().diagonal().fill(factor());
-  res.translation() = factor() * t.vector();
-  res(Dim,Dim) = Scalar(1);
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SCALING_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Transform.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Transform.h
deleted file mode 100644
index 52b8c2a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Transform.h
+++ /dev/null
@@ -1,1563 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSFORM_H
-#define EIGEN_TRANSFORM_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Transform>
-struct transform_traits
-{
-  enum
-  {
-    Dim = Transform::Dim,
-    HDim = Transform::HDim,
-    Mode = Transform::Mode,
-    IsProjective = (int(Mode)==int(Projective))
-  };
-};
-
-template< typename TransformType,
-          typename MatrixType,
-          int Case = transform_traits<TransformType>::IsProjective ? 0
-                   : int(MatrixType::RowsAtCompileTime) == int(transform_traits<TransformType>::HDim) ? 1
-                   : 2,
-          int RhsCols = MatrixType::ColsAtCompileTime>
-struct transform_right_product_impl;
-
-template< typename Other,
-          int Mode,
-          int Options,
-          int Dim,
-          int HDim,
-          int OtherRows=Other::RowsAtCompileTime,
-          int OtherCols=Other::ColsAtCompileTime>
-struct transform_left_product_impl;
-
-template< typename Lhs,
-          typename Rhs,
-          bool AnyProjective =
-            transform_traits<Lhs>::IsProjective ||
-            transform_traits<Rhs>::IsProjective>
-struct transform_transform_product_impl;
-
-template< typename Other,
-          int Mode,
-          int Options,
-          int Dim,
-          int HDim,
-          int OtherRows=Other::RowsAtCompileTime,
-          int OtherCols=Other::ColsAtCompileTime>
-struct transform_construct_from_matrix;
-
-template<typename TransformType> struct transform_take_affine_part;
-
-template<typename _Scalar, int _Dim, int _Mode, int _Options>
-struct traits<Transform<_Scalar,_Dim,_Mode,_Options> >
-{
-  typedef _Scalar Scalar;
-  typedef Eigen::Index StorageIndex;
-  typedef Dense StorageKind;
-  enum {
-    Dim1 = _Dim==Dynamic ? _Dim : _Dim + 1,
-    RowsAtCompileTime = _Mode==Projective ? Dim1 : _Dim,
-    ColsAtCompileTime = Dim1,
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    Flags = 0
-  };
-};
-
-template<int Mode> struct transform_make_affine;
-
-} // end namespace internal
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Transform
-  *
-  * \brief Represents an homogeneous transformation in a N dimensional space
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _Dim the dimension of the space
-  * \tparam _Mode the type of the transformation. Can be:
-  *              - #Affine: the transformation is stored as a (Dim+1)^2 matrix,
-  *                         where the last row is assumed to be [0 ... 0 1].
-  *              - #AffineCompact: the transformation is stored as a (Dim)x(Dim+1) matrix.
-  *              - #Projective: the transformation is stored as a (Dim+1)^2 matrix
-  *                             without any assumption.
-  *              - #Isometry: same as #Affine with the additional assumption that
-  *                           the linear part represents a rotation. This assumption is exploited
-  *                           to speed up some functions such as inverse() and rotation().
-  * \tparam _Options has the same meaning as in class Matrix. It allows to specify DontAlign and/or RowMajor.
-  *                  These Options are passed directly to the underlying matrix type.
-  *
-  * The homography is internally represented and stored by a matrix which
-  * is available through the matrix() method. To understand the behavior of
-  * this class you have to think a Transform object as its internal
-  * matrix representation. The chosen convention is right multiply:
-  *
-  * \code v' = T * v \endcode
-  *
-  * Therefore, an affine transformation matrix M is shaped like this:
-  *
-  * \f$ \left( \begin{array}{cc}
-  * linear & translation\\
-  * 0 ... 0 & 1
-  * \end{array} \right) \f$
-  *
-  * Note that for a projective transformation the last row can be anything,
-  * and then the interpretation of different parts might be slightly different.
-  *
-  * However, unlike a plain matrix, the Transform class provides many features
-  * simplifying both its assembly and usage. In particular, it can be composed
-  * with any other transformations (Transform,Translation,RotationBase,DiagonalMatrix)
-  * and can be directly used to transform implicit homogeneous vectors. All these
-  * operations are handled via the operator*. For the composition of transformations,
-  * its principle consists to first convert the right/left hand sides of the product
-  * to a compatible (Dim+1)^2 matrix and then perform a pure matrix product.
-  * Of course, internally, operator* tries to perform the minimal number of operations
-  * according to the nature of each terms. Likewise, when applying the transform
-  * to points, the latters are automatically promoted to homogeneous vectors
-  * before doing the matrix product. The conventions to homogeneous representations
-  * are performed as follow:
-  *
-  * \b Translation t (Dim)x(1):
-  * \f$ \left( \begin{array}{cc}
-  * I & t \\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *
-  * \b Rotation R (Dim)x(Dim):
-  * \f$ \left( \begin{array}{cc}
-  * R & 0\\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *<!--
-  * \b Linear \b Matrix L (Dim)x(Dim):
-  * \f$ \left( \begin{array}{cc}
-  * L & 0\\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *
-  * \b Affine \b Matrix A (Dim)x(Dim+1):
-  * \f$ \left( \begin{array}{c}
-  * A\\
-  * 0\,...\,0\,1
-  * \end{array} \right) \f$
-  *-->
-  * \b Scaling \b DiagonalMatrix S (Dim)x(Dim):
-  * \f$ \left( \begin{array}{cc}
-  * S & 0\\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *
-  * \b Column \b point v (Dim)x(1):
-  * \f$ \left( \begin{array}{c}
-  * v\\
-  * 1
-  * \end{array} \right) \f$
-  *
-  * \b Set \b of \b column \b points V1...Vn (Dim)x(n):
-  * \f$ \left( \begin{array}{ccc}
-  * v_1 & ... & v_n\\
-  * 1 & ... & 1
-  * \end{array} \right) \f$
-  *
-  * The concatenation of a Transform object with any kind of other transformation
-  * always returns a Transform object.
-  *
-  * A little exception to the "as pure matrix product" rule is the case of the
-  * transformation of non homogeneous vectors by an affine transformation. In
-  * that case the last matrix row can be ignored, and the product returns non
-  * homogeneous vectors.
-  *
-  * Since, for instance, a Dim x Dim matrix is interpreted as a linear transformation,
-  * it is not possible to directly transform Dim vectors stored in a Dim x Dim matrix.
-  * The solution is either to use a Dim x Dynamic matrix or explicitly request a
-  * vector transformation by making the vector homogeneous:
-  * \code
-  * m' = T * m.colwise().homogeneous();
-  * \endcode
-  * Note that there is zero overhead.
-  *
-  * Conversion methods from/to Qt's QMatrix and QTransform are available if the
-  * preprocessor token EIGEN_QT_SUPPORT is defined.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TRANSFORM_PLUGIN.
-  *
-  * \sa class Matrix, class Quaternion
-  */
-template<typename _Scalar, int _Dim, int _Mode, int _Options>
-class Transform
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim==Dynamic ? Dynamic : (_Dim+1)*(_Dim+1))
-  enum {
-    Mode = _Mode,
-    Options = _Options,
-    Dim = _Dim,     ///< space dimension in which the transformation holds
-    HDim = _Dim+1,  ///< size of a respective homogeneous vector
-    Rows = int(Mode)==(AffineCompact) ? Dim : HDim
-  };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  typedef Eigen::Index StorageIndex;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  /** type of the matrix used to represent the transformation */
-  typedef typename internal::make_proper_matrix_type<Scalar,Rows,HDim,Options>::type MatrixType;
-  /** constified MatrixType */
-  typedef const MatrixType ConstMatrixType;
-  /** type of the matrix used to represent the linear part of the transformation */
-  typedef Matrix<Scalar,Dim,Dim,Options> LinearMatrixType;
-  /** type of read/write reference to the linear part of the transformation */
-  typedef Block<MatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (int(Options)&RowMajor)==0> LinearPart;
-  /** type of read reference to the linear part of the transformation */
-  typedef const Block<ConstMatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (int(Options)&RowMajor)==0> ConstLinearPart;
-  /** type of read/write reference to the affine part of the transformation */
-  typedef typename internal::conditional<int(Mode)==int(AffineCompact),
-                              MatrixType&,
-                              Block<MatrixType,Dim,HDim> >::type AffinePart;
-  /** type of read reference to the affine part of the transformation */
-  typedef typename internal::conditional<int(Mode)==int(AffineCompact),
-                              const MatrixType&,
-                              const Block<const MatrixType,Dim,HDim> >::type ConstAffinePart;
-  /** type of a vector */
-  typedef Matrix<Scalar,Dim,1> VectorType;
-  /** type of a read/write reference to the translation part of the rotation */
-  typedef Block<MatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> TranslationPart;
-  /** type of a read reference to the translation part of the rotation */
-  typedef const Block<ConstMatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> ConstTranslationPart;
-  /** corresponding translation type */
-  typedef Translation<Scalar,Dim> TranslationType;
-
-  // this intermediate enum is needed to avoid an ICE with gcc 3.4 and 4.0
-  enum { TransformTimeDiagonalMode = ((Mode==int(Isometry))?Affine:int(Mode)) };
-  /** The return type of the product between a diagonal matrix and a transform */
-  typedef Transform<Scalar,Dim,TransformTimeDiagonalMode> TransformTimeDiagonalReturnType;
-
-protected:
-
-  MatrixType m_matrix;
-
-public:
-
-  /** Default constructor without initialization of the meaningful coefficients.
-    * If Mode==Affine or Mode==Isometry, then the last row is set to [0 ... 0 1] */
-  EIGEN_DEVICE_FUNC inline Transform()
-  {
-    check_template_params();
-    internal::transform_make_affine<(int(Mode)==Affine || int(Mode)==Isometry) ? Affine : AffineCompact>::run(m_matrix);
-  }
-
-  EIGEN_DEVICE_FUNC inline explicit Transform(const TranslationType& t)
-  {
-    check_template_params();
-    *this = t;
-  }
-  EIGEN_DEVICE_FUNC inline explicit Transform(const UniformScaling<Scalar>& s)
-  {
-    check_template_params();
-    *this = s;
-  }
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline explicit Transform(const RotationBase<Derived, Dim>& r)
-  {
-    check_template_params();
-    *this = r;
-  }
-
-  typedef internal::transform_take_affine_part<Transform> take_affine_part;
-
-  /** Constructs and initializes a transformation from a Dim^2 or a (Dim+1)^2 matrix. */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline explicit Transform(const EigenBase<OtherDerived>& other)
-  {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value),
-      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
-
-    check_template_params();
-    internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived());
-  }
-
-  /** Set \c *this from a Dim^2 or (Dim+1)^2 matrix. */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline Transform& operator=(const EigenBase<OtherDerived>& other)
-  {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value),
-      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
-
-    internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived());
-    return *this;
-  }
-
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar,Dim,Mode,OtherOptions>& other)
-  {
-    check_template_params();
-    // only the options change, we can directly copy the matrices
-    m_matrix = other.matrix();
-  }
-
-  template<int OtherMode,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar,Dim,OtherMode,OtherOptions>& other)
-  {
-    check_template_params();
-    // prevent conversions as:
-    // Affine | AffineCompact | Isometry = Projective
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(OtherMode==int(Projective), Mode==int(Projective)),
-                        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
-
-    // prevent conversions as:
-    // Isometry = Affine | AffineCompact
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(OtherMode==int(Affine)||OtherMode==int(AffineCompact), Mode!=int(Isometry)),
-                        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
-
-    enum { ModeIsAffineCompact = Mode == int(AffineCompact),
-           OtherModeIsAffineCompact = OtherMode == int(AffineCompact)
-    };
-
-    if(EIGEN_CONST_CONDITIONAL(ModeIsAffineCompact == OtherModeIsAffineCompact))
-    {
-      // We need the block expression because the code is compiled for all
-      // combinations of transformations and will trigger a compile time error
-      // if one tries to assign the matrices directly
-      m_matrix.template block<Dim,Dim+1>(0,0) = other.matrix().template block<Dim,Dim+1>(0,0);
-      makeAffine();
-    }
-    else if(EIGEN_CONST_CONDITIONAL(OtherModeIsAffineCompact))
-    {
-      typedef typename Transform<Scalar,Dim,OtherMode,OtherOptions>::MatrixType OtherMatrixType;
-      internal::transform_construct_from_matrix<OtherMatrixType,Mode,Options,Dim,HDim>::run(this, other.matrix());
-    }
-    else
-    {
-      // here we know that Mode == AffineCompact and OtherMode != AffineCompact.
-      // if OtherMode were Projective, the static assert above would already have caught it.
-      // So the only possibility is that OtherMode == Affine
-      linear() = other.linear();
-      translation() = other.translation();
-    }
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC Transform(const ReturnByValue<OtherDerived>& other)
-  {
-    check_template_params();
-    other.evalTo(*this);
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC Transform& operator=(const ReturnByValue<OtherDerived>& other)
-  {
-    other.evalTo(*this);
-    return *this;
-  }
-
-  #ifdef EIGEN_QT_SUPPORT
-  inline Transform(const QMatrix& other);
-  inline Transform& operator=(const QMatrix& other);
-  inline QMatrix toQMatrix(void) const;
-  inline Transform(const QTransform& other);
-  inline Transform& operator=(const QTransform& other);
-  inline QTransform toQTransform(void) const;
-  #endif
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return int(Mode)==int(Projective) ? m_matrix.cols() : (m_matrix.cols()-1); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-  /** shortcut for m_matrix(row,col);
-    * \sa MatrixBase::operator(Index,Index) const */
-  EIGEN_DEVICE_FUNC inline Scalar operator() (Index row, Index col) const { return m_matrix(row,col); }
-  /** shortcut for m_matrix(row,col);
-    * \sa MatrixBase::operator(Index,Index) */
-  EIGEN_DEVICE_FUNC inline Scalar& operator() (Index row, Index col) { return m_matrix(row,col); }
-
-  /** \returns a read-only expression of the transformation matrix */
-  EIGEN_DEVICE_FUNC inline const MatrixType& matrix() const { return m_matrix; }
-  /** \returns a writable expression of the transformation matrix */
-  EIGEN_DEVICE_FUNC inline MatrixType& matrix() { return m_matrix; }
-
-  /** \returns a read-only expression of the linear part of the transformation */
-  EIGEN_DEVICE_FUNC inline ConstLinearPart linear() const { return ConstLinearPart(m_matrix,0,0); }
-  /** \returns a writable expression of the linear part of the transformation */
-  EIGEN_DEVICE_FUNC inline LinearPart linear() { return LinearPart(m_matrix,0,0); }
-
-  /** \returns a read-only expression of the Dim x HDim affine part of the transformation */
-  EIGEN_DEVICE_FUNC inline ConstAffinePart affine() const { return take_affine_part::run(m_matrix); }
-  /** \returns a writable expression of the Dim x HDim affine part of the transformation */
-  EIGEN_DEVICE_FUNC inline AffinePart affine() { return take_affine_part::run(m_matrix); }
-
-  /** \returns a read-only expression of the translation vector of the transformation */
-  EIGEN_DEVICE_FUNC inline ConstTranslationPart translation() const { return ConstTranslationPart(m_matrix,0,Dim); }
-  /** \returns a writable expression of the translation vector of the transformation */
-  EIGEN_DEVICE_FUNC inline TranslationPart translation() { return TranslationPart(m_matrix,0,Dim); }
-
-  /** \returns an expression of the product between the transform \c *this and a matrix expression \a other.
-    *
-    * The right-hand-side \a other can be either:
-    * \li an homogeneous vector of size Dim+1,
-    * \li a set of homogeneous vectors of size Dim+1 x N,
-    * \li a transformation matrix of size Dim+1 x Dim+1.
-    *
-    * Moreover, if \c *this represents an affine transformation (i.e., Mode!=Projective), then \a other can also be:
-    * \li a point of size Dim (computes: \code this->linear() * other + this->translation()\endcode),
-    * \li a set of N points as a Dim x N matrix (computes: \code (this->linear() * other).colwise() + this->translation()\endcode),
-    *
-    * In all cases, the return type is a matrix or vector of same sizes as the right-hand-side \a other.
-    *
-    * If you want to interpret \a other as a linear or affine transformation, then first convert it to a Transform<> type,
-    * or do your own cooking.
-    *
-    * Finally, if you want to apply Affine transformations to vectors, then explicitly apply the linear part only:
-    * \code
-    * Affine3f A;
-    * Vector3f v1, v2;
-    * v2 = A.linear() * v1;
-    * \endcode
-    *
-    */
-  // note: this function is defined here because some compilers cannot find the respective declaration
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename internal::transform_right_product_impl<Transform, OtherDerived>::ResultType
-  operator * (const EigenBase<OtherDerived> &other) const
-  { return internal::transform_right_product_impl<Transform, OtherDerived>::run(*this,other.derived()); }
-
-  /** \returns the product expression of a transformation matrix \a a times a transform \a b
-    *
-    * The left hand side \a other can be either:
-    * \li a linear transformation matrix of size Dim x Dim,
-    * \li an affine transformation matrix of size Dim x Dim+1,
-    * \li a general transformation matrix of size Dim+1 x Dim+1.
-    */
-  template<typename OtherDerived> friend
-  EIGEN_DEVICE_FUNC inline const typename internal::transform_left_product_impl<OtherDerived,Mode,Options,_Dim,_Dim+1>::ResultType
-    operator * (const EigenBase<OtherDerived> &a, const Transform &b)
-  { return internal::transform_left_product_impl<OtherDerived,Mode,Options,Dim,HDim>::run(a.derived(),b); }
-
-  /** \returns The product expression of a transform \a a times a diagonal matrix \a b
-    *
-    * The rhs diagonal matrix is interpreted as an affine scaling transformation. The
-    * product results in a Transform of the same type (mode) as the lhs only if the lhs
-    * mode is no isometry. In that case, the returned transform is an affinity.
-    */
-  template<typename DiagonalDerived>
-  EIGEN_DEVICE_FUNC inline const TransformTimeDiagonalReturnType
-    operator * (const DiagonalBase<DiagonalDerived> &b) const
-  {
-    TransformTimeDiagonalReturnType res(*this);
-    res.linearExt() *= b;
-    return res;
-  }
-
-  /** \returns The product expression of a diagonal matrix \a a times a transform \a b
-    *
-    * The lhs diagonal matrix is interpreted as an affine scaling transformation. The
-    * product results in a Transform of the same type (mode) as the lhs only if the lhs
-    * mode is no isometry. In that case, the returned transform is an affinity.
-    */
-  template<typename DiagonalDerived>
-  EIGEN_DEVICE_FUNC friend inline TransformTimeDiagonalReturnType
-    operator * (const DiagonalBase<DiagonalDerived> &a, const Transform &b)
-  {
-    TransformTimeDiagonalReturnType res;
-    res.linear().noalias() = a*b.linear();
-    res.translation().noalias() = a*b.translation();
-    if (EIGEN_CONST_CONDITIONAL(Mode!=int(AffineCompact)))
-      res.matrix().row(Dim) = b.matrix().row(Dim);
-    return res;
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline Transform& operator*=(const EigenBase<OtherDerived>& other) { return *this = *this * other; }
-
-  /** Concatenates two transformations */
-  EIGEN_DEVICE_FUNC inline const Transform operator * (const Transform& other) const
-  {
-    return internal::transform_transform_product_impl<Transform,Transform>::run(*this,other);
-  }
-
-  #if EIGEN_COMP_ICC
-private:
-  // this intermediate structure permits to workaround a bug in ICC 11:
-  //   error: template instantiation resulted in unexpected function type of "Eigen::Transform<double, 3, 32, 0>
-  //             (const Eigen::Transform<double, 3, 2, 0> &) const"
-  //  (the meaning of a name may have changed since the template declaration -- the type of the template is:
-  // "Eigen::internal::transform_transform_product_impl<Eigen::Transform<double, 3, 32, 0>,
-  //     Eigen::Transform<double, 3, Mode, Options>, <expression>>::ResultType (const Eigen::Transform<double, 3, Mode, Options> &) const")
-  //
-  template<int OtherMode,int OtherOptions> struct icc_11_workaround
-  {
-    typedef internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> > ProductType;
-    typedef typename ProductType::ResultType ResultType;
-  };
-
-public:
-  /** Concatenates two different transformations */
-  template<int OtherMode,int OtherOptions>
-  inline typename icc_11_workaround<OtherMode,OtherOptions>::ResultType
-    operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const
-  {
-    typedef typename icc_11_workaround<OtherMode,OtherOptions>::ProductType ProductType;
-    return ProductType::run(*this,other);
-  }
-  #else
-  /** Concatenates two different transformations */
-  template<int OtherMode,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline typename internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::ResultType
-    operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const
-  {
-    return internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::run(*this,other);
-  }
-  #endif
-
-  /** \sa MatrixBase::setIdentity() */
-  EIGEN_DEVICE_FUNC void setIdentity() { m_matrix.setIdentity(); }
-
-  /**
-   * \brief Returns an identity transformation.
-   * \todo In the future this function should be returning a Transform expression.
-   */
-  EIGEN_DEVICE_FUNC static const Transform Identity()
-  {
-    return Transform(MatrixType::Identity());
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& scale(const MatrixBase<OtherDerived> &other);
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& prescale(const MatrixBase<OtherDerived> &other);
-
-  EIGEN_DEVICE_FUNC inline Transform& scale(const Scalar& s);
-  EIGEN_DEVICE_FUNC inline Transform& prescale(const Scalar& s);
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& translate(const MatrixBase<OtherDerived> &other);
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& pretranslate(const MatrixBase<OtherDerived> &other);
-
-  template<typename RotationType>
-  EIGEN_DEVICE_FUNC
-  inline Transform& rotate(const RotationType& rotation);
-
-  template<typename RotationType>
-  EIGEN_DEVICE_FUNC
-  inline Transform& prerotate(const RotationType& rotation);
-
-  EIGEN_DEVICE_FUNC Transform& shear(const Scalar& sx, const Scalar& sy);
-  EIGEN_DEVICE_FUNC Transform& preshear(const Scalar& sx, const Scalar& sy);
-
-  EIGEN_DEVICE_FUNC inline Transform& operator=(const TranslationType& t);
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator*=(const TranslationType& t) { return translate(t.vector()); }
-
-  EIGEN_DEVICE_FUNC inline Transform operator*(const TranslationType& t) const;
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator=(const UniformScaling<Scalar>& t);
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator*=(const UniformScaling<Scalar>& s) { return scale(s.factor()); }
-
-  EIGEN_DEVICE_FUNC
-  inline TransformTimeDiagonalReturnType operator*(const UniformScaling<Scalar>& s) const
-  {
-    TransformTimeDiagonalReturnType res = *this;
-    res.scale(s.factor());
-    return res;
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator*=(const DiagonalMatrix<Scalar,Dim>& s) { linearExt() *= s; return *this; }
-
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Transform& operator=(const RotationBase<Derived,Dim>& r);
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Transform& operator*=(const RotationBase<Derived,Dim>& r) { return rotate(r.toRotationMatrix()); }
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Transform operator*(const RotationBase<Derived,Dim>& r) const;
-
-  typedef typename internal::conditional<int(Mode)==Isometry,ConstLinearPart,const LinearMatrixType>::type RotationReturnType;
-  EIGEN_DEVICE_FUNC RotationReturnType rotation() const;
-
-  template<typename RotationMatrixType, typename ScalingMatrixType>
-  EIGEN_DEVICE_FUNC
-  void computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const;
-  template<typename ScalingMatrixType, typename RotationMatrixType>
-  EIGEN_DEVICE_FUNC
-  void computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const;
-
-  template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
-  EIGEN_DEVICE_FUNC
-  Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
-    const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale);
-
-  EIGEN_DEVICE_FUNC
-  inline Transform inverse(TransformTraits traits = (TransformTraits)Mode) const;
-
-  /** \returns a const pointer to the column major internal matrix */
-  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_matrix.data(); }
-  /** \returns a non-const pointer to the column major internal matrix */
-  EIGEN_DEVICE_FUNC Scalar* data() { return m_matrix.data(); }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type cast() const
-  { return typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit Transform(const Transform<OtherScalarType,Dim,Mode,Options>& other)
-  {
-    check_template_params();
-    m_matrix = other.matrix().template cast<Scalar>();
-  }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const Transform& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_matrix.isApprox(other.m_matrix, prec); }
-
-  /** Sets the last row to [0 ... 0 1]
-    */
-  EIGEN_DEVICE_FUNC void makeAffine()
-  {
-    internal::transform_make_affine<int(Mode)>::run(m_matrix);
-  }
-
-  /** \internal
-    * \returns the Dim x Dim linear part if the transformation is affine,
-    *          and the HDim x Dim part for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt()
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); }
-  /** \internal
-    * \returns the Dim x Dim linear part if the transformation is affine,
-    *          and the HDim x Dim part for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt() const
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); }
-
-  /** \internal
-    * \returns the translation part if the transformation is affine,
-    *          and the last column for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt()
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); }
-  /** \internal
-    * \returns the translation part if the transformation is affine,
-    *          and the last column for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt() const
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); }
-
-
-  #ifdef EIGEN_TRANSFORM_PLUGIN
-  #include EIGEN_TRANSFORM_PLUGIN
-  #endif
-
-protected:
-  #ifndef EIGEN_PARSED_BY_DOXYGEN
-    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void check_template_params()
-    {
-      EIGEN_STATIC_ASSERT((Options & (DontAlign|RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS)
-    }
-  #endif
-
-};
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,Isometry> Isometry2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,Isometry> Isometry3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,Isometry> Isometry2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,Isometry> Isometry3d;
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,Affine> Affine2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,Affine> Affine3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,Affine> Affine2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,Affine> Affine3d;
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,AffineCompact> AffineCompact2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,AffineCompact> AffineCompact3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,AffineCompact> AffineCompact2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,AffineCompact> AffineCompact3d;
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,Projective> Projective2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,Projective> Projective3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,Projective> Projective2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,Projective> Projective3d;
-
-/**************************
-*** Optional QT support ***
-**************************/
-
-#ifdef EIGEN_QT_SUPPORT
-/** Initializes \c *this from a QMatrix assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode,int Options>
-Transform<Scalar,Dim,Mode,Options>::Transform(const QMatrix& other)
-{
-  check_template_params();
-  *this = other;
-}
-
-/** Set \c *this from a QMatrix assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode,int Options>
-Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QMatrix& other)
-{
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy();
-  else
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy(),
-                0, 0, 1;
-  return *this;
-}
-
-/** \returns a QMatrix from \c *this assuming the dimension is 2.
-  *
-  * \warning this conversion might loss data if \c *this is not affine
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-QMatrix Transform<Scalar,Dim,Mode,Options>::toQMatrix(void) const
-{
-  check_template_params();
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  return QMatrix(m_matrix.coeff(0,0), m_matrix.coeff(1,0),
-                 m_matrix.coeff(0,1), m_matrix.coeff(1,1),
-                 m_matrix.coeff(0,2), m_matrix.coeff(1,2));
-}
-
-/** Initializes \c *this from a QTransform assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode,int Options>
-Transform<Scalar,Dim,Mode,Options>::Transform(const QTransform& other)
-{
-  check_template_params();
-  *this = other;
-}
-
-/** Set \c *this from a QTransform assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QTransform& other)
-{
-  check_template_params();
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy();
-  else
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy(),
-                other.m13(), other.m23(), other.m33();
-  return *this;
-}
-
-/** \returns a QTransform from \c *this assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-QTransform Transform<Scalar,Dim,Mode,Options>::toQTransform(void) const
-{
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
-    return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0),
-                      m_matrix.coeff(0,1), m_matrix.coeff(1,1),
-                      m_matrix.coeff(0,2), m_matrix.coeff(1,2));
-  else
-    return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0), m_matrix.coeff(2,0),
-                      m_matrix.coeff(0,1), m_matrix.coeff(1,1), m_matrix.coeff(2,1),
-                      m_matrix.coeff(0,2), m_matrix.coeff(1,2), m_matrix.coeff(2,2));
-}
-#endif
-
-/*********************
-*** Procedural API ***
-*********************/
-
-/** Applies on the right the non uniform scale transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \sa prescale()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::scale(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  linearExt().noalias() = (linearExt() * other.asDiagonal());
-  return *this;
-}
-
-/** Applies on the right a uniform scale of a factor \a c to \c *this
-  * and returns a reference to \c *this.
-  * \sa prescale(Scalar)
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::scale(const Scalar& s)
-{
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  linearExt() *= s;
-  return *this;
-}
-
-/** Applies on the left the non uniform scale transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \sa scale()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::prescale(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  affine().noalias() = (other.asDiagonal() * affine());
-  return *this;
-}
-
-/** Applies on the left a uniform scale of a factor \a c to \c *this
-  * and returns a reference to \c *this.
-  * \sa scale(Scalar)
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::prescale(const Scalar& s)
-{
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  m_matrix.template topRows<Dim>() *= s;
-  return *this;
-}
-
-/** Applies on the right the translation matrix represented by the vector \a other
-  * to \c *this and returns a reference to \c *this.
-  * \sa pretranslate()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::translate(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  translationExt() += linearExt() * other;
-  return *this;
-}
-
-/** Applies on the left the translation matrix represented by the vector \a other
-  * to \c *this and returns a reference to \c *this.
-  * \sa translate()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::pretranslate(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  if(EIGEN_CONST_CONDITIONAL(int(Mode)==int(Projective)))
-    affine() += other * m_matrix.row(Dim);
-  else
-    translation() += other;
-  return *this;
-}
-
-/** Applies on the right the rotation represented by the rotation \a rotation
-  * to \c *this and returns a reference to \c *this.
-  *
-  * The template parameter \a RotationType is the type of the rotation which
-  * must be known by internal::toRotationMatrix<>.
-  *
-  * Natively supported types includes:
-  *   - any scalar (2D),
-  *   - a Dim x Dim matrix expression,
-  *   - a Quaternion (3D),
-  *   - a AngleAxis (3D)
-  *
-  * This mechanism is easily extendable to support user types such as Euler angles,
-  * or a pair of Quaternion for 4D rotations.
-  *
-  * \sa rotate(Scalar), class Quaternion, class AngleAxis, prerotate(RotationType)
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename RotationType>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::rotate(const RotationType& rotation)
-{
-  linearExt() *= internal::toRotationMatrix<Scalar,Dim>(rotation);
-  return *this;
-}
-
-/** Applies on the left the rotation represented by the rotation \a rotation
-  * to \c *this and returns a reference to \c *this.
-  *
-  * See rotate() for further details.
-  *
-  * \sa rotate()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename RotationType>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::prerotate(const RotationType& rotation)
-{
-  m_matrix.template block<Dim,HDim>(0,0) = internal::toRotationMatrix<Scalar,Dim>(rotation)
-                                         * m_matrix.template block<Dim,HDim>(0,0);
-  return *this;
-}
-
-/** Applies on the right the shear transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \warning 2D only.
-  * \sa preshear()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::shear(const Scalar& sx, const Scalar& sy)
-{
-  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  VectorType tmp = linear().col(0)*sy + linear().col(1);
-  linear() << linear().col(0) + linear().col(1)*sx, tmp;
-  return *this;
-}
-
-/** Applies on the left the shear transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \warning 2D only.
-  * \sa shear()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::preshear(const Scalar& sx, const Scalar& sy)
-{
-  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0);
-  return *this;
-}
-
-/******************************************************
-*** Scaling, Translation and Rotation compatibility ***
-******************************************************/
-
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const TranslationType& t)
-{
-  linear().setIdentity();
-  translation() = t.vector();
-  makeAffine();
-  return *this;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const TranslationType& t) const
-{
-  Transform res = *this;
-  res.translate(t.vector());
-  return res;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const UniformScaling<Scalar>& s)
-{
-  m_matrix.setZero();
-  linear().diagonal().fill(s.factor());
-  makeAffine();
-  return *this;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const RotationBase<Derived,Dim>& r)
-{
-  linear() = internal::toRotationMatrix<Scalar,Dim>(r);
-  translation().setZero();
-  makeAffine();
-  return *this;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const RotationBase<Derived,Dim>& r) const
-{
-  Transform res = *this;
-  res.rotate(r.derived());
-  return res;
-}
-
-/************************
-*** Special functions ***
-************************/
-
-namespace internal {
-template<int Mode> struct transform_rotation_impl {
-  template<typename TransformType>
-  EIGEN_DEVICE_FUNC static inline
-  const typename TransformType::LinearMatrixType run(const TransformType& t)
-  {
-    typedef typename TransformType::LinearMatrixType LinearMatrixType;
-    LinearMatrixType result;
-    t.computeRotationScaling(&result, (LinearMatrixType*)0);
-    return result;
-  }
-};
-template<> struct transform_rotation_impl<Isometry> {
-  template<typename TransformType>
-  EIGEN_DEVICE_FUNC static inline
-  typename TransformType::ConstLinearPart run(const TransformType& t)
-  {
-    return t.linear();
-  }
-};
-}
-/** \returns the rotation part of the transformation
-  *
-  * If Mode==Isometry, then this method is an alias for linear(),
-  * otherwise it calls computeRotationScaling() to extract the rotation
-  * through a SVD decomposition.
-  *
-  * \svd_module
-  *
-  * \sa computeRotationScaling(), computeScalingRotation(), class SVD
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC
-typename Transform<Scalar,Dim,Mode,Options>::RotationReturnType
-Transform<Scalar,Dim,Mode,Options>::rotation() const
-{
-  return internal::transform_rotation_impl<Mode>::run(*this);
-}
-
-
-/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being
-  * not necessarily positive.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  *
-  *
-  * \svd_module
-  *
-  * \sa computeScalingRotation(), rotation(), class SVD
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename RotationMatrixType, typename ScalingMatrixType>
-EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const
-{
-  // Note that JacobiSVD is faster than BDCSVD for small matrices.
-  JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
-
-  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
-  VectorType sv(svd.singularValues());
-  sv.coeffRef(Dim-1) *= x;
-  if(scaling) *scaling = svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint();
-  if(rotation)
-  {
-    LinearMatrixType m(svd.matrixU());
-    m.col(Dim-1) *= x;
-    *rotation = m * svd.matrixV().adjoint();
-  }
-}
-
-/** decomposes the linear part of the transformation as a product scaling x rotation, the scaling being
-  * not necessarily positive.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  *
-  *
-  * \svd_module
-  *
-  * \sa computeRotationScaling(), rotation(), class SVD
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename ScalingMatrixType, typename RotationMatrixType>
-EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const
-{
-  // Note that JacobiSVD is faster than BDCSVD for small matrices.
-  JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
-
-  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
-  VectorType sv(svd.singularValues());
-  sv.coeffRef(Dim-1) *= x;
-  if(scaling) *scaling = svd.matrixU() * sv.asDiagonal() * svd.matrixU().adjoint();
-  if(rotation)
-  {
-    LinearMatrixType m(svd.matrixU());
-    m.col(Dim-1) *= x;
-    *rotation = m * svd.matrixV().adjoint();
-  }
-}
-
-/** Convenient method to set \c *this from a position, orientation and scale
-  * of a 3D object.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
-  const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale)
-{
-  linear() = internal::toRotationMatrix<Scalar,Dim>(orientation);
-  linear() *= scale.asDiagonal();
-  translation() = position;
-  makeAffine();
-  return *this;
-}
-
-namespace internal {
-
-template<int Mode>
-struct transform_make_affine
-{
-  template<typename MatrixType>
-  EIGEN_DEVICE_FUNC static void run(MatrixType &mat)
-  {
-    static const int Dim = MatrixType::ColsAtCompileTime-1;
-    mat.template block<1,Dim>(Dim,0).setZero();
-    mat.coeffRef(Dim,Dim) = typename MatrixType::Scalar(1);
-  }
-};
-
-template<>
-struct transform_make_affine<AffineCompact>
-{
-  template<typename MatrixType> EIGEN_DEVICE_FUNC static void run(MatrixType &) { }
-};
-
-// selector needed to avoid taking the inverse of a 3x4 matrix
-template<typename TransformType, int Mode=TransformType::Mode>
-struct projective_transform_inverse
-{
-  EIGEN_DEVICE_FUNC static inline void run(const TransformType&, TransformType&)
-  {}
-};
-
-template<typename TransformType>
-struct projective_transform_inverse<TransformType, Projective>
-{
-  EIGEN_DEVICE_FUNC static inline void run(const TransformType& m, TransformType& res)
-  {
-    res.matrix() = m.matrix().inverse();
-  }
-};
-
-} // end namespace internal
-
-
-/**
-  *
-  * \returns the inverse transformation according to some given knowledge
-  * on \c *this.
-  *
-  * \param hint allows to optimize the inversion process when the transformation
-  * is known to be not a general transformation (optional). The possible values are:
-  *  - #Projective if the transformation is not necessarily affine, i.e., if the
-  *    last row is not guaranteed to be [0 ... 0 1]
-  *  - #Affine if the last row can be assumed to be [0 ... 0 1]
-  *  - #Isometry if the transformation is only a concatenations of translations
-  *    and rotations.
-  *  The default is the template class parameter \c Mode.
-  *
-  * \warning unless \a traits is always set to NoShear or NoScaling, this function
-  * requires the generic inverse method of MatrixBase defined in the LU module. If
-  * you forget to include this module, then you will get hard to debug linking errors.
-  *
-  * \sa MatrixBase::inverse()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>
-Transform<Scalar,Dim,Mode,Options>::inverse(TransformTraits hint) const
-{
-  Transform res;
-  if (hint == Projective)
-  {
-    internal::projective_transform_inverse<Transform>::run(*this, res);
-  }
-  else
-  {
-    if (hint == Isometry)
-    {
-      res.matrix().template topLeftCorner<Dim,Dim>() = linear().transpose();
-    }
-    else if(hint&Affine)
-    {
-      res.matrix().template topLeftCorner<Dim,Dim>() = linear().inverse();
-    }
-    else
-    {
-      eigen_assert(false && "Invalid transform traits in Transform::Inverse");
-    }
-    // translation and remaining parts
-    res.matrix().template topRightCorner<Dim,1>()
-      = - res.matrix().template topLeftCorner<Dim,Dim>() * translation();
-    res.makeAffine(); // we do need this, because in the beginning res is uninitialized
-  }
-  return res;
-}
-
-namespace internal {
-
-/*****************************************************
-*** Specializations of take affine part            ***
-*****************************************************/
-
-template<typename TransformType> struct transform_take_affine_part {
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef typename TransformType::AffinePart AffinePart;
-  typedef typename TransformType::ConstAffinePart ConstAffinePart;
-  static inline AffinePart run(MatrixType& m)
-  { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); }
-  static inline ConstAffinePart run(const MatrixType& m)
-  { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); }
-};
-
-template<typename Scalar, int Dim, int Options>
-struct transform_take_affine_part<Transform<Scalar,Dim,AffineCompact, Options> > {
-  typedef typename Transform<Scalar,Dim,AffineCompact,Options>::MatrixType MatrixType;
-  static inline MatrixType& run(MatrixType& m) { return m; }
-  static inline const MatrixType& run(const MatrixType& m) { return m; }
-};
-
-/*****************************************************
-*** Specializations of construct from matrix       ***
-*****************************************************/
-
-template<typename Other, int Mode, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,Dim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
-  {
-    transform->linear() = other;
-    transform->translation().setZero();
-    transform->makeAffine();
-  }
-};
-
-template<typename Other, int Mode, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,HDim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
-  {
-    transform->affine() = other;
-    transform->makeAffine();
-  }
-};
-
-template<typename Other, int Mode, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, HDim,HDim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
-  { transform->matrix() = other; }
-};
-
-template<typename Other, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, AffineCompact,Options,Dim,HDim, HDim,HDim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,AffineCompact,Options> *transform, const Other& other)
-  { transform->matrix() = other.template block<Dim,HDim>(0,0); }
-};
-
-/**********************************************************
-***   Specializations of operator* with rhs EigenBase   ***
-**********************************************************/
-
-template<int LhsMode,int RhsMode>
-struct transform_product_result
-{
-  enum
-  {
-    Mode =
-      (LhsMode == (int)Projective    || RhsMode == (int)Projective    ) ? Projective :
-      (LhsMode == (int)Affine        || RhsMode == (int)Affine        ) ? Affine :
-      (LhsMode == (int)AffineCompact || RhsMode == (int)AffineCompact ) ? AffineCompact :
-      (LhsMode == (int)Isometry      || RhsMode == (int)Isometry      ) ? Isometry : Projective
-  };
-};
-
-template< typename TransformType, typename MatrixType, int RhsCols>
-struct transform_right_product_impl< TransformType, MatrixType, 0, RhsCols>
-{
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    return T.matrix() * other;
-  }
-};
-
-template< typename TransformType, typename MatrixType, int RhsCols>
-struct transform_right_product_impl< TransformType, MatrixType, 1, RhsCols>
-{
-  enum {
-    Dim = TransformType::Dim,
-    HDim = TransformType::HDim,
-    OtherRows = MatrixType::RowsAtCompileTime,
-    OtherCols = MatrixType::ColsAtCompileTime
-  };
-
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    EIGEN_STATIC_ASSERT(OtherRows==HDim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
-
-    typedef Block<ResultType, Dim, OtherCols, int(MatrixType::RowsAtCompileTime)==Dim> TopLeftLhs;
-
-    ResultType res(other.rows(),other.cols());
-    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() = T.affine() * other;
-    res.row(OtherRows-1) = other.row(OtherRows-1);
-
-    return res;
-  }
-};
-
-template< typename TransformType, typename MatrixType, int RhsCols>
-struct transform_right_product_impl< TransformType, MatrixType, 2, RhsCols>
-{
-  enum {
-    Dim = TransformType::Dim,
-    HDim = TransformType::HDim,
-    OtherRows = MatrixType::RowsAtCompileTime,
-    OtherCols = MatrixType::ColsAtCompileTime
-  };
-
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
-
-    typedef Block<ResultType, Dim, OtherCols, true> TopLeftLhs;
-    ResultType res(Replicate<typename TransformType::ConstTranslationPart, 1, OtherCols>(T.translation(),1,other.cols()));
-    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() += T.linear() * other;
-
-    return res;
-  }
-};
-
-template< typename TransformType, typename MatrixType >
-struct transform_right_product_impl< TransformType, MatrixType, 2, 1> // rhs is a vector of size Dim
-{
-  typedef typename TransformType::MatrixType TransformMatrix;
-  enum {
-    Dim = TransformType::Dim,
-    HDim = TransformType::HDim,
-    OtherRows = MatrixType::RowsAtCompileTime,
-    WorkingRows = EIGEN_PLAIN_ENUM_MIN(TransformMatrix::RowsAtCompileTime,HDim)
-  };
-
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
-
-    Matrix<typename ResultType::Scalar, Dim+1, 1> rhs;
-    rhs.template head<Dim>() = other; rhs[Dim] = typename ResultType::Scalar(1);
-    Matrix<typename ResultType::Scalar, WorkingRows, 1> res(T.matrix() * rhs);
-    return res.template head<Dim>();
-  }
-};
-
-/**********************************************************
-***   Specializations of operator* with lhs EigenBase   ***
-**********************************************************/
-
-// generic HDim x HDim matrix * T => Projective
-template<typename Other,int Mode, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, HDim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  { return ResultType(other * tr.matrix()); }
-};
-
-// generic HDim x HDim matrix * AffineCompact => Projective
-template<typename Other, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, HDim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  {
-    ResultType res;
-    res.matrix().noalias() = other.template block<HDim,Dim>(0,0) * tr.matrix();
-    res.matrix().col(Dim) += other.col(Dim);
-    return res;
-  }
-};
-
-// affine matrix * T
-template<typename Other,int Mode, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef TransformType ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  {
-    ResultType res;
-    res.affine().noalias() = other * tr.matrix();
-    res.matrix().row(Dim) = tr.matrix().row(Dim);
-    return res;
-  }
-};
-
-// affine matrix * AffineCompact
-template<typename Other, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, Dim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef TransformType ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  {
-    ResultType res;
-    res.matrix().noalias() = other.template block<Dim,Dim>(0,0) * tr.matrix();
-    res.translation() += other.col(Dim);
-    return res;
-  }
-};
-
-// linear matrix * T
-template<typename Other,int Mode, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,Dim>
-{
-  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef TransformType ResultType;
-  static ResultType run(const Other& other, const TransformType& tr)
-  {
-    TransformType res;
-    if(Mode!=int(AffineCompact))
-      res.matrix().row(Dim) = tr.matrix().row(Dim);
-    res.matrix().template topRows<Dim>().noalias()
-      = other * tr.matrix().template topRows<Dim>();
-    return res;
-  }
-};
-
-/**********************************************************
-*** Specializations of operator* with another Transform ***
-**********************************************************/
-
-template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,false >
-{
-  enum { ResultMode = transform_product_result<LhsMode,RhsMode>::Mode };
-  typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,ResultMode,LhsOptions> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    ResultType res;
-    res.linear() = lhs.linear() * rhs.linear();
-    res.translation() = lhs.linear() * rhs.translation() + lhs.translation();
-    res.makeAffine();
-    return res;
-  }
-};
-
-template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,true >
-{
-  typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,Projective> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    return ResultType( lhs.matrix() * rhs.matrix() );
-  }
-};
-
-template<typename Scalar, int Dim, int LhsOptions, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,AffineCompact,LhsOptions>,Transform<Scalar,Dim,Projective,RhsOptions>,true >
-{
-  typedef Transform<Scalar,Dim,AffineCompact,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,Projective,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,Projective> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    ResultType res;
-    res.matrix().template topRows<Dim>() = lhs.matrix() * rhs.matrix();
-    res.matrix().row(Dim) = rhs.matrix().row(Dim);
-    return res;
-  }
-};
-
-template<typename Scalar, int Dim, int LhsOptions, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,Projective,LhsOptions>,Transform<Scalar,Dim,AffineCompact,RhsOptions>,true >
-{
-  typedef Transform<Scalar,Dim,Projective,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,AffineCompact,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,Projective> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    ResultType res(lhs.matrix().template leftCols<Dim>() * rhs.matrix());
-    res.matrix().col(Dim) += lhs.matrix().col(Dim);
-    return res;
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSFORM_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Translation.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Translation.h
deleted file mode 100644
index 8c22901..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Translation.h
+++ /dev/null
@@ -1,202 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSLATION_H
-#define EIGEN_TRANSLATION_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Translation
-  *
-  * \brief Represents a translation transformation
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients.
-  * \tparam _Dim the  dimension of the space, can be a compile time value or Dynamic
-  *
-  * \note This class is not aimed to be used to store a translation transformation,
-  * but rather to make easier the constructions and updates of Transform objects.
-  *
-  * \sa class Scaling, class Transform
-  */
-template<typename _Scalar, int _Dim>
-class Translation
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim)
-  /** dimension of the space */
-  enum { Dim = _Dim };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  /** corresponding vector type */
-  typedef Matrix<Scalar,Dim,1> VectorType;
-  /** corresponding linear transformation matrix type */
-  typedef Matrix<Scalar,Dim,Dim> LinearMatrixType;
-  /** corresponding affine transformation type */
-  typedef Transform<Scalar,Dim,Affine> AffineTransformType;
-  /** corresponding isometric transformation type */
-  typedef Transform<Scalar,Dim,Isometry> IsometryTransformType;
-
-protected:
-
-  VectorType m_coeffs;
-
-public:
-
-  /** Default constructor without initialization. */
-  EIGEN_DEVICE_FUNC Translation() {}
-  /**  */
-  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy)
-  {
-    eigen_assert(Dim==2);
-    m_coeffs.x() = sx;
-    m_coeffs.y() = sy;
-  }
-  /**  */
-  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy, const Scalar& sz)
-  {
-    eigen_assert(Dim==3);
-    m_coeffs.x() = sx;
-    m_coeffs.y() = sy;
-    m_coeffs.z() = sz;
-  }
-  /** Constructs and initialize the translation transformation from a vector of translation coefficients */
-  EIGEN_DEVICE_FUNC explicit inline Translation(const VectorType& vector) : m_coeffs(vector) {}
-
-  /** \brief Returns the x-translation by value. **/
-  EIGEN_DEVICE_FUNC inline Scalar x() const { return m_coeffs.x(); }
-  /** \brief Returns the y-translation by value. **/
-  EIGEN_DEVICE_FUNC inline Scalar y() const { return m_coeffs.y(); }
-  /** \brief Returns the z-translation by value. **/
-  EIGEN_DEVICE_FUNC inline Scalar z() const { return m_coeffs.z(); }
-
-  /** \brief Returns the x-translation as a reference. **/
-  EIGEN_DEVICE_FUNC inline Scalar& x() { return m_coeffs.x(); }
-  /** \brief Returns the y-translation as a reference. **/
-  EIGEN_DEVICE_FUNC inline Scalar& y() { return m_coeffs.y(); }
-  /** \brief Returns the z-translation as a reference. **/
-  EIGEN_DEVICE_FUNC inline Scalar& z() { return m_coeffs.z(); }
-
-  EIGEN_DEVICE_FUNC const VectorType& vector() const { return m_coeffs; }
-  EIGEN_DEVICE_FUNC VectorType& vector() { return m_coeffs; }
-
-  EIGEN_DEVICE_FUNC const VectorType& translation() const { return m_coeffs; }
-  EIGEN_DEVICE_FUNC VectorType& translation() { return m_coeffs; }
-
-  /** Concatenates two translation */
-  EIGEN_DEVICE_FUNC inline Translation operator* (const Translation& other) const
-  { return Translation(m_coeffs + other.m_coeffs); }
-
-  /** Concatenates a translation and a uniform scaling */
-  EIGEN_DEVICE_FUNC inline AffineTransformType operator* (const UniformScaling<Scalar>& other) const;
-
-  /** Concatenates a translation and a linear transformation */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline AffineTransformType operator* (const EigenBase<OtherDerived>& linear) const;
-
-  /** Concatenates a translation and a rotation */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline IsometryTransformType operator*(const RotationBase<Derived,Dim>& r) const
-  { return *this * IsometryTransformType(r); }
-
-  /** \returns the concatenation of a linear transformation \a l with the translation \a t */
-  // its a nightmare to define a templated friend function outside its declaration
-  template<typename OtherDerived> friend
-  EIGEN_DEVICE_FUNC inline AffineTransformType operator*(const EigenBase<OtherDerived>& linear, const Translation& t)
-  {
-    AffineTransformType res;
-    res.matrix().setZero();
-    res.linear() = linear.derived();
-    res.translation() = linear.derived() * t.m_coeffs;
-    res.matrix().row(Dim).setZero();
-    res(Dim,Dim) = Scalar(1);
-    return res;
-  }
-
-  /** Concatenates a translation and a transformation */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode> operator* (const Transform<Scalar,Dim,Mode,Options>& t) const
-  {
-    Transform<Scalar,Dim,Mode> res = t;
-    res.pretranslate(m_coeffs);
-    return res;
-  }
-
-  /** Applies translation to vector */
-  template<typename Derived>
-  inline typename internal::enable_if<Derived::IsVectorAtCompileTime,VectorType>::type
-  operator* (const MatrixBase<Derived>& vec) const
-  { return m_coeffs + vec.derived(); }
-
-  /** \returns the inverse translation (opposite) */
-  Translation inverse() const { return Translation(-m_coeffs); }
-
-  static const Translation Identity() { return Translation(VectorType::Zero()); }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type cast() const
-  { return typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit Translation(const Translation<OtherScalarType,Dim>& other)
-  { m_coeffs = other.vector().template cast<Scalar>(); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const Translation& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_coeffs.isApprox(other.m_coeffs, prec); }
-
-};
-
-/** \addtogroup Geometry_Module */
-//@{
-typedef Translation<float, 2> Translation2f;
-typedef Translation<double,2> Translation2d;
-typedef Translation<float, 3> Translation3f;
-typedef Translation<double,3> Translation3d;
-//@}
-
-template<typename Scalar, int Dim>
-EIGEN_DEVICE_FUNC inline typename Translation<Scalar,Dim>::AffineTransformType
-Translation<Scalar,Dim>::operator* (const UniformScaling<Scalar>& other) const
-{
-  AffineTransformType res;
-  res.matrix().setZero();
-  res.linear().diagonal().fill(other.factor());
-  res.translation() = m_coeffs;
-  res(Dim,Dim) = Scalar(1);
-  return res;
-}
-
-template<typename Scalar, int Dim>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline typename Translation<Scalar,Dim>::AffineTransformType
-Translation<Scalar,Dim>::operator* (const EigenBase<OtherDerived>& linear) const
-{
-  AffineTransformType res;
-  res.matrix().setZero();
-  res.linear() = linear.derived();
-  res.translation() = m_coeffs;
-  res.matrix().row(Dim).setZero();
-  res(Dim,Dim) = Scalar(1);
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSLATION_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Umeyama.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Umeyama.h
deleted file mode 100644
index 6b75500..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/Umeyama.h
+++ /dev/null
@@ -1,166 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMEYAMA_H
-#define EIGEN_UMEYAMA_H
-
-// This file requires the user to include 
-// * Eigen/Core
-// * Eigen/LU 
-// * Eigen/SVD
-// * Eigen/Array
-
-namespace Eigen { 
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-// These helpers are required since it allows to use mixed types as parameters
-// for the Umeyama. The problem with mixed parameters is that the return type
-// cannot trivially be deduced when float and double types are mixed.
-namespace internal {
-
-// Compile time return type deduction for different MatrixBase types.
-// Different means here different alignment and parameters but the same underlying
-// real scalar type.
-template<typename MatrixType, typename OtherMatrixType>
-struct umeyama_transform_matrix_type
-{
-  enum {
-    MinRowsAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, OtherMatrixType::RowsAtCompileTime),
-
-    // When possible we want to choose some small fixed size value since the result
-    // is likely to fit on the stack. So here, EIGEN_SIZE_MIN_PREFER_DYNAMIC is not what we want.
-    HomogeneousDimension = int(MinRowsAtCompileTime) == Dynamic ? Dynamic : int(MinRowsAtCompileTime)+1
-  };
-
-  typedef Matrix<typename traits<MatrixType>::Scalar,
-    HomogeneousDimension,
-    HomogeneousDimension,
-    AutoAlign | (traits<MatrixType>::Flags & RowMajorBit ? RowMajor : ColMajor),
-    HomogeneousDimension,
-    HomogeneousDimension
-  > type;
-};
-
-}
-
-#endif
-
-/**
-* \geometry_module \ingroup Geometry_Module
-*
-* \brief Returns the transformation between two point sets.
-*
-* The algorithm is based on:
-* "Least-squares estimation of transformation parameters between two point patterns",
-* Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
-*
-* It estimates parameters \f$ c, \mathbf{R}, \f$ and \f$ \mathbf{t} \f$ such that
-* \f{align*}
-*   \frac{1}{n} \sum_{i=1}^n \vert\vert y_i - (c\mathbf{R}x_i + \mathbf{t}) \vert\vert_2^2
-* \f}
-* is minimized.
-*
-* The algorithm is based on the analysis of the covariance matrix
-* \f$ \Sigma_{\mathbf{x}\mathbf{y}} \in \mathbb{R}^{d \times d} \f$
-* of the input point sets \f$ \mathbf{x} \f$ and \f$ \mathbf{y} \f$ where 
-* \f$d\f$ is corresponding to the dimension (which is typically small).
-* The analysis is involving the SVD having a complexity of \f$O(d^3)\f$
-* though the actual computational effort lies in the covariance
-* matrix computation which has an asymptotic lower bound of \f$O(dm)\f$ when 
-* the input point sets have dimension \f$d \times m\f$.
-*
-* Currently the method is working only for floating point matrices.
-*
-* \todo Should the return type of umeyama() become a Transform?
-*
-* \param src Source points \f$ \mathbf{x} = \left( x_1, \hdots, x_n \right) \f$.
-* \param dst Destination points \f$ \mathbf{y} = \left( y_1, \hdots, y_n \right) \f$.
-* \param with_scaling Sets \f$ c=1 \f$ when <code>false</code> is passed.
-* \return The homogeneous transformation 
-* \f{align*}
-*   T = \begin{bmatrix} c\mathbf{R} & \mathbf{t} \\ \mathbf{0} & 1 \end{bmatrix}
-* \f}
-* minimizing the residual above. This transformation is always returned as an 
-* Eigen::Matrix.
-*/
-template <typename Derived, typename OtherDerived>
-typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type
-umeyama(const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, bool with_scaling = true)
-{
-  typedef typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type TransformationMatrixType;
-  typedef typename internal::traits<TransformationMatrixType>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename internal::traits<OtherDerived>::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  enum { Dimension = EIGEN_SIZE_MIN_PREFER_DYNAMIC(Derived::RowsAtCompileTime, OtherDerived::RowsAtCompileTime) };
-
-  typedef Matrix<Scalar, Dimension, 1> VectorType;
-  typedef Matrix<Scalar, Dimension, Dimension> MatrixType;
-  typedef typename internal::plain_matrix_type_row_major<Derived>::type RowMajorMatrixType;
-
-  const Index m = src.rows(); // dimension
-  const Index n = src.cols(); // number of measurements
-
-  // required for demeaning ...
-  const RealScalar one_over_n = RealScalar(1) / static_cast<RealScalar>(n);
-
-  // computation of mean
-  const VectorType src_mean = src.rowwise().sum() * one_over_n;
-  const VectorType dst_mean = dst.rowwise().sum() * one_over_n;
-
-  // demeaning of src and dst points
-  const RowMajorMatrixType src_demean = src.colwise() - src_mean;
-  const RowMajorMatrixType dst_demean = dst.colwise() - dst_mean;
-
-  // Eq. (36)-(37)
-  const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;
-
-  // Eq. (38)
-  const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose();
-
-  JacobiSVD<MatrixType> svd(sigma, ComputeFullU | ComputeFullV);
-
-  // Initialize the resulting transformation with an identity matrix...
-  TransformationMatrixType Rt = TransformationMatrixType::Identity(m+1,m+1);
-
-  // Eq. (39)
-  VectorType S = VectorType::Ones(m);
-
-  if  ( svd.matrixU().determinant() * svd.matrixV().determinant() < 0 )
-    S(m-1) = -1;
-
-  // Eq. (40) and (43)
-  Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose();
-
-  if (with_scaling)
-  {
-    // Eq. (42)
-    const Scalar c = Scalar(1)/src_var * svd.singularValues().dot(S);
-
-    // Eq. (41)
-    Rt.col(m).head(m) = dst_mean;
-    Rt.col(m).head(m).noalias() -= c*Rt.topLeftCorner(m,m)*src_mean;
-    Rt.block(0,0,m,m) *= c;
-  }
-  else
-  {
-    Rt.col(m).head(m) = dst_mean;
-    Rt.col(m).head(m).noalias() -= Rt.topLeftCorner(m,m)*src_mean;
-  }
-
-  return Rt;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_UMEYAMA_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/arch/Geometry_SIMD.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/arch/Geometry_SIMD.h
deleted file mode 100644
index 9af6a9a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Geometry/arch/Geometry_SIMD.h
+++ /dev/null
@@ -1,168 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Rohit Garg <rpg.314@gmail.com>
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GEOMETRY_SIMD_H
-#define EIGEN_GEOMETRY_SIMD_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<class Derived, class OtherDerived>
-struct quat_product<Architecture::Target, Derived, OtherDerived, float>
-{
-  enum {
-    AAlignment = traits<Derived>::Alignment,
-    BAlignment = traits<OtherDerived>::Alignment,
-    ResAlignment = traits<Quaternion<float> >::Alignment
-  };
-  static inline Quaternion<float> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
-  {
-    evaluator<typename Derived::Coefficients> ae(_a.coeffs());
-    evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
-    Quaternion<float> res;
-    const float neg_zero = numext::bit_cast<float>(0x80000000u);
-    const float arr[4] = {0.f, 0.f, 0.f, neg_zero};
-    const Packet4f mask = ploadu<Packet4f>(arr);
-    Packet4f a = ae.template packet<AAlignment,Packet4f>(0);
-    Packet4f b = be.template packet<BAlignment,Packet4f>(0);
-    Packet4f s1 = pmul(vec4f_swizzle1(a,1,2,0,2),vec4f_swizzle1(b,2,0,1,2));
-    Packet4f s2 = pmul(vec4f_swizzle1(a,3,3,3,1),vec4f_swizzle1(b,0,1,2,1));
-    pstoret<float,Packet4f,ResAlignment>(
-              &res.x(),
-              padd(psub(pmul(a,vec4f_swizzle1(b,3,3,3,3)),
-                                    pmul(vec4f_swizzle1(a,2,0,1,0),
-                                               vec4f_swizzle1(b,1,2,0,0))),
-                         pxor(mask,padd(s1,s2))));
-    
-    return res;
-  }
-};
-
-template<class Derived>
-struct quat_conj<Architecture::Target, Derived, float>
-{
-  enum {
-    ResAlignment = traits<Quaternion<float> >::Alignment
-  };
-  static inline Quaternion<float> run(const QuaternionBase<Derived>& q)
-  {
-    evaluator<typename Derived::Coefficients> qe(q.coeffs());
-    Quaternion<float> res;
-    const float neg_zero = numext::bit_cast<float>(0x80000000u);
-    const float arr[4] = {neg_zero, neg_zero, neg_zero,0.f};
-    const Packet4f mask = ploadu<Packet4f>(arr);
-    pstoret<float,Packet4f,ResAlignment>(&res.x(), pxor(mask, qe.template packet<traits<Derived>::Alignment,Packet4f>(0)));
-    return res;
-  }
-};
-
-
-template<typename VectorLhs,typename VectorRhs>
-struct cross3_impl<Architecture::Target,VectorLhs,VectorRhs,float,true>
-{
-  enum {
-    ResAlignment = traits<typename plain_matrix_type<VectorLhs>::type>::Alignment
-  };
-  static inline typename plain_matrix_type<VectorLhs>::type
-  run(const VectorLhs& lhs, const VectorRhs& rhs)
-  {
-    evaluator<VectorLhs> lhs_eval(lhs);
-    evaluator<VectorRhs> rhs_eval(rhs);
-    Packet4f a = lhs_eval.template packet<traits<VectorLhs>::Alignment,Packet4f>(0);
-    Packet4f b = rhs_eval.template packet<traits<VectorRhs>::Alignment,Packet4f>(0);
-    Packet4f mul1 = pmul(vec4f_swizzle1(a,1,2,0,3),vec4f_swizzle1(b,2,0,1,3));
-    Packet4f mul2 = pmul(vec4f_swizzle1(a,2,0,1,3),vec4f_swizzle1(b,1,2,0,3));
-    typename plain_matrix_type<VectorLhs>::type res;
-    pstoret<float,Packet4f,ResAlignment>(&res.x(),psub(mul1,mul2));
-    return res;
-  }
-};
-
-
-
-#if (defined EIGEN_VECTORIZE_SSE) || (EIGEN_ARCH_ARM64)
-
-template<class Derived, class OtherDerived>
-struct quat_product<Architecture::Target, Derived, OtherDerived, double>
-{
-  enum {
-    BAlignment = traits<OtherDerived>::Alignment,
-    ResAlignment = traits<Quaternion<double> >::Alignment
-  };
-
-  static inline Quaternion<double> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
-  {
-  Quaternion<double> res;
-
-  evaluator<typename Derived::Coefficients> ae(_a.coeffs());
-  evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
-
-  const double* a = _a.coeffs().data();
-  Packet2d b_xy = be.template packet<BAlignment,Packet2d>(0);
-  Packet2d b_zw = be.template packet<BAlignment,Packet2d>(2);
-  Packet2d a_xx = pset1<Packet2d>(a[0]);
-  Packet2d a_yy = pset1<Packet2d>(a[1]);
-  Packet2d a_zz = pset1<Packet2d>(a[2]);
-  Packet2d a_ww = pset1<Packet2d>(a[3]);
-
-  // two temporaries:
-  Packet2d t1, t2;
-
-  /*
-   * t1 = ww*xy + yy*zw
-   * t2 = zz*xy - xx*zw
-   * res.xy = t1 +/- swap(t2)
-   */
-  t1 = padd(pmul(a_ww, b_xy), pmul(a_yy, b_zw));
-  t2 = psub(pmul(a_zz, b_xy), pmul(a_xx, b_zw));
-  pstoret<double,Packet2d,ResAlignment>(&res.x(), paddsub(t1, preverse(t2)));
-  
-  /*
-   * t1 = ww*zw - yy*xy
-   * t2 = zz*zw + xx*xy
-   * res.zw = t1 -/+ swap(t2) = swap( swap(t1) +/- t2)
-   */
-  t1 = psub(pmul(a_ww, b_zw), pmul(a_yy, b_xy));
-  t2 = padd(pmul(a_zz, b_zw), pmul(a_xx, b_xy));
-  pstoret<double,Packet2d,ResAlignment>(&res.z(), preverse(paddsub(preverse(t1), t2)));
-
-  return res;
-}
-};
-
-template<class Derived>
-struct quat_conj<Architecture::Target, Derived, double>
-{
-  enum {
-    ResAlignment = traits<Quaternion<double> >::Alignment
-  };
-  static inline Quaternion<double> run(const QuaternionBase<Derived>& q)
-  {
-    evaluator<typename Derived::Coefficients> qe(q.coeffs());
-    Quaternion<double> res;
-    const double neg_zero = numext::bit_cast<double>(0x8000000000000000ull);
-    const double arr1[2] = {neg_zero, neg_zero};
-    const double arr2[2] = {neg_zero,  0.0};
-    const Packet2d mask0 = ploadu<Packet2d>(arr1);
-    const Packet2d mask2 = ploadu<Packet2d>(arr2);
-    pstoret<double,Packet2d,ResAlignment>(&res.x(), pxor(mask0, qe.template packet<traits<Derived>::Alignment,Packet2d>(0)));
-    pstoret<double,Packet2d,ResAlignment>(&res.z(), pxor(mask2, qe.template packet<traits<Derived>::Alignment,Packet2d>(2)));
-    return res;
-  }
-};
-
-#endif // end EIGEN_VECTORIZE_SSE_OR_EIGEN_ARCH_ARM64
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GEOMETRY_SIMD_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/BlockHouseholder.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/BlockHouseholder.h
deleted file mode 100644
index 39ce1c2..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/BlockHouseholder.h
+++ /dev/null
@@ -1,110 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Vincent Lejeune
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BLOCK_HOUSEHOLDER_H
-#define EIGEN_BLOCK_HOUSEHOLDER_H
-
-// This file contains some helper function to deal with block householder reflectors
-
-namespace Eigen { 
-
-namespace internal {
-  
-/** \internal */
-// template<typename TriangularFactorType,typename VectorsType,typename CoeffsType>
-// void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs)
-// {
-//   typedef typename VectorsType::Scalar Scalar;
-//   const Index nbVecs = vectors.cols();
-//   eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows()>=nbVecs);
-// 
-//   for(Index i = 0; i < nbVecs; i++)
-//   {
-//     Index rs = vectors.rows() - i;
-//     // Warning, note that hCoeffs may alias with vectors.
-//     // It is then necessary to copy it before modifying vectors(i,i). 
-//     typename CoeffsType::Scalar h = hCoeffs(i);
-//     // This hack permits to pass trough nested Block<> and Transpose<> expressions.
-//     Scalar *Vii_ptr = const_cast<Scalar*>(vectors.data() + vectors.outerStride()*i + vectors.innerStride()*i);
-//     Scalar Vii = *Vii_ptr;
-//     *Vii_ptr = Scalar(1);
-//     triFactor.col(i).head(i).noalias() = -h * vectors.block(i, 0, rs, i).adjoint()
-//                                        * vectors.col(i).tail(rs);
-//     *Vii_ptr = Vii;
-//     // FIXME add .noalias() once the triangular product can work inplace
-//     triFactor.col(i).head(i) = triFactor.block(0,0,i,i).template triangularView<Upper>()
-//                              * triFactor.col(i).head(i);
-//     triFactor(i,i) = hCoeffs(i);
-//   }
-// }
-
-/** \internal */
-// This variant avoid modifications in vectors
-template<typename TriangularFactorType,typename VectorsType,typename CoeffsType>
-void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs)
-{
-  const Index nbVecs = vectors.cols();
-  eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows()>=nbVecs);
-
-  for(Index i = nbVecs-1; i >=0 ; --i)
-  {
-    Index rs = vectors.rows() - i - 1;
-    Index rt = nbVecs-i-1;
-
-    if(rt>0)
-    {
-      triFactor.row(i).tail(rt).noalias() = -hCoeffs(i) * vectors.col(i).tail(rs).adjoint()
-                                                        * vectors.bottomRightCorner(rs, rt).template triangularView<UnitLower>();
-            
-      // FIXME use the following line with .noalias() once the triangular product can work inplace
-      // triFactor.row(i).tail(rt) = triFactor.row(i).tail(rt) * triFactor.bottomRightCorner(rt,rt).template triangularView<Upper>();
-      for(Index j=nbVecs-1; j>i; --j)
-      {
-        typename TriangularFactorType::Scalar z = triFactor(i,j);
-        triFactor(i,j) = z * triFactor(j,j);
-        if(nbVecs-j-1>0)
-          triFactor.row(i).tail(nbVecs-j-1) += z * triFactor.row(j).tail(nbVecs-j-1);
-      }
-      
-    }
-    triFactor(i,i) = hCoeffs(i);
-  }
-}
-
-/** \internal
-  * if forward then perform   mat = H0 * H1 * H2 * mat
-  * otherwise perform         mat = H2 * H1 * H0 * mat
-  */
-template<typename MatrixType,typename VectorsType,typename CoeffsType>
-void apply_block_householder_on_the_left(MatrixType& mat, const VectorsType& vectors, const CoeffsType& hCoeffs, bool forward)
-{
-  enum { TFactorSize = MatrixType::ColsAtCompileTime };
-  Index nbVecs = vectors.cols();
-  Matrix<typename MatrixType::Scalar, TFactorSize, TFactorSize, RowMajor> T(nbVecs,nbVecs);
-  
-  if(forward) make_block_householder_triangular_factor(T, vectors, hCoeffs);
-  else        make_block_householder_triangular_factor(T, vectors, hCoeffs.conjugate());  
-  const TriangularView<const VectorsType, UnitLower> V(vectors);
-
-  // A -= V T V^* A
-  Matrix<typename MatrixType::Scalar,VectorsType::ColsAtCompileTime,MatrixType::ColsAtCompileTime,
-         (VectorsType::MaxColsAtCompileTime==1 && MatrixType::MaxColsAtCompileTime!=1)?RowMajor:ColMajor,
-         VectorsType::MaxColsAtCompileTime,MatrixType::MaxColsAtCompileTime> tmp = V.adjoint() * mat;
-  // FIXME add .noalias() once the triangular product can work inplace
-  if(forward) tmp = T.template triangularView<Upper>()           * tmp;
-  else        tmp = T.template triangularView<Upper>().adjoint() * tmp;
-  mat.noalias() -= V * tmp;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BLOCK_HOUSEHOLDER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/Householder.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/Householder.h
deleted file mode 100644
index 5bc037f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/Householder.h
+++ /dev/null
@@ -1,176 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOUSEHOLDER_H
-#define EIGEN_HOUSEHOLDER_H
-
-namespace Eigen { 
-
-namespace internal {
-template<int n> struct decrement_size
-{
-  enum {
-    ret = n==Dynamic ? n : n-1
-  };
-};
-}
-
-/** Computes the elementary reflector H such that:
-  * \f$ H *this = [ beta 0 ... 0]^T \f$
-  * where the transformation H is:
-  * \f$ H = I - tau v v^*\f$
-  * and the vector v is:
-  * \f$ v^T = [1 essential^T] \f$
-  *
-  * The essential part of the vector \c v is stored in *this.
-  * 
-  * On output:
-  * \param tau the scaling factor of the Householder transformation
-  * \param beta the result of H * \c *this
-  *
-  * \sa MatrixBase::makeHouseholder(), MatrixBase::applyHouseholderOnTheLeft(),
-  *     MatrixBase::applyHouseholderOnTheRight()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::makeHouseholderInPlace(Scalar& tau, RealScalar& beta)
-{
-  VectorBlock<Derived, internal::decrement_size<Base::SizeAtCompileTime>::ret> essentialPart(derived(), 1, size()-1);
-  makeHouseholder(essentialPart, tau, beta);
-}
-
-/** Computes the elementary reflector H such that:
-  * \f$ H *this = [ beta 0 ... 0]^T \f$
-  * where the transformation H is:
-  * \f$ H = I - tau v v^*\f$
-  * and the vector v is:
-  * \f$ v^T = [1 essential^T] \f$
-  *
-  * On output:
-  * \param essential the essential part of the vector \c v
-  * \param tau the scaling factor of the Householder transformation
-  * \param beta the result of H * \c *this
-  *
-  * \sa MatrixBase::makeHouseholderInPlace(), MatrixBase::applyHouseholderOnTheLeft(),
-  *     MatrixBase::applyHouseholderOnTheRight()
-  */
-template<typename Derived>
-template<typename EssentialPart>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::makeHouseholder(
-  EssentialPart& essential,
-  Scalar& tau,
-  RealScalar& beta) const
-{
-  using std::sqrt;
-  using numext::conj;
-  
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(EssentialPart)
-  VectorBlock<const Derived, EssentialPart::SizeAtCompileTime> tail(derived(), 1, size()-1);
-  
-  RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm();
-  Scalar c0 = coeff(0);
-  const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
-
-  if(tailSqNorm <= tol && numext::abs2(numext::imag(c0))<=tol)
-  {
-    tau = RealScalar(0);
-    beta = numext::real(c0);
-    essential.setZero();
-  }
-  else
-  {
-    beta = sqrt(numext::abs2(c0) + tailSqNorm);
-    if (numext::real(c0)>=RealScalar(0))
-      beta = -beta;
-    essential = tail / (c0 - beta);
-    tau = conj((beta - c0) / beta);
-  }
-}
-
-/** Apply the elementary reflector H given by
-  * \f$ H = I - tau v v^*\f$
-  * with
-  * \f$ v^T = [1 essential^T] \f$
-  * from the left to a vector or matrix.
-  *
-  * On input:
-  * \param essential the essential part of the vector \c v
-  * \param tau the scaling factor of the Householder transformation
-  * \param workspace a pointer to working space with at least
-  *                  this->cols() entries
-  *
-  * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), 
-  *     MatrixBase::applyHouseholderOnTheRight()
-  */
-template<typename Derived>
-template<typename EssentialPart>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::applyHouseholderOnTheLeft(
-  const EssentialPart& essential,
-  const Scalar& tau,
-  Scalar* workspace)
-{
-  if(rows() == 1)
-  {
-    *this *= Scalar(1)-tau;
-  }
-  else if(tau!=Scalar(0))
-  {
-    Map<typename internal::plain_row_type<PlainObject>::type> tmp(workspace,cols());
-    Block<Derived, EssentialPart::SizeAtCompileTime, Derived::ColsAtCompileTime> bottom(derived(), 1, 0, rows()-1, cols());
-    tmp.noalias() = essential.adjoint() * bottom;
-    tmp += this->row(0);
-    this->row(0) -= tau * tmp;
-    bottom.noalias() -= tau * essential * tmp;
-  }
-}
-
-/** Apply the elementary reflector H given by
-  * \f$ H = I - tau v v^*\f$
-  * with
-  * \f$ v^T = [1 essential^T] \f$
-  * from the right to a vector or matrix.
-  *
-  * On input:
-  * \param essential the essential part of the vector \c v
-  * \param tau the scaling factor of the Householder transformation
-  * \param workspace a pointer to working space with at least
-  *                  this->rows() entries
-  *
-  * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), 
-  *     MatrixBase::applyHouseholderOnTheLeft()
-  */
-template<typename Derived>
-template<typename EssentialPart>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::applyHouseholderOnTheRight(
-  const EssentialPart& essential,
-  const Scalar& tau,
-  Scalar* workspace)
-{
-  if(cols() == 1)
-  {
-    *this *= Scalar(1)-tau;
-  }
-  else if(tau!=Scalar(0))
-  {
-    Map<typename internal::plain_col_type<PlainObject>::type> tmp(workspace,rows());
-    Block<Derived, Derived::RowsAtCompileTime, EssentialPart::SizeAtCompileTime> right(derived(), 0, 1, rows(), cols()-1);
-    tmp.noalias() = right * essential;
-    tmp += this->col(0);
-    this->col(0) -= tau * tmp;
-    right.noalias() -= tau * tmp * essential.adjoint();
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_HOUSEHOLDER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/HouseholderSequence.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/HouseholderSequence.h
deleted file mode 100644
index 022f6c3..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Householder/HouseholderSequence.h
+++ /dev/null
@@ -1,545 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOUSEHOLDER_SEQUENCE_H
-#define EIGEN_HOUSEHOLDER_SEQUENCE_H
-
-namespace Eigen {
-
-/** \ingroup Householder_Module
-  * \householder_module
-  * \class HouseholderSequence
-  * \brief Sequence of Householder reflections acting on subspaces with decreasing size
-  * \tparam VectorsType type of matrix containing the Householder vectors
-  * \tparam CoeffsType  type of vector containing the Householder coefficients
-  * \tparam Side        either OnTheLeft (the default) or OnTheRight
-  *
-  * This class represents a product sequence of Householder reflections where the first Householder reflection
-  * acts on the whole space, the second Householder reflection leaves the one-dimensional subspace spanned by
-  * the first unit vector invariant, the third Householder reflection leaves the two-dimensional subspace
-  * spanned by the first two unit vectors invariant, and so on up to the last reflection which leaves all but
-  * one dimensions invariant and acts only on the last dimension. Such sequences of Householder reflections
-  * are used in several algorithms to zero out certain parts of a matrix. Indeed, the methods
-  * HessenbergDecomposition::matrixQ(), Tridiagonalization::matrixQ(), HouseholderQR::householderQ(),
-  * and ColPivHouseholderQR::householderQ() all return a %HouseholderSequence.
-  *
-  * More precisely, the class %HouseholderSequence represents an \f$ n \times n \f$ matrix \f$ H \f$ of the
-  * form \f$ H = \prod_{i=0}^{n-1} H_i \f$ where the i-th Householder reflection is \f$ H_i = I - h_i v_i
-  * v_i^* \f$. The i-th Householder coefficient \f$ h_i \f$ is a scalar and the i-th Householder vector \f$
-  * v_i \f$ is a vector of the form
-  * \f[
-  * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
-  * \f]
-  * The last \f$ n-i \f$ entries of \f$ v_i \f$ are called the essential part of the Householder vector.
-  *
-  * Typical usages are listed below, where H is a HouseholderSequence:
-  * \code
-  * A.applyOnTheRight(H);             // A = A * H
-  * A.applyOnTheLeft(H);              // A = H * A
-  * A.applyOnTheRight(H.adjoint());   // A = A * H^*
-  * A.applyOnTheLeft(H.adjoint());    // A = H^* * A
-  * MatrixXd Q = H;                   // conversion to a dense matrix
-  * \endcode
-  * In addition to the adjoint, you can also apply the inverse (=adjoint), the transpose, and the conjugate operators.
-  *
-  * See the documentation for HouseholderSequence(const VectorsType&, const CoeffsType&) for an example.
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-
-namespace internal {
-
-template<typename VectorsType, typename CoeffsType, int Side>
-struct traits<HouseholderSequence<VectorsType,CoeffsType,Side> >
-{
-  typedef typename VectorsType::Scalar Scalar;
-  typedef typename VectorsType::StorageIndex StorageIndex;
-  typedef typename VectorsType::StorageKind StorageKind;
-  enum {
-    RowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::RowsAtCompileTime
-                                        : traits<VectorsType>::ColsAtCompileTime,
-    ColsAtCompileTime = RowsAtCompileTime,
-    MaxRowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::MaxRowsAtCompileTime
-                                           : traits<VectorsType>::MaxColsAtCompileTime,
-    MaxColsAtCompileTime = MaxRowsAtCompileTime,
-    Flags = 0
-  };
-};
-
-struct HouseholderSequenceShape {};
-
-template<typename VectorsType, typename CoeffsType, int Side>
-struct evaluator_traits<HouseholderSequence<VectorsType,CoeffsType,Side> >
-  : public evaluator_traits_base<HouseholderSequence<VectorsType,CoeffsType,Side> >
-{
-  typedef HouseholderSequenceShape Shape;
-};
-
-template<typename VectorsType, typename CoeffsType, int Side>
-struct hseq_side_dependent_impl
-{
-  typedef Block<const VectorsType, Dynamic, 1> EssentialVectorType;
-  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheLeft> HouseholderSequenceType;
-  static EIGEN_DEVICE_FUNC inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k)
-  {
-    Index start = k+1+h.m_shift;
-    return Block<const VectorsType,Dynamic,1>(h.m_vectors, start, k, h.rows()-start, 1);
-  }
-};
-
-template<typename VectorsType, typename CoeffsType>
-struct hseq_side_dependent_impl<VectorsType, CoeffsType, OnTheRight>
-{
-  typedef Transpose<Block<const VectorsType, 1, Dynamic> > EssentialVectorType;
-  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheRight> HouseholderSequenceType;
-  static inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k)
-  {
-    Index start = k+1+h.m_shift;
-    return Block<const VectorsType,1,Dynamic>(h.m_vectors, k, start, 1, h.rows()-start).transpose();
-  }
-};
-
-template<typename OtherScalarType, typename MatrixType> struct matrix_type_times_scalar_type
-{
-  typedef typename ScalarBinaryOpTraits<OtherScalarType, typename MatrixType::Scalar>::ReturnType
-    ResultScalar;
-  typedef Matrix<ResultScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
-                 0, MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime> Type;
-};
-
-} // end namespace internal
-
-template<typename VectorsType, typename CoeffsType, int Side> class HouseholderSequence
-  : public EigenBase<HouseholderSequence<VectorsType,CoeffsType,Side> >
-{
-    typedef typename internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::EssentialVectorType EssentialVectorType;
-
-  public:
-    enum {
-      RowsAtCompileTime = internal::traits<HouseholderSequence>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<HouseholderSequence>::ColsAtCompileTime,
-      MaxRowsAtCompileTime = internal::traits<HouseholderSequence>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<HouseholderSequence>::MaxColsAtCompileTime
-    };
-    typedef typename internal::traits<HouseholderSequence>::Scalar Scalar;
-
-    typedef HouseholderSequence<
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename VectorsType::ConjugateReturnType>::type,
-        VectorsType>::type,
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename CoeffsType::ConjugateReturnType>::type,
-        CoeffsType>::type,
-      Side
-    > ConjugateReturnType;
-
-    typedef HouseholderSequence<
-      VectorsType,
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename CoeffsType::ConjugateReturnType>::type,
-        CoeffsType>::type,
-      Side
-    > AdjointReturnType;
-
-    typedef HouseholderSequence<
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename VectorsType::ConjugateReturnType>::type,
-        VectorsType>::type,
-      CoeffsType,
-      Side
-    > TransposeReturnType;
-
-    typedef HouseholderSequence<
-      typename internal::add_const<VectorsType>::type,
-      typename internal::add_const<CoeffsType>::type,
-      Side
-    > ConstHouseholderSequence;
-
-    /** \brief Constructor.
-      * \param[in]  v      %Matrix containing the essential parts of the Householder vectors
-      * \param[in]  h      Vector containing the Householder coefficients
-      *
-      * Constructs the Householder sequence with coefficients given by \p h and vectors given by \p v. The
-      * i-th Householder coefficient \f$ h_i \f$ is given by \p h(i) and the essential part of the i-th
-      * Householder vector \f$ v_i \f$ is given by \p v(k,i) with \p k > \p i (the subdiagonal part of the
-      * i-th column). If \p v has fewer columns than rows, then the Householder sequence contains as many
-      * Householder reflections as there are columns.
-      *
-      * \note The %HouseholderSequence object stores \p v and \p h by reference.
-      *
-      * Example: \include HouseholderSequence_HouseholderSequence.cpp
-      * Output: \verbinclude HouseholderSequence_HouseholderSequence.out
-      *
-      * \sa setLength(), setShift()
-      */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence(const VectorsType& v, const CoeffsType& h)
-      : m_vectors(v), m_coeffs(h), m_reverse(false), m_length(v.diagonalSize()),
-        m_shift(0)
-    {
-    }
-
-    /** \brief Copy constructor. */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence(const HouseholderSequence& other)
-      : m_vectors(other.m_vectors),
-        m_coeffs(other.m_coeffs),
-        m_reverse(other.m_reverse),
-        m_length(other.m_length),
-        m_shift(other.m_shift)
-    {
-    }
-
-    /** \brief Number of rows of transformation viewed as a matrix.
-      * \returns Number of rows
-      * \details This equals the dimension of the space that the transformation acts on.
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return Side==OnTheLeft ? m_vectors.rows() : m_vectors.cols(); }
-
-    /** \brief Number of columns of transformation viewed as a matrix.
-      * \returns Number of columns
-      * \details This equals the dimension of the space that the transformation acts on.
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return rows(); }
-
-    /** \brief Essential part of a Householder vector.
-      * \param[in]  k  Index of Householder reflection
-      * \returns    Vector containing non-trivial entries of k-th Householder vector
-      *
-      * This function returns the essential part of the Householder vector \f$ v_i \f$. This is a vector of
-      * length \f$ n-i \f$ containing the last \f$ n-i \f$ entries of the vector
-      * \f[
-      * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
-      * \f]
-      * The index \f$ i \f$ equals \p k + shift(), corresponding to the k-th column of the matrix \p v
-      * passed to the constructor.
-      *
-      * \sa setShift(), shift()
-      */
-    EIGEN_DEVICE_FUNC
-    const EssentialVectorType essentialVector(Index k) const
-    {
-      eigen_assert(k >= 0 && k < m_length);
-      return internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::essentialVector(*this, k);
-    }
-
-    /** \brief %Transpose of the Householder sequence. */
-    TransposeReturnType transpose() const
-    {
-      return TransposeReturnType(m_vectors.conjugate(), m_coeffs)
-              .setReverseFlag(!m_reverse)
-              .setLength(m_length)
-              .setShift(m_shift);
-    }
-
-    /** \brief Complex conjugate of the Householder sequence. */
-    ConjugateReturnType conjugate() const
-    {
-      return ConjugateReturnType(m_vectors.conjugate(), m_coeffs.conjugate())
-             .setReverseFlag(m_reverse)
-             .setLength(m_length)
-             .setShift(m_shift);
-    }
-
-    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
-     *           returns \c *this otherwise.
-     */
-    template<bool Cond>
-    EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Cond,ConjugateReturnType,ConstHouseholderSequence>::type
-    conjugateIf() const
-    {
-      typedef typename internal::conditional<Cond,ConjugateReturnType,ConstHouseholderSequence>::type ReturnType;
-      return ReturnType(m_vectors.template conjugateIf<Cond>(), m_coeffs.template conjugateIf<Cond>());
-    }
-
-    /** \brief Adjoint (conjugate transpose) of the Householder sequence. */
-    AdjointReturnType adjoint() const
-    {
-      return AdjointReturnType(m_vectors, m_coeffs.conjugate())
-              .setReverseFlag(!m_reverse)
-              .setLength(m_length)
-              .setShift(m_shift);
-    }
-
-    /** \brief Inverse of the Householder sequence (equals the adjoint). */
-    AdjointReturnType inverse() const { return adjoint(); }
-
-    /** \internal */
-    template<typename DestType>
-    inline EIGEN_DEVICE_FUNC
-    void evalTo(DestType& dst) const
-    {
-      Matrix<Scalar, DestType::RowsAtCompileTime, 1,
-             AutoAlign|ColMajor, DestType::MaxRowsAtCompileTime, 1> workspace(rows());
-      evalTo(dst, workspace);
-    }
-
-    /** \internal */
-    template<typename Dest, typename Workspace>
-    EIGEN_DEVICE_FUNC
-    void evalTo(Dest& dst, Workspace& workspace) const
-    {
-      workspace.resize(rows());
-      Index vecs = m_length;
-      if(internal::is_same_dense(dst,m_vectors))
-      {
-        // in-place
-        dst.diagonal().setOnes();
-        dst.template triangularView<StrictlyUpper>().setZero();
-        for(Index k = vecs-1; k >= 0; --k)
-        {
-          Index cornerSize = rows() - k - m_shift;
-          if(m_reverse)
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-          else
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-
-          // clear the off diagonal vector
-          dst.col(k).tail(rows()-k-1).setZero();
-        }
-        // clear the remaining columns if needed
-        for(Index k = 0; k<cols()-vecs ; ++k)
-          dst.col(k).tail(rows()-k-1).setZero();
-      }
-      else if(m_length>BlockSize)
-      {
-        dst.setIdentity(rows(), rows());
-        if(m_reverse)
-          applyThisOnTheLeft(dst,workspace,true);
-        else
-          applyThisOnTheLeft(dst,workspace,true);
-      }
-      else
-      {
-        dst.setIdentity(rows(), rows());
-        for(Index k = vecs-1; k >= 0; --k)
-        {
-          Index cornerSize = rows() - k - m_shift;
-          if(m_reverse)
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-          else
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-        }
-      }
-    }
-
-    /** \internal */
-    template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
-    {
-      Matrix<Scalar,1,Dest::RowsAtCompileTime,RowMajor,1,Dest::MaxRowsAtCompileTime> workspace(dst.rows());
-      applyThisOnTheRight(dst, workspace);
-    }
-
-    /** \internal */
-    template<typename Dest, typename Workspace>
-    inline void applyThisOnTheRight(Dest& dst, Workspace& workspace) const
-    {
-      workspace.resize(dst.rows());
-      for(Index k = 0; k < m_length; ++k)
-      {
-        Index actual_k = m_reverse ? m_length-k-1 : k;
-        dst.rightCols(rows()-m_shift-actual_k)
-           .applyHouseholderOnTheRight(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
-      }
-    }
-
-    /** \internal */
-    template<typename Dest> inline void applyThisOnTheLeft(Dest& dst, bool inputIsIdentity = false) const
-    {
-      Matrix<Scalar,1,Dest::ColsAtCompileTime,RowMajor,1,Dest::MaxColsAtCompileTime> workspace;
-      applyThisOnTheLeft(dst, workspace, inputIsIdentity);
-    }
-
-    /** \internal */
-    template<typename Dest, typename Workspace>
-    inline void applyThisOnTheLeft(Dest& dst, Workspace& workspace, bool inputIsIdentity = false) const
-    {
-      if(inputIsIdentity && m_reverse)
-        inputIsIdentity = false;
-      // if the entries are large enough, then apply the reflectors by block
-      if(m_length>=BlockSize && dst.cols()>1)
-      {
-        // Make sure we have at least 2 useful blocks, otherwise it is point-less:
-        Index blockSize = m_length<Index(2*BlockSize) ? (m_length+1)/2 : Index(BlockSize);
-        for(Index i = 0; i < m_length; i+=blockSize)
-        {
-          Index end = m_reverse ? (std::min)(m_length,i+blockSize) : m_length-i;
-          Index k = m_reverse ? i : (std::max)(Index(0),end-blockSize);
-          Index bs = end-k;
-          Index start = k + m_shift;
-
-          typedef Block<typename internal::remove_all<VectorsType>::type,Dynamic,Dynamic> SubVectorsType;
-          SubVectorsType sub_vecs1(m_vectors.const_cast_derived(), Side==OnTheRight ? k : start,
-                                                                   Side==OnTheRight ? start : k,
-                                                                   Side==OnTheRight ? bs : m_vectors.rows()-start,
-                                                                   Side==OnTheRight ? m_vectors.cols()-start : bs);
-          typename internal::conditional<Side==OnTheRight, Transpose<SubVectorsType>, SubVectorsType&>::type sub_vecs(sub_vecs1);
-
-          Index dstStart = dst.rows()-rows()+m_shift+k;
-          Index dstRows  = rows()-m_shift-k;
-          Block<Dest,Dynamic,Dynamic> sub_dst(dst,
-                                              dstStart,
-                                              inputIsIdentity ? dstStart : 0,
-                                              dstRows,
-                                              inputIsIdentity ? dstRows : dst.cols());
-          apply_block_householder_on_the_left(sub_dst, sub_vecs, m_coeffs.segment(k, bs), !m_reverse);
-        }
-      }
-      else
-      {
-        workspace.resize(dst.cols());
-        for(Index k = 0; k < m_length; ++k)
-        {
-          Index actual_k = m_reverse ? k : m_length-k-1;
-          Index dstStart = rows()-m_shift-actual_k;
-          dst.bottomRightCorner(dstStart, inputIsIdentity ? dstStart : dst.cols())
-            .applyHouseholderOnTheLeft(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
-        }
-      }
-    }
-
-    /** \brief Computes the product of a Householder sequence with a matrix.
-      * \param[in]  other  %Matrix being multiplied.
-      * \returns    Expression object representing the product.
-      *
-      * This function computes \f$ HM \f$ where \f$ H \f$ is the Householder sequence represented by \p *this
-      * and \f$ M \f$ is the matrix \p other.
-      */
-    template<typename OtherDerived>
-    typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other) const
-    {
-      typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type
-        res(other.template cast<typename internal::matrix_type_times_scalar_type<Scalar,OtherDerived>::ResultScalar>());
-      applyThisOnTheLeft(res, internal::is_identity<OtherDerived>::value && res.rows()==res.cols());
-      return res;
-    }
-
-    template<typename _VectorsType, typename _CoeffsType, int _Side> friend struct internal::hseq_side_dependent_impl;
-
-    /** \brief Sets the length of the Householder sequence.
-      * \param [in]  length  New value for the length.
-      *
-      * By default, the length \f$ n \f$ of the Householder sequence \f$ H = H_0 H_1 \ldots H_{n-1} \f$ is set
-      * to the number of columns of the matrix \p v passed to the constructor, or the number of rows if that
-      * is smaller. After this function is called, the length equals \p length.
-      *
-      * \sa length()
-      */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence& setLength(Index length)
-    {
-      m_length = length;
-      return *this;
-    }
-
-    /** \brief Sets the shift of the Householder sequence.
-      * \param [in]  shift  New value for the shift.
-      *
-      * By default, a %HouseholderSequence object represents \f$ H = H_0 H_1 \ldots H_{n-1} \f$ and the i-th
-      * column of the matrix \p v passed to the constructor corresponds to the i-th Householder
-      * reflection. After this function is called, the object represents \f$ H = H_{\mathrm{shift}}
-      * H_{\mathrm{shift}+1} \ldots H_{n-1} \f$ and the i-th column of \p v corresponds to the (shift+i)-th
-      * Householder reflection.
-      *
-      * \sa shift()
-      */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence& setShift(Index shift)
-    {
-      m_shift = shift;
-      return *this;
-    }
-
-    EIGEN_DEVICE_FUNC
-    Index length() const { return m_length; }  /**< \brief Returns the length of the Householder sequence. */
-
-    EIGEN_DEVICE_FUNC
-    Index shift() const { return m_shift; }    /**< \brief Returns the shift of the Householder sequence. */
-
-    /* Necessary for .adjoint() and .conjugate() */
-    template <typename VectorsType2, typename CoeffsType2, int Side2> friend class HouseholderSequence;
-
-  protected:
-
-    /** \internal
-      * \brief Sets the reverse flag.
-      * \param [in]  reverse  New value of the reverse flag.
-      *
-      * By default, the reverse flag is not set. If the reverse flag is set, then this object represents
-      * \f$ H^r = H_{n-1} \ldots H_1 H_0 \f$ instead of \f$ H = H_0 H_1 \ldots H_{n-1} \f$.
-      * \note For real valued HouseholderSequence this is equivalent to transposing \f$ H \f$.
-      *
-      * \sa reverseFlag(), transpose(), adjoint()
-      */
-    HouseholderSequence& setReverseFlag(bool reverse)
-    {
-      m_reverse = reverse;
-      return *this;
-    }
-
-    bool reverseFlag() const { return m_reverse; }     /**< \internal \brief Returns the reverse flag. */
-
-    typename VectorsType::Nested m_vectors;
-    typename CoeffsType::Nested m_coeffs;
-    bool m_reverse;
-    Index m_length;
-    Index m_shift;
-    enum { BlockSize = 48 };
-};
-
-/** \brief Computes the product of a matrix with a Householder sequence.
-  * \param[in]  other  %Matrix being multiplied.
-  * \param[in]  h      %HouseholderSequence being multiplied.
-  * \returns    Expression object representing the product.
-  *
-  * This function computes \f$ MH \f$ where \f$ M \f$ is the matrix \p other and \f$ H \f$ is the
-  * Householder sequence represented by \p h.
-  */
-template<typename OtherDerived, typename VectorsType, typename CoeffsType, int Side>
-typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other, const HouseholderSequence<VectorsType,CoeffsType,Side>& h)
-{
-  typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type
-    res(other.template cast<typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::ResultScalar>());
-  h.applyThisOnTheRight(res);
-  return res;
-}
-
-/** \ingroup Householder_Module \householder_module
-  * \brief Convenience function for constructing a Householder sequence.
-  * \returns A HouseholderSequence constructed from the specified arguments.
-  */
-template<typename VectorsType, typename CoeffsType>
-HouseholderSequence<VectorsType,CoeffsType> householderSequence(const VectorsType& v, const CoeffsType& h)
-{
-  return HouseholderSequence<VectorsType,CoeffsType,OnTheLeft>(v, h);
-}
-
-/** \ingroup Householder_Module \householder_module
-  * \brief Convenience function for constructing a Householder sequence.
-  * \returns A HouseholderSequence constructed from the specified arguments.
-  * \details This function differs from householderSequence() in that the template argument \p OnTheSide of
-  * the constructed HouseholderSequence is set to OnTheRight, instead of the default OnTheLeft.
-  */
-template<typename VectorsType, typename CoeffsType>
-HouseholderSequence<VectorsType,CoeffsType,OnTheRight> rightHouseholderSequence(const VectorsType& v, const CoeffsType& h)
-{
-  return HouseholderSequence<VectorsType,CoeffsType,OnTheRight>(v, h);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_HOUSEHOLDER_SEQUENCE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
deleted file mode 100644
index a117fc1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BASIC_PRECONDITIONERS_H
-#define EIGEN_BASIC_PRECONDITIONERS_H
-
-namespace Eigen {
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A preconditioner based on the digonal entries
-  *
-  * This class allows to approximately solve for A.x = b problems assuming A is a diagonal matrix.
-  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
-    \code
-    A.diagonal().asDiagonal() . x = b
-    \endcode
-  *
-  * \tparam _Scalar the type of the scalar.
-  *
-  * \implsparsesolverconcept
-  *
-  * This preconditioner is suitable for both selfadjoint and general problems.
-  * The diagonal entries are pre-inverted and stored into a dense vector.
-  *
-  * \note A variant that has yet to be implemented would attempt to preserve the norm of each column.
-  *
-  * \sa class LeastSquareDiagonalPreconditioner, class ConjugateGradient
-  */
-template <typename _Scalar>
-class DiagonalPreconditioner
-{
-    typedef _Scalar Scalar;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-  public:
-    typedef typename Vector::StorageIndex StorageIndex;
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-
-    DiagonalPreconditioner() : m_isInitialized(false) {}
-
-    template<typename MatType>
-    explicit DiagonalPreconditioner(const MatType& mat) : m_invdiag(mat.cols())
-    {
-      compute(mat);
-    }
-
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
-
-    template<typename MatType>
-    DiagonalPreconditioner& analyzePattern(const MatType& )
-    {
-      return *this;
-    }
-
-    template<typename MatType>
-    DiagonalPreconditioner& factorize(const MatType& mat)
-    {
-      m_invdiag.resize(mat.cols());
-      for(int j=0; j<mat.outerSize(); ++j)
-      {
-        typename MatType::InnerIterator it(mat,j);
-        while(it && it.index()!=j) ++it;
-        if(it && it.index()==j && it.value()!=Scalar(0))
-          m_invdiag(j) = Scalar(1)/it.value();
-        else
-          m_invdiag(j) = Scalar(1);
-      }
-      m_isInitialized = true;
-      return *this;
-    }
-
-    template<typename MatType>
-    DiagonalPreconditioner& compute(const MatType& mat)
-    {
-      return factorize(mat);
-    }
-
-    /** \internal */
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const Rhs& b, Dest& x) const
-    {
-      x = m_invdiag.array() * b.array() ;
-    }
-
-    template<typename Rhs> inline const Solve<DiagonalPreconditioner, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "DiagonalPreconditioner is not initialized.");
-      eigen_assert(m_invdiag.size()==b.rows()
-                && "DiagonalPreconditioner::solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<DiagonalPreconditioner, Rhs>(*this, b.derived());
-    }
-
-    ComputationInfo info() { return Success; }
-
-  protected:
-    Vector m_invdiag;
-    bool m_isInitialized;
-};
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief Jacobi preconditioner for LeastSquaresConjugateGradient
-  *
-  * This class allows to approximately solve for A' A x  = A' b problems assuming A' A is a diagonal matrix.
-  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
-    \code
-    (A.adjoint() * A).diagonal().asDiagonal() * x = b
-    \endcode
-  *
-  * \tparam _Scalar the type of the scalar.
-  *
-  * \implsparsesolverconcept
-  *
-  * The diagonal entries are pre-inverted and stored into a dense vector.
-  *
-  * \sa class LeastSquaresConjugateGradient, class DiagonalPreconditioner
-  */
-template <typename _Scalar>
-class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<_Scalar>
-{
-    typedef _Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef DiagonalPreconditioner<_Scalar> Base;
-    using Base::m_invdiag;
-  public:
-
-    LeastSquareDiagonalPreconditioner() : Base() {}
-
-    template<typename MatType>
-    explicit LeastSquareDiagonalPreconditioner(const MatType& mat) : Base()
-    {
-      compute(mat);
-    }
-
-    template<typename MatType>
-    LeastSquareDiagonalPreconditioner& analyzePattern(const MatType& )
-    {
-      return *this;
-    }
-
-    template<typename MatType>
-    LeastSquareDiagonalPreconditioner& factorize(const MatType& mat)
-    {
-      // Compute the inverse squared-norm of each column of mat
-      m_invdiag.resize(mat.cols());
-      if(MatType::IsRowMajor)
-      {
-        m_invdiag.setZero();
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          for(typename MatType::InnerIterator it(mat,j); it; ++it)
-            m_invdiag(it.index()) += numext::abs2(it.value());
-        }
-        for(Index j=0; j<mat.cols(); ++j)
-          if(numext::real(m_invdiag(j))>RealScalar(0))
-            m_invdiag(j) = RealScalar(1)/numext::real(m_invdiag(j));
-      }
-      else
-      {
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          RealScalar sum = mat.col(j).squaredNorm();
-          if(sum>RealScalar(0))
-            m_invdiag(j) = RealScalar(1)/sum;
-          else
-            m_invdiag(j) = RealScalar(1);
-        }
-      }
-      Base::m_isInitialized = true;
-      return *this;
-    }
-
-    template<typename MatType>
-    LeastSquareDiagonalPreconditioner& compute(const MatType& mat)
-    {
-      return factorize(mat);
-    }
-
-    ComputationInfo info() { return Success; }
-
-  protected:
-};
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A naive preconditioner which approximates any matrix as the identity matrix
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa class DiagonalPreconditioner
-  */
-class IdentityPreconditioner
-{
-  public:
-
-    IdentityPreconditioner() {}
-
-    template<typename MatrixType>
-    explicit IdentityPreconditioner(const MatrixType& ) {}
-
-    template<typename MatrixType>
-    IdentityPreconditioner& analyzePattern(const MatrixType& ) { return *this; }
-
-    template<typename MatrixType>
-    IdentityPreconditioner& factorize(const MatrixType& ) { return *this; }
-
-    template<typename MatrixType>
-    IdentityPreconditioner& compute(const MatrixType& ) { return *this; }
-
-    template<typename Rhs>
-    inline const Rhs& solve(const Rhs& b) const { return b; }
-
-    ComputationInfo info() { return Success; }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_BASIC_PRECONDITIONERS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
deleted file mode 100644
index 153acef..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
+++ /dev/null
@@ -1,212 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BICGSTAB_H
-#define EIGEN_BICGSTAB_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Low-level bi conjugate gradient stabilized algorithm
-  * \param mat The matrix A
-  * \param rhs The right hand side vector b
-  * \param x On input and initial solution, on output the computed solution.
-  * \param precond A preconditioner being able to efficiently solve for an
-  *                approximation of Ax=b (regardless of b)
-  * \param iters On input the max number of iteration, on output the number of performed iterations.
-  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-  * \return false in the case of numerical issue, for example a break down of BiCGSTAB. 
-  */
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
-              const Preconditioner& precond, Index& iters,
-              typename Dest::RealScalar& tol_error)
-{
-  using std::sqrt;
-  using std::abs;
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,1> VectorType;
-  RealScalar tol = tol_error;
-  Index maxIters = iters;
-
-  Index n = mat.cols();
-  VectorType r  = rhs - mat * x;
-  VectorType r0 = r;
-  
-  RealScalar r0_sqnorm = r0.squaredNorm();
-  RealScalar rhs_sqnorm = rhs.squaredNorm();
-  if(rhs_sqnorm == 0)
-  {
-    x.setZero();
-    return true;
-  }
-  Scalar rho    = 1;
-  Scalar alpha  = 1;
-  Scalar w      = 1;
-  
-  VectorType v = VectorType::Zero(n), p = VectorType::Zero(n);
-  VectorType y(n),  z(n);
-  VectorType kt(n), ks(n);
-
-  VectorType s(n), t(n);
-
-  RealScalar tol2 = tol*tol*rhs_sqnorm;
-  RealScalar eps2 = NumTraits<Scalar>::epsilon()*NumTraits<Scalar>::epsilon();
-  Index i = 0;
-  Index restarts = 0;
-
-  while ( r.squaredNorm() > tol2 && i<maxIters )
-  {
-    Scalar rho_old = rho;
-
-    rho = r0.dot(r);
-    if (abs(rho) < eps2*r0_sqnorm)
-    {
-      // The new residual vector became too orthogonal to the arbitrarily chosen direction r0
-      // Let's restart with a new r0:
-      r  = rhs - mat * x;
-      r0 = r;
-      rho = r0_sqnorm = r.squaredNorm();
-      if(restarts++ == 0)
-        i = 0;
-    }
-    Scalar beta = (rho/rho_old) * (alpha / w);
-    p = r + beta * (p - w * v);
-    
-    y = precond.solve(p);
-    
-    v.noalias() = mat * y;
-
-    alpha = rho / r0.dot(v);
-    s = r - alpha * v;
-
-    z = precond.solve(s);
-    t.noalias() = mat * z;
-
-    RealScalar tmp = t.squaredNorm();
-    if(tmp>RealScalar(0))
-      w = t.dot(s) / tmp;
-    else
-      w = Scalar(0);
-    x += alpha * y + w * z;
-    r = s - w * t;
-    ++i;
-  }
-  tol_error = sqrt(r.squaredNorm()/rhs_sqnorm);
-  iters = i;
-  return true; 
-}
-
-}
-
-template< typename _MatrixType,
-          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-class BiCGSTAB;
-
-namespace internal {
-
-template< typename _MatrixType, typename _Preconditioner>
-struct traits<BiCGSTAB<_MatrixType,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A bi conjugate gradient stabilized solver for sparse square problems
-  *
-  * This class allows to solve for A.x = b sparse linear problems using a bi conjugate gradient
-  * stabilized algorithm. The vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
-  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  *
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  * 
-  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
-  * 
-  * \b Performance: when using sparse matrices, best performance is achied for a row-major sparse matrix format.
-  * Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
-  * See \ref TopicMultiThreading for details.
-  * 
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-  * \include BiCGSTAB_simple.cpp
-  * 
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * BiCGSTAB can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-  *
-  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename _MatrixType, typename _Preconditioner>
-class BiCGSTAB : public IterativeSolverBase<BiCGSTAB<_MatrixType,_Preconditioner> >
-{
-  typedef IterativeSolverBase<BiCGSTAB> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-public:
-
-  /** Default constructor. */
-  BiCGSTAB() : Base() {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit BiCGSTAB(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-
-  ~BiCGSTAB() {}
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {    
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-    
-    bool ret = internal::bicgstab(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
-
-    m_info = (!ret) ? NumericalIssue
-           : m_error <= Base::m_tolerance ? Success
-           : NoConvergence;
-  }
-
-protected:
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_BICGSTAB_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
deleted file mode 100644
index 5d8c6b4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
+++ /dev/null
@@ -1,229 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONJUGATE_GRADIENT_H
-#define EIGEN_CONJUGATE_GRADIENT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Low-level conjugate gradient algorithm
-  * \param mat The matrix A
-  * \param rhs The right hand side vector b
-  * \param x On input and initial solution, on output the computed solution.
-  * \param precond A preconditioner being able to efficiently solve for an
-  *                approximation of Ax=b (regardless of b)
-  * \param iters On input the max number of iteration, on output the number of performed iterations.
-  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-  */
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-EIGEN_DONT_INLINE
-void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
-                        const Preconditioner& precond, Index& iters,
-                        typename Dest::RealScalar& tol_error)
-{
-  using std::sqrt;
-  using std::abs;
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,1> VectorType;
-  
-  RealScalar tol = tol_error;
-  Index maxIters = iters;
-  
-  Index n = mat.cols();
-
-  VectorType residual = rhs - mat * x; //initial residual
-
-  RealScalar rhsNorm2 = rhs.squaredNorm();
-  if(rhsNorm2 == 0) 
-  {
-    x.setZero();
-    iters = 0;
-    tol_error = 0;
-    return;
-  }
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-  RealScalar threshold = numext::maxi(RealScalar(tol*tol*rhsNorm2),considerAsZero);
-  RealScalar residualNorm2 = residual.squaredNorm();
-  if (residualNorm2 < threshold)
-  {
-    iters = 0;
-    tol_error = sqrt(residualNorm2 / rhsNorm2);
-    return;
-  }
-
-  VectorType p(n);
-  p = precond.solve(residual);      // initial search direction
-
-  VectorType z(n), tmp(n);
-  RealScalar absNew = numext::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
-  Index i = 0;
-  while(i < maxIters)
-  {
-    tmp.noalias() = mat * p;                    // the bottleneck of the algorithm
-
-    Scalar alpha = absNew / p.dot(tmp);         // the amount we travel on dir
-    x += alpha * p;                             // update solution
-    residual -= alpha * tmp;                    // update residual
-    
-    residualNorm2 = residual.squaredNorm();
-    if(residualNorm2 < threshold)
-      break;
-    
-    z = precond.solve(residual);                // approximately solve for "A z = residual"
-
-    RealScalar absOld = absNew;
-    absNew = numext::real(residual.dot(z));     // update the absolute value of r
-    RealScalar beta = absNew / absOld;          // calculate the Gram-Schmidt value used to create the new search direction
-    p = z + beta * p;                           // update search direction
-    i++;
-  }
-  tol_error = sqrt(residualNorm2 / rhsNorm2);
-  iters = i;
-}
-
-}
-
-template< typename _MatrixType, int _UpLo=Lower,
-          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-class ConjugateGradient;
-
-namespace internal {
-
-template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A conjugate gradient solver for sparse (or dense) self-adjoint problems
-  *
-  * This class allows to solve for A.x = b linear problems using an iterative conjugate gradient algorithm.
-  * The matrix A must be selfadjoint. The matrix A and the vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix.
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower,
-  *               \c Upper, or \c Lower|Upper in which the full matrix entries will be considered.
-  *               Default is \c Lower, best performance is \c Lower|Upper.
-  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  *
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  * 
-  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
-  * 
-  * \b Performance: Even though the default value of \c _UpLo is \c Lower, significantly higher performance is
-  * achieved when using a complete matrix and \b Lower|Upper as the \a _UpLo template parameter. Moreover, in this
-  * case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
-  * See \ref TopicMultiThreading for details.
-  * 
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-    \code
-    int n = 10000;
-    VectorXd x(n), b(n);
-    SparseMatrix<double> A(n,n);
-    // fill A and b
-    ConjugateGradient<SparseMatrix<double>, Lower|Upper> cg;
-    cg.compute(A);
-    x = cg.solve(b);
-    std::cout << "#iterations:     " << cg.iterations() << std::endl;
-    std::cout << "estimated error: " << cg.error()      << std::endl;
-    // update b, and solve again
-    x = cg.solve(b);
-    \endcode
-  * 
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * ConjugateGradient can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-  *
-  * \sa class LeastSquaresConjugateGradient, class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
-{
-  typedef IterativeSolverBase<ConjugateGradient> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-  enum {
-    UpLo = _UpLo
-  };
-
-public:
-
-  /** Default constructor. */
-  ConjugateGradient() : Base() {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit ConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-
-  ~ConjugateGradient() {}
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {
-    typedef typename Base::MatrixWrapper MatrixWrapper;
-    typedef typename Base::ActualMatrixType ActualMatrixType;
-    enum {
-      TransposeInput  =   (!MatrixWrapper::MatrixFree)
-                      &&  (UpLo==(Lower|Upper))
-                      &&  (!MatrixType::IsRowMajor)
-                      &&  (!NumTraits<Scalar>::IsComplex)
-    };
-    typedef typename internal::conditional<TransposeInput,Transpose<const ActualMatrixType>, ActualMatrixType const&>::type RowMajorWrapper;
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(MatrixWrapper::MatrixFree,UpLo==(Lower|Upper)),MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
-    typedef typename internal::conditional<UpLo==(Lower|Upper),
-                                           RowMajorWrapper,
-                                           typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type
-                                          >::type SelfAdjointWrapper;
-
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-
-    RowMajorWrapper row_mat(matrix());
-    internal::conjugate_gradient(SelfAdjointWrapper(row_mat), b, x, Base::m_preconditioner, m_iterations, m_error);
-    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
-  }
-
-protected:
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONJUGATE_GRADIENT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
deleted file mode 100644
index 7803fd8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
+++ /dev/null
@@ -1,394 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INCOMPLETE_CHOlESKY_H
-#define EIGEN_INCOMPLETE_CHOlESKY_H
-
-#include <vector>
-#include <list>
-
-namespace Eigen {
-/**
-  * \brief Modified Incomplete Cholesky with dual threshold
-  *
-  * References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
-  *              Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
-  *
-  * \tparam Scalar the scalar type of the input matrices
-  * \tparam _UpLo The triangular part that will be used for the computations. It can be Lower
-    *               or Upper. Default is Lower.
-  * \tparam _OrderingType The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<int>,
-  *                       unless EIGEN_MPL2_ONLY is defined, in which case the default is NaturalOrdering<int>.
-  *
-  * \implsparsesolverconcept
-  *
-  * It performs the following incomplete factorization: \f$ S P A P' S \approx L L' \f$
-  * where L is a lower triangular factor, S is a diagonal scaling matrix, and P is a
-  * fill-in reducing permutation as computed by the ordering method.
-  *
-  * \b Shifting \b strategy: Let \f$ B = S P A P' S \f$  be the scaled matrix on which the factorization is carried out,
-  * and \f$ \beta \f$ be the minimum value of the diagonal. If \f$ \beta > 0 \f$ then, the factorization is directly performed
-  * on the matrix B. Otherwise, the factorization is performed on the shifted matrix \f$ B + (\sigma+|\beta| I \f$ where
-  * \f$ \sigma \f$ is the initial shift value as returned and set by setInitialShift() method. The default value is \f$ \sigma = 10^{-3} \f$.
-  * If the factorization fails, then the shift in doubled until it succeed or a maximum of ten attempts. If it still fails, as returned by
-  * the info() method, then you can either increase the initial shift, or better use another preconditioning technique.
-  *
-  */
-template <typename Scalar, int _UpLo = Lower, typename _OrderingType = AMDOrdering<int> >
-class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_UpLo,_OrderingType> >
-{
-  protected:
-    typedef SparseSolverBase<IncompleteCholesky<Scalar,_UpLo,_OrderingType> > Base;
-    using Base::m_isInitialized;
-  public:
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef _OrderingType OrderingType;
-    typedef typename OrderingType::PermutationType PermutationType;
-    typedef typename PermutationType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> FactorType;
-    typedef Matrix<Scalar,Dynamic,1> VectorSx;
-    typedef Matrix<RealScalar,Dynamic,1> VectorRx;
-    typedef Matrix<StorageIndex,Dynamic, 1> VectorIx;
-    typedef std::vector<std::list<StorageIndex> > VectorList;
-    enum { UpLo = _UpLo };
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-  public:
-
-    /** Default constructor leaving the object in a partly non-initialized stage.
-      *
-      * You must call compute() or the pair analyzePattern()/factorize() to make it valid.
-      *
-      * \sa IncompleteCholesky(const MatrixType&)
-      */
-    IncompleteCholesky() : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false) {}
-
-    /** Constructor computing the incomplete factorization for the given matrix \a matrix.
-      */
-    template<typename MatrixType>
-    IncompleteCholesky(const MatrixType& matrix) : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false)
-    {
-      compute(matrix);
-    }
-
-    /** \returns number of rows of the factored matrix */
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_L.rows(); }
-
-    /** \returns number of columns of the factored matrix */
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_L.cols(); }
-
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * It triggers an assertion if \c *this has not been initialized through the respective constructor,
-      * or a call to compute() or analyzePattern().
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Set the initial shift parameter \f$ \sigma \f$.
-      */
-    void setInitialShift(RealScalar shift) { m_initialShift = shift; }
-
-    /** \brief Computes the fill reducing permutation vector using the sparsity pattern of \a mat
-      */
-    template<typename MatrixType>
-    void analyzePattern(const MatrixType& mat)
-    {
-      OrderingType ord;
-      PermutationType pinv;
-      ord(mat.template selfadjointView<UpLo>(), pinv);
-      if(pinv.size()>0) m_perm = pinv.inverse();
-      else              m_perm.resize(0);
-      m_L.resize(mat.rows(), mat.cols());
-      m_analysisIsOk = true;
-      m_isInitialized = true;
-      m_info = Success;
-    }
-
-    /** \brief Performs the numerical factorization of the input matrix \a mat
-      *
-      * The method analyzePattern() or compute() must have been called beforehand
-      * with a matrix having the same pattern.
-      *
-      * \sa compute(), analyzePattern()
-      */
-    template<typename MatrixType>
-    void factorize(const MatrixType& mat);
-
-    /** Computes or re-computes the incomplete Cholesky factorization of the input matrix \a mat
-      *
-      * It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.
-      *
-      * \sa analyzePattern(), factorize()
-      */
-    template<typename MatrixType>
-    void compute(const MatrixType& mat)
-    {
-      analyzePattern(mat);
-      factorize(mat);
-    }
-
-    // internal
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const Rhs& b, Dest& x) const
-    {
-      eigen_assert(m_factorizationIsOk && "factorize() should be called first");
-      if (m_perm.rows() == b.rows())  x = m_perm * b;
-      else                            x = b;
-      x = m_scale.asDiagonal() * x;
-      x = m_L.template triangularView<Lower>().solve(x);
-      x = m_L.adjoint().template triangularView<Upper>().solve(x);
-      x = m_scale.asDiagonal() * x;
-      if (m_perm.rows() == b.rows())
-        x = m_perm.inverse() * x;
-    }
-
-    /** \returns the sparse lower triangular factor L */
-    const FactorType& matrixL() const { eigen_assert("m_factorizationIsOk"); return m_L; }
-
-    /** \returns a vector representing the scaling factor S */
-    const VectorRx& scalingS() const { eigen_assert("m_factorizationIsOk"); return m_scale; }
-
-    /** \returns the fill-in reducing permutation P (can be empty for a natural ordering) */
-    const PermutationType& permutationP() const { eigen_assert("m_analysisIsOk"); return m_perm; }
-
-  protected:
-    FactorType m_L;              // The lower part stored in CSC
-    VectorRx m_scale;            // The vector for scaling the matrix
-    RealScalar m_initialShift;   // The initial shift parameter
-    bool m_analysisIsOk;
-    bool m_factorizationIsOk;
-    ComputationInfo m_info;
-    PermutationType m_perm;
-
-  private:
-    inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol);
-};
-
-// Based on the following paper:
-//   C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
-//   Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
-//   http://ftp.mcs.anl.gov/pub/tech_reports/reports/P682.pdf
-template<typename Scalar, int _UpLo, typename OrderingType>
-template<typename _MatrixType>
-void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType& mat)
-{
-  using std::sqrt;
-  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
-
-  // Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of the original matrix. Other strategies will be added
-
-  // Apply the fill-reducing permutation computed in analyzePattern()
-  if (m_perm.rows() == mat.rows() ) // To detect the null permutation
-  {
-    // The temporary is needed to make sure that the diagonal entry is properly sorted
-    FactorType tmp(mat.rows(), mat.cols());
-    tmp = mat.template selfadjointView<_UpLo>().twistedBy(m_perm);
-    m_L.template selfadjointView<Lower>() = tmp.template selfadjointView<Lower>();
-  }
-  else
-  {
-    m_L.template selfadjointView<Lower>() = mat.template selfadjointView<_UpLo>();
-  }
-
-  Index n = m_L.cols();
-  Index nnz = m_L.nonZeros();
-  Map<VectorSx> vals(m_L.valuePtr(), nnz);         //values
-  Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz);  //Row indices
-  Map<VectorIx> colPtr( m_L.outerIndexPtr(), n+1); // Pointer to the beginning of each row
-  VectorIx firstElt(n-1); // for each j, points to the next entry in vals that will be used in the factorization
-  VectorList listCol(n);  // listCol(j) is a linked list of columns to update column j
-  VectorSx col_vals(n);   // Store a  nonzero values in each column
-  VectorIx col_irow(n);   // Row indices of nonzero elements in each column
-  VectorIx col_pattern(n);
-  col_pattern.fill(-1);
-  StorageIndex col_nnz;
-
-
-  // Computes the scaling factors
-  m_scale.resize(n);
-  m_scale.setZero();
-  for (Index j = 0; j < n; j++)
-    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
-    {
-      m_scale(j) += numext::abs2(vals(k));
-      if(rowIdx[k]!=j)
-        m_scale(rowIdx[k]) += numext::abs2(vals(k));
-    }
-
-  m_scale = m_scale.cwiseSqrt().cwiseSqrt();
-
-  for (Index j = 0; j < n; ++j)
-    if(m_scale(j)>(std::numeric_limits<RealScalar>::min)())
-      m_scale(j) = RealScalar(1)/m_scale(j);
-    else
-      m_scale(j) = 1;
-
-  // TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
-
-  // Scale and compute the shift for the matrix
-  RealScalar mindiag = NumTraits<RealScalar>::highest();
-  for (Index j = 0; j < n; j++)
-  {
-    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
-      vals[k] *= (m_scale(j)*m_scale(rowIdx[k]));
-    eigen_internal_assert(rowIdx[colPtr[j]]==j && "IncompleteCholesky: only the lower triangular part must be stored");
-    mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
-  }
-
-  FactorType L_save = m_L;
-
-  RealScalar shift = 0;
-  if(mindiag <= RealScalar(0.))
-    shift = m_initialShift - mindiag;
-
-  m_info = NumericalIssue;
-
-  // Try to perform the incomplete factorization using the current shift
-  int iter = 0;
-  do
-  {
-    // Apply the shift to the diagonal elements of the matrix
-    for (Index j = 0; j < n; j++)
-      vals[colPtr[j]] += shift;
-
-    // jki version of the Cholesky factorization
-    Index j=0;
-    for (; j < n; ++j)
-    {
-      // Left-looking factorization of the j-th column
-      // First, load the j-th column into col_vals
-      Scalar diag = vals[colPtr[j]];  // It is assumed that only the lower part is stored
-      col_nnz = 0;
-      for (Index i = colPtr[j] + 1; i < colPtr[j+1]; i++)
-      {
-        StorageIndex l = rowIdx[i];
-        col_vals(col_nnz) = vals[i];
-        col_irow(col_nnz) = l;
-        col_pattern(l) = col_nnz;
-        col_nnz++;
-      }
-      {
-        typename std::list<StorageIndex>::iterator k;
-        // Browse all previous columns that will update column j
-        for(k = listCol[j].begin(); k != listCol[j].end(); k++)
-        {
-          Index jk = firstElt(*k); // First element to use in the column
-          eigen_internal_assert(rowIdx[jk]==j);
-          Scalar v_j_jk = numext::conj(vals[jk]);
-
-          jk += 1;
-          for (Index i = jk; i < colPtr[*k+1]; i++)
-          {
-            StorageIndex l = rowIdx[i];
-            if(col_pattern[l]<0)
-            {
-              col_vals(col_nnz) = vals[i] * v_j_jk;
-              col_irow[col_nnz] = l;
-              col_pattern(l) = col_nnz;
-              col_nnz++;
-            }
-            else
-              col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
-          }
-          updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
-        }
-      }
-
-      // Scale the current column
-      if(numext::real(diag) <= 0)
-      {
-        if(++iter>=10)
-          return;
-
-        // increase shift
-        shift = numext::maxi(m_initialShift,RealScalar(2)*shift);
-        // restore m_L, col_pattern, and listCol
-        vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
-        rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
-        colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n+1);
-        col_pattern.fill(-1);
-        for(Index i=0; i<n; ++i)
-          listCol[i].clear();
-
-        break;
-      }
-
-      RealScalar rdiag = sqrt(numext::real(diag));
-      vals[colPtr[j]] = rdiag;
-      for (Index k = 0; k<col_nnz; ++k)
-      {
-        Index i = col_irow[k];
-        //Scale
-        col_vals(k) /= rdiag;
-        //Update the remaining diagonals with col_vals
-        vals[colPtr[i]] -= numext::abs2(col_vals(k));
-      }
-      // Select the largest p elements
-      // p is the original number of elements in the column (without the diagonal)
-      Index p = colPtr[j+1] - colPtr[j] - 1 ;
-      Ref<VectorSx> cvals = col_vals.head(col_nnz);
-      Ref<VectorIx> cirow = col_irow.head(col_nnz);
-      internal::QuickSplit(cvals,cirow, p);
-      // Insert the largest p elements in the matrix
-      Index cpt = 0;
-      for (Index i = colPtr[j]+1; i < colPtr[j+1]; i++)
-      {
-        vals[i] = col_vals(cpt);
-        rowIdx[i] = col_irow(cpt);
-        // restore col_pattern:
-        col_pattern(col_irow(cpt)) = -1;
-        cpt++;
-      }
-      // Get the first smallest row index and put it after the diagonal element
-      Index jk = colPtr(j)+1;
-      updateList(colPtr,rowIdx,vals,j,jk,firstElt,listCol);
-    }
-
-    if(j==n)
-    {
-      m_factorizationIsOk = true;
-      m_info = Success;
-    }
-  } while(m_info!=Success);
-}
-
-template<typename Scalar, int _UpLo, typename OrderingType>
-inline void IncompleteCholesky<Scalar,_UpLo, OrderingType>::updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol)
-{
-  if (jk < colPtr(col+1) )
-  {
-    Index p = colPtr(col+1) - jk;
-    Index minpos;
-    rowIdx.segment(jk,p).minCoeff(&minpos);
-    minpos += jk;
-    if (rowIdx(minpos) != rowIdx(jk))
-    {
-      //Swap
-      std::swap(rowIdx(jk),rowIdx(minpos));
-      std::swap(vals(jk),vals(minpos));
-    }
-    firstElt(col) = internal::convert_index<StorageIndex,Index>(jk);
-    listCol[rowIdx(jk)].push_back(internal::convert_index<StorageIndex,Index>(col));
-  }
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
deleted file mode 100644
index cdcf709..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
+++ /dev/null
@@ -1,453 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INCOMPLETE_LUT_H
-#define EIGEN_INCOMPLETE_LUT_H
-
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * Compute a quick-sort split of a vector
-  * On output, the vector row is permuted such that its elements satisfy
-  * abs(row(i)) >= abs(row(ncut)) if i<ncut
-  * abs(row(i)) <= abs(row(ncut)) if i>ncut
-  * \param row The vector of values
-  * \param ind The array of index for the elements in @p row
-  * \param ncut  The number of largest elements to keep
-  **/
-template <typename VectorV, typename VectorI>
-Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
-{
-  typedef typename VectorV::RealScalar RealScalar;
-  using std::swap;
-  using std::abs;
-  Index mid;
-  Index n = row.size(); /* length of the vector */
-  Index first, last ;
-
-  ncut--; /* to fit the zero-based indices */
-  first = 0;
-  last = n-1;
-  if (ncut < first || ncut > last ) return 0;
-
-  do {
-    mid = first;
-    RealScalar abskey = abs(row(mid));
-    for (Index j = first + 1; j <= last; j++) {
-      if ( abs(row(j)) > abskey) {
-        ++mid;
-        swap(row(mid), row(j));
-        swap(ind(mid), ind(j));
-      }
-    }
-    /* Interchange for the pivot element */
-    swap(row(mid), row(first));
-    swap(ind(mid), ind(first));
-
-    if (mid > ncut) last = mid - 1;
-    else if (mid < ncut ) first = mid + 1;
-  } while (mid != ncut );
-
-  return 0; /* mid is equal to ncut */
-}
-
-}// end namespace internal
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \class IncompleteLUT
-  * \brief Incomplete LU factorization with dual-threshold strategy
-  *
-  * \implsparsesolverconcept
-  *
-  * During the numerical factorization, two dropping rules are used :
-  *  1) any element whose magnitude is less than some tolerance is dropped.
-  *    This tolerance is obtained by multiplying the input tolerance @p droptol
-  *    by the average magnitude of all the original elements in the current row.
-  *  2) After the elimination of the row, only the @p fill largest elements in
-  *    the L part and the @p fill largest elements in the U part are kept
-  *    (in addition to the diagonal element ). Note that @p fill is computed from
-  *    the input parameter @p fillfactor which is used the ratio to control the fill_in
-  *    relatively to the initial number of nonzero elements.
-  *
-  * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements)
-  * and when @p fill=n/2 with @p droptol being different to zero.
-  *
-  * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization,
-  *              Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
-  *
-  * NOTE : The following implementation is derived from the ILUT implementation
-  * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota
-  *  released under the terms of the GNU LGPL:
-  *    http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README
-  * However, Yousef Saad gave us permission to relicense his ILUT code to MPL2.
-  * See the Eigen mailing list archive, thread: ILUT, date: July 8, 2012:
-  *   http://listengine.tuxfamily.org/lists.tuxfamily.org/eigen/2012/07/msg00064.html
-  * alternatively, on GMANE:
-  *   http://comments.gmane.org/gmane.comp.lib.eigen/3302
-  */
-template <typename _Scalar, typename _StorageIndex = int>
-class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar, _StorageIndex> >
-{
-  protected:
-    typedef SparseSolverBase<IncompleteLUT> Base;
-    using Base::m_isInitialized;
-  public:
-    typedef _Scalar Scalar;
-    typedef _StorageIndex StorageIndex;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> FactorType;
-
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-
-  public:
-
-    IncompleteLUT()
-      : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10),
-        m_analysisIsOk(false), m_factorizationIsOk(false)
-    {}
-
-    template<typename MatrixType>
-    explicit IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
-      : m_droptol(droptol),m_fillfactor(fillfactor),
-        m_analysisIsOk(false),m_factorizationIsOk(false)
-    {
-      eigen_assert(fillfactor != 0);
-      compute(mat);
-    }
-
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
-
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
-      return m_info;
-    }
-
-    template<typename MatrixType>
-    void analyzePattern(const MatrixType& amat);
-
-    template<typename MatrixType>
-    void factorize(const MatrixType& amat);
-
-    /**
-      * Compute an incomplete LU factorization with dual threshold on the matrix mat
-      * No pivoting is done in this version
-      *
-      **/
-    template<typename MatrixType>
-    IncompleteLUT& compute(const MatrixType& amat)
-    {
-      analyzePattern(amat);
-      factorize(amat);
-      return *this;
-    }
-
-    void setDroptol(const RealScalar& droptol);
-    void setFillfactor(int fillfactor);
-
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const Rhs& b, Dest& x) const
-    {
-      x = m_Pinv * b;
-      x = m_lu.template triangularView<UnitLower>().solve(x);
-      x = m_lu.template triangularView<Upper>().solve(x);
-      x = m_P * x;
-    }
-
-protected:
-
-    /** keeps off-diagonal entries; drops diagonal entries */
-    struct keep_diag {
-      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
-      {
-        return row!=col;
-      }
-    };
-
-protected:
-
-    FactorType m_lu;
-    RealScalar m_droptol;
-    int m_fillfactor;
-    bool m_analysisIsOk;
-    bool m_factorizationIsOk;
-    ComputationInfo m_info;
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_P;     // Fill-reducing permutation
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_Pinv;  // Inverse permutation
-};
-
-/**
- * Set control parameter droptol
- *  \param droptol   Drop any element whose magnitude is less than this tolerance
- **/
-template<typename Scalar, typename StorageIndex>
-void IncompleteLUT<Scalar,StorageIndex>::setDroptol(const RealScalar& droptol)
-{
-  this->m_droptol = droptol;
-}
-
-/**
- * Set control parameter fillfactor
- * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row.
- **/
-template<typename Scalar, typename StorageIndex>
-void IncompleteLUT<Scalar,StorageIndex>::setFillfactor(int fillfactor)
-{
-  this->m_fillfactor = fillfactor;
-}
-
-template <typename Scalar, typename StorageIndex>
-template<typename _MatrixType>
-void IncompleteLUT<Scalar,StorageIndex>::analyzePattern(const _MatrixType& amat)
-{
-  // Compute the Fill-reducing permutation
-  // Since ILUT does not perform any numerical pivoting,
-  // it is highly preferable to keep the diagonal through symmetric permutations.
-  // To this end, let's symmetrize the pattern and perform AMD on it.
-  SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat;
-  SparseMatrix<Scalar,ColMajor, StorageIndex> mat2 = amat.transpose();
-  // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice.
-  //       on the other hand for a really non-symmetric pattern, mat2*mat1 should be preferred...
-  SparseMatrix<Scalar,ColMajor, StorageIndex> AtA = mat2 + mat1;
-  AMDOrdering<StorageIndex> ordering;
-  ordering(AtA,m_P);
-  m_Pinv  = m_P.inverse(); // cache the inverse permutation
-  m_analysisIsOk = true;
-  m_factorizationIsOk = false;
-  m_isInitialized = true;
-}
-
-template <typename Scalar, typename StorageIndex>
-template<typename _MatrixType>
-void IncompleteLUT<Scalar,StorageIndex>::factorize(const _MatrixType& amat)
-{
-  using std::sqrt;
-  using std::swap;
-  using std::abs;
-  using internal::convert_index;
-
-  eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
-  Index n = amat.cols();  // Size of the matrix
-  m_lu.resize(n,n);
-  // Declare Working vectors and variables
-  Vector u(n) ;     // real values of the row -- maximum size is n --
-  VectorI ju(n);   // column position of the values in u -- maximum size  is n
-  VectorI jr(n);   // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
-
-  // Apply the fill-reducing permutation
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  SparseMatrix<Scalar,RowMajor, StorageIndex> mat;
-  mat = amat.twistedBy(m_Pinv);
-
-  // Initialization
-  jr.fill(-1);
-  ju.fill(0);
-  u.fill(0);
-
-  // number of largest elements to keep in each row:
-  Index fill_in = (amat.nonZeros()*m_fillfactor)/n + 1;
-  if (fill_in > n) fill_in = n;
-
-  // number of largest nonzero elements to keep in the L and the U part of the current row:
-  Index nnzL = fill_in/2;
-  Index nnzU = nnzL;
-  m_lu.reserve(n * (nnzL + nnzU + 1));
-
-  // global loop over the rows of the sparse matrix
-  for (Index ii = 0; ii < n; ii++)
-  {
-    // 1 - copy the lower and the upper part of the row i of mat in the working vector u
-
-    Index sizeu = 1; // number of nonzero elements in the upper part of the current row
-    Index sizel = 0; // number of nonzero elements in the lower part of the current row
-    ju(ii)    = convert_index<StorageIndex>(ii);
-    u(ii)     = 0;
-    jr(ii)    = convert_index<StorageIndex>(ii);
-    RealScalar rownorm = 0;
-
-    typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii
-    for (; j_it; ++j_it)
-    {
-      Index k = j_it.index();
-      if (k < ii)
-      {
-        // copy the lower part
-        ju(sizel) = convert_index<StorageIndex>(k);
-        u(sizel) = j_it.value();
-        jr(k) = convert_index<StorageIndex>(sizel);
-        ++sizel;
-      }
-      else if (k == ii)
-      {
-        u(ii) = j_it.value();
-      }
-      else
-      {
-        // copy the upper part
-        Index jpos = ii + sizeu;
-        ju(jpos) = convert_index<StorageIndex>(k);
-        u(jpos) = j_it.value();
-        jr(k) = convert_index<StorageIndex>(jpos);
-        ++sizeu;
-      }
-      rownorm += numext::abs2(j_it.value());
-    }
-
-    // 2 - detect possible zero row
-    if(rownorm==0)
-    {
-      m_info = NumericalIssue;
-      return;
-    }
-    // Take the 2-norm of the current row as a relative tolerance
-    rownorm = sqrt(rownorm);
-
-    // 3 - eliminate the previous nonzero rows
-    Index jj = 0;
-    Index len = 0;
-    while (jj < sizel)
-    {
-      // In order to eliminate in the correct order,
-      // we must select first the smallest column index among  ju(jj:sizel)
-      Index k;
-      Index minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment
-      k += jj;
-      if (minrow != ju(jj))
-      {
-        // swap the two locations
-        Index j = ju(jj);
-        swap(ju(jj), ju(k));
-        jr(minrow) = convert_index<StorageIndex>(jj);
-        jr(j) = convert_index<StorageIndex>(k);
-        swap(u(jj), u(k));
-      }
-      // Reset this location
-      jr(minrow) = -1;
-
-      // Start elimination
-      typename FactorType::InnerIterator ki_it(m_lu, minrow);
-      while (ki_it && ki_it.index() < minrow) ++ki_it;
-      eigen_internal_assert(ki_it && ki_it.col()==minrow);
-      Scalar fact = u(jj) / ki_it.value();
-
-      // drop too small elements
-      if(abs(fact) <= m_droptol)
-      {
-        jj++;
-        continue;
-      }
-
-      // linear combination of the current row ii and the row minrow
-      ++ki_it;
-      for (; ki_it; ++ki_it)
-      {
-        Scalar prod = fact * ki_it.value();
-        Index j     = ki_it.index();
-        Index jpos  = jr(j);
-        if (jpos == -1) // fill-in element
-        {
-          Index newpos;
-          if (j >= ii) // dealing with the upper part
-          {
-            newpos = ii + sizeu;
-            sizeu++;
-            eigen_internal_assert(sizeu<=n);
-          }
-          else // dealing with the lower part
-          {
-            newpos = sizel;
-            sizel++;
-            eigen_internal_assert(sizel<=ii);
-          }
-          ju(newpos) = convert_index<StorageIndex>(j);
-          u(newpos) = -prod;
-          jr(j) = convert_index<StorageIndex>(newpos);
-        }
-        else
-          u(jpos) -= prod;
-      }
-      // store the pivot element
-      u(len)  = fact;
-      ju(len) = convert_index<StorageIndex>(minrow);
-      ++len;
-
-      jj++;
-    } // end of the elimination on the row ii
-
-    // reset the upper part of the pointer jr to zero
-    for(Index k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1;
-
-    // 4 - partially sort and insert the elements in the m_lu matrix
-
-    // sort the L-part of the row
-    sizel = len;
-    len = (std::min)(sizel, nnzL);
-    typename Vector::SegmentReturnType ul(u.segment(0, sizel));
-    typename VectorI::SegmentReturnType jul(ju.segment(0, sizel));
-    internal::QuickSplit(ul, jul, len);
-
-    // store the largest m_fill elements of the L part
-    m_lu.startVec(ii);
-    for(Index k = 0; k < len; k++)
-      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
-
-    // store the diagonal element
-    // apply a shifting rule to avoid zero pivots (we are doing an incomplete factorization)
-    if (u(ii) == Scalar(0))
-      u(ii) = sqrt(m_droptol) * rownorm;
-    m_lu.insertBackByOuterInnerUnordered(ii, ii) = u(ii);
-
-    // sort the U-part of the row
-    // apply the dropping rule first
-    len = 0;
-    for(Index k = 1; k < sizeu; k++)
-    {
-      if(abs(u(ii+k)) > m_droptol * rownorm )
-      {
-        ++len;
-        u(ii + len)  = u(ii + k);
-        ju(ii + len) = ju(ii + k);
-      }
-    }
-    sizeu = len + 1; // +1 to take into account the diagonal element
-    len = (std::min)(sizeu, nnzU);
-    typename Vector::SegmentReturnType uu(u.segment(ii+1, sizeu-1));
-    typename VectorI::SegmentReturnType juu(ju.segment(ii+1, sizeu-1));
-    internal::QuickSplit(uu, juu, len);
-
-    // store the largest elements of the U part
-    for(Index k = ii + 1; k < ii + len; k++)
-      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
-  }
-  m_lu.finalize();
-  m_lu.makeCompressed();
-
-  m_factorizationIsOk = true;
-  m_info = Success;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_INCOMPLETE_LUT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
deleted file mode 100644
index 28a0c51..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
+++ /dev/null
@@ -1,444 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ITERATIVE_SOLVER_BASE_H
-#define EIGEN_ITERATIVE_SOLVER_BASE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType>
-struct is_ref_compatible_impl
-{
-private:
-  template <typename T0>
-  struct any_conversion
-  {
-    template <typename T> any_conversion(const volatile T&);
-    template <typename T> any_conversion(T&);
-  };
-  struct yes {int a[1];};
-  struct no  {int a[2];};
-
-  template<typename T>
-  static yes test(const Ref<const T>&, int);
-  template<typename T>
-  static no  test(any_conversion<T>, ...);
-
-public:
-  static MatrixType ms_from;
-  enum { value = sizeof(test<MatrixType>(ms_from, 0))==sizeof(yes) };
-};
-
-template<typename MatrixType>
-struct is_ref_compatible
-{
-  enum { value = is_ref_compatible_impl<typename remove_all<MatrixType>::type>::value };
-};
-
-template<typename MatrixType, bool MatrixFree = !internal::is_ref_compatible<MatrixType>::value>
-class generic_matrix_wrapper;
-
-// We have an explicit matrix at hand, compatible with Ref<>
-template<typename MatrixType>
-class generic_matrix_wrapper<MatrixType,false>
-{
-public:
-  typedef Ref<const MatrixType> ActualMatrixType;
-  template<int UpLo> struct ConstSelfAdjointViewReturnType {
-    typedef typename ActualMatrixType::template ConstSelfAdjointViewReturnType<UpLo>::Type Type;
-  };
-
-  enum {
-    MatrixFree = false
-  };
-
-  generic_matrix_wrapper()
-    : m_dummy(0,0), m_matrix(m_dummy)
-  {}
-
-  template<typename InputType>
-  generic_matrix_wrapper(const InputType &mat)
-    : m_matrix(mat)
-  {}
-
-  const ActualMatrixType& matrix() const
-  {
-    return m_matrix;
-  }
-
-  template<typename MatrixDerived>
-  void grab(const EigenBase<MatrixDerived> &mat)
-  {
-    m_matrix.~Ref<const MatrixType>();
-    ::new (&m_matrix) Ref<const MatrixType>(mat.derived());
-  }
-
-  void grab(const Ref<const MatrixType> &mat)
-  {
-    if(&(mat.derived()) != &m_matrix)
-    {
-      m_matrix.~Ref<const MatrixType>();
-      ::new (&m_matrix) Ref<const MatrixType>(mat);
-    }
-  }
-
-protected:
-  MatrixType m_dummy; // used to default initialize the Ref<> object
-  ActualMatrixType m_matrix;
-};
-
-// MatrixType is not compatible with Ref<> -> matrix-free wrapper
-template<typename MatrixType>
-class generic_matrix_wrapper<MatrixType,true>
-{
-public:
-  typedef MatrixType ActualMatrixType;
-  template<int UpLo> struct ConstSelfAdjointViewReturnType
-  {
-    typedef ActualMatrixType Type;
-  };
-
-  enum {
-    MatrixFree = true
-  };
-
-  generic_matrix_wrapper()
-    : mp_matrix(0)
-  {}
-
-  generic_matrix_wrapper(const MatrixType &mat)
-    : mp_matrix(&mat)
-  {}
-
-  const ActualMatrixType& matrix() const
-  {
-    return *mp_matrix;
-  }
-
-  void grab(const MatrixType &mat)
-  {
-    mp_matrix = &mat;
-  }
-
-protected:
-  const ActualMatrixType *mp_matrix;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief Base class for linear iterative solvers
-  *
-  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename Derived>
-class IterativeSolverBase : public SparseSolverBase<Derived>
-{
-protected:
-  typedef SparseSolverBase<Derived> Base;
-  using Base::m_isInitialized;
-
-public:
-  typedef typename internal::traits<Derived>::MatrixType MatrixType;
-  typedef typename internal::traits<Derived>::Preconditioner Preconditioner;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef typename MatrixType::RealScalar RealScalar;
-
-  enum {
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-  };
-
-public:
-
-  using Base::derived;
-
-  /** Default constructor. */
-  IterativeSolverBase()
-  {
-    init();
-  }
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    *
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    *
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit IterativeSolverBase(const EigenBase<MatrixDerived>& A)
-    : m_matrixWrapper(A.derived())
-  {
-    init();
-    compute(matrix());
-  }
-
-  ~IterativeSolverBase() {}
-
-  /** Initializes the iterative solver for the sparsity pattern of the matrix \a A for further solving \c Ax=b problems.
-    *
-    * Currently, this function mostly calls analyzePattern on the preconditioner. In the future
-    * we might, for instance, implement column reordering for faster matrix vector products.
-    */
-  template<typename MatrixDerived>
-  Derived& analyzePattern(const EigenBase<MatrixDerived>& A)
-  {
-    grab(A.derived());
-    m_preconditioner.analyzePattern(matrix());
-    m_isInitialized = true;
-    m_analysisIsOk = true;
-    m_info = m_preconditioner.info();
-    return derived();
-  }
-
-  /** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b problems.
-    *
-    * Currently, this function mostly calls factorize on the preconditioner.
-    *
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  Derived& factorize(const EigenBase<MatrixDerived>& A)
-  {
-    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-    grab(A.derived());
-    m_preconditioner.factorize(matrix());
-    m_factorizationIsOk = true;
-    m_info = m_preconditioner.info();
-    return derived();
-  }
-
-  /** Initializes the iterative solver with the matrix \a A for further solving \c Ax=b problems.
-    *
-    * Currently, this function mostly initializes/computes the preconditioner. In the future
-    * we might, for instance, implement column reordering for faster matrix vector products.
-    *
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  Derived& compute(const EigenBase<MatrixDerived>& A)
-  {
-    grab(A.derived());
-    m_preconditioner.compute(matrix());
-    m_isInitialized = true;
-    m_analysisIsOk = true;
-    m_factorizationIsOk = true;
-    m_info = m_preconditioner.info();
-    return derived();
-  }
-
-  /** \internal */
-  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return matrix().rows(); }
-
-  /** \internal */
-  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return matrix().cols(); }
-
-  /** \returns the tolerance threshold used by the stopping criteria.
-    * \sa setTolerance()
-    */
-  RealScalar tolerance() const { return m_tolerance; }
-
-  /** Sets the tolerance threshold used by the stopping criteria.
-    *
-    * This value is used as an upper bound to the relative residual error: |Ax-b|/|b|.
-    * The default value is the machine precision given by NumTraits<Scalar>::epsilon()
-    */
-  Derived& setTolerance(const RealScalar& tolerance)
-  {
-    m_tolerance = tolerance;
-    return derived();
-  }
-
-  /** \returns a read-write reference to the preconditioner for custom configuration. */
-  Preconditioner& preconditioner() { return m_preconditioner; }
-
-  /** \returns a read-only reference to the preconditioner. */
-  const Preconditioner& preconditioner() const { return m_preconditioner; }
-
-  /** \returns the max number of iterations.
-    * It is either the value set by setMaxIterations or, by default,
-    * twice the number of columns of the matrix.
-    */
-  Index maxIterations() const
-  {
-    return (m_maxIterations<0) ? 2*matrix().cols() : m_maxIterations;
-  }
-
-  /** Sets the max number of iterations.
-    * Default is twice the number of columns of the matrix.
-    */
-  Derived& setMaxIterations(Index maxIters)
-  {
-    m_maxIterations = maxIters;
-    return derived();
-  }
-
-  /** \returns the number of iterations performed during the last solve */
-  Index iterations() const
-  {
-    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
-    return m_iterations;
-  }
-
-  /** \returns the tolerance error reached during the last solve.
-    * It is a close approximation of the true relative residual error |Ax-b|/|b|.
-    */
-  RealScalar error() const
-  {
-    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
-    return m_error;
-  }
-
-  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
-    * and \a x0 as an initial solution.
-    *
-    * \sa solve(), compute()
-    */
-  template<typename Rhs,typename Guess>
-  inline const SolveWithGuess<Derived, Rhs, Guess>
-  solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const
-  {
-    eigen_assert(m_isInitialized && "Solver is not initialized.");
-    eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
-    return SolveWithGuess<Derived, Rhs, Guess>(derived(), b.derived(), x0);
-  }
-
-  /** \returns Success if the iterations converged, and NoConvergence otherwise. */
-  ComputationInfo info() const
-  {
-    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
-    return m_info;
-  }
-
-  /** \internal */
-  template<typename Rhs, typename DestDerived>
-  void _solve_with_guess_impl(const Rhs& b, SparseMatrixBase<DestDerived> &aDest) const
-  {
-    eigen_assert(rows()==b.rows());
-
-    Index rhsCols = b.cols();
-    Index size = b.rows();
-    DestDerived& dest(aDest.derived());
-    typedef typename DestDerived::Scalar DestScalar;
-    Eigen::Matrix<DestScalar,Dynamic,1> tb(size);
-    Eigen::Matrix<DestScalar,Dynamic,1> tx(cols());
-    // We do not directly fill dest because sparse expressions have to be free of aliasing issue.
-    // For non square least-square problems, b and dest might not have the same size whereas they might alias each-other.
-    typename DestDerived::PlainObject tmp(cols(),rhsCols);
-    ComputationInfo global_info = Success;
-    for(Index k=0; k<rhsCols; ++k)
-    {
-      tb = b.col(k);
-      tx = dest.col(k);
-      derived()._solve_vector_with_guess_impl(tb,tx);
-      tmp.col(k) = tx.sparseView(0);
-
-      // The call to _solve_vector_with_guess_impl updates m_info, so if it failed for a previous column
-      // we need to restore it to the worst value.
-      if(m_info==NumericalIssue)
-        global_info = NumericalIssue;
-      else if(m_info==NoConvergence)
-        global_info = NoConvergence;
-    }
-    m_info = global_info;
-    dest.swap(tmp);
-  }
-
-  template<typename Rhs, typename DestDerived>
-  typename internal::enable_if<Rhs::ColsAtCompileTime!=1 && DestDerived::ColsAtCompileTime!=1>::type
-  _solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &aDest) const
-  {
-    eigen_assert(rows()==b.rows());
-
-    Index rhsCols = b.cols();
-    DestDerived& dest(aDest.derived());
-    ComputationInfo global_info = Success;
-    for(Index k=0; k<rhsCols; ++k)
-    {
-      typename DestDerived::ColXpr xk(dest,k);
-      typename Rhs::ConstColXpr bk(b,k);
-      derived()._solve_vector_with_guess_impl(bk,xk);
-
-      // The call to _solve_vector_with_guess updates m_info, so if it failed for a previous column
-      // we need to restore it to the worst value.
-      if(m_info==NumericalIssue)
-        global_info = NumericalIssue;
-      else if(m_info==NoConvergence)
-        global_info = NoConvergence;
-    }
-    m_info = global_info;
-  }
-
-  template<typename Rhs, typename DestDerived>
-  typename internal::enable_if<Rhs::ColsAtCompileTime==1 || DestDerived::ColsAtCompileTime==1>::type
-  _solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &dest) const
-  {
-    derived()._solve_vector_with_guess_impl(b,dest.derived());
-  }
-
-  /** \internal default initial guess = 0 */
-  template<typename Rhs,typename Dest>
-  void _solve_impl(const Rhs& b, Dest& x) const
-  {
-    x.setZero();
-    derived()._solve_with_guess_impl(b,x);
-  }
-
-protected:
-  void init()
-  {
-    m_isInitialized = false;
-    m_analysisIsOk = false;
-    m_factorizationIsOk = false;
-    m_maxIterations = -1;
-    m_tolerance = NumTraits<Scalar>::epsilon();
-  }
-
-  typedef internal::generic_matrix_wrapper<MatrixType> MatrixWrapper;
-  typedef typename MatrixWrapper::ActualMatrixType ActualMatrixType;
-
-  const ActualMatrixType& matrix() const
-  {
-    return m_matrixWrapper.matrix();
-  }
-
-  template<typename InputType>
-  void grab(const InputType &A)
-  {
-    m_matrixWrapper.grab(A);
-  }
-
-  MatrixWrapper m_matrixWrapper;
-  Preconditioner m_preconditioner;
-
-  Index m_maxIterations;
-  RealScalar m_tolerance;
-
-  mutable RealScalar m_error;
-  mutable Index m_iterations;
-  mutable ComputationInfo m_info;
-  mutable bool m_analysisIsOk, m_factorizationIsOk;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_ITERATIVE_SOLVER_BASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
deleted file mode 100644
index 203fd0e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
+++ /dev/null
@@ -1,198 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
-#define EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Low-level conjugate gradient algorithm for least-square problems
-  * \param mat The matrix A
-  * \param rhs The right hand side vector b
-  * \param x On input and initial solution, on output the computed solution.
-  * \param precond A preconditioner being able to efficiently solve for an
-  *                approximation of A'Ax=b (regardless of b)
-  * \param iters On input the max number of iteration, on output the number of performed iterations.
-  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-  */
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-EIGEN_DONT_INLINE
-void least_square_conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
-                                     const Preconditioner& precond, Index& iters,
-                                     typename Dest::RealScalar& tol_error)
-{
-  using std::sqrt;
-  using std::abs;
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,1> VectorType;
-  
-  RealScalar tol = tol_error;
-  Index maxIters = iters;
-  
-  Index m = mat.rows(), n = mat.cols();
-
-  VectorType residual        = rhs - mat * x;
-  VectorType normal_residual = mat.adjoint() * residual;
-
-  RealScalar rhsNorm2 = (mat.adjoint()*rhs).squaredNorm();
-  if(rhsNorm2 == 0) 
-  {
-    x.setZero();
-    iters = 0;
-    tol_error = 0;
-    return;
-  }
-  RealScalar threshold = tol*tol*rhsNorm2;
-  RealScalar residualNorm2 = normal_residual.squaredNorm();
-  if (residualNorm2 < threshold)
-  {
-    iters = 0;
-    tol_error = sqrt(residualNorm2 / rhsNorm2);
-    return;
-  }
-  
-  VectorType p(n);
-  p = precond.solve(normal_residual);                         // initial search direction
-
-  VectorType z(n), tmp(m);
-  RealScalar absNew = numext::real(normal_residual.dot(p));  // the square of the absolute value of r scaled by invM
-  Index i = 0;
-  while(i < maxIters)
-  {
-    tmp.noalias() = mat * p;
-
-    Scalar alpha = absNew / tmp.squaredNorm();      // the amount we travel on dir
-    x += alpha * p;                                 // update solution
-    residual -= alpha * tmp;                        // update residual
-    normal_residual = mat.adjoint() * residual;     // update residual of the normal equation
-    
-    residualNorm2 = normal_residual.squaredNorm();
-    if(residualNorm2 < threshold)
-      break;
-    
-    z = precond.solve(normal_residual);             // approximately solve for "A'A z = normal_residual"
-
-    RealScalar absOld = absNew;
-    absNew = numext::real(normal_residual.dot(z));  // update the absolute value of r
-    RealScalar beta = absNew / absOld;              // calculate the Gram-Schmidt value used to create the new search direction
-    p = z + beta * p;                               // update search direction
-    i++;
-  }
-  tol_error = sqrt(residualNorm2 / rhsNorm2);
-  iters = i;
-}
-
-}
-
-template< typename _MatrixType,
-          typename _Preconditioner = LeastSquareDiagonalPreconditioner<typename _MatrixType::Scalar> >
-class LeastSquaresConjugateGradient;
-
-namespace internal {
-
-template< typename _MatrixType, typename _Preconditioner>
-struct traits<LeastSquaresConjugateGradient<_MatrixType,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A conjugate gradient solver for sparse (or dense) least-square problems
-  *
-  * This class allows to solve for A x = b linear problems using an iterative conjugate gradient algorithm.
-  * The matrix A can be non symmetric and rectangular, but the matrix A' A should be positive-definite to guaranty stability.
-  * Otherwise, the SparseLU or SparseQR classes might be preferable.
-  * The matrix A and the vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix.
-  * \tparam _Preconditioner the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  * 
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  * 
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-    \code
-    int m=1000000, n = 10000;
-    VectorXd x(n), b(m);
-    SparseMatrix<double> A(m,n);
-    // fill A and b
-    LeastSquaresConjugateGradient<SparseMatrix<double> > lscg;
-    lscg.compute(A);
-    x = lscg.solve(b);
-    std::cout << "#iterations:     " << lscg.iterations() << std::endl;
-    std::cout << "estimated error: " << lscg.error()      << std::endl;
-    // update b, and solve again
-    x = lscg.solve(b);
-    \endcode
-  * 
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * \sa class ConjugateGradient, SparseLU, SparseQR
-  */
-template< typename _MatrixType, typename _Preconditioner>
-class LeastSquaresConjugateGradient : public IterativeSolverBase<LeastSquaresConjugateGradient<_MatrixType,_Preconditioner> >
-{
-  typedef IterativeSolverBase<LeastSquaresConjugateGradient> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-public:
-
-  /** Default constructor. */
-  LeastSquaresConjugateGradient() : Base() {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit LeastSquaresConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-
-  ~LeastSquaresConjugateGradient() {}
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-
-    internal::least_square_conjugate_gradient(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
-    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
-  }
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
deleted file mode 100644
index 7b89657..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
+++ /dev/null
@@ -1,117 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVEWITHGUESS_H
-#define EIGEN_SOLVEWITHGUESS_H
-
-namespace Eigen {
-
-template<typename Decomposition, typename RhsType, typename GuessType> class SolveWithGuess;
-
-/** \class SolveWithGuess
-  * \ingroup IterativeLinearSolvers_Module
-  *
-  * \brief Pseudo expression representing a solving operation
-  *
-  * \tparam Decomposition the type of the matrix or decomposion object
-  * \tparam Rhstype the type of the right-hand side
-  *
-  * This class represents an expression of A.solve(B)
-  * and most of the time this is the only way it is used.
-  *
-  */
-namespace internal {
-
-
-template<typename Decomposition, typename RhsType, typename GuessType>
-struct traits<SolveWithGuess<Decomposition, RhsType, GuessType> >
-  : traits<Solve<Decomposition,RhsType> >
-{};
-
-}
-
-
-template<typename Decomposition, typename RhsType, typename GuessType>
-class SolveWithGuess : public internal::generic_xpr_base<SolveWithGuess<Decomposition,RhsType,GuessType>, MatrixXpr, typename internal::traits<RhsType>::StorageKind>::type
-{
-public:
-  typedef typename internal::traits<SolveWithGuess>::Scalar Scalar;
-  typedef typename internal::traits<SolveWithGuess>::PlainObject PlainObject;
-  typedef typename internal::generic_xpr_base<SolveWithGuess<Decomposition,RhsType,GuessType>, MatrixXpr, typename internal::traits<RhsType>::StorageKind>::type Base;
-  typedef typename internal::ref_selector<SolveWithGuess>::type Nested;
-
-  SolveWithGuess(const Decomposition &dec, const RhsType &rhs, const GuessType &guess)
-    : m_dec(dec), m_rhs(rhs), m_guess(guess)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  Index rows() const EIGEN_NOEXCEPT { return m_dec.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  EIGEN_DEVICE_FUNC const Decomposition& dec()   const { return m_dec; }
-  EIGEN_DEVICE_FUNC const RhsType&       rhs()   const { return m_rhs; }
-  EIGEN_DEVICE_FUNC const GuessType&     guess() const { return m_guess; }
-
-protected:
-  const Decomposition &m_dec;
-  const RhsType       &m_rhs;
-  const GuessType     &m_guess;
-
-private:
-  Scalar coeff(Index row, Index col) const;
-  Scalar coeff(Index i) const;
-};
-
-namespace internal {
-
-// Evaluator of SolveWithGuess -> eval into a temporary
-template<typename Decomposition, typename RhsType, typename GuessType>
-struct evaluator<SolveWithGuess<Decomposition,RhsType, GuessType> >
-  : public evaluator<typename SolveWithGuess<Decomposition,RhsType,GuessType>::PlainObject>
-{
-  typedef SolveWithGuess<Decomposition,RhsType,GuessType> SolveType;
-  typedef typename SolveType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  evaluator(const SolveType& solve)
-    : m_result(solve.rows(), solve.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    m_result = solve.guess();
-    solve.dec()._solve_with_guess_impl(solve.rhs(), m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-// Specialization for "dst = dec.solveWithGuess(rhs)"
-// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
-template<typename DstXprType, typename DecType, typename RhsType, typename GuessType, typename Scalar>
-struct Assignment<DstXprType, SolveWithGuess<DecType,RhsType,GuessType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef SolveWithGuess<DecType,RhsType,GuessType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst = src.guess();
-    src.dec()._solve_with_guess_impl(src.rhs(), dst/*, src.guess()*/);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVEWITHGUESS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/Jacobi/Jacobi.h b/3rdParty/my_ceres_vc17/include/Eigen/src/Jacobi/Jacobi.h
deleted file mode 100644
index 76668a5..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/Jacobi/Jacobi.h
+++ /dev/null
@@ -1,483 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_JACOBI_H
-#define EIGEN_JACOBI_H
-
-namespace Eigen {
-
-/** \ingroup Jacobi_Module
-  * \jacobi_module
-  * \class JacobiRotation
-  * \brief Rotation given by a cosine-sine pair.
-  *
-  * This class represents a Jacobi or Givens rotation.
-  * This is a 2D rotation in the plane \c J of angle \f$ \theta \f$ defined by
-  * its cosine \c c and sine \c s as follow:
-  * \f$ J = \left ( \begin{array}{cc} c & \overline s \\ -s  & \overline c \end{array} \right ) \f$
-  *
-  * You can apply the respective counter-clockwise rotation to a column vector \c v by
-  * applying its adjoint on the left: \f$ v = J^* v \f$ that translates to the following Eigen code:
-  * \code
-  * v.applyOnTheLeft(J.adjoint());
-  * \endcode
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar> class JacobiRotation
-{
-  public:
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    /** Default constructor without any initialization. */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation() {}
-
-    /** Construct a planar rotation from a cosine-sine pair (\a c, \c s). */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation(const Scalar& c, const Scalar& s) : m_c(c), m_s(s) {}
-
-    EIGEN_DEVICE_FUNC Scalar& c() { return m_c; }
-    EIGEN_DEVICE_FUNC Scalar c() const { return m_c; }
-    EIGEN_DEVICE_FUNC Scalar& s() { return m_s; }
-    EIGEN_DEVICE_FUNC Scalar s() const { return m_s; }
-
-    /** Concatenates two planar rotation */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation operator*(const JacobiRotation& other)
-    {
-      using numext::conj;
-      return JacobiRotation(m_c * other.m_c - conj(m_s) * other.m_s,
-                            conj(m_c * conj(other.m_s) + conj(m_s) * conj(other.m_c)));
-    }
-
-    /** Returns the transposed transformation */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation transpose() const { using numext::conj; return JacobiRotation(m_c, -conj(m_s)); }
-
-    /** Returns the adjoint transformation */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation adjoint() const { using numext::conj; return JacobiRotation(conj(m_c), -m_s); }
-
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC
-    bool makeJacobi(const MatrixBase<Derived>&, Index p, Index q);
-    EIGEN_DEVICE_FUNC
-    bool makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z);
-
-    EIGEN_DEVICE_FUNC
-    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r=0);
-
-  protected:
-    EIGEN_DEVICE_FUNC
-    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type);
-    EIGEN_DEVICE_FUNC
-    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type);
-
-    Scalar m_c, m_s;
-};
-
-/** Makes \c *this as a Jacobi rotation \a J such that applying \a J on both the right and left sides of the selfadjoint 2x2 matrix
-  * \f$ B = \left ( \begin{array}{cc} x & y \\ \overline y & z \end{array} \right )\f$ yields a diagonal matrix \f$ A = J^* B J \f$
-  *
-  * \sa MatrixBase::makeJacobi(const MatrixBase<Derived>&, Index, Index), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z)
-{
-  using std::sqrt;
-  using std::abs;
-
-  RealScalar deno = RealScalar(2)*abs(y);
-  if(deno < (std::numeric_limits<RealScalar>::min)())
-  {
-    m_c = Scalar(1);
-    m_s = Scalar(0);
-    return false;
-  }
-  else
-  {
-    RealScalar tau = (x-z)/deno;
-    RealScalar w = sqrt(numext::abs2(tau) + RealScalar(1));
-    RealScalar t;
-    if(tau>RealScalar(0))
-    {
-      t = RealScalar(1) / (tau + w);
-    }
-    else
-    {
-      t = RealScalar(1) / (tau - w);
-    }
-    RealScalar sign_t = t > RealScalar(0) ? RealScalar(1) : RealScalar(-1);
-    RealScalar n = RealScalar(1) / sqrt(numext::abs2(t)+RealScalar(1));
-    m_s = - sign_t * (numext::conj(y) / abs(y)) * abs(t) * n;
-    m_c = n;
-    return true;
-  }
-}
-
-/** Makes \c *this as a Jacobi rotation \c J such that applying \a J on both the right and left sides of the 2x2 selfadjoint matrix
-  * \f$ B = \left ( \begin{array}{cc} \text{this}_{pp} & \text{this}_{pq} \\ (\text{this}_{pq})^* & \text{this}_{qq} \end{array} \right )\f$ yields
-  * a diagonal matrix \f$ A = J^* B J \f$
-  *
-  * Example: \include Jacobi_makeJacobi.cpp
-  * Output: \verbinclude Jacobi_makeJacobi.out
-  *
-  * \sa JacobiRotation::makeJacobi(RealScalar, Scalar, RealScalar), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, Index p, Index q)
-{
-  return makeJacobi(numext::real(m.coeff(p,p)), m.coeff(p,q), numext::real(m.coeff(q,q)));
-}
-
-/** Makes \c *this as a Givens rotation \c G such that applying \f$ G^* \f$ to the left of the vector
-  * \f$ V = \left ( \begin{array}{c} p \\ q \end{array} \right )\f$ yields:
-  * \f$ G^* V = \left ( \begin{array}{c} r \\ 0 \end{array} \right )\f$.
-  *
-  * The value of \a r is returned if \a r is not null (the default is null).
-  * Also note that G is built such that the cosine is always real.
-  *
-  * Example: \include Jacobi_makeGivens.cpp
-  * Output: \verbinclude Jacobi_makeGivens.out
-  *
-  * This function implements the continuous Givens rotation generation algorithm
-  * found in Anderson (2000), Discontinuous Plane Rotations and the Symmetric Eigenvalue Problem.
-  * LAPACK Working Note 150, University of Tennessee, UT-CS-00-454, December 4, 2000.
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r)
-{
-  makeGivens(p, q, r, typename internal::conditional<NumTraits<Scalar>::IsComplex, internal::true_type, internal::false_type>::type());
-}
-
-
-// specialization for complexes
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type)
-{
-  using std::sqrt;
-  using std::abs;
-  using numext::conj;
-
-  if(q==Scalar(0))
-  {
-    m_c = numext::real(p)<0 ? Scalar(-1) : Scalar(1);
-    m_s = 0;
-    if(r) *r = m_c * p;
-  }
-  else if(p==Scalar(0))
-  {
-    m_c = 0;
-    m_s = -q/abs(q);
-    if(r) *r = abs(q);
-  }
-  else
-  {
-    RealScalar p1 = numext::norm1(p);
-    RealScalar q1 = numext::norm1(q);
-    if(p1>=q1)
-    {
-      Scalar ps = p / p1;
-      RealScalar p2 = numext::abs2(ps);
-      Scalar qs = q / p1;
-      RealScalar q2 = numext::abs2(qs);
-
-      RealScalar u = sqrt(RealScalar(1) + q2/p2);
-      if(numext::real(p)<RealScalar(0))
-        u = -u;
-
-      m_c = Scalar(1)/u;
-      m_s = -qs*conj(ps)*(m_c/p2);
-      if(r) *r = p * u;
-    }
-    else
-    {
-      Scalar ps = p / q1;
-      RealScalar p2 = numext::abs2(ps);
-      Scalar qs = q / q1;
-      RealScalar q2 = numext::abs2(qs);
-
-      RealScalar u = q1 * sqrt(p2 + q2);
-      if(numext::real(p)<RealScalar(0))
-        u = -u;
-
-      p1 = abs(p);
-      ps = p/p1;
-      m_c = p1/u;
-      m_s = -conj(ps) * (q/u);
-      if(r) *r = ps * u;
-    }
-  }
-}
-
-// specialization for reals
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type)
-{
-  using std::sqrt;
-  using std::abs;
-  if(q==Scalar(0))
-  {
-    m_c = p<Scalar(0) ? Scalar(-1) : Scalar(1);
-    m_s = Scalar(0);
-    if(r) *r = abs(p);
-  }
-  else if(p==Scalar(0))
-  {
-    m_c = Scalar(0);
-    m_s = q<Scalar(0) ? Scalar(1) : Scalar(-1);
-    if(r) *r = abs(q);
-  }
-  else if(abs(p) > abs(q))
-  {
-    Scalar t = q/p;
-    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
-    if(p<Scalar(0))
-      u = -u;
-    m_c = Scalar(1)/u;
-    m_s = -t * m_c;
-    if(r) *r = p * u;
-  }
-  else
-  {
-    Scalar t = p/q;
-    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
-    if(q<Scalar(0))
-      u = -u;
-    m_s = -Scalar(1)/u;
-    m_c = -t * m_s;
-    if(r) *r = q * u;
-  }
-
-}
-
-/****************************************************************************************
-*   Implementation of MatrixBase methods
-****************************************************************************************/
-
-namespace internal {
-/** \jacobi_module
-  * Applies the clock wise 2D rotation \a j to the set of 2D vectors of coordinates \a x and \a y:
-  * \f$ \left ( \begin{array}{cc} x \\ y \end{array} \right )  =  J \left ( \begin{array}{cc} x \\ y \end{array} \right ) \f$
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename VectorX, typename VectorY, typename OtherScalar>
-EIGEN_DEVICE_FUNC
-void apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j);
-}
-
-/** \jacobi_module
-  * Applies the rotation in the plane \a j to the rows \a p and \a q of \c *this, i.e., it computes B = J * B,
-  * with \f$ B = \left ( \begin{array}{cc} \text{*this.row}(p) \\ \text{*this.row}(q) \end{array} \right ) \f$.
-  *
-  * \sa class JacobiRotation, MatrixBase::applyOnTheRight(), internal::apply_rotation_in_the_plane()
-  */
-template<typename Derived>
-template<typename OtherScalar>
-EIGEN_DEVICE_FUNC
-inline void MatrixBase<Derived>::applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j)
-{
-  RowXpr x(this->row(p));
-  RowXpr y(this->row(q));
-  internal::apply_rotation_in_the_plane(x, y, j);
-}
-
-/** \ingroup Jacobi_Module
-  * Applies the rotation in the plane \a j to the columns \a p and \a q of \c *this, i.e., it computes B = B * J
-  * with \f$ B = \left ( \begin{array}{cc} \text{*this.col}(p) & \text{*this.col}(q) \end{array} \right ) \f$.
-  *
-  * \sa class JacobiRotation, MatrixBase::applyOnTheLeft(), internal::apply_rotation_in_the_plane()
-  */
-template<typename Derived>
-template<typename OtherScalar>
-EIGEN_DEVICE_FUNC
-inline void MatrixBase<Derived>::applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j)
-{
-  ColXpr x(this->col(p));
-  ColXpr y(this->col(q));
-  internal::apply_rotation_in_the_plane(x, y, j.transpose());
-}
-
-namespace internal {
-
-template<typename Scalar, typename OtherScalar,
-         int SizeAtCompileTime, int MinAlignment, bool Vectorizable>
-struct apply_rotation_in_the_plane_selector
-{
-  static EIGEN_DEVICE_FUNC
-  inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
-  {
-    for(Index i=0; i<size; ++i)
-    {
-      Scalar xi = *x;
-      Scalar yi = *y;
-      *x =  c * xi + numext::conj(s) * yi;
-      *y = -s * xi + numext::conj(c) * yi;
-      x += incrx;
-      y += incry;
-    }
-  }
-};
-
-template<typename Scalar, typename OtherScalar,
-         int SizeAtCompileTime, int MinAlignment>
-struct apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,true /* vectorizable */>
-{
-  static inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
-  {
-    enum {
-      PacketSize = packet_traits<Scalar>::size,
-      OtherPacketSize = packet_traits<OtherScalar>::size
-    };
-    typedef typename packet_traits<Scalar>::type Packet;
-    typedef typename packet_traits<OtherScalar>::type OtherPacket;
-
-    /*** dynamic-size vectorized paths ***/
-    if(SizeAtCompileTime == Dynamic && ((incrx==1 && incry==1) || PacketSize == 1))
-    {
-      // both vectors are sequentially stored in memory => vectorization
-      enum { Peeling = 2 };
-
-      Index alignedStart = internal::first_default_aligned(y, size);
-      Index alignedEnd = alignedStart + ((size-alignedStart)/PacketSize)*PacketSize;
-
-      const OtherPacket pc = pset1<OtherPacket>(c);
-      const OtherPacket ps = pset1<OtherPacket>(s);
-      conj_helper<OtherPacket,Packet,NumTraits<OtherScalar>::IsComplex,false> pcj;
-      conj_helper<OtherPacket,Packet,false,false> pm;
-
-      for(Index i=0; i<alignedStart; ++i)
-      {
-        Scalar xi = x[i];
-        Scalar yi = y[i];
-        x[i] =  c * xi + numext::conj(s) * yi;
-        y[i] = -s * xi + numext::conj(c) * yi;
-      }
-
-      Scalar* EIGEN_RESTRICT px = x + alignedStart;
-      Scalar* EIGEN_RESTRICT py = y + alignedStart;
-
-      if(internal::first_default_aligned(x, size)==alignedStart)
-      {
-        for(Index i=alignedStart; i<alignedEnd; i+=PacketSize)
-        {
-          Packet xi = pload<Packet>(px);
-          Packet yi = pload<Packet>(py);
-          pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-          pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-          px += PacketSize;
-          py += PacketSize;
-        }
-      }
-      else
-      {
-        Index peelingEnd = alignedStart + ((size-alignedStart)/(Peeling*PacketSize))*(Peeling*PacketSize);
-        for(Index i=alignedStart; i<peelingEnd; i+=Peeling*PacketSize)
-        {
-          Packet xi   = ploadu<Packet>(px);
-          Packet xi1  = ploadu<Packet>(px+PacketSize);
-          Packet yi   = pload <Packet>(py);
-          Packet yi1  = pload <Packet>(py+PacketSize);
-          pstoreu(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-          pstoreu(px+PacketSize, padd(pm.pmul(pc,xi1),pcj.pmul(ps,yi1)));
-          pstore (py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-          pstore (py+PacketSize, psub(pcj.pmul(pc,yi1),pm.pmul(ps,xi1)));
-          px += Peeling*PacketSize;
-          py += Peeling*PacketSize;
-        }
-        if(alignedEnd!=peelingEnd)
-        {
-          Packet xi = ploadu<Packet>(x+peelingEnd);
-          Packet yi = pload <Packet>(y+peelingEnd);
-          pstoreu(x+peelingEnd, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-          pstore (y+peelingEnd, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-        }
-      }
-
-      for(Index i=alignedEnd; i<size; ++i)
-      {
-        Scalar xi = x[i];
-        Scalar yi = y[i];
-        x[i] =  c * xi + numext::conj(s) * yi;
-        y[i] = -s * xi + numext::conj(c) * yi;
-      }
-    }
-
-    /*** fixed-size vectorized path ***/
-    else if(SizeAtCompileTime != Dynamic && MinAlignment>0) // FIXME should be compared to the required alignment
-    {
-      const OtherPacket pc = pset1<OtherPacket>(c);
-      const OtherPacket ps = pset1<OtherPacket>(s);
-      conj_helper<OtherPacket,Packet,NumTraits<OtherPacket>::IsComplex,false> pcj;
-      conj_helper<OtherPacket,Packet,false,false> pm;
-      Scalar* EIGEN_RESTRICT px = x;
-      Scalar* EIGEN_RESTRICT py = y;
-      for(Index i=0; i<size; i+=PacketSize)
-      {
-        Packet xi = pload<Packet>(px);
-        Packet yi = pload<Packet>(py);
-        pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-        pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-        px += PacketSize;
-        py += PacketSize;
-      }
-    }
-
-    /*** non-vectorized path ***/
-    else
-    {
-      apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,false>::run(x,incrx,y,incry,size,c,s);
-    }
-  }
-};
-
-template<typename VectorX, typename VectorY, typename OtherScalar>
-EIGEN_DEVICE_FUNC
-void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j)
-{
-  typedef typename VectorX::Scalar Scalar;
-  const bool Vectorizable =    (int(VectorX::Flags) & int(VectorY::Flags) & PacketAccessBit)
-                            && (int(packet_traits<Scalar>::size) == int(packet_traits<OtherScalar>::size));
-
-  eigen_assert(xpr_x.size() == xpr_y.size());
-  Index size = xpr_x.size();
-  Index incrx = xpr_x.derived().innerStride();
-  Index incry = xpr_y.derived().innerStride();
-
-  Scalar* EIGEN_RESTRICT x = &xpr_x.derived().coeffRef(0);
-  Scalar* EIGEN_RESTRICT y = &xpr_y.derived().coeffRef(0);
-
-  OtherScalar c = j.c();
-  OtherScalar s = j.s();
-  if (c==OtherScalar(1) && s==OtherScalar(0))
-    return;
-
-  apply_rotation_in_the_plane_selector<
-    Scalar,OtherScalar,
-    VectorX::SizeAtCompileTime,
-    EIGEN_PLAIN_ENUM_MIN(evaluator<VectorX>::Alignment, evaluator<VectorY>::Alignment),
-    Vectorizable>::run(x,incrx,y,incry,size,c,s);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_JACOBI_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/KLUSupport/KLUSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/KLUSupport/KLUSupport.h
deleted file mode 100644
index 215db35..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/KLUSupport/KLUSupport.h
+++ /dev/null
@@ -1,358 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Kyle Macfarlan <kyle.macfarlan@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_KLUSUPPORT_H
-#define EIGEN_KLUSUPPORT_H
-
-namespace Eigen {
-
-/* TODO extract L, extract U, compute det, etc... */
-
-/** \ingroup KLUSupport_Module
-  * \brief A sparse LU factorization and solver based on KLU
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
-  * using the KLU library. The sparse matrix A must be squared and full rank.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * \warning The input matrix A should be in a \b compressed and \b column-major form.
-  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class UmfPackLU, class SparseLU
-  */
-
-
-inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B [ ], klu_common *Common, double) {
-   return klu_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common);
-}
-
-inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
-   return klu_z_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), &numext::real_ref(B[0]), Common);
-}
-
-inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[], klu_common *Common, double) {
-   return klu_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common);
-}
-
-inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
-   return klu_z_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), &numext::real_ref(B[0]), 0, Common);
-}
-
-inline klu_numeric* klu_factor(int Ap [ ], int Ai [ ], double Ax [ ], klu_symbolic *Symbolic, klu_common *Common, double) {
-   return klu_factor(Ap, Ai, Ax, Symbolic, Common);
-}
-
-inline klu_numeric* klu_factor(int Ap[], int Ai[], std::complex<double> Ax[], klu_symbolic *Symbolic, klu_common *Common, std::complex<double>) {
-   return klu_z_factor(Ap, Ai, &numext::real_ref(Ax[0]), Symbolic, Common);
-}
-
-
-template<typename _MatrixType>
-class KLU : public SparseSolverBase<KLU<_MatrixType> >
-{
-  protected:
-    typedef SparseSolverBase<KLU<_MatrixType> > Base;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
-    typedef SparseMatrix<Scalar> LUMatrixType;
-    typedef SparseMatrix<Scalar,ColMajor,int> KLUMatrixType;
-    typedef Ref<const KLUMatrixType, StandardCompressedFormat> KLUMatrixRef;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    KLU()
-      : m_dummy(0,0), mp_matrix(m_dummy)
-    {
-      init();
-    }
-
-    template<typename InputMatrixType>
-    explicit KLU(const InputMatrixType& matrix)
-      : mp_matrix(matrix)
-    {
-      init();
-      compute(matrix);
-    }
-
-    ~KLU()
-    {
-      if(m_symbolic) klu_free_symbolic(&m_symbolic,&m_common);
-      if(m_numeric)  klu_free_numeric(&m_numeric,&m_common);
-    }
-
-    EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return mp_matrix.rows(); }
-    EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return mp_matrix.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-#if 0 // not implemented yet
-    inline const LUMatrixType& matrixL() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_l;
-    }
-
-    inline const LUMatrixType& matrixU() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_u;
-    }
-
-    inline const IntColVectorType& permutationP() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_p;
-    }
-
-    inline const IntRowVectorType& permutationQ() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_q;
-    }
-#endif
-    /** Computes the sparse Cholesky decomposition of \a matrix
-     *  Note that the matrix should be column-major, and in compressed format for best performance.
-     *  \sa SparseMatrix::makeCompressed().
-     */
-    template<typename InputMatrixType>
-    void compute(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
-      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
-      grab(matrix.derived());
-      analyzePattern_impl();
-      factorize_impl();
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize(), compute()
-      */
-    template<typename InputMatrixType>
-    void analyzePattern(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
-      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
-
-      grab(matrix.derived());
-
-      analyzePattern_impl();
-    }
-
-
-    /** Provides access to the control settings array used by KLU.
-      *
-      * See KLU documentation for details.
-      */
-    inline const klu_common& kluCommon() const
-    {
-      return m_common;
-    }
-
-    /** Provides access to the control settings array used by UmfPack.
-      *
-      * If this array contains NaN's, the default values are used.
-      *
-      * See KLU documentation for details.
-      */
-    inline klu_common& kluCommon()
-    {
-      return m_common;
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
-      *
-      * \sa analyzePattern(), compute()
-      */
-    template<typename InputMatrixType>
-    void factorize(const InputMatrixType& matrix)
-    {
-      eigen_assert(m_analysisIsOk && "KLU: you must first call analyzePattern()");
-      if(m_numeric)
-        klu_free_numeric(&m_numeric,&m_common);
-
-      grab(matrix.derived());
-
-      factorize_impl();
-    }
-
-    /** \internal */
-    template<typename BDerived,typename XDerived>
-    bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
-
-#if 0 // not implemented yet
-    Scalar determinant() const;
-
-    void extractData() const;
-#endif
-
-  protected:
-
-    void init()
-    {
-      m_info                  = InvalidInput;
-      m_isInitialized         = false;
-      m_numeric               = 0;
-      m_symbolic              = 0;
-      m_extractedDataAreDirty = true;
-
-      klu_defaults(&m_common);
-    }
-
-    void analyzePattern_impl()
-    {
-      m_info = InvalidInput;
-      m_analysisIsOk = false;
-      m_factorizationIsOk = false;
-      m_symbolic = klu_analyze(internal::convert_index<int>(mp_matrix.rows()),
-                                     const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()),
-                                     &m_common);
-      if (m_symbolic) {
-         m_isInitialized = true;
-         m_info = Success;
-         m_analysisIsOk = true;
-         m_extractedDataAreDirty = true;
-      }
-    }
-
-    void factorize_impl()
-    {
-
-      m_numeric = klu_factor(const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()), const_cast<Scalar*>(mp_matrix.valuePtr()),
-                                    m_symbolic, &m_common, Scalar());
-
-
-      m_info = m_numeric ? Success : NumericalIssue;
-      m_factorizationIsOk = m_numeric ? 1 : 0;
-      m_extractedDataAreDirty = true;
-    }
-
-    template<typename MatrixDerived>
-    void grab(const EigenBase<MatrixDerived> &A)
-    {
-      mp_matrix.~KLUMatrixRef();
-      ::new (&mp_matrix) KLUMatrixRef(A.derived());
-    }
-
-    void grab(const KLUMatrixRef &A)
-    {
-      if(&(A.derived()) != &mp_matrix)
-      {
-        mp_matrix.~KLUMatrixRef();
-        ::new (&mp_matrix) KLUMatrixRef(A);
-      }
-    }
-
-    // cached data to reduce reallocation, etc.
-#if 0 // not implemented yet
-    mutable LUMatrixType m_l;
-    mutable LUMatrixType m_u;
-    mutable IntColVectorType m_p;
-    mutable IntRowVectorType m_q;
-#endif
-
-    KLUMatrixType m_dummy;
-    KLUMatrixRef mp_matrix;
-
-    klu_numeric* m_numeric;
-    klu_symbolic* m_symbolic;
-    klu_common m_common;
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-    mutable bool m_extractedDataAreDirty;
-
-  private:
-    KLU(const KLU& ) { }
-};
-
-#if 0 // not implemented yet
-template<typename MatrixType>
-void KLU<MatrixType>::extractData() const
-{
-  if (m_extractedDataAreDirty)
-  {
-     eigen_assert(false && "KLU: extractData Not Yet Implemented");
-
-    // get size of the data
-    int lnz, unz, rows, cols, nz_udiag;
-    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
-
-    // allocate data
-    m_l.resize(rows,(std::min)(rows,cols));
-    m_l.resizeNonZeros(lnz);
-
-    m_u.resize((std::min)(rows,cols),cols);
-    m_u.resizeNonZeros(unz);
-
-    m_p.resize(rows);
-    m_q.resize(cols);
-
-    // extract
-    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
-                        m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
-                        m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
-
-    m_extractedDataAreDirty = false;
-  }
-}
-
-template<typename MatrixType>
-typename KLU<MatrixType>::Scalar KLU<MatrixType>::determinant() const
-{
-  eigen_assert(false && "KLU: extractData Not Yet Implemented");
-  return Scalar();
-}
-#endif
-
-template<typename MatrixType>
-template<typename BDerived,typename XDerived>
-bool KLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
-{
-  Index rhsCols = b.cols();
-  EIGEN_STATIC_ASSERT((XDerived::Flags&RowMajorBit)==0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
-
-  x = b;
-  int info = klu_solve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(), const_cast<klu_common*>(&m_common), Scalar());
-
-  m_info = info!=0 ? Success : NumericalIssue;
-  return true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_KLUSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/Determinant.h b/3rdParty/my_ceres_vc17/include/Eigen/src/LU/Determinant.h
deleted file mode 100644
index 3a41e6f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/Determinant.h
+++ /dev/null
@@ -1,117 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DETERMINANT_H
-#define EIGEN_DETERMINANT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline const typename Derived::Scalar bruteforce_det3_helper
-(const MatrixBase<Derived>& matrix, int a, int b, int c)
-{
-  return matrix.coeff(0,a)
-         * (matrix.coeff(1,b) * matrix.coeff(2,c) - matrix.coeff(1,c) * matrix.coeff(2,b));
-}
-
-template<typename Derived,
-         int DeterminantType = Derived::RowsAtCompileTime
-> struct determinant_impl
-{
-  static inline typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    if(Derived::ColsAtCompileTime==Dynamic && m.rows()==0)
-      return typename traits<Derived>::Scalar(1);
-    return m.partialPivLu().determinant();
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 1>
-{
-  static inline EIGEN_DEVICE_FUNC
-  typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    return m.coeff(0,0);
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 2>
-{
-  static inline EIGEN_DEVICE_FUNC
-  typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    return m.coeff(0,0) * m.coeff(1,1) - m.coeff(1,0) * m.coeff(0,1);
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 3>
-{
-  static inline EIGEN_DEVICE_FUNC
-  typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    return bruteforce_det3_helper(m,0,1,2)
-          - bruteforce_det3_helper(m,1,0,2)
-          + bruteforce_det3_helper(m,2,0,1);
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 4>
-{
-  typedef typename traits<Derived>::Scalar Scalar;
-  static EIGEN_DEVICE_FUNC
-  Scalar run(const Derived& m)
-  {
-    Scalar d2_01 = det2(m, 0, 1);
-    Scalar d2_02 = det2(m, 0, 2);
-    Scalar d2_03 = det2(m, 0, 3);
-    Scalar d2_12 = det2(m, 1, 2);
-    Scalar d2_13 = det2(m, 1, 3);
-    Scalar d2_23 = det2(m, 2, 3);
-    Scalar d3_0 = det3(m, 1,d2_23, 2,d2_13, 3,d2_12);
-    Scalar d3_1 = det3(m, 0,d2_23, 2,d2_03, 3,d2_02);
-    Scalar d3_2 = det3(m, 0,d2_13, 1,d2_03, 3,d2_01);
-    Scalar d3_3 = det3(m, 0,d2_12, 1,d2_02, 2,d2_01);
-    return internal::pmadd(-m(0,3),d3_0, m(1,3)*d3_1) +
-           internal::pmadd(-m(2,3),d3_2, m(3,3)*d3_3);
-  }
-protected:
-  static EIGEN_DEVICE_FUNC
-  Scalar det2(const Derived& m, Index i0, Index i1)
-  {
-    return m(i0,0) * m(i1,1) - m(i1,0) * m(i0,1);
-  }
-
-  static EIGEN_DEVICE_FUNC
-  Scalar det3(const Derived& m, Index i0, const Scalar& d0, Index i1, const Scalar& d1, Index i2, const Scalar& d2)
-  {
-    return internal::pmadd(m(i0,2), d0, internal::pmadd(-m(i1,2), d1, m(i2,2)*d2));
-  }
-};
-
-} // end namespace internal
-
-/** \lu_module
-  *
-  * \returns the determinant of this matrix
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline typename internal::traits<Derived>::Scalar MatrixBase<Derived>::determinant() const
-{
-  eigen_assert(rows() == cols());
-  typedef typename internal::nested_eval<Derived,Base::RowsAtCompileTime>::type Nested;
-  return internal::determinant_impl<typename internal::remove_all<Nested>::type>::run(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DETERMINANT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/FullPivLU.h b/3rdParty/my_ceres_vc17/include/Eigen/src/LU/FullPivLU.h
deleted file mode 100644
index ba1749f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/FullPivLU.h
+++ /dev/null
@@ -1,877 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LU_H
-#define EIGEN_LU_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _MatrixType> struct traits<FullPivLU<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-} // end namespace internal
-
-/** \ingroup LU_Module
-  *
-  * \class FullPivLU
-  *
-  * \brief LU decomposition of a matrix with complete pivoting, and related features
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the LU decomposition
-  *
-  * This class represents a LU decomposition of any matrix, with complete pivoting: the matrix A is
-  * decomposed as \f$ A = P^{-1} L U Q^{-1} \f$ where L is unit-lower-triangular, U is
-  * upper-triangular, and P and Q are permutation matrices. This is a rank-revealing LU
-  * decomposition. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any
-  * zeros are at the end.
-  *
-  * This decomposition provides the generic approach to solving systems of linear equations, computing
-  * the rank, invertibility, inverse, kernel, and determinant.
-  *
-  * This LU decomposition is very stable and well tested with large matrices. However there are use cases where the SVD
-  * decomposition is inherently more stable and/or flexible. For example, when computing the kernel of a matrix,
-  * working with the SVD allows to select the smallest singular values of the matrix, something that
-  * the LU decomposition doesn't see.
-  *
-  * The data of the LU decomposition can be directly accessed through the methods matrixLU(),
-  * permutationP(), permutationQ().
-  *
-  * As an example, here is how the original matrix can be retrieved:
-  * \include class_FullPivLU.cpp
-  * Output: \verbinclude class_FullPivLU.out
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::fullPivLu(), MatrixBase::determinant(), MatrixBase::inverse()
-  */
-template<typename _MatrixType> class FullPivLU
-  : public SolverBase<FullPivLU<_MatrixType> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<FullPivLU> Base;
-    friend class SolverBase<FullPivLU>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivLU)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename internal::plain_row_type<MatrixType, StorageIndex>::type IntRowVectorType;
-    typedef typename internal::plain_col_type<MatrixType, StorageIndex>::type IntColVectorType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationQType;
-    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationPType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via LU::compute(const MatrixType&).
-      */
-    FullPivLU();
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa FullPivLU()
-      */
-    FullPivLU(Index rows, Index cols);
-
-    /** Constructor.
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *               It is required to be nonzero.
-      */
-    template<typename InputType>
-    explicit FullPivLU(const EigenBase<InputType>& matrix);
-
-    /** \brief Constructs a LU factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa FullPivLU(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit FullPivLU(EigenBase<InputType>& matrix);
-
-    /** Computes the LU decomposition of the given matrix.
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *               It is required to be nonzero.
-      *
-      * \returns a reference to *this
-      */
-    template<typename InputType>
-    FullPivLU& compute(const EigenBase<InputType>& matrix) {
-      m_lu = matrix.derived();
-      computeInPlace();
-      return *this;
-    }
-
-    /** \returns the LU decomposition matrix: the upper-triangular part is U, the
-      * unit-lower-triangular part is L (at least for square matrices; in the non-square
-      * case, special care is needed, see the documentation of class FullPivLU).
-      *
-      * \sa matrixL(), matrixU()
-      */
-    inline const MatrixType& matrixLU() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_lu;
-    }
-
-    /** \returns the number of nonzero pivots in the LU decomposition.
-      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
-      * So that notion isn't really intrinsically interesting, but it is
-      * still useful when implementing algorithms.
-      *
-      * \sa rank()
-      */
-    inline Index nonzeroPivots() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of U.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-    /** \returns the permutation matrix P
-      *
-      * \sa permutationQ()
-      */
-    EIGEN_DEVICE_FUNC inline const PermutationPType& permutationP() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_p;
-    }
-
-    /** \returns the permutation matrix Q
-      *
-      * \sa permutationP()
-      */
-    inline const PermutationQType& permutationQ() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_q;
-    }
-
-    /** \returns the kernel of the matrix, also called its null-space. The columns of the returned matrix
-      * will form a basis of the kernel.
-      *
-      * \note If the kernel has dimension zero, then the returned matrix is a column-vector filled with zeros.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      *
-      * Example: \include FullPivLU_kernel.cpp
-      * Output: \verbinclude FullPivLU_kernel.out
-      *
-      * \sa image()
-      */
-    inline const internal::kernel_retval<FullPivLU> kernel() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return internal::kernel_retval<FullPivLU>(*this);
-    }
-
-    /** \returns the image of the matrix, also called its column-space. The columns of the returned matrix
-      * will form a basis of the image (column-space).
-      *
-      * \param originalMatrix the original matrix, of which *this is the LU decomposition.
-      *                       The reason why it is needed to pass it here, is that this allows
-      *                       a large optimization, as otherwise this method would need to reconstruct it
-      *                       from the LU decomposition.
-      *
-      * \note If the image has dimension zero, then the returned matrix is a column-vector filled with zeros.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      *
-      * Example: \include FullPivLU_image.cpp
-      * Output: \verbinclude FullPivLU_image.out
-      *
-      * \sa kernel()
-      */
-    inline const internal::image_retval<FullPivLU>
-      image(const MatrixType& originalMatrix) const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return internal::image_retval<FullPivLU>(*this, originalMatrix);
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \return a solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the LU decomposition.
-      *
-      * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
-      *          the only requirement in order for the equation to make sense is that
-      *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
-      *
-      * \returns a solution.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      * \note_about_using_kernel_to_study_multiple_solutions
-      *
-      * Example: \include FullPivLU_solve.cpp
-      * Output: \verbinclude FullPivLU_solve.out
-      *
-      * \sa TriangularView::solve(), kernel(), inverse()
-      */
-    template<typename Rhs>
-    inline const Solve<FullPivLU, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
-        the LU decomposition.
-      */
-    inline RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    /** \returns the determinant of the matrix of which
-      * *this is the LU decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the LU decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
-      *       optimized paths.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      *
-      * \sa MatrixBase::determinant()
-      */
-    typename internal::traits<MatrixType>::Scalar determinant() const;
-
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
-      * who need to determine when pivots are to be considered nonzero. This is not used for the
-      * LU decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
-      * uses a formula to automatically determine a reasonable threshold.
-      * Once you have called the present method setThreshold(const RealScalar&),
-      * your value is used instead.
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A pivot will be considered nonzero if its absolute value is strictly greater than
-      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-      * where maxpivot is the biggest pivot.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    FullPivLU& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code lu.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    FullPivLU& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
-
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    RealScalar threshold() const
-    {
-      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-      return m_usePrescribedThreshold ? m_prescribedThreshold
-      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
-      // and turns out to be identical to Higham's formula used already in LDLt.
-          : NumTraits<Scalar>::epsilon() * RealScalar(m_lu.diagonalSize());
-    }
-
-    /** \returns the rank of the matrix of which *this is the LU decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
-      Index result = 0;
-      for(Index i = 0; i < m_nonzero_pivots; ++i)
-        result += (abs(m_lu.coeff(i,i)) > premultiplied_threshold);
-      return result;
-    }
-
-    /** \returns the dimension of the kernel of the matrix of which *this is the LU decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index dimensionOfKernel() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return cols() - rank();
-    }
-
-    /** \returns true if the matrix of which *this is the LU decomposition represents an injective
-      *          linear map, i.e. has trivial kernel; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInjective() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return rank() == cols();
-    }
-
-    /** \returns true if the matrix of which *this is the LU decomposition represents a surjective
-      *          linear map; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isSurjective() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return rank() == rows();
-    }
-
-    /** \returns true if the matrix of which *this is the LU decomposition is invertible.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInvertible() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return isInjective() && (m_lu.rows() == m_lu.cols());
-    }
-
-    /** \returns the inverse of the matrix of which *this is the LU decomposition.
-      *
-      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
-      *       Use isInvertible() to first determine whether this matrix is invertible.
-      *
-      * \sa MatrixBase::inverse()
-      */
-    inline const Inverse<FullPivLU> inverse() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!");
-      return Inverse<FullPivLU>(*this);
-    }
-
-    MatrixType reconstructedMatrix() const;
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_lu;
-    PermutationPType m_p;
-    PermutationQType m_q;
-    IntColVectorType m_rowsTranspositions;
-    IntRowVectorType m_colsTranspositions;
-    Index m_nonzero_pivots;
-    RealScalar m_l1_norm;
-    RealScalar m_maxpivot, m_prescribedThreshold;
-    signed char m_det_pq;
-    bool m_isInitialized, m_usePrescribedThreshold;
-};
-
-template<typename MatrixType>
-FullPivLU<MatrixType>::FullPivLU()
-  : m_isInitialized(false), m_usePrescribedThreshold(false)
-{
-}
-
-template<typename MatrixType>
-FullPivLU<MatrixType>::FullPivLU(Index rows, Index cols)
-  : m_lu(rows, cols),
-    m_p(rows),
-    m_q(cols),
-    m_rowsTranspositions(rows),
-    m_colsTranspositions(cols),
-    m_isInitialized(false),
-    m_usePrescribedThreshold(false)
-{
-}
-
-template<typename MatrixType>
-template<typename InputType>
-FullPivLU<MatrixType>::FullPivLU(const EigenBase<InputType>& matrix)
-  : m_lu(matrix.rows(), matrix.cols()),
-    m_p(matrix.rows()),
-    m_q(matrix.cols()),
-    m_rowsTranspositions(matrix.rows()),
-    m_colsTranspositions(matrix.cols()),
-    m_isInitialized(false),
-    m_usePrescribedThreshold(false)
-{
-  compute(matrix.derived());
-}
-
-template<typename MatrixType>
-template<typename InputType>
-FullPivLU<MatrixType>::FullPivLU(EigenBase<InputType>& matrix)
-  : m_lu(matrix.derived()),
-    m_p(matrix.rows()),
-    m_q(matrix.cols()),
-    m_rowsTranspositions(matrix.rows()),
-    m_colsTranspositions(matrix.cols()),
-    m_isInitialized(false),
-    m_usePrescribedThreshold(false)
-{
-  computeInPlace();
-}
-
-template<typename MatrixType>
-void FullPivLU<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  // the permutations are stored as int indices, so just to be sure:
-  eigen_assert(m_lu.rows()<=NumTraits<int>::highest() && m_lu.cols()<=NumTraits<int>::highest());
-
-  m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
-
-  const Index size = m_lu.diagonalSize();
-  const Index rows = m_lu.rows();
-  const Index cols = m_lu.cols();
-
-  // will store the transpositions, before we accumulate them at the end.
-  // can't accumulate on-the-fly because that will be done in reverse order for the rows.
-  m_rowsTranspositions.resize(m_lu.rows());
-  m_colsTranspositions.resize(m_lu.cols());
-  Index number_of_transpositions = 0; // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i
-
-  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
-  m_maxpivot = RealScalar(0);
-
-  for(Index k = 0; k < size; ++k)
-  {
-    // First, we need to find the pivot.
-
-    // biggest coefficient in the remaining bottom-right corner (starting at row k, col k)
-    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
-    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
-    typedef typename Scoring::result_type Score;
-    Score biggest_in_corner;
-    biggest_in_corner = m_lu.bottomRightCorner(rows-k, cols-k)
-                        .unaryExpr(Scoring())
-                        .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
-    row_of_biggest_in_corner += k; // correct the values! since they were computed in the corner,
-    col_of_biggest_in_corner += k; // need to add k to them.
-
-    if(biggest_in_corner==Score(0))
-    {
-      // before exiting, make sure to initialize the still uninitialized transpositions
-      // in a sane state without destroying what we already have.
-      m_nonzero_pivots = k;
-      for(Index i = k; i < size; ++i)
-      {
-        m_rowsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-        m_colsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-      }
-      break;
-    }
-
-    RealScalar abs_pivot = internal::abs_knowing_score<Scalar>()(m_lu(row_of_biggest_in_corner, col_of_biggest_in_corner), biggest_in_corner);
-    if(abs_pivot > m_maxpivot) m_maxpivot = abs_pivot;
-
-    // Now that we've found the pivot, we need to apply the row/col swaps to
-    // bring it to the location (k,k).
-
-    m_rowsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
-    m_colsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
-    if(k != row_of_biggest_in_corner) {
-      m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner));
-      ++number_of_transpositions;
-    }
-    if(k != col_of_biggest_in_corner) {
-      m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner));
-      ++number_of_transpositions;
-    }
-
-    // Now that the pivot is at the right location, we update the remaining
-    // bottom-right corner by Gaussian elimination.
-
-    if(k<rows-1)
-      m_lu.col(k).tail(rows-k-1) /= m_lu.coeff(k,k);
-    if(k<size-1)
-      m_lu.block(k+1,k+1,rows-k-1,cols-k-1).noalias() -= m_lu.col(k).tail(rows-k-1) * m_lu.row(k).tail(cols-k-1);
-  }
-
-  // the main loop is over, we still have to accumulate the transpositions to find the
-  // permutations P and Q
-
-  m_p.setIdentity(rows);
-  for(Index k = size-1; k >= 0; --k)
-    m_p.applyTranspositionOnTheRight(k, m_rowsTranspositions.coeff(k));
-
-  m_q.setIdentity(cols);
-  for(Index k = 0; k < size; ++k)
-    m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-
-  m_isInitialized = true;
-}
-
-template<typename MatrixType>
-typename internal::traits<MatrixType>::Scalar FullPivLU<MatrixType>::determinant() const
-{
-  eigen_assert(m_isInitialized && "LU is not initialized.");
-  eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the determinant of a non-square matrix!");
-  return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod());
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: \f$ P^{-1} L U Q^{-1} \f$.
- * This function is provided for debug purposes. */
-template<typename MatrixType>
-MatrixType FullPivLU<MatrixType>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LU is not initialized.");
-  const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols());
-  // LU
-  MatrixType res(m_lu.rows(),m_lu.cols());
-  // FIXME the .toDenseMatrix() should not be needed...
-  res = m_lu.leftCols(smalldim)
-            .template triangularView<UnitLower>().toDenseMatrix()
-      * m_lu.topRows(smalldim)
-            .template triangularView<Upper>().toDenseMatrix();
-
-  // P^{-1}(LU)
-  res = m_p.inverse() * res;
-
-  // (P^{-1}LU)Q^{-1}
-  res = res * m_q.inverse();
-
-  return res;
-}
-
-/********* Implementation of kernel() **************************************************/
-
-namespace internal {
-template<typename _MatrixType>
-struct kernel_retval<FullPivLU<_MatrixType> >
-  : kernel_retval_base<FullPivLU<_MatrixType> >
-{
-  EIGEN_MAKE_KERNEL_HELPERS(FullPivLU<_MatrixType>)
-
-  enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(
-            MatrixType::MaxColsAtCompileTime,
-            MatrixType::MaxRowsAtCompileTime)
-  };
-
-  template<typename Dest> void evalTo(Dest& dst) const
-  {
-    using std::abs;
-    const Index cols = dec().matrixLU().cols(), dimker = cols - rank();
-    if(dimker == 0)
-    {
-      // The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's
-      // avoid crashing/asserting as that depends on floating point calculations. Let's
-      // just return a single column vector filled with zeros.
-      dst.setZero();
-      return;
-    }
-
-    /* Let us use the following lemma:
-      *
-      * Lemma: If the matrix A has the LU decomposition PAQ = LU,
-      * then Ker A = Q(Ker U).
-      *
-      * Proof: trivial: just keep in mind that P, Q, L are invertible.
-      */
-
-    /* Thus, all we need to do is to compute Ker U, and then apply Q.
-      *
-      * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end.
-      * Thus, the diagonal of U ends with exactly
-      * dimKer zero's. Let us use that to construct dimKer linearly
-      * independent vectors in Ker U.
-      */
-
-    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
-    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
-    Index p = 0;
-    for(Index i = 0; i < dec().nonzeroPivots(); ++i)
-      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
-        pivots.coeffRef(p++) = i;
-    eigen_internal_assert(p == rank());
-
-    // we construct a temporaty trapezoid matrix m, by taking the U matrix and
-    // permuting the rows and cols to bring the nonnegligible pivots to the top of
-    // the main diagonal. We need that to be able to apply our triangular solvers.
-    // FIXME when we get triangularView-for-rectangular-matrices, this can be simplified
-    Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, MatrixType::Options,
-           MaxSmallDimAtCompileTime, MatrixType::MaxColsAtCompileTime>
-      m(dec().matrixLU().block(0, 0, rank(), cols));
-    for(Index i = 0; i < rank(); ++i)
-    {
-      if(i) m.row(i).head(i).setZero();
-      m.row(i).tail(cols-i) = dec().matrixLU().row(pivots.coeff(i)).tail(cols-i);
-    }
-    m.block(0, 0, rank(), rank());
-    m.block(0, 0, rank(), rank()).template triangularView<StrictlyLower>().setZero();
-    for(Index i = 0; i < rank(); ++i)
-      m.col(i).swap(m.col(pivots.coeff(i)));
-
-    // ok, we have our trapezoid matrix, we can apply the triangular solver.
-    // notice that the math behind this suggests that we should apply this to the
-    // negative of the RHS, but for performance we just put the negative sign elsewhere, see below.
-    m.topLeftCorner(rank(), rank())
-     .template triangularView<Upper>().solveInPlace(
-        m.topRightCorner(rank(), dimker)
-      );
-
-    // now we must undo the column permutation that we had applied!
-    for(Index i = rank()-1; i >= 0; --i)
-      m.col(i).swap(m.col(pivots.coeff(i)));
-
-    // see the negative sign in the next line, that's what we were talking about above.
-    for(Index i = 0; i < rank(); ++i) dst.row(dec().permutationQ().indices().coeff(i)) = -m.row(i).tail(dimker);
-    for(Index i = rank(); i < cols; ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero();
-    for(Index k = 0; k < dimker; ++k) dst.coeffRef(dec().permutationQ().indices().coeff(rank()+k), k) = Scalar(1);
-  }
-};
-
-/***** Implementation of image() *****************************************************/
-
-template<typename _MatrixType>
-struct image_retval<FullPivLU<_MatrixType> >
-  : image_retval_base<FullPivLU<_MatrixType> >
-{
-  EIGEN_MAKE_IMAGE_HELPERS(FullPivLU<_MatrixType>)
-
-  enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(
-            MatrixType::MaxColsAtCompileTime,
-            MatrixType::MaxRowsAtCompileTime)
-  };
-
-  template<typename Dest> void evalTo(Dest& dst) const
-  {
-    using std::abs;
-    if(rank() == 0)
-    {
-      // The Image is just {0}, so it doesn't have a basis properly speaking, but let's
-      // avoid crashing/asserting as that depends on floating point calculations. Let's
-      // just return a single column vector filled with zeros.
-      dst.setZero();
-      return;
-    }
-
-    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
-    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
-    Index p = 0;
-    for(Index i = 0; i < dec().nonzeroPivots(); ++i)
-      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
-        pivots.coeffRef(p++) = i;
-    eigen_internal_assert(p == rank());
-
-    for(Index i = 0; i < rank(); ++i)
-      dst.col(i) = originalMatrix().col(dec().permutationQ().indices().coeff(pivots.coeff(i)));
-  }
-};
-
-/***** Implementation of solve() *****************************************************/
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void FullPivLU<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}.
-  * So we proceed as follows:
-  * Step 1: compute c = P * rhs.
-  * Step 2: replace c by the solution x to Lx = c. Exists because L is invertible.
-  * Step 3: replace c by the solution x to Ux = c. May or may not exist.
-  * Step 4: result = Q * c;
-  */
-
-  const Index rows = this->rows(),
-              cols = this->cols(),
-              nonzero_pivots = this->rank();
-  const Index smalldim = (std::min)(rows, cols);
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
-
-  // Step 1
-  c = permutationP() * rhs;
-
-  // Step 2
-  m_lu.topLeftCorner(smalldim,smalldim)
-      .template triangularView<UnitLower>()
-      .solveInPlace(c.topRows(smalldim));
-  if(rows>cols)
-    c.bottomRows(rows-cols) -= m_lu.bottomRows(rows-cols) * c.topRows(cols);
-
-  // Step 3
-  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(nonzero_pivots));
-
-  // Step 4
-  for(Index i = 0; i < nonzero_pivots; ++i)
-    dst.row(permutationQ().indices().coeff(i)) = c.row(i);
-  for(Index i = nonzero_pivots; i < m_lu.cols(); ++i)
-    dst.row(permutationQ().indices().coeff(i)).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void FullPivLU<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1},
-   * and since permutations are real and unitary, we can write this
-   * as   A^T = Q U^T L^T P,
-   * So we proceed as follows:
-   * Step 1: compute c = Q^T rhs.
-   * Step 2: replace c by the solution x to U^T x = c. May or may not exist.
-   * Step 3: replace c by the solution x to L^T x = c.
-   * Step 4: result = P^T c.
-   * If Conjugate is true, replace "^T" by "^*" above.
-   */
-
-  const Index rows = this->rows(), cols = this->cols(),
-    nonzero_pivots = this->rank();
-  const Index smalldim = (std::min)(rows, cols);
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
-
-  // Step 1
-  c = permutationQ().inverse() * rhs;
-
-  // Step 2
-  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
-      .template triangularView<Upper>()
-      .transpose()
-      .template conjugateIf<Conjugate>()
-      .solveInPlace(c.topRows(nonzero_pivots));
-
-  // Step 3
-  m_lu.topLeftCorner(smalldim, smalldim)
-      .template triangularView<UnitLower>()
-      .transpose()
-      .template conjugateIf<Conjugate>()
-      .solveInPlace(c.topRows(smalldim));
-
-  // Step 4
-  PermutationPType invp = permutationP().inverse().eval();
-  for(Index i = 0; i < smalldim; ++i)
-    dst.row(invp.indices().coeff(i)) = c.row(i);
-  for(Index i = smalldim; i < rows; ++i)
-    dst.row(invp.indices().coeff(i)).setZero();
-}
-
-#endif
-
-namespace internal {
-
-
-/***** Implementation of inverse() *****************************************************/
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<FullPivLU<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivLU<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef FullPivLU<MatrixType> LuType;
-  typedef Inverse<LuType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename MatrixType::Scalar> &)
-  {
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-} // end namespace internal
-
-/******* MatrixBase methods *****************************************************************/
-
-/** \lu_module
-  *
-  * \return the full-pivoting LU decomposition of \c *this.
-  *
-  * \sa class FullPivLU
-  */
-template<typename Derived>
-inline const FullPivLU<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::fullPivLu() const
-{
-  return FullPivLU<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LU_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/InverseImpl.h b/3rdParty/my_ceres_vc17/include/Eigen/src/LU/InverseImpl.h
deleted file mode 100644
index a40cefa..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/InverseImpl.h
+++ /dev/null
@@ -1,432 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INVERSE_IMPL_H
-#define EIGEN_INVERSE_IMPL_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************
-*** General case implementation ***
-**********************************/
-
-template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
-struct compute_inverse
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    result = matrix.partialPivLu().inverse();
-  }
-};
-
-template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
-struct compute_inverse_and_det_with_check { /* nothing! general case not supported. */ };
-
-/****************************
-*** Size 1 implementation ***
-****************************/
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    internal::evaluator<MatrixType> matrixEval(matrix);
-    result.coeffRef(0,0) = Scalar(1) / matrixEval.coeff(0,0);
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& result,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    using std::abs;
-    determinant = matrix.coeff(0,0);
-    invertible = abs(determinant) > absDeterminantThreshold;
-    if(invertible) result.coeffRef(0,0) = typename ResultType::Scalar(1) / determinant;
-  }
-};
-
-/****************************
-*** Size 2 implementation ***
-****************************/
-
-template<typename MatrixType, typename ResultType>
-EIGEN_DEVICE_FUNC 
-inline void compute_inverse_size2_helper(
-    const MatrixType& matrix, const typename ResultType::Scalar& invdet,
-    ResultType& result)
-{
-  typename ResultType::Scalar temp = matrix.coeff(0,0);
-  result.coeffRef(0,0) =  matrix.coeff(1,1) * invdet;
-  result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet;
-  result.coeffRef(0,1) = -matrix.coeff(0,1) * invdet;
-  result.coeffRef(1,1) =  temp * invdet;
-}
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 2>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    typedef typename ResultType::Scalar Scalar;
-    const Scalar invdet = typename MatrixType::Scalar(1) / matrix.determinant();
-    compute_inverse_size2_helper(matrix, invdet, result);
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 2>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    using std::abs;
-    typedef typename ResultType::Scalar Scalar;
-    determinant = matrix.determinant();
-    invertible = abs(determinant) > absDeterminantThreshold;
-    if(!invertible) return;
-    const Scalar invdet = Scalar(1) / determinant;
-    compute_inverse_size2_helper(matrix, invdet, inverse);
-  }
-};
-
-/****************************
-*** Size 3 implementation ***
-****************************/
-
-template<typename MatrixType, int i, int j>
-EIGEN_DEVICE_FUNC 
-inline typename MatrixType::Scalar cofactor_3x3(const MatrixType& m)
-{
-  enum {
-    i1 = (i+1) % 3,
-    i2 = (i+2) % 3,
-    j1 = (j+1) % 3,
-    j2 = (j+2) % 3
-  };
-  return m.coeff(i1, j1) * m.coeff(i2, j2)
-       - m.coeff(i1, j2) * m.coeff(i2, j1);
-}
-
-template<typename MatrixType, typename ResultType>
-EIGEN_DEVICE_FUNC
-inline void compute_inverse_size3_helper(
-    const MatrixType& matrix,
-    const typename ResultType::Scalar& invdet,
-    const Matrix<typename ResultType::Scalar,3,1>& cofactors_col0,
-    ResultType& result)
-{
-  // Compute cofactors in a way that avoids aliasing issues.
-  typedef typename ResultType::Scalar Scalar;
-  const Scalar c01 = cofactor_3x3<MatrixType,0,1>(matrix) * invdet;
-  const Scalar c11 = cofactor_3x3<MatrixType,1,1>(matrix) * invdet;
-  const Scalar c02 = cofactor_3x3<MatrixType,0,2>(matrix) * invdet;
-  result.coeffRef(1,2) =  cofactor_3x3<MatrixType,2,1>(matrix) * invdet;
-  result.coeffRef(2,1) =  cofactor_3x3<MatrixType,1,2>(matrix) * invdet;
-  result.coeffRef(2,2) =  cofactor_3x3<MatrixType,2,2>(matrix) * invdet;
-  result.coeffRef(1,0) =  c01;
-  result.coeffRef(1,1) =  c11;
-  result.coeffRef(2,0) =  c02;  
-  result.row(0) = cofactors_col0 * invdet;
-}
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 3>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    typedef typename ResultType::Scalar Scalar;
-    Matrix<typename MatrixType::Scalar,3,1> cofactors_col0;
-    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
-    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
-    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
-    const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
-    const Scalar invdet = Scalar(1) / det;
-    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result);
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 3>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    typedef typename ResultType::Scalar Scalar;
-    Matrix<Scalar,3,1> cofactors_col0;
-    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
-    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
-    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
-    determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
-    invertible = Eigen::numext::abs(determinant) > absDeterminantThreshold;
-    if(!invertible) return;
-    const Scalar invdet = Scalar(1) / determinant;
-    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, inverse);
-  }
-};
-
-/****************************
-*** Size 4 implementation ***
-****************************/
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC 
-inline const typename Derived::Scalar general_det3_helper
-(const MatrixBase<Derived>& matrix, int i1, int i2, int i3, int j1, int j2, int j3)
-{
-  return matrix.coeff(i1,j1)
-         * (matrix.coeff(i2,j2) * matrix.coeff(i3,j3) - matrix.coeff(i2,j3) * matrix.coeff(i3,j2));
-}
-
-template<typename MatrixType, int i, int j>
-EIGEN_DEVICE_FUNC 
-inline typename MatrixType::Scalar cofactor_4x4(const MatrixType& matrix)
-{
-  enum {
-    i1 = (i+1) % 4,
-    i2 = (i+2) % 4,
-    i3 = (i+3) % 4,
-    j1 = (j+1) % 4,
-    j2 = (j+2) % 4,
-    j3 = (j+3) % 4
-  };
-  return general_det3_helper(matrix, i1, i2, i3, j1, j2, j3)
-       + general_det3_helper(matrix, i2, i3, i1, j1, j2, j3)
-       + general_det3_helper(matrix, i3, i1, i2, j1, j2, j3);
-}
-
-template<int Arch, typename Scalar, typename MatrixType, typename ResultType>
-struct compute_inverse_size4
-{
-  EIGEN_DEVICE_FUNC
-  static void run(const MatrixType& matrix, ResultType& result)
-  {
-    result.coeffRef(0,0) =  cofactor_4x4<MatrixType,0,0>(matrix);
-    result.coeffRef(1,0) = -cofactor_4x4<MatrixType,0,1>(matrix);
-    result.coeffRef(2,0) =  cofactor_4x4<MatrixType,0,2>(matrix);
-    result.coeffRef(3,0) = -cofactor_4x4<MatrixType,0,3>(matrix);
-    result.coeffRef(0,2) =  cofactor_4x4<MatrixType,2,0>(matrix);
-    result.coeffRef(1,2) = -cofactor_4x4<MatrixType,2,1>(matrix);
-    result.coeffRef(2,2) =  cofactor_4x4<MatrixType,2,2>(matrix);
-    result.coeffRef(3,2) = -cofactor_4x4<MatrixType,2,3>(matrix);
-    result.coeffRef(0,1) = -cofactor_4x4<MatrixType,1,0>(matrix);
-    result.coeffRef(1,1) =  cofactor_4x4<MatrixType,1,1>(matrix);
-    result.coeffRef(2,1) = -cofactor_4x4<MatrixType,1,2>(matrix);
-    result.coeffRef(3,1) =  cofactor_4x4<MatrixType,1,3>(matrix);
-    result.coeffRef(0,3) = -cofactor_4x4<MatrixType,3,0>(matrix);
-    result.coeffRef(1,3) =  cofactor_4x4<MatrixType,3,1>(matrix);
-    result.coeffRef(2,3) = -cofactor_4x4<MatrixType,3,2>(matrix);
-    result.coeffRef(3,3) =  cofactor_4x4<MatrixType,3,3>(matrix);
-    result /= (matrix.col(0).cwiseProduct(result.row(0).transpose())).sum();
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 4>
- : compute_inverse_size4<Architecture::Target, typename MatrixType::Scalar,
-                            MatrixType, ResultType>
-{
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 4>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    using std::abs;
-    determinant = matrix.determinant();
-    invertible = abs(determinant) > absDeterminantThreshold;
-    if(invertible && extract_data(matrix) != extract_data(inverse)) {
-      compute_inverse<MatrixType, ResultType>::run(matrix, inverse);
-    }
-    else if(invertible) {
-      MatrixType matrix_t = matrix;
-      compute_inverse<MatrixType, ResultType>::run(matrix_t, inverse);
-    }
-  }
-};
-
-/*************************
-*** MatrixBase methods ***
-*************************/
-
-} // end namespace internal
-
-namespace internal {
-
-// Specialization for "dense = dense_xpr.inverse()"
-template<typename DstXprType, typename XprType>
-struct Assignment<DstXprType, Inverse<XprType>, internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar>, Dense2Dense>
-{
-  typedef Inverse<XprType> SrcXprType;
-  EIGEN_DEVICE_FUNC
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    
-    const int Size = EIGEN_PLAIN_ENUM_MIN(XprType::ColsAtCompileTime,DstXprType::ColsAtCompileTime);
-    EIGEN_ONLY_USED_FOR_DEBUG(Size);
-    eigen_assert(( (Size<=1) || (Size>4) || (extract_data(src.nestedExpression())!=extract_data(dst)))
-              && "Aliasing problem detected in inverse(), you need to do inverse().eval() here.");
-
-    typedef typename internal::nested_eval<XprType,XprType::ColsAtCompileTime>::type  ActualXprType;
-    typedef typename internal::remove_all<ActualXprType>::type                        ActualXprTypeCleanded;
-    
-    ActualXprType actual_xpr(src.nestedExpression());
-    
-    compute_inverse<ActualXprTypeCleanded, DstXprType>::run(actual_xpr, dst);
-  }
-};
-
-  
-} // end namespace internal
-
-/** \lu_module
-  *
-  * \returns the matrix inverse of this matrix.
-  *
-  * For small fixed sizes up to 4x4, this method uses cofactors.
-  * In the general case, this method uses class PartialPivLU.
-  *
-  * \note This matrix must be invertible, otherwise the result is undefined. If you need an
-  * invertibility check, do the following:
-  * \li for fixed sizes up to 4x4, use computeInverseAndDetWithCheck().
-  * \li for the general case, use class FullPivLU.
-  *
-  * Example: \include MatrixBase_inverse.cpp
-  * Output: \verbinclude MatrixBase_inverse.out
-  *
-  * \sa computeInverseAndDetWithCheck()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline const Inverse<Derived> MatrixBase<Derived>::inverse() const
-{
-  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsInteger,THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
-  eigen_assert(rows() == cols());
-  return Inverse<Derived>(derived());
-}
-
-/** \lu_module
-  *
-  * Computation of matrix inverse and determinant, with invertibility check.
-  *
-  * This is only for fixed-size square matrices of size up to 4x4.
-  *
-  * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
-  *
-  * \param inverse Reference to the matrix in which to store the inverse.
-  * \param determinant Reference to the variable in which to store the determinant.
-  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
-  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
-  *                                The matrix will be declared invertible if the absolute value of its
-  *                                determinant is greater than this threshold.
-  *
-  * Example: \include MatrixBase_computeInverseAndDetWithCheck.cpp
-  * Output: \verbinclude MatrixBase_computeInverseAndDetWithCheck.out
-  *
-  * \sa inverse(), computeInverseWithCheck()
-  */
-template<typename Derived>
-template<typename ResultType>
-inline void MatrixBase<Derived>::computeInverseAndDetWithCheck(
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible,
-    const RealScalar& absDeterminantThreshold
-  ) const
-{
-  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
-  eigen_assert(rows() == cols());
-  // for 2x2, it's worth giving a chance to avoid evaluating.
-  // for larger sizes, evaluating has negligible cost and limits code size.
-  typedef typename internal::conditional<
-    RowsAtCompileTime == 2,
-    typename internal::remove_all<typename internal::nested_eval<Derived, 2>::type>::type,
-    PlainObject
-  >::type MatrixType;
-  internal::compute_inverse_and_det_with_check<MatrixType, ResultType>::run
-    (derived(), absDeterminantThreshold, inverse, determinant, invertible);
-}
-
-/** \lu_module
-  *
-  * Computation of matrix inverse, with invertibility check.
-  *
-  * This is only for fixed-size square matrices of size up to 4x4.
-  *
-  * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
-  *
-  * \param inverse Reference to the matrix in which to store the inverse.
-  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
-  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
-  *                                The matrix will be declared invertible if the absolute value of its
-  *                                determinant is greater than this threshold.
-  *
-  * Example: \include MatrixBase_computeInverseWithCheck.cpp
-  * Output: \verbinclude MatrixBase_computeInverseWithCheck.out
-  *
-  * \sa inverse(), computeInverseAndDetWithCheck()
-  */
-template<typename Derived>
-template<typename ResultType>
-inline void MatrixBase<Derived>::computeInverseWithCheck(
-    ResultType& inverse,
-    bool& invertible,
-    const RealScalar& absDeterminantThreshold
-  ) const
-{
-  Scalar determinant;
-  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
-  eigen_assert(rows() == cols());
-  computeInverseAndDetWithCheck(inverse,determinant,invertible,absDeterminantThreshold);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_INVERSE_IMPL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/PartialPivLU.h b/3rdParty/my_ceres_vc17/include/Eigen/src/LU/PartialPivLU.h
deleted file mode 100644
index 34aed72..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/PartialPivLU.h
+++ /dev/null
@@ -1,624 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARTIALLU_H
-#define EIGEN_PARTIALLU_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _MatrixType> struct traits<PartialPivLU<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  typedef traits<_MatrixType> BaseTraits;
-  enum {
-    Flags = BaseTraits::Flags & RowMajorBit,
-    CoeffReadCost = Dynamic
-  };
-};
-
-template<typename T,typename Derived>
-struct enable_if_ref;
-// {
-//   typedef Derived type;
-// };
-
-template<typename T,typename Derived>
-struct enable_if_ref<Ref<T>,Derived> {
-  typedef Derived type;
-};
-
-} // end namespace internal
-
-/** \ingroup LU_Module
-  *
-  * \class PartialPivLU
-  *
-  * \brief LU decomposition of a matrix with partial pivoting, and related features
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the LU decomposition
-  *
-  * This class represents a LU decomposition of a \b square \b invertible matrix, with partial pivoting: the matrix A
-  * is decomposed as A = PLU where L is unit-lower-triangular, U is upper-triangular, and P
-  * is a permutation matrix.
-  *
-  * Typically, partial pivoting LU decomposition is only considered numerically stable for square invertible
-  * matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class
-  * does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the
-  * matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices.
-  *
-  * The guaranteed safe alternative, working for all matrices, is the full pivoting LU decomposition, provided
-  * by class FullPivLU.
-  *
-  * This is \b not a rank-revealing LU decomposition. Many features are intentionally absent from this class,
-  * such as rank computation. If you need these features, use class FullPivLU.
-  *
-  * This LU decomposition is suitable to invert invertible matrices. It is what MatrixBase::inverse() uses
-  * in the general case.
-  * On the other hand, it is \b not suitable to determine whether a given matrix is invertible.
-  *
-  * The data of the LU decomposition can be directly accessed through the methods matrixLU(), permutationP().
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::partialPivLu(), MatrixBase::determinant(), MatrixBase::inverse(), MatrixBase::computeInverse(), class FullPivLU
-  */
-template<typename _MatrixType> class PartialPivLU
-  : public SolverBase<PartialPivLU<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<PartialPivLU> Base;
-    friend class SolverBase<PartialPivLU>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(PartialPivLU)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
-    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via PartialPivLU::compute(const MatrixType&).
-      */
-    PartialPivLU();
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa PartialPivLU()
-      */
-    explicit PartialPivLU(Index size);
-
-    /** Constructor.
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *
-      * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
-      * If you need to deal with non-full rank, use class FullPivLU instead.
-      */
-    template<typename InputType>
-    explicit PartialPivLU(const EigenBase<InputType>& matrix);
-
-    /** Constructor for \link InplaceDecomposition inplace decomposition \endlink
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *
-      * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
-      * If you need to deal with non-full rank, use class FullPivLU instead.
-      */
-    template<typename InputType>
-    explicit PartialPivLU(EigenBase<InputType>& matrix);
-
-    template<typename InputType>
-    PartialPivLU& compute(const EigenBase<InputType>& matrix) {
-      m_lu = matrix.derived();
-      compute();
-      return *this;
-    }
-
-    /** \returns the LU decomposition matrix: the upper-triangular part is U, the
-      * unit-lower-triangular part is L (at least for square matrices; in the non-square
-      * case, special care is needed, see the documentation of class FullPivLU).
-      *
-      * \sa matrixL(), matrixU()
-      */
-    inline const MatrixType& matrixLU() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return m_lu;
-    }
-
-    /** \returns the permutation matrix P.
-      */
-    inline const PermutationType& permutationP() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return m_p;
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method returns the solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the LU decomposition.
-      *
-      * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
-      *          the only requirement in order for the equation to make sense is that
-      *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
-      *
-      * \returns the solution.
-      *
-      * Example: \include PartialPivLU_solve.cpp
-      * Output: \verbinclude PartialPivLU_solve.out
-      *
-      * Since this PartialPivLU class assumes anyway that the matrix A is invertible, the solution
-      * theoretically exists and is unique regardless of b.
-      *
-      * \sa TriangularView::solve(), inverse(), computeInverse()
-      */
-    template<typename Rhs>
-    inline const Solve<PartialPivLU, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
-        the LU decomposition.
-      */
-    inline RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    /** \returns the inverse of the matrix of which *this is the LU decomposition.
-      *
-      * \warning The matrix being decomposed here is assumed to be invertible. If you need to check for
-      *          invertibility, use class FullPivLU instead.
-      *
-      * \sa MatrixBase::inverse(), LU::inverse()
-      */
-    inline const Inverse<PartialPivLU> inverse() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return Inverse<PartialPivLU>(*this);
-    }
-
-    /** \returns the determinant of the matrix of which
-      * *this is the LU decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the LU decomposition has already been computed.
-      *
-      * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
-      *       optimized paths.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      *
-      * \sa MatrixBase::determinant()
-      */
-    Scalar determinant() const;
-
-    MatrixType reconstructedMatrix() const;
-
-    EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
-    EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    void _solve_impl(const RhsType &rhs, DstType &dst) const {
-     /* The decomposition PA = LU can be rewritten as A = P^{-1} L U.
-      * So we proceed as follows:
-      * Step 1: compute c = Pb.
-      * Step 2: replace c by the solution x to Lx = c.
-      * Step 3: replace c by the solution x to Ux = c.
-      */
-
-      // Step 1
-      dst = permutationP() * rhs;
-
-      // Step 2
-      m_lu.template triangularView<UnitLower>().solveInPlace(dst);
-
-      // Step 3
-      m_lu.template triangularView<Upper>().solveInPlace(dst);
-    }
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const {
-     /* The decomposition PA = LU can be rewritten as A^T = U^T L^T P.
-      * So we proceed as follows:
-      * Step 1: compute c as the solution to L^T c = b
-      * Step 2: replace c by the solution x to U^T x = c.
-      * Step 3: update  c = P^-1 c.
-      */
-
-      eigen_assert(rhs.rows() == m_lu.cols());
-
-      // Step 1
-      dst = m_lu.template triangularView<Upper>().transpose()
-                .template conjugateIf<Conjugate>().solve(rhs);
-      // Step 2
-      m_lu.template triangularView<UnitLower>().transpose()
-          .template conjugateIf<Conjugate>().solveInPlace(dst);
-      // Step 3
-      dst = permutationP().transpose() * dst;
-    }
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void compute();
-
-    MatrixType m_lu;
-    PermutationType m_p;
-    TranspositionType m_rowsTranspositions;
-    RealScalar m_l1_norm;
-    signed char m_det_p;
-    bool m_isInitialized;
-};
-
-template<typename MatrixType>
-PartialPivLU<MatrixType>::PartialPivLU()
-  : m_lu(),
-    m_p(),
-    m_rowsTranspositions(),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-}
-
-template<typename MatrixType>
-PartialPivLU<MatrixType>::PartialPivLU(Index size)
-  : m_lu(size, size),
-    m_p(size),
-    m_rowsTranspositions(size),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-}
-
-template<typename MatrixType>
-template<typename InputType>
-PartialPivLU<MatrixType>::PartialPivLU(const EigenBase<InputType>& matrix)
-  : m_lu(matrix.rows(),matrix.cols()),
-    m_p(matrix.rows()),
-    m_rowsTranspositions(matrix.rows()),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-  compute(matrix.derived());
-}
-
-template<typename MatrixType>
-template<typename InputType>
-PartialPivLU<MatrixType>::PartialPivLU(EigenBase<InputType>& matrix)
-  : m_lu(matrix.derived()),
-    m_p(matrix.rows()),
-    m_rowsTranspositions(matrix.rows()),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-  compute();
-}
-
-namespace internal {
-
-/** \internal This is the blocked version of fullpivlu_unblocked() */
-template<typename Scalar, int StorageOrder, typename PivIndex, int SizeAtCompileTime=Dynamic>
-struct partial_lu_impl
-{
-  static const int UnBlockedBound = 16;
-  static const bool UnBlockedAtCompileTime = SizeAtCompileTime!=Dynamic && SizeAtCompileTime<=UnBlockedBound;
-  static const int ActualSizeAtCompileTime = UnBlockedAtCompileTime ? SizeAtCompileTime : Dynamic;
-  // Remaining rows and columns at compile-time:
-  static const int RRows = SizeAtCompileTime==2 ? 1 : Dynamic;
-  static const int RCols = SizeAtCompileTime==2 ? 1 : Dynamic;
-  typedef Matrix<Scalar, ActualSizeAtCompileTime, ActualSizeAtCompileTime, StorageOrder> MatrixType;
-  typedef Ref<MatrixType> MatrixTypeRef;
-  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > BlockType;
-  typedef typename MatrixType::RealScalar RealScalar;
-
-  /** \internal performs the LU decomposition in-place of the matrix \a lu
-    * using an unblocked algorithm.
-    *
-    * In addition, this function returns the row transpositions in the
-    * vector \a row_transpositions which must have a size equal to the number
-    * of columns of the matrix \a lu, and an integer \a nb_transpositions
-    * which returns the actual number of transpositions.
-    *
-    * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
-    */
-  static Index unblocked_lu(MatrixTypeRef& lu, PivIndex* row_transpositions, PivIndex& nb_transpositions)
-  {
-    typedef scalar_score_coeff_op<Scalar> Scoring;
-    typedef typename Scoring::result_type Score;
-    const Index rows = lu.rows();
-    const Index cols = lu.cols();
-    const Index size = (std::min)(rows,cols);
-    // For small compile-time matrices it is worth processing the last row separately:
-    //  speedup: +100% for 2x2, +10% for others.
-    const Index endk = UnBlockedAtCompileTime ? size-1 : size;
-    nb_transpositions = 0;
-    Index first_zero_pivot = -1;
-    for(Index k = 0; k < endk; ++k)
-    {
-      int rrows = internal::convert_index<int>(rows-k-1);
-      int rcols = internal::convert_index<int>(cols-k-1);
-
-      Index row_of_biggest_in_col;
-      Score biggest_in_corner
-        = lu.col(k).tail(rows-k).unaryExpr(Scoring()).maxCoeff(&row_of_biggest_in_col);
-      row_of_biggest_in_col += k;
-
-      row_transpositions[k] = PivIndex(row_of_biggest_in_col);
-
-      if(biggest_in_corner != Score(0))
-      {
-        if(k != row_of_biggest_in_col)
-        {
-          lu.row(k).swap(lu.row(row_of_biggest_in_col));
-          ++nb_transpositions;
-        }
-
-        lu.col(k).tail(fix<RRows>(rrows)) /= lu.coeff(k,k);
-      }
-      else if(first_zero_pivot==-1)
-      {
-        // the pivot is exactly zero, we record the index of the first pivot which is exactly 0,
-        // and continue the factorization such we still have A = PLU
-        first_zero_pivot = k;
-      }
-
-      if(k<rows-1)
-        lu.bottomRightCorner(fix<RRows>(rrows),fix<RCols>(rcols)).noalias() -= lu.col(k).tail(fix<RRows>(rrows)) * lu.row(k).tail(fix<RCols>(rcols));
-    }
-
-    // special handling of the last entry
-    if(UnBlockedAtCompileTime)
-    {
-      Index k = endk;
-      row_transpositions[k] = PivIndex(k);
-      if (Scoring()(lu(k, k)) == Score(0) && first_zero_pivot == -1)
-        first_zero_pivot = k;
-    }
-
-    return first_zero_pivot;
-  }
-
-  /** \internal performs the LU decomposition in-place of the matrix represented
-    * by the variables \a rows, \a cols, \a lu_data, and \a lu_stride using a
-    * recursive, blocked algorithm.
-    *
-    * In addition, this function returns the row transpositions in the
-    * vector \a row_transpositions which must have a size equal to the number
-    * of columns of the matrix \a lu, and an integer \a nb_transpositions
-    * which returns the actual number of transpositions.
-    *
-    * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
-    *
-    * \note This very low level interface using pointers, etc. is to:
-    *   1 - reduce the number of instantiations to the strict minimum
-    *   2 - avoid infinite recursion of the instantiations with Block<Block<Block<...> > >
-    */
-  static Index blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, PivIndex* row_transpositions, PivIndex& nb_transpositions, Index maxBlockSize=256)
-  {
-    MatrixTypeRef lu = MatrixType::Map(lu_data,rows, cols, OuterStride<>(luStride));
-
-    const Index size = (std::min)(rows,cols);
-
-    // if the matrix is too small, no blocking:
-    if(UnBlockedAtCompileTime || size<=UnBlockedBound)
-    {
-      return unblocked_lu(lu, row_transpositions, nb_transpositions);
-    }
-
-    // automatically adjust the number of subdivisions to the size
-    // of the matrix so that there is enough sub blocks:
-    Index blockSize;
-    {
-      blockSize = size/8;
-      blockSize = (blockSize/16)*16;
-      blockSize = (std::min)((std::max)(blockSize,Index(8)), maxBlockSize);
-    }
-
-    nb_transpositions = 0;
-    Index first_zero_pivot = -1;
-    for(Index k = 0; k < size; k+=blockSize)
-    {
-      Index bs = (std::min)(size-k,blockSize); // actual size of the block
-      Index trows = rows - k - bs; // trailing rows
-      Index tsize = size - k - bs; // trailing size
-
-      // partition the matrix:
-      //                          A00 | A01 | A02
-      // lu  = A_0 | A_1 | A_2 =  A10 | A11 | A12
-      //                          A20 | A21 | A22
-      BlockType A_0 = lu.block(0,0,rows,k);
-      BlockType A_2 = lu.block(0,k+bs,rows,tsize);
-      BlockType A11 = lu.block(k,k,bs,bs);
-      BlockType A12 = lu.block(k,k+bs,bs,tsize);
-      BlockType A21 = lu.block(k+bs,k,trows,bs);
-      BlockType A22 = lu.block(k+bs,k+bs,trows,tsize);
-
-      PivIndex nb_transpositions_in_panel;
-      // recursively call the blocked LU algorithm on [A11^T A21^T]^T
-      // with a very small blocking size:
-      Index ret = blocked_lu(trows+bs, bs, &lu.coeffRef(k,k), luStride,
-                   row_transpositions+k, nb_transpositions_in_panel, 16);
-      if(ret>=0 && first_zero_pivot==-1)
-        first_zero_pivot = k+ret;
-
-      nb_transpositions += nb_transpositions_in_panel;
-      // update permutations and apply them to A_0
-      for(Index i=k; i<k+bs; ++i)
-      {
-        Index piv = (row_transpositions[i] += internal::convert_index<PivIndex>(k));
-        A_0.row(i).swap(A_0.row(piv));
-      }
-
-      if(trows)
-      {
-        // apply permutations to A_2
-        for(Index i=k;i<k+bs; ++i)
-          A_2.row(i).swap(A_2.row(row_transpositions[i]));
-
-        // A12 = A11^-1 A12
-        A11.template triangularView<UnitLower>().solveInPlace(A12);
-
-        A22.noalias() -= A21 * A12;
-      }
-    }
-    return first_zero_pivot;
-  }
-};
-
-/** \internal performs the LU decomposition with partial pivoting in-place.
-  */
-template<typename MatrixType, typename TranspositionType>
-void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions, typename TranspositionType::StorageIndex& nb_transpositions)
-{
-  // Special-case of zero matrix.
-  if (lu.rows() == 0 || lu.cols() == 0) {
-    nb_transpositions = 0;
-    return;
-  }
-  eigen_assert(lu.cols() == row_transpositions.size());
-  eigen_assert(row_transpositions.size() < 2 || (&row_transpositions.coeffRef(1)-&row_transpositions.coeffRef(0)) == 1);
-
-  partial_lu_impl
-    < typename MatrixType::Scalar, MatrixType::Flags&RowMajorBit?RowMajor:ColMajor,
-      typename TranspositionType::StorageIndex,
-      EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime)>
-    ::blocked_lu(lu.rows(), lu.cols(), &lu.coeffRef(0,0), lu.outerStride(), &row_transpositions.coeffRef(0), nb_transpositions);
-}
-
-} // end namespace internal
-
-template<typename MatrixType>
-void PartialPivLU<MatrixType>::compute()
-{
-  check_template_parameters();
-
-  // the row permutation is stored as int indices, so just to be sure:
-  eigen_assert(m_lu.rows()<NumTraits<int>::highest());
-
-  if(m_lu.cols()>0)
-    m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
-  else
-    m_l1_norm = RealScalar(0);
-
-  eigen_assert(m_lu.rows() == m_lu.cols() && "PartialPivLU is only for square (and moreover invertible) matrices");
-  const Index size = m_lu.rows();
-
-  m_rowsTranspositions.resize(size);
-
-  typename TranspositionType::StorageIndex nb_transpositions;
-  internal::partial_lu_inplace(m_lu, m_rowsTranspositions, nb_transpositions);
-  m_det_p = (nb_transpositions%2) ? -1 : 1;
-
-  m_p = m_rowsTranspositions;
-
-  m_isInitialized = true;
-}
-
-template<typename MatrixType>
-typename PartialPivLU<MatrixType>::Scalar PartialPivLU<MatrixType>::determinant() const
-{
-  eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-  return Scalar(m_det_p) * m_lu.diagonal().prod();
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: P^{-1} L U.
- * This function is provided for debug purpose. */
-template<typename MatrixType>
-MatrixType PartialPivLU<MatrixType>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LU is not initialized.");
-  // LU
-  MatrixType res = m_lu.template triangularView<UnitLower>().toDenseMatrix()
-                 * m_lu.template triangularView<Upper>();
-
-  // P^{-1}(LU)
-  res = m_p.inverse() * res;
-
-  return res;
-}
-
-/***** Implementation details *****************************************************/
-
-namespace internal {
-
-/***** Implementation of inverse() *****************************************************/
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<PartialPivLU<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename PartialPivLU<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef PartialPivLU<MatrixType> LuType;
-  typedef Inverse<LuType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename LuType::Scalar> &)
-  {
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-} // end namespace internal
-
-/******** MatrixBase methods *******/
-
-/** \lu_module
-  *
-  * \return the partial-pivoting LU decomposition of \c *this.
-  *
-  * \sa class PartialPivLU
-  */
-template<typename Derived>
-inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::partialPivLu() const
-{
-  return PartialPivLU<PlainObject>(eval());
-}
-
-/** \lu_module
-  *
-  * Synonym of partialPivLu().
-  *
-  * \return the partial-pivoting LU decomposition of \c *this.
-  *
-  * \sa class PartialPivLU
-  */
-template<typename Derived>
-inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::lu() const
-{
-  return PartialPivLU<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIALLU_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/PartialPivLU_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
deleted file mode 100644
index 755168a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *     LU decomposition with partial pivoting based on LAPACKE_?getrf function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_PARTIALLU_LAPACK_H
-#define EIGEN_PARTIALLU_LAPACK_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_LU_PARTPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX) \
-template<int StorageOrder> \
-struct partial_lu_impl<EIGTYPE, StorageOrder, lapack_int> \
-{ \
-  /* \internal performs the LU decomposition in-place of the matrix represented */ \
-  static lapack_int blocked_lu(Index rows, Index cols, EIGTYPE* lu_data, Index luStride, lapack_int* row_transpositions, lapack_int& nb_transpositions, lapack_int maxBlockSize=256) \
-  { \
-    EIGEN_UNUSED_VARIABLE(maxBlockSize);\
-    lapack_int matrix_order, first_zero_pivot; \
-    lapack_int m, n, lda, *ipiv, info; \
-    EIGTYPE* a; \
-/* Set up parameters for ?getrf */ \
-    matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
-    lda = convert_index<lapack_int>(luStride); \
-    a = lu_data; \
-    ipiv = row_transpositions; \
-    m = convert_index<lapack_int>(rows); \
-    n = convert_index<lapack_int>(cols); \
-    nb_transpositions = 0; \
-\
-    info = LAPACKE_##LAPACKE_PREFIX##getrf( matrix_order, m, n, (LAPACKE_TYPE*)a, lda, ipiv ); \
-\
-    for(int i=0;i<m;i++) { ipiv[i]--; if (ipiv[i]!=i) nb_transpositions++; } \
-\
-    eigen_assert(info >= 0); \
-/* something should be done with nb_transpositions */ \
-\
-    first_zero_pivot = info; \
-    return first_zero_pivot; \
-  } \
-};
-
-EIGEN_LAPACKE_LU_PARTPIV(double, double, d)
-EIGEN_LAPACKE_LU_PARTPIV(float, float, s)
-EIGEN_LAPACKE_LU_PARTPIV(dcomplex, lapack_complex_double, z)
-EIGEN_LAPACKE_LU_PARTPIV(scomplex, lapack_complex_float,  c)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIALLU_LAPACK_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/arch/InverseSize4.h b/3rdParty/my_ceres_vc17/include/Eigen/src/LU/arch/InverseSize4.h
deleted file mode 100644
index a232ffc..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/LU/arch/InverseSize4.h
+++ /dev/null
@@ -1,351 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2001 Intel Corporation
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-//
-// The algorithm below is a reimplementation of former \src\LU\Inverse_SSE.h using PacketMath.
-// inv(M) = M#/|M|, where inv(M), M# and |M| denote the inverse of M,
-// adjugate of M and determinant of M respectively. M# is computed block-wise
-// using specific formulae. For proof, see:
-// https://lxjk.github.io/2017/09/03/Fast-4x4-Matrix-Inverse-with-SSE-SIMD-Explained.html
-// Variable names are adopted from \src\LU\Inverse_SSE.h.
-//
-// The SSE code for the 4x4 float and double matrix inverse in former (deprecated) \src\LU\Inverse_SSE.h
-// comes from the following Intel's library:
-// http://software.intel.com/en-us/articles/optimized-matrix-library-for-use-with-the-intel-pentiumr-4-processors-sse2-instructions/
-//
-// Here is the respective copyright and license statement:
-//
-//   Copyright (c) 2001 Intel Corporation.
-//
-// Permition is granted to use, copy, distribute and prepare derivative works
-// of this library for any purpose and without fee, provided, that the above
-// copyright notice and this statement appear in all copies.
-// Intel makes no representations about the suitability of this software for
-// any purpose, and specifically disclaims all warranties.
-// See LEGAL.TXT for all the legal information.
-//
-// TODO: Unify implementations of different data types (i.e. float and double).
-#ifndef EIGEN_INVERSE_SIZE_4_H
-#define EIGEN_INVERSE_SIZE_4_H
-
-namespace Eigen
-{
-namespace internal
-{
-template <typename MatrixType, typename ResultType>
-struct compute_inverse_size4<Architecture::Target, float, MatrixType, ResultType>
-{
-  enum
-  {
-    MatrixAlignment = traits<MatrixType>::Alignment,
-    ResultAlignment = traits<ResultType>::Alignment,
-    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
-  };
-  typedef typename conditional<(MatrixType::Flags & LinearAccessBit), MatrixType const &, typename MatrixType::PlainObject>::type ActualMatrixType;
-
-  static void run(const MatrixType &mat, ResultType &result)
-  {
-    ActualMatrixType matrix(mat);
-
-    const float* data = matrix.data();
-    const Index stride = matrix.innerStride();
-    Packet4f _L1 = ploadt<Packet4f,MatrixAlignment>(data);
-    Packet4f _L2 = ploadt<Packet4f,MatrixAlignment>(data + stride*4);
-    Packet4f _L3 = ploadt<Packet4f,MatrixAlignment>(data + stride*8);
-    Packet4f _L4 = ploadt<Packet4f,MatrixAlignment>(data + stride*12);
-
-    // Four 2x2 sub-matrices of the input matrix
-    // input = [[A, B],
-    //          [C, D]]
-    Packet4f A, B, C, D;
-
-    if (!StorageOrdersMatch)
-    {
-      A = vec4f_unpacklo(_L1, _L2);
-      B = vec4f_unpacklo(_L3, _L4);
-      C = vec4f_unpackhi(_L1, _L2);
-      D = vec4f_unpackhi(_L3, _L4);
-    }
-    else
-    {
-      A = vec4f_movelh(_L1, _L2);
-      B = vec4f_movehl(_L2, _L1);
-      C = vec4f_movelh(_L3, _L4);
-      D = vec4f_movehl(_L4, _L3);
-    }
-
-    Packet4f AB, DC;
-
-    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
-    AB = pmul(vec4f_swizzle2(A, A, 3, 3, 0, 0), B);
-    AB = psub(AB, pmul(vec4f_swizzle2(A, A, 1, 1, 2, 2), vec4f_swizzle2(B, B, 2, 3, 0, 1)));
-
-    // DC = D#*C
-    DC = pmul(vec4f_swizzle2(D, D, 3, 3, 0, 0), C);
-    DC = psub(DC, pmul(vec4f_swizzle2(D, D, 1, 1, 2, 2), vec4f_swizzle2(C, C, 2, 3, 0, 1)));
-
-    // determinants of the sub-matrices
-    Packet4f dA, dB, dC, dD;
-
-    dA = pmul(vec4f_swizzle2(A, A, 3, 3, 1, 1), A);
-    dA = psub(dA, vec4f_movehl(dA, dA));
-
-    dB = pmul(vec4f_swizzle2(B, B, 3, 3, 1, 1), B);
-    dB = psub(dB, vec4f_movehl(dB, dB));
-
-    dC = pmul(vec4f_swizzle2(C, C, 3, 3, 1, 1), C);
-    dC = psub(dC, vec4f_movehl(dC, dC));
-
-    dD = pmul(vec4f_swizzle2(D, D, 3, 3, 1, 1), D);
-    dD = psub(dD, vec4f_movehl(dD, dD));
-
-    Packet4f d, d1, d2;
-
-    d = pmul(vec4f_swizzle2(DC, DC, 0, 2, 1, 3), AB);
-    d = padd(d, vec4f_movehl(d, d));
-    d = padd(d, vec4f_swizzle2(d, d, 1, 0, 0, 0));
-    d1 = pmul(dA, dD);
-    d2 = pmul(dB, dC);
-
-    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
-    Packet4f det = vec4f_duplane(psub(padd(d1, d2), d), 0);
-
-    // reciprocal of the determinant of the input matrix, rd = 1/det
-    Packet4f rd = pdiv(pset1<Packet4f>(1.0f), det);
-
-    // Four sub-matrices of the inverse
-    Packet4f iA, iB, iC, iD;
-
-    // iD = D*|A| - C*A#*B
-    iD = pmul(vec4f_swizzle2(C, C, 0, 0, 2, 2), vec4f_movelh(AB, AB));
-    iD = padd(iD, pmul(vec4f_swizzle2(C, C, 1, 1, 3, 3), vec4f_movehl(AB, AB)));
-    iD = psub(pmul(D, vec4f_duplane(dA, 0)), iD);
-
-    // iA = A*|D| - B*D#*C
-    iA = pmul(vec4f_swizzle2(B, B, 0, 0, 2, 2), vec4f_movelh(DC, DC));
-    iA = padd(iA, pmul(vec4f_swizzle2(B, B, 1, 1, 3, 3), vec4f_movehl(DC, DC)));
-    iA = psub(pmul(A, vec4f_duplane(dD, 0)), iA);
-
-    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
-    iB = pmul(D, vec4f_swizzle2(AB, AB, 3, 0, 3, 0));
-    iB = psub(iB, pmul(vec4f_swizzle2(D, D, 1, 0, 3, 2), vec4f_swizzle2(AB, AB, 2, 1, 2, 1)));
-    iB = psub(pmul(C, vec4f_duplane(dB, 0)), iB);
-
-    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
-    iC = pmul(A, vec4f_swizzle2(DC, DC, 3, 0, 3, 0));
-    iC = psub(iC, pmul(vec4f_swizzle2(A, A, 1, 0, 3, 2), vec4f_swizzle2(DC, DC, 2, 1, 2, 1)));
-    iC = psub(pmul(B, vec4f_duplane(dC, 0)), iC);
-
-    const float sign_mask[4] = {0.0f, numext::bit_cast<float>(0x80000000u), numext::bit_cast<float>(0x80000000u), 0.0f};
-    const Packet4f p4f_sign_PNNP = ploadu<Packet4f>(sign_mask);
-    rd = pxor(rd, p4f_sign_PNNP);
-    iA = pmul(iA, rd);
-    iB = pmul(iB, rd);
-    iC = pmul(iC, rd);
-    iD = pmul(iD, rd);
-
-    Index res_stride = result.outerStride();
-    float *res = result.data();
-
-    pstoret<float, Packet4f, ResultAlignment>(res + 0, vec4f_swizzle2(iA, iB, 3, 1, 3, 1));
-    pstoret<float, Packet4f, ResultAlignment>(res + res_stride, vec4f_swizzle2(iA, iB, 2, 0, 2, 0));
-    pstoret<float, Packet4f, ResultAlignment>(res + 2 * res_stride, vec4f_swizzle2(iC, iD, 3, 1, 3, 1));
-    pstoret<float, Packet4f, ResultAlignment>(res + 3 * res_stride, vec4f_swizzle2(iC, iD, 2, 0, 2, 0));
-  }
-};
-
-#if !(defined EIGEN_VECTORIZE_NEON && !(EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG))
-// same algorithm as above, except that each operand is split into
-// halves for two registers to hold.
-template <typename MatrixType, typename ResultType>
-struct compute_inverse_size4<Architecture::Target, double, MatrixType, ResultType>
-{
-  enum
-  {
-    MatrixAlignment = traits<MatrixType>::Alignment,
-    ResultAlignment = traits<ResultType>::Alignment,
-    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
-  };
-  typedef typename conditional<(MatrixType::Flags & LinearAccessBit),
-                               MatrixType const &,
-                               typename MatrixType::PlainObject>::type
-      ActualMatrixType;
-
-  static void run(const MatrixType &mat, ResultType &result)
-  {
-    ActualMatrixType matrix(mat);
-
-    // Four 2x2 sub-matrices of the input matrix, each is further divided into upper and lower
-    // row e.g. A1, upper row of A, A2, lower row of A
-    // input = [[A, B],  =  [[[A1, [B1,
-    //          [C, D]]        A2], B2]],
-    //                       [[C1, [D1,
-    //                         C2], D2]]]
-
-    Packet2d A1, A2, B1, B2, C1, C2, D1, D2;
-
-    const double* data = matrix.data();
-    const Index stride = matrix.innerStride();
-    if (StorageOrdersMatch)
-    {
-      A1 = ploadt<Packet2d,MatrixAlignment>(data + stride*0);
-      B1 = ploadt<Packet2d,MatrixAlignment>(data + stride*2);
-      A2 = ploadt<Packet2d,MatrixAlignment>(data + stride*4);
-      B2 = ploadt<Packet2d,MatrixAlignment>(data + stride*6);
-      C1 = ploadt<Packet2d,MatrixAlignment>(data + stride*8);
-      D1 = ploadt<Packet2d,MatrixAlignment>(data + stride*10);
-      C2 = ploadt<Packet2d,MatrixAlignment>(data + stride*12);
-      D2 = ploadt<Packet2d,MatrixAlignment>(data + stride*14);
-    }
-    else
-    {
-      Packet2d temp;
-      A1 = ploadt<Packet2d,MatrixAlignment>(data + stride*0);
-      C1 = ploadt<Packet2d,MatrixAlignment>(data + stride*2);
-      A2 = ploadt<Packet2d,MatrixAlignment>(data + stride*4);
-      C2 = ploadt<Packet2d,MatrixAlignment>(data + stride*6);
-      temp = A1;
-      A1 = vec2d_unpacklo(A1, A2);
-      A2 = vec2d_unpackhi(temp, A2);
-
-      temp = C1;
-      C1 = vec2d_unpacklo(C1, C2);
-      C2 = vec2d_unpackhi(temp, C2);
-
-      B1 = ploadt<Packet2d,MatrixAlignment>(data + stride*8);
-      D1 = ploadt<Packet2d,MatrixAlignment>(data + stride*10);
-      B2 = ploadt<Packet2d,MatrixAlignment>(data + stride*12);
-      D2 = ploadt<Packet2d,MatrixAlignment>(data + stride*14);
-
-      temp = B1;
-      B1 = vec2d_unpacklo(B1, B2);
-      B2 = vec2d_unpackhi(temp, B2);
-
-      temp = D1;
-      D1 = vec2d_unpacklo(D1, D2);
-      D2 = vec2d_unpackhi(temp, D2);
-    }
-
-    // determinants of the sub-matrices
-    Packet2d dA, dB, dC, dD;
-
-    dA = vec2d_swizzle2(A2, A2, 1);
-    dA = pmul(A1, dA);
-    dA = psub(dA, vec2d_duplane(dA, 1));
-
-    dB = vec2d_swizzle2(B2, B2, 1);
-    dB = pmul(B1, dB);
-    dB = psub(dB, vec2d_duplane(dB, 1));
-
-    dC = vec2d_swizzle2(C2, C2, 1);
-    dC = pmul(C1, dC);
-    dC = psub(dC, vec2d_duplane(dC, 1));
-
-    dD = vec2d_swizzle2(D2, D2, 1);
-    dD = pmul(D1, dD);
-    dD = psub(dD, vec2d_duplane(dD, 1));
-
-    Packet2d DC1, DC2, AB1, AB2;
-
-    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
-    AB1 = pmul(B1, vec2d_duplane(A2, 1));
-    AB2 = pmul(B2, vec2d_duplane(A1, 0));
-    AB1 = psub(AB1, pmul(B2, vec2d_duplane(A1, 1)));
-    AB2 = psub(AB2, pmul(B1, vec2d_duplane(A2, 0)));
-
-    // DC = D#*C
-    DC1 = pmul(C1, vec2d_duplane(D2, 1));
-    DC2 = pmul(C2, vec2d_duplane(D1, 0));
-    DC1 = psub(DC1, pmul(C2, vec2d_duplane(D1, 1)));
-    DC2 = psub(DC2, pmul(C1, vec2d_duplane(D2, 0)));
-
-    Packet2d d1, d2;
-
-    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
-    Packet2d det;
-
-    // reciprocal of the determinant of the input matrix, rd = 1/det
-    Packet2d rd;
-
-    d1 = pmul(AB1, vec2d_swizzle2(DC1, DC2, 0));
-    d2 = pmul(AB2, vec2d_swizzle2(DC1, DC2, 3));
-    rd = padd(d1, d2);
-    rd = padd(rd, vec2d_duplane(rd, 1));
-
-    d1 = pmul(dA, dD);
-    d2 = pmul(dB, dC);
-
-    det = padd(d1, d2);
-    det = psub(det, rd);
-    det = vec2d_duplane(det, 0);
-    rd = pdiv(pset1<Packet2d>(1.0), det);
-
-    // rows of four sub-matrices of the inverse
-    Packet2d iA1, iA2, iB1, iB2, iC1, iC2, iD1, iD2;
-
-    // iD = D*|A| - C*A#*B
-    iD1 = pmul(AB1, vec2d_duplane(C1, 0));
-    iD2 = pmul(AB1, vec2d_duplane(C2, 0));
-    iD1 = padd(iD1, pmul(AB2, vec2d_duplane(C1, 1)));
-    iD2 = padd(iD2, pmul(AB2, vec2d_duplane(C2, 1)));
-    dA = vec2d_duplane(dA, 0);
-    iD1 = psub(pmul(D1, dA), iD1);
-    iD2 = psub(pmul(D2, dA), iD2);
-
-    // iA = A*|D| - B*D#*C
-    iA1 = pmul(DC1, vec2d_duplane(B1, 0));
-    iA2 = pmul(DC1, vec2d_duplane(B2, 0));
-    iA1 = padd(iA1, pmul(DC2, vec2d_duplane(B1, 1)));
-    iA2 = padd(iA2, pmul(DC2, vec2d_duplane(B2, 1)));
-    dD = vec2d_duplane(dD, 0);
-    iA1 = psub(pmul(A1, dD), iA1);
-    iA2 = psub(pmul(A2, dD), iA2);
-
-    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
-    iB1 = pmul(D1, vec2d_swizzle2(AB2, AB1, 1));
-    iB2 = pmul(D2, vec2d_swizzle2(AB2, AB1, 1));
-    iB1 = psub(iB1, pmul(vec2d_swizzle2(D1, D1, 1), vec2d_swizzle2(AB2, AB1, 2)));
-    iB2 = psub(iB2, pmul(vec2d_swizzle2(D2, D2, 1), vec2d_swizzle2(AB2, AB1, 2)));
-    dB = vec2d_duplane(dB, 0);
-    iB1 = psub(pmul(C1, dB), iB1);
-    iB2 = psub(pmul(C2, dB), iB2);
-
-    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
-    iC1 = pmul(A1, vec2d_swizzle2(DC2, DC1, 1));
-    iC2 = pmul(A2, vec2d_swizzle2(DC2, DC1, 1));
-    iC1 = psub(iC1, pmul(vec2d_swizzle2(A1, A1, 1), vec2d_swizzle2(DC2, DC1, 2)));
-    iC2 = psub(iC2, pmul(vec2d_swizzle2(A2, A2, 1), vec2d_swizzle2(DC2, DC1, 2)));
-    dC = vec2d_duplane(dC, 0);
-    iC1 = psub(pmul(B1, dC), iC1);
-    iC2 = psub(pmul(B2, dC), iC2);
-
-    const double sign_mask1[2] = {0.0, numext::bit_cast<double>(0x8000000000000000ull)};
-    const double sign_mask2[2] = {numext::bit_cast<double>(0x8000000000000000ull), 0.0};
-    const Packet2d sign_PN = ploadu<Packet2d>(sign_mask1);
-    const Packet2d sign_NP = ploadu<Packet2d>(sign_mask2);
-    d1 = pxor(rd, sign_PN);
-    d2 = pxor(rd, sign_NP);
-
-    Index res_stride = result.outerStride();
-    double *res = result.data();
-    pstoret<double, Packet2d, ResultAlignment>(res + 0, pmul(vec2d_swizzle2(iA2, iA1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + res_stride, pmul(vec2d_swizzle2(iA2, iA1, 0), d2));
-    pstoret<double, Packet2d, ResultAlignment>(res + 2, pmul(vec2d_swizzle2(iB2, iB1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + res_stride + 2, pmul(vec2d_swizzle2(iB2, iB1, 0), d2));
-    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 0), d2));
-    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 0), d2));
-  }
-};
-#endif
-} // namespace internal
-} // namespace Eigen
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/MetisSupport/MetisSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/MetisSupport/MetisSupport.h
deleted file mode 100644
index 4c15304..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/MetisSupport/MetisSupport.h
+++ /dev/null
@@ -1,137 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef METIS_SUPPORT_H
-#define METIS_SUPPORT_H
-
-namespace Eigen {
-/**
- * Get the fill-reducing ordering from the METIS package
- * 
- * If A is the original matrix and Ap is the permuted matrix, 
- * the fill-reducing permutation is defined as follows :
- * Row (column) i of A is the matperm(i) row (column) of Ap. 
- * WARNING: As computed by METIS, this corresponds to the vector iperm (instead of perm)
- */
-template <typename StorageIndex>
-class MetisOrdering
-{
-public:
-  typedef PermutationMatrix<Dynamic,Dynamic,StorageIndex> PermutationType;
-  typedef Matrix<StorageIndex,Dynamic,1> IndexVector; 
-  
-  template <typename MatrixType>
-  void get_symmetrized_graph(const MatrixType& A)
-  {
-    Index m = A.cols(); 
-    eigen_assert((A.rows() == A.cols()) && "ONLY FOR SQUARED MATRICES");
-    // Get the transpose of the input matrix 
-    MatrixType At = A.transpose(); 
-    // Get the number of nonzeros elements in each row/col of At+A
-    Index TotNz = 0; 
-    IndexVector visited(m); 
-    visited.setConstant(-1); 
-    for (StorageIndex j = 0; j < m; j++)
-    {
-      // Compute the union structure of of A(j,:) and At(j,:)
-      visited(j) = j; // Do not include the diagonal element
-      // Get the nonzeros in row/column j of A
-      for (typename MatrixType::InnerIterator it(A, j); it; ++it)
-      {
-        Index idx = it.index(); // Get the row index (for column major) or column index (for row major)
-        if (visited(idx) != j ) 
-        {
-          visited(idx) = j; 
-          ++TotNz; 
-        }
-      }
-      //Get the nonzeros in row/column j of At
-      for (typename MatrixType::InnerIterator it(At, j); it; ++it)
-      {
-        Index idx = it.index(); 
-        if(visited(idx) != j)
-        {
-          visited(idx) = j; 
-          ++TotNz; 
-        }
-      }
-    }
-    // Reserve place for A + At
-    m_indexPtr.resize(m+1);
-    m_innerIndices.resize(TotNz); 
-
-    // Now compute the real adjacency list of each column/row 
-    visited.setConstant(-1); 
-    StorageIndex CurNz = 0; 
-    for (StorageIndex j = 0; j < m; j++)
-    {
-      m_indexPtr(j) = CurNz; 
-      
-      visited(j) = j; // Do not include the diagonal element
-      // Add the pattern of row/column j of A to A+At
-      for (typename MatrixType::InnerIterator it(A,j); it; ++it)
-      {
-        StorageIndex idx = it.index(); // Get the row index (for column major) or column index (for row major)
-        if (visited(idx) != j ) 
-        {
-          visited(idx) = j; 
-          m_innerIndices(CurNz) = idx; 
-          CurNz++; 
-        }
-      }
-      //Add the pattern of row/column j of At to A+At
-      for (typename MatrixType::InnerIterator it(At, j); it; ++it)
-      {
-        StorageIndex idx = it.index(); 
-        if(visited(idx) != j)
-        {
-          visited(idx) = j; 
-          m_innerIndices(CurNz) = idx; 
-          ++CurNz; 
-        }
-      }
-    }
-    m_indexPtr(m) = CurNz;    
-  }
-  
-  template <typename MatrixType>
-  void operator() (const MatrixType& A, PermutationType& matperm)
-  {
-     StorageIndex m = internal::convert_index<StorageIndex>(A.cols()); // must be StorageIndex, because it is passed by address to METIS
-     IndexVector perm(m),iperm(m); 
-    // First, symmetrize the matrix graph. 
-     get_symmetrized_graph(A); 
-     int output_error;
-     
-     // Call the fill-reducing routine from METIS 
-     output_error = METIS_NodeND(&m, m_indexPtr.data(), m_innerIndices.data(), NULL, NULL, perm.data(), iperm.data());
-     
-    if(output_error != METIS_OK) 
-    {
-      //FIXME The ordering interface should define a class of possible errors 
-     std::cerr << "ERROR WHILE CALLING THE METIS PACKAGE \n"; 
-     return; 
-    }
-    
-    // Get the fill-reducing permutation 
-    //NOTE:  If Ap is the permuted matrix then perm and iperm vectors are defined as follows 
-    // Row (column) i of Ap is the perm(i) row(column) of A, and row (column) i of A is the iperm(i) row(column) of Ap
-    
-     matperm.resize(m);
-     for (int j = 0; j < m; j++)
-       matperm.indices()(iperm(j)) = j;
-   
-  }
-  
-  protected:
-    IndexVector m_indexPtr; // Pointer to the adjacenccy list of each row/column
-    IndexVector m_innerIndices; // Adjacency list 
-};
-
-}// end namespace eigen 
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Amd.h b/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Amd.h
deleted file mode 100644
index 7ca3f33..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Amd.h
+++ /dev/null
@@ -1,435 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*
-NOTE: this routine has been adapted from the CSparse library:
-
-Copyright (c) 2006, Timothy A. Davis.
-http://www.suitesparse.com
-
-The author of CSparse, Timothy A. Davis., has executed a license with Google LLC
-to permit distribution of this code and derivative works as part of Eigen under
-the Mozilla Public License v. 2.0, as stated at the top of this file.
-*/
-
-#ifndef EIGEN_SPARSE_AMD_H
-#define EIGEN_SPARSE_AMD_H
-
-namespace Eigen { 
-
-namespace internal {
-  
-template<typename T> inline T amd_flip(const T& i) { return -i-2; }
-template<typename T> inline T amd_unflip(const T& i) { return i<0 ? amd_flip(i) : i; }
-template<typename T0, typename T1> inline bool amd_marked(const T0* w, const T1& j) { return w[j]<0; }
-template<typename T0, typename T1> inline void amd_mark(const T0* w, const T1& j) { return w[j] = amd_flip(w[j]); }
-
-/* clear w */
-template<typename StorageIndex>
-static StorageIndex cs_wclear (StorageIndex mark, StorageIndex lemax, StorageIndex *w, StorageIndex n)
-{
-  StorageIndex k;
-  if(mark < 2 || (mark + lemax < 0))
-  {
-    for(k = 0; k < n; k++)
-      if(w[k] != 0)
-        w[k] = 1;
-    mark = 2;
-  }
-  return (mark);     /* at this point, w[0..n-1] < mark holds */
-}
-
-/* depth-first search and postorder of a tree rooted at node j */
-template<typename StorageIndex>
-StorageIndex cs_tdfs(StorageIndex j, StorageIndex k, StorageIndex *head, const StorageIndex *next, StorageIndex *post, StorageIndex *stack)
-{
-  StorageIndex i, p, top = 0;
-  if(!head || !next || !post || !stack) return (-1);    /* check inputs */
-  stack[0] = j;                 /* place j on the stack */
-  while (top >= 0)                /* while (stack is not empty) */
-  {
-    p = stack[top];           /* p = top of stack */
-    i = head[p];              /* i = youngest child of p */
-    if(i == -1)
-    {
-      top--;                 /* p has no unordered children left */
-      post[k++] = p;        /* node p is the kth postordered node */
-    }
-    else
-    {
-      head[p] = next[i];   /* remove i from children of p */
-      stack[++top] = i;     /* start dfs on child node i */
-    }
-  }
-  return k;
-}
-
-
-/** \internal
-  * \ingroup OrderingMethods_Module 
-  * Approximate minimum degree ordering algorithm.
-  *
-  * \param[in] C the input selfadjoint matrix stored in compressed column major format.
-  * \param[out] perm the permutation P reducing the fill-in of the input matrix \a C
-  *
-  * Note that the input matrix \a C must be complete, that is both the upper and lower parts have to be stored, as well as the diagonal entries.
-  * On exit the values of C are destroyed */
-template<typename Scalar, typename StorageIndex>
-void minimum_degree_ordering(SparseMatrix<Scalar,ColMajor,StorageIndex>& C, PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm)
-{
-  using std::sqrt;
-  
-  StorageIndex d, dk, dext, lemax = 0, e, elenk, eln, i, j, k, k1,
-                k2, k3, jlast, ln, dense, nzmax, mindeg = 0, nvi, nvj, nvk, mark, wnvi,
-                ok, nel = 0, p, p1, p2, p3, p4, pj, pk, pk1, pk2, pn, q, t, h;
-  
-  StorageIndex n = StorageIndex(C.cols());
-  dense = std::max<StorageIndex> (16, StorageIndex(10 * sqrt(double(n))));   /* find dense threshold */
-  dense = (std::min)(n-2, dense);
-  
-  StorageIndex cnz = StorageIndex(C.nonZeros());
-  perm.resize(n+1);
-  t = cnz + cnz/5 + 2*n;                 /* add elbow room to C */
-  C.resizeNonZeros(t);
-  
-  // get workspace
-  ei_declare_aligned_stack_constructed_variable(StorageIndex,W,8*(n+1),0);
-  StorageIndex* len     = W;
-  StorageIndex* nv      = W +   (n+1);
-  StorageIndex* next    = W + 2*(n+1);
-  StorageIndex* head    = W + 3*(n+1);
-  StorageIndex* elen    = W + 4*(n+1);
-  StorageIndex* degree  = W + 5*(n+1);
-  StorageIndex* w       = W + 6*(n+1);
-  StorageIndex* hhead   = W + 7*(n+1);
-  StorageIndex* last    = perm.indices().data();                              /* use P as workspace for last */
-  
-  /* --- Initialize quotient graph ---------------------------------------- */
-  StorageIndex* Cp = C.outerIndexPtr();
-  StorageIndex* Ci = C.innerIndexPtr();
-  for(k = 0; k < n; k++)
-    len[k] = Cp[k+1] - Cp[k];
-  len[n] = 0;
-  nzmax = t;
-  
-  for(i = 0; i <= n; i++)
-  {
-    head[i]   = -1;                     // degree list i is empty
-    last[i]   = -1;
-    next[i]   = -1;
-    hhead[i]  = -1;                     // hash list i is empty 
-    nv[i]     = 1;                      // node i is just one node
-    w[i]      = 1;                      // node i is alive
-    elen[i]   = 0;                      // Ek of node i is empty
-    degree[i] = len[i];                 // degree of node i
-  }
-  mark = internal::cs_wclear<StorageIndex>(0, 0, w, n);         /* clear w */
-  
-  /* --- Initialize degree lists ------------------------------------------ */
-  for(i = 0; i < n; i++)
-  {
-    bool has_diag = false;
-    for(p = Cp[i]; p<Cp[i+1]; ++p)
-      if(Ci[p]==i)
-      {
-        has_diag = true;
-        break;
-      }
-   
-    d = degree[i];
-    if(d == 1 && has_diag)           /* node i is empty */
-    {
-      elen[i] = -2;                 /* element i is dead */
-      nel++;
-      Cp[i] = -1;                   /* i is a root of assembly tree */
-      w[i] = 0;
-    }
-    else if(d > dense || !has_diag)  /* node i is dense or has no structural diagonal element */
-    {
-      nv[i] = 0;                    /* absorb i into element n */
-      elen[i] = -1;                 /* node i is dead */
-      nel++;
-      Cp[i] = amd_flip (n);
-      nv[n]++;
-    }
-    else
-    {
-      if(head[d] != -1) last[head[d]] = i;
-      next[i] = head[d];           /* put node i in degree list d */
-      head[d] = i;
-    }
-  }
-  
-  elen[n] = -2;                         /* n is a dead element */
-  Cp[n] = -1;                           /* n is a root of assembly tree */
-  w[n] = 0;                             /* n is a dead element */
-  
-  while (nel < n)                         /* while (selecting pivots) do */
-  {
-    /* --- Select node of minimum approximate degree -------------------- */
-    for(k = -1; mindeg < n && (k = head[mindeg]) == -1; mindeg++) {}
-    if(next[k] != -1) last[next[k]] = -1;
-    head[mindeg] = next[k];          /* remove k from degree list */
-    elenk = elen[k];                  /* elenk = |Ek| */
-    nvk = nv[k];                      /* # of nodes k represents */
-    nel += nvk;                        /* nv[k] nodes of A eliminated */
-    
-    /* --- Garbage collection ------------------------------------------- */
-    if(elenk > 0 && cnz + mindeg >= nzmax)
-    {
-      for(j = 0; j < n; j++)
-      {
-        if((p = Cp[j]) >= 0)      /* j is a live node or element */
-        {
-          Cp[j] = Ci[p];          /* save first entry of object */
-          Ci[p] = amd_flip (j);    /* first entry is now amd_flip(j) */
-        }
-      }
-      for(q = 0, p = 0; p < cnz; ) /* scan all of memory */
-      {
-        if((j = amd_flip (Ci[p++])) >= 0)  /* found object j */
-        {
-          Ci[q] = Cp[j];       /* restore first entry of object */
-          Cp[j] = q++;          /* new pointer to object j */
-          for(k3 = 0; k3 < len[j]-1; k3++) Ci[q++] = Ci[p++];
-        }
-      }
-      cnz = q;                       /* Ci[cnz...nzmax-1] now free */
-    }
-    
-    /* --- Construct new element ---------------------------------------- */
-    dk = 0;
-    nv[k] = -nvk;                     /* flag k as in Lk */
-    p = Cp[k];
-    pk1 = (elenk == 0) ? p : cnz;      /* do in place if elen[k] == 0 */
-    pk2 = pk1;
-    for(k1 = 1; k1 <= elenk + 1; k1++)
-    {
-      if(k1 > elenk)
-      {
-        e = k;                     /* search the nodes in k */
-        pj = p;                    /* list of nodes starts at Ci[pj]*/
-        ln = len[k] - elenk;      /* length of list of nodes in k */
-      }
-      else
-      {
-        e = Ci[p++];              /* search the nodes in e */
-        pj = Cp[e];
-        ln = len[e];              /* length of list of nodes in e */
-      }
-      for(k2 = 1; k2 <= ln; k2++)
-      {
-        i = Ci[pj++];
-        if((nvi = nv[i]) <= 0) continue; /* node i dead, or seen */
-        dk += nvi;                 /* degree[Lk] += size of node i */
-        nv[i] = -nvi;             /* negate nv[i] to denote i in Lk*/
-        Ci[pk2++] = i;            /* place i in Lk */
-        if(next[i] != -1) last[next[i]] = last[i];
-        if(last[i] != -1)         /* remove i from degree list */
-        {
-          next[last[i]] = next[i];
-        }
-        else
-        {
-          head[degree[i]] = next[i];
-        }
-      }
-      if(e != k)
-      {
-        Cp[e] = amd_flip (k);      /* absorb e into k */
-        w[e] = 0;                 /* e is now a dead element */
-      }
-    }
-    if(elenk != 0) cnz = pk2;         /* Ci[cnz...nzmax] is free */
-    degree[k] = dk;                   /* external degree of k - |Lk\i| */
-    Cp[k] = pk1;                      /* element k is in Ci[pk1..pk2-1] */
-    len[k] = pk2 - pk1;
-    elen[k] = -2;                     /* k is now an element */
-    
-    /* --- Find set differences ----------------------------------------- */
-    mark = internal::cs_wclear<StorageIndex>(mark, lemax, w, n);  /* clear w if necessary */
-    for(pk = pk1; pk < pk2; pk++)    /* scan 1: find |Le\Lk| */
-    {
-      i = Ci[pk];
-      if((eln = elen[i]) <= 0) continue;/* skip if elen[i] empty */
-      nvi = -nv[i];                      /* nv[i] was negated */
-      wnvi = mark - nvi;
-      for(p = Cp[i]; p <= Cp[i] + eln - 1; p++)  /* scan Ei */
-      {
-        e = Ci[p];
-        if(w[e] >= mark)
-        {
-          w[e] -= nvi;          /* decrement |Le\Lk| */
-        }
-        else if(w[e] != 0)        /* ensure e is a live element */
-        {
-          w[e] = degree[e] + wnvi; /* 1st time e seen in scan 1 */
-        }
-      }
-    }
-    
-    /* --- Degree update ------------------------------------------------ */
-    for(pk = pk1; pk < pk2; pk++)    /* scan2: degree update */
-    {
-      i = Ci[pk];                   /* consider node i in Lk */
-      p1 = Cp[i];
-      p2 = p1 + elen[i] - 1;
-      pn = p1;
-      for(h = 0, d = 0, p = p1; p <= p2; p++)    /* scan Ei */
-      {
-        e = Ci[p];
-        if(w[e] != 0)             /* e is an unabsorbed element */
-        {
-          dext = w[e] - mark;   /* dext = |Le\Lk| */
-          if(dext > 0)
-          {
-            d += dext;         /* sum up the set differences */
-            Ci[pn++] = e;     /* keep e in Ei */
-            h += e;            /* compute the hash of node i */
-          }
-          else
-          {
-            Cp[e] = amd_flip (k);  /* aggressive absorb. e->k */
-            w[e] = 0;             /* e is a dead element */
-          }
-        }
-      }
-      elen[i] = pn - p1 + 1;        /* elen[i] = |Ei| */
-      p3 = pn;
-      p4 = p1 + len[i];
-      for(p = p2 + 1; p < p4; p++) /* prune edges in Ai */
-      {
-        j = Ci[p];
-        if((nvj = nv[j]) <= 0) continue; /* node j dead or in Lk */
-        d += nvj;                  /* degree(i) += |j| */
-        Ci[pn++] = j;             /* place j in node list of i */
-        h += j;                    /* compute hash for node i */
-      }
-      if(d == 0)                     /* check for mass elimination */
-      {
-        Cp[i] = amd_flip (k);      /* absorb i into k */
-        nvi = -nv[i];
-        dk -= nvi;                 /* |Lk| -= |i| */
-        nvk += nvi;                /* |k| += nv[i] */
-        nel += nvi;
-        nv[i] = 0;
-        elen[i] = -1;             /* node i is dead */
-      }
-      else
-      {
-        degree[i] = std::min<StorageIndex> (degree[i], d);   /* update degree(i) */
-        Ci[pn] = Ci[p3];         /* move first node to end */
-        Ci[p3] = Ci[p1];         /* move 1st el. to end of Ei */
-        Ci[p1] = k;               /* add k as 1st element in of Ei */
-        len[i] = pn - p1 + 1;     /* new len of adj. list of node i */
-        h %= n;                    /* finalize hash of i */
-        next[i] = hhead[h];      /* place i in hash bucket */
-        hhead[h] = i;
-        last[i] = h;      /* save hash of i in last[i] */
-      }
-    }                                   /* scan2 is done */
-    degree[k] = dk;                   /* finalize |Lk| */
-    lemax = std::max<StorageIndex>(lemax, dk);
-    mark = internal::cs_wclear<StorageIndex>(mark+lemax, lemax, w, n);    /* clear w */
-    
-    /* --- Supernode detection ------------------------------------------ */
-    for(pk = pk1; pk < pk2; pk++)
-    {
-      i = Ci[pk];
-      if(nv[i] >= 0) continue;         /* skip if i is dead */
-      h = last[i];                      /* scan hash bucket of node i */
-      i = hhead[h];
-      hhead[h] = -1;                    /* hash bucket will be empty */
-      for(; i != -1 && next[i] != -1; i = next[i], mark++)
-      {
-        ln = len[i];
-        eln = elen[i];
-        for(p = Cp[i]+1; p <= Cp[i] + ln-1; p++) w[Ci[p]] = mark;
-        jlast = i;
-        for(j = next[i]; j != -1; ) /* compare i with all j */
-        {
-          ok = (len[j] == ln) && (elen[j] == eln);
-          for(p = Cp[j] + 1; ok && p <= Cp[j] + ln - 1; p++)
-          {
-            if(w[Ci[p]] != mark) ok = 0;    /* compare i and j*/
-          }
-          if(ok)                     /* i and j are identical */
-          {
-            Cp[j] = amd_flip (i);  /* absorb j into i */
-            nv[i] += nv[j];
-            nv[j] = 0;
-            elen[j] = -1;         /* node j is dead */
-            j = next[j];          /* delete j from hash bucket */
-            next[jlast] = j;
-          }
-          else
-          {
-            jlast = j;             /* j and i are different */
-            j = next[j];
-          }
-        }
-      }
-    }
-    
-    /* --- Finalize new element------------------------------------------ */
-    for(p = pk1, pk = pk1; pk < pk2; pk++)   /* finalize Lk */
-    {
-      i = Ci[pk];
-      if((nvi = -nv[i]) <= 0) continue;/* skip if i is dead */
-      nv[i] = nvi;                      /* restore nv[i] */
-      d = degree[i] + dk - nvi;         /* compute external degree(i) */
-      d = std::min<StorageIndex> (d, n - nel - nvi);
-      if(head[d] != -1) last[head[d]] = i;
-      next[i] = head[d];               /* put i back in degree list */
-      last[i] = -1;
-      head[d] = i;
-      mindeg = std::min<StorageIndex> (mindeg, d);       /* find new minimum degree */
-      degree[i] = d;
-      Ci[p++] = i;                      /* place i in Lk */
-    }
-    nv[k] = nvk;                      /* # nodes absorbed into k */
-    if((len[k] = p-pk1) == 0)         /* length of adj list of element k*/
-    {
-      Cp[k] = -1;                   /* k is a root of the tree */
-      w[k] = 0;                     /* k is now a dead element */
-    }
-    if(elenk != 0) cnz = p;           /* free unused space in Lk */
-  }
-  
-  /* --- Postordering ----------------------------------------------------- */
-  for(i = 0; i < n; i++) Cp[i] = amd_flip (Cp[i]);/* fix assembly tree */
-  for(j = 0; j <= n; j++) head[j] = -1;
-  for(j = n; j >= 0; j--)              /* place unordered nodes in lists */
-  {
-    if(nv[j] > 0) continue;          /* skip if j is an element */
-    next[j] = head[Cp[j]];          /* place j in list of its parent */
-    head[Cp[j]] = j;
-  }
-  for(e = n; e >= 0; e--)              /* place elements in lists */
-  {
-    if(nv[e] <= 0) continue;         /* skip unless e is an element */
-    if(Cp[e] != -1)
-    {
-      next[e] = head[Cp[e]];      /* place e in list of its parent */
-      head[Cp[e]] = e;
-    }
-  }
-  for(k = 0, i = 0; i <= n; i++)       /* postorder the assembly tree */
-  {
-    if(Cp[i] == -1) k = internal::cs_tdfs<StorageIndex>(i, k, head, next, perm.indices().data(), w);
-  }
-  
-  perm.indices().conservativeResize(n);
-}
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_AMD_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Eigen_Colamd.h b/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
deleted file mode 100644
index 8e339a7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
+++ /dev/null
@@ -1,1863 +0,0 @@
-// // This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is modified from the colamd/symamd library. The copyright is below
-
-//   The authors of the code itself are Stefan I. Larimore and Timothy A.
-//   Davis (davis@cise.ufl.edu), University of Florida.  The algorithm was
-//   developed in collaboration with John Gilbert, Xerox PARC, and Esmond
-//   Ng, Oak Ridge National Laboratory.
-//
-//     Date:
-//
-//   September 8, 2003.  Version 2.3.
-//
-//     Acknowledgements:
-//
-//   This work was supported by the National Science Foundation, under
-//   grants DMS-9504974 and DMS-9803599.
-//
-//     Notice:
-//
-//   Copyright (c) 1998-2003 by the University of Florida.
-//   All Rights Reserved.
-//
-//   THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
-//   EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
-//
-//   Permission is hereby granted to use, copy, modify, and/or distribute
-//   this program, provided that the Copyright, this License, and the
-//   Availability of the original version is retained on all copies and made
-//   accessible to the end-user of any code or package that includes COLAMD
-//   or any modified version of COLAMD.
-//
-//     Availability:
-//
-//   The colamd/symamd library is available at
-//
-//       http://www.suitesparse.com
-
-
-#ifndef EIGEN_COLAMD_H
-#define EIGEN_COLAMD_H
-
-namespace internal {
-
-namespace Colamd {
-
-/* Ensure that debugging is turned off: */
-#ifndef COLAMD_NDEBUG
-#define COLAMD_NDEBUG
-#endif /* NDEBUG */
-
-
-/* ========================================================================== */
-/* === Knob and statistics definitions ====================================== */
-/* ========================================================================== */
-
-/* size of the knobs [ ] array.  Only knobs [0..1] are currently used. */
-const int NKnobs = 20;
-
-/* number of output statistics.  Only stats [0..6] are currently used. */
-const int NStats = 20;
-
-/* Indices into knobs and stats array. */
-enum KnobsStatsIndex {
-  /* knobs [0] and stats [0]: dense row knob and output statistic. */
-  DenseRow = 0,
-
-  /* knobs [1] and stats [1]: dense column knob and output statistic. */
-  DenseCol = 1,
-
-  /* stats [2]: memory defragmentation count output statistic */
-  DefragCount = 2,
-
-  /* stats [3]: colamd status:  zero OK, > 0 warning or notice, < 0 error */
-  Status = 3,
-
-  /* stats [4..6]: error info, or info on jumbled columns */
-  Info1 = 4,
-  Info2 = 5,
-  Info3 = 6
-};
-
-/* error codes returned in stats [3]: */
-enum Status {
-  Ok = 0,
-  OkButJumbled = 1,
-  ErrorANotPresent = -1,
-  ErrorPNotPresent = -2,
-  ErrorNrowNegative = -3,
-  ErrorNcolNegative = -4,
-  ErrorNnzNegative = -5,
-  ErrorP0Nonzero = -6,
-  ErrorATooSmall = -7,
-  ErrorColLengthNegative = -8,
-  ErrorRowIndexOutOfBounds = -9,
-  ErrorOutOfMemory = -10,
-  ErrorInternalError = -999
-};
-/* ========================================================================== */
-/* === Definitions ========================================================== */
-/* ========================================================================== */
-
-template <typename IndexType>
-IndexType ones_complement(const IndexType r) {
-  return (-(r)-1);
-}
-
-/* -------------------------------------------------------------------------- */
-const int Empty = -1;
-
-/* Row and column status */
-enum RowColumnStatus {
-  Alive = 0,
-  Dead = -1
-};
-
-/* Column status */
-enum ColumnStatus {
-  DeadPrincipal = -1,
-  DeadNonPrincipal = -2
-};
-
-/* ========================================================================== */
-/* === Colamd reporting mechanism =========================================== */
-/* ========================================================================== */
-
-// == Row and Column structures ==
-template <typename IndexType>
-struct ColStructure
-{
-  IndexType start ;   /* index for A of first row in this column, or Dead */
-  /* if column is dead */
-  IndexType length ;  /* number of rows in this column */
-  union
-  {
-    IndexType thickness ; /* number of original columns represented by this */
-    /* col, if the column is alive */
-    IndexType parent ;  /* parent in parent tree super-column structure, if */
-    /* the column is dead */
-  } shared1 ;
-  union
-  {
-    IndexType score ; /* the score used to maintain heap, if col is alive */
-    IndexType order ; /* pivot ordering of this column, if col is dead */
-  } shared2 ;
-  union
-  {
-    IndexType headhash ;  /* head of a hash bucket, if col is at the head of */
-    /* a degree list */
-    IndexType hash ;  /* hash value, if col is not in a degree list */
-    IndexType prev ;  /* previous column in degree list, if col is in a */
-    /* degree list (but not at the head of a degree list) */
-  } shared3 ;
-  union
-  {
-    IndexType degree_next ; /* next column, if col is in a degree list */
-    IndexType hash_next ;   /* next column, if col is in a hash list */
-  } shared4 ;
-
-  inline bool is_dead() const { return start < Alive; }
-
-  inline bool is_alive() const { return start >= Alive; }
-
-  inline bool is_dead_principal() const { return start == DeadPrincipal; }
-
-  inline void kill_principal() { start = DeadPrincipal; }
-
-  inline void kill_non_principal() { start = DeadNonPrincipal; }
-
-};
-
-template <typename IndexType>
-struct RowStructure
-{
-  IndexType start ;   /* index for A of first col in this row */
-  IndexType length ;  /* number of principal columns in this row */
-  union
-  {
-    IndexType degree ;  /* number of principal & non-principal columns in row */
-    IndexType p ;   /* used as a row pointer in init_rows_cols () */
-  } shared1 ;
-  union
-  {
-    IndexType mark ;  /* for computing set differences and marking dead rows*/
-    IndexType first_column ;/* first column in row (used in garbage collection) */
-  } shared2 ;
-
-  inline bool is_dead() const { return shared2.mark < Alive; }
-
-  inline bool is_alive() const { return shared2.mark >= Alive; }
-
-  inline void kill() { shared2.mark = Dead; }
-
-};
-
-/* ========================================================================== */
-/* === Colamd recommended memory size ======================================= */
-/* ========================================================================== */
-
-/*
-  The recommended length Alen of the array A passed to colamd is given by
-  the COLAMD_RECOMMENDED (nnz, n_row, n_col) macro.  It returns -1 if any
-  argument is negative.  2*nnz space is required for the row and column
-  indices of the matrix. colamd_c (n_col) + colamd_r (n_row) space is
-  required for the Col and Row arrays, respectively, which are internal to
-  colamd.  An additional n_col space is the minimal amount of "elbow room",
-  and nnz/5 more space is recommended for run time efficiency.
-
-  This macro is not needed when using symamd.
-
-  Explicit typecast to IndexType added Sept. 23, 2002, COLAMD version 2.2, to avoid
-  gcc -pedantic warning messages.
-*/
-template <typename IndexType>
-inline IndexType colamd_c(IndexType n_col)
-{ return IndexType( ((n_col) + 1) * sizeof (ColStructure<IndexType>) / sizeof (IndexType) ) ; }
-
-template <typename IndexType>
-inline IndexType  colamd_r(IndexType n_row)
-{ return IndexType(((n_row) + 1) * sizeof (RowStructure<IndexType>) / sizeof (IndexType)); }
-
-// Prototypes of non-user callable routines
-template <typename IndexType>
-static IndexType init_rows_cols (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> col [], IndexType A [], IndexType p [], IndexType stats[NStats] );
-
-template <typename IndexType>
-static void init_scoring (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType head [], double knobs[NKnobs], IndexType *p_n_row2, IndexType *p_n_col2, IndexType *p_max_deg);
-
-template <typename IndexType>
-static IndexType find_ordering (IndexType n_row, IndexType n_col, IndexType Alen, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType head [], IndexType n_col2, IndexType max_deg, IndexType pfree);
-
-template <typename IndexType>
-static void order_children (IndexType n_col, ColStructure<IndexType> Col [], IndexType p []);
-
-template <typename IndexType>
-static void detect_super_cols (ColStructure<IndexType> Col [], IndexType A [], IndexType head [], IndexType row_start, IndexType row_length ) ;
-
-template <typename IndexType>
-static IndexType garbage_collection (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType *pfree) ;
-
-template <typename IndexType>
-static inline  IndexType clear_mark (IndexType n_row, RowStructure<IndexType> Row [] ) ;
-
-/* === No debugging ========================================================= */
-
-#define COLAMD_DEBUG0(params) ;
-#define COLAMD_DEBUG1(params) ;
-#define COLAMD_DEBUG2(params) ;
-#define COLAMD_DEBUG3(params) ;
-#define COLAMD_DEBUG4(params) ;
-
-#define COLAMD_ASSERT(expression) ((void) 0)
-
-
-/**
- * \brief Returns the recommended value of Alen
- *
- * Returns recommended value of Alen for use by colamd.
- * Returns -1 if any input argument is negative.
- * The use of this routine or macro is optional.
- * Note that the macro uses its arguments   more than once,
- * so be careful for side effects, if you pass expressions as arguments to COLAMD_RECOMMENDED.
- *
- * \param nnz nonzeros in A
- * \param n_row number of rows in A
- * \param n_col number of columns in A
- * \return recommended value of Alen for use by colamd
- */
-template <typename IndexType>
-inline IndexType recommended ( IndexType nnz, IndexType n_row, IndexType n_col)
-{
-  if ((nnz) < 0 || (n_row) < 0 || (n_col) < 0)
-    return (-1);
-  else
-    return (2 * (nnz) + colamd_c (n_col) + colamd_r (n_row) + (n_col) + ((nnz) / 5));
-}
-
-/**
- * \brief set default parameters  The use of this routine is optional.
- *
- * Colamd: rows with more than (knobs [DenseRow] * n_col)
- * entries are removed prior to ordering.  Columns with more than
- * (knobs [DenseCol] * n_row) entries are removed prior to
- * ordering, and placed last in the output column ordering.
- *
- * DenseRow and DenseCol are defined as 0 and 1,
- * respectively, in colamd.h.  Default values of these two knobs
- * are both 0.5.  Currently, only knobs [0] and knobs [1] are
- * used, but future versions may use more knobs.  If so, they will
- * be properly set to their defaults by the future version of
- * colamd_set_defaults, so that the code that calls colamd will
- * not need to change, assuming that you either use
- * colamd_set_defaults, or pass a (double *) NULL pointer as the
- * knobs array to colamd or symamd.
- *
- * \param knobs parameter settings for colamd
- */
-
-static inline void set_defaults(double knobs[NKnobs])
-{
-  /* === Local variables ================================================== */
-
-  int i ;
-
-  if (!knobs)
-  {
-    return ;      /* no knobs to initialize */
-  }
-  for (i = 0 ; i < NKnobs ; i++)
-  {
-    knobs [i] = 0 ;
-  }
-  knobs [Colamd::DenseRow] = 0.5 ;  /* ignore rows over 50% dense */
-  knobs [Colamd::DenseCol] = 0.5 ;  /* ignore columns over 50% dense */
-}
-
-/**
- * \brief  Computes a column ordering using the column approximate minimum degree ordering
- *
- * Computes a column ordering (Q) of A such that P(AQ)=LU or
- * (AQ)'AQ=LL' have less fill-in and require fewer floating point
- * operations than factorizing the unpermuted matrix A or A'A,
- * respectively.
- *
- *
- * \param n_row number of rows in A
- * \param n_col number of columns in A
- * \param Alen, size of the array A
- * \param A row indices of the matrix, of size ALen
- * \param p column pointers of A, of size n_col+1
- * \param knobs parameter settings for colamd
- * \param stats colamd output statistics and error codes
- */
-template <typename IndexType>
-static bool compute_ordering(IndexType n_row, IndexType n_col, IndexType Alen, IndexType *A, IndexType *p, double knobs[NKnobs], IndexType stats[NStats])
-{
-  /* === Local variables ================================================== */
-
-  IndexType i ;     /* loop index */
-  IndexType nnz ;     /* nonzeros in A */
-  IndexType Row_size ;    /* size of Row [], in integers */
-  IndexType Col_size ;    /* size of Col [], in integers */
-  IndexType need ;      /* minimum required length of A */
-  Colamd::RowStructure<IndexType> *Row ;   /* pointer into A of Row [0..n_row] array */
-  Colamd::ColStructure<IndexType> *Col ;   /* pointer into A of Col [0..n_col] array */
-  IndexType n_col2 ;    /* number of non-dense, non-empty columns */
-  IndexType n_row2 ;    /* number of non-dense, non-empty rows */
-  IndexType ngarbage ;    /* number of garbage collections performed */
-  IndexType max_deg ;   /* maximum row degree */
-  double default_knobs [NKnobs] ; /* default knobs array */
-
-
-  /* === Check the input arguments ======================================== */
-
-  if (!stats)
-  {
-    COLAMD_DEBUG0 (("colamd: stats not present\n")) ;
-    return (false) ;
-  }
-  for (i = 0 ; i < NStats ; i++)
-  {
-    stats [i] = 0 ;
-  }
-  stats [Colamd::Status] = Colamd::Ok ;
-  stats [Colamd::Info1] = -1 ;
-  stats [Colamd::Info2] = -1 ;
-
-  if (!A)   /* A is not present */
-  {
-    stats [Colamd::Status] = Colamd::ErrorANotPresent ;
-    COLAMD_DEBUG0 (("colamd: A not present\n")) ;
-    return (false) ;
-  }
-
-  if (!p)   /* p is not present */
-  {
-    stats [Colamd::Status] = Colamd::ErrorPNotPresent ;
-    COLAMD_DEBUG0 (("colamd: p not present\n")) ;
-    return (false) ;
-  }
-
-  if (n_row < 0)  /* n_row must be >= 0 */
-  {
-    stats [Colamd::Status] = Colamd::ErrorNrowNegative ;
-    stats [Colamd::Info1] = n_row ;
-    COLAMD_DEBUG0 (("colamd: nrow negative %d\n", n_row)) ;
-    return (false) ;
-  }
-
-  if (n_col < 0)  /* n_col must be >= 0 */
-  {
-    stats [Colamd::Status] = Colamd::ErrorNcolNegative ;
-    stats [Colamd::Info1] = n_col ;
-    COLAMD_DEBUG0 (("colamd: ncol negative %d\n", n_col)) ;
-    return (false) ;
-  }
-
-  nnz = p [n_col] ;
-  if (nnz < 0)  /* nnz must be >= 0 */
-  {
-    stats [Colamd::Status] = Colamd::ErrorNnzNegative ;
-    stats [Colamd::Info1] = nnz ;
-    COLAMD_DEBUG0 (("colamd: number of entries negative %d\n", nnz)) ;
-    return (false) ;
-  }
-
-  if (p [0] != 0)
-  {
-    stats [Colamd::Status] = Colamd::ErrorP0Nonzero ;
-    stats [Colamd::Info1] = p [0] ;
-    COLAMD_DEBUG0 (("colamd: p[0] not zero %d\n", p [0])) ;
-    return (false) ;
-  }
-
-  /* === If no knobs, set default knobs =================================== */
-
-  if (!knobs)
-  {
-    set_defaults (default_knobs) ;
-    knobs = default_knobs ;
-  }
-
-  /* === Allocate the Row and Col arrays from array A ===================== */
-
-  Col_size = colamd_c (n_col) ;
-  Row_size = colamd_r (n_row) ;
-  need = 2*nnz + n_col + Col_size + Row_size ;
-
-  if (need > Alen)
-  {
-    /* not enough space in array A to perform the ordering */
-    stats [Colamd::Status] = Colamd::ErrorATooSmall ;
-    stats [Colamd::Info1] = need ;
-    stats [Colamd::Info2] = Alen ;
-    COLAMD_DEBUG0 (("colamd: Need Alen >= %d, given only Alen = %d\n", need,Alen));
-    return (false) ;
-  }
-
-  Alen -= Col_size + Row_size ;
-  Col = (ColStructure<IndexType> *) &A [Alen] ;
-  Row = (RowStructure<IndexType> *) &A [Alen + Col_size] ;
-
-  /* === Construct the row and column data structures ===================== */
-
-  if (!Colamd::init_rows_cols (n_row, n_col, Row, Col, A, p, stats))
-  {
-    /* input matrix is invalid */
-    COLAMD_DEBUG0 (("colamd: Matrix invalid\n")) ;
-    return (false) ;
-  }
-
-  /* === Initialize scores, kill dense rows/columns ======================= */
-
-  Colamd::init_scoring (n_row, n_col, Row, Col, A, p, knobs,
-		&n_row2, &n_col2, &max_deg) ;
-
-  /* === Order the supercolumns =========================================== */
-
-  ngarbage = Colamd::find_ordering (n_row, n_col, Alen, Row, Col, A, p,
-			    n_col2, max_deg, 2*nnz) ;
-
-  /* === Order the non-principal columns ================================== */
-
-  Colamd::order_children (n_col, Col, p) ;
-
-  /* === Return statistics in stats ======================================= */
-
-  stats [Colamd::DenseRow] = n_row - n_row2 ;
-  stats [Colamd::DenseCol] = n_col - n_col2 ;
-  stats [Colamd::DefragCount] = ngarbage ;
-  COLAMD_DEBUG0 (("colamd: done.\n")) ;
-  return (true) ;
-}
-
-/* ========================================================================== */
-/* === NON-USER-CALLABLE ROUTINES: ========================================== */
-/* ========================================================================== */
-
-/* There are no user-callable routines beyond this point in the file */
-
-/* ========================================================================== */
-/* === init_rows_cols ======================================================= */
-/* ========================================================================== */
-
-/*
-  Takes the column form of the matrix in A and creates the row form of the
-  matrix.  Also, row and column attributes are stored in the Col and Row
-  structs.  If the columns are un-sorted or contain duplicate row indices,
-  this routine will also sort and remove duplicate row indices from the
-  column form of the matrix.  Returns false if the matrix is invalid,
-  true otherwise.  Not user-callable.
-*/
-template <typename IndexType>
-static IndexType init_rows_cols  /* returns true if OK, or false otherwise */
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows of A */
-    IndexType n_col,      /* number of columns of A */
-    RowStructure<IndexType> Row [],    /* of size n_row+1 */
-    ColStructure<IndexType> Col [],    /* of size n_col+1 */
-    IndexType A [],     /* row indices of A, of size Alen */
-    IndexType p [],     /* pointers to columns in A, of size n_col+1 */
-    IndexType stats [NStats]  /* colamd statistics */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType col ;     /* a column index */
-  IndexType row ;     /* a row index */
-  IndexType *cp ;     /* a column pointer */
-  IndexType *cp_end ;   /* a pointer to the end of a column */
-  IndexType *rp ;     /* a row pointer */
-  IndexType *rp_end ;   /* a pointer to the end of a row */
-  IndexType last_row ;    /* previous row */
-
-  /* === Initialize columns, and check column pointers ==================== */
-
-  for (col = 0 ; col < n_col ; col++)
-  {
-    Col [col].start = p [col] ;
-    Col [col].length = p [col+1] - p [col] ;
-
-    if ((Col [col].length) < 0) // extra parentheses to work-around gcc bug 10200
-    {
-      /* column pointers must be non-decreasing */
-      stats [Colamd::Status] = Colamd::ErrorColLengthNegative ;
-      stats [Colamd::Info1] = col ;
-      stats [Colamd::Info2] = Col [col].length ;
-      COLAMD_DEBUG0 (("colamd: col %d length %d < 0\n", col, Col [col].length)) ;
-      return (false) ;
-    }
-
-    Col [col].shared1.thickness = 1 ;
-    Col [col].shared2.score = 0 ;
-    Col [col].shared3.prev = Empty ;
-    Col [col].shared4.degree_next = Empty ;
-  }
-
-  /* p [0..n_col] no longer needed, used as "head" in subsequent routines */
-
-  /* === Scan columns, compute row degrees, and check row indices ========= */
-
-  stats [Info3] = 0 ;  /* number of duplicate or unsorted row indices*/
-
-  for (row = 0 ; row < n_row ; row++)
-  {
-    Row [row].length = 0 ;
-    Row [row].shared2.mark = -1 ;
-  }
-
-  for (col = 0 ; col < n_col ; col++)
-  {
-    last_row = -1 ;
-
-    cp = &A [p [col]] ;
-    cp_end = &A [p [col+1]] ;
-
-    while (cp < cp_end)
-    {
-      row = *cp++ ;
-
-      /* make sure row indices within range */
-      if (row < 0 || row >= n_row)
-      {
-	stats [Colamd::Status] = Colamd::ErrorRowIndexOutOfBounds ;
-	stats [Colamd::Info1] = col ;
-	stats [Colamd::Info2] = row ;
-	stats [Colamd::Info3] = n_row ;
-	COLAMD_DEBUG0 (("colamd: row %d col %d out of bounds\n", row, col)) ;
-	return (false) ;
-      }
-
-      if (row <= last_row || Row [row].shared2.mark == col)
-      {
-	/* row index are unsorted or repeated (or both), thus col */
-	/* is jumbled.  This is a notice, not an error condition. */
-	stats [Colamd::Status] = Colamd::OkButJumbled ;
-	stats [Colamd::Info1] = col ;
-	stats [Colamd::Info2] = row ;
-	(stats [Colamd::Info3]) ++ ;
-	COLAMD_DEBUG1 (("colamd: row %d col %d unsorted/duplicate\n",row,col));
-      }
-
-      if (Row [row].shared2.mark != col)
-      {
-	Row [row].length++ ;
-      }
-      else
-      {
-	/* this is a repeated entry in the column, */
-	/* it will be removed */
-	Col [col].length-- ;
-      }
-
-      /* mark the row as having been seen in this column */
-      Row [row].shared2.mark = col ;
-
-      last_row = row ;
-    }
-  }
-
-  /* === Compute row pointers ============================================= */
-
-  /* row form of the matrix starts directly after the column */
-  /* form of matrix in A */
-  Row [0].start = p [n_col] ;
-  Row [0].shared1.p = Row [0].start ;
-  Row [0].shared2.mark = -1 ;
-  for (row = 1 ; row < n_row ; row++)
-  {
-    Row [row].start = Row [row-1].start + Row [row-1].length ;
-    Row [row].shared1.p = Row [row].start ;
-    Row [row].shared2.mark = -1 ;
-  }
-
-  /* === Create row form ================================================== */
-
-  if (stats [Status] == OkButJumbled)
-  {
-    /* if cols jumbled, watch for repeated row indices */
-    for (col = 0 ; col < n_col ; col++)
-    {
-      cp = &A [p [col]] ;
-      cp_end = &A [p [col+1]] ;
-      while (cp < cp_end)
-      {
-	row = *cp++ ;
-	if (Row [row].shared2.mark != col)
-	{
-	  A [(Row [row].shared1.p)++] = col ;
-	  Row [row].shared2.mark = col ;
-	}
-      }
-    }
-  }
-  else
-  {
-    /* if cols not jumbled, we don't need the mark (this is faster) */
-    for (col = 0 ; col < n_col ; col++)
-    {
-      cp = &A [p [col]] ;
-      cp_end = &A [p [col+1]] ;
-      while (cp < cp_end)
-      {
-	A [(Row [*cp++].shared1.p)++] = col ;
-      }
-    }
-  }
-
-  /* === Clear the row marks and set row degrees ========================== */
-
-  for (row = 0 ; row < n_row ; row++)
-  {
-    Row [row].shared2.mark = 0 ;
-    Row [row].shared1.degree = Row [row].length ;
-  }
-
-  /* === See if we need to re-create columns ============================== */
-
-  if (stats [Status] == OkButJumbled)
-  {
-    COLAMD_DEBUG0 (("colamd: reconstructing column form, matrix jumbled\n")) ;
-
-
-    /* === Compute col pointers ========================================= */
-
-    /* col form of the matrix starts at A [0]. */
-    /* Note, we may have a gap between the col form and the row */
-    /* form if there were duplicate entries, if so, it will be */
-    /* removed upon the first garbage collection */
-    Col [0].start = 0 ;
-    p [0] = Col [0].start ;
-    for (col = 1 ; col < n_col ; col++)
-    {
-      /* note that the lengths here are for pruned columns, i.e. */
-      /* no duplicate row indices will exist for these columns */
-      Col [col].start = Col [col-1].start + Col [col-1].length ;
-      p [col] = Col [col].start ;
-    }
-
-    /* === Re-create col form =========================================== */
-
-    for (row = 0 ; row < n_row ; row++)
-    {
-      rp = &A [Row [row].start] ;
-      rp_end = rp + Row [row].length ;
-      while (rp < rp_end)
-      {
-	A [(p [*rp++])++] = row ;
-      }
-    }
-  }
-
-  /* === Done.  Matrix is not (or no longer) jumbled ====================== */
-
-  return (true) ;
-}
-
-
-/* ========================================================================== */
-/* === init_scoring ========================================================= */
-/* ========================================================================== */
-
-/*
-  Kills dense or empty columns and rows, calculates an initial score for
-  each column, and places all columns in the degree lists.  Not user-callable.
-*/
-template <typename IndexType>
-static void init_scoring
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows of A */
-    IndexType n_col,      /* number of columns of A */
-    RowStructure<IndexType> Row [],    /* of size n_row+1 */
-    ColStructure<IndexType> Col [],    /* of size n_col+1 */
-    IndexType A [],     /* column form and row form of A */
-    IndexType head [],    /* of size n_col+1 */
-    double knobs [NKnobs],/* parameters */
-    IndexType *p_n_row2,    /* number of non-dense, non-empty rows */
-    IndexType *p_n_col2,    /* number of non-dense, non-empty columns */
-    IndexType *p_max_deg    /* maximum row degree */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType c ;     /* a column index */
-  IndexType r, row ;    /* a row index */
-  IndexType *cp ;     /* a column pointer */
-  IndexType deg ;     /* degree of a row or column */
-  IndexType *cp_end ;   /* a pointer to the end of a column */
-  IndexType *new_cp ;   /* new column pointer */
-  IndexType col_length ;    /* length of pruned column */
-  IndexType score ;     /* current column score */
-  IndexType n_col2 ;    /* number of non-dense, non-empty columns */
-  IndexType n_row2 ;    /* number of non-dense, non-empty rows */
-  IndexType dense_row_count ; /* remove rows with more entries than this */
-  IndexType dense_col_count ; /* remove cols with more entries than this */
-  IndexType min_score ;   /* smallest column score */
-  IndexType max_deg ;   /* maximum row degree */
-  IndexType next_col ;    /* Used to add to degree list.*/
-
-
-  /* === Extract knobs ==================================================== */
-
-  dense_row_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [Colamd::DenseRow] * n_col), n_col)) ;
-  dense_col_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [Colamd::DenseCol] * n_row), n_row)) ;
-  COLAMD_DEBUG1 (("colamd: densecount: %d %d\n", dense_row_count, dense_col_count)) ;
-  max_deg = 0 ;
-  n_col2 = n_col ;
-  n_row2 = n_row ;
-
-  /* === Kill empty columns =============================================== */
-
-  /* Put the empty columns at the end in their natural order, so that LU */
-  /* factorization can proceed as far as possible. */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    deg = Col [c].length ;
-    if (deg == 0)
-    {
-      /* this is a empty column, kill and order it last */
-      Col [c].shared2.order = --n_col2 ;
-      Col[c].kill_principal() ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: null columns killed: %d\n", n_col - n_col2)) ;
-
-  /* === Kill dense columns =============================================== */
-
-  /* Put the dense columns at the end, in their natural order */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    /* skip any dead columns */
-    if (Col[c].is_dead())
-    {
-      continue ;
-    }
-    deg = Col [c].length ;
-    if (deg > dense_col_count)
-    {
-      /* this is a dense column, kill and order it last */
-      Col [c].shared2.order = --n_col2 ;
-      /* decrement the row degrees */
-      cp = &A [Col [c].start] ;
-      cp_end = cp + Col [c].length ;
-      while (cp < cp_end)
-      {
-	Row [*cp++].shared1.degree-- ;
-      }
-      Col[c].kill_principal() ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: Dense and null columns killed: %d\n", n_col - n_col2)) ;
-
-  /* === Kill dense and empty rows ======================================== */
-
-  for (r = 0 ; r < n_row ; r++)
-  {
-    deg = Row [r].shared1.degree ;
-    COLAMD_ASSERT (deg >= 0 && deg <= n_col) ;
-    if (deg > dense_row_count || deg == 0)
-    {
-      /* kill a dense or empty row */
-      Row[r].kill() ;
-      --n_row2 ;
-    }
-    else
-    {
-      /* keep track of max degree of remaining rows */
-      max_deg = numext::maxi(max_deg, deg) ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: Dense and null rows killed: %d\n", n_row - n_row2)) ;
-
-  /* === Compute initial column scores ==================================== */
-
-  /* At this point the row degrees are accurate.  They reflect the number */
-  /* of "live" (non-dense) columns in each row.  No empty rows exist. */
-  /* Some "live" columns may contain only dead rows, however.  These are */
-  /* pruned in the code below. */
-
-  /* now find the initial matlab score for each column */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    /* skip dead column */
-    if (Col[c].is_dead())
-    {
-      continue ;
-    }
-    score = 0 ;
-    cp = &A [Col [c].start] ;
-    new_cp = cp ;
-    cp_end = cp + Col [c].length ;
-    while (cp < cp_end)
-    {
-      /* get a row */
-      row = *cp++ ;
-      /* skip if dead */
-      if (Row[row].is_dead())
-      {
-	continue ;
-      }
-      /* compact the column */
-      *new_cp++ = row ;
-      /* add row's external degree */
-      score += Row [row].shared1.degree - 1 ;
-      /* guard against integer overflow */
-      score = numext::mini(score, n_col) ;
-    }
-    /* determine pruned column length */
-    col_length = (IndexType) (new_cp - &A [Col [c].start]) ;
-    if (col_length == 0)
-    {
-      /* a newly-made null column (all rows in this col are "dense" */
-      /* and have already been killed) */
-      COLAMD_DEBUG2 (("Newly null killed: %d\n", c)) ;
-      Col [c].shared2.order = --n_col2 ;
-      Col[c].kill_principal() ;
-    }
-    else
-    {
-      /* set column length and set score */
-      COLAMD_ASSERT (score >= 0) ;
-      COLAMD_ASSERT (score <= n_col) ;
-      Col [c].length = col_length ;
-      Col [c].shared2.score = score ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: Dense, null, and newly-null columns killed: %d\n",
-		  n_col-n_col2)) ;
-
-  /* At this point, all empty rows and columns are dead.  All live columns */
-  /* are "clean" (containing no dead rows) and simplicial (no supercolumns */
-  /* yet).  Rows may contain dead columns, but all live rows contain at */
-  /* least one live column. */
-
-  /* === Initialize degree lists ========================================== */
-
-
-  /* clear the hash buckets */
-  for (c = 0 ; c <= n_col ; c++)
-  {
-    head [c] = Empty ;
-  }
-  min_score = n_col ;
-  /* place in reverse order, so low column indices are at the front */
-  /* of the lists.  This is to encourage natural tie-breaking */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    /* only add principal columns to degree lists */
-    if (Col[c].is_alive())
-    {
-      COLAMD_DEBUG4 (("place %d score %d minscore %d ncol %d\n",
-		      c, Col [c].shared2.score, min_score, n_col)) ;
-
-      /* === Add columns score to DList =============================== */
-
-      score = Col [c].shared2.score ;
-
-      COLAMD_ASSERT (min_score >= 0) ;
-      COLAMD_ASSERT (min_score <= n_col) ;
-      COLAMD_ASSERT (score >= 0) ;
-      COLAMD_ASSERT (score <= n_col) ;
-      COLAMD_ASSERT (head [score] >= Empty) ;
-
-      /* now add this column to dList at proper score location */
-      next_col = head [score] ;
-      Col [c].shared3.prev = Empty ;
-      Col [c].shared4.degree_next = next_col ;
-
-      /* if there already was a column with the same score, set its */
-      /* previous pointer to this new column */
-      if (next_col != Empty)
-      {
-	Col [next_col].shared3.prev = c ;
-      }
-      head [score] = c ;
-
-      /* see if this score is less than current min */
-      min_score = numext::mini(min_score, score) ;
-
-
-    }
-  }
-
-
-  /* === Return number of remaining columns, and max row degree =========== */
-
-  *p_n_col2 = n_col2 ;
-  *p_n_row2 = n_row2 ;
-  *p_max_deg = max_deg ;
-}
-
-
-/* ========================================================================== */
-/* === find_ordering ======================================================== */
-/* ========================================================================== */
-
-/*
-  Order the principal columns of the supercolumn form of the matrix
-  (no supercolumns on input).  Uses a minimum approximate column minimum
-  degree ordering method.  Not user-callable.
-*/
-template <typename IndexType>
-static IndexType find_ordering /* return the number of garbage collections */
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows of A */
-    IndexType n_col,      /* number of columns of A */
-    IndexType Alen,     /* size of A, 2*nnz + n_col or larger */
-    RowStructure<IndexType> Row [],    /* of size n_row+1 */
-    ColStructure<IndexType> Col [],    /* of size n_col+1 */
-    IndexType A [],     /* column form and row form of A */
-    IndexType head [],    /* of size n_col+1 */
-    IndexType n_col2,     /* Remaining columns to order */
-    IndexType max_deg,    /* Maximum row degree */
-    IndexType pfree     /* index of first free slot (2*nnz on entry) */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType k ;     /* current pivot ordering step */
-  IndexType pivot_col ;   /* current pivot column */
-  IndexType *cp ;     /* a column pointer */
-  IndexType *rp ;     /* a row pointer */
-  IndexType pivot_row ;   /* current pivot row */
-  IndexType *new_cp ;   /* modified column pointer */
-  IndexType *new_rp ;   /* modified row pointer */
-  IndexType pivot_row_start ; /* pointer to start of pivot row */
-  IndexType pivot_row_degree ;  /* number of columns in pivot row */
-  IndexType pivot_row_length ;  /* number of supercolumns in pivot row */
-  IndexType pivot_col_score ; /* score of pivot column */
-  IndexType needed_memory ;   /* free space needed for pivot row */
-  IndexType *cp_end ;   /* pointer to the end of a column */
-  IndexType *rp_end ;   /* pointer to the end of a row */
-  IndexType row ;     /* a row index */
-  IndexType col ;     /* a column index */
-  IndexType max_score ;   /* maximum possible score */
-  IndexType cur_score ;   /* score of current column */
-  unsigned int hash ;   /* hash value for supernode detection */
-  IndexType head_column ;   /* head of hash bucket */
-  IndexType first_col ;   /* first column in hash bucket */
-  IndexType tag_mark ;    /* marker value for mark array */
-  IndexType row_mark ;    /* Row [row].shared2.mark */
-  IndexType set_difference ;  /* set difference size of row with pivot row */
-  IndexType min_score ;   /* smallest column score */
-  IndexType col_thickness ;   /* "thickness" (no. of columns in a supercol) */
-  IndexType max_mark ;    /* maximum value of tag_mark */
-  IndexType pivot_col_thickness ; /* number of columns represented by pivot col */
-  IndexType prev_col ;    /* Used by Dlist operations. */
-  IndexType next_col ;    /* Used by Dlist operations. */
-  IndexType ngarbage ;    /* number of garbage collections performed */
-
-
-  /* === Initialization and clear mark ==================================== */
-
-  max_mark = INT_MAX - n_col ;  /* INT_MAX defined in <limits.h> */
-  tag_mark = Colamd::clear_mark (n_row, Row) ;
-  min_score = 0 ;
-  ngarbage = 0 ;
-  COLAMD_DEBUG1 (("colamd: Ordering, n_col2=%d\n", n_col2)) ;
-
-  /* === Order the columns ================================================ */
-
-  for (k = 0 ; k < n_col2 ; /* 'k' is incremented below */)
-  {
-
-    /* === Select pivot column, and order it ============================ */
-
-    /* make sure degree list isn't empty */
-    COLAMD_ASSERT (min_score >= 0) ;
-    COLAMD_ASSERT (min_score <= n_col) ;
-    COLAMD_ASSERT (head [min_score] >= Empty) ;
-
-    /* get pivot column from head of minimum degree list */
-    while (min_score < n_col && head [min_score] == Empty)
-    {
-      min_score++ ;
-    }
-    pivot_col = head [min_score] ;
-    COLAMD_ASSERT (pivot_col >= 0 && pivot_col <= n_col) ;
-    next_col = Col [pivot_col].shared4.degree_next ;
-    head [min_score] = next_col ;
-    if (next_col != Empty)
-    {
-      Col [next_col].shared3.prev = Empty ;
-    }
-
-    COLAMD_ASSERT (Col[pivot_col].is_alive()) ;
-    COLAMD_DEBUG3 (("Pivot col: %d\n", pivot_col)) ;
-
-    /* remember score for defrag check */
-    pivot_col_score = Col [pivot_col].shared2.score ;
-
-    /* the pivot column is the kth column in the pivot order */
-    Col [pivot_col].shared2.order = k ;
-
-    /* increment order count by column thickness */
-    pivot_col_thickness = Col [pivot_col].shared1.thickness ;
-    k += pivot_col_thickness ;
-    COLAMD_ASSERT (pivot_col_thickness > 0) ;
-
-    /* === Garbage_collection, if necessary ============================= */
-
-    needed_memory = numext::mini(pivot_col_score, n_col - k) ;
-    if (pfree + needed_memory >= Alen)
-    {
-      pfree = Colamd::garbage_collection (n_row, n_col, Row, Col, A, &A [pfree]) ;
-      ngarbage++ ;
-      /* after garbage collection we will have enough */
-      COLAMD_ASSERT (pfree + needed_memory < Alen) ;
-      /* garbage collection has wiped out the Row[].shared2.mark array */
-      tag_mark = Colamd::clear_mark (n_row, Row) ;
-
-    }
-
-    /* === Compute pivot row pattern ==================================== */
-
-    /* get starting location for this new merged row */
-    pivot_row_start = pfree ;
-
-    /* initialize new row counts to zero */
-    pivot_row_degree = 0 ;
-
-    /* tag pivot column as having been visited so it isn't included */
-    /* in merged pivot row */
-    Col [pivot_col].shared1.thickness = -pivot_col_thickness ;
-
-    /* pivot row is the union of all rows in the pivot column pattern */
-    cp = &A [Col [pivot_col].start] ;
-    cp_end = cp + Col [pivot_col].length ;
-    while (cp < cp_end)
-    {
-      /* get a row */
-      row = *cp++ ;
-      COLAMD_DEBUG4 (("Pivot col pattern %d %d\n", Row[row].is_alive(), row)) ;
-      /* skip if row is dead */
-      if (Row[row].is_dead())
-      {
-	continue ;
-      }
-      rp = &A [Row [row].start] ;
-      rp_end = rp + Row [row].length ;
-      while (rp < rp_end)
-      {
-	/* get a column */
-	col = *rp++ ;
-	/* add the column, if alive and untagged */
-	col_thickness = Col [col].shared1.thickness ;
-	if (col_thickness > 0 && Col[col].is_alive())
-	{
-	  /* tag column in pivot row */
-	  Col [col].shared1.thickness = -col_thickness ;
-	  COLAMD_ASSERT (pfree < Alen) ;
-	  /* place column in pivot row */
-	  A [pfree++] = col ;
-	  pivot_row_degree += col_thickness ;
-	}
-      }
-    }
-
-    /* clear tag on pivot column */
-    Col [pivot_col].shared1.thickness = pivot_col_thickness ;
-    max_deg = numext::maxi(max_deg, pivot_row_degree) ;
-
-
-    /* === Kill all rows used to construct pivot row ==================== */
-
-    /* also kill pivot row, temporarily */
-    cp = &A [Col [pivot_col].start] ;
-    cp_end = cp + Col [pivot_col].length ;
-    while (cp < cp_end)
-    {
-      /* may be killing an already dead row */
-      row = *cp++ ;
-      COLAMD_DEBUG3 (("Kill row in pivot col: %d\n", row)) ;
-      Row[row].kill() ;
-    }
-
-    /* === Select a row index to use as the new pivot row =============== */
-
-    pivot_row_length = pfree - pivot_row_start ;
-    if (pivot_row_length > 0)
-    {
-      /* pick the "pivot" row arbitrarily (first row in col) */
-      pivot_row = A [Col [pivot_col].start] ;
-      COLAMD_DEBUG3 (("Pivotal row is %d\n", pivot_row)) ;
-    }
-    else
-    {
-      /* there is no pivot row, since it is of zero length */
-      pivot_row = Empty ;
-      COLAMD_ASSERT (pivot_row_length == 0) ;
-    }
-    COLAMD_ASSERT (Col [pivot_col].length > 0 || pivot_row_length == 0) ;
-
-    /* === Approximate degree computation =============================== */
-
-    /* Here begins the computation of the approximate degree.  The column */
-    /* score is the sum of the pivot row "length", plus the size of the */
-    /* set differences of each row in the column minus the pattern of the */
-    /* pivot row itself.  The column ("thickness") itself is also */
-    /* excluded from the column score (we thus use an approximate */
-    /* external degree). */
-
-    /* The time taken by the following code (compute set differences, and */
-    /* add them up) is proportional to the size of the data structure */
-    /* being scanned - that is, the sum of the sizes of each column in */
-    /* the pivot row.  Thus, the amortized time to compute a column score */
-    /* is proportional to the size of that column (where size, in this */
-    /* context, is the column "length", or the number of row indices */
-    /* in that column).  The number of row indices in a column is */
-    /* monotonically non-decreasing, from the length of the original */
-    /* column on input to colamd. */
-
-    /* === Compute set differences ====================================== */
-
-    COLAMD_DEBUG3 (("** Computing set differences phase. **\n")) ;
-
-    /* pivot row is currently dead - it will be revived later. */
-
-    COLAMD_DEBUG3 (("Pivot row: ")) ;
-    /* for each column in pivot row */
-    rp = &A [pivot_row_start] ;
-    rp_end = rp + pivot_row_length ;
-    while (rp < rp_end)
-    {
-      col = *rp++ ;
-      COLAMD_ASSERT (Col[col].is_alive() && col != pivot_col) ;
-      COLAMD_DEBUG3 (("Col: %d\n", col)) ;
-
-      /* clear tags used to construct pivot row pattern */
-      col_thickness = -Col [col].shared1.thickness ;
-      COLAMD_ASSERT (col_thickness > 0) ;
-      Col [col].shared1.thickness = col_thickness ;
-
-      /* === Remove column from degree list =========================== */
-
-      cur_score = Col [col].shared2.score ;
-      prev_col = Col [col].shared3.prev ;
-      next_col = Col [col].shared4.degree_next ;
-      COLAMD_ASSERT (cur_score >= 0) ;
-      COLAMD_ASSERT (cur_score <= n_col) ;
-      COLAMD_ASSERT (cur_score >= Empty) ;
-      if (prev_col == Empty)
-      {
-	head [cur_score] = next_col ;
-      }
-      else
-      {
-	Col [prev_col].shared4.degree_next = next_col ;
-      }
-      if (next_col != Empty)
-      {
-	Col [next_col].shared3.prev = prev_col ;
-      }
-
-      /* === Scan the column ========================================== */
-
-      cp = &A [Col [col].start] ;
-      cp_end = cp + Col [col].length ;
-      while (cp < cp_end)
-      {
-	/* get a row */
-	row = *cp++ ;
-	/* skip if dead */
-	if (Row[row].is_dead())
-	{
-	  continue ;
-	}
-  row_mark = Row [row].shared2.mark ;
-	COLAMD_ASSERT (row != pivot_row) ;
-	set_difference = row_mark - tag_mark ;
-	/* check if the row has been seen yet */
-	if (set_difference < 0)
-	{
-	  COLAMD_ASSERT (Row [row].shared1.degree <= max_deg) ;
-	  set_difference = Row [row].shared1.degree ;
-	}
-	/* subtract column thickness from this row's set difference */
-	set_difference -= col_thickness ;
-	COLAMD_ASSERT (set_difference >= 0) ;
-	/* absorb this row if the set difference becomes zero */
-	if (set_difference == 0)
-	{
-	  COLAMD_DEBUG3 (("aggressive absorption. Row: %d\n", row)) ;
-	  Row[row].kill() ;
-	}
-	else
-	{
-	  /* save the new mark */
-	  Row [row].shared2.mark = set_difference + tag_mark ;
-	}
-      }
-    }
-
-
-    /* === Add up set differences for each column ======================= */
-
-    COLAMD_DEBUG3 (("** Adding set differences phase. **\n")) ;
-
-    /* for each column in pivot row */
-    rp = &A [pivot_row_start] ;
-    rp_end = rp + pivot_row_length ;
-    while (rp < rp_end)
-    {
-      /* get a column */
-      col = *rp++ ;
-      COLAMD_ASSERT (Col[col].is_alive() && col != pivot_col) ;
-      hash = 0 ;
-      cur_score = 0 ;
-      cp = &A [Col [col].start] ;
-      /* compact the column */
-      new_cp = cp ;
-      cp_end = cp + Col [col].length ;
-
-      COLAMD_DEBUG4 (("Adding set diffs for Col: %d.\n", col)) ;
-
-      while (cp < cp_end)
-      {
-	/* get a row */
-	row = *cp++ ;
-	COLAMD_ASSERT(row >= 0 && row < n_row) ;
-	/* skip if dead */
-	if (Row [row].is_dead())
-	{
-	  continue ;
-	}
-  row_mark = Row [row].shared2.mark ;
-	COLAMD_ASSERT (row_mark > tag_mark) ;
-	/* compact the column */
-	*new_cp++ = row ;
-	/* compute hash function */
-	hash += row ;
-	/* add set difference */
-	cur_score += row_mark - tag_mark ;
-	/* integer overflow... */
-	cur_score = numext::mini(cur_score, n_col) ;
-      }
-
-      /* recompute the column's length */
-      Col [col].length = (IndexType) (new_cp - &A [Col [col].start]) ;
-
-      /* === Further mass elimination ================================= */
-
-      if (Col [col].length == 0)
-      {
-	COLAMD_DEBUG4 (("further mass elimination. Col: %d\n", col)) ;
-	/* nothing left but the pivot row in this column */
-	Col[col].kill_principal() ;
-	pivot_row_degree -= Col [col].shared1.thickness ;
-	COLAMD_ASSERT (pivot_row_degree >= 0) ;
-	/* order it */
-	Col [col].shared2.order = k ;
-	/* increment order count by column thickness */
-	k += Col [col].shared1.thickness ;
-      }
-      else
-      {
-	/* === Prepare for supercolumn detection ==================== */
-
-	COLAMD_DEBUG4 (("Preparing supercol detection for Col: %d.\n", col)) ;
-
-	/* save score so far */
-	Col [col].shared2.score = cur_score ;
-
-	/* add column to hash table, for supercolumn detection */
-	hash %= n_col + 1 ;
-
-	COLAMD_DEBUG4 ((" Hash = %d, n_col = %d.\n", hash, n_col)) ;
-	COLAMD_ASSERT (hash <= n_col) ;
-
-	head_column = head [hash] ;
-	if (head_column > Empty)
-	{
-	  /* degree list "hash" is non-empty, use prev (shared3) of */
-	  /* first column in degree list as head of hash bucket */
-	  first_col = Col [head_column].shared3.headhash ;
-	  Col [head_column].shared3.headhash = col ;
-	}
-	else
-	{
-	  /* degree list "hash" is empty, use head as hash bucket */
-	  first_col = - (head_column + 2) ;
-	  head [hash] = - (col + 2) ;
-	}
-	Col [col].shared4.hash_next = first_col ;
-
-	/* save hash function in Col [col].shared3.hash */
-	Col [col].shared3.hash = (IndexType) hash ;
-	COLAMD_ASSERT (Col[col].is_alive()) ;
-      }
-    }
-
-    /* The approximate external column degree is now computed.  */
-
-    /* === Supercolumn detection ======================================== */
-
-    COLAMD_DEBUG3 (("** Supercolumn detection phase. **\n")) ;
-
-    Colamd::detect_super_cols (Col, A, head, pivot_row_start, pivot_row_length) ;
-
-    /* === Kill the pivotal column ====================================== */
-
-    Col[pivot_col].kill_principal() ;
-
-    /* === Clear mark =================================================== */
-
-    tag_mark += (max_deg + 1) ;
-    if (tag_mark >= max_mark)
-    {
-      COLAMD_DEBUG2 (("clearing tag_mark\n")) ;
-      tag_mark = Colamd::clear_mark (n_row, Row) ;
-    }
-
-    /* === Finalize the new pivot row, and column scores ================ */
-
-    COLAMD_DEBUG3 (("** Finalize scores phase. **\n")) ;
-
-    /* for each column in pivot row */
-    rp = &A [pivot_row_start] ;
-    /* compact the pivot row */
-    new_rp = rp ;
-    rp_end = rp + pivot_row_length ;
-    while (rp < rp_end)
-    {
-      col = *rp++ ;
-      /* skip dead columns */
-      if (Col[col].is_dead())
-      {
-	continue ;
-      }
-      *new_rp++ = col ;
-      /* add new pivot row to column */
-      A [Col [col].start + (Col [col].length++)] = pivot_row ;
-
-      /* retrieve score so far and add on pivot row's degree. */
-      /* (we wait until here for this in case the pivot */
-      /* row's degree was reduced due to mass elimination). */
-      cur_score = Col [col].shared2.score + pivot_row_degree ;
-
-      /* calculate the max possible score as the number of */
-      /* external columns minus the 'k' value minus the */
-      /* columns thickness */
-      max_score = n_col - k - Col [col].shared1.thickness ;
-
-      /* make the score the external degree of the union-of-rows */
-      cur_score -= Col [col].shared1.thickness ;
-
-      /* make sure score is less or equal than the max score */
-      cur_score = numext::mini(cur_score, max_score) ;
-      COLAMD_ASSERT (cur_score >= 0) ;
-
-      /* store updated score */
-      Col [col].shared2.score = cur_score ;
-
-      /* === Place column back in degree list ========================= */
-
-      COLAMD_ASSERT (min_score >= 0) ;
-      COLAMD_ASSERT (min_score <= n_col) ;
-      COLAMD_ASSERT (cur_score >= 0) ;
-      COLAMD_ASSERT (cur_score <= n_col) ;
-      COLAMD_ASSERT (head [cur_score] >= Empty) ;
-      next_col = head [cur_score] ;
-      Col [col].shared4.degree_next = next_col ;
-      Col [col].shared3.prev = Empty ;
-      if (next_col != Empty)
-      {
-	Col [next_col].shared3.prev = col ;
-      }
-      head [cur_score] = col ;
-
-      /* see if this score is less than current min */
-      min_score = numext::mini(min_score, cur_score) ;
-
-    }
-
-    /* === Resurrect the new pivot row ================================== */
-
-    if (pivot_row_degree > 0)
-    {
-      /* update pivot row length to reflect any cols that were killed */
-      /* during super-col detection and mass elimination */
-      Row [pivot_row].start  = pivot_row_start ;
-      Row [pivot_row].length = (IndexType) (new_rp - &A[pivot_row_start]) ;
-      Row [pivot_row].shared1.degree = pivot_row_degree ;
-      Row [pivot_row].shared2.mark = 0 ;
-      /* pivot row is no longer dead */
-    }
-  }
-
-  /* === All principal columns have now been ordered ====================== */
-
-  return (ngarbage) ;
-}
-
-
-/* ========================================================================== */
-/* === order_children ======================================================= */
-/* ========================================================================== */
-
-/*
-  The find_ordering routine has ordered all of the principal columns (the
-  representatives of the supercolumns).  The non-principal columns have not
-  yet been ordered.  This routine orders those columns by walking up the
-  parent tree (a column is a child of the column which absorbed it).  The
-  final permutation vector is then placed in p [0 ... n_col-1], with p [0]
-  being the first column, and p [n_col-1] being the last.  It doesn't look
-  like it at first glance, but be assured that this routine takes time linear
-  in the number of columns.  Although not immediately obvious, the time
-  taken by this routine is O (n_col), that is, linear in the number of
-  columns.  Not user-callable.
-*/
-template <typename IndexType>
-static inline  void order_children
-(
-  /* === Parameters ======================================================= */
-
-  IndexType n_col,      /* number of columns of A */
-  ColStructure<IndexType> Col [],    /* of size n_col+1 */
-  IndexType p []      /* p [0 ... n_col-1] is the column permutation*/
-  )
-{
-  /* === Local variables ================================================== */
-
-  IndexType i ;     /* loop counter for all columns */
-  IndexType c ;     /* column index */
-  IndexType parent ;    /* index of column's parent */
-  IndexType order ;     /* column's order */
-
-  /* === Order each non-principal column ================================== */
-
-  for (i = 0 ; i < n_col ; i++)
-  {
-    /* find an un-ordered non-principal column */
-    COLAMD_ASSERT (col_is_dead(Col, i)) ;
-    if (!Col[i].is_dead_principal() && Col [i].shared2.order == Empty)
-    {
-      parent = i ;
-      /* once found, find its principal parent */
-      do
-      {
-	parent = Col [parent].shared1.parent ;
-      } while (!Col[parent].is_dead_principal()) ;
-
-      /* now, order all un-ordered non-principal columns along path */
-      /* to this parent.  collapse tree at the same time */
-      c = i ;
-      /* get order of parent */
-      order = Col [parent].shared2.order ;
-
-      do
-      {
-	COLAMD_ASSERT (Col [c].shared2.order == Empty) ;
-
-	/* order this column */
-	Col [c].shared2.order = order++ ;
-	/* collaps tree */
-	Col [c].shared1.parent = parent ;
-
-	/* get immediate parent of this column */
-	c = Col [c].shared1.parent ;
-
-	/* continue until we hit an ordered column.  There are */
-	/* guaranteed not to be anymore unordered columns */
-	/* above an ordered column */
-      } while (Col [c].shared2.order == Empty) ;
-
-      /* re-order the super_col parent to largest order for this group */
-      Col [parent].shared2.order = order ;
-    }
-  }
-
-  /* === Generate the permutation ========================================= */
-
-  for (c = 0 ; c < n_col ; c++)
-  {
-    p [Col [c].shared2.order] = c ;
-  }
-}
-
-
-/* ========================================================================== */
-/* === detect_super_cols ==================================================== */
-/* ========================================================================== */
-
-/*
-  Detects supercolumns by finding matches between columns in the hash buckets.
-  Check amongst columns in the set A [row_start ... row_start + row_length-1].
-  The columns under consideration are currently *not* in the degree lists,
-  and have already been placed in the hash buckets.
-
-  The hash bucket for columns whose hash function is equal to h is stored
-  as follows:
-
-  if head [h] is >= 0, then head [h] contains a degree list, so:
-
-  head [h] is the first column in degree bucket h.
-  Col [head [h]].headhash gives the first column in hash bucket h.
-
-  otherwise, the degree list is empty, and:
-
-  -(head [h] + 2) is the first column in hash bucket h.
-
-  For a column c in a hash bucket, Col [c].shared3.prev is NOT a "previous
-  column" pointer.  Col [c].shared3.hash is used instead as the hash number
-  for that column.  The value of Col [c].shared4.hash_next is the next column
-  in the same hash bucket.
-
-  Assuming no, or "few" hash collisions, the time taken by this routine is
-  linear in the sum of the sizes (lengths) of each column whose score has
-  just been computed in the approximate degree computation.
-  Not user-callable.
-*/
-template <typename IndexType>
-static void detect_super_cols
-(
-  /* === Parameters ======================================================= */
-
-  ColStructure<IndexType> Col [],    /* of size n_col+1 */
-  IndexType A [],     /* row indices of A */
-  IndexType head [],    /* head of degree lists and hash buckets */
-  IndexType row_start,    /* pointer to set of columns to check */
-  IndexType row_length    /* number of columns to check */
-)
-{
-  /* === Local variables ================================================== */
-
-  IndexType hash ;      /* hash value for a column */
-  IndexType *rp ;     /* pointer to a row */
-  IndexType c ;     /* a column index */
-  IndexType super_c ;   /* column index of the column to absorb into */
-  IndexType *cp1 ;      /* column pointer for column super_c */
-  IndexType *cp2 ;      /* column pointer for column c */
-  IndexType length ;    /* length of column super_c */
-  IndexType prev_c ;    /* column preceding c in hash bucket */
-  IndexType i ;     /* loop counter */
-  IndexType *rp_end ;   /* pointer to the end of the row */
-  IndexType col ;     /* a column index in the row to check */
-  IndexType head_column ;   /* first column in hash bucket or degree list */
-  IndexType first_col ;   /* first column in hash bucket */
-
-  /* === Consider each column in the row ================================== */
-
-  rp = &A [row_start] ;
-  rp_end = rp + row_length ;
-  while (rp < rp_end)
-  {
-    col = *rp++ ;
-    if (Col[col].is_dead())
-    {
-      continue ;
-    }
-
-    /* get hash number for this column */
-    hash = Col [col].shared3.hash ;
-    COLAMD_ASSERT (hash <= n_col) ;
-
-    /* === Get the first column in this hash bucket ===================== */
-
-    head_column = head [hash] ;
-    if (head_column > Empty)
-    {
-      first_col = Col [head_column].shared3.headhash ;
-    }
-    else
-    {
-      first_col = - (head_column + 2) ;
-    }
-
-    /* === Consider each column in the hash bucket ====================== */
-
-    for (super_c = first_col ; super_c != Empty ;
-	 super_c = Col [super_c].shared4.hash_next)
-    {
-      COLAMD_ASSERT (Col [super_c].is_alive()) ;
-      COLAMD_ASSERT (Col [super_c].shared3.hash == hash) ;
-      length = Col [super_c].length ;
-
-      /* prev_c is the column preceding column c in the hash bucket */
-      prev_c = super_c ;
-
-      /* === Compare super_c with all columns after it ================ */
-
-      for (c = Col [super_c].shared4.hash_next ;
-	   c != Empty ; c = Col [c].shared4.hash_next)
-      {
-	COLAMD_ASSERT (c != super_c) ;
-	COLAMD_ASSERT (Col[c].is_alive()) ;
-	COLAMD_ASSERT (Col [c].shared3.hash == hash) ;
-
-	/* not identical if lengths or scores are different */
-	if (Col [c].length != length ||
-	    Col [c].shared2.score != Col [super_c].shared2.score)
-	{
-	  prev_c = c ;
-	  continue ;
-	}
-
-	/* compare the two columns */
-	cp1 = &A [Col [super_c].start] ;
-	cp2 = &A [Col [c].start] ;
-
-	for (i = 0 ; i < length ; i++)
-	{
-	  /* the columns are "clean" (no dead rows) */
-	  COLAMD_ASSERT ( cp1->is_alive() );
-	  COLAMD_ASSERT ( cp2->is_alive() );
-	  /* row indices will same order for both supercols, */
-	  /* no gather scatter necessary */
-	  if (*cp1++ != *cp2++)
-	  {
-	    break ;
-	  }
-	}
-
-	/* the two columns are different if the for-loop "broke" */
-	if (i != length)
-	{
-	  prev_c = c ;
-	  continue ;
-	}
-
-	/* === Got it!  two columns are identical =================== */
-
-	COLAMD_ASSERT (Col [c].shared2.score == Col [super_c].shared2.score) ;
-
-	Col [super_c].shared1.thickness += Col [c].shared1.thickness ;
-	Col [c].shared1.parent = super_c ;
-	Col[c].kill_non_principal() ;
-	/* order c later, in order_children() */
-	Col [c].shared2.order = Empty ;
-	/* remove c from hash bucket */
-	Col [prev_c].shared4.hash_next = Col [c].shared4.hash_next ;
-      }
-    }
-
-    /* === Empty this hash bucket ======================================= */
-
-    if (head_column > Empty)
-    {
-      /* corresponding degree list "hash" is not empty */
-      Col [head_column].shared3.headhash = Empty ;
-    }
-    else
-    {
-      /* corresponding degree list "hash" is empty */
-      head [hash] = Empty ;
-    }
-  }
-}
-
-
-/* ========================================================================== */
-/* === garbage_collection =================================================== */
-/* ========================================================================== */
-
-/*
-  Defragments and compacts columns and rows in the workspace A.  Used when
-  all available memory has been used while performing row merging.  Returns
-  the index of the first free position in A, after garbage collection.  The
-  time taken by this routine is linear is the size of the array A, which is
-  itself linear in the number of nonzeros in the input matrix.
-  Not user-callable.
-*/
-template <typename IndexType>
-static IndexType garbage_collection  /* returns the new value of pfree */
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows */
-    IndexType n_col,      /* number of columns */
-    RowStructure<IndexType> Row [],    /* row info */
-    ColStructure<IndexType> Col [],    /* column info */
-    IndexType A [],     /* A [0 ... Alen-1] holds the matrix */
-    IndexType *pfree      /* &A [0] ... pfree is in use */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType *psrc ;     /* source pointer */
-  IndexType *pdest ;    /* destination pointer */
-  IndexType j ;     /* counter */
-  IndexType r ;     /* a row index */
-  IndexType c ;     /* a column index */
-  IndexType length ;    /* length of a row or column */
-
-  /* === Defragment the columns =========================================== */
-
-  pdest = &A[0] ;
-  for (c = 0 ; c < n_col ; c++)
-  {
-    if (Col[c].is_alive())
-    {
-      psrc = &A [Col [c].start] ;
-
-      /* move and compact the column */
-      COLAMD_ASSERT (pdest <= psrc) ;
-      Col [c].start = (IndexType) (pdest - &A [0]) ;
-      length = Col [c].length ;
-      for (j = 0 ; j < length ; j++)
-      {
-	r = *psrc++ ;
-	if (Row[r].is_alive())
-	{
-	  *pdest++ = r ;
-	}
-      }
-      Col [c].length = (IndexType) (pdest - &A [Col [c].start]) ;
-    }
-  }
-
-  /* === Prepare to defragment the rows =================================== */
-
-  for (r = 0 ; r < n_row ; r++)
-  {
-    if (Row[r].is_alive())
-    {
-      if (Row [r].length == 0)
-      {
-        /* this row is of zero length.  cannot compact it, so kill it */
-        COLAMD_DEBUG3 (("Defrag row kill\n")) ;
-        Row[r].kill() ;
-      }
-      else
-      {
-        /* save first column index in Row [r].shared2.first_column */
-        psrc = &A [Row [r].start] ;
-        Row [r].shared2.first_column = *psrc ;
-        COLAMD_ASSERT (Row[r].is_alive()) ;
-        /* flag the start of the row with the one's complement of row */
-        *psrc = ones_complement(r) ;
-
-      }
-    }
-  }
-
-  /* === Defragment the rows ============================================== */
-
-  psrc = pdest ;
-  while (psrc < pfree)
-  {
-    /* find a negative number ... the start of a row */
-    if (*psrc++ < 0)
-    {
-      psrc-- ;
-      /* get the row index */
-      r = ones_complement(*psrc) ;
-      COLAMD_ASSERT (r >= 0 && r < n_row) ;
-      /* restore first column index */
-      *psrc = Row [r].shared2.first_column ;
-      COLAMD_ASSERT (Row[r].is_alive()) ;
-
-      /* move and compact the row */
-      COLAMD_ASSERT (pdest <= psrc) ;
-      Row [r].start = (IndexType) (pdest - &A [0]) ;
-      length = Row [r].length ;
-      for (j = 0 ; j < length ; j++)
-      {
-	c = *psrc++ ;
-	if (Col[c].is_alive())
-	{
-	  *pdest++ = c ;
-	}
-      }
-      Row [r].length = (IndexType) (pdest - &A [Row [r].start]) ;
-
-    }
-  }
-  /* ensure we found all the rows */
-  COLAMD_ASSERT (debug_rows == 0) ;
-
-  /* === Return the new value of pfree ==================================== */
-
-  return ((IndexType) (pdest - &A [0])) ;
-}
-
-
-/* ========================================================================== */
-/* === clear_mark =========================================================== */
-/* ========================================================================== */
-
-/*
-  Clears the Row [].shared2.mark array, and returns the new tag_mark.
-  Return value is the new tag_mark.  Not user-callable.
-*/
-template <typename IndexType>
-static inline  IndexType clear_mark  /* return the new value for tag_mark */
-  (
-      /* === Parameters ======================================================= */
-
-    IndexType n_row,    /* number of rows in A */
-    RowStructure<IndexType> Row [] /* Row [0 ... n_row-1].shared2.mark is set to zero */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType r ;
-
-  for (r = 0 ; r < n_row ; r++)
-  {
-    if (Row[r].is_alive())
-    {
-      Row [r].shared2.mark = 0 ;
-    }
-  }
-  return (1) ;
-}
-
-} // namespace Colamd
-
-} // namespace internal
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Ordering.h b/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Ordering.h
deleted file mode 100644
index c578970..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/OrderingMethods/Ordering.h
+++ /dev/null
@@ -1,153 +0,0 @@
- 
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012  Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORDERING_H
-#define EIGEN_ORDERING_H
-
-namespace Eigen {
-  
-#include "Eigen_Colamd.h"
-
-namespace internal {
-    
-/** \internal
-  * \ingroup OrderingMethods_Module
-  * \param[in] A the input non-symmetric matrix
-  * \param[out] symmat the symmetric pattern A^T+A from the input matrix \a A.
-  * FIXME: The values should not be considered here
-  */
-template<typename MatrixType> 
-void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat)
-{
-  MatrixType C;
-  C = A.transpose(); // NOTE: Could be  costly
-  for (int i = 0; i < C.rows(); i++) 
-  {
-      for (typename MatrixType::InnerIterator it(C, i); it; ++it)
-        it.valueRef() = typename MatrixType::Scalar(0);
-  }
-  symmat = C + A;
-}
-    
-}
-
-/** \ingroup OrderingMethods_Module
-  * \class AMDOrdering
-  *
-  * Functor computing the \em approximate \em minimum \em degree ordering
-  * If the matrix is not structurally symmetric, an ordering of A^T+A is computed
-  * \tparam  StorageIndex The type of indices of the matrix 
-  * \sa COLAMDOrdering
-  */
-template <typename StorageIndex>
-class AMDOrdering
-{
-  public:
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-    
-    /** Compute the permutation vector from a sparse matrix
-     * This routine is much faster if the input matrix is column-major     
-     */
-    template <typename MatrixType>
-    void operator()(const MatrixType& mat, PermutationType& perm)
-    {
-      // Compute the symmetric pattern
-      SparseMatrix<typename MatrixType::Scalar, ColMajor, StorageIndex> symm;
-      internal::ordering_helper_at_plus_a(mat,symm); 
-    
-      // Call the AMD routine 
-      //m_mat.prune(keep_diag());
-      internal::minimum_degree_ordering(symm, perm);
-    }
-    
-    /** Compute the permutation with a selfadjoint matrix */
-    template <typename SrcType, unsigned int SrcUpLo> 
-    void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm)
-    { 
-      SparseMatrix<typename SrcType::Scalar, ColMajor, StorageIndex> C; C = mat;
-      
-      // Call the AMD routine 
-      // m_mat.prune(keep_diag()); //Remove the diagonal elements 
-      internal::minimum_degree_ordering(C, perm);
-    }
-};
-
-/** \ingroup OrderingMethods_Module
-  * \class NaturalOrdering
-  *
-  * Functor computing the natural ordering (identity)
-  * 
-  * \note Returns an empty permutation matrix
-  * \tparam  StorageIndex The type of indices of the matrix 
-  */
-template <typename StorageIndex>
-class NaturalOrdering
-{
-  public:
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-    
-    /** Compute the permutation vector from a column-major sparse matrix */
-    template <typename MatrixType>
-    void operator()(const MatrixType& /*mat*/, PermutationType& perm)
-    {
-      perm.resize(0); 
-    }
-    
-};
-
-/** \ingroup OrderingMethods_Module
-  * \class COLAMDOrdering
-  *
-  * \tparam  StorageIndex The type of indices of the matrix 
-  * 
-  * Functor computing the \em column \em approximate \em minimum \em degree ordering 
-  * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
-  */
-template<typename StorageIndex>
-class COLAMDOrdering
-{
-  public:
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; 
-    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
-    
-    /** Compute the permutation vector \a perm form the sparse matrix \a mat
-      * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-      */
-    template <typename MatrixType>
-    void operator() (const MatrixType& mat, PermutationType& perm)
-    {
-      eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering");
-      
-      StorageIndex m = StorageIndex(mat.rows());
-      StorageIndex n = StorageIndex(mat.cols());
-      StorageIndex nnz = StorageIndex(mat.nonZeros());
-      // Get the recommended value of Alen to be used by colamd
-      StorageIndex Alen = internal::Colamd::recommended(nnz, m, n); 
-      // Set the default parameters
-      double knobs [internal::Colamd::NKnobs]; 
-      StorageIndex stats [internal::Colamd::NStats];
-      internal::Colamd::set_defaults(knobs);
-      
-      IndexVector p(n+1), A(Alen); 
-      for(StorageIndex i=0; i <= n; i++)   p(i) = mat.outerIndexPtr()[i];
-      for(StorageIndex i=0; i < nnz; i++)  A(i) = mat.innerIndexPtr()[i];
-      // Call Colamd routine to compute the ordering 
-      StorageIndex info = internal::Colamd::compute_ordering(m, n, Alen, A.data(), p.data(), knobs, stats); 
-      EIGEN_UNUSED_VARIABLE(info);
-      eigen_assert( info && "COLAMD failed " );
-      
-      perm.resize(n);
-      for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i;
-    }
-};
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/PaStiXSupport/PaStiXSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
deleted file mode 100644
index 3742687..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
+++ /dev/null
@@ -1,678 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PASTIXSUPPORT_H
-#define EIGEN_PASTIXSUPPORT_H
-
-namespace Eigen { 
-
-#if defined(DCOMPLEX)
-  #define PASTIX_COMPLEX  COMPLEX
-  #define PASTIX_DCOMPLEX DCOMPLEX
-#else
-  #define PASTIX_COMPLEX  std::complex<float>
-  #define PASTIX_DCOMPLEX std::complex<double>
-#endif
-
-/** \ingroup PaStiXSupport_Module
-  * \brief Interface to the PaStix solver
-  * 
-  * This class is used to solve the linear systems A.X = B via the PaStix library. 
-  * The matrix can be either real or complex, symmetric or not.
-  *
-  * \sa TutorialSparseDirectSolvers
-  */
-template<typename _MatrixType, bool IsStrSym = false> class PastixLU;
-template<typename _MatrixType, int Options> class PastixLLT;
-template<typename _MatrixType, int Options> class PastixLDLT;
-
-namespace internal
-{
-    
-  template<class Pastix> struct pastix_traits;
-
-  template<typename _MatrixType>
-  struct pastix_traits< PastixLU<_MatrixType> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pastix_traits< PastixLLT<_MatrixType,Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pastix_traits< PastixLDLT<_MatrixType,Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_DCOMPLEX*>(x), nbrhs, iparm, dparm); 
-  }
-
-  // Convert the matrix  to Fortran-style Numbering
-  template <typename MatrixType>
-  void c_to_fortran_numbering (MatrixType& mat)
-  {
-    if ( !(mat.outerIndexPtr()[0]) ) 
-    { 
-      int i;
-      for(i = 0; i <= mat.rows(); ++i)
-        ++mat.outerIndexPtr()[i];
-      for(i = 0; i < mat.nonZeros(); ++i)
-        ++mat.innerIndexPtr()[i];
-    }
-  }
-  
-  // Convert to C-style Numbering
-  template <typename MatrixType>
-  void fortran_to_c_numbering (MatrixType& mat)
-  {
-    // Check the Numbering
-    if ( mat.outerIndexPtr()[0] == 1 ) 
-    { // Convert to C-style numbering
-      int i;
-      for(i = 0; i <= mat.rows(); ++i)
-        --mat.outerIndexPtr()[i];
-      for(i = 0; i < mat.nonZeros(); ++i)
-        --mat.innerIndexPtr()[i];
-    }
-  }
-}
-
-// This is the base class to interface with PaStiX functions. 
-// Users should not used this class directly. 
-template <class Derived>
-class PastixBase : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    
-    typedef typename internal::pastix_traits<Derived>::MatrixType _MatrixType;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-  public:
-    
-    PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_pastixdata(0), m_size(0)
-    {
-      init();
-    }
-    
-    ~PastixBase() 
-    {
-      clean();
-    }
-    
-    template<typename Rhs,typename Dest>
-    bool _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const;
-    
-    /** Returns a reference to the integer vector IPARM of PaStiX parameters
-      * to modify the default parameters. 
-      * The statistics related to the different phases of factorization and solve are saved here as well
-      * \sa analyzePattern() factorize()
-      */
-    Array<StorageIndex,IPARM_SIZE,1>& iparm()
-    {
-      return m_iparm; 
-    }
-    
-    /** Return a reference to a particular index parameter of the IPARM vector 
-     * \sa iparm()
-     */
-    
-    int& iparm(int idxparam)
-    {
-      return m_iparm(idxparam);
-    }
-    
-     /** Returns a reference to the double vector DPARM of PaStiX parameters 
-      * The statistics related to the different phases of factorization and solve are saved here as well
-      * \sa analyzePattern() factorize()
-      */
-    Array<double,DPARM_SIZE,1>& dparm()
-    {
-      return m_dparm; 
-    }
-    
-    
-    /** Return a reference to a particular index parameter of the DPARM vector 
-     * \sa dparm()
-     */
-    double& dparm(int idxparam)
-    {
-      return m_dparm(idxparam);
-    }
-    
-    inline Index cols() const { return m_size; }
-    inline Index rows() const { return m_size; }
-    
-     /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the PaStiX reports a problem
-      *          \c InvalidInput if the input matrix is invalid
-      *
-      * \sa iparm()          
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-    
-  protected:
-
-    // Initialize the Pastix data structure, check the matrix
-    void init(); 
-    
-    // Compute the ordering and the symbolic factorization
-    void analyzePattern(ColSpMatrix& mat);
-    
-    // Compute the numerical factorization
-    void factorize(ColSpMatrix& mat);
-    
-    // Free all the data allocated by Pastix
-    void clean()
-    {
-      eigen_assert(m_initisOk && "The Pastix structure should be allocated first"); 
-      m_iparm(IPARM_START_TASK) = API_TASK_CLEAN;
-      m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
-      internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
-                             m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-    }
-    
-    void compute(ColSpMatrix& mat);
-    
-    int m_initisOk; 
-    int m_analysisIsOk;
-    int m_factorizationIsOk;
-    mutable ComputationInfo m_info; 
-    mutable pastix_data_t *m_pastixdata; // Data structure for pastix
-    mutable int m_comm; // The MPI communicator identifier
-    mutable Array<int,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters
-    mutable Array<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters
-    mutable Matrix<StorageIndex,Dynamic,1> m_perm;  // Permutation vector
-    mutable Matrix<StorageIndex,Dynamic,1> m_invp;  // Inverse permutation vector
-    mutable int m_size; // Size of the matrix 
-}; 
-
- /** Initialize the PaStiX data structure. 
-   *A first call to this function fills iparm and dparm with the default PaStiX parameters
-   * \sa iparm() dparm()
-   */
-template <class Derived>
-void PastixBase<Derived>::init()
-{
-  m_size = 0; 
-  m_iparm.setZero(IPARM_SIZE);
-  m_dparm.setZero(DPARM_SIZE);
-  
-  m_iparm(IPARM_MODIFY_PARAMETER) = API_NO;
-  pastix(&m_pastixdata, MPI_COMM_WORLD,
-         0, 0, 0, 0,
-         0, 0, 0, 1, m_iparm.data(), m_dparm.data());
-  
-  m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO;
-  m_iparm[IPARM_VERBOSE]             = API_VERBOSE_NOT;
-  m_iparm[IPARM_ORDERING]            = API_ORDER_SCOTCH;
-  m_iparm[IPARM_INCOMPLETE]          = API_NO;
-  m_iparm[IPARM_OOC_LIMIT]           = 2000;
-  m_iparm[IPARM_RHS_MAKING]          = API_RHS_B;
-  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
-  
-  m_iparm(IPARM_START_TASK) = API_TASK_INIT;
-  m_iparm(IPARM_END_TASK) = API_TASK_INIT;
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
-                         0, 0, 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER)) {
-    m_info = InvalidInput;
-    m_initisOk = false;
-  }
-  else { 
-    m_info = Success;
-    m_initisOk = true;
-  }
-}
-
-template <class Derived>
-void PastixBase<Derived>::compute(ColSpMatrix& mat)
-{
-  eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
-  
-  analyzePattern(mat);  
-  factorize(mat);
-  
-  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
-}
-
-
-template <class Derived>
-void PastixBase<Derived>::analyzePattern(ColSpMatrix& mat)
-{                         
-  eigen_assert(m_initisOk && "The initialization of PaSTiX failed");
-  
-  // clean previous calls
-  if(m_size>0)
-    clean();
-  
-  m_size = internal::convert_index<int>(mat.rows());
-  m_perm.resize(m_size);
-  m_invp.resize(m_size);
-  
-  m_iparm(IPARM_START_TASK) = API_TASK_ORDERING;
-  m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE;
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
-               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER))
-  {
-    m_info = NumericalIssue;
-    m_analysisIsOk = false;
-  }
-  else
-  { 
-    m_info = Success;
-    m_analysisIsOk = true;
-  }
-}
-
-template <class Derived>
-void PastixBase<Derived>::factorize(ColSpMatrix& mat)
-{
-//   if(&m_cpyMat != &mat) m_cpyMat = mat;
-  eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase");
-  m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT;
-  m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT;
-  m_size = internal::convert_index<int>(mat.rows());
-  
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
-               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER))
-  {
-    m_info = NumericalIssue;
-    m_factorizationIsOk = false;
-    m_isInitialized = false;
-  }
-  else
-  {
-    m_info = Success;
-    m_factorizationIsOk = true;
-    m_isInitialized = true;
-  }
-}
-
-/* Solve the system */
-template<typename Base>
-template<typename Rhs,typename Dest>
-bool PastixBase<Base>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const
-{
-  eigen_assert(m_isInitialized && "The matrix should be factorized first");
-  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
-                     THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  int rhs = 1;
-  
-  x = b; /* on return, x is overwritten by the computed solution */
-  
-  for (int i = 0; i < b.cols(); i++){
-    m_iparm[IPARM_START_TASK]          = API_TASK_SOLVE;
-    m_iparm[IPARM_END_TASK]            = API_TASK_REFINE;
-  
-    internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, internal::convert_index<int>(x.rows()), 0, 0, 0,
-                           m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data());
-  }
-  
-  // Check the returned error
-  m_info = m_iparm(IPARM_ERROR_NUMBER)==0 ? Success : NumericalIssue;
-  
-  return m_iparm(IPARM_ERROR_NUMBER)==0;
-}
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLU
-  * \brief Sparse direct LU solver based on PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B with a supernodal LU 
-  * factorization in the PaStiX library. The matrix A should be squared and nonsingular
-  * PaStiX requires that the matrix A has a symmetric structural pattern. 
-  * This interface can symmetrize the input matrix otherwise. 
-  * The vectors or matrices X and B can be either dense or sparse.
-  * 
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false
-  * NOTE : Note that if the analysis and factorization phase are called separately, 
-  * the input matrix will be symmetrized at each call, hence it is advised to 
-  * symmetrize the matrix in a end-user program and set \p IsStrSym to true
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  * 
-  */
-template<typename _MatrixType, bool IsStrSym>
-class PastixLU : public PastixBase< PastixLU<_MatrixType> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLU<MatrixType> > Base;
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    
-  public:
-    PastixLU() : Base()
-    {
-      init();
-    }
-    
-    explicit PastixLU(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-    /** Compute the LU supernodal factorization of \p matrix. 
-      * iparm and dparm can be used to tune the PaStiX parameters. 
-      * see the PaStiX user's manual
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      m_structureIsUptodate = false;
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-    /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern. 
-      * Several ordering methods can be used at this step. See the PaStiX user's manual. 
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      m_structureIsUptodate = false;
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-
-    /** Compute the LU supernodal factorization of \p matrix
-      * WARNING The matrix \p matrix should have the same structural pattern 
-      * as the same used in the analysis phase.
-      * \sa analyzePattern()
-      */ 
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-  protected:
-    
-    void init()
-    {
-      m_structureIsUptodate = false;
-      m_iparm(IPARM_SYM) = API_SYM_NO;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      if(IsStrSym)
-        out = matrix;
-      else
-      {
-        if(!m_structureIsUptodate)
-        {
-          // update the transposed structure
-          m_transposedStructure = matrix.transpose();
-          
-          // Set the elements of the matrix to zero 
-          for (Index j=0; j<m_transposedStructure.outerSize(); ++j) 
-            for(typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it)
-              it.valueRef() = 0.0;
-
-          m_structureIsUptodate = true;
-        }
-        
-        out = m_transposedStructure + matrix;
-      }
-      internal::c_to_fortran_numbering(out);
-    }
-    
-    using Base::m_iparm;
-    using Base::m_dparm;
-    
-    ColSpMatrix m_transposedStructure;
-    bool m_structureIsUptodate;
-};
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLLT
-  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization
-  * available in the PaStiX library. The matrix A should be symmetric and positive definite
-  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
-  * The vectors or matrices X and B can be either dense or sparse
-  * 
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo>
-class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLLT<MatrixType, _UpLo> > Base;
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    
-  public:
-    enum { UpLo = _UpLo };
-    PastixLLT() : Base()
-    {
-      init();
-    }
-    
-    explicit PastixLLT(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-
-    /** Compute the L factor of the LL^T supernodal factorization of \p matrix 
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-
-     /** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-      /** Compute the LL^T supernodal numerical factorization of \p matrix 
-        * \sa analyzePattern()
-        */
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-  protected:
-    using Base::m_iparm;
-    
-    void init()
-    {
-      m_iparm(IPARM_SYM) = API_SYM_YES;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      out.resize(matrix.rows(), matrix.cols());
-      // Pastix supports only lower, column-major matrices 
-      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
-      internal::c_to_fortran_numbering(out);
-    }
-};
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLDLT
-  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization
-  * available in the PaStiX library. The matrix A should be symmetric and positive definite
-  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
-  * The vectors or matrices X and B can be either dense or sparse
-  * 
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
-  */
-template<typename _MatrixType, int _UpLo>
-class PastixLDLT : public PastixBase< PastixLDLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLDLT<MatrixType, _UpLo> > Base; 
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    
-  public:
-    enum { UpLo = _UpLo };
-    PastixLDLT():Base()
-    {
-      init();
-    }
-    
-    explicit PastixLDLT(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-
-    /** Compute the L and D factors of the LDL^T factorization of \p matrix 
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-
-    /** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    { 
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-    /** Compute the LDL^T supernodal numerical factorization of \p matrix 
-      * 
-      */
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-
-  protected:
-    using Base::m_iparm;
-    
-    void init()
-    {
-      m_iparm(IPARM_SYM) = API_SYM_YES;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      // Pastix supports only lower, column-major matrices 
-      out.resize(matrix.rows(), matrix.cols());
-      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
-      internal::c_to_fortran_numbering(out);
-    }
-};
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/PardisoSupport/PardisoSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/PardisoSupport/PardisoSupport.h
deleted file mode 100644
index f89b79b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/PardisoSupport/PardisoSupport.h
+++ /dev/null
@@ -1,545 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to Intel(R) MKL PARDISO
- ********************************************************************************
-*/
-
-#ifndef EIGEN_PARDISOSUPPORT_H
-#define EIGEN_PARDISOSUPPORT_H
-
-namespace Eigen { 
-
-template<typename _MatrixType> class PardisoLU;
-template<typename _MatrixType, int Options=Upper> class PardisoLLT;
-template<typename _MatrixType, int Options=Upper> class PardisoLDLT;
-
-namespace internal
-{
-  template<typename IndexType>
-  struct pardiso_run_selector
-  {
-    static IndexType run( _MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase, IndexType n, void *a,
-                      IndexType *ia, IndexType *ja, IndexType *perm, IndexType nrhs, IndexType *iparm, IndexType msglvl, void *b, void *x)
-    {
-      IndexType error = 0;
-      ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
-      return error;
-    }
-  };
-  template<>
-  struct pardiso_run_selector<long long int>
-  {
-    typedef long long int IndexType;
-    static IndexType run( _MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase, IndexType n, void *a,
-                      IndexType *ia, IndexType *ja, IndexType *perm, IndexType nrhs, IndexType *iparm, IndexType msglvl, void *b, void *x)
-    {
-      IndexType error = 0;
-      ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
-      return error;
-    }
-  };
-
-  template<class Pardiso> struct pardiso_traits;
-
-  template<typename _MatrixType>
-  struct pardiso_traits< PardisoLU<_MatrixType> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pardiso_traits< PardisoLLT<_MatrixType, Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pardiso_traits< PardisoLDLT<_MatrixType, Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;    
-  };
-
-} // end namespace internal
-
-template<class Derived>
-class PardisoImpl : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-    
-    typedef internal::pardiso_traits<Derived> Traits;
-  public:
-    using Base::_solve_impl;
-    
-    typedef typename Traits::MatrixType MatrixType;
-    typedef typename Traits::Scalar Scalar;
-    typedef typename Traits::RealScalar RealScalar;
-    typedef typename Traits::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> SparseMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef Matrix<StorageIndex, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<StorageIndex, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
-    typedef Array<StorageIndex,64,1,DontAlign> ParameterType;
-    enum {
-      ScalarIsComplex = NumTraits<Scalar>::IsComplex,
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-
-    PardisoImpl()
-      : m_analysisIsOk(false), m_factorizationIsOk(false)
-    {
-      eigen_assert((sizeof(StorageIndex) >= sizeof(_INTEGER_t) && sizeof(StorageIndex) <= 8) && "Non-supported index type");
-      m_iparm.setZero();
-      m_msglvl = 0; // No output
-      m_isInitialized = false;
-    }
-
-    ~PardisoImpl()
-    {
-      pardisoRelease();
-    }
-
-    inline Index cols() const { return m_size; }
-    inline Index rows() const { return m_size; }
-  
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    /** \warning for advanced usage only.
-      * \returns a reference to the parameter array controlling PARDISO.
-      * See the PARDISO manual to know how to use it. */
-    ParameterType& pardisoParameterArray()
-    {
-      return m_iparm;
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    Derived& analyzePattern(const MatrixType& matrix);
-    
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    Derived& factorize(const MatrixType& matrix);
-
-    Derived& compute(const MatrixType& matrix);
-
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
-
-  protected:
-    void pardisoRelease()
-    {
-      if(m_isInitialized) // Factorization ran at least once
-      {
-        internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1, internal::convert_index<StorageIndex>(m_size),0, 0, 0, m_perm.data(), 0,
-                                                          m_iparm.data(), m_msglvl, NULL, NULL);
-        m_isInitialized = false;
-      }
-    }
-
-    void pardisoInit(int type)
-    {
-      m_type = type;
-      bool symmetric = std::abs(m_type) < 10;
-      m_iparm[0] = 1;   // No solver default
-      m_iparm[1] = 2;   // use Metis for the ordering
-      m_iparm[2] = 0;   // Reserved. Set to zero. (??Numbers of processors, value of OMP_NUM_THREADS??)
-      m_iparm[3] = 0;   // No iterative-direct algorithm
-      m_iparm[4] = 0;   // No user fill-in reducing permutation
-      m_iparm[5] = 0;   // Write solution into x, b is left unchanged
-      m_iparm[6] = 0;   // Not in use
-      m_iparm[7] = 2;   // Max numbers of iterative refinement steps
-      m_iparm[8] = 0;   // Not in use
-      m_iparm[9] = 13;  // Perturb the pivot elements with 1E-13
-      m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
-      m_iparm[11] = 0;  // Not in use
-      m_iparm[12] = symmetric ? 0 : 1;  // Maximum weighted matching algorithm is switched-off (default for symmetric).
-                                        // Try m_iparm[12] = 1 in case of inappropriate accuracy
-      m_iparm[13] = 0;  // Output: Number of perturbed pivots
-      m_iparm[14] = 0;  // Not in use
-      m_iparm[15] = 0;  // Not in use
-      m_iparm[16] = 0;  // Not in use
-      m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
-      m_iparm[18] = -1; // Output: Mflops for LU factorization
-      m_iparm[19] = 0;  // Output: Numbers of CG Iterations
-      
-      m_iparm[20] = 0;  // 1x1 pivoting
-      m_iparm[26] = 0;  // No matrix checker
-      m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
-      m_iparm[34] = 1;  // C indexing
-      m_iparm[36] = 0;  // CSR
-      m_iparm[59] = 0;  // 0 - In-Core ; 1 - Automatic switch between In-Core and Out-of-Core modes ; 2 - Out-of-Core
-      
-      memset(m_pt, 0, sizeof(m_pt));
-    }
-
-  protected:
-    // cached data to reduce reallocation, etc.
-    
-    void manageErrorCode(Index error) const
-    {
-      switch(error)
-      {
-        case 0:
-          m_info = Success;
-          break;
-        case -4:
-        case -7:
-          m_info = NumericalIssue;
-          break;
-        default:
-          m_info = InvalidInput;
-      }
-    }
-
-    mutable SparseMatrixType m_matrix;
-    mutable ComputationInfo m_info;
-    bool m_analysisIsOk, m_factorizationIsOk;
-    StorageIndex m_type, m_msglvl;
-    mutable void *m_pt[64];
-    mutable ParameterType m_iparm;
-    mutable IntColVectorType m_perm;
-    Index m_size;
-    
-};
-
-template<class Derived>
-Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
-{
-  m_size = a.rows();
-  eigen_assert(a.rows() == a.cols());
-
-  pardisoRelease();
-  m_perm.setZero(m_size);
-  derived().getMatrix(a);
-  
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 12, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
-  manageErrorCode(error);
-  m_analysisIsOk = true;
-  m_factorizationIsOk = true;
-  m_isInitialized = true;
-  return derived();
-}
-
-template<class Derived>
-Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
-{
-  m_size = a.rows();
-  eigen_assert(m_size == a.cols());
-
-  pardisoRelease();
-  m_perm.setZero(m_size);
-  derived().getMatrix(a);
-  
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 11, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
-  
-  manageErrorCode(error);
-  m_analysisIsOk = true;
-  m_factorizationIsOk = false;
-  m_isInitialized = true;
-  return derived();
-}
-
-template<class Derived>
-Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
-{
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  eigen_assert(m_size == a.rows() && m_size == a.cols());
-  
-  derived().getMatrix(a);
-
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 22, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
-  
-  manageErrorCode(error);
-  m_factorizationIsOk = true;
-  return derived();
-}
-
-template<class Derived>
-template<typename BDerived,typename XDerived>
-void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
-{
-  if(m_iparm[0] == 0) // Factorization was not computed
-  {
-    m_info = InvalidInput;
-    return;
-  }
-
-  //Index n = m_matrix.rows();
-  Index nrhs = Index(b.cols());
-  eigen_assert(m_size==b.rows());
-  eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported");
-  eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported");
-  eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));
-
-
-//  switch (transposed) {
-//    case SvNoTrans    : m_iparm[11] = 0 ; break;
-//    case SvTranspose  : m_iparm[11] = 2 ; break;
-//    case SvAdjoint    : m_iparm[11] = 1 ; break;
-//    default:
-//      //std::cerr << "Eigen: transposition  option \"" << transposed << "\" not supported by the PARDISO backend\n";
-//      m_iparm[11] = 0;
-//  }
-
-  Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
-  Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;
-  
-  // Pardiso cannot solve in-place
-  if(rhs_ptr == x.derived().data())
-  {
-    tmp = b;
-    rhs_ptr = tmp.data();
-  }
-  
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 33, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), internal::convert_index<StorageIndex>(nrhs), m_iparm.data(), m_msglvl,
-                                                            rhs_ptr, x.derived().data());
-
-  manageErrorCode(error);
-}
-
-
-/** \ingroup PardisoSupport_Module
-  * \class PardisoLU
-  * \brief A sparse direct LU factorization and solver based on the PARDISO library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
-  * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
-  * \code solver.pardisoParameterArray()[59] = 1; \endcode
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  */
-template<typename MatrixType>
-class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
-{
-  protected:
-    typedef PardisoImpl<PardisoLU> Base;
-    using Base::pardisoInit;
-    using Base::m_matrix;
-    friend class PardisoImpl< PardisoLU<MatrixType> >;
-
-  public:
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-
-    using Base::compute;
-    using Base::solve;
-
-    PardisoLU()
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
-    }
-
-    explicit PardisoLU(const MatrixType& matrix)
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
-      compute(matrix);
-    }
-  protected:
-    void getMatrix(const MatrixType& matrix)
-    {
-      m_matrix = matrix;
-      m_matrix.makeCompressed();
-    }
-};
-
-/** \ingroup PardisoSupport_Module
-  * \class PardisoLLT
-  * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
-  * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
-  * \code solver.pardisoParameterArray()[59] = 1; \endcode
-  *
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
-  *         Upper|Lower can be used to tell both triangular parts can be used as input.
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
-  */
-template<typename MatrixType, int _UpLo>
-class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
-{
-  protected:
-    typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base;
-    using Base::pardisoInit;
-    using Base::m_matrix;
-    friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >;
-
-  public:
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    enum { UpLo = _UpLo };
-    using Base::compute;
-
-    PardisoLLT()
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
-    }
-
-    explicit PardisoLLT(const MatrixType& matrix)
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
-      compute(matrix);
-    }
-    
-  protected:
-    
-    void getMatrix(const MatrixType& matrix)
-    {
-      // PARDISO supports only upper, row-major matrices
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> p_null;
-      m_matrix.resize(matrix.rows(), matrix.cols());
-      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
-      m_matrix.makeCompressed();
-    }
-};
-
-/** \ingroup PardisoSupport_Module
-  * \class PardisoLDLT
-  * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
-  * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
-  * For complex matrices, A can also be symmetric only, see the \a Options template parameter.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
-  * \code solver.pardisoParameterArray()[59] = 1; \endcode
-  *
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
-  *         Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.
-  *         Upper|Lower can be used to tell both triangular parts can be used as input.
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
-  */
-template<typename MatrixType, int Options>
-class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
-{
-  protected:
-    typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
-    using Base::pardisoInit;
-    using Base::m_matrix;
-    friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;
-
-  public:
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    using Base::compute;
-    enum { UpLo = Options&(Upper|Lower) };
-
-    PardisoLDLT()
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
-    }
-
-    explicit PardisoLDLT(const MatrixType& matrix)
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
-      compute(matrix);
-    }
-    
-    void getMatrix(const MatrixType& matrix)
-    {
-      // PARDISO supports only upper, row-major matrices
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> p_null;
-      m_matrix.resize(matrix.rows(), matrix.cols());
-      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
-      m_matrix.makeCompressed();
-    }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARDISOSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/ColPivHouseholderQR.h b/3rdParty/my_ceres_vc17/include/Eigen/src/QR/ColPivHouseholderQR.h
deleted file mode 100644
index 9b677e9..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/ColPivHouseholderQR.h
+++ /dev/null
@@ -1,674 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
-#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _MatrixType> struct traits<ColPivHouseholderQR<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-} // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  * \class ColPivHouseholderQR
-  *
-  * \brief Householder rank-revealing QR decomposition of a matrix with column-pivoting
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the QR decomposition
-  *
-  * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R
-  * such that
-  * \f[
-  *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b P is a permutation matrix, \b Q a unitary matrix and \b R an
-  * upper triangular matrix.
-  *
-  * This decomposition performs column pivoting in order to be rank-revealing and improve
-  * numerical stability. It is slower than HouseholderQR, and faster than FullPivHouseholderQR.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  * 
-  * \sa MatrixBase::colPivHouseholderQr()
-  */
-template<typename _MatrixType> class ColPivHouseholderQR
-        : public SolverBase<ColPivHouseholderQR<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<ColPivHouseholderQR> Base;
-    friend class SolverBase<ColPivHouseholderQR>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(ColPivHouseholderQR)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
-    typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-  private:
-
-    typedef typename PermutationType::StorageIndex PermIndexType;
-
-  public:
-
-    /**
-    * \brief Default Constructor.
-    *
-    * The default constructor is useful in cases in which the user intends to
-    * perform decompositions via ColPivHouseholderQR::compute(const MatrixType&).
-    */
-    ColPivHouseholderQR()
-      : m_qr(),
-        m_hCoeffs(),
-        m_colsPermutation(),
-        m_colsTranspositions(),
-        m_temp(),
-        m_colNormsUpdated(),
-        m_colNormsDirect(),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa ColPivHouseholderQR()
-      */
-    ColPivHouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_colsPermutation(PermIndexType(cols)),
-        m_colsTranspositions(cols),
-        m_temp(cols),
-        m_colNormsUpdated(cols),
-        m_colNormsDirect(cols),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This constructor computes the QR factorization of the matrix \a matrix by calling
-      * the method compute(). It is a short cut for:
-      *
-      * \code
-      * ColPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      *
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit ColPivHouseholderQR(const EigenBase<InputType>& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_colsPermutation(PermIndexType(matrix.cols())),
-        m_colsTranspositions(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_colNormsUpdated(matrix.cols()),
-        m_colNormsDirect(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa ColPivHouseholderQR(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit ColPivHouseholderQR(EigenBase<InputType>& matrix)
-      : m_qr(matrix.derived()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_colsPermutation(PermIndexType(matrix.cols())),
-        m_colsTranspositions(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_colNormsUpdated(matrix.cols()),
-        m_colNormsDirect(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      computeInPlace();
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the QR decomposition, if any exists.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns a solution.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include ColPivHouseholderQR_solve.cpp
-      * Output: \verbinclude ColPivHouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const Solve<ColPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    HouseholderSequenceType householderQ() const;
-    HouseholderSequenceType matrixQ() const
-    {
-      return householderQ();
-    }
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      */
-    const MatrixType& matrixQR() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    /** \returns a reference to the matrix where the result Householder QR is stored
-     * \warning The strict lower part of this matrix contains internal values.
-     * Only the upper triangular part should be referenced. To get it, use
-     * \code matrixR().template triangularView<Upper>() \endcode
-     * For rank-deficient matrices, use
-     * \code
-     * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>()
-     * \endcode
-     */
-    const MatrixType& matrixR() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    template<typename InputType>
-    ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns a const reference to the column permutation matrix */
-    const PermutationType& colsPermutation() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_colsPermutation;
-    }
-
-    /** \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar absDeterminant() const;
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's inherent
-      * to determinant computation.
-      *
-      * \sa absDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar logAbsDeterminant() const;
-
-    /** \returns the rank of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
-      Index result = 0;
-      for(Index i = 0; i < m_nonzero_pivots; ++i)
-        result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);
-      return result;
-    }
-
-    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index dimensionOfKernel() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return cols() - rank();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents an injective
-      *          linear map, i.e. has trivial kernel; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInjective() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return rank() == cols();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
-      *          linear map; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isSurjective() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return rank() == rows();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInvertible() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return isInjective() && isSurjective();
-    }
-
-    /** \returns the inverse of the matrix of which *this is the QR decomposition.
-      *
-      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
-      *       Use isInvertible() to first determine whether this matrix is invertible.
-      */
-    inline const Inverse<ColPivHouseholderQR> inverse() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return Inverse<ColPivHouseholderQR>(*this);
-    }
-
-    inline Index rows() const { return m_qr.rows(); }
-    inline Index cols() const { return m_qr.cols(); }
-
-    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
-      *
-      * For advanced uses only.
-      */
-    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
-
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
-      * who need to determine when pivots are to be considered nonzero. This is not used for the
-      * QR decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
-      * uses a formula to automatically determine a reasonable threshold.
-      * Once you have called the present method setThreshold(const RealScalar&),
-      * your value is used instead.
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A pivot will be considered nonzero if its absolute value is strictly greater than
-      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-      * where maxpivot is the biggest pivot.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    ColPivHouseholderQR& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code qr.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    ColPivHouseholderQR& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
-
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    RealScalar threshold() const
-    {
-      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-      return m_usePrescribedThreshold ? m_prescribedThreshold
-      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
-      // and turns out to be identical to Higham's formula used already in LDLt.
-                                      : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
-    }
-
-    /** \returns the number of nonzero pivots in the QR decomposition.
-      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
-      * So that notion isn't really intrinsically interesting, but it is
-      * still useful when implementing algorithms.
-      *
-      * \sa rank()
-      */
-    inline Index nonzeroPivots() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of R.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-    /** \brief Reports whether the QR factorization was successful.
-      *
-      * \note This function always returns \c Success. It is provided for compatibility
-      * with other factorization routines.
-      * \returns \c Success
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return Success;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    friend class CompleteOrthogonalDecomposition<MatrixType>;
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    PermutationType m_colsPermutation;
-    IntRowVectorType m_colsTranspositions;
-    RowVectorType m_temp;
-    RealRowVectorType m_colNormsUpdated;
-    RealRowVectorType m_colNormsDirect;
-    bool m_isInitialized, m_usePrescribedThreshold;
-    RealScalar m_prescribedThreshold, m_maxpivot;
-    Index m_nonzero_pivots;
-    Index m_det_pq;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return abs(m_qr.diagonal().prod());
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return m_qr.diagonal().cwiseAbs().array().log().sum();
-}
-
-/** Performs the QR factorization of the given matrix \a matrix. The result of
-  * the factorization is stored into \c *this, and a reference to \c *this
-  * is returned.
-  *
-  * \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-template<typename InputType>
-ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
-{
-  m_qr = matrix.derived();
-  computeInPlace();
-  return *this;
-}
-
-template<typename MatrixType>
-void ColPivHouseholderQR<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  // the column permutation is stored as int indices, so just to be sure:
-  eigen_assert(m_qr.cols()<=NumTraits<int>::highest());
-
-  using std::abs;
-
-  Index rows = m_qr.rows();
-  Index cols = m_qr.cols();
-  Index size = m_qr.diagonalSize();
-
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  m_colsTranspositions.resize(m_qr.cols());
-  Index number_of_transpositions = 0;
-
-  m_colNormsUpdated.resize(cols);
-  m_colNormsDirect.resize(cols);
-  for (Index k = 0; k < cols; ++k) {
-    // colNormsDirect(k) caches the most recent directly computed norm of
-    // column k.
-    m_colNormsDirect.coeffRef(k) = m_qr.col(k).norm();
-    m_colNormsUpdated.coeffRef(k) = m_colNormsDirect.coeffRef(k);
-  }
-
-  RealScalar threshold_helper =  numext::abs2<RealScalar>(m_colNormsUpdated.maxCoeff() * NumTraits<RealScalar>::epsilon()) / RealScalar(rows);
-  RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<RealScalar>::epsilon());
-
-  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
-  m_maxpivot = RealScalar(0);
-
-  for(Index k = 0; k < size; ++k)
-  {
-    // first, we look up in our table m_colNormsUpdated which column has the biggest norm
-    Index biggest_col_index;
-    RealScalar biggest_col_sq_norm = numext::abs2(m_colNormsUpdated.tail(cols-k).maxCoeff(&biggest_col_index));
-    biggest_col_index += k;
-
-    // Track the number of meaningful pivots but do not stop the decomposition to make
-    // sure that the initial matrix is properly reproduced. See bug 941.
-    if(m_nonzero_pivots==size && biggest_col_sq_norm < threshold_helper * RealScalar(rows-k))
-      m_nonzero_pivots = k;
-
-    // apply the transposition to the columns
-    m_colsTranspositions.coeffRef(k) = biggest_col_index;
-    if(k != biggest_col_index) {
-      m_qr.col(k).swap(m_qr.col(biggest_col_index));
-      std::swap(m_colNormsUpdated.coeffRef(k), m_colNormsUpdated.coeffRef(biggest_col_index));
-      std::swap(m_colNormsDirect.coeffRef(k), m_colNormsDirect.coeffRef(biggest_col_index));
-      ++number_of_transpositions;
-    }
-
-    // generate the householder vector, store it below the diagonal
-    RealScalar beta;
-    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
-
-    // apply the householder transformation to the diagonal coefficient
-    m_qr.coeffRef(k,k) = beta;
-
-    // remember the maximum absolute value of diagonal coefficients
-    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
-
-    // apply the householder transformation
-    m_qr.bottomRightCorner(rows-k, cols-k-1)
-        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1));
-
-    // update our table of norms of the columns
-    for (Index j = k + 1; j < cols; ++j) {
-      // The following implements the stable norm downgrade step discussed in
-      // http://www.netlib.org/lapack/lawnspdf/lawn176.pdf
-      // and used in LAPACK routines xGEQPF and xGEQP3.
-      // See lines 278-297 in http://www.netlib.org/lapack/explore-html/dc/df4/sgeqpf_8f_source.html
-      if (m_colNormsUpdated.coeffRef(j) != RealScalar(0)) {
-        RealScalar temp = abs(m_qr.coeffRef(k, j)) / m_colNormsUpdated.coeffRef(j);
-        temp = (RealScalar(1) + temp) * (RealScalar(1) - temp);
-        temp = temp <  RealScalar(0) ? RealScalar(0) : temp;
-        RealScalar temp2 = temp * numext::abs2<RealScalar>(m_colNormsUpdated.coeffRef(j) /
-                                                           m_colNormsDirect.coeffRef(j));
-        if (temp2 <= norm_downdate_threshold) {
-          // The updated norm has become too inaccurate so re-compute the column
-          // norm directly.
-          m_colNormsDirect.coeffRef(j) = m_qr.col(j).tail(rows - k - 1).norm();
-          m_colNormsUpdated.coeffRef(j) = m_colNormsDirect.coeffRef(j);
-        } else {
-          m_colNormsUpdated.coeffRef(j) *= numext::sqrt(temp);
-        }
-      }
-    }
-  }
-
-  m_colsPermutation.setIdentity(PermIndexType(cols));
-  for(PermIndexType k = 0; k < size/*m_nonzero_pivots*/; ++k)
-    m_colsPermutation.applyTranspositionOnTheRight(k, PermIndexType(m_colsTranspositions.coeff(k)));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-  m_isInitialized = true;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void ColPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  const Index nonzero_pivots = nonzeroPivots();
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs);
-
-  c.applyOnTheLeft(householderQ().setLength(nonzero_pivots).adjoint() );
-
-  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(nonzero_pivots));
-
-  for(Index i = 0; i < nonzero_pivots; ++i) dst.row(m_colsPermutation.indices().coeff(i)) = c.row(i);
-  for(Index i = nonzero_pivots; i < cols(); ++i) dst.row(m_colsPermutation.indices().coeff(i)).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void ColPivHouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index nonzero_pivots = nonzeroPivots();
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(m_colsPermutation.transpose()*rhs);
-
-  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
-        .template triangularView<Upper>()
-        .transpose().template conjugateIf<Conjugate>()
-        .solveInPlace(c.topRows(nonzero_pivots));
-
-  dst.topRows(nonzero_pivots) = c.topRows(nonzero_pivots);
-  dst.bottomRows(rows()-nonzero_pivots).setZero();
-
-  dst.applyOnTheLeft(householderQ().setLength(nonzero_pivots).template conjugateIf<!Conjugate>() );
-}
-#endif
-
-namespace internal {
-
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<ColPivHouseholderQR<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename ColPivHouseholderQR<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef ColPivHouseholderQR<MatrixType> QrType;
-  typedef Inverse<QrType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename QrType::Scalar> &)
-  {
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-
-} // end namespace internal
-
-/** \returns the matrix Q as a sequence of householder transformations.
-  * You can extract the meaningful part only by using:
-  * \code qr.householderQ().setLength(qr.nonzeroPivots()) \endcode*/
-template<typename MatrixType>
-typename ColPivHouseholderQR<MatrixType>::HouseholderSequenceType ColPivHouseholderQR<MatrixType>
-  ::householderQ() const
-{
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
-}
-
-/** \return the column-pivoting Householder QR decomposition of \c *this.
-  *
-  * \sa class ColPivHouseholderQR
-  */
-template<typename Derived>
-const ColPivHouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::colPivHouseholderQr() const
-{
-  return ColPivHouseholderQR<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
deleted file mode 100644
index 4e9651f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Householder QR decomposition of a matrix with column pivoting based on
- *    LAPACKE_?geqp3 function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
-#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_QR_COLPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW) \
-template<> template<typename InputType> inline \
-ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >& \
-ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >::compute( \
-              const EigenBase<InputType>& matrix) \
-\
-{ \
-  using std::abs; \
-  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
-  typedef MatrixType::RealScalar RealScalar; \
-  Index rows = matrix.rows();\
-  Index cols = matrix.cols();\
-\
-  m_qr = matrix;\
-  Index size = m_qr.diagonalSize();\
-  m_hCoeffs.resize(size);\
-\
-  m_colsTranspositions.resize(cols);\
-  /*Index number_of_transpositions = 0;*/ \
-\
-  m_nonzero_pivots = 0; \
-  m_maxpivot = RealScalar(0);\
-  m_colsPermutation.resize(cols); \
-  m_colsPermutation.indices().setZero(); \
-\
-  lapack_int lda = internal::convert_index<lapack_int,Index>(m_qr.outerStride()); \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  LAPACKE_##LAPACKE_PREFIX##geqp3( matrix_order, internal::convert_index<lapack_int,Index>(rows), internal::convert_index<lapack_int,Index>(cols), \
-                              (LAPACKE_TYPE*)m_qr.data(), lda, (lapack_int*)m_colsPermutation.indices().data(), (LAPACKE_TYPE*)m_hCoeffs.data()); \
-  m_isInitialized = true; \
-  m_maxpivot=m_qr.diagonal().cwiseAbs().maxCoeff(); \
-  m_hCoeffs.adjointInPlace(); \
-  RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold(); \
-  lapack_int *perm = m_colsPermutation.indices().data(); \
-  for(Index i=0;i<size;i++) { \
-    m_nonzero_pivots += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);\
-  } \
-  for(Index i=0;i<cols;i++) perm[i]--;\
-\
-  /*m_det_pq = (number_of_transpositions%2) ? -1 : 1;  // TODO: It's not needed now; fix upon availability in Eigen */ \
-\
-  return *this; \
-}
-
-EIGEN_LAPACKE_QR_COLPIV(double,   double,        d, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(float,    float,         s, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(dcomplex, lapack_complex_double, z, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(scomplex, lapack_complex_float,  c, ColMajor, LAPACK_COL_MAJOR)
-
-EIGEN_LAPACKE_QR_COLPIV(double,   double,        d, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(float,    float,         s, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(dcomplex, lapack_complex_double, z, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(scomplex, lapack_complex_float,  c, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/3rdParty/my_ceres_vc17/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
deleted file mode 100644
index 486d337..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
+++ /dev/null
@@ -1,635 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
-#define EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
-
-namespace Eigen {
-
-namespace internal {
-template <typename _MatrixType>
-struct traits<CompleteOrthogonalDecomposition<_MatrixType> >
-    : traits<_MatrixType> {
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-}  // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  * \class CompleteOrthogonalDecomposition
-  *
-  * \brief Complete orthogonal decomposition (COD) of a matrix.
-  *
-  * \param MatrixType the type of the matrix of which we are computing the COD.
-  *
-  * This class performs a rank-revealing complete orthogonal decomposition of a
-  * matrix  \b A into matrices \b P, \b Q, \b T, and \b Z such that
-  * \f[
-  *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \,
-  *                     \begin{bmatrix} \mathbf{T} &  \mathbf{0} \\
-  *                                     \mathbf{0} & \mathbf{0} \end{bmatrix} \, \mathbf{Z}
-  * \f]
-  * by using Householder transformations. Here, \b P is a permutation matrix,
-  * \b Q and \b Z are unitary matrices and \b T an upper triangular matrix of
-  * size rank-by-rank. \b A may be rank deficient.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  * 
-  * \sa MatrixBase::completeOrthogonalDecomposition()
-  */
-template <typename _MatrixType> class CompleteOrthogonalDecomposition
-          : public SolverBase<CompleteOrthogonalDecomposition<_MatrixType> >
-{
- public:
-  typedef _MatrixType MatrixType;
-  typedef SolverBase<CompleteOrthogonalDecomposition> Base;
-
-  template<typename Derived>
-  friend struct internal::solve_assertion;
-
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CompleteOrthogonalDecomposition)
-  enum {
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-  };
-  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime>
-      PermutationType;
-  typedef typename internal::plain_row_type<MatrixType, Index>::type
-      IntRowVectorType;
-  typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-  typedef typename internal::plain_row_type<MatrixType, RealScalar>::type
-      RealRowVectorType;
-  typedef HouseholderSequence<
-      MatrixType, typename internal::remove_all<
-                      typename HCoeffsType::ConjugateReturnType>::type>
-      HouseholderSequenceType;
-  typedef typename MatrixType::PlainObject PlainObject;
-
- private:
-  typedef typename PermutationType::Index PermIndexType;
-
- public:
-  /**
-   * \brief Default Constructor.
-   *
-   * The default constructor is useful in cases in which the user intends to
-   * perform decompositions via
-   * \c CompleteOrthogonalDecomposition::compute(const* MatrixType&).
-   */
-  CompleteOrthogonalDecomposition() : m_cpqr(), m_zCoeffs(), m_temp() {}
-
-  /** \brief Default Constructor with memory preallocation
-   *
-   * Like the default constructor but with preallocation of the internal data
-   * according to the specified problem \a size.
-   * \sa CompleteOrthogonalDecomposition()
-   */
-  CompleteOrthogonalDecomposition(Index rows, Index cols)
-      : m_cpqr(rows, cols), m_zCoeffs((std::min)(rows, cols)), m_temp(cols) {}
-
-  /** \brief Constructs a complete orthogonal decomposition from a given
-   * matrix.
-   *
-   * This constructor computes the complete orthogonal decomposition of the
-   * matrix \a matrix by calling the method compute(). The default
-   * threshold for rank determination will be used. It is a short cut for:
-   *
-   * \code
-   * CompleteOrthogonalDecomposition<MatrixType> cod(matrix.rows(),
-   *                                                 matrix.cols());
-   * cod.setThreshold(Default);
-   * cod.compute(matrix);
-   * \endcode
-   *
-   * \sa compute()
-   */
-  template <typename InputType>
-  explicit CompleteOrthogonalDecomposition(const EigenBase<InputType>& matrix)
-      : m_cpqr(matrix.rows(), matrix.cols()),
-        m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_temp(matrix.cols())
-  {
-    compute(matrix.derived());
-  }
-
-  /** \brief Constructs a complete orthogonal decomposition from a given matrix
-    *
-    * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-    *
-    * \sa CompleteOrthogonalDecomposition(const EigenBase&)
-    */
-  template<typename InputType>
-  explicit CompleteOrthogonalDecomposition(EigenBase<InputType>& matrix)
-    : m_cpqr(matrix.derived()),
-      m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
-      m_temp(matrix.cols())
-  {
-    computeInPlace();
-  } 
-
-  #ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** This method computes the minimum-norm solution X to a least squares
-   * problem \f[\mathrm{minimize} \|A X - B\|, \f] where \b A is the matrix of
-   * which \c *this is the complete orthogonal decomposition.
-   *
-   * \param b the right-hand sides of the problem to solve.
-   *
-   * \returns a solution.
-   *
-   */
-  template <typename Rhs>
-  inline const Solve<CompleteOrthogonalDecomposition, Rhs> solve(
-      const MatrixBase<Rhs>& b) const;
-  #endif
-
-  HouseholderSequenceType householderQ(void) const;
-  HouseholderSequenceType matrixQ(void) const { return m_cpqr.householderQ(); }
-
-  /** \returns the matrix \b Z.
-   */
-  MatrixType matrixZ() const {
-    MatrixType Z = MatrixType::Identity(m_cpqr.cols(), m_cpqr.cols());
-    applyZOnTheLeftInPlace<false>(Z);
-    return Z;
-  }
-
-  /** \returns a reference to the matrix where the complete orthogonal
-   * decomposition is stored
-   */
-  const MatrixType& matrixQTZ() const { return m_cpqr.matrixQR(); }
-
-  /** \returns a reference to the matrix where the complete orthogonal
-   * decomposition is stored.
-   * \warning The strict lower part and \code cols() - rank() \endcode right
-   * columns of this matrix contains internal values.
-   * Only the upper triangular part should be referenced. To get it, use
-   * \code matrixT().template triangularView<Upper>() \endcode
-   * For rank-deficient matrices, use
-   * \code
-   * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>()
-   * \endcode
-   */
-  const MatrixType& matrixT() const { return m_cpqr.matrixQR(); }
-
-  template <typename InputType>
-  CompleteOrthogonalDecomposition& compute(const EigenBase<InputType>& matrix) {
-    // Compute the column pivoted QR factorization A P = Q R.
-    m_cpqr.compute(matrix);
-    computeInPlace();
-    return *this;
-  }
-
-  /** \returns a const reference to the column permutation matrix */
-  const PermutationType& colsPermutation() const {
-    return m_cpqr.colsPermutation();
-  }
-
-  /** \returns the absolute value of the determinant of the matrix of which
-   * *this is the complete orthogonal decomposition. It has only linear
-   * complexity (that is, O(n) where n is the dimension of the square matrix)
-   * as the complete orthogonal decomposition has already been computed.
-   *
-   * \note This is only for square matrices.
-   *
-   * \warning a determinant can be very big or small, so for matrices
-   * of large enough dimension, there is a risk of overflow/underflow.
-   * One way to work around that is to use logAbsDeterminant() instead.
-   *
-   * \sa logAbsDeterminant(), MatrixBase::determinant()
-   */
-  typename MatrixType::RealScalar absDeterminant() const;
-
-  /** \returns the natural log of the absolute value of the determinant of the
-   * matrix of which *this is the complete orthogonal decomposition. It has
-   * only linear complexity (that is, O(n) where n is the dimension of the
-   * square matrix) as the complete orthogonal decomposition has already been
-   * computed.
-   *
-   * \note This is only for square matrices.
-   *
-   * \note This method is useful to work around the risk of overflow/underflow
-   * that's inherent to determinant computation.
-   *
-   * \sa absDeterminant(), MatrixBase::determinant()
-   */
-  typename MatrixType::RealScalar logAbsDeterminant() const;
-
-  /** \returns the rank of the matrix of which *this is the complete orthogonal
-   * decomposition.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline Index rank() const { return m_cpqr.rank(); }
-
-  /** \returns the dimension of the kernel of the matrix of which *this is the
-   * complete orthogonal decomposition.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline Index dimensionOfKernel() const { return m_cpqr.dimensionOfKernel(); }
-
-  /** \returns true if the matrix of which *this is the decomposition represents
-   * an injective linear map, i.e. has trivial kernel; false otherwise.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline bool isInjective() const { return m_cpqr.isInjective(); }
-
-  /** \returns true if the matrix of which *this is the decomposition represents
-   * a surjective linear map; false otherwise.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline bool isSurjective() const { return m_cpqr.isSurjective(); }
-
-  /** \returns true if the matrix of which *this is the complete orthogonal
-   * decomposition is invertible.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline bool isInvertible() const { return m_cpqr.isInvertible(); }
-
-  /** \returns the pseudo-inverse of the matrix of which *this is the complete
-   * orthogonal decomposition.
-   * \warning: Do not compute \c this->pseudoInverse()*rhs to solve a linear systems.
-   * It is more efficient and numerically stable to call \c this->solve(rhs).
-   */
-  inline const Inverse<CompleteOrthogonalDecomposition> pseudoInverse() const
-  {
-    eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
-    return Inverse<CompleteOrthogonalDecomposition>(*this);
-  }
-
-  inline Index rows() const { return m_cpqr.rows(); }
-  inline Index cols() const { return m_cpqr.cols(); }
-
-  /** \returns a const reference to the vector of Householder coefficients used
-   * to represent the factor \c Q.
-   *
-   * For advanced uses only.
-   */
-  inline const HCoeffsType& hCoeffs() const { return m_cpqr.hCoeffs(); }
-
-  /** \returns a const reference to the vector of Householder coefficients
-   * used to represent the factor \c Z.
-   *
-   * For advanced uses only.
-   */
-  const HCoeffsType& zCoeffs() const { return m_zCoeffs; }
-
-  /** Allows to prescribe a threshold to be used by certain methods, such as
-   * rank(), who need to determine when pivots are to be considered nonzero.
-   * Most be called before calling compute().
-   *
-   * When it needs to get the threshold value, Eigen calls threshold(). By
-   * default, this uses a formula to automatically determine a reasonable
-   * threshold. Once you have called the present method
-   * setThreshold(const RealScalar&), your value is used instead.
-   *
-   * \param threshold The new value to use as the threshold.
-   *
-   * A pivot will be considered nonzero if its absolute value is strictly
-   * greater than
-   *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-   * where maxpivot is the biggest pivot.
-   *
-   * If you want to come back to the default behavior, call
-   * setThreshold(Default_t)
-   */
-  CompleteOrthogonalDecomposition& setThreshold(const RealScalar& threshold) {
-    m_cpqr.setThreshold(threshold);
-    return *this;
-  }
-
-  /** Allows to come back to the default behavior, letting Eigen use its default
-   * formula for determining the threshold.
-   *
-   * You should pass the special object Eigen::Default as parameter here.
-   * \code qr.setThreshold(Eigen::Default); \endcode
-   *
-   * See the documentation of setThreshold(const RealScalar&).
-   */
-  CompleteOrthogonalDecomposition& setThreshold(Default_t) {
-    m_cpqr.setThreshold(Default);
-    return *this;
-  }
-
-  /** Returns the threshold that will be used by certain methods such as rank().
-   *
-   * See the documentation of setThreshold(const RealScalar&).
-   */
-  RealScalar threshold() const { return m_cpqr.threshold(); }
-
-  /** \returns the number of nonzero pivots in the complete orthogonal
-   * decomposition. Here nonzero is meant in the exact sense, not in a
-   * fuzzy sense. So that notion isn't really intrinsically interesting,
-   * but it is still useful when implementing algorithms.
-   *
-   * \sa rank()
-   */
-  inline Index nonzeroPivots() const { return m_cpqr.nonzeroPivots(); }
-
-  /** \returns the absolute value of the biggest pivot, i.e. the biggest
-   *          diagonal coefficient of R.
-   */
-  inline RealScalar maxPivot() const { return m_cpqr.maxPivot(); }
-
-  /** \brief Reports whether the complete orthogonal decomposition was
-   * successful.
-   *
-   * \note This function always returns \c Success. It is provided for
-   * compatibility
-   * with other factorization routines.
-   * \returns \c Success
-   */
-  ComputationInfo info() const {
-    eigen_assert(m_cpqr.m_isInitialized && "Decomposition is not initialized.");
-    return Success;
-  }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  template <typename RhsType, typename DstType>
-  void _solve_impl(const RhsType& rhs, DstType& dst) const;
-
-  template<bool Conjugate, typename RhsType, typename DstType>
-  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-#endif
-
- protected:
-  static void check_template_parameters() {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-  }
-
-  template<bool Transpose_, typename Rhs>
-  void _check_solve_assertion(const Rhs& b) const {
-      EIGEN_ONLY_USED_FOR_DEBUG(b);
-      eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
-      eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "CompleteOrthogonalDecomposition::solve(): invalid number of rows of the right hand side matrix b");
-  }
-
-  void computeInPlace();
-
-  /** Overwrites \b rhs with \f$ \mathbf{Z} * \mathbf{rhs} \f$ or
-   *  \f$ \mathbf{\overline Z} * \mathbf{rhs} \f$ if \c Conjugate 
-   *  is set to \c true.
-   */
-  template <bool Conjugate, typename Rhs>
-  void applyZOnTheLeftInPlace(Rhs& rhs) const;
-
-  /** Overwrites \b rhs with \f$ \mathbf{Z}^* * \mathbf{rhs} \f$.
-   */
-  template <typename Rhs>
-  void applyZAdjointOnTheLeftInPlace(Rhs& rhs) const;
-
-  ColPivHouseholderQR<MatrixType> m_cpqr;
-  HCoeffsType m_zCoeffs;
-  RowVectorType m_temp;
-};
-
-template <typename MatrixType>
-typename MatrixType::RealScalar
-CompleteOrthogonalDecomposition<MatrixType>::absDeterminant() const {
-  return m_cpqr.absDeterminant();
-}
-
-template <typename MatrixType>
-typename MatrixType::RealScalar
-CompleteOrthogonalDecomposition<MatrixType>::logAbsDeterminant() const {
-  return m_cpqr.logAbsDeterminant();
-}
-
-/** Performs the complete orthogonal decomposition of the given matrix \a
- * matrix. The result of the factorization is stored into \c *this, and a
- * reference to \c *this is returned.
- *
- * \sa class CompleteOrthogonalDecomposition,
- * CompleteOrthogonalDecomposition(const MatrixType&)
- */
-template <typename MatrixType>
-void CompleteOrthogonalDecomposition<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  // the column permutation is stored as int indices, so just to be sure:
-  eigen_assert(m_cpqr.cols() <= NumTraits<int>::highest());
-
-  const Index rank = m_cpqr.rank();
-  const Index cols = m_cpqr.cols();
-  const Index rows = m_cpqr.rows();
-  m_zCoeffs.resize((std::min)(rows, cols));
-  m_temp.resize(cols);
-
-  if (rank < cols) {
-    // We have reduced the (permuted) matrix to the form
-    //   [R11 R12]
-    //   [ 0  R22]
-    // where R11 is r-by-r (r = rank) upper triangular, R12 is
-    // r-by-(n-r), and R22 is empty or the norm of R22 is negligible.
-    // We now compute the complete orthogonal decomposition by applying
-    // Householder transformations from the right to the upper trapezoidal
-    // matrix X = [R11 R12] to zero out R12 and obtain the factorization
-    // [R11 R12] = [T11 0] * Z, where T11 is r-by-r upper triangular and
-    // Z = Z(0) * Z(1) ... Z(r-1) is an n-by-n orthogonal matrix.
-    // We store the data representing Z in R12 and m_zCoeffs.
-    for (Index k = rank - 1; k >= 0; --k) {
-      if (k != rank - 1) {
-        // Given the API for Householder reflectors, it is more convenient if
-        // we swap the leading parts of columns k and r-1 (zero-based) to form
-        // the matrix X_k = [X(0:k, k), X(0:k, r:n)]
-        m_cpqr.m_qr.col(k).head(k + 1).swap(
-            m_cpqr.m_qr.col(rank - 1).head(k + 1));
-      }
-      // Construct Householder reflector Z(k) to zero out the last row of X_k,
-      // i.e. choose Z(k) such that
-      // [X(k, k), X(k, r:n)] * Z(k) = [beta, 0, .., 0].
-      RealScalar beta;
-      m_cpqr.m_qr.row(k)
-          .tail(cols - rank + 1)
-          .makeHouseholderInPlace(m_zCoeffs(k), beta);
-      m_cpqr.m_qr(k, rank - 1) = beta;
-      if (k > 0) {
-        // Apply Z(k) to the first k rows of X_k
-        m_cpqr.m_qr.topRightCorner(k, cols - rank + 1)
-            .applyHouseholderOnTheRight(
-                m_cpqr.m_qr.row(k).tail(cols - rank).adjoint(), m_zCoeffs(k),
-                &m_temp(0));
-      }
-      if (k != rank - 1) {
-        // Swap X(0:k,k) back to its proper location.
-        m_cpqr.m_qr.col(k).head(k + 1).swap(
-            m_cpqr.m_qr.col(rank - 1).head(k + 1));
-      }
-    }
-  }
-}
-
-template <typename MatrixType>
-template <bool Conjugate, typename Rhs>
-void CompleteOrthogonalDecomposition<MatrixType>::applyZOnTheLeftInPlace(
-    Rhs& rhs) const {
-  const Index cols = this->cols();
-  const Index nrhs = rhs.cols();
-  const Index rank = this->rank();
-  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
-  for (Index k = rank-1; k >= 0; --k) {
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-    rhs.middleRows(rank - 1, cols - rank + 1)
-        .applyHouseholderOnTheLeft(
-            matrixQTZ().row(k).tail(cols - rank).transpose().template conjugateIf<!Conjugate>(), zCoeffs().template conjugateIf<Conjugate>()(k),
-            &temp(0));
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-  }
-}
-
-template <typename MatrixType>
-template <typename Rhs>
-void CompleteOrthogonalDecomposition<MatrixType>::applyZAdjointOnTheLeftInPlace(
-    Rhs& rhs) const {
-  const Index cols = this->cols();
-  const Index nrhs = rhs.cols();
-  const Index rank = this->rank();
-  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
-  for (Index k = 0; k < rank; ++k) {
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-    rhs.middleRows(rank - 1, cols - rank + 1)
-        .applyHouseholderOnTheLeft(
-            matrixQTZ().row(k).tail(cols - rank).adjoint(), zCoeffs()(k),
-            &temp(0));
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-  }
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template <typename _MatrixType>
-template <typename RhsType, typename DstType>
-void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl(
-    const RhsType& rhs, DstType& dst) const {
-  const Index rank = this->rank();
-  if (rank == 0) {
-    dst.setZero();
-    return;
-  }
-
-  // Compute c = Q^* * rhs
-  typename RhsType::PlainObject c(rhs);
-  c.applyOnTheLeft(matrixQ().setLength(rank).adjoint());
-
-  // Solve T z = c(1:rank, :)
-  dst.topRows(rank) = matrixT()
-                          .topLeftCorner(rank, rank)
-                          .template triangularView<Upper>()
-                          .solve(c.topRows(rank));
-
-  const Index cols = this->cols();
-  if (rank < cols) {
-    // Compute y = Z^* * [ z ]
-    //                   [ 0 ]
-    dst.bottomRows(cols - rank).setZero();
-    applyZAdjointOnTheLeftInPlace(dst);
-  }
-
-  // Undo permutation to get x = P^{-1} * y.
-  dst = colsPermutation() * dst;
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index rank = this->rank();
-
-  if (rank == 0) {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(colsPermutation().transpose()*rhs);
-
-  if (rank < cols()) {
-    applyZOnTheLeftInPlace<!Conjugate>(c);
-  }
-
-  matrixT().topLeftCorner(rank, rank)
-           .template triangularView<Upper>()
-           .transpose().template conjugateIf<Conjugate>()
-           .solveInPlace(c.topRows(rank));
-
-  dst.topRows(rank) = c.topRows(rank);
-  dst.bottomRows(rows()-rank).setZero();
-
-  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
-}
-#endif
-
-namespace internal {
-
-template<typename MatrixType>
-struct traits<Inverse<CompleteOrthogonalDecomposition<MatrixType> > >
-  : traits<typename Transpose<typename MatrixType::PlainObject>::PlainObject>
-{
-  enum { Flags = 0 };
-};
-
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<CompleteOrthogonalDecomposition<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename CompleteOrthogonalDecomposition<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef CompleteOrthogonalDecomposition<MatrixType> CodType;
-  typedef Inverse<CodType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename CodType::Scalar> &)
-  {
-    typedef Matrix<typename CodType::Scalar, CodType::RowsAtCompileTime, CodType::RowsAtCompileTime, 0, CodType::MaxRowsAtCompileTime, CodType::MaxRowsAtCompileTime> IdentityMatrixType;
-    dst = src.nestedExpression().solve(IdentityMatrixType::Identity(src.cols(), src.cols()));
-  }
-};
-
-} // end namespace internal
-
-/** \returns the matrix Q as a sequence of householder transformations */
-template <typename MatrixType>
-typename CompleteOrthogonalDecomposition<MatrixType>::HouseholderSequenceType
-CompleteOrthogonalDecomposition<MatrixType>::householderQ() const {
-  return m_cpqr.householderQ();
-}
-
-/** \return the complete orthogonal decomposition of \c *this.
-  *
-  * \sa class CompleteOrthogonalDecomposition
-  */
-template <typename Derived>
-const CompleteOrthogonalDecomposition<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::completeOrthogonalDecomposition() const {
-  return CompleteOrthogonalDecomposition<PlainObject>(eval());
-}
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/FullPivHouseholderQR.h b/3rdParty/my_ceres_vc17/include/Eigen/src/QR/FullPivHouseholderQR.h
deleted file mode 100644
index d0664a1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/FullPivHouseholderQR.h
+++ /dev/null
@@ -1,713 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
-#define EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename _MatrixType> struct traits<FullPivHouseholderQR<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType;
-
-template<typename MatrixType>
-struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-{
-  typedef typename MatrixType::PlainObject ReturnType;
-};
-
-} // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  * \class FullPivHouseholderQR
-  *
-  * \brief Householder rank-revealing QR decomposition of a matrix with full pivoting
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the QR decomposition
-  *
-  * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b P', \b Q and \b R
-  * such that 
-  * \f[
-  *  \mathbf{P} \, \mathbf{A} \, \mathbf{P}' = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b P and \b P' are permutation matrices, \b Q a unitary matrix 
-  * and \b R an upper triangular matrix.
-  *
-  * This decomposition performs a very prudent full pivoting in order to be rank-revealing and achieve optimal
-  * numerical stability. The trade-off is that it is slower than HouseholderQR and ColPivHouseholderQR.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  * 
-  * \sa MatrixBase::fullPivHouseholderQr()
-  */
-template<typename _MatrixType> class FullPivHouseholderQR
-        : public SolverBase<FullPivHouseholderQR<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<FullPivHouseholderQR> Base;
-    friend class SolverBase<FullPivHouseholderQR>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivHouseholderQR)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType> MatrixQReturnType;
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef Matrix<StorageIndex, 1,
-                   EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime,RowsAtCompileTime), RowMajor, 1,
-                   EIGEN_SIZE_MIN_PREFER_FIXED(MaxColsAtCompileTime,MaxRowsAtCompileTime)> IntDiagSizeVectorType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef typename internal::plain_col_type<MatrixType>::type ColVectorType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via FullPivHouseholderQR::compute(const MatrixType&).
-      */
-    FullPivHouseholderQR()
-      : m_qr(),
-        m_hCoeffs(),
-        m_rows_transpositions(),
-        m_cols_transpositions(),
-        m_cols_permutation(),
-        m_temp(),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa FullPivHouseholderQR()
-      */
-    FullPivHouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_rows_transpositions((std::min)(rows,cols)),
-        m_cols_transpositions((std::min)(rows,cols)),
-        m_cols_permutation(cols),
-        m_temp(cols),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This constructor computes the QR factorization of the matrix \a matrix by calling
-      * the method compute(). It is a short cut for:
-      * 
-      * \code
-      * FullPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      * 
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit FullPivHouseholderQR(const EigenBase<InputType>& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_permutation(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa FullPivHouseholderQR(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit FullPivHouseholderQR(EigenBase<InputType>& matrix)
-      : m_qr(matrix.derived()),
-        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_permutation(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      computeInPlace();
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * \c *this is the QR decomposition.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns the exact or least-square solution if the rank is greater or equal to the number of columns of A,
-      * and an arbitrary solution otherwise.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include FullPivHouseholderQR_solve.cpp
-      * Output: \verbinclude FullPivHouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const Solve<FullPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** \returns Expression object representing the matrix Q
-      */
-    MatrixQReturnType matrixQ(void) const;
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      */
-    const MatrixType& matrixQR() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    template<typename InputType>
-    FullPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns a const reference to the column permutation matrix */
-    const PermutationType& colsPermutation() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_cols_permutation;
-    }
-
-    /** \returns a const reference to the vector of indices representing the rows transpositions */
-    const IntDiagSizeVectorType& rowsTranspositions() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_rows_transpositions;
-    }
-
-    /** \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar absDeterminant() const;
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's inherent
-      * to determinant computation.
-      *
-      * \sa absDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar logAbsDeterminant() const;
-
-    /** \returns the rank of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
-      Index result = 0;
-      for(Index i = 0; i < m_nonzero_pivots; ++i)
-        result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);
-      return result;
-    }
-
-    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index dimensionOfKernel() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return cols() - rank();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents an injective
-      *          linear map, i.e. has trivial kernel; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInjective() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return rank() == cols();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
-      *          linear map; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isSurjective() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return rank() == rows();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInvertible() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return isInjective() && isSurjective();
-    }
-
-    /** \returns the inverse of the matrix of which *this is the QR decomposition.
-      *
-      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
-      *       Use isInvertible() to first determine whether this matrix is invertible.
-      */
-    inline const Inverse<FullPivHouseholderQR> inverse() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return Inverse<FullPivHouseholderQR>(*this);
-    }
-
-    inline Index rows() const { return m_qr.rows(); }
-    inline Index cols() const { return m_qr.cols(); }
-    
-    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
-      * 
-      * For advanced uses only.
-      */
-    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
-
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
-      * who need to determine when pivots are to be considered nonzero. This is not used for the
-      * QR decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
-      * uses a formula to automatically determine a reasonable threshold.
-      * Once you have called the present method setThreshold(const RealScalar&),
-      * your value is used instead.
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A pivot will be considered nonzero if its absolute value is strictly greater than
-      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-      * where maxpivot is the biggest pivot.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    FullPivHouseholderQR& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code qr.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    FullPivHouseholderQR& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
-
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    RealScalar threshold() const
-    {
-      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-      return m_usePrescribedThreshold ? m_prescribedThreshold
-      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
-      // and turns out to be identical to Higham's formula used already in LDLt.
-                                      : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
-    }
-
-    /** \returns the number of nonzero pivots in the QR decomposition.
-      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
-      * So that notion isn't really intrinsically interesting, but it is
-      * still useful when implementing algorithms.
-      *
-      * \sa rank()
-      */
-    inline Index nonzeroPivots() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of U.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    IntDiagSizeVectorType m_rows_transpositions;
-    IntDiagSizeVectorType m_cols_transpositions;
-    PermutationType m_cols_permutation;
-    RowVectorType m_temp;
-    bool m_isInitialized, m_usePrescribedThreshold;
-    RealScalar m_prescribedThreshold, m_maxpivot;
-    Index m_nonzero_pivots;
-    RealScalar m_precision;
-    Index m_det_pq;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return abs(m_qr.diagonal().prod());
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return m_qr.diagonal().cwiseAbs().array().log().sum();
-}
-
-/** Performs the QR factorization of the given matrix \a matrix. The result of
-  * the factorization is stored into \c *this, and a reference to \c *this
-  * is returned.
-  *
-  * \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-template<typename InputType>
-FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
-{
-  m_qr = matrix.derived();
-  computeInPlace();
-  return *this;
-}
-
-template<typename MatrixType>
-void FullPivHouseholderQR<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  using std::abs;
-  Index rows = m_qr.rows();
-  Index cols = m_qr.cols();
-  Index size = (std::min)(rows,cols);
-
-  
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  m_precision = NumTraits<Scalar>::epsilon() * RealScalar(size);
-
-  m_rows_transpositions.resize(size);
-  m_cols_transpositions.resize(size);
-  Index number_of_transpositions = 0;
-
-  RealScalar biggest(0);
-
-  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
-  m_maxpivot = RealScalar(0);
-
-  for (Index k = 0; k < size; ++k)
-  {
-    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
-    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
-    typedef typename Scoring::result_type Score;
-
-    Score score = m_qr.bottomRightCorner(rows-k, cols-k)
-                      .unaryExpr(Scoring())
-                      .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
-    row_of_biggest_in_corner += k;
-    col_of_biggest_in_corner += k;
-    RealScalar biggest_in_corner = internal::abs_knowing_score<Scalar>()(m_qr(row_of_biggest_in_corner, col_of_biggest_in_corner), score);
-    if(k==0) biggest = biggest_in_corner;
-
-    // if the corner is negligible, then we have less than full rank, and we can finish early
-    if(internal::isMuchSmallerThan(biggest_in_corner, biggest, m_precision))
-    {
-      m_nonzero_pivots = k;
-      for(Index i = k; i < size; i++)
-      {
-        m_rows_transpositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-        m_cols_transpositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-        m_hCoeffs.coeffRef(i) = Scalar(0);
-      }
-      break;
-    }
-
-    m_rows_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
-    m_cols_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
-    if(k != row_of_biggest_in_corner) {
-      m_qr.row(k).tail(cols-k).swap(m_qr.row(row_of_biggest_in_corner).tail(cols-k));
-      ++number_of_transpositions;
-    }
-    if(k != col_of_biggest_in_corner) {
-      m_qr.col(k).swap(m_qr.col(col_of_biggest_in_corner));
-      ++number_of_transpositions;
-    }
-
-    RealScalar beta;
-    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
-    m_qr.coeffRef(k,k) = beta;
-
-    // remember the maximum absolute value of diagonal coefficients
-    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
-
-    m_qr.bottomRightCorner(rows-k, cols-k-1)
-        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1));
-  }
-
-  m_cols_permutation.setIdentity(cols);
-  for(Index k = 0; k < size; ++k)
-    m_cols_permutation.applyTranspositionOnTheRight(k, m_cols_transpositions.coeff(k));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-  m_isInitialized = true;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  const Index l_rank = rank();
-
-  // FIXME introduce nonzeroPivots() and use it here. and more generally,
-  // make the same improvements in this dec as in FullPivLU.
-  if(l_rank==0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs);
-
-  Matrix<typename RhsType::Scalar,1,RhsType::ColsAtCompileTime> temp(rhs.cols());
-  for (Index k = 0; k < l_rank; ++k)
-  {
-    Index remainingSize = rows()-k;
-    c.row(k).swap(c.row(m_rows_transpositions.coeff(k)));
-    c.bottomRightCorner(remainingSize, rhs.cols())
-      .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1),
-                               m_hCoeffs.coeff(k), &temp.coeffRef(0));
-  }
-
-  m_qr.topLeftCorner(l_rank, l_rank)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(l_rank));
-
-  for(Index i = 0; i < l_rank; ++i) dst.row(m_cols_permutation.indices().coeff(i)) = c.row(i);
-  for(Index i = l_rank; i < cols(); ++i) dst.row(m_cols_permutation.indices().coeff(i)).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void FullPivHouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index l_rank = rank();
-
-  if(l_rank == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(m_cols_permutation.transpose()*rhs);
-
-  m_qr.topLeftCorner(l_rank, l_rank)
-         .template triangularView<Upper>()
-         .transpose().template conjugateIf<Conjugate>()
-         .solveInPlace(c.topRows(l_rank));
-
-  dst.topRows(l_rank) = c.topRows(l_rank);
-  dst.bottomRows(rows()-l_rank).setZero();
-
-  Matrix<Scalar, 1, DstType::ColsAtCompileTime> temp(dst.cols());
-  const Index size = (std::min)(rows(), cols());
-  for (Index k = size-1; k >= 0; --k)
-  {
-    Index remainingSize = rows()-k;
-
-    dst.bottomRightCorner(remainingSize, dst.cols())
-       .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1).template conjugateIf<!Conjugate>(),
-                                  m_hCoeffs.template conjugateIf<Conjugate>().coeff(k), &temp.coeffRef(0));
-
-    dst.row(k).swap(dst.row(m_rows_transpositions.coeff(k)));
-  }
-}
-#endif
-
-namespace internal {
-  
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<FullPivHouseholderQR<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivHouseholderQR<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef FullPivHouseholderQR<MatrixType> QrType;
-  typedef Inverse<QrType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename QrType::Scalar> &)
-  {    
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-
-/** \ingroup QR_Module
-  *
-  * \brief Expression type for return value of FullPivHouseholderQR::matrixQ()
-  *
-  * \tparam MatrixType type of underlying dense matrix
-  */
-template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType
-  : public ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-{
-public:
-  typedef typename FullPivHouseholderQR<MatrixType>::IntDiagSizeVectorType IntDiagSizeVectorType;
-  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-  typedef Matrix<typename MatrixType::Scalar, 1, MatrixType::RowsAtCompileTime, RowMajor, 1,
-                 MatrixType::MaxRowsAtCompileTime> WorkVectorType;
-
-  FullPivHouseholderQRMatrixQReturnType(const MatrixType&       qr,
-                                        const HCoeffsType&      hCoeffs,
-                                        const IntDiagSizeVectorType& rowsTranspositions)
-    : m_qr(qr),
-      m_hCoeffs(hCoeffs),
-      m_rowsTranspositions(rowsTranspositions)
-  {}
-
-  template <typename ResultType>
-  void evalTo(ResultType& result) const
-  {
-    const Index rows = m_qr.rows();
-    WorkVectorType workspace(rows);
-    evalTo(result, workspace);
-  }
-
-  template <typename ResultType>
-  void evalTo(ResultType& result, WorkVectorType& workspace) const
-  {
-    using numext::conj;
-    // compute the product H'_0 H'_1 ... H'_n-1,
-    // where H_k is the k-th Householder transformation I - h_k v_k v_k'
-    // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
-    const Index rows = m_qr.rows();
-    const Index cols = m_qr.cols();
-    const Index size = (std::min)(rows, cols);
-    workspace.resize(rows);
-    result.setIdentity(rows, rows);
-    for (Index k = size-1; k >= 0; k--)
-    {
-      result.block(k, k, rows-k, rows-k)
-            .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), conj(m_hCoeffs.coeff(k)), &workspace.coeffRef(k));
-      result.row(k).swap(result.row(m_rowsTranspositions.coeff(k)));
-    }
-  }
-
-  Index rows() const { return m_qr.rows(); }
-  Index cols() const { return m_qr.rows(); }
-
-protected:
-  typename MatrixType::Nested m_qr;
-  typename HCoeffsType::Nested m_hCoeffs;
-  typename IntDiagSizeVectorType::Nested m_rowsTranspositions;
-};
-
-// template<typename MatrixType>
-// struct evaluator<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-//  : public evaluator<ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> > >
-// {};
-
-} // end namespace internal
-
-template<typename MatrixType>
-inline typename FullPivHouseholderQR<MatrixType>::MatrixQReturnType FullPivHouseholderQR<MatrixType>::matrixQ() const
-{
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  return MatrixQReturnType(m_qr, m_hCoeffs, m_rows_transpositions);
-}
-
-/** \return the full-pivoting Householder QR decomposition of \c *this.
-  *
-  * \sa class FullPivHouseholderQR
-  */
-template<typename Derived>
-const FullPivHouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::fullPivHouseholderQr() const
-{
-  return FullPivHouseholderQR<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/HouseholderQR.h b/3rdParty/my_ceres_vc17/include/Eigen/src/QR/HouseholderQR.h
deleted file mode 100644
index 801739f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/HouseholderQR.h
+++ /dev/null
@@ -1,434 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010 Vincent Lejeune
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QR_H
-#define EIGEN_QR_H
-
-namespace Eigen { 
-
-namespace internal {
-template<typename _MatrixType> struct traits<HouseholderQR<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-} // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  *
-  * \class HouseholderQR
-  *
-  * \brief Householder QR decomposition of a matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the QR decomposition
-  *
-  * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R
-  * such that 
-  * \f[
-  *  \mathbf{A} = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b Q a unitary matrix and \b R an upper triangular matrix.
-  * The result is stored in a compact way compatible with LAPACK.
-  *
-  * Note that no pivoting is performed. This is \b not a rank-revealing decomposition.
-  * If you want that feature, use FullPivHouseholderQR or ColPivHouseholderQR instead.
-  *
-  * This Householder QR decomposition is faster, but less numerically stable and less feature-full than
-  * FullPivHouseholderQR or ColPivHouseholderQR.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::householderQr()
-  */
-template<typename _MatrixType> class HouseholderQR
-        : public SolverBase<HouseholderQR<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<HouseholderQR> Base;
-    friend class SolverBase<HouseholderQR>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(HouseholderQR)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags&RowMajorBit) ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via HouseholderQR::compute(const MatrixType&).
-      */
-    HouseholderQR() : m_qr(), m_hCoeffs(), m_temp(), m_isInitialized(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa HouseholderQR()
-      */
-    HouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_temp(cols),
-        m_isInitialized(false) {}
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This constructor computes the QR factorization of the matrix \a matrix by calling
-      * the method compute(). It is a short cut for:
-      * 
-      * \code
-      * HouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      * 
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit HouseholderQR(const EigenBase<InputType>& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_temp(matrix.cols()),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
-      * \c MatrixType is a Eigen::Ref.
-      *
-      * \sa HouseholderQR(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit HouseholderQR(EigenBase<InputType>& matrix)
-      : m_qr(matrix.derived()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_temp(matrix.cols()),
-        m_isInitialized(false)
-    {
-      computeInPlace();
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the QR decomposition, if any exists.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns a solution.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include HouseholderQR_solve.cpp
-      * Output: \verbinclude HouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const Solve<HouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** This method returns an expression of the unitary matrix Q as a sequence of Householder transformations.
-      *
-      * The returned expression can directly be used to perform matrix products. It can also be assigned to a dense Matrix object.
-      * Here is an example showing how to recover the full or thin matrix Q, as well as how to perform matrix products using operator*:
-      *
-      * Example: \include HouseholderQR_householderQ.cpp
-      * Output: \verbinclude HouseholderQR_householderQ.out
-      */
-    HouseholderSequenceType householderQ() const
-    {
-      eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-      return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
-    }
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      * in a LAPACK-compatible way.
-      */
-    const MatrixType& matrixQR() const
-    {
-        eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-        return m_qr;
-    }
-
-    template<typename InputType>
-    HouseholderQR& compute(const EigenBase<InputType>& matrix) {
-      m_qr = matrix.derived();
-      computeInPlace();
-      return *this;
-    }
-
-    /** \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar absDeterminant() const;
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's inherent
-      * to determinant computation.
-      *
-      * \sa absDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar logAbsDeterminant() const;
-
-    inline Index rows() const { return m_qr.rows(); }
-    inline Index cols() const { return m_qr.cols(); }
-
-    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
-      * 
-      * For advanced uses only.
-      */
-    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    RowVectorType m_temp;
-    bool m_isInitialized;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar HouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return abs(m_qr.diagonal().prod());
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar HouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return m_qr.diagonal().cwiseAbs().array().log().sum();
-}
-
-namespace internal {
-
-/** \internal */
-template<typename MatrixQR, typename HCoeffs>
-void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename MatrixQR::Scalar* tempData = 0)
-{
-  typedef typename MatrixQR::Scalar Scalar;
-  typedef typename MatrixQR::RealScalar RealScalar;
-  Index rows = mat.rows();
-  Index cols = mat.cols();
-  Index size = (std::min)(rows,cols);
-
-  eigen_assert(hCoeffs.size() == size);
-
-  typedef Matrix<Scalar,MatrixQR::ColsAtCompileTime,1> TempType;
-  TempType tempVector;
-  if(tempData==0)
-  {
-    tempVector.resize(cols);
-    tempData = tempVector.data();
-  }
-
-  for(Index k = 0; k < size; ++k)
-  {
-    Index remainingRows = rows - k;
-    Index remainingCols = cols - k - 1;
-
-    RealScalar beta;
-    mat.col(k).tail(remainingRows).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta);
-    mat.coeffRef(k,k) = beta;
-
-    // apply H to remaining part of m_qr from the left
-    mat.bottomRightCorner(remainingRows, remainingCols)
-        .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), hCoeffs.coeffRef(k), tempData+k+1);
-  }
-}
-
-/** \internal */
-template<typename MatrixQR, typename HCoeffs,
-  typename MatrixQRScalar = typename MatrixQR::Scalar,
-  bool InnerStrideIsOne = (MatrixQR::InnerStrideAtCompileTime == 1 && HCoeffs::InnerStrideAtCompileTime == 1)>
-struct householder_qr_inplace_blocked
-{
-  // This is specialized for LAPACK-supported Scalar types in HouseholderQR_LAPACKE.h
-  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index maxBlockSize=32,
-      typename MatrixQR::Scalar* tempData = 0)
-  {
-    typedef typename MatrixQR::Scalar Scalar;
-    typedef Block<MatrixQR,Dynamic,Dynamic> BlockType;
-
-    Index rows = mat.rows();
-    Index cols = mat.cols();
-    Index size = (std::min)(rows, cols);
-
-    typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixQR::MaxColsAtCompileTime,1> TempType;
-    TempType tempVector;
-    if(tempData==0)
-    {
-      tempVector.resize(cols);
-      tempData = tempVector.data();
-    }
-
-    Index blockSize = (std::min)(maxBlockSize,size);
-
-    Index k = 0;
-    for (k = 0; k < size; k += blockSize)
-    {
-      Index bs = (std::min)(size-k,blockSize);  // actual size of the block
-      Index tcols = cols - k - bs;              // trailing columns
-      Index brows = rows-k;                     // rows of the block
-
-      // partition the matrix:
-      //        A00 | A01 | A02
-      // mat  = A10 | A11 | A12
-      //        A20 | A21 | A22
-      // and performs the qr dec of [A11^T A12^T]^T
-      // and update [A21^T A22^T]^T using level 3 operations.
-      // Finally, the algorithm continue on A22
-
-      BlockType A11_21 = mat.block(k,k,brows,bs);
-      Block<HCoeffs,Dynamic,1> hCoeffsSegment = hCoeffs.segment(k,bs);
-
-      householder_qr_inplace_unblocked(A11_21, hCoeffsSegment, tempData);
-
-      if(tcols)
-      {
-        BlockType A21_22 = mat.block(k,k+bs,brows,tcols);
-        apply_block_householder_on_the_left(A21_22,A11_21,hCoeffsSegment, false); // false == backward
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void HouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  const Index rank = (std::min)(rows(), cols());
-
-  typename RhsType::PlainObject c(rhs);
-
-  c.applyOnTheLeft(householderQ().setLength(rank).adjoint() );
-
-  m_qr.topLeftCorner(rank, rank)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(rank));
-
-  dst.topRows(rank) = c.topRows(rank);
-  dst.bottomRows(cols()-rank).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void HouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index rank = (std::min)(rows(), cols());
-
-  typename RhsType::PlainObject c(rhs);
-
-  m_qr.topLeftCorner(rank, rank)
-      .template triangularView<Upper>()
-      .transpose().template conjugateIf<Conjugate>()
-      .solveInPlace(c.topRows(rank));
-
-  dst.topRows(rank) = c.topRows(rank);
-  dst.bottomRows(rows()-rank).setZero();
-
-  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
-}
-#endif
-
-/** Performs the QR factorization of the given matrix \a matrix. The result of
-  * the factorization is stored into \c *this, and a reference to \c *this
-  * is returned.
-  *
-  * \sa class HouseholderQR, HouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-void HouseholderQR<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-  
-  Index rows = m_qr.rows();
-  Index cols = m_qr.cols();
-  Index size = (std::min)(rows,cols);
-
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  internal::householder_qr_inplace_blocked<MatrixType, HCoeffsType>::run(m_qr, m_hCoeffs, 48, m_temp.data());
-
-  m_isInitialized = true;
-}
-
-/** \return the Householder QR decomposition of \c *this.
-  *
-  * \sa class HouseholderQR
-  */
-template<typename Derived>
-const HouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::householderQr() const
-{
-  return HouseholderQR<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_QR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/HouseholderQR_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
deleted file mode 100644
index 1dc7d53..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Householder QR decomposition of a matrix w/o pivoting based on
- *    LAPACKE_?geqrf function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_QR_LAPACKE_H
-#define EIGEN_QR_LAPACKE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_QR_NOPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX) \
-template<typename MatrixQR, typename HCoeffs> \
-struct householder_qr_inplace_blocked<MatrixQR, HCoeffs, EIGTYPE, true> \
-{ \
-  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index = 32, \
-      typename MatrixQR::Scalar* = 0) \
-  { \
-    lapack_int m = (lapack_int) mat.rows(); \
-    lapack_int n = (lapack_int) mat.cols(); \
-    lapack_int lda = (lapack_int) mat.outerStride(); \
-    lapack_int matrix_order = (MatrixQR::IsRowMajor) ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
-    LAPACKE_##LAPACKE_PREFIX##geqrf( matrix_order, m, n, (LAPACKE_TYPE*)mat.data(), lda, (LAPACKE_TYPE*)hCoeffs.data()); \
-    hCoeffs.adjointInPlace(); \
-  } \
-};
-
-EIGEN_LAPACKE_QR_NOPIV(double, double, d)
-EIGEN_LAPACKE_QR_NOPIV(float, float, s)
-EIGEN_LAPACKE_QR_NOPIV(dcomplex, lapack_complex_double, z)
-EIGEN_LAPACKE_QR_NOPIV(scomplex, lapack_complex_float, c)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_QR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
deleted file mode 100644
index 013c7ae..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
+++ /dev/null
@@ -1,335 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SUITESPARSEQRSUPPORT_H
-#define EIGEN_SUITESPARSEQRSUPPORT_H
-
-namespace Eigen {
-  
-  template<typename MatrixType> class SPQR; 
-  template<typename SPQRType> struct SPQRMatrixQReturnType; 
-  template<typename SPQRType> struct SPQRMatrixQTransposeReturnType; 
-  template <typename SPQRType, typename Derived> struct SPQR_QProduct;
-  namespace internal {
-    template <typename SPQRType> struct traits<SPQRMatrixQReturnType<SPQRType> >
-    {
-      typedef typename SPQRType::MatrixType ReturnType;
-    };
-    template <typename SPQRType> struct traits<SPQRMatrixQTransposeReturnType<SPQRType> >
-    {
-      typedef typename SPQRType::MatrixType ReturnType;
-    };
-    template <typename SPQRType, typename Derived> struct traits<SPQR_QProduct<SPQRType, Derived> >
-    {
-      typedef typename Derived::PlainObject ReturnType;
-    };
-  } // End namespace internal
-  
-/**
-  * \ingroup SPQRSupport_Module
-  * \class SPQR
-  * \brief Sparse QR factorization based on SuiteSparseQR library
-  *
-  * This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition
-  * of sparse matrices. The result is then used to solve linear leasts_square systems.
-  * Clearly, a QR factorization is returned such that A*P = Q*R where :
-  *
-  * P is the column permutation. Use colsPermutation() to get it.
-  *
-  * Q is the orthogonal matrix represented as Householder reflectors.
-  * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose.
-  * You can then apply it to a vector.
-  *
-  * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix.
-  * NOTE : The Index type of R is always SuiteSparse_long. You can get it with SPQR::Index
-  *
-  * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  *
-  */
-template<typename _MatrixType>
-class SPQR : public SparseSolverBase<SPQR<_MatrixType> >
-{
-  protected:
-    typedef SparseSolverBase<SPQR<_MatrixType> > Base;
-    using Base::m_isInitialized;
-  public:
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef SuiteSparse_long StorageIndex ;
-    typedef SparseMatrix<Scalar, ColMajor, StorageIndex> MatrixType;
-    typedef Map<PermutationMatrix<Dynamic, Dynamic, StorageIndex> > PermutationType;
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-  public:
-    SPQR() 
-      : m_analysisIsOk(false),
-        m_factorizationIsOk(false),
-        m_isRUpToDate(false),
-        m_ordering(SPQR_ORDERING_DEFAULT),
-        m_allow_tol(SPQR_DEFAULT_TOL),
-        m_tolerance (NumTraits<Scalar>::epsilon()),
-        m_cR(0),
-        m_E(0),
-        m_H(0),
-        m_HPinv(0),
-        m_HTau(0),
-        m_useDefaultThreshold(true)
-    { 
-      cholmod_l_start(&m_cc);
-    }
-    
-    explicit SPQR(const _MatrixType& matrix)
-      : m_analysisIsOk(false),
-        m_factorizationIsOk(false),
-        m_isRUpToDate(false),
-        m_ordering(SPQR_ORDERING_DEFAULT),
-        m_allow_tol(SPQR_DEFAULT_TOL),
-        m_tolerance (NumTraits<Scalar>::epsilon()),
-        m_cR(0),
-        m_E(0),
-        m_H(0),
-        m_HPinv(0),
-        m_HTau(0),
-        m_useDefaultThreshold(true)
-    {
-      cholmod_l_start(&m_cc);
-      compute(matrix);
-    }
-    
-    ~SPQR()
-    {
-      SPQR_free();
-      cholmod_l_finish(&m_cc);
-    }
-    void SPQR_free()
-    {
-      cholmod_l_free_sparse(&m_H, &m_cc);
-      cholmod_l_free_sparse(&m_cR, &m_cc);
-      cholmod_l_free_dense(&m_HTau, &m_cc);
-      std::free(m_E);
-      std::free(m_HPinv);
-    }
-
-    void compute(const _MatrixType& matrix)
-    {
-      if(m_isInitialized) SPQR_free();
-
-      MatrixType mat(matrix);
-      
-      /* Compute the default threshold as in MatLab, see:
-       * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
-       * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3 
-       */
-      RealScalar pivotThreshold = m_tolerance;
-      if(m_useDefaultThreshold) 
-      {
-        RealScalar max2Norm = 0.0;
-        for (int j = 0; j < mat.cols(); j++) max2Norm = numext::maxi(max2Norm, mat.col(j).norm());
-        if(max2Norm==RealScalar(0))
-          max2Norm = RealScalar(1);
-        pivotThreshold = 20 * (mat.rows() + mat.cols()) * max2Norm * NumTraits<RealScalar>::epsilon();
-      }
-      cholmod_sparse A; 
-      A = viewAsCholmod(mat);
-      m_rows = matrix.rows();
-      Index col = matrix.cols();
-      m_rank = SuiteSparseQR<Scalar>(m_ordering, pivotThreshold, col, &A, 
-                             &m_cR, &m_E, &m_H, &m_HPinv, &m_HTau, &m_cc);
-
-      if (!m_cR)
-      {
-        m_info = NumericalIssue;
-        m_isInitialized = false;
-        return;
-      }
-      m_info = Success;
-      m_isInitialized = true;
-      m_isRUpToDate = false;
-    }
-    /** 
-     * Get the number of rows of the input matrix and the Q matrix
-     */
-    inline Index rows() const {return m_rows; }
-    
-    /** 
-     * Get the number of columns of the input matrix. 
-     */
-    inline Index cols() const { return m_cR->ncol; }
-    
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
-      eigen_assert(b.cols()==1 && "This method is for vectors only");
-
-      //Compute Q^T * b
-      typename Dest::PlainObject y, y2;
-      y = matrixQ().transpose() * b;
-      
-      // Solves with the triangular matrix R
-      Index rk = this->rank();
-      y2 = y;
-      y.resize((std::max)(cols(),Index(y.rows())),y.cols());
-      y.topRows(rk) = this->matrixR().topLeftCorner(rk, rk).template triangularView<Upper>().solve(y2.topRows(rk));
-
-      // Apply the column permutation 
-      // colsPermutation() performs a copy of the permutation,
-      // so let's apply it manually:
-      for(Index i = 0; i < rk; ++i) dest.row(m_E[i]) = y.row(i);
-      for(Index i = rk; i < cols(); ++i) dest.row(m_E[i]).setZero();
-      
-//       y.bottomRows(y.rows()-rk).setZero();
-//       dest = colsPermutation() * y.topRows(cols());
-      
-      m_info = Success;
-    }
-    
-    /** \returns the sparse triangular factor R. It is a sparse matrix
-     */
-    const MatrixType matrixR() const
-    {
-      eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
-      if(!m_isRUpToDate) {
-        m_R = viewAsEigen<Scalar,ColMajor, typename MatrixType::StorageIndex>(*m_cR);
-        m_isRUpToDate = true;
-      }
-      return m_R;
-    }
-    /// Get an expression of the matrix Q
-    SPQRMatrixQReturnType<SPQR> matrixQ() const
-    {
-      return SPQRMatrixQReturnType<SPQR>(*this);
-    }
-    /// Get the permutation that was applied to columns of A
-    PermutationType colsPermutation() const
-    { 
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return PermutationType(m_E, m_cR->ncol);
-    }
-    /**
-     * Gets the rank of the matrix. 
-     * It should be equal to matrixQR().cols if the matrix is full-rank
-     */
-    Index rank() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_cc.SPQR_istat[4];
-    }
-    /// Set the fill-reducing ordering method to be used
-    void setSPQROrdering(int ord) { m_ordering = ord;}
-    /// Set the tolerance tol to treat columns with 2-norm < =tol as zero
-    void setPivotThreshold(const RealScalar& tol)
-    {
-      m_useDefaultThreshold = false;
-      m_tolerance = tol;
-    }
-    
-    /** \returns a pointer to the SPQR workspace */
-    cholmod_common *cholmodCommon() const { return &m_cc; }
-    
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the sparse QR can not be computed
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-  protected:
-    bool m_analysisIsOk;
-    bool m_factorizationIsOk;
-    mutable bool m_isRUpToDate;
-    mutable ComputationInfo m_info;
-    int m_ordering; // Ordering method to use, see SPQR's manual
-    int m_allow_tol; // Allow to use some tolerance during numerical factorization.
-    RealScalar m_tolerance; // treat columns with 2-norm below this tolerance as zero
-    mutable cholmod_sparse *m_cR; // The sparse R factor in cholmod format
-    mutable MatrixType m_R; // The sparse matrix R in Eigen format
-    mutable StorageIndex *m_E; // The permutation applied to columns
-    mutable cholmod_sparse *m_H;  //The householder vectors
-    mutable StorageIndex *m_HPinv; // The row permutation of H
-    mutable cholmod_dense *m_HTau; // The Householder coefficients
-    mutable Index m_rank; // The rank of the matrix
-    mutable cholmod_common m_cc; // Workspace and parameters
-    bool m_useDefaultThreshold;     // Use default threshold
-    Index m_rows;
-    template<typename ,typename > friend struct SPQR_QProduct;
-};
-
-template <typename SPQRType, typename Derived>
-struct SPQR_QProduct : ReturnByValue<SPQR_QProduct<SPQRType,Derived> >
-{
-  typedef typename SPQRType::Scalar Scalar;
-  typedef typename SPQRType::StorageIndex StorageIndex;
-  //Define the constructor to get reference to argument types
-  SPQR_QProduct(const SPQRType& spqr, const Derived& other, bool transpose) : m_spqr(spqr),m_other(other),m_transpose(transpose) {}
-  
-  inline Index rows() const { return m_transpose ? m_spqr.rows() : m_spqr.cols(); }
-  inline Index cols() const { return m_other.cols(); }
-  // Assign to a vector
-  template<typename ResType>
-  void evalTo(ResType& res) const
-  {
-    cholmod_dense y_cd;
-    cholmod_dense *x_cd; 
-    int method = m_transpose ? SPQR_QTX : SPQR_QX; 
-    cholmod_common *cc = m_spqr.cholmodCommon();
-    y_cd = viewAsCholmod(m_other.const_cast_derived());
-    x_cd = SuiteSparseQR_qmult<Scalar>(method, m_spqr.m_H, m_spqr.m_HTau, m_spqr.m_HPinv, &y_cd, cc);
-    res = Matrix<Scalar,ResType::RowsAtCompileTime,ResType::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x), x_cd->nrow, x_cd->ncol);
-    cholmod_l_free_dense(&x_cd, cc);
-  }
-  const SPQRType& m_spqr; 
-  const Derived& m_other; 
-  bool m_transpose; 
-  
-};
-template<typename SPQRType>
-struct SPQRMatrixQReturnType{
-  
-  SPQRMatrixQReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
-  template<typename Derived>
-  SPQR_QProduct<SPQRType, Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(),false);
-  }
-  SPQRMatrixQTransposeReturnType<SPQRType> adjoint() const
-  {
-    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
-  }
-  // To use for operations with the transpose of Q
-  SPQRMatrixQTransposeReturnType<SPQRType> transpose() const
-  {
-    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
-  }
-  const SPQRType& m_spqr;
-};
-
-template<typename SPQRType>
-struct SPQRMatrixQTransposeReturnType{
-  SPQRMatrixQTransposeReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
-  template<typename Derived>
-  SPQR_QProduct<SPQRType,Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(), true);
-  }
-  const SPQRType& m_spqr;
-};
-
-}// End namespace Eigen
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/BDCSVD.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/BDCSVD.h
deleted file mode 100644
index 17f8e44..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/BDCSVD.h
+++ /dev/null
@@ -1,1366 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-// 
-// We used the "A Divide-And-Conquer Algorithm for the Bidiagonal SVD"
-// research report written by Ming Gu and Stanley C.Eisenstat
-// The code variable names correspond to the names they used in their 
-// report
-//
-// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
-// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
-// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
-// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
-// Copyright (C) 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
-// Copyright (C) 2014-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BDCSVD_H
-#define EIGEN_BDCSVD_H
-// #define EIGEN_BDCSVD_DEBUG_VERBOSE
-// #define EIGEN_BDCSVD_SANITY_CHECKS
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-#undef eigen_internal_assert
-#define eigen_internal_assert(X) assert(X);
-#endif
-
-namespace Eigen {
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-IOFormat bdcsvdfmt(8, 0, ", ", "\n", "  [", "]");
-#endif
-  
-template<typename _MatrixType> class BDCSVD;
-
-namespace internal {
-
-template<typename _MatrixType> 
-struct traits<BDCSVD<_MatrixType> >
-        : traits<_MatrixType>
-{
-  typedef _MatrixType MatrixType;
-};  
-
-} // end namespace internal
-  
-  
-/** \ingroup SVD_Module
- *
- *
- * \class BDCSVD
- *
- * \brief class Bidiagonal Divide and Conquer SVD
- *
- * \tparam _MatrixType the type of the matrix of which we are computing the SVD decomposition
- *
- * This class first reduces the input matrix to bi-diagonal form using class UpperBidiagonalization,
- * and then performs a divide-and-conquer diagonalization. Small blocks are diagonalized using class JacobiSVD.
- * You can control the switching size with the setSwitchSize() method, default is 16.
- * For small matrice (<16), it is thus preferable to directly use JacobiSVD. For larger ones, BDCSVD is highly
- * recommended and can several order of magnitude faster.
- *
- * \warning this algorithm is unlikely to provide accurate result when compiled with unsafe math optimizations.
- * For instance, this concerns Intel's compiler (ICC), which performs such optimization by default unless
- * you compile with the \c -fp-model \c precise option. Likewise, the \c -ffast-math option of GCC or clang will
- * significantly degrade the accuracy.
- *
- * \sa class JacobiSVD
- */
-template<typename _MatrixType> 
-class BDCSVD : public SVDBase<BDCSVD<_MatrixType> >
-{
-  typedef SVDBase<BDCSVD> Base;
-    
-public:
-  using Base::rows;
-  using Base::cols;
-  using Base::computeU;
-  using Base::computeV;
-  
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-  typedef typename NumTraits<RealScalar>::Literal Literal;
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime, 
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime, 
-    DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime), 
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, 
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, 
-    MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime, MaxColsAtCompileTime), 
-    MatrixOptions = MatrixType::Options
-  };
-
-  typedef typename Base::MatrixUType MatrixUType;
-  typedef typename Base::MatrixVType MatrixVType;
-  typedef typename Base::SingularValuesType SingularValuesType;
-  
-  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> MatrixX;
-  typedef Matrix<RealScalar, Dynamic, Dynamic, ColMajor> MatrixXr;
-  typedef Matrix<RealScalar, Dynamic, 1> VectorType;
-  typedef Array<RealScalar, Dynamic, 1> ArrayXr;
-  typedef Array<Index,1,Dynamic> ArrayXi;
-  typedef Ref<ArrayXr> ArrayRef;
-  typedef Ref<ArrayXi> IndicesRef;
-
-  /** \brief Default Constructor.
-   *
-   * The default constructor is useful in cases in which the user intends to
-   * perform decompositions via BDCSVD::compute(const MatrixType&).
-   */
-  BDCSVD() : m_algoswap(16), m_isTranspose(false), m_compU(false), m_compV(false), m_numIters(0)
-  {}
-
-
-  /** \brief Default Constructor with memory preallocation
-   *
-   * Like the default constructor but with preallocation of the internal data
-   * according to the specified problem size.
-   * \sa BDCSVD()
-   */
-  BDCSVD(Index rows, Index cols, unsigned int computationOptions = 0)
-    : m_algoswap(16), m_numIters(0)
-  {
-    allocate(rows, cols, computationOptions);
-  }
-
-  /** \brief Constructor performing the decomposition of given matrix.
-   *
-   * \param matrix the matrix to decompose
-   * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-   *                           By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, 
-   *                           #ComputeFullV, #ComputeThinV.
-   *
-   * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-   * available with the (non - default) FullPivHouseholderQR preconditioner.
-   */
-  BDCSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
-    : m_algoswap(16), m_numIters(0)
-  {
-    compute(matrix, computationOptions);
-  }
-
-  ~BDCSVD() 
-  {
-  }
-  
-  /** \brief Method performing the decomposition of given matrix using custom options.
-   *
-   * \param matrix the matrix to decompose
-   * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-   *                           By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, 
-   *                           #ComputeFullV, #ComputeThinV.
-   *
-   * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-   * available with the (non - default) FullPivHouseholderQR preconditioner.
-   */
-  BDCSVD& compute(const MatrixType& matrix, unsigned int computationOptions);
-
-  /** \brief Method performing the decomposition of given matrix using current options.
-   *
-   * \param matrix the matrix to decompose
-   *
-   * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
-   */
-  BDCSVD& compute(const MatrixType& matrix)
-  {
-    return compute(matrix, this->m_computationOptions);
-  }
-
-  void setSwitchSize(int s) 
-  {
-    eigen_assert(s>3 && "BDCSVD the size of the algo switch has to be greater than 3");
-    m_algoswap = s;
-  }
- 
-private:
-  void allocate(Index rows, Index cols, unsigned int computationOptions);
-  void divide(Index firstCol, Index lastCol, Index firstRowW, Index firstColW, Index shift);
-  void computeSVDofM(Index firstCol, Index n, MatrixXr& U, VectorType& singVals, MatrixXr& V);
-  void computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, VectorType& singVals, ArrayRef shifts, ArrayRef mus);
-  void perturbCol0(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus, ArrayRef zhat);
-  void computeSingVecs(const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus, MatrixXr& U, MatrixXr& V);
-  void deflation43(Index firstCol, Index shift, Index i, Index size);
-  void deflation44(Index firstColu , Index firstColm, Index firstRowW, Index firstColW, Index i, Index j, Index size);
-  void deflation(Index firstCol, Index lastCol, Index k, Index firstRowW, Index firstColW, Index shift);
-  template<typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
-  void copyUV(const HouseholderU &householderU, const HouseholderV &householderV, const NaiveU &naiveU, const NaiveV &naivev);
-  void structured_update(Block<MatrixXr,Dynamic,Dynamic> A, const MatrixXr &B, Index n1);
-  static RealScalar secularEq(RealScalar x, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const ArrayRef& diagShifted, RealScalar shift);
-
-protected:
-  MatrixXr m_naiveU, m_naiveV;
-  MatrixXr m_computed;
-  Index m_nRec;
-  ArrayXr m_workspace;
-  ArrayXi m_workspaceI;
-  int m_algoswap;
-  bool m_isTranspose, m_compU, m_compV;
-  
-  using Base::m_singularValues;
-  using Base::m_diagSize;
-  using Base::m_computeFullU;
-  using Base::m_computeFullV;
-  using Base::m_computeThinU;
-  using Base::m_computeThinV;
-  using Base::m_matrixU;
-  using Base::m_matrixV;
-  using Base::m_info;
-  using Base::m_isInitialized;
-  using Base::m_nonzeroSingularValues;
-
-public:  
-  int m_numIters;
-}; //end class BDCSVD
-
-
-// Method to allocate and initialize matrix and attributes
-template<typename MatrixType>
-void BDCSVD<MatrixType>::allocate(Eigen::Index rows, Eigen::Index cols, unsigned int computationOptions)
-{
-  m_isTranspose = (cols > rows);
-
-  if (Base::allocate(rows, cols, computationOptions))
-    return;
-  
-  m_computed = MatrixXr::Zero(m_diagSize + 1, m_diagSize );
-  m_compU = computeV();
-  m_compV = computeU();
-  if (m_isTranspose)
-    std::swap(m_compU, m_compV);
-  
-  if (m_compU) m_naiveU = MatrixXr::Zero(m_diagSize + 1, m_diagSize + 1 );
-  else         m_naiveU = MatrixXr::Zero(2, m_diagSize + 1 );
-  
-  if (m_compV) m_naiveV = MatrixXr::Zero(m_diagSize, m_diagSize);
-  
-  m_workspace.resize((m_diagSize+1)*(m_diagSize+1)*3);
-  m_workspaceI.resize(3*m_diagSize);
-}// end allocate
-
-template<typename MatrixType>
-BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions) 
-{
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "\n\n\n======================================================================================================================\n\n\n";
-#endif
-  allocate(matrix.rows(), matrix.cols(), computationOptions);
-  using std::abs;
-
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  
-  //**** step -1 - If the problem is too small, directly falls back to JacobiSVD and return
-  if(matrix.cols() < m_algoswap)
-  {
-    // FIXME this line involves temporaries
-    JacobiSVD<MatrixType> jsvd(matrix,computationOptions);
-    m_isInitialized = true;
-    m_info = jsvd.info();
-    if (m_info == Success || m_info == NoConvergence) {
-      if(computeU()) m_matrixU = jsvd.matrixU();
-      if(computeV()) m_matrixV = jsvd.matrixV();
-      m_singularValues = jsvd.singularValues();
-      m_nonzeroSingularValues = jsvd.nonzeroSingularValues();
-    }
-    return *this;
-  }
-  
-  //**** step 0 - Copy the input matrix and apply scaling to reduce over/under-flows
-  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
-  if (!(numext::isfinite)(scale)) {
-    m_isInitialized = true;
-    m_info = InvalidInput;
-    return *this;
-  }
-
-  if(scale==Literal(0)) scale = Literal(1);
-  MatrixX copy;
-  if (m_isTranspose) copy = matrix.adjoint()/scale;
-  else               copy = matrix/scale;
-  
-  //**** step 1 - Bidiagonalization
-  // FIXME this line involves temporaries
-  internal::UpperBidiagonalization<MatrixX> bid(copy);
-
-  //**** step 2 - Divide & Conquer
-  m_naiveU.setZero();
-  m_naiveV.setZero();
-  // FIXME this line involves a temporary matrix
-  m_computed.topRows(m_diagSize) = bid.bidiagonal().toDenseMatrix().transpose();
-  m_computed.template bottomRows<1>().setZero();
-  divide(0, m_diagSize - 1, 0, 0, 0);
-  if (m_info != Success && m_info != NoConvergence) {
-    m_isInitialized = true;
-    return *this;
-  }
-    
-  //**** step 3 - Copy singular values and vectors
-  for (int i=0; i<m_diagSize; i++)
-  {
-    RealScalar a = abs(m_computed.coeff(i, i));
-    m_singularValues.coeffRef(i) = a * scale;
-    if (a<considerZero)
-    {
-      m_nonzeroSingularValues = i;
-      m_singularValues.tail(m_diagSize - i - 1).setZero();
-      break;
-    }
-    else if (i == m_diagSize - 1)
-    {
-      m_nonzeroSingularValues = i + 1;
-      break;
-    }
-  }
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-//   std::cout << "m_naiveU\n" << m_naiveU << "\n\n";
-//   std::cout << "m_naiveV\n" << m_naiveV << "\n\n";
-#endif
-  if(m_isTranspose) copyUV(bid.householderV(), bid.householderU(), m_naiveV, m_naiveU);
-  else              copyUV(bid.householderU(), bid.householderV(), m_naiveU, m_naiveV);
-
-  m_isInitialized = true;
-  return *this;
-}// end compute
-
-
-template<typename MatrixType>
-template<typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
-void BDCSVD<MatrixType>::copyUV(const HouseholderU &householderU, const HouseholderV &householderV, const NaiveU &naiveU, const NaiveV &naiveV)
-{
-  // Note exchange of U and V: m_matrixU is set from m_naiveV and vice versa
-  if (computeU())
-  {
-    Index Ucols = m_computeThinU ? m_diagSize : householderU.cols();
-    m_matrixU = MatrixX::Identity(householderU.cols(), Ucols);
-    m_matrixU.topLeftCorner(m_diagSize, m_diagSize) = naiveV.template cast<Scalar>().topLeftCorner(m_diagSize, m_diagSize);
-    householderU.applyThisOnTheLeft(m_matrixU); // FIXME this line involves a temporary buffer
-  }
-  if (computeV())
-  {
-    Index Vcols = m_computeThinV ? m_diagSize : householderV.cols();
-    m_matrixV = MatrixX::Identity(householderV.cols(), Vcols);
-    m_matrixV.topLeftCorner(m_diagSize, m_diagSize) = naiveU.template cast<Scalar>().topLeftCorner(m_diagSize, m_diagSize);
-    householderV.applyThisOnTheLeft(m_matrixV); // FIXME this line involves a temporary buffer
-  }
-}
-
-/** \internal
-  * Performs A = A * B exploiting the special structure of the matrix A. Splitting A as:
-  *  A = [A1]
-  *      [A2]
-  * such that A1.rows()==n1, then we assume that at least half of the columns of A1 and A2 are zeros.
-  * We can thus pack them prior to the the matrix product. However, this is only worth the effort if the matrix is large
-  * enough.
-  */
-template<typename MatrixType>
-void BDCSVD<MatrixType>::structured_update(Block<MatrixXr,Dynamic,Dynamic> A, const MatrixXr &B, Index n1)
-{
-  Index n = A.rows();
-  if(n>100)
-  {
-    // If the matrices are large enough, let's exploit the sparse structure of A by
-    // splitting it in half (wrt n1), and packing the non-zero columns.
-    Index n2 = n - n1;
-    Map<MatrixXr> A1(m_workspace.data()      , n1, n);
-    Map<MatrixXr> A2(m_workspace.data()+ n1*n, n2, n);
-    Map<MatrixXr> B1(m_workspace.data()+  n*n, n,  n);
-    Map<MatrixXr> B2(m_workspace.data()+2*n*n, n,  n);
-    Index k1=0, k2=0;
-    for(Index j=0; j<n; ++j)
-    {
-      if( (A.col(j).head(n1).array()!=Literal(0)).any() )
-      {
-        A1.col(k1) = A.col(j).head(n1);
-        B1.row(k1) = B.row(j);
-        ++k1;
-      }
-      if( (A.col(j).tail(n2).array()!=Literal(0)).any() )
-      {
-        A2.col(k2) = A.col(j).tail(n2);
-        B2.row(k2) = B.row(j);
-        ++k2;
-      }
-    }
-  
-    A.topRows(n1).noalias()    = A1.leftCols(k1) * B1.topRows(k1);
-    A.bottomRows(n2).noalias() = A2.leftCols(k2) * B2.topRows(k2);
-  }
-  else
-  {
-    Map<MatrixXr,Aligned> tmp(m_workspace.data(),n,n);
-    tmp.noalias() = A*B;
-    A = tmp;
-  }
-}
-
-// The divide algorithm is done "in place", we are always working on subsets of the same matrix. The divide methods takes as argument the 
-// place of the submatrix we are currently working on.
-
-//@param firstCol : The Index of the first column of the submatrix of m_computed and for m_naiveU;
-//@param lastCol : The Index of the last column of the submatrix of m_computed and for m_naiveU; 
-// lastCol + 1 - firstCol is the size of the submatrix.
-//@param firstRowW : The Index of the first row of the matrix W that we are to change. (see the reference paper section 1 for more information on W)
-//@param firstRowW : Same as firstRowW with the column.
-//@param shift : Each time one takes the left submatrix, one must add 1 to the shift. Why? Because! We actually want the last column of the U submatrix 
-// to become the first column (*coeff) and to shift all the other columns to the right. There are more details on the reference paper.
-template<typename MatrixType>
-void BDCSVD<MatrixType>::divide(Eigen::Index firstCol, Eigen::Index lastCol, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index shift)
-{
-  // requires rows = cols + 1;
-  using std::pow;
-  using std::sqrt;
-  using std::abs;
-  const Index n = lastCol - firstCol + 1;
-  const Index k = n/2;
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  RealScalar alphaK;
-  RealScalar betaK; 
-  RealScalar r0; 
-  RealScalar lambda, phi, c0, s0;
-  VectorType l, f;
-  // We use the other algorithm which is more efficient for small 
-  // matrices.
-  if (n < m_algoswap)
-  {
-    // FIXME this line involves temporaries
-    JacobiSVD<MatrixXr> b(m_computed.block(firstCol, firstCol, n + 1, n), ComputeFullU | (m_compV ? ComputeFullV : 0));
-    m_info = b.info();
-    if (m_info != Success && m_info != NoConvergence) return;
-    if (m_compU)
-      m_naiveU.block(firstCol, firstCol, n + 1, n + 1).real() = b.matrixU();
-    else 
-    {
-      m_naiveU.row(0).segment(firstCol, n + 1).real() = b.matrixU().row(0);
-      m_naiveU.row(1).segment(firstCol, n + 1).real() = b.matrixU().row(n);
-    }
-    if (m_compV) m_naiveV.block(firstRowW, firstColW, n, n).real() = b.matrixV();
-    m_computed.block(firstCol + shift, firstCol + shift, n + 1, n).setZero();
-    m_computed.diagonal().segment(firstCol + shift, n) = b.singularValues().head(n);
-    return;
-  }
-  // We use the divide and conquer algorithm
-  alphaK =  m_computed(firstCol + k, firstCol + k);
-  betaK = m_computed(firstCol + k + 1, firstCol + k);
-  // The divide must be done in that order in order to have good results. Divide change the data inside the submatrices
-  // and the divide of the right submatrice reads one column of the left submatrice. That's why we need to treat the 
-  // right submatrix before the left one. 
-  divide(k + 1 + firstCol, lastCol, k + 1 + firstRowW, k + 1 + firstColW, shift);
-  if (m_info != Success && m_info != NoConvergence) return;
-  divide(firstCol, k - 1 + firstCol, firstRowW, firstColW + 1, shift + 1);
-  if (m_info != Success && m_info != NoConvergence) return;
-
-  if (m_compU)
-  {
-    lambda = m_naiveU(firstCol + k, firstCol + k);
-    phi = m_naiveU(firstCol + k + 1, lastCol + 1);
-  } 
-  else 
-  {
-    lambda = m_naiveU(1, firstCol + k);
-    phi = m_naiveU(0, lastCol + 1);
-  }
-  r0 = sqrt((abs(alphaK * lambda) * abs(alphaK * lambda)) + abs(betaK * phi) * abs(betaK * phi));
-  if (m_compU)
-  {
-    l = m_naiveU.row(firstCol + k).segment(firstCol, k);
-    f = m_naiveU.row(firstCol + k + 1).segment(firstCol + k + 1, n - k - 1);
-  } 
-  else 
-  {
-    l = m_naiveU.row(1).segment(firstCol, k);
-    f = m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1);
-  }
-  if (m_compV) m_naiveV(firstRowW+k, firstColW) = Literal(1);
-  if (r0<considerZero)
-  {
-    c0 = Literal(1);
-    s0 = Literal(0);
-  }
-  else
-  {
-    c0 = alphaK * lambda / r0;
-    s0 = betaK * phi / r0;
-  }
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-  
-  if (m_compU)
-  {
-    MatrixXr q1 (m_naiveU.col(firstCol + k).segment(firstCol, k + 1));     
-    // we shiftW Q1 to the right
-    for (Index i = firstCol + k - 1; i >= firstCol; i--) 
-      m_naiveU.col(i + 1).segment(firstCol, k + 1) = m_naiveU.col(i).segment(firstCol, k + 1);
-    // we shift q1 at the left with a factor c0
-    m_naiveU.col(firstCol).segment( firstCol, k + 1) = (q1 * c0);
-    // last column = q1 * - s0
-    m_naiveU.col(lastCol + 1).segment(firstCol, k + 1) = (q1 * ( - s0));
-    // first column = q2 * s0
-    m_naiveU.col(firstCol).segment(firstCol + k + 1, n - k) = m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) * s0; 
-    // q2 *= c0
-    m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) *= c0;
-  } 
-  else 
-  {
-    RealScalar q1 = m_naiveU(0, firstCol + k);
-    // we shift Q1 to the right
-    for (Index i = firstCol + k - 1; i >= firstCol; i--) 
-      m_naiveU(0, i + 1) = m_naiveU(0, i);
-    // we shift q1 at the left with a factor c0
-    m_naiveU(0, firstCol) = (q1 * c0);
-    // last column = q1 * - s0
-    m_naiveU(0, lastCol + 1) = (q1 * ( - s0));
-    // first column = q2 * s0
-    m_naiveU(1, firstCol) = m_naiveU(1, lastCol + 1) *s0; 
-    // q2 *= c0
-    m_naiveU(1, lastCol + 1) *= c0;
-    m_naiveU.row(1).segment(firstCol + 1, k).setZero();
-    m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1).setZero();
-  }
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-  
-  m_computed(firstCol + shift, firstCol + shift) = r0;
-  m_computed.col(firstCol + shift).segment(firstCol + shift + 1, k) = alphaK * l.transpose().real();
-  m_computed.col(firstCol + shift).segment(firstCol + shift + k + 1, n - k - 1) = betaK * f.transpose().real();
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  ArrayXr tmp1 = (m_computed.block(firstCol+shift, firstCol+shift, n, n)).jacobiSvd().singularValues();
-#endif
-  // Second part: try to deflate singular values in combined matrix
-  deflation(firstCol, lastCol, k, firstRowW, firstColW, shift);
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  ArrayXr tmp2 = (m_computed.block(firstCol+shift, firstCol+shift, n, n)).jacobiSvd().singularValues();
-  std::cout << "\n\nj1 = " << tmp1.transpose().format(bdcsvdfmt) << "\n";
-  std::cout << "j2 = " << tmp2.transpose().format(bdcsvdfmt) << "\n\n";
-  std::cout << "err:      " << ((tmp1-tmp2).abs()>1e-12*tmp2.abs()).transpose() << "\n";
-  static int count = 0;
-  std::cout << "# " << ++count << "\n\n";
-  assert((tmp1-tmp2).matrix().norm() < 1e-14*tmp2.matrix().norm());
-//   assert(count<681);
-//   assert(((tmp1-tmp2).abs()<1e-13*tmp2.abs()).all());
-#endif
-  
-  // Third part: compute SVD of combined matrix
-  MatrixXr UofSVD, VofSVD;
-  VectorType singVals;
-  computeSVDofM(firstCol + shift, n, UofSVD, singVals, VofSVD);
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(UofSVD.allFinite());
-  assert(VofSVD.allFinite());
-#endif
-  
-  if (m_compU)
-    structured_update(m_naiveU.block(firstCol, firstCol, n + 1, n + 1), UofSVD, (n+2)/2);
-  else
-  {
-    Map<Matrix<RealScalar,2,Dynamic>,Aligned> tmp(m_workspace.data(),2,n+1);
-    tmp.noalias() = m_naiveU.middleCols(firstCol, n+1) * UofSVD;
-    m_naiveU.middleCols(firstCol, n + 1) = tmp;
-  }
-  
-  if (m_compV)  structured_update(m_naiveV.block(firstRowW, firstColW, n, n), VofSVD, (n+1)/2);
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-  
-  m_computed.block(firstCol + shift, firstCol + shift, n, n).setZero();
-  m_computed.block(firstCol + shift, firstCol + shift, n, n).diagonal() = singVals;
-}// end divide
-
-// Compute SVD of m_computed.block(firstCol, firstCol, n + 1, n); this block only has non-zeros in
-// the first column and on the diagonal and has undergone deflation, so diagonal is in increasing
-// order except for possibly the (0,0) entry. The computed SVD is stored U, singVals and V, except
-// that if m_compV is false, then V is not computed. Singular values are sorted in decreasing order.
-//
-// TODO Opportunities for optimization: better root finding algo, better stopping criterion, better
-// handling of round-off errors, be consistent in ordering
-// For instance, to solve the secular equation using FMM, see http://www.stat.uchicago.edu/~lekheng/courses/302/classics/greengard-rokhlin.pdf
-template <typename MatrixType>
-void BDCSVD<MatrixType>::computeSVDofM(Eigen::Index firstCol, Eigen::Index n, MatrixXr& U, VectorType& singVals, MatrixXr& V)
-{
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  using std::abs;
-  ArrayRef col0 = m_computed.col(firstCol).segment(firstCol, n);
-  m_workspace.head(n) =  m_computed.block(firstCol, firstCol, n, n).diagonal();
-  ArrayRef diag = m_workspace.head(n);
-  diag(0) = Literal(0);
-
-  // Allocate space for singular values and vectors
-  singVals.resize(n);
-  U.resize(n+1, n+1);
-  if (m_compV) V.resize(n, n);
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  if (col0.hasNaN() || diag.hasNaN())
-    std::cout << "\n\nHAS NAN\n\n";
-#endif
-  
-  // Many singular values might have been deflated, the zero ones have been moved to the end,
-  // but others are interleaved and we must ignore them at this stage.
-  // To this end, let's compute a permutation skipping them:
-  Index actual_n = n;
-  while(actual_n>1 && diag(actual_n-1)==Literal(0)) {--actual_n; eigen_internal_assert(col0(actual_n)==Literal(0)); }
-  Index m = 0; // size of the deflated problem
-  for(Index k=0;k<actual_n;++k)
-    if(abs(col0(k))>considerZero)
-      m_workspaceI(m++) = k;
-  Map<ArrayXi> perm(m_workspaceI.data(),m);
-  
-  Map<ArrayXr> shifts(m_workspace.data()+1*n, n);
-  Map<ArrayXr> mus(m_workspace.data()+2*n, n);
-  Map<ArrayXr> zhat(m_workspace.data()+3*n, n);
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "computeSVDofM using:\n";
-  std::cout << "  z: " << col0.transpose() << "\n";
-  std::cout << "  d: " << diag.transpose() << "\n";
-#endif
-  
-  // Compute singVals, shifts, and mus
-  computeSingVals(col0, diag, perm, singVals, shifts, mus);
-  
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "  j:        " << (m_computed.block(firstCol, firstCol, n, n)).jacobiSvd().singularValues().transpose().reverse() << "\n\n";
-  std::cout << "  sing-val: " << singVals.transpose() << "\n";
-  std::cout << "  mu:       " << mus.transpose() << "\n";
-  std::cout << "  shift:    " << shifts.transpose() << "\n";
-  
-  {
-    std::cout << "\n\n    mus:    " << mus.head(actual_n).transpose() << "\n\n";
-    std::cout << "    check1 (expect0) : " << ((singVals.array()-(shifts+mus)) / singVals.array()).head(actual_n).transpose() << "\n\n";
-    assert((((singVals.array()-(shifts+mus)) / singVals.array()).head(actual_n) >= 0).all());
-    std::cout << "    check2 (>0)      : " << ((singVals.array()-diag) / singVals.array()).head(actual_n).transpose() << "\n\n";
-    assert((((singVals.array()-diag) / singVals.array()).head(actual_n) >= 0).all());
-  }
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(singVals.allFinite());
-  assert(mus.allFinite());
-  assert(shifts.allFinite());
-#endif
-  
-  // Compute zhat
-  perturbCol0(col0, diag, perm, singVals, shifts, mus, zhat);
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "  zhat: " << zhat.transpose() << "\n";
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(zhat.allFinite());
-#endif
-  
-  computeSingVecs(zhat, diag, perm, singVals, shifts, mus, U, V);
-  
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "U^T U: " << (U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(),U.cols()))).norm() << "\n";
-  std::cout << "V^T V: " << (V.transpose() * V - MatrixXr(MatrixXr::Identity(V.cols(),V.cols()))).norm() << "\n";
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-  assert(U.allFinite());
-  assert(V.allFinite());
-//   assert((U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(),U.cols()))).norm() < 100*NumTraits<RealScalar>::epsilon() * n);
-//   assert((V.transpose() * V - MatrixXr(MatrixXr::Identity(V.cols(),V.cols()))).norm() < 100*NumTraits<RealScalar>::epsilon() * n);
-#endif
-  
-  // Because of deflation, the singular values might not be completely sorted.
-  // Fortunately, reordering them is a O(n) problem
-  for(Index i=0; i<actual_n-1; ++i)
-  {
-    if(singVals(i)>singVals(i+1))
-    {
-      using std::swap;
-      swap(singVals(i),singVals(i+1));
-      U.col(i).swap(U.col(i+1));
-      if(m_compV) V.col(i).swap(V.col(i+1));
-    }
-  }
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  {
-    bool singular_values_sorted = (((singVals.segment(1,actual_n-1)-singVals.head(actual_n-1))).array() >= 0).all();
-    if(!singular_values_sorted)
-      std::cout << "Singular values are not sorted: " << singVals.segment(1,actual_n).transpose() << "\n";
-    assert(singular_values_sorted);
-  }
-#endif
-  
-  // Reverse order so that singular values in increased order
-  // Because of deflation, the zeros singular-values are already at the end
-  singVals.head(actual_n).reverseInPlace();
-  U.leftCols(actual_n).rowwise().reverseInPlace();
-  if (m_compV) V.leftCols(actual_n).rowwise().reverseInPlace();
-  
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  JacobiSVD<MatrixXr> jsvd(m_computed.block(firstCol, firstCol, n, n) );
-  std::cout << "  * j:        " << jsvd.singularValues().transpose() << "\n\n";
-  std::cout << "  * sing-val: " << singVals.transpose() << "\n";
-//   std::cout << "  * err:      " << ((jsvd.singularValues()-singVals)>1e-13*singVals.norm()).transpose() << "\n";
-#endif
-}
-
-template <typename MatrixType>
-typename BDCSVD<MatrixType>::RealScalar BDCSVD<MatrixType>::secularEq(RealScalar mu, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const ArrayRef& diagShifted, RealScalar shift)
-{
-  Index m = perm.size();
-  RealScalar res = Literal(1);
-  for(Index i=0; i<m; ++i)
-  {
-    Index j = perm(i);
-    // The following expression could be rewritten to involve only a single division,
-    // but this would make the expression more sensitive to overflow.
-    res += (col0(j) / (diagShifted(j) - mu)) * (col0(j) / (diag(j) + shift + mu));
-  }
-  return res;
-
-}
-
-template <typename MatrixType>
-void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm,
-                                         VectorType& singVals, ArrayRef shifts, ArrayRef mus)
-{
-  using std::abs;
-  using std::swap;
-  using std::sqrt;
-
-  Index n = col0.size();
-  Index actual_n = n;
-  // Note that here actual_n is computed based on col0(i)==0 instead of diag(i)==0 as above
-  // because 1) we have diag(i)==0 => col0(i)==0 and 2) if col0(i)==0, then diag(i) is already a singular value.
-  while(actual_n>1 && col0(actual_n-1)==Literal(0)) --actual_n;
-
-  for (Index k = 0; k < n; ++k)
-  {
-    if (col0(k) == Literal(0) || actual_n==1)
-    {
-      // if col0(k) == 0, then entry is deflated, so singular value is on diagonal
-      // if actual_n==1, then the deflated problem is already diagonalized
-      singVals(k) = k==0 ? col0(0) : diag(k);
-      mus(k) = Literal(0);
-      shifts(k) = k==0 ? col0(0) : diag(k);
-      continue;
-    } 
-
-    // otherwise, use secular equation to find singular value
-    RealScalar left = diag(k);
-    RealScalar right; // was: = (k != actual_n-1) ? diag(k+1) : (diag(actual_n-1) + col0.matrix().norm());
-    if(k==actual_n-1)
-      right = (diag(actual_n-1) + col0.matrix().norm());
-    else
-    {
-      // Skip deflated singular values,
-      // recall that at this stage we assume that z[j]!=0 and all entries for which z[j]==0 have been put aside.
-      // This should be equivalent to using perm[]
-      Index l = k+1;
-      while(col0(l)==Literal(0)) { ++l; eigen_internal_assert(l<actual_n); }
-      right = diag(l);
-    }
-
-    // first decide whether it's closer to the left end or the right end
-    RealScalar mid = left + (right-left) / Literal(2);
-    RealScalar fMid = secularEq(mid, col0, diag, perm, diag, Literal(0));
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-    std::cout << "right-left = " << right-left << "\n";
-//     std::cout << "fMid = " << fMid << " " << secularEq(mid-left, col0, diag, perm, ArrayXr(diag-left), left)
-//                            << " " << secularEq(mid-right, col0, diag, perm, ArrayXr(diag-right), right)   << "\n";
-    std::cout << "     = " << secularEq(left+RealScalar(0.000001)*(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.1)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.2)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.3)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.4)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.49)    *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.5)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.51)    *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.6)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.7)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.8)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.9)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.999999)*(right-left), col0, diag, perm, diag, 0) << "\n";
-#endif
-    RealScalar shift = (k == actual_n-1 || fMid > Literal(0)) ? left : right;
-    
-    // measure everything relative to shift
-    Map<ArrayXr> diagShifted(m_workspace.data()+4*n, n);
-    diagShifted = diag - shift;
-
-    if(k!=actual_n-1)
-    {
-      // check that after the shift, f(mid) is still negative:
-      RealScalar midShifted = (right - left) / RealScalar(2);
-      if(shift==right)
-        midShifted = -midShifted;
-      RealScalar fMidShifted = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
-      if(fMidShifted>0)
-      {
-        // fMid was erroneous, fix it:
-        shift =  fMidShifted > Literal(0) ? left : right;
-        diagShifted = diag - shift;
-      }
-    }
-    
-    // initial guess
-    RealScalar muPrev, muCur;
-    if (shift == left)
-    {
-      muPrev = (right - left) * RealScalar(0.1);
-      if (k == actual_n-1) muCur = right - left;
-      else                 muCur = (right - left) * RealScalar(0.5);
-    }
-    else
-    {
-      muPrev = -(right - left) * RealScalar(0.1);
-      muCur = -(right - left) * RealScalar(0.5);
-    }
-
-    RealScalar fPrev = secularEq(muPrev, col0, diag, perm, diagShifted, shift);
-    RealScalar fCur = secularEq(muCur, col0, diag, perm, diagShifted, shift);
-    if (abs(fPrev) < abs(fCur))
-    {
-      swap(fPrev, fCur);
-      swap(muPrev, muCur);
-    }
-
-    // rational interpolation: fit a function of the form a / mu + b through the two previous
-    // iterates and use its zero to compute the next iterate
-    bool useBisection = fPrev*fCur>Literal(0);
-    while (fCur!=Literal(0) && abs(muCur - muPrev) > Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(muCur), abs(muPrev)) && abs(fCur - fPrev)>NumTraits<RealScalar>::epsilon() && !useBisection)
-    {
-      ++m_numIters;
-
-      // Find a and b such that the function f(mu) = a / mu + b matches the current and previous samples.
-      RealScalar a = (fCur - fPrev) / (Literal(1)/muCur - Literal(1)/muPrev);
-      RealScalar b = fCur - a / muCur;
-      // And find mu such that f(mu)==0:
-      RealScalar muZero = -a/b;
-      RealScalar fZero = secularEq(muZero, col0, diag, perm, diagShifted, shift);
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      assert((numext::isfinite)(fZero));
-#endif
-      
-      muPrev = muCur;
-      fPrev = fCur;
-      muCur = muZero;
-      fCur = fZero;
-      
-      if (shift == left  && (muCur < Literal(0) || muCur > right - left)) useBisection = true;
-      if (shift == right && (muCur < -(right - left) || muCur > Literal(0))) useBisection = true;
-      if (abs(fCur)>abs(fPrev)) useBisection = true;
-    }
-
-    // fall back on bisection method if rational interpolation did not work
-    if (useBisection)
-    {
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "useBisection for k = " << k << ", actual_n = " << actual_n << "\n";
-#endif
-      RealScalar leftShifted, rightShifted;
-      if (shift == left)
-      {
-        // to avoid overflow, we must have mu > max(real_min, |z(k)|/sqrt(real_max)),
-        // the factor 2 is to be more conservative
-        leftShifted = numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), Literal(2) * abs(col0(k)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
-
-        // check that we did it right:
-        eigen_internal_assert( (numext::isfinite)( (col0(k)/leftShifted)*(col0(k)/(diag(k)+shift+leftShifted)) ) );
-        // I don't understand why the case k==0 would be special there:
-        // if (k == 0) rightShifted = right - left; else
-        rightShifted = (k==actual_n-1) ? right : ((right - left) * RealScalar(0.51)); // theoretically we can take 0.5, but let's be safe
-      }
-      else
-      {
-        leftShifted = -(right - left) * RealScalar(0.51);
-        if(k+1<n)
-          rightShifted = -numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), abs(col0(k+1)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
-        else
-          rightShifted = -(std::numeric_limits<RealScalar>::min)();
-      }
-
-      RealScalar fLeft = secularEq(leftShifted, col0, diag, perm, diagShifted, shift);
-      eigen_internal_assert(fLeft<Literal(0));
-
-#if defined EIGEN_INTERNAL_DEBUGGING || defined EIGEN_BDCSVD_SANITY_CHECKS
-      RealScalar fRight = secularEq(rightShifted, col0, diag, perm, diagShifted, shift);
-#endif
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      if(!(numext::isfinite)(fLeft))
-        std::cout << "f(" << leftShifted << ") =" << fLeft << " ; " << left << " " << shift << " " << right << "\n";
-      assert((numext::isfinite)(fLeft));
-
-      if(!(numext::isfinite)(fRight))
-        std::cout << "f(" << rightShifted << ") =" << fRight << " ; " << left << " " << shift << " " << right << "\n";
-      // assert((numext::isfinite)(fRight));
-#endif
-    
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      if(!(fLeft * fRight<0))
-      {
-        std::cout << "f(leftShifted) using  leftShifted=" << leftShifted << " ;  diagShifted(1:10):" << diagShifted.head(10).transpose()  << "\n ; "
-                  << "left==shift=" << bool(left==shift) << " ; left-shift = " << (left-shift) << "\n";
-        std::cout << "k=" << k << ", " <<  fLeft << " * " << fRight << " == " << fLeft * fRight << "  ;  "
-                  << "[" << left << " .. " << right << "] -> [" << leftShifted << " " << rightShifted << "], shift=" << shift
-                  << " ,  f(right)=" << secularEq(0,     col0, diag, perm, diagShifted, shift)
-                           << " == " << secularEq(right, col0, diag, perm, diag, 0) << " == " << fRight << "\n";
-      }
-#endif
-      eigen_internal_assert(fLeft * fRight < Literal(0));
-
-      if(fLeft<Literal(0))
-      {
-        while (rightShifted - leftShifted > Literal(2) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(leftShifted), abs(rightShifted)))
-        {
-          RealScalar midShifted = (leftShifted + rightShifted) / Literal(2);
-          fMid = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
-          eigen_internal_assert((numext::isfinite)(fMid));
-
-          if (fLeft * fMid < Literal(0))
-          {
-            rightShifted = midShifted;
-          }
-          else
-          {
-            leftShifted = midShifted;
-            fLeft = fMid;
-          }
-        }
-        muCur = (leftShifted + rightShifted) / Literal(2);
-      }
-      else 
-      {
-        // We have a problem as shifting on the left or right give either a positive or negative value
-        // at the middle of [left,right]...
-        // Instead fo abbording or entering an infinite loop,
-        // let's just use the middle as the estimated zero-crossing:
-        muCur = (right - left) * RealScalar(0.5);
-        if(shift == right)
-          muCur = -muCur;
-      }
-    }
-      
-    singVals[k] = shift + muCur;
-    shifts[k] = shift;
-    mus[k] = muCur;
-
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-    if(k+1<n)
-      std::cout << "found " << singVals[k] << " == " << shift << " + " << muCur << " from " << diag(k) << " .. "  << diag(k+1) << "\n";
-#endif
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-    assert(k==0 || singVals[k]>=singVals[k-1]);
-    assert(singVals[k]>=diag(k));
-#endif
-
-    // perturb singular value slightly if it equals diagonal entry to avoid division by zero later
-    // (deflation is supposed to avoid this from happening)
-    // - this does no seem to be necessary anymore -
-//     if (singVals[k] == left) singVals[k] *= 1 + NumTraits<RealScalar>::epsilon();
-//     if (singVals[k] == right) singVals[k] *= 1 - NumTraits<RealScalar>::epsilon();
-  }
-}
-
-
-// zhat is perturbation of col0 for which singular vectors can be computed stably (see Section 3.1)
-template <typename MatrixType>
-void BDCSVD<MatrixType>::perturbCol0
-   (const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const VectorType& singVals,
-    const ArrayRef& shifts, const ArrayRef& mus, ArrayRef zhat)
-{
-  using std::sqrt;
-  Index n = col0.size();
-  Index m = perm.size();
-  if(m==0)
-  {
-    zhat.setZero();
-    return;
-  }
-  Index lastIdx = perm(m-1);
-  // The offset permits to skip deflated entries while computing zhat
-  for (Index k = 0; k < n; ++k)
-  {
-    if (col0(k) == Literal(0)) // deflated
-      zhat(k) = Literal(0);
-    else
-    {
-      // see equation (3.6)
-      RealScalar dk = diag(k);
-      RealScalar prod = (singVals(lastIdx) + dk) * (mus(lastIdx) + (shifts(lastIdx) - dk));
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      if(prod<0) {
-        std::cout << "k = " << k << " ;  z(k)=" << col0(k) << ", diag(k)=" << dk << "\n";
-        std::cout << "prod = " << "(" << singVals(lastIdx) << " + " << dk << ") * (" << mus(lastIdx) << " + (" << shifts(lastIdx) << " - " << dk << "))" << "\n";
-        std::cout << "     = " << singVals(lastIdx) + dk << " * " << mus(lastIdx) + (shifts(lastIdx) - dk) <<  "\n";
-      }
-      assert(prod>=0);
-#endif
-
-      for(Index l = 0; l<m; ++l)
-      {
-        Index i = perm(l);
-        if(i!=k)
-        {
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-          if(i>=k && (l==0 || l-1>=m))
-          {
-            std::cout << "Error in perturbCol0\n";
-            std::cout << "  " << k << "/" << n << " "  << l << "/" << m << " " << i << "/" << n << " ; " << col0(k) << " " << diag(k) << " "  <<  "\n";
-            std::cout << "  " <<diag(i) << "\n";
-            Index j = (i<k /*|| l==0*/) ? i : perm(l-1);
-            std::cout << "  " << "j=" << j << "\n";
-          }
-#endif
-          Index j = i<k ? i : perm(l-1);
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-          if(!(dk!=Literal(0) || diag(i)!=Literal(0)))
-          {
-            std::cout << "k=" << k << ", i=" << i << ", l=" << l << ", perm.size()=" << perm.size() << "\n";
-          }
-          assert(dk!=Literal(0) || diag(i)!=Literal(0));
-#endif
-          prod *= ((singVals(j)+dk) / ((diag(i)+dk))) * ((mus(j)+(shifts(j)-dk)) / ((diag(i)-dk)));
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-          assert(prod>=0);
-#endif
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-          if(i!=k && numext::abs(((singVals(j)+dk)*(mus(j)+(shifts(j)-dk)))/((diag(i)+dk)*(diag(i)-dk)) - 1) > 0.9 )
-            std::cout << "     " << ((singVals(j)+dk)*(mus(j)+(shifts(j)-dk)))/((diag(i)+dk)*(diag(i)-dk)) << " == (" << (singVals(j)+dk) << " * " << (mus(j)+(shifts(j)-dk))
-                       << ") / (" << (diag(i)+dk) << " * " << (diag(i)-dk) << ")\n";
-#endif
-        }
-      }
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "zhat(" << k << ") =  sqrt( " << prod << ")  ;  " << (singVals(lastIdx) + dk) << " * " << mus(lastIdx) + shifts(lastIdx) << " - " << dk << "\n";
-#endif
-      RealScalar tmp = sqrt(prod);
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      assert((numext::isfinite)(tmp));
-#endif
-      zhat(k) = col0(k) > Literal(0) ? RealScalar(tmp) : RealScalar(-tmp);
-    }
-  }
-}
-
-// compute singular vectors
-template <typename MatrixType>
-void BDCSVD<MatrixType>::computeSingVecs
-   (const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef &perm, const VectorType& singVals,
-    const ArrayRef& shifts, const ArrayRef& mus, MatrixXr& U, MatrixXr& V)
-{
-  Index n = zhat.size();
-  Index m = perm.size();
-  
-  for (Index k = 0; k < n; ++k)
-  {
-    if (zhat(k) == Literal(0))
-    {
-      U.col(k) = VectorType::Unit(n+1, k);
-      if (m_compV) V.col(k) = VectorType::Unit(n, k);
-    }
-    else
-    {
-      U.col(k).setZero();
-      for(Index l=0;l<m;++l)
-      {
-        Index i = perm(l);
-        U(i,k) = zhat(i)/(((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
-      }
-      U(n,k) = Literal(0);
-      U.col(k).normalize();
-    
-      if (m_compV)
-      {
-        V.col(k).setZero();
-        for(Index l=1;l<m;++l)
-        {
-          Index i = perm(l);
-          V(i,k) = diag(i) * zhat(i) / (((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
-        }
-        V(0,k) = Literal(-1);
-        V.col(k).normalize();
-      }
-    }
-  }
-  U.col(n) = VectorType::Unit(n+1, n);
-}
-
-
-// page 12_13
-// i >= 1, di almost null and zi non null.
-// We use a rotation to zero out zi applied to the left of M
-template <typename MatrixType>
-void BDCSVD<MatrixType>::deflation43(Eigen::Index firstCol, Eigen::Index shift, Eigen::Index i, Eigen::Index size)
-{
-  using std::abs;
-  using std::sqrt;
-  using std::pow;
-  Index start = firstCol + shift;
-  RealScalar c = m_computed(start, start);
-  RealScalar s = m_computed(start+i, start);
-  RealScalar r = numext::hypot(c,s);
-  if (r == Literal(0))
-  {
-    m_computed(start+i, start+i) = Literal(0);
-    return;
-  }
-  m_computed(start,start) = r;  
-  m_computed(start+i, start) = Literal(0);
-  m_computed(start+i, start+i) = Literal(0);
-  
-  JacobiRotation<RealScalar> J(c/r,-s/r);
-  if (m_compU)  m_naiveU.middleRows(firstCol, size+1).applyOnTheRight(firstCol, firstCol+i, J);
-  else          m_naiveU.applyOnTheRight(firstCol, firstCol+i, J);
-}// end deflation 43
-
-
-// page 13
-// i,j >= 1, i!=j and |di - dj| < epsilon * norm2(M)
-// We apply two rotations to have zj = 0;
-// TODO deflation44 is still broken and not properly tested
-template <typename MatrixType>
-void BDCSVD<MatrixType>::deflation44(Eigen::Index firstColu , Eigen::Index firstColm, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index i, Eigen::Index j, Eigen::Index size)
-{
-  using std::abs;
-  using std::sqrt;
-  using std::conj;
-  using std::pow;
-  RealScalar c = m_computed(firstColm+i, firstColm);
-  RealScalar s = m_computed(firstColm+j, firstColm);
-  RealScalar r = sqrt(numext::abs2(c) + numext::abs2(s));
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "deflation 4.4: " << i << "," << j << " -> " << c << " " << s << " " << r << " ; "
-    << m_computed(firstColm + i-1, firstColm)  << " "
-    << m_computed(firstColm + i, firstColm)  << " "
-    << m_computed(firstColm + i+1, firstColm) << " "
-    << m_computed(firstColm + i+2, firstColm) << "\n";
-  std::cout << m_computed(firstColm + i-1, firstColm + i-1)  << " "
-    << m_computed(firstColm + i, firstColm+i)  << " "
-    << m_computed(firstColm + i+1, firstColm+i+1) << " "
-    << m_computed(firstColm + i+2, firstColm+i+2) << "\n";
-#endif
-  if (r==Literal(0))
-  {
-    m_computed(firstColm + i, firstColm + i) = m_computed(firstColm + j, firstColm + j);
-    return;
-  }
-  c/=r;
-  s/=r;
-  m_computed(firstColm + i, firstColm) = r;
-  m_computed(firstColm + j, firstColm + j) = m_computed(firstColm + i, firstColm + i);
-  m_computed(firstColm + j, firstColm) = Literal(0);
-
-  JacobiRotation<RealScalar> J(c,-s);
-  if (m_compU)  m_naiveU.middleRows(firstColu, size+1).applyOnTheRight(firstColu + i, firstColu + j, J);
-  else          m_naiveU.applyOnTheRight(firstColu+i, firstColu+j, J);
-  if (m_compV)  m_naiveV.middleRows(firstRowW, size).applyOnTheRight(firstColW + i, firstColW + j, J);
-}// end deflation 44
-
-
-// acts on block from (firstCol+shift, firstCol+shift) to (lastCol+shift, lastCol+shift) [inclusive]
-template <typename MatrixType>
-void BDCSVD<MatrixType>::deflation(Eigen::Index firstCol, Eigen::Index lastCol, Eigen::Index k, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index shift)
-{
-  using std::sqrt;
-  using std::abs;
-  const Index length = lastCol + 1 - firstCol;
-  
-  Block<MatrixXr,Dynamic,1> col0(m_computed, firstCol+shift, firstCol+shift, length, 1);
-  Diagonal<MatrixXr> fulldiag(m_computed);
-  VectorBlock<Diagonal<MatrixXr>,Dynamic> diag(fulldiag, firstCol+shift, length);
-  
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  RealScalar maxDiag = diag.tail((std::max)(Index(1),length-1)).cwiseAbs().maxCoeff();
-  RealScalar epsilon_strict = numext::maxi<RealScalar>(considerZero,NumTraits<RealScalar>::epsilon() * maxDiag);
-  RealScalar epsilon_coarse = Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(col0.cwiseAbs().maxCoeff(), maxDiag);
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE  
-  std::cout << "\ndeflate:" << diag.head(k+1).transpose() << "  |  " << diag.segment(k+1,length-k-1).transpose() << "\n";
-#endif
-  
-  //condition 4.1
-  if (diag(0) < epsilon_coarse)
-  { 
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-    std::cout << "deflation 4.1, because " << diag(0) << " < " << epsilon_coarse << "\n";
-#endif
-    diag(0) = epsilon_coarse;
-  }
-
-  //condition 4.2
-  for (Index i=1;i<length;++i)
-    if (abs(col0(i)) < epsilon_strict)
-    {
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "deflation 4.2, set z(" << i << ") to zero because " << abs(col0(i)) << " < " << epsilon_strict << "  (diag(" << i << ")=" << diag(i) << ")\n";
-#endif
-      col0(i) = Literal(0);
-    }
-
-  //condition 4.3
-  for (Index i=1;i<length; i++)
-    if (diag(i) < epsilon_coarse)
-    {
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "deflation 4.3, cancel z(" << i << ")=" << col0(i) << " because diag(" << i << ")=" << diag(i) << " < " << epsilon_coarse << "\n";
-#endif
-      deflation43(firstCol, shift, i, length);
-    }
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "to be sorted: " << diag.transpose() << "\n\n";
-  std::cout << "            : " << col0.transpose() << "\n\n";
-#endif
-  {
-    // Check for total deflation
-    // If we have a total deflation, then we have to consider col0(0)==diag(0) as a singular value during sorting
-    bool total_deflation = (col0.tail(length-1).array()<considerZero).all();
-    
-    // Sort the diagonal entries, since diag(1:k-1) and diag(k:length) are already sorted, let's do a sorted merge.
-    // First, compute the respective permutation.
-    Index *permutation = m_workspaceI.data();
-    {
-      permutation[0] = 0;
-      Index p = 1;
-      
-      // Move deflated diagonal entries at the end.
-      for(Index i=1; i<length; ++i)
-        if(abs(diag(i))<considerZero)
-          permutation[p++] = i;
-        
-      Index i=1, j=k+1;
-      for( ; p < length; ++p)
-      {
-             if (i > k)             permutation[p] = j++;
-        else if (j >= length)       permutation[p] = i++;
-        else if (diag(i) < diag(j)) permutation[p] = j++;
-        else                        permutation[p] = i++;
-      }
-    }
-    
-    // If we have a total deflation, then we have to insert diag(0) at the right place
-    if(total_deflation)
-    {
-      for(Index i=1; i<length; ++i)
-      {
-        Index pi = permutation[i];
-        if(abs(diag(pi))<considerZero || diag(0)<diag(pi))
-          permutation[i-1] = permutation[i];
-        else
-        {
-          permutation[i-1] = 0;
-          break;
-        }
-      }
-    }
-    
-    // Current index of each col, and current column of each index
-    Index *realInd = m_workspaceI.data()+length;
-    Index *realCol = m_workspaceI.data()+2*length;
-    
-    for(int pos = 0; pos< length; pos++)
-    {
-      realCol[pos] = pos;
-      realInd[pos] = pos;
-    }
-    
-    for(Index i = total_deflation?0:1; i < length; i++)
-    {
-      const Index pi = permutation[length - (total_deflation ? i+1 : i)];
-      const Index J = realCol[pi];
-      
-      using std::swap;
-      // swap diagonal and first column entries:
-      swap(diag(i), diag(J));
-      if(i!=0 && J!=0) swap(col0(i), col0(J));
-
-      // change columns
-      if (m_compU) m_naiveU.col(firstCol+i).segment(firstCol, length + 1).swap(m_naiveU.col(firstCol+J).segment(firstCol, length + 1));
-      else         m_naiveU.col(firstCol+i).segment(0, 2)                .swap(m_naiveU.col(firstCol+J).segment(0, 2));
-      if (m_compV) m_naiveV.col(firstColW + i).segment(firstRowW, length).swap(m_naiveV.col(firstColW + J).segment(firstRowW, length));
-
-      //update real pos
-      const Index realI = realInd[i];
-      realCol[realI] = J;
-      realCol[pi] = i;
-      realInd[J] = realI;
-      realInd[i] = pi;
-    }
-  }
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "sorted: " << diag.transpose().format(bdcsvdfmt) << "\n";
-  std::cout << "      : " << col0.transpose() << "\n\n";
-#endif
-    
-  //condition 4.4
-  {
-    Index i = length-1;
-    while(i>0 && (abs(diag(i))<considerZero || abs(col0(i))<considerZero)) --i;
-    for(; i>1;--i)
-       if( (diag(i) - diag(i-1)) < NumTraits<RealScalar>::epsilon()*maxDiag )
-      {
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-        std::cout << "deflation 4.4 with i = " << i << " because " << diag(i) << " - " << diag(i-1) << " == " << (diag(i) - diag(i-1)) << " < " << NumTraits<RealScalar>::epsilon()*/*diag(i)*/maxDiag << "\n";
-#endif
-        eigen_internal_assert(abs(diag(i) - diag(i-1))<epsilon_coarse && " diagonal entries are not properly sorted");
-        deflation44(firstCol, firstCol + shift, firstRowW, firstColW, i-1, i, length);
-      }
-  }
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  for(Index j=2;j<length;++j)
-    assert(diag(j-1)<=diag(j) || abs(diag(j))<considerZero);
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-}//end deflation
-
-/** \svd_module
-  *
-  * \return the singular value decomposition of \c *this computed by Divide & Conquer algorithm
-  *
-  * \sa class BDCSVD
-  */
-template<typename Derived>
-BDCSVD<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::bdcSvd(unsigned int computationOptions) const
-{
-  return BDCSVD<PlainObject>(*this, computationOptions);
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/JacobiSVD.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/JacobiSVD.h
deleted file mode 100644
index 9d95acd..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/JacobiSVD.h
+++ /dev/null
@@ -1,812 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_JACOBISVD_H
-#define EIGEN_JACOBISVD_H
-
-namespace Eigen { 
-
-namespace internal {
-// forward declaration (needed by ICC)
-// the empty body is required by MSVC
-template<typename MatrixType, int QRPreconditioner,
-         bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex>
-struct svd_precondition_2x2_block_to_be_real {};
-
-/*** QR preconditioners (R-SVD)
- ***
- *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
- *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
- *** JacobiSVD which by itself is only able to work on square matrices.
- ***/
-
-enum { PreconditionIfMoreColsThanRows, PreconditionIfMoreRowsThanCols };
-
-template<typename MatrixType, int QRPreconditioner, int Case>
-struct qr_preconditioner_should_do_anything
-{
-  enum { a = MatrixType::RowsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime <= MatrixType::RowsAtCompileTime,
-         b = MatrixType::RowsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime != Dynamic &&
-             MatrixType::RowsAtCompileTime <= MatrixType::ColsAtCompileTime,
-         ret = !( (QRPreconditioner == NoQRPreconditioner) ||
-                  (Case == PreconditionIfMoreColsThanRows && bool(a)) ||
-                  (Case == PreconditionIfMoreRowsThanCols && bool(b)) )
-  };
-};
-
-template<typename MatrixType, int QRPreconditioner, int Case,
-         bool DoAnything = qr_preconditioner_should_do_anything<MatrixType, QRPreconditioner, Case>::ret
-> struct qr_preconditioner_impl {};
-
-template<typename MatrixType, int QRPreconditioner, int Case>
-class qr_preconditioner_impl<MatrixType, QRPreconditioner, Case, false>
-{
-public:
-  void allocate(const JacobiSVD<MatrixType, QRPreconditioner>&) {}
-  bool run(JacobiSVD<MatrixType, QRPreconditioner>&, const MatrixType&)
-  {
-    return false;
-  }
-};
-
-/*** preconditioner using FullPivHouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
-  };
-  typedef Matrix<Scalar, 1, RowsAtCompileTime, RowMajor, 1, MaxRowsAtCompileTime> WorkspaceType;
-
-  void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.matrixQ().evalTo(svd.m_matrixU, m_workspace);
-      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
-      return true;
-    }
-    return false;
-  }
-private:
-  typedef FullPivHouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  WorkspaceType m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef typename internal::make_proper_matrix_type<
-    Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime
-  >::type TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    m_adjoint.resize(svd.cols(), svd.rows());
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.matrixQ().evalTo(svd.m_matrixV, m_workspace);
-      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
-      return true;
-    }
-    else return false;
-  }
-private:
-  typedef FullPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** preconditioner using ColPivHouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
-      else if(svd.m_computeThinU)
-      {
-        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
-      }
-      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
-      return true;
-    }
-    return false;
-  }
-
-private:
-  typedef ColPivHouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  typename internal::plain_col_type<MatrixType>::type m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef typename internal::make_proper_matrix_type<
-    Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime
-  >::type TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
-    m_adjoint.resize(svd.cols(), svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
-      else if(svd.m_computeThinV)
-      {
-        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
-      }
-      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
-      return true;
-    }
-    else return false;
-  }
-
-private:
-  typedef ColPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** preconditioner using HouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
-      else if(svd.m_computeThinU)
-      {
-        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
-      }
-      if(svd.computeV()) svd.m_matrixV.setIdentity(matrix.cols(), matrix.cols());
-      return true;
-    }
-    return false;
-  }
-private:
-  typedef HouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  typename internal::plain_col_type<MatrixType>::type m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef typename internal::make_proper_matrix_type<
-    Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime
-  >::type TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
-    m_adjoint.resize(svd.cols(), svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
-      else if(svd.m_computeThinV)
-      {
-        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
-      }
-      if(svd.computeU()) svd.m_matrixU.setIdentity(matrix.rows(), matrix.rows());
-      return true;
-    }
-    else return false;
-  }
-
-private:
-  typedef HouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** 2x2 SVD implementation
- ***
- *** JacobiSVD consists in performing a series of 2x2 SVD subproblems
- ***/
-
-template<typename MatrixType, int QRPreconditioner>
-struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, false>
-{
-  typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
-  typedef typename MatrixType::RealScalar RealScalar;
-  static bool run(typename SVD::WorkMatrixType&, SVD&, Index, Index, RealScalar&) { return true; }
-};
-
-template<typename MatrixType, int QRPreconditioner>
-struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true>
-{
-  typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  static bool run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q, RealScalar& maxDiagEntry)
-  {
-    using std::sqrt;
-    using std::abs;
-    Scalar z;
-    JacobiRotation<Scalar> rot;
-    RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p,p)) + numext::abs2(work_matrix.coeff(q,p)));
-
-    const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-    const RealScalar precision = NumTraits<Scalar>::epsilon();
-
-    if(n==0)
-    {
-      // make sure first column is zero
-      work_matrix.coeffRef(p,p) = work_matrix.coeffRef(q,p) = Scalar(0);
-
-      if(abs(numext::imag(work_matrix.coeff(p,q)))>considerAsZero)
-      {
-        // work_matrix.coeff(p,q) can be zero if work_matrix.coeff(q,p) is not zero but small enough to underflow when computing n
-        z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
-        work_matrix.row(p) *= z;
-        if(svd.computeU()) svd.m_matrixU.col(p) *= conj(z);
-      }
-      if(abs(numext::imag(work_matrix.coeff(q,q)))>considerAsZero)
-      {
-        z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
-        work_matrix.row(q) *= z;
-        if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
-      }
-      // otherwise the second row is already zero, so we have nothing to do.
-    }
-    else
-    {
-      rot.c() = conj(work_matrix.coeff(p,p)) / n;
-      rot.s() = work_matrix.coeff(q,p) / n;
-      work_matrix.applyOnTheLeft(p,q,rot);
-      if(svd.computeU()) svd.m_matrixU.applyOnTheRight(p,q,rot.adjoint());
-      if(abs(numext::imag(work_matrix.coeff(p,q)))>considerAsZero)
-      {
-        z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
-        work_matrix.col(q) *= z;
-        if(svd.computeV()) svd.m_matrixV.col(q) *= z;
-      }
-      if(abs(numext::imag(work_matrix.coeff(q,q)))>considerAsZero)
-      {
-        z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
-        work_matrix.row(q) *= z;
-        if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
-      }
-    }
-
-    // update largest diagonal entry
-    maxDiagEntry = numext::maxi<RealScalar>(maxDiagEntry,numext::maxi<RealScalar>(abs(work_matrix.coeff(p,p)), abs(work_matrix.coeff(q,q))));
-    // and check whether the 2x2 block is already diagonal
-    RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
-    return abs(work_matrix.coeff(p,q))>threshold || abs(work_matrix.coeff(q,p)) > threshold;
-  }
-};
-
-template<typename _MatrixType, int QRPreconditioner> 
-struct traits<JacobiSVD<_MatrixType,QRPreconditioner> >
-        : traits<_MatrixType>
-{
-  typedef _MatrixType MatrixType;
-};
-
-} // end namespace internal
-
-/** \ingroup SVD_Module
-  *
-  *
-  * \class JacobiSVD
-  *
-  * \brief Two-sided Jacobi SVD decomposition of a rectangular matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the SVD decomposition
-  * \tparam QRPreconditioner this optional parameter allows to specify the type of QR decomposition that will be used internally
-  *                        for the R-SVD step for non-square matrices. See discussion of possible values below.
-  *
-  * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
-  *   \f[ A = U S V^* \f]
-  * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
-  * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
-  * and right \em singular \em vectors of \a A respectively.
-  *
-  * Singular values are always sorted in decreasing order.
-  *
-  * This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask for them explicitly.
-  *
-  * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
-  * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
-  * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
-  * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
-  *
-  * Here's an example demonstrating basic usage:
-  * \include JacobiSVD_basic.cpp
-  * Output: \verbinclude JacobiSVD_basic.out
-  *
-  * This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than
-  * bidiagonalizing SVD algorithms for large square matrices; however its complexity is still \f$ O(n^2p) \f$ where \a n is the smaller dimension and
-  * \a p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms.
-  * In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
-  *
-  * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to
-  * terminate in finite (and reasonable) time.
-  *
-  * The possible values for QRPreconditioner are:
-  * \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
-  * \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR.
-  *     Contrary to other QRs, it doesn't allow computing thin unitaries.
-  * \li HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses non-pivoting QR.
-  *     This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing SVD algorithms (since bidiagonalization
-  *     is inherently non-pivoting). However the resulting SVD is still more reliable than bidiagonalizing SVDs because the Jacobi-based iterarive
-  *     process is more reliable than the optimized bidiagonal SVD iterations.
-  * \li NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that you will only be computing
-  *     JacobiSVD decompositions of square matrices. Non-square matrices require a QR preconditioner. Using this option will result in
-  *     faster compilation and smaller executable code. It won't significantly speed up computation, since JacobiSVD is always checking
-  *     if QR preconditioning is needed before applying it anyway.
-  *
-  * \sa MatrixBase::jacobiSvd()
-  */
-template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
- : public SVDBase<JacobiSVD<_MatrixType,QRPreconditioner> >
-{
-    typedef SVDBase<JacobiSVD> Base;
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-      MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
-      MatrixOptions = MatrixType::Options
-    };
-
-    typedef typename Base::MatrixUType MatrixUType;
-    typedef typename Base::MatrixVType MatrixVType;
-    typedef typename Base::SingularValuesType SingularValuesType;
-    
-    typedef typename internal::plain_row_type<MatrixType>::type RowType;
-    typedef typename internal::plain_col_type<MatrixType>::type ColType;
-    typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
-                   MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime>
-            WorkMatrixType;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via JacobiSVD::compute(const MatrixType&).
-      */
-    JacobiSVD()
-    {}
-
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem size.
-      * \sa JacobiSVD()
-      */
-    JacobiSVD(Index rows, Index cols, unsigned int computationOptions = 0)
-    {
-      allocate(rows, cols, computationOptions);
-    }
-
-    /** \brief Constructor performing the decomposition of given matrix.
-     *
-     * \param matrix the matrix to decompose
-     * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-     *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-     *                           #ComputeFullV, #ComputeThinV.
-     *
-     * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-     * available with the (non-default) FullPivHouseholderQR preconditioner.
-     */
-    explicit JacobiSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
-    {
-      compute(matrix, computationOptions);
-    }
-
-    /** \brief Method performing the decomposition of given matrix using custom options.
-     *
-     * \param matrix the matrix to decompose
-     * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-     *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-     *                           #ComputeFullV, #ComputeThinV.
-     *
-     * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-     * available with the (non-default) FullPivHouseholderQR preconditioner.
-     */
-    JacobiSVD& compute(const MatrixType& matrix, unsigned int computationOptions);
-
-    /** \brief Method performing the decomposition of given matrix using current options.
-     *
-     * \param matrix the matrix to decompose
-     *
-     * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
-     */
-    JacobiSVD& compute(const MatrixType& matrix)
-    {
-      return compute(matrix, m_computationOptions);
-    }
-
-    using Base::computeU;
-    using Base::computeV;
-    using Base::rows;
-    using Base::cols;
-    using Base::rank;
-
-  private:
-    void allocate(Index rows, Index cols, unsigned int computationOptions);
-
-  protected:
-    using Base::m_matrixU;
-    using Base::m_matrixV;
-    using Base::m_singularValues;
-    using Base::m_info;
-    using Base::m_isInitialized;
-    using Base::m_isAllocated;
-    using Base::m_usePrescribedThreshold;
-    using Base::m_computeFullU;
-    using Base::m_computeThinU;
-    using Base::m_computeFullV;
-    using Base::m_computeThinV;
-    using Base::m_computationOptions;
-    using Base::m_nonzeroSingularValues;
-    using Base::m_rows;
-    using Base::m_cols;
-    using Base::m_diagSize;
-    using Base::m_prescribedThreshold;
-    WorkMatrixType m_workMatrix;
-
-    template<typename __MatrixType, int _QRPreconditioner, bool _IsComplex>
-    friend struct internal::svd_precondition_2x2_block_to_be_real;
-    template<typename __MatrixType, int _QRPreconditioner, int _Case, bool _DoAnything>
-    friend struct internal::qr_preconditioner_impl;
-
-    internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreColsThanRows> m_qr_precond_morecols;
-    internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols> m_qr_precond_morerows;
-    MatrixType m_scaledMatrix;
-};
-
-template<typename MatrixType, int QRPreconditioner>
-void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Eigen::Index rows, Eigen::Index cols, unsigned int computationOptions)
-{
-  eigen_assert(rows >= 0 && cols >= 0);
-
-  if (m_isAllocated &&
-      rows == m_rows &&
-      cols == m_cols &&
-      computationOptions == m_computationOptions)
-  {
-    return;
-  }
-
-  m_rows = rows;
-  m_cols = cols;
-  m_info = Success;
-  m_isInitialized = false;
-  m_isAllocated = true;
-  m_computationOptions = computationOptions;
-  m_computeFullU = (computationOptions & ComputeFullU) != 0;
-  m_computeThinU = (computationOptions & ComputeThinU) != 0;
-  m_computeFullV = (computationOptions & ComputeFullV) != 0;
-  m_computeThinV = (computationOptions & ComputeThinV) != 0;
-  eigen_assert(!(m_computeFullU && m_computeThinU) && "JacobiSVD: you can't ask for both full and thin U");
-  eigen_assert(!(m_computeFullV && m_computeThinV) && "JacobiSVD: you can't ask for both full and thin V");
-  eigen_assert(EIGEN_IMPLIES(m_computeThinU || m_computeThinV, MatrixType::ColsAtCompileTime==Dynamic) &&
-              "JacobiSVD: thin U and V are only available when your matrix has a dynamic number of columns.");
-  if (QRPreconditioner == FullPivHouseholderQRPreconditioner)
-  {
-      eigen_assert(!(m_computeThinU || m_computeThinV) &&
-              "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
-              "Use the ColPivHouseholderQR preconditioner instead.");
-  }
-  m_diagSize = (std::min)(m_rows, m_cols);
-  m_singularValues.resize(m_diagSize);
-  if(RowsAtCompileTime==Dynamic)
-    m_matrixU.resize(m_rows, m_computeFullU ? m_rows
-                            : m_computeThinU ? m_diagSize
-                            : 0);
-  if(ColsAtCompileTime==Dynamic)
-    m_matrixV.resize(m_cols, m_computeFullV ? m_cols
-                            : m_computeThinV ? m_diagSize
-                            : 0);
-  m_workMatrix.resize(m_diagSize, m_diagSize);
-  
-  if(m_cols>m_rows)   m_qr_precond_morecols.allocate(*this);
-  if(m_rows>m_cols)   m_qr_precond_morerows.allocate(*this);
-  if(m_rows!=m_cols)  m_scaledMatrix.resize(rows,cols);
-}
-
-template<typename MatrixType, int QRPreconditioner>
-JacobiSVD<MatrixType, QRPreconditioner>&
-JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsigned int computationOptions)
-{
-  using std::abs;
-  allocate(matrix.rows(), matrix.cols(), computationOptions);
-
-  // currently we stop when we reach precision 2*epsilon as the last bit of precision can require an unreasonable number of iterations,
-  // only worsening the precision of U and V as we accumulate more rotations
-  const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();
-
-  // limit for denormal numbers to be considered zero in order to avoid infinite loops (see bug 286)
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-
-  // Scaling factor to reduce over/under-flows
-  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
-  if (!(numext::isfinite)(scale)) {
-    m_isInitialized = true;
-    m_info = InvalidInput;
-    return *this;
-  }
-  if(scale==RealScalar(0)) scale = RealScalar(1);
-  
-  /*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
-
-  if(m_rows!=m_cols)
-  {
-    m_scaledMatrix = matrix / scale;
-    m_qr_precond_morecols.run(*this, m_scaledMatrix);
-    m_qr_precond_morerows.run(*this, m_scaledMatrix);
-  }
-  else
-  {
-    m_workMatrix = matrix.block(0,0,m_diagSize,m_diagSize) / scale;
-    if(m_computeFullU) m_matrixU.setIdentity(m_rows,m_rows);
-    if(m_computeThinU) m_matrixU.setIdentity(m_rows,m_diagSize);
-    if(m_computeFullV) m_matrixV.setIdentity(m_cols,m_cols);
-    if(m_computeThinV) m_matrixV.setIdentity(m_cols, m_diagSize);
-  }
-
-  /*** step 2. The main Jacobi SVD iteration. ***/
-  RealScalar maxDiagEntry = m_workMatrix.cwiseAbs().diagonal().maxCoeff();
-
-  bool finished = false;
-  while(!finished)
-  {
-    finished = true;
-
-    // do a sweep: for all index pairs (p,q), perform SVD of the corresponding 2x2 sub-matrix
-
-    for(Index p = 1; p < m_diagSize; ++p)
-    {
-      for(Index q = 0; q < p; ++q)
-      {
-        // if this 2x2 sub-matrix is not diagonal already...
-        // notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
-        // keep us iterating forever. Similarly, small denormal numbers are considered zero.
-        RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
-        if(abs(m_workMatrix.coeff(p,q))>threshold || abs(m_workMatrix.coeff(q,p)) > threshold)
-        {
-          finished = false;
-          // perform SVD decomposition of 2x2 sub-matrix corresponding to indices p,q to make it diagonal
-          // the complex to real operation returns true if the updated 2x2 block is not already diagonal
-          if(internal::svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner>::run(m_workMatrix, *this, p, q, maxDiagEntry))
-          {
-            JacobiRotation<RealScalar> j_left, j_right;
-            internal::real_2x2_jacobi_svd(m_workMatrix, p, q, &j_left, &j_right);
-
-            // accumulate resulting Jacobi rotations
-            m_workMatrix.applyOnTheLeft(p,q,j_left);
-            if(computeU()) m_matrixU.applyOnTheRight(p,q,j_left.transpose());
-
-            m_workMatrix.applyOnTheRight(p,q,j_right);
-            if(computeV()) m_matrixV.applyOnTheRight(p,q,j_right);
-
-            // keep track of the largest diagonal coefficient
-            maxDiagEntry = numext::maxi<RealScalar>(maxDiagEntry,numext::maxi<RealScalar>(abs(m_workMatrix.coeff(p,p)), abs(m_workMatrix.coeff(q,q))));
-          }
-        }
-      }
-    }
-  }
-
-  /*** step 3. The work matrix is now diagonal, so ensure it's positive so its diagonal entries are the singular values ***/
-
-  for(Index i = 0; i < m_diagSize; ++i)
-  {
-    // For a complex matrix, some diagonal coefficients might note have been
-    // treated by svd_precondition_2x2_block_to_be_real, and the imaginary part
-    // of some diagonal entry might not be null.
-    if(NumTraits<Scalar>::IsComplex && abs(numext::imag(m_workMatrix.coeff(i,i)))>considerAsZero)
-    {
-      RealScalar a = abs(m_workMatrix.coeff(i,i));
-      m_singularValues.coeffRef(i) = abs(a);
-      if(computeU()) m_matrixU.col(i) *= m_workMatrix.coeff(i,i)/a;
-    }
-    else
-    {
-      // m_workMatrix.coeff(i,i) is already real, no difficulty:
-      RealScalar a = numext::real(m_workMatrix.coeff(i,i));
-      m_singularValues.coeffRef(i) = abs(a);
-      if(computeU() && (a<RealScalar(0))) m_matrixU.col(i) = -m_matrixU.col(i);
-    }
-  }
-  
-  m_singularValues *= scale;
-
-  /*** step 4. Sort singular values in descending order and compute the number of nonzero singular values ***/
-
-  m_nonzeroSingularValues = m_diagSize;
-  for(Index i = 0; i < m_diagSize; i++)
-  {
-    Index pos;
-    RealScalar maxRemainingSingularValue = m_singularValues.tail(m_diagSize-i).maxCoeff(&pos);
-    if(maxRemainingSingularValue == RealScalar(0))
-    {
-      m_nonzeroSingularValues = i;
-      break;
-    }
-    if(pos)
-    {
-      pos += i;
-      std::swap(m_singularValues.coeffRef(i), m_singularValues.coeffRef(pos));
-      if(computeU()) m_matrixU.col(pos).swap(m_matrixU.col(i));
-      if(computeV()) m_matrixV.col(pos).swap(m_matrixV.col(i));
-    }
-  }
-
-  m_isInitialized = true;
-  return *this;
-}
-
-/** \svd_module
-  *
-  * \return the singular value decomposition of \c *this computed by two-sided
-  * Jacobi transformations.
-  *
-  * \sa class JacobiSVD
-  */
-template<typename Derived>
-JacobiSVD<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::jacobiSvd(unsigned int computationOptions) const
-{
-  return JacobiSVD<PlainObject>(*this, computationOptions);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_JACOBISVD_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
deleted file mode 100644
index ff0516f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Singular Value Decomposition - SVD.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_JACOBISVD_LAPACKE_H
-#define EIGEN_JACOBISVD_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_SVD(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW) \
-template<> inline \
-JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>& \
-JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) \
-{ \
-  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
-  /*typedef MatrixType::Scalar Scalar;*/ \
-  /*typedef MatrixType::RealScalar RealScalar;*/ \
-  allocate(matrix.rows(), matrix.cols(), computationOptions); \
-\
-  /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
-  m_nonzeroSingularValues = m_diagSize; \
-\
-  lapack_int lda = internal::convert_index<lapack_int>(matrix.outerStride()), ldu, ldvt; \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  char jobu, jobvt; \
-  LAPACKE_TYPE *u, *vt, dummy; \
-  jobu  = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
-  jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
-  if (computeU()) { \
-    ldu  = internal::convert_index<lapack_int>(m_matrixU.outerStride()); \
-    u    = (LAPACKE_TYPE*)m_matrixU.data(); \
-  } else { ldu=1; u=&dummy; }\
-  MatrixType localV; \
-  lapack_int vt_rows = (m_computeFullV) ? internal::convert_index<lapack_int>(m_cols) : (m_computeThinV) ? internal::convert_index<lapack_int>(m_diagSize) : 1; \
-  if (computeV()) { \
-    localV.resize(vt_rows, m_cols); \
-    ldvt  = internal::convert_index<lapack_int>(localV.outerStride()); \
-    vt   = (LAPACKE_TYPE*)localV.data(); \
-  } else { ldvt=1; vt=&dummy; }\
-  Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \
-  MatrixType m_temp; m_temp = matrix; \
-  LAPACKE_##LAPACKE_PREFIX##gesvd( matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(m_rows), internal::convert_index<lapack_int>(m_cols), (LAPACKE_TYPE*)m_temp.data(), lda, (LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
-  if (computeV()) m_matrixV = localV.adjoint(); \
- /* for(int i=0;i<m_diagSize;i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
-  m_isInitialized = true; \
-  return *this; \
-}
-
-EIGEN_LAPACKE_SVD(double,   double,                double, d, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SVD(float,    float,                 float , s, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float,  float , c, ColMajor, LAPACK_COL_MAJOR)
-
-EIGEN_LAPACKE_SVD(double,   double,                double, d, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SVD(float,    float,                 float , s, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float,  float , c, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_JACOBISVD_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/SVDBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/SVDBase.h
deleted file mode 100644
index bc7ab88..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/SVDBase.h
+++ /dev/null
@@ -1,376 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
-// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
-// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
-// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SVDBASE_H
-#define EIGEN_SVDBASE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename Derived> struct traits<SVDBase<Derived> >
- : traits<Derived>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-}
-
-/** \ingroup SVD_Module
- *
- *
- * \class SVDBase
- *
- * \brief Base class of SVD algorithms
- *
- * \tparam Derived the type of the actual SVD decomposition
- *
- * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
- *   \f[ A = U S V^* \f]
- * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
- * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
- * and right \em singular \em vectors of \a A respectively.
- *
- * Singular values are always sorted in decreasing order.
- *
- * 
- * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
- * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
- * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
- * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
- * 
- * The status of the computation can be retrived using the \a info() method. Unless \a info() returns \a Success, the results should be not
- * considered well defined.
- *  
- * If the input matrix has inf or nan coefficients, the result of the computation is undefined, and \a info() will return \a InvalidInput, but the computation is guaranteed to
- * terminate in finite (and reasonable) time.
- * \sa class BDCSVD, class JacobiSVD
- */
-template<typename Derived> class SVDBase
- : public SolverBase<SVDBase<Derived> >
-{
-public: 
-   
-  template<typename Derived_>
-  friend struct internal::solve_assertion;
-
-  typedef typename internal::traits<Derived>::MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-  typedef typename Eigen::internal::traits<SVDBase>::StorageIndex StorageIndex;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
-    MatrixOptions = MatrixType::Options
-  };
-
-  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixUType;
-  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime, MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime> MatrixVType;
-  typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
-  
-  Derived& derived() { return *static_cast<Derived*>(this); }
-  const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-  /** \returns the \a U matrix.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-   * the U matrix is n-by-n if you asked for \link Eigen::ComputeFullU ComputeFullU \endlink, and is n-by-m if you asked for \link Eigen::ComputeThinU ComputeThinU \endlink.
-   *
-   * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
-   *
-   * This method asserts that you asked for \a U to be computed.
-   */
-  const MatrixUType& matrixU() const
-  {
-    _check_compute_assertions();
-    eigen_assert(computeU() && "This SVD decomposition didn't compute U. Did you ask for it?");
-    return m_matrixU;
-  }
-
-  /** \returns the \a V matrix.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-   * the V matrix is p-by-p if you asked for \link Eigen::ComputeFullV ComputeFullV \endlink, and is p-by-m if you asked for \link Eigen::ComputeThinV ComputeThinV \endlink.
-   *
-   * The \a m first columns of \a V are the right singular vectors of the matrix being decomposed.
-   *
-   * This method asserts that you asked for \a V to be computed.
-   */
-  const MatrixVType& matrixV() const
-  {
-    _check_compute_assertions();
-    eigen_assert(computeV() && "This SVD decomposition didn't compute V. Did you ask for it?");
-    return m_matrixV;
-  }
-
-  /** \returns the vector of singular values.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the
-   * returned vector has size \a m.  Singular values are always sorted in decreasing order.
-   */
-  const SingularValuesType& singularValues() const
-  {
-    _check_compute_assertions();
-    return m_singularValues;
-  }
-
-  /** \returns the number of singular values that are not exactly 0 */
-  Index nonzeroSingularValues() const
-  {
-    _check_compute_assertions();
-    return m_nonzeroSingularValues;
-  }
-  
-  /** \returns the rank of the matrix of which \c *this is the SVD.
-    *
-    * \note This method has to determine which singular values should be considered nonzero.
-    *       For that, it uses the threshold value that you can control by calling
-    *       setThreshold(const RealScalar&).
-    */
-  inline Index rank() const
-  {
-    using std::abs;
-    _check_compute_assertions();
-    if(m_singularValues.size()==0) return 0;
-    RealScalar premultiplied_threshold = numext::maxi<RealScalar>(m_singularValues.coeff(0) * threshold(), (std::numeric_limits<RealScalar>::min)());
-    Index i = m_nonzeroSingularValues-1;
-    while(i>=0 && m_singularValues.coeff(i) < premultiplied_threshold) --i;
-    return i+1;
-  }
-  
-  /** Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(),
-    * which need to determine when singular values are to be considered nonzero.
-    * This is not used for the SVD decomposition itself.
-    *
-    * When it needs to get the threshold value, Eigen calls threshold().
-    * The default is \c NumTraits<Scalar>::epsilon()
-    *
-    * \param threshold The new value to use as the threshold.
-    *
-    * A singular value will be considered nonzero if its value is strictly greater than
-    *  \f$ \vert singular value \vert \leqslant threshold \times \vert max singular value \vert \f$.
-    *
-    * If you want to come back to the default behavior, call setThreshold(Default_t)
-    */
-  Derived& setThreshold(const RealScalar& threshold)
-  {
-    m_usePrescribedThreshold = true;
-    m_prescribedThreshold = threshold;
-    return derived();
-  }
-
-  /** Allows to come back to the default behavior, letting Eigen use its default formula for
-    * determining the threshold.
-    *
-    * You should pass the special object Eigen::Default as parameter here.
-    * \code svd.setThreshold(Eigen::Default); \endcode
-    *
-    * See the documentation of setThreshold(const RealScalar&).
-    */
-  Derived& setThreshold(Default_t)
-  {
-    m_usePrescribedThreshold = false;
-    return derived();
-  }
-
-  /** Returns the threshold that will be used by certain methods such as rank().
-    *
-    * See the documentation of setThreshold(const RealScalar&).
-    */
-  RealScalar threshold() const
-  {
-    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-    // this temporary is needed to workaround a MSVC issue
-    Index diagSize = (std::max<Index>)(1,m_diagSize);
-    return m_usePrescribedThreshold ? m_prescribedThreshold
-                                    : RealScalar(diagSize)*NumTraits<Scalar>::epsilon();
-  }
-
-  /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
-  inline bool computeU() const { return m_computeFullU || m_computeThinU; }
-  /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
-  inline bool computeV() const { return m_computeFullV || m_computeThinV; }
-
-  inline Index rows() const { return m_rows; }
-  inline Index cols() const { return m_cols; }
-  
-  #ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
-    *
-    * \param b the right-hand-side of the equation to solve.
-    *
-    * \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V.
-    *
-    * \note SVD solving is implicitly least-squares. Thus, this method serves both purposes of exact solving and least-squares solving.
-    * In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert A x - b \Vert \f$.
-    */
-  template<typename Rhs>
-  inline const Solve<Derived, Rhs>
-  solve(const MatrixBase<Rhs>& b) const;
-  #endif
-
-
-  /** \brief Reports whether previous computation was successful.
-   *
-   * \returns \c Success if computation was successful.
-   */
-  EIGEN_DEVICE_FUNC
-  ComputationInfo info() const
-  {
-    eigen_assert(m_isInitialized && "SVD is not initialized.");
-    return m_info;
-  }
-
-  #ifndef EIGEN_PARSED_BY_DOXYGEN
-  template<typename RhsType, typename DstType>
-  void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-  template<bool Conjugate, typename RhsType, typename DstType>
-  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-  #endif
-
-protected:
-
-  static void check_template_parameters()
-  {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-  }
-
-  void _check_compute_assertions() const {
-    eigen_assert(m_isInitialized && "SVD is not initialized.");
-  }
-
-  template<bool Transpose_, typename Rhs>
-  void _check_solve_assertion(const Rhs& b) const {
-      EIGEN_ONLY_USED_FOR_DEBUG(b);
-      _check_compute_assertions();
-      eigen_assert(computeU() && computeV() && "SVDBase::solve(): Both unitaries U and V are required to be computed (thin unitaries suffice).");
-      eigen_assert((Transpose_?cols():rows())==b.rows() && "SVDBase::solve(): invalid number of rows of the right hand side matrix b");
-  }
-
-  // return true if already allocated
-  bool allocate(Index rows, Index cols, unsigned int computationOptions) ;
-
-  MatrixUType m_matrixU;
-  MatrixVType m_matrixV;
-  SingularValuesType m_singularValues;
-  ComputationInfo m_info;
-  bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold;
-  bool m_computeFullU, m_computeThinU;
-  bool m_computeFullV, m_computeThinV;
-  unsigned int m_computationOptions;
-  Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize;
-  RealScalar m_prescribedThreshold;
-
-  /** \brief Default Constructor.
-   *
-   * Default constructor of SVDBase
-   */
-  SVDBase()
-    : m_info(Success),
-      m_isInitialized(false),
-      m_isAllocated(false),
-      m_usePrescribedThreshold(false),
-      m_computeFullU(false),
-      m_computeThinU(false),
-      m_computeFullV(false),
-      m_computeThinV(false),
-      m_computationOptions(0),
-      m_rows(-1), m_cols(-1), m_diagSize(0)
-  {
-    check_template_parameters();
-  }
-
-
-};
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename Derived>
-template<typename RhsType, typename DstType>
-void SVDBase<Derived>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  // A = U S V^*
-  // So A^{-1} = V S^{-1} U^*
-
-  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
-  Index l_rank = rank();
-  tmp.noalias() =  m_matrixU.leftCols(l_rank).adjoint() * rhs;
-  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
-  dst = m_matrixV.leftCols(l_rank) * tmp;
-}
-
-template<typename Derived>
-template<bool Conjugate, typename RhsType, typename DstType>
-void SVDBase<Derived>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  // A = U S V^*
-  // So  A^{-*} = U S^{-1} V^*
-  // And A^{-T} = U_conj S^{-1} V^T
-  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
-  Index l_rank = rank();
-
-  tmp.noalias() =  m_matrixV.leftCols(l_rank).transpose().template conjugateIf<Conjugate>() * rhs;
-  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
-  dst = m_matrixU.template conjugateIf<!Conjugate>().leftCols(l_rank) * tmp;
-}
-#endif
-
-template<typename MatrixType>
-bool SVDBase<MatrixType>::allocate(Index rows, Index cols, unsigned int computationOptions)
-{
-  eigen_assert(rows >= 0 && cols >= 0);
-
-  if (m_isAllocated &&
-      rows == m_rows &&
-      cols == m_cols &&
-      computationOptions == m_computationOptions)
-  {
-    return true;
-  }
-
-  m_rows = rows;
-  m_cols = cols;
-  m_info = Success;
-  m_isInitialized = false;
-  m_isAllocated = true;
-  m_computationOptions = computationOptions;
-  m_computeFullU = (computationOptions & ComputeFullU) != 0;
-  m_computeThinU = (computationOptions & ComputeThinU) != 0;
-  m_computeFullV = (computationOptions & ComputeFullV) != 0;
-  m_computeThinV = (computationOptions & ComputeThinV) != 0;
-  eigen_assert(!(m_computeFullU && m_computeThinU) && "SVDBase: you can't ask for both full and thin U");
-  eigen_assert(!(m_computeFullV && m_computeThinV) && "SVDBase: you can't ask for both full and thin V");
-  eigen_assert(EIGEN_IMPLIES(m_computeThinU || m_computeThinV, MatrixType::ColsAtCompileTime==Dynamic) &&
-	       "SVDBase: thin U and V are only available when your matrix has a dynamic number of columns.");
-
-  m_diagSize = (std::min)(m_rows, m_cols);
-  m_singularValues.resize(m_diagSize);
-  if(RowsAtCompileTime==Dynamic)
-    m_matrixU.resize(m_rows, m_computeFullU ? m_rows : m_computeThinU ? m_diagSize : 0);
-  if(ColsAtCompileTime==Dynamic)
-    m_matrixV.resize(m_cols, m_computeFullV ? m_cols : m_computeThinV ? m_diagSize : 0);
-
-  return false;
-}
-
-}// end namespace
-
-#endif // EIGEN_SVDBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/UpperBidiagonalization.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/UpperBidiagonalization.h
deleted file mode 100644
index 997defc..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SVD/UpperBidiagonalization.h
+++ /dev/null
@@ -1,414 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BIDIAGONALIZATION_H
-#define EIGEN_BIDIAGONALIZATION_H
-
-namespace Eigen { 
-
-namespace internal {
-// UpperBidiagonalization will probably be replaced by a Bidiagonalization class, don't want to make it stable API.
-// At the same time, it's useful to keep for now as it's about the only thing that is testing the BandMatrix class.
-
-template<typename _MatrixType> class UpperBidiagonalization
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      ColsAtCompileTimeMinusOne = internal::decrement_size<ColsAtCompileTime>::ret
-    };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    typedef Matrix<Scalar, 1, ColsAtCompileTime> RowVectorType;
-    typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType;
-    typedef BandMatrix<RealScalar, ColsAtCompileTime, ColsAtCompileTime, 1, 0, RowMajor> BidiagonalType;
-    typedef Matrix<Scalar, ColsAtCompileTime, 1> DiagVectorType;
-    typedef Matrix<Scalar, ColsAtCompileTimeMinusOne, 1> SuperDiagVectorType;
-    typedef HouseholderSequence<
-              const MatrixType,
-              const typename internal::remove_all<typename Diagonal<const MatrixType,0>::ConjugateReturnType>::type
-            > HouseholderUSequenceType;
-    typedef HouseholderSequence<
-              const typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type,
-              Diagonal<const MatrixType,1>,
-              OnTheRight
-            > HouseholderVSequenceType;
-    
-    /**
-    * \brief Default Constructor.
-    *
-    * The default constructor is useful in cases in which the user intends to
-    * perform decompositions via Bidiagonalization::compute(const MatrixType&).
-    */
-    UpperBidiagonalization() : m_householder(), m_bidiagonal(), m_isInitialized(false) {}
-
-    explicit UpperBidiagonalization(const MatrixType& matrix)
-      : m_householder(matrix.rows(), matrix.cols()),
-        m_bidiagonal(matrix.cols(), matrix.cols()),
-        m_isInitialized(false)
-    {
-      compute(matrix);
-    }
-    
-    UpperBidiagonalization& compute(const MatrixType& matrix);
-    UpperBidiagonalization& computeUnblocked(const MatrixType& matrix);
-    
-    const MatrixType& householder() const { return m_householder; }
-    const BidiagonalType& bidiagonal() const { return m_bidiagonal; }
-    
-    const HouseholderUSequenceType householderU() const
-    {
-      eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
-      return HouseholderUSequenceType(m_householder, m_householder.diagonal().conjugate());
-    }
-
-    const HouseholderVSequenceType householderV() // const here gives nasty errors and i'm lazy
-    {
-      eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
-      return HouseholderVSequenceType(m_householder.conjugate(), m_householder.const_derived().template diagonal<1>())
-             .setLength(m_householder.cols()-1)
-             .setShift(1);
-    }
-    
-  protected:
-    MatrixType m_householder;
-    BidiagonalType m_bidiagonal;
-    bool m_isInitialized;
-};
-
-// Standard upper bidiagonalization without fancy optimizations
-// This version should be faster for small matrix size
-template<typename MatrixType>
-void upperbidiagonalization_inplace_unblocked(MatrixType& mat,
-                                              typename MatrixType::RealScalar *diagonal,
-                                              typename MatrixType::RealScalar *upper_diagonal,
-                                              typename MatrixType::Scalar* tempData = 0)
-{
-  typedef typename MatrixType::Scalar Scalar;
-
-  Index rows = mat.rows();
-  Index cols = mat.cols();
-
-  typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixType::MaxRowsAtCompileTime,1> TempType;
-  TempType tempVector;
-  if(tempData==0)
-  {
-    tempVector.resize(rows);
-    tempData = tempVector.data();
-  }
-
-  for (Index k = 0; /* breaks at k==cols-1 below */ ; ++k)
-  {
-    Index remainingRows = rows - k;
-    Index remainingCols = cols - k - 1;
-
-    // construct left householder transform in-place in A
-    mat.col(k).tail(remainingRows)
-       .makeHouseholderInPlace(mat.coeffRef(k,k), diagonal[k]);
-    // apply householder transform to remaining part of A on the left
-    mat.bottomRightCorner(remainingRows, remainingCols)
-       .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), mat.coeff(k,k), tempData);
-
-    if(k == cols-1) break;
-
-    // construct right householder transform in-place in mat
-    mat.row(k).tail(remainingCols)
-       .makeHouseholderInPlace(mat.coeffRef(k,k+1), upper_diagonal[k]);
-    // apply householder transform to remaining part of mat on the left
-    mat.bottomRightCorner(remainingRows-1, remainingCols)
-       .applyHouseholderOnTheRight(mat.row(k).tail(remainingCols-1).adjoint(), mat.coeff(k,k+1), tempData);
-  }
-}
-
-/** \internal
-  * Helper routine for the block reduction to upper bidiagonal form.
-  *
-  * Let's partition the matrix A:
-  * 
-  *      | A00 A01 |
-  *  A = |         |
-  *      | A10 A11 |
-  *
-  * This function reduces to bidiagonal form the left \c rows x \a blockSize vertical panel [A00/A10]
-  * and the \a blockSize x \c cols horizontal panel [A00 A01] of the matrix \a A. The bottom-right block A11
-  * is updated using matrix-matrix products:
-  *   A22 -= V * Y^T - X * U^T
-  * where V and U contains the left and right Householder vectors. U and V are stored in A10, and A01
-  * respectively, and the update matrices X and Y are computed during the reduction.
-  * 
-  */
-template<typename MatrixType>
-void upperbidiagonalization_blocked_helper(MatrixType& A,
-                                           typename MatrixType::RealScalar *diagonal,
-                                           typename MatrixType::RealScalar *upper_diagonal,
-                                           Index bs,
-                                           Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic,
-                                                      traits<MatrixType>::Flags & RowMajorBit> > X,
-                                           Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic,
-                                                      traits<MatrixType>::Flags & RowMajorBit> > Y)
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename NumTraits<RealScalar>::Literal Literal;
-  enum { StorageOrder = traits<MatrixType>::Flags & RowMajorBit };
-  typedef InnerStride<int(StorageOrder) == int(ColMajor) ? 1 : Dynamic> ColInnerStride;
-  typedef InnerStride<int(StorageOrder) == int(ColMajor) ? Dynamic : 1> RowInnerStride;
-  typedef Ref<Matrix<Scalar, Dynamic, 1>, 0, ColInnerStride>    SubColumnType;
-  typedef Ref<Matrix<Scalar, 1, Dynamic>, 0, RowInnerStride>    SubRowType;
-  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder > > SubMatType;
-  
-  Index brows = A.rows();
-  Index bcols = A.cols();
-
-  Scalar tau_u, tau_u_prev(0), tau_v;
-
-  for(Index k = 0; k < bs; ++k)
-  {
-    Index remainingRows = brows - k;
-    Index remainingCols = bcols - k - 1;
-
-    SubMatType X_k1( X.block(k,0, remainingRows,k) );
-    SubMatType V_k1( A.block(k,0, remainingRows,k) );
-
-    // 1 - update the k-th column of A
-    SubColumnType v_k = A.col(k).tail(remainingRows);
-          v_k -= V_k1 * Y.row(k).head(k).adjoint();
-    if(k) v_k -= X_k1 * A.col(k).head(k);
-    
-    // 2 - construct left Householder transform in-place
-    v_k.makeHouseholderInPlace(tau_v, diagonal[k]);
-       
-    if(k+1<bcols)
-    {
-      SubMatType Y_k  ( Y.block(k+1,0, remainingCols, k+1) );
-      SubMatType U_k1 ( A.block(0,k+1, k,remainingCols) );
-      
-      // this eases the application of Householder transforAions
-      // A(k,k) will store tau_v later
-      A(k,k) = Scalar(1);
-
-      // 3 - Compute y_k^T = tau_v * ( A^T*v_k - Y_k-1*V_k-1^T*v_k - U_k-1*X_k-1^T*v_k )
-      {
-        SubColumnType y_k( Y.col(k).tail(remainingCols) );
-        
-        // let's use the beginning of column k of Y as a temporary vector
-        SubColumnType tmp( Y.col(k).head(k) );
-        y_k.noalias()  = A.block(k,k+1, remainingRows,remainingCols).adjoint() * v_k; // bottleneck
-        tmp.noalias()  = V_k1.adjoint()  * v_k;
-        y_k.noalias() -= Y_k.leftCols(k) * tmp;
-        tmp.noalias()  = X_k1.adjoint()  * v_k;
-        y_k.noalias() -= U_k1.adjoint()  * tmp;
-        y_k *= numext::conj(tau_v);
-      }
-
-      // 4 - update k-th row of A (it will become u_k)
-      SubRowType u_k( A.row(k).tail(remainingCols) );
-      u_k = u_k.conjugate();
-      {
-        u_k -= Y_k * A.row(k).head(k+1).adjoint();
-        if(k) u_k -= U_k1.adjoint() * X.row(k).head(k).adjoint();
-      }
-
-      // 5 - construct right Householder transform in-place
-      u_k.makeHouseholderInPlace(tau_u, upper_diagonal[k]);
-
-      // this eases the application of Householder transformations
-      // A(k,k+1) will store tau_u later
-      A(k,k+1) = Scalar(1);
-
-      // 6 - Compute x_k = tau_u * ( A*u_k - X_k-1*U_k-1^T*u_k - V_k*Y_k^T*u_k )
-      {
-        SubColumnType x_k ( X.col(k).tail(remainingRows-1) );
-        
-        // let's use the beginning of column k of X as a temporary vectors
-        // note that tmp0 and tmp1 overlaps
-        SubColumnType tmp0 ( X.col(k).head(k) ),
-                      tmp1 ( X.col(k).head(k+1) );
-                    
-        x_k.noalias()   = A.block(k+1,k+1, remainingRows-1,remainingCols) * u_k.transpose(); // bottleneck
-        tmp0.noalias()  = U_k1 * u_k.transpose();
-        x_k.noalias()  -= X_k1.bottomRows(remainingRows-1) * tmp0;
-        tmp1.noalias()  = Y_k.adjoint() * u_k.transpose();
-        x_k.noalias()  -= A.block(k+1,0, remainingRows-1,k+1) * tmp1;
-        x_k *= numext::conj(tau_u);
-        tau_u = numext::conj(tau_u);
-        u_k = u_k.conjugate();
-      }
-
-      if(k>0) A.coeffRef(k-1,k) = tau_u_prev;
-      tau_u_prev = tau_u;
-    }
-    else
-      A.coeffRef(k-1,k) = tau_u_prev;
-
-    A.coeffRef(k,k) = tau_v;
-  }
-  
-  if(bs<bcols)
-    A.coeffRef(bs-1,bs) = tau_u_prev;
-
-  // update A22
-  if(bcols>bs && brows>bs)
-  {
-    SubMatType A11( A.bottomRightCorner(brows-bs,bcols-bs) );
-    SubMatType A10( A.block(bs,0, brows-bs,bs) );
-    SubMatType A01( A.block(0,bs, bs,bcols-bs) );
-    Scalar tmp = A01(bs-1,0);
-    A01(bs-1,0) = Literal(1);
-    A11.noalias() -= A10 * Y.topLeftCorner(bcols,bs).bottomRows(bcols-bs).adjoint();
-    A11.noalias() -= X.topLeftCorner(brows,bs).bottomRows(brows-bs) * A01;
-    A01(bs-1,0) = tmp;
-  }
-}
-
-/** \internal
-  *
-  * Implementation of a block-bidiagonal reduction.
-  * It is based on the following paper:
-  *   The Design of a Parallel Dense Linear Algebra Software Library: Reduction to Hessenberg, Tridiagonal, and Bidiagonal Form.
-  *   by Jaeyoung Choi, Jack J. Dongarra, David W. Walker. (1995)
-  *   section 3.3
-  */
-template<typename MatrixType, typename BidiagType>
-void upperbidiagonalization_inplace_blocked(MatrixType& A, BidiagType& bidiagonal,
-                                            Index maxBlockSize=32,
-                                            typename MatrixType::Scalar* /*tempData*/ = 0)
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef Block<MatrixType,Dynamic,Dynamic> BlockType;
-
-  Index rows = A.rows();
-  Index cols = A.cols();
-  Index size = (std::min)(rows, cols);
-
-  // X and Y are work space
-  enum { StorageOrder = traits<MatrixType>::Flags & RowMajorBit };
-  Matrix<Scalar,
-         MatrixType::RowsAtCompileTime,
-         Dynamic,
-         StorageOrder,
-         MatrixType::MaxRowsAtCompileTime> X(rows,maxBlockSize);
-  Matrix<Scalar,
-         MatrixType::ColsAtCompileTime,
-         Dynamic,
-         StorageOrder,
-         MatrixType::MaxColsAtCompileTime> Y(cols,maxBlockSize);
-  Index blockSize = (std::min)(maxBlockSize,size);
-
-  Index k = 0;
-  for(k = 0; k < size; k += blockSize)
-  {
-    Index bs = (std::min)(size-k,blockSize);  // actual size of the block
-    Index brows = rows - k;                   // rows of the block
-    Index bcols = cols - k;                   // columns of the block
-
-    // partition the matrix A:
-    // 
-    //      | A00 A01 A02 |
-    //      |             |
-    // A  = | A10 A11 A12 |
-    //      |             |
-    //      | A20 A21 A22 |
-    //
-    // where A11 is a bs x bs diagonal block,
-    // and let:
-    //      | A11 A12 |
-    //  B = |         |
-    //      | A21 A22 |
-
-    BlockType B = A.block(k,k,brows,bcols);
-    
-    // This stage performs the bidiagonalization of A11, A21, A12, and updating of A22.
-    // Finally, the algorithm continue on the updated A22.
-    //
-    // However, if B is too small, or A22 empty, then let's use an unblocked strategy
-    if(k+bs==cols || bcols<48) // somewhat arbitrary threshold
-    {
-      upperbidiagonalization_inplace_unblocked(B,
-                                               &(bidiagonal.template diagonal<0>().coeffRef(k)),
-                                               &(bidiagonal.template diagonal<1>().coeffRef(k)),
-                                               X.data()
-                                              );
-      break; // We're done
-    }
-    else
-    {
-      upperbidiagonalization_blocked_helper<BlockType>( B,
-                                                        &(bidiagonal.template diagonal<0>().coeffRef(k)),
-                                                        &(bidiagonal.template diagonal<1>().coeffRef(k)),
-                                                        bs,
-                                                        X.topLeftCorner(brows,bs),
-                                                        Y.topLeftCorner(bcols,bs)
-                                                      );
-    }
-  }
-}
-
-template<typename _MatrixType>
-UpperBidiagonalization<_MatrixType>& UpperBidiagonalization<_MatrixType>::computeUnblocked(const _MatrixType& matrix)
-{
-  Index rows = matrix.rows();
-  Index cols = matrix.cols();
-  EIGEN_ONLY_USED_FOR_DEBUG(cols);
-
-  eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
-
-  m_householder = matrix;
-
-  ColVectorType temp(rows);
-
-  upperbidiagonalization_inplace_unblocked(m_householder,
-                                           &(m_bidiagonal.template diagonal<0>().coeffRef(0)),
-                                           &(m_bidiagonal.template diagonal<1>().coeffRef(0)),
-                                           temp.data());
-
-  m_isInitialized = true;
-  return *this;
-}
-
-template<typename _MatrixType>
-UpperBidiagonalization<_MatrixType>& UpperBidiagonalization<_MatrixType>::compute(const _MatrixType& matrix)
-{
-  Index rows = matrix.rows();
-  Index cols = matrix.cols();
-  EIGEN_ONLY_USED_FOR_DEBUG(rows);
-  EIGEN_ONLY_USED_FOR_DEBUG(cols);
-
-  eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
-
-  m_householder = matrix;
-  upperbidiagonalization_inplace_blocked(m_householder, m_bidiagonal);
-            
-  m_isInitialized = true;
-  return *this;
-}
-
-#if 0
-/** \return the Householder QR decomposition of \c *this.
-  *
-  * \sa class Bidiagonalization
-  */
-template<typename Derived>
-const UpperBidiagonalization<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::bidiagonalization() const
-{
-  return UpperBidiagonalization<PlainObject>(eval());
-}
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BIDIAGONALIZATION_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCholesky/SimplicialCholesky.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
deleted file mode 100644
index 9f93e32..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
+++ /dev/null
@@ -1,697 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SIMPLICIAL_CHOLESKY_H
-#define EIGEN_SIMPLICIAL_CHOLESKY_H
-
-namespace Eigen { 
-
-enum SimplicialCholeskyMode {
-  SimplicialCholeskyLLT,
-  SimplicialCholeskyLDLT
-};
-
-namespace internal {
-  template<typename CholMatrixType, typename InputMatrixType>
-  struct simplicial_cholesky_grab_input {
-    typedef CholMatrixType const * ConstCholMatrixPtr;
-    static void run(const InputMatrixType& input, ConstCholMatrixPtr &pmat, CholMatrixType &tmp)
-    {
-      tmp = input;
-      pmat = &tmp;
-    }
-  };
-  
-  template<typename MatrixType>
-  struct simplicial_cholesky_grab_input<MatrixType,MatrixType> {
-    typedef MatrixType const * ConstMatrixPtr;
-    static void run(const MatrixType& input, ConstMatrixPtr &pmat, MatrixType &/*tmp*/)
-    {
-      pmat = &input;
-    }
-  };
-} // end namespace internal
-
-/** \ingroup SparseCholesky_Module
-  * \brief A base class for direct sparse Cholesky factorizations
-  *
-  * This is a base class for LL^T and LDL^T Cholesky factorizations of sparse matrices that are
-  * selfadjoint and positive definite. These factorizations allow for solving A.X = B where
-  * X and B can be either dense or sparse.
-  * 
-  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
-  * such that the factorized matrix is P A P^-1.
-  *
-  * \tparam Derived the type of the derived class, that is the actual factorization type.
-  *
-  */
-template<typename Derived>
-class SimplicialCholeskyBase : public SparseSolverBase<Derived>
-{
-    typedef SparseSolverBase<Derived> Base;
-    using Base::m_isInitialized;
-    
-  public:
-    typedef typename internal::traits<Derived>::MatrixType MatrixType;
-    typedef typename internal::traits<Derived>::OrderingType OrderingType;
-    enum { UpLo = internal::traits<Derived>::UpLo };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
-    typedef CholMatrixType const * ConstCholMatrixPtr;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-    
-    using Base::derived;
-
-    /** Default constructor */
-    SimplicialCholeskyBase()
-      : m_info(Success),
-        m_factorizationIsOk(false),
-        m_analysisIsOk(false),
-        m_shiftOffset(0),
-        m_shiftScale(1)
-    {}
-
-    explicit SimplicialCholeskyBase(const MatrixType& matrix)
-      : m_info(Success),
-        m_factorizationIsOk(false),
-        m_analysisIsOk(false),
-        m_shiftOffset(0),
-        m_shiftScale(1)
-    {
-      derived().compute(matrix);
-    }
-
-    ~SimplicialCholeskyBase()
-    {
-    }
-
-    Derived& derived() { return *static_cast<Derived*>(this); }
-    const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    
-    inline Index cols() const { return m_matrix.cols(); }
-    inline Index rows() const { return m_matrix.rows(); }
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-    
-    /** \returns the permutation P
-      * \sa permutationPinv() */
-    const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationP() const
-    { return m_P; }
-    
-    /** \returns the inverse P^-1 of the permutation P
-      * \sa permutationP() */
-    const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationPinv() const
-    { return m_Pinv; }
-
-    /** Sets the shift parameters that will be used to adjust the diagonal coefficients during the numerical factorization.
-      *
-      * During the numerical factorization, the diagonal coefficients are transformed by the following linear model:\n
-      * \c d_ii = \a offset + \a scale * \c d_ii
-      *
-      * The default is the identity transformation with \a offset=0, and \a scale=1.
-      *
-      * \returns a reference to \c *this.
-      */
-    Derived& setShift(const RealScalar& offset, const RealScalar& scale = 1)
-    {
-      m_shiftOffset = offset;
-      m_shiftScale = scale;
-      return derived();
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal */
-    template<typename Stream>
-    void dumpMemory(Stream& s)
-    {
-      int total = 0;
-      s << "  L:        " << ((total+=(m_matrix.cols()+1) * sizeof(int) + m_matrix.nonZeros()*(sizeof(int)+sizeof(Scalar))) >> 20) << "Mb" << "\n";
-      s << "  diag:     " << ((total+=m_diag.size() * sizeof(Scalar)) >> 20) << "Mb" << "\n";
-      s << "  tree:     " << ((total+=m_parent.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  nonzeros: " << ((total+=m_nonZerosPerCol.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  perm:     " << ((total+=m_P.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  perm^-1:  " << ((total+=m_Pinv.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  TOTAL:    " << (total>> 20) << "Mb" << "\n";
-    }
-
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      eigen_assert(m_matrix.rows()==b.rows());
-
-      if(m_info!=Success)
-        return;
-
-      if(m_P.size()>0)
-        dest = m_P * b;
-      else
-        dest = b;
-
-      if(m_matrix.nonZeros()>0) // otherwise L==I
-        derived().matrixL().solveInPlace(dest);
-
-      if(m_diag.size()>0)
-        dest = m_diag.asDiagonal().inverse() * dest;
-
-      if (m_matrix.nonZeros()>0) // otherwise U==I
-        derived().matrixU().solveInPlace(dest);
-
-      if(m_P.size()>0)
-        dest = m_Pinv * dest;
-    }
-    
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
-    {
-      internal::solve_sparse_through_dense_panels(derived(), b, dest);
-    }
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-  protected:
-    
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    template<bool DoLDLT>
-    void compute(const MatrixType& matrix)
-    {
-      eigen_assert(matrix.rows()==matrix.cols());
-      Index size = matrix.cols();
-      CholMatrixType tmp(size,size);
-      ConstCholMatrixPtr pmat;
-      ordering(matrix, pmat, tmp);
-      analyzePattern_preordered(*pmat, DoLDLT);
-      factorize_preordered<DoLDLT>(*pmat);
-    }
-    
-    template<bool DoLDLT>
-    void factorize(const MatrixType& a)
-    {
-      eigen_assert(a.rows()==a.cols());
-      Index size = a.cols();
-      CholMatrixType tmp(size,size);
-      ConstCholMatrixPtr pmat;
-      
-      if(m_P.size() == 0 && (int(UpLo) & int(Upper)) == Upper)
-      {
-        // If there is no ordering, try to directly use the input matrix without any copy
-        internal::simplicial_cholesky_grab_input<CholMatrixType,MatrixType>::run(a, pmat, tmp);
-      }
-      else
-      {
-        tmp.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P);
-        pmat = &tmp;
-      }
-      
-      factorize_preordered<DoLDLT>(*pmat);
-    }
-
-    template<bool DoLDLT>
-    void factorize_preordered(const CholMatrixType& a);
-
-    void analyzePattern(const MatrixType& a, bool doLDLT)
-    {
-      eigen_assert(a.rows()==a.cols());
-      Index size = a.cols();
-      CholMatrixType tmp(size,size);
-      ConstCholMatrixPtr pmat;
-      ordering(a, pmat, tmp);
-      analyzePattern_preordered(*pmat,doLDLT);
-    }
-    void analyzePattern_preordered(const CholMatrixType& a, bool doLDLT);
-    
-    void ordering(const MatrixType& a, ConstCholMatrixPtr &pmat, CholMatrixType& ap);
-
-    /** keeps off-diagonal entries; drops diagonal entries */
-    struct keep_diag {
-      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
-      {
-        return row!=col;
-      }
-    };
-
-    mutable ComputationInfo m_info;
-    bool m_factorizationIsOk;
-    bool m_analysisIsOk;
-    
-    CholMatrixType m_matrix;
-    VectorType m_diag;                                // the diagonal coefficients (LDLT mode)
-    VectorI m_parent;                                 // elimination tree
-    VectorI m_nonZerosPerCol;
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_P;     // the permutation
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_Pinv;  // the inverse permutation
-
-    RealScalar m_shiftOffset;
-    RealScalar m_shiftScale;
-};
-
-template<typename _MatrixType, int _UpLo = Lower, typename _Ordering = AMDOrdering<typename _MatrixType::StorageIndex> > class SimplicialLLT;
-template<typename _MatrixType, int _UpLo = Lower, typename _Ordering = AMDOrdering<typename _MatrixType::StorageIndex> > class SimplicialLDLT;
-template<typename _MatrixType, int _UpLo = Lower, typename _Ordering = AMDOrdering<typename _MatrixType::StorageIndex> > class SimplicialCholesky;
-
-namespace internal {
-
-template<typename _MatrixType, int _UpLo, typename _Ordering> struct traits<SimplicialLLT<_MatrixType,_UpLo,_Ordering> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Ordering OrderingType;
-  enum { UpLo = _UpLo };
-  typedef typename MatrixType::Scalar                         Scalar;
-  typedef typename MatrixType::StorageIndex                   StorageIndex;
-  typedef SparseMatrix<Scalar, ColMajor, StorageIndex>        CholMatrixType;
-  typedef TriangularView<const CholMatrixType, Eigen::Lower>  MatrixL;
-  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::Upper>   MatrixU;
-  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
-};
-
-template<typename _MatrixType,int _UpLo, typename _Ordering> struct traits<SimplicialLDLT<_MatrixType,_UpLo,_Ordering> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Ordering OrderingType;
-  enum { UpLo = _UpLo };
-  typedef typename MatrixType::Scalar                             Scalar;
-  typedef typename MatrixType::StorageIndex                       StorageIndex;
-  typedef SparseMatrix<Scalar, ColMajor, StorageIndex>            CholMatrixType;
-  typedef TriangularView<const CholMatrixType, Eigen::UnitLower>  MatrixL;
-  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
-  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
-};
-
-template<typename _MatrixType, int _UpLo, typename _Ordering> struct traits<SimplicialCholesky<_MatrixType,_UpLo,_Ordering> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Ordering OrderingType;
-  enum { UpLo = _UpLo };
-};
-
-}
-
-/** \ingroup SparseCholesky_Module
-  * \class SimplicialLLT
-  * \brief A direct sparse LLT Cholesky factorizations
-  *
-  * This class provides a LL^T Cholesky factorizations of sparse matrices that are
-  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
-  * X and B can be either dense or sparse.
-  * 
-  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
-  * such that the factorized matrix is P A P^-1.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  * \tparam _Ordering The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa class SimplicialLDLT, class AMDOrdering, class NaturalOrdering
-  */
-template<typename _MatrixType, int _UpLo, typename _Ordering>
-    class SimplicialLLT : public SimplicialCholeskyBase<SimplicialLLT<_MatrixType,_UpLo,_Ordering> >
-{
-public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef SimplicialCholeskyBase<SimplicialLLT> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef internal::traits<SimplicialLLT> Traits;
-    typedef typename Traits::MatrixL  MatrixL;
-    typedef typename Traits::MatrixU  MatrixU;
-public:
-    /** Default constructor */
-    SimplicialLLT() : Base() {}
-    /** Constructs and performs the LLT factorization of \a matrix */
-    explicit SimplicialLLT(const MatrixType& matrix)
-        : Base(matrix) {}
-
-    /** \returns an expression of the factor L */
-    inline const MatrixL matrixL() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
-        return Traits::getL(Base::m_matrix);
-    }
-
-    /** \returns an expression of the factor U (= L^*) */
-    inline const MatrixU matrixU() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
-        return Traits::getU(Base::m_matrix);
-    }
-    
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    SimplicialLLT& compute(const MatrixType& matrix)
-    {
-      Base::template compute<false>(matrix);
-      return *this;
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& a)
-    {
-      Base::analyzePattern(a, false);
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& a)
-    {
-      Base::template factorize<false>(a);
-    }
-
-    /** \returns the determinant of the underlying matrix from the current factorization */
-    Scalar determinant() const
-    {
-      Scalar detL = Base::m_matrix.diagonal().prod();
-      return numext::abs2(detL);
-    }
-};
-
-/** \ingroup SparseCholesky_Module
-  * \class SimplicialLDLT
-  * \brief A direct sparse LDLT Cholesky factorizations without square root.
-  *
-  * This class provides a LDL^T Cholesky factorizations without square root of sparse matrices that are
-  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
-  * X and B can be either dense or sparse.
-  * 
-  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
-  * such that the factorized matrix is P A P^-1.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  * \tparam _Ordering The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa class SimplicialLLT, class AMDOrdering, class NaturalOrdering
-  */
-template<typename _MatrixType, int _UpLo, typename _Ordering>
-    class SimplicialLDLT : public SimplicialCholeskyBase<SimplicialLDLT<_MatrixType,_UpLo,_Ordering> >
-{
-public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef SimplicialCholeskyBase<SimplicialLDLT> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef internal::traits<SimplicialLDLT> Traits;
-    typedef typename Traits::MatrixL  MatrixL;
-    typedef typename Traits::MatrixU  MatrixU;
-public:
-    /** Default constructor */
-    SimplicialLDLT() : Base() {}
-
-    /** Constructs and performs the LLT factorization of \a matrix */
-    explicit SimplicialLDLT(const MatrixType& matrix)
-        : Base(matrix) {}
-
-    /** \returns a vector expression of the diagonal D */
-    inline const VectorType vectorD() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
-        return Base::m_diag;
-    }
-    /** \returns an expression of the factor L */
-    inline const MatrixL matrixL() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
-        return Traits::getL(Base::m_matrix);
-    }
-
-    /** \returns an expression of the factor U (= L^*) */
-    inline const MatrixU matrixU() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
-        return Traits::getU(Base::m_matrix);
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    SimplicialLDLT& compute(const MatrixType& matrix)
-    {
-      Base::template compute<true>(matrix);
-      return *this;
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& a)
-    {
-      Base::analyzePattern(a, true);
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& a)
-    {
-      Base::template factorize<true>(a);
-    }
-
-    /** \returns the determinant of the underlying matrix from the current factorization */
-    Scalar determinant() const
-    {
-      return Base::m_diag.prod();
-    }
-};
-
-/** \deprecated use SimplicialLDLT or class SimplicialLLT
-  * \ingroup SparseCholesky_Module
-  * \class SimplicialCholesky
-  *
-  * \sa class SimplicialLDLT, class SimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo, typename _Ordering>
-    class SimplicialCholesky : public SimplicialCholeskyBase<SimplicialCholesky<_MatrixType,_UpLo,_Ordering> >
-{
-public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef SimplicialCholeskyBase<SimplicialCholesky> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef internal::traits<SimplicialCholesky> Traits;
-    typedef internal::traits<SimplicialLDLT<MatrixType,UpLo> > LDLTTraits;
-    typedef internal::traits<SimplicialLLT<MatrixType,UpLo>  > LLTTraits;
-  public:
-    SimplicialCholesky() : Base(), m_LDLT(true) {}
-
-    explicit SimplicialCholesky(const MatrixType& matrix)
-      : Base(), m_LDLT(true)
-    {
-      compute(matrix);
-    }
-
-    SimplicialCholesky& setMode(SimplicialCholeskyMode mode)
-    {
-      switch(mode)
-      {
-      case SimplicialCholeskyLLT:
-        m_LDLT = false;
-        break;
-      case SimplicialCholeskyLDLT:
-        m_LDLT = true;
-        break;
-      default:
-        break;
-      }
-
-      return *this;
-    }
-
-    inline const VectorType vectorD() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
-        return Base::m_diag;
-    }
-    inline const CholMatrixType rawMatrix() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
-        return Base::m_matrix;
-    }
-    
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    SimplicialCholesky& compute(const MatrixType& matrix)
-    {
-      if(m_LDLT)
-        Base::template compute<true>(matrix);
-      else
-        Base::template compute<false>(matrix);
-      return *this;
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& a)
-    {
-      Base::analyzePattern(a, m_LDLT);
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& a)
-    {
-      if(m_LDLT)
-        Base::template factorize<true>(a);
-      else
-        Base::template factorize<false>(a);
-    }
-
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(Base::m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      eigen_assert(Base::m_matrix.rows()==b.rows());
-
-      if(Base::m_info!=Success)
-        return;
-
-      if(Base::m_P.size()>0)
-        dest = Base::m_P * b;
-      else
-        dest = b;
-
-      if(Base::m_matrix.nonZeros()>0) // otherwise L==I
-      {
-        if(m_LDLT)
-          LDLTTraits::getL(Base::m_matrix).solveInPlace(dest);
-        else
-          LLTTraits::getL(Base::m_matrix).solveInPlace(dest);
-      }
-
-      if(Base::m_diag.size()>0)
-        dest = Base::m_diag.real().asDiagonal().inverse() * dest;
-
-      if (Base::m_matrix.nonZeros()>0) // otherwise I==I
-      {
-        if(m_LDLT)
-          LDLTTraits::getU(Base::m_matrix).solveInPlace(dest);
-        else
-          LLTTraits::getU(Base::m_matrix).solveInPlace(dest);
-      }
-
-      if(Base::m_P.size()>0)
-        dest = Base::m_Pinv * dest;
-    }
-    
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
-    {
-      internal::solve_sparse_through_dense_panels(*this, b, dest);
-    }
-    
-    Scalar determinant() const
-    {
-      if(m_LDLT)
-      {
-        return Base::m_diag.prod();
-      }
-      else
-      {
-        Scalar detL = Diagonal<const CholMatrixType>(Base::m_matrix).prod();
-        return numext::abs2(detL);
-      }
-    }
-    
-  protected:
-    bool m_LDLT;
-};
-
-template<typename Derived>
-void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, ConstCholMatrixPtr &pmat, CholMatrixType& ap)
-{
-  eigen_assert(a.rows()==a.cols());
-  const Index size = a.rows();
-  pmat = &ap;
-  // Note that ordering methods compute the inverse permutation
-  if(!internal::is_same<OrderingType,NaturalOrdering<Index> >::value)
-  {
-    {
-      CholMatrixType C;
-      C = a.template selfadjointView<UpLo>();
-      
-      OrderingType ordering;
-      ordering(C,m_Pinv);
-    }
-
-    if(m_Pinv.size()>0) m_P = m_Pinv.inverse();
-    else                m_P.resize(0);
-    
-    ap.resize(size,size);
-    ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P);
-  }
-  else
-  {
-    m_Pinv.resize(0);
-    m_P.resize(0);
-    if(int(UpLo)==int(Lower) || MatrixType::IsRowMajor)
-    {
-      // we have to transpose the lower part to to the upper one
-      ap.resize(size,size);
-      ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>();
-    }
-    else
-      internal::simplicial_cholesky_grab_input<CholMatrixType,MatrixType>::run(a, pmat, ap);
-  }  
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SIMPLICIAL_CHOLESKY_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
deleted file mode 100644
index 72e1740..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
+++ /dev/null
@@ -1,174 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*
-NOTE: these functions have been adapted from the LDL library:
-
-LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved.
-
-The author of LDL, Timothy A. Davis., has executed a license with Google LLC
-to permit distribution of this code and derivative works as part of Eigen under
-the Mozilla Public License v. 2.0, as stated at the top of this file.
- */
-
-#ifndef EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
-#define EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
-
-namespace Eigen {
-
-template<typename Derived>
-void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT)
-{
-  const StorageIndex size = StorageIndex(ap.rows());
-  m_matrix.resize(size, size);
-  m_parent.resize(size);
-  m_nonZerosPerCol.resize(size);
-
-  ei_declare_aligned_stack_constructed_variable(StorageIndex, tags, size, 0);
-
-  for(StorageIndex k = 0; k < size; ++k)
-  {
-    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
-    m_parent[k] = -1;             /* parent of k is not yet known */
-    tags[k] = k;                  /* mark node k as visited */
-    m_nonZerosPerCol[k] = 0;      /* count of nonzeros in column k of L */
-    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if(i < k)
-      {
-        /* follow path from i to root of etree, stop at flagged node */
-        for(; tags[i] != k; i = m_parent[i])
-        {
-          /* find parent of i if not yet determined */
-          if (m_parent[i] == -1)
-            m_parent[i] = k;
-          m_nonZerosPerCol[i]++;        /* L (k,i) is nonzero */
-          tags[i] = k;                  /* mark i as visited */
-        }
-      }
-    }
-  }
-
-  /* construct Lp index array from m_nonZerosPerCol column counts */
-  StorageIndex* Lp = m_matrix.outerIndexPtr();
-  Lp[0] = 0;
-  for(StorageIndex k = 0; k < size; ++k)
-    Lp[k+1] = Lp[k] + m_nonZerosPerCol[k] + (doLDLT ? 0 : 1);
-
-  m_matrix.resizeNonZeros(Lp[size]);
-
-  m_isInitialized     = true;
-  m_info              = Success;
-  m_analysisIsOk      = true;
-  m_factorizationIsOk = false;
-}
-
-
-template<typename Derived>
-template<bool DoLDLT>
-void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap)
-{
-  using std::sqrt;
-
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  eigen_assert(ap.rows()==ap.cols());
-  eigen_assert(m_parent.size()==ap.rows());
-  eigen_assert(m_nonZerosPerCol.size()==ap.rows());
-
-  const StorageIndex size = StorageIndex(ap.rows());
-  const StorageIndex* Lp = m_matrix.outerIndexPtr();
-  StorageIndex* Li = m_matrix.innerIndexPtr();
-  Scalar* Lx = m_matrix.valuePtr();
-
-  ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0);
-  ei_declare_aligned_stack_constructed_variable(StorageIndex,  pattern, size, 0);
-  ei_declare_aligned_stack_constructed_variable(StorageIndex,  tags, size, 0);
-
-  bool ok = true;
-  m_diag.resize(DoLDLT ? size : 0);
-
-  for(StorageIndex k = 0; k < size; ++k)
-  {
-    // compute nonzero pattern of kth row of L, in topological order
-    y[k] = Scalar(0);                     // Y(0:k) is now all zero
-    StorageIndex top = size;               // stack for pattern is empty
-    tags[k] = k;                    // mark node k as visited
-    m_nonZerosPerCol[k] = 0;        // count of nonzeros in column k of L
-    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if(i <= k)
-      {
-        y[i] += numext::conj(it.value());            /* scatter A(i,k) into Y (sum duplicates) */
-        Index len;
-        for(len = 0; tags[i] != k; i = m_parent[i])
-        {
-          pattern[len++] = i;     /* L(k,i) is nonzero */
-          tags[i] = k;            /* mark i as visited */
-        }
-        while(len > 0)
-          pattern[--top] = pattern[--len];
-      }
-    }
-
-    /* compute numerical values kth row of L (a sparse triangular solve) */
-
-    RealScalar d = numext::real(y[k]) * m_shiftScale + m_shiftOffset;    // get D(k,k), apply the shift function, and clear Y(k)
-    y[k] = Scalar(0);
-    for(; top < size; ++top)
-    {
-      Index i = pattern[top];       /* pattern[top:n-1] is pattern of L(:,k) */
-      Scalar yi = y[i];             /* get and clear Y(i) */
-      y[i] = Scalar(0);
-
-      /* the nonzero entry L(k,i) */
-      Scalar l_ki;
-      if(DoLDLT)
-        l_ki = yi / numext::real(m_diag[i]);
-      else
-        yi = l_ki = yi / Lx[Lp[i]];
-
-      Index p2 = Lp[i] + m_nonZerosPerCol[i];
-      Index p;
-      for(p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p)
-        y[Li[p]] -= numext::conj(Lx[p]) * yi;
-      d -= numext::real(l_ki * numext::conj(yi));
-      Li[p] = k;                          /* store L(k,i) in column form of L */
-      Lx[p] = l_ki;
-      ++m_nonZerosPerCol[i];              /* increment count of nonzeros in col i */
-    }
-    if(DoLDLT)
-    {
-      m_diag[k] = d;
-      if(d == RealScalar(0))
-      {
-        ok = false;                         /* failure, D(k,k) is zero */
-        break;
-      }
-    }
-    else
-    {
-      Index p = Lp[k] + m_nonZerosPerCol[k]++;
-      Li[p] = k ;                /* store L(k,k) = sqrt (d) in column k */
-      if(d <= RealScalar(0)) {
-        ok = false;              /* failure, matrix is not positive definite */
-        break;
-      }
-      Lx[p] = sqrt(d) ;
-    }
-  }
-
-  m_info = ok ? Success : NumericalIssue;
-  m_factorizationIsOk = true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/AmbiVector.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/AmbiVector.h
deleted file mode 100644
index 2cb7747..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/AmbiVector.h
+++ /dev/null
@@ -1,378 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_AMBIVECTOR_H
-#define EIGEN_AMBIVECTOR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal
-  * Hybrid sparse/dense vector class designed for intensive read-write operations.
-  *
-  * See BasicSparseLLT and SparseProduct for usage examples.
-  */
-template<typename _Scalar, typename _StorageIndex>
-class AmbiVector
-{
-  public:
-    typedef _Scalar Scalar;
-    typedef _StorageIndex StorageIndex;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    explicit AmbiVector(Index size)
-      : m_buffer(0), m_zero(0), m_size(0), m_end(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
-    {
-      resize(size);
-    }
-
-    void init(double estimatedDensity);
-    void init(int mode);
-
-    Index nonZeros() const;
-
-    /** Specifies a sub-vector to work on */
-    void setBounds(Index start, Index end) { m_start = convert_index(start); m_end = convert_index(end); }
-
-    void setZero();
-
-    void restart();
-    Scalar& coeffRef(Index i);
-    Scalar& coeff(Index i);
-
-    class Iterator;
-
-    ~AmbiVector() { delete[] m_buffer; }
-
-    void resize(Index size)
-    {
-      if (m_allocatedSize < size)
-        reallocate(size);
-      m_size = convert_index(size);
-    }
-
-    StorageIndex size() const { return m_size; }
-
-  protected:
-    StorageIndex convert_index(Index idx)
-    {
-      return internal::convert_index<StorageIndex>(idx);
-    }
-
-    void reallocate(Index size)
-    {
-      // if the size of the matrix is not too large, let's allocate a bit more than needed such
-      // that we can handle dense vector even in sparse mode.
-      delete[] m_buffer;
-      if (size<1000)
-      {
-        Index allocSize = (size * sizeof(ListEl) + sizeof(Scalar) - 1)/sizeof(Scalar);
-        m_allocatedElements = convert_index((allocSize*sizeof(Scalar))/sizeof(ListEl));
-        m_buffer = new Scalar[allocSize];
-      }
-      else
-      {
-        m_allocatedElements = convert_index((size*sizeof(Scalar))/sizeof(ListEl));
-        m_buffer = new Scalar[size];
-      }
-      m_size = convert_index(size);
-      m_start = 0;
-      m_end = m_size;
-    }
-
-    void reallocateSparse()
-    {
-      Index copyElements = m_allocatedElements;
-      m_allocatedElements = (std::min)(StorageIndex(m_allocatedElements*1.5),m_size);
-      Index allocSize = m_allocatedElements * sizeof(ListEl);
-      allocSize = (allocSize + sizeof(Scalar) - 1)/sizeof(Scalar);
-      Scalar* newBuffer = new Scalar[allocSize];
-      std::memcpy(newBuffer,  m_buffer,  copyElements * sizeof(ListEl));
-      delete[] m_buffer;
-      m_buffer = newBuffer;
-    }
-
-  protected:
-    // element type of the linked list
-    struct ListEl
-    {
-      StorageIndex next;
-      StorageIndex index;
-      Scalar value;
-    };
-
-    // used to store data in both mode
-    Scalar* m_buffer;
-    Scalar m_zero;
-    StorageIndex m_size;
-    StorageIndex m_start;
-    StorageIndex m_end;
-    StorageIndex m_allocatedSize;
-    StorageIndex m_allocatedElements;
-    StorageIndex m_mode;
-
-    // linked list mode
-    StorageIndex m_llStart;
-    StorageIndex m_llCurrent;
-    StorageIndex m_llSize;
-};
-
-/** \returns the number of non zeros in the current sub vector */
-template<typename _Scalar,typename _StorageIndex>
-Index AmbiVector<_Scalar,_StorageIndex>::nonZeros() const
-{
-  if (m_mode==IsSparse)
-    return m_llSize;
-  else
-    return m_end - m_start;
-}
-
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::init(double estimatedDensity)
-{
-  if (estimatedDensity>0.1)
-    init(IsDense);
-  else
-    init(IsSparse);
-}
-
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::init(int mode)
-{
-  m_mode = mode;
-  // This is only necessary in sparse mode, but we set these unconditionally to avoid some maybe-uninitialized warnings
-  // if (m_mode==IsSparse)
-  {
-    m_llSize = 0;
-    m_llStart = -1;
-  }
-}
-
-/** Must be called whenever we might perform a write access
-  * with an index smaller than the previous one.
-  *
-  * Don't worry, this function is extremely cheap.
-  */
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::restart()
-{
-  m_llCurrent = m_llStart;
-}
-
-/** Set all coefficients of current subvector to zero */
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::setZero()
-{
-  if (m_mode==IsDense)
-  {
-    for (Index i=m_start; i<m_end; ++i)
-      m_buffer[i] = Scalar(0);
-  }
-  else
-  {
-    eigen_assert(m_mode==IsSparse);
-    m_llSize = 0;
-    m_llStart = -1;
-  }
-}
-
-template<typename _Scalar,typename _StorageIndex>
-_Scalar& AmbiVector<_Scalar,_StorageIndex>::coeffRef(Index i)
-{
-  if (m_mode==IsDense)
-    return m_buffer[i];
-  else
-  {
-    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
-    // TODO factorize the following code to reduce code generation
-    eigen_assert(m_mode==IsSparse);
-    if (m_llSize==0)
-    {
-      // this is the first element
-      m_llStart = 0;
-      m_llCurrent = 0;
-      ++m_llSize;
-      llElements[0].value = Scalar(0);
-      llElements[0].index = convert_index(i);
-      llElements[0].next = -1;
-      return llElements[0].value;
-    }
-    else if (i<llElements[m_llStart].index)
-    {
-      // this is going to be the new first element of the list
-      ListEl& el = llElements[m_llSize];
-      el.value = Scalar(0);
-      el.index = convert_index(i);
-      el.next = m_llStart;
-      m_llStart = m_llSize;
-      ++m_llSize;
-      m_llCurrent = m_llStart;
-      return el.value;
-    }
-    else
-    {
-      StorageIndex nextel = llElements[m_llCurrent].next;
-      eigen_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
-      while (nextel >= 0 && llElements[nextel].index<=i)
-      {
-        m_llCurrent = nextel;
-        nextel = llElements[nextel].next;
-      }
-
-      if (llElements[m_llCurrent].index==i)
-      {
-        // the coefficient already exists and we found it !
-        return llElements[m_llCurrent].value;
-      }
-      else
-      {
-        if (m_llSize>=m_allocatedElements)
-        {
-          reallocateSparse();
-          llElements = reinterpret_cast<ListEl*>(m_buffer);
-        }
-        eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
-        // let's insert a new coefficient
-        ListEl& el = llElements[m_llSize];
-        el.value = Scalar(0);
-        el.index = convert_index(i);
-        el.next = llElements[m_llCurrent].next;
-        llElements[m_llCurrent].next = m_llSize;
-        ++m_llSize;
-        return el.value;
-      }
-    }
-  }
-}
-
-template<typename _Scalar,typename _StorageIndex>
-_Scalar& AmbiVector<_Scalar,_StorageIndex>::coeff(Index i)
-{
-  if (m_mode==IsDense)
-    return m_buffer[i];
-  else
-  {
-    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
-    eigen_assert(m_mode==IsSparse);
-    if ((m_llSize==0) || (i<llElements[m_llStart].index))
-    {
-      return m_zero;
-    }
-    else
-    {
-      Index elid = m_llStart;
-      while (elid >= 0 && llElements[elid].index<i)
-        elid = llElements[elid].next;
-
-      if (llElements[elid].index==i)
-        return llElements[m_llCurrent].value;
-      else
-        return m_zero;
-    }
-  }
-}
-
-/** Iterator over the nonzero coefficients */
-template<typename _Scalar,typename _StorageIndex>
-class AmbiVector<_Scalar,_StorageIndex>::Iterator
-{
-  public:
-    typedef _Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    /** Default constructor
-      * \param vec the vector on which we iterate
-      * \param epsilon the minimal value used to prune zero coefficients.
-      * In practice, all coefficients having a magnitude smaller than \a epsilon
-      * are skipped.
-      */
-    explicit Iterator(const AmbiVector& vec, const RealScalar& epsilon = 0)
-      : m_vector(vec)
-    {
-      using std::abs;
-      m_epsilon = epsilon;
-      m_isDense = m_vector.m_mode==IsDense;
-      if (m_isDense)
-      {
-        m_currentEl = 0;   // this is to avoid a compilation warning
-        m_cachedValue = 0; // this is to avoid a compilation warning
-        m_cachedIndex = m_vector.m_start-1;
-        ++(*this);
-      }
-      else
-      {
-        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
-        m_currentEl = m_vector.m_llStart;
-        while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon)
-          m_currentEl = llElements[m_currentEl].next;
-        if (m_currentEl<0)
-        {
-          m_cachedValue = 0; // this is to avoid a compilation warning
-          m_cachedIndex = -1;
-        }
-        else
-        {
-          m_cachedIndex = llElements[m_currentEl].index;
-          m_cachedValue = llElements[m_currentEl].value;
-        }
-      }
-    }
-
-    StorageIndex index() const { return m_cachedIndex; }
-    Scalar value() const { return m_cachedValue; }
-
-    operator bool() const { return m_cachedIndex>=0; }
-
-    Iterator& operator++()
-    {
-      using std::abs;
-      if (m_isDense)
-      {
-        do {
-          ++m_cachedIndex;
-        } while (m_cachedIndex<m_vector.m_end && abs(m_vector.m_buffer[m_cachedIndex])<=m_epsilon);
-        if (m_cachedIndex<m_vector.m_end)
-          m_cachedValue = m_vector.m_buffer[m_cachedIndex];
-        else
-          m_cachedIndex=-1;
-      }
-      else
-      {
-        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
-        do {
-          m_currentEl = llElements[m_currentEl].next;
-        } while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon);
-        if (m_currentEl<0)
-        {
-          m_cachedIndex = -1;
-        }
-        else
-        {
-          m_cachedIndex = llElements[m_currentEl].index;
-          m_cachedValue = llElements[m_currentEl].value;
-        }
-      }
-      return *this;
-    }
-
-  protected:
-    const AmbiVector& m_vector; // the target vector
-    StorageIndex m_currentEl;   // the current element in sparse/linked-list mode
-    RealScalar m_epsilon;       // epsilon used to prune zero coefficients
-    StorageIndex m_cachedIndex; // current coordinate
-    Scalar m_cachedValue;       // current value
-    bool m_isDense;             // mode of the vector
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_AMBIVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/CompressedStorage.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/CompressedStorage.h
deleted file mode 100644
index acd986f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/CompressedStorage.h
+++ /dev/null
@@ -1,274 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPRESSED_STORAGE_H
-#define EIGEN_COMPRESSED_STORAGE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal
-  * Stores a sparse set of values as a list of values and a list of indices.
-  *
-  */
-template<typename _Scalar,typename _StorageIndex>
-class CompressedStorage
-{
-  public:
-
-    typedef _Scalar Scalar;
-    typedef _StorageIndex StorageIndex;
-
-  protected:
-
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  public:
-
-    CompressedStorage()
-      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
-    {}
-
-    explicit CompressedStorage(Index size)
-      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
-    {
-      resize(size);
-    }
-
-    CompressedStorage(const CompressedStorage& other)
-      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
-    {
-      *this = other;
-    }
-
-    CompressedStorage& operator=(const CompressedStorage& other)
-    {
-      resize(other.size());
-      if(other.size()>0)
-      {
-        internal::smart_copy(other.m_values,  other.m_values  + m_size, m_values);
-        internal::smart_copy(other.m_indices, other.m_indices + m_size, m_indices);
-      }
-      return *this;
-    }
-
-    void swap(CompressedStorage& other)
-    {
-      std::swap(m_values, other.m_values);
-      std::swap(m_indices, other.m_indices);
-      std::swap(m_size, other.m_size);
-      std::swap(m_allocatedSize, other.m_allocatedSize);
-    }
-
-    ~CompressedStorage()
-    {
-      delete[] m_values;
-      delete[] m_indices;
-    }
-
-    void reserve(Index size)
-    {
-      Index newAllocatedSize = m_size + size;
-      if (newAllocatedSize > m_allocatedSize)
-        reallocate(newAllocatedSize);
-    }
-
-    void squeeze()
-    {
-      if (m_allocatedSize>m_size)
-        reallocate(m_size);
-    }
-
-    void resize(Index size, double reserveSizeFactor = 0)
-    {
-      if (m_allocatedSize<size)
-      {
-        Index realloc_size = (std::min<Index>)(NumTraits<StorageIndex>::highest(),  size + Index(reserveSizeFactor*double(size)));
-        if(realloc_size<size)
-          internal::throw_std_bad_alloc();
-        reallocate(realloc_size);
-      }
-      m_size = size;
-    }
-
-    void append(const Scalar& v, Index i)
-    {
-      Index id = m_size;
-      resize(m_size+1, 1);
-      m_values[id] = v;
-      m_indices[id] = internal::convert_index<StorageIndex>(i);
-    }
-
-    inline Index size() const { return m_size; }
-    inline Index allocatedSize() const { return m_allocatedSize; }
-    inline void clear() { m_size = 0; }
-
-    const Scalar* valuePtr() const { return m_values; }
-    Scalar* valuePtr() { return m_values; }
-    const StorageIndex* indexPtr() const { return m_indices; }
-    StorageIndex* indexPtr() { return m_indices; }
-
-    inline Scalar& value(Index i) { eigen_internal_assert(m_values!=0); return m_values[i]; }
-    inline const Scalar& value(Index i) const { eigen_internal_assert(m_values!=0); return m_values[i]; }
-
-    inline StorageIndex& index(Index i) { eigen_internal_assert(m_indices!=0); return m_indices[i]; }
-    inline const StorageIndex& index(Index i) const { eigen_internal_assert(m_indices!=0); return m_indices[i]; }
-
-    /** \returns the largest \c k such that for all \c j in [0,k) index[\c j]\<\a key */
-    inline Index searchLowerIndex(Index key) const
-    {
-      return searchLowerIndex(0, m_size, key);
-    }
-
-    /** \returns the largest \c k in [start,end) such that for all \c j in [start,k) index[\c j]\<\a key */
-    inline Index searchLowerIndex(Index start, Index end, Index key) const
-    {
-      while(end>start)
-      {
-        Index mid = (end+start)>>1;
-        if (m_indices[mid]<key)
-          start = mid+1;
-        else
-          end = mid;
-      }
-      return start;
-    }
-
-    /** \returns the stored value at index \a key
-      * If the value does not exist, then the value \a defaultValue is returned without any insertion. */
-    inline Scalar at(Index key, const Scalar& defaultValue = Scalar(0)) const
-    {
-      if (m_size==0)
-        return defaultValue;
-      else if (key==m_indices[m_size-1])
-        return m_values[m_size-1];
-      // ^^  optimization: let's first check if it is the last coefficient
-      // (very common in high level algorithms)
-      const Index id = searchLowerIndex(0,m_size-1,key);
-      return ((id<m_size) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
-    }
-
-    /** Like at(), but the search is performed in the range [start,end) */
-    inline Scalar atInRange(Index start, Index end, Index key, const Scalar &defaultValue = Scalar(0)) const
-    {
-      if (start>=end)
-        return defaultValue;
-      else if (end>start && key==m_indices[end-1])
-        return m_values[end-1];
-      // ^^  optimization: let's first check if it is the last coefficient
-      // (very common in high level algorithms)
-      const Index id = searchLowerIndex(start,end-1,key);
-      return ((id<end) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
-    }
-
-    /** \returns a reference to the value at index \a key
-      * If the value does not exist, then the value \a defaultValue is inserted
-      * such that the keys are sorted. */
-    inline Scalar& atWithInsertion(Index key, const Scalar& defaultValue = Scalar(0))
-    {
-      Index id = searchLowerIndex(0,m_size,key);
-      if (id>=m_size || m_indices[id]!=key)
-      {
-        if (m_allocatedSize<m_size+1)
-        {
-          m_allocatedSize = 2*(m_size+1);
-          internal::scoped_array<Scalar> newValues(m_allocatedSize);
-          internal::scoped_array<StorageIndex> newIndices(m_allocatedSize);
-
-          // copy first chunk
-          internal::smart_copy(m_values,  m_values +id, newValues.ptr());
-          internal::smart_copy(m_indices, m_indices+id, newIndices.ptr());
-
-          // copy the rest
-          if(m_size>id)
-          {
-            internal::smart_copy(m_values +id,  m_values +m_size, newValues.ptr() +id+1);
-            internal::smart_copy(m_indices+id,  m_indices+m_size, newIndices.ptr()+id+1);
-          }
-          std::swap(m_values,newValues.ptr());
-          std::swap(m_indices,newIndices.ptr());
-        }
-        else if(m_size>id)
-        {
-          internal::smart_memmove(m_values +id, m_values +m_size, m_values +id+1);
-          internal::smart_memmove(m_indices+id, m_indices+m_size, m_indices+id+1);
-        }
-        m_size++;
-        m_indices[id] = internal::convert_index<StorageIndex>(key);
-        m_values[id] = defaultValue;
-      }
-      return m_values[id];
-    }
-
-    void moveChunk(Index from, Index to, Index chunkSize)
-    {
-      eigen_internal_assert(to+chunkSize <= m_size);
-      if(to>from && from+chunkSize>to)
-      {
-        // move backward
-        internal::smart_memmove(m_values+from,  m_values+from+chunkSize,  m_values+to);
-        internal::smart_memmove(m_indices+from, m_indices+from+chunkSize, m_indices+to);
-      }
-      else
-      {
-        internal::smart_copy(m_values+from,  m_values+from+chunkSize,  m_values+to);
-        internal::smart_copy(m_indices+from, m_indices+from+chunkSize, m_indices+to);
-      }
-    }
-
-    void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
-    {
-      Index k = 0;
-      Index n = size();
-      for (Index i=0; i<n; ++i)
-      {
-        if (!internal::isMuchSmallerThan(value(i), reference, epsilon))
-        {
-          value(k) = value(i);
-          index(k) = index(i);
-          ++k;
-        }
-      }
-      resize(k,0);
-    }
-
-  protected:
-
-    inline void reallocate(Index size)
-    {
-      #ifdef EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
-        EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
-      #endif
-      eigen_internal_assert(size!=m_allocatedSize);
-      internal::scoped_array<Scalar> newValues(size);
-      internal::scoped_array<StorageIndex> newIndices(size);
-      Index copySize = (std::min)(size, m_size);
-      if (copySize>0) {
-        internal::smart_copy(m_values, m_values+copySize, newValues.ptr());
-        internal::smart_copy(m_indices, m_indices+copySize, newIndices.ptr());
-      }
-      std::swap(m_values,newValues.ptr());
-      std::swap(m_indices,newIndices.ptr());
-      m_allocatedSize = size;
-    }
-
-  protected:
-    Scalar* m_values;
-    StorageIndex* m_indices;
-    Index m_size;
-    Index m_allocatedSize;
-
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPRESSED_STORAGE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
deleted file mode 100644
index 9486502..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
+++ /dev/null
@@ -1,352 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
-#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType>
-static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, bool sortedInsertion = false)
-{
-  typedef typename remove_all<Lhs>::type::Scalar LhsScalar;
-  typedef typename remove_all<Rhs>::type::Scalar RhsScalar;
-  typedef typename remove_all<ResultType>::type::Scalar ResScalar;
-
-  // make sure to call innerSize/outerSize since we fake the storage order.
-  Index rows = lhs.innerSize();
-  Index cols = rhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  ei_declare_aligned_stack_constructed_variable(bool,   mask,     rows, 0);
-  ei_declare_aligned_stack_constructed_variable(ResScalar, values,   rows, 0);
-  ei_declare_aligned_stack_constructed_variable(Index,  indices,  rows, 0);
-
-  std::memset(mask,0,sizeof(bool)*rows);
-
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-
-  // estimate the number of non zero entries
-  // given a rhs column containing Y non zeros, we assume that the respective Y columns
-  // of the lhs differs in average of one non zeros, thus the number of non zeros for
-  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
-  // per column of the lhs.
-  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
-  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
-
-  res.setZero();
-  res.reserve(Index(estimated_nnz_prod));
-  // we compute each column of the result, one after the other
-  for (Index j=0; j<cols; ++j)
-  {
-
-    res.startVec(j);
-    Index nnz = 0;
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      RhsScalar y = rhsIt.value();
-      Index k = rhsIt.index();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
-      {
-        Index i = lhsIt.index();
-        LhsScalar x = lhsIt.value();
-        if(!mask[i])
-        {
-          mask[i] = true;
-          values[i] = x * y;
-          indices[nnz] = i;
-          ++nnz;
-        }
-        else
-          values[i] += x * y;
-      }
-    }
-    if(!sortedInsertion)
-    {
-      // unordered insertion
-      for(Index k=0; k<nnz; ++k)
-      {
-        Index i = indices[k];
-        res.insertBackByOuterInnerUnordered(j,i) = values[i];
-        mask[i] = false;
-      }
-    }
-    else
-    {
-      // alternative ordered insertion code:
-      const Index t200 = rows/11; // 11 == (log2(200)*1.39)
-      const Index t = (rows*100)/139;
-
-      // FIXME reserve nnz non zeros
-      // FIXME implement faster sorting algorithms for very small nnz
-      // if the result is sparse enough => use a quick sort
-      // otherwise => loop through the entire vector
-      // In order to avoid to perform an expensive log2 when the
-      // result is clearly very sparse we use a linear bound up to 200.
-      if((nnz<200 && nnz<t200) || nnz * numext::log2(int(nnz)) < t)
-      {
-        if(nnz>1) std::sort(indices,indices+nnz);
-        for(Index k=0; k<nnz; ++k)
-        {
-          Index i = indices[k];
-          res.insertBackByOuterInner(j,i) = values[i];
-          mask[i] = false;
-        }
-      }
-      else
-      {
-        // dense path
-        for(Index i=0; i<rows; ++i)
-        {
-          if(mask[i])
-          {
-            mask[i] = false;
-            res.insertBackByOuterInner(j,i) = values[i];
-          }
-        }
-      }
-    }
-  }
-  res.finalize();
-}
-
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int ResStorageOrder = (traits<ResultType>::Flags&RowMajorBit) ? RowMajor : ColMajor>
-struct conservative_sparse_sparse_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
-{
-  typedef typename remove_all<Lhs>::type LhsCleaned;
-  typedef typename LhsCleaned::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrixAux;
-    typedef typename sparse_eval<ColMajorMatrixAux,ResultType::RowsAtCompileTime,ResultType::ColsAtCompileTime,ColMajorMatrixAux::Flags>::type ColMajorMatrix;
-
-    // If the result is tall and thin (in the extreme case a column vector)
-    // then it is faster to sort the coefficients inplace instead of transposing twice.
-    // FIXME, the following heuristic is probably not very good.
-    if(lhs.rows()>rhs.cols())
-    {
-      ColMajorMatrix resCol(lhs.rows(),rhs.cols());
-      // perform sorted insertion
-      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol, true);
-      res = resCol.markAsRValue();
-    }
-    else
-    {
-      ColMajorMatrixAux resCol(lhs.rows(),rhs.cols());
-      // resort to transpose to sort the entries
-      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrixAux>(lhs, rhs, resCol, false);
-      RowMajorMatrix resRow(resCol);
-      res = resRow.markAsRValue();
-    }
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorRhs;
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorRes;
-    RowMajorRhs rhsRow = rhs;
-    RowMajorRes resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<RowMajorRhs,Lhs,RowMajorRes>(rhsRow, lhs, resRow);
-    res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorLhs;
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorRes;
-    RowMajorLhs lhsRow = lhs;
-    RowMajorRes resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,RowMajorLhs,RowMajorRes>(rhs, lhsRow, resRow);
-    res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    RowMajorMatrix resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
-    res = resRow;
-  }
-};
-
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
-{
-  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorLhs;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRes;
-    ColMajorLhs lhsCol = lhs;
-    ColMajorRes resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<ColMajorLhs,Rhs,ColMajorRes>(lhsCol, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRhs;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRes;
-    ColMajorRhs rhsCol = rhs;
-    ColMajorRes resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Lhs,ColMajorRhs,ColMajorRes>(lhs, rhsCol, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    RowMajorMatrix resRow(lhs.rows(),rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
-    // sort the non zeros:
-    ColMajorMatrix resCol(resRow);
-    res = resCol;
-  }
-};
-
-} // end namespace internal
-
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType>
-static void sparse_sparse_to_dense_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-{
-  typedef typename remove_all<Lhs>::type::Scalar LhsScalar;
-  typedef typename remove_all<Rhs>::type::Scalar RhsScalar;
-  Index cols = rhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-
-  for (Index j=0; j<cols; ++j)
-  {
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      RhsScalar y = rhsIt.value();
-      Index k = rhsIt.index();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
-      {
-        Index i = lhsIt.index();
-        LhsScalar x = lhsIt.value();
-        res.coeffRef(i,j) += x * y;
-      }
-    }
-  }
-}
-
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor>
-struct sparse_sparse_to_dense_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    internal::sparse_sparse_to_dense_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorLhs;
-    ColMajorLhs lhsCol(lhs);
-    internal::sparse_sparse_to_dense_product_impl<ColMajorLhs,Rhs,ResultType>(lhsCol, rhs, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRhs;
-    ColMajorRhs rhsCol(rhs);
-    internal::sparse_sparse_to_dense_product_impl<Lhs,ColMajorRhs,ResultType>(lhs, rhsCol, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    Transpose<ResultType> trRes(res);
-    internal::sparse_sparse_to_dense_product_impl<Rhs,Lhs,Transpose<ResultType> >(rhs, lhs, trRes);
-  }
-};
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/MappedSparseMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/MappedSparseMatrix.h
deleted file mode 100644
index 67718c8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/MappedSparseMatrix.h
+++ /dev/null
@@ -1,67 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MAPPED_SPARSEMATRIX_H
-#define EIGEN_MAPPED_SPARSEMATRIX_H
-
-namespace Eigen {
-
-/** \deprecated Use Map<SparseMatrix<> >
-  * \class MappedSparseMatrix
-  *
-  * \brief Sparse matrix
-  *
-  * \param _Scalar the scalar type, i.e. the type of the coefficients
-  *
-  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
-  *
-  */
-namespace internal {
-template<typename _Scalar, int _Flags, typename _StorageIndex>
-struct traits<MappedSparseMatrix<_Scalar, _Flags, _StorageIndex> > : traits<SparseMatrix<_Scalar, _Flags, _StorageIndex> >
-{};
-} // end namespace internal
-
-template<typename _Scalar, int _Flags, typename _StorageIndex>
-class MappedSparseMatrix
-  : public Map<SparseMatrix<_Scalar, _Flags, _StorageIndex> >
-{
-    typedef Map<SparseMatrix<_Scalar, _Flags, _StorageIndex> > Base;
-
-  public:
-    
-    typedef typename Base::StorageIndex StorageIndex;
-    typedef typename Base::Scalar Scalar;
-
-    inline MappedSparseMatrix(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr, Scalar* valuePtr, StorageIndex* innerNonZeroPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZeroPtr)
-    {}
-
-    /** Empty destructor */
-    inline ~MappedSparseMatrix() {}
-};
-
-namespace internal {
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct evaluator<MappedSparseMatrix<_Scalar,_Options,_StorageIndex> >
-  : evaluator<SparseCompressedBase<MappedSparseMatrix<_Scalar,_Options,_StorageIndex> > >
-{
-  typedef MappedSparseMatrix<_Scalar,_Options,_StorageIndex> XprType;
-  typedef evaluator<SparseCompressedBase<XprType> > Base;
-  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MAPPED_SPARSEMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseAssign.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseAssign.h
deleted file mode 100644
index 905485c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseAssign.h
+++ /dev/null
@@ -1,270 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEASSIGN_H
-#define EIGEN_SPARSEASSIGN_H
-
-namespace Eigen { 
-
-template<typename Derived>    
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
-{
-  internal::call_assignment_no_alias(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
-{
-  // TODO use the evaluator mechanism
-  other.evalTo(derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-inline Derived& SparseMatrixBase<Derived>::operator=(const SparseMatrixBase<OtherDerived>& other)
-{
-  // by default sparse evaluation do not alias, so we can safely bypass the generic call_assignment routine
-  internal::Assignment<Derived,OtherDerived,internal::assign_op<Scalar,typename OtherDerived::Scalar> >
-          ::run(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-inline Derived& SparseMatrixBase<Derived>::operator=(const Derived& other)
-{
-  internal::call_assignment_no_alias(derived(), other.derived());
-  return derived();
-}
-
-namespace internal {
-
-template<>
-struct storage_kind_to_evaluator_kind<Sparse> {
-  typedef IteratorBased Kind;
-};
-
-template<>
-struct storage_kind_to_shape<Sparse> {
-  typedef SparseShape Shape;
-};
-
-struct Sparse2Sparse {};
-struct Sparse2Dense  {};
-
-template<> struct AssignmentKind<SparseShape, SparseShape>           { typedef Sparse2Sparse Kind; };
-template<> struct AssignmentKind<SparseShape, SparseTriangularShape> { typedef Sparse2Sparse Kind; };
-template<> struct AssignmentKind<DenseShape,  SparseShape>           { typedef Sparse2Dense  Kind; };
-template<> struct AssignmentKind<DenseShape,  SparseTriangularShape> { typedef Sparse2Dense  Kind; };
-
-
-template<typename DstXprType, typename SrcXprType>
-void assign_sparse_to_sparse(DstXprType &dst, const SrcXprType &src)
-{
-  typedef typename DstXprType::Scalar Scalar;
-  typedef internal::evaluator<DstXprType> DstEvaluatorType;
-  typedef internal::evaluator<SrcXprType> SrcEvaluatorType;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  const bool transpose = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit);
-  const Index outerEvaluationSize = (SrcEvaluatorType::Flags&RowMajorBit) ? src.rows() : src.cols();
-  if ((!transpose) && src.isRValue())
-  {
-    // eval without temporary
-    dst.resize(src.rows(), src.cols());
-    dst.setZero();
-    dst.reserve((std::min)(src.rows()*src.cols(), (std::max)(src.rows(),src.cols())*2));
-    for (Index j=0; j<outerEvaluationSize; ++j)
-    {
-      dst.startVec(j);
-      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it)
-      {
-        Scalar v = it.value();
-        dst.insertBackByOuterInner(j,it.index()) = v;
-      }
-    }
-    dst.finalize();
-  }
-  else
-  {
-    // eval through a temporary
-    eigen_assert(( ((internal::traits<DstXprType>::SupportedAccessPatterns & OuterRandomAccessPattern)==OuterRandomAccessPattern) ||
-              (!((DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit)))) &&
-              "the transpose operation is supposed to be handled in SparseMatrix::operator=");
-
-    enum { Flip = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit) };
-
-    
-    DstXprType temp(src.rows(), src.cols());
-
-    temp.reserve((std::min)(src.rows()*src.cols(), (std::max)(src.rows(),src.cols())*2));
-    for (Index j=0; j<outerEvaluationSize; ++j)
-    {
-      temp.startVec(j);
-      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it)
-      {
-        Scalar v = it.value();
-        temp.insertBackByOuterInner(Flip?it.index():j,Flip?j:it.index()) = v;
-      }
-    }
-    temp.finalize();
-
-    dst = temp.markAsRValue();
-  }
-}
-
-// Generic Sparse to Sparse assignment
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Sparse>
-{
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  {
-    assign_sparse_to_sparse(dst.derived(), src.derived());
-  }
-};
-
-// Generic Sparse to Dense assignment
-template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Dense, Weak>
-{
-  static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    if(internal::is_same<Functor,internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> >::value)
-      dst.setZero();
-    
-    internal::evaluator<SrcXprType> srcEval(src);
-    resize_if_allowed(dst, src, func);
-    internal::evaluator<DstXprType> dstEval(dst);
-    
-    const Index outerEvaluationSize = (internal::evaluator<SrcXprType>::Flags&RowMajorBit) ? src.rows() : src.cols();
-    for (Index j=0; j<outerEvaluationSize; ++j)
-      for (typename internal::evaluator<SrcXprType>::InnerIterator i(srcEval,j); i; ++i)
-        func.assignCoeff(dstEval.coeffRef(i.row(),i.col()), i.value());
-  }
-};
-
-// Specialization for dense ?= dense +/- sparse and dense ?= sparse +/- dense
-template<typename DstXprType, typename Func1, typename Func2>
-struct assignment_from_dense_op_sparse
-{
-  template<typename SrcXprType, typename InitialFunc>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
-  {
-    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
-    EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
-    #endif
-
-    call_assignment_no_alias(dst, src.lhs(), Func1());
-    call_assignment_no_alias(dst, src.rhs(), Func2());
-  }
-
-  // Specialization for dense1 = sparse + dense2; -> dense1 = dense2; dense1 += sparse;
-  template<typename Lhs, typename Rhs, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>::type
-  run(DstXprType &dst, const CwiseBinaryOp<internal::scalar_sum_op<Scalar,Scalar>, const Lhs, const Rhs> &src,
-      const internal::assign_op<typename DstXprType::Scalar,Scalar>& /*func*/)
-  {
-    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
-    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
-    #endif
-
-    // Apply the dense matrix first, then the sparse one.
-    call_assignment_no_alias(dst, src.rhs(), Func1());
-    call_assignment_no_alias(dst, src.lhs(), Func2());
-  }
-
-  // Specialization for dense1 = sparse - dense2; -> dense1 = -dense2; dense1 += sparse;
-  template<typename Lhs, typename Rhs, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>::type
-  run(DstXprType &dst, const CwiseBinaryOp<internal::scalar_difference_op<Scalar,Scalar>, const Lhs, const Rhs> &src,
-      const internal::assign_op<typename DstXprType::Scalar,Scalar>& /*func*/)
-  {
-    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
-    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
-    #endif
-
-    // Apply the dense matrix first, then the sparse one.
-    call_assignment_no_alias(dst, -src.rhs(), Func1());
-    call_assignment_no_alias(dst,  src.lhs(), add_assign_op<typename DstXprType::Scalar,typename Lhs::Scalar>());
-  }
-};
-
-#define EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(ASSIGN_OP,BINOP,ASSIGN_OP2) \
-  template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar> \
-  struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<Scalar,Scalar>, const Lhs, const Rhs>, internal::ASSIGN_OP<typename DstXprType::Scalar,Scalar>, \
-                    Sparse2Dense, \
-                    typename internal::enable_if<   internal::is_same<typename internal::evaluator_traits<Lhs>::Shape,DenseShape>::value \
-                                                 || internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>::type> \
-    : assignment_from_dense_op_sparse<DstXprType, internal::ASSIGN_OP<typename DstXprType::Scalar,typename Lhs::Scalar>, internal::ASSIGN_OP2<typename DstXprType::Scalar,typename Rhs::Scalar> > \
-  {}
-
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op,    scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op,scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op,scalar_sum_op,sub_assign_op);
-
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op,    scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op,scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op,scalar_difference_op,add_assign_op);
-
-
-// Specialization for "dst = dec.solve(rhs)"
-// NOTE we need to specialize it for Sparse2Sparse to avoid ambiguous specialization error
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Sparse2Sparse>
-{
-  typedef Solve<DecType,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec()._solve_impl(src.rhs(), dst);
-  }
-};
-
-struct Diagonal2Sparse {};
-
-template<> struct AssignmentKind<SparseShape,DiagonalShape> { typedef Diagonal2Sparse Kind; };
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Sparse>
-{
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  typedef typename DstXprType::Scalar Scalar;
-
-  template<int Options, typename AssignFunc>
-  static void run(SparseMatrix<Scalar,Options,StorageIndex> &dst, const SrcXprType &src, const AssignFunc &func)
-  { dst.assignDiagonal(src.diagonal(), func); }
-  
-  template<typename DstDerived>
-  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.derived().diagonal() = src.diagonal(); }
-  
-  template<typename DstDerived>
-  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.derived().diagonal() += src.diagonal(); }
-  
-  template<typename DstDerived>
-  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.derived().diagonal() -= src.diagonal(); }
-};
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEASSIGN_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseBlock.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseBlock.h
deleted file mode 100644
index 5b4f6cc..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseBlock.h
+++ /dev/null
@@ -1,571 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_BLOCK_H
-#define EIGEN_SPARSE_BLOCK_H
-
-namespace Eigen {
-
-// Subset of columns or rows
-template<typename XprType, int BlockRows, int BlockCols>
-class BlockImpl<XprType,BlockRows,BlockCols,true,Sparse>
-  : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,true> >
-{
-    typedef typename internal::remove_all<typename XprType::Nested>::type _MatrixTypeNested;
-    typedef Block<XprType, BlockRows, BlockCols, true> BlockType;
-public:
-    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
-protected:
-    enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
-    typedef SparseMatrixBase<BlockType> Base;
-    using Base::convert_index;
-public:
-    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
-
-    inline BlockImpl(XprType& xpr, Index i)
-      : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize)
-    {}
-
-    inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : m_matrix(xpr), m_outerStart(convert_index(IsRowMajor ? startRow : startCol)), m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols))
-    {}
-
-    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-    Index nonZeros() const
-    {
-      typedef internal::evaluator<XprType> EvaluatorType;
-      EvaluatorType matEval(m_matrix);
-      Index nnz = 0;
-      Index end = m_outerStart + m_outerSize.value();
-      for(Index j=m_outerStart; j<end; ++j)
-        for(typename EvaluatorType::InnerIterator it(matEval, j); it; ++it)
-          ++nnz;
-      return nnz;
-    }
-
-    inline const Scalar coeff(Index row, Index col) const
-    {
-      return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
-    }
-
-    inline const Scalar coeff(Index index) const
-    {
-      return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index :  m_outerStart);
-    }
-
-    inline const XprType& nestedExpression() const { return m_matrix; }
-    inline XprType& nestedExpression() { return m_matrix; }
-    Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
-    Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
-    Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-  protected:
-
-    typename internal::ref_selector<XprType>::non_const_type m_matrix;
-    Index m_outerStart;
-    const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
-
-  protected:
-    // Disable assignment with clear error message.
-    // Note that simply removing operator= yields compilation errors with ICC+MSVC
-    template<typename T>
-    BlockImpl& operator=(const T&)
-    {
-      EIGEN_STATIC_ASSERT(sizeof(T)==0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
-      return *this;
-    }
-};
-
-
-/***************************************************************************
-* specialization for SparseMatrix
-***************************************************************************/
-
-namespace internal {
-
-template<typename SparseMatrixType, int BlockRows, int BlockCols>
-class sparse_matrix_block_impl
-  : public SparseCompressedBase<Block<SparseMatrixType,BlockRows,BlockCols,true> >
-{
-    typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _MatrixTypeNested;
-    typedef Block<SparseMatrixType, BlockRows, BlockCols, true> BlockType;
-    typedef SparseCompressedBase<Block<SparseMatrixType,BlockRows,BlockCols,true> > Base;
-    using Base::convert_index;
-public:
-    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
-    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
-protected:
-    typedef typename Base::IndexVector IndexVector;
-    enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
-public:
-
-    inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index i)
-      : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize)
-    {}
-
-    inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : m_matrix(xpr), m_outerStart(convert_index(IsRowMajor ? startRow : startCol)), m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols))
-    {}
-
-    template<typename OtherDerived>
-    inline BlockType& operator=(const SparseMatrixBase<OtherDerived>& other)
-    {
-      typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _NestedMatrixType;
-      _NestedMatrixType& matrix = m_matrix;
-      // This assignment is slow if this vector set is not empty
-      // and/or it is not at the end of the nonzeros of the underlying matrix.
-
-      // 1 - eval to a temporary to avoid transposition and/or aliasing issues
-      Ref<const SparseMatrix<Scalar, IsRowMajor ? RowMajor : ColMajor, StorageIndex> > tmp(other.derived());
-      eigen_internal_assert(tmp.outerSize()==m_outerSize.value());
-
-      // 2 - let's check whether there is enough allocated memory
-      Index nnz           = tmp.nonZeros();
-      Index start         = m_outerStart==0 ? 0 : m_matrix.outerIndexPtr()[m_outerStart]; // starting position of the current block
-      Index end           = m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()]; // ending position of the current block
-      Index block_size    = end - start;                                                // available room in the current block
-      Index tail_size     = m_matrix.outerIndexPtr()[m_matrix.outerSize()] - end;
-
-      Index free_size     = m_matrix.isCompressed()
-                          ? Index(matrix.data().allocatedSize()) + block_size
-                          : block_size;
-
-      Index tmp_start = tmp.outerIndexPtr()[0];
-
-      bool update_trailing_pointers = false;
-      if(nnz>free_size)
-      {
-        // realloc manually to reduce copies
-        typename SparseMatrixType::Storage newdata(m_matrix.data().allocatedSize() - block_size + nnz);
-
-        internal::smart_copy(m_matrix.valuePtr(),       m_matrix.valuePtr() + start,      newdata.valuePtr());
-        internal::smart_copy(m_matrix.innerIndexPtr(),  m_matrix.innerIndexPtr() + start, newdata.indexPtr());
-
-        internal::smart_copy(tmp.valuePtr() + tmp_start,      tmp.valuePtr() + tmp_start + nnz,       newdata.valuePtr() + start);
-        internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,  newdata.indexPtr() + start);
-
-        internal::smart_copy(matrix.valuePtr()+end,       matrix.valuePtr()+end + tail_size,      newdata.valuePtr()+start+nnz);
-        internal::smart_copy(matrix.innerIndexPtr()+end,  matrix.innerIndexPtr()+end + tail_size, newdata.indexPtr()+start+nnz);
-
-        newdata.resize(m_matrix.outerIndexPtr()[m_matrix.outerSize()] - block_size + nnz);
-
-        matrix.data().swap(newdata);
-
-        update_trailing_pointers = true;
-      }
-      else
-      {
-        if(m_matrix.isCompressed() && nnz!=block_size)
-        {
-          // no need to realloc, simply copy the tail at its respective position and insert tmp
-          matrix.data().resize(start + nnz + tail_size);
-
-          internal::smart_memmove(matrix.valuePtr()+end,      matrix.valuePtr() + end+tail_size,      matrix.valuePtr() + start+nnz);
-          internal::smart_memmove(matrix.innerIndexPtr()+end, matrix.innerIndexPtr() + end+tail_size, matrix.innerIndexPtr() + start+nnz);
-
-          update_trailing_pointers = true;
-        }
-
-        internal::smart_copy(tmp.valuePtr() + tmp_start,      tmp.valuePtr() + tmp_start + nnz,       matrix.valuePtr() + start);
-        internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,  matrix.innerIndexPtr() + start);
-      }
-
-      // update outer index pointers and innerNonZeros
-      if(IsVectorAtCompileTime)
-      {
-        if(!m_matrix.isCompressed())
-          matrix.innerNonZeroPtr()[m_outerStart] = StorageIndex(nnz);
-        matrix.outerIndexPtr()[m_outerStart] = StorageIndex(start);
-      }
-      else
-      {
-        StorageIndex p = StorageIndex(start);
-        for(Index k=0; k<m_outerSize.value(); ++k)
-        {
-          StorageIndex nnz_k = internal::convert_index<StorageIndex>(tmp.innerVector(k).nonZeros());
-          if(!m_matrix.isCompressed())
-            matrix.innerNonZeroPtr()[m_outerStart+k] = nnz_k;
-          matrix.outerIndexPtr()[m_outerStart+k] = p;
-          p += nnz_k;
-        }
-      }
-
-      if(update_trailing_pointers)
-      {
-        StorageIndex offset = internal::convert_index<StorageIndex>(nnz - block_size);
-        for(Index k = m_outerStart + m_outerSize.value(); k<=matrix.outerSize(); ++k)
-        {
-          matrix.outerIndexPtr()[k] += offset;
-        }
-      }
-
-      return derived();
-    }
-
-    inline BlockType& operator=(const BlockType& other)
-    {
-      return operator=<BlockType>(other);
-    }
-
-    inline const Scalar* valuePtr() const
-    { return m_matrix.valuePtr(); }
-    inline Scalar* valuePtr()
-    { return m_matrix.valuePtr(); }
-
-    inline const StorageIndex* innerIndexPtr() const
-    { return m_matrix.innerIndexPtr(); }
-    inline StorageIndex* innerIndexPtr()
-    { return m_matrix.innerIndexPtr(); }
-
-    inline const StorageIndex* outerIndexPtr() const
-    { return m_matrix.outerIndexPtr() + m_outerStart; }
-    inline StorageIndex* outerIndexPtr()
-    { return m_matrix.outerIndexPtr() + m_outerStart; }
-
-    inline const StorageIndex* innerNonZeroPtr() const
-    { return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
-    inline StorageIndex* innerNonZeroPtr()
-    { return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
-
-    bool isCompressed() const { return m_matrix.innerNonZeroPtr()==0; }
-
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      return m_matrix.coeffRef(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
-    }
-
-    inline const Scalar coeff(Index row, Index col) const
-    {
-      return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
-    }
-
-    inline const Scalar coeff(Index index) const
-    {
-      return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index :  m_outerStart);
-    }
-
-    const Scalar& lastCoeff() const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(sparse_matrix_block_impl);
-      eigen_assert(Base::nonZeros()>0);
-      if(m_matrix.isCompressed())
-        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart+1]-1];
-      else
-        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart]+m_matrix.innerNonZeroPtr()[m_outerStart]-1];
-    }
-
-    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-    inline const SparseMatrixType& nestedExpression() const { return m_matrix; }
-    inline SparseMatrixType& nestedExpression() { return m_matrix; }
-    Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
-    Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
-    Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-  protected:
-
-    typename internal::ref_selector<SparseMatrixType>::non_const_type m_matrix;
-    Index m_outerStart;
-    const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
-
-};
-
-} // namespace internal
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-class BlockImpl<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true,Sparse>
-  : public internal::sparse_matrix_block_impl<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols>
-{
-public:
-  typedef _StorageIndex StorageIndex;
-  typedef SparseMatrix<_Scalar, _Options, _StorageIndex> SparseMatrixType;
-  typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base;
-  inline BlockImpl(SparseMatrixType& xpr, Index i)
-    : Base(xpr, i)
-  {}
-
-  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-    : Base(xpr, startRow, startCol, blockRows, blockCols)
-  {}
-
-  using Base::operator=;
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-class BlockImpl<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true,Sparse>
-  : public internal::sparse_matrix_block_impl<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols>
-{
-public:
-  typedef _StorageIndex StorageIndex;
-  typedef const SparseMatrix<_Scalar, _Options, _StorageIndex> SparseMatrixType;
-  typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base;
-  inline BlockImpl(SparseMatrixType& xpr, Index i)
-    : Base(xpr, i)
-  {}
-
-  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-    : Base(xpr, startRow, startCol, blockRows, blockCols)
-  {}
-
-  using Base::operator=;
-private:
-  template<typename Derived> BlockImpl(const SparseMatrixBase<Derived>& xpr, Index i);
-  template<typename Derived> BlockImpl(const SparseMatrixBase<Derived>& xpr);
-};
-
-//----------
-
-/** Generic implementation of sparse Block expression.
-  * Real-only.
-  */
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl<XprType,BlockRows,BlockCols,InnerPanel,Sparse>
-  : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,InnerPanel> >, internal::no_assignment_operator
-{
-    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-    typedef SparseMatrixBase<BlockType> Base;
-    using Base::convert_index;
-public:
-    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
-    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
-
-    typedef typename internal::remove_all<typename XprType::Nested>::type _MatrixTypeNested;
-
-    /** Column or Row constructor
-      */
-    inline BlockImpl(XprType& xpr, Index i)
-      : m_matrix(xpr),
-        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? convert_index(i) : 0),
-        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? convert_index(i) : 0),
-        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
-        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
-    {}
-
-    /** Dynamic-size constructor
-      */
-    inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : m_matrix(xpr), m_startRow(convert_index(startRow)), m_startCol(convert_index(startCol)), m_blockRows(convert_index(blockRows)), m_blockCols(convert_index(blockCols))
-    {}
-
-    inline Index rows() const { return m_blockRows.value(); }
-    inline Index cols() const { return m_blockCols.value(); }
-
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      return m_matrix.coeffRef(row + m_startRow.value(), col + m_startCol.value());
-    }
-
-    inline const Scalar coeff(Index row, Index col) const
-    {
-      return m_matrix.coeff(row + m_startRow.value(), col + m_startCol.value());
-    }
-
-    inline Scalar& coeffRef(Index index)
-    {
-      return m_matrix.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    inline const Scalar coeff(Index index) const
-    {
-      return m_matrix.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    inline const XprType& nestedExpression() const { return m_matrix; }
-    inline XprType& nestedExpression() { return m_matrix; }
-    Index startRow() const { return m_startRow.value(); }
-    Index startCol() const { return m_startCol.value(); }
-    Index blockRows() const { return m_blockRows.value(); }
-    Index blockCols() const { return m_blockCols.value(); }
-
-  protected:
-//     friend class internal::GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel>;
-    friend struct internal::unary_evaluator<Block<XprType,BlockRows,BlockCols,InnerPanel>, internal::IteratorBased, Scalar >;
-
-    Index nonZeros() const { return Dynamic; }
-
-    typename internal::ref_selector<XprType>::non_const_type m_matrix;
-    const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
-    const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
-    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
-    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
-
-  protected:
-    // Disable assignment with clear error message.
-    // Note that simply removing operator= yields compilation errors with ICC+MSVC
-    template<typename T>
-    BlockImpl& operator=(const T&)
-    {
-      EIGEN_STATIC_ASSERT(sizeof(T)==0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
-      return *this;
-    }
-
-};
-
-namespace internal {
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased >
- : public evaluator_base<Block<ArgType,BlockRows,BlockCols,InnerPanel> >
-{
-    class InnerVectorInnerIterator;
-    class OuterVectorInnerIterator;
-  public:
-    typedef Block<ArgType,BlockRows,BlockCols,InnerPanel> XprType;
-    typedef typename XprType::StorageIndex StorageIndex;
-    typedef typename XprType::Scalar Scalar;
-
-    enum {
-      IsRowMajor = XprType::IsRowMajor,
-
-      OuterVector =  (BlockCols==1 && ArgType::IsRowMajor)
-                    | // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
-                      // revert to || as soon as not needed anymore.
-                     (BlockRows==1 && !ArgType::IsRowMajor),
-
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-
-    typedef typename internal::conditional<OuterVector,OuterVectorInnerIterator,InnerVectorInnerIterator>::type InnerIterator;
-
-    explicit unary_evaluator(const XprType& op)
-      : m_argImpl(op.nestedExpression()), m_block(op)
-    {}
-
-    inline Index nonZerosEstimate() const {
-      const Index nnz = m_block.nonZeros();
-      if(nnz < 0) {
-        // Scale the non-zero estimate for the underlying expression linearly with block size.
-        // Return zero if the underlying block is empty.
-        const Index nested_sz = m_block.nestedExpression().size();        
-        return nested_sz == 0 ? 0 : m_argImpl.nonZerosEstimate() * m_block.size() / nested_sz;
-      }
-      return nnz;
-    }
-
-  protected:
-    typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
-
-    evaluator<ArgType> m_argImpl;
-    const XprType &m_block;
-};
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-class unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased>::InnerVectorInnerIterator
- : public EvalIterator
-{
-  // NOTE MSVC fails to compile if we don't explicitely "import" IsRowMajor from unary_evaluator
-  //      because the base class EvalIterator has a private IsRowMajor enum too. (bug #1786)
-  // NOTE We cannot call it IsRowMajor because it would shadow unary_evaluator::IsRowMajor
-  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
-  const XprType& m_block;
-  Index m_end;
-public:
-
-  EIGEN_STRONG_INLINE InnerVectorInnerIterator(const unary_evaluator& aEval, Index outer)
-    : EvalIterator(aEval.m_argImpl, outer + (XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol())),
-      m_block(aEval.m_block),
-      m_end(XprIsRowMajor ? aEval.m_block.startCol()+aEval.m_block.blockCols() : aEval.m_block.startRow()+aEval.m_block.blockRows())
-  {
-    while( (EvalIterator::operator bool()) && (EvalIterator::index() < (XprIsRowMajor ? m_block.startCol() : m_block.startRow())) )
-      EvalIterator::operator++();
-  }
-
-  inline StorageIndex index() const { return EvalIterator::index() - convert_index<StorageIndex>(XprIsRowMajor ? m_block.startCol() : m_block.startRow()); }
-  inline Index outer()  const { return EvalIterator::outer() - (XprIsRowMajor ? m_block.startRow() : m_block.startCol()); }
-  inline Index row()    const { return EvalIterator::row()   - m_block.startRow(); }
-  inline Index col()    const { return EvalIterator::col()   - m_block.startCol(); }
-
-  inline operator bool() const { return EvalIterator::operator bool() && EvalIterator::index() < m_end; }
-};
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-class unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased>::OuterVectorInnerIterator
-{
-  // NOTE see above
-  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
-  const unary_evaluator& m_eval;
-  Index m_outerPos;
-  const Index m_innerIndex;
-  Index m_end;
-  EvalIterator m_it;
-public:
-
-  EIGEN_STRONG_INLINE OuterVectorInnerIterator(const unary_evaluator& aEval, Index outer)
-    : m_eval(aEval),
-      m_outerPos( (XprIsRowMajor ? aEval.m_block.startCol() : aEval.m_block.startRow()) ),
-      m_innerIndex(XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol()),
-      m_end(XprIsRowMajor ? aEval.m_block.startCol()+aEval.m_block.blockCols() : aEval.m_block.startRow()+aEval.m_block.blockRows()),
-      m_it(m_eval.m_argImpl, m_outerPos)
-  {
-    EIGEN_UNUSED_VARIABLE(outer);
-    eigen_assert(outer==0);
-
-    while(m_it && m_it.index() < m_innerIndex) ++m_it;
-    if((!m_it) || (m_it.index()!=m_innerIndex))
-      ++(*this);
-  }
-
-  inline StorageIndex index() const { return convert_index<StorageIndex>(m_outerPos - (XprIsRowMajor ? m_eval.m_block.startCol() : m_eval.m_block.startRow())); }
-  inline Index outer()  const { return 0; }
-  inline Index row()    const { return XprIsRowMajor ? 0 : index(); }
-  inline Index col()    const { return XprIsRowMajor ? index() : 0; }
-
-  inline Scalar value() const { return m_it.value(); }
-  inline Scalar& valueRef() { return m_it.valueRef(); }
-
-  inline OuterVectorInnerIterator& operator++()
-  {
-    // search next non-zero entry
-    while(++m_outerPos<m_end)
-    {
-      // Restart iterator at the next inner-vector:
-      m_it.~EvalIterator();
-      ::new (&m_it) EvalIterator(m_eval.m_argImpl, m_outerPos);
-      // search for the key m_innerIndex in the current outer-vector
-      while(m_it && m_it.index() < m_innerIndex) ++m_it;
-      if(m_it && m_it.index()==m_innerIndex) break;
-    }
-    return *this;
-  }
-
-  inline operator bool() const { return m_outerPos < m_end; }
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-struct unary_evaluator<Block<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true>, IteratorBased>
-  : evaluator<SparseCompressedBase<Block<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> > >
-{
-  typedef Block<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> XprType;
-  typedef evaluator<SparseCompressedBase<XprType> > Base;
-  explicit unary_evaluator(const XprType &xpr) : Base(xpr) {}
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-struct unary_evaluator<Block<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true>, IteratorBased>
-  : evaluator<SparseCompressedBase<Block<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> > >
-{
-  typedef Block<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> XprType;
-  typedef evaluator<SparseCompressedBase<XprType> > Base;
-  explicit unary_evaluator(const XprType &xpr) : Base(xpr) {}
-};
-
-} // end namespace internal
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_BLOCK_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseColEtree.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseColEtree.h
deleted file mode 100644
index ebe02d1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseColEtree.h
+++ /dev/null
@@ -1,206 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-/* 
- 
- * NOTE: This file is the modified version of sp_coletree.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.1) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * August 1, 2008
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSE_COLETREE_H
-#define SPARSE_COLETREE_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** Find the root of the tree/set containing the vertex i : Use Path halving */ 
-template<typename Index, typename IndexVector>
-Index etree_find (Index i, IndexVector& pp)
-{
-  Index p = pp(i); // Parent 
-  Index gp = pp(p); // Grand parent 
-  while (gp != p) 
-  {
-    pp(i) = gp; // Parent pointer on find path is changed to former grand parent
-    i = gp; 
-    p = pp(i);
-    gp = pp(p);
-  }
-  return p; 
-}
-
-/** Compute the column elimination tree of a sparse matrix
-  * \param mat The matrix in column-major format. 
-  * \param parent The elimination tree
-  * \param firstRowElt The column index of the first element in each row
-  * \param perm The permutation to apply to the column of \b mat
-  */
-template <typename MatrixType, typename IndexVector>
-int coletree(const MatrixType& mat, IndexVector& parent, IndexVector& firstRowElt, typename MatrixType::StorageIndex *perm=0)
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  StorageIndex nc = convert_index<StorageIndex>(mat.cols()); // Number of columns
-  StorageIndex m = convert_index<StorageIndex>(mat.rows());
-  StorageIndex diagSize = (std::min)(nc,m);
-  IndexVector root(nc); // root of subtree of etree 
-  root.setZero();
-  IndexVector pp(nc); // disjoint sets 
-  pp.setZero(); // Initialize disjoint sets 
-  parent.resize(mat.cols());
-  //Compute first nonzero column in each row 
-  firstRowElt.resize(m);
-  firstRowElt.setConstant(nc);
-  firstRowElt.segment(0, diagSize).setLinSpaced(diagSize, 0, diagSize-1);
-  bool found_diag;
-  for (StorageIndex col = 0; col < nc; col++)
-  {
-    StorageIndex pcol = col;
-    if(perm) pcol  = perm[col];
-    for (typename MatrixType::InnerIterator it(mat, pcol); it; ++it)
-    { 
-      Index row = it.row();
-      firstRowElt(row) = (std::min)(firstRowElt(row), col);
-    }
-  }
-  /* Compute etree by Liu's algorithm for symmetric matrices,
-          except use (firstRowElt[r],c) in place of an edge (r,c) of A.
-    Thus each row clique in A'*A is replaced by a star
-    centered at its first vertex, which has the same fill. */
-  StorageIndex rset, cset, rroot;
-  for (StorageIndex col = 0; col < nc; col++) 
-  {
-    found_diag = col>=m;
-    pp(col) = col; 
-    cset = col; 
-    root(cset) = col; 
-    parent(col) = nc; 
-    /* The diagonal element is treated here even if it does not exist in the matrix
-     * hence the loop is executed once more */ 
-    StorageIndex pcol = col;
-    if(perm) pcol  = perm[col];
-    for (typename MatrixType::InnerIterator it(mat, pcol); it||!found_diag; ++it)
-    { //  A sequence of interleaved find and union is performed 
-      Index i = col;
-      if(it) i = it.index();
-      if (i == col) found_diag = true;
-      
-      StorageIndex row = firstRowElt(i);
-      if (row >= col) continue; 
-      rset = internal::etree_find(row, pp); // Find the name of the set containing row
-      rroot = root(rset);
-      if (rroot != col) 
-      {
-        parent(rroot) = col; 
-        pp(cset) = rset; 
-        cset = rset; 
-        root(cset) = col; 
-      }
-    }
-  }
-  return 0;  
-}
-
-/** 
-  * Depth-first search from vertex n.  No recursion.
-  * This routine was contributed by Cédric Doucet, CEDRAT Group, Meylan, France.
-*/
-template <typename IndexVector>
-void nr_etdfs (typename IndexVector::Scalar n, IndexVector& parent, IndexVector& first_kid, IndexVector& next_kid, IndexVector& post, typename IndexVector::Scalar postnum)
-{
-  typedef typename IndexVector::Scalar StorageIndex;
-  StorageIndex current = n, first, next;
-  while (postnum != n) 
-  {
-    // No kid for the current node
-    first = first_kid(current);
-    
-    // no kid for the current node
-    if (first == -1) 
-    {
-      // Numbering this node because it has no kid 
-      post(current) = postnum++;
-      
-      // looking for the next kid 
-      next = next_kid(current); 
-      while (next == -1) 
-      {
-        // No more kids : back to the parent node
-        current = parent(current); 
-        // numbering the parent node 
-        post(current) = postnum++;
-        
-        // Get the next kid 
-        next = next_kid(current); 
-      }
-      // stopping criterion 
-      if (postnum == n+1) return; 
-      
-      // Updating current node 
-      current = next; 
-    }
-    else 
-    {
-      current = first; 
-    }
-  }
-}
-
-
-/**
-  * \brief Post order a tree 
-  * \param n the number of nodes
-  * \param parent Input tree
-  * \param post postordered tree
-  */
-template <typename IndexVector>
-void treePostorder(typename IndexVector::Scalar n, IndexVector& parent, IndexVector& post)
-{
-  typedef typename IndexVector::Scalar StorageIndex;
-  IndexVector first_kid, next_kid; // Linked list of children 
-  StorageIndex postnum; 
-  // Allocate storage for working arrays and results 
-  first_kid.resize(n+1); 
-  next_kid.setZero(n+1);
-  post.setZero(n+1);
-  
-  // Set up structure describing children
-  first_kid.setConstant(-1); 
-  for (StorageIndex v = n-1; v >= 0; v--) 
-  {
-    StorageIndex dad = parent(v);
-    next_kid(v) = first_kid(dad); 
-    first_kid(dad) = v; 
-  }
-  
-  // Depth-first search from dummy root vertex #n
-  postnum = 0; 
-  internal::nr_etdfs(n, parent, first_kid, next_kid, post, postnum);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // SPARSE_COLETREE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCompressedBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCompressedBase.h
deleted file mode 100644
index 6a2c7a8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCompressedBase.h
+++ /dev/null
@@ -1,370 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_COMPRESSED_BASE_H
-#define EIGEN_SPARSE_COMPRESSED_BASE_H
-
-namespace Eigen { 
-
-template<typename Derived> class SparseCompressedBase;
-  
-namespace internal {
-
-template<typename Derived>
-struct traits<SparseCompressedBase<Derived> > : traits<Derived>
-{};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  * \class SparseCompressedBase
-  * \brief Common base class for sparse [compressed]-{row|column}-storage format.
-  *
-  * This class defines the common interface for all derived classes implementing the compressed sparse storage format, such as:
-  *  - SparseMatrix
-  *  - Ref<SparseMatrixType,Options>
-  *  - Map<SparseMatrixType>
-  *
-  */
-template<typename Derived>
-class SparseCompressedBase
-  : public SparseMatrixBase<Derived>
-{
-  public:
-    typedef SparseMatrixBase<Derived> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseCompressedBase)
-    using Base::operator=;
-    using Base::IsRowMajor;
-    
-    class InnerIterator;
-    class ReverseInnerIterator;
-    
-  protected:
-    typedef typename Base::IndexVector IndexVector;
-    Eigen::Map<IndexVector> innerNonZeros() { return Eigen::Map<IndexVector>(innerNonZeroPtr(), isCompressed()?0:derived().outerSize()); }
-    const  Eigen::Map<const IndexVector> innerNonZeros() const { return Eigen::Map<const IndexVector>(innerNonZeroPtr(), isCompressed()?0:derived().outerSize()); }
-        
-  public:
-    
-    /** \returns the number of non zero coefficients */
-    inline Index nonZeros() const
-    {
-      if(Derived::IsVectorAtCompileTime && outerIndexPtr()==0)
-        return derived().nonZeros();
-      else if(isCompressed())
-        return outerIndexPtr()[derived().outerSize()]-outerIndexPtr()[0];
-      else if(derived().outerSize()==0)
-        return 0;
-      else
-        return innerNonZeros().sum();
-    }
-    
-    /** \returns a const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline const Scalar* valuePtr() const { return derived().valuePtr(); }
-    /** \returns a non-const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline Scalar* valuePtr() { return derived().valuePtr(); }
-
-    /** \returns a const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline const StorageIndex* innerIndexPtr() const { return derived().innerIndexPtr(); }
-    /** \returns a non-const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline StorageIndex* innerIndexPtr() { return derived().innerIndexPtr(); }
-
-    /** \returns a const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 for SparseVector
-      * \sa valuePtr(), innerIndexPtr() */
-    inline const StorageIndex* outerIndexPtr() const { return derived().outerIndexPtr(); }
-    /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 for SparseVector
-      * \sa valuePtr(), innerIndexPtr() */
-    inline StorageIndex* outerIndexPtr() { return derived().outerIndexPtr(); }
-
-    /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline const StorageIndex* innerNonZeroPtr() const { return derived().innerNonZeroPtr(); }
-    /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline StorageIndex* innerNonZeroPtr() { return derived().innerNonZeroPtr(); }
-    
-    /** \returns whether \c *this is in compressed form. */
-    inline bool isCompressed() const { return innerNonZeroPtr()==0; }
-
-    /** \returns a read-only view of the stored coefficients as a 1D array expression.
-      *
-      * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
-      *
-      * \sa valuePtr(), isCompressed() */
-    const Map<const Array<Scalar,Dynamic,1> > coeffs() const { eigen_assert(isCompressed()); return Array<Scalar,Dynamic,1>::Map(valuePtr(),nonZeros()); }
-
-    /** \returns a read-write view of the stored coefficients as a 1D array expression
-      *
-      * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
-      *
-      * Here is an example:
-      * \include SparseMatrix_coeffs.cpp
-      * and the output is:
-      * \include SparseMatrix_coeffs.out
-      *
-      * \sa valuePtr(), isCompressed() */
-    Map<Array<Scalar,Dynamic,1> > coeffs() { eigen_assert(isCompressed()); return Array<Scalar,Dynamic,1>::Map(valuePtr(),nonZeros()); }
-
-  protected:
-    /** Default constructor. Do nothing. */
-    SparseCompressedBase() {}
-
-    /** \internal return the index of the coeff at (row,col) or just before if it does not exist.
-      * This is an analogue of std::lower_bound.
-      */
-    internal::LowerBoundIndex lower_bound(Index row, Index col) const
-    {
-      eigen_internal_assert(row>=0 && row<this->rows() && col>=0 && col<this->cols());
-
-      const Index outer = Derived::IsRowMajor ? row : col;
-      const Index inner = Derived::IsRowMajor ? col : row;
-
-      Index start = this->outerIndexPtr()[outer];
-      Index end = this->isCompressed() ? this->outerIndexPtr()[outer+1] : this->outerIndexPtr()[outer] + this->innerNonZeroPtr()[outer];
-      eigen_assert(end>=start && "you are using a non finalized sparse matrix or written coefficient does not exist");
-      internal::LowerBoundIndex p;
-      p.value = std::lower_bound(this->innerIndexPtr()+start, this->innerIndexPtr()+end,inner) - this->innerIndexPtr();
-      p.found = (p.value<end) && (this->innerIndexPtr()[p.value]==inner);
-      return p;
-    }
-
-    friend struct internal::evaluator<SparseCompressedBase<Derived> >;
-
-  private:
-    template<typename OtherDerived> explicit SparseCompressedBase(const SparseCompressedBase<OtherDerived>&);
-};
-
-template<typename Derived>
-class SparseCompressedBase<Derived>::InnerIterator
-{
-  public:
-    InnerIterator()
-      : m_values(0), m_indices(0), m_outer(0), m_id(0), m_end(0)
-    {}
-
-    InnerIterator(const InnerIterator& other)
-      : m_values(other.m_values), m_indices(other.m_indices), m_outer(other.m_outer), m_id(other.m_id), m_end(other.m_end)
-    {}
-
-    InnerIterator& operator=(const InnerIterator& other)
-    {
-      m_values = other.m_values;
-      m_indices = other.m_indices;
-      const_cast<OuterType&>(m_outer).setValue(other.m_outer.value());
-      m_id = other.m_id;
-      m_end = other.m_end;
-      return *this;
-    }
-
-    InnerIterator(const SparseCompressedBase& mat, Index outer)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer)
-    {
-      if(Derived::IsVectorAtCompileTime && mat.outerIndexPtr()==0)
-      {
-        m_id = 0;
-        m_end = mat.nonZeros();
-      }
-      else
-      {
-        m_id = mat.outerIndexPtr()[outer];
-        if(mat.isCompressed())
-          m_end = mat.outerIndexPtr()[outer+1];
-        else
-          m_end = m_id + mat.innerNonZeroPtr()[outer];
-      }
-    }
-
-    explicit InnerIterator(const SparseCompressedBase& mat)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(0), m_id(0), m_end(mat.nonZeros())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    explicit InnerIterator(const internal::CompressedStorage<Scalar,StorageIndex>& data)
-      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_id(0), m_end(data.size())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    inline InnerIterator& operator++() { m_id++; return *this; }
-    inline InnerIterator& operator+=(Index i) { m_id += i ; return *this; }
-
-    inline InnerIterator operator+(Index i) 
-    { 
-        InnerIterator result = *this;
-        result += i;
-        return result;
-    }
-
-    inline const Scalar& value() const { return m_values[m_id]; }
-    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id]); }
-
-    inline StorageIndex index() const { return m_indices[m_id]; }
-    inline Index outer() const { return m_outer.value(); }
-    inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
-    inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
-
-    inline operator bool() const { return (m_id < m_end); }
-
-  protected:
-    const Scalar* m_values;
-    const StorageIndex* m_indices;
-    typedef internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> OuterType;
-    const OuterType m_outer;
-    Index m_id;
-    Index m_end;
-  private:
-    // If you get here, then you're not using the right InnerIterator type, e.g.:
-    //   SparseMatrix<double,RowMajor> A;
-    //   SparseMatrix<double>::InnerIterator it(A,0);
-    template<typename T> InnerIterator(const SparseMatrixBase<T>&, Index outer);
-};
-
-template<typename Derived>
-class SparseCompressedBase<Derived>::ReverseInnerIterator
-{
-  public:
-    ReverseInnerIterator(const SparseCompressedBase& mat, Index outer)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer)
-    {
-      if(Derived::IsVectorAtCompileTime && mat.outerIndexPtr()==0)
-      {
-        m_start = 0;
-        m_id = mat.nonZeros();
-      }
-      else
-      {
-        m_start = mat.outerIndexPtr()[outer];
-        if(mat.isCompressed())
-          m_id = mat.outerIndexPtr()[outer+1];
-        else
-          m_id = m_start + mat.innerNonZeroPtr()[outer];
-      }
-    }
-
-    explicit ReverseInnerIterator(const SparseCompressedBase& mat)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(0), m_start(0), m_id(mat.nonZeros())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    explicit ReverseInnerIterator(const internal::CompressedStorage<Scalar,StorageIndex>& data)
-      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_start(0), m_id(data.size())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    inline ReverseInnerIterator& operator--() { --m_id; return *this; }
-    inline ReverseInnerIterator& operator-=(Index i) { m_id -= i; return *this; }
-
-    inline ReverseInnerIterator operator-(Index i) 
-    {
-        ReverseInnerIterator result = *this;
-        result -= i;
-        return result;
-    }
-
-    inline const Scalar& value() const { return m_values[m_id-1]; }
-    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id-1]); }
-
-    inline StorageIndex index() const { return m_indices[m_id-1]; }
-    inline Index outer() const { return m_outer.value(); }
-    inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
-    inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
-
-    inline operator bool() const { return (m_id > m_start); }
-
-  protected:
-    const Scalar* m_values;
-    const StorageIndex* m_indices;
-    typedef internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> OuterType;
-    const OuterType m_outer;
-    Index m_start;
-    Index m_id;
-};
-
-namespace internal {
-
-template<typename Derived>
-struct evaluator<SparseCompressedBase<Derived> >
-  : evaluator_base<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  typedef typename Derived::InnerIterator InnerIterator;
-  
-  enum {
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    Flags = Derived::Flags
-  };
-  
-  evaluator() : m_matrix(0), m_zero(0)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  explicit evaluator(const Derived &mat) : m_matrix(&mat), m_zero(0)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_matrix->nonZeros();
-  }
-  
-  operator Derived&() { return m_matrix->const_cast_derived(); }
-  operator const Derived&() const { return *m_matrix; }
-  
-  typedef typename DenseCoeffsBase<Derived,ReadOnlyAccessors>::CoeffReturnType CoeffReturnType;
-  const Scalar& coeff(Index row, Index col) const
-  {
-    Index p = find(row,col);
-
-    if(p==Dynamic)
-      return m_zero;
-    else
-      return m_matrix->const_cast_derived().valuePtr()[p];
-  }
-
-  Scalar& coeffRef(Index row, Index col)
-  {
-    Index p = find(row,col);
-    eigen_assert(p!=Dynamic && "written coefficient does not exist");
-    return m_matrix->const_cast_derived().valuePtr()[p];
-  }
-
-protected:
-
-  Index find(Index row, Index col) const
-  {
-    internal::LowerBoundIndex p = m_matrix->lower_bound(row,col);
-    return p.found ? p.value : Dynamic;
-  }
-
-  const Derived *m_matrix;
-  const Scalar m_zero;
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_COMPRESSED_BASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
deleted file mode 100644
index 9b0d3f9..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
+++ /dev/null
@@ -1,722 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_CWISE_BINARY_OP_H
-#define EIGEN_SPARSE_CWISE_BINARY_OP_H
-
-namespace Eigen { 
-
-// Here we have to handle 3 cases:
-//  1 - sparse op dense
-//  2 - dense op sparse
-//  3 - sparse op sparse
-// We also need to implement a 4th iterator for:
-//  4 - dense op dense
-// Finally, we also need to distinguish between the product and other operations :
-//                configuration      returned mode
-//  1 - sparse op dense    product      sparse
-//                         generic      dense
-//  2 - dense op sparse    product      sparse
-//                         generic      dense
-//  3 - sparse op sparse   product      sparse
-//                         generic      sparse
-//  4 - dense op dense     product      dense
-//                         generic      dense
-//
-// TODO to ease compiler job, we could specialize product/quotient with a scalar
-//      and fallback to cwise-unary evaluator using bind1st_op and bind2nd_op.
-
-template<typename BinaryOp, typename Lhs, typename Rhs>
-class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
-  : public SparseMatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  public:
-    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
-    typedef SparseMatrixBase<Derived> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
-    CwiseBinaryOpImpl()
-    {
-      EIGEN_STATIC_ASSERT((
-                (!internal::is_same<typename internal::traits<Lhs>::StorageKind,
-                                    typename internal::traits<Rhs>::StorageKind>::value)
-            ||  ((internal::evaluator<Lhs>::Flags&RowMajorBit) == (internal::evaluator<Rhs>::Flags&RowMajorBit))),
-            THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH);
-    }
-};
-
-namespace internal {
-
-  
-// Generic "sparse OP sparse"
-template<typename XprType> struct binary_sparse_evaluator;
-
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IteratorBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-protected:
-  typedef typename evaluator<Lhs>::InnerIterator  LhsIterator;
-  typedef typename evaluator<Rhs>::InnerIterator  RhsIterator;
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-public:
-
-  class InnerIterator
-  {
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor)
-    {
-      this->operator++();
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index()))
-      {
-        m_id = m_lhsIter.index();
-        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
-        ++m_lhsIter;
-        ++m_rhsIter;
-      }
-      else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index())))
-      {
-        m_id = m_lhsIter.index();
-        m_value = m_functor(m_lhsIter.value(), Scalar(0));
-        ++m_lhsIter;
-      }
-      else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index())))
-      {
-        m_id = m_rhsIter.index();
-        m_value = m_functor(Scalar(0), m_rhsIter.value());
-        ++m_rhsIter;
-      }
-      else
-      {
-        m_value = Scalar(0); // this is to avoid a compilation warning
-        m_id = -1;
-      }
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); }
-    EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_id>=0; }
-
-  protected:
-    LhsIterator m_lhsIter;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-    Scalar m_value;
-    StorageIndex m_id;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit binary_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_lhsImpl.nonZerosEstimate() + m_rhsImpl.nonZerosEstimate();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<Lhs> m_lhsImpl;
-  evaluator<Rhs> m_rhsImpl;
-};
-
-// dense op sparse
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IteratorBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-protected:
-  typedef typename evaluator<Rhs>::InnerIterator  RhsIterator;
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(Rhs::Flags)&RowMajorBit)==RowMajorBit };
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
-      : m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_value(0), m_id(-1), m_innerSize(aEval.m_expr.rhs().innerSize())
-    {
-      this->operator++();
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_id;
-      if(m_id<m_innerSize)
-      {
-        Scalar lhsVal = m_lhsEval.coeff(IsRowMajor?m_rhsIter.outer():m_id,
-                                        IsRowMajor?m_id:m_rhsIter.outer());
-        if(m_rhsIter && m_rhsIter.index()==m_id)
-        {
-          m_value = m_functor(lhsVal, m_rhsIter.value());
-          ++m_rhsIter;
-        }
-        else
-          m_value = m_functor(lhsVal, Scalar(0));
-      }
-
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
-    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
-    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
-
-  protected:
-    const evaluator<Lhs> &m_lhsEval;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-    Scalar m_value;
-    StorageIndex m_id;
-    StorageIndex m_innerSize;
-  };
-
-
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-
-  explicit binary_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()),
-      m_rhsImpl(xpr.rhs()),
-      m_expr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  inline Index nonZerosEstimate() const {
-    return m_expr.size();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<Lhs> m_lhsImpl;
-  evaluator<Rhs> m_rhsImpl;
-  const XprType &m_expr;
-};
-
-// sparse op dense
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IndexBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-protected:
-  typedef typename evaluator<Lhs>::InnerIterator  LhsIterator;
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(Lhs::Flags)&RowMajorBit)==RowMajorBit };
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_value(0), m_id(-1), m_innerSize(aEval.m_expr.lhs().innerSize())
-    {
-      this->operator++();
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_id;
-      if(m_id<m_innerSize)
-      {
-        Scalar rhsVal = m_rhsEval.coeff(IsRowMajor?m_lhsIter.outer():m_id,
-                                        IsRowMajor?m_id:m_lhsIter.outer());
-        if(m_lhsIter && m_lhsIter.index()==m_id)
-        {
-          m_value = m_functor(m_lhsIter.value(), rhsVal);
-          ++m_lhsIter;
-        }
-        else
-          m_value = m_functor(Scalar(0),rhsVal);
-      }
-
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
-    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
-
-  protected:
-    LhsIterator m_lhsIter;
-    const evaluator<Rhs> &m_rhsEval;
-    const BinaryOp& m_functor;
-    Scalar m_value;
-    StorageIndex m_id;
-    StorageIndex m_innerSize;
-  };
-
-
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-
-  explicit binary_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()),
-      m_rhsImpl(xpr.rhs()),
-      m_expr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  inline Index nonZerosEstimate() const {
-    return m_expr.size();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<Lhs> m_lhsImpl;
-  evaluator<Rhs> m_rhsImpl;
-  const XprType &m_expr;
-};
-
-template<typename T,
-         typename LhsKind   = typename evaluator_traits<typename T::Lhs>::Kind,
-         typename RhsKind   = typename evaluator_traits<typename T::Rhs>::Kind,
-         typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
-         typename RhsScalar = typename traits<typename T::Rhs>::Scalar> struct sparse_conjunction_evaluator;
-
-// "sparse .* sparse"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "dense .* sparse"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IndexBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "sparse .* dense"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IndexBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// "sparse ./ dense"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs>, IteratorBased, IndexBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// "sparse && sparse"
-template<typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs>, IteratorBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "dense && sparse"
-template<typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs>, IndexBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "sparse && dense"
-template<typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs>, IteratorBased, IndexBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// "sparse ^ sparse"
-template<typename XprType>
-struct sparse_conjunction_evaluator<XprType, IteratorBased, IteratorBased>
-  : evaluator_base<XprType>
-{
-protected:
-  typedef typename XprType::Functor BinaryOp;
-  typedef typename XprType::Lhs LhsArg;
-  typedef typename XprType::Rhs RhsArg;
-  typedef typename evaluator<LhsArg>::InnerIterator  LhsIterator;
-  typedef typename evaluator<RhsArg>::InnerIterator  RhsIterator;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename traits<XprType>::Scalar Scalar;
-public:
-
-  class InnerIterator
-  {
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor)
-    {
-      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
-      {
-        if (m_lhsIter.index() < m_rhsIter.index())
-          ++m_lhsIter;
-        else
-          ++m_rhsIter;
-      }
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_lhsIter;
-      ++m_rhsIter;
-      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
-      {
-        if (m_lhsIter.index() < m_rhsIter.index())
-          ++m_lhsIter;
-        else
-          ++m_rhsIter;
-      }
-      return *this;
-    }
-    
-    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
-    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return (m_lhsIter && m_rhsIter); }
-
-  protected:
-    LhsIterator m_lhsIter;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit sparse_conjunction_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return (std::min)(m_lhsImpl.nonZerosEstimate(), m_rhsImpl.nonZerosEstimate());
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<LhsArg> m_lhsImpl;
-  evaluator<RhsArg> m_rhsImpl;
-};
-
-// "dense ^ sparse"
-template<typename XprType>
-struct sparse_conjunction_evaluator<XprType, IndexBased, IteratorBased>
-  : evaluator_base<XprType>
-{
-protected:
-  typedef typename XprType::Functor BinaryOp;
-  typedef typename XprType::Lhs LhsArg;
-  typedef typename XprType::Rhs RhsArg;
-  typedef evaluator<LhsArg> LhsEvaluator;
-  typedef typename evaluator<RhsArg>::InnerIterator  RhsIterator;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename traits<XprType>::Scalar Scalar;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(RhsArg::Flags)&RowMajorBit)==RowMajorBit };
-
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
-      : m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_outer(outer)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_rhsIter;
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const
-    { return m_functor(m_lhsEval.coeff(IsRowMajor?m_outer:m_rhsIter.index(),IsRowMajor?m_rhsIter.index():m_outer), m_rhsIter.value()); }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_rhsIter.index(); }
-    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); }
-    EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_rhsIter; }
-    
-  protected:
-    const LhsEvaluator &m_lhsEval;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-    const Index m_outer;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit sparse_conjunction_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_rhsImpl.nonZerosEstimate();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<LhsArg> m_lhsImpl;
-  evaluator<RhsArg> m_rhsImpl;
-};
-
-// "sparse ^ dense"
-template<typename XprType>
-struct sparse_conjunction_evaluator<XprType, IteratorBased, IndexBased>
-  : evaluator_base<XprType>
-{
-protected:
-  typedef typename XprType::Functor BinaryOp;
-  typedef typename XprType::Lhs LhsArg;
-  typedef typename XprType::Rhs RhsArg;
-  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
-  typedef evaluator<RhsArg> RhsEvaluator;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename traits<XprType>::Scalar Scalar;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(LhsArg::Flags)&RowMajorBit)==RowMajorBit };
-
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_outer(outer)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_lhsIter;
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const
-    { return m_functor(m_lhsIter.value(),
-                       m_rhsEval.coeff(IsRowMajor?m_outer:m_lhsIter.index(),IsRowMajor?m_lhsIter.index():m_outer)); }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
-    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_lhsIter; }
-    
-  protected:
-    LhsIterator m_lhsIter;
-    const evaluator<RhsArg> &m_rhsEval;
-    const BinaryOp& m_functor;
-    const Index m_outer;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit sparse_conjunction_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_lhsImpl.nonZerosEstimate();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<LhsArg> m_lhsImpl;
-  evaluator<RhsArg> m_rhsImpl;
-};
-
-}
-
-/***************************************************************************
-* Implementation of SparseMatrixBase and SparseCwise functions/operators
-***************************************************************************/
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE Derived &
-SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived> &other)
-{
-  return derived() = derived() - other.derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE Derived &
-SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other)
-{
-  return derived() = derived() + other.derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator+=(const DiagonalBase<OtherDerived>& other)
-{
-  call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator-=(const DiagonalBase<OtherDerived>& other)
-{
-  call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-    
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE const typename SparseMatrixBase<Derived>::template CwiseProductDenseReturnType<OtherDerived>::Type
-SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) const
-{
-  return typename CwiseProductDenseReturnType<OtherDerived>::Type(derived(), other.derived());
-}
-
-template<typename DenseDerived, typename SparseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
-operator+(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
-}
-
-template<typename SparseDerived, typename DenseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
-operator+(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
-}
-
-template<typename DenseDerived, typename SparseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
-operator-(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
-}
-
-template<typename SparseDerived, typename DenseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
-operator-(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_CWISE_BINARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
deleted file mode 100644
index 32dac0f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
+++ /dev/null
@@ -1,150 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_CWISE_UNARY_OP_H
-#define EIGEN_SPARSE_CWISE_UNARY_OP_H
-
-namespace Eigen { 
-
-namespace internal {
-  
-template<typename UnaryOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>
-  : public evaluator_base<CwiseUnaryOp<UnaryOp,ArgType> >
-{
-  public:
-    typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
-
-    class InnerIterator;
-    
-    enum {
-      CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression())
-    {
-      EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
-      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-    }
-    
-    inline Index nonZerosEstimate() const {
-      return m_argImpl.nonZerosEstimate();
-    }
-
-  protected:
-    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
-    
-    const UnaryOp m_functor;
-    evaluator<ArgType> m_argImpl;
-};
-
-template<typename UnaryOp, typename ArgType>
-class unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::InnerIterator
-    : public unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator
-{
-  protected:
-    typedef typename XprType::Scalar Scalar;
-    typedef typename unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator Base;
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
-      : Base(unaryOp.m_argImpl,outer), m_functor(unaryOp.m_functor)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    { Base::operator++(); return *this; }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
-
-  protected:
-    const UnaryOp m_functor;
-  private:
-    Scalar& valueRef();
-};
-
-template<typename ViewOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>
-  : public evaluator_base<CwiseUnaryView<ViewOp,ArgType> >
-{
-  public:
-    typedef CwiseUnaryView<ViewOp, ArgType> XprType;
-
-    class InnerIterator;
-    
-    enum {
-      CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<ViewOp>::Cost),
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression())
-    {
-      EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<ViewOp>::Cost);
-      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-    }
-
-  protected:
-    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
-    
-    const ViewOp m_functor;
-    evaluator<ArgType> m_argImpl;
-};
-
-template<typename ViewOp, typename ArgType>
-class unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::InnerIterator
-    : public unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator
-{
-  protected:
-    typedef typename XprType::Scalar Scalar;
-    typedef typename unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator Base;
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
-      : Base(unaryOp.m_argImpl,outer), m_functor(unaryOp.m_functor)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    { Base::operator++(); return *this; }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
-    EIGEN_STRONG_INLINE Scalar& valueRef() { return m_functor(Base::valueRef()); }
-
-  protected:
-    const ViewOp m_functor;
-};
-
-} // end namespace internal
-
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-SparseMatrixBase<Derived>::operator*=(const Scalar& other)
-{
-  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
-  internal::evaluator<Derived> thisEval(derived());
-  for (Index j=0; j<outerSize(); ++j)
-    for (EvalIterator i(thisEval,j); i; ++i)
-      i.valueRef() *= other;
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-SparseMatrixBase<Derived>::operator/=(const Scalar& other)
-{
-  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
-  internal::evaluator<Derived> thisEval(derived());
-  for (Index j=0; j<outerSize(); ++j)
-    for (EvalIterator i(thisEval,j); i; ++i)
-      i.valueRef() /= other;
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_CWISE_UNARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDenseProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDenseProduct.h
deleted file mode 100644
index f005a18..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDenseProduct.h
+++ /dev/null
@@ -1,342 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEDENSEPRODUCT_H
-#define EIGEN_SPARSEDENSEPRODUCT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template <> struct product_promote_storage_type<Sparse,Dense, OuterProduct> { typedef Sparse ret; };
-template <> struct product_promote_storage_type<Dense,Sparse, OuterProduct> { typedef Sparse ret; };
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,
-         typename AlphaType,
-         int LhsStorageOrder = ((SparseLhsType::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor,
-         bool ColPerCol = ((DenseRhsType::Flags&RowMajorBit)==0) || DenseRhsType::ColsAtCompileTime==1>
-struct sparse_time_dense_product_impl;
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, true>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
-  typedef evaluator<Lhs> LhsEval;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
-  {
-    LhsEval lhsEval(lhs);
-    
-    Index n = lhs.outerSize();
-#ifdef EIGEN_HAS_OPENMP
-    Eigen::initParallel();
-    Index threads = Eigen::nbThreads();
-#endif
-    
-    for(Index c=0; c<rhs.cols(); ++c)
-    {
-#ifdef EIGEN_HAS_OPENMP
-      // This 20000 threshold has been found experimentally on 2D and 3D Poisson problems.
-      // It basically represents the minimal amount of work to be done to be worth it.
-      if(threads>1 && lhsEval.nonZerosEstimate() > 20000)
-      {
-        #pragma omp parallel for schedule(dynamic,(n+threads*4-1)/(threads*4)) num_threads(threads)
-        for(Index i=0; i<n; ++i)
-          processRow(lhsEval,rhs,res,alpha,i,c);
-      }
-      else
-#endif
-      {
-        for(Index i=0; i<n; ++i)
-          processRow(lhsEval,rhs,res,alpha,i,c);
-      }
-    }
-  }
-  
-  static void processRow(const LhsEval& lhsEval, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha, Index i, Index col)
-  {
-    typename Res::Scalar tmp(0);
-    for(LhsInnerIterator it(lhsEval,i); it ;++it)
-      tmp += it.value() * rhs.coeff(it.index(),col);
-    res.coeffRef(i,col) += alpha * tmp;
-  }
-  
-};
-
-// FIXME: what is the purpose of the following specialization? Is it for the BlockedSparse format?
-// -> let's disable it for now as it is conflicting with generic scalar*matrix and matrix*scalar operators
-// template<typename T1, typename T2/*, int _Options, typename _StrideType*/>
-// struct ScalarBinaryOpTraits<T1, Ref<T2/*, _Options, _StrideType*/> >
-// {
-//   enum {
-//     Defined = 1
-//   };
-//   typedef typename CwiseUnaryOp<scalar_multiple2_op<T1, typename T2::Scalar>, T2>::PlainObject ReturnType;
-// };
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType, ColMajor, true>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename LhsEval::InnerIterator LhsInnerIterator;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index c=0; c<rhs.cols(); ++c)
-    {
-      for(Index j=0; j<lhs.outerSize(); ++j)
-      {
-//        typename Res::Scalar rhs_j = alpha * rhs.coeff(j,c);
-        typename ScalarBinaryOpTraits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j,c));
-        for(LhsInnerIterator it(lhsEval,j); it ;++it)
-          res.coeffRef(it.index(),c) += it.value() * rhs_j;
-      }
-    }
-  }
-};
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, false>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename LhsEval::InnerIterator LhsInnerIterator;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
-  {
-    Index n = lhs.rows();
-    LhsEval lhsEval(lhs);
-
-#ifdef EIGEN_HAS_OPENMP
-    Eigen::initParallel();
-    Index threads = Eigen::nbThreads();
-    // This 20000 threshold has been found experimentally on 2D and 3D Poisson problems.
-    // It basically represents the minimal amount of work to be done to be worth it.
-    if(threads>1 && lhsEval.nonZerosEstimate()*rhs.cols() > 20000)
-    {
-      #pragma omp parallel for schedule(dynamic,(n+threads*4-1)/(threads*4)) num_threads(threads)
-      for(Index i=0; i<n; ++i)
-        processRow(lhsEval,rhs,res,alpha,i);
-    }
-    else
-#endif
-    {
-      for(Index i=0; i<n; ++i)
-        processRow(lhsEval, rhs, res, alpha, i);
-    }
-  }
-
-  static void processRow(const LhsEval& lhsEval, const DenseRhsType& rhs, Res& res, const typename Res::Scalar& alpha, Index i)
-  {
-    typename Res::RowXpr res_i(res.row(i));
-    for(LhsInnerIterator it(lhsEval,i); it ;++it)
-      res_i += (alpha*it.value()) * rhs.row(it.index());
-  }
-};
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, ColMajor, false>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
-  {
-    evaluator<Lhs> lhsEval(lhs);
-    for(Index j=0; j<lhs.outerSize(); ++j)
-    {
-      typename Rhs::ConstRowXpr rhs_j(rhs.row(j));
-      for(LhsInnerIterator it(lhsEval,j); it ;++it)
-        res.row(it.index()) += (alpha*it.value()) * rhs_j;
-    }
-  }
-};
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,typename AlphaType>
-inline void sparse_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
-{
-  sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType>::run(lhs, rhs, res, alpha);
-}
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType>
- : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SparseShape,DenseShape,ProductType> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    typedef typename nested_eval<Lhs,((Rhs::Flags&RowMajorBit)==0) ? 1 : Rhs::ColsAtCompileTime>::type LhsNested;
-    typedef typename nested_eval<Rhs,((Lhs::Flags&RowMajorBit)==0) ? 1 : Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    internal::sparse_time_dense_product(lhsNested, rhsNested, dst, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, DenseShape, ProductType>
-  : generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType>
-{};
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SparseShape,ProductType> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  template<typename Dst>
-  static void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    typedef typename nested_eval<Lhs,((Rhs::Flags&RowMajorBit)==0) ? Dynamic : 1>::type LhsNested;
-    typedef typename nested_eval<Rhs,((Lhs::Flags&RowMajorBit)==RowMajorBit) ? 1 : Lhs::RowsAtCompileTime>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    
-    // transpose everything
-    Transpose<Dst> dstT(dst);
-    internal::sparse_time_dense_product(rhsNested.transpose(), lhsNested.transpose(), dstT, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, DenseShape, SparseTriangularShape, ProductType>
-  : generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType>
-{};
-
-template<typename LhsT, typename RhsT, bool NeedToTranspose>
-struct sparse_dense_outer_product_evaluator
-{
-protected:
-  typedef typename conditional<NeedToTranspose,RhsT,LhsT>::type Lhs1;
-  typedef typename conditional<NeedToTranspose,LhsT,RhsT>::type ActualRhs;
-  typedef Product<LhsT,RhsT,DefaultProduct> ProdXprType;
-  
-  // if the actual left-hand side is a dense vector,
-  // then build a sparse-view so that we can seamlessly iterate over it.
-  typedef typename conditional<is_same<typename internal::traits<Lhs1>::StorageKind,Sparse>::value,
-            Lhs1, SparseView<Lhs1> >::type ActualLhs;
-  typedef typename conditional<is_same<typename internal::traits<Lhs1>::StorageKind,Sparse>::value,
-            Lhs1 const&, SparseView<Lhs1> >::type LhsArg;
-            
-  typedef evaluator<ActualLhs> LhsEval;
-  typedef evaluator<ActualRhs> RhsEval;
-  typedef typename evaluator<ActualLhs>::InnerIterator LhsIterator;
-  typedef typename ProdXprType::Scalar Scalar;
-  
-public:
-  enum {
-    Flags = NeedToTranspose ? RowMajorBit : 0,
-    CoeffReadCost = HugeCost
-  };
-  
-  class InnerIterator : public LhsIterator
-  {
-  public:
-    InnerIterator(const sparse_dense_outer_product_evaluator &xprEval, Index outer)
-      : LhsIterator(xprEval.m_lhsXprImpl, 0),
-        m_outer(outer),
-        m_empty(false),
-        m_factor(get(xprEval.m_rhsXprImpl, outer, typename internal::traits<ActualRhs>::StorageKind() ))
-    {}
-    
-    EIGEN_STRONG_INLINE Index outer() const { return m_outer; }
-    EIGEN_STRONG_INLINE Index row()   const { return NeedToTranspose ? m_outer : LhsIterator::index(); }
-    EIGEN_STRONG_INLINE Index col()   const { return NeedToTranspose ? LhsIterator::index() : m_outer; }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return LhsIterator::value() * m_factor; }
-    EIGEN_STRONG_INLINE operator bool() const { return LhsIterator::operator bool() && (!m_empty); }
-    
-  protected:
-    Scalar get(const RhsEval &rhs, Index outer, Dense = Dense()) const
-    {
-      return rhs.coeff(outer);
-    }
-    
-    Scalar get(const RhsEval &rhs, Index outer, Sparse = Sparse())
-    {
-      typename RhsEval::InnerIterator it(rhs, outer);
-      if (it && it.index()==0 && it.value()!=Scalar(0))
-        return it.value();
-      m_empty = true;
-      return Scalar(0);
-    }
-    
-    Index m_outer;
-    bool m_empty;
-    Scalar m_factor;
-  };
-  
-  sparse_dense_outer_product_evaluator(const Lhs1 &lhs, const ActualRhs &rhs)
-     : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  // transpose case
-  sparse_dense_outer_product_evaluator(const ActualRhs &rhs, const Lhs1 &lhs)
-     : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-    
-protected:
-  const LhsArg m_lhs;
-  evaluator<ActualLhs> m_lhsXprImpl;
-  evaluator<ActualRhs> m_rhsXprImpl;
-};
-
-// sparse * dense outer product
-template<typename Lhs, typename Rhs>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, SparseShape, DenseShape>
-  : sparse_dense_outer_product_evaluator<Lhs,Rhs, Lhs::IsRowMajor>
-{
-  typedef sparse_dense_outer_product_evaluator<Lhs,Rhs, Lhs::IsRowMajor> Base;
-  
-  typedef Product<Lhs, Rhs> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-
-  explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.lhs(), xpr.rhs())
-  {}
-  
-};
-
-template<typename Lhs, typename Rhs>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, DenseShape, SparseShape>
-  : sparse_dense_outer_product_evaluator<Lhs,Rhs, Rhs::IsRowMajor>
-{
-  typedef sparse_dense_outer_product_evaluator<Lhs,Rhs, Rhs::IsRowMajor> Base;
-  
-  typedef Product<Lhs, Rhs> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-
-  explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.lhs(), xpr.rhs())
-  {}
-  
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEDENSEPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDiagonalProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
deleted file mode 100644
index 941c03b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
+++ /dev/null
@@ -1,138 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_DIAGONAL_PRODUCT_H
-#define EIGEN_SPARSE_DIAGONAL_PRODUCT_H
-
-namespace Eigen { 
-
-// The product of a diagonal matrix with a sparse matrix can be easily
-// implemented using expression template.
-// We have two consider very different cases:
-// 1 - diag * row-major sparse
-//     => each inner vector <=> scalar * sparse vector product
-//     => so we can reuse CwiseUnaryOp::InnerIterator
-// 2 - diag * col-major sparse
-//     => each inner vector <=> densevector * sparse vector cwise product
-//     => again, we can reuse specialization of CwiseBinaryOp::InnerIterator
-//        for that particular case
-// The two other cases are symmetric.
-
-namespace internal {
-
-enum {
-  SDP_AsScalarProduct,
-  SDP_AsCwiseProduct
-};
-  
-template<typename SparseXprType, typename DiagonalCoeffType, int SDP_Tag>
-struct sparse_diagonal_product_evaluator;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, DiagonalShape, SparseShape>
-  : public sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType, Rhs::Flags&RowMajorBit?SDP_AsScalarProduct:SDP_AsCwiseProduct>
-{
-  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
-  enum { CoeffReadCost = HugeCost, Flags = Rhs::Flags&RowMajorBit, Alignment = 0 }; // FIXME CoeffReadCost & Flags
-  
-  typedef sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType, Rhs::Flags&RowMajorBit?SDP_AsScalarProduct:SDP_AsCwiseProduct> Base;
-  explicit product_evaluator(const XprType& xpr) : Base(xpr.rhs(), xpr.lhs().diagonal()) {}
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, SparseShape, DiagonalShape>
-  : public sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>, Lhs::Flags&RowMajorBit?SDP_AsCwiseProduct:SDP_AsScalarProduct>
-{
-  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
-  enum { CoeffReadCost = HugeCost, Flags = Lhs::Flags&RowMajorBit, Alignment = 0 }; // FIXME CoeffReadCost & Flags
-  
-  typedef sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>, Lhs::Flags&RowMajorBit?SDP_AsCwiseProduct:SDP_AsScalarProduct> Base;
-  explicit product_evaluator(const XprType& xpr) : Base(xpr.lhs(), xpr.rhs().diagonal().transpose()) {}
-};
-
-template<typename SparseXprType, typename DiagonalCoeffType>
-struct sparse_diagonal_product_evaluator<SparseXprType, DiagonalCoeffType, SDP_AsScalarProduct>
-{
-protected:
-  typedef typename evaluator<SparseXprType>::InnerIterator SparseXprInnerIterator;
-  typedef typename SparseXprType::Scalar Scalar;
-  
-public:
-  class InnerIterator : public SparseXprInnerIterator
-  {
-  public:
-    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
-      : SparseXprInnerIterator(xprEval.m_sparseXprImpl, outer),
-        m_coeff(xprEval.m_diagCoeffImpl.coeff(outer))
-    {}
-    
-    EIGEN_STRONG_INLINE Scalar value() const { return m_coeff * SparseXprInnerIterator::value(); }
-  protected:
-    typename DiagonalCoeffType::Scalar m_coeff;
-  };
-  
-  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagonalCoeffType &diagCoeff)
-    : m_sparseXprImpl(sparseXpr), m_diagCoeffImpl(diagCoeff)
-  {}
-
-  Index nonZerosEstimate() const { return m_sparseXprImpl.nonZerosEstimate(); }
-    
-protected:
-  evaluator<SparseXprType> m_sparseXprImpl;
-  evaluator<DiagonalCoeffType> m_diagCoeffImpl;
-};
-
-
-template<typename SparseXprType, typename DiagCoeffType>
-struct sparse_diagonal_product_evaluator<SparseXprType, DiagCoeffType, SDP_AsCwiseProduct>
-{
-  typedef typename SparseXprType::Scalar Scalar;
-  typedef typename SparseXprType::StorageIndex StorageIndex;
-  
-  typedef typename nested_eval<DiagCoeffType,SparseXprType::IsRowMajor ? SparseXprType::RowsAtCompileTime
-                                                                       : SparseXprType::ColsAtCompileTime>::type DiagCoeffNested;
-  
-  class InnerIterator
-  {
-    typedef typename evaluator<SparseXprType>::InnerIterator SparseXprIter;
-  public:
-    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
-      : m_sparseIter(xprEval.m_sparseXprEval, outer), m_diagCoeffNested(xprEval.m_diagCoeffNested)
-    {}
-    
-    inline Scalar value() const { return m_sparseIter.value() * m_diagCoeffNested.coeff(index()); }
-    inline StorageIndex index() const  { return m_sparseIter.index(); }
-    inline Index outer() const  { return m_sparseIter.outer(); }
-    inline Index col() const    { return SparseXprType::IsRowMajor ? m_sparseIter.index() : m_sparseIter.outer(); }
-    inline Index row() const    { return SparseXprType::IsRowMajor ? m_sparseIter.outer() : m_sparseIter.index(); }
-    
-    EIGEN_STRONG_INLINE InnerIterator& operator++() { ++m_sparseIter; return *this; }
-    inline operator bool() const  { return m_sparseIter; }
-    
-  protected:
-    SparseXprIter m_sparseIter;
-    DiagCoeffNested m_diagCoeffNested;
-  };
-  
-  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagCoeffType &diagCoeff)
-    : m_sparseXprEval(sparseXpr), m_diagCoeffNested(diagCoeff)
-  {}
-
-  Index nonZerosEstimate() const { return m_sparseXprEval.nonZerosEstimate(); }
-    
-protected:
-  evaluator<SparseXprType> m_sparseXprEval;
-  DiagCoeffNested m_diagCoeffNested;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_DIAGONAL_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDot.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDot.h
deleted file mode 100644
index 38bc4aa..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseDot.h
+++ /dev/null
@@ -1,98 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_DOT_H
-#define EIGEN_SPARSE_DOT_H
-
-namespace Eigen { 
-
-template<typename Derived>
-template<typename OtherDerived>
-typename internal::traits<Derived>::Scalar
-SparseMatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  eigen_assert(size() == other.size());
-  eigen_assert(other.size()>0 && "you are using a non initialized vector");
-
-  internal::evaluator<Derived> thisEval(derived());
-  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
-  Scalar res(0);
-  while (i)
-  {
-    res += numext::conj(i.value()) * other.coeff(i.index());
-    ++i;
-  }
-  return res;
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-typename internal::traits<Derived>::Scalar
-SparseMatrixBase<Derived>::dot(const SparseMatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  eigen_assert(size() == other.size());
-
-  internal::evaluator<Derived> thisEval(derived());
-  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
-  
-  internal::evaluator<OtherDerived>  otherEval(other.derived());
-  typename internal::evaluator<OtherDerived>::InnerIterator j(otherEval, 0);
-
-  Scalar res(0);
-  while (i && j)
-  {
-    if (i.index()==j.index())
-    {
-      res += numext::conj(i.value()) * j.value();
-      ++i; ++j;
-    }
-    else if (i.index()<j.index())
-      ++i;
-    else
-      ++j;
-  }
-  return res;
-}
-
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-SparseMatrixBase<Derived>::squaredNorm() const
-{
-  return numext::real((*this).cwiseAbs2().sum());
-}
-
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-SparseMatrixBase<Derived>::norm() const
-{
-  using std::sqrt;
-  return sqrt(squaredNorm());
-}
-
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-SparseMatrixBase<Derived>::blueNorm() const
-{
-  return internal::blueNorm_impl(*this);
-}
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_DOT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseFuzzy.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseFuzzy.h
deleted file mode 100644
index 7d47eb9..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseFuzzy.h
+++ /dev/null
@@ -1,29 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_FUZZY_H
-#define EIGEN_SPARSE_FUZZY_H
-
-namespace Eigen {
-  
-template<typename Derived>
-template<typename OtherDerived>
-bool SparseMatrixBase<Derived>::isApprox(const SparseMatrixBase<OtherDerived>& other, const RealScalar &prec) const
-{
-  const typename internal::nested_eval<Derived,2,PlainObject>::type actualA(derived());
-  typename internal::conditional<bool(IsRowMajor)==bool(OtherDerived::IsRowMajor),
-    const typename internal::nested_eval<OtherDerived,2,PlainObject>::type,
-    const PlainObject>::type actualB(other.derived());
-
-  return (actualA - actualB).squaredNorm() <= prec * prec * numext::mini(actualA.squaredNorm(), actualB.squaredNorm());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_FUZZY_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMap.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMap.h
deleted file mode 100644
index f99be33..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMap.h
+++ /dev/null
@@ -1,305 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_MAP_H
-#define EIGEN_SPARSE_MAP_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct traits<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  typedef traits<PlainObjectType> TraitsBase;
-  enum {
-    Flags = TraitsBase::Flags & (~NestByRefBit)
-  };
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct traits<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  typedef traits<PlainObjectType> TraitsBase;
-  enum {
-    Flags = TraitsBase::Flags & (~ (NestByRefBit | LvalueBit))
-  };
-};
-
-} // end namespace internal
-
-template<typename Derived,
-         int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors
-> class SparseMapBase;
-
-/** \ingroup SparseCore_Module
-  * class SparseMapBase
-  * \brief Common base class for Map and Ref instance of sparse matrix and vector.
-  */
-template<typename Derived>
-class SparseMapBase<Derived,ReadOnlyAccessors>
-  : public SparseCompressedBase<Derived>
-{
-  public:
-    typedef SparseCompressedBase<Derived> Base;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    enum { IsRowMajor = Base::IsRowMajor };
-    using Base::operator=;
-  protected:
-    
-    typedef typename internal::conditional<
-                         bool(internal::is_lvalue<Derived>::value),
-                         Scalar *, const Scalar *>::type ScalarPointer;
-    typedef typename internal::conditional<
-                         bool(internal::is_lvalue<Derived>::value),
-                         StorageIndex *, const StorageIndex *>::type IndexPointer;
-
-    Index   m_outerSize;
-    Index   m_innerSize;
-    Array<StorageIndex,2,1>  m_zero_nnz;
-    IndexPointer  m_outerIndex;
-    IndexPointer  m_innerIndices;
-    ScalarPointer m_values;
-    IndexPointer  m_innerNonZeros;
-
-  public:
-
-    /** \copydoc SparseMatrixBase::rows() */
-    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
-    /** \copydoc SparseMatrixBase::cols() */
-    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
-    /** \copydoc SparseMatrixBase::innerSize() */
-    inline Index innerSize() const { return m_innerSize; }
-    /** \copydoc SparseMatrixBase::outerSize() */
-    inline Index outerSize() const { return m_outerSize; }
-    /** \copydoc SparseCompressedBase::nonZeros */
-    inline Index nonZeros() const { return m_zero_nnz[1]; }
-    
-    /** \copydoc SparseCompressedBase::isCompressed */
-    bool isCompressed() const { return m_innerNonZeros==0; }
-
-    //----------------------------------------
-    // direct access interface
-    /** \copydoc SparseMatrix::valuePtr */
-    inline const Scalar* valuePtr() const { return m_values; }
-    /** \copydoc SparseMatrix::innerIndexPtr */
-    inline const StorageIndex* innerIndexPtr() const { return m_innerIndices; }
-    /** \copydoc SparseMatrix::outerIndexPtr */
-    inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
-    /** \copydoc SparseMatrix::innerNonZeroPtr */
-    inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
-    //----------------------------------------
-
-    /** \copydoc SparseMatrix::coeff */
-    inline Scalar coeff(Index row, Index col) const
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      Index start = m_outerIndex[outer];
-      Index end = isCompressed() ? m_outerIndex[outer+1] : start + m_innerNonZeros[outer];
-      if (start==end)
-        return Scalar(0);
-      else if (end>0 && inner==m_innerIndices[end-1])
-        return m_values[end-1];
-      // ^^  optimization: let's first check if it is the last coefficient
-      // (very common in high level algorithms)
-
-      const StorageIndex* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end-1],inner);
-      const Index id = r-&m_innerIndices[0];
-      return ((*r==inner) && (id<end)) ? m_values[id] : Scalar(0);
-    }
-
-    inline SparseMapBase(Index rows, Index cols, Index nnz, IndexPointer outerIndexPtr, IndexPointer innerIndexPtr,
-                              ScalarPointer valuePtr, IndexPointer innerNonZerosPtr = 0)
-      : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(outerIndexPtr),
-        m_innerIndices(innerIndexPtr), m_values(valuePtr), m_innerNonZeros(innerNonZerosPtr)
-    {}
-
-    // for vectors
-    inline SparseMapBase(Index size, Index nnz, IndexPointer innerIndexPtr, ScalarPointer valuePtr)
-      : m_outerSize(1), m_innerSize(size), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(m_zero_nnz.data()),
-        m_innerIndices(innerIndexPtr), m_values(valuePtr), m_innerNonZeros(0)
-    {}
-
-    /** Empty destructor */
-    inline ~SparseMapBase() {}
-
-  protected:
-    inline SparseMapBase() {}
-};
-
-/** \ingroup SparseCore_Module
-  * class SparseMapBase
-  * \brief Common base class for writable Map and Ref instance of sparse matrix and vector.
-  */
-template<typename Derived>
-class SparseMapBase<Derived,WriteAccessors>
-  : public SparseMapBase<Derived,ReadOnlyAccessors>
-{
-    typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
-    
-  public:
-    typedef SparseMapBase<Derived, ReadOnlyAccessors> Base;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    enum { IsRowMajor = Base::IsRowMajor };
-    
-    using Base::operator=;
-
-  public:
-    
-    //----------------------------------------
-    // direct access interface
-    using Base::valuePtr;
-    using Base::innerIndexPtr;
-    using Base::outerIndexPtr;
-    using Base::innerNonZeroPtr;
-    /** \copydoc SparseMatrix::valuePtr */
-    inline Scalar* valuePtr()              { return Base::m_values; }
-    /** \copydoc SparseMatrix::innerIndexPtr */
-    inline StorageIndex* innerIndexPtr()   { return Base::m_innerIndices; }
-    /** \copydoc SparseMatrix::outerIndexPtr */
-    inline StorageIndex* outerIndexPtr()   { return Base::m_outerIndex; }
-    /** \copydoc SparseMatrix::innerNonZeroPtr */
-    inline StorageIndex* innerNonZeroPtr() { return Base::m_innerNonZeros; }
-    //----------------------------------------
-
-    /** \copydoc SparseMatrix::coeffRef */
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      Index start = Base::m_outerIndex[outer];
-      Index end = Base::isCompressed() ? Base::m_outerIndex[outer+1] : start + Base::m_innerNonZeros[outer];
-      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
-      eigen_assert(end>start && "coeffRef cannot be called on a zero coefficient");
-      StorageIndex* r = std::lower_bound(&Base::m_innerIndices[start],&Base::m_innerIndices[end],inner);
-      const Index id = r - &Base::m_innerIndices[0];
-      eigen_assert((*r==inner) && (id<end) && "coeffRef cannot be called on a zero coefficient");
-      return const_cast<Scalar*>(Base::m_values)[id];
-    }
-    
-    inline SparseMapBase(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr,
-                         Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
-    {}
-
-    // for vectors
-    inline SparseMapBase(Index size, Index nnz, StorageIndex* innerIndexPtr, Scalar* valuePtr)
-      : Base(size, nnz, innerIndexPtr, valuePtr)
-    {}
-
-    /** Empty destructor */
-    inline ~SparseMapBase() {}
-
-  protected:
-    inline SparseMapBase() {}
-};
-
-/** \ingroup SparseCore_Module
-  *
-  * \brief Specialization of class Map for SparseMatrix-like storage.
-  *
-  * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of class SparseMatrix.
-  *
-  * \sa class Map, class SparseMatrix, class Ref<SparseMatrixType,Options>
-  */
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public SparseMapBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-#else
-template<typename SparseMatrixType>
-class Map<SparseMatrixType>
-  : public SparseMapBase<Derived,WriteAccessors>
-#endif
-{
-  public:
-    typedef SparseMapBase<Map> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
-    enum { IsRowMajor = Base::IsRowMajor };
-
-  public:
-
-    /** Constructs a read-write Map to a sparse matrix of size \a rows x \a cols, containing \a nnz non-zero coefficients,
-      * stored as a sparse format as defined by the pointers \a outerIndexPtr, \a innerIndexPtr, and \a valuePtr.
-      * If the optional parameter \a innerNonZerosPtr is the null pointer, then a standard compressed format is assumed.
-      *
-      * This constructor is available only if \c SparseMatrixType is non-const.
-      *
-      * More details on the expected storage schemes are given in the \ref TutorialSparse "manual pages".
-      */
-    inline Map(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr,
-               StorageIndex* innerIndexPtr, Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
-    {}
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** Empty destructor */
-    inline ~Map() {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public SparseMapBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-{
-  public:
-    typedef SparseMapBase<Map> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
-    enum { IsRowMajor = Base::IsRowMajor };
-
-  public:
-#endif
-    /** This is the const version of the above constructor.
-      *
-      * This constructor is available only if \c SparseMatrixType is const, e.g.:
-      * \code Map<const SparseMatrix<double> >  \endcode
-      */
-    inline Map(Index rows, Index cols, Index nnz, const StorageIndex* outerIndexPtr,
-               const StorageIndex* innerIndexPtr, const Scalar* valuePtr, const StorageIndex* innerNonZerosPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
-    {}
-
-    /** Empty destructor */
-    inline ~Map() {}
-};
-
-namespace internal {
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_MAP_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMatrix.h
deleted file mode 100644
index 616b4a0..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMatrix.h
+++ /dev/null
@@ -1,1518 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEMATRIX_H
-#define EIGEN_SPARSEMATRIX_H
-
-namespace Eigen { 
-
-/** \ingroup SparseCore_Module
-  *
-  * \class SparseMatrix
-  *
-  * \brief A versatible sparse matrix representation
-  *
-  * This class implements a more versatile variants of the common \em compressed row/column storage format.
-  * Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index.
-  * All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra
-  * space in between the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero
-  * can be done with limited memory reallocation and copies.
-  *
-  * A call to the function makeCompressed() turns the matrix into the standard \em compressed format
-  * compatible with many library.
-  *
-  * More details on this storage sceheme are given in the \ref TutorialSparse "manual pages".
-  *
-  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
-  * \tparam _Options Union of bit flags controlling the storage scheme. Currently the only possibility
-  *                 is ColMajor or RowMajor. The default is 0 which means column-major.
-  * \tparam _StorageIndex the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int.
-  *
-  * \warning In %Eigen 3.2, the undocumented type \c SparseMatrix::Index was improperly defined as the storage index type (e.g., int),
-  *          whereas it is now (starting from %Eigen 3.3) deprecated and always defined as Eigen::Index.
-  *          Codes making use of \c SparseMatrix::Index, might thus likely have to be changed to use \c SparseMatrix::StorageIndex instead.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN.
-  */
-
-namespace internal {
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct traits<SparseMatrix<_Scalar, _Options, _StorageIndex> >
-{
-  typedef _Scalar Scalar;
-  typedef _StorageIndex StorageIndex;
-  typedef Sparse StorageKind;
-  typedef MatrixXpr XprKind;
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = Dynamic,
-    MaxRowsAtCompileTime = Dynamic,
-    MaxColsAtCompileTime = Dynamic,
-    Flags = _Options | NestByRefBit | LvalueBit | CompressedAccessBit,
-    SupportedAccessPatterns = InnerRandomAccessPattern
-  };
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int DiagIndex>
-struct traits<Diagonal<SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
-{
-  typedef SparseMatrix<_Scalar, _Options, _StorageIndex> MatrixType;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-
-  typedef _Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef _StorageIndex StorageIndex;
-  typedef MatrixXpr XprKind;
-
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = 1,
-    MaxRowsAtCompileTime = Dynamic,
-    MaxColsAtCompileTime = 1,
-    Flags = LvalueBit
-  };
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int DiagIndex>
-struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
- : public traits<Diagonal<SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
-{
-  enum {
-    Flags = 0
-  };
-};
-
-} // end namespace internal
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-class SparseMatrix
-  : public SparseCompressedBase<SparseMatrix<_Scalar, _Options, _StorageIndex> >
-{
-    typedef SparseCompressedBase<SparseMatrix> Base;
-    using Base::convert_index;
-    friend class SparseVector<_Scalar,0,_StorageIndex>;
-    template<typename, typename, typename, typename, typename>
-    friend struct internal::Assignment;
-  public:
-    using Base::isCompressed;
-    using Base::nonZeros;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix)
-    using Base::operator+=;
-    using Base::operator-=;
-
-    typedef MappedSparseMatrix<Scalar,Flags> Map;
-    typedef Diagonal<SparseMatrix> DiagonalReturnType;
-    typedef Diagonal<const SparseMatrix> ConstDiagonalReturnType;
-    typedef typename Base::InnerIterator InnerIterator;
-    typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
-    
-
-    using Base::IsRowMajor;
-    typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
-    enum {
-      Options = _Options
-    };
-
-    typedef typename Base::IndexVector IndexVector;
-    typedef typename Base::ScalarVector ScalarVector;
-  protected:
-    typedef SparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0)> TransposedSparseMatrix;
-
-    Index m_outerSize;
-    Index m_innerSize;
-    StorageIndex* m_outerIndex;
-    StorageIndex* m_innerNonZeros;     // optional, if null then the data is compressed
-    Storage m_data;
-
-  public:
-    
-    /** \returns the number of rows of the matrix */
-    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
-    /** \returns the number of columns of the matrix */
-    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
-
-    /** \returns the number of rows (resp. columns) of the matrix if the storage order column major (resp. row major) */
-    inline Index innerSize() const { return m_innerSize; }
-    /** \returns the number of columns (resp. rows) of the matrix if the storage order column major (resp. row major) */
-    inline Index outerSize() const { return m_outerSize; }
-    
-    /** \returns a const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline const Scalar* valuePtr() const { return m_data.valuePtr(); }
-    /** \returns a non-const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline Scalar* valuePtr() { return m_data.valuePtr(); }
-
-    /** \returns a const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
-    /** \returns a non-const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
-
-    /** \returns a const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), innerIndexPtr() */
-    inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
-    /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), innerIndexPtr() */
-    inline StorageIndex* outerIndexPtr() { return m_outerIndex; }
-
-    /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
-    /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline StorageIndex* innerNonZeroPtr() { return m_innerNonZeros; }
-
-    /** \internal */
-    inline Storage& data() { return m_data; }
-    /** \internal */
-    inline const Storage& data() const { return m_data; }
-
-    /** \returns the value of the matrix at position \a i, \a j
-      * This function returns Scalar(0) if the element is an explicit \em zero */
-    inline Scalar coeff(Index row, Index col) const
-    {
-      eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
-      
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-      Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
-      return m_data.atInRange(m_outerIndex[outer], end, StorageIndex(inner));
-    }
-
-    /** \returns a non-const reference to the value of the matrix at position \a i, \a j
-      *
-      * If the element does not exist then it is inserted via the insert(Index,Index) function
-      * which itself turns the matrix into a non compressed form if that was not the case.
-      *
-      * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index)
-      * function if the element does not already exist.
-      */
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
-      
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      Index start = m_outerIndex[outer];
-      Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
-      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
-      if(end<=start)
-        return insert(row,col);
-      const Index p = m_data.searchLowerIndex(start,end-1,StorageIndex(inner));
-      if((p<end) && (m_data.index(p)==inner))
-        return m_data.value(p);
-      else
-        return insert(row,col);
-    }
-
-    /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
-      * The non zero coefficient must \b not already exist.
-      *
-      * If the matrix \c *this is in compressed mode, then \c *this is turned into uncompressed
-      * mode while reserving room for 2 x this->innerSize() non zeros if reserve(Index) has not been called earlier.
-      * In this case, the insertion procedure is optimized for a \e sequential insertion mode where elements are assumed to be
-      * inserted by increasing outer-indices.
-      * 
-      * If that's not the case, then it is strongly recommended to either use a triplet-list to assemble the matrix, or to first
-      * call reserve(const SizesType &) to reserve the appropriate number of non-zero elements per inner vector.
-      *
-      * Assuming memory has been appropriately reserved, this function performs a sorted insertion in O(1)
-      * if the elements of each inner vector are inserted in increasing inner index order, and in O(nnz_j) for a random insertion.
-      *
-      */
-    Scalar& insert(Index row, Index col);
-
-  public:
-
-    /** Removes all non zeros but keep allocated memory
-      *
-      * This function does not free the currently allocated memory. To release as much as memory as possible,
-      * call \code mat.data().squeeze(); \endcode after resizing it.
-      * 
-      * \sa resize(Index,Index), data()
-      */
-    inline void setZero()
-    {
-      m_data.clear();
-      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(StorageIndex));
-      if(m_innerNonZeros)
-        memset(m_innerNonZeros, 0, (m_outerSize)*sizeof(StorageIndex));
-    }
-
-    /** Preallocates \a reserveSize non zeros.
-      *
-      * Precondition: the matrix must be in compressed mode. */
-    inline void reserve(Index reserveSize)
-    {
-      eigen_assert(isCompressed() && "This function does not make sense in non compressed mode.");
-      m_data.reserve(reserveSize);
-    }
-    
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** Preallocates \a reserveSize[\c j] non zeros for each column (resp. row) \c j.
-      *
-      * This function turns the matrix in non-compressed mode.
-      * 
-      * The type \c SizesType must expose the following interface:
-        \code
-        typedef value_type;
-        const value_type& operator[](i) const;
-        \endcode
-      * for \c i in the [0,this->outerSize()[ range.
-      * Typical choices include std::vector<int>, Eigen::VectorXi, Eigen::VectorXi::Constant, etc.
-      */
-    template<class SizesType>
-    inline void reserve(const SizesType& reserveSizes);
-    #else
-    template<class SizesType>
-    inline void reserve(const SizesType& reserveSizes, const typename SizesType::value_type& enableif =
-    #if (!EIGEN_COMP_MSVC) || (EIGEN_COMP_MSVC>=1500) // MSVC 2005 fails to compile with this typename
-        typename
-    #endif
-        SizesType::value_type())
-    {
-      EIGEN_UNUSED_VARIABLE(enableif);
-      reserveInnerVectors(reserveSizes);
-    }
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-  protected:
-    template<class SizesType>
-    inline void reserveInnerVectors(const SizesType& reserveSizes)
-    {
-      if(isCompressed())
-      {
-        Index totalReserveSize = 0;
-        // turn the matrix into non-compressed mode
-        m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-        if (!m_innerNonZeros) internal::throw_std_bad_alloc();
-        
-        // temporarily use m_innerSizes to hold the new starting points.
-        StorageIndex* newOuterIndex = m_innerNonZeros;
-        
-        StorageIndex count = 0;
-        for(Index j=0; j<m_outerSize; ++j)
-        {
-          newOuterIndex[j] = count;
-          count += reserveSizes[j] + (m_outerIndex[j+1]-m_outerIndex[j]);
-          totalReserveSize += reserveSizes[j];
-        }
-        m_data.reserve(totalReserveSize);
-        StorageIndex previousOuterIndex = m_outerIndex[m_outerSize];
-        for(Index j=m_outerSize-1; j>=0; --j)
-        {
-          StorageIndex innerNNZ = previousOuterIndex - m_outerIndex[j];
-          for(Index i=innerNNZ-1; i>=0; --i)
-          {
-            m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i);
-            m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i);
-          }
-          previousOuterIndex = m_outerIndex[j];
-          m_outerIndex[j] = newOuterIndex[j];
-          m_innerNonZeros[j] = innerNNZ;
-        }
-        if(m_outerSize>0)
-          m_outerIndex[m_outerSize] = m_outerIndex[m_outerSize-1] + m_innerNonZeros[m_outerSize-1] + reserveSizes[m_outerSize-1];
-        
-        m_data.resize(m_outerIndex[m_outerSize]);
-      }
-      else
-      {
-        StorageIndex* newOuterIndex = static_cast<StorageIndex*>(std::malloc((m_outerSize+1)*sizeof(StorageIndex)));
-        if (!newOuterIndex) internal::throw_std_bad_alloc();
-        
-        StorageIndex count = 0;
-        for(Index j=0; j<m_outerSize; ++j)
-        {
-          newOuterIndex[j] = count;
-          StorageIndex alreadyReserved = (m_outerIndex[j+1]-m_outerIndex[j]) - m_innerNonZeros[j];
-          StorageIndex toReserve = std::max<StorageIndex>(reserveSizes[j], alreadyReserved);
-          count += toReserve + m_innerNonZeros[j];
-        }
-        newOuterIndex[m_outerSize] = count;
-        
-        m_data.resize(count);
-        for(Index j=m_outerSize-1; j>=0; --j)
-        {
-          Index offset = newOuterIndex[j] - m_outerIndex[j];
-          if(offset>0)
-          {
-            StorageIndex innerNNZ = m_innerNonZeros[j];
-            for(Index i=innerNNZ-1; i>=0; --i)
-            {
-              m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i);
-              m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i);
-            }
-          }
-        }
-        
-        std::swap(m_outerIndex, newOuterIndex);
-        std::free(newOuterIndex);
-      }
-      
-    }
-  public:
-
-    //--- low level purely coherent filling ---
-
-    /** \internal
-      * \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
-      * - the nonzero does not already exist
-      * - the new coefficient is the last one according to the storage order
-      *
-      * Before filling a given inner vector you must call the statVec(Index) function.
-      *
-      * After an insertion session, you should call the finalize() function.
-      *
-      * \sa insert, insertBackByOuterInner, startVec */
-    inline Scalar& insertBack(Index row, Index col)
-    {
-      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
-    }
-
-    /** \internal
-      * \sa insertBack, startVec */
-    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
-    {
-      eigen_assert(Index(m_outerIndex[outer+1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)");
-      eigen_assert( (m_outerIndex[outer+1]-m_outerIndex[outer]==0 || m_data.index(m_data.size()-1)<inner) && "Invalid ordered insertion (invalid inner index)");
-      Index p = m_outerIndex[outer+1];
-      ++m_outerIndex[outer+1];
-      m_data.append(Scalar(0), inner);
-      return m_data.value(p);
-    }
-
-    /** \internal
-      * \warning use it only if you know what you are doing */
-    inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
-    {
-      Index p = m_outerIndex[outer+1];
-      ++m_outerIndex[outer+1];
-      m_data.append(Scalar(0), inner);
-      return m_data.value(p);
-    }
-
-    /** \internal
-      * \sa insertBack, insertBackByOuterInner */
-    inline void startVec(Index outer)
-    {
-      eigen_assert(m_outerIndex[outer]==Index(m_data.size()) && "You must call startVec for each inner vector sequentially");
-      eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially");
-      m_outerIndex[outer+1] = m_outerIndex[outer];
-    }
-
-    /** \internal
-      * Must be called after inserting a set of non zero entries using the low level compressed API.
-      */
-    inline void finalize()
-    {
-      if(isCompressed())
-      {
-        StorageIndex size = internal::convert_index<StorageIndex>(m_data.size());
-        Index i = m_outerSize;
-        // find the last filled column
-        while (i>=0 && m_outerIndex[i]==0)
-          --i;
-        ++i;
-        while (i<=m_outerSize)
-        {
-          m_outerIndex[i] = size;
-          ++i;
-        }
-      }
-    }
-
-    //---
-
-    template<typename InputIterators>
-    void setFromTriplets(const InputIterators& begin, const InputIterators& end);
-
-    template<typename InputIterators,typename DupFunctor>
-    void setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
-
-    void sumupDuplicates() { collapseDuplicates(internal::scalar_sum_op<Scalar,Scalar>()); }
-
-    template<typename DupFunctor>
-    void collapseDuplicates(DupFunctor dup_func = DupFunctor());
-
-    //---
-    
-    /** \internal
-      * same as insert(Index,Index) except that the indices are given relative to the storage order */
-    Scalar& insertByOuterInner(Index j, Index i)
-    {
-      return insert(IsRowMajor ? j : i, IsRowMajor ? i : j);
-    }
-
-    /** Turns the matrix into the \em compressed format.
-      */
-    void makeCompressed()
-    {
-      if(isCompressed())
-        return;
-      
-      eigen_internal_assert(m_outerIndex!=0 && m_outerSize>0);
-      
-      Index oldStart = m_outerIndex[1];
-      m_outerIndex[1] = m_innerNonZeros[0];
-      for(Index j=1; j<m_outerSize; ++j)
-      {
-        Index nextOldStart = m_outerIndex[j+1];
-        Index offset = oldStart - m_outerIndex[j];
-        if(offset>0)
-        {
-          for(Index k=0; k<m_innerNonZeros[j]; ++k)
-          {
-            m_data.index(m_outerIndex[j]+k) = m_data.index(oldStart+k);
-            m_data.value(m_outerIndex[j]+k) = m_data.value(oldStart+k);
-          }
-        }
-        m_outerIndex[j+1] = m_outerIndex[j] + m_innerNonZeros[j];
-        oldStart = nextOldStart;
-      }
-      std::free(m_innerNonZeros);
-      m_innerNonZeros = 0;
-      m_data.resize(m_outerIndex[m_outerSize]);
-      m_data.squeeze();
-    }
-
-    /** Turns the matrix into the uncompressed mode */
-    void uncompress()
-    {
-      if(m_innerNonZeros != 0)
-        return; 
-      m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-      for (Index i = 0; i < m_outerSize; i++)
-      {
-        m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i]; 
-      }
-    }
-
-    /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerance \a epsilon */
-    void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
-    {
-      prune(default_prunning_func(reference,epsilon));
-    }
-    
-    /** Turns the matrix into compressed format, and suppresses all nonzeros which do not satisfy the predicate \a keep.
-      * The functor type \a KeepFunc must implement the following function:
-      * \code
-      * bool operator() (const Index& row, const Index& col, const Scalar& value) const;
-      * \endcode
-      * \sa prune(Scalar,RealScalar)
-      */
-    template<typename KeepFunc>
-    void prune(const KeepFunc& keep = KeepFunc())
-    {
-      // TODO optimize the uncompressed mode to avoid moving and allocating the data twice
-      makeCompressed();
-
-      StorageIndex k = 0;
-      for(Index j=0; j<m_outerSize; ++j)
-      {
-        Index previousStart = m_outerIndex[j];
-        m_outerIndex[j] = k;
-        Index end = m_outerIndex[j+1];
-        for(Index i=previousStart; i<end; ++i)
-        {
-          if(keep(IsRowMajor?j:m_data.index(i), IsRowMajor?m_data.index(i):j, m_data.value(i)))
-          {
-            m_data.value(k) = m_data.value(i);
-            m_data.index(k) = m_data.index(i);
-            ++k;
-          }
-        }
-      }
-      m_outerIndex[m_outerSize] = k;
-      m_data.resize(k,0);
-    }
-
-    /** Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched.
-      *
-      * If the sizes of the matrix are decreased, then the matrix is turned to \b uncompressed-mode
-      * and the storage of the out of bounds coefficients is kept and reserved.
-      * Call makeCompressed() to pack the entries and squeeze extra memory.
-      *
-      * \sa reserve(), setZero(), makeCompressed()
-      */
-    void conservativeResize(Index rows, Index cols) 
-    {
-      // No change
-      if (this->rows() == rows && this->cols() == cols) return;
-      
-      // If one dimension is null, then there is nothing to be preserved
-      if(rows==0 || cols==0) return resize(rows,cols);
-
-      Index innerChange = IsRowMajor ? cols - this->cols() : rows - this->rows();
-      Index outerChange = IsRowMajor ? rows - this->rows() : cols - this->cols();
-      StorageIndex newInnerSize = convert_index(IsRowMajor ? cols : rows);
-
-      // Deals with inner non zeros
-      if (m_innerNonZeros)
-      {
-        // Resize m_innerNonZeros
-        StorageIndex *newInnerNonZeros = static_cast<StorageIndex*>(std::realloc(m_innerNonZeros, (m_outerSize + outerChange) * sizeof(StorageIndex)));
-        if (!newInnerNonZeros) internal::throw_std_bad_alloc();
-        m_innerNonZeros = newInnerNonZeros;
-        
-        for(Index i=m_outerSize; i<m_outerSize+outerChange; i++)          
-          m_innerNonZeros[i] = 0;
-      } 
-      else if (innerChange < 0) 
-      {
-        // Inner size decreased: allocate a new m_innerNonZeros
-        m_innerNonZeros = static_cast<StorageIndex*>(std::malloc((m_outerSize + outerChange) * sizeof(StorageIndex)));
-        if (!m_innerNonZeros) internal::throw_std_bad_alloc();
-        for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++)
-          m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i];
-        for(Index i = m_outerSize; i < m_outerSize + outerChange; i++)
-          m_innerNonZeros[i] = 0;
-      }
-      
-      // Change the m_innerNonZeros in case of a decrease of inner size
-      if (m_innerNonZeros && innerChange < 0)
-      {
-        for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++)
-        {
-          StorageIndex &n = m_innerNonZeros[i];
-          StorageIndex start = m_outerIndex[i];
-          while (n > 0 && m_data.index(start+n-1) >= newInnerSize) --n; 
-        }
-      }
-      
-      m_innerSize = newInnerSize;
-
-      // Re-allocate outer index structure if necessary
-      if (outerChange == 0)
-        return;
-          
-      StorageIndex *newOuterIndex = static_cast<StorageIndex*>(std::realloc(m_outerIndex, (m_outerSize + outerChange + 1) * sizeof(StorageIndex)));
-      if (!newOuterIndex) internal::throw_std_bad_alloc();
-      m_outerIndex = newOuterIndex;
-      if (outerChange > 0)
-      {
-        StorageIndex lastIdx = m_outerSize == 0 ? 0 : m_outerIndex[m_outerSize];
-        for(Index i=m_outerSize; i<m_outerSize+outerChange+1; i++)          
-          m_outerIndex[i] = lastIdx; 
-      }
-      m_outerSize += outerChange;
-    }
-    
-    /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero.
-      * 
-      * This function does not free the currently allocated memory. To release as much as memory as possible,
-      * call \code mat.data().squeeze(); \endcode after resizing it.
-      * 
-      * \sa reserve(), setZero()
-      */
-    void resize(Index rows, Index cols)
-    {
-      const Index outerSize = IsRowMajor ? rows : cols;
-      m_innerSize = IsRowMajor ? cols : rows;
-      m_data.clear();
-      if (m_outerSize != outerSize || m_outerSize==0)
-      {
-        std::free(m_outerIndex);
-        m_outerIndex = static_cast<StorageIndex*>(std::malloc((outerSize + 1) * sizeof(StorageIndex)));
-        if (!m_outerIndex) internal::throw_std_bad_alloc();
-        
-        m_outerSize = outerSize;
-      }
-      if(m_innerNonZeros)
-      {
-        std::free(m_innerNonZeros);
-        m_innerNonZeros = 0;
-      }
-      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(StorageIndex));
-    }
-
-    /** \internal
-      * Resize the nonzero vector to \a size */
-    void resizeNonZeros(Index size)
-    {
-      m_data.resize(size);
-    }
-
-    /** \returns a const expression of the diagonal coefficients. */
-    const ConstDiagonalReturnType diagonal() const { return ConstDiagonalReturnType(*this); }
-    
-    /** \returns a read-write expression of the diagonal coefficients.
-      * \warning If the diagonal entries are written, then all diagonal
-      * entries \b must already exist, otherwise an assertion will be raised.
-      */
-    DiagonalReturnType diagonal() { return DiagonalReturnType(*this); }
-
-    /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */
-    inline SparseMatrix()
-      : m_outerSize(-1), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      resize(0, 0);
-    }
-
-    /** Constructs a \a rows \c x \a cols empty matrix */
-    inline SparseMatrix(Index rows, Index cols)
-      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      resize(rows, cols);
-    }
-
-    /** Constructs a sparse matrix from the sparse expression \a other */
-    template<typename OtherDerived>
-    inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other)
-      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-      check_template_parameters();
-      const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
-      if (needToTranspose)
-        *this = other.derived();
-      else
-      {
-        #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-          EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        #endif
-        internal::call_assignment_no_alias(*this, other.derived());
-      }
-    }
-    
-    /** Constructs a sparse matrix from the sparse selfadjoint view \a other */
-    template<typename OtherDerived, unsigned int UpLo>
-    inline SparseMatrix(const SparseSelfAdjointView<OtherDerived, UpLo>& other)
-      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      Base::operator=(other);
-    }
-
-    /** Copy constructor (it performs a deep copy) */
-    inline SparseMatrix(const SparseMatrix& other)
-      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    /** \brief Copy constructor with in-place evaluation */
-    template<typename OtherDerived>
-    SparseMatrix(const ReturnByValue<OtherDerived>& other)
-      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      initAssignment(other);
-      other.evalTo(*this);
-    }
-    
-    /** \brief Copy constructor with in-place evaluation */
-    template<typename OtherDerived>
-    explicit SparseMatrix(const DiagonalBase<OtherDerived>& other)
-      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    /** Swaps the content of two sparse matrices of the same type.
-      * This is a fast operation that simply swaps the underlying pointers and parameters. */
-    inline void swap(SparseMatrix& other)
-    {
-      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
-      std::swap(m_outerIndex, other.m_outerIndex);
-      std::swap(m_innerSize, other.m_innerSize);
-      std::swap(m_outerSize, other.m_outerSize);
-      std::swap(m_innerNonZeros, other.m_innerNonZeros);
-      m_data.swap(other.m_data);
-    }
-
-    /** Sets *this to the identity matrix.
-      * This function also turns the matrix into compressed mode, and drop any reserved memory. */
-    inline void setIdentity()
-    {
-      eigen_assert(rows() == cols() && "ONLY FOR SQUARED MATRICES");
-      this->m_data.resize(rows());
-      Eigen::Map<IndexVector>(this->m_data.indexPtr(), rows()).setLinSpaced(0, StorageIndex(rows()-1));
-      Eigen::Map<ScalarVector>(this->m_data.valuePtr(), rows()).setOnes();
-      Eigen::Map<IndexVector>(this->m_outerIndex, rows()+1).setLinSpaced(0, StorageIndex(rows()));
-      std::free(m_innerNonZeros);
-      m_innerNonZeros = 0;
-    }
-    inline SparseMatrix& operator=(const SparseMatrix& other)
-    {
-      if (other.isRValue())
-      {
-        swap(other.const_cast_derived());
-      }
-      else if(this!=&other)
-      {
-        #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-          EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        #endif
-        initAssignment(other);
-        if(other.isCompressed())
-        {
-          internal::smart_copy(other.m_outerIndex, other.m_outerIndex + m_outerSize + 1, m_outerIndex);
-          m_data = other.m_data;
-        }
-        else
-        {
-          Base::operator=(other);
-        }
-      }
-      return *this;
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename OtherDerived>
-    inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other)
-    { return Base::operator=(other.derived()); }
-
-    template<typename Lhs, typename Rhs>
-    inline SparseMatrix& operator=(const Product<Lhs,Rhs,AliasFreeProduct>& other);
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-    template<typename OtherDerived>
-    EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other);
-
-    friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m)
-    {
-      EIGEN_DBG_SPARSE(
-        s << "Nonzero entries:\n";
-        if(m.isCompressed())
-        {
-          for (Index i=0; i<m.nonZeros(); ++i)
-            s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
-        }
-        else
-        {
-          for (Index i=0; i<m.outerSize(); ++i)
-          {
-            Index p = m.m_outerIndex[i];
-            Index pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
-            Index k=p;
-            for (; k<pe; ++k) {
-              s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") ";
-            }
-            for (; k<m.m_outerIndex[i+1]; ++k) {
-              s << "(_,_) ";
-            }
-          }
-        }
-        s << std::endl;
-        s << std::endl;
-        s << "Outer pointers:\n";
-        for (Index i=0; i<m.outerSize(); ++i) {
-          s << m.m_outerIndex[i] << " ";
-        }
-        s << " $" << std::endl;
-        if(!m.isCompressed())
-        {
-          s << "Inner non zeros:\n";
-          for (Index i=0; i<m.outerSize(); ++i) {
-            s << m.m_innerNonZeros[i] << " ";
-          }
-          s << " $" << std::endl;
-        }
-        s << std::endl;
-      );
-      s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m);
-      return s;
-    }
-
-    /** Destructor */
-    inline ~SparseMatrix()
-    {
-      std::free(m_outerIndex);
-      std::free(m_innerNonZeros);
-    }
-
-    /** Overloaded for performance */
-    Scalar sum() const;
-    
-#   ifdef EIGEN_SPARSEMATRIX_PLUGIN
-#     include EIGEN_SPARSEMATRIX_PLUGIN
-#   endif
-
-protected:
-
-    template<typename Other>
-    void initAssignment(const Other& other)
-    {
-      resize(other.rows(), other.cols());
-      if(m_innerNonZeros)
-      {
-        std::free(m_innerNonZeros);
-        m_innerNonZeros = 0;
-      }
-    }
-
-    /** \internal
-      * \sa insert(Index,Index) */
-    EIGEN_DONT_INLINE Scalar& insertCompressed(Index row, Index col);
-
-    /** \internal
-      * A vector object that is equal to 0 everywhere but v at the position i */
-    class SingletonVector
-    {
-        StorageIndex m_index;
-        StorageIndex m_value;
-      public:
-        typedef StorageIndex value_type;
-        SingletonVector(Index i, Index v)
-          : m_index(convert_index(i)), m_value(convert_index(v))
-        {}
-
-        StorageIndex operator[](Index i) const { return i==m_index ? m_value : 0; }
-    };
-
-    /** \internal
-      * \sa insert(Index,Index) */
-    EIGEN_DONT_INLINE Scalar& insertUncompressed(Index row, Index col);
-
-public:
-    /** \internal
-      * \sa insert(Index,Index) */
-    EIGEN_STRONG_INLINE Scalar& insertBackUncompressed(Index row, Index col)
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      eigen_assert(!isCompressed());
-      eigen_assert(m_innerNonZeros[outer]<=(m_outerIndex[outer+1] - m_outerIndex[outer]));
-
-      Index p = m_outerIndex[outer] + m_innerNonZeros[outer]++;
-      m_data.index(p) = convert_index(inner);
-      return (m_data.value(p) = Scalar(0));
-    }
-protected:
-    struct IndexPosPair {
-      IndexPosPair(Index a_i, Index a_p) : i(a_i), p(a_p) {}
-      Index i;
-      Index p;
-    };
-
-    /** \internal assign \a diagXpr to the diagonal of \c *this
-      * There are different strategies:
-      *   1 - if *this is overwritten (Func==assign_op) or *this is empty, then we can work treat *this as a dense vector expression.
-      *   2 - otherwise, for each diagonal coeff,
-      *     2.a - if it already exists, then we update it,
-      *     2.b - otherwise, if *this is uncompressed and that the current inner-vector has empty room for at least 1 element, then we perform an in-place insertion.
-      *     2.c - otherwise, we'll have to reallocate and copy everything, so instead of doing so for each new element, it is recorded in a std::vector.
-      *   3 - at the end, if some entries failed to be inserted in-place, then we alloc a new buffer, copy each chunk at the right position, and insert the new elements.
-      * 
-      * TODO: some piece of code could be isolated and reused for a general in-place update strategy.
-      * TODO: if we start to defer the insertion of some elements (i.e., case 2.c executed once),
-      *       then it *might* be better to disable case 2.b since they will have to be copied anyway.
-      */
-    template<typename DiagXpr, typename Func>
-    void assignDiagonal(const DiagXpr diagXpr, const Func& assignFunc)
-    {
-      Index n = diagXpr.size();
-
-      const bool overwrite = internal::is_same<Func, internal::assign_op<Scalar,Scalar> >::value;
-      if(overwrite)
-      {
-        if((this->rows()!=n) || (this->cols()!=n))
-          this->resize(n, n);
-      }
-
-      if(m_data.size()==0 || overwrite)
-      {
-        typedef Array<StorageIndex,Dynamic,1> ArrayXI;  
-        this->makeCompressed();
-        this->resizeNonZeros(n);
-        Eigen::Map<ArrayXI>(this->innerIndexPtr(), n).setLinSpaced(0,StorageIndex(n)-1);
-        Eigen::Map<ArrayXI>(this->outerIndexPtr(), n+1).setLinSpaced(0,StorageIndex(n));
-        Eigen::Map<Array<Scalar,Dynamic,1> > values = this->coeffs();
-        values.setZero();
-        internal::call_assignment_no_alias(values, diagXpr, assignFunc);
-      }
-      else
-      {
-        bool isComp = isCompressed();
-        internal::evaluator<DiagXpr> diaEval(diagXpr);
-        std::vector<IndexPosPair> newEntries;
-
-        // 1 - try in-place update and record insertion failures
-        for(Index i = 0; i<n; ++i)
-        {
-          internal::LowerBoundIndex lb = this->lower_bound(i,i);
-          Index p = lb.value;
-          if(lb.found)
-          {
-            // the coeff already exists
-            assignFunc.assignCoeff(m_data.value(p), diaEval.coeff(i));
-          }
-          else if((!isComp) && m_innerNonZeros[i] < (m_outerIndex[i+1]-m_outerIndex[i]))
-          {
-            // non compressed mode with local room for inserting one element
-            m_data.moveChunk(p, p+1, m_outerIndex[i]+m_innerNonZeros[i]-p);
-            m_innerNonZeros[i]++;
-            m_data.value(p) = Scalar(0);
-            m_data.index(p) = StorageIndex(i);
-            assignFunc.assignCoeff(m_data.value(p), diaEval.coeff(i));
-          }
-          else
-          {
-            // defer insertion
-            newEntries.push_back(IndexPosPair(i,p));
-          }
-        }
-        // 2 - insert deferred entries
-        Index n_entries = Index(newEntries.size());
-        if(n_entries>0)
-        {
-          Storage newData(m_data.size()+n_entries);
-          Index prev_p = 0;
-          Index prev_i = 0;
-          for(Index k=0; k<n_entries;++k)
-          {
-            Index i = newEntries[k].i;
-            Index p = newEntries[k].p;
-            internal::smart_copy(m_data.valuePtr()+prev_p, m_data.valuePtr()+p, newData.valuePtr()+prev_p+k);
-            internal::smart_copy(m_data.indexPtr()+prev_p, m_data.indexPtr()+p, newData.indexPtr()+prev_p+k);
-            for(Index j=prev_i;j<i;++j)
-              m_outerIndex[j+1] += k;
-            if(!isComp)
-              m_innerNonZeros[i]++;
-            prev_p = p;
-            prev_i = i;
-            newData.value(p+k) = Scalar(0);
-            newData.index(p+k) = StorageIndex(i);
-            assignFunc.assignCoeff(newData.value(p+k), diaEval.coeff(i));
-          }
-          {
-            internal::smart_copy(m_data.valuePtr()+prev_p, m_data.valuePtr()+m_data.size(), newData.valuePtr()+prev_p+n_entries);
-            internal::smart_copy(m_data.indexPtr()+prev_p, m_data.indexPtr()+m_data.size(), newData.indexPtr()+prev_p+n_entries);
-            for(Index j=prev_i+1;j<=m_outerSize;++j)
-              m_outerIndex[j] += n_entries;
-          }
-          m_data.swap(newData);
-        }
-      }
-    }
-
-private:
-  static void check_template_parameters()
-  {
-    EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
-    EIGEN_STATIC_ASSERT((Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS);
-  }
-
-  struct default_prunning_func {
-    default_prunning_func(const Scalar& ref, const RealScalar& eps) : reference(ref), epsilon(eps) {}
-    inline bool operator() (const Index&, const Index&, const Scalar& value) const
-    {
-      return !internal::isMuchSmallerThan(value, reference, epsilon);
-    }
-    Scalar reference;
-    RealScalar epsilon;
-  };
-};
-
-namespace internal {
-
-template<typename InputIterator, typename SparseMatrixType, typename DupFunctor>
-void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat, DupFunctor dup_func)
-{
-  enum { IsRowMajor = SparseMatrixType::IsRowMajor };
-  typedef typename SparseMatrixType::Scalar Scalar;
-  typedef typename SparseMatrixType::StorageIndex StorageIndex;
-  SparseMatrix<Scalar,IsRowMajor?ColMajor:RowMajor,StorageIndex> trMat(mat.rows(),mat.cols());
-
-  if(begin!=end)
-  {
-    // pass 1: count the nnz per inner-vector
-    typename SparseMatrixType::IndexVector wi(trMat.outerSize());
-    wi.setZero();
-    for(InputIterator it(begin); it!=end; ++it)
-    {
-      eigen_assert(it->row()>=0 && it->row()<mat.rows() && it->col()>=0 && it->col()<mat.cols());
-      wi(IsRowMajor ? it->col() : it->row())++;
-    }
-
-    // pass 2: insert all the elements into trMat
-    trMat.reserve(wi);
-    for(InputIterator it(begin); it!=end; ++it)
-      trMat.insertBackUncompressed(it->row(),it->col()) = it->value();
-
-    // pass 3:
-    trMat.collapseDuplicates(dup_func);
-  }
-
-  // pass 4: transposed copy -> implicit sorting
-  mat = trMat;
-}
-
-}
-
-
-/** Fill the matrix \c *this with the list of \em triplets defined by the iterator range \a begin - \a end.
-  *
-  * A \em triplet is a tuple (i,j,value) defining a non-zero element.
-  * The input list of triplets does not have to be sorted, and can contains duplicated elements.
-  * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up.
-  * This is a \em O(n) operation, with \em n the number of triplet elements.
-  * The initial contents of \c *this is destroyed.
-  * The matrix \c *this must be properly resized beforehand using the SparseMatrix(Index,Index) constructor,
-  * or the resize(Index,Index) method. The sizes are not extracted from the triplet list.
-  *
-  * The \a InputIterators value_type must provide the following interface:
-  * \code
-  * Scalar value() const; // the value
-  * Scalar row() const;   // the row index i
-  * Scalar col() const;   // the column index j
-  * \endcode
-  * See for instance the Eigen::Triplet template class.
-  *
-  * Here is a typical usage example:
-  * \code
-    typedef Triplet<double> T;
-    std::vector<T> tripletList;
-    tripletList.reserve(estimation_of_entries);
-    for(...)
-    {
-      // ...
-      tripletList.push_back(T(i,j,v_ij));
-    }
-    SparseMatrixType m(rows,cols);
-    m.setFromTriplets(tripletList.begin(), tripletList.end());
-    // m is ready to go!
-  * \endcode
-  *
-  * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
-  * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
-  * be explicitly stored into a std::vector for instance.
-  */
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename InputIterators>
-void SparseMatrix<Scalar,_Options,_StorageIndex>::setFromTriplets(const InputIterators& begin, const InputIterators& end)
-{
-  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_StorageIndex> >(begin, end, *this, internal::scalar_sum_op<Scalar,Scalar>());
-}
-
-/** The same as setFromTriplets but when duplicates are met the functor \a dup_func is applied:
-  * \code
-  * value = dup_func(OldValue, NewValue)
-  * \endcode 
-  * Here is a C++11 example keeping the latest entry only:
-  * \code
-  * mat.setFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
-  * \endcode
-  */
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename InputIterators,typename DupFunctor>
-void SparseMatrix<Scalar,_Options,_StorageIndex>::setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
-{
-  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_StorageIndex>, DupFunctor>(begin, end, *this, dup_func);
-}
-
-/** \internal */
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename DupFunctor>
-void SparseMatrix<Scalar,_Options,_StorageIndex>::collapseDuplicates(DupFunctor dup_func)
-{
-  eigen_assert(!isCompressed());
-  // TODO, in practice we should be able to use m_innerNonZeros for that task
-  IndexVector wi(innerSize());
-  wi.fill(-1);
-  StorageIndex count = 0;
-  // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
-  for(Index j=0; j<outerSize(); ++j)
-  {
-    StorageIndex start   = count;
-    Index oldEnd  = m_outerIndex[j]+m_innerNonZeros[j];
-    for(Index k=m_outerIndex[j]; k<oldEnd; ++k)
-    {
-      Index i = m_data.index(k);
-      if(wi(i)>=start)
-      {
-        // we already meet this entry => accumulate it
-        m_data.value(wi(i)) = dup_func(m_data.value(wi(i)), m_data.value(k));
-      }
-      else
-      {
-        m_data.value(count) = m_data.value(k);
-        m_data.index(count) = m_data.index(k);
-        wi(i) = count;
-        ++count;
-      }
-    }
-    m_outerIndex[j] = start;
-  }
-  m_outerIndex[m_outerSize] = count;
-
-  // turn the matrix into compressed form
-  std::free(m_innerNonZeros);
-  m_innerNonZeros = 0;
-  m_data.resize(m_outerIndex[m_outerSize]);
-}
-
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename OtherDerived>
-EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_StorageIndex>& SparseMatrix<Scalar,_Options,_StorageIndex>::operator=(const SparseMatrixBase<OtherDerived>& other)
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-    EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-  #endif
-      
-  const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
-  if (needToTranspose)
-  {
-    #ifdef EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
-      EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
-    #endif
-    // two passes algorithm:
-    //  1 - compute the number of coeffs per dest inner vector
-    //  2 - do the actual copy/eval
-    // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed
-    typedef typename internal::nested_eval<OtherDerived,2,typename internal::plain_matrix_type<OtherDerived>::type >::type OtherCopy;
-    typedef typename internal::remove_all<OtherCopy>::type _OtherCopy;
-    typedef internal::evaluator<_OtherCopy> OtherCopyEval;
-    OtherCopy otherCopy(other.derived());
-    OtherCopyEval otherCopyEval(otherCopy);
-
-    SparseMatrix dest(other.rows(),other.cols());
-    Eigen::Map<IndexVector> (dest.m_outerIndex,dest.outerSize()).setZero();
-
-    // pass 1
-    // FIXME the above copy could be merged with that pass
-    for (Index j=0; j<otherCopy.outerSize(); ++j)
-      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it)
-        ++dest.m_outerIndex[it.index()];
-
-    // prefix sum
-    StorageIndex count = 0;
-    IndexVector positions(dest.outerSize());
-    for (Index j=0; j<dest.outerSize(); ++j)
-    {
-      StorageIndex tmp = dest.m_outerIndex[j];
-      dest.m_outerIndex[j] = count;
-      positions[j] = count;
-      count += tmp;
-    }
-    dest.m_outerIndex[dest.outerSize()] = count;
-    // alloc
-    dest.m_data.resize(count);
-    // pass 2
-    for (StorageIndex j=0; j<otherCopy.outerSize(); ++j)
-    {
-      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it)
-      {
-        Index pos = positions[it.index()]++;
-        dest.m_data.index(pos) = j;
-        dest.m_data.value(pos) = it.value();
-      }
-    }
-    this->swap(dest);
-    return *this;
-  }
-  else
-  {
-    if(other.isRValue())
-    {
-      initAssignment(other.derived());
-    }
-    // there is no special optimization
-    return Base::operator=(other.derived());
-  }
-}
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insert(Index row, Index col)
-{
-  eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
-  
-  const Index outer = IsRowMajor ? row : col;
-  const Index inner = IsRowMajor ? col : row;
-  
-  if(isCompressed())
-  {
-    if(nonZeros()==0)
-    {
-      // reserve space if not already done
-      if(m_data.allocatedSize()==0)
-        m_data.reserve(2*m_innerSize);
-      
-      // turn the matrix into non-compressed mode
-      m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-      if(!m_innerNonZeros) internal::throw_std_bad_alloc();
-      
-      memset(m_innerNonZeros, 0, (m_outerSize)*sizeof(StorageIndex));
-      
-      // pack all inner-vectors to the end of the pre-allocated space
-      // and allocate the entire free-space to the first inner-vector
-      StorageIndex end = convert_index(m_data.allocatedSize());
-      for(Index j=1; j<=m_outerSize; ++j)
-        m_outerIndex[j] = end;
-    }
-    else
-    {
-      // turn the matrix into non-compressed mode
-      m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-      if(!m_innerNonZeros) internal::throw_std_bad_alloc();
-      for(Index j=0; j<m_outerSize; ++j)
-        m_innerNonZeros[j] = m_outerIndex[j+1]-m_outerIndex[j];
-    }
-  }
-  
-  // check whether we can do a fast "push back" insertion
-  Index data_end = m_data.allocatedSize();
-  
-  // First case: we are filling a new inner vector which is packed at the end.
-  // We assume that all remaining inner-vectors are also empty and packed to the end.
-  if(m_outerIndex[outer]==data_end)
-  {
-    eigen_internal_assert(m_innerNonZeros[outer]==0);
-    
-    // pack previous empty inner-vectors to end of the used-space
-    // and allocate the entire free-space to the current inner-vector.
-    StorageIndex p = convert_index(m_data.size());
-    Index j = outer;
-    while(j>=0 && m_innerNonZeros[j]==0)
-      m_outerIndex[j--] = p;
-    
-    // push back the new element
-    ++m_innerNonZeros[outer];
-    m_data.append(Scalar(0), inner);
-    
-    // check for reallocation
-    if(data_end != m_data.allocatedSize())
-    {
-      // m_data has been reallocated
-      //  -> move remaining inner-vectors back to the end of the free-space
-      //     so that the entire free-space is allocated to the current inner-vector.
-      eigen_internal_assert(data_end < m_data.allocatedSize());
-      StorageIndex new_end = convert_index(m_data.allocatedSize());
-      for(Index k=outer+1; k<=m_outerSize; ++k)
-        if(m_outerIndex[k]==data_end)
-          m_outerIndex[k] = new_end;
-    }
-    return m_data.value(p);
-  }
-  
-  // Second case: the next inner-vector is packed to the end
-  // and the current inner-vector end match the used-space.
-  if(m_outerIndex[outer+1]==data_end && m_outerIndex[outer]+m_innerNonZeros[outer]==m_data.size())
-  {
-    eigen_internal_assert(outer+1==m_outerSize || m_innerNonZeros[outer+1]==0);
-    
-    // add space for the new element
-    ++m_innerNonZeros[outer];
-    m_data.resize(m_data.size()+1);
-    
-    // check for reallocation
-    if(data_end != m_data.allocatedSize())
-    {
-      // m_data has been reallocated
-      //  -> move remaining inner-vectors back to the end of the free-space
-      //     so that the entire free-space is allocated to the current inner-vector.
-      eigen_internal_assert(data_end < m_data.allocatedSize());
-      StorageIndex new_end = convert_index(m_data.allocatedSize());
-      for(Index k=outer+1; k<=m_outerSize; ++k)
-        if(m_outerIndex[k]==data_end)
-          m_outerIndex[k] = new_end;
-    }
-    
-    // and insert it at the right position (sorted insertion)
-    Index startId = m_outerIndex[outer];
-    Index p = m_outerIndex[outer]+m_innerNonZeros[outer]-1;
-    while ( (p > startId) && (m_data.index(p-1) > inner) )
-    {
-      m_data.index(p) = m_data.index(p-1);
-      m_data.value(p) = m_data.value(p-1);
-      --p;
-    }
-    
-    m_data.index(p) = convert_index(inner);
-    return (m_data.value(p) = Scalar(0));
-  }
-  
-  if(m_data.size() != m_data.allocatedSize())
-  {
-    // make sure the matrix is compatible to random un-compressed insertion:
-    m_data.resize(m_data.allocatedSize());
-    this->reserveInnerVectors(Array<StorageIndex,Dynamic,1>::Constant(m_outerSize, 2));
-  }
-  
-  return insertUncompressed(row,col);
-}
-    
-template<typename _Scalar, int _Options, typename _StorageIndex>
-EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insertUncompressed(Index row, Index col)
-{
-  eigen_assert(!isCompressed());
-
-  const Index outer = IsRowMajor ? row : col;
-  const StorageIndex inner = convert_index(IsRowMajor ? col : row);
-
-  Index room = m_outerIndex[outer+1] - m_outerIndex[outer];
-  StorageIndex innerNNZ = m_innerNonZeros[outer];
-  if(innerNNZ>=room)
-  {
-    // this inner vector is full, we need to reallocate the whole buffer :(
-    reserve(SingletonVector(outer,std::max<StorageIndex>(2,innerNNZ)));
-  }
-
-  Index startId = m_outerIndex[outer];
-  Index p = startId + m_innerNonZeros[outer];
-  while ( (p > startId) && (m_data.index(p-1) > inner) )
-  {
-    m_data.index(p) = m_data.index(p-1);
-    m_data.value(p) = m_data.value(p-1);
-    --p;
-  }
-  eigen_assert((p<=startId || m_data.index(p-1)!=inner) && "you cannot insert an element that already exists, you must call coeffRef to this end");
-
-  m_innerNonZeros[outer]++;
-
-  m_data.index(p) = inner;
-  return (m_data.value(p) = Scalar(0));
-}
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insertCompressed(Index row, Index col)
-{
-  eigen_assert(isCompressed());
-
-  const Index outer = IsRowMajor ? row : col;
-  const Index inner = IsRowMajor ? col : row;
-
-  Index previousOuter = outer;
-  if (m_outerIndex[outer+1]==0)
-  {
-    // we start a new inner vector
-    while (previousOuter>=0 && m_outerIndex[previousOuter]==0)
-    {
-      m_outerIndex[previousOuter] = convert_index(m_data.size());
-      --previousOuter;
-    }
-    m_outerIndex[outer+1] = m_outerIndex[outer];
-  }
-
-  // here we have to handle the tricky case where the outerIndex array
-  // starts with: [ 0 0 0 0 0 1 ...] and we are inserted in, e.g.,
-  // the 2nd inner vector...
-  bool isLastVec = (!(previousOuter==-1 && m_data.size()!=0))
-                && (std::size_t(m_outerIndex[outer+1]) == m_data.size());
-
-  std::size_t startId = m_outerIndex[outer];
-  // FIXME let's make sure sizeof(long int) == sizeof(std::size_t)
-  std::size_t p = m_outerIndex[outer+1];
-  ++m_outerIndex[outer+1];
-
-  double reallocRatio = 1;
-  if (m_data.allocatedSize()<=m_data.size())
-  {
-    // if there is no preallocated memory, let's reserve a minimum of 32 elements
-    if (m_data.size()==0)
-    {
-      m_data.reserve(32);
-    }
-    else
-    {
-      // we need to reallocate the data, to reduce multiple reallocations
-      // we use a smart resize algorithm based on the current filling ratio
-      // in addition, we use double to avoid integers overflows
-      double nnzEstimate = double(m_outerIndex[outer])*double(m_outerSize)/double(outer+1);
-      reallocRatio = (nnzEstimate-double(m_data.size()))/double(m_data.size());
-      // furthermore we bound the realloc ratio to:
-      //   1) reduce multiple minor realloc when the matrix is almost filled
-      //   2) avoid to allocate too much memory when the matrix is almost empty
-      reallocRatio = (std::min)((std::max)(reallocRatio,1.5),8.);
-    }
-  }
-  m_data.resize(m_data.size()+1,reallocRatio);
-
-  if (!isLastVec)
-  {
-    if (previousOuter==-1)
-    {
-      // oops wrong guess.
-      // let's correct the outer offsets
-      for (Index k=0; k<=(outer+1); ++k)
-        m_outerIndex[k] = 0;
-      Index k=outer+1;
-      while(m_outerIndex[k]==0)
-        m_outerIndex[k++] = 1;
-      while (k<=m_outerSize && m_outerIndex[k]!=0)
-        m_outerIndex[k++]++;
-      p = 0;
-      --k;
-      k = m_outerIndex[k]-1;
-      while (k>0)
-      {
-        m_data.index(k) = m_data.index(k-1);
-        m_data.value(k) = m_data.value(k-1);
-        k--;
-      }
-    }
-    else
-    {
-      // we are not inserting into the last inner vec
-      // update outer indices:
-      Index j = outer+2;
-      while (j<=m_outerSize && m_outerIndex[j]!=0)
-        m_outerIndex[j++]++;
-      --j;
-      // shift data of last vecs:
-      Index k = m_outerIndex[j]-1;
-      while (k>=Index(p))
-      {
-        m_data.index(k) = m_data.index(k-1);
-        m_data.value(k) = m_data.value(k-1);
-        k--;
-      }
-    }
-  }
-
-  while ( (p > startId) && (m_data.index(p-1) > inner) )
-  {
-    m_data.index(p) = m_data.index(p-1);
-    m_data.value(p) = m_data.value(p-1);
-    --p;
-  }
-
-  m_data.index(p) = inner;
-  return (m_data.value(p) = Scalar(0));
-}
-
-namespace internal {
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct evaluator<SparseMatrix<_Scalar,_Options,_StorageIndex> >
-  : evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_StorageIndex> > >
-{
-  typedef evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_StorageIndex> > > Base;
-  typedef SparseMatrix<_Scalar,_Options,_StorageIndex> SparseMatrixType;
-  evaluator() : Base() {}
-  explicit evaluator(const SparseMatrixType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMatrixBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMatrixBase.h
deleted file mode 100644
index 229449f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseMatrixBase.h
+++ /dev/null
@@ -1,398 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEMATRIXBASE_H
-#define EIGEN_SPARSEMATRIXBASE_H
-
-namespace Eigen { 
-
-/** \ingroup SparseCore_Module
-  *
-  * \class SparseMatrixBase
-  *
-  * \brief Base class of any sparse matrices or sparse expressions
-  *
-  * \tparam Derived is the derived type, e.g. a sparse matrix type, or an expression, etc.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN.
-  */
-template<typename Derived> class SparseMatrixBase
-  : public EigenBase<Derived>
-{
-  public:
-
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    
-    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
-      *
-      * It is an alias for the Scalar type */
-    typedef Scalar value_type;
-    
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-
-    /** The integer type used to \b store indices within a SparseMatrix.
-      * For a \c SparseMatrix<Scalar,Options,IndexType> it an alias of the third template parameter \c IndexType. */
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-
-    typedef typename internal::add_const_on_value_type_if_arithmetic<
-                         typename internal::packet_traits<Scalar>::type
-                     >::type PacketReturnType;
-
-    typedef SparseMatrixBase StorageBaseType;
-
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-    
-    template<typename OtherDerived>
-    Derived& operator=(const EigenBase<OtherDerived> &other);
-
-    enum {
-
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-        /**< The number of rows at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
-
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-        /**< The number of columns at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
-
-
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
-        /**< This is equal to the number of coefficients, i.e. the number of
-          * rows times the number of columns, or to \a Dynamic if this is not
-          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
-
-      MaxRowsAtCompileTime = RowsAtCompileTime,
-      MaxColsAtCompileTime = ColsAtCompileTime,
-
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<MaxRowsAtCompileTime,
-                                                      MaxColsAtCompileTime>::ret),
-
-      IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
-        /**< This is set to true if either the number of rows or the number of
-          * columns is known at compile-time to be equal to 1. Indeed, in that case,
-          * we are dealing with a column-vector (if there is only one column) or with
-          * a row-vector (if there is only one row). */
-
-      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2,
-        /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
-         * and 2 for matrices.
-         */
-
-      Flags = internal::traits<Derived>::Flags,
-        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
-          * constructed from this one. See the \ref flags "list of flags".
-          */
-
-      IsRowMajor = Flags&RowMajorBit ? 1 : 0,
-      
-      InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
-                             : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-
-      #ifndef EIGEN_PARSED_BY_DOXYGEN
-      _HasDirectAccess = (int(Flags)&DirectAccessBit) ? 1 : 0 // workaround sunCC
-      #endif
-    };
-
-    /** \internal the return type of MatrixBase::adjoint() */
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >,
-                        Transpose<const Derived>
-                     >::type AdjointReturnType;
-    typedef Transpose<Derived> TransposeReturnType;
-    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
-
-    // FIXME storage order do not match evaluator storage order
-    typedef SparseMatrix<Scalar, Flags&RowMajorBit ? RowMajor : ColMajor, StorageIndex> PlainObject;
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is the "real scalar" type; if the \a Scalar type is already real numbers
-      * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
-      * \a Scalar is \a std::complex<T> then RealScalar is \a T.
-      *
-      * \sa class NumTraits
-      */
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    /** \internal the return type of coeff()
-      */
-    typedef typename internal::conditional<_HasDirectAccess, const Scalar&, Scalar>::type CoeffReturnType;
-
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Matrix<Scalar,Dynamic,Dynamic> > ConstantReturnType;
-
-    /** type of the equivalent dense matrix */
-    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
-    /** type of the equivalent square matrix */
-    typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
-                          EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
-
-    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    inline Derived& derived() { return *static_cast<Derived*>(this); }
-    inline Derived& const_cast_derived() const
-    { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
-
-    typedef EigenBase<Derived> Base;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-#define EIGEN_DOC_UNARY_ADDONS(METHOD,OP)           /** <p>This method does not change the sparsity of \c *this: the OP is applied to explicitly stored coefficients only. \sa SparseCompressedBase::coeffs() </p> */
-#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL      /** <p> \warning This method returns a read-only expression for any sparse matrices. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
-#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND) /** <p> \warning This method returns a read-write expression for COND sparse matrices only. Otherwise, the returned expression is read-only. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
-#else
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
-#endif
-#   include "../plugins/CommonCwiseUnaryOps.h"
-#   include "../plugins/CommonCwiseBinaryOps.h"
-#   include "../plugins/MatrixCwiseUnaryOps.h"
-#   include "../plugins/MatrixCwiseBinaryOps.h"
-#   include "../plugins/BlockMethods.h"
-#   ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN
-#     include EIGEN_SPARSEMATRIXBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_UNARY_ADDONS
-#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
-
-    /** \returns the number of rows. \sa cols() */
-    inline Index rows() const { return derived().rows(); }
-    /** \returns the number of columns. \sa rows() */
-    inline Index cols() const { return derived().cols(); }
-    /** \returns the number of coefficients, which is \a rows()*cols().
-      * \sa rows(), cols(). */
-    inline Index size() const { return rows() * cols(); }
-    /** \returns true if either the number of rows or the number of columns is equal to 1.
-      * In other words, this function returns
-      * \code rows()==1 || cols()==1 \endcode
-      * \sa rows(), cols(), IsVectorAtCompileTime. */
-    inline bool isVector() const { return rows()==1 || cols()==1; }
-    /** \returns the size of the storage major dimension,
-      * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
-    Index outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); }
-    /** \returns the size of the inner dimension according to the storage order,
-      * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
-    Index innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
-
-    bool isRValue() const { return m_isRValue; }
-    Derived& markAsRValue() { m_isRValue = true; return derived(); }
-
-    SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */ }
-
-    
-    template<typename OtherDerived>
-    Derived& operator=(const ReturnByValue<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other);
-
-    inline Derived& operator=(const Derived& other);
-
-  protected:
-
-    template<typename OtherDerived>
-    inline Derived& assign(const OtherDerived& other);
-
-    template<typename OtherDerived>
-    inline void assignGeneric(const OtherDerived& other);
-
-  public:
-
-    friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
-    {
-      typedef typename Derived::Nested Nested;
-      typedef typename internal::remove_all<Nested>::type NestedCleaned;
-
-      if (Flags&RowMajorBit)
-      {
-        Nested nm(m.derived());
-        internal::evaluator<NestedCleaned> thisEval(nm);
-        for (Index row=0; row<nm.outerSize(); ++row)
-        {
-          Index col = 0;
-          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, row); it; ++it)
-          {
-            for ( ; col<it.index(); ++col)
-              s << "0 ";
-            s << it.value() << " ";
-            ++col;
-          }
-          for ( ; col<m.cols(); ++col)
-            s << "0 ";
-          s << std::endl;
-        }
-      }
-      else
-      {
-        Nested nm(m.derived());
-        internal::evaluator<NestedCleaned> thisEval(nm);
-        if (m.cols() == 1) {
-          Index row = 0;
-          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, 0); it; ++it)
-          {
-            for ( ; row<it.index(); ++row)
-              s << "0" << std::endl;
-            s << it.value() << std::endl;
-            ++row;
-          }
-          for ( ; row<m.rows(); ++row)
-            s << "0" << std::endl;
-        }
-        else
-        {
-          SparseMatrix<Scalar, RowMajorBit, StorageIndex> trans = m;
-          s << static_cast<const SparseMatrixBase<SparseMatrix<Scalar, RowMajorBit, StorageIndex> >&>(trans);
-        }
-      }
-      return s;
-    }
-
-    template<typename OtherDerived>
-    Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
-    
-    template<typename OtherDerived>
-    Derived& operator+=(const DiagonalBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    Derived& operator-=(const DiagonalBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    Derived& operator+=(const EigenBase<OtherDerived> &other);
-    template<typename OtherDerived>
-    Derived& operator-=(const EigenBase<OtherDerived> &other);
-
-    Derived& operator*=(const Scalar& other);
-    Derived& operator/=(const Scalar& other);
-
-    template<typename OtherDerived> struct CwiseProductDenseReturnType {
-      typedef CwiseBinaryOp<internal::scalar_product_op<typename ScalarBinaryOpTraits<
-                                                          typename internal::traits<Derived>::Scalar,
-                                                          typename internal::traits<OtherDerived>::Scalar
-                                                        >::ReturnType>,
-                            const Derived,
-                            const OtherDerived
-                          > Type;
-    };
-
-    template<typename OtherDerived>
-    EIGEN_STRONG_INLINE const typename CwiseProductDenseReturnType<OtherDerived>::Type
-    cwiseProduct(const MatrixBase<OtherDerived> &other) const;
-
-    // sparse * diagonal
-    template<typename OtherDerived>
-    const Product<Derived,OtherDerived>
-    operator*(const DiagonalBase<OtherDerived> &other) const
-    { return Product<Derived,OtherDerived>(derived(), other.derived()); }
-
-    // diagonal * sparse
-    template<typename OtherDerived> friend
-    const Product<OtherDerived,Derived>
-    operator*(const DiagonalBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
-    { return Product<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
-    
-    // sparse * sparse
-    template<typename OtherDerived>
-    const Product<Derived,OtherDerived,AliasFreeProduct>
-    operator*(const SparseMatrixBase<OtherDerived> &other) const;
-    
-    // sparse * dense
-    template<typename OtherDerived>
-    const Product<Derived,OtherDerived>
-    operator*(const MatrixBase<OtherDerived> &other) const
-    { return Product<Derived,OtherDerived>(derived(), other.derived()); }
-    
-    // dense * sparse
-    template<typename OtherDerived> friend
-    const Product<OtherDerived,Derived>
-    operator*(const MatrixBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
-    { return Product<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
-    
-     /** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */
-    SparseSymmetricPermutationProduct<Derived,Upper|Lower> twistedBy(const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm) const
-    {
-      return SparseSymmetricPermutationProduct<Derived,Upper|Lower>(derived(), perm);
-    }
-
-    template<typename OtherDerived>
-    Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
-
-    template<int Mode>
-    inline const TriangularView<const Derived, Mode> triangularView() const;
-    
-    template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SparseSelfAdjointView<Derived, UpLo> Type; };
-    template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SparseSelfAdjointView<const Derived, UpLo> Type; };
-
-    template<unsigned int UpLo> inline 
-    typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
-    template<unsigned int UpLo> inline
-    typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
-
-    template<typename OtherDerived> Scalar dot(const MatrixBase<OtherDerived>& other) const;
-    template<typename OtherDerived> Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
-    RealScalar squaredNorm() const;
-    RealScalar norm()  const;
-    RealScalar blueNorm() const;
-
-    TransposeReturnType transpose() { return TransposeReturnType(derived()); }
-    const ConstTransposeReturnType transpose() const { return ConstTransposeReturnType(derived()); }
-    const AdjointReturnType adjoint() const { return AdjointReturnType(transpose()); }
-
-    DenseMatrixType toDense() const
-    {
-      return DenseMatrixType(derived());
-    }
-
-    template<typename OtherDerived>
-    bool isApprox(const SparseMatrixBase<OtherDerived>& other,
-                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    template<typename OtherDerived>
-    bool isApprox(const MatrixBase<OtherDerived>& other,
-                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
-    { return toDense().isApprox(other,prec); }
-
-    /** \returns the matrix or vector obtained by evaluating this expression.
-      *
-      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
-      * a const reference, in order to avoid a useless copy.
-      */
-    inline const typename internal::eval<Derived>::type eval() const
-    { return typename internal::eval<Derived>::type(derived()); }
-
-    Scalar sum() const;
-    
-    inline const SparseView<Derived>
-    pruned(const Scalar& reference = Scalar(0), const RealScalar& epsilon = NumTraits<Scalar>::dummy_precision()) const;
-
-  protected:
-
-    bool m_isRValue;
-
-    static inline StorageIndex convert_index(const Index idx) {
-      return internal::convert_index<StorageIndex>(idx);
-    }
-  private:
-    template<typename Dest> void evalTo(Dest &) const;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEMATRIXBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparsePermutation.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparsePermutation.h
deleted file mode 100644
index ef38357..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparsePermutation.h
+++ /dev/null
@@ -1,178 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_PERMUTATION_H
-#define EIGEN_SPARSE_PERMUTATION_H
-
-// This file implements sparse * permutation products
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename ExpressionType, int Side, bool Transposed>
-struct permutation_matrix_product<ExpressionType, Side, Transposed, SparseShape>
-{
-    typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
-    typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-
-    typedef typename MatrixTypeCleaned::Scalar Scalar;
-    typedef typename MatrixTypeCleaned::StorageIndex StorageIndex;
-
-    enum {
-      SrcStorageOrder = MatrixTypeCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
-      MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
-    };
-    
-    typedef typename internal::conditional<MoveOuter,
-        SparseMatrix<Scalar,SrcStorageOrder,StorageIndex>,
-        SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,StorageIndex> >::type ReturnType;
-
-    template<typename Dest,typename PermutationType>
-    static inline void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
-    {
-      MatrixType mat(xpr);
-      if(MoveOuter)
-      {
-        SparseMatrix<Scalar,SrcStorageOrder,StorageIndex> tmp(mat.rows(), mat.cols());
-        Matrix<StorageIndex,Dynamic,1> sizes(mat.outerSize());
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          Index jp = perm.indices().coeff(j);
-          sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = StorageIndex(mat.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).nonZeros());
-        }
-        tmp.reserve(sizes);
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          Index jp = perm.indices().coeff(j);
-          Index jsrc = ((Side==OnTheRight) ^ Transposed) ? jp : j;
-          Index jdst = ((Side==OnTheLeft) ^ Transposed) ? jp : j;
-          for(typename MatrixTypeCleaned::InnerIterator it(mat,jsrc); it; ++it)
-            tmp.insertByOuterInner(jdst,it.index()) = it.value();
-        }
-        dst = tmp;
-      }
-      else
-      {
-        SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,StorageIndex> tmp(mat.rows(), mat.cols());
-        Matrix<StorageIndex,Dynamic,1> sizes(tmp.outerSize());
-        sizes.setZero();
-        PermutationMatrix<Dynamic,Dynamic,StorageIndex> perm_cpy;
-        if((Side==OnTheLeft) ^ Transposed)
-          perm_cpy = perm;
-        else
-          perm_cpy = perm.transpose();
-
-        for(Index j=0; j<mat.outerSize(); ++j)
-          for(typename MatrixTypeCleaned::InnerIterator it(mat,j); it; ++it)
-            sizes[perm_cpy.indices().coeff(it.index())]++;
-        tmp.reserve(sizes);
-        for(Index j=0; j<mat.outerSize(); ++j)
-          for(typename MatrixTypeCleaned::InnerIterator it(mat,j); it; ++it)
-            tmp.insertByOuterInner(perm_cpy.indices().coeff(it.index()),j) = it.value();
-        dst = tmp;
-      }
-    }
-};
-
-}
-
-namespace internal {
-
-template <int ProductTag> struct product_promote_storage_type<Sparse,             PermutationStorage, ProductTag> { typedef Sparse ret; };
-template <int ProductTag> struct product_promote_storage_type<PermutationStorage, Sparse,             ProductTag> { typedef Sparse ret; };
-
-// TODO, the following two overloads are only needed to define the right temporary type through 
-// typename traits<permutation_sparse_matrix_product<Rhs,Lhs,OnTheRight,false> >::ReturnType
-// whereas it should be correctly handled by traits<Product<> >::PlainObject
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, PermutationShape, SparseShape>
-  : public evaluator<typename permutation_matrix_product<Rhs,OnTheLeft,false,SparseShape>::ReturnType>
-{
-  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
-  typedef typename permutation_matrix_product<Rhs,OnTheLeft,false,SparseShape>::ReturnType PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-
-  explicit product_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<Lhs, Rhs, PermutationShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, SparseShape, PermutationShape >
-  : public evaluator<typename permutation_matrix_product<Lhs,OnTheRight,false,SparseShape>::ReturnType>
-{
-  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
-  typedef typename permutation_matrix_product<Lhs,OnTheRight,false,SparseShape>::ReturnType PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-
-  explicit product_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<Lhs, Rhs, SparseShape, PermutationShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-/** \returns the matrix with the permutation applied to the columns
-  */
-template<typename SparseDerived, typename PermDerived>
-inline const Product<SparseDerived, PermDerived, AliasFreeProduct>
-operator*(const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm)
-{ return Product<SparseDerived, PermDerived, AliasFreeProduct>(matrix.derived(), perm.derived()); }
-
-/** \returns the matrix with the permutation applied to the rows
-  */
-template<typename SparseDerived, typename PermDerived>
-inline const Product<PermDerived, SparseDerived, AliasFreeProduct>
-operator*( const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix)
-{ return  Product<PermDerived, SparseDerived, AliasFreeProduct>(perm.derived(), matrix.derived()); }
-
-
-/** \returns the matrix with the inverse permutation applied to the columns.
-  */
-template<typename SparseDerived, typename PermutationType>
-inline const Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct>
-operator*(const SparseMatrixBase<SparseDerived>& matrix, const InverseImpl<PermutationType, PermutationStorage>& tperm)
-{
-  return Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct>(matrix.derived(), tperm.derived());
-}
-
-/** \returns the matrix with the inverse permutation applied to the rows.
-  */
-template<typename SparseDerived, typename PermutationType>
-inline const Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct>
-operator*(const InverseImpl<PermutationType,PermutationStorage>& tperm, const SparseMatrixBase<SparseDerived>& matrix)
-{
-  return Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct>(tperm.derived(), matrix.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseProduct.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseProduct.h
deleted file mode 100644
index af8a774..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseProduct.h
+++ /dev/null
@@ -1,181 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEPRODUCT_H
-#define EIGEN_SPARSEPRODUCT_H
-
-namespace Eigen { 
-
-/** \returns an expression of the product of two sparse matrices.
-  * By default a conservative product preserving the symbolic non zeros is performed.
-  * The automatic pruning of the small values can be achieved by calling the pruned() function
-  * in which case a totally different product algorithm is employed:
-  * \code
-  * C = (A*B).pruned();             // suppress numerical zeros (exact)
-  * C = (A*B).pruned(ref);
-  * C = (A*B).pruned(ref,epsilon);
-  * \endcode
-  * where \c ref is a meaningful non zero reference value.
-  * */
-template<typename Derived>
-template<typename OtherDerived>
-inline const Product<Derived,OtherDerived,AliasFreeProduct>
-SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
-{
-  return Product<Derived,OtherDerived,AliasFreeProduct>(derived(), other.derived());
-}
-
-namespace internal {
-
-// sparse * sparse
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
-{
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    evalTo(dst, lhs, rhs, typename evaluator_traits<Dest>::Shape());
-  }
-
-  // dense += sparse * sparse
-  template<typename Dest,typename ActualLhs>
-  static void addTo(Dest& dst, const ActualLhs& lhs, const Rhs& rhs, typename enable_if<is_same<typename evaluator_traits<Dest>::Shape,DenseShape>::value,int*>::type* = 0)
-  {
-    typedef typename nested_eval<ActualLhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    internal::sparse_sparse_to_dense_product_selector<typename remove_all<LhsNested>::type,
-                                                      typename remove_all<RhsNested>::type, Dest>::run(lhsNested,rhsNested,dst);
-  }
-
-  // dense -= sparse * sparse
-  template<typename Dest>
-  static void subTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, typename enable_if<is_same<typename evaluator_traits<Dest>::Shape,DenseShape>::value,int*>::type* = 0)
-  {
-    addTo(dst, -lhs, rhs);
-  }
-
-protected:
-
-  // sparse = sparse * sparse
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, SparseShape)
-  {
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    internal::conservative_sparse_sparse_product_selector<typename remove_all<LhsNested>::type,
-                                                          typename remove_all<RhsNested>::type, Dest>::run(lhsNested,rhsNested,dst);
-  }
-
-  // dense = sparse * sparse
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, DenseShape)
-  {
-    dst.setZero();
-    addTo(dst, lhs, rhs);
-  }
-};
-
-// sparse * sparse-triangular
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseShape, SparseTriangularShape, ProductType>
- : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
-{};
-
-// sparse-triangular * sparse
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, SparseShape, ProductType>
- : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
-{};
-
-// dense = sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template< typename DstXprType, typename Lhs, typename Rhs>
-struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
-{
-  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    
-    generic_product_impl<Lhs, Rhs>::evalTo(dst,src.lhs(),src.rhs());
-  }
-};
-
-// dense += sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template< typename DstXprType, typename Lhs, typename Rhs>
-struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::add_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
-{
-  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
-  {
-    generic_product_impl<Lhs, Rhs>::addTo(dst,src.lhs(),src.rhs());
-  }
-};
-
-// dense -= sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template< typename DstXprType, typename Lhs, typename Rhs>
-struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::sub_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
-{
-  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
-  {
-    generic_product_impl<Lhs, Rhs>::subTo(dst,src.lhs(),src.rhs());
-  }
-};
-
-template<typename Lhs, typename Rhs, int Options>
-struct unary_evaluator<SparseView<Product<Lhs, Rhs, Options> >, IteratorBased>
- : public evaluator<typename Product<Lhs, Rhs, DefaultProduct>::PlainObject>
-{
-  typedef SparseView<Product<Lhs, Rhs, Options> > XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  explicit unary_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    using std::abs;
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(xpr.nestedExpression().lhs());
-    RhsNested rhsNested(xpr.nestedExpression().rhs());
-
-    internal::sparse_sparse_product_with_pruning_selector<typename remove_all<LhsNested>::type,
-                                                          typename remove_all<RhsNested>::type, PlainObject>::run(lhsNested,rhsNested,m_result,
-                                                                                                                  abs(xpr.reference())*xpr.epsilon());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-// sparse matrix = sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename Lhs, typename Rhs>
-SparseMatrix<Scalar,_Options,_StorageIndex>& SparseMatrix<Scalar,_Options,_StorageIndex>::operator=(const Product<Lhs,Rhs,AliasFreeProduct>& src)
-{
-  // std::cout << "in Assignment : " << DstOptions << "\n";
-  SparseMatrix dst(src.rows(),src.cols());
-  internal::generic_product_impl<Lhs, Rhs>::evalTo(dst,src.lhs(),src.rhs());
-  this->swap(dst);
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseRedux.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseRedux.h
deleted file mode 100644
index 4587749..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseRedux.h
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEREDUX_H
-#define EIGEN_SPARSEREDUX_H
-
-namespace Eigen { 
-
-template<typename Derived>
-typename internal::traits<Derived>::Scalar
-SparseMatrixBase<Derived>::sum() const
-{
-  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
-  Scalar res(0);
-  internal::evaluator<Derived> thisEval(derived());
-  for (Index j=0; j<outerSize(); ++j)
-    for (typename internal::evaluator<Derived>::InnerIterator iter(thisEval,j); iter; ++iter)
-      res += iter.value();
-  return res;
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-typename internal::traits<SparseMatrix<_Scalar,_Options,_Index> >::Scalar
-SparseMatrix<_Scalar,_Options,_Index>::sum() const
-{
-  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
-  if(this->isCompressed())
-    return Matrix<Scalar,1,Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
-  else
-    return Base::sum();
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-typename internal::traits<SparseVector<_Scalar,_Options, _Index> >::Scalar
-SparseVector<_Scalar,_Options,_Index>::sum() const
-{
-  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
-  return Matrix<Scalar,1,Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEREDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseRef.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseRef.h
deleted file mode 100644
index 748f87d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseRef.h
+++ /dev/null
@@ -1,397 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_REF_H
-#define EIGEN_SPARSE_REF_H
-
-namespace Eigen {
-
-enum {
-  StandardCompressedFormat = 2 /**< used by Ref<SparseMatrix> to specify whether the input storage must be in standard compressed form */
-};
-  
-namespace internal {
-
-template<typename Derived> class SparseRefBase;
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  enum {
-    Options = _Options,
-    Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit
-  };
-
-  template<typename Derived> struct match {
-    enum {
-      StorageOrderMatch = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
-      MatchAtCompileTime = (Derived::Flags&CompressedAccessBit) && StorageOrderMatch
-    };
-    typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
-  };
-  
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-{
-  enum {
-    Flags = (traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
-  };
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<SparseVector<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseVector<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  enum {
-    Options = _Options,
-    Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit
-  };
-
-  template<typename Derived> struct match {
-    enum {
-      MatchAtCompileTime = (Derived::Flags&CompressedAccessBit) && Derived::IsVectorAtCompileTime
-    };
-    typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
-  };
-
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-{
-  enum {
-    Flags = (traits<SparseVector<MatScalar,MatOptions,MatIndex> >::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
-  };
-};
-
-template<typename Derived>
-struct traits<SparseRefBase<Derived> > : public traits<Derived> {};
-
-template<typename Derived> class SparseRefBase
-  : public SparseMapBase<Derived>
-{
-public:
-
-  typedef SparseMapBase<Derived> Base;
-  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseRefBase)
-
-  SparseRefBase()
-    : Base(RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime, 0, 0, 0, 0, 0)
-  {}
-  
-protected:
-
-  template<typename Expression>
-  void construct(Expression& expr)
-  {
-    if(expr.outerIndexPtr()==0)
-      ::new (static_cast<Base*>(this)) Base(expr.size(), expr.nonZeros(), expr.innerIndexPtr(), expr.valuePtr());
-    else
-      ::new (static_cast<Base*>(this)) Base(expr.rows(), expr.cols(), expr.nonZeros(), expr.outerIndexPtr(), expr.innerIndexPtr(), expr.valuePtr(), expr.innerNonZeroPtr());
-  }
-};
-
-} // namespace internal
-
-
-/** 
-  * \ingroup SparseCore_Module
-  *
-  * \brief A sparse matrix expression referencing an existing sparse expression
-  *
-  * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of class SparseMatrix.
-  * \tparam Options specifies whether the a standard compressed format is required \c Options is  \c #StandardCompressedFormat, or \c 0.
-  *                The default is \c 0.
-  *
-  * \sa class Ref
-  */
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType >
-  : public internal::SparseRefBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType > >
-#else
-template<typename SparseMatrixType, int Options>
-class Ref<SparseMatrixType, Options>
-  : public SparseMapBase<Derived,WriteAccessors> // yes, that's weird to use Derived here, but that works!
-#endif
-{
-    typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-    typedef internal::traits<Ref> Traits;
-    template<int OtherOptions>
-    inline Ref(const SparseMatrix<MatScalar,OtherOptions,MatIndex>& expr);
-    template<int OtherOptions>
-    inline Ref(const MappedSparseMatrix<MatScalar,OtherOptions,MatIndex>& expr);
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<int OtherOptions>
-    inline Ref(SparseMatrix<MatScalar,OtherOptions,MatIndex>& expr)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseMatrix<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
-      Base::construct(expr.derived());
-    }
-    
-    template<int OtherOptions>
-    inline Ref(MappedSparseMatrix<MatScalar,OtherOptions,MatIndex>& expr)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseMatrix<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
-      Base::construct(expr.derived());
-    }
-    
-    template<typename Derived>
-    inline Ref(const SparseCompressedBase<Derived>& expr)
-    #else
-    /** Implicit constructor from any sparse expression (2D matrix or 1D vector) */
-    template<typename Derived>
-    inline Ref(SparseCompressedBase<Derived>& expr)
-    #endif
-    {
-      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
-      Base::construct(expr.const_cast_derived());
-    }
-};
-
-// this is the const ref version
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public internal::SparseRefBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-{
-    typedef SparseMatrix<MatScalar,MatOptions,MatIndex> TPlainObjectType;
-    typedef internal::traits<Ref> Traits;
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-    template<typename Derived>
-    inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false)
-    {
-      construct(expr.derived(), typename Traits::template match<Derived>::type());
-    }
-
-    inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
-      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
-    }
-
-    template<typename OtherRef>
-    inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
-      construct(other.derived(), typename Traits::template match<OtherRef>::type());
-    }
-
-    ~Ref() {
-      if(m_hasCopy) {
-        TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-        obj->~TPlainObjectType();
-      }
-    }
-
-  protected:
-
-    template<typename Expression>
-    void construct(const Expression& expr,internal::true_type)
-    {
-      if((Options & int(StandardCompressedFormat)) && (!expr.isCompressed()))
-      {
-        TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-        ::new (obj) TPlainObjectType(expr);
-        m_hasCopy = true;
-        Base::construct(*obj);
-      }
-      else
-      {
-        Base::construct(expr);
-      }
-    }
-
-    template<typename Expression>
-    void construct(const Expression& expr, internal::false_type)
-    {
-      TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-      ::new (obj) TPlainObjectType(expr);
-      m_hasCopy = true;
-      Base::construct(*obj);
-    }
-
-  protected:
-    typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
-    bool m_hasCopy;
-};
-
-
-
-/**
-  * \ingroup SparseCore_Module
-  *
-  * \brief A sparse vector expression referencing an existing sparse vector expression
-  *
-  * \tparam SparseVectorType the equivalent sparse vector type of the referenced data, it must be a template instance of class SparseVector.
-  *
-  * \sa class Ref
-  */
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType >
-  : public internal::SparseRefBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType > >
-#else
-template<typename SparseVectorType>
-class Ref<SparseVectorType>
-  : public SparseMapBase<Derived,WriteAccessors>
-#endif
-{
-    typedef SparseVector<MatScalar,MatOptions,MatIndex> PlainObjectType;
-    typedef internal::traits<Ref> Traits;
-    template<int OtherOptions>
-    inline Ref(const SparseVector<MatScalar,OtherOptions,MatIndex>& expr);
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<int OtherOptions>
-    inline Ref(SparseVector<MatScalar,OtherOptions,MatIndex>& expr)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseVector<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      Base::construct(expr.derived());
-    }
-
-    template<typename Derived>
-    inline Ref(const SparseCompressedBase<Derived>& expr)
-    #else
-    /** Implicit constructor from any 1D sparse vector expression */
-    template<typename Derived>
-    inline Ref(SparseCompressedBase<Derived>& expr)
-    #endif
-    {
-      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      Base::construct(expr.const_cast_derived());
-    }
-};
-
-// this is the const ref version
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public internal::SparseRefBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-{
-    typedef SparseVector<MatScalar,MatOptions,MatIndex> TPlainObjectType;
-    typedef internal::traits<Ref> Traits;
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-    template<typename Derived>
-    inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false)
-    {
-      construct(expr.derived(), typename Traits::template match<Derived>::type());
-    }
-
-    inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
-      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
-    }
-
-    template<typename OtherRef>
-    inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
-      construct(other.derived(), typename Traits::template match<OtherRef>::type());
-    }
-
-    ~Ref() {
-      if(m_hasCopy) {
-        TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-        obj->~TPlainObjectType();
-      }
-    }
-
-  protected:
-
-    template<typename Expression>
-    void construct(const Expression& expr,internal::true_type)
-    {
-      Base::construct(expr);
-    }
-
-    template<typename Expression>
-    void construct(const Expression& expr, internal::false_type)
-    {
-      TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-      ::new (obj) TPlainObjectType(expr);
-      m_hasCopy = true;
-      Base::construct(*obj);
-    }
-
-  protected:
-    typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
-    bool m_hasCopy;
-};
-
-namespace internal {
-
-// FIXME shall we introduce a general evaluatior_ref that we can specialize for any sparse object once, and thus remove this copy-pasta thing...
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_REF_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSelfAdjointView.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
deleted file mode 100644
index 85b00e1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
+++ /dev/null
@@ -1,659 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_SELFADJOINTVIEW_H
-#define EIGEN_SPARSE_SELFADJOINTVIEW_H
-
-namespace Eigen { 
-  
-/** \ingroup SparseCore_Module
-  * \class SparseSelfAdjointView
-  *
-  * \brief Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix.
-  *
-  * \param MatrixType the type of the dense matrix storing the coefficients
-  * \param Mode can be either \c #Lower or \c #Upper
-  *
-  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
-  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
-  * and most of the time this is the only way that it is used.
-  *
-  * \sa SparseMatrixBase::selfadjointView()
-  */
-namespace internal {
-  
-template<typename MatrixType, unsigned int Mode>
-struct traits<SparseSelfAdjointView<MatrixType,Mode> > : traits<MatrixType> {
-};
-
-template<int SrcMode,int DstMode,typename MatrixType,int DestOrder>
-void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm = 0);
-
-template<int Mode,typename MatrixType,int DestOrder>
-void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm = 0);
-
-}
-
-template<typename MatrixType, unsigned int _Mode> class SparseSelfAdjointView
-  : public EigenBase<SparseSelfAdjointView<MatrixType,_Mode> >
-{
-  public:
-    
-    enum {
-      Mode = _Mode,
-      TransposeMode = ((Mode & Upper) ? Lower : 0) | ((Mode & Lower) ? Upper : 0),
-      RowsAtCompileTime = internal::traits<SparseSelfAdjointView>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<SparseSelfAdjointView>::ColsAtCompileTime
-    };
-
-    typedef EigenBase<SparseSelfAdjointView> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-    typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
-    
-    explicit inline SparseSelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
-    {
-      eigen_assert(rows()==cols() && "SelfAdjointView is only for squared matrices");
-    }
-
-    inline Index rows() const { return m_matrix.rows(); }
-    inline Index cols() const { return m_matrix.cols(); }
-
-    /** \internal \returns a reference to the nested matrix */
-    const _MatrixTypeNested& matrix() const { return m_matrix; }
-    typename internal::remove_reference<MatrixTypeNested>::type& matrix() { return m_matrix; }
-
-    /** \returns an expression of the matrix product between a sparse self-adjoint matrix \c *this and a sparse matrix \a rhs.
-      *
-      * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
-      * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
-      */
-    template<typename OtherDerived>
-    Product<SparseSelfAdjointView, OtherDerived>
-    operator*(const SparseMatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<SparseSelfAdjointView, OtherDerived>(*this, rhs.derived());
-    }
-
-    /** \returns an expression of the matrix product between a sparse matrix \a lhs and a sparse self-adjoint matrix \a rhs.
-      *
-      * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
-      * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
-      */
-    template<typename OtherDerived> friend
-    Product<OtherDerived, SparseSelfAdjointView>
-    operator*(const SparseMatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
-    {
-      return Product<OtherDerived, SparseSelfAdjointView>(lhs.derived(), rhs);
-    }
-    
-    /** Efficient sparse self-adjoint matrix times dense vector/matrix product */
-    template<typename OtherDerived>
-    Product<SparseSelfAdjointView,OtherDerived>
-    operator*(const MatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<SparseSelfAdjointView,OtherDerived>(*this, rhs.derived());
-    }
-
-    /** Efficient dense vector/matrix times sparse self-adjoint matrix product */
-    template<typename OtherDerived> friend
-    Product<OtherDerived,SparseSelfAdjointView>
-    operator*(const MatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
-    {
-      return Product<OtherDerived,SparseSelfAdjointView>(lhs.derived(), rhs);
-    }
-
-    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
-      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
-      *
-      * \returns a reference to \c *this
-      *
-      * To perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
-      * call this function with u.adjoint().
-      */
-    template<typename DerivedU>
-    SparseSelfAdjointView& rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
-    
-    /** \returns an expression of P H P^-1 */
-    // TODO implement twists in a more evaluator friendly fashion
-    SparseSymmetricPermutationProduct<_MatrixTypeNested,Mode> twistedBy(const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm) const
-    {
-      return SparseSymmetricPermutationProduct<_MatrixTypeNested,Mode>(m_matrix, perm);
-    }
-
-    template<typename SrcMatrixType,int SrcMode>
-    SparseSelfAdjointView& operator=(const SparseSymmetricPermutationProduct<SrcMatrixType,SrcMode>& permutedMatrix)
-    {
-      internal::call_assignment_no_alias_no_transpose(*this, permutedMatrix);
-      return *this;
-    }
-
-    SparseSelfAdjointView& operator=(const SparseSelfAdjointView& src)
-    {
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> pnull;
-      return *this = src.twistedBy(pnull);
-    }
-
-    // Since we override the copy-assignment operator, we need to explicitly re-declare the copy-constructor
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(SparseSelfAdjointView)
-
-    template<typename SrcMatrixType,unsigned int SrcMode>
-    SparseSelfAdjointView& operator=(const SparseSelfAdjointView<SrcMatrixType,SrcMode>& src)
-    {
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> pnull;
-      return *this = src.twistedBy(pnull);
-    }
-    
-    void resize(Index rows, Index cols)
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(rows);
-      EIGEN_ONLY_USED_FOR_DEBUG(cols);
-      eigen_assert(rows == this->rows() && cols == this->cols()
-                && "SparseSelfadjointView::resize() does not actually allow to resize.");
-    }
-    
-  protected:
-
-    MatrixTypeNested m_matrix;
-    //mutable VectorI m_countPerRow;
-    //mutable VectorI m_countPerCol;
-  private:
-    template<typename Dest> void evalTo(Dest &) const;
-};
-
-/***************************************************************************
-* Implementation of SparseMatrixBase methods
-***************************************************************************/
-
-template<typename Derived>
-template<unsigned int UpLo>
-typename SparseMatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type SparseMatrixBase<Derived>::selfadjointView() const
-{
-  return SparseSelfAdjointView<const Derived, UpLo>(derived());
-}
-
-template<typename Derived>
-template<unsigned int UpLo>
-typename SparseMatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type SparseMatrixBase<Derived>::selfadjointView()
-{
-  return SparseSelfAdjointView<Derived, UpLo>(derived());
-}
-
-/***************************************************************************
-* Implementation of SparseSelfAdjointView methods
-***************************************************************************/
-
-template<typename MatrixType, unsigned int Mode>
-template<typename DerivedU>
-SparseSelfAdjointView<MatrixType,Mode>&
-SparseSelfAdjointView<MatrixType,Mode>::rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha)
-{
-  SparseMatrix<Scalar,(MatrixType::Flags&RowMajorBit)?RowMajor:ColMajor> tmp = u * u.adjoint();
-  if(alpha==Scalar(0))
-    m_matrix = tmp.template triangularView<Mode>();
-  else
-    m_matrix += alpha * tmp.template triangularView<Mode>();
-
-  return *this;
-}
-
-namespace internal {
-  
-// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
-//      in the future selfadjoint-ness should be defined by the expression traits
-//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
-template<typename MatrixType, unsigned int Mode>
-struct evaluator_traits<SparseSelfAdjointView<MatrixType,Mode> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef SparseSelfAdjointShape Shape;
-};
-
-struct SparseSelfAdjoint2Sparse {};
-
-template<> struct AssignmentKind<SparseShape,SparseSelfAdjointShape> { typedef SparseSelfAdjoint2Sparse Kind; };
-template<> struct AssignmentKind<SparseSelfAdjointShape,SparseShape> { typedef Sparse2Sparse Kind; };
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, SparseSelfAdjoint2Sparse>
-{
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  typedef internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> AssignOpType;
-
-  template<typename DestScalar,int StorageOrder>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignOpType&/*func*/)
-  {
-    internal::permute_symm_to_fullsymm<SrcXprType::Mode>(src.matrix(), dst);
-  }
-
-  // FIXME: the handling of += and -= in sparse matrices should be cleanup so that next two overloads could be reduced to:
-  template<typename DestScalar,int StorageOrder,typename AssignFunc>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignFunc& func)
-  {
-    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
-    run(tmp, src, AssignOpType());
-    call_assignment_no_alias_no_transpose(dst, tmp, func);
-  }
-
-  template<typename DestScalar,int StorageOrder>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
-                  const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
-  {
-    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
-    run(tmp, src, AssignOpType());
-    dst += tmp;
-  }
-
-  template<typename DestScalar,int StorageOrder>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
-                  const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
-  {
-    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
-    run(tmp, src, AssignOpType());
-    dst -= tmp;
-  }
-  
-  template<typename DestScalar>
-  static void run(DynamicSparseMatrix<DestScalar,ColMajor,StorageIndex>& dst, const SrcXprType &src, const AssignOpType&/*func*/)
-  {
-    // TODO directly evaluate into dst;
-    SparseMatrix<DestScalar,ColMajor,StorageIndex> tmp(dst.rows(),dst.cols());
-    internal::permute_symm_to_fullsymm<SrcXprType::Mode>(src.matrix(), tmp);
-    dst = tmp;
-  }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Implementation of sparse self-adjoint time dense matrix
-***************************************************************************/
-
-namespace internal {
-
-template<int Mode, typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
-inline void sparse_selfadjoint_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
-{
-  EIGEN_ONLY_USED_FOR_DEBUG(alpha);
-  
-  typedef typename internal::nested_eval<SparseLhsType,DenseRhsType::MaxColsAtCompileTime>::type SparseLhsTypeNested;
-  typedef typename internal::remove_all<SparseLhsTypeNested>::type SparseLhsTypeNestedCleaned;
-  typedef evaluator<SparseLhsTypeNestedCleaned> LhsEval;
-  typedef typename LhsEval::InnerIterator LhsIterator;
-  typedef typename SparseLhsType::Scalar LhsScalar;
-  
-  enum {
-    LhsIsRowMajor = (LhsEval::Flags&RowMajorBit)==RowMajorBit,
-    ProcessFirstHalf =
-              ((Mode&(Upper|Lower))==(Upper|Lower))
-          || ( (Mode&Upper) && !LhsIsRowMajor)
-          || ( (Mode&Lower) && LhsIsRowMajor),
-    ProcessSecondHalf = !ProcessFirstHalf
-  };
-  
-  SparseLhsTypeNested lhs_nested(lhs);
-  LhsEval lhsEval(lhs_nested);
-
-  // work on one column at once
-  for (Index k=0; k<rhs.cols(); ++k)
-  {
-    for (Index j=0; j<lhs.outerSize(); ++j)
-    {
-      LhsIterator i(lhsEval,j);
-      // handle diagonal coeff
-      if (ProcessSecondHalf)
-      {
-        while (i && i.index()<j) ++i;
-        if(i && i.index()==j)
-        {
-          res.coeffRef(j,k) += alpha * i.value() * rhs.coeff(j,k);
-          ++i;
-        }
-      }
-
-      // premultiplied rhs for scatters
-      typename ScalarBinaryOpTraits<AlphaType, typename DenseRhsType::Scalar>::ReturnType rhs_j(alpha*rhs(j,k));
-      // accumulator for partial scalar product
-      typename DenseResType::Scalar res_j(0);
-      for(; (ProcessFirstHalf ? i && i.index() < j : i) ; ++i)
-      {
-        LhsScalar lhs_ij = i.value();
-        if(!LhsIsRowMajor) lhs_ij = numext::conj(lhs_ij);
-        res_j += lhs_ij * rhs.coeff(i.index(),k);
-        res(i.index(),k) += numext::conj(lhs_ij) * rhs_j;
-      }
-      res.coeffRef(j,k) += alpha * res_j;
-
-      // handle diagonal coeff
-      if (ProcessFirstHalf && i && (i.index()==j))
-        res.coeffRef(j,k) += alpha * i.value() * rhs.coeff(j,k);
-    }
-  }
-}
-
-
-template<typename LhsView, typename Rhs, int ProductType>
-struct generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType>
-: generic_product_impl_base<LhsView, Rhs, generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType> >
-{
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const LhsView& lhsView, const Rhs& rhs, const typename Dest::Scalar& alpha)
-  {
-    typedef typename LhsView::_MatrixTypeNested Lhs;
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhsView.matrix());
-    RhsNested rhsNested(rhs);
-    
-    internal::sparse_selfadjoint_time_dense_product<LhsView::Mode>(lhsNested, rhsNested, dst, alpha);
-  }
-};
-
-template<typename Lhs, typename RhsView, int ProductType>
-struct generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType>
-: generic_product_impl_base<Lhs, RhsView, generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType> >
-{
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const RhsView& rhsView, const typename Dest::Scalar& alpha)
-  {
-    typedef typename RhsView::_MatrixTypeNested Rhs;
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhsView.matrix());
-    
-    // transpose everything
-    Transpose<Dest> dstT(dst);
-    internal::sparse_selfadjoint_time_dense_product<RhsView::TransposeMode>(rhsNested.transpose(), lhsNested.transpose(), dstT, alpha);
-  }
-};
-
-// NOTE: these two overloads are needed to evaluate the sparse selfadjoint view into a full sparse matrix
-// TODO: maybe the copy could be handled by generic_product_impl so that these overloads would not be needed anymore
-
-template<typename LhsView, typename Rhs, int ProductTag>
-struct product_evaluator<Product<LhsView, Rhs, DefaultProduct>, ProductTag, SparseSelfAdjointShape, SparseShape>
-  : public evaluator<typename Product<typename Rhs::PlainObject, Rhs, DefaultProduct>::PlainObject>
-{
-  typedef Product<LhsView, Rhs, DefaultProduct> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  product_evaluator(const XprType& xpr)
-    : m_lhs(xpr.lhs()), m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<typename Rhs::PlainObject, Rhs, SparseShape, SparseShape, ProductTag>::evalTo(m_result, m_lhs, xpr.rhs());
-  }
-  
-protected:
-  typename Rhs::PlainObject m_lhs;
-  PlainObject m_result;
-};
-
-template<typename Lhs, typename RhsView, int ProductTag>
-struct product_evaluator<Product<Lhs, RhsView, DefaultProduct>, ProductTag, SparseShape, SparseSelfAdjointShape>
-  : public evaluator<typename Product<Lhs, typename Lhs::PlainObject, DefaultProduct>::PlainObject>
-{
-  typedef Product<Lhs, RhsView, DefaultProduct> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  product_evaluator(const XprType& xpr)
-    : m_rhs(xpr.rhs()), m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<Lhs, typename Lhs::PlainObject, SparseShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), m_rhs);
-  }
-  
-protected:
-  typename Lhs::PlainObject m_rhs;
-  PlainObject m_result;
-};
-
-} // namespace internal
-
-/***************************************************************************
-* Implementation of symmetric copies and permutations
-***************************************************************************/
-namespace internal {
-
-template<int Mode,typename MatrixType,int DestOrder>
-void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm)
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef SparseMatrix<Scalar,DestOrder,StorageIndex> Dest;
-  typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-  typedef evaluator<MatrixType> MatEval;
-  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
-  
-  MatEval matEval(mat);
-  Dest& dest(_dest.derived());
-  enum {
-    StorageOrderMatch = int(Dest::IsRowMajor) == int(MatrixType::IsRowMajor)
-  };
-  
-  Index size = mat.rows();
-  VectorI count;
-  count.resize(size);
-  count.setZero();
-  dest.resize(size,size);
-  for(Index j = 0; j<size; ++j)
-  {
-    Index jp = perm ? perm[j] : j;
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      Index i = it.index();
-      Index r = it.row();
-      Index c = it.col();
-      Index ip = perm ? perm[i] : i;
-      if(Mode==int(Upper|Lower))
-        count[StorageOrderMatch ? jp : ip]++;
-      else if(r==c)
-        count[ip]++;
-      else if(( Mode==Lower && r>c) || ( Mode==Upper && r<c))
-      {
-        count[ip]++;
-        count[jp]++;
-      }
-    }
-  }
-  Index nnz = count.sum();
-  
-  // reserve space
-  dest.resizeNonZeros(nnz);
-  dest.outerIndexPtr()[0] = 0;
-  for(Index j=0; j<size; ++j)
-    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
-  for(Index j=0; j<size; ++j)
-    count[j] = dest.outerIndexPtr()[j];
-  
-  // copy data
-  for(StorageIndex j = 0; j<size; ++j)
-  {
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      StorageIndex i = internal::convert_index<StorageIndex>(it.index());
-      Index r = it.row();
-      Index c = it.col();
-      
-      StorageIndex jp = perm ? perm[j] : j;
-      StorageIndex ip = perm ? perm[i] : i;
-      
-      if(Mode==int(Upper|Lower))
-      {
-        Index k = count[StorageOrderMatch ? jp : ip]++;
-        dest.innerIndexPtr()[k] = StorageOrderMatch ? ip : jp;
-        dest.valuePtr()[k] = it.value();
-      }
-      else if(r==c)
-      {
-        Index k = count[ip]++;
-        dest.innerIndexPtr()[k] = ip;
-        dest.valuePtr()[k] = it.value();
-      }
-      else if(( (Mode&Lower)==Lower && r>c) || ( (Mode&Upper)==Upper && r<c))
-      {
-        if(!StorageOrderMatch)
-          std::swap(ip,jp);
-        Index k = count[jp]++;
-        dest.innerIndexPtr()[k] = ip;
-        dest.valuePtr()[k] = it.value();
-        k = count[ip]++;
-        dest.innerIndexPtr()[k] = jp;
-        dest.valuePtr()[k] = numext::conj(it.value());
-      }
-    }
-  }
-}
-
-template<int _SrcMode,int _DstMode,typename MatrixType,int DstOrder>
-void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DstOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm)
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef typename MatrixType::Scalar Scalar;
-  SparseMatrix<Scalar,DstOrder,StorageIndex>& dest(_dest.derived());
-  typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-  typedef evaluator<MatrixType> MatEval;
-  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
-
-  enum {
-    SrcOrder = MatrixType::IsRowMajor ? RowMajor : ColMajor,
-    StorageOrderMatch = int(SrcOrder) == int(DstOrder),
-    DstMode = DstOrder==RowMajor ? (_DstMode==Upper ? Lower : Upper) : _DstMode,
-    SrcMode = SrcOrder==RowMajor ? (_SrcMode==Upper ? Lower : Upper) : _SrcMode
-  };
-
-  MatEval matEval(mat);
-  
-  Index size = mat.rows();
-  VectorI count(size);
-  count.setZero();
-  dest.resize(size,size);
-  for(StorageIndex j = 0; j<size; ++j)
-  {
-    StorageIndex jp = perm ? perm[j] : j;
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if((int(SrcMode)==int(Lower) && i<j) || (int(SrcMode)==int(Upper) && i>j))
-        continue;
-                  
-      StorageIndex ip = perm ? perm[i] : i;
-      count[int(DstMode)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
-    }
-  }
-  dest.outerIndexPtr()[0] = 0;
-  for(Index j=0; j<size; ++j)
-    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
-  dest.resizeNonZeros(dest.outerIndexPtr()[size]);
-  for(Index j=0; j<size; ++j)
-    count[j] = dest.outerIndexPtr()[j];
-  
-  for(StorageIndex j = 0; j<size; ++j)
-  {
-    
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if((int(SrcMode)==int(Lower) && i<j) || (int(SrcMode)==int(Upper) && i>j))
-        continue;
-                  
-      StorageIndex jp = perm ? perm[j] : j;
-      StorageIndex ip = perm? perm[i] : i;
-      
-      Index k = count[int(DstMode)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
-      dest.innerIndexPtr()[k] = int(DstMode)==int(Lower) ? (std::max)(ip,jp) : (std::min)(ip,jp);
-      
-      if(!StorageOrderMatch) std::swap(ip,jp);
-      if( ((int(DstMode)==int(Lower) && ip<jp) || (int(DstMode)==int(Upper) && ip>jp)))
-        dest.valuePtr()[k] = numext::conj(it.value());
-      else
-        dest.valuePtr()[k] = it.value();
-    }
-  }
-}
-
-}
-
-// TODO implement twists in a more evaluator friendly fashion
-
-namespace internal {
-
-template<typename MatrixType, int Mode>
-struct traits<SparseSymmetricPermutationProduct<MatrixType,Mode> > : traits<MatrixType> {
-};
-
-}
-
-template<typename MatrixType,int Mode>
-class SparseSymmetricPermutationProduct
-  : public EigenBase<SparseSymmetricPermutationProduct<MatrixType,Mode> >
-{
-  public:
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    enum {
-      RowsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::ColsAtCompileTime
-    };
-  protected:
-    typedef PermutationMatrix<Dynamic,Dynamic,StorageIndex> Perm;
-  public:
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-    typedef typename MatrixType::Nested MatrixTypeNested;
-    typedef typename internal::remove_all<MatrixTypeNested>::type NestedExpression;
-    
-    SparseSymmetricPermutationProduct(const MatrixType& mat, const Perm& perm)
-      : m_matrix(mat), m_perm(perm)
-    {}
-    
-    inline Index rows() const { return m_matrix.rows(); }
-    inline Index cols() const { return m_matrix.cols(); }
-        
-    const NestedExpression& matrix() const { return m_matrix; }
-    const Perm& perm() const { return m_perm; }
-    
-  protected:
-    MatrixTypeNested m_matrix;
-    const Perm& m_perm;
-
-};
-
-namespace internal {
-  
-template<typename DstXprType, typename MatrixType, int Mode, typename Scalar>
-struct Assignment<DstXprType, SparseSymmetricPermutationProduct<MatrixType,Mode>, internal::assign_op<Scalar,typename MatrixType::Scalar>, Sparse2Sparse>
-{
-  typedef SparseSymmetricPermutationProduct<MatrixType,Mode> SrcXprType;
-  typedef typename DstXprType::StorageIndex DstIndex;
-  template<int Options>
-  static void run(SparseMatrix<Scalar,Options,DstIndex> &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
-  {
-    // internal::permute_symm_to_fullsymm<Mode>(m_matrix,_dest,m_perm.indices().data());
-    SparseMatrix<Scalar,(Options&RowMajor)==RowMajor ? ColMajor : RowMajor, DstIndex> tmp;
-    internal::permute_symm_to_fullsymm<Mode>(src.matrix(),tmp,src.perm().indices().data());
-    dst = tmp;
-  }
-  
-  template<typename DestType,unsigned int DestMode>
-  static void run(SparseSelfAdjointView<DestType,DestMode>& dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
-  {
-    internal::permute_symm_to_symm<Mode,DestMode>(src.matrix(),dst.matrix(),src.perm().indices().data());
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSolverBase.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSolverBase.h
deleted file mode 100644
index b4c9a42..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSolverBase.h
+++ /dev/null
@@ -1,124 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSESOLVERBASE_H
-#define EIGEN_SPARSESOLVERBASE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-  /** \internal
-  * Helper functions to solve with a sparse right-hand-side and result.
-  * The rhs is decomposed into small vertical panels which are solved through dense temporaries.
-  */
-template<typename Decomposition, typename Rhs, typename Dest>
-typename enable_if<Rhs::ColsAtCompileTime!=1 && Dest::ColsAtCompileTime!=1>::type
-solve_sparse_through_dense_panels(const Decomposition &dec, const Rhs& rhs, Dest &dest)
-{
-  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  typedef typename Dest::Scalar DestScalar;
-  // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
-  static const Index NbColsAtOnce = 4;
-  Index rhsCols = rhs.cols();
-  Index size = rhs.rows();
-  // the temporary matrices do not need more columns than NbColsAtOnce:
-  Index tmpCols = (std::min)(rhsCols, NbColsAtOnce); 
-  Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmp(size,tmpCols);
-  Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmpX(size,tmpCols);
-  for(Index k=0; k<rhsCols; k+=NbColsAtOnce)
-  {
-    Index actualCols = std::min<Index>(rhsCols-k, NbColsAtOnce);
-    tmp.leftCols(actualCols) = rhs.middleCols(k,actualCols);
-    tmpX.leftCols(actualCols) = dec.solve(tmp.leftCols(actualCols));
-    dest.middleCols(k,actualCols) = tmpX.leftCols(actualCols).sparseView();
-  }
-}
-
-// Overload for vector as rhs
-template<typename Decomposition, typename Rhs, typename Dest>
-typename enable_if<Rhs::ColsAtCompileTime==1 || Dest::ColsAtCompileTime==1>::type
-solve_sparse_through_dense_panels(const Decomposition &dec, const Rhs& rhs, Dest &dest)
-{
-  typedef typename Dest::Scalar DestScalar;
-  Index size = rhs.rows();
-  Eigen::Matrix<DestScalar,Dynamic,1> rhs_dense(rhs);
-  Eigen::Matrix<DestScalar,Dynamic,1> dest_dense(size);
-  dest_dense = dec.solve(rhs_dense);
-  dest = dest_dense.sparseView();
-}
-
-} // end namespace internal
-
-/** \class SparseSolverBase
-  * \ingroup SparseCore_Module
-  * \brief A base class for sparse solvers
-  *
-  * \tparam Derived the actual type of the solver.
-  *
-  */
-template<typename Derived>
-class SparseSolverBase : internal::noncopyable
-{
-  public:
-
-    /** Default constructor */
-    SparseSolverBase()
-      : m_isInitialized(false)
-    {}
-
-    ~SparseSolverBase()
-    {}
-
-    Derived& derived() { return *static_cast<Derived*>(this); }
-    const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    
-    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<Derived, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "Solver is not initialized.");
-      eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<Derived, Rhs>(derived(), b.derived());
-    }
-    
-    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<Derived, Rhs>
-    solve(const SparseMatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "Solver is not initialized.");
-      eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<Derived, Rhs>(derived(), b.derived());
-    }
-    
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal default implementation of solving with a sparse rhs */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
-    {
-      internal::solve_sparse_through_dense_panels(derived(), b.derived(), dest.derived());
-    }
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-
-  protected:
-    
-    mutable bool m_isInitialized;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSESOLVERBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
deleted file mode 100644
index 88820a4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
+++ /dev/null
@@ -1,198 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
-#define EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
-template<typename Lhs, typename Rhs, typename ResultType>
-static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, const typename ResultType::RealScalar& tolerance)
-{
-  // return sparse_sparse_product_with_pruning_impl2(lhs,rhs,res);
-
-  typedef typename remove_all<Rhs>::type::Scalar RhsScalar;
-  typedef typename remove_all<ResultType>::type::Scalar ResScalar;
-  typedef typename remove_all<Lhs>::type::StorageIndex StorageIndex;
-
-  // make sure to call innerSize/outerSize since we fake the storage order.
-  Index rows = lhs.innerSize();
-  Index cols = rhs.outerSize();
-  //Index size = lhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  // allocate a temporary buffer
-  AmbiVector<ResScalar,StorageIndex> tempVector(rows);
-
-  // mimics a resizeByInnerOuter:
-  if(ResultType::IsRowMajor)
-    res.resize(cols, rows);
-  else
-    res.resize(rows, cols);
-  
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-  
-  // estimate the number of non zero entries
-  // given a rhs column containing Y non zeros, we assume that the respective Y columns
-  // of the lhs differs in average of one non zeros, thus the number of non zeros for
-  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
-  // per column of the lhs.
-  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
-  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
-
-  res.reserve(estimated_nnz_prod);
-  double ratioColRes = double(estimated_nnz_prod)/(double(lhs.rows())*double(rhs.cols()));
-  for (Index j=0; j<cols; ++j)
-  {
-    // FIXME:
-    //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows());
-    // let's do a more accurate determination of the nnz ratio for the current column j of res
-    tempVector.init(ratioColRes);
-    tempVector.setZero();
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
-      tempVector.restart();
-      RhsScalar x = rhsIt.value();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, rhsIt.index()); lhsIt; ++lhsIt)
-      {
-        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
-      }
-    }
-    res.startVec(j);
-    for (typename AmbiVector<ResScalar,StorageIndex>::Iterator it(tempVector,tolerance); it; ++it)
-      res.insertBackByOuterInner(j,it.index()) = it.value();
-  }
-  res.finalize();
-}
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
-  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
-  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
-struct sparse_sparse_product_with_pruning_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance);
-    res.swap(_res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    // we need a col-major matrix to hold the result
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> SparseTemporaryType;
-    SparseTemporaryType _res(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance);
-    res = _res;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    // let's transpose the product to get a column x column product
-    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance);
-    res.swap(_res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
-    ColMajorMatrixLhs colLhs(lhs);
-    ColMajorMatrixRhs colRhs(rhs);
-    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,ColMajorMatrixRhs,ResultType>(colLhs, colRhs, res, tolerance);
-
-    // let's transpose the product to get a column x column product
-//     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
-//     SparseTemporaryType _res(res.cols(), res.rows());
-//     sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
-//     res = _res.transpose();
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixLhs;
-    RowMajorMatrixLhs rowLhs(lhs);
-    sparse_sparse_product_with_pruning_selector<RowMajorMatrixLhs,Rhs,ResultType,RowMajor,RowMajor>(rowLhs,rhs,res,tolerance);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixRhs;
-    RowMajorMatrixRhs rowRhs(rhs);
-    sparse_sparse_product_with_pruning_selector<Lhs,RowMajorMatrixRhs,ResultType,RowMajor,RowMajor,RowMajor>(lhs,rowRhs,res,tolerance);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
-    ColMajorMatrixRhs colRhs(rhs);
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,ColMajorMatrixRhs,ResultType>(lhs, colRhs, res, tolerance);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
-    ColMajorMatrixLhs colLhs(lhs);
-    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,Rhs,ResultType>(colLhs, rhs, res, tolerance);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseTranspose.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseTranspose.h
deleted file mode 100644
index 3757d4c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseTranspose.h
+++ /dev/null
@@ -1,92 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSETRANSPOSE_H
-#define EIGEN_SPARSETRANSPOSE_H
-
-namespace Eigen { 
-
-namespace internal {
-  template<typename MatrixType,int CompressedAccess=int(MatrixType::Flags&CompressedAccessBit)>
-  class SparseTransposeImpl
-    : public SparseMatrixBase<Transpose<MatrixType> >
-  {};
-  
-  template<typename MatrixType>
-  class SparseTransposeImpl<MatrixType,CompressedAccessBit>
-    : public SparseCompressedBase<Transpose<MatrixType> >
-  {
-    typedef SparseCompressedBase<Transpose<MatrixType> > Base;
-  public:
-    using Base::derived;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::StorageIndex StorageIndex;
-
-    inline Index nonZeros() const { return derived().nestedExpression().nonZeros(); }
-    
-    inline const Scalar* valuePtr() const { return derived().nestedExpression().valuePtr(); }
-    inline const StorageIndex* innerIndexPtr() const { return derived().nestedExpression().innerIndexPtr(); }
-    inline const StorageIndex* outerIndexPtr() const { return derived().nestedExpression().outerIndexPtr(); }
-    inline const StorageIndex* innerNonZeroPtr() const { return derived().nestedExpression().innerNonZeroPtr(); }
-
-    inline Scalar* valuePtr() { return derived().nestedExpression().valuePtr(); }
-    inline StorageIndex* innerIndexPtr() { return derived().nestedExpression().innerIndexPtr(); }
-    inline StorageIndex* outerIndexPtr() { return derived().nestedExpression().outerIndexPtr(); }
-    inline StorageIndex* innerNonZeroPtr() { return derived().nestedExpression().innerNonZeroPtr(); }
-  };
-}
-  
-template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>
-  : public internal::SparseTransposeImpl<MatrixType>
-{
-  protected:
-    typedef internal::SparseTransposeImpl<MatrixType> Base;
-};
-
-namespace internal {
-  
-template<typename ArgType>
-struct unary_evaluator<Transpose<ArgType>, IteratorBased>
-  : public evaluator_base<Transpose<ArgType> >
-{
-    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
-  public:
-    typedef Transpose<ArgType> XprType;
-    
-    inline Index nonZerosEstimate() const {
-      return m_argImpl.nonZerosEstimate();
-    }
-
-    class InnerIterator : public EvalIterator
-    {
-    public:
-      EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
-        : EvalIterator(unaryOp.m_argImpl,outer)
-      {}
-      
-      Index row() const { return EvalIterator::col(); }
-      Index col() const { return EvalIterator::row(); }
-    };
-    
-    enum {
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& op) :m_argImpl(op.nestedExpression()) {}
-
-  protected:
-    evaluator<ArgType> m_argImpl;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSETRANSPOSE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseTriangularView.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseTriangularView.h
deleted file mode 100644
index 9ac1202..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseTriangularView.h
+++ /dev/null
@@ -1,189 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_TRIANGULARVIEW_H
-#define EIGEN_SPARSE_TRIANGULARVIEW_H
-
-namespace Eigen {
-
-/** \ingroup SparseCore_Module
-  *
-  * \brief Base class for a triangular part in a \b sparse matrix
-  *
-  * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be instantiated.
-  * It extends class TriangularView with additional methods which are available for sparse expressions only.
-  *
-  * \sa class TriangularView, SparseMatrixBase::triangularView()
-  */
-template<typename MatrixType, unsigned int Mode> class TriangularViewImpl<MatrixType,Mode,Sparse>
-  : public SparseMatrixBase<TriangularView<MatrixType,Mode> >
-{
-    enum { SkipFirst = ((Mode&Lower) && !(MatrixType::Flags&RowMajorBit))
-                    || ((Mode&Upper) &&  (MatrixType::Flags&RowMajorBit)),
-           SkipLast = !SkipFirst,
-           SkipDiag = (Mode&ZeroDiag) ? 1 : 0,
-           HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
-    };
-    
-    typedef TriangularView<MatrixType,Mode> TriangularViewType;
-    
-  protected:
-    // dummy solve function to make TriangularView happy.
-    void solve() const;
-
-    typedef SparseMatrixBase<TriangularViewType> Base;
-  public:
-    
-    EIGEN_SPARSE_PUBLIC_INTERFACE(TriangularViewType)
-    
-    typedef typename MatrixType::Nested MatrixTypeNested;
-    typedef typename internal::remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef;
-    typedef typename internal::remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
-
-    template<typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _solve_impl(const RhsType &rhs, DstType &dst) const {
-      if(!(internal::is_same<RhsType,DstType>::value && internal::extract_data(dst) == internal::extract_data(rhs)))
-        dst = rhs;
-      this->solveInPlace(dst);
-    }
-
-    /** Applies the inverse of \c *this to the dense vector or matrix \a other, "in-place" */
-    template<typename OtherDerived> void solveInPlace(MatrixBase<OtherDerived>& other) const;
-
-    /** Applies the inverse of \c *this to the sparse vector or matrix \a other, "in-place" */
-    template<typename OtherDerived> void solveInPlace(SparseMatrixBase<OtherDerived>& other) const;
-  
-};
-
-namespace internal {
-
-template<typename ArgType, unsigned int Mode>
-struct unary_evaluator<TriangularView<ArgType,Mode>, IteratorBased>
- : evaluator_base<TriangularView<ArgType,Mode> >
-{
-  typedef TriangularView<ArgType,Mode> XprType;
-  
-protected:
-  
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
-  
-  enum { SkipFirst = ((Mode&Lower) && !(ArgType::Flags&RowMajorBit))
-                    || ((Mode&Upper) &&  (ArgType::Flags&RowMajorBit)),
-         SkipLast = !SkipFirst,
-         SkipDiag = (Mode&ZeroDiag) ? 1 : 0,
-         HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
-  };
-  
-public:
-  
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    Flags = XprType::Flags
-  };
-    
-  explicit unary_evaluator(const XprType &xpr) : m_argImpl(xpr.nestedExpression()), m_arg(xpr.nestedExpression()) {}
-  
-  inline Index nonZerosEstimate() const {
-    return m_argImpl.nonZerosEstimate();
-  }
-  
-  class InnerIterator : public EvalIterator
-  {
-      typedef EvalIterator Base;
-    public:
-
-      EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& xprEval, Index outer)
-        : Base(xprEval.m_argImpl,outer), m_returnOne(false), m_containsDiag(Base::outer()<xprEval.m_arg.innerSize())
-      {
-        if(SkipFirst)
-        {
-          while((*this) && ((HasUnitDiag||SkipDiag)  ? this->index()<=outer : this->index()<outer))
-            Base::operator++();
-          if(HasUnitDiag)
-            m_returnOne = m_containsDiag;
-        }
-        else if(HasUnitDiag && ((!Base::operator bool()) || Base::index()>=Base::outer()))
-        {
-          if((!SkipFirst) && Base::operator bool())
-            Base::operator++();
-          m_returnOne = m_containsDiag;
-        }
-      }
-
-      EIGEN_STRONG_INLINE InnerIterator& operator++()
-      {
-        if(HasUnitDiag && m_returnOne)
-          m_returnOne = false;
-        else
-        {
-          Base::operator++();
-          if(HasUnitDiag && (!SkipFirst) && ((!Base::operator bool()) || Base::index()>=Base::outer()))
-          {
-            if((!SkipFirst) && Base::operator bool())
-              Base::operator++();
-            m_returnOne = m_containsDiag;
-          }
-        }
-        return *this;
-      }
-      
-      EIGEN_STRONG_INLINE operator bool() const
-      {
-        if(HasUnitDiag && m_returnOne)
-          return true;
-        if(SkipFirst) return  Base::operator bool();
-        else
-        {
-          if (SkipDiag) return (Base::operator bool() && this->index() < this->outer());
-          else return (Base::operator bool() && this->index() <= this->outer());
-        }
-      }
-
-//       inline Index row() const { return (ArgType::Flags&RowMajorBit ? Base::outer() : this->index()); }
-//       inline Index col() const { return (ArgType::Flags&RowMajorBit ? this->index() : Base::outer()); }
-      inline StorageIndex index() const
-      {
-        if(HasUnitDiag && m_returnOne)  return internal::convert_index<StorageIndex>(Base::outer());
-        else                            return Base::index();
-      }
-      inline Scalar value() const
-      {
-        if(HasUnitDiag && m_returnOne)  return Scalar(1);
-        else                            return Base::value();
-      }
-
-    protected:
-      bool m_returnOne;
-      bool m_containsDiag;
-    private:
-      Scalar& valueRef();
-  };
-  
-protected:
-  evaluator<ArgType> m_argImpl;
-  const ArgType& m_arg;
-};
-
-} // end namespace internal
-
-template<typename Derived>
-template<int Mode>
-inline const TriangularView<const Derived, Mode>
-SparseMatrixBase<Derived>::triangularView() const
-{
-  return TriangularView<const Derived, Mode>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_TRIANGULARVIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseUtil.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseUtil.h
deleted file mode 100644
index ceb9368..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseUtil.h
+++ /dev/null
@@ -1,186 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEUTIL_H
-#define EIGEN_SPARSEUTIL_H
-
-namespace Eigen { 
-
-#ifdef NDEBUG
-#define EIGEN_DBG_SPARSE(X)
-#else
-#define EIGEN_DBG_SPARSE(X) X
-#endif
-
-#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
-template<typename OtherDerived> \
-EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SparseMatrixBase<OtherDerived>& other) \
-{ \
-  return Base::operator Op(other.derived()); \
-} \
-EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
-{ \
-  return Base::operator Op(other); \
-}
-
-#define EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
-template<typename Other> \
-EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
-{ \
-  return Base::operator Op(scalar); \
-}
-
-#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
-EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =)
-
-
-#define EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) \
-  EIGEN_GENERIC_PUBLIC_INTERFACE(Derived)
-
-  
-const int CoherentAccessPattern     = 0x1;
-const int InnerRandomAccessPattern  = 0x2 | CoherentAccessPattern;
-const int OuterRandomAccessPattern  = 0x4 | CoherentAccessPattern;
-const int RandomAccessPattern       = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern;
-
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class SparseMatrix;
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class DynamicSparseMatrix;
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class SparseVector;
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class MappedSparseMatrix;
-
-template<typename MatrixType, unsigned int UpLo>  class SparseSelfAdjointView;
-template<typename Lhs, typename Rhs>              class SparseDiagonalProduct;
-template<typename MatrixType> class SparseView;
-
-template<typename Lhs, typename Rhs>        class SparseSparseProduct;
-template<typename Lhs, typename Rhs>        class SparseTimeDenseProduct;
-template<typename Lhs, typename Rhs>        class DenseTimeSparseProduct;
-template<typename Lhs, typename Rhs, bool Transpose> class SparseDenseOuterProduct;
-
-template<typename Lhs, typename Rhs> struct SparseSparseProductReturnType;
-template<typename Lhs, typename Rhs,
-         int InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(internal::traits<Lhs>::ColsAtCompileTime,internal::traits<Rhs>::RowsAtCompileTime)> struct DenseSparseProductReturnType;
-         
-template<typename Lhs, typename Rhs,
-         int InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(internal::traits<Lhs>::ColsAtCompileTime,internal::traits<Rhs>::RowsAtCompileTime)> struct SparseDenseProductReturnType;
-template<typename MatrixType,int UpLo> class SparseSymmetricPermutationProduct;
-
-namespace internal {
-
-template<typename T,int Rows,int Cols,int Flags> struct sparse_eval;
-
-template<typename T> struct eval<T,Sparse>
-  : sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime,traits<T>::Flags>
-{};
-
-template<typename T,int Cols,int Flags> struct sparse_eval<T,1,Cols,Flags> {
-    typedef typename traits<T>::Scalar _Scalar;
-    typedef typename traits<T>::StorageIndex _StorageIndex;
-  public:
-    typedef SparseVector<_Scalar, RowMajor, _StorageIndex> type;
-};
-
-template<typename T,int Rows,int Flags> struct sparse_eval<T,Rows,1,Flags> {
-    typedef typename traits<T>::Scalar _Scalar;
-    typedef typename traits<T>::StorageIndex _StorageIndex;
-  public:
-    typedef SparseVector<_Scalar, ColMajor, _StorageIndex> type;
-};
-
-// TODO this seems almost identical to plain_matrix_type<T, Sparse>
-template<typename T,int Rows,int Cols,int Flags> struct sparse_eval {
-    typedef typename traits<T>::Scalar _Scalar;
-    typedef typename traits<T>::StorageIndex _StorageIndex;
-    enum { _Options = ((Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor };
-  public:
-    typedef SparseMatrix<_Scalar, _Options, _StorageIndex> type;
-};
-
-template<typename T,int Flags> struct sparse_eval<T,1,1,Flags> {
-    typedef typename traits<T>::Scalar _Scalar;
-  public:
-    typedef Matrix<_Scalar, 1, 1> type;
-};
-
-template<typename T> struct plain_matrix_type<T,Sparse>
-{
-  typedef typename traits<T>::Scalar _Scalar;
-  typedef typename traits<T>::StorageIndex _StorageIndex;
-  enum { _Options = ((evaluator<T>::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor };
-  public:
-    typedef SparseMatrix<_Scalar, _Options, _StorageIndex> type;
-};
-
-template<typename T>
-struct plain_object_eval<T,Sparse>
-  : sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime, evaluator<T>::Flags>
-{};
-
-template<typename Decomposition, typename RhsType>
-struct solve_traits<Decomposition,RhsType,Sparse>
-{
-  typedef typename sparse_eval<RhsType, RhsType::RowsAtCompileTime, RhsType::ColsAtCompileTime,traits<RhsType>::Flags>::type PlainObject;
-};
-
-template<typename Derived>
-struct generic_xpr_base<Derived, MatrixXpr, Sparse>
-{
-  typedef SparseMatrixBase<Derived> type;
-};
-
-struct SparseTriangularShape  { static std::string debugName() { return "SparseTriangularShape"; } };
-struct SparseSelfAdjointShape { static std::string debugName() { return "SparseSelfAdjointShape"; } };
-
-template<> struct glue_shapes<SparseShape,SelfAdjointShape> { typedef SparseSelfAdjointShape type;  };
-template<> struct glue_shapes<SparseShape,TriangularShape > { typedef SparseTriangularShape  type;  };
-
-// return type of SparseCompressedBase::lower_bound;
-struct LowerBoundIndex {
-  LowerBoundIndex() : value(-1), found(false) {}
-  LowerBoundIndex(Index val, bool ok) : value(val), found(ok) {}
-  Index value;
-  bool found;
-};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  *
-  * \class Triplet
-  *
-  * \brief A small structure to hold a non zero as a triplet (i,j,value).
-  *
-  * \sa SparseMatrix::setFromTriplets()
-  */
-template<typename Scalar, typename StorageIndex=typename SparseMatrix<Scalar>::StorageIndex >
-class Triplet
-{
-public:
-  Triplet() : m_row(0), m_col(0), m_value(0) {}
-
-  Triplet(const StorageIndex& i, const StorageIndex& j, const Scalar& v = Scalar(0))
-    : m_row(i), m_col(j), m_value(v)
-  {}
-
-  /** \returns the row index of the element */
-  const StorageIndex& row() const { return m_row; }
-
-  /** \returns the column index of the element */
-  const StorageIndex& col() const { return m_col; }
-
-  /** \returns the value of the element */
-  const Scalar& value() const { return m_value; }
-protected:
-  StorageIndex m_row, m_col;
-  Scalar m_value;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEUTIL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseVector.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseVector.h
deleted file mode 100644
index 05779be..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseVector.h
+++ /dev/null
@@ -1,478 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEVECTOR_H
-#define EIGEN_SPARSEVECTOR_H
-
-namespace Eigen { 
-
-/** \ingroup SparseCore_Module
-  * \class SparseVector
-  *
-  * \brief a sparse vector class
-  *
-  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
-  *
-  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
-  */
-
-namespace internal {
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct traits<SparseVector<_Scalar, _Options, _StorageIndex> >
-{
-  typedef _Scalar Scalar;
-  typedef _StorageIndex StorageIndex;
-  typedef Sparse StorageKind;
-  typedef MatrixXpr XprKind;
-  enum {
-    IsColVector = (_Options & RowMajorBit) ? 0 : 1,
-
-    RowsAtCompileTime = IsColVector ? Dynamic : 1,
-    ColsAtCompileTime = IsColVector ? 1 : Dynamic,
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    Flags = _Options | NestByRefBit | LvalueBit | (IsColVector ? 0 : RowMajorBit) | CompressedAccessBit,
-    SupportedAccessPatterns = InnerRandomAccessPattern
-  };
-};
-
-// Sparse-Vector-Assignment kinds:
-enum {
-  SVA_RuntimeSwitch,
-  SVA_Inner,
-  SVA_Outer
-};
-
-template< typename Dest, typename Src,
-          int AssignmentKind = !bool(Src::IsVectorAtCompileTime) ? SVA_RuntimeSwitch
-                             : Src::InnerSizeAtCompileTime==1 ? SVA_Outer
-                             : SVA_Inner>
-struct sparse_vector_assign_selector;
-
-}
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-class SparseVector
-  : public SparseCompressedBase<SparseVector<_Scalar, _Options, _StorageIndex> >
-{
-    typedef SparseCompressedBase<SparseVector> Base;
-    using Base::convert_index;
-  public:
-    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
-    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
-    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)
-    
-    typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
-    enum { IsColVector = internal::traits<SparseVector>::IsColVector };
-    
-    enum {
-      Options = _Options
-    };
-    
-    EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; }
-    EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; }
-    EIGEN_STRONG_INLINE Index innerSize() const { return m_size; }
-    EIGEN_STRONG_INLINE Index outerSize() const { return 1; }
-
-    EIGEN_STRONG_INLINE const Scalar* valuePtr() const { return m_data.valuePtr(); }
-    EIGEN_STRONG_INLINE Scalar* valuePtr() { return m_data.valuePtr(); }
-
-    EIGEN_STRONG_INLINE const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
-    EIGEN_STRONG_INLINE StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
-
-    inline const StorageIndex* outerIndexPtr() const { return 0; }
-    inline StorageIndex* outerIndexPtr() { return 0; }
-    inline const StorageIndex* innerNonZeroPtr() const { return 0; }
-    inline StorageIndex* innerNonZeroPtr() { return 0; }
-    
-    /** \internal */
-    inline Storage& data() { return m_data; }
-    /** \internal */
-    inline const Storage& data() const { return m_data; }
-
-    inline Scalar coeff(Index row, Index col) const
-    {
-      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
-      return coeff(IsColVector ? row : col);
-    }
-    inline Scalar coeff(Index i) const
-    {
-      eigen_assert(i>=0 && i<m_size);
-      return m_data.at(StorageIndex(i));
-    }
-
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
-      return coeffRef(IsColVector ? row : col);
-    }
-
-    /** \returns a reference to the coefficient value at given index \a i
-      * This operation involes a log(rho*size) binary search. If the coefficient does not
-      * exist yet, then a sorted insertion into a sequential buffer is performed.
-      *
-      * This insertion might be very costly if the number of nonzeros above \a i is large.
-      */
-    inline Scalar& coeffRef(Index i)
-    {
-      eigen_assert(i>=0 && i<m_size);
-
-      return m_data.atWithInsertion(StorageIndex(i));
-    }
-
-  public:
-
-    typedef typename Base::InnerIterator InnerIterator;
-    typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
-
-    inline void setZero() { m_data.clear(); }
-
-    /** \returns the number of non zero coefficients */
-    inline Index nonZeros() const  { return m_data.size(); }
-
-    inline void startVec(Index outer)
-    {
-      EIGEN_UNUSED_VARIABLE(outer);
-      eigen_assert(outer==0);
-    }
-
-    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
-    {
-      EIGEN_UNUSED_VARIABLE(outer);
-      eigen_assert(outer==0);
-      return insertBack(inner);
-    }
-    inline Scalar& insertBack(Index i)
-    {
-      m_data.append(0, i);
-      return m_data.value(m_data.size()-1);
-    }
-    
-    Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
-    {
-      EIGEN_UNUSED_VARIABLE(outer);
-      eigen_assert(outer==0);
-      return insertBackUnordered(inner);
-    }
-    inline Scalar& insertBackUnordered(Index i)
-    {
-      m_data.append(0, i);
-      return m_data.value(m_data.size()-1);
-    }
-
-    inline Scalar& insert(Index row, Index col)
-    {
-      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
-      
-      Index inner = IsColVector ? row : col;
-      Index outer = IsColVector ? col : row;
-      EIGEN_ONLY_USED_FOR_DEBUG(outer);
-      eigen_assert(outer==0);
-      return insert(inner);
-    }
-    Scalar& insert(Index i)
-    {
-      eigen_assert(i>=0 && i<m_size);
-      
-      Index startId = 0;
-      Index p = Index(m_data.size()) - 1;
-      // TODO smart realloc
-      m_data.resize(p+2,1);
-
-      while ( (p >= startId) && (m_data.index(p) > i) )
-      {
-        m_data.index(p+1) = m_data.index(p);
-        m_data.value(p+1) = m_data.value(p);
-        --p;
-      }
-      m_data.index(p+1) = convert_index(i);
-      m_data.value(p+1) = 0;
-      return m_data.value(p+1);
-    }
-
-    /**
-      */
-    inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }
-
-
-    inline void finalize() {}
-
-    /** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
-    void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
-    {
-      m_data.prune(reference,epsilon);
-    }
-
-    /** Resizes the sparse vector to \a rows x \a cols
-      *
-      * This method is provided for compatibility with matrices.
-      * For a column vector, \a cols must be equal to 1.
-      * For a row vector, \a rows must be equal to 1.
-      *
-      * \sa resize(Index)
-      */
-    void resize(Index rows, Index cols)
-    {
-      eigen_assert((IsColVector ? cols : rows)==1 && "Outer dimension must equal 1");
-      resize(IsColVector ? rows : cols);
-    }
-
-    /** Resizes the sparse vector to \a newSize
-      * This method deletes all entries, thus leaving an empty sparse vector
-      *
-      * \sa  conservativeResize(), setZero() */
-    void resize(Index newSize)
-    {
-      m_size = newSize;
-      m_data.clear();
-    }
-
-    /** Resizes the sparse vector to \a newSize, while leaving old values untouched.
-      *
-      * If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
-      * Call .data().squeeze() to free extra memory.
-      *
-      * \sa reserve(), setZero()
-      */
-    void conservativeResize(Index newSize)
-    {
-      if (newSize < m_size)
-      {
-        Index i = 0;
-        while (i<m_data.size() && m_data.index(i)<newSize) ++i;
-        m_data.resize(i);
-      }
-      m_size = newSize;
-    }
-
-    void resizeNonZeros(Index size) { m_data.resize(size); }
-
-    inline SparseVector() : m_size(0) { check_template_parameters(); resize(0); }
-
-    explicit inline SparseVector(Index size) : m_size(0) { check_template_parameters(); resize(size); }
-
-    inline SparseVector(Index rows, Index cols) : m_size(0) { check_template_parameters(); resize(rows,cols); }
-
-    template<typename OtherDerived>
-    inline SparseVector(const SparseMatrixBase<OtherDerived>& other)
-      : m_size(0)
-    {
-      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-      #endif
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    inline SparseVector(const SparseVector& other)
-      : Base(other), m_size(0)
-    {
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    /** Swaps the values of \c *this and \a other.
-      * Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only.
-      * \sa SparseMatrixBase::swap()
-      */
-    inline void swap(SparseVector& other)
-    {
-      std::swap(m_size, other.m_size);
-      m_data.swap(other.m_data);
-    }
-
-    template<int OtherOptions>
-    inline void swap(SparseMatrix<Scalar,OtherOptions,StorageIndex>& other)
-    {
-      eigen_assert(other.outerSize()==1);
-      std::swap(m_size, other.m_innerSize);
-      m_data.swap(other.m_data);
-    }
-
-    inline SparseVector& operator=(const SparseVector& other)
-    {
-      if (other.isRValue())
-      {
-        swap(other.const_cast_derived());
-      }
-      else
-      {
-        resize(other.size());
-        m_data = other.m_data;
-      }
-      return *this;
-    }
-
-    template<typename OtherDerived>
-    inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
-    {
-      SparseVector tmp(other.size());
-      internal::sparse_vector_assign_selector<SparseVector,OtherDerived>::run(tmp,other.derived());
-      this->swap(tmp);
-      return *this;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename Lhs, typename Rhs>
-    inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
-    {
-      return Base::operator=(product);
-    }
-    #endif
-
-    friend std::ostream & operator << (std::ostream & s, const SparseVector& m)
-    {
-      for (Index i=0; i<m.nonZeros(); ++i)
-        s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
-      s << std::endl;
-      return s;
-    }
-
-    /** Destructor */
-    inline ~SparseVector() {}
-
-    /** Overloaded for performance */
-    Scalar sum() const;
-
-  public:
-
-    /** \internal \deprecated use setZero() and reserve() */
-    EIGEN_DEPRECATED void startFill(Index reserve)
-    {
-      setZero();
-      m_data.reserve(reserve);
-    }
-
-    /** \internal \deprecated use insertBack(Index,Index) */
-    EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
-    {
-      eigen_assert(r==0 || c==0);
-      return fill(IsColVector ? r : c);
-    }
-
-    /** \internal \deprecated use insertBack(Index) */
-    EIGEN_DEPRECATED Scalar& fill(Index i)
-    {
-      m_data.append(0, i);
-      return m_data.value(m_data.size()-1);
-    }
-
-    /** \internal \deprecated use insert(Index,Index) */
-    EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
-    {
-      eigen_assert(r==0 || c==0);
-      return fillrand(IsColVector ? r : c);
-    }
-
-    /** \internal \deprecated use insert(Index) */
-    EIGEN_DEPRECATED Scalar& fillrand(Index i)
-    {
-      return insert(i);
-    }
-
-    /** \internal \deprecated use finalize() */
-    EIGEN_DEPRECATED void endFill() {}
-    
-    // These two functions were here in the 3.1 release, so let's keep them in case some code rely on them.
-    /** \internal \deprecated use data() */
-    EIGEN_DEPRECATED Storage& _data() { return m_data; }
-    /** \internal \deprecated use data() */
-    EIGEN_DEPRECATED const Storage& _data() const { return m_data; }
-    
-#   ifdef EIGEN_SPARSEVECTOR_PLUGIN
-#     include EIGEN_SPARSEVECTOR_PLUGIN
-#   endif
-
-protected:
-  
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
-      EIGEN_STATIC_ASSERT((_Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS);
-    }
-    
-    Storage m_data;
-    Index m_size;
-};
-
-namespace internal {
-
-template<typename _Scalar, int _Options, typename _Index>
-struct evaluator<SparseVector<_Scalar,_Options,_Index> >
-  : evaluator_base<SparseVector<_Scalar,_Options,_Index> >
-{
-  typedef SparseVector<_Scalar,_Options,_Index> SparseVectorType;
-  typedef evaluator_base<SparseVectorType> Base;
-  typedef typename SparseVectorType::InnerIterator InnerIterator;
-  typedef typename SparseVectorType::ReverseInnerIterator ReverseInnerIterator;
-  
-  enum {
-    CoeffReadCost = NumTraits<_Scalar>::ReadCost,
-    Flags = SparseVectorType::Flags
-  };
-
-  evaluator() : Base() {}
-  
-  explicit evaluator(const SparseVectorType &mat) : m_matrix(&mat)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_matrix->nonZeros();
-  }
-  
-  operator SparseVectorType&() { return m_matrix->const_cast_derived(); }
-  operator const SparseVectorType&() const { return *m_matrix; }
-  
-  const SparseVectorType *m_matrix;
-};
-
-template< typename Dest, typename Src>
-struct sparse_vector_assign_selector<Dest,Src,SVA_Inner> {
-  static void run(Dest& dst, const Src& src) {
-    eigen_internal_assert(src.innerSize()==src.size());
-    typedef internal::evaluator<Src> SrcEvaluatorType;
-    SrcEvaluatorType srcEval(src);
-    for(typename SrcEvaluatorType::InnerIterator it(srcEval, 0); it; ++it)
-      dst.insert(it.index()) = it.value();
-  }
-};
-
-template< typename Dest, typename Src>
-struct sparse_vector_assign_selector<Dest,Src,SVA_Outer> {
-  static void run(Dest& dst, const Src& src) {
-    eigen_internal_assert(src.outerSize()==src.size());
-    typedef internal::evaluator<Src> SrcEvaluatorType;
-    SrcEvaluatorType srcEval(src);
-    for(Index i=0; i<src.size(); ++i)
-    {
-      typename SrcEvaluatorType::InnerIterator it(srcEval, i);
-      if(it)
-        dst.insert(i) = it.value();
-    }
-  }
-};
-
-template< typename Dest, typename Src>
-struct sparse_vector_assign_selector<Dest,Src,SVA_RuntimeSwitch> {
-  static void run(Dest& dst, const Src& src) {
-    if(src.outerSize()==1)  sparse_vector_assign_selector<Dest,Src,SVA_Inner>::run(dst, src);
-    else                    sparse_vector_assign_selector<Dest,Src,SVA_Outer>::run(dst, src);
-  }
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseView.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseView.h
deleted file mode 100644
index 92b3d1f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/SparseView.h
+++ /dev/null
@@ -1,254 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEVIEW_H
-#define EIGEN_SPARSEVIEW_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename MatrixType>
-struct traits<SparseView<MatrixType> > : traits<MatrixType>
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef Sparse StorageKind;
-  enum {
-    Flags = int(traits<MatrixType>::Flags) & (RowMajorBit)
-  };
-};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  * \class SparseView
-  *
-  * \brief Expression of a dense or sparse matrix with zero or too small values removed
-  *
-  * \tparam MatrixType the type of the object of which we are removing the small entries
-  *
-  * This class represents an expression of a given dense or sparse matrix with
-  * entries smaller than \c reference * \c epsilon are removed.
-  * It is the return type of MatrixBase::sparseView() and SparseMatrixBase::pruned()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::sparseView(), SparseMatrixBase::pruned()
-  */
-template<typename MatrixType>
-class SparseView : public SparseMatrixBase<SparseView<MatrixType> >
-{
-  typedef typename MatrixType::Nested MatrixTypeNested;
-  typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
-  typedef SparseMatrixBase<SparseView > Base;
-public:
-  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView)
-  typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-  explicit SparseView(const MatrixType& mat, const Scalar& reference = Scalar(0),
-                      const RealScalar &epsilon = NumTraits<Scalar>::dummy_precision())
-    : m_matrix(mat), m_reference(reference), m_epsilon(epsilon) {}
-
-  inline Index rows() const { return m_matrix.rows(); }
-  inline Index cols() const { return m_matrix.cols(); }
-
-  inline Index innerSize() const { return m_matrix.innerSize(); }
-  inline Index outerSize() const { return m_matrix.outerSize(); }
-  
-  /** \returns the nested expression */
-  const typename internal::remove_all<MatrixTypeNested>::type&
-  nestedExpression() const { return m_matrix; }
-  
-  Scalar reference() const { return m_reference; }
-  RealScalar epsilon() const { return m_epsilon; }
-  
-protected:
-  MatrixTypeNested m_matrix;
-  Scalar m_reference;
-  RealScalar m_epsilon;
-};
-
-namespace internal {
-
-// TODO find a way to unify the two following variants
-// This is tricky because implementing an inner iterator on top of an IndexBased evaluator is
-// not easy because the evaluators do not expose the sizes of the underlying expression.
-  
-template<typename ArgType>
-struct unary_evaluator<SparseView<ArgType>, IteratorBased>
-  : public evaluator_base<SparseView<ArgType> >
-{
-    typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
-  public:
-    typedef SparseView<ArgType> XprType;
-    
-    class InnerIterator : public EvalIterator
-    {
-      protected:
-        typedef typename XprType::Scalar Scalar;
-      public:
-
-        EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
-          : EvalIterator(sve.m_argImpl,outer), m_view(sve.m_view)
-        {
-          incrementToNonZero();
-        }
-
-        EIGEN_STRONG_INLINE InnerIterator& operator++()
-        {
-          EvalIterator::operator++();
-          incrementToNonZero();
-          return *this;
-        }
-
-        using EvalIterator::value;
-
-      protected:
-        const XprType &m_view;
-
-      private:
-        void incrementToNonZero()
-        {
-          while((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.reference(), m_view.epsilon()))
-          {
-            EvalIterator::operator++();
-          }
-        }
-    };
-    
-    enum {
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
-
-  protected:
-    evaluator<ArgType> m_argImpl;
-    const XprType &m_view;
-};
-
-template<typename ArgType>
-struct unary_evaluator<SparseView<ArgType>, IndexBased>
-  : public evaluator_base<SparseView<ArgType> >
-{
-  public:
-    typedef SparseView<ArgType> XprType;
-  protected:
-    enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
-    typedef typename XprType::Scalar Scalar;
-    typedef typename XprType::StorageIndex StorageIndex;
-  public:
-    
-    class InnerIterator
-    {
-      public:
-
-        EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
-          : m_sve(sve), m_inner(0), m_outer(outer), m_end(sve.m_view.innerSize())
-        {
-          incrementToNonZero();
-        }
-
-        EIGEN_STRONG_INLINE InnerIterator& operator++()
-        {
-          m_inner++;
-          incrementToNonZero();
-          return *this;
-        }
-
-        EIGEN_STRONG_INLINE Scalar value() const
-        {
-          return (IsRowMajor) ? m_sve.m_argImpl.coeff(m_outer, m_inner)
-                              : m_sve.m_argImpl.coeff(m_inner, m_outer);
-        }
-
-        EIGEN_STRONG_INLINE StorageIndex index() const { return m_inner; }
-        inline Index row() const { return IsRowMajor ? m_outer : index(); }
-        inline Index col() const { return IsRowMajor ? index() : m_outer; }
-
-        EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
-
-      protected:
-        const unary_evaluator &m_sve;
-        Index m_inner;
-        const Index m_outer;
-        const Index m_end;
-
-      private:
-        void incrementToNonZero()
-        {
-          while((bool(*this)) && internal::isMuchSmallerThan(value(), m_sve.m_view.reference(), m_sve.m_view.epsilon()))
-          {
-            m_inner++;
-          }
-        }
-    };
-    
-    enum {
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
-
-  protected:
-    evaluator<ArgType> m_argImpl;
-    const XprType &m_view;
-};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  *
-  * \returns a sparse expression of the dense expression \c *this with values smaller than
-  * \a reference * \a epsilon removed.
-  *
-  * This method is typically used when prototyping to convert a quickly assembled dense Matrix \c D to a SparseMatrix \c S:
-  * \code
-  * MatrixXd D(n,m);
-  * SparseMatrix<double> S;
-  * S = D.sparseView();             // suppress numerical zeros (exact)
-  * S = D.sparseView(reference);
-  * S = D.sparseView(reference,epsilon);
-  * \endcode
-  * where \a reference is a meaningful non zero reference value,
-  * and \a epsilon is a tolerance factor defaulting to NumTraits<Scalar>::dummy_precision().
-  *
-  * \sa SparseMatrixBase::pruned(), class SparseView */
-template<typename Derived>
-const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& reference,
-                                                          const typename NumTraits<Scalar>::Real& epsilon) const
-{
-  return SparseView<Derived>(derived(), reference, epsilon);
-}
-
-/** \returns an expression of \c *this with values smaller than
-  * \a reference * \a epsilon removed.
-  *
-  * This method is typically used in conjunction with the product of two sparse matrices
-  * to automatically prune the smallest values as follows:
-  * \code
-  * C = (A*B).pruned();             // suppress numerical zeros (exact)
-  * C = (A*B).pruned(ref);
-  * C = (A*B).pruned(ref,epsilon);
-  * \endcode
-  * where \c ref is a meaningful non zero reference value.
-  * */
-template<typename Derived>
-const SparseView<Derived>
-SparseMatrixBase<Derived>::pruned(const Scalar& reference,
-                                  const RealScalar& epsilon) const
-{
-  return SparseView<Derived>(derived(), reference, epsilon);
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/TriangularSolver.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/TriangularSolver.h
deleted file mode 100644
index f9c56ba..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseCore/TriangularSolver.h
+++ /dev/null
@@ -1,315 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSETRIANGULARSOLVER_H
-#define EIGEN_SPARSETRIANGULARSOLVER_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Mode,
-  int UpLo = (Mode & Lower)
-           ? Lower
-           : (Mode & Upper)
-           ? Upper
-           : -1,
-  int StorageOrder = int(traits<Lhs>::Flags) & RowMajorBit>
-struct sparse_solve_triangular_selector;
-
-// forward substitution, row-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,RowMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=0; i<lhs.rows(); ++i)
-      {
-        Scalar tmp = other.coeff(i,col);
-        Scalar lastVal(0);
-        Index lastIndex = 0;
-        for(LhsIterator it(lhsEval, i); it; ++it)
-        {
-          lastVal = it.value();
-          lastIndex = it.index();
-          if(lastIndex==i)
-            break;
-          tmp -= lastVal * other.coeff(lastIndex,col);
-        }
-        if (Mode & UnitDiag)
-          other.coeffRef(i,col) = tmp;
-        else
-        {
-          eigen_assert(lastIndex==i);
-          other.coeffRef(i,col) = tmp/lastVal;
-        }
-      }
-    }
-  }
-};
-
-// backward substitution, row-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,RowMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=lhs.rows()-1 ; i>=0 ; --i)
-      {
-        Scalar tmp = other.coeff(i,col);
-        Scalar l_ii(0);
-        LhsIterator it(lhsEval, i);
-        while(it && it.index()<i)
-          ++it;
-        if(!(Mode & UnitDiag))
-        {
-          eigen_assert(it && it.index()==i);
-          l_ii = it.value();
-          ++it;
-        }
-        else if (it && it.index() == i)
-          ++it;
-        for(; it; ++it)
-        {
-          tmp -= it.value() * other.coeff(it.index(),col);
-        }
-
-        if (Mode & UnitDiag)  other.coeffRef(i,col) = tmp;
-        else                  other.coeffRef(i,col) = tmp/l_ii;
-      }
-    }
-  }
-};
-
-// forward substitution, col-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,ColMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=0; i<lhs.cols(); ++i)
-      {
-        Scalar& tmp = other.coeffRef(i,col);
-        if (tmp!=Scalar(0)) // optimization when other is actually sparse
-        {
-          LhsIterator it(lhsEval, i);
-          while(it && it.index()<i)
-            ++it;
-          if(!(Mode & UnitDiag))
-          {
-            eigen_assert(it && it.index()==i);
-            tmp /= it.value();
-          }
-          if (it && it.index()==i)
-            ++it;
-          for(; it; ++it)
-            other.coeffRef(it.index(), col) -= tmp * it.value();
-        }
-      }
-    }
-  }
-};
-
-// backward substitution, col-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,ColMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=lhs.cols()-1; i>=0; --i)
-      {
-        Scalar& tmp = other.coeffRef(i,col);
-        if (tmp!=Scalar(0)) // optimization when other is actually sparse
-        {
-          if(!(Mode & UnitDiag))
-          {
-            // TODO replace this by a binary search. make sure the binary search is safe for partially sorted elements
-            LhsIterator it(lhsEval, i);
-            while(it && it.index()!=i)
-              ++it;
-            eigen_assert(it && it.index()==i);
-            other.coeffRef(i,col) /= it.value();
-          }
-          LhsIterator it(lhsEval, i);
-          for(; it && it.index()<i; ++it)
-            other.coeffRef(it.index(), col) -= tmp * it.value();
-        }
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-template<typename ExpressionType,unsigned int Mode>
-template<typename OtherDerived>
-void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(MatrixBase<OtherDerived>& other) const
-{
-  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
-  eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
-
-  enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
-
-  typedef typename internal::conditional<copy,
-    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
-  OtherCopy otherCopy(other.derived());
-
-  internal::sparse_solve_triangular_selector<ExpressionType, typename internal::remove_reference<OtherCopy>::type, Mode>::run(derived().nestedExpression(), otherCopy);
-
-  if (copy)
-    other = otherCopy;
-}
-#endif
-
-// pure sparse path
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Mode,
-  int UpLo = (Mode & Lower)
-           ? Lower
-           : (Mode & Upper)
-           ? Upper
-           : -1,
-  int StorageOrder = int(Lhs::Flags) & (RowMajorBit)>
-struct sparse_solve_triangular_sparse_selector;
-
-// forward substitution, col-major
-template<typename Lhs, typename Rhs, int Mode, int UpLo>
-struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
-                                      typename traits<Rhs>::StorageIndex>::type StorageIndex;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    const bool IsLower = (UpLo==Lower);
-    AmbiVector<Scalar,StorageIndex> tempVector(other.rows()*2);
-    tempVector.setBounds(0,other.rows());
-
-    Rhs res(other.rows(), other.cols());
-    res.reserve(other.nonZeros());
-
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      // FIXME estimate number of non zeros
-      tempVector.init(.99/*float(other.col(col).nonZeros())/float(other.rows())*/);
-      tempVector.setZero();
-      tempVector.restart();
-      for (typename Rhs::InnerIterator rhsIt(other, col); rhsIt; ++rhsIt)
-      {
-        tempVector.coeffRef(rhsIt.index()) = rhsIt.value();
-      }
-
-      for(Index i=IsLower?0:lhs.cols()-1;
-          IsLower?i<lhs.cols():i>=0;
-          i+=IsLower?1:-1)
-      {
-        tempVector.restart();
-        Scalar& ci = tempVector.coeffRef(i);
-        if (ci!=Scalar(0))
-        {
-          // find
-          typename Lhs::InnerIterator it(lhs, i);
-          if(!(Mode & UnitDiag))
-          {
-            if (IsLower)
-            {
-              eigen_assert(it.index()==i);
-              ci /= it.value();
-            }
-            else
-              ci /= lhs.coeff(i,i);
-          }
-          tempVector.restart();
-          if (IsLower)
-          {
-            if (it.index()==i)
-              ++it;
-            for(; it; ++it)
-              tempVector.coeffRef(it.index()) -= ci * it.value();
-          }
-          else
-          {
-            for(; it && it.index()<i; ++it)
-              tempVector.coeffRef(it.index()) -= ci * it.value();
-          }
-        }
-      }
-
-
-      Index count = 0;
-      // FIXME compute a reference value to filter zeros
-      for (typename AmbiVector<Scalar,StorageIndex>::Iterator it(tempVector/*,1e-12*/); it; ++it)
-      {
-        ++ count;
-//         std::cerr << "fill " << it.index() << ", " << col << "\n";
-//         std::cout << it.value() << "  ";
-        // FIXME use insertBack
-        res.insert(it.index(), col) = it.value();
-      }
-//       std::cout << "tempVector.nonZeros() == " << int(count) << " / " << (other.rows()) << "\n";
-    }
-    res.finalize();
-    other = res.markAsRValue();
-  }
-};
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename ExpressionType,unsigned int Mode>
-template<typename OtherDerived>
-void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const
-{
-  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
-  eigen_assert( (!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
-
-//   enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
-
-//   typedef typename internal::conditional<copy,
-//     typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
-//   OtherCopy otherCopy(other.derived());
-
-  internal::sparse_solve_triangular_sparse_selector<ExpressionType, OtherDerived, Mode>::run(derived().nestedExpression(), other.derived());
-
-//   if (copy)
-//     other = otherCopy;
-}
-#endif
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSETRIANGULARSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU.h
deleted file mode 100644
index 0c8d893..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU.h
+++ /dev/null
@@ -1,923 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_SPARSE_LU_H
-#define EIGEN_SPARSE_LU_H
-
-namespace Eigen {
-
-template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::StorageIndex> > class SparseLU;
-template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
-template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType;
-
-template <bool Conjugate,class SparseLUType>
-class SparseLUTransposeView : public SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> >
-{
-protected:
-  typedef SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> > APIBase;
-  using APIBase::m_isInitialized;
-public:
-  typedef typename SparseLUType::Scalar Scalar;
-  typedef typename SparseLUType::StorageIndex StorageIndex;
-  typedef typename SparseLUType::MatrixType MatrixType;
-  typedef typename SparseLUType::OrderingType OrderingType;
-
-  enum {
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-  };
-
-  SparseLUTransposeView() : m_sparseLU(NULL) {}
-  SparseLUTransposeView(const SparseLUTransposeView& view) {
-    this->m_sparseLU = view.m_sparseLU;
-  }
-  void setIsInitialized(const bool isInitialized) {this->m_isInitialized = isInitialized;}
-  void setSparseLU(SparseLUType* sparseLU) {m_sparseLU = sparseLU;}
-  using APIBase::_solve_impl;
-  template<typename Rhs, typename Dest>
-  bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const
-  {
-    Dest& X(X_base.derived());
-    eigen_assert(m_sparseLU->info() == Success && "The matrix should be factorized first");
-    EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
-                        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-
-
-    // this ugly const_cast_derived() helps to detect aliasing when applying the permutations
-    for(Index j = 0; j < B.cols(); ++j){
-      X.col(j) = m_sparseLU->colsPermutation() * B.const_cast_derived().col(j);
-    }
-    //Forward substitution with transposed or adjoint of U
-    m_sparseLU->matrixU().template solveTransposedInPlace<Conjugate>(X);
-
-    //Backward substitution with transposed or adjoint of L
-    m_sparseLU->matrixL().template solveTransposedInPlace<Conjugate>(X);
-
-    // Permute back the solution
-    for (Index j = 0; j < B.cols(); ++j)
-      X.col(j) = m_sparseLU->rowsPermutation().transpose() * X.col(j);
-    return true;
-  }
-  inline Index rows() const { return m_sparseLU->rows(); }
-  inline Index cols() const { return m_sparseLU->cols(); }
-
-private:
-  SparseLUType *m_sparseLU;
-  SparseLUTransposeView& operator=(const SparseLUTransposeView&);
-};
-
-
-/** \ingroup SparseLU_Module
-  * \class SparseLU
-  * 
-  * \brief Sparse supernodal LU factorization for general matrices
-  * 
-  * This class implements the supernodal LU factorization for general matrices.
-  * It uses the main techniques from the sequential SuperLU package 
-  * (http://crd-legacy.lbl.gov/~xiaoye/SuperLU/). It handles transparently real 
-  * and complex arithmetic with single and double precision, depending on the 
-  * scalar type of your input matrix. 
-  * The code has been optimized to provide BLAS-3 operations during supernode-panel updates. 
-  * It benefits directly from the built-in high-performant Eigen BLAS routines. 
-  * Moreover, when the size of a supernode is very small, the BLAS calls are avoided to 
-  * enable a better optimization from the compiler. For best performance, 
-  * you should compile it with NDEBUG flag to avoid the numerous bounds checking on vectors. 
-  * 
-  * An important parameter of this class is the ordering method. It is used to reorder the columns 
-  * (and eventually the rows) of the matrix to reduce the number of new elements that are created during 
-  * numerical factorization. The cheapest method available is COLAMD. 
-  * See  \link OrderingMethods_Module the OrderingMethods module \endlink for the list of 
-  * built-in and external ordering methods. 
-  *
-  * Simple example with key steps 
-  * \code
-  * VectorXd x(n), b(n);
-  * SparseMatrix<double> A;
-  * SparseLU<SparseMatrix<double>, COLAMDOrdering<int> >   solver;
-  * // fill A and b;
-  * // Compute the ordering permutation vector from the structural pattern of A
-  * solver.analyzePattern(A); 
-  * // Compute the numerical factorization 
-  * solver.factorize(A); 
-  * //Use the factors to solve the linear system 
-  * x = solver.solve(b); 
-  * \endcode
-  * 
-  * \warning The input matrix A should be in a \b compressed and \b column-major form.
-  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
-  * 
-  * \note Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix. 
-  * For badly scaled matrices, this step can be useful to reduce the pivoting during factorization. 
-  * If this is the case for your matrices, you can try the basic scaling method at
-  *  "unsupported/Eigen/src/IterativeSolvers/Scaling.h"
-  * 
-  * \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<>
-  * \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD
-  *
-  * \implsparsesolverconcept
-  * 
-  * \sa \ref TutorialSparseSolverConcept
-  * \sa \ref OrderingMethods_Module
-  */
-template <typename _MatrixType, typename _OrderingType>
-class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >, public internal::SparseLUImpl<typename _MatrixType::Scalar, typename _MatrixType::StorageIndex>
-{
-  protected:
-    typedef SparseSolverBase<SparseLU<_MatrixType,_OrderingType> > APIBase;
-    using APIBase::m_isInitialized;
-  public:
-    using APIBase::_solve_impl;
-    
-    typedef _MatrixType MatrixType; 
-    typedef _OrderingType OrderingType;
-    typedef typename MatrixType::Scalar Scalar; 
-    typedef typename MatrixType::RealScalar RealScalar; 
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> NCMatrix;
-    typedef internal::MappedSuperNodalMatrix<Scalar, StorageIndex> SCMatrix;
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-    typedef internal::SparseLUImpl<Scalar, StorageIndex> Base;
-
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-  public:
-
-    SparseLU():m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
-    {
-      initperfvalues(); 
-    }
-    explicit SparseLU(const MatrixType& matrix)
-      : m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
-    {
-      initperfvalues(); 
-      compute(matrix);
-    }
-    
-    ~SparseLU()
-    {
-      // Free all explicit dynamic pointers 
-    }
-    
-    void analyzePattern (const MatrixType& matrix);
-    void factorize (const MatrixType& matrix);
-    void simplicialfactorize(const MatrixType& matrix);
-    
-    /**
-      * Compute the symbolic and numeric factorization of the input sparse matrix.
-      * The input matrix should be in column-major storage. 
-      */
-    void compute (const MatrixType& matrix)
-    {
-      // Analyze 
-      analyzePattern(matrix); 
-      //Factorize
-      factorize(matrix);
-    } 
-
-    /** \returns an expression of the transposed of the factored matrix.
-      *
-      * A typical usage is to solve for the transposed problem A^T x = b:
-      * \code
-      * solver.compute(A);
-      * x = solver.transpose().solve(b);
-      * \endcode
-      *
-      * \sa adjoint(), solve()
-      */
-    const SparseLUTransposeView<false,SparseLU<_MatrixType,_OrderingType> > transpose()
-    {
-      SparseLUTransposeView<false,  SparseLU<_MatrixType,_OrderingType> > transposeView;
-      transposeView.setSparseLU(this);
-      transposeView.setIsInitialized(this->m_isInitialized);
-      return transposeView;
-    }
-
-
-    /** \returns an expression of the adjoint of the factored matrix
-      *
-      * A typical usage is to solve for the adjoint problem A' x = b:
-      * \code
-      * solver.compute(A);
-      * x = solver.adjoint().solve(b);
-      * \endcode
-      *
-      * For real scalar types, this function is equivalent to transpose().
-      *
-      * \sa transpose(), solve()
-      */
-    const SparseLUTransposeView<true, SparseLU<_MatrixType,_OrderingType> > adjoint()
-    {
-      SparseLUTransposeView<true,  SparseLU<_MatrixType,_OrderingType> > adjointView;
-      adjointView.setSparseLU(this);
-      adjointView.setIsInitialized(this->m_isInitialized);
-      return adjointView;
-    }
-    
-    inline Index rows() const { return m_mat.rows(); }
-    inline Index cols() const { return m_mat.cols(); }
-    /** Indicate that the pattern of the input matrix is symmetric */
-    void isSymmetric(bool sym)
-    {
-      m_symmetricmode = sym;
-    }
-    
-    /** \returns an expression of the matrix L, internally stored as supernodes
-      * The only operation available with this expression is the triangular solve
-      * \code
-      * y = b; matrixL().solveInPlace(y);
-      * \endcode
-      */
-    SparseLUMatrixLReturnType<SCMatrix> matrixL() const
-    {
-      return SparseLUMatrixLReturnType<SCMatrix>(m_Lstore);
-    }
-    /** \returns an expression of the matrix U,
-      * The only operation available with this expression is the triangular solve
-      * \code
-      * y = b; matrixU().solveInPlace(y);
-      * \endcode
-      */
-    SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,StorageIndex> > matrixU() const
-    {
-      return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,StorageIndex> >(m_Lstore, m_Ustore);
-    }
-
-    /**
-      * \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$
-      * \sa colsPermutation()
-      */
-    inline const PermutationType& rowsPermutation() const
-    {
-      return m_perm_r;
-    }
-    /**
-      * \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$
-      * \sa rowsPermutation()
-      */
-    inline const PermutationType& colsPermutation() const
-    {
-      return m_perm_c;
-    }
-    /** Set the threshold used for a diagonal entry to be an acceptable pivot. */
-    void setPivotThreshold(const RealScalar& thresh)
-    {
-      m_diagpivotthresh = thresh; 
-    }
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
-      *
-      * \warning the destination matrix X in X = this->solve(B) must be colmun-major.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const;
-#endif // EIGEN_PARSED_BY_DOXYGEN
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance
-      *          \c InvalidInput if the input matrix is invalid
-      *
-      * \sa iparm()          
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-    
-    /**
-      * \returns A string describing the type of error
-      */
-    std::string lastErrorMessage() const
-    {
-      return m_lastError; 
-    }
-
-    template<typename Rhs, typename Dest>
-    bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const
-    {
-      Dest& X(X_base.derived());
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first");
-      EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
-                        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-      
-      // Permute the right hand side to form X = Pr*B
-      // on return, X is overwritten by the computed solution
-      X.resize(B.rows(),B.cols());
-
-      // this ugly const_cast_derived() helps to detect aliasing when applying the permutations
-      for(Index j = 0; j < B.cols(); ++j)
-        X.col(j) = rowsPermutation() * B.const_cast_derived().col(j);
-      
-      //Forward substitution with L
-      this->matrixL().solveInPlace(X);
-      this->matrixU().solveInPlace(X);
-      
-      // Permute back the solution 
-      for (Index j = 0; j < B.cols(); ++j)
-        X.col(j) = colsPermutation().inverse() * X.col(j);
-      
-      return true; 
-    }
-    
-    /**
-      * \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), signDeterminant()
-      */
-    Scalar absDeterminant()
-    {
-      using std::abs;
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      // Initialize with the determinant of the row matrix
-      Scalar det = Scalar(1.);
-      // Note that the diagonal blocks of U are stored in supernodes,
-      // which are available in the  L part :)
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.index() == j)
-          {
-            det *= abs(it.value());
-            break;
-          }
-        }
-      }
-      return det;
-    }
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix
-      * of which **this is the QR decomposition
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's
-      * inherent to the determinant computation.
-      *
-      * \sa absDeterminant(), signDeterminant()
-      */
-    Scalar logAbsDeterminant() const
-    {
-      using std::log;
-      using std::abs;
-
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      Scalar det = Scalar(0.);
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.row() < j) continue;
-          if(it.row() == j)
-          {
-            det += log(abs(it.value()));
-            break;
-          }
-        }
-      }
-      return det;
-    }
-
-    /** \returns A number representing the sign of the determinant
-      *
-      * \sa absDeterminant(), logAbsDeterminant()
-      */
-    Scalar signDeterminant()
-    {
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      // Initialize with the determinant of the row matrix
-      Index det = 1;
-      // Note that the diagonal blocks of U are stored in supernodes,
-      // which are available in the  L part :)
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.index() == j)
-          {
-            if(it.value()<0)
-              det = -det;
-            else if(it.value()==0)
-              return 0;
-            break;
-          }
-        }
-      }
-      return det * m_detPermR * m_detPermC;
-    }
-    
-    /** \returns The determinant of the matrix.
-      *
-      * \sa absDeterminant(), logAbsDeterminant()
-      */
-    Scalar determinant()
-    {
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      // Initialize with the determinant of the row matrix
-      Scalar det = Scalar(1.);
-      // Note that the diagonal blocks of U are stored in supernodes,
-      // which are available in the  L part :)
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.index() == j)
-          {
-            det *= it.value();
-            break;
-          }
-        }
-      }
-      return (m_detPermR * m_detPermC) > 0 ? det : -det;
-    }
-
-    Index nnzL() const { return m_nnzL; };
-    Index nnzU() const { return m_nnzU; };
-
-  protected:
-    // Functions 
-    void initperfvalues()
-    {
-      m_perfv.panel_size = 16;
-      m_perfv.relax = 1; 
-      m_perfv.maxsuper = 128; 
-      m_perfv.rowblk = 16; 
-      m_perfv.colblk = 8; 
-      m_perfv.fillfactor = 20;  
-    }
-      
-    // Variables 
-    mutable ComputationInfo m_info;
-    bool m_factorizationIsOk;
-    bool m_analysisIsOk;
-    std::string m_lastError;
-    NCMatrix m_mat; // The input (permuted ) matrix 
-    SCMatrix m_Lstore; // The lower triangular matrix (supernodal)
-    MappedSparseMatrix<Scalar,ColMajor,StorageIndex> m_Ustore; // The upper triangular matrix
-    PermutationType m_perm_c; // Column permutation 
-    PermutationType m_perm_r ; // Row permutation
-    IndexVector m_etree; // Column elimination tree 
-    
-    typename Base::GlobalLU_t m_glu; 
-                               
-    // SparseLU options 
-    bool m_symmetricmode;
-    // values for performance 
-    internal::perfvalues m_perfv;
-    RealScalar m_diagpivotthresh; // Specifies the threshold used for a diagonal entry to be an acceptable pivot
-    Index m_nnzL, m_nnzU; // Nonzeros in L and U factors
-    Index m_detPermR, m_detPermC; // Determinants of the permutation matrices
-  private:
-    // Disable copy constructor 
-    SparseLU (const SparseLU& );
-}; // End class SparseLU
-
-
-
-// Functions needed by the anaysis phase
-/** 
-  * Compute the column permutation to minimize the fill-in
-  * 
-  *  - Apply this permutation to the input matrix - 
-  * 
-  *  - Compute the column elimination tree on the permuted matrix 
-  * 
-  *  - Postorder the elimination tree and the column permutation
-  * 
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
-{
-  
-  //TODO  It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat.
-  
-  // Firstly, copy the whole input matrix. 
-  m_mat = mat;
-  
-  // Compute fill-in ordering
-  OrderingType ord; 
-  ord(m_mat,m_perm_c);
-  
-  // Apply the permutation to the column of the input  matrix
-  if (m_perm_c.size())
-  {
-    m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used.  
-    // Then, permute only the column pointers
-    ei_declare_aligned_stack_constructed_variable(StorageIndex,outerIndexPtr,mat.cols()+1,mat.isCompressed()?const_cast<StorageIndex*>(mat.outerIndexPtr()):0);
-    
-    // If the input matrix 'mat' is uncompressed, then the outer-indices do not match the ones of m_mat, and a copy is thus needed.
-    if(!mat.isCompressed()) 
-      IndexVector::Map(outerIndexPtr, mat.cols()+1) = IndexVector::Map(m_mat.outerIndexPtr(),mat.cols()+1);
-    
-    // Apply the permutation and compute the nnz per column.
-    for (Index i = 0; i < mat.cols(); i++)
-    {
-      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
-      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
-    }
-  }
-  
-  // Compute the column elimination tree of the permuted matrix 
-  IndexVector firstRowElt;
-  internal::coletree(m_mat, m_etree,firstRowElt); 
-     
-  // In symmetric mode, do not do postorder here
-  if (!m_symmetricmode) {
-    IndexVector post, iwork; 
-    // Post order etree
-    internal::treePostorder(StorageIndex(m_mat.cols()), m_etree, post); 
-      
-   
-    // Renumber etree in postorder 
-    Index m = m_mat.cols(); 
-    iwork.resize(m+1);
-    for (Index i = 0; i < m; ++i) iwork(post(i)) = post(m_etree(i));
-    m_etree = iwork;
-    
-    // Postmultiply A*Pc by post, i.e reorder the matrix according to the postorder of the etree
-    PermutationType post_perm(m); 
-    for (Index i = 0; i < m; i++) 
-      post_perm.indices()(i) = post(i); 
-        
-    // Combine the two permutations : postorder the permutation for future use
-    if(m_perm_c.size()) {
-      m_perm_c = post_perm * m_perm_c;
-    }
-    
-  } // end postordering 
-  
-  m_analysisIsOk = true; 
-}
-
-// Functions needed by the numerical factorization phase
-
-
-/** 
-  *  - Numerical factorization 
-  *  - Interleaved with the symbolic factorization 
-  * On exit,  info is 
-  * 
-  *    = 0: successful factorization
-  * 
-  *    > 0: if info = i, and i is
-  * 
-  *       <= A->ncol: U(i,i) is exactly zero. The factorization has
-  *          been completed, but the factor U is exactly singular,
-  *          and division by zero will occur if it is used to solve a
-  *          system of equations.
-  * 
-  *       > A->ncol: number of bytes allocated when memory allocation
-  *         failure occurred, plus A->ncol. If lwork = -1, it is
-  *         the estimated amount of space needed, plus A->ncol.  
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
-{
-  using internal::emptyIdxLU;
-  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); 
-  eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices");
-  
-  m_isInitialized = true;
-  
-  // Apply the column permutation computed in analyzepattern()
-  //   m_mat = matrix * m_perm_c.inverse(); 
-  m_mat = matrix;
-  if (m_perm_c.size()) 
-  {
-    m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers.
-    //Then, permute only the column pointers
-    const StorageIndex * outerIndexPtr;
-    if (matrix.isCompressed()) outerIndexPtr = matrix.outerIndexPtr();
-    else
-    {
-      StorageIndex* outerIndexPtr_t = new StorageIndex[matrix.cols()+1];
-      for(Index i = 0; i <= matrix.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i];
-      outerIndexPtr = outerIndexPtr_t;
-    }
-    for (Index i = 0; i < matrix.cols(); i++)
-    {
-      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
-      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
-    }
-    if(!matrix.isCompressed()) delete[] outerIndexPtr;
-  } 
-  else 
-  { //FIXME This should not be needed if the empty permutation is handled transparently
-    m_perm_c.resize(matrix.cols());
-    for(StorageIndex i = 0; i < matrix.cols(); ++i) m_perm_c.indices()(i) = i;
-  }
-  
-  Index m = m_mat.rows();
-  Index n = m_mat.cols();
-  Index nnz = m_mat.nonZeros();
-  Index maxpanel = m_perfv.panel_size * m;
-  // Allocate working storage common to the factor routines
-  Index lwork = 0;
-  Index info = Base::memInit(m, n, nnz, lwork, m_perfv.fillfactor, m_perfv.panel_size, m_glu); 
-  if (info) 
-  {
-    m_lastError = "UNABLE TO ALLOCATE WORKING MEMORY\n\n" ;
-    m_factorizationIsOk = false;
-    return ; 
-  }
-  
-  // Set up pointers for integer working arrays 
-  IndexVector segrep(m); segrep.setZero();
-  IndexVector parent(m); parent.setZero();
-  IndexVector xplore(m); xplore.setZero();
-  IndexVector repfnz(maxpanel);
-  IndexVector panel_lsub(maxpanel);
-  IndexVector xprune(n); xprune.setZero();
-  IndexVector marker(m*internal::LUNoMarker); marker.setZero();
-  
-  repfnz.setConstant(-1); 
-  panel_lsub.setConstant(-1);
-  
-  // Set up pointers for scalar working arrays 
-  ScalarVector dense; 
-  dense.setZero(maxpanel);
-  ScalarVector tempv; 
-  tempv.setZero(internal::LUnumTempV(m, m_perfv.panel_size, m_perfv.maxsuper, /*m_perfv.rowblk*/m) );
-  
-  // Compute the inverse of perm_c
-  PermutationType iperm_c(m_perm_c.inverse()); 
-  
-  // Identify initial relaxed snodes
-  IndexVector relax_end(n);
-  if ( m_symmetricmode == true ) 
-    Base::heap_relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
-  else
-    Base::relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
-  
-  
-  m_perm_r.resize(m); 
-  m_perm_r.indices().setConstant(-1);
-  marker.setConstant(-1);
-  m_detPermR = 1; // Record the determinant of the row permutation
-  
-  m_glu.supno(0) = emptyIdxLU; m_glu.xsup.setConstant(0);
-  m_glu.xsup(0) = m_glu.xlsub(0) = m_glu.xusub(0) = m_glu.xlusup(0) = Index(0);
-  
-  // Work on one 'panel' at a time. A panel is one of the following :
-  //  (a) a relaxed supernode at the bottom of the etree, or
-  //  (b) panel_size contiguous columns, <panel_size> defined by the user
-  Index jcol; 
-  Index pivrow; // Pivotal row number in the original row matrix
-  Index nseg1; // Number of segments in U-column above panel row jcol
-  Index nseg; // Number of segments in each U-column 
-  Index irep; 
-  Index i, k, jj; 
-  for (jcol = 0; jcol < n; )
-  {
-    // Adjust panel size so that a panel won't overlap with the next relaxed snode. 
-    Index panel_size = m_perfv.panel_size; // upper bound on panel width
-    for (k = jcol + 1; k < (std::min)(jcol+panel_size, n); k++)
-    {
-      if (relax_end(k) != emptyIdxLU) 
-      {
-        panel_size = k - jcol; 
-        break; 
-      }
-    }
-    if (k == n) 
-      panel_size = n - jcol; 
-      
-    // Symbolic outer factorization on a panel of columns 
-    Base::panel_dfs(m, panel_size, jcol, m_mat, m_perm_r.indices(), nseg1, dense, panel_lsub, segrep, repfnz, xprune, marker, parent, xplore, m_glu); 
-    
-    // Numeric sup-panel updates in topological order 
-    Base::panel_bmod(m, panel_size, jcol, nseg1, dense, tempv, segrep, repfnz, m_glu); 
-    
-    // Sparse LU within the panel, and below the panel diagonal 
-    for ( jj = jcol; jj< jcol + panel_size; jj++) 
-    {
-      k = (jj - jcol) * m; // Column index for w-wide arrays 
-      
-      nseg = nseg1; // begin after all the panel segments
-      //Depth-first-search for the current column
-      VectorBlock<IndexVector> panel_lsubk(panel_lsub, k, m);
-      VectorBlock<IndexVector> repfnz_k(repfnz, k, m); 
-      info = Base::column_dfs(m, jj, m_perm_r.indices(), m_perfv.maxsuper, nseg, panel_lsubk, segrep, repfnz_k, xprune, marker, parent, xplore, m_glu); 
-      if ( info ) 
-      {
-        m_lastError =  "UNABLE TO EXPAND MEMORY IN COLUMN_DFS() ";
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      // Numeric updates to this column 
-      VectorBlock<ScalarVector> dense_k(dense, k, m); 
-      VectorBlock<IndexVector> segrep_k(segrep, nseg1, m-nseg1); 
-      info = Base::column_bmod(jj, (nseg - nseg1), dense_k, tempv, segrep_k, repfnz_k, jcol, m_glu); 
-      if ( info ) 
-      {
-        m_lastError = "UNABLE TO EXPAND MEMORY IN COLUMN_BMOD() ";
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      
-      // Copy the U-segments to ucol(*)
-      info = Base::copy_to_ucol(jj, nseg, segrep, repfnz_k ,m_perm_r.indices(), dense_k, m_glu); 
-      if ( info ) 
-      {
-        m_lastError = "UNABLE TO EXPAND MEMORY IN COPY_TO_UCOL() ";
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      
-      // Form the L-segment 
-      info = Base::pivotL(jj, m_diagpivotthresh, m_perm_r.indices(), iperm_c.indices(), pivrow, m_glu);
-      if ( info ) 
-      {
-        m_lastError = "THE MATRIX IS STRUCTURALLY SINGULAR ... ZERO COLUMN AT ";
-        std::ostringstream returnInfo;
-        returnInfo << info; 
-        m_lastError += returnInfo.str();
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      
-      // Update the determinant of the row permutation matrix
-      // FIXME: the following test is not correct, we should probably take iperm_c into account and pivrow is not directly the row pivot.
-      if (pivrow != jj) m_detPermR = -m_detPermR;
-
-      // Prune columns (0:jj-1) using column jj
-      Base::pruneL(jj, m_perm_r.indices(), pivrow, nseg, segrep, repfnz_k, xprune, m_glu); 
-      
-      // Reset repfnz for this column 
-      for (i = 0; i < nseg; i++)
-      {
-        irep = segrep(i); 
-        repfnz_k(irep) = emptyIdxLU; 
-      }
-    } // end SparseLU within the panel  
-    jcol += panel_size;  // Move to the next panel
-  } // end for -- end elimination 
-  
-  m_detPermR = m_perm_r.determinant();
-  m_detPermC = m_perm_c.determinant();
-  
-  // Count the number of nonzeros in factors 
-  Base::countnz(n, m_nnzL, m_nnzU, m_glu); 
-  // Apply permutation  to the L subscripts 
-  Base::fixupL(n, m_perm_r.indices(), m_glu);
-  
-  // Create supernode matrix L 
-  m_Lstore.setInfos(m, n, m_glu.lusup, m_glu.xlusup, m_glu.lsub, m_glu.xlsub, m_glu.supno, m_glu.xsup); 
-  // Create the column major upper sparse matrix  U; 
-  new (&m_Ustore) MappedSparseMatrix<Scalar, ColMajor, StorageIndex> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() );
-  
-  m_info = Success;
-  m_factorizationIsOk = true;
-}
-
-template<typename MappedSupernodalType>
-struct SparseLUMatrixLReturnType : internal::no_assignment_operator
-{
-  typedef typename MappedSupernodalType::Scalar Scalar;
-  explicit SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL)
-  { }
-  Index rows() const { return m_mapL.rows(); }
-  Index cols() const { return m_mapL.cols(); }
-  template<typename Dest>
-  void solveInPlace( MatrixBase<Dest> &X) const
-  {
-    m_mapL.solveInPlace(X);
-  }
-  template<bool Conjugate, typename Dest>
-  void solveTransposedInPlace( MatrixBase<Dest> &X) const
-  {
-    m_mapL.template solveTransposedInPlace<Conjugate>(X);
-  }
-
-  const MappedSupernodalType& m_mapL;
-};
-
-template<typename MatrixLType, typename MatrixUType>
-struct SparseLUMatrixUReturnType : internal::no_assignment_operator
-{
-  typedef typename MatrixLType::Scalar Scalar;
-  SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU)
-  : m_mapL(mapL),m_mapU(mapU)
-  { }
-  Index rows() const { return m_mapL.rows(); }
-  Index cols() const { return m_mapL.cols(); }
-
-  template<typename Dest>   void solveInPlace(MatrixBase<Dest> &X) const
-  {
-    Index nrhs = X.cols();
-    Index n    = X.rows();
-    // Backward solve with U
-    for (Index k = m_mapL.nsuper(); k >= 0; k--)
-    {
-      Index fsupc = m_mapL.supToCol()[k];
-      Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
-      Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
-      Index luptr = m_mapL.colIndexPtr()[fsupc];
-
-      if (nsupc == 1)
-      {
-        for (Index j = 0; j < nrhs; j++)
-        {
-          X(fsupc, j) /= m_mapL.valuePtr()[luptr];
-        }
-      }
-      else
-      {
-        // FIXME: the following lines should use Block expressions and not Map!
-        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X.coeffRef(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-        U = A.template triangularView<Upper>().solve(U);
-      }
-
-      for (Index j = 0; j < nrhs; ++j)
-      {
-        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
-        {
-          typename MatrixUType::InnerIterator it(m_mapU, jcol);
-          for ( ; it; ++it)
-          {
-            Index irow = it.index();
-            X(irow, j) -= X(jcol, j) * it.value();
-          }
-        }
-      }
-    } // End For U-solve
-  }
-
-  template<bool Conjugate, typename Dest>   void solveTransposedInPlace(MatrixBase<Dest> &X) const
-  {
-    using numext::conj;
-    Index nrhs = X.cols();
-    Index n    = X.rows();
-    // Forward solve with U
-    for (Index k = 0; k <=  m_mapL.nsuper(); k++)
-    {
-      Index fsupc = m_mapL.supToCol()[k];
-      Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
-      Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
-      Index luptr = m_mapL.colIndexPtr()[fsupc];
-
-      for (Index j = 0; j < nrhs; ++j)
-      {
-        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
-        {
-          typename MatrixUType::InnerIterator it(m_mapU, jcol);
-          for ( ; it; ++it)
-          {
-            Index irow = it.index();
-            X(jcol, j) -= X(irow, j) * (Conjugate? conj(it.value()): it.value());
-          }
-        }
-      }
-      if (nsupc == 1)
-      {
-        for (Index j = 0; j < nrhs; j++)
-        {
-          X(fsupc, j) /= (Conjugate? conj(m_mapL.valuePtr()[luptr]) : m_mapL.valuePtr()[luptr]);
-        }
-      }
-      else
-      {
-        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-        if(Conjugate)
-          U = A.adjoint().template triangularView<Lower>().solve(U);
-        else
-          U = A.transpose().template triangularView<Lower>().solve(U);
-      }
-    }// End For U-solve
-  }
-
-
-  const MatrixLType& m_mapL;
-  const MatrixUType& m_mapU;
-};
-
-} // End namespace Eigen 
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLUImpl.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLUImpl.h
deleted file mode 100644
index fc0cfc4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLUImpl.h
+++ /dev/null
@@ -1,66 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef SPARSELU_IMPL_H
-#define SPARSELU_IMPL_H
-
-namespace Eigen {
-namespace internal {
-  
-/** \ingroup SparseLU_Module
-  * \class SparseLUImpl
-  * Base class for sparseLU
-  */
-template <typename Scalar, typename StorageIndex>
-class SparseLUImpl
-{
-  public:
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector; 
-    typedef Matrix<Scalar,Dynamic,Dynamic,ColMajor> ScalarMatrix;
-    typedef Map<ScalarMatrix, 0,  OuterStride<> > MappedMatrixBlock;
-    typedef typename ScalarVector::RealScalar RealScalar; 
-    typedef Ref<Matrix<Scalar,Dynamic,1> > BlockScalarVector;
-    typedef Ref<Matrix<StorageIndex,Dynamic,1> > BlockIndexVector;
-    typedef LU_GlobalLU_t<IndexVector, ScalarVector> GlobalLU_t; 
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> MatrixType; 
-    
-  protected:
-     template <typename VectorType>
-     Index expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions);
-     Index memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size,  GlobalLU_t& glu); 
-     template <typename VectorType>
-     Index memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions);
-     void heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); 
-     void relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); 
-     Index snode_dfs(const Index jcol, const Index kcol,const MatrixType& mat,  IndexVector& xprune, IndexVector& marker, GlobalLU_t& glu); 
-     Index snode_bmod (const Index jcol, const Index fsupc, ScalarVector& dense, GlobalLU_t& glu);
-     Index pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu);
-     template <typename Traits>
-     void dfs_kernel(const StorageIndex jj, IndexVector& perm_r,
-                    Index& nseg, IndexVector& panel_lsub, IndexVector& segrep,
-                    Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent,
-                    IndexVector& xplore, GlobalLU_t& glu, Index& nextl_col, Index krow, Traits& traits);
-     void panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
-    
-     void panel_bmod(const Index m, const Index w, const Index jcol, const Index nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu);
-     Index column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg,  BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
-     Index column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv, BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu); 
-     Index copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep, BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu); 
-     void pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg, const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu);
-     void countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu); 
-     void fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu); 
-     
-     template<typename , typename >
-     friend struct column_dfs_traits;
-}; 
-
-} // end namespace internal
-} // namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Memory.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Memory.h
deleted file mode 100644
index 349bfd5..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Memory.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]memory.c files in SuperLU 
- 
- * -- SuperLU routine (version 3.1) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * August 1, 2008
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-
-#ifndef EIGEN_SPARSELU_MEMORY
-#define EIGEN_SPARSELU_MEMORY
-
-namespace Eigen {
-namespace internal {
-  
-enum { LUNoMarker = 3 };
-enum {emptyIdxLU = -1};
-inline Index LUnumTempV(Index& m, Index& w, Index& t, Index& b)
-{
-  return (std::max)(m, (t+b)*w);
-}
-
-template< typename Scalar>
-inline Index LUTempSpace(Index&m, Index& w)
-{
-  return (2*w + 4 + LUNoMarker) * m * sizeof(Index) + (w + 1) * m * sizeof(Scalar);
-}
-
-
-
-
-/** 
-  * Expand the existing storage to accommodate more fill-ins
-  * \param vec Valid pointer to the vector to allocate or expand
-  * \param[in,out] length  At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector
-  * \param[in] nbElts Current number of elements in the factors
-  * \param keep_prev  1: use length  and do not expand the vector; 0: compute new_len and expand
-  * \param[in,out] num_expansions Number of times the memory has been expanded
-  */
-template <typename Scalar, typename StorageIndex>
-template <typename VectorType>
-Index  SparseLUImpl<Scalar,StorageIndex>::expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions) 
-{
-  
-  float alpha = 1.5; // Ratio of the memory increase 
-  Index new_len; // New size of the allocated memory
-  
-  if(num_expansions == 0 || keep_prev) 
-    new_len = length ; // First time allocate requested
-  else 
-    new_len = (std::max)(length+1,Index(alpha * length));
-  
-  VectorType old_vec; // Temporary vector to hold the previous values   
-  if (nbElts > 0 )
-    old_vec = vec.segment(0,nbElts); 
-  
-  //Allocate or expand the current vector
-#ifdef EIGEN_EXCEPTIONS
-  try
-#endif
-  {
-    vec.resize(new_len); 
-  }
-#ifdef EIGEN_EXCEPTIONS
-  catch(std::bad_alloc& )
-#else
-  if(!vec.size())
-#endif
-  {
-    if (!num_expansions)
-    {
-      // First time to allocate from LUMemInit()
-      // Let LUMemInit() deals with it.
-      return -1;
-    }
-    if (keep_prev)
-    {
-      // In this case, the memory length should not not be reduced
-      return new_len;
-    }
-    else 
-    {
-      // Reduce the size and increase again 
-      Index tries = 0; // Number of attempts
-      do 
-      {
-        alpha = (alpha + 1)/2;
-        new_len = (std::max)(length+1,Index(alpha * length));
-#ifdef EIGEN_EXCEPTIONS
-        try
-#endif
-        {
-          vec.resize(new_len); 
-        }
-#ifdef EIGEN_EXCEPTIONS
-        catch(std::bad_alloc& )
-#else
-        if (!vec.size())
-#endif
-        {
-          tries += 1; 
-          if ( tries > 10) return new_len; 
-        }
-      } while (!vec.size());
-    }
-  }
-  //Copy the previous values to the newly allocated space 
-  if (nbElts > 0)
-    vec.segment(0, nbElts) = old_vec;   
-   
-  
-  length  = new_len;
-  if(num_expansions) ++num_expansions;
-  return 0; 
-}
-
-/**
- * \brief  Allocate various working space for the numerical factorization phase.
- * \param m number of rows of the input matrix 
- * \param n number of columns 
- * \param annz number of initial nonzeros in the matrix 
- * \param lwork  if lwork=-1, this routine returns an estimated size of the required memory
- * \param glu persistent data to facilitate multiple factors : will be deleted later ??
- * \param fillratio estimated ratio of fill in the factors
- * \param panel_size Size of a panel
- * \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated memory when allocation failed, and 0 on success
- * \note Unlike SuperLU, this routine does not support successive factorization with the same pattern and the same row permutation
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size,  GlobalLU_t& glu)
-{
-  Index& num_expansions = glu.num_expansions; //No memory expansions so far
-  num_expansions = 0;
-  glu.nzumax = glu.nzlumax = (std::min)(fillratio * (annz+1) / n, m) * n; // estimated number of nonzeros in U 
-  glu.nzlmax = (std::max)(Index(4), fillratio) * (annz+1) / 4; // estimated  nnz in L factor
-  // Return the estimated size to the user if necessary
-  Index tempSpace;
-  tempSpace = (2*panel_size + 4 + LUNoMarker) * m * sizeof(Index) + (panel_size + 1) * m * sizeof(Scalar);
-  if (lwork == emptyIdxLU) 
-  {
-    Index estimated_size;
-    estimated_size = (5 * n + 5) * sizeof(Index)  + tempSpace
-                    + (glu.nzlmax + glu.nzumax) * sizeof(Index) + (glu.nzlumax+glu.nzumax) *  sizeof(Scalar) + n; 
-    return estimated_size;
-  }
-  
-  // Setup the required space 
-  
-  // First allocate Integer pointers for L\U factors
-  glu.xsup.resize(n+1);
-  glu.supno.resize(n+1);
-  glu.xlsub.resize(n+1);
-  glu.xlusup.resize(n+1);
-  glu.xusub.resize(n+1);
-
-  // Reserve memory for L/U factors
-  do 
-  {
-    if(     (expand<ScalarVector>(glu.lusup, glu.nzlumax, 0, 0, num_expansions)<0)
-        ||  (expand<ScalarVector>(glu.ucol,  glu.nzumax,  0, 0, num_expansions)<0)
-        ||  (expand<IndexVector> (glu.lsub,  glu.nzlmax,  0, 0, num_expansions)<0)
-        ||  (expand<IndexVector> (glu.usub,  glu.nzumax,  0, 1, num_expansions)<0) )
-    {
-      //Reduce the estimated size and retry
-      glu.nzlumax /= 2;
-      glu.nzumax /= 2;
-      glu.nzlmax /= 2;
-      if (glu.nzlumax < annz ) return glu.nzlumax; 
-    }
-  } while (!glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size());
-  
-  ++num_expansions;
-  return 0;
-  
-} // end LuMemInit
-
-/** 
- * \brief Expand the existing storage 
- * \param vec vector to expand 
- * \param[in,out] maxlen On input, previous size of vec (Number of elements to copy ). on output, new size
- * \param nbElts current number of elements in the vector.
- * \param memtype Type of the element to expand
- * \param num_expansions Number of expansions 
- * \return 0 on success, > 0 size of the memory allocated so far
- */
-template <typename Scalar, typename StorageIndex>
-template <typename VectorType>
-Index SparseLUImpl<Scalar,StorageIndex>::memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions)
-{
-  Index failed_size; 
-  if (memtype == USUB)
-     failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 1, num_expansions);
-  else
-    failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 0, num_expansions);
-
-  if (failed_size)
-    return failed_size; 
-  
-  return 0 ;  
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // EIGEN_SPARSELU_MEMORY
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Structs.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Structs.h
deleted file mode 100644
index cf5ec44..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Structs.h
+++ /dev/null
@@ -1,110 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- * NOTE: This file comes from a partly modified version of files slu_[s,d,c,z]defs.h
- * -- SuperLU routine (version 4.1) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November, 2010
- * 
- * Global data structures used in LU factorization -
- * 
- *   nsuper: #supernodes = nsuper + 1, numbered [0, nsuper].
- *   (xsup,supno): supno[i] is the supernode no to which i belongs;
- *  xsup(s) points to the beginning of the s-th supernode.
- *  e.g.   supno 0 1 2 2 3 3 3 4 4 4 4 4   (n=12)
- *          xsup 0 1 2 4 7 12
- *  Note: dfs will be performed on supernode rep. relative to the new 
- *        row pivoting ordering
- *
- *   (xlsub,lsub): lsub[*] contains the compressed subscript of
- *  rectangular supernodes; xlsub[j] points to the starting
- *  location of the j-th column in lsub[*]. Note that xlsub 
- *  is indexed by column.
- *  Storage: original row subscripts
- *
- *      During the course of sparse LU factorization, we also use
- *  (xlsub,lsub) for the purpose of symmetric pruning. For each
- *  supernode {s,s+1,...,t=s+r} with first column s and last
- *  column t, the subscript set
- *    lsub[j], j=xlsub[s], .., xlsub[s+1]-1
- *  is the structure of column s (i.e. structure of this supernode).
- *  It is used for the storage of numerical values.
- *  Furthermore,
- *    lsub[j], j=xlsub[t], .., xlsub[t+1]-1
- *  is the structure of the last column t of this supernode.
- *  It is for the purpose of symmetric pruning. Therefore, the
- *  structural subscripts can be rearranged without making physical
- *  interchanges among the numerical values.
- *
- *  However, if the supernode has only one column, then we
- *  only keep one set of subscripts. For any subscript interchange
- *  performed, similar interchange must be done on the numerical
- *  values.
- *
- *  The last column structures (for pruning) will be removed
- *  after the numercial LU factorization phase.
- *
- *   (xlusup,lusup): lusup[*] contains the numerical values of the
- *  rectangular supernodes; xlusup[j] points to the starting
- *  location of the j-th column in storage vector lusup[*]
- *  Note: xlusup is indexed by column.
- *  Each rectangular supernode is stored by column-major
- *  scheme, consistent with Fortran 2-dim array storage.
- *
- *   (xusub,ucol,usub): ucol[*] stores the numerical values of
- *  U-columns outside the rectangular supernodes. The row
- *  subscript of nonzero ucol[k] is stored in usub[k].
- *  xusub[i] points to the starting location of column i in ucol.
- *  Storage: new row subscripts; that is subscripts of PA.
- */
-
-#ifndef EIGEN_LU_STRUCTS
-#define EIGEN_LU_STRUCTS
-namespace Eigen {
-namespace internal {
-  
-typedef enum {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL} MemType; 
-
-template <typename IndexVector, typename ScalarVector>
-struct LU_GlobalLU_t {
-  typedef typename IndexVector::Scalar StorageIndex; 
-  IndexVector xsup; //First supernode column ... xsup(s) points to the beginning of the s-th supernode
-  IndexVector supno; // Supernode number corresponding to this column (column to supernode mapping)
-  ScalarVector  lusup; // nonzero values of L ordered by columns 
-  IndexVector lsub; // Compressed row indices of L rectangular supernodes. 
-  IndexVector xlusup; // pointers to the beginning of each column in lusup
-  IndexVector xlsub; // pointers to the beginning of each column in lsub
-  Index   nzlmax; // Current max size of lsub
-  Index   nzlumax; // Current max size of lusup
-  ScalarVector  ucol; // nonzero values of U ordered by columns 
-  IndexVector usub; // row indices of U columns in ucol
-  IndexVector xusub; // Pointers to the beginning of each column of U in ucol 
-  Index   nzumax; // Current max size of ucol
-  Index   n; // Number of columns in the matrix  
-  Index   num_expansions; 
-};
-
-// Values to set for performance
-struct perfvalues {
-  Index panel_size; // a panel consists of at most <panel_size> consecutive columns
-  Index relax; // To control degree of relaxing supernodes. If the number of nodes (columns) 
-                // in a subtree of the elimination tree is less than relax, this subtree is considered 
-                // as one supernode regardless of the row structures of those columns
-  Index maxsuper; // The maximum size for a supernode in complete LU
-  Index rowblk; // The minimum row dimension for 2-D blocking to be used;
-  Index colblk; // The minimum column dimension for 2-D blocking to be used;
-  Index fillfactor; // The estimated fills factors for L and U, compared with A
-}; 
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // EIGEN_LU_STRUCTS
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
deleted file mode 100644
index 0be293d..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
+++ /dev/null
@@ -1,375 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
-#define EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
-
-namespace Eigen {
-namespace internal {
-
-/** \ingroup SparseLU_Module
- * \brief a class to manipulate the L supernodal factor from the SparseLU factorization
- * 
- * This class  contain the data to easily store 
- * and manipulate the supernodes during the factorization and solution phase of Sparse LU. 
- * Only the lower triangular matrix has supernodes.
- * 
- * NOTE : This class corresponds to the SCformat structure in SuperLU
- * 
- */
-/* TODO
- * InnerIterator as for sparsematrix 
- * SuperInnerIterator to iterate through all supernodes 
- * Function for triangular solve
- */
-template <typename _Scalar, typename _StorageIndex>
-class MappedSuperNodalMatrix
-{
-  public:
-    typedef _Scalar Scalar; 
-    typedef _StorageIndex StorageIndex;
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-  public:
-    MappedSuperNodalMatrix()
-    {
-      
-    }
-    MappedSuperNodalMatrix(Index m, Index n,  ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
-             IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col )
-    {
-      setInfos(m, n, nzval, nzval_colptr, rowind, rowind_colptr, col_to_sup, sup_to_col);
-    }
-    
-    ~MappedSuperNodalMatrix()
-    {
-      
-    }
-    /**
-     * Set appropriate pointers for the lower triangular supernodal matrix
-     * These infos are available at the end of the numerical factorization
-     * FIXME This class will be modified such that it can be use in the course 
-     * of the factorization.
-     */
-    void setInfos(Index m, Index n, ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
-             IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col )
-    {
-      m_row = m;
-      m_col = n; 
-      m_nzval = nzval.data(); 
-      m_nzval_colptr = nzval_colptr.data(); 
-      m_rowind = rowind.data(); 
-      m_rowind_colptr = rowind_colptr.data(); 
-      m_nsuper = col_to_sup(n); 
-      m_col_to_sup = col_to_sup.data(); 
-      m_sup_to_col = sup_to_col.data(); 
-    }
-    
-    /**
-     * Number of rows
-     */
-    Index rows() const { return m_row; }
-    
-    /**
-     * Number of columns
-     */
-    Index cols() const { return m_col; }
-    
-    /**
-     * Return the array of nonzero values packed by column
-     * 
-     * The size is nnz
-     */
-    Scalar* valuePtr() {  return m_nzval; }
-    
-    const Scalar* valuePtr() const 
-    {
-      return m_nzval; 
-    }
-    /**
-     * Return the pointers to the beginning of each column in \ref valuePtr()
-     */
-    StorageIndex* colIndexPtr()
-    {
-      return m_nzval_colptr; 
-    }
-    
-    const StorageIndex* colIndexPtr() const
-    {
-      return m_nzval_colptr; 
-    }
-    
-    /**
-     * Return the array of compressed row indices of all supernodes
-     */
-    StorageIndex* rowIndex()  { return m_rowind; }
-    
-    const StorageIndex* rowIndex() const
-    {
-      return m_rowind; 
-    }
-    
-    /**
-     * Return the location in \em rowvaluePtr() which starts each column
-     */
-    StorageIndex* rowIndexPtr() { return m_rowind_colptr; }
-    
-    const StorageIndex* rowIndexPtr() const
-    {
-      return m_rowind_colptr; 
-    }
-    
-    /** 
-     * Return the array of column-to-supernode mapping 
-     */
-    StorageIndex* colToSup()  { return m_col_to_sup; }
-    
-    const StorageIndex* colToSup() const
-    {
-      return m_col_to_sup;       
-    }
-    /**
-     * Return the array of supernode-to-column mapping
-     */
-    StorageIndex* supToCol() { return m_sup_to_col; }
-    
-    const StorageIndex* supToCol() const
-    {
-      return m_sup_to_col;
-    }
-    
-    /**
-     * Return the number of supernodes
-     */
-    Index nsuper() const
-    {
-      return m_nsuper; 
-    }
-    
-    class InnerIterator; 
-    template<typename Dest>
-    void solveInPlace( MatrixBase<Dest>&X) const;
-    template<bool Conjugate, typename Dest>
-    void solveTransposedInPlace( MatrixBase<Dest>&X) const;
-
-    
-      
-      
-    
-  protected:
-    Index m_row; // Number of rows
-    Index m_col; // Number of columns
-    Index m_nsuper; // Number of supernodes
-    Scalar* m_nzval; //array of nonzero values packed by column
-    StorageIndex* m_nzval_colptr; //nzval_colptr[j] Stores the location in nzval[] which starts column j
-    StorageIndex* m_rowind; // Array of compressed row indices of rectangular supernodes
-    StorageIndex* m_rowind_colptr; //rowind_colptr[j] stores the location in rowind[] which starts column j
-    StorageIndex* m_col_to_sup; // col_to_sup[j] is the supernode number to which column j belongs
-    StorageIndex* m_sup_to_col; //sup_to_col[s] points to the starting column of the s-th supernode
-    
-  private :
-};
-
-/**
-  * \brief InnerIterator class to iterate over nonzero values of the current column in the supernodal matrix L
-  * 
-  */
-template<typename Scalar, typename StorageIndex>
-class MappedSuperNodalMatrix<Scalar,StorageIndex>::InnerIterator
-{
-  public:
-     InnerIterator(const MappedSuperNodalMatrix& mat, Index outer)
-      : m_matrix(mat),
-        m_outer(outer),
-        m_supno(mat.colToSup()[outer]),
-        m_idval(mat.colIndexPtr()[outer]),
-        m_startidval(m_idval),
-        m_endidval(mat.colIndexPtr()[outer+1]),
-        m_idrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]]),
-        m_endidrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]+1])
-    {}
-    inline InnerIterator& operator++()
-    { 
-      m_idval++; 
-      m_idrow++;
-      return *this;
-    }
-    inline Scalar value() const { return m_matrix.valuePtr()[m_idval]; }
-    
-    inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_idval]); }
-    
-    inline Index index() const { return m_matrix.rowIndex()[m_idrow]; }
-    inline Index row() const { return index(); }
-    inline Index col() const { return m_outer; }
-    
-    inline Index supIndex() const { return m_supno; }
-    
-    inline operator bool() const 
-    { 
-      return ( (m_idval < m_endidval) && (m_idval >= m_startidval)
-                && (m_idrow < m_endidrow) );
-    }
-    
-  protected:
-    const MappedSuperNodalMatrix& m_matrix; // Supernodal lower triangular matrix 
-    const Index m_outer;                    // Current column 
-    const Index m_supno;                    // Current SuperNode number
-    Index m_idval;                          // Index to browse the values in the current column
-    const Index m_startidval;               // Start of the column value
-    const Index m_endidval;                 // End of the column value
-    Index m_idrow;                          // Index to browse the row indices 
-    Index m_endidrow;                       // End index of row indices of the current column
-};
-
-/**
- * \brief Solve with the supernode triangular matrix
- * 
- */
-template<typename Scalar, typename Index_>
-template<typename Dest>
-void MappedSuperNodalMatrix<Scalar,Index_>::solveInPlace( MatrixBase<Dest>&X) const
-{
-    /* Explicit type conversion as the Index type of MatrixBase<Dest> may be wider than Index */
-//    eigen_assert(X.rows() <= NumTraits<Index>::highest());
-//    eigen_assert(X.cols() <= NumTraits<Index>::highest());
-    Index n    = int(X.rows());
-    Index nrhs = Index(X.cols());
-    const Scalar * Lval = valuePtr();                 // Nonzero values 
-    Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
-    work.setZero();
-    for (Index k = 0; k <= nsuper(); k ++)
-    {
-      Index fsupc = supToCol()[k];                    // First column of the current supernode 
-      Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
-      Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
-      Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
-      Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
-      Index irow;                                     //Current index row
-      
-      if (nsupc == 1 )
-      {
-        for (Index j = 0; j < nrhs; j++)
-        {
-          InnerIterator it(*this, fsupc);
-          ++it; // Skip the diagonal element
-          for (; it; ++it)
-          {
-            irow = it.row();
-            X(irow, j) -= X(fsupc, j) * it.value();
-          }
-        }
-      }
-      else
-      {
-        // The supernode has more than one column 
-        Index luptr = colIndexPtr()[fsupc]; 
-        Index lda = colIndexPtr()[fsupc+1] - luptr;
-        
-        // Triangular solve 
-        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-        U = A.template triangularView<UnitLower>().solve(U); 
-        
-        // Matrix-vector product 
-        new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-        work.topRows(nrow).noalias() = A * U;
-        
-        //Begin Scatter 
-        for (Index j = 0; j < nrhs; j++)
-        {
-          Index iptr = istart + nsupc; 
-          for (Index i = 0; i < nrow; i++)
-          {
-            irow = rowIndex()[iptr]; 
-            X(irow, j) -= work(i, j); // Scatter operation
-            work(i, j) = Scalar(0); 
-            iptr++;
-          }
-        }
-      }
-    } 
-}
-
-template<typename Scalar, typename Index_>
-template<bool Conjugate, typename Dest>
-void MappedSuperNodalMatrix<Scalar,Index_>::solveTransposedInPlace( MatrixBase<Dest>&X) const
-{
-    using numext::conj;
-  Index n    = int(X.rows());
-  Index nrhs = Index(X.cols());
-  const Scalar * Lval = valuePtr();                 // Nonzero values
-  Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
-  work.setZero();
-  for (Index k = nsuper(); k >= 0; k--)
-  {
-    Index fsupc = supToCol()[k];                    // First column of the current supernode
-    Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
-    Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
-    Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
-    Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
-    Index irow;                                     //Current index row
-
-    if (nsupc == 1 )
-    {
-      for (Index j = 0; j < nrhs; j++)
-      {
-        InnerIterator it(*this, fsupc);
-        ++it; // Skip the diagonal element
-        for (; it; ++it)
-        {
-          irow = it.row();
-          X(fsupc,j) -= X(irow, j) * (Conjugate?conj(it.value()):it.value());
-        }
-      }
-    }
-    else
-    {
-      // The supernode has more than one column
-      Index luptr = colIndexPtr()[fsupc];
-      Index lda = colIndexPtr()[fsupc+1] - luptr;
-
-      //Begin Gather
-      for (Index j = 0; j < nrhs; j++)
-      {
-        Index iptr = istart + nsupc;
-        for (Index i = 0; i < nrow; i++)
-        {
-          irow = rowIndex()[iptr];
-          work.topRows(nrow)(i,j)= X(irow,j); // Gather operation
-          iptr++;
-        }
-      }
-
-      // Matrix-vector product with transposed submatrix
-      Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-      Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-      if(Conjugate)
-        U = U - A.adjoint() * work.topRows(nrow);
-      else
-        U = U - A.transpose() * work.topRows(nrow);
-
-      // Triangular solve (of transposed diagonal block)
-      new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-      if(Conjugate)
-        U = A.adjoint().template triangularView<UnitUpper>().solve(U);
-      else
-        U = A.transpose().template triangularView<UnitUpper>().solve(U);
-
-    }
-
-  }
-}
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSELU_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Utils.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Utils.h
deleted file mode 100644
index 9e3dab4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_Utils.h
+++ /dev/null
@@ -1,80 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_SPARSELU_UTILS_H
-#define EIGEN_SPARSELU_UTILS_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Count Nonzero elements in the factors
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu)
-{
- nnzL = 0; 
- nnzU = (glu.xusub)(n); 
- Index nsuper = (glu.supno)(n); 
- Index jlen; 
- Index i, j, fsupc;
- if (n <= 0 ) return; 
- // For each supernode
- for (i = 0; i <= nsuper; i++)
- {
-   fsupc = glu.xsup(i); 
-   jlen = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
-   
-   for (j = fsupc; j < glu.xsup(i+1); j++)
-   {
-     nnzL += jlen; 
-     nnzU += j - fsupc + 1; 
-     jlen--; 
-   }
- }
-}
-
-/**
- * \brief Fix up the data storage lsub for L-subscripts. 
- * 
- * It removes the subscripts sets for structural pruning, 
- * and applies permutation to the remaining subscripts
- * 
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu)
-{
-  Index fsupc, i, j, k, jstart; 
-  
-  StorageIndex nextl = 0; 
-  Index nsuper = (glu.supno)(n); 
-  
-  // For each supernode 
-  for (i = 0; i <= nsuper; i++)
-  {
-    fsupc = glu.xsup(i); 
-    jstart = glu.xlsub(fsupc); 
-    glu.xlsub(fsupc) = nextl; 
-    for (j = jstart; j < glu.xlsub(fsupc + 1); j++)
-    {
-      glu.lsub(nextl) = perm_r(glu.lsub(j)); // Now indexed into P*A
-      nextl++;
-    }
-    for (k = fsupc+1; k < glu.xsup(i+1); k++)
-      glu.xlsub(k) = nextl; // other columns in supernode i
-  }
-  
-  glu.xlsub(n) = nextl; 
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // EIGEN_SPARSELU_UTILS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_column_bmod.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
deleted file mode 100644
index b57f068..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
+++ /dev/null
@@ -1,181 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of xcolumn_bmod.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_COLUMN_BMOD_H
-#define SPARSELU_COLUMN_BMOD_H
-
-namespace Eigen {
-
-namespace internal {
-/**
- * \brief Performs numeric block updates (sup-col) in topological order
- * 
- * \param jcol current column to update
- * \param nseg Number of segments in the U part
- * \param dense Store the full representation of the column
- * \param tempv working array 
- * \param segrep segment representative ...
- * \param repfnz ??? First nonzero column in each row ???  ...
- * \param fpanelc First column in the current panel
- * \param glu Global LU data. 
- * \return 0 - successful return 
- *         > 0 - number of bytes allocated when run out of space
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv,
-                                                     BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu)
-{
-  Index  jsupno, k, ksub, krep, ksupno; 
-  Index lptr, nrow, isub, irow, nextlu, new_next, ufirst; 
-  Index fsupc, nsupc, nsupr, luptr, kfnz, no_zeros; 
-  /* krep = representative of current k-th supernode
-    * fsupc =  first supernodal column
-    * nsupc = number of columns in a supernode
-    * nsupr = number of rows in a supernode
-    * luptr = location of supernodal LU-block in storage
-    * kfnz = first nonz in the k-th supernodal segment
-    * no_zeros = no lf leading zeros in a supernodal U-segment
-    */
-  
-  jsupno = glu.supno(jcol);
-  // For each nonzero supernode segment of U[*,j] in topological order 
-  k = nseg - 1; 
-  Index d_fsupc; // distance between the first column of the current panel and the 
-               // first column of the current snode
-  Index fst_col; // First column within small LU update
-  Index segsize; 
-  for (ksub = 0; ksub < nseg; ksub++)
-  {
-    krep = segrep(k); k--; 
-    ksupno = glu.supno(krep); 
-    if (jsupno != ksupno )
-    {
-      // outside the rectangular supernode 
-      fsupc = glu.xsup(ksupno); 
-      fst_col = (std::max)(fsupc, fpanelc); 
-      
-      // Distance from the current supernode to the current panel; 
-      // d_fsupc = 0 if fsupc > fpanelc
-      d_fsupc = fst_col - fsupc; 
-      
-      luptr = glu.xlusup(fst_col) + d_fsupc; 
-      lptr = glu.xlsub(fsupc) + d_fsupc; 
-      
-      kfnz = repfnz(krep); 
-      kfnz = (std::max)(kfnz, fpanelc); 
-      
-      segsize = krep - kfnz + 1; 
-      nsupc = krep - fst_col + 1; 
-      nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
-      nrow = nsupr - d_fsupc - nsupc;
-      Index lda = glu.xlusup(fst_col+1) - glu.xlusup(fst_col);
-      
-      
-      // Perform a triangular solver and block update, 
-      // then scatter the result of sup-col update to dense
-      no_zeros = kfnz - fst_col; 
-      if(segsize==1)
-        LU_kernel_bmod<1>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-      else
-        LU_kernel_bmod<Dynamic>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-    } // end if jsupno 
-  } // end for each segment
-  
-  // Process the supernodal portion of  L\U[*,j]
-  nextlu = glu.xlusup(jcol); 
-  fsupc = glu.xsup(jsupno);
-  
-  // copy the SPA dense into L\U[*,j]
-  Index mem; 
-  new_next = nextlu + glu.xlsub(fsupc + 1) - glu.xlsub(fsupc); 
-  Index offset = internal::first_multiple<Index>(new_next, internal::packet_traits<Scalar>::size) - new_next;
-  if(offset)
-    new_next += offset;
-  while (new_next > glu.nzlumax )
-  {
-    mem = memXpand<ScalarVector>(glu.lusup, glu.nzlumax, nextlu, LUSUP, glu.num_expansions);  
-    if (mem) return mem; 
-  }
-  
-  for (isub = glu.xlsub(fsupc); isub < glu.xlsub(fsupc+1); isub++)
-  {
-    irow = glu.lsub(isub);
-    glu.lusup(nextlu) = dense(irow);
-    dense(irow) = Scalar(0.0); 
-    ++nextlu; 
-  }
-  
-  if(offset)
-  {
-    glu.lusup.segment(nextlu,offset).setZero();
-    nextlu += offset;
-  }
-  glu.xlusup(jcol + 1) = StorageIndex(nextlu);  // close L\U(*,jcol); 
-  
-  /* For more updates within the panel (also within the current supernode),
-   * should start from the first column of the panel, or the first column
-   * of the supernode, whichever is bigger. There are two cases:
-   *  1) fsupc < fpanelc, then fst_col <-- fpanelc
-   *  2) fsupc >= fpanelc, then fst_col <-- fsupc
-   */
-  fst_col = (std::max)(fsupc, fpanelc); 
-  
-  if (fst_col  < jcol)
-  {
-    // Distance between the current supernode and the current panel
-    // d_fsupc = 0 if fsupc >= fpanelc
-    d_fsupc = fst_col - fsupc; 
-    
-    lptr = glu.xlsub(fsupc) + d_fsupc; 
-    luptr = glu.xlusup(fst_col) + d_fsupc; 
-    nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); // leading dimension
-    nsupc = jcol - fst_col; // excluding jcol 
-    nrow = nsupr - d_fsupc - nsupc; 
-    
-    // points to the beginning of jcol in snode L\U(jsupno) 
-    ufirst = glu.xlusup(jcol) + d_fsupc; 
-    Index lda = glu.xlusup(jcol+1) - glu.xlusup(jcol);
-    MappedMatrixBlock A( &(glu.lusup.data()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-    VectorBlock<ScalarVector> u(glu.lusup, ufirst, nsupc); 
-    u = A.template triangularView<UnitLower>().solve(u); 
-    
-    new (&A) MappedMatrixBlock ( &(glu.lusup.data()[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-    VectorBlock<ScalarVector> l(glu.lusup, ufirst+nsupc, nrow); 
-    l.noalias() -= A * u;
-    
-  } // End if fst_col
-  return 0; 
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_COLUMN_BMOD_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_column_dfs.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
deleted file mode 100644
index 5a2c941..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
+++ /dev/null
@@ -1,179 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]column_dfs.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_COLUMN_DFS_H
-#define SPARSELU_COLUMN_DFS_H
-
-template <typename Scalar, typename StorageIndex> class SparseLUImpl;
-namespace Eigen {
-
-namespace internal {
-
-template<typename IndexVector, typename ScalarVector>
-struct column_dfs_traits : no_assignment_operator
-{
-  typedef typename ScalarVector::Scalar Scalar;
-  typedef typename IndexVector::Scalar StorageIndex;
-  column_dfs_traits(Index jcol, Index& jsuper, typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& glu, SparseLUImpl<Scalar, StorageIndex>& luImpl)
-   : m_jcol(jcol), m_jsuper_ref(jsuper), m_glu(glu), m_luImpl(luImpl)
- {}
-  bool update_segrep(Index /*krep*/, Index /*jj*/)
-  {
-    return true;
-  }
-  void mem_expand(IndexVector& lsub, Index& nextl, Index chmark)
-  {
-    if (nextl >= m_glu.nzlmax)
-      m_luImpl.memXpand(lsub, m_glu.nzlmax, nextl, LSUB, m_glu.num_expansions); 
-    if (chmark != (m_jcol-1)) m_jsuper_ref = emptyIdxLU;
-  }
-  enum { ExpandMem = true };
-  
-  Index m_jcol;
-  Index& m_jsuper_ref;
-  typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& m_glu;
-  SparseLUImpl<Scalar, StorageIndex>& m_luImpl;
-};
-
-
-/**
- * \brief Performs a symbolic factorization on column jcol and decide the supernode boundary
- * 
- * A supernode representative is the last column of a supernode.
- * The nonzeros in U[*,j] are segments that end at supernodes representatives. 
- * The routine returns a list of the supernodal representatives 
- * in topological order of the dfs that generates them. 
- * The location of the first nonzero in each supernodal segment 
- * (supernodal entry location) is also returned. 
- * 
- * \param m number of rows in the matrix
- * \param jcol Current column 
- * \param perm_r Row permutation
- * \param maxsuper  Maximum number of column allowed in a supernode
- * \param [in,out] nseg Number of segments in current U[*,j] - new segments appended
- * \param lsub_col defines the rhs vector to start the dfs
- * \param [in,out] segrep Segment representatives - new segments appended 
- * \param repfnz  First nonzero location in each row
- * \param xprune 
- * \param marker  marker[i] == jj, if i was visited during dfs of current column jj;
- * \param parent
- * \param xplore working array
- * \param glu global LU data 
- * \return 0 success
- *         > 0 number of bytes allocated when run out of space
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg,
-                                                    BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune,
-                                                    IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu)
-{
-  
-  Index jsuper = glu.supno(jcol); 
-  Index nextl = glu.xlsub(jcol); 
-  VectorBlock<IndexVector> marker2(marker, 2*m, m); 
-  
-  
-  column_dfs_traits<IndexVector, ScalarVector> traits(jcol, jsuper, glu, *this);
-  
-  // For each nonzero in A(*,jcol) do dfs 
-  for (Index k = 0; ((k < m) ? lsub_col[k] != emptyIdxLU : false) ; k++)
-  {
-    Index krow = lsub_col(k); 
-    lsub_col(k) = emptyIdxLU; 
-    Index kmark = marker2(krow); 
-    
-    // krow was visited before, go to the next nonz; 
-    if (kmark == jcol) continue;
-    
-    dfs_kernel(StorageIndex(jcol), perm_r, nseg, glu.lsub, segrep, repfnz, xprune, marker2, parent,
-                   xplore, glu, nextl, krow, traits);
-  } // for each nonzero ... 
-  
-  Index fsupc;
-  StorageIndex nsuper = glu.supno(jcol);
-  StorageIndex jcolp1 = StorageIndex(jcol) + 1;
-  Index jcolm1 = jcol - 1;
-  
-  // check to see if j belongs in the same supernode as j-1
-  if ( jcol == 0 )
-  { // Do nothing for column 0 
-    nsuper = glu.supno(0) = 0 ;
-  }
-  else 
-  {
-    fsupc = glu.xsup(nsuper); 
-    StorageIndex jptr = glu.xlsub(jcol); // Not yet compressed
-    StorageIndex jm1ptr = glu.xlsub(jcolm1); 
-    
-    // Use supernodes of type T2 : see SuperLU paper
-    if ( (nextl-jptr != jptr-jm1ptr-1) ) jsuper = emptyIdxLU;
-    
-    // Make sure the number of columns in a supernode doesn't
-    // exceed threshold
-    if ( (jcol - fsupc) >= maxsuper) jsuper = emptyIdxLU; 
-    
-    /* If jcol starts a new supernode, reclaim storage space in
-     * glu.lsub from previous supernode. Note we only store 
-     * the subscript set of the first and last columns of 
-     * a supernode. (first for num values, last for pruning)
-     */
-    if (jsuper == emptyIdxLU)
-    { // starts a new supernode 
-      if ( (fsupc < jcolm1-1) ) 
-      { // >= 3 columns in nsuper
-        StorageIndex ito = glu.xlsub(fsupc+1);
-        glu.xlsub(jcolm1) = ito; 
-        StorageIndex istop = ito + jptr - jm1ptr; 
-        xprune(jcolm1) = istop; // initialize xprune(jcol-1)
-        glu.xlsub(jcol) = istop; 
-        
-        for (StorageIndex ifrom = jm1ptr; ifrom < nextl; ++ifrom, ++ito)
-          glu.lsub(ito) = glu.lsub(ifrom); 
-        nextl = ito;  // = istop + length(jcol)
-      }
-      nsuper++; 
-      glu.supno(jcol) = nsuper; 
-    } // if a new supernode 
-  } // end else:  jcol > 0
-  
-  // Tidy up the pointers before exit
-  glu.xsup(nsuper+1) = jcolp1; 
-  glu.supno(jcolp1) = nsuper; 
-  xprune(jcol) = StorageIndex(nextl);  // Initialize upper bound for pruning
-  glu.xlsub(jcolp1) = StorageIndex(nextl); 
-  
-  return 0; 
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
deleted file mode 100644
index c32d8d8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
+++ /dev/null
@@ -1,107 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]copy_to_ucol.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_COPY_TO_UCOL_H
-#define SPARSELU_COPY_TO_UCOL_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Performs numeric block updates (sup-col) in topological order
- * 
- * \param jcol current column to update
- * \param nseg Number of segments in the U part
- * \param segrep segment representative ...
- * \param repfnz First nonzero column in each row  ...
- * \param perm_r Row permutation 
- * \param dense Store the full representation of the column
- * \param glu Global LU data. 
- * \return 0 - successful return 
- *         > 0 - number of bytes allocated when run out of space
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep,
-                                                      BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu)
-{  
-  Index ksub, krep, ksupno; 
-    
-  Index jsupno = glu.supno(jcol);
-  
-  // For each nonzero supernode segment of U[*,j] in topological order 
-  Index k = nseg - 1, i; 
-  StorageIndex nextu = glu.xusub(jcol); 
-  Index kfnz, isub, segsize; 
-  Index new_next,irow; 
-  Index fsupc, mem; 
-  for (ksub = 0; ksub < nseg; ksub++)
-  {
-    krep = segrep(k); k--; 
-    ksupno = glu.supno(krep); 
-    if (jsupno != ksupno ) // should go into ucol(); 
-    {
-      kfnz = repfnz(krep); 
-      if (kfnz != emptyIdxLU)
-      { // Nonzero U-segment 
-        fsupc = glu.xsup(ksupno); 
-        isub = glu.xlsub(fsupc) + kfnz - fsupc; 
-        segsize = krep - kfnz + 1; 
-        new_next = nextu + segsize; 
-        while (new_next > glu.nzumax) 
-        {
-          mem = memXpand<ScalarVector>(glu.ucol, glu.nzumax, nextu, UCOL, glu.num_expansions); 
-          if (mem) return mem; 
-          mem = memXpand<IndexVector>(glu.usub, glu.nzumax, nextu, USUB, glu.num_expansions); 
-          if (mem) return mem; 
-          
-        }
-        
-        for (i = 0; i < segsize; i++)
-        {
-          irow = glu.lsub(isub); 
-          glu.usub(nextu) = perm_r(irow); // Unlike the L part, the U part is stored in its final order
-          glu.ucol(nextu) = dense(irow); 
-          dense(irow) = Scalar(0.0); 
-          nextu++;
-          isub++;
-        }
-        
-      } // end nonzero U-segment 
-      
-    } // end if jsupno 
-    
-  } // end for each segment
-  glu.xusub(jcol + 1) = nextu; // close U(*,jcol)
-  return 0; 
-}
-
-} // namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_COPY_TO_UCOL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_gemm_kernel.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
deleted file mode 100644
index e37c2fe..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
+++ /dev/null
@@ -1,280 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSELU_GEMM_KERNEL_H
-#define EIGEN_SPARSELU_GEMM_KERNEL_H
-
-namespace Eigen {
-
-namespace internal {
-
-
-/** \internal
-  * A general matrix-matrix product kernel optimized for the SparseLU factorization.
-  *  - A, B, and C must be column major
-  *  - lda and ldc must be multiples of the respective packet size
-  *  - C must have the same alignment as A
-  */
-template<typename Scalar>
-EIGEN_DONT_INLINE
-void sparselu_gemm(Index m, Index n, Index d, const Scalar* A, Index lda, const Scalar* B, Index ldb, Scalar* C, Index ldc)
-{
-  using namespace Eigen::internal;
-  
-  typedef typename packet_traits<Scalar>::type Packet;
-  enum {
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-    PacketSize = packet_traits<Scalar>::size,
-    PM = 8,                             // peeling in M
-    RN = 2,                             // register blocking
-    RK = NumberOfRegisters>=16 ? 4 : 2, // register blocking
-    BM = 4096/sizeof(Scalar),           // number of rows of A-C per chunk
-    SM = PM*PacketSize                  // step along M
-  };
-  Index d_end = (d/RK)*RK;    // number of columns of A (rows of B) suitable for full register blocking
-  Index n_end = (n/RN)*RN;    // number of columns of B-C suitable for processing RN columns at once
-  Index i0 = internal::first_default_aligned(A,m);
-  
-  eigen_internal_assert(((lda%PacketSize)==0) && ((ldc%PacketSize)==0) && (i0==internal::first_default_aligned(C,m)));
-  
-  // handle the non aligned rows of A and C without any optimization:
-  for(Index i=0; i<i0; ++i)
-  {
-    for(Index j=0; j<n; ++j)
-    {
-      Scalar c = C[i+j*ldc];
-      for(Index k=0; k<d; ++k)
-        c += B[k+j*ldb] * A[i+k*lda];
-      C[i+j*ldc] = c;
-    }
-  }
-  // process the remaining rows per chunk of BM rows
-  for(Index ib=i0; ib<m; ib+=BM)
-  {
-    Index actual_b = std::min<Index>(BM, m-ib);                 // actual number of rows
-    Index actual_b_end1 = (actual_b/SM)*SM;                   // actual number of rows suitable for peeling
-    Index actual_b_end2 = (actual_b/PacketSize)*PacketSize;   // actual number of rows suitable for vectorization
-    
-    // Let's process two columns of B-C at once
-    for(Index j=0; j<n_end; j+=RN)
-    {
-      const Scalar* Bc0 = B+(j+0)*ldb;
-      const Scalar* Bc1 = B+(j+1)*ldb;
-      
-      for(Index k=0; k<d_end; k+=RK)
-      {
-        
-        // load and expand a RN x RK block of B
-        Packet b00, b10, b20, b30, b01, b11, b21, b31;
-                  { b00 = pset1<Packet>(Bc0[0]); }
-                  { b10 = pset1<Packet>(Bc0[1]); }
-        if(RK==4) { b20 = pset1<Packet>(Bc0[2]); }
-        if(RK==4) { b30 = pset1<Packet>(Bc0[3]); }
-                  { b01 = pset1<Packet>(Bc1[0]); }
-                  { b11 = pset1<Packet>(Bc1[1]); }
-        if(RK==4) { b21 = pset1<Packet>(Bc1[2]); }
-        if(RK==4) { b31 = pset1<Packet>(Bc1[3]); }
-        
-        Packet a0, a1, a2, a3, c0, c1, t0, t1;
-        
-        const Scalar* A0 = A+ib+(k+0)*lda;
-        const Scalar* A1 = A+ib+(k+1)*lda;
-        const Scalar* A2 = A+ib+(k+2)*lda;
-        const Scalar* A3 = A+ib+(k+3)*lda;
-        
-        Scalar* C0 = C+ib+(j+0)*ldc;
-        Scalar* C1 = C+ib+(j+1)*ldc;
-        
-                  a0 = pload<Packet>(A0);
-                  a1 = pload<Packet>(A1);
-        if(RK==4)
-        {
-          a2 = pload<Packet>(A2);
-          a3 = pload<Packet>(A3);
-        }
-        else
-        {
-          // workaround "may be used uninitialized in this function" warning
-          a2 = a3 = a0;
-        }
-        
-#define KMADD(c, a, b, tmp) {tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);}
-#define WORK(I)  \
-                     c0 = pload<Packet>(C0+i+(I)*PacketSize);    \
-                     c1 = pload<Packet>(C1+i+(I)*PacketSize);    \
-                     KMADD(c0, a0, b00, t0)                      \
-                     KMADD(c1, a0, b01, t1)                      \
-                     a0 = pload<Packet>(A0+i+(I+1)*PacketSize);  \
-                     KMADD(c0, a1, b10, t0)                      \
-                     KMADD(c1, a1, b11, t1)                      \
-                     a1 = pload<Packet>(A1+i+(I+1)*PacketSize);  \
-          if(RK==4){ KMADD(c0, a2, b20, t0)                     }\
-          if(RK==4){ KMADD(c1, a2, b21, t1)                     }\
-          if(RK==4){ a2 = pload<Packet>(A2+i+(I+1)*PacketSize); }\
-          if(RK==4){ KMADD(c0, a3, b30, t0)                     }\
-          if(RK==4){ KMADD(c1, a3, b31, t1)                     }\
-          if(RK==4){ a3 = pload<Packet>(A3+i+(I+1)*PacketSize); }\
-                     pstore(C0+i+(I)*PacketSize, c0);            \
-                     pstore(C1+i+(I)*PacketSize, c1)
-        
-        // process rows of A' - C' with aggressive vectorization and peeling 
-        for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
-        {
-          EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL1");
-                    prefetch((A0+i+(5)*PacketSize));
-                    prefetch((A1+i+(5)*PacketSize));
-          if(RK==4) prefetch((A2+i+(5)*PacketSize));
-          if(RK==4) prefetch((A3+i+(5)*PacketSize));
-
-          WORK(0);
-          WORK(1);
-          WORK(2);
-          WORK(3);
-          WORK(4);
-          WORK(5);
-          WORK(6);
-          WORK(7);
-        }
-        // process the remaining rows with vectorization only
-        for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
-        {
-          WORK(0);
-        }
-#undef WORK
-        // process the remaining rows without vectorization
-        for(Index i=actual_b_end2; i<actual_b; ++i)
-        {
-          if(RK==4)
-          {
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
-            C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1]+A2[i]*Bc1[2]+A3[i]*Bc1[3];
-          }
-          else
-          {
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
-            C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1];
-          }
-        }
-        
-        Bc0 += RK;
-        Bc1 += RK;
-      } // peeled loop on k
-    } // peeled loop on the columns j
-    // process the last column (we now perform a matrix-vector product)
-    if((n-n_end)>0)
-    {
-      const Scalar* Bc0 = B+(n-1)*ldb;
-      
-      for(Index k=0; k<d_end; k+=RK)
-      {
-        
-        // load and expand a 1 x RK block of B
-        Packet b00, b10, b20, b30;
-                  b00 = pset1<Packet>(Bc0[0]);
-                  b10 = pset1<Packet>(Bc0[1]);
-        if(RK==4) b20 = pset1<Packet>(Bc0[2]);
-        if(RK==4) b30 = pset1<Packet>(Bc0[3]);
-        
-        Packet a0, a1, a2, a3, c0, t0/*, t1*/;
-        
-        const Scalar* A0 = A+ib+(k+0)*lda;
-        const Scalar* A1 = A+ib+(k+1)*lda;
-        const Scalar* A2 = A+ib+(k+2)*lda;
-        const Scalar* A3 = A+ib+(k+3)*lda;
-        
-        Scalar* C0 = C+ib+(n_end)*ldc;
-        
-                  a0 = pload<Packet>(A0);
-                  a1 = pload<Packet>(A1);
-        if(RK==4)
-        {
-          a2 = pload<Packet>(A2);
-          a3 = pload<Packet>(A3);
-        }
-        else
-        {
-          // workaround "may be used uninitialized in this function" warning
-          a2 = a3 = a0;
-        }
-        
-#define WORK(I) \
-                   c0 = pload<Packet>(C0+i+(I)*PacketSize);     \
-                   KMADD(c0, a0, b00, t0)                       \
-                   a0 = pload<Packet>(A0+i+(I+1)*PacketSize);   \
-                   KMADD(c0, a1, b10, t0)                       \
-                   a1 = pload<Packet>(A1+i+(I+1)*PacketSize);   \
-        if(RK==4){ KMADD(c0, a2, b20, t0)                      }\
-        if(RK==4){ a2 = pload<Packet>(A2+i+(I+1)*PacketSize);  }\
-        if(RK==4){ KMADD(c0, a3, b30, t0)                      }\
-        if(RK==4){ a3 = pload<Packet>(A3+i+(I+1)*PacketSize);  }\
-                   pstore(C0+i+(I)*PacketSize, c0);
-        
-        // aggressive vectorization and peeling
-        for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
-        {
-          EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL2");
-          WORK(0);
-          WORK(1);
-          WORK(2);
-          WORK(3);
-          WORK(4);
-          WORK(5);
-          WORK(6);
-          WORK(7);
-        }
-        // vectorization only
-        for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
-        {
-          WORK(0);
-        }
-        // remaining scalars
-        for(Index i=actual_b_end2; i<actual_b; ++i)
-        {
-          if(RK==4) 
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
-          else
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
-        }
-        
-        Bc0 += RK;
-#undef WORK
-      }
-    }
-    
-    // process the last columns of A, corresponding to the last rows of B
-    Index rd = d-d_end;
-    if(rd>0)
-    {
-      for(Index j=0; j<n; ++j)
-      {
-        enum {
-          Alignment = PacketSize>1 ? Aligned : 0
-        };
-        typedef Map<Matrix<Scalar,Dynamic,1>, Alignment > MapVector;
-        typedef Map<const Matrix<Scalar,Dynamic,1>, Alignment > ConstMapVector;
-        if(rd==1)       MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b);
-        
-        else if(rd==2)  MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
-                                                        + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b);
-        
-        else            MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
-                                                        + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b)
-                                                        + B[2+d_end+j*ldb] * ConstMapVector(A+(d_end+2)*lda+ib, actual_b);
-      }
-    }
-  
-  } // blocking on the rows of A and C
-}
-#undef KMADD
-
-} // namespace internal
-
-} // namespace Eigen
-
-#endif // EIGEN_SPARSELU_GEMM_KERNEL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
deleted file mode 100644
index 6f75d50..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
+++ /dev/null
@@ -1,126 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* This file is a modified version of heap_relax_snode.c file in SuperLU
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-
-#ifndef SPARSELU_HEAP_RELAX_SNODE_H
-#define SPARSELU_HEAP_RELAX_SNODE_H
-
-namespace Eigen {
-namespace internal {
-
-/** 
- * \brief Identify the initial relaxed supernodes
- * 
- * This routine applied to a symmetric elimination tree. 
- * It assumes that the matrix has been reordered according to the postorder of the etree
- * \param n The number of columns
- * \param et elimination tree 
- * \param relax_columns Maximum number of columns allowed in a relaxed snode 
- * \param descendants Number of descendants of each node in the etree
- * \param relax_end last column in a supernode
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end)
-{
-  
-  // The etree may not be postordered, but its heap ordered  
-  IndexVector post;
-  internal::treePostorder(StorageIndex(n), et, post); // Post order etree
-  IndexVector inv_post(n+1); 
-  for (StorageIndex i = 0; i < n+1; ++i) inv_post(post(i)) = i; // inv_post = post.inverse()???
-  
-  // Renumber etree in postorder 
-  IndexVector iwork(n);
-  IndexVector et_save(n+1);
-  for (Index i = 0; i < n; ++i)
-  {
-    iwork(post(i)) = post(et(i));
-  }
-  et_save = et; // Save the original etree
-  et = iwork; 
-  
-  // compute the number of descendants of each node in the etree
-  relax_end.setConstant(emptyIdxLU);
-  Index j, parent; 
-  descendants.setZero();
-  for (j = 0; j < n; j++) 
-  {
-    parent = et(j);
-    if (parent != n) // not the dummy root
-      descendants(parent) += descendants(j) + 1;
-  }
-  // Identify the relaxed supernodes by postorder traversal of the etree
-  Index snode_start; // beginning of a snode 
-  StorageIndex k;
-  Index nsuper_et_post = 0; // Number of relaxed snodes in postordered etree 
-  Index nsuper_et = 0; // Number of relaxed snodes in the original etree 
-  StorageIndex l; 
-  for (j = 0; j < n; )
-  {
-    parent = et(j);
-    snode_start = j; 
-    while ( parent != n && descendants(parent) < relax_columns ) 
-    {
-      j = parent; 
-      parent = et(j);
-    }
-    // Found a supernode in postordered etree, j is the last column 
-    ++nsuper_et_post;
-    k = StorageIndex(n);
-    for (Index i = snode_start; i <= j; ++i)
-      k = (std::min)(k, inv_post(i));
-    l = inv_post(j);
-    if ( (l - k) == (j - snode_start) )  // Same number of columns in the snode
-    {
-      // This is also a supernode in the original etree
-      relax_end(k) = l; // Record last column 
-      ++nsuper_et; 
-    }
-    else 
-    {
-      for (Index i = snode_start; i <= j; ++i) 
-      {
-        l = inv_post(i);
-        if (descendants(i) == 0) 
-        {
-          relax_end(l) = l;
-          ++nsuper_et;
-        }
-      }
-    }
-    j++;
-    // Search for a new leaf
-    while (descendants(j) != 0 && j < n) j++;
-  } // End postorder traversal of the etree
-  
-  // Recover the original etree
-  et = et_save; 
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // SPARSELU_HEAP_RELAX_SNODE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
deleted file mode 100644
index 8c1b3e8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
+++ /dev/null
@@ -1,130 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef SPARSELU_KERNEL_BMOD_H
-#define SPARSELU_KERNEL_BMOD_H
-
-namespace Eigen {
-namespace internal {
-  
-template <int SegSizeAtCompileTime> struct LU_kernel_bmod
-{
-  /** \internal
-    * \brief Performs numeric block updates from a given supernode to a single column
-    *
-    * \param segsize Size of the segment (and blocks ) to use for updates
-    * \param[in,out] dense Packed values of the original matrix
-    * \param tempv temporary vector to use for updates
-    * \param lusup array containing the supernodes
-    * \param lda Leading dimension in the supernode
-    * \param nrow Number of rows in the rectangular part of the supernode
-    * \param lsub compressed row subscripts of supernodes
-    * \param lptr pointer to the first column of the current supernode in lsub
-    * \param no_zeros Number of nonzeros elements before the diagonal part of the supernode
-    */
-  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-  static EIGEN_DONT_INLINE void run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda,
-                                    const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros);
-};
-
-template <int SegSizeAtCompileTime>
-template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-EIGEN_DONT_INLINE void LU_kernel_bmod<SegSizeAtCompileTime>::run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda,
-                                                                  const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros)
-{
-  typedef typename ScalarVector::Scalar Scalar;
-  // First, copy U[*,j] segment from dense(*) to tempv(*)
-  // The result of triangular solve is in tempv[*]; 
-    // The result of matric-vector update is in dense[*]
-  Index isub = lptr + no_zeros; 
-  Index i;
-  Index irow;
-  for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++)
-  {
-    irow = lsub(isub); 
-    tempv(i) = dense(irow); 
-    ++isub; 
-  }
-  // Dense triangular solve -- start effective triangle
-  luptr += lda * no_zeros + no_zeros; 
-  // Form Eigen matrix and vector 
-  Map<Matrix<Scalar,SegSizeAtCompileTime,SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > A( &(lusup.data()[luptr]), segsize, segsize, OuterStride<>(lda) );
-  Map<Matrix<Scalar,SegSizeAtCompileTime,1> > u(tempv.data(), segsize);
-  
-  u = A.template triangularView<UnitLower>().solve(u); 
-  
-  // Dense matrix-vector product y <-- B*x 
-  luptr += segsize;
-  const Index PacketSize = internal::packet_traits<Scalar>::size;
-  Index ldl = internal::first_multiple(nrow, PacketSize);
-  Map<Matrix<Scalar,Dynamic,SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > B( &(lusup.data()[luptr]), nrow, segsize, OuterStride<>(lda) );
-  Index aligned_offset = internal::first_default_aligned(tempv.data()+segsize, PacketSize);
-  Index aligned_with_B_offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize))%PacketSize;
-  Map<Matrix<Scalar,Dynamic,1>, 0, OuterStride<> > l(tempv.data()+segsize+aligned_offset+aligned_with_B_offset, nrow, OuterStride<>(ldl) );
-  
-  l.setZero();
-  internal::sparselu_gemm<Scalar>(l.rows(), l.cols(), B.cols(), B.data(), B.outerStride(), u.data(), u.outerStride(), l.data(), l.outerStride());
-  
-  // Scatter tempv[] into SPA dense[] as a temporary storage 
-  isub = lptr + no_zeros;
-  for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++)
-  {
-    irow = lsub(isub++); 
-    dense(irow) = tempv(i);
-  }
-  
-  // Scatter l into SPA dense[]
-  for (i = 0; i < nrow; i++)
-  {
-    irow = lsub(isub++); 
-    dense(irow) -= l(i);
-  } 
-}
-
-template <> struct LU_kernel_bmod<1>
-{
-  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-  static EIGEN_DONT_INLINE void run(const Index /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr,
-                                    const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros);
-};
-
-
-template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-EIGEN_DONT_INLINE void LU_kernel_bmod<1>::run(const Index /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr,
-                                              const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros)
-{
-  typedef typename ScalarVector::Scalar Scalar;
-  typedef typename IndexVector::Scalar StorageIndex;
-  Scalar f = dense(lsub(lptr + no_zeros));
-  luptr += lda * no_zeros + no_zeros + 1;
-  const Scalar* a(lusup.data() + luptr);
-  const StorageIndex*  irow(lsub.data()+lptr + no_zeros + 1);
-  Index i = 0;
-  for (; i+1 < nrow; i+=2)
-  {
-    Index i0 = *(irow++);
-    Index i1 = *(irow++);
-    Scalar a0 = *(a++);
-    Scalar a1 = *(a++);
-    Scalar d0 = dense.coeff(i0);
-    Scalar d1 = dense.coeff(i1);
-    d0 -= f*a0;
-    d1 -= f*a1;
-    dense.coeffRef(i0) = d0;
-    dense.coeffRef(i1) = d1;
-  }
-  if(i<nrow)
-    dense.coeffRef(*(irow++)) -= f * *(a++);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // SPARSELU_KERNEL_BMOD_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
deleted file mode 100644
index f052001..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
+++ /dev/null
@@ -1,223 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]panel_bmod.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PANEL_BMOD_H
-#define SPARSELU_PANEL_BMOD_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Performs numeric block updates (sup-panel) in topological order.
- * 
- * Before entering this routine, the original nonzeros in the panel
- * were already copied into the spa[m,w]
- * 
- * \param m number of rows in the matrix
- * \param w Panel size
- * \param jcol Starting  column of the panel
- * \param nseg Number of segments in the U part
- * \param dense Store the full representation of the panel 
- * \param tempv working array 
- * \param segrep segment representative... first row in the segment
- * \param repfnz First nonzero rows
- * \param glu Global LU data. 
- * 
- * 
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::panel_bmod(const Index m, const Index w, const Index jcol, 
-                                            const Index nseg, ScalarVector& dense, ScalarVector& tempv,
-                                            IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu)
-{
-  
-  Index ksub,jj,nextl_col; 
-  Index fsupc, nsupc, nsupr, nrow; 
-  Index krep, kfnz; 
-  Index lptr; // points to the row subscripts of a supernode 
-  Index luptr; // ...
-  Index segsize,no_zeros ; 
-  // For each nonz supernode segment of U[*,j] in topological order
-  Index k = nseg - 1; 
-  const Index PacketSize = internal::packet_traits<Scalar>::size;
-  
-  for (ksub = 0; ksub < nseg; ksub++)
-  { // For each updating supernode
-    /* krep = representative of current k-th supernode
-     * fsupc =  first supernodal column
-     * nsupc = number of columns in a supernode
-     * nsupr = number of rows in a supernode
-     */
-    krep = segrep(k); k--; 
-    fsupc = glu.xsup(glu.supno(krep)); 
-    nsupc = krep - fsupc + 1; 
-    nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
-    nrow = nsupr - nsupc; 
-    lptr = glu.xlsub(fsupc); 
-    
-    // loop over the panel columns to detect the actual number of columns and rows
-    Index u_rows = 0;
-    Index u_cols = 0;
-    for (jj = jcol; jj < jcol + w; jj++)
-    {
-      nextl_col = (jj-jcol) * m; 
-      VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-      
-      kfnz = repfnz_col(krep); 
-      if ( kfnz == emptyIdxLU ) 
-        continue; // skip any zero segment
-      
-      segsize = krep - kfnz + 1;
-      u_cols++;
-      u_rows = (std::max)(segsize,u_rows);
-    }
-    
-    if(nsupc >= 2)
-    { 
-      Index ldu = internal::first_multiple<Index>(u_rows, PacketSize);
-      Map<ScalarMatrix, Aligned,  OuterStride<> > U(tempv.data(), u_rows, u_cols, OuterStride<>(ldu));
-      
-      // gather U
-      Index u_col = 0;
-      for (jj = jcol; jj < jcol + w; jj++)
-      {
-        nextl_col = (jj-jcol) * m; 
-        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
-        
-        kfnz = repfnz_col(krep); 
-        if ( kfnz == emptyIdxLU ) 
-          continue; // skip any zero segment
-        
-        segsize = krep - kfnz + 1;
-        luptr = glu.xlusup(fsupc);    
-        no_zeros = kfnz - fsupc; 
-        
-        Index isub = lptr + no_zeros;
-        Index off = u_rows-segsize;
-        for (Index i = 0; i < off; i++) U(i,u_col) = 0;
-        for (Index i = 0; i < segsize; i++)
-        {
-          Index irow = glu.lsub(isub); 
-          U(i+off,u_col) = dense_col(irow); 
-          ++isub; 
-        }
-        u_col++;
-      }
-      // solve U = A^-1 U
-      luptr = glu.xlusup(fsupc);
-      Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc);
-      no_zeros = (krep - u_rows + 1) - fsupc;
-      luptr += lda * no_zeros + no_zeros;
-      MappedMatrixBlock A(glu.lusup.data()+luptr, u_rows, u_rows, OuterStride<>(lda) );
-      U = A.template triangularView<UnitLower>().solve(U);
-      
-      // update
-      luptr += u_rows;
-      MappedMatrixBlock B(glu.lusup.data()+luptr, nrow, u_rows, OuterStride<>(lda) );
-      eigen_assert(tempv.size()>w*ldu + nrow*w + 1);
-      
-      Index ldl = internal::first_multiple<Index>(nrow, PacketSize);
-      Index offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize)) % PacketSize;
-      MappedMatrixBlock L(tempv.data()+w*ldu+offset, nrow, u_cols, OuterStride<>(ldl));
-      
-      L.setZero();
-      internal::sparselu_gemm<Scalar>(L.rows(), L.cols(), B.cols(), B.data(), B.outerStride(), U.data(), U.outerStride(), L.data(), L.outerStride());
-      
-      // scatter U and L
-      u_col = 0;
-      for (jj = jcol; jj < jcol + w; jj++)
-      {
-        nextl_col = (jj-jcol) * m; 
-        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
-        
-        kfnz = repfnz_col(krep); 
-        if ( kfnz == emptyIdxLU ) 
-          continue; // skip any zero segment
-        
-        segsize = krep - kfnz + 1;
-        no_zeros = kfnz - fsupc; 
-        Index isub = lptr + no_zeros;
-        
-        Index off = u_rows-segsize;
-        for (Index i = 0; i < segsize; i++)
-        {
-          Index irow = glu.lsub(isub++); 
-          dense_col(irow) = U.coeff(i+off,u_col);
-          U.coeffRef(i+off,u_col) = 0;
-        }
-        
-        // Scatter l into SPA dense[]
-        for (Index i = 0; i < nrow; i++)
-        {
-          Index irow = glu.lsub(isub++); 
-          dense_col(irow) -= L.coeff(i,u_col);
-          L.coeffRef(i,u_col) = 0;
-        }
-        u_col++;
-      }
-    }
-    else // level 2 only
-    {
-      // Sequence through each column in the panel
-      for (jj = jcol; jj < jcol + w; jj++)
-      {
-        nextl_col = (jj-jcol) * m; 
-        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
-        
-        kfnz = repfnz_col(krep); 
-        if ( kfnz == emptyIdxLU ) 
-          continue; // skip any zero segment
-        
-        segsize = krep - kfnz + 1;
-        luptr = glu.xlusup(fsupc);
-        
-        Index lda = glu.xlusup(fsupc+1)-glu.xlusup(fsupc);// nsupr
-        
-        // Perform a trianglar solve and block update, 
-        // then scatter the result of sup-col update to dense[]
-        no_zeros = kfnz - fsupc; 
-              if(segsize==1)  LU_kernel_bmod<1>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-        else  if(segsize==2)  LU_kernel_bmod<2>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-        else  if(segsize==3)  LU_kernel_bmod<3>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-        else                  LU_kernel_bmod<Dynamic>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); 
-      } // End for each column in the panel 
-    }
-    
-  } // End for each updating supernode
-} // end panel bmod
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // SPARSELU_PANEL_BMOD_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
deleted file mode 100644
index 155df73..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]panel_dfs.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PANEL_DFS_H
-#define SPARSELU_PANEL_DFS_H
-
-namespace Eigen {
-
-namespace internal {
-  
-template<typename IndexVector>
-struct panel_dfs_traits
-{
-  typedef typename IndexVector::Scalar StorageIndex;
-  panel_dfs_traits(Index jcol, StorageIndex* marker)
-    : m_jcol(jcol), m_marker(marker)
-  {}
-  bool update_segrep(Index krep, StorageIndex jj)
-  {
-    if(m_marker[krep]<m_jcol)
-    {
-      m_marker[krep] = jj; 
-      return true;
-    }
-    return false;
-  }
-  void mem_expand(IndexVector& /*glu.lsub*/, Index /*nextl*/, Index /*chmark*/) {}
-  enum { ExpandMem = false };
-  Index m_jcol;
-  StorageIndex* m_marker;
-};
-
-
-template <typename Scalar, typename StorageIndex>
-template <typename Traits>
-void SparseLUImpl<Scalar,StorageIndex>::dfs_kernel(const StorageIndex jj, IndexVector& perm_r,
-                   Index& nseg, IndexVector& panel_lsub, IndexVector& segrep,
-                   Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent,
-                   IndexVector& xplore, GlobalLU_t& glu,
-                   Index& nextl_col, Index krow, Traits& traits
-                  )
-{
-  
-  StorageIndex kmark = marker(krow);
-      
-  // For each unmarked krow of jj
-  marker(krow) = jj; 
-  StorageIndex kperm = perm_r(krow); 
-  if (kperm == emptyIdxLU ) {
-    // krow is in L : place it in structure of L(*, jj)
-    panel_lsub(nextl_col++) = StorageIndex(krow);  // krow is indexed into A
-    
-    traits.mem_expand(panel_lsub, nextl_col, kmark);
-  }
-  else 
-  {
-    // krow is in U : if its supernode-representative krep
-    // has been explored, update repfnz(*)
-    // krep = supernode representative of the current row
-    StorageIndex krep = glu.xsup(glu.supno(kperm)+1) - 1; 
-    // First nonzero element in the current column:
-    StorageIndex myfnz = repfnz_col(krep); 
-    
-    if (myfnz != emptyIdxLU )
-    {
-      // Representative visited before
-      if (myfnz > kperm ) repfnz_col(krep) = kperm; 
-      
-    }
-    else 
-    {
-      // Otherwise, perform dfs starting at krep
-      StorageIndex oldrep = emptyIdxLU; 
-      parent(krep) = oldrep; 
-      repfnz_col(krep) = kperm; 
-      StorageIndex xdfs =  glu.xlsub(krep); 
-      Index maxdfs = xprune(krep); 
-      
-      StorageIndex kpar;
-      do 
-      {
-        // For each unmarked kchild of krep
-        while (xdfs < maxdfs) 
-        {
-          StorageIndex kchild = glu.lsub(xdfs); 
-          xdfs++; 
-          StorageIndex chmark = marker(kchild); 
-          
-          if (chmark != jj ) 
-          {
-            marker(kchild) = jj; 
-            StorageIndex chperm = perm_r(kchild); 
-            
-            if (chperm == emptyIdxLU) 
-            {
-              // case kchild is in L: place it in L(*, j)
-              panel_lsub(nextl_col++) = kchild;
-              traits.mem_expand(panel_lsub, nextl_col, chmark);
-            }
-            else
-            {
-              // case kchild is in U :
-              // chrep = its supernode-rep. If its rep has been explored, 
-              // update its repfnz(*)
-              StorageIndex chrep = glu.xsup(glu.supno(chperm)+1) - 1; 
-              myfnz = repfnz_col(chrep); 
-              
-              if (myfnz != emptyIdxLU) 
-              { // Visited before 
-                if (myfnz > chperm) 
-                  repfnz_col(chrep) = chperm; 
-              }
-              else 
-              { // Cont. dfs at snode-rep of kchild
-                xplore(krep) = xdfs; 
-                oldrep = krep; 
-                krep = chrep; // Go deeper down G(L)
-                parent(krep) = oldrep; 
-                repfnz_col(krep) = chperm; 
-                xdfs = glu.xlsub(krep); 
-                maxdfs = xprune(krep); 
-                
-              } // end if myfnz != -1
-            } // end if chperm == -1 
-                
-          } // end if chmark !=jj
-        } // end while xdfs < maxdfs
-        
-        // krow has no more unexplored nbrs :
-        //    Place snode-rep krep in postorder DFS, if this 
-        //    segment is seen for the first time. (Note that 
-        //    "repfnz(krep)" may change later.)
-        //    Baktrack dfs to its parent
-        if(traits.update_segrep(krep,jj))
-        //if (marker1(krep) < jcol )
-        {
-          segrep(nseg) = krep; 
-          ++nseg; 
-          //marker1(krep) = jj; 
-        }
-        
-        kpar = parent(krep); // Pop recursion, mimic recursion 
-        if (kpar == emptyIdxLU) 
-          break; // dfs done 
-        krep = kpar; 
-        xdfs = xplore(krep); 
-        maxdfs = xprune(krep); 
-
-      } while (kpar != emptyIdxLU); // Do until empty stack 
-      
-    } // end if (myfnz = -1)
-
-  } // end if (kperm == -1)   
-}
-
-/**
- * \brief Performs a symbolic factorization on a panel of columns [jcol, jcol+w)
- * 
- * A supernode representative is the last column of a supernode.
- * The nonzeros in U[*,j] are segments that end at supernodes representatives
- * 
- * The routine returns a list of the supernodal representatives 
- * in topological order of the dfs that generates them. This list is 
- * a superset of the topological order of each individual column within 
- * the panel.
- * The location of the first nonzero in each supernodal segment 
- * (supernodal entry location) is also returned. Each column has 
- * a separate list for this purpose. 
- * 
- * Two markers arrays are used for dfs :
- *    marker[i] == jj, if i was visited during dfs of current column jj;
- *    marker1[i] >= jcol, if i was visited by earlier columns in this panel; 
- * 
- * \param[in] m number of rows in the matrix
- * \param[in] w Panel size
- * \param[in] jcol Starting  column of the panel
- * \param[in] A Input matrix in column-major storage
- * \param[in] perm_r Row permutation
- * \param[out] nseg Number of U segments
- * \param[out] dense Accumulate the column vectors of the panel
- * \param[out] panel_lsub Subscripts of the row in the panel 
- * \param[out] segrep Segment representative i.e first nonzero row of each segment
- * \param[out] repfnz First nonzero location in each row
- * \param[out] xprune The pruned elimination tree
- * \param[out] marker work vector
- * \param  parent The elimination tree
- * \param xplore work vector
- * \param glu The global data structure
- * 
- */
-
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu)
-{
-  Index nextl_col; // Next available position in panel_lsub[*,jj] 
-  
-  // Initialize pointers 
-  VectorBlock<IndexVector> marker1(marker, m, m); 
-  nseg = 0; 
-  
-  panel_dfs_traits<IndexVector> traits(jcol, marker1.data());
-  
-  // For each column in the panel 
-  for (StorageIndex jj = StorageIndex(jcol); jj < jcol + w; jj++) 
-  {
-    nextl_col = (jj - jcol) * m; 
-    
-    VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero location in each row
-    VectorBlock<ScalarVector> dense_col(dense,nextl_col, m); // Accumulate a column vector here
-    
-    
-    // For each nnz in A[*, jj] do depth first search
-    for (typename MatrixType::InnerIterator it(A, jj); it; ++it)
-    {
-      Index krow = it.row(); 
-      dense_col(krow) = it.value();
-      
-      StorageIndex kmark = marker(krow); 
-      if (kmark == jj) 
-        continue; // krow visited before, go to the next nonzero
-      
-      dfs_kernel(jj, perm_r, nseg, panel_lsub, segrep, repfnz_col, xprune, marker, parent,
-                   xplore, glu, nextl_col, krow, traits);
-    }// end for nonzeros in column jj
-    
-  } // end for column jj
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_PANEL_DFS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_pivotL.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_pivotL.h
deleted file mode 100644
index a86dac9..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_pivotL.h
+++ /dev/null
@@ -1,137 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of xpivotL.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PIVOTL_H
-#define SPARSELU_PIVOTL_H
-
-namespace Eigen {
-namespace internal {
-  
-/**
- * \brief Performs the numerical pivotin on the current column of L, and the CDIV operation.
- * 
- * Pivot policy :
- * (1) Compute thresh = u * max_(i>=j) abs(A_ij);
- * (2) IF user specifies pivot row k and abs(A_kj) >= thresh THEN
- *           pivot row = k;
- *       ELSE IF abs(A_jj) >= thresh THEN
- *           pivot row = j;
- *       ELSE
- *           pivot row = m;
- * 
- *   Note: If you absolutely want to use a given pivot order, then set u=0.0.
- * 
- * \param jcol The current column of L
- * \param diagpivotthresh diagonal pivoting threshold
- * \param[in,out] perm_r Row permutation (threshold pivoting)
- * \param[in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc'
- * \param[out] pivrow  The pivot row
- * \param glu Global LU data
- * \return 0 if success, i > 0 if U(i,i) is exactly zero 
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu)
-{
-  
-  Index fsupc = (glu.xsup)((glu.supno)(jcol)); // First column in the supernode containing the column jcol
-  Index nsupc = jcol - fsupc; // Number of columns in the supernode portion, excluding jcol; nsupc >=0
-  Index lptr = glu.xlsub(fsupc); // pointer to the starting location of the row subscripts for this supernode portion
-  Index nsupr = glu.xlsub(fsupc+1) - lptr; // Number of rows in the supernode
-  Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc); // leading dimension
-  Scalar* lu_sup_ptr = &(glu.lusup.data()[glu.xlusup(fsupc)]); // Start of the current supernode
-  Scalar* lu_col_ptr = &(glu.lusup.data()[glu.xlusup(jcol)]); // Start of jcol in the supernode
-  StorageIndex* lsub_ptr = &(glu.lsub.data()[lptr]); // Start of row indices of the supernode
-  
-  // Determine the largest abs numerical value for partial pivoting 
-  Index diagind = iperm_c(jcol); // diagonal index 
-  RealScalar pivmax(-1.0);
-  Index pivptr = nsupc; 
-  Index diag = emptyIdxLU; 
-  RealScalar rtemp;
-  Index isub, icol, itemp, k; 
-  for (isub = nsupc; isub < nsupr; ++isub) {
-    using std::abs;
-    rtemp = abs(lu_col_ptr[isub]);
-    if (rtemp > pivmax) {
-      pivmax = rtemp; 
-      pivptr = isub;
-    } 
-    if (lsub_ptr[isub] == diagind) diag = isub;
-  }
-  
-  // Test for singularity
-  if ( pivmax <= RealScalar(0.0) ) {
-    // if pivmax == -1, the column is structurally empty, otherwise it is only numerically zero
-    pivrow = pivmax < RealScalar(0.0) ? diagind : lsub_ptr[pivptr];
-    perm_r(pivrow) = StorageIndex(jcol);
-    return (jcol+1);
-  }
-  
-  RealScalar thresh = diagpivotthresh * pivmax; 
-  
-  // Choose appropriate pivotal element 
-  
-  {
-    // Test if the diagonal element can be used as a pivot (given the threshold value)
-    if (diag >= 0 ) 
-    {
-      // Diagonal element exists
-      using std::abs;
-      rtemp = abs(lu_col_ptr[diag]);
-      if (rtemp != RealScalar(0.0) && rtemp >= thresh) pivptr = diag;
-    }
-    pivrow = lsub_ptr[pivptr];
-  }
-  
-  // Record pivot row
-  perm_r(pivrow) = StorageIndex(jcol);
-  // Interchange row subscripts
-  if (pivptr != nsupc )
-  {
-    std::swap( lsub_ptr[pivptr], lsub_ptr[nsupc] );
-    // Interchange numerical values as well, for the two rows in the whole snode
-    // such that L is indexed the same way as A
-    for (icol = 0; icol <= nsupc; icol++)
-    {
-      itemp = pivptr + icol * lda; 
-      std::swap(lu_sup_ptr[itemp], lu_sup_ptr[nsupc + icol * lda]);
-    }
-  }
-  // cdiv operations
-  Scalar temp = Scalar(1.0) / lu_col_ptr[nsupc];
-  for (k = nsupc+1; k < nsupr; k++)
-    lu_col_ptr[k] *= temp; 
-  return 0;
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_PIVOTL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_pruneL.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_pruneL.h
deleted file mode 100644
index ad32fed..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_pruneL.h
+++ /dev/null
@@ -1,136 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]pruneL.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PRUNEL_H
-#define SPARSELU_PRUNEL_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Prunes the L-structure.
- *
- * It prunes the L-structure  of supernodes whose L-structure contains the current pivot row "pivrow"
- * 
- * 
- * \param jcol The current column of L
- * \param[in] perm_r Row permutation
- * \param[out] pivrow  The pivot row
- * \param nseg Number of segments
- * \param segrep 
- * \param repfnz
- * \param[out] xprune 
- * \param glu Global LU data
- * 
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg,
-                                               const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu)
-{
-  // For each supernode-rep irep in U(*,j]
-  Index jsupno = glu.supno(jcol); 
-  Index i,irep,irep1; 
-  bool movnum, do_prune = false; 
-  Index kmin = 0, kmax = 0, minloc, maxloc,krow; 
-  for (i = 0; i < nseg; i++)
-  {
-    irep = segrep(i); 
-    irep1 = irep + 1; 
-    do_prune = false; 
-    
-    // Don't prune with a zero U-segment 
-    if (repfnz(irep) == emptyIdxLU) continue; 
-    
-    // If a snode overlaps with the next panel, then the U-segment
-    // is fragmented into two parts -- irep and irep1. We should let 
-    // pruning occur at the rep-column in irep1s snode. 
-    if (glu.supno(irep) == glu.supno(irep1) ) continue; // don't prune 
-    
-    // If it has not been pruned & it has a nonz in row L(pivrow,i)
-    if (glu.supno(irep) != jsupno )
-    {
-      if ( xprune (irep) >= glu.xlsub(irep1) )
-      {
-        kmin = glu.xlsub(irep);
-        kmax = glu.xlsub(irep1) - 1; 
-        for (krow = kmin; krow <= kmax; krow++)
-        {
-          if (glu.lsub(krow) == pivrow) 
-          {
-            do_prune = true; 
-            break; 
-          }
-        }
-      }
-      
-      if (do_prune) 
-      {
-        // do a quicksort-type partition
-        // movnum=true means that the num values have to be exchanged
-        movnum = false; 
-        if (irep == glu.xsup(glu.supno(irep)) ) // Snode of size 1 
-          movnum = true; 
-        
-        while (kmin <= kmax)
-        {
-          if (perm_r(glu.lsub(kmax)) == emptyIdxLU)
-            kmax--; 
-          else if ( perm_r(glu.lsub(kmin)) != emptyIdxLU)
-            kmin++;
-          else 
-          {
-            // kmin below pivrow (not yet pivoted), and kmax
-            // above pivrow: interchange the two suscripts
-            std::swap(glu.lsub(kmin), glu.lsub(kmax)); 
-            
-            // If the supernode has only one column, then we 
-            // only keep one set of subscripts. For any subscript
-            // intercnahge performed, similar interchange must be 
-            // done on the numerical values. 
-            if (movnum) 
-            {
-              minloc = glu.xlusup(irep) + ( kmin - glu.xlsub(irep) ); 
-              maxloc = glu.xlusup(irep) + ( kmax - glu.xlsub(irep) ); 
-              std::swap(glu.lusup(minloc), glu.lusup(maxloc)); 
-            }
-            kmin++;
-            kmax--;
-          }
-        } // end while 
-        
-        xprune(irep) = StorageIndex(kmin);  //Pruning 
-      } // end if do_prune 
-    } // end pruning 
-  } // End for each U-segment
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_PRUNEL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_relax_snode.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
deleted file mode 100644
index c408d01..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
+++ /dev/null
@@ -1,83 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* This file is a modified version of heap_relax_snode.c file in SuperLU
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-
-#ifndef SPARSELU_RELAX_SNODE_H
-#define SPARSELU_RELAX_SNODE_H
-
-namespace Eigen {
-
-namespace internal {
- 
-/** 
- * \brief Identify the initial relaxed supernodes
- * 
- * This routine is applied to a column elimination tree. 
- * It assumes that the matrix has been reordered according to the postorder of the etree
- * \param n  the number of columns
- * \param et elimination tree 
- * \param relax_columns Maximum number of columns allowed in a relaxed snode 
- * \param descendants Number of descendants of each node in the etree
- * \param relax_end last column in a supernode
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end)
-{
-  
-  // compute the number of descendants of each node in the etree
-  Index parent; 
-  relax_end.setConstant(emptyIdxLU);
-  descendants.setZero();
-  for (Index j = 0; j < n; j++) 
-  {
-    parent = et(j);
-    if (parent != n) // not the dummy root
-      descendants(parent) += descendants(j) + 1;
-  }
-  // Identify the relaxed supernodes by postorder traversal of the etree
-  Index snode_start; // beginning of a snode 
-  for (Index j = 0; j < n; )
-  {
-    parent = et(j);
-    snode_start = j; 
-    while ( parent != n && descendants(parent) < relax_columns ) 
-    {
-      j = parent; 
-      parent = et(j);
-    }
-    // Found a supernode in postordered etree, j is the last column 
-    relax_end(snode_start) = StorageIndex(j); // Record last column
-    j++;
-    // Search for a new leaf
-    while (descendants(j) != 0 && j < n) j++;
-  } // End postorder traversal of the etree
-  
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseQR/SparseQR.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SparseQR/SparseQR.h
deleted file mode 100644
index d1fb96f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SparseQR/SparseQR.h
+++ /dev/null
@@ -1,758 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012-2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_QR_H
-#define EIGEN_SPARSE_QR_H
-
-namespace Eigen {
-
-template<typename MatrixType, typename OrderingType> class SparseQR;
-template<typename SparseQRType> struct SparseQRMatrixQReturnType;
-template<typename SparseQRType> struct SparseQRMatrixQTransposeReturnType;
-template<typename SparseQRType, typename Derived> struct SparseQR_QProduct;
-namespace internal {
-  template <typename SparseQRType> struct traits<SparseQRMatrixQReturnType<SparseQRType> >
-  {
-    typedef typename SparseQRType::MatrixType ReturnType;
-    typedef typename ReturnType::StorageIndex StorageIndex;
-    typedef typename ReturnType::StorageKind StorageKind;
-    enum {
-      RowsAtCompileTime = Dynamic,
-      ColsAtCompileTime = Dynamic
-    };
-  };
-  template <typename SparseQRType> struct traits<SparseQRMatrixQTransposeReturnType<SparseQRType> >
-  {
-    typedef typename SparseQRType::MatrixType ReturnType;
-  };
-  template <typename SparseQRType, typename Derived> struct traits<SparseQR_QProduct<SparseQRType, Derived> >
-  {
-    typedef typename Derived::PlainObject ReturnType;
-  };
-} // End namespace internal
-
-/**
-  * \ingroup SparseQR_Module
-  * \class SparseQR
-  * \brief Sparse left-looking QR factorization with numerical column pivoting
-  * 
-  * This class implements a left-looking QR decomposition of sparse matrices
-  * with numerical column pivoting.
-  * When a column has a norm less than a given tolerance
-  * it is implicitly permuted to the end. The QR factorization thus obtained is 
-  * given by A*P = Q*R where R is upper triangular or trapezoidal. 
-  * 
-  * P is the column permutation which is the product of the fill-reducing and the
-  * numerical permutations. Use colsPermutation() to get it.
-  * 
-  * Q is the orthogonal matrix represented as products of Householder reflectors. 
-  * Use matrixQ() to get an expression and matrixQ().adjoint() to get the adjoint.
-  * You can then apply it to a vector.
-  * 
-  * R is the sparse triangular or trapezoidal matrix. The later occurs when A is rank-deficient.
-  * matrixR().topLeftCorner(rank(), rank()) always returns a triangular factor of full rank.
-  * 
-  * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<>
-  * \tparam _OrderingType The fill-reducing ordering method. See the \link OrderingMethods_Module 
-  *  OrderingMethods \endlink module for the list of built-in and external ordering methods.
-  * 
-  * \implsparsesolverconcept
-  *
-  * The numerical pivoting strategy and default threshold are the same as in SuiteSparse QR, and
-  * detailed in the following paper:
-  * <i>
-  * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
-  * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011.
-  * </i>
-  * Even though it is qualified as "rank-revealing", this strategy might fail for some 
-  * rank deficient problems. When this class is used to solve linear or least-square problems
-  * it is thus strongly recommended to check the accuracy of the computed solution. If it
-  * failed, it usually helps to increase the threshold with setPivotThreshold.
-  * 
-  * \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()).
-  * \warning For complex matrices matrixQ().transpose() will actually return the adjoint matrix.
-  * 
-  */
-template<typename _MatrixType, typename _OrderingType>
-class SparseQR : public SparseSolverBase<SparseQR<_MatrixType,_OrderingType> >
-{
-  protected:
-    typedef SparseSolverBase<SparseQR<_MatrixType,_OrderingType> > Base;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    typedef _MatrixType MatrixType;
-    typedef _OrderingType OrderingType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> QRMatrixType;
-    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
-    typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-  public:
-    SparseQR () :  m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
-    { }
-    
-    /** Construct a QR factorization of the matrix \a mat.
-      * 
-      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-      * 
-      * \sa compute()
-      */
-    explicit SparseQR(const MatrixType& mat) : m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
-    {
-      compute(mat);
-    }
-    
-    /** Computes the QR factorization of the sparse matrix \a mat.
-      * 
-      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-      * 
-      * \sa analyzePattern(), factorize()
-      */
-    void compute(const MatrixType& mat)
-    {
-      analyzePattern(mat);
-      factorize(mat);
-    }
-    void analyzePattern(const MatrixType& mat);
-    void factorize(const MatrixType& mat);
-    
-    /** \returns the number of rows of the represented matrix. 
-      */
-    inline Index rows() const { return m_pmat.rows(); }
-    
-    /** \returns the number of columns of the represented matrix. 
-      */
-    inline Index cols() const { return m_pmat.cols();}
-    
-    /** \returns a const reference to the \b sparse upper triangular matrix R of the QR factorization.
-      * \warning The entries of the returned matrix are not sorted. This means that using it in algorithms
-      *          expecting sorted entries will fail. This include random coefficient accesses (SpaseMatrix::coeff()),
-      *          and coefficient-wise operations. Matrix products and triangular solves are fine though.
-      *
-      * To sort the entries, you can assign it to a row-major matrix, and if a column-major matrix
-      * is required, you can copy it again:
-      * \code
-      * SparseMatrix<double>          R  = qr.matrixR();  // column-major, not sorted!
-      * SparseMatrix<double,RowMajor> Rr = qr.matrixR();  // row-major, sorted
-      * SparseMatrix<double>          Rc = Rr;            // column-major, sorted
-      * \endcode
-      */
-    const QRMatrixType& matrixR() const { return m_R; }
-    
-    /** \returns the number of non linearly dependent columns as determined by the pivoting threshold.
-      *
-      * \sa setPivotThreshold()
-      */
-    Index rank() const
-    {
-      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-      return m_nonzeropivots; 
-    }
-    
-    /** \returns an expression of the matrix Q as products of sparse Householder reflectors.
-    * The common usage of this function is to apply it to a dense matrix or vector
-    * \code
-    * VectorXd B1, B2;
-    * // Initialize B1
-    * B2 = matrixQ() * B1;
-    * \endcode
-    *
-    * To get a plain SparseMatrix representation of Q:
-    * \code
-    * SparseMatrix<double> Q;
-    * Q = SparseQR<SparseMatrix<double> >(A).matrixQ();
-    * \endcode
-    * Internally, this call simply performs a sparse product between the matrix Q
-    * and a sparse identity matrix. However, due to the fact that the sparse
-    * reflectors are stored unsorted, two transpositions are needed to sort
-    * them before performing the product.
-    */
-    SparseQRMatrixQReturnType<SparseQR> matrixQ() const 
-    { return SparseQRMatrixQReturnType<SparseQR>(*this); }
-    
-    /** \returns a const reference to the column permutation P that was applied to A such that A*P = Q*R
-      * It is the combination of the fill-in reducing permutation and numerical column pivoting.
-      */
-    const PermutationType& colsPermutation() const
-    { 
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_outputPerm_c;
-    }
-    
-    /** \returns A string describing the type of error.
-      * This method is provided to ease debugging, not to handle errors.
-      */
-    std::string lastErrorMessage() const { return m_lastError; }
-    
-    /** \internal */
-    template<typename Rhs, typename Dest>
-    bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-      eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
-
-      Index rank = this->rank();
-      
-      // Compute Q^* * b;
-      typename Dest::PlainObject y, b;
-      y = this->matrixQ().adjoint() * B;
-      b = y;
-      
-      // Solve with the triangular matrix R
-      y.resize((std::max<Index>)(cols(),y.rows()),y.cols());
-      y.topRows(rank) = this->matrixR().topLeftCorner(rank, rank).template triangularView<Upper>().solve(b.topRows(rank));
-      y.bottomRows(y.rows()-rank).setZero();
-      
-      // Apply the column permutation
-      if (m_perm_c.size())  dest = colsPermutation() * y.topRows(cols());
-      else                  dest = y.topRows(cols());
-      
-      m_info = Success;
-      return true;
-    }
-
-    /** Sets the threshold that is used to determine linearly dependent columns during the factorization.
-      *
-      * In practice, if during the factorization the norm of the column that has to be eliminated is below
-      * this threshold, then the entire column is treated as zero, and it is moved at the end.
-      */
-    void setPivotThreshold(const RealScalar& threshold)
-    {
-      m_useDefaultThreshold = false;
-      m_threshold = threshold;
-    }
-    
-    /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<SparseQR, Rhs> solve(const MatrixBase<Rhs>& B) const 
-    {
-      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-      eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
-      return Solve<SparseQR, Rhs>(*this, B.derived());
-    }
-    template<typename Rhs>
-    inline const Solve<SparseQR, Rhs> solve(const SparseMatrixBase<Rhs>& B) const
-    {
-          eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-          eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
-          return Solve<SparseQR, Rhs>(*this, B.derived());
-    }
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the QR factorization reports a numerical problem
-      *          \c InvalidInput if the input matrix is invalid
-      *
-      * \sa iparm()          
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-
-    /** \internal */
-    inline void _sort_matrix_Q()
-    {
-      if(this->m_isQSorted) return;
-      // The matrix Q is sorted during the transposition
-      SparseMatrix<Scalar, RowMajor, Index> mQrm(this->m_Q);
-      this->m_Q = mQrm;
-      this->m_isQSorted = true;
-    }
-
-    
-  protected:
-    bool m_analysisIsok;
-    bool m_factorizationIsok;
-    mutable ComputationInfo m_info;
-    std::string m_lastError;
-    QRMatrixType m_pmat;            // Temporary matrix
-    QRMatrixType m_R;               // The triangular factor matrix
-    QRMatrixType m_Q;               // The orthogonal reflectors
-    ScalarVector m_hcoeffs;         // The Householder coefficients
-    PermutationType m_perm_c;       // Fill-reducing  Column  permutation
-    PermutationType m_pivotperm;    // The permutation for rank revealing
-    PermutationType m_outputPerm_c; // The final column permutation
-    RealScalar m_threshold;         // Threshold to determine null Householder reflections
-    bool m_useDefaultThreshold;     // Use default threshold
-    Index m_nonzeropivots;          // Number of non zero pivots found
-    IndexVector m_etree;            // Column elimination tree
-    IndexVector m_firstRowElt;      // First element in each row
-    bool m_isQSorted;               // whether Q is sorted or not
-    bool m_isEtreeOk;               // whether the elimination tree match the initial input matrix
-    
-    template <typename, typename > friend struct SparseQR_QProduct;
-    
-};
-
-/** \brief Preprocessing step of a QR factorization 
-  * 
-  * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-  * 
-  * In this step, the fill-reducing permutation is computed and applied to the columns of A
-  * and the column elimination tree is computed as well. Only the sparsity pattern of \a mat is exploited.
-  * 
-  * \note In this step it is assumed that there is no empty row in the matrix \a mat.
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
-{
-  eigen_assert(mat.isCompressed() && "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR");
-  // Copy to a column major matrix if the input is rowmajor
-  typename internal::conditional<MatrixType::IsRowMajor,QRMatrixType,const MatrixType&>::type matCpy(mat);
-  // Compute the column fill reducing ordering
-  OrderingType ord; 
-  ord(matCpy, m_perm_c); 
-  Index n = mat.cols();
-  Index m = mat.rows();
-  Index diagSize = (std::min)(m,n);
-  
-  if (!m_perm_c.size())
-  {
-    m_perm_c.resize(n);
-    m_perm_c.indices().setLinSpaced(n, 0,StorageIndex(n-1));
-  }
-  
-  // Compute the column elimination tree of the permuted matrix
-  m_outputPerm_c = m_perm_c.inverse();
-  internal::coletree(matCpy, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
-  m_isEtreeOk = true;
-  
-  m_R.resize(m, n);
-  m_Q.resize(m, diagSize);
-  
-  // Allocate space for nonzero elements: rough estimation
-  m_R.reserve(2*mat.nonZeros()); //FIXME Get a more accurate estimation through symbolic factorization with the etree
-  m_Q.reserve(2*mat.nonZeros());
-  m_hcoeffs.resize(diagSize);
-  m_analysisIsok = true;
-}
-
-/** \brief Performs the numerical QR factorization of the input matrix
-  * 
-  * The function SparseQR::analyzePattern(const MatrixType&) must have been called beforehand with
-  * a matrix having the same sparsity pattern than \a mat.
-  * 
-  * \param mat The sparse column-major matrix
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
-{
-  using std::abs;
-  
-  eigen_assert(m_analysisIsok && "analyzePattern() should be called before this step");
-  StorageIndex m = StorageIndex(mat.rows());
-  StorageIndex n = StorageIndex(mat.cols());
-  StorageIndex diagSize = (std::min)(m,n);
-  IndexVector mark((std::max)(m,n)); mark.setConstant(-1);  // Record the visited nodes
-  IndexVector Ridx(n), Qidx(m);                             // Store temporarily the row indexes for the current column of R and Q
-  Index nzcolR, nzcolQ;                                     // Number of nonzero for the current column of R and Q
-  ScalarVector tval(m);                                     // The dense vector used to compute the current column
-  RealScalar pivotThreshold = m_threshold;
-  
-  m_R.setZero();
-  m_Q.setZero();
-  m_pmat = mat;
-  if(!m_isEtreeOk)
-  {
-    m_outputPerm_c = m_perm_c.inverse();
-    internal::coletree(m_pmat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
-    m_isEtreeOk = true;
-  }
-
-  m_pmat.uncompress(); // To have the innerNonZeroPtr allocated
-  
-  // Apply the fill-in reducing permutation lazily:
-  {
-    // If the input is row major, copy the original column indices,
-    // otherwise directly use the input matrix
-    // 
-    IndexVector originalOuterIndicesCpy;
-    const StorageIndex *originalOuterIndices = mat.outerIndexPtr();
-    if(MatrixType::IsRowMajor)
-    {
-      originalOuterIndicesCpy = IndexVector::Map(m_pmat.outerIndexPtr(),n+1);
-      originalOuterIndices = originalOuterIndicesCpy.data();
-    }
-    
-    for (int i = 0; i < n; i++)
-    {
-      Index p = m_perm_c.size() ? m_perm_c.indices()(i) : i;
-      m_pmat.outerIndexPtr()[p] = originalOuterIndices[i]; 
-      m_pmat.innerNonZeroPtr()[p] = originalOuterIndices[i+1] - originalOuterIndices[i]; 
-    }
-  }
-  
-  /* Compute the default threshold as in MatLab, see:
-   * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
-   * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3 
-   */
-  if(m_useDefaultThreshold) 
-  {
-    RealScalar max2Norm = 0.0;
-    for (int j = 0; j < n; j++) max2Norm = numext::maxi(max2Norm, m_pmat.col(j).norm());
-    if(max2Norm==RealScalar(0))
-      max2Norm = RealScalar(1);
-    pivotThreshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon();
-  }
-  
-  // Initialize the numerical permutation
-  m_pivotperm.setIdentity(n);
-  
-  StorageIndex nonzeroCol = 0; // Record the number of valid pivots
-  m_Q.startVec(0);
-
-  // Left looking rank-revealing QR factorization: compute a column of R and Q at a time
-  for (StorageIndex col = 0; col < n; ++col)
-  {
-    mark.setConstant(-1);
-    m_R.startVec(col);
-    mark(nonzeroCol) = col;
-    Qidx(0) = nonzeroCol;
-    nzcolR = 0; nzcolQ = 1;
-    bool found_diag = nonzeroCol>=m;
-    tval.setZero(); 
-    
-    // Symbolic factorization: find the nonzero locations of the column k of the factors R and Q, i.e.,
-    // all the nodes (with indexes lower than rank) reachable through the column elimination tree (etree) rooted at node k.
-    // Note: if the diagonal entry does not exist, then its contribution must be explicitly added,
-    // thus the trick with found_diag that permits to do one more iteration on the diagonal element if this one has not been found.
-    for (typename QRMatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp)
-    {
-      StorageIndex curIdx = nonzeroCol;
-      if(itp) curIdx = StorageIndex(itp.row());
-      if(curIdx == nonzeroCol) found_diag = true;
-      
-      // Get the nonzeros indexes of the current column of R
-      StorageIndex st = m_firstRowElt(curIdx); // The traversal of the etree starts here
-      if (st < 0 )
-      {
-        m_lastError = "Empty row found during numerical factorization";
-        m_info = InvalidInput;
-        return;
-      }
-
-      // Traverse the etree 
-      Index bi = nzcolR;
-      for (; mark(st) != col; st = m_etree(st))
-      {
-        Ridx(nzcolR) = st;  // Add this row to the list,
-        mark(st) = col;     // and mark this row as visited
-        nzcolR++;
-      }
-
-      // Reverse the list to get the topological ordering
-      Index nt = nzcolR-bi;
-      for(Index i = 0; i < nt/2; i++) std::swap(Ridx(bi+i), Ridx(nzcolR-i-1));
-       
-      // Copy the current (curIdx,pcol) value of the input matrix
-      if(itp) tval(curIdx) = itp.value();
-      else    tval(curIdx) = Scalar(0);
-      
-      // Compute the pattern of Q(:,k)
-      if(curIdx > nonzeroCol && mark(curIdx) != col ) 
-      {
-        Qidx(nzcolQ) = curIdx;  // Add this row to the pattern of Q,
-        mark(curIdx) = col;     // and mark it as visited
-        nzcolQ++;
-      }
-    }
-
-    // Browse all the indexes of R(:,col) in reverse order
-    for (Index i = nzcolR-1; i >= 0; i--)
-    {
-      Index curIdx = Ridx(i);
-      
-      // Apply the curIdx-th householder vector to the current column (temporarily stored into tval)
-      Scalar tdot(0);
-      
-      // First compute q' * tval
-      tdot = m_Q.col(curIdx).dot(tval);
-
-      tdot *= m_hcoeffs(curIdx);
-      
-      // Then update tval = tval - q * tau
-      // FIXME: tval -= tdot * m_Q.col(curIdx) should amount to the same (need to check/add support for efficient "dense ?= sparse")
-      for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq)
-        tval(itq.row()) -= itq.value() * tdot;
-
-      // Detect fill-in for the current column of Q
-      if(m_etree(Ridx(i)) == nonzeroCol)
-      {
-        for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq)
-        {
-          StorageIndex iQ = StorageIndex(itq.row());
-          if (mark(iQ) != col)
-          {
-            Qidx(nzcolQ++) = iQ;  // Add this row to the pattern of Q,
-            mark(iQ) = col;       // and mark it as visited
-          }
-        }
-      }
-    } // End update current column
-    
-    Scalar tau = RealScalar(0);
-    RealScalar beta = 0;
-    
-    if(nonzeroCol < diagSize)
-    {
-      // Compute the Householder reflection that eliminate the current column
-      // FIXME this step should call the Householder module.
-      Scalar c0 = nzcolQ ? tval(Qidx(0)) : Scalar(0);
-      
-      // First, the squared norm of Q((col+1):m, col)
-      RealScalar sqrNorm = 0.;
-      for (Index itq = 1; itq < nzcolQ; ++itq) sqrNorm += numext::abs2(tval(Qidx(itq)));
-      if(sqrNorm == RealScalar(0) && numext::imag(c0) == RealScalar(0))
-      {
-        beta = numext::real(c0);
-        tval(Qidx(0)) = 1;
-      }
-      else
-      {
-        using std::sqrt;
-        beta = sqrt(numext::abs2(c0) + sqrNorm);
-        if(numext::real(c0) >= RealScalar(0))
-          beta = -beta;
-        tval(Qidx(0)) = 1;
-        for (Index itq = 1; itq < nzcolQ; ++itq)
-          tval(Qidx(itq)) /= (c0 - beta);
-        tau = numext::conj((beta-c0) / beta);
-          
-      }
-    }
-
-    // Insert values in R
-    for (Index  i = nzcolR-1; i >= 0; i--)
-    {
-      Index curIdx = Ridx(i);
-      if(curIdx < nonzeroCol) 
-      {
-        m_R.insertBackByOuterInnerUnordered(col, curIdx) = tval(curIdx);
-        tval(curIdx) = Scalar(0.);
-      }
-    }
-
-    if(nonzeroCol < diagSize && abs(beta) >= pivotThreshold)
-    {
-      m_R.insertBackByOuterInner(col, nonzeroCol) = beta;
-      // The householder coefficient
-      m_hcoeffs(nonzeroCol) = tau;
-      // Record the householder reflections
-      for (Index itq = 0; itq < nzcolQ; ++itq)
-      {
-        Index iQ = Qidx(itq);
-        m_Q.insertBackByOuterInnerUnordered(nonzeroCol,iQ) = tval(iQ);
-        tval(iQ) = Scalar(0.);
-      }
-      nonzeroCol++;
-      if(nonzeroCol<diagSize)
-        m_Q.startVec(nonzeroCol);
-    }
-    else
-    {
-      // Zero pivot found: move implicitly this column to the end
-      for (Index j = nonzeroCol; j < n-1; j++) 
-        std::swap(m_pivotperm.indices()(j), m_pivotperm.indices()[j+1]);
-      
-      // Recompute the column elimination tree
-      internal::coletree(m_pmat, m_etree, m_firstRowElt, m_pivotperm.indices().data());
-      m_isEtreeOk = false;
-    }
-  }
-  
-  m_hcoeffs.tail(diagSize-nonzeroCol).setZero();
-  
-  // Finalize the column pointers of the sparse matrices R and Q
-  m_Q.finalize();
-  m_Q.makeCompressed();
-  m_R.finalize();
-  m_R.makeCompressed();
-  m_isQSorted = false;
-
-  m_nonzeropivots = nonzeroCol;
-  
-  if(nonzeroCol<n)
-  {
-    // Permute the triangular factor to put the 'dead' columns to the end
-    QRMatrixType tempR(m_R);
-    m_R = tempR * m_pivotperm;
-    
-    // Update the column permutation
-    m_outputPerm_c = m_outputPerm_c * m_pivotperm;
-  }
-  
-  m_isInitialized = true; 
-  m_factorizationIsok = true;
-  m_info = Success;
-}
-
-template <typename SparseQRType, typename Derived>
-struct SparseQR_QProduct : ReturnByValue<SparseQR_QProduct<SparseQRType, Derived> >
-{
-  typedef typename SparseQRType::QRMatrixType MatrixType;
-  typedef typename SparseQRType::Scalar Scalar;
-  // Get the references 
-  SparseQR_QProduct(const SparseQRType& qr, const Derived& other, bool transpose) : 
-  m_qr(qr),m_other(other),m_transpose(transpose) {}
-  inline Index rows() const { return m_qr.matrixQ().rows(); }
-  inline Index cols() const { return m_other.cols(); }
-  
-  // Assign to a vector
-  template<typename DesType>
-  void evalTo(DesType& res) const
-  {
-    Index m = m_qr.rows();
-    Index n = m_qr.cols();
-    Index diagSize = (std::min)(m,n);
-    res = m_other;
-    if (m_transpose)
-    {
-      eigen_assert(m_qr.m_Q.rows() == m_other.rows() && "Non conforming object sizes");
-      //Compute res = Q' * other column by column
-      for(Index j = 0; j < res.cols(); j++){
-        for (Index k = 0; k < diagSize; k++)
-        {
-          Scalar tau = Scalar(0);
-          tau = m_qr.m_Q.col(k).dot(res.col(j));
-          if(tau==Scalar(0)) continue;
-          tau = tau * m_qr.m_hcoeffs(k);
-          res.col(j) -= tau * m_qr.m_Q.col(k);
-        }
-      }
-    }
-    else
-    {
-      eigen_assert(m_qr.matrixQ().cols() == m_other.rows() && "Non conforming object sizes");
-
-      res.conservativeResize(rows(), cols());
-
-      // Compute res = Q * other column by column
-      for(Index j = 0; j < res.cols(); j++)
-      {
-        Index start_k = internal::is_identity<Derived>::value ? numext::mini(j,diagSize-1) : diagSize-1;
-        for (Index k = start_k; k >=0; k--)
-        {
-          Scalar tau = Scalar(0);
-          tau = m_qr.m_Q.col(k).dot(res.col(j));
-          if(tau==Scalar(0)) continue;
-          tau = tau * numext::conj(m_qr.m_hcoeffs(k));
-          res.col(j) -= tau * m_qr.m_Q.col(k);
-        }
-      }
-    }
-  }
-  
-  const SparseQRType& m_qr;
-  const Derived& m_other;
-  bool m_transpose; // TODO this actually means adjoint
-};
-
-template<typename SparseQRType>
-struct SparseQRMatrixQReturnType : public EigenBase<SparseQRMatrixQReturnType<SparseQRType> >
-{  
-  typedef typename SparseQRType::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = Dynamic
-  };
-  explicit SparseQRMatrixQReturnType(const SparseQRType& qr) : m_qr(qr) {}
-  template<typename Derived>
-  SparseQR_QProduct<SparseQRType, Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(),false);
-  }
-  // To use for operations with the adjoint of Q
-  SparseQRMatrixQTransposeReturnType<SparseQRType> adjoint() const
-  {
-    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
-  }
-  inline Index rows() const { return m_qr.rows(); }
-  inline Index cols() const { return m_qr.rows(); }
-  // To use for operations with the transpose of Q FIXME this is the same as adjoint at the moment
-  SparseQRMatrixQTransposeReturnType<SparseQRType> transpose() const
-  {
-    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
-  }
-  const SparseQRType& m_qr;
-};
-
-// TODO this actually represents the adjoint of Q
-template<typename SparseQRType>
-struct SparseQRMatrixQTransposeReturnType
-{
-  explicit SparseQRMatrixQTransposeReturnType(const SparseQRType& qr) : m_qr(qr) {}
-  template<typename Derived>
-  SparseQR_QProduct<SparseQRType,Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(), true);
-  }
-  const SparseQRType& m_qr;
-};
-
-namespace internal {
-  
-template<typename SparseQRType>
-struct evaluator_traits<SparseQRMatrixQReturnType<SparseQRType> >
-{
-  typedef typename SparseQRType::MatrixType MatrixType;
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef SparseShape Shape;
-};
-
-template< typename DstXprType, typename SparseQRType>
-struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Sparse>
-{
-  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
-  typedef typename DstXprType::Scalar Scalar;
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
-  {
-    typename DstXprType::PlainObject idMat(src.rows(), src.cols());
-    idMat.setIdentity();
-    // Sort the sparse householder reflectors if needed
-    const_cast<SparseQRType *>(&src.m_qr)->_sort_matrix_Q();
-    dst = SparseQR_QProduct<SparseQRType, DstXprType>(src.m_qr, idMat, false);
-  }
-};
-
-template< typename DstXprType, typename SparseQRType>
-struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Dense>
-{
-  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
-  typedef typename DstXprType::Scalar Scalar;
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
-  {
-    dst = src.m_qr.matrixQ() * DstXprType::Identity(src.m_qr.rows(), src.m_qr.rows());
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdDeque.h b/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdDeque.h
deleted file mode 100644
index 6d47e75..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdDeque.h
+++ /dev/null
@@ -1,116 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDDEQUE_H
-#define EIGEN_STDDEQUE_H
-
-#include "details.h"
-
-/**
- * This section contains a convenience MACRO which allows an easy specialization of
- * std::deque such that for data types with alignment issues the correct allocator
- * is used automatically.
- */
-#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) \
-namespace std \
-{ \
-  template<> \
-  class deque<__VA_ARGS__, std::allocator<__VA_ARGS__> >           \
-    : public deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
-  { \
-    typedef deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > deque_base; \
-  public: \
-    typedef __VA_ARGS__ value_type; \
-    typedef deque_base::allocator_type allocator_type; \
-    typedef deque_base::size_type size_type;  \
-    typedef deque_base::iterator iterator;  \
-    explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {}  \
-    template<typename InputIterator> \
-    deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : deque_base(first, last, a) {} \
-    deque(const deque& c) : deque_base(c) {}  \
-    explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \
-    deque(iterator start_, iterator end_) : deque_base(start_, end_) {}  \
-    deque& operator=(const deque& x) {  \
-      deque_base::operator=(x);  \
-      return *this;  \
-    } \
-  }; \
-}
-
-// check whether we really need the std::deque specialization
-#if !EIGEN_HAS_CXX11_CONTAINERS && !(defined(_GLIBCXX_DEQUE) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::deque::resize(size_type,const T&). */
-
-namespace std {
-
-#define EIGEN_STD_DEQUE_SPECIALIZATION_BODY \
-  public:  \
-    typedef T value_type; \
-    typedef typename deque_base::allocator_type allocator_type; \
-    typedef typename deque_base::size_type size_type;  \
-    typedef typename deque_base::iterator iterator;  \
-    typedef typename deque_base::const_iterator const_iterator;  \
-    explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {}  \
-    template<typename InputIterator> \
-    deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \
-    : deque_base(first, last, a) {} \
-    deque(const deque& c) : deque_base(c) {}  \
-    explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \
-    deque(iterator start_, iterator end_) : deque_base(start_, end_) {}  \
-    deque& operator=(const deque& x) {  \
-      deque_base::operator=(x);  \
-      return *this;  \
-    }
-
-  template<typename T>
-  class deque<T,EIGEN_ALIGNED_ALLOCATOR<T> >
-    : public deque<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                   Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> >
-{
-  typedef deque<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > deque_base;
-  EIGEN_STD_DEQUE_SPECIALIZATION_BODY
-
-  void resize(size_type new_size)
-  { resize(new_size, T()); }
-
-#if defined(_DEQUE_)
-  // workaround MSVC std::deque implementation
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (deque_base::size() < new_size)
-      deque_base::_Insert_n(deque_base::end(), new_size - deque_base::size(), x);
-    else if (new_size < deque_base::size())
-      deque_base::erase(deque_base::begin() + new_size, deque_base::end());
-  }
-  void push_back(const value_type& x)
-  { deque_base::push_back(x); } 
-  void push_front(const value_type& x)
-  { deque_base::push_front(x); }
-  using deque_base::insert;  
-  iterator insert(const_iterator position, const value_type& x)
-  { return deque_base::insert(position,x); }
-  void insert(const_iterator position, size_type new_size, const value_type& x)
-  { deque_base::insert(position, new_size, x); }
-#else
-  // default implementation which should always work.
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (new_size < deque_base::size())
-      deque_base::erase(deque_base::begin() + new_size, deque_base::end());
-    else if (new_size > deque_base::size())
-      deque_base::insert(deque_base::end(), new_size - deque_base::size(), x);
-  }
-#endif
-  };
-}
-
-#endif // check whether specialization is actually required
-
-#endif // EIGEN_STDDEQUE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdList.h b/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdList.h
deleted file mode 100644
index 8ba3fad..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdList.h
+++ /dev/null
@@ -1,106 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDLIST_H
-#define EIGEN_STDLIST_H
-
-#include "details.h"
-
-/**
- * This section contains a convenience MACRO which allows an easy specialization of
- * std::list such that for data types with alignment issues the correct allocator
- * is used automatically.
- */
-#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) \
-namespace std \
-{ \
-  template<> \
-  class list<__VA_ARGS__, std::allocator<__VA_ARGS__> >           \
-    : public list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
-  { \
-    typedef list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > list_base; \
-  public: \
-    typedef __VA_ARGS__ value_type; \
-    typedef list_base::allocator_type allocator_type; \
-    typedef list_base::size_type size_type;  \
-    typedef list_base::iterator iterator;  \
-    explicit list(const allocator_type& a = allocator_type()) : list_base(a) {}  \
-    template<typename InputIterator> \
-    list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : list_base(first, last, a) {} \
-    list(const list& c) : list_base(c) {}  \
-    explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \
-    list(iterator start_, iterator end_) : list_base(start_, end_) {}  \
-    list& operator=(const list& x) {  \
-      list_base::operator=(x);  \
-      return *this;  \
-    } \
-  }; \
-}
-
-// check whether we really need the std::list specialization
-#if !EIGEN_HAS_CXX11_CONTAINERS && !(defined(_GLIBCXX_LIST) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::list::resize(size_type,const T&). */
-
-namespace std
-{
-
-#define EIGEN_STD_LIST_SPECIALIZATION_BODY \
-  public:  \
-    typedef T value_type; \
-    typedef typename list_base::allocator_type allocator_type; \
-    typedef typename list_base::size_type size_type;  \
-    typedef typename list_base::iterator iterator;  \
-    typedef typename list_base::const_iterator const_iterator;  \
-    explicit list(const allocator_type& a = allocator_type()) : list_base(a) {}  \
-    template<typename InputIterator> \
-    list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \
-    : list_base(first, last, a) {} \
-    list(const list& c) : list_base(c) {}  \
-    explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \
-    list(iterator start_, iterator end_) : list_base(start_, end_) {}  \
-    list& operator=(const list& x) {  \
-    list_base::operator=(x);  \
-    return *this; \
-  }
-
-  template<typename T>
-  class list<T,EIGEN_ALIGNED_ALLOCATOR<T> >
-    : public list<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                  Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> >
-  {
-    typedef list<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                 Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > list_base;
-    EIGEN_STD_LIST_SPECIALIZATION_BODY
-
-    void resize(size_type new_size)
-    { resize(new_size, T()); }
-
-    void resize(size_type new_size, const value_type& x)
-    {
-      if (list_base::size() < new_size)
-        list_base::insert(list_base::end(), new_size - list_base::size(), x);
-      else
-        while (new_size < list_base::size()) list_base::pop_back();
-    }
-
-#if defined(_LIST_)
-    // workaround MSVC std::list implementation
-    void push_back(const value_type& x)
-    { list_base::push_back(x); } 
-    using list_base::insert;  
-    iterator insert(const_iterator position, const value_type& x)
-    { return list_base::insert(position,x); }
-    void insert(const_iterator position, size_type new_size, const value_type& x)
-    { list_base::insert(position, new_size, x); }
-#endif
-  };
-}
-
-#endif // check whether specialization is actually required
-
-#endif // EIGEN_STDLIST_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdVector.h b/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdVector.h
deleted file mode 100644
index 9fcf19b..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/StdVector.h
+++ /dev/null
@@ -1,131 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDVECTOR_H
-#define EIGEN_STDVECTOR_H
-
-#include "details.h"
-
-/**
- * This section contains a convenience MACRO which allows an easy specialization of
- * std::vector such that for data types with alignment issues the correct allocator
- * is used automatically.
- */
-#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) \
-namespace std \
-{ \
-  template<> \
-  class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> >  \
-    : public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
-  { \
-    typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base; \
-  public: \
-    typedef __VA_ARGS__ value_type; \
-    typedef vector_base::allocator_type allocator_type; \
-    typedef vector_base::size_type size_type;  \
-    typedef vector_base::iterator iterator;  \
-    explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {}  \
-    template<typename InputIterator> \
-    vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : vector_base(first, last, a) {} \
-    vector(const vector& c) : vector_base(c) {}  \
-    explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
-    vector(iterator start_, iterator end_) : vector_base(start_, end_) {}  \
-    vector& operator=(const vector& x) {  \
-      vector_base::operator=(x);  \
-      return *this;  \
-    } \
-  }; \
-}
-
-// Don't specialize if containers are implemented according to C++11
-#if !EIGEN_HAS_CXX11_CONTAINERS
-
-namespace std {
-
-#define EIGEN_STD_VECTOR_SPECIALIZATION_BODY \
-  public:  \
-    typedef T value_type; \
-    typedef typename vector_base::allocator_type allocator_type; \
-    typedef typename vector_base::size_type size_type;  \
-    typedef typename vector_base::iterator iterator;  \
-    typedef typename vector_base::const_iterator const_iterator;  \
-    explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {}  \
-    template<typename InputIterator> \
-    vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \
-    : vector_base(first, last, a) {} \
-    vector(const vector& c) : vector_base(c) {}  \
-    explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
-    vector(iterator start_, iterator end_) : vector_base(start_, end_) {}  \
-    vector& operator=(const vector& x) {  \
-      vector_base::operator=(x);  \
-      return *this;  \
-    }
-
-  template<typename T>
-  class vector<T,EIGEN_ALIGNED_ALLOCATOR<T> >
-    : public vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                    Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> >
-{
-  typedef vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                 Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > vector_base;
-  EIGEN_STD_VECTOR_SPECIALIZATION_BODY
-
-  void resize(size_type new_size)
-  { resize(new_size, T()); }
-
-#if defined(_VECTOR_)
-  // workaround MSVC std::vector implementation
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (vector_base::size() < new_size)
-      vector_base::_Insert_n(vector_base::end(), new_size - vector_base::size(), x);
-    else if (new_size < vector_base::size())
-      vector_base::erase(vector_base::begin() + new_size, vector_base::end());
-  }
-  void push_back(const value_type& x)
-  { vector_base::push_back(x); } 
-  using vector_base::insert;  
-  iterator insert(const_iterator position, const value_type& x)
-  { return vector_base::insert(position,x); }
-  void insert(const_iterator position, size_type new_size, const value_type& x)
-  { vector_base::insert(position, new_size, x); }
-#elif defined(_GLIBCXX_VECTOR) && (!(EIGEN_GNUC_AT_LEAST(4,1)))
-  /* Note that before gcc-4.1 we already have: std::vector::resize(size_type,const T&).
-   * However, this specialization is still needed to make the above EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION trick to work. */
-  void resize(size_type new_size, const value_type& x)
-  {
-    vector_base::resize(new_size,x);
-  }
-#elif defined(_GLIBCXX_VECTOR) && EIGEN_GNUC_AT_LEAST(4,2)
-  // workaround GCC std::vector implementation
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (new_size < vector_base::size())
-      vector_base::_M_erase_at_end(this->_M_impl._M_start + new_size);
-    else
-      vector_base::insert(vector_base::end(), new_size - vector_base::size(), x);
-  }
-#else
-  // either GCC 4.1 or non-GCC
-  // default implementation which should always work.
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (new_size < vector_base::size())
-      vector_base::erase(vector_base::begin() + new_size, vector_base::end());
-    else if (new_size > vector_base::size())
-      vector_base::insert(vector_base::end(), new_size - vector_base::size(), x);
-  }
-#endif
-  };
-}
-#endif // !EIGEN_HAS_CXX11_CONTAINERS
-
-
-#endif // EIGEN_STDVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/details.h b/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/details.h
deleted file mode 100644
index 2cfd13e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/StlSupport/details.h
+++ /dev/null
@@ -1,84 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STL_DETAILS_H
-#define EIGEN_STL_DETAILS_H
-
-#ifndef EIGEN_ALIGNED_ALLOCATOR
-  #define EIGEN_ALIGNED_ALLOCATOR Eigen::aligned_allocator
-#endif
-
-namespace Eigen {
-
-  // This one is needed to prevent reimplementing the whole std::vector.
-  template <class T>
-  class aligned_allocator_indirection : public EIGEN_ALIGNED_ALLOCATOR<T>
-  {
-  public:
-    typedef std::size_t     size_type;
-    typedef std::ptrdiff_t  difference_type;
-    typedef T*              pointer;
-    typedef const T*        const_pointer;
-    typedef T&              reference;
-    typedef const T&        const_reference;
-    typedef T               value_type;
-
-    template<class U>
-    struct rebind
-    {
-      typedef aligned_allocator_indirection<U> other;
-    };
-
-    aligned_allocator_indirection() {}
-    aligned_allocator_indirection(const aligned_allocator_indirection& ) : EIGEN_ALIGNED_ALLOCATOR<T>() {}
-    aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<T>& ) {}
-    template<class U>
-    aligned_allocator_indirection(const aligned_allocator_indirection<U>& ) {}
-    template<class U>
-    aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<U>& ) {}
-    ~aligned_allocator_indirection() {}
-  };
-
-#if EIGEN_COMP_MSVC
-
-  // sometimes, MSVC detects, at compile time, that the argument x
-  // in std::vector::resize(size_t s,T x) won't be aligned and generate an error
-  // even if this function is never called. Whence this little wrapper.
-#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) \
-  typename Eigen::internal::conditional< \
-    Eigen::internal::is_arithmetic<T>::value, \
-    T, \
-    Eigen::internal::workaround_msvc_stl_support<T> \
-  >::type
-
-  namespace internal {
-  template<typename T> struct workaround_msvc_stl_support : public T
-  {
-    inline workaround_msvc_stl_support() : T() {}
-    inline workaround_msvc_stl_support(const T& other) : T(other) {}
-    inline operator T& () { return *static_cast<T*>(this); }
-    inline operator const T& () const { return *static_cast<const T*>(this); }
-    template<typename OtherT>
-    inline T& operator=(const OtherT& other)
-    { T::operator=(other); return *this; }
-    inline workaround_msvc_stl_support& operator=(const workaround_msvc_stl_support& other)
-    { T::operator=(other); return *this; }
-  };
-  }
-
-#else
-
-#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) T
-
-#endif
-
-}
-
-#endif // EIGEN_STL_DETAILS_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/SuperLUSupport/SuperLUSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
deleted file mode 100644
index d1d3ad7..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
+++ /dev/null
@@ -1,1025 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SUPERLUSUPPORT_H
-#define EIGEN_SUPERLUSUPPORT_H
-
-namespace Eigen {
-
-#if defined(SUPERLU_MAJOR_VERSION) && (SUPERLU_MAJOR_VERSION >= 5)
-#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE)		\
-    extern "C" {                                                                                          \
-      extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *,                  \
-                                char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,           \
-                                void *, int, SuperMatrix *, SuperMatrix *,                                \
-                                FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *,                       \
-                                GlobalLU_t *, mem_usage_t *, SuperLUStat_t *, int *);                     \
-    }                                                                                                     \
-    inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A,                                \
-         int *perm_c, int *perm_r, int *etree, char *equed,                                               \
-         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                                      \
-         SuperMatrix *U, void *work, int lwork,                                                           \
-         SuperMatrix *B, SuperMatrix *X,                                                                  \
-         FLOATTYPE *recip_pivot_growth,                                                                   \
-         FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr,                                              \
-         SuperLUStat_t *stats, int *info, KEYTYPE) {                                                      \
-    mem_usage_t mem_usage;                                                                                \
-    GlobalLU_t gLU;                                                                                       \
-    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L,                                      \
-         U, work, lwork, B, X, recip_pivot_growth, rcond,                                                 \
-         ferr, berr, &gLU, &mem_usage, stats, info);                                                      \
-    return mem_usage.for_lu; /* bytes used by the factor storage */                                       \
-  }
-#else // version < 5.0
-#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE)		\
-    extern "C" {                                                                                          \
-      extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *,                  \
-                                char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,           \
-                                void *, int, SuperMatrix *, SuperMatrix *,                                \
-                                FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *,                       \
-                                mem_usage_t *, SuperLUStat_t *, int *);                                   \
-    }                                                                                                     \
-    inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A,                                \
-         int *perm_c, int *perm_r, int *etree, char *equed,                                               \
-         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                                      \
-         SuperMatrix *U, void *work, int lwork,                                                           \
-         SuperMatrix *B, SuperMatrix *X,                                                                  \
-         FLOATTYPE *recip_pivot_growth,                                                                   \
-         FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr,                                              \
-         SuperLUStat_t *stats, int *info, KEYTYPE) {                                                      \
-    mem_usage_t mem_usage;                                                                                \
-    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L,                                      \
-         U, work, lwork, B, X, recip_pivot_growth, rcond,                                                 \
-         ferr, berr, &mem_usage, stats, info);                                                            \
-    return mem_usage.for_lu; /* bytes used by the factor storage */                                       \
-  }
-#endif
-
-DECL_GSSVX(s,float,float)
-DECL_GSSVX(c,float,std::complex<float>)
-DECL_GSSVX(d,double,double)
-DECL_GSSVX(z,double,std::complex<double>)
-
-#ifdef MILU_ALPHA
-#define EIGEN_SUPERLU_HAS_ILU
-#endif
-
-#ifdef EIGEN_SUPERLU_HAS_ILU
-
-// similarly for the incomplete factorization using gsisx
-#define DECL_GSISX(PREFIX,FLOATTYPE,KEYTYPE)                                                    \
-    extern "C" {                                                                                \
-      extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *,        \
-                         char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,        \
-                         void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *, FLOATTYPE *,   \
-                         mem_usage_t *, SuperLUStat_t *, int *);                        \
-    }                                                                                           \
-    inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A,                      \
-         int *perm_c, int *perm_r, int *etree, char *equed,                                     \
-         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                            \
-         SuperMatrix *U, void *work, int lwork,                                                 \
-         SuperMatrix *B, SuperMatrix *X,                                                        \
-         FLOATTYPE *recip_pivot_growth,                                                         \
-         FLOATTYPE *rcond,                                                                      \
-         SuperLUStat_t *stats, int *info, KEYTYPE) {                                            \
-    mem_usage_t mem_usage;                                                              \
-    PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L,                            \
-         U, work, lwork, B, X, recip_pivot_growth, rcond,                                       \
-         &mem_usage, stats, info);                                                              \
-    return mem_usage.for_lu; /* bytes used by the factor storage */                             \
-  }
-
-DECL_GSISX(s,float,float)
-DECL_GSISX(c,float,std::complex<float>)
-DECL_GSISX(d,double,double)
-DECL_GSISX(z,double,std::complex<double>)
-
-#endif
-
-template<typename MatrixType>
-struct SluMatrixMapHelper;
-
-/** \internal
-  *
-  * A wrapper class for SuperLU matrices. It supports only compressed sparse matrices
-  * and dense matrices. Supernodal and other fancy format are not supported by this wrapper.
-  *
-  * This wrapper class mainly aims to avoids the need of dynamic allocation of the storage structure.
-  */
-struct SluMatrix : SuperMatrix
-{
-  SluMatrix()
-  {
-    Store = &storage;
-  }
-
-  SluMatrix(const SluMatrix& other)
-    : SuperMatrix(other)
-  {
-    Store = &storage;
-    storage = other.storage;
-  }
-
-  SluMatrix& operator=(const SluMatrix& other)
-  {
-    SuperMatrix::operator=(static_cast<const SuperMatrix&>(other));
-    Store = &storage;
-    storage = other.storage;
-    return *this;
-  }
-
-  struct
-  {
-    union {int nnz;int lda;};
-    void *values;
-    int *innerInd;
-    int *outerInd;
-  } storage;
-
-  void setStorageType(Stype_t t)
-  {
-    Stype = t;
-    if (t==SLU_NC || t==SLU_NR || t==SLU_DN)
-      Store = &storage;
-    else
-    {
-      eigen_assert(false && "storage type not supported");
-      Store = 0;
-    }
-  }
-
-  template<typename Scalar>
-  void setScalarType()
-  {
-    if (internal::is_same<Scalar,float>::value)
-      Dtype = SLU_S;
-    else if (internal::is_same<Scalar,double>::value)
-      Dtype = SLU_D;
-    else if (internal::is_same<Scalar,std::complex<float> >::value)
-      Dtype = SLU_C;
-    else if (internal::is_same<Scalar,std::complex<double> >::value)
-      Dtype = SLU_Z;
-    else
-    {
-      eigen_assert(false && "Scalar type not supported by SuperLU");
-    }
-  }
-
-  template<typename MatrixType>
-  static SluMatrix Map(MatrixBase<MatrixType>& _mat)
-  {
-    MatrixType& mat(_mat.derived());
-    eigen_assert( ((MatrixType::Flags&RowMajorBit)!=RowMajorBit) && "row-major dense matrices are not supported by SuperLU");
-    SluMatrix res;
-    res.setStorageType(SLU_DN);
-    res.setScalarType<typename MatrixType::Scalar>();
-    res.Mtype     = SLU_GE;
-
-    res.nrow      = internal::convert_index<int>(mat.rows());
-    res.ncol      = internal::convert_index<int>(mat.cols());
-
-    res.storage.lda       = internal::convert_index<int>(MatrixType::IsVectorAtCompileTime ? mat.size() : mat.outerStride());
-    res.storage.values    = (void*)(mat.data());
-    return res;
-  }
-
-  template<typename MatrixType>
-  static SluMatrix Map(SparseMatrixBase<MatrixType>& a_mat)
-  {
-    MatrixType &mat(a_mat.derived());
-    SluMatrix res;
-    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
-    {
-      res.setStorageType(SLU_NR);
-      res.nrow      = internal::convert_index<int>(mat.cols());
-      res.ncol      = internal::convert_index<int>(mat.rows());
-    }
-    else
-    {
-      res.setStorageType(SLU_NC);
-      res.nrow      = internal::convert_index<int>(mat.rows());
-      res.ncol      = internal::convert_index<int>(mat.cols());
-    }
-
-    res.Mtype       = SLU_GE;
-
-    res.storage.nnz       = internal::convert_index<int>(mat.nonZeros());
-    res.storage.values    = mat.valuePtr();
-    res.storage.innerInd  = mat.innerIndexPtr();
-    res.storage.outerInd  = mat.outerIndexPtr();
-
-    res.setScalarType<typename MatrixType::Scalar>();
-
-    // FIXME the following is not very accurate
-    if (int(MatrixType::Flags) & int(Upper))
-      res.Mtype = SLU_TRU;
-    if (int(MatrixType::Flags) & int(Lower))
-      res.Mtype = SLU_TRL;
-
-    eigen_assert(((int(MatrixType::Flags) & int(SelfAdjoint))==0) && "SelfAdjoint matrix shape not supported by SuperLU");
-
-    return res;
-  }
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MRows, int MCols>
-struct SluMatrixMapHelper<Matrix<Scalar,Rows,Cols,Options,MRows,MCols> >
-{
-  typedef Matrix<Scalar,Rows,Cols,Options,MRows,MCols> MatrixType;
-  static void run(MatrixType& mat, SluMatrix& res)
-  {
-    eigen_assert( ((Options&RowMajor)!=RowMajor) && "row-major dense matrices is not supported by SuperLU");
-    res.setStorageType(SLU_DN);
-    res.setScalarType<Scalar>();
-    res.Mtype     = SLU_GE;
-
-    res.nrow      = mat.rows();
-    res.ncol      = mat.cols();
-
-    res.storage.lda       = mat.outerStride();
-    res.storage.values    = mat.data();
-  }
-};
-
-template<typename Derived>
-struct SluMatrixMapHelper<SparseMatrixBase<Derived> >
-{
-  typedef Derived MatrixType;
-  static void run(MatrixType& mat, SluMatrix& res)
-  {
-    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
-    {
-      res.setStorageType(SLU_NR);
-      res.nrow      = mat.cols();
-      res.ncol      = mat.rows();
-    }
-    else
-    {
-      res.setStorageType(SLU_NC);
-      res.nrow      = mat.rows();
-      res.ncol      = mat.cols();
-    }
-
-    res.Mtype       = SLU_GE;
-
-    res.storage.nnz       = mat.nonZeros();
-    res.storage.values    = mat.valuePtr();
-    res.storage.innerInd  = mat.innerIndexPtr();
-    res.storage.outerInd  = mat.outerIndexPtr();
-
-    res.setScalarType<typename MatrixType::Scalar>();
-
-    // FIXME the following is not very accurate
-    if (MatrixType::Flags & Upper)
-      res.Mtype = SLU_TRU;
-    if (MatrixType::Flags & Lower)
-      res.Mtype = SLU_TRL;
-
-    eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU");
-  }
-};
-
-namespace internal {
-
-template<typename MatrixType>
-SluMatrix asSluMatrix(MatrixType& mat)
-{
-  return SluMatrix::Map(mat);
-}
-
-/** View a Super LU matrix as an Eigen expression */
-template<typename Scalar, int Flags, typename Index>
-MappedSparseMatrix<Scalar,Flags,Index> map_superlu(SluMatrix& sluMat)
-{
-  eigen_assert(((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR)
-         || ((Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC));
-
-  Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow;
-
-  return MappedSparseMatrix<Scalar,Flags,Index>(
-    sluMat.nrow, sluMat.ncol, sluMat.storage.outerInd[outerSize],
-    sluMat.storage.outerInd, sluMat.storage.innerInd, reinterpret_cast<Scalar*>(sluMat.storage.values) );
-}
-
-} // end namespace internal
-
-/** \ingroup SuperLUSupport_Module
-  * \class SuperLUBase
-  * \brief The base class for the direct and incomplete LU factorization of SuperLU
-  */
-template<typename _MatrixType, typename Derived>
-class SuperLUBase : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-  public:
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;    
-    typedef Map<PermutationMatrix<Dynamic,Dynamic,int> > PermutationMap;
-    typedef SparseMatrix<Scalar> LUMatrixType;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    SuperLUBase() {}
-
-    ~SuperLUBase()
-    {
-      clearFactors();
-    }
-    
-    inline Index rows() const { return m_matrix.rows(); }
-    inline Index cols() const { return m_matrix.cols(); }
-    
-    /** \returns a reference to the Super LU option object to configure the  Super LU algorithms. */
-    inline superlu_options_t& options() { return m_sluOptions; }
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    void compute(const MatrixType& matrix)
-    {
-      derived().analyzePattern(matrix);
-      derived().factorize(matrix);
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& /*matrix*/)
-    {
-      m_isInitialized = true;
-      m_info = Success;
-      m_analysisIsOk = true;
-      m_factorizationIsOk = false;
-    }
-    
-    template<typename Stream>
-    void dumpMemory(Stream& /*s*/)
-    {}
-    
-  protected:
-    
-    void initFactorization(const MatrixType& a)
-    {
-      set_default_options(&this->m_sluOptions);
-      
-      const Index size = a.rows();
-      m_matrix = a;
-
-      m_sluA = internal::asSluMatrix(m_matrix);
-      clearFactors();
-
-      m_p.resize(size);
-      m_q.resize(size);
-      m_sluRscale.resize(size);
-      m_sluCscale.resize(size);
-      m_sluEtree.resize(size);
-
-      // set empty B and X
-      m_sluB.setStorageType(SLU_DN);
-      m_sluB.setScalarType<Scalar>();
-      m_sluB.Mtype          = SLU_GE;
-      m_sluB.storage.values = 0;
-      m_sluB.nrow           = 0;
-      m_sluB.ncol           = 0;
-      m_sluB.storage.lda    = internal::convert_index<int>(size);
-      m_sluX                = m_sluB;
-      
-      m_extractedDataAreDirty = true;
-    }
-    
-    void init()
-    {
-      m_info = InvalidInput;
-      m_isInitialized = false;
-      m_sluL.Store = 0;
-      m_sluU.Store = 0;
-    }
-    
-    void extractData() const;
-
-    void clearFactors()
-    {
-      if(m_sluL.Store)
-        Destroy_SuperNode_Matrix(&m_sluL);
-      if(m_sluU.Store)
-        Destroy_CompCol_Matrix(&m_sluU);
-
-      m_sluL.Store = 0;
-      m_sluU.Store = 0;
-
-      memset(&m_sluL,0,sizeof m_sluL);
-      memset(&m_sluU,0,sizeof m_sluU);
-    }
-
-    // cached data to reduce reallocation, etc.
-    mutable LUMatrixType m_l;
-    mutable LUMatrixType m_u;
-    mutable IntColVectorType m_p;
-    mutable IntRowVectorType m_q;
-
-    mutable LUMatrixType m_matrix;  // copy of the factorized matrix
-    mutable SluMatrix m_sluA;
-    mutable SuperMatrix m_sluL, m_sluU;
-    mutable SluMatrix m_sluB, m_sluX;
-    mutable SuperLUStat_t m_sluStat;
-    mutable superlu_options_t m_sluOptions;
-    mutable std::vector<int> m_sluEtree;
-    mutable Matrix<RealScalar,Dynamic,1> m_sluRscale, m_sluCscale;
-    mutable Matrix<RealScalar,Dynamic,1> m_sluFerr, m_sluBerr;
-    mutable char m_sluEqued;
-
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-    mutable bool m_extractedDataAreDirty;
-    
-  private:
-    SuperLUBase(SuperLUBase& ) { }
-};
-
-
-/** \ingroup SuperLUSupport_Module
-  * \class SuperLU
-  * \brief A sparse direct LU factorization and solver based on the SuperLU library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
-  * using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  */
-template<typename _MatrixType>
-class SuperLU : public SuperLUBase<_MatrixType,SuperLU<_MatrixType> >
-{
-  public:
-    typedef SuperLUBase<_MatrixType,SuperLU> Base;
-    typedef _MatrixType MatrixType;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    typedef typename Base::IntRowVectorType IntRowVectorType;
-    typedef typename Base::IntColVectorType IntColVectorType;   
-    typedef typename Base::PermutationMap PermutationMap;
-    typedef typename Base::LUMatrixType LUMatrixType;
-    typedef TriangularView<LUMatrixType, Lower|UnitDiag>  LMatrixType;
-    typedef TriangularView<LUMatrixType,  Upper>          UMatrixType;
-
-  public:
-    using Base::_solve_impl;
-
-    SuperLU() : Base() { init(); }
-
-    explicit SuperLU(const MatrixType& matrix) : Base()
-    {
-      init();
-      Base::compute(matrix);
-    }
-
-    ~SuperLU()
-    {
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      m_info = InvalidInput;
-      m_isInitialized = false;
-      Base::analyzePattern(matrix);
-    }
-    
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& matrix);
-    
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
-    
-    inline const LMatrixType& matrixL() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_l;
-    }
-
-    inline const UMatrixType& matrixU() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_u;
-    }
-
-    inline const IntColVectorType& permutationP() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_p;
-    }
-
-    inline const IntRowVectorType& permutationQ() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_q;
-    }
-    
-    Scalar determinant() const;
-    
-  protected:
-    
-    using Base::m_matrix;
-    using Base::m_sluOptions;
-    using Base::m_sluA;
-    using Base::m_sluB;
-    using Base::m_sluX;
-    using Base::m_p;
-    using Base::m_q;
-    using Base::m_sluEtree;
-    using Base::m_sluEqued;
-    using Base::m_sluRscale;
-    using Base::m_sluCscale;
-    using Base::m_sluL;
-    using Base::m_sluU;
-    using Base::m_sluStat;
-    using Base::m_sluFerr;
-    using Base::m_sluBerr;
-    using Base::m_l;
-    using Base::m_u;
-    
-    using Base::m_analysisIsOk;
-    using Base::m_factorizationIsOk;
-    using Base::m_extractedDataAreDirty;
-    using Base::m_isInitialized;
-    using Base::m_info;
-    
-    void init()
-    {
-      Base::init();
-      
-      set_default_options(&this->m_sluOptions);
-      m_sluOptions.PrintStat        = NO;
-      m_sluOptions.ConditionNumber  = NO;
-      m_sluOptions.Trans            = NOTRANS;
-      m_sluOptions.ColPerm          = COLAMD;
-    }
-    
-    
-  private:
-    SuperLU(SuperLU& ) { }
-};
-
-template<typename MatrixType>
-void SuperLU<MatrixType>::factorize(const MatrixType& a)
-{
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  if(!m_analysisIsOk)
-  {
-    m_info = InvalidInput;
-    return;
-  }
-  
-  this->initFactorization(a);
-  
-  m_sluOptions.ColPerm = COLAMD;
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-  RealScalar ferr, berr;
-
-  StatInit(&m_sluStat);
-  SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
-                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &ferr, &berr,
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-
-  m_extractedDataAreDirty = true;
-
-  // FIXME how to better check for errors ???
-  m_info = info == 0 ? Success : NumericalIssue;
-  m_factorizationIsOk = true;
-}
-
-template<typename MatrixType>
-template<typename Rhs,typename Dest>
-void SuperLU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
-
-  const Index rhsCols = b.cols();
-  eigen_assert(m_matrix.rows()==b.rows());
-
-  m_sluOptions.Trans = NOTRANS;
-  m_sluOptions.Fact = FACTORED;
-  m_sluOptions.IterRefine = NOREFINE;
-  
-
-  m_sluFerr.resize(rhsCols);
-  m_sluBerr.resize(rhsCols);
-  
-  Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b);
-  Ref<const Matrix<typename Dest::Scalar,Dynamic,Dynamic,ColMajor> > x_ref(x);
-  
-  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
-  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
-  
-  typename Rhs::PlainObject b_cpy;
-  if(m_sluEqued!='N')
-  {
-    b_cpy = b;
-    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
-  }
-
-  StatInit(&m_sluStat);
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-  SuperLU_gssvx(&m_sluOptions, &m_sluA,
-                m_q.data(), m_p.data(),
-                &m_sluEtree[0], &m_sluEqued,
-                &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &m_sluFerr[0], &m_sluBerr[0],
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-  
-  if(x.derived().data() != x_ref.data())
-    x = x_ref;
-  
-  m_info = info==0 ? Success : NumericalIssue;
-}
-
-// the code of this extractData() function has been adapted from the SuperLU's Matlab support code,
-//
-//  Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
-//
-//  THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
-//  EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
-//
-template<typename MatrixType, typename Derived>
-void SuperLUBase<MatrixType,Derived>::extractData() const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for extracting factors, you must first call either compute() or analyzePattern()/factorize()");
-  if (m_extractedDataAreDirty)
-  {
-    int         upper;
-    int         fsupc, istart, nsupr;
-    int         lastl = 0, lastu = 0;
-    SCformat    *Lstore = static_cast<SCformat*>(m_sluL.Store);
-    NCformat    *Ustore = static_cast<NCformat*>(m_sluU.Store);
-    Scalar      *SNptr;
-
-    const Index size = m_matrix.rows();
-    m_l.resize(size,size);
-    m_l.resizeNonZeros(Lstore->nnz);
-    m_u.resize(size,size);
-    m_u.resizeNonZeros(Ustore->nnz);
-
-    int* Lcol = m_l.outerIndexPtr();
-    int* Lrow = m_l.innerIndexPtr();
-    Scalar* Lval = m_l.valuePtr();
-
-    int* Ucol = m_u.outerIndexPtr();
-    int* Urow = m_u.innerIndexPtr();
-    Scalar* Uval = m_u.valuePtr();
-
-    Ucol[0] = 0;
-    Ucol[0] = 0;
-
-    /* for each supernode */
-    for (int k = 0; k <= Lstore->nsuper; ++k)
-    {
-      fsupc   = L_FST_SUPC(k);
-      istart  = L_SUB_START(fsupc);
-      nsupr   = L_SUB_START(fsupc+1) - istart;
-      upper   = 1;
-
-      /* for each column in the supernode */
-      for (int j = fsupc; j < L_FST_SUPC(k+1); ++j)
-      {
-        SNptr = &((Scalar*)Lstore->nzval)[L_NZ_START(j)];
-
-        /* Extract U */
-        for (int i = U_NZ_START(j); i < U_NZ_START(j+1); ++i)
-        {
-          Uval[lastu] = ((Scalar*)Ustore->nzval)[i];
-          /* Matlab doesn't like explicit zero. */
-          if (Uval[lastu] != 0.0)
-            Urow[lastu++] = U_SUB(i);
-        }
-        for (int i = 0; i < upper; ++i)
-        {
-          /* upper triangle in the supernode */
-          Uval[lastu] = SNptr[i];
-          /* Matlab doesn't like explicit zero. */
-          if (Uval[lastu] != 0.0)
-            Urow[lastu++] = L_SUB(istart+i);
-        }
-        Ucol[j+1] = lastu;
-
-        /* Extract L */
-        Lval[lastl] = 1.0; /* unit diagonal */
-        Lrow[lastl++] = L_SUB(istart + upper - 1);
-        for (int i = upper; i < nsupr; ++i)
-        {
-          Lval[lastl] = SNptr[i];
-          /* Matlab doesn't like explicit zero. */
-          if (Lval[lastl] != 0.0)
-            Lrow[lastl++] = L_SUB(istart+i);
-        }
-        Lcol[j+1] = lastl;
-
-        ++upper;
-      } /* for j ... */
-
-    } /* for k ... */
-
-    // squeeze the matrices :
-    m_l.resizeNonZeros(lastl);
-    m_u.resizeNonZeros(lastu);
-
-    m_extractedDataAreDirty = false;
-  }
-}
-
-template<typename MatrixType>
-typename SuperLU<MatrixType>::Scalar SuperLU<MatrixType>::determinant() const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for computing the determinant, you must first call either compute() or analyzePattern()/factorize()");
-  
-  if (m_extractedDataAreDirty)
-    this->extractData();
-
-  Scalar det = Scalar(1);
-  for (int j=0; j<m_u.cols(); ++j)
-  {
-    if (m_u.outerIndexPtr()[j+1]-m_u.outerIndexPtr()[j] > 0)
-    {
-      int lastId = m_u.outerIndexPtr()[j+1]-1;
-      eigen_assert(m_u.innerIndexPtr()[lastId]<=j);
-      if (m_u.innerIndexPtr()[lastId]==j)
-        det *= m_u.valuePtr()[lastId];
-    }
-  }
-  if(PermutationMap(m_p.data(),m_p.size()).determinant()*PermutationMap(m_q.data(),m_q.size()).determinant()<0)
-    det = -det;
-  if(m_sluEqued!='N')
-    return det/m_sluRscale.prod()/m_sluCscale.prod();
-  else
-    return det;
-}
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-#define EIGEN_SUPERLU_HAS_ILU
-#endif
-
-#ifdef EIGEN_SUPERLU_HAS_ILU
-
-/** \ingroup SuperLUSupport_Module
-  * \class SuperILU
-  * \brief A sparse direct \b incomplete LU factorization and solver based on the SuperLU library
-  *
-  * This class allows to solve for an approximate solution of A.X = B sparse linear problems via an incomplete LU factorization
-  * using the SuperLU library. This class is aimed to be used as a preconditioner of the iterative linear solvers.
-  *
-  * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class IncompleteLUT, class ConjugateGradient, class BiCGSTAB
-  */
-
-template<typename _MatrixType>
-class SuperILU : public SuperLUBase<_MatrixType,SuperILU<_MatrixType> >
-{
-  public:
-    typedef SuperLUBase<_MatrixType,SuperILU> Base;
-    typedef _MatrixType MatrixType;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-
-  public:
-    using Base::_solve_impl;
-
-    SuperILU() : Base() { init(); }
-
-    SuperILU(const MatrixType& matrix) : Base()
-    {
-      init();
-      Base::compute(matrix);
-    }
-
-    ~SuperILU()
-    {
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      Base::analyzePattern(matrix);
-    }
-    
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& matrix);
-    
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-    
-  protected:
-    
-    using Base::m_matrix;
-    using Base::m_sluOptions;
-    using Base::m_sluA;
-    using Base::m_sluB;
-    using Base::m_sluX;
-    using Base::m_p;
-    using Base::m_q;
-    using Base::m_sluEtree;
-    using Base::m_sluEqued;
-    using Base::m_sluRscale;
-    using Base::m_sluCscale;
-    using Base::m_sluL;
-    using Base::m_sluU;
-    using Base::m_sluStat;
-    using Base::m_sluFerr;
-    using Base::m_sluBerr;
-    using Base::m_l;
-    using Base::m_u;
-    
-    using Base::m_analysisIsOk;
-    using Base::m_factorizationIsOk;
-    using Base::m_extractedDataAreDirty;
-    using Base::m_isInitialized;
-    using Base::m_info;
-
-    void init()
-    {
-      Base::init();
-      
-      ilu_set_default_options(&m_sluOptions);
-      m_sluOptions.PrintStat        = NO;
-      m_sluOptions.ConditionNumber  = NO;
-      m_sluOptions.Trans            = NOTRANS;
-      m_sluOptions.ColPerm          = MMD_AT_PLUS_A;
-      
-      // no attempt to preserve column sum
-      m_sluOptions.ILU_MILU = SILU;
-      // only basic ILU(k) support -- no direct control over memory consumption
-      // better to use ILU_DropRule = DROP_BASIC | DROP_AREA
-      // and set ILU_FillFactor to max memory growth
-      m_sluOptions.ILU_DropRule = DROP_BASIC;
-      m_sluOptions.ILU_DropTol = NumTraits<Scalar>::dummy_precision()*10;
-    }
-    
-  private:
-    SuperILU(SuperILU& ) { }
-};
-
-template<typename MatrixType>
-void SuperILU<MatrixType>::factorize(const MatrixType& a)
-{
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  if(!m_analysisIsOk)
-  {
-    m_info = InvalidInput;
-    return;
-  }
-  
-  this->initFactorization(a);
-
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-
-  StatInit(&m_sluStat);
-  SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
-                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-
-  // FIXME how to better check for errors ???
-  m_info = info == 0 ? Success : NumericalIssue;
-  m_factorizationIsOk = true;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatrixType>
-template<typename Rhs,typename Dest>
-void SuperILU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
-
-  const int rhsCols = b.cols();
-  eigen_assert(m_matrix.rows()==b.rows());
-
-  m_sluOptions.Trans = NOTRANS;
-  m_sluOptions.Fact = FACTORED;
-  m_sluOptions.IterRefine = NOREFINE;
-
-  m_sluFerr.resize(rhsCols);
-  m_sluBerr.resize(rhsCols);
-  
-  Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b);
-  Ref<const Matrix<typename Dest::Scalar,Dynamic,Dynamic,ColMajor> > x_ref(x);
-  
-  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
-  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
-
-  typename Rhs::PlainObject b_cpy;
-  if(m_sluEqued!='N')
-  {
-    b_cpy = b;
-    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
-  }
-  
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-
-  StatInit(&m_sluStat);
-  SuperLU_gsisx(&m_sluOptions, &m_sluA,
-                m_q.data(), m_p.data(),
-                &m_sluEtree[0], &m_sluEqued,
-                &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-  
-  if(x.derived().data() != x_ref.data())
-    x = x_ref;
-
-  m_info = info==0 ? Success : NumericalIssue;
-}
-#endif
-
-#endif
-
-} // end namespace Eigen
-
-#endif // EIGEN_SUPERLUSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/UmfPackSupport/UmfPackSupport.h b/3rdParty/my_ceres_vc17/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
deleted file mode 100644
index e3a333f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
+++ /dev/null
@@ -1,642 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMFPACKSUPPORT_H
-#define EIGEN_UMFPACKSUPPORT_H
-
-// for compatibility with super old version of umfpack,
-// not sure this is really needed, but this is harmless.
-#ifndef SuiteSparse_long
-#ifdef UF_long
-#define SuiteSparse_long UF_long
-#else
-#error neither SuiteSparse_long nor UF_long are defined
-#endif
-#endif
-
-namespace Eigen {
-
-/* TODO extract L, extract U, compute det, etc... */
-
-// generic double/complex<double> wrapper functions:
-
-
- // Defaults
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, int)
-{ umfpack_di_defaults(control); }
-
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, int)
-{ umfpack_zi_defaults(control); }
-
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, SuiteSparse_long)
-{ umfpack_dl_defaults(control); }
-
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_defaults(control); }
-
-// Report info
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, int)
-{ umfpack_di_report_info(control, info);}
-
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>, int)
-{ umfpack_zi_report_info(control, info);}
-
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, SuiteSparse_long)
-{ umfpack_dl_report_info(control, info);}
-
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_report_info(control, info);}
-
-// Report status
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, int)
-{ umfpack_di_report_status(control, status);}
-
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, int)
-{ umfpack_zi_report_status(control, status);}
-
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, SuiteSparse_long)
-{ umfpack_dl_report_status(control, status);}
-
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_report_status(control, status);}
-
-// report control
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, int)
-{ umfpack_di_report_control(control);}
-
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, int)
-{ umfpack_zi_report_control(control);}
-
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, SuiteSparse_long)
-{ umfpack_dl_report_control(control);}
-
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_report_control(control);}
-
-// Free numeric
-inline void umfpack_free_numeric(void **Numeric, double, int)
-{ umfpack_di_free_numeric(Numeric); *Numeric = 0; }
-
-inline void umfpack_free_numeric(void **Numeric, std::complex<double>, int)
-{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; }
-
-inline void umfpack_free_numeric(void **Numeric, double, SuiteSparse_long)
-{ umfpack_dl_free_numeric(Numeric); *Numeric = 0; }
-
-inline void umfpack_free_numeric(void **Numeric, std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_free_numeric(Numeric); *Numeric = 0; }
-
-// Free symbolic
-inline void umfpack_free_symbolic(void **Symbolic, double, int)
-{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; }
-
-inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, int)
-{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; }
-
-inline void umfpack_free_symbolic(void **Symbolic, double, SuiteSparse_long)
-{ umfpack_dl_free_symbolic(Symbolic); *Symbolic = 0; }
-
-inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_free_symbolic(Symbolic); *Symbolic = 0; }
-
-// Symbolic
-inline int umfpack_symbolic(int n_row,int n_col,
-                            const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
-                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
-}
-
-inline int umfpack_symbolic(int n_row,int n_col,
-                            const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
-                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
-}
-inline SuiteSparse_long umfpack_symbolic( SuiteSparse_long n_row,SuiteSparse_long n_col,
-                                          const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[], void **Symbolic,
-                                          const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_dl_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_symbolic( SuiteSparse_long n_row,SuiteSparse_long n_col,
-                                          const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[], void **Symbolic,
-                                          const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_zl_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
-}
-
-// Numeric
-inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
-                            void *Symbolic, void **Numeric,
-                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
-}
-
-inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
-                            void *Symbolic, void **Numeric,
-                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
-}
-inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[],
-                                        void *Symbolic, void **Numeric,
-                                        const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_dl_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[],
-                                        void *Symbolic, void **Numeric,
-                                        const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_zl_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
-}
-
-// solve
-inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
-                          double X[], const double B[], void *Numeric,
-                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
-}
-
-inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
-                          std::complex<double> X[], const std::complex<double> B[], void *Numeric,
-                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[],
-                                      double X[], const double B[], void *Numeric,
-                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_dl_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[],
-                                      std::complex<double> X[], const std::complex<double> B[], void *Numeric,
-                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_zl_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
-}
-
-// Get Lunz
-inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double)
-{
-  return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>)
-{
-  return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-inline SuiteSparse_long umfpack_get_lunz( SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row, SuiteSparse_long *n_col,
-                                          SuiteSparse_long *nz_udiag, void *Numeric, double)
-{
-  return umfpack_dl_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-inline SuiteSparse_long umfpack_get_lunz( SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row, SuiteSparse_long *n_col,
-                                          SuiteSparse_long *nz_udiag, void *Numeric, std::complex<double>)
-{
-  return umfpack_zl_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-// Get Numeric
-inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[],
-                               int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric)
-{
-  return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
-}
-
-inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[],
-                               int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric)
-{
-  double& lx0_real = numext::real_ref(Lx[0]);
-  double& ux0_real = numext::real_ref(Ux[0]);
-  double& dx0_real = numext::real_ref(Dx[0]);
-  return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
-                                Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
-}
-inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], double Lx[], SuiteSparse_long Up[], SuiteSparse_long Ui[], double Ux[],
-                                            SuiteSparse_long P[], SuiteSparse_long Q[], double Dx[], SuiteSparse_long *do_recip, double Rs[], void *Numeric)
-{
-  return umfpack_dl_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
-}
-
-inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], std::complex<double> Lx[], SuiteSparse_long Up[], SuiteSparse_long Ui[], std::complex<double> Ux[],
-                                            SuiteSparse_long P[], SuiteSparse_long Q[], std::complex<double> Dx[], SuiteSparse_long *do_recip, double Rs[], void *Numeric)
-{
-  double& lx0_real = numext::real_ref(Lx[0]);
-  double& ux0_real = numext::real_ref(Ux[0]);
-  double& dx0_real = numext::real_ref(Dx[0]);
-  return umfpack_zl_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
-                                Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
-}
-
-// Get Determinant
-inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], int)
-{
-  return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info);
-}
-
-inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], int)
-{
-  double& mx_real = numext::real_ref(*Mx);
-  return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
-}
-
-inline SuiteSparse_long umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], SuiteSparse_long)
-{
-  return umfpack_dl_get_determinant(Mx,Ex,NumericHandle,User_Info);
-}
-
-inline SuiteSparse_long umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], SuiteSparse_long)
-{
-  double& mx_real = numext::real_ref(*Mx);
-  return umfpack_zl_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
-}
-
-
-/** \ingroup UmfPackSupport_Module
-  * \brief A sparse LU factorization and solver based on UmfPack
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
-  * using the UmfPack library. The sparse matrix A must be squared and full rank.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * \warning The input matrix A should be in a \b compressed and \b column-major form.
-  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  */
-template<typename _MatrixType>
-class UmfPackLU : public SparseSolverBase<UmfPackLU<_MatrixType> >
-{
-  protected:
-    typedef SparseSolverBase<UmfPackLU<_MatrixType> > Base;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
-    typedef SparseMatrix<Scalar> LUMatrixType;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> UmfpackMatrixType;
-    typedef Ref<const UmfpackMatrixType, StandardCompressedFormat> UmfpackMatrixRef;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    typedef Array<double, UMFPACK_CONTROL, 1> UmfpackControl;
-    typedef Array<double, UMFPACK_INFO, 1> UmfpackInfo;
-
-    UmfPackLU()
-      : m_dummy(0,0), mp_matrix(m_dummy)
-    {
-      init();
-    }
-
-    template<typename InputMatrixType>
-    explicit UmfPackLU(const InputMatrixType& matrix)
-      : mp_matrix(matrix)
-    {
-      init();
-      compute(matrix);
-    }
-
-    ~UmfPackLU()
-    {
-      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(), StorageIndex());
-      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(), StorageIndex());
-    }
-
-    inline Index rows() const { return mp_matrix.rows(); }
-    inline Index cols() const { return mp_matrix.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    inline const LUMatrixType& matrixL() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_l;
-    }
-
-    inline const LUMatrixType& matrixU() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_u;
-    }
-
-    inline const IntColVectorType& permutationP() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_p;
-    }
-
-    inline const IntRowVectorType& permutationQ() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_q;
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix
-     *  Note that the matrix should be column-major, and in compressed format for best performance.
-     *  \sa SparseMatrix::makeCompressed().
-     */
-    template<typename InputMatrixType>
-    void compute(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(),StorageIndex());
-      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
-      grab(matrix.derived());
-      analyzePattern_impl();
-      factorize_impl();
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize(), compute()
-      */
-    template<typename InputMatrixType>
-    void analyzePattern(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(),StorageIndex());
-      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
-
-      grab(matrix.derived());
-
-      analyzePattern_impl();
-    }
-
-    /** Provides the return status code returned by UmfPack during the numeric
-      * factorization.
-      *
-      * \sa factorize(), compute()
-      */
-    inline int umfpackFactorizeReturncode() const
-    {
-      eigen_assert(m_numeric && "UmfPackLU: you must first call factorize()");
-      return m_fact_errorCode;
-    }
-
-    /** Provides access to the control settings array used by UmfPack.
-      *
-      * If this array contains NaN's, the default values are used.
-      *
-      * See UMFPACK documentation for details.
-      */
-    inline const UmfpackControl& umfpackControl() const
-    {
-      return m_control;
-    }
-
-    /** Provides access to the control settings array used by UmfPack.
-      *
-      * If this array contains NaN's, the default values are used.
-      *
-      * See UMFPACK documentation for details.
-      */
-    inline UmfpackControl& umfpackControl()
-    {
-      return m_control;
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
-      *
-      * \sa analyzePattern(), compute()
-      */
-    template<typename InputMatrixType>
-    void factorize(const InputMatrixType& matrix)
-    {
-      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
-      if(m_numeric)
-        umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
-
-      grab(matrix.derived());
-
-      factorize_impl();
-    }
-
-    /** Prints the current UmfPack control settings.
-      *
-      * \sa umfpackControl()
-      */
-    void printUmfpackControl()
-    {
-      umfpack_report_control(m_control.data(), Scalar(),StorageIndex());
-    }
-
-    /** Prints statistics collected by UmfPack.
-      *
-      * \sa analyzePattern(), compute()
-      */
-    void printUmfpackInfo()
-    {
-      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
-      umfpack_report_info(m_control.data(), m_umfpackInfo.data(), Scalar(),StorageIndex());
-    }
-
-    /** Prints the status of the previous factorization operation performed by UmfPack (symbolic or numerical factorization).
-      *
-      * \sa analyzePattern(), compute()
-      */
-    void printUmfpackStatus() {
-      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
-      umfpack_report_status(m_control.data(), m_fact_errorCode, Scalar(),StorageIndex());
-    }
-
-    /** \internal */
-    template<typename BDerived,typename XDerived>
-    bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
-
-    Scalar determinant() const;
-
-    void extractData() const;
-
-  protected:
-
-    void init()
-    {
-      m_info                  = InvalidInput;
-      m_isInitialized         = false;
-      m_numeric               = 0;
-      m_symbolic              = 0;
-      m_extractedDataAreDirty = true;
-
-      umfpack_defaults(m_control.data(), Scalar(),StorageIndex());
-    }
-
-    void analyzePattern_impl()
-    {
-      m_fact_errorCode = umfpack_symbolic(internal::convert_index<StorageIndex>(mp_matrix.rows()),
-                                          internal::convert_index<StorageIndex>(mp_matrix.cols()),
-                                          mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
-                                          &m_symbolic, m_control.data(), m_umfpackInfo.data());
-
-      m_isInitialized = true;
-      m_info = m_fact_errorCode ? InvalidInput : Success;
-      m_analysisIsOk = true;
-      m_factorizationIsOk = false;
-      m_extractedDataAreDirty = true;
-    }
-
-    void factorize_impl()
-    {
-
-      m_fact_errorCode = umfpack_numeric(mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
-                                         m_symbolic, &m_numeric, m_control.data(), m_umfpackInfo.data());
-
-      m_info = m_fact_errorCode == UMFPACK_OK ? Success : NumericalIssue;
-      m_factorizationIsOk = true;
-      m_extractedDataAreDirty = true;
-    }
-
-    template<typename MatrixDerived>
-    void grab(const EigenBase<MatrixDerived> &A)
-    {
-      mp_matrix.~UmfpackMatrixRef();
-      ::new (&mp_matrix) UmfpackMatrixRef(A.derived());
-    }
-
-    void grab(const UmfpackMatrixRef &A)
-    {
-      if(&(A.derived()) != &mp_matrix)
-      {
-        mp_matrix.~UmfpackMatrixRef();
-        ::new (&mp_matrix) UmfpackMatrixRef(A);
-      }
-    }
-
-    // cached data to reduce reallocation, etc.
-    mutable LUMatrixType m_l;
-    StorageIndex m_fact_errorCode;
-    UmfpackControl m_control;
-    mutable UmfpackInfo m_umfpackInfo;
-
-    mutable LUMatrixType m_u;
-    mutable IntColVectorType m_p;
-    mutable IntRowVectorType m_q;
-
-    UmfpackMatrixType m_dummy;
-    UmfpackMatrixRef mp_matrix;
-
-    void* m_numeric;
-    void* m_symbolic;
-
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-    mutable bool m_extractedDataAreDirty;
-
-  private:
-    UmfPackLU(const UmfPackLU& ) { }
-};
-
-
-template<typename MatrixType>
-void UmfPackLU<MatrixType>::extractData() const
-{
-  if (m_extractedDataAreDirty)
-  {
-    // get size of the data
-    StorageIndex lnz, unz, rows, cols, nz_udiag;
-    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
-
-    // allocate data
-    m_l.resize(rows,(std::min)(rows,cols));
-    m_l.resizeNonZeros(lnz);
-
-    m_u.resize((std::min)(rows,cols),cols);
-    m_u.resizeNonZeros(unz);
-
-    m_p.resize(rows);
-    m_q.resize(cols);
-
-    // extract
-    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
-                        m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
-                        m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
-
-    m_extractedDataAreDirty = false;
-  }
-}
-
-template<typename MatrixType>
-typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const
-{
-  Scalar det;
-  umfpack_get_determinant(&det, 0, m_numeric, 0, StorageIndex());
-  return det;
-}
-
-template<typename MatrixType>
-template<typename BDerived,typename XDerived>
-bool UmfPackLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
-{
-  Index rhsCols = b.cols();
-  eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet");
-  eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet");
-  eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve");
-
-  Scalar* x_ptr = 0;
-  Matrix<Scalar,Dynamic,1> x_tmp;
-  if(x.innerStride()!=1)
-  {
-    x_tmp.resize(x.rows());
-    x_ptr = x_tmp.data();
-  }
-  for (int j=0; j<rhsCols; ++j)
-  {
-    if(x.innerStride()==1)
-      x_ptr = &x.col(j).coeffRef(0);
-    StorageIndex errorCode = umfpack_solve(UMFPACK_A,
-                                mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
-                                x_ptr, &b.const_cast_derived().col(j).coeffRef(0),
-                                m_numeric, m_control.data(), m_umfpackInfo.data());
-    if(x.innerStride()!=1)
-      x.col(j) = x_tmp;
-    if (errorCode!=0)
-      return false;
-  }
-
-  return true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_UMFPACKSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/Image.h b/3rdParty/my_ceres_vc17/include/Eigen/src/misc/Image.h
deleted file mode 100644
index b8b8a04..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/Image.h
+++ /dev/null
@@ -1,82 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MISC_IMAGE_H
-#define EIGEN_MISC_IMAGE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \class image_retval_base
-  *
-  */
-template<typename DecompositionType>
-struct traits<image_retval_base<DecompositionType> >
-{
-  typedef typename DecompositionType::MatrixType MatrixType;
-  typedef Matrix<
-    typename MatrixType::Scalar,
-    MatrixType::RowsAtCompileTime, // the image is a subspace of the destination space, whose
-                                   // dimension is the number of rows of the original matrix
-    Dynamic,                       // we don't know at compile time the dimension of the image (the rank)
-    MatrixType::Options,
-    MatrixType::MaxRowsAtCompileTime, // the image matrix will consist of columns from the original matrix,
-    MatrixType::MaxColsAtCompileTime  // so it has the same number of rows and at most as many columns.
-  > ReturnType;
-};
-
-template<typename _DecompositionType> struct image_retval_base
- : public ReturnByValue<image_retval_base<_DecompositionType> >
-{
-  typedef _DecompositionType DecompositionType;
-  typedef typename DecompositionType::MatrixType MatrixType;
-  typedef ReturnByValue<image_retval_base> Base;
-
-  image_retval_base(const DecompositionType& dec, const MatrixType& originalMatrix)
-    : m_dec(dec), m_rank(dec.rank()),
-      m_cols(m_rank == 0 ? 1 : m_rank),
-      m_originalMatrix(originalMatrix)
-  {}
-
-  inline Index rows() const { return m_dec.rows(); }
-  inline Index cols() const { return m_cols; }
-  inline Index rank() const { return m_rank; }
-  inline const DecompositionType& dec() const { return m_dec; }
-  inline const MatrixType& originalMatrix() const { return m_originalMatrix; }
-
-  template<typename Dest> inline void evalTo(Dest& dst) const
-  {
-    static_cast<const image_retval<DecompositionType>*>(this)->evalTo(dst);
-  }
-
-  protected:
-    const DecompositionType& m_dec;
-    Index m_rank, m_cols;
-    const MatrixType& m_originalMatrix;
-};
-
-} // end namespace internal
-
-#define EIGEN_MAKE_IMAGE_HELPERS(DecompositionType) \
-  typedef typename DecompositionType::MatrixType MatrixType; \
-  typedef typename MatrixType::Scalar Scalar; \
-  typedef typename MatrixType::RealScalar RealScalar; \
-  typedef Eigen::internal::image_retval_base<DecompositionType> Base; \
-  using Base::dec; \
-  using Base::originalMatrix; \
-  using Base::rank; \
-  using Base::rows; \
-  using Base::cols; \
-  image_retval(const DecompositionType& dec, const MatrixType& originalMatrix) \
-    : Base(dec, originalMatrix) {}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MISC_IMAGE_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/Kernel.h b/3rdParty/my_ceres_vc17/include/Eigen/src/misc/Kernel.h
deleted file mode 100644
index bef5d6f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/Kernel.h
+++ /dev/null
@@ -1,79 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MISC_KERNEL_H
-#define EIGEN_MISC_KERNEL_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \class kernel_retval_base
-  *
-  */
-template<typename DecompositionType>
-struct traits<kernel_retval_base<DecompositionType> >
-{
-  typedef typename DecompositionType::MatrixType MatrixType;
-  typedef Matrix<
-    typename MatrixType::Scalar,
-    MatrixType::ColsAtCompileTime, // the number of rows in the "kernel matrix"
-                                   // is the number of cols of the original matrix
-                                   // so that the product "matrix * kernel = zero" makes sense
-    Dynamic,                       // we don't know at compile-time the dimension of the kernel
-    MatrixType::Options,
-    MatrixType::MaxColsAtCompileTime, // see explanation for 2nd template parameter
-    MatrixType::MaxColsAtCompileTime // the kernel is a subspace of the domain space,
-                                     // whose dimension is the number of columns of the original matrix
-  > ReturnType;
-};
-
-template<typename _DecompositionType> struct kernel_retval_base
- : public ReturnByValue<kernel_retval_base<_DecompositionType> >
-{
-  typedef _DecompositionType DecompositionType;
-  typedef ReturnByValue<kernel_retval_base> Base;
-
-  explicit kernel_retval_base(const DecompositionType& dec)
-    : m_dec(dec),
-      m_rank(dec.rank()),
-      m_cols(m_rank==dec.cols() ? 1 : dec.cols() - m_rank)
-  {}
-
-  inline Index rows() const { return m_dec.cols(); }
-  inline Index cols() const { return m_cols; }
-  inline Index rank() const { return m_rank; }
-  inline const DecompositionType& dec() const { return m_dec; }
-
-  template<typename Dest> inline void evalTo(Dest& dst) const
-  {
-    static_cast<const kernel_retval<DecompositionType>*>(this)->evalTo(dst);
-  }
-
-  protected:
-    const DecompositionType& m_dec;
-    Index m_rank, m_cols;
-};
-
-} // end namespace internal
-
-#define EIGEN_MAKE_KERNEL_HELPERS(DecompositionType) \
-  typedef typename DecompositionType::MatrixType MatrixType; \
-  typedef typename MatrixType::Scalar Scalar; \
-  typedef typename MatrixType::RealScalar RealScalar; \
-  typedef Eigen::internal::kernel_retval_base<DecompositionType> Base; \
-  using Base::dec; \
-  using Base::rank; \
-  using Base::rows; \
-  using Base::cols; \
-  kernel_retval(const DecompositionType& dec) : Base(dec) {}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MISC_KERNEL_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/RealSvd2x2.h b/3rdParty/my_ceres_vc17/include/Eigen/src/misc/RealSvd2x2.h
deleted file mode 100644
index abb4d3c..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/RealSvd2x2.h
+++ /dev/null
@@ -1,55 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2013-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REALSVD2X2_H
-#define EIGEN_REALSVD2X2_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType, typename RealScalar, typename Index>
-void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
-                         JacobiRotation<RealScalar> *j_left,
-                         JacobiRotation<RealScalar> *j_right)
-{
-  using std::sqrt;
-  using std::abs;
-  Matrix<RealScalar,2,2> m;
-  m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
-       numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
-  JacobiRotation<RealScalar> rot1;
-  RealScalar t = m.coeff(0,0) + m.coeff(1,1);
-  RealScalar d = m.coeff(1,0) - m.coeff(0,1);
-
-  if(abs(d) < (std::numeric_limits<RealScalar>::min)())
-  {
-    rot1.s() = RealScalar(0);
-    rot1.c() = RealScalar(1);
-  }
-  else
-  {
-    // If d!=0, then t/d cannot overflow because the magnitude of the
-    // entries forming d are not too small compared to the ones forming t.
-    RealScalar u = t / d;
-    RealScalar tmp = sqrt(RealScalar(1) + numext::abs2(u));
-    rot1.s() = RealScalar(1) / tmp;
-    rot1.c() = u / tmp;
-  }
-  m.applyOnTheLeft(0,1,rot1);
-  j_right->makeJacobi(m,0,1);
-  *j_left = rot1 * j_right->transpose();
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_REALSVD2X2_H
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/blas.h b/3rdParty/my_ceres_vc17/include/Eigen/src/misc/blas.h
deleted file mode 100644
index 25215b1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/blas.h
+++ /dev/null
@@ -1,440 +0,0 @@
-#ifndef BLAS_H
-#define BLAS_H
-
-#ifdef __cplusplus
-extern "C"
-{
-#endif
-
-#define BLASFUNC(FUNC) FUNC##_
-
-#ifdef __WIN64__
-typedef long long BLASLONG;
-typedef unsigned long long BLASULONG;
-#else
-typedef long BLASLONG;
-typedef unsigned long BLASULONG;
-#endif
-
-int    BLASFUNC(xerbla)(const char *, int *info, int);
-
-float  BLASFUNC(sdot)  (int *, float  *, int *, float  *, int *);
-float  BLASFUNC(sdsdot)(int *, float  *,        float  *, int *, float  *, int *);
-
-double BLASFUNC(dsdot) (int *, float  *, int *, float  *, int *);
-double BLASFUNC(ddot)  (int *, double *, int *, double *, int *);
-double BLASFUNC(qdot)  (int *, double *, int *, double *, int *);
-
-int  BLASFUNC(cdotuw)  (int *, float  *, int *, float  *, int *, float*);
-int  BLASFUNC(cdotcw)  (int *, float  *, int *, float  *, int *, float*);
-int  BLASFUNC(zdotuw)  (int *, double  *, int *, double  *, int *, double*);
-int  BLASFUNC(zdotcw)  (int *, double  *, int *, double  *, int *, double*);
-
-int    BLASFUNC(saxpy) (const int *, const float  *, const float  *, const int *, float  *, const int *);
-int    BLASFUNC(daxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(qaxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(caxpy) (const int *, const float  *, const float  *, const int *, float  *, const int *);
-int    BLASFUNC(zaxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(xaxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(caxpyc)(const int *, const float  *, const float  *, const int *, float  *, const int *);
-int    BLASFUNC(zaxpyc)(const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(xaxpyc)(const int *, const double *, const double *, const int *, double *, const int *);
-
-int    BLASFUNC(scopy) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(dcopy) (int *, double *, int *, double *, int *);
-int    BLASFUNC(qcopy) (int *, double *, int *, double *, int *);
-int    BLASFUNC(ccopy) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(zcopy) (int *, double *, int *, double *, int *);
-int    BLASFUNC(xcopy) (int *, double *, int *, double *, int *);
-
-int    BLASFUNC(sswap) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(dswap) (int *, double *, int *, double *, int *);
-int    BLASFUNC(qswap) (int *, double *, int *, double *, int *);
-int    BLASFUNC(cswap) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(zswap) (int *, double *, int *, double *, int *);
-int    BLASFUNC(xswap) (int *, double *, int *, double *, int *);
-
-float  BLASFUNC(sasum) (int *, float  *, int *);
-float  BLASFUNC(scasum)(int *, float  *, int *);
-double BLASFUNC(dasum) (int *, double *, int *);
-double BLASFUNC(qasum) (int *, double *, int *);
-double BLASFUNC(dzasum)(int *, double *, int *);
-double BLASFUNC(qxasum)(int *, double *, int *);
-
-int    BLASFUNC(isamax)(int *, float  *, int *);
-int    BLASFUNC(idamax)(int *, double *, int *);
-int    BLASFUNC(iqamax)(int *, double *, int *);
-int    BLASFUNC(icamax)(int *, float  *, int *);
-int    BLASFUNC(izamax)(int *, double *, int *);
-int    BLASFUNC(ixamax)(int *, double *, int *);
-
-int    BLASFUNC(ismax) (int *, float  *, int *);
-int    BLASFUNC(idmax) (int *, double *, int *);
-int    BLASFUNC(iqmax) (int *, double *, int *);
-int    BLASFUNC(icmax) (int *, float  *, int *);
-int    BLASFUNC(izmax) (int *, double *, int *);
-int    BLASFUNC(ixmax) (int *, double *, int *);
-
-int    BLASFUNC(isamin)(int *, float  *, int *);
-int    BLASFUNC(idamin)(int *, double *, int *);
-int    BLASFUNC(iqamin)(int *, double *, int *);
-int    BLASFUNC(icamin)(int *, float  *, int *);
-int    BLASFUNC(izamin)(int *, double *, int *);
-int    BLASFUNC(ixamin)(int *, double *, int *);
-
-int    BLASFUNC(ismin)(int *, float  *, int *);
-int    BLASFUNC(idmin)(int *, double *, int *);
-int    BLASFUNC(iqmin)(int *, double *, int *);
-int    BLASFUNC(icmin)(int *, float  *, int *);
-int    BLASFUNC(izmin)(int *, double *, int *);
-int    BLASFUNC(ixmin)(int *, double *, int *);
-
-float  BLASFUNC(samax) (int *, float  *, int *);
-double BLASFUNC(damax) (int *, double *, int *);
-double BLASFUNC(qamax) (int *, double *, int *);
-float  BLASFUNC(scamax)(int *, float  *, int *);
-double BLASFUNC(dzamax)(int *, double *, int *);
-double BLASFUNC(qxamax)(int *, double *, int *);
-
-float  BLASFUNC(samin) (int *, float  *, int *);
-double BLASFUNC(damin) (int *, double *, int *);
-double BLASFUNC(qamin) (int *, double *, int *);
-float  BLASFUNC(scamin)(int *, float  *, int *);
-double BLASFUNC(dzamin)(int *, double *, int *);
-double BLASFUNC(qxamin)(int *, double *, int *);
-
-float  BLASFUNC(smax)  (int *, float  *, int *);
-double BLASFUNC(dmax)  (int *, double *, int *);
-double BLASFUNC(qmax)  (int *, double *, int *);
-float  BLASFUNC(scmax) (int *, float  *, int *);
-double BLASFUNC(dzmax) (int *, double *, int *);
-double BLASFUNC(qxmax) (int *, double *, int *);
-
-float  BLASFUNC(smin)  (int *, float  *, int *);
-double BLASFUNC(dmin)  (int *, double *, int *);
-double BLASFUNC(qmin)  (int *, double *, int *);
-float  BLASFUNC(scmin) (int *, float  *, int *);
-double BLASFUNC(dzmin) (int *, double *, int *);
-double BLASFUNC(qxmin) (int *, double *, int *);
-
-int    BLASFUNC(sscal) (int *,  float  *, float  *, int *);
-int    BLASFUNC(dscal) (int *,  double *, double *, int *);
-int    BLASFUNC(qscal) (int *,  double *, double *, int *);
-int    BLASFUNC(cscal) (int *,  float  *, float  *, int *);
-int    BLASFUNC(zscal) (int *,  double *, double *, int *);
-int    BLASFUNC(xscal) (int *,  double *, double *, int *);
-int    BLASFUNC(csscal)(int *,  float  *, float  *, int *);
-int    BLASFUNC(zdscal)(int *,  double *, double *, int *);
-int    BLASFUNC(xqscal)(int *,  double *, double *, int *);
-
-float  BLASFUNC(snrm2) (int *, float  *, int *);
-float  BLASFUNC(scnrm2)(int *, float  *, int *);
-
-double BLASFUNC(dnrm2) (int *, double *, int *);
-double BLASFUNC(qnrm2) (int *, double *, int *);
-double BLASFUNC(dznrm2)(int *, double *, int *);
-double BLASFUNC(qxnrm2)(int *, double *, int *);
-
-int    BLASFUNC(srot)  (int *, float  *, int *, float  *, int *, float  *, float  *);
-int    BLASFUNC(drot)  (int *, double *, int *, double *, int *, double *, double *);
-int    BLASFUNC(qrot)  (int *, double *, int *, double *, int *, double *, double *);
-int    BLASFUNC(csrot) (int *, float  *, int *, float  *, int *, float  *, float  *);
-int    BLASFUNC(zdrot) (int *, double *, int *, double *, int *, double *, double *);
-int    BLASFUNC(xqrot) (int *, double *, int *, double *, int *, double *, double *);
-
-int    BLASFUNC(srotg) (float  *, float  *, float  *, float  *);
-int    BLASFUNC(drotg) (double *, double *, double *, double *);
-int    BLASFUNC(qrotg) (double *, double *, double *, double *);
-int    BLASFUNC(crotg) (float  *, float  *, float  *, float  *);
-int    BLASFUNC(zrotg) (double *, double *, double *, double *);
-int    BLASFUNC(xrotg) (double *, double *, double *, double *);
-
-int    BLASFUNC(srotmg)(float  *, float  *, float  *, float  *, float  *);
-int    BLASFUNC(drotmg)(double *, double *, double *, double *, double *);
-
-int    BLASFUNC(srotm) (int *, float  *, int *, float  *, int *, float  *);
-int    BLASFUNC(drotm) (int *, double *, int *, double *, int *, double *);
-int    BLASFUNC(qrotm) (int *, double *, int *, double *, int *, double *);
-
-/* Level 2 routines */
-
-int BLASFUNC(sger)(int *,    int *, float *,  float *, int *,
-		   float *,  int *, float *,  int *);
-int BLASFUNC(dger)(int *,    int *, double *, double *, int *,
-		   double *, int *, double *, int *);
-int BLASFUNC(qger)(int *,    int *, double *, double *, int *,
-		   double *, int *, double *, int *);
-int BLASFUNC(cgeru)(int *,    int *, float *,  float *, int *,
-		    float *,  int *, float *,  int *);
-int BLASFUNC(cgerc)(int *,    int *, float *,  float *, int *,
-		    float *,  int *, float *,  int *);
-int BLASFUNC(zgeru)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-int BLASFUNC(zgerc)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-int BLASFUNC(xgeru)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-int BLASFUNC(xgerc)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-
-int BLASFUNC(sgemv)(const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(cgemv)(const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(strsv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dtrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrsv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(ztrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(stpsv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(dtpsv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(qtpsv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(ctpsv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(ztpsv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(xtpsv) (char *, char *, char *, int *, double *, double *, int *);
-
-int BLASFUNC(strmv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dtrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrmv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(ztrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(stpmv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(dtpmv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(qtpmv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(ctpmv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(ztpmv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(xtpmv) (char *, char *, char *, int *, double *, double *, int *);
-
-int BLASFUNC(stbmv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(dtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(qtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(ctbmv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(ztbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(xtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-
-int BLASFUNC(stbsv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(dtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(qtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(ctbsv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(ztbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(xtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-
-int BLASFUNC(ssymv) (const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(sspmv) (char *, int *, float  *, float *,
-		     float  *, int *, float *, float *, int *);
-int BLASFUNC(dspmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-int BLASFUNC(qspmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-
-int BLASFUNC(ssyr) (const char *, const int *, const float   *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dsyr) (const char *, const int *, const double  *, const double *, const int *, double *, const int *);
-int BLASFUNC(qsyr) (const char *, const int *, const double  *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(ssyr2) (const char *, const int *, const float   *, const float  *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(qsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(csyr2) (const char *, const int *, const float   *, const float  *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(zsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(xsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(sspr) (char *, int *, float   *, float  *, int *,
-		    float  *);
-int BLASFUNC(dspr) (char *, int *, double  *, double *, int *,
-		    double *);
-int BLASFUNC(qspr) (char *, int *, double  *, double *, int *,
-		    double *);
-
-int BLASFUNC(sspr2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *);
-int BLASFUNC(dspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(qspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(cspr2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *);
-int BLASFUNC(zspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(xspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-
-int BLASFUNC(cher) (char *, int *, float   *, float  *, int *,
-		    float  *, int *);
-int BLASFUNC(zher) (char *, int *, double  *, double *, int *,
-		    double *, int *);
-int BLASFUNC(xher) (char *, int *, double  *, double *, int *,
-		    double *, int *);
-
-int BLASFUNC(chpr) (char *, int *, float   *, float  *, int *, float  *);
-int BLASFUNC(zhpr) (char *, int *, double  *, double *, int *, double *);
-int BLASFUNC(xhpr) (char *, int *, double  *, double *, int *, double *);
-
-int BLASFUNC(cher2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(zher2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *, int *);
-int BLASFUNC(xher2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *, int *);
-
-int BLASFUNC(chpr2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *);
-int BLASFUNC(zhpr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(xhpr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-
-int BLASFUNC(chemv) (const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zhemv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xhemv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(chpmv) (char *, int *, float  *, float *,
-		     float  *, int *, float *, float *, int *);
-int BLASFUNC(zhpmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-int BLASFUNC(xhpmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-
-int BLASFUNC(snorm)(char *, int *, int *, float  *, int *);
-int BLASFUNC(dnorm)(char *, int *, int *, double *, int *);
-int BLASFUNC(cnorm)(char *, int *, int *, float  *, int *);
-int BLASFUNC(znorm)(char *, int *, int *, double *, int *);
-
-int BLASFUNC(sgbmv)(char *, int *, int *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(dgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(qgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(cgbmv)(char *, int *, int *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(xgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-
-int BLASFUNC(ssbmv)(char *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(dsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(qsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(csbmv)(char *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(xsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-
-int BLASFUNC(chbmv)(char *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zhbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(xhbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-
-/* Level 3 routines */
-
-int BLASFUNC(sgemm)(const char *, const char *, const int *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(cgemm)(const char *, const char *, const int *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(cgemm3m)(char *, char *, int *, int *, int *, float *,
-	   float  *, int *, float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zgemm3m)(char *, char *, int *, int *, int *, double *,
-	   double *, int *, double *, int *, double *, double *, int *);
-int BLASFUNC(xgemm3m)(char *, char *, int *, int *, int *, double *,
-	   double *, int *, double *, int *, double *, double *, int *);
-
-int BLASFUNC(sge2mm)(char *, char *, char *, int *, int *,
-		     float *, float  *, int *, float  *, int *,
-		     float *, float  *, int *);
-int BLASFUNC(dge2mm)(char *, char *, char *, int *, int *,
-		     double *, double  *, int *, double  *, int *,
-		     double *, double  *, int *);
-int BLASFUNC(cge2mm)(char *, char *, char *, int *, int *,
-		     float *, float  *, int *, float  *, int *,
-		     float *, float  *, int *);
-int BLASFUNC(zge2mm)(char *, char *, char *, int *, int *,
-		     double *, double  *, int *, double  *, int *,
-		     double *, double  *, int *);
-
-int BLASFUNC(strsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(dtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(ztrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(strmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(dtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(ztrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(ssymm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(csymm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(csymm3m)(char *, char *, int *, int *, float  *, float  *, int *, float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zsymm3m)(char *, char *, int *, int *, double *, double *, int *, double *, int *, double *, double *, int *);
-int BLASFUNC(xsymm3m)(char *, char *, int *, int *, double *, double *, int *, double *, int *, double *, double *, int *);
-
-int BLASFUNC(ssyrk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(csyrk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(ssyr2k)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(qsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(csyr2k)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(xsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-
-int BLASFUNC(chemm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zhemm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xhemm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(chemm3m)(char *, char *, int *, int *, float  *, float  *, int *,
-	   float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zhemm3m)(char *, char *, int *, int *, double *, double *, int *,
-	   double *, int *, double *, double *, int *);
-int BLASFUNC(xhemm3m)(char *, char *, int *, int *, double *, double *, int *,
-	   double *, int *, double *, double *, int *);
-
-int BLASFUNC(cherk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zherk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xherk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(cher2k)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zher2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xher2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(cher2m)(const char *, const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zher2m)(const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(xher2m)(const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapack.h b/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapack.h
deleted file mode 100644
index 249f357..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapack.h
+++ /dev/null
@@ -1,152 +0,0 @@
-#ifndef LAPACK_H
-#define LAPACK_H
-
-#include "blas.h"
-
-#ifdef __cplusplus
-extern "C"
-{
-#endif
-
-int BLASFUNC(csymv) (const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-
-int BLASFUNC(cspmv) (char *, int *, float  *, float *,
-         float  *, int *, float *, float *, int *);
-int BLASFUNC(zspmv) (char *, int *, double  *, double *,
-         double  *, int *, double *, double *, int *);
-int BLASFUNC(xspmv) (char *, int *, double  *, double *,
-         double  *, int *, double *, double *, int *);
-
-int BLASFUNC(csyr) (char *, int *, float   *, float  *, int *,
-        float  *, int *);
-int BLASFUNC(zsyr) (char *, int *, double  *, double *, int *,
-        double *, int *);
-int BLASFUNC(xsyr) (char *, int *, double  *, double *, int *,
-        double *, int *);
-
-int BLASFUNC(cspr) (char *, int *, float   *, float  *, int *,
-        float  *);
-int BLASFUNC(zspr) (char *, int *, double  *, double *, int *,
-        double *);
-int BLASFUNC(xspr) (char *, int *, double  *, double *, int *,
-        double *);
-
-int BLASFUNC(sgemt)(char *, int *, int *, float  *, float  *, int *,
-        float  *, int *);
-int BLASFUNC(dgemt)(char *, int *, int *, double *, double *, int *,
-        double *, int *);
-int BLASFUNC(cgemt)(char *, int *, int *, float  *, float  *, int *,
-        float  *, int *);
-int BLASFUNC(zgemt)(char *, int *, int *, double *, double *, int *,
-        double *, int *);
-
-int BLASFUNC(sgema)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(dgema)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-int BLASFUNC(cgema)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(zgema)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-
-int BLASFUNC(sgems)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(dgems)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-int BLASFUNC(cgems)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(zgems)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-
-int BLASFUNC(sgetf2)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(dgetf2)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(qgetf2)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(cgetf2)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(zgetf2)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(xgetf2)(int *, int *, double *, int *, int *, int *);
-
-int BLASFUNC(sgetrf)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(dgetrf)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(qgetrf)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(cgetrf)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(zgetrf)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(xgetrf)(int *, int *, double *, int *, int *, int *);
-
-int BLASFUNC(slaswp)(int *, float  *, int *, int *, int *, int *, int *);
-int BLASFUNC(dlaswp)(int *, double *, int *, int *, int *, int *, int *);
-int BLASFUNC(qlaswp)(int *, double *, int *, int *, int *, int *, int *);
-int BLASFUNC(claswp)(int *, float  *, int *, int *, int *, int *, int *);
-int BLASFUNC(zlaswp)(int *, double *, int *, int *, int *, int *, int *);
-int BLASFUNC(xlaswp)(int *, double *, int *, int *, int *, int *, int *);
-
-int BLASFUNC(sgetrs)(char *, int *, int *, float  *, int *, int *, float  *, int *, int *);
-int BLASFUNC(dgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-int BLASFUNC(qgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-int BLASFUNC(cgetrs)(char *, int *, int *, float  *, int *, int *, float  *, int *, int *);
-int BLASFUNC(zgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-int BLASFUNC(xgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-
-int BLASFUNC(sgesv)(int *, int *, float  *, int *, int *, float *, int *, int *);
-int BLASFUNC(dgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-int BLASFUNC(qgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-int BLASFUNC(cgesv)(int *, int *, float  *, int *, int *, float *, int *, int *);
-int BLASFUNC(zgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-int BLASFUNC(xgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-
-int BLASFUNC(spotf2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dpotf2)(char *, int *, double *, int *, int *);
-int BLASFUNC(qpotf2)(char *, int *, double *, int *, int *);
-int BLASFUNC(cpotf2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zpotf2)(char *, int *, double *, int *, int *);
-int BLASFUNC(xpotf2)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(spotrf)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dpotrf)(char *, int *, double *, int *, int *);
-int BLASFUNC(qpotrf)(char *, int *, double *, int *, int *);
-int BLASFUNC(cpotrf)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zpotrf)(char *, int *, double *, int *, int *);
-int BLASFUNC(xpotrf)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(slauu2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dlauu2)(char *, int *, double *, int *, int *);
-int BLASFUNC(qlauu2)(char *, int *, double *, int *, int *);
-int BLASFUNC(clauu2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zlauu2)(char *, int *, double *, int *, int *);
-int BLASFUNC(xlauu2)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(slauum)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dlauum)(char *, int *, double *, int *, int *);
-int BLASFUNC(qlauum)(char *, int *, double *, int *, int *);
-int BLASFUNC(clauum)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zlauum)(char *, int *, double *, int *, int *);
-int BLASFUNC(xlauum)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(strti2)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(dtrti2)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(qtrti2)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(ctrti2)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(ztrti2)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(xtrti2)(char *, char *, int *, double *, int *, int *);
-
-int BLASFUNC(strtri)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(dtrtri)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(qtrtri)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(ctrtri)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(ztrtri)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(xtrtri)(char *, char *, int *, double *, int *, int *);
-
-int BLASFUNC(spotri)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dpotri)(char *, int *, double *, int *, int *);
-int BLASFUNC(qpotri)(char *, int *, double *, int *, int *);
-int BLASFUNC(cpotri)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zpotri)(char *, int *, double *, int *, int *);
-int BLASFUNC(xpotri)(char *, int *, double *, int *, int *);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapacke.h b/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapacke.h
deleted file mode 100644
index 3d8e24f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapacke.h
+++ /dev/null
@@ -1,16292 +0,0 @@
-/*****************************************************************************
-  Copyright (c) 2010, Intel Corp.
-  All rights reserved.
-
-  Redistribution and use in source and binary forms, with or without
-  modification, are permitted provided that the following conditions are met:
-
-    * Redistributions of source code must retain the above copyright notice,
-      this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in the
-      documentation and/or other materials provided with the distribution.
-    * Neither the name of Intel Corporation nor the names of its contributors
-      may be used to endorse or promote products derived from this software
-      without specific prior written permission.
-
-  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
-  THE POSSIBILITY OF SUCH DAMAGE.
-******************************************************************************
-* Contents: Native C interface to LAPACK
-* Author: Intel Corporation
-* Generated November, 2011
-*****************************************************************************/
-
-#ifndef _MKL_LAPACKE_H_
-
-#ifndef _LAPACKE_H_
-#define _LAPACKE_H_
-
-/*
-*  Turn on HAVE_LAPACK_CONFIG_H to redefine C-LAPACK datatypes
-*/
-#ifdef HAVE_LAPACK_CONFIG_H
-#include "lapacke_config.h"
-#endif
-
-#include <stdlib.h>
-
-#ifndef lapack_int
-#define lapack_int     int
-#endif
-
-#ifndef lapack_logical
-#define lapack_logical lapack_int
-#endif
-
-/* Complex types are structures equivalent to the
-* Fortran complex types COMPLEX(4) and COMPLEX(8).
-*
-* One can also redefine the types with his own types
-* for example by including in the code definitions like
-*
-* #define lapack_complex_float std::complex<float>
-* #define lapack_complex_double std::complex<double>
-*
-* or define these types in the command line:
-*
-* -Dlapack_complex_float="std::complex<float>"
-* -Dlapack_complex_double="std::complex<double>"
-*/
-
-#ifndef LAPACK_COMPLEX_CUSTOM
-
-/* Complex type (single precision) */
-#ifndef lapack_complex_float
-#include <complex.h>
-#define lapack_complex_float    float _Complex
-#endif
-
-#ifndef lapack_complex_float_real
-#define lapack_complex_float_real(z)       (creal(z))
-#endif
-
-#ifndef lapack_complex_float_imag
-#define lapack_complex_float_imag(z)       (cimag(z))
-#endif
-
-lapack_complex_float lapack_make_complex_float( float re, float im );
-
-/* Complex type (double precision) */
-#ifndef lapack_complex_double
-#include <complex.h>
-#define lapack_complex_double   double _Complex
-#endif
-
-#ifndef lapack_complex_double_real
-#define lapack_complex_double_real(z)      (creal(z))
-#endif
-
-#ifndef lapack_complex_double_imag
-#define lapack_complex_double_imag(z)       (cimag(z))
-#endif
-
-lapack_complex_double lapack_make_complex_double( double re, double im );
-
-#endif
-
-
-#ifdef __cplusplus
-extern "C" {
-#endif /* __cplusplus */
-
-#ifndef LAPACKE_malloc
-#define LAPACKE_malloc( size ) malloc( size )
-#endif
-#ifndef LAPACKE_free
-#define LAPACKE_free( p )      free( p )
-#endif
-
-#define LAPACK_C2INT( x ) (lapack_int)(*((float*)&x ))
-#define LAPACK_Z2INT( x ) (lapack_int)(*((double*)&x ))
-
-#define LAPACK_ROW_MAJOR               101
-#define LAPACK_COL_MAJOR               102
-
-#define LAPACK_WORK_MEMORY_ERROR       -1010
-#define LAPACK_TRANSPOSE_MEMORY_ERROR  -1011
-
-/* Callback logical functions of one, two, or three arguments are used
-*  to select eigenvalues to sort to the top left of the Schur form.
-*  The value is selected if function returns TRUE (non-zero). */
-
-typedef lapack_logical (*LAPACK_S_SELECT2) ( const float*, const float* );
-typedef lapack_logical (*LAPACK_S_SELECT3)
-    ( const float*, const float*, const float* );
-typedef lapack_logical (*LAPACK_D_SELECT2) ( const double*, const double* );
-typedef lapack_logical (*LAPACK_D_SELECT3)
-    ( const double*, const double*, const double* );
-
-typedef lapack_logical (*LAPACK_C_SELECT1) ( const lapack_complex_float* );
-typedef lapack_logical (*LAPACK_C_SELECT2)
-    ( const lapack_complex_float*, const lapack_complex_float* );
-typedef lapack_logical (*LAPACK_Z_SELECT1) ( const lapack_complex_double* );
-typedef lapack_logical (*LAPACK_Z_SELECT2)
-    ( const lapack_complex_double*, const lapack_complex_double* );
-
-#include "lapacke_mangling.h"
-
-#define LAPACK_lsame LAPACK_GLOBAL(lsame,LSAME)
-lapack_logical LAPACK_lsame( char* ca,  char* cb,
-                              lapack_int lca, lapack_int lcb );
-
-/* C-LAPACK function prototypes */
-
-lapack_int LAPACKE_sbdsdc( int matrix_order, char uplo, char compq,
-                           lapack_int n, float* d, float* e, float* u,
-                           lapack_int ldu, float* vt, lapack_int ldvt, float* q,
-                           lapack_int* iq );
-lapack_int LAPACKE_dbdsdc( int matrix_order, char uplo, char compq,
-                           lapack_int n, double* d, double* e, double* u,
-                           lapack_int ldu, double* vt, lapack_int ldvt,
-                           double* q, lapack_int* iq );
-
-lapack_int LAPACKE_sbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           float* d, float* e, float* vt, lapack_int ldvt,
-                           float* u, lapack_int ldu, float* c, lapack_int ldc );
-lapack_int LAPACKE_dbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           double* d, double* e, double* vt, lapack_int ldvt,
-                           double* u, lapack_int ldu, double* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_cbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           float* d, float* e, lapack_complex_float* vt,
-                           lapack_int ldvt, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_zbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           double* d, double* e, lapack_complex_double* vt,
-                           lapack_int ldvt, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* c,
-                           lapack_int ldc );
-
-lapack_int LAPACKE_sdisna( char job, lapack_int m, lapack_int n, const float* d,
-                           float* sep );
-lapack_int LAPACKE_ddisna( char job, lapack_int m, lapack_int n,
-                           const double* d, double* sep );
-
-lapack_int LAPACKE_sgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, float* ab, lapack_int ldab, float* d,
-                           float* e, float* q, lapack_int ldq, float* pt,
-                           lapack_int ldpt, float* c, lapack_int ldc );
-lapack_int LAPACKE_dgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, double* ab, lapack_int ldab,
-                           double* d, double* e, double* q, lapack_int ldq,
-                           double* pt, lapack_int ldpt, double* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_cgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, lapack_complex_float* ab,
-                           lapack_int ldab, float* d, float* e,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* pt, lapack_int ldpt,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, lapack_complex_double* ab,
-                           lapack_int ldab, double* d, double* e,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* pt, lapack_int ldpt,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* ab,
-                           lapack_int ldab, const lapack_int* ipiv, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* ab,
-                           lapack_int ldab, const lapack_int* ipiv,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* ab,
-                           lapack_int ldab, float* r, float* c, float* rowcnd,
-                           float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* ab,
-                           lapack_int ldab, double* r, double* c,
-                           double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           float* r, float* c, float* rowcnd, float* colcnd,
-                           float* amax );
-lapack_int LAPACKE_zgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           double* r, double* c, double* rowcnd, double* colcnd,
-                           double* amax );
-
-lapack_int LAPACKE_sgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku, const float* ab,
-                            lapack_int ldab, float* r, float* c, float* rowcnd,
-                            float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku, const double* ab,
-                            lapack_int ldab, double* r, double* c,
-                            double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku,
-                            const lapack_complex_float* ab, lapack_int ldab,
-                            float* r, float* c, float* rowcnd, float* colcnd,
-                            float* amax );
-lapack_int LAPACKE_zgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku,
-                            const lapack_complex_double* ab, lapack_int ldab,
-                            double* r, double* c, double* rowcnd,
-                            double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const float* ab, lapack_int ldab, const float* afb,
-                           lapack_int ldafb, const lapack_int* ipiv,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const double* ab, lapack_int ldab, const double* afb,
-                           lapack_int ldafb, const lapack_int* ipiv,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* afb, lapack_int ldafb,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* afb, lapack_int ldafb,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const float* ab, lapack_int ldab,
-                            const float* afb, lapack_int ldafb,
-                            const lapack_int* ipiv, const float* r,
-                            const float* c, const float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const double* ab, lapack_int ldab,
-                            const double* afb, lapack_int ldafb,
-                            const lapack_int* ipiv, const double* r,
-                            const double* c, const double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const lapack_complex_float* ab,
-                            lapack_int ldab, const lapack_complex_float* afb,
-                            lapack_int ldafb, const lapack_int* ipiv,
-                            const float* r, const float* c,
-                            const lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const lapack_complex_double* ab,
-                            lapack_int ldab, const lapack_complex_double* afb,
-                            lapack_int ldafb, const lapack_int* ipiv,
-                            const double* r, const double* c,
-                            const lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_sgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs, float* ab,
-                          lapack_int ldab, lapack_int* ipiv, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs, double* ab,
-                          lapack_int ldab, lapack_int* ipiv, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs,
-                          lapack_complex_float* ab, lapack_int ldab,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs,
-                          lapack_complex_double* ab, lapack_int ldab,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_sgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, float* ab, lapack_int ldab,
-                           float* afb, lapack_int ldafb, lapack_int* ipiv,
-                           char* equed, float* r, float* c, float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr,
-                           float* rpivot );
-lapack_int LAPACKE_dgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, double* ab, lapack_int ldab,
-                           double* afb, lapack_int ldafb, lapack_int* ipiv,
-                           char* equed, double* r, double* c, double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr,
-                           double* rpivot );
-lapack_int LAPACKE_cgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, lapack_complex_float* ab,
-                           lapack_int ldab, lapack_complex_float* afb,
-                           lapack_int ldafb, lapack_int* ipiv, char* equed,
-                           float* r, float* c, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr, float* rpivot );
-lapack_int LAPACKE_zgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, lapack_complex_double* ab,
-                           lapack_int ldab, lapack_complex_double* afb,
-                           lapack_int ldafb, lapack_int* ipiv, char* equed,
-                           double* r, double* c, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, double* rcond, double* ferr,
-                           double* berr, double* rpivot );
-
-lapack_int LAPACKE_sgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, float* ab, lapack_int ldab,
-                            float* afb, lapack_int ldafb, lapack_int* ipiv,
-                            char* equed, float* r, float* c, float* b,
-                            lapack_int ldb, float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, double* ab, lapack_int ldab,
-                            double* afb, lapack_int ldafb, lapack_int* ipiv,
-                            char* equed, double* r, double* c, double* b,
-                            lapack_int ldb, double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-lapack_int LAPACKE_cgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, lapack_complex_float* ab,
-                            lapack_int ldab, lapack_complex_float* afb,
-                            lapack_int ldafb, lapack_int* ipiv, char* equed,
-                            float* r, float* c, lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* rpvgrw,
-                            float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, lapack_complex_double* ab,
-                            lapack_int ldab, lapack_complex_double* afb,
-                            lapack_int ldafb, lapack_int* ipiv, char* equed,
-                            double* r, double* c, lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* rpvgrw,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_sgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, float* ab,
-                           lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_dgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, double* ab,
-                           lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_cgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_sgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const float* ab, lapack_int ldab,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const double* ab, lapack_int ldab,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_sgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* scale,
-                           lapack_int m, float* v, lapack_int ldv );
-lapack_int LAPACKE_dgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* scale,
-                           lapack_int m, double* v, lapack_int ldv );
-lapack_int LAPACKE_cgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* scale,
-                           lapack_int m, lapack_complex_float* v,
-                           lapack_int ldv );
-lapack_int LAPACKE_zgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* scale,
-                           lapack_int m, lapack_complex_double* v,
-                           lapack_int ldv );
-
-lapack_int LAPACKE_sgebal( int matrix_order, char job, lapack_int n, float* a,
-                           lapack_int lda, lapack_int* ilo, lapack_int* ihi,
-                           float* scale );
-lapack_int LAPACKE_dgebal( int matrix_order, char job, lapack_int n, double* a,
-                           lapack_int lda, lapack_int* ilo, lapack_int* ihi,
-                           double* scale );
-lapack_int LAPACKE_cgebal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ilo, lapack_int* ihi, float* scale );
-lapack_int LAPACKE_zgebal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ilo, lapack_int* ihi, double* scale );
-
-lapack_int LAPACKE_sgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* d, float* e,
-                           float* tauq, float* taup );
-lapack_int LAPACKE_dgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* d, double* e,
-                           double* tauq, double* taup );
-lapack_int LAPACKE_cgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda, float* d,
-                           float* e, lapack_complex_float* tauq,
-                           lapack_complex_float* taup );
-lapack_int LAPACKE_zgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda, double* d,
-                           double* e, lapack_complex_double* tauq,
-                           lapack_complex_double* taup );
-
-lapack_int LAPACKE_sgecon( int matrix_order, char norm, lapack_int n,
-                           const float* a, lapack_int lda, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dgecon( int matrix_order, char norm, lapack_int n,
-                           const double* a, lapack_int lda, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_cgecon( int matrix_order, char norm, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_zgecon( int matrix_order, char norm, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double anorm, double* rcond );
-
-lapack_int LAPACKE_sgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const float* a, lapack_int lda, float* r, float* c,
-                           float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const double* a, lapack_int lda, double* r,
-                           double* c, double* rowcnd, double* colcnd,
-                           double* amax );
-lapack_int LAPACKE_cgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float* r, float* c, float* rowcnd, float* colcnd,
-                           float* amax );
-lapack_int LAPACKE_zgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double* r, double* c, double* rowcnd, double* colcnd,
-                           double* amax );
-
-lapack_int LAPACKE_sgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const float* a, lapack_int lda, float* r, float* c,
-                            float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const double* a, lapack_int lda, double* r,
-                            double* c, double* rowcnd, double* colcnd,
-                            double* amax );
-lapack_int LAPACKE_cgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* r, float* c, float* rowcnd, float* colcnd,
-                            float* amax );
-lapack_int LAPACKE_zgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* r, double* c, double* rowcnd,
-                            double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_S_SELECT2 select, lapack_int n, float* a,
-                          lapack_int lda, lapack_int* sdim, float* wr,
-                          float* wi, float* vs, lapack_int ldvs );
-lapack_int LAPACKE_dgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_D_SELECT2 select, lapack_int n, double* a,
-                          lapack_int lda, lapack_int* sdim, double* wr,
-                          double* wi, double* vs, lapack_int ldvs );
-lapack_int LAPACKE_cgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_C_SELECT1 select, lapack_int n,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_int* sdim, lapack_complex_float* w,
-                          lapack_complex_float* vs, lapack_int ldvs );
-lapack_int LAPACKE_zgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_Z_SELECT1 select, lapack_int n,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_int* sdim, lapack_complex_double* w,
-                          lapack_complex_double* vs, lapack_int ldvs );
-
-lapack_int LAPACKE_sgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_S_SELECT2 select, char sense, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* sdim,
-                           float* wr, float* wi, float* vs, lapack_int ldvs,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_dgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_D_SELECT2 select, char sense, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* sdim,
-                           double* wr, double* wi, double* vs, lapack_int ldvs,
-                           double* rconde, double* rcondv );
-lapack_int LAPACKE_cgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_C_SELECT1 select, char sense, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* sdim, lapack_complex_float* w,
-                           lapack_complex_float* vs, lapack_int ldvs,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_zgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_Z_SELECT1 select, char sense, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* sdim, lapack_complex_double* w,
-                           lapack_complex_double* vs, lapack_int ldvs,
-                           double* rconde, double* rcondv );
-
-lapack_int LAPACKE_sgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, float* a, lapack_int lda, float* wr,
-                          float* wi, float* vl, lapack_int ldvl, float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_dgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, double* a, lapack_int lda, double* wr,
-                          double* wi, double* vl, lapack_int ldvl, double* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_cgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* w, lapack_complex_float* vl,
-                          lapack_int ldvl, lapack_complex_float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_zgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* w,
-                          lapack_complex_double* vl, lapack_int ldvl,
-                          lapack_complex_double* vr, lapack_int ldvr );
-
-lapack_int LAPACKE_sgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, float* a,
-                           lapack_int lda, float* wr, float* wi, float* vl,
-                           lapack_int ldvl, float* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, float* scale,
-                           float* abnrm, float* rconde, float* rcondv );
-lapack_int LAPACKE_dgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, double* a,
-                           lapack_int lda, double* wr, double* wi, double* vl,
-                           lapack_int ldvl, double* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, double* scale,
-                           double* abnrm, double* rconde, double* rcondv );
-lapack_int LAPACKE_cgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* w, lapack_complex_float* vl,
-                           lapack_int ldvl, lapack_complex_float* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           float* scale, float* abnrm, float* rconde,
-                           float* rcondv );
-lapack_int LAPACKE_zgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* w, lapack_complex_double* vl,
-                           lapack_int ldvl, lapack_complex_double* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           double* scale, double* abnrm, double* rconde,
-                           double* rcondv );
-
-lapack_int LAPACKE_sgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, float* a, lapack_int lda,
-                           float* tau );
-lapack_int LAPACKE_dgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, double* a, lapack_int lda,
-                           double* tau );
-lapack_int LAPACKE_cgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* tau );
-lapack_int LAPACKE_zgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgejsv( int matrix_order, char joba, char jobu, char jobv,
-                           char jobr, char jobt, char jobp, lapack_int m,
-                           lapack_int n, float* a, lapack_int lda, float* sva,
-                           float* u, lapack_int ldu, float* v, lapack_int ldv,
-                           float* stat, lapack_int* istat );
-lapack_int LAPACKE_dgejsv( int matrix_order, char joba, char jobu, char jobv,
-                           char jobr, char jobt, char jobp, lapack_int m,
-                           lapack_int n, double* a, lapack_int lda, double* sva,
-                           double* u, lapack_int ldu, double* v, lapack_int ldv,
-                           double* stat, lapack_int* istat );
-
-lapack_int LAPACKE_sgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs, float* a,
-                          lapack_int lda, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs, double* a,
-                          lapack_int lda, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_dgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_cgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_zgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-
-lapack_int LAPACKE_sgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_dgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_cgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_zgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-
-lapack_int LAPACKE_sgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, lapack_int* jpvt, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_dgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, lapack_int* jpvt,
-                           double rcond, lapack_int* rank );
-lapack_int LAPACKE_cgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_int* jpvt, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_zgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_int* jpvt, double rcond,
-                           lapack_int* rank );
-
-lapack_int LAPACKE_sgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* jpvt,
-                           float* tau );
-lapack_int LAPACKE_dgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* jpvt,
-                           double* tau );
-lapack_int LAPACKE_cgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* jpvt,
-                           float* tau );
-lapack_int LAPACKE_dgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* jpvt,
-                           double* tau );
-lapack_int LAPACKE_cgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const float* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const double* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs, const float* a,
-                            lapack_int lda, const float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* r,
-                            const float* c, const float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs, const double* a,
-                            lapack_int lda, const double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* r,
-                            const double* c, const double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* r,
-                            const float* c, const lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* r,
-                            const double* c, const lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_sgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, float* a, lapack_int lda, float* s,
-                           float* u, lapack_int ldu, float* vt,
-                           lapack_int ldvt );
-lapack_int LAPACKE_dgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, double* a, lapack_int lda, double* s,
-                           double* u, lapack_int ldu, double* vt,
-                           lapack_int ldvt );
-lapack_int LAPACKE_cgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float* s, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* vt,
-                           lapack_int ldvt );
-lapack_int LAPACKE_zgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double* s, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* vt,
-                           lapack_int ldvt );
-
-lapack_int LAPACKE_sgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* a, lapack_int lda, lapack_int* ipiv, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* a, lapack_int lda, lapack_int* ipiv,
-                          double* b, lapack_int ldb );
-lapack_int LAPACKE_cgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dsgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                           double* a, lapack_int lda, lapack_int* ipiv,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           lapack_int* iter );
-lapack_int LAPACKE_zcgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, lapack_int* iter );
-
-lapack_int LAPACKE_sgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, float* a, lapack_int lda,
-                           float* s, float* u, lapack_int ldu, float* vt,
-                           lapack_int ldvt, float* superb );
-lapack_int LAPACKE_dgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, double* a,
-                           lapack_int lda, double* s, double* u, lapack_int ldu,
-                           double* vt, lapack_int ldvt, double* superb );
-lapack_int LAPACKE_cgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float* s, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* vt,
-                           lapack_int ldvt, float* superb );
-lapack_int LAPACKE_zgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double* s, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* vt,
-                           lapack_int ldvt, double* superb );
-
-lapack_int LAPACKE_sgesvj( int matrix_order, char joba, char jobu, char jobv,
-                           lapack_int m, lapack_int n, float* a, lapack_int lda,
-                           float* sva, lapack_int mv, float* v, lapack_int ldv,
-                           float* stat );
-lapack_int LAPACKE_dgesvj( int matrix_order, char joba, char jobu, char jobv,
-                           lapack_int m, lapack_int n, double* a,
-                           lapack_int lda, double* sva, lapack_int mv,
-                           double* v, lapack_int ldv, double* stat );
-
-lapack_int LAPACKE_sgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, float* a,
-                           lapack_int lda, float* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, float* r, float* c,
-                           float* b, lapack_int ldb, float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr,
-                           float* rpivot );
-lapack_int LAPACKE_dgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, double* a,
-                           lapack_int lda, double* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, double* r, double* c,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr,
-                           double* rpivot );
-lapack_int LAPACKE_cgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, float* r, float* c,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr,
-                           float* rpivot );
-lapack_int LAPACKE_zgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, double* r, double* c,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr,
-                           double* rpivot );
-
-lapack_int LAPACKE_sgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs, float* a,
-                            lapack_int lda, float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* r, float* c,
-                            float* b, lapack_int ldb, float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs, double* a,
-                            lapack_int lda, double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* r, double* c,
-                            double* b, lapack_int ldb, double* x,
-                            lapack_int ldx, double* rcond, double* rpvgrw,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* r, float* c,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* r, double* c,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_sgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetri( int matrix_order, lapack_int n, float* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dgetri( int matrix_order, lapack_int n, double* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_cgetri( int matrix_order, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_zgetri( int matrix_order, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* lscale,
-                           const float* rscale, lapack_int m, float* v,
-                           lapack_int ldv );
-lapack_int LAPACKE_dggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* lscale,
-                           const double* rscale, lapack_int m, double* v,
-                           lapack_int ldv );
-lapack_int LAPACKE_cggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* lscale,
-                           const float* rscale, lapack_int m,
-                           lapack_complex_float* v, lapack_int ldv );
-lapack_int LAPACKE_zggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* lscale,
-                           const double* rscale, lapack_int m,
-                           lapack_complex_double* v, lapack_int ldv );
-
-lapack_int LAPACKE_sggbal( int matrix_order, char job, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, float* lscale,
-                           float* rscale );
-lapack_int LAPACKE_dggbal( int matrix_order, char job, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, double* lscale,
-                           double* rscale );
-lapack_int LAPACKE_cggbal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, float* lscale,
-                           float* rscale );
-lapack_int LAPACKE_zggbal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, double* lscale,
-                           double* rscale );
-
-lapack_int LAPACKE_sgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_S_SELECT3 selctg, lapack_int n, float* a,
-                          lapack_int lda, float* b, lapack_int ldb,
-                          lapack_int* sdim, float* alphar, float* alphai,
-                          float* beta, float* vsl, lapack_int ldvsl, float* vsr,
-                          lapack_int ldvsr );
-lapack_int LAPACKE_dgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_D_SELECT3 selctg, lapack_int n, double* a,
-                          lapack_int lda, double* b, lapack_int ldb,
-                          lapack_int* sdim, double* alphar, double* alphai,
-                          double* beta, double* vsl, lapack_int ldvsl,
-                          double* vsr, lapack_int ldvsr );
-lapack_int LAPACKE_cgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_C_SELECT2 selctg, lapack_int n,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* b, lapack_int ldb,
-                          lapack_int* sdim, lapack_complex_float* alpha,
-                          lapack_complex_float* beta, lapack_complex_float* vsl,
-                          lapack_int ldvsl, lapack_complex_float* vsr,
-                          lapack_int ldvsr );
-lapack_int LAPACKE_zgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_Z_SELECT2 selctg, lapack_int n,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_complex_double* b, lapack_int ldb,
-                          lapack_int* sdim, lapack_complex_double* alpha,
-                          lapack_complex_double* beta,
-                          lapack_complex_double* vsl, lapack_int ldvsl,
-                          lapack_complex_double* vsr, lapack_int ldvsr );
-
-lapack_int LAPACKE_sggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_S_SELECT3 selctg, char sense,
-                           lapack_int n, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, lapack_int* sdim, float* alphar,
-                           float* alphai, float* beta, float* vsl,
-                           lapack_int ldvsl, float* vsr, lapack_int ldvsr,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_dggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_D_SELECT3 selctg, char sense,
-                           lapack_int n, double* a, lapack_int lda, double* b,
-                           lapack_int ldb, lapack_int* sdim, double* alphar,
-                           double* alphai, double* beta, double* vsl,
-                           lapack_int ldvsl, double* vsr, lapack_int ldvsr,
-                           double* rconde, double* rcondv );
-lapack_int LAPACKE_cggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_C_SELECT2 selctg, char sense,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_int* sdim,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta,
-                           lapack_complex_float* vsl, lapack_int ldvsl,
-                           lapack_complex_float* vsr, lapack_int ldvsr,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_zggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_Z_SELECT2 selctg, char sense,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_int* sdim,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* vsl, lapack_int ldvsl,
-                           lapack_complex_double* vsr, lapack_int ldvsr,
-                           double* rconde, double* rcondv );
-
-lapack_int LAPACKE_sggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, float* a, lapack_int lda, float* b,
-                          lapack_int ldb, float* alphar, float* alphai,
-                          float* beta, float* vl, lapack_int ldvl, float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_dggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, double* a, lapack_int lda, double* b,
-                          lapack_int ldb, double* alphar, double* alphai,
-                          double* beta, double* vl, lapack_int ldvl, double* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_cggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* b, lapack_int ldb,
-                          lapack_complex_float* alpha,
-                          lapack_complex_float* beta, lapack_complex_float* vl,
-                          lapack_int ldvl, lapack_complex_float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_zggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* b,
-                          lapack_int ldb, lapack_complex_double* alpha,
-                          lapack_complex_double* beta,
-                          lapack_complex_double* vl, lapack_int ldvl,
-                          lapack_complex_double* vr, lapack_int ldvr );
-
-lapack_int LAPACKE_sggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           float* alphar, float* alphai, float* beta, float* vl,
-                           lapack_int ldvl, float* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, float* lscale,
-                           float* rscale, float* abnrm, float* bbnrm,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_dggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double* alphar, double* alphai, double* beta,
-                           double* vl, lapack_int ldvl, double* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           double* lscale, double* rscale, double* abnrm,
-                           double* bbnrm, double* rconde, double* rcondv );
-lapack_int LAPACKE_cggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta, lapack_complex_float* vl,
-                           lapack_int ldvl, lapack_complex_float* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           float* lscale, float* rscale, float* abnrm,
-                           float* bbnrm, float* rconde, float* rcondv );
-lapack_int LAPACKE_zggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* vl, lapack_int ldvl,
-                           lapack_complex_double* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, double* lscale,
-                           double* rscale, double* abnrm, double* bbnrm,
-                           double* rconde, double* rcondv );
-
-lapack_int LAPACKE_sggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* d, float* x, float* y );
-lapack_int LAPACKE_dggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, double* a, lapack_int lda, double* b,
-                           lapack_int ldb, double* d, double* x, double* y );
-lapack_int LAPACKE_cggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* d,
-                           lapack_complex_float* x, lapack_complex_float* y );
-lapack_int LAPACKE_zggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* d,
-                           lapack_complex_double* x, lapack_complex_double* y );
-
-lapack_int LAPACKE_sgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           float* a, lapack_int lda, float* b, lapack_int ldb,
-                           float* q, lapack_int ldq, float* z, lapack_int ldz );
-lapack_int LAPACKE_dgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           double* a, lapack_int lda, double* b, lapack_int ldb,
-                           double* q, lapack_int ldq, double* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_cgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* c, float* d, float* x );
-lapack_int LAPACKE_dgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, double* a, lapack_int lda, double* b,
-                           lapack_int ldb, double* c, double* d, double* x );
-lapack_int LAPACKE_cgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* c,
-                           lapack_complex_float* d, lapack_complex_float* x );
-lapack_int LAPACKE_zgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* c,
-                           lapack_complex_double* d, lapack_complex_double* x );
-
-lapack_int LAPACKE_sggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, float* a, lapack_int lda, float* taua,
-                           float* b, lapack_int ldb, float* taub );
-lapack_int LAPACKE_dggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, double* a, lapack_int lda,
-                           double* taua, double* b, lapack_int ldb,
-                           double* taub );
-lapack_int LAPACKE_cggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* taua,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* taub );
-lapack_int LAPACKE_zggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* taua,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* taub );
-
-lapack_int LAPACKE_sggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, float* a, lapack_int lda, float* taua,
-                           float* b, lapack_int ldb, float* taub );
-lapack_int LAPACKE_dggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, double* a, lapack_int lda,
-                           double* taua, double* b, lapack_int ldb,
-                           double* taub );
-lapack_int LAPACKE_cggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* taua,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* taub );
-lapack_int LAPACKE_zggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* taua,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* taub );
-
-lapack_int LAPACKE_sggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           float* alpha, float* beta, float* u, lapack_int ldu,
-                           float* v, lapack_int ldv, float* q, lapack_int ldq,
-                           lapack_int* iwork );
-lapack_int LAPACKE_dggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double* alpha, double* beta, double* u,
-                           lapack_int ldu, double* v, lapack_int ldv, double* q,
-                           lapack_int ldq, lapack_int* iwork );
-lapack_int LAPACKE_cggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           float* alpha, float* beta, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* v,
-                           lapack_int ldv, lapack_complex_float* q,
-                           lapack_int ldq, lapack_int* iwork );
-lapack_int LAPACKE_zggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           double* alpha, double* beta,
-                           lapack_complex_double* u, lapack_int ldu,
-                           lapack_complex_double* v, lapack_int ldv,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_int* iwork );
-
-lapack_int LAPACKE_sggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb, float tola,
-                           float tolb, lapack_int* k, lapack_int* l, float* u,
-                           lapack_int ldu, float* v, lapack_int ldv, float* q,
-                           lapack_int ldq );
-lapack_int LAPACKE_dggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double tola, double tolb, lapack_int* k,
-                           lapack_int* l, double* u, lapack_int ldu, double* v,
-                           lapack_int ldv, double* q, lapack_int ldq );
-lapack_int LAPACKE_cggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb, float tola,
-                           float tolb, lapack_int* k, lapack_int* l,
-                           lapack_complex_float* u, lapack_int ldu,
-                           lapack_complex_float* v, lapack_int ldv,
-                           lapack_complex_float* q, lapack_int ldq );
-lapack_int LAPACKE_zggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           double tola, double tolb, lapack_int* k,
-                           lapack_int* l, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* v,
-                           lapack_int ldv, lapack_complex_double* q,
-                           lapack_int ldq );
-
-lapack_int LAPACKE_sgtcon( char norm, lapack_int n, const float* dl,
-                           const float* d, const float* du, const float* du2,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_dgtcon( char norm, lapack_int n, const double* dl,
-                           const double* d, const double* du, const double* du2,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_cgtcon( char norm, lapack_int n,
-                           const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           const lapack_complex_float* du2,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zgtcon( char norm, lapack_int n,
-                           const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           const lapack_complex_double* du2,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* dl, const float* d,
-                           const float* du, const float* dlf, const float* df,
-                           const float* duf, const float* du2,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* dl, const double* d,
-                           const double* du, const double* dlf,
-                           const double* df, const double* duf,
-                           const double* du2, const lapack_int* ipiv,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           const lapack_complex_float* dlf,
-                           const lapack_complex_float* df,
-                           const lapack_complex_float* duf,
-                           const lapack_complex_float* du2,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           const lapack_complex_double* dlf,
-                           const lapack_complex_double* df,
-                           const lapack_complex_double* duf,
-                           const lapack_complex_double* du2,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* dl, float* d, float* du, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* dl, double* d, double* du, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_float* dl, lapack_complex_float* d,
-                          lapack_complex_float* du, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_double* dl, lapack_complex_double* d,
-                          lapack_complex_double* du, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_sgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, const float* dl,
-                           const float* d, const float* du, float* dlf,
-                           float* df, float* duf, float* du2, lapack_int* ipiv,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, const double* dl,
-                           const double* d, const double* du, double* dlf,
-                           double* df, double* duf, double* du2,
-                           lapack_int* ipiv, const double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           lapack_complex_float* dlf, lapack_complex_float* df,
-                           lapack_complex_float* duf, lapack_complex_float* du2,
-                           lapack_int* ipiv, const lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           lapack_complex_double* dlf,
-                           lapack_complex_double* df,
-                           lapack_complex_double* duf,
-                           lapack_complex_double* du2, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_sgttrf( lapack_int n, float* dl, float* d, float* du,
-                           float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_dgttrf( lapack_int n, double* dl, double* d, double* du,
-                           double* du2, lapack_int* ipiv );
-lapack_int LAPACKE_cgttrf( lapack_int n, lapack_complex_float* dl,
-                           lapack_complex_float* d, lapack_complex_float* du,
-                           lapack_complex_float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_zgttrf( lapack_int n, lapack_complex_double* dl,
-                           lapack_complex_double* d, lapack_complex_double* du,
-                           lapack_complex_double* du2, lapack_int* ipiv );
-
-lapack_int LAPACKE_sgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* dl, const float* d,
-                           const float* du, const float* du2,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* dl, const double* d,
-                           const double* du, const double* du2,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           const lapack_complex_float* du2,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           const lapack_complex_double* du2,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_chbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, lapack_complex_float* ab,
-                          lapack_int ldab, float* w, lapack_complex_float* z,
-                          lapack_int ldz );
-lapack_int LAPACKE_zhbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, lapack_complex_double* ab,
-                          lapack_int ldab, double* w, lapack_complex_double* z,
-                          lapack_int ldz );
-
-lapack_int LAPACKE_chbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_float* ab,
-                           lapack_int ldab, float* w, lapack_complex_float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_zhbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_double* ab,
-                           lapack_int ldab, double* w, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_chbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* q, lapack_int ldq, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* bb, lapack_int ldbb,
-                           lapack_complex_float* x, lapack_int ldx );
-lapack_int LAPACKE_zhbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* bb, lapack_int ldbb,
-                           lapack_complex_double* x, lapack_int ldx );
-
-lapack_int LAPACKE_chbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb,
-                          lapack_complex_float* ab, lapack_int ldab,
-                          lapack_complex_float* bb, lapack_int ldbb, float* w,
-                          lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb,
-                          lapack_complex_double* ab, lapack_int ldab,
-                          lapack_complex_double* bb, lapack_int ldbb, double* w,
-                          lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* bb, lapack_int ldbb, float* w,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* bb, lapack_int ldbb,
-                           double* w, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_chbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* bb, lapack_int ldbb,
-                           lapack_complex_float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* bb, lapack_int ldbb,
-                           lapack_complex_double* q, lapack_int ldq, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_float* ab,
-                           lapack_int ldab, float* d, float* e,
-                           lapack_complex_float* q, lapack_int ldq );
-lapack_int LAPACKE_zhbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_double* ab,
-                           lapack_int ldab, double* d, double* e,
-                           lapack_complex_double* q, lapack_int ldq );
-
-lapack_int LAPACKE_checon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zhecon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_cheequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* s, float* scond, float* amax );
-lapack_int LAPACKE_zheequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_cheev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_zheev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_double* a, lapack_int lda, double* w );
-
-lapack_int LAPACKE_cheevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_zheevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           double* w );
-
-lapack_int LAPACKE_cheevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float vl, float vu, lapack_int il,
-                           lapack_int iu, float abstol, lapack_int* m, float* w,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_zheevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double vl, double vu, lapack_int il,
-                           lapack_int iu, double abstol, lapack_int* m,
-                           double* w, lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* isuppz );
-
-lapack_int LAPACKE_cheevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float vl, float vu, lapack_int il,
-                           lapack_int iu, float abstol, lapack_int* m, float* w,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_zheevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double vl, double vu, lapack_int il,
-                           lapack_int iu, double abstol, lapack_int* m,
-                           double* w, lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chegst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zhegst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_chegv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_float* a,
-                          lapack_int lda, lapack_complex_float* b,
-                          lapack_int ldb, float* w );
-lapack_int LAPACKE_zhegv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* b,
-                          lapack_int ldb, double* w );
-
-lapack_int LAPACKE_chegvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float* w );
-lapack_int LAPACKE_zhegvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double* w );
-
-lapack_int LAPACKE_chegvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhegvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_cherfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zherfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_cherfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* s,
-                            const lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zherfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* s,
-                            const lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_chesv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zhesv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chesvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zhesvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_chesvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* s,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zhesvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* s,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_chetrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda, float* d,
-                           float* e, lapack_complex_float* tau );
-lapack_int LAPACKE_zhetrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda, double* d,
-                           double* e, lapack_complex_double* tau );
-
-lapack_int LAPACKE_chetrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zhetrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_chetri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_zhetri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv );
-
-lapack_int LAPACKE_chetrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zhetrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, float alpha,
-                          const lapack_complex_float* a, lapack_int lda,
-                          float beta, lapack_complex_float* c );
-lapack_int LAPACKE_zhfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, double alpha,
-                          const lapack_complex_double* a, lapack_int lda,
-                          double beta, lapack_complex_double* c );
-
-lapack_int LAPACKE_shgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           float* h, lapack_int ldh, float* t, lapack_int ldt,
-                           float* alphar, float* alphai, float* beta, float* q,
-                           lapack_int ldq, float* z, lapack_int ldz );
-lapack_int LAPACKE_dhgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           double* h, lapack_int ldh, double* t, lapack_int ldt,
-                           double* alphar, double* alphai, double* beta,
-                           double* q, lapack_int ldq, double* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_chgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_float* h, lapack_int ldh,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta, lapack_complex_float* q,
-                           lapack_int ldq, lapack_complex_float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_zhgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_double* h, lapack_int ldh,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zhpcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_chpev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_float* ap, float* w,
-                          lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_double* ap, double* w,
-                          lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_float* ap, float* w,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_double* ap, double* w,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_float* ap, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhpevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_double* ap, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chpgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_float* ap,
-                           const lapack_complex_float* bp );
-lapack_int LAPACKE_zhpgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_double* ap,
-                           const lapack_complex_double* bp );
-
-lapack_int LAPACKE_chpgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_float* ap,
-                          lapack_complex_float* bp, float* w,
-                          lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_double* ap,
-                          lapack_complex_double* bp, double* w,
-                          lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_float* ap,
-                           lapack_complex_float* bp, float* w,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_double* ap,
-                           lapack_complex_double* bp, double* w,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_float* ap, lapack_complex_float* bp,
-                           float vl, float vu, lapack_int il, lapack_int iu,
-                           float abstol, lapack_int* m, float* w,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_zhpgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_double* ap, lapack_complex_double* bp,
-                           double vl, double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_complex_float* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zhprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_complex_double* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_chpsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* ap,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zhpsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* ap,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_chpsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           lapack_complex_float* afp, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zhpsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           lapack_complex_double* afp, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_chptrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, float* d, float* e,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zhptrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, double* d, double* e,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_chptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zhptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_chptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, const lapack_int* ipiv );
-lapack_int LAPACKE_zhptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, const lapack_int* ipiv );
-
-lapack_int LAPACKE_chptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zhptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_shsein( int matrix_order, char job, char eigsrc, char initv,
-                           lapack_logical* select, lapack_int n, const float* h,
-                           lapack_int ldh, float* wr, const float* wi,
-                           float* vl, lapack_int ldvl, float* vr,
-                           lapack_int ldvr, lapack_int mm, lapack_int* m,
-                           lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_dhsein( int matrix_order, char job, char eigsrc, char initv,
-                           lapack_logical* select, lapack_int n,
-                           const double* h, lapack_int ldh, double* wr,
-                           const double* wi, double* vl, lapack_int ldvl,
-                           double* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m, lapack_int* ifaill,
-                           lapack_int* ifailr );
-lapack_int LAPACKE_chsein( int matrix_order, char job, char eigsrc, char initv,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* h, lapack_int ldh,
-                           lapack_complex_float* w, lapack_complex_float* vl,
-                           lapack_int ldvl, lapack_complex_float* vr,
-                           lapack_int ldvr, lapack_int mm, lapack_int* m,
-                           lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_zhsein( int matrix_order, char job, char eigsrc, char initv,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* h, lapack_int ldh,
-                           lapack_complex_double* w, lapack_complex_double* vl,
-                           lapack_int ldvl, lapack_complex_double* vr,
-                           lapack_int ldvr, lapack_int mm, lapack_int* m,
-                           lapack_int* ifaill, lapack_int* ifailr );
-
-lapack_int LAPACKE_shseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, float* h,
-                           lapack_int ldh, float* wr, float* wi, float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_dhseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, double* h,
-                           lapack_int ldh, double* wr, double* wi, double* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_chseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi,
-                           lapack_complex_float* h, lapack_int ldh,
-                           lapack_complex_float* w, lapack_complex_float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_zhseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi,
-                           lapack_complex_double* h, lapack_int ldh,
-                           lapack_complex_double* w, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_clacgv( lapack_int n, lapack_complex_float* x,
-                           lapack_int incx );
-lapack_int LAPACKE_zlacgv( lapack_int n, lapack_complex_double* x,
-                           lapack_int incx );
-
-lapack_int LAPACKE_slacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const float* a, lapack_int lda, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dlacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const double* a, lapack_int lda, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_clacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zlacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_zlag2c( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_float* sa, lapack_int ldsa );
-
-lapack_int LAPACKE_slag2d( int matrix_order, lapack_int m, lapack_int n,
-                           const float* sa, lapack_int ldsa, double* a,
-                           lapack_int lda );
-
-lapack_int LAPACKE_dlag2s( int matrix_order, lapack_int m, lapack_int n,
-                           const double* a, lapack_int lda, float* sa,
-                           lapack_int ldsa );
-
-lapack_int LAPACKE_clag2z( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_float* sa, lapack_int ldsa,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* d,
-                           float* a, lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_dlagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* d,
-                           double* a, lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_clagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* d,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* iseed );
-lapack_int LAPACKE_zlagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* d,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* iseed );
-
-float LAPACKE_slamch( char cmach );
-double LAPACKE_dlamch( char cmach );
-
-float LAPACKE_slange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const float* a, lapack_int lda );
-double LAPACKE_dlange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const double* a, lapack_int lda );
-float LAPACKE_clange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda );
-double LAPACKE_zlange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda );
-
-float LAPACKE_clanhe( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda );
-double LAPACKE_zlanhe( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda );
-
-float LAPACKE_slansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const float* a, lapack_int lda );
-double LAPACKE_dlansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const double* a, lapack_int lda );
-float LAPACKE_clansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda );
-double LAPACKE_zlansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda );
-
-float LAPACKE_slantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const float* a,
-                           lapack_int lda );
-double LAPACKE_dlantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const double* a,
-                           lapack_int lda );
-float LAPACKE_clantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda );
-double LAPACKE_zlantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda );
-
-
-lapack_int LAPACKE_slarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const float* v, lapack_int ldv,
-                           const float* t, lapack_int ldt, float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_dlarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const double* v, lapack_int ldv,
-                           const double* t, lapack_int ldt, double* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_clarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const lapack_complex_float* v,
-                           lapack_int ldv, const lapack_complex_float* t,
-                           lapack_int ldt, lapack_complex_float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_zlarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const lapack_complex_double* v,
-                           lapack_int ldv, const lapack_complex_double* t,
-                           lapack_int ldt, lapack_complex_double* c,
-                           lapack_int ldc );
-
-lapack_int LAPACKE_slarfg( lapack_int n, float* alpha, float* x,
-                           lapack_int incx, float* tau );
-lapack_int LAPACKE_dlarfg( lapack_int n, double* alpha, double* x,
-                           lapack_int incx, double* tau );
-lapack_int LAPACKE_clarfg( lapack_int n, lapack_complex_float* alpha,
-                           lapack_complex_float* x, lapack_int incx,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zlarfg( lapack_int n, lapack_complex_double* alpha,
-                           lapack_complex_double* x, lapack_int incx,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_slarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k, const float* v,
-                           lapack_int ldv, const float* tau, float* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_dlarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k, const double* v,
-                           lapack_int ldv, const double* tau, double* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_clarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k,
-                           const lapack_complex_float* v, lapack_int ldv,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zlarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k,
-                           const lapack_complex_double* v, lapack_int ldv,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_slarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const float* v, float tau, float* c,
-                           lapack_int ldc, float* work );
-lapack_int LAPACKE_dlarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const double* v, double tau, double* c,
-                           lapack_int ldc, double* work );
-lapack_int LAPACKE_clarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const lapack_complex_float* v,
-                           lapack_complex_float tau, lapack_complex_float* c,
-                           lapack_int ldc, lapack_complex_float* work );
-lapack_int LAPACKE_zlarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const lapack_complex_double* v,
-                           lapack_complex_double tau, lapack_complex_double* c,
-                           lapack_int ldc, lapack_complex_double* work );
-
-lapack_int LAPACKE_slarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           float* x );
-lapack_int LAPACKE_dlarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           double* x );
-lapack_int LAPACKE_clarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           lapack_complex_float* x );
-lapack_int LAPACKE_zlarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           lapack_complex_double* x );
-
-lapack_int LAPACKE_slaset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, float alpha, float beta, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dlaset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, double alpha, double beta, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_claset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, lapack_complex_float alpha,
-                           lapack_complex_float beta, lapack_complex_float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_zlaset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, lapack_complex_double alpha,
-                           lapack_complex_double beta, lapack_complex_double* a,
-                           lapack_int lda );
-
-lapack_int LAPACKE_slasrt( char id, lapack_int n, float* d );
-lapack_int LAPACKE_dlasrt( char id, lapack_int n, double* d );
-
-lapack_int LAPACKE_slaswp( int matrix_order, lapack_int n, float* a,
-                           lapack_int lda, lapack_int k1, lapack_int k2,
-                           const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_dlaswp( int matrix_order, lapack_int n, double* a,
-                           lapack_int lda, lapack_int k1, lapack_int k2,
-                           const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_claswp( int matrix_order, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int k1, lapack_int k2, const lapack_int* ipiv,
-                           lapack_int incx );
-lapack_int LAPACKE_zlaswp( int matrix_order, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int k1, lapack_int k2, const lapack_int* ipiv,
-                           lapack_int incx );
-
-lapack_int LAPACKE_slatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, float* d,
-                           lapack_int mode, float cond, float dmax,
-                           lapack_int kl, lapack_int ku, char pack, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dlatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, double* d,
-                           lapack_int mode, double cond, double dmax,
-                           lapack_int kl, lapack_int ku, char pack, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_clatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, float* d,
-                           lapack_int mode, float cond, float dmax,
-                           lapack_int kl, lapack_int ku, char pack,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, double* d,
-                           lapack_int mode, double cond, double dmax,
-                           lapack_int kl, lapack_int ku, char pack,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slauum( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dlauum( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_clauum( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlauum( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_sopgtr( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, const float* tau, float* q,
-                           lapack_int ldq );
-lapack_int LAPACKE_dopgtr( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, const double* tau, double* q,
-                           lapack_int ldq );
-
-lapack_int LAPACKE_sopmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const float* ap,
-                           const float* tau, float* c, lapack_int ldc );
-lapack_int LAPACKE_dopmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const double* ap,
-                           const double* tau, double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sorgbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, double* a,
-                           lapack_int lda, const double* tau );
-
-lapack_int LAPACKE_sorghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgtr( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, const float* tau );
-lapack_int LAPACKE_dorgtr( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, const double* tau );
-
-lapack_int LAPACKE_sormbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const float* a, lapack_int lda,
-                           const float* tau, float* c, lapack_int ldc );
-lapack_int LAPACKE_dormhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const double* a, lapack_int lda,
-                           const double* tau, double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const float* a, lapack_int lda,
-                           const float* tau, float* c, lapack_int ldc );
-lapack_int LAPACKE_dormrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const double* a, lapack_int lda,
-                           const double* tau, double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const float* a,
-                           lapack_int lda, const float* tau, float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_dormtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const double* a,
-                           lapack_int lda, const double* tau, double* c,
-                           lapack_int ldc );
-
-lapack_int LAPACKE_spbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const float* ab, lapack_int ldab,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_dpbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const double* ab, lapack_int ldab,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cpbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_float* ab,
-                           lapack_int ldab, float anorm, float* rcond );
-lapack_int LAPACKE_zpbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_double* ab,
-                           lapack_int ldab, double anorm, double* rcond );
-
-lapack_int LAPACKE_spbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const float* ab, lapack_int ldab,
-                           float* s, float* scond, float* amax );
-lapack_int LAPACKE_dpbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const double* ab, lapack_int ldab,
-                           double* s, double* scond, double* amax );
-lapack_int LAPACKE_cpbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_float* ab,
-                           lapack_int ldab, float* s, float* scond,
-                           float* amax );
-lapack_int LAPACKE_zpbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_double* ab,
-                           lapack_int ldab, double* s, double* scond,
-                           double* amax );
-
-lapack_int LAPACKE_spbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const float* ab,
-                           lapack_int ldab, const float* afb, lapack_int ldafb,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dpbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const double* ab,
-                           lapack_int ldab, const double* afb, lapack_int ldafb,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cpbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* afb, lapack_int ldafb,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zpbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* afb, lapack_int ldafb,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_spbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, float* bb, lapack_int ldbb );
-lapack_int LAPACKE_dpbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, double* bb, lapack_int ldbb );
-lapack_int LAPACKE_cpbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, lapack_complex_float* bb,
-                           lapack_int ldbb );
-lapack_int LAPACKE_zpbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, lapack_complex_double* bb,
-                           lapack_int ldbb );
-
-lapack_int LAPACKE_spbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs, float* ab,
-                          lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs, double* ab,
-                          lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs,
-                          lapack_complex_float* ab, lapack_int ldab,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs,
-                          lapack_complex_double* ab, lapack_int ldab,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, float* ab,
-                           lapack_int ldab, float* afb, lapack_int ldafb,
-                           char* equed, float* s, float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, double* ab,
-                           lapack_int ldab, double* afb, lapack_int ldafb,
-                           char* equed, double* s, double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* afb, lapack_int ldafb,
-                           char* equed, float* s, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* afb, lapack_int ldafb,
-                           char* equed, double* s, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, double* rcond, double* ferr,
-                           double* berr );
-
-lapack_int LAPACKE_spbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, float* ab, lapack_int ldab );
-lapack_int LAPACKE_dpbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, double* ab, lapack_int ldab );
-lapack_int LAPACKE_cpbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_float* ab,
-                           lapack_int ldab );
-lapack_int LAPACKE_zpbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_double* ab,
-                           lapack_int ldab );
-
-lapack_int LAPACKE_spbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const float* ab,
-                           lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const double* ab,
-                           lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, float* a );
-lapack_int LAPACKE_dpftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, double* a );
-lapack_int LAPACKE_cpftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, float* a );
-lapack_int LAPACKE_dpftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, double* a );
-lapack_int LAPACKE_cpftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs, const float* a,
-                           float* b, lapack_int ldb );
-lapack_int LAPACKE_dpftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs, const double* a,
-                           double* b, lapack_int ldb );
-lapack_int LAPACKE_cpftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* a,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* a,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spocon( int matrix_order, char uplo, lapack_int n,
-                           const float* a, lapack_int lda, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dpocon( int matrix_order, char uplo, lapack_int n,
-                           const double* a, lapack_int lda, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_cpocon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_zpocon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double anorm, double* rcond );
-
-lapack_int LAPACKE_spoequ( int matrix_order, lapack_int n, const float* a,
-                           lapack_int lda, float* s, float* scond,
-                           float* amax );
-lapack_int LAPACKE_dpoequ( int matrix_order, lapack_int n, const double* a,
-                           lapack_int lda, double* s, double* scond,
-                           double* amax );
-lapack_int LAPACKE_cpoequ( int matrix_order, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequ( int matrix_order, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_spoequb( int matrix_order, lapack_int n, const float* a,
-                            lapack_int lda, float* s, float* scond,
-                            float* amax );
-lapack_int LAPACKE_dpoequb( int matrix_order, lapack_int n, const double* a,
-                            lapack_int lda, double* s, double* scond,
-                            double* amax );
-lapack_int LAPACKE_cpoequb( int matrix_order, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequb( int matrix_order, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_sporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const float* af, lapack_int ldaf, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const double* af, lapack_int ldaf, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_zporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-
-lapack_int LAPACKE_sporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const float* a,
-                            lapack_int lda, const float* af, lapack_int ldaf,
-                            const float* s, const float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const double* a,
-                            lapack_int lda, const double* af, lapack_int ldaf,
-                            const double* s, const double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const float* s, const lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const double* s, const lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_sposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* a, lapack_int lda, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* a, lapack_int lda, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* a,
-                          lapack_int lda, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dsposv( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           lapack_int* iter );
-lapack_int LAPACKE_zcposv( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, lapack_int* iter );
-
-lapack_int LAPACKE_sposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* af,
-                           lapack_int ldaf, char* equed, float* s, float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_dposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* af, lapack_int ldaf, char* equed, double* s,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-lapack_int LAPACKE_cposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* af,
-                           lapack_int ldaf, char* equed, float* s,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* af,
-                           lapack_int ldaf, char* equed, double* s,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_sposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, float* a,
-                            lapack_int lda, float* af, lapack_int ldaf,
-                            char* equed, float* s, float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond,
-                            float* rpvgrw, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_dposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, double* a,
-                            lapack_int lda, double* af, lapack_int ldaf,
-                            char* equed, double* s, double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* rpvgrw, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            char* equed, float* s, lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* rpvgrw,
-                            float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            char* equed, double* s, lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* rpvgrw,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_spotrf( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dpotrf( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_cpotrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotri( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dpotri( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_cpotri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           float* b, lapack_int ldb );
-lapack_int LAPACKE_dpotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           double* b, lapack_int ldb );
-lapack_int LAPACKE_cpotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zpotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_sppcon( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, float anorm, float* rcond );
-lapack_int LAPACKE_dppcon( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, double anorm, double* rcond );
-lapack_int LAPACKE_cppcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_zppcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sppequ( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, float* s, float* scond,
-                           float* amax );
-lapack_int LAPACKE_dppequ( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, double* s, double* scond,
-                           double* amax );
-lapack_int LAPACKE_cppequ( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap, float* s,
-                           float* scond, float* amax );
-lapack_int LAPACKE_zppequ( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap, double* s,
-                           double* scond, double* amax );
-
-lapack_int LAPACKE_spprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, const float* afp,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dpprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, const double* afp,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cpprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_complex_float* afp,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zpprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_complex_double* afp,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* ap, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* ap, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* ap,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* ap,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, float* ap, float* afp, char* equed,
-                           float* s, float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, double* ap, double* afp,
-                           char* equed, double* s, double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* ap,
-                           lapack_complex_float* afp, char* equed, float* s,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* ap,
-                           lapack_complex_double* afp, char* equed, double* s,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_spptrf( int matrix_order, char uplo, lapack_int n,
-                           float* ap );
-lapack_int LAPACKE_dpptrf( int matrix_order, char uplo, lapack_int n,
-                           double* ap );
-lapack_int LAPACKE_cpptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_zpptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptri( int matrix_order, char uplo, lapack_int n,
-                           float* ap );
-lapack_int LAPACKE_dpptri( int matrix_order, char uplo, lapack_int n,
-                           double* ap );
-lapack_int LAPACKE_cpptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_zpptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dpptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_cpptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spstrf( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, lapack_int* piv, lapack_int* rank,
-                           float tol );
-lapack_int LAPACKE_dpstrf( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, lapack_int* piv, lapack_int* rank,
-                           double tol );
-lapack_int LAPACKE_cpstrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* piv, lapack_int* rank, float tol );
-lapack_int LAPACKE_zpstrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* piv, lapack_int* rank, double tol );
-
-lapack_int LAPACKE_sptcon( lapack_int n, const float* d, const float* e,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_dptcon( lapack_int n, const double* d, const double* e,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cptcon( lapack_int n, const float* d,
-                           const lapack_complex_float* e, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_zptcon( lapack_int n, const double* d,
-                           const lapack_complex_double* e, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_spteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dpteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, double* z, lapack_int ldz );
-lapack_int LAPACKE_cpteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zpteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_sptrfs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const float* d, const float* e, const float* df,
-                           const float* ef, const float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dptrfs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const double* d, const double* e, const double* df,
-                           const double* ef, const double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* ferr,
-                           double* berr );
-lapack_int LAPACKE_cptrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* d,
-                           const lapack_complex_float* e, const float* df,
-                           const lapack_complex_float* ef,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zptrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* d,
-                           const lapack_complex_double* e, const double* df,
-                           const lapack_complex_double* ef,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* d, float* e, float* b, lapack_int ldb );
-lapack_int LAPACKE_dptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* d, double* e, double* b, lapack_int ldb );
-lapack_int LAPACKE_cptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* d, lapack_complex_float* e,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* d, lapack_complex_double* e,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const float* d, const float* e,
-                           float* df, float* ef, const float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const double* d, const double* e,
-                           double* df, double* ef, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-lapack_int LAPACKE_cptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const float* d,
-                           const lapack_complex_float* e, float* df,
-                           lapack_complex_float* ef,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const double* d,
-                           const lapack_complex_double* e, double* df,
-                           lapack_complex_double* ef,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_spttrf( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dpttrf( lapack_int n, double* d, double* e );
-lapack_int LAPACKE_cpttrf( lapack_int n, float* d, lapack_complex_float* e );
-lapack_int LAPACKE_zpttrf( lapack_int n, double* d, lapack_complex_double* e );
-
-lapack_int LAPACKE_spttrs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const float* d, const float* e, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dpttrs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const double* d, const double* e, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_cpttrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* d,
-                           const lapack_complex_float* e,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpttrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* d,
-                           const lapack_complex_double* e,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, float* ab, lapack_int ldab, float* w,
-                          float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, double* ab, lapack_int ldab, double* w,
-                          double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, float* ab, lapack_int ldab, float* w,
-                           float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, double* ab, lapack_int ldab,
-                           double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd, float* ab,
-                           lapack_int ldab, float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd, double* ab,
-                           lapack_int ldab, double* q, lapack_int ldq,
-                           double vl, double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, float* ab,
-                           lapack_int ldab, const float* bb, lapack_int ldbb,
-                           float* x, lapack_int ldx );
-lapack_int LAPACKE_dsbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, double* ab,
-                           lapack_int ldab, const double* bb, lapack_int ldbb,
-                           double* x, lapack_int ldx );
-
-lapack_int LAPACKE_ssbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb, float* ab,
-                          lapack_int ldab, float* bb, lapack_int ldbb, float* w,
-                          float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb, double* ab,
-                          lapack_int ldab, double* bb, lapack_int ldbb,
-                          double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, float* ab,
-                           lapack_int ldab, float* bb, lapack_int ldbb,
-                           float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, double* ab,
-                           lapack_int ldab, double* bb, lapack_int ldbb,
-                           double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           float* ab, lapack_int ldab, float* bb,
-                           lapack_int ldbb, float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           double* ab, lapack_int ldab, double* bb,
-                           lapack_int ldbb, double* q, lapack_int ldq,
-                           double vl, double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, float* ab, lapack_int ldab, float* d,
-                           float* e, float* q, lapack_int ldq );
-lapack_int LAPACKE_dsbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, double* ab, lapack_int ldab,
-                           double* d, double* e, double* q, lapack_int ldq );
-
-lapack_int LAPACKE_ssfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, float alpha,
-                          const float* a, lapack_int lda, float beta,
-                          float* c );
-lapack_int LAPACKE_dsfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, double alpha,
-                          const double* a, lapack_int lda, double beta,
-                          double* c );
-
-lapack_int LAPACKE_sspcon( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, const lapack_int* ipiv, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dspcon( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, const lapack_int* ipiv,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cspcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zspcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sspev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          float* ap, float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          double* ap, double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           float* ap, float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           double* ap, double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, float* ap, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dspevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, double* ap, double vl, double vu,
-                           lapack_int il, lapack_int iu, double abstol,
-                           lapack_int* m, double* w, double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_sspgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, float* ap, const float* bp );
-lapack_int LAPACKE_dspgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, double* ap, const double* bp );
-
-lapack_int LAPACKE_sspgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, float* ap, float* bp,
-                          float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, double* ap, double* bp,
-                          double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, float* ap, float* bp,
-                           float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, double* ap, double* bp,
-                           double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, float* ap,
-                           float* bp, float vl, float vu, lapack_int il,
-                           lapack_int iu, float abstol, lapack_int* m, float* w,
-                           float* z, lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_dspgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, double* ap,
-                           double* bp, double vl, double vu, lapack_int il,
-                           lapack_int iu, double abstol, lapack_int* m,
-                           double* w, double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_ssprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, const float* afp,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dsprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, const double* afp,
-                           const lapack_int* ipiv, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_csprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_complex_float* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zsprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_complex_double* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* ap, lapack_int* ipiv,
-                          float* b, lapack_int ldb );
-lapack_int LAPACKE_dspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* ap, lapack_int* ipiv,
-                          double* b, lapack_int ldb );
-lapack_int LAPACKE_cspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* ap,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* ap,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_sspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, float* afp,
-                           lapack_int* ipiv, const float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, double* afp,
-                           lapack_int* ipiv, const double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           lapack_complex_float* afp, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           lapack_complex_double* afp, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_ssptrd( int matrix_order, char uplo, lapack_int n, float* ap,
-                           float* d, float* e, float* tau );
-lapack_int LAPACKE_dsptrd( int matrix_order, char uplo, lapack_int n,
-                           double* ap, double* d, double* e, double* tau );
-
-lapack_int LAPACKE_ssptrf( int matrix_order, char uplo, lapack_int n, float* ap,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_dsptrf( int matrix_order, char uplo, lapack_int n,
-                           double* ap, lapack_int* ipiv );
-lapack_int LAPACKE_csptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zsptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_ssptri( int matrix_order, char uplo, lapack_int n, float* ap,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_dsptri( int matrix_order, char uplo, lapack_int n,
-                           double* ap, const lapack_int* ipiv );
-lapack_int LAPACKE_csptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, const lapack_int* ipiv );
-lapack_int LAPACKE_zsptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, const lapack_int* ipiv );
-
-lapack_int LAPACKE_ssptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dsptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_csptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zsptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_sstebz( char range, char order, lapack_int n, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           const float* d, const float* e, lapack_int* m,
-                           lapack_int* nsplit, float* w, lapack_int* iblock,
-                           lapack_int* isplit );
-lapack_int LAPACKE_dstebz( char range, char order, lapack_int n, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, const double* d, const double* e,
-                           lapack_int* m, lapack_int* nsplit, double* w,
-                           lapack_int* iblock, lapack_int* isplit );
-
-lapack_int LAPACKE_sstedc( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dstedc( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, double* z, lapack_int ldz );
-lapack_int LAPACKE_cstedc( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zstedc( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_sstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_dstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* isuppz );
-lapack_int LAPACKE_cstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* isuppz );
-lapack_int LAPACKE_zstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* isuppz );
-
-lapack_int LAPACKE_sstein( int matrix_order, lapack_int n, const float* d,
-                           const float* e, lapack_int m, const float* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           float* z, lapack_int ldz, lapack_int* ifailv );
-lapack_int LAPACKE_dstein( int matrix_order, lapack_int n, const double* d,
-                           const double* e, lapack_int m, const double* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           double* z, lapack_int ldz, lapack_int* ifailv );
-lapack_int LAPACKE_cstein( int matrix_order, lapack_int n, const float* d,
-                           const float* e, lapack_int m, const float* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* ifailv );
-lapack_int LAPACKE_zstein( int matrix_order, lapack_int n, const double* d,
-                           const double* e, lapack_int m, const double* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifailv );
-
-lapack_int LAPACKE_sstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, lapack_int* m,
-                           float* w, float* z, lapack_int ldz, lapack_int nzc,
-                           lapack_int* isuppz, lapack_logical* tryrac );
-lapack_int LAPACKE_dstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           lapack_int* m, double* w, double* z, lapack_int ldz,
-                           lapack_int nzc, lapack_int* isuppz,
-                           lapack_logical* tryrac );
-lapack_int LAPACKE_cstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, lapack_int* m,
-                           float* w, lapack_complex_float* z, lapack_int ldz,
-                           lapack_int nzc, lapack_int* isuppz,
-                           lapack_logical* tryrac );
-lapack_int LAPACKE_zstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           lapack_int* m, double* w, lapack_complex_double* z,
-                           lapack_int ldz, lapack_int nzc, lapack_int* isuppz,
-                           lapack_logical* tryrac );
-
-lapack_int LAPACKE_ssteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dsteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, double* z, lapack_int ldz );
-lapack_int LAPACKE_csteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zsteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_ssterf( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dsterf( lapack_int n, double* d, double* e );
-
-lapack_int LAPACKE_sstev( int matrix_order, char jobz, lapack_int n, float* d,
-                          float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dstev( int matrix_order, char jobz, lapack_int n, double* d,
-                          double* e, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sstevd( int matrix_order, char jobz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dstevd( int matrix_order, char jobz, lapack_int n, double* d,
-                           double* e, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sstevr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_dstevr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* isuppz );
-
-lapack_int LAPACKE_sstevx( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dstevx( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssycon( int matrix_order, char uplo, lapack_int n,
-                           const float* a, lapack_int lda,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_dsycon( int matrix_order, char uplo, lapack_int n,
-                           const double* a, lapack_int lda,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_csycon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zsycon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_ssyequb( int matrix_order, char uplo, lapack_int n,
-                            const float* a, lapack_int lda, float* s,
-                            float* scond, float* amax );
-lapack_int LAPACKE_dsyequb( int matrix_order, char uplo, lapack_int n,
-                            const double* a, lapack_int lda, double* s,
-                            double* scond, double* amax );
-lapack_int LAPACKE_csyequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* s, float* scond, float* amax );
-lapack_int LAPACKE_zsyequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_ssyev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_dsyev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          double* a, lapack_int lda, double* w );
-
-lapack_int LAPACKE_ssyevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_dsyevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           double* a, lapack_int lda, double* w );
-
-lapack_int LAPACKE_ssyevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, float* a, lapack_int lda, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_dsyevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, double* a, lapack_int lda, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* isuppz );
-
-lapack_int LAPACKE_ssyevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, float* a, lapack_int lda, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsyevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, double* a, lapack_int lda, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssygst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, float* a, lapack_int lda,
-                           const float* b, lapack_int ldb );
-lapack_int LAPACKE_dsygst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, double* a, lapack_int lda,
-                           const double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssygv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, float* a, lapack_int lda,
-                          float* b, lapack_int ldb, float* w );
-lapack_int LAPACKE_dsygv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, double* a, lapack_int lda,
-                          double* b, lapack_int ldb, double* w );
-
-lapack_int LAPACKE_ssygvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, float* a, lapack_int lda,
-                           float* b, lapack_int ldb, float* w );
-lapack_int LAPACKE_dsygvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* w );
-
-lapack_int LAPACKE_ssygvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsygvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const float* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dsyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const double* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_csyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zsyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_ssyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const float* a,
-                            lapack_int lda, const float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* s,
-                            const float* b, lapack_int ldb, float* x,
-                            lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dsyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const double* a,
-                            lapack_int lda, const double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* s,
-                            const double* b, lapack_int ldb, double* x,
-                            lapack_int ldx, double* rcond, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-lapack_int LAPACKE_csyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* s,
-                            const lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zsyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* s,
-                            const lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_ssysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* a, lapack_int lda,
-                          lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dsysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* a, lapack_int lda,
-                          lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_csysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zsysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           float* af, lapack_int ldaf, lapack_int* ipiv,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dsysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           double* af, lapack_int ldaf, lapack_int* ipiv,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* rcond, double* ferr,
-                           double* berr );
-lapack_int LAPACKE_csysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zsysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_ssysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, float* a,
-                            lapack_int lda, float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* s, float* b,
-                            lapack_int ldb, float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dsysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, double* a,
-                            lapack_int lda, double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* s, double* b,
-                            lapack_int ldb, double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-lapack_int LAPACKE_csysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* s,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zsysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* s,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_ssytrd( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, float* d, float* e, float* tau );
-lapack_int LAPACKE_dsytrd( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, double* d, double* e, double* tau );
-
-lapack_int LAPACKE_ssytrf( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dsytrf( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_csytrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zsytrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_ssytri( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dsytri( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_csytri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_zsytri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv );
-
-lapack_int LAPACKE_ssytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dsytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_csytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zsytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd, const float* ab,
-                           lapack_int ldab, float* rcond );
-lapack_int LAPACKE_dtbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd, const double* ab,
-                           lapack_int ldab, double* rcond );
-lapack_int LAPACKE_ctbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           float* rcond );
-lapack_int LAPACKE_ztbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           double* rcond );
-
-lapack_int LAPACKE_stbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const float* ab, lapack_int ldab, const float* b,
-                           lapack_int ldb, const float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dtbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const double* ab, lapack_int ldab, const double* b,
-                           lapack_int ldb, const double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_ctbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_ztbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_stbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const float* ab, lapack_int ldab, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dtbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const double* ab, lapack_int ldab, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_ctbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          float alpha, const float* a, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dtfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          double alpha, const double* a, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_ctfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          lapack_complex_float alpha,
-                          const lapack_complex_float* a,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          lapack_complex_double alpha,
-                          const lapack_complex_double* a,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, float* a );
-lapack_int LAPACKE_dtftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, double* a );
-lapack_int LAPACKE_ctftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_ztftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_stfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* arf, float* ap );
-lapack_int LAPACKE_dtfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* arf, double* ap );
-lapack_int LAPACKE_ctfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* arf,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_ztfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* arf,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_stfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* arf, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dtfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* arf, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_ctfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* arf,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* arf,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_stgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const float* s, lapack_int lds, const float* p,
-                           lapack_int ldp, float* vl, lapack_int ldvl,
-                           float* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_dtgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const double* s, lapack_int lds, const double* p,
-                           lapack_int ldp, double* vl, lapack_int ldvl,
-                           double* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_ctgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* s, lapack_int lds,
-                           const lapack_complex_float* p, lapack_int ldp,
-                           lapack_complex_float* vl, lapack_int ldvl,
-                           lapack_complex_float* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* s, lapack_int lds,
-                           const lapack_complex_double* p, lapack_int ldp,
-                           lapack_complex_double* vl, lapack_int ldvl,
-                           lapack_complex_double* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-
-lapack_int LAPACKE_stgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb, float* q,
-                           lapack_int ldq, float* z, lapack_int ldz,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_dtgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb, double* q,
-                           lapack_int ldq, double* z, lapack_int ldz,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_ctgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_stgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           float* alphar, float* alphai, float* beta, float* q,
-                           lapack_int ldq, float* z, lapack_int ldz,
-                           lapack_int* m, float* pl, float* pr, float* dif );
-lapack_int LAPACKE_dtgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n,
-                           double* a, lapack_int lda, double* b, lapack_int ldb,
-                           double* alphar, double* alphai, double* beta,
-                           double* q, lapack_int ldq, double* z, lapack_int ldz,
-                           lapack_int* m, double* pl, double* pr, double* dif );
-lapack_int LAPACKE_ctgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta, lapack_complex_float* q,
-                           lapack_int ldq, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* m, float* pl, float* pr,
-                           float* dif );
-lapack_int LAPACKE_ztgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* m, double* pl, double* pr, double* dif );
-
-lapack_int LAPACKE_stgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, float* a, lapack_int lda,
-                           float* b, lapack_int ldb, float tola, float tolb,
-                           float* alpha, float* beta, float* u, lapack_int ldu,
-                           float* v, lapack_int ldv, float* q, lapack_int ldq,
-                           lapack_int* ncycle );
-lapack_int LAPACKE_dtgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double tola, double tolb, double* alpha,
-                           double* beta, double* u, lapack_int ldu, double* v,
-                           lapack_int ldv, double* q, lapack_int ldq,
-                           lapack_int* ncycle );
-lapack_int LAPACKE_ctgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float tola, float tolb, float* alpha,
-                           float* beta, lapack_complex_float* u, lapack_int ldu,
-                           lapack_complex_float* v, lapack_int ldv,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_int* ncycle );
-lapack_int LAPACKE_ztgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double tola, double tolb,
-                           double* alpha, double* beta,
-                           lapack_complex_double* u, lapack_int ldu,
-                           lapack_complex_double* v, lapack_int ldv,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_int* ncycle );
-
-lapack_int LAPACKE_stgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const float* a, lapack_int lda, const float* b,
-                           lapack_int ldb, const float* vl, lapack_int ldvl,
-                           const float* vr, lapack_int ldvr, float* s,
-                           float* dif, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_dtgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const double* a, lapack_int lda, const double* b,
-                           lapack_int ldb, const double* vl, lapack_int ldvl,
-                           const double* vr, lapack_int ldvr, double* s,
-                           double* dif, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ctgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* vl, lapack_int ldvl,
-                           const lapack_complex_float* vr, lapack_int ldvr,
-                           float* s, float* dif, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* vl, lapack_int ldvl,
-                           const lapack_complex_double* vr, lapack_int ldvr,
-                           double* s, double* dif, lapack_int mm,
-                           lapack_int* m );
-
-lapack_int LAPACKE_stgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n, const float* a,
-                           lapack_int lda, const float* b, lapack_int ldb,
-                           float* c, lapack_int ldc, const float* d,
-                           lapack_int ldd, const float* e, lapack_int lde,
-                           float* f, lapack_int ldf, float* scale, float* dif );
-lapack_int LAPACKE_dtgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n, const double* a,
-                           lapack_int lda, const double* b, lapack_int ldb,
-                           double* c, lapack_int ldc, const double* d,
-                           lapack_int ldd, const double* e, lapack_int lde,
-                           double* f, lapack_int ldf, double* scale,
-                           double* dif );
-lapack_int LAPACKE_ctgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* c, lapack_int ldc,
-                           const lapack_complex_float* d, lapack_int ldd,
-                           const lapack_complex_float* e, lapack_int lde,
-                           lapack_complex_float* f, lapack_int ldf,
-                           float* scale, float* dif );
-lapack_int LAPACKE_ztgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* c, lapack_int ldc,
-                           const lapack_complex_double* d, lapack_int ldd,
-                           const lapack_complex_double* e, lapack_int lde,
-                           lapack_complex_double* f, lapack_int ldf,
-                           double* scale, double* dif );
-
-lapack_int LAPACKE_stpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const float* ap, float* rcond );
-lapack_int LAPACKE_dtpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const double* ap, double* rcond );
-lapack_int LAPACKE_ctpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_float* ap,
-                           float* rcond );
-lapack_int LAPACKE_ztpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_double* ap,
-                           double* rcond );
-
-lapack_int LAPACKE_stprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* ap,
-                           const float* b, lapack_int ldb, const float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dtprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* ap,
-                           const double* b, lapack_int ldb, const double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_ctprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* ap,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_ztprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* ap,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_stptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           float* ap );
-lapack_int LAPACKE_dtptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           double* ap );
-lapack_int LAPACKE_ctptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_ztptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_stptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* ap,
-                           float* b, lapack_int ldb );
-lapack_int LAPACKE_dtptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* ap,
-                           double* b, lapack_int ldb );
-lapack_int LAPACKE_ctptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* ap,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* ap,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* ap, float* arf );
-lapack_int LAPACKE_dtpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* ap, double* arf );
-lapack_int LAPACKE_ctpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* ap,
-                           lapack_complex_float* arf );
-lapack_int LAPACKE_ztpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* ap,
-                           lapack_complex_double* arf );
-
-lapack_int LAPACKE_stpttr( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, float* a, lapack_int lda );
-lapack_int LAPACKE_dtpttr( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, double* a, lapack_int lda );
-lapack_int LAPACKE_ctpttr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztpttr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_strcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const float* a, lapack_int lda,
-                           float* rcond );
-lapack_int LAPACKE_dtrcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const double* a, lapack_int lda,
-                           double* rcond );
-lapack_int LAPACKE_ctrcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda, float* rcond );
-lapack_int LAPACKE_ztrcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda, double* rcond );
-
-lapack_int LAPACKE_strevc( int matrix_order, char side, char howmny,
-                           lapack_logical* select, lapack_int n, const float* t,
-                           lapack_int ldt, float* vl, lapack_int ldvl,
-                           float* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_dtrevc( int matrix_order, char side, char howmny,
-                           lapack_logical* select, lapack_int n,
-                           const double* t, lapack_int ldt, double* vl,
-                           lapack_int ldvl, double* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ctrevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* vl, lapack_int ldvl,
-                           lapack_complex_float* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztrevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* vl, lapack_int ldvl,
-                           lapack_complex_double* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-
-lapack_int LAPACKE_strexc( int matrix_order, char compq, lapack_int n, float* t,
-                           lapack_int ldt, float* q, lapack_int ldq,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_dtrexc( int matrix_order, char compq, lapack_int n,
-                           double* t, lapack_int ldt, double* q, lapack_int ldq,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_ctrexc( int matrix_order, char compq, lapack_int n,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztrexc( int matrix_order, char compq, lapack_int n,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_strrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* a,
-                           lapack_int lda, const float* b, lapack_int ldb,
-                           const float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dtrrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* a,
-                           lapack_int lda, const double* b, lapack_int ldb,
-                           const double* x, lapack_int ldx, double* ferr,
-                           double* berr );
-lapack_int LAPACKE_ctrrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_ztrrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_strsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n, float* t,
-                           lapack_int ldt, float* q, lapack_int ldq, float* wr,
-                           float* wi, lapack_int* m, float* s, float* sep );
-lapack_int LAPACKE_dtrsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n,
-                           double* t, lapack_int ldt, double* q, lapack_int ldq,
-                           double* wr, double* wi, lapack_int* m, double* s,
-                           double* sep );
-lapack_int LAPACKE_ctrsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* w, lapack_int* m, float* s,
-                           float* sep );
-lapack_int LAPACKE_ztrsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* w, lapack_int* m, double* s,
-                           double* sep );
-
-lapack_int LAPACKE_strsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const float* t, lapack_int ldt, const float* vl,
-                           lapack_int ldvl, const float* vr, lapack_int ldvr,
-                           float* s, float* sep, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_dtrsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const double* t, lapack_int ldt, const double* vl,
-                           lapack_int ldvl, const double* vr, lapack_int ldvr,
-                           double* s, double* sep, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_ctrsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* t, lapack_int ldt,
-                           const lapack_complex_float* vl, lapack_int ldvl,
-                           const lapack_complex_float* vr, lapack_int ldvr,
-                           float* s, float* sep, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztrsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* t, lapack_int ldt,
-                           const lapack_complex_double* vl, lapack_int ldvl,
-                           const lapack_complex_double* vr, lapack_int ldvr,
-                           double* s, double* sep, lapack_int mm,
-                           lapack_int* m );
-
-lapack_int LAPACKE_strsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const float* a, lapack_int lda, const float* b,
-                           lapack_int ldb, float* c, lapack_int ldc,
-                           float* scale );
-lapack_int LAPACKE_dtrsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const double* a, lapack_int lda, const double* b,
-                           lapack_int ldb, double* c, lapack_int ldc,
-                           double* scale );
-lapack_int LAPACKE_ctrsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* c, lapack_int ldc,
-                           float* scale );
-lapack_int LAPACKE_ztrsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* c, lapack_int ldc,
-                           double* scale );
-
-lapack_int LAPACKE_strtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           float* a, lapack_int lda );
-lapack_int LAPACKE_dtrtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           double* a, lapack_int lda );
-lapack_int LAPACKE_ctrtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztrtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_strtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* a,
-                           lapack_int lda, float* b, lapack_int ldb );
-lapack_int LAPACKE_dtrtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* a,
-                           lapack_int lda, double* b, lapack_int ldb );
-lapack_int LAPACKE_ctrtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztrtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_strttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* a, lapack_int lda,
-                           float* arf );
-lapack_int LAPACKE_dtrttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* a, lapack_int lda,
-                           double* arf );
-lapack_int LAPACKE_ctrttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* arf );
-lapack_int LAPACKE_ztrttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* arf );
-
-lapack_int LAPACKE_strttp( int matrix_order, char uplo, lapack_int n,
-                           const float* a, lapack_int lda, float* ap );
-lapack_int LAPACKE_dtrttp( int matrix_order, char uplo, lapack_int n,
-                           const double* a, lapack_int lda, double* ap );
-lapack_int LAPACKE_ctrttp( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_ztrttp( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_stzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dtzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_ctzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_ztzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cunghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zunghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cunglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zunglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungtr( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau );
-lapack_int LAPACKE_zungtr( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cunmbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cupgtr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* q, lapack_int ldq );
-lapack_int LAPACKE_zupgtr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* q, lapack_int ldq );
-
-lapack_int LAPACKE_cupmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zupmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sbdsdc_work( int matrix_order, char uplo, char compq,
-                                lapack_int n, float* d, float* e, float* u,
-                                lapack_int ldu, float* vt, lapack_int ldvt,
-                                float* q, lapack_int* iq, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dbdsdc_work( int matrix_order, char uplo, char compq,
-                                lapack_int n, double* d, double* e, double* u,
-                                lapack_int ldu, double* vt, lapack_int ldvt,
-                                double* q, lapack_int* iq, double* work,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                float* d, float* e, float* vt, lapack_int ldvt,
-                                float* u, lapack_int ldu, float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_dbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                double* d, double* e, double* vt,
-                                lapack_int ldvt, double* u, lapack_int ldu,
-                                double* c, lapack_int ldc, double* work );
-lapack_int LAPACKE_cbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                float* d, float* e, lapack_complex_float* vt,
-                                lapack_int ldvt, lapack_complex_float* u,
-                                lapack_int ldu, lapack_complex_float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_zbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                double* d, double* e, lapack_complex_double* vt,
-                                lapack_int ldvt, lapack_complex_double* u,
-                                lapack_int ldu, lapack_complex_double* c,
-                                lapack_int ldc, double* work );
-
-lapack_int LAPACKE_sdisna_work( char job, lapack_int m, lapack_int n,
-                                const float* d, float* sep );
-lapack_int LAPACKE_ddisna_work( char job, lapack_int m, lapack_int n,
-                                const double* d, double* sep );
-
-lapack_int LAPACKE_sgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, float* ab, lapack_int ldab,
-                                float* d, float* e, float* q, lapack_int ldq,
-                                float* pt, lapack_int ldpt, float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_dgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, double* ab, lapack_int ldab,
-                                double* d, double* e, double* q, lapack_int ldq,
-                                double* pt, lapack_int ldpt, double* c,
-                                lapack_int ldc, double* work );
-lapack_int LAPACKE_cgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, lapack_complex_float* ab,
-                                lapack_int ldab, float* d, float* e,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* pt, lapack_int ldpt,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, lapack_complex_double* ab,
-                                lapack_int ldab, double* d, double* e,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* pt, lapack_int ldpt,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                double anorm, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* ab,
-                                lapack_int ldab, float* r, float* c,
-                                float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* ab,
-                                lapack_int ldab, double* r, double* c,
-                                double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                float* r, float* c, float* rowcnd,
-                                float* colcnd, float* amax );
-lapack_int LAPACKE_zgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, double* r, double* c,
-                                double* rowcnd, double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku, const float* ab,
-                                 lapack_int ldab, float* r, float* c,
-                                 float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku, const double* ab,
-                                 lapack_int ldab, double* r, double* c,
-                                 double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku,
-                                 const lapack_complex_float* ab,
-                                 lapack_int ldab, float* r, float* c,
-                                 float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_zgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku,
-                                 const lapack_complex_double* ab,
-                                 lapack_int ldab, double* r, double* c,
-                                 double* rowcnd, double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const float* ab, lapack_int ldab,
-                                const float* afb, lapack_int ldafb,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const double* ab, lapack_int ldab,
-                                const double* afb, lapack_int ldafb,
-                                const lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_complex_float* afb,
-                                lapack_int ldafb, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab,
-                                const lapack_complex_double* afb,
-                                lapack_int ldafb, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, const float* ab,
-                                 lapack_int ldab, const float* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const float* r, const float* c, const float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, const double* ab,
-                                 lapack_int ldab, const double* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs,
-                                 const lapack_complex_float* ab,
-                                 lapack_int ldab,
-                                 const lapack_complex_float* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const float* r, const float* c,
-                                 const lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs,
-                                 const lapack_complex_double* ab,
-                                 lapack_int ldab,
-                                 const lapack_complex_double* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs, float* ab,
-                               lapack_int ldab, lapack_int* ipiv, float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs, double* ab,
-                               lapack_int ldab, lapack_int* ipiv, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-
-lapack_int LAPACKE_sgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, float* ab, lapack_int ldab,
-                                float* afb, lapack_int ldafb, lapack_int* ipiv,
-                                char* equed, float* r, float* c, float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, double* ab, lapack_int ldab,
-                                double* afb, lapack_int ldafb, lapack_int* ipiv,
-                                char* equed, double* r, double* c, double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, lapack_complex_float* ab,
-                                lapack_int ldab, lapack_complex_float* afb,
-                                lapack_int ldafb, lapack_int* ipiv, char* equed,
-                                float* r, float* c, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* afb,
-                                lapack_int ldafb, lapack_int* ipiv, char* equed,
-                                double* r, double* c, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_sgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, float* ab, lapack_int ldab,
-                                 float* afb, lapack_int ldafb, lapack_int* ipiv,
-                                 char* equed, float* r, float* c, float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, double* ab, lapack_int ldab,
-                                 double* afb, lapack_int ldafb,
-                                 lapack_int* ipiv, char* equed, double* r,
-                                 double* c, double* b, lapack_int ldb,
-                                 double* x, lapack_int ldx, double* rcond,
-                                 double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, lapack_complex_float* ab,
-                                 lapack_int ldab, lapack_complex_float* afb,
-                                 lapack_int ldafb, lapack_int* ipiv,
-                                 char* equed, float* r, float* c,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, lapack_complex_double* ab,
-                                 lapack_int ldab, lapack_complex_double* afb,
-                                 lapack_int ldafb, lapack_int* ipiv,
-                                 char* equed, double* r, double* c,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, float* ab,
-                                lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_dgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, double* ab,
-                                lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_cgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_int* ipiv );
-lapack_int LAPACKE_zgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_int* ipiv );
-
-lapack_int LAPACKE_sgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const float* ab, lapack_int ldab,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const double* ab, lapack_int ldab,
-                                const lapack_int* ipiv, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* scale, lapack_int m, float* v,
-                                lapack_int ldv );
-lapack_int LAPACKE_dgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* scale, lapack_int m, double* v,
-                                lapack_int ldv );
-lapack_int LAPACKE_cgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* scale, lapack_int m,
-                                lapack_complex_float* v, lapack_int ldv );
-lapack_int LAPACKE_zgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* scale, lapack_int m,
-                                lapack_complex_double* v, lapack_int ldv );
-
-lapack_int LAPACKE_sgebal_work( int matrix_order, char job, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ilo,
-                                lapack_int* ihi, float* scale );
-lapack_int LAPACKE_dgebal_work( int matrix_order, char job, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ilo,
-                                lapack_int* ihi, double* scale );
-lapack_int LAPACKE_cgebal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ilo, lapack_int* ihi,
-                                float* scale );
-lapack_int LAPACKE_zgebal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ilo, lapack_int* ihi,
-                                double* scale );
-
-lapack_int LAPACKE_sgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* d, float* e,
-                                float* tauq, float* taup, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* d, double* e,
-                                double* tauq, double* taup, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_cgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float* d, float* e, lapack_complex_float* tauq,
-                                lapack_complex_float* taup,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double* d, double* e,
-                                lapack_complex_double* tauq,
-                                lapack_complex_double* taup,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgecon_work( int matrix_order, char norm, lapack_int n,
-                                const float* a, lapack_int lda, float anorm,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgecon_work( int matrix_order, char norm, lapack_int n,
-                                const double* a, lapack_int lda, double anorm,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgecon_work( int matrix_order, char norm, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float anorm, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgecon_work( int matrix_order, char norm, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const float* a, lapack_int lda, float* r,
-                                float* c, float* rowcnd, float* colcnd,
-                                float* amax );
-lapack_int LAPACKE_dgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda, double* r,
-                                double* c, double* rowcnd, double* colcnd,
-                                double* amax );
-lapack_int LAPACKE_cgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* r, float* c, float* rowcnd,
-                                float* colcnd, float* amax );
-lapack_int LAPACKE_zgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* r, double* c, double* rowcnd,
-                                double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const float* a, lapack_int lda, float* r,
-                                 float* c, float* rowcnd, float* colcnd,
-                                 float* amax );
-lapack_int LAPACKE_dgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const double* a, lapack_int lda, double* r,
-                                 double* c, double* rowcnd, double* colcnd,
-                                 double* amax );
-lapack_int LAPACKE_cgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* r, float* c, float* rowcnd,
-                                 float* colcnd, float* amax );
-lapack_int LAPACKE_zgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* r, double* c, double* rowcnd,
-                                 double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_S_SELECT2 select, lapack_int n, float* a,
-                               lapack_int lda, lapack_int* sdim, float* wr,
-                               float* wi, float* vs, lapack_int ldvs,
-                               float* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_dgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_D_SELECT2 select, lapack_int n, double* a,
-                               lapack_int lda, lapack_int* sdim, double* wr,
-                               double* wi, double* vs, lapack_int ldvs,
-                               double* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_cgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_C_SELECT1 select, lapack_int n,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_int* sdim, lapack_complex_float* w,
-                               lapack_complex_float* vs, lapack_int ldvs,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork, lapack_logical* bwork );
-lapack_int LAPACKE_zgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_Z_SELECT1 select, lapack_int n,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_int* sdim, lapack_complex_double* w,
-                               lapack_complex_double* vs, lapack_int ldvs,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_S_SELECT2 select, char sense,
-                                lapack_int n, float* a, lapack_int lda,
-                                lapack_int* sdim, float* wr, float* wi,
-                                float* vs, lapack_int ldvs, float* rconde,
-                                float* rcondv, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_dgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_D_SELECT2 select, char sense,
-                                lapack_int n, double* a, lapack_int lda,
-                                lapack_int* sdim, double* wr, double* wi,
-                                double* vs, lapack_int ldvs, double* rconde,
-                                double* rcondv, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_cgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_C_SELECT1 select, char sense,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_int* sdim,
-                                lapack_complex_float* w,
-                                lapack_complex_float* vs, lapack_int ldvs,
-                                float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_logical* bwork );
-lapack_int LAPACKE_zgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_Z_SELECT1 select, char sense,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_int* sdim,
-                                lapack_complex_double* w,
-                                lapack_complex_double* vs, lapack_int ldvs,
-                                double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, float* a, lapack_int lda,
-                               float* wr, float* wi, float* vl, lapack_int ldvl,
-                               float* vr, lapack_int ldvr, float* work,
-                               lapack_int lwork );
-lapack_int LAPACKE_dgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, double* a, lapack_int lda,
-                               double* wr, double* wi, double* vl,
-                               lapack_int ldvl, double* vr, lapack_int ldvr,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* w,
-                               lapack_complex_float* vl, lapack_int ldvl,
-                               lapack_complex_float* vr, lapack_int ldvr,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_double* a,
-                               lapack_int lda, lapack_complex_double* w,
-                               lapack_complex_double* vl, lapack_int ldvl,
-                               lapack_complex_double* vr, lapack_int ldvr,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork );
-
-lapack_int LAPACKE_sgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, float* a,
-                                lapack_int lda, float* wr, float* wi, float* vl,
-                                lapack_int ldvl, float* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, float* scale,
-                                float* abnrm, float* rconde, float* rcondv,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, double* a,
-                                lapack_int lda, double* wr, double* wi,
-                                double* vl, lapack_int ldvl, double* vr,
-                                lapack_int ldvr, lapack_int* ilo,
-                                lapack_int* ihi, double* scale, double* abnrm,
-                                double* rconde, double* rcondv, double* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_cgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* w,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, float* scale,
-                                float* abnrm, float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork );
-lapack_int LAPACKE_zgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* w,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, double* scale,
-                                double* abnrm, double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_sgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, float* a, lapack_int lda,
-                                float* tau, float* work, lapack_int lwork );
-lapack_int LAPACKE_dgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, double* a, lapack_int lda,
-                                double* tau, double* work, lapack_int lwork );
-lapack_int LAPACKE_cgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgejsv_work( int matrix_order, char joba, char jobu,
-                                char jobv, char jobr, char jobt, char jobp,
-                                lapack_int m, lapack_int n, float* a,
-                                lapack_int lda, float* sva, float* u,
-                                lapack_int ldu, float* v, lapack_int ldv,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgejsv_work( int matrix_order, char joba, char jobu,
-                                char jobv, char jobr, char jobt, char jobp,
-                                lapack_int m, lapack_int n, double* a,
-                                lapack_int lda, double* sva, double* u,
-                                lapack_int ldu, double* v, lapack_int ldv,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work );
-lapack_int LAPACKE_dgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work );
-lapack_int LAPACKE_cgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_sgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs, float* a,
-                               lapack_int lda, float* b, lapack_int ldb,
-                               float* work, lapack_int lwork );
-lapack_int LAPACKE_dgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs, double* a,
-                               lapack_int lda, double* b, lapack_int ldb,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_cgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* s,
-                                double rcond, lapack_int* rank, double* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_cgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_zgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, double* s, double rcond,
-                                lapack_int* rank, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* s,
-                                double rcond, lapack_int* rank, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_cgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, double* s, double rcond,
-                                lapack_int* rank, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_sgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, lapack_int* jpvt,
-                                float rcond, lapack_int* rank, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, lapack_int* jpvt,
-                                double rcond, lapack_int* rank, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_cgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_int* jpvt, float rcond,
-                                lapack_int* rank, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_int* jpvt, double rcond,
-                                lapack_int* rank, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_sgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* jpvt,
-                                float* tau, float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* jpvt,
-                                double* tau, double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork );
-lapack_int LAPACKE_zgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_sgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* jpvt,
-                                float* tau, float* work );
-lapack_int LAPACKE_dgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* jpvt,
-                                double* tau, double* work );
-lapack_int LAPACKE_cgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_float* tau,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_double* tau,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work );
-lapack_int LAPACKE_dgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work );
-lapack_int LAPACKE_cgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_sgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* tau,
-                                 float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* tau,
-                                 double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* tau,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* tau,
-                                 lapack_complex_double* work,
-                                 lapack_int lwork );
-
-lapack_int LAPACKE_sgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const float* af, lapack_int ldaf,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs, const float* a,
-                                 lapack_int lda, const float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* r, const float* c, const float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs, const double* a,
-                                 lapack_int lda, const double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* r, const float* c,
-                                 const lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* s, float* u, lapack_int ldu, float* vt,
-                                lapack_int ldvt, float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* s, double* u, lapack_int ldu,
-                                double* vt, lapack_int ldvt, double* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_cgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, float* s,
-                                lapack_complex_float* u, lapack_int ldu,
-                                lapack_complex_float* vt, lapack_int ldvt,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int* iwork );
-lapack_int LAPACKE_zgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, double* s,
-                                lapack_complex_double* u, lapack_int ldu,
-                                lapack_complex_double* vt, lapack_int ldvt,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int* iwork );
-
-lapack_int LAPACKE_sgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* a, lapack_int lda, lapack_int* ipiv,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* a, lapack_int lda, lapack_int* ipiv,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_cgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dsgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                double* a, lapack_int lda, lapack_int* ipiv,
-                                double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* work, float* swork,
-                                lapack_int* iter );
-lapack_int LAPACKE_zcgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, lapack_complex_double* work,
-                                lapack_complex_float* swork, double* rwork,
-                                lapack_int* iter );
-
-lapack_int LAPACKE_sgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n, float* a,
-                                lapack_int lda, float* s, float* u,
-                                lapack_int ldu, float* vt, lapack_int ldvt,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n, double* a,
-                                lapack_int lda, double* s, double* u,
-                                lapack_int ldu, double* vt, lapack_int ldvt,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float* s, lapack_complex_float* u,
-                                lapack_int ldu, lapack_complex_float* vt,
-                                lapack_int ldvt, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double* s, lapack_complex_double* u,
-                                lapack_int ldu, lapack_complex_double* vt,
-                                lapack_int ldvt, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_sgesvj_work( int matrix_order, char joba, char jobu,
-                                char jobv, lapack_int m, lapack_int n, float* a,
-                                lapack_int lda, float* sva, lapack_int mv,
-                                float* v, lapack_int ldv, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgesvj_work( int matrix_order, char joba, char jobu,
-                                char jobv, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* sva,
-                                lapack_int mv, double* v, lapack_int ldv,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, float* a,
-                                lapack_int lda, float* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, float* r,
-                                float* c, float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, double* a,
-                                lapack_int lda, double* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, double* r,
-                                double* c, double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, float* r,
-                                float* c, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, double* r,
-                                double* c, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_sgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs, float* a,
-                                 lapack_int lda, float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* r,
-                                 float* c, float* b, lapack_int ldb, float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs, double* a,
-                                 lapack_int lda, double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* r,
-                                 double* c, double* b, lapack_int ldb,
-                                 double* x, lapack_int ldx, double* rcond,
-                                 double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* r,
-                                 float* c, lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params,
-                                 lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* r,
-                                 double* c, lapack_complex_double* b,
-                                 lapack_int ldb, lapack_complex_double* x,
-                                 lapack_int ldx, double* rcond, double* rpvgrw,
-                                 double* berr, lapack_int n_err_bnds,
-                                 double* err_bnds_norm, double* err_bnds_comp,
-                                 lapack_int nparams, double* params,
-                                 lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv );
-lapack_int LAPACKE_zgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv );
-lapack_int LAPACKE_zgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetri_work( int matrix_order, lapack_int n, float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgetri_work( int matrix_order, lapack_int n, double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgetri_work( int matrix_order, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgetri_work( int matrix_order, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_cgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* lscale, const float* rscale,
-                                lapack_int m, float* v, lapack_int ldv );
-lapack_int LAPACKE_dggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* lscale, const double* rscale,
-                                lapack_int m, double* v, lapack_int ldv );
-lapack_int LAPACKE_cggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* lscale, const float* rscale,
-                                lapack_int m, lapack_complex_float* v,
-                                lapack_int ldv );
-lapack_int LAPACKE_zggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* lscale, const double* rscale,
-                                lapack_int m, lapack_complex_double* v,
-                                lapack_int ldv );
-
-lapack_int LAPACKE_sggbal_work( int matrix_order, char job, lapack_int n,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, lapack_int* ilo,
-                                lapack_int* ihi, float* lscale, float* rscale,
-                                float* work );
-lapack_int LAPACKE_dggbal_work( int matrix_order, char job, lapack_int n,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, lapack_int* ilo,
-                                lapack_int* ihi, double* lscale, double* rscale,
-                                double* work );
-lapack_int LAPACKE_cggbal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_int* ilo, lapack_int* ihi, float* lscale,
-                                float* rscale, float* work );
-lapack_int LAPACKE_zggbal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_int* ilo, lapack_int* ihi,
-                                double* lscale, double* rscale, double* work );
-
-lapack_int LAPACKE_sgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_S_SELECT3 selctg, lapack_int n,
-                               float* a, lapack_int lda, float* b,
-                               lapack_int ldb, lapack_int* sdim, float* alphar,
-                               float* alphai, float* beta, float* vsl,
-                               lapack_int ldvsl, float* vsr, lapack_int ldvsr,
-                               float* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_dgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_D_SELECT3 selctg, lapack_int n,
-                               double* a, lapack_int lda, double* b,
-                               lapack_int ldb, lapack_int* sdim, double* alphar,
-                               double* alphai, double* beta, double* vsl,
-                               lapack_int ldvsl, double* vsr, lapack_int ldvsr,
-                               double* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_cgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_C_SELECT2 selctg, lapack_int n,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_int* sdim, lapack_complex_float* alpha,
-                               lapack_complex_float* beta,
-                               lapack_complex_float* vsl, lapack_int ldvsl,
-                               lapack_complex_float* vsr, lapack_int ldvsr,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork, lapack_logical* bwork );
-lapack_int LAPACKE_zgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_Z_SELECT2 selctg, lapack_int n,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_int* sdim, lapack_complex_double* alpha,
-                               lapack_complex_double* beta,
-                               lapack_complex_double* vsl, lapack_int ldvsl,
-                               lapack_complex_double* vsr, lapack_int ldvsr,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_S_SELECT3 selctg, char sense,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, lapack_int* sdim,
-                                float* alphar, float* alphai, float* beta,
-                                float* vsl, lapack_int ldvsl, float* vsr,
-                                lapack_int ldvsr, float* rconde, float* rcondv,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_dggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_D_SELECT3 selctg, char sense,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, lapack_int* sdim,
-                                double* alphar, double* alphai, double* beta,
-                                double* vsl, lapack_int ldvsl, double* vsr,
-                                lapack_int ldvsr, double* rconde,
-                                double* rcondv, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_cggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_C_SELECT2 selctg, char sense,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_int* sdim,
-                                lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* vsl, lapack_int ldvsl,
-                                lapack_complex_float* vsr, lapack_int ldvsr,
-                                float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int* iwork,
-                                lapack_int liwork, lapack_logical* bwork );
-lapack_int LAPACKE_zggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_Z_SELECT2 selctg, char sense,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_int* sdim,
-                                lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* vsl, lapack_int ldvsl,
-                                lapack_complex_double* vsr, lapack_int ldvsr,
-                                double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int* iwork,
-                                lapack_int liwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, float* a, lapack_int lda, float* b,
-                               lapack_int ldb, float* alphar, float* alphai,
-                               float* beta, float* vl, lapack_int ldvl,
-                               float* vr, lapack_int ldvr, float* work,
-                               lapack_int lwork );
-lapack_int LAPACKE_dggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, double* a, lapack_int lda,
-                               double* b, lapack_int ldb, double* alphar,
-                               double* alphai, double* beta, double* vl,
-                               lapack_int ldvl, double* vr, lapack_int ldvr,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_cggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* b,
-                               lapack_int ldb, lapack_complex_float* alpha,
-                               lapack_complex_float* beta,
-                               lapack_complex_float* vl, lapack_int ldvl,
-                               lapack_complex_float* vr, lapack_int ldvr,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_double* a,
-                               lapack_int lda, lapack_complex_double* b,
-                               lapack_int ldb, lapack_complex_double* alpha,
-                               lapack_complex_double* beta,
-                               lapack_complex_double* vl, lapack_int ldvl,
-                               lapack_complex_double* vr, lapack_int ldvr,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork );
-
-lapack_int LAPACKE_sggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float* alphar, float* alphai, float* beta,
-                                float* vl, lapack_int ldvl, float* vr,
-                                lapack_int ldvr, lapack_int* ilo,
-                                lapack_int* ihi, float* lscale, float* rscale,
-                                float* abnrm, float* bbnrm, float* rconde,
-                                float* rcondv, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_logical* bwork );
-lapack_int LAPACKE_dggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* alphar, double* alphai, double* beta,
-                                double* vl, lapack_int ldvl, double* vr,
-                                lapack_int ldvr, lapack_int* ilo,
-                                lapack_int* ihi, double* lscale, double* rscale,
-                                double* abnrm, double* bbnrm, double* rconde,
-                                double* rcondv, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_logical* bwork );
-lapack_int LAPACKE_cggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, float* lscale,
-                                float* rscale, float* abnrm, float* bbnrm,
-                                float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int* iwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_zggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi,
-                                double* lscale, double* rscale, double* abnrm,
-                                double* bbnrm, double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int* iwork,
-                                lapack_logical* bwork );
-
-lapack_int LAPACKE_sggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* d, float* x,
-                                float* y, float* work, lapack_int lwork );
-lapack_int LAPACKE_dggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* d, double* x,
-                                double* y, double* work, lapack_int lwork );
-lapack_int LAPACKE_cggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* d,
-                                lapack_complex_float* x,
-                                lapack_complex_float* y,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* d,
-                                lapack_complex_double* x,
-                                lapack_complex_double* y,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float* q, lapack_int ldq,
-                                float* z, lapack_int ldz );
-lapack_int LAPACKE_dgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double* q, lapack_int ldq,
-                                double* z, lapack_int ldz );
-lapack_int LAPACKE_cgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* c, float* d,
-                                float* x, float* work, lapack_int lwork );
-lapack_int LAPACKE_dgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* c, double* d,
-                                double* x, double* work, lapack_int lwork );
-lapack_int LAPACKE_cgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* c,
-                                lapack_complex_float* d,
-                                lapack_complex_float* x,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* c,
-                                lapack_complex_double* d,
-                                lapack_complex_double* x,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, float* a, lapack_int lda,
-                                float* taua, float* b, lapack_int ldb,
-                                float* taub, float* work, lapack_int lwork );
-lapack_int LAPACKE_dggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, double* a, lapack_int lda,
-                                double* taua, double* b, lapack_int ldb,
-                                double* taub, double* work, lapack_int lwork );
-lapack_int LAPACKE_cggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* taua,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* taub,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* taua,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* taub,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* taua, float* b, lapack_int ldb,
-                                float* taub, float* work, lapack_int lwork );
-lapack_int LAPACKE_dggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* taua, double* b, lapack_int ldb,
-                                double* taub, double* work, lapack_int lwork );
-lapack_int LAPACKE_cggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* taua,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* taub,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* taua,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* taub,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float* alpha, float* beta,
-                                float* u, lapack_int ldu, float* v,
-                                lapack_int ldv, float* q, lapack_int ldq,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double* alpha, double* beta,
-                                double* u, lapack_int ldu, double* v,
-                                lapack_int ldv, double* q, lapack_int ldq,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float* alpha, float* beta,
-                                lapack_complex_float* u, lapack_int ldu,
-                                lapack_complex_float* v, lapack_int ldv,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_zggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double* alpha, double* beta,
-                                lapack_complex_double* u, lapack_int ldu,
-                                lapack_complex_double* v, lapack_int ldv,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float tola,
-                                float tolb, lapack_int* k, lapack_int* l,
-                                float* u, lapack_int ldu, float* v,
-                                lapack_int ldv, float* q, lapack_int ldq,
-                                lapack_int* iwork, float* tau, float* work );
-lapack_int LAPACKE_dggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double tola,
-                                double tolb, lapack_int* k, lapack_int* l,
-                                double* u, lapack_int ldu, double* v,
-                                lapack_int ldv, double* q, lapack_int ldq,
-                                lapack_int* iwork, double* tau, double* work );
-lapack_int LAPACKE_cggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, float tola, float tolb,
-                                lapack_int* k, lapack_int* l,
-                                lapack_complex_float* u, lapack_int ldu,
-                                lapack_complex_float* v, lapack_int ldv,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_int* iwork, float* rwork,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, double tola, double tolb,
-                                lapack_int* k, lapack_int* l,
-                                lapack_complex_double* u, lapack_int ldu,
-                                lapack_complex_double* v, lapack_int ldv,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_int* iwork, double* rwork,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_sgtcon_work( char norm, lapack_int n, const float* dl,
-                                const float* d, const float* du,
-                                const float* du2, const lapack_int* ipiv,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgtcon_work( char norm, lapack_int n, const double* dl,
-                                const double* d, const double* du,
-                                const double* du2, const lapack_int* ipiv,
-                                double anorm, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgtcon_work( char norm, lapack_int n,
-                                const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                const lapack_complex_float* du2,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zgtcon_work( char norm, lapack_int n,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                const lapack_complex_double* du2,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_sgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* dl,
-                                const float* d, const float* du,
-                                const float* dlf, const float* df,
-                                const float* duf, const float* du2,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* dl,
-                                const double* d, const double* du,
-                                const double* dlf, const double* df,
-                                const double* duf, const double* du2,
-                                const lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                const lapack_complex_float* dlf,
-                                const lapack_complex_float* df,
-                                const lapack_complex_float* duf,
-                                const lapack_complex_float* du2,
-                                const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                const lapack_complex_double* dlf,
-                                const lapack_complex_double* df,
-                                const lapack_complex_double* duf,
-                                const lapack_complex_double* du2,
-                                const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* dl, float* d, float* du, float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* dl, double* d, double* du, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_float* dl,
-                               lapack_complex_float* d,
-                               lapack_complex_float* du,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_double* dl,
-                               lapack_complex_double* d,
-                               lapack_complex_double* du,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, const float* dl,
-                                const float* d, const float* du, float* dlf,
-                                float* df, float* duf, float* du2,
-                                lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, const double* dl,
-                                const double* d, const double* du, double* dlf,
-                                double* df, double* duf, double* du2,
-                                lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                lapack_complex_float* dlf,
-                                lapack_complex_float* df,
-                                lapack_complex_float* duf,
-                                lapack_complex_float* du2, lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                lapack_complex_double* dlf,
-                                lapack_complex_double* df,
-                                lapack_complex_double* duf,
-                                lapack_complex_double* du2, lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgttrf_work( lapack_int n, float* dl, float* d, float* du,
-                                float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_dgttrf_work( lapack_int n, double* dl, double* d, double* du,
-                                double* du2, lapack_int* ipiv );
-lapack_int LAPACKE_cgttrf_work( lapack_int n, lapack_complex_float* dl,
-                                lapack_complex_float* d,
-                                lapack_complex_float* du,
-                                lapack_complex_float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_zgttrf_work( lapack_int n, lapack_complex_double* dl,
-                                lapack_complex_double* d,
-                                lapack_complex_double* du,
-                                lapack_complex_double* du2, lapack_int* ipiv );
-
-lapack_int LAPACKE_sgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* dl,
-                                const float* d, const float* du,
-                                const float* du2, const lapack_int* ipiv,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* dl,
-                                const double* d, const double* du,
-                                const double* du2, const lapack_int* ipiv,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_cgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                const lapack_complex_float* du2,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                const lapack_complex_double* du2,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               float* w, lapack_complex_float* z,
-                               lapack_int ldz, lapack_complex_float* work,
-                               float* rwork );
-lapack_int LAPACKE_zhbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               double* w, lapack_complex_double* z,
-                               lapack_int ldz, lapack_complex_double* work,
-                               double* rwork );
-
-lapack_int LAPACKE_chbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* q, lapack_int ldq,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                float* rwork, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_zhbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_complex_double* q, lapack_int ldq,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                double* rwork, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_chbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_complex_float* bb, lapack_int ldbb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                const lapack_complex_double* bb,
-                                lapack_int ldbb, lapack_complex_double* x,
-                                lapack_int ldx, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_chbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               lapack_complex_float* bb, lapack_int ldbb,
-                               float* w, lapack_complex_float* z,
-                               lapack_int ldz, lapack_complex_float* work,
-                               float* rwork );
-lapack_int LAPACKE_zhbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               lapack_complex_double* bb, lapack_int ldbb,
-                               double* w, lapack_complex_double* z,
-                               lapack_int ldz, lapack_complex_double* work,
-                               double* rwork );
-
-lapack_int LAPACKE_chbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* bb, lapack_int ldbb,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_complex_double* bb, lapack_int ldbb,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, lapack_complex_float* ab,
-                                lapack_int ldab, lapack_complex_float* bb,
-                                lapack_int ldbb, lapack_complex_float* q,
-                                lapack_int ldq, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* bb,
-                                lapack_int ldbb, lapack_complex_double* q,
-                                lapack_int ldq, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                float* d, float* e, lapack_complex_float* q,
-                                lapack_int ldq, lapack_complex_float* work );
-lapack_int LAPACKE_zhbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                double* d, double* e, lapack_complex_double* q,
-                                lapack_int ldq, lapack_complex_double* work );
-
-lapack_int LAPACKE_checon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zhecon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_cheequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* s, float* scond, float* amax,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_zheequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* s, double* scond, double* amax,
-                                 lapack_complex_double* work );
-
-lapack_int LAPACKE_cheev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, float* w,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zheev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_double* a,
-                               lapack_int lda, double* w,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork );
-
-lapack_int LAPACKE_cheevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, float* w,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_zheevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, double* w,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_cheevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_int* isuppz,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_zheevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_int* isuppz,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_cheevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zheevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chegst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zhegst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_chegv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* b,
-                               lapack_int ldb, float* w,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zhegv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_complex_double* b, lapack_int ldb,
-                               double* w, lapack_complex_double* work,
-                               lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_chegvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float* w, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhegvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double* w, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chegvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhegvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_cherfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zherfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_cherfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* s, const lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zherfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* s,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_chesv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zhesv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_chesvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                lapack_int* ipiv, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zhesvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_chesvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* s,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zhesvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* s,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_chetrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float* d, float* e, lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zhetrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double* d, double* e,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_chetrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_zhetrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_chetri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zhetri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_chetrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zhetrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               float alpha, const lapack_complex_float* a,
-                               lapack_int lda, float beta,
-                               lapack_complex_float* c );
-lapack_int LAPACKE_zhfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               double alpha, const lapack_complex_double* a,
-                               lapack_int lda, double beta,
-                               lapack_complex_double* c );
-
-lapack_int LAPACKE_shgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, float* h, lapack_int ldh,
-                                float* t, lapack_int ldt, float* alphar,
-                                float* alphai, float* beta, float* q,
-                                lapack_int ldq, float* z, lapack_int ldz,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dhgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, double* h, lapack_int ldh,
-                                double* t, lapack_int ldt, double* alphar,
-                                double* alphai, double* beta, double* q,
-                                lapack_int ldq, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_chgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_float* h,
-                                lapack_int ldh, lapack_complex_float* t,
-                                lapack_int ldt, lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork );
-lapack_int LAPACKE_zhgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_double* h,
-                                lapack_int ldh, lapack_complex_double* t,
-                                lapack_int ldt, lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_chpcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zhpcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_chpev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_float* ap, float* w,
-                               lapack_complex_float* z, lapack_int ldz,
-                               lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhpev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_double* ap,
-                               double* w, lapack_complex_double* z,
-                               lapack_int ldz, lapack_complex_double* work,
-                               double* rwork );
-
-lapack_int LAPACKE_chpevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_float* ap,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhpevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_double* ap,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chpevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* ap, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhpevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* ap, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chpgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_float* ap,
-                                const lapack_complex_float* bp );
-lapack_int LAPACKE_zhpgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_double* ap,
-                                const lapack_complex_double* bp );
-
-lapack_int LAPACKE_chpgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n,
-                               lapack_complex_float* ap,
-                               lapack_complex_float* bp, float* w,
-                               lapack_complex_float* z, lapack_int ldz,
-                               lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhpgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n,
-                               lapack_complex_double* ap,
-                               lapack_complex_double* bp, double* w,
-                               lapack_complex_double* z, lapack_int ldz,
-                               lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_chpgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                lapack_complex_float* bp, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_zhpgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                lapack_complex_double* bp, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_chpgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                lapack_complex_float* bp, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhpgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                lapack_complex_double* bp, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_complex_float* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_chpsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* ap,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zhpsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* ap,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-
-lapack_int LAPACKE_chpsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* afp, lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhpsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* afp, lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_chptrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap, float* d, float* e,
-                                lapack_complex_float* tau );
-lapack_int LAPACKE_zhptrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap, double* d, double* e,
-                                lapack_complex_double* tau );
-
-lapack_int LAPACKE_chptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zhptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_chptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zhptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_chptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zhptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_shsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, lapack_logical* select,
-                                lapack_int n, const float* h, lapack_int ldh,
-                                float* wr, const float* wi, float* vl,
-                                lapack_int ldvl, float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, float* work,
-                                lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_dhsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, lapack_logical* select,
-                                lapack_int n, const double* h, lapack_int ldh,
-                                double* wr, const double* wi, double* vl,
-                                lapack_int ldvl, double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, double* work,
-                                lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_chsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, const lapack_logical* select,
-                                lapack_int n, const lapack_complex_float* h,
-                                lapack_int ldh, lapack_complex_float* w,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_zhsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, const lapack_logical* select,
-                                lapack_int n, const lapack_complex_double* h,
-                                lapack_int ldh, lapack_complex_double* w,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* ifaill, lapack_int* ifailr );
-
-lapack_int LAPACKE_shseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                float* h, lapack_int ldh, float* wr, float* wi,
-                                float* z, lapack_int ldz, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dhseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                double* h, lapack_int ldh, double* wr,
-                                double* wi, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_chseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_float* h, lapack_int ldh,
-                                lapack_complex_float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zhseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_double* h, lapack_int ldh,
-                                lapack_complex_double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_clacgv_work( lapack_int n, lapack_complex_float* x,
-                                lapack_int incx );
-lapack_int LAPACKE_zlacgv_work( lapack_int n, lapack_complex_double* x,
-                                lapack_int incx );
-
-lapack_int LAPACKE_slacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dlacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_clacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zlacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_zlag2c_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_float* sa, lapack_int ldsa );
-
-lapack_int LAPACKE_slag2d_work( int matrix_order, lapack_int m, lapack_int n,
-                                const float* sa, lapack_int ldsa, double* a,
-                                lapack_int lda );
-
-lapack_int LAPACKE_dlag2s_work( int matrix_order, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda, float* sa,
-                                lapack_int ldsa );
-
-lapack_int LAPACKE_clag2z_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_float* sa, lapack_int ldsa,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* d,
-                                float* a, lapack_int lda, lapack_int* iseed,
-                                float* work );
-lapack_int LAPACKE_dlagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* d,
-                                double* a, lapack_int lda, lapack_int* iseed,
-                                double* work );
-lapack_int LAPACKE_clagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* d,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* iseed, lapack_complex_float* work );
-lapack_int LAPACKE_zlagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* d,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* iseed,
-                                lapack_complex_double* work );
-                                
-lapack_int LAPACKE_claghe_work( int matrix_order, lapack_int n, lapack_int k,
-                                const float* d, lapack_complex_float* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zlaghe_work( int matrix_order, lapack_int n, lapack_int k,
-                                const double* d, lapack_complex_double* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_slagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const float* d, float* a, lapack_int lda,
-                                lapack_int* iseed, float* work );
-lapack_int LAPACKE_dlagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const double* d, double* a, lapack_int lda,
-                                lapack_int* iseed, double* work );
-lapack_int LAPACKE_clagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const float* d, lapack_complex_float* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zlagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const double* d, lapack_complex_double* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_slapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n, float* x,
-                                lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_dlapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n, double* x,
-                                lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_clapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_float* x, lapack_int ldx,
-                                lapack_int* k );
-lapack_int LAPACKE_zlapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_double* x, lapack_int ldx,
-                                lapack_int* k );
-
-lapack_int LAPACKE_slartgp_work( float f, float g, float* cs, float* sn,
-                                 float* r );
-lapack_int LAPACKE_dlartgp_work( double f, double g, double* cs, double* sn,
-                                 double* r );
-
-lapack_int LAPACKE_slartgs_work( float x, float y, float sigma, float* cs,
-                                 float* sn );
-lapack_int LAPACKE_dlartgs_work( double x, double y, double sigma, double* cs,
-                                 double* sn );
-                                
-float LAPACKE_slapy2_work( float x, float y );
-double LAPACKE_dlapy2_work( double x, double y );
-
-float LAPACKE_slapy3_work( float x, float y, float z );
-double LAPACKE_dlapy3_work( double x, double y, double z );
-
-float LAPACKE_slamch_work( char cmach );
-double LAPACKE_dlamch_work( char cmach );
-
-float LAPACKE_slange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* work );
-double LAPACKE_dlange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* work );
-float LAPACKE_clange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_zlange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, double* work );
-
-float LAPACKE_clanhe_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_zlanhe_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, double* work );
-
-float LAPACKE_slansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* work );
-double LAPACKE_dlansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* work );
-float LAPACKE_clansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_zlansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, double* work );
-
-float LAPACKE_slantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n, const float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_dlantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda, double* work );
-float LAPACKE_clantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* work );
-double LAPACKE_zlantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* work );
-
-lapack_int LAPACKE_slarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, const float* v,
-                                lapack_int ldv, const float* t, lapack_int ldt,
-                                float* c, lapack_int ldc, float* work,
-                                lapack_int ldwork );
-lapack_int LAPACKE_dlarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, const double* v,
-                                lapack_int ldv, const double* t, lapack_int ldt,
-                                double* c, lapack_int ldc, double* work,
-                                lapack_int ldwork );
-lapack_int LAPACKE_clarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_float* v, lapack_int ldv,
-                                const lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int ldwork );
-lapack_int LAPACKE_zlarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_double* v, lapack_int ldv,
-                                const lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work,
-                                lapack_int ldwork );
-
-lapack_int LAPACKE_slarfg_work( lapack_int n, float* alpha, float* x,
-                                lapack_int incx, float* tau );
-lapack_int LAPACKE_dlarfg_work( lapack_int n, double* alpha, double* x,
-                                lapack_int incx, double* tau );
-lapack_int LAPACKE_clarfg_work( lapack_int n, lapack_complex_float* alpha,
-                                lapack_complex_float* x, lapack_int incx,
-                                lapack_complex_float* tau );
-lapack_int LAPACKE_zlarfg_work( lapack_int n, lapack_complex_double* alpha,
-                                lapack_complex_double* x, lapack_int incx,
-                                lapack_complex_double* tau );
-
-lapack_int LAPACKE_slarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k, const float* v,
-                                lapack_int ldv, const float* tau, float* t,
-                                lapack_int ldt );
-lapack_int LAPACKE_dlarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k, const double* v,
-                                lapack_int ldv, const double* tau, double* t,
-                                lapack_int ldt );
-lapack_int LAPACKE_clarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_float* v, lapack_int ldv,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zlarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_double* v, lapack_int ldv,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_slarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const float* v, float tau,
-                                float* c, lapack_int ldc, float* work );
-lapack_int LAPACKE_dlarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const double* v, double tau,
-                                double* c, lapack_int ldc, double* work );
-lapack_int LAPACKE_clarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const lapack_complex_float* v,
-                                lapack_complex_float tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zlarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const lapack_complex_double* v,
-                                lapack_complex_double tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_slarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, float* x );
-lapack_int LAPACKE_dlarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, double* x );
-lapack_int LAPACKE_clarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, lapack_complex_float* x );
-lapack_int LAPACKE_zlarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, lapack_complex_double* x );
-
-lapack_int LAPACKE_slaset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, float alpha, float beta, float* a,
-                                lapack_int lda );
-lapack_int LAPACKE_dlaset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, double alpha, double beta,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_claset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, lapack_complex_float alpha,
-                                lapack_complex_float beta,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlaset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, lapack_complex_double alpha,
-                                lapack_complex_double beta,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slasrt_work( char id, lapack_int n, float* d );
-lapack_int LAPACKE_dlasrt_work( char id, lapack_int n, double* d );
-
-lapack_int LAPACKE_slaswp_work( int matrix_order, lapack_int n, float* a,
-                                lapack_int lda, lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_dlaswp_work( int matrix_order, lapack_int n, double* a,
-                                lapack_int lda, lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_claswp_work( int matrix_order, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_zlaswp_work( int matrix_order, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-
-lapack_int LAPACKE_slatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                float* d, lapack_int mode, float cond,
-                                float dmax, lapack_int kl, lapack_int ku,
-                                char pack, float* a, lapack_int lda,
-                                float* work );
-lapack_int LAPACKE_dlatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                double* d, lapack_int mode, double cond,
-                                double dmax, lapack_int kl, lapack_int ku,
-                                char pack, double* a, lapack_int lda,
-                                double* work );
-lapack_int LAPACKE_clatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                float* d, lapack_int mode, float cond,
-                                float dmax, lapack_int kl, lapack_int ku,
-                                char pack, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* work );
-lapack_int LAPACKE_zlatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                double* d, lapack_int mode, double cond,
-                                double dmax, lapack_int kl, lapack_int ku,
-                                char pack, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* work );
-
-lapack_int LAPACKE_slauum_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda );
-lapack_int LAPACKE_dlauum_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_clauum_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlauum_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_sopgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, const float* tau, float* q,
-                                lapack_int ldq, float* work );
-lapack_int LAPACKE_dopgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, const double* tau, double* q,
-                                lapack_int ldq, double* work );
-
-lapack_int LAPACKE_sopmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const float* ap, const float* tau, float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_dopmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const double* ap, const double* tau, double* c,
-                                lapack_int ldc, double* work );
-
-lapack_int LAPACKE_sorgbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k, float* a,
-                                lapack_int lda, const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k, double* a,
-                                lapack_int lda, const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgtr_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, const float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dorgtr_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, const double* tau,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_spbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const float* ab, lapack_int ldab,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dpbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const double* ab,
-                                lapack_int ldab, double anorm, double* rcond,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cpbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_float* ab,
-                                lapack_int ldab, float anorm, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_double* ab,
-                                lapack_int ldab, double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const float* ab, lapack_int ldab,
-                                float* s, float* scond, float* amax );
-lapack_int LAPACKE_dpbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const double* ab,
-                                lapack_int ldab, double* s, double* scond,
-                                double* amax );
-lapack_int LAPACKE_cpbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_float* ab,
-                                lapack_int ldab, float* s, float* scond,
-                                float* amax );
-lapack_int LAPACKE_zpbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_double* ab,
-                                lapack_int ldab, double* s, double* scond,
-                                double* amax );
-
-lapack_int LAPACKE_spbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs, const float* ab,
-                                lapack_int ldab, const float* afb,
-                                lapack_int ldafb, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dpbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const double* ab, lapack_int ldab,
-                                const double* afb, lapack_int ldafb,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cpbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_complex_float* afb,
-                                lapack_int ldafb, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab,
-                                const lapack_complex_double* afb,
-                                lapack_int ldafb,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, float* bb, lapack_int ldbb );
-lapack_int LAPACKE_dpbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, double* bb, lapack_int ldbb );
-lapack_int LAPACKE_cpbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, lapack_complex_float* bb,
-                                lapack_int ldbb );
-lapack_int LAPACKE_zpbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, lapack_complex_double* bb,
-                                lapack_int ldbb );
-
-lapack_int LAPACKE_spbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs, float* ab,
-                               lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs, double* ab,
-                               lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                float* ab, lapack_int ldab, float* afb,
-                                lapack_int ldafb, char* equed, float* s,
-                                float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dpbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                double* ab, lapack_int ldab, double* afb,
-                                lapack_int ldafb, char* equed, double* s,
-                                double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, double* work, lapack_int* iwork );
-lapack_int LAPACKE_cpbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* afb, lapack_int ldafb,
-                                char* equed, float* s, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zpbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_complex_double* afb, lapack_int ldafb,
-                                char* equed, double* s,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, float* ab, lapack_int ldab );
-lapack_int LAPACKE_dpbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, double* ab, lapack_int ldab );
-lapack_int LAPACKE_cpbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_complex_float* ab,
-                                lapack_int ldab );
-lapack_int LAPACKE_zpbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_complex_double* ab,
-                                lapack_int ldab );
-
-lapack_int LAPACKE_spbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs, const float* ab,
-                                lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const double* ab, lapack_int ldab, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cpbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_spftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, float* a );
-lapack_int LAPACKE_dpftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, double* a );
-lapack_int LAPACKE_cpftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, float* a );
-lapack_int LAPACKE_dpftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, double* a );
-lapack_int LAPACKE_cpftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs, const float* a,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dpftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs, const double* a,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_cpftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spocon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* a, lapack_int lda, float anorm,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dpocon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* a, lapack_int lda, double anorm,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cpocon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float anorm, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpocon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spoequ_work( int matrix_order, lapack_int n, const float* a,
-                                lapack_int lda, float* s, float* scond,
-                                float* amax );
-lapack_int LAPACKE_dpoequ_work( int matrix_order, lapack_int n, const double* a,
-                                lapack_int lda, double* s, double* scond,
-                                double* amax );
-lapack_int LAPACKE_cpoequ_work( int matrix_order, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequ_work( int matrix_order, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_spoequb_work( int matrix_order, lapack_int n, const float* a,
-                                 lapack_int lda, float* s, float* scond,
-                                 float* amax );
-lapack_int LAPACKE_dpoequb_work( int matrix_order, lapack_int n,
-                                 const double* a, lapack_int lda, double* s,
-                                 double* scond, double* amax );
-lapack_int LAPACKE_cpoequb_work( int matrix_order, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequb_work( int matrix_order, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_sporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const float* af, lapack_int ldaf,
-                                const float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const double* af,
-                                lapack_int ldaf, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const float* a,
-                                 lapack_int lda, const float* af,
-                                 lapack_int ldaf, const float* s,
-                                 const float* b, lapack_int ldb, float* x,
-                                 lapack_int ldx, float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const double* a,
-                                 lapack_int lda, const double* af,
-                                 lapack_int ldaf, const double* s,
-                                 const double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const float* s,
-                                 const lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const double* s,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* a, lapack_int lda,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* a, lapack_int lda,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_cposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* a,
-                               lapack_int lda, lapack_complex_double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dsposv_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* work, float* swork,
-                                lapack_int* iter );
-lapack_int LAPACKE_zcposv_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, lapack_complex_double* work,
-                                lapack_complex_float* swork, double* rwork,
-                                lapack_int* iter );
-
-lapack_int LAPACKE_sposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, float* a,
-                                lapack_int lda, float* af, lapack_int ldaf,
-                                char* equed, float* s, float* b, lapack_int ldb,
-                                float* x, lapack_int ldx, float* rcond,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, double* a,
-                                lapack_int lda, double* af, lapack_int ldaf,
-                                char* equed, double* s, double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                char* equed, float* s, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                char* equed, double* s,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, float* a,
-                                 lapack_int lda, float* af, lapack_int ldaf,
-                                 char* equed, float* s, float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, double* a,
-                                 lapack_int lda, double* af, lapack_int ldaf,
-                                 char* equed, double* s, double* b,
-                                 lapack_int ldb, double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 char* equed, float* s, lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params,
-                                 lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 char* equed, double* s,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_spotrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda );
-lapack_int LAPACKE_dpotrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_cpotrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotri_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda );
-lapack_int LAPACKE_dpotri_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_cpotri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dpotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zpotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_sppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, float anorm, float* rcond,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, double anorm, double* rcond,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap, float anorm,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap, double anorm,
-                                double* rcond, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_sppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, float* s, float* scond,
-                                float* amax );
-lapack_int LAPACKE_dppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, double* s, double* scond,
-                                double* amax );
-lapack_int LAPACKE_cppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap, float* s,
-                                float* scond, float* amax );
-lapack_int LAPACKE_zppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap, double* s,
-                                double* scond, double* amax );
-
-lapack_int LAPACKE_spprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap,
-                                const float* afp, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dpprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap,
-                                const double* afp, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cpprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_complex_float* afp,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* afp,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* ap, float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* ap, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* ap,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* ap,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, float* ap,
-                                float* afp, char* equed, float* s, float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, double* ap,
-                                double* afp, char* equed, double* s, double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_float* ap,
-                                lapack_complex_float* afp, char* equed,
-                                float* s, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* ap,
-                                lapack_complex_double* afp, char* equed,
-                                double* s, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_spptrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap );
-lapack_int LAPACKE_dpptrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap );
-lapack_int LAPACKE_cpptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_zpptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptri_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap );
-lapack_int LAPACKE_dpptri_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap );
-lapack_int LAPACKE_cpptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_zpptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dpptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cpptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spstrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* piv,
-                                lapack_int* rank, float tol, float* work );
-lapack_int LAPACKE_dpstrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* piv,
-                                lapack_int* rank, double tol, double* work );
-lapack_int LAPACKE_cpstrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* piv, lapack_int* rank, float tol,
-                                float* work );
-lapack_int LAPACKE_zpstrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* piv, lapack_int* rank, double tol,
-                                double* work );
-
-lapack_int LAPACKE_sptcon_work( lapack_int n, const float* d, const float* e,
-                                float anorm, float* rcond, float* work );
-lapack_int LAPACKE_dptcon_work( lapack_int n, const double* d, const double* e,
-                                double anorm, double* rcond, double* work );
-lapack_int LAPACKE_cptcon_work( lapack_int n, const float* d,
-                                const lapack_complex_float* e, float anorm,
-                                float* rcond, float* work );
-lapack_int LAPACKE_zptcon_work( lapack_int n, const double* d,
-                                const lapack_complex_double* e, double anorm,
-                                double* rcond, double* work );
-
-lapack_int LAPACKE_spteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work );
-lapack_int LAPACKE_dpteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work );
-lapack_int LAPACKE_cpteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, lapack_complex_float* z,
-                                lapack_int ldz, float* work );
-lapack_int LAPACKE_zpteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, lapack_complex_double* z,
-                                lapack_int ldz, double* work );
-
-lapack_int LAPACKE_sptrfs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const float* d, const float* e, const float* df,
-                                const float* ef, const float* b, lapack_int ldb,
-                                float* x, lapack_int ldx, float* ferr,
-                                float* berr, float* work );
-lapack_int LAPACKE_dptrfs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const double* d, const double* e,
-                                const double* df, const double* ef,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work );
-lapack_int LAPACKE_cptrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* d,
-                                const lapack_complex_float* e, const float* df,
-                                const lapack_complex_float* ef,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zptrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const lapack_complex_double* e,
-                                const double* df,
-                                const lapack_complex_double* ef,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* d, float* e, float* b, lapack_int ldb );
-lapack_int LAPACKE_dptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* d, double* e, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* d, lapack_complex_float* e,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* d, lapack_complex_double* e,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const float* d, const float* e,
-                                float* df, float* ef, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work );
-lapack_int LAPACKE_dptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const double* e, double* df, double* ef,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, double* work );
-lapack_int LAPACKE_cptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const float* d,
-                                const lapack_complex_float* e, float* df,
-                                lapack_complex_float* ef,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const lapack_complex_double* e, double* df,
-                                lapack_complex_double* ef,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spttrf_work( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dpttrf_work( lapack_int n, double* d, double* e );
-lapack_int LAPACKE_cpttrf_work( lapack_int n, float* d,
-                                lapack_complex_float* e );
-lapack_int LAPACKE_zpttrf_work( lapack_int n, double* d,
-                                lapack_complex_double* e );
-
-lapack_int LAPACKE_spttrs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const float* d, const float* e, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dpttrs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const double* d, const double* e, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cpttrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* d,
-                                const lapack_complex_float* e,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpttrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const lapack_complex_double* e,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd, float* ab,
-                               lapack_int ldab, float* w, float* z,
-                               lapack_int ldz, float* work );
-lapack_int LAPACKE_dsbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd, double* ab,
-                               lapack_int ldab, double* w, double* z,
-                               lapack_int ldz, double* work );
-
-lapack_int LAPACKE_ssbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd, float* ab,
-                                lapack_int ldab, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd, double* ab,
-                                lapack_int ldab, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                float* ab, lapack_int ldab, float* q,
-                                lapack_int ldq, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dsbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                double* ab, lapack_int ldab, double* q,
-                                lapack_int ldq, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_ssbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                float* ab, lapack_int ldab, const float* bb,
-                                lapack_int ldbb, float* x, lapack_int ldx,
-                                float* work );
-lapack_int LAPACKE_dsbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                double* ab, lapack_int ldab, const double* bb,
-                                lapack_int ldbb, double* x, lapack_int ldx,
-                                double* work );
-
-lapack_int LAPACKE_ssbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               float* ab, lapack_int ldab, float* bb,
-                               lapack_int ldbb, float* w, float* z,
-                               lapack_int ldz, float* work );
-lapack_int LAPACKE_dsbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               double* ab, lapack_int ldab, double* bb,
-                               lapack_int ldbb, double* w, double* z,
-                               lapack_int ldz, double* work );
-
-lapack_int LAPACKE_ssbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                float* ab, lapack_int ldab, float* bb,
-                                lapack_int ldbb, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                double* ab, lapack_int ldab, double* bb,
-                                lapack_int ldbb, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, float* ab, lapack_int ldab,
-                                float* bb, lapack_int ldbb, float* q,
-                                lapack_int ldq, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dsbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, double* ab, lapack_int ldab,
-                                double* bb, lapack_int ldbb, double* q,
-                                lapack_int ldq, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_ssbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd, float* ab,
-                                lapack_int ldab, float* d, float* e, float* q,
-                                lapack_int ldq, float* work );
-lapack_int LAPACKE_dsbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd, double* ab,
-                                lapack_int ldab, double* d, double* e,
-                                double* q, lapack_int ldq, double* work );
-
-lapack_int LAPACKE_ssfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               float alpha, const float* a, lapack_int lda,
-                               float beta, float* c );
-lapack_int LAPACKE_dsfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               double alpha, const double* a, lapack_int lda,
-                               double beta, double* c );
-
-lapack_int LAPACKE_sspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, const lapack_int* ipiv,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, const lapack_int* ipiv,
-                                double anorm, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_sspev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, float* ap, float* w, float* z,
-                               lapack_int ldz, float* work );
-lapack_int LAPACKE_dspev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, double* ap, double* w, double* z,
-                               lapack_int ldz, double* work );
-
-lapack_int LAPACKE_sspevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, float* ap, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dspevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, double* ap, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sspevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, float* ap, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dspevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, double* ap, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, double* work,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_sspgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, float* ap, const float* bp );
-lapack_int LAPACKE_dspgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, double* ap, const double* bp );
-
-lapack_int LAPACKE_sspgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, float* ap, float* bp,
-                               float* w, float* z, lapack_int ldz,
-                               float* work );
-lapack_int LAPACKE_dspgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, double* ap, double* bp,
-                               double* w, double* z, lapack_int ldz,
-                               double* work );
-
-lapack_int LAPACKE_sspgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, float* ap, float* bp,
-                                float* w, float* z, lapack_int ldz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dspgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, double* ap, double* bp,
-                                double* w, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sspgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, float* ap,
-                                float* bp, float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, float* z, lapack_int ldz, float* work,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_dspgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, double* ap,
-                                double* bp, double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, double* z, lapack_int ldz,
-                                double* work, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_ssprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap,
-                                const float* afp, const lapack_int* ipiv,
-                                const float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dsprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap,
-                                const double* afp, const lapack_int* ipiv,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_csprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_complex_float* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zsprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* ap, lapack_int* ipiv,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* ap, lapack_int* ipiv,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_cspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* ap,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* ap,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-
-lapack_int LAPACKE_sspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const float* ap,
-                                float* afp, lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const double* ap,
-                                double* afp, lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* afp, lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* afp, lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_ssptrd_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap, float* d, float* e, float* tau );
-lapack_int LAPACKE_dsptrd_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap, double* d, double* e, double* tau );
-
-lapack_int LAPACKE_ssptrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_dsptrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap, lapack_int* ipiv );
-lapack_int LAPACKE_csptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zsptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_ssptri_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap, const lapack_int* ipiv,
-                                float* work );
-lapack_int LAPACKE_dsptri_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap, const lapack_int* ipiv,
-                                double* work );
-lapack_int LAPACKE_csptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zsptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_ssptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dsptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap,
-                                const lapack_int* ipiv, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_csptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zsptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sstebz_work( char range, char order, lapack_int n, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, const float* d, const float* e,
-                                lapack_int* m, lapack_int* nsplit, float* w,
-                                lapack_int* iblock, lapack_int* isplit,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dstebz_work( char range, char order, lapack_int n, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, const double* d, const double* e,
-                                lapack_int* m, lapack_int* nsplit, double* w,
-                                lapack_int* iblock, lapack_int* isplit,
-                                double* work, lapack_int* iwork );
-
-lapack_int LAPACKE_sstedc_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dstedc_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_cstedc_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zstedc_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_sstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, lapack_int* isuppz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_cstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int* isuppz, double* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_sstein_work( int matrix_order, lapack_int n, const float* d,
-                                const float* e, lapack_int m, const float* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-lapack_int LAPACKE_dstein_work( int matrix_order, lapack_int n, const double* d,
-                                const double* e, lapack_int m, const double* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit, double* z,
-                                lapack_int ldz, double* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-lapack_int LAPACKE_cstein_work( int matrix_order, lapack_int n, const float* d,
-                                const float* e, lapack_int m, const float* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit,
-                                lapack_complex_float* z, lapack_int ldz,
-                                float* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-lapack_int LAPACKE_zstein_work( int matrix_order, lapack_int n, const double* d,
-                                const double* e, lapack_int m, const double* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit,
-                                lapack_complex_double* z, lapack_int ldz,
-                                double* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-
-lapack_int LAPACKE_sstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int nzc,
-                                lapack_int* isuppz, lapack_logical* tryrac,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, lapack_int nzc,
-                                lapack_int* isuppz, lapack_logical* tryrac,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_cstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int nzc, lapack_int* isuppz,
-                                lapack_logical* tryrac, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int nzc, lapack_int* isuppz,
-                                lapack_logical* tryrac, double* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_ssteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work );
-lapack_int LAPACKE_dsteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work );
-lapack_int LAPACKE_csteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, lapack_complex_float* z,
-                                lapack_int ldz, float* work );
-lapack_int LAPACKE_zsteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, lapack_complex_double* z,
-                                lapack_int ldz, double* work );
-
-lapack_int LAPACKE_ssterf_work( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dsterf_work( lapack_int n, double* d, double* e );
-
-lapack_int LAPACKE_sstev_work( int matrix_order, char jobz, lapack_int n,
-                               float* d, float* e, float* z, lapack_int ldz,
-                               float* work );
-lapack_int LAPACKE_dstev_work( int matrix_order, char jobz, lapack_int n,
-                               double* d, double* e, double* z, lapack_int ldz,
-                               double* work );
-
-lapack_int LAPACKE_sstevd_work( int matrix_order, char jobz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dstevd_work( int matrix_order, char jobz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sstevr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dstevr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, lapack_int* isuppz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sstevx_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dstevx_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, double* work,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_ssycon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* a, lapack_int lda,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dsycon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_csycon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zsycon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_ssyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const float* a, lapack_int lda, float* s,
-                                 float* scond, float* amax, float* work );
-lapack_int LAPACKE_dsyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const double* a, lapack_int lda, double* s,
-                                 double* scond, double* amax, double* work );
-lapack_int LAPACKE_csyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* s, float* scond, float* amax,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_zsyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* s, double* scond, double* amax,
-                                 lapack_complex_double* work );
-
-lapack_int LAPACKE_ssyev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, float* a, lapack_int lda, float* w,
-                               float* work, lapack_int lwork );
-lapack_int LAPACKE_dsyev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, double* a, lapack_int lda,
-                               double* w, double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssyevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* w, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsyevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* w, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssyevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, float* a,
-                                lapack_int lda, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dsyevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, double* a,
-                                lapack_int lda, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, lapack_int* isuppz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssyevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, float* a,
-                                lapack_int lda, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_dsyevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, double* a,
-                                lapack_int lda, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_ssygst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, float* a, lapack_int lda,
-                                const float* b, lapack_int ldb );
-lapack_int LAPACKE_dsygst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, double* a, lapack_int lda,
-                                const double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssygv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, float* a,
-                               lapack_int lda, float* b, lapack_int ldb,
-                               float* w, float* work, lapack_int lwork );
-lapack_int LAPACKE_dsygv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, double* a,
-                               lapack_int lda, double* b, lapack_int ldb,
-                               double* w, double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssygvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float* w, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsygvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* w, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssygvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, float* z, lapack_int ldz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dsygvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_ssyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const float* af, lapack_int ldaf,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dsyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_csyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zsyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_ssyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const float* a,
-                                 lapack_int lda, const float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* s, const float* b, lapack_int ldb,
-                                 float* x, lapack_int ldx, float* rcond,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dsyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const double* a,
-                                 lapack_int lda, const double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* s, const double* b,
-                                 lapack_int ldb, double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_csyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* s, const lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zsyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* s,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_ssysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* a, lapack_int lda,
-                               lapack_int* ipiv, float* b, lapack_int ldb,
-                               float* work, lapack_int lwork );
-lapack_int LAPACKE_dsysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* a, lapack_int lda,
-                               lapack_int* ipiv, double* b, lapack_int ldb,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_csysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zsysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const float* a,
-                                lapack_int lda, float* af, lapack_int ldaf,
-                                lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dsysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const double* a,
-                                lapack_int lda, double* af, lapack_int ldaf,
-                                lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_csysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                lapack_int* ipiv, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zsysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_ssysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, float* a,
-                                 lapack_int lda, float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* s,
-                                 float* b, lapack_int ldb, float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dsysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, double* a,
-                                 lapack_int lda, double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* s,
-                                 double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* rpvgrw,
-                                 double* berr, lapack_int n_err_bnds,
-                                 double* err_bnds_norm, double* err_bnds_comp,
-                                 lapack_int nparams, double* params,
-                                 double* work, lapack_int* iwork );
-lapack_int LAPACKE_csysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* s,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zsysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* s,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_ssytrd_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, float* d, float* e,
-                                float* tau, float* work, lapack_int lwork );
-lapack_int LAPACKE_dsytrd_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, double* d, double* e,
-                                double* tau, double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssytrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ipiv,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dsytrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ipiv,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_csytrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_zsytrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_ssytri_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda,
-                                const lapack_int* ipiv, float* work );
-lapack_int LAPACKE_dsytri_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda,
-                                const lapack_int* ipiv, double* work );
-lapack_int LAPACKE_csytri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zsytri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_ssytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dsytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_csytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zsytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const float* ab, lapack_int ldab, float* rcond,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const double* ab, lapack_int ldab,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_ztbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const float* ab,
-                                lapack_int ldab, const float* b, lapack_int ldb,
-                                const float* x, lapack_int ldx, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const double* ab,
-                                lapack_int ldab, const double* b,
-                                lapack_int ldb, const double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const lapack_complex_float* ab,
-                                lapack_int ldab, const lapack_complex_float* b,
-                                lapack_int ldb, const lapack_complex_float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, const lapack_complex_double* b,
-                                lapack_int ldb, const lapack_complex_double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const float* ab,
-                                lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dtbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const double* ab,
-                                lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_ctbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const lapack_complex_float* ab,
-                                lapack_int ldab, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_ztbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_stfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, float alpha, const float* a,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dtfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, double alpha, const double* a,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_ctfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, lapack_complex_float alpha,
-                               const lapack_complex_float* a,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, lapack_complex_double alpha,
-                               const lapack_complex_double* a,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n, float* a );
-lapack_int LAPACKE_dtftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n, double* a );
-lapack_int LAPACKE_ctftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n,
-                                lapack_complex_float* a );
-lapack_int LAPACKE_ztftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n,
-                                lapack_complex_double* a );
-
-lapack_int LAPACKE_stfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* arf, float* ap );
-lapack_int LAPACKE_dtfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* arf, double* ap );
-lapack_int LAPACKE_ctfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* arf,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_ztfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* arf,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_stfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* arf, float* a,
-                                lapack_int lda );
-lapack_int LAPACKE_dtfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* arf, double* a,
-                                lapack_int lda );
-lapack_int LAPACKE_ctfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* arf,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* arf,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_stgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const float* s, lapack_int lds, const float* p,
-                                lapack_int ldp, float* vl, lapack_int ldvl,
-                                float* vr, lapack_int ldvr, lapack_int mm,
-                                lapack_int* m, float* work );
-lapack_int LAPACKE_dtgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const double* s, lapack_int lds,
-                                const double* p, lapack_int ldp, double* vl,
-                                lapack_int ldvl, double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, double* work );
-lapack_int LAPACKE_ctgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_float* s, lapack_int lds,
-                                const lapack_complex_float* p, lapack_int ldp,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_double* s, lapack_int lds,
-                                const lapack_complex_double* p, lapack_int ldp,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float* q, lapack_int ldq, float* z,
-                                lapack_int ldz, lapack_int* ifst,
-                                lapack_int* ilst, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dtgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* q, lapack_int ldq, double* z,
-                                lapack_int ldz, lapack_int* ifst,
-                                lapack_int* ilst, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_ctgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_stgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float* alphar, float* alphai,
-                                float* beta, float* q, lapack_int ldq, float* z,
-                                lapack_int ldz, lapack_int* m, float* pl,
-                                float* pr, float* dif, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dtgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double* alphar, double* alphai,
-                                double* beta, double* q, lapack_int ldq,
-                                double* z, lapack_int ldz, lapack_int* m,
-                                double* pl, double* pr, double* dif,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_ctgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int* m, float* pl, float* pr, float* dif,
-                                lapack_complex_float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_ztgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int* m, double* pl, double* pr,
-                                double* dif, lapack_complex_double* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_stgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float tola, float tolb,
-                                float* alpha, float* beta, float* u,
-                                lapack_int ldu, float* v, lapack_int ldv,
-                                float* q, lapack_int ldq, float* work,
-                                lapack_int* ncycle );
-lapack_int LAPACKE_dtgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double tola, double tolb,
-                                double* alpha, double* beta, double* u,
-                                lapack_int ldu, double* v, lapack_int ldv,
-                                double* q, lapack_int ldq, double* work,
-                                lapack_int* ncycle );
-lapack_int LAPACKE_ctgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float tola, float tolb, float* alpha,
-                                float* beta, lapack_complex_float* u,
-                                lapack_int ldu, lapack_complex_float* v,
-                                lapack_int ldv, lapack_complex_float* q,
-                                lapack_int ldq, lapack_complex_float* work,
-                                lapack_int* ncycle );
-lapack_int LAPACKE_ztgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double tola, double tolb, double* alpha,
-                                double* beta, lapack_complex_double* u,
-                                lapack_int ldu, lapack_complex_double* v,
-                                lapack_int ldv, lapack_complex_double* q,
-                                lapack_int ldq, lapack_complex_double* work,
-                                lapack_int* ncycle );
-
-lapack_int LAPACKE_stgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const float* a, lapack_int lda, const float* b,
-                                lapack_int ldb, const float* vl,
-                                lapack_int ldvl, const float* vr,
-                                lapack_int ldvr, float* s, float* dif,
-                                lapack_int mm, lapack_int* m, float* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_dtgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const double* b, lapack_int ldb,
-                                const double* vl, lapack_int ldvl,
-                                const double* vr, lapack_int ldvr, double* s,
-                                double* dif, lapack_int mm, lapack_int* m,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                const lapack_complex_float* vl, lapack_int ldvl,
-                                const lapack_complex_float* vr, lapack_int ldvr,
-                                float* s, float* dif, lapack_int mm,
-                                lapack_int* m, lapack_complex_float* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_ztgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                const lapack_complex_double* vl,
-                                lapack_int ldvl,
-                                const lapack_complex_double* vr,
-                                lapack_int ldvr, double* s, double* dif,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, lapack_int lwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_stgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n, const float* a,
-                                lapack_int lda, const float* b, lapack_int ldb,
-                                float* c, lapack_int ldc, const float* d,
-                                lapack_int ldd, const float* e, lapack_int lde,
-                                float* f, lapack_int ldf, float* scale,
-                                float* dif, float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dtgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n, const double* a,
-                                lapack_int lda, const double* b, lapack_int ldb,
-                                double* c, lapack_int ldc, const double* d,
-                                lapack_int ldd, const double* e, lapack_int lde,
-                                double* f, lapack_int ldf, double* scale,
-                                double* dif, double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* c, lapack_int ldc,
-                                const lapack_complex_float* d, lapack_int ldd,
-                                const lapack_complex_float* e, lapack_int lde,
-                                lapack_complex_float* f, lapack_int ldf,
-                                float* scale, float* dif,
-                                lapack_complex_float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ztgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* c, lapack_int ldc,
-                                const lapack_complex_double* d, lapack_int ldd,
-                                const lapack_complex_double* e, lapack_int lde,
-                                lapack_complex_double* f, lapack_int ldf,
-                                double* scale, double* dif,
-                                lapack_complex_double* work, lapack_int lwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_stpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const float* ap,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const double* ap,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_float* ap, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_double* ap, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* ap, const float* b, lapack_int ldb,
-                                const float* x, lapack_int ldx, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* ap, const double* b,
-                                lapack_int ldb, const double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                const lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                const lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, float* ap );
-lapack_int LAPACKE_dtptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, double* ap );
-lapack_int LAPACKE_ctptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_float* ap );
-lapack_int LAPACKE_ztptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_double* ap );
-
-lapack_int LAPACKE_stptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* ap, float* b, lapack_int ldb );
-lapack_int LAPACKE_dtptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* ap, double* b, lapack_int ldb );
-lapack_int LAPACKE_ctptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* ap, float* arf );
-lapack_int LAPACKE_dtpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* ap, double* arf );
-lapack_int LAPACKE_ctpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* ap,
-                                lapack_complex_float* arf );
-lapack_int LAPACKE_ztpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* ap,
-                                lapack_complex_double* arf );
-
-lapack_int LAPACKE_stpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, float* a, lapack_int lda );
-lapack_int LAPACKE_dtpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, double* a, lapack_int lda );
-lapack_int LAPACKE_ctpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_strcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const float* a,
-                                lapack_int lda, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dtrcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const double* a,
-                                lapack_int lda, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctrcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_ztrcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* rcond, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_strevc_work( int matrix_order, char side, char howmny,
-                                lapack_logical* select, lapack_int n,
-                                const float* t, lapack_int ldt, float* vl,
-                                lapack_int ldvl, float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, float* work );
-lapack_int LAPACKE_dtrevc_work( int matrix_order, char side, char howmny,
-                                lapack_logical* select, lapack_int n,
-                                const double* t, lapack_int ldt, double* vl,
-                                lapack_int ldvl, double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, double* work );
-lapack_int LAPACKE_ctrevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztrevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_strexc_work( int matrix_order, char compq, lapack_int n,
-                                float* t, lapack_int ldt, float* q,
-                                lapack_int ldq, lapack_int* ifst,
-                                lapack_int* ilst, float* work );
-lapack_int LAPACKE_dtrexc_work( int matrix_order, char compq, lapack_int n,
-                                double* t, lapack_int ldt, double* q,
-                                lapack_int ldq, lapack_int* ifst,
-                                lapack_int* ilst, double* work );
-lapack_int LAPACKE_ctrexc_work( int matrix_order, char compq, lapack_int n,
-                                lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztrexc_work( int matrix_order, char compq, lapack_int n,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_strrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* a, lapack_int lda, const float* b,
-                                lapack_int ldb, const float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dtrrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* a, lapack_int lda,
-                                const double* b, lapack_int ldb,
-                                const double* x, lapack_int ldx, double* ferr,
-                                double* berr, double* work, lapack_int* iwork );
-lapack_int LAPACKE_ctrrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                const lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztrrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                const lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_strsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                float* t, lapack_int ldt, float* q,
-                                lapack_int ldq, float* wr, float* wi,
-                                lapack_int* m, float* s, float* sep,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dtrsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                double* t, lapack_int ldt, double* q,
-                                lapack_int ldq, double* wr, double* wi,
-                                lapack_int* m, double* s, double* sep,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_ctrsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* w, lapack_int* m,
-                                float* s, float* sep,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_ztrsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* w, lapack_int* m,
-                                double* s, double* sep,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_strsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const float* t, lapack_int ldt, const float* vl,
-                                lapack_int ldvl, const float* vr,
-                                lapack_int ldvr, float* s, float* sep,
-                                lapack_int mm, lapack_int* m, float* work,
-                                lapack_int ldwork, lapack_int* iwork );
-lapack_int LAPACKE_dtrsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const double* t, lapack_int ldt,
-                                const double* vl, lapack_int ldvl,
-                                const double* vr, lapack_int ldvr, double* s,
-                                double* sep, lapack_int mm, lapack_int* m,
-                                double* work, lapack_int ldwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctrsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_float* t, lapack_int ldt,
-                                const lapack_complex_float* vl, lapack_int ldvl,
-                                const lapack_complex_float* vr, lapack_int ldvr,
-                                float* s, float* sep, lapack_int mm,
-                                lapack_int* m, lapack_complex_float* work,
-                                lapack_int ldwork, float* rwork );
-lapack_int LAPACKE_ztrsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_double* t, lapack_int ldt,
-                                const lapack_complex_double* vl,
-                                lapack_int ldvl,
-                                const lapack_complex_double* vr,
-                                lapack_int ldvr, double* s, double* sep,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, lapack_int ldwork,
-                                double* rwork );
-
-lapack_int LAPACKE_strsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const float* a, lapack_int lda, const float* b,
-                                lapack_int ldb, float* c, lapack_int ldc,
-                                float* scale );
-lapack_int LAPACKE_dtrsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const double* b, lapack_int ldb, double* c,
-                                lapack_int ldc, double* scale );
-lapack_int LAPACKE_ctrsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* c, lapack_int ldc,
-                                float* scale );
-lapack_int LAPACKE_ztrsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* c, lapack_int ldc,
-                                double* scale );
-
-lapack_int LAPACKE_strtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, float* a, lapack_int lda );
-lapack_int LAPACKE_dtrtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, double* a, lapack_int lda );
-lapack_int LAPACKE_ctrtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda );
-lapack_int LAPACKE_ztrtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda );
-
-lapack_int LAPACKE_strtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* a, lapack_int lda, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dtrtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* a, lapack_int lda, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_ctrtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztrtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_strttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* arf );
-lapack_int LAPACKE_dtrttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* arf );
-lapack_int LAPACKE_ctrttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* arf );
-lapack_int LAPACKE_ztrttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* arf );
-
-lapack_int LAPACKE_strttp_work( int matrix_order, char uplo, lapack_int n,
-                                const float* a, lapack_int lda, float* ap );
-lapack_int LAPACKE_dtrttp_work( int matrix_order, char uplo, lapack_int n,
-                                const double* a, lapack_int lda, double* ap );
-lapack_int LAPACKE_ctrttp_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_ztrttp_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_stzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dtzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_ctzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_ztzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungtr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungtr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cupgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zupgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_cupmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zupmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_claghe( int matrix_order, lapack_int n, lapack_int k,
-                           const float* d, lapack_complex_float* a,
-                           lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_zlaghe( int matrix_order, lapack_int n, lapack_int k,
-                           const double* d, lapack_complex_double* a,
-                           lapack_int lda, lapack_int* iseed );
-
-lapack_int LAPACKE_slagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const float* d, float* a, lapack_int lda,
-                           lapack_int* iseed );
-lapack_int LAPACKE_dlagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const double* d, double* a, lapack_int lda,
-                           lapack_int* iseed );
-lapack_int LAPACKE_clagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const float* d, lapack_complex_float* a,
-                           lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_zlagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const double* d, lapack_complex_double* a,
-                           lapack_int lda, lapack_int* iseed );
-
-lapack_int LAPACKE_slapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, float* x, lapack_int ldx,
-                           lapack_int* k );
-lapack_int LAPACKE_dlapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, double* x,
-                           lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_clapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, lapack_complex_float* x,
-                           lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_zlapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, lapack_complex_double* x,
-                           lapack_int ldx, lapack_int* k );
-
-
-float LAPACKE_slapy2( float x, float y );
-double LAPACKE_dlapy2( double x, double y );
-
-float LAPACKE_slapy3( float x, float y, float z );
-double LAPACKE_dlapy3( double x, double y, double z );
-
-lapack_int LAPACKE_slartgp( float f, float g, float* cs, float* sn, float* r );
-lapack_int LAPACKE_dlartgp( double f, double g, double* cs, double* sn,
-                            double* r );
-
-lapack_int LAPACKE_slartgs( float x, float y, float sigma, float* cs,
-                            float* sn );
-lapack_int LAPACKE_dlartgs( double x, double y, double sigma, double* cs,
-                            double* sn );
-
-
-//LAPACK 3.3.0
-lapack_int LAPACKE_cbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, float* theta, float* phi,
-                           lapack_complex_float* u1, lapack_int ldu1,
-                           lapack_complex_float* u2, lapack_int ldu2,
-                           lapack_complex_float* v1t, lapack_int ldv1t,
-                           lapack_complex_float* v2t, lapack_int ldv2t,
-                           float* b11d, float* b11e, float* b12d, float* b12e,
-                           float* b21d, float* b21e, float* b22d, float* b22e );
-lapack_int LAPACKE_cbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                float* theta, float* phi,
-                                lapack_complex_float* u1, lapack_int ldu1,
-                                lapack_complex_float* u2, lapack_int ldu2,
-                                lapack_complex_float* v1t, lapack_int ldv1t,
-                                lapack_complex_float* v2t, lapack_int ldv2t,
-                                float* b11d, float* b11e, float* b12d,
-                                float* b12e, float* b21d, float* b21e,
-                                float* b22d, float* b22e, float* rwork,
-                                lapack_int lrwork );
-lapack_int LAPACKE_cheswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_cheswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_chetri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_chetri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_chetri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_chetri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_float* work, lapack_int nb );
-lapack_int LAPACKE_chetrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_float* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_chetrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_csyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_csyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, lapack_complex_float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_csyswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_csyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_csytri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_csytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_csytri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_csytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_float* work, lapack_int nb );
-lapack_int LAPACKE_csytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_float* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_csytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_cunbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_float* x11, lapack_int ldx11,
-                           lapack_complex_float* x12, lapack_int ldx12,
-                           lapack_complex_float* x21, lapack_int ldx21,
-                           lapack_complex_float* x22, lapack_int ldx22,
-                           float* theta, float* phi,
-                           lapack_complex_float* taup1,
-                           lapack_complex_float* taup2,
-                           lapack_complex_float* tauq1,
-                           lapack_complex_float* tauq2 );
-lapack_int LAPACKE_cunbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                lapack_complex_float* x11, lapack_int ldx11,
-                                lapack_complex_float* x12, lapack_int ldx12,
-                                lapack_complex_float* x21, lapack_int ldx21,
-                                lapack_complex_float* x22, lapack_int ldx22,
-                                float* theta, float* phi,
-                                lapack_complex_float* taup1,
-                                lapack_complex_float* taup2,
-                                lapack_complex_float* tauq1,
-                                lapack_complex_float* tauq2,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_cuncsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_float* x11, lapack_int ldx11,
-                           lapack_complex_float* x12, lapack_int ldx12,
-                           lapack_complex_float* x21, lapack_int ldx21,
-                           lapack_complex_float* x22, lapack_int ldx22,
-                           float* theta, lapack_complex_float* u1,
-                           lapack_int ldu1, lapack_complex_float* u2,
-                           lapack_int ldu2, lapack_complex_float* v1t,
-                           lapack_int ldv1t, lapack_complex_float* v2t,
-                           lapack_int ldv2t );
-lapack_int LAPACKE_cuncsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, lapack_complex_float* x11,
-                                lapack_int ldx11, lapack_complex_float* x12,
-                                lapack_int ldx12, lapack_complex_float* x21,
-                                lapack_int ldx21, lapack_complex_float* x22,
-                                lapack_int ldx22, float* theta,
-                                lapack_complex_float* u1, lapack_int ldu1,
-                                lapack_complex_float* u2, lapack_int ldu2,
-                                lapack_complex_float* v1t, lapack_int ldv1t,
-                                lapack_complex_float* v2t, lapack_int ldv2t,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, double* theta,
-                           double* phi, double* u1, lapack_int ldu1, double* u2,
-                           lapack_int ldu2, double* v1t, lapack_int ldv1t,
-                           double* v2t, lapack_int ldv2t, double* b11d,
-                           double* b11e, double* b12d, double* b12e,
-                           double* b21d, double* b21e, double* b22d,
-                           double* b22e );
-lapack_int LAPACKE_dbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                double* theta, double* phi, double* u1,
-                                lapack_int ldu1, double* u2, lapack_int ldu2,
-                                double* v1t, lapack_int ldv1t, double* v2t,
-                                lapack_int ldv2t, double* b11d, double* b11e,
-                                double* b12d, double* b12e, double* b21d,
-                                double* b21e, double* b22d, double* b22e,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_dorbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           double* x11, lapack_int ldx11, double* x12,
-                           lapack_int ldx12, double* x21, lapack_int ldx21,
-                           double* x22, lapack_int ldx22, double* theta,
-                           double* phi, double* taup1, double* taup2,
-                           double* tauq1, double* tauq2 );
-lapack_int LAPACKE_dorbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                double* x11, lapack_int ldx11, double* x12,
-                                lapack_int ldx12, double* x21, lapack_int ldx21,
-                                double* x22, lapack_int ldx22, double* theta,
-                                double* phi, double* taup1, double* taup2,
-                                double* tauq1, double* tauq2, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           double* x11, lapack_int ldx11, double* x12,
-                           lapack_int ldx12, double* x21, lapack_int ldx21,
-                           double* x22, lapack_int ldx22, double* theta,
-                           double* u1, lapack_int ldu1, double* u2,
-                           lapack_int ldu2, double* v1t, lapack_int ldv1t,
-                           double* v2t, lapack_int ldv2t );
-lapack_int LAPACKE_dorcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, double* x11, lapack_int ldx11,
-                                double* x12, lapack_int ldx12, double* x21,
-                                lapack_int ldx21, double* x22, lapack_int ldx22,
-                                double* theta, double* u1, lapack_int ldu1,
-                                double* u2, lapack_int ldu2, double* v1t,
-                                lapack_int ldv1t, double* v2t, lapack_int ldv2t,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dsyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            double* a, lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dsyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, double* a, lapack_int lda,
-                                 const lapack_int* ipiv, double* work );
-lapack_int LAPACKE_dsyswapr( int matrix_order, char uplo, lapack_int n,
-                             double* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_dsyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  double* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_dsytri2( int matrix_order, char uplo, lapack_int n,
-                            double* a, lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dsytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 double* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_dsytri2x( int matrix_order, char uplo, lapack_int n,
-                             double* a, lapack_int lda, const lapack_int* ipiv,
-                             lapack_int nb );
-lapack_int LAPACKE_dsytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  double* a, lapack_int lda,
-                                  const lapack_int* ipiv, double* work,
-                                  lapack_int nb );
-lapack_int LAPACKE_dsytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const double* a, lapack_int lda,
-                            const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_dsytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 double* b, lapack_int ldb, double* work );
-lapack_int LAPACKE_sbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, float* theta, float* phi,
-                           float* u1, lapack_int ldu1, float* u2,
-                           lapack_int ldu2, float* v1t, lapack_int ldv1t,
-                           float* v2t, lapack_int ldv2t, float* b11d,
-                           float* b11e, float* b12d, float* b12e, float* b21d,
-                           float* b21e, float* b22d, float* b22e );
-lapack_int LAPACKE_sbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                float* theta, float* phi, float* u1,
-                                lapack_int ldu1, float* u2, lapack_int ldu2,
-                                float* v1t, lapack_int ldv1t, float* v2t,
-                                lapack_int ldv2t, float* b11d, float* b11e,
-                                float* b12d, float* b12e, float* b21d,
-                                float* b21e, float* b22d, float* b22e,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_sorbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q, float* x11,
-                           lapack_int ldx11, float* x12, lapack_int ldx12,
-                           float* x21, lapack_int ldx21, float* x22,
-                           lapack_int ldx22, float* theta, float* phi,
-                           float* taup1, float* taup2, float* tauq1,
-                           float* tauq2 );
-lapack_int LAPACKE_sorbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                float* x11, lapack_int ldx11, float* x12,
-                                lapack_int ldx12, float* x21, lapack_int ldx21,
-                                float* x22, lapack_int ldx22, float* theta,
-                                float* phi, float* taup1, float* taup2,
-                                float* tauq1, float* tauq2, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_sorcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q, float* x11,
-                           lapack_int ldx11, float* x12, lapack_int ldx12,
-                           float* x21, lapack_int ldx21, float* x22,
-                           lapack_int ldx22, float* theta, float* u1,
-                           lapack_int ldu1, float* u2, lapack_int ldu2,
-                           float* v1t, lapack_int ldv1t, float* v2t,
-                           lapack_int ldv2t );
-lapack_int LAPACKE_sorcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, float* x11, lapack_int ldx11,
-                                float* x12, lapack_int ldx12, float* x21,
-                                lapack_int ldx21, float* x22, lapack_int ldx22,
-                                float* theta, float* u1, lapack_int ldu1,
-                                float* u2, lapack_int ldu2, float* v1t,
-                                lapack_int ldv1t, float* v2t, lapack_int ldv2t,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ssyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            float* a, lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_ssyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, float* a, lapack_int lda,
-                                 const lapack_int* ipiv, float* work );
-lapack_int LAPACKE_ssyswapr( int matrix_order, char uplo, lapack_int n,
-                             float* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_ssyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  float* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_ssytri2( int matrix_order, char uplo, lapack_int n, float* a,
-                            lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_ssytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 float* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_ssytri2x( int matrix_order, char uplo, lapack_int n,
-                             float* a, lapack_int lda, const lapack_int* ipiv,
-                             lapack_int nb );
-lapack_int LAPACKE_ssytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  float* a, lapack_int lda,
-                                  const lapack_int* ipiv, float* work,
-                                  lapack_int nb );
-lapack_int LAPACKE_ssytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const float* a, lapack_int lda,
-                            const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_ssytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 float* b, lapack_int ldb, float* work );
-lapack_int LAPACKE_zbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, double* theta,
-                           double* phi, lapack_complex_double* u1,
-                           lapack_int ldu1, lapack_complex_double* u2,
-                           lapack_int ldu2, lapack_complex_double* v1t,
-                           lapack_int ldv1t, lapack_complex_double* v2t,
-                           lapack_int ldv2t, double* b11d, double* b11e,
-                           double* b12d, double* b12e, double* b21d,
-                           double* b21e, double* b22d, double* b22e );
-lapack_int LAPACKE_zbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                double* theta, double* phi,
-                                lapack_complex_double* u1, lapack_int ldu1,
-                                lapack_complex_double* u2, lapack_int ldu2,
-                                lapack_complex_double* v1t, lapack_int ldv1t,
-                                lapack_complex_double* v2t, lapack_int ldv2t,
-                                double* b11d, double* b11e, double* b12d,
-                                double* b12e, double* b21d, double* b21e,
-                                double* b22d, double* b22e, double* rwork,
-                                lapack_int lrwork );
-lapack_int LAPACKE_zheswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_zheswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_zhetri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_zhetri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_zhetri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_zhetri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_double* work, lapack_int nb );
-lapack_int LAPACKE_zhetrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_double* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_double* b, lapack_int ldb );
-lapack_int LAPACKE_zhetrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* work );
-lapack_int LAPACKE_zsyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_zsyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, lapack_complex_double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_double* work );
-lapack_int LAPACKE_zsyswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_zsyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_zsytri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_zsytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_zsytri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_zsytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_double* work, lapack_int nb );
-lapack_int LAPACKE_zsytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_double* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_double* b, lapack_int ldb );
-lapack_int LAPACKE_zsytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* work );
-lapack_int LAPACKE_zunbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_double* x11, lapack_int ldx11,
-                           lapack_complex_double* x12, lapack_int ldx12,
-                           lapack_complex_double* x21, lapack_int ldx21,
-                           lapack_complex_double* x22, lapack_int ldx22,
-                           double* theta, double* phi,
-                           lapack_complex_double* taup1,
-                           lapack_complex_double* taup2,
-                           lapack_complex_double* tauq1,
-                           lapack_complex_double* tauq2 );
-lapack_int LAPACKE_zunbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                lapack_complex_double* x11, lapack_int ldx11,
-                                lapack_complex_double* x12, lapack_int ldx12,
-                                lapack_complex_double* x21, lapack_int ldx21,
-                                lapack_complex_double* x22, lapack_int ldx22,
-                                double* theta, double* phi,
-                                lapack_complex_double* taup1,
-                                lapack_complex_double* taup2,
-                                lapack_complex_double* tauq1,
-                                lapack_complex_double* tauq2,
-                                lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_zuncsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_double* x11, lapack_int ldx11,
-                           lapack_complex_double* x12, lapack_int ldx12,
-                           lapack_complex_double* x21, lapack_int ldx21,
-                           lapack_complex_double* x22, lapack_int ldx22,
-                           double* theta, lapack_complex_double* u1,
-                           lapack_int ldu1, lapack_complex_double* u2,
-                           lapack_int ldu2, lapack_complex_double* v1t,
-                           lapack_int ldv1t, lapack_complex_double* v2t,
-                           lapack_int ldv2t );
-lapack_int LAPACKE_zuncsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, lapack_complex_double* x11,
-                                lapack_int ldx11, lapack_complex_double* x12,
-                                lapack_int ldx12, lapack_complex_double* x21,
-                                lapack_int ldx21, lapack_complex_double* x22,
-                                lapack_int ldx22, double* theta,
-                                lapack_complex_double* u1, lapack_int ldu1,
-                                lapack_complex_double* u2, lapack_int ldu2,
-                                lapack_complex_double* v1t, lapack_int ldv1t,
-                                lapack_complex_double* v2t, lapack_int ldv2t,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork );
-//LAPACK 3.4.0
-lapack_int LAPACKE_sgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const float* v, lapack_int ldv,
-                            const float* t, lapack_int ldt, float* c,
-                            lapack_int ldc );
-lapack_int LAPACKE_dgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const double* v, lapack_int ldv,
-                            const double* t, lapack_int ldt, double* c,
-                            lapack_int ldc );
-lapack_int LAPACKE_cgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const lapack_complex_float* v,
-                            lapack_int ldv, const lapack_complex_float* t,
-                            lapack_int ldt, lapack_complex_float* c,
-                            lapack_int ldc );
-lapack_int LAPACKE_zgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const lapack_complex_double* v,
-                            lapack_int ldv, const lapack_complex_double* t,
-                            lapack_int ldt, lapack_complex_double* c,
-                            lapack_int ldc );
-
-lapack_int LAPACKE_sgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, float* a, lapack_int lda, float* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_dgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, double* a, lapack_int lda, double* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_cgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_zgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* t,
-                           lapack_int ldt );
-
-lapack_int LAPACKE_sgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_dgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_cgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_sgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_dgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_cgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb, const float* v,
-                            lapack_int ldv, const float* t, lapack_int ldt,
-                            float* a, lapack_int lda, float* b,
-                            lapack_int ldb );
-lapack_int LAPACKE_dtpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb, const double* v,
-                            lapack_int ldv, const double* t, lapack_int ldt,
-                            double* a, lapack_int lda, double* b,
-                            lapack_int ldb );
-lapack_int LAPACKE_ctpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb,
-                            const lapack_complex_float* v, lapack_int ldv,
-                            const lapack_complex_float* t, lapack_int ldt,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb,
-                            const lapack_complex_double* v, lapack_int ldv,
-                            const lapack_complex_double* t, lapack_int ldt,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_dtpqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int l, lapack_int nb, double* a,
-                           lapack_int lda, double* b, lapack_int ldb, double* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_ctpqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int l, lapack_int nb, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* t,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_int ldt );
-lapack_int LAPACKE_ztpqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int l, lapack_int nb,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* b, lapack_int ldb,
-                            float* t, lapack_int ldt );
-lapack_int LAPACKE_dtpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* b,
-                            lapack_int ldb, double* t, lapack_int ldt );
-lapack_int LAPACKE_ctpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_ztpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l, const float* v,
-                           lapack_int ldv, const float* t, lapack_int ldt,
-                           float* a, lapack_int lda, float* b, lapack_int ldb,
-                           lapack_int myldwork );
-lapack_int LAPACKE_dtprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l, const double* v,
-                           lapack_int ldv, const double* t, lapack_int ldt,
-                           double* a, lapack_int lda, double* b, lapack_int ldb,
-                           lapack_int myldwork );
-lapack_int LAPACKE_ctprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l,
-                           const lapack_complex_float* v, lapack_int ldv,
-                           const lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_int myldwork );
-lapack_int LAPACKE_ztprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l,
-                           const lapack_complex_double* v, lapack_int ldv,
-                           const lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_int myldwork );
-
-lapack_int LAPACKE_sgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const float* v, lapack_int ldv,
-                                 const float* t, lapack_int ldt, float* c,
-                                 lapack_int ldc, float* work );
-lapack_int LAPACKE_dgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const double* v, lapack_int ldv,
-                                 const double* t, lapack_int ldt, double* c,
-                                 lapack_int ldc, double* work );
-lapack_int LAPACKE_cgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const lapack_complex_float* v,
-                                 lapack_int ldv, const lapack_complex_float* t,
-                                 lapack_int ldt, lapack_complex_float* c,
-                                 lapack_int ldc, lapack_complex_float* work );
-lapack_int LAPACKE_zgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const lapack_complex_double* v,
-                                 lapack_int ldv, const lapack_complex_double* t,
-                                 lapack_int ldt, lapack_complex_double* c,
-                                 lapack_int ldc, lapack_complex_double* work );
-
-lapack_int LAPACKE_sgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, float* a, lapack_int lda,
-                                float* t, lapack_int ldt, float* work );
-lapack_int LAPACKE_dgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, double* a, lapack_int lda,
-                                double* t, lapack_int ldt, double* work );
-lapack_int LAPACKE_cgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* t,
-                                lapack_int ldt, lapack_complex_float* work );
-lapack_int LAPACKE_zgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* t,
-                                lapack_int ldt, lapack_complex_double* work );
-
-lapack_int LAPACKE_sgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_dgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_cgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_sgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_dgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_cgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb, const float* v,
-                                 lapack_int ldv, const float* t, lapack_int ldt,
-                                 float* a, lapack_int lda, float* b,
-                                 lapack_int ldb, float* work );
-lapack_int LAPACKE_dtpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb, const double* v,
-                                 lapack_int ldv, const double* t,
-                                 lapack_int ldt, double* a, lapack_int lda,
-                                 double* b, lapack_int ldb, double* work );
-lapack_int LAPACKE_ctpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb,
-                                 const lapack_complex_float* v, lapack_int ldv,
-                                 const lapack_complex_float* t, lapack_int ldt,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_ztpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb,
-                                 const lapack_complex_double* v, lapack_int ldv,
-                                 const lapack_complex_double* t, lapack_int ldt,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* work );
-
-lapack_int LAPACKE_dtpqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int l, lapack_int nb, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* t, lapack_int ldt, double* work );
-lapack_int LAPACKE_ctpqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int l, lapack_int nb,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* t,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_int ldt, lapack_complex_float* work );
-lapack_int LAPACKE_ztpqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int l, lapack_int nb,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_stpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* b,
-                                 lapack_int ldb, float* t, lapack_int ldt );
-lapack_int LAPACKE_dtpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* b,
-                                 lapack_int ldb, double* t, lapack_int ldt );
-lapack_int LAPACKE_ctpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_ztpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const float* v, lapack_int ldv, const float* t,
-                                lapack_int ldt, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, const float* mywork,
-                                lapack_int myldwork );
-lapack_int LAPACKE_dtprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const double* v, lapack_int ldv,
-                                const double* t, lapack_int ldt, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                const double* mywork, lapack_int myldwork );
-lapack_int LAPACKE_ctprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const lapack_complex_float* v, lapack_int ldv,
-                                const lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                const float* mywork, lapack_int myldwork );
-lapack_int LAPACKE_ztprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const lapack_complex_double* v, lapack_int ldv,
-                                const lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                const double* mywork, lapack_int myldwork );
-//LAPACK 3.X.X
-lapack_int LAPACKE_csyr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float alpha,
-                             const lapack_complex_float* x, lapack_int incx,
-                             lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zsyr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double alpha,
-                             const lapack_complex_double* x, lapack_int incx,
-                             lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_csyr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float alpha,
-                                  const lapack_complex_float* x,
-                                  lapack_int incx, lapack_complex_float* a,
-                                  lapack_int lda );
-lapack_int LAPACKE_zsyr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double alpha,
-                                  const lapack_complex_double* x,
-                                  lapack_int incx, lapack_complex_double* a,
-                                  lapack_int lda );
-
-
-
-#define LAPACK_sgetrf LAPACK_GLOBAL(sgetrf,SGETRF)
-#define LAPACK_dgetrf LAPACK_GLOBAL(dgetrf,DGETRF)
-#define LAPACK_cgetrf LAPACK_GLOBAL(cgetrf,CGETRF)
-#define LAPACK_zgetrf LAPACK_GLOBAL(zgetrf,ZGETRF)
-#define LAPACK_sgbtrf LAPACK_GLOBAL(sgbtrf,SGBTRF)
-#define LAPACK_dgbtrf LAPACK_GLOBAL(dgbtrf,DGBTRF)
-#define LAPACK_cgbtrf LAPACK_GLOBAL(cgbtrf,CGBTRF)
-#define LAPACK_zgbtrf LAPACK_GLOBAL(zgbtrf,ZGBTRF)
-#define LAPACK_sgttrf LAPACK_GLOBAL(sgttrf,SGTTRF)
-#define LAPACK_dgttrf LAPACK_GLOBAL(dgttrf,DGTTRF)
-#define LAPACK_cgttrf LAPACK_GLOBAL(cgttrf,CGTTRF)
-#define LAPACK_zgttrf LAPACK_GLOBAL(zgttrf,ZGTTRF)
-#define LAPACK_spotrf LAPACK_GLOBAL(spotrf,SPOTRF)
-#define LAPACK_dpotrf LAPACK_GLOBAL(dpotrf,DPOTRF)
-#define LAPACK_cpotrf LAPACK_GLOBAL(cpotrf,CPOTRF)
-#define LAPACK_zpotrf LAPACK_GLOBAL(zpotrf,ZPOTRF)
-#define LAPACK_dpstrf LAPACK_GLOBAL(dpstrf,DPSTRF)
-#define LAPACK_spstrf LAPACK_GLOBAL(spstrf,SPSTRF)
-#define LAPACK_zpstrf LAPACK_GLOBAL(zpstrf,ZPSTRF)
-#define LAPACK_cpstrf LAPACK_GLOBAL(cpstrf,CPSTRF)
-#define LAPACK_dpftrf LAPACK_GLOBAL(dpftrf,DPFTRF)
-#define LAPACK_spftrf LAPACK_GLOBAL(spftrf,SPFTRF)
-#define LAPACK_zpftrf LAPACK_GLOBAL(zpftrf,ZPFTRF)
-#define LAPACK_cpftrf LAPACK_GLOBAL(cpftrf,CPFTRF)
-#define LAPACK_spptrf LAPACK_GLOBAL(spptrf,SPPTRF)
-#define LAPACK_dpptrf LAPACK_GLOBAL(dpptrf,DPPTRF)
-#define LAPACK_cpptrf LAPACK_GLOBAL(cpptrf,CPPTRF)
-#define LAPACK_zpptrf LAPACK_GLOBAL(zpptrf,ZPPTRF)
-#define LAPACK_spbtrf LAPACK_GLOBAL(spbtrf,SPBTRF)
-#define LAPACK_dpbtrf LAPACK_GLOBAL(dpbtrf,DPBTRF)
-#define LAPACK_cpbtrf LAPACK_GLOBAL(cpbtrf,CPBTRF)
-#define LAPACK_zpbtrf LAPACK_GLOBAL(zpbtrf,ZPBTRF)
-#define LAPACK_spttrf LAPACK_GLOBAL(spttrf,SPTTRF)
-#define LAPACK_dpttrf LAPACK_GLOBAL(dpttrf,DPTTRF)
-#define LAPACK_cpttrf LAPACK_GLOBAL(cpttrf,CPTTRF)
-#define LAPACK_zpttrf LAPACK_GLOBAL(zpttrf,ZPTTRF)
-#define LAPACK_ssytrf LAPACK_GLOBAL(ssytrf,SSYTRF)
-#define LAPACK_dsytrf LAPACK_GLOBAL(dsytrf,DSYTRF)
-#define LAPACK_csytrf LAPACK_GLOBAL(csytrf,CSYTRF)
-#define LAPACK_zsytrf LAPACK_GLOBAL(zsytrf,ZSYTRF)
-#define LAPACK_chetrf LAPACK_GLOBAL(chetrf,CHETRF)
-#define LAPACK_zhetrf LAPACK_GLOBAL(zhetrf,ZHETRF)
-#define LAPACK_ssptrf LAPACK_GLOBAL(ssptrf,SSPTRF)
-#define LAPACK_dsptrf LAPACK_GLOBAL(dsptrf,DSPTRF)
-#define LAPACK_csptrf LAPACK_GLOBAL(csptrf,CSPTRF)
-#define LAPACK_zsptrf LAPACK_GLOBAL(zsptrf,ZSPTRF)
-#define LAPACK_chptrf LAPACK_GLOBAL(chptrf,CHPTRF)
-#define LAPACK_zhptrf LAPACK_GLOBAL(zhptrf,ZHPTRF)
-#define LAPACK_sgetrs LAPACK_GLOBAL(sgetrs,SGETRS)
-#define LAPACK_dgetrs LAPACK_GLOBAL(dgetrs,DGETRS)
-#define LAPACK_cgetrs LAPACK_GLOBAL(cgetrs,CGETRS)
-#define LAPACK_zgetrs LAPACK_GLOBAL(zgetrs,ZGETRS)
-#define LAPACK_sgbtrs LAPACK_GLOBAL(sgbtrs,SGBTRS)
-#define LAPACK_dgbtrs LAPACK_GLOBAL(dgbtrs,DGBTRS)
-#define LAPACK_cgbtrs LAPACK_GLOBAL(cgbtrs,CGBTRS)
-#define LAPACK_zgbtrs LAPACK_GLOBAL(zgbtrs,ZGBTRS)
-#define LAPACK_sgttrs LAPACK_GLOBAL(sgttrs,SGTTRS)
-#define LAPACK_dgttrs LAPACK_GLOBAL(dgttrs,DGTTRS)
-#define LAPACK_cgttrs LAPACK_GLOBAL(cgttrs,CGTTRS)
-#define LAPACK_zgttrs LAPACK_GLOBAL(zgttrs,ZGTTRS)
-#define LAPACK_spotrs LAPACK_GLOBAL(spotrs,SPOTRS)
-#define LAPACK_dpotrs LAPACK_GLOBAL(dpotrs,DPOTRS)
-#define LAPACK_cpotrs LAPACK_GLOBAL(cpotrs,CPOTRS)
-#define LAPACK_zpotrs LAPACK_GLOBAL(zpotrs,ZPOTRS)
-#define LAPACK_dpftrs LAPACK_GLOBAL(dpftrs,DPFTRS)
-#define LAPACK_spftrs LAPACK_GLOBAL(spftrs,SPFTRS)
-#define LAPACK_zpftrs LAPACK_GLOBAL(zpftrs,ZPFTRS)
-#define LAPACK_cpftrs LAPACK_GLOBAL(cpftrs,CPFTRS)
-#define LAPACK_spptrs LAPACK_GLOBAL(spptrs,SPPTRS)
-#define LAPACK_dpptrs LAPACK_GLOBAL(dpptrs,DPPTRS)
-#define LAPACK_cpptrs LAPACK_GLOBAL(cpptrs,CPPTRS)
-#define LAPACK_zpptrs LAPACK_GLOBAL(zpptrs,ZPPTRS)
-#define LAPACK_spbtrs LAPACK_GLOBAL(spbtrs,SPBTRS)
-#define LAPACK_dpbtrs LAPACK_GLOBAL(dpbtrs,DPBTRS)
-#define LAPACK_cpbtrs LAPACK_GLOBAL(cpbtrs,CPBTRS)
-#define LAPACK_zpbtrs LAPACK_GLOBAL(zpbtrs,ZPBTRS)
-#define LAPACK_spttrs LAPACK_GLOBAL(spttrs,SPTTRS)
-#define LAPACK_dpttrs LAPACK_GLOBAL(dpttrs,DPTTRS)
-#define LAPACK_cpttrs LAPACK_GLOBAL(cpttrs,CPTTRS)
-#define LAPACK_zpttrs LAPACK_GLOBAL(zpttrs,ZPTTRS)
-#define LAPACK_ssytrs LAPACK_GLOBAL(ssytrs,SSYTRS)
-#define LAPACK_dsytrs LAPACK_GLOBAL(dsytrs,DSYTRS)
-#define LAPACK_csytrs LAPACK_GLOBAL(csytrs,CSYTRS)
-#define LAPACK_zsytrs LAPACK_GLOBAL(zsytrs,ZSYTRS)
-#define LAPACK_chetrs LAPACK_GLOBAL(chetrs,CHETRS)
-#define LAPACK_zhetrs LAPACK_GLOBAL(zhetrs,ZHETRS)
-#define LAPACK_ssptrs LAPACK_GLOBAL(ssptrs,SSPTRS)
-#define LAPACK_dsptrs LAPACK_GLOBAL(dsptrs,DSPTRS)
-#define LAPACK_csptrs LAPACK_GLOBAL(csptrs,CSPTRS)
-#define LAPACK_zsptrs LAPACK_GLOBAL(zsptrs,ZSPTRS)
-#define LAPACK_chptrs LAPACK_GLOBAL(chptrs,CHPTRS)
-#define LAPACK_zhptrs LAPACK_GLOBAL(zhptrs,ZHPTRS)
-#define LAPACK_strtrs LAPACK_GLOBAL(strtrs,STRTRS)
-#define LAPACK_dtrtrs LAPACK_GLOBAL(dtrtrs,DTRTRS)
-#define LAPACK_ctrtrs LAPACK_GLOBAL(ctrtrs,CTRTRS)
-#define LAPACK_ztrtrs LAPACK_GLOBAL(ztrtrs,ZTRTRS)
-#define LAPACK_stptrs LAPACK_GLOBAL(stptrs,STPTRS)
-#define LAPACK_dtptrs LAPACK_GLOBAL(dtptrs,DTPTRS)
-#define LAPACK_ctptrs LAPACK_GLOBAL(ctptrs,CTPTRS)
-#define LAPACK_ztptrs LAPACK_GLOBAL(ztptrs,ZTPTRS)
-#define LAPACK_stbtrs LAPACK_GLOBAL(stbtrs,STBTRS)
-#define LAPACK_dtbtrs LAPACK_GLOBAL(dtbtrs,DTBTRS)
-#define LAPACK_ctbtrs LAPACK_GLOBAL(ctbtrs,CTBTRS)
-#define LAPACK_ztbtrs LAPACK_GLOBAL(ztbtrs,ZTBTRS)
-#define LAPACK_sgecon LAPACK_GLOBAL(sgecon,SGECON)
-#define LAPACK_dgecon LAPACK_GLOBAL(dgecon,DGECON)
-#define LAPACK_cgecon LAPACK_GLOBAL(cgecon,CGECON)
-#define LAPACK_zgecon LAPACK_GLOBAL(zgecon,ZGECON)
-#define LAPACK_sgbcon LAPACK_GLOBAL(sgbcon,SGBCON)
-#define LAPACK_dgbcon LAPACK_GLOBAL(dgbcon,DGBCON)
-#define LAPACK_cgbcon LAPACK_GLOBAL(cgbcon,CGBCON)
-#define LAPACK_zgbcon LAPACK_GLOBAL(zgbcon,ZGBCON)
-#define LAPACK_sgtcon LAPACK_GLOBAL(sgtcon,SGTCON)
-#define LAPACK_dgtcon LAPACK_GLOBAL(dgtcon,DGTCON)
-#define LAPACK_cgtcon LAPACK_GLOBAL(cgtcon,CGTCON)
-#define LAPACK_zgtcon LAPACK_GLOBAL(zgtcon,ZGTCON)
-#define LAPACK_spocon LAPACK_GLOBAL(spocon,SPOCON)
-#define LAPACK_dpocon LAPACK_GLOBAL(dpocon,DPOCON)
-#define LAPACK_cpocon LAPACK_GLOBAL(cpocon,CPOCON)
-#define LAPACK_zpocon LAPACK_GLOBAL(zpocon,ZPOCON)
-#define LAPACK_sppcon LAPACK_GLOBAL(sppcon,SPPCON)
-#define LAPACK_dppcon LAPACK_GLOBAL(dppcon,DPPCON)
-#define LAPACK_cppcon LAPACK_GLOBAL(cppcon,CPPCON)
-#define LAPACK_zppcon LAPACK_GLOBAL(zppcon,ZPPCON)
-#define LAPACK_spbcon LAPACK_GLOBAL(spbcon,SPBCON)
-#define LAPACK_dpbcon LAPACK_GLOBAL(dpbcon,DPBCON)
-#define LAPACK_cpbcon LAPACK_GLOBAL(cpbcon,CPBCON)
-#define LAPACK_zpbcon LAPACK_GLOBAL(zpbcon,ZPBCON)
-#define LAPACK_sptcon LAPACK_GLOBAL(sptcon,SPTCON)
-#define LAPACK_dptcon LAPACK_GLOBAL(dptcon,DPTCON)
-#define LAPACK_cptcon LAPACK_GLOBAL(cptcon,CPTCON)
-#define LAPACK_zptcon LAPACK_GLOBAL(zptcon,ZPTCON)
-#define LAPACK_ssycon LAPACK_GLOBAL(ssycon,SSYCON)
-#define LAPACK_dsycon LAPACK_GLOBAL(dsycon,DSYCON)
-#define LAPACK_csycon LAPACK_GLOBAL(csycon,CSYCON)
-#define LAPACK_zsycon LAPACK_GLOBAL(zsycon,ZSYCON)
-#define LAPACK_checon LAPACK_GLOBAL(checon,CHECON)
-#define LAPACK_zhecon LAPACK_GLOBAL(zhecon,ZHECON)
-#define LAPACK_sspcon LAPACK_GLOBAL(sspcon,SSPCON)
-#define LAPACK_dspcon LAPACK_GLOBAL(dspcon,DSPCON)
-#define LAPACK_cspcon LAPACK_GLOBAL(cspcon,CSPCON)
-#define LAPACK_zspcon LAPACK_GLOBAL(zspcon,ZSPCON)
-#define LAPACK_chpcon LAPACK_GLOBAL(chpcon,CHPCON)
-#define LAPACK_zhpcon LAPACK_GLOBAL(zhpcon,ZHPCON)
-#define LAPACK_strcon LAPACK_GLOBAL(strcon,STRCON)
-#define LAPACK_dtrcon LAPACK_GLOBAL(dtrcon,DTRCON)
-#define LAPACK_ctrcon LAPACK_GLOBAL(ctrcon,CTRCON)
-#define LAPACK_ztrcon LAPACK_GLOBAL(ztrcon,ZTRCON)
-#define LAPACK_stpcon LAPACK_GLOBAL(stpcon,STPCON)
-#define LAPACK_dtpcon LAPACK_GLOBAL(dtpcon,DTPCON)
-#define LAPACK_ctpcon LAPACK_GLOBAL(ctpcon,CTPCON)
-#define LAPACK_ztpcon LAPACK_GLOBAL(ztpcon,ZTPCON)
-#define LAPACK_stbcon LAPACK_GLOBAL(stbcon,STBCON)
-#define LAPACK_dtbcon LAPACK_GLOBAL(dtbcon,DTBCON)
-#define LAPACK_ctbcon LAPACK_GLOBAL(ctbcon,CTBCON)
-#define LAPACK_ztbcon LAPACK_GLOBAL(ztbcon,ZTBCON)
-#define LAPACK_sgerfs LAPACK_GLOBAL(sgerfs,SGERFS)
-#define LAPACK_dgerfs LAPACK_GLOBAL(dgerfs,DGERFS)
-#define LAPACK_cgerfs LAPACK_GLOBAL(cgerfs,CGERFS)
-#define LAPACK_zgerfs LAPACK_GLOBAL(zgerfs,ZGERFS)
-#define LAPACK_dgerfsx LAPACK_GLOBAL(dgerfsx,DGERFSX)
-#define LAPACK_sgerfsx LAPACK_GLOBAL(sgerfsx,SGERFSX)
-#define LAPACK_zgerfsx LAPACK_GLOBAL(zgerfsx,ZGERFSX)
-#define LAPACK_cgerfsx LAPACK_GLOBAL(cgerfsx,CGERFSX)
-#define LAPACK_sgbrfs LAPACK_GLOBAL(sgbrfs,SGBRFS)
-#define LAPACK_dgbrfs LAPACK_GLOBAL(dgbrfs,DGBRFS)
-#define LAPACK_cgbrfs LAPACK_GLOBAL(cgbrfs,CGBRFS)
-#define LAPACK_zgbrfs LAPACK_GLOBAL(zgbrfs,ZGBRFS)
-#define LAPACK_dgbrfsx LAPACK_GLOBAL(dgbrfsx,DGBRFSX)
-#define LAPACK_sgbrfsx LAPACK_GLOBAL(sgbrfsx,SGBRFSX)
-#define LAPACK_zgbrfsx LAPACK_GLOBAL(zgbrfsx,ZGBRFSX)
-#define LAPACK_cgbrfsx LAPACK_GLOBAL(cgbrfsx,CGBRFSX)
-#define LAPACK_sgtrfs LAPACK_GLOBAL(sgtrfs,SGTRFS)
-#define LAPACK_dgtrfs LAPACK_GLOBAL(dgtrfs,DGTRFS)
-#define LAPACK_cgtrfs LAPACK_GLOBAL(cgtrfs,CGTRFS)
-#define LAPACK_zgtrfs LAPACK_GLOBAL(zgtrfs,ZGTRFS)
-#define LAPACK_sporfs LAPACK_GLOBAL(sporfs,SPORFS)
-#define LAPACK_dporfs LAPACK_GLOBAL(dporfs,DPORFS)
-#define LAPACK_cporfs LAPACK_GLOBAL(cporfs,CPORFS)
-#define LAPACK_zporfs LAPACK_GLOBAL(zporfs,ZPORFS)
-#define LAPACK_dporfsx LAPACK_GLOBAL(dporfsx,DPORFSX)
-#define LAPACK_sporfsx LAPACK_GLOBAL(sporfsx,SPORFSX)
-#define LAPACK_zporfsx LAPACK_GLOBAL(zporfsx,ZPORFSX)
-#define LAPACK_cporfsx LAPACK_GLOBAL(cporfsx,CPORFSX)
-#define LAPACK_spprfs LAPACK_GLOBAL(spprfs,SPPRFS)
-#define LAPACK_dpprfs LAPACK_GLOBAL(dpprfs,DPPRFS)
-#define LAPACK_cpprfs LAPACK_GLOBAL(cpprfs,CPPRFS)
-#define LAPACK_zpprfs LAPACK_GLOBAL(zpprfs,ZPPRFS)
-#define LAPACK_spbrfs LAPACK_GLOBAL(spbrfs,SPBRFS)
-#define LAPACK_dpbrfs LAPACK_GLOBAL(dpbrfs,DPBRFS)
-#define LAPACK_cpbrfs LAPACK_GLOBAL(cpbrfs,CPBRFS)
-#define LAPACK_zpbrfs LAPACK_GLOBAL(zpbrfs,ZPBRFS)
-#define LAPACK_sptrfs LAPACK_GLOBAL(sptrfs,SPTRFS)
-#define LAPACK_dptrfs LAPACK_GLOBAL(dptrfs,DPTRFS)
-#define LAPACK_cptrfs LAPACK_GLOBAL(cptrfs,CPTRFS)
-#define LAPACK_zptrfs LAPACK_GLOBAL(zptrfs,ZPTRFS)
-#define LAPACK_ssyrfs LAPACK_GLOBAL(ssyrfs,SSYRFS)
-#define LAPACK_dsyrfs LAPACK_GLOBAL(dsyrfs,DSYRFS)
-#define LAPACK_csyrfs LAPACK_GLOBAL(csyrfs,CSYRFS)
-#define LAPACK_zsyrfs LAPACK_GLOBAL(zsyrfs,ZSYRFS)
-#define LAPACK_dsyrfsx LAPACK_GLOBAL(dsyrfsx,DSYRFSX)
-#define LAPACK_ssyrfsx LAPACK_GLOBAL(ssyrfsx,SSYRFSX)
-#define LAPACK_zsyrfsx LAPACK_GLOBAL(zsyrfsx,ZSYRFSX)
-#define LAPACK_csyrfsx LAPACK_GLOBAL(csyrfsx,CSYRFSX)
-#define LAPACK_cherfs LAPACK_GLOBAL(cherfs,CHERFS)
-#define LAPACK_zherfs LAPACK_GLOBAL(zherfs,ZHERFS)
-#define LAPACK_zherfsx LAPACK_GLOBAL(zherfsx,ZHERFSX)
-#define LAPACK_cherfsx LAPACK_GLOBAL(cherfsx,CHERFSX)
-#define LAPACK_ssprfs LAPACK_GLOBAL(ssprfs,SSPRFS)
-#define LAPACK_dsprfs LAPACK_GLOBAL(dsprfs,DSPRFS)
-#define LAPACK_csprfs LAPACK_GLOBAL(csprfs,CSPRFS)
-#define LAPACK_zsprfs LAPACK_GLOBAL(zsprfs,ZSPRFS)
-#define LAPACK_chprfs LAPACK_GLOBAL(chprfs,CHPRFS)
-#define LAPACK_zhprfs LAPACK_GLOBAL(zhprfs,ZHPRFS)
-#define LAPACK_strrfs LAPACK_GLOBAL(strrfs,STRRFS)
-#define LAPACK_dtrrfs LAPACK_GLOBAL(dtrrfs,DTRRFS)
-#define LAPACK_ctrrfs LAPACK_GLOBAL(ctrrfs,CTRRFS)
-#define LAPACK_ztrrfs LAPACK_GLOBAL(ztrrfs,ZTRRFS)
-#define LAPACK_stprfs LAPACK_GLOBAL(stprfs,STPRFS)
-#define LAPACK_dtprfs LAPACK_GLOBAL(dtprfs,DTPRFS)
-#define LAPACK_ctprfs LAPACK_GLOBAL(ctprfs,CTPRFS)
-#define LAPACK_ztprfs LAPACK_GLOBAL(ztprfs,ZTPRFS)
-#define LAPACK_stbrfs LAPACK_GLOBAL(stbrfs,STBRFS)
-#define LAPACK_dtbrfs LAPACK_GLOBAL(dtbrfs,DTBRFS)
-#define LAPACK_ctbrfs LAPACK_GLOBAL(ctbrfs,CTBRFS)
-#define LAPACK_ztbrfs LAPACK_GLOBAL(ztbrfs,ZTBRFS)
-#define LAPACK_sgetri LAPACK_GLOBAL(sgetri,SGETRI)
-#define LAPACK_dgetri LAPACK_GLOBAL(dgetri,DGETRI)
-#define LAPACK_cgetri LAPACK_GLOBAL(cgetri,CGETRI)
-#define LAPACK_zgetri LAPACK_GLOBAL(zgetri,ZGETRI)
-#define LAPACK_spotri LAPACK_GLOBAL(spotri,SPOTRI)
-#define LAPACK_dpotri LAPACK_GLOBAL(dpotri,DPOTRI)
-#define LAPACK_cpotri LAPACK_GLOBAL(cpotri,CPOTRI)
-#define LAPACK_zpotri LAPACK_GLOBAL(zpotri,ZPOTRI)
-#define LAPACK_dpftri LAPACK_GLOBAL(dpftri,DPFTRI)
-#define LAPACK_spftri LAPACK_GLOBAL(spftri,SPFTRI)
-#define LAPACK_zpftri LAPACK_GLOBAL(zpftri,ZPFTRI)
-#define LAPACK_cpftri LAPACK_GLOBAL(cpftri,CPFTRI)
-#define LAPACK_spptri LAPACK_GLOBAL(spptri,SPPTRI)
-#define LAPACK_dpptri LAPACK_GLOBAL(dpptri,DPPTRI)
-#define LAPACK_cpptri LAPACK_GLOBAL(cpptri,CPPTRI)
-#define LAPACK_zpptri LAPACK_GLOBAL(zpptri,ZPPTRI)
-#define LAPACK_ssytri LAPACK_GLOBAL(ssytri,SSYTRI)
-#define LAPACK_dsytri LAPACK_GLOBAL(dsytri,DSYTRI)
-#define LAPACK_csytri LAPACK_GLOBAL(csytri,CSYTRI)
-#define LAPACK_zsytri LAPACK_GLOBAL(zsytri,ZSYTRI)
-#define LAPACK_chetri LAPACK_GLOBAL(chetri,CHETRI)
-#define LAPACK_zhetri LAPACK_GLOBAL(zhetri,ZHETRI)
-#define LAPACK_ssptri LAPACK_GLOBAL(ssptri,SSPTRI)
-#define LAPACK_dsptri LAPACK_GLOBAL(dsptri,DSPTRI)
-#define LAPACK_csptri LAPACK_GLOBAL(csptri,CSPTRI)
-#define LAPACK_zsptri LAPACK_GLOBAL(zsptri,ZSPTRI)
-#define LAPACK_chptri LAPACK_GLOBAL(chptri,CHPTRI)
-#define LAPACK_zhptri LAPACK_GLOBAL(zhptri,ZHPTRI)
-#define LAPACK_strtri LAPACK_GLOBAL(strtri,STRTRI)
-#define LAPACK_dtrtri LAPACK_GLOBAL(dtrtri,DTRTRI)
-#define LAPACK_ctrtri LAPACK_GLOBAL(ctrtri,CTRTRI)
-#define LAPACK_ztrtri LAPACK_GLOBAL(ztrtri,ZTRTRI)
-#define LAPACK_dtftri LAPACK_GLOBAL(dtftri,DTFTRI)
-#define LAPACK_stftri LAPACK_GLOBAL(stftri,STFTRI)
-#define LAPACK_ztftri LAPACK_GLOBAL(ztftri,ZTFTRI)
-#define LAPACK_ctftri LAPACK_GLOBAL(ctftri,CTFTRI)
-#define LAPACK_stptri LAPACK_GLOBAL(stptri,STPTRI)
-#define LAPACK_dtptri LAPACK_GLOBAL(dtptri,DTPTRI)
-#define LAPACK_ctptri LAPACK_GLOBAL(ctptri,CTPTRI)
-#define LAPACK_ztptri LAPACK_GLOBAL(ztptri,ZTPTRI)
-#define LAPACK_sgeequ LAPACK_GLOBAL(sgeequ,SGEEQU)
-#define LAPACK_dgeequ LAPACK_GLOBAL(dgeequ,DGEEQU)
-#define LAPACK_cgeequ LAPACK_GLOBAL(cgeequ,CGEEQU)
-#define LAPACK_zgeequ LAPACK_GLOBAL(zgeequ,ZGEEQU)
-#define LAPACK_dgeequb LAPACK_GLOBAL(dgeequb,DGEEQUB)
-#define LAPACK_sgeequb LAPACK_GLOBAL(sgeequb,SGEEQUB)
-#define LAPACK_zgeequb LAPACK_GLOBAL(zgeequb,ZGEEQUB)
-#define LAPACK_cgeequb LAPACK_GLOBAL(cgeequb,CGEEQUB)
-#define LAPACK_sgbequ LAPACK_GLOBAL(sgbequ,SGBEQU)
-#define LAPACK_dgbequ LAPACK_GLOBAL(dgbequ,DGBEQU)
-#define LAPACK_cgbequ LAPACK_GLOBAL(cgbequ,CGBEQU)
-#define LAPACK_zgbequ LAPACK_GLOBAL(zgbequ,ZGBEQU)
-#define LAPACK_dgbequb LAPACK_GLOBAL(dgbequb,DGBEQUB)
-#define LAPACK_sgbequb LAPACK_GLOBAL(sgbequb,SGBEQUB)
-#define LAPACK_zgbequb LAPACK_GLOBAL(zgbequb,ZGBEQUB)
-#define LAPACK_cgbequb LAPACK_GLOBAL(cgbequb,CGBEQUB)
-#define LAPACK_spoequ LAPACK_GLOBAL(spoequ,SPOEQU)
-#define LAPACK_dpoequ LAPACK_GLOBAL(dpoequ,DPOEQU)
-#define LAPACK_cpoequ LAPACK_GLOBAL(cpoequ,CPOEQU)
-#define LAPACK_zpoequ LAPACK_GLOBAL(zpoequ,ZPOEQU)
-#define LAPACK_dpoequb LAPACK_GLOBAL(dpoequb,DPOEQUB)
-#define LAPACK_spoequb LAPACK_GLOBAL(spoequb,SPOEQUB)
-#define LAPACK_zpoequb LAPACK_GLOBAL(zpoequb,ZPOEQUB)
-#define LAPACK_cpoequb LAPACK_GLOBAL(cpoequb,CPOEQUB)
-#define LAPACK_sppequ LAPACK_GLOBAL(sppequ,SPPEQU)
-#define LAPACK_dppequ LAPACK_GLOBAL(dppequ,DPPEQU)
-#define LAPACK_cppequ LAPACK_GLOBAL(cppequ,CPPEQU)
-#define LAPACK_zppequ LAPACK_GLOBAL(zppequ,ZPPEQU)
-#define LAPACK_spbequ LAPACK_GLOBAL(spbequ,SPBEQU)
-#define LAPACK_dpbequ LAPACK_GLOBAL(dpbequ,DPBEQU)
-#define LAPACK_cpbequ LAPACK_GLOBAL(cpbequ,CPBEQU)
-#define LAPACK_zpbequ LAPACK_GLOBAL(zpbequ,ZPBEQU)
-#define LAPACK_dsyequb LAPACK_GLOBAL(dsyequb,DSYEQUB)
-#define LAPACK_ssyequb LAPACK_GLOBAL(ssyequb,SSYEQUB)
-#define LAPACK_zsyequb LAPACK_GLOBAL(zsyequb,ZSYEQUB)
-#define LAPACK_csyequb LAPACK_GLOBAL(csyequb,CSYEQUB)
-#define LAPACK_zheequb LAPACK_GLOBAL(zheequb,ZHEEQUB)
-#define LAPACK_cheequb LAPACK_GLOBAL(cheequb,CHEEQUB)
-#define LAPACK_sgesv LAPACK_GLOBAL(sgesv,SGESV)
-#define LAPACK_dgesv LAPACK_GLOBAL(dgesv,DGESV)
-#define LAPACK_cgesv LAPACK_GLOBAL(cgesv,CGESV)
-#define LAPACK_zgesv LAPACK_GLOBAL(zgesv,ZGESV)
-#define LAPACK_dsgesv LAPACK_GLOBAL(dsgesv,DSGESV)
-#define LAPACK_zcgesv LAPACK_GLOBAL(zcgesv,ZCGESV)
-#define LAPACK_sgesvx LAPACK_GLOBAL(sgesvx,SGESVX)
-#define LAPACK_dgesvx LAPACK_GLOBAL(dgesvx,DGESVX)
-#define LAPACK_cgesvx LAPACK_GLOBAL(cgesvx,CGESVX)
-#define LAPACK_zgesvx LAPACK_GLOBAL(zgesvx,ZGESVX)
-#define LAPACK_dgesvxx LAPACK_GLOBAL(dgesvxx,DGESVXX)
-#define LAPACK_sgesvxx LAPACK_GLOBAL(sgesvxx,SGESVXX)
-#define LAPACK_zgesvxx LAPACK_GLOBAL(zgesvxx,ZGESVXX)
-#define LAPACK_cgesvxx LAPACK_GLOBAL(cgesvxx,CGESVXX)
-#define LAPACK_sgbsv LAPACK_GLOBAL(sgbsv,SGBSV)
-#define LAPACK_dgbsv LAPACK_GLOBAL(dgbsv,DGBSV)
-#define LAPACK_cgbsv LAPACK_GLOBAL(cgbsv,CGBSV)
-#define LAPACK_zgbsv LAPACK_GLOBAL(zgbsv,ZGBSV)
-#define LAPACK_sgbsvx LAPACK_GLOBAL(sgbsvx,SGBSVX)
-#define LAPACK_dgbsvx LAPACK_GLOBAL(dgbsvx,DGBSVX)
-#define LAPACK_cgbsvx LAPACK_GLOBAL(cgbsvx,CGBSVX)
-#define LAPACK_zgbsvx LAPACK_GLOBAL(zgbsvx,ZGBSVX)
-#define LAPACK_dgbsvxx LAPACK_GLOBAL(dgbsvxx,DGBSVXX)
-#define LAPACK_sgbsvxx LAPACK_GLOBAL(sgbsvxx,SGBSVXX)
-#define LAPACK_zgbsvxx LAPACK_GLOBAL(zgbsvxx,ZGBSVXX)
-#define LAPACK_cgbsvxx LAPACK_GLOBAL(cgbsvxx,CGBSVXX)
-#define LAPACK_sgtsv LAPACK_GLOBAL(sgtsv,SGTSV)
-#define LAPACK_dgtsv LAPACK_GLOBAL(dgtsv,DGTSV)
-#define LAPACK_cgtsv LAPACK_GLOBAL(cgtsv,CGTSV)
-#define LAPACK_zgtsv LAPACK_GLOBAL(zgtsv,ZGTSV)
-#define LAPACK_sgtsvx LAPACK_GLOBAL(sgtsvx,SGTSVX)
-#define LAPACK_dgtsvx LAPACK_GLOBAL(dgtsvx,DGTSVX)
-#define LAPACK_cgtsvx LAPACK_GLOBAL(cgtsvx,CGTSVX)
-#define LAPACK_zgtsvx LAPACK_GLOBAL(zgtsvx,ZGTSVX)
-#define LAPACK_sposv LAPACK_GLOBAL(sposv,SPOSV)
-#define LAPACK_dposv LAPACK_GLOBAL(dposv,DPOSV)
-#define LAPACK_cposv LAPACK_GLOBAL(cposv,CPOSV)
-#define LAPACK_zposv LAPACK_GLOBAL(zposv,ZPOSV)
-#define LAPACK_dsposv LAPACK_GLOBAL(dsposv,DSPOSV)
-#define LAPACK_zcposv LAPACK_GLOBAL(zcposv,ZCPOSV)
-#define LAPACK_sposvx LAPACK_GLOBAL(sposvx,SPOSVX)
-#define LAPACK_dposvx LAPACK_GLOBAL(dposvx,DPOSVX)
-#define LAPACK_cposvx LAPACK_GLOBAL(cposvx,CPOSVX)
-#define LAPACK_zposvx LAPACK_GLOBAL(zposvx,ZPOSVX)
-#define LAPACK_dposvxx LAPACK_GLOBAL(dposvxx,DPOSVXX)
-#define LAPACK_sposvxx LAPACK_GLOBAL(sposvxx,SPOSVXX)
-#define LAPACK_zposvxx LAPACK_GLOBAL(zposvxx,ZPOSVXX)
-#define LAPACK_cposvxx LAPACK_GLOBAL(cposvxx,CPOSVXX)
-#define LAPACK_sppsv LAPACK_GLOBAL(sppsv,SPPSV)
-#define LAPACK_dppsv LAPACK_GLOBAL(dppsv,DPPSV)
-#define LAPACK_cppsv LAPACK_GLOBAL(cppsv,CPPSV)
-#define LAPACK_zppsv LAPACK_GLOBAL(zppsv,ZPPSV)
-#define LAPACK_sppsvx LAPACK_GLOBAL(sppsvx,SPPSVX)
-#define LAPACK_dppsvx LAPACK_GLOBAL(dppsvx,DPPSVX)
-#define LAPACK_cppsvx LAPACK_GLOBAL(cppsvx,CPPSVX)
-#define LAPACK_zppsvx LAPACK_GLOBAL(zppsvx,ZPPSVX)
-#define LAPACK_spbsv LAPACK_GLOBAL(spbsv,SPBSV)
-#define LAPACK_dpbsv LAPACK_GLOBAL(dpbsv,DPBSV)
-#define LAPACK_cpbsv LAPACK_GLOBAL(cpbsv,CPBSV)
-#define LAPACK_zpbsv LAPACK_GLOBAL(zpbsv,ZPBSV)
-#define LAPACK_spbsvx LAPACK_GLOBAL(spbsvx,SPBSVX)
-#define LAPACK_dpbsvx LAPACK_GLOBAL(dpbsvx,DPBSVX)
-#define LAPACK_cpbsvx LAPACK_GLOBAL(cpbsvx,CPBSVX)
-#define LAPACK_zpbsvx LAPACK_GLOBAL(zpbsvx,ZPBSVX)
-#define LAPACK_sptsv LAPACK_GLOBAL(sptsv,SPTSV)
-#define LAPACK_dptsv LAPACK_GLOBAL(dptsv,DPTSV)
-#define LAPACK_cptsv LAPACK_GLOBAL(cptsv,CPTSV)
-#define LAPACK_zptsv LAPACK_GLOBAL(zptsv,ZPTSV)
-#define LAPACK_sptsvx LAPACK_GLOBAL(sptsvx,SPTSVX)
-#define LAPACK_dptsvx LAPACK_GLOBAL(dptsvx,DPTSVX)
-#define LAPACK_cptsvx LAPACK_GLOBAL(cptsvx,CPTSVX)
-#define LAPACK_zptsvx LAPACK_GLOBAL(zptsvx,ZPTSVX)
-#define LAPACK_ssysv LAPACK_GLOBAL(ssysv,SSYSV)
-#define LAPACK_dsysv LAPACK_GLOBAL(dsysv,DSYSV)
-#define LAPACK_csysv LAPACK_GLOBAL(csysv,CSYSV)
-#define LAPACK_zsysv LAPACK_GLOBAL(zsysv,ZSYSV)
-#define LAPACK_ssysvx LAPACK_GLOBAL(ssysvx,SSYSVX)
-#define LAPACK_dsysvx LAPACK_GLOBAL(dsysvx,DSYSVX)
-#define LAPACK_csysvx LAPACK_GLOBAL(csysvx,CSYSVX)
-#define LAPACK_zsysvx LAPACK_GLOBAL(zsysvx,ZSYSVX)
-#define LAPACK_dsysvxx LAPACK_GLOBAL(dsysvxx,DSYSVXX)
-#define LAPACK_ssysvxx LAPACK_GLOBAL(ssysvxx,SSYSVXX)
-#define LAPACK_zsysvxx LAPACK_GLOBAL(zsysvxx,ZSYSVXX)
-#define LAPACK_csysvxx LAPACK_GLOBAL(csysvxx,CSYSVXX)
-#define LAPACK_chesv LAPACK_GLOBAL(chesv,CHESV)
-#define LAPACK_zhesv LAPACK_GLOBAL(zhesv,ZHESV)
-#define LAPACK_chesvx LAPACK_GLOBAL(chesvx,CHESVX)
-#define LAPACK_zhesvx LAPACK_GLOBAL(zhesvx,ZHESVX)
-#define LAPACK_zhesvxx LAPACK_GLOBAL(zhesvxx,ZHESVXX)
-#define LAPACK_chesvxx LAPACK_GLOBAL(chesvxx,CHESVXX)
-#define LAPACK_sspsv LAPACK_GLOBAL(sspsv,SSPSV)
-#define LAPACK_dspsv LAPACK_GLOBAL(dspsv,DSPSV)
-#define LAPACK_cspsv LAPACK_GLOBAL(cspsv,CSPSV)
-#define LAPACK_zspsv LAPACK_GLOBAL(zspsv,ZSPSV)
-#define LAPACK_sspsvx LAPACK_GLOBAL(sspsvx,SSPSVX)
-#define LAPACK_dspsvx LAPACK_GLOBAL(dspsvx,DSPSVX)
-#define LAPACK_cspsvx LAPACK_GLOBAL(cspsvx,CSPSVX)
-#define LAPACK_zspsvx LAPACK_GLOBAL(zspsvx,ZSPSVX)
-#define LAPACK_chpsv LAPACK_GLOBAL(chpsv,CHPSV)
-#define LAPACK_zhpsv LAPACK_GLOBAL(zhpsv,ZHPSV)
-#define LAPACK_chpsvx LAPACK_GLOBAL(chpsvx,CHPSVX)
-#define LAPACK_zhpsvx LAPACK_GLOBAL(zhpsvx,ZHPSVX)
-#define LAPACK_sgeqrf LAPACK_GLOBAL(sgeqrf,SGEQRF)
-#define LAPACK_dgeqrf LAPACK_GLOBAL(dgeqrf,DGEQRF)
-#define LAPACK_cgeqrf LAPACK_GLOBAL(cgeqrf,CGEQRF)
-#define LAPACK_zgeqrf LAPACK_GLOBAL(zgeqrf,ZGEQRF)
-#define LAPACK_sgeqpf LAPACK_GLOBAL(sgeqpf,SGEQPF)
-#define LAPACK_dgeqpf LAPACK_GLOBAL(dgeqpf,DGEQPF)
-#define LAPACK_cgeqpf LAPACK_GLOBAL(cgeqpf,CGEQPF)
-#define LAPACK_zgeqpf LAPACK_GLOBAL(zgeqpf,ZGEQPF)
-#define LAPACK_sgeqp3 LAPACK_GLOBAL(sgeqp3,SGEQP3)
-#define LAPACK_dgeqp3 LAPACK_GLOBAL(dgeqp3,DGEQP3)
-#define LAPACK_cgeqp3 LAPACK_GLOBAL(cgeqp3,CGEQP3)
-#define LAPACK_zgeqp3 LAPACK_GLOBAL(zgeqp3,ZGEQP3)
-#define LAPACK_sorgqr LAPACK_GLOBAL(sorgqr,SORGQR)
-#define LAPACK_dorgqr LAPACK_GLOBAL(dorgqr,DORGQR)
-#define LAPACK_sormqr LAPACK_GLOBAL(sormqr,SORMQR)
-#define LAPACK_dormqr LAPACK_GLOBAL(dormqr,DORMQR)
-#define LAPACK_cungqr LAPACK_GLOBAL(cungqr,CUNGQR)
-#define LAPACK_zungqr LAPACK_GLOBAL(zungqr,ZUNGQR)
-#define LAPACK_cunmqr LAPACK_GLOBAL(cunmqr,CUNMQR)
-#define LAPACK_zunmqr LAPACK_GLOBAL(zunmqr,ZUNMQR)
-#define LAPACK_sgelqf LAPACK_GLOBAL(sgelqf,SGELQF)
-#define LAPACK_dgelqf LAPACK_GLOBAL(dgelqf,DGELQF)
-#define LAPACK_cgelqf LAPACK_GLOBAL(cgelqf,CGELQF)
-#define LAPACK_zgelqf LAPACK_GLOBAL(zgelqf,ZGELQF)
-#define LAPACK_sorglq LAPACK_GLOBAL(sorglq,SORGLQ)
-#define LAPACK_dorglq LAPACK_GLOBAL(dorglq,DORGLQ)
-#define LAPACK_sormlq LAPACK_GLOBAL(sormlq,SORMLQ)
-#define LAPACK_dormlq LAPACK_GLOBAL(dormlq,DORMLQ)
-#define LAPACK_cunglq LAPACK_GLOBAL(cunglq,CUNGLQ)
-#define LAPACK_zunglq LAPACK_GLOBAL(zunglq,ZUNGLQ)
-#define LAPACK_cunmlq LAPACK_GLOBAL(cunmlq,CUNMLQ)
-#define LAPACK_zunmlq LAPACK_GLOBAL(zunmlq,ZUNMLQ)
-#define LAPACK_sgeqlf LAPACK_GLOBAL(sgeqlf,SGEQLF)
-#define LAPACK_dgeqlf LAPACK_GLOBAL(dgeqlf,DGEQLF)
-#define LAPACK_cgeqlf LAPACK_GLOBAL(cgeqlf,CGEQLF)
-#define LAPACK_zgeqlf LAPACK_GLOBAL(zgeqlf,ZGEQLF)
-#define LAPACK_sorgql LAPACK_GLOBAL(sorgql,SORGQL)
-#define LAPACK_dorgql LAPACK_GLOBAL(dorgql,DORGQL)
-#define LAPACK_cungql LAPACK_GLOBAL(cungql,CUNGQL)
-#define LAPACK_zungql LAPACK_GLOBAL(zungql,ZUNGQL)
-#define LAPACK_sormql LAPACK_GLOBAL(sormql,SORMQL)
-#define LAPACK_dormql LAPACK_GLOBAL(dormql,DORMQL)
-#define LAPACK_cunmql LAPACK_GLOBAL(cunmql,CUNMQL)
-#define LAPACK_zunmql LAPACK_GLOBAL(zunmql,ZUNMQL)
-#define LAPACK_sgerqf LAPACK_GLOBAL(sgerqf,SGERQF)
-#define LAPACK_dgerqf LAPACK_GLOBAL(dgerqf,DGERQF)
-#define LAPACK_cgerqf LAPACK_GLOBAL(cgerqf,CGERQF)
-#define LAPACK_zgerqf LAPACK_GLOBAL(zgerqf,ZGERQF)
-#define LAPACK_sorgrq LAPACK_GLOBAL(sorgrq,SORGRQ)
-#define LAPACK_dorgrq LAPACK_GLOBAL(dorgrq,DORGRQ)
-#define LAPACK_cungrq LAPACK_GLOBAL(cungrq,CUNGRQ)
-#define LAPACK_zungrq LAPACK_GLOBAL(zungrq,ZUNGRQ)
-#define LAPACK_sormrq LAPACK_GLOBAL(sormrq,SORMRQ)
-#define LAPACK_dormrq LAPACK_GLOBAL(dormrq,DORMRQ)
-#define LAPACK_cunmrq LAPACK_GLOBAL(cunmrq,CUNMRQ)
-#define LAPACK_zunmrq LAPACK_GLOBAL(zunmrq,ZUNMRQ)
-#define LAPACK_stzrzf LAPACK_GLOBAL(stzrzf,STZRZF)
-#define LAPACK_dtzrzf LAPACK_GLOBAL(dtzrzf,DTZRZF)
-#define LAPACK_ctzrzf LAPACK_GLOBAL(ctzrzf,CTZRZF)
-#define LAPACK_ztzrzf LAPACK_GLOBAL(ztzrzf,ZTZRZF)
-#define LAPACK_sormrz LAPACK_GLOBAL(sormrz,SORMRZ)
-#define LAPACK_dormrz LAPACK_GLOBAL(dormrz,DORMRZ)
-#define LAPACK_cunmrz LAPACK_GLOBAL(cunmrz,CUNMRZ)
-#define LAPACK_zunmrz LAPACK_GLOBAL(zunmrz,ZUNMRZ)
-#define LAPACK_sggqrf LAPACK_GLOBAL(sggqrf,SGGQRF)
-#define LAPACK_dggqrf LAPACK_GLOBAL(dggqrf,DGGQRF)
-#define LAPACK_cggqrf LAPACK_GLOBAL(cggqrf,CGGQRF)
-#define LAPACK_zggqrf LAPACK_GLOBAL(zggqrf,ZGGQRF)
-#define LAPACK_sggrqf LAPACK_GLOBAL(sggrqf,SGGRQF)
-#define LAPACK_dggrqf LAPACK_GLOBAL(dggrqf,DGGRQF)
-#define LAPACK_cggrqf LAPACK_GLOBAL(cggrqf,CGGRQF)
-#define LAPACK_zggrqf LAPACK_GLOBAL(zggrqf,ZGGRQF)
-#define LAPACK_sgebrd LAPACK_GLOBAL(sgebrd,SGEBRD)
-#define LAPACK_dgebrd LAPACK_GLOBAL(dgebrd,DGEBRD)
-#define LAPACK_cgebrd LAPACK_GLOBAL(cgebrd,CGEBRD)
-#define LAPACK_zgebrd LAPACK_GLOBAL(zgebrd,ZGEBRD)
-#define LAPACK_sgbbrd LAPACK_GLOBAL(sgbbrd,SGBBRD)
-#define LAPACK_dgbbrd LAPACK_GLOBAL(dgbbrd,DGBBRD)
-#define LAPACK_cgbbrd LAPACK_GLOBAL(cgbbrd,CGBBRD)
-#define LAPACK_zgbbrd LAPACK_GLOBAL(zgbbrd,ZGBBRD)
-#define LAPACK_sorgbr LAPACK_GLOBAL(sorgbr,SORGBR)
-#define LAPACK_dorgbr LAPACK_GLOBAL(dorgbr,DORGBR)
-#define LAPACK_sormbr LAPACK_GLOBAL(sormbr,SORMBR)
-#define LAPACK_dormbr LAPACK_GLOBAL(dormbr,DORMBR)
-#define LAPACK_cungbr LAPACK_GLOBAL(cungbr,CUNGBR)
-#define LAPACK_zungbr LAPACK_GLOBAL(zungbr,ZUNGBR)
-#define LAPACK_cunmbr LAPACK_GLOBAL(cunmbr,CUNMBR)
-#define LAPACK_zunmbr LAPACK_GLOBAL(zunmbr,ZUNMBR)
-#define LAPACK_sbdsqr LAPACK_GLOBAL(sbdsqr,SBDSQR)
-#define LAPACK_dbdsqr LAPACK_GLOBAL(dbdsqr,DBDSQR)
-#define LAPACK_cbdsqr LAPACK_GLOBAL(cbdsqr,CBDSQR)
-#define LAPACK_zbdsqr LAPACK_GLOBAL(zbdsqr,ZBDSQR)
-#define LAPACK_sbdsdc LAPACK_GLOBAL(sbdsdc,SBDSDC)
-#define LAPACK_dbdsdc LAPACK_GLOBAL(dbdsdc,DBDSDC)
-#define LAPACK_ssytrd LAPACK_GLOBAL(ssytrd,SSYTRD)
-#define LAPACK_dsytrd LAPACK_GLOBAL(dsytrd,DSYTRD)
-#define LAPACK_sorgtr LAPACK_GLOBAL(sorgtr,SORGTR)
-#define LAPACK_dorgtr LAPACK_GLOBAL(dorgtr,DORGTR)
-#define LAPACK_sormtr LAPACK_GLOBAL(sormtr,SORMTR)
-#define LAPACK_dormtr LAPACK_GLOBAL(dormtr,DORMTR)
-#define LAPACK_chetrd LAPACK_GLOBAL(chetrd,CHETRD)
-#define LAPACK_zhetrd LAPACK_GLOBAL(zhetrd,ZHETRD)
-#define LAPACK_cungtr LAPACK_GLOBAL(cungtr,CUNGTR)
-#define LAPACK_zungtr LAPACK_GLOBAL(zungtr,ZUNGTR)
-#define LAPACK_cunmtr LAPACK_GLOBAL(cunmtr,CUNMTR)
-#define LAPACK_zunmtr LAPACK_GLOBAL(zunmtr,ZUNMTR)
-#define LAPACK_ssptrd LAPACK_GLOBAL(ssptrd,SSPTRD)
-#define LAPACK_dsptrd LAPACK_GLOBAL(dsptrd,DSPTRD)
-#define LAPACK_sopgtr LAPACK_GLOBAL(sopgtr,SOPGTR)
-#define LAPACK_dopgtr LAPACK_GLOBAL(dopgtr,DOPGTR)
-#define LAPACK_sopmtr LAPACK_GLOBAL(sopmtr,SOPMTR)
-#define LAPACK_dopmtr LAPACK_GLOBAL(dopmtr,DOPMTR)
-#define LAPACK_chptrd LAPACK_GLOBAL(chptrd,CHPTRD)
-#define LAPACK_zhptrd LAPACK_GLOBAL(zhptrd,ZHPTRD)
-#define LAPACK_cupgtr LAPACK_GLOBAL(cupgtr,CUPGTR)
-#define LAPACK_zupgtr LAPACK_GLOBAL(zupgtr,ZUPGTR)
-#define LAPACK_cupmtr LAPACK_GLOBAL(cupmtr,CUPMTR)
-#define LAPACK_zupmtr LAPACK_GLOBAL(zupmtr,ZUPMTR)
-#define LAPACK_ssbtrd LAPACK_GLOBAL(ssbtrd,SSBTRD)
-#define LAPACK_dsbtrd LAPACK_GLOBAL(dsbtrd,DSBTRD)
-#define LAPACK_chbtrd LAPACK_GLOBAL(chbtrd,CHBTRD)
-#define LAPACK_zhbtrd LAPACK_GLOBAL(zhbtrd,ZHBTRD)
-#define LAPACK_ssterf LAPACK_GLOBAL(ssterf,SSTERF)
-#define LAPACK_dsterf LAPACK_GLOBAL(dsterf,DSTERF)
-#define LAPACK_ssteqr LAPACK_GLOBAL(ssteqr,SSTEQR)
-#define LAPACK_dsteqr LAPACK_GLOBAL(dsteqr,DSTEQR)
-#define LAPACK_csteqr LAPACK_GLOBAL(csteqr,CSTEQR)
-#define LAPACK_zsteqr LAPACK_GLOBAL(zsteqr,ZSTEQR)
-#define LAPACK_sstemr LAPACK_GLOBAL(sstemr,SSTEMR)
-#define LAPACK_dstemr LAPACK_GLOBAL(dstemr,DSTEMR)
-#define LAPACK_cstemr LAPACK_GLOBAL(cstemr,CSTEMR)
-#define LAPACK_zstemr LAPACK_GLOBAL(zstemr,ZSTEMR)
-#define LAPACK_sstedc LAPACK_GLOBAL(sstedc,SSTEDC)
-#define LAPACK_dstedc LAPACK_GLOBAL(dstedc,DSTEDC)
-#define LAPACK_cstedc LAPACK_GLOBAL(cstedc,CSTEDC)
-#define LAPACK_zstedc LAPACK_GLOBAL(zstedc,ZSTEDC)
-#define LAPACK_sstegr LAPACK_GLOBAL(sstegr,SSTEGR)
-#define LAPACK_dstegr LAPACK_GLOBAL(dstegr,DSTEGR)
-#define LAPACK_cstegr LAPACK_GLOBAL(cstegr,CSTEGR)
-#define LAPACK_zstegr LAPACK_GLOBAL(zstegr,ZSTEGR)
-#define LAPACK_spteqr LAPACK_GLOBAL(spteqr,SPTEQR)
-#define LAPACK_dpteqr LAPACK_GLOBAL(dpteqr,DPTEQR)
-#define LAPACK_cpteqr LAPACK_GLOBAL(cpteqr,CPTEQR)
-#define LAPACK_zpteqr LAPACK_GLOBAL(zpteqr,ZPTEQR)
-#define LAPACK_sstebz LAPACK_GLOBAL(sstebz,SSTEBZ)
-#define LAPACK_dstebz LAPACK_GLOBAL(dstebz,DSTEBZ)
-#define LAPACK_sstein LAPACK_GLOBAL(sstein,SSTEIN)
-#define LAPACK_dstein LAPACK_GLOBAL(dstein,DSTEIN)
-#define LAPACK_cstein LAPACK_GLOBAL(cstein,CSTEIN)
-#define LAPACK_zstein LAPACK_GLOBAL(zstein,ZSTEIN)
-#define LAPACK_sdisna LAPACK_GLOBAL(sdisna,SDISNA)
-#define LAPACK_ddisna LAPACK_GLOBAL(ddisna,DDISNA)
-#define LAPACK_ssygst LAPACK_GLOBAL(ssygst,SSYGST)
-#define LAPACK_dsygst LAPACK_GLOBAL(dsygst,DSYGST)
-#define LAPACK_chegst LAPACK_GLOBAL(chegst,CHEGST)
-#define LAPACK_zhegst LAPACK_GLOBAL(zhegst,ZHEGST)
-#define LAPACK_sspgst LAPACK_GLOBAL(sspgst,SSPGST)
-#define LAPACK_dspgst LAPACK_GLOBAL(dspgst,DSPGST)
-#define LAPACK_chpgst LAPACK_GLOBAL(chpgst,CHPGST)
-#define LAPACK_zhpgst LAPACK_GLOBAL(zhpgst,ZHPGST)
-#define LAPACK_ssbgst LAPACK_GLOBAL(ssbgst,SSBGST)
-#define LAPACK_dsbgst LAPACK_GLOBAL(dsbgst,DSBGST)
-#define LAPACK_chbgst LAPACK_GLOBAL(chbgst,CHBGST)
-#define LAPACK_zhbgst LAPACK_GLOBAL(zhbgst,ZHBGST)
-#define LAPACK_spbstf LAPACK_GLOBAL(spbstf,SPBSTF)
-#define LAPACK_dpbstf LAPACK_GLOBAL(dpbstf,DPBSTF)
-#define LAPACK_cpbstf LAPACK_GLOBAL(cpbstf,CPBSTF)
-#define LAPACK_zpbstf LAPACK_GLOBAL(zpbstf,ZPBSTF)
-#define LAPACK_sgehrd LAPACK_GLOBAL(sgehrd,SGEHRD)
-#define LAPACK_dgehrd LAPACK_GLOBAL(dgehrd,DGEHRD)
-#define LAPACK_cgehrd LAPACK_GLOBAL(cgehrd,CGEHRD)
-#define LAPACK_zgehrd LAPACK_GLOBAL(zgehrd,ZGEHRD)
-#define LAPACK_sorghr LAPACK_GLOBAL(sorghr,SORGHR)
-#define LAPACK_dorghr LAPACK_GLOBAL(dorghr,DORGHR)
-#define LAPACK_sormhr LAPACK_GLOBAL(sormhr,SORMHR)
-#define LAPACK_dormhr LAPACK_GLOBAL(dormhr,DORMHR)
-#define LAPACK_cunghr LAPACK_GLOBAL(cunghr,CUNGHR)
-#define LAPACK_zunghr LAPACK_GLOBAL(zunghr,ZUNGHR)
-#define LAPACK_cunmhr LAPACK_GLOBAL(cunmhr,CUNMHR)
-#define LAPACK_zunmhr LAPACK_GLOBAL(zunmhr,ZUNMHR)
-#define LAPACK_sgebal LAPACK_GLOBAL(sgebal,SGEBAL)
-#define LAPACK_dgebal LAPACK_GLOBAL(dgebal,DGEBAL)
-#define LAPACK_cgebal LAPACK_GLOBAL(cgebal,CGEBAL)
-#define LAPACK_zgebal LAPACK_GLOBAL(zgebal,ZGEBAL)
-#define LAPACK_sgebak LAPACK_GLOBAL(sgebak,SGEBAK)
-#define LAPACK_dgebak LAPACK_GLOBAL(dgebak,DGEBAK)
-#define LAPACK_cgebak LAPACK_GLOBAL(cgebak,CGEBAK)
-#define LAPACK_zgebak LAPACK_GLOBAL(zgebak,ZGEBAK)
-#define LAPACK_shseqr LAPACK_GLOBAL(shseqr,SHSEQR)
-#define LAPACK_dhseqr LAPACK_GLOBAL(dhseqr,DHSEQR)
-#define LAPACK_chseqr LAPACK_GLOBAL(chseqr,CHSEQR)
-#define LAPACK_zhseqr LAPACK_GLOBAL(zhseqr,ZHSEQR)
-#define LAPACK_shsein LAPACK_GLOBAL(shsein,SHSEIN)
-#define LAPACK_dhsein LAPACK_GLOBAL(dhsein,DHSEIN)
-#define LAPACK_chsein LAPACK_GLOBAL(chsein,CHSEIN)
-#define LAPACK_zhsein LAPACK_GLOBAL(zhsein,ZHSEIN)
-#define LAPACK_strevc LAPACK_GLOBAL(strevc,STREVC)
-#define LAPACK_dtrevc LAPACK_GLOBAL(dtrevc,DTREVC)
-#define LAPACK_ctrevc LAPACK_GLOBAL(ctrevc,CTREVC)
-#define LAPACK_ztrevc LAPACK_GLOBAL(ztrevc,ZTREVC)
-#define LAPACK_strsna LAPACK_GLOBAL(strsna,STRSNA)
-#define LAPACK_dtrsna LAPACK_GLOBAL(dtrsna,DTRSNA)
-#define LAPACK_ctrsna LAPACK_GLOBAL(ctrsna,CTRSNA)
-#define LAPACK_ztrsna LAPACK_GLOBAL(ztrsna,ZTRSNA)
-#define LAPACK_strexc LAPACK_GLOBAL(strexc,STREXC)
-#define LAPACK_dtrexc LAPACK_GLOBAL(dtrexc,DTREXC)
-#define LAPACK_ctrexc LAPACK_GLOBAL(ctrexc,CTREXC)
-#define LAPACK_ztrexc LAPACK_GLOBAL(ztrexc,ZTREXC)
-#define LAPACK_strsen LAPACK_GLOBAL(strsen,STRSEN)
-#define LAPACK_dtrsen LAPACK_GLOBAL(dtrsen,DTRSEN)
-#define LAPACK_ctrsen LAPACK_GLOBAL(ctrsen,CTRSEN)
-#define LAPACK_ztrsen LAPACK_GLOBAL(ztrsen,ZTRSEN)
-#define LAPACK_strsyl LAPACK_GLOBAL(strsyl,STRSYL)
-#define LAPACK_dtrsyl LAPACK_GLOBAL(dtrsyl,DTRSYL)
-#define LAPACK_ctrsyl LAPACK_GLOBAL(ctrsyl,CTRSYL)
-#define LAPACK_ztrsyl LAPACK_GLOBAL(ztrsyl,ZTRSYL)
-#define LAPACK_sgghrd LAPACK_GLOBAL(sgghrd,SGGHRD)
-#define LAPACK_dgghrd LAPACK_GLOBAL(dgghrd,DGGHRD)
-#define LAPACK_cgghrd LAPACK_GLOBAL(cgghrd,CGGHRD)
-#define LAPACK_zgghrd LAPACK_GLOBAL(zgghrd,ZGGHRD)
-#define LAPACK_sggbal LAPACK_GLOBAL(sggbal,SGGBAL)
-#define LAPACK_dggbal LAPACK_GLOBAL(dggbal,DGGBAL)
-#define LAPACK_cggbal LAPACK_GLOBAL(cggbal,CGGBAL)
-#define LAPACK_zggbal LAPACK_GLOBAL(zggbal,ZGGBAL)
-#define LAPACK_sggbak LAPACK_GLOBAL(sggbak,SGGBAK)
-#define LAPACK_dggbak LAPACK_GLOBAL(dggbak,DGGBAK)
-#define LAPACK_cggbak LAPACK_GLOBAL(cggbak,CGGBAK)
-#define LAPACK_zggbak LAPACK_GLOBAL(zggbak,ZGGBAK)
-#define LAPACK_shgeqz LAPACK_GLOBAL(shgeqz,SHGEQZ)
-#define LAPACK_dhgeqz LAPACK_GLOBAL(dhgeqz,DHGEQZ)
-#define LAPACK_chgeqz LAPACK_GLOBAL(chgeqz,CHGEQZ)
-#define LAPACK_zhgeqz LAPACK_GLOBAL(zhgeqz,ZHGEQZ)
-#define LAPACK_stgevc LAPACK_GLOBAL(stgevc,STGEVC)
-#define LAPACK_dtgevc LAPACK_GLOBAL(dtgevc,DTGEVC)
-#define LAPACK_ctgevc LAPACK_GLOBAL(ctgevc,CTGEVC)
-#define LAPACK_ztgevc LAPACK_GLOBAL(ztgevc,ZTGEVC)
-#define LAPACK_stgexc LAPACK_GLOBAL(stgexc,STGEXC)
-#define LAPACK_dtgexc LAPACK_GLOBAL(dtgexc,DTGEXC)
-#define LAPACK_ctgexc LAPACK_GLOBAL(ctgexc,CTGEXC)
-#define LAPACK_ztgexc LAPACK_GLOBAL(ztgexc,ZTGEXC)
-#define LAPACK_stgsen LAPACK_GLOBAL(stgsen,STGSEN)
-#define LAPACK_dtgsen LAPACK_GLOBAL(dtgsen,DTGSEN)
-#define LAPACK_ctgsen LAPACK_GLOBAL(ctgsen,CTGSEN)
-#define LAPACK_ztgsen LAPACK_GLOBAL(ztgsen,ZTGSEN)
-#define LAPACK_stgsyl LAPACK_GLOBAL(stgsyl,STGSYL)
-#define LAPACK_dtgsyl LAPACK_GLOBAL(dtgsyl,DTGSYL)
-#define LAPACK_ctgsyl LAPACK_GLOBAL(ctgsyl,CTGSYL)
-#define LAPACK_ztgsyl LAPACK_GLOBAL(ztgsyl,ZTGSYL)
-#define LAPACK_stgsna LAPACK_GLOBAL(stgsna,STGSNA)
-#define LAPACK_dtgsna LAPACK_GLOBAL(dtgsna,DTGSNA)
-#define LAPACK_ctgsna LAPACK_GLOBAL(ctgsna,CTGSNA)
-#define LAPACK_ztgsna LAPACK_GLOBAL(ztgsna,ZTGSNA)
-#define LAPACK_sggsvp LAPACK_GLOBAL(sggsvp,SGGSVP)
-#define LAPACK_dggsvp LAPACK_GLOBAL(dggsvp,DGGSVP)
-#define LAPACK_cggsvp LAPACK_GLOBAL(cggsvp,CGGSVP)
-#define LAPACK_zggsvp LAPACK_GLOBAL(zggsvp,ZGGSVP)
-#define LAPACK_stgsja LAPACK_GLOBAL(stgsja,STGSJA)
-#define LAPACK_dtgsja LAPACK_GLOBAL(dtgsja,DTGSJA)
-#define LAPACK_ctgsja LAPACK_GLOBAL(ctgsja,CTGSJA)
-#define LAPACK_ztgsja LAPACK_GLOBAL(ztgsja,ZTGSJA)
-#define LAPACK_sgels LAPACK_GLOBAL(sgels,SGELS)
-#define LAPACK_dgels LAPACK_GLOBAL(dgels,DGELS)
-#define LAPACK_cgels LAPACK_GLOBAL(cgels,CGELS)
-#define LAPACK_zgels LAPACK_GLOBAL(zgels,ZGELS)
-#define LAPACK_sgelsy LAPACK_GLOBAL(sgelsy,SGELSY)
-#define LAPACK_dgelsy LAPACK_GLOBAL(dgelsy,DGELSY)
-#define LAPACK_cgelsy LAPACK_GLOBAL(cgelsy,CGELSY)
-#define LAPACK_zgelsy LAPACK_GLOBAL(zgelsy,ZGELSY)
-#define LAPACK_sgelss LAPACK_GLOBAL(sgelss,SGELSS)
-#define LAPACK_dgelss LAPACK_GLOBAL(dgelss,DGELSS)
-#define LAPACK_cgelss LAPACK_GLOBAL(cgelss,CGELSS)
-#define LAPACK_zgelss LAPACK_GLOBAL(zgelss,ZGELSS)
-#define LAPACK_sgelsd LAPACK_GLOBAL(sgelsd,SGELSD)
-#define LAPACK_dgelsd LAPACK_GLOBAL(dgelsd,DGELSD)
-#define LAPACK_cgelsd LAPACK_GLOBAL(cgelsd,CGELSD)
-#define LAPACK_zgelsd LAPACK_GLOBAL(zgelsd,ZGELSD)
-#define LAPACK_sgglse LAPACK_GLOBAL(sgglse,SGGLSE)
-#define LAPACK_dgglse LAPACK_GLOBAL(dgglse,DGGLSE)
-#define LAPACK_cgglse LAPACK_GLOBAL(cgglse,CGGLSE)
-#define LAPACK_zgglse LAPACK_GLOBAL(zgglse,ZGGLSE)
-#define LAPACK_sggglm LAPACK_GLOBAL(sggglm,SGGGLM)
-#define LAPACK_dggglm LAPACK_GLOBAL(dggglm,DGGGLM)
-#define LAPACK_cggglm LAPACK_GLOBAL(cggglm,CGGGLM)
-#define LAPACK_zggglm LAPACK_GLOBAL(zggglm,ZGGGLM)
-#define LAPACK_ssyev LAPACK_GLOBAL(ssyev,SSYEV)
-#define LAPACK_dsyev LAPACK_GLOBAL(dsyev,DSYEV)
-#define LAPACK_cheev LAPACK_GLOBAL(cheev,CHEEV)
-#define LAPACK_zheev LAPACK_GLOBAL(zheev,ZHEEV)
-#define LAPACK_ssyevd LAPACK_GLOBAL(ssyevd,SSYEVD)
-#define LAPACK_dsyevd LAPACK_GLOBAL(dsyevd,DSYEVD)
-#define LAPACK_cheevd LAPACK_GLOBAL(cheevd,CHEEVD)
-#define LAPACK_zheevd LAPACK_GLOBAL(zheevd,ZHEEVD)
-#define LAPACK_ssyevx LAPACK_GLOBAL(ssyevx,SSYEVX)
-#define LAPACK_dsyevx LAPACK_GLOBAL(dsyevx,DSYEVX)
-#define LAPACK_cheevx LAPACK_GLOBAL(cheevx,CHEEVX)
-#define LAPACK_zheevx LAPACK_GLOBAL(zheevx,ZHEEVX)
-#define LAPACK_ssyevr LAPACK_GLOBAL(ssyevr,SSYEVR)
-#define LAPACK_dsyevr LAPACK_GLOBAL(dsyevr,DSYEVR)
-#define LAPACK_cheevr LAPACK_GLOBAL(cheevr,CHEEVR)
-#define LAPACK_zheevr LAPACK_GLOBAL(zheevr,ZHEEVR)
-#define LAPACK_sspev LAPACK_GLOBAL(sspev,SSPEV)
-#define LAPACK_dspev LAPACK_GLOBAL(dspev,DSPEV)
-#define LAPACK_chpev LAPACK_GLOBAL(chpev,CHPEV)
-#define LAPACK_zhpev LAPACK_GLOBAL(zhpev,ZHPEV)
-#define LAPACK_sspevd LAPACK_GLOBAL(sspevd,SSPEVD)
-#define LAPACK_dspevd LAPACK_GLOBAL(dspevd,DSPEVD)
-#define LAPACK_chpevd LAPACK_GLOBAL(chpevd,CHPEVD)
-#define LAPACK_zhpevd LAPACK_GLOBAL(zhpevd,ZHPEVD)
-#define LAPACK_sspevx LAPACK_GLOBAL(sspevx,SSPEVX)
-#define LAPACK_dspevx LAPACK_GLOBAL(dspevx,DSPEVX)
-#define LAPACK_chpevx LAPACK_GLOBAL(chpevx,CHPEVX)
-#define LAPACK_zhpevx LAPACK_GLOBAL(zhpevx,ZHPEVX)
-#define LAPACK_ssbev LAPACK_GLOBAL(ssbev,SSBEV)
-#define LAPACK_dsbev LAPACK_GLOBAL(dsbev,DSBEV)
-#define LAPACK_chbev LAPACK_GLOBAL(chbev,CHBEV)
-#define LAPACK_zhbev LAPACK_GLOBAL(zhbev,ZHBEV)
-#define LAPACK_ssbevd LAPACK_GLOBAL(ssbevd,SSBEVD)
-#define LAPACK_dsbevd LAPACK_GLOBAL(dsbevd,DSBEVD)
-#define LAPACK_chbevd LAPACK_GLOBAL(chbevd,CHBEVD)
-#define LAPACK_zhbevd LAPACK_GLOBAL(zhbevd,ZHBEVD)
-#define LAPACK_ssbevx LAPACK_GLOBAL(ssbevx,SSBEVX)
-#define LAPACK_dsbevx LAPACK_GLOBAL(dsbevx,DSBEVX)
-#define LAPACK_chbevx LAPACK_GLOBAL(chbevx,CHBEVX)
-#define LAPACK_zhbevx LAPACK_GLOBAL(zhbevx,ZHBEVX)
-#define LAPACK_sstev LAPACK_GLOBAL(sstev,SSTEV)
-#define LAPACK_dstev LAPACK_GLOBAL(dstev,DSTEV)
-#define LAPACK_sstevd LAPACK_GLOBAL(sstevd,SSTEVD)
-#define LAPACK_dstevd LAPACK_GLOBAL(dstevd,DSTEVD)
-#define LAPACK_sstevx LAPACK_GLOBAL(sstevx,SSTEVX)
-#define LAPACK_dstevx LAPACK_GLOBAL(dstevx,DSTEVX)
-#define LAPACK_sstevr LAPACK_GLOBAL(sstevr,SSTEVR)
-#define LAPACK_dstevr LAPACK_GLOBAL(dstevr,DSTEVR)
-#define LAPACK_sgees LAPACK_GLOBAL(sgees,SGEES)
-#define LAPACK_dgees LAPACK_GLOBAL(dgees,DGEES)
-#define LAPACK_cgees LAPACK_GLOBAL(cgees,CGEES)
-#define LAPACK_zgees LAPACK_GLOBAL(zgees,ZGEES)
-#define LAPACK_sgeesx LAPACK_GLOBAL(sgeesx,SGEESX)
-#define LAPACK_dgeesx LAPACK_GLOBAL(dgeesx,DGEESX)
-#define LAPACK_cgeesx LAPACK_GLOBAL(cgeesx,CGEESX)
-#define LAPACK_zgeesx LAPACK_GLOBAL(zgeesx,ZGEESX)
-#define LAPACK_sgeev LAPACK_GLOBAL(sgeev,SGEEV)
-#define LAPACK_dgeev LAPACK_GLOBAL(dgeev,DGEEV)
-#define LAPACK_cgeev LAPACK_GLOBAL(cgeev,CGEEV)
-#define LAPACK_zgeev LAPACK_GLOBAL(zgeev,ZGEEV)
-#define LAPACK_sgeevx LAPACK_GLOBAL(sgeevx,SGEEVX)
-#define LAPACK_dgeevx LAPACK_GLOBAL(dgeevx,DGEEVX)
-#define LAPACK_cgeevx LAPACK_GLOBAL(cgeevx,CGEEVX)
-#define LAPACK_zgeevx LAPACK_GLOBAL(zgeevx,ZGEEVX)
-#define LAPACK_sgesvd LAPACK_GLOBAL(sgesvd,SGESVD)
-#define LAPACK_dgesvd LAPACK_GLOBAL(dgesvd,DGESVD)
-#define LAPACK_cgesvd LAPACK_GLOBAL(cgesvd,CGESVD)
-#define LAPACK_zgesvd LAPACK_GLOBAL(zgesvd,ZGESVD)
-#define LAPACK_sgesdd LAPACK_GLOBAL(sgesdd,SGESDD)
-#define LAPACK_dgesdd LAPACK_GLOBAL(dgesdd,DGESDD)
-#define LAPACK_cgesdd LAPACK_GLOBAL(cgesdd,CGESDD)
-#define LAPACK_zgesdd LAPACK_GLOBAL(zgesdd,ZGESDD)
-#define LAPACK_dgejsv LAPACK_GLOBAL(dgejsv,DGEJSV)
-#define LAPACK_sgejsv LAPACK_GLOBAL(sgejsv,SGEJSV)
-#define LAPACK_dgesvj LAPACK_GLOBAL(dgesvj,DGESVJ)
-#define LAPACK_sgesvj LAPACK_GLOBAL(sgesvj,SGESVJ)
-#define LAPACK_sggsvd LAPACK_GLOBAL(sggsvd,SGGSVD)
-#define LAPACK_dggsvd LAPACK_GLOBAL(dggsvd,DGGSVD)
-#define LAPACK_cggsvd LAPACK_GLOBAL(cggsvd,CGGSVD)
-#define LAPACK_zggsvd LAPACK_GLOBAL(zggsvd,ZGGSVD)
-#define LAPACK_ssygv LAPACK_GLOBAL(ssygv,SSYGV)
-#define LAPACK_dsygv LAPACK_GLOBAL(dsygv,DSYGV)
-#define LAPACK_chegv LAPACK_GLOBAL(chegv,CHEGV)
-#define LAPACK_zhegv LAPACK_GLOBAL(zhegv,ZHEGV)
-#define LAPACK_ssygvd LAPACK_GLOBAL(ssygvd,SSYGVD)
-#define LAPACK_dsygvd LAPACK_GLOBAL(dsygvd,DSYGVD)
-#define LAPACK_chegvd LAPACK_GLOBAL(chegvd,CHEGVD)
-#define LAPACK_zhegvd LAPACK_GLOBAL(zhegvd,ZHEGVD)
-#define LAPACK_ssygvx LAPACK_GLOBAL(ssygvx,SSYGVX)
-#define LAPACK_dsygvx LAPACK_GLOBAL(dsygvx,DSYGVX)
-#define LAPACK_chegvx LAPACK_GLOBAL(chegvx,CHEGVX)
-#define LAPACK_zhegvx LAPACK_GLOBAL(zhegvx,ZHEGVX)
-#define LAPACK_sspgv LAPACK_GLOBAL(sspgv,SSPGV)
-#define LAPACK_dspgv LAPACK_GLOBAL(dspgv,DSPGV)
-#define LAPACK_chpgv LAPACK_GLOBAL(chpgv,CHPGV)
-#define LAPACK_zhpgv LAPACK_GLOBAL(zhpgv,ZHPGV)
-#define LAPACK_sspgvd LAPACK_GLOBAL(sspgvd,SSPGVD)
-#define LAPACK_dspgvd LAPACK_GLOBAL(dspgvd,DSPGVD)
-#define LAPACK_chpgvd LAPACK_GLOBAL(chpgvd,CHPGVD)
-#define LAPACK_zhpgvd LAPACK_GLOBAL(zhpgvd,ZHPGVD)
-#define LAPACK_sspgvx LAPACK_GLOBAL(sspgvx,SSPGVX)
-#define LAPACK_dspgvx LAPACK_GLOBAL(dspgvx,DSPGVX)
-#define LAPACK_chpgvx LAPACK_GLOBAL(chpgvx,CHPGVX)
-#define LAPACK_zhpgvx LAPACK_GLOBAL(zhpgvx,ZHPGVX)
-#define LAPACK_ssbgv LAPACK_GLOBAL(ssbgv,SSBGV)
-#define LAPACK_dsbgv LAPACK_GLOBAL(dsbgv,DSBGV)
-#define LAPACK_chbgv LAPACK_GLOBAL(chbgv,CHBGV)
-#define LAPACK_zhbgv LAPACK_GLOBAL(zhbgv,ZHBGV)
-#define LAPACK_ssbgvd LAPACK_GLOBAL(ssbgvd,SSBGVD)
-#define LAPACK_dsbgvd LAPACK_GLOBAL(dsbgvd,DSBGVD)
-#define LAPACK_chbgvd LAPACK_GLOBAL(chbgvd,CHBGVD)
-#define LAPACK_zhbgvd LAPACK_GLOBAL(zhbgvd,ZHBGVD)
-#define LAPACK_ssbgvx LAPACK_GLOBAL(ssbgvx,SSBGVX)
-#define LAPACK_dsbgvx LAPACK_GLOBAL(dsbgvx,DSBGVX)
-#define LAPACK_chbgvx LAPACK_GLOBAL(chbgvx,CHBGVX)
-#define LAPACK_zhbgvx LAPACK_GLOBAL(zhbgvx,ZHBGVX)
-#define LAPACK_sgges LAPACK_GLOBAL(sgges,SGGES)
-#define LAPACK_dgges LAPACK_GLOBAL(dgges,DGGES)
-#define LAPACK_cgges LAPACK_GLOBAL(cgges,CGGES)
-#define LAPACK_zgges LAPACK_GLOBAL(zgges,ZGGES)
-#define LAPACK_sggesx LAPACK_GLOBAL(sggesx,SGGESX)
-#define LAPACK_dggesx LAPACK_GLOBAL(dggesx,DGGESX)
-#define LAPACK_cggesx LAPACK_GLOBAL(cggesx,CGGESX)
-#define LAPACK_zggesx LAPACK_GLOBAL(zggesx,ZGGESX)
-#define LAPACK_sggev LAPACK_GLOBAL(sggev,SGGEV)
-#define LAPACK_dggev LAPACK_GLOBAL(dggev,DGGEV)
-#define LAPACK_cggev LAPACK_GLOBAL(cggev,CGGEV)
-#define LAPACK_zggev LAPACK_GLOBAL(zggev,ZGGEV)
-#define LAPACK_sggevx LAPACK_GLOBAL(sggevx,SGGEVX)
-#define LAPACK_dggevx LAPACK_GLOBAL(dggevx,DGGEVX)
-#define LAPACK_cggevx LAPACK_GLOBAL(cggevx,CGGEVX)
-#define LAPACK_zggevx LAPACK_GLOBAL(zggevx,ZGGEVX)
-#define LAPACK_dsfrk LAPACK_GLOBAL(dsfrk,DSFRK)
-#define LAPACK_ssfrk LAPACK_GLOBAL(ssfrk,SSFRK)
-#define LAPACK_zhfrk LAPACK_GLOBAL(zhfrk,ZHFRK)
-#define LAPACK_chfrk LAPACK_GLOBAL(chfrk,CHFRK)
-#define LAPACK_dtfsm LAPACK_GLOBAL(dtfsm,DTFSM)
-#define LAPACK_stfsm LAPACK_GLOBAL(stfsm,STFSM)
-#define LAPACK_ztfsm LAPACK_GLOBAL(ztfsm,ZTFSM)
-#define LAPACK_ctfsm LAPACK_GLOBAL(ctfsm,CTFSM)
-#define LAPACK_dtfttp LAPACK_GLOBAL(dtfttp,DTFTTP)
-#define LAPACK_stfttp LAPACK_GLOBAL(stfttp,STFTTP)
-#define LAPACK_ztfttp LAPACK_GLOBAL(ztfttp,ZTFTTP)
-#define LAPACK_ctfttp LAPACK_GLOBAL(ctfttp,CTFTTP)
-#define LAPACK_dtfttr LAPACK_GLOBAL(dtfttr,DTFTTR)
-#define LAPACK_stfttr LAPACK_GLOBAL(stfttr,STFTTR)
-#define LAPACK_ztfttr LAPACK_GLOBAL(ztfttr,ZTFTTR)
-#define LAPACK_ctfttr LAPACK_GLOBAL(ctfttr,CTFTTR)
-#define LAPACK_dtpttf LAPACK_GLOBAL(dtpttf,DTPTTF)
-#define LAPACK_stpttf LAPACK_GLOBAL(stpttf,STPTTF)
-#define LAPACK_ztpttf LAPACK_GLOBAL(ztpttf,ZTPTTF)
-#define LAPACK_ctpttf LAPACK_GLOBAL(ctpttf,CTPTTF)
-#define LAPACK_dtpttr LAPACK_GLOBAL(dtpttr,DTPTTR)
-#define LAPACK_stpttr LAPACK_GLOBAL(stpttr,STPTTR)
-#define LAPACK_ztpttr LAPACK_GLOBAL(ztpttr,ZTPTTR)
-#define LAPACK_ctpttr LAPACK_GLOBAL(ctpttr,CTPTTR)
-#define LAPACK_dtrttf LAPACK_GLOBAL(dtrttf,DTRTTF)
-#define LAPACK_strttf LAPACK_GLOBAL(strttf,STRTTF)
-#define LAPACK_ztrttf LAPACK_GLOBAL(ztrttf,ZTRTTF)
-#define LAPACK_ctrttf LAPACK_GLOBAL(ctrttf,CTRTTF)
-#define LAPACK_dtrttp LAPACK_GLOBAL(dtrttp,DTRTTP)
-#define LAPACK_strttp LAPACK_GLOBAL(strttp,STRTTP)
-#define LAPACK_ztrttp LAPACK_GLOBAL(ztrttp,ZTRTTP)
-#define LAPACK_ctrttp LAPACK_GLOBAL(ctrttp,CTRTTP)
-#define LAPACK_sgeqrfp LAPACK_GLOBAL(sgeqrfp,SGEQRFP)
-#define LAPACK_dgeqrfp LAPACK_GLOBAL(dgeqrfp,DGEQRFP)
-#define LAPACK_cgeqrfp LAPACK_GLOBAL(cgeqrfp,CGEQRFP)
-#define LAPACK_zgeqrfp LAPACK_GLOBAL(zgeqrfp,ZGEQRFP)
-#define LAPACK_clacgv LAPACK_GLOBAL(clacgv,CLACGV)
-#define LAPACK_zlacgv LAPACK_GLOBAL(zlacgv,ZLACGV)
-#define LAPACK_slarnv LAPACK_GLOBAL(slarnv,SLARNV)
-#define LAPACK_dlarnv LAPACK_GLOBAL(dlarnv,DLARNV)
-#define LAPACK_clarnv LAPACK_GLOBAL(clarnv,CLARNV)
-#define LAPACK_zlarnv LAPACK_GLOBAL(zlarnv,ZLARNV)
-#define LAPACK_sgeqr2 LAPACK_GLOBAL(sgeqr2,SGEQR2)
-#define LAPACK_dgeqr2 LAPACK_GLOBAL(dgeqr2,DGEQR2)
-#define LAPACK_cgeqr2 LAPACK_GLOBAL(cgeqr2,CGEQR2)
-#define LAPACK_zgeqr2 LAPACK_GLOBAL(zgeqr2,ZGEQR2)
-#define LAPACK_slacpy LAPACK_GLOBAL(slacpy,SLACPY)
-#define LAPACK_dlacpy LAPACK_GLOBAL(dlacpy,DLACPY)
-#define LAPACK_clacpy LAPACK_GLOBAL(clacpy,CLACPY)
-#define LAPACK_zlacpy LAPACK_GLOBAL(zlacpy,ZLACPY)
-#define LAPACK_sgetf2 LAPACK_GLOBAL(sgetf2,SGETF2)
-#define LAPACK_dgetf2 LAPACK_GLOBAL(dgetf2,DGETF2)
-#define LAPACK_cgetf2 LAPACK_GLOBAL(cgetf2,CGETF2)
-#define LAPACK_zgetf2 LAPACK_GLOBAL(zgetf2,ZGETF2)
-#define LAPACK_slaswp LAPACK_GLOBAL(slaswp,SLASWP)
-#define LAPACK_dlaswp LAPACK_GLOBAL(dlaswp,DLASWP)
-#define LAPACK_claswp LAPACK_GLOBAL(claswp,CLASWP)
-#define LAPACK_zlaswp LAPACK_GLOBAL(zlaswp,ZLASWP)
-#define LAPACK_slange LAPACK_GLOBAL(slange,SLANGE)
-#define LAPACK_dlange LAPACK_GLOBAL(dlange,DLANGE)
-#define LAPACK_clange LAPACK_GLOBAL(clange,CLANGE)
-#define LAPACK_zlange LAPACK_GLOBAL(zlange,ZLANGE)
-#define LAPACK_clanhe LAPACK_GLOBAL(clanhe,CLANHE)
-#define LAPACK_zlanhe LAPACK_GLOBAL(zlanhe,ZLANHE)
-#define LAPACK_slansy LAPACK_GLOBAL(slansy,SLANSY)
-#define LAPACK_dlansy LAPACK_GLOBAL(dlansy,DLANSY)
-#define LAPACK_clansy LAPACK_GLOBAL(clansy,CLANSY)
-#define LAPACK_zlansy LAPACK_GLOBAL(zlansy,ZLANSY)
-#define LAPACK_slantr LAPACK_GLOBAL(slantr,SLANTR)
-#define LAPACK_dlantr LAPACK_GLOBAL(dlantr,DLANTR)
-#define LAPACK_clantr LAPACK_GLOBAL(clantr,CLANTR)
-#define LAPACK_zlantr LAPACK_GLOBAL(zlantr,ZLANTR)
-#define LAPACK_slamch LAPACK_GLOBAL(slamch,SLAMCH)
-#define LAPACK_dlamch LAPACK_GLOBAL(dlamch,DLAMCH)
-#define LAPACK_sgelq2 LAPACK_GLOBAL(sgelq2,SGELQ2)
-#define LAPACK_dgelq2 LAPACK_GLOBAL(dgelq2,DGELQ2)
-#define LAPACK_cgelq2 LAPACK_GLOBAL(cgelq2,CGELQ2)
-#define LAPACK_zgelq2 LAPACK_GLOBAL(zgelq2,ZGELQ2)
-#define LAPACK_slarfb LAPACK_GLOBAL(slarfb,SLARFB)
-#define LAPACK_dlarfb LAPACK_GLOBAL(dlarfb,DLARFB)
-#define LAPACK_clarfb LAPACK_GLOBAL(clarfb,CLARFB)
-#define LAPACK_zlarfb LAPACK_GLOBAL(zlarfb,ZLARFB)
-#define LAPACK_slarfg LAPACK_GLOBAL(slarfg,SLARFG)
-#define LAPACK_dlarfg LAPACK_GLOBAL(dlarfg,DLARFG)
-#define LAPACK_clarfg LAPACK_GLOBAL(clarfg,CLARFG)
-#define LAPACK_zlarfg LAPACK_GLOBAL(zlarfg,ZLARFG)
-#define LAPACK_slarft LAPACK_GLOBAL(slarft,SLARFT)
-#define LAPACK_dlarft LAPACK_GLOBAL(dlarft,DLARFT)
-#define LAPACK_clarft LAPACK_GLOBAL(clarft,CLARFT)
-#define LAPACK_zlarft LAPACK_GLOBAL(zlarft,ZLARFT)
-#define LAPACK_slarfx LAPACK_GLOBAL(slarfx,SLARFX)
-#define LAPACK_dlarfx LAPACK_GLOBAL(dlarfx,DLARFX)
-#define LAPACK_clarfx LAPACK_GLOBAL(clarfx,CLARFX)
-#define LAPACK_zlarfx LAPACK_GLOBAL(zlarfx,ZLARFX)
-#define LAPACK_slatms LAPACK_GLOBAL(slatms,SLATMS)
-#define LAPACK_dlatms LAPACK_GLOBAL(dlatms,DLATMS)
-#define LAPACK_clatms LAPACK_GLOBAL(clatms,CLATMS)
-#define LAPACK_zlatms LAPACK_GLOBAL(zlatms,ZLATMS)
-#define LAPACK_slag2d LAPACK_GLOBAL(slag2d,SLAG2D)
-#define LAPACK_dlag2s LAPACK_GLOBAL(dlag2s,DLAG2S)
-#define LAPACK_clag2z LAPACK_GLOBAL(clag2z,CLAG2Z)
-#define LAPACK_zlag2c LAPACK_GLOBAL(zlag2c,ZLAG2C)
-#define LAPACK_slauum LAPACK_GLOBAL(slauum,SLAUUM)
-#define LAPACK_dlauum LAPACK_GLOBAL(dlauum,DLAUUM)
-#define LAPACK_clauum LAPACK_GLOBAL(clauum,CLAUUM)
-#define LAPACK_zlauum LAPACK_GLOBAL(zlauum,ZLAUUM)
-#define LAPACK_slagge LAPACK_GLOBAL(slagge,SLAGGE)
-#define LAPACK_dlagge LAPACK_GLOBAL(dlagge,DLAGGE)
-#define LAPACK_clagge LAPACK_GLOBAL(clagge,CLAGGE)
-#define LAPACK_zlagge LAPACK_GLOBAL(zlagge,ZLAGGE)
-#define LAPACK_slaset LAPACK_GLOBAL(slaset,SLASET)
-#define LAPACK_dlaset LAPACK_GLOBAL(dlaset,DLASET)
-#define LAPACK_claset LAPACK_GLOBAL(claset,CLASET)
-#define LAPACK_zlaset LAPACK_GLOBAL(zlaset,ZLASET)
-#define LAPACK_slasrt LAPACK_GLOBAL(slasrt,SLASRT)
-#define LAPACK_dlasrt LAPACK_GLOBAL(dlasrt,DLASRT)
-#define LAPACK_slagsy LAPACK_GLOBAL(slagsy,SLAGSY)
-#define LAPACK_dlagsy LAPACK_GLOBAL(dlagsy,DLAGSY)
-#define LAPACK_clagsy LAPACK_GLOBAL(clagsy,CLAGSY)
-#define LAPACK_zlagsy LAPACK_GLOBAL(zlagsy,ZLAGSY)
-#define LAPACK_claghe LAPACK_GLOBAL(claghe,CLAGHE)
-#define LAPACK_zlaghe LAPACK_GLOBAL(zlaghe,ZLAGHE)
-#define LAPACK_slapmr LAPACK_GLOBAL(slapmr,SLAPMR)
-#define LAPACK_dlapmr LAPACK_GLOBAL(dlapmr,DLAPMR)
-#define LAPACK_clapmr LAPACK_GLOBAL(clapmr,CLAPMR)
-#define LAPACK_zlapmr LAPACK_GLOBAL(zlapmr,ZLAPMR)
-#define LAPACK_slapy2 LAPACK_GLOBAL(slapy2,SLAPY2)
-#define LAPACK_dlapy2 LAPACK_GLOBAL(dlapy2,DLAPY2)
-#define LAPACK_slapy3 LAPACK_GLOBAL(slapy3,SLAPY3)
-#define LAPACK_dlapy3 LAPACK_GLOBAL(dlapy3,DLAPY3)
-#define LAPACK_slartgp LAPACK_GLOBAL(slartgp,SLARTGP)
-#define LAPACK_dlartgp LAPACK_GLOBAL(dlartgp,DLARTGP)
-#define LAPACK_slartgs LAPACK_GLOBAL(slartgs,SLARTGS)
-#define LAPACK_dlartgs LAPACK_GLOBAL(dlartgs,DLARTGS)
-// LAPACK 3.3.0
-#define LAPACK_cbbcsd LAPACK_GLOBAL(cbbcsd,CBBCSD)
-#define LAPACK_cheswapr LAPACK_GLOBAL(cheswapr,CHESWAPR)
-#define LAPACK_chetri2 LAPACK_GLOBAL(chetri2,CHETRI2)
-#define LAPACK_chetri2x LAPACK_GLOBAL(chetri2x,CHETRI2X)
-#define LAPACK_chetrs2 LAPACK_GLOBAL(chetrs2,CHETRS2)
-#define LAPACK_csyconv LAPACK_GLOBAL(csyconv,CSYCONV)
-#define LAPACK_csyswapr LAPACK_GLOBAL(csyswapr,CSYSWAPR)
-#define LAPACK_csytri2 LAPACK_GLOBAL(csytri2,CSYTRI2)
-#define LAPACK_csytri2x LAPACK_GLOBAL(csytri2x,CSYTRI2X)
-#define LAPACK_csytrs2 LAPACK_GLOBAL(csytrs2,CSYTRS2)
-#define LAPACK_cunbdb LAPACK_GLOBAL(cunbdb,CUNBDB)
-#define LAPACK_cuncsd LAPACK_GLOBAL(cuncsd,CUNCSD)
-#define LAPACK_dbbcsd LAPACK_GLOBAL(dbbcsd,DBBCSD)
-#define LAPACK_dorbdb LAPACK_GLOBAL(dorbdb,DORBDB)
-#define LAPACK_dorcsd LAPACK_GLOBAL(dorcsd,DORCSD)
-#define LAPACK_dsyconv LAPACK_GLOBAL(dsyconv,DSYCONV)
-#define LAPACK_dsyswapr LAPACK_GLOBAL(dsyswapr,DSYSWAPR)
-#define LAPACK_dsytri2 LAPACK_GLOBAL(dsytri2,DSYTRI2)
-#define LAPACK_dsytri2x LAPACK_GLOBAL(dsytri2x,DSYTRI2X)
-#define LAPACK_dsytrs2 LAPACK_GLOBAL(dsytrs2,DSYTRS2)
-#define LAPACK_sbbcsd LAPACK_GLOBAL(sbbcsd,SBBCSD)
-#define LAPACK_sorbdb LAPACK_GLOBAL(sorbdb,SORBDB)
-#define LAPACK_sorcsd LAPACK_GLOBAL(sorcsd,SORCSD)
-#define LAPACK_ssyconv LAPACK_GLOBAL(ssyconv,SSYCONV)
-#define LAPACK_ssyswapr LAPACK_GLOBAL(ssyswapr,SSYSWAPR)
-#define LAPACK_ssytri2 LAPACK_GLOBAL(ssytri2,SSYTRI2)
-#define LAPACK_ssytri2x LAPACK_GLOBAL(ssytri2x,SSYTRI2X)
-#define LAPACK_ssytrs2 LAPACK_GLOBAL(ssytrs2,SSYTRS2)
-#define LAPACK_zbbcsd LAPACK_GLOBAL(zbbcsd,ZBBCSD)
-#define LAPACK_zheswapr LAPACK_GLOBAL(zheswapr,ZHESWAPR)
-#define LAPACK_zhetri2 LAPACK_GLOBAL(zhetri2,ZHETRI2)
-#define LAPACK_zhetri2x LAPACK_GLOBAL(zhetri2x,ZHETRI2X)
-#define LAPACK_zhetrs2 LAPACK_GLOBAL(zhetrs2,ZHETRS2)
-#define LAPACK_zsyconv LAPACK_GLOBAL(zsyconv,ZSYCONV)
-#define LAPACK_zsyswapr LAPACK_GLOBAL(zsyswapr,ZSYSWAPR)
-#define LAPACK_zsytri2 LAPACK_GLOBAL(zsytri2,ZSYTRI2)
-#define LAPACK_zsytri2x LAPACK_GLOBAL(zsytri2x,ZSYTRI2X)
-#define LAPACK_zsytrs2 LAPACK_GLOBAL(zsytrs2,ZSYTRS2)
-#define LAPACK_zunbdb LAPACK_GLOBAL(zunbdb,ZUNBDB)
-#define LAPACK_zuncsd LAPACK_GLOBAL(zuncsd,ZUNCSD)
-// LAPACK 3.4.0
-#define LAPACK_sgemqrt LAPACK_GLOBAL(sgemqrt,SGEMQRT)
-#define LAPACK_dgemqrt LAPACK_GLOBAL(dgemqrt,DGEMQRT)
-#define LAPACK_cgemqrt LAPACK_GLOBAL(cgemqrt,CGEMQRT)
-#define LAPACK_zgemqrt LAPACK_GLOBAL(zgemqrt,ZGEMQRT)
-#define LAPACK_sgeqrt LAPACK_GLOBAL(sgeqrt,SGEQRT)
-#define LAPACK_dgeqrt LAPACK_GLOBAL(dgeqrt,DGEQRT)
-#define LAPACK_cgeqrt LAPACK_GLOBAL(cgeqrt,CGEQRT)
-#define LAPACK_zgeqrt LAPACK_GLOBAL(zgeqrt,ZGEQRT)
-#define LAPACK_sgeqrt2 LAPACK_GLOBAL(sgeqrt2,SGEQRT2)
-#define LAPACK_dgeqrt2 LAPACK_GLOBAL(dgeqrt2,DGEQRT2)
-#define LAPACK_cgeqrt2 LAPACK_GLOBAL(cgeqrt2,CGEQRT2)
-#define LAPACK_zgeqrt2 LAPACK_GLOBAL(zgeqrt2,ZGEQRT2)
-#define LAPACK_sgeqrt3 LAPACK_GLOBAL(sgeqrt3,SGEQRT3)
-#define LAPACK_dgeqrt3 LAPACK_GLOBAL(dgeqrt3,DGEQRT3)
-#define LAPACK_cgeqrt3 LAPACK_GLOBAL(cgeqrt3,CGEQRT3)
-#define LAPACK_zgeqrt3 LAPACK_GLOBAL(zgeqrt3,ZGEQRT3)
-#define LAPACK_stpmqrt LAPACK_GLOBAL(stpmqrt,STPMQRT)
-#define LAPACK_dtpmqrt LAPACK_GLOBAL(dtpmqrt,DTPMQRT)
-#define LAPACK_ctpmqrt LAPACK_GLOBAL(ctpmqrt,CTPMQRT)
-#define LAPACK_ztpmqrt LAPACK_GLOBAL(ztpmqrt,ZTPMQRT)
-#define LAPACK_dtpqrt LAPACK_GLOBAL(dtpqrt,DTPQRT)
-#define LAPACK_ctpqrt LAPACK_GLOBAL(ctpqrt,CTPQRT)
-#define LAPACK_ztpqrt LAPACK_GLOBAL(ztpqrt,ZTPQRT)
-#define LAPACK_stpqrt2 LAPACK_GLOBAL(stpqrt2,STPQRT2)
-#define LAPACK_dtpqrt2 LAPACK_GLOBAL(dtpqrt2,DTPQRT2)
-#define LAPACK_ctpqrt2 LAPACK_GLOBAL(ctpqrt2,CTPQRT2)
-#define LAPACK_ztpqrt2 LAPACK_GLOBAL(ztpqrt2,ZTPQRT2)
-#define LAPACK_stprfb LAPACK_GLOBAL(stprfb,STPRFB)
-#define LAPACK_dtprfb LAPACK_GLOBAL(dtprfb,DTPRFB)
-#define LAPACK_ctprfb LAPACK_GLOBAL(ctprfb,CTPRFB)
-#define LAPACK_ztprfb LAPACK_GLOBAL(ztprfb,ZTPRFB)
-// LAPACK 3.X.X
-#define LAPACK_csyr LAPACK_GLOBAL(csyr,CSYR)
-#define LAPACK_zsyr LAPACK_GLOBAL(zsyr,ZSYR)
-
-
-void LAPACK_sgetrf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgetrf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgetrf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_zgetrf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_sgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, float* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, double* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_zgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_sgttrf( lapack_int* n, float* dl, float* d, float* du, float* du2,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgttrf( lapack_int* n, double* dl, double* d, double* du,
-                    double* du2, lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgttrf( lapack_int* n, lapack_complex_float* dl,
-                    lapack_complex_float* d, lapack_complex_float* du,
-                    lapack_complex_float* du2, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_zgttrf( lapack_int* n, lapack_complex_double* dl,
-                    lapack_complex_double* d, lapack_complex_double* du,
-                    lapack_complex_double* du2, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_spotrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dpotrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_cpotrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_zpotrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dpstrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* piv, lapack_int* rank, double* tol,
-                    double* work, lapack_int *info );
-void LAPACK_spstrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* piv, lapack_int* rank, float* tol, float* work,
-                    lapack_int *info );
-void LAPACK_zpstrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* piv, lapack_int* rank,
-                    double* tol, double* work, lapack_int *info );
-void LAPACK_cpstrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* piv, lapack_int* rank,
-                    float* tol, float* work, lapack_int *info );
-void LAPACK_dpftrf( char* transr, char* uplo, lapack_int* n, double* a,
-                    lapack_int *info );
-void LAPACK_spftrf( char* transr, char* uplo, lapack_int* n, float* a,
-                    lapack_int *info );
-void LAPACK_zpftrf( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int *info );
-void LAPACK_cpftrf( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int *info );
-void LAPACK_spptrf( char* uplo, lapack_int* n, float* ap, lapack_int *info );
-void LAPACK_dpptrf( char* uplo, lapack_int* n, double* ap, lapack_int *info );
-void LAPACK_cpptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_zpptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_spbtrf( char* uplo, lapack_int* n, lapack_int* kd, float* ab,
-                    lapack_int* ldab, lapack_int *info );
-void LAPACK_dpbtrf( char* uplo, lapack_int* n, lapack_int* kd, double* ab,
-                    lapack_int* ldab, lapack_int *info );
-void LAPACK_cpbtrf( char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_int *info );
-void LAPACK_zpbtrf( char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_int *info );
-void LAPACK_spttrf( lapack_int* n, float* d, float* e, lapack_int *info );
-void LAPACK_dpttrf( lapack_int* n, double* d, double* e, lapack_int *info );
-void LAPACK_cpttrf( lapack_int* n, float* d, lapack_complex_float* e,
-                    lapack_int *info );
-void LAPACK_zpttrf( lapack_int* n, double* d, lapack_complex_double* e,
-                    lapack_int *info );
-void LAPACK_ssytrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ipiv, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dsytrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ipiv, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_csytrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zsytrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_chetrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zhetrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ssptrf( char* uplo, lapack_int* n, float* ap, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_dsptrf( char* uplo, lapack_int* n, double* ap, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_csptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_zsptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_chptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_zhptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_sgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* a, lapack_int* lda, const lapack_int* ipiv,
-                    float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const lapack_int* ipiv,
-                    double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_sgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const float* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const double* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_cgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_float* ab,
-                    lapack_int* ldab, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_double* ab,
-                    lapack_int* ldab, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_sgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* dl, const float* d, const float* du,
-                    const float* du2, const lapack_int* ipiv, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* dl, const double* d, const double* du,
-                    const double* du2, const lapack_int* ipiv, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du,
-                    const lapack_complex_float* du2, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du,
-                    const lapack_complex_double* du2, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_spotrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_spftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* a, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_spptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_cpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_spbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const float* ab, lapack_int* ldab, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const double* ab, lapack_int* ldab, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_spttrs( lapack_int* n, lapack_int* nrhs, const float* d,
-                    const float* e, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpttrs( lapack_int* n, lapack_int* nrhs, const double* d,
-                    const double* e, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_cpttrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const lapack_complex_float* e, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zpttrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const lapack_complex_double* e,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ssytrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, const lapack_int* ipiv, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dsytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const lapack_int* ipiv,
-                    double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_csytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zsytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_chetrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zhetrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_ssptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, const lapack_int* ipiv, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dsptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, const lapack_int* ipiv, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_csptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zsptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_chptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zhptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_strtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dtrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* a, lapack_int* lda,
-                    double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_ctrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ztrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_stptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* ap, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dtptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* ap, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_ctptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* ap,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ztptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* ap,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_stbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const float* ab,
-                    lapack_int* ldab, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dtbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const double* ab,
-                    lapack_int* ldab, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ctbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ztbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_sgecon( char* norm, lapack_int* n, const float* a, lapack_int* lda,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgecon( char* norm, lapack_int* n, const double* a, lapack_int* lda,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgecon( char* norm, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* anorm, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgecon( char* norm, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, double* anorm, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const float* ab, lapack_int* ldab, const lapack_int* ipiv,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const double* ab, lapack_int* ldab, const lapack_int* ipiv,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgtcon( char* norm, lapack_int* n, const float* dl, const float* d,
-                    const float* du, const float* du2, const lapack_int* ipiv,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgtcon( char* norm, lapack_int* n, const double* dl,
-                    const double* d, const double* du, const double* du2,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgtcon( char* norm, lapack_int* n, const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du,
-                    const lapack_complex_float* du2, const lapack_int* ipiv,
-                    float* anorm, float* rcond, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zgtcon( char* norm, lapack_int* n, const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du,
-                    const lapack_complex_double* du2, const lapack_int* ipiv,
-                    double* anorm, double* rcond, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_spocon( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dpocon( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cpocon( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* anorm, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zpocon( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, double* anorm, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sppcon( char* uplo, lapack_int* n, const float* ap, float* anorm,
-                    float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dppcon( char* uplo, lapack_int* n, const double* ap, double* anorm,
-                    double* rcond, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cppcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    float* anorm, float* rcond, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zppcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    double* anorm, double* rcond, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_spbcon( char* uplo, lapack_int* n, lapack_int* kd, const float* ab,
-                    lapack_int* ldab, float* anorm, float* rcond, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dpbcon( char* uplo, lapack_int* n, lapack_int* kd, const double* ab,
-                    lapack_int* ldab, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cpbcon( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    float* anorm, float* rcond, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zpbcon( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    double* anorm, double* rcond, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sptcon( lapack_int* n, const float* d, const float* e, float* anorm,
-                    float* rcond, float* work, lapack_int *info );
-void LAPACK_dptcon( lapack_int* n, const double* d, const double* e,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int *info );
-void LAPACK_cptcon( lapack_int* n, const float* d,
-                    const lapack_complex_float* e, float* anorm, float* rcond,
-                    float* work, lapack_int *info );
-void LAPACK_zptcon( lapack_int* n, const double* d,
-                    const lapack_complex_double* e, double* anorm,
-                    double* rcond, double* work, lapack_int *info );
-void LAPACK_ssycon( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dsycon( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_csycon( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv, float* anorm,
-                    float* rcond, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zsycon( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv, double* anorm,
-                    double* rcond, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_checon( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv, float* anorm,
-                    float* rcond, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zhecon( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv, double* anorm,
-                    double* rcond, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_sspcon( char* uplo, lapack_int* n, const float* ap,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dspcon( char* uplo, lapack_int* n, const double* ap,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cspcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zspcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_chpcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zhpcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_strcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const float* a, lapack_int* lda, float* rcond, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtrcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const double* a, lapack_int* lda, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_ctrcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    float* rcond, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztrcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    double* rcond, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const float* ap, float* rcond, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const double* ap, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_float* ap, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_double* ap, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const float* ab, lapack_int* ldab,
-                    float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dtbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const double* ab, lapack_int* ldab,
-                    double* rcond, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const lapack_complex_float* ab,
-                    lapack_int* ldab, float* rcond, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_ztbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const lapack_complex_double* ab,
-                    lapack_int* ldab, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* a, lapack_int* lda, const float* af,
-                    lapack_int* ldaf, const lapack_int* ipiv, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
-                    float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const double* af,
-                    lapack_int* ldaf, const lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const double* a, lapack_int* lda, const double* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const double* r,
-                     const double* c, const double* b, lapack_int* ldb,
-                     double* x, lapack_int* ldx, double* rcond, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const float* a, lapack_int* lda, const float* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const float* r,
-                     const float* c, const float* b, lapack_int* ldb, float* x,
-                     lapack_int* ldx, float* rcond, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const double* r, const double* c,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const float* r, const float* c,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const float* ab, lapack_int* ldab,
-                    const float* afb, lapack_int* ldafb, const lapack_int* ipiv,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const double* ab, lapack_int* ldab,
-                    const double* afb, lapack_int* ldafb,
-                    const lapack_int* ipiv, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_float* ab,
-                    lapack_int* ldab, const lapack_complex_float* afb,
-                    lapack_int* ldafb, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_double* ab,
-                    lapack_int* ldab, const lapack_complex_double* afb,
-                    lapack_int* ldafb, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_dgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, const double* ab,
-                     lapack_int* ldab, const double* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const double* r, const double* c,
-                     const double* b, lapack_int* ldb, double* x,
-                     lapack_int* ldx, double* rcond, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, const float* ab,
-                     lapack_int* ldab, const float* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const float* r, const float* c,
-                     const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params, float* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_zgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs,
-                     const lapack_complex_double* ab, lapack_int* ldab,
-                     const lapack_complex_double* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const double* r, const double* c,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs,
-                     const lapack_complex_float* ab, lapack_int* ldab,
-                     const lapack_complex_float* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const float* r, const float* c,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* dl, const float* d, const float* du,
-                    const float* dlf, const float* df, const float* duf,
-                    const float* du2, const lapack_int* ipiv, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
-                    float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* dl, const double* d, const double* du,
-                    const double* dlf, const double* df, const double* duf,
-                    const double* du2, const lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du,
-                    const lapack_complex_float* dlf,
-                    const lapack_complex_float* df,
-                    const lapack_complex_float* duf,
-                    const lapack_complex_float* du2, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du,
-                    const lapack_complex_double* dlf,
-                    const lapack_complex_double* df,
-                    const lapack_complex_double* duf,
-                    const lapack_complex_double* du2, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sporfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, const float* af, lapack_int* ldaf,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const double* af,
-                    lapack_int* ldaf, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_dporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const double* a, lapack_int* lda, const double* af,
-                     lapack_int* ldaf, const double* s, const double* b,
-                     lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params, double* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_sporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const float* a, lapack_int* lda, const float* af,
-                     lapack_int* ldaf, const float* s, const float* b,
-                     lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const double* s, const lapack_complex_double* b,
-                     lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                     double* rcond, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const float* s, const lapack_complex_float* b,
-                     lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                     float* rcond, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_spprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, const float* afp, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
-                    float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, const double* afp, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap,
-                    const lapack_complex_float* afp,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap,
-                    const lapack_complex_double* afp,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_spbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const float* ab, lapack_int* ldab, const float* afb,
-                    lapack_int* ldafb, const float* b, lapack_int* ldb,
-                    float* x, lapack_int* ldx, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const double* ab, lapack_int* ldab, const double* afb,
-                    lapack_int* ldafb, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_complex_float* afb, lapack_int* ldafb,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_complex_double* afb, lapack_int* ldafb,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sptrfs( lapack_int* n, lapack_int* nrhs, const float* d,
-                    const float* e, const float* df, const float* ef,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int *info );
-void LAPACK_dptrfs( lapack_int* n, lapack_int* nrhs, const double* d,
-                    const double* e, const double* df, const double* ef,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* ferr, double* berr, double* work,
-                    lapack_int *info );
-void LAPACK_cptrfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const lapack_complex_float* e, const float* df,
-                    const lapack_complex_float* ef,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zptrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const lapack_complex_double* e,
-                    const double* df, const lapack_complex_double* ef,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_ssyrfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, const float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const float* b, lapack_int* ldb,
-                    float* x, lapack_int* ldx, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dsyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const double* af,
-                    lapack_int* ldaf, const lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_csyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zsyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dsyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const double* a, lapack_int* lda, const double* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const double* s,
-                     const double* b, lapack_int* ldb, double* x,
-                     lapack_int* ldx, double* rcond, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_ssyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const float* a, lapack_int* lda, const float* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const float* s,
-                     const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params, float* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_zsyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const double* s,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_csyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const float* s,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_cherfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zherfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_zherfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const double* s,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cherfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const float* s,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_ssprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, const float* afp, const lapack_int* ipiv,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dsprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, const double* afp, const lapack_int* ipiv,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_csprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap,
-                    const lapack_complex_float* afp, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zsprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap,
-                    const lapack_complex_double* afp, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_chprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap,
-                    const lapack_complex_float* afp, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap,
-                    const lapack_complex_double* afp, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_strrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* a, lapack_int* lda,
-                    const float* b, lapack_int* ldb, const float* x,
-                    lapack_int* ldx, float* ferr, float* berr, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* a, lapack_int* lda,
-                    const double* b, lapack_int* ldb, const double* x,
-                    lapack_int* ldx, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* b,
-                    lapack_int* ldb, const lapack_complex_float* x,
-                    lapack_int* ldx, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* b,
-                    lapack_int* ldb, const lapack_complex_double* x,
-                    lapack_int* ldx, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* ap, const float* b,
-                    lapack_int* ldb, const float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dtprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* ap, const double* b,
-                    lapack_int* ldb, const double* x, lapack_int* ldx,
-                    double* ferr, double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* ap,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    const lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* ap,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    const lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_stbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const float* ab,
-                    lapack_int* ldab, const float* b, lapack_int* ldb,
-                    const float* x, lapack_int* ldx, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dtbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const double* ab,
-                    lapack_int* ldab, const double* b, lapack_int* ldb,
-                    const double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    const lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    const lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgetri( lapack_int* n, float* a, lapack_int* lda,
-                    const lapack_int* ipiv, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgetri( lapack_int* n, double* a, lapack_int* lda,
-                    const lapack_int* ipiv, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgetri( lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zgetri( lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_spotri( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dpotri( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_cpotri( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_zpotri( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dpftri( char* transr, char* uplo, lapack_int* n, double* a,
-                    lapack_int *info );
-void LAPACK_spftri( char* transr, char* uplo, lapack_int* n, float* a,
-                    lapack_int *info );
-void LAPACK_zpftri( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int *info );
-void LAPACK_cpftri( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int *info );
-void LAPACK_spptri( char* uplo, lapack_int* n, float* ap, lapack_int *info );
-void LAPACK_dpptri( char* uplo, lapack_int* n, double* ap, lapack_int *info );
-void LAPACK_cpptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_zpptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_ssytri( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    const lapack_int* ipiv, float* work, lapack_int *info );
-void LAPACK_dsytri( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    const lapack_int* ipiv, double* work, lapack_int *info );
-void LAPACK_csytri( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zsytri( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_chetri( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zhetri( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_ssptri( char* uplo, lapack_int* n, float* ap,
-                    const lapack_int* ipiv, float* work, lapack_int *info );
-void LAPACK_dsptri( char* uplo, lapack_int* n, double* ap,
-                    const lapack_int* ipiv, double* work, lapack_int *info );
-void LAPACK_csptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    const lapack_int* ipiv, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zsptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    const lapack_int* ipiv, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_chptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    const lapack_int* ipiv, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zhptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    const lapack_int* ipiv, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_strtri( char* uplo, char* diag, lapack_int* n, float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dtrtri( char* uplo, char* diag, lapack_int* n, double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_ctrtri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_ztrtri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dtftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    double* a, lapack_int *info );
-void LAPACK_stftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    float* a, lapack_int *info );
-void LAPACK_ztftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_double* a, lapack_int *info );
-void LAPACK_ctftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_float* a, lapack_int *info );
-void LAPACK_stptri( char* uplo, char* diag, lapack_int* n, float* ap,
-                    lapack_int *info );
-void LAPACK_dtptri( char* uplo, char* diag, lapack_int* n, double* ap,
-                    lapack_int *info );
-void LAPACK_ctptri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_float* ap, lapack_int *info );
-void LAPACK_ztptri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_double* ap, lapack_int *info );
-void LAPACK_sgeequ( lapack_int* m, lapack_int* n, const float* a,
-                    lapack_int* lda, float* r, float* c, float* rowcnd,
-                    float* colcnd, float* amax, lapack_int *info );
-void LAPACK_dgeequ( lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, double* r, double* c, double* rowcnd,
-                    double* colcnd, double* amax, lapack_int *info );
-void LAPACK_cgeequ( lapack_int* m, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* r, float* c, float* rowcnd,
-                    float* colcnd, float* amax, lapack_int *info );
-void LAPACK_zgeequ( lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* r,
-                    double* c, double* rowcnd, double* colcnd, double* amax,
-                    lapack_int *info );
-void LAPACK_dgeequb( lapack_int* m, lapack_int* n, const double* a,
-                     lapack_int* lda, double* r, double* c, double* rowcnd,
-                     double* colcnd, double* amax, lapack_int *info );
-void LAPACK_sgeequb( lapack_int* m, lapack_int* n, const float* a,
-                     lapack_int* lda, float* r, float* c, float* rowcnd,
-                     float* colcnd, float* amax, lapack_int *info );
-void LAPACK_zgeequb( lapack_int* m, lapack_int* n,
-                     const lapack_complex_double* a, lapack_int* lda, double* r,
-                     double* c, double* rowcnd, double* colcnd, double* amax,
-                     lapack_int *info );
-void LAPACK_cgeequb( lapack_int* m, lapack_int* n,
-                     const lapack_complex_float* a, lapack_int* lda, float* r,
-                     float* c, float* rowcnd, float* colcnd, float* amax,
-                     lapack_int *info );
-void LAPACK_sgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const float* ab, lapack_int* ldab, float* r,
-                    float* c, float* rowcnd, float* colcnd, float* amax,
-                    lapack_int *info );
-void LAPACK_dgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const double* ab, lapack_int* ldab,
-                    double* r, double* c, double* rowcnd, double* colcnd,
-                    double* amax, lapack_int *info );
-void LAPACK_cgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const lapack_complex_float* ab,
-                    lapack_int* ldab, float* r, float* c, float* rowcnd,
-                    float* colcnd, float* amax, lapack_int *info );
-void LAPACK_zgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const lapack_complex_double* ab,
-                    lapack_int* ldab, double* r, double* c, double* rowcnd,
-                    double* colcnd, double* amax, lapack_int *info );
-void LAPACK_dgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const double* ab, lapack_int* ldab,
-                     double* r, double* c, double* rowcnd, double* colcnd,
-                     double* amax, lapack_int *info );
-void LAPACK_sgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const float* ab, lapack_int* ldab,
-                     float* r, float* c, float* rowcnd, float* colcnd,
-                     float* amax, lapack_int *info );
-void LAPACK_zgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const lapack_complex_double* ab,
-                     lapack_int* ldab, double* r, double* c, double* rowcnd,
-                     double* colcnd, double* amax, lapack_int *info );
-void LAPACK_cgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const lapack_complex_float* ab,
-                     lapack_int* ldab, float* r, float* c, float* rowcnd,
-                     float* colcnd, float* amax, lapack_int *info );
-void LAPACK_spoequ( lapack_int* n, const float* a, lapack_int* lda, float* s,
-                    float* scond, float* amax, lapack_int *info );
-void LAPACK_dpoequ( lapack_int* n, const double* a, lapack_int* lda, double* s,
-                    double* scond, double* amax, lapack_int *info );
-void LAPACK_cpoequ( lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* s, float* scond, float* amax,
-                    lapack_int *info );
-void LAPACK_zpoequ( lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, double* s, double* scond, double* amax,
-                    lapack_int *info );
-void LAPACK_dpoequb( lapack_int* n, const double* a, lapack_int* lda, double* s,
-                     double* scond, double* amax, lapack_int *info );
-void LAPACK_spoequb( lapack_int* n, const float* a, lapack_int* lda, float* s,
-                     float* scond, float* amax, lapack_int *info );
-void LAPACK_zpoequb( lapack_int* n, const lapack_complex_double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     lapack_int *info );
-void LAPACK_cpoequb( lapack_int* n, const lapack_complex_float* a,
-                     lapack_int* lda, float* s, float* scond, float* amax,
-                     lapack_int *info );
-void LAPACK_sppequ( char* uplo, lapack_int* n, const float* ap, float* s,
-                    float* scond, float* amax, lapack_int *info );
-void LAPACK_dppequ( char* uplo, lapack_int* n, const double* ap, double* s,
-                    double* scond, double* amax, lapack_int *info );
-void LAPACK_cppequ( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    float* s, float* scond, float* amax, lapack_int *info );
-void LAPACK_zppequ( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    double* s, double* scond, double* amax, lapack_int *info );
-void LAPACK_spbequ( char* uplo, lapack_int* n, lapack_int* kd, const float* ab,
-                    lapack_int* ldab, float* s, float* scond, float* amax,
-                    lapack_int *info );
-void LAPACK_dpbequ( char* uplo, lapack_int* n, lapack_int* kd, const double* ab,
-                    lapack_int* ldab, double* s, double* scond, double* amax,
-                    lapack_int *info );
-void LAPACK_cpbequ( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_float* ab, lapack_int* ldab, float* s,
-                    float* scond, float* amax, lapack_int *info );
-void LAPACK_zpbequ( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    double* s, double* scond, double* amax, lapack_int *info );
-void LAPACK_dsyequb( char* uplo, lapack_int* n, const double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     double* work, lapack_int *info );
-void LAPACK_ssyequb( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                     float* s, float* scond, float* amax, float* work,
-                     lapack_int *info );
-void LAPACK_zsyequb( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_csyequb( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                     lapack_int* lda, float* s, float* scond, float* amax,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_zheequb( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_cheequb( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                     lapack_int* lda, float* s, float* scond, float* amax,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_sgesv( lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda,
-                   lapack_int* ipiv, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dgesv( lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda,
-                   lapack_int* ipiv, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_float* a,
-                   lapack_int* lda, lapack_int* ipiv, lapack_complex_float* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_zgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* a,
-                   lapack_int* lda, lapack_int* ipiv, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_dsgesv( lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda,
-                    lapack_int* ipiv, double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* work, float* swork,
-                    lapack_int* iter, lapack_int *info );
-void LAPACK_zcgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    lapack_complex_double* work, lapack_complex_float* swork,
-                    double* rwork, lapack_int* iter, lapack_int *info );
-void LAPACK_sgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, float* r, float* c, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, double* r, double* c,
-                    double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, float* r, float* c,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, double* r, double* c,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                     double* rcond, double* rpvgrw, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* r, float* c,
-                     float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* rpvgrw, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* r, float* c,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, float* ab, lapack_int* ldab,
-                   lapack_int* ipiv, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, double* ab, lapack_int* ldab,
-                   lapack_int* ipiv, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, lapack_complex_float* ab, lapack_int* ldab,
-                   lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_zgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, lapack_complex_double* ab,
-                   lapack_int* ldab, lapack_int* ipiv, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_sgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, float* ab,
-                    lapack_int* ldab, float* afb, lapack_int* ldafb,
-                    lapack_int* ipiv, char* equed, float* r, float* c, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, double* ab,
-                    lapack_int* ldab, double* afb, lapack_int* ldafb,
-                    lapack_int* ipiv, char* equed, double* r, double* c,
-                    double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, lapack_complex_float* ab,
-                    lapack_int* ldab, lapack_complex_float* afb,
-                    lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r,
-                    float* c, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, lapack_complex_double* ab,
-                    lapack_int* ldab, lapack_complex_double* afb,
-                    lapack_int* ldafb, lapack_int* ipiv, char* equed, double* r,
-                    double* c, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, double* ab,
-                     lapack_int* ldab, double* afb, lapack_int* ldafb,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                     double* rcond, double* rpvgrw, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, float* ab,
-                     lapack_int* ldab, float* afb, lapack_int* ldafb,
-                     lapack_int* ipiv, char* equed, float* r, float* c,
-                     float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* rpvgrw, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs,
-                     lapack_complex_double* ab, lapack_int* ldab,
-                     lapack_complex_double* afb, lapack_int* ldafb,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, lapack_complex_float* ab,
-                     lapack_int* ldab, lapack_complex_float* afb,
-                     lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r,
-                     float* c, lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgtsv( lapack_int* n, lapack_int* nrhs, float* dl, float* d,
-                   float* du, float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dgtsv( lapack_int* n, lapack_int* nrhs, double* dl, double* d,
-                   double* du, double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cgtsv( lapack_int* n, lapack_int* nrhs, lapack_complex_float* dl,
-                   lapack_complex_float* d, lapack_complex_float* du,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zgtsv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* dl,
-                   lapack_complex_double* d, lapack_complex_double* du,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_sgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* dl, const float* d, const float* du,
-                    float* dlf, float* df, float* duf, float* du2,
-                    lapack_int* ipiv, const float* b, lapack_int* ldb, float* x,
-                    lapack_int* ldx, float* rcond, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* dl, const double* d, const double* du,
-                    double* dlf, double* df, double* duf, double* du2,
-                    lapack_int* ipiv, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* rcond, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du, lapack_complex_float* dlf,
-                    lapack_complex_float* df, lapack_complex_float* duf,
-                    lapack_complex_float* du2, lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du, lapack_complex_double* dlf,
-                    lapack_complex_double* df, lapack_complex_double* duf,
-                    lapack_complex_double* du2, lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sposv( char* uplo, lapack_int* n, lapack_int* nrhs, float* a,
-                   lapack_int* lda, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dposv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
-                   lapack_int* lda, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cposv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zposv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dsposv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* work, float* swork,
-                    lapack_int* iter, lapack_int *info );
-void LAPACK_zcposv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx,
-                    lapack_complex_double* work, lapack_complex_float* swork,
-                    double* rwork, lapack_int* iter, lapack_int *info );
-void LAPACK_sposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                    char* equed, float* s, float* b, lapack_int* ldb, float* x,
-                    lapack_int* ldx, float* rcond, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                    char* equed, double* s, double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* rcond, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf, char* equed,
-                    float* s, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf, char* equed,
-                    double* s, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                     char* equed, double* s, double* b, lapack_int* ldb,
-                     double* x, lapack_int* ldx, double* rcond, double* rpvgrw,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params, double* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_sposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                     char* equed, float* s, float* b, lapack_int* ldb, float* x,
-                     lapack_int* ldx, float* rcond, float* rpvgrw, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf, char* equed,
-                     double* s, lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf, char* equed,
-                     float* s, lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sppsv( char* uplo, lapack_int* n, lapack_int* nrhs, float* ap,
-                   float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dppsv( char* uplo, lapack_int* n, lapack_int* nrhs, double* ap,
-                   double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cppsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* ap, lapack_complex_float* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_zppsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* ap, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_sppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    float* ap, float* afp, char* equed, float* s, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    double* ap, double* afp, char* equed, double* s, double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* ap, lapack_complex_float* afp,
-                    char* equed, float* s, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* ap, lapack_complex_double* afp,
-                    char* equed, double* s, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_spbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   float* ab, lapack_int* ldab, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   double* ab, lapack_int* ldab, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   lapack_complex_float* ab, lapack_int* ldab,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   lapack_complex_double* ab, lapack_int* ldab,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_spbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, float* ab, lapack_int* ldab, float* afb,
-                    lapack_int* ldafb, char* equed, float* s, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, double* ab, lapack_int* ldab, double* afb,
-                    lapack_int* ldafb, char* equed, double* s, double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, lapack_complex_float* ab,
-                    lapack_int* ldab, lapack_complex_float* afb,
-                    lapack_int* ldafb, char* equed, float* s,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, lapack_complex_double* ab,
-                    lapack_int* ldab, lapack_complex_double* afb,
-                    lapack_int* ldafb, char* equed, double* s,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sptsv( lapack_int* n, lapack_int* nrhs, float* d, float* e,
-                   float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dptsv( lapack_int* n, lapack_int* nrhs, double* d, double* e,
-                   double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cptsv( lapack_int* n, lapack_int* nrhs, float* d,
-                   lapack_complex_float* e, lapack_complex_float* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_zptsv( lapack_int* n, lapack_int* nrhs, double* d,
-                   lapack_complex_double* e, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_sptsvx( char* fact, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const float* e, float* df, float* ef, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int *info );
-void LAPACK_dptsvx( char* fact, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const double* e, double* df, double* ef,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* rcond, double* ferr, double* berr,
-                    double* work, lapack_int *info );
-void LAPACK_cptsvx( char* fact, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const lapack_complex_float* e, float* df,
-                    lapack_complex_float* ef, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zptsvx( char* fact, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const lapack_complex_double* e, double* df,
-                    lapack_complex_double* ef, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_ssysv( char* uplo, lapack_int* n, lapack_int* nrhs, float* a,
-                   lapack_int* lda, lapack_int* ipiv, float* b, lapack_int* ldb,
-                   float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dsysv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
-                   lapack_int* lda, lapack_int* ipiv, double* b,
-                   lapack_int* ldb, double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_csysv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_zsysv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_ssysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* a, lapack_int* lda, float* af,
-                    lapack_int* ldaf, lapack_int* ipiv, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dsysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, double* af,
-                    lapack_int* ldaf, lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_csysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zsysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_dsysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* s, double* b,
-                     lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params, double* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_ssysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* s, float* b,
-                     lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params, float* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_zsysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* s,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_csysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* s,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_chesv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_zhesv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_chesvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhesvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_zhesvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* s,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_chesvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* s,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sspsv( char* uplo, lapack_int* n, lapack_int* nrhs, float* ap,
-                   lapack_int* ipiv, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dspsv( char* uplo, lapack_int* n, lapack_int* nrhs, double* ap,
-                   lapack_int* ipiv, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cspsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* ap, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zspsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* ap, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_sspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, float* afp, lapack_int* ipiv,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, double* afp, lapack_int* ipiv,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* rcond, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, lapack_complex_float* afp,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, lapack_complex_double* afp,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_chpsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* ap, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zhpsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* ap, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_chpsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, lapack_complex_float* afp,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhpsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, lapack_complex_double* afp,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgeqrf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgeqrf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgeqrf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgeqrf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgeqpf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* jpvt, float* tau, float* work,
-                    lapack_int *info );
-void LAPACK_dgeqpf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* jpvt, double* tau, double* work,
-                    lapack_int *info );
-void LAPACK_cgeqpf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgeqpf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgeqp3( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* jpvt, float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgeqp3( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* jpvt, double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgeqp3( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int *info );
-void LAPACK_zgeqp3( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int *info );
-void LAPACK_sorgqr( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgqr( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungqr( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungqr( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgelqf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgelqf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgelqf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgelqf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sorglq( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorglq( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cunglq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zunglq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgeqlf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgeqlf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgeqlf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgeqlf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sorgql( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgql( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungql( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungql( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cunmql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgerqf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgerqf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgerqf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgerqf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sorgrq( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgrq( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungrq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungrq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cunmrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_stzrzf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dtzrzf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ctzrzf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ztzrzf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l, const float* a,
-                    lapack_int* lda, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dormrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l, const double* a,
-                    lapack_int* lda, const double* tau, double* c,
-                    lapack_int* ldc, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sggqrf( lapack_int* n, lapack_int* m, lapack_int* p, float* a,
-                    lapack_int* lda, float* taua, float* b, lapack_int* ldb,
-                    float* taub, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dggqrf( lapack_int* n, lapack_int* m, lapack_int* p, double* a,
-                    lapack_int* lda, double* taua, double* b, lapack_int* ldb,
-                    double* taub, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cggqrf( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* taua, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* taub,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zggqrf( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* taua, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* taub,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sggrqf( lapack_int* m, lapack_int* p, lapack_int* n, float* a,
-                    lapack_int* lda, float* taua, float* b, lapack_int* ldb,
-                    float* taub, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dggrqf( lapack_int* m, lapack_int* p, lapack_int* n, double* a,
-                    lapack_int* lda, double* taua, double* b, lapack_int* ldb,
-                    double* taub, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cggrqf( lapack_int* m, lapack_int* p, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* taua, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* taub,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zggrqf( lapack_int* m, lapack_int* p, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* taua, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* taub,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgebrd( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* d, float* e, float* tauq, float* taup, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgebrd( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* d, double* e, double* tauq, double* taup,
-                    double* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_cgebrd( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, float* d, float* e,
-                    lapack_complex_float* tauq, lapack_complex_float* taup,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgebrd( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, double* d, double* e,
-                    lapack_complex_double* tauq, lapack_complex_double* taup,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, float* ab, lapack_int* ldab,
-                    float* d, float* e, float* q, lapack_int* ldq, float* pt,
-                    lapack_int* ldpt, float* c, lapack_int* ldc, float* work,
-                    lapack_int *info );
-void LAPACK_dgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, double* ab,
-                    lapack_int* ldab, double* d, double* e, double* q,
-                    lapack_int* ldq, double* pt, lapack_int* ldpt, double* c,
-                    lapack_int* ldc, double* work, lapack_int *info );
-void LAPACK_cgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, lapack_complex_float* ab,
-                    lapack_int* ldab, float* d, float* e,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* pt, lapack_int* ldpt,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, lapack_complex_double* ab,
-                    lapack_int* ldab, double* d, double* e,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* pt, lapack_int* ldpt,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sorgbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    float* a, lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    double* a, lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k, const float* a,
-                    lapack_int* lda, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dormbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k, const double* a,
-                    lapack_int* lda, const double* tau, double* c,
-                    lapack_int* ldc, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cungbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, float* d, float* e,
-                    float* vt, lapack_int* ldvt, float* u, lapack_int* ldu,
-                    float* c, lapack_int* ldc, float* work, lapack_int *info );
-void LAPACK_dbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, double* d, double* e,
-                    double* vt, lapack_int* ldvt, double* u, lapack_int* ldu,
-                    double* c, lapack_int* ldc, double* work,
-                    lapack_int *info );
-void LAPACK_cbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, float* d, float* e,
-                    lapack_complex_float* vt, lapack_int* ldvt,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* c, lapack_int* ldc, float* work,
-                    lapack_int *info );
-void LAPACK_zbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, double* d, double* e,
-                    lapack_complex_double* vt, lapack_int* ldvt,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* c, lapack_int* ldc, double* work,
-                    lapack_int *info );
-void LAPACK_sbdsdc( char* uplo, char* compq, lapack_int* n, float* d, float* e,
-                    float* u, lapack_int* ldu, float* vt, lapack_int* ldvt,
-                    float* q, lapack_int* iq, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dbdsdc( char* uplo, char* compq, lapack_int* n, double* d,
-                    double* e, double* u, lapack_int* ldu, double* vt,
-                    lapack_int* ldvt, double* q, lapack_int* iq, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ssytrd( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    float* d, float* e, float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dsytrd( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    double* d, double* e, double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sorgtr( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    const float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dorgtr( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    const double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_chetrd( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, float* d, float* e,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zhetrd( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, double* d, double* e,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungtr( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zungtr( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ssptrd( char* uplo, lapack_int* n, float* ap, float* d, float* e,
-                    float* tau, lapack_int *info );
-void LAPACK_dsptrd( char* uplo, lapack_int* n, double* ap, double* d, double* e,
-                    double* tau, lapack_int *info );
-void LAPACK_sopgtr( char* uplo, lapack_int* n, const float* ap,
-                    const float* tau, float* q, lapack_int* ldq, float* work,
-                    lapack_int *info );
-void LAPACK_dopgtr( char* uplo, lapack_int* n, const double* ap,
-                    const double* tau, double* q, lapack_int* ldq, double* work,
-                    lapack_int *info );
-void LAPACK_sopmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const float* ap, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int *info );
-void LAPACK_dopmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const double* ap, const double* tau,
-                    double* c, lapack_int* ldc, double* work,
-                    lapack_int *info );
-void LAPACK_chptrd( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    float* d, float* e, lapack_complex_float* tau,
-                    lapack_int *info );
-void LAPACK_zhptrd( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    double* d, double* e, lapack_complex_double* tau,
-                    lapack_int *info );
-void LAPACK_cupgtr( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_complex_float* tau, lapack_complex_float* q,
-                    lapack_int* ldq, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zupgtr( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_complex_double* tau, lapack_complex_double* q,
-                    lapack_int* ldq, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_cupmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_complex_float* tau, lapack_complex_float* c,
-                    lapack_int* ldc, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zupmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_ssbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    float* ab, lapack_int* ldab, float* d, float* e, float* q,
-                    lapack_int* ldq, float* work, lapack_int *info );
-void LAPACK_dsbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    double* ab, lapack_int* ldab, double* d, double* e,
-                    double* q, lapack_int* ldq, double* work,
-                    lapack_int *info );
-void LAPACK_chbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_float* ab, lapack_int* ldab, float* d,
-                    float* e, lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zhbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_double* ab, lapack_int* ldab, double* d,
-                    double* e, lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_ssterf( lapack_int* n, float* d, float* e, lapack_int *info );
-void LAPACK_dsterf( lapack_int* n, double* d, double* e, lapack_int *info );
-void LAPACK_ssteqr( char* compz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dsteqr( char* compz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_csteqr( char* compz, lapack_int* n, float* d, float* e,
-                    lapack_complex_float* z, lapack_int* ldz, float* work,
-                    lapack_int *info );
-void LAPACK_zsteqr( char* compz, lapack_int* n, double* d, double* e,
-                    lapack_complex_double* z, lapack_int* ldz, double* work,
-                    lapack_int *info );
-void LAPACK_sstemr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    lapack_int* nzc, lapack_int* isuppz, lapack_logical* tryrac,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dstemr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, lapack_int* m, double* w, double* z,
-                    lapack_int* ldz, lapack_int* nzc, lapack_int* isuppz,
-                    lapack_logical* tryrac, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cstemr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_int* nzc, lapack_int* isuppz,
-                    lapack_logical* tryrac, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_zstemr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, lapack_int* m, double* w,
-                    lapack_complex_double* z, lapack_int* ldz, lapack_int* nzc,
-                    lapack_int* isuppz, lapack_logical* tryrac, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_sstedc( char* compz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dstedc( char* compz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cstedc( char* compz, lapack_int* n, float* d, float* e,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zstedc( char* compz, lapack_int* n, double* d, double* e,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sstegr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, lapack_int* isuppz, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_dstegr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, lapack_int* isuppz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_cstegr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_int* isuppz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_zstegr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_int* isuppz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_spteqr( char* compz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dpteqr( char* compz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_cpteqr( char* compz, lapack_int* n, float* d, float* e,
-                    lapack_complex_float* z, lapack_int* ldz, float* work,
-                    lapack_int *info );
-void LAPACK_zpteqr( char* compz, lapack_int* n, double* d, double* e,
-                    lapack_complex_double* z, lapack_int* ldz, double* work,
-                    lapack_int *info );
-void LAPACK_sstebz( char* range, char* order, lapack_int* n, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    const float* d, const float* e, lapack_int* m,
-                    lapack_int* nsplit, float* w, lapack_int* iblock,
-                    lapack_int* isplit, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dstebz( char* range, char* order, lapack_int* n, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    const double* d, const double* e, lapack_int* m,
-                    lapack_int* nsplit, double* w, lapack_int* iblock,
-                    lapack_int* isplit, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_sstein( lapack_int* n, const float* d, const float* e,
-                    lapack_int* m, const float* w, const lapack_int* iblock,
-                    const lapack_int* isplit, float* z, lapack_int* ldz,
-                    float* work, lapack_int* iwork, lapack_int* ifailv,
-                    lapack_int *info );
-void LAPACK_dstein( lapack_int* n, const double* d, const double* e,
-                    lapack_int* m, const double* w, const lapack_int* iblock,
-                    const lapack_int* isplit, double* z, lapack_int* ldz,
-                    double* work, lapack_int* iwork, lapack_int* ifailv,
-                    lapack_int *info );
-void LAPACK_cstein( lapack_int* n, const float* d, const float* e,
-                    lapack_int* m, const float* w, const lapack_int* iblock,
-                    const lapack_int* isplit, lapack_complex_float* z,
-                    lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifailv, lapack_int *info );
-void LAPACK_zstein( lapack_int* n, const double* d, const double* e,
-                    lapack_int* m, const double* w, const lapack_int* iblock,
-                    const lapack_int* isplit, lapack_complex_double* z,
-                    lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifailv, lapack_int *info );
-void LAPACK_sdisna( char* job, lapack_int* m, lapack_int* n, const float* d,
-                    float* sep, lapack_int *info );
-void LAPACK_ddisna( char* job, lapack_int* m, lapack_int* n, const double* d,
-                    double* sep, lapack_int *info );
-void LAPACK_ssygst( lapack_int* itype, char* uplo, lapack_int* n, float* a,
-                    lapack_int* lda, const float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dsygst( lapack_int* itype, char* uplo, lapack_int* n, double* a,
-                    lapack_int* lda, const double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_chegst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zhegst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_sspgst( lapack_int* itype, char* uplo, lapack_int* n, float* ap,
-                    const float* bp, lapack_int *info );
-void LAPACK_dspgst( lapack_int* itype, char* uplo, lapack_int* n, double* ap,
-                    const double* bp, lapack_int *info );
-void LAPACK_chpgst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, const lapack_complex_float* bp,
-                    lapack_int *info );
-void LAPACK_zhpgst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, const lapack_complex_double* bp,
-                    lapack_int *info );
-void LAPACK_ssbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, float* ab, lapack_int* ldab,
-                    const float* bb, lapack_int* ldbb, float* x,
-                    lapack_int* ldx, float* work, lapack_int *info );
-void LAPACK_dsbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, double* ab, lapack_int* ldab,
-                    const double* bb, lapack_int* ldbb, double* x,
-                    lapack_int* ldx, double* work, lapack_int *info );
-void LAPACK_chbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_complex_float* bb, lapack_int* ldbb,
-                    lapack_complex_float* x, lapack_int* ldx,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_complex_double* bb, lapack_int* ldbb,
-                    lapack_complex_double* x, lapack_int* ldx,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_spbstf( char* uplo, lapack_int* n, lapack_int* kb, float* bb,
-                    lapack_int* ldbb, lapack_int *info );
-void LAPACK_dpbstf( char* uplo, lapack_int* n, lapack_int* kb, double* bb,
-                    lapack_int* ldbb, lapack_int *info );
-void LAPACK_cpbstf( char* uplo, lapack_int* n, lapack_int* kb,
-                    lapack_complex_float* bb, lapack_int* ldbb,
-                    lapack_int *info );
-void LAPACK_zpbstf( char* uplo, lapack_int* n, lapack_int* kb,
-                    lapack_complex_double* bb, lapack_int* ldbb,
-                    lapack_int *info );
-void LAPACK_sgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a,
-                    lapack_int* lda, float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a,
-                    lapack_int* lda, double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sorghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, const float* a,
-                    lapack_int* lda, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dormhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, const double* a,
-                    lapack_int* lda, const double* tau, double* c,
-                    lapack_int* ldc, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zunghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* c,
-                    lapack_int* ldc, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zunmhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sgebal( char* job, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ilo, lapack_int* ihi, float* scale,
-                    lapack_int *info );
-void LAPACK_dgebal( char* job, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ilo, lapack_int* ihi, double* scale,
-                    lapack_int *info );
-void LAPACK_cgebal( char* job, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
-                    float* scale, lapack_int *info );
-void LAPACK_zgebal( char* job, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
-                    double* scale, lapack_int *info );
-void LAPACK_sgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* scale, lapack_int* m,
-                    float* v, lapack_int* ldv, lapack_int *info );
-void LAPACK_dgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* scale, lapack_int* m,
-                    double* v, lapack_int* ldv, lapack_int *info );
-void LAPACK_cgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* scale, lapack_int* m,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_zgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* scale, lapack_int* m,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_shseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, float* h, lapack_int* ldh, float* wr,
-                    float* wi, float* z, lapack_int* ldz, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dhseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, double* h, lapack_int* ldh, double* wr,
-                    double* wi, double* z, lapack_int* ldz, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_chseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_float* h, lapack_int* ldh,
-                    lapack_complex_float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zhseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_double* h, lapack_int* ldh,
-                    lapack_complex_double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_shsein( char* job, char* eigsrc, char* initv,
-                    lapack_logical* select, lapack_int* n, const float* h,
-                    lapack_int* ldh, float* wr, const float* wi, float* vl,
-                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, float* work,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_dhsein( char* job, char* eigsrc, char* initv,
-                    lapack_logical* select, lapack_int* n, const double* h,
-                    lapack_int* ldh, double* wr, const double* wi, double* vl,
-                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, double* work,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_chsein( char* job, char* eigsrc, char* initv,
-                    const lapack_logical* select, lapack_int* n,
-                    const lapack_complex_float* h, lapack_int* ldh,
-                    lapack_complex_float* w, lapack_complex_float* vl,
-                    lapack_int* ldvl, lapack_complex_float* vr,
-                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_zhsein( char* job, char* eigsrc, char* initv,
-                    const lapack_logical* select, lapack_int* n,
-                    const lapack_complex_double* h, lapack_int* ldh,
-                    lapack_complex_double* w, lapack_complex_double* vl,
-                    lapack_int* ldvl, lapack_complex_double* vr,
-                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_strevc( char* side, char* howmny, lapack_logical* select,
-                    lapack_int* n, const float* t, lapack_int* ldt, float* vl,
-                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, float* work,
-                    lapack_int *info );
-void LAPACK_dtrevc( char* side, char* howmny, lapack_logical* select,
-                    lapack_int* n, const double* t, lapack_int* ldt, double* vl,
-                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, double* work,
-                    lapack_int *info );
-void LAPACK_ctrevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* vl, lapack_int* ldvl,
-                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztrevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* vl, lapack_int* ldvl,
-                    lapack_complex_double* vr, lapack_int* ldvr, lapack_int* mm,
-                    lapack_int* m, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_strsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const float* t, lapack_int* ldt,
-                    const float* vl, lapack_int* ldvl, const float* vr,
-                    lapack_int* ldvr, float* s, float* sep, lapack_int* mm,
-                    lapack_int* m, float* work, lapack_int* ldwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtrsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const double* t, lapack_int* ldt,
-                    const double* vl, lapack_int* ldvl, const double* vr,
-                    lapack_int* ldvr, double* s, double* sep, lapack_int* mm,
-                    lapack_int* m, double* work, lapack_int* ldwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctrsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_float* t,
-                    lapack_int* ldt, const lapack_complex_float* vl,
-                    lapack_int* ldvl, const lapack_complex_float* vr,
-                    lapack_int* ldvr, float* s, float* sep, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work,
-                    lapack_int* ldwork, float* rwork, lapack_int *info );
-void LAPACK_ztrsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_double* t,
-                    lapack_int* ldt, const lapack_complex_double* vl,
-                    lapack_int* ldvl, const lapack_complex_double* vr,
-                    lapack_int* ldvr, double* s, double* sep, lapack_int* mm,
-                    lapack_int* m, lapack_complex_double* work,
-                    lapack_int* ldwork, double* rwork, lapack_int *info );
-void LAPACK_strexc( char* compq, lapack_int* n, float* t, lapack_int* ldt,
-                    float* q, lapack_int* ldq, lapack_int* ifst,
-                    lapack_int* ilst, float* work, lapack_int *info );
-void LAPACK_dtrexc( char* compq, lapack_int* n, double* t, lapack_int* ldt,
-                    double* q, lapack_int* ldq, lapack_int* ifst,
-                    lapack_int* ilst, double* work, lapack_int *info );
-void LAPACK_ctrexc( char* compq, lapack_int* n, lapack_complex_float* t,
-                    lapack_int* ldt, lapack_complex_float* q, lapack_int* ldq,
-                    lapack_int* ifst, lapack_int* ilst, lapack_int *info );
-void LAPACK_ztrexc( char* compq, lapack_int* n, lapack_complex_double* t,
-                    lapack_int* ldt, lapack_complex_double* q, lapack_int* ldq,
-                    lapack_int* ifst, lapack_int* ilst, lapack_int *info );
-void LAPACK_strsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, float* t, lapack_int* ldt, float* q,
-                    lapack_int* ldq, float* wr, float* wi, lapack_int* m,
-                    float* s, float* sep, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dtrsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, double* t, lapack_int* ldt, double* q,
-                    lapack_int* ldq, double* wr, double* wi, lapack_int* m,
-                    double* s, double* sep, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_ctrsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* w, lapack_int* m, float* s,
-                    float* sep, lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ztrsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* w, lapack_int* m, double* s,
-                    double* sep, lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_strsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const float* a, lapack_int* lda,
-                    const float* b, lapack_int* ldb, float* c, lapack_int* ldc,
-                    float* scale, lapack_int *info );
-void LAPACK_dtrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const double* a, lapack_int* lda,
-                    const double* b, lapack_int* ldb, double* c,
-                    lapack_int* ldc, double* scale, lapack_int *info );
-void LAPACK_ctrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* c, lapack_int* ldc,
-                    float* scale, lapack_int *info );
-void LAPACK_ztrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* c, lapack_int* ldc,
-                    double* scale, lapack_int *info );
-void LAPACK_sgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* q, lapack_int* ldq, float* z,
-                    lapack_int* ldz, lapack_int *info );
-void LAPACK_dgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* q, lapack_int* ldq, double* z,
-                    lapack_int* ldz, lapack_int *info );
-void LAPACK_cgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_int *info );
-void LAPACK_zgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_int *info );
-void LAPACK_sggbal( char* job, lapack_int* n, float* a, lapack_int* lda,
-                    float* b, lapack_int* ldb, lapack_int* ilo, lapack_int* ihi,
-                    float* lscale, float* rscale, float* work,
-                    lapack_int *info );
-void LAPACK_dggbal( char* job, lapack_int* n, double* a, lapack_int* lda,
-                    double* b, lapack_int* ldb, lapack_int* ilo,
-                    lapack_int* ihi, double* lscale, double* rscale,
-                    double* work, lapack_int *info );
-void LAPACK_cggbal( char* job, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int* ilo, lapack_int* ihi, float* lscale,
-                    float* rscale, float* work, lapack_int *info );
-void LAPACK_zggbal( char* job, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int* ilo, lapack_int* ihi, double* lscale,
-                    double* rscale, double* work, lapack_int *info );
-void LAPACK_sggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* lscale, const float* rscale,
-                    lapack_int* m, float* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_dggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* lscale, const double* rscale,
-                    lapack_int* m, double* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_cggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* lscale, const float* rscale,
-                    lapack_int* m, lapack_complex_float* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_zggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* lscale, const double* rscale,
-                    lapack_int* m, lapack_complex_double* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_shgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, float* h, lapack_int* ldh,
-                    float* t, lapack_int* ldt, float* alphar, float* alphai,
-                    float* beta, float* q, lapack_int* ldq, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dhgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, double* h,
-                    lapack_int* ldh, double* t, lapack_int* ldt, double* alphar,
-                    double* alphai, double* beta, double* q, lapack_int* ldq,
-                    double* z, lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_chgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, lapack_complex_float* h,
-                    lapack_int* ldh, lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, lapack_complex_double* h,
-                    lapack_int* ldh, lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_stgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const float* s, lapack_int* lds,
-                    const float* p, lapack_int* ldp, float* vl,
-                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, float* work,
-                    lapack_int *info );
-void LAPACK_dtgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const double* s, lapack_int* lds,
-                    const double* p, lapack_int* ldp, double* vl,
-                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, double* work,
-                    lapack_int *info );
-void LAPACK_ctgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_float* s,
-                    lapack_int* lds, const lapack_complex_float* p,
-                    lapack_int* ldp, lapack_complex_float* vl, lapack_int* ldvl,
-                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_double* s,
-                    lapack_int* lds, const lapack_complex_double* p,
-                    lapack_int* ldp, lapack_complex_double* vl,
-                    lapack_int* ldvl, lapack_complex_double* vr,
-                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* q, lapack_int* ldq, float* z, lapack_int* ldz,
-                    lapack_int* ifst, lapack_int* ilst, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dtgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* q, lapack_int* ldq, double* z, lapack_int* ldz,
-                    lapack_int* ifst, lapack_int* ilst, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_ctgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz, lapack_int* ifst,
-                    lapack_int* ilst, lapack_int *info );
-void LAPACK_ztgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz, lapack_int* ifst,
-                    lapack_int* ilst, lapack_int *info );
-void LAPACK_stgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* alphar, float* alphai, float* beta,
-                    float* q, lapack_int* ldq, float* z, lapack_int* ldz,
-                    lapack_int* m, float* pl, float* pr, float* dif,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dtgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* alphar, double* alphai,
-                    double* beta, double* q, lapack_int* ldq, double* z,
-                    lapack_int* ldz, lapack_int* m, double* pl, double* pr,
-                    double* dif, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_ctgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz, lapack_int* m,
-                    float* pl, float* pr, float* dif,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_ztgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz, lapack_int* m,
-                    double* pl, double* pr, double* dif,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_stgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const float* a, lapack_int* lda, const float* b,
-                    lapack_int* ldb, float* c, lapack_int* ldc, const float* d,
-                    lapack_int* ldd, const float* e, lapack_int* lde, float* f,
-                    lapack_int* ldf, float* scale, float* dif, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_dtgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const double* a, lapack_int* lda, const double* b,
-                    lapack_int* ldb, double* c, lapack_int* ldc,
-                    const double* d, lapack_int* ldd, const double* e,
-                    lapack_int* lde, double* f, lapack_int* ldf, double* scale,
-                    double* dif, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    const lapack_complex_float* d, lapack_int* ldd,
-                    const lapack_complex_float* e, lapack_int* lde,
-                    lapack_complex_float* f, lapack_int* ldf, float* scale,
-                    float* dif, lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ztgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    const lapack_complex_double* d, lapack_int* ldd,
-                    const lapack_complex_double* e, lapack_int* lde,
-                    lapack_complex_double* f, lapack_int* ldf, double* scale,
-                    double* dif, lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_stgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const float* a, lapack_int* lda,
-                    const float* b, lapack_int* ldb, const float* vl,
-                    lapack_int* ldvl, const float* vr, lapack_int* ldvr,
-                    float* s, float* dif, lapack_int* mm, lapack_int* m,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dtgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const double* a, lapack_int* lda,
-                    const double* b, lapack_int* ldb, const double* vl,
-                    lapack_int* ldvl, const double* vr, lapack_int* ldvr,
-                    double* s, double* dif, lapack_int* mm, lapack_int* m,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* b,
-                    lapack_int* ldb, const lapack_complex_float* vl,
-                    lapack_int* ldvl, const lapack_complex_float* vr,
-                    lapack_int* ldvr, float* s, float* dif, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_ztgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* b,
-                    lapack_int* ldb, const lapack_complex_double* vl,
-                    lapack_int* ldvl, const lapack_complex_double* vr,
-                    lapack_int* ldvr, double* s, double* dif, lapack_int* mm,
-                    lapack_int* m, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_sggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, float* a, lapack_int* lda,
-                    float* b, lapack_int* ldb, float* tola, float* tolb,
-                    lapack_int* k, lapack_int* l, float* u, lapack_int* ldu,
-                    float* v, lapack_int* ldv, float* q, lapack_int* ldq,
-                    lapack_int* iwork, float* tau, float* work,
-                    lapack_int *info );
-void LAPACK_dggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, double* a, lapack_int* lda,
-                    double* b, lapack_int* ldb, double* tola, double* tolb,
-                    lapack_int* k, lapack_int* l, double* u, lapack_int* ldu,
-                    double* v, lapack_int* ldv, double* q, lapack_int* ldq,
-                    lapack_int* iwork, double* tau, double* work,
-                    lapack_int *info );
-void LAPACK_cggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                    float* tola, float* tolb, lapack_int* k, lapack_int* l,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_complex_float* q, lapack_int* ldq, lapack_int* iwork,
-                    float* rwork, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                    double* tola, double* tolb, lapack_int* k, lapack_int* l,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_int* iwork, double* rwork,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_stgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* tola, float* tolb, float* alpha, float* beta,
-                    float* u, lapack_int* ldu, float* v, lapack_int* ldv,
-                    float* q, lapack_int* ldq, float* work, lapack_int* ncycle,
-                    lapack_int *info );
-void LAPACK_dtgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* tola, double* tolb, double* alpha, double* beta,
-                    double* u, lapack_int* ldu, double* v, lapack_int* ldv,
-                    double* q, lapack_int* ldq, double* work,
-                    lapack_int* ncycle, lapack_int *info );
-void LAPACK_ctgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* tola,
-                    float* tolb, float* alpha, float* beta,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* work, lapack_int* ncycle,
-                    lapack_int *info );
-void LAPACK_ztgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* tola,
-                    double* tolb, double* alpha, double* beta,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* work, lapack_int* ncycle,
-                    lapack_int *info );
-void LAPACK_sgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                   float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                   double* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_cgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_zgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_sgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb,
-                    lapack_int* jpvt, float* rcond, lapack_int* rank,
-                    float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb,
-                    lapack_int* jpvt, double* rcond, lapack_int* rank,
-                    double* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_cgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, lapack_int* jpvt,
-                    float* rcond, lapack_int* rank, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int *info );
-void LAPACK_zgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, lapack_int* jpvt,
-                    double* rcond, lapack_int* rank,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int *info );
-void LAPACK_zgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_dgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_zgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgglse( lapack_int* m, lapack_int* n, lapack_int* p, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* c,
-                    float* d, float* x, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgglse( lapack_int* m, lapack_int* n, lapack_int* p, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* c,
-                    double* d, double* x, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgglse( lapack_int* m, lapack_int* n, lapack_int* p,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* c, lapack_complex_float* d,
-                    lapack_complex_float* x, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zgglse( lapack_int* m, lapack_int* n, lapack_int* p,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* c, lapack_complex_double* d,
-                    lapack_complex_double* x, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sggglm( lapack_int* n, lapack_int* m, lapack_int* p, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* d,
-                    float* x, float* y, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dggglm( lapack_int* n, lapack_int* m, lapack_int* p, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* d,
-                    double* x, double* y, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cggglm( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* d, lapack_complex_float* x,
-                    lapack_complex_float* y, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zggglm( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* d, lapack_complex_double* x,
-                    lapack_complex_double* y, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_ssyev( char* jobz, char* uplo, lapack_int* n, float* a,
-                   lapack_int* lda, float* w, float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_dsyev( char* jobz, char* uplo, lapack_int* n, double* a,
-                   lapack_int* lda, double* w, double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_cheev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda, float* w,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zheev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda, double* w,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_int *info );
-void LAPACK_ssyevd( char* jobz, char* uplo, lapack_int* n, float* a,
-                    lapack_int* lda, float* w, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dsyevd( char* jobz, char* uplo, lapack_int* n, double* a,
-                    lapack_int* lda, double* w, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cheevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* w,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zheevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* w,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssyevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    float* a, lapack_int* lda, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dsyevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    double* a, lapack_int* lda, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, double* z, lapack_int* ldz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_cheevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_zheevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_ssyevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    float* a, lapack_int* lda, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    lapack_int* isuppz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dsyevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    double* a, lapack_int* lda, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, double* z, lapack_int* ldz,
-                    lapack_int* isuppz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cheevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_int* isuppz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zheevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_int* isuppz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sspev( char* jobz, char* uplo, lapack_int* n, float* ap, float* w,
-                   float* z, lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dspev( char* jobz, char* uplo, lapack_int* n, double* ap, double* w,
-                   double* z, lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_chpev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* ap, float* w, lapack_complex_float* z,
-                   lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                   lapack_int *info );
-void LAPACK_zhpev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* ap, double* w,
-                   lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_sspevd( char* jobz, char* uplo, lapack_int* n, float* ap, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dspevd( char* jobz, char* uplo, lapack_int* n, double* ap,
-                    double* w, double* z, lapack_int* ldz, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_chpevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* lrwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_zhpevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sspevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    float* ap, float* vl, float* vu, lapack_int* il,
-                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dspevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    double* ap, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_chpevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_zhpevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_ssbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   float* ab, lapack_int* ldab, float* w, float* z,
-                   lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dsbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   double* ab, lapack_int* ldab, double* w, double* z,
-                   lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_chbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   lapack_complex_float* ab, lapack_int* ldab, float* w,
-                   lapack_complex_float* z, lapack_int* ldz,
-                   lapack_complex_float* work, float* rwork, lapack_int *info );
-void LAPACK_zhbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   lapack_complex_double* ab, lapack_int* ldab, double* w,
-                   lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_ssbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    float* ab, lapack_int* ldab, float* w, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dsbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    double* ab, lapack_int* ldab, double* w, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_chbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_float* ab, lapack_int* ldab, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_double* ab, lapack_int* ldab, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, float* ab, lapack_int* ldab, float* q,
-                    lapack_int* ldq, float* vl, float* vu, lapack_int* il,
-                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dsbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, double* ab, lapack_int* ldab, double* q,
-                    lapack_int* ldq, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_chbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* q, lapack_int* ldq, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_zhbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* q, lapack_int* ldq, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_sstev( char* jobz, lapack_int* n, float* d, float* e, float* z,
-                   lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dstev( char* jobz, lapack_int* n, double* d, double* e, double* z,
-                   lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_sstevd( char* jobz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dstevd( char* jobz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_sstevx( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dstevx( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_sstevr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, lapack_int* isuppz, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_dstevr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, lapack_int* isuppz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sgees( char* jobvs, char* sort, LAPACK_S_SELECT2 select,
-                   lapack_int* n, float* a, lapack_int* lda, lapack_int* sdim,
-                   float* wr, float* wi, float* vs, lapack_int* ldvs,
-                   float* work, lapack_int* lwork, lapack_logical* bwork,
-                   lapack_int *info );
-void LAPACK_dgees( char* jobvs, char* sort, LAPACK_D_SELECT2 select,
-                   lapack_int* n, double* a, lapack_int* lda, lapack_int* sdim,
-                   double* wr, double* wi, double* vs, lapack_int* ldvs,
-                   double* work, lapack_int* lwork, lapack_logical* bwork,
-                   lapack_int *info );
-void LAPACK_cgees( char* jobvs, char* sort, LAPACK_C_SELECT1 select,
-                   lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                   lapack_int* sdim, lapack_complex_float* w,
-                   lapack_complex_float* vs, lapack_int* ldvs,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_logical* bwork, lapack_int *info );
-void LAPACK_zgees( char* jobvs, char* sort, LAPACK_Z_SELECT1 select,
-                   lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                   lapack_int* sdim, lapack_complex_double* w,
-                   lapack_complex_double* vs, lapack_int* ldvs,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_logical* bwork, lapack_int *info );
-void LAPACK_sgeesx( char* jobvs, char* sort, LAPACK_S_SELECT2 select,
-                    char* sense, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* sdim, float* wr, float* wi, float* vs,
-                    lapack_int* ldvs, float* rconde, float* rcondv, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_logical* bwork, lapack_int *info );
-void LAPACK_dgeesx( char* jobvs, char* sort, LAPACK_D_SELECT2 select,
-                    char* sense, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* sdim, double* wr, double* wi, double* vs,
-                    lapack_int* ldvs, double* rconde, double* rcondv,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_cgeesx( char* jobvs, char* sort, LAPACK_C_SELECT1 select,
-                    char* sense, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* sdim, lapack_complex_float* w,
-                    lapack_complex_float* vs, lapack_int* ldvs, float* rconde,
-                    float* rcondv, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_zgeesx( char* jobvs, char* sort, LAPACK_Z_SELECT1 select,
-                    char* sense, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* sdim, lapack_complex_double* w,
-                    lapack_complex_double* vs, lapack_int* ldvs, double* rconde,
-                    double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_sgeev( char* jobvl, char* jobvr, lapack_int* n, float* a,
-                   lapack_int* lda, float* wr, float* wi, float* vl,
-                   lapack_int* ldvl, float* vr, lapack_int* ldvr, float* work,
-                   lapack_int* lwork, lapack_int *info );
-void LAPACK_dgeev( char* jobvl, char* jobvr, lapack_int* n, double* a,
-                   lapack_int* lda, double* wr, double* wi, double* vl,
-                   lapack_int* ldvl, double* vr, lapack_int* ldvr, double* work,
-                   lapack_int* lwork, lapack_int *info );
-void LAPACK_cgeev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* w, lapack_complex_float* vl,
-                   lapack_int* ldvl, lapack_complex_float* vr, lapack_int* ldvr,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zgeev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* w, lapack_complex_double* vl,
-                   lapack_int* ldvl, lapack_complex_double* vr,
-                   lapack_int* ldvr, lapack_complex_double* work,
-                   lapack_int* lwork, double* rwork, lapack_int *info );
-void LAPACK_sgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, float* a, lapack_int* lda, float* wr,
-                    float* wi, float* vl, lapack_int* ldvl, float* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    float* scale, float* abnrm, float* rconde, float* rcondv,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, double* a, lapack_int* lda, double* wr,
-                    double* wi, double* vl, lapack_int* ldvl, double* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    double* scale, double* abnrm, double* rconde,
-                    double* rcondv, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* w, lapack_complex_float* vl,
-                    lapack_int* ldvl, lapack_complex_float* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    float* scale, float* abnrm, float* rconde, float* rcondv,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* w, lapack_complex_double* vl,
-                    lapack_int* ldvl, lapack_complex_double* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    double* scale, double* abnrm, double* rconde,
-                    double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int *info );
-void LAPACK_sgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    float* a, lapack_int* lda, float* s, float* u,
-                    lapack_int* ldu, float* vt, lapack_int* ldvt, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    double* a, lapack_int* lda, double* s, double* u,
-                    lapack_int* ldu, double* vt, lapack_int* ldvt, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* s,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* vt, lapack_int* ldvt,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* s,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* vt, lapack_int* ldvt,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgesdd( char* jobz, lapack_int* m, lapack_int* n, float* a,
-                    lapack_int* lda, float* s, float* u, lapack_int* ldu,
-                    float* vt, lapack_int* ldvt, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dgesdd( char* jobz, lapack_int* m, lapack_int* n, double* a,
-                    lapack_int* lda, double* s, double* u, lapack_int* ldu,
-                    double* vt, lapack_int* ldvt, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgesdd( char* jobz, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* s,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* vt, lapack_int* ldvt,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_zgesdd( char* jobz, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* s,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* vt, lapack_int* ldvt,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_dgejsv( char* joba, char* jobu, char* jobv, char* jobr, char* jobt,
-                    char* jobp, lapack_int* m, lapack_int* n, double* a,
-                    lapack_int* lda, double* sva, double* u, lapack_int* ldu,
-                    double* v, lapack_int* ldv, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_sgejsv( char* joba, char* jobu, char* jobv, char* jobr, char* jobt,
-                    char* jobp, lapack_int* m, lapack_int* n, float* a,
-                    lapack_int* lda, float* sva, float* u, lapack_int* ldu,
-                    float* v, lapack_int* ldv, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dgesvj( char* joba, char* jobu, char* jobv, lapack_int* m,
-                    lapack_int* n, double* a, lapack_int* lda, double* sva,
-                    lapack_int* mv, double* v, lapack_int* ldv, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sgesvj( char* joba, char* jobu, char* jobv, lapack_int* m,
-                    lapack_int* n, float* a, lapack_int* lda, float* sva,
-                    lapack_int* mv, float* v, lapack_int* ldv, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* alpha, float* beta, float* u, lapack_int* ldu,
-                    float* v, lapack_int* ldv, float* q, lapack_int* ldq,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* alpha, double* beta, double* u, lapack_int* ldu,
-                    double* v, lapack_int* ldv, double* q, lapack_int* ldq,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* alpha,
-                    float* beta, lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* work, float* rwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_zggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* alpha,
-                    double* beta, lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ssygv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                   float* w, float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dsygv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                   double* w, double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_chegv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb, float* w,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zhegv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb, double* w,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_int *info );
-void LAPACK_ssygvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* w, float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dsygvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* w, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_chegvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* w,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhegvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* w,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssygvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* vl, float* vu, lapack_int* il,
-                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_dsygvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_chegvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_zhegvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_sspgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   float* ap, float* bp, float* w, float* z, lapack_int* ldz,
-                   float* work, lapack_int *info );
-void LAPACK_dspgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   double* ap, double* bp, double* w, double* z,
-                   lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_chpgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* ap, lapack_complex_float* bp, float* w,
-                   lapack_complex_float* z, lapack_int* ldz,
-                   lapack_complex_float* work, float* rwork, lapack_int *info );
-void LAPACK_zhpgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* ap, lapack_complex_double* bp,
-                   double* w, lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_sspgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    float* ap, float* bp, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dspgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    double* ap, double* bp, double* w, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_chpgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, lapack_complex_float* bp,
-                    float* w, lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhpgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, lapack_complex_double* bp,
-                    double* w, lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sspgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, float* ap, float* bp, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* iwork, lapack_int* ifail,
-                    lapack_int *info );
-void LAPACK_dspgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, double* ap, double* bp, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, double* z, lapack_int* ldz,
-                    double* work, lapack_int* iwork, lapack_int* ifail,
-                    lapack_int *info );
-void LAPACK_chpgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_float* ap,
-                    lapack_complex_float* bp, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_zhpgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_double* ap,
-                    lapack_complex_double* bp, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_ssbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, float* ab, lapack_int* ldab, float* bb,
-                   lapack_int* ldbb, float* w, float* z, lapack_int* ldz,
-                   float* work, lapack_int *info );
-void LAPACK_dsbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, double* ab, lapack_int* ldab, double* bb,
-                   lapack_int* ldbb, double* w, double* z, lapack_int* ldz,
-                   double* work, lapack_int *info );
-void LAPACK_chbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
-                   lapack_complex_float* bb, lapack_int* ldbb, float* w,
-                   lapack_complex_float* z, lapack_int* ldz,
-                   lapack_complex_float* work, float* rwork, lapack_int *info );
-void LAPACK_zhbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
-                   lapack_complex_double* bb, lapack_int* ldbb, double* w,
-                   lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_ssbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, float* ab, lapack_int* ldab, float* bb,
-                    lapack_int* ldbb, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dsbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, double* ab, lapack_int* ldab, double* bb,
-                    lapack_int* ldbb, double* w, double* z, lapack_int* ldz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_chbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* bb, lapack_int* ldbb, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* bb, lapack_int* ldbb, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, float* ab, lapack_int* ldab,
-                    float* bb, lapack_int* ldbb, float* q, lapack_int* ldq,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dsbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, double* ab,
-                    lapack_int* ldab, double* bb, lapack_int* ldbb, double* q,
-                    lapack_int* ldq, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_chbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, lapack_complex_float* ab,
-                    lapack_int* ldab, lapack_complex_float* bb,
-                    lapack_int* ldbb, lapack_complex_float* q, lapack_int* ldq,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, float* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_zhbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, lapack_complex_double* ab,
-                    lapack_int* ldab, lapack_complex_double* bb,
-                    lapack_int* ldbb, lapack_complex_double* q, lapack_int* ldq,
-                    double* vl, double* vu, lapack_int* il, lapack_int* iu,
-                    double* abstol, lapack_int* m, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_sgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_S_SELECT3 selctg, lapack_int* n, float* a,
-                   lapack_int* lda, float* b, lapack_int* ldb, lapack_int* sdim,
-                   float* alphar, float* alphai, float* beta, float* vsl,
-                   lapack_int* ldvsl, float* vsr, lapack_int* ldvsr,
-                   float* work, lapack_int* lwork, lapack_logical* bwork,
-                   lapack_int *info );
-void LAPACK_dgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_D_SELECT3 selctg, lapack_int* n, double* a,
-                   lapack_int* lda, double* b, lapack_int* ldb,
-                   lapack_int* sdim, double* alphar, double* alphai,
-                   double* beta, double* vsl, lapack_int* ldvsl, double* vsr,
-                   lapack_int* ldvsr, double* work, lapack_int* lwork,
-                   lapack_logical* bwork, lapack_int *info );
-void LAPACK_cgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_C_SELECT2 selctg, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
-                   lapack_complex_float* alpha, lapack_complex_float* beta,
-                   lapack_complex_float* vsl, lapack_int* ldvsl,
-                   lapack_complex_float* vsr, lapack_int* ldvsr,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_logical* bwork, lapack_int *info );
-void LAPACK_zgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_Z_SELECT2 selctg, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb, lapack_int* sdim,
-                   lapack_complex_double* alpha, lapack_complex_double* beta,
-                   lapack_complex_double* vsl, lapack_int* ldvsl,
-                   lapack_complex_double* vsr, lapack_int* ldvsr,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_logical* bwork, lapack_int *info );
-void LAPACK_sggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_S_SELECT3 selctg, char* sense, lapack_int* n,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    lapack_int* sdim, float* alphar, float* alphai, float* beta,
-                    float* vsl, lapack_int* ldvsl, float* vsr,
-                    lapack_int* ldvsr, float* rconde, float* rcondv,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_dggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_D_SELECT3 selctg, char* sense, lapack_int* n,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    lapack_int* sdim, double* alphar, double* alphai,
-                    double* beta, double* vsl, lapack_int* ldvsl, double* vsr,
-                    lapack_int* ldvsr, double* rconde, double* rcondv,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_cggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_C_SELECT2 selctg, char* sense, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* vsl, lapack_int* ldvsl,
-                    lapack_complex_float* vsr, lapack_int* ldvsr, float* rconde,
-                    float* rcondv, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_zggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_Z_SELECT2 selctg, char* sense, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, lapack_int* sdim,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* vsl, lapack_int* ldvsl,
-                    lapack_complex_double* vsr, lapack_int* ldvsr,
-                    double* rconde, double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_sggev( char* jobvl, char* jobvr, lapack_int* n, float* a,
-                   lapack_int* lda, float* b, lapack_int* ldb, float* alphar,
-                   float* alphai, float* beta, float* vl, lapack_int* ldvl,
-                   float* vr, lapack_int* ldvr, float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_dggev( char* jobvl, char* jobvr, lapack_int* n, double* a,
-                   lapack_int* lda, double* b, lapack_int* ldb, double* alphar,
-                   double* alphai, double* beta, double* vl, lapack_int* ldvl,
-                   double* vr, lapack_int* ldvr, double* work,
-                   lapack_int* lwork, lapack_int *info );
-void LAPACK_cggev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* alpha, lapack_complex_float* beta,
-                   lapack_complex_float* vl, lapack_int* ldvl,
-                   lapack_complex_float* vr, lapack_int* ldvr,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zggev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* alpha, lapack_complex_double* beta,
-                   lapack_complex_double* vl, lapack_int* ldvl,
-                   lapack_complex_double* vr, lapack_int* ldvr,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_int *info );
-void LAPACK_sggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* alphar, float* alphai, float* beta,
-                    float* vl, lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* ilo, lapack_int* ihi, float* lscale,
-                    float* rscale, float* abnrm, float* bbnrm, float* rconde,
-                    float* rcondv, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_dggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* alphar, double* alphai,
-                    double* beta, double* vl, lapack_int* ldvl, double* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    double* lscale, double* rscale, double* abnrm,
-                    double* bbnrm, double* rconde, double* rcondv, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_cggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* vl, lapack_int* ldvl,
-                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* ilo,
-                    lapack_int* ihi, float* lscale, float* rscale, float* abnrm,
-                    float* bbnrm, float* rconde, float* rcondv,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* iwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_zggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* vl, lapack_int* ldvl,
-                    lapack_complex_double* vr, lapack_int* ldvr,
-                    lapack_int* ilo, lapack_int* ihi, double* lscale,
-                    double* rscale, double* abnrm, double* bbnrm,
-                    double* rconde, double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_logical* bwork, lapack_int *info );
-void LAPACK_dsfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, double* alpha, const double* a,
-                   lapack_int* lda, double* beta, double* c );
-void LAPACK_ssfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, float* alpha, const float* a, lapack_int* lda,
-                   float* beta, float* c );
-void LAPACK_zhfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, double* alpha, const lapack_complex_double* a,
-                   lapack_int* lda, double* beta, lapack_complex_double* c );
-void LAPACK_chfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, float* alpha, const lapack_complex_float* a,
-                   lapack_int* lda, float* beta, lapack_complex_float* c );
-void LAPACK_dtfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n, double* alpha,
-                   const double* a, double* b, lapack_int* ldb );
-void LAPACK_stfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n, float* alpha,
-                   const float* a, float* b, lapack_int* ldb );
-void LAPACK_ztfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n,
-                   lapack_complex_double* alpha, const lapack_complex_double* a,
-                   lapack_complex_double* b, lapack_int* ldb );
-void LAPACK_ctfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n,
-                   lapack_complex_float* alpha, const lapack_complex_float* a,
-                   lapack_complex_float* b, lapack_int* ldb );
-void LAPACK_dtfttp( char* transr, char* uplo, lapack_int* n, const double* arf,
-                    double* ap, lapack_int *info );
-void LAPACK_stfttp( char* transr, char* uplo, lapack_int* n, const float* arf,
-                    float* ap, lapack_int *info );
-void LAPACK_ztfttp( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* arf, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_ctfttp( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* arf, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_dtfttr( char* transr, char* uplo, lapack_int* n, const double* arf,
-                    double* a, lapack_int* lda, lapack_int *info );
-void LAPACK_stfttr( char* transr, char* uplo, lapack_int* n, const float* arf,
-                    float* a, lapack_int* lda, lapack_int *info );
-void LAPACK_ztfttr( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* arf, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_ctfttr( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* arf, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dtpttf( char* transr, char* uplo, lapack_int* n, const double* ap,
-                    double* arf, lapack_int *info );
-void LAPACK_stpttf( char* transr, char* uplo, lapack_int* n, const float* ap,
-                    float* arf, lapack_int *info );
-void LAPACK_ztpttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* ap, lapack_complex_double* arf,
-                    lapack_int *info );
-void LAPACK_ctpttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* ap, lapack_complex_float* arf,
-                    lapack_int *info );
-void LAPACK_dtpttr( char* uplo, lapack_int* n, const double* ap, double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_stpttr( char* uplo, lapack_int* n, const float* ap, float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_ztpttr( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_ctpttr( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dtrttf( char* transr, char* uplo, lapack_int* n, const double* a,
-                    lapack_int* lda, double* arf, lapack_int *info );
-void LAPACK_strttf( char* transr, char* uplo, lapack_int* n, const float* a,
-                    lapack_int* lda, float* arf, lapack_int *info );
-void LAPACK_ztrttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* arf, lapack_int *info );
-void LAPACK_ctrttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* arf, lapack_int *info );
-void LAPACK_dtrttp( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
-                    double* ap, lapack_int *info );
-void LAPACK_strttp( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                    float* ap, lapack_int *info );
-void LAPACK_ztrttp( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_ctrttp( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_sgeqrfp( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* tau, float* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_dgeqrfp( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* tau, double* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_cgeqrfp( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* tau,
-                     lapack_complex_float* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_zgeqrfp( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* tau,
-                     lapack_complex_double* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_clacgv( lapack_int* n, lapack_complex_float* x, lapack_int* incx );
-void LAPACK_zlacgv( lapack_int* n, lapack_complex_double* x, lapack_int* incx );
-void LAPACK_slarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    float* x );
-void LAPACK_dlarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    double* x );
-void LAPACK_clarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    lapack_complex_float* x );
-void LAPACK_zlarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    lapack_complex_double* x );
-void LAPACK_sgeqr2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int *info );
-void LAPACK_dgeqr2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int *info );
-void LAPACK_cgeqr2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgeqr2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slacpy( char* uplo, lapack_int* m, lapack_int* n, const float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb );
-void LAPACK_dlacpy( char* uplo, lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb );
-void LAPACK_clacpy( char* uplo, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb );
-void LAPACK_zlacpy( char* uplo, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb );
-void LAPACK_sgetf2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgetf2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgetf2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_zgetf2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_slaswp( lapack_int* n, float* a, lapack_int* lda, lapack_int* k1,
-                    lapack_int* k2, const lapack_int* ipiv, lapack_int* incx );
-void LAPACK_dlaswp( lapack_int* n, double* a, lapack_int* lda, lapack_int* k1,
-                    lapack_int* k2, const lapack_int* ipiv, lapack_int* incx );
-void LAPACK_claswp( lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
-                    lapack_int* incx );
-void LAPACK_zlaswp( lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
-                    lapack_int* incx );
-float LAPACK_slange( char* norm, lapack_int* m, lapack_int* n, const float* a,
-                    lapack_int* lda, float* work );
-double LAPACK_dlange( char* norm, lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, double* work );
-float LAPACK_clange( char* norm, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda, float* work );
-double LAPACK_zlange( char* norm, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* work );
-float LAPACK_clanhe( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda, float* work );
-double LAPACK_zlanhe( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* work );
-float LAPACK_slansy( char* norm, char* uplo, lapack_int* n, const float* a,
-                    lapack_int* lda, float* work );
-double LAPACK_dlansy( char* norm, char* uplo, lapack_int* n, const double* a,
-                    lapack_int* lda, double* work );
-float LAPACK_clansy( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda, float* work );
-double LAPACK_zlansy( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* work );
-float LAPACK_slantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const float* a, lapack_int* lda, float* work );
-double LAPACK_dlantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const double* a, lapack_int* lda, double* work );
-float LAPACK_clantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* a, lapack_int* lda,
-                    float* work );
-double LAPACK_zlantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
-                    double* work );
-float LAPACK_slamch( char* cmach );
-double LAPACK_dlamch( char* cmach );
-void LAPACK_sgelq2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int *info );
-void LAPACK_dgelq2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int *info );
-void LAPACK_cgelq2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgelq2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, const float* v,
-                    lapack_int* ldv, const float* t, lapack_int* ldt, float* c,
-                    lapack_int* ldc, float* work, lapack_int* ldwork );
-void LAPACK_dlarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k,
-                    const double* v, lapack_int* ldv, const double* t,
-                    lapack_int* ldt, double* c, lapack_int* ldc, double* work,
-                    lapack_int* ldwork );
-void LAPACK_clarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k,
-                    const lapack_complex_float* v, lapack_int* ldv,
-                    const lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* ldwork );
-void LAPACK_zlarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k,
-                    const lapack_complex_double* v, lapack_int* ldv,
-                    const lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* ldwork );
-void LAPACK_slarfg( lapack_int* n, float* alpha, float* x, lapack_int* incx,
-                    float* tau );
-void LAPACK_dlarfg( lapack_int* n, double* alpha, double* x, lapack_int* incx,
-                    double* tau );
-void LAPACK_clarfg( lapack_int* n, lapack_complex_float* alpha,
-                    lapack_complex_float* x, lapack_int* incx,
-                    lapack_complex_float* tau );
-void LAPACK_zlarfg( lapack_int* n, lapack_complex_double* alpha,
-                    lapack_complex_double* x, lapack_int* incx,
-                    lapack_complex_double* tau );
-void LAPACK_slarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const float* v, lapack_int* ldv, const float* tau, float* t,
-                    lapack_int* ldt );
-void LAPACK_dlarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const double* v, lapack_int* ldv, const double* tau,
-                    double* t, lapack_int* ldt );
-void LAPACK_clarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const lapack_complex_float* v, lapack_int* ldv,
-                    const lapack_complex_float* tau, lapack_complex_float* t,
-                    lapack_int* ldt );
-void LAPACK_zlarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const lapack_complex_double* v, lapack_int* ldv,
-                    const lapack_complex_double* tau, lapack_complex_double* t,
-                    lapack_int* ldt );
-void LAPACK_slarfx( char* side, lapack_int* m, lapack_int* n, const float* v,
-                    float* tau, float* c, lapack_int* ldc, float* work );
-void LAPACK_dlarfx( char* side, lapack_int* m, lapack_int* n, const double* v,
-                    double* tau, double* c, lapack_int* ldc, double* work );
-void LAPACK_clarfx( char* side, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* v, lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work );
-void LAPACK_zlarfx( char* side, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* v, lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work );
-void LAPACK_slatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, float* d, lapack_int* mode, float* cond,
-                    float* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    float* a, lapack_int* lda, float* work, lapack_int *info );
-void LAPACK_dlatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, double* d, lapack_int* mode, double* cond,
-                    double* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    double* a, lapack_int* lda, double* work,
-                    lapack_int *info );
-void LAPACK_clatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, float* d, lapack_int* mode, float* cond,
-                    float* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, double* d, lapack_int* mode, double* cond,
-                    double* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slag2d( lapack_int* m, lapack_int* n, const float* sa,
-                    lapack_int* ldsa, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dlag2s( lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, float* sa, lapack_int* ldsa,
-                    lapack_int *info );
-void LAPACK_clag2z( lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* sa, lapack_int* ldsa,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_zlag2c( lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_float* sa, lapack_int* ldsa,
-                    lapack_int *info );
-void LAPACK_slauum( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dlauum( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_clauum( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_zlauum( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_slagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const float* d, float* a, lapack_int* lda,
-                    lapack_int* iseed, float* work, lapack_int *info );
-void LAPACK_dlagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const double* d, double* a, lapack_int* lda,
-                    lapack_int* iseed, double* work, lapack_int *info );
-void LAPACK_clagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const float* d, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const double* d, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slaset( char* uplo, lapack_int* m, lapack_int* n, float* alpha,
-                    float* beta, float* a, lapack_int* lda );
-void LAPACK_dlaset( char* uplo, lapack_int* m, lapack_int* n, double* alpha,
-                    double* beta, double* a, lapack_int* lda );
-void LAPACK_claset( char* uplo, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* a, lapack_int* lda );
-void LAPACK_zlaset( char* uplo, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* a, lapack_int* lda );
-void LAPACK_slasrt( char* id, lapack_int* n, float* d, lapack_int *info );
-void LAPACK_dlasrt( char* id, lapack_int* n, double* d, lapack_int *info );
-void LAPACK_claghe( lapack_int* n, lapack_int* k, const float* d,
-                    lapack_complex_float* a, lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlaghe( lapack_int* n, lapack_int* k, const double* d,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int* iseed, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_slagsy( lapack_int* n, lapack_int* k, const float* d, float* a,
-                    lapack_int* lda, lapack_int* iseed, float* work,
-                    lapack_int *info );
-void LAPACK_dlagsy( lapack_int* n, lapack_int* k, const double* d, double* a,
-                    lapack_int* lda, lapack_int* iseed, double* work,
-                    lapack_int *info );
-void LAPACK_clagsy( lapack_int* n, lapack_int* k, const float* d,
-                    lapack_complex_float* a, lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlagsy( lapack_int* n, lapack_int* k, const double* d,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int* iseed, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_slapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    float* x, lapack_int* ldx, lapack_int* k );
-void LAPACK_dlapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    double* x, lapack_int* ldx, lapack_int* k );
-void LAPACK_clapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* x, lapack_int* ldx, lapack_int* k );
-void LAPACK_zlapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* x, lapack_int* ldx, lapack_int* k );
-float LAPACK_slapy2( float* x, float* y );
-double LAPACK_dlapy2( double* x, double* y );
-float LAPACK_slapy3( float* x, float* y, float* z );
-double LAPACK_dlapy3( double* x, double* y, double* z );
-void LAPACK_slartgp( float* f, float* g, float* cs, float* sn, float* r );
-void LAPACK_dlartgp( double* f, double* g, double* cs, double* sn, double* r );
-void LAPACK_slartgs( float* x, float* y, float* sigma, float* cs, float* sn );
-void LAPACK_dlartgs( double* x, double* y, double* sigma, double* cs,
-                     double* sn );
-// LAPACK 3.3.0
-void LAPACK_cbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    float* theta, float* phi,
-                    lapack_complex_float* u1, lapack_int* ldu1,
-                    lapack_complex_float* u2, lapack_int* ldu2,
-                    lapack_complex_float* v1t, lapack_int* ldv1t,
-                    lapack_complex_float* v2t, lapack_int* ldv2t,
-                    float* b11d, float* b11e, float* b12d,
-                    float* b12e, float* b21d, float* b21e,
-                    float* b22d, float* b22e, float* rwork,
-                    lapack_int* lrwork , lapack_int *info );
-void LAPACK_cheswapr( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_chetri2( char* uplo, lapack_int* n,
-                     lapack_complex_float* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_chetri2x( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_float* work, lapack_int* nb , lapack_int *info );
-void LAPACK_chetrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const lapack_complex_float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* work , lapack_int *info );
-void LAPACK_csyconv( char* uplo, char* way,
-                     lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_float* work , lapack_int *info );
-void LAPACK_csyswapr( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_csytri2( char* uplo, lapack_int* n,
-                     lapack_complex_float* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_csytri2x( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_float* work, lapack_int* nb , lapack_int *info );
-void LAPACK_csytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const lapack_complex_float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* work , lapack_int *info );
-void LAPACK_cunbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    lapack_complex_float* x11, lapack_int* ldx11,
-                    lapack_complex_float* x12, lapack_int* ldx12,
-                    lapack_complex_float* x21, lapack_int* ldx21,
-                    lapack_complex_float* x22, lapack_int* ldx22,
-                    float* theta, float* phi,
-                    lapack_complex_float* taup1,
-                    lapack_complex_float* taup2,
-                    lapack_complex_float* tauq1,
-                    lapack_complex_float* tauq2,
-                    lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_cuncsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, lapack_complex_float* x11,
-                    lapack_int* ldx11, lapack_complex_float* x12,
-                    lapack_int* ldx12, lapack_complex_float* x21,
-                    lapack_int* ldx21, lapack_complex_float* x22,
-                    lapack_int* ldx22, float* theta,
-                    lapack_complex_float* u1, lapack_int* ldu1,
-                    lapack_complex_float* u2, lapack_int* ldu2,
-                    lapack_complex_float* v1t, lapack_int* ldv1t,
-                    lapack_complex_float* v2t, lapack_int* ldv2t,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    float* rwork, lapack_int* lrwork,
-                    lapack_int* iwork , lapack_int *info );
-void LAPACK_dbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    double* theta, double* phi, double* u1,
-                    lapack_int* ldu1, double* u2, lapack_int* ldu2,
-                    double* v1t, lapack_int* ldv1t, double* v2t,
-                    lapack_int* ldv2t, double* b11d, double* b11e,
-                    double* b12d, double* b12e, double* b21d,
-                    double* b21e, double* b22d, double* b22e,
-                    double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_dorbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    double* x11, lapack_int* ldx11, double* x12,
-                    lapack_int* ldx12, double* x21, lapack_int* ldx21,
-                    double* x22, lapack_int* ldx22, double* theta,
-                    double* phi, double* taup1, double* taup2,
-                    double* tauq1, double* tauq2, double* work,
-                    lapack_int* lwork , lapack_int *info );
-void LAPACK_dorcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, double* x11, lapack_int* ldx11,
-                    double* x12, lapack_int* ldx12, double* x21,
-                    lapack_int* ldx21, double* x22, lapack_int* ldx22,
-                    double* theta, double* u1, lapack_int* ldu1,
-                    double* u2, lapack_int* ldu2, double* v1t,
-                    lapack_int* ldv1t, double* v2t, lapack_int* ldv2t,
-                    double* work, lapack_int* lwork,
-                    lapack_int* iwork , lapack_int *info );
-void LAPACK_dsyconv( char* uplo, char* way,
-                     lapack_int* n, double* a, lapack_int* lda,
-                     const lapack_int* ipiv, double* work , lapack_int *info );
-void LAPACK_dsyswapr( char* uplo, lapack_int* n,
-                      double* a, lapack_int* i1, lapack_int* i2 );
-void LAPACK_dsytri2( char* uplo, lapack_int* n,
-                     double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_dsytri2x( char* uplo, lapack_int* n,
-                      double* a, lapack_int* lda,
-                      const lapack_int* ipiv, double* work,
-                      lapack_int* nb , lapack_int *info );
-void LAPACK_dsytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const double* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     double* b, lapack_int* ldb, double* work , lapack_int *info );
-void LAPACK_sbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    float* theta, float* phi, float* u1,
-                    lapack_int* ldu1, float* u2, lapack_int* ldu2,
-                    float* v1t, lapack_int* ldv1t, float* v2t,
-                    lapack_int* ldv2t, float* b11d, float* b11e,
-                    float* b12d, float* b12e, float* b21d,
-                    float* b21e, float* b22d, float* b22e,
-                    float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_sorbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    float* x11, lapack_int* ldx11, float* x12,
-                    lapack_int* ldx12, float* x21, lapack_int* ldx21,
-                    float* x22, lapack_int* ldx22, float* theta,
-                    float* phi, float* taup1, float* taup2,
-                    float* tauq1, float* tauq2, float* work,
-                    lapack_int* lwork , lapack_int *info );
-void LAPACK_sorcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, float* x11, lapack_int* ldx11,
-                    float* x12, lapack_int* ldx12, float* x21,
-                    lapack_int* ldx21, float* x22, lapack_int* ldx22,
-                    float* theta, float* u1, lapack_int* ldu1,
-                    float* u2, lapack_int* ldu2, float* v1t,
-                    lapack_int* ldv1t, float* v2t, lapack_int* ldv2t,
-                    float* work, lapack_int* lwork,
-                    lapack_int* iwork , lapack_int *info );
-void LAPACK_ssyconv( char* uplo, char* way,
-                     lapack_int* n, float* a, lapack_int* lda,
-                     const lapack_int* ipiv, float* work , lapack_int *info );
-void LAPACK_ssyswapr( char* uplo, lapack_int* n,
-                      float* a, lapack_int* i1, lapack_int* i2 );
-void LAPACK_ssytri2( char* uplo, lapack_int* n,
-                     float* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_ssytri2x( char* uplo, lapack_int* n,
-                      float* a, lapack_int* lda,
-                      const lapack_int* ipiv, float* work,
-                      lapack_int* nb , lapack_int *info );
-void LAPACK_ssytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     float* b, lapack_int* ldb, float* work , lapack_int *info );
-void LAPACK_zbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    double* theta, double* phi,
-                    lapack_complex_double* u1, lapack_int* ldu1,
-                    lapack_complex_double* u2, lapack_int* ldu2,
-                    lapack_complex_double* v1t, lapack_int* ldv1t,
-                    lapack_complex_double* v2t, lapack_int* ldv2t,
-                    double* b11d, double* b11e, double* b12d,
-                    double* b12e, double* b21d, double* b21e,
-                    double* b22d, double* b22e, double* rwork,
-                    lapack_int* lrwork , lapack_int *info );
-void LAPACK_zheswapr( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_zhetri2( char* uplo, lapack_int* n,
-                     lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_zhetri2x( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_double* work, lapack_int* nb , lapack_int *info );
-void LAPACK_zhetrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* work , lapack_int *info );
-void LAPACK_zsyconv( char* uplo, char* way,
-                     lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_double* work , lapack_int *info );
-void LAPACK_zsyswapr( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_zsytri2( char* uplo, lapack_int* n,
-                     lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_zsytri2x( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_double* work, lapack_int* nb , lapack_int *info );
-void LAPACK_zsytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* work , lapack_int *info );
-void LAPACK_zunbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    lapack_complex_double* x11, lapack_int* ldx11,
-                    lapack_complex_double* x12, lapack_int* ldx12,
-                    lapack_complex_double* x21, lapack_int* ldx21,
-                    lapack_complex_double* x22, lapack_int* ldx22,
-                    double* theta, double* phi,
-                    lapack_complex_double* taup1,
-                    lapack_complex_double* taup2,
-                    lapack_complex_double* tauq1,
-                    lapack_complex_double* tauq2,
-                    lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_zuncsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, lapack_complex_double* x11,
-                    lapack_int* ldx11, lapack_complex_double* x12,
-                    lapack_int* ldx12, lapack_complex_double* x21,
-                    lapack_int* ldx21, lapack_complex_double* x22,
-                    lapack_int* ldx22, double* theta,
-                    lapack_complex_double* u1, lapack_int* ldu1,
-                    lapack_complex_double* u2, lapack_int* ldu2,
-                    lapack_complex_double* v1t, lapack_int* ldv1t,
-                    lapack_complex_double* v2t, lapack_int* ldv2t,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork,
-                    lapack_int* iwork , lapack_int *info );
-// LAPACK 3.4.0
-void LAPACK_sgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb, const float* v,
-                     lapack_int* ldv, const float* t, lapack_int* ldt, float* c,
-                     lapack_int* ldc, float* work, lapack_int *info );
-void LAPACK_dgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb, const double* v,
-                     lapack_int* ldv, const double* t, lapack_int* ldt,
-                     double* c, lapack_int* ldc, double* work,
-                     lapack_int *info );
-void LAPACK_cgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb,
-                     const lapack_complex_float* v, lapack_int* ldv,
-                     const lapack_complex_float* t, lapack_int* ldt,
-                     lapack_complex_float* c, lapack_int* ldc,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb,
-                     const lapack_complex_double* v, lapack_int* ldv,
-                     const lapack_complex_double* t, lapack_int* ldt,
-                     lapack_complex_double* c, lapack_int* ldc,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_sgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb, float* a,
-                    lapack_int* lda, float* t, lapack_int* ldt, float* work,
-                    lapack_int *info );
-void LAPACK_dgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb, double* a,
-                    lapack_int* lda, double* t, lapack_int* ldt, double* work,
-                    lapack_int *info );
-void LAPACK_cgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_sgeqrt2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_dgeqrt2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_cgeqrt2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_zgeqrt2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_sgeqrt3( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_dgeqrt3( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_cgeqrt3( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_zgeqrt3( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_stpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const float* v, lapack_int* ldv, const float* t,
-                     lapack_int* ldt, float* a, lapack_int* lda, float* b,
-                     lapack_int* ldb, float* work, lapack_int *info );
-void LAPACK_dtpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const double* v, lapack_int* ldv, const double* t,
-                     lapack_int* ldt, double* a, lapack_int* lda, double* b,
-                     lapack_int* ldb, double* work, lapack_int *info );
-void LAPACK_ctpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const lapack_complex_float* v, lapack_int* ldv,
-                     const lapack_complex_float* t, lapack_int* ldt,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_ztpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const lapack_complex_double* v, lapack_int* ldv,
-                     const lapack_complex_double* t, lapack_int* ldt,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_dtpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* t, lapack_int* ldt, double* work,
-                    lapack_int *info );
-void LAPACK_ctpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* t, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int* ldt,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_ztpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_stpqrt2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* b, lapack_int* ldb, float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_dtpqrt2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* b, lapack_int* ldb, double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_ctpqrt2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_ztpqrt2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_stprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const float* v, lapack_int* ldv, const float* t,
-                    lapack_int* ldt, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, const float* mywork,
-                    lapack_int* myldwork );
-void LAPACK_dtprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const double* v, lapack_int* ldv, const double* t,
-                    lapack_int* ldt, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, const double* mywork,
-                    lapack_int* myldwork );
-void LAPACK_ctprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const lapack_complex_float* v, lapack_int* ldv,
-                    const lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    const float* mywork, lapack_int* myldwork );
-void LAPACK_ztprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const lapack_complex_double* v, lapack_int* ldv,
-                    const lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    const double* mywork, lapack_int* myldwork );
-// LAPACK 3.X.X
-void LAPACK_csyr( char* uplo, lapack_int* n, lapack_complex_float* alpha,
-                      const lapack_complex_float* x, lapack_int* incx,
-                      lapack_complex_float* a, lapack_int* lda );
-void LAPACK_zsyr( char* uplo, lapack_int* n, lapack_complex_double* alpha,
-                      const lapack_complex_double* x, lapack_int* incx,
-                      lapack_complex_double* a, lapack_int* lda );
-
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-
-#endif /* _LAPACKE_H_ */
-
-#endif /* _MKL_LAPACKE_H_ */
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapacke_mangling.h b/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapacke_mangling.h
deleted file mode 100644
index 6211fd1..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/misc/lapacke_mangling.h
+++ /dev/null
@@ -1,17 +0,0 @@
-#ifndef LAPACK_HEADER_INCLUDED
-#define LAPACK_HEADER_INCLUDED
-
-#ifndef LAPACK_GLOBAL
-#if defined(LAPACK_GLOBAL_PATTERN_LC) || defined(ADD_)
-#define LAPACK_GLOBAL(lcname,UCNAME)  lcname##_
-#elif defined(LAPACK_GLOBAL_PATTERN_UC) || defined(UPPER)
-#define LAPACK_GLOBAL(lcname,UCNAME)  UCNAME
-#elif defined(LAPACK_GLOBAL_PATTERN_MC) || defined(NOCHANGE)
-#define LAPACK_GLOBAL(lcname,UCNAME)  lcname
-#else
-#define LAPACK_GLOBAL(lcname,UCNAME)  lcname##_
-#endif
-#endif
-
-#endif
-
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
deleted file mode 100644
index 0e5d544..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
+++ /dev/null
@@ -1,358 +0,0 @@
-
-/** \returns an expression of the coefficient wise product of \c *this and \a other
-  *
-  * \sa MatrixBase::cwiseProduct
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
-operator*(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient wise quotient of \c *this and \a other
-  *
-  * \sa MatrixBase::cwiseQuotient
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>
-operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise min of \c *this and \a other
-  *
-  * Example: \include Cwise_min.cpp
-  * Output: \verbinclude Cwise_min.out
-  *
-  * \sa max()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(min,min)
-
-/** \returns an expression of the coefficient-wise min of \c *this and scalar \a other
-  *
-  * \sa max()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived,
-                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-min
-#else
-(min)
-#endif
-(const Scalar &other) const
-{
-  return (min)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise max of \c *this and \a other
-  *
-  * Example: \include Cwise_max.cpp
-  * Output: \verbinclude Cwise_max.out
-  *
-  * \sa min()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(max,max)
-
-/** \returns an expression of the coefficient-wise max of \c *this and scalar \a other
-  *
-  * \sa min()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived,
-                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-max
-#else
-(max)
-#endif
-(const Scalar &other) const
-{
-  return (max)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise absdiff of \c *this and \a other
-  *
-  * Example: \include Cwise_absolute_difference.cpp
-  * Output: \verbinclude Cwise_absolute_difference.out
-  *
-  * \sa absolute_difference()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(absolute_difference,absolute_difference)
-
-/** \returns an expression of the coefficient-wise absolute_difference of \c *this and scalar \a other
-  *
-  * \sa absolute_difference()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_absolute_difference_op<Scalar,Scalar>, const Derived,
-                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-absolute_difference
-#else
-(absolute_difference)
-#endif
-(const Scalar &other) const
-{
-  return (absolute_difference)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise power of \c *this to the given array of \a exponents.
-  *
-  * This function computes the coefficient-wise power.
-  *
-  * Example: \include Cwise_array_power_array.cpp
-  * Output: \verbinclude Cwise_array_power_array.out
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(pow,pow)
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(pow,pow)
-#else
-/** \returns an expression of the coefficients of \c *this rasied to the constant power \a exponent
-  *
-  * \tparam T is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression.
-  *
-  * This function computes the coefficient-wise power. The function MatrixBase::pow() in the
-  * unsupported module MatrixFunctions computes the matrix power.
-  *
-  * Example: \include Cwise_pow.cpp
-  * Output: \verbinclude Cwise_pow.out
-  *
-  * \sa ArrayBase::pow(ArrayBase), square(), cube(), exp(), log()
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_pow_op<Scalar,T>,Derived,Constant<T> > pow(const T& exponent) const;
-#endif
-
-
-// TODO code generating macros could be moved to Macros.h and could include generation of documentation
-#define EIGEN_MAKE_CWISE_COMP_OP(OP, COMPARATOR) \
-template<typename OtherDerived> \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived> \
-OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
-{ \
-  return CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived>(derived(), other.derived()); \
-}\
-typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> > Cmp ## COMPARATOR ## ReturnType; \
-typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject>, const Derived > RCmp ## COMPARATOR ## ReturnType; \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Cmp ## COMPARATOR ## ReturnType \
-OP(const Scalar& s) const { \
-  return this->OP(Derived::PlainObject::Constant(rows(), cols(), s)); \
-} \
-EIGEN_DEVICE_FUNC friend EIGEN_STRONG_INLINE const RCmp ## COMPARATOR ## ReturnType \
-OP(const Scalar& s, const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived>& d) { \
-  return Derived::PlainObject::Constant(d.rows(), d.cols(), s).OP(d); \
-}
-
-#define EIGEN_MAKE_CWISE_COMP_R_OP(OP, R_OP, RCOMPARATOR) \
-template<typename OtherDerived> \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived> \
-OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
-{ \
-  return CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived>(other.derived(), derived()); \
-} \
-EIGEN_DEVICE_FUNC \
-inline const RCmp ## RCOMPARATOR ## ReturnType \
-OP(const Scalar& s) const { \
-  return Derived::PlainObject::Constant(rows(), cols(), s).R_OP(*this); \
-} \
-friend inline const Cmp ## RCOMPARATOR ## ReturnType \
-OP(const Scalar& s, const Derived& d) { \
-  return d.R_OP(Derived::PlainObject::Constant(d.rows(), d.cols(), s)); \
-}
-
-
-
-/** \returns an expression of the coefficient-wise \< operator of *this and \a other
-  *
-  * Example: \include Cwise_less.cpp
-  * Output: \verbinclude Cwise_less.out
-  *
-  * \sa all(), any(), operator>(), operator<=()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator<, LT)
-
-/** \returns an expression of the coefficient-wise \<= operator of *this and \a other
-  *
-  * Example: \include Cwise_less_equal.cpp
-  * Output: \verbinclude Cwise_less_equal.out
-  *
-  * \sa all(), any(), operator>=(), operator<()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator<=, LE)
-
-/** \returns an expression of the coefficient-wise \> operator of *this and \a other
-  *
-  * Example: \include Cwise_greater.cpp
-  * Output: \verbinclude Cwise_greater.out
-  *
-  * \sa all(), any(), operator>=(), operator<()
-  */
-EIGEN_MAKE_CWISE_COMP_R_OP(operator>, operator<, LT)
-
-/** \returns an expression of the coefficient-wise \>= operator of *this and \a other
-  *
-  * Example: \include Cwise_greater_equal.cpp
-  * Output: \verbinclude Cwise_greater_equal.out
-  *
-  * \sa all(), any(), operator>(), operator<=()
-  */
-EIGEN_MAKE_CWISE_COMP_R_OP(operator>=, operator<=, LE)
-
-/** \returns an expression of the coefficient-wise == operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include Cwise_equal_equal.cpp
-  * Output: \verbinclude Cwise_equal_equal.out
-  *
-  * \sa all(), any(), isApprox(), isMuchSmallerThan()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator==, EQ)
-
-/** \returns an expression of the coefficient-wise != operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include Cwise_not_equal.cpp
-  * Output: \verbinclude Cwise_not_equal.out
-  *
-  * \sa all(), any(), isApprox(), isMuchSmallerThan()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator!=, NEQ)
-
-
-#undef EIGEN_MAKE_CWISE_COMP_OP
-#undef EIGEN_MAKE_CWISE_COMP_R_OP
-
-// scalar addition
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP(operator+,sum)
-#else
-/** \returns an expression of \c *this with each coeff incremented by the constant \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  *
-  * Example: \include Cwise_plus.cpp
-  * Output: \verbinclude Cwise_plus.out
-  *
-  * \sa operator+=(), operator-()
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_sum_op<Scalar,T>,Derived,Constant<T> > operator+(const T& scalar) const;
-/** \returns an expression of \a expr with each coeff incremented by the constant \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T> friend
-const CwiseBinaryOp<internal::scalar_sum_op<T,Scalar>,Constant<T>,Derived> operator+(const T& scalar, const StorageBaseType& expr);
-#endif
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP(operator-,difference)
-#else
-/** \returns an expression of \c *this with each coeff decremented by the constant \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  *
-  * Example: \include Cwise_minus.cpp
-  * Output: \verbinclude Cwise_minus.out
-  *
-  * \sa operator+=(), operator-()
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_difference_op<Scalar,T>,Derived,Constant<T> > operator-(const T& scalar) const;
-/** \returns an expression of the constant matrix of value \a scalar decremented by the coefficients of \a expr
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T> friend
-const CwiseBinaryOp<internal::scalar_difference_op<T,Scalar>,Constant<T>,Derived> operator-(const T& scalar, const StorageBaseType& expr);
-#endif
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(operator/,quotient)
-#else
-  /**
-    * \brief Component-wise division of the scalar \a s by array elements of \a a.
-    *
-    * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
-    */
-  template<typename T> friend
-  inline const CwiseBinaryOp<internal::scalar_quotient_op<T,Scalar>,Constant<T>,Derived>
-  operator/(const T& s,const StorageBaseType& a);
-#endif
-
-/** \returns an expression of the coefficient-wise ^ operator of *this and \a other
- *
- * \warning this operator is for expression of bool only.
- *
- * Example: \include Cwise_boolean_xor.cpp
- * Output: \verbinclude Cwise_boolean_xor.out
- *
- * \sa operator&&(), select()
- */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived>
-operator^(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return CwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived>(derived(),other.derived());
-}
-
-// NOTE disabled until we agree on argument order
-#if 0
-/** \cpp11 \returns an expression of the coefficient-wise polygamma function.
-  *
-  * \specialfunctions_module
-  *
-  * It returns the \a n -th derivative of the digamma(psi) evaluated at \c *this.
-  *
-  * \warning Be careful with the order of the parameters: x.polygamma(n) is equivalent to polygamma(n,x)
-  *
-  * \sa Eigen::polygamma()
-  */
-template<typename DerivedN>
-inline const CwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const DerivedN, const Derived>
-polygamma(const EIGEN_CURRENT_STORAGE_BASE_CLASS<DerivedN> &n) const
-{
-  return CwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const DerivedN, const Derived>(n.derived(), this->derived());
-}
-#endif
-
-/** \returns an expression of the coefficient-wise zeta function.
-  *
-  * \specialfunctions_module
-  *
-  * It returns the Riemann zeta function of two arguments \c *this and \a q:
-  *
-  * \param q is the shift, it must be > 0
-  *
-  * \note *this is the exponent, it must be > 1.
-  * \note This function supports only float and double scalar types. To support other scalar types, the user has
-  * to provide implementations of zeta(T,T) for any scalar type T to be supported.
-  *
-  * This method is an alias for zeta(*this,q);
-  *
-  * \sa Eigen::zeta()
-  */
-template<typename DerivedQ>
-inline const CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ>
-zeta(const EIGEN_CURRENT_STORAGE_BASE_CLASS<DerivedQ> &q) const
-{
-  return CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ>(this->derived(), q.derived());
-}
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
deleted file mode 100644
index 13c55f4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
+++ /dev/null
@@ -1,696 +0,0 @@
-
-
-typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> AbsReturnType;
-typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> ArgReturnType;
-typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> Abs2ReturnType;
-typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> SqrtReturnType;
-typedef CwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived> RsqrtReturnType;
-typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> SignReturnType;
-typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> InverseReturnType;
-typedef CwiseUnaryOp<internal::scalar_boolean_not_op<Scalar>, const Derived> BooleanNotReturnType;
-
-typedef CwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived> ExpReturnType;
-typedef CwiseUnaryOp<internal::scalar_expm1_op<Scalar>, const Derived> Expm1ReturnType;
-typedef CwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived> LogReturnType;
-typedef CwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived> Log1pReturnType;
-typedef CwiseUnaryOp<internal::scalar_log10_op<Scalar>, const Derived> Log10ReturnType;
-typedef CwiseUnaryOp<internal::scalar_log2_op<Scalar>, const Derived> Log2ReturnType;
-typedef CwiseUnaryOp<internal::scalar_cos_op<Scalar>, const Derived> CosReturnType;
-typedef CwiseUnaryOp<internal::scalar_sin_op<Scalar>, const Derived> SinReturnType;
-typedef CwiseUnaryOp<internal::scalar_tan_op<Scalar>, const Derived> TanReturnType;
-typedef CwiseUnaryOp<internal::scalar_acos_op<Scalar>, const Derived> AcosReturnType;
-typedef CwiseUnaryOp<internal::scalar_asin_op<Scalar>, const Derived> AsinReturnType;
-typedef CwiseUnaryOp<internal::scalar_atan_op<Scalar>, const Derived> AtanReturnType;
-typedef CwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived> TanhReturnType;
-typedef CwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived> LogisticReturnType;
-typedef CwiseUnaryOp<internal::scalar_sinh_op<Scalar>, const Derived> SinhReturnType;
-#if EIGEN_HAS_CXX11_MATH
-typedef CwiseUnaryOp<internal::scalar_atanh_op<Scalar>, const Derived> AtanhReturnType;
-typedef CwiseUnaryOp<internal::scalar_asinh_op<Scalar>, const Derived> AsinhReturnType;
-typedef CwiseUnaryOp<internal::scalar_acosh_op<Scalar>, const Derived> AcoshReturnType;
-#endif
-typedef CwiseUnaryOp<internal::scalar_cosh_op<Scalar>, const Derived> CoshReturnType;
-typedef CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> SquareReturnType;
-typedef CwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived> CubeReturnType;
-typedef CwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived> RoundReturnType;
-typedef CwiseUnaryOp<internal::scalar_rint_op<Scalar>, const Derived> RintReturnType;
-typedef CwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived> FloorReturnType;
-typedef CwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived> CeilReturnType;
-typedef CwiseUnaryOp<internal::scalar_isnan_op<Scalar>, const Derived> IsNaNReturnType;
-typedef CwiseUnaryOp<internal::scalar_isinf_op<Scalar>, const Derived> IsInfReturnType;
-typedef CwiseUnaryOp<internal::scalar_isfinite_op<Scalar>, const Derived> IsFiniteReturnType;
-
-/** \returns an expression of the coefficient-wise absolute value of \c *this
-  *
-  * Example: \include Cwise_abs.cpp
-  * Output: \verbinclude Cwise_abs.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs">Math functions</a>, abs2()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const AbsReturnType
-abs() const
-{
-  return AbsReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise phase angle of \c *this
-  *
-  * Example: \include Cwise_arg.cpp
-  * Output: \verbinclude Cwise_arg.out
-  *
-  * \sa abs()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const ArgReturnType
-arg() const
-{
-  return ArgReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise squared absolute value of \c *this
-  *
-  * Example: \include Cwise_abs2.cpp
-  * Output: \verbinclude Cwise_abs2.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs2">Math functions</a>, abs(), square()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const Abs2ReturnType
-abs2() const
-{
-  return Abs2ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise exponential of *this.
-  *
-  * This function computes the coefficient-wise exponential. The function MatrixBase::exp() in the
-  * unsupported module MatrixFunctions computes the matrix exponential.
-  *
-  * Example: \include Cwise_exp.cpp
-  * Output: \verbinclude Cwise_exp.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_exp">Math functions</a>, pow(), log(), sin(), cos()
-  */
-EIGEN_DEVICE_FUNC
-inline const ExpReturnType
-exp() const
-{
-  return ExpReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise exponential of *this minus 1.
-  *
-  * In exact arithmetic, \c x.expm1() is equivalent to \c x.exp() - 1,
-  * however, with finite precision, this function is much more accurate when \c x is close to zero.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_expm1">Math functions</a>, exp()
-  */
-EIGEN_DEVICE_FUNC
-inline const Expm1ReturnType
-expm1() const
-{
-  return Expm1ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise logarithm of *this.
-  *
-  * This function computes the coefficient-wise logarithm. The function MatrixBase::log() in the
-  * unsupported module MatrixFunctions computes the matrix logarithm.
-  *
-  * Example: \include Cwise_log.cpp
-  * Output: \verbinclude Cwise_log.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log">Math functions</a>, log()
-  */
-EIGEN_DEVICE_FUNC
-inline const LogReturnType
-log() const
-{
-  return LogReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise logarithm of 1 plus \c *this.
-  *
-  * In exact arithmetic, \c x.log() is equivalent to \c (x+1).log(),
-  * however, with finite precision, this function is much more accurate when \c x is close to zero.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log1p">Math functions</a>, log()
-  */
-EIGEN_DEVICE_FUNC
-inline const Log1pReturnType
-log1p() const
-{
-  return Log1pReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise base-10 logarithm of *this.
-  *
-  * This function computes the coefficient-wise base-10 logarithm.
-  *
-  * Example: \include Cwise_log10.cpp
-  * Output: \verbinclude Cwise_log10.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log10">Math functions</a>, log()
-  */
-EIGEN_DEVICE_FUNC
-inline const Log10ReturnType
-log10() const
-{
-  return Log10ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise base-2 logarithm of *this.
-  *
-  * This function computes the coefficient-wise base-2 logarithm.
-  *
-  */
-EIGEN_DEVICE_FUNC
-inline const Log2ReturnType
-log2() const
-{
-  return Log2ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise square root of *this.
-  *
-  * This function computes the coefficient-wise square root. The function MatrixBase::sqrt() in the
-  * unsupported module MatrixFunctions computes the matrix square root.
-  *
-  * Example: \include Cwise_sqrt.cpp
-  * Output: \verbinclude Cwise_sqrt.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sqrt">Math functions</a>, pow(), square()
-  */
-EIGEN_DEVICE_FUNC
-inline const SqrtReturnType
-sqrt() const
-{
-  return SqrtReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse square root of *this.
-  *
-  * This function computes the coefficient-wise inverse square root.
-  *
-  * Example: \include Cwise_sqrt.cpp
-  * Output: \verbinclude Cwise_sqrt.out
-  *
-  * \sa pow(), square()
-  */
-EIGEN_DEVICE_FUNC
-inline const RsqrtReturnType
-rsqrt() const
-{
-  return RsqrtReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise signum of *this.
-  *
-  * This function computes the coefficient-wise signum.
-  *
-  * Example: \include Cwise_sign.cpp
-  * Output: \verbinclude Cwise_sign.out
-  *
-  * \sa pow(), square()
-  */
-EIGEN_DEVICE_FUNC
-inline const SignReturnType
-sign() const
-{
-  return SignReturnType(derived());
-}
-
-
-/** \returns an expression of the coefficient-wise cosine of *this.
-  *
-  * This function computes the coefficient-wise cosine. The function MatrixBase::cos() in the
-  * unsupported module MatrixFunctions computes the matrix cosine.
-  *
-  * Example: \include Cwise_cos.cpp
-  * Output: \verbinclude Cwise_cos.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cos">Math functions</a>, sin(), acos()
-  */
-EIGEN_DEVICE_FUNC
-inline const CosReturnType
-cos() const
-{
-  return CosReturnType(derived());
-}
-
-
-/** \returns an expression of the coefficient-wise sine of *this.
-  *
-  * This function computes the coefficient-wise sine. The function MatrixBase::sin() in the
-  * unsupported module MatrixFunctions computes the matrix sine.
-  *
-  * Example: \include Cwise_sin.cpp
-  * Output: \verbinclude Cwise_sin.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sin">Math functions</a>, cos(), asin()
-  */
-EIGEN_DEVICE_FUNC
-inline const SinReturnType
-sin() const
-{
-  return SinReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise tan of *this.
-  *
-  * Example: \include Cwise_tan.cpp
-  * Output: \verbinclude Cwise_tan.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tan">Math functions</a>, cos(), sin()
-  */
-EIGEN_DEVICE_FUNC
-inline const TanReturnType
-tan() const
-{
-  return TanReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise arc tan of *this.
-  *
-  * Example: \include Cwise_atan.cpp
-  * Output: \verbinclude Cwise_atan.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atan">Math functions</a>, tan(), asin(), acos()
-  */
-EIGEN_DEVICE_FUNC
-inline const AtanReturnType
-atan() const
-{
-  return AtanReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise arc cosine of *this.
-  *
-  * Example: \include Cwise_acos.cpp
-  * Output: \verbinclude Cwise_acos.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acos">Math functions</a>, cos(), asin()
-  */
-EIGEN_DEVICE_FUNC
-inline const AcosReturnType
-acos() const
-{
-  return AcosReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise arc sine of *this.
-  *
-  * Example: \include Cwise_asin.cpp
-  * Output: \verbinclude Cwise_asin.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asin">Math functions</a>, sin(), acos()
-  */
-EIGEN_DEVICE_FUNC
-inline const AsinReturnType
-asin() const
-{
-  return AsinReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise hyperbolic tan of *this.
-  *
-  * Example: \include Cwise_tanh.cpp
-  * Output: \verbinclude Cwise_tanh.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tanh">Math functions</a>, tan(), sinh(), cosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const TanhReturnType
-tanh() const
-{
-  return TanhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise hyperbolic sin of *this.
-  *
-  * Example: \include Cwise_sinh.cpp
-  * Output: \verbinclude Cwise_sinh.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sinh">Math functions</a>, sin(), tanh(), cosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const SinhReturnType
-sinh() const
-{
-  return SinhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise hyperbolic cos of *this.
-  *
-  * Example: \include Cwise_cosh.cpp
-  * Output: \verbinclude Cwise_cosh.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cosh">Math functions</a>, tanh(), sinh(), cosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const CoshReturnType
-cosh() const
-{
-  return CoshReturnType(derived());
-}
-
-#if EIGEN_HAS_CXX11_MATH
-/** \returns an expression of the coefficient-wise inverse hyperbolic tan of *this.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atanh">Math functions</a>, atanh(), asinh(), acosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const AtanhReturnType
-atanh() const
-{
-  return AtanhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse hyperbolic sin of *this.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asinh">Math functions</a>, atanh(), asinh(), acosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const AsinhReturnType
-asinh() const
-{
-  return AsinhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse hyperbolic cos of *this.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acosh">Math functions</a>, atanh(), asinh(), acosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const AcoshReturnType
-acosh() const
-{
-  return AcoshReturnType(derived());
-}
-#endif
-
-/** \returns an expression of the coefficient-wise logistic of *this.
-  */
-EIGEN_DEVICE_FUNC
-inline const LogisticReturnType
-logistic() const
-{
-  return LogisticReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse of *this.
-  *
-  * Example: \include Cwise_inverse.cpp
-  * Output: \verbinclude Cwise_inverse.out
-  *
-  * \sa operator/(), operator*()
-  */
-EIGEN_DEVICE_FUNC
-inline const InverseReturnType
-inverse() const
-{
-  return InverseReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise square of *this.
-  *
-  * Example: \include Cwise_square.cpp
-  * Output: \verbinclude Cwise_square.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_squareE">Math functions</a>, abs2(), cube(), pow()
-  */
-EIGEN_DEVICE_FUNC
-inline const SquareReturnType
-square() const
-{
-  return SquareReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise cube of *this.
-  *
-  * Example: \include Cwise_cube.cpp
-  * Output: \verbinclude Cwise_cube.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cube">Math functions</a>, square(), pow()
-  */
-EIGEN_DEVICE_FUNC
-inline const CubeReturnType
-cube() const
-{
-  return CubeReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise rint of *this.
-  *
-  * Example: \include Cwise_rint.cpp
-  * Output: \verbinclude Cwise_rint.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_rint">Math functions</a>, ceil(), floor()
-  */
-EIGEN_DEVICE_FUNC
-inline const RintReturnType
-rint() const
-{
-  return RintReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise round of *this.
-  *
-  * Example: \include Cwise_round.cpp
-  * Output: \verbinclude Cwise_round.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_round">Math functions</a>, ceil(), floor()
-  */
-EIGEN_DEVICE_FUNC
-inline const RoundReturnType
-round() const
-{
-  return RoundReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise floor of *this.
-  *
-  * Example: \include Cwise_floor.cpp
-  * Output: \verbinclude Cwise_floor.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_floor">Math functions</a>, ceil(), round()
-  */
-EIGEN_DEVICE_FUNC
-inline const FloorReturnType
-floor() const
-{
-  return FloorReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise ceil of *this.
-  *
-  * Example: \include Cwise_ceil.cpp
-  * Output: \verbinclude Cwise_ceil.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ceil">Math functions</a>, floor(), round()
-  */
-EIGEN_DEVICE_FUNC
-inline const CeilReturnType
-ceil() const
-{
-  return CeilReturnType(derived());
-}
-
-template<int N> struct ShiftRightXpr {
-  typedef CwiseUnaryOp<internal::scalar_shift_right_op<Scalar, N>, const Derived> Type;
-};
-
-/** \returns an expression of \c *this with the \a Scalar type arithmetically
-  * shifted right by \a N bit positions.
-  *
-  * The template parameter \a N specifies the number of bit positions to shift.
-  * 
-  * \sa shiftLeft()
-  */
-template<int N>
-EIGEN_DEVICE_FUNC
-typename ShiftRightXpr<N>::Type
-shiftRight() const
-{
-  return typename ShiftRightXpr<N>::Type(derived());
-}
-
-
-template<int N> struct ShiftLeftXpr {
-  typedef CwiseUnaryOp<internal::scalar_shift_left_op<Scalar, N>, const Derived> Type;
-};
-
-/** \returns an expression of \c *this with the \a Scalar type logically
-  * shifted left by \a N bit positions.
-  *
-  * The template parameter \a N specifies the number of bit positions to shift.
-  *
-  * \sa shiftRight()
-  */
-template<int N>
-EIGEN_DEVICE_FUNC
-typename ShiftLeftXpr<N>::Type
-shiftLeft() const
-{
-  return typename ShiftLeftXpr<N>::Type(derived());
-}
-
-/** \returns an expression of the coefficient-wise isnan of *this.
-  *
-  * Example: \include Cwise_isNaN.cpp
-  * Output: \verbinclude Cwise_isNaN.out
-  *
-  * \sa isfinite(), isinf()
-  */
-EIGEN_DEVICE_FUNC
-inline const IsNaNReturnType
-isNaN() const
-{
-  return IsNaNReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise isinf of *this.
-  *
-  * Example: \include Cwise_isInf.cpp
-  * Output: \verbinclude Cwise_isInf.out
-  *
-  * \sa isnan(), isfinite()
-  */
-EIGEN_DEVICE_FUNC
-inline const IsInfReturnType
-isInf() const
-{
-  return IsInfReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise isfinite of *this.
-  *
-  * Example: \include Cwise_isFinite.cpp
-  * Output: \verbinclude Cwise_isFinite.out
-  *
-  * \sa isnan(), isinf()
-  */
-EIGEN_DEVICE_FUNC
-inline const IsFiniteReturnType
-isFinite() const
-{
-  return IsFiniteReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise ! operator of *this
-  *
-  * \warning this operator is for expression of bool only.
-  *
-  * Example: \include Cwise_boolean_not.cpp
-  * Output: \verbinclude Cwise_boolean_not.out
-  *
-  * \sa operator!=()
-  */
-EIGEN_DEVICE_FUNC
-inline const BooleanNotReturnType
-operator!() const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return BooleanNotReturnType(derived());
-}
-
-
-// --- SpecialFunctions module ---
-
-typedef CwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived> LgammaReturnType;
-typedef CwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived> DigammaReturnType;
-typedef CwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived> ErfReturnType;
-typedef CwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived> ErfcReturnType;
-typedef CwiseUnaryOp<internal::scalar_ndtri_op<Scalar>, const Derived> NdtriReturnType;
-
-/** \cpp11 \returns an expression of the coefficient-wise ln(|gamma(*this)|).
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of lgamma(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_lgamma">Math functions</a>, digamma()
-  */
-EIGEN_DEVICE_FUNC
-inline const LgammaReturnType
-lgamma() const
-{
-  return LgammaReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise digamma (psi, derivative of lgamma).
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types. To support other scalar types,
-  * the user has to provide implementations of digamma(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_digamma">Math functions</a>, Eigen::digamma(), Eigen::polygamma(), lgamma()
-  */
-EIGEN_DEVICE_FUNC
-inline const DigammaReturnType
-digamma() const
-{
-  return DigammaReturnType(derived());
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise Gauss error
-  * function of *this.
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of erf(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erf">Math functions</a>, erfc()
-  */
-EIGEN_DEVICE_FUNC
-inline const ErfReturnType
-erf() const
-{
-  return ErfReturnType(derived());
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise Complementary error
-  * function of *this.
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of erfc(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erfc">Math functions</a>, erf()
-  */
-EIGEN_DEVICE_FUNC
-inline const ErfcReturnType
-erfc() const
-{
-  return ErfcReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse of the CDF of the Normal distribution function
-  * function of *this.
-  *
-  * \specialfunctions_module
-  * 
-  * In other words, considering `x = ndtri(y)`, it returns the argument, x, for which the area under the
-  * Gaussian probability density function (integrated from minus infinity to x) is equal to y.
-  *
-  * \note This function supports only float and double scalar types. To support other scalar types,
-  * the user has to provide implementations of ndtri(T) for any scalar type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ndtri">Math functions</a>
-  */
-EIGEN_DEVICE_FUNC
-inline const NdtriReturnType
-ndtri() const
-{
-  return NdtriReturnType(derived());
-}
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/BlockMethods.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/BlockMethods.h
deleted file mode 100644
index 63a52a6..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/BlockMethods.h
+++ /dev/null
@@ -1,1442 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-/// \internal expression type of a column */
-typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ColXpr;
-typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ConstColXpr;
-/// \internal expression type of a row */
-typedef Block<Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowXpr;
-typedef const Block<const Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowXpr;
-/// \internal expression type of a block of whole columns */
-typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ColsBlockXpr;
-typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ConstColsBlockXpr;
-/// \internal expression type of a block of whole rows */
-typedef Block<Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowsBlockXpr;
-typedef const Block<const Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowsBlockXpr;
-/// \internal expression type of a block of whole columns */
-template<int N> struct NColsBlockXpr { typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; };
-template<int N> struct ConstNColsBlockXpr { typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; };
-/// \internal expression type of a block of whole rows */
-template<int N> struct NRowsBlockXpr { typedef Block<Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; };
-template<int N> struct ConstNRowsBlockXpr { typedef const Block<const Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; };
-/// \internal expression of a block */
-typedef Block<Derived> BlockXpr;
-typedef const Block<const Derived> ConstBlockXpr;
-/// \internal expression of a block of fixed sizes */
-template<int Rows, int Cols> struct FixedBlockXpr { typedef Block<Derived,Rows,Cols> Type; };
-template<int Rows, int Cols> struct ConstFixedBlockXpr { typedef Block<const Derived,Rows,Cols> Type; };
-
-typedef VectorBlock<Derived> SegmentReturnType;
-typedef const VectorBlock<const Derived> ConstSegmentReturnType;
-template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
-template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
-
-/// \internal inner-vector
-typedef Block<Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true>       InnerVectorReturnType;
-typedef Block<const Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true> ConstInnerVectorReturnType;
-
-/// \internal set of inner-vectors
-typedef Block<Derived,Dynamic,Dynamic,true> InnerVectorsReturnType;
-typedef Block<const Derived,Dynamic,Dynamic,true> ConstInnerVectorsReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-/// \returns an expression of a block in \c *this with either dynamic or fixed sizes.
-///
-/// \param  startRow  the first row in the block
-/// \param  startCol  the first column in the block
-/// \param  blockRows number of rows in the block, specified at either run-time or compile-time
-/// \param  blockCols number of columns in the block, specified at either run-time or compile-time
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example using runtime (aka dynamic) sizes: \include MatrixBase_block_int_int_int_int.cpp
-/// Output: \verbinclude MatrixBase_block_int_int_int_int.out
-///
-/// \newin{3.4}:
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value in case \c N equals Eigen::Dynamic.
-/// Here is an example with a fixed number of rows \c NRows and dynamic number of columns \c cols:
-/// \code
-/// mat.block(i,j,fix<NRows>,cols)
-/// \endcode
-///
-/// This function thus fully covers the features offered by the following overloads block<NRows,NCols>(Index, Index),
-/// and block<NRows,NCols>(Index, Index, Index, Index) that are thus obsolete. Indeed, this generic version avoids
-/// redundancy, it preserves the argument order, and prevents the need to rely on the template keyword in templated code.
-///
-/// but with less redundancy and more consistency as it does not modify the argument order
-/// and seamlessly enable hybrid fixed/dynamic sizes.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size matrix, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block, fix, fix<N>(int)
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type(
-            derived(), startRow, startCol, internal::get_runtime_value(blockRows), internal::get_runtime_value(blockCols));
-}
-
-/// This is the const version of block(Index,Index,NRowsType,NColsType)
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type(
-            derived(), startRow, startCol, internal::get_runtime_value(blockRows), internal::get_runtime_value(blockCols));
-}
-
-
-
-/// \returns a expression of a top-right corner of \c *this with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example with dynamic sizes: \include MatrixBase_topRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_topRightCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-topRightCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(cCols), internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of topRightCorner(NRowsType, NColsType).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-topRightCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(cCols), internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size top-right corner of \c *this.
-///
-/// \tparam CRows the number of rows in the corner
-/// \tparam CCols the number of columns in the corner
-///
-/// Example: \include MatrixBase_template_int_int_topRightCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block, block<int,int>(Index,Index)
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topRightCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - CCols);
-}
-
-/// This is the const version of topRightCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topRightCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - CCols);
-}
-
-/// \returns an expression of a top-right corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_topRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topRightCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
-}
-
-/// This is the const version of topRightCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topRightCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
-}
-
-
-
-/// \returns an expression of a top-left corner of \c *this  with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_topLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_topLeftCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-topLeftCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of topLeftCorner(Index, Index).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-topLeftCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size top-left corner of \c *this.
-///
-/// The template parameters CRows and CCols are the number of rows and columns in the corner.
-///
-/// Example: \include MatrixBase_template_int_int_topLeftCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topLeftCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0);
-}
-
-/// This is the const version of topLeftCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topLeftCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0);
-}
-
-/// \returns an expression of a top-left corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_topLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topLeftCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0, cRows, cCols);
-}
-
-/// This is the const version of topLeftCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topLeftCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0, cRows, cCols);
-}
-
-
-
-/// \returns an expression of a bottom-right corner of \c *this  with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_bottomRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_bottomRightCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-bottomRightCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), cols() - internal::get_runtime_value(cCols),
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of bottomRightCorner(NRowsType, NColsType).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-bottomRightCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), cols() - internal::get_runtime_value(cCols),
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size bottom-right corner of \c *this.
-///
-/// The template parameters CRows and CCols are the number of rows and columns in the corner.
-///
-/// Example: \include MatrixBase_template_int_int_bottomRightCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomRightCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, cols() - CCols);
-}
-
-/// This is the const version of bottomRightCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomRightCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, cols() - CCols);
-}
-
-/// \returns an expression of a bottom-right corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_bottomRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomRightCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
-}
-
-/// This is the const version of bottomRightCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomRightCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
-}
-
-
-
-/// \returns an expression of a bottom-left corner of \c *this  with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_bottomLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_bottomLeftCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-bottomLeftCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), 0,
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of bottomLeftCorner(NRowsType, NColsType).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename ConstFixedBlockXpr<...,...>::Type
-#endif
-bottomLeftCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), 0,
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size bottom-left corner of \c *this.
-///
-/// The template parameters CRows and CCols are the number of rows and columns in the corner.
-///
-/// Example: \include MatrixBase_template_int_int_bottomLeftCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomLeftCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, 0);
-}
-
-/// This is the const version of bottomLeftCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomLeftCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, 0);
-}
-
-/// \returns an expression of a bottom-left corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_bottomLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomLeftCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
-}
-
-/// This is the const version of bottomLeftCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomLeftCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
-}
-
-
-
-/// \returns a block consisting of the top rows of \c *this.
-///
-/// \param n the number of rows in the block
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-///
-/// Example: \include MatrixBase_topRows_int.cpp
-/// Output: \verbinclude MatrixBase_topRows_int.out
-///
-/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-typename NRowsBlockXpr<...>::Type
-#endif
-topRows(NRowsType n)
-{
-  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(n), cols());
-}
-
-/// This is the const version of topRows(NRowsType).
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-const typename ConstNRowsBlockXpr<...>::Type
-#endif
-topRows(NRowsType n) const
-{
-  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(n), cols());
-}
-
-/// \returns a block consisting of the top rows of \c *this.
-///
-/// \tparam N the number of rows in the block as specified at compile-time
-/// \param n the number of rows in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_topRows.cpp
-/// Output: \verbinclude MatrixBase_template_int_topRows.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NRowsBlockXpr<N>::Type topRows(Index n = N)
-{
-  return typename NRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
-}
-
-/// This is the const version of topRows<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNRowsBlockXpr<N>::Type topRows(Index n = N) const
-{
-  return typename ConstNRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
-}
-
-
-
-/// \returns a block consisting of the bottom rows of \c *this.
-///
-/// \param n the number of rows in the block
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-///
-/// Example: \include MatrixBase_bottomRows_int.cpp
-/// Output: \verbinclude MatrixBase_bottomRows_int.out
-///
-/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-typename NRowsBlockXpr<...>::Type
-#endif
-bottomRows(NRowsType n)
-{
-  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
-}
-
-/// This is the const version of bottomRows(NRowsType).
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-const typename ConstNRowsBlockXpr<...>::Type
-#endif
-bottomRows(NRowsType n) const
-{
-  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
-}
-
-/// \returns a block consisting of the bottom rows of \c *this.
-///
-/// \tparam N the number of rows in the block as specified at compile-time
-/// \param n the number of rows in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_bottomRows.cpp
-/// Output: \verbinclude MatrixBase_template_int_bottomRows.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NRowsBlockXpr<N>::Type bottomRows(Index n = N)
-{
-  return typename NRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
-}
-
-/// This is the const version of bottomRows<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNRowsBlockXpr<N>::Type bottomRows(Index n = N) const
-{
-  return typename ConstNRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
-}
-
-
-
-/// \returns a block consisting of a range of rows of \c *this.
-///
-/// \param startRow the index of the first row in the block
-/// \param n the number of rows in the block
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-///
-/// Example: \include DenseBase_middleRows_int.cpp
-/// Output: \verbinclude DenseBase_middleRows_int.out
-///
-/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-typename NRowsBlockXpr<...>::Type
-#endif
-middleRows(Index startRow, NRowsType n)
-{
-  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), startRow, 0, internal::get_runtime_value(n), cols());
-}
-
-/// This is the const version of middleRows(Index,NRowsType).
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-const typename ConstNRowsBlockXpr<...>::Type
-#endif
-middleRows(Index startRow, NRowsType n) const
-{
-  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), startRow, 0, internal::get_runtime_value(n), cols());
-}
-
-/// \returns a block consisting of a range of rows of \c *this.
-///
-/// \tparam N the number of rows in the block as specified at compile-time
-/// \param startRow the index of the first row in the block
-/// \param n the number of rows in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include DenseBase_template_int_middleRows.cpp
-/// Output: \verbinclude DenseBase_template_int_middleRows.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N)
-{
-  return typename NRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
-}
-
-/// This is the const version of middleRows<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N) const
-{
-  return typename ConstNRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
-}
-
-
-
-/// \returns a block consisting of the left columns of \c *this.
-///
-/// \param n the number of columns in the block
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_leftCols_int.cpp
-/// Output: \verbinclude MatrixBase_leftCols_int.out
-///
-/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename NColsBlockXpr<...>::Type
-#endif
-leftCols(NColsType n)
-{
-  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, rows(), internal::get_runtime_value(n));
-}
-
-/// This is the const version of leftCols(NColsType).
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstNColsBlockXpr<...>::Type
-#endif
-leftCols(NColsType n) const
-{
-  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, rows(), internal::get_runtime_value(n));
-}
-
-/// \returns a block consisting of the left columns of \c *this.
-///
-/// \tparam N the number of columns in the block as specified at compile-time
-/// \param n the number of columns in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_leftCols.cpp
-/// Output: \verbinclude MatrixBase_template_int_leftCols.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NColsBlockXpr<N>::Type leftCols(Index n = N)
-{
-  return typename NColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
-}
-
-/// This is the const version of leftCols<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNColsBlockXpr<N>::Type leftCols(Index n = N) const
-{
-  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
-}
-
-
-
-/// \returns a block consisting of the right columns of \c *this.
-///
-/// \param n the number of columns in the block
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_rightCols_int.cpp
-/// Output: \verbinclude MatrixBase_rightCols_int.out
-///
-/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename NColsBlockXpr<...>::Type
-#endif
-rightCols(NColsType n)
-{
-  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
-}
-
-/// This is the const version of rightCols(NColsType).
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstNColsBlockXpr<...>::Type
-#endif
-rightCols(NColsType n) const
-{
-  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
-}
-
-/// \returns a block consisting of the right columns of \c *this.
-///
-/// \tparam N the number of columns in the block as specified at compile-time
-/// \param n the number of columns in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_rightCols.cpp
-/// Output: \verbinclude MatrixBase_template_int_rightCols.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NColsBlockXpr<N>::Type rightCols(Index n = N)
-{
-  return typename NColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
-}
-
-/// This is the const version of rightCols<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNColsBlockXpr<N>::Type rightCols(Index n = N) const
-{
-  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
-}
-
-
-
-/// \returns a block consisting of a range of columns of \c *this.
-///
-/// \param startCol the index of the first column in the block
-/// \param numCols the number of columns in the block
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include DenseBase_middleCols_int.cpp
-/// Output: \verbinclude DenseBase_middleCols_int.out
-///
-/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename NColsBlockXpr<...>::Type
-#endif
-middleCols(Index startCol, NColsType numCols)
-{
-  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
-}
-
-/// This is the const version of middleCols(Index,NColsType).
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstNColsBlockXpr<...>::Type
-#endif
-middleCols(Index startCol, NColsType numCols) const
-{
-  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
-}
-
-/// \returns a block consisting of a range of columns of \c *this.
-///
-/// \tparam N the number of columns in the block as specified at compile-time
-/// \param startCol the index of the first column in the block
-/// \param n the number of columns in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include DenseBase_template_int_middleCols.cpp
-/// Output: \verbinclude DenseBase_template_int_middleCols.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N)
-{
-  return typename NColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
-}
-
-/// This is the const version of middleCols<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N) const
-{
-  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
-}
-
-
-
-/// \returns a fixed-size expression of a block of \c *this.
-///
-/// The template parameters \a NRows and \a NCols are the number of
-/// rows and columns in the block.
-///
-/// \param startRow the first row in the block
-/// \param startCol the first column in the block
-///
-/// Example: \include MatrixBase_block_int_int.cpp
-/// Output: \verbinclude MatrixBase_block_int_int.out
-///
-/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
-/// block(Index,Index,NRowsType,NColsType), here is the one-to-one equivalence:
-/// \code
-/// mat.template block<NRows,NCols>(i,j)  <-->  mat.block(i,j,fix<NRows>,fix<NCols>)
-/// \endcode
-///
-/// \note since block is a templated member, the keyword template has to be used
-/// if the matrix type is also a template parameter: \code m.template block<3,3>(1,1); \endcode
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol)
-{
-  return typename FixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol);
-}
-
-/// This is the const version of block<>(Index, Index). */
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol) const
-{
-  return typename ConstFixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol);
-}
-
-/// \returns an expression of a block of \c *this.
-///
-/// \tparam NRows number of rows in block as specified at compile-time
-/// \tparam NCols number of columns in block as specified at compile-time
-/// \param  startRow  the first row in the block
-/// \param  startCol  the first column in the block
-/// \param  blockRows number of rows in block as specified at run-time
-/// \param  blockCols number of columns in block as specified at run-time
-///
-/// This function is mainly useful for blocks where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a blockRows should equal \a NRows unless
-/// \a NRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_block_int_int_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_block_int_int_int_int.out
-///
-/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
-/// block(Index,Index,NRowsType,NColsType), here is the one-to-one complete equivalence:
-/// \code
-/// mat.template block<NRows,NCols>(i,j,rows,cols)     <-->  mat.block(i,j,fix<NRows>(rows),fix<NCols>(cols))
-/// \endcode
-/// If we known that, e.g., NRows==Dynamic and NCols!=Dynamic, then the equivalence becomes:
-/// \code
-/// mat.template block<Dynamic,NCols>(i,j,rows,NCols)  <-->  mat.block(i,j,rows,fix<NCols>)
-/// \endcode
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol,
-                                                  Index blockRows, Index blockCols)
-{
-  return typename FixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
-}
-
-/// This is the const version of block<>(Index, Index, Index, Index).
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol,
-                                                              Index blockRows, Index blockCols) const
-{
-  return typename ConstFixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
-}
-
-/// \returns an expression of the \a i-th column of \c *this. Note that the numbering starts at 0.
-///
-/// Example: \include MatrixBase_col.cpp
-/// Output: \verbinclude MatrixBase_col.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-/**
-  * \sa row(), class Block */
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-ColXpr col(Index i)
-{
-  return ColXpr(derived(), i);
-}
-
-/// This is the const version of col().
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-ConstColXpr col(Index i) const
-{
-  return ConstColXpr(derived(), i);
-}
-
-/// \returns an expression of the \a i-th row of \c *this. Note that the numbering starts at 0.
-///
-/// Example: \include MatrixBase_row.cpp
-/// Output: \verbinclude MatrixBase_row.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-/**
-  * \sa col(), class Block */
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-RowXpr row(Index i)
-{
-  return RowXpr(derived(), i);
-}
-
-/// This is the const version of row(). */
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-ConstRowXpr row(Index i) const
-{
-  return ConstRowXpr(derived(), i);
-}
-
-/// \returns an expression of a segment (i.e. a vector block) in \c *this with either dynamic or fixed sizes.
-///
-/// \only_for_vectors
-///
-/// \param start the first coefficient in the segment
-/// \param n the number of coefficients in the segment
-/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
-///
-/// Example: \include MatrixBase_segment_int_int.cpp
-/// Output: \verbinclude MatrixBase_segment_int_int.out
-///
-/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-/// \sa block(Index,Index,NRowsType,NColsType), fix<N>, fix<N>(int), class Block
-///
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-typename FixedSegmentReturnType<...>::Type
-#endif
-segment(Index start, NType n)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), start, internal::get_runtime_value(n));
-}
-
-
-/// This is the const version of segment(Index,NType).
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-const typename ConstFixedSegmentReturnType<...>::Type
-#endif
-segment(Index start, NType n) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), start, internal::get_runtime_value(n));
-}
-
-/// \returns an expression of the first coefficients of \c *this with either dynamic or fixed sizes.
-///
-/// \only_for_vectors
-///
-/// \param n the number of coefficients in the segment
-/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
-///
-/// Example: \include MatrixBase_start_int.cpp
-/// Output: \verbinclude MatrixBase_start_int.out
-///
-/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-/// \sa class Block, block(Index,Index)
-///
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-typename FixedSegmentReturnType<...>::Type
-#endif
-head(NType n)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-              (derived(), 0, internal::get_runtime_value(n));
-}
-
-/// This is the const version of head(NType).
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-const typename ConstFixedSegmentReturnType<...>::Type
-#endif
-head(NType n) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), 0, internal::get_runtime_value(n));
-}
-
-/// \returns an expression of a last coefficients of \c *this with either dynamic or fixed sizes.
-///
-/// \only_for_vectors
-///
-/// \param n the number of coefficients in the segment
-/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
-///
-/// Example: \include MatrixBase_end_int.cpp
-/// Output: \verbinclude MatrixBase_end_int.out
-///
-/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-/// \sa class Block, block(Index,Index)
-///
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-typename FixedSegmentReturnType<...>::Type
-#endif
-tail(NType n)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
-}
-
-/// This is the const version of tail(Index).
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-const typename ConstFixedSegmentReturnType<...>::Type
-#endif
-tail(NType n) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
-}
-
-/// \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this
-///
-/// \only_for_vectors
-///
-/// \tparam N the number of coefficients in the segment as specified at compile-time
-/// \param start the index of the first element in the segment
-/// \param n the number of coefficients in the segment as specified at compile-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_segment.cpp
-/// Output: \verbinclude MatrixBase_template_int_segment.out
-///
-/// \sa segment(Index,NType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedSegmentReturnType<N>::Type segment(Index start, Index n = N)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<N>::Type(derived(), start, n);
-}
-
-/// This is the const version of segment<int>(Index).
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstFixedSegmentReturnType<N>::Type segment(Index start, Index n = N) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<N>::Type(derived(), start, n);
-}
-
-/// \returns a fixed-size expression of the first coefficients of \c *this.
-///
-/// \only_for_vectors
-///
-/// \tparam N the number of coefficients in the segment as specified at compile-time
-/// \param  n the number of coefficients in the segment as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_start.cpp
-/// Output: \verbinclude MatrixBase_template_int_start.out
-///
-/// \sa head(NType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedSegmentReturnType<N>::Type head(Index n = N)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<N>::Type(derived(), 0, n);
-}
-
-/// This is the const version of head<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstFixedSegmentReturnType<N>::Type head(Index n = N) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<N>::Type(derived(), 0, n);
-}
-
-/// \returns a fixed-size expression of the last coefficients of \c *this.
-///
-/// \only_for_vectors
-///
-/// \tparam N the number of coefficients in the segment as specified at compile-time
-/// \param  n the number of coefficients in the segment as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_end.cpp
-/// Output: \verbinclude MatrixBase_template_int_end.out
-///
-/// \sa tail(NType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedSegmentReturnType<N>::Type tail(Index n = N)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<N>::Type(derived(), size() - n);
-}
-
-/// This is the const version of tail<int>.
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstFixedSegmentReturnType<N>::Type tail(Index n = N) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<N>::Type(derived(), size() - n);
-}
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major).
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-InnerVectorReturnType innerVector(Index outer)
-{ return InnerVectorReturnType(derived(), outer); }
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major). Read-only.
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const ConstInnerVectorReturnType innerVector(Index outer) const
-{ return ConstInnerVectorReturnType(derived(), outer); }
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major).
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-InnerVectorsReturnType
-innerVectors(Index outerStart, Index outerSize)
-{
-  return Block<Derived,Dynamic,Dynamic,true>(derived(),
-                                             IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart,
-                                             IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize);
-
-}
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major). Read-only.
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const ConstInnerVectorsReturnType
-innerVectors(Index outerStart, Index outerSize) const
-{
-  return Block<const Derived,Dynamic,Dynamic,true>(derived(),
-                                                  IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart,
-                                                  IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize);
-
-}
-
-/** \returns the i-th subvector (column or vector) according to the \c Direction
-  * \sa subVectors()
-  */
-template<DirectionType Direction>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename internal::conditional<Direction==Vertical,ColXpr,RowXpr>::type
-subVector(Index i)
-{
-  return typename internal::conditional<Direction==Vertical,ColXpr,RowXpr>::type(derived(),i);
-}
-
-/** This is the const version of subVector(Index) */
-template<DirectionType Direction>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename internal::conditional<Direction==Vertical,ConstColXpr,ConstRowXpr>::type
-subVector(Index i) const
-{
-  return typename internal::conditional<Direction==Vertical,ConstColXpr,ConstRowXpr>::type(derived(),i);
-}
-
-/** \returns the number of subvectors (rows or columns) in the direction \c Direction
-  * \sa subVector(Index)
-  */
-template<DirectionType Direction>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-Index subVectors() const
-{ return (Direction==Vertical)?cols():rows(); }
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/CommonCwiseBinaryOps.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
deleted file mode 100644
index 8b6730e..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
+++ /dev/null
@@ -1,115 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is a base class plugin containing common coefficient wise functions.
-
-/** \returns an expression of the difference of \c *this and \a other
-  *
-  * \note If you want to substract a given scalar from all coefficients, see Cwise::operator-().
-  *
-  * \sa class CwiseBinaryOp, operator-=()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(operator-,difference)
-
-/** \returns an expression of the sum of \c *this and \a other
-  *
-  * \note If you want to add a given scalar to all coefficients, see Cwise::operator+().
-  *
-  * \sa class CwiseBinaryOp, operator+=()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(operator+,sum)
-
-/** \returns an expression of a custom coefficient-wise operator \a func of *this and \a other
-  *
-  * The template parameter \a CustomBinaryOp is the type of the functor
-  * of the custom operator (see class CwiseBinaryOp for an example)
-  *
-  * Here is an example illustrating the use of custom functors:
-  * \include class_CwiseBinaryOp.cpp
-  * Output: \verbinclude class_CwiseBinaryOp.out
-  *
-  * \sa class CwiseBinaryOp, operator+(), operator-(), cwiseProduct()
-  */
-template<typename CustomBinaryOp, typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
-binaryExpr(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other, const CustomBinaryOp& func = CustomBinaryOp()) const
-{
-  return CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other.derived(), func);
-}
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP(operator*,product)
-#else
-/** \returns an expression of \c *this scaled by the scalar factor \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_product_op<Scalar,T>,Derived,Constant<T> > operator*(const T& scalar) const;
-/** \returns an expression of \a expr scaled by the scalar factor \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T> friend
-const CwiseBinaryOp<internal::scalar_product_op<T,Scalar>,Constant<T>,Derived> operator*(const T& scalar, const StorageBaseType& expr);
-#endif
-
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(operator/,quotient)
-#else
-/** \returns an expression of \c *this divided by the scalar value \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,T>,Derived,Constant<T> > operator/(const T& scalar) const;
-#endif
-
-/** \returns an expression of the coefficient-wise boolean \b and operator of \c *this and \a other
-  *
-  * \warning this operator is for expression of bool only.
-  *
-  * Example: \include Cwise_boolean_and.cpp
-  * Output: \verbinclude Cwise_boolean_and.out
-  *
-  * \sa operator||(), select()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>
-operator&&(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return CwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>(derived(),other.derived());
-}
-
-/** \returns an expression of the coefficient-wise boolean \b or operator of \c *this and \a other
-  *
-  * \warning this operator is for expression of bool only.
-  *
-  * Example: \include Cwise_boolean_or.cpp
-  * Output: \verbinclude Cwise_boolean_or.out
-  *
-  * \sa operator&&(), select()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>
-operator||(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return CwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>(derived(),other.derived());
-}
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/CommonCwiseUnaryOps.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
deleted file mode 100644
index 5418dc4..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
+++ /dev/null
@@ -1,177 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is a base class plugin containing common coefficient wise functions.
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-/** \internal the return type of conjugate() */
-typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                    const CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>,
-                    const Derived&
-                  >::type ConjugateReturnType;
-/** \internal the return type of real() const */
-typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                    const CwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>,
-                    const Derived&
-                  >::type RealReturnType;
-/** \internal the return type of real() */
-typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                    CwiseUnaryView<internal::scalar_real_ref_op<Scalar>, Derived>,
-                    Derived&
-                  >::type NonConstRealReturnType;
-/** \internal the return type of imag() const */
-typedef CwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived> ImagReturnType;
-/** \internal the return type of imag() */
-typedef CwiseUnaryView<internal::scalar_imag_ref_op<Scalar>, Derived> NonConstImagReturnType;
-
-typedef CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived> NegativeReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-/// \returns an expression of the opposite of \c *this
-///
-EIGEN_DOC_UNARY_ADDONS(operator-,opposite)
-///
-EIGEN_DEVICE_FUNC
-inline const NegativeReturnType
-operator-() const { return NegativeReturnType(derived()); }
-
-
-template<class NewType> struct CastXpr { typedef typename internal::cast_return_type<Derived,const CwiseUnaryOp<internal::scalar_cast_op<Scalar, NewType>, const Derived> >::type Type; };
-
-/// \returns an expression of \c *this with the \a Scalar type casted to
-/// \a NewScalar.
-///
-/// The template parameter \a NewScalar is the type we are casting the scalars to.
-///
-EIGEN_DOC_UNARY_ADDONS(cast,conversion function)
-///
-/// \sa class CwiseUnaryOp
-///
-template<typename NewType>
-EIGEN_DEVICE_FUNC
-typename CastXpr<NewType>::Type
-cast() const
-{
-  return typename CastXpr<NewType>::Type(derived());
-}
-
-/// \returns an expression of the complex conjugate of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(conjugate,complex conjugate)
-///
-/// \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_conj">Math functions</a>, MatrixBase::adjoint()
-EIGEN_DEVICE_FUNC
-inline ConjugateReturnType
-conjugate() const
-{
-  return ConjugateReturnType(derived());
-}
-
-/// \returns an expression of the complex conjugate of \c *this if Cond==true, returns derived() otherwise.
-///
-EIGEN_DOC_UNARY_ADDONS(conjugate,complex conjugate)
-///
-/// \sa conjugate()
-template<bool Cond>
-EIGEN_DEVICE_FUNC
-inline typename internal::conditional<Cond,ConjugateReturnType,const Derived&>::type
-conjugateIf() const
-{
-  typedef typename internal::conditional<Cond,ConjugateReturnType,const Derived&>::type ReturnType;
-  return ReturnType(derived());
-}
-
-/// \returns a read-only expression of the real part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(real,real part function)
-///
-/// \sa imag()
-EIGEN_DEVICE_FUNC
-inline RealReturnType
-real() const { return RealReturnType(derived()); }
-
-/// \returns an read-only expression of the imaginary part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(imag,imaginary part function)
-///
-/// \sa real()
-EIGEN_DEVICE_FUNC
-inline const ImagReturnType
-imag() const { return ImagReturnType(derived()); }
-
-/// \brief Apply a unary operator coefficient-wise
-/// \param[in]  func  Functor implementing the unary operator
-/// \tparam  CustomUnaryOp Type of \a func
-/// \returns An expression of a custom coefficient-wise unary operator \a func of *this
-///
-/// The function \c ptr_fun() from the C++ standard library can be used to make functors out of normal functions.
-///
-/// Example:
-/// \include class_CwiseUnaryOp_ptrfun.cpp
-/// Output: \verbinclude class_CwiseUnaryOp_ptrfun.out
-///
-/// Genuine functors allow for more possibilities, for instance it may contain a state.
-///
-/// Example:
-/// \include class_CwiseUnaryOp.cpp
-/// Output: \verbinclude class_CwiseUnaryOp.out
-///
-EIGEN_DOC_UNARY_ADDONS(unaryExpr,unary function)
-///
-/// \sa unaryViewExpr, binaryExpr, class CwiseUnaryOp
-///
-template<typename CustomUnaryOp>
-EIGEN_DEVICE_FUNC
-inline const CwiseUnaryOp<CustomUnaryOp, const Derived>
-unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const
-{
-  return CwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
-}
-
-/// \returns an expression of a custom coefficient-wise unary operator \a func of *this
-///
-/// The template parameter \a CustomUnaryOp is the type of the functor
-/// of the custom unary operator.
-///
-/// Example:
-/// \include class_CwiseUnaryOp.cpp
-/// Output: \verbinclude class_CwiseUnaryOp.out
-///
-EIGEN_DOC_UNARY_ADDONS(unaryViewExpr,unary function)
-///
-/// \sa unaryExpr, binaryExpr class CwiseUnaryOp
-///
-template<typename CustomViewOp>
-EIGEN_DEVICE_FUNC
-inline const CwiseUnaryView<CustomViewOp, const Derived>
-unaryViewExpr(const CustomViewOp& func = CustomViewOp()) const
-{
-  return CwiseUnaryView<CustomViewOp, const Derived>(derived(), func);
-}
-
-/// \returns a non const expression of the real part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(real,real part function)
-///
-/// \sa imag()
-EIGEN_DEVICE_FUNC
-inline NonConstRealReturnType
-real() { return NonConstRealReturnType(derived()); }
-
-/// \returns a non const expression of the imaginary part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(imag,imaginary part function)
-///
-/// \sa real()
-EIGEN_DEVICE_FUNC
-inline NonConstImagReturnType
-imag() { return NonConstImagReturnType(derived()); }
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/IndexedViewMethods.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/IndexedViewMethods.h
deleted file mode 100644
index 5bfb19a..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/IndexedViewMethods.h
+++ /dev/null
@@ -1,262 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#if !defined(EIGEN_PARSED_BY_DOXYGEN)
-
-// This file is automatically included twice to generate const and non-const versions
-
-#ifndef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#define EIGEN_INDEXED_VIEW_METHOD_CONST const
-#define EIGEN_INDEXED_VIEW_METHOD_TYPE  ConstIndexedViewType
-#else
-#define EIGEN_INDEXED_VIEW_METHOD_CONST
-#define EIGEN_INDEXED_VIEW_METHOD_TYPE IndexedViewType
-#endif
-
-#ifndef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-protected:
-
-// define some aliases to ease readability
-
-template<typename Indices>
-struct IvcRowType : public internal::IndexedViewCompatibleType<Indices,RowsAtCompileTime> {};
-
-template<typename Indices>
-struct IvcColType : public internal::IndexedViewCompatibleType<Indices,ColsAtCompileTime> {};
-
-template<typename Indices>
-struct IvcType : public internal::IndexedViewCompatibleType<Indices,SizeAtCompileTime> {};
-
-typedef typename internal::IndexedViewCompatibleType<Index,1>::type IvcIndex;
-
-template<typename Indices>
-typename IvcRowType<Indices>::type
-ivcRow(const Indices& indices) const {
-  return internal::makeIndexedViewCompatible(indices, internal::variable_if_dynamic<Index,RowsAtCompileTime>(derived().rows()),Specialized);
-}
-
-template<typename Indices>
-typename IvcColType<Indices>::type
-ivcCol(const Indices& indices) const {
-  return internal::makeIndexedViewCompatible(indices, internal::variable_if_dynamic<Index,ColsAtCompileTime>(derived().cols()),Specialized);
-}
-
-template<typename Indices>
-typename IvcColType<Indices>::type
-ivcSize(const Indices& indices) const {
-  return internal::makeIndexedViewCompatible(indices, internal::variable_if_dynamic<Index,SizeAtCompileTime>(derived().size()),Specialized);
-}
-
-public:
-
-#endif
-
-template<typename RowIndices, typename ColIndices>
-struct EIGEN_INDEXED_VIEW_METHOD_TYPE {
-  typedef IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,
-                      typename IvcRowType<RowIndices>::type,
-                      typename IvcColType<ColIndices>::type> type;
-};
-
-// This is the generic version
-
-template<typename RowIndices, typename ColIndices>
-typename internal::enable_if<internal::valid_indexed_view_overload<RowIndices,ColIndices>::value
-  && internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::ReturnAsIndexedView,
-  typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type >::type
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type
-            (derived(), ivcRow(rowIndices), ivcCol(colIndices));
-}
-
-// The following overload returns a Block<> object
-
-template<typename RowIndices, typename ColIndices>
-typename internal::enable_if<internal::valid_indexed_view_overload<RowIndices,ColIndices>::value
-  && internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::ReturnAsBlock,
-  typename internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::BlockType>::type
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  typedef typename internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::BlockType BlockType;
-  typename IvcRowType<RowIndices>::type actualRowIndices = ivcRow(rowIndices);
-  typename IvcColType<ColIndices>::type actualColIndices = ivcCol(colIndices);
-  return BlockType(derived(),
-                   internal::first(actualRowIndices),
-                   internal::first(actualColIndices),
-                   internal::size(actualRowIndices),
-                   internal::size(actualColIndices));
-}
-
-// The following overload returns a Scalar
-
-template<typename RowIndices, typename ColIndices>
-typename internal::enable_if<internal::valid_indexed_view_overload<RowIndices,ColIndices>::value
-  && internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::ReturnAsScalar,
-  CoeffReturnType >::type
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return Base::operator()(internal::eval_expr_given_size(rowIndices,rows()),internal::eval_expr_given_size(colIndices,cols()));
-}
-
-#if EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-// The following three overloads are needed to handle raw Index[N] arrays.
-
-template<typename RowIndicesT, std::size_t RowIndicesN, typename ColIndices>
-IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN],typename IvcColType<ColIndices>::type>
-operator()(const RowIndicesT (&rowIndices)[RowIndicesN], const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN],typename IvcColType<ColIndices>::type>
-                    (derived(), rowIndices, ivcCol(colIndices));
-}
-
-template<typename RowIndices, typename ColIndicesT, std::size_t ColIndicesN>
-IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcRowType<RowIndices>::type, const ColIndicesT (&)[ColIndicesN]>
-operator()(const RowIndices& rowIndices, const ColIndicesT (&colIndices)[ColIndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcRowType<RowIndices>::type,const ColIndicesT (&)[ColIndicesN]>
-                    (derived(), ivcRow(rowIndices), colIndices);
-}
-
-template<typename RowIndicesT, std::size_t RowIndicesN, typename ColIndicesT, std::size_t ColIndicesN>
-IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN], const ColIndicesT (&)[ColIndicesN]>
-operator()(const RowIndicesT (&rowIndices)[RowIndicesN], const ColIndicesT (&colIndices)[ColIndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN],const ColIndicesT (&)[ColIndicesN]>
-                    (derived(), rowIndices, colIndices);
-}
-
-#endif // EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-// Overloads for 1D vectors/arrays
-
-template<typename Indices>
-typename internal::enable_if<
-  IsRowMajor && (!(internal::get_compile_time_incr<typename IvcType<Indices>::type>::value==1 || internal::is_valid_index_type<Indices>::value)),
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,typename IvcType<Indices>::type> >::type
-operator()(const Indices& indices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,typename IvcType<Indices>::type>
-            (derived(), IvcIndex(0), ivcCol(indices));
-}
-
-template<typename Indices>
-typename internal::enable_if<
-  (!IsRowMajor) && (!(internal::get_compile_time_incr<typename IvcType<Indices>::type>::value==1 || internal::is_valid_index_type<Indices>::value)),
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcType<Indices>::type,IvcIndex> >::type
-operator()(const Indices& indices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcType<Indices>::type,IvcIndex>
-            (derived(), ivcRow(indices), IvcIndex(0));
-}
-
-template<typename Indices>
-typename internal::enable_if<
-  (internal::get_compile_time_incr<typename IvcType<Indices>::type>::value==1) && (!internal::is_valid_index_type<Indices>::value) && (!symbolic::is_symbolic<Indices>::value),
-  VectorBlock<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,internal::array_size<Indices>::value> >::type
-operator()(const Indices& indices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  typename IvcType<Indices>::type actualIndices = ivcSize(indices);
-  return VectorBlock<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,internal::array_size<Indices>::value>
-            (derived(), internal::first(actualIndices), internal::size(actualIndices));
-}
-
-template<typename IndexType>
-typename internal::enable_if<symbolic::is_symbolic<IndexType>::value, CoeffReturnType >::type
-operator()(const IndexType& id) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return Base::operator()(internal::eval_expr_given_size(id,size()));
-}
-
-#if EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-template<typename IndicesT, std::size_t IndicesN>
-typename internal::enable_if<IsRowMajor,
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,const IndicesT (&)[IndicesN]> >::type
-operator()(const IndicesT (&indices)[IndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,const IndicesT (&)[IndicesN]>
-            (derived(), IvcIndex(0), indices);
-}
-
-template<typename IndicesT, std::size_t IndicesN>
-typename internal::enable_if<!IsRowMajor,
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const IndicesT (&)[IndicesN],IvcIndex> >::type
-operator()(const IndicesT (&indices)[IndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const IndicesT (&)[IndicesN],IvcIndex>
-            (derived(), indices, IvcIndex(0));
-}
-
-#endif // EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-#undef EIGEN_INDEXED_VIEW_METHOD_CONST
-#undef EIGEN_INDEXED_VIEW_METHOD_TYPE
-
-#ifndef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#define EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#include "IndexedViewMethods.h"
-#undef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#endif
-
-#else // EIGEN_PARSED_BY_DOXYGEN
-
-/**
-  * \returns a generic submatrix view defined by the rows and columns indexed \a rowIndices and \a colIndices respectively.
-  *
-  * Each parameter must either be:
-  *  - An integer indexing a single row or column
-  *  - Eigen::all indexing the full set of respective rows or columns in increasing order
-  *  - An ArithmeticSequence as returned by the Eigen::seq and Eigen::seqN functions
-  *  - Any %Eigen's vector/array of integers or expressions
-  *  - Plain C arrays: \c int[N]
-  *  - And more generally any type exposing the following two member functions:
-  * \code
-  * <integral type> operator[](<integral type>) const;
-  * <integral type> size() const;
-  * \endcode
-  * where \c <integral \c type>  stands for any integer type compatible with Eigen::Index (i.e. \c std::ptrdiff_t).
-  *
-  * The last statement implies compatibility with \c std::vector, \c std::valarray, \c std::array, many of the Range-v3's ranges, etc.
-  *
-  * If the submatrix can be represented using a starting position \c (i,j) and positive sizes \c (rows,columns), then this
-  * method will returns a Block object after extraction of the relevant information from the passed arguments. This is the case
-  * when all arguments are either:
-  *  - An integer
-  *  - Eigen::all
-  *  - An ArithmeticSequence with compile-time increment strictly equal to 1, as returned by Eigen::seq(a,b), and Eigen::seqN(a,N).
-  *
-  * Otherwise a more general IndexedView<Derived,RowIndices',ColIndices'> object will be returned, after conversion of the inputs
-  * to more suitable types \c RowIndices' and \c ColIndices'.
-  *
-  * For 1D vectors and arrays, you better use the operator()(const Indices&) overload, which behave the same way but taking a single parameter.
-  *
-  * See also this <a href="https://stackoverflow.com/questions/46110917/eigen-replicate-items-along-one-dimension-without-useless-allocations">question</a> and its answer for an example of how to duplicate coefficients.
-  *
-  * \sa operator()(const Indices&), class Block, class IndexedView, DenseBase::block(Index,Index,Index,Index)
-  */
-template<typename RowIndices, typename ColIndices>
-IndexedView_or_Block
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices);
-
-/** This is an overload of operator()(const RowIndices&, const ColIndices&) for 1D vectors or arrays
-  *
-  * \only_for_vectors
-  */
-template<typename Indices>
-IndexedView_or_VectorBlock
-operator()(const Indices& indices);
-
-#endif  // EIGEN_PARSED_BY_DOXYGEN
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
deleted file mode 100644
index a0feef8..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
+++ /dev/null
@@ -1,152 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is a base class plugin containing matrix specifics coefficient wise functions.
-
-/** \returns an expression of the Schur product (coefficient wise product) of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseProduct.cpp
-  * Output: \verbinclude MatrixBase_cwiseProduct.out
-  *
-  * \sa class CwiseBinaryOp, cwiseAbs2
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
-cwiseProduct(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise == operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include MatrixBase_cwiseEqual.cpp
-  * Output: \verbinclude MatrixBase_cwiseEqual.out
-  *
-  * \sa cwiseNotEqual(), isApprox(), isMuchSmallerThan()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<numext::equal_to<Scalar>, const Derived, const OtherDerived>
-cwiseEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<numext::equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise != operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include MatrixBase_cwiseNotEqual.cpp
-  * Output: \verbinclude MatrixBase_cwiseNotEqual.out
-  *
-  * \sa cwiseEqual(), isApprox(), isMuchSmallerThan()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<numext::not_equal_to<Scalar>, const Derived, const OtherDerived>
-cwiseNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<numext::not_equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise min of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseMin.cpp
-  * Output: \verbinclude MatrixBase_cwiseMin.out
-  *
-  * \sa class CwiseBinaryOp, max()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>
-cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise min of *this and scalar \a other
-  *
-  * \sa class CwiseBinaryOp, min()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
-cwiseMin(const Scalar &other) const
-{
-  return cwiseMin(Derived::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise max of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseMax.cpp
-  * Output: \verbinclude MatrixBase_cwiseMax.out
-  *
-  * \sa class CwiseBinaryOp, min()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>
-cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise max of *this and scalar \a other
-  *
-  * \sa class CwiseBinaryOp, min()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
-cwiseMax(const Scalar &other) const
-{
-  return cwiseMax(Derived::Constant(rows(), cols(), other));
-}
-
-
-/** \returns an expression of the coefficient-wise quotient of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseQuotient.cpp
-  * Output: \verbinclude MatrixBase_cwiseQuotient.out
-  *
-  * \sa class CwiseBinaryOp, cwiseProduct(), cwiseInverse()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
-cwiseQuotient(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar,internal::cmp_EQ>, const Derived, const ConstantReturnType> CwiseScalarEqualReturnType;
-
-/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * \sa cwiseEqual(const MatrixBase<OtherDerived> &) const
-  */
-EIGEN_DEVICE_FUNC
-inline const CwiseScalarEqualReturnType
-cwiseEqual(const Scalar& s) const
-{
-  return CwiseScalarEqualReturnType(derived(), Derived::Constant(rows(), cols(), s), internal::scalar_cmp_op<Scalar,Scalar,internal::cmp_EQ>());
-}
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
deleted file mode 100644
index 0514d8f..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
+++ /dev/null
@@ -1,95 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is included into the body of the base classes supporting matrix specific coefficient-wise functions.
-// This include MatrixBase and SparseMatrixBase.
-
-
-typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> CwiseAbsReturnType;
-typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> CwiseAbs2ReturnType;
-typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> CwiseArgReturnType;
-typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> CwiseSqrtReturnType;
-typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> CwiseSignReturnType;
-typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> CwiseInverseReturnType;
-
-/// \returns an expression of the coefficient-wise absolute value of \c *this
-///
-/// Example: \include MatrixBase_cwiseAbs.cpp
-/// Output: \verbinclude MatrixBase_cwiseAbs.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseAbs,absolute value)
-///
-/// \sa cwiseAbs2()
-///
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseAbsReturnType
-cwiseAbs() const { return CwiseAbsReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise squared absolute value of \c *this
-///
-/// Example: \include MatrixBase_cwiseAbs2.cpp
-/// Output: \verbinclude MatrixBase_cwiseAbs2.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseAbs2,squared absolute value)
-///
-/// \sa cwiseAbs()
-///
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseAbs2ReturnType
-cwiseAbs2() const { return CwiseAbs2ReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise square root of *this.
-///
-/// Example: \include MatrixBase_cwiseSqrt.cpp
-/// Output: \verbinclude MatrixBase_cwiseSqrt.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseSqrt,square-root)
-///
-/// \sa cwisePow(), cwiseSquare()
-///
-EIGEN_DEVICE_FUNC
-inline const CwiseSqrtReturnType
-cwiseSqrt() const { return CwiseSqrtReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise signum of *this.
-///
-/// Example: \include MatrixBase_cwiseSign.cpp
-/// Output: \verbinclude MatrixBase_cwiseSign.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseSign,sign function)
-///
-EIGEN_DEVICE_FUNC
-inline const CwiseSignReturnType
-cwiseSign() const { return CwiseSignReturnType(derived()); }
-
-
-/// \returns an expression of the coefficient-wise inverse of *this.
-///
-/// Example: \include MatrixBase_cwiseInverse.cpp
-/// Output: \verbinclude MatrixBase_cwiseInverse.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseInverse,inverse)
-///
-/// \sa cwiseProduct()
-///
-EIGEN_DEVICE_FUNC
-inline const CwiseInverseReturnType
-cwiseInverse() const { return CwiseInverseReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise phase angle of \c *this
-///
-/// Example: \include MatrixBase_cwiseArg.cpp
-/// Output: \verbinclude MatrixBase_cwiseArg.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseArg,arg)
-
-EIGEN_DEVICE_FUNC
-inline const CwiseArgReturnType
-cwiseArg() const { return CwiseArgReturnType(derived()); }
diff --git a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ReshapedMethods.h b/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ReshapedMethods.h
deleted file mode 100644
index 482a6b0..0000000
--- a/3rdParty/my_ceres_vc17/include/Eigen/src/plugins/ReshapedMethods.h
+++ /dev/null
@@ -1,149 +0,0 @@
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-/// \returns an expression of \c *this with reshaped sizes.
-///
-/// \param nRows the number of rows in the reshaped expression, specified at either run-time or compile-time, or AutoSize
-/// \param nCols the number of columns in the reshaped expression, specified at either run-time or compile-time, or AutoSize
-/// \tparam Order specifies whether the coefficients should be processed in column-major-order (ColMajor), in row-major-order (RowMajor),
-///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
-/// \tparam NRowsType the type of the value handling the number of rows, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns, typically Index.
-///
-/// Dynamic size example: \include MatrixBase_reshaped_int_int.cpp
-/// Output: \verbinclude MatrixBase_reshaped_int_int.out
-///
-/// The number of rows \a nRows and columns \a nCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value in case \c N equals Eigen::Dynamic.
-/// Here is an example with a fixed number of rows and columns:
-/// \include MatrixBase_reshaped_fixed.cpp
-/// Output: \verbinclude MatrixBase_reshaped_fixed.out
-///
-/// Finally, one of the sizes parameter can be automatically deduced from the other one by passing AutoSize as in the following example:
-/// \include MatrixBase_reshaped_auto.cpp
-/// Output: \verbinclude MatrixBase_reshaped_auto.out
-/// AutoSize does preserve compile-time sizes when possible, i.e., when the sizes of the input are known at compile time \b and
-/// that the other size is passed at compile-time using Eigen::fix<N> as above.
-///
-/// \sa class Reshaped, fix, fix<N>(int)
-///
-template<int Order = ColMajor, typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline Reshaped<Derived,...>
-reshaped(NRowsType nRows, NColsType nCols);
-
-/// This is the const version of reshaped(NRowsType,NColsType).
-template<int Order = ColMajor, typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline const Reshaped<const Derived,...>
-reshaped(NRowsType nRows, NColsType nCols) const;
-
-/// \returns an expression of \c *this with columns (or rows) stacked to a linear column vector
-///
-/// \tparam Order specifies whether the coefficients should be processed in column-major-order (ColMajor), in row-major-order (RowMajor),
-///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
-///
-/// This overloads is essentially a shortcut for `A.reshaped<Order>(AutoSize,fix<1>)`.
-///
-/// - If `Order==ColMajor` (the default), then it returns a column-vector from the stacked columns of \c *this.
-/// - If `Order==RowMajor`, then it returns a column-vector from the stacked rows of \c *this.
-/// - If `Order==AutoOrder`, then it returns a column-vector with elements stacked following the storage order of \c *this.
-///   This mode is the recommended one when the particular ordering of the element is not relevant.
-///
-/// Example:
-/// \include MatrixBase_reshaped_to_vector.cpp
-/// Output: \verbinclude MatrixBase_reshaped_to_vector.out
-///
-/// If you want more control, you can still fall back to reshaped(NRowsType,NColsType).
-///
-/// \sa reshaped(NRowsType,NColsType), class Reshaped
-///
-template<int Order = ColMajor>
-EIGEN_DEVICE_FUNC
-inline Reshaped<Derived,...>
-reshaped();
-
-/// This is the const version of reshaped().
-template<int Order = ColMajor>
-EIGEN_DEVICE_FUNC
-inline const Reshaped<const Derived,...>
-reshaped() const;
-
-#else
-
-// This file is automatically included twice to generate const and non-const versions
-
-#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
-#define EIGEN_RESHAPED_METHOD_CONST const
-#else
-#define EIGEN_RESHAPED_METHOD_CONST
-#endif
-
-#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
-
-// This part is included once
-
-#endif
-
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value>
-reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST
-{
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                  internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                  internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value>
-                (derived(),
-                 internal::get_runtime_reshape_size(nRows,internal::get_runtime_value(nCols),size()),
-                 internal::get_runtime_reshape_size(nCols,internal::get_runtime_value(nRows),size()));
-}
-
-template<int Order, typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value,
-                internal::get_compiletime_reshape_order<Flags,Order>::value>
-reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST
-{
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                  internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                  internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value,
-                  internal::get_compiletime_reshape_order<Flags,Order>::value>
-                (derived(),
-                 internal::get_runtime_reshape_size(nRows,internal::get_runtime_value(nCols),size()),
-                 internal::get_runtime_reshape_size(nCols,internal::get_runtime_value(nRows),size()));
-}
-
-// Views as linear vectors
-
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,SizeAtCompileTime,1>
-reshaped() EIGEN_RESHAPED_METHOD_CONST
-{
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,SizeAtCompileTime,1>(derived(),size(),1);
-}
-
-template<int Order>
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
-                internal::get_compiletime_reshape_order<Flags,Order>::value>
-reshaped() EIGEN_RESHAPED_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT(Order==RowMajor || Order==ColMajor || Order==AutoOrder, INVALID_TEMPLATE_PARAMETER);
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
-                  internal::get_compiletime_reshape_order<Flags,Order>::value>
-                (derived(), size(), 1);
-}
-
-#undef EIGEN_RESHAPED_METHOD_CONST
-
-#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
-#define EIGEN_RESHAPED_METHOD_2ND_PASS
-#include "ReshapedMethods.h"
-#undef EIGEN_RESHAPED_METHOD_2ND_PASS
-#endif
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
diff --git a/3rdParty/my_ceres_vc17/include/ceres/autodiff_cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/autodiff_cost_function.h
deleted file mode 100644
index 7e2fa71..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/autodiff_cost_function.h
+++ /dev/null
@@ -1,228 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//
-// Create CostFunctions as needed by the least squares framework, with
-// Jacobians computed via automatic differentiation. For more
-// information on automatic differentiation, see the wikipedia article
-// at http://en.wikipedia.org/wiki/Automatic_differentiation
-//
-// To get an auto differentiated cost function, you must define a class with a
-// templated operator() (a functor) that computes the cost function in terms of
-// the template parameter T. The autodiff framework substitutes appropriate
-// "jet" objects for T in order to compute the derivative when necessary, but
-// this is hidden, and you should write the function as if T were a scalar type
-// (e.g. a double-precision floating point number).
-//
-// The function must write the computed value in the last argument
-// (the only non-const one) and return true to indicate
-// success. Please see cost_function.h for details on how the return
-// value maybe used to impose simple constraints on the parameter
-// block.
-//
-// For example, consider a scalar error e = k - x'y, where both x and y are
-// two-dimensional column vector parameters, the prime sign indicates
-// transposition, and k is a constant. The form of this error, which is the
-// difference between a constant and an expression, is a common pattern in least
-// squares problems. For example, the value x'y might be the model expectation
-// for a series of measurements, where there is an instance of the cost function
-// for each measurement k.
-//
-// The actual cost added to the total problem is e^2, or (k - x'y)^2; however,
-// the squaring is implicitly done by the optimization framework.
-//
-// To write an auto-differentiable cost function for the above model, first
-// define the object
-//
-//   class MyScalarCostFunctor {
-//     MyScalarCostFunctor(double k): k_(k) {}
-//
-//     template <typename T>
-//     bool operator()(const T* const x , const T* const y, T* e) const {
-//       e[0] = T(k_) - x[0] * y[0] + x[1] * y[1];
-//       return true;
-//     }
-//
-//    private:
-//     double k_;
-//   };
-//
-// Note that in the declaration of operator() the input parameters x and y come
-// first, and are passed as const pointers to arrays of T. If there were three
-// input parameters, then the third input parameter would come after y. The
-// output is always the last parameter, and is also a pointer to an array. In
-// the example above, e is a scalar, so only e[0] is set.
-//
-// Then given this class definition, the auto differentiated cost function for
-// it can be constructed as follows.
-//
-//   CostFunction* cost_function
-//       = new AutoDiffCostFunction<MyScalarCostFunctor, 1, 2, 2>(
-//            new MyScalarCostFunctor(1.0));             ^  ^  ^
-//                                                       |  |  |
-//                            Dimension of residual -----+  |  |
-//                            Dimension of x ---------------+  |
-//                            Dimension of y ------------------+
-//
-// In this example, there is usually an instance for each measurement of k.
-//
-// In the instantiation above, the template parameters following
-// "MyScalarCostFunctor", "1, 2, 2", describe the functor as computing a
-// 1-dimensional output from two arguments, both 2-dimensional.
-//
-// AutoDiffCostFunction also supports cost functions with a
-// runtime-determined number of residuals. For example:
-//
-//   CostFunction* cost_function
-//       = new AutoDiffCostFunction<MyScalarCostFunctor, DYNAMIC, 2, 2>(
-//           new CostFunctorWithDynamicNumResiduals(1.0),   ^     ^  ^
-//           runtime_number_of_residuals); <----+           |     |  |
-//                                              |           |     |  |
-//                                              |           |     |  |
-//             Actual number of residuals ------+           |     |  |
-//             Indicate dynamic number of residuals --------+     |  |
-//             Dimension of x ------------------------------------+  |
-//             Dimension of y ---------------------------------------+
-//
-// WARNING #1: Since the functor will get instantiated with different types for
-// T, you must convert from other numeric types to T before mixing
-// computations with other variables of type T. In the example above, this is
-// seen where instead of using k_ directly, k_ is wrapped with T(k_).
-//
-// WARNING #2: A common beginner's error when first using autodiff cost
-// functions is to get the sizing wrong. In particular, there is a tendency to
-// set the template parameters to (dimension of residual, number of parameters)
-// instead of passing a dimension parameter for *every parameter*. In the
-// example above, that would be <MyScalarCostFunctor, 1, 2>, which is missing
-// the last '2' argument. Please be careful when setting the size parameters.
-
-#ifndef CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_
-#define CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_
-
-#include <memory>
-
-#include "ceres/internal/autodiff.h"
-#include "ceres/sized_cost_function.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// A cost function which computes the derivative of the cost with respect to
-// the parameters (a.k.a. the jacobian) using an auto differentiation framework.
-// The first template argument is the functor object, described in the header
-// comment. The second argument is the dimension of the residual (or
-// ceres::DYNAMIC to indicate it will be set at runtime), and subsequent
-// arguments describe the size of the Nth parameter, one per parameter.
-//
-// The constructors take ownership of the cost functor.
-//
-// If the number of residuals (argument kNumResiduals below) is
-// ceres::DYNAMIC, then the two-argument constructor must be used. The
-// second constructor takes a number of residuals (in addition to the
-// templated number of residuals). This allows for varying the number
-// of residuals for a single autodiff cost function at runtime.
-template <typename CostFunctor,
-          int kNumResiduals,  // Number of residuals, or ceres::DYNAMIC.
-          int... Ns>          // Number of parameters in each parameter block.
-class AutoDiffCostFunction final
-    : public SizedCostFunction<kNumResiduals, Ns...> {
- public:
-  // Takes ownership of functor by default. Uses the template-provided
-  // value for the number of residuals ("kNumResiduals").
-  explicit AutoDiffCostFunction(CostFunctor* functor,
-                                Ownership ownership = TAKE_OWNERSHIP)
-      : functor_(functor), ownership_(ownership) {
-    static_assert(kNumResiduals != DYNAMIC,
-                  "Can't run the fixed-size constructor if the number of "
-                  "residuals is set to ceres::DYNAMIC.");
-  }
-
-  // Takes ownership of functor by default. Ignores the template-provided
-  // kNumResiduals in favor of the "num_residuals" argument provided.
-  //
-  // This allows for having autodiff cost functions which return varying
-  // numbers of residuals at runtime.
-  AutoDiffCostFunction(CostFunctor* functor,
-                       int num_residuals,
-                       Ownership ownership = TAKE_OWNERSHIP)
-      : functor_(functor), ownership_(ownership) {
-    static_assert(kNumResiduals == DYNAMIC,
-                  "Can't run the dynamic-size constructor if the number of "
-                  "residuals is not ceres::DYNAMIC.");
-    SizedCostFunction<kNumResiduals, Ns...>::set_num_residuals(num_residuals);
-  }
-
-  AutoDiffCostFunction(AutoDiffCostFunction&& other)
-      : functor_(std::move(other.functor_)), ownership_(other.ownership_) {}
-
-  virtual ~AutoDiffCostFunction() {
-    // Manually release pointer if configured to not take ownership rather than
-    // deleting only if ownership is taken.
-    // This is to stay maximally compatible to old user code which may have
-    // forgotten to implement a virtual destructor, from when the
-    // AutoDiffCostFunction always took ownership.
-    if (ownership_ == DO_NOT_TAKE_OWNERSHIP) {
-      functor_.release();
-    }
-  }
-
-  // Implementation details follow; clients of the autodiff cost function should
-  // not have to examine below here.
-  //
-  // To handle variadic cost functions, some template magic is needed. It's
-  // mostly hidden inside autodiff.h.
-  bool Evaluate(double const* const* parameters,
-                double* residuals,
-                double** jacobians) const override {
-    using ParameterDims =
-        typename SizedCostFunction<kNumResiduals, Ns...>::ParameterDims;
-
-    if (!jacobians) {
-      return internal::VariadicEvaluate<ParameterDims>(
-          *functor_, parameters, residuals);
-    }
-    return internal::AutoDifferentiate<kNumResiduals, ParameterDims>(
-        *functor_,
-        parameters,
-        SizedCostFunction<kNumResiduals, Ns...>::num_residuals(),
-        residuals,
-        jacobians);
-  };
-
-  const CostFunctor& functor() const { return *functor_; }
-
- private:
-  std::unique_ptr<CostFunctor> functor_;
-  Ownership ownership_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/autodiff_first_order_function.h b/3rdParty/my_ceres_vc17/include/ceres/autodiff_first_order_function.h
deleted file mode 100644
index de7e8f1..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/autodiff_first_order_function.h
+++ /dev/null
@@ -1,151 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_AUTODIFF_FIRST_ORDER_FUNCTION_H_
-#define CERES_PUBLIC_AUTODIFF_FIRST_ORDER_FUNCTION_H_
-
-#include <memory>
-
-#include "ceres/first_order_function.h"
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/jet.h"
-#include "ceres/types.h"
-
-namespace ceres {
-
-// Create FirstOrderFunctions as needed by the GradientProblem
-// framework, with gradients computed via automatic
-// differentiation. For more information on automatic differentiation,
-// see the wikipedia article at
-// http://en.wikipedia.org/wiki/Automatic_differentiation
-//
-// To get an auto differentiated function, you must define a class
-// with a templated operator() (a functor) that computes the cost
-// function in terms of the template parameter T. The autodiff
-// framework substitutes appropriate "jet" objects for T in order to
-// compute the derivative when necessary, but this is hidden, and you
-// should write the function as if T were a scalar type (e.g. a
-// double-precision floating point number).
-//
-// The function must write the computed value in the last argument
-// (the only non-const one) and return true to indicate
-// success.
-//
-// For example, consider a scalar error e = x'y - a, where both x and y are
-// two-dimensional column vector parameters, the prime sign indicates
-// transposition, and a is a constant.
-//
-// To write an auto-differentiable FirstOrderFunction for the above model, first
-// define the object
-//
-//  class QuadraticCostFunctor {
-//   public:
-//    explicit QuadraticCostFunctor(double a) : a_(a) {}
-//    template <typename T>
-//    bool operator()(const T* const xy, T* cost) const {
-//      const T* const x = xy;
-//      const T* const y = xy + 2;
-//      *cost = x[0] * y[0] + x[1] * y[1] - T(a_);
-//      return true;
-//    }
-//
-//   private:
-//    double a_;
-//  };
-//
-// Note that in the declaration of operator() the input parameters xy come
-// first, and are passed as const pointers to arrays of T. The
-// output is the last parameter.
-//
-// Then given this class definition, the auto differentiated FirstOrderFunction
-// for it can be constructed as follows.
-//
-//    FirstOrderFunction* function =
-//      new AutoDiffFirstOrderFunction<QuadraticCostFunctor, 4>(
-//          new QuadraticCostFunctor(1.0)));
-//
-// In the instantiation above, the template parameters following
-// "QuadraticCostFunctor", "4", describe the functor as computing a
-// 1-dimensional output from a four dimensional vector.
-//
-// WARNING: Since the functor will get instantiated with different types for
-// T, you must convert from other numeric types to T before mixing
-// computations with other variables of type T. In the example above, this is
-// seen where instead of using a_ directly, a_ is wrapped with T(a_).
-
-template <typename FirstOrderFunctor, int kNumParameters>
-class AutoDiffFirstOrderFunction final : public FirstOrderFunction {
- public:
-  // Takes ownership of functor.
-  explicit AutoDiffFirstOrderFunction(FirstOrderFunctor* functor)
-      : functor_(functor) {
-    static_assert(kNumParameters > 0, "kNumParameters must be positive");
-  }
-
-  bool Evaluate(const double* const parameters,
-                double* cost,
-                double* gradient) const override {
-    if (gradient == nullptr) {
-      return (*functor_)(parameters, cost);
-    }
-
-    using JetT = Jet<double, kNumParameters>;
-    internal::FixedArray<JetT, (256 * 7) / sizeof(JetT)> x(kNumParameters);
-    for (int i = 0; i < kNumParameters; ++i) {
-      x[i].a = parameters[i];
-      x[i].v.setZero();
-      x[i].v[i] = 1.0;
-    }
-
-    JetT output;
-    output.a = kImpossibleValue;
-    output.v.setConstant(kImpossibleValue);
-
-    if (!(*functor_)(x.data(), &output)) {
-      return false;
-    }
-
-    *cost = output.a;
-    VectorRef(gradient, kNumParameters) = output.v;
-    return true;
-  }
-
-  int NumParameters() const override { return kNumParameters; }
-
-  const FirstOrderFunctor& functor() const { return *functor_; }
-
- private:
-  std::unique_ptr<FirstOrderFunctor> functor_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_AUTODIFF_FIRST_ORDER_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/autodiff_manifold.h b/3rdParty/my_ceres_vc17/include/ceres/autodiff_manifold.h
deleted file mode 100644
index 09b0aa2..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/autodiff_manifold.h
+++ /dev/null
@@ -1,259 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_AUTODIFF_MANIFOLD_H_
-#define CERES_PUBLIC_AUTODIFF_MANIFOLD_H_
-
-#include <memory>
-
-#include "ceres/internal/autodiff.h"
-#include "ceres/manifold.h"
-
-namespace ceres {
-
-// Create a Manifold with Jacobians computed via automatic differentiation. For
-// more information on manifolds, see include/ceres/manifold.h
-//
-// To get an auto differentiated manifold, you must define a class/struct with
-// templated Plus and Minus functions that compute
-//
-//   x_plus_delta = Plus(x, delta);
-//   y_minus_x    = Minus(y, x);
-//
-// Where, x, y and x_plus_y are vectors on the manifold in the ambient space (so
-// they are kAmbientSize vectors) and delta, y_minus_x are vectors in the
-// tangent space (so they are kTangentSize vectors).
-//
-// The Functor should have the signature:
-//
-// struct Functor {
-//   template <typename T>
-//   bool Plus(const T* x, const T* delta, T* x_plus_delta) const;
-//
-//   template <typename T>
-//   bool Minus(const T* y, const T* x, T* y_minus_x) const;
-// };
-//
-// Observe that the Plus and Minus operations are templated on the parameter T.
-// The autodiff framework substitutes appropriate "Jet" objects for T in order
-// to compute the derivative when necessary. This is the same mechanism that is
-// used to compute derivatives when using AutoDiffCostFunction.
-//
-// Plus and Minus should return true if the computation is successful and false
-// otherwise, in which case the result will not be used.
-//
-// Given this Functor, the corresponding Manifold can be constructed as:
-//
-// AutoDiffManifold<Functor, kAmbientSize, kTangentSize> manifold;
-//
-// As a concrete example consider the case of Quaternions. Quaternions form a
-// three dimensional manifold embedded in R^4, i.e. they have an ambient
-// dimension of 4 and their tangent space has dimension 3. The following Functor
-// (taken from autodiff_manifold_test.cc) defines the Plus and Minus operations
-// on the Quaternion manifold:
-//
-// NOTE: The following is only used for illustration purposes. Ceres Solver
-// ships with optimized production grade QuaternionManifold implementation. See
-// manifold.h.
-//
-// This functor assumes that the quaternions are laid out as [w,x,y,z] in
-// memory, i.e. the real or scalar part is the first coordinate.
-//
-// struct QuaternionFunctor {
-//   template <typename T>
-//   bool Plus(const T* x, const T* delta, T* x_plus_delta) const {
-//     const T squared_norm_delta =
-//         delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2];
-//
-//     T q_delta[4];
-//     if (squared_norm_delta > T(0.0)) {
-//       T norm_delta = sqrt(squared_norm_delta);
-//       const T sin_delta_by_delta = sin(norm_delta) / norm_delta;
-//       q_delta[0] = cos(norm_delta);
-//       q_delta[1] = sin_delta_by_delta * delta[0];
-//       q_delta[2] = sin_delta_by_delta * delta[1];
-//       q_delta[3] = sin_delta_by_delta * delta[2];
-//     } else {
-//       // We do not just use q_delta = [1,0,0,0] here because that is a
-//       // constant and when used for automatic differentiation will
-//       // lead to a zero derivative. Instead we take a first order
-//       // approximation and evaluate it at zero.
-//       q_delta[0] = T(1.0);
-//       q_delta[1] = delta[0];
-//       q_delta[2] = delta[1];
-//       q_delta[3] = delta[2];
-//     }
-//
-//     QuaternionProduct(q_delta, x, x_plus_delta);
-//     return true;
-//   }
-//
-//   template <typename T>
-//   bool Minus(const T* y, const T* x, T* y_minus_x) const {
-//     T minus_x[4] = {x[0], -x[1], -x[2], -x[3]};
-//     T ambient_y_minus_x[4];
-//     QuaternionProduct(y, minus_x, ambient_y_minus_x);
-//     T u_norm = sqrt(ambient_y_minus_x[1] * ambient_y_minus_x[1] +
-//                     ambient_y_minus_x[2] * ambient_y_minus_x[2] +
-//                     ambient_y_minus_x[3] * ambient_y_minus_x[3]);
-//     if (u_norm > 0.0) {
-//       T theta = atan2(u_norm, ambient_y_minus_x[0]);
-//       y_minus_x[0] = theta * ambient_y_minus_x[1] / u_norm;
-//       y_minus_x[1] = theta * ambient_y_minus_x[2] / u_norm;
-//       y_minus_x[2] = theta * ambient_y_minus_x[3] / u_norm;
-//     } else {
-//       // We do not use [0,0,0] here because even though the value part is
-//       // a constant, the derivative part is not.
-//       y_minus_x[0] = ambient_y_minus_x[1];
-//       y_minus_x[1] = ambient_y_minus_x[2];
-//       y_minus_x[2] = ambient_y_minus_x[3];
-//     }
-//     return true;
-//   }
-// };
-//
-// Then given this struct, the auto differentiated Quaternion Manifold can now
-// be constructed as
-//
-//   Manifold* manifold = new AutoDiffManifold<QuaternionFunctor, 4, 3>;
-
-template <typename Functor, int kAmbientSize, int kTangentSize>
-class AutoDiffManifold final : public Manifold {
- public:
-  AutoDiffManifold() : functor_(std::make_unique<Functor>()) {}
-
-  // Takes ownership of functor.
-  explicit AutoDiffManifold(Functor* functor) : functor_(functor) {}
-
-  int AmbientSize() const override { return kAmbientSize; }
-  int TangentSize() const override { return kTangentSize; }
-
-  bool Plus(const double* x,
-            const double* delta,
-            double* x_plus_delta) const override {
-    return functor_->Plus(x, delta, x_plus_delta);
-  }
-
-  bool PlusJacobian(const double* x, double* jacobian) const override;
-
-  bool Minus(const double* y,
-             const double* x,
-             double* y_minus_x) const override {
-    return functor_->Minus(y, x, y_minus_x);
-  }
-
-  bool MinusJacobian(const double* x, double* jacobian) const override;
-
-  const Functor& functor() const { return *functor_; }
-
- private:
-  std::unique_ptr<Functor> functor_;
-};
-
-namespace internal {
-
-// The following two helper structs are needed to interface the Plus and Minus
-// methods of the ManifoldFunctor with the automatic differentiation which
-// expects a Functor with operator().
-template <typename Functor>
-struct PlusWrapper {
-  explicit PlusWrapper(const Functor& functor) : functor(functor) {}
-  template <typename T>
-  bool operator()(const T* x, const T* delta, T* x_plus_delta) const {
-    return functor.Plus(x, delta, x_plus_delta);
-  }
-  const Functor& functor;
-};
-
-template <typename Functor>
-struct MinusWrapper {
-  explicit MinusWrapper(const Functor& functor) : functor(functor) {}
-  template <typename T>
-  bool operator()(const T* y, const T* x, T* y_minus_x) const {
-    return functor.Minus(y, x, y_minus_x);
-  }
-  const Functor& functor;
-};
-}  // namespace internal
-
-template <typename Functor, int kAmbientSize, int kTangentSize>
-bool AutoDiffManifold<Functor, kAmbientSize, kTangentSize>::PlusJacobian(
-    const double* x, double* jacobian) const {
-  double zero_delta[kTangentSize];
-  for (int i = 0; i < kTangentSize; ++i) {
-    zero_delta[i] = 0.0;
-  }
-
-  double x_plus_delta[kAmbientSize];
-  for (int i = 0; i < kAmbientSize; ++i) {
-    x_plus_delta[i] = 0.0;
-  }
-
-  const double* parameter_ptrs[2] = {x, zero_delta};
-
-  // PlusJacobian is D_2 Plus(x,0) so we only need to compute the Jacobian
-  // w.r.t. the second argument.
-  double* jacobian_ptrs[2] = {nullptr, jacobian};
-  return internal::AutoDifferentiate<
-      kAmbientSize,
-      internal::StaticParameterDims<kAmbientSize, kTangentSize>>(
-      internal::PlusWrapper<Functor>(*functor_),
-      parameter_ptrs,
-      kAmbientSize,
-      x_plus_delta,
-      jacobian_ptrs);
-}
-
-template <typename Functor, int kAmbientSize, int kTangentSize>
-bool AutoDiffManifold<Functor, kAmbientSize, kTangentSize>::MinusJacobian(
-    const double* x, double* jacobian) const {
-  double y_minus_x[kTangentSize];
-  for (int i = 0; i < kTangentSize; ++i) {
-    y_minus_x[i] = 0.0;
-  }
-
-  const double* parameter_ptrs[2] = {x, x};
-
-  // MinusJacobian is D_1 Minus(x,x), so we only need to compute the Jacobian
-  // w.r.t. the first argument.
-  double* jacobian_ptrs[2] = {jacobian, nullptr};
-  return internal::AutoDifferentiate<
-      kTangentSize,
-      internal::StaticParameterDims<kAmbientSize, kAmbientSize>>(
-      internal::MinusWrapper<Functor>(*functor_),
-      parameter_ptrs,
-      kTangentSize,
-      y_minus_x,
-      jacobian_ptrs);
-}
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_AUTODIFF_MANIFOLD_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/c_api.h b/3rdParty/my_ceres_vc17/include/ceres/c_api.h
deleted file mode 100644
index 30bcaaf..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/c_api.h
+++ /dev/null
@@ -1,148 +0,0 @@
-/* Ceres Solver - A fast non-linear least squares minimizer
- * Copyright 2023 Google Inc. All rights reserved.
- * http://ceres-solver.org/
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * - Redistributions of source code must retain the above copyright notice,
- *   this list of conditions and the following disclaimer.
- * - Redistributions in binary form must reproduce the above copyright notice,
- *   this list of conditions and the following disclaimer in the documentation
- *   and/or other materials provided with the distribution.
- * - Neither the name of Google Inc. nor the names of its contributors may be
- *   used to endorse or promote products derived from this software without
- *   specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
- * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- * POSSIBILITY OF SUCH DAMAGE.
- *
- * Author: mierle@gmail.com (Keir Mierle)
- *
- * A minimal C API for Ceres. Not all functionality is included. This API is
- * not intended for clients of Ceres, but is instead intended for easing the
- * process of binding Ceres to other languages.
- *
- * Currently this is a work in progress.
- */
-
-#ifndef CERES_PUBLIC_C_API_H_
-#define CERES_PUBLIC_C_API_H_
-
-// clang-format off
-#include "ceres/internal/export.h"
-#include "ceres/internal/disable_warnings.h"
-// clang-format on
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/* Init the Ceres private data. Must be called before anything else. */
-CERES_EXPORT void ceres_init();
-
-/* Equivalent to CostFunction::Evaluate() in the C++ API.
- *
- * The user may keep private information inside the opaque user_data object.
- * The pointer here is the same one passed in the ceres_add_residual_block().
- */
-typedef int (*ceres_cost_function_t)(void* user_data,
-                                     double** parameters,
-                                     double* residuals,
-                                     double** jacobians);
-
-/* Equivalent to LossFunction::Evaluate() from the C++ API. */
-typedef void (*ceres_loss_function_t)(void* user_data,
-                                      double squared_norm,
-                                      double out[3]);
-
-/* Create callback data for Ceres' stock loss functions.
- *
- * Ceres has several loss functions available by default, and these functions
- * expose those to the C API. To use the stock loss functions, call
- * ceres_create_*_loss_data(), which internally creates an instance of one of
- * the stock loss functions (for example ceres::CauchyLoss), and pass the
- * returned "loss_function_data" along with the ceres_stock_loss_function to
- * ceres_add_residual_block().
- *
- * For example:
- *
- *   void* cauchy_loss_function_data =
- *       ceres_create_cauchy_loss_function_data(1.2, 0.0);
- *   ceres_problem_add_residual_block(
- *       problem,
- *       my_cost_function,
- *       my_cost_function_data,
- *       ceres_stock_loss_function,
- *       cauchy_loss_function_data,
- *       1,
- *       2,
- *       parameter_sizes,
- *       parameter_pointers);
- *    ...
- *    ceres_free_stock_loss_function_data(cauchy_loss_function_data);
- *
- * See loss_function.h for the details of each loss function.
- */
-CERES_EXPORT void* ceres_create_huber_loss_function_data(double a);
-CERES_EXPORT void* ceres_create_softl1_loss_function_data(double a);
-CERES_EXPORT void* ceres_create_cauchy_loss_function_data(double a);
-CERES_EXPORT void* ceres_create_arctan_loss_function_data(double a);
-CERES_EXPORT void* ceres_create_tolerant_loss_function_data(double a, double b);
-
-/* Free the given stock loss function data. */
-CERES_EXPORT void ceres_free_stock_loss_function_data(void* loss_function_data);
-
-/* This is an implementation of ceres_loss_function_t contained within Ceres
- * itself, intended as a way to access the various stock Ceres loss functions
- * from the C API. This should be passed to ceres_add_residual() below, in
- * combination with a user_data pointer generated by
- * ceres_create_stock_loss_function() above. */
-CERES_EXPORT void ceres_stock_loss_function(void* user_data,
-                                            double squared_norm,
-                                            double out[3]);
-
-/* Equivalent to Problem from the C++ API. */
-struct ceres_problem_s;
-typedef struct ceres_problem_s ceres_problem_t;
-
-struct ceres_residual_block_id_s;
-typedef struct ceres_residual_block_id_s ceres_residual_block_id_t;
-
-/* Create and destroy a problem */
-/* TODO(keir): Add options for the problem. */
-CERES_EXPORT ceres_problem_t* ceres_create_problem();
-CERES_EXPORT void ceres_free_problem(ceres_problem_t* problem);
-
-/* Add a residual block. */
-CERES_EXPORT ceres_residual_block_id_t* ceres_problem_add_residual_block(
-    ceres_problem_t* problem,
-    ceres_cost_function_t cost_function,
-    void* cost_function_data,
-    ceres_loss_function_t loss_function,
-    void* loss_function_data,
-    int num_residuals,
-    int num_parameter_blocks,
-    int* parameter_block_sizes,
-    double** parameters);
-
-CERES_EXPORT void ceres_solve(ceres_problem_t* problem);
-
-/* TODO(keir): Figure out a way to pass a config in. */
-
-#ifdef __cplusplus
-}
-#endif
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif /* CERES_PUBLIC_C_API_H_ */
diff --git a/3rdParty/my_ceres_vc17/include/ceres/ceres.h b/3rdParty/my_ceres_vc17/include/ceres/ceres.h
deleted file mode 100644
index 51f9d89..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/ceres.h
+++ /dev/null
@@ -1,75 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: keir@google.com (Keir Mierle)
-//
-// This is a forwarding header containing the public symbols exported from
-// Ceres. Anything in the "ceres" namespace is available for use.
-
-#ifndef CERES_PUBLIC_CERES_H_
-#define CERES_PUBLIC_CERES_H_
-
-// IWYU pragma: begin_exports
-#include "ceres/autodiff_cost_function.h"
-#include "ceres/autodiff_first_order_function.h"
-#include "ceres/autodiff_manifold.h"
-#include "ceres/conditioned_cost_function.h"
-#include "ceres/constants.h"
-#include "ceres/context.h"
-#include "ceres/cost_function.h"
-#include "ceres/cost_function_to_functor.h"
-#include "ceres/covariance.h"
-#include "ceres/crs_matrix.h"
-#include "ceres/dynamic_autodiff_cost_function.h"
-#include "ceres/dynamic_cost_function.h"
-#include "ceres/dynamic_cost_function_to_functor.h"
-#include "ceres/dynamic_numeric_diff_cost_function.h"
-#include "ceres/evaluation_callback.h"
-#include "ceres/first_order_function.h"
-#include "ceres/gradient_checker.h"
-#include "ceres/gradient_problem.h"
-#include "ceres/gradient_problem_solver.h"
-#include "ceres/iteration_callback.h"
-#include "ceres/jet.h"
-#include "ceres/line_manifold.h"
-#include "ceres/loss_function.h"
-#include "ceres/manifold.h"
-#include "ceres/numeric_diff_cost_function.h"
-#include "ceres/numeric_diff_first_order_function.h"
-#include "ceres/numeric_diff_options.h"
-#include "ceres/ordered_groups.h"
-#include "ceres/problem.h"
-#include "ceres/product_manifold.h"
-#include "ceres/sized_cost_function.h"
-#include "ceres/solver.h"
-#include "ceres/sphere_manifold.h"
-#include "ceres/types.h"
-#include "ceres/version.h"
-// IWYU pragma: end_exports
-
-#endif  // CERES_PUBLIC_CERES_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/conditioned_cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/conditioned_cost_function.h
deleted file mode 100644
index 1edc006..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/conditioned_cost_function.h
+++ /dev/null
@@ -1,101 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: wjr@google.com (William Rucklidge)
-//
-// This file contains a cost function that can apply a transformation to
-// each residual value before they are square-summed.
-
-#ifndef CERES_PUBLIC_CONDITIONED_COST_FUNCTION_H_
-#define CERES_PUBLIC_CONDITIONED_COST_FUNCTION_H_
-
-#include <memory>
-#include <vector>
-
-#include "ceres/cost_function.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/types.h"
-
-namespace ceres {
-
-// This class allows you to apply different conditioning to the residual
-// values of a wrapped cost function. An example where this is useful is
-// where you have an existing cost function that produces N values, but you
-// want the total cost to be something other than just the sum of these
-// squared values - maybe you want to apply a different scaling to some
-// values, to change their contribution to the cost.
-//
-// Usage:
-//
-//   // my_cost_function produces N residuals
-//   CostFunction* my_cost_function = ...
-//   CHECK_EQ(N, my_cost_function->num_residuals());
-//   vector<CostFunction*> conditioners;
-//
-//   // Make N 1x1 cost functions (1 parameter, 1 residual)
-//   CostFunction* f_1 = ...
-//   conditioners.push_back(f_1);
-//   ...
-//   CostFunction* f_N = ...
-//   conditioners.push_back(f_N);
-//   ConditionedCostFunction* ccf =
-//     new ConditionedCostFunction(my_cost_function, conditioners);
-//
-// Now ccf's residual i (i=0..N-1) will be passed though the i'th conditioner.
-//
-//   ccf_residual[i] = f_i(my_cost_function_residual[i])
-//
-// and the Jacobian will be affected appropriately.
-class CERES_EXPORT ConditionedCostFunction final : public CostFunction {
- public:
-  // Builds a cost function based on a wrapped cost function, and a
-  // per-residual conditioner. Takes ownership of all of the wrapped cost
-  // functions, or not, depending on the ownership parameter. Conditioners
-  // may be nullptr, in which case the corresponding residual is not modified.
-  //
-  // The conditioners can repeat.
-  ConditionedCostFunction(CostFunction* wrapped_cost_function,
-                          const std::vector<CostFunction*>& conditioners,
-                          Ownership ownership);
-  ~ConditionedCostFunction() override;
-
-  bool Evaluate(double const* const* parameters,
-                double* residuals,
-                double** jacobians) const override;
-
- private:
-  std::unique_ptr<CostFunction> wrapped_cost_function_;
-  std::vector<CostFunction*> conditioners_;
-  Ownership ownership_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_CONDITIONED_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/constants.h b/3rdParty/my_ceres_vc17/include/ceres/constants.h
deleted file mode 100644
index 584b669..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/constants.h
+++ /dev/null
@@ -1,42 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: hellston20a@gmail.com (H S Helson Go)
-
-#ifndef CERES_PUBLIC_CONSTANTS_H_
-#define CERES_PUBLIC_CONSTANTS_H_
-
-// TODO(HSHelson): This header should no longer be necessary once C++20's
-// <numbers> (e.g. std::numbers::pi_v) becomes usable
-namespace ceres::constants {
-template <typename T>
-inline constexpr T pi_v(3.141592653589793238462643383279502884);
-inline constexpr double pi = pi_v<double>;
-}  // namespace ceres::constants
-
-#endif  // CERES_PUBLIC_CONSTANTS_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/context.h b/3rdParty/my_ceres_vc17/include/ceres/context.h
deleted file mode 100644
index fe18726..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/context.h
+++ /dev/null
@@ -1,58 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: vitus@google.com (Michael Vitus)
-
-#ifndef CERES_PUBLIC_CONTEXT_H_
-#define CERES_PUBLIC_CONTEXT_H_
-
-#include "ceres/internal/export.h"
-
-namespace ceres {
-
-// A global context for processing data in Ceres.  This provides a mechanism to
-// allow Ceres to reuse items that are expensive to create between multiple
-// calls; for example, thread pools.  The same Context can be used on multiple
-// Problems, either serially or in parallel. When using it with multiple
-// Problems at the same time, they may end up contending for resources
-// (e.g. threads) managed by the Context.
-class CERES_EXPORT Context {
- public:
-  Context();
-  Context(const Context&) = delete;
-  void operator=(const Context&) = delete;
-
-  virtual ~Context();
-
-  // Creates a context object and the caller takes ownership.
-  static Context* Create();
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_CONTEXT_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/cost_function.h
deleted file mode 100644
index 79d4912..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/cost_function.h
+++ /dev/null
@@ -1,144 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//         keir@google.m (Keir Mierle)
-//
-// This is the interface through which the least squares solver accesses the
-// residual and Jacobian of the least squares problem. Users are expected to
-// subclass CostFunction to define their own terms in the least squares problem.
-//
-// It is recommended that users define templated residual functors for use as
-// arguments for AutoDiffCostFunction (see autodiff_cost_function.h), instead of
-// directly implementing the CostFunction interface. This often results in both
-// shorter code and faster execution than hand-coded derivatives. However,
-// specialized cases may demand direct implementation of the lower-level
-// CostFunction interface; for example, this is true when calling legacy code
-// which is not templated on numeric types.
-
-#ifndef CERES_PUBLIC_COST_FUNCTION_H_
-#define CERES_PUBLIC_COST_FUNCTION_H_
-
-#include <cstdint>
-#include <vector>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-
-namespace ceres {
-
-// This class implements the computation of the cost (a.k.a. residual) terms as
-// a function of the input (control) variables, and is the interface for users
-// to describe their least squares problem to Ceres. In other words, this is the
-// modeling layer between users and the Ceres optimizer. The signature of the
-// function (number and sizes of input parameter blocks and number of outputs)
-// is stored in parameter_block_sizes_ and num_residuals_ respectively. User
-// code inheriting from this class is expected to set these two members with the
-// corresponding accessors. This information will be verified by the Problem
-// when added with AddResidualBlock().
-class CERES_EXPORT CostFunction {
- public:
-  CostFunction();
-  CostFunction(const CostFunction&) = delete;
-  void operator=(const CostFunction&) = delete;
-
-  virtual ~CostFunction();
-
-  // Inputs:
-  //
-  // parameters is an array of pointers to arrays containing the
-  // various parameter blocks. parameters has the same number of
-  // elements as parameter_block_sizes_.  Parameter blocks are in the
-  // same order as parameter_block_sizes_.i.e.,
-  //
-  //   parameters_[i] = double[parameter_block_sizes_[i]]
-  //
-  // Outputs:
-  //
-  // residuals is an array of size num_residuals_.
-  //
-  // jacobians is an array of size parameter_block_sizes_ containing
-  // pointers to storage for jacobian blocks corresponding to each
-  // parameter block. Jacobian blocks are in the same order as
-  // parameter_block_sizes, i.e. jacobians[i], is an
-  // array that contains num_residuals_* parameter_block_sizes_[i]
-  // elements. Each jacobian block is stored in row-major order, i.e.,
-  //
-  //   jacobians[i][r*parameter_block_size_[i] + c] =
-  //                              d residual[r] / d parameters[i][c]
-  //
-  // If jacobians is nullptr, then no derivatives are returned; this is
-  // the case when computing cost only. If jacobians[i] is nullptr, then
-  // the jacobian block corresponding to the i'th parameter block must
-  // not to be returned.
-  //
-  // The return value indicates whether the computation of the
-  // residuals and/or jacobians was successful or not.
-  //
-  // This can be used to communicate numerical failures in jacobian
-  // computations for instance.
-  //
-  // A more interesting and common use is to impose constraints on the
-  // parameters. If the initial values of the parameter blocks satisfy
-  // the constraints, then returning false whenever the constraints
-  // are not satisfied will prevent the solver from moving into the
-  // infeasible region. This is not a very sophisticated mechanism for
-  // enforcing constraints, but is often good enough.
-  //
-  // Note that it is important that the initial values of the
-  // parameter block must be feasible, otherwise the solver will
-  // declare a numerical problem at iteration 0.
-  virtual bool Evaluate(double const* const* parameters,
-                        double* residuals,
-                        double** jacobians) const = 0;
-
-  const std::vector<int32_t>& parameter_block_sizes() const {
-    return parameter_block_sizes_;
-  }
-
-  int num_residuals() const { return num_residuals_; }
-
- protected:
-  std::vector<int32_t>* mutable_parameter_block_sizes() {
-    return &parameter_block_sizes_;
-  }
-
-  void set_num_residuals(int num_residuals) { num_residuals_ = num_residuals; }
-
- private:
-  // Cost function signature metadata: number of inputs & their sizes,
-  // number of outputs (residuals).
-  std::vector<int32_t> parameter_block_sizes_;
-  int num_residuals_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/cost_function_to_functor.h b/3rdParty/my_ceres_vc17/include/ceres/cost_function_to_functor.h
deleted file mode 100644
index e9592ed..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/cost_function_to_functor.h
+++ /dev/null
@@ -1,171 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//
-// CostFunctionToFunctor is an adapter class that allows users to use
-// SizedCostFunction objects in templated functors which are to be used for
-// automatic differentiation. This allows the user to seamlessly mix
-// analytic, numeric and automatic differentiation.
-//
-// For example, let us assume that
-//
-//  class IntrinsicProjection : public SizedCostFunction<2, 5, 3> {
-//    public:
-//      IntrinsicProjection(const double* observation);
-//      bool Evaluate(double const* const* parameters,
-//                    double* residuals,
-//                    double** jacobians) const override;
-//  };
-//
-// is a cost function that implements the projection of a point in its
-// local coordinate system onto its image plane and subtracts it from
-// the observed point projection. It can compute its residual and
-// jacobians either via analytic or numerical differentiation.
-//
-// Now we would like to compose the action of this CostFunction with
-// the action of camera extrinsics, i.e., rotation and
-// translation. Say we have a templated function
-//
-//   template<typename T>
-//   void RotateAndTranslatePoint(const T* rotation,
-//                                const T* translation,
-//                                const T* point,
-//                                T* result);
-//
-// Then we can now do the following,
-//
-// struct CameraProjection {
-//   CameraProjection(const double* observation)
-//       : intrinsic_projection_(new IntrinsicProjection(observation)) {
-//   }
-//   template <typename T>
-//   bool operator()(const T* rotation,
-//                   const T* translation,
-//                   const T* intrinsics,
-//                   const T* point,
-//                   T* residual) const {
-//     T transformed_point[3];
-//     RotateAndTranslatePoint(rotation, translation, point, transformed_point);
-//
-//     // Note that we call intrinsic_projection_, just like it was
-//     // any other templated functor.
-//
-//     return intrinsic_projection_(intrinsics, transformed_point, residual);
-//   }
-//
-//  private:
-//   CostFunctionToFunctor<2,5,3> intrinsic_projection_;
-// };
-
-#ifndef CERES_PUBLIC_COST_FUNCTION_TO_FUNCTOR_H_
-#define CERES_PUBLIC_COST_FUNCTION_TO_FUNCTOR_H_
-
-#include <cstdint>
-#include <numeric>
-#include <tuple>
-#include <utility>
-#include <vector>
-
-#include "ceres/cost_function.h"
-#include "ceres/dynamic_cost_function_to_functor.h"
-#include "ceres/internal/export.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/internal/parameter_dims.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-template <int kNumResiduals, int... Ns>
-class CostFunctionToFunctor {
- public:
-  // Takes ownership of cost_function.
-  explicit CostFunctionToFunctor(CostFunction* cost_function)
-      : cost_functor_(cost_function) {
-    CHECK(cost_function != nullptr);
-    CHECK(kNumResiduals > 0 || kNumResiduals == DYNAMIC);
-
-    const std::vector<int32_t>& parameter_block_sizes =
-        cost_function->parameter_block_sizes();
-    const int num_parameter_blocks = ParameterDims::kNumParameterBlocks;
-    CHECK_EQ(static_cast<int>(parameter_block_sizes.size()),
-             num_parameter_blocks);
-
-    if (parameter_block_sizes.size() == num_parameter_blocks) {
-      for (int block = 0; block < num_parameter_blocks; ++block) {
-        CHECK_EQ(ParameterDims::GetDim(block), parameter_block_sizes[block])
-            << "Parameter block size mismatch. The specified static parameter "
-               "block dimension does not match the one from the cost function.";
-      }
-    }
-
-    CHECK_EQ(accumulate(
-                 parameter_block_sizes.begin(), parameter_block_sizes.end(), 0),
-             ParameterDims::kNumParameters);
-  }
-
-  template <typename T, typename... Ts>
-  bool operator()(const T* p1, Ts*... ps) const {
-    // Add one because of residual block.
-    static_assert(sizeof...(Ts) + 1 == ParameterDims::kNumParameterBlocks + 1,
-                  "Invalid number of parameter blocks specified.");
-
-    auto params = std::make_tuple(p1, ps...);
-
-    // Extract residual pointer from params. The residual pointer is the
-    // last pointer.
-    constexpr int kResidualIndex = ParameterDims::kNumParameterBlocks;
-    T* residuals = std::get<kResidualIndex>(params);
-
-    // Extract parameter block pointers from params.
-    using Indices =
-        std::make_integer_sequence<int, ParameterDims::kNumParameterBlocks>;
-    std::array<const T*, ParameterDims::kNumParameterBlocks> parameter_blocks =
-        GetParameterPointers<T>(params, Indices());
-
-    return cost_functor_(parameter_blocks.data(), residuals);
-  }
-
- private:
-  using ParameterDims = internal::StaticParameterDims<Ns...>;
-
-  template <typename T, typename Tuple, int... Indices>
-  static std::array<const T*, ParameterDims::kNumParameterBlocks>
-  GetParameterPointers(const Tuple& paramPointers,
-                       std::integer_sequence<int, Indices...>) {
-    return std::array<const T*, ParameterDims::kNumParameterBlocks>{
-        {std::get<Indices>(paramPointers)...}};
-  }
-
-  DynamicCostFunctionToFunctor cost_functor_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_COST_FUNCTION_TO_FUNCTOR_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/covariance.h b/3rdParty/my_ceres_vc17/include/ceres/covariance.h
deleted file mode 100644
index d477f31..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/covariance.h
+++ /dev/null
@@ -1,470 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_COVARIANCE_H_
-#define CERES_PUBLIC_COVARIANCE_H_
-
-#include <memory>
-#include <utility>
-#include <vector>
-
-#include "ceres/internal/config.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/types.h"
-
-namespace ceres {
-
-class Problem;
-
-namespace internal {
-class CovarianceImpl;
-}  // namespace internal
-
-// WARNING
-// =======
-// It is very easy to use this class incorrectly without understanding
-// the underlying mathematics. Please read and understand the
-// documentation completely before attempting to use it.
-//
-//
-// This class allows the user to evaluate the covariance for a
-// non-linear least squares problem and provides random access to its
-// blocks
-//
-// Background
-// ==========
-// One way to assess the quality of the solution returned by a
-// non-linear least squares solver is to analyze the covariance of the
-// solution.
-//
-// Let us consider the non-linear regression problem
-//
-//   y = f(x) + N(0, I)
-//
-// i.e., the observation y is a random non-linear function of the
-// independent variable x with mean f(x) and identity covariance. Then
-// the maximum likelihood estimate of x given observations y is the
-// solution to the non-linear least squares problem:
-//
-//  x* = arg min_x |f(x) - y|^2
-//
-// And the covariance of x* is given by
-//
-//  C(x*) = inverse[J'(x*)J(x*)]
-//
-// Here J(x*) is the Jacobian of f at x*. The above formula assumes
-// that J(x*) has full column rank.
-//
-// If J(x*) is rank deficient, then the covariance matrix C(x*) is
-// also rank deficient and is given by
-//
-//  C(x*) =  pseudoinverse[J'(x*)J(x*)]
-//
-// Note that in the above, we assumed that the covariance
-// matrix for y was identity. This is an important assumption. If this
-// is not the case and we have
-//
-//  y = f(x) + N(0, S)
-//
-// Where S is a positive semi-definite matrix denoting the covariance
-// of y, then the maximum likelihood problem to be solved is
-//
-//  x* = arg min_x f'(x) inverse[S] f(x)
-//
-// and the corresponding covariance estimate of x* is given by
-//
-//  C(x*) = inverse[J'(x*) inverse[S] J(x*)]
-//
-// So, if it is the case that the observations being fitted to have a
-// covariance matrix not equal to identity, then it is the user's
-// responsibility that the corresponding cost functions are correctly
-// scaled, e.g. in the above case the cost function for this problem
-// should evaluate S^{-1/2} f(x) instead of just f(x), where S^{-1/2}
-// is the inverse square root of the covariance matrix S.
-//
-// This class allows the user to evaluate the covariance for a
-// non-linear least squares problem and provides random access to its
-// blocks. The computation assumes that the CostFunctions compute
-// residuals such that their covariance is identity.
-//
-// Since the computation of the covariance matrix requires computing
-// the inverse of a potentially large matrix, this can involve a
-// rather large amount of time and memory. However, it is usually the
-// case that the user is only interested in a small part of the
-// covariance matrix. Quite often just the block diagonal. This class
-// allows the user to specify the parts of the covariance matrix that
-// she is interested in and then uses this information to only compute
-// and store those parts of the covariance matrix.
-//
-// Rank of the Jacobian
-// --------------------
-// As we noted above, if the jacobian is rank deficient, then the
-// inverse of J'J is not defined and instead a pseudo inverse needs to
-// be computed.
-//
-// The rank deficiency in J can be structural -- columns which are
-// always known to be zero or numerical -- depending on the exact
-// values in the Jacobian.
-//
-// Structural rank deficiency occurs when the problem contains
-// parameter blocks that are constant. This class correctly handles
-// structural rank deficiency like that.
-//
-// Numerical rank deficiency, where the rank of the matrix cannot be
-// predicted by its sparsity structure and requires looking at its
-// numerical values is more complicated. Here again there are two
-// cases.
-//
-//   a. The rank deficiency arises from overparameterization. e.g., a
-//   four dimensional quaternion used to parameterize SO(3), which is
-//   a three dimensional manifold. In cases like this, the user should
-//   use an appropriate Manifold. Not only will this lead
-//   to better numerical behaviour of the Solver, it will also expose
-//   the rank deficiency to the Covariance object so that it can
-//   handle it correctly.
-//
-//   b. More general numerical rank deficiency in the Jacobian
-//   requires the computation of the so called Singular Value
-//   Decomposition (SVD) of J'J. We do not know how to do this for
-//   large sparse matrices efficiently. For small and moderate sized
-//   problems this is done using dense linear algebra.
-//
-// Gauge Invariance
-// ----------------
-// In structure from motion (3D reconstruction) problems, the
-// reconstruction is ambiguous up to a similarity transform. This is
-// known as a Gauge Ambiguity. Handling Gauges correctly requires the
-// use of SVD or custom inversion algorithms. For small problems the
-// user can use the dense algorithm. For more details see
-//
-// Ken-ichi Kanatani, Daniel D. Morris: Gauges and gauge
-// transformations for uncertainty description of geometric structure
-// with indeterminacy. IEEE Transactions on Information Theory 47(5):
-// 2017-2028 (2001)
-//
-// Example Usage
-// =============
-//
-//  double x[3];
-//  double y[2];
-//
-//  Problem problem;
-//  problem.AddParameterBlock(x, 3);
-//  problem.AddParameterBlock(y, 2);
-//  <Build Problem>
-//  <Solve Problem>
-//
-//  Covariance::Options options;
-//  Covariance covariance(options);
-//
-//  std::vector<std::pair<const double*, const double*>> covariance_blocks;
-//  covariance_blocks.push_back(make_pair(x, x));
-//  covariance_blocks.push_back(make_pair(y, y));
-//  covariance_blocks.push_back(make_pair(x, y));
-//
-//  CHECK(covariance.Compute(covariance_blocks, &problem));
-//
-//  double covariance_xx[3 * 3];
-//  double covariance_yy[2 * 2];
-//  double covariance_xy[3 * 2];
-//  covariance.GetCovarianceBlock(x, x, covariance_xx)
-//  covariance.GetCovarianceBlock(y, y, covariance_yy)
-//  covariance.GetCovarianceBlock(x, y, covariance_xy)
-//
-class CERES_EXPORT Covariance {
- public:
-  struct CERES_EXPORT Options {
-    // Sparse linear algebra library to use when a sparse matrix
-    // factorization is being used to compute the covariance matrix.
-    //
-    // Currently this only applies to SPARSE_QR.
-    SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type =
-#if !defined(CERES_NO_SUITESPARSE)
-        SUITE_SPARSE;
-#else
-        // Eigen's QR factorization is always available.
-        EIGEN_SPARSE;
-#endif
-
-    // Ceres supports two different algorithms for covariance
-    // estimation, which represent different tradeoffs in speed,
-    // accuracy and reliability.
-    //
-    // 1. DENSE_SVD uses Eigen's JacobiSVD to perform the
-    //    computations. It computes the singular value decomposition
-    //
-    //      U * D * V' = J
-    //
-    //    and then uses it to compute the pseudo inverse of J'J as
-    //
-    //      pseudoinverse[J'J] = V * pseudoinverse[D^2] * V'
-    //
-    //    It is an accurate but slow method and should only be used
-    //    for small to moderate sized problems. It can handle
-    //    full-rank as well as rank deficient Jacobians.
-    //
-    // 2. SPARSE_QR uses the sparse QR factorization algorithm
-    //    to compute the decomposition
-    //
-    //      Q * R = J
-    //
-    //    [J'J]^-1 = [R'*R]^-1
-    //
-    // SPARSE_QR is not capable of computing the covariance if the
-    // Jacobian is rank deficient. Depending on the value of
-    // Covariance::Options::sparse_linear_algebra_library_type, either
-    // Eigen's Sparse QR factorization algorithm will be used or
-    // SuiteSparse's high performance SuiteSparseQR algorithm will be
-    // used.
-    CovarianceAlgorithmType algorithm_type = SPARSE_QR;
-
-    // During QR factorization, if a column with Euclidean norm less
-    // than column_pivot_threshold is encountered it is treated as
-    // zero.
-    //
-    // If column_pivot_threshold < 0, then an automatic default value
-    // of 20*(m+n)*eps*sqrt(max(diag(J’*J))) is used. Here m and n are
-    // the number of rows and columns of the Jacobian (J)
-    // respectively.
-    //
-    // This is an advanced option meant for users who know enough
-    // about their Jacobian matrices that they can determine a value
-    // better than the default.
-    double column_pivot_threshold = -1;
-
-    // If the Jacobian matrix is near singular, then inverting J'J
-    // will result in unreliable results, e.g, if
-    //
-    //   J = [1.0 1.0         ]
-    //       [1.0 1.0000001   ]
-    //
-    // which is essentially a rank deficient matrix, we have
-    //
-    //   inv(J'J) = [ 2.0471e+14  -2.0471e+14]
-    //              [-2.0471e+14   2.0471e+14]
-    //
-    // This is not a useful result. Therefore, by default
-    // Covariance::Compute will return false if a rank deficient
-    // Jacobian is encountered. How rank deficiency is detected
-    // depends on the algorithm being used.
-    //
-    // 1. DENSE_SVD
-    //
-    //      min_sigma / max_sigma < sqrt(min_reciprocal_condition_number)
-    //
-    //    where min_sigma and max_sigma are the minimum and maximum
-    //    singular values of J respectively.
-    //
-    // 2. SPARSE_QR
-    //
-    //      rank(J) < num_col(J)
-    //
-    //   Here rank(J) is the estimate of the rank of J returned by the
-    //   sparse QR factorization algorithm. It is a fairly reliable
-    //   indication of rank deficiency.
-    //
-    double min_reciprocal_condition_number = 1e-14;
-
-    // When using DENSE_SVD, the user has more control in dealing with
-    // singular and near singular covariance matrices.
-    //
-    // As mentioned above, when the covariance matrix is near
-    // singular, instead of computing the inverse of J'J, the
-    // Moore-Penrose pseudoinverse of J'J should be computed.
-    //
-    // If J'J has the eigen decomposition (lambda_i, e_i), where
-    // lambda_i is the i^th eigenvalue and e_i is the corresponding
-    // eigenvector, then the inverse of J'J is
-    //
-    //   inverse[J'J] = sum_i e_i e_i' / lambda_i
-    //
-    // and computing the pseudo inverse involves dropping terms from
-    // this sum that correspond to small eigenvalues.
-    //
-    // How terms are dropped is controlled by
-    // min_reciprocal_condition_number and null_space_rank.
-    //
-    // If null_space_rank is non-negative, then the smallest
-    // null_space_rank eigenvalue/eigenvectors are dropped
-    // irrespective of the magnitude of lambda_i. If the ratio of the
-    // smallest non-zero eigenvalue to the largest eigenvalue in the
-    // truncated matrix is still below
-    // min_reciprocal_condition_number, then the Covariance::Compute()
-    // will fail and return false.
-    //
-    // Setting null_space_rank = -1 drops all terms for which
-    //
-    //   lambda_i / lambda_max < min_reciprocal_condition_number.
-    //
-    // This option has no effect on the SUITE_SPARSE_QR and
-    // EIGEN_SPARSE_QR algorithms.
-    int null_space_rank = 0;
-
-    int num_threads = 1;
-
-    // Even though the residual blocks in the problem may contain loss
-    // functions, setting apply_loss_function to false will turn off
-    // the application of the loss function to the output of the cost
-    // function and in turn its effect on the covariance.
-    //
-    // TODO(sameergaarwal): Expand this based on Jim's experiments.
-    bool apply_loss_function = true;
-  };
-
-  explicit Covariance(const Options& options);
-  ~Covariance();
-
-  // Compute a part of the covariance matrix.
-  //
-  // The vector covariance_blocks, indexes into the covariance matrix
-  // block-wise using pairs of parameter blocks. This allows the
-  // covariance estimation algorithm to only compute and store these
-  // blocks.
-  //
-  // Since the covariance matrix is symmetric, if the user passes
-  // (block1, block2), then GetCovarianceBlock can be called with
-  // block1, block2 as well as block2, block1.
-  //
-  // covariance_blocks cannot contain duplicates. Bad things will
-  // happen if they do.
-  //
-  // Note that the list of covariance_blocks is only used to determine
-  // what parts of the covariance matrix are computed. The full
-  // Jacobian is used to do the computation, i.e. they do not have an
-  // impact on what part of the Jacobian is used for computation.
-  //
-  // The return value indicates the success or failure of the
-  // covariance computation. Please see the documentation for
-  // Covariance::Options for more on the conditions under which this
-  // function returns false.
-  bool Compute(const std::vector<std::pair<const double*, const double*>>&
-                   covariance_blocks,
-               Problem* problem);
-
-  // Compute a part of the covariance matrix.
-  //
-  // The vector parameter_blocks contains the parameter blocks that
-  // are used for computing the covariance matrix. From this vector
-  // all covariance pairs are generated. This allows the covariance
-  // estimation algorithm to only compute and store these blocks.
-  //
-  // parameter_blocks cannot contain duplicates. Bad things will
-  // happen if they do.
-  //
-  // Note that the list of covariance_blocks is only used to determine
-  // what parts of the covariance matrix are computed. The full
-  // Jacobian is used to do the computation, i.e. they do not have an
-  // impact on what part of the Jacobian is used for computation.
-  //
-  // The return value indicates the success or failure of the
-  // covariance computation. Please see the documentation for
-  // Covariance::Options for more on the conditions under which this
-  // function returns false.
-  bool Compute(const std::vector<const double*>& parameter_blocks,
-               Problem* problem);
-
-  // Return the block of the cross-covariance matrix corresponding to
-  // parameter_block1 and parameter_block2.
-  //
-  // Compute must be called before the first call to
-  // GetCovarianceBlock and the pair <parameter_block1,
-  // parameter_block2> OR the pair <parameter_block2,
-  // parameter_block1> must have been present in the vector
-  // covariance_blocks when Compute was called. Otherwise
-  // GetCovarianceBlock will return false.
-  //
-  // covariance_block must point to a memory location that can store a
-  // parameter_block1_size x parameter_block2_size matrix. The
-  // returned covariance will be a row-major matrix.
-  bool GetCovarianceBlock(const double* parameter_block1,
-                          const double* parameter_block2,
-                          double* covariance_block) const;
-
-  // Returns the block of the cross-covariance in the tangent space if a
-  // manifold is associated with either parameter block; else returns
-  // cross-covariance in the ambient space.
-  //
-  // Compute must be called before the first call to
-  // GetCovarianceBlock and the pair <parameter_block1,
-  // parameter_block2> OR the pair <parameter_block2,
-  // parameter_block1> must have been present in the vector
-  // covariance_blocks when Compute was called. Otherwise
-  // GetCovarianceBlock will return false.
-  //
-  // covariance_block must point to a memory location that can store a
-  // parameter_block1_local_size x parameter_block2_local_size matrix. The
-  // returned covariance will be a row-major matrix.
-  bool GetCovarianceBlockInTangentSpace(const double* parameter_block1,
-                                        const double* parameter_block2,
-                                        double* covariance_block) const;
-
-  // Return the covariance matrix corresponding to all parameter_blocks.
-  //
-  // Compute must be called before calling GetCovarianceMatrix and all
-  // parameter_blocks must have been present in the vector
-  // parameter_blocks when Compute was called. Otherwise
-  // GetCovarianceMatrix returns false.
-  //
-  // covariance_matrix must point to a memory location that can store
-  // the size of the covariance matrix. The covariance matrix will be
-  // a square matrix whose row and column count is equal to the sum of
-  // the sizes of the individual parameter blocks. The covariance
-  // matrix will be a row-major matrix.
-  bool GetCovarianceMatrix(const std::vector<const double*>& parameter_blocks,
-                           double* covariance_matrix) const;
-
-  // Return the covariance matrix corresponding to parameter_blocks
-  // in the tangent space if a manifold is associated with one of the parameter
-  // blocks else returns the covariance matrix in the ambient space.
-  //
-  // Compute must be called before calling GetCovarianceMatrix and all
-  // parameter_blocks must have been present in the vector
-  // parameters_blocks when Compute was called. Otherwise
-  // GetCovarianceMatrix returns false.
-  //
-  // covariance_matrix must point to a memory location that can store
-  // the size of the covariance matrix. The covariance matrix will be
-  // a square matrix whose row and column count is equal to the sum of
-  // the sizes of the tangent spaces of the individual parameter
-  // blocks. The covariance matrix will be a row-major matrix.
-  bool GetCovarianceMatrixInTangentSpace(
-      const std::vector<const double*>& parameter_blocks,
-      double* covariance_matrix) const;
-
- private:
-  std::unique_ptr<internal::CovarianceImpl> impl_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_COVARIANCE_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/crs_matrix.h b/3rdParty/my_ceres_vc17/include/ceres/crs_matrix.h
deleted file mode 100644
index 787b6a3..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/crs_matrix.h
+++ /dev/null
@@ -1,87 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_CRS_MATRIX_H_
-#define CERES_PUBLIC_CRS_MATRIX_H_
-
-#include <vector>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-
-namespace ceres {
-
-// A compressed row sparse matrix used primarily for communicating the
-// Jacobian matrix to the user.
-struct CERES_EXPORT CRSMatrix {
-  CRSMatrix() = default;
-
-  int num_rows{0};
-  int num_cols{0};
-
-  // A compressed row matrix stores its contents in three arrays,
-  // rows, cols and values.
-  //
-  // rows is a num_rows + 1 sized array that points into the cols and
-  // values array. For each row i:
-  //
-  // cols[rows[i]] ... cols[rows[i + 1] - 1] are the indices of the
-  // non-zero columns of row i.
-  //
-  // values[rows[i]] .. values[rows[i + 1] - 1] are the values of the
-  // corresponding entries.
-  //
-  // cols and values contain as many entries as there are non-zeros in
-  // the matrix.
-  //
-  // e.g, consider the 3x4 sparse matrix
-  //
-  //  [ 0 10  0  4 ]
-  //  [ 0  2 -3  2 ]
-  //  [ 1  2  0  0 ]
-  //
-  // The three arrays will be:
-  //
-  //
-  //            -row0-  ---row1---  -row2-
-  //  rows   = [ 0,      2,          5,     7]
-  //  cols   = [ 1,  3,  1,  2,  3,  0,  1]
-  //  values = [10,  4,  2, -3,  2,  1,  2]
-
-  std::vector<int> cols;
-  std::vector<int> rows;
-  std::vector<double> values;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_CRS_MATRIX_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/cubic_interpolation.h b/3rdParty/my_ceres_vc17/include/ceres/cubic_interpolation.h
deleted file mode 100644
index f165d2b..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/cubic_interpolation.h
+++ /dev/null
@@ -1,436 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_CUBIC_INTERPOLATION_H_
-#define CERES_PUBLIC_CUBIC_INTERPOLATION_H_
-
-#include "Eigen/Core"
-#include "ceres/internal/export.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// Given samples from a function sampled at four equally spaced points,
-//
-//   p0 = f(-1)
-//   p1 = f(0)
-//   p2 = f(1)
-//   p3 = f(2)
-//
-// Evaluate the cubic Hermite spline (also known as the Catmull-Rom
-// spline) at a point x that lies in the interval [0, 1].
-//
-// This is also the interpolation kernel (for the case of a = 0.5) as
-// proposed by R. Keys, in:
-//
-// "Cubic convolution interpolation for digital image processing".
-// IEEE Transactions on Acoustics, Speech, and Signal Processing
-// 29 (6): 1153-1160.
-//
-// For more details see
-//
-// http://en.wikipedia.org/wiki/Cubic_Hermite_spline
-// http://en.wikipedia.org/wiki/Bicubic_interpolation
-//
-// f if not nullptr will contain the interpolated function values.
-// dfdx if not nullptr will contain the interpolated derivative values.
-template <int kDataDimension>
-void CubicHermiteSpline(const Eigen::Matrix<double, kDataDimension, 1>& p0,
-                        const Eigen::Matrix<double, kDataDimension, 1>& p1,
-                        const Eigen::Matrix<double, kDataDimension, 1>& p2,
-                        const Eigen::Matrix<double, kDataDimension, 1>& p3,
-                        const double x,
-                        double* f,
-                        double* dfdx) {
-  using VType = Eigen::Matrix<double, kDataDimension, 1>;
-  const VType a = 0.5 * (-p0 + 3.0 * p1 - 3.0 * p2 + p3);
-  const VType b = 0.5 * (2.0 * p0 - 5.0 * p1 + 4.0 * p2 - p3);
-  const VType c = 0.5 * (-p0 + p2);
-  const VType d = p1;
-
-  // Use Horner's rule to evaluate the function value and its
-  // derivative.
-
-  // f = ax^3 + bx^2 + cx + d
-  if (f != nullptr) {
-    Eigen::Map<VType>(f, kDataDimension) = d + x * (c + x * (b + x * a));
-  }
-
-  // dfdx = 3ax^2 + 2bx + c
-  if (dfdx != nullptr) {
-    Eigen::Map<VType>(dfdx, kDataDimension) = c + x * (2.0 * b + 3.0 * a * x);
-  }
-}
-
-// Given as input an infinite one dimensional grid, which provides the
-// following interface.
-//
-//   class Grid {
-//    public:
-//     enum { DATA_DIMENSION = 2; };
-//     void GetValue(int n, double* f) const;
-//   };
-//
-// Here, GetValue gives the value of a function f (possibly vector
-// valued) for any integer n.
-//
-// The enum DATA_DIMENSION indicates the dimensionality of the
-// function being interpolated. For example if you are interpolating
-// rotations in axis-angle format over time, then DATA_DIMENSION = 3.
-//
-// CubicInterpolator uses cubic Hermite splines to produce a smooth
-// approximation to it that can be used to evaluate the f(x) and f'(x)
-// at any point on the real number line.
-//
-// For more details on cubic interpolation see
-//
-// http://en.wikipedia.org/wiki/Cubic_Hermite_spline
-//
-// Example usage:
-//
-//  const double data[] = {1.0, 2.0, 5.0, 6.0};
-//  Grid1D<double, 1> grid(data, 0, 4);
-//  CubicInterpolator<Grid1D<double, 1>> interpolator(grid);
-//  double f, dfdx;
-//  interpolator.Evaluator(1.5, &f, &dfdx);
-template <typename Grid>
-class CubicInterpolator {
- public:
-  explicit CubicInterpolator(const Grid& grid) : grid_(grid) {
-    // The + casts the enum into an int before doing the
-    // comparison. It is needed to prevent
-    // "-Wunnamed-type-template-args" related errors.
-    CHECK_GE(+Grid::DATA_DIMENSION, 1);
-  }
-
-  void Evaluate(double x, double* f, double* dfdx) const {
-    const int n = std::floor(x);
-    Eigen::Matrix<double, Grid::DATA_DIMENSION, 1> p0, p1, p2, p3;
-    grid_.GetValue(n - 1, p0.data());
-    grid_.GetValue(n, p1.data());
-    grid_.GetValue(n + 1, p2.data());
-    grid_.GetValue(n + 2, p3.data());
-    CubicHermiteSpline<Grid::DATA_DIMENSION>(p0, p1, p2, p3, x - n, f, dfdx);
-  }
-
-  // The following two Evaluate overloads are needed for interfacing
-  // with automatic differentiation. The first is for when a scalar
-  // evaluation is done, and the second one is for when Jets are used.
-  void Evaluate(const double& x, double* f) const { Evaluate(x, f, nullptr); }
-
-  template <typename JetT>
-  void Evaluate(const JetT& x, JetT* f) const {
-    double fx[Grid::DATA_DIMENSION], dfdx[Grid::DATA_DIMENSION];
-    Evaluate(x.a, fx, dfdx);
-    for (int i = 0; i < Grid::DATA_DIMENSION; ++i) {
-      f[i].a = fx[i];
-      f[i].v = dfdx[i] * x.v;
-    }
-  }
-
- private:
-  const Grid& grid_;
-};
-
-// An object that implements an infinite one dimensional grid needed
-// by the CubicInterpolator where the source of the function values is
-// an array of type T on the interval
-//
-//   [begin, ..., end - 1]
-//
-// Since the input array is finite and the grid is infinite, values
-// outside this interval needs to be computed. Grid1D uses the value
-// from the nearest edge.
-//
-// The function being provided can be vector valued, in which case
-// kDataDimension > 1. The dimensional slices of the function maybe
-// interleaved, or they maybe stacked, i.e, if the function has
-// kDataDimension = 2, if kInterleaved = true, then it is stored as
-//
-//   f01, f02, f11, f12 ....
-//
-// and if kInterleaved = false, then it is stored as
-//
-//  f01, f11, .. fn1, f02, f12, .. , fn2
-//
-template <typename T, int kDataDimension = 1, bool kInterleaved = true>
-struct Grid1D {
- public:
-  enum { DATA_DIMENSION = kDataDimension };
-
-  Grid1D(const T* data, const int begin, const int end)
-      : data_(data), begin_(begin), end_(end), num_values_(end - begin) {
-    CHECK_LT(begin, end);
-  }
-
-  EIGEN_STRONG_INLINE void GetValue(const int n, double* f) const {
-    const int idx = (std::min)((std::max)(begin_, n), end_ - 1) - begin_;
-    if (kInterleaved) {
-      for (int i = 0; i < kDataDimension; ++i) {
-        f[i] = static_cast<double>(data_[kDataDimension * idx + i]);
-      }
-    } else {
-      for (int i = 0; i < kDataDimension; ++i) {
-        f[i] = static_cast<double>(data_[i * num_values_ + idx]);
-      }
-    }
-  }
-
- private:
-  const T* data_;
-  const int begin_;
-  const int end_;
-  const int num_values_;
-};
-
-// Given as input an infinite two dimensional grid like object, which
-// provides the following interface:
-//
-//   struct Grid {
-//     enum { DATA_DIMENSION = 1 };
-//     void GetValue(int row, int col, double* f) const;
-//   };
-//
-// Where, GetValue gives us the value of a function f (possibly vector
-// valued) for any pairs of integers (row, col), and the enum
-// DATA_DIMENSION indicates the dimensionality of the function being
-// interpolated. For example if you are interpolating a color image
-// with three channels (Red, Green & Blue), then DATA_DIMENSION = 3.
-//
-// BiCubicInterpolator uses the cubic convolution interpolation
-// algorithm of R. Keys, to produce a smooth approximation to it that
-// can be used to evaluate the f(r,c), df(r, c)/dr and df(r,c)/dc at
-// any point in the real plane.
-//
-// For more details on the algorithm used here see:
-//
-// "Cubic convolution interpolation for digital image processing".
-// Robert G. Keys, IEEE Trans. on Acoustics, Speech, and Signal
-// Processing 29 (6): 1153-1160, 1981.
-//
-// http://en.wikipedia.org/wiki/Cubic_Hermite_spline
-// http://en.wikipedia.org/wiki/Bicubic_interpolation
-//
-// Example usage:
-//
-// const double data[] = {1.0, 3.0, -1.0, 4.0,
-//                         3.6, 2.1,  4.2, 2.0,
-//                        2.0, 1.0,  3.1, 5.2};
-//  Grid2D<double, 1>  grid(data, 3, 4);
-//  BiCubicInterpolator<Grid2D<double, 1>> interpolator(grid);
-//  double f, dfdr, dfdc;
-//  interpolator.Evaluate(1.2, 2.5, &f, &dfdr, &dfdc);
-
-template <typename Grid>
-class BiCubicInterpolator {
- public:
-  explicit BiCubicInterpolator(const Grid& grid) : grid_(grid) {
-    // The + casts the enum into an int before doing the
-    // comparison. It is needed to prevent
-    // "-Wunnamed-type-template-args" related errors.
-    CHECK_GE(+Grid::DATA_DIMENSION, 1);
-  }
-
-  // Evaluate the interpolated function value and/or its
-  // derivative. Uses the nearest point on the grid boundary if r or
-  // c is out of bounds.
-  void Evaluate(
-      double r, double c, double* f, double* dfdr, double* dfdc) const {
-    // BiCubic interpolation requires 16 values around the point being
-    // evaluated.  We will use pij, to indicate the elements of the
-    // 4x4 grid of values.
-    //
-    //          col
-    //      p00 p01 p02 p03
-    // row  p10 p11 p12 p13
-    //      p20 p21 p22 p23
-    //      p30 p31 p32 p33
-    //
-    // The point (r,c) being evaluated is assumed to lie in the square
-    // defined by p11, p12, p22 and p21.
-
-    const int row = std::floor(r);
-    const int col = std::floor(c);
-
-    Eigen::Matrix<double, Grid::DATA_DIMENSION, 1> p0, p1, p2, p3;
-
-    // Interpolate along each of the four rows, evaluating the function
-    // value and the horizontal derivative in each row.
-    Eigen::Matrix<double, Grid::DATA_DIMENSION, 1> f0, f1, f2, f3;
-    Eigen::Matrix<double, Grid::DATA_DIMENSION, 1> df0dc, df1dc, df2dc, df3dc;
-
-    grid_.GetValue(row - 1, col - 1, p0.data());
-    grid_.GetValue(row - 1, col, p1.data());
-    grid_.GetValue(row - 1, col + 1, p2.data());
-    grid_.GetValue(row - 1, col + 2, p3.data());
-    CubicHermiteSpline<Grid::DATA_DIMENSION>(
-        p0, p1, p2, p3, c - col, f0.data(), df0dc.data());
-
-    grid_.GetValue(row, col - 1, p0.data());
-    grid_.GetValue(row, col, p1.data());
-    grid_.GetValue(row, col + 1, p2.data());
-    grid_.GetValue(row, col + 2, p3.data());
-    CubicHermiteSpline<Grid::DATA_DIMENSION>(
-        p0, p1, p2, p3, c - col, f1.data(), df1dc.data());
-
-    grid_.GetValue(row + 1, col - 1, p0.data());
-    grid_.GetValue(row + 1, col, p1.data());
-    grid_.GetValue(row + 1, col + 1, p2.data());
-    grid_.GetValue(row + 1, col + 2, p3.data());
-    CubicHermiteSpline<Grid::DATA_DIMENSION>(
-        p0, p1, p2, p3, c - col, f2.data(), df2dc.data());
-
-    grid_.GetValue(row + 2, col - 1, p0.data());
-    grid_.GetValue(row + 2, col, p1.data());
-    grid_.GetValue(row + 2, col + 1, p2.data());
-    grid_.GetValue(row + 2, col + 2, p3.data());
-    CubicHermiteSpline<Grid::DATA_DIMENSION>(
-        p0, p1, p2, p3, c - col, f3.data(), df3dc.data());
-
-    // Interpolate vertically the interpolated value from each row and
-    // compute the derivative along the columns.
-    CubicHermiteSpline<Grid::DATA_DIMENSION>(f0, f1, f2, f3, r - row, f, dfdr);
-    if (dfdc != nullptr) {
-      // Interpolate vertically the derivative along the columns.
-      CubicHermiteSpline<Grid::DATA_DIMENSION>(
-          df0dc, df1dc, df2dc, df3dc, r - row, dfdc, nullptr);
-    }
-  }
-
-  // The following two Evaluate overloads are needed for interfacing
-  // with automatic differentiation. The first is for when a scalar
-  // evaluation is done, and the second one is for when Jets are used.
-  void Evaluate(const double& r, const double& c, double* f) const {
-    Evaluate(r, c, f, nullptr, nullptr);
-  }
-
-  template <typename JetT>
-  void Evaluate(const JetT& r, const JetT& c, JetT* f) const {
-    double frc[Grid::DATA_DIMENSION];
-    double dfdr[Grid::DATA_DIMENSION];
-    double dfdc[Grid::DATA_DIMENSION];
-    Evaluate(r.a, c.a, frc, dfdr, dfdc);
-    for (int i = 0; i < Grid::DATA_DIMENSION; ++i) {
-      f[i].a = frc[i];
-      f[i].v = dfdr[i] * r.v + dfdc[i] * c.v;
-    }
-  }
-
- private:
-  const Grid& grid_;
-};
-
-// An object that implements an infinite two dimensional grid needed
-// by the BiCubicInterpolator where the source of the function values
-// is an grid of type T on the grid
-//
-//   [(row_start,   col_start), ..., (row_start,   col_end - 1)]
-//   [                          ...                            ]
-//   [(row_end - 1, col_start), ..., (row_end - 1, col_end - 1)]
-//
-// Since the input grid is finite and the grid is infinite, values
-// outside this interval needs to be computed. Grid2D uses the value
-// from the nearest edge.
-//
-// The function being provided can be vector valued, in which case
-// kDataDimension > 1. The data maybe stored in row or column major
-// format and the various dimensional slices of the function maybe
-// interleaved, or they maybe stacked, i.e, if the function has
-// kDataDimension = 2, is stored in row-major format and if
-// kInterleaved = true, then it is stored as
-//
-//   f001, f002, f011, f012, ...
-//
-// A commonly occurring example are color images (RGB) where the three
-// channels are stored interleaved.
-//
-// If kInterleaved = false, then it is stored as
-//
-//  f001, f011, ..., fnm1, f002, f012, ...
-template <typename T,
-          int kDataDimension = 1,
-          bool kRowMajor = true,
-          bool kInterleaved = true>
-struct Grid2D {
- public:
-  enum { DATA_DIMENSION = kDataDimension };
-
-  Grid2D(const T* data,
-         const int row_begin,
-         const int row_end,
-         const int col_begin,
-         const int col_end)
-      : data_(data),
-        row_begin_(row_begin),
-        row_end_(row_end),
-        col_begin_(col_begin),
-        col_end_(col_end),
-        num_rows_(row_end - row_begin),
-        num_cols_(col_end - col_begin),
-        num_values_(num_rows_ * num_cols_) {
-    CHECK_GE(kDataDimension, 1);
-    CHECK_LT(row_begin, row_end);
-    CHECK_LT(col_begin, col_end);
-  }
-
-  EIGEN_STRONG_INLINE void GetValue(const int r, const int c, double* f) const {
-    const int row_idx =
-        (std::min)((std::max)(row_begin_, r), row_end_ - 1) - row_begin_;
-    const int col_idx =
-        (std::min)((std::max)(col_begin_, c), col_end_ - 1) - col_begin_;
-
-    const int n = (kRowMajor) ? num_cols_ * row_idx + col_idx
-                              : num_rows_ * col_idx + row_idx;
-
-    if (kInterleaved) {
-      for (int i = 0; i < kDataDimension; ++i) {
-        f[i] = static_cast<double>(data_[kDataDimension * n + i]);
-      }
-    } else {
-      for (int i = 0; i < kDataDimension; ++i) {
-        f[i] = static_cast<double>(data_[i * num_values_ + n]);
-      }
-    }
-  }
-
- private:
-  const T* data_;
-  const int row_begin_;
-  const int row_end_;
-  const int col_begin_;
-  const int col_end_;
-  const int num_rows_;
-  const int num_cols_;
-  const int num_values_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_CUBIC_INTERPOLATOR_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/dynamic_autodiff_cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/dynamic_autodiff_cost_function.h
deleted file mode 100644
index e47f32f..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/dynamic_autodiff_cost_function.h
+++ /dev/null
@@ -1,286 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//         mierle@gmail.com (Keir Mierle)
-
-#ifndef CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
-#define CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
-
-#include <cmath>
-#include <memory>
-#include <numeric>
-#include <vector>
-
-#include "ceres/dynamic_cost_function.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/jet.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// This autodiff implementation differs from the one found in
-// autodiff_cost_function.h by supporting autodiff on cost functions
-// with variable numbers of parameters with variable sizes. With the
-// other implementation, all the sizes (both the number of parameter
-// blocks and the size of each block) must be fixed at compile time.
-//
-// The functor API differs slightly from the API for fixed size
-// autodiff; the expected interface for the cost functors is:
-//
-//   struct MyCostFunctor {
-//     template<typename T>
-//     bool operator()(T const* const* parameters, T* residuals) const {
-//       // Use parameters[i] to access the i'th parameter block.
-//     }
-//   };
-//
-// Since the sizing of the parameters is done at runtime, you must
-// also specify the sizes after creating the dynamic autodiff cost
-// function. For example:
-//
-//   DynamicAutoDiffCostFunction<MyCostFunctor, 3> cost_function(
-//       new MyCostFunctor());
-//   cost_function.AddParameterBlock(5);
-//   cost_function.AddParameterBlock(10);
-//   cost_function.SetNumResiduals(21);
-//
-// Under the hood, the implementation evaluates the cost function
-// multiple times, computing a small set of the derivatives (four by
-// default, controlled by the Stride template parameter) with each
-// pass. There is a tradeoff with the size of the passes; you may want
-// to experiment with the stride.
-template <typename CostFunctor, int Stride = 4>
-class DynamicAutoDiffCostFunction final : public DynamicCostFunction {
- public:
-  // Takes ownership by default.
-  explicit DynamicAutoDiffCostFunction(CostFunctor* functor,
-                                       Ownership ownership = TAKE_OWNERSHIP)
-      : functor_(functor), ownership_(ownership) {}
-
-  DynamicAutoDiffCostFunction(DynamicAutoDiffCostFunction&& other)
-      : functor_(std::move(other.functor_)), ownership_(other.ownership_) {}
-
-  ~DynamicAutoDiffCostFunction() override {
-    // Manually release pointer if configured to not take ownership
-    // rather than deleting only if ownership is taken.  This is to
-    // stay maximally compatible to old user code which may have
-    // forgotten to implement a virtual destructor, from when the
-    // AutoDiffCostFunction always took ownership.
-    if (ownership_ == DO_NOT_TAKE_OWNERSHIP) {
-      functor_.release();
-    }
-  }
-
-  bool Evaluate(double const* const* parameters,
-                double* residuals,
-                double** jacobians) const override {
-    CHECK_GT(num_residuals(), 0)
-        << "You must call DynamicAutoDiffCostFunction::SetNumResiduals() "
-        << "before DynamicAutoDiffCostFunction::Evaluate().";
-
-    if (jacobians == nullptr) {
-      return (*functor_)(parameters, residuals);
-    }
-
-    // The difficulty with Jets, as implemented in Ceres, is that they were
-    // originally designed for strictly compile-sized use. At this point, there
-    // is a large body of code that assumes inside a cost functor it is
-    // acceptable to do e.g. T(1.5) and get an appropriately sized jet back.
-    //
-    // Unfortunately, it is impossible to communicate the expected size of a
-    // dynamically sized jet to the static instantiations that existing code
-    // depends on.
-    //
-    // To work around this issue, the solution here is to evaluate the
-    // jacobians in a series of passes, each one computing Stride *
-    // num_residuals() derivatives. This is done with small, fixed-size jets.
-    const int num_parameter_blocks =
-        static_cast<int>(parameter_block_sizes().size());
-    const int num_parameters = std::accumulate(
-        parameter_block_sizes().begin(), parameter_block_sizes().end(), 0);
-
-    // Allocate scratch space for the strided evaluation.
-    using JetT = Jet<double, Stride>;
-    internal::FixedArray<JetT, (256 * 7) / sizeof(JetT)> input_jets(
-        num_parameters);
-    internal::FixedArray<JetT, (256 * 7) / sizeof(JetT)> output_jets(
-        num_residuals());
-
-    // Make the parameter pack that is sent to the functor (reused).
-    internal::FixedArray<Jet<double, Stride>*> jet_parameters(
-        num_parameter_blocks, nullptr);
-    int num_active_parameters = 0;
-
-    // To handle constant parameters between non-constant parameter blocks, the
-    // start position --- a raw parameter index --- of each contiguous block of
-    // non-constant parameters is recorded in start_derivative_section.
-    std::vector<int> start_derivative_section;
-    bool in_derivative_section = false;
-    int parameter_cursor = 0;
-
-    // Discover the derivative sections and set the parameter values.
-    for (int i = 0; i < num_parameter_blocks; ++i) {
-      jet_parameters[i] = &input_jets[parameter_cursor];
-
-      const int parameter_block_size = parameter_block_sizes()[i];
-      if (jacobians[i] != nullptr) {
-        if (!in_derivative_section) {
-          start_derivative_section.push_back(parameter_cursor);
-          in_derivative_section = true;
-        }
-
-        num_active_parameters += parameter_block_size;
-      } else {
-        in_derivative_section = false;
-      }
-
-      for (int j = 0; j < parameter_block_size; ++j, parameter_cursor++) {
-        input_jets[parameter_cursor].a = parameters[i][j];
-      }
-    }
-
-    if (num_active_parameters == 0) {
-      return (*functor_)(parameters, residuals);
-    }
-    // When `num_active_parameters % Stride != 0` then it can be the case
-    // that `active_parameter_count < Stride` while parameter_cursor is less
-    // than the total number of parameters and with no remaining non-constant
-    // parameter blocks. Pushing parameter_cursor (the total number of
-    // parameters) as a final entry to start_derivative_section is required
-    // because if a constant parameter block is encountered after the
-    // last non-constant block then current_derivative_section is incremented
-    // and would otherwise index an invalid position in
-    // start_derivative_section. Setting the final element to the total number
-    // of parameters means that this can only happen at most once in the loop
-    // below.
-    start_derivative_section.push_back(parameter_cursor);
-
-    // Evaluate all of the strides. Each stride is a chunk of the derivative to
-    // evaluate, typically some size proportional to the size of the SIMD
-    // registers of the CPU.
-    int num_strides = static_cast<int>(
-        ceil(num_active_parameters / static_cast<float>(Stride)));
-
-    int current_derivative_section = 0;
-    int current_derivative_section_cursor = 0;
-
-    for (int pass = 0; pass < num_strides; ++pass) {
-      // Set most of the jet components to zero, except for
-      // non-constant #Stride parameters.
-      const int initial_derivative_section = current_derivative_section;
-      const int initial_derivative_section_cursor =
-          current_derivative_section_cursor;
-
-      int active_parameter_count = 0;
-      parameter_cursor = 0;
-
-      for (int i = 0; i < num_parameter_blocks; ++i) {
-        for (int j = 0; j < parameter_block_sizes()[i];
-             ++j, parameter_cursor++) {
-          input_jets[parameter_cursor].v.setZero();
-          if (active_parameter_count < Stride &&
-              parameter_cursor >=
-                  (start_derivative_section[current_derivative_section] +
-                   current_derivative_section_cursor)) {
-            if (jacobians[i] != nullptr) {
-              input_jets[parameter_cursor].v[active_parameter_count] = 1.0;
-              ++active_parameter_count;
-              ++current_derivative_section_cursor;
-            } else {
-              ++current_derivative_section;
-              current_derivative_section_cursor = 0;
-            }
-          }
-        }
-      }
-
-      if (!(*functor_)(&jet_parameters[0], &output_jets[0])) {
-        return false;
-      }
-
-      // Copy the pieces of the jacobians into their final place.
-      active_parameter_count = 0;
-
-      current_derivative_section = initial_derivative_section;
-      current_derivative_section_cursor = initial_derivative_section_cursor;
-
-      for (int i = 0, parameter_cursor = 0; i < num_parameter_blocks; ++i) {
-        for (int j = 0; j < parameter_block_sizes()[i];
-             ++j, parameter_cursor++) {
-          if (active_parameter_count < Stride &&
-              parameter_cursor >=
-                  (start_derivative_section[current_derivative_section] +
-                   current_derivative_section_cursor)) {
-            if (jacobians[i] != nullptr) {
-              for (int k = 0; k < num_residuals(); ++k) {
-                jacobians[i][k * parameter_block_sizes()[i] + j] =
-                    output_jets[k].v[active_parameter_count];
-              }
-              ++active_parameter_count;
-              ++current_derivative_section_cursor;
-            } else {
-              ++current_derivative_section;
-              current_derivative_section_cursor = 0;
-            }
-          }
-        }
-      }
-
-      // Only copy the residuals over once (even though we compute them on
-      // every loop).
-      if (pass == num_strides - 1) {
-        for (int k = 0; k < num_residuals(); ++k) {
-          residuals[k] = output_jets[k].a;
-        }
-      }
-    }
-    return true;
-  }
-
-  const CostFunctor& functor() const { return *functor_; }
-
- private:
-  std::unique_ptr<CostFunctor> functor_;
-  Ownership ownership_;
-};
-
-// Deduction guide that allows the user to avoid explicitly specifying the
-// template parameter of DynamicAutoDiffCostFunction. The class can instead be
-// instantiated as follows:
-//
-//   new DynamicAutoDiffCostFunction{new MyCostFunctor{}};
-//
-template <typename CostFunctor>
-DynamicAutoDiffCostFunction(CostFunctor* functor, Ownership ownership)
-    -> DynamicAutoDiffCostFunction<CostFunctor>;
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/dynamic_cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/dynamic_cost_function.h
deleted file mode 100644
index 02ce1e9..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/dynamic_cost_function.h
+++ /dev/null
@@ -1,57 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_DYNAMIC_COST_FUNCTION_H_
-#define CERES_PUBLIC_DYNAMIC_COST_FUNCTION_H_
-
-#include "ceres/cost_function.h"
-#include "ceres/internal/disable_warnings.h"
-
-namespace ceres {
-
-// A common base class for DynamicAutoDiffCostFunction and
-// DynamicNumericDiffCostFunction which depend on methods that can add
-// parameter blocks and set the number of residuals at run time.
-class CERES_EXPORT DynamicCostFunction : public CostFunction {
- public:
-  virtual void AddParameterBlock(int size) {
-    mutable_parameter_block_sizes()->push_back(size);
-  }
-
-  virtual void SetNumResiduals(int num_residuals) {
-    set_num_residuals(num_residuals);
-  }
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_DYNAMIC_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/dynamic_cost_function_to_functor.h b/3rdParty/my_ceres_vc17/include/ceres/dynamic_cost_function_to_functor.h
deleted file mode 100644
index cd124a2..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/dynamic_cost_function_to_functor.h
+++ /dev/null
@@ -1,194 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//         dgossow@google.com (David Gossow)
-
-#ifndef CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_
-#define CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_
-
-#include <memory>
-#include <numeric>
-#include <vector>
-
-#include "ceres/dynamic_cost_function.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/internal/fixed_array.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// DynamicCostFunctionToFunctor allows users to use CostFunction
-// objects in templated functors which are to be used for automatic
-// differentiation. It works similar to CostFunctionToFunctor, with the
-// difference that it allows you to wrap a cost function with dynamic numbers
-// of parameters and residuals.
-//
-// For example, let us assume that
-//
-//  class IntrinsicProjection : public CostFunction {
-//    public:
-//      IntrinsicProjection(const double* observation);
-//      bool Evaluate(double const* const* parameters,
-//                    double* residuals,
-//                    double** jacobians) const override;
-//  };
-//
-// is a cost function that implements the projection of a point in its
-// local coordinate system onto its image plane and subtracts it from
-// the observed point projection. It can compute its residual and
-// either via analytic or numerical differentiation can compute its
-// jacobians. The intrinsics are passed in as parameters[0] and the point as
-// parameters[1].
-//
-// Now we would like to compose the action of this CostFunction with
-// the action of camera extrinsics, i.e., rotation and
-// translation. Say we have a templated function
-//
-//   template<typename T>
-//   void RotateAndTranslatePoint(double const* const* parameters,
-//                                double* residuals);
-//
-// Then we can now do the following,
-//
-// struct CameraProjection {
-//   CameraProjection(const double* observation)
-//       : intrinsic_projection_.(new IntrinsicProjection(observation)) {
-//   }
-//   template <typename T>
-//   bool operator()(T const* const* parameters,
-//                   T* residual) const {
-//     const T* rotation = parameters[0];
-//     const T* translation = parameters[1];
-//     const T* intrinsics = parameters[2];
-//     const T* point = parameters[3];
-//     T transformed_point[3];
-//     RotateAndTranslatePoint(rotation, translation, point, transformed_point);
-//
-//     // Note that we call intrinsic_projection_, just like it was
-//     // any other templated functor.
-//     const T* projection_parameters[2];
-//     projection_parameters[0] = intrinsics;
-//     projection_parameters[1] = transformed_point;
-//     return intrinsic_projection_(projection_parameters, residual);
-//   }
-//
-//  private:
-//   DynamicCostFunctionToFunctor intrinsic_projection_;
-// };
-class CERES_EXPORT DynamicCostFunctionToFunctor {
- public:
-  // Takes ownership of cost_function.
-  explicit DynamicCostFunctionToFunctor(CostFunction* cost_function)
-      : cost_function_(cost_function) {
-    CHECK(cost_function != nullptr);
-  }
-
-  bool operator()(double const* const* parameters, double* residuals) const {
-    return cost_function_->Evaluate(parameters, residuals, nullptr);
-  }
-
-  template <typename JetT>
-  bool operator()(JetT const* const* inputs, JetT* output) const {
-    const std::vector<int32_t>& parameter_block_sizes =
-        cost_function_->parameter_block_sizes();
-    const int num_parameter_blocks =
-        static_cast<int>(parameter_block_sizes.size());
-    const int num_residuals = cost_function_->num_residuals();
-    const int num_parameters = std::accumulate(
-        parameter_block_sizes.begin(), parameter_block_sizes.end(), 0);
-
-    internal::FixedArray<double> parameters(num_parameters);
-    internal::FixedArray<double*> parameter_blocks(num_parameter_blocks);
-    internal::FixedArray<double> jacobians(num_residuals * num_parameters);
-    internal::FixedArray<double*> jacobian_blocks(num_parameter_blocks);
-    internal::FixedArray<double> residuals(num_residuals);
-
-    // Build a set of arrays to get the residuals and jacobians from
-    // the CostFunction wrapped by this functor.
-    double* parameter_ptr = parameters.data();
-    double* jacobian_ptr = jacobians.data();
-    for (int i = 0; i < num_parameter_blocks; ++i) {
-      parameter_blocks[i] = parameter_ptr;
-      jacobian_blocks[i] = jacobian_ptr;
-      for (int j = 0; j < parameter_block_sizes[i]; ++j) {
-        *parameter_ptr++ = inputs[i][j].a;
-      }
-      jacobian_ptr += num_residuals * parameter_block_sizes[i];
-    }
-
-    if (!cost_function_->Evaluate(parameter_blocks.data(),
-                                  residuals.data(),
-                                  jacobian_blocks.data())) {
-      return false;
-    }
-
-    // Now that we have the incoming Jets, which are carrying the
-    // partial derivatives of each of the inputs w.r.t to some other
-    // underlying parameters. The derivative of the outputs of the
-    // cost function w.r.t to the same underlying parameters can now
-    // be computed by applying the chain rule.
-    //
-    //  d output[i]               d output[i]   d input[j]
-    //  --------------  = sum_j   ----------- * ------------
-    //  d parameter[k]            d input[j]    d parameter[k]
-    //
-    // d input[j]
-    // --------------  = inputs[j], so
-    // d parameter[k]
-    //
-    //  outputJet[i]  = sum_k jacobian[i][k] * inputJet[k]
-    //
-    // The following loop, iterates over the residuals, computing one
-    // output jet at a time.
-    for (int i = 0; i < num_residuals; ++i) {
-      output[i].a = residuals[i];
-      output[i].v.setZero();
-
-      for (int j = 0; j < num_parameter_blocks; ++j) {
-        const int32_t block_size = parameter_block_sizes[j];
-        for (int k = 0; k < parameter_block_sizes[j]; ++k) {
-          output[i].v +=
-              jacobian_blocks[j][i * block_size + k] * inputs[j][k].v;
-        }
-      }
-    }
-
-    return true;
-  }
-
- private:
-  std::unique_ptr<CostFunction> cost_function_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/dynamic_numeric_diff_cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/dynamic_numeric_diff_cost_function.h
deleted file mode 100644
index d9cd945..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/dynamic_numeric_diff_cost_function.h
+++ /dev/null
@@ -1,164 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: mierle@gmail.com (Keir Mierle)
-//         sameeragarwal@google.com (Sameer Agarwal)
-//         thadh@gmail.com (Thad Hughes)
-//         tbennun@gmail.com (Tal Ben-Nun)
-
-#ifndef CERES_PUBLIC_DYNAMIC_NUMERIC_DIFF_COST_FUNCTION_H_
-#define CERES_PUBLIC_DYNAMIC_NUMERIC_DIFF_COST_FUNCTION_H_
-
-#include <cmath>
-#include <memory>
-#include <numeric>
-#include <vector>
-
-#include "ceres/dynamic_cost_function.h"
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/numeric_diff.h"
-#include "ceres/internal/parameter_dims.h"
-#include "ceres/numeric_diff_options.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// This numeric diff implementation differs from the one found in
-// numeric_diff_cost_function.h by supporting numericdiff on cost
-// functions with variable numbers of parameters with variable
-// sizes. With the other implementation, all the sizes (both the
-// number of parameter blocks and the size of each block) must be
-// fixed at compile time.
-//
-// The functor API differs slightly from the API for fixed size
-// numeric diff; the expected interface for the cost functors is:
-//
-//   struct MyCostFunctor {
-//     bool operator()(double const*
-//                     const* parameters,
-//                     double* residuals) const {
-//       // Use parameters[i] to access the i'th parameter block.
-//     }
-//   }
-//
-// Since the sizing of the parameters is done at runtime, you must
-// also specify the sizes after creating the
-// DynamicNumericDiffCostFunction. For example:
-//
-//   DynamicAutoDiffCostFunction<MyCostFunctor, CENTRAL> cost_function(
-//       new MyCostFunctor());
-//   cost_function.AddParameterBlock(5);
-//   cost_function.AddParameterBlock(10);
-//   cost_function.SetNumResiduals(21);
-template <typename CostFunctor, NumericDiffMethodType kMethod = CENTRAL>
-class DynamicNumericDiffCostFunction final : public DynamicCostFunction {
- public:
-  explicit DynamicNumericDiffCostFunction(
-      const CostFunctor* functor,
-      Ownership ownership = TAKE_OWNERSHIP,
-      const NumericDiffOptions& options = NumericDiffOptions())
-      : functor_(functor), ownership_(ownership), options_(options) {}
-
-  DynamicNumericDiffCostFunction(DynamicNumericDiffCostFunction&& other)
-      : functor_(std::move(other.functor_)), ownership_(other.ownership_) {}
-
-  ~DynamicNumericDiffCostFunction() override {
-    if (ownership_ != TAKE_OWNERSHIP) {
-      functor_.release();
-    }
-  }
-
-  bool Evaluate(double const* const* parameters,
-                double* residuals,
-                double** jacobians) const override {
-    using internal::NumericDiff;
-    CHECK_GT(num_residuals(), 0)
-        << "You must call DynamicNumericDiffCostFunction::SetNumResiduals() "
-        << "before DynamicNumericDiffCostFunction::Evaluate().";
-
-    const std::vector<int32_t>& block_sizes = parameter_block_sizes();
-    CHECK(!block_sizes.empty())
-        << "You must call DynamicNumericDiffCostFunction::AddParameterBlock() "
-        << "before DynamicNumericDiffCostFunction::Evaluate().";
-
-    const bool status =
-        internal::VariadicEvaluate<internal::DynamicParameterDims>(
-            *functor_.get(), parameters, residuals);
-    if (jacobians == nullptr || !status) {
-      return status;
-    }
-
-    // Create local space for a copy of the parameters which will get mutated.
-    int parameters_size = accumulate(block_sizes.begin(), block_sizes.end(), 0);
-    std::vector<double> parameters_copy(parameters_size);
-    std::vector<double*> parameters_references_copy(block_sizes.size());
-    parameters_references_copy[0] = &parameters_copy[0];
-    for (size_t block = 1; block < block_sizes.size(); ++block) {
-      parameters_references_copy[block] =
-          parameters_references_copy[block - 1] + block_sizes[block - 1];
-    }
-
-    // Copy the parameters into the local temp space.
-    for (size_t block = 0; block < block_sizes.size(); ++block) {
-      memcpy(parameters_references_copy[block],
-             parameters[block],
-             block_sizes[block] * sizeof(*parameters[block]));
-    }
-
-    for (size_t block = 0; block < block_sizes.size(); ++block) {
-      if (jacobians[block] != nullptr &&
-          !NumericDiff<CostFunctor,
-                       kMethod,
-                       ceres::DYNAMIC,
-                       internal::DynamicParameterDims,
-                       ceres::DYNAMIC,
-                       ceres::DYNAMIC>::
-              EvaluateJacobianForParameterBlock(functor_.get(),
-                                                residuals,
-                                                options_,
-                                                this->num_residuals(),
-                                                block,
-                                                block_sizes[block],
-                                                &parameters_references_copy[0],
-                                                jacobians[block])) {
-        return false;
-      }
-    }
-    return true;
-  }
-
- private:
-  std::unique_ptr<const CostFunctor> functor_;
-  Ownership ownership_;
-  NumericDiffOptions options_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_DYNAMIC_AUTODIFF_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/evaluation_callback.h b/3rdParty/my_ceres_vc17/include/ceres/evaluation_callback.h
deleted file mode 100644
index e582dc8..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/evaluation_callback.h
+++ /dev/null
@@ -1,84 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: mierle@gmail.com (Keir Mierle)
-
-#ifndef CERES_PUBLIC_EVALUATION_CALLBACK_H_
-#define CERES_PUBLIC_EVALUATION_CALLBACK_H_
-
-#include "ceres/internal/export.h"
-
-namespace ceres {
-
-// Using this callback interface, Ceres can notify you when it is
-// about to evaluate the residuals or jacobians. With the callback,
-// you can share computation between residual blocks by doing the
-// shared computation in PrepareForEvaluation() before Ceres calls
-// CostFunction::Evaluate(). It also enables caching results between a
-// pure residual evaluation and a residual & jacobian evaluation, via
-// the new_evaluation_point argument.
-//
-// One use case for this callback is if the cost function compute is
-// moved to the GPU. In that case, the prepare call does the actual
-// cost function evaluation, and subsequent calls from Ceres to the
-// actual cost functions merely copy the results from the GPU onto the
-// corresponding blocks for Ceres to plug into the solver.
-//
-// NOTE: Ceres provides no mechanism to share data other than the
-// notification from the callback. Users must provide access to
-// pre-computed shared data to their cost functions behind the scenes;
-// this all happens without Ceres knowing.
-//
-// One approach is to put a pointer to the shared data in each cost
-// function (recommended) or to use a global shared variable
-// (discouraged; bug-prone).  As far as Ceres is concerned, it is
-// evaluating cost functions like any other; it just so happens that
-// behind the scenes the cost functions reuse pre-computed data to
-// execute faster.
-class CERES_EXPORT EvaluationCallback {
- public:
-  virtual ~EvaluationCallback();
-
-  // Called before Ceres requests residuals or jacobians for a given setting of
-  // the parameters. User parameters (the double* values provided to the cost
-  // functions) are fixed until the next call to PrepareForEvaluation().
-  //
-  // If evaluate_jacobians == true, then the user provided CostFunctions will be
-  // asked to evaluate one or more of their Jacobians.
-  //
-  // If new_evaluation_point == true, then this is a new point that is different
-  // from the last evaluated point. Otherwise, it is the same point that was
-  // evaluated previously (either jacobian or residual) and the user can use
-  // cached results from previous evaluations.
-  virtual void PrepareForEvaluation(bool evaluate_jacobians,
-                                    bool new_evaluation_point) = 0;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_EVALUATION_CALLBACK_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/first_order_function.h b/3rdParty/my_ceres_vc17/include/ceres/first_order_function.h
deleted file mode 100644
index ea42732..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/first_order_function.h
+++ /dev/null
@@ -1,54 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_FIRST_ORDER_FUNCTION_H_
-#define CERES_PUBLIC_FIRST_ORDER_FUNCTION_H_
-
-#include "ceres/internal/export.h"
-
-namespace ceres {
-
-// A FirstOrderFunction object implements the evaluation of a function
-// and its gradient.
-class CERES_EXPORT FirstOrderFunction {
- public:
-  virtual ~FirstOrderFunction();
-
-  // cost is never null. gradient may be null. The return value
-  // indicates whether the evaluation was successful or not.
-  virtual bool Evaluate(const double* const parameters,
-                        double* cost,
-                        double* gradient) const = 0;
-  virtual int NumParameters() const = 0;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_FIRST_ORDER_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/gradient_checker.h b/3rdParty/my_ceres_vc17/include/ceres/gradient_checker.h
deleted file mode 100644
index 77f2c8e..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/gradient_checker.h
+++ /dev/null
@@ -1,154 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-// Copyright 2023 Google Inc. All Rights Reserved.
-//
-// Authors: wjr@google.com (William Rucklidge),
-//          keir@google.com (Keir Mierle),
-//          dgossow@google.com (David Gossow)
-
-#ifndef CERES_PUBLIC_GRADIENT_CHECKER_H_
-#define CERES_PUBLIC_GRADIENT_CHECKER_H_
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "ceres/cost_function.h"
-#include "ceres/dynamic_numeric_diff_cost_function.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/export.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/manifold.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// GradientChecker compares the Jacobians returned by a cost function against
-// derivatives estimated using finite differencing.
-//
-// The condition enforced is that
-//
-//    (J_actual(i, j) - J_numeric(i, j))
-//   ------------------------------------  <  relative_precision
-//   max(J_actual(i, j), J_numeric(i, j))
-//
-// where J_actual(i, j) is the Jacobian as computed by the supplied cost
-// function (by the user) multiplied by the manifold Jacobian and J_numeric is
-// the Jacobian as computed by finite differences, multiplied by the manifold
-// Jacobian as well.
-//
-// How to use: Fill in an array of pointers to parameter blocks for your
-// CostFunction, and then call Probe(). Check that the return value is 'true'.
-class CERES_EXPORT GradientChecker {
- public:
-  // This will not take ownership of the cost function or manifolds.
-  //
-  // function: The cost function to probe.
-  //
-  // manifolds: A vector of manifolds for each parameter. May be nullptr or
-  // contain nullptrs to indicate that the respective parameter blocks are
-  // Euclidean.
-  //
-  // options: Options to use for numerical differentiation.
-  GradientChecker(const CostFunction* function,
-                  const std::vector<const Manifold*>* manifolds,
-                  const NumericDiffOptions& options);
-
-  // Contains results from a call to Probe for later inspection.
-  struct CERES_EXPORT ProbeResults {
-    // The return value of the cost function.
-    bool return_value;
-
-    // Computed residual vector.
-    Vector residuals;
-
-    // The sizes of the Jacobians below are dictated by the cost function's
-    // parameter block size and residual block sizes. If a parameter block has a
-    // manifold associated with it, the size of the "local" Jacobian will be
-    // determined by the dimension of the manifold (which is the same as the
-    // dimension of the tangent space) and residual block size, otherwise it
-    // will be identical to the regular Jacobian.
-
-    // Derivatives as computed by the cost function.
-    std::vector<Matrix> jacobians;
-
-    // Derivatives as computed by the cost function in local space.
-    std::vector<Matrix> local_jacobians;
-
-    // Derivatives as computed by numerical differentiation in local space.
-    std::vector<Matrix> numeric_jacobians;
-
-    // Derivatives as computed by numerical differentiation in local space.
-    std::vector<Matrix> local_numeric_jacobians;
-
-    // Contains the maximum relative error found in the local Jacobians.
-    double maximum_relative_error;
-
-    // If an error was detected, this will contain a detailed description of
-    // that error.
-    std::string error_log;
-  };
-
-  // Call the cost function, compute alternative Jacobians using finite
-  // differencing and compare results. If manifolds are given, the Jacobians
-  // will be multiplied by the manifold Jacobians before performing the check,
-  // which effectively means that all errors along the null space of the
-  // manifold will be ignored.  Returns false if the Jacobians don't match, the
-  // cost function return false, or if a cost function returns a different
-  // residual when called with a Jacobian output argument vs. calling it
-  // without. Otherwise returns true.
-  //
-  // parameters: The parameter values at which to probe.
-  // relative_precision: A threshold for the relative difference between the
-  // Jacobians. If the Jacobians differ by more than this amount, then the
-  // probe fails.
-  // results: On return, the Jacobians (and other information) will be stored
-  // here. May be nullptr.
-  //
-  // Returns true if no problems are detected and the difference between the
-  // Jacobians is less than error_tolerance.
-  bool Probe(double const* const* parameters,
-             double relative_precision,
-             ProbeResults* results) const;
-
- private:
-  GradientChecker() = delete;
-  GradientChecker(const GradientChecker&) = delete;
-  void operator=(const GradientChecker&) = delete;
-
-  std::vector<const Manifold*> manifolds_;
-  const CostFunction* function_;
-  std::unique_ptr<CostFunction> finite_diff_cost_function_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_GRADIENT_CHECKER_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/gradient_problem.h b/3rdParty/my_ceres_vc17/include/ceres/gradient_problem.h
deleted file mode 100644
index 96d6493..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/gradient_problem.h
+++ /dev/null
@@ -1,125 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_GRADIENT_PROBLEM_H_
-#define CERES_PUBLIC_GRADIENT_PROBLEM_H_
-
-#include <memory>
-
-#include "ceres/first_order_function.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/manifold.h"
-
-namespace ceres {
-
-class FirstOrderFunction;
-
-// Instances of GradientProblem represent general non-linear
-// optimization problems that must be solved using just the value of
-// the objective function and its gradient.
-
-// Unlike the Problem class, which can only be used to model non-linear least
-// squares problems, instances of GradientProblem are not restricted in the form
-// of the objective function.
-//
-// Structurally GradientProblem is a composition of a FirstOrderFunction and
-// optionally a Manifold.
-//
-// The FirstOrderFunction is responsible for evaluating the cost and gradient of
-// the objective function.
-//
-// The Manifold is responsible for going back and forth between the ambient
-// space and the local tangent space. (See manifold.h for more details). When a
-// Manifold is not provided, then the tangent space is assumed to coincide with
-// the ambient Euclidean space that the gradient vector lives in.
-//
-// Example usage:
-//
-// The following demonstrate the problem construction for Rosenbrock's function
-//
-//   f(x,y) = (1-x)^2 + 100(y - x^2)^2;
-//
-// class Rosenbrock : public ceres::FirstOrderFunction {
-//  public:
-//   virtual ~Rosenbrock() {}
-//
-//   virtual bool Evaluate(const double* parameters,
-//                         double* cost,
-//                         double* gradient) const {
-//     const double x = parameters[0];
-//     const double y = parameters[1];
-//
-//     cost[0] = (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);
-//     if (gradient != nullptr) {
-//       gradient[0] = -2.0 * (1.0 - x) - 200.0 * (y - x * x) * 2.0 * x;
-//       gradient[1] = 200.0 * (y - x * x);
-//     }
-//     return true;
-//   };
-//
-//   virtual int NumParameters() const { return 2; };
-// };
-//
-// ceres::GradientProblem problem(new Rosenbrock());
-class CERES_EXPORT GradientProblem {
- public:
-  // Takes ownership of the function.
-  explicit GradientProblem(FirstOrderFunction* function);
-
-  // Takes ownership of the function and the manifold.
-  GradientProblem(FirstOrderFunction* function, Manifold* manifold);
-
-  int NumParameters() const;
-
-  // Dimension of the manifold (and its tangent space).
-  int NumTangentParameters() const;
-
-  // This call is not thread safe.
-  bool Evaluate(const double* parameters, double* cost, double* gradient) const;
-  bool Plus(const double* x, const double* delta, double* x_plus_delta) const;
-
-  const FirstOrderFunction* function() const { return function_.get(); }
-  FirstOrderFunction* mutable_function() { return function_.get(); }
-
-  const Manifold* manifold() const { return manifold_.get(); }
-  Manifold* mutable_manifold() { return manifold_.get(); }
-
- private:
-  std::unique_ptr<FirstOrderFunction> function_;
-  std::unique_ptr<Manifold> manifold_;
-  std::unique_ptr<double[]> scratch_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_GRADIENT_PROBLEM_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/gradient_problem_solver.h b/3rdParty/my_ceres_vc17/include/ceres/gradient_problem_solver.h
deleted file mode 100644
index f4c392f..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/gradient_problem_solver.h
+++ /dev/null
@@ -1,353 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_GRADIENT_PROBLEM_SOLVER_H_
-#define CERES_PUBLIC_GRADIENT_PROBLEM_SOLVER_H_
-
-#include <cmath>
-#include <string>
-#include <vector>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/internal/port.h"
-#include "ceres/iteration_callback.h"
-#include "ceres/types.h"
-
-namespace ceres {
-
-class GradientProblem;
-
-class CERES_EXPORT GradientProblemSolver {
- public:
-  virtual ~GradientProblemSolver();
-
-  // The options structure contains, not surprisingly, options that control how
-  // the solver operates. The defaults should be suitable for a wide range of
-  // problems; however, better performance is often obtainable with tweaking.
-  //
-  // The constants are defined inside types.h
-  struct CERES_EXPORT Options {
-    // Returns true if the options struct has a valid
-    // configuration. Returns false otherwise, and fills in *error
-    // with a message describing the problem.
-    bool IsValid(std::string* error) const;
-
-    // Minimizer options ----------------------------------------
-    LineSearchDirectionType line_search_direction_type = LBFGS;
-    LineSearchType line_search_type = WOLFE;
-    NonlinearConjugateGradientType nonlinear_conjugate_gradient_type =
-        FLETCHER_REEVES;
-
-    // The LBFGS hessian approximation is a low rank approximation to
-    // the inverse of the Hessian matrix. The rank of the
-    // approximation determines (linearly) the space and time
-    // complexity of using the approximation. Higher the rank, the
-    // better is the quality of the approximation. The increase in
-    // quality is however is bounded for a number of reasons.
-    //
-    // 1. The method only uses secant information and not actual
-    // derivatives.
-    //
-    // 2. The Hessian approximation is constrained to be positive
-    // definite.
-    //
-    // So increasing this rank to a large number will cost time and
-    // space complexity without the corresponding increase in solution
-    // quality. There are no hard and fast rules for choosing the
-    // maximum rank. The best choice usually requires some problem
-    // specific experimentation.
-    //
-    // For more theoretical and implementation details of the LBFGS
-    // method, please see:
-    //
-    // Nocedal, J. (1980). "Updating Quasi-Newton Matrices with
-    // Limited Storage". Mathematics of Computation 35 (151): 773-782.
-    int max_lbfgs_rank = 20;
-
-    // As part of the (L)BFGS update step (BFGS) / right-multiply step (L-BFGS),
-    // the initial inverse Hessian approximation is taken to be the Identity.
-    // However, Oren showed that using instead I * \gamma, where \gamma is
-    // chosen to approximate an eigenvalue of the true inverse Hessian can
-    // result in improved convergence in a wide variety of cases. Setting
-    // use_approximate_eigenvalue_bfgs_scaling to true enables this scaling.
-    //
-    // It is important to note that approximate eigenvalue scaling does not
-    // always improve convergence, and that it can in fact significantly degrade
-    // performance for certain classes of problem, which is why it is disabled
-    // by default.  In particular it can degrade performance when the
-    // sensitivity of the problem to different parameters varies significantly,
-    // as in this case a single scalar factor fails to capture this variation
-    // and detrimentally downscales parts of the jacobian approximation which
-    // correspond to low-sensitivity parameters. It can also reduce the
-    // robustness of the solution to errors in the jacobians.
-    //
-    // Oren S.S., Self-scaling variable metric (SSVM) algorithms
-    // Part II: Implementation and experiments, Management Science,
-    // 20(5), 863-874, 1974.
-    bool use_approximate_eigenvalue_bfgs_scaling = false;
-
-    // Degree of the polynomial used to approximate the objective
-    // function. Valid values are BISECTION, QUADRATIC and CUBIC.
-    //
-    // BISECTION corresponds to pure backtracking search with no
-    // interpolation.
-    LineSearchInterpolationType line_search_interpolation_type = CUBIC;
-
-    // If during the line search, the step_size falls below this
-    // value, it is truncated to zero.
-    double min_line_search_step_size = 1e-9;
-
-    // Line search parameters.
-
-    // Solving the line search problem exactly is computationally
-    // prohibitive. Fortunately, line search based optimization
-    // algorithms can still guarantee convergence if instead of an
-    // exact solution, the line search algorithm returns a solution
-    // which decreases the value of the objective function
-    // sufficiently. More precisely, we are looking for a step_size
-    // s.t.
-    //
-    //   f(step_size) <= f(0) + sufficient_decrease * f'(0) * step_size
-    //
-    double line_search_sufficient_function_decrease = 1e-4;
-
-    // In each iteration of the line search,
-    //
-    //  new_step_size >= max_line_search_step_contraction * step_size
-    //
-    // Note that by definition, for contraction:
-    //
-    //  0 < max_step_contraction < min_step_contraction < 1
-    //
-    double max_line_search_step_contraction = 1e-3;
-
-    // In each iteration of the line search,
-    //
-    //  new_step_size <= min_line_search_step_contraction * step_size
-    //
-    // Note that by definition, for contraction:
-    //
-    //  0 < max_step_contraction < min_step_contraction < 1
-    //
-    double min_line_search_step_contraction = 0.6;
-
-    // Maximum number of trial step size iterations during each line search,
-    // if a step size satisfying the search conditions cannot be found within
-    // this number of trials, the line search will terminate.
-    int max_num_line_search_step_size_iterations = 20;
-
-    // Maximum number of restarts of the line search direction algorithm before
-    // terminating the optimization. Restarts of the line search direction
-    // algorithm occur when the current algorithm fails to produce a new descent
-    // direction. This typically indicates a numerical failure, or a breakdown
-    // in the validity of the approximations used.
-    int max_num_line_search_direction_restarts = 5;
-
-    // The strong Wolfe conditions consist of the Armijo sufficient
-    // decrease condition, and an additional requirement that the
-    // step-size be chosen s.t. the _magnitude_ ('strong' Wolfe
-    // conditions) of the gradient along the search direction
-    // decreases sufficiently. Precisely, this second condition
-    // is that we seek a step_size s.t.
-    //
-    //   |f'(step_size)| <= sufficient_curvature_decrease * |f'(0)|
-    //
-    // Where f() is the line search objective and f'() is the derivative
-    // of f w.r.t step_size (d f / d step_size).
-    double line_search_sufficient_curvature_decrease = 0.9;
-
-    // During the bracketing phase of the Wolfe search, the step size is
-    // increased until either a point satisfying the Wolfe conditions is
-    // found, or an upper bound for a bracket containing a point satisfying
-    // the conditions is found.  Precisely, at each iteration of the
-    // expansion:
-    //
-    //   new_step_size <= max_step_expansion * step_size.
-    //
-    // By definition for expansion, max_step_expansion > 1.0.
-    double max_line_search_step_expansion = 10.0;
-
-    // Maximum number of iterations for the minimizer to run for.
-    int max_num_iterations = 50;
-
-    // Maximum time for which the minimizer should run for.
-    double max_solver_time_in_seconds = 1e9;
-
-    // Minimizer terminates when
-    //
-    //   (new_cost - old_cost) < function_tolerance * old_cost;
-    //
-    double function_tolerance = 1e-6;
-
-    // Minimizer terminates when
-    //
-    //   max_i |x - Project(Plus(x, -g(x))| < gradient_tolerance
-    //
-    // This value should typically be 1e-4 * function_tolerance.
-    double gradient_tolerance = 1e-10;
-
-    // Minimizer terminates when
-    //
-    //   |step|_2 <= parameter_tolerance * ( |x|_2 +  parameter_tolerance)
-    //
-    double parameter_tolerance = 1e-8;
-
-    // Logging options ---------------------------------------------------------
-
-    LoggingType logging_type = PER_MINIMIZER_ITERATION;
-
-    // By default the Minimizer progress is logged to VLOG(1), which
-    // is sent to STDERR depending on the vlog level. If this flag is
-    // set to true, and logging_type is not SILENT, the logging output
-    // is sent to STDOUT.
-    bool minimizer_progress_to_stdout = false;
-
-    // If true, the user's parameter blocks are updated at the end of
-    // every Minimizer iteration, otherwise they are updated when the
-    // Minimizer terminates. This is useful if, for example, the user
-    // wishes to visualize the state of the optimization every
-    // iteration.
-    bool update_state_every_iteration = false;
-
-    // Callbacks that are executed at the end of each iteration of the
-    // Minimizer. An iteration may terminate midway, either due to
-    // numerical failures or because one of the convergence tests has
-    // been satisfied. In this case none of the callbacks are
-    // executed.
-
-    // Callbacks are executed in the order that they are specified in
-    // this vector. By default, parameter blocks are updated only at
-    // the end of the optimization, i.e when the Minimizer
-    // terminates. This behaviour is controlled by
-    // update_state_every_variable. If the user wishes to have access
-    // to the update parameter blocks when his/her callbacks are
-    // executed, then set update_state_every_iteration to true.
-    //
-    // The solver does NOT take ownership of these pointers.
-    std::vector<IterationCallback*> callbacks;
-  };
-
-  struct CERES_EXPORT Summary {
-    // A brief one line description of the state of the solver after
-    // termination.
-    std::string BriefReport() const;
-
-    // A full multiline description of the state of the solver after
-    // termination.
-    std::string FullReport() const;
-
-    bool IsSolutionUsable() const;
-
-    // Minimizer summary -------------------------------------------------
-    TerminationType termination_type = FAILURE;
-
-    // Reason why the solver terminated.
-    std::string message = "ceres::GradientProblemSolve was not called.";
-
-    // Cost of the problem (value of the objective function) before
-    // the optimization.
-    double initial_cost = -1.0;
-
-    // Cost of the problem (value of the objective function) after the
-    // optimization.
-    double final_cost = -1.0;
-
-    // IterationSummary for each minimizer iteration in order.
-    std::vector<IterationSummary> iterations;
-
-    // Number of times the cost (and not the gradient) was evaluated.
-    int num_cost_evaluations = -1;
-
-    // Number of times the gradient (and the cost) were evaluated.
-    int num_gradient_evaluations = -1;
-
-    // Sum total of all time spent inside Ceres when Solve is called.
-    double total_time_in_seconds = -1.0;
-
-    // Time (in seconds) spent evaluating the cost.
-    double cost_evaluation_time_in_seconds = -1.0;
-
-    // Time (in seconds) spent evaluating the gradient.
-    double gradient_evaluation_time_in_seconds = -1.0;
-
-    // Time (in seconds) spent minimizing the interpolating polynomial
-    // to compute the next candidate step size as part of a line search.
-    double line_search_polynomial_minimization_time_in_seconds = -1.0;
-
-    // Number of parameters in the problem.
-    int num_parameters = -1;
-
-    // Dimension of the tangent space of the problem.
-    int num_tangent_parameters = -1;
-
-    // Type of line search direction used.
-    LineSearchDirectionType line_search_direction_type = LBFGS;
-
-    // Type of the line search algorithm used.
-    LineSearchType line_search_type = WOLFE;
-
-    //  When performing line search, the degree of the polynomial used
-    //  to approximate the objective function.
-    LineSearchInterpolationType line_search_interpolation_type = CUBIC;
-
-    // If the line search direction is NONLINEAR_CONJUGATE_GRADIENT,
-    // then this indicates the particular variant of non-linear
-    // conjugate gradient used.
-    NonlinearConjugateGradientType nonlinear_conjugate_gradient_type =
-        FLETCHER_REEVES;
-
-    // If the type of the line search direction is LBFGS, then this
-    // indicates the rank of the Hessian approximation.
-    int max_lbfgs_rank = -1;
-  };
-
-  // Once a least squares problem has been built, this function takes
-  // the problem and optimizes it based on the values of the options
-  // parameters. Upon return, a detailed summary of the work performed
-  // by the preprocessor, the non-linear minimizer and the linear
-  // solver are reported in the summary object.
-  virtual void Solve(const GradientProblemSolver::Options& options,
-                     const GradientProblem& problem,
-                     double* parameters,
-                     GradientProblemSolver::Summary* summary);
-};
-
-// Helper function which avoids going through the interface.
-CERES_EXPORT void Solve(const GradientProblemSolver::Options& options,
-                        const GradientProblem& problem,
-                        double* parameters,
-                        GradientProblemSolver::Summary* summary);
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_GRADIENT_PROBLEM_SOLVER_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/array_selector.h b/3rdParty/my_ceres_vc17/include/ceres/internal/array_selector.h
deleted file mode 100644
index 9480146..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/array_selector.h
+++ /dev/null
@@ -1,95 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: darius.rueckert@fau.de (Darius Rueckert)
-//
-
-#ifndef CERES_PUBLIC_INTERNAL_ARRAY_SELECTOR_H_
-#define CERES_PUBLIC_INTERNAL_ARRAY_SELECTOR_H_
-
-#include <array>
-#include <vector>
-
-#include "ceres/internal/fixed_array.h"
-#include "ceres/types.h"
-
-namespace ceres::internal {
-
-// StaticFixedArray selects the best array implementation based on template
-// arguments. If the size is not known at compile-time, pass
-// ceres::DYNAMIC as a size-template argument.
-//
-// Three different containers are selected in different scenarios:
-//
-//   num_elements == DYNAMIC:
-//      -> ceres::internal::FixedArray<T, max_stack_size>(size)
-
-//   num_elements != DYNAMIC  &&  num_elements <= max_stack_size
-//      -> std::array<T,num_elements>
-
-//   num_elements != DYNAMIC  &&  num_elements >  max_stack_size
-//      -> std::vector<T>(num_elements)
-//
-template <typename T,
-          int num_elements,
-          int max_num_elements_on_stack,
-          bool dynamic = (num_elements == DYNAMIC),
-          bool fits_on_stack = (num_elements <= max_num_elements_on_stack)>
-struct ArraySelector {};
-
-template <typename T,
-          int num_elements,
-          int max_num_elements_on_stack,
-          bool fits_on_stack>
-struct ArraySelector<T,
-                     num_elements,
-                     max_num_elements_on_stack,
-                     true,
-                     fits_on_stack>
-    : ceres::internal::FixedArray<T, max_num_elements_on_stack> {
-  explicit ArraySelector(int s)
-      : ceres::internal::FixedArray<T, max_num_elements_on_stack>(s) {}
-};
-
-template <typename T, int num_elements, int max_num_elements_on_stack>
-struct ArraySelector<T, num_elements, max_num_elements_on_stack, false, true>
-    : std::array<T, num_elements> {
-  explicit ArraySelector(int s) { CHECK_EQ(s, num_elements); }
-};
-
-template <typename T, int num_elements, int max_num_elements_on_stack>
-struct ArraySelector<T, num_elements, max_num_elements_on_stack, false, false>
-    : std::vector<T> {
-  explicit ArraySelector(int s) : std::vector<T>(s) {
-    CHECK_EQ(s, num_elements);
-  }
-};
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_ARRAY_SELECTOR_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/autodiff.h b/3rdParty/my_ceres_vc17/include/ceres/internal/autodiff.h
deleted file mode 100644
index 8b02a2b..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/autodiff.h
+++ /dev/null
@@ -1,363 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: keir@google.com (Keir Mierle)
-//
-// Computation of the Jacobian matrix for vector-valued functions of multiple
-// variables, using automatic differentiation based on the implementation of
-// dual numbers in jet.h. Before reading the rest of this file, it is advisable
-// to read jet.h's header comment in detail.
-//
-// The helper wrapper AutoDifferentiate() computes the jacobian of
-// functors with templated operator() taking this form:
-//
-//   struct F {
-//     template<typename T>
-//     bool operator()(const T *x, const T *y, ..., T *z) {
-//       // Compute z[] based on x[], y[], ...
-//       // return true if computation succeeded, false otherwise.
-//     }
-//   };
-//
-// All inputs and outputs may be vector-valued.
-//
-// To understand how jets are used to compute the jacobian, a
-// picture may help. Consider a vector-valued function, F, returning 3
-// dimensions and taking a vector-valued parameter of 4 dimensions:
-//
-//     y            x
-//   [ * ]    F   [ * ]
-//   [ * ]  <---  [ * ]
-//   [ * ]        [ * ]
-//                [ * ]
-//
-// Similar to the 2-parameter example for f described in jet.h, computing the
-// jacobian dy/dx is done by substituting a suitable jet object for x and all
-// intermediate steps of the computation of F. Since x is has 4 dimensions, use
-// a Jet<double, 4>.
-//
-// Before substituting a jet object for x, the dual components are set
-// appropriately for each dimension of x:
-//
-//          y                       x
-//   [ * | * * * * ]    f   [ * | 1 0 0 0 ]   x0
-//   [ * | * * * * ]  <---  [ * | 0 1 0 0 ]   x1
-//   [ * | * * * * ]        [ * | 0 0 1 0 ]   x2
-//         ---+---          [ * | 0 0 0 1 ]   x3
-//            |                   ^ ^ ^ ^
-//          dy/dx                 | | | +----- infinitesimal for x3
-//                                | | +------- infinitesimal for x2
-//                                | +--------- infinitesimal for x1
-//                                +----------- infinitesimal for x0
-//
-// The reason to set the internal 4x4 submatrix to the identity is that we wish
-// to take the derivative of y separately with respect to each dimension of x.
-// Each column of the 4x4 identity is therefore for a single component of the
-// independent variable x.
-//
-// Then the jacobian of the mapping, dy/dx, is the 3x4 sub-matrix of the
-// extended y vector, indicated in the above diagram.
-//
-// Functors with multiple parameters
-// ---------------------------------
-// In practice, it is often convenient to use a function f of two or more
-// vector-valued parameters, for example, x[3] and z[6]. Unfortunately, the jet
-// framework is designed for a single-parameter vector-valued input. The wrapper
-// in this file addresses this issue adding support for functions with one or
-// more parameter vectors.
-//
-// To support multiple parameters, all the parameter vectors are concatenated
-// into one and treated as a single parameter vector, except that since the
-// functor expects different inputs, we need to construct the jets as if they
-// were part of a single parameter vector. The extended jets are passed
-// separately for each parameter.
-//
-// For example, consider a functor F taking two vector parameters, p[2] and
-// q[3], and producing an output y[4]:
-//
-//   struct F {
-//     template<typename T>
-//     bool operator()(const T *p, const T *q, T *z) {
-//       // ...
-//     }
-//   };
-//
-// In this case, the necessary jet type is Jet<double, 5>. Here is a
-// visualization of the jet objects in this case:
-//
-//          Dual components for p ----+
-//                                    |
-//                                   -+-
-//           y                 [ * | 1 0 | 0 0 0 ]    --- p[0]
-//                             [ * | 0 1 | 0 0 0 ]    --- p[1]
-//   [ * | . . | + + + ]         |
-//   [ * | . . | + + + ]         v
-//   [ * | . . | + + + ]  <--- F(p, q)
-//   [ * | . . | + + + ]            ^
-//         ^^^   ^^^^^              |
-//        dy/dp  dy/dq            [ * | 0 0 | 1 0 0 ] --- q[0]
-//                                [ * | 0 0 | 0 1 0 ] --- q[1]
-//                                [ * | 0 0 | 0 0 1 ] --- q[2]
-//                                            --+--
-//                                              |
-//          Dual components for q --------------+
-//
-// where the 4x2 submatrix (marked with ".") and 4x3 submatrix (marked with "+"
-// of y in the above diagram are the derivatives of y with respect to p and q
-// respectively. This is how autodiff works for functors taking multiple vector
-// valued arguments (up to 6).
-//
-// Jacobian null pointers (nullptr)
-// --------------------------------
-// In general, the functions below will accept nullptr for all or some of the
-// Jacobian parameters, meaning that those Jacobians will not be computed.
-
-#ifndef CERES_PUBLIC_INTERNAL_AUTODIFF_H_
-#define CERES_PUBLIC_INTERNAL_AUTODIFF_H_
-
-#include <array>
-#include <cstddef>
-#include <utility>
-
-#include "ceres/internal/array_selector.h"
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/internal/parameter_dims.h"
-#include "ceres/internal/variadic_evaluate.h"
-#include "ceres/jet.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-// If the number of parameters exceeds this values, the corresponding jets are
-// placed on the heap. This will reduce performance by a factor of 2-5 on
-// current compilers.
-#ifndef CERES_AUTODIFF_MAX_PARAMETERS_ON_STACK
-#define CERES_AUTODIFF_MAX_PARAMETERS_ON_STACK 50
-#endif
-
-#ifndef CERES_AUTODIFF_MAX_RESIDUALS_ON_STACK
-#define CERES_AUTODIFF_MAX_RESIDUALS_ON_STACK 20
-#endif
-
-namespace ceres::internal {
-
-// Extends src by a 1st order perturbation for every dimension and puts it in
-// dst. The size of src is N. Since this is also used for perturbations in
-// blocked arrays, offset is used to shift which part of the jet the
-// perturbation occurs. This is used to set up the extended x augmented by an
-// identity matrix. The JetT type should be a Jet type, and T should be a
-// numeric type (e.g. double). For example,
-//
-//             0   1 2   3 4 5   6 7 8
-//   dst[0]  [ * | . . | 1 0 0 | . . . ]
-//   dst[1]  [ * | . . | 0 1 0 | . . . ]
-//   dst[2]  [ * | . . | 0 0 1 | . . . ]
-//
-// is what would get put in dst if N was 3, offset was 3, and the jet type JetT
-// was 8-dimensional.
-template <int j, int N, int Offset, typename T, typename JetT>
-struct Make1stOrderPerturbation {
- public:
-  inline static void Apply(const T* src, JetT* dst) {
-    if (j == 0) {
-      DCHECK(src);
-      DCHECK(dst);
-    }
-    dst[j] = JetT(src[j], j + Offset);
-    Make1stOrderPerturbation<j + 1, N, Offset, T, JetT>::Apply(src, dst);
-  }
-};
-
-template <int N, int Offset, typename T, typename JetT>
-struct Make1stOrderPerturbation<N, N, Offset, T, JetT> {
- public:
-  static void Apply(const T* /* NOT USED */, JetT* /* NOT USED */) {}
-};
-
-// Calls Make1stOrderPerturbation for every parameter block.
-//
-// Example:
-// If one having three parameter blocks with dimensions (3, 2, 4), the call
-// Make1stOrderPerturbations<integer_sequence<3, 2, 4>::Apply(params, x);
-// will result in the following calls to Make1stOrderPerturbation:
-// Make1stOrderPerturbation<0, 3, 0>::Apply(params[0], x + 0);
-// Make1stOrderPerturbation<0, 2, 3>::Apply(params[1], x + 3);
-// Make1stOrderPerturbation<0, 4, 5>::Apply(params[2], x + 5);
-template <typename Seq, int ParameterIdx = 0, int Offset = 0>
-struct Make1stOrderPerturbations;
-
-template <int N, int... Ns, int ParameterIdx, int Offset>
-struct Make1stOrderPerturbations<std::integer_sequence<int, N, Ns...>,
-                                 ParameterIdx,
-                                 Offset> {
-  template <typename T, typename JetT>
-  inline static void Apply(T const* const* parameters, JetT* x) {
-    Make1stOrderPerturbation<0, N, Offset, T, JetT>::Apply(
-        parameters[ParameterIdx], x + Offset);
-    Make1stOrderPerturbations<std::integer_sequence<int, Ns...>,
-                              ParameterIdx + 1,
-                              Offset + N>::Apply(parameters, x);
-  }
-};
-
-// End of 'recursion'. Nothing more to do.
-template <int ParameterIdx, int Total>
-struct Make1stOrderPerturbations<std::integer_sequence<int>,
-                                 ParameterIdx,
-                                 Total> {
-  template <typename T, typename JetT>
-  static void Apply(T const* const* /* NOT USED */, JetT* /* NOT USED */) {}
-};
-
-// Takes the 0th order part of src, assumed to be a Jet type, and puts it in
-// dst. This is used to pick out the "vector" part of the extended y.
-template <typename JetT, typename T>
-inline void Take0thOrderPart(int M, const JetT* src, T dst) {
-  DCHECK(src);
-  for (int i = 0; i < M; ++i) {
-    dst[i] = src[i].a;
-  }
-}
-
-// Takes N 1st order parts, starting at index N0, and puts them in the M x N
-// matrix 'dst'. This is used to pick out the "matrix" parts of the extended y.
-template <int N0, int N, typename JetT, typename T>
-inline void Take1stOrderPart(const int M, const JetT* src, T* dst) {
-  DCHECK(src);
-  DCHECK(dst);
-  for (int i = 0; i < M; ++i) {
-    Eigen::Map<Eigen::Matrix<T, N, 1>>(dst + N * i, N) =
-        src[i].v.template segment<N>(N0);
-  }
-}
-
-// Calls Take1stOrderPart for every parameter block.
-//
-// Example:
-// If one having three parameter blocks with dimensions (3, 2, 4), the call
-// Take1stOrderParts<integer_sequence<3, 2, 4>::Apply(num_outputs,
-//                                                    output,
-//                                                    jacobians);
-// will result in the following calls to Take1stOrderPart:
-// if (jacobians[0]) {
-//   Take1stOrderPart<0, 3>(num_outputs, output, jacobians[0]);
-// }
-// if (jacobians[1]) {
-//   Take1stOrderPart<3, 2>(num_outputs, output, jacobians[1]);
-// }
-// if (jacobians[2]) {
-//   Take1stOrderPart<5, 4>(num_outputs, output, jacobians[2]);
-// }
-template <typename Seq, int ParameterIdx = 0, int Offset = 0>
-struct Take1stOrderParts;
-
-template <int N, int... Ns, int ParameterIdx, int Offset>
-struct Take1stOrderParts<std::integer_sequence<int, N, Ns...>,
-                         ParameterIdx,
-                         Offset> {
-  template <typename JetT, typename T>
-  inline static void Apply(int num_outputs, JetT* output, T** jacobians) {
-    if (jacobians[ParameterIdx]) {
-      Take1stOrderPart<Offset, N>(num_outputs, output, jacobians[ParameterIdx]);
-    }
-    Take1stOrderParts<std::integer_sequence<int, Ns...>,
-                      ParameterIdx + 1,
-                      Offset + N>::Apply(num_outputs, output, jacobians);
-  }
-};
-
-// End of 'recursion'. Nothing more to do.
-template <int ParameterIdx, int Offset>
-struct Take1stOrderParts<std::integer_sequence<int>, ParameterIdx, Offset> {
-  template <typename T, typename JetT>
-  static void Apply(int /* NOT USED*/,
-                    JetT* /* NOT USED*/,
-                    T** /* NOT USED */) {}
-};
-
-template <int kNumResiduals,
-          typename ParameterDims,
-          typename Functor,
-          typename T>
-inline bool AutoDifferentiate(const Functor& functor,
-                              T const* const* parameters,
-                              int dynamic_num_outputs,
-                              T* function_value,
-                              T** jacobians) {
-  using JetT = Jet<T, ParameterDims::kNumParameters>;
-  using Parameters = typename ParameterDims::Parameters;
-
-  if (kNumResiduals != DYNAMIC) {
-    DCHECK_EQ(kNumResiduals, dynamic_num_outputs);
-  }
-
-  ArraySelector<JetT,
-                ParameterDims::kNumParameters,
-                CERES_AUTODIFF_MAX_PARAMETERS_ON_STACK>
-      parameters_as_jets(ParameterDims::kNumParameters);
-
-  // Pointers to the beginning of each parameter block
-  std::array<JetT*, ParameterDims::kNumParameterBlocks> unpacked_parameters =
-      ParameterDims::GetUnpackedParameters(parameters_as_jets.data());
-
-  // If the number of residuals is fixed, we use the template argument as the
-  // number of outputs. Otherwise we use the num_outputs parameter. Note: The
-  // ?-operator here is compile-time evaluated, therefore num_outputs is also
-  // a compile-time constant for functors with fixed residuals.
-  const int num_outputs =
-      kNumResiduals == DYNAMIC ? dynamic_num_outputs : kNumResiduals;
-  DCHECK_GT(num_outputs, 0);
-
-  ArraySelector<JetT, kNumResiduals, CERES_AUTODIFF_MAX_RESIDUALS_ON_STACK>
-      residuals_as_jets(num_outputs);
-
-  // Invalidate the output Jets, so that we can detect if the user
-  // did not assign values to all of them.
-  for (int i = 0; i < num_outputs; ++i) {
-    residuals_as_jets[i].a = kImpossibleValue;
-    residuals_as_jets[i].v.setConstant(kImpossibleValue);
-  }
-
-  Make1stOrderPerturbations<Parameters>::Apply(parameters,
-                                               parameters_as_jets.data());
-
-  if (!VariadicEvaluate<ParameterDims>(
-          functor, unpacked_parameters.data(), residuals_as_jets.data())) {
-    return false;
-  }
-
-  Take0thOrderPart(num_outputs, residuals_as_jets.data(), function_value);
-  Take1stOrderParts<Parameters>::Apply(
-      num_outputs, residuals_as_jets.data(), jacobians);
-
-  return true;
-}
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_AUTODIFF_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/config.h b/3rdParty/my_ceres_vc17/include/ceres/internal/config.h
deleted file mode 100644
index 5ebfccd..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/config.h
+++ /dev/null
@@ -1,99 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2022 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: alexs.mac@gmail.com (Alex Stewart)
-
-// Configuration options for Ceres.
-//
-// Do not edit this file, it was automatically configured by CMake when
-// Ceres was compiled with the relevant configuration for the machine
-// on which Ceres was compiled.
-//
-// Ceres Developers: All options should have the same name as their mapped
-//                   CMake options, in the preconfigured version of this file
-//                   all options should be enclosed in '@'.
-
-#ifndef CERES_PUBLIC_INTERNAL_CONFIG_H_
-#define CERES_PUBLIC_INTERNAL_CONFIG_H_
-
-// If defined, use the LGPL code in Eigen.
-#define CERES_USE_EIGEN_SPARSE
-
-// If defined, Ceres was compiled without LAPACK.
-#define CERES_NO_LAPACK
-
-// If defined, Ceres was compiled without SuiteSparse.
-#define CERES_NO_SUITESPARSE
-
-// If defined, Ceres was compiled without CUDA.
-// #define CERES_NO_CUDA
-
-// If defined, Ceres was compiled without Apple's Accelerate framework solvers.
-#define CERES_NO_ACCELERATE_SPARSE
-
-#if defined(CERES_NO_SUITESPARSE) &&              \
-    defined(CERES_NO_ACCELERATE_SPARSE) &&        \
-    !defined(CERES_USE_EIGEN_SPARSE)  // NOLINT
-// If defined Ceres was compiled without any sparse linear algebra support.
-#define CERES_NO_SPARSE
-#endif
-
-// If defined, Ceres was compiled without Schur specializations.
-// #define CERES_RESTRICT_SCHUR_SPECIALIZATION
-
-// If defined, Ceres was compiled to use Eigen instead of hardcoded BLAS
-// routines.
-// #define CERES_NO_CUSTOM_BLAS
-
-// If defined, Ceres was compiled with a version of SuiteSparse/CHOLMOD without
-// the Partition module (requires METIS).
-#define CERES_NO_CHOLMOD_PARTITION
-// If defined Ceres was compiled without support for METIS via Eigen.
-#define CERES_NO_EIGEN_METIS
-
-// If defined, Ceres was compiled with a version MSVC >= 2005 which
-// deprecated the standard POSIX names for bessel functions, replacing them
-// with underscore prefixed versions (e.g. j0() -> _j0()).
-// #define CERES_MSVC_USE_UNDERSCORE_PREFIXED_BESSEL_FUNCTIONS
-
-// CERES_NO_SPARSE should be automatically defined by config.h if Ceres was
-// compiled without any sparse back-end.  Verify that it has not subsequently
-// been inconsistently redefined.
-#if defined(CERES_NO_SPARSE)
-#if !defined(CERES_NO_SUITESPARSE)
-#error CERES_NO_SPARSE requires CERES_NO_SUITESPARSE.
-#endif
-#if !defined(CERES_NO_ACCELERATE_SPARSE)
-#error CERES_NO_SPARSE requires CERES_NO_ACCELERATE_SPARSE
-#endif
-#if defined(CERES_USE_EIGEN_SPARSE)
-#error CERES_NO_SPARSE requires !CERES_USE_EIGEN_SPARSE
-#endif
-#endif
-
-#endif  // CERES_PUBLIC_INTERNAL_CONFIG_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/disable_warnings.h b/3rdParty/my_ceres_vc17/include/ceres/internal/disable_warnings.h
deleted file mode 100644
index b6e38aa..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/disable_warnings.h
+++ /dev/null
@@ -1,44 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// This file has the sole purpose to silence warnings when including Ceres.
-
-// This is not your usual header guard. The macro CERES_WARNINGS_DISABLED
-// shows up again in reenable_warnings.h.
-#ifndef CERES_WARNINGS_DISABLED
-#define CERES_WARNINGS_DISABLED
-
-#ifdef _MSC_VER
-#pragma warning(push)
-// Disable the warning C4251 which is triggered by stl classes in
-// Ceres' public interface. To quote MSDN: "C4251 can be ignored "
-// "if you are deriving from a type in the Standard C++ Library"
-#pragma warning(disable : 4251)
-#endif
-
-#endif  // CERES_WARNINGS_DISABLED
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/eigen.h b/3rdParty/my_ceres_vc17/include/ceres/internal/eigen.h
deleted file mode 100644
index fee6b52..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/eigen.h
+++ /dev/null
@@ -1,75 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_INTERNAL_EIGEN_H_
-#define CERES_INTERNAL_EIGEN_H_
-
-#include "Eigen/Core"
-
-namespace ceres {
-
-using Vector = Eigen::Matrix<double, Eigen::Dynamic, 1>;
-using Matrix =
-    Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
-using VectorRef = Eigen::Map<Vector>;
-using MatrixRef = Eigen::Map<Matrix>;
-using ConstVectorRef = Eigen::Map<const Vector>;
-using ConstMatrixRef = Eigen::Map<const Matrix>;
-
-// Column major matrices for DenseSparseMatrix/DenseQRSolver
-using ColMajorMatrix =
-    Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::ColMajor>;
-
-using ColMajorMatrixRef =
-    Eigen::Map<ColMajorMatrix, 0, Eigen::Stride<Eigen::Dynamic, 1>>;
-
-using ConstColMajorMatrixRef =
-    Eigen::Map<const ColMajorMatrix, 0, Eigen::Stride<Eigen::Dynamic, 1>>;
-
-// C++ does not support templated typdefs, thus the need for this
-// struct so that we can support statically sized Matrix and Maps.
-template <int num_rows = Eigen::Dynamic, int num_cols = Eigen::Dynamic>
-struct EigenTypes {
-  using Matrix =
-      Eigen::Matrix<double,
-                    num_rows,
-                    num_cols,
-                    num_cols == 1 ? Eigen::ColMajor : Eigen::RowMajor>;
-
-  using MatrixRef = Eigen::Map<Matrix>;
-  using ConstMatrixRef = Eigen::Map<const Matrix>;
-  using Vector = Eigen::Matrix<double, num_rows, 1>;
-  using VectorRef = Eigen::Map<Eigen::Matrix<double, num_rows, 1>>;
-  using ConstVectorRef = Eigen::Map<const Eigen::Matrix<double, num_rows, 1>>;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_INTERNAL_EIGEN_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/euler_angles.h b/3rdParty/my_ceres_vc17/include/ceres/internal/euler_angles.h
deleted file mode 100644
index 38f2702..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/euler_angles.h
+++ /dev/null
@@ -1,199 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-
-#ifndef CERES_PUBLIC_INTERNAL_EULER_ANGLES_H_
-#define CERES_PUBLIC_INTERNAL_EULER_ANGLES_H_
-
-#include <type_traits>
-
-namespace ceres {
-namespace internal {
-
-// The EulerSystem struct represents an Euler Angle Convention in compile time.
-// It acts like a trait structure and is also used as a tag for dispatching
-// Euler angle conversion function templates
-//
-// Internally, it implements the convention laid out in "Euler angle
-// conversion", Ken Shoemake, Graphics Gems IV, where a choice of axis for the
-// first rotation (out of 3) and 3 binary choices compactly specify all 24
-// rotation conventions
-//
-//  - InnerAxis: Axis for the first rotation. This is specified by struct tags
-//  axis::X, axis::Y, and axis::Z
-//
-//  - Parity: Defines the parity of the axis permutation. The axis sequence has
-//  Even parity if the second axis of rotation is 'greater-than' the first axis
-//  of rotation according to the order X<Y<Z<X, otherwise it has Odd parity.
-//  This is specified by struct tags Even and Odd
-//
-//  - AngleConvention: Defines whether Proper Euler Angles (originally defined
-//  by Euler, which has the last axis repeated, i.e. ZYZ, ZXZ, etc), or
-//  Tait-Bryan Angles (introduced by the nautical and aerospace fields, i.e.
-//  using ZYX for roll-pitch-yaw) are used. This is specified by struct Tags
-//  ProperEuler and TaitBryan.
-//
-//  - FrameConvention: Defines whether the three rotations are be in a global
-//  frame of reference (extrinsic) or in a body centred frame of reference
-//  (intrinsic). This is specified by struct tags Extrinsic and Intrinsic
-
-namespace axis {
-struct X : std::integral_constant<int, 0> {};
-struct Y : std::integral_constant<int, 1> {};
-struct Z : std::integral_constant<int, 2> {};
-}  // namespace axis
-
-struct Even;
-struct Odd;
-
-struct ProperEuler;
-struct TaitBryan;
-
-struct Extrinsic;
-struct Intrinsic;
-
-template <typename InnerAxisType,
-          typename ParityType,
-          typename AngleConventionType,
-          typename FrameConventionType>
-struct EulerSystem {
-  static constexpr bool kIsParityOdd = std::is_same_v<ParityType, Odd>;
-  static constexpr bool kIsProperEuler =
-      std::is_same_v<AngleConventionType, ProperEuler>;
-  static constexpr bool kIsIntrinsic =
-      std::is_same_v<FrameConventionType, Intrinsic>;
-
-  static constexpr int kAxes[3] = {
-      InnerAxisType::value,
-      (InnerAxisType::value + 1 + static_cast<int>(kIsParityOdd)) % 3,
-      (InnerAxisType::value + 2 - static_cast<int>(kIsParityOdd)) % 3};
-};
-
-}  // namespace internal
-
-// Define human readable aliases to the type of the tags
-using ExtrinsicXYZ = internal::EulerSystem<internal::axis::X,
-                                           internal::Even,
-                                           internal::TaitBryan,
-                                           internal::Extrinsic>;
-using ExtrinsicXYX = internal::EulerSystem<internal::axis::X,
-                                           internal::Even,
-                                           internal::ProperEuler,
-                                           internal::Extrinsic>;
-using ExtrinsicXZY = internal::EulerSystem<internal::axis::X,
-                                           internal::Odd,
-                                           internal::TaitBryan,
-                                           internal::Extrinsic>;
-using ExtrinsicXZX = internal::EulerSystem<internal::axis::X,
-                                           internal::Odd,
-                                           internal::ProperEuler,
-                                           internal::Extrinsic>;
-using ExtrinsicYZX = internal::EulerSystem<internal::axis::Y,
-                                           internal::Even,
-                                           internal::TaitBryan,
-                                           internal::Extrinsic>;
-using ExtrinsicYZY = internal::EulerSystem<internal::axis::Y,
-                                           internal::Even,
-                                           internal::ProperEuler,
-                                           internal::Extrinsic>;
-using ExtrinsicYXZ = internal::EulerSystem<internal::axis::Y,
-                                           internal::Odd,
-                                           internal::TaitBryan,
-                                           internal::Extrinsic>;
-using ExtrinsicYXY = internal::EulerSystem<internal::axis::Y,
-                                           internal::Odd,
-                                           internal::ProperEuler,
-                                           internal::Extrinsic>;
-using ExtrinsicZXY = internal::EulerSystem<internal::axis::Z,
-                                           internal::Even,
-                                           internal::TaitBryan,
-                                           internal::Extrinsic>;
-using ExtrinsicZXZ = internal::EulerSystem<internal::axis::Z,
-                                           internal::Even,
-                                           internal::ProperEuler,
-                                           internal::Extrinsic>;
-using ExtrinsicZYX = internal::EulerSystem<internal::axis::Z,
-                                           internal::Odd,
-                                           internal::TaitBryan,
-                                           internal::Extrinsic>;
-using ExtrinsicZYZ = internal::EulerSystem<internal::axis::Z,
-                                           internal::Odd,
-                                           internal::ProperEuler,
-                                           internal::Extrinsic>;
-/* Rotating axes */
-using IntrinsicZYX = internal::EulerSystem<internal::axis::X,
-                                           internal::Even,
-                                           internal::TaitBryan,
-                                           internal::Intrinsic>;
-using IntrinsicXYX = internal::EulerSystem<internal::axis::X,
-                                           internal::Even,
-                                           internal::ProperEuler,
-                                           internal::Intrinsic>;
-using IntrinsicYZX = internal::EulerSystem<internal::axis::X,
-                                           internal::Odd,
-                                           internal::TaitBryan,
-                                           internal::Intrinsic>;
-using IntrinsicXZX = internal::EulerSystem<internal::axis::X,
-                                           internal::Odd,
-                                           internal::ProperEuler,
-                                           internal::Intrinsic>;
-using IntrinsicXZY = internal::EulerSystem<internal::axis::Y,
-                                           internal::Even,
-                                           internal::TaitBryan,
-                                           internal::Intrinsic>;
-using IntrinsicYZY = internal::EulerSystem<internal::axis::Y,
-                                           internal::Even,
-                                           internal::ProperEuler,
-                                           internal::Intrinsic>;
-using IntrinsicZXY = internal::EulerSystem<internal::axis::Y,
-                                           internal::Odd,
-                                           internal::TaitBryan,
-                                           internal::Intrinsic>;
-using IntrinsicYXY = internal::EulerSystem<internal::axis::Y,
-                                           internal::Odd,
-                                           internal::ProperEuler,
-                                           internal::Intrinsic>;
-using IntrinsicYXZ = internal::EulerSystem<internal::axis::Z,
-                                           internal::Even,
-                                           internal::TaitBryan,
-                                           internal::Intrinsic>;
-using IntrinsicZXZ = internal::EulerSystem<internal::axis::Z,
-                                           internal::Even,
-                                           internal::ProperEuler,
-                                           internal::Intrinsic>;
-using IntrinsicXYZ = internal::EulerSystem<internal::axis::Z,
-                                           internal::Odd,
-                                           internal::TaitBryan,
-                                           internal::Intrinsic>;
-using IntrinsicZYZ = internal::EulerSystem<internal::axis::Z,
-                                           internal::Odd,
-                                           internal::ProperEuler,
-                                           internal::Intrinsic>;
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_INTERNAL_EULER_ANGLES_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/export.h b/3rdParty/my_ceres_vc17/include/ceres/internal/export.h
deleted file mode 100644
index a563536..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/export.h
+++ /dev/null
@@ -1,42 +0,0 @@
-
-#ifndef CERES_EXPORT_H
-#define CERES_EXPORT_H
-
-#ifdef CERES_STATIC_DEFINE
-#  define CERES_EXPORT
-#  define CERES_NO_EXPORT
-#else
-#  ifndef CERES_EXPORT
-#    ifdef ceres_EXPORTS
-        /* We are building this library */
-#      define CERES_EXPORT __declspec(dllexport)
-#    else
-        /* We are using this library */
-#      define CERES_EXPORT __declspec(dllimport)
-#    endif
-#  endif
-
-#  ifndef CERES_NO_EXPORT
-#    define CERES_NO_EXPORT 
-#  endif
-#endif
-
-#ifndef CERES_DEPRECATED
-#  define CERES_DEPRECATED __declspec(deprecated)
-#endif
-
-#ifndef CERES_DEPRECATED_EXPORT
-#  define CERES_DEPRECATED_EXPORT CERES_EXPORT CERES_DEPRECATED
-#endif
-
-#ifndef CERES_DEPRECATED_NO_EXPORT
-#  define CERES_DEPRECATED_NO_EXPORT CERES_NO_EXPORT CERES_DEPRECATED
-#endif
-
-#if 0 /* DEFINE_NO_DEPRECATED */
-#  ifndef CERES_NO_DEPRECATED
-#    define CERES_NO_DEPRECATED
-#  endif
-#endif
-
-#endif /* CERES_EXPORT_H */
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/fixed_array.h b/3rdParty/my_ceres_vc17/include/ceres/internal/fixed_array.h
deleted file mode 100644
index 0e35f63..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/fixed_array.h
+++ /dev/null
@@ -1,465 +0,0 @@
-// Copyright 2018 The Abseil Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      https://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-// -----------------------------------------------------------------------------
-// File: fixed_array.h
-// -----------------------------------------------------------------------------
-//
-// A `FixedArray<T>` represents a non-resizable array of `T` where the length of
-// the array can be determined at run-time. It is a good replacement for
-// non-standard and deprecated uses of `alloca()` and variable length arrays
-// within the GCC extension. (See
-// https://gcc.gnu.org/onlinedocs/gcc/Variable-Length.html).
-//
-// `FixedArray` allocates small arrays inline, keeping performance fast by
-// avoiding heap operations. It also helps reduce the chances of
-// accidentally overflowing your stack if large input is passed to
-// your function.
-
-#ifndef CERES_PUBLIC_INTERNAL_FIXED_ARRAY_H_
-#define CERES_PUBLIC_INTERNAL_FIXED_ARRAY_H_
-
-#include <Eigen/Core>  // For Eigen::aligned_allocator
-#include <algorithm>
-#include <array>
-#include <cstddef>
-#include <memory>
-#include <tuple>
-#include <type_traits>
-
-#include "ceres/internal/memory.h"
-#include "glog/logging.h"
-
-namespace ceres::internal {
-
-constexpr static auto kFixedArrayUseDefault = static_cast<size_t>(-1);
-
-// The default fixed array allocator.
-//
-// As one can not easily detect if a struct contains or inherits from a fixed
-// size Eigen type, to be safe the Eigen::aligned_allocator is used by default.
-// But trivial types can never contain Eigen types, so std::allocator is used to
-// safe some heap memory.
-template <typename T>
-using FixedArrayDefaultAllocator =
-    typename std::conditional<std::is_trivial<T>::value,
-                              std::allocator<T>,
-                              Eigen::aligned_allocator<T>>::type;
-
-// -----------------------------------------------------------------------------
-// FixedArray
-// -----------------------------------------------------------------------------
-//
-// A `FixedArray` provides a run-time fixed-size array, allocating a small array
-// inline for efficiency.
-//
-// Most users should not specify an `inline_elements` argument and let
-// `FixedArray` automatically determine the number of elements
-// to store inline based on `sizeof(T)`. If `inline_elements` is specified, the
-// `FixedArray` implementation will use inline storage for arrays with a
-// length <= `inline_elements`.
-//
-// Note that a `FixedArray` constructed with a `size_type` argument will
-// default-initialize its values by leaving trivially constructible types
-// uninitialized (e.g. int, int[4], double), and others default-constructed.
-// This matches the behavior of c-style arrays and `std::array`, but not
-// `std::vector`.
-//
-// Note that `FixedArray` does not provide a public allocator; if it requires a
-// heap allocation, it will do so with global `::operator new[]()` and
-// `::operator delete[]()`, even if T provides class-scope overrides for these
-// operators.
-template <typename T,
-          size_t N = kFixedArrayUseDefault,
-          typename A = FixedArrayDefaultAllocator<T>>
-class FixedArray {
-  static_assert(!std::is_array<T>::value || std::extent<T>::value > 0,
-                "Arrays with unknown bounds cannot be used with FixedArray.");
-
-  static constexpr size_t kInlineBytesDefault = 256;
-
-  using AllocatorTraits = std::allocator_traits<A>;
-  // std::iterator_traits isn't guaranteed to be SFINAE-friendly until C++17,
-  // but this seems to be mostly pedantic.
-  template <typename Iterator>
-  using EnableIfForwardIterator = typename std::enable_if<std::is_convertible<
-      typename std::iterator_traits<Iterator>::iterator_category,
-      std::forward_iterator_tag>::value>::type;
-  static constexpr bool DefaultConstructorIsNonTrivial() {
-    return !std::is_trivially_default_constructible<StorageElement>::value;
-  }
-
- public:
-  using allocator_type = typename AllocatorTraits::allocator_type;
-  using value_type = typename AllocatorTraits::value_type;
-  using pointer = typename AllocatorTraits::pointer;
-  using const_pointer = typename AllocatorTraits::const_pointer;
-  using reference = value_type&;
-  using const_reference = const value_type&;
-  using size_type = typename AllocatorTraits::size_type;
-  using difference_type = typename AllocatorTraits::difference_type;
-  using iterator = pointer;
-  using const_iterator = const_pointer;
-  using reverse_iterator = std::reverse_iterator<iterator>;
-  using const_reverse_iterator = std::reverse_iterator<const_iterator>;
-
-  static constexpr size_type inline_elements =
-      (N == kFixedArrayUseDefault ? kInlineBytesDefault / sizeof(value_type)
-                                  : static_cast<size_type>(N));
-
-  FixedArray(const FixedArray& other,
-             const allocator_type& a = allocator_type())
-      : FixedArray(other.begin(), other.end(), a) {}
-
-  FixedArray(FixedArray&& other, const allocator_type& a = allocator_type())
-      : FixedArray(std::make_move_iterator(other.begin()),
-                   std::make_move_iterator(other.end()),
-                   a) {}
-
-  // Creates an array object that can store `n` elements.
-  // Note that trivially constructible elements will be uninitialized.
-  explicit FixedArray(size_type n, const allocator_type& a = allocator_type())
-      : storage_(n, a) {
-    if (DefaultConstructorIsNonTrivial()) {
-      ConstructRange(storage_.alloc(), storage_.begin(), storage_.end());
-    }
-  }
-
-  // Creates an array initialized with `n` copies of `val`.
-  FixedArray(size_type n,
-             const value_type& val,
-             const allocator_type& a = allocator_type())
-      : storage_(n, a) {
-    ConstructRange(storage_.alloc(), storage_.begin(), storage_.end(), val);
-  }
-
-  // Creates an array initialized with the size and contents of `init_list`.
-  FixedArray(std::initializer_list<value_type> init_list,
-             const allocator_type& a = allocator_type())
-      : FixedArray(init_list.begin(), init_list.end(), a) {}
-
-  // Creates an array initialized with the elements from the input
-  // range. The array's size will always be `std::distance(first, last)`.
-  // REQUIRES: Iterator must be a forward_iterator or better.
-  template <typename Iterator, EnableIfForwardIterator<Iterator>* = nullptr>
-  FixedArray(Iterator first,
-             Iterator last,
-             const allocator_type& a = allocator_type())
-      : storage_(std::distance(first, last), a) {
-    CopyRange(storage_.alloc(), storage_.begin(), first, last);
-  }
-
-  ~FixedArray() noexcept {
-    for (auto* cur = storage_.begin(); cur != storage_.end(); ++cur) {
-      AllocatorTraits::destroy(storage_.alloc(), cur);
-    }
-  }
-
-  // Assignments are deleted because they break the invariant that the size of a
-  // `FixedArray` never changes.
-  void operator=(FixedArray&&) = delete;
-  void operator=(const FixedArray&) = delete;
-
-  // FixedArray::size()
-  //
-  // Returns the length of the fixed array.
-  size_type size() const { return storage_.size(); }
-
-  // FixedArray::max_size()
-  //
-  // Returns the largest possible value of `std::distance(begin(), end())` for a
-  // `FixedArray<T>`. This is equivalent to the most possible addressable bytes
-  // over the number of bytes taken by T.
-  constexpr size_type max_size() const {
-    return (std::numeric_limits<difference_type>::max)() / sizeof(value_type);
-  }
-
-  // FixedArray::empty()
-  //
-  // Returns whether or not the fixed array is empty.
-  bool empty() const { return size() == 0; }
-
-  // FixedArray::memsize()
-  //
-  // Returns the memory size of the fixed array in bytes.
-  size_t memsize() const { return size() * sizeof(value_type); }
-
-  // FixedArray::data()
-  //
-  // Returns a const T* pointer to elements of the `FixedArray`. This pointer
-  // can be used to access (but not modify) the contained elements.
-  const_pointer data() const { return AsValueType(storage_.begin()); }
-
-  // Overload of FixedArray::data() to return a T* pointer to elements of the
-  // fixed array. This pointer can be used to access and modify the contained
-  // elements.
-  pointer data() { return AsValueType(storage_.begin()); }
-
-  // FixedArray::operator[]
-  //
-  // Returns a reference the ith element of the fixed array.
-  // REQUIRES: 0 <= i < size()
-  reference operator[](size_type i) {
-    DCHECK_LT(i, size());
-    return data()[i];
-  }
-
-  // Overload of FixedArray::operator()[] to return a const reference to the
-  // ith element of the fixed array.
-  // REQUIRES: 0 <= i < size()
-  const_reference operator[](size_type i) const {
-    DCHECK_LT(i, size());
-    return data()[i];
-  }
-
-  // FixedArray::front()
-  //
-  // Returns a reference to the first element of the fixed array.
-  reference front() { return *begin(); }
-
-  // Overload of FixedArray::front() to return a reference to the first element
-  // of a fixed array of const values.
-  const_reference front() const { return *begin(); }
-
-  // FixedArray::back()
-  //
-  // Returns a reference to the last element of the fixed array.
-  reference back() { return *(end() - 1); }
-
-  // Overload of FixedArray::back() to return a reference to the last element
-  // of a fixed array of const values.
-  const_reference back() const { return *(end() - 1); }
-
-  // FixedArray::begin()
-  //
-  // Returns an iterator to the beginning of the fixed array.
-  iterator begin() { return data(); }
-
-  // Overload of FixedArray::begin() to return a const iterator to the
-  // beginning of the fixed array.
-  const_iterator begin() const { return data(); }
-
-  // FixedArray::cbegin()
-  //
-  // Returns a const iterator to the beginning of the fixed array.
-  const_iterator cbegin() const { return begin(); }
-
-  // FixedArray::end()
-  //
-  // Returns an iterator to the end of the fixed array.
-  iterator end() { return data() + size(); }
-
-  // Overload of FixedArray::end() to return a const iterator to the end of the
-  // fixed array.
-  const_iterator end() const { return data() + size(); }
-
-  // FixedArray::cend()
-  //
-  // Returns a const iterator to the end of the fixed array.
-  const_iterator cend() const { return end(); }
-
-  // FixedArray::rbegin()
-  //
-  // Returns a reverse iterator from the end of the fixed array.
-  reverse_iterator rbegin() { return reverse_iterator(end()); }
-
-  // Overload of FixedArray::rbegin() to return a const reverse iterator from
-  // the end of the fixed array.
-  const_reverse_iterator rbegin() const {
-    return const_reverse_iterator(end());
-  }
-
-  // FixedArray::crbegin()
-  //
-  // Returns a const reverse iterator from the end of the fixed array.
-  const_reverse_iterator crbegin() const { return rbegin(); }
-
-  // FixedArray::rend()
-  //
-  // Returns a reverse iterator from the beginning of the fixed array.
-  reverse_iterator rend() { return reverse_iterator(begin()); }
-
-  // Overload of FixedArray::rend() for returning a const reverse iterator
-  // from the beginning of the fixed array.
-  const_reverse_iterator rend() const {
-    return const_reverse_iterator(begin());
-  }
-
-  // FixedArray::crend()
-  //
-  // Returns a reverse iterator from the beginning of the fixed array.
-  const_reverse_iterator crend() const { return rend(); }
-
-  // FixedArray::fill()
-  //
-  // Assigns the given `value` to all elements in the fixed array.
-  void fill(const value_type& val) { std::fill(begin(), end(), val); }
-
-  // Relational operators. Equality operators are elementwise using
-  // `operator==`, while order operators order FixedArrays lexicographically.
-  friend bool operator==(const FixedArray& lhs, const FixedArray& rhs) {
-    return std::equal(lhs.begin(), lhs.end(), rhs.begin(), rhs.end());
-  }
-
-  friend bool operator!=(const FixedArray& lhs, const FixedArray& rhs) {
-    return !(lhs == rhs);
-  }
-
-  friend bool operator<(const FixedArray& lhs, const FixedArray& rhs) {
-    return std::lexicographical_compare(
-        lhs.begin(), lhs.end(), rhs.begin(), rhs.end());
-  }
-
-  friend bool operator>(const FixedArray& lhs, const FixedArray& rhs) {
-    return rhs < lhs;
-  }
-
-  friend bool operator<=(const FixedArray& lhs, const FixedArray& rhs) {
-    return !(rhs < lhs);
-  }
-
-  friend bool operator>=(const FixedArray& lhs, const FixedArray& rhs) {
-    return !(lhs < rhs);
-  }
-
- private:
-  // StorageElement
-  //
-  // For FixedArrays with a C-style-array value_type, StorageElement is a POD
-  // wrapper struct called StorageElementWrapper that holds the value_type
-  // instance inside. This is needed for construction and destruction of the
-  // entire array regardless of how many dimensions it has. For all other cases,
-  // StorageElement is just an alias of value_type.
-  //
-  // Maintainer's Note: The simpler solution would be to simply wrap value_type
-  // in a struct whether it's an array or not. That causes some paranoid
-  // diagnostics to misfire, believing that 'data()' returns a pointer to a
-  // single element, rather than the packed array that it really is.
-  // e.g.:
-  //
-  //     FixedArray<char> buf(1);
-  //     sprintf(buf.data(), "foo");
-  //
-  //     error: call to int __builtin___sprintf_chk(etc...)
-  //     will always overflow destination buffer [-Werror]
-  //
-  template <typename OuterT,
-            typename InnerT = typename std::remove_extent<OuterT>::type,
-            size_t InnerN = std::extent<OuterT>::value>
-  struct StorageElementWrapper {
-    InnerT array[InnerN];
-  };
-
-  using StorageElement =
-      typename std::conditional<std::is_array<value_type>::value,
-                                StorageElementWrapper<value_type>,
-                                value_type>::type;
-
-  static pointer AsValueType(pointer ptr) { return ptr; }
-  static pointer AsValueType(StorageElementWrapper<value_type>* ptr) {
-    return std::addressof(ptr->array);
-  }
-
-  static_assert(sizeof(StorageElement) == sizeof(value_type));
-  static_assert(alignof(StorageElement) == alignof(value_type));
-
-  class NonEmptyInlinedStorage {
-   public:
-    StorageElement* data() { return reinterpret_cast<StorageElement*>(buff_); }
-    void AnnotateConstruct(size_type) {}
-    void AnnotateDestruct(size_type) {}
-
-    // #ifdef ADDRESS_SANITIZER
-    //     void* RedzoneBegin() { return &redzone_begin_; }
-    //     void* RedzoneEnd() { return &redzone_end_ + 1; }
-    // #endif  // ADDRESS_SANITIZER
-
-   private:
-    // ADDRESS_SANITIZER_REDZONE(redzone_begin_);
-    alignas(StorageElement) char buff_[sizeof(StorageElement[inline_elements])];
-    // ADDRESS_SANITIZER_REDZONE(redzone_end_);
-  };
-
-  class EmptyInlinedStorage {
-   public:
-    StorageElement* data() { return nullptr; }
-    void AnnotateConstruct(size_type) {}
-    void AnnotateDestruct(size_type) {}
-  };
-
-  using InlinedStorage =
-      typename std::conditional<inline_elements == 0,
-                                EmptyInlinedStorage,
-                                NonEmptyInlinedStorage>::type;
-
-  // Storage
-  //
-  // An instance of Storage manages the inline and out-of-line memory for
-  // instances of FixedArray. This guarantees that even when construction of
-  // individual elements fails in the FixedArray constructor body, the
-  // destructor for Storage will still be called and out-of-line memory will be
-  // properly deallocated.
-  //
-  class Storage : public InlinedStorage {
-   public:
-    Storage(size_type n, const allocator_type& a)
-        : size_alloc_(n, a), data_(InitializeData()) {}
-
-    ~Storage() noexcept {
-      if (UsingInlinedStorage(size())) {
-        InlinedStorage::AnnotateDestruct(size());
-      } else {
-        AllocatorTraits::deallocate(alloc(), AsValueType(begin()), size());
-      }
-    }
-
-    size_type size() const { return std::get<0>(size_alloc_); }
-    StorageElement* begin() const { return data_; }
-    StorageElement* end() const { return begin() + size(); }
-    allocator_type& alloc() { return std::get<1>(size_alloc_); }
-
-   private:
-    static bool UsingInlinedStorage(size_type n) {
-      return n <= inline_elements;
-    }
-
-    StorageElement* InitializeData() {
-      if (UsingInlinedStorage(size())) {
-        InlinedStorage::AnnotateConstruct(size());
-        return InlinedStorage::data();
-      } else {
-        return reinterpret_cast<StorageElement*>(
-            AllocatorTraits::allocate(alloc(), size()));
-      }
-    }
-
-    // Using std::tuple and not absl::CompressedTuple, as it has a lot of
-    // dependencies to other absl headers.
-    std::tuple<size_type, allocator_type> size_alloc_;
-    StorageElement* data_;
-  };
-
-  Storage storage_;
-};
-
-template <typename T, size_t N, typename A>
-constexpr size_t FixedArray<T, N, A>::kInlineBytesDefault;
-
-template <typename T, size_t N, typename A>
-constexpr typename FixedArray<T, N, A>::size_type
-    FixedArray<T, N, A>::inline_elements;
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_FIXED_ARRAY_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/householder_vector.h b/3rdParty/my_ceres_vc17/include/ceres/internal/householder_vector.h
deleted file mode 100644
index dd8361c..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/householder_vector.h
+++ /dev/null
@@ -1,94 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://code.google.com/p/ceres-solver/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: vitus@google.com (Michael Vitus)
-
-#ifndef CERES_PUBLIC_INTERNAL_HOUSEHOLDER_VECTOR_H_
-#define CERES_PUBLIC_INTERNAL_HOUSEHOLDER_VECTOR_H_
-
-#include "Eigen/Core"
-#include "glog/logging.h"
-
-namespace ceres::internal {
-
-// Algorithm 5.1.1 from 'Matrix Computations' by Golub et al. (Johns Hopkins
-// Studies in Mathematical Sciences) but using the nth element of the input
-// vector as pivot instead of first. This computes the vector v with v(n) = 1
-// and beta such that H = I - beta * v * v^T is orthogonal and
-// H * x = ||x||_2 * e_n.
-//
-// NOTE: Some versions of MSVC have trouble deducing the type of v if
-// you do not specify all the template arguments explicitly.
-template <typename XVectorType, typename Scalar, int N>
-void ComputeHouseholderVector(const XVectorType& x,
-                              Eigen::Matrix<Scalar, N, 1>* v,
-                              Scalar* beta) {
-  CHECK(beta != nullptr);
-  CHECK(v != nullptr);
-  CHECK_GT(x.rows(), 1);
-  CHECK_EQ(x.rows(), v->rows());
-
-  Scalar sigma = x.head(x.rows() - 1).squaredNorm();
-  *v = x;
-  (*v)(v->rows() - 1) = Scalar(1.0);
-
-  *beta = Scalar(0.0);
-  const Scalar& x_pivot = x(x.rows() - 1);
-
-  if (sigma <= Scalar(std::numeric_limits<double>::epsilon())) {
-    if (x_pivot < Scalar(0.0)) {
-      *beta = Scalar(2.0);
-    }
-    return;
-  }
-
-  const Scalar mu = sqrt(x_pivot * x_pivot + sigma);
-  Scalar v_pivot = Scalar(1.0);
-
-  if (x_pivot <= Scalar(0.0)) {
-    v_pivot = x_pivot - mu;
-  } else {
-    v_pivot = -sigma / (x_pivot + mu);
-  }
-
-  *beta = Scalar(2.0) * v_pivot * v_pivot / (sigma + v_pivot * v_pivot);
-
-  v->head(v->rows() - 1) /= v_pivot;
-}
-
-template <typename XVectorType, typename Derived>
-typename Derived::PlainObject ApplyHouseholderVector(
-    const XVectorType& y,
-    const Eigen::MatrixBase<Derived>& v,
-    const typename Derived::Scalar& beta) {
-  return (y - v * (beta * (v.transpose() * y)));
-}
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_HOUSEHOLDER_VECTOR_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/integer_sequence_algorithm.h b/3rdParty/my_ceres_vc17/include/ceres/internal/integer_sequence_algorithm.h
deleted file mode 100644
index 0c27d72..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/integer_sequence_algorithm.h
+++ /dev/null
@@ -1,199 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: jodebo_beck@gmx.de (Johannes Beck)
-//         sergiu.deitsch@gmail.com (Sergiu Deitsch)
-//
-// Algorithms to be used together with integer_sequence, like computing the sum
-// or the exclusive scan (sometimes called exclusive prefix sum) at compile
-// time.
-
-#ifndef CERES_PUBLIC_INTERNAL_INTEGER_SEQUENCE_ALGORITHM_H_
-#define CERES_PUBLIC_INTERNAL_INTEGER_SEQUENCE_ALGORITHM_H_
-
-#include <utility>
-
-#include "ceres/jet_fwd.h"
-
-namespace ceres::internal {
-
-// Implementation of calculating an exclusive scan (exclusive prefix sum) of an
-// integer sequence. Exclusive means that the i-th input element is not included
-// in the i-th sum. Calculating the exclusive scan for an input array I results
-// in the following output R:
-//
-// R[0] = 0
-// R[1] = I[0];
-// R[2] = I[0] + I[1];
-// R[3] = I[0] + I[1] + I[2];
-// ...
-//
-// In C++17 std::exclusive_scan does the same operation at runtime (but
-// cannot be used to calculate the prefix sum at compile time). See
-// https://en.cppreference.com/w/cpp/algorithm/exclusive_scan for a more
-// detailed description.
-//
-// Example for integer_sequence<int, 1, 4, 3> (seq := integer_sequence):
-//                   T  , Sum,          Ns...   ,          Rs...
-// ExclusiveScanImpl<int,   0, seq<int, 1, 4, 3>, seq<int         >>
-// ExclusiveScanImpl<int,   1, seq<int,    4, 3>, seq<int, 0      >>
-// ExclusiveScanImpl<int,   5, seq<int,       3>, seq<int, 0, 1   >>
-// ExclusiveScanImpl<int,   8, seq<int         >, seq<int, 0, 1, 5>>
-//                                                ^^^^^^^^^^^^^^^^^
-//                                                resulting sequence
-template <typename T, T Sum, typename SeqIn, typename SeqOut>
-struct ExclusiveScanImpl;
-
-template <typename T, T Sum, T N, T... Ns, T... Rs>
-struct ExclusiveScanImpl<T,
-                         Sum,
-                         std::integer_sequence<T, N, Ns...>,
-                         std::integer_sequence<T, Rs...>> {
-  using Type =
-      typename ExclusiveScanImpl<T,
-                                 Sum + N,
-                                 std::integer_sequence<T, Ns...>,
-                                 std::integer_sequence<T, Rs..., Sum>>::Type;
-};
-
-// End of 'recursion'. The resulting type is SeqOut.
-template <typename T, T Sum, typename SeqOut>
-struct ExclusiveScanImpl<T, Sum, std::integer_sequence<T>, SeqOut> {
-  using Type = SeqOut;
-};
-
-// Calculates the exclusive scan of the specified integer sequence. The last
-// element (the total) is not included in the resulting sequence so they have
-// same length. This means the exclusive scan of integer_sequence<int, 1, 2, 3>
-// will be integer_sequence<int, 0, 1, 3>.
-template <typename Seq>
-class ExclusiveScanT {
-  using T = typename Seq::value_type;
-
- public:
-  using Type =
-      typename ExclusiveScanImpl<T, T(0), Seq, std::integer_sequence<T>>::Type;
-};
-
-// Helper to use exclusive scan without typename.
-template <typename Seq>
-using ExclusiveScan = typename ExclusiveScanT<Seq>::Type;
-
-// Removes all elements from a integer sequence corresponding to specified
-// ValueToRemove.
-//
-// This type should not be used directly but instead RemoveValue.
-template <typename T, T ValueToRemove, typename... Sequence>
-struct RemoveValueImpl;
-
-// Final filtered sequence
-template <typename T, T ValueToRemove, T... Values>
-struct RemoveValueImpl<T,
-                       ValueToRemove,
-                       std::integer_sequence<T, Values...>,
-                       std::integer_sequence<T>> {
-  using type = std::integer_sequence<T, Values...>;
-};
-
-// Found a matching value
-template <typename T, T ValueToRemove, T... Head, T... Tail>
-struct RemoveValueImpl<T,
-                       ValueToRemove,
-                       std::integer_sequence<T, Head...>,
-                       std::integer_sequence<T, ValueToRemove, Tail...>>
-    : RemoveValueImpl<T,
-                      ValueToRemove,
-                      std::integer_sequence<T, Head...>,
-                      std::integer_sequence<T, Tail...>> {};
-
-// Move one element from the tail to the head
-template <typename T, T ValueToRemove, T... Head, T MiddleValue, T... Tail>
-struct RemoveValueImpl<T,
-                       ValueToRemove,
-                       std::integer_sequence<T, Head...>,
-                       std::integer_sequence<T, MiddleValue, Tail...>>
-    : RemoveValueImpl<T,
-                      ValueToRemove,
-                      std::integer_sequence<T, Head..., MiddleValue>,
-                      std::integer_sequence<T, Tail...>> {};
-
-// Start recursion by splitting the integer sequence into two separate ones
-template <typename T, T ValueToRemove, T... Tail>
-struct RemoveValueImpl<T, ValueToRemove, std::integer_sequence<T, Tail...>>
-    : RemoveValueImpl<T,
-                      ValueToRemove,
-                      std::integer_sequence<T>,
-                      std::integer_sequence<T, Tail...>> {};
-
-// RemoveValue takes an integer Sequence of arbitrary type and removes all
-// elements matching ValueToRemove.
-//
-// In contrast to RemoveValueImpl, this implementation deduces the value type
-// eliminating the need to specify it explicitly.
-//
-// As an example, RemoveValue<std::integer_sequence<int, 1, 2, 3>, 4>::type will
-// not transform the type of the original sequence. However,
-// RemoveValue<std::integer_sequence<int, 0, 0, 2>, 2>::type will generate a new
-// sequence of type std::integer_sequence<int, 0, 0> by removing the value 2.
-template <typename Sequence, typename Sequence::value_type ValueToRemove>
-struct RemoveValue
-    : RemoveValueImpl<typename Sequence::value_type, ValueToRemove, Sequence> {
-};
-
-// Convenience template alias for RemoveValue.
-template <typename Sequence, typename Sequence::value_type ValueToRemove>
-using RemoveValue_t = typename RemoveValue<Sequence, ValueToRemove>::type;
-
-// Returns true if all elements of Values are equal to HeadValue.
-//
-// Returns true if Values is empty.
-template <typename T, T HeadValue, T... Values>
-inline constexpr bool AreAllEqual_v = ((HeadValue == Values) && ...);
-
-// Predicate determining whether an integer sequence is either empty or all
-// values are equal.
-template <typename Sequence>
-struct IsEmptyOrAreAllEqual;
-
-// Empty case.
-template <typename T>
-struct IsEmptyOrAreAllEqual<std::integer_sequence<T>> : std::true_type {};
-
-// General case for sequences containing at least one value.
-template <typename T, T HeadValue, T... Values>
-struct IsEmptyOrAreAllEqual<std::integer_sequence<T, HeadValue, Values...>>
-    : std::integral_constant<bool, AreAllEqual_v<T, HeadValue, Values...>> {};
-
-// Convenience variable template for IsEmptyOrAreAllEqual.
-template <class Sequence>
-inline constexpr bool IsEmptyOrAreAllEqual_v =
-    IsEmptyOrAreAllEqual<Sequence>::value;
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_INTEGER_SEQUENCE_ALGORITHM_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/jet_traits.h b/3rdParty/my_ceres_vc17/include/ceres/internal/jet_traits.h
deleted file mode 100644
index f504a61..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/jet_traits.h
+++ /dev/null
@@ -1,195 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sergiu.deitsch@gmail.com (Sergiu Deitsch)
-//
-
-#ifndef CERES_PUBLIC_INTERNAL_JET_TRAITS_H_
-#define CERES_PUBLIC_INTERNAL_JET_TRAITS_H_
-
-#include <tuple>
-#include <type_traits>
-#include <utility>
-
-#include "ceres/internal/integer_sequence_algorithm.h"
-#include "ceres/jet_fwd.h"
-
-namespace ceres {
-namespace internal {
-
-// Predicate that determines whether any of the Types is a Jet.
-template <typename... Types>
-struct AreAnyJet : std::false_type {};
-
-template <typename T, typename... Types>
-struct AreAnyJet<T, Types...> : AreAnyJet<Types...> {};
-
-template <typename T, int N, typename... Types>
-struct AreAnyJet<Jet<T, N>, Types...> : std::true_type {};
-
-// Convenience variable template for AreAnyJet.
-template <typename... Types>
-inline constexpr bool AreAnyJet_v = AreAnyJet<Types...>::value;
-
-// Extracts the underlying floating-point from a type T.
-template <typename T, typename E = void>
-struct UnderlyingScalar {
-  using type = T;
-};
-
-template <typename T, int N>
-struct UnderlyingScalar<Jet<T, N>> : UnderlyingScalar<T> {};
-
-// Convenience template alias for UnderlyingScalar type trait.
-template <typename T>
-using UnderlyingScalar_t = typename UnderlyingScalar<T>::type;
-
-// Predicate determining whether all Types in the pack are the same.
-//
-// Specifically, the predicate applies std::is_same recursively to pairs of
-// Types in the pack.
-template <typename T1, typename... Types>
-inline constexpr bool AreAllSame_v = (std::is_same<T1, Types>::value && ...);
-
-// Determines the rank of a type. This allows to ensure that types passed as
-// arguments are compatible to each other. The rank of Jet is determined by the
-// dimensions of the dual part. The rank of a scalar is always 0.
-// Non-specialized types default to a rank of -1.
-template <typename T, typename E = void>
-struct Rank : std::integral_constant<int, -1> {};
-
-// The rank of a scalar is 0.
-template <typename T>
-struct Rank<T, std::enable_if_t<std::is_scalar<T>::value>>
-    : std::integral_constant<int, 0> {};
-
-// The rank of a Jet is given by its dimensionality.
-template <typename T, int N>
-struct Rank<Jet<T, N>> : std::integral_constant<int, N> {};
-
-// Convenience variable template for Rank.
-template <typename T>
-inline constexpr int Rank_v = Rank<T>::value;
-
-// Constructs an integer sequence of ranks for each of the Types in the pack.
-template <typename... Types>
-using Ranks_t = std::integer_sequence<int, Rank_v<Types>...>;
-
-// Returns the scalar part of a type. This overload acts as an identity.
-template <typename T>
-constexpr decltype(auto) AsScalar(T&& value) noexcept {
-  return std::forward<T>(value);
-}
-
-// Recursively unwraps the scalar part of a Jet until a non-Jet scalar type is
-// encountered.
-template <typename T, int N>
-constexpr decltype(auto) AsScalar(const Jet<T, N>& value) noexcept(
-    noexcept(AsScalar(value.a))) {
-  return AsScalar(value.a);
-}
-
-}  // namespace internal
-
-// Type trait ensuring at least one of the types is a Jet,
-// the underlying scalar types are the same and Jet dimensions match.
-//
-// The type trait can be further specialized if necessary.
-//
-// This trait is a candidate for a concept definition once C++20 features can
-// be used.
-template <typename... Types>
-// clang-format off
-struct CompatibleJetOperands : std::integral_constant
-<
-    bool,
-    // At least one of the types is a Jet
-    internal::AreAnyJet_v<Types...> &&
-    // The underlying floating-point types are exactly the same
-    internal::AreAllSame_v<internal::UnderlyingScalar_t<Types>...> &&
-    // Non-zero ranks of types are equal
-    internal::IsEmptyOrAreAllEqual_v<internal::RemoveValue_t<internal::Ranks_t<Types...>, 0>>
->
-// clang-format on
-{};
-
-// Single Jet operand is always compatible.
-template <typename T, int N>
-struct CompatibleJetOperands<Jet<T, N>> : std::true_type {};
-
-// Single non-Jet operand is always incompatible.
-template <typename T>
-struct CompatibleJetOperands<T> : std::false_type {};
-
-// Empty operands are always incompatible.
-template <>
-struct CompatibleJetOperands<> : std::false_type {};
-
-// Convenience variable template ensuring at least one of the types is a Jet,
-// the underlying scalar types are the same and Jet dimensions match.
-//
-// This trait is a candidate for a concept definition once C++20 features can
-// be used.
-template <typename... Types>
-inline constexpr bool CompatibleJetOperands_v =
-    CompatibleJetOperands<Types...>::value;
-
-// Type trait ensuring at least one of the types is a Jet,
-// the underlying scalar types are compatible among each other and Jet
-// dimensions match.
-//
-// The type trait can be further specialized if necessary.
-//
-// This trait is a candidate for a concept definition once C++20 features can
-// be used.
-template <typename... Types>
-// clang-format off
-struct PromotableJetOperands : std::integral_constant
-<
-    bool,
-    // Types can be compatible among each other
-    internal::AreAnyJet_v<Types...> &&
-    // Non-zero ranks of types are equal
-    internal::IsEmptyOrAreAllEqual_v<internal::RemoveValue_t<internal::Ranks_t<Types...>, 0>>
->
-// clang-format on
-{};
-
-// Convenience variable template ensuring at least one of the types is a Jet,
-// the underlying scalar types are compatible among each other and Jet
-// dimensions match.
-//
-// This trait is a candidate for a concept definition once C++20 features can
-// be used.
-template <typename... Types>
-inline constexpr bool PromotableJetOperands_v =
-    PromotableJetOperands<Types...>::value;
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_INTERNAL_JET_TRAITS_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/line_parameterization.h b/3rdParty/my_ceres_vc17/include/ceres/internal/line_parameterization.h
deleted file mode 100644
index f50603d..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/line_parameterization.h
+++ /dev/null
@@ -1,183 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: jodebo_beck@gmx.de (Johannes Beck)
-//
-
-#ifndef CERES_PUBLIC_INTERNAL_LINE_PARAMETERIZATION_H_
-#define CERES_PUBLIC_INTERNAL_LINE_PARAMETERIZATION_H_
-
-#include "householder_vector.h"
-
-namespace ceres {
-
-template <int AmbientSpaceDimension>
-bool LineParameterization<AmbientSpaceDimension>::Plus(
-    const double* x_ptr,
-    const double* delta_ptr,
-    double* x_plus_delta_ptr) const {
-  // We seek a box plus operator of the form
-  //
-  //   [o*, d*] = Plus([o, d], [delta_o, delta_d])
-  //
-  // where o is the origin point, d is the direction vector, delta_o is
-  // the delta of the origin point and delta_d the delta of the direction and
-  // o* and d* is the updated origin point and direction.
-  //
-  // We separate the Plus operator into the origin point and directional part
-  //   d* = Plus_d(d, delta_d)
-  //   o* = Plus_o(o, d, delta_o)
-  //
-  // The direction update function Plus_d is the same as for the homogeneous
-  // vector parameterization:
-  //
-  //   d* = H_{v(d)} [0.5 sinc(0.5 |delta_d|) delta_d, cos(0.5 |delta_d|)]^T
-  //
-  // where H is the householder matrix
-  //   H_{v} = I - (2 / |v|^2) v v^T
-  // and
-  //   v(d) = d - sign(d_n) |d| e_n.
-  //
-  // The origin point update function Plus_o is defined as
-  //
-  //   o* = o + H_{v(d)} [0.5 delta_o, 0]^T.
-
-  static constexpr int kDim = AmbientSpaceDimension;
-  using AmbientVector = Eigen::Matrix<double, kDim, 1>;
-  using AmbientVectorRef = Eigen::Map<Eigen::Matrix<double, kDim, 1>>;
-  using ConstAmbientVectorRef =
-      Eigen::Map<const Eigen::Matrix<double, kDim, 1>>;
-  using ConstTangentVectorRef =
-      Eigen::Map<const Eigen::Matrix<double, kDim - 1, 1>>;
-
-  ConstAmbientVectorRef o(x_ptr);
-  ConstAmbientVectorRef d(x_ptr + kDim);
-
-  ConstTangentVectorRef delta_o(delta_ptr);
-  ConstTangentVectorRef delta_d(delta_ptr + kDim - 1);
-  AmbientVectorRef o_plus_delta(x_plus_delta_ptr);
-  AmbientVectorRef d_plus_delta(x_plus_delta_ptr + kDim);
-
-  const double norm_delta_d = delta_d.norm();
-
-  o_plus_delta = o;
-
-  // Shortcut for zero delta direction.
-  if (norm_delta_d == 0.0) {
-    d_plus_delta = d;
-
-    if (delta_o.isZero(0.0)) {
-      return true;
-    }
-  }
-
-  // Calculate the householder transformation which is needed for f_d and f_o.
-  AmbientVector v;
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  internal::ComputeHouseholderVector<ConstAmbientVectorRef, double, kDim>(
-      d, &v, &beta);
-
-  if (norm_delta_d != 0.0) {
-    // Map the delta from the minimum representation to the over parameterized
-    // homogeneous vector. See section A6.9.2 on page 624 of Hartley & Zisserman
-    // (2nd Edition) for a detailed description.  Note there is a typo on Page
-    // 625, line 4 so check the book errata.
-    const double norm_delta_div_2 = 0.5 * norm_delta_d;
-    const double sin_delta_by_delta =
-        std::sin(norm_delta_div_2) / norm_delta_div_2;
-
-    // Apply the delta update to remain on the unit sphere. See section A6.9.3
-    // on page 625 of Hartley & Zisserman (2nd Edition) for a detailed
-    // description.
-    AmbientVector y;
-    y.template head<kDim - 1>() = 0.5 * sin_delta_by_delta * delta_d;
-    y[kDim - 1] = std::cos(norm_delta_div_2);
-
-    d_plus_delta = d.norm() * (y - v * (beta * (v.transpose() * y)));
-  }
-
-  // The null space is in the direction of the line, so the tangent space is
-  // perpendicular to the line direction. This is achieved by using the
-  // householder matrix of the direction and allow only movements
-  // perpendicular to e_n.
-  //
-  // The factor of 0.5 is used to be consistent with the line direction
-  // update.
-  AmbientVector y;
-  y << 0.5 * delta_o, 0;
-  o_plus_delta += y - v * (beta * (v.transpose() * y));
-
-  return true;
-}
-
-template <int AmbientSpaceDimension>
-bool LineParameterization<AmbientSpaceDimension>::ComputeJacobian(
-    const double* x_ptr, double* jacobian_ptr) const {
-  static constexpr int kDim = AmbientSpaceDimension;
-  using AmbientVector = Eigen::Matrix<double, kDim, 1>;
-  using ConstAmbientVectorRef =
-      Eigen::Map<const Eigen::Matrix<double, kDim, 1>>;
-  using MatrixRef = Eigen::Map<
-      Eigen::Matrix<double, 2 * kDim, 2 * (kDim - 1), Eigen::RowMajor>>;
-
-  ConstAmbientVectorRef d(x_ptr + kDim);
-  MatrixRef jacobian(jacobian_ptr);
-
-  // Clear the Jacobian as only half of the matrix is not zero.
-  jacobian.setZero();
-
-  AmbientVector v;
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  internal::ComputeHouseholderVector<ConstAmbientVectorRef, double, kDim>(
-      d, &v, &beta);
-
-  // The Jacobian is equal to J = 0.5 * H.leftCols(kDim - 1) where H is
-  // the Householder matrix (H = I - beta * v * v') for the origin point. For
-  // the line direction part the Jacobian is scaled by the norm of the
-  // direction.
-  for (int i = 0; i < kDim - 1; ++i) {
-    jacobian.block(0, i, kDim, 1) = -0.5 * beta * v(i) * v;
-    jacobian.col(i)(i) += 0.5;
-  }
-
-  jacobian.template block<kDim, kDim - 1>(kDim, kDim - 1) =
-      jacobian.template block<kDim, kDim - 1>(0, 0) * d.norm();
-  return true;
-}
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_INTERNAL_LINE_PARAMETERIZATION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/memory.h b/3rdParty/my_ceres_vc17/include/ceres/internal/memory.h
deleted file mode 100644
index e54cf2b..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/memory.h
+++ /dev/null
@@ -1,88 +0,0 @@
-// Copyright 2017 The Abseil Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      https://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//
-// -----------------------------------------------------------------------------
-// File: memory.h
-// -----------------------------------------------------------------------------
-//
-// This header file contains utility functions for managing the creation and
-// conversion of smart pointers. This file is an extension to the C++
-// standard <memory> library header file.
-
-#ifndef CERES_PUBLIC_INTERNAL_MEMORY_H_
-#define CERES_PUBLIC_INTERNAL_MEMORY_H_
-
-#include <memory>
-
-#ifdef CERES_HAVE_EXCEPTIONS
-#define CERES_INTERNAL_TRY try
-#define CERES_INTERNAL_CATCH_ANY catch (...)
-#define CERES_INTERNAL_RETHROW \
-  do {                         \
-    throw;                     \
-  } while (false)
-#else  // CERES_HAVE_EXCEPTIONS
-#define CERES_INTERNAL_TRY if (true)
-#define CERES_INTERNAL_CATCH_ANY else if (false)
-#define CERES_INTERNAL_RETHROW \
-  do {                         \
-  } while (false)
-#endif  // CERES_HAVE_EXCEPTIONS
-
-namespace ceres::internal {
-
-template <typename Allocator, typename Iterator, typename... Args>
-void ConstructRange(Allocator& alloc,
-                    Iterator first,
-                    Iterator last,
-                    const Args&... args) {
-  for (Iterator cur = first; cur != last; ++cur) {
-    CERES_INTERNAL_TRY {
-      std::allocator_traits<Allocator>::construct(
-          alloc, std::addressof(*cur), args...);
-    }
-    CERES_INTERNAL_CATCH_ANY {
-      while (cur != first) {
-        --cur;
-        std::allocator_traits<Allocator>::destroy(alloc, std::addressof(*cur));
-      }
-      CERES_INTERNAL_RETHROW;
-    }
-  }
-}
-
-template <typename Allocator, typename Iterator, typename InputIterator>
-void CopyRange(Allocator& alloc,
-               Iterator destination,
-               InputIterator first,
-               InputIterator last) {
-  for (Iterator cur = destination; first != last;
-       static_cast<void>(++cur), static_cast<void>(++first)) {
-    CERES_INTERNAL_TRY {
-      std::allocator_traits<Allocator>::construct(
-          alloc, std::addressof(*cur), *first);
-    }
-    CERES_INTERNAL_CATCH_ANY {
-      while (cur != destination) {
-        --cur;
-        std::allocator_traits<Allocator>::destroy(alloc, std::addressof(*cur));
-      }
-      CERES_INTERNAL_RETHROW;
-    }
-  }
-}
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_MEMORY_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/numeric_diff.h b/3rdParty/my_ceres_vc17/include/ceres/internal/numeric_diff.h
deleted file mode 100644
index ba28bec..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/numeric_diff.h
+++ /dev/null
@@ -1,506 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//         mierle@gmail.com (Keir Mierle)
-//         tbennun@gmail.com (Tal Ben-Nun)
-//
-// Finite differencing routines used by NumericDiffCostFunction.
-
-#ifndef CERES_PUBLIC_INTERNAL_NUMERIC_DIFF_H_
-#define CERES_PUBLIC_INTERNAL_NUMERIC_DIFF_H_
-
-#include <cstring>
-#include <utility>
-
-#include "Eigen/Dense"
-#include "Eigen/StdVector"
-#include "ceres/cost_function.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/internal/variadic_evaluate.h"
-#include "ceres/numeric_diff_options.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres::internal {
-
-// This is split from the main class because C++ doesn't allow partial template
-// specializations for member functions. The alternative is to repeat the main
-// class for differing numbers of parameters, which is also unfortunate.
-template <typename CostFunctor,
-          NumericDiffMethodType kMethod,
-          int kNumResiduals,
-          typename ParameterDims,
-          int kParameterBlock,
-          int kParameterBlockSize>
-struct NumericDiff {
-  // Mutates parameters but must restore them before return.
-  static bool EvaluateJacobianForParameterBlock(
-      const CostFunctor* functor,
-      const double* residuals_at_eval_point,
-      const NumericDiffOptions& options,
-      int num_residuals,
-      int parameter_block_index,
-      int parameter_block_size,
-      double** parameters,
-      double* jacobian) {
-    using Eigen::ColMajor;
-    using Eigen::Map;
-    using Eigen::Matrix;
-    using Eigen::RowMajor;
-
-    DCHECK(jacobian);
-
-    const int num_residuals_internal =
-        (kNumResiduals != ceres::DYNAMIC ? kNumResiduals : num_residuals);
-    const int parameter_block_index_internal =
-        (kParameterBlock != ceres::DYNAMIC ? kParameterBlock
-                                           : parameter_block_index);
-    const int parameter_block_size_internal =
-        (kParameterBlockSize != ceres::DYNAMIC ? kParameterBlockSize
-                                               : parameter_block_size);
-
-    using ResidualVector = Matrix<double, kNumResiduals, 1>;
-    using ParameterVector = Matrix<double, kParameterBlockSize, 1>;
-
-    // The convoluted reasoning for choosing the Row/Column major
-    // ordering of the matrix is an artifact of the restrictions in
-    // Eigen that prevent it from creating RowMajor matrices with a
-    // single column. In these cases, we ask for a ColMajor matrix.
-    using JacobianMatrix =
-        Matrix<double,
-               kNumResiduals,
-               kParameterBlockSize,
-               (kParameterBlockSize == 1) ? ColMajor : RowMajor>;
-
-    Map<JacobianMatrix> parameter_jacobian(
-        jacobian, num_residuals_internal, parameter_block_size_internal);
-
-    Map<ParameterVector> x_plus_delta(
-        parameters[parameter_block_index_internal],
-        parameter_block_size_internal);
-    ParameterVector x(x_plus_delta);
-    ParameterVector step_size =
-        x.array().abs() * ((kMethod == RIDDERS)
-                               ? options.ridders_relative_initial_step_size
-                               : options.relative_step_size);
-
-    // It is not a good idea to make the step size arbitrarily
-    // small. This will lead to problems with round off and numerical
-    // instability when dividing by the step size. The general
-    // recommendation is to not go down below sqrt(epsilon).
-    double min_step_size = std::sqrt(std::numeric_limits<double>::epsilon());
-
-    // For Ridders' method, the initial step size is required to be large,
-    // thus ridders_relative_initial_step_size is used.
-    if (kMethod == RIDDERS) {
-      min_step_size =
-          (std::max)(min_step_size, options.ridders_relative_initial_step_size);
-    }
-
-    // For each parameter in the parameter block, use finite differences to
-    // compute the derivative for that parameter.
-    FixedArray<double> temp_residual_array(num_residuals_internal);
-    FixedArray<double> residual_array(num_residuals_internal);
-    Map<ResidualVector> residuals(residual_array.data(),
-                                  num_residuals_internal);
-
-    for (int j = 0; j < parameter_block_size_internal; ++j) {
-      const double delta = (std::max)(min_step_size, step_size(j));
-
-      if (kMethod == RIDDERS) {
-        if (!EvaluateRiddersJacobianColumn(functor,
-                                           j,
-                                           delta,
-                                           options,
-                                           num_residuals_internal,
-                                           parameter_block_size_internal,
-                                           x.data(),
-                                           residuals_at_eval_point,
-                                           parameters,
-                                           x_plus_delta.data(),
-                                           temp_residual_array.data(),
-                                           residual_array.data())) {
-          return false;
-        }
-      } else {
-        if (!EvaluateJacobianColumn(functor,
-                                    j,
-                                    delta,
-                                    num_residuals_internal,
-                                    parameter_block_size_internal,
-                                    x.data(),
-                                    residuals_at_eval_point,
-                                    parameters,
-                                    x_plus_delta.data(),
-                                    temp_residual_array.data(),
-                                    residual_array.data())) {
-          return false;
-        }
-      }
-
-      parameter_jacobian.col(j).matrix() = residuals;
-    }
-    return true;
-  }
-
-  static bool EvaluateJacobianColumn(const CostFunctor* functor,
-                                     int parameter_index,
-                                     double delta,
-                                     int num_residuals,
-                                     int parameter_block_size,
-                                     const double* x_ptr,
-                                     const double* residuals_at_eval_point,
-                                     double** parameters,
-                                     double* x_plus_delta_ptr,
-                                     double* temp_residuals_ptr,
-                                     double* residuals_ptr) {
-    using Eigen::Map;
-    using Eigen::Matrix;
-
-    using ResidualVector = Matrix<double, kNumResiduals, 1>;
-    using ParameterVector = Matrix<double, kParameterBlockSize, 1>;
-
-    Map<const ParameterVector> x(x_ptr, parameter_block_size);
-    Map<ParameterVector> x_plus_delta(x_plus_delta_ptr, parameter_block_size);
-
-    Map<ResidualVector> residuals(residuals_ptr, num_residuals);
-    Map<ResidualVector> temp_residuals(temp_residuals_ptr, num_residuals);
-
-    // Mutate 1 element at a time and then restore.
-    x_plus_delta(parameter_index) = x(parameter_index) + delta;
-
-    if (!VariadicEvaluate<ParameterDims>(
-            *functor, parameters, residuals.data())) {
-      return false;
-    }
-
-    // Compute this column of the jacobian in 3 steps:
-    // 1. Store residuals for the forward part.
-    // 2. Subtract residuals for the backward (or 0) part.
-    // 3. Divide out the run.
-    double one_over_delta = 1.0 / delta;
-    if (kMethod == CENTRAL || kMethod == RIDDERS) {
-      // Compute the function on the other side of x(parameter_index).
-      x_plus_delta(parameter_index) = x(parameter_index) - delta;
-
-      if (!VariadicEvaluate<ParameterDims>(
-              *functor, parameters, temp_residuals.data())) {
-        return false;
-      }
-
-      residuals -= temp_residuals;
-      one_over_delta /= 2;
-    } else {
-      // Forward difference only; reuse existing residuals evaluation.
-      residuals -=
-          Map<const ResidualVector>(residuals_at_eval_point, num_residuals);
-    }
-
-    // Restore x_plus_delta.
-    x_plus_delta(parameter_index) = x(parameter_index);
-
-    // Divide out the run to get slope.
-    residuals *= one_over_delta;
-
-    return true;
-  }
-
-  // This numeric difference implementation uses adaptive differentiation
-  // on the parameters to obtain the Jacobian matrix. The adaptive algorithm
-  // is based on Ridders' method for adaptive differentiation, which creates
-  // a Romberg tableau from varying step sizes and extrapolates the
-  // intermediate results to obtain the current computational error.
-  //
-  // References:
-  // C.J.F. Ridders, Accurate computation of F'(x) and F'(x) F"(x), Advances
-  // in Engineering Software (1978), Volume 4, Issue 2, April 1982,
-  // Pages 75-76, ISSN 0141-1195,
-  // http://dx.doi.org/10.1016/S0141-1195(82)80057-0.
-  static bool EvaluateRiddersJacobianColumn(
-      const CostFunctor* functor,
-      int parameter_index,
-      double delta,
-      const NumericDiffOptions& options,
-      int num_residuals,
-      int parameter_block_size,
-      const double* x_ptr,
-      const double* residuals_at_eval_point,
-      double** parameters,
-      double* x_plus_delta_ptr,
-      double* temp_residuals_ptr,
-      double* residuals_ptr) {
-    using Eigen::aligned_allocator;
-    using Eigen::Map;
-    using Eigen::Matrix;
-
-    using ResidualVector = Matrix<double, kNumResiduals, 1>;
-    using ResidualCandidateMatrix =
-        Matrix<double, kNumResiduals, Eigen::Dynamic>;
-    using ParameterVector = Matrix<double, kParameterBlockSize, 1>;
-
-    Map<const ParameterVector> x(x_ptr, parameter_block_size);
-    Map<ParameterVector> x_plus_delta(x_plus_delta_ptr, parameter_block_size);
-
-    Map<ResidualVector> residuals(residuals_ptr, num_residuals);
-    Map<ResidualVector> temp_residuals(temp_residuals_ptr, num_residuals);
-
-    // In order for the algorithm to converge, the step size should be
-    // initialized to a value that is large enough to produce a significant
-    // change in the function.
-    // As the derivative is estimated, the step size decreases.
-    // By default, the step sizes are chosen so that the middle column
-    // of the Romberg tableau uses the input delta.
-    double current_step_size =
-        delta * pow(options.ridders_step_shrink_factor,
-                    options.max_num_ridders_extrapolations / 2);
-
-    // Double-buffering temporary differential candidate vectors
-    // from previous step size.
-    ResidualCandidateMatrix stepsize_candidates_a(
-        num_residuals, options.max_num_ridders_extrapolations);
-    ResidualCandidateMatrix stepsize_candidates_b(
-        num_residuals, options.max_num_ridders_extrapolations);
-    ResidualCandidateMatrix* current_candidates = &stepsize_candidates_a;
-    ResidualCandidateMatrix* previous_candidates = &stepsize_candidates_b;
-
-    // Represents the computational error of the derivative. This variable is
-    // initially set to a large value, and is set to the difference between
-    // current and previous finite difference extrapolations.
-    // norm_error is supposed to decrease as the finite difference tableau
-    // generation progresses, serving both as an estimate for differentiation
-    // error and as a measure of differentiation numerical stability.
-    double norm_error = (std::numeric_limits<double>::max)();
-
-    // Loop over decreasing step sizes until:
-    //  1. Error is smaller than a given value (ridders_epsilon),
-    //  2. Maximal order of extrapolation reached, or
-    //  3. Extrapolation becomes numerically unstable.
-    for (int i = 0; i < options.max_num_ridders_extrapolations; ++i) {
-      // Compute the numerical derivative at this step size.
-      if (!EvaluateJacobianColumn(functor,
-                                  parameter_index,
-                                  current_step_size,
-                                  num_residuals,
-                                  parameter_block_size,
-                                  x.data(),
-                                  residuals_at_eval_point,
-                                  parameters,
-                                  x_plus_delta.data(),
-                                  temp_residuals.data(),
-                                  current_candidates->col(0).data())) {
-        // Something went wrong; bail.
-        return false;
-      }
-
-      // Store initial results.
-      if (i == 0) {
-        residuals = current_candidates->col(0);
-      }
-
-      // Shrink differentiation step size.
-      current_step_size /= options.ridders_step_shrink_factor;
-
-      // Extrapolation factor for Richardson acceleration method (see below).
-      double richardson_factor = options.ridders_step_shrink_factor *
-                                 options.ridders_step_shrink_factor;
-      for (int k = 1; k <= i; ++k) {
-        // Extrapolate the various orders of finite differences using
-        // the Richardson acceleration method.
-        current_candidates->col(k) =
-            (richardson_factor * current_candidates->col(k - 1) -
-             previous_candidates->col(k - 1)) /
-            (richardson_factor - 1.0);
-
-        richardson_factor *= options.ridders_step_shrink_factor *
-                             options.ridders_step_shrink_factor;
-
-        // Compute the difference between the previous value and the current.
-        double candidate_error = (std::max)(
-            (current_candidates->col(k) - current_candidates->col(k - 1))
-                .norm(),
-            (current_candidates->col(k) - previous_candidates->col(k - 1))
-                .norm());
-
-        // If the error has decreased, update results.
-        if (candidate_error <= norm_error) {
-          norm_error = candidate_error;
-          residuals = current_candidates->col(k);
-
-          // If the error is small enough, stop.
-          if (norm_error < options.ridders_epsilon) {
-            break;
-          }
-        }
-      }
-
-      // After breaking out of the inner loop, declare convergence.
-      if (norm_error < options.ridders_epsilon) {
-        break;
-      }
-
-      // Check to see if the current gradient estimate is numerically unstable.
-      // If so, bail out and return the last stable result.
-      if (i > 0) {
-        double tableau_error =
-            (current_candidates->col(i) - previous_candidates->col(i - 1))
-                .norm();
-
-        // Compare current error to the chosen candidate's error.
-        if (tableau_error >= 2 * norm_error) {
-          break;
-        }
-      }
-
-      std::swap(current_candidates, previous_candidates);
-    }
-    return true;
-  }
-};
-
-// This function calls NumericDiff<...>::EvaluateJacobianForParameterBlock for
-// each parameter block.
-//
-// Example:
-// A call to
-// EvaluateJacobianForParameterBlocks<StaticParameterDims<2, 3>>(
-//        functor,
-//        residuals_at_eval_point,
-//        options,
-//        num_residuals,
-//        parameters,
-//        jacobians);
-// will result in the following calls to
-// NumericDiff<...>::EvaluateJacobianForParameterBlock:
-//
-// if (jacobians[0] != nullptr) {
-//   if (!NumericDiff<
-//           CostFunctor,
-//           method,
-//           kNumResiduals,
-//           StaticParameterDims<2, 3>,
-//           0,
-//           2>::EvaluateJacobianForParameterBlock(functor,
-//                                                 residuals_at_eval_point,
-//                                                 options,
-//                                                 num_residuals,
-//                                                 0,
-//                                                 2,
-//                                                 parameters,
-//                                                 jacobians[0])) {
-//     return false;
-//   }
-// }
-// if (jacobians[1] != nullptr) {
-//   if (!NumericDiff<
-//           CostFunctor,
-//           method,
-//           kNumResiduals,
-//           StaticParameterDims<2, 3>,
-//           1,
-//           3>::EvaluateJacobianForParameterBlock(functor,
-//                                                 residuals_at_eval_point,
-//                                                 options,
-//                                                 num_residuals,
-//                                                 1,
-//                                                 3,
-//                                                 parameters,
-//                                                 jacobians[1])) {
-//     return false;
-//   }
-// }
-template <typename ParameterDims,
-          typename Parameters = typename ParameterDims::Parameters,
-          int ParameterIdx = 0>
-struct EvaluateJacobianForParameterBlocks;
-
-template <typename ParameterDims, int N, int... Ns, int ParameterIdx>
-struct EvaluateJacobianForParameterBlocks<ParameterDims,
-                                          std::integer_sequence<int, N, Ns...>,
-                                          ParameterIdx> {
-  template <NumericDiffMethodType method,
-            int kNumResiduals,
-            typename CostFunctor>
-  static bool Apply(const CostFunctor* functor,
-                    const double* residuals_at_eval_point,
-                    const NumericDiffOptions& options,
-                    int num_residuals,
-                    double** parameters,
-                    double** jacobians) {
-    if (jacobians[ParameterIdx] != nullptr) {
-      if (!NumericDiff<
-              CostFunctor,
-              method,
-              kNumResiduals,
-              ParameterDims,
-              ParameterIdx,
-              N>::EvaluateJacobianForParameterBlock(functor,
-                                                    residuals_at_eval_point,
-                                                    options,
-                                                    num_residuals,
-                                                    ParameterIdx,
-                                                    N,
-                                                    parameters,
-                                                    jacobians[ParameterIdx])) {
-        return false;
-      }
-    }
-
-    return EvaluateJacobianForParameterBlocks<ParameterDims,
-                                              std::integer_sequence<int, Ns...>,
-                                              ParameterIdx + 1>::
-        template Apply<method, kNumResiduals>(functor,
-                                              residuals_at_eval_point,
-                                              options,
-                                              num_residuals,
-                                              parameters,
-                                              jacobians);
-  }
-};
-
-// End of 'recursion'. Nothing more to do.
-template <typename ParameterDims, int ParameterIdx>
-struct EvaluateJacobianForParameterBlocks<ParameterDims,
-                                          std::integer_sequence<int>,
-                                          ParameterIdx> {
-  template <NumericDiffMethodType method,
-            int kNumResiduals,
-            typename CostFunctor>
-  static bool Apply(const CostFunctor* /* NOT USED*/,
-                    const double* /* NOT USED*/,
-                    const NumericDiffOptions& /* NOT USED*/,
-                    int /* NOT USED*/,
-                    double** /* NOT USED*/,
-                    double** /* NOT USED*/) {
-    return true;
-  }
-};
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_NUMERIC_DIFF_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/parameter_dims.h b/3rdParty/my_ceres_vc17/include/ceres/internal/parameter_dims.h
deleted file mode 100644
index b7cf935..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/parameter_dims.h
+++ /dev/null
@@ -1,106 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: jodebo_beck@gmx.de (Johannes Beck)
-
-#ifndef CERES_PUBLIC_INTERNAL_PARAMETER_DIMS_H_
-#define CERES_PUBLIC_INTERNAL_PARAMETER_DIMS_H_
-
-#include <array>
-#include <utility>
-
-#include "ceres/internal/integer_sequence_algorithm.h"
-
-namespace ceres::internal {
-
-// Helper class that represents the parameter dimensions. The parameter
-// dimensions are either dynamic or the sizes are known at compile time. It is
-// used to pass parameter block dimensions around (e.g. between functions or
-// classes).
-//
-// As an example if one have three parameter blocks with dimensions (2, 4, 1),
-// one would use 'StaticParameterDims<2, 4, 1>' which is a synonym for
-// 'ParameterDims<false, 2, 4, 1>'.
-// For dynamic parameter dims, one would just use 'DynamicParameterDims', which
-// is a synonym for 'ParameterDims<true>'.
-template <bool IsDynamic, int... Ns>
-class ParameterDims {
- public:
-  using Parameters = std::integer_sequence<int, Ns...>;
-
-  // The parameter dimensions are only valid if all parameter block dimensions
-  // are greater than zero.
-  static constexpr bool kIsValid = ((Ns > 0) && ...);
-  static_assert(kIsValid,
-                "Invalid parameter block dimension detected. Each parameter "
-                "block dimension must be bigger than zero.");
-
-  static constexpr bool kIsDynamic = IsDynamic;
-  static constexpr int kNumParameterBlocks = sizeof...(Ns);
-  static_assert(kIsDynamic || kNumParameterBlocks > 0,
-                "At least one parameter block must be specified.");
-
-  static constexpr int kNumParameters = (Ns + ... + 0);
-
-  static constexpr int GetDim(int dim) { return params_[dim]; }
-
-  // If one has all parameters packed into a single array this function unpacks
-  // the parameters.
-  template <typename T>
-  static inline std::array<T*, kNumParameterBlocks> GetUnpackedParameters(
-      T* ptr) {
-    using Offsets = ExclusiveScan<Parameters>;
-    return GetUnpackedParameters(ptr, Offsets());
-  }
-
- private:
-  template <typename T, int... Indices>
-  static inline std::array<T*, kNumParameterBlocks> GetUnpackedParameters(
-      T* ptr, std::integer_sequence<int, Indices...>) {
-    return std::array<T*, kNumParameterBlocks>{{ptr + Indices...}};
-  }
-
-  static constexpr std::array<int, kNumParameterBlocks> params_{Ns...};
-};
-
-// Even static constexpr member variables needs to be defined (not only
-// declared). As the ParameterDims class is tempalted this definition must
-// be in the header file.
-template <bool IsDynamic, int... Ns>
-constexpr std::array<int, ParameterDims<IsDynamic, Ns...>::kNumParameterBlocks>
-    ParameterDims<IsDynamic, Ns...>::params_;
-
-// Using declarations for static and dynamic parameter dims. This makes client
-// code easier to read.
-template <int... Ns>
-using StaticParameterDims = ParameterDims<false, Ns...>;
-using DynamicParameterDims = ParameterDims<true>;
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_PARAMETER_DIMS_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/port.h b/3rdParty/my_ceres_vc17/include/ceres/internal/port.h
deleted file mode 100644
index b8cb0ff..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/port.h
+++ /dev/null
@@ -1,91 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: keir@google.com (Keir Mierle)
-
-#ifndef CERES_PUBLIC_INTERNAL_PORT_H_
-#define CERES_PUBLIC_INTERNAL_PORT_H_
-
-// A macro to mark a function/variable/class as deprecated.
-// We use compiler specific attributes rather than the c++
-// attribute because they do not mix well with each other.
-#if defined(_MSC_VER)
-#define CERES_DEPRECATED_WITH_MSG(message) __declspec(deprecated(message))
-#elif defined(__GNUC__)
-#define CERES_DEPRECATED_WITH_MSG(message) __attribute__((deprecated(message)))
-#else
-// In the worst case fall back to c++ attribute.
-#define CERES_DEPRECATED_WITH_MSG(message) [[deprecated(message)]]
-#endif
-
-#ifndef CERES_GET_FLAG
-#define CERES_GET_FLAG(X) X
-#endif
-
-// Indicates whether C++20 is currently active
-#ifndef CERES_HAS_CPP20
-#if __cplusplus >= 202002L || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L)
-#define CERES_HAS_CPP20
-#endif  // __cplusplus >= 202002L || (defined(_MSVC_LANG) && _MSVC_LANG >=
-        // 202002L)
-#endif  // !defined(CERES_HAS_CPP20)
-
-// Prevents symbols from being substituted by the corresponding macro definition
-// under the same name. For instance, min and max are defined as macros on
-// Windows (unless NOMINMAX is defined) which causes compilation errors when
-// defining or referencing symbols under the same name.
-//
-// To be robust in all cases particularly when NOMINMAX cannot be used, use this
-// macro to annotate min/max declarations/definitions. Examples:
-//
-//   int max CERES_PREVENT_MACRO_SUBSTITUTION();
-//   min CERES_PREVENT_MACRO_SUBSTITUTION(a, b);
-//   max CERES_PREVENT_MACRO_SUBSTITUTION(a, b);
-//
-// NOTE: In case the symbols for which the substitution must be prevented are
-// used within another macro, the substitution must be inhibited using parens as
-//
-//   (std::numerical_limits<double>::max)()
-//
-// since the helper macro will not work here. Do not use this technique in
-// general case, because it will prevent argument-dependent lookup (ADL).
-//
-#define CERES_PREVENT_MACRO_SUBSTITUTION  // Yes, it's empty
-
-// CERES_DISABLE_DEPRECATED_WARNING and CERES_RESTORE_DEPRECATED_WARNING allow
-// to temporarily disable deprecation warnings
-#if defined(_MSC_VER)
-#define CERES_DISABLE_DEPRECATED_WARNING \
-  _Pragma("warning(push)") _Pragma("warning(disable : 4996)")
-#define CERES_RESTORE_DEPRECATED_WARNING _Pragma("warning(pop)")
-#else  // defined(_MSC_VER)
-#define CERES_DISABLE_DEPRECATED_WARNING
-#define CERES_RESTORE_DEPRECATED_WARNING
-#endif  // defined(_MSC_VER)
-
-#endif  // CERES_PUBLIC_INTERNAL_PORT_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/reenable_warnings.h b/3rdParty/my_ceres_vc17/include/ceres/internal/reenable_warnings.h
deleted file mode 100644
index a183c25..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/reenable_warnings.h
+++ /dev/null
@@ -1,38 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-
-// This is not your usual header guard. See disable_warnings.h
-#ifdef CERES_WARNINGS_DISABLED
-#undef CERES_WARNINGS_DISABLED
-
-#ifdef _MSC_VER
-#pragma warning(pop)
-#endif
-
-#endif  // CERES_WARNINGS_DISABLED
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/sphere_manifold_functions.h b/3rdParty/my_ceres_vc17/include/ceres/internal/sphere_manifold_functions.h
deleted file mode 100644
index 4793442..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/sphere_manifold_functions.h
+++ /dev/null
@@ -1,163 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: vitus@google.com (Mike Vitus)
-//         jodebo_beck@gmx.de (Johannes Beck)
-
-#ifndef CERES_PUBLIC_INTERNAL_SPHERE_MANIFOLD_HELPERS_H_
-#define CERES_PUBLIC_INTERNAL_SPHERE_MANIFOLD_HELPERS_H_
-
-#include "ceres/constants.h"
-#include "ceres/internal/householder_vector.h"
-
-// This module contains functions to compute the SphereManifold plus and minus
-// operator and their Jacobians.
-//
-// As the parameters to these functions are shared between them, they are
-// described here: The following variable names are used:
-//  Plus(x, delta) = x + delta = x_plus_delta,
-//  Minus(y, x) = y - x = y_minus_x.
-//
-// The remaining ones are v and beta which describe the Householder
-// transformation of x, and norm_delta which is the norm of delta.
-//
-// The types of x, y, x_plus_delta and y_minus_x need to be equivalent to
-// Eigen::Matrix<double, AmbientSpaceDimension, 1> and the type of delta needs
-// to be equivalent to Eigen::Matrix<double, TangentSpaceDimension, 1>.
-//
-// The type of Jacobian plus needs to be equivalent to Eigen::Matrix<double,
-// AmbientSpaceDimension, TangentSpaceDimension, Eigen::RowMajor> and for
-// Jacobian minus Eigen::Matrix<double, TangentSpaceDimension,
-// AmbientSpaceDimension, Eigen::RowMajor>.
-//
-// For all vector / matrix inputs and outputs, template parameters are
-// used in order to allow also Eigen::Ref and Eigen block expressions to
-// be passed to the function.
-
-namespace ceres::internal {
-
-template <typename VT, typename XT, typename DeltaT, typename XPlusDeltaT>
-inline void ComputeSphereManifoldPlus(const VT& v,
-                                      double beta,
-                                      const XT& x,
-                                      const DeltaT& delta,
-                                      const double norm_delta,
-                                      XPlusDeltaT* x_plus_delta) {
-  constexpr int AmbientDim = VT::RowsAtCompileTime;
-
-  // Map the delta from the minimum representation to the over parameterized
-  // homogeneous vector. See B.2 p.25 equation (106) - (107) for more details.
-  const double sin_delta_by_delta = std::sin(norm_delta) / norm_delta;
-
-  Eigen::Matrix<double, AmbientDim, 1> y(v.size());
-  y << sin_delta_by_delta * delta, std::cos(norm_delta);
-
-  // Apply the delta update to remain on the sphere.
-  *x_plus_delta = x.norm() * ApplyHouseholderVector(y, v, beta);
-}
-
-template <typename VT, typename JacobianT>
-inline void ComputeSphereManifoldPlusJacobian(const VT& x,
-                                              JacobianT* jacobian) {
-  constexpr int AmbientSpaceDim = VT::RowsAtCompileTime;
-  using AmbientVector = Eigen::Matrix<double, AmbientSpaceDim, 1>;
-  const int ambient_size = x.size();
-  const int tangent_size = x.size() - 1;
-
-  AmbientVector v(ambient_size);
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  ComputeHouseholderVector<VT, double, AmbientSpaceDim>(x, &v, &beta);
-
-  // The Jacobian is equal to J = H.leftCols(size_ - 1) where H is the
-  // Householder matrix (H = I - beta * v * v').
-  for (int i = 0; i < tangent_size; ++i) {
-    (*jacobian).col(i) = -beta * v(i) * v;
-    (*jacobian)(i, i) += 1.0;
-  }
-  (*jacobian) *= x.norm();
-}
-
-template <typename VT, typename XT, typename YT, typename YMinusXT>
-inline void ComputeSphereManifoldMinus(
-    const VT& v, double beta, const XT& x, const YT& y, YMinusXT* y_minus_x) {
-  constexpr int AmbientSpaceDim = VT::RowsAtCompileTime;
-  constexpr int TangentSpaceDim =
-      AmbientSpaceDim == Eigen::Dynamic ? Eigen::Dynamic : AmbientSpaceDim - 1;
-  using AmbientVector = Eigen::Matrix<double, AmbientSpaceDim, 1>;
-
-  const int tangent_size = v.size() - 1;
-
-  const AmbientVector hy = ApplyHouseholderVector(y, v, beta) / x.norm();
-
-  // Calculate y - x. See B.2 p.25 equation (108).
-  const double y_last = hy[tangent_size];
-  const double hy_norm = hy.template head<TangentSpaceDim>(tangent_size).norm();
-  if (hy_norm == 0.0) {
-    y_minus_x->setZero();
-    y_minus_x->data()[tangent_size - 1] = y_last >= 0 ? 0.0 : constants::pi;
-  } else {
-    *y_minus_x = std::atan2(hy_norm, y_last) / hy_norm *
-                 hy.template head<TangentSpaceDim>(tangent_size);
-  }
-}
-
-template <typename VT, typename JacobianT>
-inline void ComputeSphereManifoldMinusJacobian(const VT& x,
-                                               JacobianT* jacobian) {
-  constexpr int AmbientSpaceDim = VT::RowsAtCompileTime;
-  using AmbientVector = Eigen::Matrix<double, AmbientSpaceDim, 1>;
-  const int ambient_size = x.size();
-  const int tangent_size = x.size() - 1;
-
-  AmbientVector v(ambient_size);
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  ComputeHouseholderVector<VT, double, AmbientSpaceDim>(x, &v, &beta);
-
-  // The Jacobian is equal to J = H.leftCols(size_ - 1) where H is the
-  // Householder matrix (H = I - beta * v * v').
-  for (int i = 0; i < tangent_size; ++i) {
-    // NOTE: The transpose is used for correctness (the product is expected to
-    // be a row vector), although here there seems to be no difference between
-    // transposing or not for Eigen (possibly a compile-time auto fix).
-    (*jacobian).row(i) = -beta * v(i) * v.transpose();
-    (*jacobian)(i, i) += 1.0;
-  }
-  (*jacobian) /= x.norm();
-}
-
-}  // namespace ceres::internal
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/ceres/internal/variadic_evaluate.h b/3rdParty/my_ceres_vc17/include/ceres/internal/variadic_evaluate.h
deleted file mode 100644
index 61af6b2..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/internal/variadic_evaluate.h
+++ /dev/null
@@ -1,134 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//         mierle@gmail.com (Keir Mierle)
-//         jodebo_beck@gmx.de (Johannes Beck)
-
-#ifndef CERES_PUBLIC_INTERNAL_VARIADIC_EVALUATE_H_
-#define CERES_PUBLIC_INTERNAL_VARIADIC_EVALUATE_H_
-
-#include <cstddef>
-#include <type_traits>
-#include <utility>
-
-#include "ceres/cost_function.h"
-#include "ceres/internal/parameter_dims.h"
-
-namespace ceres::internal {
-
-// For fixed size cost functors
-template <typename Functor, typename T, int... Indices>
-inline bool VariadicEvaluateImpl(const Functor& functor,
-                                 T const* const* input,
-                                 T* output,
-                                 std::false_type /*is_dynamic*/,
-                                 std::integer_sequence<int, Indices...>) {
-  static_assert(sizeof...(Indices) > 0,
-                "Invalid number of parameter blocks. At least one parameter "
-                "block must be specified.");
-  return functor(input[Indices]..., output);
-}
-
-// For dynamic sized cost functors
-template <typename Functor, typename T>
-inline bool VariadicEvaluateImpl(const Functor& functor,
-                                 T const* const* input,
-                                 T* output,
-                                 std::true_type /*is_dynamic*/,
-                                 std::integer_sequence<int>) {
-  return functor(input, output);
-}
-
-// For ceres cost functors (not ceres::CostFunction)
-template <typename ParameterDims, typename Functor, typename T>
-inline bool VariadicEvaluateImpl(const Functor& functor,
-                                 T const* const* input,
-                                 T* output,
-                                 const void* /* NOT USED */) {
-  using ParameterBlockIndices =
-      std::make_integer_sequence<int, ParameterDims::kNumParameterBlocks>;
-  using IsDynamic = std::integral_constant<bool, ParameterDims::kIsDynamic>;
-  return VariadicEvaluateImpl(
-      functor, input, output, IsDynamic(), ParameterBlockIndices());
-}
-
-// For ceres::CostFunction
-template <typename ParameterDims, typename Functor, typename T>
-inline bool VariadicEvaluateImpl(const Functor& functor,
-                                 T const* const* input,
-                                 T* output,
-                                 const CostFunction* /* NOT USED */) {
-  return functor.Evaluate(input, output, nullptr);
-}
-
-// Variadic evaluate is a helper function to evaluate ceres cost function or
-// functors using an input, output and the parameter dimensions. There are
-// several ways different possibilities:
-// 1) If the passed functor is a 'ceres::CostFunction' its evaluate method is
-// called.
-// 2) If the functor is not a 'ceres::CostFunction' and the specified parameter
-// dims is dynamic, the functor must have the following signature
-// 'bool(T const* const* input, T* output)'.
-// 3) If the functor is not a 'ceres::CostFunction' and the specified parameter
-// dims is not dynamic, the input is expanded by using the number of parameter
-// blocks. The signature of the functor must have the following signature
-// 'bool()(const T* i_1, const T* i_2, ... const T* i_n, T* output)'.
-template <typename ParameterDims, typename Functor, typename T>
-inline bool VariadicEvaluate(const Functor& functor,
-                             T const* const* input,
-                             T* output) {
-  return VariadicEvaluateImpl<ParameterDims>(functor, input, output, &functor);
-}
-
-// When differentiating dynamically sized CostFunctions, VariadicEvaluate
-// expects a functor with the signature:
-//
-// bool operator()(double const* const* parameters, double* cost) const
-//
-// However for NumericDiffFirstOrderFunction, the functor has the signature
-//
-// bool operator()(double const* parameters, double* cost) const
-//
-// This thin wrapper adapts the latter to the former.
-template <typename Functor>
-class FirstOrderFunctorAdapter {
- public:
-  explicit FirstOrderFunctorAdapter(const Functor& functor)
-      : functor_(functor) {}
-  bool operator()(double const* const* parameters, double* cost) const {
-    return functor_(*parameters, cost);
-  }
-
- private:
-  const Functor& functor_;
-};
-
-}  // namespace ceres::internal
-
-#endif  // CERES_PUBLIC_INTERNAL_VARIADIC_EVALUATE_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/iteration_callback.h b/3rdParty/my_ceres_vc17/include/ceres/iteration_callback.h
deleted file mode 100644
index 955e2ad..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/iteration_callback.h
+++ /dev/null
@@ -1,204 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//
-// When an iteration callback is specified, Ceres calls the callback
-// after each minimizer step (if the minimizer has not converged) and
-// passes it an IterationSummary object, defined below.
-
-#ifndef CERES_PUBLIC_ITERATION_CALLBACK_H_
-#define CERES_PUBLIC_ITERATION_CALLBACK_H_
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/types.h"
-
-namespace ceres {
-
-// This struct describes the state of the optimizer after each
-// iteration of the minimization.
-struct CERES_EXPORT IterationSummary {
-  // Current iteration number.
-  int iteration = 0;
-
-  // Step was numerically valid, i.e., all values are finite and the
-  // step reduces the value of the linearized model.
-  //
-  // Note: step_is_valid is always true when iteration = 0.
-  bool step_is_valid = false;
-
-  // Step did not reduce the value of the objective function
-  // sufficiently, but it was accepted because of the relaxed
-  // acceptance criterion used by the non-monotonic trust region
-  // algorithm.
-  //
-  // Note: step_is_nonmonotonic is always false when iteration = 0;
-  bool step_is_nonmonotonic = false;
-
-  // Whether or not the minimizer accepted this step or not. If the
-  // ordinary trust region algorithm is used, this means that the
-  // relative reduction in the objective function value was greater
-  // than Solver::Options::min_relative_decrease. However, if the
-  // non-monotonic trust region algorithm is used
-  // (Solver::Options:use_nonmonotonic_steps = true), then even if the
-  // relative decrease is not sufficient, the algorithm may accept the
-  // step and the step is declared successful.
-  //
-  // Note: step_is_successful is always true when iteration = 0.
-  bool step_is_successful = false;
-
-  // Value of the objective function.
-  double cost = 0.0;
-
-  // Change in the value of the objective function in this
-  // iteration. This can be positive or negative.
-  double cost_change = 0.0;
-
-  // Infinity norm of the gradient vector.
-  double gradient_max_norm = 0.0;
-
-  // 2-norm of the gradient vector.
-  double gradient_norm = 0.0;
-
-  // 2-norm of the size of the step computed by the optimization
-  // algorithm.
-  double step_norm = 0.0;
-
-  // For trust region algorithms, the ratio of the actual change in
-  // cost and the change in the cost of the linearized approximation.
-  double relative_decrease = 0.0;
-
-  // Size of the trust region at the end of the current iteration. For
-  // the Levenberg-Marquardt algorithm, the regularization parameter
-  // mu = 1.0 / trust_region_radius.
-  double trust_region_radius = 0.0;
-
-  // For the inexact step Levenberg-Marquardt algorithm, this is the
-  // relative accuracy with which the Newton(LM) step is solved. This
-  // number affects only the iterative solvers capable of solving
-  // linear systems inexactly. Factorization-based exact solvers
-  // ignore it.
-  double eta = 0.0;
-
-  // Step sized computed by the line search algorithm.
-  double step_size = 0.0;
-
-  // Number of function value evaluations used by the line search algorithm.
-  int line_search_function_evaluations = 0;
-
-  // Number of function gradient evaluations used by the line search algorithm.
-  int line_search_gradient_evaluations = 0;
-
-  // Number of iterations taken by the line search algorithm.
-  int line_search_iterations = 0;
-
-  // Number of iterations taken by the linear solver to solve for the
-  // Newton step.
-  int linear_solver_iterations = 0;
-
-  // All times reported below are wall times.
-
-  // Time (in seconds) spent inside the minimizer loop in the current
-  // iteration.
-  double iteration_time_in_seconds = 0.0;
-
-  // Time (in seconds) spent inside the trust region step solver.
-  double step_solver_time_in_seconds = 0.0;
-
-  // Time (in seconds) since the user called Solve().
-  double cumulative_time_in_seconds = 0.0;
-};
-
-// Interface for specifying callbacks that are executed at the end of
-// each iteration of the Minimizer. The solver uses the return value
-// of operator() to decide whether to continue solving or to
-// terminate. The user can return three values.
-//
-// SOLVER_ABORT indicates that the callback detected an abnormal
-// situation. The solver returns without updating the parameter blocks
-// (unless Solver::Options::update_state_every_iteration is set
-// true). Solver returns with Solver::Summary::termination_type set to
-// USER_ABORT.
-//
-// SOLVER_TERMINATE_SUCCESSFULLY indicates that there is no need to
-// optimize anymore (some user specified termination criterion has
-// been met). Solver returns with Solver::Summary::termination_type
-// set to USER_SUCCESS.
-//
-// SOLVER_CONTINUE indicates that the solver should continue
-// optimizing.
-//
-// For example, the following Callback is used internally by Ceres to
-// log the progress of the optimization.
-//
-// Callback for logging the state of the minimizer to STDERR or STDOUT
-// depending on the user's preferences and logging level.
-//
-//   class LoggingCallback : public IterationCallback {
-//    public:
-//     explicit LoggingCallback(bool log_to_stdout)
-//         : log_to_stdout_(log_to_stdout) {}
-//
-//     CallbackReturnType operator()(const IterationSummary& summary) {
-//       const char* kReportRowFormat =
-//           "% 4d: f:% 8e d:% 3.2e g:% 3.2e h:% 3.2e "
-//           "rho:% 3.2e mu:% 3.2e eta:% 3.2e li:% 3d";
-//       string output = StringPrintf(kReportRowFormat,
-//                                    summary.iteration,
-//                                    summary.cost,
-//                                    summary.cost_change,
-//                                    summary.gradient_max_norm,
-//                                    summary.step_norm,
-//                                    summary.relative_decrease,
-//                                    summary.trust_region_radius,
-//                                    summary.eta,
-//                                    summary.linear_solver_iterations);
-//       if (log_to_stdout_) {
-//         cout << output << endl;
-//       } else {
-//         VLOG(1) << output;
-//       }
-//       return SOLVER_CONTINUE;
-//     }
-//
-//    private:
-//     const bool log_to_stdout_;
-//   };
-//
-class CERES_EXPORT IterationCallback {
- public:
-  virtual ~IterationCallback();
-  virtual CallbackReturnType operator()(const IterationSummary& summary) = 0;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_ITERATION_CALLBACK_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/jet.h b/3rdParty/my_ceres_vc17/include/ceres/jet.h
deleted file mode 100644
index f279ba3..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/jet.h
+++ /dev/null
@@ -1,1433 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: keir@google.com (Keir Mierle)
-//
-// A simple implementation of N-dimensional dual numbers, for automatically
-// computing exact derivatives of functions.
-//
-// While a complete treatment of the mechanics of automatic differentiation is
-// beyond the scope of this header (see
-// http://en.wikipedia.org/wiki/Automatic_differentiation for details), the
-// basic idea is to extend normal arithmetic with an extra element, "e," often
-// denoted with the greek symbol epsilon, such that e != 0 but e^2 = 0. Dual
-// numbers are extensions of the real numbers analogous to complex numbers:
-// whereas complex numbers augment the reals by introducing an imaginary unit i
-// such that i^2 = -1, dual numbers introduce an "infinitesimal" unit e such
-// that e^2 = 0. Dual numbers have two components: the "real" component and the
-// "infinitesimal" component, generally written as x + y*e. Surprisingly, this
-// leads to a convenient method for computing exact derivatives without needing
-// to manipulate complicated symbolic expressions.
-//
-// For example, consider the function
-//
-//   f(x) = x^2 ,
-//
-// evaluated at 10. Using normal arithmetic, f(10) = 100, and df/dx(10) = 20.
-// Next, argument 10 with an infinitesimal to get:
-//
-//   f(10 + e) = (10 + e)^2
-//             = 100 + 2 * 10 * e + e^2
-//             = 100 + 20 * e       -+-
-//                     --            |
-//                     |             +--- This is zero, since e^2 = 0
-//                     |
-//                     +----------------- This is df/dx!
-//
-// Note that the derivative of f with respect to x is simply the infinitesimal
-// component of the value of f(x + e). So, in order to take the derivative of
-// any function, it is only necessary to replace the numeric "object" used in
-// the function with one extended with infinitesimals. The class Jet, defined in
-// this header, is one such example of this, where substitution is done with
-// templates.
-//
-// To handle derivatives of functions taking multiple arguments, different
-// infinitesimals are used, one for each variable to take the derivative of. For
-// example, consider a scalar function of two scalar parameters x and y:
-//
-//   f(x, y) = x^2 + x * y
-//
-// Following the technique above, to compute the derivatives df/dx and df/dy for
-// f(1, 3) involves doing two evaluations of f, the first time replacing x with
-// x + e, the second time replacing y with y + e.
-//
-// For df/dx:
-//
-//   f(1 + e, y) = (1 + e)^2 + (1 + e) * 3
-//               = 1 + 2 * e + 3 + 3 * e
-//               = 4 + 5 * e
-//
-//               --> df/dx = 5
-//
-// For df/dy:
-//
-//   f(1, 3 + e) = 1^2 + 1 * (3 + e)
-//               = 1 + 3 + e
-//               = 4 + e
-//
-//               --> df/dy = 1
-//
-// To take the gradient of f with the implementation of dual numbers ("jets") in
-// this file, it is necessary to create a single jet type which has components
-// for the derivative in x and y, and passing them to a templated version of f:
-//
-//   template<typename T>
-//   T f(const T &x, const T &y) {
-//     return x * x + x * y;
-//   }
-//
-//   // The "2" means there should be 2 dual number components.
-//   // It computes the partial derivative at x=10, y=20.
-//   Jet<double, 2> x(10, 0);  // Pick the 0th dual number for x.
-//   Jet<double, 2> y(20, 1);  // Pick the 1st dual number for y.
-//   Jet<double, 2> z = f(x, y);
-//
-//   LOG(INFO) << "df/dx = " << z.v[0]
-//             << "df/dy = " << z.v[1];
-//
-// Most users should not use Jet objects directly; a wrapper around Jet objects,
-// which makes computing the derivative, gradient, or jacobian of templated
-// functors simple, is in autodiff.h. Even autodiff.h should not be used
-// directly; instead autodiff_cost_function.h is typically the file of interest.
-//
-// For the more mathematically inclined, this file implements first-order
-// "jets". A 1st order jet is an element of the ring
-//
-//   T[N] = T[t_1, ..., t_N] / (t_1, ..., t_N)^2
-//
-// which essentially means that each jet consists of a "scalar" value 'a' from T
-// and a 1st order perturbation vector 'v' of length N:
-//
-//   x = a + \sum_i v[i] t_i
-//
-// A shorthand is to write an element as x = a + u, where u is the perturbation.
-// Then, the main point about the arithmetic of jets is that the product of
-// perturbations is zero:
-//
-//   (a + u) * (b + v) = ab + av + bu + uv
-//                     = ab + (av + bu) + 0
-//
-// which is what operator* implements below. Addition is simpler:
-//
-//   (a + u) + (b + v) = (a + b) + (u + v).
-//
-// The only remaining question is how to evaluate the function of a jet, for
-// which we use the chain rule:
-//
-//   f(a + u) = f(a) + f'(a) u
-//
-// where f'(a) is the (scalar) derivative of f at a.
-//
-// By pushing these things through sufficiently and suitably templated
-// functions, we can do automatic differentiation. Just be sure to turn on
-// function inlining and common-subexpression elimination, or it will be very
-// slow!
-//
-// WARNING: Most Ceres users should not directly include this file or know the
-// details of how jets work. Instead the suggested method for automatic
-// derivatives is to use autodiff_cost_function.h, which is a wrapper around
-// both jets.h and autodiff.h to make taking derivatives of cost functions for
-// use in Ceres easier.
-
-#ifndef CERES_PUBLIC_JET_H_
-#define CERES_PUBLIC_JET_H_
-
-#include <cmath>
-#include <complex>
-#include <iosfwd>
-#include <iostream>  // NOLINT
-#include <limits>
-#include <numeric>
-#include <string>
-#include <type_traits>
-
-#include "Eigen/Core"
-#include "ceres/internal/jet_traits.h"
-#include "ceres/internal/port.h"
-#include "ceres/jet_fwd.h"
-
-// Here we provide partial specializations of std::common_type for the Jet class
-// to allow determining a Jet type with a common underlying arithmetic type.
-// Such an arithmetic type can be either a scalar or an another Jet. An example
-// for a common type, say, between a float and a Jet<double, N> is a Jet<double,
-// N> (i.e., std::common_type_t<float, ceres::Jet<double, N>> and
-// ceres::Jet<double, N> refer to the same type.)
-//
-// The partial specialization are also used for determining compatible types by
-// means of SFINAE and thus allow such types to be expressed as operands of
-// logical comparison operators. Missing (partial) specialization of
-// std::common_type for a particular (custom) type will therefore disable the
-// use of comparison operators defined by Ceres.
-//
-// Since these partial specializations are used as SFINAE constraints, they
-// enable standard promotion rules between various scalar types and consequently
-// their use in comparison against a Jet without providing implicit
-// conversions from a scalar, such as an int, to a Jet (see the implementation
-// of logical comparison operators below).
-
-template <typename T, int N, typename U>
-struct std::common_type<T, ceres::Jet<U, N>> {
-  using type = ceres::Jet<common_type_t<T, U>, N>;
-};
-
-template <typename T, int N, typename U>
-struct std::common_type<ceres::Jet<T, N>, U> {
-  using type = ceres::Jet<common_type_t<T, U>, N>;
-};
-
-template <typename T, int N, typename U>
-struct std::common_type<ceres::Jet<T, N>, ceres::Jet<U, N>> {
-  using type = ceres::Jet<common_type_t<T, U>, N>;
-};
-
-namespace ceres {
-
-template <typename T, int N>
-struct Jet {
-  enum { DIMENSION = N };
-  using Scalar = T;
-
-  // Default-construct "a" because otherwise this can lead to false errors about
-  // uninitialized uses when other classes relying on default constructed T
-  // (where T is a Jet<T, N>). This usually only happens in opt mode. Note that
-  // the C++ standard mandates that e.g. default constructed doubles are
-  // initialized to 0.0; see sections 8.5 of the C++03 standard.
-  Jet() : a() { v.setConstant(Scalar()); }
-
-  // Constructor from scalar: a + 0.
-  explicit Jet(const T& value) {
-    a = value;
-    v.setConstant(Scalar());
-  }
-
-  // Constructor from scalar plus variable: a + t_i.
-  Jet(const T& value, int k) {
-    a = value;
-    v.setConstant(Scalar());
-    v[k] = T(1.0);
-  }
-
-  // Constructor from scalar and vector part
-  // The use of Eigen::DenseBase allows Eigen expressions
-  // to be passed in without being fully evaluated until
-  // they are assigned to v
-  template <typename Derived>
-  EIGEN_STRONG_INLINE Jet(const T& a, const Eigen::DenseBase<Derived>& v)
-      : a(a), v(v) {}
-
-  // Compound operators
-  Jet<T, N>& operator+=(const Jet<T, N>& y) {
-    *this = *this + y;
-    return *this;
-  }
-
-  Jet<T, N>& operator-=(const Jet<T, N>& y) {
-    *this = *this - y;
-    return *this;
-  }
-
-  Jet<T, N>& operator*=(const Jet<T, N>& y) {
-    *this = *this * y;
-    return *this;
-  }
-
-  Jet<T, N>& operator/=(const Jet<T, N>& y) {
-    *this = *this / y;
-    return *this;
-  }
-
-  // Compound with scalar operators.
-  Jet<T, N>& operator+=(const T& s) {
-    *this = *this + s;
-    return *this;
-  }
-
-  Jet<T, N>& operator-=(const T& s) {
-    *this = *this - s;
-    return *this;
-  }
-
-  Jet<T, N>& operator*=(const T& s) {
-    *this = *this * s;
-    return *this;
-  }
-
-  Jet<T, N>& operator/=(const T& s) {
-    *this = *this / s;
-    return *this;
-  }
-
-  // The scalar part.
-  T a;
-
-  // The infinitesimal part.
-  Eigen::Matrix<T, N, 1> v;
-
-  // This struct needs to have an Eigen aligned operator new as it contains
-  // fixed-size Eigen types.
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-};
-
-// Unary +
-template <typename T, int N>
-inline Jet<T, N> const& operator+(const Jet<T, N>& f) {
-  return f;
-}
-
-// TODO(keir): Try adding __attribute__((always_inline)) to these functions to
-// see if it causes a performance increase.
-
-// Unary -
-template <typename T, int N>
-inline Jet<T, N> operator-(const Jet<T, N>& f) {
-  return Jet<T, N>(-f.a, -f.v);
-}
-
-// Binary +
-template <typename T, int N>
-inline Jet<T, N> operator+(const Jet<T, N>& f, const Jet<T, N>& g) {
-  return Jet<T, N>(f.a + g.a, f.v + g.v);
-}
-
-// Binary + with a scalar: x + s
-template <typename T, int N>
-inline Jet<T, N> operator+(const Jet<T, N>& f, T s) {
-  return Jet<T, N>(f.a + s, f.v);
-}
-
-// Binary + with a scalar: s + x
-template <typename T, int N>
-inline Jet<T, N> operator+(T s, const Jet<T, N>& f) {
-  return Jet<T, N>(f.a + s, f.v);
-}
-
-// Binary -
-template <typename T, int N>
-inline Jet<T, N> operator-(const Jet<T, N>& f, const Jet<T, N>& g) {
-  return Jet<T, N>(f.a - g.a, f.v - g.v);
-}
-
-// Binary - with a scalar: x - s
-template <typename T, int N>
-inline Jet<T, N> operator-(const Jet<T, N>& f, T s) {
-  return Jet<T, N>(f.a - s, f.v);
-}
-
-// Binary - with a scalar: s - x
-template <typename T, int N>
-inline Jet<T, N> operator-(T s, const Jet<T, N>& f) {
-  return Jet<T, N>(s - f.a, -f.v);
-}
-
-// Binary *
-template <typename T, int N>
-inline Jet<T, N> operator*(const Jet<T, N>& f, const Jet<T, N>& g) {
-  return Jet<T, N>(f.a * g.a, f.a * g.v + f.v * g.a);
-}
-
-// Binary * with a scalar: x * s
-template <typename T, int N>
-inline Jet<T, N> operator*(const Jet<T, N>& f, T s) {
-  return Jet<T, N>(f.a * s, f.v * s);
-}
-
-// Binary * with a scalar: s * x
-template <typename T, int N>
-inline Jet<T, N> operator*(T s, const Jet<T, N>& f) {
-  return Jet<T, N>(f.a * s, f.v * s);
-}
-
-// Binary /
-template <typename T, int N>
-inline Jet<T, N> operator/(const Jet<T, N>& f, const Jet<T, N>& g) {
-  // This uses:
-  //
-  //   a + u   (a + u)(b - v)   (a + u)(b - v)
-  //   ----- = -------------- = --------------
-  //   b + v   (b + v)(b - v)        b^2
-  //
-  // which holds because v*v = 0.
-  const T g_a_inverse = T(1.0) / g.a;
-  const T f_a_by_g_a = f.a * g_a_inverse;
-  return Jet<T, N>(f_a_by_g_a, (f.v - f_a_by_g_a * g.v) * g_a_inverse);
-}
-
-// Binary / with a scalar: s / x
-template <typename T, int N>
-inline Jet<T, N> operator/(T s, const Jet<T, N>& g) {
-  const T minus_s_g_a_inverse2 = -s / (g.a * g.a);
-  return Jet<T, N>(s / g.a, g.v * minus_s_g_a_inverse2);
-}
-
-// Binary / with a scalar: x / s
-template <typename T, int N>
-inline Jet<T, N> operator/(const Jet<T, N>& f, T s) {
-  const T s_inverse = T(1.0) / s;
-  return Jet<T, N>(f.a * s_inverse, f.v * s_inverse);
-}
-
-// Binary comparison operators for both scalars and jets. At least one of the
-// operands must be a Jet. Promotable scalars (e.g., int, float, double etc.)
-// can appear on either side of the operator. std::common_type_t is used as an
-// SFINAE constraint to selectively enable compatible operand types. This allows
-// comparison, for instance, against int literals without implicit conversion.
-// In case the Jet arithmetic type is a Jet itself, a recursive expansion of Jet
-// value is performed.
-#define CERES_DEFINE_JET_COMPARISON_OPERATOR(op)                            \
-  template <typename Lhs,                                                   \
-            typename Rhs,                                                   \
-            std::enable_if_t<PromotableJetOperands_v<Lhs, Rhs>>* = nullptr> \
-  constexpr bool operator op(const Lhs& f, const Rhs& g) noexcept(          \
-      noexcept(internal::AsScalar(f) op internal::AsScalar(g))) {           \
-    using internal::AsScalar;                                               \
-    return AsScalar(f) op AsScalar(g);                                      \
-  }
-CERES_DEFINE_JET_COMPARISON_OPERATOR(<)   // NOLINT
-CERES_DEFINE_JET_COMPARISON_OPERATOR(<=)  // NOLINT
-CERES_DEFINE_JET_COMPARISON_OPERATOR(>)   // NOLINT
-CERES_DEFINE_JET_COMPARISON_OPERATOR(>=)  // NOLINT
-CERES_DEFINE_JET_COMPARISON_OPERATOR(==)  // NOLINT
-CERES_DEFINE_JET_COMPARISON_OPERATOR(!=)  // NOLINT
-#undef CERES_DEFINE_JET_COMPARISON_OPERATOR
-
-// Pull some functions from namespace std.
-//
-// This is necessary because we want to use the same name (e.g. 'sqrt') for
-// double-valued and Jet-valued functions, but we are not allowed to put
-// Jet-valued functions inside namespace std.
-using std::abs;
-using std::acos;
-using std::asin;
-using std::atan;
-using std::atan2;
-using std::cbrt;
-using std::ceil;
-using std::copysign;
-using std::cos;
-using std::cosh;
-using std::erf;
-using std::erfc;
-using std::exp;
-using std::exp2;
-using std::expm1;
-using std::fdim;
-using std::floor;
-using std::fma;
-using std::fmax;
-using std::fmin;
-using std::fpclassify;
-using std::hypot;
-using std::isfinite;
-using std::isinf;
-using std::isnan;
-using std::isnormal;
-using std::log;
-using std::log10;
-using std::log1p;
-using std::log2;
-using std::norm;
-using std::pow;
-using std::signbit;
-using std::sin;
-using std::sinh;
-using std::sqrt;
-using std::tan;
-using std::tanh;
-
-// MSVC (up to 1930) defines quiet comparison functions as template functions
-// which causes compilation errors due to ambiguity in the template parameter
-// type resolution for using declarations in the ceres namespace. Workaround the
-// issue by defining specific overload and bypass MSVC standard library
-// definitions.
-#if defined(_MSC_VER)
-inline bool isgreater(double lhs,
-                      double rhs) noexcept(noexcept(std::isgreater(lhs, rhs))) {
-  return std::isgreater(lhs, rhs);
-}
-inline bool isless(double lhs,
-                   double rhs) noexcept(noexcept(std::isless(lhs, rhs))) {
-  return std::isless(lhs, rhs);
-}
-inline bool islessequal(double lhs,
-                        double rhs) noexcept(noexcept(std::islessequal(lhs,
-                                                                       rhs))) {
-  return std::islessequal(lhs, rhs);
-}
-inline bool isgreaterequal(double lhs, double rhs) noexcept(
-    noexcept(std::isgreaterequal(lhs, rhs))) {
-  return std::isgreaterequal(lhs, rhs);
-}
-inline bool islessgreater(double lhs, double rhs) noexcept(
-    noexcept(std::islessgreater(lhs, rhs))) {
-  return std::islessgreater(lhs, rhs);
-}
-inline bool isunordered(double lhs,
-                        double rhs) noexcept(noexcept(std::isunordered(lhs,
-                                                                       rhs))) {
-  return std::isunordered(lhs, rhs);
-}
-#else
-using std::isgreater;
-using std::isgreaterequal;
-using std::isless;
-using std::islessequal;
-using std::islessgreater;
-using std::isunordered;
-#endif
-
-#ifdef CERES_HAS_CPP20
-using std::lerp;
-using std::midpoint;
-#endif  // defined(CERES_HAS_CPP20)
-
-// Legacy names from pre-C++11 days.
-// clang-format off
-CERES_DEPRECATED_WITH_MSG("ceres::IsFinite will be removed in a future Ceres Solver release. Please use ceres::isfinite.")
-inline bool IsFinite(double x)   { return std::isfinite(x); }
-CERES_DEPRECATED_WITH_MSG("ceres::IsInfinite will be removed in a future Ceres Solver release. Please use ceres::isinf.")
-inline bool IsInfinite(double x) { return std::isinf(x);    }
-CERES_DEPRECATED_WITH_MSG("ceres::IsNaN will be removed in a future Ceres Solver release. Please use ceres::isnan.")
-inline bool IsNaN(double x)      { return std::isnan(x);    }
-CERES_DEPRECATED_WITH_MSG("ceres::IsNormal will be removed in a future Ceres Solver release. Please use ceres::isnormal.")
-inline bool IsNormal(double x)   { return std::isnormal(x); }
-// clang-format on
-
-// In general, f(a + h) ~= f(a) + f'(a) h, via the chain rule.
-
-// abs(x + h) ~= abs(x) + sgn(x)h
-template <typename T, int N>
-inline Jet<T, N> abs(const Jet<T, N>& f) {
-  return Jet<T, N>(abs(f.a), copysign(T(1), f.a) * f.v);
-}
-
-// copysign(a, b) composes a float with the magnitude of a and the sign of b.
-// Therefore, the function can be formally defined as
-//
-//   copysign(a, b) = sgn(b)|a|
-//
-// where
-//
-//   d/dx |x| = sgn(x)
-//   d/dx sgn(x) = 2δ(x)
-//
-// sgn(x) being the signum function. Differentiating copysign(a, b) with respect
-// to a and b gives:
-//
-//   d/da sgn(b)|a| = sgn(a) sgn(b)
-//   d/db sgn(b)|a| = 2|a|δ(b)
-//
-// with the dual representation given by
-//
-//   copysign(a + da, b + db) ~= sgn(b)|a| + (sgn(a)sgn(b) da + 2|a|δ(b) db)
-//
-// where δ(b) is the Dirac delta function.
-template <typename T, int N>
-inline Jet<T, N> copysign(const Jet<T, N>& f, const Jet<T, N> g) {
-  // The Dirac delta function  δ(b) is undefined at b=0 (here it's
-  // infinite) and 0 everywhere else.
-  T d = fpclassify(g) == FP_ZERO ? std::numeric_limits<T>::infinity() : T(0);
-  T sa = copysign(T(1), f.a);  // sgn(a)
-  T sb = copysign(T(1), g.a);  // sgn(b)
-  // The second part of the infinitesimal is 2|a|δ(b) which is either infinity
-  // or 0 unless a or any of the values of the b infinitesimal are 0. In the
-  // latter case, the corresponding values become NaNs (multiplying 0 by
-  // infinity gives NaN). We drop the constant factor 2 since it does not change
-  // the result (its values will still be either 0, infinity or NaN).
-  return Jet<T, N>(copysign(f.a, g.a), sa * sb * f.v + abs(f.a) * d * g.v);
-}
-
-// log(a + h) ~= log(a) + h / a
-template <typename T, int N>
-inline Jet<T, N> log(const Jet<T, N>& f) {
-  const T a_inverse = T(1.0) / f.a;
-  return Jet<T, N>(log(f.a), f.v * a_inverse);
-}
-
-// log10(a + h) ~= log10(a) + h / (a log(10))
-template <typename T, int N>
-inline Jet<T, N> log10(const Jet<T, N>& f) {
-  // Most compilers will expand log(10) to a constant.
-  const T a_inverse = T(1.0) / (f.a * log(T(10.0)));
-  return Jet<T, N>(log10(f.a), f.v * a_inverse);
-}
-
-// log1p(a + h) ~= log1p(a) + h / (1 + a)
-template <typename T, int N>
-inline Jet<T, N> log1p(const Jet<T, N>& f) {
-  const T a_inverse = T(1.0) / (T(1.0) + f.a);
-  return Jet<T, N>(log1p(f.a), f.v * a_inverse);
-}
-
-// exp(a + h) ~= exp(a) + exp(a) h
-template <typename T, int N>
-inline Jet<T, N> exp(const Jet<T, N>& f) {
-  const T tmp = exp(f.a);
-  return Jet<T, N>(tmp, tmp * f.v);
-}
-
-// expm1(a + h) ~= expm1(a) + exp(a) h
-template <typename T, int N>
-inline Jet<T, N> expm1(const Jet<T, N>& f) {
-  const T tmp = expm1(f.a);
-  const T expa = tmp + T(1.0);  // exp(a) = expm1(a) + 1
-  return Jet<T, N>(tmp, expa * f.v);
-}
-
-// sqrt(a + h) ~= sqrt(a) + h / (2 sqrt(a))
-template <typename T, int N>
-inline Jet<T, N> sqrt(const Jet<T, N>& f) {
-  const T tmp = sqrt(f.a);
-  const T two_a_inverse = T(1.0) / (T(2.0) * tmp);
-  return Jet<T, N>(tmp, f.v * two_a_inverse);
-}
-
-// cos(a + h) ~= cos(a) - sin(a) h
-template <typename T, int N>
-inline Jet<T, N> cos(const Jet<T, N>& f) {
-  return Jet<T, N>(cos(f.a), -sin(f.a) * f.v);
-}
-
-// acos(a + h) ~= acos(a) - 1 / sqrt(1 - a^2) h
-template <typename T, int N>
-inline Jet<T, N> acos(const Jet<T, N>& f) {
-  const T tmp = -T(1.0) / sqrt(T(1.0) - f.a * f.a);
-  return Jet<T, N>(acos(f.a), tmp * f.v);
-}
-
-// sin(a + h) ~= sin(a) + cos(a) h
-template <typename T, int N>
-inline Jet<T, N> sin(const Jet<T, N>& f) {
-  return Jet<T, N>(sin(f.a), cos(f.a) * f.v);
-}
-
-// asin(a + h) ~= asin(a) + 1 / sqrt(1 - a^2) h
-template <typename T, int N>
-inline Jet<T, N> asin(const Jet<T, N>& f) {
-  const T tmp = T(1.0) / sqrt(T(1.0) - f.a * f.a);
-  return Jet<T, N>(asin(f.a), tmp * f.v);
-}
-
-// tan(a + h) ~= tan(a) + (1 + tan(a)^2) h
-template <typename T, int N>
-inline Jet<T, N> tan(const Jet<T, N>& f) {
-  const T tan_a = tan(f.a);
-  const T tmp = T(1.0) + tan_a * tan_a;
-  return Jet<T, N>(tan_a, tmp * f.v);
-}
-
-// atan(a + h) ~= atan(a) + 1 / (1 + a^2) h
-template <typename T, int N>
-inline Jet<T, N> atan(const Jet<T, N>& f) {
-  const T tmp = T(1.0) / (T(1.0) + f.a * f.a);
-  return Jet<T, N>(atan(f.a), tmp * f.v);
-}
-
-// sinh(a + h) ~= sinh(a) + cosh(a) h
-template <typename T, int N>
-inline Jet<T, N> sinh(const Jet<T, N>& f) {
-  return Jet<T, N>(sinh(f.a), cosh(f.a) * f.v);
-}
-
-// cosh(a + h) ~= cosh(a) + sinh(a) h
-template <typename T, int N>
-inline Jet<T, N> cosh(const Jet<T, N>& f) {
-  return Jet<T, N>(cosh(f.a), sinh(f.a) * f.v);
-}
-
-// tanh(a + h) ~= tanh(a) + (1 - tanh(a)^2) h
-template <typename T, int N>
-inline Jet<T, N> tanh(const Jet<T, N>& f) {
-  const T tanh_a = tanh(f.a);
-  const T tmp = T(1.0) - tanh_a * tanh_a;
-  return Jet<T, N>(tanh_a, tmp * f.v);
-}
-
-// The floor function should be used with extreme care as this operation will
-// result in a zero derivative which provides no information to the solver.
-//
-// floor(a + h) ~= floor(a) + 0
-template <typename T, int N>
-inline Jet<T, N> floor(const Jet<T, N>& f) {
-  return Jet<T, N>(floor(f.a));
-}
-
-// The ceil function should be used with extreme care as this operation will
-// result in a zero derivative which provides no information to the solver.
-//
-// ceil(a + h) ~= ceil(a) + 0
-template <typename T, int N>
-inline Jet<T, N> ceil(const Jet<T, N>& f) {
-  return Jet<T, N>(ceil(f.a));
-}
-
-// Some new additions to C++11:
-
-// cbrt(a + h) ~= cbrt(a) + h / (3 a ^ (2/3))
-template <typename T, int N>
-inline Jet<T, N> cbrt(const Jet<T, N>& f) {
-  const T derivative = T(1.0) / (T(3.0) * cbrt(f.a * f.a));
-  return Jet<T, N>(cbrt(f.a), f.v * derivative);
-}
-
-// exp2(x + h) = 2^(x+h) ~= 2^x + h*2^x*log(2)
-template <typename T, int N>
-inline Jet<T, N> exp2(const Jet<T, N>& f) {
-  const T tmp = exp2(f.a);
-  const T derivative = tmp * log(T(2));
-  return Jet<T, N>(tmp, f.v * derivative);
-}
-
-// log2(x + h) ~= log2(x) + h / (x * log(2))
-template <typename T, int N>
-inline Jet<T, N> log2(const Jet<T, N>& f) {
-  const T derivative = T(1.0) / (f.a * log(T(2)));
-  return Jet<T, N>(log2(f.a), f.v * derivative);
-}
-
-// Like sqrt(x^2 + y^2),
-// but acts to prevent underflow/overflow for small/large x/y.
-// Note that the function is non-smooth at x=y=0,
-// so the derivative is undefined there.
-template <typename T, int N>
-inline Jet<T, N> hypot(const Jet<T, N>& x, const Jet<T, N>& y) {
-  // d/da sqrt(a) = 0.5 / sqrt(a)
-  // d/dx x^2 + y^2 = 2x
-  // So by the chain rule:
-  // d/dx sqrt(x^2 + y^2) = 0.5 / sqrt(x^2 + y^2) * 2x = x / sqrt(x^2 + y^2)
-  // d/dy sqrt(x^2 + y^2) = y / sqrt(x^2 + y^2)
-  const T tmp = hypot(x.a, y.a);
-  return Jet<T, N>(tmp, x.a / tmp * x.v + y.a / tmp * y.v);
-}
-
-// Like sqrt(x^2 + y^2 + z^2),
-// but acts to prevent underflow/overflow for small/large x/y/z.
-// Note that the function is non-smooth at x=y=z=0,
-// so the derivative is undefined there.
-template <typename T, int N>
-inline Jet<T, N> hypot(const Jet<T, N>& x,
-                       const Jet<T, N>& y,
-                       const Jet<T, N>& z) {
-  // d/da sqrt(a) = 0.5 / sqrt(a)
-  // d/dx x^2 + y^2 + z^2 = 2x
-  // So by the chain rule:
-  // d/dx sqrt(x^2 + y^2 + z^2)
-  //    = 0.5 / sqrt(x^2 + y^2 + z^2) * 2x
-  //    = x / sqrt(x^2 + y^2 + z^2)
-  // d/dy sqrt(x^2 + y^2 + z^2) = y / sqrt(x^2 + y^2 + z^2)
-  // d/dz sqrt(x^2 + y^2 + z^2) = z / sqrt(x^2 + y^2 + z^2)
-  const T tmp = hypot(x.a, y.a, z.a);
-  return Jet<T, N>(tmp, x.a / tmp * x.v + y.a / tmp * y.v + z.a / tmp * z.v);
-}
-
-// Like x * y + z but rounded only once.
-template <typename T, int N>
-inline Jet<T, N> fma(const Jet<T, N>& x,
-                     const Jet<T, N>& y,
-                     const Jet<T, N>& z) {
-  // d/dx fma(x, y, z) = y
-  // d/dy fma(x, y, z) = x
-  // d/dz fma(x, y, z) = 1
-  return Jet<T, N>(fma(x.a, y.a, z.a), y.a * x.v + x.a * y.v + z.v);
-}
-
-// Return value of fmax() and fmin() on equality
-// ---------------------------------------------
-//
-// There is arguably no good answer to what fmax() & fmin() should return on
-// equality, which for Jets by definition ONLY compares the scalar parts. We
-// choose what we think is the least worst option (averaging as Jets) which
-// minimises undesirable/unexpected behaviour as used, and also supports client
-// code written against Ceres versions prior to type promotion being supported
-// in Jet comparisons (< v2.1).
-//
-// The std::max() convention of returning the first argument on equality is
-// problematic, as it means that the derivative component may or may not be
-// preserved (when comparing a Jet with a scalar) depending upon the ordering.
-//
-// Always returning the Jet in {Jet, scalar} cases on equality is problematic
-// as it is inconsistent with the behaviour that would be obtained if the scalar
-// was first cast to Jet and the {Jet, Jet} case was used. Prior to type
-// promotion (Ceres v2.1) client code would typically cast constants to Jets
-// e.g: fmax(x, T(2.0)) which means the {Jet, Jet} case predominates, and we
-// still want the result to be order independent.
-//
-// Our intuition is that preserving a non-zero derivative is best, even if
-// its value does not match either of the inputs. Averaging achieves this
-// whilst ensuring argument ordering independence. This is also the approach
-// used by the Jax library, and TensorFlow's reduce_max().
-
-// Returns the larger of the two arguments, with Jet averaging on equality.
-// NaNs are treated as missing data.
-//
-// NOTE: This function is NOT subject to any of the error conditions specified
-//       in `math_errhandling`.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline decltype(auto) fmax(const Lhs& x, const Rhs& y) {
-  using J = std::common_type_t<Lhs, Rhs>;
-  // As x == y may set FP exceptions in the presence of NaNs when used with
-  // non-default compiler options so we avoid its use here.
-  if (isnan(x) || isnan(y) || islessgreater(x, y)) {
-    return isnan(x) || isless(x, y) ? J{y} : J{x};
-  }
-  // x == y (scalar parts) return the average of their Jet representations.
-#if defined(CERES_HAS_CPP20)
-  return midpoint(J{x}, J{y});
-#else
-  return (J{x} + J{y}) * typename J::Scalar(0.5);
-#endif  // defined(CERES_HAS_CPP20)
-}
-
-// Returns the smaller of the two arguments, with Jet averaging on equality.
-// NaNs are treated as missing data.
-//
-// NOTE: This function is NOT subject to any of the error conditions specified
-//       in `math_errhandling`.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline decltype(auto) fmin(const Lhs& x, const Rhs& y) {
-  using J = std::common_type_t<Lhs, Rhs>;
-  // As x == y may set FP exceptions in the presence of NaNs when used with
-  // non-default compiler options so we avoid its use here.
-  if (isnan(x) || isnan(y) || islessgreater(x, y)) {
-    return isnan(x) || isgreater(x, y) ? J{y} : J{x};
-  }
-  // x == y (scalar parts) return the average of their Jet representations.
-#if defined(CERES_HAS_CPP20)
-  return midpoint(J{x}, J{y});
-#else
-  return (J{x} + J{y}) * typename J::Scalar(0.5);
-#endif  // defined(CERES_HAS_CPP20)
-}
-
-// Returns the positive difference (f - g) of two arguments and zero if f <= g.
-// If at least one argument is NaN, a NaN is return.
-//
-// NOTE At least one of the argument types must be a Jet, the other one can be a
-// scalar. In case both arguments are Jets, their dimensionality must match.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline decltype(auto) fdim(const Lhs& f, const Rhs& g) {
-  using J = std::common_type_t<Lhs, Rhs>;
-  if (isnan(f) || isnan(g)) {
-    return std::numeric_limits<J>::quiet_NaN();
-  }
-  return isgreater(f, g) ? J{f - g} : J{};
-}
-
-// erf is defined as an integral that cannot be expressed analytically
-// however, the derivative is trivial to compute
-// erf(x + h) = erf(x) + h * 2*exp(-x^2)/sqrt(pi)
-template <typename T, int N>
-inline Jet<T, N> erf(const Jet<T, N>& x) {
-  // We evaluate the constant as follows:
-  //   2 / sqrt(pi) = 1 / sqrt(atan(1.))
-  // On POSIX systems it is defined as M_2_SQRTPI, but this is not
-  // portable and the type may not be T.  The above expression
-  // evaluates to full precision with IEEE arithmetic and, since it's
-  // constant, the compiler can generate exactly the same code.  gcc
-  // does so even at -O0.
-  return Jet<T, N>(erf(x.a), x.v * exp(-x.a * x.a) * (T(1) / sqrt(atan(T(1)))));
-}
-
-// erfc(x) = 1-erf(x)
-// erfc(x + h) = erfc(x) + h * (-2*exp(-x^2)/sqrt(pi))
-template <typename T, int N>
-inline Jet<T, N> erfc(const Jet<T, N>& x) {
-  // See in erf() above for the evaluation of the constant in the derivative.
-  return Jet<T, N>(erfc(x.a),
-                   -x.v * exp(-x.a * x.a) * (T(1) / sqrt(atan(T(1)))));
-}
-
-// Bessel functions of the first kind with integer order equal to 0, 1, n.
-//
-// Microsoft has deprecated the j[0,1,n]() POSIX Bessel functions in favour of
-// _j[0,1,n]().  Where available on MSVC, use _j[0,1,n]() to avoid deprecated
-// function errors in client code (the specific warning is suppressed when
-// Ceres itself is built).
-inline double BesselJ0(double x) {
-  CERES_DISABLE_DEPRECATED_WARNING
-  return j0(x);
-  CERES_RESTORE_DEPRECATED_WARNING
-}
-inline double BesselJ1(double x) {
-  CERES_DISABLE_DEPRECATED_WARNING
-  return j1(x);
-  CERES_RESTORE_DEPRECATED_WARNING
-}
-inline double BesselJn(int n, double x) {
-  CERES_DISABLE_DEPRECATED_WARNING
-  return jn(n, x);
-  CERES_RESTORE_DEPRECATED_WARNING
-}
-
-// For the formulae of the derivatives of the Bessel functions see the book:
-// Olver, Lozier, Boisvert, Clark, NIST Handbook of Mathematical Functions,
-// Cambridge University Press 2010.
-//
-// Formulae are also available at http://dlmf.nist.gov
-
-// See formula http://dlmf.nist.gov/10.6#E3
-// j0(a + h) ~= j0(a) - j1(a) h
-template <typename T, int N>
-inline Jet<T, N> BesselJ0(const Jet<T, N>& f) {
-  return Jet<T, N>(BesselJ0(f.a), -BesselJ1(f.a) * f.v);
-}
-
-// See formula http://dlmf.nist.gov/10.6#E1
-// j1(a + h) ~= j1(a) + 0.5 ( j0(a) - j2(a) ) h
-template <typename T, int N>
-inline Jet<T, N> BesselJ1(const Jet<T, N>& f) {
-  return Jet<T, N>(BesselJ1(f.a),
-                   T(0.5) * (BesselJ0(f.a) - BesselJn(2, f.a)) * f.v);
-}
-
-// See formula http://dlmf.nist.gov/10.6#E1
-// j_n(a + h) ~= j_n(a) + 0.5 ( j_{n-1}(a) - j_{n+1}(a) ) h
-template <typename T, int N>
-inline Jet<T, N> BesselJn(int n, const Jet<T, N>& f) {
-  return Jet<T, N>(
-      BesselJn(n, f.a),
-      T(0.5) * (BesselJn(n - 1, f.a) - BesselJn(n + 1, f.a)) * f.v);
-}
-
-// Classification and comparison functionality referencing only the scalar part
-// of a Jet. To classify the derivatives (e.g., for sanity checks), the dual
-// part should be referenced explicitly. For instance, to check whether the
-// derivatives of a Jet 'f' are reasonable, one can use
-//
-//  isfinite(f.v.array()).all()
-//  !isnan(f.v.array()).any()
-//
-// etc., depending on the desired semantics.
-//
-// NOTE: Floating-point classification and comparison functions and operators
-// should be used with care as no derivatives can be propagated by such
-// functions directly but only by expressions resulting from corresponding
-// conditional statements. At the same time, conditional statements can possibly
-// introduce a discontinuity in the cost function making it impossible to
-// evaluate its derivative and thus the optimization problem intractable.
-
-// Determines whether the scalar part of the Jet is finite.
-template <typename T, int N>
-inline bool isfinite(const Jet<T, N>& f) {
-  return isfinite(f.a);
-}
-
-// Determines whether the scalar part of the Jet is infinite.
-template <typename T, int N>
-inline bool isinf(const Jet<T, N>& f) {
-  return isinf(f.a);
-}
-
-// Determines whether the scalar part of the Jet is NaN.
-template <typename T, int N>
-inline bool isnan(const Jet<T, N>& f) {
-  return isnan(f.a);
-}
-
-// Determines whether the scalar part of the Jet is neither zero, subnormal,
-// infinite, nor NaN.
-template <typename T, int N>
-inline bool isnormal(const Jet<T, N>& f) {
-  return isnormal(f.a);
-}
-
-// Determines whether the scalar part of the Jet f is less than the scalar
-// part of g.
-//
-// NOTE: This function does NOT set any floating-point exceptions.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline bool isless(const Lhs& f, const Rhs& g) {
-  using internal::AsScalar;
-  return isless(AsScalar(f), AsScalar(g));
-}
-
-// Determines whether the scalar part of the Jet f is greater than the scalar
-// part of g.
-//
-// NOTE: This function does NOT set any floating-point exceptions.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline bool isgreater(const Lhs& f, const Rhs& g) {
-  using internal::AsScalar;
-  return isgreater(AsScalar(f), AsScalar(g));
-}
-
-// Determines whether the scalar part of the Jet f is less than or equal to the
-// scalar part of g.
-//
-// NOTE: This function does NOT set any floating-point exceptions.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline bool islessequal(const Lhs& f, const Rhs& g) {
-  using internal::AsScalar;
-  return islessequal(AsScalar(f), AsScalar(g));
-}
-
-// Determines whether the scalar part of the Jet f is less than or greater than
-// (f < g || f > g) the scalar part of g.
-//
-// NOTE: This function does NOT set any floating-point exceptions.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline bool islessgreater(const Lhs& f, const Rhs& g) {
-  using internal::AsScalar;
-  return islessgreater(AsScalar(f), AsScalar(g));
-}
-
-// Determines whether the scalar part of the Jet f is greater than or equal to
-// the scalar part of g.
-//
-// NOTE: This function does NOT set any floating-point exceptions.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline bool isgreaterequal(const Lhs& f, const Rhs& g) {
-  using internal::AsScalar;
-  return isgreaterequal(AsScalar(f), AsScalar(g));
-}
-
-// Determines if either of the scalar parts of the arguments are NaN and
-// thus cannot be ordered with respect to each other.
-template <typename Lhs,
-          typename Rhs,
-          std::enable_if_t<CompatibleJetOperands_v<Lhs, Rhs>>* = nullptr>
-inline bool isunordered(const Lhs& f, const Rhs& g) {
-  using internal::AsScalar;
-  return isunordered(AsScalar(f), AsScalar(g));
-}
-
-// Categorize scalar part as zero, subnormal, normal, infinite, NaN, or
-// implementation-defined.
-template <typename T, int N>
-inline int fpclassify(const Jet<T, N>& f) {
-  return fpclassify(f.a);
-}
-
-// Determines whether the scalar part of the argument is negative.
-template <typename T, int N>
-inline bool signbit(const Jet<T, N>& f) {
-  return signbit(f.a);
-}
-
-// Legacy functions from the pre-C++11 days.
-template <typename T, int N>
-CERES_DEPRECATED_WITH_MSG(
-    "ceres::IsFinite will be removed in a future Ceres Solver release. Please "
-    "use ceres::isfinite.")
-inline bool IsFinite(const Jet<T, N>& f) {
-  return isfinite(f);
-}
-
-template <typename T, int N>
-CERES_DEPRECATED_WITH_MSG(
-    "ceres::IsNaN will be removed in a future Ceres Solver release. Please use "
-    "ceres::isnan.")
-inline bool IsNaN(const Jet<T, N>& f) {
-  return isnan(f);
-}
-
-template <typename T, int N>
-CERES_DEPRECATED_WITH_MSG(
-    "ceres::IsNormal will be removed in a future Ceres Solver release. Please "
-    "use ceres::isnormal.")
-inline bool IsNormal(const Jet<T, N>& f) {
-  return isnormal(f);
-}
-
-// The jet is infinite if any part of the jet is infinite.
-template <typename T, int N>
-CERES_DEPRECATED_WITH_MSG(
-    "ceres::IsInfinite will be removed in a future Ceres Solver release. "
-    "Please use ceres::isinf.")
-inline bool IsInfinite(const Jet<T, N>& f) {
-  return isinf(f);
-}
-
-#ifdef CERES_HAS_CPP20
-// Computes the linear interpolation a + t(b - a) between a and b at the value
-// t. For arguments outside of the range 0 <= t <= 1, the values are
-// extrapolated.
-//
-// Differentiating lerp(a, b, t) with respect to a, b, and t gives:
-//
-//   d/da lerp(a, b, t) = 1 - t
-//   d/db lerp(a, b, t) = t
-//   d/dt lerp(a, b, t) = b - a
-//
-// with the dual representation given by
-//
-//   lerp(a + da, b + db, t + dt)
-//      ~= lerp(a, b, t) + (1 - t) da + t db + (b - a) dt .
-template <typename T, int N>
-inline Jet<T, N> lerp(const Jet<T, N>& a,
-                      const Jet<T, N>& b,
-                      const Jet<T, N>& t) {
-  return Jet<T, N>{lerp(a.a, b.a, t.a),
-                   (T(1) - t.a) * a.v + t.a * b.v + (b.a - a.a) * t.v};
-}
-
-// Computes the midpoint a + (b - a) / 2.
-//
-// Differentiating midpoint(a, b) with respect to a and b gives:
-//
-//   d/da midpoint(a, b) = 1/2
-//   d/db midpoint(a, b) = 1/2
-//
-// with the dual representation given by
-//
-//   midpoint(a + da, b + db) ~= midpoint(a, b) + (da + db) / 2 .
-template <typename T, int N>
-inline Jet<T, N> midpoint(const Jet<T, N>& a, const Jet<T, N>& b) {
-  Jet<T, N> result{midpoint(a.a, b.a)};
-  // To avoid overflow in the differential, compute
-  // (da + db) / 2 using midpoint.
-  for (int i = 0; i < N; ++i) {
-    result.v[i] = midpoint(a.v[i], b.v[i]);
-  }
-  return result;
-}
-#endif  // defined(CERES_HAS_CPP20)
-
-// atan2(b + db, a + da) ~= atan2(b, a) + (- b da + a db) / (a^2 + b^2)
-//
-// In words: the rate of change of theta is 1/r times the rate of
-// change of (x, y) in the positive angular direction.
-template <typename T, int N>
-inline Jet<T, N> atan2(const Jet<T, N>& g, const Jet<T, N>& f) {
-  // Note order of arguments:
-  //
-  //   f = a + da
-  //   g = b + db
-
-  T const tmp = T(1.0) / (f.a * f.a + g.a * g.a);
-  return Jet<T, N>(atan2(g.a, f.a), tmp * (-g.a * f.v + f.a * g.v));
-}
-
-// Computes the square x^2 of a real number x (not the Euclidean L^2 norm as
-// the name might suggest).
-//
-// NOTE: While std::norm is primarily intended for computing the squared
-// magnitude of a std::complex<> number, the current Jet implementation does not
-// support mixing a scalar T in its real part and std::complex<T> and in the
-// infinitesimal. Mixed Jet support is necessary for the type decay from
-// std::complex<T> to T (the squared magnitude of a complex number is always
-// real) performed by std::norm.
-//
-// norm(x + h) ~= norm(x) + 2x h
-template <typename T, int N>
-inline Jet<T, N> norm(const Jet<T, N>& f) {
-  return Jet<T, N>(norm(f.a), T(2) * f.a * f.v);
-}
-
-// pow -- base is a differentiable function, exponent is a constant.
-// (a+da)^p ~= a^p + p*a^(p-1) da
-template <typename T, int N>
-inline Jet<T, N> pow(const Jet<T, N>& f, double g) {
-  T const tmp = g * pow(f.a, g - T(1.0));
-  return Jet<T, N>(pow(f.a, g), tmp * f.v);
-}
-
-// pow -- base is a constant, exponent is a differentiable function.
-// We have various special cases, see the comment for pow(Jet, Jet) for
-// analysis:
-//
-// 1. For f > 0 we have: (f)^(g + dg) ~= f^g + f^g log(f) dg
-//
-// 2. For f == 0 and g > 0 we have: (f)^(g + dg) ~= f^g
-//
-// 3. For f < 0 and integer g we have: (f)^(g + dg) ~= f^g but if dg
-// != 0, the derivatives are not defined and we return NaN.
-
-template <typename T, int N>
-inline Jet<T, N> pow(T f, const Jet<T, N>& g) {
-  Jet<T, N> result;
-
-  if (fpclassify(f) == FP_ZERO && g > 0) {
-    // Handle case 2.
-    result = Jet<T, N>(T(0.0));
-  } else {
-    if (f < 0 && g == floor(g.a)) {  // Handle case 3.
-      result = Jet<T, N>(pow(f, g.a));
-      for (int i = 0; i < N; i++) {
-        if (fpclassify(g.v[i]) != FP_ZERO) {
-          // Return a NaN when g.v != 0.
-          result.v[i] = std::numeric_limits<T>::quiet_NaN();
-        }
-      }
-    } else {
-      // Handle case 1.
-      T const tmp = pow(f, g.a);
-      result = Jet<T, N>(tmp, log(f) * tmp * g.v);
-    }
-  }
-
-  return result;
-}
-
-// pow -- both base and exponent are differentiable functions. This has a
-// variety of special cases that require careful handling.
-//
-// 1. For f > 0:
-//    (f + df)^(g + dg) ~= f^g + f^(g - 1) * (g * df + f * log(f) * dg)
-//    The numerical evaluation of f * log(f) for f > 0 is well behaved, even for
-//    extremely small values (e.g. 1e-99).
-//
-// 2. For f == 0 and g > 1: (f + df)^(g + dg) ~= 0
-//    This cases is needed because log(0) can not be evaluated in the f > 0
-//    expression. However the function f*log(f) is well behaved around f == 0
-//    and its limit as f-->0 is zero.
-//
-// 3. For f == 0 and g == 1: (f + df)^(g + dg) ~= 0 + df
-//
-// 4. For f == 0 and 0 < g < 1: The value is finite but the derivatives are not.
-//
-// 5. For f == 0 and g < 0: The value and derivatives of f^g are not finite.
-//
-// 6. For f == 0 and g == 0: The C standard incorrectly defines 0^0 to be 1
-//    "because there are applications that can exploit this definition". We
-//    (arbitrarily) decree that derivatives here will be nonfinite, since that
-//    is consistent with the behavior for f == 0, g < 0 and 0 < g < 1.
-//    Practically any definition could have been justified because mathematical
-//    consistency has been lost at this point.
-//
-// 7. For f < 0, g integer, dg == 0: (f + df)^(g + dg) ~= f^g + g * f^(g - 1) df
-//    This is equivalent to the case where f is a differentiable function and g
-//    is a constant (to first order).
-//
-// 8. For f < 0, g integer, dg != 0: The value is finite but the derivatives are
-//    not, because any change in the value of g moves us away from the point
-//    with a real-valued answer into the region with complex-valued answers.
-//
-// 9. For f < 0, g noninteger: The value and derivatives of f^g are not finite.
-
-template <typename T, int N>
-inline Jet<T, N> pow(const Jet<T, N>& f, const Jet<T, N>& g) {
-  Jet<T, N> result;
-
-  if (fpclassify(f) == FP_ZERO && g >= 1) {
-    // Handle cases 2 and 3.
-    if (g > 1) {
-      result = Jet<T, N>(T(0.0));
-    } else {
-      result = f;
-    }
-
-  } else {
-    if (f < 0 && g == floor(g.a)) {
-      // Handle cases 7 and 8.
-      T const tmp = g.a * pow(f.a, g.a - T(1.0));
-      result = Jet<T, N>(pow(f.a, g.a), tmp * f.v);
-      for (int i = 0; i < N; i++) {
-        if (fpclassify(g.v[i]) != FP_ZERO) {
-          // Return a NaN when g.v != 0.
-          result.v[i] = T(std::numeric_limits<double>::quiet_NaN());
-        }
-      }
-    } else {
-      // Handle the remaining cases. For cases 4,5,6,9 we allow the log()
-      // function to generate -HUGE_VAL or NaN, since those cases result in a
-      // nonfinite derivative.
-      T const tmp1 = pow(f.a, g.a);
-      T const tmp2 = g.a * pow(f.a, g.a - T(1.0));
-      T const tmp3 = tmp1 * log(f.a);
-      result = Jet<T, N>(tmp1, tmp2 * f.v + tmp3 * g.v);
-    }
-  }
-
-  return result;
-}
-
-// Note: This has to be in the ceres namespace for argument dependent lookup to
-// function correctly. Otherwise statements like CHECK_LE(x, 2.0) fail with
-// strange compile errors.
-template <typename T, int N>
-inline std::ostream& operator<<(std::ostream& s, const Jet<T, N>& z) {
-  s << "[" << z.a << " ; ";
-  for (int i = 0; i < N; ++i) {
-    s << z.v[i];
-    if (i != N - 1) {
-      s << ", ";
-    }
-  }
-  s << "]";
-  return s;
-}
-}  // namespace ceres
-
-namespace std {
-template <typename T, int N>
-struct numeric_limits<ceres::Jet<T, N>> {
-  static constexpr bool is_specialized = true;
-  static constexpr bool is_signed = std::numeric_limits<T>::is_signed;
-  static constexpr bool is_integer = std::numeric_limits<T>::is_integer;
-  static constexpr bool is_exact = std::numeric_limits<T>::is_exact;
-  static constexpr bool has_infinity = std::numeric_limits<T>::has_infinity;
-  static constexpr bool has_quiet_NaN = std::numeric_limits<T>::has_quiet_NaN;
-  static constexpr bool has_signaling_NaN =
-      std::numeric_limits<T>::has_signaling_NaN;
-  static constexpr bool is_iec559 = std::numeric_limits<T>::is_iec559;
-  static constexpr bool is_bounded = std::numeric_limits<T>::is_bounded;
-  static constexpr bool is_modulo = std::numeric_limits<T>::is_modulo;
-
-  // has_denorm (and has_denorm_loss, not defined for Jet) has been deprecated
-  // in C++23. However, without an intent to remove the declaration. Disable
-  // deprecation warnings temporarily just for the corresponding symbols.
-  CERES_DISABLE_DEPRECATED_WARNING
-  static constexpr std::float_denorm_style has_denorm =
-      std::numeric_limits<T>::has_denorm;
-  CERES_RESTORE_DEPRECATED_WARNING
-  static constexpr std::float_round_style round_style =
-      std::numeric_limits<T>::round_style;
-
-  static constexpr int digits = std::numeric_limits<T>::digits;
-  static constexpr int digits10 = std::numeric_limits<T>::digits10;
-  static constexpr int max_digits10 = std::numeric_limits<T>::max_digits10;
-  static constexpr int radix = std::numeric_limits<T>::radix;
-  static constexpr int min_exponent = std::numeric_limits<T>::min_exponent;
-  static constexpr int min_exponent10 = std::numeric_limits<T>::max_exponent10;
-  static constexpr int max_exponent = std::numeric_limits<T>::max_exponent;
-  static constexpr int max_exponent10 = std::numeric_limits<T>::max_exponent10;
-  static constexpr bool traps = std::numeric_limits<T>::traps;
-  static constexpr bool tinyness_before =
-      std::numeric_limits<T>::tinyness_before;
-
-  static constexpr ceres::Jet<T, N> min
-  CERES_PREVENT_MACRO_SUBSTITUTION() noexcept {
-    return ceres::Jet<T, N>((std::numeric_limits<T>::min)());
-  }
-  static constexpr ceres::Jet<T, N> lowest() noexcept {
-    return ceres::Jet<T, N>(std::numeric_limits<T>::lowest());
-  }
-  static constexpr ceres::Jet<T, N> epsilon() noexcept {
-    return ceres::Jet<T, N>(std::numeric_limits<T>::epsilon());
-  }
-  static constexpr ceres::Jet<T, N> round_error() noexcept {
-    return ceres::Jet<T, N>(std::numeric_limits<T>::round_error());
-  }
-  static constexpr ceres::Jet<T, N> infinity() noexcept {
-    return ceres::Jet<T, N>(std::numeric_limits<T>::infinity());
-  }
-  static constexpr ceres::Jet<T, N> quiet_NaN() noexcept {
-    return ceres::Jet<T, N>(std::numeric_limits<T>::quiet_NaN());
-  }
-  static constexpr ceres::Jet<T, N> signaling_NaN() noexcept {
-    return ceres::Jet<T, N>(std::numeric_limits<T>::signaling_NaN());
-  }
-  static constexpr ceres::Jet<T, N> denorm_min() noexcept {
-    return ceres::Jet<T, N>(std::numeric_limits<T>::denorm_min());
-  }
-
-  static constexpr ceres::Jet<T, N> max
-  CERES_PREVENT_MACRO_SUBSTITUTION() noexcept {
-    return ceres::Jet<T, N>((std::numeric_limits<T>::max)());
-  }
-};
-
-}  // namespace std
-
-namespace Eigen {
-
-// Creating a specialization of NumTraits enables placing Jet objects inside
-// Eigen arrays, getting all the goodness of Eigen combined with autodiff.
-template <typename T, int N>
-struct NumTraits<ceres::Jet<T, N>> {
-  using Real = ceres::Jet<T, N>;
-  using NonInteger = ceres::Jet<T, N>;
-  using Nested = ceres::Jet<T, N>;
-  using Literal = ceres::Jet<T, N>;
-
-  static typename ceres::Jet<T, N> dummy_precision() {
-    return ceres::Jet<T, N>(1e-12);
-  }
-
-  static inline Real epsilon() {
-    return Real(std::numeric_limits<T>::epsilon());
-  }
-
-  static inline int digits10() { return NumTraits<T>::digits10(); }
-  static inline int max_digits10() { return NumTraits<T>::max_digits10(); }
-
-  enum {
-    IsComplex = 0,
-    IsInteger = 0,
-    IsSigned,
-    ReadCost = 1,
-    AddCost = 1,
-    // For Jet types, multiplication is more expensive than addition.
-    MulCost = 3,
-    HasFloatingPoint = 1,
-    RequireInitialization = 1
-  };
-
-  template <bool Vectorized>
-  struct Div {
-    enum {
-#if defined(EIGEN_VECTORIZE_AVX)
-      AVX = true,
-#else
-      AVX = false,
-#endif
-
-      // Assuming that for Jets, division is as expensive as
-      // multiplication.
-      Cost = 3
-    };
-  };
-
-  static inline Real highest() { return Real((std::numeric_limits<T>::max)()); }
-  static inline Real lowest() { return Real(-(std::numeric_limits<T>::max)()); }
-};
-
-// Specifying the return type of binary operations between Jets and scalar types
-// allows you to perform matrix/array operations with Eigen matrices and arrays
-// such as addition, subtraction, multiplication, and division where one Eigen
-// matrix/array is of type Jet and the other is a scalar type. This improves
-// performance by using the optimized scalar-to-Jet binary operations but
-// is only available on Eigen versions >= 3.3
-template <typename BinaryOp, typename T, int N>
-struct ScalarBinaryOpTraits<ceres::Jet<T, N>, T, BinaryOp> {
-  using ReturnType = ceres::Jet<T, N>;
-};
-template <typename BinaryOp, typename T, int N>
-struct ScalarBinaryOpTraits<T, ceres::Jet<T, N>, BinaryOp> {
-  using ReturnType = ceres::Jet<T, N>;
-};
-
-}  // namespace Eigen
-
-#endif  // CERES_PUBLIC_JET_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/jet_fwd.h b/3rdParty/my_ceres_vc17/include/ceres/jet_fwd.h
deleted file mode 100644
index b5216da..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/jet_fwd.h
+++ /dev/null
@@ -1,44 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sergiu.deitsch@gmail.com (Sergiu Deitsch)
-//
-
-#ifndef CERES_PUBLIC_JET_FWD_H_
-#define CERES_PUBLIC_JET_FWD_H_
-
-namespace ceres {
-
-// Jet forward declaration necessary for the following partial specialization of
-// std::common_type and type traits.
-template <typename T, int N>
-struct Jet;
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_JET_FWD_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/line_manifold.h b/3rdParty/my_ceres_vc17/include/ceres/line_manifold.h
deleted file mode 100644
index dad9737..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/line_manifold.h
+++ /dev/null
@@ -1,301 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: jodebo_beck@gmx.de (Johannes Beck)
-//
-
-#ifndef CERES_PUBLIC_LINE_MANIFOLD_H_
-#define CERES_PUBLIC_LINE_MANIFOLD_H_
-
-#include <Eigen/Core>
-#include <algorithm>
-#include <array>
-#include <memory>
-#include <vector>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/internal/householder_vector.h"
-#include "ceres/internal/sphere_manifold_functions.h"
-#include "ceres/manifold.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-// This provides a manifold for lines, where the line is
-// over-parameterized by an origin point and a direction vector. So the
-// parameter vector size needs to be two times the ambient space dimension,
-// where the first half is interpreted as the origin point and the second half
-// as the direction.
-//
-// The plus operator for the line direction is the same as for the
-// SphereManifold. The update of the origin point is
-// perpendicular to the line direction before the update.
-//
-// This manifold is a special case of the affine Grassmannian
-// manifold (see https://en.wikipedia.org/wiki/Affine_Grassmannian_(manifold))
-// for the case Graff_1(R^n).
-//
-// The class works with dynamic and static ambient space dimensions. If the
-// ambient space dimensions is known at compile time use
-//
-//    LineManifold<3> manifold;
-//
-// If the ambient space dimensions is not known at compile time the template
-// parameter needs to be set to ceres::DYNAMIC and the actual dimension needs
-// to be provided as a constructor argument:
-//
-//    LineManifold<ceres::DYNAMIC> manifold(ambient_dim);
-//
-template <int AmbientSpaceDimension>
-class LineManifold final : public Manifold {
- public:
-  static_assert(AmbientSpaceDimension == DYNAMIC || AmbientSpaceDimension >= 2,
-                "The ambient space must be at least 2.");
-  static_assert(ceres::DYNAMIC == Eigen::Dynamic,
-                "ceres::DYNAMIC needs to be the same as Eigen::Dynamic.");
-
-  LineManifold();
-  explicit LineManifold(int size);
-
-  int AmbientSize() const override { return 2 * size_; }
-  int TangentSize() const override { return 2 * (size_ - 1); }
-  bool Plus(const double* x,
-            const double* delta,
-            double* x_plus_delta) const override;
-  bool PlusJacobian(const double* x, double* jacobian) const override;
-  bool Minus(const double* y,
-             const double* x,
-             double* y_minus_x) const override;
-  bool MinusJacobian(const double* x, double* jacobian) const override;
-
- private:
-  static constexpr bool IsDynamic = (AmbientSpaceDimension == ceres::DYNAMIC);
-  static constexpr int TangentSpaceDimension =
-      IsDynamic ? ceres::DYNAMIC : AmbientSpaceDimension - 1;
-
-  static constexpr int DAmbientSpaceDimension =
-      IsDynamic ? ceres::DYNAMIC : 2 * AmbientSpaceDimension;
-  static constexpr int DTangentSpaceDimension =
-      IsDynamic ? ceres::DYNAMIC : 2 * TangentSpaceDimension;
-
-  using AmbientVector = Eigen::Matrix<double, AmbientSpaceDimension, 1>;
-  using TangentVector = Eigen::Matrix<double, TangentSpaceDimension, 1>;
-  using MatrixPlusJacobian = Eigen::Matrix<double,
-                                           DAmbientSpaceDimension,
-                                           DTangentSpaceDimension,
-                                           Eigen::RowMajor>;
-  using MatrixMinusJacobian = Eigen::Matrix<double,
-                                            DTangentSpaceDimension,
-                                            DAmbientSpaceDimension,
-                                            Eigen::RowMajor>;
-
-  const int size_{AmbientSpaceDimension};
-};
-
-template <int AmbientSpaceDimension>
-LineManifold<AmbientSpaceDimension>::LineManifold()
-    : size_{AmbientSpaceDimension} {
-  static_assert(
-      AmbientSpaceDimension != Eigen::Dynamic,
-      "The size is set to dynamic. Please call the constructor with a size.");
-}
-
-template <int AmbientSpaceDimension>
-LineManifold<AmbientSpaceDimension>::LineManifold(int size) : size_{size} {
-  if (AmbientSpaceDimension != Eigen::Dynamic) {
-    CHECK_EQ(AmbientSpaceDimension, size)
-        << "Specified size by template parameter differs from the supplied "
-           "one.";
-  } else {
-    CHECK_GT(size_, 1)
-        << "The size of the manifold needs to be greater than 1.";
-  }
-}
-
-template <int AmbientSpaceDimension>
-bool LineManifold<AmbientSpaceDimension>::Plus(const double* x_ptr,
-                                               const double* delta_ptr,
-                                               double* x_plus_delta_ptr) const {
-  // We seek a box plus operator of the form
-  //
-  //   [o*, d*] = Plus([o, d], [delta_o, delta_d])
-  //
-  // where o is the origin point, d is the direction vector, delta_o is
-  // the delta of the origin point and delta_d the delta of the direction and
-  // o* and d* is the updated origin point and direction.
-  //
-  // We separate the Plus operator into the origin point and directional part
-  //   d* = Plus_d(d, delta_d)
-  //   o* = Plus_o(o, d, delta_o)
-  //
-  // The direction update function Plus_d is the same as as the SphereManifold:
-  //
-  //   d* = H_{v(d)} [sinc(|delta_d|) delta_d, cos(|delta_d|)]^T
-  //
-  // where H is the householder matrix
-  //   H_{v} = I - (2 / |v|^2) v v^T
-  // and
-  //   v(d) = d - sign(d_n) |d| e_n.
-  //
-  // The origin point update function Plus_o is defined as
-  //
-  //   o* = o + H_{v(d)} [delta_o, 0]^T.
-
-  Eigen::Map<const AmbientVector> o(x_ptr, size_);
-  Eigen::Map<const AmbientVector> d(x_ptr + size_, size_);
-
-  Eigen::Map<const TangentVector> delta_o(delta_ptr, size_ - 1);
-  Eigen::Map<const TangentVector> delta_d(delta_ptr + size_ - 1, size_ - 1);
-  Eigen::Map<AmbientVector> o_plus_delta(x_plus_delta_ptr, size_);
-  Eigen::Map<AmbientVector> d_plus_delta(x_plus_delta_ptr + size_, size_);
-
-  const double norm_delta_d = delta_d.norm();
-
-  o_plus_delta = o;
-
-  // Shortcut for zero delta direction.
-  if (norm_delta_d == 0.0) {
-    d_plus_delta = d;
-
-    if (delta_o.isZero(0.0)) {
-      return true;
-    }
-  }
-
-  // Calculate the householder transformation which is needed for f_d and f_o.
-  AmbientVector v(size_);
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  internal::ComputeHouseholderVector<Eigen::Map<const AmbientVector>,
-                                     double,
-                                     AmbientSpaceDimension>(d, &v, &beta);
-
-  if (norm_delta_d != 0.0) {
-    internal::ComputeSphereManifoldPlus(
-        v, beta, d, delta_d, norm_delta_d, &d_plus_delta);
-  }
-
-  // The null space is in the direction of the line, so the tangent space is
-  // perpendicular to the line direction. This is achieved by using the
-  // householder matrix of the direction and allow only movements
-  // perpendicular to e_n.
-  AmbientVector y(size_);
-  y << delta_o, 0;
-  o_plus_delta += internal::ApplyHouseholderVector(y, v, beta);
-
-  return true;
-}
-
-template <int AmbientSpaceDimension>
-bool LineManifold<AmbientSpaceDimension>::PlusJacobian(
-    const double* x_ptr, double* jacobian_ptr) const {
-  Eigen::Map<const AmbientVector> d(x_ptr + size_, size_);
-  Eigen::Map<MatrixPlusJacobian> jacobian(
-      jacobian_ptr, 2 * size_, 2 * (size_ - 1));
-
-  // Clear the Jacobian as only half of the matrix is not zero.
-  jacobian.setZero();
-
-  auto jacobian_d =
-      jacobian
-          .template topLeftCorner<AmbientSpaceDimension, TangentSpaceDimension>(
-              size_, size_ - 1);
-  auto jacobian_o = jacobian.template bottomRightCorner<AmbientSpaceDimension,
-                                                        TangentSpaceDimension>(
-      size_, size_ - 1);
-  internal::ComputeSphereManifoldPlusJacobian(d, &jacobian_d);
-  jacobian_o = jacobian_d;
-  return true;
-}
-
-template <int AmbientSpaceDimension>
-bool LineManifold<AmbientSpaceDimension>::Minus(const double* y_ptr,
-                                                const double* x_ptr,
-                                                double* y_minus_x) const {
-  Eigen::Map<const AmbientVector> y_o(y_ptr, size_);
-  Eigen::Map<const AmbientVector> y_d(y_ptr + size_, size_);
-  Eigen::Map<const AmbientVector> x_o(x_ptr, size_);
-  Eigen::Map<const AmbientVector> x_d(x_ptr + size_, size_);
-
-  Eigen::Map<TangentVector> y_minus_x_o(y_minus_x, size_ - 1);
-  Eigen::Map<TangentVector> y_minus_x_d(y_minus_x + size_ - 1, size_ - 1);
-
-  AmbientVector v(size_);
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  internal::ComputeHouseholderVector<Eigen::Map<const AmbientVector>,
-                                     double,
-                                     AmbientSpaceDimension>(x_d, &v, &beta);
-
-  internal::ComputeSphereManifoldMinus(v, beta, x_d, y_d, &y_minus_x_d);
-
-  AmbientVector delta_o = y_o - x_o;
-  const AmbientVector h_delta_o =
-      internal::ApplyHouseholderVector(delta_o, v, beta);
-  y_minus_x_o = h_delta_o.template head<TangentSpaceDimension>(size_ - 1);
-
-  return true;
-}
-
-template <int AmbientSpaceDimension>
-bool LineManifold<AmbientSpaceDimension>::MinusJacobian(
-    const double* x_ptr, double* jacobian_ptr) const {
-  Eigen::Map<const AmbientVector> d(x_ptr + size_, size_);
-  Eigen::Map<MatrixMinusJacobian> jacobian(
-      jacobian_ptr, 2 * (size_ - 1), 2 * size_);
-
-  // Clear the Jacobian as only half of the matrix is not zero.
-  jacobian.setZero();
-
-  auto jacobian_d =
-      jacobian
-          .template topLeftCorner<TangentSpaceDimension, AmbientSpaceDimension>(
-              size_ - 1, size_);
-  auto jacobian_o = jacobian.template bottomRightCorner<TangentSpaceDimension,
-                                                        AmbientSpaceDimension>(
-      size_ - 1, size_);
-  internal::ComputeSphereManifoldMinusJacobian(d, &jacobian_d);
-  jacobian_o = jacobian_d;
-
-  return true;
-}
-
-}  // namespace ceres
-
-// clang-format off
-#include "ceres/internal/reenable_warnings.h"
-// clang-format on
-
-#endif  // CERES_PUBLIC_LINE_MANIFOLD_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/loss_function.h b/3rdParty/my_ceres_vc17/include/ceres/loss_function.h
deleted file mode 100644
index b8582f8..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/loss_function.h
+++ /dev/null
@@ -1,433 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//
-// The LossFunction interface is the way users describe how residuals
-// are converted to cost terms for the overall problem cost function.
-// For the exact manner in which loss functions are converted to the
-// overall cost for a problem, see problem.h.
-//
-// For least squares problem where there are no outliers and standard
-// squared loss is expected, it is not necessary to create a loss
-// function; instead passing a nullptr to the problem when adding
-// residuals implies a standard squared loss.
-//
-// For least squares problems where the minimization may encounter
-// input terms that contain outliers, that is, completely bogus
-// measurements, it is important to use a loss function that reduces
-// their associated penalty.
-//
-// Consider a structure from motion problem. The unknowns are 3D
-// points and camera parameters, and the measurements are image
-// coordinates describing the expected reprojected position for a
-// point in a camera. For example, we want to model the geometry of a
-// street scene with fire hydrants and cars, observed by a moving
-// camera with unknown parameters, and the only 3D points we care
-// about are the pointy tippy-tops of the fire hydrants. Our magic
-// image processing algorithm, which is responsible for producing the
-// measurements that are input to Ceres, has found and matched all
-// such tippy-tops in all image frames, except that in one of the
-// frame it mistook a car's headlight for a hydrant. If we didn't do
-// anything special (i.e. if we used a basic quadratic loss), the
-// residual for the erroneous measurement will result in extreme error
-// due to the quadratic nature of squared loss. This results in the
-// entire solution getting pulled away from the optimum to reduce
-// the large error that would otherwise be attributed to the wrong
-// measurement.
-//
-// Using a robust loss function, the cost for large residuals is
-// reduced. In the example above, this leads to outlier terms getting
-// downweighted so they do not overly influence the final solution.
-//
-// What cost function is best?
-//
-// In general, there isn't a principled way to select a robust loss
-// function. The authors suggest starting with a non-robust cost, then
-// only experimenting with robust loss functions if standard squared
-// loss doesn't work.
-
-#ifndef CERES_PUBLIC_LOSS_FUNCTION_H_
-#define CERES_PUBLIC_LOSS_FUNCTION_H_
-
-#include <memory>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-class CERES_EXPORT LossFunction {
- public:
-  virtual ~LossFunction();
-
-  // For a residual vector with squared 2-norm 'sq_norm', this method
-  // is required to fill in the value and derivatives of the loss
-  // function (rho in this example):
-  //
-  //   out[0] = rho(sq_norm),
-  //   out[1] = rho'(sq_norm),
-  //   out[2] = rho''(sq_norm),
-  //
-  // Here the convention is that the contribution of a term to the
-  // cost function is given by 1/2 rho(s),  where
-  //
-  //   s = ||residuals||^2.
-  //
-  // Calling the method with a negative value of 's' is an error and
-  // the implementations are not required to handle that case.
-  //
-  // Most sane choices of rho() satisfy:
-  //
-  //   rho(0) = 0,
-  //   rho'(0) = 1,
-  //   rho'(s) < 1 in outlier region,
-  //   rho''(s) < 0 in outlier region,
-  //
-  // so that they mimic the least squares cost for small residuals.
-  virtual void Evaluate(double sq_norm, double out[3]) const = 0;
-};
-
-// Some common implementations follow below.
-//
-// Note: in the region of interest (i.e. s < 3) we have:
-//   TrivialLoss >= HuberLoss >= SoftLOneLoss >= CauchyLoss
-
-// This corresponds to no robustification.
-//
-//   rho(s) = s
-//
-// At s = 0: rho = [0, 1, 0].
-//
-// It is not normally necessary to use this, as passing nullptr for the
-// loss function when building the problem accomplishes the same
-// thing.
-class CERES_EXPORT TrivialLoss final : public LossFunction {
- public:
-  void Evaluate(double, double*) const override;
-};
-
-// Scaling
-// -------
-// Given one robustifier
-//   s -> rho(s)
-// one can change the length scale at which robustification takes
-// place, by adding a scale factor 'a' as follows:
-//
-//   s -> a^2 rho(s / a^2).
-//
-// The first and second derivatives are:
-//
-//   s -> rho'(s / a^2),
-//   s -> (1 / a^2) rho''(s / a^2),
-//
-// but the behaviour near s = 0 is the same as the original function,
-// i.e.
-//
-//   rho(s) = s + higher order terms,
-//   a^2 rho(s / a^2) = s + higher order terms.
-//
-// The scalar 'a' should be positive.
-//
-// The reason for the appearance of squaring is that 'a' is in the
-// units of the residual vector norm whereas 's' is a squared
-// norm. For applications it is more convenient to specify 'a' than
-// its square. The commonly used robustifiers below are described in
-// un-scaled format (a = 1) but their implementations work for any
-// non-zero value of 'a'.
-
-// Huber.
-//
-//   rho(s) = s               for s <= 1,
-//   rho(s) = 2 sqrt(s) - 1   for s >= 1.
-//
-// At s = 0: rho = [0, 1, 0].
-//
-// The scaling parameter 'a' corresponds to 'delta' on this page:
-//   http://en.wikipedia.org/wiki/Huber_Loss_Function
-class CERES_EXPORT HuberLoss final : public LossFunction {
- public:
-  explicit HuberLoss(double a) : a_(a), b_(a * a) {}
-  void Evaluate(double, double*) const override;
-
- private:
-  const double a_;
-  // b = a^2.
-  const double b_;
-};
-
-// Soft L1, similar to Huber but smooth.
-//
-//   rho(s) = 2 (sqrt(1 + s) - 1).
-//
-// At s = 0: rho = [0, 1, -1 / (2 * a^2)].
-class CERES_EXPORT SoftLOneLoss final : public LossFunction {
- public:
-  explicit SoftLOneLoss(double a) : b_(a * a), c_(1 / b_) {}
-  void Evaluate(double, double*) const override;
-
- private:
-  // b = a^2.
-  const double b_;
-  // c = 1 / a^2.
-  const double c_;
-};
-
-// Inspired by the Cauchy distribution
-//
-//   rho(s) = log(1 + s).
-//
-// At s = 0: rho = [0, 1, -1 / a^2].
-class CERES_EXPORT CauchyLoss final : public LossFunction {
- public:
-  explicit CauchyLoss(double a) : b_(a * a), c_(1 / b_) {}
-  void Evaluate(double, double*) const override;
-
- private:
-  // b = a^2.
-  const double b_;
-  // c = 1 / a^2.
-  const double c_;
-};
-
-// Loss that is capped beyond a certain level using the arc-tangent function.
-// The scaling parameter 'a' determines the level where falloff occurs.
-// For costs much smaller than 'a', the loss function is linear and behaves like
-// TrivialLoss, and for values much larger than 'a' the value asymptotically
-// approaches the constant value of a * PI / 2.
-//
-//   rho(s) = a atan(s / a).
-//
-// At s = 0: rho = [0, 1, 0].
-class CERES_EXPORT ArctanLoss final : public LossFunction {
- public:
-  explicit ArctanLoss(double a) : a_(a), b_(1 / (a * a)) {}
-  void Evaluate(double, double*) const override;
-
- private:
-  const double a_;
-  // b = 1 / a^2.
-  const double b_;
-};
-
-// Loss function that maps to approximately zero cost in a range around the
-// origin, and reverts to linear in error (quadratic in cost) beyond this range.
-// The tolerance parameter 'a' sets the nominal point at which the
-// transition occurs, and the transition size parameter 'b' sets the nominal
-// distance over which most of the transition occurs. Both a and b must be
-// greater than zero, and typically b will be set to a fraction of a.
-// The slope rho'[s] varies smoothly from about 0 at s <= a - b to
-// about 1 at s >= a + b.
-//
-// The term is computed as:
-//
-//   rho(s) = b log(1 + exp((s - a) / b)) - c0.
-//
-// where c0 is chosen so that rho(0) == 0
-//
-//   c0 = b log(1 + exp(-a / b)
-//
-// This has the following useful properties:
-//
-//   rho(s) == 0               for s = 0
-//   rho'(s) ~= 0              for s << a - b
-//   rho'(s) ~= 1              for s >> a + b
-//   rho''(s) > 0              for all s
-//
-// In addition, all derivatives are continuous, and the curvature is
-// concentrated in the range a - b to a + b.
-//
-// At s = 0: rho = [0, ~0, ~0].
-class CERES_EXPORT TolerantLoss final : public LossFunction {
- public:
-  explicit TolerantLoss(double a, double b);
-  void Evaluate(double, double*) const override;
-
- private:
-  const double a_, b_, c_;
-};
-
-// This is the Tukey biweight loss function which aggressively
-// attempts to suppress large errors.
-//
-// The term is computed as follows where the equations are scaled by a
-// factor of 2 because the cost function is given by 1/2 rho(s):
-//
-//   rho(s) = a^2 / 3 * (1 - (1 - s / a^2)^3 )   for s <= a^2,
-//   rho(s) = a^2 / 3                            for s >  a^2.
-//
-// At s = 0: rho = [0, 1, -2 / a^2]
-class CERES_EXPORT TukeyLoss final : public ceres::LossFunction {
- public:
-  explicit TukeyLoss(double a) : a_squared_(a * a) {}
-  void Evaluate(double, double*) const override;
-
- private:
-  const double a_squared_;
-};
-
-// Composition of two loss functions.  The error is the result of first
-// evaluating g followed by f to yield the composition f(g(s)).
-// The loss functions must not be nullptr.
-class CERES_EXPORT ComposedLoss final : public LossFunction {
- public:
-  explicit ComposedLoss(const LossFunction* f,
-                        Ownership ownership_f,
-                        const LossFunction* g,
-                        Ownership ownership_g);
-  ~ComposedLoss() override;
-  void Evaluate(double, double*) const override;
-
- private:
-  std::unique_ptr<const LossFunction> f_, g_;
-  const Ownership ownership_f_, ownership_g_;
-};
-
-// The discussion above has to do with length scaling: it affects the space
-// in which s is measured. Sometimes you want to simply scale the output
-// value of the robustifier. For example, you might want to weight
-// different error terms differently (e.g., weight pixel reprojection
-// errors differently from terrain errors).
-//
-// If rho is the wrapped robustifier, then this simply outputs
-// s -> a * rho(s)
-//
-// The first and second derivatives are, not surprisingly
-// s -> a * rho'(s)
-// s -> a * rho''(s)
-//
-// Since we treat the a nullptr Loss function as the Identity loss
-// function, rho = nullptr is a valid input and will result in the input
-// being scaled by a. This provides a simple way of implementing a
-// scaled ResidualBlock.
-class CERES_EXPORT ScaledLoss final : public LossFunction {
- public:
-  // Constructs a ScaledLoss wrapping another loss function. Takes
-  // ownership of the wrapped loss function or not depending on the
-  // ownership parameter.
-  ScaledLoss(const LossFunction* rho, double a, Ownership ownership)
-      : rho_(rho), a_(a), ownership_(ownership) {}
-  ScaledLoss(const ScaledLoss&) = delete;
-  void operator=(const ScaledLoss&) = delete;
-
-  ~ScaledLoss() override {
-    if (ownership_ == DO_NOT_TAKE_OWNERSHIP) {
-      rho_.release();
-    }
-  }
-  void Evaluate(double, double*) const override;
-
- private:
-  std::unique_ptr<const LossFunction> rho_;
-  const double a_;
-  const Ownership ownership_;
-};
-
-// Sometimes after the optimization problem has been constructed, we
-// wish to mutate the scale of the loss function. For example, when
-// performing estimation from data which has substantial outliers,
-// convergence can be improved by starting out with a large scale,
-// optimizing the problem and then reducing the scale. This can have
-// better convergence behaviour than just using a loss function with a
-// small scale.
-//
-// This templated class allows the user to implement a loss function
-// whose scale can be mutated after an optimization problem has been
-// constructed.
-//
-// Since we treat the a nullptr Loss function as the Identity loss
-// function, rho = nullptr is a valid input.
-//
-// Example usage
-//
-//  Problem problem;
-//
-//  // Add parameter blocks
-//
-//  CostFunction* cost_function =
-//    new AutoDiffCostFunction < UW_Camera_Mapper, 2, 9, 3>(
-//      new UW_Camera_Mapper(feature_x, feature_y));
-//
-//  LossFunctionWrapper* loss_function = new LossFunctionWrapper(
-//    new HuberLoss(1.0), TAKE_OWNERSHIP);
-//
-//  problem.AddResidualBlock(cost_function, loss_function, parameters);
-//
-//  Solver::Options options;
-//  Solger::Summary summary;
-//
-//  Solve(options, &problem, &summary)
-//
-//  loss_function->Reset(new HuberLoss(1.0), TAKE_OWNERSHIP);
-//
-//  Solve(options, &problem, &summary)
-//
-class CERES_EXPORT LossFunctionWrapper final : public LossFunction {
- public:
-  LossFunctionWrapper(LossFunction* rho, Ownership ownership)
-      : rho_(rho), ownership_(ownership) {}
-
-  LossFunctionWrapper(const LossFunctionWrapper&) = delete;
-  void operator=(const LossFunctionWrapper&) = delete;
-
-  ~LossFunctionWrapper() override {
-    if (ownership_ == DO_NOT_TAKE_OWNERSHIP) {
-      rho_.release();
-    }
-  }
-
-  void Evaluate(double sq_norm, double out[3]) const override {
-    if (rho_.get() == nullptr) {
-      out[0] = sq_norm;
-      out[1] = 1.0;
-      out[2] = 0.0;
-    } else {
-      rho_->Evaluate(sq_norm, out);
-    }
-  }
-
-  void Reset(LossFunction* rho, Ownership ownership) {
-    if (ownership_ == DO_NOT_TAKE_OWNERSHIP) {
-      rho_.release();
-    }
-    rho_.reset(rho);
-    ownership_ = ownership;
-  }
-
- private:
-  std::unique_ptr<const LossFunction> rho_;
-  Ownership ownership_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_LOSS_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/manifold.h b/3rdParty/my_ceres_vc17/include/ceres/manifold.h
deleted file mode 100644
index 9bd6459..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/manifold.h
+++ /dev/null
@@ -1,411 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_MANIFOLD_H_
-#define CERES_PUBLIC_MANIFOLD_H_
-
-#include <Eigen/Core>
-#include <algorithm>
-#include <array>
-#include <memory>
-#include <utility>
-#include <vector>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// In sensor fusion problems, often we have to model quantities that live in
-// spaces known as Manifolds, for example the rotation/orientation of a sensor
-// that is represented by a quaternion.
-//
-// Manifolds are spaces which locally look like Euclidean spaces. More
-// precisely, at each point on the manifold there is a linear space that is
-// tangent to the manifold. It has dimension equal to the intrinsic dimension of
-// the manifold itself, which is less than or equal to the ambient space in
-// which the manifold is embedded.
-//
-// For example, the tangent space to a point on a sphere in three dimensions is
-// the two dimensional plane that is tangent to the sphere at that point. There
-// are two reasons tangent spaces are interesting:
-//
-// 1. They are Eucliean spaces so the usual vector space operations apply there,
-//    which makes numerical operations easy.
-// 2. Movement in the tangent space translate into movements along the manifold.
-//    Movements perpendicular to the tangent space do not translate into
-//    movements on the manifold.
-//
-// Returning to our sphere example, moving in the 2 dimensional plane
-// tangent to the sphere and projecting back onto the sphere will move you away
-// from the point you started from but moving along the normal at the same point
-// and the projecting back onto the sphere brings you back to the point.
-//
-// The Manifold interface defines two operations (and their derivatives)
-// involving the tangent space, allowing filtering and optimization to be
-// performed on said manifold:
-//
-// 1. x_plus_delta = Plus(x, delta)
-// 2. delta = Minus(x_plus_delta, x)
-//
-// "Plus" computes the result of moving along delta in the tangent space at x,
-// and then projecting back onto the manifold that x belongs to. In Differential
-// Geometry this is known as a "Retraction". It is a generalization of vector
-// addition in Euclidean spaces.
-//
-// Given two points on the manifold, "Minus" computes the change delta to x in
-// the tangent space at x, that will take it to x_plus_delta.
-//
-// Let us now consider two examples.
-//
-// The Euclidean space R^n is the simplest example of a manifold. It has
-// dimension n (and so does its tangent space) and Plus and Minus are the
-// familiar vector sum and difference operations.
-//
-//  Plus(x, delta) = x + delta = y,
-//  Minus(y, x) = y - x = delta.
-//
-// A more interesting case is SO(3), the special orthogonal group in three
-// dimensions - the space of 3x3 rotation matrices. SO(3) is a three dimensional
-// manifold embedded in R^9 or R^(3x3). So points on SO(3) are represented using
-// 9 dimensional vectors or 3x3 matrices, and points in its tangent spaces are
-// represented by 3 dimensional vectors.
-//
-// Defining Plus and Minus are defined in terms of the matrix Exp and Log
-// operations as follows:
-//
-// Let Exp(p, q, r) = [cos(theta) + cp^2, -sr + cpq        ,  sq + cpr        ]
-//                    [sr + cpq         , cos(theta) + cq^2, -sp + cqr        ]
-//                    [-sq + cpr        , sp + cqr         , cos(theta) + cr^2]
-//
-// where: theta = sqrt(p^2 + q^2 + r^2)
-//            s = sinc(theta)
-//            c = (1 - cos(theta))/theta^2
-//
-// and Log(x) = 1/(2 sinc(theta))[x_32 - x_23, x_13 - x_31, x_21 - x_12]
-//
-// where: theta = acos((Trace(x) - 1)/2)
-//
-// Then,
-//
-// Plus(x, delta) = x Exp(delta)
-// Minus(y, x) = Log(x^T y)
-//
-// For Plus and Minus to be mathematically consistent, the following identities
-// must be satisfied at all points x on the manifold:
-//
-// 1.  Plus(x, 0) = x.
-// 2.  For all y, Plus(x, Minus(y, x)) = y.
-// 3.  For all delta, Minus(Plus(x, delta), x) = delta.
-// 4.  For all delta_1, delta_2
-//    |Minus(Plus(x, delta_1), Plus(x, delta_2)) <= |delta_1 - delta_2|
-//
-// Briefly:
-// (1) Ensures that the tangent space is "centered" at x, and the zero vector is
-//     the identity element.
-// (2) Ensures that any y can be reached from x.
-// (3) Ensures that Plus is an injective (one-to-one) map.
-// (4) Allows us to define a metric on the manifold.
-//
-// Additionally we require that Plus and Minus be sufficiently smooth. In
-// particular they need to be differentiable everywhere on the manifold.
-//
-// For more details, please see
-//
-// "Integrating Generic Sensor Fusion Algorithms with Sound State
-// Representations through Encapsulation of Manifolds"
-// By C. Hertzberg, R. Wagner, U. Frese and L. Schroder
-// https://arxiv.org/pdf/1107.1119.pdf
-class CERES_EXPORT Manifold {
- public:
-  virtual ~Manifold();
-
-  // Dimension of the ambient space in which the manifold is embedded.
-  virtual int AmbientSize() const = 0;
-
-  // Dimension of the manifold/tangent space.
-  virtual int TangentSize() const = 0;
-
-  //   x_plus_delta = Plus(x, delta),
-  //
-  // A generalization of vector addition in Euclidean space, Plus computes the
-  // result of moving along delta in the tangent space at x, and then projecting
-  // back onto the manifold that x belongs to.
-  //
-  // x and x_plus_delta are AmbientSize() vectors.
-  // delta is a TangentSize() vector.
-  //
-  // Return value indicates if the operation was successful or not.
-  virtual bool Plus(const double* x,
-                    const double* delta,
-                    double* x_plus_delta) const = 0;
-
-  // Compute the derivative of Plus(x, delta) w.r.t delta at delta = 0, i.e.
-  //
-  // (D_2 Plus)(x, 0)
-  //
-  // jacobian is a row-major AmbientSize() x TangentSize() matrix.
-  //
-  // Return value indicates whether the operation was successful or not.
-  virtual bool PlusJacobian(const double* x, double* jacobian) const = 0;
-
-  // tangent_matrix = ambient_matrix * (D_2 Plus)(x, 0)
-  //
-  // ambient_matrix is a row-major num_rows x AmbientSize() matrix.
-  // tangent_matrix is a row-major num_rows x TangentSize() matrix.
-  //
-  // Return value indicates whether the operation was successful or not.
-  //
-  // This function is only used by the GradientProblemSolver, where the
-  // dimension of the parameter block can be large and it may be more efficient
-  // to compute this product directly rather than first evaluating the Jacobian
-  // into a matrix and then doing a matrix vector product.
-  //
-  // Because this is not an often used function, we provide a default
-  // implementation for convenience. If performance becomes an issue then the
-  // user should consider implementing a specialization.
-  virtual bool RightMultiplyByPlusJacobian(const double* x,
-                                           const int num_rows,
-                                           const double* ambient_matrix,
-                                           double* tangent_matrix) const;
-
-  // y_minus_x = Minus(y, x)
-  //
-  // Given two points on the manifold, Minus computes the change to x in the
-  // tangent space at x, that will take it to y.
-  //
-  // x and y are AmbientSize() vectors.
-  // y_minus_x is a TangentSize() vector.
-  //
-  // Return value indicates if the operation was successful or not.
-  virtual bool Minus(const double* y,
-                     const double* x,
-                     double* y_minus_x) const = 0;
-
-  // Compute the derivative of Minus(y, x) w.r.t y at y = x, i.e
-  //
-  //   (D_1 Minus) (x, x)
-  //
-  // Jacobian is a row-major TangentSize() x AmbientSize() matrix.
-  //
-  // Return value indicates whether the operation was successful or not.
-  virtual bool MinusJacobian(const double* x, double* jacobian) const = 0;
-};
-
-// The Euclidean manifold is another name for the ordinary vector space R^size,
-// where the plus and minus operations are the usual vector addition and
-// subtraction:
-//   Plus(x, delta) = x + delta
-//   Minus(y, x) = y - x.
-//
-// The class works with dynamic and static ambient space dimensions. If the
-// ambient space dimensions is know at compile time use
-//
-//    EuclideanManifold<3> manifold;
-//
-// If the ambient space dimensions is not known at compile time the template
-// parameter needs to be set to ceres::DYNAMIC and the actual dimension needs
-// to be provided as a constructor argument:
-//
-//    EuclideanManifold<ceres::DYNAMIC> manifold(ambient_dim);
-template <int Size>
-class EuclideanManifold final : public Manifold {
- public:
-  static_assert(Size == ceres::DYNAMIC || Size >= 0,
-                "The size of the manifold needs to be non-negative.");
-  static_assert(ceres::DYNAMIC == Eigen::Dynamic,
-                "ceres::DYNAMIC needs to be the same as Eigen::Dynamic.");
-
-  EuclideanManifold() : size_{Size} {
-    static_assert(
-        Size != ceres::DYNAMIC,
-        "The size is set to dynamic. Please call the constructor with a size.");
-  }
-
-  explicit EuclideanManifold(int size) : size_(size) {
-    if (Size != ceres::DYNAMIC) {
-      CHECK_EQ(Size, size)
-          << "Specified size by template parameter differs from the supplied "
-             "one.";
-    } else {
-      CHECK_GE(size_, 0)
-          << "The size of the manifold needs to be non-negative.";
-    }
-  }
-
-  int AmbientSize() const override { return size_; }
-  int TangentSize() const override { return size_; }
-
-  bool Plus(const double* x_ptr,
-            const double* delta_ptr,
-            double* x_plus_delta_ptr) const override {
-    Eigen::Map<const AmbientVector> x(x_ptr, size_);
-    Eigen::Map<const AmbientVector> delta(delta_ptr, size_);
-    Eigen::Map<AmbientVector> x_plus_delta(x_plus_delta_ptr, size_);
-    x_plus_delta = x + delta;
-    return true;
-  }
-
-  bool PlusJacobian(const double* x_ptr, double* jacobian_ptr) const override {
-    Eigen::Map<MatrixJacobian> jacobian(jacobian_ptr, size_, size_);
-    jacobian.setIdentity();
-    return true;
-  }
-
-  bool RightMultiplyByPlusJacobian(const double* x,
-                                   const int num_rows,
-                                   const double* ambient_matrix,
-                                   double* tangent_matrix) const override {
-    std::copy_n(ambient_matrix, num_rows * size_, tangent_matrix);
-    return true;
-  }
-
-  bool Minus(const double* y_ptr,
-             const double* x_ptr,
-             double* y_minus_x_ptr) const override {
-    Eigen::Map<const AmbientVector> x(x_ptr, size_);
-    Eigen::Map<const AmbientVector> y(y_ptr, size_);
-    Eigen::Map<AmbientVector> y_minus_x(y_minus_x_ptr, size_);
-    y_minus_x = y - x;
-    return true;
-  }
-
-  bool MinusJacobian(const double* x_ptr, double* jacobian_ptr) const override {
-    Eigen::Map<MatrixJacobian> jacobian(jacobian_ptr, size_, size_);
-    jacobian.setIdentity();
-    return true;
-  }
-
- private:
-  static constexpr bool IsDynamic = (Size == ceres::DYNAMIC);
-  using AmbientVector = Eigen::Matrix<double, Size, 1>;
-  using MatrixJacobian = Eigen::Matrix<double, Size, Size, Eigen::RowMajor>;
-
-  int size_{};
-};
-
-// Hold a subset of the parameters inside a parameter block constant.
-class CERES_EXPORT SubsetManifold final : public Manifold {
- public:
-  SubsetManifold(int size, const std::vector<int>& constant_parameters);
-  int AmbientSize() const override;
-  int TangentSize() const override;
-
-  bool Plus(const double* x,
-            const double* delta,
-            double* x_plus_delta) const override;
-  bool PlusJacobian(const double* x, double* jacobian) const override;
-  bool RightMultiplyByPlusJacobian(const double* x,
-                                   const int num_rows,
-                                   const double* ambient_matrix,
-                                   double* tangent_matrix) const override;
-  bool Minus(const double* y,
-             const double* x,
-             double* y_minus_x) const override;
-  bool MinusJacobian(const double* x, double* jacobian) const override;
-
- private:
-  const int tangent_size_ = 0;
-  std::vector<bool> constancy_mask_;
-};
-
-// Implements the manifold for a Hamilton quaternion as defined in
-// https://en.wikipedia.org/wiki/Quaternion. Quaternions are represented as
-// unit norm 4-vectors, i.e.
-//
-// q = [q0; q1; q2; q3], |q| = 1
-//
-// is the ambient space representation.
-//
-//   q0  scalar part.
-//   q1  coefficient of i.
-//   q2  coefficient of j.
-//   q3  coefficient of k.
-//
-// where: i*i = j*j = k*k = -1 and i*j = k, j*k = i, k*i = j.
-//
-// The tangent space is R^3, which relates to the ambient space through the
-// Plus and Minus operations defined as:
-//
-// Plus(x, delta) = [cos(|delta|); sin(|delta|) * delta / |delta|] * x
-//    Minus(y, x) = to_delta(y * x^{-1})
-//
-// where "*" is the quaternion product and because q is a unit quaternion
-// (|q|=1), q^-1 = [q0; -q1; -q2; -q3]
-//
-// and to_delta( [q0; u_{3x1}] ) = u / |u| * atan2(|u|, q0)
-class CERES_EXPORT QuaternionManifold final : public Manifold {
- public:
-  int AmbientSize() const override { return 4; }
-  int TangentSize() const override { return 3; }
-
-  bool Plus(const double* x,
-            const double* delta,
-            double* x_plus_delta) const override;
-  bool PlusJacobian(const double* x, double* jacobian) const override;
-  bool Minus(const double* y,
-             const double* x,
-             double* y_minus_x) const override;
-  bool MinusJacobian(const double* x, double* jacobian) const override;
-};
-
-// Implements the quaternion manifold for Eigen's representation of the
-// Hamilton quaternion. Geometrically it is exactly the same as the
-// QuaternionManifold defined above. However, Eigen uses a different internal
-// memory layout for the elements of the quaternion than what is commonly
-// used. It stores the quaternion in memory as [q1, q2, q3, q0] or
-// [x, y, z, w] where the real (scalar) part is last.
-//
-// Since Ceres operates on parameter blocks which are raw double pointers this
-// difference is important and requires a different manifold.
-class CERES_EXPORT EigenQuaternionManifold final : public Manifold {
- public:
-  int AmbientSize() const override { return 4; }
-  int TangentSize() const override { return 3; }
-
-  bool Plus(const double* x,
-            const double* delta,
-            double* x_plus_delta) const override;
-  bool PlusJacobian(const double* x, double* jacobian) const override;
-  bool Minus(const double* y,
-             const double* x,
-             double* y_minus_x) const override;
-  bool MinusJacobian(const double* x, double* jacobian) const override;
-};
-
-}  // namespace ceres
-
-// clang-format off
-#include "ceres/internal/reenable_warnings.h"
-// clang-format on
-
-#endif  // CERES_PUBLIC_MANIFOLD_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/manifold_test_utils.h b/3rdParty/my_ceres_vc17/include/ceres/manifold_test_utils.h
deleted file mode 100644
index 3e61457..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/manifold_test_utils.h
+++ /dev/null
@@ -1,345 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#include <cmath>
-#include <limits>
-#include <memory>
-
-#include "ceres/dynamic_numeric_diff_cost_function.h"
-#include "ceres/internal/eigen.h"
-#include "ceres/manifold.h"
-#include "ceres/numeric_diff_options.h"
-#include "ceres/types.h"
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-
-namespace ceres {
-
-// Matchers and macros to simplify testing of custom Manifold objects using the
-// gtest testing framework.
-//
-// Testing a Manifold has two parts.
-//
-// 1. Checking that Manifold::Plus() and Manifold::Minus() are correctly
-//    defined. This requires per manifold tests.
-//
-// 2. The other methods of the manifold have mathematical properties that make
-//    them compatible with Plus() and Minus(), as described in [1].
-//
-// To verify these general requirements for a custom Manifold, use the
-// EXPECT_THAT_MANIFOLD_INVARIANTS_HOLD() macro from within a gtest test. Note
-// that additional domain-specific tests may also be prudent, e.g to verify the
-// behaviour of a Quaternion Manifold about pi.
-//
-// [1] "Integrating Generic Sensor Fusion Algorithms with Sound State
-//     Representations through Encapsulation of Manifolds", C. Hertzberg,
-//     R. Wagner, U. Frese and L. Schroder, https://arxiv.org/pdf/1107.1119.pdf
-
-// Verifies the general requirements for a custom Manifold are satisfied to
-// within the specified (numerical) tolerance.
-//
-// Example usage for a custom Manifold: ExampleManifold:
-//
-//    TEST(ExampleManifold, ManifoldInvariantsHold) {
-//      constexpr double kTolerance = 1.0e-9;
-//      ExampleManifold manifold;
-//      ceres::Vector x = ceres::Vector::Zero(manifold.AmbientSize());
-//      ceres::Vector y = ceres::Vector::Zero(manifold.AmbientSize());
-//      ceres::Vector delta = ceres::Vector::Zero(manifold.TangentSize());
-//      EXPECT_THAT_MANIFOLD_INVARIANTS_HOLD(manifold, x, delta, y, kTolerance);
-//    }
-#define EXPECT_THAT_MANIFOLD_INVARIANTS_HOLD(manifold, x, delta, y, tolerance) \
-  ::ceres::Vector zero_tangent =                                               \
-      ::ceres::Vector::Zero(manifold.TangentSize());                           \
-  EXPECT_THAT(manifold, ::ceres::XPlusZeroIsXAt(x, tolerance));                \
-  EXPECT_THAT(manifold, ::ceres::XMinusXIsZeroAt(x, tolerance));               \
-  EXPECT_THAT(manifold, ::ceres::MinusPlusIsIdentityAt(x, delta, tolerance));  \
-  EXPECT_THAT(manifold,                                                        \
-              ::ceres::MinusPlusIsIdentityAt(x, zero_tangent, tolerance));     \
-  EXPECT_THAT(manifold, ::ceres::PlusMinusIsIdentityAt(x, x, tolerance));      \
-  EXPECT_THAT(manifold, ::ceres::PlusMinusIsIdentityAt(x, y, tolerance));      \
-  EXPECT_THAT(manifold, ::ceres::HasCorrectPlusJacobianAt(x, tolerance));      \
-  EXPECT_THAT(manifold, ::ceres::HasCorrectMinusJacobianAt(x, tolerance));     \
-  EXPECT_THAT(manifold, ::ceres::MinusPlusJacobianIsIdentityAt(x, tolerance)); \
-  EXPECT_THAT(manifold,                                                        \
-              ::ceres::HasCorrectRightMultiplyByPlusJacobianAt(x, tolerance));
-
-// Checks that the invariant Plus(x, 0) == x holds.
-MATCHER_P2(XPlusZeroIsXAt, x, tolerance, "") {
-  const int ambient_size = arg.AmbientSize();
-  const int tangent_size = arg.TangentSize();
-
-  Vector actual = Vector::Zero(ambient_size);
-  Vector zero = Vector::Zero(tangent_size);
-  EXPECT_TRUE(arg.Plus(x.data(), zero.data(), actual.data()));
-  const double n = (actual - Vector{x}).norm();
-  const double d = x.norm();
-  const double diffnorm = (d == 0.0) ? n : (n / d);
-  if (diffnorm > tolerance) {
-    *result_listener << "\nexpected (x): " << x.transpose()
-                     << "\nactual: " << actual.transpose()
-                     << "\ndiffnorm: " << diffnorm;
-    return false;
-  }
-  return true;
-}
-
-// Checks that the invariant Minus(x, x) == 0 holds.
-MATCHER_P2(XMinusXIsZeroAt, x, tolerance, "") {
-  const int tangent_size = arg.TangentSize();
-  Vector actual = Vector::Zero(tangent_size);
-  EXPECT_TRUE(arg.Minus(x.data(), x.data(), actual.data()));
-  const double diffnorm = actual.norm();
-  if (diffnorm > tolerance) {
-    *result_listener << "\nx: " << x.transpose()  //
-                     << "\nexpected: 0 0 0"
-                     << "\nactual: " << actual.transpose()
-                     << "\ndiffnorm: " << diffnorm;
-    return false;
-  }
-  return true;
-}
-
-// Helper struct to curry Plus(x, .) so that it can be numerically
-// differentiated.
-struct PlusFunctor {
-  PlusFunctor(const Manifold& manifold, const double* x)
-      : manifold(manifold), x(x) {}
-  bool operator()(double const* const* parameters, double* x_plus_delta) const {
-    return manifold.Plus(x, parameters[0], x_plus_delta);
-  }
-
-  const Manifold& manifold;
-  const double* x;
-};
-
-// Checks that the output of PlusJacobian matches the one obtained by
-// numerically evaluating D_2 Plus(x,0).
-MATCHER_P2(HasCorrectPlusJacobianAt, x, tolerance, "") {
-  const int ambient_size = arg.AmbientSize();
-  const int tangent_size = arg.TangentSize();
-
-  NumericDiffOptions options;
-  options.ridders_relative_initial_step_size = 1e-4;
-
-  DynamicNumericDiffCostFunction<PlusFunctor, RIDDERS> cost_function(
-      new PlusFunctor(arg, x.data()), TAKE_OWNERSHIP, options);
-  cost_function.AddParameterBlock(tangent_size);
-  cost_function.SetNumResiduals(ambient_size);
-
-  Vector zero = Vector::Zero(tangent_size);
-  double* parameters[1] = {zero.data()};
-
-  Vector x_plus_zero = Vector::Zero(ambient_size);
-  Matrix expected = Matrix::Zero(ambient_size, tangent_size);
-  double* jacobians[1] = {expected.data()};
-
-  EXPECT_TRUE(
-      cost_function.Evaluate(parameters, x_plus_zero.data(), jacobians));
-
-  Matrix actual = Matrix::Random(ambient_size, tangent_size);
-  EXPECT_TRUE(arg.PlusJacobian(x.data(), actual.data()));
-
-  const double n = (actual - expected).norm();
-  const double d = expected.norm();
-  const double diffnorm = (d == 0.0) ? n : n / d;
-  if (diffnorm > tolerance) {
-    *result_listener << "\nx: " << x.transpose() << "\nexpected: \n"
-                     << expected << "\nactual:\n"
-                     << actual << "\ndiff:\n"
-                     << expected - actual << "\ndiffnorm : " << diffnorm;
-    return false;
-  }
-  return true;
-}
-
-// Checks that the invariant Minus(Plus(x, delta), x) == delta holds.
-MATCHER_P3(MinusPlusIsIdentityAt, x, delta, tolerance, "") {
-  const int ambient_size = arg.AmbientSize();
-  const int tangent_size = arg.TangentSize();
-  Vector x_plus_delta = Vector::Zero(ambient_size);
-  EXPECT_TRUE(arg.Plus(x.data(), delta.data(), x_plus_delta.data()));
-  Vector actual = Vector::Zero(tangent_size);
-  EXPECT_TRUE(arg.Minus(x_plus_delta.data(), x.data(), actual.data()));
-
-  const double n = (actual - Vector{delta}).norm();
-  const double d = delta.norm();
-  const double diffnorm = (d == 0.0) ? n : (n / d);
-  if (diffnorm > tolerance) {
-    *result_listener << "\nx: " << x.transpose()
-                     << "\nexpected: " << delta.transpose()
-                     << "\nactual:" << actual.transpose()
-                     << "\ndiff:" << (delta - actual).transpose()
-                     << "\ndiffnorm: " << diffnorm;
-    return false;
-  }
-  return true;
-}
-
-// Checks that the invariant Plus(Minus(y, x), x) == y holds.
-MATCHER_P3(PlusMinusIsIdentityAt, x, y, tolerance, "") {
-  const int ambient_size = arg.AmbientSize();
-  const int tangent_size = arg.TangentSize();
-
-  Vector y_minus_x = Vector::Zero(tangent_size);
-  EXPECT_TRUE(arg.Minus(y.data(), x.data(), y_minus_x.data()));
-
-  Vector actual = Vector::Zero(ambient_size);
-  EXPECT_TRUE(arg.Plus(x.data(), y_minus_x.data(), actual.data()));
-
-  const double n = (actual - Vector{y}).norm();
-  const double d = y.norm();
-  const double diffnorm = (d == 0.0) ? n : (n / d);
-  if (diffnorm > tolerance) {
-    *result_listener << "\nx: " << x.transpose()
-                     << "\nexpected: " << y.transpose()
-                     << "\nactual:" << actual.transpose()
-                     << "\ndiff:" << (y - actual).transpose()
-                     << "\ndiffnorm: " << diffnorm;
-    return false;
-  }
-  return true;
-}
-
-// Helper struct to curry Minus(., x) so that it can be numerically
-// differentiated.
-struct MinusFunctor {
-  MinusFunctor(const Manifold& manifold, const double* x)
-      : manifold(manifold), x(x) {}
-  bool operator()(double const* const* parameters, double* y_minus_x) const {
-    return manifold.Minus(parameters[0], x, y_minus_x);
-  }
-
-  const Manifold& manifold;
-  const double* x;
-};
-
-// Checks that the output of MinusJacobian matches the one obtained by
-// numerically evaluating D_1 Minus(x,x).
-MATCHER_P2(HasCorrectMinusJacobianAt, x, tolerance, "") {
-  const int ambient_size = arg.AmbientSize();
-  const int tangent_size = arg.TangentSize();
-
-  Vector y = x;
-  Vector y_minus_x = Vector::Zero(tangent_size);
-
-  NumericDiffOptions options;
-  options.ridders_relative_initial_step_size = 1e-4;
-  DynamicNumericDiffCostFunction<MinusFunctor, RIDDERS> cost_function(
-      new MinusFunctor(arg, x.data()), TAKE_OWNERSHIP, options);
-  cost_function.AddParameterBlock(ambient_size);
-  cost_function.SetNumResiduals(tangent_size);
-
-  double* parameters[1] = {y.data()};
-
-  Matrix expected = Matrix::Zero(tangent_size, ambient_size);
-  double* jacobians[1] = {expected.data()};
-
-  EXPECT_TRUE(cost_function.Evaluate(parameters, y_minus_x.data(), jacobians));
-
-  Matrix actual = Matrix::Random(tangent_size, ambient_size);
-  EXPECT_TRUE(arg.MinusJacobian(x.data(), actual.data()));
-
-  const double n = (actual - expected).norm();
-  const double d = expected.norm();
-  const double diffnorm = (d == 0.0) ? n : (n / d);
-  if (diffnorm > tolerance) {
-    *result_listener << "\nx: " << x.transpose() << "\nexpected: \n"
-                     << expected << "\nactual:\n"
-                     << actual << "\ndiff:\n"
-                     << expected - actual << "\ndiffnorm: " << diffnorm;
-    return false;
-  }
-  return true;
-}
-
-// Checks that D_delta Minus(Plus(x, delta), x) at delta = 0 is an identity
-// matrix.
-MATCHER_P2(MinusPlusJacobianIsIdentityAt, x, tolerance, "") {
-  const int ambient_size = arg.AmbientSize();
-  const int tangent_size = arg.TangentSize();
-
-  Matrix plus_jacobian(ambient_size, tangent_size);
-  EXPECT_TRUE(arg.PlusJacobian(x.data(), plus_jacobian.data()));
-  Matrix minus_jacobian(tangent_size, ambient_size);
-  EXPECT_TRUE(arg.MinusJacobian(x.data(), minus_jacobian.data()));
-
-  const Matrix actual = minus_jacobian * plus_jacobian;
-  const Matrix expected = Matrix::Identity(tangent_size, tangent_size);
-
-  const double n = (actual - expected).norm();
-  const double d = expected.norm();
-  const double diffnorm = n / d;
-  if (diffnorm > tolerance) {
-    *result_listener << "\nx: " << x.transpose() << "\nexpected: \n"
-                     << expected << "\nactual:\n"
-                     << actual << "\ndiff:\n"
-                     << expected - actual << "\ndiffnorm: " << diffnorm;
-
-    return false;
-  }
-  return true;
-}
-
-// Verify that the output of RightMultiplyByPlusJacobian is ambient_matrix *
-// plus_jacobian.
-MATCHER_P2(HasCorrectRightMultiplyByPlusJacobianAt, x, tolerance, "") {
-  const int ambient_size = arg.AmbientSize();
-  const int tangent_size = arg.TangentSize();
-
-  constexpr int kMinNumRows = 0;
-  constexpr int kMaxNumRows = 3;
-  for (int num_rows = kMinNumRows; num_rows <= kMaxNumRows; ++num_rows) {
-    Matrix plus_jacobian = Matrix::Random(ambient_size, tangent_size);
-    EXPECT_TRUE(arg.PlusJacobian(x.data(), plus_jacobian.data()));
-
-    Matrix ambient_matrix = Matrix::Random(num_rows, ambient_size);
-    Matrix expected = ambient_matrix * plus_jacobian;
-
-    Matrix actual = Matrix::Random(num_rows, tangent_size);
-    EXPECT_TRUE(arg.RightMultiplyByPlusJacobian(
-        x.data(), num_rows, ambient_matrix.data(), actual.data()));
-    const double n = (actual - expected).norm();
-    const double d = expected.norm();
-    const double diffnorm = (d == 0.0) ? n : (n / d);
-    if (diffnorm > tolerance) {
-      *result_listener << "\nx: " << x.transpose() << "\nambient_matrix : \n"
-                       << ambient_matrix << "\nplus_jacobian : \n"
-                       << plus_jacobian << "\nexpected: \n"
-                       << expected << "\nactual:\n"
-                       << actual << "\ndiff:\n"
-                       << expected - actual << "\ndiffnorm : " << diffnorm;
-      return false;
-    }
-  }
-  return true;
-}
-
-}  // namespace ceres
diff --git a/3rdParty/my_ceres_vc17/include/ceres/normal_prior.h b/3rdParty/my_ceres_vc17/include/ceres/normal_prior.h
deleted file mode 100644
index 5a26e01..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/normal_prior.h
+++ /dev/null
@@ -1,78 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//
-// Cost term that implements a prior on a parameter block using a
-// normal distribution.
-
-#ifndef CERES_PUBLIC_NORMAL_PRIOR_H_
-#define CERES_PUBLIC_NORMAL_PRIOR_H_
-
-#include "ceres/cost_function.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/eigen.h"
-
-namespace ceres {
-
-// Implements a cost function of the form
-//
-//   cost(x) = ||A(x - b)||^2
-//
-// where, the matrix A and the vector b are fixed and x is the
-// variable. In case the user is interested in implementing a cost
-// function of the form
-//
-//   cost(x) = (x - mu)^T S^{-1} (x - mu)
-//
-// where, mu is a vector and S is a covariance matrix, then, A =
-// S^{-1/2}, i.e the matrix A is the square root of the inverse of the
-// covariance, also known as the stiffness matrix. There are however
-// no restrictions on the shape of A. It is free to be rectangular,
-// which would be the case if the covariance matrix S is rank
-// deficient.
-
-class CERES_EXPORT NormalPrior final : public CostFunction {
- public:
-  // Check that the number of rows in the vector b are the same as the
-  // number of columns in the matrix A, crash otherwise.
-  NormalPrior(const Matrix& A, Vector b);
-  bool Evaluate(double const* const* parameters,
-                double* residuals,
-                double** jacobians) const override;
-
- private:
-  Matrix A_;
-  Vector b_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_NORMAL_PRIOR_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_cost_function.h
deleted file mode 100644
index 00a7d53..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_cost_function.h
+++ /dev/null
@@ -1,260 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: keir@google.com (Keir Mierle)
-//         sameeragarwal@google.com (Sameer Agarwal)
-//
-// Create CostFunctions as needed by the least squares framework with jacobians
-// computed via numeric (a.k.a. finite) differentiation. For more details see
-// http://en.wikipedia.org/wiki/Numerical_differentiation.
-//
-// To get an numerically differentiated cost function, you must define
-// a class with a operator() (a functor) that computes the residuals.
-//
-// The function must write the computed value in the last argument
-// (the only non-const one) and return true to indicate success.
-// Please see cost_function.h for details on how the return value
-// maybe used to impose simple constraints on the parameter block.
-//
-// For example, consider a scalar error e = k - x'y, where both x and y are
-// two-dimensional column vector parameters, the prime sign indicates
-// transposition, and k is a constant. The form of this error, which is the
-// difference between a constant and an expression, is a common pattern in least
-// squares problems. For example, the value x'y might be the model expectation
-// for a series of measurements, where there is an instance of the cost function
-// for each measurement k.
-//
-// The actual cost added to the total problem is e^2, or (k - x'k)^2; however,
-// the squaring is implicitly done by the optimization framework.
-//
-// To write an numerically-differentiable cost function for the above model,
-// first define the object
-//
-//   class MyScalarCostFunctor {
-//     explicit MyScalarCostFunctor(double k): k_(k) {}
-//
-//     bool operator()(const double* const x,
-//                     const double* const y,
-//                     double* residuals) const {
-//       residuals[0] = k_ - x[0] * y[0] - x[1] * y[1];
-//       return true;
-//     }
-//
-//    private:
-//     double k_;
-//   };
-//
-// Note that in the declaration of operator() the input parameters x
-// and y come first, and are passed as const pointers to arrays of
-// doubles. If there were three input parameters, then the third input
-// parameter would come after y. The output is always the last
-// parameter, and is also a pointer to an array. In the example above,
-// the residual is a scalar, so only residuals[0] is set.
-//
-// Then given this class definition, the numerically differentiated
-// cost function with central differences used for computing the
-// derivative can be constructed as follows.
-//
-//   CostFunction* cost_function
-//       = new NumericDiffCostFunction<MyScalarCostFunctor, CENTRAL, 1, 2, 2>(
-//           new MyScalarCostFunctor(1.0));                    ^     ^  ^  ^
-//                                                             |     |  |  |
-//                                 Finite Differencing Scheme -+     |  |  |
-//                                 Dimension of residual ------------+  |  |
-//                                 Dimension of x ----------------------+  |
-//                                 Dimension of y -------------------------+
-//
-// In this example, there is usually an instance for each measurement of k.
-//
-// In the instantiation above, the template parameters following
-// "MyScalarCostFunctor", "1, 2, 2", describe the functor as computing
-// a 1-dimensional output from two arguments, both 2-dimensional.
-//
-// NumericDiffCostFunction also supports cost functions with a
-// runtime-determined number of residuals. For example:
-//
-// clang-format off
-//
-//   CostFunction* cost_function
-//       = new NumericDiffCostFunction<MyScalarCostFunctor, CENTRAL, DYNAMIC, 2, 2>(
-//           new CostFunctorWithDynamicNumResiduals(1.0),               ^     ^  ^
-//           TAKE_OWNERSHIP,                                            |     |  |
-//           runtime_number_of_residuals); <----+                       |     |  |
-//                                              |                       |     |  |
-//                                              |                       |     |  |
-//             Actual number of residuals ------+                       |     |  |
-//             Indicate dynamic number of residuals --------------------+     |  |
-//             Dimension of x ------------------------------------------------+  |
-//             Dimension of y ---------------------------------------------------+
-// clang-format on
-//
-//
-// The central difference method is considerably more accurate at the cost of
-// twice as many function evaluations than forward difference. Consider using
-// central differences begin with, and only after that works, trying forward
-// difference to improve performance.
-//
-// WARNING #1: A common beginner's error when first using
-// NumericDiffCostFunction is to get the sizing wrong. In particular,
-// there is a tendency to set the template parameters to (dimension of
-// residual, number of parameters) instead of passing a dimension
-// parameter for *every parameter*. In the example above, that would
-// be <MyScalarCostFunctor, 1, 2>, which is missing the last '2'
-// argument. Please be careful when setting the size parameters.
-//
-////////////////////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////////////////////
-//
-// ALTERNATE INTERFACE
-//
-// For a variety of reasons, including compatibility with legacy code,
-// NumericDiffCostFunction can also take CostFunction objects as
-// input. The following describes how.
-//
-// To get a numerically differentiated cost function, define a
-// subclass of CostFunction such that the Evaluate() function ignores
-// the jacobian parameter. The numeric differentiation wrapper will
-// fill in the jacobian parameter if necessary by repeatedly calling
-// the Evaluate() function with small changes to the appropriate
-// parameters, and computing the slope. For performance, the numeric
-// differentiation wrapper class is templated on the concrete cost
-// function, even though it could be implemented only in terms of the
-// virtual CostFunction interface.
-//
-// The numerically differentiated version of a cost function for a cost function
-// can be constructed as follows:
-//
-//   CostFunction* cost_function
-//       = new NumericDiffCostFunction<MyCostFunction, CENTRAL, 1, 4, 8>(
-//           new MyCostFunction(...), TAKE_OWNERSHIP);
-//
-// where MyCostFunction has 1 residual and 2 parameter blocks with sizes 4 and 8
-// respectively. Look at the tests for a more detailed example.
-//
-// TODO(keir): Characterize accuracy; mention pitfalls; provide alternatives.
-
-#ifndef CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_
-#define CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_
-
-#include <array>
-#include <memory>
-
-#include "Eigen/Dense"
-#include "ceres/cost_function.h"
-#include "ceres/internal/numeric_diff.h"
-#include "ceres/internal/parameter_dims.h"
-#include "ceres/numeric_diff_options.h"
-#include "ceres/sized_cost_function.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-template <typename CostFunctor,
-          NumericDiffMethodType kMethod = CENTRAL,
-          int kNumResiduals = 0,  // Number of residuals, or ceres::DYNAMIC
-          int... Ns>              // Parameters dimensions for each block.
-class NumericDiffCostFunction final
-    : public SizedCostFunction<kNumResiduals, Ns...> {
- public:
-  explicit NumericDiffCostFunction(
-      CostFunctor* functor,
-      Ownership ownership = TAKE_OWNERSHIP,
-      int num_residuals = kNumResiduals,
-      const NumericDiffOptions& options = NumericDiffOptions())
-      : functor_(functor), ownership_(ownership), options_(options) {
-    if (kNumResiduals == DYNAMIC) {
-      SizedCostFunction<kNumResiduals, Ns...>::set_num_residuals(num_residuals);
-    }
-  }
-
-  NumericDiffCostFunction(NumericDiffCostFunction&& other)
-      : functor_(std::move(other.functor_)), ownership_(other.ownership_) {}
-
-  virtual ~NumericDiffCostFunction() {
-    if (ownership_ != TAKE_OWNERSHIP) {
-      functor_.release();
-    }
-  }
-
-  bool Evaluate(double const* const* parameters,
-                double* residuals,
-                double** jacobians) const override {
-    using internal::FixedArray;
-    using internal::NumericDiff;
-
-    using ParameterDims =
-        typename SizedCostFunction<kNumResiduals, Ns...>::ParameterDims;
-
-    constexpr int kNumParameters = ParameterDims::kNumParameters;
-    constexpr int kNumParameterBlocks = ParameterDims::kNumParameterBlocks;
-
-    // Get the function value (residuals) at the the point to evaluate.
-    if (!internal::VariadicEvaluate<ParameterDims>(
-            *functor_, parameters, residuals)) {
-      return false;
-    }
-
-    if (jacobians == nullptr) {
-      return true;
-    }
-
-    // Create a copy of the parameters which will get mutated.
-    FixedArray<double> parameters_copy(kNumParameters);
-    std::array<double*, kNumParameterBlocks> parameters_reference_copy =
-        ParameterDims::GetUnpackedParameters(parameters_copy.data());
-
-    for (int block = 0; block < kNumParameterBlocks; ++block) {
-      memcpy(parameters_reference_copy[block],
-             parameters[block],
-             sizeof(double) * ParameterDims::GetDim(block));
-    }
-
-    internal::EvaluateJacobianForParameterBlocks<ParameterDims>::
-        template Apply<kMethod, kNumResiduals>(
-            functor_.get(),
-            residuals,
-            options_,
-            SizedCostFunction<kNumResiduals, Ns...>::num_residuals(),
-            parameters_reference_copy.data(),
-            jacobians);
-
-    return true;
-  }
-
-  const CostFunctor& functor() const { return *functor_; }
-
- private:
-  std::unique_ptr<CostFunctor> functor_;
-  Ownership ownership_;
-  NumericDiffOptions options_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_first_order_function.h b/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_first_order_function.h
deleted file mode 100644
index ccd420c..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_first_order_function.h
+++ /dev/null
@@ -1,216 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
-#define CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
-
-#include <algorithm>
-#include <memory>
-
-#include "ceres/first_order_function.h"
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/internal/numeric_diff.h"
-#include "ceres/internal/parameter_dims.h"
-#include "ceres/internal/variadic_evaluate.h"
-#include "ceres/numeric_diff_options.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// Creates FirstOrderFunctions as needed by the GradientProblem
-// framework, with gradients computed via numeric differentiation. For
-// more information on numeric differentiation, see the wikipedia
-// article at https://en.wikipedia.org/wiki/Numerical_differentiation
-//
-// To get an numerically differentiated cost function, you must define
-// a class with an operator() (a functor) that computes the cost.
-//
-// The function must write the computed value in the last argument
-// (the only non-const one) and return true to indicate success.
-//
-// For example, consider a scalar error e = x'y - a, where both x and y are
-// two-dimensional column vector parameters, the prime sign indicates
-// transposition, and a is a constant.
-//
-// To write an numerically-differentiable cost function for the above model,
-// first define the object
-//
-//  class QuadraticCostFunctor {
-//   public:
-//    explicit QuadraticCostFunctor(double a) : a_(a) {}
-//    bool operator()(const double* const xy, double* cost) const {
-//      constexpr int kInputVectorLength = 2;
-//      const double* const x = xy;
-//      const double* const y = xy + kInputVectorLength;
-//      *cost = x[0] * y[0] + x[1] * y[1] - a_;
-//      return true;
-//    }
-//
-//   private:
-//    double a_;
-//  };
-//
-//
-// Note that in the declaration of operator() the input parameters xy
-// come first, and are passed as const pointers to array of
-// doubles. The output cost is the last parameter.
-//
-// Then given this class definition, the numerically differentiated
-// first order function with central differences used for computing the
-// derivative can be constructed as follows.
-//
-//   FirstOrderFunction* function
-//       = new NumericDiffFirstOrderFunction<MyScalarCostFunctor, CENTRAL, 4>(
-//           new QuadraticCostFunctor(1.0));                   ^     ^     ^
-//                                                             |     |     |
-//                                 Finite Differencing Scheme -+     |     |
-//                                 Dimension of xy ------------------------+
-//
-//
-// In the instantiation above, the template parameters following
-// "QuadraticCostFunctor", "CENTRAL, 4", describe the finite
-// differencing scheme as "central differencing" and the functor as
-// computing its cost from a 4 dimensional input.
-//
-// If the size of the parameter vector is not known at compile time, then an
-// alternate construction syntax can be used:
-//
-//   FirstOrderFunction* function
-//       = new NumericDiffFirstOrderFunction<MyScalarCostFunctor, CENTRAL>(
-//           new QuadraticCostFunctor(1.0), 4);
-//
-// Note that instead of passing 4 as a template argument, it is now passed as
-// the second argument to the constructor.
-template <typename FirstOrderFunctor,
-          NumericDiffMethodType kMethod,
-          int kNumParameters = DYNAMIC>
-class NumericDiffFirstOrderFunction final : public FirstOrderFunction {
- public:
-  // Constructor for the case where the parameter size is known at compile time.
-  explicit NumericDiffFirstOrderFunction(
-      FirstOrderFunctor* functor,
-      Ownership ownership = TAKE_OWNERSHIP,
-      const NumericDiffOptions& options = NumericDiffOptions())
-      : functor_(functor),
-        num_parameters_(kNumParameters),
-        ownership_(ownership),
-        options_(options) {
-    static_assert(kNumParameters != DYNAMIC,
-                  "Number of parameters must be static when defined via the "
-                  "template parameter. Use the other constructor for "
-                  "dynamically sized functions.");
-    static_assert(kNumParameters > 0, "kNumParameters must be positive");
-  }
-
-  // Constructor for the case where the parameter size is specified at run time.
-  explicit NumericDiffFirstOrderFunction(
-      FirstOrderFunctor* functor,
-      int num_parameters,
-      Ownership ownership = TAKE_OWNERSHIP,
-      const NumericDiffOptions& options = NumericDiffOptions())
-      : functor_(functor),
-        num_parameters_(num_parameters),
-        ownership_(ownership),
-        options_(options) {
-    static_assert(
-        kNumParameters == DYNAMIC,
-        "Template parameter must be DYNAMIC when using this constructor. If "
-        "you want to provide the number of parameters statically use the other "
-        "constructor.");
-    CHECK_GT(num_parameters, 0);
-  }
-
-  ~NumericDiffFirstOrderFunction() override {
-    if (ownership_ != TAKE_OWNERSHIP) {
-      functor_.release();
-    }
-  }
-
-  bool Evaluate(const double* const parameters,
-                double* cost,
-                double* gradient) const override {
-    // Get the function value (cost) at the the point to evaluate.
-    if (!(*functor_)(parameters, cost)) {
-      return false;
-    }
-
-    if (gradient == nullptr) {
-      return true;
-    }
-
-    // Create a copy of the parameters which will get mutated.
-    internal::FixedArray<double, 32> parameters_copy(num_parameters_);
-    std::copy_n(parameters, num_parameters_, parameters_copy.data());
-    double* parameters_ptr = parameters_copy.data();
-    constexpr int kNumResiduals = 1;
-    if constexpr (kNumParameters == DYNAMIC) {
-      internal::FirstOrderFunctorAdapter<FirstOrderFunctor> fofa(*functor_);
-      return internal::NumericDiff<
-          internal::FirstOrderFunctorAdapter<FirstOrderFunctor>,
-          kMethod,
-          kNumResiduals,
-          internal::DynamicParameterDims,
-          0,
-          DYNAMIC>::EvaluateJacobianForParameterBlock(&fofa,
-                                                      cost,
-                                                      options_,
-                                                      kNumResiduals,
-                                                      0,
-                                                      num_parameters_,
-                                                      &parameters_ptr,
-                                                      gradient);
-    } else {
-      return internal::EvaluateJacobianForParameterBlocks<
-          internal::StaticParameterDims<kNumParameters>>::
-          template Apply<kMethod, 1>(functor_.get(),
-                                     cost,
-                                     options_,
-                                     kNumResiduals,
-                                     &parameters_ptr,
-                                     &gradient);
-    }
-  }
-
-  int NumParameters() const override { return num_parameters_; }
-
-  const FirstOrderFunctor& functor() const { return *functor_; }
-
- private:
-  std::unique_ptr<FirstOrderFunctor> functor_;
-  const int num_parameters_;
-  const Ownership ownership_;
-  const NumericDiffOptions options_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_options.h b/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_options.h
deleted file mode 100644
index eefb7ad..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/numeric_diff_options.h
+++ /dev/null
@@ -1,76 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: tbennun@gmail.com (Tal Ben-Nun)
-//
-
-#ifndef CERES_PUBLIC_NUMERIC_DIFF_OPTIONS_H_
-#define CERES_PUBLIC_NUMERIC_DIFF_OPTIONS_H_
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-
-namespace ceres {
-
-// Options pertaining to numeric differentiation (e.g., convergence criteria,
-// step sizes).
-struct CERES_EXPORT NumericDiffOptions {
-  // Numeric differentiation step size (multiplied by parameter block's
-  // order of magnitude). If parameters are close to zero, the step size
-  // is set to sqrt(machine_epsilon).
-  double relative_step_size = 1e-6;
-
-  // Initial step size for Ridders adaptive numeric differentiation (multiplied
-  // by parameter block's order of magnitude).
-  // If parameters are close to zero, Ridders' method sets the step size
-  // directly to this value. This parameter is separate from
-  // "relative_step_size" in order to set a different default value.
-  //
-  // Note: For Ridders' method to converge, the step size should be initialized
-  // to a value that is large enough to produce a significant change in the
-  // function. As the derivative is estimated, the step size decreases.
-  double ridders_relative_initial_step_size = 1e-2;
-
-  // Maximal number of adaptive extrapolations (sampling) in Ridders' method.
-  int max_num_ridders_extrapolations = 10;
-
-  // Convergence criterion on extrapolation error for Ridders adaptive
-  // differentiation. The available error estimation methods are defined in
-  // NumericDiffErrorType and set in the "ridders_error_method" field.
-  double ridders_epsilon = 1e-12;
-
-  // The factor in which to shrink the step size with each extrapolation in
-  // Ridders' method.
-  double ridders_step_shrink_factor = 2.0;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_NUMERIC_DIFF_OPTIONS_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/ordered_groups.h b/3rdParty/my_ceres_vc17/include/ceres/ordered_groups.h
deleted file mode 100644
index d15d22d..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/ordered_groups.h
+++ /dev/null
@@ -1,197 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_ORDERED_GROUPS_H_
-#define CERES_PUBLIC_ORDERED_GROUPS_H_
-
-#include <map>
-#include <set>
-#include <unordered_map>
-#include <vector>
-
-#include "ceres/internal/export.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// A class for storing and manipulating an ordered collection of
-// groups/sets with the following semantics:
-//
-// Group ids are non-negative integer values. Elements are any type
-// that can serve as a key in a map or an element of a set.
-//
-// An element can only belong to one group at a time. A group may
-// contain an arbitrary number of elements.
-//
-// Groups are ordered by their group id.
-template <typename T>
-class OrderedGroups {
- public:
-  // Add an element to a group. If a group with this id does not
-  // exist, one is created. This method can be called any number of
-  // times for the same element. Group ids should be non-negative
-  // numbers.
-  //
-  // Return value indicates if adding the element was a success.
-  bool AddElementToGroup(const T element, const int group) {
-    if (group < 0) {
-      return false;
-    }
-
-    auto it = element_to_group_.find(element);
-    if (it != element_to_group_.end()) {
-      if (it->second == group) {
-        // Element is already in the right group, nothing to do.
-        return true;
-      }
-
-      group_to_elements_[it->second].erase(element);
-      if (group_to_elements_[it->second].size() == 0) {
-        group_to_elements_.erase(it->second);
-      }
-    }
-
-    element_to_group_[element] = group;
-    group_to_elements_[group].insert(element);
-    return true;
-  }
-
-  void Clear() {
-    group_to_elements_.clear();
-    element_to_group_.clear();
-  }
-
-  // Remove the element, no matter what group it is in. Return value
-  // indicates if the element was actually removed.
-  bool Remove(const T element) {
-    const int current_group = GroupId(element);
-    if (current_group < 0) {
-      return false;
-    }
-
-    group_to_elements_[current_group].erase(element);
-
-    if (group_to_elements_[current_group].size() == 0) {
-      // If the group is empty, then get rid of it.
-      group_to_elements_.erase(current_group);
-    }
-
-    element_to_group_.erase(element);
-    return true;
-  }
-
-  // Bulk remove elements. The return value indicates the number of
-  // elements successfully removed.
-  int Remove(const std::vector<T>& elements) {
-    if (NumElements() == 0 || elements.size() == 0) {
-      return 0;
-    }
-
-    int num_removed = 0;
-    for (int i = 0; i < elements.size(); ++i) {
-      num_removed += Remove(elements[i]);
-    }
-    return num_removed;
-  }
-
-  // Reverse the order of the groups in place.
-  void Reverse() {
-    if (NumGroups() == 0) {
-      return;
-    }
-
-    auto it = group_to_elements_.rbegin();
-    std::map<int, std::set<T>> new_group_to_elements;
-    new_group_to_elements[it->first] = it->second;
-
-    int new_group_id = it->first + 1;
-    for (++it; it != group_to_elements_.rend(); ++it) {
-      for (const auto& element : it->second) {
-        element_to_group_[element] = new_group_id;
-      }
-      new_group_to_elements[new_group_id] = it->second;
-      new_group_id++;
-    }
-
-    group_to_elements_.swap(new_group_to_elements);
-  }
-
-  // Return the group id for the element. If the element is not a
-  // member of any group, return -1.
-  int GroupId(const T element) const {
-    auto it = element_to_group_.find(element);
-    if (it == element_to_group_.end()) {
-      return -1;
-    }
-    return it->second;
-  }
-
-  bool IsMember(const T element) const {
-    auto it = element_to_group_.find(element);
-    return (it != element_to_group_.end());
-  }
-
-  // This function always succeeds, i.e., implicitly there exists a
-  // group for every integer.
-  int GroupSize(const int group) const {
-    auto it = group_to_elements_.find(group);
-    return (it == group_to_elements_.end()) ? 0 : it->second.size();
-  }
-
-  int NumElements() const { return element_to_group_.size(); }
-
-  // Number of groups with one or more elements.
-  int NumGroups() const { return group_to_elements_.size(); }
-
-  // The first group with one or more elements. Calling this when
-  // there are no groups with non-zero elements will result in a
-  // crash.
-  int MinNonZeroGroup() const {
-    CHECK_NE(NumGroups(), 0);
-    return group_to_elements_.begin()->first;
-  }
-
-  const std::map<int, std::set<T>>& group_to_elements() const {
-    return group_to_elements_;
-  }
-
-  const std::map<T, int>& element_to_group() const { return element_to_group_; }
-
- private:
-  std::map<int, std::set<T>> group_to_elements_;
-  std::unordered_map<T, int> element_to_group_;
-};
-
-// Typedef for the most commonly used version of OrderedGroups.
-using ParameterBlockOrdering = OrderedGroups<double*>;
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_ORDERED_GROUP_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/problem.h b/3rdParty/my_ceres_vc17/include/ceres/problem.h
deleted file mode 100644
index 4c6fd1b..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/problem.h
+++ /dev/null
@@ -1,554 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//         keir@google.com (Keir Mierle)
-//
-// The Problem object is used to build and hold least squares problems.
-
-#ifndef CERES_PUBLIC_PROBLEM_H_
-#define CERES_PUBLIC_PROBLEM_H_
-
-#include <array>
-#include <cstddef>
-#include <map>
-#include <memory>
-#include <set>
-#include <vector>
-
-#include "ceres/context.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/internal/port.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-class CostFunction;
-class EvaluationCallback;
-class LossFunction;
-class Manifold;
-class Solver;
-struct CRSMatrix;
-
-namespace internal {
-class Preprocessor;
-class ProblemImpl;
-class ParameterBlock;
-class ResidualBlock;
-}  // namespace internal
-
-// A ResidualBlockId is an opaque handle clients can use to remove residual
-// blocks from a Problem after adding them.
-using ResidualBlockId = internal::ResidualBlock*;
-
-// A class to represent non-linear least squares problems. Such
-// problems have a cost function that is a sum of error terms (known
-// as "residuals"), where each residual is a function of some subset
-// of the parameters. The cost function takes the form
-//
-//    N    1
-//   SUM  --- loss( || r_i1, r_i2,..., r_ik ||^2  ),
-//   i=1   2
-//
-// where
-//
-//   r_ij is residual number i, component j; the residual is a function of some
-//        subset of the parameters x1...xk. For example, in a structure from
-//        motion problem a residual might be the difference between a measured
-//        point in an image and the reprojected position for the matching
-//        camera, point pair. The residual would have two components, error in x
-//        and error in y.
-//
-//   loss(y) is the loss function; for example, squared error or Huber L1
-//           loss. If loss(y) = y, then the cost function is non-robustified
-//           least squares.
-//
-// This class is specifically designed to address the important subset of
-// "sparse" least squares problems, where each component of the residual depends
-// only on a small number number of parameters, even though the total number of
-// residuals and parameters may be very large. This property affords tremendous
-// gains in scale, allowing efficient solving of large problems that are
-// otherwise inaccessible.
-//
-// The canonical example of a sparse least squares problem is
-// "structure-from-motion" (SFM), where the parameters are points and cameras,
-// and residuals are reprojection errors. Typically a single residual will
-// depend only on 9 parameters (3 for the point, 6 for the camera).
-//
-// To create a least squares problem, use the AddResidualBlock() and
-// AddParameterBlock() methods, documented below. Here is an example least
-// squares problem containing 3 parameter blocks of sizes 3, 4 and 5
-// respectively and two residual terms of size 2 and 6:
-//
-//   double x1[] = { 1.0, 2.0, 3.0 };
-//   double x2[] = { 1.0, 2.0, 3.0, 5.0 };
-//   double x3[] = { 1.0, 2.0, 3.0, 6.0, 7.0 };
-//
-//   Problem problem;
-//
-//   problem.AddResidualBlock(new MyUnaryCostFunction(...), nullptr, x1);
-//   problem.AddResidualBlock(new MyBinaryCostFunction(...), nullptr, x2, x3);
-//
-// Please see cost_function.h for details of the CostFunction object.
-class CERES_EXPORT Problem {
- public:
-  struct CERES_EXPORT Options {
-    // These flags control whether the Problem object owns the CostFunctions,
-    // LossFunctions, and Manifolds passed into the Problem.
-    //
-    // If set to TAKE_OWNERSHIP, then the problem object will delete the
-    // corresponding object on destruction. The destructor is careful to delete
-    // the pointers only once, since sharing objects is allowed.
-    Ownership cost_function_ownership = TAKE_OWNERSHIP;
-    Ownership loss_function_ownership = TAKE_OWNERSHIP;
-    Ownership manifold_ownership = TAKE_OWNERSHIP;
-
-    // If true, trades memory for faster RemoveResidualBlock() and
-    // RemoveParameterBlock() operations.
-    //
-    // By default, RemoveParameterBlock() and RemoveResidualBlock() take time
-    // proportional to the size of the entire problem. If you only ever remove
-    // parameters or residuals from the problem occasionally, this might be
-    // acceptable. However, if you have memory to spare, enable this option to
-    // make RemoveParameterBlock() take time proportional to the number of
-    // residual blocks that depend on it, and RemoveResidualBlock() take (on
-    // average) constant time.
-    //
-    // The increase in memory usage is two-fold: an additional hash set per
-    // parameter block containing all the residuals that depend on the parameter
-    // block; and a hash set in the problem containing all residuals.
-    bool enable_fast_removal = false;
-
-    // By default, Ceres performs a variety of safety checks when constructing
-    // the problem. There is a small but measurable performance penalty to these
-    // checks, typically around 5% of construction time. If you are sure your
-    // problem construction is correct, and 5% of the problem construction time
-    // is truly an overhead you want to avoid, then you can set
-    // disable_all_safety_checks to true.
-    //
-    // WARNING: Do not set this to true, unless you are absolutely sure of what
-    // you are doing.
-    bool disable_all_safety_checks = false;
-
-    // A Ceres global context to use for solving this problem. This may help to
-    // reduce computation time as Ceres can reuse expensive objects to create.
-    // The context object can be nullptr, in which case Ceres may create one.
-    //
-    // Ceres does NOT take ownership of the pointer.
-    Context* context = nullptr;
-
-    // Using this callback interface, Ceres can notify you when it is about to
-    // evaluate the residuals or jacobians. With the callback, you can share
-    // computation between residual blocks by doing the shared computation in
-    // EvaluationCallback::PrepareForEvaluation() before Ceres calls
-    // CostFunction::Evaluate(). It also enables caching results between a pure
-    // residual evaluation and a residual & jacobian evaluation.
-    //
-    // Problem DOES NOT take ownership of the callback.
-    //
-    // NOTE: Evaluation callbacks are incompatible with inner iterations. So
-    // calling Solve with Solver::Options::use_inner_iterations = true on a
-    // Problem with a non-null evaluation callback is an error.
-    EvaluationCallback* evaluation_callback = nullptr;
-  };
-
-  // The default constructor is equivalent to the invocation
-  // Problem(Problem::Options()).
-  Problem();
-  explicit Problem(const Options& options);
-  Problem(Problem&&);
-  Problem& operator=(Problem&&);
-
-  Problem(const Problem&) = delete;
-  Problem& operator=(const Problem&) = delete;
-
-  ~Problem();
-
-  // Add a residual block to the overall cost function. The cost function
-  // carries with its information about the sizes of the parameter blocks it
-  // expects. The function checks that these match the sizes of the parameter
-  // blocks listed in parameter_blocks. The program aborts if a mismatch is
-  // detected. loss_function can be nullptr, in which case the cost of the term
-  // is just the squared norm of the residuals.
-  //
-  // The user has the option of explicitly adding the parameter blocks using
-  // AddParameterBlock. This causes additional correctness checking; however,
-  // AddResidualBlock implicitly adds the parameter blocks if they are not
-  // present, so calling AddParameterBlock explicitly is not required.
-  //
-  // The Problem object by default takes ownership of the cost_function and
-  // loss_function pointers (See Problem::Options to override this behaviour).
-  // These objects remain live for the life of the Problem object. If the user
-  // wishes to keep control over the destruction of these objects, then they can
-  // do this by setting the corresponding enums in the Options struct.
-  //
-  // Note: Even though the Problem takes ownership of cost_function and
-  // loss_function, it does not preclude the user from re-using them in another
-  // residual block. The destructor takes care to call delete on each
-  // cost_function or loss_function pointer only once, regardless of how many
-  // residual blocks refer to them.
-  //
-  // Example usage:
-  //
-  //   double x1[] = {1.0, 2.0, 3.0};
-  //   double x2[] = {1.0, 2.0, 5.0, 6.0};
-  //   double x3[] = {3.0, 6.0, 2.0, 5.0, 1.0};
-  //
-  //   Problem problem;
-  //
-  //   problem.AddResidualBlock(new MyUnaryCostFunction(...), nullptr, x1);
-  //   problem.AddResidualBlock(new MyBinaryCostFunction(...), nullptr, x2, x1);
-  //
-  // Add a residual block by listing the parameter block pointers directly
-  // instead of wapping them in a container.
-  template <typename... Ts>
-  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
-                                   LossFunction* loss_function,
-                                   double* x0,
-                                   Ts*... xs) {
-    const std::array<double*, sizeof...(Ts) + 1> parameter_blocks{{x0, xs...}};
-    return AddResidualBlock(cost_function,
-                            loss_function,
-                            parameter_blocks.data(),
-                            static_cast<int>(parameter_blocks.size()));
-  }
-
-  // Add a residual block by providing a vector of parameter blocks.
-  ResidualBlockId AddResidualBlock(
-      CostFunction* cost_function,
-      LossFunction* loss_function,
-      const std::vector<double*>& parameter_blocks);
-
-  // Add a residual block by providing a pointer to the parameter block array
-  // and the number of parameter blocks.
-  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
-                                   LossFunction* loss_function,
-                                   double* const* const parameter_blocks,
-                                   int num_parameter_blocks);
-
-  // Add a parameter block with appropriate size to the problem. Repeated calls
-  // with the same arguments are ignored. Repeated calls with the same double
-  // pointer but a different size will result in a crash.
-  void AddParameterBlock(double* values, int size);
-
-  // Add a parameter block with appropriate size and Manifold to the
-  // problem. It is okay for manifold to be nullptr.
-  //
-  // Repeated calls with the same arguments are ignored. Repeated calls
-  // with the same double pointer but a different size results in a crash
-  // (unless Solver::Options::disable_all_safety_checks is set to true).
-  //
-  // Repeated calls with the same double pointer and size but different Manifold
-  // is equivalent to calling SetManifold(manifold), i.e., any previously
-  // associated Manifold object will be replaced with the manifold.
-  void AddParameterBlock(double* values, int size, Manifold* manifold);
-
-  // Remove a parameter block from the problem. The Manifold of the parameter
-  // block, if it exists, will persist until the deletion of the problem
-  // (similar to cost/loss functions in residual block removal). Any residual
-  // blocks that depend on the parameter are also removed, as described above
-  // in RemoveResidualBlock().
-  //
-  // If Problem::Options::enable_fast_removal is true, then the removal is fast
-  // (almost constant time). Otherwise, removing a parameter block will incur a
-  // scan of the entire Problem object.
-  //
-  // WARNING: Removing a residual or parameter block will destroy the implicit
-  // ordering, rendering the jacobian or residuals returned from the solver
-  // uninterpretable. If you depend on the evaluated jacobian, do not use
-  // remove! This may change in a future release.
-  void RemoveParameterBlock(const double* values);
-
-  // Remove a residual block from the problem. Any parameters that the residual
-  // block depends on are not removed. The cost and loss functions for the
-  // residual block will not get deleted immediately; won't happen until the
-  // problem itself is deleted.
-  //
-  // WARNING: Removing a residual or parameter block will destroy the implicit
-  // ordering, rendering the jacobian or residuals returned from the solver
-  // uninterpretable. If you depend on the evaluated jacobian, do not use
-  // remove! This may change in a future release.
-  void RemoveResidualBlock(ResidualBlockId residual_block);
-
-  // Hold the indicated parameter block constant during optimization.
-  void SetParameterBlockConstant(const double* values);
-
-  // Allow the indicated parameter block to vary during optimization.
-  void SetParameterBlockVariable(double* values);
-
-  // Returns true if a parameter block is set constant, and false otherwise. A
-  // parameter block may be set constant in two ways: either by calling
-  // SetParameterBlockConstant or by associating a Manifold with a zero
-  // dimensional tangent space with it.
-  bool IsParameterBlockConstant(const double* values) const;
-
-  // Set the Manifold for the parameter block. Calling SetManifold with nullptr
-  // will clear any previously set Manifold for the parameter block.
-  //
-  // Repeated calls will result in any previously associated Manifold object to
-  // be replaced with the manifold.
-  //
-  // The manifold is owned by the Problem by default (See Problem::Options to
-  // override this behaviour).
-  //
-  // It is acceptable to set the same Manifold for multiple parameter blocks.
-  void SetManifold(double* values, Manifold* manifold);
-
-  // Get the Manifold object associated with this parameter block.
-  //
-  // If there is no Manifold object associated then nullptr is returned.
-  const Manifold* GetManifold(const double* values) const;
-
-  // Returns true if a Manifold is associated with this parameter block, false
-  // otherwise.
-  bool HasManifold(const double* values) const;
-
-  // Set the lower/upper bound for the parameter at position "index".
-  void SetParameterLowerBound(double* values, int index, double lower_bound);
-  void SetParameterUpperBound(double* values, int index, double upper_bound);
-
-  // Get the lower/upper bound for the parameter at position "index". If the
-  // parameter is not bounded by the user, then its lower bound is
-  // -std::numeric_limits<double>::max() and upper bound is
-  // std::numeric_limits<double>::max().
-  double GetParameterLowerBound(const double* values, int index) const;
-  double GetParameterUpperBound(const double* values, int index) const;
-
-  // Number of parameter blocks in the problem. Always equals
-  // parameter_blocks().size() and parameter_block_sizes().size().
-  int NumParameterBlocks() const;
-
-  // The size of the parameter vector obtained by summing over the sizes of all
-  // the parameter blocks.
-  int NumParameters() const;
-
-  // Number of residual blocks in the problem. Always equals
-  // residual_blocks().size().
-  int NumResidualBlocks() const;
-
-  // The size of the residual vector obtained by summing over the sizes of all
-  // of the residual blocks.
-  int NumResiduals() const;
-
-  // The size of the parameter block.
-  int ParameterBlockSize(const double* values) const;
-
-  // The dimension of the tangent space of the Manifold for the parameter block.
-  // If there is no Manifold associated with this parameter block, then
-  // ParameterBlockTangentSize = ParameterBlockSize.
-  int ParameterBlockTangentSize(const double* values) const;
-
-  // Is the given parameter block present in this problem or not?
-  bool HasParameterBlock(const double* values) const;
-
-  // Fills the passed parameter_blocks vector with pointers to the parameter
-  // blocks currently in the problem. After this call, parameter_block.size() ==
-  // NumParameterBlocks.
-  void GetParameterBlocks(std::vector<double*>* parameter_blocks) const;
-
-  // Fills the passed residual_blocks vector with pointers to the residual
-  // blocks currently in the problem. After this call, residual_blocks.size() ==
-  // NumResidualBlocks.
-  void GetResidualBlocks(std::vector<ResidualBlockId>* residual_blocks) const;
-
-  // Get all the parameter blocks that depend on the given residual block.
-  void GetParameterBlocksForResidualBlock(
-      const ResidualBlockId residual_block,
-      std::vector<double*>* parameter_blocks) const;
-
-  // Get the CostFunction for the given residual block.
-  const CostFunction* GetCostFunctionForResidualBlock(
-      const ResidualBlockId residual_block) const;
-
-  // Get the LossFunction for the given residual block. Returns nullptr
-  // if no loss function is associated with this residual block.
-  const LossFunction* GetLossFunctionForResidualBlock(
-      const ResidualBlockId residual_block) const;
-
-  // Get all the residual blocks that depend on the given parameter block.
-  //
-  // If Problem::Options::enable_fast_removal is true, then getting the residual
-  // blocks is fast and depends only on the number of residual
-  // blocks. Otherwise, getting the residual blocks for a parameter block will
-  // incur a scan of the entire Problem object.
-  void GetResidualBlocksForParameterBlock(
-      const double* values,
-      std::vector<ResidualBlockId>* residual_blocks) const;
-
-  // Options struct to control Problem::Evaluate.
-  struct EvaluateOptions {
-    // The set of parameter blocks for which evaluation should be
-    // performed. This vector determines the order that parameter blocks occur
-    // in the gradient vector and in the columns of the jacobian matrix. If
-    // parameter_blocks is empty, then it is assumed to be equal to vector
-    // containing ALL the parameter blocks. Generally speaking the parameter
-    // blocks will occur in the order in which they were added to the
-    // problem. But, this may change if the user removes any parameter blocks
-    // from the problem.
-    //
-    // NOTE: This vector should contain the same pointers as the ones used to
-    // add parameter blocks to the Problem. These parameter block should NOT
-    // point to new memory locations. Bad things will happen otherwise.
-    std::vector<double*> parameter_blocks;
-
-    // The set of residual blocks to evaluate. This vector determines the order
-    // in which the residuals occur, and how the rows of the jacobian are
-    // ordered. If residual_blocks is empty, then it is assumed to be equal to
-    // the vector containing ALL the residual blocks. Generally speaking the
-    // residual blocks will occur in the order in which they were added to the
-    // problem. But, this may change if the user removes any residual blocks
-    // from the problem.
-    std::vector<ResidualBlockId> residual_blocks;
-
-    // Even though the residual blocks in the problem may contain loss
-    // functions, setting apply_loss_function to false will turn off the
-    // application of the loss function to the output of the cost function. This
-    // is of use for example if the user wishes to analyse the solution quality
-    // by studying the distribution of residuals before and after the solve.
-    bool apply_loss_function = true;
-
-    int num_threads = 1;
-  };
-
-  // Evaluate Problem. Any of the output pointers can be nullptr. Which residual
-  // blocks and parameter blocks are used is controlled by the EvaluateOptions
-  // struct above.
-  //
-  // Note 1: The evaluation will use the values stored in the memory locations
-  // pointed to by the parameter block pointers used at the time of the
-  // construction of the problem. i.e.,
-  //
-  //   Problem problem;
-  //   double x = 1;
-  //   problem.AddResidualBlock(new MyCostFunction, nullptr, &x);
-  //
-  //   double cost = 0.0;
-  //   problem.Evaluate(Problem::EvaluateOptions(), &cost,
-  //                    nullptr, nullptr, nullptr);
-  //
-  // The cost is evaluated at x = 1. If you wish to evaluate the problem at x =
-  // 2, then
-  //
-  //   x = 2;
-  //   problem.Evaluate(Problem::EvaluateOptions(), &cost,
-  //                    nullptr, nullptr, nullptr);
-  //
-  // is the way to do so.
-  //
-  // Note 2: If no Manifolds are used, then the size of the gradient vector (and
-  // the number of columns in the jacobian) is the sum of the sizes of all the
-  // parameter blocks. If a parameter block has a Manifold, then it contributes
-  // "TangentSize" entries to the gradient vector (and the number of columns in
-  // the jacobian).
-  //
-  // Note 3: This function cannot be called while the problem is being solved,
-  // for example it cannot be called from an IterationCallback at the end of an
-  // iteration during a solve.
-  //
-  // Note 4: If an EvaluationCallback is associated with the problem, then its
-  // PrepareForEvaluation method will be called every time this method is called
-  // with new_point = true.
-  bool Evaluate(const EvaluateOptions& options,
-                double* cost,
-                std::vector<double>* residuals,
-                std::vector<double>* gradient,
-                CRSMatrix* jacobian);
-
-  // Evaluates the residual block, storing the scalar cost in *cost, the
-  // residual components in *residuals, and the jacobians between the parameters
-  // and residuals in jacobians[i], in row-major order.
-  //
-  // If residuals is nullptr, the residuals are not computed.
-  //
-  // If jacobians is nullptr, no Jacobians are computed. If jacobians[i] is
-  // nullptr, then the Jacobian for that parameter block is not computed.
-  //
-  // It is not okay to request the Jacobian w.r.t a parameter block that is
-  // constant.
-  //
-  // The return value indicates the success or failure. Even if the function
-  // returns false, the caller should expect the output memory locations to have
-  // been modified.
-  //
-  // The returned cost and jacobians have had robustification and Manifold
-  // applied already; for example, the jacobian for a 4-dimensional quaternion
-  // parameter using the "QuaternionParameterization" is num_residuals by 3
-  // instead of num_residuals by 4.
-  //
-  // apply_loss_function as the name implies allows the user to switch the
-  // application of the loss function on and off.
-  //
-  // If an EvaluationCallback is associated with the problem, then its
-  // PrepareForEvaluation method will be called every time this method is called
-  // with new_point = true. This conservatively assumes that the user may have
-  // changed the parameter values since the previous call to evaluate / solve.
-  // For improved efficiency, and only if you know that the parameter values
-  // have not changed between calls, see
-  // EvaluateResidualBlockAssumingParametersUnchanged().
-  bool EvaluateResidualBlock(ResidualBlockId residual_block_id,
-                             bool apply_loss_function,
-                             double* cost,
-                             double* residuals,
-                             double** jacobians) const;
-
-  // Same as EvaluateResidualBlock except that if an EvaluationCallback is
-  // associated with the problem, then its PrepareForEvaluation method will be
-  // called every time this method is called with new_point = false.
-  //
-  // This means, if an EvaluationCallback is associated with the problem then it
-  // is the user's responsibility to call PrepareForEvaluation before calling
-  // this method if necessary, i.e. iff the parameter values have been changed
-  // since the last call to evaluate / solve.'
-  //
-  // This is because, as the name implies, we assume that the parameter blocks
-  // did not change since the last time PrepareForEvaluation was called (via
-  // Solve, Evaluate or EvaluateResidualBlock).
-  bool EvaluateResidualBlockAssumingParametersUnchanged(
-      ResidualBlockId residual_block_id,
-      bool apply_loss_function,
-      double* cost,
-      double* residuals,
-      double** jacobians) const;
-
-  // Returns reference to the options with which the Problem was constructed.
-  const Options& options() const;
-
-  // Returns pointer to Problem implementation
-  internal::ProblemImpl* mutable_impl();
-
- private:
-  std::unique_ptr<internal::ProblemImpl> impl_;
-};
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_PROBLEM_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/product_manifold.h b/3rdParty/my_ceres_vc17/include/ceres/product_manifold.h
deleted file mode 100644
index ed2d1f4..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/product_manifold.h
+++ /dev/null
@@ -1,319 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//         sergiu.deitsch@gmail.com (Sergiu Deitsch)
-//
-
-#ifndef CERES_PUBLIC_PRODUCT_MANIFOLD_H_
-#define CERES_PUBLIC_PRODUCT_MANIFOLD_H_
-
-#include <algorithm>
-#include <array>
-#include <cassert>
-#include <cstddef>
-#include <numeric>
-#include <tuple>
-#include <type_traits>
-#include <utility>
-
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/fixed_array.h"
-#include "ceres/internal/port.h"
-#include "ceres/manifold.h"
-
-namespace ceres {
-
-// Construct a manifold by taking the Cartesian product of a number of other
-// manifolds. This is useful, when a parameter block is the Cartesian product
-// of two or more manifolds. For example the parameters of a camera consist of
-// a rotation and a translation, i.e., SO(3) x R^3.
-//
-// Example usage:
-//
-// ProductManifold<QuaternionManifold, EuclideanManifold<3>> se3;
-//
-// is the manifold for a rigid transformation, where the rotation is
-// represented using a quaternion.
-//
-// Manifolds can be copied and moved to ProductManifold:
-//
-// SubsetManifold manifold1(5, {2});
-// SubsetManifold manifold2(3, {0, 1});
-// ProductManifold<SubsetManifold, SubsetManifold> manifold(manifold1,
-//                                                          manifold2);
-//
-// In advanced use cases, manifolds can be dynamically allocated and passed as
-// (smart) pointers:
-//
-// ProductManifold<std::unique_ptr<QuaternionManifold>, EuclideanManifold<3>>
-//     se3{std::make_unique<QuaternionManifold>(), EuclideanManifold<3>{}};
-//
-// In C++17, the template parameters can be left out as they are automatically
-// deduced making the initialization much simpler:
-//
-// ProductManifold se3{QuaternionManifold{}, EuclideanManifold<3>{}};
-//
-// The manifold implementations must be either default constructible, copyable
-// or moveable to be usable in a ProductManifold.
-template <typename Manifold0, typename Manifold1, typename... ManifoldN>
-class ProductManifold final : public Manifold {
- public:
-  // ProductManifold constructor perfect forwards arguments to store manifolds.
-  //
-  // Either use default construction or if you need to copy or move-construct a
-  // manifold instance, you need to pass an instance as an argument for all
-  // types given as class template parameters.
-  template <typename... Args,
-            std::enable_if_t<std::is_constructible<
-                std::tuple<Manifold0, Manifold1, ManifoldN...>,
-                Args...>::value>* = nullptr>
-  explicit ProductManifold(Args&&... manifolds)
-      : ProductManifold{std::make_index_sequence<kNumManifolds>{},
-                        std::forward<Args>(manifolds)...} {}
-
-  int AmbientSize() const override { return ambient_size_; }
-  int TangentSize() const override { return tangent_size_; }
-
-  bool Plus(const double* x,
-            const double* delta,
-            double* x_plus_delta) const override {
-    return PlusImpl(
-        x, delta, x_plus_delta, std::make_index_sequence<kNumManifolds>{});
-  }
-
-  bool Minus(const double* y,
-             const double* x,
-             double* y_minus_x) const override {
-    return MinusImpl(
-        y, x, y_minus_x, std::make_index_sequence<kNumManifolds>{});
-  }
-
-  bool PlusJacobian(const double* x, double* jacobian_ptr) const override {
-    MatrixRef jacobian(jacobian_ptr, AmbientSize(), TangentSize());
-    jacobian.setZero();
-    internal::FixedArray<double> buffer(buffer_size_);
-
-    return PlusJacobianImpl(
-        x, jacobian, buffer, std::make_index_sequence<kNumManifolds>{});
-  }
-
-  bool MinusJacobian(const double* x, double* jacobian_ptr) const override {
-    MatrixRef jacobian(jacobian_ptr, TangentSize(), AmbientSize());
-    jacobian.setZero();
-    internal::FixedArray<double> buffer(buffer_size_);
-
-    return MinusJacobianImpl(
-        x, jacobian, buffer, std::make_index_sequence<kNumManifolds>{});
-  }
-
- private:
-  static constexpr std::size_t kNumManifolds = 2 + sizeof...(ManifoldN);
-
-  template <std::size_t... Indices, typename... Args>
-  explicit ProductManifold(std::index_sequence<Indices...>, Args&&... manifolds)
-      : manifolds_{std::forward<Args>(manifolds)...},
-        buffer_size_{(std::max)(
-            {(Dereference(std::get<Indices>(manifolds_)).TangentSize() *
-              Dereference(std::get<Indices>(manifolds_)).AmbientSize())...})},
-        ambient_sizes_{
-            Dereference(std::get<Indices>(manifolds_)).AmbientSize()...},
-        tangent_sizes_{
-            Dereference(std::get<Indices>(manifolds_)).TangentSize()...},
-        ambient_offsets_{ExclusiveScan(ambient_sizes_)},
-        tangent_offsets_{ExclusiveScan(tangent_sizes_)},
-        ambient_size_{
-            std::accumulate(ambient_sizes_.begin(), ambient_sizes_.end(), 0)},
-        tangent_size_{
-            std::accumulate(tangent_sizes_.begin(), tangent_sizes_.end(), 0)} {}
-
-  template <std::size_t Index0, std::size_t... Indices>
-  bool PlusImpl(const double* x,
-                const double* delta,
-                double* x_plus_delta,
-                std::index_sequence<Index0, Indices...>) const {
-    if (!Dereference(std::get<Index0>(manifolds_))
-             .Plus(x + ambient_offsets_[Index0],
-                   delta + tangent_offsets_[Index0],
-                   x_plus_delta + ambient_offsets_[Index0])) {
-      return false;
-    }
-
-    return PlusImpl(x, delta, x_plus_delta, std::index_sequence<Indices...>{});
-  }
-
-  static constexpr bool PlusImpl(const double* /*x*/,
-                                 const double* /*delta*/,
-                                 double* /*x_plus_delta*/,
-                                 std::index_sequence<>) noexcept {
-    return true;
-  }
-
-  template <std::size_t Index0, std::size_t... Indices>
-  bool MinusImpl(const double* y,
-                 const double* x,
-                 double* y_minus_x,
-                 std::index_sequence<Index0, Indices...>) const {
-    if (!Dereference(std::get<Index0>(manifolds_))
-             .Minus(y + ambient_offsets_[Index0],
-                    x + ambient_offsets_[Index0],
-                    y_minus_x + tangent_offsets_[Index0])) {
-      return false;
-    }
-
-    return MinusImpl(y, x, y_minus_x, std::index_sequence<Indices...>{});
-  }
-
-  static constexpr bool MinusImpl(const double* /*y*/,
-                                  const double* /*x*/,
-                                  double* /*y_minus_x*/,
-                                  std::index_sequence<>) noexcept {
-    return true;
-  }
-
-  template <std::size_t Index0, std::size_t... Indices>
-  bool PlusJacobianImpl(const double* x,
-                        MatrixRef& jacobian,
-                        internal::FixedArray<double>& buffer,
-                        std::index_sequence<Index0, Indices...>) const {
-    if (!Dereference(std::get<Index0>(manifolds_))
-             .PlusJacobian(x + ambient_offsets_[Index0], buffer.data())) {
-      return false;
-    }
-
-    jacobian.block(ambient_offsets_[Index0],
-                   tangent_offsets_[Index0],
-                   ambient_sizes_[Index0],
-                   tangent_sizes_[Index0]) =
-        MatrixRef(
-            buffer.data(), ambient_sizes_[Index0], tangent_sizes_[Index0]);
-
-    return PlusJacobianImpl(
-        x, jacobian, buffer, std::index_sequence<Indices...>{});
-  }
-
-  static constexpr bool PlusJacobianImpl(
-      const double* /*x*/,
-      MatrixRef& /*jacobian*/,
-      internal::FixedArray<double>& /*buffer*/,
-      std::index_sequence<>) noexcept {
-    return true;
-  }
-
-  template <std::size_t Index0, std::size_t... Indices>
-  bool MinusJacobianImpl(const double* x,
-                         MatrixRef& jacobian,
-                         internal::FixedArray<double>& buffer,
-                         std::index_sequence<Index0, Indices...>) const {
-    if (!Dereference(std::get<Index0>(manifolds_))
-             .MinusJacobian(x + ambient_offsets_[Index0], buffer.data())) {
-      return false;
-    }
-
-    jacobian.block(tangent_offsets_[Index0],
-                   ambient_offsets_[Index0],
-                   tangent_sizes_[Index0],
-                   ambient_sizes_[Index0]) =
-        MatrixRef(
-            buffer.data(), tangent_sizes_[Index0], ambient_sizes_[Index0]);
-
-    return MinusJacobianImpl(
-        x, jacobian, buffer, std::index_sequence<Indices...>{});
-  }
-
-  static constexpr bool MinusJacobianImpl(
-      const double* /*x*/,
-      MatrixRef& /*jacobian*/,
-      internal::FixedArray<double>& /*buffer*/,
-      std::index_sequence<>) noexcept {
-    return true;
-  }
-
-  template <typename T, std::size_t N>
-  static std::array<T, N> ExclusiveScan(const std::array<T, N>& values) {
-    std::array<T, N> result;
-    // TODO Replace with std::exclusive_scan once all platforms have full C++17
-    // STL support.
-    T init = 0;
-    for (std::size_t i = 0; i != N; ++i) {
-      result[i] = init;
-      init += values[i];
-    }
-    return result;
-  }
-
-  template <typename T, typename E = void>
-  struct IsDereferenceable : std::false_type {};
-
-  template <typename T>
-  struct IsDereferenceable<T, std::void_t<decltype(*std::declval<T>())>>
-      : std::true_type {};
-
-  template <typename T,
-            std::enable_if_t<!IsDereferenceable<T>::value>* = nullptr>
-  static constexpr decltype(auto) Dereference(T& value) {
-    return value;
-  }
-
-  // Support dereferenceable types such as std::unique_ptr, std::shared_ptr, raw
-  // pointers etc.
-  template <typename T,
-            std::enable_if_t<IsDereferenceable<T>::value>* = nullptr>
-  static constexpr decltype(auto) Dereference(T& value) {
-    return *value;
-  }
-
-  template <typename T>
-  static constexpr decltype(auto) Dereference(T* p) {
-    assert(p != nullptr);
-    return *p;
-  }
-
-  std::tuple<Manifold0, Manifold1, ManifoldN...> manifolds_;
-  int buffer_size_;
-  std::array<int, kNumManifolds> ambient_sizes_;
-  std::array<int, kNumManifolds> tangent_sizes_;
-  std::array<int, kNumManifolds> ambient_offsets_;
-  std::array<int, kNumManifolds> tangent_offsets_;
-  int ambient_size_;
-  int tangent_size_;
-};
-
-// C++17 deduction guide that allows the user to avoid explicitly specifying
-// the template parameters of ProductManifold. The class can instead be
-// instantiated as follows:
-//
-//   ProductManifold manifold{QuaternionManifold{}, EuclideanManifold<3>{}};
-//
-template <typename Manifold0, typename Manifold1, typename... Manifolds>
-ProductManifold(Manifold0&&, Manifold1&&, Manifolds&&...)
-    -> ProductManifold<Manifold0, Manifold1, Manifolds...>;
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_PRODUCT_MANIFOLD_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/rotation.h b/3rdParty/my_ceres_vc17/include/ceres/rotation.h
deleted file mode 100644
index 0cccfa7..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/rotation.h
+++ /dev/null
@@ -1,861 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: keir@google.com (Keir Mierle)
-//         sameeragarwal@google.com (Sameer Agarwal)
-//
-// Templated functions for manipulating rotations. The templated
-// functions are useful when implementing functors for automatic
-// differentiation.
-//
-// In the following, the Quaternions are laid out as 4-vectors, thus:
-//
-//   q[0]  scalar part.
-//   q[1]  coefficient of i.
-//   q[2]  coefficient of j.
-//   q[3]  coefficient of k.
-//
-// where: i*i = j*j = k*k = -1 and i*j = k, j*k = i, k*i = j.
-
-#ifndef CERES_PUBLIC_ROTATION_H_
-#define CERES_PUBLIC_ROTATION_H_
-
-#include <algorithm>
-#include <cmath>
-
-#include "ceres/constants.h"
-#include "ceres/internal/euler_angles.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// Trivial wrapper to index linear arrays as matrices, given a fixed
-// column and row stride. When an array "T* array" is wrapped by a
-//
-//   (const) MatrixAdapter<T, row_stride, col_stride> M"
-//
-// the expression  M(i, j) is equivalent to
-//
-//   array[i * row_stride + j * col_stride]
-//
-// Conversion functions to and from rotation matrices accept
-// MatrixAdapters to permit using row-major and column-major layouts,
-// and rotation matrices embedded in larger matrices (such as a 3x4
-// projection matrix).
-template <typename T, int row_stride, int col_stride>
-struct MatrixAdapter;
-
-// Convenience functions to create a MatrixAdapter that treats the
-// array pointed to by "pointer" as a 3x3 (contiguous) column-major or
-// row-major matrix.
-template <typename T>
-MatrixAdapter<T, 1, 3> ColumnMajorAdapter3x3(T* pointer);
-
-template <typename T>
-MatrixAdapter<T, 3, 1> RowMajorAdapter3x3(T* pointer);
-
-// Convert a value in combined axis-angle representation to a quaternion.
-// The value angle_axis is a triple whose norm is an angle in radians,
-// and whose direction is aligned with the axis of rotation,
-// and quaternion is a 4-tuple that will contain the resulting quaternion.
-// The implementation may be used with auto-differentiation up to the first
-// derivative, higher derivatives may have unexpected results near the origin.
-template <typename T>
-void AngleAxisToQuaternion(const T* angle_axis, T* quaternion);
-
-// Convert a quaternion to the equivalent combined axis-angle representation.
-// The value quaternion must be a unit quaternion - it is not normalized first,
-// and angle_axis will be filled with a value whose norm is the angle of
-// rotation in radians, and whose direction is the axis of rotation.
-// The implementation may be used with auto-differentiation up to the first
-// derivative, higher derivatives may have unexpected results near the origin.
-template <typename T>
-void QuaternionToAngleAxis(const T* quaternion, T* angle_axis);
-
-// Conversions between 3x3 rotation matrix (in column major order) and
-// quaternion rotation representations. Templated for use with
-// autodifferentiation.
-template <typename T>
-void RotationMatrixToQuaternion(const T* R, T* quaternion);
-
-template <typename T, int row_stride, int col_stride>
-void RotationMatrixToQuaternion(
-    const MatrixAdapter<const T, row_stride, col_stride>& R, T* quaternion);
-
-// Conversions between 3x3 rotation matrix (in column major order) and
-// axis-angle rotation representations. Templated for use with
-// autodifferentiation.
-template <typename T>
-void RotationMatrixToAngleAxis(const T* R, T* angle_axis);
-
-template <typename T, int row_stride, int col_stride>
-void RotationMatrixToAngleAxis(
-    const MatrixAdapter<const T, row_stride, col_stride>& R, T* angle_axis);
-
-template <typename T>
-void AngleAxisToRotationMatrix(const T* angle_axis, T* R);
-
-template <typename T, int row_stride, int col_stride>
-void AngleAxisToRotationMatrix(
-    const T* angle_axis, const MatrixAdapter<T, row_stride, col_stride>& R);
-
-// Conversions between 3x3 rotation matrix (in row major order) and
-// Euler angle (in degrees) rotation representations.
-//
-// The {pitch,roll,yaw} Euler angles are rotations around the {x,y,z}
-// axes, respectively.  They are applied in that same order, so the
-// total rotation R is Rz * Ry * Rx.
-template <typename T>
-void EulerAnglesToRotationMatrix(const T* euler, int row_stride, T* R);
-
-template <typename T, int row_stride, int col_stride>
-void EulerAnglesToRotationMatrix(
-    const T* euler, const MatrixAdapter<T, row_stride, col_stride>& R);
-
-// Convert a generic Euler Angle sequence (in radians) to a 3x3 rotation matrix.
-//
-// Euler Angles define a sequence of 3 rotations about a sequence of axes,
-// typically taken to be the X, Y, or Z axes. The last axis may be the same as
-// the first axis (e.g. ZYZ) per Euler's original definition of his angles
-// (proper Euler angles) or not (e.g. ZYX / yaw-pitch-roll), per common usage in
-// the nautical and aerospace fields (Tait-Bryan angles). The three rotations
-// may be in a global frame of reference (Extrinsic) or in a body fixed frame of
-// reference (Intrinsic) that moves with the rotating object.
-//
-// Internally, Euler Axis sequences are classified by Ken Shoemake's scheme from
-// "Euler angle conversion", Graphics Gems IV, where a choice of axis for the
-// first rotation and 3 binary choices:
-// 1. Parity of the axis permutation. The axis sequence has Even parity if the
-// second axis of rotation is 'greater-than' the first axis of rotation
-// according to the order X<Y<Z<X, otherwise it has Odd parity.
-// 2. Proper Euler Angles v.s. Tait-Bryan Angles
-// 3. Extrinsic Rotations v.s. Intrinsic Rotations
-// compactly represent all 24 possible Euler Angle Conventions
-//
-// One template parameter: EulerSystem must be explicitly given. This parameter
-// is a tag named by 'Extrinsic' or 'Intrinsic' followed by three characters in
-// the set '[XYZ]', specifying the axis sequence, e.g. ceres::ExtrinsicYZY
-// (robotic arms), ceres::IntrinsicZYX (for aerospace), etc.
-//
-// The order of elements in the input array 'euler' follows the axis sequence
-template <typename EulerSystem, typename T>
-inline void EulerAnglesToRotation(const T* euler, T* R);
-
-template <typename EulerSystem, typename T, int row_stride, int col_stride>
-void EulerAnglesToRotation(const T* euler,
-                           const MatrixAdapter<T, row_stride, col_stride>& R);
-
-// Convert a 3x3 rotation matrix to a generic Euler Angle sequence (in radians)
-//
-// Euler Angles define a sequence of 3 rotations about a sequence of axes,
-// typically taken to be the X, Y, or Z axes. The last axis may be the same as
-// the first axis (e.g. ZYZ) per Euler's original definition of his angles
-// (proper Euler angles) or not (e.g. ZYX / yaw-pitch-roll), per common usage in
-// the nautical and aerospace fields (Tait-Bryan angles). The three rotations
-// may be in a global frame of reference (Extrinsic) or in a body fixed frame of
-// reference (Intrinsic) that moves with the rotating object.
-//
-// Internally, Euler Axis sequences are classified by Ken Shoemake's scheme from
-// "Euler angle conversion", Graphics Gems IV, where a choice of axis for the
-// first rotation and 3 binary choices:
-// 1. Oddness of the axis permutation, that defines whether the second axis is
-// 'greater-than' the first axis according to the order X>Y>Z>X)
-// 2. Proper Euler Angles v.s. Tait-Bryan Angles
-// 3. Extrinsic Rotations v.s. Intrinsic Rotations
-// compactly represent all 24 possible Euler Angle Conventions
-//
-// One template parameter: EulerSystem must be explicitly given. This parameter
-// is a tag named by 'Extrinsic' or 'Intrinsic' followed by three characters in
-// the set '[XYZ]', specifying the axis sequence, e.g. ceres::ExtrinsicYZY
-// (robotic arms), ceres::IntrinsicZYX (for aerospace), etc.
-//
-// The order of elements in the output array 'euler' follows the axis sequence
-template <typename EulerSystem, typename T>
-inline void RotationMatrixToEulerAngles(const T* R, T* euler);
-
-template <typename EulerSystem, typename T, int row_stride, int col_stride>
-void RotationMatrixToEulerAngles(
-    const MatrixAdapter<const T, row_stride, col_stride>& R, T* euler);
-
-// Convert a 4-vector to a 3x3 scaled rotation matrix.
-//
-// The choice of rotation is such that the quaternion [1 0 0 0] goes to an
-// identity matrix and for small a, b, c the quaternion [1 a b c] goes to
-// the matrix
-//
-//         [  0 -c  b ]
-//   I + 2 [  c  0 -a ] + higher order terms
-//         [ -b  a  0 ]
-//
-// which corresponds to a Rodrigues approximation, the last matrix being
-// the cross-product matrix of [a b c]. Together with the property that
-// R(q1 * q2) = R(q1) * R(q2) this uniquely defines the mapping from q to R.
-//
-// No normalization of the quaternion is performed, i.e.
-// R = ||q||^2 * Q, where Q is an orthonormal matrix
-// such that det(Q) = 1 and Q*Q' = I
-//
-// WARNING: The rotation matrix is ROW MAJOR
-template <typename T>
-inline void QuaternionToScaledRotation(const T q[4], T R[3 * 3]);
-
-template <typename T, int row_stride, int col_stride>
-inline void QuaternionToScaledRotation(
-    const T q[4], const MatrixAdapter<T, row_stride, col_stride>& R);
-
-// Same as above except that the rotation matrix is normalized by the
-// Frobenius norm, so that R * R' = I (and det(R) = 1).
-//
-// WARNING: The rotation matrix is ROW MAJOR
-template <typename T>
-inline void QuaternionToRotation(const T q[4], T R[3 * 3]);
-
-template <typename T, int row_stride, int col_stride>
-inline void QuaternionToRotation(
-    const T q[4], const MatrixAdapter<T, row_stride, col_stride>& R);
-
-// Rotates a point pt by a quaternion q:
-//
-//   result = R(q) * pt
-//
-// Assumes the quaternion is unit norm. This assumption allows us to
-// write the transform as (something)*pt + pt, as is clear from the
-// formula below. If you pass in a quaternion with |q|^2 = 2 then you
-// WILL NOT get back 2 times the result you get for a unit quaternion.
-//
-// Inplace rotation is not supported. pt and result must point to different
-// memory locations, otherwise the result will be undefined.
-template <typename T>
-inline void UnitQuaternionRotatePoint(const T q[4], const T pt[3], T result[3]);
-
-// With this function you do not need to assume that q has unit norm.
-// It does assume that the norm is non-zero.
-//
-// Inplace rotation is not supported. pt and result must point to different
-// memory locations, otherwise the result will be undefined.
-template <typename T>
-inline void QuaternionRotatePoint(const T q[4], const T pt[3], T result[3]);
-
-// zw = z * w, where * is the Quaternion product between 4 vectors.
-//
-// Inplace quaternion product is not supported. The resulting quaternion zw must
-// not share the memory with the input quaternion z and w, otherwise the result
-// will be undefined.
-template <typename T>
-inline void QuaternionProduct(const T z[4], const T w[4], T zw[4]);
-
-// xy = x cross y;
-//
-// Inplace cross product is not supported. The resulting vector x_cross_y must
-// not share the memory with the input vectors x and y, otherwise the result
-// will be undefined.
-template <typename T>
-inline void CrossProduct(const T x[3], const T y[3], T x_cross_y[3]);
-
-template <typename T>
-inline T DotProduct(const T x[3], const T y[3]);
-
-// y = R(angle_axis) * x;
-//
-// Inplace rotation is not supported. pt and result must point to different
-// memory locations, otherwise the result will be undefined.
-template <typename T>
-inline void AngleAxisRotatePoint(const T angle_axis[3],
-                                 const T pt[3],
-                                 T result[3]);
-
-// --- IMPLEMENTATION
-
-template <typename T, int row_stride, int col_stride>
-struct MatrixAdapter {
-  T* pointer_;
-  explicit MatrixAdapter(T* pointer) : pointer_(pointer) {}
-
-  T& operator()(int r, int c) const {
-    return pointer_[r * row_stride + c * col_stride];
-  }
-};
-
-template <typename T>
-MatrixAdapter<T, 1, 3> ColumnMajorAdapter3x3(T* pointer) {
-  return MatrixAdapter<T, 1, 3>(pointer);
-}
-
-template <typename T>
-MatrixAdapter<T, 3, 1> RowMajorAdapter3x3(T* pointer) {
-  return MatrixAdapter<T, 3, 1>(pointer);
-}
-
-template <typename T>
-inline void AngleAxisToQuaternion(const T* angle_axis, T* quaternion) {
-  using std::fpclassify;
-  using std::hypot;
-  const T& a0 = angle_axis[0];
-  const T& a1 = angle_axis[1];
-  const T& a2 = angle_axis[2];
-  const T theta = hypot(a0, a1, a2);
-
-  // For points not at the origin, the full conversion is numerically stable.
-  if (fpclassify(theta) != FP_ZERO) {
-    const T half_theta = theta * T(0.5);
-    const T k = sin(half_theta) / theta;
-    quaternion[0] = cos(half_theta);
-    quaternion[1] = a0 * k;
-    quaternion[2] = a1 * k;
-    quaternion[3] = a2 * k;
-  } else {
-    // At the origin, sqrt() will produce NaN in the derivative since
-    // the argument is zero.  By approximating with a Taylor series,
-    // and truncating at one term, the value and first derivatives will be
-    // computed correctly when Jets are used.
-    const T k(0.5);
-    quaternion[0] = T(1.0);
-    quaternion[1] = a0 * k;
-    quaternion[2] = a1 * k;
-    quaternion[3] = a2 * k;
-  }
-}
-
-template <typename T>
-inline void QuaternionToAngleAxis(const T* quaternion, T* angle_axis) {
-  using std::fpclassify;
-  using std::hypot;
-  const T& q1 = quaternion[1];
-  const T& q2 = quaternion[2];
-  const T& q3 = quaternion[3];
-  const T sin_theta = hypot(q1, q2, q3);
-
-  // For quaternions representing non-zero rotation, the conversion
-  // is numerically stable.
-  if (fpclassify(sin_theta) != FP_ZERO) {
-    const T& cos_theta = quaternion[0];
-
-    // If cos_theta is negative, theta is greater than pi/2, which
-    // means that angle for the angle_axis vector which is 2 * theta
-    // would be greater than pi.
-    //
-    // While this will result in the correct rotation, it does not
-    // result in a normalized angle-axis vector.
-    //
-    // In that case we observe that 2 * theta ~ 2 * theta - 2 * pi,
-    // which is equivalent saying
-    //
-    //   theta - pi = atan(sin(theta - pi), cos(theta - pi))
-    //              = atan(-sin(theta), -cos(theta))
-    //
-    const T two_theta =
-        T(2.0) * ((cos_theta < T(0.0)) ? atan2(-sin_theta, -cos_theta)
-                                       : atan2(sin_theta, cos_theta));
-    const T k = two_theta / sin_theta;
-    angle_axis[0] = q1 * k;
-    angle_axis[1] = q2 * k;
-    angle_axis[2] = q3 * k;
-  } else {
-    // For zero rotation, sqrt() will produce NaN in the derivative since
-    // the argument is zero.  By approximating with a Taylor series,
-    // and truncating at one term, the value and first derivatives will be
-    // computed correctly when Jets are used.
-    const T k(2.0);
-    angle_axis[0] = q1 * k;
-    angle_axis[1] = q2 * k;
-    angle_axis[2] = q3 * k;
-  }
-}
-
-template <typename T>
-void RotationMatrixToQuaternion(const T* R, T* quaternion) {
-  RotationMatrixToQuaternion(ColumnMajorAdapter3x3(R), quaternion);
-}
-
-// This algorithm comes from "Quaternion Calculus and Fast Animation",
-// Ken Shoemake, 1987 SIGGRAPH course notes
-template <typename T, int row_stride, int col_stride>
-void RotationMatrixToQuaternion(
-    const MatrixAdapter<const T, row_stride, col_stride>& R, T* quaternion) {
-  const T trace = R(0, 0) + R(1, 1) + R(2, 2);
-  if (trace >= 0.0) {
-    T t = sqrt(trace + T(1.0));
-    quaternion[0] = T(0.5) * t;
-    t = T(0.5) / t;
-    quaternion[1] = (R(2, 1) - R(1, 2)) * t;
-    quaternion[2] = (R(0, 2) - R(2, 0)) * t;
-    quaternion[3] = (R(1, 0) - R(0, 1)) * t;
-  } else {
-    int i = 0;
-    if (R(1, 1) > R(0, 0)) {
-      i = 1;
-    }
-
-    if (R(2, 2) > R(i, i)) {
-      i = 2;
-    }
-
-    const int j = (i + 1) % 3;
-    const int k = (j + 1) % 3;
-    T t = sqrt(R(i, i) - R(j, j) - R(k, k) + T(1.0));
-    quaternion[i + 1] = T(0.5) * t;
-    t = T(0.5) / t;
-    quaternion[0] = (R(k, j) - R(j, k)) * t;
-    quaternion[j + 1] = (R(j, i) + R(i, j)) * t;
-    quaternion[k + 1] = (R(k, i) + R(i, k)) * t;
-  }
-}
-
-// The conversion of a rotation matrix to the angle-axis form is
-// numerically problematic when then rotation angle is close to zero
-// or to Pi. The following implementation detects when these two cases
-// occurs and deals with them by taking code paths that are guaranteed
-// to not perform division by a small number.
-template <typename T>
-inline void RotationMatrixToAngleAxis(const T* R, T* angle_axis) {
-  RotationMatrixToAngleAxis(ColumnMajorAdapter3x3(R), angle_axis);
-}
-
-template <typename T, int row_stride, int col_stride>
-void RotationMatrixToAngleAxis(
-    const MatrixAdapter<const T, row_stride, col_stride>& R, T* angle_axis) {
-  T quaternion[4];
-  RotationMatrixToQuaternion(R, quaternion);
-  QuaternionToAngleAxis(quaternion, angle_axis);
-  return;
-}
-
-template <typename T>
-inline void AngleAxisToRotationMatrix(const T* angle_axis, T* R) {
-  AngleAxisToRotationMatrix(angle_axis, ColumnMajorAdapter3x3(R));
-}
-
-template <typename T, int row_stride, int col_stride>
-void AngleAxisToRotationMatrix(
-    const T* angle_axis, const MatrixAdapter<T, row_stride, col_stride>& R) {
-  using std::fpclassify;
-  using std::hypot;
-  static const T kOne = T(1.0);
-  const T theta = hypot(angle_axis[0], angle_axis[1], angle_axis[2]);
-  if (fpclassify(theta) != FP_ZERO) {
-    // We want to be careful to only evaluate the square root if the
-    // norm of the angle_axis vector is greater than zero. Otherwise
-    // we get a division by zero.
-    const T wx = angle_axis[0] / theta;
-    const T wy = angle_axis[1] / theta;
-    const T wz = angle_axis[2] / theta;
-
-    const T costheta = cos(theta);
-    const T sintheta = sin(theta);
-
-    // clang-format off
-    R(0, 0) =     costheta   + wx*wx*(kOne -    costheta);
-    R(1, 0) =  wz*sintheta   + wx*wy*(kOne -    costheta);
-    R(2, 0) = -wy*sintheta   + wx*wz*(kOne -    costheta);
-    R(0, 1) =  wx*wy*(kOne - costheta)     - wz*sintheta;
-    R(1, 1) =     costheta   + wy*wy*(kOne -    costheta);
-    R(2, 1) =  wx*sintheta   + wy*wz*(kOne -    costheta);
-    R(0, 2) =  wy*sintheta   + wx*wz*(kOne -    costheta);
-    R(1, 2) = -wx*sintheta   + wy*wz*(kOne -    costheta);
-    R(2, 2) =     costheta   + wz*wz*(kOne -    costheta);
-    // clang-format on
-  } else {
-    // At zero, we switch to using the first order Taylor expansion.
-    R(0, 0) = kOne;
-    R(1, 0) = angle_axis[2];
-    R(2, 0) = -angle_axis[1];
-    R(0, 1) = -angle_axis[2];
-    R(1, 1) = kOne;
-    R(2, 1) = angle_axis[0];
-    R(0, 2) = angle_axis[1];
-    R(1, 2) = -angle_axis[0];
-    R(2, 2) = kOne;
-  }
-}
-
-template <typename EulerSystem, typename T>
-inline void EulerAnglesToRotation(const T* euler, T* R) {
-  EulerAnglesToRotation<EulerSystem>(euler, RowMajorAdapter3x3(R));
-}
-
-template <typename EulerSystem, typename T, int row_stride, int col_stride>
-void EulerAnglesToRotation(const T* euler,
-                           const MatrixAdapter<T, row_stride, col_stride>& R) {
-  using std::cos;
-  using std::sin;
-
-  const auto [i, j, k] = EulerSystem::kAxes;
-
-  T ea[3];
-  ea[1] = euler[1];
-  if constexpr (EulerSystem::kIsIntrinsic) {
-    ea[0] = euler[2];
-    ea[2] = euler[0];
-  } else {
-    ea[0] = euler[0];
-    ea[2] = euler[2];
-  }
-  if constexpr (EulerSystem::kIsParityOdd) {
-    ea[0] = -ea[0];
-    ea[1] = -ea[1];
-    ea[2] = -ea[2];
-  }
-
-  const T ci = cos(ea[0]);
-  const T cj = cos(ea[1]);
-  const T ch = cos(ea[2]);
-  const T si = sin(ea[0]);
-  const T sj = sin(ea[1]);
-  const T sh = sin(ea[2]);
-  const T cc = ci * ch;
-  const T cs = ci * sh;
-  const T sc = si * ch;
-  const T ss = si * sh;
-  if constexpr (EulerSystem::kIsProperEuler) {
-    R(i, i) = cj;
-    R(i, j) = sj * si;
-    R(i, k) = sj * ci;
-    R(j, i) = sj * sh;
-    R(j, j) = -cj * ss + cc;
-    R(j, k) = -cj * cs - sc;
-    R(k, i) = -sj * ch;
-    R(k, j) = cj * sc + cs;
-    R(k, k) = cj * cc - ss;
-  } else {
-    R(i, i) = cj * ch;
-    R(i, j) = sj * sc - cs;
-    R(i, k) = sj * cc + ss;
-    R(j, i) = cj * sh;
-    R(j, j) = sj * ss + cc;
-    R(j, k) = sj * cs - sc;
-    R(k, i) = -sj;
-    R(k, j) = cj * si;
-    R(k, k) = cj * ci;
-  }
-}
-
-template <typename EulerSystem, typename T>
-inline void RotationMatrixToEulerAngles(const T* R, T* euler) {
-  RotationMatrixToEulerAngles<EulerSystem>(RowMajorAdapter3x3(R), euler);
-}
-
-template <typename EulerSystem, typename T, int row_stride, int col_stride>
-void RotationMatrixToEulerAngles(
-    const MatrixAdapter<const T, row_stride, col_stride>& R, T* euler) {
-  using std::atan2;
-  using std::fpclassify;
-  using std::hypot;
-
-  const auto [i, j, k] = EulerSystem::kAxes;
-
-  T ea[3];
-  if constexpr (EulerSystem::kIsProperEuler) {
-    const T sy = hypot(R(i, j), R(i, k));
-    if (fpclassify(sy) != FP_ZERO) {
-      ea[0] = atan2(R(i, j), R(i, k));
-      ea[1] = atan2(sy, R(i, i));
-      ea[2] = atan2(R(j, i), -R(k, i));
-    } else {
-      ea[0] = atan2(-R(j, k), R(j, j));
-      ea[1] = atan2(sy, R(i, i));
-      ea[2] = T(0.0);
-    }
-  } else {
-    const T cy = hypot(R(i, i), R(j, i));
-    if (fpclassify(cy) != FP_ZERO) {
-      ea[0] = atan2(R(k, j), R(k, k));
-      ea[1] = atan2(-R(k, i), cy);
-      ea[2] = atan2(R(j, i), R(i, i));
-    } else {
-      ea[0] = atan2(-R(j, k), R(j, j));
-      ea[1] = atan2(-R(k, i), cy);
-      ea[2] = T(0.0);
-    }
-  }
-  if constexpr (EulerSystem::kIsParityOdd) {
-    ea[0] = -ea[0];
-    ea[1] = -ea[1];
-    ea[2] = -ea[2];
-  }
-  euler[1] = ea[1];
-  if constexpr (EulerSystem::kIsIntrinsic) {
-    euler[0] = ea[2];
-    euler[2] = ea[0];
-  } else {
-    euler[0] = ea[0];
-    euler[2] = ea[2];
-  }
-
-  // Proper euler angles are defined for angles in
-  //   [-pi, pi) x [0, pi / 2) x [-pi, pi)
-  // which is enforced here
-  if constexpr (EulerSystem::kIsProperEuler) {
-    const T kPi(constants::pi);
-    const T kTwoPi(2.0 * kPi);
-    if (euler[1] < T(0.0) || ea[1] > kPi) {
-      euler[0] += kPi;
-      euler[1] = -euler[1];
-      euler[2] -= kPi;
-    }
-
-    for (int i = 0; i < 3; ++i) {
-      if (euler[i] < -kPi) {
-        euler[i] += kTwoPi;
-      } else if (euler[i] > kPi) {
-        euler[i] -= kTwoPi;
-      }
-    }
-  }
-}
-
-template <typename T>
-inline void EulerAnglesToRotationMatrix(const T* euler,
-                                        const int row_stride_parameter,
-                                        T* R) {
-  EulerAnglesToRotationMatrix(euler, RowMajorAdapter3x3(R));
-}
-
-template <typename T, int row_stride, int col_stride>
-void EulerAnglesToRotationMatrix(
-    const T* euler, const MatrixAdapter<T, row_stride, col_stride>& R) {
-  const double kPi = 3.14159265358979323846;
-  const T degrees_to_radians(kPi / 180.0);
-
-  const T pitch(euler[0] * degrees_to_radians);
-  const T roll(euler[1] * degrees_to_radians);
-  const T yaw(euler[2] * degrees_to_radians);
-
-  const T c1 = cos(yaw);
-  const T s1 = sin(yaw);
-  const T c2 = cos(roll);
-  const T s2 = sin(roll);
-  const T c3 = cos(pitch);
-  const T s3 = sin(pitch);
-
-  R(0, 0) = c1 * c2;
-  R(0, 1) = -s1 * c3 + c1 * s2 * s3;
-  R(0, 2) = s1 * s3 + c1 * s2 * c3;
-
-  R(1, 0) = s1 * c2;
-  R(1, 1) = c1 * c3 + s1 * s2 * s3;
-  R(1, 2) = -c1 * s3 + s1 * s2 * c3;
-
-  R(2, 0) = -s2;
-  R(2, 1) = c2 * s3;
-  R(2, 2) = c2 * c3;
-}
-
-template <typename T>
-inline void QuaternionToScaledRotation(const T q[4], T R[3 * 3]) {
-  QuaternionToScaledRotation(q, RowMajorAdapter3x3(R));
-}
-
-template <typename T, int row_stride, int col_stride>
-inline void QuaternionToScaledRotation(
-    const T q[4], const MatrixAdapter<T, row_stride, col_stride>& R) {
-  // Make convenient names for elements of q.
-  T a = q[0];
-  T b = q[1];
-  T c = q[2];
-  T d = q[3];
-  // This is not to eliminate common sub-expression, but to
-  // make the lines shorter so that they fit in 80 columns!
-  T aa = a * a;
-  T ab = a * b;
-  T ac = a * c;
-  T ad = a * d;
-  T bb = b * b;
-  T bc = b * c;
-  T bd = b * d;
-  T cc = c * c;
-  T cd = c * d;
-  T dd = d * d;
-
-  // clang-format off
-  R(0, 0) = aa + bb - cc - dd; R(0, 1) = T(2) * (bc - ad);  R(0, 2) = T(2) * (ac + bd);
-  R(1, 0) = T(2) * (ad + bc);  R(1, 1) = aa - bb + cc - dd; R(1, 2) = T(2) * (cd - ab);
-  R(2, 0) = T(2) * (bd - ac);  R(2, 1) = T(2) * (ab + cd);  R(2, 2) = aa - bb - cc + dd;
-  // clang-format on
-}
-
-template <typename T>
-inline void QuaternionToRotation(const T q[4], T R[3 * 3]) {
-  QuaternionToRotation(q, RowMajorAdapter3x3(R));
-}
-
-template <typename T, int row_stride, int col_stride>
-inline void QuaternionToRotation(
-    const T q[4], const MatrixAdapter<T, row_stride, col_stride>& R) {
-  QuaternionToScaledRotation(q, R);
-
-  T normalizer = q[0] * q[0] + q[1] * q[1] + q[2] * q[2] + q[3] * q[3];
-  normalizer = T(1) / normalizer;
-
-  for (int i = 0; i < 3; ++i) {
-    for (int j = 0; j < 3; ++j) {
-      R(i, j) *= normalizer;
-    }
-  }
-}
-
-template <typename T>
-inline void UnitQuaternionRotatePoint(const T q[4],
-                                      const T pt[3],
-                                      T result[3]) {
-  DCHECK_NE(pt, result) << "Inplace rotation is not supported.";
-
-  // clang-format off
-  T uv0 = q[2] * pt[2] - q[3] * pt[1];
-  T uv1 = q[3] * pt[0] - q[1] * pt[2];
-  T uv2 = q[1] * pt[1] - q[2] * pt[0];
-  uv0 += uv0;
-  uv1 += uv1;
-  uv2 += uv2;
-  result[0] = pt[0] + q[0] * uv0;
-  result[1] = pt[1] + q[0] * uv1;
-  result[2] = pt[2] + q[0] * uv2;
-  result[0] += q[2] * uv2 - q[3] * uv1;
-  result[1] += q[3] * uv0 - q[1] * uv2;
-  result[2] += q[1] * uv1 - q[2] * uv0;
-  // clang-format on
-}
-
-template <typename T>
-inline void QuaternionRotatePoint(const T q[4], const T pt[3], T result[3]) {
-  DCHECK_NE(pt, result) << "Inplace rotation is not supported.";
-
-  // 'scale' is 1 / norm(q).
-  const T scale =
-      T(1) / sqrt(q[0] * q[0] + q[1] * q[1] + q[2] * q[2] + q[3] * q[3]);
-
-  // Make unit-norm version of q.
-  const T unit[4] = {
-      scale * q[0],
-      scale * q[1],
-      scale * q[2],
-      scale * q[3],
-  };
-
-  UnitQuaternionRotatePoint(unit, pt, result);
-}
-
-template <typename T>
-inline void QuaternionProduct(const T z[4], const T w[4], T zw[4]) {
-  DCHECK_NE(z, zw) << "Inplace quaternion product is not supported.";
-  DCHECK_NE(w, zw) << "Inplace quaternion product is not supported.";
-
-  // clang-format off
-  zw[0] = z[0] * w[0] - z[1] * w[1] - z[2] * w[2] - z[3] * w[3];
-  zw[1] = z[0] * w[1] + z[1] * w[0] + z[2] * w[3] - z[3] * w[2];
-  zw[2] = z[0] * w[2] - z[1] * w[3] + z[2] * w[0] + z[3] * w[1];
-  zw[3] = z[0] * w[3] + z[1] * w[2] - z[2] * w[1] + z[3] * w[0];
-  // clang-format on
-}
-
-// xy = x cross y;
-template <typename T>
-inline void CrossProduct(const T x[3], const T y[3], T x_cross_y[3]) {
-  DCHECK_NE(x, x_cross_y) << "Inplace cross product is not supported.";
-  DCHECK_NE(y, x_cross_y) << "Inplace cross product is not supported.";
-
-  x_cross_y[0] = x[1] * y[2] - x[2] * y[1];
-  x_cross_y[1] = x[2] * y[0] - x[0] * y[2];
-  x_cross_y[2] = x[0] * y[1] - x[1] * y[0];
-}
-
-template <typename T>
-inline T DotProduct(const T x[3], const T y[3]) {
-  return (x[0] * y[0] + x[1] * y[1] + x[2] * y[2]);
-}
-
-template <typename T>
-inline void AngleAxisRotatePoint(const T angle_axis[3],
-                                 const T pt[3],
-                                 T result[3]) {
-  DCHECK_NE(pt, result) << "Inplace rotation is not supported.";
-  using std::fpclassify;
-  using std::hypot;
-
-  const T theta = hypot(angle_axis[0], angle_axis[1], angle_axis[2]);
-
-  if (fpclassify(theta) != FP_ZERO) {
-    // Away from zero, use the rodriguez formula
-    //
-    //   result = pt costheta +
-    //            (w x pt) * sintheta +
-    //            w (w . pt) (1 - costheta)
-    //
-    // We want to be careful to only evaluate the square root if the
-    // norm of the angle_axis vector is greater than zero. Otherwise
-    // we get a division by zero.
-    //
-    const T costheta = cos(theta);
-    const T sintheta = sin(theta);
-    const T theta_inverse = T(1.0) / theta;
-
-    const T w[3] = {angle_axis[0] * theta_inverse,
-                    angle_axis[1] * theta_inverse,
-                    angle_axis[2] * theta_inverse};
-
-    // Explicitly inlined evaluation of the cross product for
-    // performance reasons.
-    const T w_cross_pt[3] = {w[1] * pt[2] - w[2] * pt[1],
-                             w[2] * pt[0] - w[0] * pt[2],
-                             w[0] * pt[1] - w[1] * pt[0]};
-    const T tmp =
-        (w[0] * pt[0] + w[1] * pt[1] + w[2] * pt[2]) * (T(1.0) - costheta);
-
-    result[0] = pt[0] * costheta + w_cross_pt[0] * sintheta + w[0] * tmp;
-    result[1] = pt[1] * costheta + w_cross_pt[1] * sintheta + w[1] * tmp;
-    result[2] = pt[2] * costheta + w_cross_pt[2] * sintheta + w[2] * tmp;
-  } else {
-    // At zero, the first order Taylor approximation of the rotation
-    // matrix R corresponding to a vector w and angle theta is
-    //
-    //   R = I + hat(w) * sin(theta)
-    //
-    // But sintheta ~ theta and theta * w = angle_axis, which gives us
-    //
-    //  R = I + hat(angle_axis)
-    //
-    // and actually performing multiplication with the point pt, gives us
-    // R * pt = pt + angle_axis x pt.
-    //
-    // Switching to the Taylor expansion at zero provides meaningful
-    // derivatives when evaluated using Jets.
-    //
-    // Explicitly inlined evaluation of the cross product for
-    // performance reasons.
-    const T w_cross_pt[3] = {angle_axis[1] * pt[2] - angle_axis[2] * pt[1],
-                             angle_axis[2] * pt[0] - angle_axis[0] * pt[2],
-                             angle_axis[0] * pt[1] - angle_axis[1] * pt[0]};
-
-    result[0] = pt[0] + w_cross_pt[0];
-    result[1] = pt[1] + w_cross_pt[1];
-    result[2] = pt[2] + w_cross_pt[2];
-  }
-}
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_ROTATION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/sized_cost_function.h b/3rdParty/my_ceres_vc17/include/ceres/sized_cost_function.h
deleted file mode 100644
index d594cfe..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/sized_cost_function.h
+++ /dev/null
@@ -1,69 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: keir@google.com (Keir Mierle)
-//
-// A convenience class for cost functions which are statically sized.
-// Compared to the dynamically-sized base class, this reduces boilerplate.
-//
-// The kNumResiduals template parameter can be a constant such as 2 or 5, or it
-// can be ceres::DYNAMIC. If kNumResiduals is ceres::DYNAMIC, then subclasses
-// are responsible for calling set_num_residuals() at runtime.
-
-#ifndef CERES_PUBLIC_SIZED_COST_FUNCTION_H_
-#define CERES_PUBLIC_SIZED_COST_FUNCTION_H_
-
-#include "ceres/cost_function.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-#include "internal/parameter_dims.h"
-
-namespace ceres {
-
-template <int kNumResiduals, int... Ns>
-class SizedCostFunction : public CostFunction {
- public:
-  static_assert(kNumResiduals > 0 || kNumResiduals == DYNAMIC,
-                "Cost functions must have at least one residual block.");
-  static_assert(internal::StaticParameterDims<Ns...>::kIsValid,
-                "Invalid parameter block dimension detected. Each parameter "
-                "block dimension must be bigger than zero.");
-
-  using ParameterDims = internal::StaticParameterDims<Ns...>;
-
-  SizedCostFunction() {
-    set_num_residuals(kNumResiduals);
-    *mutable_parameter_block_sizes() = std::vector<int32_t>{Ns...};
-  }
-
-  // Subclasses must implement Evaluate().
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_SIZED_COST_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/solver.h b/3rdParty/my_ceres_vc17/include/ceres/solver.h
deleted file mode 100644
index 68438a1..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/solver.h
+++ /dev/null
@@ -1,1126 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_SOLVER_H_
-#define CERES_PUBLIC_SOLVER_H_
-
-#include <cmath>
-#include <memory>
-#include <string>
-#include <unordered_set>
-#include <vector>
-
-#include "ceres/crs_matrix.h"
-#include "ceres/internal/config.h"
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/iteration_callback.h"
-#include "ceres/ordered_groups.h"
-#include "ceres/problem.h"
-#include "ceres/types.h"
-
-namespace ceres {
-
-// Interface for non-linear least squares solvers.
-class CERES_EXPORT Solver {
- public:
-  virtual ~Solver();
-
-  // The options structure contains, not surprisingly, options that control how
-  // the solver operates. The defaults should be suitable for a wide range of
-  // problems; however, better performance is often obtainable with tweaking.
-  //
-  // The constants are defined inside types.h
-  struct CERES_EXPORT Options {
-    // Returns true if the options struct has a valid
-    // configuration. Returns false otherwise, and fills in *error
-    // with a message describing the problem.
-    bool IsValid(std::string* error) const;
-
-    // Ceres supports the two major families of optimization strategies -
-    // Trust Region and Line Search.
-    //
-    // 1. The line search approach first finds a descent direction
-    // along which the objective function will be reduced and then
-    // computes a step size that decides how far should move along
-    // that direction. The descent direction can be computed by
-    // various methods, such as gradient descent, Newton's method and
-    // Quasi-Newton method. The step size can be determined either
-    // exactly or inexactly.
-    //
-    // 2. The trust region approach approximates the objective
-    // function using a model function (often a quadratic) over
-    // a subset of the search space known as the trust region. If the
-    // model function succeeds in minimizing the true objective
-    // function the trust region is expanded; conversely, otherwise it
-    // is contracted and the model optimization problem is solved
-    // again.
-    //
-    // Trust region methods are in some sense dual to line search methods:
-    // trust region methods first choose a step size (the size of the
-    // trust region) and then a step direction while line search methods
-    // first choose a step direction and then a step size.
-    MinimizerType minimizer_type = TRUST_REGION;
-
-    LineSearchDirectionType line_search_direction_type = LBFGS;
-    LineSearchType line_search_type = WOLFE;
-    NonlinearConjugateGradientType nonlinear_conjugate_gradient_type =
-        FLETCHER_REEVES;
-
-    // The LBFGS hessian approximation is a low rank approximation to
-    // the inverse of the Hessian matrix. The rank of the
-    // approximation determines (linearly) the space and time
-    // complexity of using the approximation. Higher the rank, the
-    // better is the quality of the approximation. The increase in
-    // quality is however is bounded for a number of reasons.
-    //
-    // 1. The method only uses secant information and not actual
-    // derivatives.
-    //
-    // 2. The Hessian approximation is constrained to be positive
-    // definite.
-    //
-    // So increasing this rank to a large number will cost time and
-    // space complexity without the corresponding increase in solution
-    // quality. There are no hard and fast rules for choosing the
-    // maximum rank. The best choice usually requires some problem
-    // specific experimentation.
-    //
-    // For more theoretical and implementation details of the LBFGS
-    // method, please see:
-    //
-    // Nocedal, J. (1980). "Updating Quasi-Newton Matrices with
-    // Limited Storage". Mathematics of Computation 35 (151): 773-782.
-    int max_lbfgs_rank = 20;
-
-    // As part of the (L)BFGS update step (BFGS) / right-multiply step (L-BFGS),
-    // the initial inverse Hessian approximation is taken to be the Identity.
-    // However, Oren showed that using instead I * \gamma, where \gamma is
-    // chosen to approximate an eigenvalue of the true inverse Hessian can
-    // result in improved convergence in a wide variety of cases. Setting
-    // use_approximate_eigenvalue_bfgs_scaling to true enables this scaling.
-    //
-    // It is important to note that approximate eigenvalue scaling does not
-    // always improve convergence, and that it can in fact significantly degrade
-    // performance for certain classes of problem, which is why it is disabled
-    // by default.  In particular it can degrade performance when the
-    // sensitivity of the problem to different parameters varies significantly,
-    // as in this case a single scalar factor fails to capture this variation
-    // and detrimentally downscales parts of the jacobian approximation which
-    // correspond to low-sensitivity parameters. It can also reduce the
-    // robustness of the solution to errors in the jacobians.
-    //
-    // Oren S.S., Self-scaling variable metric (SSVM) algorithms
-    // Part II: Implementation and experiments, Management Science,
-    // 20(5), 863-874, 1974.
-    bool use_approximate_eigenvalue_bfgs_scaling = false;
-
-    // Degree of the polynomial used to approximate the objective
-    // function. Valid values are BISECTION, QUADRATIC and CUBIC.
-    //
-    // BISECTION corresponds to pure backtracking search with no
-    // interpolation.
-    LineSearchInterpolationType line_search_interpolation_type = CUBIC;
-
-    // If during the line search, the step_size falls below this
-    // value, it is truncated to zero.
-    double min_line_search_step_size = 1e-9;
-
-    // Line search parameters.
-
-    // Solving the line search problem exactly is computationally
-    // prohibitive. Fortunately, line search based optimization
-    // algorithms can still guarantee convergence if instead of an
-    // exact solution, the line search algorithm returns a solution
-    // which decreases the value of the objective function
-    // sufficiently. More precisely, we are looking for a step_size
-    // s.t.
-    //
-    //   f(step_size) <= f(0) + sufficient_decrease * f'(0) * step_size
-    //
-    double line_search_sufficient_function_decrease = 1e-4;
-
-    // In each iteration of the line search,
-    //
-    //  new_step_size >= max_line_search_step_contraction * step_size
-    //
-    // Note that by definition, for contraction:
-    //
-    //  0 < max_step_contraction < min_step_contraction < 1
-    //
-    double max_line_search_step_contraction = 1e-3;
-
-    // In each iteration of the line search,
-    //
-    //  new_step_size <= min_line_search_step_contraction * step_size
-    //
-    // Note that by definition, for contraction:
-    //
-    //  0 < max_step_contraction < min_step_contraction < 1
-    //
-    double min_line_search_step_contraction = 0.6;
-
-    // Maximum number of trial step size iterations during each line
-    // search, if a step size satisfying the search conditions cannot
-    // be found within this number of trials, the line search will
-    // terminate.
-
-    // The minimum allowed value is 0 for trust region minimizer and 1
-    // otherwise. If 0 is specified for the trust region minimizer,
-    // then line search will not be used when solving constrained
-    // optimization problems.
-    int max_num_line_search_step_size_iterations = 20;
-
-    // Maximum number of restarts of the line search direction algorithm before
-    // terminating the optimization. Restarts of the line search direction
-    // algorithm occur when the current algorithm fails to produce a new descent
-    // direction. This typically indicates a numerical failure, or a breakdown
-    // in the validity of the approximations used.
-    int max_num_line_search_direction_restarts = 5;
-
-    // The strong Wolfe conditions consist of the Armijo sufficient
-    // decrease condition, and an additional requirement that the
-    // step-size be chosen s.t. the _magnitude_ ('strong' Wolfe
-    // conditions) of the gradient along the search direction
-    // decreases sufficiently. Precisely, this second condition
-    // is that we seek a step_size s.t.
-    //
-    //   |f'(step_size)| <= sufficient_curvature_decrease * |f'(0)|
-    //
-    // Where f() is the line search objective and f'() is the derivative
-    // of f w.r.t step_size (d f / d step_size).
-    double line_search_sufficient_curvature_decrease = 0.9;
-
-    // During the bracketing phase of the Wolfe search, the step size is
-    // increased until either a point satisfying the Wolfe conditions is
-    // found, or an upper bound for a bracket containing a point satisfying
-    // the conditions is found.  Precisely, at each iteration of the
-    // expansion:
-    //
-    //   new_step_size <= max_step_expansion * step_size.
-    //
-    // By definition for expansion, max_step_expansion > 1.0.
-    double max_line_search_step_expansion = 10.0;
-
-    TrustRegionStrategyType trust_region_strategy_type = LEVENBERG_MARQUARDT;
-
-    // Type of dogleg strategy to use.
-    DoglegType dogleg_type = TRADITIONAL_DOGLEG;
-
-    // The classical trust region methods are descent methods, in that
-    // they only accept a point if it strictly reduces the value of
-    // the objective function.
-    //
-    // Relaxing this requirement allows the algorithm to be more
-    // efficient in the long term at the cost of some local increase
-    // in the value of the objective function.
-    //
-    // This is because allowing for non-decreasing objective function
-    // values in a principled manner allows the algorithm to "jump over
-    // boulders" as the method is not restricted to move into narrow
-    // valleys while preserving its convergence properties.
-    //
-    // Setting use_nonmonotonic_steps to true enables the
-    // non-monotonic trust region algorithm as described by Conn,
-    // Gould & Toint in "Trust Region Methods", Section 10.1.
-    //
-    // The parameter max_consecutive_nonmonotonic_steps controls the
-    // window size used by the step selection algorithm to accept
-    // non-monotonic steps.
-    //
-    // Even though the value of the objective function may be larger
-    // than the minimum value encountered over the course of the
-    // optimization, the final parameters returned to the user are the
-    // ones corresponding to the minimum cost over all iterations.
-    bool use_nonmonotonic_steps = false;
-    int max_consecutive_nonmonotonic_steps = 5;
-
-    // Maximum number of iterations for the minimizer to run for.
-    int max_num_iterations = 50;
-
-    // Maximum time for which the minimizer should run for.
-    double max_solver_time_in_seconds = 1e9;
-
-    // Number of threads used by Ceres for evaluating the cost and
-    // jacobians.
-    int num_threads = 1;
-
-    // Trust region minimizer settings.
-    double initial_trust_region_radius = 1e4;
-    double max_trust_region_radius = 1e16;
-
-    // Minimizer terminates when the trust region radius becomes
-    // smaller than this value.
-    double min_trust_region_radius = 1e-32;
-
-    // Lower bound for the relative decrease before a step is
-    // accepted.
-    double min_relative_decrease = 1e-3;
-
-    // For the Levenberg-Marquadt algorithm, the scaled diagonal of
-    // the normal equations J'J is used to control the size of the
-    // trust region. Extremely small and large values along the
-    // diagonal can make this regularization scheme
-    // fail. max_lm_diagonal and min_lm_diagonal, clamp the values of
-    // diag(J'J) from above and below. In the normal course of
-    // operation, the user should not have to modify these parameters.
-    double min_lm_diagonal = 1e-6;
-    double max_lm_diagonal = 1e32;
-
-    // Sometimes due to numerical conditioning problems or linear
-    // solver flakiness, the trust region strategy may return a
-    // numerically invalid step that can be fixed by reducing the
-    // trust region size. So the TrustRegionMinimizer allows for a few
-    // successive invalid steps before it declares NUMERICAL_FAILURE.
-    int max_num_consecutive_invalid_steps = 5;
-
-    // Minimizer terminates when
-    //
-    //   (new_cost - old_cost) < function_tolerance * old_cost;
-    //
-    double function_tolerance = 1e-6;
-
-    // Minimizer terminates when
-    //
-    //   max_i |x - Project(Plus(x, -g(x))| < gradient_tolerance
-    //
-    // This value should typically be 1e-4 * function_tolerance.
-    double gradient_tolerance = 1e-10;
-
-    // Minimizer terminates when
-    //
-    //   |step|_2 <= parameter_tolerance * ( |x|_2 +  parameter_tolerance)
-    //
-    double parameter_tolerance = 1e-8;
-
-    // Linear least squares solver options -------------------------------------
-
-    LinearSolverType linear_solver_type =
-#if defined(CERES_NO_SPARSE)
-        DENSE_QR;
-#else
-        SPARSE_NORMAL_CHOLESKY;
-#endif
-
-    // Type of preconditioner to use with the iterative linear solvers.
-    PreconditionerType preconditioner_type = JACOBI;
-
-    // Type of clustering algorithm to use for visibility based
-    // preconditioning. This option is used only when the
-    // preconditioner_type is CLUSTER_JACOBI or CLUSTER_TRIDIAGONAL.
-    VisibilityClusteringType visibility_clustering_type = CANONICAL_VIEWS;
-
-    // Subset preconditioner is a preconditioner for problems with
-    // general sparsity. Given a subset of residual blocks of a
-    // problem, it uses the corresponding subset of the rows of the
-    // Jacobian to construct a preconditioner.
-    //
-    // Suppose the Jacobian J has been horizontally partitioned as
-    //
-    // J = [P]
-    //     [Q]
-    //
-    // Where, Q is the set of rows corresponding to the residual
-    // blocks in residual_blocks_for_subset_preconditioner.
-    //
-    // The preconditioner is the inverse of the matrix Q'Q.
-    //
-    // Obviously, the efficacy of the preconditioner depends on how
-    // well the matrix Q approximates J'J, or how well the chosen
-    // residual blocks approximate the non-linear least squares
-    // problem.
-    //
-    // If Solver::Options::preconditioner_type == SUBSET, then
-    // residual_blocks_for_subset_preconditioner must be non-empty.
-    std::unordered_set<ResidualBlockId>
-        residual_blocks_for_subset_preconditioner;
-
-    // Ceres supports using multiple dense linear algebra libraries for dense
-    // matrix factorizations. Currently EIGEN, LAPACK and CUDA are the valid
-    // choices. EIGEN is always available, LAPACK refers to the system BLAS +
-    // LAPACK library which may or may not be available. CUDA refers to Nvidia's
-    // GPU based dense linear algebra library, which may or may not be
-    // available.
-    //
-    // This setting affects the DENSE_QR, DENSE_NORMAL_CHOLESKY and DENSE_SCHUR
-    // solvers. For small to moderate sized problem EIGEN is a fine choice but
-    // for large problems, an optimized LAPACK + BLAS or CUDA implementation can
-    // make a substantial difference in performance.
-    DenseLinearAlgebraLibraryType dense_linear_algebra_library_type = EIGEN;
-
-    // Ceres supports using multiple sparse linear algebra libraries for sparse
-    // matrix ordering and factorizations.
-    SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type =
-#if !defined(CERES_NO_SUITESPARSE)
-        SUITE_SPARSE;
-#elif !defined(CERES_NO_ACCELERATE_SPARSE)
-        ACCELERATE_SPARSE;
-#elif defined(CERES_USE_EIGEN_SPARSE)
-        EIGEN_SPARSE;
-#else
-        NO_SPARSE;
-#endif
-
-    // The order in which variables are eliminated in a linear solver
-    // can have a significant impact on the efficiency and accuracy of
-    // the method. e.g., when doing sparse Cholesky factorization,
-    // there are matrices for which a good ordering will give a
-    // Cholesky factor with O(n) storage, where as a bad ordering will
-    // result in an completely dense factor.
-    //
-    // Sparse direct solvers like SPARSE_NORMAL_CHOLESKY and
-    // SPARSE_SCHUR use a fill reducing ordering of the columns and
-    // rows of the matrix being factorized before computing the
-    // numeric factorization.
-    //
-    // This enum controls the type of algorithm used to compute
-    // this fill reducing ordering. There is no single algorithm
-    // that works on all matrices, so determining which algorithm
-    // works better is a matter of empirical experimentation.
-    //
-    // The exact behaviour of this setting is affected by the value of
-    // linear_solver_ordering as described below.
-    LinearSolverOrderingType linear_solver_ordering_type = AMD;
-
-    // Besides specifying the fill reducing ordering via
-    // linear_solver_ordering_type, Ceres allows the user to provide varying
-    // amounts of hints to the linear solver about the variable elimination
-    // ordering to use. This can range from no hints, where the solver is free
-    // to decide the best possible ordering based on the user's choices like the
-    // linear solver being used, to an exact order in which the variables should
-    // be eliminated, and a variety of possibilities in between.
-    //
-    // Instances of the ParameterBlockOrdering class are used to communicate
-    // this information to Ceres.
-    //
-    // Formally an ordering is an ordered partitioning of the parameter blocks,
-    // i.e, each parameter block belongs to exactly one group, and each group
-    // has a unique non-negative integer associated with it, that determines its
-    // order in the set of groups.
-    //
-    // e.g. Consider the linear system
-    //
-    //   x + y = 3
-    //   2x + 3y = 7
-    //
-    // There are two ways in which it can be solved. First eliminating x from
-    // the two equations, solving for y and then back substituting for x, or
-    // first eliminating y, solving for x and back substituting for y. The user
-    // can construct three orderings here.
-    //
-    //   {0: x}, {1: y} - eliminate x first.
-    //   {0: y}, {1: x} - eliminate y first.
-    //   {0: x, y}      - Solver gets to decide the elimination order.
-    //
-    // Thus, to have Ceres determine the ordering automatically, put all the
-    // variables in group 0 and to control the ordering for every variable
-    // create groups 0 ... N-1, one per variable, in the desired
-    // order.
-    //
-    // linear_solver_ordering == nullptr and an ordering where all the parameter
-    // blocks are in one elimination group mean the same thing - the solver is
-    // free to choose what it thinks is the best elimination ordering. Therefore
-    // in the following we will only consider the case where
-    // linear_solver_ordering is nullptr.
-    //
-    // The exact interpretation of this information depends on the values of
-    // linear_solver_ordering_type and linear_solver_type/preconditioner_type
-    // and sparse_linear_algebra_type.
-    //
-    // Bundle Adjustment
-    // =================
-    //
-    // If the user is using one of the Schur solvers (DENSE_SCHUR,
-    // SPARSE_SCHUR, ITERATIVE_SCHUR) and chooses to specify an
-    // ordering, it must have one important property. The lowest
-    // numbered elimination group must form an independent set in the
-    // graph corresponding to the Hessian, or in other words, no two
-    // parameter blocks in in the first elimination group should
-    // co-occur in the same residual block. For the best performance,
-    // this elimination group should be as large as possible. For
-    // standard bundle adjustment problems, this corresponds to the
-    // first elimination group containing all the 3d points, and the
-    // second containing the all the cameras parameter blocks.
-    //
-    // If the user leaves the choice to Ceres, then the solver uses an
-    // approximate maximum independent set algorithm to identify the first
-    // elimination group.
-    //
-    // sparse_linear_algebra_library_type = SUITE_SPARSE
-    // =================================================
-    //
-    // linear_solver_ordering_type = AMD
-    // ---------------------------------
-    //
-    // A Constrained Approximate Minimum Degree (CAMD) ordering used where the
-    // parameter blocks in the lowest numbered group are eliminated first, and
-    // then the parameter blocks in the next lowest numbered group and so
-    // on. Within each group, CAMD free to order the parameter blocks as it
-    // chooses.
-    //
-    // linear_solver_ordering_type = NESDIS
-    // -------------------------------------
-    //
-    // a. linear_solver_type = SPARSE_NORMAL_CHOLESKY or
-    //    linear_solver_type = CGNR and preconditioner_type = SUBSET
-    //
-    // The value of linear_solver_ordering is ignored and a Nested Dissection
-    // algorithm is used to compute a fill reducing ordering.
-    //
-    // b. linear_solver_type = SPARSE_SCHUR/DENSE_SCHUR/ITERATIVE_SCHUR
-    //
-    // ONLY the lowest group are used to compute the Schur complement, and
-    // Nested Dissection is used to compute a fill reducing ordering for the
-    // Schur Complement (or its preconditioner).
-    //
-    // sparse_linear_algebra_library_type = EIGEN_SPARSE or ACCELERATE_SPARSE
-    // ======================================================================
-    //
-    // a. linear_solver_type = SPARSE_NORMAL_CHOLESKY or
-    //    linear_solver_type = CGNR and preconditioner_type = SUBSET
-    //
-    // then the value of linear_solver_ordering is ignored and AMD or NESDIS is
-    // used to compute a fill reducing ordering as requested by the user.
-    //
-    // b. linear_solver_type = SPARSE_SCHUR/DENSE_SCHUR/ITERATIVE_SCHUR
-    //
-    // ONLY the lowest group are used to compute the Schur complement, and AMD
-    // or NESDIS is used to compute a fill reducing ordering for the Schur
-    // Complement (or its preconditioner).
-    std::shared_ptr<ParameterBlockOrdering> linear_solver_ordering;
-
-    // Use an explicitly computed Schur complement matrix with
-    // ITERATIVE_SCHUR.
-    //
-    // By default this option is disabled and ITERATIVE_SCHUR
-    // evaluates matrix-vector products between the Schur
-    // complement and a vector implicitly by exploiting the algebraic
-    // expression for the Schur complement.
-    //
-    // The cost of this evaluation scales with the number of non-zeros
-    // in the Jacobian.
-    //
-    // For small to medium sized problems there is a sweet spot where
-    // computing the Schur complement is cheap enough that it is much
-    // more efficient to explicitly compute it and use it for evaluating
-    // the matrix-vector products.
-    //
-    // Enabling this option tells ITERATIVE_SCHUR to use an explicitly
-    // computed Schur complement.
-    //
-    // NOTE: This option can only be used with the SCHUR_JACOBI
-    // preconditioner.
-    bool use_explicit_schur_complement = false;
-
-    // Sparse Cholesky factorization algorithms use a fill-reducing
-    // ordering to permute the columns of the Jacobian matrix. There
-    // are two ways of doing this.
-
-    // 1. Compute the Jacobian matrix in some order and then have the
-    //    factorization algorithm permute the columns of the Jacobian.
-
-    // 2. Compute the Jacobian with its columns already permuted.
-
-    // The first option incurs a significant memory penalty. The
-    // factorization algorithm has to make a copy of the permuted
-    // Jacobian matrix, thus Ceres pre-permutes the columns of the
-    // Jacobian matrix and generally speaking, there is no performance
-    // penalty for doing so.
-
-    // Some non-linear least squares problems are symbolically dense but
-    // numerically sparse. i.e. at any given state only a small number
-    // of jacobian entries are non-zero, but the position and number of
-    // non-zeros is different depending on the state. For these problems
-    // it can be useful to factorize the sparse jacobian at each solver
-    // iteration instead of including all of the zero entries in a single
-    // general factorization.
-    //
-    // If your problem does not have this property (or you do not know),
-    // then it is probably best to keep this false, otherwise it will
-    // likely lead to worse performance.
-
-    // This settings only affects the SPARSE_NORMAL_CHOLESKY solver.
-    bool dynamic_sparsity = false;
-
-    // If use_mixed_precision_solves is true, the Gauss-Newton matrix
-    // is computed in double precision, but its factorization is
-    // computed in single precision. This can result in significant
-    // time and memory savings at the cost of some accuracy in the
-    // Gauss-Newton step. Iterative refinement is used to recover some
-    // of this accuracy back.
-    //
-    // If use_mixed_precision_solves is true, we recommend setting
-    // max_num_refinement_iterations to 2-3.
-    //
-    // This options is available when linear solver uses sparse or dense
-    // cholesky factorization, except when sparse_linear_algebra_library_type =
-    // SUITE_SPARSE.
-    bool use_mixed_precision_solves = false;
-
-    // Number steps of the iterative refinement process to run when
-    // computing the Gauss-Newton step.
-    int max_num_refinement_iterations = 0;
-
-    // Minimum number of iterations for which the linear solver should
-    // run, even if the convergence criterion is satisfied.
-    int min_linear_solver_iterations = 0;
-
-    // Maximum number of iterations for which the linear solver should
-    // run. If the solver does not converge in less than
-    // max_linear_solver_iterations, then it returns MAX_ITERATIONS,
-    // as its termination type.
-    int max_linear_solver_iterations = 500;
-
-    // Maximum number of iterations performed by SCHUR_POWER_SERIES_EXPANSION.
-    // Each iteration corresponds to one more term in the power series expansion
-    // od the inverse of the Schur complement.  This value controls the maximum
-    // number of iterations whether it is used as a preconditioner or just to
-    // initialize the solution for ITERATIVE_SCHUR.
-    int max_num_spse_iterations = 5;
-
-    // Use SCHUR_POWER_SERIES_EXPANSION to initialize the solution for
-    // ITERATIVE_SCHUR. This option can be set true regardless of what
-    // preconditioner is being used.
-    bool use_spse_initialization = false;
-
-    // When use_spse_initialization is true, this parameter along with
-    // max_num_spse_iterations controls the number of
-    // SCHUR_POWER_SERIES_EXPANSION iterations performed for initialization. It
-    // is not used to control the preconditioner.
-    double spse_tolerance = 0.1;
-
-    // Forcing sequence parameter. The truncated Newton solver uses
-    // this number to control the relative accuracy with which the
-    // Newton step is computed.
-    //
-    // This constant is passed to ConjugateGradientsSolver which uses
-    // it to terminate the iterations when
-    //
-    //  (Q_i - Q_{i-1})/Q_i < eta/i
-    double eta = 1e-1;
-
-    // Normalize the jacobian using Jacobi scaling before calling
-    // the linear least squares solver.
-    bool jacobi_scaling = true;
-
-    // Some non-linear least squares problems have additional
-    // structure in the way the parameter blocks interact that it is
-    // beneficial to modify the way the trust region step is computed.
-    //
-    // e.g., consider the following regression problem
-    //
-    //   y = a_1 exp(b_1 x) + a_2 exp(b_3 x^2 + c_1)
-    //
-    // Given a set of pairs{(x_i, y_i)}, the user wishes to estimate
-    // a_1, a_2, b_1, b_2, and c_1.
-    //
-    // Notice here that the expression on the left is linear in a_1
-    // and a_2, and given any value for b_1, b_2 and c_1, it is
-    // possible to use linear regression to estimate the optimal
-    // values of a_1 and a_2. Indeed, its possible to analytically
-    // eliminate the variables a_1 and a_2 from the problem all
-    // together. Problems like these are known as separable least
-    // squares problem and the most famous algorithm for solving them
-    // is the Variable Projection algorithm invented by Golub &
-    // Pereyra.
-    //
-    // Similar structure can be found in the matrix factorization with
-    // missing data problem. There the corresponding algorithm is
-    // known as Wiberg's algorithm.
-    //
-    // Ruhe & Wedin (Algorithms for Separable Nonlinear Least Squares
-    // Problems, SIAM Reviews, 22(3), 1980) present an analysis of
-    // various algorithms for solving separable non-linear least
-    // squares problems and refer to "Variable Projection" as
-    // Algorithm I in their paper.
-    //
-    // Implementing Variable Projection is tedious and expensive, and
-    // they present a simpler algorithm, which they refer to as
-    // Algorithm II, where once the Newton/Trust Region step has been
-    // computed for the whole problem (a_1, a_2, b_1, b_2, c_1) and
-    // additional optimization step is performed to estimate a_1 and
-    // a_2 exactly.
-    //
-    // This idea can be generalized to cases where the residual is not
-    // linear in a_1 and a_2, i.e., Solve for the trust region step
-    // for the full problem, and then use it as the starting point to
-    // further optimize just a_1 and a_2. For the linear case, this
-    // amounts to doing a single linear least squares solve. For
-    // non-linear problems, any method for solving the a_1 and a_2
-    // optimization problems will do. The only constraint on a_1 and
-    // a_2 is that they do not co-occur in any residual block.
-    //
-    // This idea can be further generalized, by not just optimizing
-    // (a_1, a_2), but decomposing the graph corresponding to the
-    // Hessian matrix's sparsity structure in a collection of
-    // non-overlapping independent sets and optimizing each of them.
-    //
-    // Setting "use_inner_iterations" to true enables the use of this
-    // non-linear generalization of Ruhe & Wedin's Algorithm II.  This
-    // version of Ceres has a higher iteration complexity, but also
-    // displays better convergence behaviour per iteration. Setting
-    // Solver::Options::num_threads to the maximum number possible is
-    // highly recommended.
-    bool use_inner_iterations = false;
-
-    // If inner_iterations is true, then the user has two choices.
-    //
-    // 1. Let the solver heuristically decide which parameter blocks
-    //    to optimize in each inner iteration. To do this leave
-    //    Solver::Options::inner_iteration_ordering untouched.
-    //
-    // 2. Specify a collection of of ordered independent sets. Where
-    //    the lower numbered groups are optimized before the higher
-    //    number groups. Each group must be an independent set. Not
-    //    all parameter blocks need to be present in the ordering.
-    std::shared_ptr<ParameterBlockOrdering> inner_iteration_ordering;
-
-    // Generally speaking, inner iterations make significant progress
-    // in the early stages of the solve and then their contribution
-    // drops down sharply, at which point the time spent doing inner
-    // iterations is not worth it.
-    //
-    // Once the relative decrease in the objective function due to
-    // inner iterations drops below inner_iteration_tolerance, the use
-    // of inner iterations in subsequent trust region minimizer
-    // iterations is disabled.
-    double inner_iteration_tolerance = 1e-3;
-
-    LoggingType logging_type = PER_MINIMIZER_ITERATION;
-
-    // By default the Minimizer progress is logged to VLOG(1), which
-    // is sent to STDERR depending on the vlog level. If this flag is
-    // set to true, and logging_type is not SILENT, the logging output
-    // is sent to STDOUT.
-    bool minimizer_progress_to_stdout = false;
-
-    // List of iterations at which the minimizer should dump the trust
-    // region problem. Useful for testing and benchmarking. If empty
-    // (default), no problems are dumped.
-    std::vector<int> trust_region_minimizer_iterations_to_dump;
-
-    // Directory to which the problems should be written to. Should be
-    // non-empty if trust_region_minimizer_iterations_to_dump is
-    // non-empty and trust_region_problem_dump_format_type is not
-    // CONSOLE.
-    std::string trust_region_problem_dump_directory = "/tmp";
-    DumpFormatType trust_region_problem_dump_format_type = TEXTFILE;
-
-    // Finite differences options ----------------------------------------------
-
-    // Check all jacobians computed by each residual block with finite
-    // differences. This is expensive since it involves computing the
-    // derivative by normal means (e.g. user specified, autodiff,
-    // etc), then also computing it using finite differences. The
-    // results are compared, and if they differ substantially, details
-    // are printed to the log.
-    bool check_gradients = false;
-
-    // Relative precision to check for in the gradient checker. If the
-    // relative difference between an element in a jacobian exceeds
-    // this number, then the jacobian for that cost term is dumped.
-    double gradient_check_relative_precision = 1e-8;
-
-    // WARNING: This option only applies to the to the numeric
-    // differentiation used for checking the user provided derivatives
-    // when when Solver::Options::check_gradients is true. If you are
-    // using NumericDiffCostFunction and are interested in changing
-    // the step size for numeric differentiation in your cost
-    // function, please have a look at
-    // include/ceres/numeric_diff_options.h.
-    //
-    // Relative shift used for taking numeric derivatives when
-    // Solver::Options::check_gradients is true.
-    //
-    // For finite differencing, each dimension is evaluated at
-    // slightly shifted values; for the case of central difference,
-    // this is what gets evaluated:
-    //
-    //   delta = gradient_check_numeric_derivative_relative_step_size;
-    //   f_initial  = f(x)
-    //   f_forward  = f((1 + delta) * x)
-    //   f_backward = f((1 - delta) * x)
-    //
-    // The finite differencing is done along each dimension. The
-    // reason to use a relative (rather than absolute) step size is
-    // that this way, numeric differentiation works for functions where
-    // the arguments are typically large (e.g. 1e9) and when the
-    // values are small (e.g. 1e-5). It is possible to construct
-    // "torture cases" which break this finite difference heuristic,
-    // but they do not come up often in practice.
-    //
-    // TODO(keir): Pick a smarter number than the default above! In
-    // theory a good choice is sqrt(eps) * x, which for doubles means
-    // about 1e-8 * x. However, I have found this number too
-    // optimistic. This number should be exposed for users to change.
-    double gradient_check_numeric_derivative_relative_step_size = 1e-6;
-
-    // If update_state_every_iteration is true, then Ceres Solver will
-    // guarantee that at the end of every iteration and before any
-    // user provided IterationCallback is called, the parameter blocks
-    // are updated to the current best solution found by the
-    // solver. Thus the IterationCallback can inspect the values of
-    // the parameter blocks for purposes of computation, visualization
-    // or termination.
-
-    // If update_state_every_iteration is false then there is no such
-    // guarantee, and user provided IterationCallbacks should not
-    // expect to look at the parameter blocks and interpret their
-    // values.
-    bool update_state_every_iteration = false;
-
-    // Callbacks that are executed at the end of each iteration of the
-    // Minimizer. An iteration may terminate midway, either due to
-    // numerical failures or because one of the convergence tests has
-    // been satisfied. In this case none of the callbacks are
-    // executed.
-
-    // Callbacks are executed in the order that they are specified in
-    // this vector. By default, parameter blocks are updated only at the
-    // end of the optimization, i.e when the Minimizer terminates. This
-    // behaviour is controlled by update_state_every_iteration. If the
-    // user wishes to have access to the updated parameter blocks when
-    // his/her callbacks are executed, then set
-    // update_state_every_iteration to true.
-    //
-    // The solver does NOT take ownership of these pointers.
-    std::vector<IterationCallback*> callbacks;
-  };
-
-  struct CERES_EXPORT Summary {
-    // A brief one line description of the state of the solver after
-    // termination.
-    std::string BriefReport() const;
-
-    // A full multiline description of the state of the solver after
-    // termination.
-    std::string FullReport() const;
-
-    bool IsSolutionUsable() const;
-
-    // Minimizer summary -------------------------------------------------
-    MinimizerType minimizer_type = TRUST_REGION;
-
-    TerminationType termination_type = FAILURE;
-
-    // Reason why the solver terminated.
-    std::string message = "ceres::Solve was not called.";
-
-    // Cost of the problem (value of the objective function) before
-    // the optimization.
-    double initial_cost = -1.0;
-
-    // Cost of the problem (value of the objective function) after the
-    // optimization.
-    double final_cost = -1.0;
-
-    // The part of the total cost that comes from residual blocks that
-    // were held fixed by the preprocessor because all the parameter
-    // blocks that they depend on were fixed.
-    double fixed_cost = -1.0;
-
-    // IterationSummary for each minimizer iteration in order.
-    std::vector<IterationSummary> iterations;
-
-    // Number of minimizer iterations in which the step was accepted. Unless
-    // use_nonmonotonic_steps is true this is also the number of steps in which
-    // the objective function value/cost went down.
-    int num_successful_steps = -1;
-
-    // Number of minimizer iterations in which the step was rejected
-    // either because it did not reduce the cost enough or the step
-    // was not numerically valid.
-    int num_unsuccessful_steps = -1;
-
-    // Number of times inner iterations were performed.
-    int num_inner_iteration_steps = -1;
-
-    // Total number of iterations inside the line search algorithm
-    // across all invocations. We call these iterations "steps" to
-    // distinguish them from the outer iterations of the line search
-    // and trust region minimizer algorithms which call the line
-    // search algorithm as a subroutine.
-    int num_line_search_steps = -1;
-
-    // All times reported below are wall times.
-
-    // When the user calls Solve, before the actual optimization
-    // occurs, Ceres performs a number of preprocessing steps. These
-    // include error checks, memory allocations, and reorderings. This
-    // time is accounted for as preprocessing time.
-    double preprocessor_time_in_seconds = -1.0;
-
-    // Time spent in the TrustRegionMinimizer.
-    double minimizer_time_in_seconds = -1.0;
-
-    // After the Minimizer is finished, some time is spent in
-    // re-evaluating residuals etc. This time is accounted for in the
-    // postprocessor time.
-    double postprocessor_time_in_seconds = -1.0;
-
-    // Some total of all time spent inside Ceres when Solve is called.
-    double total_time_in_seconds = -1.0;
-
-    // Time (in seconds) spent in the linear solver computing the
-    // trust region step.
-    double linear_solver_time_in_seconds = -1.0;
-
-    // Number of times the Newton step was computed by solving a
-    // linear system. This does not include linear solves used by
-    // inner iterations.
-    int num_linear_solves = -1;
-
-    // Time (in seconds) spent evaluating the residual vector.
-    double residual_evaluation_time_in_seconds = -1.0;
-
-    // Number of residual only evaluations.
-    int num_residual_evaluations = -1;
-
-    // Time (in seconds) spent evaluating the jacobian matrix.
-    double jacobian_evaluation_time_in_seconds = -1.0;
-
-    // Number of Jacobian (and residual) evaluations.
-    int num_jacobian_evaluations = -1;
-
-    // Time (in seconds) spent doing inner iterations.
-    double inner_iteration_time_in_seconds = -1.0;
-
-    // Cumulative timing information for line searches performed as part of the
-    // solve.  Note that in addition to the case when the Line Search minimizer
-    // is used, the Trust Region minimizer also uses a line search when
-    // solving a constrained problem.
-
-    // Time (in seconds) spent evaluating the univariate cost function as part
-    // of a line search.
-    double line_search_cost_evaluation_time_in_seconds = -1.0;
-
-    // Time (in seconds) spent evaluating the gradient of the univariate cost
-    // function as part of a line search.
-    double line_search_gradient_evaluation_time_in_seconds = -1.0;
-
-    // Time (in seconds) spent minimizing the interpolating polynomial
-    // to compute the next candidate step size as part of a line search.
-    double line_search_polynomial_minimization_time_in_seconds = -1.0;
-
-    // Total time (in seconds) spent performing line searches.
-    double line_search_total_time_in_seconds = -1.0;
-
-    // Number of parameter blocks in the problem.
-    int num_parameter_blocks = -1;
-
-    // Number of parameters in the problem.
-    int num_parameters = -1;
-
-    // Dimension of the tangent space of the problem (or the number of
-    // columns in the Jacobian for the problem). This is different
-    // from num_parameters if a parameter block is associated with a
-    // Manifold.
-    int num_effective_parameters = -1;
-
-    // Number of residual blocks in the problem.
-    int num_residual_blocks = -1;
-
-    // Number of residuals in the problem.
-    int num_residuals = -1;
-
-    // Number of parameter blocks in the problem after the inactive
-    // and constant parameter blocks have been removed. A parameter
-    // block is inactive if no residual block refers to it.
-    int num_parameter_blocks_reduced = -1;
-
-    // Number of parameters in the reduced problem.
-    int num_parameters_reduced = -1;
-
-    // Dimension of the tangent space of the reduced problem (or the
-    // number of columns in the Jacobian for the reduced
-    // problem). This is different from num_parameters_reduced if a
-    // parameter block in the reduced problem is associated with a
-    // Manifold.
-    int num_effective_parameters_reduced = -1;
-
-    // Number of residual blocks in the reduced problem.
-    int num_residual_blocks_reduced = -1;
-
-    //  Number of residuals in the reduced problem.
-    int num_residuals_reduced = -1;
-
-    // Is the reduced problem bounds constrained.
-    bool is_constrained = false;
-
-    //  Number of threads specified by the user for Jacobian and
-    //  residual evaluation.
-    int num_threads_given = -1;
-
-    // Number of threads actually used by the solver for Jacobian and
-    // residual evaluation.
-    int num_threads_used = -1;
-
-    // Type of the linear solver requested by the user.
-    LinearSolverType linear_solver_type_given =
-#if defined(CERES_NO_SPARSE)
-        DENSE_QR;
-#else
-        SPARSE_NORMAL_CHOLESKY;
-#endif
-    // Type of the linear solver actually used. This may be different
-    // from linear_solver_type_given if Ceres determines that the
-    // problem structure is not compatible with the linear solver
-    // requested or if the linear solver requested by the user is not
-    // available, e.g. The user requested SPARSE_NORMAL_CHOLESKY but
-    // no sparse linear algebra library was available.
-    LinearSolverType linear_solver_type_used =
-#if defined(CERES_NO_SPARSE)
-        DENSE_QR;
-#else
-        SPARSE_NORMAL_CHOLESKY;
-#endif
-
-    bool mixed_precision_solves_used = false;
-
-    LinearSolverOrderingType linear_solver_ordering_type = AMD;
-
-    // Size of the elimination groups given by the user as hints to
-    // the linear solver.
-    std::vector<int> linear_solver_ordering_given;
-
-    // Size of the parameter groups used by the solver when ordering
-    // the columns of the Jacobian.  This maybe different from
-    // linear_solver_ordering_given if the user left
-    // linear_solver_ordering_given blank and asked for an automatic
-    // ordering, or if the problem contains some constant or inactive
-    // parameter blocks.
-    std::vector<int> linear_solver_ordering_used;
-
-    // For Schur type linear solvers, this string describes the
-    // template specialization which was detected in the problem and
-    // should be used.
-    std::string schur_structure_given;
-
-    // This is the Schur template specialization that was actually
-    // instantiated and used. The reason this will be different from
-    // schur_structure_given is because the corresponding template
-    // specialization does not exist.
-    //
-    // Template specializations can be added to ceres by editing
-    // internal/ceres/generate_template_specializations.py
-    std::string schur_structure_used;
-
-    // True if the user asked for inner iterations to be used as part
-    // of the optimization.
-    bool inner_iterations_given = false;
-
-    // True if the user asked for inner iterations to be used as part
-    // of the optimization and the problem structure was such that
-    // they were actually performed. e.g., in a problem with just one
-    // parameter block, inner iterations are not performed.
-    bool inner_iterations_used = false;
-
-    // Size of the parameter groups given by the user for performing
-    // inner iterations.
-    std::vector<int> inner_iteration_ordering_given;
-
-    // Size of the parameter groups given used by the solver for
-    // performing inner iterations. This maybe different from
-    // inner_iteration_ordering_given if the user left
-    // inner_iteration_ordering_given blank and asked for an automatic
-    // ordering, or if the problem contains some constant or inactive
-    // parameter blocks.
-    std::vector<int> inner_iteration_ordering_used;
-
-    // Type of the preconditioner requested by the user.
-    PreconditionerType preconditioner_type_given = IDENTITY;
-
-    // Type of the preconditioner actually used. This may be different
-    // from linear_solver_type_given if Ceres determines that the
-    // problem structure is not compatible with the linear solver
-    // requested or if the linear solver requested by the user is not
-    // available.
-    PreconditionerType preconditioner_type_used = IDENTITY;
-
-    // Type of clustering algorithm used for visibility based
-    // preconditioning. Only meaningful when the preconditioner_type_used
-    // is CLUSTER_JACOBI or CLUSTER_TRIDIAGONAL.
-    VisibilityClusteringType visibility_clustering_type = CANONICAL_VIEWS;
-
-    //  Type of trust region strategy.
-    TrustRegionStrategyType trust_region_strategy_type = LEVENBERG_MARQUARDT;
-
-    //  Type of dogleg strategy used for solving the trust region
-    //  problem.
-    DoglegType dogleg_type = TRADITIONAL_DOGLEG;
-
-    //  Type of the dense linear algebra library used.
-    DenseLinearAlgebraLibraryType dense_linear_algebra_library_type = EIGEN;
-
-    // Type of the sparse linear algebra library used.
-    SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type =
-        NO_SPARSE;
-
-    // Type of line search direction used.
-    LineSearchDirectionType line_search_direction_type = LBFGS;
-
-    // Type of the line search algorithm used.
-    LineSearchType line_search_type = WOLFE;
-
-    //  When performing line search, the degree of the polynomial used
-    //  to approximate the objective function.
-    LineSearchInterpolationType line_search_interpolation_type = CUBIC;
-
-    // If the line search direction is NONLINEAR_CONJUGATE_GRADIENT,
-    // then this indicates the particular variant of non-linear
-    // conjugate gradient used.
-    NonlinearConjugateGradientType nonlinear_conjugate_gradient_type =
-        FLETCHER_REEVES;
-
-    // If the type of the line search direction is LBFGS, then this
-    // indicates the rank of the Hessian approximation.
-    int max_lbfgs_rank = -1;
-  };
-
-  // Once a least squares problem has been built, this function takes
-  // the problem and optimizes it based on the values of the options
-  // parameters. Upon return, a detailed summary of the work performed
-  // by the preprocessor, the non-linear minimizer and the linear
-  // solver are reported in the summary object.
-  virtual void Solve(const Options& options,
-                     Problem* problem,
-                     Solver::Summary* summary);
-};
-
-// Helper function which avoids going through the interface.
-CERES_EXPORT void Solve(const Solver::Options& options,
-                        Problem* problem,
-                        Solver::Summary* summary);
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_SOLVER_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/sphere_manifold.h b/3rdParty/my_ceres_vc17/include/ceres/sphere_manifold.h
deleted file mode 100644
index 1c7458b..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/sphere_manifold.h
+++ /dev/null
@@ -1,236 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: vitus@google.com (Mike Vitus)
-//         jodebo_beck@gmx.de (Johannes Beck)
-
-#ifndef CERES_PUBLIC_SPHERE_MANIFOLD_H_
-#define CERES_PUBLIC_SPHERE_MANIFOLD_H_
-
-#include <Eigen/Core>
-#include <algorithm>
-#include <array>
-#include <memory>
-#include <vector>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-#include "ceres/internal/householder_vector.h"
-#include "ceres/internal/sphere_manifold_functions.h"
-#include "ceres/manifold.h"
-#include "ceres/types.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// This provides a manifold on a sphere meaning that the norm of the vector
-// stays the same. Such cases often arises in Structure for Motion
-// problems. One example where they are used is in representing points whose
-// triangulation is ill-conditioned. Here it is advantageous to use an
-// over-parameterization since homogeneous vectors can represent points at
-// infinity.
-//
-// The plus operator is defined as
-//  Plus(x, delta) =
-//    [sin(0.5 * |delta|) * delta / |delta|, cos(0.5 * |delta|)] * x
-//
-// The minus operator is defined as
-//  Minus(x, y) = 2 atan2(nhy, y[-1]) / nhy * hy[0 : size_ - 1]
-// with nhy = norm(hy[0 : size_ - 1])
-//
-// with * defined as an operator which applies the update orthogonal to x to
-// remain on the sphere. The ambient space dimension is required to be greater
-// than 1.
-//
-// The class works with dynamic and static ambient space dimensions. If the
-// ambient space dimensions is known at compile time use
-//
-//    SphereManifold<3> manifold;
-//
-// If the ambient space dimensions is not known at compile time the template
-// parameter needs to be set to ceres::DYNAMIC and the actual dimension needs
-// to be provided as a constructor argument:
-//
-//    SphereManifold<ceres::DYNAMIC> manifold(ambient_dim);
-//
-// See  section B.2 (p.25) in "Integrating Generic Sensor Fusion Algorithms
-// with Sound State Representations through Encapsulation of Manifolds" by C.
-// Hertzberg, R. Wagner, U. Frese and L. Schroder for more details
-// (https://arxiv.org/pdf/1107.1119.pdf)
-template <int AmbientSpaceDimension>
-class SphereManifold final : public Manifold {
- public:
-  static_assert(
-      AmbientSpaceDimension == ceres::DYNAMIC || AmbientSpaceDimension > 1,
-      "The size of the homogeneous vector needs to be greater than 1.");
-  static_assert(ceres::DYNAMIC == Eigen::Dynamic,
-                "ceres::DYNAMIC needs to be the same as Eigen::Dynamic.");
-
-  SphereManifold();
-  explicit SphereManifold(int size);
-
-  int AmbientSize() const override {
-    return AmbientSpaceDimension == ceres::DYNAMIC ? size_
-                                                   : AmbientSpaceDimension;
-  }
-  int TangentSize() const override { return AmbientSize() - 1; }
-
-  bool Plus(const double* x,
-            const double* delta,
-            double* x_plus_delta) const override;
-  bool PlusJacobian(const double* x, double* jacobian) const override;
-
-  bool Minus(const double* y,
-             const double* x,
-             double* y_minus_x) const override;
-  bool MinusJacobian(const double* x, double* jacobian) const override;
-
- private:
-  static constexpr int TangentSpaceDimension =
-      AmbientSpaceDimension > 0 ? AmbientSpaceDimension - 1 : Eigen::Dynamic;
-
-  // NOTE: Eigen does not allow to have a RowMajor column vector.
-  // In that case, change the storage order
-  static constexpr int SafeRowMajor =
-      TangentSpaceDimension == 1 ? Eigen::ColMajor : Eigen::RowMajor;
-
-  using AmbientVector = Eigen::Matrix<double, AmbientSpaceDimension, 1>;
-  using TangentVector = Eigen::Matrix<double, TangentSpaceDimension, 1>;
-  using MatrixPlusJacobian = Eigen::Matrix<double,
-                                           AmbientSpaceDimension,
-                                           TangentSpaceDimension,
-                                           SafeRowMajor>;
-  using MatrixMinusJacobian = Eigen::Matrix<double,
-                                            TangentSpaceDimension,
-                                            AmbientSpaceDimension,
-                                            Eigen::RowMajor>;
-
-  const int size_{};
-};
-
-template <int AmbientSpaceDimension>
-SphereManifold<AmbientSpaceDimension>::SphereManifold()
-    : size_{AmbientSpaceDimension} {
-  static_assert(
-      AmbientSpaceDimension != Eigen::Dynamic,
-      "The size is set to dynamic. Please call the constructor with a size.");
-}
-
-template <int AmbientSpaceDimension>
-SphereManifold<AmbientSpaceDimension>::SphereManifold(int size) : size_{size} {
-  if (AmbientSpaceDimension != Eigen::Dynamic) {
-    CHECK_EQ(AmbientSpaceDimension, size)
-        << "Specified size by template parameter differs from the supplied "
-           "one.";
-  } else {
-    CHECK_GT(size_, 1)
-        << "The size of the manifold needs to be greater than 1.";
-  }
-}
-
-template <int AmbientSpaceDimension>
-bool SphereManifold<AmbientSpaceDimension>::Plus(
-    const double* x_ptr,
-    const double* delta_ptr,
-    double* x_plus_delta_ptr) const {
-  Eigen::Map<const AmbientVector> x(x_ptr, size_);
-  Eigen::Map<const TangentVector> delta(delta_ptr, size_ - 1);
-  Eigen::Map<AmbientVector> x_plus_delta(x_plus_delta_ptr, size_);
-
-  const double norm_delta = delta.norm();
-
-  if (norm_delta == 0.0) {
-    x_plus_delta = x;
-    return true;
-  }
-
-  AmbientVector v(size_);
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  internal::ComputeHouseholderVector<Eigen::Map<const AmbientVector>,
-                                     double,
-                                     AmbientSpaceDimension>(x, &v, &beta);
-
-  internal::ComputeSphereManifoldPlus(
-      v, beta, x, delta, norm_delta, &x_plus_delta);
-
-  return true;
-}
-
-template <int AmbientSpaceDimension>
-bool SphereManifold<AmbientSpaceDimension>::PlusJacobian(
-    const double* x_ptr, double* jacobian_ptr) const {
-  Eigen::Map<const AmbientVector> x(x_ptr, size_);
-  Eigen::Map<MatrixPlusJacobian> jacobian(jacobian_ptr, size_, size_ - 1);
-  internal::ComputeSphereManifoldPlusJacobian(x, &jacobian);
-
-  return true;
-}
-
-template <int AmbientSpaceDimension>
-bool SphereManifold<AmbientSpaceDimension>::Minus(const double* y_ptr,
-                                                  const double* x_ptr,
-                                                  double* y_minus_x_ptr) const {
-  AmbientVector y = Eigen::Map<const AmbientVector>(y_ptr, size_);
-  Eigen::Map<const AmbientVector> x(x_ptr, size_);
-  Eigen::Map<TangentVector> y_minus_x(y_minus_x_ptr, size_ - 1);
-
-  // Apply hoseholder transformation.
-  AmbientVector v(size_);
-  double beta;
-
-  // NOTE: The explicit template arguments are needed here because
-  // ComputeHouseholderVector is templated and some versions of MSVC
-  // have trouble deducing the type of v automatically.
-  internal::ComputeHouseholderVector<Eigen::Map<const AmbientVector>,
-                                     double,
-                                     AmbientSpaceDimension>(x, &v, &beta);
-  internal::ComputeSphereManifoldMinus(v, beta, x, y, &y_minus_x);
-  return true;
-}
-
-template <int AmbientSpaceDimension>
-bool SphereManifold<AmbientSpaceDimension>::MinusJacobian(
-    const double* x_ptr, double* jacobian_ptr) const {
-  Eigen::Map<const AmbientVector> x(x_ptr, size_);
-  Eigen::Map<MatrixMinusJacobian> jacobian(jacobian_ptr, size_ - 1, size_);
-
-  internal::ComputeSphereManifoldMinusJacobian(x, &jacobian);
-  return true;
-}
-
-}  // namespace ceres
-
-// clang-format off
-#include "ceres/internal/reenable_warnings.h"
-// clang-format on
-
-#endif  // CERES_PUBLIC_SPHERE_MANIFOLD_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/tiny_solver.h b/3rdParty/my_ceres_vc17/include/ceres/tiny_solver.h
deleted file mode 100644
index 9242cd0..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/tiny_solver.h
+++ /dev/null
@@ -1,398 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: mierle@gmail.com (Keir Mierle)
-//
-// WARNING WARNING WARNING
-// WARNING WARNING WARNING  Tiny solver is experimental and will change.
-// WARNING WARNING WARNING
-//
-// A tiny least squares solver using Levenberg-Marquardt, intended for solving
-// small dense problems with low latency and low overhead. The implementation
-// takes care to do all allocation up front, so that no memory is allocated
-// during solving. This is especially useful when solving many similar problems;
-// for example, inverse pixel distortion for every pixel on a grid.
-//
-// Note: This code has no dependencies beyond Eigen, including on other parts of
-// Ceres, so it is possible to take this file alone and put it in another
-// project without the rest of Ceres.
-//
-// Algorithm based off of:
-//
-// [1] K. Madsen, H. Nielsen, O. Tingleoff.
-//     Methods for Non-linear Least Squares Problems.
-//     http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf
-
-#ifndef CERES_PUBLIC_TINY_SOLVER_H_
-#define CERES_PUBLIC_TINY_SOLVER_H_
-
-#include <cassert>
-#include <cmath>
-
-#include "Eigen/Dense"
-
-namespace ceres {
-
-// To use tiny solver, create a class or struct that allows computing the cost
-// function (described below). This is similar to a ceres::CostFunction, but is
-// different to enable statically allocating all memory for the solver
-// (specifically, enum sizes). Key parts are the Scalar typedef, the enums to
-// describe problem sizes (needed to remove all heap allocations), and the
-// operator() overload to evaluate the cost and (optionally) jacobians.
-//
-//   struct TinySolverCostFunctionTraits {
-//     typedef double Scalar;
-//     enum {
-//       NUM_RESIDUALS = <int> OR Eigen::Dynamic,
-//       NUM_PARAMETERS = <int> OR Eigen::Dynamic,
-//     };
-//     bool operator()(const double* parameters,
-//                     double* residuals,
-//                     double* jacobian) const;
-//
-//     int NumResiduals() const;  -- Needed if NUM_RESIDUALS == Eigen::Dynamic.
-//     int NumParameters() const; -- Needed if NUM_PARAMETERS == Eigen::Dynamic.
-//   };
-//
-// For operator(), the size of the objects is:
-//
-//   double* parameters -- NUM_PARAMETERS or NumParameters()
-//   double* residuals  -- NUM_RESIDUALS or NumResiduals()
-//   double* jacobian   -- NUM_RESIDUALS * NUM_PARAMETERS in column-major format
-//                         (Eigen's default); or nullptr if no jacobian
-//                         requested.
-//
-// An example (fully statically sized):
-//
-//   struct MyCostFunctionExample {
-//     typedef double Scalar;
-//     enum {
-//       NUM_RESIDUALS = 2,
-//       NUM_PARAMETERS = 3,
-//     };
-//     bool operator()(const double* parameters,
-//                     double* residuals,
-//                     double* jacobian) const {
-//       residuals[0] = x + 2*y + 4*z;
-//       residuals[1] = y * z;
-//       if (jacobian) {
-//         jacobian[0 * 2 + 0] = 1;   // First column (x).
-//         jacobian[0 * 2 + 1] = 0;
-//
-//         jacobian[1 * 2 + 0] = 2;   // Second column (y).
-//         jacobian[1 * 2 + 1] = z;
-//
-//         jacobian[2 * 2 + 0] = 4;   // Third column (z).
-//         jacobian[2 * 2 + 1] = y;
-//       }
-//       return true;
-//     }
-//   };
-//
-// The solver supports either statically or dynamically sized cost
-// functions. If the number of residuals is dynamic then the Function
-// must define:
-//
-//   int NumResiduals() const;
-//
-// If the number of parameters is dynamic then the Function must
-// define:
-//
-//   int NumParameters() const;
-//
-template <typename Function,
-          typename LinearSolver =
-              Eigen::LDLT<Eigen::Matrix<typename Function::Scalar,  //
-                                        Function::NUM_PARAMETERS,   //
-                                        Function::NUM_PARAMETERS>>>
-class TinySolver {
- public:
-  // This class needs to have an Eigen aligned operator new as it contains
-  // fixed-size Eigen types.
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-
-  enum {
-    NUM_RESIDUALS = Function::NUM_RESIDUALS,
-    NUM_PARAMETERS = Function::NUM_PARAMETERS
-  };
-  using Scalar = typename Function::Scalar;
-  using Parameters = typename Eigen::Matrix<Scalar, NUM_PARAMETERS, 1>;
-
-  enum Status {
-    // max_norm |J'(x) * f(x)| < gradient_tolerance
-    GRADIENT_TOO_SMALL,
-    //  ||dx|| <= parameter_tolerance * (||x|| + parameter_tolerance)
-    RELATIVE_STEP_SIZE_TOO_SMALL,
-    // cost_threshold > ||f(x)||^2 / 2
-    COST_TOO_SMALL,
-    // num_iterations >= max_num_iterations
-    HIT_MAX_ITERATIONS,
-    // (new_cost - old_cost) < function_tolerance * old_cost
-    COST_CHANGE_TOO_SMALL,
-
-    // TODO(sameeragarwal): Deal with numerical failures.
-  };
-
-  struct Options {
-    int max_num_iterations = 50;
-
-    // max_norm |J'(x) * f(x)| < gradient_tolerance
-    Scalar gradient_tolerance = 1e-10;
-
-    //  ||dx|| <= parameter_tolerance * (||x|| + parameter_tolerance)
-    Scalar parameter_tolerance = 1e-8;
-
-    // (new_cost - old_cost) < function_tolerance * old_cost
-    Scalar function_tolerance = 1e-6;
-
-    // cost_threshold > ||f(x)||^2 / 2
-    Scalar cost_threshold = std::numeric_limits<Scalar>::epsilon();
-
-    Scalar initial_trust_region_radius = 1e4;
-  };
-
-  struct Summary {
-    // 1/2 ||f(x_0)||^2
-    Scalar initial_cost = -1;
-    // 1/2 ||f(x)||^2
-    Scalar final_cost = -1;
-    // max_norm(J'f(x))
-    Scalar gradient_max_norm = -1;
-    int iterations = -1;
-    Status status = HIT_MAX_ITERATIONS;
-  };
-
-  bool Update(const Function& function, const Parameters& x) {
-    if (!function(x.data(), residuals_.data(), jacobian_.data())) {
-      return false;
-    }
-
-    residuals_ = -residuals_;
-
-    // On the first iteration, compute a diagonal (Jacobi) scaling
-    // matrix, which we store as a vector.
-    if (summary.iterations == 0) {
-      // jacobi_scaling = 1 / (1 + diagonal(J'J))
-      //
-      // 1 is added to the denominator to regularize small diagonal
-      // entries.
-      jacobi_scaling_ = 1.0 / (1.0 + jacobian_.colwise().norm().array());
-    }
-
-    // This explicitly computes the normal equations, which is numerically
-    // unstable. Nevertheless, it is often good enough and is fast.
-    //
-    // TODO(sameeragarwal): Refactor this to allow for DenseQR
-    // factorization.
-    jacobian_ = jacobian_ * jacobi_scaling_.asDiagonal();
-    jtj_ = jacobian_.transpose() * jacobian_;
-    g_ = jacobian_.transpose() * residuals_;
-    summary.gradient_max_norm = g_.array().abs().maxCoeff();
-    cost_ = residuals_.squaredNorm() / 2;
-    return true;
-  }
-
-  const Summary& Solve(const Function& function, Parameters* x_and_min) {
-    Initialize<NUM_RESIDUALS, NUM_PARAMETERS>(function);
-    assert(x_and_min);
-    Parameters& x = *x_and_min;
-    summary = Summary();
-    summary.iterations = 0;
-
-    // TODO(sameeragarwal): Deal with failure here.
-    Update(function, x);
-    summary.initial_cost = cost_;
-    summary.final_cost = cost_;
-
-    if (summary.gradient_max_norm < options.gradient_tolerance) {
-      summary.status = GRADIENT_TOO_SMALL;
-      return summary;
-    }
-
-    if (cost_ < options.cost_threshold) {
-      summary.status = COST_TOO_SMALL;
-      return summary;
-    }
-
-    Scalar u = 1.0 / options.initial_trust_region_radius;
-    Scalar v = 2;
-
-    for (summary.iterations = 1;
-         summary.iterations < options.max_num_iterations;
-         summary.iterations++) {
-      jtj_regularized_ = jtj_;
-      const Scalar min_diagonal = 1e-6;
-      const Scalar max_diagonal = 1e32;
-      for (int i = 0; i < dx_.rows(); ++i) {
-        jtj_regularized_(i, i) +=
-            u * (std::min)((std::max)(jtj_(i, i), min_diagonal), max_diagonal);
-      }
-
-      // TODO(sameeragarwal): Check for failure and deal with it.
-      linear_solver_.compute(jtj_regularized_);
-      lm_step_ = linear_solver_.solve(g_);
-      dx_ = jacobi_scaling_.asDiagonal() * lm_step_;
-
-      // Adding parameter_tolerance to x.norm() ensures that this
-      // works if x is near zero.
-      const Scalar parameter_tolerance =
-          options.parameter_tolerance *
-          (x.norm() + options.parameter_tolerance);
-      if (dx_.norm() < parameter_tolerance) {
-        summary.status = RELATIVE_STEP_SIZE_TOO_SMALL;
-        break;
-      }
-      x_new_ = x + dx_;
-
-      // TODO(keir): Add proper handling of errors from user eval of cost
-      // functions.
-      function(&x_new_[0], &f_x_new_[0], nullptr);
-
-      const Scalar cost_change = (2 * cost_ - f_x_new_.squaredNorm());
-      // TODO(sameeragarwal): Better more numerically stable evaluation.
-      const Scalar model_cost_change = lm_step_.dot(2 * g_ - jtj_ * lm_step_);
-
-      // rho is the ratio of the actual reduction in error to the reduction
-      // in error that would be obtained if the problem was linear. See [1]
-      // for details.
-      Scalar rho(cost_change / model_cost_change);
-      if (rho > 0) {
-        // Accept the Levenberg-Marquardt step because the linear
-        // model fits well.
-        x = x_new_;
-
-        if (std::abs(cost_change) < options.function_tolerance) {
-          cost_ = f_x_new_.squaredNorm() / 2;
-          summary.status = COST_CHANGE_TOO_SMALL;
-          break;
-        }
-
-        // TODO(sameeragarwal): Deal with failure.
-        Update(function, x);
-        if (summary.gradient_max_norm < options.gradient_tolerance) {
-          summary.status = GRADIENT_TOO_SMALL;
-          break;
-        }
-
-        if (cost_ < options.cost_threshold) {
-          summary.status = COST_TOO_SMALL;
-          break;
-        }
-
-        Scalar tmp = Scalar(2 * rho - 1);
-        u = u * (std::max)(Scalar(1 / 3.), Scalar(1) - tmp * tmp * tmp);
-        v = 2;
-
-      } else {
-        // Reject the update because either the normal equations failed to solve
-        // or the local linear model was not good (rho < 0).
-
-        // Additionally if the cost change is too small, then terminate.
-        if (std::abs(cost_change) < options.function_tolerance) {
-          // Terminate
-          summary.status = COST_CHANGE_TOO_SMALL;
-          break;
-        }
-
-        // Reduce the size of the trust region.
-        u *= v;
-        v *= 2;
-      }
-    }
-
-    summary.final_cost = cost_;
-    return summary;
-  }
-
-  Options options;
-  Summary summary;
-
- private:
-  // Preallocate everything, including temporary storage needed for solving the
-  // linear system. This allows reusing the intermediate storage across solves.
-  LinearSolver linear_solver_;
-  Scalar cost_;
-  Parameters dx_, x_new_, g_, jacobi_scaling_, lm_step_;
-  Eigen::Matrix<Scalar, NUM_RESIDUALS, 1> residuals_, f_x_new_;
-  Eigen::Matrix<Scalar, NUM_RESIDUALS, NUM_PARAMETERS> jacobian_;
-  Eigen::Matrix<Scalar, NUM_PARAMETERS, NUM_PARAMETERS> jtj_, jtj_regularized_;
-
-  // The following definitions are needed for template metaprogramming.
-  template <bool Condition, typename T>
-  struct enable_if;
-
-  template <typename T>
-  struct enable_if<true, T> {
-    using type = T;
-  };
-
-  // The number of parameters and residuals are dynamically sized.
-  template <int R, int P>
-  typename enable_if<(R == Eigen::Dynamic && P == Eigen::Dynamic), void>::type
-  Initialize(const Function& function) {
-    Initialize(function.NumResiduals(), function.NumParameters());
-  }
-
-  // The number of parameters is dynamically sized and the number of
-  // residuals is statically sized.
-  template <int R, int P>
-  typename enable_if<(R == Eigen::Dynamic && P != Eigen::Dynamic), void>::type
-  Initialize(const Function& function) {
-    Initialize(function.NumResiduals(), P);
-  }
-
-  // The number of parameters is statically sized and the number of
-  // residuals is dynamically sized.
-  template <int R, int P>
-  typename enable_if<(R != Eigen::Dynamic && P == Eigen::Dynamic), void>::type
-  Initialize(const Function& function) {
-    Initialize(R, function.NumParameters());
-  }
-
-  // The number of parameters and residuals are statically sized.
-  template <int R, int P>
-  typename enable_if<(R != Eigen::Dynamic && P != Eigen::Dynamic), void>::type
-  Initialize(const Function& /* function */) {}
-
-  void Initialize(int num_residuals, int num_parameters) {
-    dx_.resize(num_parameters);
-    x_new_.resize(num_parameters);
-    g_.resize(num_parameters);
-    jacobi_scaling_.resize(num_parameters);
-    lm_step_.resize(num_parameters);
-    residuals_.resize(num_residuals);
-    f_x_new_.resize(num_residuals);
-    jacobian_.resize(num_residuals, num_parameters);
-    jtj_.resize(num_parameters, num_parameters);
-    jtj_regularized_.resize(num_parameters, num_parameters);
-  }
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_TINY_SOLVER_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/tiny_solver_autodiff_function.h b/3rdParty/my_ceres_vc17/include/ceres/tiny_solver_autodiff_function.h
deleted file mode 100644
index 1b9bd96..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/tiny_solver_autodiff_function.h
+++ /dev/null
@@ -1,206 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: mierle@gmail.com (Keir Mierle)
-//
-// WARNING WARNING WARNING
-// WARNING WARNING WARNING  Tiny solver is experimental and will change.
-// WARNING WARNING WARNING
-
-#ifndef CERES_PUBLIC_TINY_SOLVER_AUTODIFF_FUNCTION_H_
-#define CERES_PUBLIC_TINY_SOLVER_AUTODIFF_FUNCTION_H_
-
-#include <memory>
-#include <type_traits>
-
-#include "Eigen/Core"
-#include "ceres/jet.h"
-#include "ceres/types.h"  // For kImpossibleValue.
-
-namespace ceres {
-
-// An adapter around autodiff-style CostFunctors to enable easier use of
-// TinySolver. See the example below showing how to use it:
-//
-//   // Example for cost functor with static residual size.
-//   // Same as an autodiff cost functor, but taking only 1 parameter.
-//   struct MyFunctor {
-//     template<typename T>
-//     bool operator()(const T* const parameters, T* residuals) const {
-//       const T& x = parameters[0];
-//       const T& y = parameters[1];
-//       const T& z = parameters[2];
-//       residuals[0] = x + 2.*y + 4.*z;
-//       residuals[1] = y * z;
-//       return true;
-//     }
-//   };
-//
-//   typedef TinySolverAutoDiffFunction<MyFunctor, 2, 3>
-//       AutoDiffFunction;
-//
-//   MyFunctor my_functor;
-//   AutoDiffFunction f(my_functor);
-//
-//   Vec3 x = ...;
-//   TinySolver<AutoDiffFunction> solver;
-//   solver.Solve(f, &x);
-//
-//   // Example for cost functor with dynamic residual size.
-//   // NumResiduals() supplies dynamic size of residuals.
-//   // Same functionality as in tiny_solver.h but with autodiff.
-//   struct MyFunctorWithDynamicResiduals {
-//     int NumResiduals() const {
-//       return 2;
-//     }
-//
-//     template<typename T>
-//     bool operator()(const T* const parameters, T* residuals) const {
-//       const T& x = parameters[0];
-//       const T& y = parameters[1];
-//       const T& z = parameters[2];
-//       residuals[0] = x + static_cast<T>(2.)*y + static_cast<T>(4.)*z;
-//       residuals[1] = y * z;
-//       return true;
-//     }
-//   };
-//
-//   typedef TinySolverAutoDiffFunction<MyFunctorWithDynamicResiduals,
-//                                      Eigen::Dynamic,
-//                                      3>
-//       AutoDiffFunctionWithDynamicResiduals;
-//
-//   MyFunctorWithDynamicResiduals my_functor_dyn;
-//   AutoDiffFunctionWithDynamicResiduals f(my_functor_dyn);
-//
-//   Vec3 x = ...;
-//   TinySolver<AutoDiffFunctionWithDynamicResiduals> solver;
-//   solver.Solve(f, &x);
-//
-// WARNING: The cost function adapter is not thread safe.
-template <typename CostFunctor,
-          int kNumResiduals,
-          int kNumParameters,
-          typename T = double>
-class TinySolverAutoDiffFunction {
- public:
-  // This class needs to have an Eigen aligned operator new as it contains
-  // as a member a Jet type, which itself has a fixed-size Eigen type as member.
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-
-  explicit TinySolverAutoDiffFunction(const CostFunctor& cost_functor)
-      : cost_functor_(cost_functor) {
-    Initialize<kNumResiduals>(cost_functor);
-  }
-
-  using Scalar = T;
-  enum {
-    NUM_PARAMETERS = kNumParameters,
-    NUM_RESIDUALS = kNumResiduals,
-  };
-
-  // This is similar to AutoDifferentiate(), but since there is only one
-  // parameter block it is easier to inline to avoid overhead.
-  bool operator()(const T* parameters, T* residuals, T* jacobian) const {
-    if (jacobian == nullptr) {
-      // No jacobian requested, so just directly call the cost function with
-      // doubles, skipping jets and derivatives.
-      return cost_functor_(parameters, residuals);
-    }
-    // Initialize the input jets with passed parameters.
-    for (int i = 0; i < kNumParameters; ++i) {
-      jet_parameters_[i].a = parameters[i];  // Scalar part.
-      jet_parameters_[i].v.setZero();        // Derivative part.
-      jet_parameters_[i].v[i] = T(1.0);
-    }
-
-    // Initialize the output jets such that we can detect user errors.
-    for (int i = 0; i < num_residuals_; ++i) {
-      jet_residuals_[i].a = kImpossibleValue;
-      jet_residuals_[i].v.setConstant(kImpossibleValue);
-    }
-
-    // Execute the cost function, but with jets to find the derivative.
-    if (!cost_functor_(jet_parameters_, jet_residuals_.data())) {
-      return false;
-    }
-
-    // Copy the jacobian out of the derivative part of the residual jets.
-    Eigen::Map<Eigen::Matrix<T, kNumResiduals, kNumParameters>> jacobian_matrix(
-        jacobian, num_residuals_, kNumParameters);
-    for (int r = 0; r < num_residuals_; ++r) {
-      residuals[r] = jet_residuals_[r].a;
-      // Note that while this looks like a fast vectorized write, in practice it
-      // unfortunately thrashes the cache since the writes to the column-major
-      // jacobian are strided (e.g. rows are non-contiguous).
-      jacobian_matrix.row(r) = jet_residuals_[r].v;
-    }
-    return true;
-  }
-
-  int NumResiduals() const {
-    return num_residuals_;  // Set by Initialize.
-  }
-
- private:
-  const CostFunctor& cost_functor_;
-
-  // The number of residuals at runtime.
-  // This will be overridden if NUM_RESIDUALS == Eigen::Dynamic.
-  int num_residuals_ = kNumResiduals;
-
-  // To evaluate the cost function with jets, temporary storage is needed. These
-  // are the buffers that are used during evaluation; parameters for the input,
-  // and jet_residuals_ are where the final cost and derivatives end up.
-  //
-  // Since this buffer is used for evaluation, the adapter is not thread safe.
-  using JetType = Jet<T, kNumParameters>;
-  mutable JetType jet_parameters_[kNumParameters];
-  // Eigen::Matrix serves as static or dynamic container.
-  mutable Eigen::Matrix<JetType, kNumResiduals, 1> jet_residuals_;
-
-  // The number of residuals is dynamically sized and the number of
-  // parameters is statically sized.
-  template <int R>
-  typename std::enable_if<(R == Eigen::Dynamic), void>::type Initialize(
-      const CostFunctor& function) {
-    jet_residuals_.resize(function.NumResiduals());
-    num_residuals_ = function.NumResiduals();
-  }
-
-  // The number of parameters and residuals are statically sized.
-  template <int R>
-  typename std::enable_if<(R != Eigen::Dynamic), void>::type Initialize(
-      const CostFunctor& /* function */) {
-    num_residuals_ = kNumResiduals;
-  }
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_TINY_SOLVER_AUTODIFF_FUNCTION_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/tiny_solver_cost_function_adapter.h b/3rdParty/my_ceres_vc17/include/ceres/tiny_solver_cost_function_adapter.h
deleted file mode 100644
index 166f03f..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/tiny_solver_cost_function_adapter.h
+++ /dev/null
@@ -1,142 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-
-#ifndef CERES_PUBLIC_TINY_SOLVER_COST_FUNCTION_ADAPTER_H_
-#define CERES_PUBLIC_TINY_SOLVER_COST_FUNCTION_ADAPTER_H_
-
-#include "Eigen/Core"
-#include "ceres/cost_function.h"
-#include "glog/logging.h"
-
-namespace ceres {
-
-// An adapter class that lets users of TinySolver use
-// ceres::CostFunction objects that have exactly one parameter block.
-//
-// The adapter allows for the number of residuals and the size of the
-// parameter block to be specified at compile or run-time.
-//
-// WARNING: This object is not thread-safe.
-//
-// Example usage:
-//
-//  CostFunction* cost_function = ...
-//
-// Number of residuals and parameter block size known at compile time:
-//
-//   TinySolverCostFunctionAdapter<kNumResiduals, kNumParameters>
-//   cost_function_adapter(*cost_function);
-//
-// Number of residuals known at compile time and the parameter block
-// size not known at compile time.
-//
-//   TinySolverCostFunctionAdapter<kNumResiduals, Eigen::Dynamic>
-//   cost_function_adapter(*cost_function);
-//
-// Number of residuals not known at compile time and the parameter
-// block size known at compile time.
-//
-//   TinySolverCostFunctionAdapter<Eigen::Dynamic, kParameterBlockSize>
-//   cost_function_adapter(*cost_function);
-//
-// Number of residuals not known at compile time and the parameter
-// block size not known at compile time.
-//
-//   TinySolverCostFunctionAdapter cost_function_adapter(*cost_function);
-//
-template <int kNumResiduals = Eigen::Dynamic,
-          int kNumParameters = Eigen::Dynamic>
-class TinySolverCostFunctionAdapter {
- public:
-  using Scalar = double;
-  enum ComponentSizeType {
-    NUM_PARAMETERS = kNumParameters,
-    NUM_RESIDUALS = kNumResiduals
-  };
-
-  // This struct needs to have an Eigen aligned operator new as it contains
-  // fixed-size Eigen types.
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-
-  explicit TinySolverCostFunctionAdapter(const CostFunction& cost_function)
-      : cost_function_(cost_function) {
-    CHECK_EQ(cost_function_.parameter_block_sizes().size(), 1)
-        << "Only CostFunctions with exactly one parameter blocks are allowed.";
-
-    const int parameter_block_size = cost_function_.parameter_block_sizes()[0];
-    if (NUM_PARAMETERS == Eigen::Dynamic || NUM_RESIDUALS == Eigen::Dynamic) {
-      if (NUM_RESIDUALS != Eigen::Dynamic) {
-        CHECK_EQ(cost_function_.num_residuals(), NUM_RESIDUALS);
-      }
-      if (NUM_PARAMETERS != Eigen::Dynamic) {
-        CHECK_EQ(parameter_block_size, NUM_PARAMETERS);
-      }
-
-      row_major_jacobian_.resize(cost_function_.num_residuals(),
-                                 parameter_block_size);
-    }
-  }
-
-  bool operator()(const double* parameters,
-                  double* residuals,
-                  double* jacobian) const {
-    if (!jacobian) {
-      return cost_function_.Evaluate(&parameters, residuals, nullptr);
-    }
-
-    double* jacobians[1] = {row_major_jacobian_.data()};
-    if (!cost_function_.Evaluate(&parameters, residuals, jacobians)) {
-      return false;
-    }
-
-    // The Function object used by TinySolver takes its Jacobian in a
-    // column-major layout, and the CostFunction objects use row-major
-    // Jacobian matrices. So the following bit of code does the
-    // conversion from row-major Jacobians to column-major Jacobians.
-    Eigen::Map<Eigen::Matrix<double, NUM_RESIDUALS, NUM_PARAMETERS>>
-        col_major_jacobian(jacobian, NumResiduals(), NumParameters());
-    col_major_jacobian = row_major_jacobian_;
-    return true;
-  }
-
-  int NumResiduals() const { return cost_function_.num_residuals(); }
-  int NumParameters() const {
-    return cost_function_.parameter_block_sizes()[0];
-  }
-
- private:
-  const CostFunction& cost_function_;
-  mutable Eigen::Matrix<double, NUM_RESIDUALS, NUM_PARAMETERS, Eigen::RowMajor>
-      row_major_jacobian_;
-};
-
-}  // namespace ceres
-
-#endif  // CERES_PUBLIC_TINY_SOLVER_COST_FUNCTION_ADAPTER_H_
diff --git a/3rdParty/my_ceres_vc17/include/ceres/types.h b/3rdParty/my_ceres_vc17/include/ceres/types.h
deleted file mode 100644
index 6e19c51..0000000
--- a/3rdParty/my_ceres_vc17/include/ceres/types.h
+++ /dev/null
@@ -1,565 +0,0 @@
-// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2023 Google Inc. All rights reserved.
-// http://ceres-solver.org/
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-// * Redistributions of source code must retain the above copyright notice,
-//   this list of conditions and the following disclaimer.
-// * Redistributions in binary form must reproduce the above copyright notice,
-//   this list of conditions and the following disclaimer in the documentation
-//   and/or other materials provided with the distribution.
-// * Neither the name of Google Inc. nor the names of its contributors may be
-//   used to endorse or promote products derived from this software without
-//   specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: sameeragarwal@google.com (Sameer Agarwal)
-//
-// Enums and other top level class definitions.
-//
-// Note: internal/types.cc defines stringification routines for some
-// of these enums. Please update those routines if you extend or
-// remove enums from here.
-
-#ifndef CERES_PUBLIC_TYPES_H_
-#define CERES_PUBLIC_TYPES_H_
-
-#include <string>
-
-#include "ceres/internal/disable_warnings.h"
-#include "ceres/internal/export.h"
-
-namespace ceres {
-
-// Argument type used in interfaces that can optionally take ownership
-// of a passed in argument. If TAKE_OWNERSHIP is passed, the called
-// object takes ownership of the pointer argument, and will call
-// delete on it upon completion.
-enum Ownership {
-  DO_NOT_TAKE_OWNERSHIP,
-  TAKE_OWNERSHIP,
-};
-
-// TODO(keir): Considerably expand the explanations of each solver type.
-enum LinearSolverType {
-  // These solvers are for general rectangular systems formed from the
-  // normal equations A'A x = A'b. They are direct solvers and do not
-  // assume any special problem structure.
-
-  // Solve the normal equations using a dense Cholesky solver; based
-  // on Eigen.
-  DENSE_NORMAL_CHOLESKY,
-
-  // Solve the normal equations using a dense QR solver; based on
-  // Eigen.
-  DENSE_QR,
-
-  // Solve the normal equations using a sparse cholesky solver;
-  SPARSE_NORMAL_CHOLESKY,
-
-  // Specialized solvers, specific to problems with a generalized
-  // bi-partitite structure.
-
-  // Solves the reduced linear system using a dense Cholesky solver;
-  // based on Eigen.
-  DENSE_SCHUR,
-
-  // Solves the reduced linear system using a sparse Cholesky solver;
-  // based on CHOLMOD.
-  SPARSE_SCHUR,
-
-  // Solves the reduced linear system using Conjugate Gradients, based
-  // on a new Ceres implementation.  Suitable for large scale
-  // problems.
-  ITERATIVE_SCHUR,
-
-  // Conjugate gradients on the normal equations.
-  CGNR
-};
-
-enum PreconditionerType {
-  // Trivial preconditioner - the identity matrix.
-  IDENTITY,
-
-  // Block diagonal of the Gauss-Newton Hessian.
-  JACOBI,
-
-  // Note: The following four preconditioners can only be used with
-  // the ITERATIVE_SCHUR solver. They are well suited for Structure
-  // from Motion problems.
-
-  // Block diagonal of the Schur complement. This preconditioner may
-  // only be used with the ITERATIVE_SCHUR solver.
-  SCHUR_JACOBI,
-
-  // Use power series expansion to approximate the inversion of Schur complement
-  // as a preconditioner.
-  SCHUR_POWER_SERIES_EXPANSION,
-
-  // Visibility clustering based preconditioners.
-  //
-  // The following two preconditioners use the visibility structure of
-  // the scene to determine the sparsity structure of the
-  // preconditioner. This is done using a clustering algorithm. The
-  // available visibility clustering algorithms are described below.
-  CLUSTER_JACOBI,
-  CLUSTER_TRIDIAGONAL,
-
-  // Subset preconditioner is a general purpose preconditioner
-  // linear least squares problems. Given a set of residual blocks,
-  // it uses the corresponding subset of the rows of the Jacobian to
-  // construct a preconditioner.
-  //
-  // Suppose the Jacobian J has been horizontally partitioned as
-  //
-  // J = [P]
-  //     [Q]
-  //
-  // Where, Q is the set of rows corresponding to the residual
-  // blocks in residual_blocks_for_subset_preconditioner.
-  //
-  // The preconditioner is the inverse of the matrix Q'Q.
-  //
-  // Obviously, the efficacy of the preconditioner depends on how
-  // well the matrix Q approximates J'J, or how well the chosen
-  // residual blocks approximate the non-linear least squares
-  // problem.
-  SUBSET
-};
-
-enum VisibilityClusteringType {
-  // Canonical views algorithm as described in
-  //
-  // "Scene Summarization for Online Image Collections", Ian Simon, Noah
-  // Snavely, Steven M. Seitz, ICCV 2007.
-  //
-  // This clustering algorithm can be quite slow, but gives high
-  // quality clusters. The original visibility based clustering paper
-  // used this algorithm.
-  CANONICAL_VIEWS,
-
-  // The classic single linkage algorithm. It is extremely fast as
-  // compared to CANONICAL_VIEWS, but can give slightly poorer
-  // results. For problems with large number of cameras though, this
-  // is generally a pretty good option.
-  //
-  // If you are using SCHUR_JACOBI preconditioner and have SuiteSparse
-  // available, CLUSTER_JACOBI and CLUSTER_TRIDIAGONAL in combination
-  // with the SINGLE_LINKAGE algorithm will generally give better
-  // results.
-  SINGLE_LINKAGE
-};
-
-enum SparseLinearAlgebraLibraryType {
-  // High performance sparse Cholesky factorization and approximate
-  // minimum degree ordering.
-  SUITE_SPARSE,
-
-  // Eigen's sparse linear algebra routines. In particular Ceres uses
-  // the Simplicial LDLT routines.
-  EIGEN_SPARSE,
-
-  // Apple's Accelerate framework sparse linear algebra routines.
-  ACCELERATE_SPARSE,
-
-  // Nvidia's cuSPARSE library.
-  CUDA_SPARSE,
-
-  // No sparse linear solver should be used.  This does not necessarily
-  // imply that Ceres was built without any sparse library, although that
-  // is the likely use case, merely that one should not be used.
-  NO_SPARSE
-};
-
-// The order in which variables are eliminated in a linear solver
-// can have a significant of impact on the efficiency and accuracy
-// of the method. e.g., when doing sparse Cholesky factorization,
-// there are matrices for which a good ordering will give a
-// Cholesky factor with O(n) storage, where as a bad ordering will
-// result in an completely dense factor.
-//
-// So sparse direct solvers like SPARSE_NORMAL_CHOLESKY and
-// SPARSE_SCHUR and preconditioners like SUBSET, CLUSTER_JACOBI &
-// CLUSTER_TRIDIAGONAL use a fill reducing ordering of the columns and
-// rows of the matrix being factorized before actually the numeric
-// factorization.
-//
-// This enum controls the class of algorithm used to compute this
-// fill reducing ordering. There is no single algorithm that works
-// on all matrices, so determining which algorithm works better is a
-// matter of empirical experimentation.
-enum LinearSolverOrderingType {
-  // Approximate Minimum Degree.
-  AMD,
-  // Nested Dissection.
-  NESDIS
-};
-
-enum DenseLinearAlgebraLibraryType {
-  EIGEN,
-  LAPACK,
-  CUDA,
-};
-
-// Logging options
-// The options get progressively noisier.
-enum LoggingType {
-  SILENT,
-  PER_MINIMIZER_ITERATION,
-};
-
-enum MinimizerType {
-  LINE_SEARCH,
-  TRUST_REGION,
-};
-
-enum LineSearchDirectionType {
-  // Negative of the gradient.
-  STEEPEST_DESCENT,
-
-  // A generalization of the Conjugate Gradient method to non-linear
-  // functions. The generalization can be performed in a number of
-  // different ways, resulting in a variety of search directions. The
-  // precise choice of the non-linear conjugate gradient algorithm
-  // used is determined by NonlinerConjuateGradientType.
-  NONLINEAR_CONJUGATE_GRADIENT,
-
-  // BFGS, and it's limited memory approximation L-BFGS, are quasi-Newton
-  // algorithms that approximate the Hessian matrix by iteratively refining
-  // an initial estimate with rank-one updates using the gradient at each
-  // iteration. They are a generalisation of the Secant method and satisfy
-  // the Secant equation.  The Secant equation has an infinium of solutions
-  // in multiple dimensions, as there are N*(N+1)/2 degrees of freedom in a
-  // symmetric matrix but only N conditions are specified by the Secant
-  // equation. The requirement that the Hessian approximation be positive
-  // definite imposes another N additional constraints, but that still leaves
-  // remaining degrees-of-freedom.  (L)BFGS methods uniquely determine the
-  // approximate Hessian by imposing the additional constraints that the
-  // approximation at the next iteration must be the 'closest' to the current
-  // approximation (the nature of how this proximity is measured is actually
-  // the defining difference between a family of quasi-Newton methods including
-  // (L)BFGS & DFP). (L)BFGS is currently regarded as being the best known
-  // general quasi-Newton method.
-  //
-  // The principal difference between BFGS and L-BFGS is that whilst BFGS
-  // maintains a full, dense approximation to the (inverse) Hessian, L-BFGS
-  // maintains only a window of the last M observations of the parameters and
-  // gradients. Using this observation history, the calculation of the next
-  // search direction can be computed without requiring the construction of the
-  // full dense inverse Hessian approximation. This is particularly important
-  // for problems with a large number of parameters, where storage of an N-by-N
-  // matrix in memory would be prohibitive.
-  //
-  // For more details on BFGS see:
-  //
-  // Broyden, C.G., "The Convergence of a Class of Double-rank Minimization
-  // Algorithms,"; J. Inst. Maths. Applics., Vol. 6, pp 76-90, 1970.
-  //
-  // Fletcher, R., "A New Approach to Variable Metric Algorithms,"
-  // Computer Journal, Vol. 13, pp 317-322, 1970.
-  //
-  // Goldfarb, D., "A Family of Variable Metric Updates Derived by Variational
-  // Means," Mathematics of Computing, Vol. 24, pp 23-26, 1970.
-  //
-  // Shanno, D.F., "Conditioning of Quasi-Newton Methods for Function
-  // Minimization," Mathematics of Computing, Vol. 24, pp 647-656, 1970.
-  //
-  // For more details on L-BFGS see:
-  //
-  // Nocedal, J. (1980). "Updating Quasi-Newton Matrices with Limited
-  // Storage". Mathematics of Computation 35 (151): 773-782.
-  //
-  // Byrd, R. H.; Nocedal, J.; Schnabel, R. B. (1994).
-  // "Representations of Quasi-Newton Matrices and their use in
-  // Limited Memory Methods". Mathematical Programming 63 (4):
-  // 129-156.
-  //
-  // A general reference for both methods:
-  //
-  // Nocedal J., Wright S., Numerical Optimization, 2nd Ed. Springer, 1999.
-  LBFGS,
-  BFGS,
-};
-
-// Nonlinear conjugate gradient methods are a generalization of the
-// method of Conjugate Gradients for linear systems. The
-// generalization can be carried out in a number of different ways
-// leading to number of different rules for computing the search
-// direction. Ceres provides a number of different variants. For more
-// details see Numerical Optimization by Nocedal & Wright.
-enum NonlinearConjugateGradientType {
-  FLETCHER_REEVES,
-  POLAK_RIBIERE,
-  HESTENES_STIEFEL,
-};
-
-enum LineSearchType {
-  // Backtracking line search with polynomial interpolation or
-  // bisection.
-  ARMIJO,
-  WOLFE,
-};
-
-// Ceres supports different strategies for computing the trust region
-// step.
-enum TrustRegionStrategyType {
-  // The default trust region strategy is to use the step computation
-  // used in the Levenberg-Marquardt algorithm. For more details see
-  // levenberg_marquardt_strategy.h
-  LEVENBERG_MARQUARDT,
-
-  // Powell's dogleg algorithm interpolates between the Cauchy point
-  // and the Gauss-Newton step. It is particularly useful if the
-  // LEVENBERG_MARQUARDT algorithm is making a large number of
-  // unsuccessful steps. For more details see dogleg_strategy.h.
-  //
-  // NOTES:
-  //
-  // 1. This strategy has not been experimented with or tested as
-  // extensively as LEVENBERG_MARQUARDT, and therefore it should be
-  // considered EXPERIMENTAL for now.
-  //
-  // 2. For now this strategy should only be used with exact
-  // factorization based linear solvers, i.e., SPARSE_SCHUR,
-  // DENSE_SCHUR, DENSE_QR and SPARSE_NORMAL_CHOLESKY.
-  DOGLEG
-};
-
-// Ceres supports two different dogleg strategies.
-// The "traditional" dogleg method by Powell and the
-// "subspace" method described in
-// R. H. Byrd, R. B. Schnabel, and G. A. Shultz,
-// "Approximate solution of the trust region problem by minimization
-//  over two-dimensional subspaces", Mathematical Programming,
-// 40 (1988), pp. 247--263
-enum DoglegType {
-  // The traditional approach constructs a dogleg path
-  // consisting of two line segments and finds the furthest
-  // point on that path that is still inside the trust region.
-  TRADITIONAL_DOGLEG,
-
-  // The subspace approach finds the exact minimum of the model
-  // constrained to the subspace spanned by the dogleg path.
-  SUBSPACE_DOGLEG
-};
-
-enum TerminationType {
-  // Minimizer terminated because one of the convergence criterion set
-  // by the user was satisfied.
-  //
-  // 1.  (new_cost - old_cost) < function_tolerance * old_cost;
-  // 2.  max_i |gradient_i| < gradient_tolerance
-  // 3.  |step|_2 <= parameter_tolerance * ( |x|_2 +  parameter_tolerance)
-  //
-  // The user's parameter blocks will be updated with the solution.
-  CONVERGENCE,
-
-  // The solver ran for maximum number of iterations or maximum amount
-  // of time specified by the user, but none of the convergence
-  // criterion specified by the user were met. The user's parameter
-  // blocks will be updated with the solution found so far.
-  NO_CONVERGENCE,
-
-  // The minimizer terminated because of an error.  The user's
-  // parameter blocks will not be updated.
-  FAILURE,
-
-  // Using an IterationCallback object, user code can control the
-  // minimizer. The following enums indicate that the user code was
-  // responsible for termination.
-  //
-  // Minimizer terminated successfully because a user
-  // IterationCallback returned SOLVER_TERMINATE_SUCCESSFULLY.
-  //
-  // The user's parameter blocks will be updated with the solution.
-  USER_SUCCESS,
-
-  // Minimizer terminated because because a user IterationCallback
-  // returned SOLVER_ABORT.
-  //
-  // The user's parameter blocks will not be updated.
-  USER_FAILURE
-};
-
-// Enums used by the IterationCallback instances to indicate to the
-// solver whether it should continue solving, the user detected an
-// error or the solution is good enough and the solver should
-// terminate.
-enum CallbackReturnType {
-  // Continue solving to next iteration.
-  SOLVER_CONTINUE,
-
-  // Terminate solver, and do not update the parameter blocks upon
-  // return. Unless the user has set
-  // Solver:Options:::update_state_every_iteration, in which case the
-  // state would have been updated every iteration
-  // anyways. Solver::Summary::termination_type is set to USER_ABORT.
-  SOLVER_ABORT,
-
-  // Terminate solver, update state and
-  // return. Solver::Summary::termination_type is set to USER_SUCCESS.
-  SOLVER_TERMINATE_SUCCESSFULLY
-};
-
-// The format in which linear least squares problems should be logged
-// when Solver::Options::lsqp_iterations_to_dump is non-empty.
-enum DumpFormatType {
-  // Print the linear least squares problem in a human readable format
-  // to stderr. The Jacobian is printed as a dense matrix. The vectors
-  // D, x and f are printed as dense vectors. This should only be used
-  // for small problems.
-  CONSOLE,
-
-  // Write out the linear least squares problem to the directory
-  // pointed to by Solver::Options::lsqp_dump_directory as text files
-  // which can be read into MATLAB/Octave. The Jacobian is dumped as a
-  // text file containing (i,j,s) triplets, the vectors D, x and f are
-  // dumped as text files containing a list of their values.
-  //
-  // A MATLAB/octave script called lm_iteration_???.m is also output,
-  // which can be used to parse and load the problem into memory.
-  TEXTFILE
-};
-
-// For SizedCostFunction and AutoDiffCostFunction, DYNAMIC can be
-// specified for the number of residuals. If specified, then the
-// number of residuas for that cost function can vary at runtime.
-enum DimensionType {
-  DYNAMIC = -1,
-};
-
-// The differentiation method used to compute numerical derivatives in
-// NumericDiffCostFunction and DynamicNumericDiffCostFunction.
-enum NumericDiffMethodType {
-  // Compute central finite difference: f'(x) ~ (f(x+h) - f(x-h)) / 2h.
-  CENTRAL,
-
-  // Compute forward finite difference: f'(x) ~ (f(x+h) - f(x)) / h.
-  FORWARD,
-
-  // Adaptive numerical differentiation using Ridders' method. Provides more
-  // accurate and robust derivatives at the expense of additional cost
-  // function evaluations.
-  RIDDERS
-};
-
-enum LineSearchInterpolationType {
-  BISECTION,
-  QUADRATIC,
-  CUBIC,
-};
-
-enum CovarianceAlgorithmType {
-  DENSE_SVD,
-  SPARSE_QR,
-};
-
-// It is a near impossibility that user code generates this exact
-// value in normal operation, thus we will use it to fill arrays
-// before passing them to user code. If on return an element of the
-// array still contains this value, we will assume that the user code
-// did not write to that memory location.
-const double kImpossibleValue = 1e302;
-
-CERES_EXPORT const char* LinearSolverTypeToString(LinearSolverType type);
-CERES_EXPORT bool StringToLinearSolverType(std::string value,
-                                           LinearSolverType* type);
-
-CERES_EXPORT const char* PreconditionerTypeToString(PreconditionerType type);
-CERES_EXPORT bool StringToPreconditionerType(std::string value,
-                                             PreconditionerType* type);
-
-CERES_EXPORT const char* VisibilityClusteringTypeToString(
-    VisibilityClusteringType type);
-CERES_EXPORT bool StringToVisibilityClusteringType(
-    std::string value, VisibilityClusteringType* type);
-
-CERES_EXPORT const char* SparseLinearAlgebraLibraryTypeToString(
-    SparseLinearAlgebraLibraryType type);
-CERES_EXPORT bool StringToSparseLinearAlgebraLibraryType(
-    std::string value, SparseLinearAlgebraLibraryType* type);
-
-CERES_EXPORT const char* LinearSolverOrderingTypeToString(
-    LinearSolverOrderingType type);
-CERES_EXPORT bool StringToLinearSolverOrderingType(
-    std::string value, LinearSolverOrderingType* type);
-
-CERES_EXPORT const char* DenseLinearAlgebraLibraryTypeToString(
-    DenseLinearAlgebraLibraryType type);
-CERES_EXPORT bool StringToDenseLinearAlgebraLibraryType(
-    std::string value, DenseLinearAlgebraLibraryType* type);
-
-CERES_EXPORT const char* TrustRegionStrategyTypeToString(
-    TrustRegionStrategyType type);
-CERES_EXPORT bool StringToTrustRegionStrategyType(
-    std::string value, TrustRegionStrategyType* type);
-
-CERES_EXPORT const char* DoglegTypeToString(DoglegType type);
-CERES_EXPORT bool StringToDoglegType(std::string value, DoglegType* type);
-
-CERES_EXPORT const char* MinimizerTypeToString(MinimizerType type);
-CERES_EXPORT bool StringToMinimizerType(std::string value, MinimizerType* type);
-
-CERES_EXPORT const char* LineSearchDirectionTypeToString(
-    LineSearchDirectionType type);
-CERES_EXPORT bool StringToLineSearchDirectionType(
-    std::string value, LineSearchDirectionType* type);
-
-CERES_EXPORT const char* LineSearchTypeToString(LineSearchType type);
-CERES_EXPORT bool StringToLineSearchType(std::string value,
-                                         LineSearchType* type);
-
-CERES_EXPORT const char* NonlinearConjugateGradientTypeToString(
-    NonlinearConjugateGradientType type);
-CERES_EXPORT bool StringToNonlinearConjugateGradientType(
-    std::string value, NonlinearConjugateGradientType* type);
-
-CERES_EXPORT const char* LineSearchInterpolationTypeToString(
-    LineSearchInterpolationType type);
-CERES_EXPORT bool StringToLineSearchInterpolationType(
-    std::string value, LineSearchInterpolationType* type);
-
-CERES_EXPORT const char* CovarianceAlgorithmTypeToString(
-    CovarianceAlgorithmType type);
-CERES_EXPORT bool StringToCovarianceAlgorithmType(
-    std::string value, CovarianceAlgorithmType* type);
-
-CERES_EXPORT const char* NumericDiffMethodTypeToString(
-    NumericDiffMethodType type);
-CERES_EXPORT bool StringToNumericDiffMethodType(std::string value,
-                                                NumericDiffMethodType* type);
-
-CERES_EXPORT const char* LoggingTypeToString(LoggingType type);
-CERES_EXPORT bool StringtoLoggingType(std::string value, LoggingType* type);
-
-CERES_EXPORT const char* DumpFormatTypeToString(DumpFormatType type);
-CERES_EXPORT bool StringtoDumpFormatType(std::string value,
-                                         DumpFormatType* type);
-CERES_EXPORT bool StringtoDumpFormatType(std::string value, LoggingType* type);
-
-CERES_EXPORT const char* TerminationTypeToString(TerminationType type);
-
-CERES_EXPORT bool IsSchurType(LinearSolverType type);
-CERES_EXPORT bool IsSparseLinearAlgebraLibraryTypeAvailable(
-    SparseLinearAlgebraLibraryType type);
-CERES_EXPORT bool IsDenseLinearAlgebraLibraryTypeAvailable(
-    DenseLinearAlgebraLibraryType type);
-
-}  // namespace ceres
-
-#include "ceres/internal/reenable_warnings.h"
-
-#endif  // CERES_PUBLIC_TYPES_H_
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Cholesky b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Cholesky
deleted file mode 100644
index a318ceb..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Cholesky
+++ /dev/null
@@ -1,45 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLESKY_MODULE_H
-#define EIGEN_CHOLESKY_MODULE_H
-
-#include "Core"
-#include "Jacobi"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Cholesky_Module Cholesky module
-  *
-  *
-  *
-  * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
-  * Those decompositions are also accessible via the following methods:
-  *  - MatrixBase::llt()
-  *  - MatrixBase::ldlt()
-  *  - SelfAdjointView::llt()
-  *  - SelfAdjointView::ldlt()
-  *
-  * \code
-  * #include <Eigen/Cholesky>
-  * \endcode
-  */
-
-#include "src/Cholesky/LLT.h"
-#include "src/Cholesky/LDLT.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/Cholesky/LLT_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CHOLESKY_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/CholmodSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/CholmodSupport
deleted file mode 100644
index bed8924..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/CholmodSupport
+++ /dev/null
@@ -1,48 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
-#define EIGEN_CHOLMODSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-  #include <cholmod.h>
-}
-
-/** \ingroup Support_modules
-  * \defgroup CholmodSupport_Module CholmodSupport module
-  *
-  * This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  * It provides the two following main factorization classes:
-  * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
-  * - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
-  *
-  * For the sake of completeness, this module also propose the two following classes:
-  * - class CholmodSimplicialLLT
-  * - class CholmodSimplicialLDLT
-  * Note that these classes does not bring any particular advantage compared to the built-in
-  * SimplicialLLT and SimplicialLDLT factorization classes.
-  *
-  * \code
-  * #include <Eigen/CholmodSupport>
-  * \endcode
-  *
-  * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be linked to the cholmod library and its dependencies.
-  * The dependencies depend on how cholmod has been compiled.
-  * For a cmake based project, you can use our FindCholmod.cmake module to help you in this task.
-  *
-  */
-
-#include "src/CholmodSupport/CholmodSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CHOLMODSUPPORT_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Core b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Core
deleted file mode 100644
index 5921e15..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Core
+++ /dev/null
@@ -1,384 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CORE_H
-#define EIGEN_CORE_H
-
-// first thing Eigen does: stop the compiler from reporting useless warnings.
-#include "src/Core/util/DisableStupidWarnings.h"
-
-// then include this file where all our macros are defined. It's really important to do it first because
-// it's where we do all the compiler/OS/arch detections and define most defaults.
-#include "src/Core/util/Macros.h"
-
-// This detects SSE/AVX/NEON/etc. and configure alignment settings
-#include "src/Core/util/ConfigureVectorization.h"
-
-// We need cuda_runtime.h/hip_runtime.h to ensure that
-// the EIGEN_USING_STD macro works properly on the device side
-#if defined(EIGEN_CUDACC)
-  #include <cuda_runtime.h>
-#elif defined(EIGEN_HIPCC)
-  #include <hip/hip_runtime.h>
-#endif
-
-
-#ifdef EIGEN_EXCEPTIONS
-  #include <new>
-#endif
-
-// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
-// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
-#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6) && EIGEN_GNUC_AT_MOST(5,5)
-  #pragma GCC optimize ("-fno-ipa-cp-clone")
-#endif
-
-// Prevent ICC from specializing std::complex operators that silently fail
-// on device. This allows us to use our own device-compatible specializations
-// instead.
-#if defined(EIGEN_COMP_ICC) && defined(EIGEN_GPU_COMPILE_PHASE) \
-    && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_)
-#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1
-#endif
-#include <complex>
-
-// this include file manages BLAS and MKL related macros
-// and inclusion of their respective header files
-#include "src/Core/util/MKL_support.h"
-
-
-#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
-  #define EIGEN_HAS_GPU_FP16
-#endif
-
-#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16)
-  #define EIGEN_HAS_GPU_BF16
-#endif
-
-#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
-  #define EIGEN_HAS_OPENMP
-#endif
-
-#ifdef EIGEN_HAS_OPENMP
-#include <omp.h>
-#endif
-
-// MSVC for windows mobile does not have the errno.h file
-#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
-#define EIGEN_HAS_ERRNO
-#endif
-
-#ifdef EIGEN_HAS_ERRNO
-#include <cerrno>
-#endif
-#include <cstddef>
-#include <cstdlib>
-#include <cmath>
-#include <cassert>
-#include <functional>
-#include <sstream>
-#ifndef EIGEN_NO_IO
-  #include <iosfwd>
-#endif
-#include <cstring>
-#include <string>
-#include <limits>
-#include <climits> // for CHAR_BIT
-// for min/max:
-#include <algorithm>
-
-#if EIGEN_HAS_CXX11
-#include <array>
-#endif
-
-// for std::is_nothrow_move_assignable
-#ifdef EIGEN_INCLUDE_TYPE_TRAITS
-#include <type_traits>
-#endif
-
-// for outputting debug info
-#ifdef EIGEN_DEBUG_ASSIGN
-#include <iostream>
-#endif
-
-// required for __cpuid, needs to be included after cmath
-#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
-  #include <intrin.h>
-#endif
-
-#if defined(EIGEN_USE_SYCL)
-  #undef min
-  #undef max
-  #undef isnan
-  #undef isinf
-  #undef isfinite
-  #include <CL/sycl.hpp>
-  #include <map>
-  #include <memory>
-  #include <utility>
-  #include <thread>
-  #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0
-  #define EIGEN_SYCL_LOCAL_THREAD_DIM0 16
-  #endif
-  #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1
-  #define EIGEN_SYCL_LOCAL_THREAD_DIM1 16
-  #endif
-#endif
-
-
-#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT
-// This will generate an error message:
-#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
-#endif
-
-namespace Eigen {
-
-// we use size_t frequently and we'll never remember to prepend it with std:: every time just to
-// ensure QNX/QCC support
-using std::size_t;
-// gcc 4.6.0 wants std:: for ptrdiff_t
-using std::ptrdiff_t;
-
-}
-
-/** \defgroup Core_Module Core module
-  * This is the main module of Eigen providing dense matrix and vector support
-  * (both fixed and dynamic size) with all the features corresponding to a BLAS library
-  * and much more...
-  *
-  * \code
-  * #include <Eigen/Core>
-  * \endcode
-  */
-
-#include "src/Core/util/Constants.h"
-#include "src/Core/util/Meta.h"
-#include "src/Core/util/ForwardDeclarations.h"
-#include "src/Core/util/StaticAssert.h"
-#include "src/Core/util/XprHelper.h"
-#include "src/Core/util/Memory.h"
-#include "src/Core/util/IntegralConstant.h"
-#include "src/Core/util/SymbolicIndex.h"
-
-#include "src/Core/NumTraits.h"
-#include "src/Core/MathFunctions.h"
-#include "src/Core/GenericPacketMath.h"
-#include "src/Core/MathFunctionsImpl.h"
-#include "src/Core/arch/Default/ConjHelper.h"
-// Generic half float support
-#include "src/Core/arch/Default/Half.h"
-#include "src/Core/arch/Default/BFloat16.h"
-#include "src/Core/arch/Default/TypeCasting.h"
-#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
-
-#if defined EIGEN_VECTORIZE_AVX512
-  #include "src/Core/arch/SSE/PacketMath.h"
-  #include "src/Core/arch/SSE/TypeCasting.h"
-  #include "src/Core/arch/SSE/Complex.h"
-  #include "src/Core/arch/AVX/PacketMath.h"
-  #include "src/Core/arch/AVX/TypeCasting.h"
-  #include "src/Core/arch/AVX/Complex.h"
-  #include "src/Core/arch/AVX512/PacketMath.h"
-  #include "src/Core/arch/AVX512/TypeCasting.h"
-  #include "src/Core/arch/AVX512/Complex.h"
-  #include "src/Core/arch/SSE/MathFunctions.h"
-  #include "src/Core/arch/AVX/MathFunctions.h"
-  #include "src/Core/arch/AVX512/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_AVX
-  // Use AVX for floats and doubles, SSE for integers
-  #include "src/Core/arch/SSE/PacketMath.h"
-  #include "src/Core/arch/SSE/TypeCasting.h"
-  #include "src/Core/arch/SSE/Complex.h"
-  #include "src/Core/arch/AVX/PacketMath.h"
-  #include "src/Core/arch/AVX/TypeCasting.h"
-  #include "src/Core/arch/AVX/Complex.h"
-  #include "src/Core/arch/SSE/MathFunctions.h"
-  #include "src/Core/arch/AVX/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_SSE
-  #include "src/Core/arch/SSE/PacketMath.h"
-  #include "src/Core/arch/SSE/TypeCasting.h"
-  #include "src/Core/arch/SSE/MathFunctions.h"
-  #include "src/Core/arch/SSE/Complex.h"
-#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
-  #include "src/Core/arch/AltiVec/PacketMath.h"
-  #include "src/Core/arch/AltiVec/MathFunctions.h"
-  #include "src/Core/arch/AltiVec/Complex.h"
-#elif defined EIGEN_VECTORIZE_NEON
-  #include "src/Core/arch/NEON/PacketMath.h"
-  #include "src/Core/arch/NEON/TypeCasting.h"
-  #include "src/Core/arch/NEON/MathFunctions.h"
-  #include "src/Core/arch/NEON/Complex.h"
-#elif defined EIGEN_VECTORIZE_SVE
-  #include "src/Core/arch/SVE/PacketMath.h"
-  #include "src/Core/arch/SVE/TypeCasting.h"
-  #include "src/Core/arch/SVE/MathFunctions.h"
-#elif defined EIGEN_VECTORIZE_ZVECTOR
-  #include "src/Core/arch/ZVector/PacketMath.h"
-  #include "src/Core/arch/ZVector/MathFunctions.h"
-  #include "src/Core/arch/ZVector/Complex.h"
-#elif defined EIGEN_VECTORIZE_MSA
-  #include "src/Core/arch/MSA/PacketMath.h"
-  #include "src/Core/arch/MSA/MathFunctions.h"
-  #include "src/Core/arch/MSA/Complex.h"
-#endif
-
-#if defined EIGEN_VECTORIZE_GPU
-  #include "src/Core/arch/GPU/PacketMath.h"
-  #include "src/Core/arch/GPU/MathFunctions.h"
-  #include "src/Core/arch/GPU/TypeCasting.h"
-#endif
-
-#if defined(EIGEN_USE_SYCL)
-  #include "src/Core/arch/SYCL/SyclMemoryModel.h"
-  #include "src/Core/arch/SYCL/InteropHeaders.h"
-#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
-  #include "src/Core/arch/SYCL/PacketMath.h"
-  #include "src/Core/arch/SYCL/MathFunctions.h"
-  #include "src/Core/arch/SYCL/TypeCasting.h"
-#endif
-#endif
-
-#include "src/Core/arch/Default/Settings.h"
-// This file provides generic implementations valid for scalar as well
-#include "src/Core/arch/Default/GenericPacketMathFunctions.h"
-
-#include "src/Core/functors/TernaryFunctors.h"
-#include "src/Core/functors/BinaryFunctors.h"
-#include "src/Core/functors/UnaryFunctors.h"
-#include "src/Core/functors/NullaryFunctors.h"
-#include "src/Core/functors/StlFunctors.h"
-#include "src/Core/functors/AssignmentFunctors.h"
-
-// Specialized functors to enable the processing of complex numbers
-// on CUDA devices
-#ifdef EIGEN_CUDACC
-#include "src/Core/arch/CUDA/Complex.h"
-#endif
-
-#include "src/Core/util/IndexedViewHelper.h"
-#include "src/Core/util/ReshapedHelper.h"
-#include "src/Core/ArithmeticSequence.h"
-#ifndef EIGEN_NO_IO
-  #include "src/Core/IO.h"
-#endif
-#include "src/Core/DenseCoeffsBase.h"
-#include "src/Core/DenseBase.h"
-#include "src/Core/MatrixBase.h"
-#include "src/Core/EigenBase.h"
-
-#include "src/Core/Product.h"
-#include "src/Core/CoreEvaluators.h"
-#include "src/Core/AssignEvaluator.h"
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
-                                // at least confirmed with Doxygen 1.5.5 and 1.5.6
-  #include "src/Core/Assign.h"
-#endif
-
-#include "src/Core/ArrayBase.h"
-#include "src/Core/util/BlasUtil.h"
-#include "src/Core/DenseStorage.h"
-#include "src/Core/NestByValue.h"
-
-// #include "src/Core/ForceAlignedAccess.h"
-
-#include "src/Core/ReturnByValue.h"
-#include "src/Core/NoAlias.h"
-#include "src/Core/PlainObjectBase.h"
-#include "src/Core/Matrix.h"
-#include "src/Core/Array.h"
-#include "src/Core/CwiseTernaryOp.h"
-#include "src/Core/CwiseBinaryOp.h"
-#include "src/Core/CwiseUnaryOp.h"
-#include "src/Core/CwiseNullaryOp.h"
-#include "src/Core/CwiseUnaryView.h"
-#include "src/Core/SelfCwiseBinaryOp.h"
-#include "src/Core/Dot.h"
-#include "src/Core/StableNorm.h"
-#include "src/Core/Stride.h"
-#include "src/Core/MapBase.h"
-#include "src/Core/Map.h"
-#include "src/Core/Ref.h"
-#include "src/Core/Block.h"
-#include "src/Core/VectorBlock.h"
-#include "src/Core/IndexedView.h"
-#include "src/Core/Reshaped.h"
-#include "src/Core/Transpose.h"
-#include "src/Core/DiagonalMatrix.h"
-#include "src/Core/Diagonal.h"
-#include "src/Core/DiagonalProduct.h"
-#include "src/Core/Redux.h"
-#include "src/Core/Visitor.h"
-#include "src/Core/Fuzzy.h"
-#include "src/Core/Swap.h"
-#include "src/Core/CommaInitializer.h"
-#include "src/Core/GeneralProduct.h"
-#include "src/Core/Solve.h"
-#include "src/Core/Inverse.h"
-#include "src/Core/SolverBase.h"
-#include "src/Core/PermutationMatrix.h"
-#include "src/Core/Transpositions.h"
-#include "src/Core/TriangularMatrix.h"
-#include "src/Core/SelfAdjointView.h"
-#include "src/Core/products/GeneralBlockPanelKernel.h"
-#include "src/Core/products/Parallelizer.h"
-#include "src/Core/ProductEvaluators.h"
-#include "src/Core/products/GeneralMatrixVector.h"
-#include "src/Core/products/GeneralMatrixMatrix.h"
-#include "src/Core/SolveTriangular.h"
-#include "src/Core/products/GeneralMatrixMatrixTriangular.h"
-#include "src/Core/products/SelfadjointMatrixVector.h"
-#include "src/Core/products/SelfadjointMatrixMatrix.h"
-#include "src/Core/products/SelfadjointProduct.h"
-#include "src/Core/products/SelfadjointRank2Update.h"
-#include "src/Core/products/TriangularMatrixVector.h"
-#include "src/Core/products/TriangularMatrixMatrix.h"
-#include "src/Core/products/TriangularSolverMatrix.h"
-#include "src/Core/products/TriangularSolverVector.h"
-#include "src/Core/BandMatrix.h"
-#include "src/Core/CoreIterators.h"
-#include "src/Core/ConditionEstimator.h"
-
-#if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
-  #include "src/Core/arch/AltiVec/MatrixProduct.h"
-#elif defined EIGEN_VECTORIZE_NEON
-  #include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
-#endif
-
-#include "src/Core/BooleanRedux.h"
-#include "src/Core/Select.h"
-#include "src/Core/VectorwiseOp.h"
-#include "src/Core/PartialReduxEvaluator.h"
-#include "src/Core/Random.h"
-#include "src/Core/Replicate.h"
-#include "src/Core/Reverse.h"
-#include "src/Core/ArrayWrapper.h"
-#include "src/Core/StlIterators.h"
-
-#ifdef EIGEN_USE_BLAS
-#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
-#include "src/Core/products/GeneralMatrixVector_BLAS.h"
-#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
-#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
-#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
-#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
-#include "src/Core/products/TriangularMatrixVector_BLAS.h"
-#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
-#endif // EIGEN_USE_BLAS
-
-#ifdef EIGEN_USE_MKL_VML
-#include "src/Core/Assign_MKL.h"
-#endif
-
-#include "src/Core/GlobalFunctions.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CORE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Dense b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Dense
deleted file mode 100644
index 5768910..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Dense
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "Core"
-#include "LU"
-#include "Cholesky"
-#include "QR"
-#include "SVD"
-#include "Geometry"
-#include "Eigenvalues"
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Eigen b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Eigen
deleted file mode 100644
index 654c8dc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Eigen
+++ /dev/null
@@ -1,2 +0,0 @@
-#include "Dense"
-#include "Sparse"
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Eigenvalues b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Eigenvalues
deleted file mode 100644
index 5467a2e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Eigenvalues
+++ /dev/null
@@ -1,60 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EIGENVALUES_MODULE_H
-#define EIGEN_EIGENVALUES_MODULE_H
-
-#include "Core"
-
-#include "Cholesky"
-#include "Jacobi"
-#include "Householder"
-#include "LU"
-#include "Geometry"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Eigenvalues_Module Eigenvalues module
-  *
-  *
-  *
-  * This module mainly provides various eigenvalue solvers.
-  * This module also provides some MatrixBase methods, including:
-  *  - MatrixBase::eigenvalues(),
-  *  - MatrixBase::operatorNorm()
-  *
-  * \code
-  * #include <Eigen/Eigenvalues>
-  * \endcode
-  */
-
-#include "src/misc/RealSvd2x2.h"
-#include "src/Eigenvalues/Tridiagonalization.h"
-#include "src/Eigenvalues/RealSchur.h"
-#include "src/Eigenvalues/EigenSolver.h"
-#include "src/Eigenvalues/SelfAdjointEigenSolver.h"
-#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h"
-#include "src/Eigenvalues/HessenbergDecomposition.h"
-#include "src/Eigenvalues/ComplexSchur.h"
-#include "src/Eigenvalues/ComplexEigenSolver.h"
-#include "src/Eigenvalues/RealQZ.h"
-#include "src/Eigenvalues/GeneralizedEigenSolver.h"
-#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/Eigenvalues/RealSchur_LAPACKE.h"
-#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
-#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_EIGENVALUES_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Geometry b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Geometry
deleted file mode 100644
index bc78110..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Geometry
+++ /dev/null
@@ -1,59 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GEOMETRY_MODULE_H
-#define EIGEN_GEOMETRY_MODULE_H
-
-#include "Core"
-
-#include "SVD"
-#include "LU"
-#include <limits>
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Geometry_Module Geometry module
-  *
-  * This module provides support for:
-  *  - fixed-size homogeneous transformations
-  *  - translation, scaling, 2D and 3D rotations
-  *  - \link Quaternion quaternions \endlink
-  *  - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3)
-  *  - orthognal vector generation (\ref MatrixBase::unitOrthogonal)
-  *  - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
-  *  - \link AlignedBox axis aligned bounding boxes \endlink
-  *  - \link umeyama least-square transformation fitting \endlink
-  *
-  * \code
-  * #include <Eigen/Geometry>
-  * \endcode
-  */
-
-#include "src/Geometry/OrthoMethods.h"
-#include "src/Geometry/EulerAngles.h"
-
-#include "src/Geometry/Homogeneous.h"
-#include "src/Geometry/RotationBase.h"
-#include "src/Geometry/Rotation2D.h"
-#include "src/Geometry/Quaternion.h"
-#include "src/Geometry/AngleAxis.h"
-#include "src/Geometry/Transform.h"
-#include "src/Geometry/Translation.h"
-#include "src/Geometry/Scaling.h"
-#include "src/Geometry/Hyperplane.h"
-#include "src/Geometry/ParametrizedLine.h"
-#include "src/Geometry/AlignedBox.h"
-#include "src/Geometry/Umeyama.h"
-
-// Use the SSE optimized version whenever possible.
-#if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON)
-#include "src/Geometry/arch/Geometry_SIMD.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_GEOMETRY_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Householder b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Householder
deleted file mode 100644
index f2fa799..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Householder
+++ /dev/null
@@ -1,29 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOUSEHOLDER_MODULE_H
-#define EIGEN_HOUSEHOLDER_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Householder_Module Householder module
-  * This module provides Householder transformations.
-  *
-  * \code
-  * #include <Eigen/Householder>
-  * \endcode
-  */
-
-#include "src/Householder/Householder.h"
-#include "src/Householder/HouseholderSequence.h"
-#include "src/Householder/BlockHouseholder.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_HOUSEHOLDER_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/IterativeLinearSolvers b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/IterativeLinearSolvers
deleted file mode 100644
index 957d575..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/IterativeLinearSolvers
+++ /dev/null
@@ -1,48 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
-#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** 
-  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
-  *
-  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
-  * Those solvers are accessible via the following classes:
-  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
-  *  - LeastSquaresConjugateGradient for rectangular least-square problems,
-  *  - BiCGSTAB for general square matrices.
-  *
-  * These iterative solvers are associated with some preconditioners:
-  *  - IdentityPreconditioner - not really useful
-  *  - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
-  *  - IncompleteLUT - incomplete LU factorization with dual thresholding
-  *
-  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
-  *
-    \code
-    #include <Eigen/IterativeLinearSolvers>
-    \endcode
-  */
-
-#include "src/IterativeLinearSolvers/SolveWithGuess.h"
-#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
-#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
-#include "src/IterativeLinearSolvers/ConjugateGradient.h"
-#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
-#include "src/IterativeLinearSolvers/BiCGSTAB.h"
-#include "src/IterativeLinearSolvers/IncompleteLUT.h"
-#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Jacobi b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Jacobi
deleted file mode 100644
index 43edc7a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Jacobi
+++ /dev/null
@@ -1,32 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_JACOBI_MODULE_H
-#define EIGEN_JACOBI_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup Jacobi_Module Jacobi module
-  * This module provides Jacobi and Givens rotations.
-  *
-  * \code
-  * #include <Eigen/Jacobi>
-  * \endcode
-  *
-  * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
-  *  - MatrixBase::applyOnTheLeft()
-  *  - MatrixBase::applyOnTheRight().
-  */
-
-#include "src/Jacobi/Jacobi.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_JACOBI_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/KLUSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/KLUSupport
deleted file mode 100644
index b23d905..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/KLUSupport
+++ /dev/null
@@ -1,41 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_KLUSUPPORT_MODULE_H
-#define EIGEN_KLUSUPPORT_MODULE_H
-
-#include <Eigen/SparseCore>
-
-#include <Eigen/src/Core/util/DisableStupidWarnings.h>
-
-extern "C" {
-#include <btf.h>
-#include <klu.h>
-   }
-
-/** \ingroup Support_modules
-  * \defgroup KLUSupport_Module KLUSupport module
-  *
-  * This module provides an interface to the KLU library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  * It provides the following factorization class:
-  * - class KLU: a sparse LU factorization, well-suited for circuit simulation.
-  *
-  * \code
-  * #include <Eigen/KLUSupport>
-  * \endcode
-  *
-  * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must be linked to the klu library and its dependencies.
-  * The dependencies depend on how umfpack has been compiled.
-  * For a cmake based project, you can use our FindKLU.cmake module to help you in this task.
-  *
-  */
-
-#include "src/KLUSupport/KLUSupport.h"
-
-#include <Eigen/src/Core/util/ReenableStupidWarnings.h>
-
-#endif // EIGEN_KLUSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/LU b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/LU
deleted file mode 100644
index 1236ceb..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/LU
+++ /dev/null
@@ -1,47 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LU_MODULE_H
-#define EIGEN_LU_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup LU_Module LU module
-  * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
-  * This module defines the following MatrixBase methods:
-  *  - MatrixBase::inverse()
-  *  - MatrixBase::determinant()
-  *
-  * \code
-  * #include <Eigen/LU>
-  * \endcode
-  */
-
-#include "src/misc/Kernel.h"
-#include "src/misc/Image.h"
-#include "src/LU/FullPivLU.h"
-#include "src/LU/PartialPivLU.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/LU/PartialPivLU_LAPACKE.h"
-#endif
-#include "src/LU/Determinant.h"
-#include "src/LU/InverseImpl.h"
-
-#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON
-  #include "src/LU/arch/InverseSize4.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_LU_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/MetisSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/MetisSupport
deleted file mode 100644
index 85c41bf..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/MetisSupport
+++ /dev/null
@@ -1,35 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_METISSUPPORT_MODULE_H
-#define EIGEN_METISSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <metis.h>
-}
-
-
-/** \ingroup Support_modules
-  * \defgroup MetisSupport_Module MetisSupport module
-  *
-  * \code
-  * #include <Eigen/MetisSupport>
-  * \endcode
-  * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis). 
-  * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
-  */
-
-
-#include "src/MetisSupport/MetisSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_METISSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/OrderingMethods b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/OrderingMethods
deleted file mode 100644
index 29691a6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/OrderingMethods
+++ /dev/null
@@ -1,70 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
-#define EIGEN_ORDERINGMETHODS_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** 
-  * \defgroup OrderingMethods_Module OrderingMethods module
-  *
-  * This module is currently for internal use only
-  * 
-  * It defines various built-in and external ordering methods for sparse matrices. 
-  * They are typically used to reduce the number of elements during 
-  * the sparse matrix decomposition (LLT, LU, QR).
-  * Precisely, in a preprocessing step, a permutation matrix P is computed using 
-  * those ordering methods and applied to the columns of the matrix. 
-  * Using for instance the sparse Cholesky decomposition, it is expected that 
-  * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
-  * 
-  * 
-  * Usage : 
-  * \code
-  * #include <Eigen/OrderingMethods>
-  * \endcode
-  * 
-  * A simple usage is as a template parameter in the sparse decomposition classes : 
-  * 
-  * \code 
-  * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
-  * \endcode 
-  * 
-  * \code 
-  * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
-  * \endcode
-  * 
-  * It is possible as well to call directly a particular ordering method for your own purpose, 
-  * \code 
-  * AMDOrdering<int> ordering;
-  * PermutationMatrix<Dynamic, Dynamic, int> perm;
-  * SparseMatrix<double> A; 
-  * //Fill the matrix ...
-  * 
-  * ordering(A, perm); // Call AMD
-  * \endcode
-  * 
-  * \note Some of these methods (like AMD or METIS), need the sparsity pattern 
-  * of the input matrix to be symmetric. When the matrix is structurally unsymmetric, 
-  * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
-  * If your matrix is already symmetric (at leat in structure), you can avoid that
-  * by calling the method with a SelfAdjointView type.
-  * 
-  * \code
-  *  // Call the ordering on the pattern of the lower triangular matrix A
-  * ordering(A.selfadjointView<Lower>(), perm);
-  * \endcode
-  */
-
-#include "src/OrderingMethods/Amd.h"
-#include "src/OrderingMethods/Ordering.h"
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_ORDERINGMETHODS_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/PaStiXSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/PaStiXSupport
deleted file mode 100644
index 234619a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/PaStiXSupport
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
-#define EIGEN_PASTIXSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <pastix_nompi.h>
-#include <pastix.h>
-}
-
-#ifdef complex
-#undef complex
-#endif
-
-/** \ingroup Support_modules
-  * \defgroup PaStiXSupport_Module PaStiXSupport module
-  * 
-  * This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
-  * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
-  * It provides the two following main factorization classes:
-  * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
-  * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
-  * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
-  * 
-  * \code
-  * #include <Eigen/PaStiXSupport>
-  * \endcode
-  *
-  * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be linked to the PaSTiX library and its dependencies.
-  * This wrapper resuires PaStiX version 5.x compiled without MPI support.
-  * The dependencies depend on how PaSTiX has been compiled.
-  * For a cmake based project, you can use our FindPaSTiX.cmake module to help you in this task.
-  *
-  */
-
-#include "src/PaStiXSupport/PaStiXSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_PASTIXSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/PardisoSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/PardisoSupport
deleted file mode 100644
index 340edf5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/PardisoSupport
+++ /dev/null
@@ -1,35 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
-#define EIGEN_PARDISOSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include <mkl_pardiso.h>
-
-/** \ingroup Support_modules
-  * \defgroup PardisoSupport_Module PardisoSupport module
-  *
-  * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
-  *
-  * \code
-  * #include <Eigen/PardisoSupport>
-  * \endcode
-  *
-  * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies.
-  * See this \ref TopicUsingIntelMKL "page" for more information on MKL-Eigen integration.
-  * 
-  */
-
-#include "src/PardisoSupport/PardisoSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_PARDISOSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/QR b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/QR
deleted file mode 100644
index 8465b62..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/QR
+++ /dev/null
@@ -1,50 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QR_MODULE_H
-#define EIGEN_QR_MODULE_H
-
-#include "Core"
-
-#include "Cholesky"
-#include "Jacobi"
-#include "Householder"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup QR_Module QR module
-  *
-  *
-  *
-  * This module provides various QR decompositions
-  * This module also provides some MatrixBase methods, including:
-  *  - MatrixBase::householderQr()
-  *  - MatrixBase::colPivHouseholderQr()
-  *  - MatrixBase::fullPivHouseholderQr()
-  *
-  * \code
-  * #include <Eigen/QR>
-  * \endcode
-  */
-
-#include "src/QR/HouseholderQR.h"
-#include "src/QR/FullPivHouseholderQR.h"
-#include "src/QR/ColPivHouseholderQR.h"
-#include "src/QR/CompleteOrthogonalDecomposition.h"
-#ifdef EIGEN_USE_LAPACKE
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/QR/HouseholderQR_LAPACKE.h"
-#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_QR_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/QtAlignedMalloc b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/QtAlignedMalloc
deleted file mode 100644
index 6fe8237..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/QtAlignedMalloc
+++ /dev/null
@@ -1,39 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QTMALLOC_MODULE_H
-#define EIGEN_QTMALLOC_MODULE_H
-
-#include "Core"
-
-#if (!EIGEN_MALLOC_ALREADY_ALIGNED)
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-void *qMalloc(std::size_t size)
-{
-  return Eigen::internal::aligned_malloc(size);
-}
-
-void qFree(void *ptr)
-{
-  Eigen::internal::aligned_free(ptr);
-}
-
-void *qRealloc(void *ptr, std::size_t size)
-{
-  void* newPtr = Eigen::internal::aligned_malloc(size);
-  std::memcpy(newPtr, ptr, size);
-  Eigen::internal::aligned_free(ptr);
-  return newPtr;
-}
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif
-
-#endif // EIGEN_QTMALLOC_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SPQRSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SPQRSupport
deleted file mode 100644
index f70390c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SPQRSupport
+++ /dev/null
@@ -1,34 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPQRSUPPORT_MODULE_H
-#define EIGEN_SPQRSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include "SuiteSparseQR.hpp"
-
-/** \ingroup Support_modules
-  * \defgroup SPQRSupport_Module SuiteSparseQR module
-  * 
-  * This module provides an interface to the SPQR library, which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  *
-  * \code
-  * #include <Eigen/SPQRSupport>
-  * \endcode
-  *
-  * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).
-  * For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules
-  *
-  */
-
-#include "src/CholmodSupport/CholmodSupport.h"
-#include "src/SPQRSupport/SuiteSparseQRSupport.h"
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SVD b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SVD
deleted file mode 100644
index 3451794..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SVD
+++ /dev/null
@@ -1,50 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SVD_MODULE_H
-#define EIGEN_SVD_MODULE_H
-
-#include "QR"
-#include "Householder"
-#include "Jacobi"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup SVD_Module SVD module
-  *
-  *
-  *
-  * This module provides SVD decomposition for matrices (both real and complex).
-  * Two decomposition algorithms are provided:
-  *  - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.
-  *  - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems.
-  * These decompositions are accessible via the respective classes and following MatrixBase methods:
-  *  - MatrixBase::jacobiSvd()
-  *  - MatrixBase::bdcSvd()
-  *
-  * \code
-  * #include <Eigen/SVD>
-  * \endcode
-  */
-
-#include "src/misc/RealSvd2x2.h"
-#include "src/SVD/UpperBidiagonalization.h"
-#include "src/SVD/SVDBase.h"
-#include "src/SVD/JacobiSVD.h"
-#include "src/SVD/BDCSVD.h"
-#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
-#ifdef EIGEN_USE_MKL
-#include "mkl_lapacke.h"
-#else
-#include "src/misc/lapacke.h"
-#endif
-#include "src/SVD/JacobiSVD_LAPACKE.h"
-#endif
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SVD_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Sparse b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Sparse
deleted file mode 100644
index a2ef7a6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/Sparse
+++ /dev/null
@@ -1,34 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_MODULE_H
-#define EIGEN_SPARSE_MODULE_H
-
-/** \defgroup Sparse_Module Sparse meta-module
-  *
-  * Meta-module including all related modules:
-  * - \ref SparseCore_Module
-  * - \ref OrderingMethods_Module
-  * - \ref SparseCholesky_Module
-  * - \ref SparseLU_Module
-  * - \ref SparseQR_Module
-  * - \ref IterativeLinearSolvers_Module
-  *
-    \code
-    #include <Eigen/Sparse>
-    \endcode
-  */
-
-#include "SparseCore"
-#include "OrderingMethods"
-#include "SparseCholesky"
-#include "SparseLU"
-#include "SparseQR"
-#include "IterativeLinearSolvers"
-
-#endif // EIGEN_SPARSE_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseCholesky b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseCholesky
deleted file mode 100644
index d2b1f12..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseCholesky
+++ /dev/null
@@ -1,37 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
-#define EIGEN_SPARSECHOLESKY_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** 
-  * \defgroup SparseCholesky_Module SparseCholesky module
-  *
-  * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.
-  * Those decompositions are accessible via the following classes:
-  *  - SimplicialLLt,
-  *  - SimplicialLDLt
-  *
-  * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
-  *
-  * \code
-  * #include <Eigen/SparseCholesky>
-  * \endcode
-  */
-
-#include "src/SparseCholesky/SimplicialCholesky.h"
-#include "src/SparseCholesky/SimplicialCholesky_impl.h"
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPARSECHOLESKY_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseCore b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseCore
deleted file mode 100644
index 76966c4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseCore
+++ /dev/null
@@ -1,69 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSECORE_MODULE_H
-#define EIGEN_SPARSECORE_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include <vector>
-#include <map>
-#include <cstdlib>
-#include <cstring>
-#include <algorithm>
-
-/** 
-  * \defgroup SparseCore_Module SparseCore module
-  *
-  * This module provides a sparse matrix representation, and basic associated matrix manipulations
-  * and operations.
-  *
-  * See the \ref TutorialSparse "Sparse tutorial"
-  *
-  * \code
-  * #include <Eigen/SparseCore>
-  * \endcode
-  *
-  * This module depends on: Core.
-  */
-
-#include "src/SparseCore/SparseUtil.h"
-#include "src/SparseCore/SparseMatrixBase.h"
-#include "src/SparseCore/SparseAssign.h"
-#include "src/SparseCore/CompressedStorage.h"
-#include "src/SparseCore/AmbiVector.h"
-#include "src/SparseCore/SparseCompressedBase.h"
-#include "src/SparseCore/SparseMatrix.h"
-#include "src/SparseCore/SparseMap.h"
-#include "src/SparseCore/MappedSparseMatrix.h"
-#include "src/SparseCore/SparseVector.h"
-#include "src/SparseCore/SparseRef.h"
-#include "src/SparseCore/SparseCwiseUnaryOp.h"
-#include "src/SparseCore/SparseCwiseBinaryOp.h"
-#include "src/SparseCore/SparseTranspose.h"
-#include "src/SparseCore/SparseBlock.h"
-#include "src/SparseCore/SparseDot.h"
-#include "src/SparseCore/SparseRedux.h"
-#include "src/SparseCore/SparseView.h"
-#include "src/SparseCore/SparseDiagonalProduct.h"
-#include "src/SparseCore/ConservativeSparseSparseProduct.h"
-#include "src/SparseCore/SparseSparseProductWithPruning.h"
-#include "src/SparseCore/SparseProduct.h"
-#include "src/SparseCore/SparseDenseProduct.h"
-#include "src/SparseCore/SparseSelfAdjointView.h"
-#include "src/SparseCore/SparseTriangularView.h"
-#include "src/SparseCore/TriangularSolver.h"
-#include "src/SparseCore/SparsePermutation.h"
-#include "src/SparseCore/SparseFuzzy.h"
-#include "src/SparseCore/SparseSolverBase.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPARSECORE_MODULE_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseLU b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseLU
deleted file mode 100644
index 37c4a5c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseLU
+++ /dev/null
@@ -1,50 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSELU_MODULE_H
-#define EIGEN_SPARSELU_MODULE_H
-
-#include "SparseCore"
-
-/** 
-  * \defgroup SparseLU_Module SparseLU module
-  * This module defines a supernodal factorization of general sparse matrices.
-  * The code is fully optimized for supernode-panel updates with specialized kernels.
-  * Please, see the documentation of the SparseLU class for more details.
-  */
-
-// Ordering interface
-#include "OrderingMethods"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#include "src/SparseLU/SparseLU_gemm_kernel.h"
-
-#include "src/SparseLU/SparseLU_Structs.h"
-#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
-#include "src/SparseLU/SparseLUImpl.h"
-#include "src/SparseCore/SparseColEtree.h"
-#include "src/SparseLU/SparseLU_Memory.h"
-#include "src/SparseLU/SparseLU_heap_relax_snode.h"
-#include "src/SparseLU/SparseLU_relax_snode.h"
-#include "src/SparseLU/SparseLU_pivotL.h"
-#include "src/SparseLU/SparseLU_panel_dfs.h"
-#include "src/SparseLU/SparseLU_kernel_bmod.h"
-#include "src/SparseLU/SparseLU_panel_bmod.h"
-#include "src/SparseLU/SparseLU_column_dfs.h"
-#include "src/SparseLU/SparseLU_column_bmod.h"
-#include "src/SparseLU/SparseLU_copy_to_ucol.h"
-#include "src/SparseLU/SparseLU_pruneL.h"
-#include "src/SparseLU/SparseLU_Utils.h"
-#include "src/SparseLU/SparseLU.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPARSELU_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseQR b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseQR
deleted file mode 100644
index f5fc5fa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SparseQR
+++ /dev/null
@@ -1,36 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEQR_MODULE_H
-#define EIGEN_SPARSEQR_MODULE_H
-
-#include "SparseCore"
-#include "OrderingMethods"
-#include "src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup SparseQR_Module SparseQR module
-  * \brief Provides QR decomposition for sparse matrices
-  * 
-  * This module provides a simplicial version of the left-looking Sparse QR decomposition. 
-  * The columns of the input matrix should be reordered to limit the fill-in during the 
-  * decomposition. Built-in methods (COLAMD, AMD) or external  methods (METIS) can be used to this end.
-  * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list 
-  * of built-in and external ordering methods.
-  * 
-  * \code
-  * #include <Eigen/SparseQR>
-  * \endcode
-  * 
-  * 
-  */
-
-#include "src/SparseCore/SparseColEtree.h"
-#include "src/SparseQR/SparseQR.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdDeque b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdDeque
deleted file mode 100644
index bc68397..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdDeque
+++ /dev/null
@@ -1,27 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDDEQUE_MODULE_H
-#define EIGEN_STDDEQUE_MODULE_H
-
-#include "Core"
-#include <deque>
-
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
-
-#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
-
-#else
-
-#include "src/StlSupport/StdDeque.h"
-
-#endif
-
-#endif // EIGEN_STDDEQUE_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdList b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdList
deleted file mode 100644
index 4c6262c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdList
+++ /dev/null
@@ -1,26 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDLIST_MODULE_H
-#define EIGEN_STDLIST_MODULE_H
-
-#include "Core"
-#include <list>
-
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
-
-#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
-
-#else
-
-#include "src/StlSupport/StdList.h"
-
-#endif
-
-#endif // EIGEN_STDLIST_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdVector b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdVector
deleted file mode 100644
index 0c4697a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/StdVector
+++ /dev/null
@@ -1,27 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDVECTOR_MODULE_H
-#define EIGEN_STDVECTOR_MODULE_H
-
-#include "Core"
-#include <vector>
-
-#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
-
-#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
-
-#else
-
-#include "src/StlSupport/StdVector.h"
-
-#endif
-
-#endif // EIGEN_STDVECTOR_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SuperLUSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SuperLUSupport
deleted file mode 100644
index 59312a8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/SuperLUSupport
+++ /dev/null
@@ -1,64 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
-#define EIGEN_SUPERLUSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-#ifdef EMPTY
-#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
-#endif
-
-typedef int int_t;
-#include <slu_Cnames.h>
-#include <supermatrix.h>
-#include <slu_util.h>
-
-// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
-// so we remove it in favor of a SUPERLU_EMPTY token.
-// If EMPTY was already defined then we don't undef it.
-
-#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
-# undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
-#elif defined(EMPTY)
-# undef EMPTY
-#endif
-
-#define SUPERLU_EMPTY (-1)
-
-namespace Eigen { struct SluMatrix; }
-
-/** \ingroup Support_modules
-  * \defgroup SuperLUSupport_Module SuperLUSupport module
-  *
-  * This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
-  * It provides the following factorization class:
-  * - class SuperLU: a supernodal sequential LU factorization.
-  * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods).
-  *
-  * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
-  *
-  * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
-  *
-  * \code
-  * #include <Eigen/SuperLUSupport>
-  * \endcode
-  *
-  * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be linked to the superlu library and its dependencies.
-  * The dependencies depend on how superlu has been compiled.
-  * For a cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
-  *
-  */
-
-#include "src/SuperLUSupport/SuperLUSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SUPERLUSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/UmfPackSupport b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/UmfPackSupport
deleted file mode 100644
index 00eec80..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/UmfPackSupport
+++ /dev/null
@@ -1,40 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
-#define EIGEN_UMFPACKSUPPORT_MODULE_H
-
-#include "SparseCore"
-
-#include "src/Core/util/DisableStupidWarnings.h"
-
-extern "C" {
-#include <umfpack.h>
-}
-
-/** \ingroup Support_modules
-  * \defgroup UmfPackSupport_Module UmfPackSupport module
-  *
-  * This module provides an interface to the UmfPack library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
-  * It provides the following factorization class:
-  * - class UmfPackLU: a multifrontal sequential LU factorization.
-  *
-  * \code
-  * #include <Eigen/UmfPackSupport>
-  * \endcode
-  *
-  * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be linked to the umfpack library and its dependencies.
-  * The dependencies depend on how umfpack has been compiled.
-  * For a cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
-  *
-  */
-
-#include "src/UmfPackSupport/UmfPackSupport.h"
-
-#include "src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_UMFPACKSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LDLT.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LDLT.h
deleted file mode 100644
index 1013ca0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LDLT.h
+++ /dev/null
@@ -1,688 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LDLT_H
-#define EIGEN_LDLT_H
-
-namespace Eigen {
-
-namespace internal {
-  template<typename _MatrixType, int _UpLo> struct traits<LDLT<_MatrixType, _UpLo> >
-   : traits<_MatrixType>
-  {
-    typedef MatrixXpr XprKind;
-    typedef SolverStorage StorageKind;
-    typedef int StorageIndex;
-    enum { Flags = 0 };
-  };
-
-  template<typename MatrixType, int UpLo> struct LDLT_Traits;
-
-  // PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
-  enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
-}
-
-/** \ingroup Cholesky_Module
-  *
-  * \class LDLT
-  *
-  * \brief Robust Cholesky decomposition of a matrix with pivoting
-  *
-  * \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
-  * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
-  *             The other triangular part won't be read.
-  *
-  * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
-  * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
-  * is lower triangular with a unit diagonal and D is a diagonal matrix.
-  *
-  * The decomposition uses pivoting to ensure stability, so that D will have
-  * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
-  * on D also stabilizes the computation.
-  *
-  * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
-  * decomposition to determine whether a system of equations has a solution.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
-  */
-template<typename _MatrixType, int _UpLo> class LDLT
-        : public SolverBase<LDLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<LDLT> Base;
-    friend class SolverBase<LDLT>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-      UpLo = _UpLo
-    };
-    typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
-
-    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
-    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
-
-    typedef internal::LDLT_Traits<MatrixType,UpLo> Traits;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via LDLT::compute(const MatrixType&).
-      */
-    LDLT()
-      : m_matrix(),
-        m_transpositions(),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa LDLT()
-      */
-    explicit LDLT(Index size)
-      : m_matrix(size, size),
-        m_transpositions(size),
-        m_temporary(size),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {}
-
-    /** \brief Constructor with decomposition
-      *
-      * This calculates the decomposition for the input \a matrix.
-      *
-      * \sa LDLT(Index size)
-      */
-    template<typename InputType>
-    explicit LDLT(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.rows(), matrix.cols()),
-        m_transpositions(matrix.rows()),
-        m_temporary(matrix.rows()),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a LDLT factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa LDLT(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit LDLT(EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_transpositions(matrix.rows()),
-        m_temporary(matrix.rows()),
-        m_sign(internal::ZeroSign),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** Clear any existing decomposition
-     * \sa rankUpdate(w,sigma)
-     */
-    void setZero()
-    {
-      m_isInitialized = false;
-    }
-
-    /** \returns a view of the upper triangular matrix U */
-    inline typename Traits::MatrixU matrixU() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return Traits::getU(m_matrix);
-    }
-
-    /** \returns a view of the lower triangular matrix L */
-    inline typename Traits::MatrixL matrixL() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return Traits::getL(m_matrix);
-    }
-
-    /** \returns the permutation matrix P as a transposition sequence.
-      */
-    inline const TranspositionType& transpositionsP() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_transpositions;
-    }
-
-    /** \returns the coefficients of the diagonal matrix D */
-    inline Diagonal<const MatrixType> vectorD() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_matrix.diagonal();
-    }
-
-    /** \returns true if the matrix is positive (semidefinite) */
-    inline bool isPositive() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
-    }
-
-    /** \returns true if the matrix is negative (semidefinite) */
-    inline bool isNegative(void) const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
-      *
-      * \note_about_checking_solutions
-      *
-      * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
-      * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
-      * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
-      * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
-      * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
-      * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular.
-      *
-      * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
-      */
-    template<typename Rhs>
-    inline const Solve<LDLT, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    template<typename Derived>
-    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
-
-    template<typename InputType>
-    LDLT& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of
-     *  which \c *this is the LDLT decomposition.
-     */
-    RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    template <typename Derived>
-    LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
-
-    /** \returns the internal LDLT decomposition matrix
-      *
-      * TODO: document the storage layout
-      */
-    inline const MatrixType& matrixLDLT() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_matrix;
-    }
-
-    MatrixType reconstructedMatrix() const;
-
-    /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
-      *
-      * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
-      * \code x = decomposition.adjoint().solve(b) \endcode
-      */
-    const LDLT& adjoint() const { return *this; };
-
-    EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the factorization failed because of a zero pivot.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "LDLT is not initialized.");
-      return m_info;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    /** \internal
-      * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
-      * The strict upper part is used during the decomposition, the strict lower
-      * part correspond to the coefficients of L (its diagonal is equal to 1 and
-      * is not stored), and the diagonal entries correspond to D.
-      */
-    MatrixType m_matrix;
-    RealScalar m_l1_norm;
-    TranspositionType m_transpositions;
-    TmpMatrixType m_temporary;
-    internal::SignMatrix m_sign;
-    bool m_isInitialized;
-    ComputationInfo m_info;
-};
-
-namespace internal {
-
-template<int UpLo> struct ldlt_inplace;
-
-template<> struct ldlt_inplace<Lower>
-{
-  template<typename MatrixType, typename TranspositionType, typename Workspace>
-  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
-  {
-    using std::abs;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename TranspositionType::StorageIndex IndexType;
-    eigen_assert(mat.rows()==mat.cols());
-    const Index size = mat.rows();
-    bool found_zero_pivot = false;
-    bool ret = true;
-
-    if (size <= 1)
-    {
-      transpositions.setIdentity();
-      if(size==0) sign = ZeroSign;
-      else if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
-      else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
-      else sign = ZeroSign;
-      return true;
-    }
-
-    for (Index k = 0; k < size; ++k)
-    {
-      // Find largest diagonal element
-      Index index_of_biggest_in_corner;
-      mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
-      index_of_biggest_in_corner += k;
-
-      transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
-      if(k != index_of_biggest_in_corner)
-      {
-        // apply the transposition while taking care to consider only
-        // the lower triangular part
-        Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element
-        mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
-        mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
-        std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
-        for(Index i=k+1;i<index_of_biggest_in_corner;++i)
-        {
-          Scalar tmp = mat.coeffRef(i,k);
-          mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
-          mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
-        }
-        if(NumTraits<Scalar>::IsComplex)
-          mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
-      }
-
-      // partition the matrix:
-      //       A00 |  -  |  -
-      // lu  = A10 | A11 |  -
-      //       A20 | A21 | A22
-      Index rs = size - k - 1;
-      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
-      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
-      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
-
-      if(k>0)
-      {
-        temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
-        mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
-        if(rs>0)
-          A21.noalias() -= A20 * temp.head(k);
-      }
-
-      // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
-      // was smaller than the cutoff value. However, since LDLT is not rank-revealing
-      // we should only make sure that we do not introduce INF or NaN values.
-      // Remark that LAPACK also uses 0 as the cutoff value.
-      RealScalar realAkk = numext::real(mat.coeffRef(k,k));
-      bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
-
-      if(k==0 && !pivot_is_valid)
-      {
-        // The entire diagonal is zero, there is nothing more to do
-        // except filling the transpositions, and checking whether the matrix is zero.
-        sign = ZeroSign;
-        for(Index j = 0; j<size; ++j)
-        {
-          transpositions.coeffRef(j) = IndexType(j);
-          ret = ret && (mat.col(j).tail(size-j-1).array()==Scalar(0)).all();
-        }
-        return ret;
-      }
-
-      if((rs>0) && pivot_is_valid)
-        A21 /= realAkk;
-      else if(rs>0)
-        ret = ret && (A21.array()==Scalar(0)).all();
-
-      if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
-      else if(!pivot_is_valid) found_zero_pivot = true;
-
-      if (sign == PositiveSemiDef) {
-        if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
-      } else if (sign == NegativeSemiDef) {
-        if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
-      } else if (sign == ZeroSign) {
-        if (realAkk > static_cast<RealScalar>(0)) sign = PositiveSemiDef;
-        else if (realAkk < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
-      }
-    }
-
-    return ret;
-  }
-
-  // Reference for the algorithm: Davis and Hager, "Multiple Rank
-  // Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
-  // Trivial rearrangements of their computations (Timothy E. Holy)
-  // allow their algorithm to work for rank-1 updates even if the
-  // original matrix is not of full rank.
-  // Here only rank-1 updates are implemented, to reduce the
-  // requirement for intermediate storage and improve accuracy
-  template<typename MatrixType, typename WDerived>
-  static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
-  {
-    using numext::isfinite;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-
-    const Index size = mat.rows();
-    eigen_assert(mat.cols() == size && w.size()==size);
-
-    RealScalar alpha = 1;
-
-    // Apply the update
-    for (Index j = 0; j < size; j++)
-    {
-      // Check for termination due to an original decomposition of low-rank
-      if (!(isfinite)(alpha))
-        break;
-
-      // Update the diagonal terms
-      RealScalar dj = numext::real(mat.coeff(j,j));
-      Scalar wj = w.coeff(j);
-      RealScalar swj2 = sigma*numext::abs2(wj);
-      RealScalar gamma = dj*alpha + swj2;
-
-      mat.coeffRef(j,j) += swj2/alpha;
-      alpha += swj2/dj;
-
-
-      // Update the terms of L
-      Index rs = size-j-1;
-      w.tail(rs) -= wj * mat.col(j).tail(rs);
-      if(gamma != 0)
-        mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
-    }
-    return true;
-  }
-
-  template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
-  static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
-  {
-    // Apply the permutation to the input w
-    tmp = transpositions * w;
-
-    return ldlt_inplace<Lower>::updateInPlace(mat,tmp,sigma);
-  }
-};
-
-template<> struct ldlt_inplace<Upper>
-{
-  template<typename MatrixType, typename TranspositionType, typename Workspace>
-  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
-  {
-    Transpose<MatrixType> matt(mat);
-    return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
-  }
-
-  template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
-  static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
-  {
-    Transpose<MatrixType> matt(mat);
-    return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
-  }
-};
-
-template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
-{
-  typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
-};
-
-template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
-{
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
-  typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
-};
-
-} // end namespace internal
-
-/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
-  */
-template<typename MatrixType, int _UpLo>
-template<typename InputType>
-LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
-{
-  check_template_parameters();
-
-  eigen_assert(a.rows()==a.cols());
-  const Index size = a.rows();
-
-  m_matrix = a.derived();
-
-  // Compute matrix L1 norm = max abs column sum.
-  m_l1_norm = RealScalar(0);
-  // TODO move this code to SelfAdjointView
-  for (Index col = 0; col < size; ++col) {
-    RealScalar abs_col_sum;
-    if (_UpLo == Lower)
-      abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
-    else
-      abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
-    if (abs_col_sum > m_l1_norm)
-      m_l1_norm = abs_col_sum;
-  }
-
-  m_transpositions.resize(size);
-  m_isInitialized = false;
-  m_temporary.resize(size);
-  m_sign = internal::ZeroSign;
-
-  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;
-
-  m_isInitialized = true;
-  return *this;
-}
-
-/** Update the LDLT decomposition:  given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
- * \param w a vector to be incorporated into the decomposition.
- * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1.
- * \sa setZero()
-  */
-template<typename MatrixType, int _UpLo>
-template<typename Derived>
-LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,_UpLo>::RealScalar& sigma)
-{
-  typedef typename TranspositionType::StorageIndex IndexType;
-  const Index size = w.rows();
-  if (m_isInitialized)
-  {
-    eigen_assert(m_matrix.rows()==size);
-  }
-  else
-  {
-    m_matrix.resize(size,size);
-    m_matrix.setZero();
-    m_transpositions.resize(size);
-    for (Index i = 0; i < size; i++)
-      m_transpositions.coeffRef(i) = IndexType(i);
-    m_temporary.resize(size);
-    m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
-    m_isInitialized = true;
-  }
-
-  internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
-
-  return *this;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType, int _UpLo>
-template<typename RhsType, typename DstType>
-void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  _solve_impl_transposed<true>(rhs, dst);
-}
-
-template<typename _MatrixType,int _UpLo>
-template<bool Conjugate, typename RhsType, typename DstType>
-void LDLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  // dst = P b
-  dst = m_transpositions * rhs;
-
-  // dst = L^-1 (P b)
-  // dst = L^-*T (P b)
-  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
-
-  // dst = D^-* (L^-1 P b)
-  // dst = D^-1 (L^-*T P b)
-  // more precisely, use pseudo-inverse of D (see bug 241)
-  using std::abs;
-  const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
-  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
-  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
-  // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
-  // However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
-  // diagonal element is not well justified and leads to numerical issues in some cases.
-  // Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
-  // Using numeric_limits::min() gives us more robustness to denormals.
-  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
-  for (Index i = 0; i < vecD.size(); ++i)
-  {
-    if(abs(vecD(i)) > tolerance)
-      dst.row(i) /= vecD(i);
-    else
-      dst.row(i).setZero();
-  }
-
-  // dst = L^-* (D^-* L^-1 P b)
-  // dst = L^-T (D^-1 L^-*T P b)
-  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
-
-  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
-  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
-  dst = m_transpositions.transpose() * dst;
-}
-#endif
-
-/** \internal use x = ldlt_object.solve(x);
-  *
-  * This is the \em in-place version of solve().
-  *
-  * \param bAndX represents both the right-hand side matrix b and result x.
-  *
-  * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
-  *
-  * This version avoids a copy when the right hand side matrix b is not
-  * needed anymore.
-  *
-  * \sa LDLT::solve(), MatrixBase::ldlt()
-  */
-template<typename MatrixType,int _UpLo>
-template<typename Derived>
-bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
-{
-  eigen_assert(m_isInitialized && "LDLT is not initialized.");
-  eigen_assert(m_matrix.rows() == bAndX.rows());
-
-  bAndX = this->solve(bAndX);
-
-  return true;
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: P^T L D L^* P.
- * This function is provided for debug purpose. */
-template<typename MatrixType, int _UpLo>
-MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LDLT is not initialized.");
-  const Index size = m_matrix.rows();
-  MatrixType res(size,size);
-
-  // P
-  res.setIdentity();
-  res = transpositionsP() * res;
-  // L^* P
-  res = matrixU() * res;
-  // D(L^*P)
-  res = vectorD().real().asDiagonal() * res;
-  // L(DL^*P)
-  res = matrixL() * res;
-  // P^T (LDL^*P)
-  res = transpositionsP().transpose() * res;
-
-  return res;
-}
-
-/** \cholesky_module
-  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
-  * \sa MatrixBase::ldlt()
-  */
-template<typename MatrixType, unsigned int UpLo>
-inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
-SelfAdjointView<MatrixType, UpLo>::ldlt() const
-{
-  return LDLT<PlainObject,UpLo>(m_matrix);
-}
-
-/** \cholesky_module
-  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
-  * \sa SelfAdjointView::ldlt()
-  */
-template<typename Derived>
-inline const LDLT<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::ldlt() const
-{
-  return LDLT<PlainObject>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LDLT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LLT.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LLT.h
deleted file mode 100644
index 8c9b2b3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LLT.h
+++ /dev/null
@@ -1,558 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LLT_H
-#define EIGEN_LLT_H
-
-namespace Eigen {
-
-namespace internal{
-
-template<typename _MatrixType, int _UpLo> struct traits<LLT<_MatrixType, _UpLo> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-template<typename MatrixType, int UpLo> struct LLT_Traits;
-}
-
-/** \ingroup Cholesky_Module
-  *
-  * \class LLT
-  *
-  * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
-  * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
-  *               The other triangular part won't be read.
-  *
-  * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
-  * matrix A such that A = LL^* = U^*U, where L is lower triangular.
-  *
-  * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
-  * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
-  * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
-  * situations like generalised eigen problems with hermitian matrices.
-  *
-  * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices,
-  * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
-  * has a solution.
-  *
-  * Example: \include LLT_example.cpp
-  * Output: \verbinclude LLT_example.out
-  *
-  * \b Performance: for best performance, it is recommended to use a column-major storage format
-  * with the Lower triangular part (the default), or, equivalently, a row-major storage format
-  * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
-  * step, and rank-updates can be up to 3 times slower.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
-  * Therefore, the strict lower part does not have to store correct values.
-  *
-  * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
-  */
-template<typename _MatrixType, int _UpLo> class LLT
-        : public SolverBase<LLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<LLT> Base;
-    friend class SolverBase<LLT>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
-    enum {
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    enum {
-      PacketSize = internal::packet_traits<Scalar>::size,
-      AlignmentMask = int(PacketSize)-1,
-      UpLo = _UpLo
-    };
-
-    typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via LLT::compute(const MatrixType&).
-      */
-    LLT() : m_matrix(), m_isInitialized(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa LLT()
-      */
-    explicit LLT(Index size) : m_matrix(size, size),
-                    m_isInitialized(false) {}
-
-    template<typename InputType>
-    explicit LLT(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.rows(), matrix.cols()),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a LLT factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
-      * \c MatrixType is a Eigen::Ref.
-      *
-      * \sa LLT(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit LLT(EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \returns a view of the upper triangular matrix U */
-    inline typename Traits::MatrixU matrixU() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return Traits::getU(m_matrix);
-    }
-
-    /** \returns a view of the lower triangular matrix L */
-    inline typename Traits::MatrixL matrixL() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return Traits::getL(m_matrix);
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * Since this LLT class assumes anyway that the matrix A is invertible, the solution
-      * theoretically exists and is unique regardless of b.
-      *
-      * Example: \include LLT_solve.cpp
-      * Output: \verbinclude LLT_solve.out
-      *
-      * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
-      */
-    template<typename Rhs>
-    inline const Solve<LLT, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    template<typename Derived>
-    void solveInPlace(const MatrixBase<Derived> &bAndX) const;
-
-    template<typename InputType>
-    LLT& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of
-      *  which \c *this is the Cholesky decomposition.
-      */
-    RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    /** \returns the LLT decomposition matrix
-      *
-      * TODO: document the storage layout
-      */
-    inline const MatrixType& matrixLLT() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return m_matrix;
-    }
-
-    MatrixType reconstructedMatrix() const;
-
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears not to be positive definite.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "LLT is not initialized.");
-      return m_info;
-    }
-
-    /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
-      *
-      * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
-      * \code x = decomposition.adjoint().solve(b) \endcode
-      */
-    const LLT& adjoint() const EIGEN_NOEXCEPT { return *this; };
-
-    inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    template<typename VectorType>
-    LLT & rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    /** \internal
-      * Used to compute and store L
-      * The strict upper part is not used and even not initialized.
-      */
-    MatrixType m_matrix;
-    RealScalar m_l1_norm;
-    bool m_isInitialized;
-    ComputationInfo m_info;
-};
-
-namespace internal {
-
-template<typename Scalar, int UpLo> struct llt_inplace;
-
-template<typename MatrixType, typename VectorType>
-static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
-{
-  using std::sqrt;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename MatrixType::ColXpr ColXpr;
-  typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
-  typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
-  typedef Matrix<Scalar,Dynamic,1> TempVectorType;
-  typedef typename TempVectorType::SegmentReturnType TempVecSegment;
-
-  Index n = mat.cols();
-  eigen_assert(mat.rows()==n && vec.size()==n);
-
-  TempVectorType temp;
-
-  if(sigma>0)
-  {
-    // This version is based on Givens rotations.
-    // It is faster than the other one below, but only works for updates,
-    // i.e., for sigma > 0
-    temp = sqrt(sigma) * vec;
-
-    for(Index i=0; i<n; ++i)
-    {
-      JacobiRotation<Scalar> g;
-      g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
-
-      Index rs = n-i-1;
-      if(rs>0)
-      {
-        ColXprSegment x(mat.col(i).tail(rs));
-        TempVecSegment y(temp.tail(rs));
-        apply_rotation_in_the_plane(x, y, g);
-      }
-    }
-  }
-  else
-  {
-    temp = vec;
-    RealScalar beta = 1;
-    for(Index j=0; j<n; ++j)
-    {
-      RealScalar Ljj = numext::real(mat.coeff(j,j));
-      RealScalar dj = numext::abs2(Ljj);
-      Scalar wj = temp.coeff(j);
-      RealScalar swj2 = sigma*numext::abs2(wj);
-      RealScalar gamma = dj*beta + swj2;
-
-      RealScalar x = dj + swj2/beta;
-      if (x<=RealScalar(0))
-        return j;
-      RealScalar nLjj = sqrt(x);
-      mat.coeffRef(j,j) = nLjj;
-      beta += swj2/dj;
-
-      // Update the terms of L
-      Index rs = n-j-1;
-      if(rs)
-      {
-        temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
-        if(gamma != 0)
-          mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
-      }
-    }
-  }
-  return -1;
-}
-
-template<typename Scalar> struct llt_inplace<Scalar, Lower>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename MatrixType>
-  static Index unblocked(MatrixType& mat)
-  {
-    using std::sqrt;
-
-    eigen_assert(mat.rows()==mat.cols());
-    const Index size = mat.rows();
-    for(Index k = 0; k < size; ++k)
-    {
-      Index rs = size-k-1; // remaining size
-
-      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
-      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
-      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
-
-      RealScalar x = numext::real(mat.coeff(k,k));
-      if (k>0) x -= A10.squaredNorm();
-      if (x<=RealScalar(0))
-        return k;
-      mat.coeffRef(k,k) = x = sqrt(x);
-      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
-      if (rs>0) A21 /= x;
-    }
-    return -1;
-  }
-
-  template<typename MatrixType>
-  static Index blocked(MatrixType& m)
-  {
-    eigen_assert(m.rows()==m.cols());
-    Index size = m.rows();
-    if(size<32)
-      return unblocked(m);
-
-    Index blockSize = size/8;
-    blockSize = (blockSize/16)*16;
-    blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
-
-    for (Index k=0; k<size; k+=blockSize)
-    {
-      // partition the matrix:
-      //       A00 |  -  |  -
-      // lu  = A10 | A11 |  -
-      //       A20 | A21 | A22
-      Index bs = (std::min)(blockSize, size-k);
-      Index rs = size - k - bs;
-      Block<MatrixType,Dynamic,Dynamic> A11(m,k,   k,   bs,bs);
-      Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k,   rs,bs);
-      Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs);
-
-      Index ret;
-      if((ret=unblocked(A11))>=0) return k+ret;
-      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
-      if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck
-    }
-    return -1;
-  }
-
-  template<typename MatrixType, typename VectorType>
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
-  {
-    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
-  }
-};
-
-template<typename Scalar> struct llt_inplace<Scalar, Upper>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  template<typename MatrixType>
-  static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat)
-  {
-    Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::unblocked(matt);
-  }
-  template<typename MatrixType>
-  static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat)
-  {
-    Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::blocked(matt);
-  }
-  template<typename MatrixType, typename VectorType>
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
-  {
-    Transpose<MatrixType> matt(mat);
-    return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
-  }
-};
-
-template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
-{
-  typedef const TriangularView<const MatrixType, Lower> MatrixL;
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
-  static bool inplace_decomposition(MatrixType& m)
-  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }
-};
-
-template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
-{
-  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
-  typedef const TriangularView<const MatrixType, Upper> MatrixU;
-  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
-  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
-  static bool inplace_decomposition(MatrixType& m)
-  { return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; }
-};
-
-} // end namespace internal
-
-/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
-  *
-  * \returns a reference to *this
-  *
-  * Example: \include TutorialLinAlgComputeTwice.cpp
-  * Output: \verbinclude TutorialLinAlgComputeTwice.out
-  */
-template<typename MatrixType, int _UpLo>
-template<typename InputType>
-LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
-{
-  check_template_parameters();
-
-  eigen_assert(a.rows()==a.cols());
-  const Index size = a.rows();
-  m_matrix.resize(size, size);
-  if (!internal::is_same_dense(m_matrix, a.derived()))
-    m_matrix = a.derived();
-
-  // Compute matrix L1 norm = max abs column sum.
-  m_l1_norm = RealScalar(0);
-  // TODO move this code to SelfAdjointView
-  for (Index col = 0; col < size; ++col) {
-    RealScalar abs_col_sum;
-    if (_UpLo == Lower)
-      abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
-    else
-      abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
-    if (abs_col_sum > m_l1_norm)
-      m_l1_norm = abs_col_sum;
-  }
-
-  m_isInitialized = true;
-  bool ok = Traits::inplace_decomposition(m_matrix);
-  m_info = ok ? Success : NumericalIssue;
-
-  return *this;
-}
-
-/** Performs a rank one update (or dowdate) of the current decomposition.
-  * If A = LL^* before the rank one update,
-  * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
-  * of same dimension.
-  */
-template<typename _MatrixType, int _UpLo>
-template<typename VectorType>
-LLT<_MatrixType,_UpLo> & LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
-  eigen_assert(v.size()==m_matrix.cols());
-  eigen_assert(m_isInitialized);
-  if(internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix,v,sigma)>=0)
-    m_info = NumericalIssue;
-  else
-    m_info = Success;
-
-  return *this;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType,int _UpLo>
-template<typename RhsType, typename DstType>
-void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  _solve_impl_transposed<true>(rhs, dst);
-}
-
-template<typename _MatrixType,int _UpLo>
-template<bool Conjugate, typename RhsType, typename DstType>
-void LLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-    dst = rhs;
-
-    matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
-    matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
-}
-#endif
-
-/** \internal use x = llt_object.solve(x);
-  *
-  * This is the \em in-place version of solve().
-  *
-  * \param bAndX represents both the right-hand side matrix b and result x.
-  *
-  * This version avoids a copy when the right hand side matrix b is not needed anymore.
-  *
-  * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
-  * This function will const_cast it, so constness isn't honored here.
-  *
-  * \sa LLT::solve(), MatrixBase::llt()
-  */
-template<typename MatrixType, int _UpLo>
-template<typename Derived>
-void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
-{
-  eigen_assert(m_isInitialized && "LLT is not initialized.");
-  eigen_assert(m_matrix.rows()==bAndX.rows());
-  matrixL().solveInPlace(bAndX);
-  matrixU().solveInPlace(bAndX);
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: L L^*.
- * This function is provided for debug purpose. */
-template<typename MatrixType, int _UpLo>
-MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LLT is not initialized.");
-  return matrixL() * matrixL().adjoint().toDenseMatrix();
-}
-
-/** \cholesky_module
-  * \returns the LLT decomposition of \c *this
-  * \sa SelfAdjointView::llt()
-  */
-template<typename Derived>
-inline const LLT<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::llt() const
-{
-  return LLT<PlainObject>(derived());
-}
-
-/** \cholesky_module
-  * \returns the LLT decomposition of \c *this
-  * \sa SelfAdjointView::llt()
-  */
-template<typename MatrixType, unsigned int UpLo>
-inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
-SelfAdjointView<MatrixType, UpLo>::llt() const
-{
-  return LLT<PlainObject,UpLo>(m_matrix);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LLT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h
deleted file mode 100644
index bc6489e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h
+++ /dev/null
@@ -1,99 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *     LLt decomposition based on LAPACKE_?potrf function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_LLT_LAPACKE_H
-#define EIGEN_LLT_LAPACKE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Scalar> struct lapacke_llt;
-
-#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \
-template<> struct lapacke_llt<EIGTYPE> \
-{ \
-  template<typename MatrixType> \
-  static inline Index potrf(MatrixType& m, char uplo) \
-  { \
-    lapack_int matrix_order; \
-    lapack_int size, lda, info, StorageOrder; \
-    EIGTYPE* a; \
-    eigen_assert(m.rows()==m.cols()); \
-    /* Set up parameters for ?potrf */ \
-    size = convert_index<lapack_int>(m.rows()); \
-    StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
-    matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
-    a = &(m.coeffRef(0,0)); \
-    lda = convert_index<lapack_int>(m.outerStride()); \
-\
-    info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
-    info = (info==0) ? -1 : info>0 ? info-1 : size; \
-    return info; \
-  } \
-}; \
-template<> struct llt_inplace<EIGTYPE, Lower> \
-{ \
-  template<typename MatrixType> \
-  static Index blocked(MatrixType& m) \
-  { \
-    return lapacke_llt<EIGTYPE>::potrf(m, 'L'); \
-  } \
-  template<typename MatrixType, typename VectorType> \
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
-  { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \
-}; \
-template<> struct llt_inplace<EIGTYPE, Upper> \
-{ \
-  template<typename MatrixType> \
-  static Index blocked(MatrixType& m) \
-  { \
-    return lapacke_llt<EIGTYPE>::potrf(m, 'U'); \
-  } \
-  template<typename MatrixType, typename VectorType> \
-  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
-  { \
-    Transpose<MatrixType> matt(mat); \
-    return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
-  } \
-};
-
-EIGEN_LAPACKE_LLT(double, double, d)
-EIGEN_LAPACKE_LLT(float, float, s)
-EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z)
-EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_LLT_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
deleted file mode 100644
index adaf528..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
+++ /dev/null
@@ -1,682 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CHOLMODSUPPORT_H
-#define EIGEN_CHOLMODSUPPORT_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar> struct cholmod_configure_matrix;
-
-template<> struct cholmod_configure_matrix<double> {
-  template<typename CholmodType>
-  static void run(CholmodType& mat) {
-    mat.xtype = CHOLMOD_REAL;
-    mat.dtype = CHOLMOD_DOUBLE;
-  }
-};
-
-template<> struct cholmod_configure_matrix<std::complex<double> > {
-  template<typename CholmodType>
-  static void run(CholmodType& mat) {
-    mat.xtype = CHOLMOD_COMPLEX;
-    mat.dtype = CHOLMOD_DOUBLE;
-  }
-};
-
-// Other scalar types are not yet supported by Cholmod
-// template<> struct cholmod_configure_matrix<float> {
-//   template<typename CholmodType>
-//   static void run(CholmodType& mat) {
-//     mat.xtype = CHOLMOD_REAL;
-//     mat.dtype = CHOLMOD_SINGLE;
-//   }
-// };
-//
-// template<> struct cholmod_configure_matrix<std::complex<float> > {
-//   template<typename CholmodType>
-//   static void run(CholmodType& mat) {
-//     mat.xtype = CHOLMOD_COMPLEX;
-//     mat.dtype = CHOLMOD_SINGLE;
-//   }
-// };
-
-} // namespace internal
-
-/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
-  * Note that the data are shared.
-  */
-template<typename _Scalar, int _Options, typename _StorageIndex>
-cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> > mat)
-{
-  cholmod_sparse res;
-  res.nzmax   = mat.nonZeros();
-  res.nrow    = mat.rows();
-  res.ncol    = mat.cols();
-  res.p       = mat.outerIndexPtr();
-  res.i       = mat.innerIndexPtr();
-  res.x       = mat.valuePtr();
-  res.z       = 0;
-  res.sorted  = 1;
-  if(mat.isCompressed())
-  {
-    res.packed  = 1;
-    res.nz = 0;
-  }
-  else
-  {
-    res.packed  = 0;
-    res.nz = mat.innerNonZeroPtr();
-  }
-
-  res.dtype   = 0;
-  res.stype   = -1;
-
-  if (internal::is_same<_StorageIndex,int>::value)
-  {
-    res.itype = CHOLMOD_INT;
-  }
-  else if (internal::is_same<_StorageIndex,SuiteSparse_long>::value)
-  {
-    res.itype = CHOLMOD_LONG;
-  }
-  else
-  {
-    eigen_assert(false && "Index type not supported yet");
-  }
-
-  // setup res.xtype
-  internal::cholmod_configure_matrix<_Scalar>::run(res);
-
-  res.stype = 0;
-
-  return res;
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
-{
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
-  return res;
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar,_Options,_Index>& mat)
-{
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
-  return res;
-}
-
-/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
-  * The data are not copied but shared. */
-template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
-cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
-{
-  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.matrix().const_cast_derived()));
-
-  if(UpLo==Upper) res.stype =  1;
-  if(UpLo==Lower) res.stype = -1;
-  // swap stype for rowmajor matrices (only works for real matrices)
-  EIGEN_STATIC_ASSERT((_Options & RowMajorBit) == 0 || NumTraits<_Scalar>::IsComplex == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  if(_Options & RowMajorBit) res.stype *=-1;
-
-  return res;
-}
-
-/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
-  * The data are not copied but shared. */
-template<typename Derived>
-cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
-{
-  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  typedef typename Derived::Scalar Scalar;
-
-  cholmod_dense res;
-  res.nrow   = mat.rows();
-  res.ncol   = mat.cols();
-  res.nzmax  = res.nrow * res.ncol;
-  res.d      = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
-  res.x      = (void*)(mat.derived().data());
-  res.z      = 0;
-
-  internal::cholmod_configure_matrix<Scalar>::run(res);
-
-  return res;
-}
-
-/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
-  * The data are not copied but shared. */
-template<typename Scalar, int Flags, typename StorageIndex>
-MappedSparseMatrix<Scalar,Flags,StorageIndex> viewAsEigen(cholmod_sparse& cm)
-{
-  return MappedSparseMatrix<Scalar,Flags,StorageIndex>
-         (cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
-          static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
-}
-
-namespace internal {
-
-// template specializations for int and long that call the correct cholmod method
-
-#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name) \
-    template<typename _StorageIndex> inline ret cm_ ## name       (cholmod_common &Common) { return cholmod_ ## name   (&Common); } \
-    template<>                       inline ret cm_ ## name<SuiteSparse_long> (cholmod_common &Common) { return cholmod_l_ ## name (&Common); }
-
-#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1) \
-    template<typename _StorageIndex> inline ret cm_ ## name       (t1& a1, cholmod_common &Common) { return cholmod_ ## name   (&a1, &Common); } \
-    template<>                       inline ret cm_ ## name<SuiteSparse_long> (t1& a1, cholmod_common &Common) { return cholmod_l_ ## name (&a1, &Common); }
-
-EIGEN_CHOLMOD_SPECIALIZE0(int, start)
-EIGEN_CHOLMOD_SPECIALIZE0(int, finish)
-
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense,  cholmod_dense*,  X)
-EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
-
-EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
-
-template<typename _StorageIndex> inline cholmod_dense*  cm_solve         (int sys, cholmod_factor& L, cholmod_dense&  B, cholmod_common &Common) { return cholmod_solve     (sys, &L, &B, &Common); }
-template<>                       inline cholmod_dense*  cm_solve<SuiteSparse_long>   (int sys, cholmod_factor& L, cholmod_dense&  B, cholmod_common &Common) { return cholmod_l_solve   (sys, &L, &B, &Common); }
-
-template<typename _StorageIndex> inline cholmod_sparse* cm_spsolve       (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_spsolve   (sys, &L, &B, &Common); }
-template<>                       inline cholmod_sparse* cm_spsolve<SuiteSparse_long> (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_l_spsolve (sys, &L, &B, &Common); }
-
-template<typename _StorageIndex>
-inline int  cm_factorize_p       (cholmod_sparse*  A, double beta[2], _StorageIndex* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_factorize_p   (A, beta, fset, fsize, L, &Common); }
-template<>
-inline int  cm_factorize_p<SuiteSparse_long> (cholmod_sparse*  A, double beta[2], SuiteSparse_long* fset,          std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_l_factorize_p (A, beta, fset, fsize, L, &Common); }
-
-#undef EIGEN_CHOLMOD_SPECIALIZE0
-#undef EIGEN_CHOLMOD_SPECIALIZE1
-
-}  // namespace internal
-
-
-enum CholmodMode {
-  CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt
-};
-
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodBase
-  * \brief The base class for the direct Cholesky factorization of Cholmod
-  * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo, typename Derived>
-class CholmodBase : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-  public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef MatrixType CholMatrixType;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    CholmodBase()
-      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
-      m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
-      internal::cm_start<StorageIndex>(m_cholmod);
-    }
-
-    explicit CholmodBase(const MatrixType& matrix)
-      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
-      m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
-      internal::cm_start<StorageIndex>(m_cholmod);
-      compute(matrix);
-    }
-
-    ~CholmodBase()
-    {
-      if(m_cholmodFactor)
-        internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
-      internal::cm_finish<StorageIndex>(m_cholmod);
-    }
-
-    inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
-    inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    Derived& compute(const MatrixType& matrix)
-    {
-      analyzePattern(matrix);
-      factorize(matrix);
-      return derived();
-    }
-
-    /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      if(m_cholmodFactor)
-      {
-        internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
-        m_cholmodFactor = 0;
-      }
-      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
-      m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);
-
-      this->m_isInitialized = true;
-      this->m_info = Success;
-      m_analysisIsOk = true;
-      m_factorizationIsOk = false;
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& matrix)
-    {
-      eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
-      internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);
-
-      // If the factorization failed, minor is the column at which it did. On success minor == n.
-      this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
-      m_factorizationIsOk = true;
-    }
-
-    /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
-     *  See the Cholmod user guide for details. */
-    cholmod_common& cholmod() { return m_cholmod; }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      const Index size = m_cholmodFactor->n;
-      EIGEN_UNUSED_VARIABLE(size);
-      eigen_assert(size==b.rows());
-
-      // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
-      Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b.derived());
-
-      cholmod_dense b_cd = viewAsCholmod(b_ref);
-      cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
-      if(!x_cd)
-      {
-        this->m_info = NumericalIssue;
-        return;
-      }
-      // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
-      // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
-      dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
-      internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
-    }
-
-    /** \internal */
-    template<typename RhsDerived, typename DestDerived>
-    void _solve_impl(const SparseMatrixBase<RhsDerived> &b, SparseMatrixBase<DestDerived> &dest) const
-    {
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      const Index size = m_cholmodFactor->n;
-      EIGEN_UNUSED_VARIABLE(size);
-      eigen_assert(size==b.rows());
-
-      // note: cs stands for Cholmod Sparse
-      Ref<SparseMatrix<typename RhsDerived::Scalar,ColMajor,typename RhsDerived::StorageIndex> > b_ref(b.const_cast_derived());
-      cholmod_sparse b_cs = viewAsCholmod(b_ref);
-      cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
-      if(!x_cs)
-      {
-        this->m_info = NumericalIssue;
-        return;
-      }
-      // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
-      // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's sparse solver)
-      dest.derived() = viewAsEigen<typename DestDerived::Scalar,ColMajor,typename DestDerived::StorageIndex>(*x_cs);
-      internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
-    }
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-
-
-    /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
-      *
-      * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
-      * \c d_ii = \a offset + \c d_ii
-      *
-      * The default is \a offset=0.
-      *
-      * \returns a reference to \c *this.
-      */
-    Derived& setShift(const RealScalar& offset)
-    {
-      m_shiftOffset[0] = double(offset);
-      return derived();
-    }
-
-    /** \returns the determinant of the underlying matrix from the current factorization */
-    Scalar determinant() const
-    {
-      using std::exp;
-      return exp(logDeterminant());
-    }
-
-    /** \returns the log determinant of the underlying matrix from the current factorization */
-    Scalar logDeterminant() const
-    {
-      using std::log;
-      using numext::real;
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-
-      RealScalar logDet = 0;
-      Scalar *x = static_cast<Scalar*>(m_cholmodFactor->x);
-      if (m_cholmodFactor->is_super)
-      {
-        // Supernodal factorization stored as a packed list of dense column-major blocs,
-        // as described by the following structure:
-
-        // super[k] == index of the first column of the j-th super node
-        StorageIndex *super = static_cast<StorageIndex*>(m_cholmodFactor->super);
-        // pi[k] == offset to the description of row indices
-        StorageIndex *pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
-        // px[k] == offset to the respective dense block
-        StorageIndex *px = static_cast<StorageIndex*>(m_cholmodFactor->px);
-
-        Index nb_super_nodes = m_cholmodFactor->nsuper;
-        for (Index k=0; k < nb_super_nodes; ++k)
-        {
-          StorageIndex ncols = super[k + 1] - super[k];
-          StorageIndex nrows = pi[k + 1] - pi[k];
-
-          Map<const Array<Scalar,1,Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows+1));
-          logDet += sk.real().log().sum();
-        }
-      }
-      else
-      {
-        // Simplicial factorization stored as standard CSC matrix.
-        StorageIndex *p = static_cast<StorageIndex*>(m_cholmodFactor->p);
-        Index size = m_cholmodFactor->n;
-        for (Index k=0; k<size; ++k)
-          logDet += log(real( x[p[k]] ));
-      }
-      if (m_cholmodFactor->is_ll)
-        logDet *= 2.0;
-      return logDet;
-    };
-
-    template<typename Stream>
-    void dumpMemory(Stream& /*s*/)
-    {}
-
-  protected:
-    mutable cholmod_common m_cholmod;
-    cholmod_factor* m_cholmodFactor;
-    double m_shiftOffset[2];
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-};
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodSimplicialLLT
-  * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
-  * using the Cholmod library.
-  * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
-  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodSimplicialLLT() : Base() { init(); }
-
-    CholmodSimplicialLLT(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodSimplicialLLT() {}
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 0;
-      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-      m_cholmod.final_ll = 1;
-    }
-};
-
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodSimplicialLDLT
-  * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
-  * using the Cholmod library.
-  * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
-  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodSimplicialLDLT() : Base() { init(); }
-
-    CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodSimplicialLDLT() {}
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 1;
-      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-    }
-};
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodSupernodalLLT
-  * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
-  * using the Cholmod library.
-  * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
-  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodSupernodalLLT() : Base() { init(); }
-
-    CholmodSupernodalLLT(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodSupernodalLLT() {}
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 1;
-      m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
-    }
-};
-
-/** \ingroup CholmodSupport_Module
-  * \class CholmodDecomposition
-  * \brief A general Cholesky factorization and solver based on Cholmod
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
-  * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * This variant permits to change the underlying Cholesky method at runtime.
-  * On the other hand, it does not provide access to the result of the factorization.
-  * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  *
-  * \implsparsesolverconcept
-  *
-  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
-  *
-  * \warning Only double precision real and complex scalar types are supported by Cholmod.
-  *
-  * \sa \ref TutorialSparseSolverConcept
-  */
-template<typename _MatrixType, int _UpLo = Lower>
-class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
-{
-    typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base;
-    using Base::m_cholmod;
-
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    CholmodDecomposition() : Base() { init(); }
-
-    CholmodDecomposition(const MatrixType& matrix) : Base()
-    {
-      init();
-      this->compute(matrix);
-    }
-
-    ~CholmodDecomposition() {}
-
-    void setMode(CholmodMode mode)
-    {
-      switch(mode)
-      {
-        case CholmodAuto:
-          m_cholmod.final_asis = 1;
-          m_cholmod.supernodal = CHOLMOD_AUTO;
-          break;
-        case CholmodSimplicialLLt:
-          m_cholmod.final_asis = 0;
-          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-          m_cholmod.final_ll = 1;
-          break;
-        case CholmodSupernodalLLt:
-          m_cholmod.final_asis = 1;
-          m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
-          break;
-        case CholmodLDLt:
-          m_cholmod.final_asis = 1;
-          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
-          break;
-        default:
-          break;
-      }
-    }
-  protected:
-    void init()
-    {
-      m_cholmod.final_asis = 1;
-      m_cholmod.supernodal = CHOLMOD_AUTO;
-    }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CHOLMODSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArithmeticSequence.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
deleted file mode 100644
index b6200fa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
+++ /dev/null
@@ -1,413 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARITHMETIC_SEQUENCE_H
-#define EIGEN_ARITHMETIC_SEQUENCE_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if (!EIGEN_HAS_CXX11) || !((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48)
-template<typename T> struct aseq_negate {};
-
-template<> struct aseq_negate<Index> {
-  typedef Index type;
-};
-
-template<int N> struct aseq_negate<FixedInt<N> > {
-  typedef FixedInt<-N> type;
-};
-
-// Compilation error in the following case:
-template<> struct aseq_negate<FixedInt<DynamicIndex> > {};
-
-template<typename FirstType,typename SizeType,typename IncrType,
-         bool FirstIsSymbolic=symbolic::is_symbolic<FirstType>::value,
-         bool SizeIsSymbolic =symbolic::is_symbolic<SizeType>::value>
-struct aseq_reverse_first_type {
-  typedef Index type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct aseq_reverse_first_type<FirstType,SizeType,IncrType,true,true> {
-  typedef symbolic::AddExpr<FirstType,
-                            symbolic::ProductExpr<symbolic::AddExpr<SizeType,symbolic::ValueExpr<FixedInt<-1> > >,
-                                                  symbolic::ValueExpr<IncrType> >
-                           > type;
-};
-
-template<typename SizeType,typename IncrType,typename EnableIf = void>
-struct aseq_reverse_first_type_aux {
-  typedef Index type;
-};
-
-template<typename SizeType,typename IncrType>
-struct aseq_reverse_first_type_aux<SizeType,IncrType,typename internal::enable_if<bool((SizeType::value+IncrType::value)|0x1)>::type> {
-  typedef FixedInt<(SizeType::value-1)*IncrType::value> type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct aseq_reverse_first_type<FirstType,SizeType,IncrType,true,false> {
-  typedef typename aseq_reverse_first_type_aux<SizeType,IncrType>::type Aux;
-  typedef symbolic::AddExpr<FirstType,symbolic::ValueExpr<Aux> > type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct aseq_reverse_first_type<FirstType,SizeType,IncrType,false,true> {
-  typedef symbolic::AddExpr<symbolic::ProductExpr<symbolic::AddExpr<SizeType,symbolic::ValueExpr<FixedInt<-1> > >,
-                                                  symbolic::ValueExpr<IncrType> >,
-                            symbolic::ValueExpr<> > type;
-};
-#endif
-
-// Helper to cleanup the type of the increment:
-template<typename T> struct cleanup_seq_incr {
-  typedef typename cleanup_index_type<T,DynamicIndex>::type type;
-};
-
-}
-
-//--------------------------------------------------------------------------------
-// seq(first,last,incr) and seqN(first,size,incr)
-//--------------------------------------------------------------------------------
-
-template<typename FirstType=Index,typename SizeType=Index,typename IncrType=internal::FixedInt<1> >
-class ArithmeticSequence;
-
-template<typename FirstType,typename SizeType,typename IncrType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                   typename internal::cleanup_index_type<SizeType>::type,
-                   typename internal::cleanup_seq_incr<IncrType>::type >
-seqN(FirstType first, SizeType size, IncrType incr);
-
-/** \class ArithmeticSequence
-  * \ingroup Core_Module
-  *
-  * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by
-  * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride)
-  * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i.
-  *
-  * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments
-  * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the
-  * only way it is used.
-  *
-  * \tparam FirstType type of the first element, usually an Index,
-  *                   but internally it can be a symbolic expression
-  * \tparam SizeType type representing the size of the sequence, usually an Index
-  *                  or a compile time integral constant. Internally, it can also be a symbolic expression
-  * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is compile-time 1)
-  *
-  * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView
-  */
-template<typename FirstType,typename SizeType,typename IncrType>
-class ArithmeticSequence
-{
-public:
-  ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {}
-  ArithmeticSequence(FirstType first, SizeType size, IncrType incr) : m_first(first), m_size(size), m_incr(incr) {}
-
-  enum {
-    SizeAtCompileTime = internal::get_fixed_value<SizeType>::value,
-    IncrAtCompileTime = internal::get_fixed_value<IncrType,DynamicIndex>::value
-  };
-
-  /** \returns the size, i.e., number of elements, of the sequence */
-  Index size()  const { return m_size; }
-
-  /** \returns the first element \f$ a_0 \f$ in the sequence */
-  Index first()  const { return m_first; }
-
-  /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */
-  Index operator[](Index i) const { return m_first + i * m_incr; }
-
-  const FirstType& firstObject() const { return m_first; }
-  const SizeType&  sizeObject()  const { return m_size; }
-  const IncrType&  incrObject()  const { return m_incr; }
-
-protected:
-  FirstType m_first;
-  SizeType  m_size;
-  IncrType  m_incr;
-
-public:
-
-#if EIGEN_HAS_CXX11 && ((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48)
-  auto reverse() const -> decltype(Eigen::seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr)) {
-    return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr);
-  }
-#else
-protected:
-  typedef typename internal::aseq_negate<IncrType>::type ReverseIncrType;
-  typedef typename internal::aseq_reverse_first_type<FirstType,SizeType,IncrType>::type ReverseFirstType;
-public:
-  ArithmeticSequence<ReverseFirstType,SizeType,ReverseIncrType>
-  reverse() const {
-    return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr);
-  }
-#endif
-};
-
-/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
-  *
-  * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
-template<typename FirstType,typename SizeType,typename IncrType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type,typename internal::cleanup_seq_incr<IncrType>::type >
-seqN(FirstType first, SizeType size, IncrType incr)  {
-  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type,typename internal::cleanup_seq_incr<IncrType>::type>(first,size,incr);
-}
-
-/** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment
-  *
-  * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */
-template<typename FirstType,typename SizeType>
-ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type >
-seqN(FirstType first, SizeType size)  {
-  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type>(first,size);
-}
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a incr
-  *
-  * It is essentially an alias to:
-  * \code
-  * seqN(f, (l-f+incr)/incr, incr);
-  * \endcode
-  *
-  * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
-  */
-template<typename FirstType,typename LastType, typename IncrType>
-auto seq(FirstType f, LastType l, IncrType incr);
-
-/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
-  *
-  * It is essentially an alias to:
-  * \code
-  * seqN(f,l-f+1);
-  * \endcode
-  *
-  * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
-  */
-template<typename FirstType,typename LastType>
-auto seq(FirstType f, LastType l);
-
-#else // EIGEN_PARSED_BY_DOXYGEN
-
-#if EIGEN_HAS_CXX11
-template<typename FirstType,typename LastType>
-auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-                                                   (  typename internal::cleanup_index_type<LastType>::type(l)
-                                                    - typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())))
-{
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              (typename internal::cleanup_index_type<LastType>::type(l)
-               -typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
-}
-
-template<typename FirstType,typename LastType, typename IncrType>
-auto seq(FirstType f, LastType l, IncrType incr)
-  -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-                   (   typename internal::cleanup_index_type<LastType>::type(l)
-                     - typename internal::cleanup_index_type<FirstType>::type(f)+typename internal::cleanup_seq_incr<IncrType>::type(incr)
-                   ) / typename internal::cleanup_seq_incr<IncrType>::type(incr),
-                   typename internal::cleanup_seq_incr<IncrType>::type(incr)))
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              ( typename internal::cleanup_index_type<LastType>::type(l)
-               -typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr)) / CleanedIncrType(incr),
-              CleanedIncrType(incr));
-}
-
-#else // EIGEN_HAS_CXX11
-
-template<typename FirstType,typename LastType>
-typename internal::enable_if<!(symbolic::is_symbolic<FirstType>::value || symbolic::is_symbolic<LastType>::value),
-                             ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,Index> >::type
-seq(FirstType f, LastType l)
-{
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              Index((typename internal::cleanup_index_type<LastType>::type(l)-typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())));
-}
-
-template<typename FirstTypeDerived,typename LastType>
-typename internal::enable_if<!symbolic::is_symbolic<LastType>::value,
-    ArithmeticSequence<FirstTypeDerived, symbolic::AddExpr<symbolic::AddExpr<symbolic::NegateExpr<FirstTypeDerived>,symbolic::ValueExpr<> >,
-                                                            symbolic::ValueExpr<internal::FixedInt<1> > > > >::type
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, LastType l)
-{
-  return seqN(f.derived(),(typename internal::cleanup_index_type<LastType>::type(l)-f.derived()+fix<1>()));
-}
-
-template<typename FirstType,typename LastTypeDerived>
-typename internal::enable_if<!symbolic::is_symbolic<FirstType>::value,
-    ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                        symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::ValueExpr<> >,
-                                          symbolic::ValueExpr<internal::FixedInt<1> > > > >::type
-seq(FirstType f, const symbolic::BaseExpr<LastTypeDerived> &l)
-{
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),(l.derived()-typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
-}
-
-template<typename FirstTypeDerived,typename LastTypeDerived>
-ArithmeticSequence<FirstTypeDerived,
-                    symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::NegateExpr<FirstTypeDerived> >,symbolic::ValueExpr<internal::FixedInt<1> > > >
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<LastTypeDerived> &l)
-{
-  return seqN(f.derived(),(l.derived()-f.derived()+fix<1>()));
-}
-
-
-template<typename FirstType,typename LastType, typename IncrType>
-typename internal::enable_if<!(symbolic::is_symbolic<FirstType>::value || symbolic::is_symbolic<LastType>::value),
-    ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,Index,typename internal::cleanup_seq_incr<IncrType>::type> >::type
-seq(FirstType f, LastType l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              Index((typename internal::cleanup_index_type<LastType>::type(l)-typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr)), incr);
-}
-
-template<typename FirstTypeDerived,typename LastType, typename IncrType>
-typename internal::enable_if<!symbolic::is_symbolic<LastType>::value,
-    ArithmeticSequence<FirstTypeDerived,
-                        symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<symbolic::NegateExpr<FirstTypeDerived>,
-                                                                                   symbolic::ValueExpr<> >,
-                                                                 symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                                              symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                        typename internal::cleanup_seq_incr<IncrType>::type> >::type
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, LastType l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(f.derived(),(typename internal::cleanup_index_type<LastType>::type(l)-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
-}
-
-template<typename FirstType,typename LastTypeDerived, typename IncrType>
-typename internal::enable_if<!symbolic::is_symbolic<FirstType>::value,
-    ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
-                        symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::ValueExpr<> >,
-                                                                 symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                                               symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                        typename internal::cleanup_seq_incr<IncrType>::type> >::type
-seq(FirstType f, const symbolic::BaseExpr<LastTypeDerived> &l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              (l.derived()-typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
-}
-
-template<typename FirstTypeDerived,typename LastTypeDerived, typename IncrType>
-ArithmeticSequence<FirstTypeDerived,
-                    symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,
-                                                                               symbolic::NegateExpr<FirstTypeDerived> >,
-                                                             symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                                          symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
-                    typename internal::cleanup_seq_incr<IncrType>::type>
-seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<LastTypeDerived> &l, IncrType incr)
-{
-  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
-  return seqN(f.derived(),(l.derived()-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
-}
-#endif // EIGEN_HAS_CXX11
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-
-#if EIGEN_HAS_CXX11 || defined(EIGEN_PARSED_BY_DOXYGEN)
-/** \cpp11
-  * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
-  *
-  * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
-  * 
-  * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
-template<typename SizeType,typename IncrType>
-auto lastN(SizeType size, IncrType incr)
--> decltype(seqN(Eigen::last-(size-fix<1>())*incr, size, incr))
-{
-  return seqN(Eigen::last-(size-fix<1>())*incr, size, incr);
-}
-
-/** \cpp11
-  * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
-  *
-  *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
-  * 
-  * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
-template<typename SizeType>
-auto lastN(SizeType size)
--> decltype(seqN(Eigen::last+fix<1>()-size, size))
-{
-  return seqN(Eigen::last+fix<1>()-size, size);
-}
-#endif
-
-namespace internal {
-
-// Convert a symbolic span into a usable one (i.e., remove last/end "keywords")
-template<typename T>
-struct make_size_type {
-  typedef typename internal::conditional<symbolic::is_symbolic<T>::value, Index, T>::type type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType,int XprSize>
-struct IndexedViewCompatibleType<ArithmeticSequence<FirstType,SizeType,IncrType>, XprSize> {
-  typedef ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType> type;
-};
-
-template<typename FirstType,typename SizeType,typename IncrType>
-ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType>
-makeIndexedViewCompatible(const ArithmeticSequence<FirstType,SizeType,IncrType>& ids, Index size,SpecializedType) {
-  return ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType>(
-            eval_expr_given_size(ids.firstObject(),size),eval_expr_given_size(ids.sizeObject(),size),ids.incrObject());
-}
-
-template<typename FirstType,typename SizeType,typename IncrType>
-struct get_compile_time_incr<ArithmeticSequence<FirstType,SizeType,IncrType> > {
-  enum { value = get_fixed_value<IncrType,DynamicIndex>::value };
-};
-
-} // end namespace internal
-
-/** \namespace Eigen::indexing
-  * \ingroup Core_Module
-  * 
-  * The sole purpose of this namespace is to be able to import all functions
-  * and symbols that are expected to be used within operator() for indexing
-  * and slicing. If you already imported the whole Eigen namespace:
-  * \code using namespace Eigen; \endcode
-  * then you are already all set. Otherwise, if you don't want/cannot import
-  * the whole Eigen namespace, the following line:
-  * \code using namespace Eigen::indexing; \endcode
-  * is equivalent to:
-  * \code
-  using Eigen::all;
-  using Eigen::seq;
-  using Eigen::seqN;
-  using Eigen::lastN; // c++11 only
-  using Eigen::last;
-  using Eigen::lastp1;
-  using Eigen::fix;
-  \endcode
-  */
-namespace indexing {
-  using Eigen::all;
-  using Eigen::seq;
-  using Eigen::seqN;
-  #if EIGEN_HAS_CXX11
-  using Eigen::lastN;
-  #endif
-  using Eigen::last;
-  using Eigen::lastp1;
-  using Eigen::fix;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARITHMETIC_SEQUENCE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Array.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Array.h
deleted file mode 100644
index 20c789b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Array.h
+++ /dev/null
@@ -1,417 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARRAY_H
-#define EIGEN_ARRAY_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-  typedef ArrayXpr XprKind;
-  typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
-};
-}
-
-/** \class Array
-  * \ingroup Core_Module
-  *
-  * \brief General-purpose arrays with easy API for coefficient-wise operations
-  *
-  * The %Array class is very similar to the Matrix class. It provides
-  * general-purpose one- and two-dimensional arrays. The difference between the
-  * %Array and the %Matrix class is primarily in the API: the API for the
-  * %Array class provides easy access to coefficient-wise operations, while the
-  * API for the %Matrix class provides easy access to linear-algebra
-  * operations.
-  *
-  * See documentation of class Matrix for detailed information on the template parameters
-  * storage layout.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
-  *
-  * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
-  */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-class Array
-  : public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-  public:
-
-    typedef PlainObjectBase<Array> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Array)
-
-    enum { Options = _Options };
-    typedef typename Base::PlainObject PlainObject;
-
-  protected:
-    template <typename Derived, typename OtherDerived, bool IsVector>
-    friend struct internal::conservative_resize_like_impl;
-
-    using Base::m_storage;
-
-  public:
-
-    using Base::base;
-    using Base::coeff;
-    using Base::coeffRef;
-
-    /**
-      * The usage of
-      *   using Base::operator=;
-      * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
-      * the usage of 'using'. This should be done only for operator=.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
-    {
-      return Base::operator=(other);
-    }
-
-    /** Set all the entries to \a value.
-      * \sa DenseBase::setConstant(), DenseBase::fill()
-      */
-    /* This overload is needed because the usage of
-      *   using Base::operator=;
-      * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
-      * the usage of 'using'. This should be done only for operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const Scalar &value)
-    {
-      Base::setConstant(value);
-      return *this;
-    }
-
-    /** Copies the value of the expression \a other into \c *this with automatic resizing.
-      *
-      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
-      * it will be initialized.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other)
-    {
-      return Base::_set(other);
-    }
-
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array& operator=(const Array& other)
-    {
-      return Base::_set(other);
-    }
-
-    /** Default constructor.
-      *
-      * For fixed-size matrices, does nothing.
-      *
-      * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
-      * is called a null matrix. This constructor is the unique way to create null matrices: resizing
-      * a matrix to 0 is not supported.
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array() : Base()
-    {
-      Base::_check_template_params();
-      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    // FIXME is it still needed ??
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    Array(internal::constructor_without_unaligned_array_assert)
-      : Base(internal::constructor_without_unaligned_array_assert())
-    {
-      Base::_check_template_params();
-      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-#endif
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
-      : Base(std::move(other))
-    {
-      Base::_check_template_params();
-    }
-    EIGEN_DEVICE_FUNC
-    Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
-    {
-      Base::operator=(std::move(other));
-      return *this;
-    }
-#endif
-
-    #if EIGEN_HAS_CXX11
-    /** \copydoc PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-     *
-     * Example: \include Array_variadic_ctor_cxx11.cpp
-     * Output: \verbinclude Array_variadic_ctor_cxx11.out
-     *
-     * \sa Array(const std::initializer_list<std::initializer_list<Scalar>>&)
-     * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&)
-     */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      : Base(a0, a1, a2, a3, args...) {}
-
-    /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11
-      *
-      * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
-      *
-      * Example: \include Array_initializer_list_23_cxx11.cpp
-      * Output: \verbinclude Array_initializer_list_23_cxx11.out
-      *
-      * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered.
-      *
-      * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed.
-      * Therefore <code> Array<int,Dynamic,1>{{1,2,3,4,5}}</code> is legal and the more verbose syntax
-      * <code>Array<int,Dynamic,1>{{1},{2},{3},{4},{5}}</code> can be avoided:
-      *
-      * Example: \include Array_initializer_list_vector_cxx11.cpp
-      * Output: \verbinclude Array_initializer_list_vector_cxx11.out
-      *
-      * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes,
-      * and implicit transposition is allowed for compile-time 1D arrays only.
-      *
-      * \sa  Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
-    #endif // end EIGEN_HAS_CXX11
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE explicit Array(const T& x)
-    {
-      Base::_check_template_params();
-      Base::template _init1<T>(x);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
-    {
-      Base::_check_template_params();
-      this->template _init2<T0,T1>(val0, val1);
-    }
-
-    #else
-    /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
-    EIGEN_DEVICE_FUNC explicit Array(const Scalar *data);
-    /** Constructs a vector or row-vector with given dimension. \only_for_vectors
-      *
-      * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
-      * it is redundant to pass the dimension here, so it makes more sense to use the default
-      * constructor Array() instead.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE explicit Array(Index dim);
-    /** constructs an initialized 1x1 Array with the given coefficient
-      * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */
-    Array(const Scalar& value);
-    /** constructs an uninitialized array with \a rows rows and \a cols columns.
-      *
-      * This is useful for dynamic-size arrays. For fixed-size arrays,
-      * it is redundant to pass these parameters, so one should use the default constructor
-      * Array() instead. */
-    Array(Index rows, Index cols);
-    /** constructs an initialized 2D vector with given coefficients
-      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */
-    Array(const Scalar& val0, const Scalar& val1);
-    #endif  // end EIGEN_PARSED_BY_DOXYGEN
-
-    /** constructs an initialized 3D vector with given coefficients
-      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
-      m_storage.data()[0] = val0;
-      m_storage.data()[1] = val1;
-      m_storage.data()[2] = val2;
-    }
-    /** constructs an initialized 4D vector with given coefficients
-      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
-      m_storage.data()[0] = val0;
-      m_storage.data()[1] = val1;
-      m_storage.data()[2] = val2;
-      m_storage.data()[3] = val3;
-    }
-
-    /** Copy constructor */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const Array& other)
-            : Base(other)
-    { }
-
-  private:
-    struct PrivateType {};
-  public:
-
-    /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
-                              typename internal::enable_if<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
-                                                           PrivateType>::type = PrivateType())
-      : Base(other.derived())
-    { }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT{ return 1; }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); }
-
-    #ifdef EIGEN_ARRAY_PLUGIN
-    #include EIGEN_ARRAY_PLUGIN
-    #endif
-
-  private:
-
-    template<typename MatrixType, typename OtherDerived, bool SwapPointers>
-    friend struct internal::matrix_swap_impl;
-};
-
-/** \defgroup arraytypedefs Global array typedefs
-  * \ingroup Core_Module
-  *
-  * %Eigen defines several typedef shortcuts for most common 1D and 2D array types.
-  *
-  * The general patterns are the following:
-  *
-  * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
-  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
-  * for complex double.
-  *
-  * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats.
-  *
-  * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
-  * a fixed-size 1D array of 4 complex floats.
-  *
-  * With \cpp11, template alias are also defined for common sizes.
-  * They follow the same pattern as above except that the scalar type suffix is replaced by a
-  * template parameter, i.e.:
-  *   - `ArrayRowsCols<Type>` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size.
-  *   - `ArraySize<Type>` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays.
-  *
-  * \sa class Array
-  */
-
-#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix;  \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Size, 1>    Array##SizeSuffix##TypeSuffix;
-
-#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix;  \
-/** \ingroup arraytypedefs */                                    \
-typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
-
-#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \
-EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
-
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int,                  i)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float,                f)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double,               d)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>,  cf)
-EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
-
-#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
-#undef EIGEN_MAKE_ARRAY_TYPEDEFS
-#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
-
-#if EIGEN_HAS_CXX11
-
-#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix)               \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##SizeSuffix##SizeSuffix = Array<Type, Size, Size>;    \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##SizeSuffix = Array<Type, Size, 1>;
-
-#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size)                     \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##Size##X = Array<Type, Size, Dynamic>;                \
-/** \ingroup arraytypedefs */                                     \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Array##X##Size = Array<Type, Dynamic, Size>;
-
-EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2)
-EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3)
-EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4)
-EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3)
-EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
-
-#undef EIGEN_MAKE_ARRAY_TYPEDEFS
-#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
-
-#endif // EIGEN_HAS_CXX11
-
-#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
-using Eigen::Matrix##SizeSuffix##TypeSuffix; \
-using Eigen::Vector##SizeSuffix##TypeSuffix; \
-using Eigen::RowVector##SizeSuffix##TypeSuffix;
-
-#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
-
-#define EIGEN_USING_ARRAY_TYPEDEFS \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
-EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARRAY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArrayBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArrayBase.h
deleted file mode 100644
index ea3dd1c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArrayBase.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARRAYBASE_H
-#define EIGEN_ARRAYBASE_H
-
-namespace Eigen { 
-
-template<typename ExpressionType> class MatrixWrapper;
-
-/** \class ArrayBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for all 1D and 2D array, and related expressions
-  *
-  * An array is similar to a dense vector or matrix. While matrices are mathematical
-  * objects with well defined linear algebra operators, an array is just a collection
-  * of scalar values arranged in a one or two dimensionnal fashion. As the main consequence,
-  * all operations applied to an array are performed coefficient wise. Furthermore,
-  * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
-  * constructors allowing to easily write generic code working for both scalar values
-  * and arrays.
-  *
-  * This class is the base that is inherited by all array expression types.
-  *
-  * \tparam Derived is the derived type, e.g., an array or an expression type.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
-  *
-  * \sa class MatrixBase, \ref TopicClassHierarchy
-  */
-template<typename Derived> class ArrayBase
-  : public DenseBase<Derived>
-{
-  public:
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** The base class for a given storage type. */
-    typedef ArrayBase StorageBaseType;
-
-    typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    typedef DenseBase<Derived> Base;
-    using Base::RowsAtCompileTime;
-    using Base::ColsAtCompileTime;
-    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-    
-    using Base::derived;
-    using Base::const_cast_derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-    using Base::lazyAssign;
-    using Base::operator-;
-    using Base::operator=;
-    using Base::operator+=;
-    using Base::operator-=;
-    using Base::operator*=;
-    using Base::operator/=;
-
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Base::PlainObject PlainObject;
-
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#   include "../plugins/MatrixCwiseUnaryOps.h"
-#   include "../plugins/ArrayCwiseUnaryOps.h"
-#   include "../plugins/CommonCwiseBinaryOps.h"
-#   include "../plugins/MatrixCwiseBinaryOps.h"
-#   include "../plugins/ArrayCwiseBinaryOps.h"
-#   ifdef EIGEN_ARRAYBASE_PLUGIN
-#     include EIGEN_ARRAYBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_UNARY_ADDONS
-
-    /** Special case of the template operator=, in order to prevent the compiler
-      * from generating a default operator= (issue hit with g++ 4.1)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const ArrayBase& other)
-    {
-      internal::call_assignment(derived(), other.derived());
-      return derived();
-    }
-    
-    /** Set all the entries to \a value.
-      * \sa DenseBase::setConstant(), DenseBase::fill() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const Scalar &value)
-    { Base::setConstant(value); return derived(); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator+=(const Scalar& scalar);
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator-=(const Scalar& scalar);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator+=(const ArrayBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator-=(const ArrayBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator*=(const ArrayBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator/=(const ArrayBase<OtherDerived>& other);
-
-  public:
-    EIGEN_DEVICE_FUNC
-    ArrayBase<Derived>& array() { return *this; }
-    EIGEN_DEVICE_FUNC
-    const ArrayBase<Derived>& array() const { return *this; }
-
-    /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
-      * \sa MatrixBase::array() */
-    EIGEN_DEVICE_FUNC
-    MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
-    EIGEN_DEVICE_FUNC
-    const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
-
-//     template<typename Dest>
-//     inline void evalTo(Dest& dst) const { dst = matrix(); }
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase)
-
-  private:
-    explicit ArrayBase(Index);
-    ArrayBase(Index,Index);
-    template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&);
-  protected:
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-};
-
-/** replaces \c *this by \c *this - \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this + \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this * \a other coefficient wise.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this / \a other coefficient wise.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARRAYBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArrayWrapper.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArrayWrapper.h
deleted file mode 100644
index 2e9555b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ArrayWrapper.h
+++ /dev/null
@@ -1,209 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARRAYWRAPPER_H
-#define EIGEN_ARRAYWRAPPER_H
-
-namespace Eigen {
-
-/** \class ArrayWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a mathematical vector or matrix as an array object
-  *
-  * This class is the return type of MatrixBase::array(), and most of the time
-  * this is the only way it is use.
-  *
-  * \sa MatrixBase::array(), class MatrixWrapper
-  */
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<ArrayWrapper<ExpressionType> >
-  : public traits<typename remove_all<typename ExpressionType::Nested>::type >
-{
-  typedef ArrayXpr XprKind;
-  // Let's remove NestByRefBit
-  enum {
-    Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
-    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
-    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
-  };
-};
-}
-
-template<typename ExpressionType>
-class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
-{
-  public:
-    typedef ArrayBase<ArrayWrapper> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
-    typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<ExpressionType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
-
-    using Base::coeffRef;
-
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return m_expression.data(); }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return m_expression.coeffRef(rowId, colId);
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      return m_expression.coeffRef(index);
-    }
-
-    template<typename Dest>
-    EIGEN_DEVICE_FUNC
-    inline void evalTo(Dest& dst) const { dst = m_expression; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<NestedExpressionType>::type&
-    nestedExpression() const
-    {
-      return m_expression;
-    }
-
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index)  */
-    EIGEN_DEVICE_FUNC
-    void resize(Index newSize) { m_expression.resize(newSize); }
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index,Index)*/
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
-
-  protected:
-    NestedExpressionType m_expression;
-};
-
-/** \class MatrixWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Expression of an array as a mathematical vector or matrix
-  *
-  * This class is the return type of ArrayBase::matrix(), and most of the time
-  * this is the only way it is use.
-  *
-  * \sa MatrixBase::matrix(), class ArrayWrapper
-  */
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<MatrixWrapper<ExpressionType> >
- : public traits<typename remove_all<typename ExpressionType::Nested>::type >
-{
-  typedef MatrixXpr XprKind;
-  // Let's remove NestByRefBit
-  enum {
-    Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
-    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
-    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
-  };
-};
-}
-
-template<typename ExpressionType>
-class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
-{
-  public:
-    typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
-    typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<ExpressionType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
-
-    using Base::coeffRef;
-
-    EIGEN_DEVICE_FUNC
-    explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return m_expression.data(); }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return m_expression.derived().coeffRef(rowId, colId);
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      return m_expression.coeffRef(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<NestedExpressionType>::type&
-    nestedExpression() const
-    {
-      return m_expression;
-    }
-
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index)  */
-    EIGEN_DEVICE_FUNC
-    void resize(Index newSize) { m_expression.resize(newSize); }
-    /** Forwards the resizing request to the nested expression
-      * \sa DenseBase::resize(Index,Index)*/
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
-
-  protected:
-    NestedExpressionType m_expression;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARRAYWRAPPER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Assign.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Assign.h
deleted file mode 100644
index 655412e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Assign.h
+++ /dev/null
@@ -1,90 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ASSIGN_H
-#define EIGEN_ASSIGN_H
-
-namespace Eigen {
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
-  ::lazyAssign(const DenseBase<OtherDerived>& other)
-{
-  enum{
-    SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value
-  };
-
-  EIGEN_STATIC_ASSERT_LVALUE(Derived)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
-  EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  eigen_assert(rows() == other.rows() && cols() == other.cols());
-  internal::call_assignment_no_alias(derived(),other.derived());
-  
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
-{
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
-{
-  other.derived().evalTo(derived());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ASSIGN_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/AssignEvaluator.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/AssignEvaluator.h
deleted file mode 100644
index 7d76f0c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/AssignEvaluator.h
+++ /dev/null
@@ -1,1010 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ASSIGN_EVALUATOR_H
-#define EIGEN_ASSIGN_EVALUATOR_H
-
-namespace Eigen {
-
-// This implementation is based on Assign.h
-
-namespace internal {
-
-/***************************************************************************
-* Part 1 : the logic deciding a strategy for traversal and unrolling       *
-***************************************************************************/
-
-// copy_using_evaluator_traits is based on assign_traits
-
-template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc, int MaxPacketSize = -1>
-struct copy_using_evaluator_traits
-{
-  typedef typename DstEvaluator::XprType Dst;
-  typedef typename Dst::Scalar DstScalar;
-
-  enum {
-    DstFlags = DstEvaluator::Flags,
-    SrcFlags = SrcEvaluator::Flags
-  };
-
-public:
-  enum {
-    DstAlignment = DstEvaluator::Alignment,
-    SrcAlignment = SrcEvaluator::Alignment,
-    DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
-    JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
-  };
-
-private:
-  enum {
-    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
-              : int(DstFlags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
-              : int(Dst::RowsAtCompileTime),
-    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
-              : int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
-              : int(Dst::MaxRowsAtCompileTime),
-    RestrictedInnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(InnerSize,MaxPacketSize),
-    RestrictedLinearSize = EIGEN_SIZE_MIN_PREFER_FIXED(Dst::SizeAtCompileTime,MaxPacketSize),
-    OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
-    MaxSizeAtCompileTime = Dst::SizeAtCompileTime
-  };
-
-  // TODO distinguish between linear traversal and inner-traversals
-  typedef typename find_best_packet<DstScalar,RestrictedLinearSize>::type LinearPacketType;
-  typedef typename find_best_packet<DstScalar,RestrictedInnerSize>::type InnerPacketType;
-
-  enum {
-    LinearPacketSize = unpacket_traits<LinearPacketType>::size,
-    InnerPacketSize = unpacket_traits<InnerPacketType>::size
-  };
-
-public:
-  enum {
-    LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
-    InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
-  };
-
-private:
-  enum {
-    DstIsRowMajor = DstFlags&RowMajorBit,
-    SrcIsRowMajor = SrcFlags&RowMajorBit,
-    StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
-    MightVectorize = bool(StorageOrdersAgree)
-                  && (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
-                  && bool(functor_traits<AssignFunc>::PacketAccess),
-    MayInnerVectorize  = MightVectorize
-                       && int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
-                       && int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
-                       && (EIGEN_UNALIGNED_VECTORIZE  || int(JointAlignment)>=int(InnerRequiredAlignment)),
-    MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
-    MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess)
-                       && (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
-      /* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
-         so it's only good for large enough sizes. */
-    MaySliceVectorize  = bool(MightVectorize) && bool(DstHasDirectAccess)
-                       && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize)))
-      /* slice vectorization can be slow, so we only want it if the slices are big, which is
-         indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
-         in a fixed-size matrix
-         However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
-  };
-
-public:
-  enum {
-    Traversal =  int(Dst::SizeAtCompileTime) == 0 ? int(AllAtOnceTraversal) // If compile-size is zero, traversing will fail at compile-time.
-              : (int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize)) ? int(LinearVectorizedTraversal)
-              : int(MayInnerVectorize)   ? int(InnerVectorizedTraversal)
-              : int(MayLinearVectorize)  ? int(LinearVectorizedTraversal)
-              : int(MaySliceVectorize)   ? int(SliceVectorizedTraversal)
-              : int(MayLinearize)        ? int(LinearTraversal)
-                                         : int(DefaultTraversal),
-    Vectorized = int(Traversal) == InnerVectorizedTraversal
-              || int(Traversal) == LinearVectorizedTraversal
-              || int(Traversal) == SliceVectorizedTraversal
-  };
-
-  typedef typename conditional<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType>::type PacketType;
-
-private:
-  enum {
-    ActualPacketSize    = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize
-                        : Vectorized ? InnerPacketSize
-                        : 1,
-    UnrollingLimit      = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
-    MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
-                       && int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
-    MayUnrollInner      = int(InnerSize) != Dynamic
-                       && int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
-  };
-
-public:
-  enum {
-    Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
-                ? (
-                    int(MayUnrollCompletely) ? int(CompleteUnrolling)
-                  : int(MayUnrollInner)      ? int(InnerUnrolling)
-                                             : int(NoUnrolling)
-                  )
-              : int(Traversal) == int(LinearVectorizedTraversal)
-                ? ( bool(MayUnrollCompletely) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)))
-                          ? int(CompleteUnrolling)
-                          : int(NoUnrolling) )
-              : int(Traversal) == int(LinearTraversal)
-                ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
-                                              : int(NoUnrolling) )
-#if EIGEN_UNALIGNED_VECTORIZE
-              : int(Traversal) == int(SliceVectorizedTraversal)
-                ? ( bool(MayUnrollInner) ? int(InnerUnrolling)
-                                         : int(NoUnrolling) )
-#endif
-              : int(NoUnrolling)
-  };
-
-#ifdef EIGEN_DEBUG_ASSIGN
-  static void debug()
-  {
-    std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
-    std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
-    std::cerr.setf(std::ios::hex, std::ios::basefield);
-    std::cerr << "DstFlags" << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
-    std::cerr << "SrcFlags" << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
-    std::cerr.unsetf(std::ios::hex);
-    EIGEN_DEBUG_VAR(DstAlignment)
-    EIGEN_DEBUG_VAR(SrcAlignment)
-    EIGEN_DEBUG_VAR(LinearRequiredAlignment)
-    EIGEN_DEBUG_VAR(InnerRequiredAlignment)
-    EIGEN_DEBUG_VAR(JointAlignment)
-    EIGEN_DEBUG_VAR(InnerSize)
-    EIGEN_DEBUG_VAR(InnerMaxSize)
-    EIGEN_DEBUG_VAR(LinearPacketSize)
-    EIGEN_DEBUG_VAR(InnerPacketSize)
-    EIGEN_DEBUG_VAR(ActualPacketSize)
-    EIGEN_DEBUG_VAR(StorageOrdersAgree)
-    EIGEN_DEBUG_VAR(MightVectorize)
-    EIGEN_DEBUG_VAR(MayLinearize)
-    EIGEN_DEBUG_VAR(MayInnerVectorize)
-    EIGEN_DEBUG_VAR(MayLinearVectorize)
-    EIGEN_DEBUG_VAR(MaySliceVectorize)
-    std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
-    EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
-    EIGEN_DEBUG_VAR(DstEvaluator::CoeffReadCost)
-    EIGEN_DEBUG_VAR(Dst::SizeAtCompileTime)
-    EIGEN_DEBUG_VAR(UnrollingLimit)
-    EIGEN_DEBUG_VAR(MayUnrollCompletely)
-    EIGEN_DEBUG_VAR(MayUnrollInner)
-    std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
-    std::cerr << std::endl;
-  }
-#endif
-};
-
-/***************************************************************************
-* Part 2 : meta-unrollers
-***************************************************************************/
-
-/************************
-*** Default traversal ***
-************************/
-
-template<typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
-{
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-
-  enum {
-    outer = Index / DstXprType::InnerSizeAtCompileTime,
-    inner = Index % DstXprType::InnerSizeAtCompileTime
-  };
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    kernel.assignCoeffByOuterInner(outer, inner);
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
-};
-
-template<typename Kernel, int Index_, int Stop>
-struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
-  {
-    kernel.assignCoeffByOuterInner(outer, Index_);
-    copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_+1, Stop>::run(kernel, outer);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { }
-};
-
-/***********************
-*** Linear traversal ***
-***********************/
-
-template<typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel)
-  {
-    kernel.assignCoeff(Index);
-    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
-};
-
-/**************************
-*** Inner vectorization ***
-**************************/
-
-template<typename Kernel, int Index, int Stop>
-struct copy_using_evaluator_innervec_CompleteUnrolling
-{
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-  typedef typename Kernel::PacketType PacketType;
-
-  enum {
-    outer = Index / DstXprType::InnerSizeAtCompileTime,
-    inner = Index % DstXprType::InnerSizeAtCompileTime,
-    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
-    DstAlignment = Kernel::AssignmentTraits::DstAlignment
-  };
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
-    enum { NextIndex = Index + unpacket_traits<PacketType>::size };
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
-  }
-};
-
-template<typename Kernel, int Stop>
-struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
-};
-
-template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
-struct copy_using_evaluator_innervec_InnerUnrolling
-{
-  typedef typename Kernel::PacketType PacketType;
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
-  {
-    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
-    enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
-    copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel, outer);
-  }
-};
-
-template<typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
-struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
-};
-
-/***************************************************************************
-* Part 3 : implementation of all cases
-***************************************************************************/
-
-// dense_assignment_loop is based on assign_impl
-
-template<typename Kernel,
-         int Traversal = Kernel::AssignmentTraits::Traversal,
-         int Unrolling = Kernel::AssignmentTraits::Unrolling>
-struct dense_assignment_loop;
-
-/************************
-***** Special Cases *****
-************************/
-
-// Zero-sized assignment is a no-op.
-template<typename Kernel, int Unrolling>
-struct dense_assignment_loop<Kernel, AllAtOnceTraversal, Unrolling>
-{
-  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel& /*kernel*/)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    EIGEN_STATIC_ASSERT(int(DstXprType::SizeAtCompileTime) == 0,
-      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT)
-  }
-};
-
-/************************
-*** Default traversal ***
-************************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel &kernel)
-  {
-    for(Index outer = 0; outer < kernel.outerSize(); ++outer) {
-      for(Index inner = 0; inner < kernel.innerSize(); ++inner) {
-        kernel.assignCoeffByOuterInner(outer, inner);
-      }
-    }
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-
-    const Index outerSize = kernel.outerSize();
-    for(Index outer = 0; outer < outerSize; ++outer)
-      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
-  }
-};
-
-/***************************
-*** Linear vectorization ***
-***************************/
-
-
-// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
-// of the non vectorizable beginning and ending parts
-
-template <bool IsAligned = false>
-struct unaligned_dense_assignment_loop
-{
-  // if IsAligned = true, then do nothing
-  template <typename Kernel>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {}
-};
-
-template <>
-struct unaligned_dense_assignment_loop<false>
-{
-  // MSVC must not inline this functions. If it does, it fails to optimize the
-  // packet access path.
-  // FIXME check which version exhibits this issue
-#if EIGEN_COMP_MSVC
-  template <typename Kernel>
-  static EIGEN_DONT_INLINE void run(Kernel &kernel,
-                                    Index start,
-                                    Index end)
-#else
-  template <typename Kernel>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
-                                      Index start,
-                                      Index end)
-#endif
-  {
-    for (Index index = start; index < end; ++index)
-      kernel.assignCoeff(index);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    const Index size = kernel.size();
-    typedef typename Kernel::Scalar Scalar;
-    typedef typename Kernel::PacketType PacketType;
-    enum {
-      requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
-      packetSize = unpacket_traits<PacketType>::size,
-      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
-      dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
-                                                            : int(Kernel::AssignmentTraits::DstAlignment),
-      srcAlignment = Kernel::AssignmentTraits::JointAlignment
-    };
-    const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned<requestedAlignment>(kernel.dstDataPtr(), size);
-    const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
-
-    unaligned_dense_assignment_loop<dstIsAligned!=0>::run(kernel, 0, alignedStart);
-
-    for(Index index = alignedStart; index < alignedEnd; index += packetSize)
-      kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
-
-    unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::PacketType PacketType;
-
-    enum { size = DstXprType::SizeAtCompileTime,
-           packetSize =unpacket_traits<PacketType>::size,
-           alignedSize = (int(size)/packetSize)*packetSize };
-
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
-    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel);
-  }
-};
-
-/**************************
-*** Inner vectorization ***
-**************************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
-{
-  typedef typename Kernel::PacketType PacketType;
-  enum {
-    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
-    DstAlignment = Kernel::AssignmentTraits::DstAlignment
-  };
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    const Index innerSize = kernel.innerSize();
-    const Index outerSize = kernel.outerSize();
-    const Index packetSize = unpacket_traits<PacketType>::size;
-    for(Index outer = 0; outer < outerSize; ++outer)
-      for(Index inner = 0; inner < innerSize; inner+=packetSize)
-        kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::AssignmentTraits Traits;
-    const Index outerSize = kernel.outerSize();
-    for(Index outer = 0; outer < outerSize; ++outer)
-      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime,
-                                                   Traits::SrcAlignment, Traits::DstAlignment>::run(kernel, outer);
-  }
-};
-
-/***********************
-*** Linear traversal ***
-***********************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    const Index size = kernel.size();
-    for(Index i = 0; i < size; ++i)
-      kernel.assignCoeff(i);
-  }
-};
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
-  }
-};
-
-/**************************
-*** Slice vectorization ***
-***************************/
-
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::Scalar Scalar;
-    typedef typename Kernel::PacketType PacketType;
-    enum {
-      packetSize = unpacket_traits<PacketType>::size,
-      requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
-      alignable = packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar),
-      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
-      dstAlignment = alignable ? int(requestedAlignment)
-                               : int(Kernel::AssignmentTraits::DstAlignment)
-    };
-    const Scalar *dst_ptr = kernel.dstDataPtr();
-    if((!bool(dstIsAligned)) && (UIntPtr(dst_ptr) % sizeof(Scalar))>0)
-    {
-      // the pointer is not aligned-on scalar, so alignment is not possible
-      return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);
-    }
-    const Index packetAlignedMask = packetSize - 1;
-    const Index innerSize = kernel.innerSize();
-    const Index outerSize = kernel.outerSize();
-    const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0;
-    Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, innerSize);
-
-    for(Index outer = 0; outer < outerSize; ++outer)
-    {
-      const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
-      // do the non-vectorizable part of the assignment
-      for(Index inner = 0; inner<alignedStart ; ++inner)
-        kernel.assignCoeffByOuterInner(outer, inner);
-
-      // do the vectorizable part of the assignment
-      for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
-        kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
-
-      // do the non-vectorizable part of the assignment
-      for(Index inner = alignedEnd; inner<innerSize ; ++inner)
-        kernel.assignCoeffByOuterInner(outer, inner);
-
-      alignedStart = numext::mini((alignedStart+alignedStep)%packetSize, innerSize);
-    }
-  }
-};
-
-#if EIGEN_UNALIGNED_VECTORIZE
-template<typename Kernel>
-struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
-  {
-    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
-    typedef typename Kernel::PacketType PacketType;
-
-    enum { innerSize = DstXprType::InnerSizeAtCompileTime,
-           packetSize =unpacket_traits<PacketType>::size,
-           vectorizableSize = (int(innerSize) / int(packetSize)) * int(packetSize),
-           size = DstXprType::SizeAtCompileTime };
-
-    for(Index outer = 0; outer < kernel.outerSize(); ++outer)
-    {
-      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
-      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, innerSize>::run(kernel, outer);
-    }
-  }
-};
-#endif
-
-
-/***************************************************************************
-* Part 4 : Generic dense assignment kernel
-***************************************************************************/
-
-// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
-// to another dense writable evaluator.
-// It is parametrized by the two evaluators, and the actual assignment functor.
-// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
-// One can customize the assignment using this generic dense_assignment_kernel with different
-// functors, or by completely overloading it, by-passing a functor.
-template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
-class generic_dense_assignment_kernel
-{
-protected:
-  typedef typename DstEvaluatorTypeT::XprType DstXprType;
-  typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
-public:
-
-  typedef DstEvaluatorTypeT DstEvaluatorType;
-  typedef SrcEvaluatorTypeT SrcEvaluatorType;
-  typedef typename DstEvaluatorType::Scalar Scalar;
-  typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
-  typedef typename AssignmentTraits::PacketType PacketType;
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
-    : m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr)
-  {
-    #ifdef EIGEN_DEBUG_ASSIGN
-    AssignmentTraits::debug();
-    #endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_dstExpr.size(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index innerSize() const EIGEN_NOEXCEPT { return m_dstExpr.innerSize(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerSize() const EIGEN_NOEXCEPT { return m_dstExpr.outerSize(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dstExpr.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_dstExpr.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerStride() const EIGEN_NOEXCEPT { return m_dstExpr.outerStride(); }
-
-  EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() EIGEN_NOEXCEPT { return m_dst; }
-  EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const EIGEN_NOEXCEPT { return m_src; }
-
-  /// Assign src(row,col) to dst(row,col) through the assignment functor.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col)
-  {
-    m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col));
-  }
-
-  /// \sa assignCoeff(Index,Index)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index)
-  {
-    m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
-  }
-
-  /// \sa assignCoeff(Index,Index)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner)
-  {
-    Index row = rowIndexByOuterInner(outer, inner);
-    Index col = colIndexByOuterInner(outer, inner);
-    assignCoeff(row, col);
-  }
-
-
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
-  {
-    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode,PacketType>(row,col));
-  }
-
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index)
-  {
-    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,PacketType>(index));
-  }
-
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
-  {
-    Index row = rowIndexByOuterInner(outer, inner);
-    Index col = colIndexByOuterInner(outer, inner);
-    assignPacket<StoreMode,LoadMode,PacketType>(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner)
-  {
-    typedef typename DstEvaluatorType::ExpressionTraits Traits;
-    return int(Traits::RowsAtCompileTime) == 1 ? 0
-      : int(Traits::ColsAtCompileTime) == 1 ? inner
-      : int(DstEvaluatorType::Flags)&RowMajorBit ? outer
-      : inner;
-  }
-
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner)
-  {
-    typedef typename DstEvaluatorType::ExpressionTraits Traits;
-    return int(Traits::ColsAtCompileTime) == 1 ? 0
-      : int(Traits::RowsAtCompileTime) == 1 ? inner
-      : int(DstEvaluatorType::Flags)&RowMajorBit ? inner
-      : outer;
-  }
-
-  EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const
-  {
-    return m_dstExpr.data();
-  }
-
-protected:
-  DstEvaluatorType& m_dst;
-  const SrcEvaluatorType& m_src;
-  const Functor &m_functor;
-  // TODO find a way to avoid the needs of the original expression
-  DstXprType& m_dstExpr;
-};
-
-// Special kernel used when computing small products whose operands have dynamic dimensions.  It ensures that the
-// PacketSize used is no larger than 4, thereby increasing the chance that vectorized instructions will be used
-// when computing the product.
-
-template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor>
-class restricted_packet_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn> Base;
- public:
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::DstXprType DstXprType;
-    typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, 4> AssignmentTraits;
-    typedef typename AssignmentTraits::PacketType PacketType;
-
-    EIGEN_DEVICE_FUNC restricted_packet_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {
-  }
- };
-
-/***************************************************************************
-* Part 5 : Entry point for dense rectangular assignment
-***************************************************************************/
-
-template<typename DstXprType,typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const Functor &/*func*/)
-{
-  EIGEN_ONLY_USED_FOR_DEBUG(dst);
-  EIGEN_ONLY_USED_FOR_DEBUG(src);
-  eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-}
-
-template<typename DstXprType,typename SrcXprType, typename T1, typename T2>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::assign_op<T1,T2> &/*func*/)
-{
-  Index dstRows = src.rows();
-  Index dstCols = src.cols();
-  if(((dst.rows()!=dstRows) || (dst.cols()!=dstCols)))
-    dst.resize(dstRows, dstCols);
-  eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
-}
-
-template<typename DstXprType, typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
-{
-  typedef evaluator<DstXprType> DstEvaluatorType;
-  typedef evaluator<SrcXprType> SrcEvaluatorType;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
-  // we need to resize the destination after the source evaluator has been created.
-  resize_if_allowed(dst, src, func);
-
-  DstEvaluatorType dstEvaluator(dst);
-
-  typedef generic_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
-  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-  dense_assignment_loop<Kernel>::run(kernel);
-}
-
-// Specialization for filling the destination with a constant value.
-#ifndef EIGEN_GPU_COMPILE_PHASE
-template<typename DstXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<typename DstXprType::Scalar>, DstXprType>& src, const internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>& func)
-{
-  resize_if_allowed(dst, src, func);
-  std::fill_n(dst.data(), dst.size(), src.functor()());
-}
-#endif
-
-template<typename DstXprType, typename SrcXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src)
-{
-  call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
-}
-
-/***************************************************************************
-* Part 6 : Generic assignment
-***************************************************************************/
-
-// Based on the respective shapes of the destination and source,
-// the class AssignmentKind determine the kind of assignment mechanism.
-// AssignmentKind must define a Kind typedef.
-template<typename DstShape, typename SrcShape> struct AssignmentKind;
-
-// Assignment kind defined in this file:
-struct Dense2Dense {};
-struct EigenBase2EigenBase {};
-
-template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
-template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
-
-// This is the main assignment class
-template< typename DstXprType, typename SrcXprType, typename Functor,
-          typename Kind = typename AssignmentKind< typename evaluator_traits<DstXprType>::Shape , typename evaluator_traits<SrcXprType>::Shape >::Kind,
-          typename EnableIf = void>
-struct Assignment;
-
-
-// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic transposition.
-// Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite complicated.
-// So this intermediate function removes everything related to "assume-aliasing" such that Assignment
-// does not has to bother about these annoying details.
-
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(Dst& dst, const Src& src)
-{
-  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(const Dst& dst, const Src& src)
-{
-  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-
-// Deal with "assume-aliasing"
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
-{
-  typename plain_matrix_type<Src>::type tmp(src);
-  call_assignment_no_alias(dst, tmp, func);
-}
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
-{
-  call_assignment_no_alias(dst, src, func);
-}
-
-// by-pass "assume-aliasing"
-// When there is no aliasing, we require that 'dst' has been properly resized
-template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
-{
-  call_assignment_no_alias(dst.expression(), src, func);
-}
-
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
-{
-  enum {
-    NeedToTranspose = (    (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1)
-                        || (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)
-                      ) && int(Dst::SizeAtCompileTime) != 1
-  };
-
-  typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst>::type ActualDstTypeCleaned;
-  typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst&>::type ActualDstType;
-  ActualDstType actualDst(dst);
-
-  // TODO check whether this is the right place to perform these checks:
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned,Src)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar);
-
-  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
-}
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_restricted_packet_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
-{
-    typedef evaluator<Dst> DstEvaluatorType;
-    typedef evaluator<Src> SrcEvaluatorType;
-    typedef restricted_packet_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Func> Kernel;
-
-    EIGEN_STATIC_ASSERT_LVALUE(Dst)
-    EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
-
-    SrcEvaluatorType srcEvaluator(src);
-    resize_if_allowed(dst, src, func);
-
-    DstEvaluatorType dstEvaluator(dst);
-    Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-    dense_assignment_loop<Kernel>::run(kernel);
-}
-
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias(Dst& dst, const Src& src)
-{
-  call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-
-template<typename Dst, typename Src, typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
-{
-  // TODO check whether this is the right place to perform these checks:
-  EIGEN_STATIC_ASSERT_LVALUE(Dst)
-  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst,Src)
-  EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
-
-  Assignment<Dst,Src,Func>::run(dst, src, func);
-}
-template<typename Dst, typename Src>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
-{
-  call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
-}
-
-// forward declaration
-template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
-
-// Generic Dense to Dense assignment
-// Note that the last template argument "Weak" is needed to make it possible to perform
-// both partial specialization+SFINAE without ambiguous specialization
-template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak>
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-#ifndef EIGEN_NO_DEBUG
-    internal::check_for_aliasing(dst, src);
-#endif
-
-    call_dense_assignment_loop(dst, src, func);
-  }
-};
-
-// Generic assignment through evalTo.
-// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
-// Note that the last template argument "Weak" is needed to make it possible to perform
-// both partial specialization+SFINAE without ambiguous specialization
-template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.evalTo(dst);
-  }
-
-  // NOTE The following two functions are templated to avoid their instantiation if not needed
-  //      This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
-  template<typename SrcScalarType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.addTo(dst);
-  }
-
-  template<typename SrcScalarType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    src.subTo(dst);
-  }
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_ASSIGN_EVALUATOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Assign_MKL.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Assign_MKL.h
deleted file mode 100644
index c6140d1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Assign_MKL.h
+++ /dev/null
@@ -1,178 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
- Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
- 
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to Intel(R) MKL
- *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
- ********************************************************************************
-*/
-
-#ifndef EIGEN_ASSIGN_VML_H
-#define EIGEN_ASSIGN_VML_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Dst, typename Src>
-class vml_assign_traits
-{
-  private:
-    enum {
-      DstHasDirectAccess = Dst::Flags & DirectAccessBit,
-      SrcHasDirectAccess = Src::Flags & DirectAccessBit,
-      StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
-      InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
-                : int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
-                : int(Dst::RowsAtCompileTime),
-      InnerMaxSize  = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
-                    : int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
-                    : int(Dst::MaxRowsAtCompileTime),
-      MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
-
-      MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
-      MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
-      VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
-      LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD
-    };
-  public:
-    enum {
-      EnableVml = MightEnableVml && LargeEnough,
-      Traversal = MightLinearize ? LinearTraversal : DefaultTraversal
-    };
-};
-
-#define EIGEN_PP_EXPAND(ARG) ARG
-#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
-#define EIGEN_VMLMODE_EXPAND_xLA , VML_HA
-#else
-#define EIGEN_VMLMODE_EXPAND_xLA , VML_LA
-#endif
-
-#define EIGEN_VMLMODE_EXPAND_x_
-
-#define EIGEN_VMLMODE_PREFIX_xLA vm
-#define EIGEN_VMLMODE_PREFIX_x_  v
-#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x,VMLMODE)
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                                           \
-  template< typename DstXprType, typename SrcXprNested>                                                                         \
-  struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>,   \
-                   Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> {              \
-    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType;                                            \
-    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) {                       \
-      resize_if_allowed(dst, src, func);                                                                                        \
-      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                                       \
-      if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) {                                              \
-        VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(),                                                        \
-              (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) );                                           \
-      } else {                                                                                                                  \
-        const Index outerSize = dst.outerSize();                                                                                \
-        for(Index outer = 0; outer < outerSize; ++outer) {                                                                      \
-          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) :                             \
-                                                      &(src.nestedExpression().coeffRef(0, outer));                             \
-          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));                           \
-          VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr,                                                                      \
-                (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                                             \
-        }                                                                                                                       \
-      }                                                                                                                         \
-    }                                                                                                                           \
-  };                                                                                                                            \
-
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                         \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),s##VMLOP), float, float, VMLMODE)           \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),d##VMLOP), double, double, VMLMODE)
-
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)                                                         \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),c##VMLOP), scomplex, MKL_Complex8, VMLMODE) \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),z##VMLOP), dcomplex, MKL_Complex16, VMLMODE)
-  
-#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE)                                                              \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                               \
-  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
-
-  
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin,   Sin,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin,  Asin,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh,  Sinh,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos,   Cos,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos,  Acos,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh,  Cosh,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan,   Tan,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan,  Atan,  LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh,  Tanh,  LA)
-// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,   Abs,    _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp,   Exp,   LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log,   Ln,    LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt,  Sqrt,  _)
-
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr,   _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg,      _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round,  _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor,  _)
-EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil,  Ceil,   _)
-
-#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                                           \
-  template< typename DstXprType, typename SrcXprNested, typename Plain>                                                       \
-  struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested,                       \
-                    const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>,    \
-                   Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> {            \
-    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested,                                           \
-                    const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType;                         \
-    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) {                     \
-      resize_if_allowed(dst, src, func);                                                                                      \
-      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                                     \
-      VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other);                                       \
-      if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal)                                              \
-      {                                                                                                                       \
-        VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent,                                                        \
-              (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) );                                         \
-      } else {                                                                                                                \
-        const Index outerSize = dst.outerSize();                                                                              \
-        for(Index outer = 0; outer < outerSize; ++outer) {                                                                    \
-          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) :                                        \
-                                                      &(src.lhs().coeffRef(0, outer));                                        \
-          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));                         \
-          VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent,                                                          \
-                 (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                                          \
-        }                                                                                                                     \
-      }                                                                                                                       \
-    }                                                                                                                         \
-  };
-  
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float,    float,         LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double,   double,        LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8,  LA)
-EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_ASSIGN_VML_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/BandMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/BandMatrix.h
deleted file mode 100644
index 878c024..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/BandMatrix.h
+++ /dev/null
@@ -1,353 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BANDMATRIX_H
-#define EIGEN_BANDMATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Derived>
-class BandMatrixBase : public EigenBase<Derived>
-{
-  public:
-
-    enum {
-      Flags = internal::traits<Derived>::Flags,
-      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-      Supers = internal::traits<Derived>::Supers,
-      Subs   = internal::traits<Derived>::Subs,
-      Options = internal::traits<Derived>::Options
-    };
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
-    typedef typename DenseMatrixType::StorageIndex StorageIndex;
-    typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
-    typedef EigenBase<Derived> Base;
-
-  protected:
-    enum {
-      DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
-                            ? 1 + Supers + Subs
-                            : Dynamic,
-      SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
-    };
-
-  public:
-
-    using Base::derived;
-    using Base::rows;
-    using Base::cols;
-
-    /** \returns the number of super diagonals */
-    inline Index supers() const { return derived().supers(); }
-
-    /** \returns the number of sub diagonals */
-    inline Index subs() const { return derived().subs(); }
-
-    /** \returns an expression of the underlying coefficient matrix */
-    inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
-
-    /** \returns an expression of the underlying coefficient matrix */
-    inline CoefficientsType& coeffs() { return derived().coeffs(); }
-
-    /** \returns a vector expression of the \a i -th column,
-      * only the meaningful part is returned.
-      * \warning the internal storage must be column major. */
-    inline Block<CoefficientsType,Dynamic,1> col(Index i)
-    {
-      EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-      Index start = 0;
-      Index len = coeffs().rows();
-      if (i<=supers())
-      {
-        start = supers()-i;
-        len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
-      }
-      else if (i>=rows()-subs())
-        len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
-      return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1);
-    }
-
-    /** \returns a vector expression of the main diagonal */
-    inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
-    { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
-
-    /** \returns a vector expression of the main diagonal (const version) */
-    inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
-    { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
-
-    template<int Index> struct DiagonalIntReturnType {
-      enum {
-        ReturnOpposite = (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)),
-        Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
-        ActualIndex = ReturnOpposite ? -Index : Index,
-        DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
-                     ? Dynamic
-                     : (ActualIndex<0
-                     ? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
-                     : EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
-      };
-      typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
-      typedef typename internal::conditional<Conjugate,
-                 CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
-                 BuildType>::type Type;
-    };
-
-    /** \returns a vector expression of the \a N -th sub or super diagonal */
-    template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
-    {
-      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
-    }
-
-    /** \returns a vector expression of the \a N -th sub or super diagonal */
-    template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
-    {
-      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
-    }
-
-    /** \returns a vector expression of the \a i -th sub or super diagonal */
-    inline Block<CoefficientsType,1,Dynamic> diagonal(Index i)
-    {
-      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
-      return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
-    }
-
-    /** \returns a vector expression of the \a i -th sub or super diagonal */
-    inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const
-    {
-      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
-      return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
-    }
-
-    template<typename Dest> inline void evalTo(Dest& dst) const
-    {
-      dst.resize(rows(),cols());
-      dst.setZero();
-      dst.diagonal() = diagonal();
-      for (Index i=1; i<=supers();++i)
-        dst.diagonal(i) = diagonal(i);
-      for (Index i=1; i<=subs();++i)
-        dst.diagonal(-i) = diagonal(-i);
-    }
-
-    DenseMatrixType toDenseMatrix() const
-    {
-      DenseMatrixType res(rows(),cols());
-      evalTo(res);
-      return res;
-    }
-
-  protected:
-
-    inline Index diagonalLength(Index i) const
-    { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
-};
-
-/**
-  * \class BandMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Represents a rectangular matrix with a banded storage
-  *
-  * \tparam _Scalar Numeric type, i.e. float, double, int
-  * \tparam _Rows Number of rows, or \b Dynamic
-  * \tparam _Cols Number of columns, or \b Dynamic
-  * \tparam _Supers Number of super diagonal
-  * \tparam _Subs Number of sub diagonal
-  * \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
-  *                  The former controls \ref TopicStorageOrders "storage order", and defaults to
-  *                  column-major. The latter controls whether the matrix represents a selfadjoint
-  *                  matrix in which case either Supers of Subs have to be null.
-  *
-  * \sa class TridiagonalMatrix
-  */
-
-template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
-struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef _Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef Eigen::Index StorageIndex;
-  enum {
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = _Rows,
-    MaxColsAtCompileTime = _Cols,
-    Flags = LvalueBit,
-    Supers = _Supers,
-    Subs = _Subs,
-    Options = _Options,
-    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
-  };
-  typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor> CoefficientsType;
-};
-
-template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options>
-class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> >
-{
-  public:
-
-    typedef typename internal::traits<BandMatrix>::Scalar Scalar;
-    typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
-    typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
-
-    explicit inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
-      : m_coeffs(1+supers+subs,cols),
-        m_rows(rows), m_supers(supers), m_subs(subs)
-    {
-    }
-
-    /** \returns the number of columns */
-    inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
-
-    /** \returns the number of rows */
-    inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
-
-    /** \returns the number of super diagonals */
-    inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
-
-    /** \returns the number of sub diagonals */
-    inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
-
-    inline const CoefficientsType& coeffs() const { return m_coeffs; }
-    inline CoefficientsType& coeffs() { return m_coeffs; }
-
-  protected:
-
-    CoefficientsType m_coeffs;
-    internal::variable_if_dynamic<Index, Rows>   m_rows;
-    internal::variable_if_dynamic<Index, Supers> m_supers;
-    internal::variable_if_dynamic<Index, Subs>   m_subs;
-};
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-class BandMatrixWrapper;
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef typename _CoefficientsType::Scalar Scalar;
-  typedef typename _CoefficientsType::StorageKind StorageKind;
-  typedef typename _CoefficientsType::StorageIndex StorageIndex;
-  enum {
-    CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
-    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = _Rows,
-    MaxColsAtCompileTime = _Cols,
-    Flags = LvalueBit,
-    Supers = _Supers,
-    Subs = _Subs,
-    Options = _Options,
-    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
-  };
-  typedef _CoefficientsType CoefficientsType;
-};
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  public:
-
-    typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
-    typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
-    typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
-
-    explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
-      : m_coeffs(coeffs),
-        m_rows(rows), m_supers(supers), m_subs(subs)
-    {
-      EIGEN_UNUSED_VARIABLE(cols);
-      //internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
-    }
-
-    /** \returns the number of columns */
-    inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
-
-    /** \returns the number of rows */
-    inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
-
-    /** \returns the number of super diagonals */
-    inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
-
-    /** \returns the number of sub diagonals */
-    inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
-
-    inline const CoefficientsType& coeffs() const { return m_coeffs; }
-
-  protected:
-
-    const CoefficientsType& m_coeffs;
-    internal::variable_if_dynamic<Index, _Rows>   m_rows;
-    internal::variable_if_dynamic<Index, _Supers> m_supers;
-    internal::variable_if_dynamic<Index, _Subs>   m_subs;
-};
-
-/**
-  * \class TridiagonalMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Represents a tridiagonal matrix with a compact banded storage
-  *
-  * \tparam Scalar Numeric type, i.e. float, double, int
-  * \tparam Size Number of rows and cols, or \b Dynamic
-  * \tparam Options Can be 0 or \b SelfAdjoint
-  *
-  * \sa class BandMatrix
-  */
-template<typename Scalar, int Size, int Options>
-class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
-{
-    typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
-    typedef typename Base::StorageIndex StorageIndex;
-  public:
-    explicit TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
-
-    inline typename Base::template DiagonalIntReturnType<1>::Type super()
-    { return Base::template diagonal<1>(); }
-    inline const typename Base::template DiagonalIntReturnType<1>::Type super() const
-    { return Base::template diagonal<1>(); }
-    inline typename Base::template DiagonalIntReturnType<-1>::Type sub()
-    { return Base::template diagonal<-1>(); }
-    inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const
-    { return Base::template diagonal<-1>(); }
-  protected:
-};
-
-
-struct BandShape {};
-
-template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
-struct evaluator_traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
-  : public evaluator_traits_base<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef BandShape Shape;
-};
-
-template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
-struct evaluator_traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-  : public evaluator_traits_base<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
-{
-  typedef BandShape Shape;
-};
-
-template<> struct AssignmentKind<DenseShape,BandShape> { typedef EigenBase2EigenBase Kind; };
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BANDMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Block.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Block.h
deleted file mode 100644
index 3206d66..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Block.h
+++ /dev/null
@@ -1,448 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BLOCK_H
-#define EIGEN_BLOCK_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType>
-{
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename traits<XprType>::StorageKind StorageKind;
-  typedef typename traits<XprType>::XprKind XprKind;
-  typedef typename ref_selector<XprType>::type XprTypeNested;
-  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
-  enum{
-    MatrixRows = traits<XprType>::RowsAtCompileTime,
-    MatrixCols = traits<XprType>::ColsAtCompileTime,
-    RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
-    ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
-    MaxRowsAtCompileTime = BlockRows==0 ? 0
-                         : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
-                         : int(traits<XprType>::MaxRowsAtCompileTime),
-    MaxColsAtCompileTime = BlockCols==0 ? 0
-                         : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
-                         : int(traits<XprType>::MaxColsAtCompileTime),
-
-    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
-    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
-               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
-               : XprTypeIsRowMajor,
-    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
-    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
-                             ? int(inner_stride_at_compile_time<XprType>::ret)
-                             : int(outer_stride_at_compile_time<XprType>::ret),
-    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
-                             ? int(outer_stride_at_compile_time<XprType>::ret)
-                             : int(inner_stride_at_compile_time<XprType>::ret),
-
-    // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
-    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
-    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
-    Flags = (traits<XprType>::Flags & (DirectAccessBit | (InnerPanel?CompressedAccessBit:0))) | FlagsLvalueBit | FlagsRowMajorBit,
-    // FIXME DirectAccessBit should not be handled by expressions
-    //
-    // Alignment is needed by MapBase's assertions
-    // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the respective evaluator
-    Alignment = 0
-  };
-};
-
-template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
-         bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense;
-
-} // end namespace internal
-
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl;
-
-/** \class Block
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a fixed-size or dynamic-size block
-  *
-  * \tparam XprType the type of the expression in which we are taking a block
-  * \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
-  * \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
-  * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
-  *         to set of columns of a column major matrix (optional). The parameter allows to determine
-  *         at compile time whether aligned access is possible on the block expression.
-  *
-  * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
-  * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
-  * most of the time this is the only way it is used.
-  *
-  * However, if you want to directly maniputate block expressions,
-  * for instance if you want to write a function returning such an expression, you
-  * will need to use this class.
-  *
-  * Here is an example illustrating the dynamic case:
-  * \include class_Block.cpp
-  * Output: \verbinclude class_Block.out
-  *
-  * \note Even though this expression has dynamic size, in the case where \a XprType
-  * has fixed size, this expression inherits a fixed maximal size which means that evaluating
-  * it does not cause a dynamic memory allocation.
-  *
-  * Here is an example illustrating the fixed-size case:
-  * \include class_FixedBlock.cpp
-  * Output: \verbinclude class_FixedBlock.out
-  *
-  * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
-  */
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block
-  : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind>
-{
-    typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
-  public:
-    //typedef typename Impl::Base Base;
-    typedef Impl Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
-
-    typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-    /** Column or Row constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Block(XprType& xpr, Index i) : Impl(xpr,i)
-    {
-      eigen_assert( (i>=0) && (
-          ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
-        ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
-    }
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Block(XprType& xpr, Index startRow, Index startCol)
-      : Impl(xpr, startRow, startCol)
-    {
-      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
-      eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows()
-             && startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
-    }
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Block(XprType& xpr,
-          Index startRow, Index startCol,
-          Index blockRows, Index blockCols)
-      : Impl(xpr, startRow, startCol, blockRows, blockCols)
-    {
-      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
-          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
-      eigen_assert(startRow >= 0 && blockRows >= 0 && startRow  <= xpr.rows() - blockRows
-          && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
-    }
-};
-
-// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense
-// that must be specialized for direct and non-direct access...
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
-  : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
-{
-    typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
-    typedef typename XprType::StorageIndex StorageIndex;
-  public:
-    typedef Impl Base;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : Impl(xpr, startRow, startCol, blockRows, blockCols) {}
-};
-
-namespace internal {
-
-/** \internal Internal implementation of dense Blocks in the general case. */
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense
-  : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
-{
-    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-  public:
-
-    typedef typename internal::dense_xpr_base<BlockType>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
-
-    // class InnerIterator; // FIXME apparently never used
-
-    /** Column or Row constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline BlockImpl_dense(XprType& xpr, Index i)
-      : m_xpr(xpr),
-        // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
-        // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
-        // all other cases are invalid.
-        // The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
-        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
-        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
-        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
-        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
-    {}
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
-                    m_blockRows(BlockRows), m_blockCols(BlockCols)
-    {}
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline BlockImpl_dense(XprType& xpr,
-          Index startRow, Index startCol,
-          Index blockRows, Index blockCols)
-      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
-                    m_blockRows(blockRows), m_blockCols(blockCols)
-    {}
-
-    EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
-    EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index rowId, Index colId)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(XprType)
-      return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
-    {
-      return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index index)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(XprType)
-      return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const CoeffReturnType coeff(Index index) const
-    {
-      return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                         m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    template<int LoadMode>
-    inline PacketScalar packet(Index rowId, Index colId) const
-    {
-      return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
-    {
-      m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
-    }
-
-    template<int LoadMode>
-    inline PacketScalar packet(Index index) const
-    {
-      return m_xpr.template packet<Unaligned>
-              (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index index, const PacketScalar& val)
-    {
-      m_xpr.template writePacket<Unaligned>
-         (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \sa MapBase::data() */
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const;
-    EIGEN_DEVICE_FUNC inline Index innerStride() const;
-    EIGEN_DEVICE_FUNC inline Index outerStride() const;
-    #endif
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
-    {
-      return m_xpr;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    XprType& nestedExpression() { return m_xpr; }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startRow() const EIGEN_NOEXCEPT
-    {
-      return m_startRow.value();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startCol() const EIGEN_NOEXCEPT
-    {
-      return m_startCol.value();
-    }
-
-  protected:
-
-    XprTypeNested m_xpr;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
-    const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
-    const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
-};
-
-/** \internal Internal implementation of dense Blocks in the direct access case.*/
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
-  : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
-{
-    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-    enum {
-      XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
-    };
-  public:
-
-    typedef MapBase<BlockType> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
-
-    /** Column or Row constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr, Index i)
-      : Base(xpr.data() + i * (    ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
-                                || ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
-             BlockRows==1 ? 1 : xpr.rows(),
-             BlockCols==1 ? 1 : xpr.cols()),
-        m_xpr(xpr),
-        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
-        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)
-    {
-      init();
-    }
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
-      : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
-        m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
-    {
-      init();
-    }
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr,
-          Index startRow, Index startCol,
-          Index blockRows, Index blockCols)
-      : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
-        m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
-    {
-      init();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const EIGEN_NOEXCEPT
-    {
-      return m_xpr;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    XprType& nestedExpression() { return m_xpr; }
-
-    /** \sa MapBase::innerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index innerStride() const EIGEN_NOEXCEPT
-    {
-      return internal::traits<BlockType>::HasSameStorageOrderAsXprType
-             ? m_xpr.innerStride()
-             : m_xpr.outerStride();
-    }
-
-    /** \sa MapBase::outerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index outerStride() const EIGEN_NOEXCEPT
-    {
-      return internal::traits<BlockType>::HasSameStorageOrderAsXprType
-                    ? m_xpr.outerStride()
-                    : m_xpr.innerStride();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startRow() const EIGEN_NOEXCEPT { return m_startRow.value(); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    StorageIndex startCol() const EIGEN_NOEXCEPT { return m_startCol.value(); }
-
-  #ifndef __SUNPRO_CC
-  // FIXME sunstudio is not friendly with the above friend...
-  // META-FIXME there is no 'friend' keyword around here. Is this obsolete?
-  protected:
-  #endif
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal used by allowAligned() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
-      : Base(data, blockRows, blockCols), m_xpr(xpr)
-    {
-      init();
-    }
-    #endif
-
-  protected:
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void init()
-    {
-      m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
-                    ? m_xpr.outerStride()
-                    : m_xpr.innerStride();
-    }
-
-    XprTypeNested m_xpr;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
-    const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
-    Index m_outerStride;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BLOCK_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/BooleanRedux.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/BooleanRedux.h
deleted file mode 100644
index 852de8b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/BooleanRedux.h
+++ /dev/null
@@ -1,162 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ALLANDANY_H
-#define EIGEN_ALLANDANY_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Derived, int UnrollCount, int Rows>
-struct all_unroller
-{
-  enum {
-    col = (UnrollCount-1) / Rows,
-    row = (UnrollCount-1) % Rows
-  };
-
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
-  {
-    return all_unroller<Derived, UnrollCount-1, Rows>::run(mat) && mat.coeff(row, col);
-  }
-};
-
-template<typename Derived, int Rows>
-struct all_unroller<Derived, 0, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &/*mat*/) { return true; }
-};
-
-template<typename Derived, int Rows>
-struct all_unroller<Derived, Dynamic, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
-};
-
-template<typename Derived, int UnrollCount, int Rows>
-struct any_unroller
-{
-  enum {
-    col = (UnrollCount-1) / Rows,
-    row = (UnrollCount-1) % Rows
-  };
-  
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
-  {
-    return any_unroller<Derived, UnrollCount-1, Rows>::run(mat) || mat.coeff(row, col);
-  }
-};
-
-template<typename Derived, int Rows>
-struct any_unroller<Derived, 0, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived & /*mat*/) { return false; }
-};
-
-template<typename Derived, int Rows>
-struct any_unroller<Derived, Dynamic, Rows>
-{
-  EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
-};
-
-} // end namespace internal
-
-/** \returns true if all coefficients are true
-  *
-  * Example: \include MatrixBase_all.cpp
-  * Output: \verbinclude MatrixBase_all.out
-  *
-  * \sa any(), Cwise::operator<()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::all() const
-{
-  typedef internal::evaluator<Derived> Evaluator;
-  enum {
-    unroll = SizeAtCompileTime != Dynamic
-          && SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT
-  };
-  Evaluator evaluator(derived());
-  if(unroll)
-    return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
-  else
-  {
-    for(Index j = 0; j < cols(); ++j)
-      for(Index i = 0; i < rows(); ++i)
-        if (!evaluator.coeff(i, j)) return false;
-    return true;
-  }
-}
-
-/** \returns true if at least one coefficient is true
-  *
-  * \sa all()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::any() const
-{
-  typedef internal::evaluator<Derived> Evaluator;
-  enum {
-    unroll = SizeAtCompileTime != Dynamic
-          && SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT
-  };
-  Evaluator evaluator(derived());
-  if(unroll)
-    return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
-  else
-  {
-    for(Index j = 0; j < cols(); ++j)
-      for(Index i = 0; i < rows(); ++i)
-        if (evaluator.coeff(i, j)) return true;
-    return false;
-  }
-}
-
-/** \returns the number of coefficients which evaluate to true
-  *
-  * \sa all(), any()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Eigen::Index DenseBase<Derived>::count() const
-{
-  return derived().template cast<bool>().template cast<Index>().sum();
-}
-
-/** \returns true is \c *this contains at least one Not A Number (NaN).
-  *
-  * \sa allFinite()
-  */
-template<typename Derived>
-inline bool DenseBase<Derived>::hasNaN() const
-{
-#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
-  return derived().array().isNaN().any();
-#else
-  return !((derived().array()==derived().array()).all());
-#endif
-}
-
-/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
-  *
-  * \sa hasNaN()
-  */
-template<typename Derived>
-inline bool DenseBase<Derived>::allFinite() const
-{
-#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
-  return derived().array().isFinite().all();
-#else
-  return !((derived()-derived()).hasNaN());
-#endif
-}
-    
-} // end namespace Eigen
-
-#endif // EIGEN_ALLANDANY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CommaInitializer.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CommaInitializer.h
deleted file mode 100644
index c0e29c7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CommaInitializer.h
+++ /dev/null
@@ -1,164 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMMAINITIALIZER_H
-#define EIGEN_COMMAINITIALIZER_H
-
-namespace Eigen { 
-
-/** \class CommaInitializer
-  * \ingroup Core_Module
-  *
-  * \brief Helper class used by the comma initializer operator
-  *
-  * This class is internally used to implement the comma initializer feature. It is
-  * the return type of MatrixBase::operator<<, and most of the time this is the only
-  * way it is used.
-  *
-  * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
-  */
-template<typename XprType>
-struct CommaInitializer
-{
-  typedef typename XprType::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  inline CommaInitializer(XprType& xpr, const Scalar& s)
-    : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
-  {
-    eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0
-      && "Cannot comma-initialize a 0x0 matrix (operator<<)");
-    m_xpr.coeffRef(0,0) = s;
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
-    : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
-  {
-    eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols()
-      && "Cannot comma-initialize a 0x0 matrix (operator<<)");
-    m_xpr.block(0, 0, other.rows(), other.cols()) = other;
-  }
-
-  /* Copy/Move constructor which transfers ownership. This is crucial in 
-   * absence of return value optimization to avoid assertions during destruction. */
-  // FIXME in C++11 mode this could be replaced by a proper RValue constructor
-  EIGEN_DEVICE_FUNC
-  inline CommaInitializer(const CommaInitializer& o)
-  : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
-    // Mark original object as finished. In absence of R-value references we need to const_cast:
-    const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
-    const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
-    const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
-  }
-
-  /* inserts a scalar value in the target matrix */
-  EIGEN_DEVICE_FUNC
-  CommaInitializer& operator,(const Scalar& s)
-  {
-    if (m_col==m_xpr.cols())
-    {
-      m_row+=m_currentBlockRows;
-      m_col = 0;
-      m_currentBlockRows = 1;
-      eigen_assert(m_row<m_xpr.rows()
-        && "Too many rows passed to comma initializer (operator<<)");
-    }
-    eigen_assert(m_col<m_xpr.cols()
-      && "Too many coefficients passed to comma initializer (operator<<)");
-    eigen_assert(m_currentBlockRows==1);
-    m_xpr.coeffRef(m_row, m_col++) = s;
-    return *this;
-  }
-
-  /* inserts a matrix expression in the target matrix */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
-  {
-    if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
-    {
-      m_row+=m_currentBlockRows;
-      m_col = 0;
-      m_currentBlockRows = other.rows();
-      eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
-        && "Too many rows passed to comma initializer (operator<<)");
-    }
-    eigen_assert((m_col + other.cols() <= m_xpr.cols())
-      && "Too many coefficients passed to comma initializer (operator<<)");
-    eigen_assert(m_currentBlockRows==other.rows());
-    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>
-                    (m_row, m_col, other.rows(), other.cols()) = other;
-    m_col += other.cols();
-    return *this;
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline ~CommaInitializer()
-#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
-  EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
-#endif
-  {
-    finished();
-  }
-
-  /** \returns the built matrix once all its coefficients have been set.
-    * Calling finished is 100% optional. Its purpose is to write expressions
-    * like this:
-    * \code
-    * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
-    * \endcode
-    */
-  EIGEN_DEVICE_FUNC
-  inline XprType& finished() {
-      eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
-           && m_col == m_xpr.cols()
-           && "Too few coefficients passed to comma initializer (operator<<)");
-      return m_xpr;
-  }
-
-  XprType& m_xpr;           // target expression
-  Index m_row;              // current row id
-  Index m_col;              // current col id
-  Index m_currentBlockRows; // current block height
-};
-
-/** \anchor MatrixBaseCommaInitRef
-  * Convenient operator to set the coefficients of a matrix.
-  *
-  * The coefficients must be provided in a row major order and exactly match
-  * the size of the matrix. Otherwise an assertion is raised.
-  *
-  * Example: \include MatrixBase_set.cpp
-  * Output: \verbinclude MatrixBase_set.out
-  * 
-  * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
-  *
-  * \sa CommaInitializer::finished(), class CommaInitializer
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s)
-{
-  return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
-}
-
-/** \sa operator<<(const Scalar&) */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline CommaInitializer<Derived>
-DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other)
-{
-  return CommaInitializer<Derived>(*static_cast<Derived *>(this), other);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMMAINITIALIZER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ConditionEstimator.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ConditionEstimator.h
deleted file mode 100644
index 51a2e5f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ConditionEstimator.h
+++ /dev/null
@@ -1,175 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONDITIONESTIMATOR_H
-#define EIGEN_CONDITIONESTIMATOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Vector, typename RealVector, bool IsComplex>
-struct rcond_compute_sign {
-  static inline Vector run(const Vector& v) {
-    const RealVector v_abs = v.cwiseAbs();
-    return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
-            .select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
-  }
-};
-
-// Partial specialization to avoid elementwise division for real vectors.
-template <typename Vector>
-struct rcond_compute_sign<Vector, Vector, false> {
-  static inline Vector run(const Vector& v) {
-    return (v.array() < static_cast<typename Vector::RealScalar>(0))
-           .select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
-  }
-};
-
-/**
-  * \returns an estimate of ||inv(matrix)||_1 given a decomposition of
-  * \a matrix that implements .solve() and .adjoint().solve() methods.
-  *
-  * This function implements Algorithms 4.1 and 5.1 from
-  *   http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
-  * which also forms the basis for the condition number estimators in
-  * LAPACK. Since at most 10 calls to the solve method of dec are
-  * performed, the total cost is O(dims^2), as opposed to O(dims^3)
-  * needed to compute the inverse matrix explicitly.
-  *
-  * The most common usage is in estimating the condition number
-  * ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
-  * computed directly in O(n^2) operations.
-  *
-  * Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
-  * LLT.
-  *
-  * \sa FullPivLU, PartialPivLU, LDLT, LLT.
-  */
-template <typename Decomposition>
-typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec)
-{
-  typedef typename Decomposition::MatrixType MatrixType;
-  typedef typename Decomposition::Scalar Scalar;
-  typedef typename Decomposition::RealScalar RealScalar;
-  typedef typename internal::plain_col_type<MatrixType>::type Vector;
-  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
-  const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
-
-  eigen_assert(dec.rows() == dec.cols());
-  const Index n = dec.rows();
-  if (n == 0)
-    return 0;
-
-  // Disable Index to float conversion warning
-#ifdef __INTEL_COMPILER
-  #pragma warning push
-  #pragma warning ( disable : 2259 )
-#endif
-  Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
-#ifdef __INTEL_COMPILER
-  #pragma warning pop
-#endif
-
-  // lower_bound is a lower bound on
-  //   ||inv(matrix)||_1  = sup_v ||inv(matrix) v||_1 / ||v||_1
-  // and is the objective maximized by the ("super-") gradient ascent
-  // algorithm below.
-  RealScalar lower_bound = v.template lpNorm<1>();
-  if (n == 1)
-    return lower_bound;
-
-  // Gradient ascent algorithm follows: We know that the optimum is achieved at
-  // one of the simplices v = e_i, so in each iteration we follow a
-  // super-gradient to move towards the optimal one.
-  RealScalar old_lower_bound = lower_bound;
-  Vector sign_vector(n);
-  Vector old_sign_vector;
-  Index v_max_abs_index = -1;
-  Index old_v_max_abs_index = v_max_abs_index;
-  for (int k = 0; k < 4; ++k)
-  {
-    sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
-    if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
-      // Break if the solution stagnated.
-      break;
-    }
-    // v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
-    v = dec.adjoint().solve(sign_vector);
-    v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
-    if (v_max_abs_index == old_v_max_abs_index) {
-      // Break if the solution stagnated.
-      break;
-    }
-    // Move to the new simplex e_j, where j = v_max_abs_index.
-    v = dec.solve(Vector::Unit(n, v_max_abs_index));  // v = inv(matrix) * e_j.
-    lower_bound = v.template lpNorm<1>();
-    if (lower_bound <= old_lower_bound) {
-      // Break if the gradient step did not increase the lower_bound.
-      break;
-    }
-    if (!is_complex) {
-      old_sign_vector = sign_vector;
-    }
-    old_v_max_abs_index = v_max_abs_index;
-    old_lower_bound = lower_bound;
-  }
-  // The following calculates an independent estimate of ||matrix||_1 by
-  // multiplying matrix by a vector with entries of slowly increasing
-  // magnitude and alternating sign:
-  //   v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
-  // This improvement to Hager's algorithm above is due to Higham. It was
-  // added to make the algorithm more robust in certain corner cases where
-  // large elements in the matrix might otherwise escape detection due to
-  // exact cancellation (especially when op and op_adjoint correspond to a
-  // sequence of backsubstitutions and permutations), which could cause
-  // Hager's algorithm to vastly underestimate ||matrix||_1.
-  Scalar alternating_sign(RealScalar(1));
-  for (Index i = 0; i < n; ++i) {
-    // The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
-    v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
-    alternating_sign = -alternating_sign;
-  }
-  v = dec.solve(v);
-  const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
-  return numext::maxi(lower_bound, alternate_lower_bound);
-}
-
-/** \brief Reciprocal condition number estimator.
-  *
-  * Computing a decomposition of a dense matrix takes O(n^3) operations, while
-  * this method estimates the condition number quickly and reliably in O(n^2)
-  * operations.
-  *
-  * \returns an estimate of the reciprocal condition number
-  * (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
-  * its decomposition. Supports the following decompositions: FullPivLU,
-  * PartialPivLU, LDLT, and LLT.
-  *
-  * \sa FullPivLU, PartialPivLU, LDLT, LLT.
-  */
-template <typename Decomposition>
-typename Decomposition::RealScalar
-rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
-{
-  typedef typename Decomposition::RealScalar RealScalar;
-  eigen_assert(dec.rows() == dec.cols());
-  if (dec.rows() == 0)              return NumTraits<RealScalar>::infinity();
-  if (matrix_norm == RealScalar(0)) return RealScalar(0);
-  if (dec.rows() == 1)              return RealScalar(1);
-  const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
-  return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
-                                               : (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
-}
-
-}  // namespace internal
-
-}  // namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CoreEvaluators.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CoreEvaluators.h
deleted file mode 100644
index 0ff8c8d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CoreEvaluators.h
+++ /dev/null
@@ -1,1741 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_COREEVALUATORS_H
-#define EIGEN_COREEVALUATORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// This class returns the evaluator kind from the expression storage kind.
-// Default assumes index based accessors
-template<typename StorageKind>
-struct storage_kind_to_evaluator_kind {
-  typedef IndexBased Kind;
-};
-
-// This class returns the evaluator shape from the expression storage kind.
-// It can be Dense, Sparse, Triangular, Diagonal, SelfAdjoint, Band, etc.
-template<typename StorageKind> struct storage_kind_to_shape;
-
-template<> struct storage_kind_to_shape<Dense>                  { typedef DenseShape Shape;           };
-template<> struct storage_kind_to_shape<SolverStorage>          { typedef SolverShape Shape;           };
-template<> struct storage_kind_to_shape<PermutationStorage>     { typedef PermutationShape Shape;     };
-template<> struct storage_kind_to_shape<TranspositionsStorage>  { typedef TranspositionsShape Shape;  };
-
-// Evaluators have to be specialized with respect to various criteria such as:
-//  - storage/structure/shape
-//  - scalar type
-//  - etc.
-// Therefore, we need specialization of evaluator providing additional template arguments for each kind of evaluators.
-// We currently distinguish the following kind of evaluators:
-// - unary_evaluator    for expressions taking only one arguments (CwiseUnaryOp, CwiseUnaryView, Transpose, MatrixWrapper, ArrayWrapper, Reverse, Replicate)
-// - binary_evaluator   for expression taking two arguments (CwiseBinaryOp)
-// - ternary_evaluator   for expression taking three arguments (CwiseTernaryOp)
-// - product_evaluator  for linear algebra products (Product); special case of binary_evaluator because it requires additional tags for dispatching.
-// - mapbase_evaluator  for Map, Block, Ref
-// - block_evaluator    for Block (special dispatching to a mapbase_evaluator or unary_evaluator)
-
-template< typename T,
-          typename Arg1Kind   = typename evaluator_traits<typename T::Arg1>::Kind,
-          typename Arg2Kind   = typename evaluator_traits<typename T::Arg2>::Kind,
-          typename Arg3Kind   = typename evaluator_traits<typename T::Arg3>::Kind,
-          typename Arg1Scalar = typename traits<typename T::Arg1>::Scalar,
-          typename Arg2Scalar = typename traits<typename T::Arg2>::Scalar,
-          typename Arg3Scalar = typename traits<typename T::Arg3>::Scalar> struct ternary_evaluator;
-
-template< typename T,
-          typename LhsKind   = typename evaluator_traits<typename T::Lhs>::Kind,
-          typename RhsKind   = typename evaluator_traits<typename T::Rhs>::Kind,
-          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
-          typename RhsScalar = typename traits<typename T::Rhs>::Scalar> struct binary_evaluator;
-
-template< typename T,
-          typename Kind   = typename evaluator_traits<typename T::NestedExpression>::Kind,
-          typename Scalar = typename T::Scalar> struct unary_evaluator;
-
-// evaluator_traits<T> contains traits for evaluator<T>
-
-template<typename T>
-struct evaluator_traits_base
-{
-  // by default, get evaluator kind and shape from storage
-  typedef typename storage_kind_to_evaluator_kind<typename traits<T>::StorageKind>::Kind Kind;
-  typedef typename storage_kind_to_shape<typename traits<T>::StorageKind>::Shape Shape;
-};
-
-// Default evaluator traits
-template<typename T>
-struct evaluator_traits : public evaluator_traits_base<T>
-{
-};
-
-template<typename T, typename Shape = typename evaluator_traits<T>::Shape >
-struct evaluator_assume_aliasing {
-  static const bool value = false;
-};
-
-// By default, we assume a unary expression:
-template<typename T>
-struct evaluator : public unary_evaluator<T>
-{
-  typedef unary_evaluator<T> Base;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const T& xpr) : Base(xpr) {}
-};
-
-
-// TODO: Think about const-correctness
-template<typename T>
-struct evaluator<const T>
-  : evaluator<T>
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const T& xpr) : evaluator<T>(xpr) {}
-};
-
-// ---------- base class for all evaluators ----------
-
-template<typename ExpressionType>
-struct evaluator_base
-{
-  // TODO that's not very nice to have to propagate all these traits. They are currently only needed to handle outer,inner indices.
-  typedef traits<ExpressionType> ExpressionTraits;
-
-  enum {
-    Alignment = 0
-  };
-  // noncopyable:
-  // Don't make this class inherit noncopyable as this kills EBO (Empty Base Optimization)
-  // and make complex evaluator much larger than then should do.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE evaluator_base() {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~evaluator_base() {}
-private:
-  EIGEN_DEVICE_FUNC evaluator_base(const evaluator_base&);
-  EIGEN_DEVICE_FUNC const evaluator_base& operator=(const evaluator_base&);
-};
-
-// -------------------- Matrix and Array --------------------
-//
-// evaluator<PlainObjectBase> is a common base class for the
-// Matrix and Array evaluators.
-// Here we directly specialize evaluator. This is not really a unary expression, and it is, by definition, dense,
-// so no need for more sophisticated dispatching.
-
-// this helper permits to completely eliminate m_outerStride if it is known at compiletime.
-template<typename Scalar,int OuterStride> class plainobjectbase_evaluator_data {
-public:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr)
-  {
-#ifndef EIGEN_INTERNAL_DEBUGGING
-    EIGEN_UNUSED_VARIABLE(outerStride);
-#endif
-    eigen_internal_assert(outerStride==OuterStride);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index outerStride() const EIGEN_NOEXCEPT { return OuterStride; }
-  const Scalar *data;
-};
-
-template<typename Scalar> class plainobjectbase_evaluator_data<Scalar,Dynamic> {
-public:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr), m_outerStride(outerStride) {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Index outerStride() const { return m_outerStride; }
-  const Scalar *data;
-protected:
-  Index m_outerStride;
-};
-
-template<typename Derived>
-struct evaluator<PlainObjectBase<Derived> >
-  : evaluator_base<Derived>
-{
-  typedef PlainObjectBase<Derived> PlainObjectType;
-  typedef typename PlainObjectType::Scalar Scalar;
-  typedef typename PlainObjectType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    IsRowMajor = PlainObjectType::IsRowMajor,
-    IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime,
-    RowsAtCompileTime = PlainObjectType::RowsAtCompileTime,
-    ColsAtCompileTime = PlainObjectType::ColsAtCompileTime,
-
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    Flags = traits<Derived>::EvaluatorFlags,
-    Alignment = traits<Derived>::Alignment
-  };
-  enum {
-    // We do not need to know the outer stride for vectors
-    OuterStrideAtCompileTime = IsVectorAtCompileTime  ? 0
-                                                      : int(IsRowMajor) ? ColsAtCompileTime
-                                                                        : RowsAtCompileTime
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  evaluator()
-    : m_d(0,OuterStrideAtCompileTime)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const PlainObjectType& m)
-    : m_d(m.data(),IsVectorAtCompileTime ? 0 : m.outerStride())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    if (IsRowMajor)
-      return m_d.data[row * m_d.outerStride() + col];
-    else
-      return m_d.data[row + col * m_d.outerStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.data[index];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    if (IsRowMajor)
-      return const_cast<Scalar*>(m_d.data)[row * m_d.outerStride() + col];
-    else
-      return const_cast<Scalar*>(m_d.data)[row + col * m_d.outerStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return const_cast<Scalar*>(m_d.data)[index];
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    if (IsRowMajor)
-      return ploadt<PacketType, LoadMode>(m_d.data + row * m_d.outerStride() + col);
-    else
-      return ploadt<PacketType, LoadMode>(m_d.data + row + col * m_d.outerStride());
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return ploadt<PacketType, LoadMode>(m_d.data + index);
-  }
-
-  template<int StoreMode,typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    if (IsRowMajor)
-      return pstoret<Scalar, PacketType, StoreMode>
-	            (const_cast<Scalar*>(m_d.data) + row * m_d.outerStride() + col, x);
-    else
-      return pstoret<Scalar, PacketType, StoreMode>
-                    (const_cast<Scalar*>(m_d.data) + row + col * m_d.outerStride(), x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    return pstoret<Scalar, PacketType, StoreMode>(const_cast<Scalar*>(m_d.data) + index, x);
-  }
-
-protected:
-
-  plainobjectbase_evaluator_data<Scalar,OuterStrideAtCompileTime> m_d;
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-struct evaluator<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
-  : evaluator<PlainObjectBase<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > >
-{
-  typedef Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  evaluator() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& m)
-    : evaluator<PlainObjectBase<XprType> >(m)
-  { }
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-struct evaluator<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
-  : evaluator<PlainObjectBase<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > >
-{
-  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  evaluator() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& m)
-    : evaluator<PlainObjectBase<XprType> >(m)
-  { }
-};
-
-// -------------------- Transpose --------------------
-
-template<typename ArgType>
-struct unary_evaluator<Transpose<ArgType>, IndexBased>
-  : evaluator_base<Transpose<ArgType> >
-{
-  typedef Transpose<ArgType> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    Flags = evaluator<ArgType>::Flags ^ RowMajorBit,
-    Alignment = evaluator<ArgType>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& t) : m_argImpl(t.nestedExpression()) {}
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(col, row);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(col, row);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename XprType::Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(index);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(col, row);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(index);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode,PacketType>(col, row, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode,PacketType>(index, x);
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-};
-
-// -------------------- CwiseNullaryOp --------------------
-// Like Matrix and Array, this is not really a unary expression, so we directly specialize evaluator.
-// Likewise, there is not need to more sophisticated dispatching here.
-
-template<typename Scalar,typename NullaryOp,
-         bool has_nullary = has_nullary_operator<NullaryOp>::value,
-         bool has_unary   = has_unary_operator<NullaryOp>::value,
-         bool has_binary  = has_binary_operator<NullaryOp>::value>
-struct nullary_wrapper
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const { return op(i,j); }
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
-
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const { return op.template packetOp<T>(i,j); }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,true,false,false>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType=0, IndexType=0) const { return op(); }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType=0, IndexType=0) const { return op.template packetOp<T>(); }
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,false,false,true>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j=0) const { return op(i,j); }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j=0) const { return op.template packetOp<T>(i,j); }
-};
-
-// We need the following specialization for vector-only functors assigned to a runtime vector,
-// for instance, using linspace and assigning a RowVectorXd to a MatrixXd or even a row of a MatrixXd.
-// In this case, i==0 and j is used for the actual iteration.
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,false,true,false>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
-    eigen_assert(i==0 || j==0);
-    return op(i+j);
-  }
-  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
-    eigen_assert(i==0 || j==0);
-    return op.template packetOp<T>(i+j);
-  }
-
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
-  template <typename T, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,false,false,false> {};
-
-#if 0 && EIGEN_COMP_MSVC>0
-// Disable this ugly workaround. This is now handled in traits<Ref>::match,
-// but this piece of code might still become handly if some other weird compilation
-// erros pop up again.
-
-// MSVC exhibits a weird compilation error when
-// compiling:
-//    Eigen::MatrixXf A = MatrixXf::Random(3,3);
-//    Ref<const MatrixXf> R = 2.f*A;
-// and that has_*ary_operator<scalar_constant_op<float>> have not been instantiated yet.
-// The "problem" is that evaluator<2.f*A> is instantiated by traits<Ref>::match<2.f*A>
-// and at that time has_*ary_operator<T> returns true regardless of T.
-// Then nullary_wrapper is badly instantiated as nullary_wrapper<.,.,true,true,true>.
-// The trick is thus to defer the proper instantiation of nullary_wrapper when coeff(),
-// and packet() are really instantiated as implemented below:
-
-// This is a simple wrapper around Index to enforce the re-instantiation of
-// has_*ary_operator when needed.
-template<typename T> struct nullary_wrapper_workaround_msvc {
-  nullary_wrapper_workaround_msvc(const T&);
-  operator T()const;
-};
-
-template<typename Scalar,typename NullaryOp>
-struct nullary_wrapper<Scalar,NullaryOp,true,true,true>
-{
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i,j);
-  }
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i);
-  }
-
-  template <typename T, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i,j);
-  }
-  template <typename T, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const {
-    return nullary_wrapper<Scalar,NullaryOp,
-    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
-    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i);
-  }
-};
-#endif // MSVC workaround
-
-template<typename NullaryOp, typename PlainObjectType>
-struct evaluator<CwiseNullaryOp<NullaryOp,PlainObjectType> >
-  : evaluator_base<CwiseNullaryOp<NullaryOp,PlainObjectType> >
-{
-  typedef CwiseNullaryOp<NullaryOp,PlainObjectType> XprType;
-  typedef typename internal::remove_all<PlainObjectType>::type PlainObjectTypeCleaned;
-
-  enum {
-    CoeffReadCost = internal::functor_traits<NullaryOp>::Cost,
-
-    Flags = (evaluator<PlainObjectTypeCleaned>::Flags
-          &  (  HereditaryBits
-              | (functor_has_linear_access<NullaryOp>::ret  ? LinearAccessBit : 0)
-              | (functor_traits<NullaryOp>::PacketAccess    ? PacketAccessBit : 0)))
-          | (functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit),
-    Alignment = AlignedMax
-  };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& n)
-    : m_functor(n.functor()), m_wrapper()
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(IndexType row, IndexType col) const
-  {
-    return m_wrapper(m_functor, row, col);
-  }
-
-  template <typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(IndexType index) const
-  {
-    return m_wrapper(m_functor,index);
-  }
-
-  template<int LoadMode, typename PacketType, typename IndexType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(IndexType row, IndexType col) const
-  {
-    return m_wrapper.template packetOp<PacketType>(m_functor, row, col);
-  }
-
-  template<int LoadMode, typename PacketType, typename IndexType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(IndexType index) const
-  {
-    return m_wrapper.template packetOp<PacketType>(m_functor, index);
-  }
-
-protected:
-  const NullaryOp m_functor;
-  const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper;
-};
-
-// -------------------- CwiseUnaryOp --------------------
-
-template<typename UnaryOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IndexBased >
-  : evaluator_base<CwiseUnaryOp<UnaryOp, ArgType> >
-{
-  typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
-
-    Flags = evaluator<ArgType>::Flags
-          & (HereditaryBits | LinearAccessBit | (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
-    Alignment = evaluator<ArgType>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& op) : m_d(op)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(index));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(row, col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(index));
-  }
-
-protected:
-
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const UnaryOp& func() const { return op; }
-    UnaryOp op;
-    evaluator<ArgType> argImpl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- CwiseTernaryOp --------------------
-
-// this is a ternary expression
-template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
-struct evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
-  : public ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
-{
-  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
-  typedef ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
-struct ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>, IndexBased, IndexBased>
-  : evaluator_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
-{
-  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<Arg1>::CoeffReadCost) + int(evaluator<Arg2>::CoeffReadCost) + int(evaluator<Arg3>::CoeffReadCost) + int(functor_traits<TernaryOp>::Cost),
-
-    Arg1Flags = evaluator<Arg1>::Flags,
-    Arg2Flags = evaluator<Arg2>::Flags,
-    Arg3Flags = evaluator<Arg3>::Flags,
-    SameType = is_same<typename Arg1::Scalar,typename Arg2::Scalar>::value && is_same<typename Arg1::Scalar,typename Arg3::Scalar>::value,
-    StorageOrdersAgree = (int(Arg1Flags)&RowMajorBit)==(int(Arg2Flags)&RowMajorBit) && (int(Arg1Flags)&RowMajorBit)==(int(Arg3Flags)&RowMajorBit),
-    Flags0 = (int(Arg1Flags) | int(Arg2Flags) | int(Arg3Flags)) & (
-        HereditaryBits
-        | (int(Arg1Flags) & int(Arg2Flags) & int(Arg3Flags) &
-           ( (StorageOrdersAgree ? LinearAccessBit : 0)
-           | (functor_traits<TernaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
-           )
-        )
-     ),
-    Flags = (Flags0 & ~RowMajorBit) | (Arg1Flags & RowMajorBit),
-    Alignment = EIGEN_PLAIN_ENUM_MIN(
-        EIGEN_PLAIN_ENUM_MIN(evaluator<Arg1>::Alignment, evaluator<Arg2>::Alignment),
-        evaluator<Arg3>::Alignment)
-  };
-
-  EIGEN_DEVICE_FUNC explicit ternary_evaluator(const XprType& xpr) : m_d(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<TernaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.arg1Impl.coeff(row, col), m_d.arg2Impl.coeff(row, col), m_d.arg3Impl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.arg1Impl.coeff(index), m_d.arg2Impl.coeff(index), m_d.arg3Impl.coeff(index));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode,PacketType>(row, col),
-                               m_d.arg2Impl.template packet<LoadMode,PacketType>(row, col),
-                               m_d.arg3Impl.template packet<LoadMode,PacketType>(row, col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode,PacketType>(index),
-                               m_d.arg2Impl.template packet<LoadMode,PacketType>(index),
-                               m_d.arg3Impl.template packet<LoadMode,PacketType>(index));
-  }
-
-protected:
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), arg1Impl(xpr.arg1()), arg2Impl(xpr.arg2()), arg3Impl(xpr.arg3()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TernaryOp& func() const { return op; }
-    TernaryOp op;
-    evaluator<Arg1> arg1Impl;
-    evaluator<Arg2> arg2Impl;
-    evaluator<Arg3> arg3Impl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- CwiseBinaryOp --------------------
-
-// this is a binary expression
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-  : public binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IndexBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-
-    LhsFlags = evaluator<Lhs>::Flags,
-    RhsFlags = evaluator<Rhs>::Flags,
-    SameType = is_same<typename Lhs::Scalar,typename Rhs::Scalar>::value,
-    StorageOrdersAgree = (int(LhsFlags)&RowMajorBit)==(int(RhsFlags)&RowMajorBit),
-    Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
-        HereditaryBits
-      | (int(LhsFlags) & int(RhsFlags) &
-           ( (StorageOrdersAgree ? LinearAccessBit : 0)
-           | (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
-           )
-        )
-     ),
-    Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
-    Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<Lhs>::Alignment,evaluator<Rhs>::Alignment)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit binary_evaluator(const XprType& xpr) : m_d(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.lhsImpl.coeff(row, col), m_d.rhsImpl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.lhsImpl.coeff(index), m_d.rhsImpl.coeff(index));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode,PacketType>(row, col),
-                               m_d.rhsImpl.template packet<LoadMode,PacketType>(row, col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode,PacketType>(index),
-                               m_d.rhsImpl.template packet<LoadMode,PacketType>(index));
-  }
-
-protected:
-
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), lhsImpl(xpr.lhs()), rhsImpl(xpr.rhs()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const BinaryOp& func() const { return op; }
-    BinaryOp op;
-    evaluator<Lhs> lhsImpl;
-    evaluator<Rhs> rhsImpl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- CwiseUnaryView --------------------
-
-template<typename UnaryOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryView<UnaryOp, ArgType>, IndexBased>
-  : evaluator_base<CwiseUnaryView<UnaryOp, ArgType> >
-{
-  typedef CwiseUnaryView<UnaryOp, ArgType> XprType;
-
-  enum {
-    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
-
-    Flags = (evaluator<ArgType>::Flags & (HereditaryBits | LinearAccessBit | DirectAccessBit)),
-
-    Alignment = 0 // FIXME it is not very clear why alignment is necessarily lost...
-  };
-
-  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op) : m_d(op)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_d.func()(m_d.argImpl.coeff(index));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_d.func()(m_d.argImpl.coeffRef(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_d.func()(m_d.argImpl.coeffRef(index));
-  }
-
-protected:
-
-  // this helper permits to completely eliminate the functor if it is empty
-  struct Data
-  {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const UnaryOp& func() const { return op; }
-    UnaryOp op;
-    evaluator<ArgType> argImpl;
-  };
-
-  Data m_d;
-};
-
-// -------------------- Map --------------------
-
-// FIXME perhaps the PlainObjectType could be provided by Derived::PlainObject ?
-// but that might complicate template specialization
-template<typename Derived, typename PlainObjectType>
-struct mapbase_evaluator;
-
-template<typename Derived, typename PlainObjectType>
-struct mapbase_evaluator : evaluator_base<Derived>
-{
-  typedef Derived  XprType;
-  typedef typename XprType::PointerType PointerType;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    IsRowMajor = XprType::RowsAtCompileTime,
-    ColsAtCompileTime = XprType::ColsAtCompileTime,
-    CoeffReadCost = NumTraits<Scalar>::ReadCost
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit mapbase_evaluator(const XprType& map)
-    : m_data(const_cast<PointerType>(map.data())),
-      m_innerStride(map.innerStride()),
-      m_outerStride(map.outerStride())
-  {
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(evaluator<Derived>::Flags&PacketAccessBit, internal::inner_stride_at_compile_time<Derived>::ret==1),
-                        PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_data[col * colStride() + row * rowStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_data[index * m_innerStride.value()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_data[col * colStride() + row * rowStride()];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_data[index * m_innerStride.value()];
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    PointerType ptr = m_data + row * rowStride() + col * colStride();
-    return internal::ploadt<PacketType, LoadMode>(ptr);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return internal::ploadt<PacketType, LoadMode>(m_data + index * m_innerStride.value());
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    PointerType ptr = m_data + row * rowStride() + col * colStride();
-    return internal::pstoret<Scalar, PacketType, StoreMode>(ptr, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    internal::pstoret<Scalar, PacketType, StoreMode>(m_data + index * m_innerStride.value(), x);
-  }
-protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index rowStride() const EIGEN_NOEXCEPT {
-    return XprType::IsRowMajor ? m_outerStride.value() : m_innerStride.value();
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index colStride() const EIGEN_NOEXCEPT {
-     return XprType::IsRowMajor ? m_innerStride.value() : m_outerStride.value();
-  }
-
-  PointerType m_data;
-  const internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_innerStride;
-  const internal::variable_if_dynamic<Index, XprType::OuterStrideAtCompileTime> m_outerStride;
-};
-
-template<typename PlainObjectType, int MapOptions, typename StrideType>
-struct evaluator<Map<PlainObjectType, MapOptions, StrideType> >
-  : public mapbase_evaluator<Map<PlainObjectType, MapOptions, StrideType>, PlainObjectType>
-{
-  typedef Map<PlainObjectType, MapOptions, StrideType> XprType;
-  typedef typename XprType::Scalar Scalar;
-  // TODO: should check for smaller packet types once we can handle multi-sized packet types
-  typedef typename packet_traits<Scalar>::type PacketScalar;
-
-  enum {
-    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
-                             ? int(PlainObjectType::InnerStrideAtCompileTime)
-                             : int(StrideType::InnerStrideAtCompileTime),
-    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
-                             ? int(PlainObjectType::OuterStrideAtCompileTime)
-                             : int(StrideType::OuterStrideAtCompileTime),
-    HasNoInnerStride = InnerStrideAtCompileTime == 1,
-    HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0,
-    HasNoStride = HasNoInnerStride && HasNoOuterStride,
-    IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic,
-
-    PacketAccessMask = bool(HasNoInnerStride) ? ~int(0) : ~int(PacketAccessBit),
-    LinearAccessMask = bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime) ? ~int(0) : ~int(LinearAccessBit),
-    Flags = int( evaluator<PlainObjectType>::Flags) & (LinearAccessMask&PacketAccessMask),
-
-    Alignment = int(MapOptions)&int(AlignedMask)
-  };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& map)
-    : mapbase_evaluator<XprType, PlainObjectType>(map)
-  { }
-};
-
-// -------------------- Ref --------------------
-
-template<typename PlainObjectType, int RefOptions, typename StrideType>
-struct evaluator<Ref<PlainObjectType, RefOptions, StrideType> >
-  : public mapbase_evaluator<Ref<PlainObjectType, RefOptions, StrideType>, PlainObjectType>
-{
-  typedef Ref<PlainObjectType, RefOptions, StrideType> XprType;
-
-  enum {
-    Flags = evaluator<Map<PlainObjectType, RefOptions, StrideType> >::Flags,
-    Alignment = evaluator<Map<PlainObjectType, RefOptions, StrideType> >::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& ref)
-    : mapbase_evaluator<XprType, PlainObjectType>(ref)
-  { }
-};
-
-// -------------------- Block --------------------
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel,
-         bool HasDirectAccess = internal::has_direct_access<ArgType>::ret> struct block_evaluator;
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
-  : block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel>
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-  typedef typename XprType::Scalar Scalar;
-  // TODO: should check for smaller packet types once we can handle multi-sized packet types
-  typedef typename packet_traits<Scalar>::type PacketScalar;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-
-    RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
-    ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
-
-    ArgTypeIsRowMajor = (int(evaluator<ArgType>::Flags)&RowMajorBit) != 0,
-    IsRowMajor = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? 1
-               : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
-               : ArgTypeIsRowMajor,
-    HasSameStorageOrderAsArgType = (IsRowMajor == ArgTypeIsRowMajor),
-    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-    InnerStrideAtCompileTime = HasSameStorageOrderAsArgType
-                             ? int(inner_stride_at_compile_time<ArgType>::ret)
-                             : int(outer_stride_at_compile_time<ArgType>::ret),
-    OuterStrideAtCompileTime = HasSameStorageOrderAsArgType
-                             ? int(outer_stride_at_compile_time<ArgType>::ret)
-                             : int(inner_stride_at_compile_time<ArgType>::ret),
-    MaskPacketAccessBit = (InnerStrideAtCompileTime == 1 || HasSameStorageOrderAsArgType) ? PacketAccessBit : 0,
-
-    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (evaluator<ArgType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0,
-    FlagsRowMajorBit = XprType::Flags&RowMajorBit,
-    Flags0 = evaluator<ArgType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
-                                           DirectAccessBit |
-                                           MaskPacketAccessBit),
-    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit,
-
-    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
-    Alignment0 = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic)
-                             && (OuterStrideAtCompileTime!=0)
-                             && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0)) ? int(PacketAlignment) : 0,
-    Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ArgType>::Alignment, Alignment0)
-  };
-  typedef block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> block_evaluator_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& block) : block_evaluator_type(block)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-};
-
-// no direct-access => dispatch to a unary evaluator
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /*HasDirectAccess*/ false>
-  : unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit block_evaluator(const XprType& block)
-    : unary_evaluator<XprType>(block)
-  {}
-};
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBased>
-  : evaluator_base<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& block)
-    : m_argImpl(block.nestedExpression()),
-      m_startRow(block.startRow()),
-      m_startCol(block.startCol()),
-      m_linear_offset(ForwardLinearAccess?(ArgType::IsRowMajor ? block.startRow()*block.nestedExpression().cols() + block.startCol() : block.startCol()*block.nestedExpression().rows() + block.startRow()):0)
-  { }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    RowsAtCompileTime = XprType::RowsAtCompileTime,
-    ForwardLinearAccess = (InnerPanel || int(XprType::IsRowMajor)==int(ArgType::IsRowMajor)) && bool(evaluator<ArgType>::Flags&LinearAccessBit)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(m_startRow.value() + row, m_startCol.value() + col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return linear_coeff_impl(index, bool_constant<ForwardLinearAccess>());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(m_startRow.value() + row, m_startCol.value() + col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return linear_coeffRef_impl(index, bool_constant<ForwardLinearAccess>());
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    if (ForwardLinearAccess)
-      return m_argImpl.template packet<LoadMode,PacketType>(m_linear_offset.value() + index);
-    else
-      return packet<LoadMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
-                                         RowsAtCompileTime == 1 ? index : 0);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    return m_argImpl.template writePacket<StoreMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    if (ForwardLinearAccess)
-      return m_argImpl.template writePacket<StoreMode,PacketType>(m_linear_offset.value() + index, x);
-    else
-      return writePacket<StoreMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
-                                              RowsAtCompileTime == 1 ? index : 0,
-                                              x);
-  }
-
-protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType linear_coeff_impl(Index index, internal::true_type /* ForwardLinearAccess */) const
-  {
-    return m_argImpl.coeff(m_linear_offset.value() + index);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType linear_coeff_impl(Index index, internal::false_type /* not ForwardLinearAccess */) const
-  {
-    return coeff(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& linear_coeffRef_impl(Index index, internal::true_type /* ForwardLinearAccess */)
-  {
-    return m_argImpl.coeffRef(m_linear_offset.value() + index);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& linear_coeffRef_impl(Index index, internal::false_type /* not ForwardLinearAccess */)
-  {
-    return coeffRef(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
-  }
-
-  evaluator<ArgType> m_argImpl;
-  const variable_if_dynamic<Index, (ArgType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
-  const variable_if_dynamic<Index, (ArgType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
-  const variable_if_dynamic<Index, ForwardLinearAccess ? Dynamic : 0> m_linear_offset;
-};
-
-// TODO: This evaluator does not actually use the child evaluator;
-// all action is via the data() as returned by the Block expression.
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /* HasDirectAccess */ true>
-  : mapbase_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>,
-                      typename Block<ArgType, BlockRows, BlockCols, InnerPanel>::PlainObject>
-{
-  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
-  typedef typename XprType::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit block_evaluator(const XprType& block)
-    : mapbase_evaluator<XprType, typename XprType::PlainObject>(block)
-  {
-    // TODO: for the 3.3 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
-    eigen_assert(((internal::UIntPtr(block.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
-  }
-};
-
-
-// -------------------- Select --------------------
-// NOTE shall we introduce a ternary_evaluator?
-
-// TODO enable vectorization for Select
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
-struct evaluator<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
-  : evaluator_base<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
-{
-  typedef Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> XprType;
-  enum {
-    CoeffReadCost = evaluator<ConditionMatrixType>::CoeffReadCost
-                  + EIGEN_PLAIN_ENUM_MAX(evaluator<ThenMatrixType>::CoeffReadCost,
-                                         evaluator<ElseMatrixType>::CoeffReadCost),
-
-    Flags = (unsigned int)evaluator<ThenMatrixType>::Flags & evaluator<ElseMatrixType>::Flags & HereditaryBits,
-
-    Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ThenMatrixType>::Alignment, evaluator<ElseMatrixType>::Alignment)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& select)
-    : m_conditionImpl(select.conditionMatrix()),
-      m_thenImpl(select.thenMatrix()),
-      m_elseImpl(select.elseMatrix())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    if (m_conditionImpl.coeff(row, col))
-      return m_thenImpl.coeff(row, col);
-    else
-      return m_elseImpl.coeff(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    if (m_conditionImpl.coeff(index))
-      return m_thenImpl.coeff(index);
-    else
-      return m_elseImpl.coeff(index);
-  }
-
-protected:
-  evaluator<ConditionMatrixType> m_conditionImpl;
-  evaluator<ThenMatrixType> m_thenImpl;
-  evaluator<ElseMatrixType> m_elseImpl;
-};
-
-
-// -------------------- Replicate --------------------
-
-template<typename ArgType, int RowFactor, int ColFactor>
-struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
-  : evaluator_base<Replicate<ArgType, RowFactor, ColFactor> >
-{
-  typedef Replicate<ArgType, RowFactor, ColFactor> XprType;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  enum {
-    Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor
-  };
-  typedef typename internal::nested_eval<ArgType,Factor>::type ArgTypeNested;
-  typedef typename internal::remove_all<ArgTypeNested>::type ArgTypeNestedCleaned;
-
-  enum {
-    CoeffReadCost = evaluator<ArgTypeNestedCleaned>::CoeffReadCost,
-    LinearAccessMask = XprType::IsVectorAtCompileTime ? LinearAccessBit : 0,
-    Flags = (evaluator<ArgTypeNestedCleaned>::Flags & (HereditaryBits|LinearAccessMask) & ~RowMajorBit) | (traits<XprType>::Flags & RowMajorBit),
-
-    Alignment = evaluator<ArgTypeNestedCleaned>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& replicate)
-    : m_arg(replicate.nestedExpression()),
-      m_argImpl(m_arg),
-      m_rows(replicate.nestedExpression().rows()),
-      m_cols(replicate.nestedExpression().cols())
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    // try to avoid using modulo; this is a pure optimization strategy
-    const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
-                           : RowFactor==1 ? row
-                           : row % m_rows.value();
-    const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0
-                           : ColFactor==1 ? col
-                           : col % m_cols.value();
-
-    return m_argImpl.coeff(actual_row, actual_col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    // try to avoid using modulo; this is a pure optimization strategy
-    const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
-                                  ? (ColFactor==1 ?  index : index%m_cols.value())
-                                  : (RowFactor==1 ?  index : index%m_rows.value());
-
-    return m_argImpl.coeff(actual_index);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
-                           : RowFactor==1 ? row
-                           : row % m_rows.value();
-    const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0
-                           : ColFactor==1 ? col
-                           : col % m_cols.value();
-
-    return m_argImpl.template packet<LoadMode,PacketType>(actual_row, actual_col);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
-                                  ? (ColFactor==1 ?  index : index%m_cols.value())
-                                  : (RowFactor==1 ?  index : index%m_rows.value());
-
-    return m_argImpl.template packet<LoadMode,PacketType>(actual_index);
-  }
-
-protected:
-  const ArgTypeNested m_arg;
-  evaluator<ArgTypeNestedCleaned> m_argImpl;
-  const variable_if_dynamic<Index, ArgType::RowsAtCompileTime> m_rows;
-  const variable_if_dynamic<Index, ArgType::ColsAtCompileTime> m_cols;
-};
-
-// -------------------- MatrixWrapper and ArrayWrapper --------------------
-//
-// evaluator_wrapper_base<T> is a common base class for the
-// MatrixWrapper and ArrayWrapper evaluators.
-
-template<typename XprType>
-struct evaluator_wrapper_base
-  : evaluator_base<XprType>
-{
-  typedef typename remove_all<typename XprType::NestedExpressionType>::type ArgType;
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    Flags = evaluator<ArgType>::Flags,
-    Alignment = evaluator<ArgType>::Alignment
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator_wrapper_base(const ArgType& arg) : m_argImpl(arg) {}
-
-  typedef typename ArgType::Scalar Scalar;
-  typedef typename ArgType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(row, col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(index);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(row, col);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    return m_argImpl.template packet<LoadMode,PacketType>(index);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode>(row, col, x);
-  }
-
-  template<int StoreMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    m_argImpl.template writePacket<StoreMode>(index, x);
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-};
-
-template<typename TArgType>
-struct unary_evaluator<MatrixWrapper<TArgType> >
-  : evaluator_wrapper_base<MatrixWrapper<TArgType> >
-{
-  typedef MatrixWrapper<TArgType> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& wrapper)
-    : evaluator_wrapper_base<MatrixWrapper<TArgType> >(wrapper.nestedExpression())
-  { }
-};
-
-template<typename TArgType>
-struct unary_evaluator<ArrayWrapper<TArgType> >
-  : evaluator_wrapper_base<ArrayWrapper<TArgType> >
-{
-  typedef ArrayWrapper<TArgType> XprType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& wrapper)
-    : evaluator_wrapper_base<ArrayWrapper<TArgType> >(wrapper.nestedExpression())
-  { }
-};
-
-
-// -------------------- Reverse --------------------
-
-// defined in Reverse.h:
-template<typename PacketType, bool ReversePacket> struct reverse_packet_cond;
-
-template<typename ArgType, int Direction>
-struct unary_evaluator<Reverse<ArgType, Direction> >
-  : evaluator_base<Reverse<ArgType, Direction> >
-{
-  typedef Reverse<ArgType, Direction> XprType;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    IsRowMajor = XprType::IsRowMajor,
-    IsColMajor = !IsRowMajor,
-    ReverseRow = (Direction == Vertical)   || (Direction == BothDirections),
-    ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
-    ReversePacket = (Direction == BothDirections)
-                    || ((Direction == Vertical)   && IsColMajor)
-                    || ((Direction == Horizontal) && IsRowMajor),
-
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-
-    // let's enable LinearAccess only with vectorization because of the product overhead
-    // FIXME enable DirectAccess with negative strides?
-    Flags0 = evaluator<ArgType>::Flags,
-    LinearAccess = ( (Direction==BothDirections) && (int(Flags0)&PacketAccessBit) )
-                  || ((ReverseRow && XprType::ColsAtCompileTime==1) || (ReverseCol && XprType::RowsAtCompileTime==1))
-                 ? LinearAccessBit : 0,
-
-    Flags = int(Flags0) & (HereditaryBits | PacketAccessBit | LinearAccess),
-
-    Alignment = 0 // FIXME in some rare cases, Alignment could be preserved, like a Vector4f.
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit unary_evaluator(const XprType& reverse)
-    : m_argImpl(reverse.nestedExpression()),
-      m_rows(ReverseRow ? reverse.nestedExpression().rows() : 1),
-      m_cols(ReverseCol ? reverse.nestedExpression().cols() : 1)
-  { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(ReverseRow ? m_rows.value() - row - 1 : row,
-                           ReverseCol ? m_cols.value() - col - 1 : col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(m_rows.value() * m_cols.value() - index - 1);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(ReverseRow ? m_rows.value() - row - 1 : row,
-                              ReverseCol ? m_cols.value() - col - 1 : col);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(m_rows.value() * m_cols.value() - index - 1);
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index row, Index col) const
-  {
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
-      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1
-    };
-    typedef internal::reverse_packet_cond<PacketType,ReversePacket> reverse_packet;
-    return reverse_packet::run(m_argImpl.template packet<LoadMode,PacketType>(
-                                  ReverseRow ? m_rows.value() - row - OffsetRow : row,
-                                  ReverseCol ? m_cols.value() - col - OffsetCol : col));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  PacketType packet(Index index) const
-  {
-    enum { PacketSize = unpacket_traits<PacketType>::size };
-    return preverse(m_argImpl.template packet<LoadMode,PacketType>(m_rows.value() * m_cols.value() - index - PacketSize));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index row, Index col, const PacketType& x)
-  {
-    // FIXME we could factorize some code with packet(i,j)
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
-      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1
-    };
-    typedef internal::reverse_packet_cond<PacketType,ReversePacket> reverse_packet;
-    m_argImpl.template writePacket<LoadMode>(
-                                  ReverseRow ? m_rows.value() - row - OffsetRow : row,
-                                  ReverseCol ? m_cols.value() - col - OffsetCol : col,
-                                  reverse_packet::run(x));
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketType& x)
-  {
-    enum { PacketSize = unpacket_traits<PacketType>::size };
-    m_argImpl.template writePacket<LoadMode>
-      (m_rows.value() * m_cols.value() - index - PacketSize, preverse(x));
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-
-  // If we do not reverse rows, then we do not need to know the number of rows; same for columns
-  // Nonetheless, in this case it is important to set to 1 such that the coeff(index) method works fine for vectors.
-  const variable_if_dynamic<Index, ReverseRow ? ArgType::RowsAtCompileTime : 1> m_rows;
-  const variable_if_dynamic<Index, ReverseCol ? ArgType::ColsAtCompileTime : 1> m_cols;
-};
-
-
-// -------------------- Diagonal --------------------
-
-template<typename ArgType, int DiagIndex>
-struct evaluator<Diagonal<ArgType, DiagIndex> >
-  : evaluator_base<Diagonal<ArgType, DiagIndex> >
-{
-  typedef Diagonal<ArgType, DiagIndex> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-
-    Flags = (unsigned int)(evaluator<ArgType>::Flags & (HereditaryBits | DirectAccessBit) & ~RowMajorBit) | LinearAccessBit,
-
-    Alignment = 0
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit evaluator(const XprType& diagonal)
-    : m_argImpl(diagonal.nestedExpression()),
-      m_index(diagonal.index())
-  { }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index) const
-  {
-    return m_argImpl.coeff(row + rowOffset(), row + colOffset());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index index) const
-  {
-    return m_argImpl.coeff(index + rowOffset(), index + colOffset());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index)
-  {
-    return m_argImpl.coeffRef(row + rowOffset(), row + colOffset());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    return m_argImpl.coeffRef(index + rowOffset(), index + colOffset());
-  }
-
-protected:
-  evaluator<ArgType> m_argImpl;
-  const internal::variable_if_dynamicindex<Index, XprType::DiagIndex> m_index;
-
-private:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index rowOffset() const { return m_index.value() > 0 ? 0 : -m_index.value(); }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-  Index colOffset() const { return m_index.value() > 0 ? m_index.value() : 0; }
-};
-
-
-//----------------------------------------------------------------------
-// deprecated code
-//----------------------------------------------------------------------
-
-// -------------------- EvalToTemp --------------------
-
-// expression class for evaluating nested expression to a temporary
-
-template<typename ArgType> class EvalToTemp;
-
-template<typename ArgType>
-struct traits<EvalToTemp<ArgType> >
-  : public traits<ArgType>
-{ };
-
-template<typename ArgType>
-class EvalToTemp
-  : public dense_xpr_base<EvalToTemp<ArgType> >::type
-{
- public:
-
-  typedef typename dense_xpr_base<EvalToTemp>::type Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(EvalToTemp)
-
-  explicit EvalToTemp(const ArgType& arg)
-    : m_arg(arg)
-  { }
-
-  const ArgType& arg() const
-  {
-    return m_arg;
-  }
-
-  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT
-  {
-    return m_arg.rows();
-  }
-
-  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT
-  {
-    return m_arg.cols();
-  }
-
- private:
-  const ArgType& m_arg;
-};
-
-template<typename ArgType>
-struct evaluator<EvalToTemp<ArgType> >
-  : public evaluator<typename ArgType::PlainObject>
-{
-  typedef EvalToTemp<ArgType>                   XprType;
-  typedef typename ArgType::PlainObject         PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
-    : m_result(xpr.arg())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-  }
-
-  // This constructor is used when nesting an EvalTo evaluator in another evaluator
-  EIGEN_DEVICE_FUNC evaluator(const ArgType& arg)
-    : m_result(arg)
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COREEVALUATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CoreIterators.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CoreIterators.h
deleted file mode 100644
index b967196..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CoreIterators.h
+++ /dev/null
@@ -1,132 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COREITERATORS_H
-#define EIGEN_COREITERATORS_H
-
-namespace Eigen { 
-
-/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
- */
-
-namespace internal {
-
-template<typename XprType, typename EvaluatorKind>
-class inner_iterator_selector;
-
-}
-
-/** \class InnerIterator
-  * \brief An InnerIterator allows to loop over the element of any matrix expression.
-  * 
-  * \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed.
-  * 
-  * TODO: add a usage example
-  */
-template<typename XprType>
-class InnerIterator
-{
-protected:
-  typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
-  typedef internal::evaluator<XprType> EvaluatorType;
-  typedef typename internal::traits<XprType>::Scalar Scalar;
-public:
-  /** Construct an iterator over the \a outerId -th row or column of \a xpr */
-  InnerIterator(const XprType &xpr, const Index &outerId)
-    : m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize())
-  {}
-  
-  /// \returns the value of the current coefficient.
-  EIGEN_STRONG_INLINE Scalar value() const          { return m_iter.value(); }
-  /** Increment the iterator \c *this to the next non-zero coefficient.
-    * Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
-    */
-  EIGEN_STRONG_INLINE InnerIterator& operator++()   { m_iter.operator++(); return *this; }
-  EIGEN_STRONG_INLINE InnerIterator& operator+=(Index i) { m_iter.operator+=(i); return *this; }
-  EIGEN_STRONG_INLINE InnerIterator operator+(Index i) 
-  { InnerIterator result(*this); result+=i; return result; }
-    
-
-  /// \returns the column or row index of the current coefficient.
-  EIGEN_STRONG_INLINE Index index() const           { return m_iter.index(); }
-  /// \returns the row index of the current coefficient.
-  EIGEN_STRONG_INLINE Index row() const             { return m_iter.row(); }
-  /// \returns the column index of the current coefficient.
-  EIGEN_STRONG_INLINE Index col() const             { return m_iter.col(); }
-  /// \returns \c true if the iterator \c *this still references a valid coefficient.
-  EIGEN_STRONG_INLINE operator bool() const         { return m_iter; }
-  
-protected:
-  EvaluatorType m_eval;
-  IteratorType m_iter;
-private:
-  // If you get here, then you're not using the right InnerIterator type, e.g.:
-  //   SparseMatrix<double,RowMajor> A;
-  //   SparseMatrix<double>::InnerIterator it(A,0);
-  template<typename T> InnerIterator(const EigenBase<T>&,Index outer);
-};
-
-namespace internal {
-
-// Generic inner iterator implementation for dense objects
-template<typename XprType>
-class inner_iterator_selector<XprType, IndexBased>
-{
-protected:
-  typedef evaluator<XprType> EvaluatorType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
-  
-public:
-  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
-    : m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize)
-  {}
-
-  EIGEN_STRONG_INLINE Scalar value() const
-  {
-    return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner)
-                        : m_eval.coeff(m_inner, m_outer);
-  }
-
-  EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; }
-
-  EIGEN_STRONG_INLINE Index index() const { return m_inner; }
-  inline Index row() const { return IsRowMajor ? m_outer : index(); }
-  inline Index col() const { return IsRowMajor ? index() : m_outer; }
-
-  EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
-
-protected:
-  const EvaluatorType& m_eval;
-  Index m_inner;
-  const Index m_outer;
-  const Index m_end;
-};
-
-// For iterator-based evaluator, inner-iterator is already implemented as
-// evaluator<>::InnerIterator
-template<typename XprType>
-class inner_iterator_selector<XprType, IteratorBased>
- : public evaluator<XprType>::InnerIterator
-{
-protected:
-  typedef typename evaluator<XprType>::InnerIterator Base;
-  typedef evaluator<XprType> EvaluatorType;
-  
-public:
-  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/)
-    : Base(eval, outerId)
-  {}  
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COREITERATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h
deleted file mode 100644
index 2202b1c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h
+++ /dev/null
@@ -1,183 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_BINARY_OP_H
-#define EIGEN_CWISE_BINARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  // we must not inherit from traits<Lhs> since it has
-  // the potential to cause problems with MSVC
-  typedef typename remove_all<Lhs>::type Ancestor;
-  typedef typename traits<Ancestor>::XprKind XprKind;
-  enum {
-    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
-    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
-  };
-
-  // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
-  // we still want to handle the case when the result type is different.
-  typedef typename result_of<
-                     BinaryOp(
-                       const typename Lhs::Scalar&,
-                       const typename Rhs::Scalar&
-                     )
-                   >::type Scalar;
-  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,
-                                              typename traits<Rhs>::StorageKind,
-                                              BinaryOp>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
-                                      typename traits<Rhs>::StorageIndex>::type StorageIndex;
-  typedef typename Lhs::Nested LhsNested;
-  typedef typename Rhs::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  enum {
-    Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value
-  };
-};
-} // end namespace internal
-
-template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
-class CwiseBinaryOpImpl;
-
-/** \class CwiseBinaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
-  *
-  * \tparam BinaryOp template functor implementing the operator
-  * \tparam LhsType the type of the left-hand side
-  * \tparam RhsType the type of the right-hand side
-  *
-  * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
-  * It is the return type of binary operators, by which we mean only those binary operators where
-  * both the left-hand side and the right-hand side are Eigen expressions.
-  * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
-  *
-  * Most of the time, this is the only way that it is used, so you typically don't have to name
-  * CwiseBinaryOp types explicitly.
-  *
-  * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
-  */
-template<typename BinaryOp, typename LhsType, typename RhsType>
-class CwiseBinaryOp :
-  public CwiseBinaryOpImpl<
-          BinaryOp, LhsType, RhsType,
-          typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
-                                                        typename internal::traits<RhsType>::StorageKind,
-                                                        BinaryOp>::ret>,
-  internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::remove_all<BinaryOp>::type Functor;
-    typedef typename internal::remove_all<LhsType>::type Lhs;
-    typedef typename internal::remove_all<RhsType>::type Rhs;
-
-    typedef typename CwiseBinaryOpImpl<
-        BinaryOp, LhsType, RhsType,
-        typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
-                                                      typename internal::traits<Rhs>::StorageKind,
-                                                      BinaryOp>::ret>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
-
-    typedef typename internal::ref_selector<LhsType>::type LhsNested;
-    typedef typename internal::ref_selector<RhsType>::type RhsNested;
-    typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
-    typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
-
-#if EIGEN_COMP_MSVC && EIGEN_HAS_CXX11
-    //Required for Visual Studio or the Copy constructor will probably not get inlined!
-    EIGEN_STRONG_INLINE
-    CwiseBinaryOp(const CwiseBinaryOp<BinaryOp,LhsType,RhsType>&) = default;
-#endif
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
-      : m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
-    {
-      EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
-      // require the sizes to match
-      EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
-      eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT {
-      // return the fixed size type if available to enable compile time optimizations
-      return internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic ? m_rhs.rows() : m_lhs.rows();
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT {
-      // return the fixed size type if available to enable compile time optimizations
-      return internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic ? m_rhs.cols() : m_lhs.cols();
-    }
-
-    /** \returns the left hand side nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const _LhsNested& lhs() const { return m_lhs; }
-    /** \returns the right hand side nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const _RhsNested& rhs() const { return m_rhs; }
-    /** \returns the functor representing the binary operation */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const BinaryOp& functor() const { return m_functor; }
-
-  protected:
-    LhsNested m_lhs;
-    RhsNested m_rhs;
-    const BinaryOp m_functor;
-};
-
-// Generic API dispatcher
-template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
-class CwiseBinaryOpImpl
-  : public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
-};
-
-/** replaces \c *this by \c *this - \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-/** replaces \c *this by \c *this + \a other.
-  *
-  * \returns a reference to \c *this
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
-MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_BINARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
deleted file mode 100644
index 289ec51..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
+++ /dev/null
@@ -1,1001 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_NULLARY_OP_H
-#define EIGEN_CWISE_NULLARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename NullaryOp, typename PlainObjectType>
-struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
-{
-  enum {
-    Flags = traits<PlainObjectType>::Flags & RowMajorBit
-  };
-};
-
-} // namespace internal
-
-/** \class CwiseNullaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression of a matrix where all coefficients are defined by a functor
-  *
-  * \tparam NullaryOp template functor implementing the operator
-  * \tparam PlainObjectType the underlying plain matrix/array type
-  *
-  * This class represents an expression of a generic nullary operator.
-  * It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
-  * and most of the time this is the only way it is used.
-  *
-  * However, if you want to write a function returning such an expression, you
-  * will need to use this class.
-  *
-  * The functor NullaryOp must expose one of the following method:
-    <table class="manual">
-    <tr            ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries (e.g., random numbers)</td></tr>
-    <tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr>
-    <tr            ><td>\c operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)</td></tr>
-    </table>
-  * It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors.
-  *
-  * See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
-  * C++11 random number generators.
-  *
-  * A nullary expression can also be used to implement custom sophisticated matrix manipulations
-  * that cannot be covered by the existing set of natively supported matrix manipulations.
-  * See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
-  * on the behavior of CwiseNullaryOp.
-  *
-  * \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
-  */
-template<typename NullaryOp, typename PlainObjectType>
-class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
-
-    EIGEN_DEVICE_FUNC
-    CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
-      : m_rows(rows), m_cols(cols), m_functor(func)
-    {
-      eigen_assert(rows >= 0
-            && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
-            &&  cols >= 0
-            && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const { return m_rows.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const { return m_cols.value(); }
-
-    /** \returns the functor representing the nullary operation */
-    EIGEN_DEVICE_FUNC
-    const NullaryOp& functor() const { return m_functor; }
-
-  protected:
-    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
-    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
-    const NullaryOp m_functor;
-};
-
-
-/** \returns an expression of a matrix defined by a custom functor \a func
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-template<typename CustomNullaryOp>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const CwiseNullaryOp<CustomNullaryOp,typename DenseBase<Derived>::PlainObject>
-#else
-const CwiseNullaryOp<CustomNullaryOp,PlainObject>
-#endif
-DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
-{
-  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
-}
-
-/** \returns an expression of a matrix defined by a custom functor \a func
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * Here is an example with C++11 random generators: \include random_cpp11.cpp
-  * Output: \verbinclude random_cpp11.out
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-template<typename CustomNullaryOp>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
-#else
-const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-#endif
-DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, PlainObject>(1, size, func);
-  else return CwiseNullaryOp<CustomNullaryOp, PlainObject>(size, 1, func);
-}
-
-/** \returns an expression of a matrix defined by a custom functor \a func
-  *
-  * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-template<typename CustomNullaryOp>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
-#else
-const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-#endif
-DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
-{
-  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
-}
-
-/** \returns an expression of a constant matrix of value \a value
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this DenseBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
-{
-  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
-}
-
-/** \returns an expression of a constant matrix of value \a value
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this DenseBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Zero() should be used
-  * instead.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Constant(Index size, const Scalar& value)
-{
-  return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
-}
-
-/** \returns an expression of a constant matrix of value \a value
-  *
-  * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * The template parameter \a CustomNullaryOp is the type of the functor.
-  *
-  * \sa class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Constant(const Scalar& value)
-{
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value));
-}
-
-/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&)
-  *
-  * \only_for_vectors
-  *
-  * Example: \include DenseBase_LinSpaced_seq_deprecated.cpp
-  * Output: \verbinclude DenseBase_LinSpaced_seq_deprecated.out
-  *
-  * \sa LinSpaced(Index,const Scalar&, const Scalar&), setLinSpaced(Index,const Scalar&,const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low,high,size));
-}
-
-/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&)
-  *
-  * \sa LinSpaced(const Scalar&, const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar>(low,high,Derived::SizeAtCompileTime));
-}
-
-/**
-  * \brief Sets a linearly spaced vector.
-  *
-  * The function generates 'size' equally spaced values in the closed interval [low,high].
-  * When size is set to 1, a vector of length 1 containing 'high' is returned.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include DenseBase_LinSpaced.cpp
-  * Output: \verbinclude DenseBase_LinSpaced.out
-  *
-  * For integer scalar types, an even spacing is possible if and only if the length of the range,
-  * i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the
-  * number of values \c high-low+1 (meaning each value can be repeated the same number of time).
-  * If one of these two considions is not satisfied, then \c high is lowered to the largest value
-  * satisfying one of this constraint.
-  * Here are some examples:
-  *
-  * Example: \include DenseBase_LinSpacedInt.cpp
-  * Output: \verbinclude DenseBase_LinSpacedInt.out
-  *
-  * \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low,high,size));
-}
-
-/**
-  * \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&)
-  * Special version for fixed size types which does not require the size parameter.
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
-DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar>(low,high,Derived::SizeAtCompileTime));
-}
-
-/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
-(const Scalar& val, const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = 0; i < rows(); ++i)
-      if(!internal::isApprox(self.coeff(i, j), val, prec))
-        return false;
-  return true;
-}
-
-/** This is just an alias for isApproxToConstant().
-  *
-  * \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant
-(const Scalar& val, const RealScalar& prec) const
-{
-  return isApproxToConstant(val, prec);
-}
-
-/** Alias for setConstant(): sets all coefficients in this expression to \a val.
-  *
-  * \sa setConstant(), Constant(), class CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
-{
-  setConstant(val);
-}
-
-/** Sets all coefficients in this expression to value \a val.
-  *
-  * \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
-{
-  return derived() = Constant(rows(), cols(), val);
-}
-
-/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include Matrix_setConstant_int.cpp
-  * Output: \verbinclude Matrix_setConstant_int.out
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
-{
-  resize(size);
-  return setConstant(val);
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  * \param val the value to which all coefficients are set
-  *
-  * Example: \include Matrix_setConstant_int_int.cpp
-  * Output: \verbinclude Matrix_setConstant_int_int.out
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
-{
-  resize(rows, cols);
-  return setConstant(val);
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to the given value \a val. For the parameter
-  * of type NoChange_t, just pass the special value \c NoChange.
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(NoChange_t, Index cols, const Scalar& val)
-{
-  return setConstant(rows(), cols, val);
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to the given value \a val. For the parameter
-  * of type NoChange_t, just pass the special value \c NoChange.
-  *
-  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setConstant(Index rows, NoChange_t, const Scalar& val)
-{
-  return setConstant(rows, cols(), val);
-}
-
-
-/**
-  * \brief Sets a linearly spaced vector.
-  *
-  * The function generates 'size' equally spaced values in the closed interval [low,high].
-  * When size is set to 1, a vector of length 1 containing 'high' is returned.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include DenseBase_setLinSpaced.cpp
-  * Output: \verbinclude DenseBase_setLinSpaced.out
-  *
-  * For integer scalar types, do not miss the explanations on the definition
-  * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
-  *
-  * \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar>(low,high,newSize));
-}
-
-/**
-  * \brief Sets a linearly spaced vector.
-  *
-  * The function fills \c *this with equally spaced values in the closed interval [low,high].
-  * When size is set to 1, a vector of length 1 containing 'high' is returned.
-  *
-  * \only_for_vectors
-  *
-  * For integer scalar types, do not miss the explanations on the definition
-  * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
-  *
-  * \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return setLinSpaced(size(), low, high);
-}
-
-// zero:
-
-/** \returns an expression of a zero matrix.
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_zero_int_int.cpp
-  * Output: \verbinclude MatrixBase_zero_int_int.out
-  *
-  * \sa Zero(), Zero(Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Zero(Index rows, Index cols)
-{
-  return Constant(rows, cols, Scalar(0));
-}
-
-/** \returns an expression of a zero vector.
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Zero() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_zero_int.cpp
-  * Output: \verbinclude MatrixBase_zero_int.out
-  *
-  * \sa Zero(), Zero(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Zero(Index size)
-{
-  return Constant(size, Scalar(0));
-}
-
-/** \returns an expression of a fixed-size zero matrix or vector.
-  *
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * Example: \include MatrixBase_zero.cpp
-  * Output: \verbinclude MatrixBase_zero.out
-  *
-  * \sa Zero(Index), Zero(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Zero()
-{
-  return Constant(Scalar(0));
-}
-
-/** \returns true if *this is approximately equal to the zero matrix,
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isZero.cpp
-  * Output: \verbinclude MatrixBase_isZero.out
-  *
-  * \sa class CwiseNullaryOp, Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = 0; i < rows(); ++i)
-      if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec))
-        return false;
-  return true;
-}
-
-/** Sets all coefficients in this expression to zero.
-  *
-  * Example: \include MatrixBase_setZero.cpp
-  * Output: \verbinclude MatrixBase_setZero.out
-  *
-  * \sa class CwiseNullaryOp, Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
-{
-  return setConstant(Scalar(0));
-}
-
-/** Resizes to the given \a size, and sets all coefficients in this expression to zero.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include Matrix_setZero_int.cpp
-  * Output: \verbinclude Matrix_setZero_int.out
-  *
-  * \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(Index newSize)
-{
-  resize(newSize);
-  return setConstant(Scalar(0));
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to zero.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setZero_int_int.cpp
-  * Output: \verbinclude Matrix_setZero_int_int.out
-  *
-  * \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(Index rows, Index cols)
-{
-  resize(rows, cols);
-  return setConstant(Scalar(0));
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to zero. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
-  * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(NoChange_t, Index cols)
-{
-  return setZero(rows(), cols);
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to zero. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
-  * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Zero()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setZero(Index rows, NoChange_t)
-{
-  return setZero(rows, cols());
-}
-
-// ones:
-
-/** \returns an expression of a matrix where all coefficients equal one.
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_ones_int_int.cpp
-  * Output: \verbinclude MatrixBase_ones_int_int.out
-  *
-  * \sa Ones(), Ones(Index), isOnes(), class Ones
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Ones(Index rows, Index cols)
-{
-  return Constant(rows, cols, Scalar(1));
-}
-
-/** \returns an expression of a vector where all coefficients equal one.
-  *
-  * The parameter \a newSize is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Ones() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_ones_int.cpp
-  * Output: \verbinclude MatrixBase_ones_int.out
-  *
-  * \sa Ones(), Ones(Index,Index), isOnes(), class Ones
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Ones(Index newSize)
-{
-  return Constant(newSize, Scalar(1));
-}
-
-/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
-  *
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * Example: \include MatrixBase_ones.cpp
-  * Output: \verbinclude MatrixBase_ones.out
-  *
-  * \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
-DenseBase<Derived>::Ones()
-{
-  return Constant(Scalar(1));
-}
-
-/** \returns true if *this is approximately equal to the matrix where all coefficients
-  *          are equal to 1, within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isOnes.cpp
-  * Output: \verbinclude MatrixBase_isOnes.out
-  *
-  * \sa class CwiseNullaryOp, Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes
-(const RealScalar& prec) const
-{
-  return isApproxToConstant(Scalar(1), prec);
-}
-
-/** Sets all coefficients in this expression to one.
-  *
-  * Example: \include MatrixBase_setOnes.cpp
-  * Output: \verbinclude MatrixBase_setOnes.out
-  *
-  * \sa class CwiseNullaryOp, Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
-{
-  return setConstant(Scalar(1));
-}
-
-/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include Matrix_setOnes_int.cpp
-  * Output: \verbinclude Matrix_setOnes_int.out
-  *
-  * \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(Index newSize)
-{
-  resize(newSize);
-  return setConstant(Scalar(1));
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to one.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setOnes_int_int.cpp
-  * Output: \verbinclude Matrix_setOnes_int_int.out
-  *
-  * \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
-{
-  resize(rows, cols);
-  return setConstant(Scalar(1));
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to one. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
- * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(NoChange_t, Index), class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(Index rows, NoChange_t)
-{
-  return setOnes(rows, cols());
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to one. For the parameter of type NoChange_t,
-  * just pass the special value \c NoChange.
-  *
- * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(Index, NoChange_t) class CwiseNullaryOp, MatrixBase::Ones()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setOnes(NoChange_t, Index cols)
-{
-  return setOnes(rows(), cols);
-}
-
-// Identity:
-
-/** \returns an expression of the identity matrix (not necessarily square).
-  *
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_identity_int_int.cpp
-  * Output: \verbinclude MatrixBase_identity_int_int.out
-  *
-  * \sa Identity(), setIdentity(), isIdentity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
-MatrixBase<Derived>::Identity(Index rows, Index cols)
-{
-  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
-}
-
-/** \returns an expression of the identity matrix (not necessarily square).
-  *
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variant taking size arguments.
-  *
-  * Example: \include MatrixBase_identity.cpp
-  * Output: \verbinclude MatrixBase_identity.out
-  *
-  * \sa Identity(Index,Index), setIdentity(), isIdentity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
-MatrixBase<Derived>::Identity()
-{
-  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-  return MatrixBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op<Scalar>());
-}
-
-/** \returns true if *this is approximately equal to the identity matrix
-  *          (not necessarily square),
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isIdentity.cpp
-  * Output: \verbinclude MatrixBase_isIdentity.out
-  *
-  * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isIdentity
-(const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index j = 0; j < cols(); ++j)
-  {
-    for(Index i = 0; i < rows(); ++i)
-    {
-      if(i == j)
-      {
-        if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
-          return false;
-      }
-      else
-      {
-        if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
-          return false;
-      }
-    }
-  }
-  return true;
-}
-
-namespace internal {
-
-template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
-struct setIdentity_impl
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
-  {
-    return m = Derived::Identity(m.rows(), m.cols());
-  }
-};
-
-template<typename Derived>
-struct setIdentity_impl<Derived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
-  {
-    m.setZero();
-    const Index size = numext::mini(m.rows(), m.cols());
-    for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
-    return m;
-  }
-};
-
-} // end namespace internal
-
-/** Writes the identity expression (not necessarily square) into *this.
-  *
-  * Example: \include MatrixBase_setIdentity.cpp
-  * Output: \verbinclude MatrixBase_setIdentity.out
-  *
-  * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
-{
-  return internal::setIdentity_impl<Derived>::run(derived());
-}
-
-/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
-  *
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setIdentity_int_int.cpp
-  * Output: \verbinclude Matrix_setIdentity_int_int.out
-  *
-  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
-{
-  derived().resize(rows, cols);
-  return setIdentity();
-}
-
-/** \returns an expression of the i-th unit (basis) vector.
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
-}
-
-/** \returns an expression of the i-th unit (basis) vector.
-  *
-  * \only_for_vectors
-  *
-  * This variant is for fixed-size vector only.
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return BasisReturnType(SquareMatrixType::Identity(),i);
-}
-
-/** \returns an expression of the X axis unit vector (1{,0}^*)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
-{ return Derived::Unit(0); }
-
-/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
-{ return Derived::Unit(1); }
-
-/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
-{ return Derived::Unit(2); }
-
-/** \returns an expression of the W axis unit vector (0,0,0,1)
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
-{ return Derived::Unit(3); }
-
-/** \brief Set the coefficients of \c *this to the i-th unit (basis) vector
-  *
-  * \param i index of the unique coefficient to be set to 1
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  eigen_assert(i<size());
-  derived().setZero();
-  derived().coeffRef(i) = Scalar(1);
-  return derived();
-}
-
-/** \brief Resizes to the given \a newSize, and writes the i-th unit (basis) vector into *this.
-  *
-  * \param newSize the new size of the vector
-  * \param i index of the unique coefficient to be set to 1
-  *
-  * \only_for_vectors
-  *
-  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index newSize, Index i)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  eigen_assert(i<newSize);
-  derived().resize(newSize);
-  return setUnit(i);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_NULLARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h
deleted file mode 100644
index 9f3576f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h
+++ /dev/null
@@ -1,197 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_TERNARY_OP_H
-#define EIGEN_CWISE_TERNARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
-struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
-  // we must not inherit from traits<Arg1> since it has
-  // the potential to cause problems with MSVC
-  typedef typename remove_all<Arg1>::type Ancestor;
-  typedef typename traits<Ancestor>::XprKind XprKind;
-  enum {
-    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
-    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
-    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
-  };
-
-  // even though we require Arg1, Arg2, and Arg3 to have the same scalar type
-  // (see CwiseTernaryOp constructor),
-  // we still want to handle the case when the result type is different.
-  typedef typename result_of<TernaryOp(
-      const typename Arg1::Scalar&, const typename Arg2::Scalar&,
-      const typename Arg3::Scalar&)>::type Scalar;
-
-  typedef typename internal::traits<Arg1>::StorageKind StorageKind;
-  typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
-
-  typedef typename Arg1::Nested Arg1Nested;
-  typedef typename Arg2::Nested Arg2Nested;
-  typedef typename Arg3::Nested Arg3Nested;
-  typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
-  typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
-  typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
-  enum { Flags = _Arg1Nested::Flags & RowMajorBit };
-};
-}  // end namespace internal
-
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
-          typename StorageKind>
-class CwiseTernaryOpImpl;
-
-/** \class CwiseTernaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression where a coefficient-wise ternary operator is
- * applied to two expressions
-  *
-  * \tparam TernaryOp template functor implementing the operator
-  * \tparam Arg1Type the type of the first argument
-  * \tparam Arg2Type the type of the second argument
-  * \tparam Arg3Type the type of the third argument
-  *
-  * This class represents an expression where a coefficient-wise ternary
- * operator is applied to three expressions.
-  * It is the return type of ternary operators, by which we mean only those
- * ternary operators where
-  * all three arguments are Eigen expressions.
-  * For example, the return type of betainc(matrix1, matrix2, matrix3) is a
- * CwiseTernaryOp.
-  *
-  * Most of the time, this is the only way that it is used, so you typically
- * don't have to name
-  * CwiseTernaryOp types explicitly.
-  *
-  * \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
- * MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
- * class CwiseUnaryOp, class CwiseNullaryOp
-  */
-template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
-          typename Arg3Type>
-class CwiseTernaryOp : public CwiseTernaryOpImpl<
-                           TernaryOp, Arg1Type, Arg2Type, Arg3Type,
-                           typename internal::traits<Arg1Type>::StorageKind>,
-                       internal::no_assignment_operator
-{
- public:
-  typedef typename internal::remove_all<Arg1Type>::type Arg1;
-  typedef typename internal::remove_all<Arg2Type>::type Arg2;
-  typedef typename internal::remove_all<Arg3Type>::type Arg3;
-
-  typedef typename CwiseTernaryOpImpl<
-      TernaryOp, Arg1Type, Arg2Type, Arg3Type,
-      typename internal::traits<Arg1Type>::StorageKind>::Base Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
-
-  typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
-  typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
-  typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
-  typedef typename internal::remove_reference<Arg1Nested>::type _Arg1Nested;
-  typedef typename internal::remove_reference<Arg2Nested>::type _Arg2Nested;
-  typedef typename internal::remove_reference<Arg3Nested>::type _Arg3Nested;
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
-                                     const Arg3& a3,
-                                     const TernaryOp& func = TernaryOp())
-      : m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
-    // require the sizes to match
-    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
-    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
-
-    // The index types should match
-    EIGEN_STATIC_ASSERT((internal::is_same<
-                         typename internal::traits<Arg1Type>::StorageKind,
-                         typename internal::traits<Arg2Type>::StorageKind>::value),
-                        STORAGE_KIND_MUST_MATCH)
-    EIGEN_STATIC_ASSERT((internal::is_same<
-                         typename internal::traits<Arg1Type>::StorageKind,
-                         typename internal::traits<Arg3Type>::StorageKind>::value),
-                        STORAGE_KIND_MUST_MATCH)
-
-    eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
-                 a1.rows() == a3.rows() && a1.cols() == a3.cols());
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Index rows() const {
-    // return the fixed size type if available to enable compile time
-    // optimizations
-    if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                RowsAtCompileTime == Dynamic &&
-        internal::traits<typename internal::remove_all<Arg2Nested>::type>::
-                RowsAtCompileTime == Dynamic)
-      return m_arg3.rows();
-    else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                     RowsAtCompileTime == Dynamic &&
-             internal::traits<typename internal::remove_all<Arg3Nested>::type>::
-                     RowsAtCompileTime == Dynamic)
-      return m_arg2.rows();
-    else
-      return m_arg1.rows();
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Index cols() const {
-    // return the fixed size type if available to enable compile time
-    // optimizations
-    if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                ColsAtCompileTime == Dynamic &&
-        internal::traits<typename internal::remove_all<Arg2Nested>::type>::
-                ColsAtCompileTime == Dynamic)
-      return m_arg3.cols();
-    else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
-                     ColsAtCompileTime == Dynamic &&
-             internal::traits<typename internal::remove_all<Arg3Nested>::type>::
-                     ColsAtCompileTime == Dynamic)
-      return m_arg2.cols();
-    else
-      return m_arg1.cols();
-  }
-
-  /** \returns the first argument nested expression */
-  EIGEN_DEVICE_FUNC
-  const _Arg1Nested& arg1() const { return m_arg1; }
-  /** \returns the first argument nested expression */
-  EIGEN_DEVICE_FUNC
-  const _Arg2Nested& arg2() const { return m_arg2; }
-  /** \returns the third argument nested expression */
-  EIGEN_DEVICE_FUNC
-  const _Arg3Nested& arg3() const { return m_arg3; }
-  /** \returns the functor representing the ternary operation */
-  EIGEN_DEVICE_FUNC
-  const TernaryOp& functor() const { return m_functor; }
-
- protected:
-  Arg1Nested m_arg1;
-  Arg2Nested m_arg2;
-  Arg3Nested m_arg3;
-  const TernaryOp m_functor;
-};
-
-// Generic API dispatcher
-template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
-          typename StorageKind>
-class CwiseTernaryOpImpl
-    : public internal::generic_xpr_base<
-          CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type {
- public:
-  typedef typename internal::generic_xpr_base<
-      CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type Base;
-};
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CWISE_TERNARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h
deleted file mode 100644
index e68c4f7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h
+++ /dev/null
@@ -1,103 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_UNARY_OP_H
-#define EIGEN_CWISE_UNARY_OP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename UnaryOp, typename XprType>
-struct traits<CwiseUnaryOp<UnaryOp, XprType> >
- : traits<XprType>
-{
-  typedef typename result_of<
-                     UnaryOp(const typename XprType::Scalar&)
-                   >::type Scalar;
-  typedef typename XprType::Nested XprTypeNested;
-  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
-  enum {
-    Flags = _XprTypeNested::Flags & RowMajorBit
-  };
-};
-}
-
-template<typename UnaryOp, typename XprType, typename StorageKind>
-class CwiseUnaryOpImpl;
-
-/** \class CwiseUnaryOp
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression where a coefficient-wise unary operator is applied to an expression
-  *
-  * \tparam UnaryOp template functor implementing the operator
-  * \tparam XprType the type of the expression to which we are applying the unary operator
-  *
-  * This class represents an expression where a unary operator is applied to an expression.
-  * It is the return type of all operations taking exactly 1 input expression, regardless of the
-  * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
-  * is considered unary, because only the right-hand side is an expression, and its
-  * return type is a specialization of CwiseUnaryOp.
-  *
-  * Most of the time, this is the only way that it is used, so you typically don't have to name
-  * CwiseUnaryOp types explicitly.
-  *
-  * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
-  */
-template<typename UnaryOp, typename XprType>
-class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>, internal::no_assignment_operator
-{
-  public:
-
-    typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
-    typedef typename internal::ref_selector<XprType>::type XprTypeNested;
-    typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
-      : m_xpr(xpr), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
-
-    /** \returns the functor representing the unary operation */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const UnaryOp& functor() const { return m_functor; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<XprTypeNested>::type&
-    nestedExpression() const { return m_xpr; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    typename internal::remove_all<XprTypeNested>::type&
-    nestedExpression() { return m_xpr; }
-
-  protected:
-    XprTypeNested m_xpr;
-    const UnaryOp m_functor;
-};
-
-// Generic API dispatcher
-template<typename UnaryOp, typename XprType, typename StorageKind>
-class CwiseUnaryOpImpl
-  : public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_UNARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseUnaryView.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseUnaryView.h
deleted file mode 100644
index a06d762..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/CwiseUnaryView.h
+++ /dev/null
@@ -1,132 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CWISE_UNARY_VIEW_H
-#define EIGEN_CWISE_UNARY_VIEW_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename ViewOp, typename MatrixType>
-struct traits<CwiseUnaryView<ViewOp, MatrixType> >
- : traits<MatrixType>
-{
-  typedef typename result_of<
-                     ViewOp(const typename traits<MatrixType>::Scalar&)
-                   >::type Scalar;
-  typedef typename MatrixType::Nested MatrixTypeNested;
-  typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = traits<_MatrixTypeNested>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
-    MatrixTypeInnerStride =  inner_stride_at_compile_time<MatrixType>::ret,
-    // need to cast the sizeof's from size_t to int explicitly, otherwise:
-    // "error: no integral type can represent all of the enumerator values
-    InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
-                             ? int(Dynamic)
-                             : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
-    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
-                             ? int(Dynamic)
-                             : outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
-  };
-};
-}
-
-template<typename ViewOp, typename MatrixType, typename StorageKind>
-class CwiseUnaryViewImpl;
-
-/** \class CwiseUnaryView
-  * \ingroup Core_Module
-  *
-  * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
-  *
-  * \tparam ViewOp template functor implementing the view
-  * \tparam MatrixType the type of the matrix we are applying the unary operator
-  *
-  * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
-  * It is the return type of real() and imag(), and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
-  */
-template<typename ViewOp, typename MatrixType>
-class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
-{
-  public:
-
-    typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
-    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-    explicit EIGEN_DEVICE_FUNC inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
-      : m_matrix(mat), m_functor(func) {}
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    /** \returns the functor representing unary operation */
-    EIGEN_DEVICE_FUNC const ViewOp& functor() const { return m_functor; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC const typename internal::remove_all<MatrixTypeNested>::type&
-    nestedExpression() const { return m_matrix; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC typename internal::remove_reference<MatrixTypeNested>::type&
-    nestedExpression() { return m_matrix; }
-
-  protected:
-    MatrixTypeNested m_matrix;
-    ViewOp m_functor;
-};
-
-// Generic API dispatcher
-template<typename ViewOp, typename XprType, typename StorageKind>
-class CwiseUnaryViewImpl
-  : public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type Base;
-};
-
-template<typename ViewOp, typename MatrixType>
-class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
-  : public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
-{
-  public:
-
-    typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
-    typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
-
-    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
-
-    EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const
-    {
-      return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const
-    {
-      return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
-    }
-  protected:
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CWISE_UNARY_VIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseBase.h
deleted file mode 100644
index 9b16db6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseBase.h
+++ /dev/null
@@ -1,701 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DENSEBASE_H
-#define EIGEN_DENSEBASE_H
-
-namespace Eigen {
-
-namespace internal {
-
-// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
-// This dummy function simply aims at checking that at compile time.
-static inline void check_DenseIndex_is_signed() {
-  EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
-}
-
-} // end namespace internal
-
-/** \class DenseBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for all dense matrices, vectors, and arrays
-  *
-  * This class is the base that is inherited by all dense objects (matrix, vector, arrays,
-  * and related expression types). The common Eigen API for dense objects is contained in this class.
-  *
-  * \tparam Derived is the derived type, e.g., a matrix type or an expression.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived> class DenseBase
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  : public DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value>
-#else
-  : public DenseCoeffsBase<Derived,DirectWriteAccessors>
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-{
-  public:
-
-    /** Inner iterator type to iterate over the coefficients of a row or column.
-      * \sa class InnerIterator
-      */
-    typedef Eigen::InnerIterator<Derived> InnerIterator;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-
-    /**
-      * \brief The type used to store indices
-      * \details This typedef is relevant for types that store multiple indices such as
-      *          PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
-      * \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
-     */
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-
-    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-
-    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
-      *
-      * It is an alias for the Scalar type */
-    typedef Scalar value_type;
-
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value> Base;
-
-    using Base::derived;
-    using Base::const_cast_derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::rowIndexByOuterInner;
-    using Base::colIndexByOuterInner;
-    using Base::coeff;
-    using Base::coeffByOuterInner;
-    using Base::operator();
-    using Base::operator[];
-    using Base::x;
-    using Base::y;
-    using Base::z;
-    using Base::w;
-    using Base::stride;
-    using Base::innerStride;
-    using Base::outerStride;
-    using Base::rowStride;
-    using Base::colStride;
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-    enum {
-
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-        /**< The number of rows at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
-
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-        /**< The number of columns at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
-
-
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
-        /**< This is equal to the number of coefficients, i.e. the number of
-          * rows times the number of columns, or to \a Dynamic if this is not
-          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
-
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-        /**< This value is equal to the maximum possible number of rows that this expression
-          * might have. If this expression might have an arbitrarily high number of rows,
-          * this value is set to \a Dynamic.
-          *
-          * This value is useful to know when evaluating an expression, in order to determine
-          * whether it is possible to avoid doing a dynamic memory allocation.
-          *
-          * \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime
-          */
-
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-        /**< This value is equal to the maximum possible number of columns that this expression
-          * might have. If this expression might have an arbitrarily high number of columns,
-          * this value is set to \a Dynamic.
-          *
-          * This value is useful to know when evaluating an expression, in order to determine
-          * whether it is possible to avoid doing a dynamic memory allocation.
-          *
-          * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
-          */
-
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
-                                                      internal::traits<Derived>::MaxColsAtCompileTime>::ret),
-        /**< This value is equal to the maximum possible number of coefficients that this expression
-          * might have. If this expression might have an arbitrarily high number of coefficients,
-          * this value is set to \a Dynamic.
-          *
-          * This value is useful to know when evaluating an expression, in order to determine
-          * whether it is possible to avoid doing a dynamic memory allocation.
-          *
-          * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
-          */
-
-      IsVectorAtCompileTime = internal::traits<Derived>::RowsAtCompileTime == 1
-                           || internal::traits<Derived>::ColsAtCompileTime == 1,
-        /**< This is set to true if either the number of rows or the number of
-          * columns is known at compile-time to be equal to 1. Indeed, in that case,
-          * we are dealing with a column-vector (if there is only one column) or with
-          * a row-vector (if there is only one row). */
-
-      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2,
-        /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
-         * and 2 for matrices.
-         */
-
-      Flags = internal::traits<Derived>::Flags,
-        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
-          * constructed from this one. See the \ref flags "list of flags".
-          */
-
-      IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */
-
-      InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
-                             : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-
-      InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
-      OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
-    };
-
-    typedef typename internal::find_best_packet<Scalar,SizeAtCompileTime>::type PacketScalar;
-
-    enum { IsPlainObjectBase = 0 };
-
-    /** The plain matrix type corresponding to this expression.
-      * \sa PlainObject */
-    typedef Matrix<typename internal::traits<Derived>::Scalar,
-                internal::traits<Derived>::RowsAtCompileTime,
-                internal::traits<Derived>::ColsAtCompileTime,
-                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
-                internal::traits<Derived>::MaxRowsAtCompileTime,
-                internal::traits<Derived>::MaxColsAtCompileTime
-          > PlainMatrix;
-
-    /** The plain array type corresponding to this expression.
-      * \sa PlainObject */
-    typedef Array<typename internal::traits<Derived>::Scalar,
-                internal::traits<Derived>::RowsAtCompileTime,
-                internal::traits<Derived>::ColsAtCompileTime,
-                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
-                internal::traits<Derived>::MaxRowsAtCompileTime,
-                internal::traits<Derived>::MaxColsAtCompileTime
-          > PlainArray;
-
-    /** \brief The plain matrix or array type corresponding to this expression.
-      *
-      * This is not necessarily exactly the return type of eval(). In the case of plain matrices,
-      * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
-      * that the return type of eval() is either PlainObject or const PlainObject&.
-      */
-    typedef typename internal::conditional<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
-                                 PlainMatrix, PlainArray>::type PlainObject;
-
-    /** \returns the number of nonzero coefficients which is in practice the number
-      * of stored coefficients. */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index nonZeros() const { return size(); }
-
-    /** \returns the outer size.
-      *
-      * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
-      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
-      * column-major matrix, and the number of rows for a row-major matrix. */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index outerSize() const
-    {
-      return IsVectorAtCompileTime ? 1
-           : int(IsRowMajor) ? this->rows() : this->cols();
-    }
-
-    /** \returns the inner size.
-      *
-      * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
-      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
-      * column-major matrix, and the number of columns for a row-major matrix. */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index innerSize() const
-    {
-      return IsVectorAtCompileTime ? this->size()
-           : int(IsRowMajor) ? this->cols() : this->rows();
-    }
-
-    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
-      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
-      * nothing else.
-      */
-    EIGEN_DEVICE_FUNC
-    void resize(Index newSize)
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(newSize);
-      eigen_assert(newSize == this->size()
-                && "DenseBase::resize() does not actually allow to resize.");
-    }
-    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
-      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
-      * nothing else.
-      */
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols)
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(rows);
-      EIGEN_ONLY_USED_FOR_DEBUG(cols);
-      eigen_assert(rows == this->rows() && cols == this->cols()
-                && "DenseBase::resize() does not actually allow to resize.");
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
-    /** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
-    EIGEN_DEPRECATED typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> SequentialLinSpacedReturnType;
-    /** \internal Represents a vector with linearly spaced coefficients that allows random access. */
-    typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> RandomAccessLinSpacedReturnType;
-    /** \internal the return type of MatrixBase::eigenvalues() */
-    typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-    /** Copies \a other into *this. \returns a reference to *this. */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const DenseBase<OtherDerived>& other);
-
-    /** Special case of the template operator=, in order to prevent the compiler
-      * from generating a default operator= (issue hit with g++ 4.1)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const DenseBase& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const EigenBase<OtherDerived> &other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator+=(const EigenBase<OtherDerived> &other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator-=(const EigenBase<OtherDerived> &other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const ReturnByValue<OtherDerived>& func);
-
-    /** \internal
-      * Copies \a other into *this without evaluating other. \returns a reference to *this. */
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    Derived& lazyAssign(const DenseBase<OtherDerived>& other);
-
-    EIGEN_DEVICE_FUNC
-    CommaInitializer<Derived> operator<< (const Scalar& s);
-
-    template<unsigned int Added,unsigned int Removed>
-    /** \deprecated it now returns \c *this */
-    EIGEN_DEPRECATED
-    const Derived& flagged() const
-    { return derived(); }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
-
-    typedef Transpose<Derived> TransposeReturnType;
-    EIGEN_DEVICE_FUNC
-    TransposeReturnType transpose();
-    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
-    EIGEN_DEVICE_FUNC
-    ConstTransposeReturnType transpose() const;
-    EIGEN_DEVICE_FUNC
-    void transposeInPlace();
-
-    EIGEN_DEVICE_FUNC static const ConstantReturnType
-    Constant(Index rows, Index cols, const Scalar& value);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType
-    Constant(Index size, const Scalar& value);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType
-    Constant(const Scalar& value);
-
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
-
-    EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(Index size, const Scalar& low, const Scalar& high);
-    EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
-    LinSpaced(const Scalar& low, const Scalar& high);
-
-    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
-    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-    NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
-    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
-    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-    NullaryExpr(Index size, const CustomNullaryOp& func);
-    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
-    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
-    NullaryExpr(const CustomNullaryOp& func);
-
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero();
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
-    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
-
-    EIGEN_DEVICE_FUNC void fill(const Scalar& value);
-    EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
-    EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
-    EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
-    EIGEN_DEVICE_FUNC Derived& setZero();
-    EIGEN_DEVICE_FUNC Derived& setOnes();
-    EIGEN_DEVICE_FUNC Derived& setRandom();
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC
-    bool isApprox(const DenseBase<OtherDerived>& other,
-                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC
-    bool isMuchSmallerThan(const RealScalar& other,
-                           const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC
-    bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
-                           const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    inline bool hasNaN() const;
-    inline bool allFinite() const;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator*=(const Scalar& other);
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator/=(const Scalar& other);
-
-    typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
-    /** \returns the matrix or vector obtained by evaluating this expression.
-      *
-      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
-      * a const reference, in order to avoid a useless copy.
-      *
-      * \warning Be careful with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE EvalReturnType eval() const
-    {
-      // Even though MSVC does not honor strong inlining when the return type
-      // is a dynamic matrix, we desperately need strong inlining for fixed
-      // size types on MSVC.
-      return typename internal::eval<Derived>::type(derived());
-    }
-
-    /** swaps *this with the expression \a other.
-      *
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      eigen_assert(rows()==other.rows() && cols()==other.cols());
-      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    /** swaps *this with the matrix or array \a other.
-      *
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(PlainObjectBase<OtherDerived>& other)
-    {
-      eigen_assert(rows()==other.rows() && cols()==other.cols());
-      call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC inline const NestByValue<Derived> nestByValue() const;
-    EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
-    EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
-    template<bool Enable> EIGEN_DEVICE_FUNC
-    inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
-    template<bool Enable> EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
-
-    EIGEN_DEVICE_FUNC Scalar sum() const;
-    EIGEN_DEVICE_FUNC Scalar mean() const;
-    EIGEN_DEVICE_FUNC Scalar trace() const;
-
-    EIGEN_DEVICE_FUNC Scalar prod() const;
-
-    template<int NaNPropagation>
-    EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
-    template<int NaNPropagation>
-    EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
-
-
-    // By default, the fastest version with undefined NaN propagation semantics is
-    // used.
-    // TODO(rmlarsen): Replace with default template argument when we move to
-    // c++11 or beyond.
-    EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff() const {
-      return minCoeff<PropagateFast>();
-    }
-    EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff() const {
-      return maxCoeff<PropagateFast>();
-    }
-
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
-    template<int NaNPropagation, typename IndexType>
-    EIGEN_DEVICE_FUNC
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
-
-    // TODO(rmlarsen): Replace these methods with a default template argument.
-    template<typename IndexType>
-    EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const {
-      return minCoeff<PropagateFast>(row, col);
-    }
-    template<typename IndexType>
-    EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const {
-      return maxCoeff<PropagateFast>(row, col);
-    }
-    template<typename IndexType>
-     EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const {
-      return minCoeff<PropagateFast>(index);
-    }
-    template<typename IndexType>
-    EIGEN_DEVICE_FUNC inline
-    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const {
-      return maxCoeff<PropagateFast>(index);
-    }
-  
-    template<typename BinaryOp>
-    EIGEN_DEVICE_FUNC
-    Scalar redux(const BinaryOp& func) const;
-
-    template<typename Visitor>
-    EIGEN_DEVICE_FUNC
-    void visit(Visitor& func) const;
-
-    /** \returns a WithFormat proxy object allowing to print a matrix the with given
-      * format \a fmt.
-      *
-      * See class IOFormat for some examples.
-      *
-      * \sa class IOFormat, class WithFormat
-      */
-    inline const WithFormat<Derived> format(const IOFormat& fmt) const
-    {
-      return WithFormat<Derived>(derived(), fmt);
-    }
-
-    /** \returns the unique coefficient of a 1x1 expression */
-    EIGEN_DEVICE_FUNC
-    CoeffReturnType value() const
-    {
-      EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
-      eigen_assert(this->rows() == 1 && this->cols() == 1);
-      return derived().coeff(0,0);
-    }
-
-    EIGEN_DEVICE_FUNC bool all() const;
-    EIGEN_DEVICE_FUNC bool any() const;
-    EIGEN_DEVICE_FUNC Index count() const;
-
-    typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
-    typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
-    typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
-    typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
-
-    /** \returns a VectorwiseOp wrapper of *this for broadcasting and partial reductions
-    *
-    * Example: \include MatrixBase_rowwise.cpp
-    * Output: \verbinclude MatrixBase_rowwise.out
-    *
-    * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-    */
-    //Code moved here due to a CUDA compiler bug
-    EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const {
-      return ConstRowwiseReturnType(derived());
-    }
-    EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
-
-    /** \returns a VectorwiseOp wrapper of *this broadcasting and partial reductions
-    *
-    * Example: \include MatrixBase_colwise.cpp
-    * Output: \verbinclude MatrixBase_colwise.out
-    *
-    * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-    */
-    EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const {
-      return ConstColwiseReturnType(derived());
-    }
-    EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
-
-    typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>,PlainObject> RandomReturnType;
-    static const RandomReturnType Random(Index rows, Index cols);
-    static const RandomReturnType Random(Index size);
-    static const RandomReturnType Random();
-
-    template<typename ThenDerived,typename ElseDerived>
-    inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived,ElseDerived>
-    select(const DenseBase<ThenDerived>& thenMatrix,
-           const DenseBase<ElseDerived>& elseMatrix) const;
-
-    template<typename ThenDerived>
-    inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
-    select(const DenseBase<ThenDerived>& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const;
-
-    template<typename ElseDerived>
-    inline EIGEN_DEVICE_FUNC const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
-    select(const typename ElseDerived::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
-
-    template<int p> RealScalar lpNorm() const;
-
-    template<int RowFactor, int ColFactor>
-    EIGEN_DEVICE_FUNC
-    const Replicate<Derived,RowFactor,ColFactor> replicate() const;
-    /**
-    * \return an expression of the replication of \c *this
-    *
-    * Example: \include MatrixBase_replicate_int_int.cpp
-    * Output: \verbinclude MatrixBase_replicate_int_int.out
-    *
-    * \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
-    */
-    //Code moved here due to a CUDA compiler bug
-    EIGEN_DEVICE_FUNC
-    const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const
-    {
-      return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
-    }
-
-    typedef Reverse<Derived, BothDirections> ReverseReturnType;
-    typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
-    EIGEN_DEVICE_FUNC ReverseReturnType reverse();
-    /** This is the const version of reverse(). */
-    //Code moved here due to a CUDA compiler bug
-    EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const
-    {
-      return ConstReverseReturnType(derived());
-    }
-    EIGEN_DEVICE_FUNC void reverseInPlace();
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
-      * iterator type as returned by the begin() and end() methods.
-      */
-    typedef random_access_iterator_type iterator;
-    /** This is the const version of iterator (aka read-only) */
-    typedef random_access_iterator_type const_iterator;
-    #else
-    typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit,
-                                            internal::pointer_based_stl_iterator<Derived>,
-                                            internal::generic_randaccess_stl_iterator<Derived>
-                                          >::type iterator_type;
-
-    typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit,
-                                            internal::pointer_based_stl_iterator<const Derived>,
-                                            internal::generic_randaccess_stl_iterator<const Derived>
-                                          >::type const_iterator_type;
-
-    // Stl-style iterators are supported only for vectors.
-
-    typedef typename internal::conditional< IsVectorAtCompileTime,
-                                            iterator_type,
-                                            void
-                                          >::type iterator;
-
-    typedef typename internal::conditional< IsVectorAtCompileTime,
-                                            const_iterator_type,
-                                            void
-                                          >::type const_iterator;
-    #endif
-
-    inline iterator begin();
-    inline const_iterator begin() const;
-    inline const_iterator cbegin() const;
-    inline iterator end();
-    inline const_iterator end() const;
-    inline const_iterator cend() const;
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
-#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#   include "../plugins/CommonCwiseUnaryOps.h"
-#   include "../plugins/BlockMethods.h"
-#   include "../plugins/IndexedViewMethods.h"
-#   include "../plugins/ReshapedMethods.h"
-#   ifdef EIGEN_DENSEBASE_PLUGIN
-#     include EIGEN_DENSEBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
-#undef EIGEN_DOC_UNARY_ADDONS
-
-    // disable the use of evalTo for dense objects with a nice compilation error
-    template<typename Dest>
-    EIGEN_DEVICE_FUNC
-    inline void evalTo(Dest& ) const
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
-    }
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(DenseBase)
-    /** Default constructor. Do nothing. */
-    EIGEN_DEVICE_FUNC DenseBase()
-    {
-      /* Just checks for self-consistency of the flags.
-       * Only do it when debugging Eigen, as this borders on paranoia and could slow compilation down
-       */
-#ifdef EIGEN_INTERNAL_DEBUGGING
-      EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
-                        && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
-                          INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
-#endif
-    }
-
-  private:
-    EIGEN_DEVICE_FUNC explicit DenseBase(int);
-    EIGEN_DEVICE_FUNC DenseBase(int,int);
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_DENSEBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h
deleted file mode 100644
index 37fcdb5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h
+++ /dev/null
@@ -1,685 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DENSECOEFFSBASE_H
-#define EIGEN_DENSECOEFFSBASE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename T> struct add_const_on_value_type_if_arithmetic
-{
-  typedef typename conditional<is_arithmetic<T>::value, T, typename add_const_on_value_type<T>::type>::type type;
-};
-}
-
-/** \brief Base class providing read-only coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #ReadOnlyAccessors Constant indicating read-only access
-  *
-  * This class defines the \c operator() \c const function and friends, which can be used to read specific
-  * entries of a matrix or array.
-  *
-  * \sa DenseCoeffsBase<Derived, WriteAccessors>, DenseCoeffsBase<Derived, DirectAccessors>,
-  *     \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
-{
-  public:
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-
-    // Explanation for this CoeffReturnType typedef.
-    // - This is the return type of the coeff() method.
-    // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
-    // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
-    // - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems
-    // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
-    // not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
-    typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
-                         const Scalar&,
-                         typename internal::conditional<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>::type
-                     >::type CoeffReturnType;
-
-    typedef typename internal::add_const_on_value_type_if_arithmetic<
-                         typename internal::packet_traits<Scalar>::type
-                     >::type PacketReturnType;
-
-    typedef EigenBase<Derived> Base;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
-    {
-      return int(Derived::RowsAtCompileTime) == 1 ? 0
-          : int(Derived::ColsAtCompileTime) == 1 ? inner
-          : int(Derived::Flags)&RowMajorBit ? outer
-          : inner;
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
-    {
-      return int(Derived::ColsAtCompileTime) == 1 ? 0
-          : int(Derived::RowsAtCompileTime) == 1 ? inner
-          : int(Derived::Flags)&RowMajorBit ? inner
-          : outer;
-    }
-
-    /** Short version: don't use this function, use
-      * \link operator()(Index,Index) const \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator()(Index,Index) const \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameters \a row and \a col are in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator()(Index,Index) const \endlink.
-      *
-      * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
-    {
-      eigen_internal_assert(row >= 0 && row < rows()
-                         && col >= 0 && col < cols());
-      return internal::evaluator<Derived>(derived()).coeff(row,col);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
-    {
-      return coeff(rowIndexByOuterInner(outer, inner),
-                   colIndexByOuterInner(outer, inner));
-    }
-
-    /** \returns the coefficient at given the given row and column.
-      *
-      * \sa operator()(Index,Index), operator[](Index)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
-    {
-      eigen_assert(row >= 0 && row < rows()
-          && col >= 0 && col < cols());
-      return coeff(row, col);
-    }
-
-    /** Short version: don't use this function, use
-      * \link operator[](Index) const \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator[](Index) const \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameter \a index is in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator[](Index) const \endlink.
-      *
-      * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    coeff(Index index) const
-    {
-      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
-                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
-      eigen_internal_assert(index >= 0 && index < size());
-      return internal::evaluator<Derived>(derived()).coeff(index);
-    }
-
-
-    /** \returns the coefficient at given index.
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
-      * z() const, w() const
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    operator[](Index index) const
-    {
-      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
-                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
-      eigen_assert(index >= 0 && index < size());
-      return coeff(index);
-    }
-
-    /** \returns the coefficient at given index.
-      *
-      * This is synonymous to operator[](Index) const.
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
-      * z() const, w() const
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    operator()(Index index) const
-    {
-      eigen_assert(index >= 0 && index < size());
-      return coeff(index);
-    }
-
-    /** equivalent to operator[](0).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    x() const { return (*this)[0]; }
-
-    /** equivalent to operator[](1).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    y() const
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
-      return (*this)[1];
-    }
-
-    /** equivalent to operator[](2).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    z() const
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
-      return (*this)[2];
-    }
-
-    /** equivalent to operator[](3).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE CoeffReturnType
-    w() const
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
-      return (*this)[3];
-    }
-
-    /** \internal
-      * \returns the packet of coefficients starting at the given row and column. It is your responsibility
-      * to ensure that a packet really starts there. This method is only available on expressions having the
-      * PacketAccessBit.
-      *
-      * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
-      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
-      * starting at an address which is a multiple of the packet size.
-      */
-
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const
-    {
-      typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
-      eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
-      return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(row,col);
-    }
-
-
-    /** \internal */
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const
-    {
-      return packet<LoadMode>(rowIndexByOuterInner(outer, inner),
-                              colIndexByOuterInner(outer, inner));
-    }
-
-    /** \internal
-      * \returns the packet of coefficients starting at the given index. It is your responsibility
-      * to ensure that a packet really starts there. This method is only available on expressions having the
-      * PacketAccessBit and the LinearAccessBit.
-      *
-      * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
-      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
-      * starting at an address which is a multiple of the packet size.
-      */
-
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-    {
-      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
-                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
-      typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
-      eigen_internal_assert(index >= 0 && index < size());
-      return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(index);
-    }
-
-  protected:
-    // explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase.
-    // But some methods are only available in the DirectAccess case.
-    // So we add dummy methods here with these names, so that "using... " doesn't fail.
-    // It's not private so that the child class DenseBase can access them, and it's not public
-    // either since it's an implementation detail, so has to be protected.
-    void coeffRef();
-    void coeffRefByOuterInner();
-    void writePacket();
-    void writePacketByOuterInner();
-    void copyCoeff();
-    void copyCoeffByOuterInner();
-    void copyPacket();
-    void copyPacketByOuterInner();
-    void stride();
-    void innerStride();
-    void outerStride();
-    void rowStride();
-    void colStride();
-};
-
-/** \brief Base class providing read/write coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #WriteAccessors Constant indicating read/write access
-  *
-  * This class defines the non-const \c operator() function and friends, which can be used to write specific
-  * entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which
-  * defines the const variant for reading specific entries.
-  *
-  * \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
-{
-  public:
-
-    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    using Base::coeff;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-    using Base::rowIndexByOuterInner;
-    using Base::colIndexByOuterInner;
-    using Base::operator[];
-    using Base::operator();
-    using Base::x;
-    using Base::y;
-    using Base::z;
-    using Base::w;
-
-    /** Short version: don't use this function, use
-      * \link operator()(Index,Index) \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator()(Index,Index) \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameters \a row and \a col are in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator()(Index,Index) \endlink.
-      *
-      * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
-    {
-      eigen_internal_assert(row >= 0 && row < rows()
-                         && col >= 0 && col < cols());
-      return internal::evaluator<Derived>(derived()).coeffRef(row,col);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    coeffRefByOuterInner(Index outer, Index inner)
-    {
-      return coeffRef(rowIndexByOuterInner(outer, inner),
-                      colIndexByOuterInner(outer, inner));
-    }
-
-    /** \returns a reference to the coefficient at given the given row and column.
-      *
-      * \sa operator[](Index)
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    operator()(Index row, Index col)
-    {
-      eigen_assert(row >= 0 && row < rows()
-          && col >= 0 && col < cols());
-      return coeffRef(row, col);
-    }
-
-
-    /** Short version: don't use this function, use
-      * \link operator[](Index) \endlink instead.
-      *
-      * Long version: this function is similar to
-      * \link operator[](Index) \endlink, but without the assertion.
-      * Use this for limiting the performance cost of debugging code when doing
-      * repeated coefficient access. Only use this when it is guaranteed that the
-      * parameters \a row and \a col are in range.
-      *
-      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
-      * function equivalent to \link operator[](Index) \endlink.
-      *
-      * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    coeffRef(Index index)
-    {
-      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
-                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
-      eigen_internal_assert(index >= 0 && index < size());
-      return internal::evaluator<Derived>(derived()).coeffRef(index);
-    }
-
-    /** \returns a reference to the coefficient at given index.
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    operator[](Index index)
-    {
-      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
-                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
-      eigen_assert(index >= 0 && index < size());
-      return coeffRef(index);
-    }
-
-    /** \returns a reference to the coefficient at given index.
-      *
-      * This is synonymous to operator[](Index).
-      *
-      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
-      *
-      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
-      */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    operator()(Index index)
-    {
-      eigen_assert(index >= 0 && index < size());
-      return coeffRef(index);
-    }
-
-    /** equivalent to operator[](0).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    x() { return (*this)[0]; }
-
-    /** equivalent to operator[](1).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    y()
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
-      return (*this)[1];
-    }
-
-    /** equivalent to operator[](2).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    z()
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
-      return (*this)[2];
-    }
-
-    /** equivalent to operator[](3).  */
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar&
-    w()
-    {
-      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
-      return (*this)[3];
-    }
-};
-
-/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #DirectAccessors Constant indicating direct access
-  *
-  * This class defines functions to work with strides which can be used to access entries directly. This class
-  * inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
-  * \c operator() .
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
-{
-  public:
-
-    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-
-    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
-      *
-      * \sa outerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const
-    {
-      return derived().innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
-      *          in a column-major matrix).
-      *
-      * \sa innerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const
-    {
-      return derived().outerStride();
-    }
-
-    // FIXME shall we remove it ?
-    EIGEN_CONSTEXPR inline Index stride() const
-    {
-      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive rows.
-      *
-      * \sa innerStride(), outerStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rowStride() const
-    {
-      return Derived::IsRowMajor ? outerStride() : innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive columns.
-      *
-      * \sa innerStride(), outerStride(), rowStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index colStride() const
-    {
-      return Derived::IsRowMajor ? innerStride() : outerStride();
-    }
-};
-
-/** \brief Base class providing direct read/write coefficient access to matrices and arrays.
-  * \ingroup Core_Module
-  * \tparam Derived Type of the derived class
-  *
-  * \note #DirectWriteAccessors Constant indicating direct access
-  *
-  * This class defines functions to work with strides which can be used to access entries directly. This class
-  * inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
-  * \c operator().
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class DenseCoeffsBase<Derived, DirectWriteAccessors>
-  : public DenseCoeffsBase<Derived, WriteAccessors>
-{
-  public:
-
-    typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::derived;
-
-    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
-      *
-      * \sa outerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT
-    {
-      return derived().innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
-      *          in a column-major matrix).
-      *
-      * \sa innerStride(), rowStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT
-    {
-      return derived().outerStride();
-    }
-
-    // FIXME shall we remove it ?
-    EIGEN_CONSTEXPR inline Index stride() const EIGEN_NOEXCEPT
-    {
-      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive rows.
-      *
-      * \sa innerStride(), outerStride(), colStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rowStride() const EIGEN_NOEXCEPT
-    {
-      return Derived::IsRowMajor ? outerStride() : innerStride();
-    }
-
-    /** \returns the pointer increment between two consecutive columns.
-      *
-      * \sa innerStride(), outerStride(), rowStride()
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index colStride() const EIGEN_NOEXCEPT
-    {
-      return Derived::IsRowMajor ? innerStride() : outerStride();
-    }
-};
-
-namespace internal {
-
-template<int Alignment, typename Derived, bool JustReturnZero>
-struct first_aligned_impl
-{
-  static EIGEN_CONSTEXPR inline Index run(const Derived&) EIGEN_NOEXCEPT
-  { return 0; }
-};
-
-template<int Alignment, typename Derived>
-struct first_aligned_impl<Alignment, Derived, false>
-{
-  static inline Index run(const Derived& m)
-  {
-    return internal::first_aligned<Alignment>(m.data(), m.size());
-  }
-};
-
-/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect to \a Alignment for vectorization.
-  *
-  * \tparam Alignment requested alignment in Bytes.
-  *
-  * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
-  * documentation.
-  */
-template<int Alignment, typename Derived>
-static inline Index first_aligned(const DenseBase<Derived>& m)
-{
-  enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
-  return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
-}
-
-template<typename Derived>
-static inline Index first_default_aligned(const DenseBase<Derived>& m)
-{
-  typedef typename Derived::Scalar Scalar;
-  typedef typename packet_traits<Scalar>::type DefaultPacketType;
-  return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment),Derived>(m);
-}
-
-template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
-struct inner_stride_at_compile_time
-{
-  enum { ret = traits<Derived>::InnerStrideAtCompileTime };
-};
-
-template<typename Derived>
-struct inner_stride_at_compile_time<Derived, false>
-{
-  enum { ret = 0 };
-};
-
-template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
-struct outer_stride_at_compile_time
-{
-  enum { ret = traits<Derived>::OuterStrideAtCompileTime };
-};
-
-template<typename Derived>
-struct outer_stride_at_compile_time<Derived, false>
-{
-  enum { ret = 0 };
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_DENSECOEFFSBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseStorage.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseStorage.h
deleted file mode 100644
index 08ef6c5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DenseStorage.h
+++ /dev/null
@@ -1,652 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIXSTORAGE_H
-#define EIGEN_MATRIXSTORAGE_H
-
-#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) X; EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
-#else
-  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-struct constructor_without_unaligned_array_assert {};
-
-template<typename T, int Size>
-EIGEN_DEVICE_FUNC
-void check_static_allocation_size()
-{
-  // if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
-  #if EIGEN_STACK_ALLOCATION_LIMIT
-  EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
-  #endif
-}
-
-/** \internal
-  * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
-  * to 16 bytes boundary if the total size is a multiple of 16 bytes.
-  */
-template <typename T, int Size, int MatrixOrArrayOptions,
-          int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
-                        : compute_default_alignment<T,Size>::value >
-struct plain_array
-{
-  T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
-  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
-#elif EIGEN_GNUC_AT_LEAST(4,7)
-  // GCC 4.7 is too aggressive in its optimizations and remove the alignment test based on the fact the array is declared to be aligned.
-  // See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
-  // Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
-  template<typename PtrType>
-  EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
-  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
-    eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
-              && "this assertion is explained here: " \
-              "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
-              " **** READ THIS WEB PAGE !!! ****");
-#else
-  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
-    eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \
-              && "this assertion is explained here: " \
-              "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
-              " **** READ THIS WEB PAGE !!! ****");
-#endif
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 8>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 16>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 32>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int Size, int MatrixOrArrayOptions>
-struct plain_array<T, Size, MatrixOrArrayOptions, 64>
-{
-  EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
-
-  EIGEN_DEVICE_FUNC
-  plain_array()
-  {
-    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
-    check_static_allocation_size<T,Size>();
-  }
-
-  EIGEN_DEVICE_FUNC
-  plain_array(constructor_without_unaligned_array_assert)
-  {
-    check_static_allocation_size<T,Size>();
-  }
-};
-
-template <typename T, int MatrixOrArrayOptions, int Alignment>
-struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
-{
-  T array[1];
-  EIGEN_DEVICE_FUNC plain_array() {}
-  EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
-};
-
-struct plain_array_helper {
-  template<typename T, int Size, int MatrixOrArrayOptions, int Alignment>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  static void copy(const plain_array<T, Size, MatrixOrArrayOptions, Alignment>& src, const Eigen::Index size,
-                         plain_array<T, Size, MatrixOrArrayOptions, Alignment>& dst) {
-    smart_copy(src.array, src.array + size, dst.array);
-  }
-  
-  template<typename T, int Size, int MatrixOrArrayOptions, int Alignment>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  static void swap(plain_array<T, Size, MatrixOrArrayOptions, Alignment>& a, const Eigen::Index a_size,
-                   plain_array<T, Size, MatrixOrArrayOptions, Alignment>& b, const Eigen::Index b_size) {
-    if (a_size < b_size) {
-      std::swap_ranges(b.array, b.array + a_size, a.array);
-      smart_move(b.array + a_size, b.array + b_size, a.array + a_size);
-    } else if (a_size > b_size) {
-      std::swap_ranges(a.array, a.array + b_size, b.array);
-      smart_move(a.array + b_size, a.array + a_size, b.array + b_size);
-    } else {
-      std::swap_ranges(a.array, a.array + a_size, b.array);
-    }
-  }
-};
-
-} // end namespace internal
-
-/** \internal
-  *
-  * \class DenseStorage
-  * \ingroup Core_Module
-  *
-  * \brief Stores the data of a matrix
-  *
-  * This class stores the data of fixed-size, dynamic-size or mixed matrices
-  * in a way as compact as possible.
-  *
-  * \sa Matrix
-  */
-template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage;
-
-// purely fixed-size matrix
-template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage
-{
-    internal::plain_array<T,Size,_Options> m_data;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
-    }
-    EIGEN_DEVICE_FUNC
-    explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()) {}
-#if !EIGEN_HAS_CXX11 || defined(EIGEN_DENSE_STORAGE_CTOR_PLUGIN)
-    EIGEN_DEVICE_FUNC
-    DenseStorage(const DenseStorage& other) : m_data(other.m_data) {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
-    }
-#else
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) = default;
-#endif
-#if !EIGEN_HAS_CXX11
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other) m_data = other.m_data;
-      return *this;
-    }
-#else
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) = default;
-#endif
-#if EIGEN_HAS_RVALUE_REFERENCES
-#if !EIGEN_HAS_CXX11
-    EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-    {
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      if (this != &other)
-        m_data = std::move(other.m_data);
-      return *this;
-    }
-#else
-    EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&&) = default;
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&&) = default;
-#endif
-#endif
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
-      EIGEN_UNUSED_VARIABLE(size);
-      EIGEN_UNUSED_VARIABLE(rows);
-      EIGEN_UNUSED_VARIABLE(cols);
-    }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      numext::swap(m_data, other.m_data);
-    }
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// null matrix
-template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
-{
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {}
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; }
-    EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
-    EIGEN_DEVICE_FUNC const T *data() const { return 0; }
-    EIGEN_DEVICE_FUNC T *data() { return 0; }
-};
-
-// more specializations for null matrices; these are necessary to resolve ambiguities
-template<typename T, int _Options> class DenseStorage<T, 0, Dynamic, Dynamic, _Options>
-: public DenseStorage<T, 0, 0, 0, _Options> { };
-
-template<typename T, int _Rows, int _Options> class DenseStorage<T, 0, _Rows, Dynamic, _Options>
-: public DenseStorage<T, 0, 0, 0, _Options> { };
-
-template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic, _Cols, _Options>
-: public DenseStorage<T, 0, 0, 0, _Options> { };
-
-// dynamic-size matrix with fixed-size storage
-template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
-{
-    internal::plain_array<T,Size,_Options> m_data;
-    Index m_rows;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows), m_cols(other.m_cols)
-    {
-      internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        m_rows = other.m_rows;
-        m_cols = other.m_cols;
-        internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
-      }
-      return *this;
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
-    {
-      internal::plain_array_helper::swap(m_data, m_rows * m_cols, other.m_data, other.m_rows * other.m_cols);
-      numext::swap(m_rows,other.m_rows);
-      numext::swap(m_cols,other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC Index rows() const {return m_rows;}
-    EIGEN_DEVICE_FUNC Index cols() const {return m_cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
-    EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// dynamic-size matrix with fixed-size storage and fixed width
-template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
-{
-    internal::plain_array<T,Size,_Options> m_data;
-    Index m_rows;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows)
-    {
-      internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data);
-    }
-    
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        m_rows = other.m_rows;
-        internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data);
-      }
-      return *this;
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
-    { 
-      internal::plain_array_helper::swap(m_data, m_rows * _Cols, other.m_data, other.m_rows * _Cols);
-      numext::swap(m_rows, other.m_rows);
-    }
-    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols(void) const EIGEN_NOEXCEPT {return _Cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
-    EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// dynamic-size matrix with fixed-size storage and fixed height
-template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
-{
-    internal::plain_array<T,Size,_Options> m_data;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) 
-      : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(other.m_cols)
-    {
-      internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        m_cols = other.m_cols;
-        internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data);
-      }
-      return *this;
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      internal::plain_array_helper::swap(m_data, _Rows * m_cols, other.m_data, _Rows * other.m_cols);
-      numext::swap(m_cols, other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows(void) const EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
-    EIGEN_DEVICE_FUNC void resize(Index, Index, Index cols) { m_cols = cols; }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
-};
-
-// purely dynamic matrix.
-template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
-{
-    T *m_data;
-    Index m_rows;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
-       : m_data(0), m_rows(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols))
-      , m_rows(other.m_rows)
-      , m_cols(other.m_cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*m_cols)
-      internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        DenseStorage tmp(other);
-        this->swap(tmp);
-      }
-      return *this;
-    }
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-      , m_rows(std::move(other.m_rows))
-      , m_cols(std::move(other.m_cols))
-    {
-      other.m_data = nullptr;
-      other.m_rows = 0;
-      other.m_cols = 0;
-    }
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      numext::swap(m_data, other.m_data);
-      numext::swap(m_rows, other.m_rows);
-      numext::swap(m_cols, other.m_cols);
-      return *this;
-    }
-#endif
-    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
-    {
-      numext::swap(m_data,other.m_data);
-      numext::swap(m_rows,other.m_rows);
-      numext::swap(m_cols,other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
-    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
-    void conservativeResize(Index size, Index rows, Index cols)
-    {
-      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
-      m_rows = rows;
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
-    {
-      if(size != m_rows*m_cols)
-      {
-        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols);
-        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
-        else
-          m_data = 0;
-        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      }
-      m_rows = rows;
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data; }
-};
-
-// matrix with dynamic width and fixed height (so that matrix has dynamic size).
-template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
-{
-    T *m_data;
-    Index m_cols;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
-    explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0);
-      EIGEN_UNUSED_VARIABLE(rows);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols))
-      , m_cols(other.m_cols)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*_Rows)
-      internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        DenseStorage tmp(other);
-        this->swap(tmp);
-      }
-      return *this;
-    }
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-      , m_cols(std::move(other.m_cols))
-    {
-      other.m_data = nullptr;
-      other.m_cols = 0;
-    }
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      numext::swap(m_data, other.m_data);
-      numext::swap(m_cols, other.m_cols);
-      return *this;
-    }
-#endif
-    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      numext::swap(m_data,other.m_data);
-      numext::swap(m_cols,other.m_cols);
-    }
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
-    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
-    EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
-    {
-      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
-    {
-      if(size != _Rows*m_cols)
-      {
-        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols);
-        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
-        else
-          m_data = 0;
-        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      }
-      m_cols = cols;
-    }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data; }
-};
-
-// matrix with dynamic height and fixed width (so that matrix has dynamic size).
-template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
-{
-    T *m_data;
-    Index m_rows;
-  public:
-    EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
-    explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
-    EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols);
-      EIGEN_UNUSED_VARIABLE(cols);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
-      : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols))
-      , m_rows(other.m_rows)
-    {
-      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*_Cols)
-      internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data);
-    }
-    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
-    {
-      if (this != &other)
-      {
-        DenseStorage tmp(other);
-        this->swap(tmp);
-      }
-      return *this;
-    }
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
-      : m_data(std::move(other.m_data))
-      , m_rows(std::move(other.m_rows))
-    {
-      other.m_data = nullptr;
-      other.m_rows = 0;
-    }
-    EIGEN_DEVICE_FUNC
-    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
-    {
-      numext::swap(m_data, other.m_data);
-      numext::swap(m_rows, other.m_rows);
-      return *this;
-    }
-#endif
-    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
-    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
-      numext::swap(m_data,other.m_data);
-      numext::swap(m_rows,other.m_rows);
-    }
-    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
-    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) {return _Cols;}
-    void conservativeResize(Index size, Index rows, Index)
-    {
-      m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
-      m_rows = rows;
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
-    {
-      if(size != m_rows*_Cols)
-      {
-        internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows);
-        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
-          m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
-        else
-          m_data = 0;
-        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      }
-      m_rows = rows;
-    }
-    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
-    EIGEN_DEVICE_FUNC T *data() { return m_data; }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Diagonal.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Diagonal.h
deleted file mode 100644
index 3112d2c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Diagonal.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DIAGONAL_H
-#define EIGEN_DIAGONAL_H
-
-namespace Eigen {
-
-/** \class Diagonal
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
-  *
-  * \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
-  * \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
-  *              A positive value means a superdiagonal, a negative value means a subdiagonal.
-  *              You can also use DynamicIndex so the index can be set at runtime.
-  *
-  * The matrix is not required to be square.
-  *
-  * This class represents an expression of the main diagonal, or any sub/super diagonal
-  * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
-  * time this is the only way it is used.
-  *
-  * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
-  */
-
-namespace internal {
-template<typename MatrixType, int DiagIndex>
-struct traits<Diagonal<MatrixType,DiagIndex> >
- : traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  typedef typename MatrixType::StorageKind StorageKind;
-  enum {
-    RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
-                      : (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
-                                              MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
-    ColsAtCompileTime = 1,
-    MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
-                         : DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
-                                                                              MatrixType::MaxColsAtCompileTime)
-                         : (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
-                                                 MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
-    MaxColsAtCompileTime = 1,
-    MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
-    MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
-    InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
-    OuterStrideAtCompileTime = 0
-  };
-};
-}
-
-template<typename MatrixType, int _DiagIndex> class Diagonal
-   : public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type
-{
-  public:
-
-    enum { DiagIndex = _DiagIndex };
-    typedef typename internal::dense_xpr_base<Diagonal>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
-
-    EIGEN_DEVICE_FUNC
-    explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index)
-    {
-      eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() );
-    }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
-
-    EIGEN_DEVICE_FUNC
-    inline Index rows() const
-    {
-      return m_index.value()<0 ? numext::mini<Index>(m_matrix.cols(),m_matrix.rows()+m_index.value())
-                               : numext::mini<Index>(m_matrix.rows(),m_matrix.cols()-m_index.value());
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return 1; }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT {
-      return m_matrix.outerStride() + 1;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return 0; }
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<MatrixType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index row, Index) const
-    {
-      return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline CoeffReturnType coeff(Index row, Index) const
-    {
-      return m_matrix.coeff(row+rowOffset(), row+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index idx)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index idx) const
-    {
-      return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline CoeffReturnType coeff(Index idx) const
-    {
-      return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline const typename internal::remove_all<typename MatrixType::Nested>::type&
-    nestedExpression() const
-    {
-      return m_matrix;
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Index index() const
-    {
-      return m_index.value();
-    }
-
-  protected:
-    typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
-    const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
-
-  private:
-    // some compilers may fail to optimize std::max etc in case of compile-time constants...
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index absDiagIndex() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rowOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? 0 : -m_index.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index colOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : 0; }
-    // trigger a compile-time error if someone try to call packet
-    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
-    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
-};
-
-/** \returns an expression of the main diagonal of the matrix \c *this
-  *
-  * \c *this is not required to be square.
-  *
-  * Example: \include MatrixBase_diagonal.cpp
-  * Output: \verbinclude MatrixBase_diagonal.out
-  *
-  * \sa class Diagonal */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalReturnType
-MatrixBase<Derived>::diagonal()
-{
-  return DiagonalReturnType(derived());
-}
-
-/** This is the const version of diagonal(). */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalReturnType
-MatrixBase<Derived>::diagonal() const
-{
-  return ConstDiagonalReturnType(derived());
-}
-
-/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
-  *
-  * \c *this is not required to be square.
-  *
-  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
-  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
-  *
-  * Example: \include MatrixBase_diagonal_int.cpp
-  * Output: \verbinclude MatrixBase_diagonal_int.out
-  *
-  * \sa MatrixBase::diagonal(), class Diagonal */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
-MatrixBase<Derived>::diagonal(Index index)
-{
-  return DiagonalDynamicIndexReturnType(derived(), index);
-}
-
-/** This is the const version of diagonal(Index). */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
-MatrixBase<Derived>::diagonal(Index index) const
-{
-  return ConstDiagonalDynamicIndexReturnType(derived(), index);
-}
-
-/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
-  *
-  * \c *this is not required to be square.
-  *
-  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
-  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
-  *
-  * Example: \include MatrixBase_diagonal_template_int.cpp
-  * Output: \verbinclude MatrixBase_diagonal_template_int.out
-  *
-  * \sa MatrixBase::diagonal(), class Diagonal */
-template<typename Derived>
-template<int Index_>
-EIGEN_DEVICE_FUNC
-inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
-MatrixBase<Derived>::diagonal()
-{
-  return typename DiagonalIndexReturnType<Index_>::Type(derived());
-}
-
-/** This is the const version of diagonal<int>(). */
-template<typename Derived>
-template<int Index_>
-EIGEN_DEVICE_FUNC
-inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
-MatrixBase<Derived>::diagonal() const
-{
-  return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DIAGONAL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DiagonalMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DiagonalMatrix.h
deleted file mode 100644
index 542685c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DiagonalMatrix.h
+++ /dev/null
@@ -1,391 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DIAGONALMATRIX_H
-#define EIGEN_DIAGONALMATRIX_H
-
-namespace Eigen { 
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename Derived>
-class DiagonalBase : public EigenBase<Derived>
-{
-  public:
-    typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
-    typedef typename DiagonalVectorType::Scalar Scalar;
-    typedef typename DiagonalVectorType::RealScalar RealScalar;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-
-    enum {
-      RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-      ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-      MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-      MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-      IsVectorAtCompileTime = 0,
-      Flags = NoPreferredStorageOrderBit
-    };
-
-    typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
-    typedef DenseMatrixType DenseType;
-    typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
-
-    EIGEN_DEVICE_FUNC
-    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    EIGEN_DEVICE_FUNC
-    inline Derived& derived() { return *static_cast<Derived*>(this); }
-
-    EIGEN_DEVICE_FUNC
-    DenseMatrixType toDenseMatrix() const { return derived(); }
-
-    EIGEN_DEVICE_FUNC
-    inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
-    EIGEN_DEVICE_FUNC
-    inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
-
-    EIGEN_DEVICE_FUNC
-    inline Index rows() const { return diagonal().size(); }
-    EIGEN_DEVICE_FUNC
-    inline Index cols() const { return diagonal().size(); }
-
-    template<typename MatrixDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived,MatrixDerived,LazyProduct>
-    operator*(const MatrixBase<MatrixDerived> &matrix) const
-    {
-      return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
-    }
-
-    typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
-    EIGEN_DEVICE_FUNC
-    inline const InverseReturnType
-    inverse() const
-    {
-      return InverseReturnType(diagonal().cwiseInverse());
-    }
-    
-    EIGEN_DEVICE_FUNC
-    inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
-    operator*(const Scalar& scalar) const
-    {
-      return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
-    }
-    EIGEN_DEVICE_FUNC
-    friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
-    operator*(const Scalar& scalar, const DiagonalBase& other)
-    {
-      return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    inline unspecified_expression_type
-    #else
-    inline const DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(DiagonalVectorType,typename OtherDerived::DiagonalVectorType,sum) >
-    #endif
-    operator+(const DiagonalBase<OtherDerived>& other) const
-    {
-      return (diagonal() + other.diagonal()).asDiagonal();
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    inline unspecified_expression_type
-    #else
-    inline const DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(DiagonalVectorType,typename OtherDerived::DiagonalVectorType,difference) >
-    #endif
-    operator-(const DiagonalBase<OtherDerived>& other) const
-    {
-      return (diagonal() - other.diagonal()).asDiagonal();
-    }
-};
-
-#endif
-
-/** \class DiagonalMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Represents a diagonal matrix with its storage
-  *
-  * \param _Scalar the type of coefficients
-  * \param SizeAtCompileTime the dimension of the matrix, or Dynamic
-  * \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
-  *        to SizeAtCompileTime. Most of the time, you do not need to specify it.
-  *
-  * \sa class DiagonalWrapper
-  */
-
-namespace internal {
-template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
-struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
- : traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
-  typedef DiagonalShape StorageKind;
-  enum {
-    Flags = LvalueBit | NoPreferredStorageOrderBit
-  };
-};
-}
-template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
-class DiagonalMatrix
-  : public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  public:
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
-    typedef const DiagonalMatrix& Nested;
-    typedef _Scalar Scalar;
-    typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
-    typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
-    #endif
-
-  protected:
-
-    DiagonalVectorType m_diagonal;
-
-  public:
-
-    /** const version of diagonal(). */
-    EIGEN_DEVICE_FUNC
-    inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
-    /** \returns a reference to the stored vector of diagonal coefficients. */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalVectorType& diagonal() { return m_diagonal; }
-
-    /** Default constructor without initialization */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix() {}
-
-    /** Constructs a diagonal matrix with given dimension  */
-    EIGEN_DEVICE_FUNC
-    explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
-
-    /** 2D constructor. */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
-
-    /** 3D constructor. */
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
-
-    #if EIGEN_HAS_CXX11
-    /** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients. \cpp11
-      * 
-      * There exists C++98 anologue constructors for fixed-size diagonal matrices having 2 or 3 coefficients.
-      * 
-      * \warning To construct a diagonal matrix of fixed size, the number of values passed to this 
-      * constructor must match the fixed dimension of \c *this.
-      * 
-      * \sa DiagonalMatrix(const Scalar&, const Scalar&)
-      * \sa DiagonalMatrix(const Scalar&, const Scalar&, const Scalar&)
-      */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    DiagonalMatrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const ArgTypes&... args)
-      : m_diagonal(a0, a1, a2, args...) {}
-
-    /** \brief Constructs a DiagonalMatrix and initializes it by elements given by an initializer list of initializer
-      * lists \cpp11
-      */
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE DiagonalMatrix(const std::initializer_list<std::initializer_list<Scalar>>& list)
-      : m_diagonal(list) {}
-    #endif  // EIGEN_HAS_CXX11
-
-    /** Copy constructor. */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
-    inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
-    #endif
-
-    /** generic constructor from expression of the diagonal coefficients */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
-    {}
-
-    /** Copy operator. */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
-    {
-      m_diagonal = other.diagonal();
-      return *this;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    DiagonalMatrix& operator=(const DiagonalMatrix& other)
-    {
-      m_diagonal = other.diagonal();
-      return *this;
-    }
-    #endif
-
-    /** Resizes to given size. */
-    EIGEN_DEVICE_FUNC
-    inline void resize(Index size) { m_diagonal.resize(size); }
-    /** Sets all coefficients to zero. */
-    EIGEN_DEVICE_FUNC
-    inline void setZero() { m_diagonal.setZero(); }
-    /** Resizes and sets all coefficients to zero. */
-    EIGEN_DEVICE_FUNC
-    inline void setZero(Index size) { m_diagonal.setZero(size); }
-    /** Sets this matrix to be the identity matrix of the current size. */
-    EIGEN_DEVICE_FUNC
-    inline void setIdentity() { m_diagonal.setOnes(); }
-    /** Sets this matrix to be the identity matrix of the given size. */
-    EIGEN_DEVICE_FUNC
-    inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
-};
-
-/** \class DiagonalWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a diagonal matrix
-  *
-  * \param _DiagonalVectorType the type of the vector of diagonal coefficients
-  *
-  * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
-  * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
-  * and most of the time this is the only way that it is used.
-  *
-  * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
-  */
-
-namespace internal {
-template<typename _DiagonalVectorType>
-struct traits<DiagonalWrapper<_DiagonalVectorType> >
-{
-  typedef _DiagonalVectorType DiagonalVectorType;
-  typedef typename DiagonalVectorType::Scalar Scalar;
-  typedef typename DiagonalVectorType::StorageIndex StorageIndex;
-  typedef DiagonalShape StorageKind;
-  typedef typename traits<DiagonalVectorType>::XprKind XprKind;
-  enum {
-    RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
-    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
-    Flags =  (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
-  };
-};
-}
-
-template<typename _DiagonalVectorType>
-class DiagonalWrapper
-  : public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator
-{
-  public:
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef _DiagonalVectorType DiagonalVectorType;
-    typedef DiagonalWrapper Nested;
-    #endif
-
-    /** Constructor from expression of diagonal coefficients to wrap. */
-    EIGEN_DEVICE_FUNC
-    explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
-
-    /** \returns a const reference to the wrapped expression of diagonal coefficients. */
-    EIGEN_DEVICE_FUNC
-    const DiagonalVectorType& diagonal() const { return m_diagonal; }
-
-  protected:
-    typename DiagonalVectorType::Nested m_diagonal;
-};
-
-/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
-  *
-  * \only_for_vectors
-  *
-  * Example: \include MatrixBase_asDiagonal.cpp
-  * Output: \verbinclude MatrixBase_asDiagonal.out
-  *
-  * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
-  **/
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const DiagonalWrapper<const Derived>
-MatrixBase<Derived>::asDiagonal() const
-{
-  return DiagonalWrapper<const Derived>(derived());
-}
-
-/** \returns true if *this is approximately equal to a diagonal matrix,
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isDiagonal.cpp
-  * Output: \verbinclude MatrixBase_isDiagonal.out
-  *
-  * \sa asDiagonal()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
-{
-  if(cols() != rows()) return false;
-  RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
-  for(Index j = 0; j < cols(); ++j)
-  {
-    RealScalar absOnDiagonal = numext::abs(coeff(j,j));
-    if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
-  }
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = 0; i < j; ++i)
-    {
-      if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
-      if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
-    }
-  return true;
-}
-
-namespace internal {
-
-template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
-
-struct Diagonal2Dense {};
-
-template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
-
-// Diagonal matrix to Dense assignment
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
-{
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    
-    dst.setZero();
-    dst.diagonal() = src.diagonal();
-  }
-  
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.diagonal() += src.diagonal(); }
-  
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.diagonal() -= src.diagonal(); }
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_DIAGONALMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DiagonalProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DiagonalProduct.h
deleted file mode 100644
index 7911d1c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/DiagonalProduct.h
+++ /dev/null
@@ -1,28 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DIAGONALPRODUCT_H
-#define EIGEN_DIAGONALPRODUCT_H
-
-namespace Eigen { 
-
-/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
-  */
-template<typename Derived>
-template<typename DiagonalDerived>
-EIGEN_DEVICE_FUNC inline const Product<Derived, DiagonalDerived, LazyProduct>
-MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
-{
-  return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DIAGONALPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Dot.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Dot.h
deleted file mode 100644
index 5c3441b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Dot.h
+++ /dev/null
@@ -1,318 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DOT_H
-#define EIGEN_DOT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot
-// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
-// looking at the static assertions. Thus this is a trick to get better compile errors.
-template<typename T, typename U,
-// the NeedToTranspose condition here is taken straight from Assign.h
-         bool NeedToTranspose = T::IsVectorAtCompileTime
-                && U::IsVectorAtCompileTime
-                && ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1)
-                      |  // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
-                         // revert to || as soon as not needed anymore.
-                    (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
->
-struct dot_nocheck
-{
-  typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
-  typedef typename conj_prod::result_type ResScalar;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE
-  static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
-  {
-    return a.template binaryExpr<conj_prod>(b).sum();
-  }
-};
-
-template<typename T, typename U>
-struct dot_nocheck<T, U, true>
-{
-  typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
-  typedef typename conj_prod::result_type ResScalar;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE
-  static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
-  {
-    return a.transpose().template binaryExpr<conj_prod>(b).sum();
-  }
-};
-
-} // end namespace internal
-
-/** \fn MatrixBase::dot
-  * \returns the dot product of *this with other.
-  *
-  * \only_for_vectors
-  *
-  * \note If the scalar type is complex numbers, then this function returns the hermitian
-  * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
-  * second variable.
-  *
-  * \sa squaredNorm(), norm()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE
-typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
-MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
-#if !(defined(EIGEN_NO_STATIC_ASSERT) && defined(EIGEN_NO_DEBUG))
-  typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
-  EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
-#endif
-  
-  eigen_assert(size() == other.size());
-
-  return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
-}
-
-//---------- implementation of L2 norm and related functions ----------
-
-/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm.
-  * In both cases, it consists in the sum of the square of all the matrix entries.
-  * For vectors, this is also equals to the dot product of \c *this with itself.
-  *
-  * \sa dot(), norm(), lpNorm()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
-{
-  return numext::real((*this).cwiseAbs2().sum());
-}
-
-/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
-  * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
-  * For vectors, this is also equals to the square root of the dot product of \c *this with itself.
-  *
-  * \sa lpNorm(), dot(), squaredNorm()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
-{
-  return numext::sqrt(squaredNorm());
-}
-
-/** \returns an expression of the quotient of \c *this by its own norm.
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0),
-  *          then this function returns a copy of the input.
-  *
-  * \only_for_vectors
-  *
-  * \sa norm(), normalize()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::normalized() const
-{
-  typedef typename internal::nested_eval<Derived,2>::type _Nested;
-  _Nested n(derived());
-  RealScalar z = n.squaredNorm();
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
-  if(z>RealScalar(0))
-    return n / numext::sqrt(z);
-  else
-    return n;
-}
-
-/** Normalizes the vector, i.e. divides it by its own norm.
-  *
-  * \only_for_vectors
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
-  *
-  * \sa norm(), normalized()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize()
-{
-  RealScalar z = squaredNorm();
-  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
-  if(z>RealScalar(0))
-    derived() /= numext::sqrt(z);
-}
-
-/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
-  *
-  * \only_for_vectors
-  *
-  * This method is analogue to the normalized() method, but it reduces the risk of
-  * underflow and overflow when computing the norm.
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0),
-  *          then this function returns a copy of the input.
-  *
-  * \sa stableNorm(), stableNormalize(), normalized()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::stableNormalized() const
-{
-  typedef typename internal::nested_eval<Derived,3>::type _Nested;
-  _Nested n(derived());
-  RealScalar w = n.cwiseAbs().maxCoeff();
-  RealScalar z = (n/w).squaredNorm();
-  if(z>RealScalar(0))
-    return n / (numext::sqrt(z)*w);
-  else
-    return n;
-}
-
-/** Normalizes the vector while avoid underflow and overflow
-  *
-  * \only_for_vectors
-  *
-  * This method is analogue to the normalize() method, but it reduces the risk of
-  * underflow and overflow when computing the norm.
-  *
-  * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
-  *
-  * \sa stableNorm(), stableNormalized(), normalize()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize()
-{
-  RealScalar w = cwiseAbs().maxCoeff();
-  RealScalar z = (derived()/w).squaredNorm();
-  if(z>RealScalar(0))
-    derived() /= numext::sqrt(z)*w;
-}
-
-//---------- implementation of other norms ----------
-
-namespace internal {
-
-template<typename Derived, int p>
-struct lpNorm_selector
-{
-  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const MatrixBase<Derived>& m)
-  {
-    EIGEN_USING_STD(pow)
-    return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
-  }
-};
-
-template<typename Derived>
-struct lpNorm_selector<Derived, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
-  {
-    return m.cwiseAbs().sum();
-  }
-};
-
-template<typename Derived>
-struct lpNorm_selector<Derived, 2>
-{
-  EIGEN_DEVICE_FUNC
-  static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
-  {
-    return m.norm();
-  }
-};
-
-template<typename Derived>
-struct lpNorm_selector<Derived, Infinity>
-{
-  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const MatrixBase<Derived>& m)
-  {
-    if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
-      return RealScalar(0);
-    return m.cwiseAbs().maxCoeff();
-  }
-};
-
-} // end namespace internal
-
-/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
-  *          of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
-  *          norm, that is the maximum of the absolute values of the coefficients of \c *this.
-  *
-  * In all cases, if \c *this is empty, then the value 0 is returned.
-  *
-  * \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
-  *
-  * \sa norm()
-  */
-template<typename Derived>
-template<int p>
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_DEVICE_FUNC inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-#else
-EIGEN_DEVICE_FUNC MatrixBase<Derived>::RealScalar
-#endif
-MatrixBase<Derived>::lpNorm() const
-{
-  return internal::lpNorm_selector<Derived, p>::run(*this);
-}
-
-//---------- implementation of isOrthogonal / isUnitary ----------
-
-/** \returns true if *this is approximately orthogonal to \a other,
-  *          within the precision given by \a prec.
-  *
-  * Example: \include MatrixBase_isOrthogonal.cpp
-  * Output: \verbinclude MatrixBase_isOrthogonal.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-bool MatrixBase<Derived>::isOrthogonal
-(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,2>::type nested(derived());
-  typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
-  return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
-}
-
-/** \returns true if *this is approximately an unitary matrix,
-  *          within the precision given by \a prec. In the case where the \a Scalar
-  *          type is real numbers, a unitary matrix is an orthogonal matrix, whence the name.
-  *
-  * \note This can be used to check whether a family of vectors forms an orthonormal basis.
-  *       Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an
-  *       orthonormal basis.
-  *
-  * Example: \include MatrixBase_isUnitary.cpp
-  * Output: \verbinclude MatrixBase_isUnitary.out
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
-{
-  typename internal::nested_eval<Derived,1>::type self(derived());
-  for(Index i = 0; i < cols(); ++i)
-  {
-    if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
-      return false;
-    for(Index j = 0; j < i; ++j)
-      if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
-        return false;
-  }
-  return true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DOT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/EigenBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/EigenBase.h
deleted file mode 100644
index 6b3c7d3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/EigenBase.h
+++ /dev/null
@@ -1,160 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EIGENBASE_H
-#define EIGEN_EIGENBASE_H
-
-namespace Eigen {
-
-/** \class EigenBase
-  * \ingroup Core_Module
-  *
-  * Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
-  *
-  * In other words, an EigenBase object is an object that can be copied into a MatrixBase.
-  *
-  * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc.
-  *
-  * Notice that this class is trivial, it is only used to disambiguate overloaded functions.
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived> struct EigenBase
-{
-//   typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
-
-  /** \brief The interface type of indices
-    * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
-    * \sa StorageIndex, \ref TopicPreprocessorDirectives.
-    * DEPRECATED: Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
-    * Deprecation is not marked with a doxygen comment because there are too many existing usages to add the deprecation attribute.
-    */
-  typedef Eigen::Index Index;
-
-  // FIXME is it needed?
-  typedef typename internal::traits<Derived>::StorageKind StorageKind;
-
-  /** \returns a reference to the derived object */
-  EIGEN_DEVICE_FUNC
-  Derived& derived() { return *static_cast<Derived*>(this); }
-  /** \returns a const reference to the derived object */
-  EIGEN_DEVICE_FUNC
-  const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-  EIGEN_DEVICE_FUNC
-  inline Derived& const_cast_derived() const
-  { return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
-  EIGEN_DEVICE_FUNC
-  inline const Derived& const_derived() const
-  { return *static_cast<const Derived*>(this); }
-
-  /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
-  /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
-  /** \returns the number of coefficients, which is rows()*cols().
-    * \sa rows(), cols(), SizeAtCompileTime. */
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index size() const EIGEN_NOEXCEPT { return rows() * cols(); }
-
-  /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC
-  inline void evalTo(Dest& dst) const
-  { derived().evalTo(dst); }
-
-  /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC
-  inline void addTo(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    typename Dest::PlainObject res(rows(),cols());
-    evalTo(res);
-    dst += res;
-  }
-
-  /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC
-  inline void subTo(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    typename Dest::PlainObject res(rows(),cols());
-    evalTo(res);
-    dst -= res;
-  }
-
-  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    dst = dst * this->derived();
-  }
-
-  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const
-  {
-    // This is the default implementation,
-    // derived class can reimplement it in a more optimized way.
-    dst = this->derived() * dst;
-  }
-
-};
-
-/***************************************************************************
-* Implementation of matrix base methods
-***************************************************************************/
-
-/** \brief Copies the generic expression \a other into *this.
-  *
-  * \details The expression must provide a (templated) evalTo(Derived& dst) const
-  * function which does the actual job. In practice, this allows any user to write
-  * its own special matrix without having to modify MatrixBase
-  *
-  * \returns a reference to *this.
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_EIGENBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h
deleted file mode 100644
index 817a43a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h
+++ /dev/null
@@ -1,150 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FORCEALIGNEDACCESS_H
-#define EIGEN_FORCEALIGNEDACCESS_H
-
-namespace Eigen {
-
-/** \class ForceAlignedAccess
-  * \ingroup Core_Module
-  *
-  * \brief Enforce aligned packet loads and stores regardless of what is requested
-  *
-  * \param ExpressionType the type of the object of which we are forcing aligned packet access
-  *
-  * This class is the return type of MatrixBase::forceAlignedAccess()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::forceAlignedAccess()
-  */
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<ForceAlignedAccess<ExpressionType> > : public traits<ExpressionType>
-{};
-}
-
-template<typename ExpressionType> class ForceAlignedAccess
-  : public internal::dense_xpr_base< ForceAlignedAccess<ExpressionType> >::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
-
-    EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
-
-    EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
-    {
-      return m_expression.coeff(row, col);
-    }
-
-    EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
-    {
-      return m_expression.const_cast_derived().coeffRef(row, col);
-    }
-
-    EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
-    {
-      return m_expression.coeff(index);
-    }
-
-    EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
-    {
-      return m_expression.const_cast_derived().coeffRef(index);
-    }
-
-    template<int LoadMode>
-    inline const PacketScalar packet(Index row, Index col) const
-    {
-      return m_expression.template packet<Aligned>(row, col);
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index row, Index col, const PacketScalar& x)
-    {
-      m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x);
-    }
-
-    template<int LoadMode>
-    inline const PacketScalar packet(Index index) const
-    {
-      return m_expression.template packet<Aligned>(index);
-    }
-
-    template<int LoadMode>
-    inline void writePacket(Index index, const PacketScalar& x)
-    {
-      m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
-    }
-
-    EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
-
-  protected:
-    const ExpressionType& m_expression;
-
-  private:
-    ForceAlignedAccess& operator=(const ForceAlignedAccess&);
-};
-
-/** \returns an expression of *this with forced aligned access
-  * \sa forceAlignedAccessIf(),class ForceAlignedAccess
-  */
-template<typename Derived>
-inline const ForceAlignedAccess<Derived>
-MatrixBase<Derived>::forceAlignedAccess() const
-{
-  return ForceAlignedAccess<Derived>(derived());
-}
-
-/** \returns an expression of *this with forced aligned access
-  * \sa forceAlignedAccessIf(), class ForceAlignedAccess
-  */
-template<typename Derived>
-inline ForceAlignedAccess<Derived>
-MatrixBase<Derived>::forceAlignedAccess()
-{
-  return ForceAlignedAccess<Derived>(derived());
-}
-
-/** \returns an expression of *this with forced aligned access if \a Enable is true.
-  * \sa forceAlignedAccess(), class ForceAlignedAccess
-  */
-template<typename Derived>
-template<bool Enable>
-inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type
-MatrixBase<Derived>::forceAlignedAccessIf() const
-{
-  return derived();  // FIXME This should not work but apparently is never used
-}
-
-/** \returns an expression of *this with forced aligned access if \a Enable is true.
-  * \sa forceAlignedAccess(), class ForceAlignedAccess
-  */
-template<typename Derived>
-template<bool Enable>
-inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
-MatrixBase<Derived>::forceAlignedAccessIf()
-{
-  return derived();  // FIXME This should not work but apparently is never used
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FORCEALIGNEDACCESS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Fuzzy.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Fuzzy.h
deleted file mode 100644
index 43aa49b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Fuzzy.h
+++ /dev/null
@@ -1,155 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FUZZY_H
-#define EIGEN_FUZZY_H
-
-namespace Eigen { 
-
-namespace internal
-{
-
-template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isApprox_selector
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
-  {
-    typename internal::nested_eval<Derived,2>::type nested(x);
-    typename internal::nested_eval<OtherDerived,2>::type otherNested(y);
-    return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
-  }
-};
-
-template<typename Derived, typename OtherDerived>
-struct isApprox_selector<Derived, OtherDerived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
-  {
-    return x.matrix() == y.matrix();
-  }
-};
-
-template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isMuchSmallerThan_object_selector
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
-  {
-    return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
-  }
-};
-
-template<typename Derived, typename OtherDerived>
-struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&)
-  {
-    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
-  }
-};
-
-template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
-struct isMuchSmallerThan_scalar_selector
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
-  {
-    return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
-  }
-};
-
-template<typename Derived>
-struct isMuchSmallerThan_scalar_selector<Derived, true>
-{
-  EIGEN_DEVICE_FUNC
-  static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&)
-  {
-    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
-  }
-};
-
-} // end namespace internal
-
-
-/** \returns \c true if \c *this is approximately equal to \a other, within the precision
-  * determined by \a prec.
-  *
-  * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
-  * are considered to be approximately equal within precision \f$ p \f$ if
-  * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
-  * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
-  * L2 norm).
-  *
-  * \note Because of the multiplicativeness of this comparison, one can't use this function
-  * to check whether \c *this is approximately equal to the zero matrix or vector.
-  * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
-  * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const
-  * RealScalar&, RealScalar) instead.
-  *
-  * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApprox(
-  const DenseBase<OtherDerived>& other,
-  const RealScalar& prec
-) const
-{
-  return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
-}
-
-/** \returns \c true if the norm of \c *this is much smaller than \a other,
-  * within the precision determined by \a prec.
-  *
-  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
-  * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
-  * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
-  *
-  * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason,
-  * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm
-  * of a reference matrix of same dimensions.
-  *
-  * \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(
-  const typename NumTraits<Scalar>::Real& other,
-  const RealScalar& prec
-) const
-{
-  return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
-}
-
-/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
-  * within the precision determined by \a prec.
-  *
-  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
-  * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
-  * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
-  * For matrices, the comparison is done using the Hilbert-Schmidt norm.
-  *
-  * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(
-  const DenseBase<OtherDerived>& other,
-  const RealScalar& prec
-) const
-{
-  return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FUZZY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GeneralProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GeneralProduct.h
deleted file mode 100644
index 6906aa7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GeneralProduct.h
+++ /dev/null
@@ -1,465 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_PRODUCT_H
-#define EIGEN_GENERAL_PRODUCT_H
-
-namespace Eigen {
-
-enum {
-  Large = 2,
-  Small = 3
-};
-
-// Define the threshold value to fallback from the generic matrix-matrix product
-// implementation (heavy) to the lightweight coeff-based product one.
-// See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
-// in products/GeneralMatrixMatrix.h for more details.
-// TODO This threshold should also be used in the compile-time selector below.
-#ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD
-// This default value has been obtained on a Haswell architecture.
-#define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20
-#endif
-
-namespace internal {
-
-template<int Rows, int Cols, int Depth> struct product_type_selector;
-
-template<int Size, int MaxSize> struct product_size_category
-{
-  enum {
-    #ifndef EIGEN_GPU_COMPILE_PHASE
-    is_large = MaxSize == Dynamic ||
-               Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
-               (Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
-    #else
-    is_large = 0,
-    #endif
-    value = is_large  ? Large
-          : Size == 1 ? 1
-                      : Small
-  };
-};
-
-template<typename Lhs, typename Rhs> struct product_type
-{
-  typedef typename remove_all<Lhs>::type _Lhs;
-  typedef typename remove_all<Rhs>::type _Rhs;
-  enum {
-    MaxRows = traits<_Lhs>::MaxRowsAtCompileTime,
-    Rows    = traits<_Lhs>::RowsAtCompileTime,
-    MaxCols = traits<_Rhs>::MaxColsAtCompileTime,
-    Cols    = traits<_Rhs>::ColsAtCompileTime,
-    MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::MaxColsAtCompileTime,
-                                           traits<_Rhs>::MaxRowsAtCompileTime),
-    Depth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::ColsAtCompileTime,
-                                        traits<_Rhs>::RowsAtCompileTime)
-  };
-
-  // the splitting into different lines of code here, introducing the _select enums and the typedef below,
-  // is to work around an internal compiler error with gcc 4.1 and 4.2.
-private:
-  enum {
-    rows_select = product_size_category<Rows,MaxRows>::value,
-    cols_select = product_size_category<Cols,MaxCols>::value,
-    depth_select = product_size_category<Depth,MaxDepth>::value
-  };
-  typedef product_type_selector<rows_select, cols_select, depth_select> selector;
-
-public:
-  enum {
-    value = selector::ret,
-    ret = selector::ret
-  };
-#ifdef EIGEN_DEBUG_PRODUCT
-  static void debug()
-  {
-      EIGEN_DEBUG_VAR(Rows);
-      EIGEN_DEBUG_VAR(Cols);
-      EIGEN_DEBUG_VAR(Depth);
-      EIGEN_DEBUG_VAR(rows_select);
-      EIGEN_DEBUG_VAR(cols_select);
-      EIGEN_DEBUG_VAR(depth_select);
-      EIGEN_DEBUG_VAR(value);
-  }
-#endif
-};
-
-/* The following allows to select the kind of product at compile time
- * based on the three dimensions of the product.
- * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
-// FIXME I'm not sure the current mapping is the ideal one.
-template<int M, int N>  struct product_type_selector<M,N,1>              { enum { ret = OuterProduct }; };
-template<int M>         struct product_type_selector<M, 1, 1>            { enum { ret = LazyCoeffBasedProductMode }; };
-template<int N>         struct product_type_selector<1, N, 1>            { enum { ret = LazyCoeffBasedProductMode }; };
-template<int Depth>     struct product_type_selector<1,    1,    Depth>  { enum { ret = InnerProduct }; };
-template<>              struct product_type_selector<1,    1,    1>      { enum { ret = InnerProduct }; };
-template<>              struct product_type_selector<Small,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<1,    Small,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small,Small,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small, Large, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
-template<>              struct product_type_selector<1,    Large,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<1,    Large,Large>  { enum { ret = GemvProduct }; };
-template<>              struct product_type_selector<1,    Small,Large>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large,1,    Large>  { enum { ret = GemvProduct }; };
-template<>              struct product_type_selector<Small,1,    Large>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small,Small,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Large,Small,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Small,Large,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Large,Large,Large>  { enum { ret = GemmProduct }; };
-template<>              struct product_type_selector<Large,Small,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Small,Large,Small>  { enum { ret = CoeffBasedProductMode }; };
-template<>              struct product_type_selector<Large,Large,Small>  { enum { ret = GemmProduct }; };
-
-} // end namespace internal
-
-/***********************************************************************
-*  Implementation of Inner Vector Vector Product
-***********************************************************************/
-
-// FIXME : maybe the "inner product" could return a Scalar
-// instead of a 1x1 matrix ??
-// Pro: more natural for the user
-// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix
-// product ends up to a row-vector times col-vector product... To tackle this use
-// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
-
-/***********************************************************************
-*  Implementation of Outer Vector Vector Product
-***********************************************************************/
-
-/***********************************************************************
-*  Implementation of General Matrix Vector Product
-***********************************************************************/
-
-/*  According to the shape/flags of the matrix we have to distinghish 3 different cases:
- *   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
- *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
- *   3 - all other cases are handled using a simple loop along the outer-storage direction.
- *  Therefore we need a lower level meta selector.
- *  Furthermore, if the matrix is the rhs, then the product has to be transposed.
- */
-namespace internal {
-
-template<int Side, int StorageOrder, bool BlasCompatible>
-struct gemv_dense_selector;
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
-
-template<typename Scalar,int Size,int MaxSize>
-struct gemv_static_vector_if<Scalar,Size,MaxSize,false>
-{
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; }
-};
-
-template<typename Scalar,int Size>
-struct gemv_static_vector_if<Scalar,Size,Dynamic,true>
-{
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { return 0; }
-};
-
-template<typename Scalar,int Size,int MaxSize>
-struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
-{
-  enum {
-    ForceAlignment  = internal::packet_traits<Scalar>::Vectorizable,
-    PacketSize      = internal::packet_traits<Scalar>::size
-  };
-  #if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
-  internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0,EIGEN_PLAIN_ENUM_MIN(AlignedMax,PacketSize)> m_data;
-  EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
-  #else
-  // Some architectures cannot align on the stack,
-  // => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
-  internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
-  EIGEN_STRONG_INLINE Scalar* data() {
-    return ForceAlignment
-            ? reinterpret_cast<Scalar*>((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
-            : m_data.array;
-  }
-  #endif
-};
-
-// The vector is on the left => transposition
-template<int StorageOrder, bool BlasCompatible>
-struct gemv_dense_selector<OnTheLeft,StorageOrder,BlasCompatible>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    Transpose<Dest> destT(dest);
-    enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
-    gemv_dense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
-      ::run(rhs.transpose(), lhs.transpose(), destT, alpha);
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar   LhsScalar;
-    typedef typename Rhs::Scalar   RhsScalar;
-    typedef typename Dest::Scalar  ResScalar;
-    typedef typename Dest::RealScalar  RealScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  
-    typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
-
-    ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
-    ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
-
-    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
-
-    // make sure Dest is a compile-time vector type (bug 1166)
-    typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest;
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1),
-      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
-      MightCannotUseDest = ((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime!=0)
-    };
-
-    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
-    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
-
-    if(!MightCannotUseDest)
-    {
-      // shortcut if we are sure to be able to use dest directly,
-      // this ease the compiler to generate cleaner and more optimzized code for most common cases
-      general_matrix_vector_product
-          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
-          actualLhs.rows(), actualLhs.cols(),
-          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
-          dest.data(), 1,
-          compatibleAlpha);
-    }
-    else
-    {
-      gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
-
-      const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
-      const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
-
-      ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
-                                                    evalToDest ? dest.data() : static_dest.data());
-
-      if(!evalToDest)
-      {
-        #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-        Index size = dest.size();
-        EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-        #endif
-        if(!alphaIsCompatible)
-        {
-          MappedDest(actualDestPtr, dest.size()).setZero();
-          compatibleAlpha = RhsScalar(1);
-        }
-        else
-          MappedDest(actualDestPtr, dest.size()) = dest;
-      }
-
-      general_matrix_vector_product
-          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
-          actualLhs.rows(), actualLhs.cols(),
-          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
-          actualDestPtr, 1,
-          compatibleAlpha);
-
-      if (!evalToDest)
-      {
-        if(!alphaIsCompatible)
-          dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
-        else
-          dest = MappedDest(actualDestPtr, dest.size());
-      }
-    }
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,RowMajor,true>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar   LhsScalar;
-    typedef typename Rhs::Scalar   RhsScalar;
-    typedef typename Dest::Scalar  ResScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-    typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
-    typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
-
-    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime==0
-    };
-
-    gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
-
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
-        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
-
-    if(!DirectlyUseRhs)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = actualRhs.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
-    }
-
-    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
-    general_matrix_vector_product
-        <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
-        actualLhs.rows(), actualLhs.cols(),
-        LhsMapper(actualLhs.data(), actualLhs.outerStride()),
-        RhsMapper(actualRhsPtr, 1),
-        dest.data(), dest.col(0).innerStride(), //NOTE  if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166)
-        actualAlpha);
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,ColMajor,false>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
-    // TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp
-    typename nested_eval<Rhs,1>::type actual_rhs(rhs);
-    const Index size = rhs.rows();
-    for(Index k=0; k<size; ++k)
-      dest += (alpha*actual_rhs.coeff(k)) * lhs.col(k);
-  }
-};
-
-template<> struct gemv_dense_selector<OnTheRight,RowMajor,false>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
-    typename nested_eval<Rhs,Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
-    const Index rows = dest.rows();
-    for(Index i=0; i<rows; ++i)
-      dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_rhs.transpose())).sum();
-  }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Implementation of matrix base methods
-***************************************************************************/
-
-/** \returns the matrix product of \c *this and \a other.
-  *
-  * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
-  *
-  * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const Product<Derived, OtherDerived>
-MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
-{
-  // A note regarding the function declaration: In MSVC, this function will sometimes
-  // not be inlined since DenseStorage is an unwindable object for dynamic
-  // matrices and product types are holding a member to store the result.
-  // Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
-  enum {
-    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
-                   || OtherDerived::RowsAtCompileTime==Dynamic
-                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
-    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
-    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
-  };
-  // note to the lost user:
-  //    * for a dot product use: v1.dot(v2)
-  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
-    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
-    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
-  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
-#ifdef EIGEN_DEBUG_PRODUCT
-  internal::product_type<Derived,OtherDerived>::debug();
-#endif
-
-  return Product<Derived, OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
-  *
-  * The returned product will behave like any other expressions: the coefficients of the product will be
-  * computed once at a time as requested. This might be useful in some extremely rare cases when only
-  * a small and no coherent fraction of the result's coefficients have to be computed.
-  *
-  * \warning This version of the matrix product can be much much slower. So use it only if you know
-  * what you are doing and that you measured a true speed improvement.
-  *
-  * \sa operator*(const MatrixBase&)
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const Product<Derived,OtherDerived,LazyProduct>
-MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
-{
-  enum {
-    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
-                   || OtherDerived::RowsAtCompileTime==Dynamic
-                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
-    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
-    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
-  };
-  // note to the lost user:
-  //    * for a dot product use: v1.dot(v2)
-  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
-    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
-  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
-    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
-  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
-
-  return Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GenericPacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GenericPacketMath.h
deleted file mode 100644
index cf677a1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GenericPacketMath.h
+++ /dev/null
@@ -1,1040 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERIC_PACKET_MATH_H
-#define EIGEN_GENERIC_PACKET_MATH_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * \file GenericPacketMath.h
-  *
-  * Default implementation for types not supported by the vectorization.
-  * In practice these functions are provided to make easier the writing
-  * of generic vectorized code.
-  */
-
-#ifndef EIGEN_DEBUG_ALIGNED_LOAD
-#define EIGEN_DEBUG_ALIGNED_LOAD
-#endif
-
-#ifndef EIGEN_DEBUG_UNALIGNED_LOAD
-#define EIGEN_DEBUG_UNALIGNED_LOAD
-#endif
-
-#ifndef EIGEN_DEBUG_ALIGNED_STORE
-#define EIGEN_DEBUG_ALIGNED_STORE
-#endif
-
-#ifndef EIGEN_DEBUG_UNALIGNED_STORE
-#define EIGEN_DEBUG_UNALIGNED_STORE
-#endif
-
-struct default_packet_traits
-{
-  enum {
-    HasHalfPacket = 0,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 0,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 1,
-    HasBlend     = 0,
-    // This flag is used to indicate whether packet comparison is supported.
-    // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
-    HasCmp       = 0,
-
-    HasDiv    = 0,
-    HasSqrt   = 0,
-    HasRsqrt  = 0,
-    HasExp    = 0,
-    HasExpm1  = 0,
-    HasLog    = 0,
-    HasLog1p  = 0,
-    HasLog10  = 0,
-    HasPow    = 0,
-
-    HasSin    = 0,
-    HasCos    = 0,
-    HasTan    = 0,
-    HasASin   = 0,
-    HasACos   = 0,
-    HasATan   = 0,
-    HasSinh   = 0,
-    HasCosh   = 0,
-    HasTanh   = 0,
-    HasLGamma = 0,
-    HasDiGamma = 0,
-    HasZeta = 0,
-    HasPolygamma = 0,
-    HasErf = 0,
-    HasErfc = 0,
-    HasNdtri = 0,
-    HasBessel = 0,
-    HasIGamma = 0,
-    HasIGammaDerA = 0,
-    HasGammaSampleDerAlpha = 0,
-    HasIGammac = 0,
-    HasBetaInc = 0,
-
-    HasRound  = 0,
-    HasRint   = 0,
-    HasFloor  = 0,
-    HasCeil   = 0,
-    HasSign   = 0
-  };
-};
-
-template<typename T> struct packet_traits : default_packet_traits
-{
-  typedef T type;
-  typedef T half;
-  enum {
-    Vectorizable = 0,
-    size = 1,
-    AlignedOnScalar = 0,
-    HasHalfPacket = 0
-  };
-  enum {
-    HasAdd    = 0,
-    HasSub    = 0,
-    HasMul    = 0,
-    HasNegate = 0,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasConj   = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<typename T> struct packet_traits<const T> : packet_traits<T> { };
-
-template<typename T> struct unpacket_traits
-{
-  typedef T type;
-  typedef T half;
-  enum
-  {
-    size = 1,
-    alignment = 1,
-    vectorizable = false,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<typename T> struct unpacket_traits<const T> : unpacket_traits<T> { };
-
-template <typename Src, typename Tgt> struct type_casting_traits {
-  enum {
-    VectorizedCast = 0,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-/** \internal Wrapper to ensure that multiple packet types can map to the same
-    same underlying vector type. */
-template<typename T, int unique_id = 0>
-struct eigen_packet_wrapper
-{
-  EIGEN_ALWAYS_INLINE operator T&() { return m_val; }
-  EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; }
-  EIGEN_ALWAYS_INLINE eigen_packet_wrapper() {}
-  EIGEN_ALWAYS_INLINE eigen_packet_wrapper(const T &v) : m_val(v) {}
-  EIGEN_ALWAYS_INLINE eigen_packet_wrapper& operator=(const T &v) {
-    m_val = v;
-    return *this;
-  }
-
-  T m_val;
-};
-
-
-/** \internal A convenience utility for determining if the type is a scalar.
- * This is used to enable some generic packet implementations.
- */
-template<typename Packet>
-struct is_scalar {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  enum {
-    value = internal::is_same<Packet, Scalar>::value
-  };
-};
-
-/** \internal \returns static_cast<TgtType>(a) (coeff-wise) */
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a) {
-  return static_cast<TgtPacket>(a);
-}
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a, const SrcPacket& /*b*/) {
-  return static_cast<TgtPacket>(a);
-}
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/) {
-  return static_cast<TgtPacket>(a);
-}
-template <typename SrcPacket, typename TgtPacket>
-EIGEN_DEVICE_FUNC inline TgtPacket
-pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/,
-      const SrcPacket& /*e*/, const SrcPacket& /*f*/, const SrcPacket& /*g*/, const SrcPacket& /*h*/) {
-  return static_cast<TgtPacket>(a);
-}
-
-/** \internal \returns reinterpret_cast<Target>(a) */
-template <typename Target, typename Packet>
-EIGEN_DEVICE_FUNC inline Target
-preinterpret(const Packet& a); /* { return reinterpret_cast<const Target&>(a); } */
-
-/** \internal \returns a + b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-padd(const Packet& a, const Packet& b) { return a+b; }
-// Avoid compiler warning for boolean algebra.
-template<> EIGEN_DEVICE_FUNC inline bool
-padd(const bool& a, const bool& b) { return a || b; }
-
-/** \internal \returns a - b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-psub(const Packet& a, const Packet& b) { return a-b; }
-
-/** \internal \returns -a (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pnegate(const Packet& a) { return -a; }
-
-template<> EIGEN_DEVICE_FUNC inline bool
-pnegate(const bool& a) { return !a; }
-
-/** \internal \returns conj(a) (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pconj(const Packet& a) { return numext::conj(a); }
-
-/** \internal \returns a * b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmul(const Packet& a, const Packet& b) { return a*b; }
-// Avoid compiler warning for boolean algebra.
-template<> EIGEN_DEVICE_FUNC inline bool
-pmul(const bool& a, const bool& b) { return a && b; }
-
-/** \internal \returns a / b (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pdiv(const Packet& a, const Packet& b) { return a/b; }
-
-// In the generic case, memset to all one bits.
-template<typename Packet, typename EnableIf = void>
-struct ptrue_impl {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/){
-    Packet b;
-    memset(static_cast<void*>(&b), 0xff, sizeof(Packet));
-    return b;
-  }
-};
-
-// For non-trivial scalars, set to Scalar(1) (i.e. a non-zero value).
-// Although this is technically not a valid bitmask, the scalar path for pselect
-// uses a comparison to zero, so this should still work in most cases. We don't
-// have another option, since the scalar type requires initialization.
-template<typename T>
-struct ptrue_impl<T, 
-    typename internal::enable_if<is_scalar<T>::value && NumTraits<T>::RequireInitialization>::type > {
-  static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/){
-    return T(1);
-  }
-};
-
-/** \internal \returns one bits. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-ptrue(const Packet& a) {
-  return ptrue_impl<Packet>::run(a);
-}
-
-// In the general case, memset to zero.
-template<typename Packet, typename EnableIf = void>
-struct pzero_impl {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/) {
-    Packet b;
-    memset(static_cast<void*>(&b), 0x00, sizeof(Packet));
-    return b;
-  }
-};
-
-// For scalars, explicitly set to Scalar(0), since the underlying representation
-// for zero may not consist of all-zero bits.
-template<typename T>
-struct pzero_impl<T,
-    typename internal::enable_if<is_scalar<T>::value>::type> {
-  static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/) {
-    return T(0);
-  }
-};
-
-/** \internal \returns packet of zeros */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pzero(const Packet& a) {
-  return pzero_impl<Packet>::run(a);
-}
-
-/** \internal \returns a <= b as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_le(const Packet& a, const Packet& b)  { return a<=b ? ptrue(a) : pzero(a); }
-
-/** \internal \returns a < b as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_lt(const Packet& a, const Packet& b)  { return a<b ? ptrue(a) : pzero(a); }
-
-/** \internal \returns a == b as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_eq(const Packet& a, const Packet& b) { return a==b ? ptrue(a) : pzero(a); }
-
-/** \internal \returns a < b or a==NaN or b==NaN as a bit mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pcmp_lt_or_nan(const Packet& a, const Packet& b) { return a>=b ? pzero(a) : ptrue(a); }
-
-template<typename T>
-struct bit_and {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
-    return a & b;
-  }
-};
-
-template<typename T>
-struct bit_or {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
-    return a | b;
-  }
-};
-
-template<typename T>
-struct bit_xor {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
-    return a ^ b;
-  }
-};
-
-template<typename T>
-struct bit_not {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a) const {
-    return ~a;
-  }
-};
-
-// Use operators &, |, ^, ~.
-template<typename T>
-struct operator_bitwise_helper {
-  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) { return bit_and<T>()(a, b); }
-  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { return bit_or<T>()(a, b); }
-  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) { return bit_xor<T>()(a, b); }
-  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { return bit_not<T>()(a); }
-};
-
-// Apply binary operations byte-by-byte
-template<typename T>
-struct bytewise_bitwise_helper {
-  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) {
-    return binary(a, b, bit_and<unsigned char>());
-  }
-  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { 
-    return binary(a, b, bit_or<unsigned char>());
-   }
-  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) {
-    return binary(a, b, bit_xor<unsigned char>());
-  }
-  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { 
-    return unary(a,bit_not<unsigned char>());
-   }
-  
- private:
-  template<typename Op>
-  EIGEN_DEVICE_FUNC static inline T unary(const T& a, Op op) {
-    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
-    T c;
-    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
-    for (size_t i = 0; i < sizeof(T); ++i) {
-      *c_ptr++ = op(*a_ptr++);
-    }
-    return c;
-  }
-
-  template<typename Op>
-  EIGEN_DEVICE_FUNC static inline T binary(const T& a, const T& b, Op op) {
-    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
-    const unsigned char* b_ptr = reinterpret_cast<const unsigned char*>(&b);
-    T c;
-    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
-    for (size_t i = 0; i < sizeof(T); ++i) {
-      *c_ptr++ = op(*a_ptr++, *b_ptr++);
-    }
-    return c;
-  }
-};
-
-// In the general case, use byte-by-byte manipulation.
-template<typename T, typename EnableIf = void>
-struct bitwise_helper : public bytewise_bitwise_helper<T> {};
-
-// For integers or non-trivial scalars, use binary operators.
-template<typename T>
-struct bitwise_helper<T,
-  typename internal::enable_if<
-    is_scalar<T>::value && (NumTraits<T>::IsInteger || NumTraits<T>::RequireInitialization)>::type
-  > : public operator_bitwise_helper<T> {};
-
-/** \internal \returns the bitwise and of \a a and \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pand(const Packet& a, const Packet& b) {
-  return bitwise_helper<Packet>::bitwise_and(a, b);
-}
-
-/** \internal \returns the bitwise or of \a a and \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-por(const Packet& a, const Packet& b) {
-  return bitwise_helper<Packet>::bitwise_or(a, b);
-}
-
-/** \internal \returns the bitwise xor of \a a and \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pxor(const Packet& a, const Packet& b) {
-  return bitwise_helper<Packet>::bitwise_xor(a, b);
-}
-
-/** \internal \returns the bitwise not of \a a */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pnot(const Packet& a) {
-  return bitwise_helper<Packet>::bitwise_not(a);
-}
-
-/** \internal \returns the bitwise and of \a a and not \a b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pandnot(const Packet& a, const Packet& b) { return pand(a, pnot(b)); }
-
-// In the general case, use bitwise select.
-template<typename Packet, typename EnableIf = void>
-struct pselect_impl {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
-    return por(pand(a,mask),pandnot(b,mask));
-  }
-};
-
-// For scalars, use ternary select.
-template<typename Packet>
-struct pselect_impl<Packet, 
-    typename internal::enable_if<is_scalar<Packet>::value>::type > {
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
-    return numext::equal_strict(mask, Packet(0)) ? b : a;
-  }
-};
-
-/** \internal \returns \a or \b for each field in packet according to \mask */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pselect(const Packet& mask, const Packet& a, const Packet& b) {
-  return pselect_impl<Packet>::run(mask, a, b);
-}
-
-template<> EIGEN_DEVICE_FUNC inline bool pselect<bool>(
-    const bool& cond, const bool& a, const bool& b) {
-  return cond ? a : b;
-}
-
-/** \internal \returns the min or of \a a and \a b (coeff-wise)
-    If either \a a or \a b are NaN, the result is implementation defined. */
-template<int NaNPropagation>
-struct pminmax_impl {
-  template <typename Packet, typename Op>
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
-    return op(a,b);
-  }
-};
-
-/** \internal \returns the min or max of \a a and \a b (coeff-wise)
-    If either \a a or \a b are NaN, NaN is returned. */
-template<>
-struct pminmax_impl<PropagateNaN> {
-  template <typename Packet, typename Op>
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet not_nan_mask_b = pcmp_eq(b, b);
-  return pselect(not_nan_mask_a,
-                 pselect(not_nan_mask_b, op(a, b), b),
-                 a);
-  }
-};
-
-/** \internal \returns the min or max of \a a and \a b (coeff-wise)
-    If both \a a and \a b are NaN, NaN is returned.
-    Equivalent to std::fmin(a, b).  */
-template<>
-struct pminmax_impl<PropagateNumbers> {
-  template <typename Packet, typename Op>
-  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet not_nan_mask_b = pcmp_eq(b, b);
-  return pselect(not_nan_mask_a,
-                 pselect(not_nan_mask_b, op(a, b), a),
-                 b);
-  }
-};
-
-
-#ifndef SYCL_DEVICE_ONLY
-#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) Func
-#else
-#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) \
-[](const Type& a, const Type& b) { \
-        return Func(a, b);}
-#endif
-
-/** \internal \returns the min of \a a and \a b  (coeff-wise).
-    If \a a or \b b is NaN, the return value is implementation defined. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmin(const Packet& a, const Packet& b) { return numext::mini(a,b); }
-
-/** \internal \returns the min of \a a and \a b  (coeff-wise).
-    NaNPropagation determines the NaN propagation semantics. */
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) {
-  return pminmax_impl<NaNPropagation>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet, (pmin<Packet>)));
-}
-
-/** \internal \returns the max of \a a and \a b  (coeff-wise)
-    If \a a or \b b is NaN, the return value is implementation defined. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmax(const Packet& a, const Packet& b) { return numext::maxi(a, b); }
-
-/** \internal \returns the max of \a a and \a b  (coeff-wise).
-    NaNPropagation determines the NaN propagation semantics. */
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline Packet pmax(const Packet& a, const Packet& b) {
-  return pminmax_impl<NaNPropagation>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet,(pmax<Packet>)));
-}
-
-/** \internal \returns the absolute value of \a a */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pabs(const Packet& a) { return numext::abs(a); }
-template<> EIGEN_DEVICE_FUNC inline unsigned int
-pabs(const unsigned int& a) { return a; }
-template<> EIGEN_DEVICE_FUNC inline unsigned long
-pabs(const unsigned long& a) { return a; }
-template<> EIGEN_DEVICE_FUNC inline unsigned long long
-pabs(const unsigned long long& a) { return a; }
-
-/** \internal \returns the addsub value of \a a,b */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-paddsub(const Packet& a, const Packet& b) {
-  return pselect(peven_mask(a), padd(a, b), psub(a, b));
- }
-
-/** \internal \returns the phase angle of \a a */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-parg(const Packet& a) { using numext::arg; return arg(a); }
-
-
-/** \internal \returns \a a logically shifted by N bits to the right */
-template<int N> EIGEN_DEVICE_FUNC inline int
-parithmetic_shift_right(const int& a) { return a >> N; }
-template<int N> EIGEN_DEVICE_FUNC inline long int
-parithmetic_shift_right(const long int& a) { return a >> N; }
-
-/** \internal \returns \a a arithmetically shifted by N bits to the right */
-template<int N> EIGEN_DEVICE_FUNC inline int
-plogical_shift_right(const int& a) { return static_cast<int>(static_cast<unsigned int>(a) >> N); }
-template<int N> EIGEN_DEVICE_FUNC inline long int
-plogical_shift_right(const long int& a) { return static_cast<long>(static_cast<unsigned long>(a) >> N); }
-
-/** \internal \returns \a a shifted by N bits to the left */
-template<int N> EIGEN_DEVICE_FUNC inline int
-plogical_shift_left(const int& a) { return a << N; }
-template<int N> EIGEN_DEVICE_FUNC inline long int
-plogical_shift_left(const long int& a) { return a << N; }
-
-/** \internal \returns the significant and exponent of the underlying floating point numbers
-  * See https://en.cppreference.com/w/cpp/numeric/math/frexp
-  */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline Packet pfrexp(const Packet& a, Packet& exponent) {
-  int exp;
-  EIGEN_USING_STD(frexp);
-  Packet result = static_cast<Packet>(frexp(a, &exp));
-  exponent = static_cast<Packet>(exp);
-  return result;
-}
-
-/** \internal \returns a * 2^((int)exponent)
-  * See https://en.cppreference.com/w/cpp/numeric/math/ldexp
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pldexp(const Packet &a, const Packet &exponent) {
-  EIGEN_USING_STD(ldexp)
-  return static_cast<Packet>(ldexp(a, static_cast<int>(exponent)));
-}
-
-/** \internal \returns the min of \a a and \a b  (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pabsdiff(const Packet& a, const Packet& b) { return pselect(pcmp_lt(a, b), psub(b, a), psub(a, b)); }
-
-/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
-
-/** \internal \returns a packet version of \a *from, (un-aligned load) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
-
-/** \internal \returns a packet version of \a *from, (un-aligned masked load)
- * There is no generic implementation. We only have implementations for specialized
- * cases. Generic case should not be called.
- */
-template<typename Packet> EIGEN_DEVICE_FUNC inline
-typename enable_if<unpacket_traits<Packet>::masked_load_available, Packet>::type
-ploadu(const typename unpacket_traits<Packet>::type* from, typename unpacket_traits<Packet>::mask_t umask);
-
-/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pset1(const typename unpacket_traits<Packet>::type& a) { return a; }
-
-/** \internal \returns a packet with constant coefficients set from bits */
-template<typename Packet,typename BitsType> EIGEN_DEVICE_FUNC inline Packet
-pset1frombits(BitsType a);
-
-/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pload1(const typename unpacket_traits<Packet>::type  *a) { return pset1<Packet>(*a); }
-
-/** \internal \returns a packet with elements of \a *from duplicated.
-  * For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and
-  * duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
-  * Currently, this function is only used for scalar * complex products.
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
-ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
-
-/** \internal \returns a packet with elements of \a *from quadrupled.
-  * For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and
-  * replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]}
-  * Currently, this function is only used in matrix products.
-  * For packet-size smaller or equal to 4, this function is equivalent to pload1
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-ploadquad(const typename unpacket_traits<Packet>::type* from)
-{ return pload1<Packet>(from); }
-
-/** \internal equivalent to
-  * \code
-  * a0 = pload1(a+0);
-  * a1 = pload1(a+1);
-  * a2 = pload1(a+2);
-  * a3 = pload1(a+3);
-  * \endcode
-  * \sa pset1, pload1, ploaddup, pbroadcast2
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC
-inline void pbroadcast4(const typename unpacket_traits<Packet>::type *a,
-                        Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  a0 = pload1<Packet>(a+0);
-  a1 = pload1<Packet>(a+1);
-  a2 = pload1<Packet>(a+2);
-  a3 = pload1<Packet>(a+3);
-}
-
-/** \internal equivalent to
-  * \code
-  * a0 = pload1(a+0);
-  * a1 = pload1(a+1);
-  * \endcode
-  * \sa pset1, pload1, ploaddup, pbroadcast4
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC
-inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a,
-                        Packet& a0, Packet& a1)
-{
-  a0 = pload1<Packet>(a+0);
-  a1 = pload1<Packet>(a+1);
-}
-
-/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */
-template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
-plset(const typename unpacket_traits<Packet>::type& a) { return a; }
-
-/** \internal \returns a packet with constant coefficients \a a, e.g.: (x, 0, x, 0),
-     where x is the value of all 1-bits. */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-peven_mask(const Packet& /*a*/) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  const size_t n = unpacket_traits<Packet>::size;
-  EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
-  for(size_t i = 0; i < n; ++i) {
-    memset(elements+i, ((i & 1) == 0 ? 0xff : 0), sizeof(Scalar));
-  }
-  return ploadu<Packet>(elements);
-}
-
-
-/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
-template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from)
-{ (*to) = from; }
-
-/** \internal copy the packet \a from to \a *to, (un-aligned store) */
-template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from)
-{  (*to) = from; }
-
-/** \internal copy the packet \a from to \a *to, (un-aligned store with a mask)
- * There is no generic implementation. We only have implementations for specialized
- * cases. Generic case should not be called.
- */
-template<typename Scalar, typename Packet>
-EIGEN_DEVICE_FUNC inline
-typename enable_if<unpacket_traits<Packet>::masked_store_available, void>::type
-pstoreu(Scalar* to, const Packet& from, typename unpacket_traits<Packet>::mask_t umask);
-
- template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/)
- { return ploadu<Packet>(from); }
-
- template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/)
- { pstore(to, from); }
-
-/** \internal tries to do cache prefetching of \a addr */
-template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr)
-{
-#if defined(EIGEN_HIP_DEVICE_COMPILE)
-  // do nothing
-#elif defined(EIGEN_CUDA_ARCH)
-#if defined(__LP64__) || EIGEN_OS_WIN64
-  // 64-bit pointer operand constraint for inlined asm
-  asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr));
-#else
-  // 32-bit pointer operand constraint for inlined asm
-  asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
-#endif
-#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
-  __builtin_prefetch(addr);
-#endif
-}
-
-/** \internal \returns the reversed elements of \a a*/
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a)
-{ return a; }
-
-/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
-{
-  return Packet(numext::imag(a),numext::real(a));
-}
-
-/**************************
-* Special math functions
-***************************/
-
-/** \internal \returns the sine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet psin(const Packet& a) { EIGEN_USING_STD(sin); return sin(a); }
-
-/** \internal \returns the cosine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pcos(const Packet& a) { EIGEN_USING_STD(cos); return cos(a); }
-
-/** \internal \returns the tan of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet ptan(const Packet& a) { EIGEN_USING_STD(tan); return tan(a); }
-
-/** \internal \returns the arc sine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pasin(const Packet& a) { EIGEN_USING_STD(asin); return asin(a); }
-
-/** \internal \returns the arc cosine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pacos(const Packet& a) { EIGEN_USING_STD(acos); return acos(a); }
-
-/** \internal \returns the arc tangent of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet patan(const Packet& a) { EIGEN_USING_STD(atan); return atan(a); }
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet psinh(const Packet& a) { EIGEN_USING_STD(sinh); return sinh(a); }
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pcosh(const Packet& a) { EIGEN_USING_STD(cosh); return cosh(a); }
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet ptanh(const Packet& a) { EIGEN_USING_STD(tanh); return tanh(a); }
-
-/** \internal \returns the exp of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pexp(const Packet& a) { EIGEN_USING_STD(exp); return exp(a); }
-
-/** \internal \returns the expm1 of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pexpm1(const Packet& a) { return numext::expm1(a); }
-
-/** \internal \returns the log of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog(const Packet& a) { EIGEN_USING_STD(log); return log(a); }
-
-/** \internal \returns the log1p of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog1p(const Packet& a) { return numext::log1p(a); }
-
-/** \internal \returns the log10 of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog10(const Packet& a) { EIGEN_USING_STD(log10); return log10(a); }
-
-/** \internal \returns the log10 of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plog2(const Packet& a) {
-  typedef typename internal::unpacket_traits<Packet>::type Scalar;
-  return pmul(pset1<Packet>(Scalar(EIGEN_LOG2E)), plog(a)); 
-}
-
-/** \internal \returns the square-root of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet psqrt(const Packet& a) { return numext::sqrt(a); }
-
-/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet prsqrt(const Packet& a) {
-  typedef typename internal::unpacket_traits<Packet>::type Scalar;
-  return pdiv(pset1<Packet>(Scalar(1)), psqrt(a));
-}
-
-/** \internal \returns the rounded value of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pround(const Packet& a) { using numext::round; return round(a); }
-
-/** \internal \returns the floor of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
-
-/** \internal \returns the rounded value of \a a (coeff-wise) with current
- * rounding mode */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet print(const Packet& a) { using numext::rint; return rint(a); }
-
-/** \internal \returns the ceil of \a a (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
-
-/** \internal \returns the first element of a packet */
-template<typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
-pfirst(const Packet& a)
-{ return a; }
-
-/** \internal \returns the sum of the elements of upper and lower half of \a a if \a a is larger than 4.
-  * For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7}
-  * For packet-size smaller or equal to 4, this boils down to a noop.
-  */
-template<typename Packet>
-EIGEN_DEVICE_FUNC inline typename conditional<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>::type
-predux_half_dowto4(const Packet& a)
-{ return a; }
-
-// Slow generic implementation of Packet reduction.
-template <typename Packet, typename Op>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
-predux_helper(const Packet& a, Op op) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  const size_t n = unpacket_traits<Packet>::size;
-  EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
-  pstoreu<Scalar>(elements, a);
-  for(size_t k = n / 2; k > 0; k /= 2)  {
-    for(size_t i = 0; i < k; ++i) {
-      elements[i] = op(elements[i], elements[i + k]);
-    }
-  }
-  return elements[0];
-}
-
-/** \internal \returns the sum of the elements of \a a*/
-template<typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
-predux(const Packet& a)
-{
-  return a;
-}
-
-/** \internal \returns the product of the elements of \a a */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(
-    const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmul<Scalar>)));
-}
-
-/** \internal \returns the min of the elements of \a a */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
-    const Packet &a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<PropagateFast, Scalar>)));
-}
-
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
-    const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<NaNPropagation, Scalar>)));
-}
-
-/** \internal \returns the min of the elements of \a a */
-template <typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
-    const Packet &a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<PropagateFast, Scalar>)));
-}
-
-template <int NaNPropagation, typename Packet>
-EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
-    const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar; 
-  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<NaNPropagation, Scalar>)));
-}
-
-#undef EIGEN_BINARY_OP_NAN_PROPAGATION
-
-/** \internal \returns true if all coeffs of \a a means "true"
-  * It is supposed to be called on values returned by pcmp_*.
-  */
-// not needed yet
-// template<typename Packet> EIGEN_DEVICE_FUNC inline bool predux_all(const Packet& a)
-// { return bool(a); }
-
-/** \internal \returns true if any coeffs of \a a means "true"
-  * It is supposed to be called on values returned by pcmp_*.
-  */
-template<typename Packet> EIGEN_DEVICE_FUNC inline bool predux_any(const Packet& a)
-{
-  // Dirty but generic implementation where "true" is assumed to be non 0 and all the sames.
-  // It is expected that "true" is either:
-  //  - Scalar(1)
-  //  - bits full of ones (NaN for floats),
-  //  - or first bit equals to 1 (1 for ints, smallest denormal for floats).
-  // For all these cases, taking the sum is just fine, and this boils down to a no-op for scalars.
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  return numext::not_equal_strict(predux(a), Scalar(0));
-}
-
-/***************************************************************************
-* The following functions might not have to be overwritten for vectorized types
-***************************************************************************/
-
-/** \internal copy a packet with constant coefficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */
-// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type)
-template<typename Packet>
-inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename unpacket_traits<Packet>::type& a)
-{
-  pstore(to, pset1<Packet>(a));
-}
-
-/** \internal \returns a * b + c (coeff-wise) */
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pmadd(const Packet&  a,
-         const Packet&  b,
-         const Packet&  c)
-{ return padd(pmul(a, b),c); }
-
-/** \internal \returns a packet version of \a *from.
-  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
-template<typename Packet, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits<Packet>::type* from)
-{
-  if(Alignment >= unpacket_traits<Packet>::alignment)
-    return pload<Packet>(from);
-  else
-    return ploadu<Packet>(from);
-}
-
-/** \internal copy the packet \a from to \a *to.
-  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
-template<typename Scalar, typename Packet, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from)
-{
-  if(Alignment >= unpacket_traits<Packet>::alignment)
-    pstore(to, from);
-  else
-    pstoreu(to, from);
-}
-
-/** \internal \returns a packet version of \a *from.
-  * Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the
-  * hardware if available to speedup the loading of data that won't be modified
-  * by the current computation.
-  */
-template<typename Packet, int LoadMode>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from)
-{
-  return ploadt<Packet, LoadMode>(from);
-}
-
-/***************************************************************************
-* Fast complex products (GCC generates a function call which is very slow)
-***************************************************************************/
-
-// Eigen+CUDA does not support complexes.
-#if !defined(EIGEN_GPUCC)
-
-template<> inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b)
-{ return std::complex<float>(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); }
-
-template<> inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b)
-{ return std::complex<double>(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); }
-
-#endif
-
-
-/***************************************************************************
- * PacketBlock, that is a collection of N packets where the number of words
- * in the packet is a multiple of N.
-***************************************************************************/
-template <typename Packet,int N=unpacket_traits<Packet>::size> struct PacketBlock {
-  Packet packet[N];
-};
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet,1>& /*kernel*/) {
-  // Nothing to do in the scalar case, i.e. a 1x1 matrix.
-}
-
-/***************************************************************************
- * Selector, i.e. vector of N boolean values used to select (i.e. blend)
- * words from 2 packets.
-***************************************************************************/
-template <size_t N> struct Selector {
-  bool select[N];
-};
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
-pblend(const Selector<unpacket_traits<Packet>::size>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
-  return ifPacket.select[0] ? thenPacket : elsePacket;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERIC_PACKET_MATH_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GlobalFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GlobalFunctions.h
deleted file mode 100644
index 629af94..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/GlobalFunctions.h
+++ /dev/null
@@ -1,194 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GLOBAL_FUNCTIONS_H
-#define EIGEN_GLOBAL_FUNCTIONS_H
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
-  /** \returns an expression of the coefficient-wise DOC_OP of \a x
-
-    DOC_DETAILS
-
-    \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp
-    */ \
-  template<typename Derived> \
-  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
-  NAME(const Eigen::ArrayBase<Derived>& x);
-
-#else
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
-  template<typename Derived> \
-  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
-  (NAME)(const Eigen::ArrayBase<Derived>& x) { \
-    return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
-  }
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
-  \
-  template<typename Derived> \
-  struct NAME##_retval<ArrayBase<Derived> > \
-  { \
-    typedef const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> type; \
-  }; \
-  template<typename Derived> \
-  struct NAME##_impl<ArrayBase<Derived> > \
-  { \
-    static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
-    { \
-      return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived()); \
-    } \
-  };
-
-namespace Eigen
-{
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op,imaginary part,\sa ArrayBase::imag)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op,inverse,\sa ArrayBase::inverse)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op,sine,\sa ArrayBase::sin)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op,cosine,\sa ArrayBase::cos)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op,tangent,\sa ArrayBase::tan)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op,arc-tangent,\sa ArrayBase::atan)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op,arc-sine,\sa ArrayBase::asin)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op,arc-consine,\sa ArrayBase::acos)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
-#if EIGEN_HAS_CXX11_MATH
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asinh,scalar_asinh_op,inverse hyperbolic sine,\sa ArrayBase::asinh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acosh,scalar_acosh_op,inverse hyperbolic cosine,\sa ArrayBase::acosh)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atanh,scalar_atanh_op,inverse hyperbolic tangent,\sa ArrayBase::atanh)
-#endif
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic,scalar_logistic_op,logistic function,\sa ArrayBase::logistic)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op,complement error function,\sa ArrayBase::erfc)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ndtri,scalar_ndtri_op,inverse normal distribution function,\sa ArrayBase::ndtri)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op,exponential,\sa ArrayBase::exp)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(expm1,scalar_expm1_op,exponential of a value minus 1,\sa ArrayBase::expm1)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op,natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p,scalar_log1p_op,natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op,base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log10)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log2,scalar_log2_op,base 2 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log2)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op,absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op,squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op,complex argument,\sa ArrayBase::arg DOXCOMMA MatrixBase::cwiseArg)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op,square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt,scalar_rsqrt_op,reciprocal square root,\sa ArrayBase::rsqrt)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op,square (power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op,cube (power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rint,scalar_rint_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op,nearest integer not greater than the giben value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op,nearest integer not less than the giben value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op,not-a-number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
-  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
-
-  /** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
-    *
-    * \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar).
-    *
-    * \sa ArrayBase::pow()
-    *
-    * \relates ArrayBase
-    */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-  template<typename Derived,typename ScalarExponent>
-  inline const CwiseBinaryOp<internal::scalar_pow_op<Derived::Scalar,ScalarExponent>,Derived,Constant<ScalarExponent> >
-  pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
-#else
-  template <typename Derived,typename ScalarExponent>
-  EIGEN_DEVICE_FUNC inline
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(
-    const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<typename Derived::Scalar
-                                                 EIGEN_COMMA ScalarExponent EIGEN_COMMA
-                                                 EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent)>::type,pow))
-  pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent)
-  {
-    typedef typename internal::promote_scalar_arg<typename Derived::Scalar,ScalarExponent,
-                                                  EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent)>::type PromotedExponent;
-    return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedExponent,pow)(x.derived(),
-           typename internal::plain_constant_type<Derived,PromotedExponent>::type(x.derived().rows(), x.derived().cols(), internal::scalar_constant_op<PromotedExponent>(exponent)));
-  }
-#endif
-
-  /** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
-    *
-    * This function computes the coefficient-wise power.
-    *
-    * Example: \include Cwise_array_power_array.cpp
-    * Output: \verbinclude Cwise_array_power_array.out
-    *
-    * \sa ArrayBase::pow()
-    *
-    * \relates ArrayBase
-    */
-  template<typename Derived,typename ExponentDerived>
-  inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
-  pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
-  {
-    return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
-      x.derived(),
-      exponents.derived()
-    );
-  }
-
-  /** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
-    *
-    * This function computes the coefficient-wise power between a scalar and an array of exponents.
-    *
-    * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
-    *
-    * Example: \include Cwise_scalar_power_array.cpp
-    * Output: \verbinclude Cwise_scalar_power_array.out
-    *
-    * \sa ArrayBase::pow()
-    *
-    * \relates ArrayBase
-    */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-  template<typename Scalar,typename Derived>
-  inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar,Derived::Scalar>,Constant<Scalar>,Derived>
-  pow(const Scalar& x,const Eigen::ArrayBase<Derived>& x);
-#else
-  template <typename Scalar, typename Derived>
-  EIGEN_DEVICE_FUNC inline
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(
-    const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<typename Derived::Scalar
-                                                 EIGEN_COMMA Scalar EIGEN_COMMA
-                                                 EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type,Derived,pow))
-  pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents) {
-    typedef typename internal::promote_scalar_arg<typename Derived::Scalar,Scalar,
-                                                  EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type PromotedScalar;
-    return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedScalar,Derived,pow)(
-           typename internal::plain_constant_type<Derived,PromotedScalar>::type(exponents.derived().rows(), exponents.derived().cols(), internal::scalar_constant_op<PromotedScalar>(x)), exponents.derived());
-  }
-#endif
-
-
-  namespace internal
-  {
-    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
-    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
-    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
-  }
-}
-
-// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...)
-
-#endif // EIGEN_GLOBAL_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/IO.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/IO.h
deleted file mode 100644
index e81c315..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/IO.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_IO_H
-#define EIGEN_IO_H
-
-namespace Eigen { 
-
-enum { DontAlignCols = 1 };
-enum { StreamPrecision = -1,
-       FullPrecision = -2 };
-
-namespace internal {
-template<typename Derived>
-std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt);
-}
-
-/** \class IOFormat
-  * \ingroup Core_Module
-  *
-  * \brief Stores a set of parameters controlling the way matrices are printed
-  *
-  * List of available parameters:
-  *  - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c FullPrecision.
-  *                 The default is the special value \c StreamPrecision which means to use the
-  *                 stream's own precision setting, as set for instance using \c cout.precision(3). The other special value
-  *                 \c FullPrecision means that the number of digits will be computed to match the full precision of each floating-point
-  *                 type.
-  *  - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c DontAlignCols which
-  *             allows to disable the alignment of columns, resulting in faster code.
-  *  - \b coeffSeparator string printed between two coefficients of the same row
-  *  - \b rowSeparator string printed between two rows
-  *  - \b rowPrefix string printed at the beginning of each row
-  *  - \b rowSuffix string printed at the end of each row
-  *  - \b matPrefix string printed at the beginning of the matrix
-  *  - \b matSuffix string printed at the end of the matrix
-  *  - \b fill character printed to fill the empty space in aligned columns
-  *
-  * Example: \include IOFormat.cpp
-  * Output: \verbinclude IOFormat.out
-  *
-  * \sa DenseBase::format(), class WithFormat
-  */
-struct IOFormat
-{
-  /** Default constructor, see class IOFormat for the meaning of the parameters */
-  IOFormat(int _precision = StreamPrecision, int _flags = 0,
-    const std::string& _coeffSeparator = " ",
-    const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
-    const std::string& _matPrefix="", const std::string& _matSuffix="", const char _fill=' ')
-  : matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
-    rowSpacer(""), coeffSeparator(_coeffSeparator), fill(_fill), precision(_precision), flags(_flags)
-  {
-    // TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline
-    // don't add rowSpacer if columns are not to be aligned
-    if((flags & DontAlignCols))
-      return;
-    int i = int(matSuffix.length())-1;
-    while (i>=0 && matSuffix[i]!='\n')
-    {
-      rowSpacer += ' ';
-      i--;
-    }
-  }
-  std::string matPrefix, matSuffix;
-  std::string rowPrefix, rowSuffix, rowSeparator, rowSpacer;
-  std::string coeffSeparator;
-  char fill;
-  int precision;
-  int flags;
-};
-
-/** \class WithFormat
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression providing matrix output with given format
-  *
-  * \tparam ExpressionType the type of the object on which IO stream operations are performed
-  *
-  * This class represents an expression with stream operators controlled by a given IOFormat.
-  * It is the return type of DenseBase::format()
-  * and most of the time this is the only way it is used.
-  *
-  * See class IOFormat for some examples.
-  *
-  * \sa DenseBase::format(), class IOFormat
-  */
-template<typename ExpressionType>
-class WithFormat
-{
-  public:
-
-    WithFormat(const ExpressionType& matrix, const IOFormat& format)
-      : m_matrix(matrix), m_format(format)
-    {}
-
-    friend std::ostream & operator << (std::ostream & s, const WithFormat& wf)
-    {
-      return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format);
-    }
-
-  protected:
-    typename ExpressionType::Nested m_matrix;
-    IOFormat m_format;
-};
-
-namespace internal {
-
-// NOTE: This helper is kept for backward compatibility with previous code specializing
-//       this internal::significant_decimals_impl structure. In the future we should directly
-//       call digits10() which has been introduced in July 2016 in 3.3.
-template<typename Scalar>
-struct significant_decimals_impl
-{
-  static inline int run()
-  {
-    return NumTraits<Scalar>::digits10();
-  }
-};
-
-/** \internal
-  * print the matrix \a _m to the output stream \a s using the output format \a fmt */
-template<typename Derived>
-std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt)
-{
-  using internal::is_same;
-  using internal::conditional;
-
-  if(_m.size() == 0)
-  {
-    s << fmt.matPrefix << fmt.matSuffix;
-    return s;
-  }
-  
-  typename Derived::Nested m = _m;
-  typedef typename Derived::Scalar Scalar;
-  typedef typename
-      conditional<
-          is_same<Scalar, char>::value ||
-            is_same<Scalar, unsigned char>::value ||
-            is_same<Scalar, numext::int8_t>::value ||
-            is_same<Scalar, numext::uint8_t>::value,
-          int,
-          typename conditional<
-              is_same<Scalar, std::complex<char> >::value ||
-                is_same<Scalar, std::complex<unsigned char> >::value ||
-                is_same<Scalar, std::complex<numext::int8_t> >::value ||
-                is_same<Scalar, std::complex<numext::uint8_t> >::value,
-              std::complex<int>,
-              const Scalar&
-            >::type
-        >::type PrintType;
-
-  Index width = 0;
-
-  std::streamsize explicit_precision;
-  if(fmt.precision == StreamPrecision)
-  {
-    explicit_precision = 0;
-  }
-  else if(fmt.precision == FullPrecision)
-  {
-    if (NumTraits<Scalar>::IsInteger)
-    {
-      explicit_precision = 0;
-    }
-    else
-    {
-      explicit_precision = significant_decimals_impl<Scalar>::run();
-    }
-  }
-  else
-  {
-    explicit_precision = fmt.precision;
-  }
-
-  std::streamsize old_precision = 0;
-  if(explicit_precision) old_precision = s.precision(explicit_precision);
-
-  bool align_cols = !(fmt.flags & DontAlignCols);
-  if(align_cols)
-  {
-    // compute the largest width
-    for(Index j = 0; j < m.cols(); ++j)
-      for(Index i = 0; i < m.rows(); ++i)
-      {
-        std::stringstream sstr;
-        sstr.copyfmt(s);
-        sstr << static_cast<PrintType>(m.coeff(i,j));
-        width = std::max<Index>(width, Index(sstr.str().length()));
-      }
-  }
-  std::streamsize old_width = s.width();
-  char old_fill_character = s.fill();
-  s << fmt.matPrefix;
-  for(Index i = 0; i < m.rows(); ++i)
-  {
-    if (i)
-      s << fmt.rowSpacer;
-    s << fmt.rowPrefix;
-    if(width) {
-      s.fill(fmt.fill);
-      s.width(width);
-    }
-    s << static_cast<PrintType>(m.coeff(i, 0));
-    for(Index j = 1; j < m.cols(); ++j)
-    {
-      s << fmt.coeffSeparator;
-      if(width) {
-        s.fill(fmt.fill);
-        s.width(width);
-      }
-      s << static_cast<PrintType>(m.coeff(i, j));
-    }
-    s << fmt.rowSuffix;
-    if( i < m.rows() - 1)
-      s << fmt.rowSeparator;
-  }
-  s << fmt.matSuffix;
-  if(explicit_precision) s.precision(old_precision);
-  if(width) {
-    s.fill(old_fill_character);
-    s.width(old_width);
-  }
-  return s;
-}
-
-} // end namespace internal
-
-/** \relates DenseBase
-  *
-  * Outputs the matrix, to the given stream.
-  *
-  * If you wish to print the matrix with a format different than the default, use DenseBase::format().
-  *
-  * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers.
-  * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters.
-  *
-  * \sa DenseBase::format()
-  */
-template<typename Derived>
-std::ostream & operator <<
-(std::ostream & s,
- const DenseBase<Derived> & m)
-{
-  return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_IO_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/IndexedView.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/IndexedView.h
deleted file mode 100644
index 0847625..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/IndexedView.h
+++ /dev/null
@@ -1,237 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INDEXED_VIEW_H
-#define EIGEN_INDEXED_VIEW_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename XprType, typename RowIndices, typename ColIndices>
-struct traits<IndexedView<XprType, RowIndices, ColIndices> >
- : traits<XprType>
-{
-  enum {
-    RowsAtCompileTime = int(array_size<RowIndices>::value),
-    ColsAtCompileTime = int(array_size<ColIndices>::value),
-    MaxRowsAtCompileTime = RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime) : Dynamic,
-    MaxColsAtCompileTime = ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime) : Dynamic,
-
-    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
-    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
-               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
-               : XprTypeIsRowMajor,
-
-    RowIncr = int(get_compile_time_incr<RowIndices>::value),
-    ColIncr = int(get_compile_time_incr<ColIndices>::value),
-    InnerIncr = IsRowMajor ? ColIncr : RowIncr,
-    OuterIncr = IsRowMajor ? RowIncr : ColIncr,
-
-    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
-    XprInnerStride = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType>::ret) : int(outer_stride_at_compile_time<XprType>::ret),
-    XprOuterstride = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType>::ret) : int(inner_stride_at_compile_time<XprType>::ret),
-
-    InnerSize = XprTypeIsRowMajor ? ColsAtCompileTime : RowsAtCompileTime,
-    IsBlockAlike = InnerIncr==1 && OuterIncr==1,
-    IsInnerPannel = HasSameStorageOrderAsXprType && is_same<AllRange<InnerSize>,typename conditional<XprTypeIsRowMajor,ColIndices,RowIndices>::type>::value,
-
-    InnerStrideAtCompileTime = InnerIncr<0 || InnerIncr==DynamicIndex || XprInnerStride==Dynamic ? Dynamic : XprInnerStride * InnerIncr,
-    OuterStrideAtCompileTime = OuterIncr<0 || OuterIncr==DynamicIndex || XprOuterstride==Dynamic ? Dynamic : XprOuterstride * OuterIncr,
-
-    ReturnAsScalar = is_same<RowIndices,SingleRange>::value && is_same<ColIndices,SingleRange>::value,
-    ReturnAsBlock = (!ReturnAsScalar) && IsBlockAlike,
-    ReturnAsIndexedView = (!ReturnAsScalar) && (!ReturnAsBlock),
-
-    // FIXME we deal with compile-time strides if and only if we have DirectAccessBit flag,
-    // but this is too strict regarding negative strides...
-    DirectAccessMask = (int(InnerIncr)!=UndefinedIncr && int(OuterIncr)!=UndefinedIncr && InnerIncr>=0 && OuterIncr>=0) ? DirectAccessBit : 0,
-    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
-    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
-    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
-    Flags = (traits<XprType>::Flags & (HereditaryBits | DirectAccessMask )) | FlagsLvalueBit | FlagsRowMajorBit | FlagsLinearAccessBit
-  };
-
-  typedef Block<XprType,RowsAtCompileTime,ColsAtCompileTime,IsInnerPannel> BlockType;
-};
-
-}
-
-template<typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
-class IndexedViewImpl;
-
-
-/** \class IndexedView
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a non-sequential sub-matrix defined by arbitrary sequences of row and column indices
-  *
-  * \tparam XprType the type of the expression in which we are taking the intersections of sub-rows and sub-columns
-  * \tparam RowIndices the type of the object defining the sequence of row indices
-  * \tparam ColIndices the type of the object defining the sequence of column indices
-  *
-  * This class represents an expression of a sub-matrix (or sub-vector) defined as the intersection
-  * of sub-sets of rows and columns, that are themself defined by generic sequences of row indices \f$ \{r_0,r_1,..r_{m-1}\} \f$
-  * and column indices \f$ \{c_0,c_1,..c_{n-1} \}\f$. Let \f$ A \f$  be the nested matrix, then the resulting matrix \f$ B \f$ has \c m
-  * rows and \c n columns, and its entries are given by: \f$ B(i,j) = A(r_i,c_j) \f$.
-  *
-  * The \c RowIndices and \c ColIndices types must be compatible with the following API:
-  * \code
-  * <integral type> operator[](Index) const;
-  * Index size() const;
-  * \endcode
-  *
-  * Typical supported types thus include:
-  *  - std::vector<int>
-  *  - std::valarray<int>
-  *  - std::array<int>
-  *  - Plain C arrays: int[N]
-  *  - Eigen::ArrayXi
-  *  - decltype(ArrayXi::LinSpaced(...))
-  *  - Any view/expressions of the previous types
-  *  - Eigen::ArithmeticSequence
-  *  - Eigen::internal::AllRange      (helper for Eigen::all)
-  *  - Eigen::internal::SingleRange  (helper for single index)
-  *  - etc.
-  *
-  * In typical usages of %Eigen, this class should never be used directly. It is the return type of
-  * DenseBase::operator()(const RowIndices&, const ColIndices&).
-  *
-  * \sa class Block
-  */
-template<typename XprType, typename RowIndices, typename ColIndices>
-class IndexedView : public IndexedViewImpl<XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind>
-{
-public:
-  typedef typename IndexedViewImpl<XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind>::Base Base;
-  EIGEN_GENERIC_PUBLIC_INTERFACE(IndexedView)
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedView)
-
-  typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
-  typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-  template<typename T0, typename T1>
-  IndexedView(XprType& xpr, const T0& rowIndices, const T1& colIndices)
-    : m_xpr(xpr), m_rowIndices(rowIndices), m_colIndices(colIndices)
-  {}
-
-  /** \returns number of rows */
-  Index rows() const { return internal::size(m_rowIndices); }
-
-  /** \returns number of columns */
-  Index cols() const { return internal::size(m_colIndices); }
-
-  /** \returns the nested expression */
-  const typename internal::remove_all<XprType>::type&
-  nestedExpression() const { return m_xpr; }
-
-  /** \returns the nested expression */
-  typename internal::remove_reference<XprType>::type&
-  nestedExpression() { return m_xpr; }
-
-  /** \returns a const reference to the object storing/generating the row indices */
-  const RowIndices& rowIndices() const { return m_rowIndices; }
-
-  /** \returns a const reference to the object storing/generating the column indices */
-  const ColIndices& colIndices() const { return m_colIndices; }
-
-protected:
-  MatrixTypeNested m_xpr;
-  RowIndices m_rowIndices;
-  ColIndices m_colIndices;
-};
-
-
-// Generic API dispatcher
-template<typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
-class IndexedViewImpl
-  : public internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices> >::type Base;
-};
-
-namespace internal {
-
-
-template<typename ArgType, typename RowIndices, typename ColIndices>
-struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
-  : evaluator_base<IndexedView<ArgType, RowIndices, ColIndices> >
-{
-  typedef IndexedView<ArgType, RowIndices, ColIndices> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of row/col index */,
-
-    FlagsLinearAccessBit = (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1) ? LinearAccessBit : 0,
-
-    FlagsRowMajorBit = traits<XprType>::FlagsRowMajorBit, 
-
-    Flags = (evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit /*| LinearAccessBit | DirectAccessBit*/)) | FlagsLinearAccessBit | FlagsRowMajorBit,
-
-    Alignment = 0
-  };
-
-  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeff(Index row, Index col) const
-  {
-    return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index row, Index col)
-  {
-    return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Scalar& coeffRef(Index index)
-  {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
-    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar& coeffRef(Index index) const
-  {
-    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
-    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const CoeffReturnType coeff(Index index) const
-  {
-    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
-    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    return m_argImpl.coeff( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
-  }
-
-protected:
-
-  evaluator<ArgType> m_argImpl;
-  const XprType& m_xpr;
-
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_INDEXED_VIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Inverse.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Inverse.h
deleted file mode 100644
index c514438..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Inverse.h
+++ /dev/null
@@ -1,117 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INVERSE_H
-#define EIGEN_INVERSE_H
-
-namespace Eigen {
-
-template<typename XprType,typename StorageKind> class InverseImpl;
-
-namespace internal {
-
-template<typename XprType>
-struct traits<Inverse<XprType> >
-  : traits<typename XprType::PlainObject>
-{
-  typedef typename XprType::PlainObject PlainObject;
-  typedef traits<PlainObject> BaseTraits;
-  enum {
-    Flags = BaseTraits::Flags & RowMajorBit
-  };
-};
-
-} // end namespace internal
-
-/** \class Inverse
-  *
-  * \brief Expression of the inverse of another expression
-  *
-  * \tparam XprType the type of the expression we are taking the inverse
-  *
-  * This class represents an abstract expression of A.inverse()
-  * and most of the time this is the only way it is used.
-  *
-  */
-template<typename XprType>
-class Inverse : public InverseImpl<XprType,typename internal::traits<XprType>::StorageKind>
-{
-public:
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename XprType::Scalar                            Scalar;
-  typedef typename internal::ref_selector<XprType>::type      XprTypeNested;
-  typedef typename internal::remove_all<XprTypeNested>::type  XprTypeNestedCleaned;
-  typedef typename internal::ref_selector<Inverse>::type Nested;
-  typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-  explicit EIGEN_DEVICE_FUNC Inverse(const XprType &xpr)
-    : m_xpr(xpr)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR  Index rows() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR  Index cols() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
-
-  EIGEN_DEVICE_FUNC const XprTypeNestedCleaned& nestedExpression() const { return m_xpr; }
-
-protected:
-  XprTypeNested m_xpr;
-};
-
-// Generic API dispatcher
-template<typename XprType, typename StorageKind>
-class InverseImpl
-  : public internal::generic_xpr_base<Inverse<XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<Inverse<XprType> >::type Base;
-  typedef typename XprType::Scalar Scalar;
-private:
-
-  Scalar coeff(Index row, Index col) const;
-  Scalar coeff(Index i) const;
-};
-
-namespace internal {
-
-/** \internal
-  * \brief Default evaluator for Inverse expression.
-  *
-  * This default evaluator for Inverse expression simply evaluate the inverse into a temporary
-  * by a call to internal::call_assignment_no_alias.
-  * Therefore, inverse implementers only have to specialize Assignment<Dst,Inverse<...>, ...> for
-  * there own nested expression.
-  *
-  * \sa class Inverse
-  */
-template<typename ArgType>
-struct unary_evaluator<Inverse<ArgType> >
-  : public evaluator<typename Inverse<ArgType>::PlainObject>
-{
-  typedef Inverse<ArgType> InverseType;
-  typedef typename InverseType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum { Flags = Base::Flags | EvalBeforeNestingBit };
-
-  unary_evaluator(const InverseType& inv_xpr)
-    : m_result(inv_xpr.rows(), inv_xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    internal::call_assignment_no_alias(m_result, inv_xpr);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_INVERSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Map.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Map.h
deleted file mode 100644
index 218cc15..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Map.h
+++ /dev/null
@@ -1,171 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MAP_H
-#define EIGEN_MAP_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename PlainObjectType, int MapOptions, typename StrideType>
-struct traits<Map<PlainObjectType, MapOptions, StrideType> >
-  : public traits<PlainObjectType>
-{
-  typedef traits<PlainObjectType> TraitsBase;
-  enum {
-    PlainObjectTypeInnerSize = ((traits<PlainObjectType>::Flags&RowMajorBit)==RowMajorBit)
-                             ? PlainObjectType::ColsAtCompileTime
-                             : PlainObjectType::RowsAtCompileTime,
-
-    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
-                             ? int(PlainObjectType::InnerStrideAtCompileTime)
-                             : int(StrideType::InnerStrideAtCompileTime),
-    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
-                             ? (InnerStrideAtCompileTime==Dynamic || PlainObjectTypeInnerSize==Dynamic
-                                ? Dynamic
-                                : int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize))
-                             : int(StrideType::OuterStrideAtCompileTime),
-    Alignment = int(MapOptions)&int(AlignedMask),
-    Flags0 = TraitsBase::Flags & (~NestByRefBit),
-    Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
-  };
-private:
-  enum { Options }; // Expressions don't have Options
-};
-}
-
-/** \class Map
-  * \ingroup Core_Module
-  *
-  * \brief A matrix or vector expression mapping an existing array of data.
-  *
-  * \tparam PlainObjectType the equivalent matrix type of the mapped data
-  * \tparam MapOptions specifies the pointer alignment in bytes. It can be: \c #Aligned128, \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
-  *                The default is \c #Unaligned.
-  * \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout
-  *                   of an ordinary, contiguous array. This can be overridden by specifying strides.
-  *                   The type passed here must be a specialization of the Stride template, see examples below.
-  *
-  * This class represents a matrix or vector expression mapping an existing array of data.
-  * It can be used to let Eigen interface without any overhead with non-Eigen data structures,
-  * such as plain C arrays or structures from other libraries. By default, it assumes that the
-  * data is laid out contiguously in memory. You can however override this by explicitly specifying
-  * inner and outer strides.
-  *
-  * Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix:
-  * \include Map_simple.cpp
-  * Output: \verbinclude Map_simple.out
-  *
-  * If you need to map non-contiguous arrays, you can do so by specifying strides:
-  *
-  * Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer
-  * increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time
-  * fixed value.
-  * \include Map_inner_stride.cpp
-  * Output: \verbinclude Map_inner_stride.out
-  *
-  * Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping
-  * as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns.
-  * Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is
-  * a short version of \c OuterStride<Dynamic> because the default template parameter of OuterStride
-  * is  \c Dynamic
-  * \include Map_outer_stride.cpp
-  * Output: \verbinclude Map_outer_stride.out
-  *
-  * For more details and for an example of specifying both an inner and an outer stride, see class Stride.
-  *
-  * \b Tip: to change the array of data mapped by a Map object, you can use the C++
-  * placement new syntax:
-  *
-  * Example: \include Map_placement_new.cpp
-  * Output: \verbinclude Map_placement_new.out
-  *
-  * This class is the return type of PlainObjectBase::Map() but can also be used directly.
-  *
-  * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
-  */
-template<typename PlainObjectType, int MapOptions, typename StrideType> class Map
-  : public MapBase<Map<PlainObjectType, MapOptions, StrideType> >
-{
-  public:
-
-    typedef MapBase<Map> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Map)
-
-    typedef typename Base::PointerType PointerType;
-    typedef PointerType PointerArgType;
-    EIGEN_DEVICE_FUNC
-    inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const
-    {
-      return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const
-    {
-      return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
-           : internal::traits<Map>::OuterStrideAtCompileTime != Dynamic ? Index(internal::traits<Map>::OuterStrideAtCompileTime)
-           : IsVectorAtCompileTime ? (this->size() * innerStride())
-           : int(Flags)&RowMajorBit ? (this->cols() * innerStride())
-           : (this->rows() * innerStride());
-    }
-
-    /** Constructor in the fixed-size case.
-      *
-      * \param dataPtr pointer to the array to map
-      * \param stride optional Stride object, passing the strides.
-      */
-    EIGEN_DEVICE_FUNC
-    explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
-      : Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
-    {
-      PlainObjectType::Base::_check_template_params();
-    }
-
-    /** Constructor in the dynamic-size vector case.
-      *
-      * \param dataPtr pointer to the array to map
-      * \param size the size of the vector expression
-      * \param stride optional Stride object, passing the strides.
-      */
-    EIGEN_DEVICE_FUNC
-    inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
-      : Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
-    {
-      PlainObjectType::Base::_check_template_params();
-    }
-
-    /** Constructor in the dynamic-size matrix case.
-      *
-      * \param dataPtr pointer to the array to map
-      * \param rows the number of rows of the matrix expression
-      * \param cols the number of columns of the matrix expression
-      * \param stride optional Stride object, passing the strides.
-      */
-    EIGEN_DEVICE_FUNC
-    inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType())
-      : Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride)
-    {
-      PlainObjectType::Base::_check_template_params();
-    }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
-
-  protected:
-    StrideType m_stride;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_MAP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MapBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MapBase.h
deleted file mode 100644
index d856447..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MapBase.h
+++ /dev/null
@@ -1,310 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MAPBASE_H
-#define EIGEN_MAPBASE_H
-
-#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \
-      EIGEN_STATIC_ASSERT((int(internal::evaluator<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
-                          YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT)
-
-namespace Eigen {
-
-/** \ingroup Core_Module
-  *
-  * \brief Base class for dense Map and Block expression with direct access
-  *
-  * This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense
-  * Map and Block objects with direct access.
-  * Typical users do not have to directly deal with this class.
-  *
-  * This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN.
-  * See \link TopicCustomizing_Plugins customizing Eigen \endlink for details.
-  *
-  * The \c Derived class has to provide the following two methods describing the memory layout:
-  *  \code Index innerStride() const; \endcode
-  *  \code Index outerStride() const; \endcode
-  *
-  * \sa class Map, class Block
-  */
-template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
-  : public internal::dense_xpr_base<Derived>::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<Derived>::type Base;
-    enum {
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      InnerStrideAtCompileTime = internal::traits<Derived>::InnerStrideAtCompileTime,
-      SizeAtCompileTime = Base::SizeAtCompileTime
-    };
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef typename internal::conditional<
-                         bool(internal::is_lvalue<Derived>::value),
-                         Scalar *,
-                         const Scalar *>::type
-                     PointerType;
-
-    using Base::derived;
-//    using Base::RowsAtCompileTime;
-//    using Base::ColsAtCompileTime;
-//    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-    using Base::IsRowMajor;
-
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-    using Base::lazyAssign;
-    using Base::eval;
-
-    using Base::innerStride;
-    using Base::outerStride;
-    using Base::rowStride;
-    using Base::colStride;
-
-    // bug 217 - compile error on ICC 11.1
-    using Base::operator=;
-
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-    /** \copydoc DenseBase::rows() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_rows.value(); }
-    /** \copydoc DenseBase::cols() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_cols.value(); }
-
-    /** Returns a pointer to the first coefficient of the matrix or vector.
-      *
-      * \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride().
-      *
-      * \sa innerStride(), outerStride()
-      */
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; }
-
-    /** \copydoc PlainObjectBase::coeff(Index,Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeff(Index rowId, Index colId) const
-    {
-      return m_data[colId * colStride() + rowId * rowStride()];
-    }
-
-    /** \copydoc PlainObjectBase::coeff(Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeff(Index index) const
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return m_data[index * innerStride()];
-    }
-
-    /** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return this->m_data[colId * colStride() + rowId * rowStride()];
-    }
-
-    /** \copydoc PlainObjectBase::coeffRef(Index) const */
-    EIGEN_DEVICE_FUNC
-    inline const Scalar& coeffRef(Index index) const
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return this->m_data[index * innerStride()];
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    inline PacketScalar packet(Index rowId, Index colId) const
-    {
-      return internal::ploadt<PacketScalar, LoadMode>
-               (m_data + (colId * colStride() + rowId * rowStride()));
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    inline PacketScalar packet(Index index) const
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
-    }
-
-    /** \internal Constructor for fixed size matrices or vectors */
-    EIGEN_DEVICE_FUNC
-    explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
-    {
-      EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
-      checkSanity<Derived>();
-    }
-
-    /** \internal Constructor for dynamically sized vectors */
-    EIGEN_DEVICE_FUNC
-    inline MapBase(PointerType dataPtr, Index vecSize)
-            : m_data(dataPtr),
-              m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
-              m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime))
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-      eigen_assert(vecSize >= 0);
-      eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
-      checkSanity<Derived>();
-    }
-
-    /** \internal Constructor for dynamically sized matrices */
-    EIGEN_DEVICE_FUNC
-    inline MapBase(PointerType dataPtr, Index rows, Index cols)
-            : m_data(dataPtr), m_rows(rows), m_cols(cols)
-    {
-      eigen_assert( (dataPtr == 0)
-              || (   rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
-                  && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
-      checkSanity<Derived>();
-    }
-
-    #ifdef EIGEN_MAPBASE_PLUGIN
-    #include EIGEN_MAPBASE_PLUGIN
-    #endif
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
-
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) const
-    {
-#if EIGEN_MAX_ALIGN_BYTES>0
-      // innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value:
-      const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime);
-      EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride);
-      eigen_assert((   ((internal::UIntPtr(m_data) % internal::traits<Derived>::Alignment) == 0)
-                    || (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
-#endif
-    }
-
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    void checkSanity(typename internal::enable_if<internal::traits<T>::Alignment==0,void*>::type = 0) const
-    {}
-
-    PointerType m_data;
-    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
-    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
-};
-
-/** \ingroup Core_Module
-  *
-  * \brief Base class for non-const dense Map and Block expression with direct access
-  *
-  * This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of
-  * dense Map and Block objects with direct access.
-  * It inherits MapBase<Derived, ReadOnlyAccessors> which defines the const variant for reading specific entries.
-  *
-  * \sa class Map, class Block
-  */
-template<typename Derived> class MapBase<Derived, WriteAccessors>
-  : public MapBase<Derived, ReadOnlyAccessors>
-{
-    typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
-  public:
-
-    typedef MapBase<Derived, ReadOnlyAccessors> Base;
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::PacketScalar PacketScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    typedef typename Base::PointerType PointerType;
-
-    using Base::derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-
-    using Base::innerStride;
-    using Base::outerStride;
-    using Base::rowStride;
-    using Base::colStride;
-
-    typedef typename internal::conditional<
-                    internal::is_lvalue<Derived>::value,
-                    Scalar,
-                    const Scalar
-                  >::type ScalarWithConstIfNotLvalue;
-
-    EIGEN_DEVICE_FUNC
-    inline const Scalar* data() const { return this->m_data; }
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col)
-    {
-      return this->m_data[col * colStride() + row * rowStride()];
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      return this->m_data[index * innerStride()];
-    }
-
-    template<int StoreMode>
-    inline void writePacket(Index row, Index col, const PacketScalar& val)
-    {
-      internal::pstoret<Scalar, PacketScalar, StoreMode>
-               (this->m_data + (col * colStride() + row * rowStride()), val);
-    }
-
-    template<int StoreMode>
-    inline void writePacket(Index index, const PacketScalar& val)
-    {
-      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
-      internal::pstoret<Scalar, PacketScalar, StoreMode>
-                (this->m_data + index * innerStride(), val);
-    }
-
-    EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
-    EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
-    EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {}
-
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const MapBase& other)
-    {
-      ReadOnlyMapBase::Base::operator=(other);
-      return derived();
-    }
-
-    // In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
-    // see bugs 821 and 920.
-    using ReadOnlyMapBase::Base::operator=;
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
-};
-
-#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS
-
-} // end namespace Eigen
-
-#endif // EIGEN_MAPBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MathFunctions.h
deleted file mode 100644
index 61b78f4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MathFunctions.h
+++ /dev/null
@@ -1,2057 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATHFUNCTIONS_H
-#define EIGEN_MATHFUNCTIONS_H
-
-// TODO this should better be moved to NumTraits
-// Source: WolframAlpha
-#define EIGEN_PI    3.141592653589793238462643383279502884197169399375105820974944592307816406L
-#define EIGEN_LOG2E 1.442695040888963407359924681001892137426645954152985934135449406931109219L
-#define EIGEN_LN2   0.693147180559945309417232121458176568075500134360255254120680009493393621L
-
-namespace Eigen {
-
-// On WINCE, std::abs is defined for int only, so let's defined our own overloads:
-// This issue has been confirmed with MSVC 2008 only, but the issue might exist for more recent versions too.
-#if EIGEN_OS_WINCE && EIGEN_COMP_MSVC && EIGEN_COMP_MSVC<=1500
-long        abs(long        x) { return (labs(x));  }
-double      abs(double      x) { return (fabs(x));  }
-float       abs(float       x) { return (fabsf(x)); }
-long double abs(long double x) { return (fabsl(x)); }
-#endif
-
-namespace internal {
-
-/** \internal \class global_math_functions_filtering_base
-  *
-  * What it does:
-  * Defines a typedef 'type' as follows:
-  * - if type T has a member typedef Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl, then
-  *   global_math_functions_filtering_base<T>::type is a typedef for it.
-  * - otherwise, global_math_functions_filtering_base<T>::type is a typedef for T.
-  *
-  * How it's used:
-  * To allow to defined the global math functions (like sin...) in certain cases, like the Array expressions.
-  * When you do sin(array1+array2), the object array1+array2 has a complicated expression type, all what you want to know
-  * is that it inherits ArrayBase. So we implement a partial specialization of sin_impl for ArrayBase<Derived>.
-  * So we must make sure to use sin_impl<ArrayBase<Derived> > and not sin_impl<Derived>, otherwise our partial specialization
-  * won't be used. How does sin know that? That's exactly what global_math_functions_filtering_base tells it.
-  *
-  * How it's implemented:
-  * SFINAE in the style of enable_if. Highly susceptible of breaking compilers. With GCC, it sure does work, but if you replace
-  * the typename dummy by an integer template parameter, it doesn't work anymore!
-  */
-
-template<typename T, typename dummy = void>
-struct global_math_functions_filtering_base
-{
-  typedef T type;
-};
-
-template<typename T> struct always_void { typedef void type; };
-
-template<typename T>
-struct global_math_functions_filtering_base
-  <T,
-   typename always_void<typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl>::type
-  >
-{
-  typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type;
-};
-
-#define EIGEN_MATHFUNC_IMPL(func, scalar) Eigen::internal::func##_impl<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>
-#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename Eigen::internal::func##_retval<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>::type
-
-/****************************************************************************
-* Implementation of real                                                 *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct real_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return x;
-  }
-};
-
-template<typename Scalar>
-struct real_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    using std::real;
-    return real(x);
-  }
-};
-
-template<typename Scalar> struct real_impl : real_default_impl<Scalar> {};
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T>
-struct real_impl<std::complex<T> >
-{
-  typedef T RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline T run(const std::complex<T>& x)
-  {
-    return x.real();
-  }
-};
-#endif
-
-template<typename Scalar>
-struct real_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of imag                                                 *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct imag_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar&)
-  {
-    return RealScalar(0);
-  }
-};
-
-template<typename Scalar>
-struct imag_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    using std::imag;
-    return imag(x);
-  }
-};
-
-template<typename Scalar> struct imag_impl : imag_default_impl<Scalar> {};
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T>
-struct imag_impl<std::complex<T> >
-{
-  typedef T RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline T run(const std::complex<T>& x)
-  {
-    return x.imag();
-  }
-};
-#endif
-
-template<typename Scalar>
-struct imag_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of real_ref                                             *
-****************************************************************************/
-
-template<typename Scalar>
-struct real_ref_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar& run(Scalar& x)
-  {
-    return reinterpret_cast<RealScalar*>(&x)[0];
-  }
-  EIGEN_DEVICE_FUNC
-  static inline const RealScalar& run(const Scalar& x)
-  {
-    return reinterpret_cast<const RealScalar*>(&x)[0];
-  }
-};
-
-template<typename Scalar>
-struct real_ref_retval
-{
-  typedef typename NumTraits<Scalar>::Real & type;
-};
-
-/****************************************************************************
-* Implementation of imag_ref                                             *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex>
-struct imag_ref_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar& run(Scalar& x)
-  {
-    return reinterpret_cast<RealScalar*>(&x)[1];
-  }
-  EIGEN_DEVICE_FUNC
-  static inline const RealScalar& run(const Scalar& x)
-  {
-    return reinterpret_cast<RealScalar*>(&x)[1];
-  }
-};
-
-template<typename Scalar>
-struct imag_ref_default_impl<Scalar, false>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Scalar run(Scalar&)
-  {
-    return Scalar(0);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline const Scalar run(const Scalar&)
-  {
-    return Scalar(0);
-  }
-};
-
-template<typename Scalar>
-struct imag_ref_impl : imag_ref_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
-
-template<typename Scalar>
-struct imag_ref_retval
-{
-  typedef typename NumTraits<Scalar>::Real & type;
-};
-
-/****************************************************************************
-* Implementation of conj                                                 *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct conj_default_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    return x;
-  }
-};
-
-template<typename Scalar>
-struct conj_default_impl<Scalar,true>
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    using std::conj;
-    return conj(x);
-  }
-};
-
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct conj_impl : conj_default_impl<Scalar, IsComplex> {};
-
-template<typename Scalar>
-struct conj_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of abs2                                                 *
-****************************************************************************/
-
-template<typename Scalar,bool IsComplex>
-struct abs2_impl_default
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return x*x;
-  }
-};
-
-template<typename Scalar>
-struct abs2_impl_default<Scalar, true> // IsComplex
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return x.real()*x.real() + x.imag()*x.imag();
-  }
-};
-
-template<typename Scalar>
-struct abs2_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return abs2_impl_default<Scalar,NumTraits<Scalar>::IsComplex>::run(x);
-  }
-};
-
-template<typename Scalar>
-struct abs2_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of sqrt/rsqrt                                             *
-****************************************************************************/
-
-template<typename Scalar>
-struct sqrt_impl
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE Scalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(sqrt);
-    return sqrt(x);
-  }
-};
-
-// Complex sqrt defined in MathFunctionsImpl.h.
-template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& a_x);
-
-// Custom implementation is faster than `std::sqrt`, works on
-// GPU, and correctly handles special cases (unlike MSVC).
-template<typename T>
-struct sqrt_impl<std::complex<T> >
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x)
-  {
-    return complex_sqrt<T>(x);
-  }
-};
-
-template<typename Scalar>
-struct sqrt_retval
-{
-  typedef Scalar type;
-};
-
-// Default implementation relies on numext::sqrt, at bottom of file.
-template<typename T>
-struct rsqrt_impl;
-
-// Complex rsqrt defined in MathFunctionsImpl.h.
-template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_rsqrt(const std::complex<T>& a_x);
-
-template<typename T>
-struct rsqrt_impl<std::complex<T> >
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x)
-  {
-    return complex_rsqrt<T>(x);
-  }
-};
-
-template<typename Scalar>
-struct rsqrt_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of norm1                                                *
-****************************************************************************/
-
-template<typename Scalar, bool IsComplex>
-struct norm1_default_impl;
-
-template<typename Scalar>
-struct norm1_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(abs);
-    return abs(x.real()) + abs(x.imag());
-  }
-};
-
-template<typename Scalar>
-struct norm1_default_impl<Scalar, false>
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(abs);
-    return abs(x);
-  }
-};
-
-template<typename Scalar>
-struct norm1_impl : norm1_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
-
-template<typename Scalar>
-struct norm1_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of hypot                                                *
-****************************************************************************/
-
-template<typename Scalar> struct hypot_impl;
-
-template<typename Scalar>
-struct hypot_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of cast                                                 *
-****************************************************************************/
-
-template<typename OldType, typename NewType, typename EnableIf = void>
-struct cast_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline NewType run(const OldType& x)
-  {
-    return static_cast<NewType>(x);
-  }
-};
-
-// Casting from S -> Complex<T> leads to an implicit conversion from S to T,
-// generating warnings on clang.  Here we explicitly cast the real component.
-template<typename OldType, typename NewType>
-struct cast_impl<OldType, NewType,
-  typename internal::enable_if<
-    !NumTraits<OldType>::IsComplex && NumTraits<NewType>::IsComplex
-  >::type>
-{
-  EIGEN_DEVICE_FUNC
-  static inline NewType run(const OldType& x)
-  {
-    typedef typename NumTraits<NewType>::Real NewReal;
-    return static_cast<NewType>(static_cast<NewReal>(x));
-  }
-};
-
-// here, for once, we're plainly returning NewType: we don't want cast to do weird things.
-
-template<typename OldType, typename NewType>
-EIGEN_DEVICE_FUNC
-inline NewType cast(const OldType& x)
-{
-  return cast_impl<OldType, NewType>::run(x);
-}
-
-/****************************************************************************
-* Implementation of round                                                   *
-****************************************************************************/
-
-template<typename Scalar>
-struct round_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
-#if EIGEN_HAS_CXX11_MATH
-    EIGEN_USING_STD(round);
-#endif
-    return Scalar(round(x));
-  }
-};
-
-#if !EIGEN_HAS_CXX11_MATH
-#if EIGEN_HAS_C99_MATH
-// Use ::roundf for float.
-template<>
-struct round_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static inline float run(const float& x)
-  {
-    return ::roundf(x);
-  }
-};
-#else
-template<typename Scalar>
-struct round_using_floor_ceil_impl
-{
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
-    // Without C99 round/roundf, resort to floor/ceil.
-    EIGEN_USING_STD(floor);
-    EIGEN_USING_STD(ceil);
-    // If not enough precision to resolve a decimal at all, return the input.
-    // Otherwise, adding 0.5 can trigger an increment by 1.
-    const Scalar limit = Scalar(1ull << (NumTraits<Scalar>::digits() - 1));
-    if (x >= limit || x <= -limit) {
-      return x;
-    }
-    return (x > Scalar(0)) ? Scalar(floor(x + Scalar(0.5))) : Scalar(ceil(x - Scalar(0.5)));
-  }
-};
-
-template<>
-struct round_impl<float> : round_using_floor_ceil_impl<float> {};
-
-template<>
-struct round_impl<double> : round_using_floor_ceil_impl<double> {};
-#endif // EIGEN_HAS_C99_MATH
-#endif // !EIGEN_HAS_CXX11_MATH
-
-template<typename Scalar>
-struct round_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of rint                                                    *
-****************************************************************************/
-
-template<typename Scalar>
-struct rint_impl {
-  EIGEN_DEVICE_FUNC
-  static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
-#if EIGEN_HAS_CXX11_MATH
-      EIGEN_USING_STD(rint);
-#endif
-    return rint(x);
-  }
-};
-
-#if !EIGEN_HAS_CXX11_MATH
-template<>
-struct rint_impl<double> {
-  EIGEN_DEVICE_FUNC
-  static inline double run(const double& x)
-  {
-    return ::rint(x);
-  }
-};
-template<>
-struct rint_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static inline float run(const float& x)
-  {
-    return ::rintf(x);
-  }
-};
-#endif
-
-template<typename Scalar>
-struct rint_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of arg                                                     *
-****************************************************************************/
-
-// Visual Studio 2017 has a bug where arg(float) returns 0 for negative inputs.
-// This seems to be fixed in VS 2019.
-#if EIGEN_HAS_CXX11_MATH && (!EIGEN_COMP_MSVC || EIGEN_COMP_MSVC >= 1920)
-// std::arg is only defined for types of std::complex, or integer types or float/double/long double
-template<typename Scalar,
-          bool HasStdImpl = NumTraits<Scalar>::IsComplex || is_integral<Scalar>::value
-                            || is_same<Scalar, float>::value || is_same<Scalar, double>::value
-                            || is_same<Scalar, long double>::value >
-struct arg_default_impl;
-
-template<typename Scalar>
-struct arg_default_impl<Scalar, true> {
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    #if defined(EIGEN_HIP_DEVICE_COMPILE)
-    // HIP does not seem to have a native device side implementation for the math routine "arg"
-    using std::arg;
-    #else
-    EIGEN_USING_STD(arg);
-    #endif
-    return static_cast<RealScalar>(arg(x));
-  }
-};
-
-// Must be non-complex floating-point type (e.g. half/bfloat16).
-template<typename Scalar>
-struct arg_default_impl<Scalar, false> {
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return (x < Scalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
-  }
-};
-#else
-template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
-struct arg_default_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    return (x < RealScalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
-  }
-};
-
-template<typename Scalar>
-struct arg_default_impl<Scalar,true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline RealScalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(arg);
-    return arg(x);
-  }
-};
-#endif
-template<typename Scalar> struct arg_impl : arg_default_impl<Scalar> {};
-
-template<typename Scalar>
-struct arg_retval
-{
-  typedef typename NumTraits<Scalar>::Real type;
-};
-
-/****************************************************************************
-* Implementation of expm1                                                   *
-****************************************************************************/
-
-// This implementation is based on GSL Math's expm1.
-namespace std_fallback {
-  // fallback expm1 implementation in case there is no expm1(Scalar) function in namespace of Scalar,
-  // or that there is no suitable std::expm1 function available. Implementation
-  // attributed to Kahan. See: http://www.plunk.org/~hatch/rightway.php.
-  template<typename Scalar>
-  EIGEN_DEVICE_FUNC inline Scalar expm1(const Scalar& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    EIGEN_USING_STD(exp);
-    Scalar u = exp(x);
-    if (numext::equal_strict(u, Scalar(1))) {
-      return x;
-    }
-    Scalar um1 = u - RealScalar(1);
-    if (numext::equal_strict(um1, Scalar(-1))) {
-      return RealScalar(-1);
-    }
-
-    EIGEN_USING_STD(log);
-    Scalar logu = log(u);
-    return numext::equal_strict(u, logu) ? u : (u - RealScalar(1)) * x / logu;
-  }
-}
-
-template<typename Scalar>
-struct expm1_impl {
-  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    #if EIGEN_HAS_CXX11_MATH
-    using std::expm1;
-    #else
-    using std_fallback::expm1;
-    #endif
-    return expm1(x);
-  }
-};
-
-template<typename Scalar>
-struct expm1_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of log                                                     *
-****************************************************************************/
-
-// Complex log defined in MathFunctionsImpl.h.
-template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_log(const std::complex<T>& z);
-
-template<typename Scalar>
-struct log_impl {
-  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_USING_STD(log);
-    return static_cast<Scalar>(log(x));
-  }
-};
-
-template<typename Scalar>
-struct log_impl<std::complex<Scalar> > {
-  EIGEN_DEVICE_FUNC static inline std::complex<Scalar> run(const std::complex<Scalar>& z)
-  {
-    return complex_log(z);
-  }
-};
-
-/****************************************************************************
-* Implementation of log1p                                                   *
-****************************************************************************/
-
-namespace std_fallback {
-  // fallback log1p implementation in case there is no log1p(Scalar) function in namespace of Scalar,
-  // or that there is no suitable std::log1p function available
-  template<typename Scalar>
-  EIGEN_DEVICE_FUNC inline Scalar log1p(const Scalar& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    EIGEN_USING_STD(log);
-    Scalar x1p = RealScalar(1) + x;
-    Scalar log_1p = log_impl<Scalar>::run(x1p);
-    const bool is_small = numext::equal_strict(x1p, Scalar(1));
-    const bool is_inf = numext::equal_strict(x1p, log_1p);
-    return (is_small || is_inf) ? x : x * (log_1p / (x1p - RealScalar(1)));
-  }
-}
-
-template<typename Scalar>
-struct log1p_impl {
-  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
-  {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
-    #if EIGEN_HAS_CXX11_MATH
-    using std::log1p;
-    #else
-    using std_fallback::log1p;
-    #endif
-    return log1p(x);
-  }
-};
-
-// Specialization for complex types that are not supported by std::log1p.
-template <typename RealScalar>
-struct log1p_impl<std::complex<RealScalar> > {
-  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
-      const std::complex<RealScalar>& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
-    return std_fallback::log1p(x);
-  }
-};
-
-template<typename Scalar>
-struct log1p_retval
-{
-  typedef Scalar type;
-};
-
-/****************************************************************************
-* Implementation of pow                                                  *
-****************************************************************************/
-
-template<typename ScalarX,typename ScalarY, bool IsInteger = NumTraits<ScalarX>::IsInteger&&NumTraits<ScalarY>::IsInteger>
-struct pow_impl
-{
-  //typedef Scalar retval;
-  typedef typename ScalarBinaryOpTraits<ScalarX,ScalarY,internal::scalar_pow_op<ScalarX,ScalarY> >::ReturnType result_type;
-  static EIGEN_DEVICE_FUNC inline result_type run(const ScalarX& x, const ScalarY& y)
-  {
-    EIGEN_USING_STD(pow);
-    return pow(x, y);
-  }
-};
-
-template<typename ScalarX,typename ScalarY>
-struct pow_impl<ScalarX,ScalarY, true>
-{
-  typedef ScalarX result_type;
-  static EIGEN_DEVICE_FUNC inline ScalarX run(ScalarX x, ScalarY y)
-  {
-    ScalarX res(1);
-    eigen_assert(!NumTraits<ScalarY>::IsSigned || y >= 0);
-    if(y & 1) res *= x;
-    y >>= 1;
-    while(y)
-    {
-      x *= x;
-      if(y&1) res *= x;
-      y >>= 1;
-    }
-    return res;
-  }
-};
-
-/****************************************************************************
-* Implementation of random                                               *
-****************************************************************************/
-
-template<typename Scalar,
-         bool IsComplex,
-         bool IsInteger>
-struct random_default_impl {};
-
-template<typename Scalar>
-struct random_impl : random_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
-
-template<typename Scalar>
-struct random_retval
-{
-  typedef Scalar type;
-};
-
-template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y);
-template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random();
-
-template<typename Scalar>
-struct random_default_impl<Scalar, false, false>
-{
-  static inline Scalar run(const Scalar& x, const Scalar& y)
-  {
-    return x + (y-x) * Scalar(std::rand()) / Scalar(RAND_MAX);
-  }
-  static inline Scalar run()
-  {
-    return run(Scalar(NumTraits<Scalar>::IsSigned ? -1 : 0), Scalar(1));
-  }
-};
-
-enum {
-  meta_floor_log2_terminate,
-  meta_floor_log2_move_up,
-  meta_floor_log2_move_down,
-  meta_floor_log2_bogus
-};
-
-template<unsigned int n, int lower, int upper> struct meta_floor_log2_selector
-{
-  enum { middle = (lower + upper) / 2,
-         value = (upper <= lower + 1) ? int(meta_floor_log2_terminate)
-               : (n < (1 << middle)) ? int(meta_floor_log2_move_down)
-               : (n==0) ? int(meta_floor_log2_bogus)
-               : int(meta_floor_log2_move_up)
-  };
-};
-
-template<unsigned int n,
-         int lower = 0,
-         int upper = sizeof(unsigned int) * CHAR_BIT - 1,
-         int selector = meta_floor_log2_selector<n, lower, upper>::value>
-struct meta_floor_log2 {};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_down>
-{
-  enum { value = meta_floor_log2<n, lower, meta_floor_log2_selector<n, lower, upper>::middle>::value };
-};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_up>
-{
-  enum { value = meta_floor_log2<n, meta_floor_log2_selector<n, lower, upper>::middle, upper>::value };
-};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_terminate>
-{
-  enum { value = (n >= ((unsigned int)(1) << (lower+1))) ? lower+1 : lower };
-};
-
-template<unsigned int n, int lower, int upper>
-struct meta_floor_log2<n, lower, upper, meta_floor_log2_bogus>
-{
-  // no value, error at compile time
-};
-
-template<typename Scalar>
-struct random_default_impl<Scalar, false, true>
-{
-  static inline Scalar run(const Scalar& x, const Scalar& y)
-  {
-    if (y <= x)
-      return x;
-    // ScalarU is the unsigned counterpart of Scalar, possibly Scalar itself.
-    typedef typename make_unsigned<Scalar>::type ScalarU;
-    // ScalarX is the widest of ScalarU and unsigned int.
-    // We'll deal only with ScalarX and unsigned int below thus avoiding signed
-    // types and arithmetic and signed overflows (which are undefined behavior).
-    typedef typename conditional<(ScalarU(-1) > unsigned(-1)), ScalarU, unsigned>::type ScalarX;
-    // The following difference doesn't overflow, provided our integer types are two's
-    // complement and have the same number of padding bits in signed and unsigned variants.
-    // This is the case in most modern implementations of C++.
-    ScalarX range = ScalarX(y) - ScalarX(x);
-    ScalarX offset = 0;
-    ScalarX divisor = 1;
-    ScalarX multiplier = 1;
-    const unsigned rand_max = RAND_MAX;
-    if (range <= rand_max) divisor = (rand_max + 1) / (range + 1);
-    else                   multiplier = 1 + range / (rand_max + 1);
-    // Rejection sampling.
-    do {
-      offset = (unsigned(std::rand()) * multiplier) / divisor;
-    } while (offset > range);
-    return Scalar(ScalarX(x) + offset);
-  }
-
-  static inline Scalar run()
-  {
-#ifdef EIGEN_MAKING_DOCS
-    return run(Scalar(NumTraits<Scalar>::IsSigned ? -10 : 0), Scalar(10));
-#else
-    enum { rand_bits = meta_floor_log2<(unsigned int)(RAND_MAX)+1>::value,
-           scalar_bits = sizeof(Scalar) * CHAR_BIT,
-           shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)),
-           offset = NumTraits<Scalar>::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0
-    };
-    return Scalar((std::rand() >> shift) - offset);
-#endif
-  }
-};
-
-template<typename Scalar>
-struct random_default_impl<Scalar, true, false>
-{
-  static inline Scalar run(const Scalar& x, const Scalar& y)
-  {
-    return Scalar(random(x.real(), y.real()),
-                  random(x.imag(), y.imag()));
-  }
-  static inline Scalar run()
-  {
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    return Scalar(random<RealScalar>(), random<RealScalar>());
-  }
-};
-
-template<typename Scalar>
-inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y)
-{
-  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(x, y);
-}
-
-template<typename Scalar>
-inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
-{
-  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run();
-}
-
-// Implementation of is* functions
-
-// std::is* do not work with fast-math and gcc, std::is* are available on MSVC 2013 and newer, as well as in clang.
-#if (EIGEN_HAS_CXX11_MATH && !(EIGEN_COMP_GNUC_STRICT && __FINITE_MATH_ONLY__)) || (EIGEN_COMP_MSVC>=1800) || (EIGEN_COMP_CLANG)
-#define EIGEN_USE_STD_FPCLASSIFY 1
-#else
-#define EIGEN_USE_STD_FPCLASSIFY 0
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<internal::is_integral<T>::value,bool>::type
-isnan_impl(const T&) { return false; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<internal::is_integral<T>::value,bool>::type
-isinf_impl(const T&) { return false; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<internal::is_integral<T>::value,bool>::type
-isfinite_impl(const T&) { return true; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
-isfinite_impl(const T& x)
-{
-  #if defined(EIGEN_GPU_COMPILE_PHASE)
-    return (::isfinite)(x);
-  #elif EIGEN_USE_STD_FPCLASSIFY
-    using std::isfinite;
-    return isfinite EIGEN_NOT_A_MACRO (x);
-  #else
-    return x<=NumTraits<T>::highest() && x>=NumTraits<T>::lowest();
-  #endif
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
-isinf_impl(const T& x)
-{
-  #if defined(EIGEN_GPU_COMPILE_PHASE)
-    return (::isinf)(x);
-  #elif EIGEN_USE_STD_FPCLASSIFY
-    using std::isinf;
-    return isinf EIGEN_NOT_A_MACRO (x);
-  #else
-    return x>NumTraits<T>::highest() || x<NumTraits<T>::lowest();
-  #endif
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
-isnan_impl(const T& x)
-{
-  #if defined(EIGEN_GPU_COMPILE_PHASE)
-    return (::isnan)(x);
-  #elif EIGEN_USE_STD_FPCLASSIFY
-    using std::isnan;
-    return isnan EIGEN_NOT_A_MACRO (x);
-  #else
-    return x != x;
-  #endif
-}
-
-#if (!EIGEN_USE_STD_FPCLASSIFY)
-
-#if EIGEN_COMP_MSVC
-
-template<typename T> EIGEN_DEVICE_FUNC bool isinf_msvc_helper(T x)
-{
-  return _fpclass(x)==_FPCLASS_NINF || _fpclass(x)==_FPCLASS_PINF;
-}
-
-//MSVC defines a _isnan builtin function, but for double only
-EIGEN_DEVICE_FUNC inline bool isnan_impl(const long double& x) { return _isnan(x)!=0; }
-EIGEN_DEVICE_FUNC inline bool isnan_impl(const double& x)      { return _isnan(x)!=0; }
-EIGEN_DEVICE_FUNC inline bool isnan_impl(const float& x)       { return _isnan(x)!=0; }
-
-EIGEN_DEVICE_FUNC inline bool isinf_impl(const long double& x) { return isinf_msvc_helper(x); }
-EIGEN_DEVICE_FUNC inline bool isinf_impl(const double& x)      { return isinf_msvc_helper(x); }
-EIGEN_DEVICE_FUNC inline bool isinf_impl(const float& x)       { return isinf_msvc_helper(x); }
-
-#elif (defined __FINITE_MATH_ONLY__ && __FINITE_MATH_ONLY__ && EIGEN_COMP_GNUC)
-
-#if EIGEN_GNUC_AT_LEAST(5,0)
-  #define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((optimize("no-finite-math-only")))
-#else
-  // NOTE the inline qualifier and noinline attribute are both needed: the former is to avoid linking issue (duplicate symbol),
-  //      while the second prevent too aggressive optimizations in fast-math mode:
-  #define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((noinline,optimize("no-finite-math-only")))
-#endif
-
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const long double& x) { return __builtin_isnan(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const double& x)      { return __builtin_isnan(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const float& x)       { return __builtin_isnan(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const double& x)      { return __builtin_isinf(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const float& x)       { return __builtin_isinf(x); }
-template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const long double& x) { return __builtin_isinf(x); }
-
-#undef EIGEN_TMP_NOOPT_ATTRIB
-
-#endif
-
-#endif
-
-// The following overload are defined at the end of this file
-template<typename T> EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x);
-template<typename T> EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x);
-template<typename T> EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x);
-
-template<typename T> T generic_fast_tanh_float(const T& a_x);
-} // end namespace internal
-
-/****************************************************************************
-* Generic math functions                                                    *
-****************************************************************************/
-
-namespace numext {
-
-#if (!defined(EIGEN_GPUCC) || defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
-{
-  EIGEN_USING_STD(min)
-  return min EIGEN_NOT_A_MACRO (x,y);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
-{
-  EIGEN_USING_STD(max)
-  return max EIGEN_NOT_A_MACRO (x,y);
-}
-#else
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
-{
-  return y < x ? y : x;
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y)
-{
-  return fminf(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE double mini(const double& x, const double& y)
-{
-  return fmin(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE long double mini(const long double& x, const long double& y)
-{
-#if defined(EIGEN_HIPCC)
-  // no "fminl" on HIP yet
-  return (x < y) ? x : y;
-#else
-  return fminl(x, y);
-#endif
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
-{
-  return x < y ? y : x;
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y)
-{
-  return fmaxf(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE double maxi(const double& x, const double& y)
-{
-  return fmax(x, y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE long double maxi(const long double& x, const long double& y)
-{
-#if defined(EIGEN_HIPCC)
-  // no "fmaxl" on HIP yet
-  return (x > y) ? x : y;
-#else
-  return fmaxl(x, y);
-#endif
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-
-
-#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_char)   \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_short)  \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_int)    \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
-#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_char)   \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_short)  \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_int)    \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
-#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)  \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)   \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
-#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)  \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)   \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
-#define SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC)
-#define SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC)
-#define SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
-  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC,cl::sycl::cl_double)
-#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(NAME, FUNC) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
-  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC,cl::sycl::cl_double)
-#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(NAME, FUNC, RET_TYPE) \
-  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_float) \
-  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_double)
-
-#define SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \
-template<>                                               \
-  EIGEN_DEVICE_FUNC                                      \
-  EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE& x) { \
-    return cl::sycl::FUNC(x);                            \
-  }
-
-#define SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, TYPE) \
-  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, TYPE, TYPE)
-
-#define SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE1, ARG_TYPE2) \
-  template<>                                                                  \
-  EIGEN_DEVICE_FUNC                                                           \
-  EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE1& x, const ARG_TYPE2& y) { \
-    return cl::sycl::FUNC(x, y);                                              \
-  }
-
-#define SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \
-  SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE, ARG_TYPE)
-
-#define SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, TYPE) \
-  SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, TYPE, TYPE)
-
-SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(mini, min)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(mini, fmin)
-SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(maxi, max)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(maxi, fmax)
-
-#endif
-
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x)
-{
-  return internal::real_ref_impl<Scalar>::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(arg, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
-{
-  return internal::imag_ref_impl<Scalar>::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
-}
-
-EIGEN_DEVICE_FUNC
-inline bool abs2(bool x) { return x; }
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE T absdiff(const T& x, const T& y)
-{
-  return x > y ? x - y : y - x;
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE float absdiff(const float& x, const float& y)
-{
-  return fabsf(x - y);
-}
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE double absdiff(const double& x, const double& y)
-{
-  return fabs(x - y);
-}
-
-#if !defined(EIGEN_GPUCC)
-// HIP and CUDA do not support long double.
-template<>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE long double absdiff(const long double& x, const long double& y) {
-  return fabsl(x - y);
-}
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y)
-{
-  return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-  SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(hypot, hypot)
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log1p, log1p)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float log1p(const float &x) { return ::log1pf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double log1p(const double &x) { return ::log1p(x); }
-#endif
-
-template<typename ScalarX,typename ScalarY>
-EIGEN_DEVICE_FUNC
-inline typename internal::pow_impl<ScalarX,ScalarY>::result_type pow(const ScalarX& x, const ScalarY& y)
-{
-  return internal::pow_impl<ScalarX,ScalarY>::run(x, y);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(pow, pow)
-#endif
-
-template<typename T> EIGEN_DEVICE_FUNC bool (isnan)   (const T &x) { return internal::isnan_impl(x); }
-template<typename T> EIGEN_DEVICE_FUNC bool (isinf)   (const T &x) { return internal::isinf_impl(x); }
-template<typename T> EIGEN_DEVICE_FUNC bool (isfinite)(const T &x) { return internal::isfinite_impl(x); }
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isnan, isnan, bool)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isinf, isinf, bool)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isfinite, isfinite, bool)
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(rint, Scalar) rint(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(rint, Scalar)::run(x);
-}
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(round, Scalar) round(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(round, Scalar)::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(round, round)
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-T (floor)(const T& x)
-{
-  EIGEN_USING_STD(floor)
-  return floor(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(floor, floor)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float floor(const float &x) { return ::floorf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double floor(const double &x) { return ::floor(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-T (ceil)(const T& x)
-{
-  EIGEN_USING_STD(ceil);
-  return ceil(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(ceil, ceil)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float ceil(const float &x) { return ::ceilf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double ceil(const double &x) { return ::ceil(x); }
-#endif
-
-
-/** Log base 2 for 32 bits positive integers.
-  * Conveniently returns 0 for x==0. */
-inline int log2(int x)
-{
-  eigen_assert(x>=0);
-  unsigned int v(x);
-  static const int table[32] = { 0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30, 8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31 };
-  v |= v >> 1;
-  v |= v >> 2;
-  v |= v >> 4;
-  v |= v >> 8;
-  v |= v >> 16;
-  return table[(v * 0x07C4ACDDU) >> 27];
-}
-
-/** \returns the square root of \a x.
-  *
-  * It is essentially equivalent to
-  * \code using std::sqrt; return sqrt(x); \endcode
-  * but slightly faster for float/double and some compilers (e.g., gcc), thanks to
-  * specializations when SSE is enabled.
-  *
-  * It's usage is justified in performance critical functions, like norm/normalize.
-  */
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-EIGEN_ALWAYS_INLINE EIGEN_MATHFUNC_RETVAL(sqrt, Scalar) sqrt(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(sqrt, Scalar)::run(x);
-}
-
-// Boolean specialization, avoids implicit float to bool conversion (-Wimplicit-conversion-floating-point-to-bool).
-template<>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
-bool sqrt<bool>(const bool &x) { return x; }
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sqrt, sqrt)
-#endif
-
-/** \returns the reciprocal square root of \a x. **/
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T rsqrt(const T& x)
-{
-  return internal::rsqrt_impl<T>::run(x);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T log(const T &x) {
-  return internal::log_impl<T>::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log, log)
-#endif
-
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float log(const float &x) { return ::logf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double log(const double &x) { return ::log(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-typename internal::enable_if<NumTraits<T>::IsSigned || NumTraits<T>::IsComplex,typename NumTraits<T>::Real>::type
-abs(const T &x) {
-  EIGEN_USING_STD(abs);
-  return abs(x);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-typename internal::enable_if<!(NumTraits<T>::IsSigned || NumTraits<T>::IsComplex),typename NumTraits<T>::Real>::type
-abs(const T &x) {
-  return x;
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(abs, abs)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(abs, fabs)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float abs(const float &x) { return ::fabsf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double abs(const double &x) { return ::fabs(x); }
-
-template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float abs(const std::complex<float>& x) {
-  return ::hypotf(x.real(), x.imag());
-}
-
-template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double abs(const std::complex<double>& x) {
-  return ::hypot(x.real(), x.imag());
-}
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T exp(const T &x) {
-  EIGEN_USING_STD(exp);
-  return exp(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(exp, exp)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float exp(const float &x) { return ::expf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double exp(const double &x) { return ::exp(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-std::complex<float> exp(const std::complex<float>& x) {
-  float com = ::expf(x.real());
-  float res_real = com * ::cosf(x.imag());
-  float res_imag = com * ::sinf(x.imag());
-  return std::complex<float>(res_real, res_imag);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-std::complex<double> exp(const std::complex<double>& x) {
-  double com = ::exp(x.real());
-  double res_real = com * ::cos(x.imag());
-  double res_imag = com * ::sin(x.imag());
-  return std::complex<double>(res_real, res_imag);
-}
-#endif
-
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-inline EIGEN_MATHFUNC_RETVAL(expm1, Scalar) expm1(const Scalar& x)
-{
-  return EIGEN_MATHFUNC_IMPL(expm1, Scalar)::run(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(expm1, expm1)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float expm1(const float &x) { return ::expm1f(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double expm1(const double &x) { return ::expm1(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T cos(const T &x) {
-  EIGEN_USING_STD(cos);
-  return cos(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cos,cos)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float cos(const float &x) { return ::cosf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double cos(const double &x) { return ::cos(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T sin(const T &x) {
-  EIGEN_USING_STD(sin);
-  return sin(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sin, sin)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float sin(const float &x) { return ::sinf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double sin(const double &x) { return ::sin(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T tan(const T &x) {
-  EIGEN_USING_STD(tan);
-  return tan(x);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tan, tan)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float tan(const float &x) { return ::tanf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double tan(const double &x) { return ::tan(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T acos(const T &x) {
-  EIGEN_USING_STD(acos);
-  return acos(x);
-}
-
-#if EIGEN_HAS_CXX11_MATH
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T acosh(const T &x) {
-  EIGEN_USING_STD(acosh);
-  return static_cast<T>(acosh(x));
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acos, acos)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acosh, acosh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float acos(const float &x) { return ::acosf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double acos(const double &x) { return ::acos(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T asin(const T &x) {
-  EIGEN_USING_STD(asin);
-  return asin(x);
-}
-
-#if EIGEN_HAS_CXX11_MATH
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T asinh(const T &x) {
-  EIGEN_USING_STD(asinh);
-  return static_cast<T>(asinh(x));
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asin, asin)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asinh, asinh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float asin(const float &x) { return ::asinf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double asin(const double &x) { return ::asin(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T atan(const T &x) {
-  EIGEN_USING_STD(atan);
-  return static_cast<T>(atan(x));
-}
-
-#if EIGEN_HAS_CXX11_MATH
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T atanh(const T &x) {
-  EIGEN_USING_STD(atanh);
-  return static_cast<T>(atanh(x));
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atan, atan)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atanh, atanh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float atan(const float &x) { return ::atanf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double atan(const double &x) { return ::atan(x); }
-#endif
-
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T cosh(const T &x) {
-  EIGEN_USING_STD(cosh);
-  return static_cast<T>(cosh(x));
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cosh, cosh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float cosh(const float &x) { return ::coshf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double cosh(const double &x) { return ::cosh(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T sinh(const T &x) {
-  EIGEN_USING_STD(sinh);
-  return static_cast<T>(sinh(x));
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sinh, sinh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float sinh(const float &x) { return ::sinhf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double sinh(const double &x) { return ::sinh(x); }
-#endif
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T tanh(const T &x) {
-  EIGEN_USING_STD(tanh);
-  return tanh(x);
-}
-
-#if (!defined(EIGEN_GPUCC)) && EIGEN_FAST_MATH && !defined(SYCL_DEVICE_ONLY)
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float tanh(float x) { return internal::generic_fast_tanh_float(x); }
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tanh, tanh)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float tanh(const float &x) { return ::tanhf(x); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double tanh(const double &x) { return ::tanh(x); }
-#endif
-
-template <typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T fmod(const T& a, const T& b) {
-  EIGEN_USING_STD(fmod);
-  return fmod(a, b);
-}
-
-#if defined(SYCL_DEVICE_ONLY)
-SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(fmod, fmod)
-#endif
-
-#if defined(EIGEN_GPUCC)
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float fmod(const float& a, const float& b) {
-  return ::fmodf(a, b);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double fmod(const double& a, const double& b) {
-  return ::fmod(a, b);
-}
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY
-#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY
-#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY
-#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
-#undef SYCL_SPECIALIZE_INTEGER_TYPES_BINARY
-#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
-#undef SYCL_SPECIALIZE_FLOATING_TYPES_BINARY
-#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY
-#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE
-#undef SYCL_SPECIALIZE_GEN_UNARY_FUNC
-#undef SYCL_SPECIALIZE_UNARY_FUNC
-#undef SYCL_SPECIALIZE_GEN1_BINARY_FUNC
-#undef SYCL_SPECIALIZE_GEN2_BINARY_FUNC
-#undef SYCL_SPECIALIZE_BINARY_FUNC
-#endif
-
-} // end namespace numext
-
-namespace internal {
-
-template<typename T>
-EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x)
-{
-  return (numext::isfinite)(numext::real(x)) && (numext::isfinite)(numext::imag(x));
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x)
-{
-  return (numext::isnan)(numext::real(x)) || (numext::isnan)(numext::imag(x));
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x)
-{
-  return ((numext::isinf)(numext::real(x)) || (numext::isinf)(numext::imag(x))) && (!(numext::isnan)(x));
-}
-
-/****************************************************************************
-* Implementation of fuzzy comparisons                                       *
-****************************************************************************/
-
-template<typename Scalar,
-         bool IsComplex,
-         bool IsInteger>
-struct scalar_fuzzy_default_impl {};
-
-template<typename Scalar>
-struct scalar_fuzzy_default_impl<Scalar, false, false>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
-  {
-    return numext::abs(x) <= numext::abs(y) * prec;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
-  {
-    return numext::abs(x - y) <= numext::mini(numext::abs(x), numext::abs(y)) * prec;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec)
-  {
-    return x <= y || isApprox(x, y, prec);
-  }
-};
-
-template<typename Scalar>
-struct scalar_fuzzy_default_impl<Scalar, false, true>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const Scalar& x, const Scalar&, const RealScalar&)
-  {
-    return x == Scalar(0);
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar&)
-  {
-    return x == y;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar&)
-  {
-    return x <= y;
-  }
-};
-
-template<typename Scalar>
-struct scalar_fuzzy_default_impl<Scalar, true, false>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
-  {
-    return numext::abs2(x) <= numext::abs2(y) * prec * prec;
-  }
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
-  {
-    return numext::abs2(x - y) <= numext::mini(numext::abs2(x), numext::abs2(y)) * prec * prec;
-  }
-};
-
-template<typename Scalar>
-struct scalar_fuzzy_impl : scalar_fuzzy_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
-
-template<typename Scalar, typename OtherScalar> EIGEN_DEVICE_FUNC
-inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y,
-                              const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
-{
-  return scalar_fuzzy_impl<Scalar>::template isMuchSmallerThan<OtherScalar>(x, y, precision);
-}
-
-template<typename Scalar> EIGEN_DEVICE_FUNC
-inline bool isApprox(const Scalar& x, const Scalar& y,
-                     const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
-{
-  return scalar_fuzzy_impl<Scalar>::isApprox(x, y, precision);
-}
-
-template<typename Scalar> EIGEN_DEVICE_FUNC
-inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y,
-                               const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
-{
-  return scalar_fuzzy_impl<Scalar>::isApproxOrLessThan(x, y, precision);
-}
-
-/******************************************
-***  The special case of the  bool type ***
-******************************************/
-
-template<> struct random_impl<bool>
-{
-  static inline bool run()
-  {
-    return random<int>(0,1)==0 ? false : true;
-  }
-
-  static inline bool run(const bool& a, const bool& b)
-  {
-    return random<int>(a, b)==0 ? false : true;
-  }
-};
-
-template<> struct scalar_fuzzy_impl<bool>
-{
-  typedef bool RealScalar;
-
-  template<typename OtherScalar> EIGEN_DEVICE_FUNC
-  static inline bool isMuchSmallerThan(const bool& x, const bool&, const bool&)
-  {
-    return !x;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline bool isApprox(bool x, bool y, bool)
-  {
-    return x == y;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline bool isApproxOrLessThan(const bool& x, const bool& y, const bool&)
-  {
-    return (!x) || y;
-  }
-
-};
-
-} // end namespace internal
-
-// Default implementations that rely on other numext implementations
-namespace internal {
-
-// Specialization for complex types that are not supported by std::expm1.
-template <typename RealScalar>
-struct expm1_impl<std::complex<RealScalar> > {
-  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
-      const std::complex<RealScalar>& x) {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
-    RealScalar xr = x.real();
-    RealScalar xi = x.imag();
-    // expm1(z) = exp(z) - 1
-    //          = exp(x +  i * y) - 1
-    //          = exp(x) * (cos(y) + i * sin(y)) - 1
-    //          = exp(x) * cos(y) - 1 + i * exp(x) * sin(y)
-    // Imag(expm1(z)) = exp(x) * sin(y)
-    // Real(expm1(z)) = exp(x) * cos(y) - 1
-    //          = exp(x) * cos(y) - 1.
-    //          = expm1(x) + exp(x) * (cos(y) - 1)
-    //          = expm1(x) + exp(x) * (2 * sin(y / 2) ** 2)
-    RealScalar erm1 = numext::expm1<RealScalar>(xr);
-    RealScalar er = erm1 + RealScalar(1.);
-    RealScalar sin2 = numext::sin(xi / RealScalar(2.));
-    sin2 = sin2 * sin2;
-    RealScalar s = numext::sin(xi);
-    RealScalar real_part = erm1 - RealScalar(2.) * er * sin2;
-    return std::complex<RealScalar>(real_part, er * s);
-  }
-};
-
-template<typename T>
-struct rsqrt_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE T run(const T& x) {
-    return T(1)/numext::sqrt(x);
-  }
-};
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T>
-struct conj_impl<std::complex<T>, true>
-{
-  EIGEN_DEVICE_FUNC
-  static inline std::complex<T> run(const std::complex<T>& x)
-  {
-    return std::complex<T>(numext::real(x), -numext::imag(x));
-  }
-};
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATHFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h
deleted file mode 100644
index 4eaaaa7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h
+++ /dev/null
@@ -1,200 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATHFUNCTIONSIMPL_H
-#define EIGEN_MATHFUNCTIONSIMPL_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
-    Doesn't do anything fancy, just a 13/6-degree rational interpolant which
-    is accurate up to a couple of ulps in the (approximate) range [-8, 8],
-    outside of which tanh(x) = +/-1 in single precision. The input is clamped
-    to the range [-c, c]. The value c is chosen as the smallest value where
-    the approximation evaluates to exactly 1. In the reange [-0.0004, 0.0004]
-    the approxmation tanh(x) ~= x is used for better accuracy as x tends to zero.
-
-    This implementation works on both scalars and packets.
-*/
-template<typename T>
-T generic_fast_tanh_float(const T& a_x)
-{
-  // Clamp the inputs to the range [-c, c]
-#ifdef EIGEN_VECTORIZE_FMA
-  const T plus_clamp = pset1<T>(7.99881172180175781f);
-  const T minus_clamp = pset1<T>(-7.99881172180175781f);
-#else
-  const T plus_clamp = pset1<T>(7.90531110763549805f);
-  const T minus_clamp = pset1<T>(-7.90531110763549805f);
-#endif
-  const T tiny = pset1<T>(0.0004f);
-  const T x = pmax(pmin(a_x, plus_clamp), minus_clamp);
-  const T tiny_mask = pcmp_lt(pabs(a_x), tiny);
-  // The monomial coefficients of the numerator polynomial (odd).
-  const T alpha_1 = pset1<T>(4.89352455891786e-03f);
-  const T alpha_3 = pset1<T>(6.37261928875436e-04f);
-  const T alpha_5 = pset1<T>(1.48572235717979e-05f);
-  const T alpha_7 = pset1<T>(5.12229709037114e-08f);
-  const T alpha_9 = pset1<T>(-8.60467152213735e-11f);
-  const T alpha_11 = pset1<T>(2.00018790482477e-13f);
-  const T alpha_13 = pset1<T>(-2.76076847742355e-16f);
-
-  // The monomial coefficients of the denominator polynomial (even).
-  const T beta_0 = pset1<T>(4.89352518554385e-03f);
-  const T beta_2 = pset1<T>(2.26843463243900e-03f);
-  const T beta_4 = pset1<T>(1.18534705686654e-04f);
-  const T beta_6 = pset1<T>(1.19825839466702e-06f);
-
-  // Since the polynomials are odd/even, we need x^2.
-  const T x2 = pmul(x, x);
-
-  // Evaluate the numerator polynomial p.
-  T p = pmadd(x2, alpha_13, alpha_11);
-  p = pmadd(x2, p, alpha_9);
-  p = pmadd(x2, p, alpha_7);
-  p = pmadd(x2, p, alpha_5);
-  p = pmadd(x2, p, alpha_3);
-  p = pmadd(x2, p, alpha_1);
-  p = pmul(x, p);
-
-  // Evaluate the denominator polynomial q.
-  T q = pmadd(x2, beta_6, beta_4);
-  q = pmadd(x2, q, beta_2);
-  q = pmadd(x2, q, beta_0);
-
-  // Divide the numerator by the denominator.
-  return pselect(tiny_mask, x, pdiv(p, q));
-}
-
-template<typename RealScalar>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y)
-{
-  // IEEE IEC 6059 special cases.
-  if ((numext::isinf)(x) || (numext::isinf)(y))
-    return NumTraits<RealScalar>::infinity();
-  if ((numext::isnan)(x) || (numext::isnan)(y))
-    return NumTraits<RealScalar>::quiet_NaN();
-    
-  EIGEN_USING_STD(sqrt);
-  RealScalar p, qp;
-  p = numext::maxi(x,y);
-  if(p==RealScalar(0)) return RealScalar(0);
-  qp = numext::mini(y,x) / p;
-  return p * sqrt(RealScalar(1) + qp*qp);
-}
-
-template<typename Scalar>
-struct hypot_impl
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  static EIGEN_DEVICE_FUNC
-  inline RealScalar run(const Scalar& x, const Scalar& y)
-  {
-    EIGEN_USING_STD(abs);
-    return positive_real_hypot<RealScalar>(abs(x), abs(y));
-  }
-};
-
-// Generic complex sqrt implementation that correctly handles corner cases
-// according to https://en.cppreference.com/w/cpp/numeric/complex/sqrt
-template<typename T>
-EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& z) {
-  // Computes the principal sqrt of the input.
-  //
-  // For a complex square root of the number x + i*y. We want to find real
-  // numbers u and v such that
-  //    (u + i*v)^2 = x + i*y  <=>
-  //    u^2 - v^2 + i*2*u*v = x + i*v.
-  // By equating the real and imaginary parts we get:
-  //    u^2 - v^2 = x
-  //    2*u*v = y.
-  //
-  // For x >= 0, this has the numerically stable solution
-  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
-  //    v = y / (2 * u)
-  // and for x < 0,
-  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
-  //    u = y / (2 * v)
-  //
-  // Letting w = sqrt(0.5 * (|x| + |z|)),
-  //   if x == 0: u = w, v = sign(y) * w
-  //   if x > 0:  u = w, v = y / (2 * w)
-  //   if x < 0:  u = |y| / (2 * w), v = sign(y) * w
-
-  const T x = numext::real(z);
-  const T y = numext::imag(z);
-  const T zero = T(0);
-  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + numext::hypot(x, y)));
-
-  return
-    (numext::isinf)(y) ? std::complex<T>(NumTraits<T>::infinity(), y)
-      : x == zero ? std::complex<T>(w, y < zero ? -w : w)
-      : x > zero ? std::complex<T>(w, y / (2 * w))
-      : std::complex<T>(numext::abs(y) / (2 * w), y < zero ? -w : w );
-}
-
-// Generic complex rsqrt implementation.
-template<typename T>
-EIGEN_DEVICE_FUNC std::complex<T> complex_rsqrt(const std::complex<T>& z) {
-  // Computes the principal reciprocal sqrt of the input.
-  //
-  // For a complex reciprocal square root of the number z = x + i*y. We want to
-  // find real numbers u and v such that
-  //    (u + i*v)^2 = 1 / (x + i*y)  <=>
-  //    u^2 - v^2 + i*2*u*v = x/|z|^2 - i*v/|z|^2.
-  // By equating the real and imaginary parts we get:
-  //    u^2 - v^2 = x/|z|^2
-  //    2*u*v = y/|z|^2.
-  //
-  // For x >= 0, this has the numerically stable solution
-  //    u = sqrt(0.5 * (x + |z|)) / |z|
-  //    v = -y / (2 * u * |z|)
-  // and for x < 0,
-  //    v = -sign(y) * sqrt(0.5 * (-x + |z|)) / |z|
-  //    u = -y / (2 * v * |z|)
-  //
-  // Letting w = sqrt(0.5 * (|x| + |z|)),
-  //   if x == 0: u = w / |z|, v = -sign(y) * w / |z|
-  //   if x > 0:  u = w / |z|, v = -y / (2 * w * |z|)
-  //   if x < 0:  u = |y| / (2 * w * |z|), v = -sign(y) * w / |z|
-
-  const T x = numext::real(z);
-  const T y = numext::imag(z);
-  const T zero = T(0);
-
-  const T abs_z = numext::hypot(x, y);
-  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + abs_z));
-  const T woz = w / abs_z;
-  // Corner cases consistent with 1/sqrt(z) on gcc/clang.
-  return
-    abs_z == zero ? std::complex<T>(NumTraits<T>::infinity(), NumTraits<T>::quiet_NaN())
-      : ((numext::isinf)(x) || (numext::isinf)(y)) ? std::complex<T>(zero, zero)
-      : x == zero ? std::complex<T>(woz, y < zero ? woz : -woz)
-      : x > zero ? std::complex<T>(woz, -y / (2 * w * abs_z))
-      : std::complex<T>(numext::abs(y) / (2 * w * abs_z), y < zero ? woz : -woz );
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC std::complex<T> complex_log(const std::complex<T>& z) {
-  // Computes complex log.
-  T a = numext::abs(z);
-  EIGEN_USING_STD(atan2);
-  T b = atan2(z.imag(), z.real());
-  return std::complex<T>(numext::log(a), b);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATHFUNCTIONSIMPL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Matrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Matrix.h
deleted file mode 100644
index f0e59a9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Matrix.h
+++ /dev/null
@@ -1,565 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_H
-#define EIGEN_MATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-private:
-  enum { size = internal::size_at_compile_time<_Rows,_Cols>::ret };
-  typedef typename find_best_packet<_Scalar,size>::type PacketScalar;
-  enum {
-      row_major_bit = _Options&RowMajor ? RowMajorBit : 0,
-      is_dynamic_size_storage = _MaxRows==Dynamic || _MaxCols==Dynamic,
-      max_size = is_dynamic_size_storage ? Dynamic : _MaxRows*_MaxCols,
-      default_alignment = compute_default_alignment<_Scalar,max_size>::value,
-      actual_alignment = ((_Options&DontAlign)==0) ? default_alignment : 0,
-      required_alignment = unpacket_traits<PacketScalar>::alignment,
-      packet_access_bit = (packet_traits<_Scalar>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
-    };
-
-public:
-  typedef _Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef Eigen::Index StorageIndex;
-  typedef MatrixXpr XprKind;
-  enum {
-    RowsAtCompileTime = _Rows,
-    ColsAtCompileTime = _Cols,
-    MaxRowsAtCompileTime = _MaxRows,
-    MaxColsAtCompileTime = _MaxCols,
-    Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
-    Options = _Options,
-    InnerStrideAtCompileTime = 1,
-    OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime,
-
-    // FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase
-    EvaluatorFlags = LinearAccessBit | DirectAccessBit | packet_access_bit | row_major_bit,
-    Alignment = actual_alignment
-  };
-};
-}
-
-/** \class Matrix
-  * \ingroup Core_Module
-  *
-  * \brief The matrix class, also used for vectors and row-vectors
-  *
-  * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen.
-  * Vectors are matrices with one column, and row-vectors are matrices with one row.
-  *
-  * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
-  *
-  * The first three template parameters are required:
-  * \tparam _Scalar Numeric type, e.g. float, double, int or std::complex<float>.
-  *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
-  * \tparam _Rows Number of rows, or \b Dynamic
-  * \tparam _Cols Number of columns, or \b Dynamic
-  *
-  * The remaining template parameters are optional -- in most cases you don't have to worry about them.
-  * \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of either
-  *                 \b #AutoAlign or \b #DontAlign.
-  *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
-  *                 for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size.
-  * \tparam _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note").
-  * \tparam _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note").
-  *
-  * Eigen provides a number of typedefs covering the usual cases. Here are some examples:
-  *
-  * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix<double, 2, 2>)
-  * \li \c Vector4f is a vector of 4 floats (\c Matrix<float, 4, 1>)
-  * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix<int, 1, 3>)
-  *
-  * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>)
-  * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>)
-  *
-  * \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix<float, 2, Dynamic>)
-  * \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix<double, Dynamic, 3>)
-  *
-  * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs.
-  *
-  * You can access elements of vectors and matrices using normal subscripting:
-  *
-  * \code
-  * Eigen::VectorXd v(10);
-  * v[0] = 0.1;
-  * v[1] = 0.2;
-  * v(0) = 0.3;
-  * v(1) = 0.4;
-  *
-  * Eigen::MatrixXi m(10, 10);
-  * m(0, 1) = 1;
-  * m(0, 2) = 2;
-  * m(0, 3) = 3;
-  * \endcode
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
-  *
-  * <i><b>Some notes:</b></i>
-  *
-  * <dl>
-  * <dt><b>\anchor dense Dense versus sparse:</b></dt>
-  * <dd>This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the Sparse module.
-  *
-  * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary contiguous array.
-  * This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero coefficients.</dd>
-  *
-  * <dt><b>\anchor fixedsize Fixed-size versus dynamic-size:</b></dt>
-  * <dd>Fixed-size means that the numbers of rows and columns are known are compile-time. In this case, Eigen allocates the array
-  * of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices, typically up to 4x4, sometimes up
-  * to 16x16. Larger matrices should be declared as dynamic-size even if one happens to know their size at compile-time.
-  *
-  * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they are runtime
-  * variables, and the array of coefficients is allocated dynamically on the heap.
-  *
-  * Note that \em dense matrices, be they Fixed-size or Dynamic-size, <em>do not</em> expand dynamically in the sense of a std::map.
-  * If you want this behavior, see the Sparse module.</dd>
-  *
-  * <dt><b>\anchor maxrows _MaxRows and _MaxCols:</b></dt>
-  * <dd>In most cases, one just leaves these parameters to the default values.
-  * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases
-  * when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot
-  * exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case _MaxRows and _MaxCols
-  * are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
-  * </dl>
-  *
-  * <i><b>ABI and storage layout</b></i>
-  *
-  * The table below summarizes the ABI of some possible Matrix instances which is fixed thorough the lifetime of Eigen 3.
-  * <table  class="manual">
-  * <tr><th>Matrix type</th><th>Equivalent C structure</th></tr>
-  * <tr><td>\code Matrix<T,Dynamic,Dynamic> \endcode</td><td>\code
-  * struct {
-  *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
-  *   Eigen::Index rows, cols;
-  *  };
-  * \endcode</td></tr>
-  * <tr class="alt"><td>\code
-  * Matrix<T,Dynamic,1>
-  * Matrix<T,1,Dynamic> \endcode</td><td>\code
-  * struct {
-  *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
-  *   Eigen::Index size;
-  *  };
-  * \endcode</td></tr>
-  * <tr><td>\code Matrix<T,Rows,Cols> \endcode</td><td>\code
-  * struct {
-  *   T data[Rows*Cols];        // with (size_t(data)%A(Rows*Cols*sizeof(T)))==0
-  *  };
-  * \endcode</td></tr>
-  * <tr class="alt"><td>\code Matrix<T,Dynamic,Dynamic,0,MaxRows,MaxCols> \endcode</td><td>\code
-  * struct {
-  *   T data[MaxRows*MaxCols];  // with (size_t(data)%A(MaxRows*MaxCols*sizeof(T)))==0
-  *   Eigen::Index rows, cols;
-  *  };
-  * \endcode</td></tr>
-  * </table>
-  * Note that in this table Rows, Cols, MaxRows and MaxCols are all positive integers. A(S) is defined to the largest possible power-of-two
-  * smaller to EIGEN_MAX_STATIC_ALIGN_BYTES.
-  *
-  * \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
-  * \ref TopicStorageOrders
-  */
-
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-class Matrix
-  : public PlainObjectBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-{
-  public:
-
-    /** \brief Base class typedef.
-      * \sa PlainObjectBase
-      */
-    typedef PlainObjectBase<Matrix> Base;
-
-    enum { Options = _Options };
-
-    EIGEN_DENSE_PUBLIC_INTERFACE(Matrix)
-
-    typedef typename Base::PlainObject PlainObject;
-
-    using Base::base;
-    using Base::coeffRef;
-
-    /**
-      * \brief Assigns matrices to each other.
-      *
-      * \note This is a special case of the templated operator=. Its purpose is
-      * to prevent a default operator= from hiding the templated operator=.
-      *
-      * \callgraph
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
-    {
-      return Base::_set(other);
-    }
-
-    /** \internal
-      * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
-      *
-      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
-      * it will be initialized.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase<OtherDerived>& other)
-    {
-      return Base::_set(other);
-    }
-
-    /* Here, doxygen failed to copy the brief information when using \copydoc */
-
-    /**
-      * \brief Copies the generic expression \a other into *this.
-      * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived> &other)
-    {
-      return Base::operator=(other);
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func)
-    {
-      return Base::operator=(func);
-    }
-
-    /** \brief Default constructor.
-      *
-      * For fixed-size matrices, does nothing.
-      *
-      * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
-      * is called a null matrix. This constructor is the unique way to create null matrices: resizing
-      * a matrix to 0 is not supported.
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix() : Base()
-    {
-      Base::_check_template_params();
-      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-    // FIXME is it still needed
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    explicit Matrix(internal::constructor_without_unaligned_array_assert)
-      : Base(internal::constructor_without_unaligned_array_assert())
-    { Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
-      : Base(std::move(other))
-    {
-      Base::_check_template_params();
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
-    {
-      Base::operator=(std::move(other));
-      return *this;
-    }
-#endif
-
-#if EIGEN_HAS_CXX11
-    /** \copydoc PlainObjectBase(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&... args)
-     *
-     * Example: \include Matrix_variadic_ctor_cxx11.cpp
-     * Output: \verbinclude Matrix_variadic_ctor_cxx11.out
-     *
-     * \sa Matrix(const std::initializer_list<std::initializer_list<Scalar>>&)
-     */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
-      : Base(a0, a1, a2, a3, args...) {}
-
-    /** \brief Constructs a Matrix and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11
-      *
-      * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
-      *
-      * Example: \include Matrix_initializer_list_23_cxx11.cpp
-      * Output: \verbinclude Matrix_initializer_list_23_cxx11.out
-      *
-      * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered.
-      *
-      * In the case of a compile-time column vector, implicit transposition from a single row is allowed.
-      * Therefore <code>VectorXd{{1,2,3,4,5}}</code> is legal and the more verbose syntax
-      * <code>RowVectorXd{{1},{2},{3},{4},{5}}</code> can be avoided:
-      *
-      * Example: \include Matrix_initializer_list_vector_cxx11.cpp
-      * Output: \verbinclude Matrix_initializer_list_vector_cxx11.out
-      *
-      * In the case of fixed-sized matrices, the initializer list sizes must exactly match the matrix sizes,
-      * and implicit transposition is allowed for compile-time vectors only.
-      *
-      * \sa Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
-      */
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE Matrix(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
-#endif // end EIGEN_HAS_CXX11
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-    // This constructor is for both 1x1 matrices and dynamic vectors
-    template<typename T>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    explicit Matrix(const T& x)
-    {
-      Base::_check_template_params();
-      Base::template _init1<T>(x);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Matrix(const T0& x, const T1& y)
-    {
-      Base::_check_template_params();
-      Base::template _init2<T0,T1>(x, y);
-    }
-
-
-#else
-    /** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */
-    EIGEN_DEVICE_FUNC
-    explicit Matrix(const Scalar *data);
-
-    /** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
-      *
-      * This is useful for dynamic-size vectors. For fixed-size vectors,
-      * it is redundant to pass these parameters, so one should use the default constructor
-      * Matrix() instead.
-      *
-      * \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance,
-      * calling Matrix<double,1,1>(1) will call the initialization constructor: Matrix(const Scalar&).
-      * For fixed-size \c 1x1 matrices it is therefore recommended to use the default
-      * constructor Matrix() instead, especially when using one of the non standard
-      * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
-      */
-    EIGEN_STRONG_INLINE explicit Matrix(Index dim);
-    /** \brief Constructs an initialized 1x1 matrix with the given coefficient
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
-    Matrix(const Scalar& x);
-    /** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
-      *
-      * This is useful for dynamic-size matrices. For fixed-size matrices,
-      * it is redundant to pass these parameters, so one should use the default constructor
-      * Matrix() instead.
-      *
-      * \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance,
-      * calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y).
-      * For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default
-      * constructor Matrix() instead, especially when using one of the non standard
-      * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
-      */
-    EIGEN_DEVICE_FUNC
-    Matrix(Index rows, Index cols);
-
-    /** \brief Constructs an initialized 2D vector with given coefficients
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
-    Matrix(const Scalar& x, const Scalar& y);
-    #endif  // end EIGEN_PARSED_BY_DOXYGEN
-
-    /** \brief Constructs an initialized 3D vector with given coefficients
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3)
-      m_storage.data()[0] = x;
-      m_storage.data()[1] = y;
-      m_storage.data()[2] = z;
-    }
-    /** \brief Constructs an initialized 4D vector with given coefficients
-      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
-    {
-      Base::_check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4)
-      m_storage.data()[0] = x;
-      m_storage.data()[1] = y;
-      m_storage.data()[2] = z;
-      m_storage.data()[3] = w;
-    }
-
-
-    /** \brief Copy constructor */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other)
-    { }
-
-    /** \brief Copy constructor for generic expressions.
-      * \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
-      : Base(other.derived())
-    { }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return 1; }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); }
-
-    /////////// Geometry module ///////////
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
-
-    // allow to extend Matrix outside Eigen
-    #ifdef EIGEN_MATRIX_PLUGIN
-    #include EIGEN_MATRIX_PLUGIN
-    #endif
-
-  protected:
-    template <typename Derived, typename OtherDerived, bool IsVector>
-    friend struct internal::conservative_resize_like_impl;
-
-    using Base::m_storage;
-};
-
-/** \defgroup matrixtypedefs Global matrix typedefs
-  *
-  * \ingroup Core_Module
-  *
-  * %Eigen defines several typedef shortcuts for most common matrix and vector types.
-  *
-  * The general patterns are the following:
-  *
-  * \c MatrixSizeType where \c Size can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
-  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
-  * for complex double.
-  *
-  * For example, \c Matrix3d is a fixed-size 3x3 matrix type of doubles, and \c MatrixXf is a dynamic-size matrix of floats.
-  *
-  * There are also \c VectorSizeType and \c RowVectorSizeType which are self-explanatory. For example, \c Vector4cf is
-  * a fixed-size vector of 4 complex floats.
-  *
-  * With \cpp11, template alias are also defined for common sizes.
-  * They follow the same pattern as above except that the scalar type suffix is replaced by a
-  * template parameter, i.e.:
-  *   - `MatrixSize<Type>` where `Size` can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size.
-  *   - `MatrixXSize<Type>` and `MatrixSizeX<Type>` where `Size` can be \c 2,\c 3,\c 4 for hybrid dynamic/fixed matrices.
-  *   - `VectorSize<Type>` and `RowVectorSize<Type>` for column and row vectors.
-  *
-  * With \cpp11, you can also use fully generic column and row vector types: `Vector<Type,Size>` and `RowVector<Type,Size>`.
-  *
-  * \sa class Matrix
-  */
-
-#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Size, Size> Matrix##SizeSuffix##TypeSuffix;  \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Size, 1>    Vector##SizeSuffix##TypeSuffix;  \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, 1, Size>    RowVector##SizeSuffix##TypeSuffix;
-
-#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix;  \
-/** \ingroup matrixtypedefs */                                    \
-typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix;
-
-#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
-EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
-EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
-EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
-
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int,                  i)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float,                f)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double,               d)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<float>,  cf)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
-
-#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
-#undef EIGEN_MAKE_TYPEDEFS
-#undef EIGEN_MAKE_FIXED_TYPEDEFS
-
-#if EIGEN_HAS_CXX11
-
-#define EIGEN_MAKE_TYPEDEFS(Size, SizeSuffix)                     \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Matrix##SizeSuffix = Matrix<Type, Size, Size>;              \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Vector##SizeSuffix = Matrix<Type, Size, 1>;                 \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using RowVector##SizeSuffix = Matrix<Type, 1, Size>;
-
-#define EIGEN_MAKE_FIXED_TYPEDEFS(Size)                           \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Matrix##Size##X = Matrix<Type, Size, Dynamic>;              \
-/** \ingroup matrixtypedefs */                                    \
-/** \brief \cpp11 */                                              \
-template <typename Type>                                          \
-using Matrix##X##Size = Matrix<Type, Dynamic, Size>;
-
-EIGEN_MAKE_TYPEDEFS(2, 2)
-EIGEN_MAKE_TYPEDEFS(3, 3)
-EIGEN_MAKE_TYPEDEFS(4, 4)
-EIGEN_MAKE_TYPEDEFS(Dynamic, X)
-EIGEN_MAKE_FIXED_TYPEDEFS(2)
-EIGEN_MAKE_FIXED_TYPEDEFS(3)
-EIGEN_MAKE_FIXED_TYPEDEFS(4)
-
-/** \ingroup matrixtypedefs
-  * \brief \cpp11 */
-template <typename Type, int Size>
-using Vector = Matrix<Type, Size, 1>;
-
-/** \ingroup matrixtypedefs
-  * \brief \cpp11 */
-template <typename Type, int Size>
-using RowVector = Matrix<Type, 1, Size>;
-
-#undef EIGEN_MAKE_TYPEDEFS
-#undef EIGEN_MAKE_FIXED_TYPEDEFS
-
-#endif // EIGEN_HAS_CXX11
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MatrixBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MatrixBase.h
deleted file mode 100644
index 45c3a59..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/MatrixBase.h
+++ /dev/null
@@ -1,547 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIXBASE_H
-#define EIGEN_MATRIXBASE_H
-
-namespace Eigen {
-
-/** \class MatrixBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for all dense matrices, vectors, and expressions
-  *
-  * This class is the base that is inherited by all matrix, vector, and related expression
-  * types. Most of the Eigen API is contained in this class, and its base classes. Other important
-  * classes for the Eigen API are Matrix, and VectorwiseOp.
-  *
-  * Note that some methods are defined in other modules such as the \ref LU_Module LU module
-  * for all functions related to matrix inversions.
-  *
-  * \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc.
-  *
-  * When writing a function taking Eigen objects as argument, if you want your function
-  * to take as argument any matrix, vector, or expression, just let it take a
-  * MatrixBase argument. As an example, here is a function printFirstRow which, given
-  * a matrix, vector, or expression \a x, prints the first row of \a x.
-  *
-  * \code
-    template<typename Derived>
-    void printFirstRow(const Eigen::MatrixBase<Derived>& x)
-    {
-      cout << x.row(0) << endl;
-    }
-  * \endcode
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
-  *
-  * \sa \blank \ref TopicClassHierarchy
-  */
-template<typename Derived> class MatrixBase
-  : public DenseBase<Derived>
-{
-  public:
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef MatrixBase StorageBaseType;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    typedef DenseBase<Derived> Base;
-    using Base::RowsAtCompileTime;
-    using Base::ColsAtCompileTime;
-    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-
-    using Base::derived;
-    using Base::const_cast_derived;
-    using Base::rows;
-    using Base::cols;
-    using Base::size;
-    using Base::coeff;
-    using Base::coeffRef;
-    using Base::lazyAssign;
-    using Base::eval;
-    using Base::operator-;
-    using Base::operator+=;
-    using Base::operator-=;
-    using Base::operator*=;
-    using Base::operator/=;
-
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-    typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
-    typedef typename Base::RowXpr RowXpr;
-    typedef typename Base::ColXpr ColXpr;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** type of the equivalent square matrix */
-    typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
-                          EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-    /** \returns the size of the main diagonal, which is min(rows(),cols()).
-      * \sa rows(), cols(), SizeAtCompileTime. */
-    EIGEN_DEVICE_FUNC
-    inline Index diagonalSize() const { return (numext::mini)(rows(),cols()); }
-
-    typedef typename Base::PlainObject PlainObject;
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
-    /** \internal the return type of MatrixBase::adjoint() */
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
-                        ConstTransposeReturnType
-                     >::type AdjointReturnType;
-    /** \internal Return type of eigenvalues() */
-    typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType;
-    /** \internal the return type of identity */
-    typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,PlainObject> IdentityReturnType;
-    /** \internal the return type of unit vectors */
-    typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>,
-                  internal::traits<Derived>::RowsAtCompileTime,
-                  internal::traits<Derived>::ColsAtCompileTime> BasisReturnType;
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#   include "../plugins/CommonCwiseBinaryOps.h"
-#   include "../plugins/MatrixCwiseUnaryOps.h"
-#   include "../plugins/MatrixCwiseBinaryOps.h"
-#   ifdef EIGEN_MATRIXBASE_PLUGIN
-#     include EIGEN_MATRIXBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_UNARY_ADDONS
-
-    /** Special case of the template operator=, in order to prevent the compiler
-      * from generating a default operator= (issue hit with g++ 4.1)
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const MatrixBase& other);
-
-    // We cannot inherit here via Base::operator= since it is causing
-    // trouble with MSVC.
-
-    template <typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator=(const DenseBase<OtherDerived>& other);
-
-    template <typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const EigenBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    Derived& operator=(const ReturnByValue<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator+=(const MatrixBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator-=(const MatrixBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived,OtherDerived>
-    operator*(const MatrixBase<OtherDerived> &other) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived,OtherDerived,LazyProduct>
-    lazyProduct(const MatrixBase<OtherDerived> &other) const;
-
-    template<typename OtherDerived>
-    Derived& operator*=(const EigenBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    void applyOnTheLeft(const EigenBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    void applyOnTheRight(const EigenBase<OtherDerived>& other);
-
-    template<typename DiagonalDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<Derived, DiagonalDerived, LazyProduct>
-    operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
-    dot(const MatrixBase<OtherDerived>& other) const;
-
-    EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
-    EIGEN_DEVICE_FUNC RealScalar norm() const;
-    RealScalar stableNorm() const;
-    RealScalar blueNorm() const;
-    RealScalar hypotNorm() const;
-    EIGEN_DEVICE_FUNC const PlainObject normalized() const;
-    EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const;
-    EIGEN_DEVICE_FUNC void normalize();
-    EIGEN_DEVICE_FUNC void stableNormalize();
-
-    EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const;
-    EIGEN_DEVICE_FUNC void adjointInPlace();
-
-    typedef Diagonal<Derived> DiagonalReturnType;
-    EIGEN_DEVICE_FUNC
-    DiagonalReturnType diagonal();
-
-    typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
-    EIGEN_DEVICE_FUNC
-    ConstDiagonalReturnType diagonal() const;
-
-    template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
-    template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
-
-    template<int Index>
-    EIGEN_DEVICE_FUNC
-    typename DiagonalIndexReturnType<Index>::Type diagonal();
-
-    template<int Index>
-    EIGEN_DEVICE_FUNC
-    typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
-
-    typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
-    typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
-
-    EIGEN_DEVICE_FUNC
-    DiagonalDynamicIndexReturnType diagonal(Index index);
-    EIGEN_DEVICE_FUNC
-    ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
-
-    template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
-    template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
-
-    template<unsigned int Mode>
-    EIGEN_DEVICE_FUNC
-    typename TriangularViewReturnType<Mode>::Type triangularView();
-    template<unsigned int Mode>
-    EIGEN_DEVICE_FUNC
-    typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
-
-    template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; };
-    template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; };
-
-    template<unsigned int UpLo>
-    EIGEN_DEVICE_FUNC
-    typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
-    template<unsigned int UpLo>
-    EIGEN_DEVICE_FUNC
-    typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
-
-    const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
-                                         const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
-    EIGEN_DEVICE_FUNC static const IdentityReturnType Identity();
-    EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i);
-    EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i);
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitX();
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitY();
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ();
-    EIGEN_DEVICE_FUNC static const BasisReturnType UnitW();
-
-    EIGEN_DEVICE_FUNC
-    const DiagonalWrapper<const Derived> asDiagonal() const;
-    const PermutationWrapper<const Derived> asPermutation() const;
-
-    EIGEN_DEVICE_FUNC
-    Derived& setIdentity();
-    EIGEN_DEVICE_FUNC
-    Derived& setIdentity(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setUnit(Index i);
-    EIGEN_DEVICE_FUNC Derived& setUnit(Index newSize, Index i);
-
-    bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    template<typename OtherDerived>
-    bool isOrthogonal(const MatrixBase<OtherDerived>& other,
-                      const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-    bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
-      * \warning When using floating point scalar values you probably should rather use a
-      *          fuzzy comparison such as isApprox()
-      * \sa isApprox(), operator!= */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase<OtherDerived>& other) const
-    { return cwiseEqual(other).all(); }
-
-    /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
-      * \warning When using floating point scalar values you probably should rather use a
-      *          fuzzy comparison such as isApprox()
-      * \sa isApprox(), operator== */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase<OtherDerived>& other) const
-    { return cwiseNotEqual(other).any(); }
-
-    NoAlias<Derived,Eigen::MatrixBase > EIGEN_DEVICE_FUNC noalias();
-
-    // TODO forceAlignedAccess is temporarily disabled
-    // Need to find a nicer workaround.
-    inline const Derived& forceAlignedAccess() const { return derived(); }
-    inline Derived& forceAlignedAccess() { return derived(); }
-    template<bool Enable> inline const Derived& forceAlignedAccessIf() const { return derived(); }
-    template<bool Enable> inline Derived& forceAlignedAccessIf() { return derived(); }
-
-    EIGEN_DEVICE_FUNC Scalar trace() const;
-
-    template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
-
-    EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
-    EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; }
-
-    /** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
-      * \sa ArrayBase::matrix() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return ArrayWrapper<Derived>(derived()); }
-    /** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
-      * \sa ArrayBase::matrix() */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return ArrayWrapper<const Derived>(derived()); }
-
-/////////// LU module ///////////
-
-    inline const FullPivLU<PlainObject> fullPivLu() const;
-    inline const PartialPivLU<PlainObject> partialPivLu() const;
-
-    inline const PartialPivLU<PlainObject> lu() const;
-
-    EIGEN_DEVICE_FUNC
-    inline const Inverse<Derived> inverse() const;
-
-    template<typename ResultType>
-    inline void computeInverseAndDetWithCheck(
-      ResultType& inverse,
-      typename ResultType::Scalar& determinant,
-      bool& invertible,
-      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
-    ) const;
-
-    template<typename ResultType>
-    inline void computeInverseWithCheck(
-      ResultType& inverse,
-      bool& invertible,
-      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
-    ) const;
-
-    EIGEN_DEVICE_FUNC
-    Scalar determinant() const;
-
-/////////// Cholesky module ///////////
-
-    inline const LLT<PlainObject>  llt() const;
-    inline const LDLT<PlainObject> ldlt() const;
-
-/////////// QR module ///////////
-
-    inline const HouseholderQR<PlainObject> householderQr() const;
-    inline const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
-    inline const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
-    inline const CompleteOrthogonalDecomposition<PlainObject> completeOrthogonalDecomposition() const;
-
-/////////// Eigenvalues module ///////////
-
-    inline EigenvaluesReturnType eigenvalues() const;
-    inline RealScalar operatorNorm() const;
-
-/////////// SVD module ///////////
-
-    inline JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
-    inline BDCSVD<PlainObject>    bdcSvd(unsigned int computationOptions = 0) const;
-
-/////////// Geometry module ///////////
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /// \internal helper struct to form the return type of the cross product
-    template<typename OtherDerived> struct cross_product_return_type {
-      typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
-      typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type;
-    };
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    inline typename cross_product_return_type<OtherDerived>::type
-#else
-    inline PlainObject
-#endif
-    cross(const MatrixBase<OtherDerived>& other) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    inline PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
-
-    EIGEN_DEVICE_FUNC
-    inline PlainObject unitOrthogonal(void) const;
-
-    EIGEN_DEVICE_FUNC
-    inline Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
-
-    // put this as separate enum value to work around possible GCC 4.3 bug (?)
-    enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits<Derived>::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical)
-                                          : ColsAtCompileTime==1 ? Vertical : Horizontal };
-    typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
-    EIGEN_DEVICE_FUNC
-    inline HomogeneousReturnType homogeneous() const;
-
-    enum {
-      SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
-    };
-    typedef Block<const Derived,
-                  internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
-                  internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
-    typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType;
-    EIGEN_DEVICE_FUNC
-    inline const HNormalizedReturnType hnormalized() const;
-
-////////// Householder module ///////////
-
-    EIGEN_DEVICE_FUNC
-    void makeHouseholderInPlace(Scalar& tau, RealScalar& beta);
-    template<typename EssentialPart>
-    EIGEN_DEVICE_FUNC
-    void makeHouseholder(EssentialPart& essential,
-                         Scalar& tau, RealScalar& beta) const;
-    template<typename EssentialPart>
-    EIGEN_DEVICE_FUNC
-    void applyHouseholderOnTheLeft(const EssentialPart& essential,
-                                   const Scalar& tau,
-                                   Scalar* workspace);
-    template<typename EssentialPart>
-    EIGEN_DEVICE_FUNC
-    void applyHouseholderOnTheRight(const EssentialPart& essential,
-                                    const Scalar& tau,
-                                    Scalar* workspace);
-
-///////// Jacobi module /////////
-
-    template<typename OtherScalar>
-    EIGEN_DEVICE_FUNC
-    void applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j);
-    template<typename OtherScalar>
-    EIGEN_DEVICE_FUNC
-    void applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j);
-
-///////// SparseCore module /////////
-
-    template<typename OtherDerived>
-    EIGEN_STRONG_INLINE const typename SparseMatrixBase<OtherDerived>::template CwiseProductDenseReturnType<Derived>::Type
-    cwiseProduct(const SparseMatrixBase<OtherDerived> &other) const
-    {
-      return other.cwiseProduct(derived());
-    }
-
-///////// MatrixFunctions module /////////
-
-    typedef typename internal::stem_function<Scalar>::type StemFunction;
-#define EIGEN_MATRIX_FUNCTION(ReturnType, Name, Description) \
-    /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
-    const ReturnType<Derived> Name() const;
-#define EIGEN_MATRIX_FUNCTION_1(ReturnType, Name, Description, Argument) \
-    /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
-    const ReturnType<Derived> Name(Argument) const;
-
-    EIGEN_MATRIX_FUNCTION(MatrixExponentialReturnValue, exp, exponential)
-    /** \brief Helper function for the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>.*/
-    const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine)
-#if EIGEN_HAS_CXX11_MATH
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, atanh, inverse hyperbolic cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, acosh, inverse hyperbolic cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, asinh, inverse hyperbolic sine)
-#endif
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine)
-    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine)
-    EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root)
-    EIGEN_MATRIX_FUNCTION(MatrixLogarithmReturnValue, log, logarithm)
-    EIGEN_MATRIX_FUNCTION_1(MatrixPowerReturnValue,        pow, power to \c p, const RealScalar& p)
-    EIGEN_MATRIX_FUNCTION_1(MatrixComplexPowerReturnValue, pow, power to \c p, const std::complex<RealScalar>& p)
-
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MatrixBase)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MatrixBase)
-
-  private:
-    EIGEN_DEVICE_FUNC explicit MatrixBase(int);
-    EIGEN_DEVICE_FUNC MatrixBase(int,int);
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&);
-  protected:
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-    // mixing arrays and matrices is not legal
-    template<typename OtherDerived> Derived& operator-=(const ArrayBase<OtherDerived>& )
-    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
-};
-
-
-/***************************************************************************
-* Implementation of matrix base methods
-***************************************************************************/
-
-/** replaces \c *this by \c *this * \a other.
-  *
-  * \returns a reference to \c *this
-  *
-  * Example: \include MatrixBase_applyOnTheRight.cpp
-  * Output: \verbinclude MatrixBase_applyOnTheRight.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-inline Derived&
-MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
-{
-  other.derived().applyThisOnTheRight(derived());
-  return derived();
-}
-
-/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
-  *
-  * Example: \include MatrixBase_applyOnTheRight.cpp
-  * Output: \verbinclude MatrixBase_applyOnTheRight.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
-{
-  other.derived().applyThisOnTheRight(derived());
-}
-
-/** replaces \c *this by \a other * \c *this.
-  *
-  * Example: \include MatrixBase_applyOnTheLeft.cpp
-  * Output: \verbinclude MatrixBase_applyOnTheLeft.out
-  */
-template<typename Derived>
-template<typename OtherDerived>
-inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
-{
-  other.derived().applyThisOnTheLeft(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIXBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NestByValue.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NestByValue.h
deleted file mode 100644
index b427576..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NestByValue.h
+++ /dev/null
@@ -1,85 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NESTBYVALUE_H
-#define EIGEN_NESTBYVALUE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename ExpressionType>
-struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType>
-{
-  enum {
-    Flags = traits<ExpressionType>::Flags & ~NestByRefBit
-  };
-};
-}
-
-/** \class NestByValue
-  * \ingroup Core_Module
-  *
-  * \brief Expression which must be nested by value
-  *
-  * \tparam ExpressionType the type of the object of which we are requiring nesting-by-value
-  *
-  * This class is the return type of MatrixBase::nestByValue()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::nestByValue()
-  */
-template<typename ExpressionType> class NestByValue
-  : public internal::dense_xpr_base< NestByValue<ExpressionType> >::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<NestByValue>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
-
-    EIGEN_DEVICE_FUNC explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
-
-    EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
-
-    EIGEN_DEVICE_FUNC const ExpressionType& nestedExpression() const { return m_expression; }
-
-  protected:
-    const ExpressionType m_expression;
-};
-
-/** \returns an expression of the temporary version of *this.
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const NestByValue<Derived>
-DenseBase<Derived>::nestByValue() const
-{
-  return NestByValue<Derived>(derived());
-}
-
-namespace internal {
-
-// Evaluator of Solve -> eval into a temporary
-template<typename ArgType>
-struct evaluator<NestByValue<ArgType> >
-  : public evaluator<ArgType>
-{
-  typedef evaluator<ArgType> Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const NestByValue<ArgType>& xpr)
-    : Base(xpr.nestedExpression())
-  {}
-};
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_NESTBYVALUE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NoAlias.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NoAlias.h
deleted file mode 100644
index 570283d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NoAlias.h
+++ /dev/null
@@ -1,109 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NOALIAS_H
-#define EIGEN_NOALIAS_H
-
-namespace Eigen {
-
-/** \class NoAlias
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression providing an operator = assuming no aliasing
-  *
-  * \tparam ExpressionType the type of the object on which to do the lazy assignment
-  *
-  * This class represents an expression with special assignment operators
-  * assuming no aliasing between the target expression and the source expression.
-  * More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression.
-  * It is the return type of MatrixBase::noalias()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::noalias()
-  */
-template<typename ExpressionType, template <typename> class StorageBase>
-class NoAlias
-{
-  public:
-    typedef typename ExpressionType::Scalar Scalar;
-    
-    EIGEN_DEVICE_FUNC
-    explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
-    
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
-    {
-      call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-      return m_expression;
-    }
-    
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
-    {
-      call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-      return m_expression;
-    }
-    
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
-    {
-      call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-      return m_expression;
-    }
-
-    EIGEN_DEVICE_FUNC
-    ExpressionType& expression() const
-    {
-      return m_expression;
-    }
-
-  protected:
-    ExpressionType& m_expression;
-};
-
-/** \returns a pseudo expression of \c *this with an operator= assuming
-  * no aliasing between \c *this and the source expression.
-  *
-  * More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag.
-  * Currently, even though several expressions may alias, only product
-  * expressions have this flag. Therefore, noalias() is only useful when
-  * the source expression contains a matrix product.
-  *
-  * Here are some examples where noalias is useful:
-  * \code
-  * D.noalias()  = A * B;
-  * D.noalias() += A.transpose() * B;
-  * D.noalias() -= 2 * A * B.adjoint();
-  * \endcode
-  *
-  * On the other hand the following example will lead to a \b wrong result:
-  * \code
-  * A.noalias() = A * B;
-  * \endcode
-  * because the result matrix A is also an operand of the matrix product. Therefore,
-  * there is no alternative than evaluating A * B in a temporary, that is the default
-  * behavior when you write:
-  * \code
-  * A = A * B;
-  * \endcode
-  *
-  * \sa class NoAlias
-  */
-template<typename Derived>
-NoAlias<Derived,MatrixBase> EIGEN_DEVICE_FUNC MatrixBase<Derived>::noalias()
-{
-  return NoAlias<Derived, Eigen::MatrixBase >(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_NOALIAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NumTraits.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NumTraits.h
deleted file mode 100644
index 72eac5a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/NumTraits.h
+++ /dev/null
@@ -1,335 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NUMTRAITS_H
-#define EIGEN_NUMTRAITS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// default implementation of digits10(), based on numeric_limits if specialized,
-// 0 for integer types, and log10(epsilon()) otherwise.
-template< typename T,
-          bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
-          bool is_integer = NumTraits<T>::IsInteger>
-struct default_digits10_impl
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return std::numeric_limits<T>::digits10; }
-};
-
-template<typename T>
-struct default_digits10_impl<T,false,false> // Floating point
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() {
-    using std::log10;
-    using std::ceil;
-    typedef typename NumTraits<T>::Real Real;
-    return int(ceil(-log10(NumTraits<Real>::epsilon())));
-  }
-};
-
-template<typename T>
-struct default_digits10_impl<T,false,true> // Integer
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return 0; }
-};
-
-
-// default implementation of digits(), based on numeric_limits if specialized,
-// 0 for integer types, and log2(epsilon()) otherwise.
-template< typename T,
-          bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
-          bool is_integer = NumTraits<T>::IsInteger>
-struct default_digits_impl
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return std::numeric_limits<T>::digits; }
-};
-
-template<typename T>
-struct default_digits_impl<T,false,false> // Floating point
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() {
-    using std::log;
-    using std::ceil;
-    typedef typename NumTraits<T>::Real Real;
-    return int(ceil(-log(NumTraits<Real>::epsilon())/log(static_cast<Real>(2))));
-  }
-};
-
-template<typename T>
-struct default_digits_impl<T,false,true> // Integer
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int run() { return 0; }
-};
-
-} // end namespace internal
-
-namespace numext {
-/** \internal bit-wise cast without changing the underlying bit representation. */
-
-// TODO: Replace by std::bit_cast (available in C++20)
-template <typename Tgt, typename Src>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) {
-#if EIGEN_HAS_TYPE_TRAITS
-  // The behaviour of memcpy is not specified for non-trivially copyable types
-  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Src>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Tgt>::value && std::is_default_constructible<Tgt>::value,
-                      THIS_TYPE_IS_NOT_SUPPORTED);
-#endif
-
-  EIGEN_STATIC_ASSERT(sizeof(Src) == sizeof(Tgt), THIS_TYPE_IS_NOT_SUPPORTED);
-  Tgt tgt;
-  EIGEN_USING_STD(memcpy)
-  memcpy(&tgt, &src, sizeof(Tgt));
-  return tgt;
-}
-}  // namespace numext
-
-/** \class NumTraits
-  * \ingroup Core_Module
-  *
-  * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
-  *
-  * \tparam T the numeric type at hand
-  *
-  * This class stores enums, typedefs and static methods giving information about a numeric type.
-  *
-  * The provided data consists of:
-  * \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
-  *     then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
-  *     is a typedef to \a U.
-  * \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
-  *     such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
-  *     \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
-  *     take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
-  *     only intended as a helper for code that needs to explicitly promote types.
-  * \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
-  *     Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
-  * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
-  *     this means, just use \a T here.
-  * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
-  *     type, and to 0 otherwise.
-  * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int,
-  *     and to \c 0 otherwise.
-  * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed
-  *     to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers.
-  *     Stay vague here. No need to do architecture-specific stuff. If you don't know what this means, just use \c Eigen::HugeCost.
-  * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
-  * \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
-  *     be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
-  * \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">std::numeric_limits::epsilon()</a>,
-  *     it returns a \a Real instead of a \a T.
-  * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
-  *     value by the fuzzy comparison operators.
-  * \li highest() and lowest() functions returning the highest and lowest possible values respectively.
-  * \li digits() function returning the number of radix digits (non-sign digits for integers, mantissa for floating-point). This is
-  *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits">std::numeric_limits<T>::digits</a>
-  *     which is used as the default implementation if specialized.
-  * \li digits10() function returning the number of decimal digits that can be represented without change. This is
-  *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">std::numeric_limits<T>::digits10</a>
-  *     which is used as the default implementation if specialized.
-  * \li min_exponent() and max_exponent() functions returning the highest and lowest possible values, respectively,
-  *     such that the radix raised to the power exponent-1 is a normalized floating-point number.  These are equivalent to
-  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/min_exponent">std::numeric_limits<T>::min_exponent</a>/
-  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_exponent">std::numeric_limits<T>::max_exponent</a>.
-  * \li infinity() function returning a representation of positive infinity, if available.
-  * \li quiet_NaN function returning a non-signaling "not-a-number", if available.
-  */
-
-template<typename T> struct GenericNumTraits
-{
-  enum {
-    IsInteger = std::numeric_limits<T>::is_integer,
-    IsSigned = std::numeric_limits<T>::is_signed,
-    IsComplex = 0,
-    RequireInitialization = internal::is_arithmetic<T>::value ? 0 : 1,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-
-  typedef T Real;
-  typedef typename internal::conditional<
-                     IsInteger,
-                     typename internal::conditional<sizeof(T)<=2, float, double>::type,
-                     T
-                   >::type NonInteger;
-  typedef T Nested;
-  typedef T Literal;
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real epsilon()
-  {
-    return numext::numeric_limits<T>::epsilon();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int digits10()
-  {
-    return internal::default_digits10_impl<T>::run();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int digits()
-  {
-    return internal::default_digits_impl<T>::run();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int min_exponent()
-  {
-    return numext::numeric_limits<T>::min_exponent;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int max_exponent()
-  {
-    return numext::numeric_limits<T>::max_exponent;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real dummy_precision()
-  {
-    // make sure to override this for floating-point types
-    return Real(0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T highest() {
-    return (numext::numeric_limits<T>::max)();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T lowest()  {
-    return IsInteger ? (numext::numeric_limits<T>::min)()
-                     : static_cast<T>(-(numext::numeric_limits<T>::max)());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T infinity() {
-    return numext::numeric_limits<T>::infinity();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline T quiet_NaN() {
-    return numext::numeric_limits<T>::quiet_NaN();
-  }
-};
-
-template<typename T> struct NumTraits : GenericNumTraits<T>
-{};
-
-template<> struct NumTraits<float>
-  : GenericNumTraits<float>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline float dummy_precision() { return 1e-5f; }
-};
-
-template<> struct NumTraits<double> : GenericNumTraits<double>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline double dummy_precision() { return 1e-12; }
-};
-
-template<> struct NumTraits<long double>
-  : GenericNumTraits<long double>
-{
-  EIGEN_CONSTEXPR
-  static inline long double dummy_precision() { return 1e-15l; }
-};
-
-template<typename _Real> struct NumTraits<std::complex<_Real> >
-  : GenericNumTraits<std::complex<_Real> >
-{
-  typedef _Real Real;
-  typedef typename NumTraits<_Real>::Literal Literal;
-  enum {
-    IsComplex = 1,
-    RequireInitialization = NumTraits<_Real>::RequireInitialization,
-    ReadCost = 2 * NumTraits<_Real>::ReadCost,
-    AddCost = 2 * NumTraits<Real>::AddCost,
-    MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real epsilon() { return NumTraits<Real>::epsilon(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline int digits10() { return NumTraits<Real>::digits10(); }
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
-{
-  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> ArrayType;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Array<RealScalar, Rows, Cols, Options, MaxRows, MaxCols> Real;
-  typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
-  typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
-  typedef ArrayType & Nested;
-  typedef typename NumTraits<Scalar>::Literal Literal;
-
-  enum {
-    IsComplex = NumTraits<Scalar>::IsComplex,
-    IsInteger = NumTraits<Scalar>::IsInteger,
-    IsSigned  = NumTraits<Scalar>::IsSigned,
-    RequireInitialization = 1,
-    ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::ReadCost),
-    AddCost  = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::AddCost),
-    MulCost  = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::MulCost)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
-
-  EIGEN_CONSTEXPR
-  static inline int digits10() { return NumTraits<Scalar>::digits10(); }
-};
-
-template<> struct NumTraits<std::string>
-  : GenericNumTraits<std::string>
-{
-  enum {
-    RequireInitialization = 1,
-    ReadCost = HugeCost,
-    AddCost  = HugeCost,
-    MulCost  = HugeCost
-  };
-
-  EIGEN_CONSTEXPR
-  static inline int digits10() { return 0; }
-
-private:
-  static inline std::string epsilon();
-  static inline std::string dummy_precision();
-  static inline std::string lowest();
-  static inline std::string highest();
-  static inline std::string infinity();
-  static inline std::string quiet_NaN();
-};
-
-// Empty specialization for void to allow template specialization based on NumTraits<T>::Real with T==void and SFINAE.
-template<> struct NumTraits<void> {};
-
-template<> struct NumTraits<bool> : GenericNumTraits<bool> {};
-
-} // end namespace Eigen
-
-#endif // EIGEN_NUMTRAITS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
deleted file mode 100644
index 29abf35..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
+++ /dev/null
@@ -1,232 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARTIALREDUX_H
-#define EIGEN_PARTIALREDUX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-/***************************************************************************
-*
-* This file provides evaluators for partial reductions.
-* There are two modes:
-*
-*  - scalar path: simply calls the respective function on the column or row.
-*    -> nothing special here, all the tricky part is handled by the return
-*       types of VectorwiseOp's members. They embed the functor calling the
-*       respective DenseBase's member function.
-*
-*  - vectorized path: implements a packet-wise reductions followed by
-*    some (optional) processing of the outcome, e.g., division by n for mean.
-*
-* For the vectorized path let's observe that the packet-size and outer-unrolling
-* are both decided by the assignement logic. So all we have to do is to decide
-* on the inner unrolling.
-*
-* For the unrolling, we can reuse "internal::redux_vec_unroller" from Redux.h,
-* but be need to be careful to specify correct increment.
-*
-***************************************************************************/
-
-
-/* logic deciding a strategy for unrolling of vectorized paths */
-template<typename Func, typename Evaluator>
-struct packetwise_redux_traits
-{
-  enum {
-    OuterSize = int(Evaluator::IsRowMajor) ? Evaluator::RowsAtCompileTime : Evaluator::ColsAtCompileTime,
-    Cost = OuterSize == Dynamic ? HugeCost
-         : OuterSize * Evaluator::CoeffReadCost + (OuterSize-1) * functor_traits<Func>::Cost,
-    Unrolling = Cost <= EIGEN_UNROLLING_LIMIT ? CompleteUnrolling : NoUnrolling
-  };
-
-};
-
-/* Value to be returned when size==0 , by default let's return 0 */
-template<typename PacketType,typename Func>
-EIGEN_DEVICE_FUNC
-PacketType packetwise_redux_empty_value(const Func& ) { return pset1<PacketType>(0); }
-
-/* For products the default is 1 */
-template<typename PacketType,typename Scalar>
-EIGEN_DEVICE_FUNC
-PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar,Scalar>& ) { return pset1<PacketType>(1); }
-
-/* Perform the actual reduction */
-template<typename Func, typename Evaluator,
-         int Unrolling = packetwise_redux_traits<Func, Evaluator>::Unrolling
->
-struct packetwise_redux_impl;
-
-/* Perform the actual reduction with unrolling */
-template<typename Func, typename Evaluator>
-struct packetwise_redux_impl<Func, Evaluator, CompleteUnrolling>
-{
-  typedef redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
-  typedef typename Evaluator::Scalar Scalar;
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  PacketType run(const Evaluator &eval, const Func& func, Index /*size*/)
-  {
-    return redux_vec_unroller<Func, Evaluator, 0, packetwise_redux_traits<Func, Evaluator>::OuterSize>::template run<PacketType>(eval,func);
-  }
-};
-
-/* Add a specialization of redux_vec_unroller for size==0 at compiletime.
- * This specialization is not required for general reductions, which is
- * why it is defined here.
- */
-template<typename Func, typename Evaluator, int Start>
-struct redux_vec_unroller<Func, Evaluator, Start, 0>
-{
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &, const Func& f)
-  {
-    return packetwise_redux_empty_value<PacketType>(f);
-  }
-};
-
-/* Perform the actual reduction for dynamic sizes */
-template<typename Func, typename Evaluator>
-struct packetwise_redux_impl<Func, Evaluator, NoUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static PacketType run(const Evaluator &eval, const Func& func, Index size)
-  {
-    if(size==0)
-      return packetwise_redux_empty_value<PacketType>(func);
-    
-    const Index size4 = (size-1)&(~3);
-    PacketType p = eval.template packetByOuterInner<Unaligned,PacketType>(0,0);
-    Index i = 1;
-    // This loop is optimized for instruction pipelining:
-    // - each iteration generates two independent instructions
-    // - thanks to branch prediction and out-of-order execution we have independent instructions across loops
-    for(; i<size4; i+=4)
-      p = func.packetOp(p,
-            func.packetOp(
-              func.packetOp(eval.template packetByOuterInner<Unaligned,PacketType>(i+0,0),eval.template packetByOuterInner<Unaligned,PacketType>(i+1,0)),
-              func.packetOp(eval.template packetByOuterInner<Unaligned,PacketType>(i+2,0),eval.template packetByOuterInner<Unaligned,PacketType>(i+3,0))));
-    for(; i<size; ++i)
-      p = func.packetOp(p, eval.template packetByOuterInner<Unaligned,PacketType>(i,0));
-    return p;
-  }
-};
-
-template< typename ArgType, typename MemberOp, int Direction>
-struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
-  : evaluator_base<PartialReduxExpr<ArgType, MemberOp, Direction> >
-{
-  typedef PartialReduxExpr<ArgType, MemberOp, Direction> XprType;
-  typedef typename internal::nested_eval<ArgType,1>::type ArgTypeNested;
-  typedef typename internal::add_const_on_value_type<ArgTypeNested>::type ConstArgTypeNested;
-  typedef typename internal::remove_all<ArgTypeNested>::type ArgTypeNestedCleaned;
-  typedef typename ArgType::Scalar InputScalar;
-  typedef typename XprType::Scalar Scalar;
-  enum {
-    TraversalSize = Direction==int(Vertical) ? int(ArgType::RowsAtCompileTime) :  int(ArgType::ColsAtCompileTime)
-  };
-  typedef typename MemberOp::template Cost<int(TraversalSize)> CostOpType;
-  enum {
-    CoeffReadCost = TraversalSize==Dynamic ? HugeCost
-                  : TraversalSize==0 ? 1
-                  : int(TraversalSize) * int(evaluator<ArgType>::CoeffReadCost) + int(CostOpType::value),
-    
-    _ArgFlags = evaluator<ArgType>::Flags,
-
-    _Vectorizable =  bool(int(_ArgFlags)&PacketAccessBit)
-                  && bool(MemberOp::Vectorizable)
-                  && (Direction==int(Vertical) ? bool(_ArgFlags&RowMajorBit) : (_ArgFlags&RowMajorBit)==0)
-                  && (TraversalSize!=0),
-                  
-    Flags = (traits<XprType>::Flags&RowMajorBit)
-          | (evaluator<ArgType>::Flags&(HereditaryBits&(~RowMajorBit)))
-          | (_Vectorizable ? PacketAccessBit : 0)
-          | LinearAccessBit,
-    
-    Alignment = 0 // FIXME this will need to be improved once PartialReduxExpr is vectorized
-  };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType xpr)
-    : m_arg(xpr.nestedExpression()), m_functor(xpr.functor())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(TraversalSize==Dynamic ? HugeCost : (TraversalSize==0 ? 1 : int(CostOpType::value)));
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar coeff(Index i, Index j) const
-  {
-    return coeff(Direction==Vertical ? j : i);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar coeff(Index index) const
-  {
-    return m_functor(m_arg.template subVector<DirectionType(Direction)>(index));
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketType packet(Index i, Index j) const
-  {
-    return packet<LoadMode,PacketType>(Direction==Vertical ? j : i);
-  }
-  
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-  PacketType packet(Index idx) const
-  {
-    enum { PacketSize = internal::unpacket_traits<PacketType>::size };
-    typedef Block<const ArgTypeNestedCleaned,
-                  Direction==Vertical ? int(ArgType::RowsAtCompileTime) : int(PacketSize),
-                  Direction==Vertical ? int(PacketSize) : int(ArgType::ColsAtCompileTime),
-                  true /* InnerPanel */> PanelType;
-    
-    PanelType panel(m_arg,
-                    Direction==Vertical ? 0 : idx,
-                    Direction==Vertical ? idx : 0,
-                    Direction==Vertical ? m_arg.rows() : Index(PacketSize),
-                    Direction==Vertical ? Index(PacketSize) : m_arg.cols());
-
-    // FIXME
-    // See bug 1612, currently if PacketSize==1 (i.e. complex<double> with 128bits registers) then the storage-order of panel get reversed
-    // and methods like packetByOuterInner do not make sense anymore in this context.
-    // So let's just by pass "vectorization" in this case:
-    if(PacketSize==1)
-      return internal::pset1<PacketType>(coeff(idx));
-    
-    typedef typename internal::redux_evaluator<PanelType> PanelEvaluator;
-    PanelEvaluator panel_eval(panel);
-    typedef typename MemberOp::BinaryOp BinaryOp;
-    PacketType p = internal::packetwise_redux_impl<BinaryOp,PanelEvaluator>::template run<PacketType>(panel_eval,m_functor.binaryFunc(),m_arg.outerSize());
-    return p;
-  }
-
-protected:
-  ConstArgTypeNested m_arg;
-  const MemberOp m_functor;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIALREDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PermutationMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PermutationMatrix.h
deleted file mode 100644
index 69401bf..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PermutationMatrix.h
+++ /dev/null
@@ -1,605 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PERMUTATIONMATRIX_H
-#define EIGEN_PERMUTATIONMATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-enum PermPermProduct_t {PermPermProduct};
-
-} // end namespace internal
-
-/** \class PermutationBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for permutations
-  *
-  * \tparam Derived the derived class
-  *
-  * This class is the base class for all expressions representing a permutation matrix,
-  * internally stored as a vector of integers.
-  * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix
-  * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have:
-  *  \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f]
-  * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have:
-  *  \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f]
-  *
-  * Permutation matrices are square and invertible.
-  *
-  * Notice that in addition to the member functions and operators listed here, there also are non-member
-  * operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase)
-  * on either side.
-  *
-  * \sa class PermutationMatrix, class PermutationWrapper
-  */
-template<typename Derived>
-class PermutationBase : public EigenBase<Derived>
-{
-    typedef internal::traits<Derived> Traits;
-    typedef EigenBase<Derived> Base;
-  public:
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    enum {
-      Flags = Traits::Flags,
-      RowsAtCompileTime = Traits::RowsAtCompileTime,
-      ColsAtCompileTime = Traits::ColsAtCompileTime,
-      MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
-    };
-    typedef typename Traits::StorageIndex StorageIndex;
-    typedef Matrix<StorageIndex,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
-            DenseMatrixType;
-    typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,StorageIndex>
-            PlainPermutationType;
-    typedef PlainPermutationType PlainObject;
-    using Base::derived;
-    typedef Inverse<Derived> InverseReturnType;
-    typedef void Scalar;
-    #endif
-
-    /** Copies the other permutation into *this */
-    template<typename OtherDerived>
-    Derived& operator=(const PermutationBase<OtherDerived>& other)
-    {
-      indices() = other.indices();
-      return derived();
-    }
-
-    /** Assignment from the Transpositions \a tr */
-    template<typename OtherDerived>
-    Derived& operator=(const TranspositionsBase<OtherDerived>& tr)
-    {
-      setIdentity(tr.size());
-      for(Index k=size()-1; k>=0; --k)
-        applyTranspositionOnTheRight(k,tr.coeff(k));
-      return derived();
-    }
-
-    /** \returns the number of rows */
-    inline EIGEN_DEVICE_FUNC Index rows() const { return Index(indices().size()); }
-
-    /** \returns the number of columns */
-    inline EIGEN_DEVICE_FUNC Index cols() const { return Index(indices().size()); }
-
-    /** \returns the size of a side of the respective square matrix, i.e., the number of indices */
-    inline EIGEN_DEVICE_FUNC Index size() const { return Index(indices().size()); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename DenseDerived>
-    void evalTo(MatrixBase<DenseDerived>& other) const
-    {
-      other.setZero();
-      for (Index i=0; i<rows(); ++i)
-        other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
-    }
-    #endif
-
-    /** \returns a Matrix object initialized from this permutation matrix. Notice that it
-      * is inefficient to return this Matrix object by value. For efficiency, favor using
-      * the Matrix constructor taking EigenBase objects.
-      */
-    DenseMatrixType toDenseMatrix() const
-    {
-      return derived();
-    }
-
-    /** const version of indices(). */
-    const IndicesType& indices() const { return derived().indices(); }
-    /** \returns a reference to the stored array representing the permutation. */
-    IndicesType& indices() { return derived().indices(); }
-
-    /** Resizes to given size.
-      */
-    inline void resize(Index newSize)
-    {
-      indices().resize(newSize);
-    }
-
-    /** Sets *this to be the identity permutation matrix */
-    void setIdentity()
-    {
-      StorageIndex n = StorageIndex(size());
-      for(StorageIndex i = 0; i < n; ++i)
-        indices().coeffRef(i) = i;
-    }
-
-    /** Sets *this to be the identity permutation matrix of given size.
-      */
-    void setIdentity(Index newSize)
-    {
-      resize(newSize);
-      setIdentity();
-    }
-
-    /** Multiplies *this by the transposition \f$(ij)\f$ on the left.
-      *
-      * \returns a reference to *this.
-      *
-      * \warning This is much slower than applyTranspositionOnTheRight(Index,Index):
-      * this has linear complexity and requires a lot of branching.
-      *
-      * \sa applyTranspositionOnTheRight(Index,Index)
-      */
-    Derived& applyTranspositionOnTheLeft(Index i, Index j)
-    {
-      eigen_assert(i>=0 && j>=0 && i<size() && j<size());
-      for(Index k = 0; k < size(); ++k)
-      {
-        if(indices().coeff(k) == i) indices().coeffRef(k) = StorageIndex(j);
-        else if(indices().coeff(k) == j) indices().coeffRef(k) = StorageIndex(i);
-      }
-      return derived();
-    }
-
-    /** Multiplies *this by the transposition \f$(ij)\f$ on the right.
-      *
-      * \returns a reference to *this.
-      *
-      * This is a fast operation, it only consists in swapping two indices.
-      *
-      * \sa applyTranspositionOnTheLeft(Index,Index)
-      */
-    Derived& applyTranspositionOnTheRight(Index i, Index j)
-    {
-      eigen_assert(i>=0 && j>=0 && i<size() && j<size());
-      std::swap(indices().coeffRef(i), indices().coeffRef(j));
-      return derived();
-    }
-
-    /** \returns the inverse permutation matrix.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    inline InverseReturnType inverse() const
-    { return InverseReturnType(derived()); }
-    /** \returns the tranpose permutation matrix.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    inline InverseReturnType transpose() const
-    { return InverseReturnType(derived()); }
-
-    /**** multiplication helpers to hopefully get RVO ****/
-
-  
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  protected:
-    template<typename OtherDerived>
-    void assignTranspose(const PermutationBase<OtherDerived>& other)
-    {
-      for (Index i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i;
-    }
-    template<typename Lhs,typename Rhs>
-    void assignProduct(const Lhs& lhs, const Rhs& rhs)
-    {
-      eigen_assert(lhs.cols() == rhs.rows());
-      for (Index i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
-    }
-#endif
-
-  public:
-
-    /** \returns the product permutation matrix.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    template<typename Other>
-    inline PlainPermutationType operator*(const PermutationBase<Other>& other) const
-    { return PlainPermutationType(internal::PermPermProduct, derived(), other.derived()); }
-
-    /** \returns the product of a permutation with another inverse permutation.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    template<typename Other>
-    inline PlainPermutationType operator*(const InverseImpl<Other,PermutationStorage>& other) const
-    { return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); }
-
-    /** \returns the product of an inverse permutation with another permutation.
-      *
-      * \note \blank \note_try_to_help_rvo
-      */
-    template<typename Other> friend
-    inline PlainPermutationType operator*(const InverseImpl<Other, PermutationStorage>& other, const PermutationBase& perm)
-    { return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); }
-    
-    /** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the permutation.
-      *
-      * This function is O(\c n) procedure allocating a buffer of \c n booleans.
-      */
-    Index determinant() const
-    {
-      Index res = 1;
-      Index n = size();
-      Matrix<bool,RowsAtCompileTime,1,0,MaxRowsAtCompileTime> mask(n);
-      mask.fill(false);
-      Index r = 0;
-      while(r < n)
-      {
-        // search for the next seed
-        while(r<n && mask[r]) r++;
-        if(r>=n)
-          break;
-        // we got one, let's follow it until we are back to the seed
-        Index k0 = r++;
-        mask.coeffRef(k0) = true;
-        for(Index k=indices().coeff(k0); k!=k0; k=indices().coeff(k))
-        {
-          mask.coeffRef(k) = true;
-          res = -res;
-        }
-      }
-      return res;
-    }
-
-  protected:
-
-};
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
- : traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  typedef PermutationStorage StorageKind;
-  typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
-  typedef _StorageIndex StorageIndex;
-  typedef void Scalar;
-};
-}
-
-/** \class PermutationMatrix
-  * \ingroup Core_Module
-  *
-  * \brief Permutation matrix
-  *
-  * \tparam SizeAtCompileTime the number of rows/cols, or Dynamic
-  * \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
-  * \tparam _StorageIndex the integer type of the indices
-  *
-  * This class represents a permutation matrix, internally stored as a vector of integers.
-  *
-  * \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
-  */
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
-{
-    typedef PermutationBase<PermutationMatrix> Base;
-    typedef internal::traits<PermutationMatrix> Traits;
-  public:
-
-    typedef const PermutationMatrix& Nested;
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename Traits::StorageIndex StorageIndex;
-    #endif
-
-    inline PermutationMatrix()
-    {}
-
-    /** Constructs an uninitialized permutation matrix of given size.
-      */
-    explicit inline PermutationMatrix(Index size) : m_indices(size)
-    {
-      eigen_internal_assert(size <= NumTraits<StorageIndex>::highest());
-    }
-
-    /** Copy constructor. */
-    template<typename OtherDerived>
-    inline PermutationMatrix(const PermutationBase<OtherDerived>& other)
-      : m_indices(other.indices()) {}
-
-    /** Generic constructor from expression of the indices. The indices
-      * array has the meaning that the permutations sends each integer i to indices[i].
-      *
-      * \warning It is your responsibility to check that the indices array that you passes actually
-      * describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the
-      * array's size.
-      */
-    template<typename Other>
-    explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
-    {}
-
-    /** Convert the Transpositions \a tr to a permutation matrix */
-    template<typename Other>
-    explicit PermutationMatrix(const TranspositionsBase<Other>& tr)
-      : m_indices(tr.size())
-    {
-      *this = tr;
-    }
-
-    /** Copies the other permutation into *this */
-    template<typename Other>
-    PermutationMatrix& operator=(const PermutationBase<Other>& other)
-    {
-      m_indices = other.indices();
-      return *this;
-    }
-
-    /** Assignment from the Transpositions \a tr */
-    template<typename Other>
-    PermutationMatrix& operator=(const TranspositionsBase<Other>& tr)
-    {
-      return Base::operator=(tr.derived());
-    }
-
-    /** const version of indices(). */
-    const IndicesType& indices() const { return m_indices; }
-    /** \returns a reference to the stored array representing the permutation. */
-    IndicesType& indices() { return m_indices; }
-
-
-    /**** multiplication helpers to hopefully get RVO ****/
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename Other>
-    PermutationMatrix(const InverseImpl<Other,PermutationStorage>& other)
-      : m_indices(other.derived().nestedExpression().size())
-    {
-      eigen_internal_assert(m_indices.size() <= NumTraits<StorageIndex>::highest());
-      StorageIndex end = StorageIndex(m_indices.size());
-      for (StorageIndex i=0; i<end;++i)
-        m_indices.coeffRef(other.derived().nestedExpression().indices().coeff(i)) = i;
-    }
-    template<typename Lhs,typename Rhs>
-    PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
-      : m_indices(lhs.indices().size())
-    {
-      Base::assignProduct(lhs,rhs);
-    }
-#endif
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
-struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
- : traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
-{
-  typedef PermutationStorage StorageKind;
-  typedef Map<const Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
-  typedef _StorageIndex StorageIndex;
-  typedef void Scalar;
-};
-}
-
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
-class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess>
-  : public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
-{
-    typedef PermutationBase<Map> Base;
-    typedef internal::traits<Map> Traits;
-  public:
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-    #endif
-
-    inline Map(const StorageIndex* indicesPtr)
-      : m_indices(indicesPtr)
-    {}
-
-    inline Map(const StorageIndex* indicesPtr, Index size)
-      : m_indices(indicesPtr,size)
-    {}
-
-    /** Copies the other permutation into *this */
-    template<typename Other>
-    Map& operator=(const PermutationBase<Other>& other)
-    { return Base::operator=(other.derived()); }
-
-    /** Assignment from the Transpositions \a tr */
-    template<typename Other>
-    Map& operator=(const TranspositionsBase<Other>& tr)
-    { return Base::operator=(tr.derived()); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    Map& operator=(const Map& other)
-    {
-      m_indices = other.m_indices;
-      return *this;
-    }
-    #endif
-
-    /** const version of indices(). */
-    const IndicesType& indices() const { return m_indices; }
-    /** \returns a reference to the stored array representing the permutation. */
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-template<typename _IndicesType> class TranspositionsWrapper;
-namespace internal {
-template<typename _IndicesType>
-struct traits<PermutationWrapper<_IndicesType> >
-{
-  typedef PermutationStorage StorageKind;
-  typedef void Scalar;
-  typedef typename _IndicesType::Scalar StorageIndex;
-  typedef _IndicesType IndicesType;
-  enum {
-    RowsAtCompileTime = _IndicesType::SizeAtCompileTime,
-    ColsAtCompileTime = _IndicesType::SizeAtCompileTime,
-    MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
-    MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
-    Flags = 0
-  };
-};
-}
-
-/** \class PermutationWrapper
-  * \ingroup Core_Module
-  *
-  * \brief Class to view a vector of integers as a permutation matrix
-  *
-  * \tparam _IndicesType the type of the vector of integer (can be any compatible expression)
-  *
-  * This class allows to view any vector expression of integers as a permutation matrix.
-  *
-  * \sa class PermutationBase, class PermutationMatrix
-  */
-template<typename _IndicesType>
-class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesType> >
-{
-    typedef PermutationBase<PermutationWrapper> Base;
-    typedef internal::traits<PermutationWrapper> Traits;
-  public:
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename Traits::IndicesType IndicesType;
-    #endif
-
-    inline PermutationWrapper(const IndicesType& indices)
-      : m_indices(indices)
-    {}
-
-    /** const version of indices(). */
-    const typename internal::remove_all<typename IndicesType::Nested>::type&
-    indices() const { return m_indices; }
-
-  protected:
-
-    typename IndicesType::Nested m_indices;
-};
-
-
-/** \returns the matrix with the permutation applied to the columns.
-  */
-template<typename MatrixDerived, typename PermutationDerived>
-EIGEN_DEVICE_FUNC
-const Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
-operator*(const MatrixBase<MatrixDerived> &matrix,
-          const PermutationBase<PermutationDerived>& permutation)
-{
-  return Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
-            (matrix.derived(), permutation.derived());
-}
-
-/** \returns the matrix with the permutation applied to the rows.
-  */
-template<typename PermutationDerived, typename MatrixDerived>
-EIGEN_DEVICE_FUNC
-const Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
-operator*(const PermutationBase<PermutationDerived> &permutation,
-          const MatrixBase<MatrixDerived>& matrix)
-{
-  return Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
-            (permutation.derived(), matrix.derived());
-}
-
-
-template<typename PermutationType>
-class InverseImpl<PermutationType, PermutationStorage>
-  : public EigenBase<Inverse<PermutationType> >
-{
-    typedef typename PermutationType::PlainPermutationType PlainPermutationType;
-    typedef internal::traits<PermutationType> PermTraits;
-  protected:
-    InverseImpl() {}
-  public:
-    typedef Inverse<PermutationType> InverseType;
-    using EigenBase<Inverse<PermutationType> >::derived;
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    typedef typename PermutationType::DenseMatrixType DenseMatrixType;
-    enum {
-      RowsAtCompileTime = PermTraits::RowsAtCompileTime,
-      ColsAtCompileTime = PermTraits::ColsAtCompileTime,
-      MaxRowsAtCompileTime = PermTraits::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = PermTraits::MaxColsAtCompileTime
-    };
-    #endif
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename DenseDerived>
-    void evalTo(MatrixBase<DenseDerived>& other) const
-    {
-      other.setZero();
-      for (Index i=0; i<derived().rows();++i)
-        other.coeffRef(i, derived().nestedExpression().indices().coeff(i)) = typename DenseDerived::Scalar(1);
-    }
-    #endif
-
-    /** \return the equivalent permutation matrix */
-    PlainPermutationType eval() const { return derived(); }
-
-    DenseMatrixType toDenseMatrix() const { return derived(); }
-
-    /** \returns the matrix with the inverse permutation applied to the columns.
-      */
-    template<typename OtherDerived> friend
-    const Product<OtherDerived, InverseType, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix, const InverseType& trPerm)
-    {
-      return Product<OtherDerived, InverseType, AliasFreeProduct>(matrix.derived(), trPerm.derived());
-    }
-
-    /** \returns the matrix with the inverse permutation applied to the rows.
-      */
-    template<typename OtherDerived>
-    const Product<InverseType, OtherDerived, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix) const
-    {
-      return Product<InverseType, OtherDerived, AliasFreeProduct>(derived(), matrix.derived());
-    }
-};
-
-template<typename Derived>
-const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() const
-{
-  return derived();
-}
-
-namespace internal {
-
-template<> struct AssignmentKind<DenseShape,PermutationShape> { typedef EigenBase2EigenBase Kind; };
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PERMUTATIONMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PlainObjectBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PlainObjectBase.h
deleted file mode 100644
index e2ddbd1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/PlainObjectBase.h
+++ /dev/null
@@ -1,1128 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DENSESTORAGEBASE_H
-#define EIGEN_DENSESTORAGEBASE_H
-
-#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO)
-# define EIGEN_INITIALIZE_COEFFS
-# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(Index i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
-#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN)
-# define EIGEN_INITIALIZE_COEFFS
-# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(Index i=0;i<base().size();++i) coeffRef(i)=std::numeric_limits<Scalar>::quiet_NaN();
-#else
-# undef EIGEN_INITIALIZE_COEFFS
-# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
-  template<typename Index>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE void run(Index, Index)
-  {
-  }
-};
-
-template<> struct check_rows_cols_for_overflow<Dynamic> {
-  template<typename Index>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols)
-  {
-    // http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
-    // we assume Index is signed
-    Index max_index = (std::size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
-    bool error = (rows == 0 || cols == 0) ? false
-               : (rows > max_index / cols);
-    if (error)
-      throw_std_bad_alloc();
-  }
-};
-
-template <typename Derived,
-          typename OtherDerived = Derived,
-          bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)>
-struct conservative_resize_like_impl;
-
-template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
-
-} // end namespace internal
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-namespace doxygen {
-
-// This is a workaround to doxygen not being able to understand the inheritance logic
-// when it is hidden by the dense_xpr_base helper struct.
-// Moreover, doxygen fails to include members that are not documented in the declaration body of
-// MatrixBase if we inherits MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >,
-// this is why we simply inherits MatrixBase, though this does not make sense.
-
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename Derived> struct dense_xpr_base_dispatcher;
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-    : public MatrixBase {};
-/** This class is just a workaround for Doxygen and it does not not actually exist. */
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
-    : public ArrayBase {};
-
-} // namespace doxygen
-
-/** \class PlainObjectBase
-  * \ingroup Core_Module
-  * \brief %Dense storage base class for matrices and arrays.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
-  *
-  * \tparam Derived is the derived type, e.g., a Matrix or Array
-  *
-  * \sa \ref TopicClassHierarchy
-  */
-template<typename Derived>
-class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher<Derived>
-#else
-template<typename Derived>
-class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
-#endif
-{
-  public:
-    enum { Options = internal::traits<Derived>::Options };
-    typedef typename internal::dense_xpr_base<Derived>::type Base;
-
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Derived DenseType;
-
-    using Base::RowsAtCompileTime;
-    using Base::ColsAtCompileTime;
-    using Base::SizeAtCompileTime;
-    using Base::MaxRowsAtCompileTime;
-    using Base::MaxColsAtCompileTime;
-    using Base::MaxSizeAtCompileTime;
-    using Base::IsVectorAtCompileTime;
-    using Base::Flags;
-
-    typedef Eigen::Map<Derived, Unaligned>  MapType;
-    typedef const Eigen::Map<const Derived, Unaligned> ConstMapType;
-    typedef Eigen::Map<Derived, AlignedMax> AlignedMapType;
-    typedef const Eigen::Map<const Derived, AlignedMax> ConstAlignedMapType;
-    template<typename StrideType> struct StridedMapType { typedef Eigen::Map<Derived, Unaligned, StrideType> type; };
-    template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; };
-    template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, AlignedMax, StrideType> type; };
-    template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, AlignedMax, StrideType> type; };
-
-  protected:
-    DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
-
-  public:
-    enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits<Derived>::Alignment>0) };
-    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
-
-    EIGEN_DEVICE_FUNC
-    Base& base() { return *static_cast<Base*>(this); }
-    EIGEN_DEVICE_FUNC
-    const Base& base() const { return *static_cast<const Base*>(this); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_storage.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_storage.cols(); }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index,Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
-    {
-      if(Flags & RowMajorBit)
-        return m_storage.data()[colId + rowId * m_storage.cols()];
-      else // column-major
-        return m_storage.data()[rowId + colId * m_storage.rows()];
-    }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
-    {
-      return m_storage.data()[index];
-    }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
-    {
-      if(Flags & RowMajorBit)
-        return m_storage.data()[colId + rowId * m_storage.cols()];
-      else // column-major
-        return m_storage.data()[rowId + colId * m_storage.rows()];
-    }
-
-    /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const
-      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
-      *
-      * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
-    {
-      return m_storage.data()[index];
-    }
-
-    /** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
-      * It is provided for convenience. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      if(Flags & RowMajorBit)
-        return m_storage.data()[colId + rowId * m_storage.cols()];
-      else // column-major
-        return m_storage.data()[rowId + colId * m_storage.rows()];
-    }
-
-    /** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
-      * It is provided for convenience. */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
-    {
-      return m_storage.data()[index];
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
-    {
-      return internal::ploadt<PacketScalar, LoadMode>
-               (m_storage.data() + (Flags & RowMajorBit
-                                   ? colId + rowId * m_storage.cols()
-                                   : rowId + colId * m_storage.rows()));
-    }
-
-    /** \internal */
-    template<int LoadMode>
-    EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
-    {
-      return internal::ploadt<PacketScalar, LoadMode>(m_storage.data() + index);
-    }
-
-    /** \internal */
-    template<int StoreMode>
-    EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val)
-    {
-      internal::pstoret<Scalar, PacketScalar, StoreMode>
-              (m_storage.data() + (Flags & RowMajorBit
-                                   ? colId + rowId * m_storage.cols()
-                                   : rowId + colId * m_storage.rows()), val);
-    }
-
-    /** \internal */
-    template<int StoreMode>
-    EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val)
-    {
-      internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val);
-    }
-
-    /** \returns a const pointer to the data array of this matrix */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const
-    { return m_storage.data(); }
-
-    /** \returns a pointer to the data array of this matrix */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data()
-    { return m_storage.data(); }
-
-    /** Resizes \c *this to a \a rows x \a cols matrix.
-      *
-      * This method is intended for dynamic-size matrices, although it is legal to call it on any
-      * matrix as long as fixed dimensions are left unchanged. If you only want to change the number
-      * of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t).
-      *
-      * If the current number of coefficients of \c *this exactly matches the
-      * product \a rows * \a cols, then no memory allocation is performed and
-      * the current values are left unchanged. In all other cases, including
-      * shrinking, the data is reallocated and all previous values are lost.
-      *
-      * Example: \include Matrix_resize_int_int.cpp
-      * Output: \verbinclude Matrix_resize_int_int.out
-      *
-      * \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
-    {
-      eigen_assert(   EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime)
-                   && EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime)
-                   && EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime)
-                   && EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime)
-                   && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
-      internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
-      #ifdef EIGEN_INITIALIZE_COEFFS
-        Index size = rows*cols;
-        bool size_changed = size != this->size();
-        m_storage.resize(size, rows, cols);
-        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-      #else
-        m_storage.resize(rows*cols, rows, cols);
-      #endif
-    }
-
-    /** Resizes \c *this to a vector of length \a size
-      *
-      * \only_for_vectors. This method does not work for
-      * partially dynamic matrices when the static dimension is anything other
-      * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
-      *
-      * Example: \include Matrix_resize_int.cpp
-      * Output: \verbinclude Matrix_resize_int.out
-      *
-      * \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
-      */
-    EIGEN_DEVICE_FUNC
-    inline void resize(Index size)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
-      eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0);
-      #ifdef EIGEN_INITIALIZE_COEFFS
-        bool size_changed = size != this->size();
-      #endif
-      if(RowsAtCompileTime == 1)
-        m_storage.resize(size, 1, size);
-      else
-        m_storage.resize(size, size, 1);
-      #ifdef EIGEN_INITIALIZE_COEFFS
-        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-      #endif
-    }
-
-    /** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the special value \c NoChange
-      * as in the example below.
-      *
-      * Example: \include Matrix_resize_NoChange_int.cpp
-      * Output: \verbinclude Matrix_resize_NoChange_int.out
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC
-    inline void resize(NoChange_t, Index cols)
-    {
-      resize(rows(), cols);
-    }
-
-    /** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
-      * as in the example below.
-      *
-      * Example: \include Matrix_resize_int_NoChange.cpp
-      * Output: \verbinclude Matrix_resize_int_NoChange.out
-      *
-      * \sa resize(Index,Index)
-      */
-    EIGEN_DEVICE_FUNC
-    inline void resize(Index rows, NoChange_t)
-    {
-      resize(rows, cols());
-    }
-
-    /** Resizes \c *this to have the same dimensions as \a other.
-      * Takes care of doing all the checking that's needed.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
-    {
-      const OtherDerived& other = _other.derived();
-      internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.rows(), other.cols());
-      const Index othersize = other.rows()*other.cols();
-      if(RowsAtCompileTime == 1)
-      {
-        eigen_assert(other.rows() == 1 || other.cols() == 1);
-        resize(1, othersize);
-      }
-      else if(ColsAtCompileTime == 1)
-      {
-        eigen_assert(other.rows() == 1 || other.cols() == 1);
-        resize(othersize, 1);
-      }
-      else resize(other.rows(), other.cols());
-    }
-
-    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
-      *
-      * The method is intended for matrices of dynamic size. If you only want to change the number
-      * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
-      * conservativeResize(Index, NoChange_t).
-      *
-      * Matrices are resized relative to the top-left element. In case values need to be
-      * appended to the matrix they will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
-    {
-      internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
-    }
-
-    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
-      *
-      * As opposed to conservativeResize(Index rows, Index cols), this version leaves
-      * the number of columns unchanged.
-      *
-      * In case the matrix is growing, new rows will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
-    {
-      // Note: see the comment in conservativeResize(Index,Index)
-      conservativeResize(rows, cols());
-    }
-
-    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
-      *
-      * As opposed to conservativeResize(Index rows, Index cols), this version leaves
-      * the number of rows unchanged.
-      *
-      * In case the matrix is growing, new columns will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
-    {
-      // Note: see the comment in conservativeResize(Index,Index)
-      conservativeResize(rows(), cols);
-    }
-
-    /** Resizes the vector to \a size while retaining old values.
-      *
-      * \only_for_vectors. This method does not work for
-      * partially dynamic matrices when the static dimension is anything other
-      * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
-      *
-      * When values are appended, they will be uninitialized.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResize(Index size)
-    {
-      internal::conservative_resize_like_impl<Derived>::run(*this, size);
-    }
-
-    /** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched.
-      *
-      * The method is intended for matrices of dynamic size. If you only want to change the number
-      * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
-      * conservativeResize(Index, NoChange_t).
-      *
-      * Matrices are resized relative to the top-left element. In case values need to be
-      * appended to the matrix they will copied from \c other.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other)
-    {
-      internal::conservative_resize_like_impl<Derived,OtherDerived>::run(*this, other);
-    }
-
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other)
-    {
-      return _set(other);
-    }
-
-    /** \sa MatrixBase::lazyAssign() */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other)
-    {
-      _resize_to_match(other);
-      return Base::lazyAssign(other.derived());
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func)
-    {
-      resize(func.rows(), func.cols());
-      return Base::operator=(func);
-    }
-
-    // Prevent user from trying to instantiate PlainObjectBase objects
-    // by making all its constructor protected. See bug 1074.
-  protected:
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
-    {
-//       _check_template_params();
-//       EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    // FIXME is it still needed ?
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert)
-      : m_storage(internal::constructor_without_unaligned_array_assert())
-    {
-//       _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-#endif
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
-      : m_storage( std::move(other.m_storage) )
-    {
-    }
-
-    EIGEN_DEVICE_FUNC
-    PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
-    {
-      _check_template_params();
-      m_storage = std::move(other.m_storage);
-      return *this;
-    }
-#endif
-
-    /** Copy constructor */
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other)
-      : Base(), m_storage(other.m_storage) { }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
-      : m_storage(size, rows, cols)
-    {
-//       _check_template_params();
-//       EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-    #if EIGEN_HAS_CXX11
-    /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients. \cpp11
-      *
-      * \only_for_vectors
-      *
-      * This constructor is for 1D array or vectors with more than 4 coefficients.
-      * There exists C++98 analogue constructors for fixed-size array/vector having 1, 2, 3, or 4 coefficients.
-      *
-      * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
-      * constructor must match the the fixed number of rows (resp. columns) of \c *this.
-      */
-    template <typename... ArgTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
-      : m_storage()
-    {
-      _check_template_params();
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, sizeof...(args) + 4);
-      m_storage.data()[0] = a0;
-      m_storage.data()[1] = a1;
-      m_storage.data()[2] = a2;
-      m_storage.data()[3] = a3;
-      Index i = 4;
-      auto x = {(m_storage.data()[i++] = args, 0)...};
-      static_cast<void>(x);
-    }
-
-    /** \brief Constructs a Matrix or Array and initializes it by elements given by an initializer list of initializer
-      * lists \cpp11
-      */
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE PlainObjectBase(const std::initializer_list<std::initializer_list<Scalar>>& list)
-      : m_storage()
-    {
-      _check_template_params();
-
-      size_t list_size = 0;
-      if (list.begin() != list.end()) {
-        list_size = list.begin()->size();
-      }
-
-      // This is to allow syntax like VectorXi {{1, 2, 3, 4}}
-      if (ColsAtCompileTime == 1 && list.size() == 1) {
-        eigen_assert(list_size == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
-        resize(list_size, ColsAtCompileTime);
-        std::copy(list.begin()->begin(), list.begin()->end(), m_storage.data());
-      } else {
-        eigen_assert(list.size() == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
-        eigen_assert(list_size == static_cast<size_t>(ColsAtCompileTime) || ColsAtCompileTime == Dynamic);
-        resize(list.size(), list_size);
-
-        Index row_index = 0;
-        for (const std::initializer_list<Scalar>& row : list) {
-          eigen_assert(list_size == row.size());
-          Index col_index = 0;
-          for (const Scalar& e : row) {
-            coeffRef(row_index, col_index) = e;
-            ++col_index;
-          }
-          ++row_index;
-        }
-      }
-    }
-    #endif  // end EIGEN_HAS_CXX11
-
-    /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
-      : m_storage()
-    {
-      _check_template_params();
-      resizeLike(other);
-      _set_noalias(other);
-    }
-
-    /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
-      : m_storage()
-    {
-      _check_template_params();
-      resizeLike(other);
-      *this = other.derived();
-    }
-    /** \brief Copy constructor with in-place evaluation */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue<OtherDerived>& other)
-    {
-      _check_template_params();
-      // FIXME this does not automatically transpose vectors if necessary
-      resize(other.rows(), other.cols());
-      other.evalTo(this->derived());
-    }
-
-  public:
-
-    /** \brief Copies the generic expression \a other into *this.
-      * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
-    {
-      _resize_to_match(other);
-      Base::operator=(other.derived());
-      return this->derived();
-    }
-
-    /** \name Map
-      * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
-      * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
-      * \a data pointers.
-      *
-      * Here is an example using strides:
-      * \include Matrix_Map_stride.cpp
-      * Output: \verbinclude Matrix_Map_stride.out
-      *
-      * \see class Map
-      */
-    //@{
-    static inline ConstMapType Map(const Scalar* data)
-    { return ConstMapType(data); }
-    static inline MapType Map(Scalar* data)
-    { return MapType(data); }
-    static inline ConstMapType Map(const Scalar* data, Index size)
-    { return ConstMapType(data, size); }
-    static inline MapType Map(Scalar* data, Index size)
-    { return MapType(data, size); }
-    static inline ConstMapType Map(const Scalar* data, Index rows, Index cols)
-    { return ConstMapType(data, rows, cols); }
-    static inline MapType Map(Scalar* data, Index rows, Index cols)
-    { return MapType(data, rows, cols); }
-
-    static inline ConstAlignedMapType MapAligned(const Scalar* data)
-    { return ConstAlignedMapType(data); }
-    static inline AlignedMapType MapAligned(Scalar* data)
-    { return AlignedMapType(data); }
-    static inline ConstAlignedMapType MapAligned(const Scalar* data, Index size)
-    { return ConstAlignedMapType(data, size); }
-    static inline AlignedMapType MapAligned(Scalar* data, Index size)
-    { return AlignedMapType(data, size); }
-    static inline ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols)
-    { return ConstAlignedMapType(data, rows, cols); }
-    static inline AlignedMapType MapAligned(Scalar* data, Index rows, Index cols)
-    { return AlignedMapType(data, rows, cols); }
-
-    template<int Outer, int Inner>
-    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-
-    template<int Outer, int Inner>
-    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, const Stride<Outer, Inner>& stride)
-    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
-    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-    template<int Outer, int Inner>
-    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
-    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
-    //@}
-
-    using Base::setConstant;
-    EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val);
-    EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& val);
-    EIGEN_DEVICE_FUNC Derived& setConstant(NoChange_t, Index cols, const Scalar& val);
-    EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, NoChange_t, const Scalar& val);
-
-    using Base::setZero;
-    EIGEN_DEVICE_FUNC Derived& setZero(Index size);
-    EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setZero(NoChange_t, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setZero(Index rows, NoChange_t);
-
-    using Base::setOnes;
-    EIGEN_DEVICE_FUNC Derived& setOnes(Index size);
-    EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setOnes(NoChange_t, Index cols);
-    EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, NoChange_t);
-
-    using Base::setRandom;
-    Derived& setRandom(Index size);
-    Derived& setRandom(Index rows, Index cols);
-    Derived& setRandom(NoChange_t, Index cols);
-    Derived& setRandom(Index rows, NoChange_t);
-
-    #ifdef EIGEN_PLAINOBJECTBASE_PLUGIN
-    #include EIGEN_PLAINOBJECTBASE_PLUGIN
-    #endif
-
-  protected:
-    /** \internal Resizes *this in preparation for assigning \a other to it.
-      * Takes care of doing all the checking that's needed.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
-    {
-      #ifdef EIGEN_NO_AUTOMATIC_RESIZING
-      eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
-                 : (rows() == other.rows() && cols() == other.cols())))
-        && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
-      EIGEN_ONLY_USED_FOR_DEBUG(other);
-      #else
-      resizeLike(other);
-      #endif
-    }
-
-    /**
-      * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
-      *
-      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
-      * it will be initialized.
-      *
-      * Note that copying a row-vector into a vector (and conversely) is allowed.
-      * The resizing, if any, is then done in the appropriate way so that row-vectors
-      * remain row-vectors and vectors remain vectors.
-      *
-      * \sa operator=(const MatrixBase<OtherDerived>&), _set_noalias()
-      *
-      * \internal
-      */
-    // aliasing is dealt once in internal::call_assignment
-    // so at this stage we have to assume aliasing... and resising has to be done later.
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& _set(const DenseBase<OtherDerived>& other)
-    {
-      internal::call_assignment(this->derived(), other.derived());
-      return this->derived();
-    }
-
-    /** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
-      * is the case when creating a new matrix) so one can enforce lazy evaluation.
-      *
-      * \sa operator=(const MatrixBase<OtherDerived>&), _set()
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other)
-    {
-      // I don't think we need this resize call since the lazyAssign will anyways resize
-      // and lazyAssign will be called by the assign selector.
-      //_resize_to_match(other);
-      // the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
-      // it wouldn't allow to copy a row-vector into a column-vector.
-      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-      return this->derived();
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
-    {
-      const bool t0_is_integer_alike = internal::is_valid_index_type<T0>::value;
-      const bool t1_is_integer_alike = internal::is_valid_index_type<T1>::value;
-      EIGEN_STATIC_ASSERT(t0_is_integer_alike &&
-                          t1_is_integer_alike,
-                          FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
-      resize(rows,cols);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
-      m_storage.data()[0] = Scalar(val0);
-      m_storage.data()[1] = Scalar(val1);
-    }
-
-    template<typename T0, typename T1>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1,
-                                    typename internal::enable_if<    (!internal::is_same<Index,Scalar>::value)
-                                                                  && (internal::is_same<T0,Index>::value)
-                                                                  && (internal::is_same<T1,Index>::value)
-                                                                  && Base::SizeAtCompileTime==2,T1>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
-      m_storage.data()[0] = Scalar(val0);
-      m_storage.data()[1] = Scalar(val1);
-    }
-
-    // The argument is convertible to the Index type and we either have a non 1x1 Matrix, or a dynamic-sized Array,
-    // then the argument is meant to be the size of the object.
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(Index size, typename internal::enable_if<    (Base::SizeAtCompileTime!=1 || !internal::is_convertible<T, Scalar>::value)
-                                                                              && ((!internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value || Base::SizeAtCompileTime==Dynamic)),T>::type* = 0)
-    {
-      // NOTE MSVC 2008 complains if we directly put bool(NumTraits<T>::IsInteger) as the EIGEN_STATIC_ASSERT argument.
-      const bool is_integer_alike = internal::is_valid_index_type<T>::value;
-      EIGEN_UNUSED_VARIABLE(is_integer_alike);
-      EIGEN_STATIC_ASSERT(is_integer_alike,
-                          FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
-      resize(size);
-    }
-
-    // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitly converted)
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Scalar& val0, typename internal::enable_if<Base::SizeAtCompileTime==1 && internal::is_convertible<T, Scalar>::value,T>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
-      m_storage.data()[0] = val0;
-    }
-
-    // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type match the index type)
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Index& val0,
-                                    typename internal::enable_if<    (!internal::is_same<Index,Scalar>::value)
-                                                                  && (internal::is_same<Index,T>::value)
-                                                                  && Base::SizeAtCompileTime==1
-                                                                  && internal::is_convertible<T, Scalar>::value,T*>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
-      m_storage.data()[0] = Scalar(val0);
-    }
-
-    // Initialize a fixed size matrix from a pointer to raw data
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Scalar* data){
-      this->_set_noalias(ConstMapType(data));
-    }
-
-    // Initialize an arbitrary matrix from a dense expression
-    template<typename T, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const DenseBase<OtherDerived>& other){
-      this->_set_noalias(other);
-    }
-
-    // Initialize an arbitrary matrix from an object convertible to the Derived type.
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Derived& other){
-      this->_set_noalias(other);
-    }
-
-    // Initialize an arbitrary matrix from a generic Eigen expression
-    template<typename T, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const EigenBase<OtherDerived>& other){
-      this->derived() = other;
-    }
-
-    template<typename T, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const ReturnByValue<OtherDerived>& other)
-    {
-      resize(other.rows(), other.cols());
-      other.evalTo(this->derived());
-    }
-
-    template<typename T, typename OtherDerived, int ColsAtCompileTime>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
-    {
-      this->derived() = r;
-    }
-
-    // For fixed-size Array<Scalar,...>
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Scalar& val0,
-                                    typename internal::enable_if<    Base::SizeAtCompileTime!=Dynamic
-                                                                  && Base::SizeAtCompileTime!=1
-                                                                  && internal::is_convertible<T, Scalar>::value
-                                                                  && internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T>::type* = 0)
-    {
-      Base::setConstant(val0);
-    }
-
-    // For fixed-size Array<Index,...>
-    template<typename T>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _init1(const Index& val0,
-                                    typename internal::enable_if<    (!internal::is_same<Index,Scalar>::value)
-                                                                  && (internal::is_same<Index,T>::value)
-                                                                  && Base::SizeAtCompileTime!=Dynamic
-                                                                  && Base::SizeAtCompileTime!=1
-                                                                  && internal::is_convertible<T, Scalar>::value
-                                                                  && internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T*>::type* = 0)
-    {
-      Base::setConstant(val0);
-    }
-
-    template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
-    friend struct internal::matrix_swap_impl;
-
-  public:
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal
-      * \brief Override DenseBase::swap() since for dynamic-sized matrices
-      * of same type it is enough to swap the data pointers.
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(DenseBase<OtherDerived> & other)
-    {
-      enum { SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime==Dynamic };
-      internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.derived());
-    }
-
-    /** \internal
-      * \brief const version forwarded to DenseBase::swap
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void swap(DenseBase<OtherDerived> const & other)
-    { Base::swap(other.derived()); }
-
-    EIGEN_DEVICE_FUNC
-    static EIGEN_STRONG_INLINE void _check_template_params()
-    {
-      EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (int(Options)&RowMajor)==RowMajor)
-                        && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (int(Options)&RowMajor)==0)
-                        && ((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0))
-                        && ((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0))
-                        && ((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0))
-                        && ((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0))
-                        && (MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic)
-                        && (MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic)
-                        && (Options & (DontAlign|RowMajor)) == Options),
-        INVALID_MATRIX_TEMPLATE_PARAMETERS)
-    }
-
-    enum { IsPlainObjectBase = 1 };
-#endif
-  public:
-    // These apparently need to be down here for nvcc+icc to prevent duplicate
-    // Map symbol.
-    template<typename PlainObjectType, int MapOptions, typename StrideType> friend class Eigen::Map;
-    friend class Eigen::Map<Derived, Unaligned>;
-    friend class Eigen::Map<const Derived, Unaligned>;
-#if EIGEN_MAX_ALIGN_BYTES>0
-    // for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class twice.
-    friend class Eigen::Map<Derived, AlignedMax>;
-    friend class Eigen::Map<const Derived, AlignedMax>;
-#endif
-};
-
-namespace internal {
-
-template <typename Derived, typename OtherDerived, bool IsVector>
-struct conservative_resize_like_impl
-{
-  #if EIGEN_HAS_TYPE_TRAITS
-  static const bool IsRelocatable = std::is_trivially_copyable<typename Derived::Scalar>::value;
-  #else
-  static const bool IsRelocatable = !NumTraits<typename Derived::Scalar>::RequireInitialization;
-  #endif
-  static void run(DenseBase<Derived>& _this, Index rows, Index cols)
-  {
-    if (_this.rows() == rows && _this.cols() == cols) return;
-    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
-
-    if ( IsRelocatable
-          && (( Derived::IsRowMajor && _this.cols() == cols) ||  // row-major and we change only the number of rows
-              (!Derived::IsRowMajor && _this.rows() == rows) ))  // column-major and we change only the number of columns
-    {
-      internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime>::run(rows, cols);
-      _this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
-    }
-    else
-    {
-      // The storage order does not allow us to use reallocation.
-      Derived tmp(rows,cols);
-      const Index common_rows = numext::mini(rows, _this.rows());
-      const Index common_cols = numext::mini(cols, _this.cols());
-      tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
-      _this.derived().swap(tmp);
-    }
-  }
-
-  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
-  {
-    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
-
-    // Note: Here is space for improvement. Basically, for conservativeResize(Index,Index),
-    // neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the
-    // dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or
-    // conservativeResize(NoChange_t, Index cols). For these methods new static asserts like
-    // EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good.
-    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
-    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived)
-
-    if ( IsRelocatable &&
-          (( Derived::IsRowMajor && _this.cols() == other.cols()) ||  // row-major and we change only the number of rows
-           (!Derived::IsRowMajor && _this.rows() == other.rows()) ))  // column-major and we change only the number of columns
-    {
-      const Index new_rows = other.rows() - _this.rows();
-      const Index new_cols = other.cols() - _this.cols();
-      _this.derived().m_storage.conservativeResize(other.size(),other.rows(),other.cols());
-      if (new_rows>0)
-        _this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows);
-      else if (new_cols>0)
-        _this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols);
-    }
-    else
-    {
-      // The storage order does not allow us to use reallocation.
-      Derived tmp(other);
-      const Index common_rows = numext::mini(tmp.rows(), _this.rows());
-      const Index common_cols = numext::mini(tmp.cols(), _this.cols());
-      tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
-      _this.derived().swap(tmp);
-    }
-  }
-};
-
-// Here, the specialization for vectors inherits from the general matrix case
-// to allow calling .conservativeResize(rows,cols) on vectors.
-template <typename Derived, typename OtherDerived>
-struct conservative_resize_like_impl<Derived,OtherDerived,true>
-  : conservative_resize_like_impl<Derived,OtherDerived,false>
-{
-  typedef conservative_resize_like_impl<Derived,OtherDerived,false> Base;
-  using Base::run;
-  using Base::IsRelocatable;
-
-  static void run(DenseBase<Derived>& _this, Index size)
-  {
-    const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
-    const Index new_cols = Derived::RowsAtCompileTime==1 ? size : 1;
-    if(IsRelocatable)
-      _this.derived().m_storage.conservativeResize(size,new_rows,new_cols);
-    else
-      Base::run(_this.derived(), new_rows, new_cols);
-  }
-
-  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
-  {
-    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
-
-    const Index num_new_elements = other.size() - _this.size();
-
-    const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : other.rows();
-    const Index new_cols = Derived::RowsAtCompileTime==1 ? other.cols() : 1;
-    if(IsRelocatable)
-      _this.derived().m_storage.conservativeResize(other.size(),new_rows,new_cols);
-    else
-      Base::run(_this.derived(), new_rows, new_cols);
-
-    if (num_new_elements > 0)
-      _this.tail(num_new_elements) = other.tail(num_new_elements);
-  }
-};
-
-template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
-struct matrix_swap_impl
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(MatrixTypeA& a, MatrixTypeB& b)
-  {
-    a.base().swap(b);
-  }
-};
-
-template<typename MatrixTypeA, typename MatrixTypeB>
-struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(MatrixTypeA& a, MatrixTypeB& b)
-  {
-    static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_DENSESTORAGEBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Product.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Product.h
deleted file mode 100644
index 70a6c10..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Product.h
+++ /dev/null
@@ -1,191 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PRODUCT_H
-#define EIGEN_PRODUCT_H
-
-namespace Eigen {
-
-template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Option>
-struct traits<Product<Lhs, Rhs, Option> >
-{
-  typedef typename remove_all<Lhs>::type LhsCleaned;
-  typedef typename remove_all<Rhs>::type RhsCleaned;
-  typedef traits<LhsCleaned> LhsTraits;
-  typedef traits<RhsCleaned> RhsTraits;
-
-  typedef MatrixXpr XprKind;
-
-  typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
-  typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
-                                                typename RhsTraits::StorageKind,
-                                                internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;
-  typedef typename promote_index_type<typename LhsTraits::StorageIndex,
-                                      typename RhsTraits::StorageIndex>::type StorageIndex;
-
-  enum {
-    RowsAtCompileTime    = LhsTraits::RowsAtCompileTime,
-    ColsAtCompileTime    = RhsTraits::ColsAtCompileTime,
-    MaxRowsAtCompileTime = LhsTraits::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime,
-
-    // FIXME: only needed by GeneralMatrixMatrixTriangular
-    InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime),
-
-    // The storage order is somewhat arbitrary here. The correct one will be determined through the evaluator.
-    Flags = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? RowMajorBit
-          : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
-          : (   ((LhsTraits::Flags&NoPreferredStorageOrderBit) && (RhsTraits::Flags&RowMajorBit))
-             || ((RhsTraits::Flags&NoPreferredStorageOrderBit) && (LhsTraits::Flags&RowMajorBit)) ) ? RowMajorBit
-          : NoPreferredStorageOrderBit
-  };
-};
-
-} // end namespace internal
-
-/** \class Product
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the product of two arbitrary matrices or vectors
-  *
-  * \tparam _Lhs the type of the left-hand side expression
-  * \tparam _Rhs the type of the right-hand side expression
-  *
-  * This class represents an expression of the product of two arbitrary matrices.
-  *
-  * The other template parameters are:
-  * \tparam Option     can be DefaultProduct, AliasFreeProduct, or LazyProduct
-  *
-  */
-template<typename _Lhs, typename _Rhs, int Option>
-class Product : public ProductImpl<_Lhs,_Rhs,Option,
-                                   typename internal::product_promote_storage_type<typename internal::traits<_Lhs>::StorageKind,
-                                                                                   typename internal::traits<_Rhs>::StorageKind,
-                                                                                   internal::product_type<_Lhs,_Rhs>::ret>::ret>
-{
-  public:
-
-    typedef _Lhs Lhs;
-    typedef _Rhs Rhs;
-
-    typedef typename ProductImpl<
-        Lhs, Rhs, Option,
-        typename internal::product_promote_storage_type<typename internal::traits<Lhs>::StorageKind,
-                                                        typename internal::traits<Rhs>::StorageKind,
-                                                        internal::product_type<Lhs,Rhs>::ret>::ret>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
-
-    typedef typename internal::ref_selector<Lhs>::type LhsNested;
-    typedef typename internal::ref_selector<Rhs>::type RhsNested;
-    typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
-    typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
-    {
-      eigen_assert(lhs.cols() == rhs.rows()
-        && "invalid matrix product"
-        && "if you wanted a coeff-wise or a dot product use the respective explicit functions");
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const LhsNestedCleaned& lhs() const { return m_lhs; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const RhsNestedCleaned& rhs() const { return m_rhs; }
-
-  protected:
-
-    LhsNested m_lhs;
-    RhsNested m_rhs;
-};
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Option, int ProductTag = internal::product_type<Lhs,Rhs>::ret>
-class dense_product_base
- : public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
-{};
-
-/** Conversion to scalar for inner-products */
-template<typename Lhs, typename Rhs, int Option>
-class dense_product_base<Lhs, Rhs, Option, InnerProduct>
- : public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
-{
-  typedef Product<Lhs,Rhs,Option> ProductXpr;
-  typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
-public:
-  using Base::derived;
-  typedef typename Base::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator const Scalar() const
-  {
-    return internal::evaluator<ProductXpr>(derived()).coeff(0,0);
-  }
-};
-
-} // namespace internal
-
-// Generic API dispatcher
-template<typename Lhs, typename Rhs, int Option, typename StorageKind>
-class ProductImpl : public internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type
-{
-  public:
-    typedef typename internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type Base;
-};
-
-template<typename Lhs, typename Rhs, int Option>
-class ProductImpl<Lhs,Rhs,Option,Dense>
-  : public internal::dense_product_base<Lhs,Rhs,Option>
-{
-    typedef Product<Lhs, Rhs, Option> Derived;
-
-  public:
-
-    typedef typename internal::dense_product_base<Lhs, Rhs, Option> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-  protected:
-    enum {
-      IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) &&
-                   (ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic),
-      EnableCoeff = IsOneByOne || Option==LazyProduct
-    };
-
-  public:
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const
-    {
-      EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
-      eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
-
-      return internal::evaluator<Derived>(derived()).coeff(row,col);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index i) const
-    {
-      EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
-      eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
-
-      return internal::evaluator<Derived>(derived()).coeff(i);
-    }
-
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ProductEvaluators.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ProductEvaluators.h
deleted file mode 100644
index 8cf294b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ProductEvaluators.h
+++ /dev/null
@@ -1,1179 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_PRODUCTEVALUATORS_H
-#define EIGEN_PRODUCTEVALUATORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * Evaluator of a product expression.
-  * Since products require special treatments to handle all possible cases,
-  * we simply defer the evaluation logic to a product_evaluator class
-  * which offers more partial specialization possibilities.
-  *
-  * \sa class product_evaluator
-  */
-template<typename Lhs, typename Rhs, int Options>
-struct evaluator<Product<Lhs, Rhs, Options> >
- : public product_evaluator<Product<Lhs, Rhs, Options> >
-{
-  typedef Product<Lhs, Rhs, Options> XprType;
-  typedef product_evaluator<XprType> Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
-// TODO we should apply that rule only if that's really helpful
-template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
-struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
-                                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
-                                               const Product<Lhs, Rhs, DefaultProduct> > >
-{
-  static const bool value = true;
-};
-template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
-struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
-                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
-                               const Product<Lhs, Rhs, DefaultProduct> > >
- : public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> >
-{
-  typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
-                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
-                               const Product<Lhs, Rhs, DefaultProduct> > XprType;
-  typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
-    : Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
-  {}
-};
-
-
-template<typename Lhs, typename Rhs, int DiagIndex>
-struct evaluator<Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> >
- : public evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> >
-{
-  typedef Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> XprType;
-  typedef evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> > Base;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
-    : Base(Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>(
-        Product<Lhs, Rhs, LazyProduct>(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()),
-        xpr.index() ))
-  {}
-};
-
-
-// Helper class to perform a matrix product with the destination at hand.
-// Depending on the sizes of the factors, there are different evaluation strategies
-// as controlled by internal::product_type.
-template< typename Lhs, typename Rhs,
-          typename LhsShape = typename evaluator_traits<Lhs>::Shape,
-          typename RhsShape = typename evaluator_traits<Rhs>::Shape,
-          int ProductType = internal::product_type<Lhs,Rhs>::value>
-struct generic_product_impl;
-
-template<typename Lhs, typename Rhs>
-struct evaluator_assume_aliasing<Product<Lhs, Rhs, DefaultProduct> > {
-  static const bool value = true;
-};
-
-// This is the default evaluator implementation for products:
-// It creates a temporary and call generic_product_impl
-template<typename Lhs, typename Rhs, int Options, int ProductTag, typename LhsShape, typename RhsShape>
-struct product_evaluator<Product<Lhs, Rhs, Options>, ProductTag, LhsShape, RhsShape>
-  : public evaluator<typename Product<Lhs, Rhs, Options>::PlainObject>
-{
-  typedef Product<Lhs, Rhs, Options> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit product_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-
-// FIXME shall we handle nested_eval here?,
-// if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in permutation_matrix_product, transposition_matrix_product, etc.)
-//     typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
-//     typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
-//     typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
-//     typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
-//
-//     const LhsNested lhs(xpr.lhs());
-//     const RhsNested rhs(xpr.rhs());
-//
-//     generic_product_impl<LhsNestedCleaned, RhsNestedCleaned>::evalTo(m_result, lhs, rhs);
-
-    generic_product_impl<Lhs, Rhs, LhsShape, RhsShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-// The following three shortcuts are enabled only if the scalar types match exactly.
-// TODO: we could enable them for different scalar types when the product is not vectorized.
-
-// Dense = Product
-template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
-  typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
-{
-  typedef Product<Lhs,Rhs,Options> SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    // FIXME shall we handle nested_eval here?
-    generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
-  }
-};
-
-// Dense += Product
-template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar,Scalar>, Dense2Dense,
-  typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
-{
-  typedef Product<Lhs,Rhs,Options> SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
-  {
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    // FIXME shall we handle nested_eval here?
-    generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
-  }
-};
-
-// Dense -= Product
-template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar,Scalar>, Dense2Dense,
-  typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
-{
-  typedef Product<Lhs,Rhs,Options> SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
-  {
-    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
-    // FIXME shall we handle nested_eval here?
-    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
-  }
-};
-
-
-// Dense ?= scalar * Product
-// TODO we should apply that rule if that's really helpful
-// for instance, this is not good for inner products
-template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain>
-struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>, const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
-                                           const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense>
-{
-  typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>,
-                        const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
-                        const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
-  {
-    call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func);
-  }
-};
-
-//----------------------------------------
-// Catch "Dense ?= xpr + Product<>" expression to save one temporary
-// FIXME we could probably enable these rules for any product, i.e., not only Dense and DefaultProduct
-
-template<typename OtherXpr, typename Lhs, typename Rhs>
-struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
-                                               const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
-  static const bool value = true;
-};
-
-template<typename OtherXpr, typename Lhs, typename Rhs>
-struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_difference_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
-                                               const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
-  static const bool value = true;
-};
-
-template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
-struct assignment_from_xpr_op_product
-{
-  template<typename SrcXprType, typename InitialFunc>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
-  {
-    call_assignment_no_alias(dst, src.lhs(), Func1());
-    call_assignment_no_alias(dst, src.rhs(), Func2());
-  }
-};
-
-#define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP,BINOP,ASSIGN_OP2) \
-  template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar> \
-  struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<OtherScalar,ProdScalar>, const OtherXpr, \
-                                            const Product<Lhs,Rhs,DefaultProduct> >, internal::ASSIGN_OP<DstScalar,SrcScalar>, Dense2Dense> \
-    : assignment_from_xpr_op_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::ASSIGN_OP<DstScalar,OtherScalar>, internal::ASSIGN_OP2<DstScalar,ProdScalar> > \
-  {}
-
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_sum_op,sub_assign_op);
-
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_difference_op,add_assign_op);
-
-//----------------------------------------
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,InnerProduct>
-{
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    dst.coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    dst.coeffRef(0,0) += (lhs.transpose().cwiseProduct(rhs)).sum();
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { dst.coeffRef(0,0) -= (lhs.transpose().cwiseProduct(rhs)).sum(); }
-};
-
-
-/***********************************************************************
-*  Implementation of outer dense * dense vector product
-***********************************************************************/
-
-// Column major result
-template<typename Dst, typename Lhs, typename Rhs, typename Func>
-void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&)
-{
-  evaluator<Rhs> rhsEval(rhs);
-  ei_declare_local_nested_eval(Lhs,lhs,Rhs::SizeAtCompileTime,actual_lhs);
-  // FIXME if cols is large enough, then it might be useful to make sure that lhs is sequentially stored
-  // FIXME not very good if rhs is real and lhs complex while alpha is real too
-  const Index cols = dst.cols();
-  for (Index j=0; j<cols; ++j)
-    func(dst.col(j), rhsEval.coeff(Index(0),j) * actual_lhs);
-}
-
-// Row major result
-template<typename Dst, typename Lhs, typename Rhs, typename Func>
-void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&)
-{
-  evaluator<Lhs> lhsEval(lhs);
-  ei_declare_local_nested_eval(Rhs,rhs,Lhs::SizeAtCompileTime,actual_rhs);
-  // FIXME if rows is large enough, then it might be useful to make sure that rhs is sequentially stored
-  // FIXME not very good if lhs is real and rhs complex while alpha is real too
-  const Index rows = dst.rows();
-  for (Index i=0; i<rows; ++i)
-    func(dst.row(i), lhsEval.coeff(i,Index(0)) * actual_rhs);
-}
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,OuterProduct>
-{
-  template<typename T> struct is_row_major : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  // TODO it would be nice to be able to exploit our *_assign_op functors for that purpose
-  struct set  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived()  = src; } };
-  struct add  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
-  struct sub  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
-  struct adds {
-    Scalar m_scale;
-    explicit adds(const Scalar& s) : m_scale(s) {}
-    template<typename Dst, typename Src> void EIGEN_DEVICE_FUNC operator()(const Dst& dst, const Src& src) const {
-      dst.const_cast_derived() += m_scale * src;
-    }
-  };
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major<Dst>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major<Dst>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major<Dst>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major<Dst>());
-  }
-
-};
-
-
-// This base class provides default implementations for evalTo, addTo, subTo, in terms of scaleAndAddTo
-template<typename Lhs, typename Rhs, typename Derived>
-struct generic_product_impl_base
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { scaleAndAddTo(dst,lhs, rhs, Scalar(1)); }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }
-
-};
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct> >
-{
-  typedef typename nested_eval<Lhs,1>::type LhsNested;
-  typedef typename nested_eval<Rhs,1>::type RhsNested;
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
-  typedef typename internal::remove_all<typename internal::conditional<int(Side)==OnTheRight,LhsNested,RhsNested>::type>::type MatrixType;
-
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    // Fallback to inner product if both the lhs and rhs is a runtime vector.
-    if (lhs.rows() == 1 && rhs.cols() == 1) {
-      dst.coeffRef(0,0) += alpha * lhs.row(0).conjugate().dot(rhs.col(0));
-      return;
-    }
-    LhsNested actual_lhs(lhs);
-    RhsNested actual_rhs(rhs);
-    internal::gemv_dense_selector<Side,
-                            (int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
-                            bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)
-                           >::run(actual_lhs, actual_rhs, dst, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // Same as: dst.noalias() = lhs.lazyProduct(rhs);
-    // but easier on the compiler side
-    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // dst.noalias() += lhs.lazyProduct(rhs);
-    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
-  }
-
-  template<typename Dst>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // dst.noalias() -= lhs.lazyProduct(rhs);
-    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
-  }
-
-  // This is a special evaluation path called from generic_product_impl<...,GemmProduct> in file GeneralMatrixMatrix.h
-  // This variant tries to extract scalar multiples from both the LHS and RHS and factor them out. For instance:
-  //   dst {,+,-}= (s1*A)*(B*s2)
-  // will be rewritten as:
-  //   dst {,+,-}= (s1*s2) * (A.lazyProduct(B))
-  // There are at least four benefits of doing so:
-  //  1 - huge performance gain for heap-allocated matrix types as it save costly allocations.
-  //  2 - it is faster than simply by-passing the heap allocation through stack allocation.
-  //  3 - it makes this fallback consistent with the heavy GEMM routine.
-  //  4 - it fully by-passes huge stack allocation attempts when multiplying huge fixed-size matrices.
-  //      (see https://stackoverflow.com/questions/54738495)
-  // For small fixed sizes matrices, howver, the gains are less obvious, it is sometimes x2 faster, but sometimes x3 slower,
-  // and the behavior depends also a lot on the compiler... This is why this re-writting strategy is currently
-  // enabled only when falling back from the main GEMM.
-  template<typename Dst, typename Func>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void eval_dynamic(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Func &func)
-  {
-    enum {
-      HasScalarFactor = blas_traits<Lhs>::HasScalarFactor || blas_traits<Rhs>::HasScalarFactor,
-      ConjLhs = blas_traits<Lhs>::NeedToConjugate,
-      ConjRhs = blas_traits<Rhs>::NeedToConjugate
-    };
-    // FIXME: in c++11 this should be auto, and extractScalarFactor should also return auto
-    //        this is important for real*complex_mat
-    Scalar actualAlpha = combine_scalar_factors<Scalar>(lhs, rhs);
-
-    eval_dynamic_impl(dst,
-                      blas_traits<Lhs>::extract(lhs).template conjugateIf<ConjLhs>(),
-                      blas_traits<Rhs>::extract(rhs).template conjugateIf<ConjRhs>(),
-                      func,
-                      actualAlpha,
-                      typename conditional<HasScalarFactor,true_type,false_type>::type());
-  }
-
-protected:
-
-  template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar&  s /* == 1 */, false_type)
-  {
-    EIGEN_UNUSED_VARIABLE(s);
-    eigen_internal_assert(s==Scalar(1));
-    call_restricted_packet_assignment_no_alias(dst, lhs.lazyProduct(rhs), func);
-  }
-
-  template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar& s, true_type)
-  {
-    call_restricted_packet_assignment_no_alias(dst, s * lhs.lazyProduct(rhs), func);
-  }
-};
-
-// This specialization enforces the use of a coefficient-based evaluation strategy
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,LazyCoeffBasedProductMode>
-  : generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> {};
-
-// Case 2: Evaluate coeff by coeff
-//
-// This is mostly taken from CoeffBasedProduct.h
-// The main difference is that we add an extra argument to the etor_product_*_impl::run() function
-// for the inner dimension of the product, because evaluator object do not know their size.
-
-template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
-struct etor_product_coeff_impl;
-
-template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, DenseShape>
-    : evaluator_base<Product<Lhs, Rhs, LazyProduct> >
-{
-  typedef Product<Lhs, Rhs, LazyProduct> XprType;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit product_evaluator(const XprType& xpr)
-    : m_lhs(xpr.lhs()),
-      m_rhs(xpr.rhs()),
-      m_lhsImpl(m_lhs),     // FIXME the creation of the evaluator objects should result in a no-op, but check that!
-      m_rhsImpl(m_rhs),     //       Moreover, they are only useful for the packet path, so we could completely disable them when not needed,
-                            //       or perhaps declare them on the fly on the packet method... We have experiment to check what's best.
-      m_innerDim(xpr.lhs().cols())
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::AddCost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-#if 0
-    std::cerr << "LhsOuterStrideBytes=  " << LhsOuterStrideBytes << "\n";
-    std::cerr << "RhsOuterStrideBytes=  " << RhsOuterStrideBytes << "\n";
-    std::cerr << "LhsAlignment=         " << LhsAlignment << "\n";
-    std::cerr << "RhsAlignment=         " << RhsAlignment << "\n";
-    std::cerr << "CanVectorizeLhs=      " << CanVectorizeLhs << "\n";
-    std::cerr << "CanVectorizeRhs=      " << CanVectorizeRhs << "\n";
-    std::cerr << "CanVectorizeInner=    " << CanVectorizeInner << "\n";
-    std::cerr << "EvalToRowMajor=       " << EvalToRowMajor << "\n";
-    std::cerr << "Alignment=            " << Alignment << "\n";
-    std::cerr << "Flags=                " << Flags << "\n";
-#endif
-  }
-
-  // Everything below here is taken from CoeffBasedProduct.h
-
-  typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
-  typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
-
-  typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
-  typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
-
-  typedef evaluator<LhsNestedCleaned> LhsEtorType;
-  typedef evaluator<RhsNestedCleaned> RhsEtorType;
-
-  enum {
-    RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
-    ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
-    InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
-    MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
-  };
-
-  typedef typename find_best_packet<Scalar,RowsAtCompileTime>::type LhsVecPacketType;
-  typedef typename find_best_packet<Scalar,ColsAtCompileTime>::type RhsVecPacketType;
-
-  enum {
-
-    LhsCoeffReadCost = LhsEtorType::CoeffReadCost,
-    RhsCoeffReadCost = RhsEtorType::CoeffReadCost,
-    CoeffReadCost = InnerSize==0 ? NumTraits<Scalar>::ReadCost
-                  : InnerSize == Dynamic ? HugeCost
-                    : InnerSize * (NumTraits<Scalar>::MulCost + int(LhsCoeffReadCost) + int(RhsCoeffReadCost))
-                    + (InnerSize - 1) * NumTraits<Scalar>::AddCost,
-
-    Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
-
-    LhsFlags = LhsEtorType::Flags,
-    RhsFlags = RhsEtorType::Flags,
-
-    LhsRowMajor = LhsFlags & RowMajorBit,
-    RhsRowMajor = RhsFlags & RowMajorBit,
-
-    LhsVecPacketSize = unpacket_traits<LhsVecPacketType>::size,
-    RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
-
-    // Here, we don't care about alignment larger than the usable packet size.
-    LhsAlignment = EIGEN_PLAIN_ENUM_MIN(LhsEtorType::Alignment,LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
-    RhsAlignment = EIGEN_PLAIN_ENUM_MIN(RhsEtorType::Alignment,RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
-
-    SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
-
-    CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime!=1),
-    CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime!=1),
-
-    EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
-                    : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
-                    : (bool(RhsRowMajor) && !CanVectorizeLhs),
-
-    Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & ~RowMajorBit)
-          | (EvalToRowMajor ? RowMajorBit : 0)
-          // TODO enable vectorization for mixed types
-          | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0)
-          | (XprType::IsVectorAtCompileTime ? LinearAccessBit : 0),
-
-    LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
-    RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
-
-    Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment)
-              : bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment)
-              : 0,
-
-    /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
-     * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
-     * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
-     * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
-     */
-    CanVectorizeInner =    SameType
-                        && LhsRowMajor
-                        && (!RhsRowMajor)
-                        && (int(LhsFlags) & int(RhsFlags) & ActualPacketAccessBit)
-                        && (int(InnerSize) % packet_traits<Scalar>::size == 0)
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
-  {
-    return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
-  }
-
-  /* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
-   * which is why we don't set the LinearAccessBit.
-   * TODO: this seems possible when the result is a vector
-   */
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const CoeffReturnType coeff(Index index) const
-  {
-    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
-    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
-    return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const PacketType packet(Index row, Index col) const
-  {
-    PacketType res;
-    typedef etor_product_packet_impl<bool(int(Flags)&RowMajorBit) ? RowMajor : ColMajor,
-                                     Unroll ? int(InnerSize) : Dynamic,
-                                     LhsEtorType, RhsEtorType, PacketType, LoadMode> PacketImpl;
-    PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res);
-    return res;
-  }
-
-  template<int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const PacketType packet(Index index) const
-  {
-    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
-    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
-    return packet<LoadMode,PacketType>(row,col);
-  }
-
-protected:
-  typename internal::add_const_on_value_type<LhsNested>::type m_lhs;
-  typename internal::add_const_on_value_type<RhsNested>::type m_rhs;
-
-  LhsEtorType m_lhsImpl;
-  RhsEtorType m_rhsImpl;
-
-  // TODO: Get rid of m_innerDim if known at compile time
-  Index m_innerDim;
-};
-
-template<typename Lhs, typename Rhs>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, LazyCoeffBasedProductMode, DenseShape, DenseShape>
-  : product_evaluator<Product<Lhs, Rhs, LazyProduct>, CoeffBasedProductMode, DenseShape, DenseShape>
-{
-  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
-  typedef Product<Lhs, Rhs, LazyProduct> BaseProduct;
-  typedef product_evaluator<BaseProduct, CoeffBasedProductMode, DenseShape, DenseShape> Base;
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit product_evaluator(const XprType& xpr)
-    : Base(BaseProduct(xpr.lhs(),xpr.rhs()))
-  {}
-};
-
-/****************************************
-*** Coeff based product, Packet path  ***
-****************************************/
-
-template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
-  {
-    etor_product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
-    res =  pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex-1))), rhs.template packet<LoadMode,Packet>(Index(UnrollingIndex-1), col), res);
-  }
-};
-
-template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
-  {
-    etor_product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
-    res =  pmadd(lhs.template packet<LoadMode,Packet>(row, Index(UnrollingIndex-1)), pset1<Packet>(rhs.coeff(Index(UnrollingIndex-1), col)), res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, 1, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
-  {
-    res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))),rhs.template packet<LoadMode,Packet>(Index(0), col));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, 1, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
-  {
-    res = pmul(lhs.template packet<LoadMode,Packet>(row, Index(0)), pset1<Packet>(rhs.coeff(Index(0), col)));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-    for(Index i = 0; i < innerDim; ++i)
-      res =  pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode,Packet>(i, col), res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
-struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
-  {
-    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
-    for(Index i = 0; i < innerDim; ++i)
-      res =  pmadd(lhs.template packet<LoadMode,Packet>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
-  }
-};
-
-
-/***************************************************************************
-* Triangular products
-***************************************************************************/
-template<int Mode, bool LhsIsTriangular,
-         typename Lhs, bool LhsIsVector,
-         typename Rhs, bool RhsIsVector>
-struct triangular_product_impl;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    triangular_product_impl<Lhs::Mode,true,typename Lhs::MatrixType,false,Rhs, Rhs::ColsAtCompileTime==1>
-        ::run(dst, lhs.nestedExpression(), rhs, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag>
-: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    triangular_product_impl<Rhs::Mode,false,Lhs,Lhs::RowsAtCompileTime==1, typename Rhs::MatrixType, false>::run(dst, lhs, rhs.nestedExpression(), alpha);
-  }
-};
-
-
-/***************************************************************************
-* SelfAdjoint products
-***************************************************************************/
-template <typename Lhs, int LhsMode, bool LhsIsVector,
-          typename Rhs, int RhsMode, bool RhsIsVector>
-struct selfadjoint_product_impl;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC
-  void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    selfadjoint_product_impl<typename Lhs::MatrixType,Lhs::Mode,false,Rhs,0,Rhs::IsVectorAtCompileTime>::run(dst, lhs.nestedExpression(), rhs, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag>
-: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    selfadjoint_product_impl<Lhs,0,Lhs::IsVectorAtCompileTime,typename Rhs::MatrixType,Rhs::Mode,false>::run(dst, lhs, rhs.nestedExpression(), alpha);
-  }
-};
-
-
-/***************************************************************************
-* Diagonal products
-***************************************************************************/
-
-template<typename MatrixType, typename DiagonalType, typename Derived, int ProductOrder>
-struct diagonal_product_evaluator_base
-  : evaluator_base<Derived>
-{
-   typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
-public:
-  enum {
-    CoeffReadCost = int(NumTraits<Scalar>::MulCost) + int(evaluator<MatrixType>::CoeffReadCost) + int(evaluator<DiagonalType>::CoeffReadCost),
-
-    MatrixFlags = evaluator<MatrixType>::Flags,
-    DiagFlags = evaluator<DiagonalType>::Flags,
-
-    _StorageOrder = (Derived::MaxRowsAtCompileTime==1 && Derived::MaxColsAtCompileTime!=1) ? RowMajor
-                  : (Derived::MaxColsAtCompileTime==1 && Derived::MaxRowsAtCompileTime!=1) ? ColMajor
-                  : MatrixFlags & RowMajorBit ? RowMajor : ColMajor,
-    _SameStorageOrder = _StorageOrder == (MatrixFlags & RowMajorBit ? RowMajor : ColMajor),
-
-    _ScalarAccessOnDiag =  !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
-                           ||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)),
-    _SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
-    // FIXME currently we need same types, but in the future the next rule should be the one
-    //_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagFlags)&PacketAccessBit))),
-    _Vectorizable =   bool(int(MatrixFlags)&PacketAccessBit)
-                  &&  _SameTypes
-                  && (_SameStorageOrder || (MatrixFlags&LinearAccessBit)==LinearAccessBit)
-                  && (_ScalarAccessOnDiag || (bool(int(DiagFlags)&PacketAccessBit))),
-    _LinearAccessMask = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
-    Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixFlags)) | (_Vectorizable ? PacketAccessBit : 0),
-    Alignment = evaluator<MatrixType>::Alignment,
-
-    AsScalarProduct =     (DiagonalType::SizeAtCompileTime==1)
-                      ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::RowsAtCompileTime==1 && ProductOrder==OnTheLeft)
-                      ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime==1 && ProductOrder==OnTheRight)
-  };
-
-  EIGEN_DEVICE_FUNC diagonal_product_evaluator_base(const MatrixType &mat, const DiagonalType &diag)
-    : m_diagImpl(diag), m_matImpl(mat)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
-  {
-    if(AsScalarProduct)
-      return m_diagImpl.coeff(0) * m_matImpl.coeff(idx);
-    else
-      return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx);
-  }
-
-protected:
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::true_type) const
-  {
-    return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
-                          internal::pset1<PacketType>(m_diagImpl.coeff(id)));
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::false_type) const
-  {
-    enum {
-      InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
-      DiagonalPacketLoadMode = EIGEN_PLAIN_ENUM_MIN(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment)) // FIXME hardcoded 16!!
-    };
-    return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
-                          m_diagImpl.template packet<DiagonalPacketLoadMode,PacketType>(id));
-  }
-
-  evaluator<DiagonalType> m_diagImpl;
-  evaluator<MatrixType>   m_matImpl;
-};
-
-// diagonal * dense
-template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalShape, DenseShape>
-  : diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft>
-{
-  typedef diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft> Base;
-  using Base::m_diagImpl;
-  using Base::m_matImpl;
-  using Base::coeff;
-  typedef typename Base::Scalar Scalar;
-
-  typedef Product<Lhs, Rhs, ProductKind> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef typename Lhs::DiagonalVectorType DiagonalType;
-
-
-  enum { StorageOrder = Base::_StorageOrder };
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.rhs(), xpr.lhs().diagonal())
-  {
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
-  {
-    return m_diagImpl.coeff(row) * m_matImpl.coeff(row, col);
-  }
-
-#ifndef EIGEN_GPUCC
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
-  {
-    // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
-    // See also similar calls below.
-    return this->template packet_impl<LoadMode,PacketType>(row,col, row,
-                                 typename internal::conditional<int(StorageOrder)==RowMajor, internal::true_type, internal::false_type>::type());
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index idx) const
-  {
-    return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
-  }
-#endif
-};
-
-// dense * diagonal
-template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape, DiagonalShape>
-  : diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight>
-{
-  typedef diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight> Base;
-  using Base::m_diagImpl;
-  using Base::m_matImpl;
-  using Base::coeff;
-  typedef typename Base::Scalar Scalar;
-
-  typedef Product<Lhs, Rhs, ProductKind> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-
-  enum { StorageOrder = Base::_StorageOrder };
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.lhs(), xpr.rhs().diagonal())
-  {
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
-  {
-    return m_matImpl.coeff(row, col) * m_diagImpl.coeff(col);
-  }
-
-#ifndef EIGEN_GPUCC
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
-  {
-    return this->template packet_impl<LoadMode,PacketType>(row,col, col,
-                                 typename internal::conditional<int(StorageOrder)==ColMajor, internal::true_type, internal::false_type>::type());
-  }
-
-  template<int LoadMode,typename PacketType>
-  EIGEN_STRONG_INLINE PacketType packet(Index idx) const
-  {
-    return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
-  }
-#endif
-};
-
-/***************************************************************************
-* Products with permutation matrices
-***************************************************************************/
-
-/** \internal
-  * \class permutation_matrix_product
-  * Internal helper class implementing the product between a permutation matrix and a matrix.
-  * This class is specialized for DenseShape below and for SparseShape in SparseCore/SparsePermutation.h
-  */
-template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
-struct permutation_matrix_product;
-
-template<typename ExpressionType, int Side, bool Transposed>
-struct permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
-{
-    typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
-    typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-
-    template<typename Dest, typename PermutationType>
-    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
-    {
-      MatrixType mat(xpr);
-      const Index n = Side==OnTheLeft ? mat.rows() : mat.cols();
-      // FIXME we need an is_same for expression that is not sensitive to constness. For instance
-      // is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
-      //if(is_same<MatrixTypeCleaned,Dest>::value && extract_data(dst) == extract_data(mat))
-      if(is_same_dense(dst, mat))
-      {
-        // apply the permutation inplace
-        Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(perm.size());
-        mask.fill(false);
-        Index r = 0;
-        while(r < perm.size())
-        {
-          // search for the next seed
-          while(r<perm.size() && mask[r]) r++;
-          if(r>=perm.size())
-            break;
-          // we got one, let's follow it until we are back to the seed
-          Index k0 = r++;
-          Index kPrev = k0;
-          mask.coeffRef(k0) = true;
-          for(Index k=perm.indices().coeff(k0); k!=k0; k=perm.indices().coeff(k))
-          {
-                  Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
-            .swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
-                       (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
-
-            mask.coeffRef(k) = true;
-            kPrev = k;
-          }
-        }
-      }
-      else
-      {
-        for(Index i = 0; i < n; ++i)
-        {
-          Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
-               (dst, ((Side==OnTheLeft) ^ Transposed) ? perm.indices().coeff(i) : i)
-
-          =
-
-          Block<const MatrixTypeCleaned,Side==OnTheLeft ? 1 : MatrixTypeCleaned::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixTypeCleaned::ColsAtCompileTime>
-               (mat, ((Side==OnTheRight) ^ Transposed) ? perm.indices().coeff(i) : i);
-        }
-      }
-    }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, PermutationShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    permutation_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, MatrixShape, PermutationShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    permutation_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Inverse<Lhs>, Rhs, PermutationShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Inverse<Lhs>& lhs, const Rhs& rhs)
-  {
-    permutation_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Inverse<Rhs>, MatrixShape, PermutationShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Inverse<Rhs>& rhs)
-  {
-    permutation_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
-  }
-};
-
-
-/***************************************************************************
-* Products with transpositions matrices
-***************************************************************************/
-
-// FIXME could we unify Transpositions and Permutation into a single "shape"??
-
-/** \internal
-  * \class transposition_matrix_product
-  * Internal helper class implementing the product between a permutation matrix and a matrix.
-  */
-template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
-struct transposition_matrix_product
-{
-  typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
-  typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-
-  template<typename Dest, typename TranspositionType>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const TranspositionType& tr, const ExpressionType& xpr)
-  {
-    MatrixType mat(xpr);
-    typedef typename TranspositionType::StorageIndex StorageIndex;
-    const Index size = tr.size();
-    StorageIndex j = 0;
-
-    if(!is_same_dense(dst,mat))
-      dst = mat;
-
-    for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)
-      if(Index(j=tr.coeff(k))!=k)
-      {
-        if(Side==OnTheLeft)        dst.row(k).swap(dst.row(j));
-        else if(Side==OnTheRight)  dst.col(k).swap(dst.col(j));
-      }
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, TranspositionsShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    transposition_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Rhs, MatrixShape, TranspositionsShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    transposition_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
-  }
-};
-
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Transpose<Lhs>, Rhs, TranspositionsShape, MatrixShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Transpose<Lhs>& lhs, const Rhs& rhs)
-  {
-    transposition_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
-struct generic_product_impl<Lhs, Transpose<Rhs>, MatrixShape, TranspositionsShape, ProductTag>
-{
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Transpose<Rhs>& rhs)
-  {
-    transposition_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PRODUCT_EVALUATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Random.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Random.h
deleted file mode 100644
index dab2ac8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Random.h
+++ /dev/null
@@ -1,218 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_RANDOM_H
-#define EIGEN_RANDOM_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Scalar> struct scalar_random_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op)
-  inline const Scalar operator() () const { return random<Scalar>(); }
-};
-
-template<typename Scalar>
-struct functor_traits<scalar_random_op<Scalar> >
-{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false }; };
-
-} // end namespace internal
-
-/** \returns a random matrix expression
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * The parameters \a rows and \a cols are the number of rows and of columns of
-  * the returned matrix. Must be compatible with this MatrixBase type.
-  *
-  * \not_reentrant
-  * 
-  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
-  * it is redundant to pass \a rows and \a cols as arguments, so Random() should be used
-  * instead.
-  * 
-  *
-  * Example: \include MatrixBase_random_int_int.cpp
-  * Output: \verbinclude MatrixBase_random_int_int.out
-  *
-  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
-  * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
-  * behavior with expressions involving random matrices.
-  * 
-  * See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators.
-  *
-  * \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random()
-  */
-template<typename Derived>
-inline const typename DenseBase<Derived>::RandomReturnType
-DenseBase<Derived>::Random(Index rows, Index cols)
-{
-  return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>());
-}
-
-/** \returns a random vector expression
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * The parameter \a size is the size of the returned vector.
-  * Must be compatible with this MatrixBase type.
-  *
-  * \only_for_vectors
-  * \not_reentrant
-  *
-  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
-  * it is redundant to pass \a size as argument, so Random() should be used
-  * instead.
-  *
-  * Example: \include MatrixBase_random_int.cpp
-  * Output: \verbinclude MatrixBase_random_int.out
-  *
-  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
-  * a temporary vector whenever it is nested in a larger expression. This prevents unexpected
-  * behavior with expressions involving random matrices.
-  *
-  * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random()
-  */
-template<typename Derived>
-inline const typename DenseBase<Derived>::RandomReturnType
-DenseBase<Derived>::Random(Index size)
-{
-  return NullaryExpr(size, internal::scalar_random_op<Scalar>());
-}
-
-/** \returns a fixed-size random matrix or vector expression
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
-  * need to use the variants taking size arguments.
-  *
-  * Example: \include MatrixBase_random.cpp
-  * Output: \verbinclude MatrixBase_random.out
-  *
-  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
-  * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
-  * behavior with expressions involving random matrices.
-  * 
-  * \not_reentrant
-  *
-  * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index)
-  */
-template<typename Derived>
-inline const typename DenseBase<Derived>::RandomReturnType
-DenseBase<Derived>::Random()
-{
-  return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>());
-}
-
-/** Sets all coefficients in this expression to random values.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * \not_reentrant
-  * 
-  * Example: \include MatrixBase_setRandom.cpp
-  * Output: \verbinclude MatrixBase_setRandom.out
-  *
-  * \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index)
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Derived& DenseBase<Derived>::setRandom()
-{
-  return *this = Random(rows(), cols());
-}
-
-/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  * 
-  * \only_for_vectors
-  * \not_reentrant
-  *
-  * Example: \include Matrix_setRandom_int.cpp
-  * Output: \verbinclude Matrix_setRandom_int.out
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(Index newSize)
-{
-  resize(newSize);
-  return setRandom();
-}
-
-/** Resizes to the given size, and sets all coefficients in this expression to random values.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * \not_reentrant
-  * 
-  * \param rows the new number of rows
-  * \param cols the new number of columns
-  *
-  * Example: \include Matrix_setRandom_int_int.cpp
-  * Output: \verbinclude Matrix_setRandom_int_int.out
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
-{
-  resize(rows, cols);
-  return setRandom();
-}
-
-/** Resizes to the given size, changing only the number of columns, and sets all
-  * coefficients in this expression to random values. For the parameter of type
-  * NoChange_t, just pass the special value \c NoChange.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * \not_reentrant
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index), setRandom(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(NoChange_t, Index cols)
-{
-  return setRandom(rows(), cols);
-}
-
-/** Resizes to the given size, changing only the number of rows, and sets all
-  * coefficients in this expression to random values. For the parameter of type
-  * NoChange_t, just pass the special value \c NoChange.
-  *
-  * Numbers are uniformly spread through their whole definition range for integer types,
-  * and in the [-1:1] range for floating point scalar types.
-  *
-  * \not_reentrant
-  *
-  * \sa DenseBase::setRandom(), setRandom(Index), setRandom(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Random()
-  */
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-PlainObjectBase<Derived>::setRandom(Index rows, NoChange_t)
-{
-  return setRandom(rows, cols());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_RANDOM_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Redux.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Redux.h
deleted file mode 100644
index b6790d1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Redux.h
+++ /dev/null
@@ -1,515 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REDUX_H
-#define EIGEN_REDUX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// TODO
-//  * implement other kind of vectorization
-//  * factorize code
-
-/***************************************************************************
-* Part 1 : the logic deciding a strategy for vectorization and unrolling
-***************************************************************************/
-
-template<typename Func, typename Evaluator>
-struct redux_traits
-{
-public:
-    typedef typename find_best_packet<typename Evaluator::Scalar,Evaluator::SizeAtCompileTime>::type PacketType;
-  enum {
-    PacketSize = unpacket_traits<PacketType>::size,
-    InnerMaxSize = int(Evaluator::IsRowMajor)
-                 ? Evaluator::MaxColsAtCompileTime
-                 : Evaluator::MaxRowsAtCompileTime,
-    OuterMaxSize = int(Evaluator::IsRowMajor)
-                 ? Evaluator::MaxRowsAtCompileTime
-                 : Evaluator::MaxColsAtCompileTime,
-    SliceVectorizedWork = int(InnerMaxSize)==Dynamic ? Dynamic
-                        : int(OuterMaxSize)==Dynamic ? (int(InnerMaxSize)>=int(PacketSize) ? Dynamic : 0)
-                        : (int(InnerMaxSize)/int(PacketSize)) * int(OuterMaxSize)
-  };
-
-  enum {
-    MightVectorize = (int(Evaluator::Flags)&ActualPacketAccessBit)
-                  && (functor_traits<Func>::PacketAccess),
-    MayLinearVectorize = bool(MightVectorize) && (int(Evaluator::Flags)&LinearAccessBit),
-    MaySliceVectorize  = bool(MightVectorize) && (int(SliceVectorizedWork)==Dynamic || int(SliceVectorizedWork)>=3)
-  };
-
-public:
-  enum {
-    Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
-              : int(MaySliceVectorize)  ? int(SliceVectorizedTraversal)
-                                        : int(DefaultTraversal)
-  };
-
-public:
-  enum {
-    Cost = Evaluator::SizeAtCompileTime == Dynamic ? HugeCost
-         : int(Evaluator::SizeAtCompileTime) * int(Evaluator::CoeffReadCost) + (Evaluator::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
-    UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize))
-  };
-
-public:
-  enum {
-    Unrolling = Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling
-  };
-  
-#ifdef EIGEN_DEBUG_ASSIGN
-  static void debug()
-  {
-    std::cerr << "Xpr: " << typeid(typename Evaluator::XprType).name() << std::endl;
-    std::cerr.setf(std::ios::hex, std::ios::basefield);
-    EIGEN_DEBUG_VAR(Evaluator::Flags)
-    std::cerr.unsetf(std::ios::hex);
-    EIGEN_DEBUG_VAR(InnerMaxSize)
-    EIGEN_DEBUG_VAR(OuterMaxSize)
-    EIGEN_DEBUG_VAR(SliceVectorizedWork)
-    EIGEN_DEBUG_VAR(PacketSize)
-    EIGEN_DEBUG_VAR(MightVectorize)
-    EIGEN_DEBUG_VAR(MayLinearVectorize)
-    EIGEN_DEBUG_VAR(MaySliceVectorize)
-    std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
-    EIGEN_DEBUG_VAR(UnrollingLimit)
-    std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
-    std::cerr << std::endl;
-  }
-#endif
-};
-
-/***************************************************************************
-* Part 2 : unrollers
-***************************************************************************/
-
-/*** no vectorization ***/
-
-template<typename Func, typename Evaluator, int Start, int Length>
-struct redux_novec_unroller
-{
-  enum {
-    HalfLength = Length/2
-  };
-
-  typedef typename Evaluator::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func& func)
-  {
-    return func(redux_novec_unroller<Func, Evaluator, Start, HalfLength>::run(eval,func),
-                redux_novec_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::run(eval,func));
-  }
-};
-
-template<typename Func, typename Evaluator, int Start>
-struct redux_novec_unroller<Func, Evaluator, Start, 1>
-{
-  enum {
-    outer = Start / Evaluator::InnerSizeAtCompileTime,
-    inner = Start % Evaluator::InnerSizeAtCompileTime
-  };
-
-  typedef typename Evaluator::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func&)
-  {
-    return eval.coeffByOuterInner(outer, inner);
-  }
-};
-
-// This is actually dead code and will never be called. It is required
-// to prevent false warnings regarding failed inlining though
-// for 0 length run() will never be called at all.
-template<typename Func, typename Evaluator, int Start>
-struct redux_novec_unroller<Func, Evaluator, Start, 0>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  EIGEN_DEVICE_FUNC 
-  static EIGEN_STRONG_INLINE Scalar run(const Evaluator&, const Func&) { return Scalar(); }
-};
-
-/*** vectorization ***/
-
-template<typename Func, typename Evaluator, int Start, int Length>
-struct redux_vec_unroller
-{
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func& func)
-  {
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      HalfLength = Length/2
-    };
-
-    return func.packetOp(
-            redux_vec_unroller<Func, Evaluator, Start, HalfLength>::template run<PacketType>(eval,func),
-            redux_vec_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::template run<PacketType>(eval,func) );
-  }
-};
-
-template<typename Func, typename Evaluator, int Start>
-struct redux_vec_unroller<Func, Evaluator, Start, 1>
-{
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func&)
-  {
-    enum {
-      PacketSize = unpacket_traits<PacketType>::size,
-      index = Start * PacketSize,
-      outer = index / int(Evaluator::InnerSizeAtCompileTime),
-      inner = index % int(Evaluator::InnerSizeAtCompileTime),
-      alignment = Evaluator::Alignment
-    };
-    return eval.template packetByOuterInner<alignment,PacketType>(outer, inner);
-  }
-};
-
-/***************************************************************************
-* Part 3 : implementation of all cases
-***************************************************************************/
-
-template<typename Func, typename Evaluator,
-         int Traversal = redux_traits<Func, Evaluator>::Traversal,
-         int Unrolling = redux_traits<Func, Evaluator>::Unrolling
->
-struct redux_impl;
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
-  {
-    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
-    Scalar res;
-    res = eval.coeffByOuterInner(0, 0);
-    for(Index i = 1; i < xpr.innerSize(); ++i)
-      res = func(res, eval.coeffByOuterInner(0, i));
-    for(Index i = 1; i < xpr.outerSize(); ++i)
-      for(Index j = 0; j < xpr.innerSize(); ++j)
-        res = func(res, eval.coeffByOuterInner(i, j));
-    return res;
-  }
-};
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func,Evaluator, DefaultTraversal, CompleteUnrolling>
-  : redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime>
-{
-  typedef redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
-  typedef typename Evaluator::Scalar Scalar;
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  Scalar run(const Evaluator &eval, const Func& func, const XprType& /*xpr*/)
-  {
-    return Base::run(eval,func);
-  }
-};
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
-
-  template<typename XprType>
-  static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
-  {
-    const Index size = xpr.size();
-    
-    const Index packetSize = redux_traits<Func, Evaluator>::PacketSize;
-    const int packetAlignment = unpacket_traits<PacketScalar>::alignment;
-    enum {
-      alignment0 = (bool(Evaluator::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned),
-      alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Evaluator::Alignment)
-    };
-    const Index alignedStart = internal::first_default_aligned(xpr);
-    const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize);
-    const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize);
-    const Index alignedEnd2 = alignedStart + alignedSize2;
-    const Index alignedEnd  = alignedStart + alignedSize;
-    Scalar res;
-    if(alignedSize)
-    {
-      PacketScalar packet_res0 = eval.template packet<alignment,PacketScalar>(alignedStart);
-      if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop
-      {
-        PacketScalar packet_res1 = eval.template packet<alignment,PacketScalar>(alignedStart+packetSize);
-        for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize)
-        {
-          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment,PacketScalar>(index));
-          packet_res1 = func.packetOp(packet_res1, eval.template packet<alignment,PacketScalar>(index+packetSize));
-        }
-
-        packet_res0 = func.packetOp(packet_res0,packet_res1);
-        if(alignedEnd>alignedEnd2)
-          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment,PacketScalar>(alignedEnd2));
-      }
-      res = func.predux(packet_res0);
-
-      for(Index index = 0; index < alignedStart; ++index)
-        res = func(res,eval.coeff(index));
-
-      for(Index index = alignedEnd; index < size; ++index)
-        res = func(res,eval.coeff(index));
-    }
-    else // too small to vectorize anything.
-         // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
-    {
-      res = eval.coeff(0);
-      for(Index index = 1; index < size; ++index)
-        res = func(res,eval.coeff(index));
-    }
-
-    return res;
-  }
-};
-
-// NOTE: for SliceVectorizedTraversal we simply bypass unrolling
-template<typename Func, typename Evaluator, int Unrolling>
-struct redux_impl<Func, Evaluator, SliceVectorizedTraversal, Unrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
-
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
-  {
-    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
-    const Index innerSize = xpr.innerSize();
-    const Index outerSize = xpr.outerSize();
-    enum {
-      packetSize = redux_traits<Func, Evaluator>::PacketSize
-    };
-    const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
-    Scalar res;
-    if(packetedInnerSize)
-    {
-      PacketType packet_res = eval.template packet<Unaligned,PacketType>(0,0);
-      for(Index j=0; j<outerSize; ++j)
-        for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize))
-          packet_res = func.packetOp(packet_res, eval.template packetByOuterInner<Unaligned,PacketType>(j,i));
-
-      res = func.predux(packet_res);
-      for(Index j=0; j<outerSize; ++j)
-        for(Index i=packetedInnerSize; i<innerSize; ++i)
-          res = func(res, eval.coeffByOuterInner(j,i));
-    }
-    else // too small to vectorize anything.
-         // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
-    {
-      res = redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>::run(eval, func, xpr);
-    }
-
-    return res;
-  }
-};
-
-template<typename Func, typename Evaluator>
-struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, CompleteUnrolling>
-{
-  typedef typename Evaluator::Scalar Scalar;
-
-  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
-  enum {
-    PacketSize = redux_traits<Func, Evaluator>::PacketSize,
-    Size = Evaluator::SizeAtCompileTime,
-    VectorizedSize = (int(Size) / int(PacketSize)) * int(PacketSize)
-  };
-
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
-  Scalar run(const Evaluator &eval, const Func& func, const XprType &xpr)
-  {
-    EIGEN_ONLY_USED_FOR_DEBUG(xpr)
-    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
-    if (VectorizedSize > 0) {
-      Scalar res = func.predux(redux_vec_unroller<Func, Evaluator, 0, Size / PacketSize>::template run<PacketType>(eval,func));
-      if (VectorizedSize != Size)
-        res = func(res,redux_novec_unroller<Func, Evaluator, VectorizedSize, Size-VectorizedSize>::run(eval,func));
-      return res;
-    }
-    else {
-      return redux_novec_unroller<Func, Evaluator, 0, Size>::run(eval,func);
-    }
-  }
-};
-
-// evaluator adaptor
-template<typename _XprType>
-class redux_evaluator : public internal::evaluator<_XprType>
-{
-  typedef internal::evaluator<_XprType> Base;
-public:
-  typedef _XprType XprType;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit redux_evaluator(const XprType &xpr) : Base(xpr) {}
-  
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename XprType::PacketScalar PacketScalar;
-  
-  enum {
-    MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = XprType::MaxColsAtCompileTime,
-    // TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator
-    Flags = Base::Flags & ~DirectAccessBit,
-    IsRowMajor = XprType::IsRowMajor,
-    SizeAtCompileTime = XprType::SizeAtCompileTime,
-    InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime
-  };
-  
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
-  { return Base::coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
-  
-  template<int LoadMode, typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketType packetByOuterInner(Index outer, Index inner) const
-  { return Base::template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
-  
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Part 4 : public API
-***************************************************************************/
-
-
-/** \returns the result of a full redux operation on the whole matrix or vector using \a func
-  *
-  * The template parameter \a BinaryOp is the type of the functor \a func which must be
-  * an associative operator. Both current C++98 and C++11 functor styles are handled.
-  *
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
-  */
-template<typename Derived>
-template<typename Func>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::redux(const Func& func) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
-  ThisEvaluator thisEval(derived());
-
-  // The initial expression is passed to the reducer as an additional argument instead of
-  // passing it as a member of redux_evaluator to help  
-  return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func, derived());
-}
-
-/** \returns the minimum of all coefficients of \c *this.
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is minimum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  */
-template<typename Derived>
-template<int NaNPropagation>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::minCoeff() const
-{
-  return derived().redux(Eigen::internal::scalar_min_op<Scalar,Scalar, NaNPropagation>());
-}
-
-/** \returns the maximum of all coefficients of \c *this. 
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  */
-template<typename Derived>
-template<int NaNPropagation>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::maxCoeff() const
-{
-  return derived().redux(Eigen::internal::scalar_max_op<Scalar,Scalar, NaNPropagation>());
-}
-
-/** \returns the sum of all coefficients of \c *this
-  *
-  * If \c *this is empty, then the value 0 is returned.
-  *
-  * \sa trace(), prod(), mean()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::sum() const
-{
-  if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
-    return Scalar(0);
-  return derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>());
-}
-
-/** \returns the mean of all coefficients of *this
-*
-* \sa trace(), prod(), sum()
-*/
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::mean() const
-{
-#ifdef __INTEL_COMPILER
-  #pragma warning push
-  #pragma warning ( disable : 2259 )
-#endif
-  return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size());
-#ifdef __INTEL_COMPILER
-  #pragma warning pop
-#endif
-}
-
-/** \returns the product of all coefficients of *this
-  *
-  * Example: \include MatrixBase_prod.cpp
-  * Output: \verbinclude MatrixBase_prod.out
-  *
-  * \sa sum(), mean(), trace()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::prod() const
-{
-  if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
-    return Scalar(1);
-  return derived().redux(Eigen::internal::scalar_product_op<Scalar>());
-}
-
-/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.
-  *
-  * \c *this can be any matrix, not necessarily square.
-  *
-  * \sa diagonal(), sum()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
-MatrixBase<Derived>::trace() const
-{
-  return derived().diagonal().sum();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Ref.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Ref.h
deleted file mode 100644
index c2a37ea..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Ref.h
+++ /dev/null
@@ -1,381 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REF_H
-#define EIGEN_REF_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename _PlainObjectType, int _Options, typename _StrideType>
-struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
-  : public traits<Map<_PlainObjectType, _Options, _StrideType> >
-{
-  typedef _PlainObjectType PlainObjectType;
-  typedef _StrideType StrideType;
-  enum {
-    Options = _Options,
-    Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit,
-    Alignment = traits<Map<_PlainObjectType, _Options, _StrideType> >::Alignment
-  };
-
-  template<typename Derived> struct match {
-    enum {
-      IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime,
-      HasDirectAccess = internal::has_direct_access<Derived>::ret,
-      StorageOrderMatch = IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
-      InnerStrideMatch = int(StrideType::InnerStrideAtCompileTime)==int(Dynamic)
-                      || int(StrideType::InnerStrideAtCompileTime)==int(Derived::InnerStrideAtCompileTime)
-                      || (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
-      OuterStrideMatch = IsVectorAtCompileTime
-                      || int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
-      // NOTE, this indirection of evaluator<Derived>::Alignment is needed
-      // to workaround a very strange bug in MSVC related to the instantiation
-      // of has_*ary_operator in evaluator<CwiseNullaryOp>.
-      // This line is surprisingly very sensitive. For instance, simply adding parenthesis
-      // as "DerivedAlignment = (int(evaluator<Derived>::Alignment))," will make MSVC fail...
-      DerivedAlignment = int(evaluator<Derived>::Alignment),
-      AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (DerivedAlignment >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment
-      ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
-      MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch
-    };
-    typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
-  };
-
-};
-
-template<typename Derived>
-struct traits<RefBase<Derived> > : public traits<Derived> {};
-
-}
-
-template<typename Derived> class RefBase
- : public MapBase<Derived>
-{
-  typedef typename internal::traits<Derived>::PlainObjectType PlainObjectType;
-  typedef typename internal::traits<Derived>::StrideType StrideType;
-
-public:
-
-  typedef MapBase<Derived> Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const
-  {
-    return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const
-  {
-    return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
-         : IsVectorAtCompileTime ? this->size()
-         : int(Flags)&RowMajorBit ? this->cols()
-         : this->rows();
-  }
-
-  EIGEN_DEVICE_FUNC RefBase()
-    : Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime),
-      // Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values:
-      m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime,
-               StrideType::InnerStrideAtCompileTime==Dynamic?0:StrideType::InnerStrideAtCompileTime)
-  {}
-
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)
-
-protected:
-
-  typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
-
-  // Resolves inner stride if default 0.
-  static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveInnerStride(Index inner) {
-    return inner == 0 ? 1 : inner;
-  }
-
-  // Resolves outer stride if default 0.
-  static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveOuterStride(Index inner, Index outer, Index rows, Index cols, bool isVectorAtCompileTime, bool isRowMajor) {
-    return outer == 0 ? isVectorAtCompileTime ? inner * rows * cols : isRowMajor ? inner * cols : inner * rows : outer;
-  }
-
-  // Returns true if construction is valid, false if there is a stride mismatch,
-  // and fails if there is a size mismatch.
-  template<typename Expression>
-  EIGEN_DEVICE_FUNC bool construct(Expression& expr)
-  {
-    // Check matrix sizes.  If this is a compile-time vector, we do allow
-    // implicitly transposing.
-    EIGEN_STATIC_ASSERT(
-      EIGEN_PREDICATE_SAME_MATRIX_SIZE(PlainObjectType, Expression)
-      // If it is a vector, the transpose sizes might match.
-      || ( PlainObjectType::IsVectorAtCompileTime
-            && ((int(PlainObjectType::RowsAtCompileTime)==Eigen::Dynamic
-              || int(Expression::ColsAtCompileTime)==Eigen::Dynamic
-              || int(PlainObjectType::RowsAtCompileTime)==int(Expression::ColsAtCompileTime))
-            &&  (int(PlainObjectType::ColsAtCompileTime)==Eigen::Dynamic
-              || int(Expression::RowsAtCompileTime)==Eigen::Dynamic
-              || int(PlainObjectType::ColsAtCompileTime)==int(Expression::RowsAtCompileTime)))),
-      YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES
-    )
-
-    // Determine runtime rows and columns.
-    Index rows = expr.rows();
-    Index cols = expr.cols();
-    if(PlainObjectType::RowsAtCompileTime==1)
-    {
-      eigen_assert(expr.rows()==1 || expr.cols()==1);
-      rows = 1;
-      cols = expr.size();
-    }
-    else if(PlainObjectType::ColsAtCompileTime==1)
-    {
-      eigen_assert(expr.rows()==1 || expr.cols()==1);
-      rows = expr.size();
-      cols = 1;
-    }
-    // Verify that the sizes are valid.
-    eigen_assert(
-      (PlainObjectType::RowsAtCompileTime == Dynamic) || (PlainObjectType::RowsAtCompileTime == rows));
-    eigen_assert(
-      (PlainObjectType::ColsAtCompileTime == Dynamic) || (PlainObjectType::ColsAtCompileTime == cols));
-
-
-    // If this is a vector, we might be transposing, which means that stride should swap.
-    const bool transpose = PlainObjectType::IsVectorAtCompileTime && (rows != expr.rows());
-    // If the storage format differs, we also need to swap the stride.
-    const bool row_major = ((PlainObjectType::Flags)&RowMajorBit) != 0;
-    const bool expr_row_major = (Expression::Flags&RowMajorBit) != 0;
-    const bool storage_differs =  (row_major != expr_row_major);
-
-    const bool swap_stride = (transpose != storage_differs);
-
-    // Determine expr's actual strides, resolving any defaults if zero.
-    const Index expr_inner_actual = resolveInnerStride(expr.innerStride());
-    const Index expr_outer_actual = resolveOuterStride(expr_inner_actual,
-                                                       expr.outerStride(),
-                                                       expr.rows(),
-                                                       expr.cols(),
-                                                       Expression::IsVectorAtCompileTime != 0,
-                                                       expr_row_major);
-
-    // If this is a column-major row vector or row-major column vector, the inner-stride
-    // is arbitrary, so set it to either the compile-time inner stride or 1.
-    const bool row_vector = (rows == 1);
-    const bool col_vector = (cols == 1);
-    const Index inner_stride =
-        ( (!row_major && row_vector) || (row_major && col_vector) ) ?
-            ( StrideType::InnerStrideAtCompileTime > 0 ? Index(StrideType::InnerStrideAtCompileTime) : 1)
-            : swap_stride ? expr_outer_actual : expr_inner_actual;
-
-    // If this is a column-major column vector or row-major row vector, the outer-stride
-    // is arbitrary, so set it to either the compile-time outer stride or vector size.
-    const Index outer_stride =
-      ( (!row_major && col_vector) || (row_major && row_vector) ) ?
-          ( StrideType::OuterStrideAtCompileTime > 0 ? Index(StrideType::OuterStrideAtCompileTime) : rows * cols * inner_stride)
-          : swap_stride ? expr_inner_actual : expr_outer_actual;
-
-    // Check if given inner/outer strides are compatible with compile-time strides.
-    const bool inner_valid = (StrideType::InnerStrideAtCompileTime == Dynamic)
-        || (resolveInnerStride(Index(StrideType::InnerStrideAtCompileTime)) == inner_stride);
-    if (!inner_valid) {
-      return false;
-    }
-
-    const bool outer_valid = (StrideType::OuterStrideAtCompileTime == Dynamic)
-        || (resolveOuterStride(
-              inner_stride,
-              Index(StrideType::OuterStrideAtCompileTime),
-              rows, cols, PlainObjectType::IsVectorAtCompileTime != 0,
-              row_major)
-            == outer_stride);
-    if (!outer_valid) {
-      return false;
-    }
-
-    ::new (static_cast<Base*>(this)) Base(expr.data(), rows, cols);
-    ::new (&m_stride) StrideBase(
-      (StrideType::OuterStrideAtCompileTime == 0) ? 0 : outer_stride,
-      (StrideType::InnerStrideAtCompileTime == 0) ? 0 : inner_stride );
-    return true;
-  }
-
-  StrideBase m_stride;
-};
-
-/** \class Ref
-  * \ingroup Core_Module
-  *
-  * \brief A matrix or vector expression mapping an existing expression
-  *
-  * \tparam PlainObjectType the equivalent matrix type of the mapped data
-  * \tparam Options specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
-  *                 The default is \c #Unaligned.
-  * \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1),
-  *                   but accepts a variable outer stride (leading dimension).
-  *                   This can be overridden by specifying strides.
-  *                   The type passed here must be a specialization of the Stride template, see examples below.
-  *
-  * This class provides a way to write non-template functions taking Eigen objects as parameters while limiting the number of copies.
-  * A Ref<> object can represent either a const expression or a l-value:
-  * \code
-  * // in-out argument:
-  * void foo1(Ref<VectorXf> x);
-  *
-  * // read-only const argument:
-  * void foo2(const Ref<const VectorXf>& x);
-  * \endcode
-  *
-  * In the in-out case, the input argument must satisfy the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered.
-  * By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout.
-  * Likewise, a Ref<MatrixXf> can reference any column-major dense matrix expression of float whose column's elements are contiguously stored with
-  * the possibility to have a constant space in-between each column, i.e. the inner stride must be equal to 1, but the outer stride (or leading dimension)
-  * can be greater than the number of rows.
-  *
-  * In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function.
-  * Here are some examples:
-  * \code
-  * MatrixXf A;
-  * VectorXf a;
-  * foo1(a.head());             // OK
-  * foo1(A.col());              // OK
-  * foo1(A.row());              // Compilation error because here innerstride!=1
-  * foo2(A.row());              // Compilation error because A.row() is a 1xN object while foo2 is expecting a Nx1 object
-  * foo2(A.row().transpose());  // The row is copied into a contiguous temporary
-  * foo2(2*a);                  // The expression is evaluated into a temporary
-  * foo2(A.col().segment(2,4)); // No temporary
-  * \endcode
-  *
-  * The range of inputs that can be referenced without temporary can be enlarged using the last two template parameters.
-  * Here is an example accepting an innerstride!=1:
-  * \code
-  * // in-out argument:
-  * void foo3(Ref<VectorXf,0,InnerStride<> > x);
-  * foo3(A.row());              // OK
-  * \endcode
-  * The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involve more
-  * expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overload internally calling a
-  * template function, e.g.:
-  * \code
-  * // in the .h:
-  * void foo(const Ref<MatrixXf>& A);
-  * void foo(const Ref<MatrixXf,0,Stride<> >& A);
-  *
-  * // in the .cpp:
-  * template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
-  *     ... // crazy code goes here
-  * }
-  * void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
-  * void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
-  * \endcode
-  *
-  * See also the following stackoverflow questions for further references:
-  *  - <a href="http://stackoverflow.com/questions/21132538/correct-usage-of-the-eigenref-class">Correct usage of the Eigen::Ref<> class</a>
-  *
-  * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
-  */
-template<typename PlainObjectType, int Options, typename StrideType> class Ref
-  : public RefBase<Ref<PlainObjectType, Options, StrideType> >
-{
-  private:
-    typedef internal::traits<Ref> Traits;
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0);
-  public:
-
-    typedef RefBase<Ref> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
-
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      // Construction must pass since we will not create temprary storage in the non-const case.
-      const bool success = Base::construct(expr.derived());
-      EIGEN_UNUSED_VARIABLE(success)
-      eigen_assert(success);
-    }
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
-    #else
-    /** Implicit constructor from any dense expression */
-    template<typename Derived>
-    inline Ref(DenseBase<Derived>& expr)
-    #endif
-    {
-      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      // Construction must pass since we will not create temporary storage in the non-const case.
-      const bool success = Base::construct(expr.const_cast_derived());
-      EIGEN_UNUSED_VARIABLE(success)
-      eigen_assert(success);
-    }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)
-
-};
-
-// this is the const ref version
-template<typename TPlainObjectType, int Options, typename StrideType> class Ref<const TPlainObjectType, Options, StrideType>
-  : public RefBase<Ref<const TPlainObjectType, Options, StrideType> >
-{
-    typedef internal::traits<Ref> Traits;
-  public:
-
-    typedef RefBase<Ref> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
-
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
-                                 typename internal::enable_if<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>::type* = 0)
-    {
-//      std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
-//      std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";
-//      std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n";
-      construct(expr.derived(), typename Traits::template match<Derived>::type());
-    }
-
-    EIGEN_DEVICE_FUNC inline Ref(const Ref& other) : Base(other) {
-      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
-    }
-
-    template<typename OtherRef>
-    EIGEN_DEVICE_FUNC inline Ref(const RefBase<OtherRef>& other) {
-      construct(other.derived(), typename Traits::template match<OtherRef>::type());
-    }
-
-  protected:
-
-    template<typename Expression>
-    EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type)
-    {
-      // Check if we can use the underlying expr's storage directly, otherwise call the copy version.
-      if (!Base::construct(expr)) {
-        construct(expr, internal::false_type());
-      }
-    }
-
-    template<typename Expression>
-    EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
-    {
-      internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar,Scalar>());
-      Base::construct(m_object);
-    }
-
-  protected:
-    TPlainObjectType m_object;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_REF_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Replicate.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Replicate.h
deleted file mode 100644
index ab5be7e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Replicate.h
+++ /dev/null
@@ -1,142 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REPLICATE_H
-#define EIGEN_REPLICATE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename MatrixType,int RowFactor,int ColFactor>
-struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
- : traits<MatrixType>
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename traits<MatrixType>::XprKind XprKind;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic
-                      ? Dynamic
-                      : RowFactor * MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = ColFactor==Dynamic || int(MatrixType::ColsAtCompileTime)==Dynamic
-                      ? Dynamic
-                      : ColFactor * MatrixType::ColsAtCompileTime,
-   //FIXME we don't propagate the max sizes !!!
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    IsRowMajor = MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1 ? 1
-               : MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1 ? 0
-               : (MatrixType::Flags & RowMajorBit) ? 1 : 0,
-
-    // FIXME enable DirectAccess with negative strides?
-    Flags = IsRowMajor ? RowMajorBit : 0
-  };
-};
-}
-
-/**
-  * \class Replicate
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the multiple replication of a matrix or vector
-  *
-  * \tparam MatrixType the type of the object we are replicating
-  * \tparam RowFactor number of repetitions at compile time along the vertical direction, can be Dynamic.
-  * \tparam ColFactor number of repetitions at compile time along the horizontal direction, can be Dynamic.
-  *
-  * This class represents an expression of the multiple replication of a matrix or vector.
-  * It is the return type of DenseBase::replicate() and most of the time
-  * this is the only way it is used.
-  *
-  * \sa DenseBase::replicate()
-  */
-template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
-  : public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type
-{
-    typedef typename internal::traits<Replicate>::MatrixTypeNested MatrixTypeNested;
-    typedef typename internal::traits<Replicate>::_MatrixTypeNested _MatrixTypeNested;
-  public:
-
-    typedef typename internal::dense_xpr_base<Replicate>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-    template<typename OriginalMatrixType>
-    EIGEN_DEVICE_FUNC
-    inline explicit Replicate(const OriginalMatrixType& matrix)
-      : m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
-                          THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
-      eigen_assert(RowFactor!=Dynamic && ColFactor!=Dynamic);
-    }
-
-    template<typename OriginalMatrixType>
-    EIGEN_DEVICE_FUNC
-    inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
-      : m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
-                          THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
-
-    EIGEN_DEVICE_FUNC
-    const _MatrixTypeNested& nestedExpression() const
-    {
-      return m_matrix;
-    }
-
-  protected:
-    MatrixTypeNested m_matrix;
-    const internal::variable_if_dynamic<Index, RowFactor> m_rowFactor;
-    const internal::variable_if_dynamic<Index, ColFactor> m_colFactor;
-};
-
-/**
-  * \return an expression of the replication of \c *this
-  *
-  * Example: \include MatrixBase_replicate.cpp
-  * Output: \verbinclude MatrixBase_replicate.out
-  *
-  * \sa VectorwiseOp::replicate(), DenseBase::replicate(Index,Index), class Replicate
-  */
-template<typename Derived>
-template<int RowFactor, int ColFactor>
-EIGEN_DEVICE_FUNC const Replicate<Derived,RowFactor,ColFactor>
-DenseBase<Derived>::replicate() const
-{
-  return Replicate<Derived,RowFactor,ColFactor>(derived());
-}
-
-/**
-  * \return an expression of the replication of each column (or row) of \c *this
-  *
-  * Example: \include DirectionWise_replicate_int.cpp
-  * Output: \verbinclude DirectionWise_replicate_int.out
-  *
-  * \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate
-  */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC const typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
-VectorwiseOp<ExpressionType,Direction>::replicate(Index factor) const
-{
-  return typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
-          (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REPLICATE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Reshaped.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Reshaped.h
deleted file mode 100644
index 52de73b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Reshaped.h
+++ /dev/null
@@ -1,454 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2014 yoco <peter.xiau@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_RESHAPED_H
-#define EIGEN_RESHAPED_H
-
-namespace Eigen {
-
-/** \class Reshaped
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a fixed-size or dynamic-size reshape
-  *
-  * \tparam XprType the type of the expression in which we are taking a reshape
-  * \tparam Rows the number of rows of the reshape we are taking at compile time (optional)
-  * \tparam Cols the number of columns of the reshape we are taking at compile time (optional)
-  * \tparam Order can be ColMajor or RowMajor, default is ColMajor.
-  *
-  * This class represents an expression of either a fixed-size or dynamic-size reshape.
-  * It is the return type of DenseBase::reshaped(NRowsType,NColsType) and
-  * most of the time this is the only way it is used.
-  *
-  * However, in C++98, if you want to directly maniputate reshaped expressions,
-  * for instance if you want to write a function returning such an expression, you
-  * will need to use this class. In C++11, it is advised to use the \em auto
-  * keyword for such use cases.
-  *
-  * Here is an example illustrating the dynamic case:
-  * \include class_Reshaped.cpp
-  * Output: \verbinclude class_Reshaped.out
-  *
-  * Here is an example illustrating the fixed-size case:
-  * \include class_FixedReshaped.cpp
-  * Output: \verbinclude class_FixedReshaped.out
-  *
-  * \sa DenseBase::reshaped(NRowsType,NColsType)
-  */
-
-namespace internal {
-
-template<typename XprType, int Rows, int Cols, int Order>
-struct traits<Reshaped<XprType, Rows, Cols, Order> > : traits<XprType>
-{
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename traits<XprType>::StorageKind StorageKind;
-  typedef typename traits<XprType>::XprKind XprKind;
-  enum{
-    MatrixRows = traits<XprType>::RowsAtCompileTime,
-    MatrixCols = traits<XprType>::ColsAtCompileTime,
-    RowsAtCompileTime = Rows,
-    ColsAtCompileTime = Cols,
-    MaxRowsAtCompileTime = Rows,
-    MaxColsAtCompileTime = Cols,
-    XpxStorageOrder = ((int(traits<XprType>::Flags) & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor,
-    ReshapedStorageOrder = (RowsAtCompileTime == 1 && ColsAtCompileTime != 1) ? RowMajor
-                         : (ColsAtCompileTime == 1 && RowsAtCompileTime != 1) ? ColMajor
-                         : XpxStorageOrder,
-    HasSameStorageOrderAsXprType = (ReshapedStorageOrder == XpxStorageOrder),
-    InnerSize = (ReshapedStorageOrder==int(RowMajor)) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
-                             ? int(inner_stride_at_compile_time<XprType>::ret)
-                             : Dynamic,
-    OuterStrideAtCompileTime = Dynamic,
-
-    HasDirectAccess = internal::has_direct_access<XprType>::ret
-                    && (Order==int(XpxStorageOrder))
-                    && ((evaluator<XprType>::Flags&LinearAccessBit)==LinearAccessBit),
-
-    MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
-                       && (InnerStrideAtCompileTime == 1)
-                        ? PacketAccessBit : 0,
-    //MaskAlignedBit = ((OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
-    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
-    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
-    FlagsRowMajorBit = (ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0,
-    FlagsDirectAccessBit = HasDirectAccess ? DirectAccessBit : 0,
-    Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) | MaskPacketAccessBit),
-
-    Flags = (Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit | FlagsDirectAccessBit)
-  };
-};
-
-template<typename XprType, int Rows, int Cols, int Order, bool HasDirectAccess> class ReshapedImpl_dense;
-
-} // end namespace internal
-
-template<typename XprType, int Rows, int Cols, int Order, typename StorageKind> class ReshapedImpl;
-
-template<typename XprType, int Rows, int Cols, int Order> class Reshaped
-  : public ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind>
-{
-    typedef ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind> Impl;
-  public:
-    //typedef typename Impl::Base Base;
-    typedef Impl Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Reshaped)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reshaped)
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline Reshaped(XprType& xpr)
-      : Impl(xpr)
-    {
-      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
-      eigen_assert(Rows * Cols == xpr.rows() * xpr.cols());
-    }
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline Reshaped(XprType& xpr,
-          Index reshapeRows, Index reshapeCols)
-      : Impl(xpr, reshapeRows, reshapeCols)
-    {
-      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==reshapeRows)
-          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==reshapeCols));
-      eigen_assert(reshapeRows * reshapeCols == xpr.rows() * xpr.cols());
-    }
-};
-
-// The generic default implementation for dense reshape simply forward to the internal::ReshapedImpl_dense
-// that must be specialized for direct and non-direct access...
-template<typename XprType, int Rows, int Cols, int Order>
-class ReshapedImpl<XprType, Rows, Cols, Order, Dense>
-  : public internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,internal::traits<Reshaped<XprType,Rows,Cols,Order> >::HasDirectAccess>
-{
-    typedef internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,internal::traits<Reshaped<XprType,Rows,Cols,Order> >::HasDirectAccess> Impl;
-  public:
-    typedef Impl Base;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl)
-    EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr) : Impl(xpr) {}
-    EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr, Index reshapeRows, Index reshapeCols)
-      : Impl(xpr, reshapeRows, reshapeCols) {}
-};
-
-namespace internal {
-
-/** \internal Internal implementation of dense Reshaped in the general case. */
-template<typename XprType, int Rows, int Cols, int Order>
-class ReshapedImpl_dense<XprType,Rows,Cols,Order,false>
-  : public internal::dense_xpr_base<Reshaped<XprType, Rows, Cols, Order> >::type
-{
-    typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
-  public:
-
-    typedef typename internal::dense_xpr_base<ReshapedType>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
-
-    typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
-    typedef typename internal::remove_all<XprType>::type NestedExpression;
-
-    class InnerIterator;
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr)
-      : m_xpr(xpr), m_rows(Rows), m_cols(Cols)
-    {}
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
-      : m_xpr(xpr), m_rows(nRows), m_cols(nCols)
-    {}
-
-    EIGEN_DEVICE_FUNC Index rows() const { return m_rows; }
-    EIGEN_DEVICE_FUNC Index cols() const { return m_cols; }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \sa MapBase::data() */
-    EIGEN_DEVICE_FUNC inline const Scalar* data() const;
-    EIGEN_DEVICE_FUNC inline Index innerStride() const;
-    EIGEN_DEVICE_FUNC inline Index outerStride() const;
-    #endif
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<XprType>::type&
-    nestedExpression() const { return m_xpr; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC
-    typename internal::remove_reference<XprType>::type&
-    nestedExpression() { return m_xpr; }
-
-  protected:
-
-    MatrixTypeNested m_xpr;
-    const internal::variable_if_dynamic<Index, Rows> m_rows;
-    const internal::variable_if_dynamic<Index, Cols> m_cols;
-};
-
-
-/** \internal Internal implementation of dense Reshaped in the direct access case. */
-template<typename XprType, int Rows, int Cols, int Order>
-class ReshapedImpl_dense<XprType, Rows, Cols, Order, true>
-  : public MapBase<Reshaped<XprType, Rows, Cols, Order> >
-{
-    typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
-    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
-  public:
-
-    typedef MapBase<ReshapedType> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr)
-      : Base(xpr.data()), m_xpr(xpr)
-    {}
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC
-    inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
-      : Base(xpr.data(), nRows, nCols),
-        m_xpr(xpr)
-    {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
-    {
-      return m_xpr;
-    }
-
-    EIGEN_DEVICE_FUNC
-    XprType& nestedExpression() { return m_xpr; }
-
-    /** \sa MapBase::innerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const
-    {
-      return m_xpr.innerStride();
-    }
-
-    /** \sa MapBase::outerStride() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const
-    {
-      return ((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows();
-    }
-
-  protected:
-
-    XprTypeNested m_xpr;
-};
-
-// Evaluators
-template<typename ArgType, int Rows, int Cols, int Order, bool HasDirectAccess> struct reshaped_evaluator;
-
-template<typename ArgType, int Rows, int Cols, int Order>
-struct evaluator<Reshaped<ArgType, Rows, Cols, Order> >
-  : reshaped_evaluator<ArgType, Rows, Cols, Order, traits<Reshaped<ArgType,Rows,Cols,Order> >::HasDirectAccess>
-{
-  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
-  typedef typename XprType::Scalar Scalar;
-  // TODO: should check for smaller packet types
-  typedef typename packet_traits<Scalar>::type PacketScalar;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    HasDirectAccess = traits<XprType>::HasDirectAccess,
-
-//     RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
-//     ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
-//     MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
-//     MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
-//
-//     InnerStrideAtCompileTime = traits<XprType>::HasSameStorageOrderAsXprType
-//                              ? int(inner_stride_at_compile_time<ArgType>::ret)
-//                              : Dynamic,
-//     OuterStrideAtCompileTime = Dynamic,
-
-    FlagsLinearAccessBit = (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1 || HasDirectAccess) ? LinearAccessBit : 0,
-    FlagsRowMajorBit = (traits<XprType>::ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0,
-    FlagsDirectAccessBit =  HasDirectAccess ? DirectAccessBit : 0,
-    Flags0 = evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit),
-    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit | FlagsDirectAccessBit,
-
-    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
-    Alignment = evaluator<ArgType>::Alignment
-  };
-  typedef reshaped_evaluator<ArgType, Rows, Cols, Order, HasDirectAccess> reshaped_evaluator_type;
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : reshaped_evaluator_type(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-};
-
-template<typename ArgType, int Rows, int Cols, int Order>
-struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ false>
-  : evaluator_base<Reshaped<ArgType, Rows, Cols, Order> >
-{
-  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
-
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of index computations */,
-
-    Flags = (evaluator<ArgType>::Flags & (HereditaryBits /*| LinearAccessBit | DirectAccessBit*/)),
-
-    Alignment = 0
-  };
-
-  EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  typedef std::pair<Index, Index> RowCol;
-
-  inline RowCol index_remap(Index rowId, Index colId) const
-  {
-    if(Order==ColMajor)
-    {
-      const Index nth_elem_idx = colId * m_xpr.rows() + rowId;
-      return RowCol(nth_elem_idx % m_xpr.nestedExpression().rows(),
-                    nth_elem_idx / m_xpr.nestedExpression().rows());
-    }
-    else
-    {
-      const Index nth_elem_idx = colId + rowId * m_xpr.cols();
-      return RowCol(nth_elem_idx / m_xpr.nestedExpression().cols(),
-                    nth_elem_idx % m_xpr.nestedExpression().cols());
-    }
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline Scalar& coeffRef(Index rowId, Index colId)
-  {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline const Scalar& coeffRef(Index rowId, Index colId) const
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.coeff(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline Scalar& coeffRef(Index index)
-  {
-    EIGEN_STATIC_ASSERT_LVALUE(XprType)
-    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
-                                       Rows == 1 ? index : 0);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline const Scalar& coeffRef(Index index) const
-  {
-    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
-                                       Rows == 1 ? index : 0);
-    return m_argImpl.coeffRef(row_col.first, row_col.second);
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline const CoeffReturnType coeff(Index index) const
-  {
-    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
-                                       Rows == 1 ? index : 0);
-    return m_argImpl.coeff(row_col.first, row_col.second);
-  }
-#if 0
-  EIGEN_DEVICE_FUNC
-  template<int LoadMode>
-  inline PacketScalar packet(Index rowId, Index colId) const
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second);
-
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC
-  inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
-  {
-    const RowCol row_col = index_remap(rowId, colId);
-    m_argImpl.const_cast_derived().template writePacket<Unaligned>
-            (row_col.first, row_col.second, val);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC
-  inline PacketScalar packet(Index index) const
-  {
-    const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index,
-                                        RowsAtCompileTime == 1 ? index : 0);
-    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC
-  inline void writePacket(Index index, const PacketScalar& val)
-  {
-    const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index,
-                                        RowsAtCompileTime == 1 ? index : 0);
-    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second, val);
-  }
-#endif
-protected:
-
-  evaluator<ArgType> m_argImpl;
-  const XprType& m_xpr;
-
-};
-
-template<typename ArgType, int Rows, int Cols, int Order>
-struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ true>
-: mapbase_evaluator<Reshaped<ArgType, Rows, Cols, Order>,
-                      typename Reshaped<ArgType, Rows, Cols, Order>::PlainObject>
-{
-  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
-  typedef typename XprType::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr)
-    : mapbase_evaluator<XprType, typename XprType::PlainObject>(xpr)
-  {
-    // TODO: for the 3.4 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
-    eigen_assert(((internal::UIntPtr(xpr.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_RESHAPED_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ReturnByValue.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ReturnByValue.h
deleted file mode 100644
index 4dad13e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/ReturnByValue.h
+++ /dev/null
@@ -1,119 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_RETURNBYVALUE_H
-#define EIGEN_RETURNBYVALUE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Derived>
-struct traits<ReturnByValue<Derived> >
-  : public traits<typename traits<Derived>::ReturnType>
-{
-  enum {
-    // We're disabling the DirectAccess because e.g. the constructor of
-    // the Block-with-DirectAccess expression requires to have a coeffRef method.
-    // Also, we don't want to have to implement the stride stuff.
-    Flags = (traits<typename traits<Derived>::ReturnType>::Flags
-             | EvalBeforeNestingBit) & ~DirectAccessBit
-  };
-};
-
-/* The ReturnByValue object doesn't even have a coeff() method.
- * So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix.
- * So internal::nested always gives the plain return matrix type.
- *
- * FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ??
- * Answer: EvalBeforeNestingBit should be deprecated since we have the evaluators
- */
-template<typename Derived,int n,typename PlainObject>
-struct nested_eval<ReturnByValue<Derived>, n, PlainObject>
-{
-  typedef typename traits<Derived>::ReturnType type;
-};
-
-} // end namespace internal
-
-/** \class ReturnByValue
-  * \ingroup Core_Module
-  *
-  */
-template<typename Derived> class ReturnByValue
-  : public internal::dense_xpr_base< ReturnByValue<Derived> >::type, internal::no_assignment_operator
-{
-  public:
-    typedef typename internal::traits<Derived>::ReturnType ReturnType;
-
-    typedef typename internal::dense_xpr_base<ReturnByValue>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue)
-
-    template<typename Dest>
-    EIGEN_DEVICE_FUNC
-    inline void evalTo(Dest& dst) const
-    { static_cast<const Derived*>(this)->evalTo(dst); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return static_cast<const Derived*>(this)->rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return static_cast<const Derived*>(this)->cols(); }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT
-    class Unusable{
-      Unusable(const Unusable&) {}
-      Unusable& operator=(const Unusable&) {return *this;}
-    };
-    const Unusable& coeff(Index) const { return *reinterpret_cast<const Unusable*>(this); }
-    const Unusable& coeff(Index,Index) const { return *reinterpret_cast<const Unusable*>(this); }
-    Unusable& coeffRef(Index) { return *reinterpret_cast<Unusable*>(this); }
-    Unusable& coeffRef(Index,Index) { return *reinterpret_cast<Unusable*>(this); }
-#undef Unusable
-#endif
-};
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
-{
-  other.evalTo(derived());
-  return derived();
-}
-
-namespace internal {
-
-// Expression is evaluated in a temporary; default implementation of Assignment is bypassed so that
-// when a ReturnByValue expression is assigned, the evaluator is not constructed.
-// TODO: Finalize port to new regime; ReturnByValue should not exist in the expression world
-
-template<typename Derived>
-struct evaluator<ReturnByValue<Derived> >
-  : public evaluator<typename internal::traits<Derived>::ReturnType>
-{
-  typedef ReturnByValue<Derived> XprType;
-  typedef typename internal::traits<Derived>::ReturnType PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    xpr.evalTo(m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_RETURNBYVALUE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Reverse.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Reverse.h
deleted file mode 100644
index 28cdd76..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Reverse.h
+++ /dev/null
@@ -1,217 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com>
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REVERSE_H
-#define EIGEN_REVERSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType, int Direction>
-struct traits<Reverse<MatrixType, Direction> >
- : traits<MatrixType>
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename traits<MatrixType>::XprKind XprKind;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Flags = _MatrixTypeNested::Flags & (RowMajorBit | LvalueBit)
-  };
-};
-
-template<typename PacketType, bool ReversePacket> struct reverse_packet_cond
-{
-  static inline PacketType run(const PacketType& x) { return preverse(x); }
-};
-
-template<typename PacketType> struct reverse_packet_cond<PacketType,false>
-{
-  static inline PacketType run(const PacketType& x) { return x; }
-};
-
-} // end namespace internal
-
-/** \class Reverse
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the reverse of a vector or matrix
-  *
-  * \tparam MatrixType the type of the object of which we are taking the reverse
-  * \tparam Direction defines the direction of the reverse operation, can be Vertical, Horizontal, or BothDirections
-  *
-  * This class represents an expression of the reverse of a vector.
-  * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::reverse(), VectorwiseOp::reverse()
-  */
-template<typename MatrixType, int Direction> class Reverse
-  : public internal::dense_xpr_base< Reverse<MatrixType, Direction> >::type
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<Reverse>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-    using Base::IsRowMajor;
-
-  protected:
-    enum {
-      PacketSize = internal::packet_traits<Scalar>::size,
-      IsColMajor = !IsRowMajor,
-      ReverseRow = (Direction == Vertical)   || (Direction == BothDirections),
-      ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
-      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
-      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1,
-      ReversePacket = (Direction == BothDirections)
-                    || ((Direction == Vertical)   && IsColMajor)
-                    || ((Direction == Horizontal) && IsRowMajor)
-    };
-    typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet;
-  public:
-
-    EIGEN_DEVICE_FUNC explicit inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse)
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    EIGEN_DEVICE_FUNC inline Index innerStride() const
-    {
-      return -m_matrix.innerStride();
-    }
-
-    EIGEN_DEVICE_FUNC const typename internal::remove_all<typename MatrixType::Nested>::type&
-    nestedExpression() const
-    {
-      return m_matrix;
-    }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-};
-
-/** \returns an expression of the reverse of *this.
-  *
-  * Example: \include MatrixBase_reverse.cpp
-  * Output: \verbinclude MatrixBase_reverse.out
-  *
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ReverseReturnType
-DenseBase<Derived>::reverse()
-{
-  return ReverseReturnType(derived());
-}
-
-
-//reverse const overload moved DenseBase.h due to a CUDA compiler bug
-
-/** This is the "in place" version of reverse: it reverses \c *this.
-  *
-  * In most cases it is probably better to simply use the reversed expression
-  * of a matrix. However, when reversing the matrix data itself is really needed,
-  * then this "in-place" version is probably the right choice because it provides
-  * the following additional benefits:
-  *  - less error prone: doing the same operation with .reverse() requires special care:
-  *    \code m = m.reverse().eval(); \endcode
-  *  - this API enables reverse operations without the need for a temporary
-  *  - it allows future optimizations (cache friendliness, etc.)
-  *
-  * \sa VectorwiseOp::reverseInPlace(), reverse() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::reverseInPlace()
-{
-  if(cols()>rows())
-  {
-    Index half = cols()/2;
-    leftCols(half).swap(rightCols(half).reverse());
-    if((cols()%2)==1)
-    {
-      Index half2 = rows()/2;
-      col(half).head(half2).swap(col(half).tail(half2).reverse());
-    }
-  }
-  else
-  {
-    Index half = rows()/2;
-    topRows(half).swap(bottomRows(half).reverse());
-    if((rows()%2)==1)
-    {
-      Index half2 = cols()/2;
-      row(half).head(half2).swap(row(half).tail(half2).reverse());
-    }
-  }
-}
-
-namespace internal {
-
-template<int Direction>
-struct vectorwise_reverse_inplace_impl;
-
-template<>
-struct vectorwise_reverse_inplace_impl<Vertical>
-{
-  template<typename ExpressionType>
-  static void run(ExpressionType &xpr)
-  {
-    const int HalfAtCompileTime = ExpressionType::RowsAtCompileTime==Dynamic?Dynamic:ExpressionType::RowsAtCompileTime/2;
-    Index half = xpr.rows()/2;
-    xpr.topRows(fix<HalfAtCompileTime>(half))
-       .swap(xpr.bottomRows(fix<HalfAtCompileTime>(half)).colwise().reverse());
-  }
-};
-
-template<>
-struct vectorwise_reverse_inplace_impl<Horizontal>
-{
-  template<typename ExpressionType>
-  static void run(ExpressionType &xpr)
-  {
-    const int HalfAtCompileTime = ExpressionType::ColsAtCompileTime==Dynamic?Dynamic:ExpressionType::ColsAtCompileTime/2;
-    Index half = xpr.cols()/2;
-    xpr.leftCols(fix<HalfAtCompileTime>(half))
-       .swap(xpr.rightCols(fix<HalfAtCompileTime>(half)).rowwise().reverse());
-  }
-};
-
-} // end namespace internal
-
-/** This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of \c *this.
-  *
-  * In most cases it is probably better to simply use the reversed expression
-  * of a matrix. However, when reversing the matrix data itself is really needed,
-  * then this "in-place" version is probably the right choice because it provides
-  * the following additional benefits:
-  *  - less error prone: doing the same operation with .reverse() requires special care:
-  *    \code m = m.reverse().eval(); \endcode
-  *  - this API enables reverse operations without the need for a temporary
-  *
-  * \sa DenseBase::reverseInPlace(), reverse() */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC void VectorwiseOp<ExpressionType,Direction>::reverseInPlace()
-{
-  internal::vectorwise_reverse_inplace_impl<Direction>::run(m_matrix);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REVERSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Select.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Select.h
deleted file mode 100644
index 7c86bf8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Select.h
+++ /dev/null
@@ -1,164 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELECT_H
-#define EIGEN_SELECT_H
-
-namespace Eigen {
-
-/** \class Select
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a coefficient wise version of the C++ ternary operator ?:
-  *
-  * \param ConditionMatrixType the type of the \em condition expression which must be a boolean matrix
-  * \param ThenMatrixType the type of the \em then expression
-  * \param ElseMatrixType the type of the \em else expression
-  *
-  * This class represents an expression of a coefficient wise version of the C++ ternary operator ?:.
-  * It is the return type of DenseBase::select() and most of the time this is the only way it is used.
-  *
-  * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const
-  */
-
-namespace internal {
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
-struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
- : traits<ThenMatrixType>
-{
-  typedef typename traits<ThenMatrixType>::Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef typename traits<ThenMatrixType>::XprKind XprKind;
-  typedef typename ConditionMatrixType::Nested ConditionMatrixNested;
-  typedef typename ThenMatrixType::Nested ThenMatrixNested;
-  typedef typename ElseMatrixType::Nested ElseMatrixNested;
-  enum {
-    RowsAtCompileTime = ConditionMatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime,
-    Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & RowMajorBit
-  };
-};
-}
-
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
-class Select : public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type,
-               internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<Select>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Select)
-
-    inline EIGEN_DEVICE_FUNC
-    Select(const ConditionMatrixType& a_conditionMatrix,
-           const ThenMatrixType& a_thenMatrix,
-           const ElseMatrixType& a_elseMatrix)
-      : m_condition(a_conditionMatrix), m_then(a_thenMatrix), m_else(a_elseMatrix)
-    {
-      eigen_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows());
-      eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols());
-    }
-
-    inline EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_condition.rows(); }
-    inline EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_condition.cols(); }
-
-    inline EIGEN_DEVICE_FUNC
-    const Scalar coeff(Index i, Index j) const
-    {
-      if (m_condition.coeff(i,j))
-        return m_then.coeff(i,j);
-      else
-        return m_else.coeff(i,j);
-    }
-
-    inline EIGEN_DEVICE_FUNC
-    const Scalar coeff(Index i) const
-    {
-      if (m_condition.coeff(i))
-        return m_then.coeff(i);
-      else
-        return m_else.coeff(i);
-    }
-
-    inline EIGEN_DEVICE_FUNC const ConditionMatrixType& conditionMatrix() const
-    {
-      return m_condition;
-    }
-
-    inline EIGEN_DEVICE_FUNC const ThenMatrixType& thenMatrix() const
-    {
-      return m_then;
-    }
-
-    inline EIGEN_DEVICE_FUNC const ElseMatrixType& elseMatrix() const
-    {
-      return m_else;
-    }
-
-  protected:
-    typename ConditionMatrixType::Nested m_condition;
-    typename ThenMatrixType::Nested m_then;
-    typename ElseMatrixType::Nested m_else;
-};
-
-
-/** \returns a matrix where each coefficient (i,j) is equal to \a thenMatrix(i,j)
-  * if \c *this(i,j), and \a elseMatrix(i,j) otherwise.
-  *
-  * Example: \include MatrixBase_select.cpp
-  * Output: \verbinclude MatrixBase_select.out
-  *
-  * \sa class Select
-  */
-template<typename Derived>
-template<typename ThenDerived,typename ElseDerived>
-inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived,ElseDerived>
-DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
-                            const DenseBase<ElseDerived>& elseMatrix) const
-{
-  return Select<Derived,ThenDerived,ElseDerived>(derived(), thenMatrix.derived(), elseMatrix.derived());
-}
-
-/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
-  * the \em else expression being a scalar value.
-  *
-  * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
-  */
-template<typename Derived>
-template<typename ThenDerived>
-inline EIGEN_DEVICE_FUNC const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
-DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
-                           const typename ThenDerived::Scalar& elseScalar) const
-{
-  return Select<Derived,ThenDerived,typename ThenDerived::ConstantReturnType>(
-    derived(), thenMatrix.derived(), ThenDerived::Constant(rows(),cols(),elseScalar));
-}
-
-/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
-  * the \em then expression being a scalar value.
-  *
-  * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
-  */
-template<typename Derived>
-template<typename ElseDerived>
-inline EIGEN_DEVICE_FUNC const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
-DenseBase<Derived>::select(const typename ElseDerived::Scalar& thenScalar,
-                           const DenseBase<ElseDerived>& elseMatrix) const
-{
-  return Select<Derived,typename ElseDerived::ConstantReturnType,ElseDerived>(
-    derived(), ElseDerived::Constant(rows(),cols(),thenScalar), elseMatrix.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELECT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SelfAdjointView.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SelfAdjointView.h
deleted file mode 100644
index 8ce3b37..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SelfAdjointView.h
+++ /dev/null
@@ -1,365 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINTMATRIX_H
-#define EIGEN_SELFADJOINTMATRIX_H
-
-namespace Eigen {
-
-/** \class SelfAdjointView
-  * \ingroup Core_Module
-  *
-  *
-  * \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix
-  *
-  * \param MatrixType the type of the dense matrix storing the coefficients
-  * \param TriangularPart can be either \c #Lower or \c #Upper
-  *
-  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
-  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
-  * and most of the time this is the only way that it is used.
-  *
-  * \sa class TriangularBase, MatrixBase::selfadjointView()
-  */
-
-namespace internal {
-template<typename MatrixType, unsigned int UpLo>
-struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-  typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
-  typedef MatrixType ExpressionType;
-  typedef typename MatrixType::PlainObject FullMatrixType;
-  enum {
-    Mode = UpLo | SelfAdjoint,
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags =  MatrixTypeNestedCleaned::Flags & (HereditaryBits|FlagsLvalueBit)
-           & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)) // FIXME these flags should be preserved
-  };
-};
-}
-
-
-template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
-  : public TriangularBase<SelfAdjointView<_MatrixType, UpLo> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef TriangularBase<SelfAdjointView> Base;
-    typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
-    typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
-    typedef MatrixTypeNestedCleaned NestedExpression;
-
-    /** \brief The type of coefficients in this matrix */
-    typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
-    typedef SelfAdjointView<typename internal::add_const<MatrixType>::type, UpLo> ConstSelfAdjointView;
-
-    enum {
-      Mode = internal::traits<SelfAdjointView>::Mode,
-      Flags = internal::traits<SelfAdjointView>::Flags,
-      TransposeMode = ((int(Mode) & int(Upper)) ? Lower : 0) | ((int(Mode) & int(Lower)) ? Upper : 0)
-    };
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    EIGEN_DEVICE_FUNC
-    explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
-    {
-      EIGEN_STATIC_ASSERT(UpLo==Lower || UpLo==Upper,SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return m_matrix.outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return m_matrix.innerStride(); }
-
-    /** \sa MatrixBase::coeff()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar coeff(Index row, Index col) const
-    {
-      Base::check_coordinates_internal(row, col);
-      return m_matrix.coeff(row, col);
-    }
-
-    /** \sa MatrixBase::coeffRef()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(SelfAdjointView);
-      Base::check_coordinates_internal(row, col);
-      return m_matrix.coeffRef(row, col);
-    }
-
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    const MatrixTypeNestedCleaned& _expression() const { return m_matrix; }
-
-    EIGEN_DEVICE_FUNC
-    const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
-    EIGEN_DEVICE_FUNC
-    MatrixTypeNestedCleaned& nestedExpression() { return m_matrix; }
-
-    /** Efficient triangular matrix times vector/matrix product */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<SelfAdjointView,OtherDerived>
-    operator*(const MatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<SelfAdjointView,OtherDerived>(*this, rhs.derived());
-    }
-
-    /** Efficient vector/matrix times triangular matrix product */
-    template<typename OtherDerived> friend
-    EIGEN_DEVICE_FUNC
-    const Product<OtherDerived,SelfAdjointView>
-    operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView& rhs)
-    {
-      return Product<OtherDerived,SelfAdjointView>(lhs.derived(),rhs);
-    }
-
-    friend EIGEN_DEVICE_FUNC
-    const SelfAdjointView<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,MatrixType,product),UpLo>
-    operator*(const Scalar& s, const SelfAdjointView& mat)
-    {
-      return (s*mat.nestedExpression()).template selfadjointView<UpLo>();
-    }
-
-    /** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this:
-      * \f$ this = this + \alpha u v^* + conj(\alpha) v u^* \f$
-      * \returns a reference to \c *this
-      *
-      * The vectors \a u and \c v \b must be column vectors, however they can be
-      * a adjoint expression without any overhead. Only the meaningful triangular
-      * part of the matrix is updated, the rest is left unchanged.
-      *
-      * \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
-      */
-    template<typename DerivedU, typename DerivedV>
-    EIGEN_DEVICE_FUNC
-    SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha = Scalar(1));
-
-    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
-      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
-      *
-      * \returns a reference to \c *this
-      *
-      * Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
-      * call this function with u.adjoint().
-      *
-      * \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
-      */
-    template<typename DerivedU>
-    EIGEN_DEVICE_FUNC
-    SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
-
-    /** \returns an expression of a triangular view extracted from the current selfadjoint view of a given triangular part
-      *
-      * The parameter \a TriMode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
-      * \c #Lower, \c #StrictlyLower, \c #UnitLower.
-      *
-      * If \c TriMode references the same triangular part than \c *this, then this method simply return a \c TriangularView of the nested expression,
-      * otherwise, the nested expression is first transposed, thus returning a \c TriangularView<Transpose<MatrixType>> object.
-      *
-      * \sa MatrixBase::triangularView(), class TriangularView
-      */
-    template<unsigned int TriMode>
-    EIGEN_DEVICE_FUNC
-    typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
-                                   TriangularView<MatrixType,TriMode>,
-                                   TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type
-    triangularView() const
-    {
-      typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::ConstTransposeReturnType>::type tmp1(m_matrix);
-      typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::AdjointReturnType>::type tmp2(tmp1);
-      return typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
-                                   TriangularView<MatrixType,TriMode>,
-                                   TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type(tmp2);
-    }
-
-    typedef SelfAdjointView<const MatrixConjugateReturnType,UpLo> ConjugateReturnType;
-    /** \sa MatrixBase::conjugate() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConjugateReturnType conjugate() const
-    { return ConjugateReturnType(m_matrix.conjugate()); }
-
-    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
-     *           returns \c *this otherwise.
-     */
-    template<bool Cond>
-    EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Cond,ConjugateReturnType,ConstSelfAdjointView>::type
-    conjugateIf() const
-    {
-      typedef typename internal::conditional<Cond,ConjugateReturnType,ConstSelfAdjointView>::type ReturnType;
-      return ReturnType(m_matrix.template conjugateIf<Cond>());
-    }
-
-    typedef SelfAdjointView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
-    /** \sa MatrixBase::adjoint() const */
-    EIGEN_DEVICE_FUNC
-    inline const AdjointReturnType adjoint() const
-    { return AdjointReturnType(m_matrix.adjoint()); }
-
-    typedef SelfAdjointView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
-     /** \sa MatrixBase::transpose() */
-    EIGEN_DEVICE_FUNC
-    inline TransposeReturnType transpose()
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      typename MatrixType::TransposeReturnType tmp(m_matrix);
-      return TransposeReturnType(tmp);
-    }
-
-    typedef SelfAdjointView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
-    /** \sa MatrixBase::transpose() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConstTransposeReturnType transpose() const
-    {
-      return ConstTransposeReturnType(m_matrix.transpose());
-    }
-
-    /** \returns a const expression of the main diagonal of the matrix \c *this
-      *
-      * This method simply returns the diagonal of the nested expression, thus by-passing the SelfAdjointView decorator.
-      *
-      * \sa MatrixBase::diagonal(), class Diagonal */
-    EIGEN_DEVICE_FUNC
-    typename MatrixType::ConstDiagonalReturnType diagonal() const
-    {
-      return typename MatrixType::ConstDiagonalReturnType(m_matrix);
-    }
-
-/////////// Cholesky module ///////////
-
-    const LLT<PlainObject, UpLo> llt() const;
-    const LDLT<PlainObject, UpLo> ldlt() const;
-
-/////////// Eigenvalue module ///////////
-
-    /** Real part of #Scalar */
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    /** Return type of eigenvalues() */
-    typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType;
-
-    EIGEN_DEVICE_FUNC
-    EigenvaluesReturnType eigenvalues() const;
-    EIGEN_DEVICE_FUNC
-    RealScalar operatorNorm() const;
-
-  protected:
-    MatrixTypeNested m_matrix;
-};
-
-
-// template<typename OtherDerived, typename MatrixType, unsigned int UpLo>
-// internal::selfadjoint_matrix_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >
-// operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView<MatrixType,UpLo>& rhs)
-// {
-//   return internal::matrix_selfadjoint_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >(lhs.derived(),rhs);
-// }
-
-// selfadjoint to dense matrix
-
-namespace internal {
-
-// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
-//      in the future selfadjoint-ness should be defined by the expression traits
-//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
-template<typename MatrixType, unsigned int Mode>
-struct evaluator_traits<SelfAdjointView<MatrixType,Mode> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef SelfAdjointShape Shape;
-};
-
-template<int UpLo, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version>
-class triangular_dense_assignment_kernel<UpLo,SelfAdjoint,SetOpposite,DstEvaluatorTypeT,SrcEvaluatorTypeT,Functor,Version>
-  : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
-  typedef typename Base::DstXprType DstXprType;
-  typedef typename Base::SrcXprType SrcXprType;
-  using Base::m_dst;
-  using Base::m_src;
-  using Base::m_functor;
-public:
-
-  typedef typename Base::DstEvaluatorType DstEvaluatorType;
-  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
-  typedef typename Base::Scalar Scalar;
-  typedef typename Base::AssignmentTraits AssignmentTraits;
-
-
-  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {}
-
-  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
-  {
-    eigen_internal_assert(row!=col);
-    Scalar tmp = m_src.coeff(row,col);
-    m_functor.assignCoeff(m_dst.coeffRef(row,col), tmp);
-    m_functor.assignCoeff(m_dst.coeffRef(col,row), numext::conj(tmp));
-  }
-
-  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
-  {
-    Base::assignCoeff(id,id);
-  }
-
-  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index, Index)
-  { eigen_internal_assert(false && "should never be called"); }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Implementation of MatrixBase methods
-***************************************************************************/
-
-/** This is the const version of MatrixBase::selfadjointView() */
-template<typename Derived>
-template<unsigned int UpLo>
-EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
-MatrixBase<Derived>::selfadjointView() const
-{
-  return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
-}
-
-/** \returns an expression of a symmetric/self-adjoint view extracted from the upper or lower triangular part of the current matrix
-  *
-  * The parameter \a UpLo can be either \c #Upper or \c #Lower
-  *
-  * Example: \include MatrixBase_selfadjointView.cpp
-  * Output: \verbinclude MatrixBase_selfadjointView.out
-  *
-  * \sa class SelfAdjointView
-  */
-template<typename Derived>
-template<unsigned int UpLo>
-EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
-MatrixBase<Derived>::selfadjointView()
-{
-  return typename SelfAdjointViewReturnType<UpLo>::Type(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINTMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h
deleted file mode 100644
index 7c89c2e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h
+++ /dev/null
@@ -1,47 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFCWISEBINARYOP_H
-#define EIGEN_SELFCWISEBINARYOP_H
-
-namespace Eigen { 
-
-// TODO generalize the scalar type of 'other'
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
-{
-  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFCWISEBINARYOP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Solve.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Solve.h
deleted file mode 100644
index 23d5cb7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Solve.h
+++ /dev/null
@@ -1,188 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVE_H
-#define EIGEN_SOLVE_H
-
-namespace Eigen {
-
-template<typename Decomposition, typename RhsType, typename StorageKind> class SolveImpl;
-
-/** \class Solve
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression representing a solving operation
-  *
-  * \tparam Decomposition the type of the matrix or decomposition object
-  * \tparam Rhstype the type of the right-hand side
-  *
-  * This class represents an expression of A.solve(B)
-  * and most of the time this is the only way it is used.
-  *
-  */
-namespace internal {
-
-// this solve_traits class permits to determine the evaluation type with respect to storage kind (Dense vs Sparse)
-template<typename Decomposition, typename RhsType,typename StorageKind> struct solve_traits;
-
-template<typename Decomposition, typename RhsType>
-struct solve_traits<Decomposition,RhsType,Dense>
-{
-  typedef typename make_proper_matrix_type<typename RhsType::Scalar,
-                 Decomposition::ColsAtCompileTime,
-                 RhsType::ColsAtCompileTime,
-                 RhsType::PlainObject::Options,
-                 Decomposition::MaxColsAtCompileTime,
-                 RhsType::MaxColsAtCompileTime>::type PlainObject;
-};
-
-template<typename Decomposition, typename RhsType>
-struct traits<Solve<Decomposition, RhsType> >
-  : traits<typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject>
-{
-  typedef typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject PlainObject;
-  typedef typename promote_index_type<typename Decomposition::StorageIndex, typename RhsType::StorageIndex>::type StorageIndex;
-  typedef traits<PlainObject> BaseTraits;
-  enum {
-    Flags = BaseTraits::Flags & RowMajorBit,
-    CoeffReadCost = HugeCost
-  };
-};
-
-}
-
-
-template<typename Decomposition, typename RhsType>
-class Solve : public SolveImpl<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>
-{
-public:
-  typedef typename internal::traits<Solve>::PlainObject PlainObject;
-  typedef typename internal::traits<Solve>::StorageIndex StorageIndex;
-
-  Solve(const Decomposition &dec, const RhsType &rhs)
-    : m_dec(dec), m_rhs(rhs)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dec.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  EIGEN_DEVICE_FUNC const Decomposition& dec() const { return m_dec; }
-  EIGEN_DEVICE_FUNC const RhsType&       rhs() const { return m_rhs; }
-
-protected:
-  const Decomposition &m_dec;
-  const RhsType       &m_rhs;
-};
-
-
-// Specialization of the Solve expression for dense results
-template<typename Decomposition, typename RhsType>
-class SolveImpl<Decomposition,RhsType,Dense>
-  : public MatrixBase<Solve<Decomposition,RhsType> >
-{
-  typedef Solve<Decomposition,RhsType> Derived;
-
-public:
-
-  typedef MatrixBase<Solve<Decomposition,RhsType> > Base;
-  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
-
-private:
-
-  Scalar coeff(Index row, Index col) const;
-  Scalar coeff(Index i) const;
-};
-
-// Generic API dispatcher
-template<typename Decomposition, typename RhsType, typename StorageKind>
-class SolveImpl : public internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type
-{
-  public:
-    typedef typename internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type Base;
-};
-
-namespace internal {
-
-// Evaluator of Solve -> eval into a temporary
-template<typename Decomposition, typename RhsType>
-struct evaluator<Solve<Decomposition,RhsType> >
-  : public evaluator<typename Solve<Decomposition,RhsType>::PlainObject>
-{
-  typedef Solve<Decomposition,RhsType> SolveType;
-  typedef typename SolveType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum { Flags = Base::Flags | EvalBeforeNestingBit };
-
-  EIGEN_DEVICE_FUNC explicit evaluator(const SolveType& solve)
-    : m_result(solve.rows(), solve.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    solve.dec()._solve_impl(solve.rhs(), m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-// Specialization for "dst = dec.solve(rhs)"
-// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef Solve<DecType,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec()._solve_impl(src.rhs(), dst);
-  }
-};
-
-// Specialization for "dst = dec.transpose().solve(rhs)"
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef Solve<Transpose<const DecType>,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec().nestedExpression().template _solve_impl_transposed<false>(src.rhs(), dst);
-  }
-};
-
-// Specialization for "dst = dec.adjoint().solve(rhs)"
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>,
-                  internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec().nestedExpression().nestedExpression().template _solve_impl_transposed<true>(src.rhs(), dst);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SolveTriangular.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SolveTriangular.h
deleted file mode 100644
index dfbf995..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SolveTriangular.h
+++ /dev/null
@@ -1,235 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVETRIANGULAR_H
-#define EIGEN_SOLVETRIANGULAR_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Forward declarations:
-// The following two routines are implemented in the products/TriangularSolver*.h files
-template<typename LhsScalar, typename RhsScalar, typename Index, int Side, int Mode, bool Conjugate, int StorageOrder>
-struct triangular_solve_vector;
-
-template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherStorageOrder, int OtherInnerStride>
-struct triangular_solve_matrix;
-
-// small helper struct extracting some traits on the underlying solver operation
-template<typename Lhs, typename Rhs, int Side>
-class trsolve_traits
-{
-  private:
-    enum {
-      RhsIsVectorAtCompileTime = (Side==OnTheLeft ? Rhs::ColsAtCompileTime : Rhs::RowsAtCompileTime)==1
-    };
-  public:
-    enum {
-      Unrolling   = (RhsIsVectorAtCompileTime && Rhs::SizeAtCompileTime != Dynamic && Rhs::SizeAtCompileTime <= 8)
-                  ? CompleteUnrolling : NoUnrolling,
-      RhsVectors  = RhsIsVectorAtCompileTime ? 1 : Dynamic
-    };
-};
-
-template<typename Lhs, typename Rhs,
-  int Side, // can be OnTheLeft/OnTheRight
-  int Mode, // can be Upper/Lower | UnitDiag
-  int Unrolling = trsolve_traits<Lhs,Rhs,Side>::Unrolling,
-  int RhsVectors = trsolve_traits<Lhs,Rhs,Side>::RhsVectors
-  >
-struct triangular_solver_selector;
-
-template<typename Lhs, typename Rhs, int Side, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,1>
-{
-  typedef typename Lhs::Scalar LhsScalar;
-  typedef typename Rhs::Scalar RhsScalar;
-  typedef blas_traits<Lhs> LhsProductTraits;
-  typedef typename LhsProductTraits::ExtractType ActualLhsType;
-  typedef Map<Matrix<RhsScalar,Dynamic,1>, Aligned> MappedRhs;
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    ActualLhsType actualLhs = LhsProductTraits::extract(lhs);
-
-    // FIXME find a way to allow an inner stride if packet_traits<Scalar>::size==1
-
-    bool useRhsDirectly = Rhs::InnerStrideAtCompileTime==1 || rhs.innerStride()==1;
-
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhs,rhs.size(),
-                                                  (useRhsDirectly ? rhs.data() : 0));
-
-    if(!useRhsDirectly)
-      MappedRhs(actualRhs,rhs.size()) = rhs;
-
-    triangular_solve_vector<LhsScalar, RhsScalar, Index, Side, Mode, LhsProductTraits::NeedToConjugate,
-                            (int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor>
-      ::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs);
-
-    if(!useRhsDirectly)
-      rhs = MappedRhs(actualRhs, rhs.size());
-  }
-};
-
-// the rhs is a matrix
-template<typename Lhs, typename Rhs, int Side, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef blas_traits<Lhs> LhsProductTraits;
-  typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType;
-
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsProductTraits::extract(lhs);
-
-    const Index size = lhs.rows();
-    const Index othersize = Side==OnTheLeft? rhs.cols() : rhs.rows();
-
-    typedef internal::gemm_blocking_space<(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
-              Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxRowsAtCompileTime,4> BlockingType;
-
-    BlockingType blocking(rhs.rows(), rhs.cols(), size, 1, false);
-
-    triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor,
-                               (Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor, Rhs::InnerStrideAtCompileTime>
-      ::run(size, othersize, &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.innerStride(), rhs.outerStride(), blocking);
-  }
-};
-
-/***************************************************************************
-* meta-unrolling implementation
-***************************************************************************/
-
-template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size,
-         bool Stop = LoopIndex==Size>
-struct triangular_solver_unroller;
-
-template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
-struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,false> {
-  enum {
-    IsLower = ((Mode&Lower)==Lower),
-    DiagIndex  = IsLower ? LoopIndex : Size - LoopIndex - 1,
-    StartIndex = IsLower ? 0         : DiagIndex+1
-  };
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    if (LoopIndex>0)
-      rhs.coeffRef(DiagIndex) -= lhs.row(DiagIndex).template segment<LoopIndex>(StartIndex).transpose()
-                                .cwiseProduct(rhs.template segment<LoopIndex>(StartIndex)).sum();
-
-    if(!(Mode & UnitDiag))
-      rhs.coeffRef(DiagIndex) /= lhs.coeff(DiagIndex,DiagIndex);
-
-    triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex+1,Size>::run(lhs,rhs);
-  }
-};
-
-template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
-struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,true> {
-  static EIGEN_DEVICE_FUNC void run(const Lhs&, Rhs&) {}
-};
-
-template<typename Lhs, typename Rhs, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,OnTheLeft,Mode,CompleteUnrolling,1> {
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  { triangular_solver_unroller<Lhs,Rhs,Mode,0,Rhs::SizeAtCompileTime>::run(lhs,rhs); }
-};
-
-template<typename Lhs, typename Rhs, int Mode>
-struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
-  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
-  {
-    Transpose<const Lhs> trLhs(lhs);
-    Transpose<Rhs> trRhs(rhs);
-
-    triangular_solver_unroller<Transpose<const Lhs>,Transpose<Rhs>,
-                              ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag),
-                              0,Rhs::SizeAtCompileTime>::run(trLhs,trRhs);
-  }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* TriangularView methods
-***************************************************************************/
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatrixType, unsigned int Mode>
-template<int Side, typename OtherDerived>
-EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
-{
-  OtherDerived& other = _other.const_cast_derived();
-  eigen_assert( derived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols())) );
-  eigen_assert((!(int(Mode) & int(ZeroDiag))) && bool(int(Mode) & (int(Upper) | int(Lower))));
-  // If solving for a 0x0 matrix, nothing to do, simply return.
-  if (derived().cols() == 0)
-    return;
-
-  enum { copy = (internal::traits<OtherDerived>::Flags & RowMajorBit)  && OtherDerived::IsVectorAtCompileTime && OtherDerived::SizeAtCompileTime!=1};
-  typedef typename internal::conditional<copy,
-    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
-  OtherCopy otherCopy(other);
-
-  internal::triangular_solver_selector<MatrixType, typename internal::remove_reference<OtherCopy>::type,
-    Side, Mode>::run(derived().nestedExpression(), otherCopy);
-
-  if (copy)
-    other = otherCopy;
-}
-
-template<typename Derived, unsigned int Mode>
-template<int Side, typename Other>
-const internal::triangular_solve_retval<Side,TriangularView<Derived,Mode>,Other>
-TriangularViewImpl<Derived,Mode,Dense>::solve(const MatrixBase<Other>& other) const
-{
-  return internal::triangular_solve_retval<Side,TriangularViewType,Other>(derived(), other.derived());
-}
-#endif
-
-namespace internal {
-
-
-template<int Side, typename TriangularType, typename Rhs>
-struct traits<triangular_solve_retval<Side, TriangularType, Rhs> >
-{
-  typedef typename internal::plain_matrix_type_column_major<Rhs>::type ReturnType;
-};
-
-template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval
- : public ReturnByValue<triangular_solve_retval<Side, TriangularType, Rhs> >
-{
-  typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned;
-  typedef ReturnByValue<triangular_solve_retval> Base;
-
-  triangular_solve_retval(const TriangularType& tri, const Rhs& rhs)
-    : m_triangularMatrix(tri), m_rhs(rhs)
-  {}
-
-  inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_rhs.rows(); }
-  inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  template<typename Dest> inline void evalTo(Dest& dst) const
-  {
-    if(!is_same_dense(dst,m_rhs))
-      dst = m_rhs;
-    m_triangularMatrix.template solveInPlace<Side>(dst);
-  }
-
-  protected:
-    const TriangularType& m_triangularMatrix;
-    typename Rhs::Nested m_rhs;
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVETRIANGULAR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SolverBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SolverBase.h
deleted file mode 100644
index 5014610..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/SolverBase.h
+++ /dev/null
@@ -1,168 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVERBASE_H
-#define EIGEN_SOLVERBASE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Derived>
-struct solve_assertion {
-    template<bool Transpose_, typename Rhs>
-    static void run(const Derived& solver, const Rhs& b) { solver.template _check_solve_assertion<Transpose_>(b); }
-};
-
-template<typename Derived>
-struct solve_assertion<Transpose<Derived> >
-{
-    typedef Transpose<Derived> type;
-
-    template<bool Transpose_, typename Rhs>
-    static void run(const type& transpose, const Rhs& b)
-    {
-        internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<true>(transpose.nestedExpression(), b);
-    }
-};
-
-template<typename Scalar, typename Derived>
-struct solve_assertion<CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > >
-{
-    typedef CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > type;
-
-    template<bool Transpose_, typename Rhs>
-    static void run(const type& adjoint, const Rhs& b)
-    {
-        internal::solve_assertion<typename internal::remove_all<Transpose<Derived> >::type>::template run<true>(adjoint.nestedExpression(), b);
-    }
-};
-} // end namespace internal
-
-/** \class SolverBase
-  * \brief A base class for matrix decomposition and solvers
-  *
-  * \tparam Derived the actual type of the decomposition/solver.
-  *
-  * Any matrix decomposition inheriting this base class provide the following API:
-  *
-  * \code
-  * MatrixType A, b, x;
-  * DecompositionType dec(A);
-  * x = dec.solve(b);             // solve A   * x = b
-  * x = dec.transpose().solve(b); // solve A^T * x = b
-  * x = dec.adjoint().solve(b);   // solve A'  * x = b
-  * \endcode
-  *
-  * \warning Currently, any other usage of transpose() and adjoint() are not supported and will produce compilation errors.
-  *
-  * \sa class PartialPivLU, class FullPivLU, class HouseholderQR, class ColPivHouseholderQR, class FullPivHouseholderQR, class CompleteOrthogonalDecomposition, class LLT, class LDLT, class SVDBase
-  */
-template<typename Derived>
-class SolverBase : public EigenBase<Derived>
-{
-  public:
-
-    typedef EigenBase<Derived> Base;
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef Scalar CoeffReturnType;
-
-    template<typename Derived_>
-    friend struct internal::solve_assertion;
-
-    enum {
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                          internal::traits<Derived>::ColsAtCompileTime>::ret),
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
-                                                             internal::traits<Derived>::MaxColsAtCompileTime>::ret),
-      IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
-                           || internal::traits<Derived>::MaxColsAtCompileTime == 1,
-      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2
-    };
-
-    /** Default constructor */
-    SolverBase()
-    {}
-
-    ~SolverBase()
-    {}
-
-    using Base::derived;
-
-    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      */
-    template<typename Rhs>
-    inline const Solve<Derived, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<false>(derived(), b);
-      return Solve<Derived, Rhs>(derived(), b.derived());
-    }
-
-    /** \internal the return type of transpose() */
-    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
-    /** \returns an expression of the transposed of the factored matrix.
-      *
-      * A typical usage is to solve for the transposed problem A^T x = b:
-      * \code x = dec.transpose().solve(b); \endcode
-      *
-      * \sa adjoint(), solve()
-      */
-    inline ConstTransposeReturnType transpose() const
-    {
-      return ConstTransposeReturnType(derived());
-    }
-
-    /** \internal the return type of adjoint() */
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
-                        ConstTransposeReturnType
-                     >::type AdjointReturnType;
-    /** \returns an expression of the adjoint of the factored matrix
-      *
-      * A typical usage is to solve for the adjoint problem A' x = b:
-      * \code x = dec.adjoint().solve(b); \endcode
-      *
-      * For real scalar types, this function is equivalent to transpose().
-      *
-      * \sa transpose(), solve()
-      */
-    inline AdjointReturnType adjoint() const
-    {
-      return AdjointReturnType(derived().transpose());
-    }
-
-  protected:
-
-    template<bool Transpose_, typename Rhs>
-    void _check_solve_assertion(const Rhs& b) const {
-        EIGEN_ONLY_USED_FOR_DEBUG(b);
-        eigen_assert(derived().m_isInitialized && "Solver is not initialized.");
-        eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b");
-    }
-};
-
-namespace internal {
-
-template<typename Derived>
-struct generic_xpr_base<Derived, MatrixXpr, SolverStorage>
-{
-  typedef SolverBase<Derived> type;
-
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVERBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/StableNorm.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/StableNorm.h
deleted file mode 100644
index 4a3f0cc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/StableNorm.h
+++ /dev/null
@@ -1,251 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STABLENORM_H
-#define EIGEN_STABLENORM_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename ExpressionType, typename Scalar>
-inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
-{
-  Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
-  
-  if(maxCoeff>scale)
-  {
-    ssq = ssq * numext::abs2(scale/maxCoeff);
-    Scalar tmp = Scalar(1)/maxCoeff;
-    if(tmp > NumTraits<Scalar>::highest())
-    {
-      invScale = NumTraits<Scalar>::highest();
-      scale = Scalar(1)/invScale;
-    }
-    else if(maxCoeff>NumTraits<Scalar>::highest()) // we got a INF
-    {
-      invScale = Scalar(1);
-      scale = maxCoeff;
-    }
-    else
-    {
-      scale = maxCoeff;
-      invScale = tmp;
-    }
-  }
-  else if(maxCoeff!=maxCoeff) // we got a NaN
-  {
-    scale = maxCoeff;
-  }
-  
-  // TODO if the maxCoeff is much much smaller than the current scale,
-  // then we can neglect this sub vector
-  if(scale>Scalar(0)) // if scale==0, then bl is 0 
-    ssq += (bl*invScale).squaredNorm();
-}
-
-template<typename VectorType, typename RealScalar>
-void stable_norm_impl_inner_step(const VectorType &vec, RealScalar& ssq, RealScalar& scale, RealScalar& invScale)
-{
-  typedef typename VectorType::Scalar Scalar;
-  const Index blockSize = 4096;
-  
-  typedef typename internal::nested_eval<VectorType,2>::type VectorTypeCopy;
-  typedef typename internal::remove_all<VectorTypeCopy>::type VectorTypeCopyClean;
-  const VectorTypeCopy copy(vec);
-  
-  enum {
-    CanAlign = (   (int(VectorTypeCopyClean::Flags)&DirectAccessBit)
-                || (int(internal::evaluator<VectorTypeCopyClean>::Alignment)>0) // FIXME Alignment)>0 might not be enough
-               ) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT)
-                 && (EIGEN_MAX_STATIC_ALIGN_BYTES>0) // if we cannot allocate on the stack, then let's not bother about this optimization
-  };
-  typedef typename internal::conditional<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<VectorTypeCopyClean>::Alignment>,
-                                                   typename VectorTypeCopyClean::ConstSegmentReturnType>::type SegmentWrapper;
-  Index n = vec.size();
-  
-  Index bi = internal::first_default_aligned(copy);
-  if (bi>0)
-    internal::stable_norm_kernel(copy.head(bi), ssq, scale, invScale);
-  for (; bi<n; bi+=blockSize)
-    internal::stable_norm_kernel(SegmentWrapper(copy.segment(bi,numext::mini(blockSize, n - bi))), ssq, scale, invScale);
-}
-
-template<typename VectorType>
-typename VectorType::RealScalar
-stable_norm_impl(const VectorType &vec, typename enable_if<VectorType::IsVectorAtCompileTime>::type* = 0 )
-{
-  using std::sqrt;
-  using std::abs;
-
-  Index n = vec.size();
-
-  if(n==1)
-    return abs(vec.coeff(0));
-
-  typedef typename VectorType::RealScalar RealScalar;
-  RealScalar scale(0);
-  RealScalar invScale(1);
-  RealScalar ssq(0); // sum of squares
-
-  stable_norm_impl_inner_step(vec, ssq, scale, invScale);
-  
-  return scale * sqrt(ssq);
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar
-stable_norm_impl(const MatrixType &mat, typename enable_if<!MatrixType::IsVectorAtCompileTime>::type* = 0 )
-{
-  using std::sqrt;
-
-  typedef typename MatrixType::RealScalar RealScalar;
-  RealScalar scale(0);
-  RealScalar invScale(1);
-  RealScalar ssq(0); // sum of squares
-
-  for(Index j=0; j<mat.outerSize(); ++j)
-    stable_norm_impl_inner_step(mat.innerVector(j), ssq, scale, invScale);
-  return scale * sqrt(ssq);
-}
-
-template<typename Derived>
-inline typename NumTraits<typename traits<Derived>::Scalar>::Real
-blueNorm_impl(const EigenBase<Derived>& _vec)
-{
-  typedef typename Derived::RealScalar RealScalar;  
-  using std::pow;
-  using std::sqrt;
-  using std::abs;
-
-  // This program calculates the machine-dependent constants
-  // bl, b2, slm, s2m, relerr overfl
-  // from the "basic" machine-dependent numbers
-  // nbig, ibeta, it, iemin, iemax, rbig.
-  // The following define the basic machine-dependent constants.
-  // For portability, the PORT subprograms "ilmaeh" and "rlmach"
-  // are used. For any specific computer, each of the assignment
-  // statements can be replaced
-  static const int ibeta = std::numeric_limits<RealScalar>::radix;  // base for floating-point numbers
-  static const int it    = NumTraits<RealScalar>::digits();  // number of base-beta digits in mantissa
-  static const int iemin = NumTraits<RealScalar>::min_exponent();  // minimum exponent
-  static const int iemax = NumTraits<RealScalar>::max_exponent();  // maximum exponent
-  static const RealScalar rbig   = NumTraits<RealScalar>::highest();  // largest floating-point number
-  static const RealScalar b1     = RealScalar(pow(RealScalar(ibeta),RealScalar(-((1-iemin)/2))));  // lower boundary of midrange
-  static const RealScalar b2     = RealScalar(pow(RealScalar(ibeta),RealScalar((iemax + 1 - it)/2)));  // upper boundary of midrange
-  static const RealScalar s1m    = RealScalar(pow(RealScalar(ibeta),RealScalar((2-iemin)/2)));  // scaling factor for lower range
-  static const RealScalar s2m    = RealScalar(pow(RealScalar(ibeta),RealScalar(- ((iemax+it)/2))));  // scaling factor for upper range
-  static const RealScalar eps    = RealScalar(pow(double(ibeta), 1-it));
-  static const RealScalar relerr = sqrt(eps);  // tolerance for neglecting asml
-
-  const Derived& vec(_vec.derived());
-  Index n = vec.size();
-  RealScalar ab2 = b2 / RealScalar(n);
-  RealScalar asml = RealScalar(0);
-  RealScalar amed = RealScalar(0);
-  RealScalar abig = RealScalar(0);
-
-  for(Index j=0; j<vec.outerSize(); ++j)
-  {
-    for(typename Derived::InnerIterator iter(vec, j); iter; ++iter)
-    {
-      RealScalar ax = abs(iter.value());
-      if(ax > ab2)     abig += numext::abs2(ax*s2m);
-      else if(ax < b1) asml += numext::abs2(ax*s1m);
-      else             amed += numext::abs2(ax);
-    }
-  }
-  if(amed!=amed)
-    return amed;  // we got a NaN
-  if(abig > RealScalar(0))
-  {
-    abig = sqrt(abig);
-    if(abig > rbig) // overflow, or *this contains INF values
-      return abig;  // return INF
-    if(amed > RealScalar(0))
-    {
-      abig = abig/s2m;
-      amed = sqrt(amed);
-    }
-    else
-      return abig/s2m;
-  }
-  else if(asml > RealScalar(0))
-  {
-    if (amed > RealScalar(0))
-    {
-      abig = sqrt(amed);
-      amed = sqrt(asml) / s1m;
-    }
-    else
-      return sqrt(asml)/s1m;
-  }
-  else
-    return sqrt(amed);
-  asml = numext::mini(abig, amed);
-  abig = numext::maxi(abig, amed);
-  if(asml <= abig*relerr)
-    return abig;
-  else
-    return abig * sqrt(RealScalar(1) + numext::abs2(asml/abig));
-}
-
-} // end namespace internal
-
-/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
-  * This version use a blockwise two passes algorithm:
-  *  1 - find the absolute largest coefficient \c s
-  *  2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way
-  *
-  * For architecture/scalar types supporting vectorization, this version
-  * is faster than blueNorm(). Otherwise the blueNorm() is much faster.
-  *
-  * \sa norm(), blueNorm(), hypotNorm()
-  */
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::stableNorm() const
-{
-  return internal::stable_norm_impl(derived());
-}
-
-/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
-  * A Portable Fortran Program to Find the Euclidean Norm of a Vector,
-  * ACM TOMS, Vol 4, Issue 1, 1978.
-  *
-  * For architecture/scalar types without vectorization, this version
-  * is much faster than stableNorm(). Otherwise the stableNorm() is faster.
-  *
-  * \sa norm(), stableNorm(), hypotNorm()
-  */
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::blueNorm() const
-{
-  return internal::blueNorm_impl(*this);
-}
-
-/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow.
-  * This version use a concatenation of hypot() calls, and it is very slow.
-  *
-  * \sa norm(), stableNorm()
-  */
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-MatrixBase<Derived>::hypotNorm() const
-{
-  if(size()==1)
-    return numext::abs(coeff(0,0));
-  else
-    return this->cwiseAbs().redux(internal::scalar_hypot_op<RealScalar>());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_STABLENORM_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/StlIterators.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/StlIterators.h
deleted file mode 100644
index 09041db..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/StlIterators.h
+++ /dev/null
@@ -1,463 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STLITERATORS_H
-#define EIGEN_STLITERATORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename IteratorType>
-struct indexed_based_stl_iterator_traits;
-
-template<typename  Derived>
-class indexed_based_stl_iterator_base
-{
-protected:
-  typedef indexed_based_stl_iterator_traits<Derived> traits;
-  typedef typename traits::XprType XprType;
-  typedef indexed_based_stl_iterator_base<typename traits::non_const_iterator> non_const_iterator;
-  typedef indexed_based_stl_iterator_base<typename traits::const_iterator> const_iterator;
-  typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
-  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
-  friend class indexed_based_stl_iterator_base<typename traits::const_iterator>;
-  friend class indexed_based_stl_iterator_base<typename traits::non_const_iterator>;
-public:
-  typedef Index difference_type;
-  typedef std::random_access_iterator_tag iterator_category;
-
-  indexed_based_stl_iterator_base() EIGEN_NO_THROW : mp_xpr(0), m_index(0) {}
-  indexed_based_stl_iterator_base(XprType& xpr, Index index) EIGEN_NO_THROW : mp_xpr(&xpr), m_index(index) {}
-
-  indexed_based_stl_iterator_base(const non_const_iterator& other) EIGEN_NO_THROW
-    : mp_xpr(other.mp_xpr), m_index(other.m_index)
-  {}
-
-  indexed_based_stl_iterator_base& operator=(const non_const_iterator& other)
-  {
-    mp_xpr = other.mp_xpr;
-    m_index = other.m_index;
-    return *this;
-  }
-
-  Derived& operator++() { ++m_index; return derived(); }
-  Derived& operator--() { --m_index; return derived(); }
-
-  Derived operator++(int) { Derived prev(derived()); operator++(); return prev;}
-  Derived operator--(int) { Derived prev(derived()); operator--(); return prev;}
-
-  friend Derived operator+(const indexed_based_stl_iterator_base& a, Index b) { Derived ret(a.derived()); ret += b; return ret; }
-  friend Derived operator-(const indexed_based_stl_iterator_base& a, Index b) { Derived ret(a.derived()); ret -= b; return ret; }
-  friend Derived operator+(Index a, const indexed_based_stl_iterator_base& b) { Derived ret(b.derived()); ret += a; return ret; }
-  friend Derived operator-(Index a, const indexed_based_stl_iterator_base& b) { Derived ret(b.derived()); ret -= a; return ret; }
-  
-  Derived& operator+=(Index b) { m_index += b; return derived(); }
-  Derived& operator-=(Index b) { m_index -= b; return derived(); }
-
-  difference_type operator-(const indexed_based_stl_iterator_base& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return m_index - other.m_index;
-  }
-
-  difference_type operator-(const other_iterator& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return m_index - other.m_index;
-  }
-
-  bool operator==(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator<=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-  bool operator> (const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator>=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-
-  bool operator==(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator<=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-  bool operator> (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator>=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-
-protected:
-
-  Derived& derived() { return static_cast<Derived&>(*this); }
-  const Derived& derived() const { return static_cast<const Derived&>(*this); }
-
-  XprType *mp_xpr;
-  Index m_index;
-};
-
-template<typename  Derived>
-class indexed_based_stl_reverse_iterator_base
-{
-protected:
-  typedef indexed_based_stl_iterator_traits<Derived> traits;
-  typedef typename traits::XprType XprType;
-  typedef indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator> non_const_iterator;
-  typedef indexed_based_stl_reverse_iterator_base<typename traits::const_iterator> const_iterator;
-  typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
-  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
-  friend class indexed_based_stl_reverse_iterator_base<typename traits::const_iterator>;
-  friend class indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator>;
-public:
-  typedef Index difference_type;
-  typedef std::random_access_iterator_tag iterator_category;
-
-  indexed_based_stl_reverse_iterator_base() : mp_xpr(0), m_index(0) {}
-  indexed_based_stl_reverse_iterator_base(XprType& xpr, Index index) : mp_xpr(&xpr), m_index(index) {}
-
-  indexed_based_stl_reverse_iterator_base(const non_const_iterator& other)
-    : mp_xpr(other.mp_xpr), m_index(other.m_index)
-  {}
-
-  indexed_based_stl_reverse_iterator_base& operator=(const non_const_iterator& other)
-  {
-    mp_xpr = other.mp_xpr;
-    m_index = other.m_index;
-    return *this;
-  }
-
-  Derived& operator++() { --m_index; return derived(); }
-  Derived& operator--() { ++m_index; return derived(); }
-
-  Derived operator++(int) { Derived prev(derived()); operator++(); return prev;}
-  Derived operator--(int) { Derived prev(derived()); operator--(); return prev;}
-
-  friend Derived operator+(const indexed_based_stl_reverse_iterator_base& a, Index b) { Derived ret(a.derived()); ret += b; return ret; }
-  friend Derived operator-(const indexed_based_stl_reverse_iterator_base& a, Index b) { Derived ret(a.derived()); ret -= b; return ret; }
-  friend Derived operator+(Index a, const indexed_based_stl_reverse_iterator_base& b) { Derived ret(b.derived()); ret += a; return ret; }
-  friend Derived operator-(Index a, const indexed_based_stl_reverse_iterator_base& b) { Derived ret(b.derived()); ret -= a; return ret; }
-  
-  Derived& operator+=(Index b) { m_index -= b; return derived(); }
-  Derived& operator-=(Index b) { m_index += b; return derived(); }
-
-  difference_type operator-(const indexed_based_stl_reverse_iterator_base& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return other.m_index - m_index;
-  }
-
-  difference_type operator-(const other_iterator& other) const
-  {
-    eigen_assert(mp_xpr == other.mp_xpr);
-    return other.m_index - m_index;
-  }
-
-  bool operator==(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator<=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-  bool operator> (const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator>=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-
-  bool operator==(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
-  bool operator!=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
-  bool operator< (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
-  bool operator<=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
-  bool operator> (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
-  bool operator>=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
-
-protected:
-
-  Derived& derived() { return static_cast<Derived&>(*this); }
-  const Derived& derived() const { return static_cast<const Derived&>(*this); }
-
-  XprType *mp_xpr;
-  Index m_index;
-};
-
-template<typename XprType>
-class pointer_based_stl_iterator
-{
-  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
-  typedef pointer_based_stl_iterator<typename internal::remove_const<XprType>::type> non_const_iterator;
-  typedef pointer_based_stl_iterator<typename internal::add_const<XprType>::type> const_iterator;
-  typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
-  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
-  friend class pointer_based_stl_iterator<typename internal::add_const<XprType>::type>;
-  friend class pointer_based_stl_iterator<typename internal::remove_const<XprType>::type>;
-public:
-  typedef Index difference_type;
-  typedef typename XprType::Scalar value_type;
-  typedef std::random_access_iterator_tag iterator_category;
-  typedef typename internal::conditional<bool(is_lvalue), value_type*, const value_type*>::type pointer;
-  typedef typename internal::conditional<bool(is_lvalue), value_type&, const value_type&>::type reference;
-
-
-  pointer_based_stl_iterator() EIGEN_NO_THROW : m_ptr(0) {}
-  pointer_based_stl_iterator(XprType& xpr, Index index) EIGEN_NO_THROW : m_incr(xpr.innerStride())
-  {
-    m_ptr = xpr.data() + index * m_incr.value();
-  }
-
-  pointer_based_stl_iterator(const non_const_iterator& other) EIGEN_NO_THROW
-    : m_ptr(other.m_ptr), m_incr(other.m_incr)
-  {}
-
-  pointer_based_stl_iterator& operator=(const non_const_iterator& other) EIGEN_NO_THROW
-  {
-    m_ptr = other.m_ptr;
-    m_incr.setValue(other.m_incr);
-    return *this;
-  }
-
-  reference operator*()         const { return *m_ptr;   }
-  reference operator[](Index i) const { return *(m_ptr+i*m_incr.value()); }
-  pointer   operator->()        const { return m_ptr;    }
-
-  pointer_based_stl_iterator& operator++() { m_ptr += m_incr.value(); return *this; }
-  pointer_based_stl_iterator& operator--() { m_ptr -= m_incr.value(); return *this; }
-
-  pointer_based_stl_iterator operator++(int) { pointer_based_stl_iterator prev(*this); operator++(); return prev;}
-  pointer_based_stl_iterator operator--(int) { pointer_based_stl_iterator prev(*this); operator--(); return prev;}
-
-  friend pointer_based_stl_iterator operator+(const pointer_based_stl_iterator& a, Index b) { pointer_based_stl_iterator ret(a); ret += b; return ret; }
-  friend pointer_based_stl_iterator operator-(const pointer_based_stl_iterator& a, Index b) { pointer_based_stl_iterator ret(a); ret -= b; return ret; }
-  friend pointer_based_stl_iterator operator+(Index a, const pointer_based_stl_iterator& b) { pointer_based_stl_iterator ret(b); ret += a; return ret; }
-  friend pointer_based_stl_iterator operator-(Index a, const pointer_based_stl_iterator& b) { pointer_based_stl_iterator ret(b); ret -= a; return ret; }
-  
-  pointer_based_stl_iterator& operator+=(Index b) { m_ptr += b*m_incr.value(); return *this; }
-  pointer_based_stl_iterator& operator-=(Index b) { m_ptr -= b*m_incr.value(); return *this; }
-
-  difference_type operator-(const pointer_based_stl_iterator& other) const {
-    return (m_ptr - other.m_ptr)/m_incr.value();
-  }
-
-  difference_type operator-(const other_iterator& other) const {
-    return (m_ptr - other.m_ptr)/m_incr.value();
-  }
-
-  bool operator==(const pointer_based_stl_iterator& other) const { return m_ptr == other.m_ptr; }
-  bool operator!=(const pointer_based_stl_iterator& other) const { return m_ptr != other.m_ptr; }
-  bool operator< (const pointer_based_stl_iterator& other) const { return m_ptr <  other.m_ptr; }
-  bool operator<=(const pointer_based_stl_iterator& other) const { return m_ptr <= other.m_ptr; }
-  bool operator> (const pointer_based_stl_iterator& other) const { return m_ptr >  other.m_ptr; }
-  bool operator>=(const pointer_based_stl_iterator& other) const { return m_ptr >= other.m_ptr; }
-
-  bool operator==(const other_iterator& other) const { return m_ptr == other.m_ptr; }
-  bool operator!=(const other_iterator& other) const { return m_ptr != other.m_ptr; }
-  bool operator< (const other_iterator& other) const { return m_ptr <  other.m_ptr; }
-  bool operator<=(const other_iterator& other) const { return m_ptr <= other.m_ptr; }
-  bool operator> (const other_iterator& other) const { return m_ptr >  other.m_ptr; }
-  bool operator>=(const other_iterator& other) const { return m_ptr >= other.m_ptr; }
-
-protected:
-
-  pointer m_ptr;
-  internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_incr;
-};
-
-template<typename _XprType>
-struct indexed_based_stl_iterator_traits<generic_randaccess_stl_iterator<_XprType> >
-{
-  typedef _XprType XprType;
-  typedef generic_randaccess_stl_iterator<typename internal::remove_const<XprType>::type> non_const_iterator;
-  typedef generic_randaccess_stl_iterator<typename internal::add_const<XprType>::type> const_iterator;
-};
-
-template<typename XprType>
-class generic_randaccess_stl_iterator : public indexed_based_stl_iterator_base<generic_randaccess_stl_iterator<XprType> >
-{
-public:
-  typedef typename XprType::Scalar value_type;
-
-protected:
-
-  enum {
-    has_direct_access = (internal::traits<XprType>::Flags & DirectAccessBit) ? 1 : 0,
-    is_lvalue  = internal::is_lvalue<XprType>::value
-  };
-
-  typedef indexed_based_stl_iterator_base<generic_randaccess_stl_iterator> Base;
-  using Base::m_index;
-  using Base::mp_xpr;
-
-  // TODO currently const Transpose/Reshape expressions never returns const references,
-  // so lets return by value too.
-  //typedef typename internal::conditional<bool(has_direct_access), const value_type&, const value_type>::type read_only_ref_t;
-  typedef const value_type read_only_ref_t;
-
-public:
-  
-  typedef typename internal::conditional<bool(is_lvalue), value_type *, const value_type *>::type pointer;
-  typedef typename internal::conditional<bool(is_lvalue), value_type&, read_only_ref_t>::type reference;
-  
-  generic_randaccess_stl_iterator() : Base() {}
-  generic_randaccess_stl_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
-  generic_randaccess_stl_iterator(const typename Base::non_const_iterator& other) : Base(other) {}
-  using Base::operator=;
-
-  reference operator*()         const { return   (*mp_xpr)(m_index);   }
-  reference operator[](Index i) const { return   (*mp_xpr)(m_index+i); }
-  pointer   operator->()        const { return &((*mp_xpr)(m_index)); }
-};
-
-template<typename _XprType, DirectionType Direction>
-struct indexed_based_stl_iterator_traits<subvector_stl_iterator<_XprType,Direction> >
-{
-  typedef _XprType XprType;
-  typedef subvector_stl_iterator<typename internal::remove_const<XprType>::type, Direction> non_const_iterator;
-  typedef subvector_stl_iterator<typename internal::add_const<XprType>::type, Direction> const_iterator;
-};
-
-template<typename XprType, DirectionType Direction>
-class subvector_stl_iterator : public indexed_based_stl_iterator_base<subvector_stl_iterator<XprType,Direction> >
-{
-protected:
-
-  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
-
-  typedef indexed_based_stl_iterator_base<subvector_stl_iterator> Base;
-  using Base::m_index;
-  using Base::mp_xpr;
-
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr>::type SubVectorType;
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr>::type ConstSubVectorType;
-
-
-public:
-  typedef typename internal::conditional<bool(is_lvalue), SubVectorType, ConstSubVectorType>::type reference;
-  typedef typename reference::PlainObject value_type;
-
-private:
-  class subvector_stl_iterator_ptr
-  {
-  public:
-      subvector_stl_iterator_ptr(const reference &subvector) : m_subvector(subvector) {}
-      reference* operator->() { return &m_subvector; }
-  private:
-      reference m_subvector;
-  };
-public:
-
-  typedef subvector_stl_iterator_ptr pointer;
-  
-  subvector_stl_iterator() : Base() {}
-  subvector_stl_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
-
-  reference operator*()         const { return (*mp_xpr).template subVector<Direction>(m_index); }
-  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index+i); }
-  pointer   operator->()        const { return (*mp_xpr).template subVector<Direction>(m_index); }
-};
-
-template<typename _XprType, DirectionType Direction>
-struct indexed_based_stl_iterator_traits<subvector_stl_reverse_iterator<_XprType,Direction> >
-{
-  typedef _XprType XprType;
-  typedef subvector_stl_reverse_iterator<typename internal::remove_const<XprType>::type, Direction> non_const_iterator;
-  typedef subvector_stl_reverse_iterator<typename internal::add_const<XprType>::type, Direction> const_iterator;
-};
-
-template<typename XprType, DirectionType Direction>
-class subvector_stl_reverse_iterator : public indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator<XprType,Direction> >
-{
-protected:
-
-  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
-
-  typedef indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator> Base;
-  using Base::m_index;
-  using Base::mp_xpr;
-
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr>::type SubVectorType;
-  typedef typename internal::conditional<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr>::type ConstSubVectorType;
-
-
-public:
-  typedef typename internal::conditional<bool(is_lvalue), SubVectorType, ConstSubVectorType>::type reference;
-  typedef typename reference::PlainObject value_type;
-
-private:
-  class subvector_stl_reverse_iterator_ptr
-  {
-  public:
-      subvector_stl_reverse_iterator_ptr(const reference &subvector) : m_subvector(subvector) {}
-      reference* operator->() { return &m_subvector; }
-  private:
-      reference m_subvector;
-  };
-public:
-
-  typedef subvector_stl_reverse_iterator_ptr pointer;
-  
-  subvector_stl_reverse_iterator() : Base() {}
-  subvector_stl_reverse_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
-
-  reference operator*()         const { return (*mp_xpr).template subVector<Direction>(m_index); }
-  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index+i); }
-  pointer   operator->()        const { return (*mp_xpr).template subVector<Direction>(m_index); }
-};
-
-} // namespace internal
-
-
-/** returns an iterator to the first element of the 1D vector or array
-  * \only_for_vectors
-  * \sa end(), cbegin()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::iterator DenseBase<Derived>::begin()
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return iterator(derived(), 0);
-}
-
-/** const version of begin() */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::begin() const
-{
-  return cbegin();
-}
-
-/** returns a read-only const_iterator to the first element of the 1D vector or array
-  * \only_for_vectors
-  * \sa cend(), begin()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cbegin() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return const_iterator(derived(), 0);
-}
-
-/** returns an iterator to the element following the last element of the 1D vector or array
-  * \only_for_vectors
-  * \sa begin(), cend()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::iterator DenseBase<Derived>::end()
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return iterator(derived(), size());
-}
-
-/** const version of end() */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::end() const
-{
-  return cend();
-}
-
-/** returns a read-only const_iterator to the element following the last element of the 1D vector or array
-  * \only_for_vectors
-  * \sa begin(), cend()
-  */
-template<typename Derived>
-inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cend() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return const_iterator(derived(), size());
-}
-
-} // namespace Eigen
-
-#endif // EIGEN_STLITERATORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Stride.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Stride.h
deleted file mode 100644
index 6494d51..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Stride.h
+++ /dev/null
@@ -1,116 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STRIDE_H
-#define EIGEN_STRIDE_H
-
-namespace Eigen {
-
-/** \class Stride
-  * \ingroup Core_Module
-  *
-  * \brief Holds strides information for Map
-  *
-  * This class holds the strides information for mapping arrays with strides with class Map.
-  *
-  * It holds two values: the inner stride and the outer stride.
-  *
-  * The inner stride is the pointer increment between two consecutive entries within a given row of a
-  * row-major matrix or within a given column of a column-major matrix.
-  *
-  * The outer stride is the pointer increment between two consecutive rows of a row-major matrix or
-  * between two consecutive columns of a column-major matrix.
-  *
-  * These two values can be passed either at compile-time as template parameters, or at runtime as
-  * arguments to the constructor.
-  *
-  * Indeed, this class takes two template parameters:
-  *  \tparam _OuterStrideAtCompileTime the outer stride, or Dynamic if you want to specify it at runtime.
-  *  \tparam _InnerStrideAtCompileTime the inner stride, or Dynamic if you want to specify it at runtime.
-  *
-  * Here is an example:
-  * \include Map_general_stride.cpp
-  * Output: \verbinclude Map_general_stride.out
-  *
-  * Both strides can be negative, however, a negative stride of -1 cannot be specified at compiletime
-  * because of the ambiguity with Dynamic which is defined to -1 (historically, negative strides were
-  * not allowed).
-  *
-  * \sa class InnerStride, class OuterStride, \ref TopicStorageOrders
-  */
-template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime>
-class Stride
-{
-  public:
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    enum {
-      InnerStrideAtCompileTime = _InnerStrideAtCompileTime,
-      OuterStrideAtCompileTime = _OuterStrideAtCompileTime
-    };
-
-    /** Default constructor, for use when strides are fixed at compile time */
-    EIGEN_DEVICE_FUNC
-    Stride()
-      : m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime)
-    {
-      // FIXME: for Eigen 4 we should use DynamicIndex instead of Dynamic.
-      // FIXME: for Eigen 4 we should also unify this API with fix<>
-      eigen_assert(InnerStrideAtCompileTime != Dynamic && OuterStrideAtCompileTime != Dynamic);
-    }
-
-    /** Constructor allowing to pass the strides at runtime */
-    EIGEN_DEVICE_FUNC
-    Stride(Index outerStride, Index innerStride)
-      : m_outer(outerStride), m_inner(innerStride)
-    {
-    }
-
-    /** Copy constructor */
-    EIGEN_DEVICE_FUNC
-    Stride(const Stride& other)
-      : m_outer(other.outer()), m_inner(other.inner())
-    {}
-
-    /** \returns the outer stride */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outer() const { return m_outer.value(); }
-    /** \returns the inner stride */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index inner() const { return m_inner.value(); }
-
-  protected:
-    internal::variable_if_dynamic<Index, OuterStrideAtCompileTime> m_outer;
-    internal::variable_if_dynamic<Index, InnerStrideAtCompileTime> m_inner;
-};
-
-/** \brief Convenience specialization of Stride to specify only an inner stride
-  * See class Map for some examples */
-template<int Value>
-class InnerStride : public Stride<0, Value>
-{
-    typedef Stride<0, Value> Base;
-  public:
-    EIGEN_DEVICE_FUNC InnerStride() : Base() {}
-    EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {} // FIXME making this explicit could break valid code
-};
-
-/** \brief Convenience specialization of Stride to specify only an outer stride
-  * See class Map for some examples */
-template<int Value>
-class OuterStride : public Stride<Value, 0>
-{
-    typedef Stride<Value, 0> Base;
-  public:
-    EIGEN_DEVICE_FUNC OuterStride() : Base() {}
-    EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} // FIXME making this explicit could break valid code
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_STRIDE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Swap.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Swap.h
deleted file mode 100644
index 180a4e5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Swap.h
+++ /dev/null
@@ -1,68 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SWAP_H
-#define EIGEN_SWAP_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// Overload default assignPacket behavior for swapping them
-template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
-class generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, Specialized>
- : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn> Base;
-  using Base::m_dst;
-  using Base::m_src;
-  using Base::m_functor;
-  
-public:
-  typedef typename Base::Scalar Scalar;
-  typedef typename Base::DstXprType DstXprType;
-  typedef swap_assign_op<Scalar> Functor;
-  
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  generic_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {}
-  
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
-  {
-    PacketType tmp = m_src.template packet<LoadMode,PacketType>(row,col);
-    const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(row,col, m_dst.template packet<StoreMode,PacketType>(row,col));
-    m_dst.template writePacket<StoreMode>(row,col,tmp);
-  }
-  
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE void assignPacket(Index index)
-  {
-    PacketType tmp = m_src.template packet<LoadMode,PacketType>(index);
-    const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(index, m_dst.template packet<StoreMode,PacketType>(index));
-    m_dst.template writePacket<StoreMode>(index,tmp);
-  }
-  
-  // TODO find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I mean no CRTP (Gael)
-  template<int StoreMode, int LoadMode, typename PacketType>
-  EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
-  {
-    Index row = Base::rowIndexByOuterInner(outer, inner); 
-    Index col = Base::colIndexByOuterInner(outer, inner);
-    assignPacket<StoreMode,LoadMode,PacketType>(row, col);
-  }
-};
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SWAP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Transpose.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Transpose.h
deleted file mode 100644
index 2bc658f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Transpose.h
+++ /dev/null
@@ -1,464 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSPOSE_H
-#define EIGEN_TRANSPOSE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename MatrixType>
-struct traits<Transpose<MatrixType> > : public traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedPlain;
-  enum {
-    RowsAtCompileTime = MatrixType::ColsAtCompileTime,
-    ColsAtCompileTime = MatrixType::RowsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags0 = traits<MatrixTypeNestedPlain>::Flags & ~(LvalueBit | NestByRefBit),
-    Flags1 = Flags0 | FlagsLvalueBit,
-    Flags = Flags1 ^ RowMajorBit,
-    InnerStrideAtCompileTime = inner_stride_at_compile_time<MatrixType>::ret,
-    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
-  };
-};
-}
-
-template<typename MatrixType, typename StorageKind> class TransposeImpl;
-
-/** \class Transpose
-  * \ingroup Core_Module
-  *
-  * \brief Expression of the transpose of a matrix
-  *
-  * \tparam MatrixType the type of the object of which we are taking the transpose
-  *
-  * This class represents an expression of the transpose of a matrix.
-  * It is the return type of MatrixBase::transpose() and MatrixBase::adjoint()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::transpose(), MatrixBase::adjoint()
-  */
-template<typename MatrixType> class Transpose
-  : public TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>
-{
-  public:
-
-    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-
-    typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
-    EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
-    typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-    EIGEN_DEVICE_FUNC
-    explicit EIGEN_STRONG_INLINE Transpose(MatrixType& matrix) : m_matrix(matrix) {}
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<MatrixTypeNested>::type&
-    nestedExpression() const { return m_matrix; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    typename internal::remove_reference<MatrixTypeNested>::type&
-    nestedExpression() { return m_matrix; }
-
-    /** \internal */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void resize(Index nrows, Index ncols) {
-      m_matrix.resize(ncols,nrows);
-    }
-
-  protected:
-    typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
-};
-
-namespace internal {
-
-template<typename MatrixType, bool HasDirectAccess = has_direct_access<MatrixType>::ret>
-struct TransposeImpl_base
-{
-  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
-};
-
-template<typename MatrixType>
-struct TransposeImpl_base<MatrixType, false>
-{
-  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
-};
-
-} // end namespace internal
-
-// Generic API dispatcher
-template<typename XprType, typename StorageKind>
-class TransposeImpl
-  : public internal::generic_xpr_base<Transpose<XprType> >::type
-{
-public:
-  typedef typename internal::generic_xpr_base<Transpose<XprType> >::type Base;
-};
-
-template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
-  : public internal::TransposeImpl_base<MatrixType>::type
-{
-  public:
-
-    typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
-    using Base::coeffRef;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Index innerStride() const { return derived().nestedExpression().innerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Index outerStride() const { return derived().nestedExpression().outerStride(); }
-
-    typedef typename internal::conditional<
-                       internal::is_lvalue<MatrixType>::value,
-                       Scalar,
-                       const Scalar
-                     >::type ScalarWithConstIfNotLvalue;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Scalar* data() const { return derived().nestedExpression().data(); }
-
-    // FIXME: shall we keep the const version of coeffRef?
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Scalar& coeffRef(Index rowId, Index colId) const
-    {
-      return derived().nestedExpression().coeffRef(colId, rowId);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Scalar& coeffRef(Index index) const
-    {
-      return derived().nestedExpression().coeffRef(index);
-    }
-  protected:
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TransposeImpl)
-};
-
-/** \returns an expression of the transpose of *this.
-  *
-  * Example: \include MatrixBase_transpose.cpp
-  * Output: \verbinclude MatrixBase_transpose.out
-  *
-  * \warning If you want to replace a matrix by its own transpose, do \b NOT do this:
-  * \code
-  * m = m.transpose(); // bug!!! caused by aliasing effect
-  * \endcode
-  * Instead, use the transposeInPlace() method:
-  * \code
-  * m.transposeInPlace();
-  * \endcode
-  * which gives Eigen good opportunities for optimization, or alternatively you can also do:
-  * \code
-  * m = m.transpose().eval();
-  * \endcode
-  *
-  * \sa transposeInPlace(), adjoint() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-Transpose<Derived>
-DenseBase<Derived>::transpose()
-{
-  return TransposeReturnType(derived());
-}
-
-/** This is the const version of transpose().
-  *
-  * Make sure you read the warning for transpose() !
-  *
-  * \sa transposeInPlace(), adjoint() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename DenseBase<Derived>::ConstTransposeReturnType
-DenseBase<Derived>::transpose() const
-{
-  return ConstTransposeReturnType(derived());
-}
-
-/** \returns an expression of the adjoint (i.e. conjugate transpose) of *this.
-  *
-  * Example: \include MatrixBase_adjoint.cpp
-  * Output: \verbinclude MatrixBase_adjoint.out
-  *
-  * \warning If you want to replace a matrix by its own adjoint, do \b NOT do this:
-  * \code
-  * m = m.adjoint(); // bug!!! caused by aliasing effect
-  * \endcode
-  * Instead, use the adjointInPlace() method:
-  * \code
-  * m.adjointInPlace();
-  * \endcode
-  * which gives Eigen good opportunities for optimization, or alternatively you can also do:
-  * \code
-  * m = m.adjoint().eval();
-  * \endcode
-  *
-  * \sa adjointInPlace(), transpose(), conjugate(), class Transpose, class internal::scalar_conjugate_op */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::AdjointReturnType
-MatrixBase<Derived>::adjoint() const
-{
-  return AdjointReturnType(this->transpose());
-}
-
-/***************************************************************************
-* "in place" transpose implementation
-***************************************************************************/
-
-namespace internal {
-
-template<typename MatrixType,
-  bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic,
-  bool MatchPacketSize =
-        (int(MatrixType::RowsAtCompileTime) == int(internal::packet_traits<typename MatrixType::Scalar>::size))
-    &&  (internal::evaluator<MatrixType>::Flags&PacketAccessBit) >
-struct inplace_transpose_selector;
-
-template<typename MatrixType>
-struct inplace_transpose_selector<MatrixType,true,false> { // square matrix
-  static void run(MatrixType& m) {
-    m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose().template triangularView<StrictlyUpper>());
-  }
-};
-
-template<typename MatrixType>
-struct inplace_transpose_selector<MatrixType,true,true> { // PacketSize x PacketSize
-  static void run(MatrixType& m) {
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
-    const Index PacketSize = internal::packet_traits<Scalar>::size;
-    const Index Alignment = internal::evaluator<MatrixType>::Alignment;
-    PacketBlock<Packet> A;
-    for (Index i=0; i<PacketSize; ++i)
-      A.packet[i] = m.template packetByOuterInner<Alignment>(i,0);
-    internal::ptranspose(A);
-    for (Index i=0; i<PacketSize; ++i)
-      m.template writePacket<Alignment>(m.rowIndexByOuterInner(i,0), m.colIndexByOuterInner(i,0), A.packet[i]);
-  }
-};
-
-
-template <typename MatrixType, Index Alignment>
-void BlockedInPlaceTranspose(MatrixType& m) {
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
-  const Index PacketSize = internal::packet_traits<Scalar>::size;
-  eigen_assert(m.rows() == m.cols());
-  int row_start = 0;
-  for (; row_start + PacketSize <= m.rows(); row_start += PacketSize) {
-    for (int col_start = row_start; col_start + PacketSize <= m.cols(); col_start += PacketSize) {
-      PacketBlock<Packet> A;
-      if (row_start == col_start) {
-        for (Index i=0; i<PacketSize; ++i)
-          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i,col_start);
-        internal::ptranspose(A);
-        for (Index i=0; i<PacketSize; ++i)
-          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start), m.colIndexByOuterInner(row_start + i,col_start), A.packet[i]);
-      } else {
-        PacketBlock<Packet> B;
-        for (Index i=0; i<PacketSize; ++i) {
-          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i,col_start);
-          B.packet[i] = m.template packetByOuterInner<Alignment>(col_start + i, row_start);
-        }
-        internal::ptranspose(A);
-        internal::ptranspose(B);
-        for (Index i=0; i<PacketSize; ++i) {
-          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start), m.colIndexByOuterInner(row_start + i,col_start), B.packet[i]);
-          m.template writePacket<Alignment>(m.rowIndexByOuterInner(col_start + i, row_start), m.colIndexByOuterInner(col_start + i,row_start), A.packet[i]);
-        }
-      }
-    }
-  }
-  for (Index row = row_start; row < m.rows(); ++row) {
-    m.matrix().row(row).head(row).swap(
-        m.matrix().col(row).head(row).transpose());
-  }
-}
-
-template<typename MatrixType,bool MatchPacketSize>
-struct inplace_transpose_selector<MatrixType,false,MatchPacketSize> { // non square or dynamic matrix
-  static void run(MatrixType& m) {
-    typedef typename MatrixType::Scalar Scalar;
-    if (m.rows() == m.cols()) {
-      const Index PacketSize = internal::packet_traits<Scalar>::size;
-      if (!NumTraits<Scalar>::IsComplex && m.rows() >= PacketSize) {
-        if ((m.rows() % PacketSize) == 0)
-          BlockedInPlaceTranspose<MatrixType,internal::evaluator<MatrixType>::Alignment>(m);
-        else
-          BlockedInPlaceTranspose<MatrixType,Unaligned>(m);
-      }
-      else {
-        m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose().template triangularView<StrictlyUpper>());
-      }
-    } else {
-      m = m.transpose().eval();
-    }
-  }
-};
-
-
-} // end namespace internal
-
-/** This is the "in place" version of transpose(): it replaces \c *this by its own transpose.
-  * Thus, doing
-  * \code
-  * m.transposeInPlace();
-  * \endcode
-  * has the same effect on m as doing
-  * \code
-  * m = m.transpose().eval();
-  * \endcode
-  * and is faster and also safer because in the latter line of code, forgetting the eval() results
-  * in a bug caused by \ref TopicAliasing "aliasing".
-  *
-  * Notice however that this method is only useful if you want to replace a matrix by its own transpose.
-  * If you just need the transpose of a matrix, use transpose().
-  *
-  * \note if the matrix is not square, then \c *this must be a resizable matrix.
-  * This excludes (non-square) fixed-size matrices, block-expressions and maps.
-  *
-  * \sa transpose(), adjoint(), adjointInPlace() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::transposeInPlace()
-{
-  eigen_assert((rows() == cols() || (RowsAtCompileTime == Dynamic && ColsAtCompileTime == Dynamic))
-               && "transposeInPlace() called on a non-square non-resizable matrix");
-  internal::inplace_transpose_selector<Derived>::run(derived());
-}
-
-/***************************************************************************
-* "in place" adjoint implementation
-***************************************************************************/
-
-/** This is the "in place" version of adjoint(): it replaces \c *this by its own transpose.
-  * Thus, doing
-  * \code
-  * m.adjointInPlace();
-  * \endcode
-  * has the same effect on m as doing
-  * \code
-  * m = m.adjoint().eval();
-  * \endcode
-  * and is faster and also safer because in the latter line of code, forgetting the eval() results
-  * in a bug caused by aliasing.
-  *
-  * Notice however that this method is only useful if you want to replace a matrix by its own adjoint.
-  * If you just need the adjoint of a matrix, use adjoint().
-  *
-  * \note if the matrix is not square, then \c *this must be a resizable matrix.
-  * This excludes (non-square) fixed-size matrices, block-expressions and maps.
-  *
-  * \sa transpose(), adjoint(), transposeInPlace() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline void MatrixBase<Derived>::adjointInPlace()
-{
-  derived() = adjoint().eval();
-}
-
-#ifndef EIGEN_NO_DEBUG
-
-// The following is to detect aliasing problems in most common cases.
-
-namespace internal {
-
-template<bool DestIsTransposed, typename OtherDerived>
-struct check_transpose_aliasing_compile_time_selector
-{
-  enum { ret = bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed };
-};
-
-template<bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
-struct check_transpose_aliasing_compile_time_selector<DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> >
-{
-  enum { ret =    bool(blas_traits<DerivedA>::IsTransposed) != DestIsTransposed
-               || bool(blas_traits<DerivedB>::IsTransposed) != DestIsTransposed
-  };
-};
-
-template<typename Scalar, bool DestIsTransposed, typename OtherDerived>
-struct check_transpose_aliasing_run_time_selector
-{
-  static bool run(const Scalar* dest, const OtherDerived& src)
-  {
-    return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src));
-  }
-};
-
-template<typename Scalar, bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
-struct check_transpose_aliasing_run_time_selector<Scalar,DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> >
-{
-  static bool run(const Scalar* dest, const CwiseBinaryOp<BinOp,DerivedA,DerivedB>& src)
-  {
-    return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.lhs())))
-        || ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.rhs())));
-  }
-};
-
-// the following selector, checkTransposeAliasing_impl, based on MightHaveTransposeAliasing,
-// is because when the condition controlling the assert is known at compile time, ICC emits a warning.
-// This is actually a good warning: in expressions that don't have any transposing, the condition is
-// known at compile time to be false, and using that, we can avoid generating the code of the assert again
-// and again for all these expressions that don't need it.
-
-template<typename Derived, typename OtherDerived,
-         bool MightHaveTransposeAliasing
-                 = check_transpose_aliasing_compile_time_selector
-                     <blas_traits<Derived>::IsTransposed,OtherDerived>::ret
-        >
-struct checkTransposeAliasing_impl
-{
-    static void run(const Derived& dst, const OtherDerived& other)
-    {
-        eigen_assert((!check_transpose_aliasing_run_time_selector
-                      <typename Derived::Scalar,blas_traits<Derived>::IsTransposed,OtherDerived>
-                      ::run(extract_data(dst), other))
-          && "aliasing detected during transposition, use transposeInPlace() "
-             "or evaluate the rhs into a temporary using .eval()");
-
-    }
-};
-
-template<typename Derived, typename OtherDerived>
-struct checkTransposeAliasing_impl<Derived, OtherDerived, false>
-{
-    static void run(const Derived&, const OtherDerived&)
-    {
-    }
-};
-
-template<typename Dst, typename Src>
-void check_for_aliasing(const Dst &dst, const Src &src)
-{
-  if((!Dst::IsVectorAtCompileTime) && dst.rows()>1 && dst.cols()>1)
-    internal::checkTransposeAliasing_impl<Dst, Src>::run(dst, src);
-}
-
-} // end namespace internal
-
-#endif // EIGEN_NO_DEBUG
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSPOSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Transpositions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Transpositions.h
deleted file mode 100644
index 38a7b01..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Transpositions.h
+++ /dev/null
@@ -1,386 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSPOSITIONS_H
-#define EIGEN_TRANSPOSITIONS_H
-
-namespace Eigen {
-
-template<typename Derived>
-class TranspositionsBase
-{
-    typedef internal::traits<Derived> Traits;
-
-  public:
-
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    EIGEN_DEVICE_FUNC
-    Derived& derived() { return *static_cast<Derived*>(this); }
-    EIGEN_DEVICE_FUNC
-    const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    Derived& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      indices() = other.indices();
-      return derived();
-    }
-
-    /** \returns the number of transpositions */
-    EIGEN_DEVICE_FUNC
-    Index size() const { return indices().size(); }
-    /** \returns the number of rows of the equivalent permutation matrix */
-    EIGEN_DEVICE_FUNC
-    Index rows() const { return indices().size(); }
-    /** \returns the number of columns of the equivalent permutation matrix */
-    EIGEN_DEVICE_FUNC
-    Index cols() const { return indices().size(); }
-
-    /** Direct access to the underlying index vector */
-    EIGEN_DEVICE_FUNC
-    inline const StorageIndex& coeff(Index i) const { return indices().coeff(i); }
-    /** Direct access to the underlying index vector */
-    inline StorageIndex& coeffRef(Index i) { return indices().coeffRef(i); }
-    /** Direct access to the underlying index vector */
-    inline const StorageIndex& operator()(Index i) const { return indices()(i); }
-    /** Direct access to the underlying index vector */
-    inline StorageIndex& operator()(Index i) { return indices()(i); }
-    /** Direct access to the underlying index vector */
-    inline const StorageIndex& operator[](Index i) const { return indices()(i); }
-    /** Direct access to the underlying index vector */
-    inline StorageIndex& operator[](Index i) { return indices()(i); }
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return derived().indices(); }
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return derived().indices(); }
-
-    /** Resizes to given size. */
-    inline void resize(Index newSize)
-    {
-      indices().resize(newSize);
-    }
-
-    /** Sets \c *this to represents an identity transformation */
-    void setIdentity()
-    {
-      for(StorageIndex i = 0; i < indices().size(); ++i)
-        coeffRef(i) = i;
-    }
-
-    // FIXME: do we want such methods ?
-    // might be useful when the target matrix expression is complex, e.g.:
-    // object.matrix().block(..,..,..,..) = trans * object.matrix().block(..,..,..,..);
-    /*
-    template<typename MatrixType>
-    void applyForwardToRows(MatrixType& mat) const
-    {
-      for(Index k=0 ; k<size() ; ++k)
-        if(m_indices(k)!=k)
-          mat.row(k).swap(mat.row(m_indices(k)));
-    }
-
-    template<typename MatrixType>
-    void applyBackwardToRows(MatrixType& mat) const
-    {
-      for(Index k=size()-1 ; k>=0 ; --k)
-        if(m_indices(k)!=k)
-          mat.row(k).swap(mat.row(m_indices(k)));
-    }
-    */
-
-    /** \returns the inverse transformation */
-    inline Transpose<TranspositionsBase> inverse() const
-    { return Transpose<TranspositionsBase>(derived()); }
-
-    /** \returns the tranpose transformation */
-    inline Transpose<TranspositionsBase> transpose() const
-    { return Transpose<TranspositionsBase>(derived()); }
-
-  protected:
-};
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
- : traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
-{
-  typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
-  typedef TranspositionsStorage StorageKind;
-};
-}
-
-/** \class Transpositions
-  * \ingroup Core_Module
-  *
-  * \brief Represents a sequence of transpositions (row/column interchange)
-  *
-  * \tparam SizeAtCompileTime the number of transpositions, or Dynamic
-  * \tparam MaxSizeAtCompileTime the maximum number of transpositions, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
-  *
-  * This class represents a permutation transformation as a sequence of \em n transpositions
-  * \f$[T_{n-1} \ldots T_{i} \ldots T_{0}]\f$. It is internally stored as a vector of integers \c indices.
-  * Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges
-  * the rows \c i and \c indices[i] of the matrix \c M.
-  * A transposition applied on the right (e.g., \f$ M T_{i}\f$) yields a column interchange.
-  *
-  * Compared to the class PermutationMatrix, such a sequence of transpositions is what is
-  * computed during a decomposition with pivoting, and it is faster when applying the permutation in-place.
-  *
-  * To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example:
-  * \code
-  * Transpositions tr;
-  * MatrixXf mat;
-  * mat = tr * mat;
-  * \endcode
-  * In this example, we detect that the matrix appears on both side, and so the transpositions
-  * are applied in-place without any temporary or extra copy.
-  *
-  * \sa class PermutationMatrix
-  */
-
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
-class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
-{
-    typedef internal::traits<Transpositions> Traits;
-  public:
-
-    typedef TranspositionsBase<Transpositions> Base;
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-
-    inline Transpositions() {}
-
-    /** Copy constructor. */
-    template<typename OtherDerived>
-    inline Transpositions(const TranspositionsBase<OtherDerived>& other)
-      : m_indices(other.indices()) {}
-
-    /** Generic constructor from expression of the transposition indices. */
-    template<typename Other>
-    explicit inline Transpositions(const MatrixBase<Other>& indices) : m_indices(indices)
-    {}
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    Transpositions& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      return Base::operator=(other);
-    }
-
-    /** Constructs an uninitialized permutation matrix of given size.
-      */
-    inline Transpositions(Index size) : m_indices(size)
-    {}
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return m_indices; }
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-
-namespace internal {
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
-struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,_PacketAccess> >
- : traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
-{
-  typedef Map<const Matrix<_StorageIndex,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType;
-  typedef _StorageIndex StorageIndex;
-  typedef TranspositionsStorage StorageKind;
-};
-}
-
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int PacketAccess>
-class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess>
- : public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess> >
-{
-    typedef internal::traits<Map> Traits;
-  public:
-
-    typedef TranspositionsBase<Map> Base;
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-
-    explicit inline Map(const StorageIndex* indicesPtr)
-      : m_indices(indicesPtr)
-    {}
-
-    inline Map(const StorageIndex* indicesPtr, Index size)
-      : m_indices(indicesPtr,size)
-    {}
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    Map& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      return Base::operator=(other);
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is a special case of the templated operator=. Its purpose is to
-      * prevent a default operator= from hiding the templated operator=.
-      */
-    Map& operator=(const Map& other)
-    {
-      m_indices = other.m_indices;
-      return *this;
-    }
-    #endif
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return m_indices; }
-
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    IndicesType m_indices;
-};
-
-namespace internal {
-template<typename _IndicesType>
-struct traits<TranspositionsWrapper<_IndicesType> >
- : traits<PermutationWrapper<_IndicesType> >
-{
-  typedef TranspositionsStorage StorageKind;
-};
-}
-
-template<typename _IndicesType>
-class TranspositionsWrapper
- : public TranspositionsBase<TranspositionsWrapper<_IndicesType> >
-{
-    typedef internal::traits<TranspositionsWrapper> Traits;
-  public:
-
-    typedef TranspositionsBase<TranspositionsWrapper> Base;
-    typedef typename Traits::IndicesType IndicesType;
-    typedef typename IndicesType::Scalar StorageIndex;
-
-    explicit inline TranspositionsWrapper(IndicesType& indices)
-      : m_indices(indices)
-    {}
-
-    /** Copies the \a other transpositions into \c *this */
-    template<typename OtherDerived>
-    TranspositionsWrapper& operator=(const TranspositionsBase<OtherDerived>& other)
-    {
-      return Base::operator=(other);
-    }
-
-    /** const version of indices(). */
-    EIGEN_DEVICE_FUNC
-    const IndicesType& indices() const { return m_indices; }
-
-    /** \returns a reference to the stored array representing the transpositions. */
-    EIGEN_DEVICE_FUNC
-    IndicesType& indices() { return m_indices; }
-
-  protected:
-
-    typename IndicesType::Nested m_indices;
-};
-
-
-
-/** \returns the \a matrix with the \a transpositions applied to the columns.
-  */
-template<typename MatrixDerived, typename TranspositionsDerived>
-EIGEN_DEVICE_FUNC
-const Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
-operator*(const MatrixBase<MatrixDerived> &matrix,
-          const TranspositionsBase<TranspositionsDerived>& transpositions)
-{
-  return Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
-            (matrix.derived(), transpositions.derived());
-}
-
-/** \returns the \a matrix with the \a transpositions applied to the rows.
-  */
-template<typename TranspositionsDerived, typename MatrixDerived>
-EIGEN_DEVICE_FUNC
-const Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
-operator*(const TranspositionsBase<TranspositionsDerived> &transpositions,
-          const MatrixBase<MatrixDerived>& matrix)
-{
-  return Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
-            (transpositions.derived(), matrix.derived());
-}
-
-// Template partial specialization for transposed/inverse transpositions
-
-namespace internal {
-
-template<typename Derived>
-struct traits<Transpose<TranspositionsBase<Derived> > >
- : traits<Derived>
-{};
-
-} // end namespace internal
-
-template<typename TranspositionsDerived>
-class Transpose<TranspositionsBase<TranspositionsDerived> >
-{
-    typedef TranspositionsDerived TranspositionType;
-    typedef typename TranspositionType::IndicesType IndicesType;
-  public:
-
-    explicit Transpose(const TranspositionType& t) : m_transpositions(t) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index size() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
-
-    /** \returns the \a matrix with the inverse transpositions applied to the columns.
-      */
-    template<typename OtherDerived> friend
-    const Product<OtherDerived, Transpose, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trt)
-    {
-      return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt);
-    }
-
-    /** \returns the \a matrix with the inverse transpositions applied to the rows.
-      */
-    template<typename OtherDerived>
-    const Product<Transpose, OtherDerived, AliasFreeProduct>
-    operator*(const MatrixBase<OtherDerived>& matrix) const
-    {
-      return Product<Transpose, OtherDerived, AliasFreeProduct>(*this, matrix.derived());
-    }
-
-    EIGEN_DEVICE_FUNC
-    const TranspositionType& nestedExpression() const { return m_transpositions; }
-
-  protected:
-    const TranspositionType& m_transpositions;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSPOSITIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/TriangularMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/TriangularMatrix.h
deleted file mode 100644
index fdb8bc1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/TriangularMatrix.h
+++ /dev/null
@@ -1,1001 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULARMATRIX_H
-#define EIGEN_TRIANGULARMATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval;
-
-}
-
-/** \class TriangularBase
-  * \ingroup Core_Module
-  *
-  * \brief Base class for triangular part in a matrix
-  */
-template<typename Derived> class TriangularBase : public EigenBase<Derived>
-{
-  public:
-
-    enum {
-      Mode = internal::traits<Derived>::Mode,
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
-
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
-      /**< This is equal to the number of coefficients, i.e. the number of
-          * rows times the number of columns, or to \a Dynamic if this is not
-          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
-
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
-                                                   internal::traits<Derived>::MaxColsAtCompileTime>::ret)
-
-    };
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-    typedef typename internal::traits<Derived>::FullMatrixType DenseMatrixType;
-    typedef DenseMatrixType DenseType;
-    typedef Derived const& Nested;
-
-    EIGEN_DEVICE_FUNC
-    inline TriangularBase() { eigen_assert(!((int(Mode) & int(UnitDiag)) && (int(Mode) & int(ZeroDiag)))); }
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index outerStride() const EIGEN_NOEXCEPT { return derived().outerStride(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index innerStride() const EIGEN_NOEXCEPT { return derived().innerStride(); }
-
-    // dummy resize function
-    EIGEN_DEVICE_FUNC
-    void resize(Index rows, Index cols)
-    {
-      EIGEN_UNUSED_VARIABLE(rows);
-      EIGEN_UNUSED_VARIABLE(cols);
-      eigen_assert(rows==this->rows() && cols==this->cols());
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar coeff(Index row, Index col) const  { return derived().coeff(row,col); }
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index col) { return derived().coeffRef(row,col); }
-
-    /** \see MatrixBase::copyCoeff(row,col)
-      */
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, Other& other)
-    {
-      derived().coeffRef(row, col) = other.coeff(row, col);
-    }
-
-    EIGEN_DEVICE_FUNC
-    inline Scalar operator()(Index row, Index col) const
-    {
-      check_coordinates(row, col);
-      return coeff(row,col);
-    }
-    EIGEN_DEVICE_FUNC
-    inline Scalar& operator()(Index row, Index col)
-    {
-      check_coordinates(row, col);
-      return coeffRef(row,col);
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    EIGEN_DEVICE_FUNC
-    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    EIGEN_DEVICE_FUNC
-    inline Derived& derived() { return *static_cast<Derived*>(this); }
-    #endif // not EIGEN_PARSED_BY_DOXYGEN
-
-    template<typename DenseDerived>
-    EIGEN_DEVICE_FUNC
-    void evalTo(MatrixBase<DenseDerived> &other) const;
-    template<typename DenseDerived>
-    EIGEN_DEVICE_FUNC
-    void evalToLazy(MatrixBase<DenseDerived> &other) const;
-
-    EIGEN_DEVICE_FUNC
-    DenseMatrixType toDenseMatrix() const
-    {
-      DenseMatrixType res(rows(), cols());
-      evalToLazy(res);
-      return res;
-    }
-
-  protected:
-
-    void check_coordinates(Index row, Index col) const
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(row);
-      EIGEN_ONLY_USED_FOR_DEBUG(col);
-      eigen_assert(col>=0 && col<cols() && row>=0 && row<rows());
-      const int mode = int(Mode) & ~SelfAdjoint;
-      EIGEN_ONLY_USED_FOR_DEBUG(mode);
-      eigen_assert((mode==Upper && col>=row)
-                || (mode==Lower && col<=row)
-                || ((mode==StrictlyUpper || mode==UnitUpper) && col>row)
-                || ((mode==StrictlyLower || mode==UnitLower) && col<row));
-    }
-
-    #ifdef EIGEN_INTERNAL_DEBUGGING
-    void check_coordinates_internal(Index row, Index col) const
-    {
-      check_coordinates(row, col);
-    }
-    #else
-    void check_coordinates_internal(Index , Index ) const {}
-    #endif
-
-};
-
-/** \class TriangularView
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a triangular part in a matrix
-  *
-  * \param MatrixType the type of the object in which we are taking the triangular part
-  * \param Mode the kind of triangular matrix expression to construct. Can be #Upper,
-  *             #Lower, #UnitUpper, #UnitLower, #StrictlyUpper, or #StrictlyLower.
-  *             This is in fact a bit field; it must have either #Upper or #Lower,
-  *             and additionally it may have #UnitDiag or #ZeroDiag or neither.
-  *
-  * This class represents a triangular part of a matrix, not necessarily square. Strictly speaking, for rectangular
-  * matrices one should speak of "trapezoid" parts. This class is the return type
-  * of MatrixBase::triangularView() and SparseMatrixBase::triangularView(), and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::triangularView()
-  */
-namespace internal {
-template<typename MatrixType, unsigned int _Mode>
-struct traits<TriangularView<MatrixType, _Mode> > : traits<MatrixType>
-{
-  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef;
-  typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
-  typedef typename MatrixType::PlainObject FullMatrixType;
-  typedef MatrixType ExpressionType;
-  enum {
-    Mode = _Mode,
-    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
-    Flags = (MatrixTypeNestedCleaned::Flags & (HereditaryBits | FlagsLvalueBit) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)))
-  };
-};
-}
-
-template<typename _MatrixType, unsigned int _Mode, typename StorageKind> class TriangularViewImpl;
-
-template<typename _MatrixType, unsigned int _Mode> class TriangularView
-  : public TriangularViewImpl<_MatrixType, _Mode, typename internal::traits<_MatrixType>::StorageKind >
-{
-  public:
-
-    typedef TriangularViewImpl<_MatrixType, _Mode, typename internal::traits<_MatrixType>::StorageKind > Base;
-    typedef typename internal::traits<TriangularView>::Scalar Scalar;
-    typedef _MatrixType MatrixType;
-
-  protected:
-    typedef typename internal::traits<TriangularView>::MatrixTypeNested MatrixTypeNested;
-    typedef typename internal::traits<TriangularView>::MatrixTypeNestedNonRef MatrixTypeNestedNonRef;
-
-    typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
-    typedef TriangularView<typename internal::add_const<MatrixType>::type, _Mode> ConstTriangularView;
-
-  public:
-
-    typedef typename internal::traits<TriangularView>::StorageKind StorageKind;
-    typedef typename internal::traits<TriangularView>::MatrixTypeNestedCleaned NestedExpression;
-
-    enum {
-      Mode = _Mode,
-      Flags = internal::traits<TriangularView>::Flags,
-      TransposeMode = (Mode & Upper ? Lower : 0)
-                    | (Mode & Lower ? Upper : 0)
-                    | (Mode & (UnitDiag))
-                    | (Mode & (ZeroDiag)),
-      IsVectorAtCompileTime = false
-    };
-
-    EIGEN_DEVICE_FUNC
-    explicit inline TriangularView(MatrixType& matrix) : m_matrix(matrix)
-    {}
-
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TriangularView)
-
-    /** \copydoc EigenBase::rows() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    /** \copydoc EigenBase::cols() */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-    /** \returns a const reference to the nested expression */
-    EIGEN_DEVICE_FUNC
-    const NestedExpression& nestedExpression() const { return m_matrix; }
-
-    /** \returns a reference to the nested expression */
-    EIGEN_DEVICE_FUNC
-    NestedExpression& nestedExpression() { return m_matrix; }
-
-    typedef TriangularView<const MatrixConjugateReturnType,Mode> ConjugateReturnType;
-    /** \sa MatrixBase::conjugate() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConjugateReturnType conjugate() const
-    { return ConjugateReturnType(m_matrix.conjugate()); }
-
-    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
-     *           returns \c *this otherwise.
-     */
-    template<bool Cond>
-    EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Cond,ConjugateReturnType,ConstTriangularView>::type
-    conjugateIf() const
-    {
-      typedef typename internal::conditional<Cond,ConjugateReturnType,ConstTriangularView>::type ReturnType;
-      return ReturnType(m_matrix.template conjugateIf<Cond>());
-    }
-
-    typedef TriangularView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
-    /** \sa MatrixBase::adjoint() const */
-    EIGEN_DEVICE_FUNC
-    inline const AdjointReturnType adjoint() const
-    { return AdjointReturnType(m_matrix.adjoint()); }
-
-    typedef TriangularView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
-     /** \sa MatrixBase::transpose() */
-    EIGEN_DEVICE_FUNC
-    inline TransposeReturnType transpose()
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
-      typename MatrixType::TransposeReturnType tmp(m_matrix);
-      return TransposeReturnType(tmp);
-    }
-
-    typedef TriangularView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
-    /** \sa MatrixBase::transpose() const */
-    EIGEN_DEVICE_FUNC
-    inline const ConstTransposeReturnType transpose() const
-    {
-      return ConstTransposeReturnType(m_matrix.transpose());
-    }
-
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    inline const Solve<TriangularView, Other>
-    solve(const MatrixBase<Other>& other) const
-    { return Solve<TriangularView, Other>(*this, other.derived()); }
-
-  // workaround MSVC ICE
-  #if EIGEN_COMP_MSVC
-    template<int Side, typename Other>
-    EIGEN_DEVICE_FUNC
-    inline const internal::triangular_solve_retval<Side,TriangularView, Other>
-    solve(const MatrixBase<Other>& other) const
-    { return Base::template solve<Side>(other); }
-  #else
-    using Base::solve;
-  #endif
-
-    /** \returns a selfadjoint view of the referenced triangular part which must be either \c #Upper or \c #Lower.
-      *
-      * This is a shortcut for \code this->nestedExpression().selfadjointView<(*this)::Mode>() \endcode
-      * \sa MatrixBase::selfadjointView() */
-    EIGEN_DEVICE_FUNC
-    SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView()
-    {
-      EIGEN_STATIC_ASSERT((Mode&(UnitDiag|ZeroDiag))==0,PROGRAMMING_ERROR);
-      return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix);
-    }
-
-    /** This is the const version of selfadjointView() */
-    EIGEN_DEVICE_FUNC
-    const SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView() const
-    {
-      EIGEN_STATIC_ASSERT((Mode&(UnitDiag|ZeroDiag))==0,PROGRAMMING_ERROR);
-      return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix);
-    }
-
-
-    /** \returns the determinant of the triangular matrix
-      * \sa MatrixBase::determinant() */
-    EIGEN_DEVICE_FUNC
-    Scalar determinant() const
-    {
-      if (Mode & UnitDiag)
-        return 1;
-      else if (Mode & ZeroDiag)
-        return 0;
-      else
-        return m_matrix.diagonal().prod();
-    }
-
-  protected:
-
-    MatrixTypeNested m_matrix;
-};
-
-/** \ingroup Core_Module
-  *
-  * \brief Base class for a triangular part in a \b dense matrix
-  *
-  * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be instantiated.
-  * It extends class TriangularView with additional methods which available for dense expressions only.
-  *
-  * \sa class TriangularView, MatrixBase::triangularView()
-  */
-template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_MatrixType,_Mode,Dense>
-  : public TriangularBase<TriangularView<_MatrixType, _Mode> >
-{
-  public:
-
-    typedef TriangularView<_MatrixType, _Mode> TriangularViewType;
-    typedef TriangularBase<TriangularViewType> Base;
-    typedef typename internal::traits<TriangularViewType>::Scalar Scalar;
-
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::PlainObject DenseMatrixType;
-    typedef DenseMatrixType PlainObject;
-
-  public:
-    using Base::evalToLazy;
-    using Base::derived;
-
-    typedef typename internal::traits<TriangularViewType>::StorageKind StorageKind;
-
-    enum {
-      Mode = _Mode,
-      Flags = internal::traits<TriangularViewType>::Flags
-    };
-
-    /** \returns the outer-stride of the underlying dense matrix
-      * \sa DenseCoeffsBase::outerStride() */
-    EIGEN_DEVICE_FUNC
-    inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
-    /** \returns the inner-stride of the underlying dense matrix
-      * \sa DenseCoeffsBase::innerStride() */
-    EIGEN_DEVICE_FUNC
-    inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
-
-    /** \sa MatrixBase::operator+=() */
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator+=(const DenseBase<Other>& other) {
-      internal::call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename Other::Scalar>());
-      return derived();
-    }
-    /** \sa MatrixBase::operator-=() */
-    template<typename Other>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator-=(const DenseBase<Other>& other) {
-      internal::call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename Other::Scalar>());
-      return derived();
-    }
-
-    /** \sa MatrixBase::operator*=() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator*=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() * other; }
-    /** \sa DenseBase::operator/=() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType&  operator/=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() / other; }
-
-    /** \sa MatrixBase::fill() */
-    EIGEN_DEVICE_FUNC
-    void fill(const Scalar& value) { setConstant(value); }
-    /** \sa MatrixBase::setConstant() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& setConstant(const Scalar& value)
-    { return *this = MatrixType::Constant(derived().rows(), derived().cols(), value); }
-    /** \sa MatrixBase::setZero() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& setZero() { return setConstant(Scalar(0)); }
-    /** \sa MatrixBase::setOnes() */
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& setOnes() { return setConstant(Scalar(1)); }
-
-    /** \sa MatrixBase::coeff()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar coeff(Index row, Index col) const
-    {
-      Base::check_coordinates_internal(row, col);
-      return derived().nestedExpression().coeff(row, col);
-    }
-
-    /** \sa MatrixBase::coeffRef()
-      * \warning the coordinates must fit into the referenced triangular part
-      */
-    EIGEN_DEVICE_FUNC
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(TriangularViewType);
-      Base::check_coordinates_internal(row, col);
-      return derived().nestedExpression().coeffRef(row, col);
-    }
-
-    /** Assigns a triangular matrix to a triangular part of a dense matrix */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& operator=(const TriangularBase<OtherDerived>& other);
-
-    /** Shortcut for\code *this = other.other.triangularView<(*this)::Mode>() \endcode */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& operator=(const MatrixBase<OtherDerived>& other);
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    EIGEN_DEVICE_FUNC
-    TriangularViewType& operator=(const TriangularViewImpl& other)
-    { return *this = other.derived().nestedExpression(); }
-
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    void lazyAssign(const TriangularBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    void lazyAssign(const MatrixBase<OtherDerived>& other);
-#endif
-
-    /** Efficient triangular matrix times vector/matrix product */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const Product<TriangularViewType,OtherDerived>
-    operator*(const MatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<TriangularViewType,OtherDerived>(derived(), rhs.derived());
-    }
-
-    /** Efficient vector/matrix times triangular matrix product */
-    template<typename OtherDerived> friend
-    EIGEN_DEVICE_FUNC
-    const Product<OtherDerived,TriangularViewType>
-    operator*(const MatrixBase<OtherDerived>& lhs, const TriangularViewImpl& rhs)
-    {
-      return Product<OtherDerived,TriangularViewType>(lhs.derived(),rhs.derived());
-    }
-
-    /** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
-      *
-      * This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if
-      * \a Side==OnTheLeft (the default), or the right-inverse-multiply  \a other * inverse(\c *this) if
-      * \a Side==OnTheRight.
-      *
-      * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
-      *
-      * The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
-      * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
-      * is an upper (resp. lower) triangular matrix.
-      *
-      * Example: \include Triangular_solve.cpp
-      * Output: \verbinclude Triangular_solve.out
-      *
-      * This function returns an expression of the inverse-multiply and can works in-place if it is assigned
-      * to the same matrix or vector \a other.
-      *
-      * For users coming from BLAS, this function (and more specifically solveInPlace()) offer
-      * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
-      *
-      * \sa TriangularView::solveInPlace()
-      */
-    template<int Side, typename Other>
-    inline const internal::triangular_solve_retval<Side,TriangularViewType, Other>
-    solve(const MatrixBase<Other>& other) const;
-
-    /** "in-place" version of TriangularView::solve() where the result is written in \a other
-      *
-      * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
-      * This function will const_cast it, so constness isn't honored here.
-      *
-      * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
-      *
-      * See TriangularView:solve() for the details.
-      */
-    template<int Side, typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    void solveInPlace(const MatrixBase<OtherDerived>& other) const;
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    void solveInPlace(const MatrixBase<OtherDerived>& other) const
-    { return solveInPlace<OnTheLeft>(other); }
-
-    /** Swaps the coefficients of the common triangular parts of two matrices */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-    void swap(TriangularBase<OtherDerived> &other)
-#else
-    void swap(TriangularBase<OtherDerived> const & other)
-#endif
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
-      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    /** Shortcut for \code (*this).swap(other.triangularView<(*this)::Mode>()) \endcode */
-    template<typename OtherDerived>
-    /** \deprecated */
-    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
-    void swap(MatrixBase<OtherDerived> const & other)
-    {
-      EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
-      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
-    }
-
-    template<typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _solve_impl(const RhsType &rhs, DstType &dst) const {
-      if(!internal::is_same_dense(dst,rhs))
-        dst = rhs;
-      this->solveInPlace(dst);
-    }
-
-    template<typename ProductType>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha, bool beta);
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TriangularViewImpl)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TriangularViewImpl)
-
-};
-
-/***************************************************************************
-* Implementation of triangular evaluation/assignment
-***************************************************************************/
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-// FIXME should we keep that possibility
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>&
-TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const MatrixBase<OtherDerived>& other)
-{
-  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-// FIXME should we keep that possibility
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const MatrixBase<OtherDerived>& other)
-{
-  internal::call_assignment_no_alias(derived(), other.template triangularView<Mode>());
-}
-
-
-
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>&
-TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const TriangularBase<OtherDerived>& other)
-{
-  eigen_assert(Mode == int(OtherDerived::Mode));
-  internal::call_assignment(derived(), other.derived());
-  return derived();
-}
-
-template<typename MatrixType, unsigned int Mode>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const TriangularBase<OtherDerived>& other)
-{
-  eigen_assert(Mode == int(OtherDerived::Mode));
-  internal::call_assignment_no_alias(derived(), other.derived());
-}
-#endif
-
-/***************************************************************************
-* Implementation of TriangularBase methods
-***************************************************************************/
-
-/** Assigns a triangular or selfadjoint matrix to a dense matrix.
-  * If the matrix is triangular, the opposite part is set to zero. */
-template<typename Derived>
-template<typename DenseDerived>
-EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
-{
-  evalToLazy(other.derived());
-}
-
-/***************************************************************************
-* Implementation of TriangularView methods
-***************************************************************************/
-
-/***************************************************************************
-* Implementation of MatrixBase methods
-***************************************************************************/
-
-/**
-  * \returns an expression of a triangular view extracted from the current matrix
-  *
-  * The parameter \a Mode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
-  * \c #Lower, \c #StrictlyLower, \c #UnitLower.
-  *
-  * Example: \include MatrixBase_triangularView.cpp
-  * Output: \verbinclude MatrixBase_triangularView.out
-  *
-  * \sa class TriangularView
-  */
-template<typename Derived>
-template<unsigned int Mode>
-EIGEN_DEVICE_FUNC
-typename MatrixBase<Derived>::template TriangularViewReturnType<Mode>::Type
-MatrixBase<Derived>::triangularView()
-{
-  return typename TriangularViewReturnType<Mode>::Type(derived());
-}
-
-/** This is the const version of MatrixBase::triangularView() */
-template<typename Derived>
-template<unsigned int Mode>
-EIGEN_DEVICE_FUNC
-typename MatrixBase<Derived>::template ConstTriangularViewReturnType<Mode>::Type
-MatrixBase<Derived>::triangularView() const
-{
-  return typename ConstTriangularViewReturnType<Mode>::Type(derived());
-}
-
-/** \returns true if *this is approximately equal to an upper triangular matrix,
-  *          within the precision given by \a prec.
-  *
-  * \sa isLowerTriangular()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isUpperTriangular(const RealScalar& prec) const
-{
-  RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
-  for(Index j = 0; j < cols(); ++j)
-  {
-    Index maxi = numext::mini(j, rows()-1);
-    for(Index i = 0; i <= maxi; ++i)
-    {
-      RealScalar absValue = numext::abs(coeff(i,j));
-      if(absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue;
-    }
-  }
-  RealScalar threshold = maxAbsOnUpperPart * prec;
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = j+1; i < rows(); ++i)
-      if(numext::abs(coeff(i, j)) > threshold) return false;
-  return true;
-}
-
-/** \returns true if *this is approximately equal to a lower triangular matrix,
-  *          within the precision given by \a prec.
-  *
-  * \sa isUpperTriangular()
-  */
-template<typename Derived>
-bool MatrixBase<Derived>::isLowerTriangular(const RealScalar& prec) const
-{
-  RealScalar maxAbsOnLowerPart = static_cast<RealScalar>(-1);
-  for(Index j = 0; j < cols(); ++j)
-    for(Index i = j; i < rows(); ++i)
-    {
-      RealScalar absValue = numext::abs(coeff(i,j));
-      if(absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue;
-    }
-  RealScalar threshold = maxAbsOnLowerPart * prec;
-  for(Index j = 1; j < cols(); ++j)
-  {
-    Index maxi = numext::mini(j, rows()-1);
-    for(Index i = 0; i < maxi; ++i)
-      if(numext::abs(coeff(i, j)) > threshold) return false;
-  }
-  return true;
-}
-
-
-/***************************************************************************
-****************************************************************************
-* Evaluators and Assignment of triangular expressions
-***************************************************************************
-***************************************************************************/
-
-namespace internal {
-
-
-// TODO currently a triangular expression has the form TriangularView<.,.>
-//      in the future triangular-ness should be defined by the expression traits
-//      such that Transpose<TriangularView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
-template<typename MatrixType, unsigned int Mode>
-struct evaluator_traits<TriangularView<MatrixType,Mode> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef typename glue_shapes<typename evaluator_traits<MatrixType>::Shape, TriangularShape>::type Shape;
-};
-
-template<typename MatrixType, unsigned int Mode>
-struct unary_evaluator<TriangularView<MatrixType,Mode>, IndexBased>
- : evaluator<typename internal::remove_all<MatrixType>::type>
-{
-  typedef TriangularView<MatrixType,Mode> XprType;
-  typedef evaluator<typename internal::remove_all<MatrixType>::type> Base;
-  EIGEN_DEVICE_FUNC
-  unary_evaluator(const XprType &xpr) : Base(xpr.nestedExpression()) {}
-};
-
-// Additional assignment kinds:
-struct Triangular2Triangular    {};
-struct Triangular2Dense         {};
-struct Dense2Triangular         {};
-
-
-template<typename Kernel, unsigned int Mode, int UnrollCount, bool ClearOpposite> struct triangular_assignment_loop;
-
-
-/** \internal Specialization of the dense assignment kernel for triangular matrices.
-  * The main difference is that the triangular, diagonal, and opposite parts are processed through three different functions.
-  * \tparam UpLo must be either Lower or Upper
-  * \tparam Mode must be either 0, UnitDiag, ZeroDiag, or SelfAdjoint
-  */
-template<int UpLo, int Mode, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
-class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
-{
-protected:
-  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
-  typedef typename Base::DstXprType DstXprType;
-  typedef typename Base::SrcXprType SrcXprType;
-  using Base::m_dst;
-  using Base::m_src;
-  using Base::m_functor;
-public:
-
-  typedef typename Base::DstEvaluatorType DstEvaluatorType;
-  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
-  typedef typename Base::Scalar Scalar;
-  typedef typename Base::AssignmentTraits AssignmentTraits;
-
-
-  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
-    : Base(dst, src, func, dstExpr)
-  {}
-
-#ifdef EIGEN_INTERNAL_DEBUGGING
-  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
-  {
-    eigen_internal_assert(row!=col);
-    Base::assignCoeff(row,col);
-  }
-#else
-  using Base::assignCoeff;
-#endif
-
-  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
-  {
-         if(Mode==UnitDiag && SetOpposite) m_functor.assignCoeff(m_dst.coeffRef(id,id), Scalar(1));
-    else if(Mode==ZeroDiag && SetOpposite) m_functor.assignCoeff(m_dst.coeffRef(id,id), Scalar(0));
-    else if(Mode==0)                       Base::assignCoeff(id,id);
-  }
-
-  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index row, Index col)
-  {
-    eigen_internal_assert(row!=col);
-    if(SetOpposite)
-      m_functor.assignCoeff(m_dst.coeffRef(row,col), Scalar(0));
-  }
-};
-
-template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType, typename Functor>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
-{
-  typedef evaluator<DstXprType> DstEvaluatorType;
-  typedef evaluator<SrcXprType> SrcEvaluatorType;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  Index dstRows = src.rows();
-  Index dstCols = src.cols();
-  if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-    dst.resize(dstRows, dstCols);
-  DstEvaluatorType dstEvaluator(dst);
-
-  typedef triangular_dense_assignment_kernel< Mode&(Lower|Upper),Mode&(UnitDiag|ZeroDiag|SelfAdjoint),SetOpposite,
-                                              DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
-  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
-
-  enum {
-      unroll = DstXprType::SizeAtCompileTime != Dynamic
-            && SrcEvaluatorType::CoeffReadCost < HugeCost
-            && DstXprType::SizeAtCompileTime * (int(DstEvaluatorType::CoeffReadCost) + int(SrcEvaluatorType::CoeffReadCost)) / 2 <= EIGEN_UNROLLING_LIMIT
-    };
-
-  triangular_assignment_loop<Kernel, Mode, unroll ? int(DstXprType::SizeAtCompileTime) : Dynamic, SetOpposite>::run(kernel);
-}
-
-template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src)
-{
-  call_triangular_assignment_loop<Mode,SetOpposite>(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
-}
-
-template<> struct AssignmentKind<TriangularShape,TriangularShape> { typedef Triangular2Triangular Kind; };
-template<> struct AssignmentKind<DenseShape,TriangularShape>      { typedef Triangular2Dense      Kind; };
-template<> struct AssignmentKind<TriangularShape,DenseShape>      { typedef Dense2Triangular      Kind; };
-
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Triangular>
-{
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    eigen_assert(int(DstXprType::Mode) == int(SrcXprType::Mode));
-
-    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
-  }
-};
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Dense>
-{
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    call_triangular_assignment_loop<SrcXprType::Mode, (int(SrcXprType::Mode) & int(SelfAdjoint)) == 0>(dst, src, func);
-  }
-};
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Dense2Triangular>
-{
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
-  }
-};
-
-
-template<typename Kernel, unsigned int Mode, int UnrollCount, bool SetOpposite>
-struct triangular_assignment_loop
-{
-  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
-  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
-  typedef typename DstEvaluatorType::XprType DstXprType;
-
-  enum {
-    col = (UnrollCount-1) / DstXprType::RowsAtCompileTime,
-    row = (UnrollCount-1) % DstXprType::RowsAtCompileTime
-  };
-
-  typedef typename Kernel::Scalar Scalar;
-
-  EIGEN_DEVICE_FUNC
-  static inline void run(Kernel &kernel)
-  {
-    triangular_assignment_loop<Kernel, Mode, UnrollCount-1, SetOpposite>::run(kernel);
-
-    if(row==col)
-      kernel.assignDiagonalCoeff(row);
-    else if( ((Mode&Lower) && row>col) || ((Mode&Upper) && row<col) )
-      kernel.assignCoeff(row,col);
-    else if(SetOpposite)
-      kernel.assignOppositeCoeff(row,col);
-  }
-};
-
-// prevent buggy user code from causing an infinite recursion
-template<typename Kernel, unsigned int Mode, bool SetOpposite>
-struct triangular_assignment_loop<Kernel, Mode, 0, SetOpposite>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(Kernel &) {}
-};
-
-
-
-// TODO: experiment with a recursive assignment procedure splitting the current
-//       triangular part into one rectangular and two triangular parts.
-
-
-template<typename Kernel, unsigned int Mode, bool SetOpposite>
-struct triangular_assignment_loop<Kernel, Mode, Dynamic, SetOpposite>
-{
-  typedef typename Kernel::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  static inline void run(Kernel &kernel)
-  {
-    for(Index j = 0; j < kernel.cols(); ++j)
-    {
-      Index maxi = numext::mini(j, kernel.rows());
-      Index i = 0;
-      if (((Mode&Lower) && SetOpposite) || (Mode&Upper))
-      {
-        for(; i < maxi; ++i)
-          if(Mode&Upper) kernel.assignCoeff(i, j);
-          else           kernel.assignOppositeCoeff(i, j);
-      }
-      else
-        i = maxi;
-
-      if(i<kernel.rows()) // then i==j
-        kernel.assignDiagonalCoeff(i++);
-
-      if (((Mode&Upper) && SetOpposite) || (Mode&Lower))
-      {
-        for(; i < kernel.rows(); ++i)
-          if(Mode&Lower) kernel.assignCoeff(i, j);
-          else           kernel.assignOppositeCoeff(i, j);
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-/** Assigns a triangular or selfadjoint matrix to a dense matrix.
-  * If the matrix is triangular, the opposite part is set to zero. */
-template<typename Derived>
-template<typename DenseDerived>
-EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalToLazy(MatrixBase<DenseDerived> &other) const
-{
-  other.derived().resize(this->rows(), this->cols());
-  internal::call_triangular_assignment_loop<Derived::Mode, (int(Derived::Mode) & int(SelfAdjoint)) == 0 /* SetOpposite */>(other.derived(), derived().nestedExpression());
-}
-
-namespace internal {
-
-// Triangular = Product
-template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
-{
-  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename SrcXprType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst._assignProduct(src, Scalar(1), false);
-  }
-};
-
-// Triangular += Product
-template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
-{
-  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,typename SrcXprType::Scalar> &)
-  {
-    dst._assignProduct(src, Scalar(1), true);
-  }
-};
-
-// Triangular -= Product
-template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
-struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
-{
-  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,typename SrcXprType::Scalar> &)
-  {
-    dst._assignProduct(src, Scalar(-1), true);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULARMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/VectorBlock.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/VectorBlock.h
deleted file mode 100644
index 71c5b95..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/VectorBlock.h
+++ /dev/null
@@ -1,96 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_VECTORBLOCK_H
-#define EIGEN_VECTORBLOCK_H
-
-namespace Eigen { 
-
-namespace internal {
-template<typename VectorType, int Size>
-struct traits<VectorBlock<VectorType, Size> >
-  : public traits<Block<VectorType,
-                     traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
-                     traits<VectorType>::Flags & RowMajorBit ? Size : 1> >
-{
-};
-}
-
-/** \class VectorBlock
-  * \ingroup Core_Module
-  *
-  * \brief Expression of a fixed-size or dynamic-size sub-vector
-  *
-  * \tparam VectorType the type of the object in which we are taking a sub-vector
-  * \tparam Size size of the sub-vector we are taking at compile time (optional)
-  *
-  * This class represents an expression of either a fixed-size or dynamic-size sub-vector.
-  * It is the return type of DenseBase::segment(Index,Index) and DenseBase::segment<int>(Index) and
-  * most of the time this is the only way it is used.
-  *
-  * However, if you want to directly manipulate sub-vector expressions,
-  * for instance if you want to write a function returning such an expression, you
-  * will need to use this class.
-  *
-  * Here is an example illustrating the dynamic case:
-  * \include class_VectorBlock.cpp
-  * Output: \verbinclude class_VectorBlock.out
-  *
-  * \note Even though this expression has dynamic size, in the case where \a VectorType
-  * has fixed size, this expression inherits a fixed maximal size which means that evaluating
-  * it does not cause a dynamic memory allocation.
-  *
-  * Here is an example illustrating the fixed-size case:
-  * \include class_FixedVectorBlock.cpp
-  * Output: \verbinclude class_FixedVectorBlock.out
-  *
-  * \sa class Block, DenseBase::segment(Index,Index,Index,Index), DenseBase::segment(Index,Index)
-  */
-template<typename VectorType, int Size> class VectorBlock
-  : public Block<VectorType,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1>
-{
-    typedef Block<VectorType,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
-                     internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1> Base;
-    enum {
-      IsColVector = !(internal::traits<VectorType>::Flags & RowMajorBit)
-    };
-  public:
-    EIGEN_DENSE_PUBLIC_INTERFACE(VectorBlock)
-
-    using Base::operator=;
-
-    /** Dynamic-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    VectorBlock(VectorType& vector, Index start, Index size)
-      : Base(vector,
-             IsColVector ? start : 0, IsColVector ? 0 : start,
-             IsColVector ? size  : 1, IsColVector ? 1 : size)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock);
-    }
-
-    /** Fixed-size constructor
-      */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    VectorBlock(VectorType& vector, Index start)
-      : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock);
-    }
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_VECTORBLOCK_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/VectorwiseOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/VectorwiseOp.h
deleted file mode 100644
index 870f4f1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/VectorwiseOp.h
+++ /dev/null
@@ -1,784 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARTIAL_REDUX_H
-#define EIGEN_PARTIAL_REDUX_H
-
-namespace Eigen {
-
-/** \class PartialReduxExpr
-  * \ingroup Core_Module
-  *
-  * \brief Generic expression of a partially reduxed matrix
-  *
-  * \tparam MatrixType the type of the matrix we are applying the redux operation
-  * \tparam MemberOp type of the member functor
-  * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal)
-  *
-  * This class represents an expression of a partial redux operator of a matrix.
-  * It is the return type of some VectorwiseOp functions,
-  * and most of the time this is the only way it is used.
-  *
-  * \sa class VectorwiseOp
-  */
-
-template< typename MatrixType, typename MemberOp, int Direction>
-class PartialReduxExpr;
-
-namespace internal {
-template<typename MatrixType, typename MemberOp, int Direction>
-struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
- : traits<MatrixType>
-{
-  typedef typename MemberOp::result_type Scalar;
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename traits<MatrixType>::XprKind XprKind;
-  typedef typename MatrixType::Scalar InputScalar;
-  enum {
-    RowsAtCompileTime = Direction==Vertical   ? 1 : MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = Direction==Vertical   ? 1 : MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
-    Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0,
-    TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime :  MatrixType::ColsAtCompileTime
-  };
-};
-}
-
-template< typename MatrixType, typename MemberOp, int Direction>
-class PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type,
-                         internal::no_assignment_operator
-{
-  public:
-
-    typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr)
-
-    EIGEN_DEVICE_FUNC
-    explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
-      : m_matrix(mat), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return (Direction==Vertical   ? 1 : m_matrix.rows()); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return (Direction==Horizontal ? 1 : m_matrix.cols()); }
-
-    EIGEN_DEVICE_FUNC
-    typename MatrixType::Nested nestedExpression() const { return m_matrix; }
-
-    EIGEN_DEVICE_FUNC
-    const MemberOp& functor() const { return m_functor; }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-    const MemberOp m_functor;
-};
-
-template<typename A,typename B> struct partial_redux_dummy_func;
-
-#define EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,VECTORIZABLE,BINARYOP)                \
-  template <typename ResultType,typename Scalar>                                                            \
-  struct member_##MEMBER {                                                                  \
-    EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER)                                                \
-    typedef ResultType result_type;                                                         \
-    typedef BINARYOP<Scalar,Scalar> BinaryOp;   \
-    template<int Size> struct Cost { enum { value = COST }; };             \
-    enum { Vectorizable = VECTORIZABLE };                                                   \
-    template<typename XprType>                                                              \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                   \
-    ResultType operator()(const XprType& mat) const                                         \
-    { return mat.MEMBER(); }                                                                \
-    BinaryOp binaryFunc() const { return BinaryOp(); }                                      \
-  }
-
-#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \
-  EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,0,partial_redux_dummy_func)
-
-namespace internal {
-
-EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost );
-EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
-EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
-
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_sum_op);
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_min_op);
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_max_op);
-EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost, 1, internal::scalar_product_op);
-
-template <int p, typename ResultType,typename Scalar>
-struct member_lpnorm {
-  typedef ResultType result_type;
-  enum { Vectorizable = 0 };
-  template<int Size> struct Cost
-  { enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; };
-  EIGEN_DEVICE_FUNC member_lpnorm() {}
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const
-  { return mat.template lpNorm<p>(); }
-};
-
-template <typename BinaryOpT, typename Scalar>
-struct member_redux {
-  typedef BinaryOpT BinaryOp;
-  typedef typename result_of<
-                     BinaryOp(const Scalar&,const Scalar&)
-                   >::type  result_type;
-
-  enum { Vectorizable = functor_traits<BinaryOp>::PacketAccess };
-  template<int Size> struct Cost { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; };
-  EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {}
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const
-  { return mat.redux(m_functor); }
-  const BinaryOp& binaryFunc() const { return m_functor; }
-  const BinaryOp m_functor;
-};
-}
-
-/** \class VectorwiseOp
-  * \ingroup Core_Module
-  *
-  * \brief Pseudo expression providing broadcasting and partial reduction operations
-  *
-  * \tparam ExpressionType the type of the object on which to do partial reductions
-  * \tparam Direction indicates whether to operate on columns (#Vertical) or rows (#Horizontal)
-  *
-  * This class represents a pseudo expression with broadcasting and partial reduction features.
-  * It is the return type of DenseBase::colwise() and DenseBase::rowwise()
-  * and most of the time this is the only way it is explicitly used.
-  *
-  * To understand the logic of rowwise/colwise expression, let's consider a generic case `A.colwise().foo()`
-  * where `foo` is any method of `VectorwiseOp`. This expression is equivalent to applying `foo()` to each
-  * column of `A` and then re-assemble the outputs in a matrix expression:
-  * \code [A.col(0).foo(), A.col(1).foo(), ..., A.col(A.cols()-1).foo()] \endcode
-  *
-  * Example: \include MatrixBase_colwise.cpp
-  * Output: \verbinclude MatrixBase_colwise.out
-  *
-  * The begin() and end() methods are obviously exceptions to the previous rule as they
-  * return STL-compatible begin/end iterators to the rows or columns of the nested expression.
-  * Typical use cases include for-range-loop and calls to STL algorithms:
-  *
-  * Example: \include MatrixBase_colwise_iterator_cxx11.cpp
-  * Output: \verbinclude MatrixBase_colwise_iterator_cxx11.out
-  *
-  * For a partial reduction on an empty input, some rules apply.
-  * For the sake of clarity, let's consider a vertical reduction:
-  *   - If the number of columns is zero, then a 1x0 row-major vector expression is returned.
-  *   - Otherwise, if the number of rows is zero, then
-  *       - a row vector of zeros is returned for sum-like reductions (sum, squaredNorm, norm, etc.)
-  *       - a row vector of ones is returned for a product reduction (e.g., <code>MatrixXd(n,0).colwise().prod()</code>)
-  *       - an assert is triggered for all other reductions (minCoeff,maxCoeff,redux(bin_op))
-  *
-  * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr
-  */
-template<typename ExpressionType, int Direction> class VectorwiseOp
-{
-  public:
-
-    typedef typename ExpressionType::Scalar Scalar;
-    typedef typename ExpressionType::RealScalar RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
-    typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned;
-
-    template<template<typename OutScalar,typename InputScalar> class Functor,
-                      typename ReturnScalar=Scalar> struct ReturnType
-    {
-      typedef PartialReduxExpr<ExpressionType,
-                               Functor<ReturnScalar,Scalar>,
-                               Direction
-                              > Type;
-    };
-
-    template<typename BinaryOp> struct ReduxReturnType
-    {
-      typedef PartialReduxExpr<ExpressionType,
-                               internal::member_redux<BinaryOp,Scalar>,
-                               Direction
-                              > Type;
-    };
-
-    enum {
-      isVertical   = (Direction==Vertical) ? 1 : 0,
-      isHorizontal = (Direction==Horizontal) ? 1 : 0
-    };
-
-  protected:
-
-    template<typename OtherDerived> struct ExtendedType {
-      typedef Replicate<OtherDerived,
-                        isVertical   ? 1 : ExpressionType::RowsAtCompileTime,
-                        isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
-    };
-
-    /** \internal
-      * Replicates a vector to match the size of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    typename ExtendedType<OtherDerived>::Type
-    extendedTo(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxColsAtCompileTime==1),
-                          YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
-                          YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
-      return typename ExtendedType<OtherDerived>::Type
-                      (other.derived(),
-                       isVertical   ? 1 : m_matrix.rows(),
-                       isHorizontal ? 1 : m_matrix.cols());
-    }
-
-    template<typename OtherDerived> struct OppositeExtendedType {
-      typedef Replicate<OtherDerived,
-                        isHorizontal ? 1 : ExpressionType::RowsAtCompileTime,
-                        isVertical   ? 1 : ExpressionType::ColsAtCompileTime> Type;
-    };
-
-    /** \internal
-      * Replicates a vector in the opposite direction to match the size of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    typename OppositeExtendedType<OtherDerived>::Type
-    extendedToOpposite(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
-                          YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
-      EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
-                          YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
-      return typename OppositeExtendedType<OtherDerived>::Type
-                      (other.derived(),
-                       isHorizontal  ? 1 : m_matrix.rows(),
-                       isVertical    ? 1 : m_matrix.cols());
-    }
-
-  public:
-    EIGEN_DEVICE_FUNC
-    explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
-
-    /** \internal */
-    EIGEN_DEVICE_FUNC
-    inline const ExpressionType& _expression() const { return m_matrix; }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
-      * iterator type over the columns or rows as returned by the begin() and end() methods.
-      */
-    random_access_iterator_type iterator;
-    /** This is the const version of iterator (aka read-only) */
-    random_access_iterator_type const_iterator;
-    #else
-    typedef internal::subvector_stl_iterator<ExpressionType,               DirectionType(Direction)> iterator;
-    typedef internal::subvector_stl_iterator<const ExpressionType,         DirectionType(Direction)> const_iterator;
-    typedef internal::subvector_stl_reverse_iterator<ExpressionType,       DirectionType(Direction)> reverse_iterator;
-    typedef internal::subvector_stl_reverse_iterator<const ExpressionType, DirectionType(Direction)> const_reverse_iterator;
-    #endif
-
-    /** returns an iterator to the first row (rowwise) or column (colwise) of the nested expression.
-      * \sa end(), cbegin()
-      */
-    iterator                 begin()       { return iterator      (m_matrix, 0); }
-    /** const version of begin() */
-    const_iterator           begin() const { return const_iterator(m_matrix, 0); }
-    /** const version of begin() */
-    const_iterator          cbegin() const { return const_iterator(m_matrix, 0); }
-
-    /** returns a reverse iterator to the last row (rowwise) or column (colwise) of the nested expression.
-      * \sa rend(), crbegin()
-      */
-    reverse_iterator        rbegin()       { return reverse_iterator       (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
-	/** const version of rbegin() */
-    const_reverse_iterator  rbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
-	/** const version of rbegin() */
-	const_reverse_iterator crbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
-
-    /** returns an iterator to the row (resp. column) following the last row (resp. column) of the nested expression
-      * \sa begin(), cend()
-      */
-    iterator                 end()         { return iterator      (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
-    /** const version of end() */
-    const_iterator           end()  const  { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
-    /** const version of end() */
-    const_iterator          cend()  const  { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
-
-    /** returns a reverse iterator to the row (resp. column) before the first row (resp. column) of the nested expression
-      * \sa begin(), cend()
-      */
-    reverse_iterator        rend()         { return reverse_iterator       (m_matrix, -1); }
-    /** const version of rend() */
-    const_reverse_iterator  rend()  const  { return const_reverse_iterator (m_matrix, -1); }
-    /** const version of rend() */
-    const_reverse_iterator crend()  const  { return const_reverse_iterator (m_matrix, -1); }
-
-    /** \returns a row or column vector expression of \c *this reduxed by \a func
-      *
-      * The template parameter \a BinaryOp is the type of the functor
-      * of the custom redux operator. Note that func must be an associative operator.
-      *
-      * \warning the size along the reduction direction must be strictly positive,
-      *          otherwise an assertion is triggered.
-      *
-      * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise()
-      */
-    template<typename BinaryOp>
-    EIGEN_DEVICE_FUNC
-    const typename ReduxReturnType<BinaryOp>::Type
-    redux(const BinaryOp& func = BinaryOp()) const
-    {
-      eigen_assert(redux_length()>0 && "you are using an empty matrix");
-      return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func));
-    }
-
-    typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType;
-    typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType;
-    typedef PartialReduxExpr<const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const ExpressionTypeNestedCleaned>,internal::member_sum<RealScalar,RealScalar>,Direction> SquaredNormReturnType;
-    typedef CwiseUnaryOp<internal::scalar_sqrt_op<RealScalar>, const SquaredNormReturnType> NormReturnType;
-    typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType;
-    typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType;
-    typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType;
-    typedef typename ReturnType<internal::member_sum>::Type SumReturnType;
-    typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SumReturnType,Scalar,quotient) MeanReturnType;
-    typedef typename ReturnType<internal::member_all>::Type AllReturnType;
-    typedef typename ReturnType<internal::member_any>::Type AnyReturnType;
-    typedef PartialReduxExpr<ExpressionType, internal::member_count<Index,Scalar>, Direction> CountReturnType;
-    typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
-    typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType;
-    typedef Reverse<ExpressionType, Direction> ReverseReturnType;
-
-    template<int p> struct LpNormReturnType {
-      typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar,Scalar>,Direction> Type;
-    };
-
-    /** \returns a row (or column) vector expression of the smallest coefficient
-      * of each column (or row) of the referenced expression.
-      *
-      * \warning the size along the reduction direction must be strictly positive,
-      *          otherwise an assertion is triggered.
-      *
-      * \warning the result is undefined if \c *this contains NaN.
-      *
-      * Example: \include PartialRedux_minCoeff.cpp
-      * Output: \verbinclude PartialRedux_minCoeff.out
-      *
-      * \sa DenseBase::minCoeff() */
-    EIGEN_DEVICE_FUNC
-    const MinCoeffReturnType minCoeff() const
-    {
-      eigen_assert(redux_length()>0 && "you are using an empty matrix");
-      return MinCoeffReturnType(_expression());
-    }
-
-    /** \returns a row (or column) vector expression of the largest coefficient
-      * of each column (or row) of the referenced expression.
-      *
-      * \warning the size along the reduction direction must be strictly positive,
-      *          otherwise an assertion is triggered.
-      *
-      * \warning the result is undefined if \c *this contains NaN.
-      *
-      * Example: \include PartialRedux_maxCoeff.cpp
-      * Output: \verbinclude PartialRedux_maxCoeff.out
-      *
-      * \sa DenseBase::maxCoeff() */
-    EIGEN_DEVICE_FUNC
-    const MaxCoeffReturnType maxCoeff() const
-    {
-      eigen_assert(redux_length()>0 && "you are using an empty matrix");
-      return MaxCoeffReturnType(_expression());
-    }
-
-    /** \returns a row (or column) vector expression of the squared norm
-      * of each column (or row) of the referenced expression.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * Example: \include PartialRedux_squaredNorm.cpp
-      * Output: \verbinclude PartialRedux_squaredNorm.out
-      *
-      * \sa DenseBase::squaredNorm() */
-    EIGEN_DEVICE_FUNC
-    const SquaredNormReturnType squaredNorm() const
-    { return SquaredNormReturnType(m_matrix.cwiseAbs2()); }
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * Example: \include PartialRedux_norm.cpp
-      * Output: \verbinclude PartialRedux_norm.out
-      *
-      * \sa DenseBase::norm() */
-    EIGEN_DEVICE_FUNC
-    const NormReturnType norm() const
-    { return NormReturnType(squaredNorm()); }
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * Example: \include PartialRedux_norm.cpp
-      * Output: \verbinclude PartialRedux_norm.out
-      *
-      * \sa DenseBase::norm() */
-    template<int p>
-    EIGEN_DEVICE_FUNC
-    const typename LpNormReturnType<p>::Type lpNorm() const
-    { return typename LpNormReturnType<p>::Type(_expression()); }
-
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression, using
-      * Blue's algorithm.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * \sa DenseBase::blueNorm() */
-    EIGEN_DEVICE_FUNC
-    const BlueNormReturnType blueNorm() const
-    { return BlueNormReturnType(_expression()); }
-
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression, avoiding
-      * underflow and overflow.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * \sa DenseBase::stableNorm() */
-    EIGEN_DEVICE_FUNC
-    const StableNormReturnType stableNorm() const
-    { return StableNormReturnType(_expression()); }
-
-
-    /** \returns a row (or column) vector expression of the norm
-      * of each column (or row) of the referenced expression, avoiding
-      * underflow and overflow using a concatenation of hypot() calls.
-      * This is a vector with real entries, even if the original matrix has complex entries.
-      *
-      * \sa DenseBase::hypotNorm() */
-    EIGEN_DEVICE_FUNC
-    const HypotNormReturnType hypotNorm() const
-    { return HypotNormReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression of the sum
-      * of each column (or row) of the referenced expression.
-      *
-      * Example: \include PartialRedux_sum.cpp
-      * Output: \verbinclude PartialRedux_sum.out
-      *
-      * \sa DenseBase::sum() */
-    EIGEN_DEVICE_FUNC
-    const SumReturnType sum() const
-    { return SumReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression of the mean
-    * of each column (or row) of the referenced expression.
-    *
-    * \sa DenseBase::mean() */
-    EIGEN_DEVICE_FUNC
-    const MeanReturnType mean() const
-    { return sum() / Scalar(Direction==Vertical?m_matrix.rows():m_matrix.cols()); }
-
-    /** \returns a row (or column) vector expression representing
-      * whether \b all coefficients of each respective column (or row) are \c true.
-      * This expression can be assigned to a vector with entries of type \c bool.
-      *
-      * \sa DenseBase::all() */
-    EIGEN_DEVICE_FUNC
-    const AllReturnType all() const
-    { return AllReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression representing
-      * whether \b at \b least one coefficient of each respective column (or row) is \c true.
-      * This expression can be assigned to a vector with entries of type \c bool.
-      *
-      * \sa DenseBase::any() */
-    EIGEN_DEVICE_FUNC
-    const AnyReturnType any() const
-    { return AnyReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression representing
-      * the number of \c true coefficients of each respective column (or row).
-      * This expression can be assigned to a vector whose entries have the same type as is used to
-      * index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t .
-      *
-      * Example: \include PartialRedux_count.cpp
-      * Output: \verbinclude PartialRedux_count.out
-      *
-      * \sa DenseBase::count() */
-    EIGEN_DEVICE_FUNC
-    const CountReturnType count() const
-    { return CountReturnType(_expression()); }
-
-    /** \returns a row (or column) vector expression of the product
-      * of each column (or row) of the referenced expression.
-      *
-      * Example: \include PartialRedux_prod.cpp
-      * Output: \verbinclude PartialRedux_prod.out
-      *
-      * \sa DenseBase::prod() */
-    EIGEN_DEVICE_FUNC
-    const ProdReturnType prod() const
-    { return ProdReturnType(_expression()); }
-
-
-    /** \returns a matrix expression
-      * where each column (or row) are reversed.
-      *
-      * Example: \include Vectorwise_reverse.cpp
-      * Output: \verbinclude Vectorwise_reverse.out
-      *
-      * \sa DenseBase::reverse() */
-    EIGEN_DEVICE_FUNC
-    const ConstReverseReturnType reverse() const
-    { return ConstReverseReturnType( _expression() ); }
-
-    /** \returns a writable matrix expression
-      * where each column (or row) are reversed.
-      *
-      * \sa reverse() const */
-    EIGEN_DEVICE_FUNC
-    ReverseReturnType reverse()
-    { return ReverseReturnType( _expression() ); }
-
-    typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType;
-    EIGEN_DEVICE_FUNC
-    const ReplicateReturnType replicate(Index factor) const;
-
-    /**
-      * \return an expression of the replication of each column (or row) of \c *this
-      *
-      * Example: \include DirectionWise_replicate.cpp
-      * Output: \verbinclude DirectionWise_replicate.out
-      *
-      * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate
-      */
-    // NOTE implemented here because of sunstudio's compilation errors
-    // isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator
-    template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical>
-    EIGEN_DEVICE_FUNC
-    replicate(Index factor = Factor) const
-    {
-      return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)>
-          (_expression(),isVertical?factor:1,isHorizontal?factor:1);
-    }
-
-/////////// Artithmetic operators ///////////
-
-    /** Copies the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME
-      return m_matrix = extendedTo(other.derived());
-    }
-
-    /** Adds the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator+=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix += extendedTo(other.derived());
-    }
-
-    /** Substracts the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator-=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix -= extendedTo(other.derived());
-    }
-
-    /** Multiples each subvector of \c *this by the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator*=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      m_matrix *= extendedTo(other.derived());
-      return m_matrix;
-    }
-
-    /** Divides each subvector of \c *this by the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    ExpressionType& operator/=(const DenseBase<OtherDerived>& other)
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      m_matrix /= extendedTo(other.derived());
-      return m_matrix;
-    }
-
-    /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
-    template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    operator+(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix + extendedTo(other.derived());
-    }
-
-    /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    operator-(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix - extendedTo(other.derived());
-    }
-
-    /** Returns the expression where each subvector is the product of the vector \a other
-      * by the corresponding subvector of \c *this */
-    template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_product_op<Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    EIGEN_DEVICE_FUNC
-    operator*(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix * extendedTo(other.derived());
-    }
-
-    /** Returns the expression where each subvector is the quotient of the corresponding
-      * subvector of \c *this by the vector \a other */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename ExtendedType<OtherDerived>::Type>
-    operator/(const DenseBase<OtherDerived>& other) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
-      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
-      return m_matrix / extendedTo(other.derived());
-    }
-
-    /** \returns an expression where each column (or row) of the referenced matrix are normalized.
-      * The referenced matrix is \b not modified.
-      * \sa MatrixBase::normalized(), normalize()
-      */
-    EIGEN_DEVICE_FUNC
-    CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
-                  const ExpressionTypeNestedCleaned,
-                  const typename OppositeExtendedType<NormReturnType>::Type>
-    normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); }
-
-
-    /** Normalize in-place each row or columns of the referenced matrix.
-      * \sa MatrixBase::normalize(), normalized()
-      */
-    EIGEN_DEVICE_FUNC void normalize() {
-      m_matrix = this->normalized();
-    }
-
-    EIGEN_DEVICE_FUNC inline void reverseInPlace();
-
-/////////// Geometry module ///////////
-
-    typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType;
-    EIGEN_DEVICE_FUNC
-    HomogeneousReturnType homogeneous() const;
-
-    typedef typename ExpressionType::PlainObject CrossReturnType;
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const;
-
-    enum {
-      HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime
-                                             : internal::traits<ExpressionType>::ColsAtCompileTime,
-      HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1
-    };
-    typedef Block<const ExpressionType,
-                  Direction==Vertical   ? int(HNormalized_SizeMinusOne)
-                                        : int(internal::traits<ExpressionType>::RowsAtCompileTime),
-                  Direction==Horizontal ? int(HNormalized_SizeMinusOne)
-                                        : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
-            HNormalized_Block;
-    typedef Block<const ExpressionType,
-                  Direction==Vertical   ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime),
-                  Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
-            HNormalized_Factors;
-    typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>,
-                const HNormalized_Block,
-                const Replicate<HNormalized_Factors,
-                  Direction==Vertical   ? HNormalized_SizeMinusOne : 1,
-                  Direction==Horizontal ? HNormalized_SizeMinusOne : 1> >
-            HNormalizedReturnType;
-
-    EIGEN_DEVICE_FUNC
-    const HNormalizedReturnType hnormalized() const;
-
-#   ifdef EIGEN_VECTORWISEOP_PLUGIN
-#     include EIGEN_VECTORWISEOP_PLUGIN
-#   endif
-
-  protected:
-    Index redux_length() const
-    {
-      return Direction==Vertical ? m_matrix.rows() : m_matrix.cols();
-    }
-    ExpressionTypeNested m_matrix;
-};
-
-//const colwise moved to DenseBase.h due to CUDA compiler bug
-
-
-/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
-  *
-  * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ColwiseReturnType
-DenseBase<Derived>::colwise()
-{
-  return ColwiseReturnType(derived());
-}
-
-//const rowwise moved to DenseBase.h due to CUDA compiler bug
-
-
-/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
-  *
-  * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::RowwiseReturnType
-DenseBase<Derived>::rowwise()
-{
-  return RowwiseReturnType(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIAL_REDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Visitor.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Visitor.h
deleted file mode 100644
index 00bcca8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/Visitor.h
+++ /dev/null
@@ -1,381 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_VISITOR_H
-#define EIGEN_VISITOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Visitor, typename Derived, int UnrollCount>
-struct visitor_impl
-{
-  enum {
-    col = (UnrollCount-1) / Derived::RowsAtCompileTime,
-    row = (UnrollCount-1) % Derived::RowsAtCompileTime
-  };
-
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived &mat, Visitor& visitor)
-  {
-    visitor_impl<Visitor, Derived, UnrollCount-1>::run(mat, visitor);
-    visitor(mat.coeff(row, col), row, col);
-  }
-};
-
-template<typename Visitor, typename Derived>
-struct visitor_impl<Visitor, Derived, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived &mat, Visitor& visitor)
-  {
-    return visitor.init(mat.coeff(0, 0), 0, 0);
-  }
-};
-
-// This specialization enables visitors on empty matrices at compile-time
-template<typename Visitor, typename Derived>
-struct visitor_impl<Visitor, Derived, 0> {
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived &/*mat*/, Visitor& /*visitor*/)
-  {}
-};
-
-template<typename Visitor, typename Derived>
-struct visitor_impl<Visitor, Derived, Dynamic>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const Derived& mat, Visitor& visitor)
-  {
-    visitor.init(mat.coeff(0,0), 0, 0);
-    for(Index i = 1; i < mat.rows(); ++i)
-      visitor(mat.coeff(i, 0), i, 0);
-    for(Index j = 1; j < mat.cols(); ++j)
-      for(Index i = 0; i < mat.rows(); ++i)
-        visitor(mat.coeff(i, j), i, j);
-  }
-};
-
-// evaluator adaptor
-template<typename XprType>
-class visitor_evaluator
-{
-public:
-  EIGEN_DEVICE_FUNC
-  explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  enum {
-    RowsAtCompileTime = XprType::RowsAtCompileTime,
-    CoeffReadCost = internal::evaluator<XprType>::CoeffReadCost
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_xpr.size(); }
-
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
-  { return m_evaluator.coeff(row, col); }
-
-protected:
-  internal::evaluator<XprType> m_evaluator;
-  const XprType &m_xpr;
-};
-} // end namespace internal
-
-/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
-  *
-  * The template parameter \a Visitor is the type of the visitor and provides the following interface:
-  * \code
-  * struct MyVisitor {
-  *   // called for the first coefficient
-  *   void init(const Scalar& value, Index i, Index j);
-  *   // called for all other coefficients
-  *   void operator() (const Scalar& value, Index i, Index j);
-  * };
-  * \endcode
-  *
-  * \note compared to one or two \em for \em loops, visitors offer automatic
-  * unrolling for small fixed size matrix.
-  *
-  * \note if the matrix is empty, then the visitor is left unchanged.
-  *
-  * \sa minCoeff(Index*,Index*), maxCoeff(Index*,Index*), DenseBase::redux()
-  */
-template<typename Derived>
-template<typename Visitor>
-EIGEN_DEVICE_FUNC
-void DenseBase<Derived>::visit(Visitor& visitor) const
-{
-  if(size()==0)
-    return;
-
-  typedef typename internal::visitor_evaluator<Derived> ThisEvaluator;
-  ThisEvaluator thisEval(derived());
-
-  enum {
-    unroll =  SizeAtCompileTime != Dynamic
-           && SizeAtCompileTime * int(ThisEvaluator::CoeffReadCost) + (SizeAtCompileTime-1) * int(internal::functor_traits<Visitor>::Cost) <= EIGEN_UNROLLING_LIMIT
-  };
-  return internal::visitor_impl<Visitor, ThisEvaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(thisEval, visitor);
-}
-
-namespace internal {
-
-/** \internal
-  * \brief Base class to implement min and max visitors
-  */
-template <typename Derived>
-struct coeff_visitor
-{
-  // default initialization to avoid countless invalid maybe-uninitialized warnings by gcc
-  EIGEN_DEVICE_FUNC
-  coeff_visitor() : row(-1), col(-1), res(0) {}
-  typedef typename Derived::Scalar Scalar;
-  Index row, col;
-  Scalar res;
-  EIGEN_DEVICE_FUNC
-  inline void init(const Scalar& value, Index i, Index j)
-  {
-    res = value;
-    row = i;
-    col = j;
-  }
-};
-
-/** \internal
-  * \brief Visitor computing the min coefficient with its value and coordinates
-  *
-  * \sa DenseBase::minCoeff(Index*, Index*)
-  */
-template <typename Derived, int NaNPropagation>
-struct min_coeff_visitor : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if(value < this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct min_coeff_visitor<Derived, PropagateNumbers> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(this->res) || (!(numext::isnan)(value) && value < this->res))
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct min_coeff_visitor<Derived, PropagateNaN> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(value) || value < this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template<typename Scalar, int NaNPropagation>
-    struct functor_traits<min_coeff_visitor<Scalar, NaNPropagation> > {
-  enum {
-    Cost = NumTraits<Scalar>::AddCost
-  };
-};
-
-/** \internal
-  * \brief Visitor computing the max coefficient with its value and coordinates
-  *
-  * \sa DenseBase::maxCoeff(Index*, Index*)
-  */
-template <typename Derived, int NaNPropagation>
-struct max_coeff_visitor : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if(value > this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct max_coeff_visitor<Derived, PropagateNumbers> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(this->res) || (!(numext::isnan)(value) && value > this->res))
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template <typename Derived>
-struct max_coeff_visitor<Derived, PropagateNaN> : coeff_visitor<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  EIGEN_DEVICE_FUNC
-  void operator() (const Scalar& value, Index i, Index j)
-  {
-    if((numext::isnan)(value) || value > this->res)
-    {
-      this->res = value;
-      this->row = i;
-      this->col = j;
-    }
-  }
-};
-
-template<typename Scalar, int NaNPropagation>
-struct functor_traits<max_coeff_visitor<Scalar, NaNPropagation> > {
-  enum {
-    Cost = NumTraits<Scalar>::AddCost
-  };
-};
-
-} // end namespace internal
-
-/** \fn DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
-  * \returns the minimum of all coefficients of *this and puts in *row and *col its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visit(), DenseBase::minCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  internal::min_coeff_visitor<Derived, NaNPropagation> minVisitor;
-  this->visit(minVisitor);
-  *rowId = minVisitor.row;
-  if (colId) *colId = minVisitor.col;
-  return minVisitor.res;
-}
-
-/** \returns the minimum of all coefficients of *this and puts in *index its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::minCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::minCoeff(IndexType* index) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-      internal::min_coeff_visitor<Derived, NaNPropagation> minVisitor;
-  this->visit(minVisitor);
-  *index = IndexType((RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row);
-  return minVisitor.res;
-}
-
-/** \fn DenseBase<Derived>::maxCoeff(IndexType* rowId, IndexType* colId) const
-  * \returns the maximum of all coefficients of *this and puts in *row and *col its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::maxCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  internal::max_coeff_visitor<Derived, NaNPropagation> maxVisitor;
-  this->visit(maxVisitor);
-  *rowPtr = maxVisitor.row;
-  if (colPtr) *colPtr = maxVisitor.col;
-  return maxVisitor.res;
-}
-
-/** \returns the maximum of all coefficients of *this and puts in *index its location.
-  *
-  * In case \c *this contains NaN, NaNPropagation determines the behavior:
-  *   NaNPropagation == PropagateFast : undefined
-  *   NaNPropagation == PropagateNaN : result is NaN
-  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
-  * \warning the matrix must be not empty, otherwise an assertion is triggered.
-  *
-  * \sa DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff()
-  */
-template<typename Derived>
-template<int NaNPropagation, typename IndexType>
-EIGEN_DEVICE_FUNC
-typename internal::traits<Derived>::Scalar
-DenseBase<Derived>::maxCoeff(IndexType* index) const
-{
-  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
-
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-      internal::max_coeff_visitor<Derived, NaNPropagation> maxVisitor;
-  this->visit(maxVisitor);
-  *index = (RowsAtCompileTime==1) ? maxVisitor.col : maxVisitor.row;
-  return maxVisitor.res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_VISITOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
deleted file mode 100644
index ab7bd6c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
+++ /dev/null
@@ -1,372 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_AVX_H
-#define EIGEN_COMPLEX_AVX_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet4cf
-{
-  EIGEN_STRONG_INLINE Packet4cf() {}
-  EIGEN_STRONG_INLINE explicit Packet4cf(const __m256& a) : v(a) {}
-  __m256  v;
-};
-
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet4cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet4cf> {
-  typedef std::complex<float> type;
-  typedef Packet2cf half;
-  typedef Packet8f as_real;
-  enum {
-    size=4,
-    alignment=Aligned32,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4cf padd<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_add_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf psub<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_sub_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf pnegate(const Packet4cf& a)
-{
-  return Packet4cf(pnegate(a.v));
-}
-template<> EIGEN_STRONG_INLINE Packet4cf pconj(const Packet4cf& a)
-{
-  const __m256 mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000));
-  return Packet4cf(_mm256_xor_ps(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf pmul<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
-{
-  __m256 tmp1 = _mm256_mul_ps(_mm256_moveldup_ps(a.v), b.v);
-  __m256 tmp2 = _mm256_mul_ps(_mm256_movehdup_ps(a.v), _mm256_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1)));
-  __m256 result = _mm256_addsub_ps(tmp1, tmp2);
-  return Packet4cf(result);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4cf pcmp_eq(const Packet4cf& a, const Packet4cf& b) {
-  __m256 eq = _mm256_cmp_ps(a.v, b.v, _CMP_EQ_OQ);
-  return Packet4cf(_mm256_and_ps(eq, _mm256_permute_ps(eq, 0xb1)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf ptrue<Packet4cf>(const Packet4cf& a) { return Packet4cf(ptrue(Packet8f(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet4cf pand   <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_and_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf por    <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_or_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf pxor   <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_xor_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cf pandnot<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_andnot_ps(b.v,a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet4cf pload <Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet4cf(pload<Packet8f>(&numext::real_ref(*from))); }
-template<> EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cf(ploadu<Packet8f>(&numext::real_ref(*from))); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from)
-{
-  return Packet4cf(_mm256_castpd_ps(_mm256_broadcast_sd((const double*)(const void*)&from)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from)
-{
-  // FIXME The following might be optimized using _mm256_movedup_pd
-  Packet2cf a = ploaddup<Packet2cf>(from);
-  Packet2cf b = ploaddup<Packet2cf>(from+1);
-  return  Packet4cf(_mm256_insertf128_ps(_mm256_castps128_ps256(a.v), b.v, 1));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from, Index stride)
-{
-  return Packet4cf(_mm256_set_ps(std::imag(from[3*stride]), std::real(from[3*stride]),
-                                 std::imag(from[2*stride]), std::real(from[2*stride]),
-                                 std::imag(from[1*stride]), std::real(from[1*stride]),
-                                 std::imag(from[0*stride]), std::real(from[0*stride])));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, Index stride)
-{
-  __m128 low = _mm256_extractf128_ps(from.v, 0);
-  to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 0)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1)));
-  to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 2)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3)));
-
-  __m128 high = _mm256_extractf128_ps(from.v, 1);
-  to[stride*2] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 0)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1)));
-  to[stride*3] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 2)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3)));
-
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet4cf>(const Packet4cf& a)
-{
-  return pfirst(Packet2cf(_mm256_castps256_ps128(a.v)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf preverse(const Packet4cf& a) {
-  __m128 low  = _mm256_extractf128_ps(a.v, 0);
-  __m128 high = _mm256_extractf128_ps(a.v, 1);
-  __m128d lowd  = _mm_castps_pd(low);
-  __m128d highd = _mm_castps_pd(high);
-  low  = _mm_castpd_ps(_mm_shuffle_pd(lowd,lowd,0x1));
-  high = _mm_castpd_ps(_mm_shuffle_pd(highd,highd,0x1));
-  __m256 result = _mm256_setzero_ps();
-  result = _mm256_insertf128_ps(result, low, 1);
-  result = _mm256_insertf128_ps(result, high, 0);
-  return Packet4cf(result);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet4cf>(const Packet4cf& a)
-{
-  return predux(padd(Packet2cf(_mm256_extractf128_ps(a.v,0)),
-                     Packet2cf(_mm256_extractf128_ps(a.v,1))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const Packet4cf& a)
-{
-  return predux_mul(pmul(Packet2cf(_mm256_extractf128_ps(a.v, 0)),
-                         Packet2cf(_mm256_extractf128_ps(a.v, 1))));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf,Packet8f)
-
-template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
-{
-  Packet4cf num = pmul(a, pconj(b));
-  __m256 tmp = _mm256_mul_ps(b.v, b.v);
-  __m256 tmp2    = _mm256_shuffle_ps(tmp,tmp,0xB1);
-  __m256 denom = _mm256_add_ps(tmp, tmp2);
-  return Packet4cf(_mm256_div_ps(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x)
-{
-  return Packet4cf(_mm256_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1)));
-}
-
-//---------- double ----------
-struct Packet2cd
-{
-  EIGEN_STRONG_INLINE Packet2cd() {}
-  EIGEN_STRONG_INLINE explicit Packet2cd(const __m256d& a) : v(a) {}
-  __m256d  v;
-};
-
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet2cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 2,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet2cd> {
-  typedef std::complex<double> type;
-  typedef Packet1cd half;
-  typedef Packet4d as_real;
-  enum {
-    size=2,
-    alignment=Aligned32,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2cd padd<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_add_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd psub<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_sub_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pnegate(const Packet2cd& a) { return Packet2cd(pnegate(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pconj(const Packet2cd& a)
-{
-  const __m256d mask = _mm256_castsi256_pd(_mm256_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0));
-  return Packet2cd(_mm256_xor_pd(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd pmul<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
-{
-  __m256d tmp1 = _mm256_shuffle_pd(a.v,a.v,0x0);
-  __m256d even = _mm256_mul_pd(tmp1, b.v);
-  __m256d tmp2 = _mm256_shuffle_pd(a.v,a.v,0xF);
-  __m256d tmp3 = _mm256_shuffle_pd(b.v,b.v,0x5);
-  __m256d odd  = _mm256_mul_pd(tmp2, tmp3);
-  return Packet2cd(_mm256_addsub_pd(even, odd));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cd pcmp_eq(const Packet2cd& a, const Packet2cd& b) {
-  __m256d eq = _mm256_cmp_pd(a.v, b.v, _CMP_EQ_OQ);
-  return Packet2cd(pand(eq, _mm256_permute_pd(eq, 0x5)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd ptrue<Packet2cd>(const Packet2cd& a) { return Packet2cd(ptrue(Packet4d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet2cd pand   <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_and_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd por    <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_or_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pxor   <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_xor_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cd pandnot<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_andnot_pd(b.v,a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cd pload <Packet2cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cd(pload<Packet4d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cd ploadu<Packet2cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cd(ploadu<Packet4d>((const double*)from)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cd pset1<Packet2cd>(const std::complex<double>& from)
-{
-  // in case casting to a __m128d* is really not safe, then we can still fallback to this version: (much slower though)
-//   return Packet2cd(_mm256_loadu2_m128d((const double*)&from,(const double*)&from));
-    return Packet2cd(_mm256_broadcast_pd((const __m128d*)(const void*)&from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd ploaddup<Packet2cd>(const std::complex<double>* from) { return pset1<Packet2cd>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet2cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet2cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from, Index stride)
-{
-  return Packet2cd(_mm256_set_pd(std::imag(from[1*stride]), std::real(from[1*stride]),
-				 std::imag(from[0*stride]), std::real(from[0*stride])));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from, Index stride)
-{
-  __m128d low = _mm256_extractf128_pd(from.v, 0);
-  to[stride*0] = std::complex<double>(_mm_cvtsd_f64(low), _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1)));
-  __m128d high = _mm256_extractf128_pd(from.v, 1);
-  to[stride*1] = std::complex<double>(_mm_cvtsd_f64(high), _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet2cd>(const Packet2cd& a)
-{
-  __m128d low = _mm256_extractf128_pd(a.v, 0);
-  EIGEN_ALIGN16 double res[2];
-  _mm_store_pd(res, low);
-  return std::complex<double>(res[0],res[1]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd preverse(const Packet2cd& a) {
-  __m256d result = _mm256_permute2f128_pd(a.v, a.v, 1);
-  return Packet2cd(result);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet2cd>(const Packet2cd& a)
-{
-  return predux(padd(Packet1cd(_mm256_extractf128_pd(a.v,0)),
-                     Packet1cd(_mm256_extractf128_pd(a.v,1))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet2cd>(const Packet2cd& a)
-{
-  return predux(pmul(Packet1cd(_mm256_extractf128_pd(a.v,0)),
-                     Packet1cd(_mm256_extractf128_pd(a.v,1))));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd,Packet4d)
-
-template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
-{
-  Packet2cd num = pmul(a, pconj(b));
-  __m256d tmp = _mm256_mul_pd(b.v, b.v);
-  __m256d denom = _mm256_hadd_pd(tmp, tmp);
-  return Packet2cd(_mm256_div_pd(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x)
-{
-  return Packet2cd(_mm256_shuffle_pd(x.v, x.v, 0x5));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4cf,4>& kernel) {
-  __m256d P0 = _mm256_castps_pd(kernel.packet[0].v);
-  __m256d P1 = _mm256_castps_pd(kernel.packet[1].v);
-  __m256d P2 = _mm256_castps_pd(kernel.packet[2].v);
-  __m256d P3 = _mm256_castps_pd(kernel.packet[3].v);
-
-  __m256d T0 = _mm256_shuffle_pd(P0, P1, 15);
-  __m256d T1 = _mm256_shuffle_pd(P0, P1, 0);
-  __m256d T2 = _mm256_shuffle_pd(P2, P3, 15);
-  __m256d T3 = _mm256_shuffle_pd(P2, P3, 0);
-
-  kernel.packet[1].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 32));
-  kernel.packet[3].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 49));
-  kernel.packet[0].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 32));
-  kernel.packet[2].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 49));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2cd,2>& kernel) {
-  __m256d tmp = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 0+(2<<4));
-  kernel.packet[1].v = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 1+(3<<4));
- kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cd psqrt<Packet2cd>(const Packet2cd& a) {
-  return psqrt_complex<Packet2cd>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cf psqrt<Packet4cf>(const Packet4cf& a) {
-  return psqrt_complex<Packet4cf>(a);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h
deleted file mode 100644
index 67041c8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h
+++ /dev/null
@@ -1,228 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_AVX_H
-#define EIGEN_MATH_FUNCTIONS_AVX_H
-
-/* The sin and cos functions of this file are loosely derived from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-namespace Eigen {
-
-namespace internal {
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-psin<Packet8f>(const Packet8f& _x) {
-  return psin_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-pcos<Packet8f>(const Packet8f& _x) {
-  return pcos_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-plog<Packet8f>(const Packet8f& _x) {
-  return plog_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
-plog<Packet4d>(const Packet4d& _x) {
-  return plog_double(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-plog2<Packet8f>(const Packet8f& _x) {
-  return plog2_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
-plog2<Packet4d>(const Packet4d& _x) {
-  return plog2_double(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f plog1p<Packet8f>(const Packet8f& _x) {
-  return generic_plog1p(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f pexpm1<Packet8f>(const Packet8f& _x) {
-  return generic_expm1(_x);
-}
-
-// Exponential function. Works by writing "x = m*log(2) + r" where
-// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
-// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-pexp<Packet8f>(const Packet8f& _x) {
-  return pexp_float(_x);
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
-ptanh<Packet8f>(const Packet8f& _x) {
-  return internal::generic_fast_tanh_float(_x);
-}
-
-// Exponential function for doubles.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
-pexp<Packet4d>(const Packet4d& _x) {
-  return pexp_double(_x);
-}
-
-// Functions for sqrt.
-// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
-// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
-// exact solution. It does not handle +inf, or denormalized numbers correctly.
-// The main advantage of this approach is not just speed, but also the fact that
-// it can be inlined and pipelined with other computations, further reducing its
-// effective latency. This is similar to Quake3's fast inverse square root.
-// For detail see here: http://www.beyond3d.com/content/articles/8/
-#if EIGEN_FAST_MATH
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f psqrt<Packet8f>(const Packet8f& _x) {
-  Packet8f minus_half_x = pmul(_x, pset1<Packet8f>(-0.5f));
-  Packet8f denormal_mask = pandnot(
-      pcmp_lt(_x, pset1<Packet8f>((std::numeric_limits<float>::min)())),
-      pcmp_lt(_x, pzero(_x)));
-
-  // Compute approximate reciprocal sqrt.
-  Packet8f x = _mm256_rsqrt_ps(_x);
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(minus_half_x, pmul(x,x), pset1<Packet8f>(1.5f)));
-  // Flush results for denormals to zero.
-  return pandnot(pmul(_x,x), denormal_mask);
-}
-
-#else
-
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f psqrt<Packet8f>(const Packet8f& _x) {
-  return _mm256_sqrt_ps(_x);
-}
-
-#endif
-
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4d psqrt<Packet4d>(const Packet4d& _x) {
-  return _mm256_sqrt_pd(_x);
-}
-
-#if EIGEN_FAST_MATH
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f prsqrt<Packet8f>(const Packet8f& _x) {
-  _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inf, 0x7f800000);
-  _EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f);
-  _EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f);
-  _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000);
-
-  Packet8f neg_half = pmul(_x, p8f_minus_half);
-
-  // select only the inverse sqrt of positive normal inputs (denormals are
-  // flushed to zero and cause infs as well).
-  Packet8f lt_min_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_LT_OQ);
-  Packet8f inf_mask =  _mm256_cmp_ps(_x, p8f_inf, _CMP_EQ_OQ);
-  Packet8f not_normal_finite_mask = _mm256_or_ps(lt_min_mask, inf_mask);
-
-  // Compute an approximate result using the rsqrt intrinsic.
-  Packet8f y_approx = _mm256_rsqrt_ps(_x);
-
-  // Do a single step of Newton-Raphson iteration to improve the approximation.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet8f y_newton = pmul(y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p8f_one_point_five));
-
-  // Select the result of the Newton-Raphson step for positive normal arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(x) = +inf if
-  // x is zero or a positive denormalized float (equivalent to flushing positive
-  // denormalized inputs to zero).
-  return pselect<Packet8f>(not_normal_finite_mask, y_approx, y_newton);
-}
-
-#else
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet8f prsqrt<Packet8f>(const Packet8f& _x) {
-  _EIGEN_DECLARE_CONST_Packet8f(one, 1.0f);
-  return _mm256_div_ps(p8f_one, _mm256_sqrt_ps(_x));
-}
-#endif
-
-template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4d prsqrt<Packet4d>(const Packet4d& _x) {
-  _EIGEN_DECLARE_CONST_Packet4d(one, 1.0);
-  return _mm256_div_pd(p4d_one, _mm256_sqrt_pd(_x));
-}
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, psin)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pcos)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, plog)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, plog2)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, plog1p)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pexpm1)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pexp)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, ptanh)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, psqrt)
-F16_PACKET_FUNCTION(Packet8f, Packet8h, prsqrt)
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pfrexp(const Packet8h& a, Packet8h& exponent) {
-  Packet8f fexponent;
-  const Packet8h out = float2half(pfrexp<Packet8f>(half2float(a), fexponent));
-  exponent = float2half(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pldexp(const Packet8h& a, const Packet8h& exponent) {
-  return float2half(pldexp<Packet8f>(half2float(a), half2float(exponent)));
-}
-
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, psin)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pcos)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog2)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog1p)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pexpm1)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pexp)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, ptanh)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, psqrt)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, prsqrt)
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pfrexp(const Packet8bf& a, Packet8bf& exponent) {
-  Packet8f fexponent;
-  const Packet8bf out = F32ToBf16(pfrexp<Packet8f>(Bf16ToF32(a), fexponent));
-  exponent = F32ToBf16(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pldexp(const Packet8bf& a, const Packet8bf& exponent) {
-  return F32ToBf16(pldexp<Packet8f>(Bf16ToF32(a), Bf16ToF32(exponent)));
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
deleted file mode 100644
index 7fc32fd..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
+++ /dev/null
@@ -1,1574 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_AVX_H
-#define EIGEN_PACKET_MATH_AVX_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#if !defined(EIGEN_VECTORIZE_AVX512) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
-#endif
-
-#ifdef EIGEN_VECTORIZE_FMA
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-#endif
-
-typedef __m256  Packet8f;
-typedef __m256i Packet8i;
-typedef __m256d Packet4d;
-typedef eigen_packet_wrapper<__m128i, 2> Packet8h;
-typedef eigen_packet_wrapper<__m128i, 3> Packet8bf;
-
-template<> struct is_arithmetic<__m256>  { enum { value = true }; };
-template<> struct is_arithmetic<__m256i> { enum { value = true }; };
-template<> struct is_arithmetic<__m256d> { enum { value = true }; };
-template<> struct is_arithmetic<Packet8h> { enum { value = true }; };
-template<> struct is_arithmetic<Packet8bf> { enum { value = true }; };
-
-#define _EIGEN_DECLARE_CONST_Packet8f(NAME,X) \
-  const Packet8f p8f_##NAME = pset1<Packet8f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4d(NAME,X) \
-  const Packet4d p4d_##NAME = pset1<Packet4d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(NAME,X) \
-  const Packet8f p8f_##NAME = _mm256_castsi256_ps(pset1<Packet8i>(X))
-
-#define _EIGEN_DECLARE_CONST_Packet8i(NAME,X) \
-  const Packet8i p8i_##NAME = pset1<Packet8i>(X)
-
-// Use the packet_traits defined in AVX512/PacketMath.h instead if we're going
-// to leverage AVX512 instructions.
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> struct packet_traits<float>  : default_packet_traits
-{
-  typedef Packet8f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasLog1p = 1,
-    HasExpm1 = 1,
-    HasExp = 1,
-    HasNdtri = 1,
-    HasBessel = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasBlend = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
-};
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet4d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=4,
-    HasHalfPacket = 1,
-
-    HasCmp  = 1,
-    HasDiv  = 1,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasBlend = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
-};
-
-template <>
-struct packet_traits<Eigen::half> : default_packet_traits {
-  typedef Packet8h type;
-  // There is no half-size packet for Packet8h.
-  typedef Packet8h half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasCmp    = 1,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasSin    = EIGEN_FAST_MATH,
-    HasCos    = EIGEN_FAST_MATH,
-    HasNegate = 1,
-    HasAbs    = 1,
-    HasAbs2   = 0,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasSetLinear = 0,
-    HasLog    = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasExp    = 1,
-    HasSqrt   = 1,
-    HasRsqrt  = 1,
-    HasTanh   = EIGEN_FAST_MATH,
-    HasErf    = EIGEN_FAST_MATH,
-    HasBlend  = 0,
-    HasRound  = 1,
-    HasFloor  = 1,
-    HasCeil   = 1,
-    HasRint   = 1,
-    HasBessel = 1,
-    HasNdtri  = 1
-  };
-};
-
-template <>
-struct packet_traits<bfloat16> : default_packet_traits {
-  typedef Packet8bf type;
-  // There is no half-size packet for current Packet8bf.
-  // TODO: support as SSE path.
-  typedef Packet8bf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasCmp = 1,
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasNegate = 1,
-    HasAbs    = 1,
-    HasAbs2   = 0,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasSetLinear = 0,
-    HasLog = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasBlend = 0,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasBessel = 1,
-    HasNdtri  = 1
-  };
-};
-#endif
-
-template<> struct scalar_div_cost<float,true> { enum { value = 14 }; };
-template<> struct scalar_div_cost<double,true> { enum { value = 16 }; };
-
-/* Proper support for integers is only provided by AVX2. In the meantime, we'll
-   use SSE instructions and packets to deal with integers.
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet8i type;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=8
-  };
-};
-*/
-
-template<> struct unpacket_traits<Packet8f> {
-  typedef float     type;
-  typedef Packet4f  half;
-  typedef Packet8i  integer_packet;
-  typedef uint8_t   mask_t;
-  enum {size=8, alignment=Aligned32, vectorizable=true, masked_load_available=true, masked_store_available=true};
-};
-template<> struct unpacket_traits<Packet4d> {
-  typedef double type;
-  typedef Packet2d half;
-  enum {size=4, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet8i> { typedef int    type; typedef Packet4i half; enum {size=8, alignment=Aligned32, vectorizable=false, masked_load_available=false, masked_store_available=false}; };
-template<> struct unpacket_traits<Packet8bf> { typedef bfloat16 type; typedef Packet8bf half; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; };
-
-// Helper function for bit packing snippet of low precision comparison.
-// It packs the flags from 16x16 to 8x16.
-EIGEN_STRONG_INLINE __m128i Pack16To8(Packet8f rf) {
-  return _mm_packs_epi32(_mm256_extractf128_si256(_mm256_castps_si256(rf), 0),
-                         _mm256_extractf128_si256(_mm256_castps_si256(rf), 1));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float&  from) { return _mm256_set1_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet4d pset1<Packet4d>(const double& from) { return _mm256_set1_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet8i pset1<Packet8i>(const int&    from) { return _mm256_set1_epi32(from); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pset1frombits<Packet8f>(unsigned int from) { return _mm256_castsi256_ps(pset1<Packet8i>(from)); }
-template<> EIGEN_STRONG_INLINE Packet4d pset1frombits<Packet4d>(uint64_t from) { return _mm256_castsi256_pd(_mm256_set1_epi64x(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pzero(const Packet8f& /*a*/) { return _mm256_setzero_ps(); }
-template<> EIGEN_STRONG_INLINE Packet4d pzero(const Packet4d& /*a*/) { return _mm256_setzero_pd(); }
-template<> EIGEN_STRONG_INLINE Packet8i pzero(const Packet8i& /*a*/) { return _mm256_setzero_si256(); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8f peven_mask(const Packet8f& /*a*/) { return _mm256_castsi256_ps(_mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1)); }
-template<> EIGEN_STRONG_INLINE Packet8i peven_mask(const Packet8i& /*a*/) { return _mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1); }
-template<> EIGEN_STRONG_INLINE Packet4d peven_mask(const Packet4d& /*a*/) { return _mm256_castsi256_pd(_mm256_set_epi32(0, 0, -1, -1, 0, 0, -1, -1)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pload1<Packet8f>(const float*  from) { return _mm256_broadcast_ss(from); }
-template<> EIGEN_STRONG_INLINE Packet4d pload1<Packet4d>(const double* from) { return _mm256_broadcast_sd(from); }
-
-template<> EIGEN_STRONG_INLINE Packet8f plset<Packet8f>(const float& a) { return _mm256_add_ps(_mm256_set1_ps(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); }
-template<> EIGEN_STRONG_INLINE Packet4d plset<Packet4d>(const double& a) { return _mm256_add_pd(_mm256_set1_pd(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_add_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d padd<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_add_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i padd<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_add_epi32(a,b);
-#else
-  __m128i lo = _mm_add_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  __m128i hi = _mm_add_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f psub<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_sub_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d psub<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_sub_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i psub<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_sub_epi32(a,b);
-#else
-  __m128i lo = _mm_sub_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  __m128i hi = _mm_sub_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a)
-{
-  return _mm256_sub_ps(_mm256_set1_ps(0.0),a);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pnegate(const Packet4d& a)
-{
-  return _mm256_sub_pd(_mm256_set1_pd(0.0),a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pconj(const Packet8f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4d pconj(const Packet4d& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8i pconj(const Packet8i& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet8f pmul<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_mul_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pmul<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_mul_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pmul<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_mullo_epi32(a,b);
-#else
-  const __m128i lo = _mm_mullo_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  const __m128i hi = _mm_mullo_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pdiv<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_div_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pdiv<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_div_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& /*a*/, const Packet8i& /*b*/)
-{ eigen_assert(false && "packet integer division are not supported by AVX");
-  return pset1<Packet8i>(0);
-}
-
-#ifdef EIGEN_VECTORIZE_FMA
-template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
-#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
-  // Clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
-  //  and even register spilling with clang>=6.0 (bug 1637).
-  // Gcc stupidly generates a vfmadd132ps instruction.
-  // So let's enforce it to generate a vfmadd231ps instruction since the most common use
-  //  case is to accumulate the result of the product.
-  Packet8f res = c;
-  __asm__("vfmadd231ps %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  return _mm256_fmadd_ps(a,b,c);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
-#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
-  // see above
-  Packet4d res = c;
-  __asm__("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  return _mm256_fmadd_pd(a,b,c);
-#endif
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_le(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_LE_OQ); }
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_lt(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_LT_OQ); }
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_lt_or_nan(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a, b, _CMP_NGE_UQ); }
-template<> EIGEN_STRONG_INLINE Packet8f pcmp_eq(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_EQ_OQ); }
-
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_le(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_LE_OQ); }
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_LT_OQ); }
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt_or_nan(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a, b, _CMP_NGE_UQ); }
-template<> EIGEN_STRONG_INLINE Packet4d pcmp_eq(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_EQ_OQ); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8i pcmp_eq(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_cmpeq_epi32(a,b);
-#else
-  __m128i lo = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
-  __m128i hi = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pmin<Packet8f>(const Packet8f& a, const Packet8f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may flip
-  // the argument order in calls to _mm_min_ps/_mm_max_ps, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  Packet8f res;
-  asm("vminps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::min.
-  return _mm256_min_ps(b,a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4d pmin<Packet4d>(const Packet4d& a, const Packet4d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // See pmin above
-  Packet4d res;
-  asm("vminpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::min.
-  return _mm256_min_pd(b,a);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pmax<Packet8f>(const Packet8f& a, const Packet8f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // See pmin above
-  Packet8f res;
-  asm("vmaxps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::max.
-  return _mm256_max_ps(b,a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4d pmax<Packet4d>(const Packet4d& a, const Packet4d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // See pmin above
-  Packet4d res;
-  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  return res;
-#else
-  // Arguments are swapped to match NaN propagation behavior of std::max.
-  return _mm256_max_pd(b,a);
-#endif
-}
-
-// Add specializations for min/max with prescribed NaN progation.
-template<>
-EIGEN_STRONG_INLINE Packet8f pmin<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmin<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet4d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8f pmax<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmax<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet4d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8f pmin<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmin<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet4d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8f pmax<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet8f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4d pmax<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet4d>);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f print<Packet8f>(const Packet8f& a) { return _mm256_round_ps(a, _MM_FROUND_CUR_DIRECTION); }
-template<> EIGEN_STRONG_INLINE Packet4d print<Packet4d>(const Packet4d& a) { return _mm256_round_pd(a, _MM_FROUND_CUR_DIRECTION); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pceil<Packet8f>(const Packet8f& a) { return _mm256_ceil_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet4d pceil<Packet4d>(const Packet4d& a) { return _mm256_ceil_pd(a); }
-
-template<> EIGEN_STRONG_INLINE Packet8f pfloor<Packet8f>(const Packet8f& a) { return _mm256_floor_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet4d pfloor<Packet4d>(const Packet4d& a) { return _mm256_floor_pd(a); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8i ptrue<Packet8i>(const Packet8i& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  // vpcmpeqd has lower latency than the more general vcmpps
-  return _mm256_cmpeq_epi32(a,a);
-#else
-  const __m256 b = _mm256_castsi256_ps(a);
-  return _mm256_castps_si256(_mm256_cmp_ps(b,b,_CMP_TRUE_UQ));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f ptrue<Packet8f>(const Packet8f& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  // vpcmpeqd has lower latency than the more general vcmpps
-  const __m256i b = _mm256_castps_si256(a);
-  return _mm256_castsi256_ps(_mm256_cmpeq_epi32(b,b));
-#else
-  return _mm256_cmp_ps(a,a,_CMP_TRUE_UQ);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4d ptrue<Packet4d>(const Packet4d& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  // vpcmpeqq has lower latency than the more general vcmppd
-  const __m256i b = _mm256_castpd_si256(a);
-  return _mm256_castsi256_pd(_mm256_cmpeq_epi64(b,b));
-#else
-  return _mm256_cmp_pd(a,a,_CMP_TRUE_UQ);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pand<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_and_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pand<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_and_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pand<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_and_si256(a,b);
-#else
-  return _mm256_castps_si256(_mm256_and_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f por<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_or_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d por<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_or_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i por<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_or_si256(a,b);
-#else
-  return _mm256_castps_si256(_mm256_or_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pxor<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_xor_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4d pxor<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_xor_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8i pxor<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_xor_si256(a,b);
-#else
-  return _mm256_castps_si256(_mm256_xor_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pandnot<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_andnot_ps(b,a); }
-template<> EIGEN_STRONG_INLINE Packet4d pandnot<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_andnot_pd(b,a); }
-template<> EIGEN_STRONG_INLINE Packet8i pandnot<Packet8i>(const Packet8i& a, const Packet8i& b) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_andnot_si256(b,a);
-#else
-  return _mm256_castps_si256(_mm256_andnot_ps(_mm256_castsi256_ps(b),_mm256_castsi256_ps(a)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pround<Packet8f>(const Packet8f& a)
-{
-  const Packet8f mask = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x80000000u));
-  const Packet8f prev0dot5 = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
-  return _mm256_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pround<Packet4d>(const Packet4d& a)
-{
-  const Packet4d mask = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
-  const Packet4d prev0dot5 = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
-  return _mm256_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pselect<Packet8f>(const Packet8f& mask, const Packet8f& a, const Packet8f& b)
-{ return _mm256_blendv_ps(b,a,mask); }
-template<> EIGEN_STRONG_INLINE Packet4d pselect<Packet4d>(const Packet4d& mask, const Packet4d& a, const Packet4d& b)
-{ return _mm256_blendv_pd(b,a,mask); }
-
-template<int N> EIGEN_STRONG_INLINE Packet8i parithmetic_shift_right(Packet8i a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_srai_epi32(a, N);
-#else
-  __m128i lo = _mm_srai_epi32(_mm256_extractf128_si256(a, 0), N);
-  __m128i hi = _mm_srai_epi32(_mm256_extractf128_si256(a, 1), N);
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet8i plogical_shift_right(Packet8i a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_srli_epi32(a, N);
-#else
-  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(a, 0), N);
-  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(a, 1), N);
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet8i plogical_shift_left(Packet8i a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  return _mm256_slli_epi32(a, N);
-#else
-  __m128i lo = _mm_slli_epi32(_mm256_extractf128_si256(a, 0), N);
-  __m128i hi = _mm_slli_epi32(_mm256_extractf128_si256(a, 1), N);
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pload<Packet8f>(const float*   from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet4d pload<Packet4d>(const double*  from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet8i pload<Packet8i>(const int*     from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet4d ploadu<Packet4d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet8i ploadu<Packet8i>(const int* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from, uint8_t umask) {
-  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
-  const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
-  mask = por<Packet8i>(mask, bit_mask);
-  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_maskload_ps(from, mask);
-}
-
-// Loads 4 floats from memory a returns the packet {a0, a0  a1, a1, a2, a2, a3, a3}
-template<> EIGEN_STRONG_INLINE Packet8f ploaddup<Packet8f>(const float* from)
-{
-  // TODO try to find a way to avoid the need of a temporary register
-//   Packet8f tmp  = _mm256_castps128_ps256(_mm_loadu_ps(from));
-//   tmp = _mm256_insertf128_ps(tmp, _mm_movehl_ps(_mm256_castps256_ps128(tmp),_mm256_castps256_ps128(tmp)), 1);
-//   return _mm256_unpacklo_ps(tmp,tmp);
-
-  // _mm256_insertf128_ps is very slow on Haswell, thus:
-  Packet8f tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
-  // mimic an "inplace" permutation of the lower 128bits using a blend
-  tmp = _mm256_blend_ps(tmp,_mm256_castps128_ps256(_mm_permute_ps( _mm256_castps256_ps128(tmp), _MM_SHUFFLE(1,0,1,0))), 15);
-  // then we can perform a consistent permutation on the global register to get everything in shape:
-  return  _mm256_permute_ps(tmp, _MM_SHUFFLE(3,3,2,2));
-}
-// Loads 2 doubles from memory a returns the packet {a0, a0  a1, a1}
-template<> EIGEN_STRONG_INLINE Packet4d ploaddup<Packet4d>(const double* from)
-{
-  Packet4d tmp = _mm256_broadcast_pd((const __m128d*)(const void*)from);
-  return  _mm256_permute_pd(tmp, 3<<2);
-}
-
-// Loads 2 floats from memory a returns the packet {a0, a0  a0, a0, a1, a1, a1, a1}
-template<> EIGEN_STRONG_INLINE Packet8f ploadquad<Packet8f>(const float* from)
-{
-  Packet8f tmp = _mm256_castps128_ps256(_mm_broadcast_ss(from));
-  return _mm256_insertf128_ps(tmp, _mm_broadcast_ss(from+1), 1);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet8f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet8i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet8f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*       to, const Packet8i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet8f& from, uint8_t umask) {
-  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
-  const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
-  mask = por<Packet8i>(mask, bit_mask);
-  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
-  EIGEN_DEBUG_UNALIGNED_STORE return _mm256_maskstore_ps(to, mask, from);
-}
-
-// NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available
-// NOTE: for the record the following seems to be slower: return _mm256_i32gather_ps(from, _mm256_set1_epi32(stride), 4);
-template<> EIGEN_DEVICE_FUNC inline Packet8f pgather<float, Packet8f>(const float* from, Index stride)
-{
-  return _mm256_set_ps(from[7*stride], from[6*stride], from[5*stride], from[4*stride],
-                       from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-template<> EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const double* from, Index stride)
-{
-  return _mm256_set_pd(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, Index stride)
-{
-  __m128 low = _mm256_extractf128_ps(from, 0);
-  to[stride*0] = _mm_cvtss_f32(low);
-  to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1));
-  to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 2));
-  to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3));
-
-  __m128 high = _mm256_extractf128_ps(from, 1);
-  to[stride*4] = _mm_cvtss_f32(high);
-  to[stride*5] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1));
-  to[stride*6] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 2));
-  to[stride*7] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to, const Packet4d& from, Index stride)
-{
-  __m128d low = _mm256_extractf128_pd(from, 0);
-  to[stride*0] = _mm_cvtsd_f64(low);
-  to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1));
-  __m128d high = _mm256_extractf128_pd(from, 1);
-  to[stride*2] = _mm_cvtsd_f64(high);
-  to[stride*3] = _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore1<Packet8f>(float* to, const float& a)
-{
-  Packet8f pa = pset1<Packet8f>(a);
-  pstore(to, pa);
-}
-template<> EIGEN_STRONG_INLINE void pstore1<Packet4d>(double* to, const double& a)
-{
-  Packet4d pa = pset1<Packet4d>(a);
-  pstore(to, pa);
-}
-template<> EIGEN_STRONG_INLINE void pstore1<Packet8i>(int* to, const int& a)
-{
-  Packet8i pa = pset1<Packet8i>(a);
-  pstore(to, pa);
-}
-
-#ifndef EIGEN_VECTORIZE_AVX512
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-#endif
-
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet8f>(const Packet8f& a) {
-  return _mm_cvtss_f32(_mm256_castps256_ps128(a));
-}
-template<> EIGEN_STRONG_INLINE double pfirst<Packet4d>(const Packet4d& a) {
-  return _mm_cvtsd_f64(_mm256_castpd256_pd128(a));
-}
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet8i>(const Packet8i& a) {
-  return _mm_cvtsi128_si32(_mm256_castsi256_si128(a));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet8f preverse(const Packet8f& a)
-{
-  __m256 tmp = _mm256_shuffle_ps(a,a,0x1b);
-  return _mm256_permute2f128_ps(tmp, tmp, 1);
-}
-template<> EIGEN_STRONG_INLINE Packet4d preverse(const Packet4d& a)
-{
-   __m256d tmp = _mm256_shuffle_pd(a,a,5);
-  return _mm256_permute2f128_pd(tmp, tmp, 1);
-  #if 0
-  // This version is unlikely to be faster as _mm256_shuffle_ps and _mm256_permute_pd
-  // exhibit the same latency/throughput, but it is here for future reference/benchmarking...
-  __m256d swap_halves = _mm256_permute2f128_pd(a,a,1);
-    return _mm256_permute_pd(swap_halves,5);
-  #endif
-}
-
-// pabs should be ok
-template<> EIGEN_STRONG_INLINE Packet8f pabs(const Packet8f& a)
-{
-  const Packet8f mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF));
-  return _mm256_and_ps(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pabs(const Packet4d& a)
-{
-  const Packet4d mask = _mm256_castsi256_pd(_mm256_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF));
-  return _mm256_and_pd(a,mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pfrexp<Packet8f>(const Packet8f& a, Packet8f& exponent) {
-  return pfrexp_generic(a,exponent);
-}
-
-// Extract exponent without existence of Packet4l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet4d pfrexp_generic_get_biased_exponent(const Packet4d& a) {
-  const Packet4d cst_exp_mask  = pset1frombits<Packet4d>(static_cast<uint64_t>(0x7ff0000000000000ull));
-  __m256i a_expo = _mm256_castpd_si256(pand(a, cst_exp_mask));
-#ifdef EIGEN_VECTORIZE_AVX2
-  a_expo = _mm256_srli_epi64(a_expo, 52);
-  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
-  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
-#else
-  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
-  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
-  lo = _mm_srli_epi64(lo, 52);
-  hi = _mm_srli_epi64(hi, 52);
-#endif
-  Packet2d exponent_lo = _mm_cvtepi32_pd(vec4i_swizzle1(lo, 0, 2, 1, 3));
-  Packet2d exponent_hi = _mm_cvtepi32_pd(vec4i_swizzle1(hi, 0, 2, 1, 3));
-  Packet4d exponent = _mm256_insertf128_pd(_mm256_setzero_pd(), exponent_lo, 0);
-  exponent = _mm256_insertf128_pd(exponent, exponent_hi, 1);
-  return exponent;
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4d pfrexp<Packet4d>(const Packet4d& a, Packet4d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pldexp<Packet8f>(const Packet8f& a, const Packet8f& exponent) {
-  return pldexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4d pldexp<Packet4d>(const Packet4d& a, const Packet4d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet4d max_exponent = pset1<Packet4d>(2099.0);
-  const Packet4i e = _mm256_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-  
-  // Split 2^e into four factors and multiply.
-  const Packet4i bias = pset1<Packet4i>(1023);
-  Packet4i b = parithmetic_shift_right<2>(e);  // floor(e/4)
-  
-  // 2^b
-  Packet4i hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
-  Packet4i lo = _mm_slli_epi64(hi, 52);
-  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
-  Packet4d c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
-  Packet4d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
-  
-  // 2^(e - 3b)
-  b = psub(psub(psub(e, b), b), b);  // e - 3b
-  hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
-  lo = _mm_slli_epi64(hi, 52);
-  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
-  c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
-  out = pmul(out, c); // a * 2^e
-  return out;
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet8f>(const Packet8f& a)
-{
-  return predux(Packet4f(_mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1))));
-}
-template<> EIGEN_STRONG_INLINE double predux<Packet4d>(const Packet4d& a)
-{
-  return predux(Packet2d(_mm_add_pd(_mm256_castpd256_pd128(a),_mm256_extractf128_pd(a,1))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f predux_half_dowto4<Packet8f>(const Packet8f& a)
-{
-  return _mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet8f>(const Packet8f& a)
-{
-  Packet8f tmp;
-  tmp = _mm256_mul_ps(a, _mm256_permute2f128_ps(a,a,1));
-  tmp = _mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
-  return pfirst(_mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet4d>(const Packet4d& a)
-{
-  Packet4d tmp;
-  tmp = _mm256_mul_pd(a, _mm256_permute2f128_pd(a,a,1));
-  return pfirst(_mm256_mul_pd(tmp, _mm256_shuffle_pd(tmp,tmp,1)));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_min<Packet8f>(const Packet8f& a)
-{
-  Packet8f tmp = _mm256_min_ps(a, _mm256_permute2f128_ps(a,a,1));
-  tmp = _mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
-  return pfirst(_mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_min<Packet4d>(const Packet4d& a)
-{
-  Packet4d tmp = _mm256_min_pd(a, _mm256_permute2f128_pd(a,a,1));
-  return pfirst(_mm256_min_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_max<Packet8f>(const Packet8f& a)
-{
-  Packet8f tmp = _mm256_max_ps(a, _mm256_permute2f128_ps(a,a,1));
-  tmp = _mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
-  return pfirst(_mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
-}
-
-template<> EIGEN_STRONG_INLINE double predux_max<Packet4d>(const Packet4d& a)
-{
-  Packet4d tmp = _mm256_max_pd(a, _mm256_permute2f128_pd(a,a,1));
-  return pfirst(_mm256_max_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1)));
-}
-
-// not needed yet
-// template<> EIGEN_STRONG_INLINE bool predux_all(const Packet8f& x)
-// {
-//   return _mm256_movemask_ps(x)==0xFF;
-// }
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet8f& x)
-{
-  return _mm256_movemask_ps(x)!=0;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8f,8>& kernel) {
-  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-  __m256 T4 = _mm256_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
-  __m256 T5 = _mm256_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
-  __m256 T6 = _mm256_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
-  __m256 T7 = _mm256_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
-  __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0));
-  __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2));
-  __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0));
-  __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2));
-  __m256 S4 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(1,0,1,0));
-  __m256 S5 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(3,2,3,2));
-  __m256 S6 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(1,0,1,0));
-  __m256 S7 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(3,2,3,2));
-  kernel.packet[0] = _mm256_permute2f128_ps(S0, S4, 0x20);
-  kernel.packet[1] = _mm256_permute2f128_ps(S1, S5, 0x20);
-  kernel.packet[2] = _mm256_permute2f128_ps(S2, S6, 0x20);
-  kernel.packet[3] = _mm256_permute2f128_ps(S3, S7, 0x20);
-  kernel.packet[4] = _mm256_permute2f128_ps(S0, S4, 0x31);
-  kernel.packet[5] = _mm256_permute2f128_ps(S1, S5, 0x31);
-  kernel.packet[6] = _mm256_permute2f128_ps(S2, S6, 0x31);
-  kernel.packet[7] = _mm256_permute2f128_ps(S3, S7, 0x31);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8f,4>& kernel) {
-  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-
-  __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0));
-  __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2));
-  __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0));
-  __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2));
-
-  kernel.packet[0] = _mm256_permute2f128_ps(S0, S1, 0x20);
-  kernel.packet[1] = _mm256_permute2f128_ps(S2, S3, 0x20);
-  kernel.packet[2] = _mm256_permute2f128_ps(S0, S1, 0x31);
-  kernel.packet[3] = _mm256_permute2f128_ps(S2, S3, 0x31);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4d,4>& kernel) {
-  __m256d T0 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 15);
-  __m256d T1 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
-  __m256d T2 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 15);
-  __m256d T3 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
-
-  kernel.packet[1] = _mm256_permute2f128_pd(T0, T2, 32);
-  kernel.packet[3] = _mm256_permute2f128_pd(T0, T2, 49);
-  kernel.packet[0] = _mm256_permute2f128_pd(T1, T3, 32);
-  kernel.packet[2] = _mm256_permute2f128_pd(T1, T3, 49);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pblend(const Selector<8>& ifPacket, const Packet8f& thenPacket, const Packet8f& elsePacket) {
-  const __m256 zero = _mm256_setzero_ps();
-  const __m256 select = _mm256_set_ps(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m256 false_mask = _mm256_cmp_ps(select, zero, _CMP_EQ_UQ);
-  return _mm256_blendv_ps(thenPacket, elsePacket, false_mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4d pblend(const Selector<4>& ifPacket, const Packet4d& thenPacket, const Packet4d& elsePacket) {
-  const __m256d zero = _mm256_setzero_pd();
-  const __m256d select = _mm256_set_pd(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m256d false_mask = _mm256_cmp_pd(select, zero, _CMP_EQ_UQ);
-  return _mm256_blendv_pd(thenPacket, elsePacket, false_mask);
-}
-
-// Packet math for Eigen::half
-
-template<> struct unpacket_traits<Packet8h> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet8h half; };
-
-template<> EIGEN_STRONG_INLINE Packet8h pset1<Packet8h>(const Eigen::half& from) {
-  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8h>(const Packet8h& from) {
-  return numext::bit_cast<Eigen::half>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pload<Packet8h>(const Eigen::half* from) {
-  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h ploadu<Packet8h>(const Eigen::half* from) {
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8h& from) {
-  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8h& from) {
-  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h
-ploaddup<Packet8h>(const Eigen::half*  from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
-  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
-  return _mm_set_epi16(d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h
-ploadquad<Packet8h>(const Eigen::half* from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  return _mm_set_epi16(b, b, b, b, a, a, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h ptrue(const Packet8h& a) {
- return _mm_cmpeq_epi32(a, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pabs(const Packet8h& a) {
-  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_andnot_si128(sign_mask, a);
-}
-
-EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm256_cvtph_ps(a);
-#else
-  EIGEN_ALIGN32 Eigen::half aux[8];
-  pstore(aux, a);
-  float f0(aux[0]);
-  float f1(aux[1]);
-  float f2(aux[2]);
-  float f3(aux[3]);
-  float f4(aux[4]);
-  float f5(aux[5]);
-  float f6(aux[6]);
-  float f7(aux[7]);
-
-  return _mm256_set_ps(f7, f6, f5, f4, f3, f2, f1, f0);
-#endif
-}
-
-EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
-#else
-  EIGEN_ALIGN32 float aux[8];
-  pstore(aux, a);
-  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[0]));
-  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[1]));
-  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[2]));
-  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[3]));
-  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[4]));
-  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[5]));
-  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[6]));
-  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[7]));
-  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pmin<Packet8h>(const Packet8h& a,
-                                            const Packet8h& b) {
-  return float2half(pmin<Packet8f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h pmax<Packet8h>(const Packet8h& a,
-                                            const Packet8h& b) {
-  return float2half(pmax<Packet8f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h plset<Packet8h>(const half& a) {
-  return float2half(plset<Packet8f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h por(const Packet8h& a,const Packet8h& b) {
-  // in some cases Packet4i is a wrapper around __m128i, so we either need to
-  // cast to Packet4i to directly call the intrinsics as below:
-  return _mm_or_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8h pxor(const Packet8h& a,const Packet8h& b) {
-  return _mm_xor_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8h pand(const Packet8h& a,const Packet8h& b) {
-  return _mm_and_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8h pandnot(const Packet8h& a,const Packet8h& b) {
-  return _mm_andnot_si128(b,a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pselect(const Packet8h& mask, const Packet8h& a, const Packet8h& b) {
-  return _mm_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pround<Packet8h>(const Packet8h& a) {
-  return float2half(pround<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h print<Packet8h>(const Packet8h& a) {
-  return float2half(print<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pceil<Packet8h>(const Packet8h& a) {
-  return float2half(pceil<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pfloor<Packet8h>(const Packet8h& a) {
-  return float2half(pfloor<Packet8f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_eq(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_eq(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_le(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_le(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_lt(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_lt(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcmp_lt_or_nan(const Packet8h& a,const Packet8h& b) {
-  return Pack16To8(pcmp_lt_or_nan(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pconj(const Packet8h& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet8h pnegate(const Packet8h& a) {
-  Packet8h sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_xor_si128(a, sign_mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = padd(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h psub<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = psub(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pmul<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = pmul(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pdiv<Packet8h>(const Packet8h& a, const Packet8h& b) {
-  Packet8f af = half2float(a);
-  Packet8f bf = half2float(b);
-  Packet8f rf = pdiv(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pgather<Eigen::half, Packet8h>(const Eigen::half* from, Index stride)
-{
-  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0*stride]);
-  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1*stride]);
-  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2*stride]);
-  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3*stride]);
-  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4*stride]);
-  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5*stride]);
-  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6*stride]);
-  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7*stride]);
-  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8h>(Eigen::half* to, const Packet8h& from, Index stride)
-{
-  EIGEN_ALIGN32 Eigen::half aux[8];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux_max<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_min<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux_min<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet8h>(const Packet8h& a) {
-  Packet8f af = half2float(a);
-  float reduced = predux_mul<Packet8f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h preverse(const Packet8h& a)
-{
-  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-  return _mm_shuffle_epi8(a,m);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8h,8>& kernel) {
-  __m128i a = kernel.packet[0];
-  __m128i b = kernel.packet[1];
-  __m128i c = kernel.packet[2];
-  __m128i d = kernel.packet[3];
-  __m128i e = kernel.packet[4];
-  __m128i f = kernel.packet[5];
-  __m128i g = kernel.packet[6];
-  __m128i h = kernel.packet[7];
-
-  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
-  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
-  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
-  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
-  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
-  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
-  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
-  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
-
-  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
-  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
-  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
-  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
-  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
-  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
-  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
-  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
-
-  __m128i a0b0c0d0e0f0g0h0 = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
-  __m128i a1b1c1d1e1f1g1h1 = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
-  __m128i a2b2c2d2e2f2g2h2 = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
-  __m128i a3b3c3d3e3f3g3h3 = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
-  __m128i a4b4c4d4e4f4g4h4 = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
-  __m128i a5b5c5d5e5f5g5h5 = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
-  __m128i a6b6c6d6e6f6g6h6 = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
-  __m128i a7b7c7d7e7f7g7h7 = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
-
-  kernel.packet[0] = a0b0c0d0e0f0g0h0;
-  kernel.packet[1] = a1b1c1d1e1f1g1h1;
-  kernel.packet[2] = a2b2c2d2e2f2g2h2;
-  kernel.packet[3] = a3b3c3d3e3f3g3h3;
-  kernel.packet[4] = a4b4c4d4e4f4g4h4;
-  kernel.packet[5] = a5b5c5d5e5f5g5h5;
-  kernel.packet[6] = a6b6c6d6e6f6g6h6;
-  kernel.packet[7] = a7b7c7d7e7f7g7h7;
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8h,4>& kernel) {
-  EIGEN_ALIGN32 Eigen::half in[4][8];
-  pstore<Eigen::half>(in[0], kernel.packet[0]);
-  pstore<Eigen::half>(in[1], kernel.packet[1]);
-  pstore<Eigen::half>(in[2], kernel.packet[2]);
-  pstore<Eigen::half>(in[3], kernel.packet[3]);
-
-  EIGEN_ALIGN32 Eigen::half out[4][8];
-
-  for (int i = 0; i < 4; ++i) {
-    for (int j = 0; j < 4; ++j) {
-      out[i][j] = in[j][2*i];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+4] = in[j][2*i+1];
-    }
-  }
-
-  kernel.packet[0] = pload<Packet8h>(out[0]);
-  kernel.packet[1] = pload<Packet8h>(out[1]);
-  kernel.packet[2] = pload<Packet8h>(out[2]);
-  kernel.packet[3] = pload<Packet8h>(out[3]);
-}
-
-// BFloat16 implementation.
-
-EIGEN_STRONG_INLINE Packet8f Bf16ToF32(const Packet8bf& a) {
-#ifdef EIGEN_VECTORIZE_AVX2
-  __m256i extend = _mm256_cvtepu16_epi32(a);
-  return _mm256_castsi256_ps(_mm256_slli_epi32(extend, 16));
-#else
-  __m128i lo = _mm_cvtepu16_epi32(a);
-  __m128i hi = _mm_cvtepu16_epi32(_mm_srli_si128(a, 8));
-  __m128i lo_shift = _mm_slli_epi32(lo, 16);
-  __m128i hi_shift = _mm_slli_epi32(hi, 16);
-  return _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(lo_shift), hi_shift, 1));
-#endif
-}
-
-// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16(const Packet8f& a) {
-  Packet8bf r;
-
-  __m256i input = _mm256_castps_si256(a);
-
-#ifdef EIGEN_VECTORIZE_AVX2
-  // uint32_t lsb = (input >> 16);
-  __m256i t = _mm256_srli_epi32(input, 16);
-  // uint32_t lsb = lsb & 1;
-  t = _mm256_and_si256(t, _mm256_set1_epi32(1));
-  // uint32_t rounding_bias = 0x7fff + lsb;
-  t = _mm256_add_epi32(t, _mm256_set1_epi32(0x7fff));
-  // input += rounding_bias;
-  t = _mm256_add_epi32(t, input);
-  // input = input >> 16;
-  t = _mm256_srli_epi32(t, 16);
-  // Check NaN before converting back to bf16
-  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
-  __m256i nan = _mm256_set1_epi32(0x7fc0);
-  t = _mm256_blendv_epi8(nan, t, _mm256_castps_si256(mask));
-  // output = numext::bit_cast<uint16_t>(input);
-  return _mm_packus_epi32(_mm256_extractf128_si256(t, 0),
-                         _mm256_extractf128_si256(t, 1));
-#else
-  // uint32_t lsb = (input >> 16);
-  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(input, 0), 16);
-  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(input, 1), 16);
-  // uint32_t lsb = lsb & 1;
-  lo = _mm_and_si128(lo, _mm_set1_epi32(1));
-  hi = _mm_and_si128(hi, _mm_set1_epi32(1));
-  // uint32_t rounding_bias = 0x7fff + lsb;
-  lo = _mm_add_epi32(lo, _mm_set1_epi32(0x7fff));
-  hi = _mm_add_epi32(hi, _mm_set1_epi32(0x7fff));
-  // input += rounding_bias;
-  lo = _mm_add_epi32(lo, _mm256_extractf128_si256(input, 0));
-  hi = _mm_add_epi32(hi, _mm256_extractf128_si256(input, 1));
-  // input = input >> 16;
-  lo = _mm_srli_epi32(lo, 16);
-  hi = _mm_srli_epi32(hi, 16);
-  // Check NaN before converting back to bf16
-  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
-  __m128i nan = _mm_set1_epi32(0x7fc0);
-  lo = _mm_blendv_epi8(nan, lo, _mm_castps_si128(_mm256_castps256_ps128(mask)));
-  hi = _mm_blendv_epi8(nan, hi, _mm_castps_si128(_mm256_extractf128_ps(mask, 1)));
-  // output = numext::bit_cast<uint16_t>(input);
-  return _mm_packus_epi32(lo, hi);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16& from) {
-  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 pfirst<Packet8bf>(const Packet8bf& from) {
-  return numext::bit_cast<bfloat16>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16* from) {
-  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from) {
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet8bf& from) {
-  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet8bf& from) {
-  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf
-ploaddup<Packet8bf>(const bfloat16* from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
-  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
-  return _mm_set_epi16(d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf
-ploadquad<Packet8bf>(const bfloat16* from) {
-  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
-  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
-  return _mm_set_epi16(b, b, b, b, a, a, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ptrue(const Packet8bf& a) {
- return _mm_cmpeq_epi32(a, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pabs(const Packet8bf& a) {
-  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_andnot_si128(sign_mask, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a,
-                                                const Packet8bf& b) {
-  return F32ToBf16(pmin<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a,
-                                                const Packet8bf& b) {
-  return F32ToBf16(pmax<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
-  return F32ToBf16(plset<Packet8f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf por(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_or_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pxor(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_xor_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pand(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_and_si128(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pandnot(const Packet8bf& a,const Packet8bf& b) {
-  return _mm_andnot_si128(b,a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pselect(const Packet8bf& mask, const Packet8bf& a, const Packet8bf& b) {
-  return _mm_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf>(const Packet8bf& a)
-{
-  return F32ToBf16(pround<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf print<Packet8bf>(const Packet8bf& a) {
-  return F32ToBf16(print<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf>(const Packet8bf& a) {
-  return F32ToBf16(pceil<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf>(const Packet8bf& a) {
-  return F32ToBf16(pfloor<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a,const Packet8bf& b) {
-  return Pack16To8(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pconj(const Packet8bf& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet8bf pnegate(const Packet8bf& a) {
-  Packet8bf sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm_xor_si128(a, sign_mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(padd<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(psub<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(pmul<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return F32ToBf16(pdiv<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride)
-{
-  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0*stride]);
-  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1*stride]);
-  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2*stride]);
-  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3*stride]);
-  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4*stride]);
-  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5*stride]);
-  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6*stride]);
-  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7*stride]);
-  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride)
-{
-  EIGEN_ALIGN32 bfloat16 aux[8];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux_max<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux_min<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet8bf>(const Packet8bf& a) {
-  return static_cast<bfloat16>(predux_mul<Packet8f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a)
-{
-  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-  return _mm_shuffle_epi8(a,m);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8bf,8>& kernel) {
-  __m128i a = kernel.packet[0];
-  __m128i b = kernel.packet[1];
-  __m128i c = kernel.packet[2];
-  __m128i d = kernel.packet[3];
-  __m128i e = kernel.packet[4];
-  __m128i f = kernel.packet[5];
-  __m128i g = kernel.packet[6];
-  __m128i h = kernel.packet[7];
-
-  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
-  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
-  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
-  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
-  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
-  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
-  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
-  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
-
-  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
-  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
-  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
-  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
-  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
-  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
-  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
-  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
-
-  kernel.packet[0] = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
-  kernel.packet[1] = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
-  kernel.packet[2] = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
-  kernel.packet[3] = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
-  kernel.packet[4] = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
-  kernel.packet[5] = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
-  kernel.packet[6] = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
-  kernel.packet[7] = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet8bf,4>& kernel) {
-  __m128i a = kernel.packet[0];
-  __m128i b = kernel.packet[1];
-  __m128i c = kernel.packet[2];
-  __m128i d = kernel.packet[3];
-
-  __m128i ab_03 = _mm_unpacklo_epi16(a, b);
-  __m128i cd_03 = _mm_unpacklo_epi16(c, d);
-  __m128i ab_47 = _mm_unpackhi_epi16(a, b);
-  __m128i cd_47 = _mm_unpackhi_epi16(c, d);
-
-  kernel.packet[0] = _mm_unpacklo_epi32(ab_03, cd_03);
-  kernel.packet[1] = _mm_unpackhi_epi32(ab_03, cd_03);
-  kernel.packet[2] = _mm_unpacklo_epi32(ab_47, cd_47);
-  kernel.packet[3] = _mm_unpackhi_epi32(ab_47, cd_47);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h
deleted file mode 100644
index d507fb6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h
+++ /dev/null
@@ -1,115 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_AVX_H
-#define EIGEN_TYPE_CASTING_AVX_H
-
-namespace Eigen {
-
-namespace internal {
-
-// For now we use SSE to handle integers, so we can't use AVX instructions to cast
-// from int to float
-template <>
-struct type_casting_traits<float, int> {
-  enum {
-    VectorizedCast = 0,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<int, float> {
-  enum {
-    VectorizedCast = 0,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-
-#ifndef EIGEN_VECTORIZE_AVX512
-
-template <>
-struct type_casting_traits<Eigen::half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-
-template <>
-struct type_casting_traits<float, Eigen::half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<bfloat16, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<float, bfloat16> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-#endif  // EIGEN_VECTORIZE_AVX512
-
-template<> EIGEN_STRONG_INLINE Packet8i pcast<Packet8f, Packet8i>(const Packet8f& a) {
-  return _mm256_cvttps_epi32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8i, Packet8f>(const Packet8i& a) {
-  return _mm256_cvtepi32_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i,Packet8f>(const Packet8f& a) {
-  return _mm256_castps_si256(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f preinterpret<Packet8f,Packet8i>(const Packet8i& a) {
-  return _mm256_castsi256_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8h, Packet8f>(const Packet8h& a) {
-  return half2float(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8bf, Packet8f>(const Packet8bf& a) {
-  return Bf16ToF32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8h pcast<Packet8f, Packet8h>(const Packet8f& a) {
-  return float2half(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcast<Packet8f, Packet8bf>(const Packet8f& a) {
-  return F32ToBf16(a);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_AVX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
deleted file mode 100644
index 49c72b3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
+++ /dev/null
@@ -1,422 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_AVX512_H
-#define EIGEN_COMPLEX_AVX512_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet8cf
-{
-  EIGEN_STRONG_INLINE Packet8cf() {}
-  EIGEN_STRONG_INLINE explicit Packet8cf(const __m512& a) : v(a) {}
-  __m512  v;
-};
-
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet8cf type;
-  typedef Packet4cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet8cf> {
-  typedef std::complex<float> type;
-  typedef Packet4cf half;
-  typedef Packet16f as_real;
-  enum {
-    size = 8,
-    alignment=unpacket_traits<Packet16f>::alignment,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet8cf ptrue<Packet8cf>(const Packet8cf& a) { return Packet8cf(ptrue(Packet16f(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet8cf padd<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(_mm512_add_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf psub<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(_mm512_sub_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf pnegate(const Packet8cf& a)
-{
-  return Packet8cf(pnegate(a.v));
-}
-template<> EIGEN_STRONG_INLINE Packet8cf pconj(const Packet8cf& a)
-{
-  const __m512 mask = _mm512_castsi512_ps(_mm512_setr_epi32(
-    0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,
-    0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000));
-  return Packet8cf(pxor(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pmul<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
-{
-  __m512 tmp2 = _mm512_mul_ps(_mm512_movehdup_ps(a.v), _mm512_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1)));
-  return Packet8cf(_mm512_fmaddsub_ps(_mm512_moveldup_ps(a.v), b.v, tmp2));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pand   <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pand(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf por    <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(por(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf pxor   <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pxor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet8cf pandnot<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pandnot(a.v,b.v)); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8cf pcmp_eq(const Packet8cf& a, const Packet8cf& b) {
-  __m512 eq = pcmp_eq<Packet16f>(a.v, b.v);
-  return Packet8cf(pand(eq, _mm512_permute_ps(eq, 0xB1)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pload <Packet8cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet8cf(pload<Packet16f>(&numext::real_ref(*from))); }
-template<> EIGEN_STRONG_INLINE Packet8cf ploadu<Packet8cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet8cf(ploadu<Packet16f>(&numext::real_ref(*from))); }
-
-
-template<> EIGEN_STRONG_INLINE Packet8cf pset1<Packet8cf>(const std::complex<float>& from)
-{
-  return Packet8cf(_mm512_castpd_ps(pload1<Packet8d>((const double*)(const void*)&from)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf ploaddup<Packet8cf>(const std::complex<float>* from)
-{
-  return Packet8cf( _mm512_castpd_ps( ploaddup<Packet8d>((const double*)(const void*)from )) );
-}
-template<> EIGEN_STRONG_INLINE Packet8cf ploadquad<Packet8cf>(const std::complex<float>* from)
-{
-  return Packet8cf( _mm512_castpd_ps( ploadquad<Packet8d>((const double*)(const void*)from )) );
-}
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet8cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet8cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet8cf pgather<std::complex<float>, Packet8cf>(const std::complex<float>* from, Index stride)
-{
-  return Packet8cf(_mm512_castpd_ps(pgather<double,Packet8d>((const double*)(const void*)from, stride)));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet8cf>(std::complex<float>* to, const Packet8cf& from, Index stride)
-{
-  pscatter((double*)(void*)to, _mm512_castps_pd(from.v), stride);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet8cf>(const Packet8cf& a)
-{
-  return pfirst(Packet2cf(_mm512_castps512_ps128(a.v)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf preverse(const Packet8cf& a) {
-  return Packet8cf(_mm512_castsi512_ps(
-            _mm512_permutexvar_epi64( _mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7),
-                                      _mm512_castps_si512(a.v))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet8cf>(const Packet8cf& a)
-{
-  return predux(padd(Packet4cf(extract256<0>(a.v)),
-                     Packet4cf(extract256<1>(a.v))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet8cf>(const Packet8cf& a)
-{
-  return predux_mul(pmul(Packet4cf(extract256<0>(a.v)),
-                         Packet4cf(extract256<1>(a.v))));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4cf predux_half_dowto4<Packet8cf>(const Packet8cf& a) {
-  __m256 lane0 = extract256<0>(a.v);
-  __m256 lane1 = extract256<1>(a.v);
-  __m256 res = _mm256_add_ps(lane0, lane1);
-  return Packet4cf(res);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet8cf,Packet16f)
-
-template<> EIGEN_STRONG_INLINE Packet8cf pdiv<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
-{
-  Packet8cf num = pmul(a, pconj(b));
-  __m512 tmp = _mm512_mul_ps(b.v, b.v);
-  __m512 tmp2    = _mm512_shuffle_ps(tmp,tmp,0xB1);
-  __m512 denom = _mm512_add_ps(tmp, tmp2);
-  return Packet8cf(_mm512_div_ps(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf pcplxflip<Packet8cf>(const Packet8cf& x)
-{
-  return Packet8cf(_mm512_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1)));
-}
-
-//---------- double ----------
-struct Packet4cd
-{
-  EIGEN_STRONG_INLINE Packet4cd() {}
-  EIGEN_STRONG_INLINE explicit Packet4cd(const __m512d& a) : v(a) {}
-  __m512d  v;
-};
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet4cd type;
-  typedef Packet2cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet4cd> {
-  typedef std::complex<double> type;
-  typedef Packet2cd half;
-  typedef Packet8d as_real;
-  enum {
-    size = 4,
-    alignment = unpacket_traits<Packet8d>::alignment,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4cd padd<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(_mm512_add_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd psub<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(_mm512_sub_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pnegate(const Packet4cd& a) { return Packet4cd(pnegate(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pconj(const Packet4cd& a)
-{
-  const __m512d mask = _mm512_castsi512_pd(
-          _mm512_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0,
-                           0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0));
-  return Packet4cd(pxor(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd pmul<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
-{
-  __m512d tmp1 = _mm512_shuffle_pd(a.v,a.v,0x0);
-  __m512d tmp2 = _mm512_shuffle_pd(a.v,a.v,0xFF);
-  __m512d tmp3 = _mm512_shuffle_pd(b.v,b.v,0x55);
-  __m512d odd  = _mm512_mul_pd(tmp2, tmp3);
-  return Packet4cd(_mm512_fmaddsub_pd(tmp1, b.v, odd));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd ptrue<Packet4cd>(const Packet4cd& a) { return Packet4cd(ptrue(Packet8d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet4cd pand   <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pand(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd por    <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(por(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pxor   <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pxor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet4cd pandnot<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pandnot(a.v,b.v)); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4cd pcmp_eq(const Packet4cd& a, const Packet4cd& b) {
-  __m512d eq = pcmp_eq<Packet8d>(a.v, b.v);
-  return Packet4cd(pand(eq, _mm512_permute_pd(eq, 0x55)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd pload <Packet4cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet4cd(pload<Packet8d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet4cd ploadu<Packet4cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cd(ploadu<Packet8d>((const double*)from)); }
-
-template<> EIGEN_STRONG_INLINE Packet4cd pset1<Packet4cd>(const std::complex<double>& from)
-{
-  #ifdef EIGEN_VECTORIZE_AVX512DQ
-  return Packet4cd(_mm512_broadcast_f64x2(pset1<Packet1cd>(from).v));
-  #else
-  return Packet4cd(_mm512_castps_pd(_mm512_broadcast_f32x4( _mm_castpd_ps(pset1<Packet1cd>(from).v))));
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd ploaddup<Packet4cd>(const std::complex<double>* from) {
-  return Packet4cd(_mm512_insertf64x4(
-          _mm512_castpd256_pd512(ploaddup<Packet2cd>(from).v), ploaddup<Packet2cd>(from+1).v, 1));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet4cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet4cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet4cd pgather<std::complex<double>, Packet4cd>(const std::complex<double>* from, Index stride)
-{
-  return Packet4cd(_mm512_insertf64x4(_mm512_castpd256_pd512(
-            _mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+0*stride).v), ploadu<Packet1cd>(from+1*stride).v,1)),
-            _mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+2*stride).v), ploadu<Packet1cd>(from+3*stride).v,1), 1));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet4cd>(std::complex<double>* to, const Packet4cd& from, Index stride)
-{
-  __m512i fromi = _mm512_castpd_si512(from.v);
-  double* tod = (double*)(void*)to;
-  _mm_storeu_pd(tod+0*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,0)) );
-  _mm_storeu_pd(tod+2*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,1)) );
-  _mm_storeu_pd(tod+4*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,2)) );
-  _mm_storeu_pd(tod+6*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,3)) );
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet4cd>(const Packet4cd& a)
-{
-  __m128d low = extract128<0>(a.v);
-  EIGEN_ALIGN16 double res[2];
-  _mm_store_pd(res, low);
-  return std::complex<double>(res[0],res[1]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd preverse(const Packet4cd& a) {
-  return Packet4cd(_mm512_shuffle_f64x2(a.v, a.v, (shuffle_mask<3,2,1,0>::mask)));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet4cd>(const Packet4cd& a)
-{
-  return predux(padd(Packet2cd(_mm512_extractf64x4_pd(a.v,0)),
-                     Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet4cd>(const Packet4cd& a)
-{
-  return predux_mul(pmul(Packet2cd(_mm512_extractf64x4_pd(a.v,0)),
-                         Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
-}
-
-template<> struct conj_helper<Packet4cd, Packet4cd, false,true>
-{
-  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
-  { return padd(pmul(x,y),c); }
-
-  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
-  {
-    return internal::pmul(a, pconj(b));
-  }
-};
-
-template<> struct conj_helper<Packet4cd, Packet4cd, true,false>
-{
-  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
-  { return padd(pmul(x,y),c); }
-
-  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
-  {
-    return internal::pmul(pconj(a), b);
-  }
-};
-
-template<> struct conj_helper<Packet4cd, Packet4cd, true,true>
-{
-  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
-  { return padd(pmul(x,y),c); }
-
-  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
-  {
-    return pconj(internal::pmul(a, b));
-  }
-};
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cd,Packet8d)
-
-template<> EIGEN_STRONG_INLINE Packet4cd pdiv<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
-{
-  Packet4cd num = pmul(a, pconj(b));
-  __m512d tmp = _mm512_mul_pd(b.v, b.v);
-  __m512d denom =  padd(_mm512_permute_pd(tmp,0x55), tmp);
-  return Packet4cd(_mm512_div_pd(num.v, denom));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd pcplxflip<Packet4cd>(const Packet4cd& x)
-{
-  return Packet4cd(_mm512_permute_pd(x.v,0x55));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8cf,4>& kernel) {
-  PacketBlock<Packet8d,4> pb;
-  
-  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
-  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
-  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
-  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
-  ptranspose(pb);
-  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
-  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
-  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
-  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8cf,8>& kernel) {
-  PacketBlock<Packet8d,8> pb;
-  
-  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
-  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
-  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
-  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
-  pb.packet[4] = _mm512_castps_pd(kernel.packet[4].v);
-  pb.packet[5] = _mm512_castps_pd(kernel.packet[5].v);
-  pb.packet[6] = _mm512_castps_pd(kernel.packet[6].v);
-  pb.packet[7] = _mm512_castps_pd(kernel.packet[7].v);
-  ptranspose(pb);
-  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
-  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
-  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
-  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
-  kernel.packet[4].v = _mm512_castpd_ps(pb.packet[4]);
-  kernel.packet[5].v = _mm512_castpd_ps(pb.packet[5]);
-  kernel.packet[6].v = _mm512_castpd_ps(pb.packet[6]);
-  kernel.packet[7].v = _mm512_castpd_ps(pb.packet[7]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4cd,4>& kernel) {
-  __m512d T0 = _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<0,1,0,1>::mask)); // [a0 a1 b0 b1]
-  __m512d T1 = _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<2,3,2,3>::mask)); // [a2 a3 b2 b3]
-  __m512d T2 = _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<0,1,0,1>::mask)); // [c0 c1 d0 d1]
-  __m512d T3 = _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<2,3,2,3>::mask)); // [c2 c3 d2 d3]
-
-  kernel.packet[3] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<1,3,1,3>::mask))); // [a3 b3 c3 d3]
-  kernel.packet[2] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<0,2,0,2>::mask))); // [a2 b2 c2 d2]
-  kernel.packet[1] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<1,3,1,3>::mask))); // [a1 b1 c1 d1]
-  kernel.packet[0] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<0,2,0,2>::mask))); // [a0 b0 c0 d0]
-}
-
-template<> EIGEN_STRONG_INLINE Packet4cd psqrt<Packet4cd>(const Packet4cd& a) {
-  return psqrt_complex<Packet4cd>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8cf psqrt<Packet8cf>(const Packet8cf& a) {
-  return psqrt_complex<Packet8cf>(a);
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_AVX512_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
deleted file mode 100644
index 6fd726d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
+++ /dev/null
@@ -1,362 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Pedro Gonnet (pedro.gonnet@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
-#define THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
-
-namespace Eigen {
-
-namespace internal {
-
-// Disable the code for older versions of gcc that don't support many of the required avx512 instrinsics.
-#if EIGEN_GNUC_AT_LEAST(5, 3) || EIGEN_COMP_CLANG  || EIGEN_COMP_MSVC >= 1923
-
-#define _EIGEN_DECLARE_CONST_Packet16f(NAME, X) \
-  const Packet16f p16f_##NAME = pset1<Packet16f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet16f_FROM_INT(NAME, X) \
-  const Packet16f p16f_##NAME =  preinterpret<Packet16f,Packet16i>(pset1<Packet16i>(X))
-
-#define _EIGEN_DECLARE_CONST_Packet8d(NAME, X) \
-  const Packet8d p8d_##NAME = pset1<Packet8d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet8d_FROM_INT64(NAME, X) \
-  const Packet8d p8d_##NAME = _mm512_castsi512_pd(_mm512_set1_epi64(X))
-
-#define _EIGEN_DECLARE_CONST_Packet16bf(NAME, X) \
-  const Packet16bf p16bf_##NAME = pset1<Packet16bf>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet16bf_FROM_INT(NAME, X) \
-  const Packet16bf p16bf_##NAME =  preinterpret<Packet16bf,Packet16i>(pset1<Packet16i>(X))
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-plog<Packet16f>(const Packet16f& _x) {
-  return plog_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-plog<Packet8d>(const Packet8d& _x) {
-  return plog_double(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, plog)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog)
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-plog2<Packet16f>(const Packet16f& _x) {
-  return plog2_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-plog2<Packet8d>(const Packet8d& _x) {
-  return plog2_double(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, plog2)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog2)
-
-// Exponential function. Works by writing "x = m*log(2) + r" where
-// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
-// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-pexp<Packet16f>(const Packet16f& _x) {
-  _EIGEN_DECLARE_CONST_Packet16f(1, 1.0f);
-  _EIGEN_DECLARE_CONST_Packet16f(half, 0.5f);
-  _EIGEN_DECLARE_CONST_Packet16f(127, 127.0f);
-
-  _EIGEN_DECLARE_CONST_Packet16f(exp_hi, 88.3762626647950f);
-  _EIGEN_DECLARE_CONST_Packet16f(exp_lo, -88.3762626647949f);
-
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_LOG2EF, 1.44269504088896341f);
-
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p0, 1.9875691500E-4f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p1, 1.3981999507E-3f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p2, 8.3334519073E-3f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p3, 4.1665795894E-2f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p4, 1.6666665459E-1f);
-  _EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p5, 5.0000001201E-1f);
-
-  // Clamp x.
-  Packet16f x = pmax(pmin(_x, p16f_exp_hi), p16f_exp_lo);
-
-  // Express exp(x) as exp(m*ln(2) + r), start by extracting
-  // m = floor(x/ln(2) + 0.5).
-  Packet16f m = _mm512_floor_ps(pmadd(x, p16f_cephes_LOG2EF, p16f_half));
-
-  // Get r = x - m*ln(2). Note that we can do this without losing more than one
-  // ulp precision due to the FMA instruction.
-  _EIGEN_DECLARE_CONST_Packet16f(nln2, -0.6931471805599453f);
-  Packet16f r = _mm512_fmadd_ps(m, p16f_nln2, x);
-  Packet16f r2 = pmul(r, r);
-  Packet16f r3 = pmul(r2, r);
-
-  // Evaluate the polynomial approximant,improved by instruction-level parallelism.
-  Packet16f y, y1, y2;
-  y  = pmadd(p16f_cephes_exp_p0, r, p16f_cephes_exp_p1);
-  y1 = pmadd(p16f_cephes_exp_p3, r, p16f_cephes_exp_p4);
-  y2 = padd(r, p16f_1);
-  y  = pmadd(y, r, p16f_cephes_exp_p2);
-  y1 = pmadd(y1, r, p16f_cephes_exp_p5);
-  y  = pmadd(y, r3, y1);
-  y  = pmadd(y, r2, y2);
-
-  // Build emm0 = 2^m.
-  Packet16i emm0 = _mm512_cvttps_epi32(padd(m, p16f_127));
-  emm0 = _mm512_slli_epi32(emm0, 23);
-
-  // Return 2^m * exp(r).
-  return pmax(pmul(y, _mm512_castsi512_ps(emm0)), _x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-pexp<Packet8d>(const Packet8d& _x) {
-  return pexp_double(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pexp)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexp)
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pfrexp(const Packet16h& a, Packet16h& exponent) {
-  Packet16f fexponent;
-  const Packet16h out = float2half(pfrexp<Packet16f>(half2float(a), fexponent));
-  exponent = float2half(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pldexp(const Packet16h& a, const Packet16h& exponent) {
-  return float2half(pldexp<Packet16f>(half2float(a), half2float(exponent)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pfrexp(const Packet16bf& a, Packet16bf& exponent) {
-  Packet16f fexponent;
-  const Packet16bf out = F32ToBf16(pfrexp<Packet16f>(Bf16ToF32(a), fexponent));
-  exponent = F32ToBf16(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pldexp(const Packet16bf& a, const Packet16bf& exponent) {
-  return F32ToBf16(pldexp<Packet16f>(Bf16ToF32(a), Bf16ToF32(exponent)));
-}
-
-// Functions for sqrt.
-// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
-// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
-// exact solution. The main advantage of this approach is not just speed, but
-// also the fact that it can be inlined and pipelined with other computations,
-// further reducing its effective latency.
-#if EIGEN_FAST_MATH
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-psqrt<Packet16f>(const Packet16f& _x) {
-  Packet16f neg_half = pmul(_x, pset1<Packet16f>(-.5f));
-  __mmask16 denormal_mask = _mm512_kand(
-      _mm512_cmp_ps_mask(_x, pset1<Packet16f>((std::numeric_limits<float>::min)()),
-                        _CMP_LT_OQ),
-      _mm512_cmp_ps_mask(_x, _mm512_setzero_ps(), _CMP_GE_OQ));
-
-  Packet16f x = _mm512_rsqrt14_ps(_x);
-
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet16f>(1.5f)));
-
-  // Flush results for denormals to zero.
-  return _mm512_mask_blend_ps(denormal_mask, pmul(_x,x), _mm512_setzero_ps());
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-psqrt<Packet8d>(const Packet8d& _x) {
-  Packet8d neg_half = pmul(_x, pset1<Packet8d>(-.5));
-  __mmask16 denormal_mask = _mm512_kand(
-      _mm512_cmp_pd_mask(_x, pset1<Packet8d>((std::numeric_limits<double>::min)()),
-                        _CMP_LT_OQ),
-      _mm512_cmp_pd_mask(_x, _mm512_setzero_pd(), _CMP_GE_OQ));
-
-  Packet8d x = _mm512_rsqrt14_pd(_x);
-
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet8d>(1.5)));
-
-  // Do a second step of Newton's iteration.
-  x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet8d>(1.5)));
-
-  return _mm512_mask_blend_pd(denormal_mask, pmul(_x,x), _mm512_setzero_pd());
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet16f psqrt<Packet16f>(const Packet16f& x) {
-  return _mm512_sqrt_ps(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d psqrt<Packet8d>(const Packet8d& x) {
-  return _mm512_sqrt_pd(x);
-}
-#endif
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, psqrt)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, psqrt)
-
-// prsqrt for float.
-#if defined(EIGEN_VECTORIZE_AVX512ER)
-
-template <>
-EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
-  return _mm512_rsqrt28_ps(x);
-}
-#elif EIGEN_FAST_MATH
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-prsqrt<Packet16f>(const Packet16f& _x) {
-  _EIGEN_DECLARE_CONST_Packet16f_FROM_INT(inf, 0x7f800000);
-  _EIGEN_DECLARE_CONST_Packet16f(one_point_five, 1.5f);
-  _EIGEN_DECLARE_CONST_Packet16f(minus_half, -0.5f);
-
-  Packet16f neg_half = pmul(_x, p16f_minus_half);
-
-  // Identity infinite, negative and denormal arguments.
-  __mmask16 inf_mask = _mm512_cmp_ps_mask(_x, p16f_inf, _CMP_EQ_OQ);
-  __mmask16 not_pos_mask = _mm512_cmp_ps_mask(_x, _mm512_setzero_ps(), _CMP_LE_OQ);
-  __mmask16 not_finite_pos_mask = not_pos_mask | inf_mask;
-
-  // Compute an approximate result using the rsqrt intrinsic, forcing +inf
-  // for denormals for consistency with AVX and SSE implementations.
-  Packet16f y_approx = _mm512_rsqrt14_ps(_x);
-
-  // Do a single step of Newton-Raphson iteration to improve the approximation.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet16f y_newton = pmul(y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p16f_one_point_five));
-
-  // Select the result of the Newton-Raphson step for positive finite arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(0) = +inf.
-  return _mm512_mask_blend_ps(not_finite_pos_mask, y_newton, y_approx);
-}
-#else
-
-template <>
-EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
-  _EIGEN_DECLARE_CONST_Packet16f(one, 1.0f);
-  return _mm512_div_ps(p16f_one, _mm512_sqrt_ps(x));
-}
-#endif
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, prsqrt)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, prsqrt)
-
-// prsqrt for double.
-#if EIGEN_FAST_MATH
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
-prsqrt<Packet8d>(const Packet8d& _x) {
-  _EIGEN_DECLARE_CONST_Packet8d(one_point_five, 1.5);
-  _EIGEN_DECLARE_CONST_Packet8d(minus_half, -0.5);
-  _EIGEN_DECLARE_CONST_Packet8d_FROM_INT64(inf, 0x7ff0000000000000LL);
-
-  Packet8d neg_half = pmul(_x, p8d_minus_half);
-
-  // Identity infinite, negative and denormal arguments.
-  __mmask8 inf_mask = _mm512_cmp_pd_mask(_x, p8d_inf, _CMP_EQ_OQ);
-  __mmask8 not_pos_mask = _mm512_cmp_pd_mask(_x, _mm512_setzero_pd(), _CMP_LE_OQ);
-  __mmask8 not_finite_pos_mask = not_pos_mask | inf_mask;
-
-  // Compute an approximate result using the rsqrt intrinsic, forcing +inf
-  // for denormals for consistency with AVX and SSE implementations.
-#if defined(EIGEN_VECTORIZE_AVX512ER)
-  Packet8d y_approx = _mm512_rsqrt28_pd(_x);
-#else
-  Packet8d y_approx = _mm512_rsqrt14_pd(_x);
-#endif
-  // Do one or two steps of Newton-Raphson's to improve the approximation, depending on the
-  // starting accuracy (either 2^-14 or 2^-28, depending on whether AVX512ER is available).
-  // The Newton-Raphson algorithm has quadratic convergence and roughly doubles the number
-  // of correct digits for each step.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet8d y_newton = pmul(y_approx, pmadd(neg_half, pmul(y_approx, y_approx), p8d_one_point_five));
-#if !defined(EIGEN_VECTORIZE_AVX512ER)
-  y_newton = pmul(y_newton, pmadd(y_newton, pmul(neg_half, y_newton), p8d_one_point_five));
-#endif
-  // Select the result of the Newton-Raphson step for positive finite arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(0) = +inf.
-  return _mm512_mask_blend_pd(not_finite_pos_mask, y_newton, y_approx);
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet8d prsqrt<Packet8d>(const Packet8d& x) {
-  _EIGEN_DECLARE_CONST_Packet8d(one, 1.0f);
-  return _mm512_div_pd(p8d_one, _mm512_sqrt_pd(x));
-}
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet16f plog1p<Packet16f>(const Packet16f& _x) {
-  return generic_plog1p(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, plog1p)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog1p)
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet16f pexpm1<Packet16f>(const Packet16f& _x) {
-  return generic_expm1(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pexpm1)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexpm1)
-
-#endif
-
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-psin<Packet16f>(const Packet16f& _x) {
-  return psin_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-pcos<Packet16f>(const Packet16f& _x) {
-  return pcos_float(_x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
-ptanh<Packet16f>(const Packet16f& _x) {
-  return internal::generic_fast_tanh_float(_x);
-}
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, psin)
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pcos)
-F16_PACKET_FUNCTION(Packet16f, Packet16h, ptanh)
-
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, psin)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pcos)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, ptanh)
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
deleted file mode 100644
index 34d49ab..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
+++ /dev/null
@@ -1,2303 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner (benoit.steiner.goog@gmail.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_AVX512_H
-#define EIGEN_PACKET_MATH_AVX512_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-#endif
-
-#ifdef EIGEN_VECTORIZE_FMA
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-#endif
-
-typedef __m512 Packet16f;
-typedef __m512i Packet16i;
-typedef __m512d Packet8d;
-typedef eigen_packet_wrapper<__m256i, 1> Packet16h;
-typedef eigen_packet_wrapper<__m256i, 2> Packet16bf;
-
-template <>
-struct is_arithmetic<__m512> {
-  enum { value = true };
-};
-template <>
-struct is_arithmetic<__m512i> {
-  enum { value = true };
-};
-template <>
-struct is_arithmetic<__m512d> {
-  enum { value = true };
-};
-
-template<> struct is_arithmetic<Packet16h> { enum { value = true }; };
-
-template <>
-struct packet_traits<half> : default_packet_traits {
-  typedef Packet16h type;
-  // There is no half-size packet for Packet16h.
-  typedef Packet16h half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasCmp    = 1,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 1,
-    HasAbs2   = 0,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasSetLinear = 0,
-    HasLog    = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasExp    = 1,
-    HasSqrt   = 1,
-    HasRsqrt  = 1,
-    HasSin    = EIGEN_FAST_MATH,
-    HasCos    = EIGEN_FAST_MATH,
-    HasTanh   = EIGEN_FAST_MATH,
-    HasErf    = EIGEN_FAST_MATH,
-    HasBlend = 0,
-    HasRound  = 1,
-    HasFloor  = 1,
-    HasCeil   = 1,
-    HasRint   = 1,
-    HasBessel = 1,
-    HasNdtri  = 1
-  };
-};
-
-template<> struct packet_traits<float>  : default_packet_traits
-{
-  typedef Packet16f type;
-  typedef Packet8f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasAbs = 1,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 1,
-    HasBlend = 0,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
-    HasLog = 1,
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasNdtri = 1,
-    HasBessel  = 1,
-    HasExp = 1,
-    HasSqrt = EIGEN_FAST_MATH,
-    HasRsqrt = EIGEN_FAST_MATH,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
- };
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet8d type;
-  typedef Packet4d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
-    HasLog  = 1,
-    HasExp = 1,
-    HasSqrt = EIGEN_FAST_MATH,
-    HasRsqrt = EIGEN_FAST_MATH,
-#endif
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1
-  };
-};
-
-/* TODO Implement AVX512 for integers
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet16i type;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=8
-  };
-};
-*/
-
-template <>
-struct unpacket_traits<Packet16f> {
-  typedef float type;
-  typedef Packet8f half;
-  typedef Packet16i integer_packet;
-  typedef uint16_t mask_t;
-  enum { size = 16, alignment=Aligned64, vectorizable=true, masked_load_available=true, masked_store_available=true };
-};
-template <>
-struct unpacket_traits<Packet8d> {
-  typedef double type;
-  typedef Packet4d half;
-  enum { size = 8, alignment=Aligned64, vectorizable=true, masked_load_available=false, masked_store_available=false };
-};
-template <>
-struct unpacket_traits<Packet16i> {
-  typedef int type;
-  typedef Packet8i half;
-  enum { size = 16, alignment=Aligned64, vectorizable=false, masked_load_available=false, masked_store_available=false };
-};
-
-template<>
-struct unpacket_traits<Packet16h> {
-  typedef Eigen::half type;
-  typedef Packet8h half;
-  enum {size=16, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pset1<Packet16f>(const float& from) {
-  return _mm512_set1_ps(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pset1<Packet8d>(const double& from) {
-  return _mm512_set1_pd(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pset1<Packet16i>(const int& from) {
-  return _mm512_set1_epi32(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pset1frombits<Packet16f>(unsigned int from) {
-  return _mm512_castsi512_ps(_mm512_set1_epi32(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d pset1frombits<Packet8d>(const numext::uint64_t from) {
-  return _mm512_castsi512_pd(_mm512_set1_epi64(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pzero(const Packet16f& /*a*/) { return _mm512_setzero_ps(); }
-template<> EIGEN_STRONG_INLINE Packet8d pzero(const Packet8d& /*a*/) { return _mm512_setzero_pd(); }
-template<> EIGEN_STRONG_INLINE Packet16i pzero(const Packet16i& /*a*/) { return _mm512_setzero_si512(); }
-
-template<> EIGEN_STRONG_INLINE Packet16f peven_mask(const Packet16f& /*a*/) {
-  return _mm512_castsi512_ps(_mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1,
-                                              0, -1, 0, -1, 0, -1, 0, -1));
-}
-template<> EIGEN_STRONG_INLINE Packet16i peven_mask(const Packet16i& /*a*/) {
-  return _mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1,
-                          0, -1, 0, -1, 0, -1, 0, -1);
-}
-template<> EIGEN_STRONG_INLINE Packet8d peven_mask(const Packet8d& /*a*/) {
-  return _mm512_castsi512_pd(_mm512_set_epi32(0, 0, -1, -1, 0, 0, -1, -1,
-                                              0, 0, -1, -1, 0, 0, -1, -1));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pload1<Packet16f>(const float* from) {
-  return _mm512_broadcastss_ps(_mm_load_ps1(from));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pload1<Packet8d>(const double* from) {
-  return _mm512_set1_pd(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f plset<Packet16f>(const float& a) {
-  return _mm512_add_ps(
-      _mm512_set1_ps(a),
-      _mm512_set_ps(15.0f, 14.0f, 13.0f, 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f,
-                    4.0f, 3.0f, 2.0f, 1.0f, 0.0f));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d plset<Packet8d>(const double& a) {
-  return _mm512_add_pd(_mm512_set1_pd(a),
-                       _mm512_set_pd(7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f padd<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_add_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d padd<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_add_pd(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i padd<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_add_epi32(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f psub<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_sub_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d psub<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_sub_pd(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i psub<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_sub_epi32(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pnegate(const Packet16f& a) {
-  return _mm512_sub_ps(_mm512_set1_ps(0.0), a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pnegate(const Packet8d& a) {
-  return _mm512_sub_pd(_mm512_set1_pd(0.0), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pconj(const Packet16f& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pconj(const Packet8d& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pconj(const Packet16i& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pmul<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_mul_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmul<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_mul_pd(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pmul<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_mullo_epi32(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pdiv<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  return _mm512_div_ps(a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pdiv<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  return _mm512_div_pd(a, b);
-}
-
-#ifdef EIGEN_VECTORIZE_FMA
-template <>
-EIGEN_STRONG_INLINE Packet16f pmadd(const Packet16f& a, const Packet16f& b,
-                                    const Packet16f& c) {
-  return _mm512_fmadd_ps(a, b, c);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmadd(const Packet8d& a, const Packet8d& b,
-                                   const Packet8d& c) {
-  return _mm512_fmadd_pd(a, b, c);
-}
-#endif
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet16f pselect(const Packet16f& mask,
-                                           const Packet16f& a,
-                                           const Packet16f& b) {
-  __mmask16 mask16 = _mm512_cmp_epi32_mask(
-      _mm512_castps_si512(mask), _mm512_setzero_epi32(), _MM_CMPINT_EQ);
-  return _mm512_mask_blend_ps(mask16, a, b);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet8d pselect(const Packet8d& mask,
-                                          const Packet8d& a,
-                                          const Packet8d& b) {
-  __mmask8 mask8 = _mm512_cmp_epi64_mask(_mm512_castpd_si512(mask),
-                                         _mm512_setzero_epi32(), _MM_CMPINT_EQ);
-  return _mm512_mask_blend_pd(mask8, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pmin<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm512_min_ps(b, a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmin<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm512_min_pd(b, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pmax<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm512_max_ps(b, a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pmax<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm512_max_pd(b, a);
-}
-
-// Add specializations for min/max with prescribed NaN progation.
-template<>
-EIGEN_STRONG_INLINE Packet16f pmin<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmin<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet8d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet16f pmax<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmax<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet8d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet16f pmin<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmin<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet8d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet16f pmax<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet16f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet8d pmax<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet8d>);
-}
-
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-template<int I_> EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) { return _mm512_extractf32x8_ps(x,I_); }
-template<int I_> EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) { return _mm512_extractf64x2_pd(x,I_); }
-EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) { return _mm512_insertf32x8(_mm512_castps256_ps512(a),b,1); }
-#else
-// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
-template<int I_> EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) {
-  return  _mm256_castsi256_ps(_mm512_extracti64x4_epi64( _mm512_castps_si512(x),I_));
-}
-
-// AVX512F does not define _mm512_extractf64x2_pd to extract _m128 from _m512
-template<int I_> EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) {
-  return _mm_castsi128_pd(_mm512_extracti32x4_epi32( _mm512_castpd_si512(x),I_));
-}
-
-EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) {
-  return _mm512_castsi512_ps(_mm512_inserti64x4(_mm512_castsi256_si512(_mm256_castps_si256(a)),
-                                                _mm256_castps_si256(b),1));
-}
-#endif
-
-// Helper function for bit packing snippet of low precision comparison.
-// It packs the flags from 32x16 to 16x16.
-EIGEN_STRONG_INLINE __m256i Pack32To16(Packet16f rf) {
-  // Split data into small pieces and handle with AVX instructions
-  // to guarantee internal order of vector.
-  // Operation:
-  //   dst[15:0]    := Saturate16(rf[31:0])
-  //   dst[31:16]   := Saturate16(rf[63:32])
-  //   ...
-  //   dst[255:240] := Saturate16(rf[255:224])
-  __m256i lo = _mm256_castps_si256(extract256<0>(rf));
-  __m256i hi = _mm256_castps_si256(extract256<1>(rf));
-  __m128i result_lo = _mm_packs_epi32(_mm256_extractf128_si256(lo, 0),
-                                      _mm256_extractf128_si256(lo, 1));
-  __m128i result_hi = _mm_packs_epi32(_mm256_extractf128_si256(hi, 0),
-                                      _mm256_extractf128_si256(hi, 1));
-  return _mm256_insertf128_si256(_mm256_castsi128_si256(result_lo), result_hi, 1);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pcmp_eq(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_EQ_OQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-template<> EIGEN_STRONG_INLINE Packet16f pcmp_le(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LE_OQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pcmp_lt(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LT_OQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pcmp_lt_or_nan(const Packet16f& a, const Packet16f& b) {
-  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_NGE_UQ);
-  return _mm512_castsi512_ps(
-      _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16i pcmp_eq(const Packet16i& a, const Packet16i& b) {
-  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _CMP_EQ_OQ);
-  return _mm512_mask_set1_epi32(_mm512_set1_epi32(0), mask, 0xffffffffu);
-}
-
-
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_eq(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_EQ_OQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_le(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LE_OQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_lt(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LT_OQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pcmp_lt_or_nan(const Packet8d& a, const Packet8d& b) {
-  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_NGE_UQ);
-  return _mm512_castsi512_pd(
-      _mm512_mask_set1_epi64(_mm512_set1_epi64(0), mask, 0xffffffffffffffffu));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f print<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_CUR_DIRECTION); }
-template<> EIGEN_STRONG_INLINE Packet8d print<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_CUR_DIRECTION); }
-
-template<> EIGEN_STRONG_INLINE Packet16f pceil<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_TO_POS_INF); }
-template<> EIGEN_STRONG_INLINE Packet8d pceil<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_TO_POS_INF); }
-
-template<> EIGEN_STRONG_INLINE Packet16f pfloor<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_TO_NEG_INF); }
-template<> EIGEN_STRONG_INLINE Packet8d pfloor<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_TO_NEG_INF); }
-
-template <>
-EIGEN_STRONG_INLINE Packet16i ptrue<Packet16i>(const Packet16i& /*a*/) {
-  return _mm512_set1_epi32(0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f ptrue<Packet16f>(const Packet16f& a) {
-  return _mm512_castsi512_ps(ptrue<Packet16i>(_mm512_castps_si512(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d ptrue<Packet8d>(const Packet8d& a) {
-  return _mm512_castsi512_pd(ptrue<Packet16i>(_mm512_castpd_si512(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i pand<Packet16i>(const Packet16i& a,
-                                              const Packet16i& b) {
-  return _mm512_and_si512(a,b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pand<Packet16f>(const Packet16f& a,
-                                              const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_and_ps(a, b);
-#else
-  return _mm512_castsi512_ps(pand(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pand<Packet8d>(const Packet8d& a,
-                                            const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_and_pd(a, b);
-#else
-  Packet8d res = _mm512_undefined_pd();
-  Packet4d lane0_a = _mm512_extractf64x4_pd(a, 0);
-  Packet4d lane0_b = _mm512_extractf64x4_pd(b, 0);
-  res = _mm512_insertf64x4(res, _mm256_and_pd(lane0_a, lane0_b), 0);
-
-  Packet4d lane1_a = _mm512_extractf64x4_pd(a, 1);
-  Packet4d lane1_b = _mm512_extractf64x4_pd(b, 1);
-  return _mm512_insertf64x4(res, _mm256_and_pd(lane1_a, lane1_b), 1);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i por<Packet16i>(const Packet16i& a, const Packet16i& b) {
-  return _mm512_or_si512(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f por<Packet16f>(const Packet16f& a, const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_or_ps(a, b);
-#else
-  return _mm512_castsi512_ps(por(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d por<Packet8d>(const Packet8d& a,
-                                           const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_or_pd(a, b);
-#else
-  return _mm512_castsi512_pd(por(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i pxor<Packet16i>(const Packet16i& a, const Packet16i& b) {
-  return _mm512_xor_si512(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pxor<Packet16f>(const Packet16f& a, const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_xor_ps(a, b);
-#else
-  return _mm512_castsi512_ps(pxor(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8d pxor<Packet8d>(const Packet8d& a, const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_xor_pd(a, b);
-#else
-  return _mm512_castsi512_pd(pxor(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16i pandnot<Packet16i>(const Packet16i& a, const Packet16i& b) {
-  return _mm512_andnot_si512(b, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pandnot<Packet16f>(const Packet16f& a, const Packet16f& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_andnot_ps(b, a);
-#else
-  return _mm512_castsi512_ps(pandnot(_mm512_castps_si512(a),_mm512_castps_si512(b)));
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pandnot<Packet8d>(const Packet8d& a,const Packet8d& b) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_andnot_pd(b, a);
-#else
-  return _mm512_castsi512_pd(pandnot(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pround<Packet16f>(const Packet16f& a)
-{
-  // Work-around for default std::round rounding mode.
-  const Packet16f mask = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x80000000u));
-  const Packet16f prev0dot5 = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
-  return _mm512_roundscale_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-template<> EIGEN_STRONG_INLINE Packet8d pround<Packet8d>(const Packet8d& a)
-{
-  // Work-around for default std::round rounding mode.
-  const Packet8d mask = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
-  const Packet8d prev0dot5 = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
-  return _mm512_roundscale_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet16i parithmetic_shift_right(Packet16i a) {
-  return _mm512_srai_epi32(a, N);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet16i plogical_shift_right(Packet16i a) {
-  return _mm512_srli_epi32(a, N);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet16i plogical_shift_left(Packet16i a) {
-  return _mm512_slli_epi32(a, N);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f pload<Packet16f>(const float* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_ps(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pload<Packet8d>(const double* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_pd(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i pload<Packet16i>(const int* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_si512(
-      reinterpret_cast<const __m512i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_ps(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d ploadu<Packet8d>(const double* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_pd(from);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16i ploadu<Packet16i>(const int* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_si512(
-      reinterpret_cast<const __m512i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from, uint16_t umask) {
-  __mmask16 mask = static_cast<__mmask16>(umask);
-  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_maskz_loadu_ps(mask, from);
-}
-
-// Loads 8 floats from memory a returns the packet
-// {a0, a0  a1, a1, a2, a2, a3, a3, a4, a4, a5, a5, a6, a6, a7, a7}
-template <>
-EIGEN_STRONG_INLINE Packet16f ploaddup<Packet16f>(const float* from) {
-  // an unaligned load is required here as there is no requirement
-  // on the alignment of input pointer 'from'
-  __m256i low_half = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
-  __m512 even_elements = _mm512_castsi512_ps(_mm512_cvtepu32_epi64(low_half));
-  __m512 pairs = _mm512_permute_ps(even_elements, _MM_SHUFFLE(2, 2, 0, 0));
-  return pairs;
-}
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-// FIXME: this does not look optimal, better load a Packet4d and shuffle...
-// Loads 4 doubles from memory a returns the packet {a0, a0  a1, a1, a2, a2, a3,
-// a3}
-template <>
-EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
- __m512d x = _mm512_setzero_pd();
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[0]), 0);
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[1]), 1);
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[2]), 2);
-  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[3]), 3);
-  return x;
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
-  __m512d x = _mm512_setzero_pd();
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<0, _mm_load_sd(from+0));
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<2, _mm_load_sd(from+1));
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<4, _mm_load_sd(from+2));
-  x = _mm512_mask_broadcastsd_pd(x, 0x3<<6, _mm_load_sd(from+3));
-  return x;
-}
-#endif
-
-// Loads 4 floats from memory a returns the packet
-// {a0, a0  a0, a0, a1, a1, a1, a1, a2, a2, a2, a2, a3, a3, a3, a3}
-template <>
-EIGEN_STRONG_INLINE Packet16f ploadquad<Packet16f>(const float* from) {
-  Packet16f tmp = _mm512_castps128_ps512(ploadu<Packet4f>(from));
-  const Packet16i scatter_mask = _mm512_set_epi32(3,3,3,3, 2,2,2,2, 1,1,1,1, 0,0,0,0);
-  return _mm512_permutexvar_ps(scatter_mask, tmp);
-}
-
-// Loads 2 doubles from memory a returns the packet
-// {a0, a0  a0, a0, a1, a1, a1, a1}
-template <>
-EIGEN_STRONG_INLINE Packet8d ploadquad<Packet8d>(const double* from) {
-  __m256d lane0 = _mm256_set1_pd(*from);
-  __m256d lane1 = _mm256_set1_pd(*(from+1));
-  __m512d tmp = _mm512_undefined_pd();
-  tmp = _mm512_insertf64x4(tmp, lane0, 0);
-  return _mm512_insertf64x4(tmp, lane1, 1);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet16f& from) {
-  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_ps(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet8d& from) {
-  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_pd(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet16i& from) {
-  EIGEN_DEBUG_ALIGNED_STORE _mm512_storeu_si512(reinterpret_cast<__m512i*>(to),
-                                                from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_ps(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet8d& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_pd(to, from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet16i& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_si512(
-      reinterpret_cast<__m512i*>(to), from);
-}
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from, uint16_t umask) {
-  __mmask16 mask = static_cast<__mmask16>(umask);
-  EIGEN_DEBUG_UNALIGNED_STORE return _mm512_mask_storeu_ps(to, mask, from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet16f pgather<float, Packet16f>(const float* from,
-                                                             Index stride) {
-  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
-  Packet16i stride_multiplier =
-      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
-  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
-
-  return _mm512_i32gather_ps(indices, from, 4);
-}
-template <>
-EIGEN_DEVICE_FUNC inline Packet8d pgather<double, Packet8d>(const double* from,
-                                                            Index stride) {
-  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
-  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
-  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
-
-  return _mm512_i32gather_pd(indices, from, 8);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<float, Packet16f>(float* to,
-                                                         const Packet16f& from,
-                                                         Index stride) {
-  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
-  Packet16i stride_multiplier =
-      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
-  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
-  _mm512_i32scatter_ps(to, indices, from, 4);
-}
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<double, Packet8d>(double* to,
-                                                         const Packet8d& from,
-                                                         Index stride) {
-  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
-  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
-  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
-  _mm512_i32scatter_pd(to, indices, from, 8);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore1<Packet16f>(float* to, const float& a) {
-  Packet16f pa = pset1<Packet16f>(a);
-  pstore(to, pa);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore1<Packet8d>(double* to, const double& a) {
-  Packet8d pa = pset1<Packet8d>(a);
-  pstore(to, pa);
-}
-template <>
-EIGEN_STRONG_INLINE void pstore1<Packet16i>(int* to, const int& a) {
-  Packet16i pa = pset1<Packet16i>(a);
-  pstore(to, pa);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-
-template <>
-EIGEN_STRONG_INLINE float pfirst<Packet16f>(const Packet16f& a) {
-  return _mm_cvtss_f32(_mm512_extractf32x4_ps(a, 0));
-}
-template <>
-EIGEN_STRONG_INLINE double pfirst<Packet8d>(const Packet8d& a) {
-  return _mm_cvtsd_f64(_mm256_extractf128_pd(_mm512_extractf64x4_pd(a, 0), 0));
-}
-template <>
-EIGEN_STRONG_INLINE int pfirst<Packet16i>(const Packet16i& a) {
-  return _mm_extract_epi32(_mm512_extracti32x4_epi32(a, 0), 0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f preverse(const Packet16f& a)
-{
-  return _mm512_permutexvar_ps(_mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8d preverse(const Packet8d& a)
-{
-  return _mm512_permutexvar_pd(_mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pabs(const Packet16f& a)
-{
-  // _mm512_abs_ps intrinsic not found, so hack around it
-  return _mm512_castsi512_ps(_mm512_and_si512(_mm512_castps_si512(a), _mm512_set1_epi32(0x7fffffff)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pabs(const Packet8d& a) {
-  // _mm512_abs_ps intrinsic not found, so hack around it
-  return _mm512_castsi512_pd(_mm512_and_si512(_mm512_castpd_si512(a),
-                                   _mm512_set1_epi64(0x7fffffffffffffff)));
-}
-
-template<>
-EIGEN_STRONG_INLINE Packet16f pfrexp<Packet16f>(const Packet16f& a, Packet16f& exponent){
-  return pfrexp_generic(a, exponent);
-}
-
-// Extract exponent without existence of Packet8l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet8d pfrexp_generic_get_biased_exponent(const Packet8d& a) {
-  const Packet8d cst_exp_mask  = pset1frombits<Packet8d>(static_cast<uint64_t>(0x7ff0000000000000ull));
-  #ifdef EIGEN_VECTORIZE_AVX512DQ
-  return _mm512_cvtepi64_pd(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52));
-  #else
-  return _mm512_cvtepi32_pd(_mm512_cvtepi64_epi32(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52)));
-  #endif
-}
-
-template<>
-EIGEN_STRONG_INLINE Packet8d pfrexp<Packet8d>(const Packet8d& a, Packet8d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pldexp<Packet16f>(const Packet16f& a, const Packet16f& exponent) {
-  return pldexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8d pldexp<Packet8d>(const Packet8d& a, const Packet8d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet8d max_exponent = pset1<Packet8d>(2099.0);
-  const Packet8i e = _mm512_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-  
-  // Split 2^e into four factors and multiply.
-  const Packet8i bias = pset1<Packet8i>(1023);
-  Packet8i b = parithmetic_shift_right<2>(e);  // floor(e/4)
-  
-  // 2^b
-  const Packet8i permute_idx = _mm256_setr_epi32(0, 4, 1, 5, 2, 6, 3, 7);
-  Packet8i hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
-  Packet8i lo = _mm256_slli_epi64(hi, 52);
-  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
-  Packet8d c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
-  Packet8d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
-  
-  // 2^(e - 3b)
-  b = psub(psub(psub(e, b), b), b);  // e - 3b
-  hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
-  lo = _mm256_slli_epi64(hi, 52);
-  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
-  c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
-  out = pmul(out, c);  // a * 2^e
-  return out;
-}
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
-#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)                           \
-  __m256 OUTPUT##_0 = _mm512_extractf32x8_ps(INPUT, 0);                    \
-  __m256 OUTPUT##_1 = _mm512_extractf32x8_ps(INPUT, 1)
-#else
-#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)                \
-  __m256 OUTPUT##_0 = _mm256_insertf128_ps(                     \
-      _mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 0)), \
-      _mm512_extractf32x4_ps(INPUT, 1), 1);                     \
-  __m256 OUTPUT##_1 = _mm256_insertf128_ps(                     \
-      _mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 2)), \
-      _mm512_extractf32x4_ps(INPUT, 3), 1);
-#endif
-
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB) \
-  OUTPUT = _mm512_insertf32x8(_mm512_castps256_ps512(INPUTA), INPUTB, 1);
-#else
-#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB)                    \
-  OUTPUT = _mm512_undefined_ps();                                           \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 0), 0); \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 1), 1); \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 0), 2); \
-  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 1), 3);
-#endif
-
-template <>
-EIGEN_STRONG_INLINE float predux<Packet16f>(const Packet16f& a) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  __m256 lane0 = _mm512_extractf32x8_ps(a, 0);
-  __m256 lane1 = _mm512_extractf32x8_ps(a, 1);
-  Packet8f x = _mm256_add_ps(lane0, lane1);
-  return predux<Packet8f>(x);
-#else
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 sum = _mm_add_ps(_mm_add_ps(lane0, lane1), _mm_add_ps(lane2, lane3));
-  sum = _mm_hadd_ps(sum, sum);
-  sum = _mm_hadd_ps(sum, _mm_permute_ps(sum, 1));
-  return _mm_cvtss_f32(sum);
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE double predux<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d sum = _mm256_add_pd(lane0, lane1);
-  __m256d tmp0 = _mm256_hadd_pd(sum, _mm256_permute2f128_pd(sum, sum, 1));
-  return _mm_cvtsd_f64(_mm256_castpd256_pd128(_mm256_hadd_pd(tmp0, tmp0)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8f predux_half_dowto4<Packet16f>(const Packet16f& a) {
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-  __m256 lane0 = _mm512_extractf32x8_ps(a, 0);
-  __m256 lane1 = _mm512_extractf32x8_ps(a, 1);
-  return _mm256_add_ps(lane0, lane1);
-#else
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 sum0 = _mm_add_ps(lane0, lane2);
-  __m128 sum1 = _mm_add_ps(lane1, lane3);
-  return _mm256_insertf128_ps(_mm256_castps128_ps256(sum0), sum1, 1);
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE Packet4d predux_half_dowto4<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  return _mm256_add_pd(lane0, lane1);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_mul<Packet16f>(const Packet16f& a) {
-//#ifdef EIGEN_VECTORIZE_AVX512DQ
-#if 0
-  Packet8f lane0 = _mm512_extractf32x8_ps(a, 0);
-  Packet8f lane1 = _mm512_extractf32x8_ps(a, 1);
-  Packet8f res = pmul(lane0, lane1);
-  res = pmul(res, _mm256_permute2f128_ps(res, res, 1));
-  res = pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-#else
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 res = pmul(pmul(lane0, lane1), pmul(lane2, lane3));
-  res = pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-#endif
-}
-template <>
-EIGEN_STRONG_INLINE double predux_mul<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d res = pmul(lane0, lane1);
-  res = pmul(res, _mm256_permute2f128_pd(res, res, 1));
-  return pfirst(pmul(res, _mm256_shuffle_pd(res, res, 1)));
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_min<Packet16f>(const Packet16f& a) {
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 res = _mm_min_ps(_mm_min_ps(lane0, lane1), _mm_min_ps(lane2, lane3));
-  res = _mm_min_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(_mm_min_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-}
-template <>
-EIGEN_STRONG_INLINE double predux_min<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d res = _mm256_min_pd(lane0, lane1);
-  res = _mm256_min_pd(res, _mm256_permute2f128_pd(res, res, 1));
-  return pfirst(_mm256_min_pd(res, _mm256_shuffle_pd(res, res, 1)));
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_max<Packet16f>(const Packet16f& a) {
-  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
-  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
-  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
-  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
-  __m128 res = _mm_max_ps(_mm_max_ps(lane0, lane1), _mm_max_ps(lane2, lane3));
-  res = _mm_max_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
-  return pfirst(_mm_max_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
-}
-
-template <>
-EIGEN_STRONG_INLINE double predux_max<Packet8d>(const Packet8d& a) {
-  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
-  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
-  __m256d res = _mm256_max_pd(lane0, lane1);
-  res = _mm256_max_pd(res, _mm256_permute2f128_pd(res, res, 1));
-  return pfirst(_mm256_max_pd(res, _mm256_shuffle_pd(res, res, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet16f& x)
-{
-  Packet16i xi = _mm512_castps_si512(x);
-  __mmask16 tmp = _mm512_test_epi32_mask(xi,xi);
-  return !_mm512_kortestz(tmp,tmp);
-}
-
-
-
-#define PACK_OUTPUT(OUTPUT, INPUT, INDEX, STRIDE) \
-  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[INDEX], INPUT[INDEX + STRIDE]);
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 16>& kernel) {
-  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
-  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
-  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
-  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
-  __m512 T8 = _mm512_unpacklo_ps(kernel.packet[8], kernel.packet[9]);
-  __m512 T9 = _mm512_unpackhi_ps(kernel.packet[8], kernel.packet[9]);
-  __m512 T10 = _mm512_unpacklo_ps(kernel.packet[10], kernel.packet[11]);
-  __m512 T11 = _mm512_unpackhi_ps(kernel.packet[10], kernel.packet[11]);
-  __m512 T12 = _mm512_unpacklo_ps(kernel.packet[12], kernel.packet[13]);
-  __m512 T13 = _mm512_unpackhi_ps(kernel.packet[12], kernel.packet[13]);
-  __m512 T14 = _mm512_unpacklo_ps(kernel.packet[14], kernel.packet[15]);
-  __m512 T15 = _mm512_unpackhi_ps(kernel.packet[14], kernel.packet[15]);
-  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S4 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S5 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S6 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S7 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S8 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S9 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S10 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S11 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S12 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S13 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S14 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S15 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(3, 2, 3, 2));
-
-  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
-  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
-  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
-  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
-  EIGEN_EXTRACT_8f_FROM_16f(S4, S4);
-  EIGEN_EXTRACT_8f_FROM_16f(S5, S5);
-  EIGEN_EXTRACT_8f_FROM_16f(S6, S6);
-  EIGEN_EXTRACT_8f_FROM_16f(S7, S7);
-  EIGEN_EXTRACT_8f_FROM_16f(S8, S8);
-  EIGEN_EXTRACT_8f_FROM_16f(S9, S9);
-  EIGEN_EXTRACT_8f_FROM_16f(S10, S10);
-  EIGEN_EXTRACT_8f_FROM_16f(S11, S11);
-  EIGEN_EXTRACT_8f_FROM_16f(S12, S12);
-  EIGEN_EXTRACT_8f_FROM_16f(S13, S13);
-  EIGEN_EXTRACT_8f_FROM_16f(S14, S14);
-  EIGEN_EXTRACT_8f_FROM_16f(S15, S15);
-
-  PacketBlock<Packet8f, 32> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S4_0, 0x20);
-  tmp.packet[1] = _mm256_permute2f128_ps(S1_0, S5_0, 0x20);
-  tmp.packet[2] = _mm256_permute2f128_ps(S2_0, S6_0, 0x20);
-  tmp.packet[3] = _mm256_permute2f128_ps(S3_0, S7_0, 0x20);
-  tmp.packet[4] = _mm256_permute2f128_ps(S0_0, S4_0, 0x31);
-  tmp.packet[5] = _mm256_permute2f128_ps(S1_0, S5_0, 0x31);
-  tmp.packet[6] = _mm256_permute2f128_ps(S2_0, S6_0, 0x31);
-  tmp.packet[7] = _mm256_permute2f128_ps(S3_0, S7_0, 0x31);
-
-  tmp.packet[8] = _mm256_permute2f128_ps(S0_1, S4_1, 0x20);
-  tmp.packet[9] = _mm256_permute2f128_ps(S1_1, S5_1, 0x20);
-  tmp.packet[10] = _mm256_permute2f128_ps(S2_1, S6_1, 0x20);
-  tmp.packet[11] = _mm256_permute2f128_ps(S3_1, S7_1, 0x20);
-  tmp.packet[12] = _mm256_permute2f128_ps(S0_1, S4_1, 0x31);
-  tmp.packet[13] = _mm256_permute2f128_ps(S1_1, S5_1, 0x31);
-  tmp.packet[14] = _mm256_permute2f128_ps(S2_1, S6_1, 0x31);
-  tmp.packet[15] = _mm256_permute2f128_ps(S3_1, S7_1, 0x31);
-
-  // Second set of _m256 outputs
-  tmp.packet[16] = _mm256_permute2f128_ps(S8_0, S12_0, 0x20);
-  tmp.packet[17] = _mm256_permute2f128_ps(S9_0, S13_0, 0x20);
-  tmp.packet[18] = _mm256_permute2f128_ps(S10_0, S14_0, 0x20);
-  tmp.packet[19] = _mm256_permute2f128_ps(S11_0, S15_0, 0x20);
-  tmp.packet[20] = _mm256_permute2f128_ps(S8_0, S12_0, 0x31);
-  tmp.packet[21] = _mm256_permute2f128_ps(S9_0, S13_0, 0x31);
-  tmp.packet[22] = _mm256_permute2f128_ps(S10_0, S14_0, 0x31);
-  tmp.packet[23] = _mm256_permute2f128_ps(S11_0, S15_0, 0x31);
-
-  tmp.packet[24] = _mm256_permute2f128_ps(S8_1, S12_1, 0x20);
-  tmp.packet[25] = _mm256_permute2f128_ps(S9_1, S13_1, 0x20);
-  tmp.packet[26] = _mm256_permute2f128_ps(S10_1, S14_1, 0x20);
-  tmp.packet[27] = _mm256_permute2f128_ps(S11_1, S15_1, 0x20);
-  tmp.packet[28] = _mm256_permute2f128_ps(S8_1, S12_1, 0x31);
-  tmp.packet[29] = _mm256_permute2f128_ps(S9_1, S13_1, 0x31);
-  tmp.packet[30] = _mm256_permute2f128_ps(S10_1, S14_1, 0x31);
-  tmp.packet[31] = _mm256_permute2f128_ps(S11_1, S15_1, 0x31);
-
-  // Pack them into the output
-  PACK_OUTPUT(kernel.packet, tmp.packet, 0, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 1, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 2, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 3, 16);
-
-  PACK_OUTPUT(kernel.packet, tmp.packet, 4, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 5, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 6, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 7, 16);
-
-  PACK_OUTPUT(kernel.packet, tmp.packet, 8, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 9, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 10, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 11, 16);
-
-  PACK_OUTPUT(kernel.packet, tmp.packet, 12, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 13, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 14, 16);
-  PACK_OUTPUT(kernel.packet, tmp.packet, 15, 16);
-}
-#define PACK_OUTPUT_2(OUTPUT, INPUT, INDEX, STRIDE)         \
-  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[2 * INDEX], \
-                           INPUT[2 * INDEX + STRIDE]);
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 4>& kernel) {
-  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
-  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
-  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
-
-  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
-  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
-  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
-
-  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
-  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
-  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
-  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
-
-  PacketBlock<Packet8f, 8> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S1_0, 0x20);
-  tmp.packet[1] = _mm256_permute2f128_ps(S2_0, S3_0, 0x20);
-  tmp.packet[2] = _mm256_permute2f128_ps(S0_0, S1_0, 0x31);
-  tmp.packet[3] = _mm256_permute2f128_ps(S2_0, S3_0, 0x31);
-
-  tmp.packet[4] = _mm256_permute2f128_ps(S0_1, S1_1, 0x20);
-  tmp.packet[5] = _mm256_permute2f128_ps(S2_1, S3_1, 0x20);
-  tmp.packet[6] = _mm256_permute2f128_ps(S0_1, S1_1, 0x31);
-  tmp.packet[7] = _mm256_permute2f128_ps(S2_1, S3_1, 0x31);
-
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 0, 1);
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 1, 1);
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 2, 1);
-  PACK_OUTPUT_2(kernel.packet, tmp.packet, 3, 1);
-}
-
-#define PACK_OUTPUT_SQ_D(OUTPUT, INPUT, INDEX, STRIDE)                \
-  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX], 0); \
-  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX + STRIDE], 1);
-
-#define PACK_OUTPUT_D(OUTPUT, INPUT, INDEX, STRIDE)                         \
-  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX)], 0); \
-  OUTPUT[INDEX] =                                                           \
-      _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX) + STRIDE], 1);
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 4>& kernel) {
-  __m512d T0 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
-  __m512d T1 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0xff);
-  __m512d T2 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
-  __m512d T3 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0xff);
-
-  PacketBlock<Packet4d, 8> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x20);
-  tmp.packet[1] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x20);
-  tmp.packet[2] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x31);
-  tmp.packet[3] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x31);
-
-  tmp.packet[4] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x20);
-  tmp.packet[5] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x20);
-  tmp.packet[6] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x31);
-  tmp.packet[7] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x31);
-
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 0, 1);
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 1, 1);
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 2, 1);
-  PACK_OUTPUT_D(kernel.packet, tmp.packet, 3, 1);
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 8>& kernel) {
-  __m512d T0 = _mm512_unpacklo_pd(kernel.packet[0], kernel.packet[1]);
-  __m512d T1 = _mm512_unpackhi_pd(kernel.packet[0], kernel.packet[1]);
-  __m512d T2 = _mm512_unpacklo_pd(kernel.packet[2], kernel.packet[3]);
-  __m512d T3 = _mm512_unpackhi_pd(kernel.packet[2], kernel.packet[3]);
-  __m512d T4 = _mm512_unpacklo_pd(kernel.packet[4], kernel.packet[5]);
-  __m512d T5 = _mm512_unpackhi_pd(kernel.packet[4], kernel.packet[5]);
-  __m512d T6 = _mm512_unpacklo_pd(kernel.packet[6], kernel.packet[7]);
-  __m512d T7 = _mm512_unpackhi_pd(kernel.packet[6], kernel.packet[7]);
-
-  PacketBlock<Packet4d, 16> tmp;
-
-  tmp.packet[0] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x20);
-  tmp.packet[1] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x20);
-  tmp.packet[2] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
-                                         _mm512_extractf64x4_pd(T2, 0), 0x31);
-  tmp.packet[3] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
-                                         _mm512_extractf64x4_pd(T3, 0), 0x31);
-
-  tmp.packet[4] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x20);
-  tmp.packet[5] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x20);
-  tmp.packet[6] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
-                                         _mm512_extractf64x4_pd(T2, 1), 0x31);
-  tmp.packet[7] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
-                                         _mm512_extractf64x4_pd(T3, 1), 0x31);
-
-  tmp.packet[8] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 0),
-                                         _mm512_extractf64x4_pd(T6, 0), 0x20);
-  tmp.packet[9] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 0),
-                                         _mm512_extractf64x4_pd(T7, 0), 0x20);
-  tmp.packet[10] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 0),
-                                          _mm512_extractf64x4_pd(T6, 0), 0x31);
-  tmp.packet[11] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 0),
-                                          _mm512_extractf64x4_pd(T7, 0), 0x31);
-
-  tmp.packet[12] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 1),
-                                          _mm512_extractf64x4_pd(T6, 1), 0x20);
-  tmp.packet[13] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 1),
-                                          _mm512_extractf64x4_pd(T7, 1), 0x20);
-  tmp.packet[14] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T4, 1),
-                                          _mm512_extractf64x4_pd(T6, 1), 0x31);
-  tmp.packet[15] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T5, 1),
-                                          _mm512_extractf64x4_pd(T7, 1), 0x31);
-
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 0, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 1, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 2, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 3, 8);
-
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 4, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 5, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 6, 8);
-  PACK_OUTPUT_SQ_D(kernel.packet, tmp.packet, 7, 8);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16f pblend(const Selector<16>& /*ifPacket*/,
-                                     const Packet16f& /*thenPacket*/,
-                                     const Packet16f& /*elsePacket*/) {
-  assert(false && "To be implemented");
-  return Packet16f();
-}
-template <>
-EIGEN_STRONG_INLINE Packet8d pblend(const Selector<8>& ifPacket,
-                                    const Packet8d& thenPacket,
-                                    const Packet8d& elsePacket) {
-  __mmask8 m = (ifPacket.select[0]   )
-             | (ifPacket.select[1]<<1)
-             | (ifPacket.select[2]<<2)
-             | (ifPacket.select[3]<<3)
-             | (ifPacket.select[4]<<4)
-             | (ifPacket.select[5]<<5)
-             | (ifPacket.select[6]<<6)
-             | (ifPacket.select[7]<<7);
-  return _mm512_mask_blend_pd(m, elsePacket, thenPacket);
-}
-
-// Packet math for Eigen::half
-template<> EIGEN_STRONG_INLINE Packet16h pset1<Packet16h>(const Eigen::half& from) {
-  return _mm256_set1_epi16(from.x);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet16h>(const Packet16h& from) {
-  return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm256_extract_epi16(from, 0)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pload<Packet16h>(const Eigen::half* from) {
-  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h ploadu<Packet16h>(const Eigen::half* from) {
-  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet16h& from) {
-  // (void*) -> workaround clang warning:
-  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
-  _mm256_store_si256((__m256i*)(void*)to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet16h& from) {
-  // (void*) -> workaround clang warning:
-  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
-  _mm256_storeu_si256((__m256i*)(void*)to, from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h
-ploaddup<Packet16h>(const Eigen::half*  from) {
-  unsigned short a = from[0].x;
-  unsigned short b = from[1].x;
-  unsigned short c = from[2].x;
-  unsigned short d = from[3].x;
-  unsigned short e = from[4].x;
-  unsigned short f = from[5].x;
-  unsigned short g = from[6].x;
-  unsigned short h = from[7].x;
-  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h
-ploadquad(const Eigen::half* from) {
-  unsigned short a = from[0].x;
-  unsigned short b = from[1].x;
-  unsigned short c = from[2].x;
-  unsigned short d = from[3].x;
-  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
-}
-
-EIGEN_STRONG_INLINE Packet16f half2float(const Packet16h& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm512_cvtph_ps(a);
-#else
-  EIGEN_ALIGN64 half aux[16];
-  pstore(aux, a);
-  float f0(aux[0]);
-  float f1(aux[1]);
-  float f2(aux[2]);
-  float f3(aux[3]);
-  float f4(aux[4]);
-  float f5(aux[5]);
-  float f6(aux[6]);
-  float f7(aux[7]);
-  float f8(aux[8]);
-  float f9(aux[9]);
-  float fa(aux[10]);
-  float fb(aux[11]);
-  float fc(aux[12]);
-  float fd(aux[13]);
-  float fe(aux[14]);
-  float ff(aux[15]);
-
-  return _mm512_set_ps(
-      ff, fe, fd, fc, fb, fa, f9, f8, f7, f6, f5, f4, f3, f2, f1, f0);
-#endif
-}
-
-EIGEN_STRONG_INLINE Packet16h float2half(const Packet16f& a) {
-#ifdef EIGEN_HAS_FP16_C
-  return _mm512_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
-#else
-  EIGEN_ALIGN64 float aux[16];
-  pstore(aux, a);
-  half h0(aux[0]);
-  half h1(aux[1]);
-  half h2(aux[2]);
-  half h3(aux[3]);
-  half h4(aux[4]);
-  half h5(aux[5]);
-  half h6(aux[6]);
-  half h7(aux[7]);
-  half h8(aux[8]);
-  half h9(aux[9]);
-  half ha(aux[10]);
-  half hb(aux[11]);
-  half hc(aux[12]);
-  half hd(aux[13]);
-  half he(aux[14]);
-  half hf(aux[15]);
-
-  return _mm256_set_epi16(
-      hf.x, he.x, hd.x, hc.x, hb.x, ha.x, h9.x, h8.x,
-      h7.x, h6.x, h5.x, h4.x, h3.x, h2.x, h1.x, h0.x);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h ptrue(const Packet16h& a) {
-  return ptrue(Packet8i(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pabs(const Packet16h& a) {
-  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm256_andnot_si256(sign_mask, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pmin<Packet16h>(const Packet16h& a,
-                                              const Packet16h& b) {
-  return float2half(pmin<Packet16f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h pmax<Packet16h>(const Packet16h& a,
-                                              const Packet16h& b) {
-  return float2half(pmax<Packet16f>(half2float(a), half2float(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16h plset<Packet16h>(const half& a) {
-  return float2half(plset<Packet16f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h por(const Packet16h& a,const Packet16h& b) {
-  // in some cases Packet8i is a wrapper around __m256i, so we need to
-  // cast to Packet8i to call the correct overload.
-  return por(Packet8i(a),Packet8i(b));
-}
-template<> EIGEN_STRONG_INLINE Packet16h pxor(const Packet16h& a,const Packet16h& b) {
-  return pxor(Packet8i(a),Packet8i(b));
-}
-template<> EIGEN_STRONG_INLINE Packet16h pand(const Packet16h& a,const Packet16h& b) {
-  return pand(Packet8i(a),Packet8i(b));
-}
-template<> EIGEN_STRONG_INLINE Packet16h pandnot(const Packet16h& a,const Packet16h& b) {
-  return pandnot(Packet8i(a),Packet8i(b));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pselect(const Packet16h& mask, const Packet16h& a, const Packet16h& b) {
-  return _mm256_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pround<Packet16h>(const Packet16h& a) {
-  return float2half(pround<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h print<Packet16h>(const Packet16h& a) {
-  return float2half(print<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pceil<Packet16h>(const Packet16h& a) {
-  return float2half(pceil<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pfloor<Packet16h>(const Packet16h& a) {
-  return float2half(pfloor<Packet16f>(half2float(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_eq(const Packet16h& a,const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  return Pack32To16(pcmp_eq(af, bf));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_le(const Packet16h& a,const Packet16h& b) {
-  return Pack32To16(pcmp_le(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_lt(const Packet16h& a,const Packet16h& b) {
-  return Pack32To16(pcmp_lt(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pcmp_lt_or_nan(const Packet16h& a,const Packet16h& b) {
-  return Pack32To16(pcmp_lt_or_nan(half2float(a), half2float(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pconj(const Packet16h& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet16h pnegate(const Packet16h& a) {
-  Packet16h sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
-  return _mm256_xor_si256(a, sign_mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h padd<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = padd(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h psub<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = psub(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pmul<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = pmul(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pdiv<Packet16h>(const Packet16h& a, const Packet16h& b) {
-  Packet16f af = half2float(a);
-  Packet16f bf = half2float(b);
-  Packet16f rf = pdiv(af, bf);
-  return float2half(rf);
-}
-
-template<> EIGEN_STRONG_INLINE half predux<Packet16h>(const Packet16h& from) {
-  Packet16f from_float = half2float(from);
-  return half(predux(from_float));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8h predux_half_dowto4<Packet16h>(const Packet16h& a) {
-  Packet8h lane0 = _mm256_extractf128_si256(a, 0);
-  Packet8h lane1 = _mm256_extractf128_si256(a, 1);
-  return padd<Packet8h>(lane0, lane1);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_max<Packet16h>(const Packet16h& a) {
-  Packet16f af = half2float(a);
-  float reduced = predux_max<Packet16f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half predux_min<Packet16h>(const Packet16h& a) {
-  Packet16f af = half2float(a);
-  float reduced = predux_min<Packet16f>(af);
-  return Eigen::half(reduced);
-}
-
-template<> EIGEN_STRONG_INLINE half predux_mul<Packet16h>(const Packet16h& from) {
-  Packet16f from_float = half2float(from);
-  return half(predux_mul(from_float));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h preverse(const Packet16h& a)
-{
-  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-  return _mm256_insertf128_si256(
-                    _mm256_castsi128_si256(_mm_shuffle_epi8(_mm256_extractf128_si256(a,1),m)),
-                                           _mm_shuffle_epi8(_mm256_extractf128_si256(a,0),m), 1);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16h pgather<Eigen::half, Packet16h>(const Eigen::half* from, Index stride)
-{
-  return _mm256_set_epi16(
-      from[15*stride].x, from[14*stride].x, from[13*stride].x, from[12*stride].x,
-      from[11*stride].x, from[10*stride].x, from[9*stride].x, from[8*stride].x,
-      from[7*stride].x, from[6*stride].x, from[5*stride].x, from[4*stride].x,
-      from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<half, Packet16h>(half* to, const Packet16h& from, Index stride)
-{
-  EIGEN_ALIGN64 half aux[16];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-  to[stride*8] = aux[8];
-  to[stride*9] = aux[9];
-  to[stride*10] = aux[10];
-  to[stride*11] = aux[11];
-  to[stride*12] = aux[12];
-  to[stride*13] = aux[13];
-  to[stride*14] = aux[14];
-  to[stride*15] = aux[15];
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet16h,16>& kernel) {
-  __m256i a = kernel.packet[0];
-  __m256i b = kernel.packet[1];
-  __m256i c = kernel.packet[2];
-  __m256i d = kernel.packet[3];
-  __m256i e = kernel.packet[4];
-  __m256i f = kernel.packet[5];
-  __m256i g = kernel.packet[6];
-  __m256i h = kernel.packet[7];
-  __m256i i = kernel.packet[8];
-  __m256i j = kernel.packet[9];
-  __m256i k = kernel.packet[10];
-  __m256i l = kernel.packet[11];
-  __m256i m = kernel.packet[12];
-  __m256i n = kernel.packet[13];
-  __m256i o = kernel.packet[14];
-  __m256i p = kernel.packet[15];
-
-  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
-  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
-  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
-  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
-  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
-  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
-  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
-  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
-
-  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
-  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
-  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
-  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
-  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
-  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
-  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
-  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
-
-  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
-  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
-  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
-  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
-  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
-  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
-  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
-  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
-
-  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
-  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
-  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
-  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
-  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
-  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
-  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
-  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
-
-  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
-  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
-  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
-  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
-  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
-  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
-  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
-  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
-  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
-  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
-  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
-  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
-  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
-  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
-  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
-  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
-
-  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
-  __m256i a_p_0 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
-  __m256i a_p_1 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
-  __m256i a_p_2 = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
-  __m256i a_p_3 = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
-  __m256i a_p_4 = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
-  __m256i a_p_5 = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
-  __m256i a_p_6 = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
-  __m256i a_p_7 = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
-  __m256i a_p_8 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
-  __m256i a_p_9 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
-  __m256i a_p_a = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
-  __m256i a_p_b = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
-  __m256i a_p_c = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
-  __m256i a_p_d = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
-  __m256i a_p_e = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
-  __m256i a_p_f = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
-
-  kernel.packet[0] = a_p_0;
-  kernel.packet[1] = a_p_1;
-  kernel.packet[2] = a_p_2;
-  kernel.packet[3] = a_p_3;
-  kernel.packet[4] = a_p_4;
-  kernel.packet[5] = a_p_5;
-  kernel.packet[6] = a_p_6;
-  kernel.packet[7] = a_p_7;
-  kernel.packet[8] = a_p_8;
-  kernel.packet[9] = a_p_9;
-  kernel.packet[10] = a_p_a;
-  kernel.packet[11] = a_p_b;
-  kernel.packet[12] = a_p_c;
-  kernel.packet[13] = a_p_d;
-  kernel.packet[14] = a_p_e;
-  kernel.packet[15] = a_p_f;
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet16h,8>& kernel) {
-  EIGEN_ALIGN64 half in[8][16];
-  pstore<half>(in[0], kernel.packet[0]);
-  pstore<half>(in[1], kernel.packet[1]);
-  pstore<half>(in[2], kernel.packet[2]);
-  pstore<half>(in[3], kernel.packet[3]);
-  pstore<half>(in[4], kernel.packet[4]);
-  pstore<half>(in[5], kernel.packet[5]);
-  pstore<half>(in[6], kernel.packet[6]);
-  pstore<half>(in[7], kernel.packet[7]);
-
-  EIGEN_ALIGN64 half out[8][16];
-
-  for (int i = 0; i < 8; ++i) {
-    for (int j = 0; j < 8; ++j) {
-      out[i][j] = in[j][2*i];
-    }
-    for (int j = 0; j < 8; ++j) {
-      out[i][j+8] = in[j][2*i+1];
-    }
-  }
-
-  kernel.packet[0] = pload<Packet16h>(out[0]);
-  kernel.packet[1] = pload<Packet16h>(out[1]);
-  kernel.packet[2] = pload<Packet16h>(out[2]);
-  kernel.packet[3] = pload<Packet16h>(out[3]);
-  kernel.packet[4] = pload<Packet16h>(out[4]);
-  kernel.packet[5] = pload<Packet16h>(out[5]);
-  kernel.packet[6] = pload<Packet16h>(out[6]);
-  kernel.packet[7] = pload<Packet16h>(out[7]);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet16h,4>& kernel) {
-  EIGEN_ALIGN64 half in[4][16];
-  pstore<half>(in[0], kernel.packet[0]);
-  pstore<half>(in[1], kernel.packet[1]);
-  pstore<half>(in[2], kernel.packet[2]);
-  pstore<half>(in[3], kernel.packet[3]);
-
-  EIGEN_ALIGN64 half out[4][16];
-
-  for (int i = 0; i < 4; ++i) {
-    for (int j = 0; j < 4; ++j) {
-      out[i][j] = in[j][4*i];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+4] = in[j][4*i+1];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+8] = in[j][4*i+2];
-    }
-    for (int j = 0; j < 4; ++j) {
-      out[i][j+12] = in[j][4*i+3];
-    }
-  }
-
-  kernel.packet[0] = pload<Packet16h>(out[0]);
-  kernel.packet[1] = pload<Packet16h>(out[1]);
-  kernel.packet[2] = pload<Packet16h>(out[2]);
-  kernel.packet[3] = pload<Packet16h>(out[3]);
-}
-
-template <> struct is_arithmetic<Packet16bf> { enum { value = true }; };
-
-template <>
-struct packet_traits<bfloat16> : default_packet_traits {
-  typedef Packet16bf type;
-  typedef Packet8bf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-    HasBlend = 0,
-    HasInsert = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
-#ifdef EIGEN_VECTORIZE_AVX512DQ
-    HasLog = 1,  // Currently fails test with bad accuracy.
-    HasLog1p  = 1,
-    HasExpm1  = 1,
-    HasNdtri = 1,
-    HasBessel  = 1,
-#endif
-    HasExp = 1,
-    HasSqrt = EIGEN_FAST_MATH,
-    HasRsqrt = EIGEN_FAST_MATH,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasCmp  = 1,
-    HasDiv = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet16bf>
-{
-  typedef bfloat16 type;
-  enum {size=16, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
-  typedef Packet8bf half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pset1<Packet16bf>(const bfloat16& from) {
-  return _mm256_set1_epi16(from.value);
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 pfirst<Packet16bf>(const Packet16bf& from) {
-  bfloat16 t;
-  t.value = static_cast<unsigned short>(_mm256_extract_epi16(from, 0));
-  return t;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pload<Packet16bf>(const bfloat16* from) {
-  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf ploadu<Packet16bf>(const bfloat16* from) {
-  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to,
-                                          const Packet16bf& from) {
-  _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to,
-                                           const Packet16bf& from) {
-  _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf
-ploaddup<Packet16bf>(const bfloat16* from) {
-  Packet16bf r;
-  unsigned short a = from[0].value;
-  unsigned short b = from[1].value;
-  unsigned short c = from[2].value;
-  unsigned short d = from[3].value;
-  unsigned short e = from[4].value;
-  unsigned short f = from[5].value;
-  unsigned short g = from[6].value;
-  unsigned short h = from[7].value;
-  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf
-ploadquad(const bfloat16* from) {
-  Packet16bf r;
-  unsigned short a = from[0].value;
-  unsigned short b = from[1].value;
-  unsigned short c = from[2].value;
-  unsigned short d = from[3].value;
-  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
-}
-
-EIGEN_STRONG_INLINE Packet16f Bf16ToF32(const Packet16bf& a) {
-  return _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16));
-}
-
-// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
-EIGEN_STRONG_INLINE Packet16bf F32ToBf16(const Packet16f& a) {
-  Packet16bf r;
-
-#if defined(EIGEN_VECTORIZE_AVX512BF16) && EIGEN_GNUC_AT_LEAST(10, 1)
-  // Since GCC 10.1 supports avx512bf16 and C style explicit cast
-  // (C++ static_cast is not supported yet), do converion via intrinsic
-  // and register path for performance.
-  r = (__m256i)(_mm512_cvtneps_pbh(a));
-
-#else
-  __m512i t;
-  __m512i input = _mm512_castps_si512(a);
-  __m512i nan = _mm512_set1_epi32(0x7fc0);
-
-  // uint32_t lsb = (input >> 16) & 1;
-  t = _mm512_and_si512(_mm512_srli_epi32(input, 16), _mm512_set1_epi32(1));
-  // uint32_t rounding_bias = 0x7fff + lsb;
-  t = _mm512_add_epi32(t, _mm512_set1_epi32(0x7fff));
-  // input += rounding_bias;
-  t = _mm512_add_epi32(t, input);
-  // input = input >> 16;
-  t = _mm512_srli_epi32(t, 16);
-
-  // Check NaN before converting back to bf16
-  __mmask16 mask = _mm512_cmp_ps_mask(a, a, _CMP_ORD_Q);
-
-  t = _mm512_mask_blend_epi32(mask, nan, t);
-  // output.value = static_cast<uint16_t>(input);
-  r = _mm512_cvtepi32_epi16(t);
-#endif // EIGEN_VECTORIZE_AVX512BF16
-
-  return r;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf ptrue(const Packet16bf& a) {
-  return ptrue<Packet8i>(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf por(const Packet16bf& a, const Packet16bf& b) {
-  return por<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pxor(const Packet16bf& a, const Packet16bf& b) {
-  return pxor<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pand(const Packet16bf& a, const Packet16bf& b) {
-  return pand<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pandnot(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return pandnot<Packet8i>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pselect(const Packet16bf& mask,
-                                       const Packet16bf& a,
-                                       const Packet16bf& b) {
-  // Input mask is expected to be all 0/1, handle it with 8-bit
-  // intrinsic for performance.
-  return _mm256_blendv_epi8(b, a, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf pround<Packet16bf>(const Packet16bf& a)
-{
-  return F32ToBf16(pround<Packet16f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf print<Packet16bf>(const Packet16bf& a) {
-  return F32ToBf16(print<Packet16f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf pceil<Packet16bf>(const Packet16bf& a) {
-  return F32ToBf16(pceil<Packet16f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16bf pfloor<Packet16bf>(const Packet16bf& a) {
-  return F32ToBf16(pfloor<Packet16f>(Bf16ToF32(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_eq(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return Pack32To16(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_le(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return Pack32To16(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_lt(const Packet16bf& a,
-                                       const Packet16bf& b) {
-  return Pack32To16(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pcmp_lt_or_nan(const Packet16bf& a,
-                                              const Packet16bf& b) {
-  return Pack32To16(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pnegate(const Packet16bf& a) {
-  Packet16bf sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
-  return _mm256_xor_si256(a, sign_mask);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pconj(const Packet16bf& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pabs(const Packet16bf& a) {
-  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
-  return _mm256_andnot_si256(sign_mask, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf padd<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(padd<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf psub<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(psub<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pmul<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pmul<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pdiv<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pdiv<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pmin<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pmin<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pmax<Packet16bf>(const Packet16bf& a,
-                                                const Packet16bf& b) {
-  return F32ToBf16(pmax<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf plset<Packet16bf>(const bfloat16& a) {
-  return F32ToBf16(plset<Packet16f>(static_cast<float>(a)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8bf predux_half_dowto4<Packet16bf>(const Packet16bf& a) {
-  Packet8bf lane0 = _mm256_extractf128_si256(a, 0);
-  Packet8bf lane1 = _mm256_extractf128_si256(a, 1);
-  return padd<Packet8bf>(lane0, lane1);
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux<Packet16bf>(const Packet16bf& p) {
-  return static_cast<bfloat16>(predux<Packet16f>(Bf16ToF32(p)));
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet16bf>(const Packet16bf& from) {
-  return static_cast<bfloat16>(predux_mul<Packet16f>(Bf16ToF32(from)));
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux_min<Packet16bf>(const Packet16bf& from) {
-  return static_cast<bfloat16>(predux_min<Packet16f>(Bf16ToF32(from)));
-}
-
-template <>
-EIGEN_STRONG_INLINE bfloat16 predux_max<Packet16bf>(const Packet16bf& from) {
-  return static_cast<bfloat16>(predux_max<Packet16f>(Bf16ToF32(from)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf preverse(const Packet16bf& a) {
-  __m256i m = _mm256_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1,
-                               14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
-
-  Packet16bf res;
-  // Swap hi and lo first because shuffle is in 128-bit lanes.
-  res = _mm256_permute2x128_si256(a, a, 1);
-  // Shuffle 8-bit values in src within 2*128-bit lanes.
-  return _mm256_shuffle_epi8(res, m);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet16bf pgather<bfloat16, Packet16bf>(const bfloat16* from,
-                                                             Index stride) {
-  return _mm256_set_epi16(
-      from[15*stride].value, from[14*stride].value, from[13*stride].value, from[12*stride].value,
-      from[11*stride].value, from[10*stride].value, from[9*stride].value, from[8*stride].value,
-      from[7*stride].value, from[6*stride].value, from[5*stride].value, from[4*stride].value,
-      from[3*stride].value, from[2*stride].value, from[1*stride].value, from[0*stride].value);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet16bf>(bfloat16* to,
-                                                        const Packet16bf& from,
-                                                        Index stride) {
-  EIGEN_ALIGN64 bfloat16 aux[16];
-  pstore(aux, from);
-  to[stride*0] = aux[0];
-  to[stride*1] = aux[1];
-  to[stride*2] = aux[2];
-  to[stride*3] = aux[3];
-  to[stride*4] = aux[4];
-  to[stride*5] = aux[5];
-  to[stride*6] = aux[6];
-  to[stride*7] = aux[7];
-  to[stride*8] = aux[8];
-  to[stride*9] = aux[9];
-  to[stride*10] = aux[10];
-  to[stride*11] = aux[11];
-  to[stride*12] = aux[12];
-  to[stride*13] = aux[13];
-  to[stride*14] = aux[14];
-  to[stride*15] = aux[15];
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf,16>& kernel) {
-  __m256i a = kernel.packet[0];
-  __m256i b = kernel.packet[1];
-  __m256i c = kernel.packet[2];
-  __m256i d = kernel.packet[3];
-  __m256i e = kernel.packet[4];
-  __m256i f = kernel.packet[5];
-  __m256i g = kernel.packet[6];
-  __m256i h = kernel.packet[7];
-  __m256i i = kernel.packet[8];
-  __m256i j = kernel.packet[9];
-  __m256i k = kernel.packet[10];
-  __m256i l = kernel.packet[11];
-  __m256i m = kernel.packet[12];
-  __m256i n = kernel.packet[13];
-  __m256i o = kernel.packet[14];
-  __m256i p = kernel.packet[15];
-
-  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
-  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
-  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
-  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
-  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
-  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
-  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
-  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
-
-  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
-  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
-  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
-  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
-  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
-  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
-  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
-  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
-
-  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
-  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
-  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
-  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
-  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
-  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
-  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
-  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
-
-  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
-  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
-  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
-  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
-  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
-  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
-  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
-  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
-
-  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
-  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
-  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
-  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
-  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
-  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
-  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
-  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
-  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
-  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
-  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
-  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
-  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
-  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
-  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
-  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
-
-  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
-  kernel.packet[0] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
-  kernel.packet[1] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
-  kernel.packet[2] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
-  kernel.packet[3] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
-  kernel.packet[4] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
-  kernel.packet[5] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
-  kernel.packet[6] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
-  kernel.packet[7] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
-  kernel.packet[8] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
-  kernel.packet[9] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
-  kernel.packet[10] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
-  kernel.packet[11] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
-  kernel.packet[12] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
-  kernel.packet[13] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
-  kernel.packet[14] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
-  kernel.packet[15] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf,4>& kernel) {
-  __m256i a = kernel.packet[0];
-  __m256i b = kernel.packet[1];
-  __m256i c = kernel.packet[2];
-  __m256i d = kernel.packet[3];
-
-  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
-  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
-  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
-  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
-
-  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
-  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
-  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
-  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
-
-  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
-  kernel.packet[0] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x20);
-  kernel.packet[1] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x20);
-  kernel.packet[2] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x31);
-  kernel.packet[3] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x31);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_AVX512_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h
deleted file mode 100644
index 3304127..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h
+++ /dev/null
@@ -1,89 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_AVX512_H
-#define EIGEN_TYPE_CASTING_AVX512_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_STRONG_INLINE Packet16i pcast<Packet16f, Packet16i>(const Packet16f& a) {
-  return _mm512_cvttps_epi32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16i, Packet16f>(const Packet16i& a) {
-  return _mm512_cvtepi32_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16i preinterpret<Packet16i, Packet16f>(const Packet16f& a) {
-  return _mm512_castps_si512(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet16i>(const Packet16i& a) {
-  return _mm512_castsi512_ps(a);
-}
-
-template <>
-struct type_casting_traits<half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16h, Packet16f>(const Packet16h& a) {
-  return half2float(a);
-}
-
-template <>
-struct type_casting_traits<float, half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16h pcast<Packet16f, Packet16h>(const Packet16f& a) {
-  return float2half(a);
-}
-
-template <>
-struct type_casting_traits<bfloat16, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16bf, Packet16f>(const Packet16bf& a) {
-  return Bf16ToF32(a);
-}
-
-template <>
-struct type_casting_traits<float, bfloat16> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet16bf pcast<Packet16f, Packet16bf>(const Packet16f& a) {
-  return F32ToBf16(a);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_AVX512_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
deleted file mode 100644
index f424f11..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
+++ /dev/null
@@ -1,417 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010-2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX32_ALTIVEC_H
-#define EIGEN_COMPLEX32_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-static Packet4ui  p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
-#ifdef __VSX__
-#if defined(_BIG_ENDIAN)
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-#else
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-#endif
-#endif
-
-//---------- float ----------
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE explicit Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
-
-  EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b)
-  {
-    Packet4f v1, v2;
-
-    // Permute and multiply the real parts of a and b
-    v1 = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
-    // Get the imaginary parts of a
-    v2 = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
-    // multiply a_re * b
-    v1 = vec_madd(v1, b.v, p4f_ZERO);
-    // multiply a_im * b and get the conjugate result
-    v2 = vec_madd(v2, b.v, p4f_ZERO);
-    v2 = reinterpret_cast<Packet4f>(pxor(v2, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR)));
-    // permute back to a proper order
-    v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
-
-    return Packet2cf(padd<Packet4f>(v1, v2));
-  }
-
-  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
-    v = pmul(Packet2cf(*this), b).v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const {
-    return Packet2cf(*this) *= b;
-  }
-
-  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const {
-    return Packet2cf(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const {
-    return Packet2cf(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(void) const {
-    return Packet2cf(-v);
-  }
-
-  Packet4f  v;
-};
-
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  typedef Packet4f as_real;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-#ifdef __VSX__
-    HasBlend  = 1,
-#endif
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2cf half; typedef Packet4f as_real; };
-
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-  if((std::ptrdiff_t(&from) % 16) == 0)
-    res.v = pload<Packet4f>((const float *)&from);
-  else
-    res.v = ploadu<Packet4f>((const float *)&from);
-  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>*        from) { return Packet2cf(pload<Packet4f>((const float *) from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>*       from) { return Packet2cf(ploadu<Packet4f>((const float*) from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>*     from) { return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstore((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstoreu((float*)to, from.v); }
-
-EIGEN_STRONG_INLINE Packet2cf pload2(const std::complex<float>* from0, const std::complex<float>* from1)
-{
-  Packet4f res0, res1;
-#ifdef __VSX__
-  __asm__ ("lxsdx %x0,%y1" : "=wa" (res0) : "Z" (*from0));
-  __asm__ ("lxsdx %x0,%y1" : "=wa" (res1) : "Z" (*from1));
-#ifdef _BIG_ENDIAN
-  __asm__ ("xxpermdi %x0, %x1, %x2, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
-#else
-  __asm__ ("xxpermdi %x0, %x2, %x1, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
-#endif
-#else
-  *reinterpret_cast<std::complex<float> *>(&res0) = *from0;
-  *reinterpret_cast<std::complex<float> *>(&res1) = *from1;
-  res0 = vec_perm(res0, res1, p16uc_TRANSPOSE64_HI);
-#endif
-  return Packet2cf(res0);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
-{
-  EIGEN_ALIGN16 std::complex<float> af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
-  return pload<Packet2cf>(af);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  EIGEN_ALIGN16 std::complex<float> af[2];
-  pstore<std::complex<float> >((std::complex<float> *) af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v + b.v); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v - b.v); }
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pxor<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pandnot<Packet4f>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr)    { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
-{
-  EIGEN_ALIGN16 std::complex<float> res[2];
-  pstore((float *)&res, a.v);
-
-  return res[0];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{
-  Packet4f rev_a;
-  rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
-  return Packet2cf(rev_a);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  b = vec_sld(a.v, a.v, 8);
-  b = padd<Packet4f>(a.v, b);
-  return pfirst<Packet2cf>(Packet2cf(b));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  Packet2cf prod;
-  b = vec_sld(a.v, a.v, 8);
-  prod = pmul<Packet2cf>(a, Packet2cf(b));
-
-  return pfirst<Packet2cf>(prod);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for AltiVec
-  Packet2cf res = pmul(a, pconj(b));
-  Packet4f s = pmul<Packet4f>(b.v, b.v);
-  return Packet2cf(pdiv(res.v, padd<Packet4f>(s, vec_perm(s, s, p16uc_COMPLEX32_REV))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
-{
-  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
-{
-  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
-  Packet4f eq = reinterpret_cast<Packet4f>(vec_cmpeq(a.v,b.v));
-  return Packet2cf(vec_and(eq, vec_perm(eq, eq, p16uc_COMPLEX32_REV)));
-}
-
-#ifdef __VSX__
-template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  Packet2cf result;
-  result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
-  return result;
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a)
-{
-  return psqrt_complex<Packet2cf>(a);
-}
-
-//---------- double ----------
-#ifdef __VSX__
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
-
-  EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b)
-  {
-    Packet2d a_re, a_im, v1, v2;
-
-    // Permute and multiply the real parts of a and b
-    a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
-    // Get the imaginary parts of a
-    a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
-    // multiply a_re * b
-    v1 = vec_madd(a_re, b.v, p2d_ZERO);
-    // multiply a_im * b and get the conjugate result
-    v2 = vec_madd(a_im, b.v, p2d_ZERO);
-    v2 = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(v2), reinterpret_cast<Packet4ui>(v2), 8));
-    v2 = pxor(v2, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR1));
-
-    return Packet1cd(padd<Packet2d>(v1, v2));
-  }
-
-  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
-    v = pmul(Packet1cd(*this), b).v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const {
-    return Packet1cd(*this) *= b;
-  }
-
-  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const {
-    return Packet1cd(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const {
-    return Packet1cd(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(void) const {
-    return Packet1cd(-v);
-  }
-
-  Packet2d v;
-};
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  typedef Packet2d as_real;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet1cd half; typedef Packet2d as_real; };
-
-template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { return Packet1cd(pload<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { return Packet1cd(ploadu<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { pstoreu((double*)to, from.v); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
-{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index)
-{
-  return pload<Packet1cd>(from);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index)
-{
-  pstore<std::complex<double> >(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR2))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pand   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pand(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd por    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(por(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pxor   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pxor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pandnot(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>*     from)  { return pset1<Packet1cd>(*from); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr)    { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
-{
-  EIGEN_ALIGN16 std::complex<double> res[2];
-  pstore<std::complex<double> >(res, a);
-
-  return res[0];
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for AltiVec
-  Packet1cd res = pmul(a,pconj(b));
-  Packet2d s = pmul<Packet2d>(b.v, b.v);
-  return Packet1cd(pdiv(res.v, padd<Packet2d>(s, vec_perm(s, s, p16uc_REVERSE64))));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
-{
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
-{
-  Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a)==re(b), im(a)==im(b)]
-  Packet2d eq = reinterpret_cast<Packet2d>(vec_cmpeq(a.v,b.v));
-  // Swap real/imag elements in the mask in to get:
-  // [im(a)==im(b), re(a)==re(b)]
-  Packet2d eq_swapped = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(eq), reinterpret_cast<Packet4ui>(eq), 8));
-  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet1cd(vec_and(eq, eq_swapped));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a)
-{
-  return psqrt_complex<Packet1cd>(a);
-}
-
-#endif // __VSX__
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX32_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
deleted file mode 100644
index 3a7a329..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
+++ /dev/null
@@ -1,90 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog<Packet4f>(const Packet4f& _x)
-{
-  return plog_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexp<Packet4f>(const Packet4f& _x)
-{
-  return pexp_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psin<Packet4f>(const Packet4f& _x)
-{
-  return psin_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pcos<Packet4f>(const Packet4f& _x)
-{
-  return pcos_float(_x);
-}
-
-#ifndef EIGEN_COMP_CLANG
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& x)
-{
-  return  vec_rsqrt(x);
-}
-#endif
-
-#ifdef __VSX__
-#ifndef EIGEN_COMP_CLANG
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d prsqrt<Packet2d>(const Packet2d& x)
-{
-  return  vec_rsqrt(x);
-}
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& x)
-{
-  return  vec_sqrt(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d psqrt<Packet2d>(const Packet2d& x)
-{
-  return  vec_sqrt(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d pexp<Packet2d>(const Packet2d& _x)
-{
-  return pexp_double(_x);
-}
-#endif
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
deleted file mode 100644
index 3f79b97..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
+++ /dev/null
@@ -1,2937 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
-// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_PRODUCT_ALTIVEC_H
-#define EIGEN_MATRIX_PRODUCT_ALTIVEC_H
-
-#ifndef EIGEN_ALTIVEC_USE_CUSTOM_PACK
-#define EIGEN_ALTIVEC_USE_CUSTOM_PACK    1
-#endif
-
-#include "MatrixProductCommon.h"
-
-// Since LLVM doesn't support dynamic dispatching, force either always MMA or VSX
-#if EIGEN_COMP_LLVM
-#if !defined(EIGEN_ALTIVEC_DISABLE_MMA) && !defined(EIGEN_ALTIVEC_MMA_ONLY)
-#ifdef __MMA__
-#define EIGEN_ALTIVEC_MMA_ONLY
-#else
-#define EIGEN_ALTIVEC_DISABLE_MMA
-#endif
-#endif
-#endif
-
-#ifdef __has_builtin
-#if __has_builtin(__builtin_mma_assemble_acc)
-  #define ALTIVEC_MMA_SUPPORT
-#endif
-#endif
-
-#if defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-  #include "MatrixProductMMA.h"
-#endif
-
-/**************************************************************************************************
- * TODO                                                                                           *
- * - Check StorageOrder on dhs_pack (the innermost second loop seems unvectorized when it could). *
- * - Check the possibility of transposing as GETREAL and GETIMAG when needed.                     *
- **************************************************************************************************/
-namespace Eigen {
-
-namespace internal {
-
-/**************************
- * Constants and typedefs *
- **************************/
-template<typename Scalar>
-struct quad_traits
-{
-  typedef typename packet_traits<Scalar>::type    vectortype;
-  typedef PacketBlock<vectortype,4>                     type;
-  typedef vectortype                                 rhstype;
-  enum
-  {
-    vectorsize = packet_traits<Scalar>::size,
-    size = 4,
-    rows = 4
-  };
-};
-
-template<>
-struct quad_traits<double>
-{
-  typedef Packet2d                        vectortype;
-  typedef PacketBlock<vectortype,4>             type;
-  typedef PacketBlock<Packet2d,2>            rhstype;
-  enum
-  {
-    vectorsize = packet_traits<double>::size,
-    size = 2,
-    rows = 4
-  };
-};
-
-// MatrixProduct decomposes real/imaginary vectors into a real vector and an imaginary vector, this turned out
-// to be faster than Eigen's usual approach of having real/imaginary pairs on a single vector. This constants then
-// are responsible to extract from convert between Eigen's and MatrixProduct approach.
-
-const static Packet16uc p16uc_GETREAL32 = {  0,  1,  2,  3,
-                                             8,  9, 10, 11,
-                                            16, 17, 18, 19,
-                                            24, 25, 26, 27};
-
-const static Packet16uc p16uc_GETIMAG32 = {  4,  5,  6,  7,
-                                            12, 13, 14, 15,
-                                            20, 21, 22, 23,
-                                            28, 29, 30, 31};
-const static Packet16uc p16uc_GETREAL64 = {  0,  1,  2,  3,  4,  5,  6,  7,
-                                            16, 17, 18, 19, 20, 21, 22, 23};
-
-//[a,ai],[b,bi] = [ai,bi]
-const static Packet16uc p16uc_GETIMAG64 = {  8,  9, 10, 11, 12, 13, 14, 15,
-                                            24, 25, 26, 27, 28, 29, 30, 31};
-
-/*********************************************
- * Single precision real and complex packing *
- * *******************************************/
-
-/**
- * Symm packing is related to packing of symmetric adjoint blocks, as expected the packing leaves
- * the diagonal real, whatever is below it is copied from the respective upper diagonal element and 
- * conjugated. There's no PanelMode available for symm packing.
- *
- * Packing in general is supposed to leave the lhs block and the rhs block easy to be read by gemm using 
- * its respective rank-update instructions. The float32/64 versions are different because at this moment
- * the size of the accumulator is fixed at 512-bits so you can't have a 4x4 accumulator of 64-bit elements.
- * 
- * As mentioned earlier MatrixProduct breaks complex numbers into a real vector and a complex vector so packing has
- * to take that into account, at the moment, we run pack the real part and then the imaginary part, this is the main
- * reason why packing for complex is broken down into several different parts, also the reason why we endup having a
- * float32/64 and complex float32/64 version.
- **/
-template<typename Scalar, typename Index, int StorageOrder>
-EIGEN_ALWAYS_INLINE std::complex<Scalar> getAdjointVal(Index i, Index j, const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder>& dt)
-{
-  std::complex<Scalar> v;
-  if(i < j)
-  {
-    v.real( dt(j,i).real());
-    v.imag(-dt(j,i).imag());
-  } else if(i > j)
-  {
-    v.real( dt(i,j).real());
-    v.imag( dt(i,j).imag());
-  } else {
-    v.real( dt(i,j).real());
-    v.imag((Scalar)0.0);
-  }
-  return v;
-}
-
-template<typename Scalar, typename Index, int StorageOrder, int N>
-EIGEN_STRONG_INLINE void symm_pack_complex_rhs_helper(std::complex<Scalar>* blockB, const std::complex<Scalar>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-{
-  const Index depth = k2 + rows;
-  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> rhs(_rhs, rhsStride);
-  const Index vectorSize = N*quad_traits<Scalar>::vectorsize;
-  const Index vectorDelta = vectorSize * rows;
-  Scalar* blockBf = reinterpret_cast<Scalar *>(blockB);
-
-  Index rir = 0, rii, j = 0;
-  for(; j + vectorSize <= cols; j+=vectorSize)
-  {
-    rii = rir + vectorDelta;
-
-    for(Index i = k2; i < depth; i++)
-    {
-      for(Index k = 0; k < vectorSize; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(i, j + k, rhs);
-
-        blockBf[rir + k] = v.real();
-        blockBf[rii + k] = v.imag();
-      }
-      rir += vectorSize;
-      rii += vectorSize;
-    }
-
-    rir += vectorDelta;
-  }
-  if (j < cols)
-  {
-    rii = rir + ((cols - j) * rows);
-
-    for(Index i = k2; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < cols; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(i, k, rhs);
-
-        blockBf[rir] = v.real();
-        blockBf[rii] = v.imag();
-
-        rir += 1;
-        rii += 1;
-      }
-    }
-  }
-}
-
-template<typename Scalar, typename Index, int StorageOrder>
-EIGEN_STRONG_INLINE void symm_pack_complex_lhs_helper(std::complex<Scalar>* blockA, const std::complex<Scalar>* _lhs, Index lhsStride, Index cols, Index rows)
-{
-  const Index depth = cols;
-  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> lhs(_lhs, lhsStride);
-  const Index vectorSize = quad_traits<Scalar>::vectorsize;
-  const Index vectorDelta = vectorSize * depth;
-  Scalar* blockAf = (Scalar *)(blockA);
-
-  Index rir = 0, rii, j = 0;
-  for(; j + vectorSize <= rows; j+=vectorSize)
-  {
-    rii = rir + vectorDelta;
-
-    for(Index i = 0; i < depth; i++)
-    {
-      for(Index k = 0; k < vectorSize; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(j+k, i, lhs);
-
-        blockAf[rir + k] = v.real();
-        blockAf[rii + k] = v.imag();
-      }
-      rir += vectorSize;
-      rii += vectorSize;
-    }
-
-    rir += vectorDelta;
-  }
-
-  if (j < rows)
-  {
-    rii = rir + ((rows - j) * depth);
-
-    for(Index i = 0; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < rows; k++)
-      {
-        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(k, i, lhs);
-
-        blockAf[rir] = v.real();
-        blockAf[rii] = v.imag();
-
-        rir += 1;
-        rii += 1;
-      }
-    }
-  }
-}
-
-template<typename Scalar, typename Index, int StorageOrder, int N>
-EIGEN_STRONG_INLINE void symm_pack_rhs_helper(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-{
-  const Index depth = k2 + rows;
-  const_blas_data_mapper<Scalar, Index, StorageOrder> rhs(_rhs, rhsStride);
-  const Index vectorSize = quad_traits<Scalar>::vectorsize;
-
-  Index ri = 0, j = 0;
-  for(; j + N*vectorSize <= cols; j+=N*vectorSize)
-  {
-    Index i = k2;
-    for(; i < depth; i++)
-    {
-      for(Index k = 0; k < N*vectorSize; k++)
-      {
-        if(i <= j+k)
-          blockB[ri + k] = rhs(j+k, i);
-        else
-          blockB[ri + k] = rhs(i, j+k);
-      }
-      ri += N*vectorSize;
-    }
-  }
-
-  if (j < cols)
-  {
-    for(Index i = k2; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < cols; k++)
-      {
-        if(k <= i)
-          blockB[ri] = rhs(i, k);
-        else
-          blockB[ri] = rhs(k, i);
-        ri += 1;
-      }
-    }
-  }
-}
-
-template<typename Scalar, typename Index, int StorageOrder>
-EIGEN_STRONG_INLINE void symm_pack_lhs_helper(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
-{
-  const Index depth = cols;
-  const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs, lhsStride);
-  const Index vectorSize = quad_traits<Scalar>::vectorsize;
-
-  Index ri = 0, j = 0;
-  for(; j + vectorSize <= rows; j+=vectorSize)
-  {
-    Index i = 0;
-
-    for(; i < depth; i++)
-    {
-      for(Index k = 0; k < vectorSize; k++)
-      {
-        if(i <= j+k)
-          blockA[ri + k] = lhs(j+k, i);
-        else
-          blockA[ri + k] = lhs(i, j+k);
-      }
-      ri += vectorSize;
-    }
-  }
-
-  if (j < rows)
-  {
-    for(Index i = 0; i < depth; i++)
-    {
-      Index k = j;
-      for(; k < rows; k++)
-      {
-        if(i <= k)
-          blockA[ri] = lhs(k, i);
-        else
-          blockA[ri] = lhs(i, k);
-        ri += 1;
-      }
-    }
-  }
-}
-
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<std::complex<float>, Index, nr, StorageOrder>
-{
-  void operator()(std::complex<float>* blockB, const std::complex<float>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_complex_rhs_helper<float, Index, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<std::complex<float>, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(std::complex<float>* blockA, const std::complex<float>* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_complex_lhs_helper<float, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-// *********** symm_pack std::complex<float64> ***********
-
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<std::complex<double>, Index, nr, StorageOrder>
-{
-  void operator()(std::complex<double>* blockB, const std::complex<double>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_complex_rhs_helper<double, Index, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<std::complex<double>, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(std::complex<double>* blockA, const std::complex<double>* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_complex_lhs_helper<double, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-// *********** symm_pack float32 ***********
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<float, Index, nr, StorageOrder>
-{
-  void operator()(float* blockB, const float* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_rhs_helper<float, Index, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<float, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(float* blockA, const float* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_lhs_helper<float, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-// *********** symm_pack float64 ***********
-template<typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs<double, Index, nr, StorageOrder>
-{
-  void operator()(double* blockB, const double* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    symm_pack_rhs_helper<double, Index, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
-  }
-};
-
-template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs<double, Index, Pack1, Pack2_dummy, StorageOrder>
-{
-  void operator()(double* blockA, const double* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    symm_pack_lhs_helper<double, Index, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
-  }
-};
-
-/**
- * PanelMode
- * Packing might be called several times before being multiplied by gebp_kernel, this happens because 
- * on special occasions it fills part of block with other parts of the matrix. Two variables control
- * how PanelMode should behave: offset and stride. The idea is that those variables represent whatever
- * is going to be the real offset and stride in the future and this is what you should obey. The process
- * is to behave as you would with normal packing but leave the start of each part with the correct offset
- * and the end as well respecting the real stride the block will have. Gebp is aware of both blocks stride
- * and offset and behaves accordingly.
- **/
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,4>& block)
-{
-  const Index size = 16 / sizeof(Scalar);
-  pstore<Scalar>(to + (0 * size), block.packet[0]);
-  pstore<Scalar>(to + (1 * size), block.packet[1]);
-  pstore<Scalar>(to + (2 * size), block.packet[2]);
-  pstore<Scalar>(to + (3 * size), block.packet[3]);
-}
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,2>& block)
-{
-  const Index size = 16 / sizeof(Scalar);
-  pstore<Scalar>(to + (0 * size), block.packet[0]);
-  pstore<Scalar>(to + (1 * size), block.packet[1]);
-}
-
-// General template for lhs & rhs complex packing.
-template<typename Scalar, typename Index, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode, bool UseLhs>
-struct dhs_cpack {
-  EIGEN_STRONG_INLINE void operator()(std::complex<Scalar>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<Scalar>::vectorsize;
-    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
-    Index rir = ((PanelMode) ? (vectorSize*offset) : 0), rii;
-    Scalar* blockAt = reinterpret_cast<Scalar *>(blockA);
-    Index j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      rii = rir + vectorDelta;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet,4> blockr, blocki;
-        PacketBlock<PacketC,8> cblock;
-
-        if (UseLhs) {
-          bload<DataMapper, PacketC, Index, 2, 0, StorageOrder>(cblock, lhs, j, i);
-        } else {
-          bload<DataMapper, PacketC, Index, 2, 0, StorageOrder>(cblock, lhs, i, j);
-        }
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, p16uc_GETREAL32);
-        blockr.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[5].v, p16uc_GETREAL32);
-        blockr.packet[2] = vec_perm(cblock.packet[2].v, cblock.packet[6].v, p16uc_GETREAL32);
-        blockr.packet[3] = vec_perm(cblock.packet[3].v, cblock.packet[7].v, p16uc_GETREAL32);
-
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, p16uc_GETIMAG32);
-        blocki.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[5].v, p16uc_GETIMAG32);
-        blocki.packet[2] = vec_perm(cblock.packet[2].v, cblock.packet[6].v, p16uc_GETIMAG32);
-        blocki.packet[3] = vec_perm(cblock.packet[3].v, cblock.packet[7].v, p16uc_GETIMAG32);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-          blocki.packet[1] = -blocki.packet[1];
-          blocki.packet[2] = -blocki.packet[2];
-          blocki.packet[3] = -blocki.packet[3];
-        }
-
-        if(((StorageOrder == RowMajor) && UseLhs) || (((StorageOrder == ColMajor) && !UseLhs)))
-        {
-          ptranspose(blockr);
-          ptranspose(blocki);
-        }
-
-        storeBlock<Scalar, Packet, Index>(blockAt + rir, blockr);
-        storeBlock<Scalar, Packet, Index>(blockAt + rii, blocki);
-
-        rir += 4*vectorSize;
-        rii += 4*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        PacketBlock<Packet,1> blockr, blocki;
-        PacketBlock<PacketC,2> cblock;
-
-        if(((StorageOrder == ColMajor) && UseLhs) || (((StorageOrder == RowMajor) && !UseLhs)))
-        {
-          if (UseLhs) {
-            cblock.packet[0] = lhs.template loadPacket<PacketC>(j + 0, i);
-            cblock.packet[1] = lhs.template loadPacket<PacketC>(j + 2, i);
-          } else {
-            cblock.packet[0] = lhs.template loadPacket<PacketC>(i, j + 0);
-            cblock.packet[1] = lhs.template loadPacket<PacketC>(i, j + 2);
-          }
-        } else {
-          std::complex<Scalar> lhs0, lhs1;
-          if (UseLhs) {
-            lhs0 = lhs(j + 0, i);
-            lhs1 = lhs(j + 1, i);
-            cblock.packet[0] = pload2(&lhs0, &lhs1);
-            lhs0 = lhs(j + 2, i);
-            lhs1 = lhs(j + 3, i);
-            cblock.packet[1] = pload2(&lhs0, &lhs1);
-          } else {
-            lhs0 = lhs(i, j + 0);
-            lhs1 = lhs(i, j + 1);
-            cblock.packet[0] = pload2(&lhs0, &lhs1);
-            lhs0 = lhs(i, j + 2);
-            lhs1 = lhs(i, j + 3);
-            cblock.packet[1] = pload2(&lhs0, &lhs1);
-          }
-        }
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL32);
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG32);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-        }
-
-        pstore<Scalar>(blockAt + rir, blockr.packet[0]);
-        pstore<Scalar>(blockAt + rii, blocki.packet[0]);
-
-        rir += vectorSize;
-        rii += vectorSize;
-      }
-
-      rir += ((PanelMode) ? (vectorSize*(2*stride - depth)) : vectorDelta);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) rir += (offset*(rows - j - vectorSize));
-      rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          if (UseLhs) {
-            blockAt[rir] = lhs(k, i).real();
-
-            if(Conjugate)
-              blockAt[rii] = -lhs(k, i).imag();
-            else
-              blockAt[rii] =  lhs(k, i).imag();
-          } else {
-            blockAt[rir] = lhs(i, k).real();
-
-            if(Conjugate)
-              blockAt[rii] = -lhs(i, k).imag();
-            else
-              blockAt[rii] =  lhs(i, k).imag();
-          }
-
-          rir += 1;
-          rii += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for lhs & rhs packing.
-template<typename Scalar, typename Index, typename DataMapper, typename Packet, int StorageOrder, bool PanelMode, bool UseLhs>
-struct dhs_pack{
-  EIGEN_STRONG_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<Scalar>::vectorsize;
-    Index ri = 0, j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      if(PanelMode) ri += vectorSize*offset;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet,4> block;
-
-        if (UseLhs) {
-          bload<DataMapper, Packet, Index, 4, 0, StorageOrder>(block, lhs, j, i);
-        } else {
-          bload<DataMapper, Packet, Index, 4, 0, StorageOrder>(block, lhs, i, j);
-        }
-        if(((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs))
-        {
-          ptranspose(block);
-        }
-
-        storeBlock<Scalar, Packet, Index>(blockA + ri, block);
-
-        ri += 4*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        if(((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs))
-        {
-          if (UseLhs) {
-            blockA[ri+0] = lhs(j+0, i);
-            blockA[ri+1] = lhs(j+1, i);
-            blockA[ri+2] = lhs(j+2, i);
-            blockA[ri+3] = lhs(j+3, i);
-          } else {
-            blockA[ri+0] = lhs(i, j+0);
-            blockA[ri+1] = lhs(i, j+1);
-            blockA[ri+2] = lhs(i, j+2);
-            blockA[ri+3] = lhs(i, j+3);
-          }
-        } else {
-          Packet lhsV;
-          if (UseLhs) {
-            lhsV = lhs.template loadPacket<Packet>(j, i);
-          } else {
-            lhsV = lhs.template loadPacket<Packet>(i, j);
-          }
-          pstore<Scalar>(blockA + ri, lhsV);
-        }
-
-        ri += vectorSize;
-      }
-
-      if(PanelMode) ri += vectorSize*(stride - offset - depth);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) ri += offset*(rows - j);
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          if (UseLhs) {
-            blockA[ri] = lhs(k, i);
-          } else {
-            blockA[ri] = lhs(i, k);
-          }
-          ri += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for lhs packing, float64 specialization.
-template<typename Index, typename DataMapper, int StorageOrder, bool PanelMode>
-struct dhs_pack<double, Index, DataMapper, Packet2d, StorageOrder, PanelMode, true>
-{
-  EIGEN_STRONG_INLINE void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    Index ri = 0, j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      if(PanelMode) ri += vectorSize*offset;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet2d,2> block;
-        if(StorageOrder == RowMajor)
-        {
-          block.packet[0] = lhs.template loadPacket<Packet2d>(j + 0, i);
-          block.packet[1] = lhs.template loadPacket<Packet2d>(j + 1, i);
-
-          ptranspose(block);
-        } else {
-          block.packet[0] = lhs.template loadPacket<Packet2d>(j, i + 0);
-          block.packet[1] = lhs.template loadPacket<Packet2d>(j, i + 1);
-        }
-
-        storeBlock<double, Packet2d, Index>(blockA + ri, block);
-
-        ri += 2*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        if(StorageOrder == RowMajor)
-        {
-          blockA[ri+0] = lhs(j+0, i);
-          blockA[ri+1] = lhs(j+1, i);
-        } else {
-          Packet2d lhsV = lhs.template loadPacket<Packet2d>(j, i);
-          pstore<double>(blockA + ri, lhsV);
-        }
-
-        ri += vectorSize;
-      }
-
-      if(PanelMode) ri += vectorSize*(stride - offset - depth);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) ri += offset*(rows - j);
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          blockA[ri] = lhs(k, i);
-          ri += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for rhs packing, float64 specialization.
-template<typename Index, typename DataMapper, int StorageOrder, bool PanelMode>
-struct dhs_pack<double, Index, DataMapper, Packet2d, StorageOrder, PanelMode, false>
-{
-  EIGEN_STRONG_INLINE void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    Index ri = 0, j = 0;
-
-    for(; j + 2*vectorSize <= cols; j+=2*vectorSize)
-    {
-      Index i = 0;
-
-      if(PanelMode) ri += offset*(2*vectorSize);
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet2d,4> block;
-        if(StorageOrder == ColMajor)
-        {
-          PacketBlock<Packet2d,2> block1, block2;
-          block1.packet[0] = rhs.template loadPacket<Packet2d>(i, j + 0);
-          block1.packet[1] = rhs.template loadPacket<Packet2d>(i, j + 1);
-          block2.packet[0] = rhs.template loadPacket<Packet2d>(i, j + 2);
-          block2.packet[1] = rhs.template loadPacket<Packet2d>(i, j + 3);
-
-          ptranspose(block1);
-          ptranspose(block2);
-
-          pstore<double>(blockB + ri    , block1.packet[0]);
-          pstore<double>(blockB + ri + 2, block2.packet[0]);
-          pstore<double>(blockB + ri + 4, block1.packet[1]);
-          pstore<double>(blockB + ri + 6, block2.packet[1]);
-        } else {
-          block.packet[0] = rhs.template loadPacket<Packet2d>(i + 0, j + 0); //[a1 a2]
-          block.packet[1] = rhs.template loadPacket<Packet2d>(i + 0, j + 2); //[a3 a4]
-          block.packet[2] = rhs.template loadPacket<Packet2d>(i + 1, j + 0); //[b1 b2]
-          block.packet[3] = rhs.template loadPacket<Packet2d>(i + 1, j + 2); //[b3 b4]
-
-          storeBlock<double, Packet2d, Index>(blockB + ri, block);
-        }
-
-        ri += 4*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        if(StorageOrder == ColMajor)
-        {
-          blockB[ri+0] = rhs(i, j+0);
-          blockB[ri+1] = rhs(i, j+1);
-
-          ri += vectorSize;
-
-          blockB[ri+0] = rhs(i, j+2);
-          blockB[ri+1] = rhs(i, j+3);
-        } else {
-          Packet2d rhsV = rhs.template loadPacket<Packet2d>(i, j);
-          pstore<double>(blockB + ri, rhsV);
-
-          ri += vectorSize;
-
-          rhsV = rhs.template loadPacket<Packet2d>(i, j + 2);
-          pstore<double>(blockB + ri, rhsV);
-        }
-        ri += vectorSize;
-      }
-
-      if(PanelMode) ri += (2*vectorSize)*(stride - offset - depth);
-    }
-
-    if (j < cols)
-    {
-      if(PanelMode) ri += offset*(cols - j);
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < cols; k++)
-        {
-          blockB[ri] = rhs(i, k);
-          ri += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for lhs complex packing, float64 specialization.
-template<typename Index, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
-struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, true>
-{
-  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
-    Index rir = ((PanelMode) ? (vectorSize*offset) : 0), rii;
-    double* blockAt = reinterpret_cast<double *>(blockA);
-    Index j = 0;
-
-    for(; j + vectorSize <= rows; j+=vectorSize)
-    {
-      Index i = 0;
-
-      rii = rir + vectorDelta;
-
-      for(; i + vectorSize <= depth; i+=vectorSize)
-      {
-        PacketBlock<Packet,2> blockr, blocki;
-        PacketBlock<PacketC,4> cblock;
-
-        if(StorageOrder == ColMajor)
-        {
-          cblock.packet[0] = lhs.template loadPacket<PacketC>(j, i + 0); //[a1 a1i]
-          cblock.packet[1] = lhs.template loadPacket<PacketC>(j, i + 1); //[b1 b1i]
-
-          cblock.packet[2] = lhs.template loadPacket<PacketC>(j + 1, i + 0); //[a2 a2i]
-          cblock.packet[3] = lhs.template loadPacket<PacketC>(j + 1, i + 1); //[b2 b2i]
-
-          blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[2].v, p16uc_GETREAL64); //[a1 a2]
-          blockr.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[3].v, p16uc_GETREAL64); //[b1 b2]
-
-          blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[2].v, p16uc_GETIMAG64);
-          blocki.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[3].v, p16uc_GETIMAG64);
-        } else {
-          cblock.packet[0] = lhs.template loadPacket<PacketC>(j + 0, i); //[a1 a1i]
-          cblock.packet[1] = lhs.template loadPacket<PacketC>(j + 1, i); //[a2 a2i]
-
-          cblock.packet[2] = lhs.template loadPacket<PacketC>(j + 0, i + 1); //[b1 b1i]
-          cblock.packet[3] = lhs.template loadPacket<PacketC>(j + 1, i + 1); //[b2 b2i
-
-          blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL64); //[a1 a2]
-          blockr.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETREAL64); //[b1 b2]
-
-          blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG64);
-          blocki.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETIMAG64);
-        }
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-          blocki.packet[1] = -blocki.packet[1];
-        }
-
-        storeBlock<double, Packet, Index>(blockAt + rir, blockr);
-        storeBlock<double, Packet, Index>(blockAt + rii, blocki);
-
-        rir += 2*vectorSize;
-        rii += 2*vectorSize;
-      }
-      for(; i < depth; i++)
-      {
-        PacketBlock<Packet,1> blockr, blocki;
-        PacketBlock<PacketC,2> cblock;
-
-        cblock.packet[0] = lhs.template loadPacket<PacketC>(j + 0, i);
-        cblock.packet[1] = lhs.template loadPacket<PacketC>(j + 1, i);
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL64);
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG64);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-        }
-
-        pstore<double>(blockAt + rir, blockr.packet[0]);
-        pstore<double>(blockAt + rii, blocki.packet[0]);
-
-        rir += vectorSize;
-        rii += vectorSize;
-      }
-
-      rir += ((PanelMode) ? (vectorSize*(2*stride - depth)) : vectorDelta);
-    }
-
-    if (j < rows)
-    {
-      if(PanelMode) rir += (offset*(rows - j - vectorSize));
-      rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < rows; k++)
-        {
-          blockAt[rir] = lhs(k, i).real();
-
-          if(Conjugate)
-            blockAt[rii] = -lhs(k, i).imag();
-          else
-            blockAt[rii] =  lhs(k, i).imag();
-
-          rir += 1;
-          rii += 1;
-        }
-      }
-    }
-  }
-};
-
-// General template for rhs complex packing, float64 specialization.
-template<typename Index, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
-struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, false>
-{
-  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-  {
-    const Index vectorSize = quad_traits<double>::vectorsize;
-    const Index vectorDelta = 2*vectorSize * ((PanelMode) ? stride : depth);
-    Index rir = ((PanelMode) ? (2*vectorSize*offset) : 0), rii;
-    double* blockBt = reinterpret_cast<double *>(blockB);
-    Index j = 0;
-
-    for(; j + 2*vectorSize <= cols; j+=2*vectorSize)
-    {
-      Index i = 0;
-
-      rii = rir + vectorDelta;
-
-      for(; i < depth; i++)
-      {
-        PacketBlock<PacketC,4> cblock;
-        PacketBlock<Packet,2> blockr, blocki;
-
-        bload<DataMapper, PacketC, Index, 2, 0, ColMajor>(cblock, rhs, i, j);
-
-        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL64);
-        blockr.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETREAL64);
-
-        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG64);
-        blocki.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETIMAG64);
-
-        if(Conjugate)
-        {
-          blocki.packet[0] = -blocki.packet[0];
-          blocki.packet[1] = -blocki.packet[1];
-        }
-
-        storeBlock<double, Packet, Index>(blockBt + rir, blockr);
-        storeBlock<double, Packet, Index>(blockBt + rii, blocki);
-
-        rir += 2*vectorSize;
-        rii += 2*vectorSize;
-      }
-
-      rir += ((PanelMode) ? (2*vectorSize*(2*stride - depth)) : vectorDelta);
-    }
-
-    if (j < cols)
-    {
-      if(PanelMode) rir += (offset*(cols - j - 2*vectorSize));
-      rii = rir + (((PanelMode) ? stride : depth) * (cols - j));
-
-      for(Index i = 0; i < depth; i++)
-      {
-        Index k = j;
-        for(; k < cols; k++)
-        {
-          blockBt[rir] = rhs(i, k).real();
-
-          if(Conjugate)
-            blockBt[rii] = -rhs(i, k).imag();
-          else
-            blockBt[rii] =  rhs(i, k).imag();
-
-          rir += 1;
-          rii += 1;
-        }
-      }
-    }
-  }
-};
-
-/**************
- * GEMM utils *
- **************/
-
-// 512-bits rank1-update of acc. It can either positive or negative accumulate (useful for complex gemm).
-template<typename Packet, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,4>* acc, const Packet& lhsV, const Packet* rhsV)
-{
-  if(NegativeAccumulate)
-  {
-    acc->packet[0] = vec_nmsub(lhsV, rhsV[0], acc->packet[0]);
-    acc->packet[1] = vec_nmsub(lhsV, rhsV[1], acc->packet[1]);
-    acc->packet[2] = vec_nmsub(lhsV, rhsV[2], acc->packet[2]);
-    acc->packet[3] = vec_nmsub(lhsV, rhsV[3], acc->packet[3]);
-  } else {
-    acc->packet[0] = vec_madd(lhsV, rhsV[0], acc->packet[0]);
-    acc->packet[1] = vec_madd(lhsV, rhsV[1], acc->packet[1]);
-    acc->packet[2] = vec_madd(lhsV, rhsV[2], acc->packet[2]);
-    acc->packet[3] = vec_madd(lhsV, rhsV[3], acc->packet[3]);
-  }
-}
-
-template<typename Packet, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,1>* acc, const Packet& lhsV, const Packet* rhsV)
-{
-  if(NegativeAccumulate)
-  {
-    acc->packet[0] = vec_nmsub(lhsV, rhsV[0], acc->packet[0]);
-  } else {
-    acc->packet[0] = vec_madd(lhsV, rhsV[0], acc->packet[0]);
-  }
-}
-
-template<int N, typename Scalar, typename Packet, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, const Packet* rhsV)
-{
-  Packet lhsV = pload<Packet>(lhs);
-
-  pger_common<Packet, NegativeAccumulate>(acc, lhsV, rhsV);
-}
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs, Packet &lhsV, Index remaining_rows)
-{
-#ifdef _ARCH_PWR9
-  lhsV = vec_xl_len((Scalar *)lhs, remaining_rows * sizeof(Scalar));
-#else
-  Index i = 0;
-  do {
-    lhsV[i] = lhs[i];
-  } while (++i < remaining_rows);
-#endif
-}
-
-template<int N, typename Scalar, typename Packet, typename Index, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, const Packet* rhsV, Index remaining_rows)
-{
-  Packet lhsV;
-  loadPacketRemaining<Scalar, Packet, Index>(lhs, lhsV, remaining_rows);
-
-  pger_common<Packet, NegativeAccumulate>(acc, lhsV, rhsV);
-}
-
-// 512-bits rank1-update of complex acc. It takes decoupled accumulators as entries. It also takes cares of mixed types real * complex and complex * real.
-template<int N, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgerc_common(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Packet &lhsV, const Packet &lhsVi, const Packet* rhsV, const Packet* rhsVi)
-{
-  pger_common<Packet, false>(accReal, lhsV, rhsV);
-  if(LhsIsReal)
-  {
-    pger_common<Packet, ConjugateRhs>(accImag, lhsV, rhsVi);
-    EIGEN_UNUSED_VARIABLE(lhsVi);
-  } else {
-    if (!RhsIsReal) {
-      pger_common<Packet, ConjugateLhs == ConjugateRhs>(accReal, lhsVi, rhsVi);
-      pger_common<Packet, ConjugateRhs>(accImag, lhsV, rhsVi);
-    } else {
-      EIGEN_UNUSED_VARIABLE(rhsVi);
-    }
-    pger_common<Packet, ConjugateLhs>(accImag, lhsVi, rhsV);
-  }
-}
-
-template<int N, typename Scalar, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi)
-{
-  Packet lhsV = ploadLhs<Scalar, Packet>(lhs_ptr);
-  Packet lhsVi;
-  if(!LhsIsReal) lhsVi = ploadLhs<Scalar, Packet>(lhs_ptr_imag);
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-
-  pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
-}
-
-template<typename Scalar, typename Packet, typename Index, bool LhsIsReal>
-EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, Packet &lhsV, Packet &lhsVi, Index remaining_rows)
-{
-#ifdef _ARCH_PWR9
-  lhsV = vec_xl_len((Scalar *)lhs_ptr, remaining_rows * sizeof(Scalar));
-  if(!LhsIsReal) lhsVi = vec_xl_len((Scalar *)lhs_ptr_imag, remaining_rows * sizeof(Scalar));
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-#else
-  Index i = 0;
-  do {
-    lhsV[i] = lhs_ptr[i];
-    if(!LhsIsReal) lhsVi[i] = lhs_ptr_imag[i];
-  } while (++i < remaining_rows);
-  if(LhsIsReal) EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-#endif
-}
-
-template<int N, typename Scalar, typename Packet, typename Index, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi, Index remaining_rows)
-{
-  Packet lhsV, lhsVi;
-  loadPacketRemaining<Scalar, Packet, Index, LhsIsReal>(lhs_ptr, lhs_ptr_imag, lhsV, lhsVi, remaining_rows);
-
-  pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
-}
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadLhs(const Scalar* lhs)
-{
-  return ploadu<Packet>(lhs);
-}
-
-// Zero the accumulator on PacketBlock.
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,4>& acc)
-{
-  acc.packet[0] = pset1<Packet>((Scalar)0);
-  acc.packet[1] = pset1<Packet>((Scalar)0);
-  acc.packet[2] = pset1<Packet>((Scalar)0);
-  acc.packet[3] = pset1<Packet>((Scalar)0);
-}
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,1>& acc)
-{
-  acc.packet[0] = pset1<Packet>((Scalar)0);
-}
-
-// Scale the PacketBlock vectors by alpha.
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmadd(pAlpha, accZ.packet[0], acc.packet[0]);
-  acc.packet[1] = pmadd(pAlpha, accZ.packet[1], acc.packet[1]);
-  acc.packet[2] = pmadd(pAlpha, accZ.packet[2], acc.packet[2]);
-  acc.packet[3] = pmadd(pAlpha, accZ.packet[3], acc.packet[3]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,1>& acc, PacketBlock<Packet,1>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmadd(pAlpha, accZ.packet[0], acc.packet[0]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmul<Packet>(accZ.packet[0], pAlpha);
-  acc.packet[1] = pmul<Packet>(accZ.packet[1], pAlpha);
-  acc.packet[2] = pmul<Packet>(accZ.packet[2], pAlpha);
-  acc.packet[3] = pmul<Packet>(accZ.packet[3], pAlpha);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,1>& acc, PacketBlock<Packet,1>& accZ, const Packet& pAlpha)
-{
-  acc.packet[0] = pmul<Packet>(accZ.packet[0], pAlpha);
-}
-
-// Complex version of PacketBlock scaling.
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag)
-{
-  bscalec_common<Packet>(cReal, aReal, bReal);
-
-  bscalec_common<Packet>(cImag, aImag, bReal);
-
-  pger_common<Packet, true>(&cReal, bImag, aImag.packet);
-
-  pger_common<Packet, false>(&cImag, bImag, aReal.packet);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void band(PacketBlock<Packet,4>& acc, const Packet& pMask)
-{
-  acc.packet[0] = pand(acc.packet[0], pMask);
-  acc.packet[1] = pand(acc.packet[1], pMask);
-  acc.packet[2] = pand(acc.packet[2], pMask);
-  acc.packet[3] = pand(acc.packet[3], pMask);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,4>& aReal, PacketBlock<Packet,4>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,4>& cReal, PacketBlock<Packet,4>& cImag, const Packet& pMask)
-{
-  band<Packet>(aReal, pMask);
-  band<Packet>(aImag, pMask);
-
-  bscalec<Packet,4>(aReal, aImag, bReal, bImag, cReal, cImag);
-}
-
-// Load a PacketBlock, the N parameters make tunning gemm easier so we can add more accumulators as needed.
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,4>& acc, const DataMapper& res, Index row, Index col)
-{
-  if (StorageOrder == RowMajor) {
-    acc.packet[0] = res.template loadPacket<Packet>(row + 0, col + N*accCols);
-    acc.packet[1] = res.template loadPacket<Packet>(row + 1, col + N*accCols);
-    acc.packet[2] = res.template loadPacket<Packet>(row + 2, col + N*accCols);
-    acc.packet[3] = res.template loadPacket<Packet>(row + 3, col + N*accCols);
-  } else {
-    acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
-    acc.packet[1] = res.template loadPacket<Packet>(row + N*accCols, col + 1);
-    acc.packet[2] = res.template loadPacket<Packet>(row + N*accCols, col + 2);
-    acc.packet[3] = res.template loadPacket<Packet>(row + N*accCols, col + 3);
-  }
-}
-
-// An overload of bload when you have a PacketBLock with 8 vectors.
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,8>& acc, const DataMapper& res, Index row, Index col)
-{
-  if (StorageOrder == RowMajor) {
-    acc.packet[0] = res.template loadPacket<Packet>(row + 0, col + N*accCols);
-    acc.packet[1] = res.template loadPacket<Packet>(row + 1, col + N*accCols);
-    acc.packet[2] = res.template loadPacket<Packet>(row + 2, col + N*accCols);
-    acc.packet[3] = res.template loadPacket<Packet>(row + 3, col + N*accCols);
-    acc.packet[4] = res.template loadPacket<Packet>(row + 0, col + (N+1)*accCols);
-    acc.packet[5] = res.template loadPacket<Packet>(row + 1, col + (N+1)*accCols);
-    acc.packet[6] = res.template loadPacket<Packet>(row + 2, col + (N+1)*accCols);
-    acc.packet[7] = res.template loadPacket<Packet>(row + 3, col + (N+1)*accCols);
-  } else {
-    acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
-    acc.packet[1] = res.template loadPacket<Packet>(row + N*accCols, col + 1);
-    acc.packet[2] = res.template loadPacket<Packet>(row + N*accCols, col + 2);
-    acc.packet[3] = res.template loadPacket<Packet>(row + N*accCols, col + 3);
-    acc.packet[4] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 0);
-    acc.packet[5] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 1);
-    acc.packet[6] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 2);
-    acc.packet[7] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 3);
-  }
-}
-
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,2>& acc, const DataMapper& res, Index row, Index col)
-{
-  acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
-  acc.packet[1] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 0);
-}
-
-const static Packet4i mask41 = { -1,  0,  0,  0 };
-const static Packet4i mask42 = { -1, -1,  0,  0 };
-const static Packet4i mask43 = { -1, -1, -1,  0 };
-
-const static Packet2l mask21 = { -1, 0 };
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE Packet bmask(const int remaining_rows)
-{
-  if (remaining_rows == 0) {
-    return pset1<Packet>(float(0.0));  // Not used
-  } else {
-    switch (remaining_rows) {
-      case 1:  return Packet(mask41);
-      case 2:  return Packet(mask42);
-      default: return Packet(mask43);
-    }
-  }
-}
-
-template<>
-EIGEN_ALWAYS_INLINE Packet2d bmask<Packet2d>(const int remaining_rows)
-{
-  if (remaining_rows == 0) {
-    return pset1<Packet2d>(double(0.0));  // Not used
-  } else {
-    return Packet2d(mask21);
-  }
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha, const Packet& pMask)
-{
-  band<Packet>(accZ, pMask);
-
-  bscale<Packet>(acc, accZ, pAlpha);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void pbroadcast4_old(const __UNPACK_TYPE__(Packet)* a, Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  pbroadcast4<Packet>(a, a0, a1, a2, a3);
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void pbroadcast4_old<Packet2d>(const double* a, Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-  a1 = pload<Packet2d>(a);
-  a3 = pload<Packet2d>(a + 2);
-  a0 = vec_splat(a1, 0);
-  a1 = vec_splat(a1, 1);
-  a2 = vec_splat(a3, 0);
-  a3 = vec_splat(a3, 1);
-}
-
-// PEEL loop factor.
-#define PEEL 7
-
-template<typename Scalar, typename Packet, typename Index>
-EIGEN_ALWAYS_INLINE void MICRO_EXTRA_COL(
-  const Scalar* &lhs_ptr,
-  const Scalar* &rhs_ptr,
-  PacketBlock<Packet,1> &accZero,
-  Index remaining_rows,
-  Index remaining_cols)
-{
-  Packet rhsV[1];
-  rhsV[0] = pset1<Packet>(rhs_ptr[0]);
-  pger<1,Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
-  lhs_ptr += remaining_rows;
-  rhs_ptr += remaining_cols;
-}
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows>
-EIGEN_STRONG_INLINE void gemm_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr = lhs_base + row*strideA + remaining_rows*offsetA;
-  PacketBlock<Packet,1> accZero;
-
-  bsetzero<Scalar, Packet>(accZero);
-
-  Index remaining_depth = (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL <= remaining_depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    EIGEN_POWER_PREFETCH(lhs_ptr);
-    for (int l = 0; l < PEEL; l++) {
-      MICRO_EXTRA_COL<Scalar, Packet, Index>(lhs_ptr, rhs_ptr, accZero, remaining_rows, remaining_cols);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_EXTRA_COL<Scalar, Packet, Index>(lhs_ptr, rhs_ptr, accZero, remaining_rows, remaining_cols);
-  }
-  for(; k < depth; k++)
-  {
-    Packet rhsV[1];
-    rhsV[0] = pset1<Packet>(rhs_ptr[0]);
-    pger<1, Scalar, Packet, Index, false>(&accZero, lhs_ptr, rhsV, remaining_rows);
-    lhs_ptr += remaining_rows;
-    rhs_ptr += remaining_cols;
-  }
-
-  accZero.packet[0] = vec_mul(pAlpha, accZero.packet[0]);
-  for(Index i = 0; i < remaining_rows; i++) {
-    res(row + i, col) += accZero.packet[0][i];
-  }
-}
-
-template<typename Scalar, typename Packet, typename Index, const Index accRows>
-EIGEN_ALWAYS_INLINE void MICRO_EXTRA_ROW(
-  const Scalar* &lhs_ptr,
-  const Scalar* &rhs_ptr,
-  PacketBlock<Packet,4> &accZero,
-  Index remaining_rows)
-{
-  Packet rhsV[4];
-  pbroadcast4<Packet>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-  pger<4, Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
-  lhs_ptr += remaining_rows;
-  rhs_ptr += accRows;
-}
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr = lhs_base + row*strideA + remaining_rows*offsetA;
-  PacketBlock<Packet,4> accZero, acc;
-
-  bsetzero<Scalar, Packet>(accZero);
-
-  Index remaining_depth = (col + accRows < cols) ? depth : (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL <= remaining_depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    EIGEN_POWER_PREFETCH(lhs_ptr);
-    for (int l = 0; l < PEEL; l++) {
-      MICRO_EXTRA_ROW<Scalar, Packet, Index, accRows>(lhs_ptr, rhs_ptr, accZero, remaining_rows);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_EXTRA_ROW<Scalar, Packet, Index, accRows>(lhs_ptr, rhs_ptr, accZero, remaining_rows);
-  }
-
-  if ((remaining_depth == depth) && (rows >= accCols))
-  {
-    for(Index j = 0; j < 4; j++) {
-      acc.packet[j] = res.template loadPacket<Packet>(row, col + j);
-    }
-    bscale<Packet>(acc, accZero, pAlpha, pMask);
-    res.template storePacketBlock<Packet,4>(row, col, acc);
-  } else {
-    for(; k < depth; k++)
-    {
-      Packet rhsV[4];
-      pbroadcast4<Packet>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-      pger<4, Scalar, Packet, Index, false>(&accZero, lhs_ptr, rhsV, remaining_rows);
-      lhs_ptr += remaining_rows;
-      rhs_ptr += accRows;
-    }
-
-    for(Index j = 0; j < 4; j++) {
-      accZero.packet[j] = vec_mul(pAlpha, accZero.packet[j]);
-    }
-    for(Index j = 0; j < 4; j++) {
-      for(Index i = 0; i < remaining_rows; i++) {
-        res(row + i, col + j) += accZero.packet[j][i];
-      }
-    }
-  }
-}
-
-#define MICRO_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
-
-#define MICRO_UNROLL_WORK(func, func2, peel) \
-    MICRO_UNROLL(func2); \
-    func(0,peel) func(1,peel) func(2,peel) func(3,peel) \
-    func(4,peel) func(5,peel) func(6,peel) func(7,peel)
-
-#define MICRO_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr##iter); \
-    lhs_ptr##iter += accCols; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-  }
-
-#define MICRO_WORK_ONE(iter, peel) \
-  if (unroll_factor > iter) { \
-    pger_common<Packet, false>(&accZero##iter, lhsV##iter, rhsV##peel); \
-  }
-
-#define MICRO_TYPE_PEEL4(func, func2, peel) \
-  if (PEEL > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
-    pbroadcast4<Packet>(rhs_ptr + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
-    MICRO_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-  }
-
-#define MICRO_TYPE_PEEL1(func, func2, peel) \
-  if (PEEL > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
-    rhsV##peel[0] = pset1<Packet>(rhs_ptr[remaining_cols * peel]); \
-    MICRO_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-  }
-
-#define MICRO_UNROLL_TYPE_PEEL(M, func, func1, func2) \
-  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M], rhsV8[M], rhsV9[M]; \
-  func(func1,func2,0); func(func1,func2,1); \
-  func(func1,func2,2); func(func1,func2,3); \
-  func(func1,func2,4); func(func1,func2,5); \
-  func(func1,func2,6); func(func1,func2,7); \
-  func(func1,func2,8); func(func1,func2,9);
-
-#define MICRO_UNROLL_TYPE_ONE(M, func, func1, func2) \
-  Packet rhsV0[M]; \
-  func(func1,func2,0);
-
-#define MICRO_ONE_PEEL4 \
-  MICRO_UNROLL_TYPE_PEEL(4, MICRO_TYPE_PEEL4, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += (accRows * PEEL);
-
-#define MICRO_ONE4 \
-  MICRO_UNROLL_TYPE_ONE(4, MICRO_TYPE_PEEL4, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += accRows;
-
-#define MICRO_ONE_PEEL1 \
-  MICRO_UNROLL_TYPE_PEEL(1, MICRO_TYPE_PEEL1, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += (remaining_cols * PEEL);
-
-#define MICRO_ONE1 \
-  MICRO_UNROLL_TYPE_ONE(1, MICRO_TYPE_PEEL1, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
-  rhs_ptr += remaining_cols;
-
-#define MICRO_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzero<Scalar, Packet>(accZero##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accZero##iter); \
-  }
-
-#define MICRO_DST_PTR MICRO_UNROLL(MICRO_DST_PTR_ONE)
-
-#define MICRO_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols + accCols*offsetA; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr##iter); \
-  }
-
-#define MICRO_SRC_PTR MICRO_UNROLL(MICRO_SRC_PTR_ONE)
-
-#define MICRO_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr##iter); \
-  }
-
-#define MICRO_PREFETCH MICRO_UNROLL(MICRO_PREFETCH_ONE)
-
-#define MICRO_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    acc.packet[0] = res.template loadPacket<Packet>(row + iter*accCols, col + 0); \
-    acc.packet[1] = res.template loadPacket<Packet>(row + iter*accCols, col + 1); \
-    acc.packet[2] = res.template loadPacket<Packet>(row + iter*accCols, col + 2); \
-    acc.packet[3] = res.template loadPacket<Packet>(row + iter*accCols, col + 3); \
-    bscale<Packet>(acc, accZero##iter, pAlpha); \
-    res.template storePacketBlock<Packet,4>(row + iter*accCols, col, acc); \
-  }
-
-#define MICRO_STORE MICRO_UNROLL(MICRO_STORE_ONE)
-
-#define MICRO_COL_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    acc.packet[0] = res.template loadPacket<Packet>(row + iter*accCols, col + 0); \
-    bscale<Packet>(acc, accZero##iter, pAlpha); \
-    res.template storePacketBlock<Packet,1>(row + iter*accCols, col, acc); \
-  }
-
-#define MICRO_COL_STORE MICRO_UNROLL(MICRO_COL_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index col,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr0 = NULL, *  lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, * lhs_ptr7 = NULL;
-  PacketBlock<Packet,4> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
-  PacketBlock<Packet,4> acc;
-
-  MICRO_SRC_PTR
-  MICRO_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL <= depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    MICRO_PREFETCH
-    MICRO_ONE_PEEL4
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_ONE4
-  }
-  MICRO_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_col_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr0 = NULL, * lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, *lhs_ptr7 = NULL;
-  PacketBlock<Packet,1> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
-  PacketBlock<Packet,1> acc;
-
-  MICRO_SRC_PTR
-  MICRO_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL <= depth; k+= PEEL)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    MICRO_PREFETCH
-    MICRO_ONE_PEEL1
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_ONE1
-  }
-  MICRO_COL_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlpha)
-{
-#define MAX_UNROLL 6
-  while(row + MAX_UNROLL*accCols <= rows) {
-    gemm_unrolled_col_iteration<MAX_UNROLL, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-  }
-  switch( (rows-row)/accCols ) {
-#if MAX_UNROLL > 7
-    case 7:
-      gemm_unrolled_col_iteration<7, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 6
-    case 6:
-      gemm_unrolled_col_iteration<6, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 5
-   case 5:
-      gemm_unrolled_col_iteration<5, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 4
-   case 4:
-      gemm_unrolled_col_iteration<4, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-      break;
-#endif
-#if MAX_UNROLL > 3
-   case 3:
-     gemm_unrolled_col_iteration<3, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-     break;
-#endif
-#if MAX_UNROLL > 2
-   case 2:
-     gemm_unrolled_col_iteration<2, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-     break;
-#endif
-#if MAX_UNROLL > 1
-   case 1:
-     gemm_unrolled_col_iteration<1, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
-     break;
-#endif
-   default:
-     break;
-  }
-#undef MAX_UNROLL
-}
-
-/****************
- * GEMM kernels *
- * **************/
-template<typename Scalar, typename Index, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlpha = pset1<Packet>(alpha);
-      const Packet pMask  = bmask<Packet>((const int)(remaining_rows));
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-        Index row = 0;
-
-#define MAX_UNROLL 6
-        while(row + MAX_UNROLL*accCols <= rows) {
-          gemm_unrolled_iteration<MAX_UNROLL, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_UNROLL > 7
-          case 7:
-            gemm_unrolled_iteration<7, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 6
-          case 6:
-            gemm_unrolled_iteration<6, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 5
-          case 5:
-            gemm_unrolled_iteration<5, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 4
-          case 4:
-            gemm_unrolled_iteration<4, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 3
-          case 3:
-            gemm_unrolled_iteration<3, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 2
-          case 2:
-            gemm_unrolled_iteration<2, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_UNROLL > 1
-          case 1:
-            gemm_unrolled_iteration<1, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
-        }
-    }
-
-    if(remaining_cols > 0)
-    {
-      const Scalar* rhs_base = blockB + col*strideB + remaining_cols*offsetB;
-      const Scalar* lhs_base = blockA;
-
-      for(; col < cols; col++)
-      {
-        Index row = 0;
-
-        gemm_unrolled_col<Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, rows, col, remaining_cols, pAlpha);
-
-        if (remaining_rows > 0)
-        {
-          gemm_extra_col<Scalar, Packet, DataMapper, Index, accRows>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_rows, remaining_cols, pAlpha);
-        }
-        rhs_base++;
-      }
-    }
-}
-
-#define accColsC (accCols / 2)
-#define advanceRows ((LhsIsReal) ? 1 : 2)
-#define advanceCols ((RhsIsReal) ? 1 : 2)
-
-// PEEL_COMPLEX loop factor.
-#define PEEL_COMPLEX 3
-
-template<typename Scalar, typename Packet, typename Index, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_COL(
-  const Scalar* &lhs_ptr_real, const Scalar* &lhs_ptr_imag,
-  const Scalar* &rhs_ptr_real, const Scalar* &rhs_ptr_imag,
-  PacketBlock<Packet,1> &accReal, PacketBlock<Packet,1> &accImag,
-  Index remaining_rows,
-  Index remaining_cols)
-{
-  Packet rhsV[1], rhsVi[1];
-  rhsV[0] = pset1<Packet>(rhs_ptr_real[0]);
-  if(!RhsIsReal) rhsVi[0] = pset1<Packet>(rhs_ptr_imag[0]);
-  pgerc<1, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
-  lhs_ptr_real += remaining_rows;
-  if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  rhs_ptr_real += remaining_cols;
-  if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-}
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) rhs_ptr_imag = rhs_base + remaining_cols*strideB;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  const Scalar* lhs_ptr_real = lhs_base + advanceRows*row*strideA + remaining_rows*offsetA;
-  const Scalar* lhs_ptr_imag;
-  if(!LhsIsReal) lhs_ptr_imag = lhs_ptr_real + remaining_rows*strideA;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  PacketBlock<Packet,1> accReal, accImag;
-  PacketBlock<Packet,1> taccReal, taccImag;
-  PacketBlock<Packetc,1> acc0, acc1;
-
-  bsetzero<Scalar, Packet>(accReal);
-  bsetzero<Scalar, Packet>(accImag);
-
-  Index remaining_depth = (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= remaining_depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    EIGEN_POWER_PREFETCH(lhs_ptr_real);
-    if(!LhsIsReal) {
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag);
-    }
-    for (int l = 0; l < PEEL_COMPLEX; l++) {
-      MICRO_COMPLEX_EXTRA_COL<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows, remaining_cols);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_COMPLEX_EXTRA_COL<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows, remaining_cols);
-  }
-
-  for(; k < depth; k++)
-  {
-    Packet rhsV[1], rhsVi[1];
-    rhsV[0] = pset1<Packet>(rhs_ptr_real[0]);
-    if(!RhsIsReal) rhsVi[0] = pset1<Packet>(rhs_ptr_imag[0]);
-    pgerc<1, Scalar, Packet, Index, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi, remaining_rows);
-    lhs_ptr_real += remaining_rows;
-    if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-    rhs_ptr_real += remaining_cols;
-    if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
-  }
-
-  bscalec<Packet,1>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag);
-  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc0, acc1);
-
-  if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1))
-  {
-    res(row + 0, col + 0) += pfirst<Packetc>(acc0.packet[0]);
-  } else {
-    acc0.packet[0] += res.template loadPacket<Packetc>(row + 0, col + 0);
-    res.template storePacketBlock<Packetc,1>(row + 0, col + 0, acc0);
-    if(remaining_rows > accColsC) {
-      res(row + accColsC, col + 0) += pfirst<Packetc>(acc1.packet[0]);
-    }
-  }
-}
-
-template<typename Scalar, typename Packet, typename Index, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_ROW(
-  const Scalar* &lhs_ptr_real, const Scalar* &lhs_ptr_imag,
-  const Scalar* &rhs_ptr_real, const Scalar* &rhs_ptr_imag,
-  PacketBlock<Packet,4> &accReal, PacketBlock<Packet,4> &accImag,
-  Index remaining_rows)
-{
-  Packet rhsV[4], rhsVi[4];
-  pbroadcast4_old<Packet>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-  if(!RhsIsReal) pbroadcast4_old<Packet>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
-  pgerc<4, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
-  lhs_ptr_real += remaining_rows;
-  if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  rhs_ptr_real += accRows;
-  if(!RhsIsReal) rhs_ptr_imag += accRows;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-}
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) rhs_ptr_imag = rhs_base + accRows*strideB;
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  const Scalar* lhs_ptr_real = lhs_base + advanceRows*row*strideA + remaining_rows*offsetA;
-  const Scalar* lhs_ptr_imag;
-  if(!LhsIsReal) lhs_ptr_imag = lhs_ptr_real + remaining_rows*strideA;
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  PacketBlock<Packet,4> accReal, accImag;
-  PacketBlock<Packet,4> taccReal, taccImag;
-  PacketBlock<Packetc,4> acc0, acc1;
-  PacketBlock<Packetc,8> tRes;
-
-  bsetzero<Scalar, Packet>(accReal);
-  bsetzero<Scalar, Packet>(accImag);
-
-  Index remaining_depth = (col + accRows < cols) ? depth : (depth & -accRows);
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= remaining_depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    EIGEN_POWER_PREFETCH(lhs_ptr_real);
-    if(!LhsIsReal) {
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag);
-    }
-    for (int l = 0; l < PEEL_COMPLEX; l++) {
-      MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows);
-    }
-  }
-  for(; k < remaining_depth; k++)
-  {
-    MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows);
-  }
-
-  if ((remaining_depth == depth) && (rows >= accCols))
-  {
-    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row, col);
-    bscalec<Packet>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
-    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1);
-    res.template storePacketBlock<Packetc,4>(row + 0, col, acc0);
-    res.template storePacketBlock<Packetc,4>(row + accColsC, col, acc1);
-  } else {
-    for(; k < depth; k++)
-    {
-      Packet rhsV[4], rhsVi[4];
-      pbroadcast4_old<Packet>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-      if(!RhsIsReal) pbroadcast4_old<Packet>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
-      pgerc<4, Scalar, Packet, Index, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi, remaining_rows);
-      lhs_ptr_real += remaining_rows;
-      if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
-      rhs_ptr_real += accRows;
-      if(!RhsIsReal) rhs_ptr_imag += accRows;
-    }
-
-    bscalec<Packet,4>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag);
-    bcouple_common<Packet, Packetc>(taccReal, taccImag, acc0, acc1);
-
-    if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1))
-    {
-      for(Index j = 0; j < 4; j++) {
-        res(row + 0, col + j) += pfirst<Packetc>(acc0.packet[j]);
-      }
-    } else {
-      for(Index j = 0; j < 4; j++) {
-        PacketBlock<Packetc,1> acc2;
-        acc2.packet[0] = res.template loadPacket<Packetc>(row + 0, col + j) + acc0.packet[j];
-        res.template storePacketBlock<Packetc,1>(row + 0, col + j, acc2);
-        if(remaining_rows > accColsC) {
-          res(row + accColsC, col + j) += pfirst<Packetc>(acc1.packet[j]);
-        }
-      }
-    }
-  }
-}
-
-#define MICRO_COMPLEX_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4)
-
-#define MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
-    MICRO_COMPLEX_UNROLL(func2); \
-    func(0,peel) func(1,peel) func(2,peel) func(3,peel) func(4,peel)
-
-#define MICRO_COMPLEX_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter); \
-    lhs_ptr_real##iter += accCols; \
-    if(!LhsIsReal) { \
-      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_imag##iter); \
-      lhs_ptr_imag##iter += accCols; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-    EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-  }
-
-#define MICRO_COMPLEX_WORK_ONE4(iter, peel) \
-  if (unroll_factor > iter) { \
-    pgerc_common<4, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_WORK_ONE1(iter, peel) \
-  if (unroll_factor > iter) { \
-    pgerc_common<1, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_TYPE_PEEL4(func, func2, peel) \
-  if (PEEL_COMPLEX > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
-    pbroadcast4_old<Packet>(rhs_ptr_real + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
-    if(!RhsIsReal) { \
-      pbroadcast4_old<Packet>(rhs_ptr_imag + (accRows * peel), rhsVi##peel[0], rhsVi##peel[1], rhsVi##peel[2], rhsVi##peel[3]); \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-    } \
-    MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_TYPE_PEEL1(func, func2, peel) \
-  if (PEEL_COMPLEX > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
-    rhsV##peel[0] = pset1<Packet>(rhs_ptr_real[remaining_cols * peel]); \
-    if(!RhsIsReal) { \
-      rhsVi##peel[0] = pset1<Packet>(rhs_ptr_imag[remaining_cols * peel]); \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-    } \
-    MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_UNROLL_TYPE_PEEL(M, func, func1, func2) \
-  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M], rhsV8[M], rhsV9[M]; \
-  Packet rhsVi0[M], rhsVi1[M], rhsVi2[M], rhsVi3[M], rhsVi4[M], rhsVi5[M], rhsVi6[M], rhsVi7[M], rhsVi8[M], rhsVi9[M]; \
-  func(func1,func2,0); func(func1,func2,1); \
-  func(func1,func2,2); func(func1,func2,3); \
-  func(func1,func2,4); func(func1,func2,5); \
-  func(func1,func2,6); func(func1,func2,7); \
-  func(func1,func2,8); func(func1,func2,9);
-
-#define MICRO_COMPLEX_UNROLL_TYPE_ONE(M, func, func1, func2) \
-  Packet rhsV0[M], rhsVi0[M];\
-  func(func1,func2,0);
-
-#define MICRO_COMPLEX_ONE_PEEL4 \
-  MICRO_COMPLEX_UNROLL_TYPE_PEEL(4, MICRO_COMPLEX_TYPE_PEEL4, MICRO_COMPLEX_WORK_ONE4, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += (accRows * PEEL_COMPLEX); \
-  if(!RhsIsReal) rhs_ptr_imag += (accRows * PEEL_COMPLEX);
-
-#define MICRO_COMPLEX_ONE4 \
-  MICRO_COMPLEX_UNROLL_TYPE_ONE(4, MICRO_COMPLEX_TYPE_PEEL4, MICRO_COMPLEX_WORK_ONE4, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += accRows; \
-  if(!RhsIsReal) rhs_ptr_imag += accRows;
-
-#define MICRO_COMPLEX_ONE_PEEL1 \
-  MICRO_COMPLEX_UNROLL_TYPE_PEEL(1, MICRO_COMPLEX_TYPE_PEEL1, MICRO_COMPLEX_WORK_ONE1, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += (remaining_cols * PEEL_COMPLEX); \
-  if(!RhsIsReal) rhs_ptr_imag += (remaining_cols * PEEL_COMPLEX);
-
-#define MICRO_COMPLEX_ONE1 \
-  MICRO_COMPLEX_UNROLL_TYPE_ONE(1, MICRO_COMPLEX_TYPE_PEEL1, MICRO_COMPLEX_WORK_ONE1, MICRO_COMPLEX_LOAD_ONE); \
-  rhs_ptr_real += remaining_cols; \
-  if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
-
-#define MICRO_COMPLEX_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzero<Scalar, Packet>(accReal##iter); \
-    bsetzero<Scalar, Packet>(accImag##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accReal##iter); \
-    EIGEN_UNUSED_VARIABLE(accImag##iter); \
-  }
-
-#define MICRO_COMPLEX_DST_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_DST_PTR_ONE)
-
-#define MICRO_COMPLEX_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols + accCols*offsetA; \
-    if(!LhsIsReal) { \
-      lhs_ptr_imag##iter = lhs_ptr_real##iter + accCols*strideA; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_real##iter); \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-  }
-
-#define MICRO_COMPLEX_SRC_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_SRC_PTR_ONE)
-
-#define MICRO_COMPLEX_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr_real##iter); \
-    if(!LhsIsReal) { \
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag##iter); \
-    } \
-  }
-
-#define MICRO_COMPLEX_PREFETCH MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_PREFETCH_ONE)
-
-#define MICRO_COMPLEX_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row + iter*accCols, col); \
-    bscalec<Packet,4>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag); \
-    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1); \
-    res.template storePacketBlock<Packetc,4>(row + iter*accCols + 0, col, acc0); \
-    res.template storePacketBlock<Packetc,4>(row + iter*accCols + accColsC, col, acc1); \
-  }
-
-#define MICRO_COMPLEX_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_STORE_ONE)
-
-#define MICRO_COMPLEX_COL_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row + iter*accCols, col); \
-    bscalec<Packet,1>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag); \
-    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1); \
-    res.template storePacketBlock<Packetc,1>(row + iter*accCols + 0, col, acc0); \
-    res.template storePacketBlock<Packetc,1>(row + iter*accCols + accColsC, col, acc1); \
-  }
-
-#define MICRO_COMPLEX_COL_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_COL_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index col,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) {
-    rhs_ptr_imag = rhs_base + accRows*strideB;
-  } else {
-    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
-  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
-  PacketBlock<Packet,4> accReal0, accImag0, accReal1, accImag1;
-  PacketBlock<Packet,4> accReal2, accImag2, accReal3, accImag3;
-  PacketBlock<Packet,4> accReal4, accImag4;
-  PacketBlock<Packet,4> taccReal, taccImag;
-  PacketBlock<Packetc,4> acc0, acc1;
-  PacketBlock<Packetc,8> tRes;
-
-  MICRO_COMPLEX_SRC_PTR
-  MICRO_COMPLEX_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    MICRO_COMPLEX_PREFETCH
-    MICRO_COMPLEX_ONE_PEEL4
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_COMPLEX_ONE4
-  }
-  MICRO_COMPLEX_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_col_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) {
-    rhs_ptr_imag = rhs_base + remaining_cols*strideB;
-  } else {
-    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
-  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
-  PacketBlock<Packet,1> accReal0, accImag0, accReal1, accImag1;
-  PacketBlock<Packet,1> accReal2, accImag2, accReal3, accImag3;
-  PacketBlock<Packet,1> accReal4, accImag4;
-  PacketBlock<Packet,1> taccReal, taccImag;
-  PacketBlock<Packetc,1> acc0, acc1;
-  PacketBlock<Packetc,2> tRes;
-
-  MICRO_COMPLEX_SRC_PTR
-  MICRO_COMPLEX_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_COMPLEX <= depth; k+= PEEL_COMPLEX)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    MICRO_COMPLEX_PREFETCH
-    MICRO_COMPLEX_ONE_PEEL1
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_COMPLEX_ONE1
-  }
-  MICRO_COMPLEX_COL_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-#define MAX_COMPLEX_UNROLL 3
-  while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
-    gemm_complex_unrolled_col_iteration<MAX_COMPLEX_UNROLL, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-  }
-  switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_UNROLL > 4
-   case 4:
-     gemm_complex_unrolled_col_iteration<4, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-#if MAX_COMPLEX_UNROLL > 3
-   case 3:
-     gemm_complex_unrolled_col_iteration<3, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-#if MAX_COMPLEX_UNROLL > 2
-   case 2:
-     gemm_complex_unrolled_col_iteration<2, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-#if MAX_COMPLEX_UNROLL > 1
-   case 1:
-     gemm_complex_unrolled_col_iteration<1, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
-     break;
-#endif
-   default:
-     break;
-  }
-#undef MAX_COMPLEX_UNROLL
-}
-
-template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Index, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlphaReal = pset1<Packet>(alpha.real());
-      const Packet pAlphaImag = pset1<Packet>(alpha.imag());
-      const Packet pMask = bmask<Packet>((const int)(remaining_rows));
-
-      const Scalar* blockA = (Scalar *) blockAc;
-      const Scalar* blockB = (Scalar *) blockBc;
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-        Index row = 0;
-
-#define MAX_COMPLEX_UNROLL 3
-        while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
-          gemm_complex_unrolled_iteration<MAX_COMPLEX_UNROLL, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_UNROLL > 4
-          case 4:
-            gemm_complex_unrolled_iteration<4, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_UNROLL > 3
-          case 3:
-            gemm_complex_unrolled_iteration<3, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_UNROLL > 2
-          case 2:
-            gemm_complex_unrolled_iteration<2, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_UNROLL > 1
-          case 1:
-            gemm_complex_unrolled_iteration<1, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_COMPLEX_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
-        }
-      }
-
-      if(remaining_cols > 0)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + remaining_cols*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        for(; col < cols; col++)
-        {
-          Index row = 0;
-
-          gemm_complex_unrolled_col<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, col, remaining_cols, pAlphaReal, pAlphaImag);
-
-          if (remaining_rows > 0)
-          {
-            gemm_complex_extra_col<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_rows, remaining_cols, pAlphaReal, pAlphaImag);
-          }
-          rhs_base++;
-        }
-      }
-}
-
-#undef accColsC
-#undef advanceCols
-#undef advanceRows
-
-/************************************
- * ppc64le template specializations *
- * **********************************/
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-    dhs_pack<double, Index, DataMapper, Packet2d, ColMajor, PanelMode, true> pack;
-    pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-    dhs_pack<double, Index, DataMapper, Packet2d, RowMajor, PanelMode, true> pack;
-    pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<double, Index, DataMapper, Packet2d, ColMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<double, Index, DataMapper, Packet2d, RowMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-#endif
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, RowMajor, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, ColMajor, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, ColMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_pack<float, Index, DataMapper, Packet4f, RowMajor, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-#endif
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<float, Index, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, true> pack;
-  pack(blockA, lhs, depth, rows, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-  ::operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  dhs_cpack<double, Index, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, false> pack;
-  pack(blockB, rhs, depth, cols, stride, offset);
-}
-
-// ********* gebp specializations *********
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef typename quad_traits<float>::vectortype   Packet;
-  typedef typename quad_traits<float>::rhstype      RhsPacket;
-
-  void operator()(const DataMapper& res, const float* blockA, const float* blockB,
-                  Index rows, Index depth, Index cols, float alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const float* blockA, const float* blockB,
-               Index rows, Index depth, Index cols, float alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const float*, const float*, Index, Index, Index, float, Index, Index, Index, Index);
-
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemmMMA<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemmMMA<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #endif
-      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef Packet4f   Packet;
-  typedef Packet2cf  Packetc;
-  typedef Packet4f   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<float>* blockA, const std::complex<float>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<float> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<float>* blockA, const std::complex<float>* blockB,
-               Index rows, Index depth, Index cols, std::complex<float> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<float>*, const std::complex<float>*,
-          Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #endif
-      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef Packet4f   Packet;
-  typedef Packet2cf  Packetc;
-  typedef Packet4f   RhsPacket;
-
-  void operator()(const DataMapper& res, const float* blockA, const std::complex<float>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<float> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const float* blockA, const std::complex<float>* blockB,
-               Index rows, Index depth, Index cols, std::complex<float> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const float*, const std::complex<float>*,
-          Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef Packet4f   Packet;
-  typedef Packet2cf  Packetc;
-  typedef Packet4f   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<float>* blockA, const float* blockB,
-                  Index rows, Index depth, Index cols, std::complex<float> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<float>* blockA, const float* blockB,
-               Index rows, Index depth, Index cols, std::complex<float> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<float>::rows;
-    const Index accCols = quad_traits<float>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<float>*, const float*,
-          Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef typename quad_traits<double>::vectortype  Packet;
-  typedef typename quad_traits<double>::rhstype     RhsPacket;
-
-  void operator()(const DataMapper& res, const double* blockA, const double* blockB,
-                  Index rows, Index depth, Index cols, double alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const double* blockA, const double* blockB,
-               Index rows, Index depth, Index cols, double alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const double*, const double*, Index, Index, Index, double, Index, Index, Index, Index);
-
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemmMMA<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemmMMA<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #endif
-      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef quad_traits<double>::vectortype   Packet;
-  typedef Packet1cd  Packetc;
-  typedef quad_traits<double>::rhstype   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<double>* blockA, const std::complex<double>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<double> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<double>* blockA, const std::complex<double>* blockB,
-               Index rows, Index depth, Index cols, std::complex<double> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<double>*, const std::complex<double>*,
-          Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef quad_traits<double>::vectortype   Packet;
-  typedef Packet1cd  Packetc;
-  typedef quad_traits<double>::rhstype   RhsPacket;
-
-  void operator()(const DataMapper& res, const std::complex<double>* blockA, const double* blockB,
-                  Index rows, Index depth, Index cols, std::complex<double> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const std::complex<double>* blockA, const double* blockB,
-               Index rows, Index depth, Index cols, std::complex<double> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const std::complex<double>*, const double*,
-          Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-{
-  typedef quad_traits<double>::vectortype   Packet;
-  typedef Packet1cd  Packetc;
-  typedef quad_traits<double>::rhstype   RhsPacket;
-
-  void operator()(const DataMapper& res, const double* blockA, const std::complex<double>* blockB,
-                  Index rows, Index depth, Index cols, std::complex<double> alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-void gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
-  ::operator()(const DataMapper& res, const double* blockA, const std::complex<double>* blockB,
-               Index rows, Index depth, Index cols, std::complex<double> alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    const Index accRows = quad_traits<double>::rows;
-    const Index accCols = quad_traits<double>::size;
-    void (*gemm_function)(const DataMapper&, const double*, const std::complex<double>*,
-          Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #ifdef EIGEN_ALTIVEC_MMA_ONLY
-       //generate with MMA only
-       gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-         gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-       else{
-         gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-       }
-     #else
-       gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-     #endif
-       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
-  }
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_PRODUCT_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
deleted file mode 100644
index 33d5434..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
+++ /dev/null
@@ -1,221 +0,0 @@
-//#define EIGEN_POWER_USE_PREFETCH  // Use prefetching in gemm routines
-#ifdef EIGEN_POWER_USE_PREFETCH
-#define EIGEN_POWER_PREFETCH(p)  prefetch(p)
-#else
-#define EIGEN_POWER_PREFETCH(p)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows>
-EIGEN_STRONG_INLINE void gemm_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlpha);
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask);
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlpha);
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE Packet bmask(const int remaining_rows);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index remaining_rows,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_col(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index rows,
-  Index col,
-  Index remaining_cols,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag);
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadLhs(const Scalar* lhs);
-
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,4>& acc, const DataMapper& res, Index row, Index col);
-
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,8>& acc, const DataMapper& res, Index row, Index col);
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha);
-
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag);
-
-const static Packet16uc p16uc_SETCOMPLEX32_FIRST = {  0,  1,  2,  3,
-                                                     16, 17, 18, 19,
-                                                      4,  5,  6,  7,
-                                                     20, 21, 22, 23};
-
-const static Packet16uc p16uc_SETCOMPLEX32_SECOND = {  8,  9, 10, 11,
-                                                      24, 25, 26, 27,
-                                                      12, 13, 14, 15,
-                                                      28, 29, 30, 31};
-//[a,b],[ai,bi] = [a,ai] - This is equivalent to p16uc_GETREAL64
-const static Packet16uc p16uc_SETCOMPLEX64_FIRST = {  0,  1,  2,  3,  4,  5,  6,  7,
-                                                     16, 17, 18, 19, 20, 21, 22, 23};
-
-//[a,b],[ai,bi] = [b,bi] - This is equivalent to p16uc_GETIMAG64
-const static Packet16uc p16uc_SETCOMPLEX64_SECOND = {  8,  9, 10, 11, 12, 13, 14, 15,
-                                                      24, 25, 26, 27, 28, 29, 30, 31};
-
-
-// Grab two decouples real/imaginary PacketBlocks and return two coupled (real/imaginary pairs) PacketBlocks.
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,4>& taccReal, PacketBlock<Packet,4>& taccImag, PacketBlock<Packetc, 4>& acc1, PacketBlock<Packetc, 4>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_FIRST);
-  acc1.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX32_FIRST);
-  acc1.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX32_FIRST);
-  acc1.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX32_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_SECOND);
-  acc2.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX32_SECOND);
-  acc2.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX32_SECOND);
-  acc2.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX32_SECOND);
-}
-
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,4>& taccReal, PacketBlock<Packet,4>& taccImag, PacketBlock<Packetc,8>& tRes, PacketBlock<Packetc, 4>& acc1, PacketBlock<Packetc, 4>& acc2)
-{
-  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc1, acc2);
-
-  acc1.packet[0] = padd<Packetc>(tRes.packet[0], acc1.packet[0]);
-  acc1.packet[1] = padd<Packetc>(tRes.packet[1], acc1.packet[1]);
-  acc1.packet[2] = padd<Packetc>(tRes.packet[2], acc1.packet[2]);
-  acc1.packet[3] = padd<Packetc>(tRes.packet[3], acc1.packet[3]);
-
-  acc2.packet[0] = padd<Packetc>(tRes.packet[4], acc2.packet[0]);
-  acc2.packet[1] = padd<Packetc>(tRes.packet[5], acc2.packet[1]);
-  acc2.packet[2] = padd<Packetc>(tRes.packet[6], acc2.packet[2]);
-  acc2.packet[3] = padd<Packetc>(tRes.packet[7], acc2.packet[3]);
-}
-
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,1>& taccReal, PacketBlock<Packet,1>& taccImag, PacketBlock<Packetc, 1>& acc1, PacketBlock<Packetc, 1>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_SECOND);
-}
-
-template<typename Packet, typename Packetc>
-EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,1>& taccReal, PacketBlock<Packet,1>& taccImag, PacketBlock<Packetc,2>& tRes, PacketBlock<Packetc, 1>& acc1, PacketBlock<Packetc, 1>& acc2)
-{
-  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc1, acc2);
-
-  acc1.packet[0] = padd<Packetc>(tRes.packet[0], acc1.packet[0]);
-
-  acc2.packet[0] = padd<Packetc>(tRes.packet[1], acc2.packet[0]);
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void bcouple_common<Packet2d, Packet1cd>(PacketBlock<Packet2d,4>& taccReal, PacketBlock<Packet2d,4>& taccImag, PacketBlock<Packet1cd, 4>& acc1, PacketBlock<Packet1cd, 4>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_FIRST);
-  acc1.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX64_FIRST);
-  acc1.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX64_FIRST);
-  acc1.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX64_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_SECOND);
-  acc2.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX64_SECOND);
-  acc2.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX64_SECOND);
-  acc2.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX64_SECOND);
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void bcouple_common<Packet2d, Packet1cd>(PacketBlock<Packet2d,1>& taccReal, PacketBlock<Packet2d,1>& taccImag, PacketBlock<Packet1cd, 1>& acc1, PacketBlock<Packet1cd, 1>& acc2)
-{
-  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_FIRST);
-
-  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_SECOND);
-}
-
-// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadRhs(const Scalar* rhs)
-{
-  return ploadu<Packet>(rhs);
-}
-
-} // end namespace internal
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
deleted file mode 100644
index 6540c6f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
+++ /dev/null
@@ -1,629 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
-// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
-#define EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
-
-#pragma GCC target("cpu=power10")
-
-#ifdef __has_builtin
-#if !__has_builtin(__builtin_vsx_assemble_pair)
-#define __builtin_vsx_assemble_pair __builtin_mma_assemble_pair
-#endif
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void bsetzeroMMA(__vector_quad* acc)
-{
-  __builtin_mma_xxsetaccz(acc);
-}
-
-template<typename DataMapper, typename Index, typename Packet, const Index accCols>
-EIGEN_ALWAYS_INLINE void storeAccumulator(Index i, Index j, const DataMapper& data, const Packet& alpha, __vector_quad* acc)
-{
-  PacketBlock<Packet, 4> result;
-  __builtin_mma_disassemble_acc(&result.packet, acc);
-
-  PacketBlock<Packet, 4> tRes;
-  bload<DataMapper, Packet, Index, accCols, 0, ColMajor>(tRes, data, i, j);
-
-  bscale<Packet>(tRes, result, alpha);
-
-  data.template storePacketBlock<Packet, 4>(i, j, tRes);
-}
-
-template<typename DataMapper, typename Index, typename Packet, typename Packetc, const Index accColsC, int N>
-EIGEN_ALWAYS_INLINE void storeComplexAccumulator(Index i, Index j, const DataMapper& data, const Packet& alphaReal, const Packet& alphaImag, __vector_quad* accReal, __vector_quad* accImag)
-{
-  PacketBlock<Packet, 4> resultReal, resultImag;
-  __builtin_mma_disassemble_acc(&resultReal.packet, accReal);
-  __builtin_mma_disassemble_acc(&resultImag.packet, accImag);
-
-  PacketBlock<Packetc, 8> tRes;
-  bload<DataMapper, Packetc, Index, accColsC, N, ColMajor>(tRes, data, i, j);
-
-  PacketBlock<Packet,4> taccReal, taccImag;
-  bscalec<Packet,4>(resultReal, resultImag, alphaReal, alphaImag, taccReal, taccImag);
-
-  PacketBlock<Packetc, 4> acc1, acc2;
-  bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc1, acc2);
-
-  data.template storePacketBlock<Packetc, 4>(i + N*accColsC, j, acc1);
-  data.template storePacketBlock<Packetc, 4>(i + (N+1)*accColsC, j, acc2);
-}
-
-// Defaults to float32, since Eigen still supports C++03 we can't use default template arguments
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const RhsPacket& a, const LhsPacket& b)
-{
-  if(NegativeAccumulate)
-  {
-    __builtin_mma_xvf32gernp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-  } else {
-    __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-  }
-}
-
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const PacketBlock<Packet2d,2>& a, const Packet2d& b)
-{
-  __vector_pair* a0 = (__vector_pair *)(&a.packet[0]);
-  if(NegativeAccumulate)
-  {
-    __builtin_mma_xvf64gernp(acc, *a0, (__vector unsigned char)b);
-  } else {
-    __builtin_mma_xvf64gerpp(acc, *a0, (__vector unsigned char)b);
-  }
-}
-
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const __vector_pair& a, const Packet2d& b)
-{
-  if(NegativeAccumulate)
-  {
-    __builtin_mma_xvf64gernp(acc, (__vector_pair)a, (__vector unsigned char)b);
-  } else {
-    __builtin_mma_xvf64gerpp(acc, (__vector_pair)a, (__vector unsigned char)b);
-  }
-}
-
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad*, const __vector_pair&, const Packet4f&)
-{
-  // Just for compilation
-}
-
-template<typename Scalar, typename Packet, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void pgercMMA(__vector_quad* accReal, __vector_quad* accImag, const Packet& lhsV, const Packet& lhsVi, const RhsPacket& rhsV, const RhsPacket& rhsVi)
-{
-  pgerMMA<Packet, RhsPacket, false>(accReal,  rhsV,  lhsV);
-  if(LhsIsReal) {
-    pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi,  lhsV);
-  } else {
-    if(!RhsIsReal) {
-      pgerMMA<Packet, RhsPacket, ConjugateLhs == ConjugateRhs>(accReal, rhsVi, lhsVi);
-      pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi,  lhsV);
-    } else {
-      EIGEN_UNUSED_VARIABLE(rhsVi);
-    }
-    pgerMMA<Packet, RhsPacket, ConjugateLhs>(accImag,  rhsV, lhsVi);
-  }
-}
-
-// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA(const Scalar* rhs, Packet& rhsV)
-{
-  rhsV = ploadRhs<Scalar, Packet>((const Scalar*)(rhs));
-} 
-
-template<>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA<double, PacketBlock<Packet2d, 2> >(const double* rhs, PacketBlock<Packet2d, 2>& rhsV)
-{
-  rhsV.packet[0] = ploadRhs<double, Packet2d>((const double *)((Packet2d *)rhs      ));
-  rhsV.packet[1] = ploadRhs<double, Packet2d>((const double *)(((Packet2d *)rhs) + 1));
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA<double, __vector_pair>(const double* rhs, __vector_pair& rhsV)
-{
-#if EIGEN_COMP_LLVM
-  __builtin_vsx_assemble_pair(&rhsV,
-    (__vector unsigned char)(ploadRhs<double, Packet2d>((const double *)(((Packet2d *)rhs) + 1))),
-    (__vector unsigned char)(ploadRhs<double, Packet2d>((const double *)((Packet2d *)rhs      ))));
-#else
-  __asm__ ("lxvp %x0,%1" : "=wa" (rhsV) : "Y" (*rhs));
-#endif
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void ploadRhsMMA(const float*, __vector_pair&)
-{
-  // Just for compilation
-}
-
-// PEEL_MMA loop factor.
-#define PEEL_MMA 7
-
-#define MICRO_MMA_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
-
-#define MICRO_MMA_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr##iter); \
-    lhs_ptr##iter += accCols; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-  }
-
-#define MICRO_MMA_WORK_ONE(iter, type, peel) \
-  if (unroll_factor > iter) { \
-    pgerMMA<Packet, type, false>(&accZero##iter, rhsV##peel, lhsV##iter); \
-  }
-
-#define MICRO_MMA_TYPE_PEEL(func, func2, type, peel) \
-  if (PEEL_MMA > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
-    ploadRhsMMA<Scalar, type>(rhs_ptr + (accRows * peel), rhsV##peel); \
-    MICRO_MMA_UNROLL(func2); \
-    func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel) \
-    func(4,type,peel) func(5,type,peel) func(6,type,peel) func(7,type,peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-  }
-
-#define MICRO_MMA_UNROLL_TYPE_PEEL(func, func2, type) \
-  type rhsV0, rhsV1, rhsV2, rhsV3, rhsV4, rhsV5, rhsV6, rhsV7, rhsV8, rhsV9; \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,0); MICRO_MMA_TYPE_PEEL(func,func2,type,1); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,2); MICRO_MMA_TYPE_PEEL(func,func2,type,3); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,4); MICRO_MMA_TYPE_PEEL(func,func2,type,5); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,6); MICRO_MMA_TYPE_PEEL(func,func2,type,7); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,8); MICRO_MMA_TYPE_PEEL(func,func2,type,9);
-
-#define MICRO_MMA_UNROLL_TYPE_ONE(func, func2, type) \
-  type rhsV0; \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,0);
-
-#define MICRO_MMA_ONE_PEEL \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_MMA_UNROLL_TYPE_PEEL(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_MMA_UNROLL_TYPE_PEEL(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr += (accRows * PEEL_MMA);
-
-#define MICRO_MMA_ONE \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_MMA_UNROLL_TYPE_ONE(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_MMA_UNROLL_TYPE_ONE(MICRO_MMA_WORK_ONE, MICRO_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr += accRows;
-
-#define MICRO_MMA_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzeroMMA<Scalar, Packet>(&accZero##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accZero##iter); \
-  }
-
-#define MICRO_MMA_DST_PTR MICRO_MMA_UNROLL(MICRO_MMA_DST_PTR_ONE)
-
-#define MICRO_MMA_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols + accCols*offsetA; \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr##iter); \
-  }
-
-#define MICRO_MMA_SRC_PTR MICRO_MMA_UNROLL(MICRO_MMA_SRC_PTR_ONE)
-
-#define MICRO_MMA_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr##iter); \
-  }
-
-#define MICRO_MMA_PREFETCH MICRO_MMA_UNROLL(MICRO_MMA_PREFETCH_ONE)
-
-#define MICRO_MMA_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    storeAccumulator<DataMapper, Index, Packet, accCols>(row + iter*accCols, col, res, pAlpha, &accZero##iter); \
-  }
-
-#define MICRO_MMA_STORE MICRO_MMA_UNROLL(MICRO_MMA_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_unrolled_MMA_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index& row,
-  Index col,
-  const Packet& pAlpha)
-{
-  const Scalar* rhs_ptr = rhs_base;
-  const Scalar* lhs_ptr0 = NULL, * lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, * lhs_ptr7 = NULL;
-  __vector_quad accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
-
-  MICRO_MMA_SRC_PTR
-  MICRO_MMA_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_MMA <= depth; k+= PEEL_MMA)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr);
-    MICRO_MMA_PREFETCH
-    MICRO_MMA_ONE_PEEL
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_MMA_ONE
-  }
-  MICRO_MMA_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename Scalar, typename Index, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
-void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlpha = pset1<Packet>(alpha);
-      const Packet pMask  = bmask<Packet>((const int)(remaining_rows));
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        Index row = 0;
-#define MAX_MMA_UNROLL 7
-        while(row + MAX_MMA_UNROLL*accCols <= rows) {
-          gemm_unrolled_MMA_iteration<MAX_MMA_UNROLL, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_MMA_UNROLL > 7
-          case 7:
-            gemm_unrolled_MMA_iteration<7, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 6
-          case 6:
-            gemm_unrolled_MMA_iteration<6, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 5
-          case 5:
-            gemm_unrolled_MMA_iteration<5, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 4
-          case 4:
-            gemm_unrolled_MMA_iteration<4, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 3
-          case 3:
-            gemm_unrolled_MMA_iteration<3, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 2
-          case 2:
-            gemm_unrolled_MMA_iteration<2, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-#if MAX_MMA_UNROLL > 1
-          case 1:
-            gemm_unrolled_MMA_iteration<1, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_MMA_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
-        }
-      }
-
-      if(remaining_cols > 0)
-      {
-        const Scalar* rhs_base = blockB + col*strideB + remaining_cols*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        for(; col < cols; col++)
-        {
-          Index row = 0;
-
-          gemm_unrolled_col<Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, rows, col, remaining_cols, pAlpha);
-
-          if (remaining_rows > 0)
-          {
-            gemm_extra_col<Scalar, Packet, DataMapper, Index, accRows>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_rows, remaining_cols, pAlpha);
-          }
-          rhs_base++;
-        }
-      }
-}
-
-#define accColsC (accCols / 2)
-#define advanceRows ((LhsIsReal) ? 1 : 2)
-#define advanceCols ((RhsIsReal) ? 1 : 2)
-
-// PEEL_COMPLEX_MMA loop factor.
-#define PEEL_COMPLEX_MMA 7
-
-#define MICRO_COMPLEX_MMA_UNROLL(func) \
-  func(0) func(1) func(2) func(3) func(4)
-
-#define MICRO_COMPLEX_MMA_LOAD_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter); \
-    lhs_ptr_real##iter += accCols; \
-    if(!LhsIsReal) { \
-      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_imag##iter); \
-      lhs_ptr_imag##iter += accCols; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
-    EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_WORK_ONE(iter, type, peel) \
-  if (unroll_factor > iter) { \
-    pgercMMA<Scalar, Packet, type, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_MMA_TYPE_PEEL(func, func2, type, peel) \
-  if (PEEL_COMPLEX_MMA > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
-    ploadRhsMMA<Scalar, type>(rhs_ptr_real + (accRows * peel), rhsV##peel); \
-    if(!RhsIsReal) { \
-      ploadRhsMMA<Scalar, type>(rhs_ptr_imag + (accRows * peel), rhsVi##peel); \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-    } \
-    MICRO_COMPLEX_MMA_UNROLL(func2); \
-    func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel) func(4,type,peel) \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
-  }
-
-#define MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(func, func2, type) \
-  type rhsV0, rhsV1, rhsV2, rhsV3, rhsV4, rhsV5, rhsV6, rhsV7, rhsV8, rhsV9; \
-  type rhsVi0, rhsVi1, rhsVi2, rhsVi3, rhsVi4, rhsVi5, rhsVi6, rhsVi7, rhsVi8, rhsVi9; \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,0); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,1); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,2); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,3); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,4); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,5); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,6); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,7); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,8); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,9);
-
-#define MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(func, func2, type) \
-  type rhsV0, rhsVi0; \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,0);
-
-#define MICRO_COMPLEX_MMA_ONE_PEEL \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr_real += (accRows * PEEL_COMPLEX_MMA); \
-  if(!RhsIsReal) rhs_ptr_imag += (accRows * PEEL_COMPLEX_MMA);
-
-#define MICRO_COMPLEX_MMA_ONE \
-  if (sizeof(Scalar) == sizeof(float)) { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, RhsPacket); \
-  } else { \
-    MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_MMA_LOAD_ONE, __vector_pair); \
-  } \
-  rhs_ptr_real += accRows; \
-  if(!RhsIsReal) rhs_ptr_imag += accRows;
-
-#define MICRO_COMPLEX_MMA_DST_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    bsetzeroMMA<Scalar, Packet>(&accReal##iter); \
-    bsetzeroMMA<Scalar, Packet>(&accImag##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accReal##iter); \
-    EIGEN_UNUSED_VARIABLE(accImag##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_DST_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_DST_PTR_ONE)
-
-#define MICRO_COMPLEX_MMA_SRC_PTR_ONE(iter) \
-  if (unroll_factor > iter) { \
-    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols + accCols*offsetA; \
-    if(!LhsIsReal) { \
-      lhs_ptr_imag##iter = lhs_ptr_real##iter + accCols*strideA; \
-    } else { \
-      EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_real##iter); \
-    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_SRC_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_SRC_PTR_ONE)
-
-#define MICRO_COMPLEX_MMA_PREFETCH_ONE(iter) \
-  if (unroll_factor > iter) { \
-    EIGEN_POWER_PREFETCH(lhs_ptr_real##iter); \
-    if(!LhsIsReal) { \
-      EIGEN_POWER_PREFETCH(lhs_ptr_imag##iter); \
-    } \
-  }
-
-#define MICRO_COMPLEX_MMA_PREFETCH MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_PREFETCH_ONE)
-
-#define MICRO_COMPLEX_MMA_STORE_ONE(iter) \
-  if (unroll_factor > iter) { \
-    storeComplexAccumulator<DataMapper, Index, Packet, Packetc, accColsC, 0>(row + iter*accCols, col, res, pAlphaReal, pAlphaImag, &accReal##iter, &accImag##iter); \
-  }
-
-#define MICRO_COMPLEX_MMA_STORE MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_STORE_ONE)
-
-template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_unrolled_MMA_iteration(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index& row,
-  Index col,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag)
-{
-  const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag;
-  if(!RhsIsReal) {
-    rhs_ptr_imag = rhs_base + accRows*strideB;
-  } else {
-    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-  }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
-  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
-  __vector_quad accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3, accReal4, accImag4;
-
-  MICRO_COMPLEX_MMA_SRC_PTR
-  MICRO_COMPLEX_MMA_DST_PTR
-
-  Index k = 0;
-  for(; k + PEEL_COMPLEX_MMA <= depth; k+= PEEL_COMPLEX_MMA)
-  {
-    EIGEN_POWER_PREFETCH(rhs_ptr_real);
-    if(!RhsIsReal) {
-      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-    }
-    MICRO_COMPLEX_MMA_PREFETCH
-    MICRO_COMPLEX_MMA_ONE_PEEL
-  }
-  for(; k < depth; k++)
-  {
-    MICRO_COMPLEX_MMA_ONE
-  }
-  MICRO_COMPLEX_MMA_STORE
-
-  row += unroll_factor*accCols;
-}
-
-template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Index, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-void gemm_complexMMA(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
-      const Index remaining_cols = cols % accRows;
-
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
-
-      const Packet pAlphaReal = pset1<Packet>(alpha.real());
-      const Packet pAlphaImag = pset1<Packet>(alpha.imag());
-      const Packet pMask = bmask<Packet>((const int)(remaining_rows));
-
-      const Scalar* blockA = (Scalar *) blockAc;
-      const Scalar* blockB = (Scalar *) blockBc;
-
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
-        const Scalar* lhs_base = blockA;
-        Index row = 0;
-
-#define MAX_COMPLEX_MMA_UNROLL 4
-        while(row + MAX_COMPLEX_MMA_UNROLL*accCols <= rows) {
-          gemm_complex_unrolled_MMA_iteration<MAX_COMPLEX_MMA_UNROLL, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-        }
-        switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_MMA_UNROLL > 4
-          case 4:
-            gemm_complex_unrolled_MMA_iteration<4, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 3
-          case 3:
-            gemm_complex_unrolled_MMA_iteration<3, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 2
-          case 2:
-            gemm_complex_unrolled_MMA_iteration<2, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 1
-          case 1:
-            gemm_complex_unrolled_MMA_iteration<1, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
-            break;
-#endif
-          default:
-            break;
-        }
-#undef MAX_COMPLEX_MMA_UNROLL
-
-        if(remaining_rows > 0)
-        {
-          gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
-        }
-      }
-
-      if(remaining_cols > 0)
-      {
-        const Scalar* rhs_base = blockB + advanceCols*col*strideB + remaining_cols*offsetB;
-        const Scalar* lhs_base = blockA;
-
-        for(; col < cols; col++)
-        {
-          Index row = 0;
-
-          gemm_complex_unrolled_col<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, col, remaining_cols, pAlphaReal, pAlphaImag);
-
-          if (remaining_rows > 0)
-          {
-            gemm_complex_extra_col<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_rows, remaining_cols, pAlphaReal, pAlphaImag);
-          }
-          rhs_base++;
-        }
-      }
-}
-
-#undef accColsC
-#undef advanceRows
-#undef advanceCols
-
-#pragma GCC reset_options
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
deleted file mode 100644
index 2a44054..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
+++ /dev/null
@@ -1,2711 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_ALTIVEC_H
-#define EIGEN_PACKET_MATH_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-// NOTE Altivec has 32 registers, but Eigen only accepts a value of 8 or 16
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS  32
-#endif
-
-typedef __vector float                   Packet4f;
-typedef __vector int                     Packet4i;
-typedef __vector unsigned int            Packet4ui;
-typedef __vector __bool int              Packet4bi;
-typedef __vector short int               Packet8s;
-typedef __vector unsigned short int      Packet8us;
-typedef __vector signed char             Packet16c;
-typedef __vector unsigned char           Packet16uc;
-typedef eigen_packet_wrapper<__vector unsigned short int,0> Packet8bf;
-
-// We don't want to write the same code all the time, but we need to reuse the constants
-// and it doesn't really work to declare them global, so we define macros instead
-#define _EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = {X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = vec_splat_s32(X)
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet4ui(NAME,X) \
-  Packet4ui p4ui_##NAME = {X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet8us(NAME,X) \
-  Packet8us p8us_##NAME = {X, X, X, X, X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet16uc(NAME,X) \
-  Packet16uc p16uc_##NAME = {X, X, X, X, X, X, X, X, X, X, X, X, X, X, X, X}
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
-  Packet2d p2d_##NAME = pset1<Packet2d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
-  Packet2l p2l_##NAME = pset1<Packet2l>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
-
-#define DST_CHAN 1
-#define DST_CTRL(size, count, stride) (((size) << 24) | ((count) << 16) | (stride))
-#define __UNPACK_TYPE__(PACKETNAME) typename unpacket_traits<PACKETNAME>::type 
-
-// These constants are endian-agnostic
-static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); //{ 1, 1, 1, 1}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16,-16); //{ -16, -16, -16, -16}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
-static _EIGEN_DECLARE_CONST_FAST_Packet4ui(SIGN, 0x80000000u);
-static _EIGEN_DECLARE_CONST_FAST_Packet4ui(PREV0DOT5, 0x3EFFFFFFu);
-static _EIGEN_DECLARE_CONST_FAST_Packet8us(ONE,1); //{ 1, 1, 1, 1, 1, 1, 1, 1}
-static _EIGEN_DECLARE_CONST_FAST_Packet16uc(ONE,1);
-static Packet4f p4f_MZERO = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
-#ifndef __VSX__
-static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
-#endif
-
-static Packet4f  p4f_COUNTDOWN  = { 0.0, 1.0, 2.0, 3.0 };
-static Packet4i  p4i_COUNTDOWN  = { 0, 1, 2, 3 };
-static Packet8s  p8s_COUNTDOWN  = { 0, 1, 2, 3, 4, 5, 6, 7 };
-static Packet8us p8us_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7 };
-
-static Packet16c  p16c_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7,
-                                    8, 9, 10, 11, 12, 13, 14, 15};
-static Packet16uc p16uc_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7, 
-                                    8, 9, 10, 11, 12, 13, 14, 15};
-
-static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
-static Packet16uc p16uc_REVERSE16 = { 14,15, 12,13, 10,11, 8,9, 6,7, 4,5, 2,3, 0,1 };
-static Packet16uc p16uc_REVERSE8 = { 15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,0 };
-
-static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
-static Packet16uc p16uc_DUPLICATE16_HI = { 0,1,0,1, 2,3,2,3, 4,5,4,5, 6,7,6,7 };
-static Packet16uc p16uc_DUPLICATE8_HI = { 0,0, 1,1, 2,2, 3,3, 4,4, 5,5, 6,6, 7,7 };
-static const Packet16uc p16uc_DUPLICATE16_EVEN= { 0,1 ,0,1, 4,5, 4,5, 8,9, 8,9, 12,13, 12,13 };
-static const Packet16uc p16uc_DUPLICATE16_ODD = { 2,3 ,2,3, 6,7, 6,7, 10,11, 10,11, 14,15, 14,15 };
-
-static Packet16uc p16uc_QUADRUPLICATE16_HI = { 0,1,0,1,0,1,0,1, 2,3,2,3,2,3,2,3 };
-
-// Handle endianness properly while loading constants
-// Define global static constants:
-#ifdef _BIG_ENDIAN
-static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
-#ifdef __VSX__
-static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-#endif
-static Packet16uc p16uc_PSET32_WODD   = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
-static Packet16uc p16uc_PSET32_WEVEN  = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
-static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
-#else
-static Packet16uc p16uc_FORWARD = p16uc_REVERSE32;
-static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 1), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
-static Packet16uc p16uc_PSET32_WEVEN = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
-static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 0), (Packet16uc)p4i_ZERO, 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
-#endif // _BIG_ENDIAN
-
-static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
-static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
-static Packet16uc p16uc_TRANSPOSE64_HI = p16uc_PSET64_HI + p16uc_HALF64_0_16;                                         //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
-static Packet16uc p16uc_TRANSPOSE64_LO = p16uc_PSET64_LO + p16uc_HALF64_0_16;                                         //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
-
-static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);                                         //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
-
-#ifdef _BIG_ENDIAN
-static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);                                            //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-#else
-static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_PSET64_HI, p16uc_PSET64_LO, 8);                                            //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-#endif // _BIG_ENDIAN
-
-#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
-  #define EIGEN_PPC_PREFETCH(ADDR) __builtin_prefetch(ADDR);
-#else
-  #define EIGEN_PPC_PREFETCH(ADDR) asm( "   dcbt [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
-#endif
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasAbs = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-#ifdef __VSX__
-    HasSqrt = 1,
-#if !EIGEN_COMP_CLANG
-    HasRsqrt = 1,
-#else
-    HasRsqrt = 0,
-#endif
-#else
-    HasSqrt = 0,
-    HasRsqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-template <>
-struct packet_traits<bfloat16> : default_packet_traits {
-  typedef Packet8bf type;
-  typedef Packet8bf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasAbs = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-#ifdef __VSX__
-    HasSqrt = 1,
-#if !EIGEN_COMP_CLANG
-    HasRsqrt = 1,
-#else
-    HasRsqrt = 0,
-#endif
-#else
-    HasSqrt = 0,
-    HasRsqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-#endif
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<int> : default_packet_traits {
-  typedef Packet4i type;
-  typedef Packet4i half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasAdd   = 1,
-    HasSub   = 1,
-    HasShift = 1,
-    HasMul   = 1,
-    HasDiv   = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<short int> : default_packet_traits {
-  typedef Packet8s type;
-  typedef Packet8s half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<unsigned short int> : default_packet_traits {
-  typedef Packet8us type;
-  typedef Packet8us half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<signed char> : default_packet_traits {
-  typedef Packet16c type;
-  typedef Packet16c half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<unsigned char> : default_packet_traits {
-  typedef Packet16uc type;
-  typedef Packet16uc half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 0,
-    HasBlend = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet4f>
-{
-  typedef float     type;
-  typedef Packet4f  half;
-  typedef Packet4i  integer_packet;
-  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet4i>
-{
-  typedef int       type;
-  typedef Packet4i  half;
-  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet8s>
-{
-  typedef short int type;
-  typedef Packet8s  half;
-  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet8us>
-{
-  typedef unsigned short int type;
-  typedef Packet8us          half;
-  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-template<> struct unpacket_traits<Packet16c>
-{
-  typedef signed char type;
-  typedef Packet16c  half;
-  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet16uc>
-{
-  typedef unsigned char type;
-  typedef Packet16uc  half;
-  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-template<> struct unpacket_traits<Packet8bf>
-{
-  typedef bfloat16 type;
-  typedef Packet8bf          half;
-  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-inline std::ostream & operator <<(std::ostream & s, const Packet16c & v)
-{
-  union {
-    Packet16c   v;
-    signed char n[16];
-  } vt;
-  vt.v = v;
-  for (int i=0; i< 16; i++)
-    s << vt.n[i] << ", ";
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet16uc & v)
-{
-  union {
-    Packet16uc   v;
-    unsigned char n[16];
-  } vt;
-  vt.v = v;
-  for (int i=0; i< 16; i++)
-    s << vt.n[i] << ", ";
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
-{
-  union {
-    Packet4f   v;
-    float n[4];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4i & v)
-{
-  union {
-    Packet4i   v;
-    int n[4];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
-{
-  union {
-    Packet4ui   v;
-    unsigned int n[4];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
-  return s;
-}
-
-template <typename Packet>
-EIGEN_STRONG_INLINE Packet pload_common(const __UNPACK_TYPE__(Packet)* from)
-{
-  // some versions of GCC throw "unused-but-set-parameter".
-  // ignoring these warnings for now.
-  EIGEN_UNUSED_VARIABLE(from);
-  EIGEN_DEBUG_ALIGNED_LOAD
-#ifdef __VSX__
-  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
-#else
-  return vec_ld(0, from);
-#endif
-}
-
-// Need to define them first or we get specialization after instantiation errors
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
-{
-  return pload_common<Packet4f>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from)
-{
-  return pload_common<Packet4i>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const short int* from)
-{
-  return pload_common<Packet8s>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const unsigned short int* from)
-{
-  return pload_common<Packet8us>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const signed char*     from)
-{
-  return pload_common<Packet16c>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const unsigned char*     from)
-{
-  return pload_common<Packet16uc>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16*     from)
-{
-  return pload_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-
-template <typename Packet>
-EIGEN_STRONG_INLINE void pstore_common(__UNPACK_TYPE__(Packet)* to, const Packet& from){
-  // some versions of GCC throw "unused-but-set-parameter" (float *to).
-  // ignoring these warnings for now.
-  EIGEN_UNUSED_VARIABLE(to);
-  EIGEN_DEBUG_ALIGNED_STORE
-#ifdef __VSX__
-  vec_xst(from, 0, to);
-#else
-  vec_st(from, 0, to);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from)
-{
-  pstore_common<Packet4f>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from)
-{
-  pstore_common<Packet4i>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<short int>(short int*       to, const Packet8s& from)
-{
-  pstore_common<Packet8s>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<unsigned short int>(unsigned short int*       to, const Packet8us& from)
-{
-  pstore_common<Packet8us>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16*       to, const Packet8bf& from)
-{
-  pstore_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<signed char>(signed char*       to, const Packet16c& from)
-{
-  pstore_common<Packet16c>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<unsigned char>(unsigned char*       to, const Packet16uc& from)
-{
-  pstore_common<Packet16uc>(to, from);
-}
-
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet pset1_size4(const __UNPACK_TYPE__(Packet)& from)
-{
-  Packet v = {from, from, from, from};
-  return v;
-}
-
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet pset1_size8(const __UNPACK_TYPE__(Packet)& from)
-{
-  Packet v = {from, from, from, from, from, from, from, from};
-  return v;
-}
-
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet pset1_size16(const __UNPACK_TYPE__(Packet)& from)
-{
-  Packet v = {from, from, from, from, from, from, from, from, from, from, from, from, from, from, from, from};
-  return v;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) {
-  return pset1_size4<Packet4f>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from)   {
-  return pset1_size4<Packet4i>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const short int&    from)   {
-  return pset1_size8<Packet8s>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const unsigned short int&    from)   {
-  return pset1_size8<Packet8us>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const signed char&    from)   {
-  return pset1_size16<Packet16c>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const unsigned char&    from)   {
-  return pset1_size16<Packet16uc>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) {
-  return reinterpret_cast<Packet4f>(pset1<Packet4i>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16&    from)   {
-  return pset1_size8<Packet8us>(reinterpret_cast<const unsigned short int&>(from));
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE void
-pbroadcast4_common(const __UNPACK_TYPE__(Packet) *a,
-                      Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  a3 = pload<Packet>(a);
-  a0 = vec_splat(a3, 0);
-  a1 = vec_splat(a3, 1);
-  a2 = vec_splat(a3, 2);
-  a3 = vec_splat(a3, 3);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  pbroadcast4_common<Packet4f>(a, a0, a1, a2, a3);
-}
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4i>(const int *a,
-                      Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
-{
-  pbroadcast4_common<Packet4i>(a, a0, a1, a2, a3);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_common(const __UNPACK_TYPE__(Packet)* from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[4];
-  a[0] = from[0*stride];
-  a[1] = from[1*stride];
-  a[2] = from[2*stride];
-  a[3] = from[3*stride];
-  return pload<Packet>(a);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  return pgather_common<Packet4f>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
-{
-  return pgather_common<Packet4i>(from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_size8(const __UNPACK_TYPE__(Packet)* from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[8];
-  a[0] = from[0*stride];
-  a[1] = from[1*stride];
-  a[2] = from[2*stride];
-  a[3] = from[3*stride];
-  a[4] = from[4*stride];
-  a[5] = from[5*stride];
-  a[6] = from[6*stride];
-  a[7] = from[7*stride];
-  return pload<Packet>(a);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet8s pgather<short int, Packet8s>(const short int* from, Index stride)
-{
-  return pgather_size8<Packet8s>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet8us pgather<unsigned short int, Packet8us>(const unsigned short int* from, Index stride)
-{
-  return pgather_size8<Packet8us>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride)
-{
-  return pgather_size8<Packet8bf>(from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_size16(const __UNPACK_TYPE__(Packet)* from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[16];
-  a[0] = from[0*stride];
-  a[1] = from[1*stride];
-  a[2] = from[2*stride];
-  a[3] = from[3*stride];
-  a[4] = from[4*stride];
-  a[5] = from[5*stride];
-  a[6] = from[6*stride];
-  a[7] = from[7*stride];
-  a[8] = from[8*stride];
-  a[9] = from[9*stride];
-  a[10] = from[10*stride];
-  a[11] = from[11*stride];
-  a[12] = from[12*stride];
-  a[13] = from[13*stride];
-  a[14] = from[14*stride];
-  a[15] = from[15*stride];
-  return pload<Packet>(a);
-}
-
-
-template<> EIGEN_DEVICE_FUNC inline Packet16c pgather<signed char, Packet16c>(const signed char* from, Index stride)
-{
-  return pgather_size16<Packet16c>(from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet16uc pgather<unsigned char, Packet16uc>(const unsigned char* from, Index stride)
-{
-  return pgather_size16<Packet16uc>(from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_size4(__UNPACK_TYPE__(Packet)* to, const Packet& from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[4];
-  pstore<__UNPACK_TYPE__(Packet)>(a, from);
-  to[0*stride] = a[0];
-  to[1*stride] = a[1];
-  to[2*stride] = a[2];
-  to[3*stride] = a[3];
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  pscatter_size4<Packet4f>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
-{
-  pscatter_size4<Packet4i>(to, from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_size8(__UNPACK_TYPE__(Packet)* to, const Packet& from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[8];
-  pstore<__UNPACK_TYPE__(Packet)>(a, from);
-  to[0*stride] = a[0];
-  to[1*stride] = a[1];
-  to[2*stride] = a[2];
-  to[3*stride] = a[3];
-  to[4*stride] = a[4];
-  to[5*stride] = a[5];
-  to[6*stride] = a[6];
-  to[7*stride] = a[7];
-}
-
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<short int, Packet8s>(short int* to, const Packet8s& from, Index stride)
-{
-  pscatter_size8<Packet8s>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<unsigned short int, Packet8us>(unsigned short int* to, const Packet8us& from, Index stride)
-{
-  pscatter_size8<Packet8us>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride)
-{
-  pscatter_size8<Packet8bf>(to, from, stride);
-}
-
-template<typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_size16(__UNPACK_TYPE__(Packet)* to, const Packet& from, Index stride)
-{
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[16];
-  pstore<__UNPACK_TYPE__(Packet)>(a, from);
-  to[0*stride] = a[0];
-  to[1*stride] = a[1];
-  to[2*stride] = a[2];
-  to[3*stride] = a[3];
-  to[4*stride] = a[4];
-  to[5*stride] = a[5];
-  to[6*stride] = a[6];
-  to[7*stride] = a[7];
-  to[8*stride] = a[8];
-  to[9*stride] = a[9];
-  to[10*stride] = a[10];
-  to[11*stride] = a[11];
-  to[12*stride] = a[12];
-  to[13*stride] = a[13];
-  to[14*stride] = a[14];
-  to[15*stride] = a[15];
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<signed char, Packet16c>(signed char* to, const Packet16c& from, Index stride)
-{
-  pscatter_size16<Packet16c>(to, from, stride);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<unsigned char, Packet16uc>(unsigned char* to, const Packet16uc& from, Index stride)
-{
-  pscatter_size16<Packet16uc>(to, from, stride);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f   plset<Packet4f>(const float&     a) { return pset1<Packet4f>(a) + p4f_COUNTDOWN;  }
-template<> EIGEN_STRONG_INLINE Packet4i   plset<Packet4i>(const int&       a) { return pset1<Packet4i>(a) + p4i_COUNTDOWN;  }
-template<> EIGEN_STRONG_INLINE Packet8s   plset<Packet8s>(const short int& a) { return pset1<Packet8s>(a) + p8s_COUNTDOWN; }
-template<> EIGEN_STRONG_INLINE Packet8us  plset<Packet8us>(const unsigned short int& a) { return pset1<Packet8us>(a) + p8us_COUNTDOWN; }
-template<> EIGEN_STRONG_INLINE Packet16c  plset<Packet16c>(const signed char& a)   { return pset1<Packet16c>(a) + p16c_COUNTDOWN; }
-template<> EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const unsigned char& a)   { return pset1<Packet16uc>(a) + p16uc_COUNTDOWN; }
-
-template<> EIGEN_STRONG_INLINE Packet4f   padd<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet4i   padd<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet4ui   padd<Packet4ui>  (const Packet4ui&   a, const Packet4ui&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet8s   padd<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet8us  padd<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet16c  padd<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return a + b; }
-template<> EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return a + b; }
-
-template<> EIGEN_STRONG_INLINE Packet4f   psub<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet4i   psub<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet8s   psub<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet8us  psub<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet16c  psub<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return a - b; }
-template<> EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return a - b; }
-
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return p4f_ZERO - a; }
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return p4i_ZERO - a; }
-
-template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4f   pmul<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return vec_madd(a,b, p4f_MZERO); }
-template<> EIGEN_STRONG_INLINE Packet4i   pmul<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a * b; }
-template<> EIGEN_STRONG_INLINE Packet8s   pmul<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return vec_mul(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us  pmul<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return vec_mul(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c  pmul<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return vec_mul(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_mul(a,b); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-#ifndef __VSX__  // VSX actually provides a div instruction
-  Packet4f t, y_0, y_1;
-
-  // Altivec does not offer a divide instruction, we have to do a reciprocal approximation
-  y_0 = vec_re(b);
-
-  // Do one Newton-Raphson iteration to get the needed accuracy
-  t   = vec_nmsub(y_0, b, p4f_ONE);
-  y_1 = vec_madd(y_0, t, y_0);
-
-  return vec_madd(a, y_1, p4f_MZERO);
-#else
-  return vec_div(a, b);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
-{ eigen_assert(false && "packet integer division are not supported by AltiVec");
-  return pset1<Packet4i>(0);
-}
-
-// for some weird raisons, it has to be overloaded for packet of integers
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a,b,c); }
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return a*b + c; }
-template<> EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c) { return vec_madd(a,b,c); }
-template<> EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c) { return vec_madd(a,b,c); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  #ifdef __VSX__
-  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
-  Packet4f ret;
-  __asm__ ("xvcmpgesp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
-  #else
-  return vec_min(a, b);
-  #endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_min(a, b); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  #ifdef __VSX__
-  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
-  Packet4f ret;
-  __asm__ ("xvcmpgtsp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
-  #else
-  return vec_max(a, b);
-  #endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_max(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) {
-  Packet4f c = reinterpret_cast<Packet4f>(vec_cmpge(a,b));
-  return vec_nor(c,c);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_le(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_lt(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_eq(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_le(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_lt(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_eq(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_le(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_lt(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_eq(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_le(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_lt(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_eq(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmpeq(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8bf pand<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return pand<Packet8us>(a, b);
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8bf por<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  return por<Packet8us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8bf pxor<Packet8bf>(const Packet8bf& a, const Packet8bf& b) { 
-  return pxor<Packet8us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_andc(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_andc(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
-  return vec_sel(b, a, reinterpret_cast<Packet4ui>(mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
-{
-    Packet4f t = vec_add(reinterpret_cast<Packet4f>(vec_or(vec_and(reinterpret_cast<Packet4ui>(a), p4ui_SIGN), p4ui_PREV0DOT5)), a);
-    Packet4f res;
-
-#ifdef __VSX__
-    __asm__("xvrspiz %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (t));
-#else
-    __asm__("vrfiz %0, %1\n\t"
-        : "=v" (res)
-        : "v" (t));
-#endif
-
-    return res;
-}
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const  Packet4f& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return vec_floor(a); }
-template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
-{
-    Packet4f res;
-
-    __asm__("xvrspic %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (a));
-
-    return res;
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE Packet ploadu_common(const __UNPACK_TYPE__(Packet)* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD
-#ifdef _BIG_ENDIAN
-  Packet16uc MSQ, LSQ;
-  Packet16uc mask;
-  MSQ = vec_ld(0, (unsigned char *)from);          // most significant quadword
-  LSQ = vec_ld(15, (unsigned char *)from);         // least significant quadword
-  mask = vec_lvsl(0, from);                        // create the permute mask
-  //TODO: Add static_cast here
-  return (Packet) vec_perm(MSQ, LSQ, mask);           // align the data
-#else
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
-{
-  return ploadu_common<Packet4f>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
-{
-  return ploadu_common<Packet4i>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const short int* from)
-{
-  return ploadu_common<Packet8s>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const unsigned short int* from)
-{
-  return ploadu_common<Packet8us>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from)
-{
-  return ploadu_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-template<> EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const signed char* from)
-{
-  return ploadu_common<Packet16c>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const unsigned char* from)
-{
-  return ploadu_common<Packet16uc>(from);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE Packet ploaddup_common(const __UNPACK_TYPE__(Packet)*   from)
-{
-  Packet p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet>(from);
-  else                                  p = ploadu<Packet>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
-}
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*   from)
-{
-  return ploaddup_common<Packet4f>(from);
-}
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
-{
-  return ploaddup_common<Packet4i>(from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const short int*     from)
-{
-  Packet8s p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8s>(from);
-  else                                  p = ploadu<Packet8s>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const unsigned short int*     from)
-{
-  Packet8us p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8us>(from);
-  else                                  p = ploadu<Packet8us>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const short int*     from)
-{
-  Packet8s p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8s>(from);
-  else                                  p = ploadu<Packet8s>(from);
-  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const unsigned short int*     from)
-{
-  Packet8us p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8us>(from);
-  else                                  p = ploadu<Packet8us>(from);
-  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ploadquad<Packet8bf>(const bfloat16*     from)
-{
-  return ploadquad<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const signed char*     from)
-{
-  Packet16c p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet16c>(from);
-  else                                  p = ploadu<Packet16c>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE8_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const unsigned char*     from)
-{
-  Packet16uc p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet16uc>(from);
-  else                                  p = ploadu<Packet16uc>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE8_HI);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE void pstoreu_common(__UNPACK_TYPE__(Packet)*  to, const Packet& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE
-#ifdef _BIG_ENDIAN
-  // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
-  // Warning: not thread safe!
-  Packet16uc MSQ, LSQ, edges;
-  Packet16uc edgeAlign, align;
-
-  MSQ = vec_ld(0, (unsigned char *)to);                     // most significant quadword
-  LSQ = vec_ld(15, (unsigned char *)to);                    // least significant quadword
-  edgeAlign = vec_lvsl(0, to);                              // permute map to extract edges
-  edges=vec_perm(LSQ,MSQ,edgeAlign);                        // extract the edges
-  align = vec_lvsr( 0, to );                                // permute map to misalign data
-  MSQ = vec_perm(edges,(Packet16uc)from,align);             // misalign the data (MSQ)
-  LSQ = vec_perm((Packet16uc)from,edges,align);             // misalign the data (LSQ)
-  vec_st( LSQ, 15, (unsigned char *)to );                   // Store the LSQ part first
-  vec_st( MSQ, 0, (unsigned char *)to );                   // Store the MSQ part second
-#else
-  vec_xst(from, 0, to);
-#endif
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*  to, const Packet4f& from)
-{
-  pstoreu_common<Packet4f>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*      to, const Packet4i& from)
-{
-  pstoreu_common<Packet4i>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<short int>(short int*      to, const Packet8s& from)
-{
-  pstoreu_common<Packet8s>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<unsigned short int>(unsigned short int*      to, const Packet8us& from)
-{
-  pstoreu_common<Packet8us>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16*      to, const Packet8bf& from)
-{
-  pstoreu_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<signed char>(signed char*      to, const Packet16c& from)
-{
-  pstoreu_common<Packet16c>(to, from);
-}
-template<> EIGEN_STRONG_INLINE void pstoreu<unsigned char>(unsigned char*      to, const Packet16uc& from)
-{
-  pstoreu_common<Packet16uc>(to, from);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr)    { EIGEN_PPC_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*     addr)    { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { EIGEN_ALIGN16 float x; vec_ste(a, 0, &x); return x; }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { EIGEN_ALIGN16 int   x; vec_ste(a, 0, &x); return x; }
-
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) pfirst_common(const Packet& a) {
-  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) x;
-  vec_ste(a, 0, &x);
-  return x;
-}
-
-template<> EIGEN_STRONG_INLINE short int pfirst<Packet8s>(const Packet8s& a) {
-  return pfirst_common<Packet8s>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int pfirst<Packet8us>(const Packet8us& a) {
-  return pfirst_common<Packet8us>(a);
-}
-
-template<> EIGEN_STRONG_INLINE signed char pfirst<Packet16c>(const Packet16c& a)
-{
-  return pfirst_common<Packet16c>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char pfirst<Packet16uc>(const Packet16uc& a)
-{
-  return pfirst_common<Packet16uc>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  return reinterpret_cast<Packet4f>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
-{
-  return reinterpret_cast<Packet4i>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-template<> EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a)
-{
-  return reinterpret_cast<Packet8s>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
-}
-template<> EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a)
-{
-  return reinterpret_cast<Packet8us>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
-}
-template<> EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a)
-{
-  return vec_perm(a, a, p16uc_REVERSE8);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a)
-{
-  return vec_perm(a, a, p16uc_REVERSE8);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a)
-{
-  return preverse<Packet8us>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8bf  pabs(const Packet8bf& a) {
-  _EIGEN_DECLARE_CONST_FAST_Packet8us(abs_mask,0x7FFF);
-  return pand<Packet8us>(p8us_abs_mask, a);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a)
-{ return vec_sra(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right(const Packet4i& a)
-{ return vec_sr(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left(const Packet4i& a)
-{ return vec_sl(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
-template<int N> EIGEN_STRONG_INLINE Packet4f plogical_shift_left(const Packet4f& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  Packet4ui r = vec_sl(reinterpret_cast<Packet4ui>(a), p4ui_mask);
-  return reinterpret_cast<Packet4f>(r);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4f plogical_shift_right(const Packet4f& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  Packet4ui r = vec_sr(reinterpret_cast<Packet4ui>(a), p4ui_mask);
-  return reinterpret_cast<Packet4f>(r);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(const Packet4ui& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  return vec_sr(a, p4ui_mask);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(const Packet4ui& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
-  return vec_sl(a, p4ui_mask);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_left(const Packet8us& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
-  return vec_sl(a, p8us_mask);
-}
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_right(const Packet8us& a)
-{
-  const _EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
-  return vec_sr(a, p8us_mask);
-}
-
-EIGEN_STRONG_INLINE Packet4f Bf16ToF32Even(const Packet8bf& bf){
-  return plogical_shift_left<16>(reinterpret_cast<Packet4f>(bf.m_val));
-}
-
-EIGEN_STRONG_INLINE Packet4f Bf16ToF32Odd(const Packet8bf& bf){
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(high_mask, 0xFFFF0000);
-  return pand<Packet4f>(
-    reinterpret_cast<Packet4f>(bf.m_val),
-    reinterpret_cast<Packet4f>(p4ui_high_mask)
-  );
-}
-
-// Simple interleaving of bool masks, prevents true values from being
-// converted to NaNs.
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16Bool(Packet4f even, Packet4f odd) {
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(high_mask, 0xFFFF0000);
-  Packet4f bf_odd, bf_even;
-  bf_odd = pand(reinterpret_cast<Packet4f>(p4ui_high_mask), odd);
-  bf_even = plogical_shift_right<16>(even);
-  return reinterpret_cast<Packet8us>(por<Packet4f>(bf_even, bf_odd));
-}
-
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f p4f){
-  Packet4ui input = reinterpret_cast<Packet4ui>(p4f);
-  Packet4ui lsb = plogical_shift_right<16>(input);
-  lsb = pand<Packet4ui>(lsb, reinterpret_cast<Packet4ui>(p4i_ONE));
-
-  _EIGEN_DECLARE_CONST_FAST_Packet4ui(BIAS,0x7FFFu);
-  Packet4ui rounding_bias = padd<Packet4ui>(lsb, p4ui_BIAS);
-  input = padd<Packet4ui>(input, rounding_bias);
-
-  //Test NaN and Subnormal - Begin
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(exp_mask, 0x7F800000);
-  Packet4ui exp = pand<Packet4ui>(p4ui_exp_mask, reinterpret_cast<Packet4ui>(p4f));
-
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(mantissa_mask, 0x7FFFFF);
-  Packet4ui mantissa = pand<Packet4ui>(p4ui_mantissa_mask, reinterpret_cast<Packet4ui>(p4f));
-
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(max_exp, 0x7F800000);
-  Packet4bi is_max_exp = vec_cmpeq(exp, p4ui_max_exp);
-  Packet4bi is_zero_exp = vec_cmpeq(exp, reinterpret_cast<Packet4ui>(p4i_ZERO));
-
-  Packet4bi is_mant_zero = vec_cmpeq(mantissa, reinterpret_cast<Packet4ui>(p4i_ZERO));
-  Packet4ui nan_selector = pandnot<Packet4ui>(
-      reinterpret_cast<Packet4ui>(is_max_exp),
-      reinterpret_cast<Packet4ui>(is_mant_zero)
-  );
-
-  Packet4ui subnormal_selector = pandnot<Packet4ui>(
-      reinterpret_cast<Packet4ui>(is_zero_exp),
-      reinterpret_cast<Packet4ui>(is_mant_zero)
-  );
-
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(nan, 0x7FC00000);
-  input = vec_sel(input, p4ui_nan, nan_selector);
-  input = vec_sel(input, reinterpret_cast<Packet4ui>(p4f), subnormal_selector);
-  //Test NaN and Subnormal - End
-
-  input = plogical_shift_right<16>(input);
-  return reinterpret_cast<Packet8us>(input);
-}
-
-EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f even, Packet4f odd){
-  Packet4f bf_odd, bf_even;
-  bf_odd = reinterpret_cast<Packet4f>(F32ToBf16(odd).m_val);
-  bf_odd = plogical_shift_left<16>(bf_odd);
-  bf_even = reinterpret_cast<Packet4f>(F32ToBf16(even).m_val);
-  return reinterpret_cast<Packet8us>(por<Packet4f>(bf_even, bf_odd));
-}
-#define BF16_TO_F32_UNARY_OP_WRAPPER(OP, A) \
-  Packet4f a_even = Bf16ToF32Even(A);\
-  Packet4f a_odd = Bf16ToF32Odd(A);\
-  Packet4f op_even = OP(a_even);\
-  Packet4f op_odd = OP(a_odd);\
-  return F32ToBf16(op_even, op_odd);\
-
-#define BF16_TO_F32_BINARY_OP_WRAPPER(OP, A, B) \
-  Packet4f a_even = Bf16ToF32Even(A);\
-  Packet4f a_odd = Bf16ToF32Odd(A);\
-  Packet4f b_even = Bf16ToF32Even(B);\
-  Packet4f b_odd = Bf16ToF32Odd(B);\
-  Packet4f op_even = OP(a_even, b_even);\
-  Packet4f op_odd = OP(a_odd, b_odd);\
-  return F32ToBf16(op_even, op_odd);\
-
-#define BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(OP, A, B) \
-  Packet4f a_even = Bf16ToF32Even(A);\
-  Packet4f a_odd = Bf16ToF32Odd(A);\
-  Packet4f b_even = Bf16ToF32Even(B);\
-  Packet4f b_odd = Bf16ToF32Odd(B);\
-  Packet4f op_even = OP(a_even, b_even);\
-  Packet4f op_odd = OP(a_odd, b_odd);\
-  return F32ToBf16Bool(op_even, op_odd);\
-
-template<> EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(padd<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pmul<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pdiv<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pnegate<Packet8bf>(const Packet8bf& a) {
-  BF16_TO_F32_UNARY_OP_WRAPPER(pnegate<Packet4f>, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(psub<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psqrt<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(vec_sqrt, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf prsqrt<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(prsqrt<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pexp<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pexp_float, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
-  return pldexp_generic(a,exponent);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pldexp<Packet8bf> (const Packet8bf& a, const Packet8bf& exponent){
-  BF16_TO_F32_BINARY_OP_WRAPPER(pldexp<Packet4f>, a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
-  return pfrexp_generic(a,exponent);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pfrexp<Packet8bf> (const Packet8bf& a, Packet8bf& e){
-  Packet4f a_even = Bf16ToF32Even(a);
-  Packet4f a_odd = Bf16ToF32Odd(a);
-  Packet4f e_even;
-  Packet4f e_odd;
-  Packet4f op_even = pfrexp<Packet4f>(a_even, e_even);
-  Packet4f op_odd = pfrexp<Packet4f>(a_odd, e_odd);
-  e = F32ToBf16(e_even, e_odd);
-  return F32ToBf16(op_even, op_odd);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf psin<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(psin_float, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcos<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pcos_float, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf plog<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(plog_float, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pfloor<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pceil<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pround<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf print<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(print<Packet4f>, a);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pmadd(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
-  Packet4f a_even = Bf16ToF32Even(a);
-  Packet4f a_odd = Bf16ToF32Odd(a);
-  Packet4f b_even = Bf16ToF32Even(b);
-  Packet4f b_odd = Bf16ToF32Odd(b);
-  Packet4f c_even = Bf16ToF32Even(c);
-  Packet4f c_odd = Bf16ToF32Odd(c);
-  Packet4f pmadd_even = pmadd<Packet4f>(a_even, b_even, c_even);
-  Packet4f pmadd_odd = pmadd<Packet4f>(a_odd, b_odd, c_odd);
-  return F32ToBf16(pmadd_even, pmadd_odd);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pmin<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER(pmax<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt<Packet4f>, a, b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt_or_nan<Packet4f>, a, b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_le<Packet4f>, a, b);
-}
-template<> EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a, const Packet8bf& b) {
-  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_eq<Packet4f>, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 pfirst(const Packet8bf& a) {
-  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16((pfirst<Packet8us>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf ploaddup<Packet8bf>(const  bfloat16*     from)
-{
-  return ploaddup<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
-  bfloat16 countdown[8] = { bfloat16(0), bfloat16(1), bfloat16(2), bfloat16(3),
-                            bfloat16(4), bfloat16(5), bfloat16(6), bfloat16(7) };
-  return padd<Packet8bf>(pset1<Packet8bf>(a), pload<Packet8bf>(countdown));
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, sum;
-  b   = vec_sld(a, a, 8);
-  sum = a + b;
-  b   = vec_sld(sum, sum, 4);
-  sum += b;
-  return pfirst(sum);
-}
-
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i sum;
-  sum = vec_sums(a, p4i_ZERO);
-#ifdef _BIG_ENDIAN
-  sum = vec_sld(sum, p4i_ZERO, 12);
-#else
-  sum = vec_sld(p4i_ZERO, sum, 4);
-#endif
-  return pfirst(sum);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = redux_even + redux_odd;
-  return bfloat16(f32_result);
-}
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size8(const Packet& a)
-{
-  union{
-    Packet v;
-    __UNPACK_TYPE__(Packet) n[8];
-  } vt;
-  vt.v = a;
-
-  EIGEN_ALIGN16 int first_loader[4] = { vt.n[0], vt.n[1], vt.n[2], vt.n[3] };
-  EIGEN_ALIGN16 int second_loader[4] = { vt.n[4], vt.n[5], vt.n[6], vt.n[7] };
-  Packet4i first_half  = pload<Packet4i>(first_loader);
-  Packet4i second_half = pload<Packet4i>(second_loader);
-
-  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_half) + predux(second_half));
-}
-
-template<> EIGEN_STRONG_INLINE short int predux<Packet8s>(const Packet8s& a)
-{
-  return predux_size8<Packet8s>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux<Packet8us>(const Packet8us& a)
-{
-  return predux_size8<Packet8us>(a);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size16(const Packet& a)
-{
-  union{
-    Packet v;
-    __UNPACK_TYPE__(Packet) n[16];
-  } vt;
-  vt.v = a;
-
-  EIGEN_ALIGN16 int first_loader[4] = { vt.n[0], vt.n[1], vt.n[2], vt.n[3] };
-  EIGEN_ALIGN16 int second_loader[4] = { vt.n[4], vt.n[5], vt.n[6], vt.n[7] };
-  EIGEN_ALIGN16 int third_loader[4] = { vt.n[8], vt.n[9], vt.n[10], vt.n[11] };
-  EIGEN_ALIGN16 int fourth_loader[4] = { vt.n[12], vt.n[13], vt.n[14], vt.n[15] };
-
-  Packet4i first_quarter = pload<Packet4i>(first_loader);
-  Packet4i second_quarter = pload<Packet4i>(second_loader);
-  Packet4i third_quarter = pload<Packet4i>(third_loader);
-  Packet4i fourth_quarter = pload<Packet4i>(fourth_loader);
-
-  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_quarter) + predux(second_quarter)
-		                  + predux(third_quarter) + predux(fourth_quarter));
-}
-
-template<> EIGEN_STRONG_INLINE signed char predux<Packet16c>(const Packet16c& a)
-{
-  return predux_size16<Packet16c>(a);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux<Packet16uc>(const Packet16uc& a)
-{
-  return predux_size16<Packet16uc>(a);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  Packet4f prod;
-  prod = pmul(a, vec_sld(a, a, 8));
-  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
-}
-
-template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
-{
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  return aux[0] * aux[1] * aux[2] * aux[3];
-}
-
-template<> EIGEN_STRONG_INLINE short int predux_mul<Packet8s>(const Packet8s& a)
-{
-  Packet8s pair, quad, octo;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux_mul<Packet8us>(const Packet8us& a)
-{
-  Packet8us pair, quad, octo;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux_mul<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux_mul<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = redux_even * redux_odd;
-  return bfloat16(f32_result);
-}
-
-
-template<> EIGEN_STRONG_INLINE signed char predux_mul<Packet16c>(const Packet16c& a)
-{
-  Packet16c pair, quad, octo, result;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-  result = vec_mul(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux_mul<Packet16uc>(const Packet16uc& a)
-{
-  Packet16uc pair, quad, octo, result;
-
-  pair = vec_mul(a, vec_sld(a, a, 8));
-  quad = vec_mul(pair, vec_sld(pair, pair, 4));
-  octo = vec_mul(quad, vec_sld(quad, quad, 2));
-  result = vec_mul(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-// min
-template<typename Packet> EIGEN_STRONG_INLINE
-__UNPACK_TYPE__(Packet) predux_min4(const Packet& a)
-{
-  Packet b, res;
-  b = vec_min(a, vec_sld(a, a, 8));
-  res = vec_min(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  return predux_min4<Packet4f>(a);
-}
-
-template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
-{
-  return predux_min4<Packet4i>(a);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux_min<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux_min<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = (std::min)(redux_even, redux_odd);
-  return bfloat16(f32_result);
-}
-
-template<> EIGEN_STRONG_INLINE short int predux_min<Packet8s>(const Packet8s& a)
-{
-  Packet8s pair, quad, octo;
-  
-  //pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
-  pair = vec_min(a, vec_sld(a, a, 8)); 
-
-  //quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux_min<Packet8us>(const Packet8us& a)
-{
-  Packet8us pair, quad, octo;
-  
-  //pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
-  pair = vec_min(a, vec_sld(a, a, 8)); 
-
-  //quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE signed char predux_min<Packet16c>(const Packet16c& a)
-{
-  Packet16c pair, quad, octo, result;
-
-  pair = vec_min(a, vec_sld(a, a, 8));
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  result = vec_min(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux_min<Packet16uc>(const Packet16uc& a)
-{
-  Packet16uc pair, quad, octo, result;
-
-  pair = vec_min(a, vec_sld(a, a, 8));
-  quad = vec_min(pair, vec_sld(pair, pair, 4));
-  octo = vec_min(quad, vec_sld(quad, quad, 2));
-  result = vec_min(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-// max
-template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_max4(const Packet& a)
-{
-  Packet b, res;
-  b = vec_max(a, vec_sld(a, a, 8));
-  res = vec_max(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  return predux_max4<Packet4f>(a);
-}
-
-template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
-{
-  return predux_max4<Packet4i>(a);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet8bf>(const Packet8bf& a)
-{
-  float redux_even = predux_max<Packet4f>(Bf16ToF32Even(a));
-  float redux_odd  = predux_max<Packet4f>(Bf16ToF32Odd(a));
-  float f32_result = (std::max)(redux_even, redux_odd);
-  return bfloat16(f32_result);
-}
-
-template<> EIGEN_STRONG_INLINE short int predux_max<Packet8s>(const Packet8s& a)
-{
-  Packet8s pair, quad, octo;
-  
-  //pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
-  pair = vec_max(a, vec_sld(a, a, 8)); 
-
-  //quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned short int predux_max<Packet8us>(const Packet8us& a)
-{
-  Packet8us pair, quad, octo;
-  
-  //pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
-  pair = vec_max(a, vec_sld(a, a, 8)); 
-
-  //quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-
-  //octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  return pfirst(octo);
-}
-
-template<> EIGEN_STRONG_INLINE signed char predux_max<Packet16c>(const Packet16c& a)
-{
-  Packet16c pair, quad, octo, result;
-
-  pair = vec_max(a, vec_sld(a, a, 8));
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  result = vec_max(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE unsigned char predux_max<Packet16uc>(const Packet16uc& a)
-{
-  Packet16uc pair, quad, octo, result;
-
-  pair = vec_max(a, vec_sld(a, a, 8));
-  quad = vec_max(pair, vec_sld(pair, pair, 4));
-  octo = vec_max(quad, vec_sld(quad, quad, 2));
-  result = vec_max(octo, vec_sld(octo, octo, 1));
-
-  return pfirst(result);
-}
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
-{
-  return vec_any_ne(x, pzero(x));
-}
-
-template <typename T> EIGEN_DEVICE_FUNC inline void
-ptranpose_common(PacketBlock<T,4>& kernel){
-  T t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  ptranpose_common<Packet4f>(kernel);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4i,4>& kernel) {
-  ptranpose_common<Packet4i>(kernel);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8s,4>& kernel) {
-  Packet8s t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8us,4>& kernel) {
-  Packet8us t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8bf,4>& kernel) {
-  Packet8us t0, t1, t2, t3;
-
-  t0 = vec_mergeh(kernel.packet[0].m_val, kernel.packet[2].m_val);
-  t1 = vec_mergel(kernel.packet[0].m_val, kernel.packet[2].m_val);
-  t2 = vec_mergeh(kernel.packet[1].m_val, kernel.packet[3].m_val);
-  t3 = vec_mergel(kernel.packet[1].m_val, kernel.packet[3].m_val);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16c,4>& kernel) {
-  Packet16c t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16uc,4>& kernel) {
-  Packet16uc t0, t1, t2, t3;
-  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8s,8>& kernel) {
-  Packet8s v[8], sum[8];
-
-  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
-  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
-  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
-  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
-  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
-  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
-  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
-  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
-  sum[0] = vec_mergeh(v[0], v[4]);
-  sum[1] = vec_mergel(v[0], v[4]);
-  sum[2] = vec_mergeh(v[1], v[5]);
-  sum[3] = vec_mergel(v[1], v[5]);
-  sum[4] = vec_mergeh(v[2], v[6]);
-  sum[5] = vec_mergel(v[2], v[6]);
-  sum[6] = vec_mergeh(v[3], v[7]);
-  sum[7] = vec_mergel(v[3], v[7]);
-
-  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
-  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
-  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
-  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
-  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
-  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
-  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
-  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8us,8>& kernel) {
-  Packet8us v[8], sum[8];
-
-  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
-  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
-  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
-  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
-  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
-  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
-  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
-  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
-  sum[0] = vec_mergeh(v[0], v[4]);
-  sum[1] = vec_mergel(v[0], v[4]);
-  sum[2] = vec_mergeh(v[1], v[5]);
-  sum[3] = vec_mergel(v[1], v[5]);
-  sum[4] = vec_mergeh(v[2], v[6]);
-  sum[5] = vec_mergel(v[2], v[6]);
-  sum[6] = vec_mergeh(v[3], v[7]);
-  sum[7] = vec_mergel(v[3], v[7]);
-
-  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
-  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
-  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
-  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
-  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
-  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
-  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
-  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet8bf,8>& kernel) {
-  Packet8bf v[8], sum[8];
-
-  v[0] = vec_mergeh(kernel.packet[0].m_val, kernel.packet[4].m_val);
-  v[1] = vec_mergel(kernel.packet[0].m_val, kernel.packet[4].m_val);
-  v[2] = vec_mergeh(kernel.packet[1].m_val, kernel.packet[5].m_val);
-  v[3] = vec_mergel(kernel.packet[1].m_val, kernel.packet[5].m_val);
-  v[4] = vec_mergeh(kernel.packet[2].m_val, kernel.packet[6].m_val);
-  v[5] = vec_mergel(kernel.packet[2].m_val, kernel.packet[6].m_val);
-  v[6] = vec_mergeh(kernel.packet[3].m_val, kernel.packet[7].m_val);
-  v[7] = vec_mergel(kernel.packet[3].m_val, kernel.packet[7].m_val);
-  sum[0] = vec_mergeh(v[0].m_val, v[4].m_val);
-  sum[1] = vec_mergel(v[0].m_val, v[4].m_val);
-  sum[2] = vec_mergeh(v[1].m_val, v[5].m_val);
-  sum[3] = vec_mergel(v[1].m_val, v[5].m_val);
-  sum[4] = vec_mergeh(v[2].m_val, v[6].m_val);
-  sum[5] = vec_mergel(v[2].m_val, v[6].m_val);
-  sum[6] = vec_mergeh(v[3].m_val, v[7].m_val);
-  sum[7] = vec_mergel(v[3].m_val, v[7].m_val);
-
-  kernel.packet[0] = vec_mergeh(sum[0].m_val, sum[4].m_val);
-  kernel.packet[1] = vec_mergel(sum[0].m_val, sum[4].m_val);
-  kernel.packet[2] = vec_mergeh(sum[1].m_val, sum[5].m_val);
-  kernel.packet[3] = vec_mergel(sum[1].m_val, sum[5].m_val);
-  kernel.packet[4] = vec_mergeh(sum[2].m_val, sum[6].m_val);
-  kernel.packet[5] = vec_mergel(sum[2].m_val, sum[6].m_val);
-  kernel.packet[6] = vec_mergeh(sum[3].m_val, sum[7].m_val);
-  kernel.packet[7] = vec_mergel(sum[3].m_val, sum[7].m_val);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16c,16>& kernel) {
-  Packet16c step1[16], step2[16], step3[16];
-
-  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
-  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
-  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
-  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
-  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
-  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
-  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
-  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
-  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
-  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
-  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
-  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
-  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
-  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
-  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
-  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
-
-  step2[0]  = vec_mergeh(step1[0], step1[8]);
-  step2[1]  = vec_mergel(step1[0], step1[8]);
-  step2[2]  = vec_mergeh(step1[1], step1[9]);
-  step2[3]  = vec_mergel(step1[1], step1[9]);
-  step2[4]  = vec_mergeh(step1[2], step1[10]);
-  step2[5]  = vec_mergel(step1[2], step1[10]);
-  step2[6]  = vec_mergeh(step1[3], step1[11]);
-  step2[7]  = vec_mergel(step1[3], step1[11]);
-  step2[8]  = vec_mergeh(step1[4], step1[12]);
-  step2[9]  = vec_mergel(step1[4], step1[12]);
-  step2[10] = vec_mergeh(step1[5], step1[13]);
-  step2[11] = vec_mergel(step1[5], step1[13]);
-  step2[12] = vec_mergeh(step1[6], step1[14]);
-  step2[13] = vec_mergel(step1[6], step1[14]);
-  step2[14] = vec_mergeh(step1[7], step1[15]);
-  step2[15] = vec_mergel(step1[7], step1[15]);
-
-  step3[0]  = vec_mergeh(step2[0], step2[8]);
-  step3[1]  = vec_mergel(step2[0], step2[8]);
-  step3[2]  = vec_mergeh(step2[1], step2[9]);
-  step3[3]  = vec_mergel(step2[1], step2[9]);
-  step3[4]  = vec_mergeh(step2[2], step2[10]);
-  step3[5]  = vec_mergel(step2[2], step2[10]);
-  step3[6]  = vec_mergeh(step2[3], step2[11]);
-  step3[7]  = vec_mergel(step2[3], step2[11]);
-  step3[8]  = vec_mergeh(step2[4], step2[12]);
-  step3[9]  = vec_mergel(step2[4], step2[12]);
-  step3[10] = vec_mergeh(step2[5], step2[13]);
-  step3[11] = vec_mergel(step2[5], step2[13]);
-  step3[12] = vec_mergeh(step2[6], step2[14]);
-  step3[13] = vec_mergel(step2[6], step2[14]);
-  step3[14] = vec_mergeh(step2[7], step2[15]);
-  step3[15] = vec_mergel(step2[7], step2[15]);
-
-  kernel.packet[0]  = vec_mergeh(step3[0], step3[8]);
-  kernel.packet[1]  = vec_mergel(step3[0], step3[8]);
-  kernel.packet[2]  = vec_mergeh(step3[1], step3[9]);
-  kernel.packet[3]  = vec_mergel(step3[1], step3[9]);
-  kernel.packet[4]  = vec_mergeh(step3[2], step3[10]);
-  kernel.packet[5]  = vec_mergel(step3[2], step3[10]);
-  kernel.packet[6]  = vec_mergeh(step3[3], step3[11]);
-  kernel.packet[7]  = vec_mergel(step3[3], step3[11]);
-  kernel.packet[8]  = vec_mergeh(step3[4], step3[12]);
-  kernel.packet[9]  = vec_mergel(step3[4], step3[12]);
-  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
-  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
-  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
-  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
-  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
-  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16uc,16>& kernel) {
-  Packet16uc step1[16], step2[16], step3[16];
-
-  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
-  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
-  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
-  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
-  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
-  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
-  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
-  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
-  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
-  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
-  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
-  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
-  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
-  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
-  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
-  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
-
-  step2[0]  = vec_mergeh(step1[0], step1[8]);
-  step2[1]  = vec_mergel(step1[0], step1[8]);
-  step2[2]  = vec_mergeh(step1[1], step1[9]);
-  step2[3]  = vec_mergel(step1[1], step1[9]);
-  step2[4]  = vec_mergeh(step1[2], step1[10]);
-  step2[5]  = vec_mergel(step1[2], step1[10]);
-  step2[6]  = vec_mergeh(step1[3], step1[11]);
-  step2[7]  = vec_mergel(step1[3], step1[11]);
-  step2[8]  = vec_mergeh(step1[4], step1[12]);
-  step2[9]  = vec_mergel(step1[4], step1[12]);
-  step2[10] = vec_mergeh(step1[5], step1[13]);
-  step2[11] = vec_mergel(step1[5], step1[13]);
-  step2[12] = vec_mergeh(step1[6], step1[14]);
-  step2[13] = vec_mergel(step1[6], step1[14]);
-  step2[14] = vec_mergeh(step1[7], step1[15]);
-  step2[15] = vec_mergel(step1[7], step1[15]);
-
-  step3[0]  = vec_mergeh(step2[0], step2[8]);
-  step3[1]  = vec_mergel(step2[0], step2[8]);
-  step3[2]  = vec_mergeh(step2[1], step2[9]);
-  step3[3]  = vec_mergel(step2[1], step2[9]);
-  step3[4]  = vec_mergeh(step2[2], step2[10]);
-  step3[5]  = vec_mergel(step2[2], step2[10]);
-  step3[6]  = vec_mergeh(step2[3], step2[11]);
-  step3[7]  = vec_mergel(step2[3], step2[11]);
-  step3[8]  = vec_mergeh(step2[4], step2[12]);
-  step3[9]  = vec_mergel(step2[4], step2[12]);
-  step3[10] = vec_mergeh(step2[5], step2[13]);
-  step3[11] = vec_mergel(step2[5], step2[13]);
-  step3[12] = vec_mergeh(step2[6], step2[14]);
-  step3[13] = vec_mergel(step2[6], step2[14]);
-  step3[14] = vec_mergeh(step2[7], step2[15]);
-  step3[15] = vec_mergel(step2[7], step2[15]);
-
-  kernel.packet[0]  = vec_mergeh(step3[0], step3[8]);
-  kernel.packet[1]  = vec_mergel(step3[0], step3[8]);
-  kernel.packet[2]  = vec_mergeh(step3[1], step3[9]);
-  kernel.packet[3]  = vec_mergel(step3[1], step3[9]);
-  kernel.packet[4]  = vec_mergeh(step3[2], step3[10]);
-  kernel.packet[5]  = vec_mergel(step3[2], step3[10]);
-  kernel.packet[6]  = vec_mergeh(step3[3], step3[11]);
-  kernel.packet[7]  = vec_mergel(step3[3], step3[11]);
-  kernel.packet[8]  = vec_mergeh(step3[4], step3[12]);
-  kernel.packet[9]  = vec_mergel(step3[4], step3[12]);
-  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
-  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
-  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
-  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
-  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
-  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
-}
-
-template<typename Packet> EIGEN_STRONG_INLINE
-Packet pblend4(const Selector<4>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
-  Packet4ui mask = reinterpret_cast<Packet4ui>(vec_cmpeq(reinterpret_cast<Packet4ui>(select), reinterpret_cast<Packet4ui>(p4i_ONE)));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
-  return pblend4<Packet4i>(ifPacket, thenPacket, elsePacket);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  return pblend4<Packet4f>(ifPacket, thenPacket, elsePacket);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8s pblend(const Selector<8>& ifPacket, const Packet8s& thenPacket, const Packet8s& elsePacket) {
-  Packet8us select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7] };
-  Packet8us mask = reinterpret_cast<Packet8us>(vec_cmpeq(select, p8us_ONE));
-  Packet8s result = vec_sel(elsePacket, thenPacket, mask);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pblend(const Selector<8>& ifPacket, const Packet8us& thenPacket, const Packet8us& elsePacket) {
-  Packet8us select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7] };
-  Packet8us mask = reinterpret_cast<Packet8us>(vec_cmpeq(reinterpret_cast<Packet8us>(select), p8us_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pblend(const Selector<8>& ifPacket, const Packet8bf& thenPacket, const Packet8bf& elsePacket) {
-  return pblend<Packet8us>(ifPacket, thenPacket, elsePacket);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16c pblend(const Selector<16>& ifPacket, const Packet16c& thenPacket, const Packet16c& elsePacket) {
-  Packet16uc select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7],
-                       ifPacket.select[8], ifPacket.select[9], ifPacket.select[10], ifPacket.select[11],
-                       ifPacket.select[12], ifPacket.select[13], ifPacket.select[14], ifPacket.select[15] };
-
-  Packet16uc mask = reinterpret_cast<Packet16uc>(vec_cmpeq(reinterpret_cast<Packet16uc>(select), p16uc_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16uc pblend(const Selector<16>& ifPacket, const Packet16uc& thenPacket, const Packet16uc& elsePacket) {
-  Packet16uc select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
-                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7],
-                       ifPacket.select[8], ifPacket.select[9], ifPacket.select[10], ifPacket.select[11],
-                       ifPacket.select[12], ifPacket.select[13], ifPacket.select[14], ifPacket.select[15] };
-
-  Packet16uc mask = reinterpret_cast<Packet16uc>(vec_cmpeq(reinterpret_cast<Packet16uc>(select), p16uc_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-template <>
-struct type_casting_traits<float, int> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<int, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<bfloat16, unsigned short int> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<unsigned short int, bfloat16> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
-  return vec_cts(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
-  return vec_ctu(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
-  return vec_ctf(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
-  return vec_ctf(a,0);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8us pcast<Packet8bf, Packet8us>(const Packet8bf& a) {
-  Packet4f float_even = Bf16ToF32Even(a);
-  Packet4f float_odd = Bf16ToF32Odd(a);
-  Packet4ui int_even = pcast<Packet4f, Packet4ui>(float_even);
-  Packet4ui int_odd = pcast<Packet4f, Packet4ui>(float_odd);
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
-  Packet4ui low_even = pand<Packet4ui>(int_even, p4ui_low_mask);
-  Packet4ui low_odd = pand<Packet4ui>(int_odd, p4ui_low_mask);
-
-  //Check values that are bigger than USHRT_MAX (0xFFFF)
-  Packet4bi overflow_selector;
-  if(vec_any_gt(int_even, p4ui_low_mask)){
-    overflow_selector = vec_cmpgt(int_even, p4ui_low_mask);
-    low_even = vec_sel(low_even, p4ui_low_mask, overflow_selector);
-  }
-  if(vec_any_gt(int_odd, p4ui_low_mask)){
-    overflow_selector = vec_cmpgt(int_odd, p4ui_low_mask);
-    low_odd = vec_sel(low_even, p4ui_low_mask, overflow_selector);
-  }
-
-  low_odd = plogical_shift_left<16>(low_odd);
-
-  Packet4ui int_final = por<Packet4ui>(low_even, low_odd);
-  return reinterpret_cast<Packet8us>(int_final);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pcast<Packet8us, Packet8bf>(const Packet8us& a) {
-  //short -> int -> float -> bfloat16
-  const _EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
-  Packet4ui int_cast = reinterpret_cast<Packet4ui>(a);
-  Packet4ui int_even = pand<Packet4ui>(int_cast, p4ui_low_mask);
-  Packet4ui int_odd = plogical_shift_right<16>(int_cast);
-  Packet4f float_even = pcast<Packet4ui, Packet4f>(int_even);
-  Packet4f float_odd = pcast<Packet4ui, Packet4f>(int_odd);
-  return F32ToBf16(float_even, float_odd);
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet4f>(const Packet4f& a) {
-  return reinterpret_cast<Packet4i>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f,Packet4i>(const Packet4i& a) {
-  return reinterpret_cast<Packet4f>(a);
-}
-
-
-
-//---------- double ----------
-#ifdef __VSX__
-typedef __vector double              Packet2d;
-typedef __vector unsigned long long  Packet2ul;
-typedef __vector long long           Packet2l;
-#if EIGEN_COMP_CLANG
-typedef Packet2ul                    Packet2bl;
-#else
-typedef __vector __bool long         Packet2bl;
-#endif
-
-static Packet2l  p2l_ONE  = { 1, 1 };
-static Packet2l  p2l_ZERO = reinterpret_cast<Packet2l>(p4i_ZERO);
-static Packet2ul p2ul_SIGN = { 0x8000000000000000ull, 0x8000000000000000ull };
-static Packet2ul p2ul_PREV0DOT5 = { 0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull };
-static Packet2d  p2d_ONE  = { 1.0, 1.0 };
-static Packet2d  p2d_ZERO = reinterpret_cast<Packet2d>(p4f_ZERO);
-static Packet2d  p2d_MZERO = { numext::bit_cast<double>(0x8000000000000000ull),
-                               numext::bit_cast<double>(0x8000000000000000ull) };
-
-#ifdef _BIG_ENDIAN
-static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ZERO), reinterpret_cast<Packet4f>(p2d_ONE), 8));
-#else
-static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ONE), reinterpret_cast<Packet4f>(p2d_ZERO), 8));
-#endif
-
-template<int index> Packet2d vec_splat_dbl(Packet2d& a)
-{
-  return vec_splat(a, index);
-}
-
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 1,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 1,
-    HasMin  = 1,
-    HasMax  = 1,
-    HasAbs  = 1,
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 0,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2d half; };
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2l & v)
-{
-  union {
-    Packet2l   v;
-    int64_t n[2];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
-{
-  union {
-    Packet2d   v;
-    double n[2];
-  } vt;
-  vt.v = v;
-  s << vt.n[0] << ", " << vt.n[1];
-  return s;
-}
-
-// Need to define them first or we get specialization after instantiation errors
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD
-  return vec_xl(0, const_cast<double *>(from)); // cast needed by Clang
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<double>(double*   to, const Packet2d& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE
-  vec_xst(from, 0, to);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double&  from) {
-  Packet2d v = {from, from};
-  return v;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(unsigned long from) {
-  Packet2l v = {static_cast<long long>(from), static_cast<long long>(from)};
-  return reinterpret_cast<Packet2d>(v);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet2d>(const double *a,
-                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-  //This way is faster than vec_splat (at least for doubles in Power 9)
-  a0 = pset1<Packet2d>(a[0]);
-  a1 = pset1<Packet2d>(a[1]);
-  a2 = pset1<Packet2d>(a[2]);
-  a3 = pset1<Packet2d>(a[3]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
-  EIGEN_ALIGN16 double af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
- return pload<Packet2d>(af);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  EIGEN_ALIGN16 double af[2];
-  pstore<double>(af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return pset1<Packet2d>(a) + p2d_COUNTDOWN; }
-
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return a + b; }
-
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return a - b; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return p2d_ZERO - a; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_MZERO); }
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_div(a,b); }
-
-// for some weird raisons, it has to be overloaded for packet of integers
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b)
-{
-  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
-  Packet2d ret;
-  __asm__ ("xvcmpgedp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
- }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b)
-{
-  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
-  Packet2d ret;
-  __asm__ ("xvcmpgtdp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
-  return ret;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmple(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmplt(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmpeq(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) {
-  Packet2d c = reinterpret_cast<Packet2d>(vec_cmpge(a,b));
-  return vec_nor(c,c);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a)
-{
-    Packet2d t = vec_add(reinterpret_cast<Packet2d>(vec_or(vec_and(reinterpret_cast<Packet2ul>(a), p2ul_SIGN), p2ul_PREV0DOT5)), a);
-    Packet2d res;
-
-    __asm__("xvrdpiz %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (t));
-
-    return res;
-}
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const  Packet2d& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return vec_floor(a); }
-template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a)
-{
-    Packet2d res;
-
-    __asm__("xvrdpic %x0, %x1\n\t"
-        : "=&wa" (res)
-        : "wa" (a));
-
-    return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return vec_xl(0, const_cast<double*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*   from)
-{
-  Packet2d p;
-  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet2d>(from);
-  else                                  p = ploadu<Packet2d>(from);
-  return vec_splat_dbl<0>(p);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double*  to, const Packet2d& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE
-  vec_xst(from, 0, to);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_PPC_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE double  pfirst<Packet2d>(const Packet2d& a) { EIGEN_ALIGN16 double x[2]; pstore<double>(x, a); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
-{
-  return reinterpret_cast<Packet2d>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE64));
-}
-template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vec_abs(a); }
-
-// VSX support varies between different compilers and even different
-// versions of the same compiler.  For gcc version >= 4.9.3, we can use
-// vec_cts to efficiently convert Packet2d to Packet2l.  Otherwise, use
-// a slow version that works with older compilers. 
-// Update: apparently vec_cts/vec_ctf intrinsics for 64-bit doubles
-// are buggy, https://gcc.gnu.org/bugzilla/show_bug.cgi?id=70963
-template<>
-inline Packet2l pcast<Packet2d, Packet2l>(const Packet2d& x) {
-#if EIGEN_GNUC_AT_LEAST(5, 4) || \
-    (EIGEN_GNUC_AT(6, 1) && __GNUC_PATCHLEVEL__ >= 1)
-  return vec_cts(x, 0);    // TODO: check clang version.
-#else
-  double tmp[2];
-  memcpy(tmp, &x, sizeof(tmp));
-  Packet2l l = { static_cast<long long>(tmp[0]),
-                 static_cast<long long>(tmp[1]) };
-  return l;
-#endif
-}
-
-template<>
-inline Packet2d pcast<Packet2l, Packet2d>(const Packet2l& x) {
-  unsigned long long tmp[2];
-  memcpy(tmp, &x, sizeof(tmp));
-  Packet2d d = { static_cast<double>(tmp[0]),
-                 static_cast<double>(tmp[1]) };
-  return d;
-}
-
-
-// Packet2l shifts.
-// For POWER8 we simply use vec_sr/l. 
-//
-// Things are more complicated for POWER7. There is actually a
-// vec_xxsxdi intrinsic but it is not supported by some gcc versions.
-// So we need to shift by N % 32 and rearrage bytes.
-#ifdef __POWER8_VECTOR__
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
-  const Packet2ul shift = { N, N };
-  return vec_sl(a, shift); 
-}
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
-  const Packet2ul shift = { N, N };
-  return vec_sr(a, shift); 
-}
-
-#else
-
-// Shifts [A, B, C, D] to [B, 0, D, 0].
-// Used to implement left shifts for Packet2l.
-EIGEN_ALWAYS_INLINE Packet4i shift_even_left(const Packet4i& a) {
-  static const Packet16uc perm = {
-      0x14, 0x15, 0x16, 0x17, 0x00, 0x01, 0x02, 0x03, 
-      0x1c, 0x1d, 0x1e, 0x1f, 0x08, 0x09, 0x0a, 0x0b };
-  #ifdef  _BIG_ENDIAN
-    return vec_perm(p4i_ZERO, a, perm);
-  #else
-    return vec_perm(a, p4i_ZERO, perm);
-  #endif
-}
-
-// Shifts [A, B, C, D] to [0, A, 0, C].
-// Used to implement right shifts for Packet2l.
-EIGEN_ALWAYS_INLINE Packet4i shift_odd_right(const Packet4i& a) {
-  static const Packet16uc perm = {
-      0x04, 0x05, 0x06, 0x07, 0x10, 0x11, 0x12, 0x13, 
-      0x0c, 0x0d, 0x0e, 0x0f, 0x18, 0x19, 0x1a, 0x1b };
-  #ifdef  _BIG_ENDIAN
-    return vec_perm(p4i_ZERO, a, perm);
-  #else
-    return vec_perm(a, p4i_ZERO, perm);
-  #endif
-}
-
-template<int N, typename EnableIf = void>
-struct plogical_shift_left_impl;
-
-template<int N>
-struct plogical_shift_left_impl<N, typename enable_if<(N < 32) && (N >= 0)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned n = static_cast<unsigned>(N);
-    const Packet4ui shift = {n, n, n, n};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    static const unsigned m = static_cast<unsigned>(32 - N);
-    const Packet4ui shift_right = {m, m, m, m};
-    const Packet4i out_hi = vec_sl(ai, shift);
-    const Packet4i out_lo = shift_even_left(vec_sr(ai, shift_right));
-    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
-  }
-};
-
-template<int N>
-struct plogical_shift_left_impl<N, typename enable_if<(N >= 32)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned m = static_cast<unsigned>(N - 32);
-    const Packet4ui shift = {m, m, m, m};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    return reinterpret_cast<Packet2l>(shift_even_left(vec_sl(ai, shift)));
-  }
-};
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
-  return plogical_shift_left_impl<N>::run(a); 
-}
-
-template<int N, typename EnableIf = void>
-struct plogical_shift_right_impl;
-
-template<int N>
-struct plogical_shift_right_impl<N, typename enable_if<(N < 32) && (N >= 0)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned n = static_cast<unsigned>(N);
-    const Packet4ui shift = {n, n, n, n};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    static const unsigned m = static_cast<unsigned>(32 - N);
-    const Packet4ui shift_left = {m, m, m, m};
-    const Packet4i out_lo = vec_sr(ai, shift);
-    const Packet4i out_hi = shift_odd_right(vec_sl(ai, shift_left));
-    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
-  }
-};
-
-template<int N>
-struct plogical_shift_right_impl<N, typename enable_if<(N >= 32)>::type> {
-  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
-    static const unsigned m = static_cast<unsigned>(N - 32);
-    const Packet4ui shift = {m, m, m, m};
-    const Packet4i ai = reinterpret_cast<Packet4i>(a);
-    return reinterpret_cast<Packet2l>(shift_odd_right(vec_sr(ai, shift)));
-  }
-};
-
-template<int N>
-EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
-  return plogical_shift_right_impl<N>::run(a); 
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
-  const Packet2l e = pcast<Packet2d, Packet2l>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-
-  // Split 2^e into four factors and multiply:
-  const Packet2l  bias = { 1023, 1023 };
-  Packet2l b = plogical_shift_right<2>(e);  // floor(e/4)
-  Packet2d c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias));
-  Packet2d out = pmul(pmul(pmul(a, c), c), c); // a * 2^(3b)
-  b = psub(psub(psub(e, b), b), b);  // e - 3b
-  c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias)); // 2^(e - 3b)
-  out = pmul(out, c); // a * 2^e
-  return out;
-}
-
-
-// Extract exponent without existence of Packet2l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
-  return pcast<Packet2l, Packet2d>(plogical_shift_right<52>(reinterpret_cast<Packet2l>(pabs(a))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d> (const Packet2d& a, Packet2d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{
-  Packet2d b, sum;
-  b   = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(a), reinterpret_cast<Packet4f>(a), 8));
-  sum = a + b;
-  return pfirst<Packet2d>(sum);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmin(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
-}
-
-// max
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmax(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2d,2>& kernel) {
-  Packet2d t0, t1;
-  t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
-  t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
-  kernel.packet[0] = t0;
-  kernel.packet[1] = t1;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
-  Packet2l select = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2bl mask = reinterpret_cast<Packet2bl>( vec_cmpeq(reinterpret_cast<Packet2d>(select), reinterpret_cast<Packet2d>(p2l_ONE)) );
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-
-#endif // __VSX__
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
deleted file mode 100644
index deb4c86..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2021 C. Antonio Sanchez <cantonios@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_CUDA_H
-#define EIGEN_COMPLEX_CUDA_H
-
-// clang-format off
-// Many std::complex methods such as operator+, operator-, operator* and
-// operator/ are not constexpr. Due to this, GCC and older versions of clang do
-// not treat them as device functions and thus Eigen functors making use of
-// these operators fail to compile. Here, we manually specialize these
-// operators and functors for complex types when building for CUDA to enable
-// their use on-device.
-
-#if defined(EIGEN_CUDACC) && defined(EIGEN_GPU_COMPILE_PHASE)
-    
-// ICC already specializes std::complex<float> and std::complex<double>
-// operators, preventing us from making them device functions here.
-// This will lead to silent runtime errors if the operators are used on device.
-//
-// To allow std::complex operator use on device, define _OVERRIDE_COMPLEX_SPECIALIZATION_
-// prior to first inclusion of <complex>.  This prevents ICC from adding
-// its own specializations, so our custom ones below can be used instead.
-#if !(defined(EIGEN_COMP_ICC) && defined(_USE_COMPLEX_SPECIALIZATION_))
-
-// Import Eigen's internal operator specializations.
-#define EIGEN_USING_STD_COMPLEX_OPERATORS           \
-  using Eigen::complex_operator_detail::operator+;  \
-  using Eigen::complex_operator_detail::operator-;  \
-  using Eigen::complex_operator_detail::operator*;  \
-  using Eigen::complex_operator_detail::operator/;  \
-  using Eigen::complex_operator_detail::operator+=; \
-  using Eigen::complex_operator_detail::operator-=; \
-  using Eigen::complex_operator_detail::operator*=; \
-  using Eigen::complex_operator_detail::operator/=; \
-  using Eigen::complex_operator_detail::operator==; \
-  using Eigen::complex_operator_detail::operator!=;
-
-namespace Eigen {
-
-// Specialized std::complex overloads.
-namespace complex_operator_detail {
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_multiply(const std::complex<T>& a, const std::complex<T>& b) {
-  const T a_real = numext::real(a);
-  const T a_imag = numext::imag(a);
-  const T b_real = numext::real(b);
-  const T b_imag = numext::imag(b);
-  return std::complex<T>(
-      a_real * b_real - a_imag * b_imag,
-      a_imag * b_real + a_real * b_imag);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_divide_fast(const std::complex<T>& a, const std::complex<T>& b) {
-  const T a_real = numext::real(a);
-  const T a_imag = numext::imag(a);
-  const T b_real = numext::real(b);
-  const T b_imag = numext::imag(b);
-  const T norm = (b_real * b_real + b_imag * b_imag);
-  return std::complex<T>((a_real * b_real + a_imag * b_imag) / norm,
-                          (a_imag * b_real - a_real * b_imag) / norm);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_divide_stable(const std::complex<T>& a, const std::complex<T>& b) {
-  const T a_real = numext::real(a);
-  const T a_imag = numext::imag(a);
-  const T b_real = numext::real(b);
-  const T b_imag = numext::imag(b);
-  // Smith's complex division (https://arxiv.org/pdf/1210.4539.pdf),
-  // guards against over/under-flow.
-  const bool scale_imag = numext::abs(b_imag) <= numext::abs(b_real);
-  const T rscale = scale_imag ? T(1) : b_real / b_imag;
-  const T iscale = scale_imag ? b_imag / b_real : T(1);
-  const T denominator = b_real * rscale + b_imag * iscale;
-  return std::complex<T>((a_real * rscale + a_imag * iscale) / denominator, 
-                         (a_imag * rscale - a_real * iscale) / denominator);
-}
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<T> complex_divide(const std::complex<T>& a, const std::complex<T>& b) {
-#if EIGEN_FAST_MATH
-  return complex_divide_fast(a, b);
-#else
-  return complex_divide_stable(a, b);
-#endif
-}
-
-// NOTE: We cannot specialize compound assignment operators with Scalar T,
-//         (i.e.  operator@=(const T&), for @=+,-,*,/)
-//       since they are already specialized for float/double/long double within
-//       the standard <complex> header. We also do not specialize the stream
-//       operators.
-#define EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(T)                                    \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const std::complex<T>& a) { return a; }                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const std::complex<T>& a) {                                           \
-  return std::complex<T>(-numext::real(a), -numext::imag(a));                                   \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return std::complex<T>(numext::real(a) + numext::real(b), numext::imag(a) + numext::imag(b)); \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) + b, numext::imag(a));                                 \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator+(const T& a, const std::complex<T>& b) {                               \
-  return std::complex<T>(a + numext::real(b), numext::imag(b));                                 \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return std::complex<T>(numext::real(a) - numext::real(b), numext::imag(a) - numext::imag(b)); \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) - b, numext::imag(a));                                 \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator-(const T& a, const std::complex<T>& b) {                               \
-  return std::complex<T>(a - numext::real(b), -numext::imag(b));                                \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator*(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return complex_multiply(a, b);                                                                \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator*(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) * b, numext::imag(a) * b);                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator*(const T& a, const std::complex<T>& b) {                               \
-  return std::complex<T>(a * numext::real(b), a * numext::imag(b));                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator/(const std::complex<T>& a, const std::complex<T>& b) {                 \
-  return complex_divide(a, b);                                                                  \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator/(const std::complex<T>& a, const T& b) {                               \
-  return std::complex<T>(numext::real(a) / b, numext::imag(a) / b);                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T> operator/(const T& a, const std::complex<T>& b) {                               \
-  return complex_divide(std::complex<T>(a, 0), b);                                              \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator+=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  numext::real_ref(a) += numext::real(b);                                                       \
-  numext::imag_ref(a) += numext::imag(b);                                                       \
-  return a;                                                                                     \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator-=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  numext::real_ref(a) -= numext::real(b);                                                       \
-  numext::imag_ref(a) -= numext::imag(b);                                                       \
-  return a;                                                                                     \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator*=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  a = complex_multiply(a, b);                                                                   \
-  return a;                                                                                     \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-std::complex<T>& operator/=(std::complex<T>& a, const std::complex<T>& b) {                     \
-  a = complex_divide(a, b);                                                                     \
-  return  a;                                                                                    \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator==(const std::complex<T>& a, const std::complex<T>& b) {                           \
-  return numext::real(a) == numext::real(b) && numext::imag(a) == numext::imag(b);              \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator==(const std::complex<T>& a, const T& b) {                                         \
-  return numext::real(a) == b && numext::imag(a) == 0;                                          \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator==(const T& a, const std::complex<T>& b) {                                         \
-  return a  == numext::real(b) && 0 == numext::imag(b);                                         \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator!=(const std::complex<T>& a, const std::complex<T>& b) {                           \
-  return !(a == b);                                                                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator!=(const std::complex<T>& a, const T& b) {                                         \
-  return !(a == b);                                                                             \
-}                                                                                               \
-                                                                                                \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
-bool operator!=(const T& a, const std::complex<T>& b) {                                         \
-  return !(a == b);                                                                             \
-}
-
-// Do not specialize for long double, since that reduces to double on device.
-EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(float)
-EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(double)
-
-#undef EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS
-
-  
-}  // namespace complex_operator_detail
-
-EIGEN_USING_STD_COMPLEX_OPERATORS
-
-namespace numext {
-EIGEN_USING_STD_COMPLEX_OPERATORS
-}  // namespace numext
-
-namespace internal {
-EIGEN_USING_STD_COMPLEX_OPERATORS
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // !(EIGEN_COMP_ICC && _USE_COMPLEX_SPECIALIZATION_)
-
-#endif  // EIGEN_CUDACC && EIGEN_GPU_COMPILE_PHASE
-
-#endif  // EIGEN_COMPLEX_CUDA_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
deleted file mode 100644
index 1c28f4f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
+++ /dev/null
@@ -1,700 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef EIGEN_BFLOAT16_H
-#define EIGEN_BFLOAT16_H
-
-#define BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, METHOD)         \
-  template <>                                                       \
-  EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED  \
-  PACKET_BF16 METHOD<PACKET_BF16>(const PACKET_BF16& _x) {          \
-    return F32ToBf16(METHOD<PACKET_F>(Bf16ToF32(_x)));              \
-  }
-
-namespace Eigen {
-
-struct bfloat16;
-
-namespace bfloat16_impl {
-
-// Make our own __bfloat16_raw definition.
-struct __bfloat16_raw {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() : value(0) {}
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(unsigned short raw) : value(raw) {}
-  unsigned short value;
-};
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(unsigned short value);
-template <bool AssumeArgumentIsNormalOrInfinityOrZero>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne(float ff);
-// Forward declarations of template specializations, to avoid Visual C++ 2019 errors, saying:
-// > error C2908: explicit specialization; 'float_to_bfloat16_rtne' has already been instantiated
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff);
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff);
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h);
-
-struct bfloat16_base : public __bfloat16_raw {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base() {}
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base(const __bfloat16_raw& h) : __bfloat16_raw(h) {}
-};
-
-} // namespace bfloat16_impl
-
-// Class definition.
-struct bfloat16 : public bfloat16_impl::bfloat16_base {
-
-  typedef bfloat16_impl::__bfloat16_raw __bfloat16_raw;
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const __bfloat16_raw& h) : bfloat16_impl::bfloat16_base(h) {}
-
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(bool b)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::raw_uint16_to_bfloat16(b ? 0x3f80 : 0)) {}
-
-  template<class T>
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(T val)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<internal::is_integral<T>::value>(static_cast<float>(val))) {}
-
-  explicit EIGEN_DEVICE_FUNC bfloat16(float f)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(f)) {}
-
-  // Following the convention of numpy, converting between complex and
-  // float will lead to loss of imag value.
-  template<typename RealScalar>
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const std::complex<RealScalar>& val)
-      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(static_cast<float>(val.real()))) {}
-
-  EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
-    return bfloat16_impl::bfloat16_to_float(*this);
-  }
-};
-} // namespace Eigen
-
-namespace std {
-template<>
-struct numeric_limits<Eigen::bfloat16> {
-  static const bool is_specialized = true;
-  static const bool is_signed = true;
-  static const bool is_integer = false;
-  static const bool is_exact = false;
-  static const bool has_infinity = true;
-  static const bool has_quiet_NaN = true;
-  static const bool has_signaling_NaN = true;
-  static const float_denorm_style has_denorm = std::denorm_absent;
-  static const bool has_denorm_loss = false;
-  static const std::float_round_style round_style = numeric_limits<float>::round_style;
-  static const bool is_iec559 = false;
-  static const bool is_bounded = true;
-  static const bool is_modulo = false;
-  static const int digits = 8;
-  static const int digits10 = 2;
-  static const int max_digits10 = 4;
-  static const int radix = 2;
-  static const int min_exponent = numeric_limits<float>::min_exponent;
-  static const int min_exponent10 = numeric_limits<float>::min_exponent10;
-  static const int max_exponent = numeric_limits<float>::max_exponent;
-  static const int max_exponent10 = numeric_limits<float>::max_exponent10;
-  static const bool traps = numeric_limits<float>::traps;
-  static const bool tinyness_before = numeric_limits<float>::tinyness_before;
-
-  static Eigen::bfloat16 (min)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0080); }
-  static Eigen::bfloat16 lowest() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0xff7f); }
-  static Eigen::bfloat16 (max)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f7f); }
-  static Eigen::bfloat16 epsilon() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x3c00); }
-  static Eigen::bfloat16 round_error() { return Eigen::bfloat16(0x3f00); }
-  static Eigen::bfloat16 infinity() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f80); }
-  static Eigen::bfloat16 quiet_NaN() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0); }
-  static Eigen::bfloat16 signaling_NaN() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f81); }
-  static Eigen::bfloat16 denorm_min() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0001); }
-};
-
-// If std::numeric_limits<T> is specialized, should also specialize
-// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
-// std::numeric_limits<const volatile T>
-// https://stackoverflow.com/a/16519653/
-template<>
-struct numeric_limits<const Eigen::bfloat16> : numeric_limits<Eigen::bfloat16> {};
-template<>
-struct numeric_limits<volatile Eigen::bfloat16> : numeric_limits<Eigen::bfloat16> {};
-template<>
-struct numeric_limits<const volatile Eigen::bfloat16> : numeric_limits<Eigen::bfloat16> {};
-} // namespace std
-
-namespace Eigen {
-
-namespace bfloat16_impl {
-
-// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
-// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
-// of the functions, while the latter can only deal with one of them.
-#if !defined(EIGEN_HAS_NATIVE_BF16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC) // Emulate support for bfloat16 floats
-
-#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
-// We need to provide emulated *host-side* BF16 operators for clang.
-#pragma push_macro("EIGEN_DEVICE_FUNC")
-#undef EIGEN_DEVICE_FUNC
-#if defined(EIGEN_HAS_CUDA_BF16) && defined(EIGEN_HAS_NATIVE_BF16)
-#define EIGEN_DEVICE_FUNC __host__
-#else // both host and device need emulated ops.
-#define EIGEN_DEVICE_FUNC __host__ __device__
-#endif
-#endif
-
-// Definitions for CPUs, mostly working through conversion
-// to/from fp32.
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) + float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const bfloat16& a, const int& b) {
-  return bfloat16(float(a) + static_cast<float>(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const int& a, const bfloat16& b) {
-  return bfloat16(static_cast<float>(a) + float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator * (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) * float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator - (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) - float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator / (const bfloat16& a, const bfloat16& b) {
-  return bfloat16(float(a) / float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator - (const bfloat16& a) {
-  bfloat16 result;
-  result.value = a.value ^ 0x8000;
-  return result;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator += (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) + float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator *= (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) * float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator -= (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) - float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator /= (bfloat16& a, const bfloat16& b) {
-  a = bfloat16(float(a) / float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a) {
-  a += bfloat16(1);
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a) {
-  a -= bfloat16(1);
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a, int) {
-  bfloat16 original_value = a;
-  ++a;
-  return original_value;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a, int) {
-  bfloat16 original_value = a;
-  --a;
-  return original_value;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const bfloat16& a, const bfloat16& b) {
-  return numext::equal_strict(float(a),float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const bfloat16& a, const bfloat16& b) {
-  return numext::not_equal_strict(float(a), float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const bfloat16& a, const bfloat16& b) {
-  return float(a) < float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const bfloat16& a, const bfloat16& b) {
-  return float(a) <= float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const bfloat16& a, const bfloat16& b) {
-  return float(a) > float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const bfloat16& a, const bfloat16& b) {
-  return float(a) >= float(b);
-}
-
-#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
-#pragma pop_macro("EIGEN_DEVICE_FUNC")
-#endif
-#endif  // Emulate support for bfloat16 floats
-
-// Division by an index. Do it in full float precision to avoid accuracy
-// issues in converting the denominator to bfloat16.
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator / (const bfloat16& a, Index b) {
-  return bfloat16(static_cast<float>(a) / static_cast<float>(b));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw truncate_to_bfloat16(const float v) {
-  __bfloat16_raw output;
-  if (Eigen::numext::isnan EIGEN_NOT_A_MACRO(v)) {
-    output.value = std::signbit(v) ? 0xFFC0: 0x7FC0;
-    return output;
-  }
-  const uint16_t* p = reinterpret_cast<const uint16_t*>(&v);
-#if defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
-  output.value = p[0];
-#else
-  output.value = p[1];
-#endif
-  return output;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(numext::uint16_t value) {
-  return __bfloat16_raw(value);
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR numext::uint16_t raw_bfloat16_as_uint16(const __bfloat16_raw& bf) {
-  return bf.value;
-}
-
-// float_to_bfloat16_rtne template specialization that does not make any
-// assumption about the value of its function argument (ff).
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff) {
-#if (defined(EIGEN_HAS_CUDA_BF16) && defined(EIGEN_HAS_HIP_BF16))
-  // Nothing to do here
-#else
-  __bfloat16_raw output;
-
-  if (Eigen::numext::isnan EIGEN_NOT_A_MACRO(ff)) {
-    // If the value is a NaN, squash it to a qNaN with msb of fraction set,
-    // this makes sure after truncation we don't end up with an inf.
-    //
-    // qNaN magic: All exponent bits set + most significant bit of fraction
-    // set.
-    output.value = std::signbit(ff) ? 0xFFC0: 0x7FC0;
-  } else {
-    // Fast rounding algorithm that rounds a half value to nearest even. This
-    // reduces expected error when we convert a large number of floats. Here
-    // is how it works:
-    //
-    // Definitions:
-    // To convert a float 32 to bfloat16, a float 32 can be viewed as 32 bits
-    // with the following tags:
-    //
-    // Sign |  Exp (8 bits) | Frac (23 bits)
-    //  S     EEEEEEEE         FFFFFFLRTTTTTTTTTTTTTTT
-    //
-    //  S: Sign bit.
-    //  E: Exponent bits.
-    //  F: First 6 bits of fraction.
-    //  L: Least significant bit of resulting bfloat16 if we truncate away the
-    //  rest of the float32. This is also the 7th bit of fraction
-    //  R: Rounding bit, 8th bit of fraction.
-    //  T: Sticky bits, rest of fraction, 15 bits.
-    //
-    // To round half to nearest even, there are 3 cases where we want to round
-    // down (simply truncate the result of the bits away, which consists of
-    // rounding bit and sticky bits) and two cases where we want to round up
-    // (truncate then add one to the result).
-    //
-    // The fast converting algorithm simply adds lsb (L) to 0x7fff (15 bits of
-    // 1s) as the rounding bias, adds the rounding bias to the input, then
-    // truncates the last 16 bits away.
-    //
-    // To understand how it works, we can analyze this algorithm case by case:
-    //
-    // 1. L = 0, R = 0:
-    //   Expect: round down, this is less than half value.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 0 = 0x7fff
-    //   - Adding rounding bias to input may create any carry, depending on
-    //   whether there is any value set to 1 in T bits.
-    //   - R may be set to 1 if there is a carry.
-    //   - L remains 0.
-    //   - Note that this case also handles Inf and -Inf, where all fraction
-    //   bits, including L, R and Ts are all 0. The output remains Inf after
-    //   this algorithm.
-    //
-    // 2. L = 1, R = 0:
-    //   Expect: round down, this is less than half value.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 1 = 0x8000
-    //   - Adding rounding bias to input doesn't change sticky bits but
-    //   adds 1 to rounding bit.
-    //   - L remains 1.
-    //
-    // 3. L = 0, R = 1, all of T are 0:
-    //   Expect: round down, this is exactly at half, the result is already
-    //   even (L=0).
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 0 = 0x7fff
-    //   - Adding rounding bias to input sets all sticky bits to 1, but
-    //   doesn't create a carry.
-    //   - R remains 1.
-    //   - L remains 0.
-    //
-    // 4. L = 1, R = 1:
-    //   Expect: round up, this is exactly at half, the result needs to be
-    //   round to the next even number.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 1 = 0x8000
-    //   - Adding rounding bias to input doesn't change sticky bits, but
-    //   creates a carry from rounding bit.
-    //   - The carry sets L to 0, creates another carry bit and propagate
-    //   forward to F bits.
-    //   - If all the F bits are 1, a carry then propagates to the exponent
-    //   bits, which then creates the minimum value with the next exponent
-    //   value. Note that we won't have the case where exponents are all 1,
-    //   since that's either a NaN (handled in the other if condition) or inf
-    //   (handled in case 1).
-    //
-    // 5. L = 0, R = 1, any of T is 1:
-    //   Expect: round up, this is greater than half.
-    //
-    //   Algorithm:
-    //   - Rounding bias: 0x7fff + 0 = 0x7fff
-    //   - Adding rounding bias to input creates a carry from sticky bits,
-    //   sets rounding bit to 0, then create another carry.
-    //   - The second carry sets L to 1.
-    //
-    // Examples:
-    //
-    //  Exact half value that is already even:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0     1000000000000000
-    //
-    //     This falls into case 3. We truncate the rest of 16 bits and no
-    //     carry is created into F and L:
-    //
-    //    Output:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
-    //
-    //  Exact half value, round to next even number:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 0 1     1000000000000000
-    //
-    //     This falls into case 4. We create a carry from R and T,
-    //     which then propagates into L and F:
-    //
-    //    Output:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
-    //
-    //
-    //  Max denormal value round to min normal value:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     0 0 0 0 0 0 0 0      1 1 1 1 1 1 1     1111111111111111
-    //
-    //     This falls into case 4. We create a carry from R and T,
-    //     propagate into L and F, which then propagates into exponent
-    //     bits:
-    //
-    //    Output:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     0 0 0 0 0 0 0 1      0 0 0 0 0 0 0
-    //
-    //  Max normal value round to Inf:
-    //    Input:
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
-    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
-    //     0     1 1 1 1 1 1 1 0      1 1 1 1 1 1 1     1111111111111111
-    //
-    //     This falls into case 4. We create a carry from R and T,
-    //     propagate into L and F, which then propagates into exponent
-    //     bits:
-    //
-    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
-    //     S     E E E E E E E E      F F F F F F L
-    //     0     1 1 1 1 1 1 1 1      0 0 0 0 0 0 0
-
-    // At this point, ff must be either a normal float, or +/-infinity.
-    output = float_to_bfloat16_rtne<true>(ff);
-  }
-  return output;
-#endif
-}
-
-// float_to_bfloat16_rtne template specialization that assumes that its function
-// argument (ff) is either a normal floating point number, or +/-infinity, or
-// zero. Used to improve the runtime performance of conversion from an integer
-// type to bfloat16.
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff) {
-#if (defined(EIGEN_HAS_CUDA_BF16) && defined(EIGEN_HAS_HIP_BF16))
-    // Nothing to do here
-#else
-    numext::uint32_t input = numext::bit_cast<numext::uint32_t>(ff);
-    __bfloat16_raw output;
-
-    // Least significant bit of resulting bfloat.
-    numext::uint32_t lsb = (input >> 16) & 1;
-    numext::uint32_t rounding_bias = 0x7fff + lsb;
-    input += rounding_bias;
-    output.value = static_cast<numext::uint16_t>(input >> 16);
-    return output;
-#endif
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h) {
-    float result = 0;
-    unsigned short* q = reinterpret_cast<unsigned short*>(&result);
-#if defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
-    q[0] = h.value;
-#else
-    q[1] = h.value;
-#endif
-    return result;
-}
-// --- standard functions ---
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const bfloat16& a) {
-  EIGEN_USING_STD(isinf);
-  return (isinf)(float(a));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const bfloat16& a) {
-  EIGEN_USING_STD(isnan);
-  return (isnan)(float(a));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const bfloat16& a) {
-  return !(isinf EIGEN_NOT_A_MACRO (a)) && !(isnan EIGEN_NOT_A_MACRO (a));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 abs(const bfloat16& a) {
-  bfloat16 result;
-  result.value = a.value & 0x7FFF;
-  return result;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 exp(const bfloat16& a) {
-   return bfloat16(::expf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 expm1(const bfloat16& a) {
-  return bfloat16(numext::expm1(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log(const bfloat16& a) {
-  return bfloat16(::logf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log1p(const bfloat16& a) {
-  return bfloat16(numext::log1p(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log10(const bfloat16& a) {
-  return bfloat16(::log10f(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log2(const bfloat16& a) {
-  return bfloat16(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sqrt(const bfloat16& a) {
-    return bfloat16(::sqrtf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 pow(const bfloat16& a, const bfloat16& b) {
-  return bfloat16(::powf(float(a), float(b)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sin(const bfloat16& a) {
-  return bfloat16(::sinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cos(const bfloat16& a) {
-  return bfloat16(::cosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tan(const bfloat16& a) {
-  return bfloat16(::tanf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asin(const bfloat16& a) {
-  return bfloat16(::asinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acos(const bfloat16& a) {
-  return bfloat16(::acosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atan(const bfloat16& a) {
-  return bfloat16(::atanf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sinh(const bfloat16& a) {
-  return bfloat16(::sinhf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cosh(const bfloat16& a) {
-  return bfloat16(::coshf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tanh(const bfloat16& a) {
-  return bfloat16(::tanhf(float(a)));
-}
-#if EIGEN_HAS_CXX11_MATH
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asinh(const bfloat16& a) {
-  return bfloat16(::asinhf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acosh(const bfloat16& a) {
-  return bfloat16(::acoshf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atanh(const bfloat16& a) {
-  return bfloat16(::atanhf(float(a)));
-}
-#endif
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 floor(const bfloat16& a) {
-  return bfloat16(::floorf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 ceil(const bfloat16& a) {
-  return bfloat16(::ceilf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 rint(const bfloat16& a) {
-  return bfloat16(::rintf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 round(const bfloat16& a) {
-  return bfloat16(::roundf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmod(const bfloat16& a, const bfloat16& b) {
-  return bfloat16(::fmodf(float(a), float(b)));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 (min)(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f2 < f1 ? b : a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 (max)(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f1 < f2 ? b : a;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmin(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return bfloat16(::fminf(f1, f2));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmax(const bfloat16& a, const bfloat16& b) {
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return bfloat16(::fmaxf(f1, f2));
-}
-
-#ifndef EIGEN_NO_IO
-EIGEN_ALWAYS_INLINE std::ostream& operator << (std::ostream& os, const bfloat16& v) {
-  os << static_cast<float>(v);
-  return os;
-}
-#endif
-
-} // namespace bfloat16_impl
-
-namespace internal {
-
-template<>
-struct random_default_impl<bfloat16, false, false>
-{
-  static inline bfloat16 run(const bfloat16& x, const bfloat16& y)
-  {
-    return x + (y-x) * bfloat16(float(std::rand()) / float(RAND_MAX));
-  }
-  static inline bfloat16 run()
-  {
-    return run(bfloat16(-1.f), bfloat16(1.f));
-  }
-};
-
-template<> struct is_arithmetic<bfloat16> { enum { value = true }; };
-
-} // namespace internal
-
-template<> struct NumTraits<Eigen::bfloat16>
-    : GenericNumTraits<Eigen::bfloat16>
-{
-  enum {
-    IsSigned = true,
-    IsInteger = false,
-    IsComplex = false,
-    RequireInitialization = false
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 epsilon() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x3c00);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 dummy_precision() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x3D4D);  // bfloat16(5e-2f);
-
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 highest() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x7F7F);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 lowest() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0xFF7F);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 infinity() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x7f80);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 quiet_NaN() {
-    return bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0);
-  }
-};
-
-} // namespace Eigen
-
-namespace Eigen {
-namespace numext {
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool (isnan)(const Eigen::bfloat16& h) {
-  return (bfloat16_impl::isnan)(h);
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool (isinf)(const Eigen::bfloat16& h) {
-  return (bfloat16_impl::isinf)(h);
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool (isfinite)(const Eigen::bfloat16& h) {
-  return (bfloat16_impl::isfinite)(h);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bit_cast<Eigen::bfloat16, uint16_t>(const uint16_t& src) {
-  return Eigen::bfloat16(Eigen::bfloat16_impl::raw_uint16_to_bfloat16(src));
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::bfloat16>(const Eigen::bfloat16& src) {
-  return Eigen::bfloat16_impl::raw_bfloat16_as_uint16(src);
-}
-
-}  // namespace numext
-}  // namespace Eigen
-
-#if EIGEN_HAS_STD_HASH
-namespace std {
-template <>
-struct hash<Eigen::bfloat16> {
-  EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::bfloat16& a) const {
-    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
-  }
-};
-} // namespace std
-#endif
-
-
-#endif // EIGEN_BFLOAT16_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h
deleted file mode 100644
index 53830b5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h
+++ /dev/null
@@ -1,117 +0,0 @@
-
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARCH_CONJ_HELPER_H
-#define EIGEN_ARCH_CONJ_HELPER_H
-
-#define EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(PACKET_CPLX, PACKET_REAL)      \
-  template <>                                                           \
-  struct conj_helper<PACKET_REAL, PACKET_CPLX, false, false> {          \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_REAL& x,         \
-                                          const PACKET_CPLX& y,         \
-                                          const PACKET_CPLX& c) const { \
-      return padd(c, this->pmul(x, y));                                 \
-    }                                                                   \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_REAL& x,          \
-                                         const PACKET_CPLX& y) const {  \
-      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x, y.v));   \
-    }                                                                   \
-  };                                                                    \
-                                                                        \
-  template <>                                                           \
-  struct conj_helper<PACKET_CPLX, PACKET_REAL, false, false> {          \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_CPLX& x,         \
-                                          const PACKET_REAL& y,         \
-                                          const PACKET_CPLX& c) const { \
-      return padd(c, this->pmul(x, y));                                 \
-    }                                                                   \
-    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_CPLX& x,          \
-                                         const PACKET_REAL& y) const {  \
-      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x.v, y));   \
-    }                                                                   \
-  };
-
-namespace Eigen {
-namespace internal {
-
-template<bool Conjugate> struct conj_if;
-
-template<> struct conj_if<true> {
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const { return numext::conj(x); }
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T pconj(const T& x) const { return internal::pconj(x); }
-};
-
-template<> struct conj_if<false> {
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator()(const T& x) const { return x; }
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& pconj(const T& x) const { return x; }
-};
-
-// Generic Implementation, assume scalars since the packet-version is
-// specialized below.
-template<typename LhsType, typename RhsType, bool ConjLhs, bool ConjRhs>
-struct conj_helper {
-  typedef typename ScalarBinaryOpTraits<LhsType, RhsType>::ReturnType ResultType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmadd(const LhsType& x, const RhsType& y, const ResultType& c) const
-  { return this->pmul(x, y) + c; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmul(const LhsType& x, const RhsType& y) const
-  { return conj_if<ConjLhs>()(x) * conj_if<ConjRhs>()(y); }
-};
-
-template<typename LhsScalar, typename RhsScalar>
-struct conj_helper<LhsScalar, RhsScalar, true, true> {
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar>::ReturnType ResultType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmadd(const LhsScalar& x, const RhsScalar& y, const ResultType& c) const
-  { return this->pmul(x, y) + c; }
-
-  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
-  pmul(const LhsScalar& x, const RhsScalar& y) const
-  { return numext::conj(x * y); }
-};
-
-// Implementation with equal type, use packet operations.
-template<typename Packet, bool ConjLhs, bool ConjRhs>
-struct conj_helper<Packet, Packet, ConjLhs, ConjRhs>
-{
-  typedef Packet ResultType;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const
-  { return Eigen::internal::pmadd(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y), c); }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const
-  { return Eigen::internal::pmul(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y)); }
-};
-
-template<typename Packet>
-struct conj_helper<Packet, Packet, true, true>
-{
-  typedef Packet ResultType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const
-  { return Eigen::internal::pmadd(pconj(x), pconj(y), c); }
-  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const
-  { return pconj(Eigen::internal::pmul(x, y)); }
-};
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_ARCH_CONJ_HELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
deleted file mode 100644
index c9fbaf6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
+++ /dev/null
@@ -1,1649 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The exp and log functions of this file initially come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
-#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-// Creates a Scalar integer type with same bit-width.
-template<typename T> struct make_integer;
-template<> struct make_integer<float>    { typedef numext::int32_t type; };
-template<> struct make_integer<double>   { typedef numext::int64_t type; };
-template<> struct make_integer<half>     { typedef numext::int16_t type; };
-template<> struct make_integer<bfloat16> { typedef numext::int16_t type; };
-
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC  
-Packet pfrexp_generic_get_biased_exponent(const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-  enum { mantissa_bits = numext::numeric_limits<Scalar>::digits - 1};
-  return pcast<PacketI, Packet>(plogical_shift_right<mantissa_bits>(preinterpret<PacketI>(pabs(a))));
-}
-
-// Safely applies frexp, correctly handles denormals.
-// Assumes IEEE floating point format.
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pfrexp_generic(const Packet& a, Packet& exponent) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename make_unsigned<typename make_integer<Scalar>::type>::type ScalarUI;
-  enum {
-    TotalBits = sizeof(Scalar) * CHAR_BIT,
-    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
-    ExponentBits = int(TotalBits) - int(MantissaBits) - 1
-  };
-
-  EIGEN_CONSTEXPR ScalarUI scalar_sign_mantissa_mask = 
-      ~(((ScalarUI(1) << int(ExponentBits)) - ScalarUI(1)) << int(MantissaBits)); // ~0x7f800000
-  const Packet sign_mantissa_mask = pset1frombits<Packet>(static_cast<ScalarUI>(scalar_sign_mantissa_mask)); 
-  const Packet half = pset1<Packet>(Scalar(0.5));
-  const Packet zero = pzero(a);
-  const Packet normal_min = pset1<Packet>((numext::numeric_limits<Scalar>::min)()); // Minimum normal value, 2^-126
-  
-  // To handle denormals, normalize by multiplying by 2^(int(MantissaBits)+1).
-  const Packet is_denormal = pcmp_lt(pabs(a), normal_min);
-  EIGEN_CONSTEXPR ScalarUI scalar_normalization_offset = ScalarUI(int(MantissaBits) + 1); // 24
-  // The following cannot be constexpr because bfloat16(uint16_t) is not constexpr.
-  const Scalar scalar_normalization_factor = Scalar(ScalarUI(1) << int(scalar_normalization_offset)); // 2^24
-  const Packet normalization_factor = pset1<Packet>(scalar_normalization_factor);  
-  const Packet normalized_a = pselect(is_denormal, pmul(a, normalization_factor), a);
-  
-  // Determine exponent offset: -126 if normal, -126-24 if denormal
-  const Scalar scalar_exponent_offset = -Scalar((ScalarUI(1)<<(int(ExponentBits)-1)) - ScalarUI(2)); // -126
-  Packet exponent_offset = pset1<Packet>(scalar_exponent_offset);
-  const Packet normalization_offset = pset1<Packet>(-Scalar(scalar_normalization_offset)); // -24
-  exponent_offset = pselect(is_denormal, padd(exponent_offset, normalization_offset), exponent_offset);
-  
-  // Determine exponent and mantissa from normalized_a.
-  exponent = pfrexp_generic_get_biased_exponent(normalized_a);
-  // Zero, Inf and NaN return 'a' unmodified, exponent is zero
-  // (technically the exponent is unspecified for inf/NaN, but GCC/Clang set it to zero)
-  const Scalar scalar_non_finite_exponent = Scalar((ScalarUI(1) << int(ExponentBits)) - ScalarUI(1));  // 255
-  const Packet non_finite_exponent = pset1<Packet>(scalar_non_finite_exponent);
-  const Packet is_zero_or_not_finite = por(pcmp_eq(a, zero), pcmp_eq(exponent, non_finite_exponent));
-  const Packet m = pselect(is_zero_or_not_finite, a, por(pand(normalized_a, sign_mantissa_mask), half));
-  exponent = pselect(is_zero_or_not_finite, zero, padd(exponent, exponent_offset));  
-  return m;
-}
-
-// Safely applies ldexp, correctly handles overflows, underflows and denormals.
-// Assumes IEEE floating point format.
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pldexp_generic(const Packet& a, const Packet& exponent) {
-  // We want to return a * 2^exponent, allowing for all possible integer
-  // exponents without overflowing or underflowing in intermediate
-  // computations.
-  //
-  // Since 'a' and the output can be denormal, the maximum range of 'exponent'
-  // to consider for a float is:
-  //   -255-23 -> 255+23
-  // Below -278 any finite float 'a' will become zero, and above +278 any
-  // finite float will become inf, including when 'a' is the smallest possible 
-  // denormal.
-  //
-  // Unfortunately, 2^(278) cannot be represented using either one or two
-  // finite normal floats, so we must split the scale factor into at least
-  // three parts. It turns out to be faster to split 'exponent' into four
-  // factors, since [exponent>>2] is much faster to compute that [exponent/3].
-  //
-  // Set e = min(max(exponent, -278), 278);
-  //     b = floor(e/4);
-  //   out = ((((a * 2^(b)) * 2^(b)) * 2^(b)) * 2^(e-3*b))
-  //
-  // This will avoid any intermediate overflows and correctly handle 0, inf,
-  // NaN cases.
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename unpacket_traits<PacketI>::type ScalarI;
-  enum {
-    TotalBits = sizeof(Scalar) * CHAR_BIT,
-    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
-    ExponentBits = int(TotalBits) - int(MantissaBits) - 1
-  };
-
-  const Packet max_exponent = pset1<Packet>(Scalar((ScalarI(1)<<int(ExponentBits)) + ScalarI(int(MantissaBits) - 1)));  // 278
-  const PacketI bias = pset1<PacketI>((ScalarI(1)<<(int(ExponentBits)-1)) - ScalarI(1));  // 127
-  const PacketI e = pcast<Packet, PacketI>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
-  PacketI b = parithmetic_shift_right<2>(e); // floor(e/4);
-  Packet c = preinterpret<Packet>(plogical_shift_left<int(MantissaBits)>(padd(b, bias)));  // 2^b
-  Packet out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
-  b = psub(psub(psub(e, b), b), b); // e - 3b
-  c = preinterpret<Packet>(plogical_shift_left<int(MantissaBits)>(padd(b, bias)));  // 2^(e-3*b)
-  out = pmul(out, c);
-  return out;
-}
-
-// Explicitly multiplies 
-//    a * (2^e)
-// clamping e to the range
-// [NumTraits<Scalar>::min_exponent()-2, NumTraits<Scalar>::max_exponent()]
-//
-// This is approx 7x faster than pldexp_impl, but will prematurely over/underflow
-// if 2^e doesn't fit into a normal floating-point Scalar.
-//
-// Assumes IEEE floating point format
-template<typename Packet>
-struct pldexp_fast_impl {
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename unpacket_traits<PacketI>::type ScalarI;
-  enum {
-    TotalBits = sizeof(Scalar) * CHAR_BIT,
-    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
-    ExponentBits = int(TotalBits) - int(MantissaBits) - 1
-  };
-  
-  static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-  Packet run(const Packet& a, const Packet& exponent) {
-    const Packet bias = pset1<Packet>(Scalar((ScalarI(1)<<(int(ExponentBits)-1)) - ScalarI(1)));  // 127
-    const Packet limit = pset1<Packet>(Scalar((ScalarI(1)<<int(ExponentBits)) - ScalarI(1)));     // 255
-    // restrict biased exponent between 0 and 255 for float.
-    const PacketI e = pcast<Packet, PacketI>(pmin(pmax(padd(exponent, bias), pzero(limit)), limit)); // exponent + 127
-    // return a * (2^e)
-    return pmul(a, preinterpret<Packet>(plogical_shift_left<int(MantissaBits)>(e)));
-  }
-};
-
-// Natural or base 2 logarithm.
-// Computes log(x) as log(2^e * m) = C*e + log(m), where the constant C =log(2)
-// and m is in the range [sqrt(1/2),sqrt(2)). In this range, the logarithm can
-// be easily approximated by a polynomial centered on m=1 for stability.
-// TODO(gonnet): Further reduce the interval allowing for lower-degree
-//               polynomial interpolants -> ... -> profit!
-template <typename Packet, bool base2>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_impl_float(const Packet _x)
-{
-  Packet x = _x;
-
-  const Packet cst_1              = pset1<Packet>(1.0f);
-  const Packet cst_neg_half       = pset1<Packet>(-0.5f);
-  // The smallest non denormalized float number.
-  const Packet cst_min_norm_pos   = pset1frombits<Packet>( 0x00800000u);
-  const Packet cst_minus_inf      = pset1frombits<Packet>( 0xff800000u);
-  const Packet cst_pos_inf        = pset1frombits<Packet>( 0x7f800000u);
-
-  // Polynomial coefficients.
-  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.707106781186547524f);
-  const Packet cst_cephes_log_p0 = pset1<Packet>(7.0376836292E-2f);
-  const Packet cst_cephes_log_p1 = pset1<Packet>(-1.1514610310E-1f);
-  const Packet cst_cephes_log_p2 = pset1<Packet>(1.1676998740E-1f);
-  const Packet cst_cephes_log_p3 = pset1<Packet>(-1.2420140846E-1f);
-  const Packet cst_cephes_log_p4 = pset1<Packet>(+1.4249322787E-1f);
-  const Packet cst_cephes_log_p5 = pset1<Packet>(-1.6668057665E-1f);
-  const Packet cst_cephes_log_p6 = pset1<Packet>(+2.0000714765E-1f);
-  const Packet cst_cephes_log_p7 = pset1<Packet>(-2.4999993993E-1f);
-  const Packet cst_cephes_log_p8 = pset1<Packet>(+3.3333331174E-1f);
-
-  // Truncate input values to the minimum positive normal.
-  x = pmax(x, cst_min_norm_pos);
-
-  Packet e;
-  // extract significant in the range [0.5,1) and exponent
-  x = pfrexp(x,e);
-
-  // part2: Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
-  // and shift by -1. The values are then centered around 0, which improves
-  // the stability of the polynomial evaluation.
-  //   if( x < SQRTHF ) {
-  //     e -= 1;
-  //     x = x + x - 1.0;
-  //   } else { x = x - 1.0; }
-  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
-  Packet tmp = pand(x, mask);
-  x = psub(x, cst_1);
-  e = psub(e, pand(cst_1, mask));
-  x = padd(x, tmp);
-
-  Packet x2 = pmul(x, x);
-  Packet x3 = pmul(x2, x);
-
-  // Evaluate the polynomial approximant of degree 8 in three parts, probably
-  // to improve instruction-level parallelism.
-  Packet y, y1, y2;
-  y  = pmadd(cst_cephes_log_p0, x, cst_cephes_log_p1);
-  y1 = pmadd(cst_cephes_log_p3, x, cst_cephes_log_p4);
-  y2 = pmadd(cst_cephes_log_p6, x, cst_cephes_log_p7);
-  y  = pmadd(y, x, cst_cephes_log_p2);
-  y1 = pmadd(y1, x, cst_cephes_log_p5);
-  y2 = pmadd(y2, x, cst_cephes_log_p8);
-  y  = pmadd(y, x3, y1);
-  y  = pmadd(y, x3, y2);
-  y  = pmul(y, x3);
-
-  y = pmadd(cst_neg_half, x2, y);
-  x = padd(x, y);
-
-  // Add the logarithm of the exponent back to the result of the interpolation.
-  if (base2) {
-    const Packet cst_log2e = pset1<Packet>(static_cast<float>(EIGEN_LOG2E));
-    x = pmadd(x, cst_log2e, e);
-  } else {
-    const Packet cst_ln2 = pset1<Packet>(static_cast<float>(EIGEN_LN2));
-    x = pmadd(e, cst_ln2, x);
-  }
-
-  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
-  Packet iszero_mask  = pcmp_eq(_x,pzero(_x));
-  Packet pos_inf_mask = pcmp_eq(_x,cst_pos_inf);
-  // Filter out invalid inputs, i.e.:
-  //  - negative arg will be NAN
-  //  - 0 will be -INF
-  //  - +INF will be +INF
-  return pselect(iszero_mask, cst_minus_inf,
-                              por(pselect(pos_inf_mask,cst_pos_inf,x), invalid_mask));
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_float(const Packet _x)
-{
-  return plog_impl_float<Packet, /* base2 */ false>(_x);
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_float(const Packet _x)
-{
-  return plog_impl_float<Packet, /* base2 */ true>(_x);
-}
-
-/* Returns the base e (2.718...) or base 2 logarithm of x.
- * The argument is separated into its exponent and fractional parts.
- * The logarithm of the fraction in the interval [sqrt(1/2), sqrt(2)],
- * is approximated by
- *
- *     log(1+x) = x - 0.5 x**2 + x**3 P(x)/Q(x).
- *
- * for more detail see: http://www.netlib.org/cephes/
- */
-template <typename Packet, bool base2>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_impl_double(const Packet _x)
-{
-  Packet x = _x;
-
-  const Packet cst_1              = pset1<Packet>(1.0);
-  const Packet cst_neg_half       = pset1<Packet>(-0.5);
-  // The smallest non denormalized double.
-  const Packet cst_min_norm_pos   = pset1frombits<Packet>( static_cast<uint64_t>(0x0010000000000000ull));
-  const Packet cst_minus_inf      = pset1frombits<Packet>( static_cast<uint64_t>(0xfff0000000000000ull));
-  const Packet cst_pos_inf        = pset1frombits<Packet>( static_cast<uint64_t>(0x7ff0000000000000ull));
-
-
- // Polynomial Coefficients for log(1+x) = x - x**2/2 + x**3 P(x)/Q(x)
- //                             1/sqrt(2) <= x < sqrt(2)
-  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.70710678118654752440E0);
-  const Packet cst_cephes_log_p0 = pset1<Packet>(1.01875663804580931796E-4);
-  const Packet cst_cephes_log_p1 = pset1<Packet>(4.97494994976747001425E-1);
-  const Packet cst_cephes_log_p2 = pset1<Packet>(4.70579119878881725854E0);
-  const Packet cst_cephes_log_p3 = pset1<Packet>(1.44989225341610930846E1);
-  const Packet cst_cephes_log_p4 = pset1<Packet>(1.79368678507819816313E1);
-  const Packet cst_cephes_log_p5 = pset1<Packet>(7.70838733755885391666E0);
-
-  const Packet cst_cephes_log_q0 = pset1<Packet>(1.0);
-  const Packet cst_cephes_log_q1 = pset1<Packet>(1.12873587189167450590E1);
-  const Packet cst_cephes_log_q2 = pset1<Packet>(4.52279145837532221105E1);
-  const Packet cst_cephes_log_q3 = pset1<Packet>(8.29875266912776603211E1);
-  const Packet cst_cephes_log_q4 = pset1<Packet>(7.11544750618563894466E1);
-  const Packet cst_cephes_log_q5 = pset1<Packet>(2.31251620126765340583E1);
-
-  // Truncate input values to the minimum positive normal.
-  x = pmax(x, cst_min_norm_pos);
-
-  Packet e;
-  // extract significant in the range [0.5,1) and exponent
-  x = pfrexp(x,e);
-  
-  // Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
-  // and shift by -1. The values are then centered around 0, which improves
-  // the stability of the polynomial evaluation.
-  //   if( x < SQRTHF ) {
-  //     e -= 1;
-  //     x = x + x - 1.0;
-  //   } else { x = x - 1.0; }
-  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
-  Packet tmp = pand(x, mask);
-  x = psub(x, cst_1);
-  e = psub(e, pand(cst_1, mask));
-  x = padd(x, tmp);
-
-  Packet x2 = pmul(x, x);
-  Packet x3 = pmul(x2, x);
-
-  // Evaluate the polynomial approximant , probably to improve instruction-level parallelism.
-  // y = x - 0.5*x^2 + x^3 * polevl( x, P, 5 ) / p1evl( x, Q, 5 ) );
-  Packet y, y1, y_;
-  y  = pmadd(cst_cephes_log_p0, x, cst_cephes_log_p1);
-  y1 = pmadd(cst_cephes_log_p3, x, cst_cephes_log_p4);
-  y  = pmadd(y, x, cst_cephes_log_p2);
-  y1 = pmadd(y1, x, cst_cephes_log_p5);
-  y_ = pmadd(y, x3, y1);
-
-  y  = pmadd(cst_cephes_log_q0, x, cst_cephes_log_q1);
-  y1 = pmadd(cst_cephes_log_q3, x, cst_cephes_log_q4);
-  y  = pmadd(y, x, cst_cephes_log_q2);
-  y1 = pmadd(y1, x, cst_cephes_log_q5);
-  y  = pmadd(y, x3, y1);
-
-  y_ = pmul(y_, x3);
-  y  = pdiv(y_, y);
-
-  y = pmadd(cst_neg_half, x2, y);
-  x = padd(x, y);
-
-  // Add the logarithm of the exponent back to the result of the interpolation.
-  if (base2) {
-    const Packet cst_log2e = pset1<Packet>(static_cast<double>(EIGEN_LOG2E));
-    x = pmadd(x, cst_log2e, e);
-  } else {
-    const Packet cst_ln2 = pset1<Packet>(static_cast<double>(EIGEN_LN2));
-    x = pmadd(e, cst_ln2, x);
-  }
-
-  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
-  Packet iszero_mask  = pcmp_eq(_x,pzero(_x));
-  Packet pos_inf_mask = pcmp_eq(_x,cst_pos_inf);
-  // Filter out invalid inputs, i.e.:
-  //  - negative arg will be NAN
-  //  - 0 will be -INF
-  //  - +INF will be +INF
-  return pselect(iszero_mask, cst_minus_inf,
-                              por(pselect(pos_inf_mask,cst_pos_inf,x), invalid_mask));
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_double(const Packet _x)
-{
-  return plog_impl_double<Packet, /* base2 */ false>(_x);
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_double(const Packet _x)
-{
-  return plog_impl_double<Packet, /* base2 */ true>(_x);
-}
-
-/** \internal \returns log(1 + x) computed using W. Kahan's formula.
-    See: http://www.plunk.org/~hatch/rightway.php
- */
-template<typename Packet>
-Packet generic_plog1p(const Packet& x)
-{
-  typedef typename unpacket_traits<Packet>::type ScalarType;
-  const Packet one = pset1<Packet>(ScalarType(1));
-  Packet xp1 = padd(x, one);
-  Packet small_mask = pcmp_eq(xp1, one);
-  Packet log1 = plog(xp1);
-  Packet inf_mask = pcmp_eq(xp1, log1);
-  Packet log_large = pmul(x, pdiv(log1, psub(xp1, one)));
-  return pselect(por(small_mask, inf_mask), x, log_large);
-}
-
-/** \internal \returns exp(x)-1 computed using W. Kahan's formula.
-    See: http://www.plunk.org/~hatch/rightway.php
- */
-template<typename Packet>
-Packet generic_expm1(const Packet& x)
-{
-  typedef typename unpacket_traits<Packet>::type ScalarType;
-  const Packet one = pset1<Packet>(ScalarType(1));
-  const Packet neg_one = pset1<Packet>(ScalarType(-1));
-  Packet u = pexp(x);
-  Packet one_mask = pcmp_eq(u, one);
-  Packet u_minus_one = psub(u, one);
-  Packet neg_one_mask = pcmp_eq(u_minus_one, neg_one);
-  Packet logu = plog(u);
-  // The following comparison is to catch the case where
-  // exp(x) = +inf. It is written in this way to avoid having
-  // to form the constant +inf, which depends on the packet
-  // type.
-  Packet pos_inf_mask = pcmp_eq(logu, u);
-  Packet expm1 = pmul(u_minus_one, pdiv(x, logu));
-  expm1 = pselect(pos_inf_mask, u, expm1);
-  return pselect(one_mask,
-                 x,
-                 pselect(neg_one_mask,
-                         neg_one,
-                         expm1));
-}
-
-
-// Exponential function. Works by writing "x = m*log(2) + r" where
-// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
-// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_float(const Packet _x)
-{
-  const Packet cst_1      = pset1<Packet>(1.0f);
-  const Packet cst_half   = pset1<Packet>(0.5f);
-  const Packet cst_exp_hi = pset1<Packet>( 88.723f);
-  const Packet cst_exp_lo = pset1<Packet>(-88.723f);
-
-  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.44269504088896341f);
-  const Packet cst_cephes_exp_p0 = pset1<Packet>(1.9875691500E-4f);
-  const Packet cst_cephes_exp_p1 = pset1<Packet>(1.3981999507E-3f);
-  const Packet cst_cephes_exp_p2 = pset1<Packet>(8.3334519073E-3f);
-  const Packet cst_cephes_exp_p3 = pset1<Packet>(4.1665795894E-2f);
-  const Packet cst_cephes_exp_p4 = pset1<Packet>(1.6666665459E-1f);
-  const Packet cst_cephes_exp_p5 = pset1<Packet>(5.0000001201E-1f);
-
-  // Clamp x.
-  Packet x = pmax(pmin(_x, cst_exp_hi), cst_exp_lo);
-
-  // Express exp(x) as exp(m*ln(2) + r), start by extracting
-  // m = floor(x/ln(2) + 0.5).
-  Packet m = pfloor(pmadd(x, cst_cephes_LOG2EF, cst_half));
-
-  // Get r = x - m*ln(2). If no FMA instructions are available, m*ln(2) is
-  // subtracted out in two parts, m*C1+m*C2 = m*ln(2), to avoid accumulating
-  // truncation errors.
-  const Packet cst_cephes_exp_C1 = pset1<Packet>(-0.693359375f);
-  const Packet cst_cephes_exp_C2 = pset1<Packet>(2.12194440e-4f);
-  Packet r = pmadd(m, cst_cephes_exp_C1, x);
-  r = pmadd(m, cst_cephes_exp_C2, r);
-
-  Packet r2 = pmul(r, r);
-  Packet r3 = pmul(r2, r);
-
-  // Evaluate the polynomial approximant,improved by instruction-level parallelism.
-  Packet y, y1, y2;
-  y  = pmadd(cst_cephes_exp_p0, r, cst_cephes_exp_p1);
-  y1 = pmadd(cst_cephes_exp_p3, r, cst_cephes_exp_p4);
-  y2 = padd(r, cst_1);
-  y  = pmadd(y, r, cst_cephes_exp_p2);
-  y1 = pmadd(y1, r, cst_cephes_exp_p5);
-  y  = pmadd(y, r3, y1);
-  y  = pmadd(y, r2, y2);
-
-  // Return 2^m * exp(r).
-  // TODO: replace pldexp with faster implementation since y in [-1, 1).
-  return pmax(pldexp(y,m), _x);
-}
-
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_double(const Packet _x)
-{
-  Packet x = _x;
-
-  const Packet cst_1 = pset1<Packet>(1.0);
-  const Packet cst_2 = pset1<Packet>(2.0);
-  const Packet cst_half = pset1<Packet>(0.5);
-
-  const Packet cst_exp_hi = pset1<Packet>(709.784);
-  const Packet cst_exp_lo = pset1<Packet>(-709.784);
-
-  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.4426950408889634073599);
-  const Packet cst_cephes_exp_p0 = pset1<Packet>(1.26177193074810590878e-4);
-  const Packet cst_cephes_exp_p1 = pset1<Packet>(3.02994407707441961300e-2);
-  const Packet cst_cephes_exp_p2 = pset1<Packet>(9.99999999999999999910e-1);
-  const Packet cst_cephes_exp_q0 = pset1<Packet>(3.00198505138664455042e-6);
-  const Packet cst_cephes_exp_q1 = pset1<Packet>(2.52448340349684104192e-3);
-  const Packet cst_cephes_exp_q2 = pset1<Packet>(2.27265548208155028766e-1);
-  const Packet cst_cephes_exp_q3 = pset1<Packet>(2.00000000000000000009e0);
-  const Packet cst_cephes_exp_C1 = pset1<Packet>(0.693145751953125);
-  const Packet cst_cephes_exp_C2 = pset1<Packet>(1.42860682030941723212e-6);
-
-  Packet tmp, fx;
-
-  // clamp x
-  x = pmax(pmin(x, cst_exp_hi), cst_exp_lo);
-  // Express exp(x) as exp(g + n*log(2)).
-  fx = pmadd(cst_cephes_LOG2EF, x, cst_half);
-
-  // Get the integer modulus of log(2), i.e. the "n" described above.
-  fx = pfloor(fx);
-
-  // Get the remainder modulo log(2), i.e. the "g" described above. Subtract
-  // n*log(2) out in two steps, i.e. n*C1 + n*C2, C1+C2=log2 to get the last
-  // digits right.
-  tmp = pmul(fx, cst_cephes_exp_C1);
-  Packet z = pmul(fx, cst_cephes_exp_C2);
-  x = psub(x, tmp);
-  x = psub(x, z);
-
-  Packet x2 = pmul(x, x);
-
-  // Evaluate the numerator polynomial of the rational interpolant.
-  Packet px = cst_cephes_exp_p0;
-  px = pmadd(px, x2, cst_cephes_exp_p1);
-  px = pmadd(px, x2, cst_cephes_exp_p2);
-  px = pmul(px, x);
-
-  // Evaluate the denominator polynomial of the rational interpolant.
-  Packet qx = cst_cephes_exp_q0;
-  qx = pmadd(qx, x2, cst_cephes_exp_q1);
-  qx = pmadd(qx, x2, cst_cephes_exp_q2);
-  qx = pmadd(qx, x2, cst_cephes_exp_q3);
-
-  // I don't really get this bit, copied from the SSE2 routines, so...
-  // TODO(gonnet): Figure out what is going on here, perhaps find a better
-  // rational interpolant?
-  x = pdiv(px, psub(qx, px));
-  x = pmadd(cst_2, x, cst_1);
-
-  // Construct the result 2^n * exp(g) = e * x. The max is used to catch
-  // non-finite values in the input.
-  // TODO: replace pldexp with faster implementation since x in [-1, 1).
-  return pmax(pldexp(x,fx), _x);
-}
-
-// The following code is inspired by the following stack-overflow answer:
-//   https://stackoverflow.com/questions/30463616/payne-hanek-algorithm-implementation-in-c/30465751#30465751
-// It has been largely optimized:
-//  - By-pass calls to frexp.
-//  - Aligned loads of required 96 bits of 2/pi. This is accomplished by
-//    (1) balancing the mantissa and exponent to the required bits of 2/pi are
-//    aligned on 8-bits, and (2) replicating the storage of the bits of 2/pi.
-//  - Avoid a branch in rounding and extraction of the remaining fractional part.
-// Overall, I measured a speed up higher than x2 on x86-64.
-inline float trig_reduce_huge (float xf, int *quadrant)
-{
-  using Eigen::numext::int32_t;
-  using Eigen::numext::uint32_t;
-  using Eigen::numext::int64_t;
-  using Eigen::numext::uint64_t;
-
-  const double pio2_62 = 3.4061215800865545e-19;    // pi/2 * 2^-62
-  const uint64_t zero_dot_five = uint64_t(1) << 61; // 0.5 in 2.62-bit fixed-point foramt
-
-  // 192 bits of 2/pi for Payne-Hanek reduction
-  // Bits are introduced by packet of 8 to enable aligned reads.
-  static const uint32_t two_over_pi [] = 
-  {
-    0x00000028, 0x000028be, 0x0028be60, 0x28be60db,
-    0xbe60db93, 0x60db9391, 0xdb939105, 0x9391054a,
-    0x91054a7f, 0x054a7f09, 0x4a7f09d5, 0x7f09d5f4,
-    0x09d5f47d, 0xd5f47d4d, 0xf47d4d37, 0x7d4d3770,
-    0x4d377036, 0x377036d8, 0x7036d8a5, 0x36d8a566,
-    0xd8a5664f, 0xa5664f10, 0x664f10e4, 0x4f10e410,
-    0x10e41000, 0xe4100000
-  };
-  
-  uint32_t xi = numext::bit_cast<uint32_t>(xf);
-  // Below, -118 = -126 + 8.
-  //   -126 is to get the exponent,
-  //   +8 is to enable alignment of 2/pi's bits on 8 bits.
-  // This is possible because the fractional part of x as only 24 meaningful bits.
-  uint32_t e = (xi >> 23) - 118;
-  // Extract the mantissa and shift it to align it wrt the exponent
-  xi = ((xi & 0x007fffffu)| 0x00800000u) << (e & 0x7);
-
-  uint32_t i = e >> 3;
-  uint32_t twoopi_1  = two_over_pi[i-1];
-  uint32_t twoopi_2  = two_over_pi[i+3];
-  uint32_t twoopi_3  = two_over_pi[i+7];
-
-  // Compute x * 2/pi in 2.62-bit fixed-point format.
-  uint64_t p;
-  p = uint64_t(xi) * twoopi_3;
-  p = uint64_t(xi) * twoopi_2 + (p >> 32);
-  p = (uint64_t(xi * twoopi_1) << 32) + p;
-
-  // Round to nearest: add 0.5 and extract integral part.
-  uint64_t q = (p + zero_dot_five) >> 62;
-  *quadrant = int(q);
-  // Now it remains to compute "r = x - q*pi/2" with high accuracy,
-  // since we have p=x/(pi/2) with high accuracy, we can more efficiently compute r as:
-  //   r = (p-q)*pi/2,
-  // where the product can be be carried out with sufficient accuracy using double precision.
-  p -= q<<62;
-  return float(double(int64_t(p)) * pio2_62);
-}
-
-template<bool ComputeSine,typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-#if EIGEN_GNUC_AT_LEAST(4,4) && EIGEN_COMP_GNUC_STRICT
-__attribute__((optimize("-fno-unsafe-math-optimizations")))
-#endif
-Packet psincos_float(const Packet& _x)
-{
-  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
-
-  const Packet  cst_2oPI            = pset1<Packet>(0.636619746685028076171875f); // 2/PI
-  const Packet  cst_rounding_magic  = pset1<Packet>(12582912); // 2^23 for rounding
-  const PacketI csti_1              = pset1<PacketI>(1);
-  const Packet  cst_sign_mask       = pset1frombits<Packet>(0x80000000u);
-
-  Packet x = pabs(_x);
-
-  // Scale x by 2/Pi to find x's octant.
-  Packet y = pmul(x, cst_2oPI);
-
-  // Rounding trick:
-  Packet y_round = padd(y, cst_rounding_magic);
-  EIGEN_OPTIMIZATION_BARRIER(y_round)
-  PacketI y_int = preinterpret<PacketI>(y_round); // last 23 digits represent integer (if abs(x)<2^24)
-  y = psub(y_round, cst_rounding_magic); // nearest integer to x*4/pi
-
-  // Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4
-  // using "Extended precision modular arithmetic"
-  #if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD)
-  // This version requires true FMA for high accuracy
-  // It provides a max error of 1ULP up to (with absolute_error < 5.9605e-08):
-  const float huge_th = ComputeSine ? 117435.992f : 71476.0625f;
-  x = pmadd(y, pset1<Packet>(-1.57079601287841796875f), x);
-  x = pmadd(y, pset1<Packet>(-3.1391647326017846353352069854736328125e-07f), x);
-  x = pmadd(y, pset1<Packet>(-5.390302529957764765544681040410068817436695098876953125e-15f), x);
-  #else
-  // Without true FMA, the previous set of coefficients maintain 1ULP accuracy
-  // up to x<15.7 (for sin), but accuracy is immediately lost for x>15.7.
-  // We thus use one more iteration to maintain 2ULPs up to reasonably large inputs.
-
-  // The following set of coefficients maintain 1ULP up to 9.43 and 14.16 for sin and cos respectively.
-  // and 2 ULP up to:
-  const float huge_th = ComputeSine ? 25966.f : 18838.f;
-  x = pmadd(y, pset1<Packet>(-1.5703125), x); // = 0xbfc90000
-  EIGEN_OPTIMIZATION_BARRIER(x)
-  x = pmadd(y, pset1<Packet>(-0.000483989715576171875), x); // = 0xb9fdc000
-  EIGEN_OPTIMIZATION_BARRIER(x)
-  x = pmadd(y, pset1<Packet>(1.62865035235881805419921875e-07), x); // = 0x342ee000
-  x = pmadd(y, pset1<Packet>(5.5644315544167710640977020375430583953857421875e-11), x); // = 0x2e74b9ee
-
-  // For the record, the following set of coefficients maintain 2ULP up
-  // to a slightly larger range:
-  // const float huge_th = ComputeSine ? 51981.f : 39086.125f;
-  // but it slightly fails to maintain 1ULP for two values of sin below pi.
-  // x = pmadd(y, pset1<Packet>(-3.140625/2.), x);
-  // x = pmadd(y, pset1<Packet>(-0.00048351287841796875), x);
-  // x = pmadd(y, pset1<Packet>(-3.13855707645416259765625e-07), x);
-  // x = pmadd(y, pset1<Packet>(-6.0771006282767103812147979624569416046142578125e-11), x);
-
-  // For the record, with only 3 iterations it is possible to maintain
-  // 1 ULP up to 3PI (maybe more) and 2ULP up to 255.
-  // The coefficients are: 0xbfc90f80, 0xb7354480, 0x2e74b9ee
-  #endif
-
-  if(predux_any(pcmp_le(pset1<Packet>(huge_th),pabs(_x))))
-  {
-    const int PacketSize = unpacket_traits<Packet>::size;
-    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float vals[PacketSize];
-    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float x_cpy[PacketSize];
-    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) int y_int2[PacketSize];
-    pstoreu(vals, pabs(_x));
-    pstoreu(x_cpy, x);
-    pstoreu(y_int2, y_int);
-    for(int k=0; k<PacketSize;++k)
-    {
-      float val = vals[k];
-      if(val>=huge_th && (numext::isfinite)(val))
-        x_cpy[k] = trig_reduce_huge(val,&y_int2[k]);
-    }
-    x = ploadu<Packet>(x_cpy);
-    y_int = ploadu<PacketI>(y_int2);
-  }
-
-  // Compute the sign to apply to the polynomial.
-  // sin: sign = second_bit(y_int) xor signbit(_x)
-  // cos: sign = second_bit(y_int+1)
-  Packet sign_bit = ComputeSine ? pxor(_x, preinterpret<Packet>(plogical_shift_left<30>(y_int)))
-                                : preinterpret<Packet>(plogical_shift_left<30>(padd(y_int,csti_1)));
-  sign_bit = pand(sign_bit, cst_sign_mask); // clear all but left most bit
-
-  // Get the polynomial selection mask from the second bit of y_int
-  // We'll calculate both (sin and cos) polynomials and then select from the two.
-  Packet poly_mask = preinterpret<Packet>(pcmp_eq(pand(y_int, csti_1), pzero(y_int)));
-
-  Packet x2 = pmul(x,x);
-
-  // Evaluate the cos(x) polynomial. (-Pi/4 <= x <= Pi/4)
-  Packet y1 =        pset1<Packet>(2.4372266125283204019069671630859375e-05f);
-  y1 = pmadd(y1, x2, pset1<Packet>(-0.00138865201734006404876708984375f     ));
-  y1 = pmadd(y1, x2, pset1<Packet>(0.041666619479656219482421875f           ));
-  y1 = pmadd(y1, x2, pset1<Packet>(-0.5f));
-  y1 = pmadd(y1, x2, pset1<Packet>(1.f));
-
-  // Evaluate the sin(x) polynomial. (Pi/4 <= x <= Pi/4)
-  // octave/matlab code to compute those coefficients:
-  //    x = (0:0.0001:pi/4)';
-  //    A = [x.^3 x.^5 x.^7];
-  //    w = ((1.-(x/(pi/4)).^2).^5)*2000+1;         # weights trading relative accuracy
-  //    c = (A'*diag(w)*A)\(A'*diag(w)*(sin(x)-x)); # weighted LS, linear coeff forced to 1
-  //    printf('%.64f\n %.64f\n%.64f\n', c(3), c(2), c(1))
-  //
-  Packet y2 =        pset1<Packet>(-0.0001959234114083702898469196984621021329076029360294342041015625f);
-  y2 = pmadd(y2, x2, pset1<Packet>( 0.0083326873655616851693794799871284340042620897293090820312500000f));
-  y2 = pmadd(y2, x2, pset1<Packet>(-0.1666666203982298255503735617821803316473960876464843750000000000f));
-  y2 = pmul(y2, x2);
-  y2 = pmadd(y2, x, x);
-
-  // Select the correct result from the two polynomials.
-  y = ComputeSine ? pselect(poly_mask,y2,y1)
-                  : pselect(poly_mask,y1,y2);
-
-  // Update the sign and filter huge inputs
-  return pxor(y, sign_bit);
-}
-
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psin_float(const Packet& x)
-{
-  return psincos_float<true>(x);
-}
-
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pcos_float(const Packet& x)
-{
-  return psincos_float<false>(x);
-}
-
-
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psqrt_complex(const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  typedef typename Scalar::value_type RealScalar;
-  typedef typename unpacket_traits<Packet>::as_real RealPacket;
-
-  // Computes the principal sqrt of the complex numbers in the input.
-  //
-  // For example, for packets containing 2 complex numbers stored in interleaved format
-  //    a = [a0, a1] = [x0, y0, x1, y1],
-  // where x0 = real(a0), y0 = imag(a0) etc., this function returns
-  //    b = [b0, b1] = [u0, v0, u1, v1],
-  // such that b0^2 = a0, b1^2 = a1.
-  //
-  // To derive the formula for the complex square roots, let's consider the equation for
-  // a single complex square root of the number x + i*y. We want to find real numbers
-  // u and v such that
-  //    (u + i*v)^2 = x + i*y  <=>
-  //    u^2 - v^2 + i*2*u*v = x + i*v.
-  // By equating the real and imaginary parts we get:
-  //    u^2 - v^2 = x
-  //    2*u*v = y.
-  //
-  // For x >= 0, this has the numerically stable solution
-  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
-  //    v = 0.5 * (y / u)
-  // and for x < 0,
-  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
-  //    u = 0.5 * (y / v)
-  //
-  //  To avoid unnecessary over- and underflow, we compute sqrt(x^2 + y^2) as
-  //     l = max(|x|, |y|) * sqrt(1 + (min(|x|, |y|) / max(|x|, |y|))^2) ,
-
-  // In the following, without lack of generality, we have annotated the code, assuming
-  // that the input is a packet of 2 complex numbers.
-  //
-  // Step 1. Compute l = [l0, l0, l1, l1], where
-  //    l0 = sqrt(x0^2 + y0^2),  l1 = sqrt(x1^2 + y1^2)
-  // To avoid over- and underflow, we use the stable formula for each hypotenuse
-  //    l0 = (min0 == 0 ? max0 : max0 * sqrt(1 + (min0/max0)**2)),
-  // where max0 = max(|x0|, |y0|), min0 = min(|x0|, |y0|), and similarly for l1.
-
-  RealPacket a_abs = pabs(a.v);           // [|x0|, |y0|, |x1|, |y1|]
-  RealPacket a_abs_flip = pcplxflip(Packet(a_abs)).v; // [|y0|, |x0|, |y1|, |x1|]
-  RealPacket a_max = pmax(a_abs, a_abs_flip);
-  RealPacket a_min = pmin(a_abs, a_abs_flip);
-  RealPacket a_min_zero_mask = pcmp_eq(a_min, pzero(a_min));
-  RealPacket a_max_zero_mask = pcmp_eq(a_max, pzero(a_max));
-  RealPacket r = pdiv(a_min, a_max);
-  const RealPacket cst_one  = pset1<RealPacket>(RealScalar(1));
-  RealPacket l = pmul(a_max, psqrt(padd(cst_one, pmul(r, r))));  // [l0, l0, l1, l1]
-  // Set l to a_max if a_min is zero.
-  l = pselect(a_min_zero_mask, a_max, l);
-
-  // Step 2. Compute [rho0, *, rho1, *], where
-  // rho0 = sqrt(0.5 * (l0 + |x0|)), rho1 =  sqrt(0.5 * (l1 + |x1|))
-  // We don't care about the imaginary parts computed here. They will be overwritten later.
-  const RealPacket cst_half = pset1<RealPacket>(RealScalar(0.5));
-  Packet rho;
-  rho.v = psqrt(pmul(cst_half, padd(a_abs, l)));
-
-  // Step 3. Compute [rho0, eta0, rho1, eta1], where
-  // eta0 = (y0 / l0) / 2, and eta1 = (y1 / l1) / 2.
-  // set eta = 0 of input is 0 + i0.
-  RealPacket eta = pandnot(pmul(cst_half, pdiv(a.v, pcplxflip(rho).v)), a_max_zero_mask);
-  RealPacket real_mask = peven_mask(a.v);
-  Packet positive_real_result;
-  // Compute result for inputs with positive real part.
-  positive_real_result.v = pselect(real_mask, rho.v, eta);
-
-  // Step 4. Compute solution for inputs with negative real part:
-  //         [|eta0|, sign(y0)*rho0, |eta1|, sign(y1)*rho1]
-  const RealScalar neg_zero = RealScalar(numext::bit_cast<float>(0x80000000u));
-  const RealPacket cst_imag_sign_mask = pset1<Packet>(Scalar(RealScalar(0.0), neg_zero)).v;
-  RealPacket imag_signs = pand(a.v, cst_imag_sign_mask);
-  Packet negative_real_result;
-  // Notice that rho is positive, so taking it's absolute value is a noop.
-  negative_real_result.v = por(pabs(pcplxflip(positive_real_result).v), imag_signs);
-
-  // Step 5. Select solution branch based on the sign of the real parts.
-  Packet negative_real_mask;
-  negative_real_mask.v = pcmp_lt(pand(real_mask, a.v), pzero(a.v));
-  negative_real_mask.v = por(negative_real_mask.v, pcplxflip(negative_real_mask).v);
-  Packet result = pselect(negative_real_mask, negative_real_result, positive_real_result);
-
-  // Step 6. Handle special cases for infinities:
-  // * If z is (x,+∞), the result is (+∞,+∞) even if x is NaN
-  // * If z is (x,-∞), the result is (+∞,-∞) even if x is NaN
-  // * If z is (-∞,y), the result is (0*|y|,+∞) for finite or NaN y
-  // * If z is (+∞,y), the result is (+∞,0*|y|) for finite or NaN y
-  const RealPacket cst_pos_inf = pset1<RealPacket>(NumTraits<RealScalar>::infinity());
-  Packet is_inf;
-  is_inf.v = pcmp_eq(a_abs, cst_pos_inf);
-  Packet is_real_inf;
-  is_real_inf.v = pand(is_inf.v, real_mask);
-  is_real_inf = por(is_real_inf, pcplxflip(is_real_inf));
-  // prepare packet of (+∞,0*|y|) or (0*|y|,+∞), depending on the sign of the infinite real part.
-  Packet real_inf_result;
-  real_inf_result.v = pmul(a_abs, pset1<Packet>(Scalar(RealScalar(1.0), RealScalar(0.0))).v);
-  real_inf_result.v = pselect(negative_real_mask.v, pcplxflip(real_inf_result).v, real_inf_result.v);
-  // prepare packet of (+∞,+∞) or (+∞,-∞), depending on the sign of the infinite imaginary part.
-  Packet is_imag_inf;
-  is_imag_inf.v = pandnot(is_inf.v, real_mask);
-  is_imag_inf = por(is_imag_inf, pcplxflip(is_imag_inf));
-  Packet imag_inf_result;
-  imag_inf_result.v = por(pand(cst_pos_inf, real_mask), pandnot(a.v, real_mask));
-
-  return  pselect(is_imag_inf, imag_inf_result,
-                  pselect(is_real_inf, real_inf_result,result));
-}
-
-// TODO(rmlarsen): The following set of utilities for double word arithmetic
-// should perhaps be refactored as a separate file, since it would be generally
-// useful for special function implementation etc. Writing the algorithms in
-// terms if a double word type would also make the code more readable.
-
-// This function splits x into the nearest integer n and fractional part r,
-// such that x = n + r holds exactly.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void absolute_split(const Packet& x, Packet& n, Packet& r) {
-  n = pround(x);
-  r = psub(x, n);
-}
-
-// This function computes the sum {s, r}, such that x + y = s_hi + s_lo
-// holds exactly, and s_hi = fl(x+y), if |x| >= |y|.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void fast_twosum(const Packet& x, const Packet& y, Packet& s_hi, Packet& s_lo) {
-  s_hi = padd(x, y);
-  const Packet t = psub(s_hi, x);
-  s_lo = psub(y, t);
-}
-
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-// This function implements the extended precision product of
-// a pair of floating point numbers. Given {x, y}, it computes the pair
-// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
-// p_hi = fl(x * y).
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x, const Packet& y,
-             Packet& p_hi, Packet& p_lo) {
-  p_hi = pmul(x, y);
-  p_lo = pmadd(x, y, pnegate(p_hi));
-}
-
-#else
-
-// This function implements the Veltkamp splitting. Given a floating point
-// number x it returns the pair {x_hi, x_lo} such that x_hi + x_lo = x holds
-// exactly and that half of the significant of x fits in x_hi.
-// This is Algorithm 3 from Jean-Michel Muller, "Elementary Functions",
-// 3rd edition, Birkh\"auser, 2016.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void veltkamp_splitting(const Packet& x, Packet& x_hi, Packet& x_lo) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  EIGEN_CONSTEXPR int shift = (NumTraits<Scalar>::digits() + 1) / 2;
-  const Scalar shift_scale = Scalar(uint64_t(1) << shift);  // Scalar constructor not necessarily constexpr.
-  const Packet gamma = pmul(pset1<Packet>(shift_scale + Scalar(1)), x);
-  Packet rho = psub(x, gamma);
-  x_hi = padd(rho, gamma);
-  x_lo = psub(x, x_hi);
-}
-
-// This function implements Dekker's algorithm for products x * y.
-// Given floating point numbers {x, y} computes the pair
-// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
-// p_hi = fl(x * y).
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x, const Packet& y,
-             Packet& p_hi, Packet& p_lo) {
-  Packet x_hi, x_lo, y_hi, y_lo;
-  veltkamp_splitting(x, x_hi, x_lo);
-  veltkamp_splitting(y, y_hi, y_lo);
-
-  p_hi = pmul(x, y);
-  p_lo = pmadd(x_hi, y_hi, pnegate(p_hi));
-  p_lo = pmadd(x_hi, y_lo, p_lo);
-  p_lo = pmadd(x_lo, y_hi, p_lo);
-  p_lo = pmadd(x_lo, y_lo, p_lo);
-}
-
-#endif  // EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-
-
-// This function implements Dekker's algorithm for the addition
-// of two double word numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
-// It returns the result as a pair {s_hi, s_lo} such that
-// x_hi + x_lo + y_hi + y_lo = s_hi + s_lo holds exactly.
-// This is Algorithm 5 from Jean-Michel Muller, "Elementary Functions",
-// 3rd edition, Birkh\"auser, 2016.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-  void twosum(const Packet& x_hi, const Packet& x_lo,
-              const Packet& y_hi, const Packet& y_lo,
-              Packet& s_hi, Packet& s_lo) {
-  const Packet x_greater_mask = pcmp_lt(pabs(y_hi), pabs(x_hi));
-  Packet r_hi_1, r_lo_1;
-  fast_twosum(x_hi, y_hi,r_hi_1, r_lo_1);
-  Packet r_hi_2, r_lo_2;
-  fast_twosum(y_hi, x_hi,r_hi_2, r_lo_2);
-  const Packet r_hi = pselect(x_greater_mask, r_hi_1, r_hi_2);
-
-  const Packet s1 = padd(padd(y_lo, r_lo_1), x_lo);
-  const Packet s2 = padd(padd(x_lo, r_lo_2), y_lo);
-  const Packet s = pselect(x_greater_mask, s1, s2);
-
-  fast_twosum(r_hi, s, s_hi, s_lo);
-}
-
-// This is a version of twosum for double word numbers,
-// which assumes that |x_hi| >= |y_hi|.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-  void fast_twosum(const Packet& x_hi, const Packet& x_lo,
-              const Packet& y_hi, const Packet& y_lo,
-              Packet& s_hi, Packet& s_lo) {
-  Packet r_hi, r_lo;
-  fast_twosum(x_hi, y_hi, r_hi, r_lo);
-  const Packet s = padd(padd(y_lo, r_lo), x_lo);
-  fast_twosum(r_hi, s, s_hi, s_lo);
-}
-
-// This is a version of twosum for adding a floating point number x to
-// double word number {y_hi, y_lo} number, with the assumption
-// that |x| >= |y_hi|.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void fast_twosum(const Packet& x,
-                 const Packet& y_hi, const Packet& y_lo,
-                 Packet& s_hi, Packet& s_lo) {
-  Packet r_hi, r_lo;
-  fast_twosum(x, y_hi, r_hi, r_lo);
-  const Packet s = padd(y_lo, r_lo);
-  fast_twosum(r_hi, s, s_hi, s_lo);
-}
-
-// This function implements the multiplication of a double word
-// number represented by {x_hi, x_lo} by a floating point number y.
-// It returns the result as a pair {p_hi, p_lo} such that
-// (x_hi + x_lo) * y = p_hi + p_lo hold with a relative error
-// of less than 2*2^{-2p}, where p is the number of significand bit
-// in the floating point type.
-// This is Algorithm 7 from Jean-Michel Muller, "Elementary Functions",
-// 3rd edition, Birkh\"auser, 2016.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x_hi, const Packet& x_lo, const Packet& y,
-             Packet& p_hi, Packet& p_lo) {
-  Packet c_hi, c_lo1;
-  twoprod(x_hi, y, c_hi, c_lo1);
-  const Packet c_lo2 = pmul(x_lo, y);
-  Packet t_hi, t_lo1;
-  fast_twosum(c_hi, c_lo2, t_hi, t_lo1);
-  const Packet t_lo2 = padd(t_lo1, c_lo1);
-  fast_twosum(t_hi, t_lo2, p_hi, p_lo);
-}
-
-// This function implements the multiplication of two double word
-// numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
-// It returns the result as a pair {p_hi, p_lo} such that
-// (x_hi + x_lo) * (y_hi + y_lo) = p_hi + p_lo holds with a relative error
-// of less than 2*2^{-2p}, where p is the number of significand bit
-// in the floating point type.
-template<typename Packet>
-EIGEN_STRONG_INLINE
-void twoprod(const Packet& x_hi, const Packet& x_lo,
-             const Packet& y_hi, const Packet& y_lo,
-             Packet& p_hi, Packet& p_lo) {
-  Packet p_hi_hi, p_hi_lo;
-  twoprod(x_hi, x_lo, y_hi, p_hi_hi, p_hi_lo);
-  Packet p_lo_hi, p_lo_lo;
-  twoprod(x_hi, x_lo, y_lo, p_lo_hi, p_lo_lo);
-  fast_twosum(p_hi_hi, p_hi_lo, p_lo_hi, p_lo_lo, p_hi, p_lo);
-}
-
-// This function computes the reciprocal of a floating point number
-// with extra precision and returns the result as a double word.
-template <typename Packet>
-void doubleword_reciprocal(const Packet& x, Packet& recip_hi, Packet& recip_lo) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  // 1. Approximate the reciprocal as the reciprocal of the high order element.
-  Packet approx_recip = prsqrt(x);
-  approx_recip = pmul(approx_recip, approx_recip);
-
-  // 2. Run one step of Newton-Raphson iteration in double word arithmetic
-  // to get the bottom half. The NR iteration for reciprocal of 'a' is
-  //    x_{i+1} = x_i * (2 - a * x_i)
-
-  // -a*x_i
-  Packet t1_hi, t1_lo;
-  twoprod(pnegate(x), approx_recip, t1_hi, t1_lo);
-  // 2 - a*x_i
-  Packet t2_hi, t2_lo;
-  fast_twosum(pset1<Packet>(Scalar(2)), t1_hi, t2_hi, t2_lo);
-  Packet t3_hi, t3_lo;
-  fast_twosum(t2_hi, padd(t2_lo, t1_lo), t3_hi, t3_lo);
-  // x_i * (2 - a * x_i)
-  twoprod(t3_hi, t3_lo, approx_recip, recip_hi, recip_lo);
-}
-
-
-// This function computes log2(x) and returns the result as a double word.
-template <typename Scalar>
-struct accurate_log2 {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
-    log2_x_hi = plog2(x);
-    log2_x_lo = pzero(x);
-  }
-};
-
-// This specialization uses a more accurate algorithm to compute log2(x) for
-// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~6.42e-10.
-// This additional accuracy is needed to counter the error-magnification
-// inherent in multiplying by a potentially large exponent in pow(x,y).
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-template <>
-struct accurate_log2<float> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  void operator()(const Packet& z, Packet& log2_x_hi, Packet& log2_x_lo) {
-    // The function log(1+x)/x is approximated in the interval
-    // [1/sqrt(2)-1;sqrt(2)-1] by a degree 10 polynomial of the form
-    //  Q(x) = (C0 + x * (C1 + x * (C2 + x * (C3 + x * P(x))))),
-    // where the degree 6 polynomial P(x) is evaluated in single precision,
-    // while the remaining 4 terms of Q(x), as well as the final multiplication by x
-    // to reconstruct log(1+x) are evaluated in extra precision using
-    // double word arithmetic. C0 through C3 are extra precise constants
-    // stored as double words.
-    //
-    // The polynomial coefficients were calculated using Sollya commands:
-    // > n = 10;
-    // > f = log2(1+x)/x;
-    // > interval = [sqrt(0.5)-1;sqrt(2)-1];
-    // > p = fpminimax(f,n,[|double,double,double,double,single...|],interval,relative,floating);
-    
-    const Packet p6 = pset1<Packet>( 9.703654795885e-2f);
-    const Packet p5 = pset1<Packet>(-0.1690667718648f);
-    const Packet p4 = pset1<Packet>( 0.1720575392246f);
-    const Packet p3 = pset1<Packet>(-0.1789081543684f);
-    const Packet p2 = pset1<Packet>( 0.2050433009862f);
-    const Packet p1 = pset1<Packet>(-0.2404672354459f);
-    const Packet p0 = pset1<Packet>( 0.2885761857032f);
-
-    const Packet C3_hi = pset1<Packet>(-0.360674142838f);
-    const Packet C3_lo = pset1<Packet>(-6.13283912543e-09f);
-    const Packet C2_hi = pset1<Packet>(0.480897903442f);
-    const Packet C2_lo = pset1<Packet>(-1.44861207474e-08f);
-    const Packet C1_hi = pset1<Packet>(-0.721347510815f);
-    const Packet C1_lo = pset1<Packet>(-4.84483164698e-09f);
-    const Packet C0_hi = pset1<Packet>(1.44269502163f);
-    const Packet C0_lo = pset1<Packet>(2.01711713999e-08f);
-    const Packet one = pset1<Packet>(1.0f);
-
-    const Packet x = psub(z, one);
-    // Evaluate P(x) in working precision.
-    // We evaluate it in multiple parts to improve instruction level
-    // parallelism.
-    Packet x2 = pmul(x,x);
-    Packet p_even = pmadd(p6, x2, p4);
-    p_even = pmadd(p_even, x2, p2);
-    p_even = pmadd(p_even, x2, p0);
-    Packet p_odd = pmadd(p5, x2, p3);
-    p_odd = pmadd(p_odd, x2, p1);
-    Packet p = pmadd(p_odd, x, p_even);
-
-    // Now evaluate the low-order tems of Q(x) in double word precision.
-    // In the following, due to the alternating signs and the fact that
-    // |x| < sqrt(2)-1, we can assume that |C*_hi| >= q_i, and use
-    // fast_twosum instead of the slower twosum.
-    Packet q_hi, q_lo;
-    Packet t_hi, t_lo;
-    // C3 + x * p(x)
-    twoprod(p, x, t_hi, t_lo);
-    fast_twosum(C3_hi, C3_lo, t_hi, t_lo, q_hi, q_lo);
-    // C2 + x * p(x)
-    twoprod(q_hi, q_lo, x, t_hi, t_lo);
-    fast_twosum(C2_hi, C2_lo, t_hi, t_lo, q_hi, q_lo);
-    // C1 + x * p(x)
-    twoprod(q_hi, q_lo, x, t_hi, t_lo);
-    fast_twosum(C1_hi, C1_lo, t_hi, t_lo, q_hi, q_lo);
-    // C0 + x * p(x)
-    twoprod(q_hi, q_lo, x, t_hi, t_lo);
-    fast_twosum(C0_hi, C0_lo, t_hi, t_lo, q_hi, q_lo);
-
-    // log(z) ~= x * Q(x)
-    twoprod(q_hi, q_lo, x, log2_x_hi, log2_x_lo);
-  }
-};
-
-// This specialization uses a more accurate algorithm to compute log2(x) for
-// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~1.27e-18.
-// This additional accuracy is needed to counter the error-magnification
-// inherent in multiplying by a potentially large exponent in pow(x,y).
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-
-template <>
-struct accurate_log2<double> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
-    // We use a transformation of variables:
-    //    r = c * (x-1) / (x+1),
-    // such that
-    //    log2(x) = log2((1 + r/c) / (1 - r/c)) = f(r).
-    // The function f(r) can be approximated well using an odd polynomial
-    // of the form
-    //   P(r) = ((Q(r^2) * r^2 + C) * r^2 + 1) * r,
-    // For the implementation of log2<double> here, Q is of degree 6 with
-    // coefficient represented in working precision (double), while C is a
-    // constant represented in extra precision as a double word to achieve
-    // full accuracy.
-    //
-    // The polynomial coefficients were computed by the Sollya script:
-    //
-    // c = 2 / log(2);
-    // trans = c * (x-1)/(x+1);
-    // itrans = (1+x/c)/(1-x/c);
-    // interval=[trans(sqrt(0.5)); trans(sqrt(2))];
-    // print(interval);
-    // f = log2(itrans(x));
-    // p=fpminimax(f,[|1,3,5,7,9,11,13,15,17|],[|1,DD,double...|],interval,relative,floating);
-    const Packet q12 = pset1<Packet>(2.87074255468000586e-9);
-    const Packet q10 = pset1<Packet>(2.38957980901884082e-8);
-    const Packet q8 = pset1<Packet>(2.31032094540014656e-7);
-    const Packet q6 = pset1<Packet>(2.27279857398537278e-6);
-    const Packet q4 = pset1<Packet>(2.31271023278625638e-5);
-    const Packet q2 = pset1<Packet>(2.47556738444535513e-4);
-    const Packet q0 = pset1<Packet>(2.88543873228900172e-3);
-    const Packet C_hi = pset1<Packet>(0.0400377511598501157);
-    const Packet C_lo = pset1<Packet>(-4.77726582251425391e-19);
-    const Packet one = pset1<Packet>(1.0);
-
-    const Packet cst_2_log2e_hi = pset1<Packet>(2.88539008177792677);
-    const Packet cst_2_log2e_lo = pset1<Packet>(4.07660016854549667e-17);
-    // c * (x - 1)
-    Packet num_hi, num_lo;
-    twoprod(cst_2_log2e_hi, cst_2_log2e_lo, psub(x, one), num_hi, num_lo);
-    // TODO(rmlarsen): Investigate if using the division algorithm by
-    // Muller et al. is faster/more accurate.
-    // 1 / (x + 1)
-    Packet denom_hi, denom_lo;
-    doubleword_reciprocal(padd(x, one), denom_hi, denom_lo);
-    // r =  c * (x-1) / (x+1),
-    Packet r_hi, r_lo;
-    twoprod(num_hi, num_lo, denom_hi, denom_lo, r_hi, r_lo);
-    // r2 = r * r
-    Packet r2_hi, r2_lo;
-    twoprod(r_hi, r_lo, r_hi, r_lo, r2_hi, r2_lo);
-    // r4 = r2 * r2
-    Packet r4_hi, r4_lo;
-    twoprod(r2_hi, r2_lo, r2_hi, r2_lo, r4_hi, r4_lo);
-
-    // Evaluate Q(r^2) in working precision. We evaluate it in two parts
-    // (even and odd in r^2) to improve instruction level parallelism.
-    Packet q_even = pmadd(q12, r4_hi, q8);
-    Packet q_odd = pmadd(q10, r4_hi, q6);
-    q_even = pmadd(q_even, r4_hi, q4);
-    q_odd = pmadd(q_odd, r4_hi, q2);
-    q_even = pmadd(q_even, r4_hi, q0);
-    Packet q = pmadd(q_odd, r2_hi, q_even);
-
-    // Now evaluate the low order terms of P(x) in double word precision.
-    // In the following, due to the increasing magnitude of the coefficients
-    // and r being constrained to [-0.5, 0.5] we can use fast_twosum instead
-    // of the slower twosum.
-    // Q(r^2) * r^2
-    Packet p_hi, p_lo;
-    twoprod(r2_hi, r2_lo, q, p_hi, p_lo);
-    // Q(r^2) * r^2 + C
-    Packet p1_hi, p1_lo;
-    fast_twosum(C_hi, C_lo, p_hi, p_lo, p1_hi, p1_lo);
-    // (Q(r^2) * r^2 + C) * r^2
-    Packet p2_hi, p2_lo;
-    twoprod(r2_hi, r2_lo, p1_hi, p1_lo, p2_hi, p2_lo);
-    // ((Q(r^2) * r^2 + C) * r^2 + 1)
-    Packet p3_hi, p3_lo;
-    fast_twosum(one, p2_hi, p2_lo, p3_hi, p3_lo);
-
-    // log(z) ~= ((Q(r^2) * r^2 + C) * r^2 + 1) * r
-    twoprod(p3_hi, p3_lo, r_hi, r_lo, log2_x_hi, log2_x_lo);
-  }
-};
-
-// This function computes exp2(x) (i.e. 2**x).
-template <typename Scalar>
-struct fast_accurate_exp2 {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  Packet operator()(const Packet& x) {
-    // TODO(rmlarsen): Add a pexp2 packetop.
-    return pexp(pmul(pset1<Packet>(Scalar(EIGEN_LN2)), x));
-  }
-};
-
-// This specialization uses a faster algorithm to compute exp2(x) for floats
-// in [-0.5;0.5] with a relative accuracy of 1 ulp.
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-template <>
-struct fast_accurate_exp2<float> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  Packet operator()(const Packet& x) {
-    // This function approximates exp2(x) by a degree 6 polynomial of the form
-    // Q(x) = 1 + x * (C + x * P(x)), where the degree 4 polynomial P(x) is evaluated in
-    // single precision, and the remaining steps are evaluated with extra precision using
-    // double word arithmetic. C is an extra precise constant stored as a double word.
-    //
-    // The polynomial coefficients were calculated using Sollya commands:
-    // > n = 6;
-    // > f = 2^x;
-    // > interval = [-0.5;0.5];
-    // > p = fpminimax(f,n,[|1,double,single...|],interval,relative,floating);
-
-    const Packet p4 = pset1<Packet>(1.539513905e-4f);
-    const Packet p3 = pset1<Packet>(1.340007293e-3f);
-    const Packet p2 = pset1<Packet>(9.618283249e-3f);
-    const Packet p1 = pset1<Packet>(5.550328270e-2f);
-    const Packet p0 = pset1<Packet>(0.2402264923f);
-
-    const Packet C_hi = pset1<Packet>(0.6931471825f);
-    const Packet C_lo = pset1<Packet>(2.36836577e-08f);
-    const Packet one = pset1<Packet>(1.0f);
-
-    // Evaluate P(x) in working precision.
-    // We evaluate even and odd parts of the polynomial separately
-    // to gain some instruction level parallelism.
-    Packet x2 = pmul(x,x);
-    Packet p_even = pmadd(p4, x2, p2);
-    Packet p_odd = pmadd(p3, x2, p1);
-    p_even = pmadd(p_even, x2, p0);
-    Packet p = pmadd(p_odd, x, p_even);
-
-    // Evaluate the remaining terms of Q(x) with extra precision using
-    // double word arithmetic.
-    Packet p_hi, p_lo;
-    // x * p(x)
-    twoprod(p, x, p_hi, p_lo);
-    // C + x * p(x)
-    Packet q1_hi, q1_lo;
-    twosum(p_hi, p_lo, C_hi, C_lo, q1_hi, q1_lo);
-    // x * (C + x * p(x))
-    Packet q2_hi, q2_lo;
-    twoprod(q1_hi, q1_lo, x, q2_hi, q2_lo);
-    // 1 + x * (C + x * p(x))
-    Packet q3_hi, q3_lo;
-    // Since |q2_hi| <= sqrt(2)-1 < 1, we can use fast_twosum
-    // for adding it to unity here.
-    fast_twosum(one, q2_hi, q3_hi, q3_lo);
-    return padd(q3_hi, padd(q2_lo, q3_lo));
-  }
-};
-
-// in [-0.5;0.5] with a relative accuracy of 1 ulp.
-// The minimax polynomial used was calculated using the Sollya tool.
-// See sollya.org.
-template <>
-struct fast_accurate_exp2<double> {
-  template <typename Packet>
-  EIGEN_STRONG_INLINE
-  Packet operator()(const Packet& x) {
-    // This function approximates exp2(x) by a degree 10 polynomial of the form
-    // Q(x) = 1 + x * (C + x * P(x)), where the degree 8 polynomial P(x) is evaluated in
-    // single precision, and the remaining steps are evaluated with extra precision using
-    // double word arithmetic. C is an extra precise constant stored as a double word.
-    //
-    // The polynomial coefficients were calculated using Sollya commands:
-    // > n = 11;
-    // > f = 2^x;
-    // > interval = [-0.5;0.5];
-    // > p = fpminimax(f,n,[|1,DD,double...|],interval,relative,floating);
-
-    const Packet p9 = pset1<Packet>(4.431642109085495276e-10);
-    const Packet p8 = pset1<Packet>(7.073829923303358410e-9);
-    const Packet p7 = pset1<Packet>(1.017822306737031311e-7);
-    const Packet p6 = pset1<Packet>(1.321543498017646657e-6);
-    const Packet p5 = pset1<Packet>(1.525273342728892877e-5);
-    const Packet p4 = pset1<Packet>(1.540353045780084423e-4);
-    const Packet p3 = pset1<Packet>(1.333355814685869807e-3);
-    const Packet p2 = pset1<Packet>(9.618129107593478832e-3);
-    const Packet p1 = pset1<Packet>(5.550410866481961247e-2);
-    const Packet p0 = pset1<Packet>(0.240226506959101332);
-    const Packet C_hi = pset1<Packet>(0.693147180559945286); 
-    const Packet C_lo = pset1<Packet>(4.81927865669806721e-17);
-    const Packet one = pset1<Packet>(1.0);
-
-    // Evaluate P(x) in working precision.
-    // We evaluate even and odd parts of the polynomial separately
-    // to gain some instruction level parallelism.
-    Packet x2 = pmul(x,x);
-    Packet p_even = pmadd(p8, x2, p6);
-    Packet p_odd = pmadd(p9, x2, p7);
-    p_even = pmadd(p_even, x2, p4);
-    p_odd = pmadd(p_odd, x2, p5);
-    p_even = pmadd(p_even, x2, p2);
-    p_odd = pmadd(p_odd, x2, p3);
-    p_even = pmadd(p_even, x2, p0);
-    p_odd = pmadd(p_odd, x2, p1);
-    Packet p = pmadd(p_odd, x, p_even);
-
-    // Evaluate the remaining terms of Q(x) with extra precision using
-    // double word arithmetic.
-    Packet p_hi, p_lo;
-    // x * p(x)
-    twoprod(p, x, p_hi, p_lo);
-    // C + x * p(x)
-    Packet q1_hi, q1_lo;
-    twosum(p_hi, p_lo, C_hi, C_lo, q1_hi, q1_lo);
-    // x * (C + x * p(x))
-    Packet q2_hi, q2_lo;
-    twoprod(q1_hi, q1_lo, x, q2_hi, q2_lo);
-    // 1 + x * (C + x * p(x))
-    Packet q3_hi, q3_lo;
-    // Since |q2_hi| <= sqrt(2)-1 < 1, we can use fast_twosum
-    // for adding it to unity here.
-    fast_twosum(one, q2_hi, q3_hi, q3_lo);
-    return padd(q3_hi, padd(q2_lo, q3_lo));
-  }
-};
-
-// This function implements the non-trivial case of pow(x,y) where x is
-// positive and y is (possibly) non-integer.
-// Formally, pow(x,y) = exp2(y * log2(x)), where exp2(x) is shorthand for 2^x.
-// TODO(rmlarsen): We should probably add this as a packet up 'ppow', to make it
-// easier to specialize or turn off for specific types and/or backends.x
-template <typename Packet>
-EIGEN_STRONG_INLINE Packet generic_pow_impl(const Packet& x, const Packet& y) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-  // Split x into exponent e_x and mantissa m_x.
-  Packet e_x;
-  Packet m_x = pfrexp(x, e_x);
-
-  // Adjust m_x to lie in [1/sqrt(2):sqrt(2)] to minimize absolute error in log2(m_x).
-  EIGEN_CONSTEXPR Scalar sqrt_half = Scalar(0.70710678118654752440);
-  const Packet m_x_scale_mask = pcmp_lt(m_x, pset1<Packet>(sqrt_half));
-  m_x = pselect(m_x_scale_mask, pmul(pset1<Packet>(Scalar(2)), m_x), m_x);
-  e_x = pselect(m_x_scale_mask, psub(e_x, pset1<Packet>(Scalar(1))), e_x);
-
-  // Compute log2(m_x) with 6 extra bits of accuracy.
-  Packet rx_hi, rx_lo;
-  accurate_log2<Scalar>()(m_x, rx_hi, rx_lo);
-
-  // Compute the two terms {y * e_x, y * r_x} in f = y * log2(x) with doubled
-  // precision using double word arithmetic.
-  Packet f1_hi, f1_lo, f2_hi, f2_lo;
-  twoprod(e_x, y, f1_hi, f1_lo);
-  twoprod(rx_hi, rx_lo, y, f2_hi, f2_lo);
-  // Sum the two terms in f using double word arithmetic. We know
-  // that |e_x| > |log2(m_x)|, except for the case where e_x==0.
-  // This means that we can use fast_twosum(f1,f2).
-  // In the case e_x == 0, e_x * y = f1 = 0, so we don't lose any
-  // accuracy by violating the assumption of fast_twosum, because
-  // it's a no-op.
-  Packet f_hi, f_lo;
-  fast_twosum(f1_hi, f1_lo, f2_hi, f2_lo, f_hi, f_lo);
-
-  // Split f into integer and fractional parts.
-  Packet n_z, r_z;
-  absolute_split(f_hi, n_z, r_z);
-  r_z = padd(r_z, f_lo);
-  Packet n_r;
-  absolute_split(r_z, n_r, r_z);
-  n_z = padd(n_z, n_r);
-
-  // We now have an accurate split of f = n_z + r_z and can compute
-  //   x^y = 2**{n_z + r_z) = exp2(r_z) * 2**{n_z}.
-  // Since r_z is in [-0.5;0.5], we compute the first factor to high accuracy
-  // using a specialized algorithm. Multiplication by the second factor can
-  // be done exactly using pldexp(), since it is an integer power of 2.
-  const Packet e_r = fast_accurate_exp2<Scalar>()(r_z);
-  return pldexp(e_r, n_z);
-}
-
-// Generic implementation of pow(x,y).
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet generic_pow(const Packet& x, const Packet& y) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
-
-  const Packet cst_pos_inf = pset1<Packet>(NumTraits<Scalar>::infinity());
-  const Packet cst_zero = pset1<Packet>(Scalar(0));
-  const Packet cst_one = pset1<Packet>(Scalar(1));
-  const Packet cst_nan = pset1<Packet>(NumTraits<Scalar>::quiet_NaN());
-
-  const Packet abs_x = pabs(x);
-  // Predicates for sign and magnitude of x.
-  const Packet x_is_zero = pcmp_eq(x, cst_zero);
-  const Packet x_is_neg = pcmp_lt(x, cst_zero);
-  const Packet abs_x_is_inf = pcmp_eq(abs_x, cst_pos_inf);
-  const Packet abs_x_is_one =  pcmp_eq(abs_x, cst_one);
-  const Packet abs_x_is_gt_one = pcmp_lt(cst_one, abs_x);
-  const Packet abs_x_is_lt_one = pcmp_lt(abs_x, cst_one);
-  const Packet x_is_one =  pandnot(abs_x_is_one, x_is_neg);
-  const Packet x_is_neg_one =  pand(abs_x_is_one, x_is_neg);
-  const Packet x_is_nan = pandnot(ptrue(x), pcmp_eq(x, x));
-
-  // Predicates for sign and magnitude of y.
-  const Packet y_is_one = pcmp_eq(y, cst_one);
-  const Packet y_is_zero = pcmp_eq(y, cst_zero);
-  const Packet y_is_neg = pcmp_lt(y, cst_zero);
-  const Packet y_is_pos = pandnot(ptrue(y), por(y_is_zero, y_is_neg));
-  const Packet y_is_nan = pandnot(ptrue(y), pcmp_eq(y, y));
-  const Packet abs_y_is_inf = pcmp_eq(pabs(y), cst_pos_inf);
-  EIGEN_CONSTEXPR Scalar huge_exponent =
-      (NumTraits<Scalar>::max_exponent() * Scalar(EIGEN_LN2)) /
-       NumTraits<Scalar>::epsilon();
-  const Packet abs_y_is_huge = pcmp_le(pset1<Packet>(huge_exponent), pabs(y));
-
-  // Predicates for whether y is integer and/or even.
-  const Packet y_is_int = pcmp_eq(pfloor(y), y);
-  const Packet y_div_2 = pmul(y, pset1<Packet>(Scalar(0.5)));
-  const Packet y_is_even = pcmp_eq(pround(y_div_2), y_div_2);
-
-  // Predicates encoding special cases for the value of pow(x,y)
-  const Packet invalid_negative_x = pandnot(pandnot(pandnot(x_is_neg, abs_x_is_inf),
-                                                    y_is_int),
-                                            abs_y_is_inf);
-  const Packet pow_is_one = por(por(x_is_one, y_is_zero),
-                                pand(x_is_neg_one,
-                                     por(abs_y_is_inf, pandnot(y_is_even, invalid_negative_x))));
-  const Packet pow_is_nan = por(invalid_negative_x, por(x_is_nan, y_is_nan));
-  const Packet pow_is_zero = por(por(por(pand(x_is_zero, y_is_pos),
-                                         pand(abs_x_is_inf, y_is_neg)),
-                                     pand(pand(abs_x_is_lt_one, abs_y_is_huge),
-                                          y_is_pos)),
-                                 pand(pand(abs_x_is_gt_one, abs_y_is_huge),
-                                      y_is_neg));
-  const Packet pow_is_inf = por(por(por(pand(x_is_zero, y_is_neg),
-                                        pand(abs_x_is_inf, y_is_pos)),
-                                    pand(pand(abs_x_is_lt_one, abs_y_is_huge),
-                                         y_is_neg)),
-                                pand(pand(abs_x_is_gt_one, abs_y_is_huge),
-                                     y_is_pos));
-
-  // General computation of pow(x,y) for positive x or negative x and integer y.
-  const Packet negate_pow_abs = pandnot(x_is_neg, y_is_even);
-  const Packet pow_abs = generic_pow_impl(abs_x, y);
-  return pselect(y_is_one, x,
-                 pselect(pow_is_one, cst_one,
-                         pselect(pow_is_nan, cst_nan,
-                                 pselect(pow_is_inf, cst_pos_inf,
-                                         pselect(pow_is_zero, cst_zero,
-                                                 pselect(negate_pow_abs, pnegate(pow_abs), pow_abs))))));
-}
-
-
-
-/* polevl (modified for Eigen)
- *
- *      Evaluate polynomial
- *
- *
- *
- * SYNOPSIS:
- *
- * int N;
- * Scalar x, y, coef[N+1];
- *
- * y = polevl<decltype(x), N>( x, coef);
- *
- *
- *
- * DESCRIPTION:
- *
- * Evaluates polynomial of degree N:
- *
- *                     2          N
- * y  =  C  + C x + C x  +...+ C x
- *        0    1     2          N
- *
- * Coefficients are stored in reverse order:
- *
- * coef[0] = C  , ..., coef[N] = C  .
- *            N                   0
- *
- *  The function p1evl() assumes that coef[N] = 1.0 and is
- * omitted from the array.  Its calling arguments are
- * otherwise the same as polevl().
- *
- *
- * The Eigen implementation is templatized.  For best speed, store
- * coef as a const array (constexpr), e.g.
- *
- * const double coef[] = {1.0, 2.0, 3.0, ...};
- *
- */
-template <typename Packet, int N>
-struct ppolevl {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const typename unpacket_traits<Packet>::type coeff[]) {
-    EIGEN_STATIC_ASSERT((N > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    return pmadd(ppolevl<Packet, N-1>::run(x, coeff), x, pset1<Packet>(coeff[N]));
-  }
-};
-
-template <typename Packet>
-struct ppolevl<Packet, 0> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const typename unpacket_traits<Packet>::type coeff[]) {
-    EIGEN_UNUSED_VARIABLE(x);
-    return pset1<Packet>(coeff[0]);
-  }
-};
-
-/* chbevl (modified for Eigen)
- *
- *     Evaluate Chebyshev series
- *
- *
- *
- * SYNOPSIS:
- *
- * int N;
- * Scalar x, y, coef[N], chebevl();
- *
- * y = chbevl( x, coef, N );
- *
- *
- *
- * DESCRIPTION:
- *
- * Evaluates the series
- *
- *        N-1
- *         - '
- *  y  =   >   coef[i] T (x/2)
- *         -            i
- *        i=0
- *
- * of Chebyshev polynomials Ti at argument x/2.
- *
- * Coefficients are stored in reverse order, i.e. the zero
- * order term is last in the array.  Note N is the number of
- * coefficients, not the order.
- *
- * If coefficients are for the interval a to b, x must
- * have been transformed to x -> 2(2x - b - a)/(b-a) before
- * entering the routine.  This maps x from (a, b) to (-1, 1),
- * over which the Chebyshev polynomials are defined.
- *
- * If the coefficients are for the inverted interval, in
- * which (a, b) is mapped to (1/b, 1/a), the transformation
- * required is x -> 2(2ab/x - b - a)/(b-a).  If b is infinity,
- * this becomes x -> 4a/x - 1.
- *
- *
- *
- * SPEED:
- *
- * Taking advantage of the recurrence properties of the
- * Chebyshev polynomials, the routine requires one more
- * addition per loop than evaluating a nested polynomial of
- * the same degree.
- *
- */
-
-template <typename Packet, int N>
-struct pchebevl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Packet run(Packet x, const typename unpacket_traits<Packet>::type coef[]) {
-    typedef typename unpacket_traits<Packet>::type Scalar;
-    Packet b0 = pset1<Packet>(coef[0]);
-    Packet b1 = pset1<Packet>(static_cast<Scalar>(0.f));
-    Packet b2;
-
-    for (int i = 1; i < N; i++) {
-      b2 = b1;
-      b1 = b0;
-      b0 = psub(pmadd(x, b1, pset1<Packet>(coef[i])), b2);
-    }
-
-    return pmul(pset1<Packet>(static_cast<Scalar>(0.5f)), psub(b0, b2));
-  }
-};
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
deleted file mode 100644
index 177a04e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
+++ /dev/null
@@ -1,110 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2019 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
-#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
-
-namespace Eigen {
-namespace internal {
-
-// Forward declarations of the generic math functions
-// implemented in GenericPacketMathFunctions.h
-// This is needed to workaround a circular dependency.
-
-/***************************************************************************
- * Some generic implementations to be used by implementors
-***************************************************************************/
-
-/** Default implementation of pfrexp.
-  * It is expected to be called by implementers of template<> pfrexp.
-  */
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pfrexp_generic(const Packet& a, Packet& exponent);
-
-// Extracts the biased exponent value from Packet p, and casts the results to
-// a floating-point Packet type. Used by pfrexp_generic. Override this if
-// there is no unpacket_traits<Packet>::integer_packet.
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pfrexp_generic_get_biased_exponent(const Packet& p);
-
-/** Default implementation of pldexp.
-  * It is expected to be called by implementers of template<> pldexp.
-  */
-template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-Packet pldexp_generic(const Packet& a, const Packet& exponent);
-
-/** \internal \returns log(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_float(const Packet _x);
-
-/** \internal \returns log2(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_float(const Packet _x);
-
-/** \internal \returns log(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog_double(const Packet _x);
-
-/** \internal \returns log2(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet plog2_double(const Packet _x);
-
-/** \internal \returns log(1 + x) */
-template<typename Packet>
-Packet generic_plog1p(const Packet& x);
-
-/** \internal \returns exp(x)-1 */
-template<typename Packet>
-Packet generic_expm1(const Packet& x);
-
-/** \internal \returns exp(x) for single precision float */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_float(const Packet _x);
-
-/** \internal \returns exp(x) for double precision real numbers */
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pexp_double(const Packet _x);
-
-/** \internal \returns sin(x) for single precision float */
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psin_float(const Packet& x);
-
-/** \internal \returns cos(x) for single precision float */
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pcos_float(const Packet& x);
-
-/** \internal \returns sqrt(x) for complex types */
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet psqrt_complex(const Packet& a);
-
-template <typename Packet, int N> struct ppolevl;
-
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/Half.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/Half.h
deleted file mode 100644
index 9f8e8cc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/Half.h
+++ /dev/null
@@ -1,942 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-//
-// The conversion routines are Copyright (c) Fabian Giesen, 2016.
-// The original license follows:
-//
-// Copyright (c) Fabian Giesen, 2016
-// All rights reserved.
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted.
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-
-// Standard 16-bit float type, mostly useful for GPUs. Defines a new
-// type Eigen::half (inheriting either from CUDA's or HIP's __half struct) with
-// operator overloads such that it behaves basically as an arithmetic
-// type. It will be quite slow on CPUs (so it is recommended to stay
-// in fp32 for CPUs, except for simple parameter conversions, I/O
-// to disk and the likes), but fast on GPUs.
-
-
-#ifndef EIGEN_HALF_H
-#define EIGEN_HALF_H
-
-#include <sstream>
-
-#if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-// When compiling with GPU support, the "__half_raw" base class as well as
-// some other routines are defined in the GPU compiler header files
-// (cuda_fp16.h, hip_fp16.h), and they are not tagged constexpr
-// As a consequence, we get compile failures when compiling Eigen with
-// GPU support. Hence the need to disable EIGEN_CONSTEXPR when building
-// Eigen with GPU support
-  #pragma push_macro("EIGEN_CONSTEXPR")
-  #undef EIGEN_CONSTEXPR
-  #define EIGEN_CONSTEXPR
-#endif
-
-#define F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, METHOD)           \
-  template <>                                                       \
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED                \
-  PACKET_F16 METHOD<PACKET_F16>(const PACKET_F16& _x) {             \
-    return float2half(METHOD<PACKET_F>(half2float(_x)));            \
-  }
-
-namespace Eigen {
-
-struct half;
-
-namespace half_impl {
-
-// We want to use the __half_raw struct from the HIP header file only during the device compile phase.
-// This is required because of a quirk in the way TensorFlow GPU builds are done.
-// When compiling TensorFlow source code with GPU support, files that
-//  * contain GPU kernels (i.e. *.cu.cc files) are compiled via hipcc
-//  * do not contain GPU kernels ( i.e. *.cc files) are compiled via gcc (typically)
-//
-// Tensorflow uses the Eigen::half type as its FP16 type, and there are functions that
-//  * are defined in a file that gets compiled via hipcc AND
-//  * have Eigen::half as a pass-by-value argument AND
-//  * are called in a file that gets compiled via gcc
-//
-// In the scenario described above the caller and callee will see different versions
-// of the Eigen::half base class __half_raw, and they will be compiled by different compilers
-//
-// There appears to be an ABI mismatch between gcc and clang (which is called by hipcc) that results in
-// the callee getting corrupted values for the Eigen::half argument.
-//
-// Making the host side compile phase of hipcc use the same Eigen::half impl, as the gcc compile, resolves
-// this error, and hence the following convoluted #if condition
-#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
-// Make our own __half_raw definition that is similar to CUDA's.
-struct __half_raw {
-#if (defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE))
-  // Eigen::half can be used as the datatype for shared memory declarations (in Eigen and TF)
-  // The element type for shared memory cannot have non-trivial constructors
-  // and hence the following special casing (which skips the zero-initilization).
-  // Note that this check gets done even in the host compilation phase, and
-  // hence the need for this
-  EIGEN_DEVICE_FUNC __half_raw() {}
-#else
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw() : x(0) {}
-#endif
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw(numext::uint16_t raw) : x(numext::bit_cast<__fp16>(raw)) {
-  }
-  __fp16 x;
-#else
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw(numext::uint16_t raw) : x(raw) {}
-  numext::uint16_t x;
-#endif
-};
-
-#elif defined(EIGEN_HAS_HIP_FP16)
-  // Nothing to do here
-  // HIP fp16 header file has a definition for __half_raw
-#elif defined(EIGEN_HAS_CUDA_FP16)
-  #if EIGEN_CUDA_SDK_VER < 90000
-    // In CUDA < 9.0, __half is the equivalent of CUDA 9's __half_raw
-    typedef __half __half_raw;
-  #endif // defined(EIGEN_HAS_CUDA_FP16)
-#elif defined(SYCL_DEVICE_ONLY)
-  typedef cl::sycl::half __half_raw;
-#endif
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x);
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff);
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h);
-
-struct half_base : public __half_raw {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base() {}
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half_raw& h) : __half_raw(h) {}
-
-#if defined(EIGEN_HAS_GPU_FP16)
- #if defined(EIGEN_HAS_HIP_FP16)
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half& h) { x = __half_as_ushort(h); }
- #elif defined(EIGEN_HAS_CUDA_FP16)
-  #if EIGEN_CUDA_SDK_VER >= 90000
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half& h) : __half_raw(*(__half_raw*)&h) {}
-  #endif
- #endif
-#endif
-};
-
-} // namespace half_impl
-
-// Class definition.
-struct half : public half_impl::half_base {
-
-  // Writing this out as separate #if-else blocks to make the code easier to follow
-  // The same applies to most #if-else blocks in this file
-#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
-  // Use the same base class for the following two scenarios
-  // * when compiling without GPU support enabled
-  // * during host compile phase when compiling with GPU support enabled
-  typedef half_impl::__half_raw __half_raw;
-#elif defined(EIGEN_HAS_HIP_FP16)
-  // Nothing to do here
-  // HIP fp16 header file has a definition for __half_raw
-#elif defined(EIGEN_HAS_CUDA_FP16)
-  // Note that EIGEN_CUDA_SDK_VER is set to 0 even when compiling with HIP, so
-  // (EIGEN_CUDA_SDK_VER < 90000) is true even for HIP!  So keeping this within
-  // #if defined(EIGEN_HAS_CUDA_FP16) is needed
-  #if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
-    typedef half_impl::__half_raw __half_raw;
-  #endif
-#endif
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half_raw& h) : half_impl::half_base(h) {}
-
-#if defined(EIGEN_HAS_GPU_FP16)
- #if defined(EIGEN_HAS_HIP_FP16)
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
- #elif defined(EIGEN_HAS_CUDA_FP16)
-  #if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
-  #endif
- #endif
-#endif
-
-
-  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(bool b)
-      : half_impl::half_base(half_impl::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
-  template<class T>
-  explicit EIGEN_DEVICE_FUNC half(T val)
-      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(val))) {}
-  explicit EIGEN_DEVICE_FUNC half(float f)
-      : half_impl::half_base(half_impl::float_to_half_rtne(f)) {}
-
-  // Following the convention of numpy, converting between complex and
-  // float will lead to loss of imag value.
-  template<typename RealScalar>
-  explicit EIGEN_DEVICE_FUNC half(std::complex<RealScalar> c)
-      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(c.real()))) {}
-
-   EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
-    return half_impl::half_to_float(*this);
-  }
-
-#if defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE)
-  EIGEN_DEVICE_FUNC operator __half() const {
-    ::__half_raw hr;
-    hr.x = x;
-    return __half(hr);
-  }
-#endif
-};
-
-} // end namespace Eigen
-
-namespace std {
-template<>
-struct numeric_limits<Eigen::half> {
-  static const bool is_specialized = true;
-  static const bool is_signed = true;
-  static const bool is_integer = false;
-  static const bool is_exact = false;
-  static const bool has_infinity = true;
-  static const bool has_quiet_NaN = true;
-  static const bool has_signaling_NaN = true;
-  static const float_denorm_style has_denorm = denorm_present;
-  static const bool has_denorm_loss = false;
-  static const std::float_round_style round_style = std::round_to_nearest;
-  static const bool is_iec559 = false;
-  static const bool is_bounded = false;
-  static const bool is_modulo = false;
-  static const int digits = 11;
-  static const int digits10 = 3;      // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
-  static const int max_digits10 = 5;  // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
-  static const int radix = 2;
-  static const int min_exponent = -13;
-  static const int min_exponent10 = -4;
-  static const int max_exponent = 16;
-  static const int max_exponent10 = 4;
-  static const bool traps = true;
-  static const bool tinyness_before = false;
-
-  static Eigen::half (min)() { return Eigen::half_impl::raw_uint16_to_half(0x400); }
-  static Eigen::half lowest() { return Eigen::half_impl::raw_uint16_to_half(0xfbff); }
-  static Eigen::half (max)() { return Eigen::half_impl::raw_uint16_to_half(0x7bff); }
-  static Eigen::half epsilon() { return Eigen::half_impl::raw_uint16_to_half(0x0800); }
-  static Eigen::half round_error() { return Eigen::half(0.5); }
-  static Eigen::half infinity() { return Eigen::half_impl::raw_uint16_to_half(0x7c00); }
-  static Eigen::half quiet_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
-  static Eigen::half signaling_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7d00); }
-  static Eigen::half denorm_min() { return Eigen::half_impl::raw_uint16_to_half(0x1); }
-};
-
-// If std::numeric_limits<T> is specialized, should also specialize
-// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
-// std::numeric_limits<const volatile T>
-// https://stackoverflow.com/a/16519653/
-template<>
-struct numeric_limits<const Eigen::half> : numeric_limits<Eigen::half> {};
-template<>
-struct numeric_limits<volatile Eigen::half> : numeric_limits<Eigen::half> {};
-template<>
-struct numeric_limits<const volatile Eigen::half> : numeric_limits<Eigen::half> {};
-} // end namespace std
-
-namespace Eigen {
-
-namespace half_impl {
-
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && \
-     EIGEN_CUDA_ARCH >= 530) ||                                  \
-    (defined(EIGEN_HAS_HIP_FP16) && defined(HIP_DEVICE_COMPILE))
-// Note: We deliberatly do *not* define this to 1 even if we have Arm's native
-// fp16 type since GPU halfs are rather different from native CPU halfs.
-// TODO: Rename to something like EIGEN_HAS_NATIVE_GPU_FP16
-#define EIGEN_HAS_NATIVE_FP16
-#endif
-
-// Intrinsics for native fp16 support. Note that on current hardware,
-// these are no faster than fp32 arithmetic (you need to use the half2
-// versions to get the ALU speed increased), but you do save the
-// conversion steps back and forth.
-
-#if defined(EIGEN_HAS_NATIVE_FP16)
-EIGEN_STRONG_INLINE __device__ half operator + (const half& a, const half& b) {
-#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
-  return __hadd(::__half(a), ::__half(b));
-#else
-  return __hadd(a, b);
-#endif
-}
-EIGEN_STRONG_INLINE __device__ half operator * (const half& a, const half& b) {
-  return __hmul(a, b);
-}
-EIGEN_STRONG_INLINE __device__ half operator - (const half& a, const half& b) {
-  return __hsub(a, b);
-}
-EIGEN_STRONG_INLINE __device__ half operator / (const half& a, const half& b) {
-#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
-  return __hdiv(a, b);
-#else
-  float num = __half2float(a);
-  float denom = __half2float(b);
-  return __float2half(num / denom);
-#endif
-}
-EIGEN_STRONG_INLINE __device__ half operator - (const half& a) {
-  return __hneg(a);
-}
-EIGEN_STRONG_INLINE __device__ half& operator += (half& a, const half& b) {
-  a = a + b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ half& operator *= (half& a, const half& b) {
-  a = a * b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ half& operator -= (half& a, const half& b) {
-  a = a - b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ half& operator /= (half& a, const half& b) {
-  a = a / b;
-  return a;
-}
-EIGEN_STRONG_INLINE __device__ bool operator == (const half& a, const half& b) {
-  return __heq(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator != (const half& a, const half& b) {
-  return __hne(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator < (const half& a, const half& b) {
-  return __hlt(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator <= (const half& a, const half& b) {
-  return __hle(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator > (const half& a, const half& b) {
-  return __hgt(a, b);
-}
-EIGEN_STRONG_INLINE __device__ bool operator >= (const half& a, const half& b) {
-  return __hge(a, b);
-}
-#endif
-
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
-  return half(vaddh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
-  return half(vmulh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
-  return half(vsubh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
-  return half(vdivh_f16(a.x, b.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
-  return half(vnegh_f16(a.x));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
-  a = half(vaddh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
-  a = half(vmulh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
-  a = half(vsubh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
-  a = half(vdivh_f16(a.x, b.x));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
-  return vceqh_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
-  return !vceqh_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
-  return vclth_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
-  return vcleh_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
-  return vcgth_f16(a.x, b.x);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
-  return vcgeh_f16(a.x, b.x);
-}
-// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
-// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
-// of the functions, while the latter can only deal with one of them.
-#elif !defined(EIGEN_HAS_NATIVE_FP16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC) // Emulate support for half floats
-
-#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
-// We need to provide emulated *host-side* FP16 operators for clang.
-#pragma push_macro("EIGEN_DEVICE_FUNC")
-#undef EIGEN_DEVICE_FUNC
-#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_HAS_NATIVE_FP16)
-#define EIGEN_DEVICE_FUNC __host__
-#else // both host and device need emulated ops.
-#define EIGEN_DEVICE_FUNC __host__ __device__
-#endif
-#endif
-
-// Definitions for CPUs and older HIP+CUDA, mostly working through conversion
-// to/from fp32.
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
-  return half(float(a) + float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
-  return half(float(a) * float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
-  return half(float(a) - float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
-  return half(float(a) / float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
-  half result;
-  result.x = a.x ^ 0x8000;
-  return result;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
-  a = half(float(a) + float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
-  a = half(float(a) * float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
-  a = half(float(a) - float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
-  a = half(float(a) / float(b));
-  return a;
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
-  return numext::equal_strict(float(a),float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
-  return numext::not_equal_strict(float(a), float(b));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
-  return float(a) < float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
-  return float(a) <= float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
-  return float(a) > float(b);
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
-  return float(a) >= float(b);
-}
-
-#if defined(__clang__) && defined(__CUDA__)
-#pragma pop_macro("EIGEN_DEVICE_FUNC")
-#endif
-#endif  // Emulate support for half floats
-
-// Division by an index. Do it in full float precision to avoid accuracy
-// issues in converting the denominator to half.
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, Index b) {
-  return half(static_cast<float>(a) / static_cast<float>(b));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a) {
-  a += half(1);
-  return a;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a) {
-  a -= half(1);
-  return a;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a, int) {
-  half original_value = a;
-  ++a;
-  return original_value;
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a, int) {
-  half original_value = a;
-  --a;
-  return original_value;
-}
-
-// Conversion routines, including fallbacks for the host or older CUDA.
-// Note that newer Intel CPUs (Haswell or newer) have vectorized versions of
-// these in hardware. If we need more performance on older/other CPUs, they are
-// also possible to vectorize directly.
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x) {
-  // We cannot simply do a "return __half_raw(x)" here, because __half_raw is union type
-  // in the hip_fp16 header file, and that will trigger a compile error
-  // On the other hand, having anything but a return statement also triggers a compile error
-  // because this is constexpr function.
-  // Fortunately, since we need to disable EIGEN_CONSTEXPR for GPU anyway, we can get out
-  // of this catch22 by having separate bodies for GPU / non GPU
-#if defined(EIGEN_HAS_GPU_FP16)
-   __half_raw h;
-   h.x = x;
-  return h;
-#else
-  return __half_raw(x);
-#endif
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC numext::uint16_t raw_half_as_uint16(const __half_raw& h) {
-  // HIP/CUDA/Default have a member 'x' of type uint16_t.
-  // For ARM64 native half, the member 'x' is of type __fp16, so we need to bit-cast.
-  // For SYCL, cl::sycl::half is _Float16, so cast directly.
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return numext::bit_cast<numext::uint16_t>(h.x);
-#elif defined(SYCL_DEVICE_ONLY)
-  return numext::bit_cast<numext::uint16_t>(h);
-#else
-  return h.x;
-#endif
-}
-
-union float32_bits {
-  unsigned int u;
-  float f;
-};
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  __half tmp_ff = __float2half(ff);
-  return *(__half_raw*)&tmp_ff;
-
-#elif defined(EIGEN_HAS_FP16_C)
-  __half_raw h;
-  h.x = _cvtss_sh(ff, 0);
-  return h;
-
-#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  __half_raw h;
-  h.x = static_cast<__fp16>(ff);
-  return h;
-
-#else
-  float32_bits f; f.f = ff;
-
-  const float32_bits f32infty = { 255 << 23 };
-  const float32_bits f16max = { (127 + 16) << 23 };
-  const float32_bits denorm_magic = { ((127 - 15) + (23 - 10) + 1) << 23 };
-  unsigned int sign_mask = 0x80000000u;
-  __half_raw o;
-  o.x = static_cast<numext::uint16_t>(0x0u);
-
-  unsigned int sign = f.u & sign_mask;
-  f.u ^= sign;
-
-  // NOTE all the integer compares in this function can be safely
-  // compiled into signed compares since all operands are below
-  // 0x80000000. Important if you want fast straight SSE2 code
-  // (since there's no unsigned PCMPGTD).
-
-  if (f.u >= f16max.u) {  // result is Inf or NaN (all exponent bits set)
-    o.x = (f.u > f32infty.u) ? 0x7e00 : 0x7c00; // NaN->qNaN and Inf->Inf
-  } else {  // (De)normalized number or zero
-    if (f.u < (113 << 23)) {  // resulting FP16 is subnormal or zero
-      // use a magic value to align our 10 mantissa bits at the bottom of
-      // the float. as long as FP addition is round-to-nearest-even this
-      // just works.
-      f.f += denorm_magic.f;
-
-      // and one integer subtract of the bias later, we have our final float!
-      o.x = static_cast<numext::uint16_t>(f.u - denorm_magic.u);
-    } else {
-      unsigned int mant_odd = (f.u >> 13) & 1; // resulting mantissa is odd
-
-      // update exponent, rounding bias part 1
-      // Equivalent to `f.u += ((unsigned int)(15 - 127) << 23) + 0xfff`, but
-      // without arithmetic overflow.
-      f.u += 0xc8000fffU;
-      // rounding bias part 2
-      f.u += mant_odd;
-      // take the bits!
-      o.x = static_cast<numext::uint16_t>(f.u >> 13);
-    }
-  }
-
-  o.x |= static_cast<numext::uint16_t>(sign >> 16);
-  return o;
-#endif
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __half2float(h);
-#elif defined(EIGEN_HAS_FP16_C)
-  return _cvtsh_ss(h.x);
-#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return static_cast<float>(h.x);
-#else
-  const float32_bits magic = { 113 << 23 };
-  const unsigned int shifted_exp = 0x7c00 << 13; // exponent mask after shift
-  float32_bits o;
-
-  o.u = (h.x & 0x7fff) << 13;             // exponent/mantissa bits
-  unsigned int exp = shifted_exp & o.u;   // just the exponent
-  o.u += (127 - 15) << 23;                // exponent adjust
-
-  // handle exponent special cases
-  if (exp == shifted_exp) {     // Inf/NaN?
-    o.u += (128 - 16) << 23;    // extra exp adjust
-  } else if (exp == 0) {        // Zero/Denormal?
-    o.u += 1 << 23;             // extra exp adjust
-    o.f -= magic.f;             // renormalize
-  }
-
-  o.u |= (h.x & 0x8000) << 16;    // sign bit
-  return o.f;
-#endif
-}
-
-// --- standard functions ---
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const half& a) {
-#ifdef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
-  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) == 0x7c00;
-#else
-  return (a.x & 0x7fff) == 0x7c00;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const half& a) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __hisnan(a);
-#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) > 0x7c00;
-#else
-  return (a.x & 0x7fff) > 0x7c00;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const half& a) {
-  return !(isinf EIGEN_NOT_A_MACRO (a)) && !(isnan EIGEN_NOT_A_MACRO (a));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half abs(const half& a) {
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  return half(vabsh_f16(a.x));
-#else
-  half result;
-  result.x = a.x & 0x7FFF;
-  return result;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half exp(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hexp(a));
-#else
-   return half(::expf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half expm1(const half& a) {
-  return half(numext::expm1(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log(const half& a) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return half(::hlog(a));
-#else
-  return half(::logf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log1p(const half& a) {
-  return half(numext::log1p(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log10(const half& a) {
-  return half(::log10f(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log2(const half& a) {
-  return half(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sqrt(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hsqrt(a));
-#else
-    return half(::sqrtf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half pow(const half& a, const half& b) {
-  return half(::powf(float(a), float(b)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sin(const half& a) {
-  return half(::sinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half cos(const half& a) {
-  return half(::cosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tan(const half& a) {
-  return half(::tanf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tanh(const half& a) {
-  return half(::tanhf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half asin(const half& a) {
-  return half(::asinf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half acos(const half& a) {
-  return half(::acosf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half floor(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hfloor(a));
-#else
-  return half(::floorf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half ceil(const half& a) {
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-  return half(hceil(a));
-#else
-  return half(::ceilf(float(a)));
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half rint(const half& a) {
-  return half(::rintf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half round(const half& a) {
-  return half(::roundf(float(a)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half fmod(const half& a, const half& b) {
-  return half(::fmodf(float(a), float(b)));
-}
-
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (min)(const half& a, const half& b) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __hlt(b, a) ? b : a;
-#else
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f2 < f1 ? b : a;
-#endif
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (max)(const half& a, const half& b) {
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-  return __hlt(a, b) ? b : a;
-#else
-  const float f1 = static_cast<float>(a);
-  const float f2 = static_cast<float>(b);
-  return f1 < f2 ? b : a;
-#endif
-}
-
-#ifndef EIGEN_NO_IO
-EIGEN_ALWAYS_INLINE std::ostream& operator << (std::ostream& os, const half& v) {
-  os << static_cast<float>(v);
-  return os;
-}
-#endif
-
-} // end namespace half_impl
-
-// import Eigen::half_impl::half into Eigen namespace
-// using half_impl::half;
-
-namespace internal {
-
-template<>
-struct random_default_impl<half, false, false>
-{
-  static inline half run(const half& x, const half& y)
-  {
-    return x + (y-x) * half(float(std::rand()) / float(RAND_MAX));
-  }
-  static inline half run()
-  {
-    return run(half(-1.f), half(1.f));
-  }
-};
-
-template<> struct is_arithmetic<half> { enum { value = true }; };
-
-} // end namespace internal
-
-template<> struct NumTraits<Eigen::half>
-    : GenericNumTraits<Eigen::half>
-{
-  enum {
-    IsSigned = true,
-    IsInteger = false,
-    IsComplex = false,
-    RequireInitialization = false
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half epsilon() {
-    return half_impl::raw_uint16_to_half(0x0800);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half dummy_precision() {
-    return half_impl::raw_uint16_to_half(0x211f); //  Eigen::half(1e-2f);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half highest() {
-    return half_impl::raw_uint16_to_half(0x7bff);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half lowest() {
-    return half_impl::raw_uint16_to_half(0xfbff);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half infinity() {
-    return half_impl::raw_uint16_to_half(0x7c00);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half quiet_NaN() {
-    return half_impl::raw_uint16_to_half(0x7e00);
-  }
-};
-
-} // end namespace Eigen
-
-#if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  #pragma pop_macro("EIGEN_CONSTEXPR")
-#endif
-
-namespace Eigen {
-namespace numext {
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isnan)(const Eigen::half& h) {
-  return (half_impl::isnan)(h);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isinf)(const Eigen::half& h) {
-  return (half_impl::isinf)(h);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isfinite)(const Eigen::half& h) {
-  return (half_impl::isfinite)(h);
-}
-
-#endif
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bit_cast<Eigen::half, uint16_t>(const uint16_t& src) {
-  return Eigen::half(Eigen::half_impl::raw_uint16_to_half(src));
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::half>(const Eigen::half& src) {
-  return Eigen::half_impl::raw_half_as_uint16(src);
-}
-
-}  // namespace numext
-}  // namespace Eigen
-
-// Add the missing shfl* intrinsics.
-// The __shfl* functions are only valid on HIP or _CUDA_ARCH_ >= 300.
-//   CUDA defines them for (__CUDA_ARCH__ >= 300 || !defined(__CUDA_ARCH__))
-//
-// HIP and CUDA prior to SDK 9.0 define
-//    __shfl, __shfl_up, __shfl_down, __shfl_xor for int and float
-// CUDA since 9.0 deprecates those and instead defines
-//    __shfl_sync, __shfl_up_sync, __shfl_down_sync, __shfl_xor_sync,
-//    with native support for __half and __nv_bfloat16
-//
-// Note that the following are __device__ - only functions.
-#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 300)) \
-    || defined(EIGEN_HIPCC)
-
-#if defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 90000
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_sync(unsigned mask, Eigen::half var, int srcLane, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_sync(mask, h, srcLane, width));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up_sync(unsigned mask, Eigen::half var, unsigned int delta, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_up_sync(mask, h, delta, width));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down_sync(unsigned mask, Eigen::half var, unsigned int delta, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_down_sync(mask, h, delta, width));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor_sync(unsigned mask, Eigen::half var, int laneMask, int width=warpSize) {
-  const __half h = var;
-  return static_cast<Eigen::half>(__shfl_xor_sync(mask, h, laneMask, width));
-}
-
-#else // HIP or CUDA SDK < 9.0
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl(Eigen::half var, int srcLane, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl(ivar, srcLane, width)));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up(Eigen::half var, unsigned int delta, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_up(ivar, delta, width)));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down(Eigen::half var, unsigned int delta, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_down(ivar, delta, width)));
-}
-
-__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor(Eigen::half var, int laneMask, int width=warpSize) {
-  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
-  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_xor(ivar, laneMask, width)));
-}
-
-#endif // HIP vs CUDA
-#endif // __shfl*
-
-// ldg() has an overload for __half_raw, but we also need one for Eigen::half.
-#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 350)) \
-    || defined(EIGEN_HIPCC)
-EIGEN_STRONG_INLINE __device__ Eigen::half __ldg(const Eigen::half* ptr) {
-  return Eigen::half_impl::raw_uint16_to_half(__ldg(reinterpret_cast<const Eigen::numext::uint16_t*>(ptr)));
-}
-#endif // __ldg
-
-#if EIGEN_HAS_STD_HASH
-namespace std {
-template <>
-struct hash<Eigen::half> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::half& a) const {
-    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
-  }
-};
-} // end namespace std
-#endif
-
-#endif // EIGEN_HALF_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/Settings.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/Settings.h
deleted file mode 100644
index a5c3ada..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/Settings.h
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-/* All the parameters defined in this file can be specialized in the
- * architecture specific files, and/or by the user.
- * More to come... */
-
-#ifndef EIGEN_DEFAULT_SETTINGS_H
-#define EIGEN_DEFAULT_SETTINGS_H
-
-/** Defines the maximal loop size to enable meta unrolling of loops.
-  * Note that the value here is expressed in Eigen's own notion of "number of FLOPS",
-  * it does not correspond to the number of iterations or the number of instructions
-  */
-#ifndef EIGEN_UNROLLING_LIMIT
-#define EIGEN_UNROLLING_LIMIT 110
-#endif
-
-/** Defines the threshold between a "small" and a "large" matrix.
-  * This threshold is mainly used to select the proper product implementation.
-  */
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-/** Defines the maximal width of the blocks used in the triangular product and solver
-  * for vectors (level 2 blas xTRMV and xTRSV). The default is 8.
-  */
-#ifndef EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH
-#define EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH 8
-#endif
-
-
-/** Defines the default number of registers available for that architecture.
-  * Currently it must be 8 or 16. Other values will fail.
-  */
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 8
-#endif
-
-#endif // EIGEN_DEFAULT_SETTINGS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h
deleted file mode 100644
index fb8183b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h
+++ /dev/null
@@ -1,120 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERIC_TYPE_CASTING_H
-#define EIGEN_GENERIC_TYPE_CASTING_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<>
-struct scalar_cast_op<float, Eigen::half> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::half result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half operator() (const float& a) const {
-    #if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-      (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-      return __float2half(a);
-    #else
-      return Eigen::half(a);
-    #endif
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<float, Eigen::half> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<int, Eigen::half> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::half result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half operator() (const int& a) const {
-    #if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-      (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-      return __float2half(static_cast<float>(a));
-    #else
-      return Eigen::half(static_cast<float>(a));
-    #endif
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<int, Eigen::half> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<Eigen::half, float> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef float result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator() (const Eigen::half& a) const {
-    #if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-      (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-      return __half2float(a);
-    #else
-      return static_cast<float>(a);
-    #endif
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<Eigen::half, float> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<float, Eigen::bfloat16> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::bfloat16 result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::bfloat16 operator() (const float& a) const {
-    return Eigen::bfloat16(a);
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<float, Eigen::bfloat16> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<int, Eigen::bfloat16> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef Eigen::bfloat16 result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::bfloat16 operator() (const int& a) const {
-    return Eigen::bfloat16(static_cast<float>(a));
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<int, Eigen::bfloat16> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-template<>
-struct scalar_cast_op<Eigen::bfloat16, float> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef float result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator() (const Eigen::bfloat16& a) const {
-    return static_cast<float>(a);
-  }
-};
-
-template<>
-struct functor_traits<scalar_cast_op<Eigen::bfloat16, float> >
-{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
-
-
-}
-}
-
-#endif  // EIGEN_GENERIC_TYPE_CASTING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h
deleted file mode 100644
index d2b3a25..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h
+++ /dev/null
@@ -1,103 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_GPU_H
-#define EIGEN_MATH_FUNCTIONS_GPU_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 plog<float4>(const float4& a)
-{
-  return make_float4(logf(a.x), logf(a.y), logf(a.z), logf(a.w));
-}
-
-template<>  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 plog<double2>(const double2& a)
-{
-  using ::log;
-  return make_double2(log(a.x), log(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 plog1p<float4>(const float4& a)
-{
-  return make_float4(log1pf(a.x), log1pf(a.y), log1pf(a.z), log1pf(a.w));
-}
-
-template<>  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 plog1p<double2>(const double2& a)
-{
-  return make_double2(log1p(a.x), log1p(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pexp<float4>(const float4& a)
-{
-  return make_float4(expf(a.x), expf(a.y), expf(a.z), expf(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pexp<double2>(const double2& a)
-{
-  using ::exp;
-  return make_double2(exp(a.x), exp(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pexpm1<float4>(const float4& a)
-{
-  return make_float4(expm1f(a.x), expm1f(a.y), expm1f(a.z), expm1f(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pexpm1<double2>(const double2& a)
-{
-  return make_double2(expm1(a.x), expm1(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 psqrt<float4>(const float4& a)
-{
-  return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 psqrt<double2>(const double2& a)
-{
-  using ::sqrt;
-  return make_double2(sqrt(a.x), sqrt(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 prsqrt<float4>(const float4& a)
-{
-  return make_float4(rsqrtf(a.x), rsqrtf(a.y), rsqrtf(a.z), rsqrtf(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 prsqrt<double2>(const double2& a)
-{
-  return make_double2(rsqrt(a.x), rsqrt(a.y));
-}
-
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATH_FUNCTIONS_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
deleted file mode 100644
index 689110d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
+++ /dev/null
@@ -1,1685 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_GPU_H
-#define EIGEN_PACKET_MATH_GPU_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Read-only data cached load available.
-#if defined(EIGEN_HIP_DEVICE_COMPILE) || (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350)
-#define EIGEN_GPU_HAS_LDG 1
-#endif
-
-// FP16 math available.
-#if (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530)
-#define EIGEN_CUDA_HAS_FP16_ARITHMETIC 1
-#endif
-
-#if defined(EIGEN_HIP_DEVICE_COMPILE) || defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-#define EIGEN_GPU_HAS_FP16_ARITHMETIC 1
-#endif
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
-
-template<> struct is_arithmetic<float4>  { enum { value = true }; };
-template<> struct is_arithmetic<double2> { enum { value = true }; };
-
-template<> struct packet_traits<float> : default_packet_traits
-{
-  typedef float4 type;
-  typedef float4 half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=4,
-    HasHalfPacket = 0,
-
-    HasDiv  = 1,
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasLGamma = 1,
-    HasDiGamma = 1,
-    HasZeta = 1,
-    HasPolygamma = 1,
-    HasErf = 1,
-    HasErfc = 1,
-    HasNdtri = 1,
-    HasBessel = 1,
-    HasIGamma = 1,
-    HasIGammaDerA = 1,
-    HasGammaSampleDerAlpha = 1,
-    HasIGammac = 1,
-    HasBetaInc = 1,
-
-    HasBlend = 0,
-    HasFloor = 1,
-  };
-};
-
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef double2 type;
-  typedef double2 half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 0,
-
-    HasDiv  = 1,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasLGamma = 1,
-    HasDiGamma = 1,
-    HasZeta = 1,
-    HasPolygamma = 1,
-    HasErf = 1,
-    HasErfc = 1,
-    HasNdtri = 1,
-    HasBessel = 1,
-    HasIGamma = 1,
-    HasIGammaDerA = 1,
-    HasGammaSampleDerAlpha = 1,
-    HasIGammac = 1,
-    HasBetaInc = 1,
-
-    HasBlend = 0,
-    HasFloor = 1,
-  };
-};
-
-
-template<> struct unpacket_traits<float4>  { typedef float  type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef float4 half; };
-template<> struct unpacket_traits<double2> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef double2 half; };
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pset1<float4>(const float&  from) {
-  return make_float4(from, from, from, from);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pset1<double2>(const double& from) {
-  return make_double2(from, from);
-}
-
-// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
-// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
-// of the functions, while the latter can only deal with one of them.
-#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-namespace {
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_and(const float& a,
-                                                        const float& b) {
-  return __int_as_float(__float_as_int(a) & __float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_and(const double& a,
-                                                         const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) &
-                              __double_as_longlong(b));
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_or(const float& a,
-                                                       const float& b) {
-  return __int_as_float(__float_as_int(a) | __float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_or(const double& a,
-                                                        const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) |
-                              __double_as_longlong(b));
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_xor(const float& a,
-                                                        const float& b) {
-  return __int_as_float(__float_as_int(a) ^ __float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_xor(const double& a,
-                                                         const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) ^
-                              __double_as_longlong(b));
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_andnot(const float& a,
-                                                           const float& b) {
-  return __int_as_float(__float_as_int(a) & ~__float_as_int(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_andnot(const double& a,
-                                                            const double& b) {
-  return __longlong_as_double(__double_as_longlong(a) &
-                              ~__double_as_longlong(b));
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float eq_mask(const float& a,
-                                                    const float& b) {
-  return __int_as_float(a == b ? 0xffffffffu : 0u);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double eq_mask(const double& a,
-                                                     const double& b) {
-  return __longlong_as_double(a == b ? 0xffffffffffffffffull : 0ull);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float lt_mask(const float& a,
-                                                    const float& b) {
-  return __int_as_float(a < b ? 0xffffffffu : 0u);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double lt_mask(const double& a,
-                                                     const double& b) {
-  return __longlong_as_double(a < b ? 0xffffffffffffffffull : 0ull);
-}
-
-}  // namespace
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pand<float4>(const float4& a,
-                                                          const float4& b) {
-  return make_float4(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y),
-                     bitwise_and(a.z, b.z), bitwise_and(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pand<double2>(const double2& a,
-                                                            const double2& b) {
-  return make_double2(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 por<float4>(const float4& a,
-                                                         const float4& b) {
-  return make_float4(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y),
-                     bitwise_or(a.z, b.z), bitwise_or(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 por<double2>(const double2& a,
-                                                           const double2& b) {
-  return make_double2(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pxor<float4>(const float4& a,
-                                                          const float4& b) {
-  return make_float4(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y),
-                     bitwise_xor(a.z, b.z), bitwise_xor(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pxor<double2>(const double2& a,
-                                                            const double2& b) {
-  return make_double2(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pandnot<float4>(const float4& a,
-                                                             const float4& b) {
-  return make_float4(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y),
-                     bitwise_andnot(a.z, b.z), bitwise_andnot(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pandnot<double2>(const double2& a, const double2& b) {
-  return make_double2(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_eq<float4>(const float4& a,
-                                                             const float4& b) {
-  return make_float4(eq_mask(a.x, b.x), eq_mask(a.y, b.y), eq_mask(a.z, b.z),
-                     eq_mask(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_lt<float4>(const float4& a,
-                                                             const float4& b) {
-  return make_float4(lt_mask(a.x, b.x), lt_mask(a.y, b.y), lt_mask(a.z, b.z),
-                     lt_mask(a.w, b.w));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pcmp_eq<double2>(const double2& a, const double2& b) {
-  return make_double2(eq_mask(a.x, b.x), eq_mask(a.y, b.y));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pcmp_lt<double2>(const double2& a, const double2& b) {
-  return make_double2(lt_mask(a.x, b.x), lt_mask(a.y, b.y));
-}
-#endif // defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plset<float4>(const float& a) {
-  return make_float4(a, a+1, a+2, a+3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plset<double2>(const double& a) {
-  return make_double2(a, a+1);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 padd<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x+b.x, a.y+b.y, a.z+b.z, a.w+b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 padd<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x+b.x, a.y+b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 psub<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x-b.x, a.y-b.y, a.z-b.z, a.w-b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 psub<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x-b.x, a.y-b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pnegate(const float4& a) {
-  return make_float4(-a.x, -a.y, -a.z, -a.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pnegate(const double2& a) {
-  return make_double2(-a.x, -a.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pconj(const float4& a) { return a; }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pconj(const double2& a) { return a; }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmul<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x*b.x, a.y*b.y, a.z*b.z, a.w*b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmul<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x*b.x, a.y*b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pdiv<float4>(const float4& a, const float4& b) {
-  return make_float4(a.x/b.x, a.y/b.y, a.z/b.z, a.w/b.w);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pdiv<double2>(const double2& a, const double2& b) {
-  return make_double2(a.x/b.x, a.y/b.y);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmin<float4>(const float4& a, const float4& b) {
-  return make_float4(fminf(a.x, b.x), fminf(a.y, b.y), fminf(a.z, b.z), fminf(a.w, b.w));
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmin<double2>(const double2& a, const double2& b) {
-  return make_double2(fmin(a.x, b.x), fmin(a.y, b.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmax<float4>(const float4& a, const float4& b) {
-  return make_float4(fmaxf(a.x, b.x), fmaxf(a.y, b.y), fmaxf(a.z, b.z), fmaxf(a.w, b.w));
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmax<double2>(const double2& a, const double2& b) {
-  return make_double2(fmax(a.x, b.x), fmax(a.y, b.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pload<float4>(const float* from) {
-  return *reinterpret_cast<const float4*>(from);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pload<double2>(const double* from) {
-  return *reinterpret_cast<const double2*>(from);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploadu<float4>(const float* from) {
-  return make_float4(from[0], from[1], from[2], from[3]);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploadu<double2>(const double* from) {
-  return make_double2(from[0], from[1]);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploaddup<float4>(const float*   from) {
-  return make_float4(from[0], from[0], from[1], from[1]);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploaddup<double2>(const double*  from) {
-  return make_double2(from[0], from[0]);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<float>(float*   to, const float4& from) {
-  *reinterpret_cast<float4*>(to) = from;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<double>(double* to, const double2& from) {
-  *reinterpret_cast<double2*>(to) = from;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<float>(float*  to, const float4& from) {
-  to[0] = from.x;
-  to[1] = from.y;
-  to[2] = from.z;
-  to[3] = from.w;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const double2& from) {
-  to[0] = from.x;
-  to[1] = from.y;
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Aligned>(const float* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return __ldg((const float4*)from);
-#else
-  return make_float4(from[0], from[1], from[2], from[3]);
-#endif
-}
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Aligned>(const double* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return __ldg((const double2*)from);
-#else
-  return make_double2(from[0], from[1]);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Unaligned>(const float* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return make_float4(__ldg(from+0), __ldg(from+1), __ldg(from+2), __ldg(from+3));
-#else
-  return make_float4(from[0], from[1], from[2], from[3]);
-#endif
-}
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Unaligned>(const double* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return make_double2(__ldg(from+0), __ldg(from+1));
-#else
-  return make_double2(from[0], from[1]);
-#endif
-}
-
-template<> EIGEN_DEVICE_FUNC inline float4 pgather<float, float4>(const float* from, Index stride) {
-  return make_float4(from[0*stride], from[1*stride], from[2*stride], from[3*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline double2 pgather<double, double2>(const double* from, Index stride) {
-  return make_double2(from[0*stride], from[1*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, float4>(float* to, const float4& from, Index stride) {
-  to[stride*0] = from.x;
-  to[stride*1] = from.y;
-  to[stride*2] = from.z;
-  to[stride*3] = from.w;
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, double2>(double* to, const double2& from, Index stride) {
-  to[stride*0] = from.x;
-  to[stride*1] = from.y;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  pfirst<float4>(const float4& a) {
-  return a.x;
-}
-template<> EIGEN_DEVICE_FUNC inline double pfirst<double2>(const double2& a) {
-  return a.x;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux<float4>(const float4& a) {
-  return a.x + a.y + a.z + a.w;
-}
-template<> EIGEN_DEVICE_FUNC inline double predux<double2>(const double2& a) {
-  return a.x + a.y;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux_max<float4>(const float4& a) {
-  return fmaxf(fmaxf(a.x, a.y), fmaxf(a.z, a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double predux_max<double2>(const double2& a) {
-  return fmax(a.x, a.y);
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux_min<float4>(const float4& a) {
-  return fminf(fminf(a.x, a.y), fminf(a.z, a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double predux_min<double2>(const double2& a) {
-  return fmin(a.x, a.y);
-}
-
-template<> EIGEN_DEVICE_FUNC inline float  predux_mul<float4>(const float4& a) {
-  return a.x * a.y * a.z * a.w;
-}
-template<> EIGEN_DEVICE_FUNC inline double predux_mul<double2>(const double2& a) {
-  return a.x * a.y;
-}
-
-template<> EIGEN_DEVICE_FUNC inline float4  pabs<float4>(const float4& a) {
-  return make_float4(fabsf(a.x), fabsf(a.y), fabsf(a.z), fabsf(a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double2 pabs<double2>(const double2& a) {
-  return make_double2(fabs(a.x), fabs(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC inline float4  pfloor<float4>(const float4& a) {
-  return make_float4(floorf(a.x), floorf(a.y), floorf(a.z), floorf(a.w));
-}
-template<> EIGEN_DEVICE_FUNC inline double2 pfloor<double2>(const double2& a) {
-  return make_double2(floor(a.x), floor(a.y));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<float4,4>& kernel) {
-  float tmp = kernel.packet[0].y;
-  kernel.packet[0].y = kernel.packet[1].x;
-  kernel.packet[1].x = tmp;
-
-  tmp = kernel.packet[0].z;
-  kernel.packet[0].z = kernel.packet[2].x;
-  kernel.packet[2].x = tmp;
-
-  tmp = kernel.packet[0].w;
-  kernel.packet[0].w = kernel.packet[3].x;
-  kernel.packet[3].x = tmp;
-
-  tmp = kernel.packet[1].z;
-  kernel.packet[1].z = kernel.packet[2].y;
-  kernel.packet[2].y = tmp;
-
-  tmp = kernel.packet[1].w;
-  kernel.packet[1].w = kernel.packet[3].y;
-  kernel.packet[3].y = tmp;
-
-  tmp = kernel.packet[2].w;
-  kernel.packet[2].w = kernel.packet[3].z;
-  kernel.packet[3].z = tmp;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<double2,2>& kernel) {
-  double tmp = kernel.packet[0].y;
-  kernel.packet[0].y = kernel.packet[1].x;
-  kernel.packet[1].x = tmp;
-}
-
-#endif // defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
-
-// Packet4h2 must be defined in the macro without EIGEN_CUDA_ARCH, meaning
-// its corresponding packet_traits<Eigen::half> must be visible on host.
-#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
-
-typedef ulonglong2 Packet4h2;
-template<> struct unpacket_traits<Packet4h2> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4h2 half; };
-template<> struct is_arithmetic<Packet4h2> { enum { value = true }; };
-
-template<> struct unpacket_traits<half2> { typedef Eigen::half type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef half2 half; };
-template<> struct is_arithmetic<half2> { enum { value = true }; };
-
-template<> struct packet_traits<Eigen::half> : default_packet_traits
-{
-  typedef Packet4h2 type;
-  typedef Packet4h2 half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=8,
-    HasHalfPacket = 0,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasSqrt   = 1,
-    HasRsqrt  = 1,
-    HasExp    = 1,
-    HasExpm1  = 1,
-    HasLog    = 1,
-    HasLog1p  = 1
-  };
-};
-
-namespace {
-// This is equivalent to make_half2, which is undocumented and doesn't seem to always exist.
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 combine_half(const __half& a, const __half& b) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __halves2half2(a, b);
-#else
-  // Round-about way since __halves2half2 is a __device__ function.
-  return __floats2half2_rn(__half2float(a), __half2float(b));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE __half get_half2_low(const half2& a) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __low2half(a);
-#else
-  return __float2half(__low2float(a));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE __half get_half2_high(const half2& a) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __high2half(a);
-#else
-  return __float2half(__high2float(a));
-#endif
-}
-} // namespace
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pset1<half2>(const Eigen::half& from) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  return __half2half2(from);
-#else
-  const float f = __half2float(from);
-  return __floats2half2_rn(f, f);
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pset1<Packet4h2>(const Eigen::half& from) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = pset1<half2>(from);
-  p_alias[1] = pset1<half2>(from);
-  p_alias[2] = pset1<half2>(from);
-  p_alias[3] = pset1<half2>(from);
-  return r;
-}
-
-// We now need this visible on both host and device.
-// #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-namespace {
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pload(const Eigen::half* from) {
-  return *reinterpret_cast<const half2*>(from);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploadu(const Eigen::half* from) {
-  return combine_half(from[0], from[1]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploaddup(const Eigen::half*  from) {
-  return combine_half(from[0], from[0]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore(Eigen::half* to,
-                                                  const half2& from) {
-  *reinterpret_cast<half2*>(to) = from;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu(Eigen::half* to,
-                                                   const half2& from) {
-  to[0] = get_half2_low(from);
-  to[1] = get_half2_high(from);
-}
-
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_aligned(
-    const Eigen::half* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  // Input is guaranteed to be properly aligned.
-  return __ldg(reinterpret_cast<const half2*>(from));
-#else
-  return combine_half(*(from+0), *(from+1));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_unaligned(
-    const Eigen::half* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  return __halves2half2(__ldg(from+0), __ldg(from+1));
-#else
-  return combine_half(*(from+0), *(from+1));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pgather(const Eigen::half* from,
-                                                    Index stride) {
-  return combine_half(from[0*stride], from[1*stride]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter(
-    Eigen::half* to, const half2& from, Index stride) {
-  to[stride*0] = get_half2_low(from);
-  to[stride*1] = get_half2_high(from);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst(const half2& a) {
-  return get_half2_low(a);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pabs(const half2& a) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half result1 = half_impl::raw_uint16_to_half(a1.x & 0x7FFF);
-  half result2 = half_impl::raw_uint16_to_half(a2.x & 0x7FFF);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ptrue(const half2& /*a*/) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  return pset1<half2>(true_half);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pzero(const half2& /*a*/) {
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  return pset1<half2>(false_half);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<half2,2>& kernel) {
-  __half a1 = get_half2_low(kernel.packet[0]);
-  __half a2 = get_half2_high(kernel.packet[0]);
-  __half b1 = get_half2_low(kernel.packet[1]);
-  __half b2 = get_half2_high(kernel.packet[1]);
-  kernel.packet[0] = combine_half(a1, b1);
-  kernel.packet[1] = combine_half(a2, b2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plset(const Eigen::half& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __halves2half2(a, __hadd(a, __float2half(1.0f)));
-#else
-  float f = __half2float(a) + 1.0f;
-  return combine_half(a, __float2half(f));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pselect(const half2& mask,
-                                                    const half2& a,
-                                                    const half2& b) {
-  half mask_low = get_half2_low(mask);
-  half mask_high = get_half2_high(mask);
-  half result_low = mask_low == half(0) ? get_half2_low(b) : get_half2_low(a);
-  half result_high = mask_high == half(0) ? get_half2_high(b) : get_half2_high(a);
-  return combine_half(result_low, result_high);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_eq(const half2& a,
-                                                    const half2& b) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half eq1 = __half2float(a1) == __half2float(b1) ? true_half : false_half;
-  half eq2 = __half2float(a2) == __half2float(b2) ? true_half : false_half;
-  return combine_half(eq1, eq2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_lt(const half2& a,
-                                                    const half2& b) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half eq1 = __half2float(a1) < __half2float(b1) ? true_half : false_half;
-  half eq2 = __half2float(a2) < __half2float(b2) ? true_half : false_half;
-  return combine_half(eq1, eq2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pand(const half2& a,
-                                                 const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x & b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x & b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 por(const half2& a,
-                                                const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x | b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x | b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pxor(const half2& a,
-                                                 const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x ^ b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x ^ b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pandnot(const half2& a,
-                                                    const half2& b) {
-  half a1 = get_half2_low(a);
-  half a2 = get_half2_high(a);
-  half b1 = get_half2_low(b);
-  half b2 = get_half2_high(b);
-  half result1 = half_impl::raw_uint16_to_half(a1.x & ~b1.x);
-  half result2 = half_impl::raw_uint16_to_half(a2.x & ~b2.x);
-  return combine_half(result1, result2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hadd2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 + b1;
-  float r2 = a2 + b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psub(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hsub2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 - b1;
-  float r2 = a2 - b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pnegate(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hneg2(a);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return __floats2half2_rn(-a1, -a2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pconj(const half2& a) { return a; }
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hmul2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 * b1;
-  float r2 = a2 * b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmadd(const half2& a,
-                                                  const half2& b,
-                                                  const half2& c) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-   return __hfma2(a, b, c);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float c1 = __low2float(c);
-  float c2 = __high2float(c);
-  float r1 = a1 * b1 + c1;
-  float r2 = a2 * b2 + c2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv(const half2& a,
-                                                 const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __h2div(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 / b1;
-  float r2 = a2 / b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin(const half2& a,
-                                                 const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 < b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 < b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax(const half2& a,
-                                                 const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 > b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 > b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hadd(__low2half(a), __high2half(a));
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return Eigen::half(__float2half(a1 + a2));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  __half first = __low2half(a);
-  __half second = __high2half(a);
-  return __hgt(first, second) ? first : second;
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return a1 > a2 ? get_half2_low(a) : get_half2_high(a);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  __half first = __low2half(a);
-  __half second = __high2half(a);
-  return __hlt(first, second) ? first : second;
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return a1 < a2 ? get_half2_low(a) : get_half2_high(a);
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul(const half2& a) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hmul(__low2half(a), __high2half(a));
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  return Eigen::half(__float2half(a1 * a2));
-#endif
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog1p(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = log1pf(a1);
-  float r2 = log1pf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexpm1(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = expm1f(a1);
-  float r2 = expm1f(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-#if (EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)) || \
-  defined(EIGEN_HIP_DEVICE_COMPILE)
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 plog(const half2& a) {
-  return h2log(a);
-}
-
- EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 pexp(const half2& a) {
-  return h2exp(a);
-}
-
- EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 psqrt(const half2& a) {
-  return h2sqrt(a);
-}
-
- EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-half2 prsqrt(const half2& a) {
-  return h2rsqrt(a);
-}
-
-#else
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = logf(a1);
-  float r2 = logf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexp(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = expf(a1);
-  float r2 = expf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psqrt(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = sqrtf(a1);
-  float r2 = sqrtf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 prsqrt(const half2& a) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float r1 = rsqrtf(a1);
-  float r2 = rsqrtf(a2);
-  return __floats2half2_rn(r1, r2);
-}
-#endif
-} // namespace
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pload<Packet4h2>(const Eigen::half* from) {
-  return *reinterpret_cast<const Packet4h2*>(from);
-}
-
-// unaligned load;
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-ploadu<Packet4h2>(const Eigen::half* from) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = ploadu(from + 0);
-  p_alias[1] = ploadu(from + 2);
-  p_alias[2] = ploadu(from + 4);
-  p_alias[3] = ploadu(from + 6);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-ploaddup<Packet4h2>(const Eigen::half* from) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = ploaddup(from + 0);
-  p_alias[1] = ploaddup(from + 1);
-  p_alias[2] = ploaddup(from + 2);
-  p_alias[3] = ploaddup(from + 3);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<Eigen::half>(
-    Eigen::half* to, const Packet4h2& from) {
-  *reinterpret_cast<Packet4h2*>(to) = from;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(
-    Eigen::half* to, const Packet4h2& from) {
-  const half2* from_alias = reinterpret_cast<const half2*>(&from);
-  pstoreu(to + 0,from_alias[0]);
-  pstoreu(to + 2,from_alias[1]);
-  pstoreu(to + 4,from_alias[2]);
-  pstoreu(to + 6,from_alias[3]);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2
-ploadt_ro<Packet4h2, Aligned>(const Eigen::half* from) {
-#if defined(EIGEN_GPU_HAS_LDG)
-  Packet4h2 r;
-  r = __ldg(reinterpret_cast<const Packet4h2*>(from));
-  return r;
-#else
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  r_alias[0] = ploadt_ro_aligned(from + 0);
-  r_alias[1] = ploadt_ro_aligned(from + 2);
-  r_alias[2] = ploadt_ro_aligned(from + 4);
-  r_alias[3] = ploadt_ro_aligned(from + 6);
-  return r;
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2
-ploadt_ro<Packet4h2, Unaligned>(const Eigen::half* from) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  r_alias[0] = ploadt_ro_unaligned(from + 0);
-  r_alias[1] = ploadt_ro_unaligned(from + 2);
-  r_alias[2] = ploadt_ro_unaligned(from + 4);
-  r_alias[3] = ploadt_ro_unaligned(from + 6);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pgather<Eigen::half, Packet4h2>(const Eigen::half* from, Index stride) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = combine_half(from[0 * stride], from[1 * stride]);
-  p_alias[1] = combine_half(from[2 * stride], from[3 * stride]);
-  p_alias[2] = combine_half(from[4 * stride], from[5 * stride]);
-  p_alias[3] = combine_half(from[6 * stride], from[7 * stride]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h2>(
-    Eigen::half* to, const Packet4h2& from, Index stride) {
-  const half2* from_alias = reinterpret_cast<const half2*>(&from);
-  pscatter(to + stride * 0, from_alias[0], stride);
-  pscatter(to + stride * 2, from_alias[1], stride);
-  pscatter(to + stride * 4, from_alias[2], stride);
-  pscatter(to + stride * 6, from_alias[3], stride);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h2>(
-    const Packet4h2& a) {
-  return pfirst(*(reinterpret_cast<const half2*>(&a)));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pabs<Packet4h2>(
-    const Packet4h2& a) {
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  p_alias[0] = pabs(a_alias[0]);
-  p_alias[1] = pabs(a_alias[1]);
-  p_alias[2] = pabs(a_alias[2]);
-  p_alias[3] = pabs(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 ptrue<Packet4h2>(
-    const Packet4h2& /*a*/) {
-  half true_half = half_impl::raw_uint16_to_half(0xffffu);
-  return pset1<Packet4h2>(true_half);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pzero<Packet4h2>(const Packet4h2& /*a*/) {
-  half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  return pset1<Packet4h2>(false_half);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_double(
-    double* d_row0, double* d_row1, double* d_row2, double* d_row3,
-    double* d_row4, double* d_row5, double* d_row6, double* d_row7) {
-  double d_tmp;
-  d_tmp = d_row0[1];
-  d_row0[1] = d_row4[0];
-  d_row4[0] = d_tmp;
-
-  d_tmp = d_row1[1];
-  d_row1[1] = d_row5[0];
-  d_row5[0] = d_tmp;
-
-  d_tmp = d_row2[1];
-  d_row2[1] = d_row6[0];
-  d_row6[0] = d_tmp;
-
-  d_tmp = d_row3[1];
-  d_row3[1] = d_row7[0];
-  d_row7[0] = d_tmp;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_half2(
-    half2* f_row0, half2* f_row1, half2* f_row2, half2* f_row3) {
-  half2 f_tmp;
-  f_tmp = f_row0[1];
-  f_row0[1] = f_row2[0];
-  f_row2[0] = f_tmp;
-
-  f_tmp = f_row1[1];
-  f_row1[1] = f_row3[0];
-  f_row3[0] = f_tmp;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose_half(half2& f0, half2& f1) {
-  __half a1 = get_half2_low(f0);
-  __half a2 = get_half2_high(f0);
-  __half b1 = get_half2_low(f1);
-  __half b2 = get_half2_high(f1);
-  f0 = combine_half(a1, b1);
-  f1 = combine_half(a2, b2);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet4h2,8>& kernel) {
-  double* d_row0 = reinterpret_cast<double*>(&kernel.packet[0]);
-  double* d_row1 = reinterpret_cast<double*>(&kernel.packet[1]);
-  double* d_row2 = reinterpret_cast<double*>(&kernel.packet[2]);
-  double* d_row3 = reinterpret_cast<double*>(&kernel.packet[3]);
-  double* d_row4 = reinterpret_cast<double*>(&kernel.packet[4]);
-  double* d_row5 = reinterpret_cast<double*>(&kernel.packet[5]);
-  double* d_row6 = reinterpret_cast<double*>(&kernel.packet[6]);
-  double* d_row7 = reinterpret_cast<double*>(&kernel.packet[7]);
-  ptranspose_double(d_row0, d_row1, d_row2, d_row3,
-                    d_row4, d_row5, d_row6, d_row7);
-
-
-  half2* f_row0 = reinterpret_cast<half2*>(d_row0);
-  half2* f_row1 = reinterpret_cast<half2*>(d_row1);
-  half2* f_row2 = reinterpret_cast<half2*>(d_row2);
-  half2* f_row3 = reinterpret_cast<half2*>(d_row3);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-  f_row0 = reinterpret_cast<half2*>(d_row0 + 1);
-  f_row1 = reinterpret_cast<half2*>(d_row1 + 1);
-  f_row2 = reinterpret_cast<half2*>(d_row2 + 1);
-  f_row3 = reinterpret_cast<half2*>(d_row3 + 1);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-  f_row0 = reinterpret_cast<half2*>(d_row4);
-  f_row1 = reinterpret_cast<half2*>(d_row5);
-  f_row2 = reinterpret_cast<half2*>(d_row6);
-  f_row3 = reinterpret_cast<half2*>(d_row7);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-  f_row0 = reinterpret_cast<half2*>(d_row4 + 1);
-  f_row1 = reinterpret_cast<half2*>(d_row5 + 1);
-  f_row2 = reinterpret_cast<half2*>(d_row6 + 1);
-  f_row3 = reinterpret_cast<half2*>(d_row7 + 1);
-  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
-  ptranspose_half(f_row0[0], f_row1[0]);
-  ptranspose_half(f_row0[1], f_row1[1]);
-  ptranspose_half(f_row2[0], f_row3[0]);
-  ptranspose_half(f_row2[1], f_row3[1]);
-
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-plset<Packet4h2>(const Eigen::half& a) {
-#if defined(EIGEN_HIP_DEVICE_COMPILE)
-
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = __halves2half2(a, __hadd(a, __float2half(1.0f)));
-  p_alias[1] = __halves2half2(__hadd(a, __float2half(2.0f)),
-                              __hadd(a, __float2half(3.0f)));
-  p_alias[2] = __halves2half2(__hadd(a, __float2half(4.0f)),
-                              __hadd(a, __float2half(5.0f)));
-  p_alias[3] = __halves2half2(__hadd(a, __float2half(6.0f)),
-                              __hadd(a, __float2half(7.0f)));
-  return r;
-#elif defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-
-  half2 b = pset1<half2>(a);
-  half2 c;
-  half2 half_offset0 = __halves2half2(__float2half(0.0f),__float2half(2.0f));
-  half2 half_offset1 = __halves2half2(__float2half(4.0f),__float2half(6.0f));
-
-  c = __hadd2(b, half_offset0);
-  r_alias[0] = plset(__low2half(c));
-  r_alias[1] = plset(__high2half(c));
-
-  c = __hadd2(b, half_offset1);
-  r_alias[2] = plset(__low2half(c));
-  r_alias[3] = plset(__high2half(c));
-
-  return r;
-
-#else
-  float f = __half2float(a);
-  Packet4h2 r;
-  half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = combine_half(a, __float2half(f + 1.0f));
-  p_alias[1] = combine_half(__float2half(f + 2.0f), __float2half(f + 3.0f));
-  p_alias[2] = combine_half(__float2half(f + 4.0f), __float2half(f + 5.0f));
-  p_alias[3] = combine_half(__float2half(f + 6.0f), __float2half(f + 7.0f));
-  return r;
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pselect<Packet4h2>(const Packet4h2& mask, const Packet4h2& a,
-                   const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* mask_alias = reinterpret_cast<const half2*>(&mask);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pselect(mask_alias[0], a_alias[0], b_alias[0]);
-  r_alias[1] = pselect(mask_alias[1], a_alias[1], b_alias[1]);
-  r_alias[2] = pselect(mask_alias[2], a_alias[2], b_alias[2]);
-  r_alias[3] = pselect(mask_alias[3], a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pcmp_eq<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pcmp_eq(a_alias[0], b_alias[0]);
-  r_alias[1] = pcmp_eq(a_alias[1], b_alias[1]);
-  r_alias[2] = pcmp_eq(a_alias[2], b_alias[2]);
-  r_alias[3] = pcmp_eq(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pand<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pand(a_alias[0], b_alias[0]);
-  r_alias[1] = pand(a_alias[1], b_alias[1]);
-  r_alias[2] = pand(a_alias[2], b_alias[2]);
-  r_alias[3] = pand(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 por<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = por(a_alias[0], b_alias[0]);
-  r_alias[1] = por(a_alias[1], b_alias[1]);
-  r_alias[2] = por(a_alias[2], b_alias[2]);
-  r_alias[3] = por(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pxor<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pxor(a_alias[0], b_alias[0]);
-  r_alias[1] = pxor(a_alias[1], b_alias[1]);
-  r_alias[2] = pxor(a_alias[2], b_alias[2]);
-  r_alias[3] = pxor(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pandnot<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pandnot(a_alias[0], b_alias[0]);
-  r_alias[1] = pandnot(a_alias[1], b_alias[1]);
-  r_alias[2] = pandnot(a_alias[2], b_alias[2]);
-  r_alias[3] = pandnot(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 padd<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = padd(a_alias[0], b_alias[0]);
-  r_alias[1] = padd(a_alias[1], b_alias[1]);
-  r_alias[2] = padd(a_alias[2], b_alias[2]);
-  r_alias[3] = padd(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psub<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = psub(a_alias[0], b_alias[0]);
-  r_alias[1] = psub(a_alias[1], b_alias[1]);
-  r_alias[2] = psub(a_alias[2], b_alias[2]);
-  r_alias[3] = psub(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pnegate(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = pnegate(a_alias[0]);
-  r_alias[1] = pnegate(a_alias[1]);
-  r_alias[2] = pnegate(a_alias[2]);
-  r_alias[3] = pnegate(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pconj(const Packet4h2& a) {
-  return a;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmul<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pmul(a_alias[0], b_alias[0]);
-  r_alias[1] = pmul(a_alias[1], b_alias[1]);
-  r_alias[2] = pmul(a_alias[2], b_alias[2]);
-  r_alias[3] = pmul(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmadd<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b, const Packet4h2& c) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  const half2* c_alias = reinterpret_cast<const half2*>(&c);
-  r_alias[0] = pmadd(a_alias[0], b_alias[0], c_alias[0]);
-  r_alias[1] = pmadd(a_alias[1], b_alias[1], c_alias[1]);
-  r_alias[2] = pmadd(a_alias[2], b_alias[2], c_alias[2]);
-  r_alias[3] = pmadd(a_alias[3], b_alias[3], c_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pdiv<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pdiv(a_alias[0], b_alias[0]);
-  r_alias[1] = pdiv(a_alias[1], b_alias[1]);
-  r_alias[2] = pdiv(a_alias[2], b_alias[2]);
-  r_alias[3] = pdiv(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmin<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pmin(a_alias[0], b_alias[0]);
-  r_alias[1] = pmin(a_alias[1], b_alias[1]);
-  r_alias[2] = pmin(a_alias[2], b_alias[2]);
-  r_alias[3] = pmin(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmax<Packet4h2>(
-    const Packet4h2& a, const Packet4h2& b) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  const half2* b_alias = reinterpret_cast<const half2*>(&b);
-  r_alias[0] = pmax(a_alias[0], b_alias[0]);
-  r_alias[1] = pmax(a_alias[1], b_alias[1]);
-  r_alias[2] = pmax(a_alias[2], b_alias[2]);
-  r_alias[3] = pmax(a_alias[3], b_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-
-  return predux(a_alias[0]) + predux(a_alias[1]) +
-         predux(a_alias[2]) + predux(a_alias[3]);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  half2 m0 = combine_half(predux_max(a_alias[0]),
-                            predux_max(a_alias[1]));
-  half2 m1 = combine_half(predux_max(a_alias[2]),
-                            predux_max(a_alias[3]));
-  __half first  = predux_max(m0);
-  __half second = predux_max(m1);
-#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-  return (__hgt(first, second) ? first : second);
-#else
-  float ffirst  = __half2float(first);
-  float fsecond = __half2float(second);
-  return (ffirst > fsecond)? first: second;
-#endif
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  half2 m0 = combine_half(predux_min(a_alias[0]),
-                            predux_min(a_alias[1]));
-  half2 m1 = combine_half(predux_min(a_alias[2]),
-                            predux_min(a_alias[3]));
-  __half first  = predux_min(m0);
-  __half second = predux_min(m1);
-#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
-  return (__hlt(first, second) ? first : second);
-#else
-  float ffirst  = __half2float(first);
-  float fsecond = __half2float(second);
-  return (ffirst < fsecond)? first: second;
-#endif
-}
-
-// likely overflow/underflow
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4h2>(
-    const Packet4h2& a) {
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  return predux_mul(pmul(pmul(a_alias[0], a_alias[1]),
-                                       pmul(a_alias[2], a_alias[3])));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-plog1p<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = plog1p(a_alias[0]);
-  r_alias[1] = plog1p(a_alias[1]);
-  r_alias[2] = plog1p(a_alias[2]);
-  r_alias[3] = plog1p(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-pexpm1<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = pexpm1(a_alias[0]);
-  r_alias[1] = pexpm1(a_alias[1]);
-  r_alias[2] = pexpm1(a_alias[2]);
-  r_alias[3] = pexpm1(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 plog<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = plog(a_alias[0]);
-  r_alias[1] = plog(a_alias[1]);
-  r_alias[2] = plog(a_alias[2]);
-  r_alias[3] = plog(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pexp<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = pexp(a_alias[0]);
-  r_alias[1] = pexp(a_alias[1]);
-  r_alias[2] = pexp(a_alias[2]);
-  r_alias[3] = pexp(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psqrt<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = psqrt(a_alias[0]);
-  r_alias[1] = psqrt(a_alias[1]);
-  r_alias[2] = psqrt(a_alias[2]);
-  r_alias[3] = psqrt(a_alias[3]);
-  return r;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
-prsqrt<Packet4h2>(const Packet4h2& a) {
-  Packet4h2 r;
-  half2* r_alias = reinterpret_cast<half2*>(&r);
-  const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  r_alias[0] = prsqrt(a_alias[0]);
-  r_alias[1] = prsqrt(a_alias[1]);
-  r_alias[2] = prsqrt(a_alias[2]);
-  r_alias[3] = prsqrt(a_alias[3]);
-  return r;
-}
-
-// The following specialized padd, pmul, pdiv, pmin, pmax, pset1 are needed for
-// the implementation of GPU half reduction.
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd<half2>(const half2& a,
-                                                        const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hadd2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 + b1;
-  float r2 = a2 + b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul<half2>(const half2& a,
-                                                        const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __hmul2(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 * b1;
-  float r2 = a2 * b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv<half2>(const half2& a,
-                                                        const half2& b) {
-#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
-  return __h2div(a, b);
-#else
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  float r1 = a1 / b1;
-  float r2 = a2 / b2;
-  return __floats2half2_rn(r1, r2);
-#endif
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin<half2>(const half2& a,
-                                                        const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 < b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 < b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax<half2>(const half2& a,
-                                                        const half2& b) {
-  float a1 = __low2float(a);
-  float a2 = __high2float(a);
-  float b1 = __low2float(b);
-  float b2 = __high2float(b);
-  __half r1 = a1 > b1 ? get_half2_low(a) : get_half2_low(b);
-  __half r2 = a2 > b2 ? get_half2_high(a) : get_half2_high(b);
-  return combine_half(r1, r2);
-}
-
-// #endif // defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
-
-#endif // defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
-
-#undef EIGEN_GPU_HAS_LDG
-#undef EIGEN_CUDA_HAS_FP16_ARITHMETIC
-#undef EIGEN_GPU_HAS_FP16_ARITHMETIC
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-
-#endif // EIGEN_PACKET_MATH_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
deleted file mode 100644
index 7545462..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
+++ /dev/null
@@ -1,80 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_GPU_H
-#define EIGEN_TYPE_CASTING_GPU_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
-
-
-template <>
-struct type_casting_traits<Eigen::half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 2
-  };
-};
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<half2, float4>(const half2& a, const half2& b) {
-  float2 r1 = __half22float2(a);
-  float2 r2 = __half22float2(b);
-  return make_float4(r1.x, r1.y, r2.x, r2.y);
-}
-
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pcast<float4, Packet4h2>(const float4& a, const float4& b) {
-  Packet4h2 r;
-  half2* r_alias=reinterpret_cast<half2*>(&r);
-  r_alias[0]=__floats2half2_rn(a.x,a.y);
-  r_alias[1]=__floats2half2_rn(a.z,a.w);
-  r_alias[2]=__floats2half2_rn(b.x,b.y);
-  r_alias[3]=__floats2half2_rn(b.z,b.w);
-  return r;
-}
-
-template <>
-struct type_casting_traits<float, Eigen::half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 2,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<Packet4h2, float4>(const Packet4h2& a) {
-  // Simply discard the second half of the input
-  float4 r;
-  const half2* a_alias=reinterpret_cast<const half2*>(&a);
-  float2 r1 = __half22float2(a_alias[0]);
-  float2 r2 = __half22float2(a_alias[1]);
-  r.x=static_cast<float>(r1.x);
-  r.y=static_cast<float>(r1.y);
-  r.z=static_cast<float>(r2.x);
-  r.w=static_cast<float>(r2.y);
-  return r;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcast<float4, half2>(const float4& a) {
-  // Simply discard the second half of the input
-  return __floats2half2_rn(a.x, a.y);
-}
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h
deleted file mode 100644
index 25375a0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * math_constants.h - 
- *  HIP equivalent of the CUDA header of the same name
- */
-
-#ifndef __MATH_CONSTANTS_H__
-#define __MATH_CONSTANTS_H__
-
-/* single precision constants */
-
-#define HIPRT_INF_F        __int_as_float(0x7f800000)
-#define HIPRT_NAN_F        __int_as_float(0x7fffffff)
-#define HIPRT_MIN_DENORM_F __int_as_float(0x00000001)
-#define HIPRT_MAX_NORMAL_F __int_as_float(0x7f7fffff)
-#define HIPRT_NEG_ZERO_F   __int_as_float(0x80000000)
-#define HIPRT_ZERO_F       0.0f
-#define HIPRT_ONE_F        1.0f
-
-/* double precision constants */
-#define HIPRT_INF          __hiloint2double(0x7ff00000, 0x00000000)
-#define HIPRT_NAN          __hiloint2double(0xfff80000, 0x00000000)
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
deleted file mode 100644
index 53dacfa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
+++ /dev/null
@@ -1,648 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Wave Computing, Inc.
-// Written by:
-//   Chris Larsen
-//   Alexey Frunze (afrunze@wavecomp.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_MSA_H
-#define EIGEN_COMPLEX_MSA_H
-
-#include <iostream>
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet2cf {
-  EIGEN_STRONG_INLINE Packet2cf() {
-  }
-  EIGEN_STRONG_INLINE explicit Packet2cf(const std::complex<float>& a,
-                                         const std::complex<float>& b) {
-    Packet4f t = { std::real(a), std::imag(a), std::real(b), std::imag(b) };
-    v = t;
-  }
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {
-  }
-  EIGEN_STRONG_INLINE Packet2cf(const Packet2cf& a) : v(a.v) {
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator=(const Packet2cf& b) {
-    v = b.v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf conjugate(void) const {
-    return Packet2cf((Packet4f)__builtin_msa_bnegi_d((v2u64)v, 63));
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
-    Packet4f v1, v2;
-
-    // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
-    v1 = (Packet4f)__builtin_msa_ilvev_w((v4i32)v, (v4i32)v);
-    // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
-    v2 = (Packet4f)__builtin_msa_ilvod_w((v4i32)v, (v4i32)v);
-    // Multiply the real a with b
-    v1 = pmul(v1, b.v);
-    // Multiply the imag a with b
-    v2 = pmul(v2, b.v);
-    // Conjugate v2
-    v2 = Packet2cf(v2).conjugate().v;
-    // Swap real/imag elements in v2.
-    v2 = (Packet4f)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(1, 0, 3, 2));
-    // Add and return the result
-    v = padd(v1, v2);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const {
-    return Packet2cf(*this) *= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const {
-    return Packet2cf(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const {
-    return Packet2cf(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf& operator/=(const Packet2cf& b) {
-    *this *= b.conjugate();
-    Packet4f s = pmul<Packet4f>(b.v, b.v);
-    s = padd(s, (Packet4f)__builtin_msa_shf_w((v4i32)s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-    v = pdiv(v, s);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator/(const Packet2cf& b) const {
-    return Packet2cf(*this) /= b;
-  }
-  EIGEN_STRONG_INLINE Packet2cf operator-(void) const {
-    return Packet2cf(pnegate(v));
-  }
-
-  Packet4f v;
-};
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2cf& value) {
-  os << "[ (" << value.v[0] << ", " << value.v[1]
-     << "i),"
-        "  ("
-     << value.v[2] << ", " << value.v[3] << "i) ]";
-  return os;
-}
-
-template <>
-struct packet_traits<std::complex<float> > : default_packet_traits {
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasNegate = 1,
-    HasAbs = 0,
-    HasAbs2 = 0,
-    HasMin = 0,
-    HasMax = 0,
-    HasSetLinear = 0,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet2cf> {
-  typedef std::complex<float> type;
-  enum { size = 2, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet2cf half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
-  EIGEN_MSA_DEBUG;
-
-  float f0 = from.real(), f1 = from.imag();
-  Packet4f v0 = { f0, f0, f0, f0 };
-  Packet4f v1 = { f1, f1, f1, f1 };
-  return Packet2cf((Packet4f)__builtin_msa_ilvr_w((Packet4i)v1, (Packet4i)v0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a + b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a - b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return -a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a.conjugate();
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a * b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(pand(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(por(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(pxor(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(pandnot(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
-  EIGEN_MSA_DEBUG;
-
-  return pset1<Packet2cf>(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to,
-                                                      const Packet2cf& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE pstore<float>((float*)to, from.v);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to,
-                                                       const Packet2cf& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<float>((float*)to, from.v);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(
-    const std::complex<float>* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf(from[0 * stride], from[1 * stride]);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to,
-                                                                       const Packet2cf& from,
-                                                                       Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = std::complex<float>(from.v[0], from.v[1]);
-  to += stride;
-  *to = std::complex<float>(from.v[2], from.v[3]);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
-  EIGEN_MSA_DEBUG;
-
-  prefetch(reinterpret_cast<const float*>(addr));
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return std::complex<float>(a.v[0], a.v[1]);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f value = (Packet4f)preverse((Packet2d)a.v);
-  value += a.v;
-  return std::complex<float>(value[0], value[1]);
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
-  EIGEN_MSA_DEBUG;
-
-  return std::complex<float>((a.v[0] * a.v[2]) - (a.v[1] * a.v[3]),
-                             (a.v[0] * a.v[3]) + (a.v[1] * a.v[2]));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a / b;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2cf, 2>& value) {
-  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f tmp =
-      (Packet4f)__builtin_msa_ilvl_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
-  kernel.packet[0].v =
-      (Packet4f)__builtin_msa_ilvr_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
-  kernel.packet[1].v = tmp;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket,
-                                     const Packet2cf& elsePacket) {
-  return (Packet2cf)(Packet4f)pblend<Packet2d>(ifPacket, (Packet2d)thenPacket.v,
-                                               (Packet2d)elsePacket.v);
-}
-
-//---------- double ----------
-
-struct Packet1cd {
-  EIGEN_STRONG_INLINE Packet1cd() {
-  }
-  EIGEN_STRONG_INLINE explicit Packet1cd(const std::complex<double>& a) {
-    v[0] = std::real(a);
-    v[1] = std::imag(a);
-  }
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {
-  }
-  EIGEN_STRONG_INLINE Packet1cd(const Packet1cd& a) : v(a.v) {
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator=(const Packet1cd& b) {
-    v = b.v;
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd conjugate(void) const {
-    static const v2u64 p2ul_CONJ_XOR = { 0x0, 0x8000000000000000 };
-    return (Packet1cd)pxor(v, (Packet2d)p2ul_CONJ_XOR);
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
-    Packet2d v1, v2;
-
-    // Get the real values of a | a1_re | a1_re
-    v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)v, (v2i64)v);
-    // Get the imag values of a | a1_im | a1_im
-    v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)v, (v2i64)v);
-    // Multiply the real a with b
-    v1 = pmul(v1, b.v);
-    // Multiply the imag a with b
-    v2 = pmul(v2, b.v);
-    // Conjugate v2
-    v2 = Packet1cd(v2).conjugate().v;
-    // Swap real/imag elements in v2.
-    v2 = (Packet2d)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-    // Add and return the result
-    v = padd(v1, v2);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const {
-    return Packet1cd(*this) *= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
-    v = padd(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const {
-    return Packet1cd(*this) += b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
-    v = psub(v, b.v);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const {
-    return Packet1cd(*this) -= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd& operator/=(const Packet1cd& b) {
-    *this *= b.conjugate();
-    Packet2d s = pmul<Packet2d>(b.v, b.v);
-    s = padd(s, preverse<Packet2d>(s));
-    v = pdiv(v, s);
-    return *this;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator/(const Packet1cd& b) const {
-    return Packet1cd(*this) /= b;
-  }
-  EIGEN_STRONG_INLINE Packet1cd operator-(void) const {
-    return Packet1cd(pnegate(v));
-  }
-
-  Packet2d v;
-};
-
-inline std::ostream& operator<<(std::ostream& os, const Packet1cd& value) {
-  os << "[ (" << value.v[0] << ", " << value.v[1] << "i) ]";
-  return os;
-}
-
-template <>
-struct packet_traits<std::complex<double> > : default_packet_traits {
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasNegate = 1,
-    HasAbs = 0,
-    HasAbs2 = 0,
-    HasMin = 0,
-    HasMax = 0,
-    HasSetLinear = 0
-  };
-};
-
-template <>
-struct unpacket_traits<Packet1cd> {
-  typedef std::complex<double> type;
-  enum { size = 1, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet1cd half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a + b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a - b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return -a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a.conjugate();
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a * b;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(pand(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(por(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(pxor(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(pandnot(a.v, b.v));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
-  EIGEN_MSA_DEBUG;
-
-  return pset1<Packet1cd>(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to,
-                                                       const Packet1cd& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE pstore<double>((double*)to, from.v);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to,
-                                                        const Packet1cd& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<double>((double*)to, from.v);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
-  EIGEN_MSA_DEBUG;
-
-  prefetch(reinterpret_cast<const double*>(addr));
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(
-    const std::complex<double>* from, Index stride __attribute__((unused))) {
-  EIGEN_MSA_DEBUG;
-
-  Packet1cd res;
-  res.v[0] = std::real(from[0]);
-  res.v[1] = std::imag(from[0]);
-  return res;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to,
-                                                                        const Packet1cd& from,
-                                                                        Index stride
-                                                                        __attribute__((unused))) {
-  EIGEN_MSA_DEBUG;
-
-  pstore(to, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return std::complex<double>(a.v[0], a.v[1]);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return pfirst(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
-  EIGEN_MSA_DEBUG;
-
-  return pfirst(a);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
-
-template <>
-EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
-  EIGEN_MSA_DEBUG;
-
-  return a / b;
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip /*<Packet1cd>*/ (const Packet1cd& x) {
-  EIGEN_MSA_DEBUG;
-
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet1cd, 2>& value) {
-  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
-  return os;
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d v1, v2;
-
-  v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
-  // Get the imag values of a
-  v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
-
-  kernel.packet[0].v = v1;
-  kernel.packet[1].v = v2;
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_COMPLEX_MSA_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h
deleted file mode 100644
index f5181b9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h
+++ /dev/null
@@ -1,387 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Copyright (C) 2018 Wave Computing, Inc.
-// Written by:
-//   Chris Larsen
-//   Alexey Frunze (afrunze@wavecomp.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The sin, cos, exp, and log functions of this file come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-/* The tanh function of this file is an adaptation of
- * template<typename T> T generic_fast_tanh_float(const T&)
- * from MathFunctionsImpl.h.
- */
-
-#ifndef EIGEN_MATH_FUNCTIONS_MSA_H
-#define EIGEN_MATH_FUNCTIONS_MSA_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-plog<Packet4f>(const Packet4f& _x) {
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292e-2f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, -1.1514610310e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, -1.2420140846e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, +1.4249322787e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, -1.6668057665e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, +2.0000714765e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, -2.4999993993e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, +3.3333331174e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
-  static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
-
-  // Convert negative argument into NAN (quiet negative, to be specific).
-  Packet4f zero = (Packet4f)__builtin_msa_ldi_w(0);
-  Packet4i neg_mask = __builtin_msa_fclt_w(_x, zero);
-  Packet4i zero_mask = __builtin_msa_fceq_w(_x, zero);
-  Packet4f non_neg_x_or_nan = padd(_x, (Packet4f)neg_mask);  // Add 0.0 or NAN.
-  Packet4f x = non_neg_x_or_nan;
-
-  // Extract exponent from x = mantissa * 2**exponent, where 1.0 <= mantissa < 2.0.
-  // N.B. the exponent is one less of what frexpf() would return.
-  Packet4i e_int = __builtin_msa_ftint_s_w(__builtin_msa_flog2_w(x));
-  // Multiply x by 2**(-exponent-1) to get 0.5 <= x < 1.0 as from frexpf().
-  x = __builtin_msa_fexp2_w(x, (Packet4i)__builtin_msa_nori_b((v16u8)e_int, 0));
-
-  /*
-     if (x < SQRTHF) {
-       x = x + x - 1.0;
-     } else {
-       e += 1;
-       x = x - 1.0;
-     }
-  */
-  Packet4f xx = padd(x, x);
-  Packet4i ge_mask = __builtin_msa_fcle_w(p4f_cephes_SQRTHF, x);
-  e_int = psub(e_int, ge_mask);
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)ge_mask, (v16u8)xx, (v16u8)x);
-  x = psub(x, p4f_1);
-  Packet4f e = __builtin_msa_ffint_s_w(e_int);
-
-  Packet4f x2 = pmul(x, x);
-  Packet4f x3 = pmul(x2, x);
-
-  Packet4f y, y1, y2;
-  y = pmadd(p4f_cephes_log_p0, x, p4f_cephes_log_p1);
-  y1 = pmadd(p4f_cephes_log_p3, x, p4f_cephes_log_p4);
-  y2 = pmadd(p4f_cephes_log_p6, x, p4f_cephes_log_p7);
-  y = pmadd(y, x, p4f_cephes_log_p2);
-  y1 = pmadd(y1, x, p4f_cephes_log_p5);
-  y2 = pmadd(y2, x, p4f_cephes_log_p8);
-  y = pmadd(y, x3, y1);
-  y = pmadd(y, x3, y2);
-  y = pmul(y, x3);
-
-  y = pmadd(e, p4f_cephes_log_q1, y);
-  x = __builtin_msa_fmsub_w(x, x2, p4f_half);
-  x = padd(x, y);
-  x = pmadd(e, p4f_cephes_log_q2, x);
-
-  // x is now the logarithm result candidate. We still need to handle the
-  // extreme arguments of zero and positive infinity, though.
-  // N.B. if the argument is +INFINITY, x is NAN because the polynomial terms
-  // contain infinities of both signs (see the coefficients and code above).
-  // INFINITY - INFINITY is NAN.
-
-  // If the argument is +INFINITY, make it the new result candidate.
-  // To achieve that we choose the smaller of the result candidate and the
-  // argument.
-  // This is correct for all finite pairs of values (the logarithm is smaller
-  // than the argument).
-  // This is also correct in the special case when the argument is +INFINITY
-  // and the result candidate is NAN. This is because the fmin.df instruction
-  // prefers non-NANs to NANs.
-  x = __builtin_msa_fmin_w(x, non_neg_x_or_nan);
-
-  // If the argument is zero (including -0.0), the result becomes -INFINITY.
-  Packet4i neg_infs = __builtin_msa_slli_w(zero_mask, 23);
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)zero_mask, (v16u8)x, (v16u8)neg_infs);
-
-  return x;
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-pexp<Packet4f>(const Packet4f& _x) {
-  // Limiting single-precision pexp's argument to [-128, +128] lets pexp
-  // reach 0 and INFINITY naturally.
-  static _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -128.0f);
-  static _EIGEN_DECLARE_CONST_Packet4f(exp_hi, +128.0f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894e-2f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
-
-  Packet4f x = _x;
-
-  // Clamp x.
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(x, p4f_exp_lo), (v16u8)x,
-                                     (v16u8)p4f_exp_lo);
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_exp_hi, x), (v16u8)x,
-                                     (v16u8)p4f_exp_hi);
-
-  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
-  Packet4f x2_add = (Packet4f)__builtin_msa_binsli_w((v4u32)p4f_half, (v4u32)x, 0);
-  Packet4f x2 = pmadd(x, p4f_cephes_LOG2EF, x2_add);
-  Packet4i x2_int = __builtin_msa_ftrunc_s_w(x2);
-  Packet4f x2_int_f = __builtin_msa_ffint_s_w(x2_int);
-
-  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C1);
-  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C2);
-
-  Packet4f z = pmul(x, x);
-
-  Packet4f y = p4f_cephes_exp_p0;
-  y = pmadd(y, x, p4f_cephes_exp_p1);
-  y = pmadd(y, x, p4f_cephes_exp_p2);
-  y = pmadd(y, x, p4f_cephes_exp_p3);
-  y = pmadd(y, x, p4f_cephes_exp_p4);
-  y = pmadd(y, x, p4f_cephes_exp_p5);
-  y = pmadd(y, z, x);
-  y = padd(y, p4f_1);
-
-  // y *= 2**exponent.
-  y = __builtin_msa_fexp2_w(y, x2_int);
-
-  return y;
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& _x) {
-  static _EIGEN_DECLARE_CONST_Packet4f(tanh_tiny, 1e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(tanh_hi, 9.0f);
-  // The monomial coefficients of the numerator polynomial (odd).
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_1, 4.89352455891786e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_3, 6.37261928875436e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_5, 1.48572235717979e-5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_7, 5.12229709037114e-8f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_9, -8.60467152213735e-11f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_11, 2.00018790482477e-13f);
-  static _EIGEN_DECLARE_CONST_Packet4f(alpha_13, -2.76076847742355e-16f);
-  // The monomial coefficients of the denominator polynomial (even).
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_0, 4.89352518554385e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_2, 2.26843463243900e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_4, 1.18534705686654e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(beta_6, 1.19825839466702e-6f);
-
-  Packet4f x = pabs(_x);
-  Packet4i tiny_mask = __builtin_msa_fclt_w(x, p4f_tanh_tiny);
-
-  // Clamp the inputs to the range [-9, 9] since anything outside
-  // this range is -/+1.0f in single-precision.
-  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_tanh_hi, x), (v16u8)x,
-                                     (v16u8)p4f_tanh_hi);
-
-  // Since the polynomials are odd/even, we need x**2.
-  Packet4f x2 = pmul(x, x);
-
-  // Evaluate the numerator polynomial p.
-  Packet4f p = pmadd(x2, p4f_alpha_13, p4f_alpha_11);
-  p = pmadd(x2, p, p4f_alpha_9);
-  p = pmadd(x2, p, p4f_alpha_7);
-  p = pmadd(x2, p, p4f_alpha_5);
-  p = pmadd(x2, p, p4f_alpha_3);
-  p = pmadd(x2, p, p4f_alpha_1);
-  p = pmul(x, p);
-
-  // Evaluate the denominator polynomial q.
-  Packet4f q = pmadd(x2, p4f_beta_6, p4f_beta_4);
-  q = pmadd(x2, q, p4f_beta_2);
-  q = pmadd(x2, q, p4f_beta_0);
-
-  // Divide the numerator by the denominator.
-  p = pdiv(p, q);
-
-  // Reinstate the sign.
-  p = (Packet4f)__builtin_msa_binsli_w((v4u32)p, (v4u32)_x, 0);
-
-  // When the argument is very small in magnitude it's more accurate to just return it.
-  p = (Packet4f)__builtin_msa_bsel_v((v16u8)tiny_mask, (v16u8)p, (v16u8)_x);
-
-  return p;
-}
-
-template <bool sine>
-Packet4f psincos_inner_msa_float(const Packet4f& _x) {
-  static _EIGEN_DECLARE_CONST_Packet4f(sincos_max_arg, 13176795.0f);  // Approx. (2**24) / (4/Pi).
-  static _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP1, -0.78515625f);
-  static _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP2, -2.4187564849853515625e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP3, -3.77489497744594108e-8f);
-  static _EIGEN_DECLARE_CONST_Packet4f(sincof_p0, -1.9515295891e-4f);
-  static _EIGEN_DECLARE_CONST_Packet4f(sincof_p1, 8.3321608736e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(sincof_p2, -1.6666654611e-1f);
-  static _EIGEN_DECLARE_CONST_Packet4f(coscof_p0, 2.443315711809948e-5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(coscof_p1, -1.388731625493765e-3f);
-  static _EIGEN_DECLARE_CONST_Packet4f(coscof_p2, 4.166664568298827e-2f);
-  static _EIGEN_DECLARE_CONST_Packet4f(cephes_FOPI, 1.27323954473516f);  // 4/Pi.
-  static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-  static _EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
-
-  Packet4f x = pabs(_x);
-
-  // Translate infinite arguments into NANs.
-  Packet4f zero_or_nan_if_inf = psub(_x, _x);
-  x = padd(x, zero_or_nan_if_inf);
-  // Prevent sin/cos from generating values larger than 1.0 in magnitude
-  // for very large arguments by setting x to 0.0.
-  Packet4i small_or_nan_mask = __builtin_msa_fcult_w(x, p4f_sincos_max_arg);
-  x = pand(x, (Packet4f)small_or_nan_mask);
-
-  // Scale x by 4/Pi to find x's octant.
-  Packet4f y = pmul(x, p4f_cephes_FOPI);
-  // Get the octant. We'll reduce x by this number of octants or by one more than it.
-  Packet4i y_int = __builtin_msa_ftrunc_s_w(y);
-  // x's from even-numbered octants will translate to octant 0: [0, +Pi/4].
-  // x's from odd-numbered octants will translate to octant -1: [-Pi/4, 0].
-  // Adjustment for odd-numbered octants: octant = (octant + 1) & (~1).
-  Packet4i y_int1 = __builtin_msa_addvi_w(y_int, 1);
-  Packet4i y_int2 = (Packet4i)__builtin_msa_bclri_w((Packet4ui)y_int1, 0); // bclri = bit-clear
-  y = __builtin_msa_ffint_s_w(y_int2);
-
-  // Compute the sign to apply to the polynomial.
-  Packet4i sign_mask = sine ? pxor(__builtin_msa_slli_w(y_int1, 29), (Packet4i)_x)
-                            : __builtin_msa_slli_w(__builtin_msa_addvi_w(y_int, 3), 29);
-
-  // Get the polynomial selection mask.
-  // We'll calculate both (sin and cos) polynomials and then select from the two.
-  Packet4i poly_mask = __builtin_msa_ceqi_w(__builtin_msa_slli_w(y_int2, 30), 0);
-
-  // Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4.
-  // The magic pass: "Extended precision modular arithmetic"
-  // x = ((x - y * DP1) - y * DP2) - y * DP3
-  Packet4f tmp1 = pmul(y, p4f_minus_cephes_DP1);
-  Packet4f tmp2 = pmul(y, p4f_minus_cephes_DP2);
-  Packet4f tmp3 = pmul(y, p4f_minus_cephes_DP3);
-  x = padd(x, tmp1);
-  x = padd(x, tmp2);
-  x = padd(x, tmp3);
-
-  // Evaluate the cos(x) polynomial.
-  y = p4f_coscof_p0;
-  Packet4f z = pmul(x, x);
-  y = pmadd(y, z, p4f_coscof_p1);
-  y = pmadd(y, z, p4f_coscof_p2);
-  y = pmul(y, z);
-  y = pmul(y, z);
-  y = __builtin_msa_fmsub_w(y, z, p4f_half);
-  y = padd(y, p4f_1);
-
-  // Evaluate the sin(x) polynomial.
-  Packet4f y2 = p4f_sincof_p0;
-  y2 = pmadd(y2, z, p4f_sincof_p1);
-  y2 = pmadd(y2, z, p4f_sincof_p2);
-  y2 = pmul(y2, z);
-  y2 = pmadd(y2, x, x);
-
-  // Select the correct result from the two polynomials.
-  y = sine ? (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y, (v16u8)y2)
-           : (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y2, (v16u8)y);
-
-  // Update the sign.
-  sign_mask = pxor(sign_mask, (Packet4i)y);
-  y = (Packet4f)__builtin_msa_binsli_w((v4u32)y, (v4u32)sign_mask, 0); // binsli = bit-insert-left
-  return y;
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-psin<Packet4f>(const Packet4f& x) {
-  return psincos_inner_msa_float</* sine */ true>(x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-pcos<Packet4f>(const Packet4f& x) {
-  return psincos_inner_msa_float</* sine */ false>(x);
-}
-
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2d
-pexp<Packet2d>(const Packet2d& _x) {
-  // Limiting double-precision pexp's argument to [-1024, +1024] lets pexp
-  // reach 0 and INFINITY naturally.
-  static _EIGEN_DECLARE_CONST_Packet2d(exp_lo, -1024.0);
-  static _EIGEN_DECLARE_CONST_Packet2d(exp_hi, +1024.0);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
-  static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
-  static _EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
-  static _EIGEN_DECLARE_CONST_Packet2d(1, 1.0);
-  static _EIGEN_DECLARE_CONST_Packet2d(2, 2.0);
-
-  Packet2d x = _x;
-
-  // Clamp x.
-  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(x, p2d_exp_lo), (v16u8)x,
-                                     (v16u8)p2d_exp_lo);
-  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(p2d_exp_hi, x), (v16u8)x,
-                                     (v16u8)p2d_exp_hi);
-
-  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
-  Packet2d x2_add = (Packet2d)__builtin_msa_binsli_d((v2u64)p2d_half, (v2u64)x, 0);
-  Packet2d x2 = pmadd(x, p2d_cephes_LOG2EF, x2_add);
-  Packet2l x2_long = __builtin_msa_ftrunc_s_d(x2);
-  Packet2d x2_long_d = __builtin_msa_ffint_s_d(x2_long);
-
-  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C1);
-  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C2);
-
-  x2 = pmul(x, x);
-
-  Packet2d px = p2d_cephes_exp_p0;
-  px = pmadd(px, x2, p2d_cephes_exp_p1);
-  px = pmadd(px, x2, p2d_cephes_exp_p2);
-  px = pmul(px, x);
-
-  Packet2d qx = p2d_cephes_exp_q0;
-  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
-
-  x = pdiv(px, psub(qx, px));
-  x = pmadd(p2d_2, x, p2d_1);
-
-  // x *= 2**exponent.
-  x = __builtin_msa_fexp2_d(x, x2_long);
-
-  return x;
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_MSA_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h
deleted file mode 100644
index afe8f33..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h
+++ /dev/null
@@ -1,1233 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Wave Computing, Inc.
-// Written by:
-//   Chris Larsen
-//   Alexey Frunze (afrunze@wavecomp.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_MSA_H
-#define EIGEN_PACKET_MATH_MSA_H
-
-#include <iostream>
-#include <string>
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-#endif
-
-#if 0
-#define EIGEN_MSA_DEBUG                                                             \
-  static bool firstTime = true;                                                     \
-  do {                                                                              \
-    if (firstTime) {                                                                \
-      std::cout << __FILE__ << ':' << __LINE__ << ':' << __FUNCTION__ << std::endl; \
-      firstTime = false;                                                            \
-    }                                                                               \
-  } while (0)
-#else
-#define EIGEN_MSA_DEBUG
-#endif
-
-#define EIGEN_MSA_SHF_I8(a, b, c, d) (((d) << 6) | ((c) << 4) | ((b) << 2) | (a))
-
-typedef v4f32 Packet4f;
-typedef v4i32 Packet4i;
-typedef v4u32 Packet4ui;
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME, X) const Packet4f p4f_##NAME = { X, X, X, X }
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME, X) const Packet4i p4i_##NAME = { X, X, X, X }
-#define _EIGEN_DECLARE_CONST_Packet4ui(NAME, X) const Packet4ui p4ui_##NAME = { X, X, X, X }
-
-inline std::ostream& operator<<(std::ostream& os, const Packet4f& value) {
-  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet4i& value) {
-  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet4ui& value) {
-  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
-  return os;
-}
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;  // Packet2f intrinsics not implemented yet
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,  // Packet2f intrinsics not implemented yet
-    // FIXME check the Has*
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<int32_t> : default_packet_traits {
-  typedef Packet4i type;
-  typedef Packet4i half;  // Packet2i intrinsics not implemented yet
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,  // Packet2i intrinsics not implemented yet
-    // FIXME check the Has*
-    HasDiv = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet4f> {
-  typedef float type;
-  enum { size = 4, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet4f half;
-};
-
-template <>
-struct unpacket_traits<Packet4i> {
-  typedef int32_t type;
-  enum { size = 4, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet4i half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f v = { from, from, from, from };
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fill_w(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  float f = *from;
-  Packet4f v = { f, f, f, f };
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pload1<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fill_w(*from);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fadd_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_addv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
-  EIGEN_MSA_DEBUG;
-
-  static const Packet4f countdown = { 0.0f, 1.0f, 2.0f, 3.0f };
-  return padd(pset1<Packet4f>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a) {
-  EIGEN_MSA_DEBUG;
-
-  static const Packet4i countdown = { 0, 1, 2, 3 };
-  return padd(pset1<Packet4i>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsub_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_subv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_bnegi_w((v4u32)a, 31);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_addvi_w((v4i32)__builtin_msa_nori_b((v16u8)a, 0), 1);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmul_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_mulv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fdiv_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_div_s_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmadd_w(c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
-  EIGEN_MSA_DEBUG;
-
-  // Use "asm" construct to avoid __builtin_msa_maddv_w GNU C bug.
-  Packet4i value = c;
-  __asm__("maddv.w %w[value], %w[a], %w[b]\n"
-          // Outputs
-          : [value] "+f"(value)
-          // Inputs
-          : [a] "f"(a), [b] "f"(b));
-  return value;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_and_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4i)__builtin_msa_and_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_or_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4i)__builtin_msa_or_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4i)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-  return pand(a, (Packet4f)__builtin_msa_xori_b((v16u8)b, 255));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return pand(a, (Packet4i)__builtin_msa_xori_b((v16u8)b, 255));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmin_w(a, b);
-#else
-  // This prefers NaNs to numbers.
-  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
-  Packet4i aMinOrNaN = por(__builtin_msa_fclt_w(a, b), aNaN);
-  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_min_s_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmax_w(a, b);
-#else
-  // This prefers NaNs to numbers.
-  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
-  Packet4i aMaxOrNaN = por(__builtin_msa_fclt_w(b, a), aNaN);
-  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_max_s_w(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_LOAD return __builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4i)__builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
-  EIGEN_MSA_DEBUG;
-
-  float f0 = from[0], f1 = from[1];
-  Packet4f v0 = { f0, f0, f0, f0 };
-  Packet4f v1 = { f1, f1, f1, f1 };
-  return (Packet4f)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from) {
-  EIGEN_MSA_DEBUG;
-
-  int32_t i0 = from[0], i1 = from[1];
-  Packet4i v0 = { i0, i0, i0, i0 };
-  Packet4i v1 = { i1, i1, i1, i1 };
-  return (Packet4i)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w(from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w(from, to, 0);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  float f = *from;
-  Packet4f v = { f, f, f, f };
-  v[1] = from[stride];
-  v[2] = from[2 * stride];
-  v[3] = from[3 * stride];
-  return v;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  int32_t i = *from;
-  Packet4i v = { i, i, i, i };
-  v[1] = from[stride];
-  v[2] = from[2 * stride];
-  v[3] = from[3 * stride];
-  return v;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from,
-                                                        Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = from[0];
-  to += stride;
-  *to = from[1];
-  to += stride;
-  *to = from[2];
-  to += stride;
-  *to = from[3];
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from,
-                                                          Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = from[0];
-  to += stride;
-  *to = from[1];
-  to += stride;
-  *to = from[2];
-  to += stride;
-  *to = from[3];
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
-  EIGEN_MSA_DEBUG;
-
-  __builtin_prefetch(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) {
-  EIGEN_MSA_DEBUG;
-
-  __builtin_prefetch(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet4f)__builtin_msa_bclri_w((v4u32)a, 31);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i zero = __builtin_msa_ldi_w(0);
-  return __builtin_msa_add_a_w(zero, a);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f s = padd(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  s = padd(s, (Packet4f)__builtin_msa_shf_w((v4i32)s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return s[0];
-}
-
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i s = padd(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  s = padd(s, __builtin_msa_shf_w(s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return s[0];
-}
-
-// Other reduction functions:
-// mul
-template <>
-EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4f p = pmul(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  p = pmul(p, (Packet4f)__builtin_msa_shf_w((v4i32)p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return p[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i p = pmul(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  p = pmul(p, __builtin_msa_shf_w(p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return p[0];
-}
-
-// min
-template <>
-EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  // Swap 64-bit halves of a.
-  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-#if !EIGEN_FAST_MATH
-  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
-  // masks of all zeroes/ones in low 64 bits.
-  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
-  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
-  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
-#endif
-  // Continue with min computation.
-  Packet4f v = __builtin_msa_fmin_w(a, swapped);
-  v = __builtin_msa_fmin_w(
-      v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-#if !EIGEN_FAST_MATH
-  // Based on the mask select between v and 4 qNaNs.
-  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
-  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
-#endif
-  return v[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i m = pmin(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  m = pmin(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return m[0];
-}
-
-// max
-template <>
-EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  // Swap 64-bit halves of a.
-  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-#if !EIGEN_FAST_MATH
-  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
-  // masks of all zeroes/ones in low 64 bits.
-  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
-  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
-  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
-#endif
-  // Continue with max computation.
-  Packet4f v = __builtin_msa_fmax_w(a, swapped);
-  v = __builtin_msa_fmax_w(
-      v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-#if !EIGEN_FAST_MATH
-  // Based on the mask select between v and 4 qNaNs.
-  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
-  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
-#endif
-  return v[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet4i m = pmax(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
-  m = pmax(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
-  return m[0];
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4f, 4>& value) {
-  os << "[ " << value.packet[0] << "," << std::endl
-     << "  " << value.packet[1] << "," << std::endl
-     << "  " << value.packet[2] << "," << std::endl
-     << "  " << value.packet[3] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  v4i32 tmp1, tmp2, tmp3, tmp4;
-
-  tmp1 = __builtin_msa_ilvr_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
-  tmp2 = __builtin_msa_ilvr_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
-  tmp3 = __builtin_msa_ilvl_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
-  tmp4 = __builtin_msa_ilvl_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
-
-  kernel.packet[0] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[1] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[2] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
-  kernel.packet[3] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4i, 4>& value) {
-  os << "[ " << value.packet[0] << "," << std::endl
-     << "  " << value.packet[1] << "," << std::endl
-     << "  " << value.packet[2] << "," << std::endl
-     << "  " << value.packet[3] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  v4i32 tmp1, tmp2, tmp3, tmp4;
-
-  tmp1 = __builtin_msa_ilvr_w(kernel.packet[1], kernel.packet[0]);
-  tmp2 = __builtin_msa_ilvr_w(kernel.packet[3], kernel.packet[2]);
-  tmp3 = __builtin_msa_ilvl_w(kernel.packet[1], kernel.packet[0]);
-  tmp4 = __builtin_msa_ilvl_w(kernel.packet[3], kernel.packet[2]);
-
-  kernel.packet[0] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[1] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
-  kernel.packet[2] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
-  kernel.packet[3] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsqrt_w(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  return __builtin_msa_frsqrt_w(a);
-#else
-  Packet4f ones = __builtin_msa_ffint_s_w(__builtin_msa_ldi_w(1));
-  return pdiv(ones, psqrt(a));
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
-  Packet4f v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.w %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
-  Packet4f v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.w %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
-  Packet4f v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.w %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket,
-                                    const Packet4f& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2],
-                       ifPacket.select[3] };
-  Packet4i mask = __builtin_msa_ceqi_w((Packet4i)select, 0);
-  return (Packet4f)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket,
-                                    const Packet4i& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2],
-                       ifPacket.select[3] };
-  Packet4i mask = __builtin_msa_ceqi_w((Packet4i)select, 0);
-  return (Packet4i)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
-}
-
-//---------- double ----------
-
-typedef v2f64 Packet2d;
-typedef v2i64 Packet2l;
-typedef v2u64 Packet2ul;
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME, X) const Packet2d p2d_##NAME = { X, X }
-#define _EIGEN_DECLARE_CONST_Packet2l(NAME, X) const Packet2l p2l_##NAME = { X, X }
-#define _EIGEN_DECLARE_CONST_Packet2ul(NAME, X) const Packet2ul p2ul_##NAME = { X, X }
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2d& value) {
-  os << "[ " << value[0] << ", " << value[1] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2l& value) {
-  os << "[ " << value[0] << ", " << value[1] << " ]";
-  return os;
-}
-
-inline std::ostream& operator<<(std::ostream& os, const Packet2ul& value) {
-  os << "[ " << value[0] << ", " << value[1] << " ]";
-  return os;
-}
-
-template <>
-struct packet_traits<double> : default_packet_traits {
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-    // FIXME check the Has*
-    HasDiv = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct unpacket_traits<Packet2d> {
-  typedef double type;
-  enum { size = 2, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
-  typedef Packet2d half;
-};
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d value = { from, from };
-  return value;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fadd_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
-  EIGEN_MSA_DEBUG;
-
-  static const Packet2d countdown = { 0.0, 1.0 };
-  return padd(pset1<Packet2d>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsub_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_bnegi_d((v2u64)a, 63);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmul_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fdiv_d(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fmadd_d(c, a, b);
-}
-
-// Logical Operations are not supported for float, so we have to reinterpret casts using MSA
-// intrinsics
-template <>
-EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_and_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_or_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-  return pand(a, (Packet2d)__builtin_msa_xori_b((v16u8)b, 255));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmin_d(a, b);
-#else
-  // This prefers NaNs to numbers.
-  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
-  v2i64 aMinOrNaN = por(__builtin_msa_fclt_d(a, b), aNaN);
-  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  // This prefers numbers to NaNs.
-  return __builtin_msa_fmax_d(a, b);
-#else
-  // This prefers NaNs to numbers.
-  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
-  v2i64 aMaxOrNaN = por(__builtin_msa_fclt_d(b, a), aNaN);
-  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d value = { *from, *from };
-  return value;
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
-  EIGEN_MSA_DEBUG;
-
-  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d value;
-  value[0] = *from;
-  from += stride;
-  value[1] = *from;
-  return value;
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from,
-                                                         Index stride) {
-  EIGEN_MSA_DEBUG;
-
-  *to = from[0];
-  to += stride;
-  *to = from[1];
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
-  EIGEN_MSA_DEBUG;
-
-  __builtin_prefetch(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return a[0];
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return (Packet2d)__builtin_msa_bclri_d((v2u64)a, 63);
-}
-
-template <>
-EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d s = padd(a, preverse(a));
-  return s[0];
-}
-
-// Other reduction functions:
-// mul
-template <>
-EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d p = pmul(a, preverse(a));
-  return p[0];
-}
-
-// min
-template <>
-EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-  Packet2d v = __builtin_msa_fmin_d(a, swapped);
-  return v[0];
-#else
-  double a0 = a[0], a1 = a[1];
-  return ((numext::isnan)(a0) || a0 < a1) ? a0 : a1;
-#endif
-}
-
-// max
-template <>
-EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
-  Packet2d v = __builtin_msa_fmax_d(a, swapped);
-  return v[0];
-#else
-  double a0 = a[0], a1 = a[1];
-  return ((numext::isnan)(a0) || a0 > a1) ? a0 : a1;
-#endif
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-  return __builtin_msa_fsqrt_d(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
-  EIGEN_MSA_DEBUG;
-
-#if EIGEN_FAST_MATH
-  return __builtin_msa_frsqrt_d(a);
-#else
-  Packet2d ones = __builtin_msa_ffint_s_d(__builtin_msa_ldi_d(1));
-  return pdiv(ones, psqrt(a));
-#endif
-}
-
-inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2d, 2>& value) {
-  os << "[ " << value.packet[0] << "," << std::endl << "  " << value.packet[1] << " ]";
-  return os;
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
-  EIGEN_MSA_DEBUG;
-
-  Packet2d trn1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
-  Packet2d trn2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
-  kernel.packet[0] = trn1;
-  kernel.packet[1] = trn2;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
-  Packet2d v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.d %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
-  Packet2d v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.d %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
-  Packet2d v = a;
-  int32_t old_mode, new_mode;
-  asm volatile(
-      "cfcmsa  %[old_mode], $1\n"
-      "ori     %[new_mode], %[old_mode], 3\n"
-      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
-      "ctcmsa  $1, %[new_mode]\n"
-      "frint.d %w[v], %w[v]\n"
-      "ctcmsa  $1, %[old_mode]\n"
-      :  // outputs
-      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
-      [v] "+f"(v)
-      :  // inputs
-      :  // clobbers
-  );
-  return v;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket,
-                                    const Packet2d& elsePacket) {
-  Packet2ul select = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2l mask = __builtin_msa_ceqi_d((Packet2l)select, 0);
-  return (Packet2d)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_PACKET_MATH_MSA_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
deleted file mode 100644
index f40af7f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
+++ /dev/null
@@ -1,584 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_NEON_H
-#define EIGEN_COMPLEX_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-inline uint32x4_t p4ui_CONJ_XOR()
-{
-// See bug 1325, clang fails to call vld1q_u64.
-#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML
-  uint32x4_t ret = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
-  return ret;
-#else
-  static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
-  return vld1q_u32( conj_XOR_DATA );
-#endif
-}
-
-inline uint32x2_t p2ui_CONJ_XOR()
-{
-  static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000 };
-  return vld1_u32( conj_XOR_DATA );
-}
-
-//---------- float ----------
-
-struct Packet1cf
-{
-  EIGEN_STRONG_INLINE Packet1cf() {}
-  EIGEN_STRONG_INLINE explicit Packet1cf(const Packet2f& a) : v(a) {}
-  Packet2f v;
-};
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
-  Packet4f v;
-};
-
-template<> struct packet_traits<std::complex<float> > : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet1cf half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasMul       = 1,
-    HasDiv       = 1,
-    HasNegate    = 1,
-    HasAbs       = 0,
-    HasAbs2      = 0,
-    HasMin       = 0,
-    HasMax       = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet1cf>
-{
-  typedef std::complex<float> type;
-  typedef Packet1cf half;
-  typedef Packet2f as_real;
-  enum
-  {
-    size = 1,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2cf>
-{
-  typedef std::complex<float> type;
-  typedef Packet1cf half;
-  typedef Packet4f as_real;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet1cf pcast<float,Packet1cf>(const float& a)
-{ return Packet1cf(vset_lane_f32(a, vdup_n_f32(0.f), 0)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pcast<Packet2f,Packet2cf>(const Packet2f& a)
-{ return Packet2cf(vreinterpretq_f32_u64(vmovl_u32(vreinterpret_u32_f32(a)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pset1<Packet1cf>(const std::complex<float>& from)
-{ return Packet1cf(vld1_f32(reinterpret_cast<const float*>(&from))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
-{
-  const float32x2_t r64 = vld1_f32(reinterpret_cast<const float*>(&from));
-  return Packet2cf(vcombine_f32(r64, r64));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf padd<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(padd<Packet2f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(padd<Packet4f>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf psub<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(psub<Packet2f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(psub<Packet4f>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pnegate(const Packet1cf& a) { return Packet1cf(pnegate<Packet2f>(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate<Packet4f>(a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pconj(const Packet1cf& a)
-{
-  const Packet2ui b = vreinterpret_u32_f32(a.v);
-  return Packet1cf(vreinterpret_f32_u32(veor_u32(b, p2ui_CONJ_XOR())));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
-{
-  const Packet4ui b = vreinterpretq_u32_f32(a.v);
-  return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR())));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf pmul<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{
-  Packet2f v1, v2;
-
-  // Get the real values of a | a1_re | a1_re |
-  v1 = vdup_lane_f32(a.v, 0);
-  // Get the imag values of a | a1_im | a1_im |
-  v2 = vdup_lane_f32(a.v, 1);
-  // Multiply the real a with b
-  v1 = vmul_f32(v1, b.v);
-  // Multiply the imag a with b
-  v2 = vmul_f32(v2, b.v);
-  // Conjugate v2
-  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
-  // Swap real/imag elements in v2.
-  v2 = vrev64_f32(v2);
-  // Add and return the result
-  return Packet1cf(vadd_f32(v1, v2));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  Packet4f v1, v2;
-
-  // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
-  v1 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 0), vdup_lane_f32(vget_high_f32(a.v), 0));
-  // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
-  v2 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 1), vdup_lane_f32(vget_high_f32(a.v), 1));
-  // Multiply the real a with b
-  v1 = vmulq_f32(v1, b.v);
-  // Multiply the imag a with b
-  v2 = vmulq_f32(v2, b.v);
-  // Conjugate v2
-  v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR()));
-  // Swap real/imag elements in v2.
-  v2 = vrev64q_f32(v2);
-  // Add and return the result
-  return Packet2cf(vaddq_f32(v1, v2));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf pcmp_eq(const Packet1cf& a, const Packet1cf& b)
-{
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a[0])==re(b[0]), im(a[0])==im(b[0])]
-  Packet2f eq = pcmp_eq<Packet2f>(a.v, b.v);
-  // Swap real/imag elements in the mask in to get:
-  // [im(a[0])==im(b[0]), re(a[0])==re(b[0])]
-  Packet2f eq_swapped = vrev64_f32(eq);
-  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet1cf(pand<Packet2f>(eq, eq_swapped));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b)
-{
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a[0])==re(b[0]), im(a[0])==im(b[0]), re(a[1])==re(b[1]), im(a[1])==im(b[1])]
-  Packet4f eq = pcmp_eq<Packet4f>(a.v, b.v);
-  // Swap real/imag elements in the mask in to get:
-  // [im(a[0])==im(b[0]), re(a[0])==re(b[0]), im(a[1])==im(b[1]), re(a[1])==re(b[1])]
-  Packet4f eq_swapped = vrev64q_f32(eq);
-  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet2cf(pand<Packet4f>(eq, eq_swapped));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf pand<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf por<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pxor<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pandnot<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{ return Packet1cf(vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{ return Packet2cf(vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pload<Packet1cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cf(pload<Packet2f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(reinterpret_cast<const float*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf ploadu<Packet1cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cf(ploadu<Packet2f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(reinterpret_cast<const float*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf ploaddup<Packet1cf>(const std::complex<float>* from)
-{ return pset1<Packet1cf>(*from); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from)
-{ return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *to, const Packet1cf& from)
-{ EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *to, const Packet2cf& from)
-{ EIGEN_DEBUG_ALIGNED_STORE pstore(reinterpret_cast<float*>(to), from.v); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *to, const Packet1cf& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *to, const Packet2cf& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<float*>(to), from.v); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cf pgather<std::complex<float>, Packet1cf>(
-    const std::complex<float>* from, Index stride)
-{
-  const Packet2f tmp = vdup_n_f32(std::real(from[0*stride]));
-  return Packet1cf(vset_lane_f32(std::imag(from[0*stride]), tmp, 1));
-}
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(
-    const std::complex<float>* from, Index stride)
-{
-  Packet4f res = vdupq_n_f32(std::real(from[0*stride]));
-  res = vsetq_lane_f32(std::imag(from[0*stride]), res, 1);
-  res = vsetq_lane_f32(std::real(from[1*stride]), res, 2);
-  res = vsetq_lane_f32(std::imag(from[1*stride]), res, 3);
-  return Packet2cf(res);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet1cf>(
-    std::complex<float>* to, const Packet1cf& from, Index stride)
-{ to[stride*0] = std::complex<float>(vget_lane_f32(from.v, 0), vget_lane_f32(from.v, 1)); }
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(
-    std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  to[stride*0] = std::complex<float>(vgetq_lane_f32(from.v, 0), vgetq_lane_f32(from.v, 1));
-  to[stride*1] = std::complex<float>(vgetq_lane_f32(from.v, 2), vgetq_lane_f32(from.v, 3));
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *addr)
-{ EIGEN_ARM_PREFETCH(reinterpret_cast<const float*>(addr)); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet1cf>(const Packet1cf& a)
-{
-  EIGEN_ALIGN16 std::complex<float> x;
-  vst1_f32(reinterpret_cast<float*>(&x), a.v);
-  return x;
-}
-template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a)
-{
-  EIGEN_ALIGN16 std::complex<float> x[2];
-  vst1q_f32(reinterpret_cast<float*>(x), a.v);
-  return x[0];
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf preverse(const Packet1cf& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{ return Packet2cf(vcombine_f32(vget_high_f32(a.v), vget_low_f32(a.v))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cf pcplxflip<Packet1cf>(const Packet1cf& a)
-{ return Packet1cf(vrev64_f32(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a)
-{ return Packet2cf(vrev64q_f32(a.v)); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet1cf>(const Packet1cf& a)
-{
-  std::complex<float> s;
-  vst1_f32((float *)&s, a.v);
-  return s;
-}
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> s;
-  vst1_f32(reinterpret_cast<float*>(&s), vadd_f32(vget_low_f32(a.v), vget_high_f32(a.v)));
-  return s;
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet1cf>(const Packet1cf& a)
-{
-  std::complex<float> s;
-  vst1_f32((float *)&s, a.v);
-  return s;
-}
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  float32x2_t a1, a2, v1, v2, prod;
-  std::complex<float> s;
-
-  a1 = vget_low_f32(a.v);
-  a2 = vget_high_f32(a.v);
-   // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
-  v1 = vdup_lane_f32(a1, 0);
-  // Get the real values of a | a1_im | a1_im | a2_im | a2_im |
-  v2 = vdup_lane_f32(a1, 1);
-  // Multiply the real a with b
-  v1 = vmul_f32(v1, a2);
-  // Multiply the imag a with b
-  v2 = vmul_f32(v2, a2);
-  // Conjugate v2
-  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
-  // Swap real/imag elements in v2.
-  v2 = vrev64_f32(v2);
-  // Add v1, v2
-  prod = vadd_f32(v1, v2);
-
-  vst1_f32(reinterpret_cast<float*>(&s), prod);
-
-  return s;
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cf,Packet2f)
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet1cf pdiv<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
-{
-  // TODO optimize it for NEON
-  Packet1cf res = pmul(a, pconj(b));
-  Packet2f s, rev_s;
-
-  // this computes the norm
-  s = vmul_f32(b.v, b.v);
-  rev_s = vrev64_f32(s);
-
-  return Packet1cf(pdiv<Packet2f>(res.v, vadd_f32(s, rev_s)));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for NEON
-  Packet2cf res = pmul(a,pconj(b));
-  Packet4f s, rev_s;
-
-  // this computes the norm
-  s = vmulq_f32(b.v, b.v);
-  rev_s = vrev64q_f32(s);
-
-  return Packet2cf(pdiv<Packet4f>(res.v, vaddq_f32(s, rev_s)));
-}
-
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1cf, 1>& /*kernel*/) {}
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel)
-{
-  Packet4f tmp = vcombine_f32(vget_high_f32(kernel.packet[0].v), vget_high_f32(kernel.packet[1].v));
-  kernel.packet[0].v = vcombine_f32(vget_low_f32(kernel.packet[0].v), vget_low_f32(kernel.packet[1].v));
-  kernel.packet[1].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cf psqrt<Packet1cf>(const Packet1cf& a) {
-  return psqrt_complex<Packet1cf>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
-  return psqrt_complex<Packet2cf>(a);
-}
-
-//---------- double ----------
-#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
-
-// See bug 1325, clang fails to call vld1q_u64.
-#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML
-  static uint64x2_t p2ul_CONJ_XOR = {0x0, 0x8000000000000000};
-#else
-  const uint64_t  p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
-  static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
-#endif
-
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
-  Packet2d v;
-};
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet1cd>
-{
-  typedef std::complex<double> type;
-  typedef Packet1cd half;
-  typedef Packet2d as_real;
-  enum
-  {
-    size=1,
-    alignment=Aligned16,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>(reinterpret_cast<const double*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>(reinterpret_cast<const double*>(from))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
-{
-  /* here we really have to use unaligned loads :( */
-  return ploadu<Packet1cd>(&from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(padd<Packet2d>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(psub<Packet2d>(a.v, b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a)
-{ return Packet1cd(pnegate<Packet2d>(a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a)
-{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  Packet2d v1, v2;
-
-  // Get the real values of a
-  v1 = vdupq_lane_f64(vget_low_f64(a.v), 0);
-  // Get the imag values of a
-  v2 = vdupq_lane_f64(vget_high_f64(a.v), 0);
-  // Multiply the real a with b
-  v1 = vmulq_f64(v1, b.v);
-  // Multiply the imag a with b
-  v2 = vmulq_f64(v2, b.v);
-  // Conjugate v2
-  v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR));
-  // Swap real/imag elements in v2.
-  v2 = preverse<Packet2d>(v2);
-  // Add and return the result
-  return Packet1cd(vaddq_f64(v1, v2));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b)
-{
-  // Compare real and imaginary parts of a and b to get the mask vector:
-  // [re(a)==re(b), im(a)==im(b)]
-  Packet2d eq = pcmp_eq<Packet2d>(a.v, b.v);
-  // Swap real/imag elements in the mask in to get:
-  // [im(a)==im(b), re(a)==re(b)]
-  Packet2d eq_swapped = vreinterpretq_f64_u32(vrev64q_u32(vreinterpretq_u32_f64(eq)));
-  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
-  return Packet1cd(pand<Packet2d>(eq, eq_swapped));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{ return Packet1cd(vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from)
-{ return pset1<Packet1cd>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *to, const Packet1cd& from)
-{ EIGEN_DEBUG_ALIGNED_STORE pstore(reinterpret_cast<double*>(to), from.v); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *to, const Packet1cd& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<double*>(to), from.v); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *addr)
-{ EIGEN_ARM_PREFETCH(reinterpret_cast<const double*>(addr)); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(
-    const std::complex<double>* from, Index stride)
-{
-  Packet2d res = pset1<Packet2d>(0.0);
-  res = vsetq_lane_f64(std::real(from[0*stride]), res, 0);
-  res = vsetq_lane_f64(std::imag(from[0*stride]), res, 1);
-  return Packet1cd(res);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(
-    std::complex<double>* to, const Packet1cd& from, Index stride)
-{ to[stride*0] = std::complex<double>(vgetq_lane_f64(from.v, 0), vgetq_lane_f64(from.v, 1)); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
-{
-  EIGEN_ALIGN16 std::complex<double> res;
-  pstore<std::complex<double> >(&res, a);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for NEON
-  Packet1cd res = pmul(a,pconj(b));
-  Packet2d s = pmul<Packet2d>(b.v, b.v);
-  Packet2d rev_s = preverse<Packet2d>(s);
-
-  return Packet1cd(pdiv(res.v, padd<Packet2d>(s,rev_s)));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
-{ return Packet1cd(preverse(Packet2d(x.v))); }
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
-{
-  Packet2d tmp = vcombine_f64(vget_high_f64(kernel.packet[0].v), vget_high_f64(kernel.packet[1].v));
-  kernel.packet[0].v = vcombine_f64(vget_low_f64(kernel.packet[0].v), vget_low_f64(kernel.packet[1].v));
-  kernel.packet[1].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
-  return psqrt_complex<Packet1cd>(a);
-}
-
-#endif // EIGEN_ARCH_ARM64
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
deleted file mode 100644
index 3481f33..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
+++ /dev/null
@@ -1,183 +0,0 @@
-namespace Eigen {
-namespace internal {
-  
-#if EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
-
-// Clang seems to excessively spill registers in the GEBP kernel on 32-bit arm.
-// Here we specialize gebp_traits to eliminate these register spills.
-// See #2138.
-template<>
-struct gebp_traits <float,float,false,false,Architecture::NEON,GEBPPacketFull>
- : gebp_traits<float,float,false,false,Architecture::Generic,GEBPPacketFull>
-{
-  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
-  { 
-    // This volatile inline ASM both acts as a barrier to prevent reordering,
-    // as well as enforces strict register use.
-    asm volatile(
-      "vmla.f32 %q[r], %q[c], %q[alpha]"
-      : [r] "+w" (r)
-      : [c] "w" (c),
-        [alpha] "w" (alpha)
-      : );
-  }
-
-  template <typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const Packet4f& b,
-                                Packet4f& c, Packet4f& tmp,
-                                const LaneIdType&) const {
-    acc(a, b, c);
-  }
-  
-  template <typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const QuadPacket<Packet4f>& b,
-                                Packet4f& c, Packet4f& tmp,
-                                const LaneIdType& lane) const {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-};
-
-#endif // EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
-
-#if EIGEN_ARCH_ARM64
-
-template<>
-struct gebp_traits <float,float,false,false,Architecture::NEON,GEBPPacketFull>
- : gebp_traits<float,float,false,false,Architecture::Generic,GEBPPacketFull>
-{
-  typedef float RhsPacket;
-  typedef float32x4_t RhsPacketx4;
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = *b;
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    dest = vld1q_f32(b);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = *b;
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  {
-    c = vfmaq_n_f32(c, a, b);
-  }
-
-  // NOTE: Template parameter inference failed when compiled with Android NDK:
-  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  { madd_helper<0>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<1>&) const
-  { madd_helper<1>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<2>&) const
-  { madd_helper<2>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<3>&) const
-  { madd_helper<3>(a, b, c); }
-
- private:
-  template<int LaneID>
-  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const
-  {
-    #if EIGEN_COMP_GNUC_STRICT && !(EIGEN_GNUC_AT_LEAST(9,0))
-    // workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
-    // vfmaq_laneq_f32 is implemented through a costly dup
-         if(LaneID==0)  asm("fmla %0.4s, %1.4s, %2.s[0]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    else if(LaneID==1)  asm("fmla %0.4s, %1.4s, %2.s[1]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    else if(LaneID==2)  asm("fmla %0.4s, %1.4s, %2.s[2]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    else if(LaneID==3)  asm("fmla %0.4s, %1.4s, %2.s[3]\n" : "+w" (c) : "w" (a), "w" (b) :  );
-    #else
-    c = vfmaq_laneq_f32(c, a, b, LaneID);
-    #endif
-  }
-};
-
-
-template<>
-struct gebp_traits <double,double,false,false,Architecture::NEON>
- : gebp_traits<double,double,false,false,Architecture::Generic>
-{
-  typedef double RhsPacket;
-
-  struct RhsPacketx4 {
-    float64x2_t B_0, B_1;
-  };
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = *b;
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    dest.B_0 = vld1q_f64(b);
-    dest.B_1 = vld1q_f64(b+2);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  {
-    c = vfmaq_n_f64(c, a, b);
-  }
-
-  // NOTE: Template parameter inference failed when compiled with Android NDK:
-  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
-
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
-  { madd_helper<0>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<1>&) const
-  { madd_helper<1>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<2>&) const
-  { madd_helper<2>(a, b, c); }
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<3>&) const
-  { madd_helper<3>(a, b, c); }
-
- private:
-  template <int LaneID>
-  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const
-  {
-    #if EIGEN_COMP_GNUC_STRICT && !(EIGEN_GNUC_AT_LEAST(9,0))
-    // workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
-    // vfmaq_laneq_f64 is implemented through a costly dup
-         if(LaneID==0)  asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w" (c) : "w" (a), "w" (b.B_0) :  );
-    else if(LaneID==1)  asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w" (c) : "w" (a), "w" (b.B_0) :  );
-    else if(LaneID==2)  asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w" (c) : "w" (a), "w" (b.B_1) :  );
-    else if(LaneID==3)  asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w" (c) : "w" (a), "w" (b.B_1) :  );
-    #else
-         if(LaneID==0) c = vfmaq_laneq_f64(c, a, b.B_0, 0);
-    else if(LaneID==1) c = vfmaq_laneq_f64(c, a, b.B_0, 1);
-    else if(LaneID==2) c = vfmaq_laneq_f64(c, a, b.B_1, 0);
-    else if(LaneID==3) c = vfmaq_laneq_f64(c, a, b.B_1, 1);
-    #endif
-  }
-};
-
-#endif // EIGEN_ARCH_ARM64
-
-}  // namespace internal
-}  // namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h
deleted file mode 100644
index fa6615a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h
+++ /dev/null
@@ -1,75 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_NEON_H
-#define EIGEN_MATH_FUNCTIONS_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f pexp<Packet2f>(const Packet2f& x)
-{ return pexp_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f pexp<Packet4f>(const Packet4f& x)
-{ return pexp_float(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f plog<Packet2f>(const Packet2f& x)
-{ return plog_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f plog<Packet4f>(const Packet4f& x)
-{ return plog_float(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f psin<Packet2f>(const Packet2f& x)
-{ return psin_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f psin<Packet4f>(const Packet4f& x)
-{ return psin_float(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f pcos<Packet2f>(const Packet2f& x)
-{ return pcos_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f pcos<Packet4f>(const Packet4f& x)
-{ return pcos_float(x); }
-
-// Hyperbolic Tangent function.
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2f ptanh<Packet2f>(const Packet2f& x)
-{ return internal::generic_fast_tanh_float(x); }
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f ptanh<Packet4f>(const Packet4f& x)
-{ return internal::generic_fast_tanh_float(x); }
-
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, psin)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pcos)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, plog)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pexp)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, ptanh)
-
-template <>
-EIGEN_STRONG_INLINE Packet4bf pfrexp(const Packet4bf& a, Packet4bf& exponent) {
-  Packet4f fexponent;
-  const Packet4bf out = F32ToBf16(pfrexp<Packet4f>(Bf16ToF32(a), fexponent));
-  exponent = F32ToBf16(fexponent);
-  return out;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4bf pldexp(const Packet4bf& a, const Packet4bf& exponent) {
-  return F32ToBf16(pldexp<Packet4f>(Bf16ToF32(a), Bf16ToF32(exponent)));
-}
-
-//---------- double ----------
-
-#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2d pexp<Packet2d>(const Packet2d& x)
-{ return pexp_double(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet2d plog<Packet2d>(const Packet2d& x)
-{ return plog_double(x); }
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATH_FUNCTIONS_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
deleted file mode 100644
index d2aeef4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
+++ /dev/null
@@ -1,4587 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
-// Heavily based on Gael's SSE version.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_NEON_H
-#define EIGEN_PACKET_MATH_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#if EIGEN_ARCH_ARM64
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-#else
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
-#endif
-#endif
-
-#if EIGEN_COMP_MSVC_STRICT
-
-// In MSVC's arm_neon.h header file, all NEON vector types
-// are aliases to the same underlying type __n128.
-// We thus have to wrap them to make them different C++ types.
-// (See also bug 1428)
-typedef eigen_packet_wrapper<float32x2_t,0>  Packet2f;
-typedef eigen_packet_wrapper<float32x4_t,1>  Packet4f;
-typedef eigen_packet_wrapper<int32_t    ,2>  Packet4c;
-typedef eigen_packet_wrapper<int8x8_t   ,3>  Packet8c;
-typedef eigen_packet_wrapper<int8x16_t  ,4>  Packet16c;
-typedef eigen_packet_wrapper<uint32_t   ,5>  Packet4uc;
-typedef eigen_packet_wrapper<uint8x8_t  ,6>  Packet8uc;
-typedef eigen_packet_wrapper<uint8x16_t ,7>  Packet16uc;
-typedef eigen_packet_wrapper<int16x4_t  ,8>  Packet4s;
-typedef eigen_packet_wrapper<int16x8_t  ,9>  Packet8s;
-typedef eigen_packet_wrapper<uint16x4_t ,10> Packet4us;
-typedef eigen_packet_wrapper<uint16x8_t ,11> Packet8us;
-typedef eigen_packet_wrapper<int32x2_t  ,12> Packet2i;
-typedef eigen_packet_wrapper<int32x4_t  ,13> Packet4i;
-typedef eigen_packet_wrapper<uint32x2_t ,14> Packet2ui;
-typedef eigen_packet_wrapper<uint32x4_t ,15> Packet4ui;
-typedef eigen_packet_wrapper<int64x2_t  ,16> Packet2l;
-typedef eigen_packet_wrapper<uint64x2_t ,17> Packet2ul;
-
-#else
-
-typedef float32x2_t                          Packet2f;
-typedef float32x4_t                          Packet4f;
-typedef eigen_packet_wrapper<int32_t    ,2>  Packet4c;
-typedef int8x8_t                             Packet8c;
-typedef int8x16_t                            Packet16c;
-typedef eigen_packet_wrapper<uint32_t   ,5>  Packet4uc;
-typedef uint8x8_t                            Packet8uc;
-typedef uint8x16_t                           Packet16uc;
-typedef int16x4_t                            Packet4s;
-typedef int16x8_t                            Packet8s;
-typedef uint16x4_t                           Packet4us;
-typedef uint16x8_t                           Packet8us;
-typedef int32x2_t                            Packet2i;
-typedef int32x4_t                            Packet4i;
-typedef uint32x2_t                           Packet2ui;
-typedef uint32x4_t                           Packet4ui;
-typedef int64x2_t                            Packet2l;
-typedef uint64x2_t                           Packet2ul;
-
-#endif // EIGEN_COMP_MSVC_STRICT
-
-EIGEN_STRONG_INLINE Packet4f shuffle1(const Packet4f& m, int mask){
-  const float* a = reinterpret_cast<const float*>(&m);
-  Packet4f res = {*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3 )), *(a + ((mask >> 6) & 3))};
-  return res;
-}
-
-// fuctionally equivalent to _mm_shuffle_ps in SSE when interleave
-// == false (i.e. shuffle<false>(m, n, mask) equals _mm_shuffle_ps(m, n, mask)),
-// interleave m and n when interleave == true. Currently used in LU/arch/InverseSize4.h
-// to enable a shared implementation for fast inversion of matrices of size 4. 
-template<bool interleave> 
-EIGEN_STRONG_INLINE Packet4f shuffle2(const Packet4f &m, const Packet4f &n, int mask)
-{
-  const float* a = reinterpret_cast<const float*>(&m);
-  const float* b = reinterpret_cast<const float*>(&n);
-  Packet4f res = {*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(b + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3))};
-  return res;
-}
-
-template<> 
-EIGEN_STRONG_INLINE Packet4f shuffle2<true>(const Packet4f &m, const Packet4f &n, int mask) 
-{
-  const float* a = reinterpret_cast<const float*>(&m);
-  const float* b = reinterpret_cast<const float*>(&n);
-  Packet4f res = {*(a + (mask & 3)), *(b + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3))};
-  return res;
-}
-
-EIGEN_STRONG_INLINE static int eigen_neon_shuffle_mask(int p, int q, int r, int s) {return ((s)<<6|(r)<<4|(q)<<2|(p));}
-
-EIGEN_STRONG_INLINE Packet4f vec4f_swizzle1(const Packet4f& a, int p, int q, int r, int s)
-{ 
-  return shuffle1(a, eigen_neon_shuffle_mask(p, q, r, s));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_swizzle2(const Packet4f& a, const Packet4f& b, int p, int q, int r, int s)
-{ 
-  return shuffle2<false>(a,b,eigen_neon_shuffle_mask(p, q, r, s));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<false>(a,b,eigen_neon_shuffle_mask(0, 1, 0, 1));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<false>(b,a,eigen_neon_shuffle_mask(2, 3, 2, 3));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<true>(a,b,eigen_neon_shuffle_mask(0, 0, 1, 1));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b)
-{
-  return shuffle2<true>(a,b,eigen_neon_shuffle_mask(2, 2, 3, 3));
-}
-#define vec4f_duplane(a, p) \
-  vdupq_lane_f32(vget_low_f32(a), p)
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  const Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = vreinterpretq_f32_u32(pset1<int32_t>(X))
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  const Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-#if EIGEN_ARCH_ARM64
-  // __builtin_prefetch tends to do nothing on ARM64 compilers because the
-  // prefetch instructions there are too detailed for __builtin_prefetch to map
-  // meaningfully to them.
-  #define EIGEN_ARM_PREFETCH(ADDR)  __asm__ __volatile__("prfm pldl1keep, [%[addr]]\n" ::[addr] "r"(ADDR) : );
-#elif EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
-  #define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
-#elif defined __pld
-  #define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
-#elif EIGEN_ARCH_ARM32
-  #define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ("pld [%[addr]]\n" :: [addr] "r" (ADDR) : );
-#else
-  // by default no explicit prefetching
-  #define EIGEN_ARM_PREFETCH(ADDR)
-#endif
-
-template <>
-struct packet_traits<float> : default_packet_traits
-{
-  typedef Packet4f type;
-  typedef Packet2f half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasDiv   = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-
-    HasSin  = EIGEN_FAST_MATH,
-    HasCos  = EIGEN_FAST_MATH,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf  = EIGEN_FAST_MATH,
-    HasBessel = 0,  // Issues with accuracy.
-    HasNdtri = 0
-  };
-};
-
-template <>
-struct packet_traits<int8_t> : default_packet_traits
-{
-  typedef Packet16c type;
-  typedef Packet8c half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint8_t> : default_packet_traits
-{
-  typedef Packet16uc type;
-  typedef Packet8uc half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 16,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 1,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasSqrt = 1
-  };
-};
-
-template <>
-struct packet_traits<int16_t> : default_packet_traits
-{
-  typedef Packet8s type;
-  typedef Packet4s half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint16_t> : default_packet_traits
-{
-  typedef Packet8us type;
-  typedef Packet4us half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 0,
-    HasAbsDiff   = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-    HasSqrt = 1
-  };
-};
-
-template <>
-struct packet_traits<int32_t> : default_packet_traits
-{
-  typedef Packet4i type;
-  typedef Packet2i half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint32_t> : default_packet_traits
-{
-  typedef Packet4ui type;
-  typedef Packet2ui half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 1,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 0,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasSqrt = 1
-  };
-};
-
-template <>
-struct packet_traits<int64_t> : default_packet_traits
-{
-  typedef Packet2l type;
-  typedef Packet2l half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-template <>
-struct packet_traits<uint64_t> : default_packet_traits
-{
-  typedef Packet2ul type;
-  typedef Packet2ul half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 0,
-    HasAbs       = 0,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0
-  };
-};
-
-#if EIGEN_GNUC_AT_MOST(4, 4) && !EIGEN_COMP_LLVM
-// workaround gcc 4.2, 4.3 and 4.4 compilation issue
-EIGEN_STRONG_INLINE float32x4_t vld1q_f32(const float* x) { return ::vld1q_f32((const float32_t*)x); }
-EIGEN_STRONG_INLINE float32x2_t vld1_f32(const float* x) { return ::vld1_f32 ((const float32_t*)x); }
-EIGEN_STRONG_INLINE float32x2_t vld1_dup_f32(const float* x) { return ::vld1_dup_f32 ((const float32_t*)x); }
-EIGEN_STRONG_INLINE void vst1q_f32(float* to, float32x4_t from) { ::vst1q_f32((float32_t*)to,from); }
-EIGEN_STRONG_INLINE void vst1_f32 (float* to, float32x2_t from) { ::vst1_f32 ((float32_t*)to,from); }
-#endif
-
-template<> struct unpacket_traits<Packet2f>
-{
-  typedef float type;
-  typedef Packet2f half;
-  typedef Packet2i integer_packet;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4f>
-{
-  typedef float type;
-  typedef Packet2f half;
-  typedef Packet4i integer_packet;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4c>
-{
-  typedef int8_t type;
-  typedef Packet4c half;
-  enum
-  {
-    size = 4,
-    alignment = Unaligned,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8c>
-{
-  typedef int8_t type;
-  typedef Packet4c half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet16c>
-{
-  typedef int8_t type;
-  typedef Packet8c half;
-  enum
-  {
-    size = 16,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4uc>
-{
-  typedef uint8_t type;
-  typedef Packet4uc half;
-  enum
-  {
-    size = 4,
-    alignment = Unaligned,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8uc>
-{
-  typedef uint8_t type;
-  typedef Packet4uc half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet16uc>
-{
-  typedef uint8_t type;
-  typedef Packet8uc half;
-  enum
-  {
-    size = 16,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false};
-};
-template<> struct unpacket_traits<Packet4s>
-{
-  typedef int16_t type;
-  typedef Packet4s half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8s>
-{
-  typedef int16_t type;
-  typedef Packet4s half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4us>
-{
-  typedef uint16_t type;
-  typedef Packet4us half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet8us>
-{
-  typedef uint16_t type;
-  typedef Packet4us half;
-  enum
-  {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2i>
-{
-  typedef int32_t type;
-  typedef Packet2i half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4i>
-{
-  typedef int32_t type;
-  typedef Packet2i half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2ui>
-{
-  typedef uint32_t type;
-  typedef Packet2ui half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet4ui>
-{
-  typedef uint32_t type;
-  typedef Packet2ui half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2l>
-{
-  typedef int64_t type;
-  typedef Packet2l half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-template<> struct unpacket_traits<Packet2ul>
-{
-  typedef uint64_t type;
-  typedef Packet2ul half;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2f pset1<Packet2f>(const float& from) { return vdup_n_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) { return vdupq_n_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c pset1<Packet4c>(const int8_t& from)
-{ return vget_lane_s32(vreinterpret_s32_s8(vdup_n_s8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c pset1<Packet8c>(const int8_t& from) { return vdup_n_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const int8_t& from) { return vdupq_n_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet4uc pset1<Packet4uc>(const uint8_t& from)
-{ return vget_lane_u32(vreinterpret_u32_u8(vdup_n_u8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc pset1<Packet8uc>(const uint8_t& from) { return vdup_n_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const uint8_t& from) { return vdupq_n_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet4s pset1<Packet4s>(const int16_t& from) { return vdup_n_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const int16_t& from) { return vdupq_n_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet4us pset1<Packet4us>(const uint16_t& from) { return vdup_n_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const uint16_t& from) { return vdupq_n_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet2i pset1<Packet2i>(const int32_t& from) { return vdup_n_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) { return vdupq_n_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet2ui pset1<Packet2ui>(const uint32_t& from) { return vdup_n_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui pset1<Packet4ui>(const uint32_t& from) { return vdupq_n_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet2l pset1<Packet2l>(const int64_t& from) { return vdupq_n_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul pset1<Packet2ul>(const uint64_t& from) { return vdupq_n_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pset1frombits<Packet2f>(unsigned int from)
-{ return vreinterpret_f32_u32(vdup_n_u32(from)); }
-template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from)
-{ return vreinterpretq_f32_u32(vdupq_n_u32(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet2f plset<Packet2f>(const float& a)
-{
-  const float c[] = {0.0f,1.0f};
-  return vadd_f32(pset1<Packet2f>(a), vld1_f32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a)
-{
-  const float c[] = {0.0f,1.0f,2.0f,3.0f};
-  return vaddq_f32(pset1<Packet4f>(a), vld1q_f32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4c plset<Packet4c>(const int8_t& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(vreinterpret_s8_u32(vdup_n_u32(0x03020100)), vdup_n_s8(a))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c plset<Packet8c>(const int8_t& a)
-{
-  const int8_t c[] = {0,1,2,3,4,5,6,7};
-  return vadd_s8(pset1<Packet8c>(a), vld1_s8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet16c plset<Packet16c>(const int8_t& a)
-{
-  const int8_t c[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15};
-  return vaddq_s8(pset1<Packet16c>(a), vld1q_s8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4uc plset<Packet4uc>(const uint8_t& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(vreinterpret_u8_u32(vdup_n_u32(0x03020100)), vdup_n_u8(a))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc plset<Packet8uc>(const uint8_t& a)
-{
-  const uint8_t c[] = {0,1,2,3,4,5,6,7};
-  return vadd_u8(pset1<Packet8uc>(a), vld1_u8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const uint8_t& a)
-{
-  const uint8_t c[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15};
-  return vaddq_u8(pset1<Packet16uc>(a), vld1q_u8(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4s plset<Packet4s>(const int16_t& a)
-{
-  const int16_t c[] = {0,1,2,3};
-  return vadd_s16(pset1<Packet4s>(a), vld1_s16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4us plset<Packet4us>(const uint16_t& a)
-{
-  const uint16_t c[] = {0,1,2,3};
-  return vadd_u16(pset1<Packet4us>(a), vld1_u16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet8s plset<Packet8s>(const int16_t& a)
-{
-  const int16_t c[] = {0,1,2,3,4,5,6,7};
-  return vaddq_s16(pset1<Packet8s>(a), vld1q_s16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet8us plset<Packet8us>(const uint16_t& a)
-{
-  const uint16_t c[] = {0,1,2,3,4,5,6,7};
-  return vaddq_u16(pset1<Packet8us>(a), vld1q_u16(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2i plset<Packet2i>(const int32_t& a)
-{
-  const int32_t c[] = {0,1};
-  return vadd_s32(pset1<Packet2i>(a), vld1_s32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a)
-{
-  const int32_t c[] = {0,1,2,3};
-  return vaddq_s32(pset1<Packet4i>(a), vld1q_s32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2ui plset<Packet2ui>(const uint32_t& a)
-{
-  const uint32_t c[] = {0,1};
-  return vadd_u32(pset1<Packet2ui>(a), vld1_u32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet4ui plset<Packet4ui>(const uint32_t& a)
-{
-  const uint32_t c[] = {0,1,2,3};
-  return vaddq_u32(pset1<Packet4ui>(a), vld1q_u32(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2l plset<Packet2l>(const int64_t& a)
-{
-  const int64_t c[] = {0,1};
-  return vaddq_s64(pset1<Packet2l>(a), vld1q_s64(c));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul plset<Packet2ul>(const uint64_t& a)
-{
-  const uint64_t c[] = {0,1};
-  return vaddq_u64(pset1<Packet2ul>(a), vld1q_u64(c));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f padd<Packet2f>(const Packet2f& a, const Packet2f& b) { return vadd_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return vaddq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c padd<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c padd<Packet8c>(const Packet8c& a, const Packet8c& b) { return vadd_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c padd<Packet16c>(const Packet16c& a, const Packet16c& b) { return vaddq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc padd<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc padd<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vadd_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vaddq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s padd<Packet4s>(const Packet4s& a, const Packet4s& b) { return vadd_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s padd<Packet8s>(const Packet8s& a, const Packet8s& b) { return vaddq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us padd<Packet4us>(const Packet4us& a, const Packet4us& b) { return vadd_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us padd<Packet8us>(const Packet8us& a, const Packet8us& b) { return vaddq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i padd<Packet2i>(const Packet2i& a, const Packet2i& b) { return vadd_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return vaddq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui padd<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vadd_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vaddq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l padd<Packet2l>(const Packet2l& a, const Packet2l& b) { return vaddq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul padd<Packet2ul>(const Packet2ul& a, const Packet2ul& b) { return vaddq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f psub<Packet2f>(const Packet2f& a, const Packet2f& b) { return vsub_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return vsubq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c psub<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vsub_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c psub<Packet8c>(const Packet8c& a, const Packet8c& b) { return vsub_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c psub<Packet16c>(const Packet16c& a, const Packet16c& b) { return vsubq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc psub<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vsub_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc psub<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vsub_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vsubq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s psub<Packet4s>(const Packet4s& a, const Packet4s& b) { return vsub_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s psub<Packet8s>(const Packet8s& a, const Packet8s& b) { return vsubq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us psub<Packet4us>(const Packet4us& a, const Packet4us& b) { return vsub_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us psub<Packet8us>(const Packet8us& a, const Packet8us& b) { return vsubq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i psub<Packet2i>(const Packet2i& a, const Packet2i& b) { return vsub_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return vsubq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui psub<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vsub_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui psub<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vsubq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l psub<Packet2l>(const Packet2l& a, const Packet2l& b) { return vsubq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul psub<Packet2ul>(const Packet2ul& a, const Packet2ul& b) { return vsubq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b);
-template<> EIGEN_STRONG_INLINE Packet2f paddsub<Packet2f>(const Packet2f& a, const Packet2f & b) {
-  Packet2f mask = {numext::bit_cast<float>(0x80000000u), 0.0f};
-  return padd(a, pxor(mask, b));
-}
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
-template<> EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  Packet4f mask = {numext::bit_cast<float>(0x80000000u), 0.0f, numext::bit_cast<float>(0x80000000u), 0.0f};
-  return padd(a, pxor(mask, b));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pnegate(const Packet2f& a) { return vneg_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return vnegq_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4c pnegate(const Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vneg_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c pnegate(const Packet8c& a) { return vneg_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet16c pnegate(const Packet16c& a) { return vnegq_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet4s pnegate(const Packet4s& a) { return vneg_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a) { return vnegq_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet2i pnegate(const Packet2i& a) { return vneg_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return vnegq_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet2l pnegate(const Packet2l& a) {
-#if EIGEN_ARCH_ARM64
-  return vnegq_s64(a);
-#else
-  return vcombine_s64(
-      vdup_n_s64(-vgetq_lane_s64(a, 0)),
-      vdup_n_s64(-vgetq_lane_s64(a, 1)));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pconj(const Packet2f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4c pconj(const Packet4c& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8c pconj(const Packet8c& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16c pconj(const Packet16c& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4uc pconj(const Packet4uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8uc pconj(const Packet8uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16uc pconj(const Packet16uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4s pconj(const Packet4s& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8s pconj(const Packet8s& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4us pconj(const Packet4us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8us pconj(const Packet8us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2i pconj(const Packet2i& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2ui pconj(const Packet2ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4ui pconj(const Packet4ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2l pconj(const Packet2l& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2ul pconj(const Packet2ul& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmul<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmul_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmulq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c pmul<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmul_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmul<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmul_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmul<Packet16c>(const Packet16c& a, const Packet16c& b) { return vmulq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmul<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmul_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmul<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmul_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vmulq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmul<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmul_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmul<Packet8s>(const Packet8s& a, const Packet8s& b) { return vmulq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmul<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmul_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmul<Packet8us>(const Packet8us& a, const Packet8us& b) { return vmulq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmul<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmul_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmulq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmul<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmul_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmul<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vmulq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pmul<Packet2l>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s64(
-    vdup_n_s64(vgetq_lane_s64(a, 0)*vgetq_lane_s64(b, 0)),
-    vdup_n_s64(vgetq_lane_s64(a, 1)*vgetq_lane_s64(b, 1)));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pmul<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u64(
-    vdup_n_u64(vgetq_lane_u64(a, 0)*vgetq_lane_u64(b, 0)),
-    vdup_n_u64(vgetq_lane_u64(a, 1)*vgetq_lane_u64(b, 1)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pdiv<Packet2f>(const Packet2f& a, const Packet2f& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vdiv_f32(a,b);
-#else
-  Packet2f inv, restep, div;
-
-  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
-  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
-  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
-  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
-  // Newton-Raphson and vrecpsq_f32()
-  inv = vrecpe_f32(b);
-
-  // This returns a differential, by which we will have to multiply inv to get a better
-  // approximation of 1/b.
-  restep = vrecps_f32(b, inv);
-  inv = vmul_f32(restep, inv);
-
-  // Finally, multiply a by 1/b and get the wanted result of the division.
-  div = vmul_f32(a, inv);
-
-  return div;
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vdivq_f32(a,b);
-#else
-  Packet4f inv, restep, div;
-
-  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
-  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
-  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
-  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
-  // Newton-Raphson and vrecpsq_f32()
-  inv = vrecpeq_f32(b);
-
-  // This returns a differential, by which we will have to multiply inv to get a better
-  // approximation of 1/b.
-  restep = vrecpsq_f32(b, inv);
-  inv = vmulq_f32(restep, inv);
-
-  // Finally, multiply a by 1/b and get the wanted result of the division.
-  div = vmulq_f32(a, inv);
-
-  return div;
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4c pdiv<Packet4c>(const Packet4c& /*a*/, const Packet4c& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4c>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pdiv<Packet8c>(const Packet8c& /*a*/, const Packet8c& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8c>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet16c pdiv<Packet16c>(const Packet16c& /*a*/, const Packet16c& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet16c>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4uc pdiv<Packet4uc>(const Packet4uc& /*a*/, const Packet4uc& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4uc>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pdiv<Packet8uc>(const Packet8uc& /*a*/, const Packet8uc& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8uc>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc pdiv<Packet16uc>(const Packet16uc& /*a*/, const Packet16uc& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet16uc>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4s pdiv<Packet4s>(const Packet4s& /*a*/, const Packet4s& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4s>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8s pdiv<Packet8s>(const Packet8s& /*a*/, const Packet8s& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8s>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4us pdiv<Packet4us>(const Packet4us& /*a*/, const Packet4us& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4us>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet8us pdiv<Packet8us>(const Packet8us& /*a*/, const Packet8us& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet8us>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet2i pdiv<Packet2i>(const Packet2i& /*a*/, const Packet2i& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2i>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4i>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet2ui pdiv<Packet2ui>(const Packet2ui& /*a*/, const Packet2ui& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2ui>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet4ui pdiv<Packet4ui>(const Packet4ui& /*a*/, const Packet4ui& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet4ui>(0);
-}
-template<> EIGEN_STRONG_INLINE Packet2l pdiv<Packet2l>(const Packet2l& /*a*/, const Packet2l& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2l>(0LL);
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pdiv<Packet2ul>(const Packet2ul& /*a*/, const Packet2ul& /*b*/)
-{
-  eigen_assert(false && "packet integer division are not supported by NEON");
-  return pset1<Packet2ul>(0ULL);
-}
-
-
-#ifdef __ARM_FEATURE_FMA
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
-{ return vfmaq_f32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c)
-{ return vfma_f32(c,a,b); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
-{
-  return vmlaq_f32(c,a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c)
-{
-  return vmla_f32(c,a,b);
-}
-#endif
-
-// No FMA instruction for int, so use MLA unconditionally.
-template<> EIGEN_STRONG_INLINE Packet4c pmadd(const Packet4c& a, const Packet4c& b, const Packet4c& c)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmla_s8(
-      vreinterpret_s8_s32(vdup_n_s32(c)),
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmadd(const Packet8c& a, const Packet8c& b, const Packet8c& c)
-{ return vmla_s8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmadd(const Packet16c& a, const Packet16c& b, const Packet16c& c)
-{ return vmlaq_s8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmadd(const Packet4uc& a, const Packet4uc& b, const Packet4uc& c)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmla_u8(
-      vreinterpret_u8_u32(vdup_n_u32(c)),
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmadd(const Packet8uc& a, const Packet8uc& b, const Packet8uc& c)
-{ return vmla_u8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmadd(const Packet16uc& a, const Packet16uc& b, const Packet16uc& c)
-{ return vmlaq_u8(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmadd(const Packet4s& a, const Packet4s& b, const Packet4s& c)
-{ return vmla_s16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c)
-{ return vmlaq_s16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmadd(const Packet4us& a, const Packet4us& b, const Packet4us& c)
-{ return vmla_u16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c)
-{ return vmlaq_u16(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmadd(const Packet2i& a, const Packet2i& b, const Packet2i& c)
-{ return vmla_s32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c)
-{ return vmlaq_s32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmadd(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c)
-{ return vmla_u32(c,a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmadd(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c)
-{ return vmlaq_u32(c,a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pabsdiff<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vabd_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pabsdiff<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vabdq_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4c pabsdiff<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vabd_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pabsdiff<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vabd_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pabsdiff<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vabdq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pabsdiff<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vabd_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pabsdiff<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vabd_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pabsdiff<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vabdq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pabsdiff<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vabd_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pabsdiff<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vabdq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pabsdiff<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vabd_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pabsdiff<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vabdq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pabsdiff<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vabd_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pabsdiff<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vabdq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pabsdiff<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vabd_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pabsdiff<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vabdq_u32(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmin<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmin_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vminq_f32(a,b); }
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) { return vminnmq_f32(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2f pmin<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) { return vminnm_f32(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) { return pmin<Packet4f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmin<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) { return pmin<Packet2f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4c pmin<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmin_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmin<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmin_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) { return vminq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmin<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmin_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmin<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmin_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vminq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmin<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmin_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) { return vminq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmin<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmin_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) { return vminq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmin<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmin_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vminq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmin<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmin_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmin<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vminq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pmin<Packet2l>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s64(
-      vdup_n_s64((std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
-      vdup_n_s64((std::min)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pmin<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u64(
-      vdup_n_u64((std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
-      vdup_n_u64((std::min)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pmax<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmax_f32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmaxq_f32(a,b); }
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) { return vmaxnmq_f32(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2f pmax<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) { return vmaxnm_f32(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) { return pmax<Packet4f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pmax<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) { return pmax<Packet2f>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4c pmax<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vmax_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pmax<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmax_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) { return vmaxq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pmax<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vmax_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pmax<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmax_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vmaxq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pmax<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmax_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) { return vmaxq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pmax<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmax_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) { return vmaxq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pmax<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmax_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmaxq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pmax<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmax_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pmax<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vmaxq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pmax<Packet2l>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s64(
-      vdup_n_s64((std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
-      vdup_n_s64((std::max)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pmax<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u64(
-      vdup_n_u64((std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
-      vdup_n_u64((std::max)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_le<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vcle_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vcleq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4c pcmp_le<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_u8(vcle_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pcmp_le<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vreinterpret_s8_u8(vcle_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_le<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vreinterpretq_s8_u8(vcleq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4uc pcmp_le<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vcle_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pcmp_le<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vcle_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_le<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vcleq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pcmp_le<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vreinterpret_s16_u16(vcle_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_le<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vreinterpretq_s16_u16(vcleq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4us pcmp_le<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vcle_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_le<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vcleq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pcmp_le<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vreinterpret_s32_u32(vcle_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_le<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vreinterpretq_s32_u32(vcleq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2ui pcmp_le<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vcle_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pcmp_le<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vcleq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pcmp_le<Packet2l>(const Packet2l& a, const Packet2l& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vreinterpretq_s64_u64(vcleq_s64(a,b));
-#else
-  return vcombine_s64(
-      vdup_n_s64(vgetq_lane_s64(a, 0) <= vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
-      vdup_n_s64(vgetq_lane_s64(a, 1) <= vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pcmp_le<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vcleq_u64(a,b);
-#else
-  return vcombine_u64(
-      vdup_n_u64(vgetq_lane_u64(a, 0) <= vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
-      vdup_n_u64(vgetq_lane_u64(a, 1) <= vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_lt<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vclt_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vcltq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4c pcmp_lt<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_u8(vclt_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pcmp_lt<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vreinterpret_s8_u8(vclt_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_lt<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vreinterpretq_s8_u8(vcltq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4uc pcmp_lt<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vclt_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pcmp_lt<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vclt_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_lt<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vcltq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pcmp_lt<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vreinterpret_s16_u16(vclt_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_lt<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vreinterpretq_s16_u16(vcltq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4us pcmp_lt<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vclt_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_lt<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vcltq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pcmp_lt<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vreinterpret_s32_u32(vclt_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vreinterpretq_s32_u32(vcltq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2ui pcmp_lt<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vclt_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pcmp_lt<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vcltq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pcmp_lt<Packet2l>(const Packet2l& a, const Packet2l& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vreinterpretq_s64_u64(vcltq_s64(a,b));
-#else
-  return vcombine_s64(
-      vdup_n_s64(vgetq_lane_s64(a, 0) < vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
-      vdup_n_s64(vgetq_lane_s64(a, 1) < vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pcmp_lt<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vcltq_u64(a,b);
-#else
-  return vcombine_u64(
-      vdup_n_u64(vgetq_lane_u64(a, 0) < vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
-      vdup_n_u64(vgetq_lane_u64(a, 1) < vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_eq<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vceq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vceqq_f32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4c pcmp_eq<Packet4c>(const Packet4c& a, const Packet4c& b)
-{
-  return vget_lane_s32(vreinterpret_s32_u8(vceq_s8(
-      vreinterpret_s8_s32(vdup_n_s32(a)),
-      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c pcmp_eq<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vreinterpret_s8_u8(vceq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet16c pcmp_eq<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vreinterpretq_s8_u8(vceqq_s8(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4uc pcmp_eq<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vceq_u8(
-      vreinterpret_u8_u32(vdup_n_u32(a)),
-      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pcmp_eq<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vceq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pcmp_eq<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vceqq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pcmp_eq<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vreinterpret_s16_u16(vceq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet8s pcmp_eq<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vreinterpretq_s16_u16(vceqq_s16(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4us pcmp_eq<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vceq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pcmp_eq<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vceqq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pcmp_eq<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vreinterpret_s32_u32(vceq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vreinterpretq_s32_u32(vceqq_s32(a,b)); }
-template<> EIGEN_STRONG_INLINE Packet2ui pcmp_eq<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vceq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pcmp_eq<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vceqq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pcmp_eq<Packet2l>(const Packet2l& a, const Packet2l& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vreinterpretq_s64_u64(vceqq_s64(a,b));
-#else
-  return vcombine_s64(
-      vdup_n_s64(vgetq_lane_s64(a, 0) == vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
-      vdup_n_s64(vgetq_lane_s64(a, 1) == vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pcmp_eq<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{
-#if EIGEN_ARCH_ARM64
-  return vceqq_u64(a,b);
-#else
-  return vcombine_u64(
-      vdup_n_u64(vgetq_lane_u64(a, 0) == vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
-      vdup_n_u64(vgetq_lane_u64(a, 1) == vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pcmp_lt_or_nan<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vmvn_u32(vcge_f32(a,b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vmvnq_u32(vcgeq_f32(a,b))); }
-
-// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
-template<> EIGEN_STRONG_INLINE Packet2f pand<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c pand<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a & b; }
-template<> EIGEN_STRONG_INLINE Packet8c pand<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return vand_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pand<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vandq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pand<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a & b; }
-template<> EIGEN_STRONG_INLINE Packet8uc pand<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vand_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pand<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vandq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pand<Packet4s>(const Packet4s& a, const Packet4s& b) { return vand_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pand<Packet8s>(const Packet8s& a, const Packet8s& b) { return vandq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pand<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vand_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vandq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pand<Packet2i>(const Packet2i& a, const Packet2i& b) { return vand_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vandq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pand<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vand_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vandq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pand<Packet2l>(const Packet2l& a, const Packet2l& b) { return vandq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul pand<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return vandq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f por<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c por<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a | b; }
-template<> EIGEN_STRONG_INLINE Packet8c por<Packet8c>(const Packet8c& a, const Packet8c& b) { return vorr_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c por<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return vorrq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc por<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a | b; }
-template<> EIGEN_STRONG_INLINE Packet8uc por<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vorr_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc por<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vorrq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s por<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vorr_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vorrq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us por<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vorr_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vorrq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i por<Packet2i>(const Packet2i& a, const Packet2i& b) { return vorr_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vorrq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui por<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vorr_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui por<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vorrq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l por<Packet2l>(const Packet2l& a, const Packet2l& b)
-{ return vorrq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul por<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return vorrq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c pxor<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a ^ b; }
-template<> EIGEN_STRONG_INLINE Packet8c pxor<Packet8c>(const Packet8c& a, const Packet8c& b)
-{ return veor_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pxor<Packet16c>(const Packet16c& a, const Packet16c& b)
-{ return veorq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pxor<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a ^ b; }
-template<> EIGEN_STRONG_INLINE Packet8uc pxor<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return veor_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pxor<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return veorq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pxor<Packet4s>(const Packet4s& a, const Packet4s& b) { return veor_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pxor<Packet8s>(const Packet8s& a, const Packet8s& b) { return veorq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pxor<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return veor_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pxor<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return veorq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pxor<Packet2i>(const Packet2i& a, const Packet2i& b) { return veor_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return veorq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pxor<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return veor_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pxor<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return veorq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pxor<Packet2l>(const Packet2l& a, const Packet2l& b)
-{ return veorq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul pxor<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return veorq_u64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pandnot<Packet2f>(const Packet2f& a, const Packet2f& b)
-{ return vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b)
-{ return vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
-template<> EIGEN_STRONG_INLINE Packet4c pandnot<Packet4c>(const Packet4c& a, const Packet4c& b)
-{ return a & ~b; }
-template<> EIGEN_STRONG_INLINE Packet8c pandnot<Packet8c>(const Packet8c& a, const Packet8c& b) { return vbic_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16c pandnot<Packet16c>(const Packet16c& a, const Packet16c& b) { return vbicq_s8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4uc pandnot<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
-{ return a & ~b; }
-template<> EIGEN_STRONG_INLINE Packet8uc pandnot<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
-{ return vbic_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16uc pandnot<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
-{ return vbicq_u8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4s pandnot<Packet4s>(const Packet4s& a, const Packet4s& b)
-{ return vbic_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8s pandnot<Packet8s>(const Packet8s& a, const Packet8s& b)
-{ return vbicq_s16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4us pandnot<Packet4us>(const Packet4us& a, const Packet4us& b)
-{ return vbic_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet8us pandnot<Packet8us>(const Packet8us& a, const Packet8us& b)
-{ return vbicq_u16(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2i pandnot<Packet2i>(const Packet2i& a, const Packet2i& b)
-{ return vbic_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b)
-{ return vbicq_s32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ui pandnot<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
-{ return vbic_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4ui pandnot<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
-{ return vbicq_u32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2l pandnot<Packet2l>(const Packet2l& a, const Packet2l& b)
-{ return vbicq_s64(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2ul pandnot<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
-{ return vbicq_u64(a,b); }
-
-
-template<int N> EIGEN_STRONG_INLINE Packet4c parithmetic_shift_right(Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vshr_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8c parithmetic_shift_right(Packet8c a) { return vshr_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16c parithmetic_shift_right(Packet16c a) { return vshrq_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4uc parithmetic_shift_right(Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vshr_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8uc parithmetic_shift_right(Packet8uc a) { return vshr_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16uc parithmetic_shift_right(Packet16uc a) { return vshrq_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4s parithmetic_shift_right(Packet4s a) { return vshr_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8s parithmetic_shift_right(Packet8s a) { return vshrq_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4us parithmetic_shift_right(Packet4us a) { return vshr_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8us parithmetic_shift_right(Packet8us a) { return vshrq_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2i parithmetic_shift_right(Packet2i a) { return vshr_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(Packet4i a) { return vshrq_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ui parithmetic_shift_right(Packet2ui a) { return vshr_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4ui parithmetic_shift_right(Packet4ui a) { return vshrq_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2l parithmetic_shift_right(Packet2l a) { return vshrq_n_s64(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ul parithmetic_shift_right(Packet2ul a) { return vshrq_n_u64(a,N); }
-
-template<int N> EIGEN_STRONG_INLINE Packet4c plogical_shift_right(Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_u8(vshr_n_u8(vreinterpret_u8_s32(vdup_n_s32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8c plogical_shift_right(Packet8c a)
-{ return vreinterpret_s8_u8(vshr_n_u8(vreinterpret_u8_s8(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet16c plogical_shift_right(Packet16c a)
-{ return vreinterpretq_s8_u8(vshrq_n_u8(vreinterpretq_u8_s8(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet4uc plogical_shift_right(Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_s8(vshr_n_s8(vreinterpret_s8_u32(vdup_n_u32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8uc plogical_shift_right(Packet8uc a) { return vshr_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16uc plogical_shift_right(Packet16uc a) { return vshrq_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4s plogical_shift_right(Packet4s a)
-{ return vreinterpret_s16_u16(vshr_n_u16(vreinterpret_u16_s16(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet8s plogical_shift_right(Packet8s a)
-{ return vreinterpretq_s16_u16(vshrq_n_u16(vreinterpretq_u16_s16(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet4us plogical_shift_right(Packet4us a) { return vshr_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_right(Packet8us a) { return vshrq_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2i plogical_shift_right(Packet2i a)
-{ return vreinterpret_s32_u32(vshr_n_u32(vreinterpret_u32_s32(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right(Packet4i a)
-{ return vreinterpretq_s32_u32(vshrq_n_u32(vreinterpretq_u32_s32(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet2ui plogical_shift_right(Packet2ui a) { return vshr_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(Packet4ui a) { return vshrq_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2l plogical_shift_right(Packet2l a)
-{ return vreinterpretq_s64_u64(vshrq_n_u64(vreinterpretq_u64_s64(a),N)); }
-template<int N> EIGEN_STRONG_INLINE Packet2ul plogical_shift_right(Packet2ul a) { return vshrq_n_u64(a,N); }
-
-template<int N> EIGEN_STRONG_INLINE Packet4c plogical_shift_left(Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vshl_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8c plogical_shift_left(Packet8c a) { return vshl_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16c plogical_shift_left(Packet16c a) { return vshlq_n_s8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4uc plogical_shift_left(Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vshl_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0); }
-template<int N> EIGEN_STRONG_INLINE Packet8uc plogical_shift_left(Packet8uc a) { return vshl_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet16uc plogical_shift_left(Packet16uc a) { return vshlq_n_u8(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4s plogical_shift_left(Packet4s a) { return vshl_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8s plogical_shift_left(Packet8s a) { return vshlq_n_s16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4us plogical_shift_left(Packet4us a) { return vshl_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_left(Packet8us a) { return vshlq_n_u16(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2i plogical_shift_left(Packet2i a) { return vshl_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left(Packet4i a) { return vshlq_n_s32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ui plogical_shift_left(Packet2ui a) { return vshl_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(Packet4ui a) { return vshlq_n_u32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2l plogical_shift_left(Packet2l a) { return vshlq_n_s64(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet2ul plogical_shift_left(Packet2ul a) { return vshlq_n_u64(a,N); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pload<Packet2f>(const float* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c pload<Packet4c>(const int8_t* from)
-{
-  Packet4c res;
-  memcpy(&res, from, sizeof(Packet4c));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8c pload<Packet8c>(const int8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const int8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet4uc pload<Packet4uc>(const uint8_t* from)
-{
-  Packet4uc res;
-  memcpy(&res, from, sizeof(Packet4uc));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8uc pload<Packet8uc>(const uint8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const uint8_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet4s pload<Packet4s>(const int16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const int16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet4us pload<Packet4us>(const uint16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const uint16_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet2i pload<Packet2i>(const int32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet2ui pload<Packet2ui>(const uint32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui pload<Packet4ui>(const uint32_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet2l pload<Packet2l>(const int64_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul pload<Packet2ul>(const uint64_t* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2f ploadu<Packet2f>(const float* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c ploadu<Packet4c>(const int8_t* from)
-{
-  Packet4c res;
-  memcpy(&res, from, sizeof(Packet4c));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8c ploadu<Packet8c>(const int8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const int8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s8(from); }
-template<> EIGEN_STRONG_INLINE Packet4uc ploadu<Packet4uc>(const uint8_t* from)
-{
-  Packet4uc res;
-  memcpy(&res, from, sizeof(Packet4uc));
-  return res;
-}
-template<> EIGEN_STRONG_INLINE Packet8uc ploadu<Packet8uc>(const uint8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const uint8_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u8(from); }
-template<> EIGEN_STRONG_INLINE Packet4s ploadu<Packet4s>(const int16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const int16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s16(from); }
-template<> EIGEN_STRONG_INLINE Packet4us ploadu<Packet4us>(const uint16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const uint16_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u16(from); }
-template<> EIGEN_STRONG_INLINE Packet2i ploadu<Packet2i>(const int32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet2ui ploadu<Packet2ui>(const uint32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui ploadu<Packet4ui>(const uint32_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet2l ploadu<Packet2l>(const int64_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul ploadu<Packet2ul>(const uint64_t* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2f ploaddup<Packet2f>(const float* from)
-{ return vld1_dup_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
-{ return vcombine_f32(vld1_dup_f32(from), vld1_dup_f32(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet4c ploaddup<Packet4c>(const int8_t* from)
-{
-  const int8x8_t a = vreinterpret_s8_s32(vdup_n_s32(pload<Packet4c>(from)));
-  return vget_lane_s32(vreinterpret_s32_s8(vzip_s8(a,a).val[0]), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8c ploaddup<Packet8c>(const int8_t* from)
-{
-  const int8x8_t a = vld1_s8(from);
-  return vzip_s8(a,a).val[0];
-}
-template<> EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const int8_t* from)
-{
-  const int8x8_t a = vld1_s8(from);
-  const int8x8x2_t b = vzip_s8(a,a);
-  return vcombine_s8(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet4uc ploaddup<Packet4uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vreinterpret_u8_u32(vdup_n_u32(pload<Packet4uc>(from)));
-  return vget_lane_u32(vreinterpret_u32_u8(vzip_u8(a,a).val[0]), 0);
-}
-template<> EIGEN_STRONG_INLINE Packet8uc ploaddup<Packet8uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vld1_u8(from);
-  return vzip_u8(a,a).val[0];
-}
-template<> EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vld1_u8(from);
-  const uint8x8x2_t b = vzip_u8(a,a);
-  return vcombine_u8(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet4s ploaddup<Packet4s>(const int16_t* from)
-{
-  return vreinterpret_s16_u32(vzip_u32(vreinterpret_u32_s16(vld1_dup_s16(from)),
-      vreinterpret_u32_s16(vld1_dup_s16(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const int16_t* from)
-{
-  const int16x4_t a = vld1_s16(from);
-  const int16x4x2_t b = vzip_s16(a,a);
-  return vcombine_s16(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet4us ploaddup<Packet4us>(const uint16_t* from)
-{
-  return vreinterpret_u16_u32(vzip_u32(vreinterpret_u32_u16(vld1_dup_u16(from)),
-      vreinterpret_u32_u16(vld1_dup_u16(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const uint16_t* from)
-{
-  const uint16x4_t a = vld1_u16(from);
-  const uint16x4x2_t b = vzip_u16(a,a);
-  return vcombine_u16(b.val[0], b.val[1]);
-}
-template<> EIGEN_STRONG_INLINE Packet2i ploaddup<Packet2i>(const int32_t* from)
-{ return vld1_dup_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from)
-{ return vcombine_s32(vld1_dup_s32(from), vld1_dup_s32(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet2ui ploaddup<Packet2ui>(const uint32_t* from)
-{ return vld1_dup_u32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui ploaddup<Packet4ui>(const uint32_t* from)
-{ return vcombine_u32(vld1_dup_u32(from), vld1_dup_u32(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet2l ploaddup<Packet2l>(const int64_t* from)
-{ return vld1q_dup_s64(from); }
-template<> EIGEN_STRONG_INLINE Packet2ul ploaddup<Packet2ul>(const uint64_t* from)
-{ return vld1q_dup_u64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet4f ploadquad<Packet4f>(const float* from) { return vld1q_dup_f32(from); }
-template<> EIGEN_STRONG_INLINE Packet4c ploadquad<Packet4c>(const int8_t* from)
-{ return vget_lane_s32(vreinterpret_s32_s8(vld1_dup_s8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c ploadquad<Packet8c>(const int8_t* from)
-{
-  return vreinterpret_s8_u32(vzip_u32(
-      vreinterpret_u32_s8(vld1_dup_s8(from)),
-      vreinterpret_u32_s8(vld1_dup_s8(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet16c ploadquad<Packet16c>(const int8_t* from)
-{
-  const int8x8_t a = vreinterpret_s8_u32(vzip_u32(
-      vreinterpret_u32_s8(vld1_dup_s8(from)),
-      vreinterpret_u32_s8(vld1_dup_s8(from+1))).val[0]);
-  const int8x8_t b = vreinterpret_s8_u32(vzip_u32(
-      vreinterpret_u32_s8(vld1_dup_s8(from+2)),
-      vreinterpret_u32_s8(vld1_dup_s8(from+3))).val[0]);
-  return vcombine_s8(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet4uc ploadquad<Packet4uc>(const uint8_t* from)
-{ return vget_lane_u32(vreinterpret_u32_u8(vld1_dup_u8(from)), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc ploadquad<Packet8uc>(const uint8_t* from)
-{
-  return vreinterpret_u8_u32(vzip_u32(
-      vreinterpret_u32_u8(vld1_dup_u8(from)),
-      vreinterpret_u32_u8(vld1_dup_u8(from+1))).val[0]);
-}
-template<> EIGEN_STRONG_INLINE Packet16uc ploadquad<Packet16uc>(const uint8_t* from)
-{
-  const uint8x8_t a = vreinterpret_u8_u32(vzip_u32(
-      vreinterpret_u32_u8(vld1_dup_u8(from)),
-      vreinterpret_u32_u8(vld1_dup_u8(from+1))).val[0]);
-  const uint8x8_t b = vreinterpret_u8_u32(vzip_u32(
-      vreinterpret_u32_u8(vld1_dup_u8(from+2)),
-      vreinterpret_u32_u8(vld1_dup_u8(from+3))).val[0]);
-  return vcombine_u8(a,b);
-}
-template<> EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const int16_t* from)
-{ return vcombine_s16(vld1_dup_s16(from), vld1_dup_s16(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const uint16_t* from)
-{ return vcombine_u16(vld1_dup_u16(from), vld1_dup_u16(from+1)); }
-template<> EIGEN_STRONG_INLINE Packet4i ploadquad<Packet4i>(const int32_t* from) { return vld1q_dup_s32(from); }
-template<> EIGEN_STRONG_INLINE Packet4ui ploadquad<Packet4ui>(const uint32_t* from) { return vld1q_dup_u32(from); }
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet2f& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet4c& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet8c& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet16c& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet4uc& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet8uc& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet16uc& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet4s& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet8s& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet4us& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet8us& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet2i& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet2ui& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet4ui& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet2l& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s64(to,from); }
-template<> EIGEN_STRONG_INLINE void pstore<uint64_t>(uint64_t* to, const Packet2ul& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u64(to,from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet2f& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_f32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet4c& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet8c& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet16c& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet4uc& from)
-{ memcpy(to, &from, sizeof(from)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet8uc& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet16uc& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u8(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet4s& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet8s& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet4us& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet8us& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u16(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet2i& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet2ui& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet4ui& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u32(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet2l& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s64(to,from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<uint64_t>(uint64_t* to, const Packet2ul& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u64(to,from); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pgather<float, Packet2f>(const float* from, Index stride)
-{
-  Packet2f res = vld1_dup_f32(from);
-  res = vld1_lane_f32(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  Packet4f res = vld1q_dup_f32(from);
-  res = vld1q_lane_f32(from + 1*stride, res, 1);
-  res = vld1q_lane_f32(from + 2*stride, res, 2);
-  res = vld1q_lane_f32(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c pgather<int8_t, Packet4c>(const int8_t* from, Index stride)
-{
-  Packet4c res;
-  for (int i = 0; i != 4; i++)
-    reinterpret_cast<int8_t*>(&res)[i] = *(from + i * stride);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pgather<int8_t, Packet8c>(const int8_t* from, Index stride)
-{
-  Packet8c res = vld1_dup_s8(from);
-  res = vld1_lane_s8(from + 1*stride, res, 1);
-  res = vld1_lane_s8(from + 2*stride, res, 2);
-  res = vld1_lane_s8(from + 3*stride, res, 3);
-  res = vld1_lane_s8(from + 4*stride, res, 4);
-  res = vld1_lane_s8(from + 5*stride, res, 5);
-  res = vld1_lane_s8(from + 6*stride, res, 6);
-  res = vld1_lane_s8(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pgather<int8_t, Packet16c>(const int8_t* from, Index stride)
-{
-  Packet16c res = vld1q_dup_s8(from);
-  res = vld1q_lane_s8(from + 1*stride, res, 1);
-  res = vld1q_lane_s8(from + 2*stride, res, 2);
-  res = vld1q_lane_s8(from + 3*stride, res, 3);
-  res = vld1q_lane_s8(from + 4*stride, res, 4);
-  res = vld1q_lane_s8(from + 5*stride, res, 5);
-  res = vld1q_lane_s8(from + 6*stride, res, 6);
-  res = vld1q_lane_s8(from + 7*stride, res, 7);
-  res = vld1q_lane_s8(from + 8*stride, res, 8);
-  res = vld1q_lane_s8(from + 9*stride, res, 9);
-  res = vld1q_lane_s8(from + 10*stride, res, 10);
-  res = vld1q_lane_s8(from + 11*stride, res, 11);
-  res = vld1q_lane_s8(from + 12*stride, res, 12);
-  res = vld1q_lane_s8(from + 13*stride, res, 13);
-  res = vld1q_lane_s8(from + 14*stride, res, 14);
-  res = vld1q_lane_s8(from + 15*stride, res, 15);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc pgather<uint8_t, Packet4uc>(const uint8_t* from, Index stride)
-{
-  Packet4uc res;
-  for (int i = 0; i != 4; i++)
-    reinterpret_cast<uint8_t*>(&res)[i] = *(from + i * stride);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pgather<uint8_t, Packet8uc>(const uint8_t* from, Index stride)
-{
-  Packet8uc res = vld1_dup_u8(from);
-  res = vld1_lane_u8(from + 1*stride, res, 1);
-  res = vld1_lane_u8(from + 2*stride, res, 2);
-  res = vld1_lane_u8(from + 3*stride, res, 3);
-  res = vld1_lane_u8(from + 4*stride, res, 4);
-  res = vld1_lane_u8(from + 5*stride, res, 5);
-  res = vld1_lane_u8(from + 6*stride, res, 6);
-  res = vld1_lane_u8(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pgather<uint8_t, Packet16uc>(const uint8_t* from, Index stride)
-{
-  Packet16uc res = vld1q_dup_u8(from);
-  res = vld1q_lane_u8(from + 1*stride, res, 1);
-  res = vld1q_lane_u8(from + 2*stride, res, 2);
-  res = vld1q_lane_u8(from + 3*stride, res, 3);
-  res = vld1q_lane_u8(from + 4*stride, res, 4);
-  res = vld1q_lane_u8(from + 5*stride, res, 5);
-  res = vld1q_lane_u8(from + 6*stride, res, 6);
-  res = vld1q_lane_u8(from + 7*stride, res, 7);
-  res = vld1q_lane_u8(from + 8*stride, res, 8);
-  res = vld1q_lane_u8(from + 9*stride, res, 9);
-  res = vld1q_lane_u8(from + 10*stride, res, 10);
-  res = vld1q_lane_u8(from + 11*stride, res, 11);
-  res = vld1q_lane_u8(from + 12*stride, res, 12);
-  res = vld1q_lane_u8(from + 13*stride, res, 13);
-  res = vld1q_lane_u8(from + 14*stride, res, 14);
-  res = vld1q_lane_u8(from + 15*stride, res, 15);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pgather<int16_t, Packet4s>(const int16_t* from, Index stride)
-{
-  Packet4s res = vld1_dup_s16(from);
-  res = vld1_lane_s16(from + 1*stride, res, 1);
-  res = vld1_lane_s16(from + 2*stride, res, 2);
-  res = vld1_lane_s16(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pgather<int16_t, Packet8s>(const int16_t* from, Index stride)
-{
-  Packet8s res = vld1q_dup_s16(from);
-  res = vld1q_lane_s16(from + 1*stride, res, 1);
-  res = vld1q_lane_s16(from + 2*stride, res, 2);
-  res = vld1q_lane_s16(from + 3*stride, res, 3);
-  res = vld1q_lane_s16(from + 4*stride, res, 4);
-  res = vld1q_lane_s16(from + 5*stride, res, 5);
-  res = vld1q_lane_s16(from + 6*stride, res, 6);
-  res = vld1q_lane_s16(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pgather<uint16_t, Packet4us>(const uint16_t* from, Index stride)
-{
-  Packet4us res = vld1_dup_u16(from);
-  res = vld1_lane_u16(from + 1*stride, res, 1);
-  res = vld1_lane_u16(from + 2*stride, res, 2);
-  res = vld1_lane_u16(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pgather<uint16_t, Packet8us>(const uint16_t* from, Index stride)
-{
-  Packet8us res = vld1q_dup_u16(from);
-  res = vld1q_lane_u16(from + 1*stride, res, 1);
-  res = vld1q_lane_u16(from + 2*stride, res, 2);
-  res = vld1q_lane_u16(from + 3*stride, res, 3);
-  res = vld1q_lane_u16(from + 4*stride, res, 4);
-  res = vld1q_lane_u16(from + 5*stride, res, 5);
-  res = vld1q_lane_u16(from + 6*stride, res, 6);
-  res = vld1q_lane_u16(from + 7*stride, res, 7);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pgather<int32_t, Packet2i>(const int32_t* from, Index stride)
-{
-  Packet2i res = vld1_dup_s32(from);
-  res = vld1_lane_s32(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride)
-{
-  Packet4i res = vld1q_dup_s32(from);
-  res = vld1q_lane_s32(from + 1*stride, res, 1);
-  res = vld1q_lane_s32(from + 2*stride, res, 2);
-  res = vld1q_lane_s32(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pgather<uint32_t, Packet2ui>(const uint32_t* from, Index stride)
-{
-  Packet2ui res = vld1_dup_u32(from);
-  res = vld1_lane_u32(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pgather<uint32_t, Packet4ui>(const uint32_t* from, Index stride)
-{
-  Packet4ui res = vld1q_dup_u32(from);
-  res = vld1q_lane_u32(from + 1*stride, res, 1);
-  res = vld1q_lane_u32(from + 2*stride, res, 2);
-  res = vld1q_lane_u32(from + 3*stride, res, 3);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pgather<int64_t, Packet2l>(const int64_t* from, Index stride)
-{
-  Packet2l res = vld1q_dup_s64(from);
-  res = vld1q_lane_s64(from + 1*stride, res, 1);
-  return res;
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pgather<uint64_t, Packet2ul>(const uint64_t* from, Index stride)
-{
-  Packet2ul res = vld1q_dup_u64(from);
-  res = vld1q_lane_u64(from + 1*stride, res, 1);
-  return res;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet2f>(float* to, const Packet2f& from, Index stride)
-{
-  vst1_lane_f32(to + stride*0, from, 0);
-  vst1_lane_f32(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  vst1q_lane_f32(to + stride*0, from, 0);
-  vst1q_lane_f32(to + stride*1, from, 1);
-  vst1q_lane_f32(to + stride*2, from, 2);
-  vst1q_lane_f32(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet4c>(int8_t* to, const Packet4c& from, Index stride)
-{
-  for (int i = 0; i != 4; i++)
-    *(to + i * stride) = reinterpret_cast<const int8_t*>(&from)[i];
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet8c>(int8_t* to, const Packet8c& from, Index stride)
-{
-  vst1_lane_s8(to + stride*0, from, 0);
-  vst1_lane_s8(to + stride*1, from, 1);
-  vst1_lane_s8(to + stride*2, from, 2);
-  vst1_lane_s8(to + stride*3, from, 3);
-  vst1_lane_s8(to + stride*4, from, 4);
-  vst1_lane_s8(to + stride*5, from, 5);
-  vst1_lane_s8(to + stride*6, from, 6);
-  vst1_lane_s8(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet16c>(int8_t* to, const Packet16c& from, Index stride)
-{
-  vst1q_lane_s8(to + stride*0, from, 0);
-  vst1q_lane_s8(to + stride*1, from, 1);
-  vst1q_lane_s8(to + stride*2, from, 2);
-  vst1q_lane_s8(to + stride*3, from, 3);
-  vst1q_lane_s8(to + stride*4, from, 4);
-  vst1q_lane_s8(to + stride*5, from, 5);
-  vst1q_lane_s8(to + stride*6, from, 6);
-  vst1q_lane_s8(to + stride*7, from, 7);
-  vst1q_lane_s8(to + stride*8, from, 8);
-  vst1q_lane_s8(to + stride*9, from, 9);
-  vst1q_lane_s8(to + stride*10, from, 10);
-  vst1q_lane_s8(to + stride*11, from, 11);
-  vst1q_lane_s8(to + stride*12, from, 12);
-  vst1q_lane_s8(to + stride*13, from, 13);
-  vst1q_lane_s8(to + stride*14, from, 14);
-  vst1q_lane_s8(to + stride*15, from, 15);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet4uc>(uint8_t* to, const Packet4uc& from, Index stride)
-{
-  for (int i = 0; i != 4; i++)
-    *(to + i * stride) = reinterpret_cast<const uint8_t*>(&from)[i];
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet8uc>(uint8_t* to, const Packet8uc& from, Index stride)
-{
-  vst1_lane_u8(to + stride*0, from, 0);
-  vst1_lane_u8(to + stride*1, from, 1);
-  vst1_lane_u8(to + stride*2, from, 2);
-  vst1_lane_u8(to + stride*3, from, 3);
-  vst1_lane_u8(to + stride*4, from, 4);
-  vst1_lane_u8(to + stride*5, from, 5);
-  vst1_lane_u8(to + stride*6, from, 6);
-  vst1_lane_u8(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet16uc>(uint8_t* to, const Packet16uc& from, Index stride)
-{
-  vst1q_lane_u8(to + stride*0, from, 0);
-  vst1q_lane_u8(to + stride*1, from, 1);
-  vst1q_lane_u8(to + stride*2, from, 2);
-  vst1q_lane_u8(to + stride*3, from, 3);
-  vst1q_lane_u8(to + stride*4, from, 4);
-  vst1q_lane_u8(to + stride*5, from, 5);
-  vst1q_lane_u8(to + stride*6, from, 6);
-  vst1q_lane_u8(to + stride*7, from, 7);
-  vst1q_lane_u8(to + stride*8, from, 8);
-  vst1q_lane_u8(to + stride*9, from, 9);
-  vst1q_lane_u8(to + stride*10, from, 10);
-  vst1q_lane_u8(to + stride*11, from, 11);
-  vst1q_lane_u8(to + stride*12, from, 12);
-  vst1q_lane_u8(to + stride*13, from, 13);
-  vst1q_lane_u8(to + stride*14, from, 14);
-  vst1q_lane_u8(to + stride*15, from, 15);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet4s>(int16_t* to, const Packet4s& from, Index stride)
-{
-  vst1_lane_s16(to + stride*0, from, 0);
-  vst1_lane_s16(to + stride*1, from, 1);
-  vst1_lane_s16(to + stride*2, from, 2);
-  vst1_lane_s16(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet8s>(int16_t* to, const Packet8s& from, Index stride)
-{
-  vst1q_lane_s16(to + stride*0, from, 0);
-  vst1q_lane_s16(to + stride*1, from, 1);
-  vst1q_lane_s16(to + stride*2, from, 2);
-  vst1q_lane_s16(to + stride*3, from, 3);
-  vst1q_lane_s16(to + stride*4, from, 4);
-  vst1q_lane_s16(to + stride*5, from, 5);
-  vst1q_lane_s16(to + stride*6, from, 6);
-  vst1q_lane_s16(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet4us>(uint16_t* to, const Packet4us& from, Index stride)
-{
-  vst1_lane_u16(to + stride*0, from, 0);
-  vst1_lane_u16(to + stride*1, from, 1);
-  vst1_lane_u16(to + stride*2, from, 2);
-  vst1_lane_u16(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet8us>(uint16_t* to, const Packet8us& from, Index stride)
-{
-  vst1q_lane_u16(to + stride*0, from, 0);
-  vst1q_lane_u16(to + stride*1, from, 1);
-  vst1q_lane_u16(to + stride*2, from, 2);
-  vst1q_lane_u16(to + stride*3, from, 3);
-  vst1q_lane_u16(to + stride*4, from, 4);
-  vst1q_lane_u16(to + stride*5, from, 5);
-  vst1q_lane_u16(to + stride*6, from, 6);
-  vst1q_lane_u16(to + stride*7, from, 7);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet2i>(int32_t* to, const Packet2i& from, Index stride)
-{
-  vst1_lane_s32(to + stride*0, from, 0);
-  vst1_lane_s32(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from, Index stride)
-{
-  vst1q_lane_s32(to + stride*0, from, 0);
-  vst1q_lane_s32(to + stride*1, from, 1);
-  vst1q_lane_s32(to + stride*2, from, 2);
-  vst1q_lane_s32(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet2ui>(uint32_t* to, const Packet2ui& from, Index stride)
-{
-  vst1_lane_u32(to + stride*0, from, 0);
-  vst1_lane_u32(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from, Index stride)
-{
-  vst1q_lane_u32(to + stride*0, from, 0);
-  vst1q_lane_u32(to + stride*1, from, 1);
-  vst1q_lane_u32(to + stride*2, from, 2);
-  vst1q_lane_u32(to + stride*3, from, 3);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int64_t, Packet2l>(int64_t* to, const Packet2l& from, Index stride)
-{
-  vst1q_lane_s64(to + stride*0, from, 0);
-  vst1q_lane_s64(to + stride*1, from, 1);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint64_t, Packet2ul>(uint64_t* to, const Packet2ul& from, Index stride)
-{
-  vst1q_lane_u64(to + stride*0, from, 0);
-  vst1q_lane_u64(to + stride*1, from, 1);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int8_t>(const int8_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint8_t>(const uint8_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int16_t>(const int16_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint16_t>(const uint16_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<int64_t>(const int64_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<uint64_t>(const uint64_t* addr) { EIGEN_ARM_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE float pfirst<Packet2f>(const Packet2f& a) { return vget_lane_f32(a,0); }
-template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { return vgetq_lane_f32(a,0); }
-template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet4c>(const Packet4c& a) { return static_cast<int8_t>(a & 0xff); }
-template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet8c>(const Packet8c& a) { return vget_lane_s8(a,0); }
-template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet16c>(const Packet16c& a) { return vgetq_lane_s8(a,0); }
-template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet4uc>(const Packet4uc& a) { return static_cast<uint8_t>(a & 0xff); }
-template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet8uc>(const Packet8uc& a) { return vget_lane_u8(a,0); }
-template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet16uc>(const Packet16uc& a) { return vgetq_lane_u8(a,0); }
-template<> EIGEN_STRONG_INLINE int16_t pfirst<Packet4s>(const Packet4s& a) { return vget_lane_s16(a,0); }
-template<> EIGEN_STRONG_INLINE int16_t pfirst<Packet8s>(const Packet8s& a) { return vgetq_lane_s16(a,0); }
-template<> EIGEN_STRONG_INLINE uint16_t pfirst<Packet4us>(const Packet4us& a) { return vget_lane_u16(a,0); }
-template<> EIGEN_STRONG_INLINE uint16_t pfirst<Packet8us>(const Packet8us& a) { return vgetq_lane_u16(a,0); }
-template<> EIGEN_STRONG_INLINE int32_t pfirst<Packet2i>(const Packet2i& a) { return vget_lane_s32(a,0); }
-template<> EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) { return vgetq_lane_s32(a,0); }
-template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet2ui>(const Packet2ui& a) { return vget_lane_u32(a,0); }
-template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) { return vgetq_lane_u32(a,0); }
-template<> EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) { return vgetq_lane_s64(a,0); }
-template<> EIGEN_STRONG_INLINE uint64_t pfirst<Packet2ul>(const Packet2ul& a) { return vgetq_lane_u64(a,0); }
-
-template<> EIGEN_STRONG_INLINE Packet2f preverse(const Packet2f& a) { return vrev64_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  const float32x4_t a_r64 = vrev64q_f32(a);
-  return vcombine_f32(vget_high_f32(a_r64), vget_low_f32(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4c preverse(const Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vrev64_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c preverse(const Packet8c& a) { return vrev64_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a)
-{
-  const int8x16_t a_r64 = vrev64q_s8(a);
-  return vcombine_s8(vget_high_s8(a_r64), vget_low_s8(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4uc preverse(const Packet4uc& a)
-{ return vget_lane_u32(vreinterpret_u32_u8(vrev64_u8(vreinterpret_u8_u32(vdup_n_u32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8uc preverse(const Packet8uc& a) { return vrev64_u8(a); }
-template<> EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a)
-{
-  const uint8x16_t a_r64 = vrev64q_u8(a);
-  return vcombine_u8(vget_high_u8(a_r64), vget_low_u8(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4s preverse(const Packet4s& a) { return vrev64_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a)
-{
-  const int16x8_t a_r64 = vrev64q_s16(a);
-  return vcombine_s16(vget_high_s16(a_r64), vget_low_s16(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet4us preverse(const Packet4us& a) { return vrev64_u16(a); }
-template<> EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a)
-{
-  const uint16x8_t a_r64 = vrev64q_u16(a);
-  return vcombine_u16(vget_high_u16(a_r64), vget_low_u16(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet2i preverse(const Packet2i& a) { return vrev64_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
-{
-  const int32x4_t a_r64 = vrev64q_s32(a);
-  return vcombine_s32(vget_high_s32(a_r64), vget_low_s32(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet2ui preverse(const Packet2ui& a) { return vrev64_u32(a); }
-template<> EIGEN_STRONG_INLINE Packet4ui preverse(const Packet4ui& a)
-{
-  const uint32x4_t a_r64 = vrev64q_u32(a);
-  return vcombine_u32(vget_high_u32(a_r64), vget_low_u32(a_r64));
-}
-template<> EIGEN_STRONG_INLINE Packet2l preverse(const Packet2l& a)
-{ return vcombine_s64(vget_high_s64(a), vget_low_s64(a)); }
-template<> EIGEN_STRONG_INLINE Packet2ul preverse(const Packet2ul& a)
-{ return vcombine_u64(vget_high_u64(a), vget_low_u64(a)); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pabs(const Packet2f& a) { return vabs_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vabsq_f32(a); }
-template<> EIGEN_STRONG_INLINE Packet4c pabs<Packet4c>(const Packet4c& a)
-{ return vget_lane_s32(vreinterpret_s32_s8(vabs_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
-template<> EIGEN_STRONG_INLINE Packet8c pabs(const Packet8c& a) { return vabs_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) { return vabsq_s8(a); }
-template<> EIGEN_STRONG_INLINE Packet4uc pabs(const Packet4uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8uc pabs(const Packet8uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4s pabs(const Packet4s& a) { return vabs_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) { return vabsq_s16(a); }
-template<> EIGEN_STRONG_INLINE Packet4us pabs(const Packet4us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2i pabs(const Packet2i& a) { return vabs_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vabsq_s32(a); }
-template<> EIGEN_STRONG_INLINE Packet2ui pabs(const Packet2ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4ui pabs(const Packet4ui& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2l pabs(const Packet2l& a) {
-#if EIGEN_ARCH_ARM64
-  return vabsq_s64(a);
-#else
-  return vcombine_s64(
-      vdup_n_s64((std::abs)(vgetq_lane_s64(a, 0))),
-      vdup_n_s64((std::abs)(vgetq_lane_s64(a, 1))));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2ul pabs(const Packet2ul& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2f pfrexp<Packet2f>(const Packet2f& a, Packet2f& exponent)
-{ return pfrexp_generic(a,exponent); }
-template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent)
-{ return pfrexp_generic(a,exponent); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pldexp<Packet2f>(const Packet2f& a, const Packet2f& exponent)
-{ return pldexp_generic(a,exponent); }
-template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent)
-{ return pldexp_generic(a,exponent); }
-
-template<> EIGEN_STRONG_INLINE float predux<Packet2f>(const Packet2f& a) { return vget_lane_f32(vpadd_f32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  const float32x2_t sum = vadd_f32(vget_low_f32(a), vget_high_f32(a));
-  return vget_lane_f32(vpadd_f32(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux<Packet4c>(const Packet4c& a)
-{
-  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
-  int8x8_t sum = vpadd_s8(a_dup, a_dup);
-  sum = vpadd_s8(sum, sum);
-  return vget_lane_s8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux<Packet8c>(const Packet8c& a)
-{
-  int8x8_t sum = vpadd_s8(a,a);
-  sum = vpadd_s8(sum, sum);
-  sum = vpadd_s8(sum, sum);
-  return vget_lane_s8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux<Packet16c>(const Packet16c& a)
-{
-  int8x8_t sum = vadd_s8(vget_low_s8(a), vget_high_s8(a));
-  sum = vpadd_s8(sum, sum);
-  sum = vpadd_s8(sum, sum);
-  sum = vpadd_s8(sum, sum);
-  return vget_lane_s8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux<Packet4uc>(const Packet4uc& a)
-{
-  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t sum = vpadd_u8(a_dup, a_dup);
-  sum = vpadd_u8(sum, sum);
-  return vget_lane_u8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t sum = vpadd_u8(a,a);
-  sum = vpadd_u8(sum, sum);
-  sum = vpadd_u8(sum, sum);
-  return vget_lane_u8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux<Packet16uc>(const Packet16uc& a)
-{
-  uint8x8_t sum = vadd_u8(vget_low_u8(a), vget_high_u8(a));
-  sum = vpadd_u8(sum, sum);
-  sum = vpadd_u8(sum, sum);
-  sum = vpadd_u8(sum, sum);
-  return vget_lane_u8(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t sum = vpadd_s16(a,a);
-  return vget_lane_s16(vpadd_s16(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux<Packet8s>(const Packet8s& a)
-{
-  int16x4_t sum = vadd_s16(vget_low_s16(a), vget_high_s16(a));
-  sum = vpadd_s16(sum, sum);
-  sum = vpadd_s16(sum, sum);
-  return vget_lane_s16(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t sum = vpadd_u16(a,a);
-  return vget_lane_u16(vpadd_u16(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t sum = vadd_u16(vget_low_u16(a), vget_high_u16(a));
-  sum = vpadd_u16(sum, sum);
-  sum = vpadd_u16(sum, sum);
-  return vget_lane_u16(sum, 0);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux<Packet2i>(const Packet2i& a) { return vget_lane_s32(vpadd_s32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a)
-{
-  const int32x2_t sum = vadd_s32(vget_low_s32(a), vget_high_s32(a));
-  return vget_lane_s32(vpadd_s32(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE uint32_t predux<Packet2ui>(const Packet2ui& a) { return vget_lane_u32(vpadd_u32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a)
-{
-  const uint32x2_t sum = vadd_u32(vget_low_u32(a), vget_high_u32(a));
-  return vget_lane_u32(vpadd_u32(sum, sum), 0);
-}
-template<> EIGEN_STRONG_INLINE int64_t predux<Packet2l>(const Packet2l& a)
-{ return vgetq_lane_s64(a, 0) + vgetq_lane_s64(a, 1); }
-template<> EIGEN_STRONG_INLINE uint64_t predux<Packet2ul>(const Packet2ul& a)
-{ return vgetq_lane_u64(a, 0) + vgetq_lane_u64(a, 1); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c predux_half_dowto4(const Packet8c& a)
-{
-  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(a,
-      vreinterpret_s8_s32(vrev64_s32(vreinterpret_s32_s8(a))))), 0);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c predux_half_dowto4(const Packet16c& a)
-{ return vadd_s8(vget_high_s8(a), vget_low_s8(a)); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc predux_half_dowto4(const Packet8uc& a)
-{
-  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(a,
-      vreinterpret_u8_u32(vrev64_u32(vreinterpret_u32_u8(a))))), 0);
-}
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc predux_half_dowto4(const Packet16uc& a)
-{ return vadd_u8(vget_high_u8(a), vget_low_u8(a)); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s predux_half_dowto4(const Packet8s& a)
-{ return vadd_s16(vget_high_s16(a), vget_low_s16(a)); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us predux_half_dowto4(const Packet8us& a)
-{ return vadd_u16(vget_high_u16(a), vget_low_u16(a)); }
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet2f>(const Packet2f& a)
-{ return vget_lane_f32(a, 0) * vget_lane_f32(a, 1); }
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{ return predux_mul(vmul_f32(vget_low_f32(a), vget_high_f32(a))); }
-template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet4c>(const Packet4c& a)
-{
-  int8x8_t prod = vreinterpret_s8_s32(vdup_n_s32(a));
-  prod = vmul_s8(prod, vrev16_s8(prod));
-  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet8c>(const Packet8c& a)
-{
-  int8x8_t prod = vmul_s8(a, vrev16_s8(a));
-  prod = vmul_s8(prod, vrev32_s8(prod));
-  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 4);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet16c>(const Packet16c& a)
-{ return predux_mul(vmul_s8(vget_low_s8(a), vget_high_s8(a))); }
-template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet4uc>(const Packet4uc& a)
-{
-  uint8x8_t prod = vreinterpret_u8_u32(vdup_n_u32(a));
-  prod = vmul_u8(prod, vrev16_u8(prod));
-  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t prod = vmul_u8(a, vrev16_u8(a));
-  prod = vmul_u8(prod, vrev32_u8(prod));
-  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 4);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet16uc>(const Packet16uc& a)
-{ return predux_mul(vmul_u8(vget_low_u8(a), vget_high_u8(a))); }
-template<> EIGEN_STRONG_INLINE int16_t predux_mul<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t prod = vmul_s16(a, vrev32_s16(a));
-  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_mul<Packet8s>(const Packet8s& a)
-{
-  int16x4_t prod;
-
-  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
-  prod = vmul_s16(vget_low_s16(a), vget_high_s16(a));
-  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
-  prod = vmul_s16(prod, vrev32_s16(prod));
-  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
-  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_mul<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t prod = vmul_u16(a, vrev32_u16(a));
-  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_mul<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t prod;
-
-  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
-  prod = vmul_u16(vget_low_u16(a), vget_high_u16(a));
-  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
-  prod = vmul_u16(prod, vrev32_u16(prod));
-  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
-  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet2i>(const Packet2i& a)
-{ return vget_lane_s32(a, 0) * vget_lane_s32(a, 1); }
-template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a)
-{ return predux_mul(vmul_s32(vget_low_s32(a), vget_high_s32(a))); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet2ui>(const Packet2ui& a)
-{ return vget_lane_u32(a, 0) * vget_lane_u32(a, 1); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet4ui>(const Packet4ui& a)
-{ return predux_mul(vmul_u32(vget_low_u32(a), vget_high_u32(a))); }
-template<> EIGEN_STRONG_INLINE int64_t predux_mul<Packet2l>(const Packet2l& a)
-{ return vgetq_lane_s64(a, 0) * vgetq_lane_s64(a, 1); }
-template<> EIGEN_STRONG_INLINE uint64_t predux_mul<Packet2ul>(const Packet2ul& a)
-{ return vgetq_lane_u64(a, 0) * vgetq_lane_u64(a, 1); }
-
-// min
-template<> EIGEN_STRONG_INLINE float predux_min<Packet2f>(const Packet2f& a)
-{ return vget_lane_f32(vpmin_f32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  const float32x2_t min = vmin_f32(vget_low_f32(a), vget_high_f32(a));
-  return vget_lane_f32(vpmin_f32(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet4c>(const Packet4c& a)
-{
-  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
-  int8x8_t min = vpmin_s8(a_dup, a_dup);
-  min = vpmin_s8(min, min);
-  return vget_lane_s8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet8c>(const Packet8c& a)
-{
-  int8x8_t min = vpmin_s8(a,a);
-  min = vpmin_s8(min, min);
-  min = vpmin_s8(min, min);
-  return vget_lane_s8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet16c>(const Packet16c& a)
-{
-  int8x8_t min = vmin_s8(vget_low_s8(a), vget_high_s8(a));
-  min = vpmin_s8(min, min);
-  min = vpmin_s8(min, min);
-  min = vpmin_s8(min, min);
-  return vget_lane_s8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet4uc>(const Packet4uc& a)
-{
-  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t min = vpmin_u8(a_dup, a_dup);
-  min = vpmin_u8(min, min);
-  return vget_lane_u8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t min = vpmin_u8(a,a);
-  min = vpmin_u8(min, min);
-  min = vpmin_u8(min, min);
-  return vget_lane_u8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet16uc>(const Packet16uc& a)
-{
-  uint8x8_t min = vmin_u8(vget_low_u8(a), vget_high_u8(a));
-  min = vpmin_u8(min, min);
-  min = vpmin_u8(min, min);
-  min = vpmin_u8(min, min);
-  return vget_lane_u8(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t min = vpmin_s16(a,a);
-  return vget_lane_s16(vpmin_s16(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet8s>(const Packet8s& a)
-{
-  int16x4_t min = vmin_s16(vget_low_s16(a), vget_high_s16(a));
-  min = vpmin_s16(min, min);
-  min = vpmin_s16(min, min);
-  return vget_lane_s16(min, 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t min = vpmin_u16(a,a);
-  return vget_lane_u16(vpmin_u16(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t min = vmin_u16(vget_low_u16(a), vget_high_u16(a));
-  min = vpmin_u16(min, min);
-  min = vpmin_u16(min, min);
-  return vget_lane_u16(min, 0);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet2i>(const Packet2i& a)
-{ return vget_lane_s32(vpmin_s32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a)
-{
-  const int32x2_t min = vmin_s32(vget_low_s32(a), vget_high_s32(a));
-  return vget_lane_s32(vpmin_s32(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet2ui>(const Packet2ui& a)
-{ return vget_lane_u32(vpmin_u32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a)
-{
-  const uint32x2_t min = vmin_u32(vget_low_u32(a), vget_high_u32(a));
-  return vget_lane_u32(vpmin_u32(min, min), 0);
-}
-template<> EIGEN_STRONG_INLINE int64_t predux_min<Packet2l>(const Packet2l& a)
-{ return (std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1)); }
-template<> EIGEN_STRONG_INLINE uint64_t predux_min<Packet2ul>(const Packet2ul& a)
-{ return (std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1)); }
-
-// max
-template<> EIGEN_STRONG_INLINE float predux_max<Packet2f>(const Packet2f& a)
-{ return vget_lane_f32(vpmax_f32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  const float32x2_t max = vmax_f32(vget_low_f32(a), vget_high_f32(a));
-  return vget_lane_f32(vpmax_f32(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet4c>(const Packet4c& a)
-{
-  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
-  int8x8_t max = vpmax_s8(a_dup, a_dup);
-  max = vpmax_s8(max, max);
-  return vget_lane_s8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet8c>(const Packet8c& a)
-{
-  int8x8_t max = vpmax_s8(a,a);
-  max = vpmax_s8(max, max);
-  max = vpmax_s8(max, max);
-  return vget_lane_s8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet16c>(const Packet16c& a)
-{
-  int8x8_t max = vmax_s8(vget_low_s8(a), vget_high_s8(a));
-  max = vpmax_s8(max, max);
-  max = vpmax_s8(max, max);
-  max = vpmax_s8(max, max);
-  return vget_lane_s8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet4uc>(const Packet4uc& a)
-{
-  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t max = vpmax_u8(a_dup, a_dup);
-  max = vpmax_u8(max, max);
-  return vget_lane_u8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet8uc>(const Packet8uc& a)
-{
-  uint8x8_t max = vpmax_u8(a,a);
-  max = vpmax_u8(max, max);
-  max = vpmax_u8(max, max);
-  return vget_lane_u8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet16uc>(const Packet16uc& a)
-{
-  uint8x8_t max = vmax_u8(vget_low_u8(a), vget_high_u8(a));
-  max = vpmax_u8(max, max);
-  max = vpmax_u8(max, max);
-  max = vpmax_u8(max, max);
-  return vget_lane_u8(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet4s>(const Packet4s& a)
-{
-  const int16x4_t max = vpmax_s16(a,a);
-  return vget_lane_s16(vpmax_s16(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet8s>(const Packet8s& a)
-{
-  int16x4_t max = vmax_s16(vget_low_s16(a), vget_high_s16(a));
-  max = vpmax_s16(max, max);
-  max = vpmax_s16(max, max);
-  return vget_lane_s16(max, 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet4us>(const Packet4us& a)
-{
-  const uint16x4_t max = vpmax_u16(a,a);
-  return vget_lane_u16(vpmax_u16(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet8us>(const Packet8us& a)
-{
-  uint16x4_t max = vmax_u16(vget_low_u16(a), vget_high_u16(a));
-  max = vpmax_u16(max, max);
-  max = vpmax_u16(max, max);
-  return vget_lane_u16(max, 0);
-}
-template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet2i>(const Packet2i& a)
-{ return vget_lane_s32(vpmax_s32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a)
-{
-  const int32x2_t max = vmax_s32(vget_low_s32(a), vget_high_s32(a));
-  return vget_lane_s32(vpmax_s32(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet2ui>(const Packet2ui& a)
-{ return vget_lane_u32(vpmax_u32(a,a), 0); }
-template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a)
-{
-  const uint32x2_t max = vmax_u32(vget_low_u32(a), vget_high_u32(a));
-  return vget_lane_u32(vpmax_u32(max, max), 0);
-}
-template<> EIGEN_STRONG_INLINE int64_t predux_max<Packet2l>(const Packet2l& a)
-{ return (std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1)); }
-template<> EIGEN_STRONG_INLINE uint64_t predux_max<Packet2ul>(const Packet2ul& a)
-{ return (std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1)); }
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
-{
-  uint32x2_t tmp = vorr_u32(vget_low_u32( vreinterpretq_u32_f32(x)),
-                            vget_high_u32(vreinterpretq_u32_f32(x)));
-  return vget_lane_u32(vpmax_u32(tmp, tmp), 0);
-}
-
-// Helpers for ptranspose.
-namespace detail {
-  
-template<typename Packet>
-void zip_in_place(Packet& p1, Packet& p2);
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet2f>(Packet2f& p1, Packet2f& p2) {
-  const float32x2x2_t tmp = vzip_f32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4f>(Packet4f& p1, Packet4f& p2) {
-  const float32x4x2_t tmp = vzipq_f32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8c>(Packet8c& p1, Packet8c& p2) {
-  const int8x8x2_t tmp = vzip_s8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet16c>(Packet16c& p1, Packet16c& p2) {
-  const int8x16x2_t tmp = vzipq_s8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8uc>(Packet8uc& p1, Packet8uc& p2) {
-  const uint8x8x2_t tmp = vzip_u8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet16uc>(Packet16uc& p1, Packet16uc& p2) {
-  const uint8x16x2_t tmp = vzipq_u8(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet2i>(Packet2i& p1, Packet2i& p2) {
-  const int32x2x2_t tmp = vzip_s32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4i>(Packet4i& p1, Packet4i& p2) {
-  const int32x4x2_t tmp = vzipq_s32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet2ui>(Packet2ui& p1, Packet2ui& p2) {
-  const uint32x2x2_t tmp = vzip_u32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4ui>(Packet4ui& p1, Packet4ui& p2) {
-  const uint32x4x2_t tmp = vzipq_u32(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4s>(Packet4s& p1, Packet4s& p2) {
-  const int16x4x2_t tmp = vzip_s16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8s>(Packet8s& p1, Packet8s& p2) {
-  const int16x8x2_t tmp = vzipq_s16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4us>(Packet4us& p1, Packet4us& p2) {
-  const uint16x4x2_t tmp = vzip_u16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet8us>(Packet8us& p1, Packet8us& p2) {
-  const uint16x8x2_t tmp = vzipq_u16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 2>& kernel) {
-  zip_in_place(kernel.packet[0], kernel.packet[1]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 4>& kernel) {
-  zip_in_place(kernel.packet[0], kernel.packet[2]);
-  zip_in_place(kernel.packet[1], kernel.packet[3]);
-  zip_in_place(kernel.packet[0], kernel.packet[1]);
-  zip_in_place(kernel.packet[2], kernel.packet[3]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 8>& kernel) {
-  zip_in_place(kernel.packet[0], kernel.packet[4]);
-  zip_in_place(kernel.packet[1], kernel.packet[5]);
-  zip_in_place(kernel.packet[2], kernel.packet[6]);
-  zip_in_place(kernel.packet[3], kernel.packet[7]);
-
-  zip_in_place(kernel.packet[0], kernel.packet[2]);
-  zip_in_place(kernel.packet[1], kernel.packet[3]);
-  zip_in_place(kernel.packet[4], kernel.packet[6]);
-  zip_in_place(kernel.packet[5], kernel.packet[7]);
-  
-  zip_in_place(kernel.packet[0], kernel.packet[1]);
-  zip_in_place(kernel.packet[2], kernel.packet[3]);
-  zip_in_place(kernel.packet[4], kernel.packet[5]);
-  zip_in_place(kernel.packet[6], kernel.packet[7]);
-}
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 16>& kernel) {
-  EIGEN_UNROLL_LOOP
-  for (int i=0; i<4; ++i) {
-    const int m = (1 << i);
-    EIGEN_UNROLL_LOOP
-    for (int j=0; j<m; ++j) {
-      const int n = (1 << (3-i));
-      EIGEN_UNROLL_LOOP
-      for (int k=0; k<n; ++k) {
-        const int idx = 2*j*n+k;
-        zip_in_place(kernel.packet[idx], kernel.packet[idx + n]);
-      }
-    }
-  }
-}
-
-} // namespace detail
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2f, 2>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4c, 4>& kernel)
-{
-  const int8x8_t a = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[2], vdup_n_s32(kernel.packet[0]), 1));
-  const int8x8_t b = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[3], vdup_n_s32(kernel.packet[1]), 1));
-
-  const int8x8x2_t zip8 = vzip_s8(a,b);
-  const int16x4x2_t zip16 = vzip_s16(vreinterpret_s16_s8(zip8.val[0]), vreinterpret_s16_s8(zip8.val[1]));
-
-  kernel.packet[0] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 0);
-  kernel.packet[1] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 1);
-  kernel.packet[2] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 0);
-  kernel.packet[3] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 1);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 16>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4uc, 4>& kernel)
-{
-  const uint8x8_t a = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[2], vdup_n_u32(kernel.packet[0]), 1));
-  const uint8x8_t b = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[3], vdup_n_u32(kernel.packet[1]), 1));
-
-  const uint8x8x2_t zip8 = vzip_u8(a,b);
-  const uint16x4x2_t zip16 = vzip_u16(vreinterpret_u16_u8(zip8.val[0]), vreinterpret_u16_u8(zip8.val[1]));
-
-  kernel.packet[0] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 0);
-  kernel.packet[1] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 1);
-  kernel.packet[2] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 0);
-  kernel.packet[3] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 1);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 16>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4s, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4us, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 8>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2i, 2>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
-    detail::ptranspose_impl(kernel);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2ui, 2>& kernel) {
-  detail::zip_in_place(kernel.packet[0], kernel.packet[1]);
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4ui, 4>& kernel) {
-  detail::ptranspose_impl(kernel);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet2l, 2>& kernel)
-{
-#if EIGEN_ARCH_ARM64
-  const int64x2_t tmp1 = vzip1q_s64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[1] = vzip2q_s64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[0] = tmp1;
-#else
-  const int64x1_t tmp[2][2] = {
-    { vget_low_s64(kernel.packet[0]), vget_high_s64(kernel.packet[0]) },
-    { vget_low_s64(kernel.packet[1]), vget_high_s64(kernel.packet[1]) }
-  };
-
-  kernel.packet[0] = vcombine_s64(tmp[0][0], tmp[1][0]);
-  kernel.packet[1] = vcombine_s64(tmp[0][1], tmp[1][1]);
-#endif
-}
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet2ul, 2>& kernel)
-{
-#if EIGEN_ARCH_ARM64
-  const uint64x2_t tmp1 = vzip1q_u64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[1] = vzip2q_u64(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[0] = tmp1;
-#else
-  const uint64x1_t tmp[2][2] = {
-    { vget_low_u64(kernel.packet[0]), vget_high_u64(kernel.packet[0]) },
-    { vget_low_u64(kernel.packet[1]), vget_high_u64(kernel.packet[1]) }
-  };
-
-  kernel.packet[0] = vcombine_u64(tmp[0][0], tmp[1][0]);
-  kernel.packet[1] = vcombine_u64(tmp[0][1], tmp[1][1]);
-#endif
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pselect( const Packet2f& mask, const Packet2f& a, const Packet2f& b)
-{ return vbsl_f32(vreinterpret_u32_f32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b)
-{ return vbslq_f32(vreinterpretq_u32_f32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pselect(const Packet8c& mask, const Packet8c& a, const Packet8c& b)
-{ return vbsl_s8(vreinterpret_u8_s8(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pselect(const Packet16c& mask, const Packet16c& a, const Packet16c& b)
-{ return vbslq_s8(vreinterpretq_u8_s8(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pselect(const Packet8uc& mask, const Packet8uc& a, const Packet8uc& b)
-{ return vbsl_u8(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pselect(const Packet16uc& mask, const Packet16uc& a, const Packet16uc& b)
-{ return vbslq_u8(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pselect(const Packet4s& mask, const Packet4s& a, const Packet4s& b)
-{ return vbsl_s16(vreinterpret_u16_s16(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pselect(const Packet8s& mask, const Packet8s& a, const Packet8s& b)
-{ return vbslq_s16(vreinterpretq_u16_s16(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pselect(const Packet4us& mask, const Packet4us& a, const Packet4us& b)
-{ return vbsl_u16(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pselect(const Packet8us& mask, const Packet8us& a, const Packet8us& b)
-{ return vbslq_u16(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pselect(const Packet2i& mask, const Packet2i& a, const Packet2i& b)
-{ return vbsl_s32(vreinterpret_u32_s32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b)
-{ return vbslq_s32(vreinterpretq_u32_s32(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pselect(const Packet2ui& mask, const Packet2ui& a, const Packet2ui& b)
-{ return vbsl_u32(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pselect(const Packet4ui& mask, const Packet4ui& a, const Packet4ui& b)
-{ return vbslq_u32(mask, a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pselect(const Packet2l& mask, const Packet2l& a, const Packet2l& b)
-{ return vbslq_s64(vreinterpretq_u64_s64(mask), a, b); }
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pselect(const Packet2ul& mask, const Packet2ul& a, const Packet2ul& b)
-{ return vbslq_u64(mask, a, b); }
-
-// Use armv8 rounding intinsics if available.
-#if EIGEN_ARCH_ARMV8
-template<> EIGEN_STRONG_INLINE Packet2f print<Packet2f>(const Packet2f& a)
-{ return vrndn_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
-{ return vrndnq_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pfloor<Packet2f>(const Packet2f& a)
-{ return vrndm_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{ return vrndmq_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a)
-{ return vrndp_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
-{ return vrndpq_f32(a); }
-
-#else
-
-template<> EIGEN_STRONG_INLINE Packet4f print(const Packet4f& a) {
-  // Adds and subtracts signum(a) * 2^23 to force rounding.
-  const Packet4f limit = pset1<Packet4f>(static_cast<float>(1<<23));
-  const Packet4f abs_a = pabs(a);
-  Packet4f r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f print(const Packet2f& a) {
-  // Adds and subtracts signum(a) * 2^23 to force rounding.
-  const Packet2f limit = pset1<Packet2f>(static_cast<float>(1<<23));
-  const Packet2f abs_a = pabs(a);
-  Packet2f r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If greater, subtract one.
-  Packet4f mask = pcmp_lt(a, tmp);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pfloor<Packet2f>(const Packet2f& a)
-{
-  const Packet2f cst_1 = pset1<Packet2f>(1.0f);
-  Packet2f tmp  = print<Packet2f>(a);
-  // If greater, subtract one.
-  Packet2f mask = pcmp_lt(a, tmp);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If smaller, add one.
-  Packet4f mask = pcmp_lt(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a)
-{
-  const Packet2f cst_1 = pset1<Packet2f>(1.0);
-  Packet2f tmp  = print<Packet2f>(a);
-  // If smaller, add one.
-  Packet2f mask = pcmp_lt(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-
-#endif
-
-/**
- * Computes the integer square root
- * @remarks The calculation is performed using an algorithm which iterates through each binary digit of the result
- *   and tests whether setting that digit to 1 would cause the square of the value to be greater than the argument
- *   value. The algorithm is described in detail here: http://ww1.microchip.com/downloads/en/AppNotes/91040a.pdf .
- */
-template<> EIGEN_STRONG_INLINE Packet4uc psqrt(const Packet4uc& a) {
-  uint8x8_t x = vreinterpret_u8_u32(vdup_n_u32(a));
-  uint8x8_t res = vdup_n_u8(0);
-  uint8x8_t add = vdup_n_u8(0x8);
-  for (int i = 0; i < 4; i++)
-  {
-    const uint8x8_t temp = vorr_u8(res, add);
-    res = vbsl_u8(vcge_u8(x, vmul_u8(temp, temp)), temp, res);
-    add = vshr_n_u8(add, 1);
-  }
-  return vget_lane_u32(vreinterpret_u32_u8(res), 0);
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet8uc psqrt(const Packet8uc& a) {
-  uint8x8_t res = vdup_n_u8(0);
-  uint8x8_t add = vdup_n_u8(0x8);
-  for (int i = 0; i < 4; i++)
-  {
-    const uint8x8_t temp = vorr_u8(res, add);
-    res = vbsl_u8(vcge_u8(a, vmul_u8(temp, temp)), temp, res);
-    add = vshr_n_u8(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet16uc psqrt(const Packet16uc& a) {
-  uint8x16_t res = vdupq_n_u8(0);
-  uint8x16_t add = vdupq_n_u8(0x8);
-  for (int i = 0; i < 4; i++)
-  {
-    const uint8x16_t temp = vorrq_u8(res, add);
-    res = vbslq_u8(vcgeq_u8(a, vmulq_u8(temp, temp)), temp, res);
-    add = vshrq_n_u8(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet4us psqrt(const Packet4us& a) {
-  uint16x4_t res = vdup_n_u16(0);
-  uint16x4_t add = vdup_n_u16(0x80);
-  for (int i = 0; i < 8; i++)
-  {
-    const uint16x4_t temp = vorr_u16(res, add);
-    res = vbsl_u16(vcge_u16(a, vmul_u16(temp, temp)), temp, res);
-    add = vshr_n_u16(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet8us psqrt(const Packet8us& a) {
-  uint16x8_t res = vdupq_n_u16(0);
-  uint16x8_t add = vdupq_n_u16(0x80);
-  for (int i = 0; i < 8; i++)
-  {
-    const uint16x8_t temp = vorrq_u16(res, add);
-    res = vbslq_u16(vcgeq_u16(a, vmulq_u16(temp, temp)), temp, res);
-    add = vshrq_n_u16(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet2ui psqrt(const Packet2ui& a) {
-  uint32x2_t res = vdup_n_u32(0);
-  uint32x2_t add = vdup_n_u32(0x8000);
-  for (int i = 0; i < 16; i++)
-  {
-    const uint32x2_t temp = vorr_u32(res, add);
-    res = vbsl_u32(vcge_u32(a, vmul_u32(temp, temp)), temp, res);
-    add = vshr_n_u32(add, 1);
-  }
-  return res;
-}
-/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
-template<> EIGEN_STRONG_INLINE Packet4ui psqrt(const Packet4ui& a) {
-  uint32x4_t res = vdupq_n_u32(0);
-  uint32x4_t add = vdupq_n_u32(0x8000);
-  for (int i = 0; i < 16; i++)
-  {
-    const uint32x4_t temp = vorrq_u32(res, add);
-    res = vbslq_u32(vcgeq_u32(a, vmulq_u32(temp, temp)), temp, res);
-    add = vshrq_n_u32(add, 1);
-  }
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
-  // Compute approximate reciprocal sqrt.
-  Packet4f x = vrsqrteq_f32(a);
-  // Do Newton iterations for 1/sqrt(x).
-  x = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, x), x), x);
-  x = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, x), x), x);
-  const Packet4f infinity = pset1<Packet4f>(NumTraits<float>::infinity());
-  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f prsqrt(const Packet2f& a) {
-  // Compute approximate reciprocal sqrt.
-  Packet2f x = vrsqrte_f32(a);
-  // Do Newton iterations for 1/sqrt(x).
-  x = vmul_f32(vrsqrts_f32(vmul_f32(a, x), x), x);
-  x = vmul_f32(vrsqrts_f32(vmul_f32(a, x), x), x);
-  const Packet2f infinity = pset1<Packet2f>(NumTraits<float>::infinity());
-  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
-}
-
-// Unfortunately vsqrt_f32 is only available for A64.
-#if EIGEN_ARCH_ARM64
-template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& _x){return vsqrtq_f32(_x);}
-template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& _x){return vsqrt_f32(_x); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
-  const Packet4f infinity = pset1<Packet4f>(NumTraits<float>::infinity());
-  const Packet4f is_zero_or_inf = por(pcmp_eq(a, pzero(a)), pcmp_eq(a, infinity));
-  return pselect(is_zero_or_inf, a, pmul(a, prsqrt(a)));
-}
-template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& a) {
-  const Packet2f infinity = pset1<Packet2f>(NumTraits<float>::infinity());
-  const Packet2f is_zero_or_inf = por(pcmp_eq(a, pzero(a)), pcmp_eq(a, infinity));
-  return pselect(is_zero_or_inf, a, pmul(a, prsqrt(a)));
-}
-#endif
-
-//---------- bfloat16 ----------
-// TODO: Add support for native armv8.6-a bfloat16_t
-
-// TODO: Guard if we have native bfloat16 support
-typedef eigen_packet_wrapper<uint16x4_t, 19> Packet4bf;
-
-template<> struct is_arithmetic<Packet4bf> { enum { value = true }; };
-
-template<> struct packet_traits<bfloat16> : default_packet_traits
-{
-  typedef Packet4bf type;
-  typedef Packet4bf half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-    HasDiv       = 1,
-    HasFloor     = 1,
-    HasCeil      = 1,
-    HasRint      = 1,
-
-    HasSin  = EIGEN_FAST_MATH,
-    HasCos  = EIGEN_FAST_MATH,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf  = EIGEN_FAST_MATH,
-    HasBessel = 0,  // Issues with accuracy.
-    HasNdtri = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet4bf>
-{
-  typedef bfloat16 type;
-  typedef Packet4bf half;
-  enum
-  {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-namespace detail {  
-template<>
-EIGEN_ALWAYS_INLINE void zip_in_place<Packet4bf>(Packet4bf& p1, Packet4bf& p2) {
-  const uint16x4x2_t tmp = vzip_u16(p1, p2);
-  p1 = tmp.val[0];
-  p2 = tmp.val[1];
-}
-} // namespace detail
-
-EIGEN_STRONG_INLINE Packet4bf F32ToBf16(const Packet4f& p)
-{
-  // See the scalar implemention in BFloat16.h for a comprehensible explanation
-  // of this fast rounding algorithm
-  Packet4ui input = reinterpret_cast<Packet4ui>(p);
-
-  // lsb = (input >> 16) & 1
-  Packet4ui lsb =  vandq_u32(vshrq_n_u32(input, 16), vdupq_n_u32(1));
-
-  // rounding_bias = 0x7fff + lsb
-  Packet4ui rounding_bias = vaddq_u32(lsb, vdupq_n_u32(0x7fff));
-
-  // input += rounding_bias
-  input = vaddq_u32(input, rounding_bias);
-
-  // input = input >> 16
-  input = vshrq_n_u32(input, 16);
-
-  // Replace float-nans by bfloat16-nans, that is 0x7fc0
-  const Packet4ui bf16_nan = vdupq_n_u32(0x7fc0);
-  const Packet4ui mask = vceqq_f32(p, p);
-  input = vbslq_u32(mask, input, bf16_nan);
-
-  // output = static_cast<uint16_t>(input)
-  return vmovn_u32(input);
-}
-
-EIGEN_STRONG_INLINE Packet4f Bf16ToF32(const Packet4bf& p)
-{
-  return reinterpret_cast<Packet4f>(vshlq_n_u32(vmovl_u16(p), 16));
-}
-
-EIGEN_STRONG_INLINE Packet4bf F32MaskToBf16Mask(const Packet4f& p) {
-  return vmovn_u32(vreinterpretq_u32_f32(p));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pset1<Packet4bf>(const bfloat16& from) {
-  return pset1<Packet4us>(from.value);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 pfirst<Packet4bf>(const Packet4bf& from) {
-  return bfloat16_impl::raw_uint16_to_bfloat16(static_cast<uint16_t>(pfirst<Packet4us>(from)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pload<Packet4bf>(const bfloat16* from)
-{
-  return pload<Packet4us>(reinterpret_cast<const uint16_t*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf ploadu<Packet4bf>(const bfloat16* from)
-{
-  return ploadu<Packet4us>(reinterpret_cast<const uint16_t*>(from));
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet4bf& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE vst1_u16(reinterpret_cast<uint16_t*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet4bf& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(reinterpret_cast<uint16_t*>(to), from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf ploaddup<Packet4bf>(const bfloat16* from)
-{
-  return ploaddup<Packet4us>(reinterpret_cast<const uint16_t*>(from));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pabs(const Packet4bf& a) {
-  return F32ToBf16(pabs<Packet4f>(Bf16ToF32(a)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNumbers, Packet4bf>(const Packet4bf &a,
-                                                                            const Packet4bf &b)
-{
-  return F32ToBf16(pmin<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-template <> EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNaN, Packet4bf>(const Packet4bf &a,
-                                                                        const Packet4bf &b)
-{
-  return F32ToBf16(pmin<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmin<Packet4bf>(const Packet4bf &a,
-                                                          const Packet4bf &b)
-{
-  return F32ToBf16(pmin<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNumbers, Packet4bf>(const Packet4bf &a,
-                                                                            const Packet4bf &b)
-{
-  return F32ToBf16(pmax<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-template <> EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNaN, Packet4bf>(const Packet4bf &a,
-                                                                        const Packet4bf &b)
-{
-  return F32ToBf16(pmax<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template <> EIGEN_STRONG_INLINE Packet4bf pmax<Packet4bf>(const Packet4bf &a,
-                                                          const Packet4bf &b)
-{
-  return F32ToBf16(pmax<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf plset<Packet4bf>(const bfloat16& a)
-{
-  return F32ToBf16(plset<Packet4f>(static_cast<float>(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf por(const Packet4bf& a,const Packet4bf& b) {
-  return por<Packet4us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pxor(const Packet4bf& a,const Packet4bf& b) {
-  return pxor<Packet4us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pand(const Packet4bf& a,const Packet4bf& b) {
-  return pand<Packet4us>(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pandnot(const Packet4bf& a,const Packet4bf& b) {
-  return pandnot<Packet4us>(a, b);
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4bf pselect(const Packet4bf& mask, const Packet4bf& a,
-                                                      const Packet4bf& b)
-{
-  return pselect<Packet4us>(mask, a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf print<Packet4bf>(const Packet4bf& a)
-{
-  return F32ToBf16(print<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pfloor<Packet4bf>(const Packet4bf& a)
-{
-  return F32ToBf16(pfloor<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pceil<Packet4bf>(const Packet4bf& a)
-{
-  return F32ToBf16(pceil<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pconj(const Packet4bf& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4bf padd<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(padd<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf psub<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(psub<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pmul<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(pmul<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pdiv<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
-  return F32ToBf16(pdiv<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<>
-EIGEN_STRONG_INLINE Packet4bf pgather<bfloat16, Packet4bf>(const bfloat16* from, Index stride)
-{
-  return pgather<uint16_t, Packet4us>(reinterpret_cast<const uint16_t*>(from), stride);
-}
-
-template<>
-EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet4bf>(bfloat16* to, const Packet4bf& from, Index stride)
-{
-  pscatter<uint16_t, Packet4us>(reinterpret_cast<uint16_t*>(to), from, stride);
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux_max<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux_min<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet4bf>(const Packet4bf& a)
-{
-  return static_cast<bfloat16>(predux_mul<Packet4f>(Bf16ToF32(a)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf preverse<Packet4bf>(const Packet4bf& a)
-{
-  return preverse<Packet4us>(a);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4bf, 4>& kernel)
-{
-  detail::ptranspose_impl(kernel);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pabsdiff<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32ToBf16(pabsdiff<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_eq<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_eq<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_lt<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_lt<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_lt_or_nan<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_lt_or_nan<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pcmp_le<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
-{
-  return F32MaskToBf16Mask(pcmp_le<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4bf pnegate<Packet4bf>(const Packet4bf& a)
-{
-  return pxor<Packet4us>(a, pset1<Packet4us>(static_cast<uint16_t>(0x8000)));
-}
-
-//---------- double ----------
-
-// Clang 3.5 in the iOS toolchain has an ICE triggered by NEON intrisics for double.
-// Confirmed at least with __apple_build_version__ = 6000054.
-#ifdef __apple_build_version__
-// Let's hope that by the time __apple_build_version__ hits the 601* range, the bug will be fixed.
-// https://gist.github.com/yamaya/2924292 suggests that the 3 first digits are only updated with
-// major toolchain updates.
-#define EIGEN_APPLE_DOUBLE_NEON_BUG (__apple_build_version__ < 6010000)
-#else
-#define EIGEN_APPLE_DOUBLE_NEON_BUG 0
-#endif
-
-#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
-
-// Bug 907: workaround missing declarations of the following two functions in the ADK
-// Defining these functions as templates ensures that if these intrinsics are
-// already defined in arm_neon.h, then our workaround doesn't cause a conflict
-// and has lower priority in overload resolution.
-template <typename T> uint64x2_t vreinterpretq_u64_f64(T a) { return (uint64x2_t) a; }
-
-template <typename T> float64x2_t vreinterpretq_f64_u64(T a) { return (float64x2_t) a; }
-
-typedef float64x2_t Packet2d;
-typedef float64x1_t Packet1d;
-
-// fuctionally equivalent to _mm_shuffle_pd in SSE (i.e. shuffle(m, n, mask) equals _mm_shuffle_pd(m,n,mask))
-// Currently used in LU/arch/InverseSize4.h to enable a shared implementation
-// for fast inversion of matrices of size 4.
-EIGEN_STRONG_INLINE Packet2d shuffle(const Packet2d& m, const Packet2d& n, int mask)
-{
-  const double* a = reinterpret_cast<const double*>(&m);
-  const double* b = reinterpret_cast<const double*>(&n);
-  Packet2d res = {*(a + (mask & 1)), *(b + ((mask >> 1) & 1))};
-  return res;
-}
-
-EIGEN_STRONG_INLINE Packet2d vec2d_swizzle2(const Packet2d& a, const Packet2d& b, int mask)
-{
-  return shuffle(a, b, mask);
-}
-EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a,const Packet2d& b)
-{
-  return shuffle(a, b, 0);
-}
-EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a,const Packet2d& b)
-{
-  return shuffle(a, b, 3);
-}
-#define vec2d_duplane(a, p) \
-  vdupq_laneq_f64(a, p)
-
-template<> struct packet_traits<double>  : default_packet_traits
-{
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum
-  {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasCmp       = 1,
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 1,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 1,
-    HasArg       = 0,
-    HasAbs2      = 1,
-    HasAbsDiff   = 1,
-    HasMin       = 1,
-    HasMax       = 1,
-    HasConj      = 1,
-    HasSetLinear = 0,
-    HasBlend     = 0,
-
-    HasDiv   = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = 0,
-    HasErf  = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet2d>
-{
-  typedef double type;
-  typedef Packet2d half;
-  typedef Packet2l integer_packet;
-  enum
-  {
-    size = 2,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double&  from) { return vdupq_n_f64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a)
-{
-  const double c[] = {0.0,1.0};
-  return vaddq_f64(pset1<Packet2d>(a), vld1q_f64(c));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vaddq_f64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vsubq_f64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& , const Packet2d& );
-template<> EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b){
-  const Packet2d mask = {numext::bit_cast<double>(0x8000000000000000ull),0.0};
-  return padd(a, pxor(mask, b));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return vnegq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmulq_f64(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vdivq_f64(a,b); }
-
-#ifdef __ARM_FEATURE_FMA
-// See bug 936. See above comment about FMA for float.
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c)
-{ return vfmaq_f64(c,a,b); }
-#else
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c)
-{ return vmlaq_f64(c,a,b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vminq_f64(a,b); }
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) { return vminnmq_f64(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxnmq_f64(a, b); }
-
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) { return pmin<Packet2d>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxq_f64(a,b); }
-
-
-template<> EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) { return pmax<Packet2d>(a, b); }
-
-// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vcleq_f64(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vcltq_f64(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u32(vmvnq_u32(vreinterpretq_u32_u64(vcgeq_f64(a,b)))); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b)
-{ return vreinterpretq_f64_u64(vceqq_f64(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f64(from); }
-
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) { return vld1q_dup_f64(from); }
-template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from)
-{ EIGEN_DEBUG_ALIGNED_STORE vst1q_f64(to,from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from)
-{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_f64(to,from); }
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
-  Packet2d res = pset1<Packet2d>(0.0);
-  res = vld1q_lane_f64(from + 0*stride, res, 0);
-  res = vld1q_lane_f64(from + 1*stride, res, 1);
-  return res;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  vst1q_lane_f64(to + stride*0, from, 0);
-  vst1q_lane_f64(to + stride*1, from, 1);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ARM_PREFETCH(addr); }
-
-// FIXME only store the 2 first elements ?
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(a,0); }
-
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
-{ return vcombine_f64(vget_high_f64(a), vget_low_f64(a)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vabsq_f64(a); }
-
-#if EIGEN_COMP_CLANG && defined(__apple_build_version__)
-// workaround ICE, see bug 907
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{ return (vget_low_f64(a) + vget_high_f64(a))[0]; }
-#else
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{ return vget_lane_f64(vget_low_f64(a) + vget_high_f64(a), 0); }
-#endif
-
-// Other reduction functions:
-// mul
-#if EIGEN_COMP_CLANG && defined(__apple_build_version__)
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{ return (vget_low_f64(a) * vget_high_f64(a))[0]; }
-#else
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{ return vget_lane_f64(vget_low_f64(a) * vget_high_f64(a), 0); }
-#endif
-
-// min
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{ return vgetq_lane_f64(vpminq_f64(a,a), 0); }
-
-// max
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{ return vgetq_lane_f64(vpmaxq_f64(a,a), 0); }
-
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet2d, 2>& kernel)
-{
-  const float64x2_t tmp1 = vzip1q_f64(kernel.packet[0], kernel.packet[1]);
-  const float64x2_t tmp2 = vzip2q_f64(kernel.packet[0], kernel.packet[1]);
-
-  kernel.packet[0] = tmp1;
-  kernel.packet[1] = tmp2;
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pselect( const Packet2d& mask, const Packet2d& a, const Packet2d& b)
-{ return vbslq_f64(vreinterpretq_u64_f64(mask), a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a)
-{ return vrndnq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a)
-{ return vrndmq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a)
-{ return vrndpq_f64(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent)
-{ return pldexp_generic(a, exponent); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent)
-{ return pfrexp_generic(a,exponent); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from)
-{ return vreinterpretq_f64_u64(vdupq_n_u64(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
-  // Compute approximate reciprocal sqrt.
-  Packet2d x = vrsqrteq_f64(a);
-  // Do Newton iterations for 1/sqrt(x).
-  x = vmulq_f64(vrsqrtsq_f64(vmulq_f64(a, x), x), x);
-  x = vmulq_f64(vrsqrtsq_f64(vmulq_f64(a, x), x), x);
-  x = vmulq_f64(vrsqrtsq_f64(vmulq_f64(a, x), x), x);
-  const Packet2d infinity = pset1<Packet2d>(NumTraits<double>::infinity());
-  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& _x){ return vsqrtq_f64(_x); }
-
-#endif // EIGEN_ARCH_ARM64
-
-// Do we have an fp16 types and supporting Neon intrinsics?
-#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-typedef float16x4_t Packet4hf;
-typedef float16x8_t Packet8hf;
-
-template <>
-struct packet_traits<Eigen::half> : default_packet_traits {
-  typedef Packet8hf type;
-  typedef Packet4hf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 8,
-    HasHalfPacket = 1,
-
-    HasCmp = 1,
-    HasCast = 1,
-    HasAdd = 1,
-    HasSub = 1,
-    HasShift = 1,
-    HasMul = 1,
-    HasNegate = 1,
-    HasAbs = 1,
-    HasArg = 0,
-    HasAbs2 = 1,
-    HasAbsDiff = 0,
-    HasMin = 1,
-    HasMax = 1,
-    HasConj = 1,
-    HasSetLinear = 0,
-    HasBlend = 0,
-    HasInsert = 1,
-    HasReduxp = 1,
-    HasDiv = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasRint = 1,
-    HasSin = 0,
-    HasCos = 0,
-    HasLog = 0,
-    HasExp = 0,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasErf = EIGEN_FAST_MATH,
-    HasBessel = 0,  // Issues with accuracy.
-    HasNdtri = 0
-  };
-};
-
-template <>
-struct unpacket_traits<Packet4hf> {
-  typedef Eigen::half type;
-  typedef Packet4hf half;
-  enum {
-    size = 4,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template <>
-struct unpacket_traits<Packet8hf> {
-  typedef Eigen::half type;
-  typedef Packet4hf half;
-  enum {
-    size = 8,
-    alignment = Aligned16,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf predux_half_dowto4<Packet8hf>(const Packet8hf& a) {
-  return vadd_f16(vget_low_f16(a), vget_high_f16(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pset1<Packet8hf>(const Eigen::half& from) {
-  return vdupq_n_f16(from.x);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pset1<Packet4hf>(const Eigen::half& from) {
-  return vdup_n_f16(from.x);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf plset<Packet8hf>(const Eigen::half& a) {
-  const float16_t f[] = {0, 1, 2, 3, 4, 5, 6, 7};
-  Packet8hf countdown = vld1q_f16(f);
-  return vaddq_f16(pset1<Packet8hf>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf plset<Packet4hf>(const Eigen::half& a) {
-  const float16_t f[] = {0, 1, 2, 3};
-  Packet4hf countdown = vld1_f16(f);
-  return vadd_f16(pset1<Packet4hf>(a), countdown);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf padd<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vaddq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf padd<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vadd_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf psub<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vsubq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf psub<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vsub_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pnegate(const Packet8hf& a) {
-  return vnegq_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pnegate(const Packet4hf& a) {
-  return vneg_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pconj(const Packet8hf& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pconj(const Packet4hf& a) {
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmul<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vmulq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmul<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vmul_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pdiv<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vdivq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pdiv<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vdiv_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmadd(const Packet8hf& a, const Packet8hf& b, const Packet8hf& c) {
-  return vfmaq_f16(c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmadd(const Packet4hf& a, const Packet4hf& b, const Packet4hf& c) {
-  return vfma_f16(c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmin<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vminq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmin<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vmin_f16(a, b);
-}
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return vminnm_f16(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return vminnmq_f16(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return pmin<Packet4hf>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return pmin<Packet8hf>(a, b); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pmax<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vmaxq_f16(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pmax<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vmax_f16(a, b);
-}
-
-#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
-// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
-template<> EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return vmaxnm_f16(a, b); }
-template<> EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return vmaxnmq_f16(a, b); }
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return pmax<Packet4hf>(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return pmax<Packet8hf>(a, b); }
-
-#define EIGEN_MAKE_ARM_FP16_CMP_8(name)                                               \
-  template <>                                                                         \
-  EIGEN_STRONG_INLINE Packet8hf pcmp_##name(const Packet8hf& a, const Packet8hf& b) { \
-    return vreinterpretq_f16_u16(vc##name##q_f16(a, b));                              \
-  }
-
-#define EIGEN_MAKE_ARM_FP16_CMP_4(name)                                               \
-  template <>                                                                         \
-  EIGEN_STRONG_INLINE Packet4hf pcmp_##name(const Packet4hf& a, const Packet4hf& b) { \
-    return vreinterpret_f16_u16(vc##name##_f16(a, b));                                \
-  }
-
-EIGEN_MAKE_ARM_FP16_CMP_8(eq)
-EIGEN_MAKE_ARM_FP16_CMP_8(lt)
-EIGEN_MAKE_ARM_FP16_CMP_8(le)
-
-EIGEN_MAKE_ARM_FP16_CMP_4(eq)
-EIGEN_MAKE_ARM_FP16_CMP_4(lt)
-EIGEN_MAKE_ARM_FP16_CMP_4(le)
-
-#undef EIGEN_MAKE_ARM_FP16_CMP_8
-#undef EIGEN_MAKE_ARM_FP16_CMP_4
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pcmp_lt_or_nan<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vmvnq_u16(vcgeq_f16(a, b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pcmp_lt_or_nan<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vmvn_u16(vcge_f16(a, b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf print<Packet8hf>(const Packet8hf& a)
-{ return vrndnq_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf print<Packet4hf>(const Packet4hf& a)
-{ return vrndn_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pfloor<Packet8hf>(const Packet8hf& a)
-{ return vrndmq_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pfloor<Packet4hf>(const Packet4hf& a)
-{ return vrndm_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pceil<Packet8hf>(const Packet8hf& a)
-{ return vrndpq_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pceil<Packet4hf>(const Packet4hf& a)
-{ return vrndp_f16(a); }
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf psqrt<Packet8hf>(const Packet8hf& a) {
-  return vsqrtq_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf psqrt<Packet4hf>(const Packet4hf& a) {
-  return vsqrt_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pand<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vandq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pand<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vand_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf por<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vorrq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf por<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vorr_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pxor<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(veorq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pxor<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(veor_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pandnot<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
-  return vreinterpretq_f16_u16(vbicq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pandnot<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
-  return vreinterpret_f16_u16(vbic_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pload<Packet8hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pload<Packet4hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_ALIGNED_LOAD return vld1_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf ploadu<Packet8hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf ploadu<Packet4hf>(const Eigen::half* from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f16(reinterpret_cast<const float16_t*>(from));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf ploaddup<Packet8hf>(const Eigen::half* from) {
-  Packet8hf packet;
-  packet[0] = from[0].x;
-  packet[1] = from[0].x;
-  packet[2] = from[1].x;
-  packet[3] = from[1].x;
-  packet[4] = from[2].x;
-  packet[5] = from[2].x;
-  packet[6] = from[3].x;
-  packet[7] = from[3].x;
-  return packet;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf ploaddup<Packet4hf>(const Eigen::half* from) {
-  float16x4_t packet;
-  float16_t* tmp;
-  tmp = (float16_t*)&packet;
-  tmp[0] = from[0].x;
-  tmp[1] = from[0].x;
-  tmp[2] = from[1].x;
-  tmp[3] = from[1].x;
-  return packet;
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf ploadquad<Packet8hf>(const Eigen::half* from) {
-  Packet4hf lo, hi;
-  lo = vld1_dup_f16(reinterpret_cast<const float16_t*>(from));
-  hi = vld1_dup_f16(reinterpret_cast<const float16_t*>(from+1));
-  return vcombine_f16(lo, hi);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertfirst(const Packet8hf& a, Eigen::half b) { return vsetq_lane_f16(b.x, a, 0); }
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertfirst(const Packet4hf& a, Eigen::half b) { return vset_lane_f16(b.x, a, 0); }
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pselect(const Packet8hf& mask, const Packet8hf& a, const Packet8hf& b) {
-  return vbslq_f16(vreinterpretq_u16_f16(mask), a, b);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pselect(const Packet4hf& mask, const Packet4hf& a, const Packet4hf& b) {
-  return vbsl_f16(vreinterpret_u16_f16(mask), a, b);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertlast(const Packet8hf& a, Eigen::half b) { return vsetq_lane_f16(b.x, a, 7); }
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertlast(const Packet4hf& a, Eigen::half b) { return vset_lane_f16(b.x, a, 3); }
-
-template <>
-EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
-  EIGEN_DEBUG_ALIGNED_STORE vst1q_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
-  EIGEN_DEBUG_ALIGNED_STORE vst1_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE vst1q_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
-  EIGEN_DEBUG_UNALIGNED_STORE vst1_f16(reinterpret_cast<float16_t*>(to), from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pgather<Eigen::half, Packet8hf>(const Eigen::half* from, Index stride) {
-  Packet8hf res = pset1<Packet8hf>(Eigen::half(0.f));
-  res = vsetq_lane_f16(from[0 * stride].x, res, 0);
-  res = vsetq_lane_f16(from[1 * stride].x, res, 1);
-  res = vsetq_lane_f16(from[2 * stride].x, res, 2);
-  res = vsetq_lane_f16(from[3 * stride].x, res, 3);
-  res = vsetq_lane_f16(from[4 * stride].x, res, 4);
-  res = vsetq_lane_f16(from[5 * stride].x, res, 5);
-  res = vsetq_lane_f16(from[6 * stride].x, res, 6);
-  res = vsetq_lane_f16(from[7 * stride].x, res, 7);
-  return res;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pgather<Eigen::half, Packet4hf>(const Eigen::half* from, Index stride) {
-  Packet4hf res = pset1<Packet4hf>(Eigen::half(0.f));
-  res = vset_lane_f16(from[0 * stride].x, res, 0);
-  res = vset_lane_f16(from[1 * stride].x, res, 1);
-  res = vset_lane_f16(from[2 * stride].x, res, 2);
-  res = vset_lane_f16(from[3 * stride].x, res, 3);
-  return res;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8hf>(Eigen::half* to, const Packet8hf& from, Index stride) {
-  to[stride * 0].x = vgetq_lane_f16(from, 0);
-  to[stride * 1].x = vgetq_lane_f16(from, 1);
-  to[stride * 2].x = vgetq_lane_f16(from, 2);
-  to[stride * 3].x = vgetq_lane_f16(from, 3);
-  to[stride * 4].x = vgetq_lane_f16(from, 4);
-  to[stride * 5].x = vgetq_lane_f16(from, 5);
-  to[stride * 6].x = vgetq_lane_f16(from, 6);
-  to[stride * 7].x = vgetq_lane_f16(from, 7);
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4hf>(Eigen::half* to, const Packet4hf& from, Index stride) {
-  to[stride * 0].x = vget_lane_f16(from, 0);
-  to[stride * 1].x = vget_lane_f16(from, 1);
-  to[stride * 2].x = vget_lane_f16(from, 2);
-  to[stride * 3].x = vget_lane_f16(from, 3);
-}
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<Eigen::half>(const Eigen::half* addr) {
-  EIGEN_ARM_PREFETCH(addr);
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8hf>(const Packet8hf& a) {
-  float16_t x[8];
-  vst1q_f16(x, a);
-  Eigen::half h;
-  h.x = x[0];
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4hf>(const Packet4hf& a) {
-  float16_t x[4];
-  vst1_f16(x, a);
-  Eigen::half h;
-  h.x = x[0];
-  return h;
-}
-
-template<> EIGEN_STRONG_INLINE Packet8hf preverse(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi;
-  Packet8hf a_r64;
-
-  a_r64 = vrev64q_f16(a);
-  a_lo = vget_low_f16(a_r64);
-  a_hi = vget_high_f16(a_r64);
-  return vcombine_f16(a_hi, a_lo);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf preverse<Packet4hf>(const Packet4hf& a) {
-  return vrev64_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet8hf pabs<Packet8hf>(const Packet8hf& a) {
-  return vabsq_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4hf pabs<Packet4hf>(const Packet4hf& a) {
-  return vabs_f16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, sum;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  sum = vpadd_f16(a_lo, a_hi);
-  sum = vpadd_f16(sum, sum);
-  sum = vpadd_f16(sum, sum);
-
-  Eigen::half h;
-  h.x = vget_lane_f16(sum, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux<Packet4hf>(const Packet4hf& a) {
-  float16x4_t sum;
-
-  sum = vpadd_f16(a, a);
-  sum = vpadd_f16(sum, sum);
-  Eigen::half h;
-  h.x = vget_lane_f16(sum, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, prod;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  prod = vmul_f16(a_lo, a_hi);
-  prod = vmul_f16(prod, vrev64_f16(prod));
-
-  Eigen::half h;
-  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4hf>(const Packet4hf& a) {
-  float16x4_t prod;
-  prod = vmul_f16(a, vrev64_f16(a));
-  Eigen::half h;
-  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_min<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, min;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  min = vpmin_f16(a_lo, a_hi);
-  min = vpmin_f16(min, min);
-  min = vpmin_f16(min, min);
-
-  Eigen::half h;
-  h.x = vget_lane_f16(min, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4hf>(const Packet4hf& a) {
-  Packet4hf tmp;
-  tmp = vpmin_f16(a, a);
-  tmp = vpmin_f16(tmp, tmp);
-  Eigen::half h;
-  h.x = vget_lane_f16(tmp, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8hf>(const Packet8hf& a) {
-  float16x4_t a_lo, a_hi, max;
-
-  a_lo = vget_low_f16(a);
-  a_hi = vget_high_f16(a);
-  max = vpmax_f16(a_lo, a_hi);
-  max = vpmax_f16(max, max);
-  max = vpmax_f16(max, max);
-
-  Eigen::half h;
-  h.x = vget_lane_f16(max, 0);
-  return h;
-}
-
-template <>
-EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4hf>(const Packet4hf& a) {
-  Packet4hf tmp;
-  tmp = vpmax_f16(a, a);
-  tmp = vpmax_f16(tmp, tmp);
-  Eigen::half h;
-  h.x = vget_lane_f16(tmp, 0);
-  return h;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 4>& kernel)
-{
-  const float16x8x2_t zip16_1 = vzipq_f16(kernel.packet[0], kernel.packet[1]);
-  const float16x8x2_t zip16_2 = vzipq_f16(kernel.packet[2], kernel.packet[3]);
-
-  const float32x4x2_t zip32_1 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[0]), vreinterpretq_f32_f16(zip16_2.val[0]));
-  const float32x4x2_t zip32_2 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[1]), vreinterpretq_f32_f16(zip16_2.val[1]));
-
-  kernel.packet[0] = vreinterpretq_f16_f32(zip32_1.val[0]);
-  kernel.packet[1] = vreinterpretq_f16_f32(zip32_1.val[1]);
-  kernel.packet[2] = vreinterpretq_f16_f32(zip32_2.val[0]);
-  kernel.packet[3] = vreinterpretq_f16_f32(zip32_2.val[1]);
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4hf, 4>& kernel) {
-  EIGEN_ALIGN16 float16x4x4_t tmp_x4;
-  float16_t* tmp = (float16_t*)&kernel;
-  tmp_x4 = vld4_f16(tmp);
-
-  kernel.packet[0] = tmp_x4.val[0];
-  kernel.packet[1] = tmp_x4.val[1];
-  kernel.packet[2] = tmp_x4.val[2];
-  kernel.packet[3] = tmp_x4.val[3];
-}
-
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 8>& kernel) {
-  float16x8x2_t T_1[4];
-
-  T_1[0] = vuzpq_f16(kernel.packet[0], kernel.packet[1]);
-  T_1[1] = vuzpq_f16(kernel.packet[2], kernel.packet[3]);
-  T_1[2] = vuzpq_f16(kernel.packet[4], kernel.packet[5]);
-  T_1[3] = vuzpq_f16(kernel.packet[6], kernel.packet[7]);
-
-  float16x8x2_t T_2[4];
-  T_2[0] = vuzpq_f16(T_1[0].val[0], T_1[1].val[0]);
-  T_2[1] = vuzpq_f16(T_1[0].val[1], T_1[1].val[1]);
-  T_2[2] = vuzpq_f16(T_1[2].val[0], T_1[3].val[0]);
-  T_2[3] = vuzpq_f16(T_1[2].val[1], T_1[3].val[1]);
-
-  float16x8x2_t T_3[4];
-  T_3[0] = vuzpq_f16(T_2[0].val[0], T_2[2].val[0]);
-  T_3[1] = vuzpq_f16(T_2[0].val[1], T_2[2].val[1]);
-  T_3[2] = vuzpq_f16(T_2[1].val[0], T_2[3].val[0]);
-  T_3[3] = vuzpq_f16(T_2[1].val[1], T_2[3].val[1]);
-
-  kernel.packet[0] = T_3[0].val[0];
-  kernel.packet[1] = T_3[2].val[0];
-  kernel.packet[2] = T_3[1].val[0];
-  kernel.packet[3] = T_3[3].val[0];
-  kernel.packet[4] = T_3[0].val[1];
-  kernel.packet[5] = T_3[2].val[1];
-  kernel.packet[6] = T_3[1].val[1];
-  kernel.packet[7] = T_3[3].val[1];
-}
-#endif // end EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
deleted file mode 100644
index 54f9733..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
+++ /dev/null
@@ -1,1419 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
-// Copyright (C) 2020 Antonio Sanchez <cantonios@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_NEON_H
-#define EIGEN_TYPE_CASTING_NEON_H
-
-namespace Eigen {
-
-namespace internal {
-
-//==============================================================================
-// pcast, SrcType = float
-//==============================================================================
-template <>
-struct type_casting_traits<float, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet4f, Packet4f>(const Packet4f& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet2f, Packet2f>(const Packet2f& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<float, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-struct type_casting_traits<float, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-// If float64 exists, first convert to that to keep as much precision as possible.
-#if EIGEN_ARCH_ARM64
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
-  // Discard second half of input.
-  return vcvtq_s64_f64(vcvt_f64_f32(vget_low_f32(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
-  // Discard second half of input.
-  return vcvtq_u64_f64(vcvt_f64_f32(vget_low_f32(a)));
-}
-#else
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
-  // Discard second half of input.
-  return vmovl_s32(vget_low_s32(vcvtq_s32_f32(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
-  // Discard second half of input.
-  return vmovl_u32(vget_low_u32(vcvtq_u32_f32(a)));
-}
-#endif  // EIGEN_ARCH_ARM64
-
-template <>
-struct type_casting_traits<float, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
-  return vcvtq_s32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet2f, Packet2i>(const Packet2f& a) {
-  return vcvt_s32_f32(a);
-}
-
-template <>
-struct type_casting_traits<float, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
-  return vcvtq_u32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet2f, Packet2ui>(const Packet2f& a) {
-  return vcvt_u32_f32(a);
-}
-
-template <>
-struct type_casting_traits<float, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet4f, Packet8s>(const Packet4f& a, const Packet4f& b) {
-  return vcombine_s16(vmovn_s32(vcvtq_s32_f32(a)), vmovn_s32(vcvtq_s32_f32(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet2f, Packet4s>(const Packet2f& a, const Packet2f& b) {
-  return vmovn_s32(vcombine_s32(vcvt_s32_f32(a), vcvt_s32_f32(b)));
-}
-
-template <>
-struct type_casting_traits<float, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet4f, Packet8us>(const Packet4f& a, const Packet4f& b) {
-  return vcombine_u16(vmovn_u32(vcvtq_u32_f32(a)), vmovn_u32(vcvtq_u32_f32(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet2f, Packet4us>(const Packet2f& a, const Packet2f& b) {
-  return vmovn_u32(vcombine_u32(vcvt_u32_f32(a), vcvt_u32_f32(b)));
-}
-
-template <>
-struct type_casting_traits<float, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet4f, Packet16c>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
-                                                         const Packet4f& d) {
-  const int16x8_t ab_s16 = pcast<Packet4f, Packet8s>(a, b);
-  const int16x8_t cd_s16 = pcast<Packet4f, Packet8s>(c, d);
-  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet2f, Packet8c>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
-                                                       const Packet2f& d) {
-  const int16x4_t ab_s16 = pcast<Packet2f, Packet4s>(a, b);
-  const int16x4_t cd_s16 = pcast<Packet2f, Packet4s>(c, d);
-  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
-}
-
-template <>
-struct type_casting_traits<float, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet4f, Packet16uc>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
-                                                           const Packet4f& d) {
-  const uint16x8_t ab_u16 = pcast<Packet4f, Packet8us>(a, b);
-  const uint16x8_t cd_u16 = pcast<Packet4f, Packet8us>(c, d);
-  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet2f, Packet8uc>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
-                                                         const Packet2f& d) {
-  const uint16x4_t ab_u16 = pcast<Packet2f, Packet4us>(a, b);
-  const uint16x4_t cd_u16 = pcast<Packet2f, Packet4us>(c, d);
-  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
-}
-
-//==============================================================================
-// pcast, SrcType = int8_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int8_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet16c, Packet4f>(const Packet16c& a) {
-  // Discard all but first 4 bytes.
-  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a)))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet8c, Packet2f>(const Packet8c& a) {
-  // Discard all but first 2 bytes.
-  return vcvt_f32_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a)))));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet16c, Packet2l>(const Packet16c& a) {
-  // Discard all but first two bytes.
-  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))))));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet16c, Packet2ul>(const Packet16c& a) {
-  return vreinterpretq_u64_s64(pcast<Packet16c, Packet2l>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet16c, Packet4i>(const Packet16c& a) {
-  // Discard all but first 4 bytes.
-  return vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet8c, Packet2i>(const Packet8c& a) {
-  // Discard all but first 2 bytes.
-  return vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a))));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet16c, Packet4ui>(const Packet16c& a) {
-  return vreinterpretq_u32_s32(pcast<Packet16c, Packet4i>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet8c, Packet2ui>(const Packet8c& a) {
-  return vreinterpret_u32_s32(pcast<Packet8c, Packet2i>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet16c, Packet8s>(const Packet16c& a) {
-  // Discard second half of input.
-  return vmovl_s8(vget_low_s8(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet8c, Packet4s>(const Packet8c& a) {
-  // Discard second half of input.
-  return vget_low_s16(vmovl_s8(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet16c, Packet8us>(const Packet16c& a) {
-  return vreinterpretq_u16_s16(pcast<Packet16c, Packet8s>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet8c, Packet4us>(const Packet8c& a) {
-  return vreinterpret_u16_s16(pcast<Packet8c, Packet4s>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet16c, Packet16c>(const Packet16c& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet8c, Packet8c>(const Packet8c& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4c pcast<Packet4c, Packet4c>(const Packet4c& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet16c, Packet16uc>(const Packet16c& a) {
-  return vreinterpretq_u8_s8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet8c, Packet8uc>(const Packet8c& a) {
-  return vreinterpret_u8_s8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4uc pcast<Packet4c, Packet4uc>(const Packet4c& a) {
-  return static_cast<Packet4uc>(a);
-}
-
-//==============================================================================
-// pcast, SrcType = uint8_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint8_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet16uc, Packet4f>(const Packet16uc& a) {
-  // Discard all but first 4 bytes.
-  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a)))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet8uc, Packet2f>(const Packet8uc& a) {
-  // Discard all but first 2 bytes.
-  return vcvt_f32_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a)))));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet16uc, Packet2ul>(const Packet16uc& a) {
-  // Discard all but first two bytes.
-  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet16uc, Packet2l>(const Packet16uc& a) {
-  return vreinterpretq_s64_u64(pcast<Packet16uc, Packet2ul>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet16uc, Packet4ui>(const Packet16uc& a) {
-  // Discard all but first 4 bytes.
-  return vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet8uc, Packet2ui>(const Packet8uc& a) {
-  // Discard all but first 2 bytes.
-  return vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a))));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet16uc, Packet4i>(const Packet16uc& a) {
-  return vreinterpretq_s32_u32(pcast<Packet16uc, Packet4ui>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet8uc, Packet2i>(const Packet8uc& a) {
-  return vreinterpret_s32_u32(pcast<Packet8uc, Packet2ui>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet16uc, Packet8us>(const Packet16uc& a) {
-  // Discard second half of input.
-  return vmovl_u8(vget_low_u8(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet8uc, Packet4us>(const Packet8uc& a) {
-  // Discard second half of input.
-  return vget_low_u16(vmovl_u8(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet16uc, Packet8s>(const Packet16uc& a) {
-  return vreinterpretq_s16_u16(pcast<Packet16uc, Packet8us>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet8uc, Packet4s>(const Packet8uc& a) {
-  return vreinterpret_s16_u16(pcast<Packet8uc, Packet4us>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet16uc, Packet16uc>(const Packet16uc& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet8uc, Packet8uc>(const Packet8uc& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4uc pcast<Packet4uc, Packet4uc>(const Packet4uc& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet16uc, Packet16c>(const Packet16uc& a) {
-  return vreinterpretq_s8_u8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet8uc, Packet8c>(const Packet8uc& a) {
-  return vreinterpret_s8_u8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4c pcast<Packet4uc, Packet4c>(const Packet4uc& a) {
-  return static_cast<Packet4c>(a);
-}
-
-//==============================================================================
-// pcast, SrcType = int16_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int16_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet8s, Packet4f>(const Packet8s& a) {
-  // Discard second half of input.
-  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet4s, Packet2f>(const Packet4s& a) {
-  // Discard second half of input.
-  return vcvt_f32_s32(vget_low_s32(vmovl_s16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet8s, Packet2l>(const Packet8s& a) {
-  // Discard all but first two values.
-  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(a))));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet8s, Packet2ul>(const Packet8s& a) {
-  return vreinterpretq_u64_s64(pcast<Packet8s, Packet2l>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet8s, Packet4i>(const Packet8s& a) {
-  // Discard second half of input.
-  return vmovl_s16(vget_low_s16(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet4s, Packet2i>(const Packet4s& a) {
-  // Discard second half of input.
-  return vget_low_s32(vmovl_s16(a));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet8s, Packet4ui>(const Packet8s& a) {
-  return vreinterpretq_u32_s32(pcast<Packet8s, Packet4i>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet4s, Packet2ui>(const Packet4s& a) {
-  return vreinterpret_u32_s32(pcast<Packet4s, Packet2i>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet8s, Packet8s>(const Packet8s& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet4s, Packet4s>(const Packet4s& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet8s, Packet8us>(const Packet8s& a) {
-  return vreinterpretq_u16_s16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet4s, Packet4us>(const Packet4s& a) {
-  return vreinterpret_u16_s16(a);
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet8s, Packet16c>(const Packet8s& a, const Packet8s& b) {
-  return vcombine_s8(vmovn_s16(a), vmovn_s16(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet4s, Packet8c>(const Packet4s& a, const Packet4s& b) {
-  return vmovn_s16(vcombine_s16(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet8s, Packet16uc>(const Packet8s& a, const Packet8s& b) {
-  return vcombine_u8(vmovn_u16(vreinterpretq_u16_s16(a)), vmovn_u16(vreinterpretq_u16_s16(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet4s, Packet8uc>(const Packet4s& a, const Packet4s& b) {
-  return vmovn_u16(vcombine_u16(vreinterpret_u16_s16(a), vreinterpret_u16_s16(b)));
-}
-
-//==============================================================================
-// pcast, SrcType = uint16_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint16_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet8us, Packet4f>(const Packet8us& a) {
-  // Discard second half of input.
-  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(a)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet4us, Packet2f>(const Packet4us& a) {
-  // Discard second half of input.
-  return vcvt_f32_u32(vget_low_u32(vmovl_u16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet8us, Packet2ul>(const Packet8us& a) {
-  // Discard all but first two values.
-  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(a))));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet8us, Packet2l>(const Packet8us& a) {
-  return vreinterpretq_s64_u64(pcast<Packet8us, Packet2ul>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet8us, Packet4ui>(const Packet8us& a) {
-  // Discard second half of input.
-  return vmovl_u16(vget_low_u16(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet4us, Packet2ui>(const Packet4us& a) {
-  // Discard second half of input.
-  return vget_low_u32(vmovl_u16(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet8us, Packet4i>(const Packet8us& a) {
-  return vreinterpretq_s32_u32(pcast<Packet8us, Packet4ui>(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet4us, Packet2i>(const Packet4us& a) {
-  return vreinterpret_s32_u32(pcast<Packet4us, Packet2ui>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet8us, Packet8us>(const Packet8us& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet4us, Packet4us>(const Packet4us& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet8us, Packet8s>(const Packet8us& a) {
-  return vreinterpretq_s16_u16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet4us, Packet4s>(const Packet4us& a) {
-  return vreinterpret_s16_u16(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet8us, Packet16uc>(const Packet8us& a, const Packet8us& b) {
-  return vcombine_u8(vmovn_u16(a), vmovn_u16(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet4us, Packet8uc>(const Packet4us& a, const Packet4us& b) {
-  return vmovn_u16(vcombine_u16(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet8us, Packet16c>(const Packet8us& a, const Packet8us& b) {
-  return vreinterpretq_s8_u8(pcast<Packet8us, Packet16uc>(a, b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet4us, Packet8c>(const Packet4us& a, const Packet4us& b) {
-  return vreinterpret_s8_u8(pcast<Packet4us, Packet8uc>(a, b));
-}
-
-//==============================================================================
-// pcast, SrcType = int32_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int32_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
-  return vcvtq_f32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet2i, Packet2f>(const Packet2i& a) {
-  return vcvt_f32_s32(a);
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4i, Packet2l>(const Packet4i& a) {
-  // Discard second half of input.
-  return vmovl_s32(vget_low_s32(a));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4i, Packet2ul>(const Packet4i& a) {
-  return vreinterpretq_u64_s64(pcast<Packet4i, Packet2l>(a));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet4i, Packet4i>(const Packet4i& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet2i, Packet2i>(const Packet2i& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet4i, Packet4ui>(const Packet4i& a) {
-  return vreinterpretq_u32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet2i, Packet2ui>(const Packet2i& a) {
-  return vreinterpret_u32_s32(a);
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet4i, Packet8s>(const Packet4i& a, const Packet4i& b) {
-  return vcombine_s16(vmovn_s32(a), vmovn_s32(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet2i, Packet4s>(const Packet2i& a, const Packet2i& b) {
-  return vmovn_s32(vcombine_s32(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet4i, Packet8us>(const Packet4i& a, const Packet4i& b) {
-  return vcombine_u16(vmovn_u32(vreinterpretq_u32_s32(a)), vmovn_u32(vreinterpretq_u32_s32(b)));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet2i, Packet4us>(const Packet2i& a, const Packet2i& b) {
-  return vmovn_u32(vreinterpretq_u32_s32(vcombine_s32(a, b)));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet4i, Packet16c>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
-                                                         const Packet4i& d) {
-  const int16x8_t ab_s16 = pcast<Packet4i, Packet8s>(a, b);
-  const int16x8_t cd_s16 = pcast<Packet4i, Packet8s>(c, d);
-  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet2i, Packet8c>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
-                                                       const Packet2i& d) {
-  const int16x4_t ab_s16 = vmovn_s32(vcombine_s32(a, b));
-  const int16x4_t cd_s16 = vmovn_s32(vcombine_s32(c, d));
-  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet4i, Packet16uc>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
-                                                           const Packet4i& d) {
-  const uint16x8_t ab_u16 = pcast<Packet4i, Packet8us>(a, b);
-  const uint16x8_t cd_u16 = pcast<Packet4i, Packet8us>(c, d);
-  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet2i, Packet8uc>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
-                                                         const Packet2i& d) {
-  const uint16x4_t ab_u16 = pcast<Packet2i, Packet4us>(a, b);
-  const uint16x4_t cd_u16 = pcast<Packet2i, Packet4us>(c, d);
-  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
-}
-
-//==============================================================================
-// pcast, SrcType = uint32_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint32_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
-  return vcvtq_f32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pcast<Packet2ui, Packet2f>(const Packet2ui& a) {
-  return vcvt_f32_u32(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet4ui, Packet2ul>(const Packet4ui& a) {
-  // Discard second half of input.
-  return vmovl_u32(vget_low_u32(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet4ui, Packet2l>(const Packet4ui& a) {
-  return vreinterpretq_s64_u64(pcast<Packet4ui, Packet2ul>(a));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet4ui, Packet4ui>(const Packet4ui& a) {
-  return a;
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui pcast<Packet2ui, Packet2ui>(const Packet2ui& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet4ui, Packet4i>(const Packet4ui& a) {
-  return vreinterpretq_s32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i pcast<Packet2ui, Packet2i>(const Packet2ui& a) {
-  return vreinterpret_s32_u32(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet4ui, Packet8us>(const Packet4ui& a, const Packet4ui& b) {
-  return vcombine_u16(vmovn_u32(a), vmovn_u32(b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4us pcast<Packet2ui, Packet4us>(const Packet2ui& a, const Packet2ui& b) {
-  return vmovn_u32(vcombine_u32(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet4ui, Packet8s>(const Packet4ui& a, const Packet4ui& b) {
-  return vreinterpretq_s16_u16(pcast<Packet4ui, Packet8us>(a, b));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4s pcast<Packet2ui, Packet4s>(const Packet2ui& a, const Packet2ui& b) {
-  return vreinterpret_s16_u16(pcast<Packet2ui, Packet4us>(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet4ui, Packet16uc>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
-                                                            const Packet4ui& d) {
-  const uint16x8_t ab_u16 = vcombine_u16(vmovn_u32(a), vmovn_u32(b));
-  const uint16x8_t cd_u16 = vcombine_u16(vmovn_u32(c), vmovn_u32(d));
-  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc pcast<Packet2ui, Packet8uc>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
-                                                          const Packet2ui& d) {
-  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(a, b));
-  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(c, d));
-  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet4ui, Packet16c>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
-                                                          const Packet4ui& d) {
-  return vreinterpretq_s8_u8(pcast<Packet4ui, Packet16uc>(a, b, c, d));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c pcast<Packet2ui, Packet8c>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
-                                                        const Packet2ui& d) {
-  return vreinterpret_s8_u8(pcast<Packet2ui, Packet8uc>(a, b, c, d));
-}
-
-//==============================================================================
-// pcast, SrcType = int64_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::int64_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet2l, Packet4f>(const Packet2l& a, const Packet2l& b) {
-  return vcvtq_f32_s32(vcombine_s32(vmovn_s64(a), vmovn_s64(b)));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet2l, Packet2l>(const Packet2l& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet2l, Packet2ul>(const Packet2l& a) {
-  return vreinterpretq_u64_s64(a);
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet2l, Packet4i>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_s32(vmovn_s64(a), vmovn_s64(b));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet2l, Packet4ui>(const Packet2l& a, const Packet2l& b) {
-  return vcombine_u32(vmovn_u64(vreinterpretq_u64_s64(a)), vmovn_u64(vreinterpretq_u64_s64(b)));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet2l, Packet8s>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                       const Packet2l& d) {
-  const int32x4_t ab_s32 = pcast<Packet2l, Packet4i>(a, b);
-  const int32x4_t cd_s32 = pcast<Packet2l, Packet4i>(c, d);
-  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet2l, Packet8us>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                         const Packet2l& d) {
-  const uint32x4_t ab_u32 = pcast<Packet2l, Packet4ui>(a, b);
-  const uint32x4_t cd_u32 = pcast<Packet2l, Packet4ui>(c, d);
-  return vcombine_u16(vmovn_u32(ab_u32), vmovn_u32(cd_u32));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet2l, Packet16c>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                         const Packet2l& d, const Packet2l& e, const Packet2l& f,
-                                                         const Packet2l& g, const Packet2l& h) {
-  const int16x8_t abcd_s16 = pcast<Packet2l, Packet8s>(a, b, c, d);
-  const int16x8_t efgh_s16 = pcast<Packet2l, Packet8s>(e, f, g, h);
-  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet2l, Packet16uc>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
-                                                           const Packet2l& d, const Packet2l& e, const Packet2l& f,
-                                                           const Packet2l& g, const Packet2l& h) {
-  const uint16x8_t abcd_u16 = pcast<Packet2l, Packet8us>(a, b, c, d);
-  const uint16x8_t efgh_u16 = pcast<Packet2l, Packet8us>(e, f, g, h);
-  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
-}
-
-//==============================================================================
-// pcast, SrcType = uint64_t
-//==============================================================================
-template <>
-struct type_casting_traits<numext::uint64_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet2ul, Packet4f>(const Packet2ul& a, const Packet2ul& b) {
-  return vcvtq_f32_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet2ul, Packet2ul>(const Packet2ul& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet2ul, Packet2l>(const Packet2ul& a) {
-  return vreinterpretq_s64_u64(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet2ul, Packet4ui>(const Packet2ul& a, const Packet2ul& b) {
-  return vcombine_u32(vmovn_u64(a), vmovn_u64(b));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet2ul, Packet4i>(const Packet2ul& a, const Packet2ul& b) {
-  return vreinterpretq_s32_u32(pcast<Packet2ul, Packet4ui>(a, b));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet2ul, Packet8us>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                          const Packet2ul& d) {
-  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
-  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(vmovn_u64(c), vmovn_u64(d)));
-  return vcombine_u16(ab_u16, cd_u16);
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet2ul, Packet8s>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                        const Packet2ul& d) {
-  return vreinterpretq_s16_u16(pcast<Packet2ul, Packet8us>(a, b, c, d));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet2ul, Packet16uc>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                            const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
-                                                            const Packet2ul& g, const Packet2ul& h) {
-  const uint16x8_t abcd_u16 = pcast<Packet2ul, Packet8us>(a, b, c, d);
-  const uint16x8_t efgh_u16 = pcast<Packet2ul, Packet8us>(e, f, g, h);
-  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet2ul, Packet16c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
-                                                          const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
-                                                          const Packet2ul& g, const Packet2ul& h) {
-  return vreinterpretq_s8_u8(pcast<Packet2ul, Packet16uc>(a, b, c, d, e, f, g, h));
-}
-
-//==============================================================================
-// preinterpret
-//==============================================================================
-template <>
-EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2i>(const Packet2i& a) {
-  return vreinterpret_f32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2ui>(const Packet2ui& a) {
-  return vreinterpret_f32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
-  return vreinterpretq_f32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4ui>(const Packet4ui& a) {
-  return vreinterpretq_f32_u32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet4uc>(const Packet4uc& a) {
-  return static_cast<Packet4c>(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet8uc>(const Packet8uc& a) {
-  return vreinterpret_s8_u8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16c preinterpret<Packet16c, Packet16uc>(const Packet16uc& a) {
-  return vreinterpretq_s8_u8(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet4c>(const Packet4c& a) {
-  return static_cast<Packet4uc>(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet8c>(const Packet8c& a) {
-  return vreinterpret_u8_s8(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet16uc preinterpret<Packet16uc, Packet16c>(const Packet16c& a) {
-  return vreinterpretq_u8_s8(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet4us>(const Packet4us& a) {
-  return vreinterpret_s16_u16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8us>(const Packet8us& a) {
-  return vreinterpretq_s16_u16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet4s>(const Packet4s& a) {
-  return vreinterpret_u16_s16(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8us preinterpret<Packet8us, Packet8s>(const Packet8s& a) {
-  return vreinterpretq_u16_s16(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2f>(const Packet2f& a) {
-  return vreinterpret_s32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2ui>(const Packet2ui& a) {
-  return vreinterpret_s32_u32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
-  return vreinterpretq_s32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
-  return vreinterpretq_s32_u32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2f>(const Packet2f& a) {
-  return vreinterpret_u32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2i>(const Packet2i& a) {
-  return vreinterpret_u32_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4f>(const Packet4f& a) {
-  return vreinterpretq_u32_f32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
-  return vreinterpretq_u32_s32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2ul>(const Packet2ul& a) {
-  return vreinterpretq_s64_u64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2l>(const Packet2l& a) {
-  return vreinterpretq_u64_s64(a);
-}
-
-#if EIGEN_ARCH_ARM64
-
-//==============================================================================
-// pcast/preinterpret, Double
-//==============================================================================
-
-template <>
-struct type_casting_traits<double, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet2d, Packet2d>(const Packet2d& a) {
-  return a;
-}
-
-template <>
-struct type_casting_traits<double, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
-  return vcombine_f32(vcvt_f32_f64(a), vcvt_f32_f64(b));
-}
-
-template <>
-struct type_casting_traits<double, numext::int64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2l pcast<Packet2d, Packet2l>(const Packet2d& a) {
-  return vcvtq_s64_f64(a);
-}
-
-template <>
-struct type_casting_traits<double, numext::uint64_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2ul pcast<Packet2d, Packet2ul>(const Packet2d& a) {
-  return vcvtq_u64_f64(a);
-}
-
-template <>
-struct type_casting_traits<double, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4i pcast<Packet2d, Packet4i>(const Packet2d& a, const Packet2d& b) {
-  return vcombine_s32(vmovn_s64(vcvtq_s64_f64(a)), vmovn_s64(vcvtq_s64_f64(b)));
-}
-
-template <>
-struct type_casting_traits<double, numext::uint32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet4ui pcast<Packet2d, Packet4ui>(const Packet2d& a, const Packet2d& b) {
-  return vcombine_u32(vmovn_u64(vcvtq_u64_f64(a)), vmovn_u64(vcvtq_u64_f64(b)));
-}
-
-template <>
-struct type_casting_traits<double, numext::int16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8s pcast<Packet2d, Packet8s>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                       const Packet2d& d) {
-  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
-  const int32x4_t cd_s32 = pcast<Packet2d, Packet4i>(c, d);
-  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
-}
-
-template <>
-struct type_casting_traits<double, numext::uint16_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet8us pcast<Packet2d, Packet8us>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                         const Packet2d& d) {
-  const uint32x4_t ab_u32 = pcast<Packet2d, Packet4ui>(a, b);
-  const uint32x4_t cd_u32 = pcast<Packet2d, Packet4ui>(c, d);
-  return vcombine_u16(vmovn_u32(ab_u32), vmovn_u32(cd_u32));
-}
-
-template <>
-struct type_casting_traits<double, numext::int8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16c pcast<Packet2d, Packet16c>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                         const Packet2d& d, const Packet2d& e, const Packet2d& f,
-                                                         const Packet2d& g, const Packet2d& h) {
-  const int16x8_t abcd_s16 = pcast<Packet2d, Packet8s>(a, b, c, d);
-  const int16x8_t efgh_s16 = pcast<Packet2d, Packet8s>(e, f, g, h);
-  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
-}
-
-template <>
-struct type_casting_traits<double, numext::uint8_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet16uc pcast<Packet2d, Packet16uc>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
-                                                           const Packet2d& d, const Packet2d& e, const Packet2d& f,
-                                                           const Packet2d& g, const Packet2d& h) {
-  const uint16x8_t abcd_u16 = pcast<Packet2d, Packet8us>(a, b, c, d);
-  const uint16x8_t efgh_u16 = pcast<Packet2d, Packet8us>(e, f, g, h);
-  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
-}
-
-template <>
-struct type_casting_traits<float, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
-  // Discard second-half of input.
-  return vcvt_f64_f32(vget_low_f32(a));
-}
-
-template <>
-struct type_casting_traits<numext::int8_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet16c, Packet2d>(const Packet16c& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet8c, Packet2f>(vget_low_s8(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint8_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet16uc, Packet2d>(const Packet16uc& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet8uc, Packet2f>(vget_low_u8(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int16_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet8s, Packet2d>(const Packet8s& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet4s, Packet2f>(vget_low_s16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint16_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet8us, Packet2d>(const Packet8us& a) {
-  // Discard all but first two values.
-  return vcvt_f64_f32(pcast<Packet4us, Packet2f>(vget_low_u16(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int32_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet4i, Packet2d>(const Packet4i& a) {
-  // Discard second half of input.
-  return vcvtq_f64_s64(vmovl_s32(vget_low_s32(a)));
-}
-
-template <>
-struct type_casting_traits<numext::uint32_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet4ui, Packet2d>(const Packet4ui& a) {
-  // Discard second half of input.
-  return vcvtq_f64_u64(vmovl_u32(vget_low_u32(a)));
-}
-
-template <>
-struct type_casting_traits<numext::int64_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet2l, Packet2d>(const Packet2l& a) {
-  return vcvtq_f64_s64(a);
-}
-
-template <>
-struct type_casting_traits<numext::uint64_t, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-template <>
-EIGEN_STRONG_INLINE Packet2d pcast<Packet2ul, Packet2d>(const Packet2ul& a) {
-  return vcvtq_f64_u64(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
-  return vreinterpretq_f64_s64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2ul>(const Packet2ul& a) {
-  return vreinterpretq_f64_u64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
-  return vreinterpretq_s64_f64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2d>(const Packet2d& a) {
-  return vreinterpretq_u64_f64(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
-  return vreinterpretq_f64_s32(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
-  return vreinterpretq_s32_f64(a);
-}
-
-#endif  // EIGEN_ARCH_ARM64
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_TYPE_CASTING_NEON_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
deleted file mode 100644
index 8fe22da..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
+++ /dev/null
@@ -1,351 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_SSE_H
-#define EIGEN_COMPLEX_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- float ----------
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const __m128& a) : v(a) {}
-  Packet4f v;
-};
-
-// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
-// to leverage AVX instructions.
-#ifndef EIGEN_VECTORIZE_AVX
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0,
-    HasBlend  = 1
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet2cf> {
-  typedef std::complex<float> type;
-  typedef Packet2cf half;
-  typedef Packet4f as_real;
-  enum {
-    size=2,
-    alignment=Aligned16,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_add_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_sub_ps(a.v,b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a)
-{
-  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000));
-  return Packet2cf(_mm_xor_ps(a.v,mask));
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
-{
-  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000));
-  return Packet2cf(_mm_xor_ps(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  #ifdef EIGEN_VECTORIZE_SSE3
-  return Packet2cf(_mm_addsub_ps(_mm_mul_ps(_mm_moveldup_ps(a.v), b.v),
-                                 _mm_mul_ps(_mm_movehdup_ps(a.v),
-                                            vec4f_swizzle1(b.v, 1, 0, 3, 2))));
-//   return Packet2cf(_mm_addsub_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v),
-//                                  _mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3),
-//                                             vec4f_swizzle1(b.v, 1, 0, 3, 2))));
-  #else
-  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x00000000,0x80000000,0x00000000));
-  return Packet2cf(_mm_add_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v),
-                              _mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3),
-                                                    vec4f_swizzle1(b.v, 1, 0, 3, 2)), mask)));
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf ptrue  <Packet2cf>(const Packet2cf& a) { return Packet2cf(ptrue(Packet4f(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_and_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_or_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_xor_ps(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_andnot_ps(b.v,a.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&numext::real_ref(*from))); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&numext::real_ref(*from))); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-#ifdef EIGEN_VECTORIZE_SSE3
-  res.v = _mm_castpd_ps(_mm_loaddup_pd(reinterpret_cast<double const*>(&from)));
-#else
-  res.v = _mm_castpd_ps(_mm_load_sd(reinterpret_cast<double const*>(&from)));
-  res.v = _mm_movelh_ps(res.v, res.v);
-#endif
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), Packet4f(from.v)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), Packet4f(from.v)); }
-
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
-{
-  return Packet2cf(_mm_set_ps(std::imag(from[1*stride]), std::real(from[1*stride]),
-                              std::imag(from[0*stride]), std::real(from[0*stride])));
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 0)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 1)));
-  to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 2)),
-                                     _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 3)));
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
-{
-  #if EIGEN_GNUC_AT_MOST(4,3)
-  // Workaround gcc 4.2 ICE - this is not performance wise ideal, but who cares...
-  // This workaround also fix invalid code generation with gcc 4.3
-  EIGEN_ALIGN16 std::complex<float> res[2];
-  _mm_store_ps((float*)res, a.v);
-  return res[0];
-  #else
-  std::complex<float> res;
-  _mm_storel_pi((__m64*)&res, a.v);
-  return res;
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) { return Packet2cf(_mm_castpd_ps(preverse(Packet2d(_mm_castps_pd(a.v))))); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  return pfirst(Packet2cf(_mm_add_ps(a.v, _mm_movehl_ps(a.v,a.v))));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  return pfirst(pmul(a, Packet2cf(_mm_movehl_ps(a.v,a.v))));
-}
-
-EIGEN_STRONG_INLINE Packet2cf pcplxflip/* <Packet2cf> */(const Packet2cf& x)
-{
-  return Packet2cf(vec4f_swizzle1(x.v, 1, 0, 3, 2));
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for SSE3 and 4
-  Packet2cf res = pmul(a, pconj(b));
-  __m128 s = _mm_mul_ps(b.v,b.v);
-  return Packet2cf(_mm_div_ps(res.v,_mm_add_ps(s,vec4f_swizzle1(s, 1, 0, 3, 2))));
-}
-
-
-
-//---------- double ----------
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const __m128d& a) : v(a) {}
-  Packet2d v;
-};
-
-// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
-// to leverage AVX instructions.
-#ifndef EIGEN_VECTORIZE_AVX
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 0,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasSqrt   = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-#endif
-
-template<> struct unpacket_traits<Packet1cd> {
-  typedef std::complex<double> type;
-  typedef Packet1cd half;
-  typedef Packet2d as_real;
-  enum {
-    size=1,
-    alignment=Aligned16,
-    vectorizable=true,
-    masked_load_available=false,
-    masked_store_available=false
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_add_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_sub_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a)
-{
-  const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0));
-  return Packet1cd(_mm_xor_pd(a.v,mask));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  #ifdef EIGEN_VECTORIZE_SSE3
-  return Packet1cd(_mm_addsub_pd(_mm_mul_pd(_mm_movedup_pd(a.v), b.v),
-                                 _mm_mul_pd(vec2d_swizzle1(a.v, 1, 1),
-                                            vec2d_swizzle1(b.v, 1, 0))));
-  #else
-  const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0));
-  return Packet1cd(_mm_add_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v),
-                              _mm_xor_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 1, 1),
-                                                    vec2d_swizzle1(b.v, 1, 0)), mask)));
-  #endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd ptrue  <Packet1cd>(const Packet1cd& a) { return Packet1cd(ptrue(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pand   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_and_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd por    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_or_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pxor   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_xor_pd(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_andnot_pd(b.v,a.v)); }
-
-// FIXME force unaligned load, this is a temporary fix
-template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from)
-{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
-{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) { return pset1<Packet1cd>(*from); }
-
-// FIXME force unaligned store, this is a temporary fix
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, Packet2d(from.v)); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, Packet2d(from.v)); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
-{
-  EIGEN_ALIGN16 double res[2];
-  _mm_store_pd(res, a.v);
-  return std::complex<double>(res[0],res[1]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for SSE3 and 4
-  Packet1cd res = pmul(a,pconj(b));
-  __m128d s = _mm_mul_pd(b.v,b.v);
-  return Packet1cd(_mm_div_pd(res.v, _mm_add_pd(s,_mm_shuffle_pd(s, s, 0x1))));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/* <Packet1cd> */(const Packet1cd& x)
-{
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2cf,2>& kernel) {
-  __m128d w1 = _mm_castps_pd(kernel.packet[0].v);
-  __m128d w2 = _mm_castps_pd(kernel.packet[1].v);
-
-  __m128 tmp = _mm_castpd_ps(_mm_unpackhi_pd(w1, w2));
-  kernel.packet[0].v = _mm_castpd_ps(_mm_unpacklo_pd(w1, w2));
-  kernel.packet[1].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b)
-{
-  __m128 eq = _mm_cmpeq_ps(a.v, b.v);
-  return Packet2cf(pand<Packet4f>(eq, vec4f_swizzle1(eq, 1, 0, 3, 2)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b)
-{
-  __m128d eq = _mm_cmpeq_pd(a.v, b.v);
-  return Packet1cd(pand<Packet2d>(eq, vec2d_swizzle1(eq, 1, 0)));
-}
-
-template<>  EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  __m128d result = pblend<Packet2d>(ifPacket, _mm_castps_pd(thenPacket.v), _mm_castps_pd(elsePacket.v));
-  return Packet2cf(_mm_castpd_ps(result));
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
-  return psqrt_complex<Packet1cd>(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
-  return psqrt_complex<Packet2cf>(a);
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h
deleted file mode 100644
index 8736d0d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h
+++ /dev/null
@@ -1,199 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The sin and cos and functions of this file come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-#ifndef EIGEN_MATH_FUNCTIONS_SSE_H
-#define EIGEN_MATH_FUNCTIONS_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog<Packet4f>(const Packet4f& _x) {
-  return plog_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d plog<Packet2d>(const Packet2d& _x) {
-  return plog_double(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog2<Packet4f>(const Packet4f& _x) {
-  return plog2_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d plog2<Packet2d>(const Packet2d& _x) {
-  return plog2_double(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f plog1p<Packet4f>(const Packet4f& _x) {
-  return generic_plog1p(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexpm1<Packet4f>(const Packet4f& _x) {
-  return generic_expm1(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexp<Packet4f>(const Packet4f& _x)
-{
-  return pexp_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d pexp<Packet2d>(const Packet2d& x)
-{
-  return pexp_double(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psin<Packet4f>(const Packet4f& _x)
-{
-  return psin_float(_x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pcos<Packet4f>(const Packet4f& _x)
-{
-  return pcos_float(_x);
-}
-
-#if EIGEN_FAST_MATH
-
-// Functions for sqrt.
-// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
-// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
-// exact solution. It does not handle +inf, or denormalized numbers correctly.
-// The main advantage of this approach is not just speed, but also the fact that
-// it can be inlined and pipelined with other computations, further reducing its
-// effective latency. This is similar to Quake3's fast inverse square root.
-// For detail see here: http://www.beyond3d.com/content/articles/8/
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& _x)
-{
-  Packet4f minus_half_x = pmul(_x, pset1<Packet4f>(-0.5f));
-  Packet4f denormal_mask = pandnot(
-      pcmp_lt(_x, pset1<Packet4f>((std::numeric_limits<float>::min)())),
-      pcmp_lt(_x, pzero(_x)));
-
-  // Compute approximate reciprocal sqrt.
-  Packet4f x = _mm_rsqrt_ps(_x);
-  // Do a single step of Newton's iteration.
-  x = pmul(x, pmadd(minus_half_x, pmul(x,x), pset1<Packet4f>(1.5f)));
-  // Flush results for denormals to zero.
-  return pandnot(pmul(_x,x), denormal_mask);
-}
-
-#else
-
-template<>EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& x) { return _mm_sqrt_ps(x); }
-
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d psqrt<Packet2d>(const Packet2d& x) { return _mm_sqrt_pd(x); }
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet16b psqrt<Packet16b>(const Packet16b& x) { return x; }
-
-#if EIGEN_FAST_MATH
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& _x) {
-  _EIGEN_DECLARE_CONST_Packet4f(one_point_five, 1.5f);
-  _EIGEN_DECLARE_CONST_Packet4f(minus_half, -0.5f);
-  _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inf, 0x7f800000u);
-  _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(flt_min, 0x00800000u);
-
-  Packet4f neg_half = pmul(_x, p4f_minus_half);
-
-  // Identity infinite, zero, negative and denormal arguments.
-  Packet4f lt_min_mask = _mm_cmplt_ps(_x, p4f_flt_min);
-  Packet4f inf_mask = _mm_cmpeq_ps(_x, p4f_inf);
-  Packet4f not_normal_finite_mask = _mm_or_ps(lt_min_mask, inf_mask);
-
-  // Compute an approximate result using the rsqrt intrinsic.
-  Packet4f y_approx = _mm_rsqrt_ps(_x);
-
-  // Do a single step of Newton-Raphson iteration to improve the approximation.
-  // This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
-  // It is essential to evaluate the inner term like this because forming
-  // y_n^2 may over- or underflow.
-  Packet4f y_newton = pmul(
-      y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p4f_one_point_five));
-
-  // Select the result of the Newton-Raphson step for positive normal arguments.
-  // For other arguments, choose the output of the intrinsic. This will
-  // return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(x) = +inf if
-  // x is zero or a positive denormalized float (equivalent to flushing positive
-  // denormalized inputs to zero).
-  return pselect<Packet4f>(not_normal_finite_mask, y_approx, y_newton);
-}
-
-#else
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& x) {
-  // Unfortunately we can't use the much faster mm_rsqrt_ps since it only provides an approximation.
-  return _mm_div_ps(pset1<Packet4f>(1.0f), _mm_sqrt_ps(x));
-}
-
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d prsqrt<Packet2d>(const Packet2d& x) {
-  return _mm_div_pd(pset1<Packet2d>(1.0), _mm_sqrt_pd(x));
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-
-} // end namespace internal
-
-namespace numext {
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float sqrt(const float &x)
-{
-  return internal::pfirst(internal::Packet4f(_mm_sqrt_ss(_mm_set_ss(x))));
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double sqrt(const double &x)
-{
-#if EIGEN_COMP_GNUC_STRICT
-  // This works around a GCC bug generating poor code for _mm_sqrt_pd
-  // See https://gitlab.com/libeigen/eigen/commit/8dca9f97e38970
-  return internal::pfirst(internal::Packet2d(__builtin_ia32_sqrtsd(_mm_set_sd(x))));
-#else
-  return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x))));
-#endif
-}
-
-} // end namespace numex
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATH_FUNCTIONS_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
deleted file mode 100644
index db102c7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
+++ /dev/null
@@ -1,1505 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_SSE_H
-#define EIGEN_PACKET_MATH_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#if !defined(EIGEN_VECTORIZE_AVX) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
-// 32 bits =>  8 registers
-// 64 bits => 16 registers
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*))
-#endif
-
-#ifdef EIGEN_VECTORIZE_FMA
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-#endif
-
-#if ((defined EIGEN_VECTORIZE_AVX) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_MINGW) && (__GXX_ABI_VERSION < 1004)) || EIGEN_OS_QNX
-// With GCC's default ABI version, a __m128 or __m256 are the same types and therefore we cannot
-// have overloads for both types without linking error.
-// One solution is to increase ABI version using -fabi-version=4 (or greater).
-// Otherwise, we workaround this inconvenience by wrapping 128bit types into the following helper
-// structure:
-typedef eigen_packet_wrapper<__m128>  Packet4f;
-typedef eigen_packet_wrapper<__m128d> Packet2d;
-#else
-typedef __m128  Packet4f;
-typedef __m128d Packet2d;
-#endif
-
-typedef eigen_packet_wrapper<__m128i, 0> Packet4i;
-typedef eigen_packet_wrapper<__m128i, 1> Packet16b;
-
-template<> struct is_arithmetic<__m128>  { enum { value = true }; };
-template<> struct is_arithmetic<__m128i> { enum { value = true }; };
-template<> struct is_arithmetic<__m128d> { enum { value = true }; };
-template<> struct is_arithmetic<Packet4i>  { enum { value = true }; };
-template<> struct is_arithmetic<Packet16b>  { enum { value = true }; };
-
-template<int p, int q, int r, int s>
-struct shuffle_mask{
- enum { mask = (s)<<6|(r)<<4|(q)<<2|(p) };
-};
-
-// TODO: change the implementation of all swizzle* ops from macro to template,
-#define vec4f_swizzle1(v,p,q,r,s) \
-  Packet4f(_mm_castsi128_ps(_mm_shuffle_epi32( _mm_castps_si128(v), (shuffle_mask<p,q,r,s>::mask))))
-
-#define vec4i_swizzle1(v,p,q,r,s) \
-  Packet4i(_mm_shuffle_epi32( v, (shuffle_mask<p,q,r,s>::mask)))
-
-#define vec2d_swizzle1(v,p,q) \
-  Packet2d(_mm_castsi128_pd(_mm_shuffle_epi32( _mm_castpd_si128(v), (shuffle_mask<2*p,2*p+1,2*q,2*q+1>::mask))))
-
-#define vec4f_swizzle2(a,b,p,q,r,s) \
-  Packet4f(_mm_shuffle_ps( (a), (b), (shuffle_mask<p,q,r,s>::mask)))
-
-#define vec4i_swizzle2(a,b,p,q,r,s) \
-  Packet4i(_mm_castps_si128( (_mm_shuffle_ps( _mm_castsi128_ps(a), _mm_castsi128_ps(b), (shuffle_mask<p,q,r,s>::mask)))))
-
-EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_movelh_ps(a,b));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_movehl_ps(a,b));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_unpacklo_ps(a,b));
-}
-EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b)
-{
-  return Packet4f(_mm_unpackhi_ps(a,b));
-}
-#define vec4f_duplane(a,p) \
-  vec4f_swizzle2(a,a,p,p,p,p)
-
-#define vec2d_swizzle2(a,b,mask) \
-  Packet2d(_mm_shuffle_pd(a,b,mask))
-
-EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a, const Packet2d& b)
-{
-  return Packet2d(_mm_unpacklo_pd(a,b));
-}
-EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a, const Packet2d& b)
-{
-  return Packet2d(_mm_unpackhi_pd(a,b));
-}
-#define vec2d_duplane(a,p) \
-  vec2d_swizzle2(a,a,(p<<1)|p)
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  const Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
-  const Packet2d p2d_##NAME = pset1<Packet2d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = pset1frombits<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  const Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-
-// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
-// to leverage AVX instructions.
-#ifndef EIGEN_VECTORIZE_AVX
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasCmp  = 1,
-    HasDiv = 1,
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasLog1p = 1,
-    HasExpm1 = 1,
-    HasNdtri = 1,
-    HasExp = 1,
-    HasBessel = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasBlend = 1,
-    HasCeil = 1,
-    HasFloor = 1,
-#ifdef EIGEN_VECTORIZE_SSE4_1
-    HasRound = 1,
-#endif
-    HasRint = 1
-  };
-};
-template <>
-struct packet_traits<double> : default_packet_traits {
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 0,
-
-    HasCmp  = 1,
-    HasDiv  = 1,
-    HasLog  = 1,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasBlend = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-#ifdef EIGEN_VECTORIZE_SSE4_1
-    HasRound = 1,
-#endif
-    HasRint = 1
-  };
-};
-#endif
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet4i type;
-  typedef Packet4i half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=4,
-
-    HasShift = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct packet_traits<bool> : default_packet_traits
-{
-  typedef Packet16b type;
-  typedef Packet16b half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    HasHalfPacket = 0,
-    size=16,
-
-    HasAdd       = 1,
-    HasSub       = 1,
-    HasShift     = 0,
-    HasMul       = 1,
-    HasNegate    = 1,
-    HasAbs       = 0,
-    HasAbs2      = 0,
-    HasMin       = 0,
-    HasMax       = 0,
-    HasConj      = 0,
-    HasSqrt      = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet4f> {
-  typedef float     type;
-  typedef Packet4f  half;
-  typedef Packet4i  integer_packet;
-  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet2d> {
-  typedef double    type;
-  typedef Packet2d  half;
-  enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet4i> {
-  typedef int       type;
-  typedef Packet4i  half;
-  enum {size=4, alignment=Aligned16, vectorizable=false, masked_load_available=false, masked_store_available=false};
-};
-template<> struct unpacket_traits<Packet16b> {
-  typedef bool       type;
-  typedef Packet16b  half;
-  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-};
-
-#ifndef EIGEN_VECTORIZE_AVX
-template<> struct scalar_div_cost<float,true> { enum { value = 7 }; };
-template<> struct scalar_div_cost<double,true> { enum { value = 8 }; };
-#endif
-
-#if EIGEN_COMP_MSVC==1500
-// Workaround MSVC 9 internal compiler error.
-// TODO: It has been detected with win64 builds (amd64), so let's check whether it also happens in 32bits+SSE mode
-// TODO: let's check whether there does not exist a better fix, like adding a pset0() function. (it crashed on pset1(0)).
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) { return _mm_set_ps(from,from,from,from); }
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return _mm_set_pd(from,from); }
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from) { return _mm_set_epi32(from,from,from,from); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) { return _mm_set_ps1(from); }
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return _mm_set1_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from) { return _mm_set1_epi32(from); }
-#endif
-template<> EIGEN_STRONG_INLINE Packet16b pset1<Packet16b>(const bool&    from) { return _mm_set1_epi8(static_cast<char>(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) { return _mm_castsi128_ps(pset1<Packet4i>(from)); }
-template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from) { return _mm_castsi128_pd(_mm_set1_epi64x(from)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f peven_mask(const Packet4f& /*a*/) { return _mm_castsi128_ps(_mm_set_epi32(0, -1, 0, -1)); }
-template<> EIGEN_STRONG_INLINE Packet4i peven_mask(const Packet4i& /*a*/) { return _mm_set_epi32(0, -1, 0, -1); }
-template<> EIGEN_STRONG_INLINE Packet2d peven_mask(const Packet2d& /*a*/) { return _mm_castsi128_pd(_mm_set_epi32(0, 0, -1, -1)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pzero(const Packet4f& /*a*/) { return _mm_setzero_ps(); }
-template<> EIGEN_STRONG_INLINE Packet2d pzero(const Packet2d& /*a*/) { return _mm_setzero_pd(); }
-template<> EIGEN_STRONG_INLINE Packet4i pzero(const Packet4i& /*a*/) { return _mm_setzero_si128(); }
-
-// GCC generates a shufps instruction for _mm_set1_ps/_mm_load1_ps instead of the more efficient pshufd instruction.
-// However, using inrinsics for pset1 makes gcc to generate crappy code in some cases (see bug 203)
-// Using inline assembly is also not an option because then gcc fails to reorder properly the instructions.
-// Therefore, we introduced the pload1 functions to be used in product kernels for which bug 203 does not apply.
-// Also note that with AVX, we want it to generate a vbroadcastss.
-#if EIGEN_COMP_GNUC_STRICT && (!defined __AVX__)
-template<> EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float *from) {
-  return vec4f_swizzle1(_mm_load_ss(from),0,0,0,0);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) { return _mm_add_ps(pset1<Packet4f>(a), _mm_set_ps(3,2,1,0)); }
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return _mm_add_pd(pset1<Packet2d>(a),_mm_set_pd(1,0)); }
-template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) { return _mm_add_epi32(pset1<Packet4i>(a),_mm_set_epi32(3,2,1,0)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_add_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_add_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_add_epi32(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet16b padd<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_or_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_sub_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_sub_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_sub_epi32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b psub<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_xor_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
-template<> EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE3
-  return _mm_addsub_ps(a,b);
-#else
-  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x0,0x80000000,0x0));
-  return padd(a, pxor(mask, b));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& , const Packet2d& );
-template<> EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b) 
-{
-#ifdef EIGEN_VECTORIZE_SSE3  
-  return _mm_addsub_pd(a,b); 
-#else
-  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0,0x80000000,0x0,0x0)); 
-  return padd(a, pxor(mask, b));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
-{
-  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000));
-  return _mm_xor_ps(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a)
-{
-  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0,0x80000000,0x0,0x80000000));
-  return _mm_xor_pd(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a)
-{
-  return psub(Packet4i(_mm_setr_epi32(0,0,0,0)), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet16b pnegate(const Packet16b& a)
-{
-  return psub(pset1<Packet16b>(false), a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_mul_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_mul_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_mullo_epi32(a,b);
-#else
-  // this version is slightly faster than 4 scalar products
-  return vec4i_swizzle1(
-            vec4i_swizzle2(
-              _mm_mul_epu32(a,b),
-              _mm_mul_epu32(vec4i_swizzle1(a,1,0,3,2),
-                            vec4i_swizzle1(b,1,0,3,2)),
-              0,2,0,2),
-            0,2,1,3);
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet16b pmul<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_and_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_div_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_div_pd(a,b); }
-
-// for some weird raisons, it has to be overloaded for packet of integers
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd(pmul(a,b), c); }
-#ifdef EIGEN_VECTORIZE_FMA
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fmadd_ps(a,b,c); }
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fmadd_pd(a,b,c); }
-#endif
-
-#ifdef EIGEN_VECTORIZE_SSE4_1
-template<> EIGEN_DEVICE_FUNC inline Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
-  return _mm_blendv_ps(b,a,mask);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b) {
-  return _mm_castps_si128(_mm_blendv_ps(_mm_castsi128_ps(b),_mm_castsi128_ps(a),_mm_castsi128_ps(mask)));
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2d pselect(const Packet2d& mask, const Packet2d& a, const Packet2d& b) {  return _mm_blendv_pd(b,a,mask); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet16b pselect(const Packet16b& mask, const Packet16b& a, const Packet16b& b) {
-  return _mm_blendv_epi8(b,a,mask);
-}
-#else
-template<> EIGEN_DEVICE_FUNC inline Packet16b pselect(const Packet16b& mask, const Packet16b& a, const Packet16b& b) {
-  Packet16b a_part = _mm_and_si128(mask, a);
-  Packet16b b_part = _mm_andnot_si128(mask, b);
-  return _mm_or_si128(a_part, b_part);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4i ptrue<Packet4i>(const Packet4i& a) { return _mm_cmpeq_epi32(a, a); }
-template<> EIGEN_STRONG_INLINE Packet16b ptrue<Packet16b>(const Packet16b& a) { return _mm_cmpeq_epi8(a, a); }
-template<> EIGEN_STRONG_INLINE Packet4f
-ptrue<Packet4f>(const Packet4f& a) {
-  Packet4i b = _mm_castps_si128(a);
-  return _mm_castsi128_ps(_mm_cmpeq_epi32(b, b));
-}
-template<> EIGEN_STRONG_INLINE Packet2d
-ptrue<Packet2d>(const Packet2d& a) {
-  Packet4i b = _mm_castpd_si128(a);
-  return _mm_castsi128_pd(_mm_cmpeq_epi32(b, b));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_and_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_and_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_and_si128(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b pand<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_and_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_or_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_or_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_or_si128(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b por<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_or_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_xor_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_xor_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_xor_si128(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b pxor<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_xor_si128(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_andnot_ps(b,a); }
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_andnot_pd(b,a); }
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_andnot_si128(b,a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) { return _mm_cmple_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) { return _mm_cmplt_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) { return _mm_cmpnge_ps(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) { return _mm_cmpeq_ps(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) { return _mm_cmple_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) { return _mm_cmplt_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) { return _mm_cmpnge_pd(a,b); }
-template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) { return _mm_cmpeq_pd(a,b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) { return _mm_cmplt_epi32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) { return _mm_cmpeq_epi32(a,b); }
-template<> EIGEN_STRONG_INLINE Packet16b pcmp_eq(const Packet16b& a, const Packet16b& b) { return _mm_cmpeq_epi8(a,b); }
-template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return por(pcmp_lt(a,b), pcmp_eq(a,b)); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_min_ps, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet4f res;
-  asm("vminps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet4f res = b;
-  asm("minps %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm_min_ps(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_min_pd, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet2d res;
-  asm("vminpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet2d res = b;
-  asm("minpd %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::min.
-  return _mm_min_pd(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_min_epi32(a,b);
-#else
-  // after some bench, this version *is* faster than a scalar implementation
-  Packet4i mask = _mm_cmplt_epi32(a,b);
-  return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
-#endif
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_max_ps, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet4f res;
-  asm("vmaxps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet4f res = b;
-  asm("maxps %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm_max_ps(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
-#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
-  // There appears to be a bug in GCC, by which the optimizer may
-  // flip the argument order in calls to _mm_max_pd, so we have to
-  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
-  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
-  #ifdef EIGEN_VECTORIZE_AVX
-  Packet2d res;
-  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
-  #else
-  Packet2d res = b;
-  asm("maxpd %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
-  #endif
-  return res;
-#else
-  // Arguments are reversed to match NaN propagation behavior of std::max.
-  return _mm_max_pd(b, a);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_max_epi32(a,b);
-#else
-  // after some bench, this version *is* faster than a scalar implementation
-  Packet4i mask = _mm_cmpgt_epi32(a,b);
-  return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
-#endif
-}
-
-template <typename Packet, typename Op>
-EIGEN_STRONG_INLINE Packet pminmax_propagate_numbers(const Packet& a, const Packet& b, Op op) {
-  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
-  // always return a if either a or b is NaN.
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet m = op(a, b);
-  return pselect<Packet>(not_nan_mask_a, m, b);
-}
-
-template <typename Packet, typename Op>
-EIGEN_STRONG_INLINE Packet pminmax_propagate_nan(const Packet& a, const Packet& b, Op op) {
-  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
-  // always return a if either a or b is NaN.
-  Packet not_nan_mask_a = pcmp_eq(a, a);
-  Packet m = op(b, a);
-  return pselect<Packet>(not_nan_mask_a, m, a);
-}
-
-// Add specializations for min/max with prescribed NaN progation.
-template<>
-EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_numbers(a, b, pmin<Packet2d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_numbers(a, b, pmax<Packet2d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_nan(a, b, pmin<Packet2d>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet4f>);
-}
-template<>
-EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
-  return pminmax_propagate_nan(a, b, pmax<Packet2d>);
-}
-
-template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a) { return _mm_srai_epi32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right   (const Packet4i& a) { return _mm_srli_epi32(a,N); }
-template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left    (const Packet4i& a) { return _mm_slli_epi32(a,N); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a)
-{
-  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF));
-  return _mm_and_ps(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a)
-{
-  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF));
-  return _mm_and_pd(a,mask);
-}
-template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a)
-{
-  #ifdef EIGEN_VECTORIZE_SSSE3
-  return _mm_abs_epi32(a);
-  #else
-  Packet4i aux = _mm_srai_epi32(a,31);
-  return _mm_sub_epi32(_mm_xor_si128(a,aux),aux);
-  #endif
-}
-
-#ifdef EIGEN_VECTORIZE_SSE4_1
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
-{
-  // Unfortunatly _mm_round_ps doesn't have a rounding mode to implement numext::round.
-  const Packet4f mask = pset1frombits<Packet4f>(0x80000000u);
-  const Packet4f prev0dot5 = pset1frombits<Packet4f>(0x3EFFFFFFu);
-  return _mm_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a)
-{
-  const Packet2d mask = _mm_castsi128_pd(_mm_set_epi64x(0x8000000000000000ull, 0x8000000000000000ull));
-  const Packet2d prev0dot5 = _mm_castsi128_pd(_mm_set_epi64x(0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull));
-  return _mm_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a) { return _mm_round_ps(a, _MM_FROUND_CUR_DIRECTION); }
-template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a) { return _mm_round_pd(a, _MM_FROUND_CUR_DIRECTION); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) { return _mm_ceil_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) { return _mm_ceil_pd(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return _mm_floor_ps(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return _mm_floor_pd(a); }
-#else
-template<> EIGEN_STRONG_INLINE Packet4f print(const Packet4f& a) {
-  // Adds and subtracts signum(a) * 2^23 to force rounding.
-  const Packet4f limit = pset1<Packet4f>(static_cast<float>(1<<23));
-  const Packet4f abs_a = pabs(a);
-  Packet4f r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d print(const Packet2d& a) {
-  // Adds and subtracts signum(a) * 2^52 to force rounding.
-  const Packet2d limit = pset1<Packet2d>(static_cast<double>(1ull<<52));
-  const Packet2d abs_a = pabs(a);
-  Packet2d r = padd(abs_a, limit);
-  // Don't compile-away addition and subtraction.
-  EIGEN_OPTIMIZATION_BARRIER(r);
-  r = psub(r, limit);
-  // If greater than limit, simply return a.  Otherwise, account for sign.
-  r = pselect(pcmp_lt(abs_a, limit),
-              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
-  return r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If greater, subtract one.
-  Packet4f mask = _mm_cmpgt_ps(tmp, a);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a)
-{
-  const Packet2d cst_1 = pset1<Packet2d>(1.0);
-  Packet2d tmp  = print<Packet2d>(a);
-  // If greater, subtract one.
-  Packet2d mask = _mm_cmpgt_pd(tmp, a);
-  mask = pand(mask, cst_1);
-  return psub(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
-{
-  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
-  Packet4f tmp  = print<Packet4f>(a);
-  // If smaller, add one.
-  Packet4f mask = _mm_cmplt_ps(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a)
-{
-  const Packet2d cst_1 = pset1<Packet2d>(1.0);
-  Packet2d tmp  = print<Packet2d>(a);
-  // If smaller, add one.
-  Packet2d mask = _mm_cmplt_pd(tmp, a);
-  mask = pand(mask, cst_1);
-  return padd(tmp, mask);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float*   from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_ps(from); }
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double*  from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_pd(from); }
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
-template<> EIGEN_STRONG_INLINE Packet16b pload<Packet16b>(const bool*     from) { EIGEN_DEBUG_ALIGNED_LOAD return  _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
-
-#if EIGEN_COMP_MSVC
-  template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float*  from) {
-    EIGEN_DEBUG_UNALIGNED_LOAD
-    #if (EIGEN_COMP_MSVC==1600)
-    // NOTE Some version of MSVC10 generates bad code when using _mm_loadu_ps
-    // (i.e., it does not generate an unaligned load!!
-    __m128 res = _mm_loadl_pi(_mm_set1_ps(0.0f), (const __m64*)(from));
-    res = _mm_loadh_pi(res, (const __m64*)(from+2));
-    return res;
-    #else
-    return _mm_loadu_ps(from);
-    #endif
-  }
-#else
-// NOTE: with the code below, MSVC's compiler crashes!
-
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_ps(from);
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_pd(from);
-}
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-template<> EIGEN_STRONG_INLINE Packet16b ploadu<Packet16b>(const bool*     from) {
-  EIGEN_DEBUG_UNALIGNED_LOAD
-  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*   from)
-{
-  return vec4f_swizzle1(_mm_castpd_ps(_mm_load_sd(reinterpret_cast<const double*>(from))), 0, 0, 1, 1);
-}
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*  from)
-{ return pset1<Packet2d>(from[0]); }
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
-{
-  Packet4i tmp;
-  tmp = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(from));
-  return vec4i_swizzle1(tmp, 0, 0, 1, 1);
-}
-
-// Loads 8 bools from memory and returns the packet
-// {b0, b0, b1, b1, b2, b2, b3, b3, b4, b4, b5, b5, b6, b6, b7, b7}
-template<> EIGEN_STRONG_INLINE Packet16b ploaddup<Packet16b>(const bool*     from)
-{
-  __m128i tmp = _mm_castpd_si128(pload1<Packet2d>(reinterpret_cast<const double*>(from)));
-  return  _mm_unpacklo_epi8(tmp, tmp);
-}
-
-// Loads 4 bools from memory and returns the packet
-// {b0, b0  b0, b0, b1, b1, b1, b1, b2, b2, b2, b2, b3, b3, b3, b3}
-template<> EIGEN_STRONG_INLINE Packet16b
-ploadquad<Packet16b>(const bool* from) {
-  __m128i tmp = _mm_castps_si128(pload1<Packet4f>(reinterpret_cast<const float*>(from)));
-  tmp = _mm_unpacklo_epi8(tmp, tmp);
-  return  _mm_unpacklo_epi16(tmp, tmp);
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
-template<> EIGEN_STRONG_INLINE void pstore<bool>(bool*     to, const Packet16b& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
-
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_pd(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_ps(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*       to, const Packet4i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<bool>(bool*     to, const Packet16b& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
- return _mm_set_ps(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
- return _mm_set_pd(from[1*stride], from[0*stride]);
-}
-template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
-{
- return _mm_set_epi32(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet16b pgather<bool, Packet16b>(const bool* from, Index stride)
-{
-  return _mm_set_epi8(from[15*stride], from[14*stride], from[13*stride], from[12*stride],
-                      from[11*stride], from[10*stride], from[9*stride], from[8*stride],
-                      from[7*stride], from[6*stride], from[5*stride], from[4*stride],
-                      from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  to[stride*0] = _mm_cvtss_f32(from);
-  to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 1));
-  to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 2));
-  to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 3));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  to[stride*0] = _mm_cvtsd_f64(from);
-  to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(from, from, 1));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
-{
-  to[stride*0] = _mm_cvtsi128_si32(from);
-  to[stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1));
-  to[stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2));
-  to[stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3));
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<bool, Packet16b>(bool* to, const Packet16b& from, Index stride)
-{
-  to[4*stride*0] = _mm_cvtsi128_si32(from);
-  to[4*stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1));
-  to[4*stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2));
-  to[4*stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3));
-}
-
-
-// some compilers might be tempted to perform multiple moves instead of using a vector path.
-template<> EIGEN_STRONG_INLINE void pstore1<Packet4f>(float* to, const float& a)
-{
-  Packet4f pa = _mm_set_ss(a);
-  pstore(to, Packet4f(vec4f_swizzle1(pa,0,0,0,0)));
-}
-// some compilers might be tempted to perform multiple moves instead of using a vector path.
-template<> EIGEN_STRONG_INLINE void pstore1<Packet2d>(double* to, const double& a)
-{
-  Packet2d pa = _mm_set_sd(a);
-  pstore(to, Packet2d(vec2d_swizzle1(pa,0,0)));
-}
-
-#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
-typedef const void * SsePrefetchPtrType;
-#else
-typedef const char * SsePrefetchPtrType;
-#endif
-
-#ifndef EIGEN_VECTORIZE_AVX
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
-#endif
-
-#if EIGEN_COMP_MSVC_STRICT && EIGEN_OS_WIN64
-// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
-// Direct of the struct members fixed bug #62.
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { return a.m128_f32[0]; }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return a.m128d_f64[0]; }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; }
-#elif EIGEN_COMP_MSVC_STRICT
-// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { float x = _mm_cvtss_f32(a); return x; }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double x = _mm_cvtsd_f64(a); return x; }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; }
-#else
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { return _mm_cvtss_f32(a); }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return _mm_cvtsd_f64(a); }
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { return _mm_cvtsi128_si32(a); }
-#endif
-template<> EIGEN_STRONG_INLINE bool   pfirst<Packet16b>(const Packet16b& a) { int x = _mm_cvtsi128_si32(a); return static_cast<bool>(x & 1); }
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return _mm_shuffle_ps(a,a,0x1B); }
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return _mm_shuffle_pd(a,a,0x1); }
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return _mm_shuffle_epi32(a,0x1B); }
-template<> EIGEN_STRONG_INLINE Packet16b preverse(const Packet16b& a) {
-#ifdef EIGEN_VECTORIZE_SSSE3
-  __m128i mask = _mm_set_epi8(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
-  return _mm_shuffle_epi8(a, mask);
-#else
-  Packet16b tmp = _mm_shuffle_epi32(a, _MM_SHUFFLE(0, 1, 2, 3));
-  tmp = _mm_shufflehi_epi16(_mm_shufflelo_epi16(tmp, _MM_SHUFFLE(2, 3, 0, 1)), _MM_SHUFFLE(2, 3, 0, 1));
-  return _mm_or_si128(_mm_slli_epi16(tmp, 8), _mm_srli_epi16(tmp, 8));
-#endif
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
-  return pfrexp_generic(a,exponent);
-}
-
-// Extract exponent without existence of Packet2l.
-template<>
-EIGEN_STRONG_INLINE  
-Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
-  const Packet2d cst_exp_mask  = pset1frombits<Packet2d>(static_cast<uint64_t>(0x7ff0000000000000ull));
-  __m128i a_expo = _mm_srli_epi64(_mm_castpd_si128(pand(a, cst_exp_mask)), 52);
-  return _mm_cvtepi32_pd(vec4i_swizzle1(a_expo, 0, 2, 1, 3));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
-  return pfrexp_generic(a, exponent);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
-  return pldexp_generic(a,exponent);
-}
-
-// We specialize pldexp here, since the generic implementation uses Packet2l, which is not well
-// supported by SSE, and has more range than is needed for exponents.
-template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
-  // Clamp exponent to [-2099, 2099]
-  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
-  const Packet2d e = pmin(pmax(exponent, pnegate(max_exponent)), max_exponent);
-  
-  // Convert e to integer and swizzle to low-order bits.
-  const Packet4i ei = vec4i_swizzle1(_mm_cvtpd_epi32(e), 0, 3, 1, 3);
-  
-  // Split 2^e into four factors and multiply:
-  const Packet4i bias = _mm_set_epi32(0, 1023, 0, 1023);
-  Packet4i b = parithmetic_shift_right<2>(ei);  // floor(e/4)
-  Packet2d c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));  // 2^b
-  Packet2d out = pmul(pmul(pmul(a, c), c), c); // a * 2^(3b)
-  b = psub(psub(psub(ei, b), b), b);  // e - 3b
-  c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));  // 2^(e - 3b)
-  out = pmul(out, c);  // a * 2^e
-  return out;
-}
-
-// with AVX, the default implementations based on pload1 are faster
-#ifndef __AVX__
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  a3 = pload<Packet4f>(a);
-  a0 = vec4f_swizzle1(a3, 0,0,0,0);
-  a1 = vec4f_swizzle1(a3, 1,1,1,1);
-  a2 = vec4f_swizzle1(a3, 2,2,2,2);
-  a3 = vec4f_swizzle1(a3, 3,3,3,3);
-}
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet2d>(const double *a,
-                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-#ifdef EIGEN_VECTORIZE_SSE3
-  a0 = _mm_loaddup_pd(a+0);
-  a1 = _mm_loaddup_pd(a+1);
-  a2 = _mm_loaddup_pd(a+2);
-  a3 = _mm_loaddup_pd(a+3);
-#else
-  a1 = pload<Packet2d>(a);
-  a0 = vec2d_swizzle1(a1, 0,0);
-  a1 = vec2d_swizzle1(a1, 1,1);
-  a3 = pload<Packet2d>(a+2);
-  a2 = vec2d_swizzle1(a3, 0,0);
-  a3 = vec2d_swizzle1(a3, 1,1);
-#endif
-}
-#endif
-
-EIGEN_STRONG_INLINE void punpackp(Packet4f* vecs)
-{
-  vecs[1] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x55));
-  vecs[2] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xAA));
-  vecs[3] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xFF));
-  vecs[0] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x00));
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
-  // (from Nehalem to Haswell)
-// #ifdef EIGEN_VECTORIZE_SSE3
-//   Packet4f tmp = _mm_add_ps(a, vec4f_swizzle1(a,2,3,2,3));
-//   return pfirst<Packet4f>(_mm_hadd_ps(tmp, tmp));
-// #else
-  Packet4f tmp = _mm_add_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_add_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-// #endif
-}
-
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{
-  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
-  // (from Nehalem to Haswell)
-// #ifdef EIGEN_VECTORIZE_SSE3
-//   return pfirst<Packet2d>(_mm_hadd_pd(a, a));
-// #else
-  return pfirst<Packet2d>(_mm_add_sd(a, _mm_unpackhi_pd(a,a)));
-// #endif
-}
-
-#ifdef EIGEN_VECTORIZE_SSSE3
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i tmp0 = _mm_hadd_epi32(a,a);
-  return pfirst<Packet4i>(_mm_hadd_epi32(tmp0,tmp0));
-}
-
-#else
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i tmp = _mm_add_epi32(a, _mm_unpackhi_epi64(a,a));
-  return pfirst(tmp) + pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1));
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE bool predux<Packet16b>(const Packet16b& a) {
-  Packet4i tmp = _mm_or_si128(a, _mm_unpackhi_epi64(a,a));
-  return (pfirst(tmp) != 0) || (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) != 0);
-}
-
-// Other reduction functions:
-
-
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp = _mm_mul_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_mul_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{
-  return pfirst<Packet2d>(_mm_mul_sd(a, _mm_unpackhi_pd(a,a)));
-}
-template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
-{
-  // after some experiments, it is seems this is the fastest way to implement it
-  // for GCC (eg., reusing pmul is very slow !)
-  // TODO try to call _mm_mul_epu32 directly
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  return  (aux[0] * aux[1]) * (aux[2] * aux[3]);
-}
-
-template<> EIGEN_STRONG_INLINE bool predux_mul<Packet16b>(const Packet16b& a) {
-  Packet4i tmp = _mm_and_si128(a, _mm_unpackhi_epi64(a,a));
-  return ((pfirst<Packet4i>(tmp) == 0x01010101) &&
-          (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) == 0x01010101));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp = _mm_min_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_min_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{
-  return pfirst<Packet2d>(_mm_min_sd(a, _mm_unpackhi_pd(a,a)));
-}
-template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  Packet4i tmp = _mm_min_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
-  return pfirst<Packet4i>(_mm_min_epi32(tmp,_mm_shuffle_epi32(tmp, 1)));
-#else
-  // after some experiments, it is seems this is the fastest way to implement it
-  // for GCC (eg., it does not like using std::min after the pstore !!)
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  int aux0 = aux[0]<aux[1] ? aux[0] : aux[1];
-  int aux2 = aux[2]<aux[3] ? aux[2] : aux[3];
-  return aux0<aux2 ? aux0 : aux2;
-#endif // EIGEN_VECTORIZE_SSE4_1
-}
-
-// max
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  Packet4f tmp = _mm_max_ps(a, _mm_movehl_ps(a,a));
-  return pfirst<Packet4f>(_mm_max_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
-}
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{
-  return pfirst<Packet2d>(_mm_max_sd(a, _mm_unpackhi_pd(a,a)));
-}
-template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
-{
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  Packet4i tmp = _mm_max_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
-  return pfirst<Packet4i>(_mm_max_epi32(tmp,_mm_shuffle_epi32(tmp, 1)));
-#else
-  // after some experiments, it is seems this is the fastest way to implement it
-  // for GCC (eg., it does not like using std::min after the pstore !!)
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  int aux0 = aux[0]>aux[1] ? aux[0] : aux[1];
-  int aux2 = aux[2]>aux[3] ? aux[2] : aux[3];
-  return aux0>aux2 ? aux0 : aux2;
-#endif // EIGEN_VECTORIZE_SSE4_1
-}
-
-// not needed yet
-// template<> EIGEN_STRONG_INLINE bool predux_all(const Packet4f& x)
-// {
-//   return _mm_movemask_ps(x) == 0xF;
-// }
-
-template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
-{
-  return _mm_movemask_ps(x) != 0x0;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  _MM_TRANSPOSE4_PS(kernel.packet[0], kernel.packet[1], kernel.packet[2], kernel.packet[3]);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2d,2>& kernel) {
-  __m128d tmp = _mm_unpackhi_pd(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[0] = _mm_unpacklo_pd(kernel.packet[0], kernel.packet[1]);
-  kernel.packet[1] = tmp;
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4i,4>& kernel) {
-  __m128i T0 = _mm_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
-  __m128i T1 = _mm_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
-  __m128i T2 = _mm_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
-  __m128i T3 = _mm_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
-
-  kernel.packet[0] = _mm_unpacklo_epi64(T0, T1);
-  kernel.packet[1] = _mm_unpackhi_epi64(T0, T1);
-  kernel.packet[2] = _mm_unpacklo_epi64(T2, T3);
-  kernel.packet[3] = _mm_unpackhi_epi64(T2, T3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16b,4>& kernel) {
-  __m128i T0 =  _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]);
-  __m128i T1 =  _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]);
-  __m128i T2 =  _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]);
-  __m128i T3 =  _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]);
-  kernel.packet[0] = _mm_unpacklo_epi16(T0, T2);
-  kernel.packet[1] = _mm_unpackhi_epi16(T0, T2);
-  kernel.packet[2] = _mm_unpacklo_epi16(T1, T3);
-  kernel.packet[3] = _mm_unpackhi_epi16(T1, T3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet16b,16>& kernel) {
-  // If we number the elements in the input thus:
-  // kernel.packet[ 0] = {00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0a, 0b, 0c, 0d, 0e, 0f}
-  // kernel.packet[ 1] = {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1a, 1b, 1c, 1d, 1e, 1f}
-  // ...
-  // kernel.packet[15] = {f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, fa, fb, fc, fd, fe, ff},
-  //
-  // the desired output is:
-  // kernel.packet[ 0] = {00, 10, 20, 30, 40, 50, 60, 70, 80, 90, a0, b0, c0, d0, e0, f0}
-  // kernel.packet[ 1] = {01, 11, 21, 31, 41, 51, 61, 71, 81, 91, a1, b1, c1, d1, e1, f1}
-  // ...
-  // kernel.packet[15] = {0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, af, bf, cf, df, ef, ff},
-  __m128i t0 =  _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]); // 00 10 01 11 02 12 03 13 04 14 05 15 06 16 07 17
-  __m128i t1 =  _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]); // 08 18 09 19 0a 1a 0b 1b 0c 1c 0d 1d 0e 1e 0f 1f
-  __m128i t2 =  _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]); // 20 30 21 31 22 32 ...                     27 37
-  __m128i t3 =  _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]); // 28 38 29 39 2a 3a ...                     2f 3f
-  __m128i t4 =  _mm_unpacklo_epi8(kernel.packet[4], kernel.packet[5]); // 40 50 41 51 42 52                         47 57
-  __m128i t5 =  _mm_unpackhi_epi8(kernel.packet[4], kernel.packet[5]); // 48 58 49 59 4a 5a
-  __m128i t6 =  _mm_unpacklo_epi8(kernel.packet[6], kernel.packet[7]);
-  __m128i t7 =  _mm_unpackhi_epi8(kernel.packet[6], kernel.packet[7]);
-  __m128i t8 =  _mm_unpacklo_epi8(kernel.packet[8], kernel.packet[9]);
-  __m128i t9 =  _mm_unpackhi_epi8(kernel.packet[8], kernel.packet[9]);
-  __m128i ta =  _mm_unpacklo_epi8(kernel.packet[10], kernel.packet[11]);
-  __m128i tb =  _mm_unpackhi_epi8(kernel.packet[10], kernel.packet[11]);
-  __m128i tc =  _mm_unpacklo_epi8(kernel.packet[12], kernel.packet[13]);
-  __m128i td =  _mm_unpackhi_epi8(kernel.packet[12], kernel.packet[13]);
-  __m128i te =  _mm_unpacklo_epi8(kernel.packet[14], kernel.packet[15]);
-  __m128i tf =  _mm_unpackhi_epi8(kernel.packet[14], kernel.packet[15]);
-
-  __m128i s0 =  _mm_unpacklo_epi16(t0, t2); // 00 10 20 30 01 11 21 31 02 12 22 32 03 13 23 33
-  __m128i s1 =  _mm_unpackhi_epi16(t0, t2); // 04 14 24 34
-  __m128i s2 =  _mm_unpacklo_epi16(t1, t3); // 08 18 28 38 ...
-  __m128i s3 =  _mm_unpackhi_epi16(t1, t3); // 0c 1c 2c 3c ...
-  __m128i s4 =  _mm_unpacklo_epi16(t4, t6); // 40 50 60 70 41 51 61 71 42 52 62 72 43 53 63 73
-  __m128i s5 =  _mm_unpackhi_epi16(t4, t6); // 44 54 64 74 ...
-  __m128i s6 =  _mm_unpacklo_epi16(t5, t7);
-  __m128i s7 =  _mm_unpackhi_epi16(t5, t7);
-  __m128i s8 =  _mm_unpacklo_epi16(t8, ta);
-  __m128i s9 =  _mm_unpackhi_epi16(t8, ta);
-  __m128i sa =  _mm_unpacklo_epi16(t9, tb);
-  __m128i sb =  _mm_unpackhi_epi16(t9, tb);
-  __m128i sc =  _mm_unpacklo_epi16(tc, te);
-  __m128i sd =  _mm_unpackhi_epi16(tc, te);
-  __m128i se =  _mm_unpacklo_epi16(td, tf);
-  __m128i sf =  _mm_unpackhi_epi16(td, tf);
-
-  __m128i u0 =  _mm_unpacklo_epi32(s0, s4); // 00 10 20 30 40 50 60 70 01 11 21 31 41 51 61 71
-  __m128i u1 =  _mm_unpackhi_epi32(s0, s4); // 02 12 22 32 42 52 62 72 03 13 23 33 43 53 63 73
-  __m128i u2 =  _mm_unpacklo_epi32(s1, s5);
-  __m128i u3 =  _mm_unpackhi_epi32(s1, s5);
-  __m128i u4 =  _mm_unpacklo_epi32(s2, s6);
-  __m128i u5 =  _mm_unpackhi_epi32(s2, s6);
-  __m128i u6 =  _mm_unpacklo_epi32(s3, s7);
-  __m128i u7 =  _mm_unpackhi_epi32(s3, s7);
-  __m128i u8 =  _mm_unpacklo_epi32(s8, sc);
-  __m128i u9 =  _mm_unpackhi_epi32(s8, sc);
-  __m128i ua =  _mm_unpacklo_epi32(s9, sd);
-  __m128i ub =  _mm_unpackhi_epi32(s9, sd);
-  __m128i uc =  _mm_unpacklo_epi32(sa, se);
-  __m128i ud =  _mm_unpackhi_epi32(sa, se);
-  __m128i ue =  _mm_unpacklo_epi32(sb, sf);
-  __m128i uf =  _mm_unpackhi_epi32(sb, sf);
-
-  kernel.packet[0]  = _mm_unpacklo_epi64(u0, u8);
-  kernel.packet[1]  = _mm_unpackhi_epi64(u0, u8);
-  kernel.packet[2]  = _mm_unpacklo_epi64(u1, u9);
-  kernel.packet[3]  = _mm_unpackhi_epi64(u1, u9);
-  kernel.packet[4]  = _mm_unpacklo_epi64(u2, ua);
-  kernel.packet[5]  = _mm_unpackhi_epi64(u2, ua);
-  kernel.packet[6]  = _mm_unpacklo_epi64(u3, ub);
-  kernel.packet[7]  = _mm_unpackhi_epi64(u3, ub);
-  kernel.packet[8]  = _mm_unpacklo_epi64(u4, uc);
-  kernel.packet[9]  = _mm_unpackhi_epi64(u4, uc);
-  kernel.packet[10] = _mm_unpacklo_epi64(u5, ud);
-  kernel.packet[11] = _mm_unpackhi_epi64(u5, ud);
-  kernel.packet[12] = _mm_unpacklo_epi64(u6, ue);
-  kernel.packet[13] = _mm_unpackhi_epi64(u6, ue);
-  kernel.packet[14] = _mm_unpacklo_epi64(u7, uf);
-  kernel.packet[15] = _mm_unpackhi_epi64(u7, uf);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
-  const __m128i zero = _mm_setzero_si128();
-  const __m128i select = _mm_set_epi32(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m128i false_mask = _mm_cmpeq_epi32(select, zero);
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_blendv_epi8(thenPacket, elsePacket, false_mask);
-#else
-  return _mm_or_si128(_mm_andnot_si128(false_mask, thenPacket), _mm_and_si128(false_mask, elsePacket));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  const __m128 zero = _mm_setzero_ps();
-  const __m128 select = _mm_set_ps(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
-  __m128 false_mask = _mm_cmpeq_ps(select, zero);
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_blendv_ps(thenPacket, elsePacket, false_mask);
-#else
-  return _mm_or_ps(_mm_andnot_ps(false_mask, thenPacket), _mm_and_ps(false_mask, elsePacket));
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
-  const __m128d zero = _mm_setzero_pd();
-  const __m128d select = _mm_set_pd(ifPacket.select[1], ifPacket.select[0]);
-  __m128d false_mask = _mm_cmpeq_pd(select, zero);
-#ifdef EIGEN_VECTORIZE_SSE4_1
-  return _mm_blendv_pd(thenPacket, elsePacket, false_mask);
-#else
-  return _mm_or_pd(_mm_andnot_pd(false_mask, thenPacket), _mm_and_pd(false_mask, elsePacket));
-#endif
-}
-
-// Scalar path for pmadd with FMA to ensure consistency with vectorized path.
-#ifdef EIGEN_VECTORIZE_FMA
-template<> EIGEN_STRONG_INLINE float pmadd(const float& a, const float& b, const float& c) {
-  return ::fmaf(a,b,c);
-}
-template<> EIGEN_STRONG_INLINE double pmadd(const double& a, const double& b, const double& c) {
-  return ::fma(a,b,c);
-}
-#endif
-
-
-// Packet math for Eigen::half
-// Disable the following code since it's broken on too many platforms / compilers.
-//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
-#if 0
-
-typedef struct {
-  __m64 x;
-} Packet4h;
-
-
-template<> struct is_arithmetic<Packet4h> { enum { value = true }; };
-
-template <>
-struct packet_traits<Eigen::half> : default_packet_traits {
-  typedef Packet4h type;
-  // There is no half-size packet for Packet4h.
-  typedef Packet4h half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 0,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasConj   = 0,
-    HasSetLinear = 0,
-    HasSqrt = 0,
-    HasRsqrt = 0,
-    HasExp = 0,
-    HasLog = 0,
-    HasBlend = 0
-  };
-};
-
-
-template<> struct unpacket_traits<Packet4h> { typedef Eigen::half type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4h half; };
-
-template<> EIGEN_STRONG_INLINE Packet4h pset1<Packet4h>(const Eigen::half& from) {
-  Packet4h result;
-  result.x = _mm_set1_pi16(from.x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h>(const Packet4h& from) {
-  return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm_cvtsi64_si32(from.x)));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pconj(const Packet4h& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4h padd<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha + hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha + hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha + hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha + hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h psub<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha - hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha - hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha - hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha - hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pmul<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha * hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha * hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha * hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha * hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pdiv<Packet4h>(const Packet4h& a, const Packet4h& b) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  __int64_t b64 = _mm_cvtm64_si64(b.x);
-
-  Eigen::half h[4];
-
-  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
-  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
-  h[0] = ha / hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
-  h[1] = ha / hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
-  h[2] = ha / hb;
-  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
-  h[3] = ha / hb;
-  Packet4h result;
-  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pload<Packet4h>(const Eigen::half* from) {
-  Packet4h result;
-  result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h ploadu<Packet4h>(const Eigen::half* from) {
-  Packet4h result;
-  result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4h& from) {
-  __int64_t r = _mm_cvtm64_si64(from.x);
-  *(reinterpret_cast<__int64_t*>(to)) = r;
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4h& from) {
-  __int64_t r = _mm_cvtm64_si64(from.x);
-  *(reinterpret_cast<__int64_t*>(to)) = r;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h
-ploadquad<Packet4h>(const Eigen::half* from) {
-  return pset1<Packet4h>(*from);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4h pgather<Eigen::half, Packet4h>(const Eigen::half* from, Index stride)
-{
-  Packet4h result;
-  result.x = _mm_set_pi16(from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h>(Eigen::half* to, const Packet4h& from, Index stride)
-{
-  __int64_t a = _mm_cvtm64_si64(from.x);
-  to[stride*0].x = static_cast<unsigned short>(a);
-  to[stride*1].x = static_cast<unsigned short>(a >> 16);
-  to[stride*2].x = static_cast<unsigned short>(a >> 32);
-  to[stride*3].x = static_cast<unsigned short>(a >> 48);
-}
-
-EIGEN_STRONG_INLINE void
-ptranspose(PacketBlock<Packet4h,4>& kernel) {
-  __m64 T0 = _mm_unpacklo_pi16(kernel.packet[0].x, kernel.packet[1].x);
-  __m64 T1 = _mm_unpacklo_pi16(kernel.packet[2].x, kernel.packet[3].x);
-  __m64 T2 = _mm_unpackhi_pi16(kernel.packet[0].x, kernel.packet[1].x);
-  __m64 T3 = _mm_unpackhi_pi16(kernel.packet[2].x, kernel.packet[3].x);
-
-  kernel.packet[0].x = _mm_unpacklo_pi32(T0, T1);
-  kernel.packet[1].x = _mm_unpackhi_pi32(T0, T1);
-  kernel.packet[2].x = _mm_unpacklo_pi32(T2, T3);
-  kernel.packet[3].x = _mm_unpackhi_pi32(T2, T3);
-}
-
-#endif
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
-// PGI++ does not define the following intrinsics in C++ mode.
-static inline __m128  _mm_castpd_ps   (__m128d x) { return reinterpret_cast<__m128&>(x);  }
-static inline __m128i _mm_castpd_si128(__m128d x) { return reinterpret_cast<__m128i&>(x); }
-static inline __m128d _mm_castps_pd   (__m128  x) { return reinterpret_cast<__m128d&>(x); }
-static inline __m128i _mm_castps_si128(__m128  x) { return reinterpret_cast<__m128i&>(x); }
-static inline __m128  _mm_castsi128_ps(__m128i x) { return reinterpret_cast<__m128&>(x);  }
-static inline __m128d _mm_castsi128_pd(__m128i x) { return reinterpret_cast<__m128d&>(x); }
-#endif
-
-#endif // EIGEN_PACKET_MATH_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h
deleted file mode 100644
index d2a0037..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h
+++ /dev/null
@@ -1,142 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_SSE_H
-#define EIGEN_TYPE_CASTING_SSE_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_VECTORIZE_AVX
-template <>
-struct type_casting_traits<float, int> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<int, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<double, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 2,
-    TgtCoeffRatio = 1
-  };
-};
-
-template <>
-struct type_casting_traits<float, double> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 2
-  };
-};
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
-  return _mm_cvttps_epi32(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
-  return _mm_cvtepi32_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
-  return _mm_shuffle_ps(_mm_cvtpd_ps(a), _mm_cvtpd_ps(b), (1 << 2) | (1 << 6));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
-  // Simply discard the second half of the input
-  return _mm_cvtps_pd(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet4f>(const Packet4f& a) {
-  return _mm_castps_si128(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f,Packet4i>(const Packet4i& a) {
-  return _mm_castsi128_ps(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d,Packet4i>(const Packet4i& a) {
-  return _mm_castsi128_pd(a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet2d>(const Packet2d& a) {
-  return _mm_castpd_si128(a);
-}
-
-// Disable the following code since it's broken on too many platforms / compilers.
-//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
-#if 0
-
-template <>
-struct type_casting_traits<Eigen::half, float> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4h, Packet4f>(const Packet4h& a) {
-  __int64_t a64 = _mm_cvtm64_si64(a.x);
-  Eigen::half h = raw_uint16_to_half(static_cast<unsigned short>(a64));
-  float f1 = static_cast<float>(h);
-  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
-  float f2 = static_cast<float>(h);
-  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
-  float f3 = static_cast<float>(h);
-  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
-  float f4 = static_cast<float>(h);
-  return _mm_set_ps(f4, f3, f2, f1);
-}
-
-template <>
-struct type_casting_traits<float, Eigen::half> {
-  enum {
-    VectorizedCast = 1,
-    SrcCoeffRatio = 1,
-    TgtCoeffRatio = 1
-  };
-};
-
-template<> EIGEN_STRONG_INLINE Packet4h pcast<Packet4f, Packet4h>(const Packet4f& a) {
-  EIGEN_ALIGN16 float aux[4];
-  pstore(aux, a);
-  Eigen::half h0(aux[0]);
-  Eigen::half h1(aux[1]);
-  Eigen::half h2(aux[2]);
-  Eigen::half h3(aux[3]);
-
-  Packet4h result;
-  result.x = _mm_set_pi16(h3.x, h2.x, h1.x, h0.x);
-  return result;
-}
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_SSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h
deleted file mode 100644
index b139ea2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h
+++ /dev/null
@@ -1,44 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATH_FUNCTIONS_SVE_H
-#define EIGEN_MATH_FUNCTIONS_SVE_H
-
-namespace Eigen {
-namespace internal {
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf pexp<PacketXf>(const PacketXf& x) {
-  return pexp_float(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf plog<PacketXf>(const PacketXf& x) {
-  return plog_float(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf psin<PacketXf>(const PacketXf& x) {
-  return psin_float(x);
-}
-
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf pcos<PacketXf>(const PacketXf& x) {
-  return pcos_float(x);
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_STRONG_INLINE EIGEN_UNUSED PacketXf ptanh<PacketXf>(const PacketXf& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-}  // end namespace internal
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_SVE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h
deleted file mode 100644
index 9060b37..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h
+++ /dev/null
@@ -1,752 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_SVE_H
-#define EIGEN_PACKET_MATH_SVE_H
-
-namespace Eigen
-{
-namespace internal
-{
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
-
-template <typename Scalar, int SVEVectorLength>
-struct sve_packet_size_selector {
-  enum { size = SVEVectorLength / (sizeof(Scalar) * CHAR_BIT) };
-};
-
-/********************************* int32 **************************************/
-typedef svint32_t PacketXi __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
-
-template <>
-struct packet_traits<numext::int32_t> : default_packet_traits {
-  typedef PacketXi type;
-  typedef PacketXi half;  // Half not implemented yet
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasShift = 1,
-    HasMul = 1,
-    HasNegate = 1,
-    HasAbs = 1,
-    HasArg = 0,
-    HasAbs2 = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasConj = 1,
-    HasSetLinear = 0,
-    HasBlend = 0,
-    HasReduxp = 0  // Not implemented in SVE
-  };
-};
-
-template <>
-struct unpacket_traits<PacketXi> {
-  typedef numext::int32_t type;
-  typedef PacketXi half;  // Half not yet implemented
-  enum {
-    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
-    alignment = Aligned64,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template <>
-EIGEN_STRONG_INLINE void prefetch<numext::int32_t>(const numext::int32_t* addr)
-{
-  svprfw(svptrue_b32(), addr, SV_PLDL1KEEP);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pset1<PacketXi>(const numext::int32_t& from)
-{
-  return svdup_n_s32(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi plset<PacketXi>(const numext::int32_t& a)
-{
-  numext::int32_t c[packet_traits<numext::int32_t>::size];
-  for (int i = 0; i < packet_traits<numext::int32_t>::size; i++) c[i] = i;
-  return svadd_s32_z(svptrue_b32(), pset1<PacketXi>(a), svld1_s32(svptrue_b32(), c));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi padd<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svadd_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi psub<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svsub_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pnegate(const PacketXi& a)
-{
-  return svneg_s32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pconj(const PacketXi& a)
-{
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmul<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svmul_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pdiv<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdiv_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmadd(const PacketXi& a, const PacketXi& b, const PacketXi& c)
-{
-  return svmla_s32_z(svptrue_b32(), c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmin<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svmin_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pmax<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svmax_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcmp_le<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdup_n_s32_z(svcmplt_s32(svptrue_b32(), a, b), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcmp_lt<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdup_n_s32_z(svcmplt_s32(svptrue_b32(), a, b), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcmp_eq<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svdup_n_s32_z(svcmpeq_s32(svptrue_b32(), a, b), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ptrue<PacketXi>(const PacketXi& /*a*/)
-{
-  return svdup_n_s32_z(svptrue_b32(), 0xffffffffu);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pzero<PacketXi>(const PacketXi& /*a*/)
-{
-  return svdup_n_s32_z(svptrue_b32(), 0);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pand<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svand_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi por<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svorr_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pxor<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return sveor_s32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pandnot<PacketXi>(const PacketXi& a, const PacketXi& b)
-{
-  return svbic_s32_z(svptrue_b32(), a, b);
-}
-
-template <int N>
-EIGEN_STRONG_INLINE PacketXi parithmetic_shift_right(PacketXi a)
-{
-  return svasrd_n_s32_z(svptrue_b32(), a, N);
-}
-
-template <int N>
-EIGEN_STRONG_INLINE PacketXi plogical_shift_right(PacketXi a)
-{
-  return svreinterpret_s32_u32(svlsr_u32_z(svptrue_b32(), svreinterpret_u32_s32(a), svdup_n_u32_z(svptrue_b32(), N)));
-}
-
-template <int N>
-EIGEN_STRONG_INLINE PacketXi plogical_shift_left(PacketXi a)
-{
-  return svlsl_s32_z(svptrue_b32(), a, svdup_n_u32_z(svptrue_b32(), N));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pload<PacketXi>(const numext::int32_t* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ploadu<PacketXi>(const numext::int32_t* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ploaddup<PacketXi>(const numext::int32_t* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi ploadquad<PacketXi>(const numext::int32_t* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
-  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<numext::int32_t>(numext::int32_t* to, const PacketXi& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<numext::int32_t>(numext::int32_t* to, const PacketXi& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline PacketXi pgather<numext::int32_t, PacketXi>(const numext::int32_t* from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  return svld1_gather_s32index_s32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<numext::int32_t, PacketXi>(numext::int32_t* to, const PacketXi& from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  svst1_scatter_s32index_s32(svptrue_b32(), to, indices, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t pfirst<PacketXi>(const PacketXi& a)
-{
-  // svlasta returns the first element if all predicate bits are 0
-  return svlasta_s32(svpfalse_b(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi preverse(const PacketXi& a)
-{
-  return svrev_s32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pabs(const PacketXi& a)
-{
-  return svabs_s32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux<PacketXi>(const PacketXi& a)
-{
-  return static_cast<numext::int32_t>(svaddv_s32(svptrue_b32(), a));
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux_mul<PacketXi>(const PacketXi& a)
-{
-  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0),
-                      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
-
-  // Multiply the vector by its reverse
-  svint32_t prod = svmul_s32_z(svptrue_b32(), a, svrev_s32(a));
-  svint32_t half_prod;
-
-  // Extract the high half of the vector. Depending on the VL more reductions need to be done
-  if (EIGEN_ARM64_SVE_VL >= 2048) {
-    half_prod = svtbl_s32(prod, svindex_u32(32, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 1024) {
-    half_prod = svtbl_s32(prod, svindex_u32(16, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 512) {
-    half_prod = svtbl_s32(prod, svindex_u32(8, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 256) {
-    half_prod = svtbl_s32(prod, svindex_u32(4, 1));
-    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-  }
-  // Last reduction
-  half_prod = svtbl_s32(prod, svindex_u32(2, 1));
-  prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
-
-  // The reduction is done to the first element.
-  return pfirst<PacketXi>(prod);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux_min<PacketXi>(const PacketXi& a)
-{
-  return svminv_s32(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE numext::int32_t predux_max<PacketXi>(const PacketXi& a)
-{
-  return svmaxv_s32(svptrue_b32(), a);
-}
-
-template <int N>
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXi, N>& kernel) {
-  int buffer[packet_traits<numext::int32_t>::size * N] = {0};
-  int i = 0;
-
-  PacketXi stride_index = svindex_s32(0, N);
-
-  for (i = 0; i < N; i++) {
-    svst1_scatter_s32index_s32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
-  }
-  for (i = 0; i < N; i++) {
-    kernel.packet[i] = svld1_s32(svptrue_b32(), buffer + i * packet_traits<numext::int32_t>::size);
-  }
-}
-
-/********************************* float32 ************************************/
-
-typedef svfloat32_t PacketXf __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef PacketXf type;
-  typedef PacketXf half;
-
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasShift = 1,
-    HasMul = 1,
-    HasNegate = 1,
-    HasAbs = 1,
-    HasArg = 0,
-    HasAbs2 = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasConj = 1,
-    HasSetLinear = 0,
-    HasBlend = 0,
-    HasReduxp = 0,  // Not implemented in SVE
-
-    HasDiv = 1,
-    HasFloor = 1,
-
-    HasSin = EIGEN_FAST_MATH,
-    HasCos = EIGEN_FAST_MATH,
-    HasLog = 1,
-    HasExp = 1,
-    HasSqrt = 0,
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH
-  };
-};
-
-template <>
-struct unpacket_traits<PacketXf> {
-  typedef float type;
-  typedef PacketXf half;  // Half not yet implemented
-  typedef PacketXi integer_packet;
-
-  enum {
-    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
-    alignment = Aligned64,
-    vectorizable = true,
-    masked_load_available = false,
-    masked_store_available = false
-  };
-};
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pset1<PacketXf>(const float& from)
-{
-  return svdup_n_f32(from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pset1frombits<PacketXf>(numext::uint32_t from)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svptrue_b32(), from));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf plset<PacketXf>(const float& a)
-{
-  float c[packet_traits<float>::size];
-  for (int i = 0; i < packet_traits<float>::size; i++) c[i] = i;
-  return svadd_f32_z(svptrue_b32(), pset1<PacketXf>(a), svld1_f32(svptrue_b32(), c));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf padd<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svadd_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf psub<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svsub_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pnegate(const PacketXf& a)
-{
-  return svneg_f32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pconj(const PacketXf& a)
-{
-  return a;
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmul<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmul_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pdiv<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svdiv_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmadd(const PacketXf& a, const PacketXf& b, const PacketXf& c)
-{
-  return svmla_f32_z(svptrue_b32(), c, a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmin<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmin_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmin<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return pmin<PacketXf>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmin<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svminnm_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmax<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmax_f32_z(svptrue_b32(), a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmax<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return pmax<PacketXf>(a, b);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pmax<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svmaxnm_f32_z(svptrue_b32(), a, b);
-}
-
-// Float comparisons in SVE return svbool (predicate). Use svdup to set active
-// lanes to 1 (0xffffffffu) and inactive lanes to 0.
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_le<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svcmplt_f32(svptrue_b32(), a, b), 0xffffffffu));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_lt<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svcmplt_f32(svptrue_b32(), a, b), 0xffffffffu));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_eq<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svcmpeq_f32(svptrue_b32(), a, b), 0xffffffffu));
-}
-
-// Do a predicate inverse (svnot_b_z) on the predicate resulted from the
-// greater/equal comparison (svcmpge_f32). Then fill a float vector with the
-// active elements.
-template <>
-EIGEN_STRONG_INLINE PacketXf pcmp_lt_or_nan<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svnot_b_z(svptrue_b32(), svcmpge_f32(svptrue_b32(), a, b)), 0xffffffffu));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pfloor<PacketXf>(const PacketXf& a)
-{
-  return svrintm_f32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ptrue<PacketXf>(const PacketXf& /*a*/)
-{
-  return svreinterpret_f32_u32(svdup_n_u32_z(svptrue_b32(), 0xffffffffu));
-}
-
-// Logical Operations are not supported for float, so reinterpret casts
-template <>
-EIGEN_STRONG_INLINE PacketXf pand<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svand_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf por<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svorr_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pxor<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(sveor_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pandnot<PacketXf>(const PacketXf& a, const PacketXf& b)
-{
-  return svreinterpret_f32_u32(svbic_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pload<PacketXf>(const float* from)
-{
-  EIGEN_DEBUG_ALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ploadu<PacketXf>(const float* from)
-{
-  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ploaddup<PacketXf>(const float* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf ploadquad<PacketXf>(const float* from)
-{
-  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
-  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
-  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstore<float>(float* to, const PacketXf& from)
-{
-  EIGEN_DEBUG_ALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const PacketXf& from)
-{
-  EIGEN_DEBUG_UNALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline PacketXf pgather<float, PacketXf>(const float* from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  return svld1_gather_s32index_f32(svptrue_b32(), from, indices);
-}
-
-template <>
-EIGEN_DEVICE_FUNC inline void pscatter<float, PacketXf>(float* to, const PacketXf& from, Index stride)
-{
-  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
-  svint32_t indices = svindex_s32(0, stride);
-  svst1_scatter_s32index_f32(svptrue_b32(), to, indices, from);
-}
-
-template <>
-EIGEN_STRONG_INLINE float pfirst<PacketXf>(const PacketXf& a)
-{
-  // svlasta returns the first element if all predicate bits are 0
-  return svlasta_f32(svpfalse_b(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf preverse(const PacketXf& a)
-{
-  return svrev_f32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pabs(const PacketXf& a)
-{
-  return svabs_f32_z(svptrue_b32(), a);
-}
-
-// TODO(tellenbach): Should this go into MathFunctions.h? If so, change for 
-// all vector extensions and the generic version.
-template <>
-EIGEN_STRONG_INLINE PacketXf pfrexp<PacketXf>(const PacketXf& a, PacketXf& exponent)
-{
-  return pfrexp_generic(a, exponent);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux<PacketXf>(const PacketXf& a)
-{
-  return svaddv_f32(svptrue_b32(), a);
-}
-
-// Other reduction functions:
-// mul
-// Only works for SVE Vls multiple of 128
-template <>
-EIGEN_STRONG_INLINE float predux_mul<PacketXf>(const PacketXf& a)
-{
-  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0),
-                      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
-  // Multiply the vector by its reverse
-  svfloat32_t prod = svmul_f32_z(svptrue_b32(), a, svrev_f32(a));
-  svfloat32_t half_prod;
-
-  // Extract the high half of the vector. Depending on the VL more reductions need to be done
-  if (EIGEN_ARM64_SVE_VL >= 2048) {
-    half_prod = svtbl_f32(prod, svindex_u32(32, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 1024) {
-    half_prod = svtbl_f32(prod, svindex_u32(16, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 512) {
-    half_prod = svtbl_f32(prod, svindex_u32(8, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  if (EIGEN_ARM64_SVE_VL >= 256) {
-    half_prod = svtbl_f32(prod, svindex_u32(4, 1));
-    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-  }
-  // Last reduction
-  half_prod = svtbl_f32(prod, svindex_u32(2, 1));
-  prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
-
-  // The reduction is done to the first element.
-  return pfirst<PacketXf>(prod);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_min<PacketXf>(const PacketXf& a)
-{
-  return svminv_f32(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE float predux_max<PacketXf>(const PacketXf& a)
-{
-  return svmaxv_f32(svptrue_b32(), a);
-}
-
-template<int N>
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXf, N>& kernel)
-{
-  float buffer[packet_traits<float>::size * N] = {0};
-  int i = 0;
-
-  PacketXi stride_index = svindex_s32(0, N);
-
-  for (i = 0; i < N; i++) {
-    svst1_scatter_s32index_f32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
-  }
-
-  for (i = 0; i < N; i++) {
-    kernel.packet[i] = svld1_f32(svptrue_b32(), buffer + i * packet_traits<float>::size);
-  }
-}
-
-template<>
-EIGEN_STRONG_INLINE PacketXf pldexp<PacketXf>(const PacketXf& a, const PacketXf& exponent)
-{
-  return pldexp_generic(a, exponent);
-}
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_PACKET_MATH_SVE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h
deleted file mode 100644
index 7ba5d9c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TYPE_CASTING_SVE_H
-#define EIGEN_TYPE_CASTING_SVE_H
-
-namespace Eigen {
-namespace internal {
-
-template <>
-struct type_casting_traits<float, numext::int32_t> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-struct type_casting_traits<numext::int32_t, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_STRONG_INLINE PacketXf pcast<PacketXi, PacketXf>(const PacketXi& a) {
-  return svcvt_f32_s32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi pcast<PacketXf, PacketXi>(const PacketXf& a) {
-  return svcvt_s32_f32_z(svptrue_b32(), a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXf preinterpret<PacketXf, PacketXi>(const PacketXi& a) {
-  return svreinterpret_f32_s32(a);
-}
-
-template <>
-EIGEN_STRONG_INLINE PacketXi preinterpret<PacketXi, PacketXf>(const PacketXf& a) {
-  return svreinterpret_s32_f32(a);
-}
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif // EIGEN_TYPE_CASTING_SVE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h
deleted file mode 100644
index 10856ff..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h
+++ /dev/null
@@ -1,232 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * InteropHeaders.h
- *
- * \brief:
- *  InteropHeaders
- *
- *****************************************************************/
-
-#ifndef EIGEN_INTEROP_HEADERS_SYCL_H
-#define EIGEN_INTEROP_HEADERS_SYCL_H
-
-namespace Eigen {
-
-#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
-
-namespace internal {
-
-template <int has_blend, int lengths>
-struct sycl_packet_traits : default_packet_traits {
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = lengths,
-    HasHalfPacket = 0,
-    HasDiv = 1,
-    HasLog = 1,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasSin = 1,
-    HasCos = 1,
-    HasTan = 1,
-    HasASin = 1,
-    HasACos = 1,
-    HasATan = 1,
-    HasSinh = 1,
-    HasCosh = 1,
-    HasTanh = 1,
-    HasLGamma = 0,
-    HasDiGamma = 0,
-    HasZeta = 0,
-    HasPolygamma = 0,
-    HasErf = 0,
-    HasErfc = 0,
-    HasNdtri = 0,
-    HasIGamma = 0,
-    HasIGammac = 0,
-    HasBetaInc = 0,
-    HasBlend = has_blend,
-    // This flag is used to indicate whether packet comparison is supported.
-    // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
-    HasCmp = 1,
-    HasMax = 1,
-    HasMin = 1,
-    HasMul = 1,
-    HasAdd = 1,
-    HasFloor = 1,
-    HasRound = 1,
-    HasRint = 1,
-    HasLog1p = 1,
-    HasExpm1 = 1,
-    HasCeil = 1,
-  };
-};
-
-#ifdef SYCL_DEVICE_ONLY
-#define SYCL_PACKET_TRAITS(packet_type, has_blend, unpacket_type, lengths) \
-  template <>                                                              \
-  struct packet_traits<unpacket_type>                                      \
-      : sycl_packet_traits<has_blend, lengths> {                           \
-    typedef packet_type type;                                              \
-    typedef packet_type half;                                              \
-  };
-
-SYCL_PACKET_TRAITS(cl::sycl::cl_float4, 1, float, 4)
-SYCL_PACKET_TRAITS(cl::sycl::cl_float4, 1, const float, 4)
-SYCL_PACKET_TRAITS(cl::sycl::cl_double2, 0, double, 2)
-SYCL_PACKET_TRAITS(cl::sycl::cl_double2, 0, const double, 2)
-#undef SYCL_PACKET_TRAITS
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#define SYCL_ARITHMETIC(packet_type)  \
-  template <>                         \
-  struct is_arithmetic<packet_type> { \
-    enum { value = true };            \
-  };
-SYCL_ARITHMETIC(cl::sycl::cl_float4)
-SYCL_ARITHMETIC(cl::sycl::cl_double2)
-#undef SYCL_ARITHMETIC
-
-#define SYCL_UNPACKET_TRAITS(packet_type, unpacket_type, lengths)        \
-  template <>                                                            \
-  struct unpacket_traits<packet_type> {                                  \
-    typedef unpacket_type type;                                          \
-    enum { size = lengths, vectorizable = true, alignment = Aligned16 }; \
-    typedef packet_type half;                                            \
-  };
-SYCL_UNPACKET_TRAITS(cl::sycl::cl_float4, float, 4)
-SYCL_UNPACKET_TRAITS(cl::sycl::cl_double2, double, 2)
-
-#undef SYCL_UNPACKET_TRAITS
-#endif
-
-}  // end namespace internal
-
-#endif
-
-namespace TensorSycl {
-namespace internal {
-
-template <typename PacketReturnType, int PacketSize>
-struct PacketWrapper;
-// This function should never get called on the device
-#ifndef SYCL_DEVICE_ONLY
-template <typename PacketReturnType, int PacketSize>
-struct PacketWrapper {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC static Scalar scalarize(Index, PacketReturnType &) {
-    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
-    abort();
-  }
-  EIGEN_DEVICE_FUNC static PacketReturnType convert_to_packet_type(Scalar in,
-                                                                   Scalar) {
-    return ::Eigen::internal::template plset<PacketReturnType>(in);
-  }
-  EIGEN_DEVICE_FUNC static void set_packet(PacketReturnType, Scalar *) {
-    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
-    abort();
-  }
-};
-
-#elif defined(SYCL_DEVICE_ONLY)
-template <typename PacketReturnType>
-struct PacketWrapper<PacketReturnType, 4> {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType &in) {
-    switch (index) {
-      case 0:
-        return in.x();
-      case 1:
-        return in.y();
-      case 2:
-        return in.z();
-      case 3:
-        return in.w();
-      default:
-      //INDEX MUST BE BETWEEN 0 and 3.There is no abort function in SYCL kernel. so we cannot use abort here. 
-      // The code will never reach here
-      __builtin_unreachable();
-    }
-    __builtin_unreachable();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(
-      Scalar in, Scalar other) {
-    return PacketReturnType(in, other, other, other);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
-    lhs = PacketReturnType(rhs[0], rhs[1], rhs[2], rhs[3]);
-  }
-};
-
-template <typename PacketReturnType>
-struct PacketWrapper<PacketReturnType, 1> {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index, PacketReturnType &in) {
-    return in;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(Scalar in,
-                                                                   Scalar) {
-    return PacketReturnType(in);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
-    lhs = rhs[0];
-  }
-};
-
-template <typename PacketReturnType>
-struct PacketWrapper<PacketReturnType, 2> {
-  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
-      Scalar;
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType &in) {
-    switch (index) {
-      case 0:
-        return in.x();
-      case 1:
-        return in.y();
-      default:
-        //INDEX MUST BE BETWEEN 0 and 1.There is no abort function in SYCL kernel. so we cannot use abort here. 
-      // The code will never reach here
-        __builtin_unreachable();
-    }
-    __builtin_unreachable();
-  }
-  
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(
-      Scalar in, Scalar other) {
-    return PacketReturnType(in, other);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
-    lhs = PacketReturnType(rhs[0], rhs[1]);
-  }
-};
-
-#endif
-
-}  // end namespace internal
-}  // end namespace TensorSycl
-}  // end namespace Eigen
-
-#endif  // EIGEN_INTEROP_HEADERS_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h
deleted file mode 100644
index 2ab0f2a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h
+++ /dev/null
@@ -1,301 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * MathFunctions.h
- *
- * \brief:
- *  MathFunctions
- *
- *****************************************************************/
-
-#ifndef EIGEN_MATH_FUNCTIONS_SYCL_H
-#define EIGEN_MATH_FUNCTIONS_SYCL_H
-namespace Eigen {
-
-namespace internal {
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#if defined(SYCL_DEVICE_ONLY)
-#define SYCL_PLOG(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::log(a);                                           \
-  }
-
-SYCL_PLOG(cl::sycl::cl_float4)
-SYCL_PLOG(cl::sycl::cl_double2)
-#undef SYCL_PLOG
-
-#define SYCL_PLOG1P(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog1p<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::log1p(a);                                           \
-  }
-
-SYCL_PLOG1P(cl::sycl::cl_float4)
-SYCL_PLOG1P(cl::sycl::cl_double2)
-#undef SYCL_PLOG1P
-
-#define SYCL_PLOG10(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog10<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::log10(a);                                           \
-  }
-
-SYCL_PLOG10(cl::sycl::cl_float4)
-SYCL_PLOG10(cl::sycl::cl_double2)
-#undef SYCL_PLOG10
-
-#define SYCL_PEXP(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pexp<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::exp(a);                                           \
-  }
-
-SYCL_PEXP(cl::sycl::cl_float4)
-SYCL_PEXP(cl::sycl::cl_float)
-SYCL_PEXP(cl::sycl::cl_double2)
-#undef SYCL_PEXP
-
-#define SYCL_PEXPM1(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pexpm1<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::expm1(a);                                           \
-  }
-
-SYCL_PEXPM1(cl::sycl::cl_float4)
-SYCL_PEXPM1(cl::sycl::cl_double2)
-#undef SYCL_PEXPM1
-
-#define SYCL_PSQRT(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psqrt<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::sqrt(a);                                           \
-  }
-
-SYCL_PSQRT(cl::sycl::cl_float4)
-SYCL_PSQRT(cl::sycl::cl_double2)
-#undef SYCL_PSQRT
-
-#define SYCL_PRSQRT(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type prsqrt<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::rsqrt(a);                                           \
-  }
-
-SYCL_PRSQRT(cl::sycl::cl_float4)
-SYCL_PRSQRT(cl::sycl::cl_double2)
-#undef SYCL_PRSQRT
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-#define SYCL_PSIN(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psin<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::sin(a);                                           \
-  }
-
-SYCL_PSIN(cl::sycl::cl_float4)
-SYCL_PSIN(cl::sycl::cl_double2)
-#undef SYCL_PSIN
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-#define SYCL_PCOS(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pcos<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::cos(a);                                           \
-  }
-
-SYCL_PCOS(cl::sycl::cl_float4)
-SYCL_PCOS(cl::sycl::cl_double2)
-#undef SYCL_PCOS
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-#define SYCL_PTAN(packet_type)                                         \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ptan<packet_type>( \
-      const packet_type& a) {                                          \
-    return cl::sycl::tan(a);                                           \
-  }
-
-SYCL_PTAN(cl::sycl::cl_float4)
-SYCL_PTAN(cl::sycl::cl_double2)
-#undef SYCL_PTAN
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-#define SYCL_PASIN(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pasin<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::asin(a);                                           \
-  }
-
-SYCL_PASIN(cl::sycl::cl_float4)
-SYCL_PASIN(cl::sycl::cl_double2)
-#undef SYCL_PASIN
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-#define SYCL_PACOS(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pacos<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::acos(a);                                           \
-  }
-
-SYCL_PACOS(cl::sycl::cl_float4)
-SYCL_PACOS(cl::sycl::cl_double2)
-#undef SYCL_PACOS
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-#define SYCL_PATAN(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type patan<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::atan(a);                                           \
-  }
-
-SYCL_PATAN(cl::sycl::cl_float4)
-SYCL_PATAN(cl::sycl::cl_double2)
-#undef SYCL_PATAN
-
-/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
-#define SYCL_PSINH(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psinh<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::sinh(a);                                           \
-  }
-
-SYCL_PSINH(cl::sycl::cl_float4)
-SYCL_PSINH(cl::sycl::cl_double2)
-#undef SYCL_PSINH
-
-/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
-#define SYCL_PCOSH(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pcosh<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::cosh(a);                                           \
-  }
-
-SYCL_PCOSH(cl::sycl::cl_float4)
-SYCL_PCOSH(cl::sycl::cl_double2)
-#undef SYCL_PCOSH
-
-/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
-#define SYCL_PTANH(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ptanh<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::tanh(a);                                           \
-  }
-
-SYCL_PTANH(cl::sycl::cl_float4)
-SYCL_PTANH(cl::sycl::cl_double2)
-#undef SYCL_PTANH
-
-#define SYCL_PCEIL(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pceil<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::ceil(a);                                           \
-  }
-
-SYCL_PCEIL(cl::sycl::cl_float4)
-SYCL_PCEIL(cl::sycl::cl_double2)
-#undef SYCL_PCEIL
-
-#define SYCL_PROUND(packet_type)                                         \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pround<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::round(a);                                           \
-  }
-
-SYCL_PROUND(cl::sycl::cl_float4)
-SYCL_PROUND(cl::sycl::cl_double2)
-#undef SYCL_PROUND
-
-#define SYCL_PRINT(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type print<packet_type>( \
-      const packet_type& a) {                                           \
-    return cl::sycl::rint(a);                                           \
-  }
-
-SYCL_PRINT(cl::sycl::cl_float4)
-SYCL_PRINT(cl::sycl::cl_double2)
-#undef SYCL_PRINT
-
-#define SYCL_FLOOR(packet_type)                                          \
-  template <>                                                            \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pfloor<packet_type>( \
-      const packet_type& a) {                                            \
-    return cl::sycl::floor(a);                                           \
-  }
-
-SYCL_FLOOR(cl::sycl::cl_float4)
-SYCL_FLOOR(cl::sycl::cl_double2)
-#undef SYCL_FLOOR
-
-#define SYCL_PMIN(packet_type, expr)                                   \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pmin<packet_type>( \
-      const packet_type& a, const packet_type& b) {                    \
-    return expr;                                                       \
-  }
-
-SYCL_PMIN(cl::sycl::cl_float4, cl::sycl::fmin(a, b))
-SYCL_PMIN(cl::sycl::cl_double2, cl::sycl::fmin(a, b))
-#undef SYCL_PMIN
-
-#define SYCL_PMAX(packet_type, expr)                                   \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pmax<packet_type>( \
-      const packet_type& a, const packet_type& b) {                    \
-    return expr;                                                       \
-  }
-
-SYCL_PMAX(cl::sycl::cl_float4, cl::sycl::fmax(a, b))
-SYCL_PMAX(cl::sycl::cl_double2, cl::sycl::fmax(a, b))
-#undef SYCL_PMAX
-
-#define SYCL_PLDEXP(packet_type)                                             \
-  template <>                                                                \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pldexp(                  \
-      const packet_type& a, const packet_type& exponent) {                   \
-    return cl::sycl::ldexp(                                                  \
-        a, exponent.template convert<cl::sycl::cl_int,                       \
-                                     cl::sycl::rounding_mode::automatic>()); \
-  }
-
-SYCL_PLDEXP(cl::sycl::cl_float4)
-SYCL_PLDEXP(cl::sycl::cl_double2)
-#undef SYCL_PLDEXP
-
-#endif
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h
deleted file mode 100644
index 87badc0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h
+++ /dev/null
@@ -1,670 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * PacketMath.h
- *
- * \brief:
- *  PacketMath
- *
- *****************************************************************/
-
-#ifndef EIGEN_PACKET_MATH_SYCL_H
-#define EIGEN_PACKET_MATH_SYCL_H
-#include <type_traits>
-namespace Eigen {
-
-namespace internal {
-#ifdef SYCL_DEVICE_ONLY
-
-#define SYCL_PLOADT_RO(address_space_target)                                 \
-  template <typename packet_type, int Alignment>                             \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type ploadt_ro(               \
-      typename cl::sycl::multi_ptr<                                          \
-          const typename unpacket_traits<packet_type>::type,                 \
-          cl::sycl::access::address_space::address_space_target>::pointer_t  \
-          from) {                                                            \
-    typedef typename unpacket_traits<packet_type>::type scalar;              \
-    typedef cl::sycl::multi_ptr<                                             \
-        scalar, cl::sycl::access::address_space::address_space_target>       \
-        multi_ptr;                                                           \
-    auto res = packet_type(                                                  \
-        static_cast<typename unpacket_traits<packet_type>::type>(0));        \
-    res.load(0, multi_ptr(const_cast<typename multi_ptr::pointer_t>(from))); \
-    return res;                                                              \
-  }
-
-SYCL_PLOADT_RO(global_space)
-SYCL_PLOADT_RO(local_space)
-#undef SYCL_PLOADT_RO
-#endif
-
-template <typename packet_type, int Alignment, typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type
-ploadt_ro(const Eigen::TensorSycl::internal::RangeAccess<
-          cl::sycl::access::mode::read_write, T>& from) {
-  return ploadt_ro<packet_type, Alignment>(from.get_pointer());
-}
-
-#ifdef SYCL_DEVICE_ONLY
-#define SYCL_PLOAD(address_space_target, Alignment, AlignedType)            \
-  template <typename packet_type>                                           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pload##AlignedType(     \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from) {                                                           \
-    return ploadt_ro<packet_type, Alignment>(from);                         \
-  }
-
-// global space
-SYCL_PLOAD(global_space, Unaligned, u)
-SYCL_PLOAD(global_space, Aligned, )
-// local space
-SYCL_PLOAD(local_space, Unaligned, u)
-SYCL_PLOAD(local_space, Aligned, )
-
-#undef SYCL_PLOAD
-#endif
-
-#define SYCL_PLOAD(Alignment, AlignedType)                              \
-  template <typename packet_type>                                       \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pload##AlignedType( \
-      const Eigen::TensorSycl::internal::RangeAccess<                   \
-          cl::sycl::access::mode::read_write,                           \
-          typename unpacket_traits<packet_type>::type>                  \
-          from) {                                                       \
-    return ploadt_ro<packet_type, Alignment>(from);                     \
-  }
-SYCL_PLOAD(Unaligned, u)
-SYCL_PLOAD(Aligned, )
-#undef SYCL_PLOAD
-
-#ifdef SYCL_DEVICE_ONLY
-/** \internal \returns a packet version of \a *from.
- * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
-#define SYCL_PLOADT(address_space_target)                                   \
-  template <typename packet_type, int Alignment>                            \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type ploadt(                 \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from) {                                                           \
-    if (Alignment >= unpacket_traits<packet_type>::alignment)               \
-      return pload<packet_type>(from);                                      \
-    else                                                                    \
-      return ploadu<packet_type>(from);                                     \
-  }
-
-// global space
-SYCL_PLOADT(global_space)
-// local space
-SYCL_PLOADT(local_space)
-#undef SYCL_PLOADT
-#endif
-
-template <typename packet_type, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type
-ploadt(const Eigen::TensorSycl::internal::RangeAccess<
-       cl::sycl::access::mode::read_write,
-       typename unpacket_traits<packet_type>::type>& from) {
-  return ploadt<packet_type, Alignment>(from.get_pointer());
-}
-#ifdef SYCL_DEVICE_ONLY
-
-// private_space
-#define SYCL_PLOADT_RO_SPECIAL(packet_type, Alignment)                 \
-  template <>                                                          \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type                    \
-  ploadt_ro<packet_type, Alignment>(                                   \
-      const typename unpacket_traits<packet_type>::type* from) {       \
-    typedef typename unpacket_traits<packet_type>::type scalar;        \
-    auto res = packet_type(static_cast<scalar>(0));                    \
-    res.template load<cl::sycl::access::address_space::private_space>( \
-        0, const_cast<scalar*>(from));                                 \
-    return res;                                                        \
-  }
-
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_float4, Aligned)
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_double2, Aligned)
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_float4, Unaligned)
-SYCL_PLOADT_RO_SPECIAL(cl::sycl::cl_double2, Unaligned)
-
-#define SYCL_PLOAD_SPECIAL(packet_type, alignment_type)                    \
-  template <>                                                              \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pload##alignment_type( \
-      const typename unpacket_traits<packet_type>::type* from) {           \
-    typedef typename unpacket_traits<packet_type>::type scalar;            \
-    auto res = packet_type(static_cast<scalar>(0));                        \
-    res.template load<cl::sycl::access::address_space::private_space>(     \
-        0, const_cast<scalar*>(from));                                     \
-    return res;                                                            \
-  }
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_float4, )
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_double2, )
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_float4, u)
-SYCL_PLOAD_SPECIAL(cl::sycl::cl_double2, u)
-
-#undef SYCL_PLOAD_SPECIAL
-
-#define SYCL_PSTORE(scalar, packet_type, address_space_target, alignment)   \
-  template <>                                                               \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstore##alignment(             \
-      typename cl::sycl::multi_ptr<                                         \
-          scalar,                                                           \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          to,                                                               \
-      const packet_type& from) {                                            \
-    typedef cl::sycl::multi_ptr<                                            \
-        scalar, cl::sycl::access::address_space::address_space_target>      \
-        multi_ptr;                                                          \
-    from.store(0, multi_ptr(to));                                           \
-  }
-
-// global space
-SYCL_PSTORE(float, cl::sycl::cl_float4, global_space, )
-SYCL_PSTORE(float, cl::sycl::cl_float4, global_space, u)
-SYCL_PSTORE(double, cl::sycl::cl_double2, global_space, )
-SYCL_PSTORE(double, cl::sycl::cl_double2, global_space, u)
-SYCL_PSTORE(float, cl::sycl::cl_float4, local_space, )
-SYCL_PSTORE(float, cl::sycl::cl_float4, local_space, u)
-SYCL_PSTORE(double, cl::sycl::cl_double2, local_space, )
-SYCL_PSTORE(double, cl::sycl::cl_double2, local_space, u)
-
-SYCL_PSTORE(float, cl::sycl::cl_float4, private_space, )
-SYCL_PSTORE(float, cl::sycl::cl_float4, private_space, u)
-SYCL_PSTORE(double, cl::sycl::cl_double2, private_space, )
-SYCL_PSTORE(double, cl::sycl::cl_double2, private_space, u)
-#undef SYCL_PSTORE
-
-#define SYCL_PSTORE_T(address_space_target)                                 \
-  template <typename scalar, typename packet_type, int Alignment>           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(                       \
-      typename cl::sycl::multi_ptr<                                         \
-          scalar,                                                           \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          to,                                                               \
-      const packet_type& from) {                                            \
-    if (Alignment)                                                          \
-      pstore(to, from);                                                     \
-    else                                                                    \
-      pstoreu(to, from);                                                    \
-  }
-
-SYCL_PSTORE_T(global_space)
-
-SYCL_PSTORE_T(local_space)
-
-#undef SYCL_PSTORE_T
-
-#define SYCL_PSET1(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pset1<packet_type>( \
-      const typename unpacket_traits<packet_type>::type& from) {        \
-    return packet_type(from);                                           \
-  }
-
-// global space
-SYCL_PSET1(cl::sycl::cl_float4)
-SYCL_PSET1(cl::sycl::cl_double2)
-
-#undef SYCL_PSET1
-
-template <typename packet_type>
-struct get_base_packet {
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type
-  get_ploaddup(sycl_multi_pointer) {}
-
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type
-  get_pgather(sycl_multi_pointer, Index) {}
-};
-
-template <>
-struct get_base_packet<cl::sycl::cl_float4> {
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_ploaddup(
-      sycl_multi_pointer from) {
-    return cl::sycl::cl_float4(from[0], from[0], from[1], from[1]);
-  }
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_pgather(
-      sycl_multi_pointer from, Index stride) {
-    return cl::sycl::cl_float4(from[0 * stride], from[1 * stride],
-                               from[2 * stride], from[3 * stride]);
-  }
-
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(
-      sycl_multi_pointer to, const cl::sycl::cl_float4& from, Index stride) {
-    auto tmp = stride;
-    to[0] = from.x();
-    to[tmp] = from.y();
-    to[tmp += stride] = from.z();
-    to[tmp += stride] = from.w();
-  }
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 set_plset(
-      const float& a) {
-    return cl::sycl::cl_float4(static_cast<float>(a), static_cast<float>(a + 1),
-                               static_cast<float>(a + 2),
-                               static_cast<float>(a + 3));
-  }
-};
-
-template <>
-struct get_base_packet<cl::sycl::cl_double2> {
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2
-  get_ploaddup(const sycl_multi_pointer from) {
-    return cl::sycl::cl_double2(from[0], from[0]);
-  }
-
-  template <typename sycl_multi_pointer, typename Index>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 get_pgather(
-      const sycl_multi_pointer from, Index stride) {
-    return cl::sycl::cl_double2(from[0 * stride], from[1 * stride]);
-  }
-
-  template <typename sycl_multi_pointer>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(
-      sycl_multi_pointer to, const cl::sycl::cl_double2& from, Index stride) {
-    to[0] = from.x();
-    to[stride] = from.y();
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 set_plset(
-      const double& a) {
-    return cl::sycl::cl_double2(static_cast<double>(a),
-                                static_cast<double>(a + 1));
-  }
-};
-
-#define SYCL_PLOAD_DUP(address_space_target)                                \
-  template <typename packet_type>                                           \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ploaddup(               \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from) {                                                           \
-    return get_base_packet<packet_type>::get_ploaddup(from);                \
-  }
-
-// global space
-SYCL_PLOAD_DUP(global_space)
-// local_space
-SYCL_PLOAD_DUP(local_space)
-#undef SYCL_PLOAD_DUP
-
-#define SYCL_PLOAD_DUP_SPECILIZE(packet_type)                              \
-  template <>                                                              \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ploaddup<packet_type>( \
-      const typename unpacket_traits<packet_type>::type* from) {           \
-    return get_base_packet<packet_type>::get_ploaddup(from);               \
-  }
-
-SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_float4)
-SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_double2)
-
-#undef SYCL_PLOAD_DUP_SPECILIZE
-
-#define SYCL_PLSET(packet_type)                                         \
-  template <>                                                           \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type plset<packet_type>( \
-      const typename unpacket_traits<packet_type>::type& a) {           \
-    return get_base_packet<packet_type>::set_plset(a);                  \
-  }
-
-SYCL_PLSET(cl::sycl::cl_float4)
-SYCL_PLSET(cl::sycl::cl_double2)
-
-#undef SYCL_PLSET
-
-#define SYCL_PGATHER(address_space_target)                                  \
-  template <typename Scalar, typename packet_type>                          \
-  EIGEN_DEVICE_FUNC inline packet_type pgather(                             \
-      typename cl::sycl::multi_ptr<                                         \
-          const typename unpacket_traits<packet_type>::type,                \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          from,                                                             \
-      Index stride) {                                                       \
-    return get_base_packet<packet_type>::get_pgather(from, stride);         \
-  }
-
-// global space
-SYCL_PGATHER(global_space)
-// local space
-SYCL_PGATHER(local_space)
-
-#undef SYCL_PGATHER
-
-#define SYCL_PGATHER_SPECILIZE(scalar, packet_type)                            \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type                            \
-  pgather<scalar, packet_type>(                                                \
-      const typename unpacket_traits<packet_type>::type* from, Index stride) { \
-    return get_base_packet<packet_type>::get_pgather(from, stride);            \
-  }
-
-SYCL_PGATHER_SPECILIZE(float, cl::sycl::cl_float4)
-SYCL_PGATHER_SPECILIZE(double, cl::sycl::cl_double2)
-
-#undef SYCL_PGATHER_SPECILIZE
-
-#define SYCL_PSCATTER(address_space_target)                                 \
-  template <typename Scalar, typename packet_type>                          \
-  EIGEN_DEVICE_FUNC inline void pscatter(                                   \
-      typename cl::sycl::multi_ptr<                                         \
-          typename unpacket_traits<packet_type>::type,                      \
-          cl::sycl::access::address_space::address_space_target>::pointer_t \
-          to,                                                               \
-      const packet_type& from, Index stride) {                              \
-    get_base_packet<packet_type>::set_pscatter(to, from, stride);           \
-  }
-
-// global space
-SYCL_PSCATTER(global_space)
-// local space
-SYCL_PSCATTER(local_space)
-
-#undef SYCL_PSCATTER
-
-#define SYCL_PSCATTER_SPECILIZE(scalar, packet_type)                        \
-  template <>                                                               \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<scalar, packet_type>( \
-      typename unpacket_traits<packet_type>::type * to,                     \
-      const packet_type& from, Index stride) {                              \
-    get_base_packet<packet_type>::set_pscatter(to, from, stride);           \
-  }
-
-SYCL_PSCATTER_SPECILIZE(float, cl::sycl::cl_float4)
-SYCL_PSCATTER_SPECILIZE(double, cl::sycl::cl_double2)
-
-#undef SYCL_PSCATTER_SPECILIZE
-
-#define SYCL_PMAD(packet_type)                                            \
-  template <>                                                             \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pmadd(                \
-      const packet_type& a, const packet_type& b, const packet_type& c) { \
-    return cl::sycl::mad(a, b, c);                                        \
-  }
-
-SYCL_PMAD(cl::sycl::cl_float4)
-SYCL_PMAD(cl::sycl::cl_double2)
-#undef SYCL_PMAD
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float pfirst<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return a.x();
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double pfirst<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return a.x();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return a.x() + a.y() + a.z() + a.w();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return a.x() + a.y();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_max<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return cl::sycl::fmax(cl::sycl::fmax(a.x(), a.y()),
-                        cl::sycl::fmax(a.z(), a.w()));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_max<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return cl::sycl::fmax(a.x(), a.y());
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_min<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return cl::sycl::fmin(cl::sycl::fmin(a.x(), a.y()),
-                        cl::sycl::fmin(a.z(), a.w()));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_min<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return cl::sycl::fmin(a.x(), a.y());
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_mul<cl::sycl::cl_float4>(
-    const cl::sycl::cl_float4& a) {
-  return a.x() * a.y() * a.z() * a.w();
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_mul<cl::sycl::cl_double2>(
-    const cl::sycl::cl_double2& a) {
-  return a.x() * a.y();
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
-pabs<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
-  return cl::sycl::cl_float4(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()),
-                             cl::sycl::fabs(a.z()), cl::sycl::fabs(a.w()));
-}
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2
-pabs<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
-  return cl::sycl::cl_double2(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()));
-}
-
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_le(const Packet &a,
-                                                          const Packet &b) {
-  return ((a <= b)
-              .template convert<typename unpacket_traits<Packet>::type,
-                                cl::sycl::rounding_mode::automatic>());
-}
-
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_lt(const Packet &a,
-                                                          const Packet &b) {
-  return ((a < b)
-              .template convert<typename unpacket_traits<Packet>::type,
-                                cl::sycl::rounding_mode::automatic>());
-}
-
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_eq(const Packet &a,
-                                                          const Packet &b) {
-  return ((a == b)
-              .template convert<typename unpacket_traits<Packet>::type,
-                                cl::sycl::rounding_mode::automatic>());
-}
-
-#define SYCL_PCMP(OP, TYPE)                                                    \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TYPE pcmp_##OP<TYPE>(const TYPE &a,    \
-                                                             const TYPE &b) {  \
-    return sycl_pcmp_##OP<TYPE>(a, b);                                         \
-  }
-
-SYCL_PCMP(le, cl::sycl::cl_float4)
-SYCL_PCMP(lt, cl::sycl::cl_float4)
-SYCL_PCMP(eq, cl::sycl::cl_float4)
-SYCL_PCMP(le, cl::sycl::cl_double2)
-SYCL_PCMP(lt, cl::sycl::cl_double2)
-SYCL_PCMP(eq, cl::sycl::cl_double2)
-#undef SYCL_PCMP
-
-template <typename T> struct convert_to_integer;
-
-template <> struct convert_to_integer<float> {
-  using type = std::int32_t;
-  using packet_type = cl::sycl::cl_int4;
-};
-template <> struct convert_to_integer<double> {
-  using type = std::int64_t;
-  using packet_type = cl::sycl::cl_long2;
-};
-
-template <typename PacketIn>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename convert_to_integer<
-    typename unpacket_traits<PacketIn>::type>::packet_type
-vector_as_int(const PacketIn &p) {
-  return (
-      p.template convert<typename convert_to_integer<
-                             typename unpacket_traits<PacketIn>::type>::type,
-                         cl::sycl::rounding_mode::automatic>());
-}
-
-template <typename packetOut, typename PacketIn>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packetOut
-convert_vector(const PacketIn &p) {
-  return (p.template convert<typename unpacket_traits<packetOut>::type,
-                             cl::sycl::rounding_mode::automatic>());
-}
-
-#define SYCL_PAND(TYPE)                                                        \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE pand<TYPE>(const TYPE &a,         \
-                                                        const TYPE &b) {       \
-    return convert_vector<TYPE>(vector_as_int(a) & vector_as_int(b));          \
-  }
-SYCL_PAND(cl::sycl::cl_float4)
-SYCL_PAND(cl::sycl::cl_double2)
-#undef SYCL_PAND
-
-#define SYCL_POR(TYPE)                                                         \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE por<TYPE>(const TYPE &a,          \
-                                                       const TYPE &b) {        \
-    return convert_vector<TYPE>(vector_as_int(a) | vector_as_int(b));          \
-  }
-
-SYCL_POR(cl::sycl::cl_float4)
-SYCL_POR(cl::sycl::cl_double2)
-#undef SYCL_POR
-
-#define SYCL_PXOR(TYPE)                                                        \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE pxor<TYPE>(const TYPE &a,         \
-                                                        const TYPE &b) {       \
-    return convert_vector<TYPE>(vector_as_int(a) ^ vector_as_int(b));          \
-  }
-
-SYCL_PXOR(cl::sycl::cl_float4)
-SYCL_PXOR(cl::sycl::cl_double2)
-#undef SYCL_PXOR
-
-#define SYCL_PANDNOT(TYPE)                                                     \
-  template <>                                                                  \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TYPE pandnot<TYPE>(const TYPE &a,      \
-                                                           const TYPE &b) {    \
-    return convert_vector<TYPE>(vector_as_int(a) & (~vector_as_int(b)));       \
-  }
-SYCL_PANDNOT(cl::sycl::cl_float4)
-SYCL_PANDNOT(cl::sycl::cl_double2)
-#undef SYCL_PANDNOT
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(
-    PacketBlock<cl::sycl::cl_float4, 4>& kernel) {
-  float tmp = kernel.packet[0].y();
-  kernel.packet[0].y() = kernel.packet[1].x();
-  kernel.packet[1].x() = tmp;
-
-  tmp = kernel.packet[0].z();
-  kernel.packet[0].z() = kernel.packet[2].x();
-  kernel.packet[2].x() = tmp;
-
-  tmp = kernel.packet[0].w();
-  kernel.packet[0].w() = kernel.packet[3].x();
-  kernel.packet[3].x() = tmp;
-
-  tmp = kernel.packet[1].z();
-  kernel.packet[1].z() = kernel.packet[2].y();
-  kernel.packet[2].y() = tmp;
-
-  tmp = kernel.packet[1].w();
-  kernel.packet[1].w() = kernel.packet[3].y();
-  kernel.packet[3].y() = tmp;
-
-  tmp = kernel.packet[2].w();
-  kernel.packet[2].w() = kernel.packet[3].z();
-  kernel.packet[3].z() = tmp;
-}
-
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(
-    PacketBlock<cl::sycl::cl_double2, 2>& kernel) {
-  double tmp = kernel.packet[0].y();
-  kernel.packet[0].y() = kernel.packet[1].x();
-  kernel.packet[1].x() = tmp;
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4 pblend(
-    const Selector<unpacket_traits<cl::sycl::cl_float4>::size>& ifPacket,
-    const cl::sycl::cl_float4& thenPacket,
-    const cl::sycl::cl_float4& elsePacket) {
-  cl::sycl::cl_int4 condition(
-      ifPacket.select[0] ? 0 : -1, ifPacket.select[1] ? 0 : -1,
-      ifPacket.select[2] ? 0 : -1, ifPacket.select[3] ? 0 : -1);
-  return cl::sycl::select(thenPacket, elsePacket, condition);
-}
-
-template <>
-inline cl::sycl::cl_double2 pblend(
-    const Selector<unpacket_traits<cl::sycl::cl_double2>::size>& ifPacket,
-    const cl::sycl::cl_double2& thenPacket,
-    const cl::sycl::cl_double2& elsePacket) {
-  cl::sycl::cl_long2 condition(ifPacket.select[0] ? 0 : -1,
-                               ifPacket.select[1] ? 0 : -1);
-  return cl::sycl::select(thenPacket, elsePacket, condition);
-}
-#endif  // SYCL_DEVICE_ONLY
-
-#define SYCL_PSTORE(alignment)                                  \
-  template <typename packet_type>                               \
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstore##alignment( \
-      const Eigen::TensorSycl::internal::RangeAccess<           \
-          cl::sycl::access::mode::read_write,                   \
-          typename unpacket_traits<packet_type>::type>& to,     \
-      const packet_type& from) {                                \
-    pstore##alignment(to.get_pointer(), from);                  \
-  }
-
-// global space
-SYCL_PSTORE()
-SYCL_PSTORE(u)
-
-#undef SYCL_PSTORE
-
-template <typename scalar, typename packet_type, int Alignment>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(
-    Eigen::TensorSycl::internal::RangeAccess<
-        cl::sycl::access::mode::read_write,
-        typename unpacket_traits<packet_type>::type>
-        to,
-    const packet_type& from) {
-  pstoret<scalar, packet_type, Alignment>(to.get_pointer(), from);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_PACKET_MATH_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
deleted file mode 100644
index f81e59d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
+++ /dev/null
@@ -1,694 +0,0 @@
-/***************************************************************************
- *  Copyright (C) 2017 Codeplay Software Limited
- *  This Source Code Form is subject to the terms of the Mozilla
- *  Public License v. 2.0. If a copy of the MPL was not distributed
- *  with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
- *
- *
- *  SyclMemoryModel.h
- *
- *  Description:
- *    Interface for SYCL buffers to behave as a non-dereferenceable pointer
- *    Interface for Placeholder accessor to behave as a pointer on both host
- *    and device
- *
- * Authors:
- *
- *    Ruyman Reyes   Codeplay Software Ltd.
- *    Mehdi Goli     Codeplay Software Ltd.
- *    Vanya Yaneva   Codeplay Software Ltd.
- *
- **************************************************************************/
-
-#if defined(EIGEN_USE_SYCL) && \
-    !defined(EIGEN_CXX11_TENSOR_TENSOR_SYCL_STORAGE_MEMORY_H)
-#define EIGEN_CXX11_TENSOR_TENSOR_SYCL_STORAGE_MEMORY_H
-
-#include <CL/sycl.hpp>
-#ifdef EIGEN_EXCEPTIONS
-#include <stdexcept>
-#endif
-#include <cstddef>
-#include <queue>
-#include <set>
-#include <unordered_map>
-
-namespace Eigen {
-namespace TensorSycl {
-namespace internal {
-
-using sycl_acc_target = cl::sycl::access::target;
-using sycl_acc_mode = cl::sycl::access::mode;
-
-/**
- * Default values for template arguments
- */
-using buffer_data_type_t = uint8_t;
-const sycl_acc_target default_acc_target = sycl_acc_target::global_buffer;
-const sycl_acc_mode default_acc_mode = sycl_acc_mode::read_write;
-
-/**
- * PointerMapper
- *  Associates fake pointers with buffers.
- *
- */
-class PointerMapper {
- public:
-  using base_ptr_t = std::intptr_t;
-
-  /* Structure of a virtual pointer
-   *
-   * |================================================|
-   * |               POINTER ADDRESS                  |
-   * |================================================|
-   */
-  struct virtual_pointer_t {
-    /* Type for the pointers
-     */
-    base_ptr_t m_contents;
-
-    /** Conversions from virtual_pointer_t to
-     * void * should just reinterpret_cast the integer number
-     */
-    operator void *() const { return reinterpret_cast<void *>(m_contents); }
-
-    /**
-     * Convert back to the integer number.
-     */
-    operator base_ptr_t() const { return m_contents; }
-
-    /**
-     * Add a certain value to the pointer to create a
-     * new pointer to that offset
-     */
-    virtual_pointer_t operator+(size_t off) { return m_contents + off; }
-
-    /* Numerical order for sorting pointers in containers. */
-    bool operator<(virtual_pointer_t rhs) const {
-      return (static_cast<base_ptr_t>(m_contents) <
-              static_cast<base_ptr_t>(rhs.m_contents));
-    }
-
-    bool operator>(virtual_pointer_t rhs) const {
-      return (static_cast<base_ptr_t>(m_contents) >
-              static_cast<base_ptr_t>(rhs.m_contents));
-    }
-
-    /**
-     * Numerical order for sorting pointers in containers
-     */
-    bool operator==(virtual_pointer_t rhs) const {
-      return (static_cast<base_ptr_t>(m_contents) ==
-              static_cast<base_ptr_t>(rhs.m_contents));
-    }
-
-    /**
-     * Simple forward to the equality overload.
-     */
-    bool operator!=(virtual_pointer_t rhs) const {
-      return !(this->operator==(rhs));
-    }
-
-    /**
-     * Converts a void * into a virtual pointer structure.
-     * Note that this will only work if the void * was
-     * already a virtual_pointer_t, but we have no way of
-     * checking
-     */
-    virtual_pointer_t(const void *ptr)
-        : m_contents(reinterpret_cast<base_ptr_t>(ptr)){};
-
-    /**
-     * Creates a virtual_pointer_t from the given integer
-     * number
-     */
-    virtual_pointer_t(base_ptr_t u) : m_contents(u){};
-  };
-
-  /* Definition of a null pointer
-   */
-  const virtual_pointer_t null_virtual_ptr = nullptr;
-
-  /**
-   * Whether if a pointer is null or not.
-   * A pointer is nullptr if the value is of null_virtual_ptr
-   */
-  static inline bool is_nullptr(virtual_pointer_t ptr) {
-    return (static_cast<void *>(ptr) == nullptr);
-  }
-
-  /* basic type for all buffers
-   */
-  using buffer_t = cl::sycl::buffer_mem;
-
-  /**
-   * Node that stores information about a device allocation.
-   * Nodes are sorted by size to organise a free list of nodes
-   * that can be recovered.
-   */
-  struct pMapNode_t {
-    buffer_t m_buffer;
-    size_t m_size;
-    bool m_free;
-
-    pMapNode_t(buffer_t b, size_t size, bool f)
-        : m_buffer{b}, m_size{size}, m_free{f} {
-      m_buffer.set_final_data(nullptr);
-    }
-
-    bool operator<=(const pMapNode_t &rhs) { return (m_size <= rhs.m_size); }
-  };
-
-  /** Storage of the pointer / buffer tree
-   */
-  using pointerMap_t = std::map<virtual_pointer_t, pMapNode_t>;
-
-  /**
-   * Obtain the insertion point in the pointer map for
-   * a pointer of the given size.
-   * \param requiredSize Size attemted to reclaim
-   */
-  typename pointerMap_t::iterator get_insertion_point(size_t requiredSize) {
-    typename pointerMap_t::iterator retVal;
-    bool reuse = false;
-    if (!m_freeList.empty()) {
-      // try to re-use an existing block
-      for (auto freeElem : m_freeList) {
-        if (freeElem->second.m_size >= requiredSize) {
-          retVal = freeElem;
-          reuse = true;
-          // Element is not going to be free anymore
-          m_freeList.erase(freeElem);
-          break;
-        }
-      }
-    }
-    if (!reuse) {
-      retVal = std::prev(m_pointerMap.end());
-    }
-    return retVal;
-  }
-
-  /**
-   * Returns an iterator to the node that stores the information
-   * of the given virtual pointer from the given pointer map structure.
-   * If pointer is not found, throws std::out_of_range.
-   * If the pointer map structure is empty, throws std::out_of_range
-   *
-   * \param pMap the pointerMap_t structure storing all the pointers
-   * \param virtual_pointer_ptr The virtual pointer to obtain the node of
-   * \throws std::out:of_range if the pointer is not found or pMap is empty
-   */
-  typename pointerMap_t::iterator get_node(const virtual_pointer_t ptr) {
-    if (this->count() == 0) {
-      m_pointerMap.clear();
-      EIGEN_THROW_X(std::out_of_range("There are no pointers allocated\n"));
-
-    }
-    if (is_nullptr(ptr)) {
-      m_pointerMap.clear();
-      EIGEN_THROW_X(std::out_of_range("Cannot access null pointer\n"));
-    }
-    // The previous element to the lower bound is the node that
-    // holds this memory address
-    auto node = m_pointerMap.lower_bound(ptr);
-    // If the value of the pointer is not the one of the node
-    // then we return the previous one
-    if (node == std::end(m_pointerMap)) {
-      --node;
-    } else if (node->first != ptr) {
-      if (node == std::begin(m_pointerMap)) {
-        m_pointerMap.clear();
-        EIGEN_THROW_X(
-            std::out_of_range("The pointer is not registered in the map\n"));
-
-      }
-      --node;
-    }
-
-    return node;
-  }
-
-  /* get_buffer.
-   * Returns a buffer from the map using the pointer address
-   */
-  template <typename buffer_data_type = buffer_data_type_t>
-  cl::sycl::buffer<buffer_data_type, 1> get_buffer(
-      const virtual_pointer_t ptr) {
-    using sycl_buffer_t = cl::sycl::buffer<buffer_data_type, 1>;
-
-    // get_node() returns a `buffer_mem`, so we need to cast it to a `buffer<>`.
-    // We can do this without the `buffer_mem` being a pointer, as we
-    // only declare member variables in the base class (`buffer_mem`) and not in
-    // the child class (`buffer<>).
-    auto node = get_node(ptr);
-    eigen_assert(node->first == ptr || node->first < ptr);
-    eigen_assert(ptr < static_cast<virtual_pointer_t>(node->second.m_size +
-                                                      node->first));
-    return *(static_cast<sycl_buffer_t *>(&node->second.m_buffer));
-  }
-
-  /**
-   * @brief Returns an accessor to the buffer of the given virtual pointer
-   * @param accessMode
-   * @param accessTarget
-   * @param ptr The virtual pointer
-   */
-  template <sycl_acc_mode access_mode = default_acc_mode,
-            sycl_acc_target access_target = default_acc_target,
-            typename buffer_data_type = buffer_data_type_t>
-  cl::sycl::accessor<buffer_data_type, 1, access_mode, access_target>
-  get_access(const virtual_pointer_t ptr) {
-    auto buf = get_buffer<buffer_data_type>(ptr);
-    return buf.template get_access<access_mode, access_target>();
-  }
-
-  /**
-   * @brief Returns an accessor to the buffer of the given virtual pointer
-   *        in the given command group scope
-   * @param accessMode
-   * @param accessTarget
-   * @param ptr The virtual pointer
-   * @param cgh Reference to the command group scope
-   */
-  template <sycl_acc_mode access_mode = default_acc_mode,
-            sycl_acc_target access_target = default_acc_target,
-            typename buffer_data_type = buffer_data_type_t>
-  cl::sycl::accessor<buffer_data_type, 1, access_mode, access_target>
-  get_access(const virtual_pointer_t ptr, cl::sycl::handler &cgh) {
-    auto buf = get_buffer<buffer_data_type>(ptr);
-    return buf.template get_access<access_mode, access_target>(cgh);
-  }
-
-  /*
-   * Returns the offset from the base address of this pointer.
-   */
-  inline std::ptrdiff_t get_offset(const virtual_pointer_t ptr) {
-    // The previous element to the lower bound is the node that
-    // holds this memory address
-    auto node = get_node(ptr);
-    auto start = node->first;
-    eigen_assert(start == ptr || start < ptr);
-    eigen_assert(ptr < start + node->second.m_size);
-    return (ptr - start);
-  }
-
-  /*
-   * Returns the number of elements by which the given pointer is offset from
-   * the base address.
-   */
-  template <typename buffer_data_type>
-  inline size_t get_element_offset(const virtual_pointer_t ptr) {
-    return get_offset(ptr) / sizeof(buffer_data_type);
-  }
-
-  /**
-   * Constructs the PointerMapper structure.
-   */
-  PointerMapper(base_ptr_t baseAddress = 4096)
-      : m_pointerMap{}, m_freeList{}, m_baseAddress{baseAddress} {
-    if (m_baseAddress == 0) {
-      EIGEN_THROW_X(std::invalid_argument("Base address cannot be zero\n"));
-    }
-  };
-
-  /**
-   * PointerMapper cannot be copied or moved
-   */
-  PointerMapper(const PointerMapper &) = delete;
-
-  /**
-   * Empty the pointer list
-   */
-  inline void clear() {
-    m_freeList.clear();
-    m_pointerMap.clear();
-  }
-
-  /* add_pointer.
-   * Adds an existing pointer to the map and returns the virtual pointer id.
-   */
-  inline virtual_pointer_t add_pointer(const buffer_t &b) {
-    return add_pointer_impl(b);
-  }
-
-  /* add_pointer.
-   * Adds a pointer to the map and returns the virtual pointer id.
-   */
-  inline virtual_pointer_t add_pointer(buffer_t &&b) {
-    return add_pointer_impl(b);
-  }
-
-  /**
-   * @brief Fuses the given node with the previous nodes in the
-   *        pointer map if they are free
-   *
-   * @param node A reference to the free node to be fused
-   */
-  void fuse_forward(typename pointerMap_t::iterator &node) {
-    while (node != std::prev(m_pointerMap.end())) {
-      // if following node is free
-      // remove it and extend the current node with its size
-      auto fwd_node = std::next(node);
-      if (!fwd_node->second.m_free) {
-        break;
-      }
-      auto fwd_size = fwd_node->second.m_size;
-      m_freeList.erase(fwd_node);
-      m_pointerMap.erase(fwd_node);
-
-      node->second.m_size += fwd_size;
-    }
-  }
-
-  /**
-   * @brief Fuses the given node with the following nodes in the
-   *        pointer map if they are free
-   *
-   * @param node A reference to the free node to be fused
-   */
-  void fuse_backward(typename pointerMap_t::iterator &node) {
-    while (node != m_pointerMap.begin()) {
-      // if previous node is free, extend it
-      // with the size of the current one
-      auto prev_node = std::prev(node);
-      if (!prev_node->second.m_free) {
-        break;
-      }
-      prev_node->second.m_size += node->second.m_size;
-
-      // remove the current node
-      m_freeList.erase(node);
-      m_pointerMap.erase(node);
-
-      // point to the previous node
-      node = prev_node;
-    }
-  }
-
-  /* remove_pointer.
-   * Removes the given pointer from the map.
-   * The pointer is allowed to be reused only if ReUse if true.
-   */
-  template <bool ReUse = true>
-  void remove_pointer(const virtual_pointer_t ptr) {
-    if (is_nullptr(ptr)) {
-      return;
-    }
-    auto node = this->get_node(ptr);
-
-    node->second.m_free = true;
-    m_freeList.emplace(node);
-
-    // Fuse the node
-    // with free nodes before and after it
-    fuse_forward(node);
-    fuse_backward(node);
-
-    // If after fusing the node is the last one
-    // simply remove it (since it is free)
-    if (node == std::prev(m_pointerMap.end())) {
-      m_freeList.erase(node);
-      m_pointerMap.erase(node);
-    }
-  }
-
-  /* count.
-   * Return the number of active pointers (i.e, pointers that
-   * have been malloc but not freed).
-   */
-  size_t count() const { return (m_pointerMap.size() - m_freeList.size()); }
-
- private:
-  /* add_pointer_impl.
-   * Adds a pointer to the map and returns the virtual pointer id.
-   * BufferT is either a const buffer_t& or a buffer_t&&.
-   */
-  template <class BufferT>
-  virtual_pointer_t add_pointer_impl(BufferT b) {
-    virtual_pointer_t retVal = nullptr;
-    size_t bufSize = b.get_count();
-    pMapNode_t p{b, bufSize, false};
-    // If this is the first pointer:
-    if (m_pointerMap.empty()) {
-      virtual_pointer_t initialVal{m_baseAddress};
-      m_pointerMap.emplace(initialVal, p);
-      return initialVal;
-    }
-
-    auto lastElemIter = get_insertion_point(bufSize);
-    // We are recovering an existing free node
-    if (lastElemIter->second.m_free) {
-      lastElemIter->second.m_buffer = b;
-      lastElemIter->second.m_free = false;
-
-      // If the recovered node is bigger than the inserted one
-      // add a new free node with the remaining space
-      if (lastElemIter->second.m_size > bufSize) {
-        // create a new node with the remaining space
-        auto remainingSize = lastElemIter->second.m_size - bufSize;
-        pMapNode_t p2{b, remainingSize, true};
-
-        // update size of the current node
-        lastElemIter->second.m_size = bufSize;
-
-        // add the new free node
-        auto newFreePtr = lastElemIter->first + bufSize;
-        auto freeNode = m_pointerMap.emplace(newFreePtr, p2).first;
-        m_freeList.emplace(freeNode);
-      }
-
-      retVal = lastElemIter->first;
-    } else {
-      size_t lastSize = lastElemIter->second.m_size;
-      retVal = lastElemIter->first + lastSize;
-      m_pointerMap.emplace(retVal, p);
-    }
-    return retVal;
-  }
-
-  /**
-   * Compare two iterators to pointer map entries according to
-   * the size of the allocation on the device.
-   */
-  struct SortBySize {
-    bool operator()(typename pointerMap_t::iterator a,
-                    typename pointerMap_t::iterator b) const {
-      return ((a->first < b->first) && (a->second <= b->second)) ||
-             ((a->first < b->first) && (b->second <= a->second));
-    }
-  };
-
-  /* Maps the pointer addresses to buffer and size pairs.
-   */
-  pointerMap_t m_pointerMap;
-
-  /* List of free nodes available for re-using
-   */
-  std::set<typename pointerMap_t::iterator, SortBySize> m_freeList;
-
-  /* Base address used when issuing the first virtual pointer, allows users
-   * to specify alignment. Cannot be zero. */
-  std::intptr_t m_baseAddress;
-};
-
-/* remove_pointer.
- * Removes the given pointer from the map.
- * The pointer is allowed to be reused only if ReUse if true.
- */
-template <>
-inline void PointerMapper::remove_pointer<false>(const virtual_pointer_t ptr) {
-  if (is_nullptr(ptr)) {
-    return;
-  }
-  m_pointerMap.erase(this->get_node(ptr));
-}
-
-/**
- * Malloc-like interface to the pointer-mapper.
- * Given a size, creates a byte-typed buffer and returns a
- * fake pointer to keep track of it.
- * \param size Size in bytes of the desired allocation
- * \throw cl::sycl::exception if error while creating the buffer
- */
-inline void *SYCLmalloc(size_t size, PointerMapper &pMap) {
-  if (size == 0) {
-    return nullptr;
-  }
-  // Create a generic buffer of the given size
-  using buffer_t = cl::sycl::buffer<buffer_data_type_t, 1>;
-  auto thePointer = pMap.add_pointer(buffer_t(cl::sycl::range<1>{size}));
-  // Store the buffer on the global list
-  return static_cast<void *>(thePointer);
-}
-
-/**
- * Free-like interface to the pointer mapper.
- * Given a fake-pointer created with the virtual-pointer malloc,
- * destroys the buffer and remove it from the list.
- * If ReUse is false, the pointer is not added to the freeList,
- * it should be false only for sub-buffers.
- */
-template <bool ReUse = true, typename PointerMapper>
-inline void SYCLfree(void *ptr, PointerMapper &pMap) {
-  pMap.template remove_pointer<ReUse>(ptr);
-}
-
-/**
- * Clear all the memory allocated by SYCL.
- */
-template <typename PointerMapper>
-inline void SYCLfreeAll(PointerMapper &pMap) {
-  pMap.clear();
-}
-
-template <cl::sycl::access::mode AcMd, typename T>
-struct RangeAccess {
-  static const auto global_access = cl::sycl::access::target::global_buffer;
-  static const auto is_place_holder = cl::sycl::access::placeholder::true_t;
-  typedef T scalar_t;
-  typedef scalar_t &ref_t;
-  typedef typename cl::sycl::global_ptr<scalar_t>::pointer_t ptr_t;
-
-  // the accessor type does not necessarily the same as T
-  typedef cl::sycl::accessor<scalar_t, 1, AcMd, global_access, is_place_holder>
-      accessor;
-
-  typedef RangeAccess<AcMd, T> self_t;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE RangeAccess(accessor access,
-                                                    size_t offset,
-                                                    std::intptr_t virtual_ptr)
-      : access_(access), offset_(offset), virtual_ptr_(virtual_ptr) {}
-
-  RangeAccess(cl::sycl::buffer<scalar_t, 1> buff =
-                  cl::sycl::buffer<scalar_t, 1>(cl::sycl::range<1>(1)))
-      : access_{accessor{buff}}, offset_(0), virtual_ptr_(-1) {}
-
-  // This should be only used for null constructor on the host side
-  RangeAccess(std::nullptr_t) : RangeAccess() {}
-  // This template parameter must be removed and scalar_t should be replaced
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptr_t get_pointer() const {
-    return (access_.get_pointer().get() + offset_);
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t &operator+=(Index offset) {
-    offset_ += (offset);
-    return *this;
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t operator+(Index offset) const {
-    return self_t(access_, offset_ + offset, virtual_ptr_);
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t operator-(Index offset) const {
-    return self_t(access_, offset_ - offset, virtual_ptr_);
-  }
-  template <typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t &operator-=(Index offset) {
-    offset_ -= offset;
-    return *this;
-  }
-
-  // THIS IS FOR NULL COMPARISON ONLY
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator==(
-      const RangeAccess &lhs, std::nullptr_t) {
-    return ((lhs.virtual_ptr_ == -1));
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator!=(
-      const RangeAccess &lhs, std::nullptr_t i) {
-    return !(lhs == i);
-  }
-
-  // THIS IS FOR NULL COMPARISON ONLY
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator==(
-      std::nullptr_t, const RangeAccess &rhs) {
-    return ((rhs.virtual_ptr_ == -1));
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend bool operator!=(
-      std::nullptr_t i, const RangeAccess &rhs) {
-    return !(i == rhs);
-  }
-  // Prefix operator (Increment and return value)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t &operator++() {
-    offset_++;
-    return (*this);
-  }
-
-  // Postfix operator (Return value and increment)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE self_t operator++(int i) {
-    EIGEN_UNUSED_VARIABLE(i);
-    self_t temp_iterator(*this);
-    offset_++;
-    return temp_iterator;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t get_size() const {
-    return (access_.get_count() - offset_);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t get_offset() const {
-    return offset_;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_offset(std::ptrdiff_t offset) {
-    offset_ = offset;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator*() const {
-    return *get_pointer();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator*() {
-    return *get_pointer();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptr_t operator->() = delete;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator[](int x) {
-    return *(get_pointer() + x);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ref_t operator[](int x) const {
-    return *(get_pointer() + x);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_t *get_virtual_pointer() const {
-    return reinterpret_cast<scalar_t *>(virtual_ptr_ +
-                                        (offset_ * sizeof(scalar_t)));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit operator bool() const {
-    return (virtual_ptr_ != -1);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator RangeAccess<AcMd, const T>() {
-    return RangeAccess<AcMd, const T>(access_, offset_, virtual_ptr_);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  operator RangeAccess<AcMd, const T>() const {
-    return RangeAccess<AcMd, const T>(access_, offset_, virtual_ptr_);
-  }
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(
-      cl::sycl::handler &cgh) const {
-    cgh.require(access_);
-  }
-
- private:
-  accessor access_;
-  size_t offset_;
-  std::intptr_t virtual_ptr_;  // the location of the buffer in the map
-};
-
-template <cl::sycl::access::mode AcMd, typename T>
-struct RangeAccess<AcMd, const T> : RangeAccess<AcMd, T> {
-  typedef RangeAccess<AcMd, T> Base;
-  using Base::Base;
-};
-
-}  // namespace internal
-}  // namespace TensorSycl
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_SYCL_STORAGE_MEMORY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h
deleted file mode 100644
index 9208ab2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h
+++ /dev/null
@@ -1,85 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * TypeCasting.h
- *
- * \brief:
- *  TypeCasting
- *
- *****************************************************************/
-
-#ifndef EIGEN_TYPE_CASTING_SYCL_H
-#define EIGEN_TYPE_CASTING_SYCL_H
-
-namespace Eigen {
-
-namespace internal {
-#ifdef SYCL_DEVICE_ONLY
-template <>
-struct type_casting_traits<float, int> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_int4
-pcast<cl::sycl::cl_float4, cl::sycl::cl_int4>(const cl::sycl::cl_float4& a) {
-  return a
-      .template convert<cl::sycl::cl_int, cl::sycl::rounding_mode::automatic>();
-}
-
-template <>
-struct type_casting_traits<int, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
-pcast<cl::sycl::cl_int4, cl::sycl::cl_float4>(const cl::sycl::cl_int4& a) {
-  return a.template convert<cl::sycl::cl_float,
-                            cl::sycl::rounding_mode::automatic>();
-}
-
-template <>
-struct type_casting_traits<double, float> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
-pcast<cl::sycl::cl_double2, cl::sycl::cl_float4>(
-    const cl::sycl::cl_double2& a, const cl::sycl::cl_double2& b) {
-  auto a1 = a.template convert<cl::sycl::cl_float,
-                               cl::sycl::rounding_mode::automatic>();
-  auto b1 = b.template convert<cl::sycl::cl_float,
-                               cl::sycl::rounding_mode::automatic>();
-  return cl::sycl::float4(a1.x(), a1.y(), b1.x(), b1.y());
-}
-
-template <>
-struct type_casting_traits<float, double> {
-  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
-};
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2
-pcast<cl::sycl::cl_float4, cl::sycl::cl_double2>(const cl::sycl::cl_float4& a) {
-  // Simply discard the second half of the input
-  return cl::sycl::cl_double2(a.x(), a.y());
-}
-
-#endif
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_TYPE_CASTING_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
deleted file mode 100644
index 0b9b33d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
+++ /dev/null
@@ -1,426 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX32_ALTIVEC_H
-#define EIGEN_COMPLEX32_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-static Packet4ui  p4ui_CONJ_XOR = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 }; //vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);
-#endif
-
-static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
-
-struct Packet1cd
-{
-  EIGEN_STRONG_INLINE Packet1cd() {}
-  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
-  Packet2d v;
-};
-
-struct Packet2cf
-{
-  EIGEN_STRONG_INLINE Packet2cf() {}
-  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-  union {
-    Packet4f v;
-    Packet1cd cd[2];
-  };
-#else
-  Packet4f v;
-#endif
-};
-
-template<> struct packet_traits<std::complex<float> >  : default_packet_traits
-{
-  typedef Packet2cf type;
-  typedef Packet2cf half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 2,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasBlend  = 1,
-    HasSetLinear = 0
-  };
-};
-
-
-template<> struct packet_traits<std::complex<double> >  : default_packet_traits
-{
-  typedef Packet1cd type;
-  typedef Packet1cd half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 1,
-    HasHalfPacket = 0,
-
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasDiv    = 1,
-    HasNegate = 1,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasSetLinear = 0
-  };
-};
-
-template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float>  type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2cf half; };
-template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet1cd half; };
-
-/* Forward declaration */
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel);
-
-/* complex<double> first */
-template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
-
-template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
-{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
-
-template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index stride EIGEN_UNUSED)
-{
-  return pload<Packet1cd>(from);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index stride EIGEN_UNUSED)
-{
-  pstore<std::complex<double> >(to, from);
-}
-template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
-template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  Packet2d a_re, a_im, v1, v2;
-
-  // Permute and multiply the real parts of a and b
-  a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
-  // Get the imaginary parts of a
-  a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
-  // multiply a_re * b
-  v1 = vec_madd(a_re, b.v, p2d_ZERO);
-  // multiply a_im * b and get the conjugate result
-  v2 = vec_madd(a_im, b.v, p2d_ZERO);
-  v2 = (Packet2d) vec_sld((Packet4ui)v2, (Packet4ui)v2, 8);
-  v2 = (Packet2d) vec_xor((Packet2d)v2, (Packet2d) p2ul_CONJ_XOR1);
-
-  return Packet1cd(v1 + v2);
-}
-template<> EIGEN_STRONG_INLINE Packet1cd pand    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd por     <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_or(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pxor    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_xor(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet1cd pandnot <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v, vec_nor(b.v,b.v))); }
-template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>*     from) {  return pset1<Packet1cd>(*from); }
-template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
-  Packet2d eq = vec_cmpeq (a.v, b.v);
-  Packet2d tmp = { eq[1], eq[0] };
-  return (Packet1cd)pand<Packet2d>(eq, tmp);
-}
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *   addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
-{
-  std::complex<double> EIGEN_ALIGN16 res;
-  pstore<std::complex<double> >(&res, a);
-
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
-template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
-{
-  return pfirst(a);
-}
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
-
-template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
-{
-  // TODO optimize it for AltiVec
-  Packet1cd res = pmul(a,pconj(b));
-  Packet2d s = vec_madd(b.v, b.v, p2d_ZERO_);
-  return Packet1cd(pdiv(res.v, s + vec_perm(s, s, p16uc_REVERSE64)));
-}
-
-EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
-{
-  return Packet1cd(preverse(Packet2d(x.v)));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
-{
-  Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-/* complex<float> follows */
-template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from)  { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from)  { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from)); }
-template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *     to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
-template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *     to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
-
-template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> EIGEN_ALIGN16 res[2];
-  pstore<std::complex<float> >(res, a);
-
-  return res[0];
-}
-
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-  res.cd[0] = Packet1cd(vec_ld2f((const float *)&from));
-  res.cd[1] = res.cd[0];
-  return res;
-}
-#else
-template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
-{
-  Packet2cf res;
-  if((std::ptrdiff_t(&from) % 16) == 0)
-    res.v = pload<Packet4f>((const float *)&from);
-  else
-    res.v = ploadu<Packet4f>((const float *)&from);
-  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
-  return res;
-}
-#endif
-
-template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
-{
-  std::complex<float> EIGEN_ALIGN16 af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
-  return pload<Packet2cf>(af);
-}
-template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
-{
-  std::complex<float> EIGEN_ALIGN16 af[2];
-  pstore<std::complex<float> >((std::complex<float> *) af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(padd<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(psub<Packet4f>(a.v, b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(Packet4f(a.v))); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pxor<Packet4f>(a.v,b.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pandnot<Packet4f>(a.v,b.v)); }
-
-template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>*      from) {  return pset1<Packet2cf>(*from); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *     addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
-  Packet4f eq = pcmp_eq<Packet4f> (a.v, b.v);
-  Packet2cf res;
-  Packet2d tmp1 = { eq.v4f[0][1], eq.v4f[0][0] };
-  Packet2d tmp2 = { eq.v4f[1][1], eq.v4f[1][0] };
-  res.v.v4f[0] = pand<Packet2d>(eq.v4f[0], tmp1);
-  res.v.v4f[1] = pand<Packet2d>(eq.v4f[1], tmp2);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
-{
-  Packet2cf res;
-  res.v.v4f[0] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0]))).v;
-  res.v.v4f[1] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1]))).v;
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  Packet2cf res;
-  res.v.v4f[0] = pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[0]))).v;
-  res.v.v4f[1] = pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[1]))).v;
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{
-  Packet2cf res;
-  res.cd[0] = a.cd[1];
-  res.cd[1] = a.cd[0];
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> res;
-  Packet1cd b = padd<Packet1cd>(a.cd[0], a.cd[1]);
-  vec_st2f(b.v, (float*)&res);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  std::complex<float> res;
-  Packet1cd b = pmul<Packet1cd>(a.cd[0], a.cd[1]);
-  vec_st2f(b.v, (float*)&res);
-  return res;
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for AltiVec
-  Packet2cf res;
-  res.cd[0] = pdiv<Packet1cd>(a.cd[0], b.cd[0]);
-  res.cd[1] = pdiv<Packet1cd>(a.cd[1], b.cd[1]);
-  return res;
-}
-
-EIGEN_STRONG_INLINE Packet2cf pcplxflip/*<Packet2cf>*/(const Packet2cf& x)
-{
-  Packet2cf res;
-  res.cd[0] = pcplxflip(x.cd[0]);
-  res.cd[1] = pcplxflip(x.cd[1]);
-  return res;
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
-{
-  Packet1cd tmp = kernel.packet[0].cd[1];
-  kernel.packet[0].cd[1] = kernel.packet[1].cd[0];
-  kernel.packet[1].cd[0] = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  Packet2cf result;
-  const Selector<4> ifPacket4 = { ifPacket.select[0], ifPacket.select[0], ifPacket.select[1], ifPacket.select[1] };
-  result.v = pblend<Packet4f>(ifPacket4, thenPacket.v, elsePacket.v);
-  return result;
-}
-#else
-template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
-  Packet4f eq = vec_cmpeq (a.v, b.v);
-  Packet4f tmp = { eq[1], eq[0], eq[3], eq[2] };
-  return (Packet2cf)pand<Packet4f>(eq, tmp);
-}
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
-template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  Packet4f a_re, a_im, prod, prod_im;
-
-  // Permute and multiply the real parts of a and b
-  a_re = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
-  
-  // Get the imaginary parts of a
-  a_im = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
-
-  // multiply a_im * b and get the conjugate result
-  prod_im = a_im * b.v;
-  prod_im = pxor<Packet4f>(prod_im, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR));
-  // permute back to a proper order
-  prod_im = vec_perm(prod_im, prod_im, p16uc_COMPLEX32_REV);
-
-  // multiply a_re * b, add prod_im
-  prod = pmadd<Packet4f>(a_re, b.v, prod_im);
- 
-  return Packet2cf(prod);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
-{
-  Packet4f rev_a;
-  rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
-  return Packet2cf(rev_a);
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  b = vec_sld(a.v, a.v, 8);
-  b = padd<Packet4f>(a.v, b);
-  return pfirst<Packet2cf>(Packet2cf(b));
-}
-
-template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
-{
-  Packet4f b;
-  Packet2cf prod;
-  b = vec_sld(a.v, a.v, 8);
-  prod = pmul<Packet2cf>(a, Packet2cf(b));
-
-  return pfirst<Packet2cf>(prod);
-}
-
-EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
-
-template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
-{
-  // TODO optimize it for AltiVec
-  Packet2cf res = pmul(a, pconj(b));
-  Packet4f s = pmul<Packet4f>(b.v, b.v);
-  return Packet2cf(pdiv(res.v, padd<Packet4f>(s, vec_perm(s, s, p16uc_COMPLEX32_REV))));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
-{
-  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
-}
-
-EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
-{
-  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
-  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
-  kernel.packet[0].v = tmp;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
-  Packet2cf result;
-  result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
-  return result;
-}
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX32_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h
deleted file mode 100644
index 1635e12..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h
+++ /dev/null
@@ -1,233 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007 Julien Pommier
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* The sin, cos, exp, and log functions of this file come from
- * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
- */
-
-#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H
-
-namespace Eigen {
-
-namespace internal {
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-static _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
-static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
-static _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
-static _EIGEN_DECLARE_CONST_Packet4i(23, 23);
-
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000);
-
-/* the smallest non denormalized float number */
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos,  0x00800000);
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf,     0xff800000); // -1.f/0.f
-static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan,     0xffffffff);
-  
-/* natural logarithm computed for 4 simultaneous float
-  return NaN for x <= 0
-*/
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
-
-static _EIGEN_DECLARE_CONST_Packet4f(exp_hi,  88.3762626647950f);
-static _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f);
-
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
-
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f);
-static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f);
-#endif
-
-static _EIGEN_DECLARE_CONST_Packet2d(1 , 1.0);
-static _EIGEN_DECLARE_CONST_Packet2d(2 , 2.0);
-static _EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
-
-static _EIGEN_DECLARE_CONST_Packet2d(exp_hi,  709.437);
-static _EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
-
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
-static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d pexp<Packet2d>(const Packet2d& _x)
-{
-  Packet2d x = _x;
-
-  Packet2d tmp, fx;
-  Packet2l emm0;
-
-  // clamp x
-  x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo);
-  /* express exp(x) as exp(g + n*log(2)) */
-  fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half);
-
-  fx = vec_floor(fx);
-
-  tmp = pmul(fx, p2d_cephes_exp_C1);
-  Packet2d z = pmul(fx, p2d_cephes_exp_C2);
-  x = psub(x, tmp);
-  x = psub(x, z);
-
-  Packet2d x2 = pmul(x,x);
-
-  Packet2d px = p2d_cephes_exp_p0;
-  px = pmadd(px, x2, p2d_cephes_exp_p1);
-  px = pmadd(px, x2, p2d_cephes_exp_p2);
-  px = pmul (px, x);
-
-  Packet2d qx = p2d_cephes_exp_q0;
-  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
-  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
-
-  x = pdiv(px,psub(qx,px));
-  x = pmadd(p2d_2,x,p2d_1);
-
-  // build 2^n
-  emm0 = vec_ctsl(fx, 0);
-
-  static const Packet2l p2l_1023 = { 1023, 1023 };
-  static const Packet2ul p2ul_52 = { 52, 52 };
-
-  emm0 = emm0 + p2l_1023;
-  emm0 = emm0 << reinterpret_cast<Packet2l>(p2ul_52);
-
-  // Altivec's max & min operators just drop silent NaNs. Check NaNs in 
-  // inputs and return them unmodified.
-  Packet2ul isnumber_mask = reinterpret_cast<Packet2ul>(vec_cmpeq(_x, _x));
-  return vec_sel(_x, pmax(pmul(x, reinterpret_cast<Packet2d>(emm0)), _x),
-                 isnumber_mask);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f pexp<Packet4f>(const Packet4f& _x)
-{
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  Packet4f x = _x;
-
-  Packet4f tmp, fx;
-  Packet4i emm0;
-
-  // clamp x
-  x = pmax(pmin(x, p4f_exp_hi), p4f_exp_lo);
-
-  // express exp(x) as exp(g + n*log(2))
-  fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half);
-
-  fx = pfloor(fx);
-
-  tmp = pmul(fx, p4f_cephes_exp_C1);
-  Packet4f z = pmul(fx, p4f_cephes_exp_C2);
-  x = psub(x, tmp);
-  x = psub(x, z);
-
-  z = pmul(x,x);
-
-  Packet4f y = p4f_cephes_exp_p0;
-  y = pmadd(y, x, p4f_cephes_exp_p1);
-  y = pmadd(y, x, p4f_cephes_exp_p2);
-  y = pmadd(y, x, p4f_cephes_exp_p3);
-  y = pmadd(y, x, p4f_cephes_exp_p4);
-  y = pmadd(y, x, p4f_cephes_exp_p5);
-  y = pmadd(y, z, x);
-  y = padd(y, p4f_1);
-
-  // build 2^n
-  emm0 = (Packet4i){ (int)fx[0], (int)fx[1], (int)fx[2], (int)fx[3] };
-  emm0 = emm0 + p4i_0x7f;
-  emm0 = emm0 << reinterpret_cast<Packet4i>(p4i_23);
-
-  return pmax(pmul(y, reinterpret_cast<Packet4f>(emm0)), _x);
-#else
-  Packet4f res;
-  res.v4f[0] = pexp<Packet2d>(_x.v4f[0]);
-  res.v4f[1] = pexp<Packet2d>(_x.v4f[1]);
-  return res;
-#endif
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d psqrt<Packet2d>(const Packet2d& x)
-{
-  return vec_sqrt(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f psqrt<Packet4f>(const Packet4f& x)
-{
-  Packet4f res;
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  res = vec_sqrt(x);
-#else
-  res.v4f[0] = psqrt<Packet2d>(x.v4f[0]);
-  res.v4f[1] = psqrt<Packet2d>(x.v4f[1]);
-#endif
-  return res;
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet2d prsqrt<Packet2d>(const Packet2d& x) {
-  return pset1<Packet2d>(1.0) / psqrt<Packet2d>(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
-Packet4f prsqrt<Packet4f>(const Packet4f& x) {
-  Packet4f res;
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  res = pset1<Packet4f>(1.0) / psqrt<Packet4f>(x);
-#else
-  res.v4f[0] = prsqrt<Packet2d>(x.v4f[0]);
-  res.v4f[1] = prsqrt<Packet2d>(x.v4f[1]);
-#endif
-  return res;
-}
-
-// Hyperbolic Tangent function.
-template <>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
-ptanh<Packet4f>(const Packet4f& x) {
-  return internal::generic_fast_tanh_float(x);
-}
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_MATH_FUNCTIONS_ALTIVEC_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
deleted file mode 100644
index 1f55a90..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
+++ /dev/null
@@ -1,1060 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PACKET_MATH_ZVECTOR_H
-#define EIGEN_PACKET_MATH_ZVECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
-#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16
-#endif
-
-#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-#endif
-
-#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
-#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS  32
-#endif
-
-typedef __vector int                 Packet4i;
-typedef __vector unsigned int        Packet4ui;
-typedef __vector __bool int          Packet4bi;
-typedef __vector short int           Packet8i;
-typedef __vector unsigned char       Packet16uc;
-typedef __vector double              Packet2d;
-typedef __vector unsigned long long  Packet2ul;
-typedef __vector long long           Packet2l;
-
-// Z14 has builtin support for float vectors
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-typedef __vector float               Packet4f;
-#else
-typedef struct {
-	Packet2d  v4f[2];
-} Packet4f;
-#endif
-
-typedef union {
-  numext::int32_t   i[4];
-  numext::uint32_t ui[4];
-  numext::int64_t   l[2];
-  numext::uint64_t ul[2];
-  double    d[2];
-  float     f[4];
-  Packet4i  v4i;
-  Packet4ui v4ui;
-  Packet2l  v2l;
-  Packet2ul v2ul;
-  Packet2d  v2d;
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-  Packet4f  v4f;
-#endif
-} Packet;
-
-// We don't want to write the same code all the time, but we need to reuse the constants
-// and it doesn't really work to declare them global, so we define macros instead
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = reinterpret_cast<Packet4i>(vec_splat_s32(X))
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet2d(NAME,X) \
-  Packet2d p2d_##NAME = reinterpret_cast<Packet2d>(vec_splat_s64(X))
-
-#define _EIGEN_DECLARE_CONST_FAST_Packet2l(NAME,X) \
-  Packet2l p2l_##NAME = reinterpret_cast<Packet2l>(vec_splat_s64(X))
-
-#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
-  Packet4i p4i_##NAME = pset1<Packet4i>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
-  Packet2d p2d_##NAME = pset1<Packet2d>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
-  Packet2l p2l_##NAME = pset1<Packet2l>(X)
-
-// These constants are endian-agnostic
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE, 1); //{ 1, 1, 1, 1}
-
-static _EIGEN_DECLARE_CONST_FAST_Packet2d(ZERO, 0);
-static _EIGEN_DECLARE_CONST_FAST_Packet2l(ZERO, 0);
-static _EIGEN_DECLARE_CONST_FAST_Packet2l(ONE, 1);
-
-static Packet2d p2d_ONE = { 1.0, 1.0 };
-static Packet2d p2d_ZERO_ = { numext::bit_cast<double>0x8000000000000000ull),
-                              numext::bit_cast<double>0x8000000000000000ull) };
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-#define _EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(vec_splat_s32(X))
-
-#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
-  Packet4f p4f_##NAME = pset1<Packet4f>(X)
-
-#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
-  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
-
-static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
-static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
-static Packet4f p4f_MZERO = { 0x80000000, 0x80000000, 0x80000000, 0x80000000};
-#endif
-
-static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
-static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
-static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet16uc>(p2d_ZERO), reinterpret_cast<Packet16uc>(p2d_ONE), 8));
-
-static Packet16uc p16uc_PSET64_HI = { 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
-static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
-
-// Mask alignment
-#define _EIGEN_MASK_ALIGNMENT	0xfffffffffffffff0
-
-#define _EIGEN_ALIGNED_PTR(x)	((std::ptrdiff_t)(x) & _EIGEN_MASK_ALIGNMENT)
-
-// Handle endianness properly while loading constants
-// Define global static constants:
-
-static Packet16uc p16uc_FORWARD =   { 0,1,2,3, 4,5,6,7, 8,9,10,11, 12,13,14,15 };
-static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
-static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-
-static Packet16uc p16uc_PSET32_WODD   = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
-static Packet16uc p16uc_PSET32_WEVEN  = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
-/*static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
-
-static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };*/
-static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
-/*static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16);                                         //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
-static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16);                                         //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};*/
-static Packet16uc p16uc_TRANSPOSE64_HI = { 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
-static Packet16uc p16uc_TRANSPOSE64_LO = { 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
-
-static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);                                         //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
-
-static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);                                            //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
-
-
-#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
-  #define EIGEN_ZVECTOR_PREFETCH(ADDR) __builtin_prefetch(ADDR);
-#else
-  #define EIGEN_ZVECTOR_PREFETCH(ADDR) asm( "   pfd [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
-#endif
-
-template<> struct packet_traits<int>    : default_packet_traits
-{
-  typedef Packet4i type;
-  typedef Packet4i half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 1,
-    HasBlend = 1
-  };
-};
-
-template <>
-struct packet_traits<float> : default_packet_traits {
-  typedef Packet4f type;
-  typedef Packet4f half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size = 4,
-    HasHalfPacket = 0,
-
-    HasAdd = 1,
-    HasSub = 1,
-    HasMul = 1,
-    HasDiv = 1,
-    HasMin = 1,
-    HasMax = 1,
-    HasAbs = 1,
-    HasSin = 0,
-    HasCos = 0,
-    HasLog = 0,
-    HasExp = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasTanh = 1,
-    HasErf = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct packet_traits<double> : default_packet_traits
-{
-  typedef Packet2d type;
-  typedef Packet2d half;
-  enum {
-    Vectorizable = 1,
-    AlignedOnScalar = 1,
-    size=2,
-    HasHalfPacket = 1,
-
-    HasAdd  = 1,
-    HasSub  = 1,
-    HasMul  = 1,
-    HasDiv  = 1,
-    HasMin  = 1,
-    HasMax  = 1,
-    HasAbs  = 1,
-    HasSin  = 0,
-    HasCos  = 0,
-    HasLog  = 0,
-    HasExp  = 1,
-    HasSqrt = 1,
-    HasRsqrt = 1,
-    HasRound = 1,
-    HasFloor = 1,
-    HasCeil = 1,
-    HasNegate = 1,
-    HasBlend = 1
-  };
-};
-
-template<> struct unpacket_traits<Packet4i> { typedef int    type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4i half; };
-template<> struct unpacket_traits<Packet4f> { typedef float  type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4f half; };
-template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2d half; };
-
-/* Forward declaration */
-EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f,4>& kernel);
- 
-inline std::ostream & operator <<(std::ostream & s, const Packet4i & v)
-{
-  Packet vt;
-  vt.v4i = v;
-  s << vt.i[0] << ", " << vt.i[1] << ", " << vt.i[2] << ", " << vt.i[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
-{
-  Packet vt;
-  vt.v4ui = v;
-  s << vt.ui[0] << ", " << vt.ui[1] << ", " << vt.ui[2] << ", " << vt.ui[3];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2l & v)
-{
-  Packet vt;
-  vt.v2l = v;
-  s << vt.l[0] << ", " << vt.l[1];
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2ul & v)
-{
-  Packet vt;
-  vt.v2ul = v;
-  s << vt.ul[0] << ", " << vt.ul[1] ;
-  return s;
-}
-
-inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
-{
-  Packet vt;
-  vt.v2d = v;
-  s << vt.d[0] << ", " << vt.d[1];
-  return s;
-}
-
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
-{
-  Packet vt;
-  vt.v4f = v;
-  s << vt.f[0] << ", " << vt.f[1] << ", " << vt.f[2] << ", " << vt.f[3];
-  return s;
-}
-#endif
-
-template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet *vfrom;
-  vfrom = (Packet *) from;
-  return vfrom->v4i;
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet *vfrom;
-  vfrom = (Packet *) from;
-  return vfrom->v2d;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  Packet *vto;
-  vto = (Packet *) to;
-  vto->v4i = from;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<double>(double*   to, const Packet2d& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  Packet *vto;
-  vto = (Packet *) to;
-  vto->v2d = from;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from)
-{
-  return vec_splats(from);
-}
-template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
-  return vec_splats(from);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4i>(const int *a,
-                      Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
-{
-  a3 = pload<Packet4i>(a);
-  a0 = vec_splat(a3, 0);
-  a1 = vec_splat(a3, 1);
-  a2 = vec_splat(a3, 2);
-  a3 = vec_splat(a3, 3);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet2d>(const double *a,
-                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
-{
-  a1 = pload<Packet2d>(a);
-  a0 = vec_splat(a1, 0);
-  a1 = vec_splat(a1, 1);
-  a3 = pload<Packet2d>(a+2);
-  a2 = vec_splat(a3, 0);
-  a3 = vec_splat(a3, 1);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
-{
-  int EIGEN_ALIGN16 ai[4];
-  ai[0] = from[0*stride];
-  ai[1] = from[1*stride];
-  ai[2] = from[2*stride];
-  ai[3] = from[3*stride];
- return pload<Packet4i>(ai);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
-{
-  double EIGEN_ALIGN16 af[2];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
- return pload<Packet2d>(af);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
-{
-  int EIGEN_ALIGN16 ai[4];
-  pstore<int>((int *)ai, from);
-  to[0*stride] = ai[0];
-  to[1*stride] = ai[1];
-  to[2*stride] = ai[2];
-  to[3*stride] = ai[3];
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
-{
-  double EIGEN_ALIGN16 af[2];
-  pstore<double>(af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a + b); }
-template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a + b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a - b); }
-template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a - b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a * b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a * b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a / b); }
-template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a / b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return (-a); }
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return (-a); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd<Packet4i>(pmul<Packet4i>(a, b), c); }
-template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
-
-template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a)    { return padd<Packet4i>(pset1<Packet4i>(a), p4i_COUNTDOWN); }
-template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return padd<Packet2d>(pset1<Packet2d>(a), p2d_COUNTDOWN); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_min(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_max(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return pand<Packet4i>(a, vec_nor(b, b)); }
-template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
-
-template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) { return vec_round(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const  Packet2d& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return vec_floor(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int*       from) { return pload<Packet4i>(from); }
-template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double*    from) { return pload<Packet2d>(from); }
-
-
-template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
-{
-  Packet4i p = pload<Packet4i>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*   from)
-{
-  Packet2d p = pload<Packet2d>(from);
-  return vec_perm(p, p, p16uc_PSET64_HI);
-}
-
-template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*        to, const Packet4i& from) { pstore<int>(to, from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<double>(double*  to, const Packet2d& from) { pstore<double>(to, from); }
-
-template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int    EIGEN_ALIGN16 x[4]; pstore(x, a); return x[0]; }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore(x, a); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
-{
-  return reinterpret_cast<Packet4i>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-
-template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
-{
-  return reinterpret_cast<Packet2d>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE64));
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pabs<Packet4i>(const Packet4i& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE Packet2d pabs<Packet2d>(const Packet2d& a) { return vec_abs(a); }
-
-template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
-{
-  Packet4i b, sum;
-  b   = vec_sld(a, a, 8);
-  sum = padd<Packet4i>(a, b);
-  b   = vec_sld(sum, sum, 4);
-  sum = padd<Packet4i>(sum, b);
-  return pfirst(sum);
-}
-
-template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
-{
-  Packet2d b, sum;
-  b   = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8));
-  sum = padd<Packet2d>(a, b);
-  return pfirst(sum);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
-{
-  EIGEN_ALIGN16 int aux[4];
-  pstore(aux, a);
-  return aux[0] * aux[1] * aux[2] * aux[3];
-}
-
-template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
-{
-  Packet4i b, res;
-  b   = pmin<Packet4i>(a, vec_sld(a, a, 8));
-  res = pmin<Packet4i>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmin<Packet2d>(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
-}
-
-// max
-template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
-{
-  Packet4i b, res;
-  b = pmax<Packet4i>(a, vec_sld(a, a, 8));
-  res = pmax<Packet4i>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-// max
-template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
-{
-  return pfirst(pmax<Packet2d>(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4i,4>& kernel) {
-  Packet4i t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  Packet4i t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  Packet4i t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  Packet4i t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet2d,2>& kernel) {
-  Packet2d t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
-  Packet2d t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
-  kernel.packet[0] = t0;
-  kernel.packet[1] = t1;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
-  Packet4ui mask = vec_cmpeq(select, reinterpret_cast<Packet4ui>(p4i_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-
-template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
-  Packet2ul select = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2ul mask = vec_cmpeq(select, reinterpret_cast<Packet2ul>(p2l_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-/* z13 has no vector float support so we emulate that with double
-   z14 has proper vector float support.
-*/
-#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
-/* Helper function to simulate a vec_splat_packet4f
- */
-template<int element> EIGEN_STRONG_INLINE Packet4f vec_splat_packet4f(const Packet4f&   from)
-{
-  Packet4f splat;
-  switch (element) {
-  case 0:
-    splat.v4f[0] = vec_splat(from.v4f[0], 0);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  case 1:
-    splat.v4f[0] = vec_splat(from.v4f[0], 1);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  case 2:
-    splat.v4f[0] = vec_splat(from.v4f[1], 0);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  case 3:
-    splat.v4f[0] = vec_splat(from.v4f[1], 1);
-    splat.v4f[1] = splat.v4f[0];
-    break;
-  }
-  return splat;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float*   from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet4f vfrom;
-  vfrom.v4f[0] = vec_ld2f(&from[0]);
-  vfrom.v4f[1] = vec_ld2f(&from[2]);
-  return vfrom;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  vec_st2f(from.v4f[0], &to[0]);
-  vec_st2f(from.v4f[1], &to[2]);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&    from)
-{
-  Packet4f to;
-  to.v4f[0] = pset1<Packet2d>(static_cast<const double&>(from));
-  to.v4f[1] = to.v4f[0];
-  return to;
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  a3 = pload<Packet4f>(a);
-  a0 = vec_splat_packet4f<0>(a3);
-  a1 = vec_splat_packet4f<1>(a3);
-  a2 = vec_splat_packet4f<2>(a3);
-  a3 = vec_splat_packet4f<3>(a3);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  float EIGEN_ALIGN16 ai[4];
-  ai[0] = from[0*stride];
-  ai[1] = from[1*stride];
-  ai[2] = from[2*stride];
-  ai[3] = from[3*stride];
- return pload<Packet4f>(ai);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  float EIGEN_ALIGN16 ai[4];
-  pstore<float>((float *)ai, from);
-  to[0*stride] = ai[0];
-  to[1*stride] = ai[1];
-  to[2*stride] = ai[2];
-  to[3*stride] = ai[3];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] + b.v4f[0];
-  c.v4f[1] = a.v4f[1] + b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] - b.v4f[0];
-  c.v4f[1] = a.v4f[1] - b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] * b.v4f[0];
-  c.v4f[1] = a.v4f[1] * b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f c;
-  c.v4f[0] = a.v4f[0] / b.v4f[0];
-  c.v4f[1] = a.v4f[1] / b.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
-{
-  Packet4f c;
-  c.v4f[0] = -a.v4f[0];
-  c.v4f[1] = -a.v4f[1];
-  return c;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
-{
-  Packet4f res;
-  res.v4f[0] = vec_madd(a.v4f[0], b.v4f[0], c.v4f[0]);
-  res.v4f[1] = vec_madd(a.v4f[1], b.v4f[1], c.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pmin(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pmin(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pmax(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pmax(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pand(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pand(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = por(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = por(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pxor(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pxor(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pandnot(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pandnot(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = vec_round(a.v4f[0]);
-  res.v4f[1] = vec_round(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const  Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = vec_ceil(a.v4f[0]);
-  res.v4f[1] = vec_ceil(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = vec_floor(a.v4f[0]);
-  res.v4f[1] = vec_floor(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*    from)
-{
-  Packet4f p = pload<Packet4f>(from);
-  p.v4f[1] = vec_splat(p.v4f[0], 1);
-  p.v4f[0] = vec_splat(p.v4f[0], 0);
-  return p;
-}
-
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { float  EIGEN_ALIGN16 x[2]; vec_st2f(a.v4f[0], &x[0]); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  Packet4f rev;
-  rev.v4f[0] = preverse<Packet2d>(a.v4f[1]);
-  rev.v4f[1] = preverse<Packet2d>(a.v4f[0]);
-  return rev;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>(const Packet4f& a)
-{
-  Packet4f res;
-  res.v4f[0] = pabs(a.v4f[0]);
-  res.v4f[1] = pabs(a.v4f[1]);
-  return res;
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet2d sum;
-  sum = padd<Packet2d>(a.v4f[0], a.v4f[1]);
-  double first = predux<Packet2d>(sum);
-  return static_cast<float>(first);
-}
-
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  // Return predux_mul<Packet2d> of the subvectors product
-  return static_cast<float>(pfirst(predux_mul(pmul(a.v4f[0], a.v4f[1]))));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  Packet2d b, res;
-  b   = pmin<Packet2d>(a.v4f[0], a.v4f[1]);
-  res = pmin<Packet2d>(b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
-  return static_cast<float>(pfirst(res));
-}
-
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  Packet2d b, res;
-  b   = pmax<Packet2d>(a.v4f[0], a.v4f[1]);
-  res = pmax<Packet2d>(b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
-  return static_cast<float>(pfirst(res));
-}
-
-/* Split the Packet4f PacketBlock into 4 Packet2d PacketBlocks and transpose each one
- */
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  PacketBlock<Packet2d,2> t0,t1,t2,t3;
-  // copy top-left 2x2 Packet2d block
-  t0.packet[0] = kernel.packet[0].v4f[0];
-  t0.packet[1] = kernel.packet[1].v4f[0];
-
-  // copy top-right 2x2 Packet2d block
-  t1.packet[0] = kernel.packet[0].v4f[1];
-  t1.packet[1] = kernel.packet[1].v4f[1];
-
-  // copy bottom-left 2x2 Packet2d block
-  t2.packet[0] = kernel.packet[2].v4f[0];
-  t2.packet[1] = kernel.packet[3].v4f[0];
-
-  // copy bottom-right 2x2 Packet2d block
-  t3.packet[0] = kernel.packet[2].v4f[1];
-  t3.packet[1] = kernel.packet[3].v4f[1];
-
-  // Transpose all 2x2 blocks
-  ptranspose(t0);
-  ptranspose(t1);
-  ptranspose(t2);
-  ptranspose(t3);
-
-  // Copy back transposed blocks, but exchange t1 and t2 due to transposition
-  kernel.packet[0].v4f[0] = t0.packet[0];
-  kernel.packet[0].v4f[1] = t2.packet[0];
-  kernel.packet[1].v4f[0] = t0.packet[1];
-  kernel.packet[1].v4f[1] = t2.packet[1];
-  kernel.packet[2].v4f[0] = t1.packet[0];
-  kernel.packet[2].v4f[1] = t3.packet[0];
-  kernel.packet[3].v4f[0] = t1.packet[1];
-  kernel.packet[3].v4f[1] = t3.packet[1];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  Packet2ul select_hi = { ifPacket.select[0], ifPacket.select[1] };
-  Packet2ul select_lo = { ifPacket.select[2], ifPacket.select[3] };
-  Packet2ul mask_hi = vec_cmpeq(select_hi, reinterpret_cast<Packet2ul>(p2l_ONE));
-  Packet2ul mask_lo = vec_cmpeq(select_lo, reinterpret_cast<Packet2ul>(p2l_ONE));
-  Packet4f result;
-  result.v4f[0] = vec_sel(elsePacket.v4f[0], thenPacket.v4f[0], mask_hi);
-  result.v4f[1] = vec_sel(elsePacket.v4f[1], thenPacket.v4f[1], mask_lo);
-  return result;
-}
-
-template<> Packet4f EIGEN_STRONG_INLINE pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pcmp_le(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pcmp_le(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> Packet4f EIGEN_STRONG_INLINE pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pcmp_lt(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pcmp_lt(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-template<> Packet4f EIGEN_STRONG_INLINE pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b)
-{
-  Packet4f res;
-  res.v4f[0] = pcmp_eq(a.v4f[0], b.v4f[0]);
-  res.v4f[1] = pcmp_eq(a.v4f[1], b.v4f[1]);
-  return res;
-}
-
-#else
-template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_LOAD
-  Packet *vfrom;
-  vfrom = (Packet *) from;
-  return vfrom->v4f;
-}
-
-template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from)
-{
-  // FIXME: No intrinsic yet
-  EIGEN_DEBUG_ALIGNED_STORE
-  Packet *vto;
-  vto = (Packet *) to;
-  vto->v4f = from;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from)
-{
-  return vec_splats(from);
-}
-
-template<> EIGEN_STRONG_INLINE void
-pbroadcast4<Packet4f>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
-{
-  a3 = pload<Packet4f>(a);
-  a0 = vec_splat(a3, 0);
-  a1 = vec_splat(a3, 1);
-  a2 = vec_splat(a3, 2);
-  a3 = vec_splat(a3, 3);
-}
-
-template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
-{
-  float EIGEN_ALIGN16 af[4];
-  af[0] = from[0*stride];
-  af[1] = from[1*stride];
-  af[2] = from[2*stride];
-  af[3] = from[3*stride];
- return pload<Packet4f>(af);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
-{
-  float EIGEN_ALIGN16 af[4];
-  pstore<float>((float*)af, from);
-  to[0*stride] = af[0];
-  to[1*stride] = af[1];
-  to[2*stride] = af[2];
-  to[3*stride] = af[3];
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a + b); }
-template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a - b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a * b); }
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a / b); }
-template<> EIGEN_STRONG_INLINE Packet4f pnegate<Packet4f>(const Packet4f& a) { return (-a); }
-template<> EIGEN_STRONG_INLINE Packet4f pconj<Packet4f>  (const Packet4f& a) { return a; }
-template<> EIGEN_STRONG_INLINE Packet4f pmadd<Packet4f>  (const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a, b, c); }
-template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_min(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_max(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>    (const Packet4f& a, const Packet4f& b) { return vec_or(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_xor(a, b); }
-template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, vec_nor(b, b)); }
-template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f> (const Packet4f& a) { return vec_round(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>  (const Packet4f& a) { return vec_ceil(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f> (const Packet4f& a) { return vec_floor(a); }
-template<> EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>   (const Packet4f& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; pstore(x, a); return x[0]; }
-
-template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
-{
-  Packet4f p = pload<Packet4f>(from);
-  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
-{
-  return reinterpret_cast<Packet4f>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
-}
-
-template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, sum;
-  b   = vec_sld(a, a, 8);
-  sum = padd<Packet4f>(a, b);
-  b   = vec_sld(sum, sum, 4);
-  sum = padd<Packet4f>(sum, b);
-  return pfirst(sum);
-}
-
-// Other reduction functions:
-// mul
-template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{
-  Packet4f prod;
-  prod = pmul(a, vec_sld(a, a, 8));
-  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
-}
-
-// min
-template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, res;
-  b   = pmin<Packet4f>(a, vec_sld(a, a, 8));
-  res = pmin<Packet4f>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-// max
-template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
-{
-  Packet4f b, res;
-  b = pmax<Packet4f>(a, vec_sld(a, a, 8));
-  res = pmax<Packet4f>(b, vec_sld(b, b, 4));
-  return pfirst(res);
-}
-
-EIGEN_DEVICE_FUNC inline void
-ptranspose(PacketBlock<Packet4f,4>& kernel) {
-  Packet4f t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
-  Packet4f t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
-  Packet4f t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
-  Packet4f t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
-  kernel.packet[0] = vec_mergeh(t0, t2);
-  kernel.packet[1] = vec_mergel(t0, t2);
-  kernel.packet[2] = vec_mergeh(t1, t3);
-  kernel.packet[3] = vec_mergel(t1, t3);
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
-  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
-  Packet4ui mask = vec_cmpeq(select, reinterpret_cast<Packet4ui>(p4i_ONE));
-  return vec_sel(elsePacket, thenPacket, mask);
-}
-
-#endif
-
-template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
-template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f> (const float* from) { return pload<Packet4f>(from); }
-template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) { pstore<float>(to, from); }
-template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>  (const float& a)  { return padd<Packet4f>(pset1<Packet4f>(a), p4f_COUNTDOWN); }
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PACKET_MATH_ZVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h
deleted file mode 100644
index bf64ef4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h
+++ /dev/null
@@ -1,177 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ASSIGNMENT_FUNCTORS_H
-#define EIGEN_ASSIGNMENT_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-  
-/** \internal
-  * \brief Template functor for scalar/packet assignment
-  *
-  */
-template<typename DstScalar,typename SrcScalar> struct assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a = b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,b); }
-};
-
-// Empty overload for void type (used by PermutationMatrix)
-template<typename DstScalar> struct assign_op<DstScalar,void> {};
-
-template<typename DstScalar,typename SrcScalar>
-struct functor_traits<assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::Vectorizable && packet_traits<SrcScalar>::Vectorizable
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with addition
-  *
-  */
-template<typename DstScalar,typename SrcScalar> struct add_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(add_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a += b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar,typename SrcScalar>
-struct functor_traits<add_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasAdd
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with subtraction
-  *
-  */
-template<typename DstScalar,typename SrcScalar> struct sub_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(sub_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a -= b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar,typename SrcScalar>
-struct functor_traits<sub_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasSub
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with multiplication
-  *
-  */
-template<typename DstScalar, typename SrcScalar=DstScalar>
-struct mul_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(mul_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a *= b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pmul(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar, typename SrcScalar>
-struct functor_traits<mul_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasMul
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with diviving
-  *
-  */
-template<typename DstScalar, typename SrcScalar=DstScalar> struct div_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(div_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a /= b; }
-  
-  template<int Alignment, typename Packet>
-  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
-  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pdiv(internal::ploadt<Packet,Alignment>(a),b)); }
-};
-template<typename DstScalar, typename SrcScalar>
-struct functor_traits<div_assign_op<DstScalar,SrcScalar> > {
-  enum {
-    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
-    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasDiv
-  };
-};
-
-/** \internal
-  * \brief Template functor for scalar/packet assignment with swapping
-  *
-  * It works as follow. For a non-vectorized evaluation loop, we have:
-  *   for(i) func(A.coeffRef(i), B.coeff(i));
-  * where B is a SwapWrapper expression. The trick is to make SwapWrapper::coeff behaves like a non-const coeffRef.
-  * Actually, SwapWrapper might not even be needed since even if B is a plain expression, since it has to be writable
-  * B.coeff already returns a const reference to the underlying scalar value.
-  * 
-  * The case of a vectorized loop is more tricky:
-  *   for(i,j) func.assignPacket<A_Align>(&A.coeffRef(i,j), B.packet<B_Align>(i,j));
-  * Here, B must be a SwapWrapper whose packet function actually returns a proxy object holding a Scalar*,
-  * the actual alignment and Packet type.
-  *
-  */
-template<typename Scalar> struct swap_assign_op {
-
-  EIGEN_EMPTY_STRUCT_CTOR(swap_assign_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const
-  {
-#ifdef EIGEN_GPUCC
-    // FIXME is there some kind of cuda::swap?
-    Scalar t=b; const_cast<Scalar&>(b)=a; a=t;
-#else
-    using std::swap;
-    swap(a,const_cast<Scalar&>(b));
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<swap_assign_op<Scalar> > {
-  enum {
-    Cost = 3 * NumTraits<Scalar>::ReadCost,
-    PacketAccess = 
-    #if defined(EIGEN_VECTORIZE_AVX) && EIGEN_COMP_CLANG && (EIGEN_COMP_CLANG<800 || defined(__apple_build_version__))
-    // This is a partial workaround for a bug in clang generating bad code
-    // when mixing 256/512 bits loads and 128 bits moves.
-    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1684
-    //     https://bugs.llvm.org/show_bug.cgi?id=40815
-    0
-    #else
-    packet_traits<Scalar>::Vectorizable
-    #endif
-  };
-};
-
-} // namespace internal
-
-} // namespace Eigen
-
-#endif // EIGEN_ASSIGNMENT_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h
deleted file mode 100644
index 63f09ab..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h
+++ /dev/null
@@ -1,541 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BINARY_FUNCTORS_H
-#define EIGEN_BINARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- associative binary functors ----------
-
-template<typename Arg1, typename Arg2>
-struct binary_op_base
-{
-  typedef Arg1 first_argument_type;
-  typedef Arg2 second_argument_type;
-};
-
-/** \internal
-  * \brief Template functor to compute the sum of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum()
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_sum_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_sum_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
-#else
-  scalar_sum_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a + b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::padd(a,b); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  { return internal::predux(a); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_sum_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2, // rough estimate!
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd
-    // TODO vectorize mixed sum
-  };
-};
-
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_sum_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a || b; }
-
-
-/** \internal
-  * \brief Template functor to compute the product of two scalars
-  *
-  * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_product_op  : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_product_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
-#else
-  scalar_product_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pmul(a,b); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  { return internal::predux_mul(a); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (int(NumTraits<LhsScalar>::MulCost) + int(NumTraits<RhsScalar>::MulCost))/2, // rough estimate!
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
-    // TODO vectorize mixed product
-  };
-};
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_product_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a && b; }
-
-
-/** \internal
-  * \brief Template functor to compute the conjugate product of two scalars
-  *
-  * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_conj_product_op  : binary_op_base<LhsScalar,RhsScalar>
-{
-
-  enum {
-    Conj = NumTraits<LhsScalar>::IsComplex
-  };
-  
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_conj_product_op>::ReturnType result_type;
-  
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const
-  { return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); }
-  
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  { return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = NumTraits<LhsScalar>::MulCost,
-    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the min of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
-  */
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct scalar_min_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_min_op>::ReturnType result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const {
-    return internal::pmin<NaNPropagation>(a, b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  {
-    return internal::pmin<NaNPropagation>(a,b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  {
-    return internal::predux_min<NaNPropagation>(a);
-  }
-};
-
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct functor_traits<scalar_min_op<LhsScalar,RhsScalar, NaNPropagation> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the max of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
-  */
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct scalar_max_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_max_op>::ReturnType result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const {
-    return internal::pmax<NaNPropagation>(a,b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
-  {
-    return internal::pmax<NaNPropagation>(a,b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
-  {
-    return internal::predux_max<NaNPropagation>(a);
-  }
-};
-
-template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
-struct functor_traits<scalar_max_op<LhsScalar,RhsScalar, NaNPropagation> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax
-  };
-};
-
-/** \internal
-  * \brief Template functors for comparison of two scalars
-  * \todo Implement packet-comparisons
-  */
-template<typename LhsScalar, typename RhsScalar, ComparisonName cmp> struct scalar_cmp_op;
-
-template<typename LhsScalar, typename RhsScalar, ComparisonName cmp>
-struct functor_traits<scalar_cmp_op<LhsScalar,RhsScalar, cmp> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = false
-  };
-};
-
-template<ComparisonName Cmp, typename LhsScalar, typename RhsScalar>
-struct result_of<scalar_cmp_op<LhsScalar, RhsScalar, Cmp>(LhsScalar,RhsScalar)> {
-  typedef bool type;
-};
-
-
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_EQ> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a==b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LT> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LE> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<=b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GT> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GE> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>=b;}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_UNORD> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return !(a<=b || b<=a);}
-};
-template<typename LhsScalar, typename RhsScalar>
-struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_NEQ> : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef bool result_type;
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a!=b;}
-};
-
-/** \internal
-  * \brief Template functor to compute the hypot of two \b positive \b and \b real scalars
-  *
-  * \sa MatrixBase::stableNorm(), class Redux
-  */
-template<typename Scalar>
-struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar>
-{
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar &x, const Scalar &y) const
-  {
-    // This functor is used by hypotNorm only for which it is faster to first apply abs
-    // on all coefficients prior to reduction through hypot.
-    // This way we avoid calling abs on positive and real entries, and this also permits
-    // to seamlessly handle complexes. Otherwise we would have to handle both real and complexes
-    // through the same functor...
-    return internal::positive_real_hypot(x,y);
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_hypot_op<Scalar,Scalar> > {
-  enum
-  {
-    Cost = 3 * NumTraits<Scalar>::AddCost +
-           2 * NumTraits<Scalar>::MulCost +
-           2 * scalar_div_cost<Scalar,false>::value,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the pow of two scalars
-  * See the specification of pow in https://en.cppreference.com/w/cpp/numeric/math/pow
-  */
-template<typename Scalar, typename Exponent>
-struct scalar_pow_op  : binary_op_base<Scalar,Exponent>
-{
-  typedef typename ScalarBinaryOpTraits<Scalar,Exponent,scalar_pow_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_pow_op)
-#else
-  scalar_pow_op() {
-    typedef Scalar LhsScalar;
-    typedef Exponent RhsScalar;
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC
-  inline result_type operator() (const Scalar& a, const Exponent& b) const { return numext::pow(a, b); }
-
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  {
-    return generic_pow(a,b);
-  }
-};
-
-template<typename Scalar, typename Exponent>
-struct functor_traits<scalar_pow_op<Scalar,Exponent> > {
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = (!NumTraits<Scalar>::IsComplex && !NumTraits<Scalar>::IsInteger &&
-                    packet_traits<Scalar>::HasExp && packet_traits<Scalar>::HasLog &&
-                    packet_traits<Scalar>::HasRound && packet_traits<Scalar>::HasCmp &&
-                    // Temporarly disable packet access for half/bfloat16 until
-                    // accuracy is improved.
-                    !is_same<Scalar, half>::value && !is_same<Scalar, bfloat16>::value
-                    )
-  };
-};
-
-//---------- non associative binary functors ----------
-
-/** \internal
-  * \brief Template functor to compute the difference of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::operator-
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_difference_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_difference_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
-#else
-  scalar_difference_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a - b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::psub(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_difference_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2,
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the quotient of two scalars
-  *
-  * \sa class CwiseBinaryOp, Cwise::operator/()
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_quotient_op  : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_quotient_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
-#else
-  scalar_quotient_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pdiv(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > {
-  typedef typename scalar_quotient_op<LhsScalar,RhsScalar>::result_type result_type;
-  enum {
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv,
-    Cost = scalar_div_cost<result_type,PacketAccess>::value
-  };
-};
-
-
-
-/** \internal
-  * \brief Template functor to compute the and of two booleans
-  *
-  * \sa class CwiseBinaryOp, ArrayBase::operator&&
-  */
-struct scalar_boolean_and_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a && b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pand(a,b); }
-};
-template<> struct functor_traits<scalar_boolean_and_op> {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = true
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the or of two booleans
-  *
-  * \sa class CwiseBinaryOp, ArrayBase::operator||
-  */
-struct scalar_boolean_or_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a || b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::por(a,b); }
-};
-template<> struct functor_traits<scalar_boolean_or_op> {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = true
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the xor of two booleans
- *
- * \sa class CwiseBinaryOp, ArrayBase::operator^
- */
-struct scalar_boolean_xor_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_xor_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a ^ b; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pxor(a,b); }
-};
-template<> struct functor_traits<scalar_boolean_xor_op> {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = true
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the absolute difference of two scalars
-  *
-  * \sa class CwiseBinaryOp, MatrixBase::absolute_difference
-  */
-template<typename LhsScalar,typename RhsScalar>
-struct scalar_absolute_difference_op : binary_op_base<LhsScalar,RhsScalar>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_absolute_difference_op>::ReturnType result_type;
-#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_absolute_difference_op)
-#else
-  scalar_absolute_difference_op() {
-    EIGEN_SCALAR_BINARY_OP_PLUGIN
-  }
-#endif
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
-  { return numext::absdiff(a,b); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
-  { return internal::pabsdiff(a,b); }
-};
-template<typename LhsScalar,typename RhsScalar>
-struct functor_traits<scalar_absolute_difference_op<LhsScalar,RhsScalar> > {
-  enum {
-    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
-    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAbsDiff
-  };
-};
-
-
-
-//---------- binary functors bound to a constant, thus appearing as a unary functor ----------
-
-// The following two classes permits to turn any binary functor into a unary one with one argument bound to a constant value.
-// They are analogues to std::binder1st/binder2nd but with the following differences:
-//  - they are compatible with packetOp
-//  - they are portable across C++ versions (the std::binder* are deprecated in C++11)
-template<typename BinaryOp> struct bind1st_op : BinaryOp {
-
-  typedef typename BinaryOp::first_argument_type  first_argument_type;
-  typedef typename BinaryOp::second_argument_type second_argument_type;
-  typedef typename BinaryOp::result_type          result_type;
-
-  EIGEN_DEVICE_FUNC explicit bind1st_op(const first_argument_type &val) : m_value(val) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const second_argument_type& b) const { return BinaryOp::operator()(m_value,b); }
-
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& b) const
-  { return BinaryOp::packetOp(internal::pset1<Packet>(m_value), b); }
-
-  first_argument_type m_value;
-};
-template<typename BinaryOp> struct functor_traits<bind1st_op<BinaryOp> > : functor_traits<BinaryOp> {};
-
-
-template<typename BinaryOp> struct bind2nd_op : BinaryOp {
-
-  typedef typename BinaryOp::first_argument_type  first_argument_type;
-  typedef typename BinaryOp::second_argument_type second_argument_type;
-  typedef typename BinaryOp::result_type          result_type;
-
-  EIGEN_DEVICE_FUNC explicit bind2nd_op(const second_argument_type &val) : m_value(val) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const first_argument_type& a) const { return BinaryOp::operator()(a,m_value); }
-
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return BinaryOp::packetOp(a,internal::pset1<Packet>(m_value)); }
-
-  second_argument_type m_value;
-};
-template<typename BinaryOp> struct functor_traits<bind2nd_op<BinaryOp> > : functor_traits<BinaryOp> {};
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BINARY_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h
deleted file mode 100644
index 192f225..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h
+++ /dev/null
@@ -1,189 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NULLARY_FUNCTORS_H
-#define EIGEN_NULLARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar>
-struct scalar_constant_op {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() () const { return m_other; }
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetOp() const { return internal::pset1<PacketType>(m_other); }
-  const Scalar m_other;
-};
-template<typename Scalar>
-struct functor_traits<scalar_constant_op<Scalar> >
-{ enum { Cost = 0 /* as the constant value should be loaded in register only once for the whole expression */,
-         PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
-
-template<typename Scalar> struct scalar_identity_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op)
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType row, IndexType col) const { return row==col ? Scalar(1) : Scalar(0); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_identity_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; };
-
-template <typename Scalar, bool IsInteger> struct linspaced_op_impl;
-
-template <typename Scalar>
-struct linspaced_op_impl<Scalar,/*IsInteger*/false>
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
-    m_low(low), m_high(high), m_size1(num_steps==1 ? 1 : num_steps-1), m_step(num_steps==1 ? Scalar() : Scalar((high-low)/RealScalar(num_steps-1))),
-    m_flip(numext::abs(high)<numext::abs(low))
-  {}
-
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const {
-    if(m_flip)
-      return (i==0)? m_low : Scalar(m_high - RealScalar(m_size1-i)*m_step);
-    else
-      return (i==m_size1)? m_high : Scalar(m_low + RealScalar(i)*m_step);
-  }
-
-  template<typename Packet, typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const
-  {
-    // Principle:
-    // [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
-    if(m_flip)
-    {
-      Packet pi = plset<Packet>(Scalar(i-m_size1));
-      Packet res = padd(pset1<Packet>(m_high), pmul(pset1<Packet>(m_step), pi));
-      if (EIGEN_PREDICT_TRUE(i != 0)) return res;
-      Packet mask = pcmp_lt(pset1<Packet>(0), plset<Packet>(0));
-      return pselect<Packet>(mask, res, pset1<Packet>(m_low));
-    }
-    else
-    {
-      Packet pi = plset<Packet>(Scalar(i));
-      Packet res = padd(pset1<Packet>(m_low), pmul(pset1<Packet>(m_step), pi));
-      if(EIGEN_PREDICT_TRUE(i != m_size1-unpacket_traits<Packet>::size+1)) return res;
-      Packet mask = pcmp_lt(plset<Packet>(0), pset1<Packet>(unpacket_traits<Packet>::size-1));
-      return pselect<Packet>(mask, res, pset1<Packet>(m_high));
-    }
-  }
-
-  const Scalar m_low;
-  const Scalar m_high;
-  const Index m_size1;
-  const Scalar m_step;
-  const bool m_flip;
-};
-
-template <typename Scalar>
-struct linspaced_op_impl<Scalar,/*IsInteger*/true>
-{
-  EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
-    m_low(low),
-    m_multiplier((high-low)/convert_index<Scalar>(num_steps<=1 ? 1 : num_steps-1)),
-    m_divisor(convert_index<Scalar>((high>=low?num_steps:-num_steps)+(high-low))/((numext::abs(high-low)+1)==0?1:(numext::abs(high-low)+1))),
-    m_use_divisor(num_steps>1 && (numext::abs(high-low)+1)<num_steps)
-  {}
-
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Scalar operator() (IndexType i) const
-  {
-    if(m_use_divisor) return m_low + convert_index<Scalar>(i)/m_divisor;
-    else              return m_low + convert_index<Scalar>(i)*m_multiplier;
-  }
-
-  const Scalar m_low;
-  const Scalar m_multiplier;
-  const Scalar m_divisor;
-  const bool m_use_divisor;
-};
-
-// ----- Linspace functor ----------------------------------------------------------------
-
-// Forward declaration (we default to random access which does not really give
-// us a speed gain when using packet access but it allows to use the functor in
-// nested expressions).
-template <typename Scalar> struct linspaced_op;
-template <typename Scalar> struct functor_traits< linspaced_op<Scalar> >
-{
-  enum
-  {
-    Cost = 1,
-    PacketAccess =   (!NumTraits<Scalar>::IsInteger) && packet_traits<Scalar>::HasSetLinear && packet_traits<Scalar>::HasBlend,
-                  /*&& ((!NumTraits<Scalar>::IsInteger) || packet_traits<Scalar>::HasDiv),*/ // <- vectorization for integer is currently disabled
-    IsRepeatable = true
-  };
-};
-template <typename Scalar> struct linspaced_op
-{
-  EIGEN_DEVICE_FUNC linspaced_op(const Scalar& low, const Scalar& high, Index num_steps)
-    : impl((num_steps==1 ? high : low),high,num_steps)
-  {}
-
-  template<typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const { return impl(i); }
-
-  template<typename Packet,typename IndexType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const { return impl.template packetOp<Packet>(i); }
-
-  // This proxy object handles the actual required temporaries and the different
-  // implementations (integer vs. floating point).
-  const linspaced_op_impl<Scalar,NumTraits<Scalar>::IsInteger> impl;
-};
-
-// Linear access is automatically determined from the operator() prototypes available for the given functor.
-// If it exposes an operator()(i,j), then we assume the i and j coefficients are required independently
-// and linear access is not possible. In all other cases, linear access is enabled.
-// Users should not have to deal with this structure.
-template<typename Functor> struct functor_has_linear_access { enum { ret = !has_binary_operator<Functor>::value }; };
-
-// For unreliable compilers, let's specialize the has_*ary_operator
-// helpers so that at least built-in nullary functors work fine.
-#if !( (EIGEN_COMP_MSVC>1600) || (EIGEN_GNUC_AT_LEAST(4,8)) || (EIGEN_COMP_ICC>=1600))
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 1}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; };
-
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 1}; };
-
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 1}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; };
-
-template<typename Scalar,typename IndexType>
-struct has_nullary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 1}; };
-template<typename Scalar,typename IndexType>
-struct has_unary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; };
-template<typename Scalar,typename IndexType>
-struct has_binary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; };
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_NULLARY_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/StlFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/StlFunctors.h
deleted file mode 100644
index 4570c9b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/StlFunctors.h
+++ /dev/null
@@ -1,166 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STL_FUNCTORS_H
-#define EIGEN_STL_FUNCTORS_H
-
-namespace Eigen {
-
-// Portable replacements for certain functors.
-namespace numext {
-
-template<typename T = void>
-struct equal_to {
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC bool operator()(const T& lhs, const T& rhs) const {
-    return lhs == rhs;
-  }
-};
-
-template<typename T = void>
-struct not_equal_to {
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC bool operator()(const T& lhs, const T& rhs) const {
-    return lhs != rhs;
-  }
-};
-
-}
-
-
-namespace internal {
-
-// default functor traits for STL functors:
-
-template<typename T>
-struct functor_traits<std::multiplies<T> >
-{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::divides<T> >
-{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::plus<T> >
-{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::minus<T> >
-{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::negate<T> >
-{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::logical_or<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::logical_and<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::logical_not<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::greater<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::less<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::greater_equal<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::less_equal<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::equal_to<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<numext::equal_to<T> >
-  : functor_traits<std::equal_to<T> > {};
-
-template<typename T>
-struct functor_traits<std::not_equal_to<T> >
-{ enum { Cost = 1, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<numext::not_equal_to<T> >
-  : functor_traits<std::not_equal_to<T> > {};
-
-#if (EIGEN_COMP_CXXVER < 11)
-// std::binder* are deprecated since c++11 and will be removed in c++17
-template<typename T>
-struct functor_traits<std::binder2nd<T> >
-{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
-
-template<typename T>
-struct functor_traits<std::binder1st<T> >
-{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
-#endif
-
-#if (EIGEN_COMP_CXXVER < 17)
-// std::unary_negate is deprecated since c++17 and will be removed in c++20
-template<typename T>
-struct functor_traits<std::unary_negate<T> >
-{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
-
-// std::binary_negate is deprecated since c++17 and will be removed in c++20
-template<typename T>
-struct functor_traits<std::binary_negate<T> >
-{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
-#endif
-
-#ifdef EIGEN_STDEXT_SUPPORT
-
-template<typename T0,typename T1>
-struct functor_traits<std::project1st<T0,T1> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::project2nd<T0,T1> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::select2nd<std::pair<T0,T1> > >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::select1st<std::pair<T0,T1> > >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-template<typename T0,typename T1>
-struct functor_traits<std::unary_compose<T0,T1> >
-{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; };
-
-template<typename T0,typename T1,typename T2>
-struct functor_traits<std::binary_compose<T0,T1,T2> >
-{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; };
-
-#endif // EIGEN_STDEXT_SUPPORT
-
-// allow to add new functors and specializations of functor_traits from outside Eigen.
-// this macro is really needed because functor_traits must be specialized after it is declared but before it is used...
-#ifdef EIGEN_FUNCTORS_PLUGIN
-#include EIGEN_FUNCTORS_PLUGIN
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_STL_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h
deleted file mode 100644
index b254e96..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h
+++ /dev/null
@@ -1,25 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TERNARY_FUNCTORS_H
-#define EIGEN_TERNARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-//---------- associative ternary functors ----------
-
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TERNARY_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h
deleted file mode 100644
index 16136d1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h
+++ /dev/null
@@ -1,1131 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UNARY_FUNCTORS_H
-#define EIGEN_UNARY_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * \brief Template functor to compute the opposite of a scalar
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::operator-
-  */
-template<typename Scalar> struct scalar_opposite_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::pnegate(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_opposite_op<Scalar> >
-{ enum {
-    Cost = NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasNegate };
-};
-
-/** \internal
-  * \brief Template functor to compute the absolute value of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::abs
-  */
-template<typename Scalar> struct scalar_abs_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::pabs(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_abs_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasAbs
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the score of a scalar, to chose a pivot
-  *
-  * \sa class CwiseUnaryOp
-  */
-template<typename Scalar> struct scalar_score_coeff_op : scalar_abs_op<Scalar>
-{
-  typedef void Score_is_abs;
-};
-template<typename Scalar>
-struct functor_traits<scalar_score_coeff_op<Scalar> > : functor_traits<scalar_abs_op<Scalar> > {};
-
-/* Avoid recomputing abs when we know the score and they are the same. Not a true Eigen functor.  */
-template<typename Scalar, typename=void> struct abs_knowing_score
-{
-  EIGEN_EMPTY_STRUCT_CTOR(abs_knowing_score)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  template<typename Score>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a, const Score&) const { return numext::abs(a); }
-};
-template<typename Scalar> struct abs_knowing_score<Scalar, typename scalar_score_coeff_op<Scalar>::Score_is_abs>
-{
-  EIGEN_EMPTY_STRUCT_CTOR(abs_knowing_score)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  template<typename Scal>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scal&, const result_type& a) const { return a; }
-};
-
-/** \internal
-  * \brief Template functor to compute the squared absolute value of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::abs2
-  */
-template<typename Scalar> struct scalar_abs2_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::pmul(a,a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_abs2_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; };
-
-/** \internal
-  * \brief Template functor to compute the conjugate of a complex value
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::conjugate()
-  */
-template<typename Scalar> struct scalar_conjugate_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op)
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::conj(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_conjugate_op<Scalar> >
-{
-  enum {
-    Cost = 0,
-    // Yes the cost is zero even for complexes because in most cases for which
-    // the cost is used, conjugation turns to be a no-op. Some examples:
-    //   cost(a*conj(b)) == cost(a*b)
-    //   cost(a+conj(b)) == cost(a+b)
-    //   <etc.
-    // If we don't set it to zero, then:
-    //   A.conjugate().lazyProduct(B.conjugate())
-    // will bake its operands. We definitely don't want that!
-    PacketAccess = packet_traits<Scalar>::HasConj
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the phase angle of a complex
-  *
-  * \sa class CwiseUnaryOp, Cwise::arg
-  */
-template<typename Scalar> struct scalar_arg_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_arg_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::arg(a); }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::parg(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_arg_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::IsComplex ? 5 * NumTraits<Scalar>::MulCost : NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasArg
-  };
-};
-/** \internal
-  * \brief Template functor to cast a scalar to another type
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::cast()
-  */
-template<typename Scalar, typename NewType>
-struct scalar_cast_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
-  typedef NewType result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); }
-};
-template<typename Scalar, typename NewType>
-struct functor_traits<scalar_cast_op<Scalar,NewType> >
-{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to arithmetically shift a scalar right by a number of bits
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::shift_right()
-  */
-template<typename Scalar, int N>
-struct scalar_shift_right_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_shift_right_op)
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const
-  { return a >> N; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::parithmetic_shift_right<N>(a); }
-};
-template<typename Scalar, int N>
-struct functor_traits<scalar_shift_right_op<Scalar,N> >
-{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; };
-
-/** \internal
-  * \brief Template functor to logically shift a scalar left by a number of bits
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::shift_left()
-  */
-template<typename Scalar, int N>
-struct scalar_shift_left_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_shift_left_op)
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const
-  { return a << N; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
-  { return internal::plogical_shift_left<N>(a); }
-};
-template<typename Scalar, int N>
-struct functor_traits<scalar_shift_left_op<Scalar,N> >
-{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; };
-
-/** \internal
-  * \brief Template functor to extract the real part of a complex
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::real()
-  */
-template<typename Scalar>
-struct scalar_real_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_real_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to extract the imaginary part of a complex
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::imag()
-  */
-template<typename Scalar>
-struct scalar_imag_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_imag_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to extract the real part of a complex as a reference
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::real()
-  */
-template<typename Scalar>
-struct scalar_real_ref_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_real_ref_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  * \brief Template functor to extract the imaginary part of a complex as a reference
-  *
-  * \sa class CwiseUnaryOp, MatrixBase::imag()
-  */
-template<typename Scalar>
-struct scalar_imag_ref_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op)
-  typedef typename NumTraits<Scalar>::Real result_type;
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_imag_ref_op<Scalar> >
-{ enum { Cost = 0, PacketAccess = false }; };
-
-/** \internal
-  *
-  * \brief Template functor to compute the exponential of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::exp()
-  */
-template<typename Scalar> struct scalar_exp_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::exp(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexp(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_exp_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasExp,
-    // The following numbers are based on the AVX implementation.
-#ifdef EIGEN_VECTORIZE_FMA
-    // Haswell can issue 2 add/mul/madd per cycle.
-    Cost =
-    (sizeof(Scalar) == 4
-     // float: 8 pmadd, 4 pmul, 2 padd/psub, 6 other
-     ? (8 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost)
-     // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
-     : (14 * NumTraits<Scalar>::AddCost +
-        6 * NumTraits<Scalar>::MulCost +
-        scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
-#else
-    Cost =
-    (sizeof(Scalar) == 4
-     // float: 7 pmadd, 6 pmul, 4 padd/psub, 10 other
-     ? (21 * NumTraits<Scalar>::AddCost + 13 * NumTraits<Scalar>::MulCost)
-     // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
-     : (23 * NumTraits<Scalar>::AddCost +
-        12 * NumTraits<Scalar>::MulCost +
-        scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
-#endif
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the exponential of a scalar - 1.
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::expm1()
-  */
-template<typename Scalar> struct scalar_expm1_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_expm1_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::expm1(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexpm1(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_expm1_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasExpm1,
-    Cost = functor_traits<scalar_exp_op<Scalar> >::Cost // TODO measure cost of expm1
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the logarithm of a scalar
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::log()
-  */
-template<typename Scalar> struct scalar_log_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_log_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasLog,
-    Cost =
-    (PacketAccess
-     // The following numbers are based on the AVX implementation.
-#ifdef EIGEN_VECTORIZE_FMA
-     // 8 pmadd, 6 pmul, 8 padd/psub, 16 other, can issue 2 add/mul/madd per cycle.
-     ? (20 * NumTraits<Scalar>::AddCost + 7 * NumTraits<Scalar>::MulCost)
-#else
-     // 8 pmadd, 6 pmul, 8 padd/psub, 20 other
-     ? (36 * NumTraits<Scalar>::AddCost + 14 * NumTraits<Scalar>::MulCost)
-#endif
-     // Measured cost of std::log.
-     : sizeof(Scalar)==4 ? 40 : 85)
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the logarithm of 1 plus a scalar value
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::log1p()
-  */
-template<typename Scalar> struct scalar_log1p_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log1p_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log1p(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog1p(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_log1p_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasLog1p,
-    Cost = functor_traits<scalar_log_op<Scalar> >::Cost // TODO measure cost of log1p
-  };
-};
-
-/** \internal
-  *
-  * \brief Template functor to compute the base-10 logarithm of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::log10()
-  */
-template<typename Scalar> struct scalar_log10_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log10_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { EIGEN_USING_STD(log10) return log10(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog10(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_log10_op<Scalar> >
-{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog10 }; };
-
-/** \internal
-  *
-  * \brief Template functor to compute the base-2 logarithm of a scalar
-  *
-  * \sa class CwiseUnaryOp, Cwise::log2()
-  */
-template<typename Scalar> struct scalar_log2_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_log2_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return Scalar(EIGEN_LOG2E) * numext::log(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog2(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_log2_op<Scalar> >
-{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; };
-
-/** \internal
-  * \brief Template functor to compute the square root of a scalar
-  * \sa class CwiseUnaryOp, Cwise::sqrt()
-  */
-template<typename Scalar> struct scalar_sqrt_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sqrt(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_sqrt_op<Scalar> > {
-  enum {
-#if EIGEN_FAST_MATH
-    // The following numbers are based on the AVX implementation.
-    Cost = (sizeof(Scalar) == 8 ? 28
-                                // 4 pmul, 1 pmadd, 3 other
-                                : (3 * NumTraits<Scalar>::AddCost +
-                                   5 * NumTraits<Scalar>::MulCost)),
-#else
-    // The following numbers are based on min VSQRT throughput on Haswell.
-    Cost = (sizeof(Scalar) == 8 ? 28 : 14),
-#endif
-    PacketAccess = packet_traits<Scalar>::HasSqrt
-  };
-};
-
-// Boolean specialization to eliminate -Wimplicit-conversion-floating-point-to-bool warnings.
-template<> struct scalar_sqrt_op<bool> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
-  template <typename Packet>
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return a; }
-};
-template <>
-struct functor_traits<scalar_sqrt_op<bool> > {
-  enum { Cost = 1, PacketAccess = packet_traits<bool>::Vectorizable };
-};
-
-/** \internal
-  * \brief Template functor to compute the reciprocal square root of a scalar
-  * \sa class CwiseUnaryOp, Cwise::rsqrt()
-  */
-template<typename Scalar> struct scalar_rsqrt_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_rsqrt_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::rsqrt(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::prsqrt(a); }
-};
-
-template<typename Scalar>
-struct functor_traits<scalar_rsqrt_op<Scalar> >
-{ enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasRsqrt
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the cosine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::cos()
-  */
-template<typename Scalar> struct scalar_cos_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return numext::cos(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcos(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_cos_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasCos
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the sine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::sin()
-  */
-template<typename Scalar> struct scalar_sin_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sin(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psin(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_sin_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasSin
-  };
-};
-
-
-/** \internal
-  * \brief Template functor to compute the tan of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::tan()
-  */
-template<typename Scalar> struct scalar_tan_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::tan(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::ptan(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_tan_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasTan
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the arc cosine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::acos()
-  */
-template<typename Scalar> struct scalar_acos_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::acos(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pacos(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_acos_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasACos
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the arc sine of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::asin()
-  */
-template<typename Scalar> struct scalar_asin_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::asin(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pasin(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_asin_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasASin
-  };
-};
-
-
-/** \internal
-  * \brief Template functor to compute the atan of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::atan()
-  */
-template<typename Scalar> struct scalar_atan_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_atan_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::atan(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::patan(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_atan_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasATan
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the tanh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::tanh()
-  */
-template <typename Scalar>
-struct scalar_tanh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::tanh(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const { return ptanh(x); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_tanh_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasTanh,
-    Cost = ( (EIGEN_FAST_MATH && is_same<Scalar,float>::value)
-// The following numbers are based on the AVX implementation,
-#ifdef EIGEN_VECTORIZE_FMA
-                // Haswell can issue 2 add/mul/madd per cycle.
-                // 9 pmadd, 2 pmul, 1 div, 2 other
-                ? (2 * NumTraits<Scalar>::AddCost +
-                   6 * NumTraits<Scalar>::MulCost +
-                   scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
-#else
-                ? (11 * NumTraits<Scalar>::AddCost +
-                   11 * NumTraits<Scalar>::MulCost +
-                   scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
-#endif
-                // This number assumes a naive implementation of tanh
-                : (6 * NumTraits<Scalar>::AddCost +
-                   3 * NumTraits<Scalar>::MulCost +
-                   2 * scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value +
-                   functor_traits<scalar_exp_op<Scalar> >::Cost))
-  };
-};
-
-#if EIGEN_HAS_CXX11_MATH
-/** \internal
-  * \brief Template functor to compute the atanh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::atanh()
-  */
-template <typename Scalar>
-struct scalar_atanh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_atanh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::atanh(a); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_atanh_op<Scalar> > {
-  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
-};
-#endif
-
-/** \internal
-  * \brief Template functor to compute the sinh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::sinh()
-  */
-template<typename Scalar> struct scalar_sinh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sinh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sinh(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psinh(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_sinh_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasSinh
-  };
-};
-
-#if EIGEN_HAS_CXX11_MATH
-/** \internal
-  * \brief Template functor to compute the asinh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::asinh()
-  */
-template <typename Scalar>
-struct scalar_asinh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::asinh(a); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_asinh_op<Scalar> > {
-  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
-};
-#endif
-
-/** \internal
-  * \brief Template functor to compute the cosh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::cosh()
-  */
-template<typename Scalar> struct scalar_cosh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cosh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::cosh(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcosh(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_cosh_op<Scalar> >
-{
-  enum {
-    Cost = 5 * NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasCosh
-  };
-};
-
-#if EIGEN_HAS_CXX11_MATH
-/** \internal
-  * \brief Template functor to compute the acosh of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::acosh()
-  */
-template <typename Scalar>
-struct scalar_acosh_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_acosh_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::acosh(a); }
-};
-
-template <typename Scalar>
-struct functor_traits<scalar_acosh_op<Scalar> > {
-  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
-};
-#endif
-
-/** \internal
-  * \brief Template functor to compute the inverse of a scalar
-  * \sa class CwiseUnaryOp, Cwise::inverse()
-  */
-template<typename Scalar>
-struct scalar_inverse_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return internal::pdiv(pset1<Packet>(Scalar(1)),a); }
-};
-template <typename Scalar>
-struct functor_traits<scalar_inverse_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasDiv,
-    Cost = scalar_div_cost<Scalar, PacketAccess>::value
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the square of a scalar
-  * \sa class CwiseUnaryOp, Cwise::square()
-  */
-template<typename Scalar>
-struct scalar_square_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return internal::pmul(a,a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_square_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
-
-// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
-template<>
-struct scalar_square_op<bool> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op)
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
-  template<typename Packet>
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return a; }
-};
-template<>
-struct functor_traits<scalar_square_op<bool> >
-{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
-
-/** \internal
-  * \brief Template functor to compute the cube of a scalar
-  * \sa class CwiseUnaryOp, Cwise::cube()
-  */
-template<typename Scalar>
-struct scalar_cube_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op)
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a*a; }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return internal::pmul(a,pmul(a,a)); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_cube_op<Scalar> >
-{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
-
-// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
-template<>
-struct scalar_cube_op<bool> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op)
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
-  template<typename Packet>
-  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
-  { return a; }
-};
-template<>
-struct functor_traits<scalar_cube_op<bool> >
-{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
-
-/** \internal
-  * \brief Template functor to compute the rounded value of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::round()
-  */
-template<typename Scalar> struct scalar_round_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_round_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::round(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pround(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_round_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasRound
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the floor of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::floor()
-  */
-template<typename Scalar> struct scalar_floor_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_floor_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::floor(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pfloor(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_floor_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasFloor
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the rounded (with current rounding mode)  value of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::rint()
-  */
-template<typename Scalar> struct scalar_rint_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_rint_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::rint(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::print(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_rint_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasRint
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the ceil of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::ceil()
-  */
-template<typename Scalar> struct scalar_ceil_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_ceil_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::ceil(a); }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pceil(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_ceil_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = packet_traits<Scalar>::HasCeil
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute whether a scalar is NaN
-  * \sa class CwiseUnaryOp, ArrayBase::isnan()
-  */
-template<typename Scalar> struct scalar_isnan_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_isnan_op)
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
-#if defined(SYCL_DEVICE_ONLY)
-    return numext::isnan(a);
-#else
-    return (numext::isnan)(a);
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_isnan_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to check whether a scalar is +/-inf
-  * \sa class CwiseUnaryOp, ArrayBase::isinf()
-  */
-template<typename Scalar> struct scalar_isinf_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_isinf_op)
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
-#if defined(SYCL_DEVICE_ONLY)
-    return numext::isinf(a);
-#else
-    return (numext::isinf)(a);
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_isinf_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to check whether a scalar has a finite value
-  * \sa class CwiseUnaryOp, ArrayBase::isfinite()
-  */
-template<typename Scalar> struct scalar_isfinite_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_isfinite_op)
-  typedef bool result_type;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
-#if defined(SYCL_DEVICE_ONLY)
-    return numext::isfinite(a);
-#else
-    return (numext::isfinite)(a);
-#endif
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_isfinite_op<Scalar> >
-{
-  enum {
-    Cost = NumTraits<Scalar>::MulCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the logical not of a boolean
-  *
-  * \sa class CwiseUnaryOp, ArrayBase::operator!
-  */
-template<typename Scalar> struct scalar_boolean_not_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_not_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a) const { return !a; }
-};
-template<typename Scalar>
-struct functor_traits<scalar_boolean_not_op<Scalar> > {
-  enum {
-    Cost = NumTraits<bool>::AddCost,
-    PacketAccess = false
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the signum of a scalar
-  * \sa class CwiseUnaryOp, Cwise::sign()
-  */
-template<typename Scalar,bool is_complex=(NumTraits<Scalar>::IsComplex!=0), bool is_integer=(NumTraits<Scalar>::IsInteger!=0) > struct scalar_sign_op;
-template<typename Scalar>
-struct scalar_sign_op<Scalar, false, true> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const
-  {
-      return Scalar( (a>Scalar(0)) - (a<Scalar(0)) );
-  }
-  //TODO
-  //template <typename Packet>
-  //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); }
-};
-
-template<typename Scalar>
-struct scalar_sign_op<Scalar, false, false> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const
-  {
-    return (numext::isnan)(a) ? a : Scalar( (a>Scalar(0)) - (a<Scalar(0)) );
-  }
-  //TODO
-  //template <typename Packet>
-  //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); }
-};
-
-template<typename Scalar, bool is_integer>
-struct scalar_sign_op<Scalar,true, is_integer> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op)
-  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const
-  {
-    typedef typename NumTraits<Scalar>::Real real_type;
-    real_type aa = numext::abs(a);
-    if (aa==real_type(0))
-      return Scalar(0);
-    aa = real_type(1)/aa;
-    return Scalar(a.real()*aa, a.imag()*aa );
-  }
-  //TODO
-  //template <typename Packet>
-  //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_sign_op<Scalar> >
-{ enum {
-    Cost =
-        NumTraits<Scalar>::IsComplex
-        ? ( 8*NumTraits<Scalar>::MulCost  ) // roughly
-        : ( 3*NumTraits<Scalar>::AddCost),
-    PacketAccess = packet_traits<Scalar>::HasSign
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the logistic function of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::logistic()
-  */
-template <typename T>
-struct scalar_logistic_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_logistic_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
-    return packetOp(x);
-  }
-
-  template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Packet packetOp(const Packet& x) const {
-    const Packet one = pset1<Packet>(T(1));
-    return pdiv(one, padd(one, pexp(pnegate(x))));
-  }
-};
-
-#ifndef EIGEN_GPU_COMPILE_PHASE
-/** \internal
-  * \brief Template specialization of the logistic function for float.
-  *
-  *  Uses just a 9/10-degree rational interpolant which
-  *  interpolates 1/(1+exp(-x)) - 0.5 up to a couple of ulps in the range
-  *  [-9, 18]. Below -9 we use the more accurate approximation
-  *  1/(1+exp(-x)) ~= exp(x), and above 18 the logistic function is 1 withing
-  *  one ulp. The shifted logistic is interpolated because it was easier to
-  *  make the fit converge.
-  *
-  */
-template <>
-struct scalar_logistic_op<float> {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_logistic_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator()(const float& x) const {
-    return packetOp(x);
-  }
-
-  template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Packet packetOp(const Packet& _x) const {
-    const Packet cutoff_lower = pset1<Packet>(-9.f);
-    const Packet lt_mask = pcmp_lt<Packet>(_x, cutoff_lower);
-    const bool any_small = predux_any(lt_mask);
-
-    // The upper cut-off is the smallest x for which the rational approximation evaluates to 1.
-    // Choosing this value saves us a few instructions clamping the results at the end.
-#ifdef EIGEN_VECTORIZE_FMA
-    const Packet cutoff_upper = pset1<Packet>(15.7243833541870117f);
-#else
-    const Packet cutoff_upper = pset1<Packet>(15.6437711715698242f);
-#endif
-    const Packet x = pmin(_x, cutoff_upper);
-
-    // The monomial coefficients of the numerator polynomial (odd).
-    const Packet alpha_1 = pset1<Packet>(2.48287947061529e-01f);
-    const Packet alpha_3 = pset1<Packet>(8.51377133304701e-03f);
-    const Packet alpha_5 = pset1<Packet>(6.08574864600143e-05f);
-    const Packet alpha_7 = pset1<Packet>(1.15627324459942e-07f);
-    const Packet alpha_9 = pset1<Packet>(4.37031012579801e-11f);
-
-    // The monomial coefficients of the denominator polynomial (even).
-    const Packet beta_0 = pset1<Packet>(9.93151921023180e-01f);
-    const Packet beta_2 = pset1<Packet>(1.16817656904453e-01f);
-    const Packet beta_4 = pset1<Packet>(1.70198817374094e-03f);
-    const Packet beta_6 = pset1<Packet>(6.29106785017040e-06f);
-    const Packet beta_8 = pset1<Packet>(5.76102136993427e-09f);
-    const Packet beta_10 = pset1<Packet>(6.10247389755681e-13f);
-
-    // Since the polynomials are odd/even, we need x^2.
-    const Packet x2 = pmul(x, x);
-
-    // Evaluate the numerator polynomial p.
-    Packet p = pmadd(x2, alpha_9, alpha_7);
-    p = pmadd(x2, p, alpha_5);
-    p = pmadd(x2, p, alpha_3);
-    p = pmadd(x2, p, alpha_1);
-    p = pmul(x, p);
-
-    // Evaluate the denominator polynomial q.
-    Packet q = pmadd(x2, beta_10, beta_8);
-    q = pmadd(x2, q, beta_6);
-    q = pmadd(x2, q, beta_4);
-    q = pmadd(x2, q, beta_2);
-    q = pmadd(x2, q, beta_0);
-    // Divide the numerator by the denominator and shift it up.
-    const Packet logistic = padd(pdiv(p, q), pset1<Packet>(0.5f));
-    if (EIGEN_PREDICT_FALSE(any_small)) {
-      const Packet exponential = pexp(_x);
-      return pselect(lt_mask, exponential, logistic);
-    } else {
-      return logistic;
-    }
-  }
-};
-#endif  // #ifndef EIGEN_GPU_COMPILE_PHASE
-
-template <typename T>
-struct functor_traits<scalar_logistic_op<T> > {
-  enum {
-    // The cost estimate for float here here is for the common(?) case where
-    // all arguments are greater than -9.
-    Cost = scalar_div_cost<T, packet_traits<T>::HasDiv>::value +
-           (internal::is_same<T, float>::value
-                ? NumTraits<T>::AddCost * 15 + NumTraits<T>::MulCost * 11
-                : NumTraits<T>::AddCost * 2 +
-                      functor_traits<scalar_exp_op<T> >::Cost),
-    PacketAccess =
-        packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
-        (internal::is_same<T, float>::value
-             ? packet_traits<T>::HasMul && packet_traits<T>::HasMax &&
-                   packet_traits<T>::HasMin
-             : packet_traits<T>::HasNegate && packet_traits<T>::HasExp)
-  };
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
deleted file mode 100644
index f35b760..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
+++ /dev/null
@@ -1,2645 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_BLOCK_PANEL_H
-#define EIGEN_GENERAL_BLOCK_PANEL_H
-
-
-namespace Eigen {
-
-namespace internal {
-
-enum GEBPPacketSizeType {
-  GEBPPacketFull = 0,
-  GEBPPacketHalf,
-  GEBPPacketQuarter
-};
-
-template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false, int Arch=Architecture::Target, int _PacketSize=GEBPPacketFull>
-class gebp_traits;
-
-
-/** \internal \returns b if a<=0, and returns a otherwise. */
-inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b)
-{
-  return a<=0 ? b : a;
-}
-
-#if defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
-#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) EIGEN_DEFAULT_L1_CACHE_SIZE
-#else
-#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) val
-#endif // defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
-
-#if defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
-#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) EIGEN_DEFAULT_L2_CACHE_SIZE
-#else
-#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) val
-#endif // defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
-
-#if defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
-#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) EIGEN_DEFAULT_L3_CACHE_SIZE
-#else
-#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) val
-#endif // defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
-  
-#if EIGEN_ARCH_i386_OR_x86_64
-const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(32*1024);
-const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(256*1024);
-const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(2*1024*1024);
-#elif EIGEN_ARCH_PPC
-const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(64*1024);
-const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512*1024);
-const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(4*1024*1024);
-#else
-const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(16*1024);
-const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512*1024);
-const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(512*1024);
-#endif
-
-#undef EIGEN_SET_DEFAULT_L1_CACHE_SIZE
-#undef EIGEN_SET_DEFAULT_L2_CACHE_SIZE
-#undef EIGEN_SET_DEFAULT_L3_CACHE_SIZE
-
-/** \internal */
-struct CacheSizes {
-  CacheSizes(): m_l1(-1),m_l2(-1),m_l3(-1) {
-    int l1CacheSize, l2CacheSize, l3CacheSize;
-    queryCacheSizes(l1CacheSize, l2CacheSize, l3CacheSize);
-    m_l1 = manage_caching_sizes_helper(l1CacheSize, defaultL1CacheSize);
-    m_l2 = manage_caching_sizes_helper(l2CacheSize, defaultL2CacheSize);
-    m_l3 = manage_caching_sizes_helper(l3CacheSize, defaultL3CacheSize);
-  }
-
-  std::ptrdiff_t m_l1;
-  std::ptrdiff_t m_l2;
-  std::ptrdiff_t m_l3;
-};
-
-/** \internal */
-inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1, std::ptrdiff_t* l2, std::ptrdiff_t* l3)
-{
-  static CacheSizes m_cacheSizes;
-
-  if(action==SetAction)
-  {
-    // set the cpu cache size and cache all block sizes from a global cache size in byte
-    eigen_internal_assert(l1!=0 && l2!=0);
-    m_cacheSizes.m_l1 = *l1;
-    m_cacheSizes.m_l2 = *l2;
-    m_cacheSizes.m_l3 = *l3;
-  }
-  else if(action==GetAction)
-  {
-    eigen_internal_assert(l1!=0 && l2!=0);
-    *l1 = m_cacheSizes.m_l1;
-    *l2 = m_cacheSizes.m_l2;
-    *l3 = m_cacheSizes.m_l3;
-  }
-  else
-  {
-    eigen_internal_assert(false);
-  }
-}
-
-/* Helper for computeProductBlockingSizes.
- *
- * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
- * this function computes the blocking size parameters along the respective dimensions
- * for matrix products and related algorithms. The blocking sizes depends on various
- * parameters:
- * - the L1 and L2 cache sizes,
- * - the register level blocking sizes defined by gebp_traits,
- * - the number of scalars that fit into a packet (when vectorization is enabled).
- *
- * \sa setCpuCacheSizes */
-
-template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
-void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index num_threads = 1)
-{
-  typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-  // Explanations:
-  // Let's recall that the product algorithms form mc x kc vertical panels A' on the lhs and
-  // kc x nc blocks B' on the rhs. B' has to fit into L2/L3 cache. Moreover, A' is processed
-  // per mr x kc horizontal small panels where mr is the blocking size along the m dimension
-  // at the register level. This small horizontal panel has to stay within L1 cache.
-  std::ptrdiff_t l1, l2, l3;
-  manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  #ifdef EIGEN_VECTORIZE_AVX512
-  // We need to find a rationale for that, but without this adjustment,
-  // performance with AVX512 is pretty bad, like -20% slower.
-  // One reason is that with increasing packet-size, the blocking size k
-  // has to become pretty small if we want that 1 lhs panel fit within L1.
-  // For instance, with the 3pX4 kernel and double, the size of the lhs+rhs panels are:
-  //   k*(3*64 + 4*8) Bytes, with l1=32kBytes, and k%8=0, we have k=144.
-  // This is quite small for a good reuse of the accumulation registers.
-  l1 *= 4;
-  #endif
-
-  if (num_threads > 1) {
-    typedef typename Traits::ResScalar ResScalar;
-    enum {
-      kdiv = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
-      ksub = Traits::mr * Traits::nr * sizeof(ResScalar),
-      kr = 8,
-      mr = Traits::mr,
-      nr = Traits::nr
-    };
-    // Increasing k gives us more time to prefetch the content of the "C"
-    // registers. However once the latency is hidden there is no point in
-    // increasing the value of k, so we'll cap it at 320 (value determined
-    // experimentally).
-    // To avoid that k vanishes, we make k_cache at least as big as kr
-    const Index k_cache = numext::maxi<Index>(kr, (numext::mini<Index>)((l1-ksub)/kdiv, 320));
-    if (k_cache < k) {
-      k = k_cache - (k_cache % kr);
-      eigen_internal_assert(k > 0);
-    }
-
-    const Index n_cache = (l2-l1) / (nr * sizeof(RhsScalar) * k);
-    const Index n_per_thread = numext::div_ceil(n, num_threads);
-    if (n_cache <= n_per_thread) {
-      // Don't exceed the capacity of the l2 cache.
-      eigen_internal_assert(n_cache >= static_cast<Index>(nr));
-      n = n_cache - (n_cache % nr);
-      eigen_internal_assert(n > 0);
-    } else {
-      n = (numext::mini<Index>)(n, (n_per_thread + nr - 1) - ((n_per_thread + nr - 1) % nr));
-    }
-
-    if (l3 > l2) {
-      // l3 is shared between all cores, so we'll give each thread its own chunk of l3.
-      const Index m_cache = (l3-l2) / (sizeof(LhsScalar) * k * num_threads);
-      const Index m_per_thread = numext::div_ceil(m, num_threads);
-      if(m_cache < m_per_thread && m_cache >= static_cast<Index>(mr)) {
-        m = m_cache - (m_cache % mr);
-        eigen_internal_assert(m > 0);
-      } else {
-        m = (numext::mini<Index>)(m, (m_per_thread + mr - 1) - ((m_per_thread + mr - 1) % mr));
-      }
-    }
-  }
-  else {
-    // In unit tests we do not want to use extra large matrices,
-    // so we reduce the cache size to check the blocking strategy is not flawed
-#ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
-    l1 = 9*1024;
-    l2 = 32*1024;
-    l3 = 512*1024;
-#endif
-
-    // Early return for small problems because the computation below are time consuming for small problems.
-    // Perhaps it would make more sense to consider k*n*m??
-    // Note that for very tiny problem, this function should be bypassed anyway
-    // because we use the coefficient-based implementation for them.
-    if((numext::maxi)(k,(numext::maxi)(m,n))<48)
-      return;
-
-    typedef typename Traits::ResScalar ResScalar;
-    enum {
-      k_peeling = 8,
-      k_div = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
-      k_sub = Traits::mr * Traits::nr * sizeof(ResScalar)
-    };
-
-    // ---- 1st level of blocking on L1, yields kc ----
-
-    // Blocking on the third dimension (i.e., k) is chosen so that an horizontal panel
-    // of size mr x kc of the lhs plus a vertical panel of kc x nr of the rhs both fits within L1 cache.
-    // We also include a register-level block of the result (mx x nr).
-    // (In an ideal world only the lhs panel would stay in L1)
-    // Moreover, kc has to be a multiple of 8 to be compatible with loop peeling, leading to a maximum blocking size of:
-    const Index max_kc = numext::maxi<Index>(((l1-k_sub)/k_div) & (~(k_peeling-1)),1);
-    const Index old_k = k;
-    if(k>max_kc)
-    {
-      // We are really blocking on the third dimension:
-      // -> reduce blocking size to make sure the last block is as large as possible
-      //    while keeping the same number of sweeps over the result.
-      k = (k%max_kc)==0 ? max_kc
-                        : max_kc - k_peeling * ((max_kc-1-(k%max_kc))/(k_peeling*(k/max_kc+1)));
-
-      eigen_internal_assert(((old_k/k) == (old_k/max_kc)) && "the number of sweeps has to remain the same");
-    }
-
-    // ---- 2nd level of blocking on max(L2,L3), yields nc ----
-
-    // TODO find a reliable way to get the actual amount of cache per core to use for 2nd level blocking, that is:
-    //      actual_l2 = max(l2, l3/nb_core_sharing_l3)
-    // The number below is quite conservative: it is better to underestimate the cache size rather than overestimating it)
-    // For instance, it corresponds to 6MB of L3 shared among 4 cores.
-    #ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
-    const Index actual_l2 = l3;
-    #else
-    const Index actual_l2 = 1572864; // == 1.5 MB
-    #endif
-
-    // Here, nc is chosen such that a block of kc x nc of the rhs fit within half of L2.
-    // The second half is implicitly reserved to access the result and lhs coefficients.
-    // When k<max_kc, then nc can arbitrarily growth. In practice, it seems to be fruitful
-    // to limit this growth: we bound nc to growth by a factor x1.5.
-    // However, if the entire lhs block fit within L1, then we are not going to block on the rows at all,
-    // and it becomes fruitful to keep the packed rhs blocks in L1 if there is enough remaining space.
-    Index max_nc;
-    const Index lhs_bytes = m * k * sizeof(LhsScalar);
-    const Index remaining_l1 = l1- k_sub - lhs_bytes;
-    if(remaining_l1 >= Index(Traits::nr*sizeof(RhsScalar))*k)
-    {
-      // L1 blocking
-      max_nc = remaining_l1 / (k*sizeof(RhsScalar));
-    }
-    else
-    {
-      // L2 blocking
-      max_nc = (3*actual_l2)/(2*2*max_kc*sizeof(RhsScalar));
-    }
-    // WARNING Below, we assume that Traits::nr is a power of two.
-    Index nc = numext::mini<Index>(actual_l2/(2*k*sizeof(RhsScalar)), max_nc) & (~(Traits::nr-1));
-    if(n>nc)
-    {
-      // We are really blocking over the columns:
-      // -> reduce blocking size to make sure the last block is as large as possible
-      //    while keeping the same number of sweeps over the packed lhs.
-      //    Here we allow one more sweep if this gives us a perfect match, thus the commented "-1"
-      n = (n%nc)==0 ? nc
-                    : (nc - Traits::nr * ((nc/*-1*/-(n%nc))/(Traits::nr*(n/nc+1))));
-    }
-    else if(old_k==k)
-    {
-      // So far, no blocking at all, i.e., kc==k, and nc==n.
-      // In this case, let's perform a blocking over the rows such that the packed lhs data is kept in cache L1/L2
-      // TODO: part of this blocking strategy is now implemented within the kernel itself, so the L1-based heuristic here should be obsolete.
-      Index problem_size = k*n*sizeof(LhsScalar);
-      Index actual_lm = actual_l2;
-      Index max_mc = m;
-      if(problem_size<=1024)
-      {
-        // problem is small enough to keep in L1
-        // Let's choose m such that lhs's block fit in 1/3 of L1
-        actual_lm = l1;
-      }
-      else if(l3!=0 && problem_size<=32768)
-      {
-        // we have both L2 and L3, and problem is small enough to be kept in L2
-        // Let's choose m such that lhs's block fit in 1/3 of L2
-        actual_lm = l2;
-        max_mc = (numext::mini<Index>)(576,max_mc);
-      }
-      Index mc = (numext::mini<Index>)(actual_lm/(3*k*sizeof(LhsScalar)), max_mc);
-      if (mc > Traits::mr) mc -= mc % Traits::mr;
-      else if (mc==0) return;
-      m = (m%mc)==0 ? mc
-                    : (mc - Traits::mr * ((mc/*-1*/-(m%mc))/(Traits::mr*(m/mc+1))));
-    }
-  }
-}
-
-template <typename Index>
-inline bool useSpecificBlockingSizes(Index& k, Index& m, Index& n)
-{
-#ifdef EIGEN_TEST_SPECIFIC_BLOCKING_SIZES
-  if (EIGEN_TEST_SPECIFIC_BLOCKING_SIZES) {
-    k = numext::mini<Index>(k, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K);
-    m = numext::mini<Index>(m, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M);
-    n = numext::mini<Index>(n, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N);
-    return true;
-  }
-#else
-  EIGEN_UNUSED_VARIABLE(k)
-  EIGEN_UNUSED_VARIABLE(m)
-  EIGEN_UNUSED_VARIABLE(n)
-#endif
-  return false;
-}
-
-/** \brief Computes the blocking parameters for a m x k times k x n matrix product
-  *
-  * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension.
-  * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension.
-  * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension.
-  *
-  * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
-  * this function computes the blocking size parameters along the respective dimensions
-  * for matrix products and related algorithms.
-  *
-  * The blocking size parameters may be evaluated:
-  *   - either by a heuristic based on cache sizes;
-  *   - or using fixed prescribed values (for testing purposes).
-  *
-  * \sa setCpuCacheSizes */
-
-template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
-void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
-{
-  if (!useSpecificBlockingSizes(k, m, n)) {
-    evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor, Index>(k, m, n, num_threads);
-  }
-}
-
-template<typename LhsScalar, typename RhsScalar, typename Index>
-inline void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
-{
-  computeProductBlockingSizes<LhsScalar,RhsScalar,1,Index>(k, m, n, num_threads);
-}
-
-template <typename RhsPacket, typename RhsPacketx4, int registers_taken>
-struct RhsPanelHelper {
- private:
-  static const int remaining_registers = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS - registers_taken;
- public:
-  typedef typename conditional<remaining_registers>=4, RhsPacketx4, RhsPacket>::type type;
-};
-
-template <typename Packet>
-struct QuadPacket
-{
-  Packet B_0, B1, B2, B3;
-  const Packet& get(const FixedInt<0>&) const { return B_0; }
-  const Packet& get(const FixedInt<1>&) const { return B1; }
-  const Packet& get(const FixedInt<2>&) const { return B2; }
-  const Packet& get(const FixedInt<3>&) const { return B3; }
-};
-
-template <int N, typename T1, typename T2, typename T3>
-struct packet_conditional { typedef T3 type; };
-
-template <typename T1, typename T2, typename T3>
-struct packet_conditional<GEBPPacketFull, T1, T2, T3> { typedef T1 type; };
-
-template <typename T1, typename T2, typename T3>
-struct packet_conditional<GEBPPacketHalf, T1, T2, T3> { typedef T2 type; };
-
-#define PACKET_DECL_COND_PREFIX(prefix, name, packet_size)         \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<name ## Scalar>::type, \
-                                      typename packet_traits<name ## Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
-  prefix ## name ## Packet
-
-#define PACKET_DECL_COND(name, packet_size)                        \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<name ## Scalar>::type, \
-                                      typename packet_traits<name ## Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
-  name ## Packet
-
-#define PACKET_DECL_COND_SCALAR_PREFIX(prefix, packet_size)        \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<Scalar>::type, \
-                                      typename packet_traits<Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type \
-  prefix ## ScalarPacket
-
-#define PACKET_DECL_COND_SCALAR(packet_size)                       \
-  typedef typename packet_conditional<packet_size,                 \
-                                      typename packet_traits<Scalar>::type, \
-                                      typename packet_traits<Scalar>::half, \
-                                      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type \
-  ScalarPacket
-
-/* Vectorization logic
- *  real*real: unpack rhs to constant packets, ...
- * 
- *  cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i),
- *          storing each res packet into two packets (2x2),
- *          at the end combine them: swap the second and addsub them 
- *  cf*cf : same but with 2x4 blocks
- *  cplx*real : unpack rhs to constant packets, ...
- *  real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual
- */
-template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs, int Arch, int _PacketSize>
-class gebp_traits
-{
-public:
-  typedef _LhsScalar LhsScalar;
-  typedef _RhsScalar RhsScalar;
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-
-  enum {
-    ConjLhs = _ConjLhs,
-    ConjRhs = _ConjRhs,
-    Vectorizable = unpacket_traits<_LhsPacket>::vectorizable && unpacket_traits<_RhsPacket>::vectorizable,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-    ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-
-    // register block size along the N direction must be 1 or 4
-    nr = 4,
-
-    // register block size along the M direction (currently, this one cannot be modified)
-    default_mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*LhsPacketSize,
-#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX) \
-    && ((!EIGEN_COMP_MSVC) || (EIGEN_COMP_MSVC>=1914))
-    // we assume 16 registers or more
-    // See bug 992, if the scalar type is not vectorizable but that EIGEN_HAS_SINGLE_INSTRUCTION_MADD is defined,
-    // then using 3*LhsPacketSize triggers non-implemented paths in syrk.
-    // Bug 1515: MSVC prior to v19.14 yields to register spilling.
-    mr = Vectorizable ? 3*LhsPacketSize : default_mr,
-#else
-    mr = default_mr,
-#endif
-    
-    LhsProgress = LhsPacketSize,
-    RhsProgress = 1
-  };
-
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-  typedef LhsPacket LhsPacket4Packing;
-
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-  typedef ResPacket AccPacket;
-  
-  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
-  {
-    p = pset1<ResPacket>(ResScalar(0));
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    dest = pset1<RhsPacketType>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {
-  }
-
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = ploadquad<RhsPacket>(b);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = pload<LhsPacketType>(a);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploadu<LhsPacketType>(a);
-  }
-
-  template<typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
-  {
-    conj_helper<LhsPacketType,RhsPacketType,ConjLhs,ConjRhs> cj;
-    // It would be a lot cleaner to call pmadd all the time. Unfortunately if we
-    // let gcc allocate the register in which to store the result of the pmul
-    // (in the case where there is no FMA) gcc fails to figure out how to avoid
-    // spilling register.
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-    EIGEN_UNUSED_VARIABLE(tmp);
-    c = cj.pmadd(a,b,c);
-#else
-    tmp = b; tmp = cj.pmul(a,tmp); c = padd(c,tmp);
-#endif
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-
-  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
-  {
-    r = pmadd(c,alpha,r);
-  }
-  
-  template<typename ResPacketHalf>
-  EIGEN_STRONG_INLINE void acc(const ResPacketHalf& c, const ResPacketHalf& alpha, ResPacketHalf& r) const
-  {
-    r = pmadd(c,alpha,r);
-  }
-
-};
-
-template<typename RealScalar, bool _ConjLhs, int Arch, int _PacketSize>
-class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false, Arch, _PacketSize>
-{
-public:
-  typedef std::complex<RealScalar> LhsScalar;
-  typedef RealScalar RhsScalar;
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-
-  enum {
-    ConjLhs = _ConjLhs,
-    ConjRhs = false,
-    Vectorizable = unpacket_traits<_LhsPacket>::vectorizable && unpacket_traits<_RhsPacket>::vectorizable,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-    ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-    nr = 4,
-#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
-    // we assume 16 registers
-    mr = 3*LhsPacketSize,
-#else
-    mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*LhsPacketSize,
-#endif
-
-    LhsProgress = LhsPacketSize,
-    RhsProgress = 1
-  };
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-  typedef LhsPacket LhsPacket4Packing;
-
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-
-  typedef ResPacket AccPacket;
-
-  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
-  {
-    p = pset1<ResPacket>(ResScalar(0));
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    dest = pset1<RhsPacketType>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-  
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    loadRhsQuad_impl(b,dest, typename conditional<RhsPacketSize==16,true_type,false_type>::type());
-  }
-
-  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const true_type&) const
-  {
-    // FIXME we can do better!
-    // what we want here is a ploadheight
-    RhsScalar tmp[4] = {b[0],b[0],b[1],b[1]};
-    dest = ploadquad<RhsPacket>(tmp);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const false_type&) const
-  {
-    eigen_internal_assert(RhsPacketSize<=8);
-    dest = pset1<RhsPacket>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
-  {
-    dest = pload<LhsPacket>(a);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploadu<LhsPacketType>(a);
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
-  {
-    madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const true_type&) const
-  {
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-    EIGEN_UNUSED_VARIABLE(tmp);
-    c.v = pmadd(a.v,b,c.v);
-#else
-    tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp);
-#endif
-  }
-
-  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
-  {
-    c += a * b;
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-
-  template <typename ResPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const
-  {
-    conj_helper<ResPacketType,ResPacketType,ConjLhs,false> cj;
-    r = cj.pmadd(c,alpha,r);
-  }
-
-protected:
-};
-
-template<typename Packet>
-struct DoublePacket
-{
-  Packet first;
-  Packet second;
-};
-
-template<typename Packet>
-DoublePacket<Packet> padd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b)
-{
-  DoublePacket<Packet> res;
-  res.first  = padd(a.first, b.first);
-  res.second = padd(a.second,b.second);
-  return res;
-}
-
-// note that for DoublePacket<RealPacket> the "4" in "downto4"
-// corresponds to the number of complexes, so it means "8"
-// it terms of real coefficients.
-
-template<typename Packet>
-const DoublePacket<Packet>&
-predux_half_dowto4(const DoublePacket<Packet> &a,
-                   typename enable_if<unpacket_traits<Packet>::size<=8>::type* = 0)
-{
-  return a;
-}
-
-template<typename Packet>
-DoublePacket<typename unpacket_traits<Packet>::half>
-predux_half_dowto4(const DoublePacket<Packet> &a,
-                   typename enable_if<unpacket_traits<Packet>::size==16>::type* = 0)
-{
-  // yes, that's pretty hackish :(
-  DoublePacket<typename unpacket_traits<Packet>::half> res;
-  typedef std::complex<typename unpacket_traits<Packet>::type> Cplx;
-  typedef typename packet_traits<Cplx>::type CplxPacket;
-  res.first  = predux_half_dowto4(CplxPacket(a.first)).v;
-  res.second = predux_half_dowto4(CplxPacket(a.second)).v;
-  return res;
-}
-
-// same here, "quad" actually means "8" in terms of real coefficients
-template<typename Scalar, typename RealPacket>
-void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
-                            typename enable_if<unpacket_traits<RealPacket>::size<=8>::type* = 0)
-{
-  dest.first  = pset1<RealPacket>(numext::real(*b));
-  dest.second = pset1<RealPacket>(numext::imag(*b));
-}
-
-template<typename Scalar, typename RealPacket>
-void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
-                            typename enable_if<unpacket_traits<RealPacket>::size==16>::type* = 0)
-{
-  // yes, that's pretty hackish too :(
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  RealScalar r[4] = {numext::real(b[0]), numext::real(b[0]), numext::real(b[1]), numext::real(b[1])};
-  RealScalar i[4] = {numext::imag(b[0]), numext::imag(b[0]), numext::imag(b[1]), numext::imag(b[1])};
-  dest.first  = ploadquad<RealPacket>(r);
-  dest.second = ploadquad<RealPacket>(i);
-}
-
-
-template<typename Packet> struct unpacket_traits<DoublePacket<Packet> > {
-  typedef DoublePacket<typename unpacket_traits<Packet>::half> half;
-};
-// template<typename Packet>
-// DoublePacket<Packet> pmadd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b)
-// {
-//   DoublePacket<Packet> res;
-//   res.first  = padd(a.first, b.first);
-//   res.second = padd(a.second,b.second);
-//   return res;
-// }
-
-template<typename RealScalar, bool _ConjLhs, bool _ConjRhs, int Arch, int _PacketSize>
-class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs, Arch, _PacketSize >
-{
-public:
-  typedef std::complex<RealScalar>  Scalar;
-  typedef std::complex<RealScalar>  LhsScalar;
-  typedef std::complex<RealScalar>  RhsScalar;
-  typedef std::complex<RealScalar>  ResScalar;
-  
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-  PACKET_DECL_COND(Real, _PacketSize);
-  PACKET_DECL_COND_SCALAR(_PacketSize);
-
-  enum {
-    ConjLhs = _ConjLhs,
-    ConjRhs = _ConjRhs,
-    Vectorizable = unpacket_traits<RealPacket>::vectorizable
-                && unpacket_traits<ScalarPacket>::vectorizable,
-    ResPacketSize   = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<RhsScalar>::size : 1,
-    RealPacketSize  = Vectorizable ? unpacket_traits<RealPacket>::size : 1,
-
-    // FIXME: should depend on NumberOfRegisters
-    nr = 4,
-    mr = ResPacketSize,
-
-    LhsProgress = ResPacketSize,
-    RhsProgress = 1
-  };
-  
-  typedef DoublePacket<RealPacket>                 DoublePacketType;
-
-  typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type LhsPacket4Packing;
-  typedef typename conditional<Vectorizable,RealPacket,  Scalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,DoublePacketType,Scalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket;
-  typedef typename conditional<Vectorizable,DoublePacketType,Scalar>::type AccPacket;
-
-  // this actualy holds 8 packets!
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-  
-  EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); }
-
-  EIGEN_STRONG_INLINE void initAcc(DoublePacketType& p)
-  {
-    p.first   = pset1<RealPacket>(RealScalar(0));
-    p.second  = pset1<RealPacket>(RealScalar(0));
-  }
-
-  // Scalar path
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ScalarPacket& dest) const
-  {
-    dest = pset1<ScalarPacket>(*b);
-  }
-
-  // Vectorized path
-  template<typename RealPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const
-  {
-    dest.first  = pset1<RealPacketType>(numext::real(*b));
-    dest.second = pset1<RealPacketType>(numext::imag(*b));
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    loadRhs(b, dest.B_0);
-    loadRhs(b + 1, dest.B1);
-    loadRhs(b + 2, dest.B2);
-    loadRhs(b + 3, dest.B3);
-  }
-
-  // Scalar path
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, ScalarPacket& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  // Vectorized path
-  template<typename RealPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
-  
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, ResPacket& dest) const
-  {
-    loadRhs(b,dest);
-  }
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, DoublePacketType& dest) const
-  {
-    loadQuadToDoublePacket(b,dest);
-  }
-
-  // nothing special here
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
-  {
-    dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a));
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploadu<LhsPacketType>((const typename unpacket_traits<LhsPacketType>::type*)(a));
-  }
-
-  template<typename LhsPacketType, typename RhsPacketType, typename ResPacketType, typename TmpType, typename LaneIdType>
-  EIGEN_STRONG_INLINE
-  typename enable_if<!is_same<RhsPacketType,RhsPacketx4>::value>::type
-  madd(const LhsPacketType& a, const RhsPacketType& b, DoublePacket<ResPacketType>& c, TmpType& /*tmp*/, const LaneIdType&) const
-  {
-    c.first   = padd(pmul(a,b.first), c.first);
-    c.second  = padd(pmul(a,b.second),c.second);
-  }
-
-  template<typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/, const LaneIdType&) const
-  {
-    c = cj.pmadd(a,b,c);
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-  
-  EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
-  
-  template<typename RealPacketType, typename ResPacketType>
-  EIGEN_STRONG_INLINE void acc(const DoublePacket<RealPacketType>& c, const ResPacketType& alpha, ResPacketType& r) const
-  {
-    // assemble c
-    ResPacketType tmp;
-    if((!ConjLhs)&&(!ConjRhs))
-    {
-      tmp = pcplxflip(pconj(ResPacketType(c.second)));
-      tmp = padd(ResPacketType(c.first),tmp);
-    }
-    else if((!ConjLhs)&&(ConjRhs))
-    {
-      tmp = pconj(pcplxflip(ResPacketType(c.second)));
-      tmp = padd(ResPacketType(c.first),tmp);
-    }
-    else if((ConjLhs)&&(!ConjRhs))
-    {
-      tmp = pcplxflip(ResPacketType(c.second));
-      tmp = padd(pconj(ResPacketType(c.first)),tmp);
-    }
-    else if((ConjLhs)&&(ConjRhs))
-    {
-      tmp = pcplxflip(ResPacketType(c.second));
-      tmp = psub(pconj(ResPacketType(c.first)),tmp);
-    }
-    
-    r = pmadd(tmp,alpha,r);
-  }
-
-protected:
-  conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
-};
-
-template<typename RealScalar, bool _ConjRhs, int Arch, int _PacketSize>
-class gebp_traits<RealScalar, std::complex<RealScalar>, false, _ConjRhs, Arch, _PacketSize >
-{
-public:
-  typedef std::complex<RealScalar>  Scalar;
-  typedef RealScalar  LhsScalar;
-  typedef Scalar      RhsScalar;
-  typedef Scalar      ResScalar;
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Real, _PacketSize);
-  PACKET_DECL_COND_SCALAR_PREFIX(_, _PacketSize);
-
-#undef PACKET_DECL_COND_SCALAR_PREFIX
-#undef PACKET_DECL_COND_PREFIX
-#undef PACKET_DECL_COND_SCALAR
-#undef PACKET_DECL_COND
-
-  enum {
-    ConjLhs = false,
-    ConjRhs = _ConjRhs,
-    Vectorizable = unpacket_traits<_RealPacket>::vectorizable
-                && unpacket_traits<_ScalarPacket>::vectorizable,
-    LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-    RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-    ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1,
-    
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-    // FIXME: should depend on NumberOfRegisters
-    nr = 4,
-    mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*ResPacketSize,
-
-    LhsProgress = ResPacketSize,
-    RhsProgress = 1
-  };
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-  typedef LhsPacket LhsPacket4Packing;
-  typedef QuadPacket<RhsPacket> RhsPacketx4;
-  typedef ResPacket AccPacket;
-
-  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
-  {
-    p = pset1<ResPacket>(ResScalar(0));
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    dest = pset1<RhsPacketType>(*b);
-  }
-
-  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
-  {
-    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
-  }
-
-  template<typename RhsPacketType>
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
-  {
-    loadRhs(b, dest);
-  }
-
-  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
-  {}
-
-  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
-  {
-    dest = ploaddup<LhsPacket>(a);
-  }
-  
-  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
-  {
-    dest = ploadquad<RhsPacket>(b);
-  }
-
-  template<typename LhsPacketType>
-  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
-  {
-    dest = ploaddup<LhsPacketType>(a);
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
-  {
-    madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
-  }
-
-  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const true_type&) const
-  {
-#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
-    EIGEN_UNUSED_VARIABLE(tmp);
-    c.v = pmadd(a,b.v,c.v);
-#else
-    tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp);
-#endif
-    
-  }
-
-  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
-  {
-    c += a * b;
-  }
-
-  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
-  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
-  {
-    madd(a, b.get(lane), c, tmp, lane);
-  }
-
-  template <typename ResPacketType, typename AccPacketType>
-  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const
-  {
-    conj_helper<ResPacketType,ResPacketType,false,ConjRhs> cj;
-    r = cj.pmadd(alpha,c,r);
-  }
-
-protected:
-
-};
-
-/* optimized General packed Block * packed Panel product kernel
- *
- * Mixing type logic: C += A * B
- *  |  A  |  B  | comments
- *  |real |cplx | no vectorization yet, would require to pack A with duplication
- *  |cplx |real | easy vectorization
- */
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct gebp_kernel
-{
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target,GEBPPacketHalf> HalfTraits;
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target,GEBPPacketQuarter> QuarterTraits;
-  
-  typedef typename Traits::ResScalar ResScalar;
-  typedef typename Traits::LhsPacket LhsPacket;
-  typedef typename Traits::RhsPacket RhsPacket;
-  typedef typename Traits::ResPacket ResPacket;
-  typedef typename Traits::AccPacket AccPacket;
-  typedef typename Traits::RhsPacketx4 RhsPacketx4;
-
-  typedef typename RhsPanelHelper<RhsPacket, RhsPacketx4, 15>::type RhsPanel15;
-
-  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
-
-  typedef typename SwappedTraits::ResScalar SResScalar;
-  typedef typename SwappedTraits::LhsPacket SLhsPacket;
-  typedef typename SwappedTraits::RhsPacket SRhsPacket;
-  typedef typename SwappedTraits::ResPacket SResPacket;
-  typedef typename SwappedTraits::AccPacket SAccPacket;
-
-  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
-  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
-  typedef typename HalfTraits::ResPacket ResPacketHalf;
-  typedef typename HalfTraits::AccPacket AccPacketHalf;
-
-  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
-  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
-  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
-  typedef typename QuarterTraits::AccPacket AccPacketQuarter;
-
-  typedef typename DataMapper::LinearMapper LinearMapper;
-
-  enum {
-    Vectorizable  = Traits::Vectorizable,
-    LhsProgress   = Traits::LhsProgress,
-    LhsProgressHalf      = HalfTraits::LhsProgress,
-    LhsProgressQuarter   = QuarterTraits::LhsProgress,
-    RhsProgress   = Traits::RhsProgress,
-    RhsProgressHalf      = HalfTraits::RhsProgress,
-    RhsProgressQuarter   = QuarterTraits::RhsProgress,
-    ResPacketSize = Traits::ResPacketSize
-  };
-
-  EIGEN_DONT_INLINE
-  void operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
-                  Index rows, Index depth, Index cols, ResScalar alpha,
-                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
-};
-
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs,
-int SwappedLhsProgress = gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target>::LhsProgress>
-struct last_row_process_16_packets
-{
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
-  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
-
-  typedef typename Traits::ResScalar ResScalar;
-  typedef typename SwappedTraits::LhsPacket SLhsPacket;
-  typedef typename SwappedTraits::RhsPacket SRhsPacket;
-  typedef typename SwappedTraits::ResPacket SResPacket;
-  typedef typename SwappedTraits::AccPacket SAccPacket;
-
-  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits &straits, const LhsScalar* blA,
-                  const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
-                  ResScalar alpha, SAccPacket &C0)
-    {
-      EIGEN_UNUSED_VARIABLE(res);
-      EIGEN_UNUSED_VARIABLE(straits);
-      EIGEN_UNUSED_VARIABLE(blA);
-      EIGEN_UNUSED_VARIABLE(blB);
-      EIGEN_UNUSED_VARIABLE(depth);
-      EIGEN_UNUSED_VARIABLE(endk);
-      EIGEN_UNUSED_VARIABLE(i);
-      EIGEN_UNUSED_VARIABLE(j2);
-      EIGEN_UNUSED_VARIABLE(alpha);
-      EIGEN_UNUSED_VARIABLE(C0);
-    }
-};
-
-
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-struct last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper,  mr,  nr, ConjugateLhs,  ConjugateRhs, 16> {
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
-  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
-
-  typedef typename Traits::ResScalar ResScalar;
-  typedef typename SwappedTraits::LhsPacket SLhsPacket;
-  typedef typename SwappedTraits::RhsPacket SRhsPacket;
-  typedef typename SwappedTraits::ResPacket SResPacket;
-  typedef typename SwappedTraits::AccPacket SAccPacket;
-
-  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits &straits, const LhsScalar* blA,
-                  const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
-                  ResScalar alpha, SAccPacket &C0)
-  {
-    typedef typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half SResPacketQuarter;
-    typedef typename unpacket_traits<typename unpacket_traits<SLhsPacket>::half>::half SLhsPacketQuarter;
-    typedef typename unpacket_traits<typename unpacket_traits<SRhsPacket>::half>::half SRhsPacketQuarter;
-    typedef typename unpacket_traits<typename unpacket_traits<SAccPacket>::half>::half SAccPacketQuarter;
-
-    SResPacketQuarter R = res.template gatherPacket<SResPacketQuarter>(i, j2);
-    SResPacketQuarter alphav = pset1<SResPacketQuarter>(alpha);
-
-    if (depth - endk > 0)
-      {
-	// We have to handle the last row(s) of the rhs, which
-	// correspond to a half-packet
-	SAccPacketQuarter c0 = predux_half_dowto4(predux_half_dowto4(C0));
-
-	for (Index kk = endk; kk < depth; kk++)
-	  {
-	    SLhsPacketQuarter a0;
-	    SRhsPacketQuarter b0;
-	    straits.loadLhsUnaligned(blB, a0);
-	    straits.loadRhs(blA, b0);
-	    straits.madd(a0,b0,c0,b0, fix<0>);
-	    blB += SwappedTraits::LhsProgress/4;
-	    blA += 1;
-	  }
-	straits.acc(c0, alphav, R);
-      }
-    else
-      {
-	straits.acc(predux_half_dowto4(predux_half_dowto4(C0)), alphav, R);
-      }
-    res.scatterPacket(i, j2, R);
-  }
-};
-
-template<int nr, Index LhsProgress, Index RhsProgress, typename LhsScalar, typename RhsScalar, typename ResScalar, typename AccPacket, typename LhsPacket, typename RhsPacket, typename ResPacket, typename GEBPTraits, typename LinearMapper, typename DataMapper>
-struct lhs_process_one_packet
-{
-  typedef typename GEBPTraits::RhsPacketx4 RhsPacketx4;
-
-  EIGEN_STRONG_INLINE void peeled_kc_onestep(Index K, const LhsScalar* blA, const RhsScalar* blB, GEBPTraits traits, LhsPacket *A0, RhsPacketx4 *rhs_panel, RhsPacket *T0, AccPacket *C0, AccPacket *C1, AccPacket *C2, AccPacket *C3)
-  {
-    EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1X4");
-    EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!");
-    traits.loadLhs(&blA[(0+1*K)*LhsProgress], *A0);
-    traits.loadRhs(&blB[(0+4*K)*RhsProgress], *rhs_panel);
-    traits.madd(*A0, *rhs_panel, *C0, *T0, fix<0>);
-    traits.madd(*A0, *rhs_panel, *C1, *T0, fix<1>);
-    traits.madd(*A0, *rhs_panel, *C2, *T0, fix<2>);
-    traits.madd(*A0, *rhs_panel, *C3, *T0, fix<3>);
-    #if EIGEN_GNUC_AT_LEAST(6,0) && defined(EIGEN_VECTORIZE_SSE)
-    __asm__  ("" : "+x,m" (*A0));
-    #endif
-    EIGEN_ASM_COMMENT("end step of gebp micro kernel 1X4");
-  }
-
-  EIGEN_STRONG_INLINE void operator()(
-    const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB, ResScalar alpha,
-    Index peelStart, Index peelEnd, Index strideA, Index strideB, Index offsetA, Index offsetB,
-    int prefetch_res_offset, Index peeled_kc, Index pk, Index cols, Index depth, Index packet_cols4)
-  {
-    GEBPTraits traits;
-
-    // loops on each largest micro horizontal panel of lhs
-    // (LhsProgress x depth)
-    for(Index i=peelStart; i<peelEnd; i+=LhsProgress)
-    {
-      // loops on each largest micro vertical panel of rhs (depth * nr)
-      for(Index j2=0; j2<packet_cols4; j2+=nr)
-      {
-        // We select a LhsProgress x nr micro block of res
-        // which is entirely stored into 1 x nr registers.
-
-        const LhsScalar* blA = &blockA[i*strideA+offsetA*(LhsProgress)];
-        prefetch(&blA[0]);
-
-        // gets res block as register
-        AccPacket C0, C1, C2, C3;
-        traits.initAcc(C0);
-        traits.initAcc(C1);
-        traits.initAcc(C2);
-        traits.initAcc(C3);
-        // To improve instruction pipelining, let's double the accumulation registers:
-        //  even k will accumulate in C*, while odd k will accumulate in D*.
-        // This trick is crutial to get good performance with FMA, otherwise it is 
-        // actually faster to perform separated MUL+ADD because of a naturally
-        // better instruction-level parallelism.
-        AccPacket D0, D1, D2, D3;
-        traits.initAcc(D0);
-        traits.initAcc(D1);
-        traits.initAcc(D2);
-        traits.initAcc(D3);
-
-        LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
-        LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
-        LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
-        LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
-
-        r0.prefetch(prefetch_res_offset);
-        r1.prefetch(prefetch_res_offset);
-        r2.prefetch(prefetch_res_offset);
-        r3.prefetch(prefetch_res_offset);
-
-        // performs "inner" products
-        const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-        prefetch(&blB[0]);
-        LhsPacket A0, A1;
-
-        for(Index k=0; k<peeled_kc; k+=pk)
-        {
-          EIGEN_ASM_COMMENT("begin gebp micro kernel 1/half/quarterX4");
-          RhsPacketx4 rhs_panel;
-          RhsPacket T0;
-
-          internal::prefetch(blB+(48+0));
-          peeled_kc_onestep(0, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(1, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-          peeled_kc_onestep(2, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(3, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-          internal::prefetch(blB+(48+16));
-          peeled_kc_onestep(4, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(5, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-          peeled_kc_onestep(6, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          peeled_kc_onestep(7, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
-
-          blB += pk*4*RhsProgress;
-          blA += pk*LhsProgress;
-
-          EIGEN_ASM_COMMENT("end gebp micro kernel 1/half/quarterX4");
-        }
-        C0 = padd(C0,D0);
-        C1 = padd(C1,D1);
-        C2 = padd(C2,D2);
-        C3 = padd(C3,D3);
-
-        // process remaining peeled loop
-        for(Index k=peeled_kc; k<depth; k++)
-        {
-          RhsPacketx4 rhs_panel;
-          RhsPacket T0;
-          peeled_kc_onestep(0, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
-          blB += 4*RhsProgress;
-          blA += LhsProgress;
-        }
-
-        ResPacket R0, R1;
-        ResPacket alphav = pset1<ResPacket>(alpha);
-
-        R0 = r0.template loadPacket<ResPacket>(0);
-        R1 = r1.template loadPacket<ResPacket>(0);
-        traits.acc(C0, alphav, R0);
-        traits.acc(C1,  alphav, R1);
-        r0.storePacket(0, R0);
-        r1.storePacket(0, R1);
-
-        R0 = r2.template loadPacket<ResPacket>(0);
-        R1 = r3.template loadPacket<ResPacket>(0);
-        traits.acc(C2,  alphav, R0);
-        traits.acc(C3,  alphav, R1);
-        r2.storePacket(0, R0);
-        r3.storePacket(0, R1);
-      }
-
-      // Deal with remaining columns of the rhs
-      for(Index j2=packet_cols4; j2<cols; j2++)
-      {
-        // One column at a time
-        const LhsScalar* blA = &blockA[i*strideA+offsetA*(LhsProgress)];
-        prefetch(&blA[0]);
-
-        // gets res block as register
-        AccPacket C0;
-        traits.initAcc(C0);
-
-        LinearMapper r0 = res.getLinearMapper(i, j2);
-
-        // performs "inner" products
-        const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-        LhsPacket A0;
-
-        for(Index k= 0; k<peeled_kc; k+=pk)
-        {
-          EIGEN_ASM_COMMENT("begin gebp micro kernel 1/half/quarterX1");
-          RhsPacket B_0;
-
-#define EIGEN_GEBGP_ONESTEP(K)                                          \
-	      do {                                                      \
-		EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1/half/quarterX1"); \
-		EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-    /* FIXME: why unaligned???? */ \
-		traits.loadLhsUnaligned(&blA[(0+1*K)*LhsProgress], A0); \
-		traits.loadRhs(&blB[(0+K)*RhsProgress], B_0);		\
-		traits.madd(A0, B_0, C0, B_0, fix<0>);				\
-		EIGEN_ASM_COMMENT("end step of gebp micro kernel 1/half/quarterX1"); \
-	      } while(false);
-
-          EIGEN_GEBGP_ONESTEP(0);
-          EIGEN_GEBGP_ONESTEP(1);
-          EIGEN_GEBGP_ONESTEP(2);
-          EIGEN_GEBGP_ONESTEP(3);
-          EIGEN_GEBGP_ONESTEP(4);
-          EIGEN_GEBGP_ONESTEP(5);
-          EIGEN_GEBGP_ONESTEP(6);
-          EIGEN_GEBGP_ONESTEP(7);
-
-          blB += pk*RhsProgress;
-          blA += pk*LhsProgress;
-
-          EIGEN_ASM_COMMENT("end gebp micro kernel 1/half/quarterX1");
-        }
-
-        // process remaining peeled loop
-        for(Index k=peeled_kc; k<depth; k++)
-        {
-          RhsPacket B_0;
-          EIGEN_GEBGP_ONESTEP(0);
-          blB += RhsProgress;
-          blA += LhsProgress;
-        }
-#undef EIGEN_GEBGP_ONESTEP
-        ResPacket R0;
-        ResPacket alphav = pset1<ResPacket>(alpha);
-        R0 = r0.template loadPacket<ResPacket>(0);
-        traits.acc(C0, alphav, R0);
-        r0.storePacket(0, R0);
-      }
-    }
-  }
-};
-
-template<int nr, Index LhsProgress, Index RhsProgress, typename LhsScalar, typename RhsScalar, typename ResScalar, typename AccPacket, typename LhsPacket, typename RhsPacket, typename ResPacket, typename GEBPTraits, typename LinearMapper, typename DataMapper>
-struct lhs_process_fraction_of_packet : lhs_process_one_packet<nr, LhsProgress, RhsProgress, LhsScalar, RhsScalar, ResScalar, AccPacket, LhsPacket, RhsPacket, ResPacket, GEBPTraits, LinearMapper, DataMapper>
-{
-
-EIGEN_STRONG_INLINE void peeled_kc_onestep(Index K, const LhsScalar* blA, const RhsScalar* blB, GEBPTraits traits, LhsPacket *A0, RhsPacket *B_0, RhsPacket *B1, RhsPacket *B2, RhsPacket *B3, AccPacket *C0, AccPacket *C1, AccPacket *C2, AccPacket *C3)
-  {
-        EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1X4");
-        EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!");
-        traits.loadLhsUnaligned(&blA[(0+1*K)*(LhsProgress)], *A0);
-        traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], *B_0, *B1, *B2, *B3);
-        traits.madd(*A0, *B_0, *C0, *B_0);
-        traits.madd(*A0, *B1,  *C1, *B1);
-        traits.madd(*A0, *B2,  *C2, *B2);
-        traits.madd(*A0, *B3,  *C3, *B3);
-        EIGEN_ASM_COMMENT("end step of gebp micro kernel 1X4");
-  }
-};
-
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_DONT_INLINE
-void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,ConjugateRhs>
-  ::operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
-               Index rows, Index depth, Index cols, ResScalar alpha,
-               Index strideA, Index strideB, Index offsetA, Index offsetB)
-  {
-    Traits traits;
-    SwappedTraits straits;
-    
-    if(strideA==-1) strideA = depth;
-    if(strideB==-1) strideB = depth;
-    conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
-    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-    const Index peeled_mc3 = mr>=3*Traits::LhsProgress ? (rows/(3*LhsProgress))*(3*LhsProgress) : 0;
-    const Index peeled_mc2 = mr>=2*Traits::LhsProgress ? peeled_mc3+((rows-peeled_mc3)/(2*LhsProgress))*(2*LhsProgress) : 0;
-    const Index peeled_mc1 = mr>=1*Traits::LhsProgress ? peeled_mc2+((rows-peeled_mc2)/(1*LhsProgress))*(1*LhsProgress) : 0;
-    const Index peeled_mc_half = mr>=LhsProgressHalf ? peeled_mc1+((rows-peeled_mc1)/(LhsProgressHalf))*(LhsProgressHalf) : 0;
-    const Index peeled_mc_quarter = mr>=LhsProgressQuarter ? peeled_mc_half+((rows-peeled_mc_half)/(LhsProgressQuarter))*(LhsProgressQuarter) : 0;
-    enum { pk = 8 }; // NOTE Such a large peeling factor is important for large matrices (~ +5% when >1000 on Haswell)
-    const Index peeled_kc  = depth & ~(pk-1);
-    const int prefetch_res_offset = 32/sizeof(ResScalar);    
-//     const Index depth2     = depth & ~1;
-
-    //---------- Process 3 * LhsProgress rows at once ----------
-    // This corresponds to 3*LhsProgress x nr register blocks.
-    // Usually, make sense only with FMA
-    if(mr>=3*Traits::LhsProgress)
-    {
-      // Here, the general idea is to loop on each largest micro horizontal panel of the lhs (3*Traits::LhsProgress x depth)
-      // and on each largest micro vertical panel of the rhs (depth * nr).
-      // Blocking sizes, i.e., 'depth' has been computed so that the micro horizontal panel of the lhs fit in L1.
-      // However, if depth is too small, we can extend the number of rows of these horizontal panels.
-      // This actual number of rows is computed as follow:
-      const Index l1 = defaultL1CacheSize; // in Bytes, TODO, l1 should be passed to this function.
-      // The max(1, ...) here is needed because we may be using blocking params larger than what our known l1 cache size
-      // suggests we should be using: either because our known l1 cache size is inaccurate (e.g. on Android, we can only guess),
-      // or because we are testing specific blocking sizes.
-      const Index actual_panel_rows = (3*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 3*LhsProgress) ));
-      for(Index i1=0; i1<peeled_mc3; i1+=actual_panel_rows)
-      {
-        const Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc3);
-        for(Index j2=0; j2<packet_cols4; j2+=nr)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
-          {
-          
-          // We selected a 3*Traits::LhsProgress x nr micro block of res which is entirely
-          // stored into 3 x nr registers.
-          
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C1, C2,  C3,
-                    C4, C5, C6,  C7,
-                    C8, C9, C10, C11;
-          traits.initAcc(C0);  traits.initAcc(C1);  traits.initAcc(C2);  traits.initAcc(C3);
-          traits.initAcc(C4);  traits.initAcc(C5);  traits.initAcc(C6);  traits.initAcc(C7);
-          traits.initAcc(C8);  traits.initAcc(C9);  traits.initAcc(C10); traits.initAcc(C11);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
-          LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
-          LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
-          LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
-
-          r0.prefetch(0);
-          r1.prefetch(0);
-          r2.prefetch(0);
-          r3.prefetch(0);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-          prefetch(&blB[0]);
-          LhsPacket A0, A1;
-
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX4");
-            // 15 registers are taken (12 for acc, 2 for lhs).
-            RhsPanel15 rhs_panel;
-            RhsPacket T0;
-            LhsPacket A2;
-            #if EIGEN_COMP_GNUC_STRICT && EIGEN_ARCH_ARM64 && defined(EIGEN_VECTORIZE_NEON) && !(EIGEN_GNUC_AT_LEAST(9,0))
-            // see http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1633
-            // without this workaround A0, A1, and A2 are loaded in the same register,
-            // which is not good for pipelining
-            #define EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND __asm__  ("" : "+w,m" (A0), "+w,m" (A1), "+w,m" (A2));
-            #else
-            #define EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND
-            #endif
-#define EIGEN_GEBP_ONESTEP(K)                                                     \
-            do {                                                                  \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX4");          \
-              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-              internal::prefetch(blA + (3 * K + 16) * LhsProgress);               \
-              if (EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) {                            \
-                internal::prefetch(blB + (4 * K + 16) * RhsProgress);             \
-              } /* Bug 953 */                                                     \
-              traits.loadLhs(&blA[(0 + 3 * K) * LhsProgress], A0);                \
-              traits.loadLhs(&blA[(1 + 3 * K) * LhsProgress], A1);                \
-              traits.loadLhs(&blA[(2 + 3 * K) * LhsProgress], A2);                \
-              EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND \
-              traits.loadRhs(blB + (0+4*K) * Traits::RhsProgress, rhs_panel);     \
-              traits.madd(A0, rhs_panel, C0, T0, fix<0>);                         \
-              traits.madd(A1, rhs_panel, C4, T0, fix<0>);                         \
-              traits.madd(A2, rhs_panel, C8, T0, fix<0>);                         \
-              traits.updateRhs(blB + (1+4*K) * Traits::RhsProgress, rhs_panel);   \
-              traits.madd(A0, rhs_panel, C1, T0, fix<1>);                         \
-              traits.madd(A1, rhs_panel, C5, T0, fix<1>);                         \
-              traits.madd(A2, rhs_panel, C9, T0, fix<1>);                         \
-              traits.updateRhs(blB + (2+4*K) * Traits::RhsProgress, rhs_panel);   \
-              traits.madd(A0, rhs_panel, C2, T0, fix<2>);                         \
-              traits.madd(A1, rhs_panel, C6, T0, fix<2>);                         \
-              traits.madd(A2, rhs_panel, C10, T0, fix<2>);                        \
-              traits.updateRhs(blB + (3+4*K) * Traits::RhsProgress, rhs_panel);   \
-              traits.madd(A0, rhs_panel, C3, T0, fix<3>);                         \
-              traits.madd(A1, rhs_panel, C7, T0, fix<3>);                         \
-              traits.madd(A2, rhs_panel, C11, T0, fix<3>);                        \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX4");            \
-            } while (false)
-
-            internal::prefetch(blB);
-            EIGEN_GEBP_ONESTEP(0);
-            EIGEN_GEBP_ONESTEP(1);
-            EIGEN_GEBP_ONESTEP(2);
-            EIGEN_GEBP_ONESTEP(3);
-            EIGEN_GEBP_ONESTEP(4);
-            EIGEN_GEBP_ONESTEP(5);
-            EIGEN_GEBP_ONESTEP(6);
-            EIGEN_GEBP_ONESTEP(7);
-
-            blB += pk*4*RhsProgress;
-            blA += pk*3*Traits::LhsProgress;
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 3pX4");
-          }
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPanel15 rhs_panel;
-            RhsPacket T0;
-            LhsPacket A2;
-            EIGEN_GEBP_ONESTEP(0);
-            blB += 4*RhsProgress;
-            blA += 3*Traits::LhsProgress;
-          }
-
-#undef EIGEN_GEBP_ONESTEP
-
-          ResPacket R0, R1, R2;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r0.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          traits.acc(C8, alphav, R2);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          r0.storePacket(2 * Traits::ResPacketSize, R2);
-
-          R0 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r1.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C1, alphav, R0);
-          traits.acc(C5, alphav, R1);
-          traits.acc(C9, alphav, R2);
-          r1.storePacket(0 * Traits::ResPacketSize, R0);
-          r1.storePacket(1 * Traits::ResPacketSize, R1);
-          r1.storePacket(2 * Traits::ResPacketSize, R2);
-
-          R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r2.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C2, alphav, R0);
-          traits.acc(C6, alphav, R1);
-          traits.acc(C10, alphav, R2);
-          r2.storePacket(0 * Traits::ResPacketSize, R0);
-          r2.storePacket(1 * Traits::ResPacketSize, R1);
-          r2.storePacket(2 * Traits::ResPacketSize, R2);
-
-          R0 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r3.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C3, alphav, R0);
-          traits.acc(C7, alphav, R1);
-          traits.acc(C11, alphav, R2);
-          r3.storePacket(0 * Traits::ResPacketSize, R0);
-          r3.storePacket(1 * Traits::ResPacketSize, R1);
-          r3.storePacket(2 * Traits::ResPacketSize, R2);          
-          }
-        }
-
-        // Deal with remaining columns of the rhs
-        for(Index j2=packet_cols4; j2<cols; j2++)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
-          {
-          // One column at a time
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*Traits::LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C4, C8;
-          traits.initAcc(C0);
-          traits.initAcc(C4);
-          traits.initAcc(C8);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2);
-          r0.prefetch(0);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-          LhsPacket A0, A1, A2;
-          
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX1");
-            RhsPacket B_0;
-#define EIGEN_GEBGP_ONESTEP(K)                                                    \
-            do {                                                                  \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX1");          \
-              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-              traits.loadLhs(&blA[(0 + 3 * K) * LhsProgress], A0);                \
-              traits.loadLhs(&blA[(1 + 3 * K) * LhsProgress], A1);                \
-              traits.loadLhs(&blA[(2 + 3 * K) * LhsProgress], A2);                \
-              traits.loadRhs(&blB[(0 + K) * RhsProgress], B_0);                   \
-              traits.madd(A0, B_0, C0, B_0, fix<0>);                              \
-              traits.madd(A1, B_0, C4, B_0, fix<0>);                              \
-              traits.madd(A2, B_0, C8, B_0, fix<0>);                              \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX1");            \
-            } while (false)
-
-            EIGEN_GEBGP_ONESTEP(0);
-            EIGEN_GEBGP_ONESTEP(1);
-            EIGEN_GEBGP_ONESTEP(2);
-            EIGEN_GEBGP_ONESTEP(3);
-            EIGEN_GEBGP_ONESTEP(4);
-            EIGEN_GEBGP_ONESTEP(5);
-            EIGEN_GEBGP_ONESTEP(6);
-            EIGEN_GEBGP_ONESTEP(7);
-
-            blB += int(pk) * int(RhsProgress);
-            blA += int(pk) * 3 * int(Traits::LhsProgress);
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 3pX1");
-          }
-
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPacket B_0;
-            EIGEN_GEBGP_ONESTEP(0);
-            blB += RhsProgress;
-            blA += 3*Traits::LhsProgress;
-          }
-#undef EIGEN_GEBGP_ONESTEP
-          ResPacket R0, R1, R2;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r0.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          traits.acc(C8, alphav, R2);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          r0.storePacket(2 * Traits::ResPacketSize, R2);          
-          }
-        }
-      }
-    }
-
-    //---------- Process 2 * LhsProgress rows at once ----------
-    if(mr>=2*Traits::LhsProgress)
-    {
-      const Index l1 = defaultL1CacheSize; // in Bytes, TODO, l1 should be passed to this function.
-      // The max(1, ...) here is needed because we may be using blocking params larger than what our known l1 cache size
-      // suggests we should be using: either because our known l1 cache size is inaccurate (e.g. on Android, we can only guess),
-      // or because we are testing specific blocking sizes.
-      Index actual_panel_rows = (2*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 2*LhsProgress) ));
-
-      for(Index i1=peeled_mc3; i1<peeled_mc2; i1+=actual_panel_rows)
-      {
-        Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc2);
-        for(Index j2=0; j2<packet_cols4; j2+=nr)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
-          {
-          
-          // We selected a 2*Traits::LhsProgress x nr micro block of res which is entirely
-          // stored into 2 x nr registers.
-          
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C1, C2, C3,
-                    C4, C5, C6, C7;
-          traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3);
-          traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
-          LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
-          LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
-          LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
-
-          r0.prefetch(prefetch_res_offset);
-          r1.prefetch(prefetch_res_offset);
-          r2.prefetch(prefetch_res_offset);
-          r3.prefetch(prefetch_res_offset);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-          prefetch(&blB[0]);
-          LhsPacket A0, A1;
-
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX4");
-            RhsPacketx4 rhs_panel;
-            RhsPacket T0;
-
-          // NOTE: the begin/end asm comments below work around bug 935!
-          // but they are not enough for gcc>=6 without FMA (bug 1637)
-          #if EIGEN_GNUC_AT_LEAST(6,0) && defined(EIGEN_VECTORIZE_SSE)
-            #define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND __asm__  ("" : [a0] "+x,m" (A0),[a1] "+x,m" (A1));
-          #else
-            #define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND
-          #endif
-#define EIGEN_GEBGP_ONESTEP(K)                                            \
-            do {                                                          \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX4");  \
-              traits.loadLhs(&blA[(0 + 2 * K) * LhsProgress], A0);        \
-              traits.loadLhs(&blA[(1 + 2 * K) * LhsProgress], A1);        \
-              traits.loadRhs(&blB[(0 + 4 * K) * RhsProgress], rhs_panel); \
-              traits.madd(A0, rhs_panel, C0, T0, fix<0>);                 \
-              traits.madd(A1, rhs_panel, C4, T0, fix<0>);                 \
-              traits.madd(A0, rhs_panel, C1, T0, fix<1>);                 \
-              traits.madd(A1, rhs_panel, C5, T0, fix<1>);                 \
-              traits.madd(A0, rhs_panel, C2, T0, fix<2>);                 \
-              traits.madd(A1, rhs_panel, C6, T0, fix<2>);                 \
-              traits.madd(A0, rhs_panel, C3, T0, fix<3>);                 \
-              traits.madd(A1, rhs_panel, C7, T0, fix<3>);                 \
-              EIGEN_GEBP_2PX4_SPILLING_WORKAROUND                         \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX4");    \
-            } while (false)
-
-            internal::prefetch(blB+(48+0));
-            EIGEN_GEBGP_ONESTEP(0);
-            EIGEN_GEBGP_ONESTEP(1);
-            EIGEN_GEBGP_ONESTEP(2);
-            EIGEN_GEBGP_ONESTEP(3);
-            internal::prefetch(blB+(48+16));
-            EIGEN_GEBGP_ONESTEP(4);
-            EIGEN_GEBGP_ONESTEP(5);
-            EIGEN_GEBGP_ONESTEP(6);
-            EIGEN_GEBGP_ONESTEP(7);
-
-            blB += pk*4*RhsProgress;
-            blA += pk*(2*Traits::LhsProgress);
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 2pX4");
-          }
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPacketx4 rhs_panel;
-            RhsPacket T0;
-            EIGEN_GEBGP_ONESTEP(0);
-            blB += 4*RhsProgress;
-            blA += 2*Traits::LhsProgress;
-          }
-#undef EIGEN_GEBGP_ONESTEP
-
-          ResPacket R0, R1, R2, R3;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R3 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          traits.acc(C1, alphav, R2);
-          traits.acc(C5, alphav, R3);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          r1.storePacket(0 * Traits::ResPacketSize, R2);
-          r1.storePacket(1 * Traits::ResPacketSize, R3);
-
-          R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          R2 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R3 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          traits.acc(C2,  alphav, R0);
-          traits.acc(C6,  alphav, R1);
-          traits.acc(C3,  alphav, R2);
-          traits.acc(C7,  alphav, R3);
-          r2.storePacket(0 * Traits::ResPacketSize, R0);
-          r2.storePacket(1 * Traits::ResPacketSize, R1);
-          r3.storePacket(0 * Traits::ResPacketSize, R2);
-          r3.storePacket(1 * Traits::ResPacketSize, R3);
-          }
-        }
-      
-        // Deal with remaining columns of the rhs
-        for(Index j2=packet_cols4; j2<cols; j2++)
-        {
-          for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
-          {
-          // One column at a time
-          const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)];
-          prefetch(&blA[0]);
-
-          // gets res block as register
-          AccPacket C0, C4;
-          traits.initAcc(C0);
-          traits.initAcc(C4);
-
-          LinearMapper r0 = res.getLinearMapper(i, j2);
-          r0.prefetch(prefetch_res_offset);
-
-          // performs "inner" products
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-          LhsPacket A0, A1;
-
-          for(Index k=0; k<peeled_kc; k+=pk)
-          {
-            EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1");
-            RhsPacket B_0, B1;
-        
-#define EIGEN_GEBGP_ONESTEP(K) \
-            do {                                                                  \
-              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1");          \
-              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
-              traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0);                      \
-              traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1);                      \
-              traits.loadRhs(&blB[(0+K)*RhsProgress], B_0);                       \
-              traits.madd(A0, B_0, C0, B1, fix<0>);                               \
-              traits.madd(A1, B_0, C4, B_0, fix<0>);                              \
-              EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX1");            \
-            } while(false)
-        
-            EIGEN_GEBGP_ONESTEP(0);
-            EIGEN_GEBGP_ONESTEP(1);
-            EIGEN_GEBGP_ONESTEP(2);
-            EIGEN_GEBGP_ONESTEP(3);
-            EIGEN_GEBGP_ONESTEP(4);
-            EIGEN_GEBGP_ONESTEP(5);
-            EIGEN_GEBGP_ONESTEP(6);
-            EIGEN_GEBGP_ONESTEP(7);
-
-            blB += int(pk) * int(RhsProgress);
-            blA += int(pk) * 2 * int(Traits::LhsProgress);
-
-            EIGEN_ASM_COMMENT("end gebp micro kernel 2pX1");
-          }
-
-          // process remaining peeled loop
-          for(Index k=peeled_kc; k<depth; k++)
-          {
-            RhsPacket B_0, B1;
-            EIGEN_GEBGP_ONESTEP(0);
-            blB += RhsProgress;
-            blA += 2*Traits::LhsProgress;
-          }
-#undef EIGEN_GEBGP_ONESTEP
-          ResPacket R0, R1;
-          ResPacket alphav = pset1<ResPacket>(alpha);
-
-          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
-          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
-          traits.acc(C0, alphav, R0);
-          traits.acc(C4, alphav, R1);
-          r0.storePacket(0 * Traits::ResPacketSize, R0);
-          r0.storePacket(1 * Traits::ResPacketSize, R1);
-          }
-        }
-      }
-    }
-    //---------- Process 1 * LhsProgress rows at once ----------
-    if(mr>=1*Traits::LhsProgress)
-    {
-      lhs_process_one_packet<nr, LhsProgress, RhsProgress, LhsScalar, RhsScalar, ResScalar, AccPacket, LhsPacket, RhsPacket, ResPacket, Traits, LinearMapper, DataMapper> p;
-      p(res, blockA, blockB, alpha, peeled_mc2, peeled_mc1, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
-    }
-    //---------- Process LhsProgressHalf rows at once ----------
-    if((LhsProgressHalf < LhsProgress) && mr>=LhsProgressHalf)
-    {
-      lhs_process_fraction_of_packet<nr, LhsProgressHalf, RhsProgressHalf, LhsScalar, RhsScalar, ResScalar, AccPacketHalf, LhsPacketHalf, RhsPacketHalf, ResPacketHalf, HalfTraits, LinearMapper, DataMapper> p;
-      p(res, blockA, blockB, alpha, peeled_mc1, peeled_mc_half, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
-    }
-    //---------- Process LhsProgressQuarter rows at once ----------
-    if((LhsProgressQuarter < LhsProgressHalf) && mr>=LhsProgressQuarter)
-    {
-      lhs_process_fraction_of_packet<nr, LhsProgressQuarter, RhsProgressQuarter, LhsScalar, RhsScalar, ResScalar, AccPacketQuarter, LhsPacketQuarter, RhsPacketQuarter, ResPacketQuarter, QuarterTraits, LinearMapper, DataMapper> p;
-      p(res, blockA, blockB, alpha, peeled_mc_half, peeled_mc_quarter, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
-    }
-    //---------- Process remaining rows, 1 at once ----------
-    if(peeled_mc_quarter<rows)
-    {
-      // loop on each panel of the rhs
-      for(Index j2=0; j2<packet_cols4; j2+=nr)
-      {
-        // loop on each row of the lhs (1*LhsProgress x depth)
-        for(Index i=peeled_mc_quarter; i<rows; i+=1)
-        {
-          const LhsScalar* blA = &blockA[i*strideA+offsetA];
-          prefetch(&blA[0]);
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
-
-          // If LhsProgress is 8 or 16, it assumes that there is a
-          // half or quarter packet, respectively, of the same size as
-          // nr (which is currently 4) for the return type.
-          const int SResPacketHalfSize = unpacket_traits<typename unpacket_traits<SResPacket>::half>::size;
-          const int SResPacketQuarterSize = unpacket_traits<typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half>::size;
-          if ((SwappedTraits::LhsProgress % 4) == 0 &&
-              (SwappedTraits::LhsProgress<=16) &&
-              (SwappedTraits::LhsProgress!=8  || SResPacketHalfSize==nr) &&
-              (SwappedTraits::LhsProgress!=16 || SResPacketQuarterSize==nr))
-          {
-            SAccPacket C0, C1, C2, C3;
-            straits.initAcc(C0);
-            straits.initAcc(C1);
-            straits.initAcc(C2);
-            straits.initAcc(C3);
-
-            const Index spk   = (std::max)(1,SwappedTraits::LhsProgress/4);
-            const Index endk  = (depth/spk)*spk;
-            const Index endk4 = (depth/(spk*4))*(spk*4);
-
-            Index k=0;
-            for(; k<endk4; k+=4*spk)
-            {
-              SLhsPacket A0,A1;
-              SRhsPacket B_0,B_1;
-
-              straits.loadLhsUnaligned(blB+0*SwappedTraits::LhsProgress, A0);
-              straits.loadLhsUnaligned(blB+1*SwappedTraits::LhsProgress, A1);
-
-              straits.loadRhsQuad(blA+0*spk, B_0);
-              straits.loadRhsQuad(blA+1*spk, B_1);
-              straits.madd(A0,B_0,C0,B_0, fix<0>);
-              straits.madd(A1,B_1,C1,B_1, fix<0>);
-
-              straits.loadLhsUnaligned(blB+2*SwappedTraits::LhsProgress, A0);
-              straits.loadLhsUnaligned(blB+3*SwappedTraits::LhsProgress, A1);
-              straits.loadRhsQuad(blA+2*spk, B_0);
-              straits.loadRhsQuad(blA+3*spk, B_1);
-              straits.madd(A0,B_0,C2,B_0, fix<0>);
-              straits.madd(A1,B_1,C3,B_1, fix<0>);
-
-              blB += 4*SwappedTraits::LhsProgress;
-              blA += 4*spk;
-            }
-            C0 = padd(padd(C0,C1),padd(C2,C3));
-            for(; k<endk; k+=spk)
-            {
-              SLhsPacket A0;
-              SRhsPacket B_0;
-
-              straits.loadLhsUnaligned(blB, A0);
-              straits.loadRhsQuad(blA, B_0);
-              straits.madd(A0,B_0,C0,B_0, fix<0>);
-
-              blB += SwappedTraits::LhsProgress;
-              blA += spk;
-            }
-            if(SwappedTraits::LhsProgress==8)
-            {
-              // Special case where we have to first reduce the accumulation register C0
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SResPacket>::half,SResPacket>::type SResPacketHalf;
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SLhsPacket>::half,SLhsPacket>::type SLhsPacketHalf;
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SRhsPacket>::half,SRhsPacket>::type SRhsPacketHalf;
-              typedef typename conditional<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SAccPacket>::half,SAccPacket>::type SAccPacketHalf;
-
-              SResPacketHalf R = res.template gatherPacket<SResPacketHalf>(i, j2);
-              SResPacketHalf alphav = pset1<SResPacketHalf>(alpha);
-
-              if(depth-endk>0)
-              {
-                // We have to handle the last row of the rhs which corresponds to a half-packet
-                SLhsPacketHalf a0;
-                SRhsPacketHalf b0;
-                straits.loadLhsUnaligned(blB, a0);
-                straits.loadRhs(blA, b0);
-                SAccPacketHalf c0 = predux_half_dowto4(C0);
-                straits.madd(a0,b0,c0,b0, fix<0>);
-                straits.acc(c0, alphav, R);
-              }
-              else
-              {
-                straits.acc(predux_half_dowto4(C0), alphav, R);
-              }
-              res.scatterPacket(i, j2, R);
-            }
-            else if (SwappedTraits::LhsProgress==16)
-            {
-              // Special case where we have to first reduce the
-              // accumulation register C0. We specialize the block in
-              // template form, so that LhsProgress < 16 paths don't
-              // fail to compile
-              last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> p;
-	            p(res, straits, blA, blB, depth, endk, i, j2,alpha, C0);
-            }
-            else
-            {
-              SResPacket R = res.template gatherPacket<SResPacket>(i, j2);
-              SResPacket alphav = pset1<SResPacket>(alpha);
-              straits.acc(C0, alphav, R);
-              res.scatterPacket(i, j2, R);
-            }
-          }
-          else // scalar path
-          {
-            // get a 1 x 4 res block as registers
-            ResScalar C0(0), C1(0), C2(0), C3(0);
-
-            for(Index k=0; k<depth; k++)
-            {
-              LhsScalar A0;
-              RhsScalar B_0, B_1;
-
-              A0 = blA[k];
-
-              B_0 = blB[0];
-              B_1 = blB[1];
-              C0 = cj.pmadd(A0,B_0,C0);
-              C1 = cj.pmadd(A0,B_1,C1);
-
-              B_0 = blB[2];
-              B_1 = blB[3];
-              C2 = cj.pmadd(A0,B_0,C2);
-              C3 = cj.pmadd(A0,B_1,C3);
-
-              blB += 4;
-            }
-            res(i, j2 + 0) += alpha * C0;
-            res(i, j2 + 1) += alpha * C1;
-            res(i, j2 + 2) += alpha * C2;
-            res(i, j2 + 3) += alpha * C3;
-          }
-        }
-      }
-      // remaining columns
-      for(Index j2=packet_cols4; j2<cols; j2++)
-      {
-        // loop on each row of the lhs (1*LhsProgress x depth)
-        for(Index i=peeled_mc_quarter; i<rows; i+=1)
-        {
-          const LhsScalar* blA = &blockA[i*strideA+offsetA];
-          prefetch(&blA[0]);
-          // gets a 1 x 1 res block as registers
-          ResScalar C0(0);
-          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
-          for(Index k=0; k<depth; k++)
-          {
-            LhsScalar A0 = blA[k];
-            RhsScalar B_0 = blB[k];
-            C0 = cj.pmadd(A0, B_0, C0);
-          }
-          res(i, j2) += alpha * C0;
-        }
-      }
-    }
-  }
-
-
-// pack a block of the lhs
-// The traversal is as follow (mr==4):
-//   0  4  8 12 ...
-//   1  5  9 13 ...
-//   2  6 10 14 ...
-//   3  7 11 15 ...
-//
-//  16 20 24 28 ...
-//  17 21 25 29 ...
-//  18 22 26 30 ...
-//  19 23 27 31 ...
-//
-//  32 33 34 35 ...
-//  36 36 38 39 ...
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-{
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
-  ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  typedef typename unpacket_traits<Packet>::half HalfPacket;
-  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
-  enum { PacketSize = unpacket_traits<Packet>::size,
-         HalfPacketSize = unpacket_traits<HalfPacket>::size,
-         QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
-         HasHalf = (int)HalfPacketSize < (int)PacketSize,
-         HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
-
-  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
-  EIGEN_UNUSED_VARIABLE(stride);
-  EIGEN_UNUSED_VARIABLE(offset);
-  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-  eigen_assert( ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) || (Pack1<=4) );
-  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-  Index count = 0;
-
-  const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0;
-  const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0;
-  const Index peeled_mc1 = Pack1>=1*PacketSize ? peeled_mc2+((rows-peeled_mc2)/(1*PacketSize))*(1*PacketSize) : 0;
-  const Index peeled_mc_half = Pack1>=HalfPacketSize ? peeled_mc1+((rows-peeled_mc1)/(HalfPacketSize))*(HalfPacketSize) : 0;
-  const Index peeled_mc_quarter = Pack1>=QuarterPacketSize ? (rows/(QuarterPacketSize))*(QuarterPacketSize) : 0;
-  const Index last_lhs_progress = rows > peeled_mc_quarter ? (rows - peeled_mc_quarter) & ~1 : 0;
-  const Index peeled_mc0 = Pack2>=PacketSize ? peeled_mc_quarter
-                         : Pack2>1 && last_lhs_progress ? (rows/last_lhs_progress)*last_lhs_progress : 0;
-
-  Index i=0;
-
-  // Pack 3 packets
-  if(Pack1>=3*PacketSize)
-  {
-    for(; i<peeled_mc3; i+=3*PacketSize)
-    {
-      if(PanelMode) count += (3*PacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        Packet A, B, C;
-        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
-        B = lhs.template loadPacket<Packet>(i+1*PacketSize, k);
-        C = lhs.template loadPacket<Packet>(i+2*PacketSize, k);
-        pstore(blockA+count, cj.pconj(A)); count+=PacketSize;
-        pstore(blockA+count, cj.pconj(B)); count+=PacketSize;
-        pstore(blockA+count, cj.pconj(C)); count+=PacketSize;
-      }
-      if(PanelMode) count += (3*PacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack 2 packets
-  if(Pack1>=2*PacketSize)
-  {
-    for(; i<peeled_mc2; i+=2*PacketSize)
-    {
-      if(PanelMode) count += (2*PacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        Packet A, B;
-        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
-        B = lhs.template loadPacket<Packet>(i+1*PacketSize, k);
-        pstore(blockA+count, cj.pconj(A)); count+=PacketSize;
-        pstore(blockA+count, cj.pconj(B)); count+=PacketSize;
-      }
-      if(PanelMode) count += (2*PacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack 1 packets
-  if(Pack1>=1*PacketSize)
-  {
-    for(; i<peeled_mc1; i+=1*PacketSize)
-    {
-      if(PanelMode) count += (1*PacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        Packet A;
-        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
-        pstore(blockA+count, cj.pconj(A));
-        count+=PacketSize;
-      }
-      if(PanelMode) count += (1*PacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack half packets
-  if(HasHalf && Pack1>=HalfPacketSize)
-  {
-    for(; i<peeled_mc_half; i+=HalfPacketSize)
-    {
-      if(PanelMode) count += (HalfPacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        HalfPacket A;
-        A = lhs.template loadPacket<HalfPacket>(i+0*(HalfPacketSize), k);
-        pstoreu(blockA+count, cj.pconj(A));
-        count+=HalfPacketSize;
-      }
-      if(PanelMode) count += (HalfPacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack quarter packets
-  if(HasQuarter && Pack1>=QuarterPacketSize)
-  {
-    for(; i<peeled_mc_quarter; i+=QuarterPacketSize)
-    {
-      if(PanelMode) count += (QuarterPacketSize) * offset;
-
-      for(Index k=0; k<depth; k++)
-      {
-        QuarterPacket A;
-        A = lhs.template loadPacket<QuarterPacket>(i+0*(QuarterPacketSize), k);
-        pstoreu(blockA+count, cj.pconj(A));
-        count+=QuarterPacketSize;
-      }
-      if(PanelMode) count += (QuarterPacketSize) * (stride-offset-depth);
-    }
-  }
-  // Pack2 may be *smaller* than PacketSize—that happens for
-  // products like real * complex, where we have to go half the
-  // progress on the lhs in order to duplicate those operands to
-  // address both real & imaginary parts on the rhs. This portion will
-  // pack those half ones until they match the number expected on the
-  // last peeling loop at this point (for the rhs).
-  if(Pack2<PacketSize && Pack2>1)
-  {
-    for(; i<peeled_mc0; i+=last_lhs_progress)
-    {
-      if(PanelMode) count += last_lhs_progress * offset;
-
-      for(Index k=0; k<depth; k++)
-        for(Index w=0; w<last_lhs_progress; w++)
-          blockA[count++] = cj(lhs(i+w, k));
-
-      if(PanelMode) count += last_lhs_progress * (stride-offset-depth);
-    }
-  }
-  // Pack scalars
-  for(; i<rows; i++)
-  {
-    if(PanelMode) count += offset;
-    for(Index k=0; k<depth; k++)
-      blockA[count++] = cj(lhs(i, k));
-    if(PanelMode) count += (stride-offset-depth);
-  }
-}
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-{
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
-};
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
-EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
-  ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
-{
-  typedef typename unpacket_traits<Packet>::half HalfPacket;
-  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
-  enum { PacketSize = unpacket_traits<Packet>::size,
-         HalfPacketSize = unpacket_traits<HalfPacket>::size,
-         QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
-         HasHalf = (int)HalfPacketSize < (int)PacketSize,
-         HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
-
-  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
-  EIGEN_UNUSED_VARIABLE(stride);
-  EIGEN_UNUSED_VARIABLE(offset);
-  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-  Index count = 0;
-  bool gone_half = false, gone_quarter = false, gone_last = false;
-
-  Index i = 0;
-  int pack = Pack1;
-  int psize = PacketSize;
-  while(pack>0)
-  {
-    Index remaining_rows = rows-i;
-    Index peeled_mc = gone_last ? Pack2>1 ? (rows/pack)*pack : 0 : i+(remaining_rows/pack)*pack;
-    Index starting_pos = i;
-    for(; i<peeled_mc; i+=pack)
-    {
-      if(PanelMode) count += pack * offset;
-
-      Index k=0;
-      if(pack>=psize && psize >= QuarterPacketSize)
-      {
-        const Index peeled_k = (depth/psize)*psize;
-        for(; k<peeled_k; k+=psize)
-        {
-          for (Index m = 0; m < pack; m += psize)
-          {
-            if (psize == PacketSize) {
-              PacketBlock<Packet> kernel;
-              for (int p = 0; p < psize; ++p) kernel.packet[p] = lhs.template loadPacket<Packet>(i+p+m, k);
-              ptranspose(kernel);
-              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel.packet[p]));
-            } else if (HasHalf && psize == HalfPacketSize) {
-              gone_half = true;
-              PacketBlock<HalfPacket> kernel_half;
-              for (int p = 0; p < psize; ++p) kernel_half.packet[p] = lhs.template loadPacket<HalfPacket>(i+p+m, k);
-              ptranspose(kernel_half);
-              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_half.packet[p]));
-            } else if (HasQuarter && psize == QuarterPacketSize) {
-              gone_quarter = true;
-              PacketBlock<QuarterPacket> kernel_quarter;
-              for (int p = 0; p < psize; ++p) kernel_quarter.packet[p] = lhs.template loadPacket<QuarterPacket>(i+p+m, k);
-              ptranspose(kernel_quarter);
-              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_quarter.packet[p]));
-	    }
-          }
-          count += psize*pack;
-        }
-      }
-
-      for(; k<depth; k++)
-      {
-        Index w=0;
-        for(; w<pack-3; w+=4)
-        {
-          Scalar a(cj(lhs(i+w+0, k))),
-                 b(cj(lhs(i+w+1, k))),
-                 c(cj(lhs(i+w+2, k))),
-                 d(cj(lhs(i+w+3, k)));
-          blockA[count++] = a;
-          blockA[count++] = b;
-          blockA[count++] = c;
-          blockA[count++] = d;
-        }
-        if(pack%4)
-          for(;w<pack;++w)
-            blockA[count++] = cj(lhs(i+w, k));
-      }
-
-      if(PanelMode) count += pack * (stride-offset-depth);
-    }
-
-    pack -= psize;
-    Index left = rows - i;
-    if (pack <= 0) {
-      if (!gone_last &&
-          (starting_pos == i || left >= psize/2 || left >= psize/4) &&
-          ((psize/2 == HalfPacketSize && HasHalf && !gone_half) ||
-           (psize/2 == QuarterPacketSize && HasQuarter && !gone_quarter))) {
-        psize /= 2;
-        pack = psize;
-        continue;
-      }
-      // Pack2 may be *smaller* than PacketSize—that happens for
-      // products like real * complex, where we have to go half the
-      // progress on the lhs in order to duplicate those operands to
-      // address both real & imaginary parts on the rhs. This portion will
-      // pack those half ones until they match the number expected on the
-      // last peeling loop at this point (for the rhs).
-      if (Pack2 < PacketSize && !gone_last) {
-        gone_last = true;
-        psize = pack = left & ~1;
-      }
-    }
-  }
-
-  for(; i<rows; i++)
-  {
-    if(PanelMode) count += offset;
-    for(Index k=0; k<depth; k++)
-      blockA[count++] = cj(lhs(i, k));
-    if(PanelMode) count += (stride-offset-depth);
-  }
-}
-
-// copy a complete panel of the rhs
-// this version is optimized for column major matrices
-// The traversal order is as follow: (nr==4):
-//  0  1  2  3   12 13 14 15   24 27
-//  4  5  6  7   16 17 18 19   25 28
-//  8  9 10 11   20 21 22 23   26 29
-//  .  .  .  .    .  .  .  .    .  .
-template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-{
-  typedef typename packet_traits<Scalar>::type Packet;
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  enum { PacketSize = packet_traits<Scalar>::size };
-  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
-};
-
-template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
-  ::operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
-{
-  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
-  EIGEN_UNUSED_VARIABLE(stride);
-  EIGEN_UNUSED_VARIABLE(offset);
-  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-  Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
-  Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-  Index count = 0;
-  const Index peeled_k = (depth/PacketSize)*PacketSize;
-//   if(nr>=8)
-//   {
-//     for(Index j2=0; j2<packet_cols8; j2+=8)
-//     {
-//       // skip what we have before
-//       if(PanelMode) count += 8 * offset;
-//       const Scalar* b0 = &rhs[(j2+0)*rhsStride];
-//       const Scalar* b1 = &rhs[(j2+1)*rhsStride];
-//       const Scalar* b2 = &rhs[(j2+2)*rhsStride];
-//       const Scalar* b3 = &rhs[(j2+3)*rhsStride];
-//       const Scalar* b4 = &rhs[(j2+4)*rhsStride];
-//       const Scalar* b5 = &rhs[(j2+5)*rhsStride];
-//       const Scalar* b6 = &rhs[(j2+6)*rhsStride];
-//       const Scalar* b7 = &rhs[(j2+7)*rhsStride];
-//       Index k=0;
-//       if(PacketSize==8) // TODO enable vectorized transposition for PacketSize==4
-//       {
-//         for(; k<peeled_k; k+=PacketSize) {
-//           PacketBlock<Packet> kernel;
-//           for (int p = 0; p < PacketSize; ++p) {
-//             kernel.packet[p] = ploadu<Packet>(&rhs[(j2+p)*rhsStride+k]);
-//           }
-//           ptranspose(kernel);
-//           for (int p = 0; p < PacketSize; ++p) {
-//             pstoreu(blockB+count, cj.pconj(kernel.packet[p]));
-//             count+=PacketSize;
-//           }
-//         }
-//       }
-//       for(; k<depth; k++)
-//       {
-//         blockB[count+0] = cj(b0[k]);
-//         blockB[count+1] = cj(b1[k]);
-//         blockB[count+2] = cj(b2[k]);
-//         blockB[count+3] = cj(b3[k]);
-//         blockB[count+4] = cj(b4[k]);
-//         blockB[count+5] = cj(b5[k]);
-//         blockB[count+6] = cj(b6[k]);
-//         blockB[count+7] = cj(b7[k]);
-//         count += 8;
-//       }
-//       // skip what we have after
-//       if(PanelMode) count += 8 * (stride-offset-depth);
-//     }
-//   }
-
-  if(nr>=4)
-  {
-    for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
-    {
-      // skip what we have before
-      if(PanelMode) count += 4 * offset;
-      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
-      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
-      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
-      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
-
-      Index k=0;
-      if((PacketSize%4)==0) // TODO enable vectorized transposition for PacketSize==2 ??
-      {
-        for(; k<peeled_k; k+=PacketSize) {
-          PacketBlock<Packet,(PacketSize%4)==0?4:PacketSize> kernel;
-          kernel.packet[0           ] = dm0.template loadPacket<Packet>(k);
-          kernel.packet[1%PacketSize] = dm1.template loadPacket<Packet>(k);
-          kernel.packet[2%PacketSize] = dm2.template loadPacket<Packet>(k);
-          kernel.packet[3%PacketSize] = dm3.template loadPacket<Packet>(k);
-          ptranspose(kernel);
-          pstoreu(blockB+count+0*PacketSize, cj.pconj(kernel.packet[0]));
-          pstoreu(blockB+count+1*PacketSize, cj.pconj(kernel.packet[1%PacketSize]));
-          pstoreu(blockB+count+2*PacketSize, cj.pconj(kernel.packet[2%PacketSize]));
-          pstoreu(blockB+count+3*PacketSize, cj.pconj(kernel.packet[3%PacketSize]));
-          count+=4*PacketSize;
-        }
-      }
-      for(; k<depth; k++)
-      {
-        blockB[count+0] = cj(dm0(k));
-        blockB[count+1] = cj(dm1(k));
-        blockB[count+2] = cj(dm2(k));
-        blockB[count+3] = cj(dm3(k));
-        count += 4;
-      }
-      // skip what we have after
-      if(PanelMode) count += 4 * (stride-offset-depth);
-    }
-  }
-
-  // copy the remaining columns one at a time (nr==1)
-  for(Index j2=packet_cols4; j2<cols; ++j2)
-  {
-    if(PanelMode) count += offset;
-    const LinearMapper dm0 = rhs.getLinearMapper(0, j2);
-    for(Index k=0; k<depth; k++)
-    {
-      blockB[count] = cj(dm0(k));
-      count += 1;
-    }
-    if(PanelMode) count += (stride-offset-depth);
-  }
-}
-
-// this version is optimized for row major matrices
-template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
-struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
-{
-  typedef typename packet_traits<Scalar>::type Packet;
-  typedef typename unpacket_traits<Packet>::half HalfPacket;
-  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
-  typedef typename DataMapper::LinearMapper LinearMapper;
-  enum { PacketSize = packet_traits<Scalar>::size,
-         HalfPacketSize = unpacket_traits<HalfPacket>::size,
-		 QuarterPacketSize = unpacket_traits<QuarterPacket>::size};
-  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0)
-  {
-    EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
-    EIGEN_UNUSED_VARIABLE(stride);
-    EIGEN_UNUSED_VARIABLE(offset);
-    eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
-    const bool HasHalf = (int)HalfPacketSize < (int)PacketSize;
-    const bool HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize;
-    conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
-    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-    Index count = 0;
-
-  //   if(nr>=8)
-  //   {
-  //     for(Index j2=0; j2<packet_cols8; j2+=8)
-  //     {
-  //       // skip what we have before
-  //       if(PanelMode) count += 8 * offset;
-  //       for(Index k=0; k<depth; k++)
-  //       {
-  //         if (PacketSize==8) {
-  //           Packet A = ploadu<Packet>(&rhs[k*rhsStride + j2]);
-  //           pstoreu(blockB+count, cj.pconj(A));
-  //         } else if (PacketSize==4) {
-  //           Packet A = ploadu<Packet>(&rhs[k*rhsStride + j2]);
-  //           Packet B = ploadu<Packet>(&rhs[k*rhsStride + j2 + PacketSize]);
-  //           pstoreu(blockB+count, cj.pconj(A));
-  //           pstoreu(blockB+count+PacketSize, cj.pconj(B));
-  //         } else {
-  //           const Scalar* b0 = &rhs[k*rhsStride + j2];
-  //           blockB[count+0] = cj(b0[0]);
-  //           blockB[count+1] = cj(b0[1]);
-  //           blockB[count+2] = cj(b0[2]);
-  //           blockB[count+3] = cj(b0[3]);
-  //           blockB[count+4] = cj(b0[4]);
-  //           blockB[count+5] = cj(b0[5]);
-  //           blockB[count+6] = cj(b0[6]);
-  //           blockB[count+7] = cj(b0[7]);
-  //         }
-  //         count += 8;
-  //       }
-  //       // skip what we have after
-  //       if(PanelMode) count += 8 * (stride-offset-depth);
-  //     }
-  //   }
-    if(nr>=4)
-    {
-      for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
-      {
-        // skip what we have before
-        if(PanelMode) count += 4 * offset;
-        for(Index k=0; k<depth; k++)
-        {
-          if (PacketSize==4) {
-            Packet A = rhs.template loadPacket<Packet>(k, j2);
-            pstoreu(blockB+count, cj.pconj(A));
-            count += PacketSize;
-          } else if (HasHalf && HalfPacketSize==4) {
-            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
-            pstoreu(blockB+count, cj.pconj(A));
-            count += HalfPacketSize;
-          } else if (HasQuarter && QuarterPacketSize==4) {
-            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
-            pstoreu(blockB+count, cj.pconj(A));
-            count += QuarterPacketSize;
-          } else {
-            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
-            blockB[count+0] = cj(dm0(0));
-            blockB[count+1] = cj(dm0(1));
-            blockB[count+2] = cj(dm0(2));
-            blockB[count+3] = cj(dm0(3));
-            count += 4;
-          }
-        }
-        // skip what we have after
-        if(PanelMode) count += 4 * (stride-offset-depth);
-      }
-    }
-    // copy the remaining columns one at a time (nr==1)
-    for(Index j2=packet_cols4; j2<cols; ++j2)
-    {
-      if(PanelMode) count += offset;
-      for(Index k=0; k<depth; k++)
-      {
-        blockB[count] = cj(rhs(k, j2));
-        count += 1;
-      }
-      if(PanelMode) count += stride-offset-depth;
-    }
-  }
-};
-
-} // end namespace internal
-
-/** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
-  * \sa setCpuCacheSize */
-inline std::ptrdiff_t l1CacheSize()
-{
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  return l1;
-}
-
-/** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
-  * \sa setCpuCacheSize */
-inline std::ptrdiff_t l2CacheSize()
-{
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  return l2;
-}
-
-/** \returns the currently set level 3 cpu cache size (in bytes) used to estimate the ideal blocking size paramete\
-rs.                                                                                                                
-* \sa setCpuCacheSize */
-inline std::ptrdiff_t l3CacheSize()
-{
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-  return l3;
-}
-
-/** Set the cpu L1 and L2 cache sizes (in bytes).
-  * These values are use to adjust the size of the blocks
-  * for the algorithms working per blocks.
-  *
-  * \sa computeProductBlockingSizes */
-inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2, std::ptrdiff_t l3)
-{
-  internal::manage_caching_sizes(SetAction, &l1, &l2, &l3);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_BLOCK_PANEL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
deleted file mode 100644
index caa65fc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
+++ /dev/null
@@ -1,517 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename _LhsScalar, typename _RhsScalar> class level3_blocking;
-
-/* Specialization for a row-major destination matrix => simple transposition of the product */
-template<
-  typename Index,
-  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-  int ResInnerStride>
-struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride>
-{
-  typedef gebp_traits<RhsScalar,LhsScalar> Traits;
-
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  static EIGEN_STRONG_INLINE void run(
-    Index rows, Index cols, Index depth,
-    const LhsScalar* lhs, Index lhsStride,
-    const RhsScalar* rhs, Index rhsStride,
-    ResScalar* res, Index resIncr, Index resStride,
-    ResScalar alpha,
-    level3_blocking<RhsScalar,LhsScalar>& blocking,
-    GemmParallelInfo<Index>* info = 0)
-  {
-    // transpose the product such that the result is column major
-    general_matrix_matrix_product<Index,
-      RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
-      LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
-      ColMajor,ResInnerStride>
-    ::run(cols,rows,depth,rhs,rhsStride,lhs,lhsStride,res,resIncr,resStride,alpha,blocking,info);
-  }
-};
-
-/*  Specialization for a col-major destination matrix
- *    => Blocking algorithm following Goto's paper */
-template<
-  typename Index,
-  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-  int ResInnerStride>
-struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride>
-{
-
-typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-static void run(Index rows, Index cols, Index depth,
-  const LhsScalar* _lhs, Index lhsStride,
-  const RhsScalar* _rhs, Index rhsStride,
-  ResScalar* _res, Index resIncr, Index resStride,
-  ResScalar alpha,
-  level3_blocking<LhsScalar,RhsScalar>& blocking,
-  GemmParallelInfo<Index>* info = 0)
-{
-  typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
-  typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
-  typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor,Unaligned,ResInnerStride> ResMapper;
-  LhsMapper lhs(_lhs, lhsStride);
-  RhsMapper rhs(_rhs, rhsStride);
-  ResMapper res(_res, resStride, resIncr);
-
-  Index kc = blocking.kc();                   // cache block size along the K direction
-  Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-  Index nc = (std::min)(cols,blocking.nc());  // cache block size along the N direction
-
-  gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-  gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
-  gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
-
-#ifdef EIGEN_HAS_OPENMP
-  if(info)
-  {
-    // this is the parallel version!
-    int tid = omp_get_thread_num();
-    int threads = omp_get_num_threads();
-
-    LhsScalar* blockA = blocking.blockA();
-    eigen_internal_assert(blockA!=0);
-
-    std::size_t sizeB = kc*nc;
-    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, 0);
-
-    // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs...
-    for(Index k=0; k<depth; k+=kc)
-    {
-      const Index actual_kc = (std::min)(k+kc,depth)-k; // => rows of B', and cols of the A'
-
-      // In order to reduce the chance that a thread has to wait for the other,
-      // let's start by packing B'.
-      pack_rhs(blockB, rhs.getSubMapper(k,0), actual_kc, nc);
-
-      // Pack A_k to A' in a parallel fashion:
-      // each thread packs the sub block A_k,i to A'_i where i is the thread id.
-
-      // However, before copying to A'_i, we have to make sure that no other thread is still using it,
-      // i.e., we test that info[tid].users equals 0.
-      // Then, we set info[tid].users to the number of threads to mark that all other threads are going to use it.
-      while(info[tid].users!=0) {}
-      info[tid].users = threads;
-
-      pack_lhs(blockA+info[tid].lhs_start*actual_kc, lhs.getSubMapper(info[tid].lhs_start,k), actual_kc, info[tid].lhs_length);
-
-      // Notify the other threads that the part A'_i is ready to go.
-      info[tid].sync = k;
-
-      // Computes C_i += A' * B' per A'_i
-      for(int shift=0; shift<threads; ++shift)
-      {
-        int i = (tid+shift)%threads;
-
-        // At this point we have to make sure that A'_i has been updated by the thread i,
-        // we use testAndSetOrdered to mimic a volatile access.
-        // However, no need to wait for the B' part which has been updated by the current thread!
-        if (shift>0) {
-          while(info[i].sync!=k) {
-          }
-        }
-
-        gebp(res.getSubMapper(info[i].lhs_start, 0), blockA+info[i].lhs_start*actual_kc, blockB, info[i].lhs_length, actual_kc, nc, alpha);
-      }
-
-      // Then keep going as usual with the remaining B'
-      for(Index j=nc; j<cols; j+=nc)
-      {
-        const Index actual_nc = (std::min)(j+nc,cols)-j;
-
-        // pack B_k,j to B'
-        pack_rhs(blockB, rhs.getSubMapper(k,j), actual_kc, actual_nc);
-
-        // C_j += A' * B'
-        gebp(res.getSubMapper(0, j), blockA, blockB, rows, actual_kc, actual_nc, alpha);
-      }
-
-      // Release all the sub blocks A'_i of A' for the current thread,
-      // i.e., we simply decrement the number of users by 1
-      for(Index i=0; i<threads; ++i)
-#if !EIGEN_HAS_CXX11_ATOMIC
-        #pragma omp atomic
-#endif
-        info[i].users -= 1;
-    }
-  }
-  else
-#endif // EIGEN_HAS_OPENMP
-  {
-    EIGEN_UNUSED_VARIABLE(info);
-
-    // this is the sequential version!
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*nc;
-
-    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
-
-    const bool pack_rhs_once = mc!=rows && kc==depth && nc==cols;
-
-    // For each horizontal panel of the rhs, and corresponding panel of the lhs...
-    for(Index i2=0; i2<rows; i2+=mc)
-    {
-      const Index actual_mc = (std::min)(i2+mc,rows)-i2;
-
-      for(Index k2=0; k2<depth; k2+=kc)
-      {
-        const Index actual_kc = (std::min)(k2+kc,depth)-k2;
-
-        // OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs.
-        // => Pack lhs's panel into a sequential chunk of memory (L2/L3 caching)
-        // Note that this panel will be read as many times as the number of blocks in the rhs's
-        // horizontal panel which is, in practice, a very low number.
-        pack_lhs(blockA, lhs.getSubMapper(i2,k2), actual_kc, actual_mc);
-
-        // For each kc x nc block of the rhs's horizontal panel...
-        for(Index j2=0; j2<cols; j2+=nc)
-        {
-          const Index actual_nc = (std::min)(j2+nc,cols)-j2;
-
-          // We pack the rhs's block into a sequential chunk of memory (L2 caching)
-          // Note that this block will be read a very high number of times, which is equal to the number of
-          // micro horizontal panel of the large rhs's panel (e.g., rows/12 times).
-          if((!pack_rhs_once) || i2==0)
-            pack_rhs(blockB, rhs.getSubMapper(k2,j2), actual_kc, actual_nc);
-
-          // Everything is packed, we can now call the panel * block kernel:
-          gebp(res.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, alpha);
-        }
-      }
-    }
-  }
-}
-
-};
-
-/*********************************************************************************
-*  Specialization of generic_product_impl for "large" GEMM, i.e.,
-*  implementation of the high level wrapper to general_matrix_matrix_product
-**********************************************************************************/
-
-template<typename Scalar, typename Index, typename Gemm, typename Lhs, typename Rhs, typename Dest, typename BlockingType>
-struct gemm_functor
-{
-  gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, const Scalar& actualAlpha, BlockingType& blocking)
-    : m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking)
-  {}
-
-  void initParallelSession(Index num_threads) const
-  {
-    m_blocking.initParallel(m_lhs.rows(), m_rhs.cols(), m_lhs.cols(), num_threads);
-    m_blocking.allocateA();
-  }
-
-  void operator() (Index row, Index rows, Index col=0, Index cols=-1, GemmParallelInfo<Index>* info=0) const
-  {
-    if(cols==-1)
-      cols = m_rhs.cols();
-
-    Gemm::run(rows, cols, m_lhs.cols(),
-              &m_lhs.coeffRef(row,0), m_lhs.outerStride(),
-              &m_rhs.coeffRef(0,col), m_rhs.outerStride(),
-              (Scalar*)&(m_dest.coeffRef(row,col)), m_dest.innerStride(), m_dest.outerStride(),
-              m_actualAlpha, m_blocking, info);
-  }
-
-  typedef typename Gemm::Traits Traits;
-
-  protected:
-    const Lhs& m_lhs;
-    const Rhs& m_rhs;
-    Dest& m_dest;
-    Scalar m_actualAlpha;
-    BlockingType& m_blocking;
-};
-
-template<int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor=1,
-bool FiniteAtCompileTime = MaxRows!=Dynamic && MaxCols!=Dynamic && MaxDepth != Dynamic> class gemm_blocking_space;
-
-template<typename _LhsScalar, typename _RhsScalar>
-class level3_blocking
-{
-    typedef _LhsScalar LhsScalar;
-    typedef _RhsScalar RhsScalar;
-
-  protected:
-    LhsScalar* m_blockA;
-    RhsScalar* m_blockB;
-
-    Index m_mc;
-    Index m_nc;
-    Index m_kc;
-
-  public:
-
-    level3_blocking()
-      : m_blockA(0), m_blockB(0), m_mc(0), m_nc(0), m_kc(0)
-    {}
-
-    inline Index mc() const { return m_mc; }
-    inline Index nc() const { return m_nc; }
-    inline Index kc() const { return m_kc; }
-
-    inline LhsScalar* blockA() { return m_blockA; }
-    inline RhsScalar* blockB() { return m_blockB; }
-};
-
-template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor>
-class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, true /* == FiniteAtCompileTime */>
-  : public level3_blocking<
-      typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type,
-      typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type>
-{
-    enum {
-      Transpose = StorageOrder==RowMajor,
-      ActualRows = Transpose ? MaxCols : MaxRows,
-      ActualCols = Transpose ? MaxRows : MaxCols
-    };
-    typedef typename conditional<Transpose,_RhsScalar,_LhsScalar>::type LhsScalar;
-    typedef typename conditional<Transpose,_LhsScalar,_RhsScalar>::type RhsScalar;
-    typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-    enum {
-      SizeA = ActualRows * MaxDepth,
-      SizeB = ActualCols * MaxDepth
-    };
-
-#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
-    EIGEN_ALIGN_MAX LhsScalar m_staticA[SizeA];
-    EIGEN_ALIGN_MAX RhsScalar m_staticB[SizeB];
-#else
-    EIGEN_ALIGN_MAX char m_staticA[SizeA * sizeof(LhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES-1];
-    EIGEN_ALIGN_MAX char m_staticB[SizeB * sizeof(RhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES-1];
-#endif
-
-  public:
-
-    gemm_blocking_space(Index /*rows*/, Index /*cols*/, Index /*depth*/, Index /*num_threads*/, bool /*full_rows = false*/)
-    {
-      this->m_mc = ActualRows;
-      this->m_nc = ActualCols;
-      this->m_kc = MaxDepth;
-#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
-      this->m_blockA = m_staticA;
-      this->m_blockB = m_staticB;
-#else
-      this->m_blockA = reinterpret_cast<LhsScalar*>((internal::UIntPtr(m_staticA) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
-      this->m_blockB = reinterpret_cast<RhsScalar*>((internal::UIntPtr(m_staticB) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
-#endif
-    }
-
-    void initParallel(Index, Index, Index, Index)
-    {}
-
-    inline void allocateA() {}
-    inline void allocateB() {}
-    inline void allocateAll() {}
-};
-
-template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor>
-class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, false>
-  : public level3_blocking<
-      typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type,
-      typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type>
-{
-    enum {
-      Transpose = StorageOrder==RowMajor
-    };
-    typedef typename conditional<Transpose,_RhsScalar,_LhsScalar>::type LhsScalar;
-    typedef typename conditional<Transpose,_LhsScalar,_RhsScalar>::type RhsScalar;
-    typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-    Index m_sizeA;
-    Index m_sizeB;
-
-  public:
-
-    gemm_blocking_space(Index rows, Index cols, Index depth, Index num_threads, bool l3_blocking)
-    {
-      this->m_mc = Transpose ? cols : rows;
-      this->m_nc = Transpose ? rows : cols;
-      this->m_kc = depth;
-
-      if(l3_blocking)
-      {
-        computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, this->m_nc, num_threads);
-      }
-      else  // no l3 blocking
-      {
-        Index n = this->m_nc;
-        computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, n, num_threads);
-      }
-
-      m_sizeA = this->m_mc * this->m_kc;
-      m_sizeB = this->m_kc * this->m_nc;
-    }
-
-    void initParallel(Index rows, Index cols, Index depth, Index num_threads)
-    {
-      this->m_mc = Transpose ? cols : rows;
-      this->m_nc = Transpose ? rows : cols;
-      this->m_kc = depth;
-
-      eigen_internal_assert(this->m_blockA==0 && this->m_blockB==0);
-      Index m = this->m_mc;
-      computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, m, this->m_nc, num_threads);
-      m_sizeA = this->m_mc * this->m_kc;
-      m_sizeB = this->m_kc * this->m_nc;
-    }
-
-    void allocateA()
-    {
-      if(this->m_blockA==0)
-        this->m_blockA = aligned_new<LhsScalar>(m_sizeA);
-    }
-
-    void allocateB()
-    {
-      if(this->m_blockB==0)
-        this->m_blockB = aligned_new<RhsScalar>(m_sizeB);
-    }
-
-    void allocateAll()
-    {
-      allocateA();
-      allocateB();
-    }
-
-    ~gemm_blocking_space()
-    {
-      aligned_delete(this->m_blockA, m_sizeA);
-      aligned_delete(this->m_blockB, m_sizeB);
-    }
-};
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs>
-struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  typedef typename Lhs::Scalar LhsScalar;
-  typedef typename Rhs::Scalar RhsScalar;
-
-  typedef internal::blas_traits<Lhs> LhsBlasTraits;
-  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-  typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
-
-  typedef internal::blas_traits<Rhs> RhsBlasTraits;
-  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-  enum {
-    MaxDepthAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(Lhs::MaxColsAtCompileTime,Rhs::MaxRowsAtCompileTime)
-  };
-
-  typedef generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> lazyproduct;
-
-  template<typename Dst>
-  static void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=404 for a discussion and helper program
-    // to determine the following heuristic.
-    // EIGEN_GEMM_TO_COEFFBASED_THRESHOLD is typically defined to 20 in GeneralProduct.h,
-    // unless it has been specialized by the user or for a given architecture.
-    // Note that the condition rhs.rows()>0 was required because lazy product is (was?) not happy with empty inputs.
-    // I'm not sure it is still required.
-    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
-      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::assign_op<typename Dst::Scalar,Scalar>());
-    else
-    {
-      dst.setZero();
-      scaleAndAddTo(dst, lhs, rhs, Scalar(1));
-    }
-  }
-
-  template<typename Dst>
-  static void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
-      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::add_assign_op<typename Dst::Scalar,Scalar>());
-    else
-      scaleAndAddTo(dst,lhs, rhs, Scalar(1));
-  }
-
-  template<typename Dst>
-  static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
-      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::sub_assign_op<typename Dst::Scalar,Scalar>());
-    else
-      scaleAndAddTo(dst, lhs, rhs, Scalar(-1));
-  }
-
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
-    if(a_lhs.cols()==0 || a_lhs.rows()==0 || a_rhs.cols()==0)
-      return;
-
-    if (dst.cols() == 1)
-    {
-      // Fallback to GEMV if either the lhs or rhs is a runtime vector
-      typename Dest::ColXpr dst_vec(dst.col(0));
-      return internal::generic_product_impl<Lhs,typename Rhs::ConstColXpr,DenseShape,DenseShape,GemvProduct>
-        ::scaleAndAddTo(dst_vec, a_lhs, a_rhs.col(0), alpha);
-    }
-    else if (dst.rows() == 1)
-    {
-      // Fallback to GEMV if either the lhs or rhs is a runtime vector
-      typename Dest::RowXpr dst_vec(dst.row(0));
-      return internal::generic_product_impl<typename Lhs::ConstRowXpr,Rhs,DenseShape,DenseShape,GemvProduct>
-        ::scaleAndAddTo(dst_vec, a_lhs.row(0), a_rhs, alpha);
-    }
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    Scalar actualAlpha = combine_scalar_factors(alpha, a_lhs, a_rhs);
-
-    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,LhsScalar,RhsScalar,
-            Dest::MaxRowsAtCompileTime,Dest::MaxColsAtCompileTime,MaxDepthAtCompileTime> BlockingType;
-
-    typedef internal::gemm_functor<
-      Scalar, Index,
-      internal::general_matrix_matrix_product<
-        Index,
-        LhsScalar, (ActualLhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(LhsBlasTraits::NeedToConjugate),
-        RhsScalar, (ActualRhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate),
-        (Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,
-        Dest::InnerStrideAtCompileTime>,
-      ActualLhsTypeCleaned, ActualRhsTypeCleaned, Dest, BlockingType> GemmFunctor;
-
-    BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true);
-    internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime>32 || Dest::MaxRowsAtCompileTime==Dynamic)>
-        (GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), a_lhs.rows(), a_rhs.cols(), a_lhs.cols(), Dest::Flags&RowMajorBit);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
deleted file mode 100644
index 6ba0d9b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
+++ /dev/null
@@ -1,317 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
-
-namespace Eigen { 
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs>
-struct selfadjoint_rank1_update;
-
-namespace internal {
-
-/**********************************************************************
-* This file implements a general A * B product while
-* evaluating only one triangular part of the product.
-* This is a more general version of self adjoint product (C += A A^T)
-* as the level 3 SYRK Blas routine.
-**********************************************************************/
-
-// forward declarations (defined at the end of this file)
-template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int ResInnerStride, int UpLo>
-struct tribb_kernel;
-  
-/* Optimized matrix-matrix product evaluating only one triangular half */
-template <typename Index,
-          typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-                              int ResStorageOrder, int ResInnerStride, int  UpLo, int Version = Specialized>
-struct general_matrix_matrix_triangular_product;
-
-// as usual if the result is row major => we transpose the product
-template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-                          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-                          int ResInnerStride, int  UpLo, int Version>
-struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride,UpLo,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride,
-                                      const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resIncr, Index resStride,
-                                      const ResScalar& alpha, level3_blocking<RhsScalar,LhsScalar>& blocking)
-  {
-    general_matrix_matrix_triangular_product<Index,
-        RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
-        LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
-        ColMajor, ResInnerStride, UpLo==Lower?Upper:Lower>
-      ::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resIncr,resStride,alpha,blocking);
-  }
-};
-
-template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-                          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-                          int ResInnerStride, int  UpLo, int Version>
-struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,UpLo,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
-                                      const RhsScalar* _rhs, Index rhsStride,
-                                      ResScalar* _res, Index resIncr, Index resStride,
-                                      const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)
-  {
-    typedef gebp_traits<LhsScalar,RhsScalar> Traits;
-
-    typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();
-    Index mc = (std::min)(size,blocking.mc());
-
-    // !!! mc must be a multiple of nr:
-    if(mc > Traits::nr)
-      mc = (mc/Traits::nr)*Traits::nr;
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*size;
-
-    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
-
-    gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
-    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
-    tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, ResInnerStride, UpLo> sybb;
-
-    for(Index k2=0; k2<depth; k2+=kc)
-    {
-      const Index actual_kc = (std::min)(k2+kc,depth)-k2;
-
-      // note that the actual rhs is the transpose/adjoint of mat
-      pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, size);
-
-      for(Index i2=0; i2<size; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,size)-i2;
-
-        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        // the selected actual_mc * size panel of res is split into three different part:
-        //  1 - before the diagonal => processed with gebp or skipped
-        //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
-        //  3 - after the diagonal => processed with gebp or skipped
-        if (UpLo==Lower)
-          gebp(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc,
-               (std::min)(size,i2), alpha, -1, -1, 0, 0);
-
-        sybb(_res+resStride*i2 + resIncr*i2, resIncr, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha);
-
-        if (UpLo==Upper)
-        {
-          Index j2 = i2+actual_mc;
-          gebp(res.getSubMapper(i2, j2), blockA, blockB+actual_kc*j2, actual_mc,
-               actual_kc, (std::max)(Index(0), size-j2), alpha, -1, -1, 0, 0);
-        }
-      }
-    }
-  }
-};
-
-// Optimized packed Block * packed Block product kernel evaluating only one given triangular part
-// This kernel is built on top of the gebp kernel:
-// - the current destination block is processed per panel of actual_mc x BlockSize
-//   where BlockSize is set to the minimal value allowing gebp to be as fast as possible
-// - then, as usual, each panel is split into three parts along the diagonal,
-//   the sub blocks above and below the diagonal are processed as usual,
-//   while the triangular block overlapping the diagonal is evaluated into a
-//   small temporary buffer which is then accumulated into the result using a
-//   triangular traversal.
-template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int ResInnerStride, int UpLo>
-struct tribb_kernel
-{
-  typedef gebp_traits<LhsScalar,RhsScalar,ConjLhs,ConjRhs> Traits;
-  typedef typename Traits::ResScalar ResScalar;
-
-  enum {
-    BlockSize  = meta_least_common_multiple<EIGEN_PLAIN_ENUM_MAX(mr,nr),EIGEN_PLAIN_ENUM_MIN(mr,nr)>::ret
-  };
-  void operator()(ResScalar* _res, Index resIncr, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha)
-  {
-    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned> BufferMapper;
-    ResMapper res(_res, resStride, resIncr);
-    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel1;
-    gebp_kernel<LhsScalar, RhsScalar, Index, BufferMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel2;
-
-    Matrix<ResScalar,BlockSize,BlockSize,ColMajor> buffer((internal::constructor_without_unaligned_array_assert()));
-
-    // let's process the block per panel of actual_mc x BlockSize,
-    // again, each is split into three parts, etc.
-    for (Index j=0; j<size; j+=BlockSize)
-    {
-      Index actualBlockSize = std::min<Index>(BlockSize,size - j);
-      const RhsScalar* actual_b = blockB+j*depth;
-
-      if(UpLo==Upper)
-        gebp_kernel1(res.getSubMapper(0, j), blockA, actual_b, j, depth, actualBlockSize, alpha,
-                     -1, -1, 0, 0);
-      
-      // selfadjoint micro block
-      {
-        Index i = j;
-        buffer.setZero();
-        // 1 - apply the kernel on the temporary buffer
-        gebp_kernel2(BufferMapper(buffer.data(), BlockSize), blockA+depth*i, actual_b, actualBlockSize, depth, actualBlockSize, alpha,
-                     -1, -1, 0, 0);
-
-        // 2 - triangular accumulation
-        for(Index j1=0; j1<actualBlockSize; ++j1)
-        {
-          typename ResMapper::LinearMapper r = res.getLinearMapper(i,j+j1);
-          for(Index i1=UpLo==Lower ? j1 : 0;
-              UpLo==Lower ? i1<actualBlockSize : i1<=j1; ++i1)
-            r(i1) += buffer(i1,j1);
-        }
-      }
-
-      if(UpLo==Lower)
-      {
-        Index i = j+actualBlockSize;
-        gebp_kernel1(res.getSubMapper(i, j), blockA+depth*i, actual_b, size-i, 
-                     depth, actualBlockSize, alpha, -1, -1, 0, 0);
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-// high level API
-
-template<typename MatrixType, typename ProductType, int UpLo, bool IsOuterProduct>
-struct general_product_to_triangular_selector;
-
-
-template<typename MatrixType, typename ProductType, int UpLo>
-struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true>
-{
-  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    
-    typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs;
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
-    typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
-    typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
-    
-    typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
-    typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
-    typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
-
-    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
-
-    if(!beta)
-      mat.template triangularView<UpLo>().setZero();
-
-    enum {
-      StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-      UseLhsDirectly = _ActualLhs::InnerStrideAtCompileTime==1,
-      UseRhsDirectly = _ActualRhs::InnerStrideAtCompileTime==1
-    };
-    
-    internal::gemv_static_vector_if<Scalar,Lhs::SizeAtCompileTime,Lhs::MaxSizeAtCompileTime,!UseLhsDirectly> static_lhs;
-    ei_declare_aligned_stack_constructed_variable(Scalar, actualLhsPtr, actualLhs.size(),
-      (UseLhsDirectly ? const_cast<Scalar*>(actualLhs.data()) : static_lhs.data()));
-    if(!UseLhsDirectly) Map<typename _ActualLhs::PlainObject>(actualLhsPtr, actualLhs.size()) = actualLhs;
-    
-    internal::gemv_static_vector_if<Scalar,Rhs::SizeAtCompileTime,Rhs::MaxSizeAtCompileTime,!UseRhsDirectly> static_rhs;
-    ei_declare_aligned_stack_constructed_variable(Scalar, actualRhsPtr, actualRhs.size(),
-      (UseRhsDirectly ? const_cast<Scalar*>(actualRhs.data()) : static_rhs.data()));
-    if(!UseRhsDirectly) Map<typename _ActualRhs::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
-    
-    
-    selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
-                              LhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
-                              RhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex>
-          ::run(actualLhs.size(), mat.data(), mat.outerStride(), actualLhsPtr, actualRhsPtr, actualAlpha);
-  }
-};
-
-template<typename MatrixType, typename ProductType, int UpLo>
-struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
-{
-  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
-  {
-    typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs;
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
-    typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
-    typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
-    
-    typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
-    typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
-    typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
-
-    typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
-
-    if(!beta)
-      mat.template triangularView<UpLo>().setZero();
-
-    enum {
-      IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
-      LhsIsRowMajor = _ActualLhs::Flags&RowMajorBit ? 1 : 0,
-      RhsIsRowMajor = _ActualRhs::Flags&RowMajorBit ? 1 : 0,
-      SkipDiag = (UpLo&(UnitDiag|ZeroDiag))!=0
-    };
-
-    Index size = mat.cols();
-    if(SkipDiag)
-      size--;
-    Index depth = actualLhs.cols();
-
-    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,typename Lhs::Scalar,typename Rhs::Scalar,
-          MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, _ActualRhs::MaxColsAtCompileTime> BlockingType;
-
-    BlockingType blocking(size, size, depth, 1, false);
-
-    internal::general_matrix_matrix_triangular_product<Index,
-      typename Lhs::Scalar, LhsIsRowMajor ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
-      typename Rhs::Scalar, RhsIsRowMajor ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
-      IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime, UpLo&(Lower|Upper)>
-      ::run(size, depth,
-            &actualLhs.coeffRef(SkipDiag&&(UpLo&Lower)==Lower ? 1 : 0,0), actualLhs.outerStride(),
-            &actualRhs.coeffRef(0,SkipDiag&&(UpLo&Upper)==Upper ? 1 : 0), actualRhs.outerStride(),
-            mat.data() + (SkipDiag ? (bool(IsRowMajor) != ((UpLo&Lower)==Lower) ? mat.innerStride() : mat.outerStride() ) : 0),
-            mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
-  }
-};
-
-template<typename MatrixType, unsigned int UpLo>
-template<typename ProductType>
-EIGEN_DEVICE_FUNC TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha, bool beta)
-{
-  EIGEN_STATIC_ASSERT((UpLo&UnitDiag)==0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
-  eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
-
-  general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha, beta);
-
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
deleted file mode 100644
index 9a650ec..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
+++ /dev/null
@@ -1,145 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Level 3 BLAS SYRK/HERK implementation.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Index, typename Scalar, int AStorageOrder, bool ConjugateA, int ResStorageOrder, int UpLo>
-struct general_matrix_matrix_rankupdate :
-       general_matrix_matrix_triangular_product<
-         Index,Scalar,AStorageOrder,ConjugateA,Scalar,AStorageOrder,ConjugateA,ResStorageOrder,1,UpLo,BuiltIn> {};
-
-
-// try to go to BLAS specialization
-#define EIGEN_BLAS_RANKUPDATE_SPECIALIZE(Scalar) \
-template <typename Index, int LhsStorageOrder, bool ConjugateLhs, \
-                          int RhsStorageOrder, bool ConjugateRhs, int  UpLo> \
-struct general_matrix_matrix_triangular_product<Index,Scalar,LhsStorageOrder,ConjugateLhs, \
-               Scalar,RhsStorageOrder,ConjugateRhs,ColMajor,1,UpLo,Specialized> { \
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \
-                          const Scalar* rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride, Scalar alpha, level3_blocking<Scalar, Scalar>& blocking) \
-  { \
-    if ( lhs==rhs && ((UpLo&(Lower|Upper))==UpLo) ) { \
-      general_matrix_matrix_rankupdate<Index,Scalar,LhsStorageOrder,ConjugateLhs,ColMajor,UpLo> \
-      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha,blocking); \
-    } else { \
-      general_matrix_matrix_triangular_product<Index, \
-        Scalar, LhsStorageOrder, ConjugateLhs, \
-        Scalar, RhsStorageOrder, ConjugateRhs, \
-        ColMajor, 1, UpLo, BuiltIn> \
-      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resIncr,resStride,alpha,blocking); \
-    } \
-  } \
-};
-
-EIGEN_BLAS_RANKUPDATE_SPECIALIZE(double)
-EIGEN_BLAS_RANKUPDATE_SPECIALIZE(float)
-// TODO handle complex cases
-// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(dcomplex)
-// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(scomplex)
-
-// SYRK for float/double
-#define EIGEN_BLAS_RANKUPDATE_R(EIGTYPE, BLASTYPE, BLASFUNC) \
-template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
-struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
-  enum { \
-    IsLower = (UpLo&Lower) == Lower, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = ((AStorageOrder==ColMajor) && ConjugateA) ? 1 : 0 \
-  }; \
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
-                          const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-  /* typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;*/ \
-\
-   BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
-   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'T':'N'); \
-   EIGTYPE beta(1); \
-   BLASFUNC(&uplo, &trans, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), lhs, &lda, (const BLASTYPE*)&numext::real_ref(beta), res, &ldc); \
-  } \
-};
-
-// HERK for complex data
-#define EIGEN_BLAS_RANKUPDATE_C(EIGTYPE, BLASTYPE, RTYPE, BLASFUNC) \
-template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
-struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
-  enum { \
-    IsLower = (UpLo&Lower) == Lower, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = (((AStorageOrder==ColMajor) && ConjugateA) || ((AStorageOrder==RowMajor) && !ConjugateA)) ? 1 : 0 \
-  }; \
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
-                          const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, AStorageOrder> MatrixType; \
-\
-   BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
-   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'C':'N'); \
-   RTYPE alpha_, beta_; \
-   const EIGTYPE* a_ptr; \
-\
-   alpha_ = alpha.real(); \
-   beta_ = 1.0; \
-/* Copy with conjugation in some cases*/ \
-   MatrixType a; \
-   if (conjA) { \
-     Map<const MatrixType, 0, OuterStride<> > mapA(lhs,n,k,OuterStride<>(lhsStride)); \
-     a = mapA.conjugate(); \
-     lda = a.outerStride(); \
-     a_ptr = a.data(); \
-   } else a_ptr=lhs; \
-   BLASFUNC(&uplo, &trans, &n, &k, &alpha_, (BLASTYPE*)a_ptr, &lda, &beta_, (BLASTYPE*)res, &ldc); \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk)
-EIGEN_BLAS_RANKUPDATE_R(float,  float,  ssyrk)
-#else
-EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk_)
-EIGEN_BLAS_RANKUPDATE_R(float,  float,  ssyrk_)
-#endif
-
-// TODO hanlde complex cases
-// EIGEN_BLAS_RANKUPDATE_C(dcomplex, double, double, zherk_)
-// EIGEN_BLAS_RANKUPDATE_C(scomplex, float,  float, cherk_)
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
deleted file mode 100644
index 71abf40..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   General matrix-matrix product functionality based on ?GEMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
-#define EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements general matrix-matrix multiplication using BLAS
-* gemm function via partial specialization of
-* general_matrix_matrix_product::run(..) method for float, double,
-* std::complex<float> and std::complex<double> types
-**********************************************************************/
-
-// gemm specialization
-
-#define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, BLASTYPE, BLASFUNC) \
-template< \
-  typename Index, \
-  int LhsStorageOrder, bool ConjugateLhs, \
-  int RhsStorageOrder, bool ConjugateRhs> \
-struct general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1> \
-{ \
-typedef gebp_traits<EIGTYPE,EIGTYPE> Traits; \
-\
-static void run(Index rows, Index cols, Index depth, \
-  const EIGTYPE* _lhs, Index lhsStride, \
-  const EIGTYPE* _rhs, Index rhsStride, \
-  EIGTYPE* res, Index resIncr, Index resStride, \
-  EIGTYPE alpha, \
-  level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/, \
-  GemmParallelInfo<Index>* /*info = 0*/) \
-{ \
-  using std::conj; \
-\
-  EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-  eigen_assert(resIncr == 1); \
-  char transa, transb; \
-  BlasIndex m, n, k, lda, ldb, ldc; \
-  const EIGTYPE *a, *b; \
-  EIGTYPE beta(1); \
-  MatrixX##EIGPREFIX a_tmp, b_tmp; \
-\
-/* Set transpose options */ \
-  transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
-  transb = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
-\
-/* Set m, n, k */ \
-  m = convert_index<BlasIndex>(rows);  \
-  n = convert_index<BlasIndex>(cols);  \
-  k = convert_index<BlasIndex>(depth); \
-\
-/* Set lda, ldb, ldc */ \
-  lda = convert_index<BlasIndex>(lhsStride); \
-  ldb = convert_index<BlasIndex>(rhsStride); \
-  ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-  if ((LhsStorageOrder==ColMajor) && (ConjugateLhs)) { \
-    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,k,OuterStride<>(lhsStride)); \
-    a_tmp = lhs.conjugate(); \
-    a = a_tmp.data(); \
-    lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-  } else a = _lhs; \
-\
-  if ((RhsStorageOrder==ColMajor) && (ConjugateRhs)) { \
-    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,k,n,OuterStride<>(rhsStride)); \
-    b_tmp = rhs.conjugate(); \
-    b = b_tmp.data(); \
-    ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-  } else b = _rhs; \
-\
-  BLASFUNC(&transa, &transb, &m, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-}};
-
-#ifdef EIGEN_USE_MKL
-GEMM_SPECIALIZATION(double,   d,  double, dgemm)
-GEMM_SPECIALIZATION(float,    f,  float,  sgemm)
-GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, zgemm)
-GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8,  cgemm)
-#else
-GEMM_SPECIALIZATION(double,   d,  double, dgemm_)
-GEMM_SPECIALIZATION(float,    f,  float,  sgemm_)
-GEMM_SPECIALIZATION(dcomplex, cd, double, zgemm_)
-GEMM_SPECIALIZATION(scomplex, cf, float,  cgemm_)
-#endif
-
-} // end namespase internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
deleted file mode 100644
index dfb6aeb..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
+++ /dev/null
@@ -1,518 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERAL_MATRIX_VECTOR_H
-#define EIGEN_GENERAL_MATRIX_VECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-enum GEMVPacketSizeType {
-  GEMVPacketFull = 0,
-  GEMVPacketHalf,
-  GEMVPacketQuarter
-};
-
-template <int N, typename T1, typename T2, typename T3>
-struct gemv_packet_cond { typedef T3 type; };
-
-template <typename T1, typename T2, typename T3>
-struct gemv_packet_cond<GEMVPacketFull, T1, T2, T3> { typedef T1 type; };
-
-template <typename T1, typename T2, typename T3>
-struct gemv_packet_cond<GEMVPacketHalf, T1, T2, T3> { typedef T2 type; };
-
-template<typename LhsScalar, typename RhsScalar, int _PacketSize=GEMVPacketFull>
-class gemv_traits
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-#define PACKET_DECL_COND_PREFIX(prefix, name, packet_size)                        \
-  typedef typename gemv_packet_cond<packet_size,                                  \
-                                    typename packet_traits<name ## Scalar>::type, \
-                                    typename packet_traits<name ## Scalar>::half, \
-                                    typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
-  prefix ## name ## Packet
-
-  PACKET_DECL_COND_PREFIX(_, Lhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Rhs, _PacketSize);
-  PACKET_DECL_COND_PREFIX(_, Res, _PacketSize);
-#undef PACKET_DECL_COND_PREFIX
-
-public:
-  enum {
-        Vectorizable = unpacket_traits<_LhsPacket>::vectorizable &&
-        unpacket_traits<_RhsPacket>::vectorizable &&
-        int(unpacket_traits<_LhsPacket>::size)==int(unpacket_traits<_RhsPacket>::size),
-        LhsPacketSize = Vectorizable ? unpacket_traits<_LhsPacket>::size : 1,
-        RhsPacketSize = Vectorizable ? unpacket_traits<_RhsPacket>::size : 1,
-        ResPacketSize = Vectorizable ? unpacket_traits<_ResPacket>::size : 1
-  };
-
-  typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
-  typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
-  typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
-};
-
-
-/* Optimized col-major matrix * vector product:
- * This algorithm processes the matrix per vertical panels,
- * which are then processed horizontaly per chunck of 8*PacketSize x 1 vertical segments.
- *
- * Mixing type logic: C += alpha * A * B
- *  |  A  |  B  |alpha| comments
- *  |real |cplx |cplx | no vectorization
- *  |real |cplx |real | alpha is converted to a cplx when calling the run function, no vectorization
- *  |cplx |real |cplx | invalid, the caller has to do tmp: = A * B; C += alpha*tmp
- *  |cplx |real |real | optimal case, vectorization possible via real-cplx mul
- *
- * The same reasoning apply for the transposed case.
- */
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
-{
-  typedef gemv_traits<LhsScalar,RhsScalar> Traits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketHalf> HalfTraits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketQuarter> QuarterTraits;
-
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  typedef typename Traits::LhsPacket LhsPacket;
-  typedef typename Traits::RhsPacket RhsPacket;
-  typedef typename Traits::ResPacket ResPacket;
-
-  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
-  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
-  typedef typename HalfTraits::ResPacket ResPacketHalf;
-
-  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
-  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
-  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
-
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(
-  Index rows, Index cols,
-  const LhsMapper& lhs,
-  const RhsMapper& rhs,
-        ResScalar* res, Index resIncr,
-  RhsScalar alpha);
-};
-
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run(
-  Index rows, Index cols,
-  const LhsMapper& alhs,
-  const RhsMapper& rhs,
-        ResScalar* res, Index resIncr,
-  RhsScalar alpha)
-{
-  EIGEN_UNUSED_VARIABLE(resIncr);
-  eigen_internal_assert(resIncr==1);
-
-  // The following copy tells the compiler that lhs's attributes are not modified outside this function
-  // This helps GCC to generate propoer code.
-  LhsMapper lhs(alhs);
-
-  conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
-  conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
-  conj_helper<LhsPacketHalf,RhsPacketHalf,ConjugateLhs,ConjugateRhs> pcj_half;
-  conj_helper<LhsPacketQuarter,RhsPacketQuarter,ConjugateLhs,ConjugateRhs> pcj_quarter;
-
-  const Index lhsStride = lhs.stride();
-  // TODO: for padded aligned inputs, we could enable aligned reads
-  enum { LhsAlignment = Unaligned,
-         ResPacketSize = Traits::ResPacketSize,
-         ResPacketSizeHalf = HalfTraits::ResPacketSize,
-         ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
-         LhsPacketSize = Traits::LhsPacketSize,
-         HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
-         HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
-  };
-
-  const Index n8 = rows-8*ResPacketSize+1;
-  const Index n4 = rows-4*ResPacketSize+1;
-  const Index n3 = rows-3*ResPacketSize+1;
-  const Index n2 = rows-2*ResPacketSize+1;
-  const Index n1 = rows-1*ResPacketSize+1;
-  const Index n_half = rows-1*ResPacketSizeHalf+1;
-  const Index n_quarter = rows-1*ResPacketSizeQuarter+1;
-
-  // TODO: improve the following heuristic:
-  const Index block_cols = cols<128 ? cols : (lhsStride*sizeof(LhsScalar)<32000?16:4);
-  ResPacket palpha = pset1<ResPacket>(alpha);
-  ResPacketHalf palpha_half = pset1<ResPacketHalf>(alpha);
-  ResPacketQuarter palpha_quarter = pset1<ResPacketQuarter>(alpha);
-
-  for(Index j2=0; j2<cols; j2+=block_cols)
-  {
-    Index jend = numext::mini(j2+block_cols,cols);
-    Index i=0;
-    for(; i<n8; i+=ResPacketSize*8)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0)),
-                c2 = pset1<ResPacket>(ResScalar(0)),
-                c3 = pset1<ResPacket>(ResScalar(0)),
-                c4 = pset1<ResPacket>(ResScalar(0)),
-                c5 = pset1<ResPacket>(ResScalar(0)),
-                c6 = pset1<ResPacket>(ResScalar(0)),
-                c7 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
-        c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*3,j),b0,c3);
-        c4 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*4,j),b0,c4);
-        c5 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*5,j),b0,c5);
-        c6 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*6,j),b0,c6);
-        c7 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*7,j),b0,c7);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
-      pstoreu(res+i+ResPacketSize*3, pmadd(c3,palpha,ploadu<ResPacket>(res+i+ResPacketSize*3)));
-      pstoreu(res+i+ResPacketSize*4, pmadd(c4,palpha,ploadu<ResPacket>(res+i+ResPacketSize*4)));
-      pstoreu(res+i+ResPacketSize*5, pmadd(c5,palpha,ploadu<ResPacket>(res+i+ResPacketSize*5)));
-      pstoreu(res+i+ResPacketSize*6, pmadd(c6,palpha,ploadu<ResPacket>(res+i+ResPacketSize*6)));
-      pstoreu(res+i+ResPacketSize*7, pmadd(c7,palpha,ploadu<ResPacket>(res+i+ResPacketSize*7)));
-    }
-    if(i<n4)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0)),
-                c2 = pset1<ResPacket>(ResScalar(0)),
-                c3 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
-        c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*3,j),b0,c3);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
-      pstoreu(res+i+ResPacketSize*3, pmadd(c3,palpha,ploadu<ResPacket>(res+i+ResPacketSize*3)));
-
-      i+=ResPacketSize*4;
-    }
-    if(i<n3)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0)),
-                c2 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
-
-      i+=ResPacketSize*3;
-    }
-    if(i<n2)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-                c1 = pset1<ResPacket>(ResScalar(0));
-
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
-        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
-      i+=ResPacketSize*2;
-    }
-    if(i<n1)
-    {
-      ResPacket c0 = pset1<ResPacket>(ResScalar(0));
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
-        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      }
-      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
-      i+=ResPacketSize;
-    }
-    if(HasHalf && i<n_half)
-    {
-      ResPacketHalf c0 = pset1<ResPacketHalf>(ResScalar(0));
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacketHalf b0 = pset1<RhsPacketHalf>(rhs(j,0));
-        c0 = pcj_half.pmadd(lhs.template load<LhsPacketHalf,LhsAlignment>(i+0,j),b0,c0);
-      }
-      pstoreu(res+i+ResPacketSizeHalf*0, pmadd(c0,palpha_half,ploadu<ResPacketHalf>(res+i+ResPacketSizeHalf*0)));
-      i+=ResPacketSizeHalf;
-    }
-    if(HasQuarter && i<n_quarter)
-    {
-      ResPacketQuarter c0 = pset1<ResPacketQuarter>(ResScalar(0));
-      for(Index j=j2; j<jend; j+=1)
-      {
-        RhsPacketQuarter b0 = pset1<RhsPacketQuarter>(rhs(j,0));
-        c0 = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter,LhsAlignment>(i+0,j),b0,c0);
-      }
-      pstoreu(res+i+ResPacketSizeQuarter*0, pmadd(c0,palpha_quarter,ploadu<ResPacketQuarter>(res+i+ResPacketSizeQuarter*0)));
-      i+=ResPacketSizeQuarter;
-    }
-    for(;i<rows;++i)
-    {
-      ResScalar c0(0);
-      for(Index j=j2; j<jend; j+=1)
-        c0 += cj.pmul(lhs(i,j), rhs(j,0));
-      res[i] += alpha*c0;
-    }
-  }
-}
-
-/* Optimized row-major matrix * vector product:
- * This algorithm processes 4 rows at once that allows to both reduce
- * the number of load/stores of the result by a factor 4 and to reduce
- * the instruction dependency. Moreover, we know that all bands have the
- * same alignment pattern.
- *
- * Mixing type logic:
- *  - alpha is always a complex (or converted to a complex)
- *  - no vectorization
- */
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
-{
-  typedef gemv_traits<LhsScalar,RhsScalar> Traits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketHalf> HalfTraits;
-  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketQuarter> QuarterTraits;
-
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-
-  typedef typename Traits::LhsPacket LhsPacket;
-  typedef typename Traits::RhsPacket RhsPacket;
-  typedef typename Traits::ResPacket ResPacket;
-
-  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
-  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
-  typedef typename HalfTraits::ResPacket ResPacketHalf;
-
-  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
-  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
-  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
-
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(
-  Index rows, Index cols,
-  const LhsMapper& lhs,
-  const RhsMapper& rhs,
-        ResScalar* res, Index resIncr,
-  ResScalar alpha);
-};
-
-template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
-EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run(
-  Index rows, Index cols,
-  const LhsMapper& alhs,
-  const RhsMapper& rhs,
-  ResScalar* res, Index resIncr,
-  ResScalar alpha)
-{
-  // The following copy tells the compiler that lhs's attributes are not modified outside this function
-  // This helps GCC to generate propoer code.
-  LhsMapper lhs(alhs);
-
-  eigen_internal_assert(rhs.stride()==1);
-  conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
-  conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
-  conj_helper<LhsPacketHalf,RhsPacketHalf,ConjugateLhs,ConjugateRhs> pcj_half;
-  conj_helper<LhsPacketQuarter,RhsPacketQuarter,ConjugateLhs,ConjugateRhs> pcj_quarter;
-
-  // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
-  //       processing 8 rows at once might be counter productive wrt cache.
-  const Index n8 = lhs.stride()*sizeof(LhsScalar)>32000 ? 0 : rows-7;
-  const Index n4 = rows-3;
-  const Index n2 = rows-1;
-
-  // TODO: for padded aligned inputs, we could enable aligned reads
-  enum { LhsAlignment = Unaligned,
-         ResPacketSize = Traits::ResPacketSize,
-         ResPacketSizeHalf = HalfTraits::ResPacketSize,
-         ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
-         LhsPacketSize = Traits::LhsPacketSize,
-         LhsPacketSizeHalf = HalfTraits::LhsPacketSize,
-         LhsPacketSizeQuarter = QuarterTraits::LhsPacketSize,
-         HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
-         HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
-  };
-
-  Index i=0;
-  for(; i<n8; i+=8)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-              c1 = pset1<ResPacket>(ResScalar(0)),
-              c2 = pset1<ResPacket>(ResScalar(0)),
-              c3 = pset1<ResPacket>(ResScalar(0)),
-              c4 = pset1<ResPacket>(ResScalar(0)),
-              c5 = pset1<ResPacket>(ResScalar(0)),
-              c6 = pset1<ResPacket>(ResScalar(0)),
-              c7 = pset1<ResPacket>(ResScalar(0));
-
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
-
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
-      c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+2,j),b0,c2);
-      c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+3,j),b0,c3);
-      c4 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+4,j),b0,c4);
-      c5 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+5,j),b0,c5);
-      c6 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+6,j),b0,c6);
-      c7 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+7,j),b0,c7);
-    }
-    ResScalar cc0 = predux(c0);
-    ResScalar cc1 = predux(c1);
-    ResScalar cc2 = predux(c2);
-    ResScalar cc3 = predux(c3);
-    ResScalar cc4 = predux(c4);
-    ResScalar cc5 = predux(c5);
-    ResScalar cc6 = predux(c6);
-    ResScalar cc7 = predux(c7);
-    for(; j<cols; ++j)
-    {
-      RhsScalar b0 = rhs(j,0);
-
-      cc0 += cj.pmul(lhs(i+0,j), b0);
-      cc1 += cj.pmul(lhs(i+1,j), b0);
-      cc2 += cj.pmul(lhs(i+2,j), b0);
-      cc3 += cj.pmul(lhs(i+3,j), b0);
-      cc4 += cj.pmul(lhs(i+4,j), b0);
-      cc5 += cj.pmul(lhs(i+5,j), b0);
-      cc6 += cj.pmul(lhs(i+6,j), b0);
-      cc7 += cj.pmul(lhs(i+7,j), b0);
-    }
-    res[(i+0)*resIncr] += alpha*cc0;
-    res[(i+1)*resIncr] += alpha*cc1;
-    res[(i+2)*resIncr] += alpha*cc2;
-    res[(i+3)*resIncr] += alpha*cc3;
-    res[(i+4)*resIncr] += alpha*cc4;
-    res[(i+5)*resIncr] += alpha*cc5;
-    res[(i+6)*resIncr] += alpha*cc6;
-    res[(i+7)*resIncr] += alpha*cc7;
-  }
-  for(; i<n4; i+=4)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-              c1 = pset1<ResPacket>(ResScalar(0)),
-              c2 = pset1<ResPacket>(ResScalar(0)),
-              c3 = pset1<ResPacket>(ResScalar(0));
-
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
-
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
-      c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+2,j),b0,c2);
-      c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+3,j),b0,c3);
-    }
-    ResScalar cc0 = predux(c0);
-    ResScalar cc1 = predux(c1);
-    ResScalar cc2 = predux(c2);
-    ResScalar cc3 = predux(c3);
-    for(; j<cols; ++j)
-    {
-      RhsScalar b0 = rhs(j,0);
-
-      cc0 += cj.pmul(lhs(i+0,j), b0);
-      cc1 += cj.pmul(lhs(i+1,j), b0);
-      cc2 += cj.pmul(lhs(i+2,j), b0);
-      cc3 += cj.pmul(lhs(i+3,j), b0);
-    }
-    res[(i+0)*resIncr] += alpha*cc0;
-    res[(i+1)*resIncr] += alpha*cc1;
-    res[(i+2)*resIncr] += alpha*cc2;
-    res[(i+3)*resIncr] += alpha*cc3;
-  }
-  for(; i<n2; i+=2)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
-              c1 = pset1<ResPacket>(ResScalar(0));
-
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
-
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
-      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
-    }
-    ResScalar cc0 = predux(c0);
-    ResScalar cc1 = predux(c1);
-    for(; j<cols; ++j)
-    {
-      RhsScalar b0 = rhs(j,0);
-
-      cc0 += cj.pmul(lhs(i+0,j), b0);
-      cc1 += cj.pmul(lhs(i+1,j), b0);
-    }
-    res[(i+0)*resIncr] += alpha*cc0;
-    res[(i+1)*resIncr] += alpha*cc1;
-  }
-  for(; i<rows; ++i)
-  {
-    ResPacket c0 = pset1<ResPacket>(ResScalar(0));
-    ResPacketHalf c0_h = pset1<ResPacketHalf>(ResScalar(0));
-    ResPacketQuarter c0_q = pset1<ResPacketQuarter>(ResScalar(0));
-    Index j=0;
-    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
-    {
-      RhsPacket b0 = rhs.template load<RhsPacket,Unaligned>(j,0);
-      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i,j),b0,c0);
-    }
-    ResScalar cc0 = predux(c0);
-    if (HasHalf) {
-      for(; j+LhsPacketSizeHalf<=cols; j+=LhsPacketSizeHalf)
-        {
-          RhsPacketHalf b0 = rhs.template load<RhsPacketHalf,Unaligned>(j,0);
-          c0_h = pcj_half.pmadd(lhs.template load<LhsPacketHalf,LhsAlignment>(i,j),b0,c0_h);
-        }
-      cc0 += predux(c0_h);
-    }
-    if (HasQuarter) {
-      for(; j+LhsPacketSizeQuarter<=cols; j+=LhsPacketSizeQuarter)
-        {
-          RhsPacketQuarter b0 = rhs.template load<RhsPacketQuarter,Unaligned>(j,0);
-          c0_q = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter,LhsAlignment>(i,j),b0,c0_q);
-        }
-      cc0 += predux(c0_q);
-    }
-    for(; j<cols; ++j)
-    {
-      cc0 += cj.pmul(lhs(i,j), rhs(j,0));
-    }
-    res[i*resIncr] += alpha*cc0;
-  }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_VECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
deleted file mode 100644
index 6e36c2b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
+++ /dev/null
@@ -1,136 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   General matrix-vector product functionality based on ?GEMV.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
-#define EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements general matrix-vector multiplication using BLAS
-* gemv function via partial specialization of
-* general_matrix_vector_product::run(..) method for float, double,
-* std::complex<float> and std::complex<double> types
-**********************************************************************/
-
-// gemv specialization
-
-template<typename Index, typename LhsScalar, int StorageOrder, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs>
-struct general_matrix_vector_product_gemv;
-
-#define EIGEN_BLAS_GEMV_SPECIALIZE(Scalar) \
-template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
-struct general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ColMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,ConjugateRhs,Specialized> { \
-static void run( \
-  Index rows, Index cols, \
-  const const_blas_data_mapper<Scalar,Index,ColMajor> &lhs, \
-  const const_blas_data_mapper<Scalar,Index,RowMajor> &rhs, \
-  Scalar* res, Index resIncr, Scalar alpha) \
-{ \
-  if (ConjugateLhs) { \
-    general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ColMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,ConjugateRhs,BuiltIn>::run( \
-      rows, cols, lhs, rhs, res, resIncr, alpha); \
-  } else { \
-    general_matrix_vector_product_gemv<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
-      rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha); \
-  } \
-} \
-}; \
-template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
-struct general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,RowMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ConjugateRhs,Specialized> { \
-static void run( \
-  Index rows, Index cols, \
-  const const_blas_data_mapper<Scalar,Index,RowMajor> &lhs, \
-  const const_blas_data_mapper<Scalar,Index,ColMajor> &rhs, \
-  Scalar* res, Index resIncr, Scalar alpha) \
-{ \
-    general_matrix_vector_product_gemv<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
-      rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha); \
-} \
-}; \
-
-EIGEN_BLAS_GEMV_SPECIALIZE(double)
-EIGEN_BLAS_GEMV_SPECIALIZE(float)
-EIGEN_BLAS_GEMV_SPECIALIZE(dcomplex)
-EIGEN_BLAS_GEMV_SPECIALIZE(scomplex)
-
-#define EIGEN_BLAS_GEMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASFUNC) \
-template<typename Index, int LhsStorageOrder, bool ConjugateLhs, bool ConjugateRhs> \
-struct general_matrix_vector_product_gemv<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,ConjugateRhs> \
-{ \
-typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> GEMVVector;\
-\
-static void run( \
-  Index rows, Index cols, \
-  const EIGTYPE* lhs, Index lhsStride, \
-  const EIGTYPE* rhs, Index rhsIncr, \
-  EIGTYPE* res, Index resIncr, EIGTYPE alpha) \
-{ \
-  BlasIndex m=convert_index<BlasIndex>(rows), n=convert_index<BlasIndex>(cols), \
-            lda=convert_index<BlasIndex>(lhsStride), incx=convert_index<BlasIndex>(rhsIncr), incy=convert_index<BlasIndex>(resIncr); \
-  const EIGTYPE beta(1); \
-  const EIGTYPE *x_ptr; \
-  char trans=(LhsStorageOrder==ColMajor) ? 'N' : (ConjugateLhs) ? 'C' : 'T'; \
-  if (LhsStorageOrder==RowMajor) { \
-    m = convert_index<BlasIndex>(cols); \
-    n = convert_index<BlasIndex>(rows); \
-  }\
-  GEMVVector x_tmp; \
-  if (ConjugateRhs) { \
-    Map<const GEMVVector, 0, InnerStride<> > map_x(rhs,cols,1,InnerStride<>(incx)); \
-    x_tmp=map_x.conjugate(); \
-    x_ptr=x_tmp.data(); \
-    incx=1; \
-  } else x_ptr=rhs; \
-  BLASFUNC(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
-}\
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_GEMV_SPECIALIZATION(double,   double, dgemv)
-EIGEN_BLAS_GEMV_SPECIALIZATION(float,    float,  sgemv)
-EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, zgemv)
-EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, MKL_Complex8 , cgemv)
-#else
-EIGEN_BLAS_GEMV_SPECIALIZATION(double,   double, dgemv_)
-EIGEN_BLAS_GEMV_SPECIALIZATION(float,    float,  sgemv_)
-EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, double, zgemv_)
-EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, float,  cgemv_)
-#endif
-
-} // end namespase internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/Parallelizer.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/Parallelizer.h
deleted file mode 100644
index 8f91879..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/Parallelizer.h
+++ /dev/null
@@ -1,180 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARALLELIZER_H
-#define EIGEN_PARALLELIZER_H
-
-#if EIGEN_HAS_CXX11_ATOMIC
-#include <atomic>
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal */
-inline void manage_multi_threading(Action action, int* v)
-{
-  static int m_maxThreads = -1;
-  EIGEN_UNUSED_VARIABLE(m_maxThreads)
-
-  if(action==SetAction)
-  {
-    eigen_internal_assert(v!=0);
-    m_maxThreads = *v;
-  }
-  else if(action==GetAction)
-  {
-    eigen_internal_assert(v!=0);
-    #ifdef EIGEN_HAS_OPENMP
-    if(m_maxThreads>0)
-      *v = m_maxThreads;
-    else
-      *v = omp_get_max_threads();
-    #else
-    *v = 1;
-    #endif
-  }
-  else
-  {
-    eigen_internal_assert(false);
-  }
-}
-
-}
-
-/** Must be call first when calling Eigen from multiple threads */
-inline void initParallel()
-{
-  int nbt;
-  internal::manage_multi_threading(GetAction, &nbt);
-  std::ptrdiff_t l1, l2, l3;
-  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
-}
-
-/** \returns the max number of threads reserved for Eigen
-  * \sa setNbThreads */
-inline int nbThreads()
-{
-  int ret;
-  internal::manage_multi_threading(GetAction, &ret);
-  return ret;
-}
-
-/** Sets the max number of threads reserved for Eigen
-  * \sa nbThreads */
-inline void setNbThreads(int v)
-{
-  internal::manage_multi_threading(SetAction, &v);
-}
-
-namespace internal {
-
-template<typename Index> struct GemmParallelInfo
-{
-  GemmParallelInfo() : sync(-1), users(0), lhs_start(0), lhs_length(0) {}
-
-  // volatile is not enough on all architectures (see bug 1572)
-  // to guarantee that when thread A says to thread B that it is
-  // done with packing a block, then all writes have been really
-  // carried out... C++11 memory model+atomic guarantees this.
-#if EIGEN_HAS_CXX11_ATOMIC
-  std::atomic<Index> sync;
-  std::atomic<int> users;
-#else
-  Index volatile sync;
-  int volatile users;
-#endif
-
-  Index lhs_start;
-  Index lhs_length;
-};
-
-template<bool Condition, typename Functor, typename Index>
-void parallelize_gemm(const Functor& func, Index rows, Index cols, Index depth, bool transpose)
-{
-  // TODO when EIGEN_USE_BLAS is defined,
-  // we should still enable OMP for other scalar types
-  // Without C++11, we have to disable GEMM's parallelization on
-  // non x86 architectures because there volatile is not enough for our purpose.
-  // See bug 1572.
-#if (! defined(EIGEN_HAS_OPENMP)) || defined(EIGEN_USE_BLAS) || ((!EIGEN_HAS_CXX11_ATOMIC) && !(EIGEN_ARCH_i386_OR_x86_64))
-  // FIXME the transpose variable is only needed to properly split
-  // the matrix product when multithreading is enabled. This is a temporary
-  // fix to support row-major destination matrices. This whole
-  // parallelizer mechanism has to be redesigned anyway.
-  EIGEN_UNUSED_VARIABLE(depth);
-  EIGEN_UNUSED_VARIABLE(transpose);
-  func(0,rows, 0,cols);
-#else
-
-  // Dynamically check whether we should enable or disable OpenMP.
-  // The conditions are:
-  // - the max number of threads we can create is greater than 1
-  // - we are not already in a parallel code
-  // - the sizes are large enough
-
-  // compute the maximal number of threads from the size of the product:
-  // This first heuristic takes into account that the product kernel is fully optimized when working with nr columns at once.
-  Index size = transpose ? rows : cols;
-  Index pb_max_threads = std::max<Index>(1,size / Functor::Traits::nr);
-
-  // compute the maximal number of threads from the total amount of work:
-  double work = static_cast<double>(rows) * static_cast<double>(cols) *
-      static_cast<double>(depth);
-  double kMinTaskSize = 50000;  // FIXME improve this heuristic.
-  pb_max_threads = std::max<Index>(1, std::min<Index>(pb_max_threads, static_cast<Index>( work / kMinTaskSize ) ));
-
-  // compute the number of threads we are going to use
-  Index threads = std::min<Index>(nbThreads(), pb_max_threads);
-
-  // if multi-threading is explicitly disabled, not useful, or if we already are in a parallel session,
-  // then abort multi-threading
-  // FIXME omp_get_num_threads()>1 only works for openmp, what if the user does not use openmp?
-  if((!Condition) || (threads==1) || (omp_get_num_threads()>1))
-    return func(0,rows, 0,cols);
-
-  Eigen::initParallel();
-  func.initParallelSession(threads);
-
-  if(transpose)
-    std::swap(rows,cols);
-
-  ei_declare_aligned_stack_constructed_variable(GemmParallelInfo<Index>,info,threads,0);
-
-  #pragma omp parallel num_threads(threads)
-  {
-    Index i = omp_get_thread_num();
-    // Note that the actual number of threads might be lower than the number of request ones.
-    Index actual_threads = omp_get_num_threads();
-
-    Index blockCols = (cols / actual_threads) & ~Index(0x3);
-    Index blockRows = (rows / actual_threads);
-    blockRows = (blockRows/Functor::Traits::mr)*Functor::Traits::mr;
-
-    Index r0 = i*blockRows;
-    Index actualBlockRows = (i+1==actual_threads) ? rows-r0 : blockRows;
-
-    Index c0 = i*blockCols;
-    Index actualBlockCols = (i+1==actual_threads) ? cols-c0 : blockCols;
-
-    info[i].lhs_start = r0;
-    info[i].lhs_length = actualBlockRows;
-
-    if(transpose) func(c0, actualBlockCols, 0, rows, info);
-    else          func(0, rows, c0, actualBlockCols, info);
-  }
-#endif
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARALLELIZER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
deleted file mode 100644
index 33ecf10..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
+++ /dev/null
@@ -1,544 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_H
-#define EIGEN_SELFADJOINT_MATRIX_MATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// pack a selfadjoint block diagonal for use with the gebp_kernel
-template<typename Scalar, typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
-struct symm_pack_lhs
-{
-  template<int BlockRows> inline
-  void pack(Scalar* blockA, const const_blas_data_mapper<Scalar,Index,StorageOrder>& lhs, Index cols, Index i, Index& count)
-  {
-    // normal copy
-    for(Index k=0; k<i; k++)
-      for(Index w=0; w<BlockRows; w++)
-        blockA[count++] = lhs(i+w,k);           // normal
-    // symmetric copy
-    Index h = 0;
-    for(Index k=i; k<i+BlockRows; k++)
-    {
-      for(Index w=0; w<h; w++)
-        blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
-
-      blockA[count++] = numext::real(lhs(k,k));   // real (diagonal)
-
-      for(Index w=h+1; w<BlockRows; w++)
-        blockA[count++] = lhs(i+w, k);          // normal
-      ++h;
-    }
-    // transposed copy
-    for(Index k=i+BlockRows; k<cols; k++)
-      for(Index w=0; w<BlockRows; w++)
-        blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
-  }
-  void operator()(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
-  {
-    typedef typename unpacket_traits<typename packet_traits<Scalar>::type>::half HalfPacket;
-    typedef typename unpacket_traits<typename unpacket_traits<typename packet_traits<Scalar>::type>::half>::half QuarterPacket;
-    enum { PacketSize = packet_traits<Scalar>::size,
-           HalfPacketSize = unpacket_traits<HalfPacket>::size,
-           QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
-           HasHalf = (int)HalfPacketSize < (int)PacketSize,
-           HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
-
-    const_blas_data_mapper<Scalar,Index,StorageOrder> lhs(_lhs,lhsStride);
-    Index count = 0;
-    //Index peeled_mc3 = (rows/Pack1)*Pack1;
-    
-    const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0;
-    const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0;
-    const Index peeled_mc1 = Pack1>=1*PacketSize ? peeled_mc2+((rows-peeled_mc2)/(1*PacketSize))*(1*PacketSize) : 0;
-    const Index peeled_mc_half = Pack1>=HalfPacketSize ? peeled_mc1+((rows-peeled_mc1)/(HalfPacketSize))*(HalfPacketSize) : 0;
-    const Index peeled_mc_quarter = Pack1>=QuarterPacketSize ? peeled_mc_half+((rows-peeled_mc_half)/(QuarterPacketSize))*(QuarterPacketSize) : 0;
-    
-    if(Pack1>=3*PacketSize)
-      for(Index i=0; i<peeled_mc3; i+=3*PacketSize)
-        pack<3*PacketSize>(blockA, lhs, cols, i, count);
-    
-    if(Pack1>=2*PacketSize)
-      for(Index i=peeled_mc3; i<peeled_mc2; i+=2*PacketSize)
-        pack<2*PacketSize>(blockA, lhs, cols, i, count);
-    
-    if(Pack1>=1*PacketSize)
-      for(Index i=peeled_mc2; i<peeled_mc1; i+=1*PacketSize)
-        pack<1*PacketSize>(blockA, lhs, cols, i, count);
-
-    if(HasHalf && Pack1>=HalfPacketSize)
-      for(Index i=peeled_mc1; i<peeled_mc_half; i+=HalfPacketSize)
-        pack<HalfPacketSize>(blockA, lhs, cols, i, count);
-
-    if(HasQuarter && Pack1>=QuarterPacketSize)
-      for(Index i=peeled_mc_half; i<peeled_mc_quarter; i+=QuarterPacketSize)
-        pack<QuarterPacketSize>(blockA, lhs, cols, i, count);
-
-    // do the same with mr==1
-    for(Index i=peeled_mc_quarter; i<rows; i++)
-    {
-      for(Index k=0; k<i; k++)
-        blockA[count++] = lhs(i, k);                   // normal
-
-      blockA[count++] = numext::real(lhs(i, i));       // real (diagonal)
-
-      for(Index k=i+1; k<cols; k++)
-        blockA[count++] = numext::conj(lhs(k, i));     // transposed
-    }
-  }
-};
-
-template<typename Scalar, typename Index, int nr, int StorageOrder>
-struct symm_pack_rhs
-{
-  enum { PacketSize = packet_traits<Scalar>::size };
-  void operator()(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
-  {
-    Index end_k = k2 + rows;
-    Index count = 0;
-    const_blas_data_mapper<Scalar,Index,StorageOrder> rhs(_rhs,rhsStride);
-    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
-    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
-
-    // first part: normal case
-    for(Index j2=0; j2<k2; j2+=nr)
-    {
-      for(Index k=k2; k<end_k; k++)
-      {
-        blockB[count+0] = rhs(k,j2+0);
-        blockB[count+1] = rhs(k,j2+1);
-        if (nr>=4)
-        {
-          blockB[count+2] = rhs(k,j2+2);
-          blockB[count+3] = rhs(k,j2+3);
-        }
-        if (nr>=8)
-        {
-          blockB[count+4] = rhs(k,j2+4);
-          blockB[count+5] = rhs(k,j2+5);
-          blockB[count+6] = rhs(k,j2+6);
-          blockB[count+7] = rhs(k,j2+7);
-        }
-        count += nr;
-      }
-    }
-
-    // second part: diagonal block
-    Index end8 = nr>=8 ? (std::min)(k2+rows,packet_cols8) : k2;
-    if(nr>=8)
-    {
-      for(Index j2=k2; j2<end8; j2+=8)
-      {
-        // again we can split vertically in three different parts (transpose, symmetric, normal)
-        // transpose
-        for(Index k=k2; k<j2; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          blockB[count+4] = numext::conj(rhs(j2+4,k));
-          blockB[count+5] = numext::conj(rhs(j2+5,k));
-          blockB[count+6] = numext::conj(rhs(j2+6,k));
-          blockB[count+7] = numext::conj(rhs(j2+7,k));
-          count += 8;
-        }
-        // symmetric
-        Index h = 0;
-        for(Index k=j2; k<j2+8; k++)
-        {
-          // normal
-          for (Index w=0 ; w<h; ++w)
-            blockB[count+w] = rhs(k,j2+w);
-
-          blockB[count+h] = numext::real(rhs(k,k));
-
-          // transpose
-          for (Index w=h+1 ; w<8; ++w)
-            blockB[count+w] = numext::conj(rhs(j2+w,k));
-          count += 8;
-          ++h;
-        }
-        // normal
-        for(Index k=j2+8; k<end_k; k++)
-        {
-          blockB[count+0] = rhs(k,j2+0);
-          blockB[count+1] = rhs(k,j2+1);
-          blockB[count+2] = rhs(k,j2+2);
-          blockB[count+3] = rhs(k,j2+3);
-          blockB[count+4] = rhs(k,j2+4);
-          blockB[count+5] = rhs(k,j2+5);
-          blockB[count+6] = rhs(k,j2+6);
-          blockB[count+7] = rhs(k,j2+7);
-          count += 8;
-        }
-      }
-    }
-    if(nr>=4)
-    {
-      for(Index j2=end8; j2<(std::min)(k2+rows,packet_cols4); j2+=4)
-      {
-        // again we can split vertically in three different parts (transpose, symmetric, normal)
-        // transpose
-        for(Index k=k2; k<j2; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          count += 4;
-        }
-        // symmetric
-        Index h = 0;
-        for(Index k=j2; k<j2+4; k++)
-        {
-          // normal
-          for (Index w=0 ; w<h; ++w)
-            blockB[count+w] = rhs(k,j2+w);
-
-          blockB[count+h] = numext::real(rhs(k,k));
-
-          // transpose
-          for (Index w=h+1 ; w<4; ++w)
-            blockB[count+w] = numext::conj(rhs(j2+w,k));
-          count += 4;
-          ++h;
-        }
-        // normal
-        for(Index k=j2+4; k<end_k; k++)
-        {
-          blockB[count+0] = rhs(k,j2+0);
-          blockB[count+1] = rhs(k,j2+1);
-          blockB[count+2] = rhs(k,j2+2);
-          blockB[count+3] = rhs(k,j2+3);
-          count += 4;
-        }
-      }
-    }
-
-    // third part: transposed
-    if(nr>=8)
-    {
-      for(Index j2=k2+rows; j2<packet_cols8; j2+=8)
-      {
-        for(Index k=k2; k<end_k; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          blockB[count+4] = numext::conj(rhs(j2+4,k));
-          blockB[count+5] = numext::conj(rhs(j2+5,k));
-          blockB[count+6] = numext::conj(rhs(j2+6,k));
-          blockB[count+7] = numext::conj(rhs(j2+7,k));
-          count += 8;
-        }
-      }
-    }
-    if(nr>=4)
-    {
-      for(Index j2=(std::max)(packet_cols8,k2+rows); j2<packet_cols4; j2+=4)
-      {
-        for(Index k=k2; k<end_k; k++)
-        {
-          blockB[count+0] = numext::conj(rhs(j2+0,k));
-          blockB[count+1] = numext::conj(rhs(j2+1,k));
-          blockB[count+2] = numext::conj(rhs(j2+2,k));
-          blockB[count+3] = numext::conj(rhs(j2+3,k));
-          count += 4;
-        }
-      }
-    }
-
-    // copy the remaining columns one at a time (=> the same with nr==1)
-    for(Index j2=packet_cols4; j2<cols; ++j2)
-    {
-      // transpose
-      Index half = (std::min)(end_k,j2);
-      for(Index k=k2; k<half; k++)
-      {
-        blockB[count] = numext::conj(rhs(j2,k));
-        count += 1;
-      }
-
-      if(half==j2 && half<k2+rows)
-      {
-        blockB[count] = numext::real(rhs(j2,j2));
-        count += 1;
-      }
-      else
-        half--;
-
-      // normal
-      for(Index k=half+1; k<k2+rows; k++)
-      {
-        blockB[count] = rhs(k,j2);
-        count += 1;
-      }
-    }
-  }
-};
-
-/* Optimized selfadjoint matrix * matrix (_SYMM) product built on top of
- * the general matrix matrix product.
- */
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
-          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs,
-          int ResStorageOrder, int ResInnerStride>
-struct product_selfadjoint_matrix;
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
-          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs,
-          int ResInnerStride>
-struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,LhsSelfAdjoint,ConjugateLhs, RhsStorageOrder,RhsSelfAdjoint,ConjugateRhs,RowMajor,ResInnerStride>
-{
-
-  static EIGEN_STRONG_INLINE void run(
-    Index rows, Index cols,
-    const Scalar* lhs, Index lhsStride,
-    const Scalar* rhs, Index rhsStride,
-    Scalar* res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    product_selfadjoint_matrix<Scalar, Index,
-      EIGEN_LOGICAL_XOR(RhsSelfAdjoint,RhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
-      RhsSelfAdjoint, NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(RhsSelfAdjoint,ConjugateRhs),
-      EIGEN_LOGICAL_XOR(LhsSelfAdjoint,LhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
-      LhsSelfAdjoint, NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsSelfAdjoint,ConjugateLhs),
-      ColMajor,ResInnerStride>
-      ::run(cols, rows,  rhs, rhsStride,  lhs, lhsStride,  res, resIncr, resStride,  alpha, blocking);
-  }
-};
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor,ResInnerStride>
-{
-
-  static EIGEN_DONT_INLINE void run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor,ResInnerStride>::run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index size = rows;
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, (LhsStorageOrder == RowMajor) ? ColMajor : RowMajor> LhsTransposeMapper;
-    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    LhsTransposeMapper lhs_transpose(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-    // kc must be smaller than mc
-    kc = (std::min)(kc,mc);
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    symm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
-    gemm_pack_lhs<Scalar, Index, LhsTransposeMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder==RowMajor?ColMajor:RowMajor, true> pack_lhs_transposed;
-
-    for(Index k2=0; k2<size; k2+=kc)
-    {
-      const Index actual_kc = (std::min)(k2+kc,size)-k2;
-
-      // we have selected one row panel of rhs and one column panel of lhs
-      // pack rhs's panel into a sequential chunk of memory
-      // and expand each coeff to a constant packet for further reuse
-      pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, cols);
-
-      // the select lhs's panel has to be split in three different parts:
-      //  1 - the transposed panel above the diagonal block => transposed packed copy
-      //  2 - the diagonal block => special packed copy
-      //  3 - the panel below the diagonal block => generic packed copy
-      for(Index i2=0; i2<k2; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,k2)-i2;
-        // transposed packed copy
-        pack_lhs_transposed(blockA, lhs_transpose.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-      // the block diagonal
-      {
-        const Index actual_mc = (std::min)(k2+kc,size)-k2;
-        // symmetric packed copy
-        pack_lhs(blockA, &lhs(k2,k2), lhsStride, actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(k2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-
-      for(Index i2=k2+kc; i2<size; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,size)-i2;
-        gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder,false>()
-          (blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-    }
-  }
-
-// matrix * selfadjoint product
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor,ResInnerStride>
-{
-
-  static EIGEN_DONT_INLINE void run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride>
-EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor,ResInnerStride>::run(
-    Index rows, Index cols,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index size = cols;
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    ResMapper res(_res,resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    symm_pack_rhs<Scalar, Index, Traits::nr,RhsStorageOrder> pack_rhs;
-
-    for(Index k2=0; k2<size; k2+=kc)
-    {
-      const Index actual_kc = (std::min)(k2+kc,size)-k2;
-
-      pack_rhs(blockB, _rhs, rhsStride, actual_kc, cols, k2);
-
-      // => GEPP
-      for(Index i2=0; i2<rows; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(i2+mc,rows)-i2;
-        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
-      }
-    }
-  }
-
-} // end namespace internal
-
-/***************************************************************************
-* Wrapper to product_selfadjoint_matrix
-***************************************************************************/
-
-namespace internal {
-  
-template<typename Lhs, int LhsMode, typename Rhs, int RhsMode>
-struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,RhsMode,false>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  typedef internal::blas_traits<Lhs> LhsBlasTraits;
-  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-  typedef internal::blas_traits<Rhs> RhsBlasTraits;
-  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  
-  enum {
-    LhsIsUpper = (LhsMode&(Upper|Lower))==Upper,
-    LhsIsSelfAdjoint = (LhsMode&SelfAdjoint)==SelfAdjoint,
-    RhsIsUpper = (RhsMode&(Upper|Lower))==Upper,
-    RhsIsSelfAdjoint = (RhsMode&SelfAdjoint)==SelfAdjoint
-  };
-  
-  template<typename Dest>
-  static void run(Dest &dst, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
-                               * RhsBlasTraits::extractScalarFactor(a_rhs);
-
-    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
-              Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,1> BlockingType;
-
-    BlockingType blocking(lhs.rows(), rhs.cols(), lhs.cols(), 1, false);
-
-    internal::product_selfadjoint_matrix<Scalar, Index,
-      EIGEN_LOGICAL_XOR(LhsIsUpper,internal::traits<Lhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint,
-      NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsIsUpper,bool(LhsBlasTraits::NeedToConjugate)),
-      EIGEN_LOGICAL_XOR(RhsIsUpper,internal::traits<Rhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, RhsIsSelfAdjoint,
-      NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(RhsIsUpper,bool(RhsBlasTraits::NeedToConjugate)),
-      internal::traits<Dest>::Flags&RowMajorBit  ? RowMajor : ColMajor,
-      Dest::InnerStrideAtCompileTime>
-      ::run(
-        lhs.rows(), rhs.cols(),                 // sizes
-        &lhs.coeffRef(0,0), lhs.outerStride(),  // lhs info
-        &rhs.coeffRef(0,0), rhs.outerStride(),  // rhs info
-        &dst.coeffRef(0,0), dst.innerStride(), dst.outerStride(),  // result info
-        actualAlpha, blocking                   // alpha
-      );
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
deleted file mode 100644
index 61396db..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
+++ /dev/null
@@ -1,295 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
-#define EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-/* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */
-
-#define EIGEN_BLAS_SYMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor,1> \
-{\
-\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='L', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-\
-/* Set transpose options */ \
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows);  \
-    n = convert_index<BlasIndex>(cols);  \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(lhsStride); \
-    ldb = convert_index<BlasIndex>(rhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (LhsStorageOrder==RowMajor) uplo='U'; \
-    a = _lhs; \
-\
-    if (RhsStorageOrder==RowMajor) { \
-      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
-      b_tmp = rhs.adjoint(); \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } else b = _rhs; \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-\
-  } \
-};
-
-
-#define EIGEN_BLAS_HEMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor,1> \
-{\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='L', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-    Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> a_tmp; \
-\
-/* Set transpose options */ \
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows); \
-    n = convert_index<BlasIndex>(cols); \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(lhsStride); \
-    ldb = convert_index<BlasIndex>(rhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (((LhsStorageOrder==ColMajor) && ConjugateLhs) || ((LhsStorageOrder==RowMajor) && (!ConjugateLhs))) { \
-      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder>, 0, OuterStride<> > lhs(_lhs,m,m,OuterStride<>(lhsStride)); \
-      a_tmp = lhs.conjugate(); \
-      a = a_tmp.data(); \
-      lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-    } else a = _lhs; \
-    if (LhsStorageOrder==RowMajor) uplo='U'; \
-\
-    if (RhsStorageOrder==ColMajor && (!ConjugateRhs)) { \
-       b = _rhs; } \
-    else { \
-      if (RhsStorageOrder==ColMajor && ConjugateRhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,m,n,OuterStride<>(rhsStride)); \
-        b_tmp = rhs.conjugate(); \
-      } else \
-      if (ConjugateRhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
-        b_tmp = rhs.adjoint(); \
-      } else { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
-        b_tmp = rhs.transpose(); \
-      } \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-\
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_SYMM_L(double, double, d, dsymm)
-EIGEN_BLAS_SYMM_L(float, float, f, ssymm)
-EIGEN_BLAS_HEMM_L(dcomplex, MKL_Complex16, cd, zhemm)
-EIGEN_BLAS_HEMM_L(scomplex, MKL_Complex8, cf, chemm)
-#else
-EIGEN_BLAS_SYMM_L(double, double, d, dsymm_)
-EIGEN_BLAS_SYMM_L(float, float, f, ssymm_)
-EIGEN_BLAS_HEMM_L(dcomplex, double, cd, zhemm_)
-EIGEN_BLAS_HEMM_L(scomplex, float, cf, chemm_)
-#endif
-
-/* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */
-
-#define EIGEN_BLAS_SYMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor,1> \
-{\
-\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='R', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-\
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows);  \
-    n = convert_index<BlasIndex>(cols);  \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(rhsStride); \
-    ldb = convert_index<BlasIndex>(lhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (RhsStorageOrder==RowMajor) uplo='U'; \
-    a = _rhs; \
-\
-    if (LhsStorageOrder==RowMajor) { \
-      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(rhsStride)); \
-      b_tmp = lhs.adjoint(); \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } else b = _lhs; \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-\
-  } \
-};
-
-
-#define EIGEN_BLAS_HEMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor,1> \
-{\
-  static void run( \
-    Index rows, Index cols, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resIncr, Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
-  { \
-    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-    eigen_assert(resIncr == 1); \
-    char side='R', uplo='L'; \
-    BlasIndex m, n, lda, ldb, ldc; \
-    const EIGTYPE *a, *b; \
-    EIGTYPE beta(1); \
-    MatrixX##EIGPREFIX b_tmp; \
-    Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> a_tmp; \
-\
-/* Set m, n, k */ \
-    m = convert_index<BlasIndex>(rows); \
-    n = convert_index<BlasIndex>(cols); \
-\
-/* Set lda, ldb, ldc */ \
-    lda = convert_index<BlasIndex>(rhsStride); \
-    ldb = convert_index<BlasIndex>(lhsStride); \
-    ldc = convert_index<BlasIndex>(resStride); \
-\
-/* Set a, b, c */ \
-    if (((RhsStorageOrder==ColMajor) && ConjugateRhs) || ((RhsStorageOrder==RowMajor) && (!ConjugateRhs))) { \
-      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder>, 0, OuterStride<> > rhs(_rhs,n,n,OuterStride<>(rhsStride)); \
-      a_tmp = rhs.conjugate(); \
-      a = a_tmp.data(); \
-      lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-    } else a = _rhs; \
-    if (RhsStorageOrder==RowMajor) uplo='U'; \
-\
-    if (LhsStorageOrder==ColMajor && (!ConjugateLhs)) { \
-       b = _lhs; } \
-    else { \
-      if (LhsStorageOrder==ColMajor && ConjugateLhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,n,OuterStride<>(lhsStride)); \
-        b_tmp = lhs.conjugate(); \
-      } else \
-      if (ConjugateLhs) { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
-        b_tmp = lhs.adjoint(); \
-      } else { \
-        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
-        b_tmp = lhs.transpose(); \
-      } \
-      b = b_tmp.data(); \
-      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-    } \
-\
-    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_SYMM_R(double, double, d, dsymm)
-EIGEN_BLAS_SYMM_R(float, float, f, ssymm)
-EIGEN_BLAS_HEMM_R(dcomplex, MKL_Complex16, cd, zhemm)
-EIGEN_BLAS_HEMM_R(scomplex, MKL_Complex8, cf, chemm)
-#else
-EIGEN_BLAS_SYMM_R(double, double, d, dsymm_)
-EIGEN_BLAS_SYMM_R(float, float, f, ssymm_)
-EIGEN_BLAS_HEMM_R(dcomplex, double, cd, zhemm_)
-EIGEN_BLAS_HEMM_R(scomplex, float, cf, chemm_)
-#endif
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h
deleted file mode 100644
index d38fd72..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h
+++ /dev/null
@@ -1,262 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H
-#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/* Optimized selfadjoint matrix * vector product:
- * This algorithm processes 2 columns at once that allows to both reduce
- * the number of load/stores of the result by a factor 2 and to reduce
- * the instruction dependency.
- */
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version=Specialized>
-struct selfadjoint_matrix_vector_product;
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version>
-struct selfadjoint_matrix_vector_product
-
-{
-static EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC
-void run(
-  Index size,
-  const Scalar*  lhs, Index lhsStride,
-  const Scalar*  rhs,
-  Scalar* res,
-  Scalar alpha);
-};
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version>
-EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC
-void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Version>::run(
-  Index size,
-  const Scalar*  lhs, Index lhsStride,
-  const Scalar*  rhs,
-  Scalar* res,
-  Scalar alpha)
-{
-  typedef typename packet_traits<Scalar>::type Packet;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  const Index PacketSize = sizeof(Packet)/sizeof(Scalar);
-
-  enum {
-    IsRowMajor = StorageOrder==RowMajor ? 1 : 0,
-    IsLower = UpLo == Lower ? 1 : 0,
-    FirstTriangular = IsRowMajor == IsLower
-  };
-
-  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs,  IsRowMajor), ConjugateRhs> cj0;
-  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1;
-  conj_helper<RealScalar,Scalar,false, ConjugateRhs> cjd;
-
-  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs,  IsRowMajor), ConjugateRhs> pcj0;
-  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1;
-
-  Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;
-
-  Index bound = numext::maxi(Index(0), size-8) & 0xfffffffe;
-  if (FirstTriangular)
-    bound = size - bound;
-
-  for (Index j=FirstTriangular ? bound : 0;
-       j<(FirstTriangular ? size : bound);j+=2)
-  {
-    const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
-    const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride;
-
-    Scalar t0 = cjAlpha * rhs[j];
-    Packet ptmp0 = pset1<Packet>(t0);
-    Scalar t1 = cjAlpha * rhs[j+1];
-    Packet ptmp1 = pset1<Packet>(t1);
-
-    Scalar t2(0);
-    Packet ptmp2 = pset1<Packet>(t2);
-    Scalar t3(0);
-    Packet ptmp3 = pset1<Packet>(t3);
-
-    Index starti = FirstTriangular ? 0 : j+2;
-    Index endi   = FirstTriangular ? j : size;
-    Index alignedStart = (starti) + internal::first_default_aligned(&res[starti], endi-starti);
-    Index alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize);
-
-    res[j]   += cjd.pmul(numext::real(A0[j]), t0);
-    res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1);
-    if(FirstTriangular)
-    {
-      res[j]   += cj0.pmul(A1[j],   t1);
-      t3       += cj1.pmul(A1[j],   rhs[j]);
-    }
-    else
-    {
-      res[j+1] += cj0.pmul(A0[j+1],t0);
-      t2 += cj1.pmul(A0[j+1], rhs[j+1]);
-    }
-
-    for (Index i=starti; i<alignedStart; ++i)
-    {
-      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);
-      t2 += cj1.pmul(A0[i], rhs[i]);
-      t3 += cj1.pmul(A1[i], rhs[i]);
-    }
-    // Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)
-    // gcc 4.2 does this optimization automatically.
-    const Scalar* EIGEN_RESTRICT a0It  = A0  + alignedStart;
-    const Scalar* EIGEN_RESTRICT a1It  = A1  + alignedStart;
-    const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart;
-          Scalar* EIGEN_RESTRICT resIt = res + alignedStart;
-    for (Index i=alignedStart; i<alignedEnd; i+=PacketSize)
-    {
-      Packet A0i = ploadu<Packet>(a0It);  a0It  += PacketSize;
-      Packet A1i = ploadu<Packet>(a1It);  a1It  += PacketSize;
-      Packet Bi  = ploadu<Packet>(rhsIt); rhsIt += PacketSize; // FIXME should be aligned in most cases
-      Packet Xi  = pload <Packet>(resIt);
-
-      Xi    = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi));
-      ptmp2 = pcj1.pmadd(A0i,  Bi, ptmp2);
-      ptmp3 = pcj1.pmadd(A1i,  Bi, ptmp3);
-      pstore(resIt,Xi); resIt += PacketSize;
-    }
-    for (Index i=alignedEnd; i<endi; i++)
-    {
-      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);
-      t2 += cj1.pmul(A0[i], rhs[i]);
-      t3 += cj1.pmul(A1[i], rhs[i]);
-    }
-
-    res[j]   += alpha * (t2 + predux(ptmp2));
-    res[j+1] += alpha * (t3 + predux(ptmp3));
-  }
-  for (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++)
-  {
-    const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
-
-    Scalar t1 = cjAlpha * rhs[j];
-    Scalar t2(0);
-    res[j] += cjd.pmul(numext::real(A0[j]), t1);
-    for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++)
-    {
-      res[i] += cj0.pmul(A0[i], t1);
-      t2 += cj1.pmul(A0[i], rhs[i]);
-    }
-    res[j] += alpha * t2;
-  }
-}
-
-} // end namespace internal 
-
-/***************************************************************************
-* Wrapper to product_selfadjoint_vector
-***************************************************************************/
-
-namespace internal {
-
-template<typename Lhs, int LhsMode, typename Rhs>
-struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,0,true>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  typedef internal::blas_traits<Lhs> LhsBlasTraits;
-  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-  typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
-  
-  typedef internal::blas_traits<Rhs> RhsBlasTraits;
-  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-  typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-  enum { LhsUpLo = LhsMode&(Upper|Lower) };
-
-  template<typename Dest>
-  static EIGEN_DEVICE_FUNC
-  void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
-  {
-    typedef typename Dest::Scalar ResScalar;
-    typedef typename Rhs::Scalar RhsScalar;
-    typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
-    
-    eigen_assert(dest.rows()==a_lhs.rows() && dest.cols()==a_rhs.cols());
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
-                               * RhsBlasTraits::extractScalarFactor(a_rhs);
-
-    enum {
-      EvalToDest = (Dest::InnerStrideAtCompileTime==1),
-      UseRhs = (ActualRhsTypeCleaned::InnerStrideAtCompileTime==1)
-    };
-    
-    internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest;
-    internal::gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!UseRhs> static_rhs;
-
-    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
-                                                  EvalToDest ? dest.data() : static_dest.data());
-                                                  
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(),
-        UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data());
-    
-    if(!EvalToDest)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = dest.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      MappedDest(actualDestPtr, dest.size()) = dest;
-    }
-      
-    if(!UseRhs)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = rhs.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, rhs.size()) = rhs;
-    }
-      
-      
-    internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-                                                int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run
-      (
-        lhs.rows(),                             // size
-        &lhs.coeffRef(0,0),  lhs.outerStride(), // lhs info
-        actualRhsPtr,                           // rhs info
-        actualDestPtr,                          // result info
-        actualAlpha                             // scale factor
-      );
-    
-    if(!EvalToDest)
-      dest = MappedDest(actualDestPtr, dest.size());
-  }
-};
-
-template<typename Lhs, typename Rhs, int RhsMode>
-struct selfadjoint_product_impl<Lhs,0,true,Rhs,RhsMode,false>
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  enum { RhsUpLo = RhsMode&(Upper|Lower)  };
-
-  template<typename Dest>
-  static void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
-  {
-    // let's simply transpose the product
-    Transpose<Dest> destT(dest);
-    selfadjoint_product_impl<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false,
-                             Transpose<const Lhs>, 0, true>::run(destT, a_rhs.transpose(), a_lhs.transpose(), alpha);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
deleted file mode 100644
index 1238345..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
+++ /dev/null
@@ -1,118 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Selfadjoint matrix-vector product functionality based on ?SYMV/HEMV.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
-#define EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements selfadjoint matrix-vector multiplication using BLAS
-**********************************************************************/
-
-// symv/hemv specialization
-
-template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs>
-struct selfadjoint_matrix_vector_product_symv :
-  selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn> {};
-
-#define EIGEN_BLAS_SYMV_SPECIALIZE(Scalar) \
-template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
-struct selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Specialized> { \
-static void run( \
-  Index size, const Scalar*  lhs, Index lhsStride, \
-  const Scalar* _rhs, Scalar* res, Scalar alpha) { \
-    enum {\
-      IsColMajor = StorageOrder==ColMajor \
-    }; \
-    if (IsColMajor == ConjugateLhs) {\
-      selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn>::run( \
-        size, lhs, lhsStride, _rhs, res, alpha);  \
-    } else {\
-      selfadjoint_matrix_vector_product_symv<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs>::run( \
-        size, lhs, lhsStride, _rhs, res, alpha);  \
-    }\
-  } \
-}; \
-
-EIGEN_BLAS_SYMV_SPECIALIZE(double)
-EIGEN_BLAS_SYMV_SPECIALIZE(float)
-EIGEN_BLAS_SYMV_SPECIALIZE(dcomplex)
-EIGEN_BLAS_SYMV_SPECIALIZE(scomplex)
-
-#define EIGEN_BLAS_SYMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASFUNC) \
-template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
-struct selfadjoint_matrix_vector_product_symv<EIGTYPE,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs> \
-{ \
-typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> SYMVVector;\
-\
-static void run( \
-Index size, const EIGTYPE*  lhs, Index lhsStride, \
-const EIGTYPE* _rhs, EIGTYPE* res, EIGTYPE alpha) \
-{ \
-  enum {\
-    IsRowMajor = StorageOrder==RowMajor ? 1 : 0, \
-    IsLower = UpLo == Lower ? 1 : 0 \
-  }; \
-  BlasIndex n=convert_index<BlasIndex>(size), lda=convert_index<BlasIndex>(lhsStride), incx=1, incy=1; \
-  EIGTYPE beta(1); \
-  const EIGTYPE *x_ptr; \
-  char uplo=(IsRowMajor) ? (IsLower ? 'U' : 'L') : (IsLower ? 'L' : 'U'); \
-  SYMVVector x_tmp; \
-  if (ConjugateRhs) { \
-    Map<const SYMVVector, 0 > map_x(_rhs,size,1); \
-    x_tmp=map_x.conjugate(); \
-    x_ptr=x_tmp.data(); \
-  } else x_ptr=_rhs; \
-  BLASFUNC(&uplo, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
-}\
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_SYMV_SPECIALIZATION(double,   double, dsymv)
-EIGEN_BLAS_SYMV_SPECIALIZATION(float,    float,  ssymv)
-EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv)
-EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, MKL_Complex8,  chemv)
-#else
-EIGEN_BLAS_SYMV_SPECIALIZATION(double,   double, dsymv_)
-EIGEN_BLAS_SYMV_SPECIALIZATION(float,    float,  ssymv_)
-EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, double, zhemv_)
-EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, float,  chemv_)
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h
deleted file mode 100644
index a21be80..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h
+++ /dev/null
@@ -1,133 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINT_PRODUCT_H
-#define EIGEN_SELFADJOINT_PRODUCT_H
-
-/**********************************************************************
-* This file implements a self adjoint product: C += A A^T updating only
-* half of the selfadjoint matrix C.
-* It corresponds to the level 3 SYRK and level 2 SYR Blas routines.
-**********************************************************************/
-
-namespace Eigen { 
-
-
-template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
-struct selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo,ConjLhs,ConjRhs>
-{
-  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
-  {
-    internal::conj_if<ConjRhs> cj;
-    typedef Map<const Matrix<Scalar,Dynamic,1> > OtherMap;
-    typedef typename internal::conditional<ConjLhs,typename OtherMap::ConjugateReturnType,const OtherMap&>::type ConjLhsType;
-    for (Index i=0; i<size; ++i)
-    {
-      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+(UpLo==Lower ? i : 0), (UpLo==Lower ? size-i : (i+1)))
-          += (alpha * cj(vecY[i])) * ConjLhsType(OtherMap(vecX+(UpLo==Lower ? i : 0),UpLo==Lower ? size-i : (i+1)));
-    }
-  }
-};
-
-template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
-struct selfadjoint_rank1_update<Scalar,Index,RowMajor,UpLo,ConjLhs,ConjRhs>
-{
-  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
-  {
-    selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo==Lower?Upper:Lower,ConjRhs,ConjLhs>::run(size,mat,stride,vecY,vecX,alpha);
-  }
-};
-
-template<typename MatrixType, typename OtherType, int UpLo, bool OtherIsVector = OtherType::IsVectorAtCompileTime>
-struct selfadjoint_product_selector;
-
-template<typename MatrixType, typename OtherType, int UpLo>
-struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,true>
-{
-  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    typedef internal::blas_traits<OtherType> OtherBlasTraits;
-    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
-    typedef typename internal::remove_all<ActualOtherType>::type _ActualOtherType;
-    typename internal::add_const_on_value_type<ActualOtherType>::type actualOther = OtherBlasTraits::extract(other.derived());
-
-    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
-
-    enum {
-      StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-      UseOtherDirectly = _ActualOtherType::InnerStrideAtCompileTime==1
-    };
-    internal::gemv_static_vector_if<Scalar,OtherType::SizeAtCompileTime,OtherType::MaxSizeAtCompileTime,!UseOtherDirectly> static_other;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, actualOtherPtr, other.size(),
-      (UseOtherDirectly ? const_cast<Scalar*>(actualOther.data()) : static_other.data()));
-      
-    if(!UseOtherDirectly)
-      Map<typename _ActualOtherType::PlainObject>(actualOtherPtr, actualOther.size()) = actualOther;
-    
-    selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
-                              OtherBlasTraits::NeedToConjugate  && NumTraits<Scalar>::IsComplex,
-                            (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex>
-          ::run(other.size(), mat.data(), mat.outerStride(), actualOtherPtr, actualOtherPtr, actualAlpha);
-  }
-};
-
-template<typename MatrixType, typename OtherType, int UpLo>
-struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,false>
-{
-  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    typedef internal::blas_traits<OtherType> OtherBlasTraits;
-    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
-    typedef typename internal::remove_all<ActualOtherType>::type _ActualOtherType;
-    typename internal::add_const_on_value_type<ActualOtherType>::type actualOther = OtherBlasTraits::extract(other.derived());
-
-    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
-
-    enum {
-      IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
-      OtherIsRowMajor = _ActualOtherType::Flags&RowMajorBit ? 1 : 0
-    };
-
-    Index size = mat.cols();
-    Index depth = actualOther.cols();
-
-    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,Scalar,Scalar,
-              MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, _ActualOtherType::MaxColsAtCompileTime> BlockingType;
-
-    BlockingType blocking(size, size, depth, 1, false);
-
-
-    internal::general_matrix_matrix_triangular_product<Index,
-      Scalar, OtherIsRowMajor ? RowMajor : ColMajor,   OtherBlasTraits::NeedToConjugate  && NumTraits<Scalar>::IsComplex,
-      Scalar, OtherIsRowMajor ? ColMajor : RowMajor, (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex,
-      IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime, UpLo>
-      ::run(size, depth,
-            actualOther.data(), actualOther.outerStride(), actualOther.data(), actualOther.outerStride(),
-            mat.data(), mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
-  }
-};
-
-// high level API
-
-template<typename MatrixType, unsigned int UpLo>
-template<typename DerivedU>
-EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
-::rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha)
-{
-  selfadjoint_product_selector<MatrixType,DerivedU,UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha);
-
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINT_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h
deleted file mode 100644
index f752a0b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h
+++ /dev/null
@@ -1,94 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINTRANK2UPTADE_H
-#define EIGEN_SELFADJOINTRANK2UPTADE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/* Optimized selfadjoint matrix += alpha * uv' + conj(alpha)*vu'
- * It corresponds to the Level2 syr2 BLAS routine
- */
-
-template<typename Scalar, typename Index, typename UType, typename VType, int UpLo>
-struct selfadjoint_rank2_update_selector;
-
-template<typename Scalar, typename Index, typename UType, typename VType>
-struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Lower>
-{
-  static EIGEN_DEVICE_FUNC
-  void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
-  {
-    const Index size = u.size();
-    for (Index i=0; i<size; ++i)
-    {
-      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+i, size-i) +=
-                        (numext::conj(alpha) * numext::conj(u.coeff(i))) * v.tail(size-i)
-                      + (alpha * numext::conj(v.coeff(i))) * u.tail(size-i);
-    }
-  }
-};
-
-template<typename Scalar, typename Index, typename UType, typename VType>
-struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Upper>
-{
-  static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
-  {
-    const Index size = u.size();
-    for (Index i=0; i<size; ++i)
-      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i, i+1) +=
-                        (numext::conj(alpha)  * numext::conj(u.coeff(i))) * v.head(i+1)
-                      + (alpha * numext::conj(v.coeff(i))) * u.head(i+1);
-  }
-};
-
-template<bool Cond, typename T> struct conj_expr_if
-  : conditional<!Cond, const T&,
-      CwiseUnaryOp<scalar_conjugate_op<typename traits<T>::Scalar>,T> > {};
-
-} // end namespace internal
-
-template<typename MatrixType, unsigned int UpLo>
-template<typename DerivedU, typename DerivedV>
-EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
-::rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha)
-{
-  typedef internal::blas_traits<DerivedU> UBlasTraits;
-  typedef typename UBlasTraits::DirectLinearAccessType ActualUType;
-  typedef typename internal::remove_all<ActualUType>::type _ActualUType;
-  typename internal::add_const_on_value_type<ActualUType>::type actualU = UBlasTraits::extract(u.derived());
-
-  typedef internal::blas_traits<DerivedV> VBlasTraits;
-  typedef typename VBlasTraits::DirectLinearAccessType ActualVType;
-  typedef typename internal::remove_all<ActualVType>::type _ActualVType;
-  typename internal::add_const_on_value_type<ActualVType>::type actualV = VBlasTraits::extract(v.derived());
-
-  // If MatrixType is row major, then we use the routine for lower triangular in the upper triangular case and
-  // vice versa, and take the complex conjugate of all coefficients and vector entries.
-
-  enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 };
-  Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived())
-                             * numext::conj(VBlasTraits::extractScalarFactor(v.derived()));
-  if (IsRowMajor)
-    actualAlpha = numext::conj(actualAlpha);
-
-  typedef typename internal::remove_all<typename internal::conj_expr_if<int(IsRowMajor) ^ int(UBlasTraits::NeedToConjugate), _ActualUType>::type>::type UType;
-  typedef typename internal::remove_all<typename internal::conj_expr_if<int(IsRowMajor) ^ int(VBlasTraits::NeedToConjugate), _ActualVType>::type>::type VType;
-  internal::selfadjoint_rank2_update_selector<Scalar, Index, UType, VType,
-    (IsRowMajor ? int(UpLo==Upper ? Lower : Upper) : UpLo)>
-    ::run(_expression().const_cast_derived().data(),_expression().outerStride(),UType(actualU),VType(actualV),actualAlpha);
-
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINTRANK2UPTADE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
deleted file mode 100644
index f0c6050..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
+++ /dev/null
@@ -1,472 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_H
-#define EIGEN_TRIANGULAR_MATRIX_MATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// template<typename Scalar, int mr, int StorageOrder, bool Conjugate, int Mode>
-// struct gemm_pack_lhs_triangular
-// {
-//   Matrix<Scalar,mr,mr,
-//   void operator()(Scalar* blockA, const EIGEN_RESTRICT Scalar* _lhs, int lhsStride, int depth, int rows)
-//   {
-//     conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-//     const_blas_data_mapper<Scalar, StorageOrder> lhs(_lhs,lhsStride);
-//     int count = 0;
-//     const int peeled_mc = (rows/mr)*mr;
-//     for(int i=0; i<peeled_mc; i+=mr)
-//     {
-//       for(int k=0; k<depth; k++)
-//         for(int w=0; w<mr; w++)
-//           blockA[count++] = cj(lhs(i+w, k));
-//     }
-//     for(int i=peeled_mc; i<rows; i++)
-//     {
-//       for(int k=0; k<depth; k++)
-//         blockA[count++] = cj(lhs(i, k));
-//     }
-//   }
-// };
-
-/* Optimized triangular matrix * matrix (_TRMM++) product built on top of
- * the general matrix matrix product.
- */
-template <typename Scalar, typename Index,
-          int Mode, bool LhsIsTriangular,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResStorageOrder, int ResInnerStride,
-          int Version = Specialized>
-struct product_triangular_matrix_matrix;
-
-template <typename Scalar, typename Index,
-          int Mode, bool LhsIsTriangular,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-struct product_triangular_matrix_matrix<Scalar,Index,Mode,LhsIsTriangular,
-                                           LhsStorageOrder,ConjugateLhs,
-                                           RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride,Version>
-{
-  static EIGEN_STRONG_INLINE void run(
-    Index rows, Index cols, Index depth,
-    const Scalar* lhs, Index lhsStride,
-    const Scalar* rhs, Index rhsStride,
-    Scalar* res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    product_triangular_matrix_matrix<Scalar, Index,
-      (Mode&(UnitDiag|ZeroDiag)) | ((Mode&Upper) ? Lower : Upper),
-      (!LhsIsTriangular),
-      RhsStorageOrder==RowMajor ? ColMajor : RowMajor,
-      ConjugateRhs,
-      LhsStorageOrder==RowMajor ? ColMajor : RowMajor,
-      ConjugateLhs,
-      ColMajor, ResInnerStride>
-      ::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride, res, resIncr, resStride, alpha, blocking);
-  }
-};
-
-// implements col-major += alpha * op(triangular) * op(general)
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
-                                           LhsStorageOrder,ConjugateLhs,
-                                           RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>
-{
-  
-  typedef gebp_traits<Scalar,Scalar> Traits;
-  enum {
-    SmallPanelWidth   = 2 * EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-    IsLower = (Mode&Lower) == Lower,
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1
-  };
-
-  static EIGEN_DONT_INLINE void run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
-                                                        LhsStorageOrder,ConjugateLhs,
-                                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    // strip zeros
-    Index diagSize  = (std::min)(_rows,_depth);
-    Index rows      = IsLower ? _rows : diagSize;
-    Index depth     = IsLower ? diagSize : _depth;
-    Index cols      = _cols;
-    
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-    // The small panel size must not be larger than blocking size.
-    // Usually this should never be the case because SmallPanelWidth^2 is very small
-    // compared to L2 cache size, but let's be safe:
-    Index panelWidth = (std::min)(Index(SmallPanelWidth),(std::min)(kc,mc));
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    // To work around an "error: member reference base type 'Matrix<...>
-    // (Eigen::internal::constructor_without_unaligned_array_assert (*)())' is
-    // not a structure or union" compilation error in nvcc (tested V8.0.61),
-    // create a dummy internal::constructor_without_unaligned_array_assert
-    // object to pass to the Matrix constructor.
-    internal::constructor_without_unaligned_array_assert a;
-    Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,LhsStorageOrder> triangularBuffer(a);
-    triangularBuffer.setZero();
-    if((Mode&ZeroDiag)==ZeroDiag)
-      triangularBuffer.diagonal().setZero();
-    else
-      triangularBuffer.diagonal().setOnes();
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
-
-    for(Index k2=IsLower ? depth : 0;
-        IsLower ? k2>0 : k2<depth;
-        IsLower ? k2-=kc : k2+=kc)
-    {
-      Index actual_kc = (std::min)(IsLower ? k2 : depth-k2, kc);
-      Index actual_k2 = IsLower ? k2-actual_kc : k2;
-
-      // align blocks with the end of the triangular part for trapezoidal lhs
-      if((!IsLower)&&(k2<rows)&&(k2+actual_kc>rows))
-      {
-        actual_kc = rows-k2;
-        k2 = k2+actual_kc-kc;
-      }
-
-      pack_rhs(blockB, rhs.getSubMapper(actual_k2,0), actual_kc, cols);
-
-      // the selected lhs's panel has to be split in three different parts:
-      //  1 - the part which is zero => skip it
-      //  2 - the diagonal block => special kernel
-      //  3 - the dense panel below (lower case) or above (upper case) the diagonal block => GEPP
-
-      // the block diagonal, if any:
-      if(IsLower || actual_k2<rows)
-      {
-        // for each small vertical panels of lhs
-        for (Index k1=0; k1<actual_kc; k1+=panelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-k1, panelWidth);
-          Index lengthTarget = IsLower ? actual_kc-k1-actualPanelWidth : k1;
-          Index startBlock   = actual_k2+k1;
-          Index blockBOffset = k1;
-
-          // => GEBP with the micro triangular block
-          // The trick is to pack this micro block while filling the opposite triangular part with zeros.
-          // To this end we do an extra triangular copy to a small temporary buffer
-          for (Index k=0;k<actualPanelWidth;++k)
-          {
-            if (SetDiag)
-              triangularBuffer.coeffRef(k,k) = lhs(startBlock+k,startBlock+k);
-            for (Index i=IsLower ? k+1 : 0; IsLower ? i<actualPanelWidth : i<k; ++i)
-              triangularBuffer.coeffRef(i,k) = lhs(startBlock+i,startBlock+k);
-          }
-          pack_lhs(blockA, LhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()), actualPanelWidth, actualPanelWidth);
-
-          gebp_kernel(res.getSubMapper(startBlock, 0), blockA, blockB,
-                      actualPanelWidth, actualPanelWidth, cols, alpha,
-                      actualPanelWidth, actual_kc, 0, blockBOffset);
-
-          // GEBP with remaining micro panel
-          if (lengthTarget>0)
-          {
-            Index startTarget  = IsLower ? actual_k2+k1+actualPanelWidth : actual_k2;
-
-            pack_lhs(blockA, lhs.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget);
-
-            gebp_kernel(res.getSubMapper(startTarget, 0), blockA, blockB,
-                        lengthTarget, actualPanelWidth, cols, alpha,
-                        actualPanelWidth, actual_kc, 0, blockBOffset);
-          }
-        }
-      }
-      // the part below (lower case) or above (upper case) the diagonal => GEPP
-      {
-        Index start = IsLower ? k2 : 0;
-        Index end   = IsLower ? rows : (std::min)(actual_k2,rows);
-        for(Index i2=start; i2<end; i2+=mc)
-        {
-          const Index actual_mc = (std::min)(i2+mc,end)-i2;
-          gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr,Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder,false>()
-            (blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
-
-          gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc,
-                      actual_kc, cols, alpha, -1, -1, 0, 0);
-        }
-      }
-    }
-  }
-
-// implements col-major += alpha * op(general) * op(triangular)
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
-                                        LhsStorageOrder,ConjugateLhs,
-                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>
-{
-  typedef gebp_traits<Scalar,Scalar> Traits;
-  enum {
-    SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-    IsLower = (Mode&Lower) == Lower,
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1
-  };
-
-  static EIGEN_DONT_INLINE void run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* res,        Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
-};
-
-template <typename Scalar, typename Index, int Mode,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResInnerStride, int Version>
-EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
-                                                        LhsStorageOrder,ConjugateLhs,
-                                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
-    Index _rows, Index _cols, Index _depth,
-    const Scalar* _lhs, Index lhsStride,
-    const Scalar* _rhs, Index rhsStride,
-    Scalar* _res,       Index resIncr, Index resStride,
-    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
-  {
-    const Index PacketBytes = packet_traits<Scalar>::size*sizeof(Scalar);
-    // strip zeros
-    Index diagSize  = (std::min)(_cols,_depth);
-    Index rows      = _rows;
-    Index depth     = IsLower ? _depth : diagSize;
-    Index cols      = IsLower ? diagSize : _cols;
-    
-    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
-    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(_lhs,lhsStride);
-    RhsMapper rhs(_rhs,rhsStride);
-    ResMapper res(_res, resStride, resIncr);
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols+EIGEN_MAX_ALIGN_BYTES/sizeof(Scalar);
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    internal::constructor_without_unaligned_array_assert a;
-    Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,RhsStorageOrder> triangularBuffer(a);
-    triangularBuffer.setZero();
-    if((Mode&ZeroDiag)==ZeroDiag)
-      triangularBuffer.diagonal().setZero();
-    else
-      triangularBuffer.diagonal().setOnes();
-
-    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder,false,true> pack_rhs_panel;
-
-    for(Index k2=IsLower ? 0 : depth;
-        IsLower ? k2<depth  : k2>0;
-        IsLower ? k2+=kc   : k2-=kc)
-    {
-      Index actual_kc = (std::min)(IsLower ? depth-k2 : k2, kc);
-      Index actual_k2 = IsLower ? k2 : k2-actual_kc;
-
-      // align blocks with the end of the triangular part for trapezoidal rhs
-      if(IsLower && (k2<cols) && (actual_k2+actual_kc>cols))
-      {
-        actual_kc = cols-k2;
-        k2 = actual_k2 + actual_kc - kc;
-      }
-
-      // remaining size
-      Index rs = IsLower ? (std::min)(cols,actual_k2) : cols - k2;
-      // size of the triangular part
-      Index ts = (IsLower && actual_k2>=cols) ? 0 : actual_kc;
-
-      Scalar* geb = blockB+ts*ts;
-      geb = geb + internal::first_aligned<PacketBytes>(geb,PacketBytes/sizeof(Scalar));
-
-      pack_rhs(geb, rhs.getSubMapper(actual_k2,IsLower ? 0 : k2), actual_kc, rs);
-
-      // pack the triangular part of the rhs padding the unrolled blocks with zeros
-      if(ts>0)
-      {
-        for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-          Index actual_j2 = actual_k2 + j2;
-          Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
-          Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2;
-          // general part
-          pack_rhs_panel(blockB+j2*actual_kc,
-                         rhs.getSubMapper(actual_k2+panelOffset, actual_j2),
-                         panelLength, actualPanelWidth,
-                         actual_kc, panelOffset);
-
-          // append the triangular part via a temporary buffer
-          for (Index j=0;j<actualPanelWidth;++j)
-          {
-            if (SetDiag)
-              triangularBuffer.coeffRef(j,j) = rhs(actual_j2+j,actual_j2+j);
-            for (Index k=IsLower ? j+1 : 0; IsLower ? k<actualPanelWidth : k<j; ++k)
-              triangularBuffer.coeffRef(k,j) = rhs(actual_j2+k,actual_j2+j);
-          }
-
-          pack_rhs_panel(blockB+j2*actual_kc,
-                         RhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()),
-                         actualPanelWidth, actualPanelWidth,
-                         actual_kc, j2);
-        }
-      }
-
-      for (Index i2=0; i2<rows; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(mc,rows-i2);
-        pack_lhs(blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
-
-        // triangular kernel
-        if(ts>0)
-        {
-          for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
-          {
-            Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-            Index panelLength = IsLower ? actual_kc-j2 : j2+actualPanelWidth;
-            Index blockOffset = IsLower ? j2 : 0;
-
-            gebp_kernel(res.getSubMapper(i2, actual_k2 + j2),
-                        blockA, blockB+j2*actual_kc,
-                        actual_mc, panelLength, actualPanelWidth,
-                        alpha,
-                        actual_kc, actual_kc,  // strides
-                        blockOffset, blockOffset);// offsets
-          }
-        }
-        gebp_kernel(res.getSubMapper(i2, IsLower ? 0 : k2),
-                    blockA, geb, actual_mc, actual_kc, rs,
-                    alpha,
-                    -1, -1, 0, 0);
-      }
-    }
-  }
-
-/***************************************************************************
-* Wrapper to product_triangular_matrix_matrix
-***************************************************************************/
-
-} // end namespace internal
-
-namespace internal {
-template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs>
-struct triangular_product_impl<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
-{
-  template<typename Dest> static void run(Dest& dst, const Lhs &a_lhs, const Rhs &a_rhs, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar  LhsScalar;
-    typedef typename Rhs::Scalar  RhsScalar;
-    typedef typename Dest::Scalar Scalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-    
-    typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
-
-    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(a_lhs);
-    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(a_rhs);
-    Scalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
-
-    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
-              Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,4> BlockingType;
-
-    enum { IsLower = (Mode&Lower) == Lower };
-    Index stripedRows  = ((!LhsIsTriangular) || (IsLower))  ? lhs.rows() : (std::min)(lhs.rows(),lhs.cols());
-    Index stripedCols  = ((LhsIsTriangular)  || (!IsLower)) ? rhs.cols() : (std::min)(rhs.cols(),rhs.rows());
-    Index stripedDepth = LhsIsTriangular ? ((!IsLower) ? lhs.cols() : (std::min)(lhs.cols(),lhs.rows()))
-                                         : ((IsLower)  ? rhs.rows() : (std::min)(rhs.rows(),rhs.cols()));
-
-    BlockingType blocking(stripedRows, stripedCols, stripedDepth, 1, false);
-
-    internal::product_triangular_matrix_matrix<Scalar, Index,
-      Mode, LhsIsTriangular,
-      (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
-      (internal::traits<ActualRhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
-      (internal::traits<Dest          >::Flags&RowMajorBit) ? RowMajor : ColMajor, Dest::InnerStrideAtCompileTime>
-      ::run(
-        stripedRows, stripedCols, stripedDepth,   // sizes
-        &lhs.coeffRef(0,0), lhs.outerStride(),    // lhs info
-        &rhs.coeffRef(0,0), rhs.outerStride(),    // rhs info
-        &dst.coeffRef(0,0), dst.innerStride(), dst.outerStride(),    // result info
-        actualAlpha, blocking
-      );
-
-    // Apply correction if the diagonal is unit and a scalar factor was nested:
-    if ((Mode&UnitDiag)==UnitDiag)
-    {
-      if (LhsIsTriangular && lhs_alpha!=LhsScalar(1))
-      {
-        Index diagSize = (std::min)(lhs.rows(),lhs.cols());
-        dst.topRows(diagSize) -= ((lhs_alpha-LhsScalar(1))*a_rhs).topRows(diagSize);
-      }
-      else if ((!LhsIsTriangular) && rhs_alpha!=RhsScalar(1))
-      {
-        Index diagSize = (std::min)(rhs.rows(),rhs.cols());
-        dst.leftCols(diagSize) -= (rhs_alpha-RhsScalar(1))*a_lhs.leftCols(diagSize);
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
deleted file mode 100644
index a98d12e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
+++ /dev/null
@@ -1,317 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Triangular matrix * matrix product functionality based on ?TRMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
-#define EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-template <typename Scalar, typename Index,
-          int Mode, bool LhsIsTriangular,
-          int LhsStorageOrder, bool ConjugateLhs,
-          int RhsStorageOrder, bool ConjugateRhs,
-          int ResStorageOrder>
-struct product_triangular_matrix_matrix_trmm :
-       product_triangular_matrix_matrix<Scalar,Index,Mode,
-          LhsIsTriangular,LhsStorageOrder,ConjugateLhs,
-          RhsStorageOrder, ConjugateRhs, ResStorageOrder, 1, BuiltIn> {};
-
-
-// try to go to BLAS specialization
-#define EIGEN_BLAS_TRMM_SPECIALIZE(Scalar, LhsIsTriangular) \
-template <typename Index, int Mode, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_triangular_matrix_matrix<Scalar,Index, Mode, LhsIsTriangular, \
-           LhsStorageOrder,ConjugateLhs, RhsStorageOrder,ConjugateRhs,ColMajor,1,Specialized> { \
-  static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,\
-    const Scalar* _rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride, Scalar alpha, level3_blocking<Scalar,Scalar>& blocking) { \
-      EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
-      eigen_assert(resIncr == 1); \
-      product_triangular_matrix_matrix_trmm<Scalar,Index,Mode, \
-        LhsIsTriangular,LhsStorageOrder,ConjugateLhs, \
-        RhsStorageOrder, ConjugateRhs, ColMajor>::run( \
-          _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \
-  } \
-};
-
-EIGEN_BLAS_TRMM_SPECIALIZE(double, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(double, false)
-EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, false)
-EIGEN_BLAS_TRMM_SPECIALIZE(float, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(float, false)
-EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, true)
-EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, false)
-
-// implements col-major += alpha * op(triangular) * op(general)
-#define EIGEN_BLAS_TRMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, int Mode, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
-         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = ((LhsStorageOrder==ColMajor) && ConjugateLhs) ? 1 : 0 \
-  }; \
-\
-  static void run( \
-    Index _rows, Index _cols, Index _depth, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
-  { \
-   Index diagSize  = (std::min)(_rows,_depth); \
-   Index rows      = IsLower ? _rows : diagSize; \
-   Index depth     = IsLower ? diagSize : _depth; \
-   Index cols      = _cols; \
-\
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
-\
-/* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/ \
-   if (rows != depth) { \
-\
-     /* FIXME handle mkl_domain_get_max_threads */ \
-     /*int nthr = mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS);*/ int nthr = 1;\
-\
-     if (((nthr==1) && (((std::max)(rows,depth)-diagSize)/(double)diagSize < 0.5))) { \
-     /* Most likely no benefit to call TRMM or GEMM from BLAS */ \
-       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,true, \
-       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, 1, BuiltIn>::run( \
-           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, 1, resStride, alpha, blocking); \
-     /*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/ \
-     } else { \
-     /* Make sense to call GEMM */ \
-       Map<const MatrixLhs, 0, OuterStride<> > lhsMap(_lhs,rows,depth,OuterStride<>(lhsStride)); \
-       MatrixLhs aa_tmp=lhsMap.template triangularView<Mode>(); \
-       BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride()); \
-       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth, 1, true); \
-       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1>::run( \
-       rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, 1, resStride, alpha, gemm_blocking, 0); \
-\
-     /*std::cout << "TRMM_L: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/ \
-     } \
-     return; \
-   } \
-   char side = 'L', transa, uplo, diag = 'N'; \
-   EIGTYPE *b; \
-   const EIGTYPE *a; \
-   BlasIndex m, n, lda, ldb; \
-\
-/* Set m, n */ \
-   m = convert_index<BlasIndex>(diagSize); \
-   n = convert_index<BlasIndex>(cols); \
-\
-/* Set trans */ \
-   transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
-\
-/* Set b, ldb */ \
-   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols,OuterStride<>(rhsStride)); \
-   MatrixX##EIGPREFIX b_tmp; \
-\
-   if (ConjugateRhs) b_tmp = rhs.conjugate(); else b_tmp = rhs; \
-   b = b_tmp.data(); \
-   ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-\
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (LhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
-   MatrixLhs a_tmp; \
-\
-   if ((conjA!=0) || (SetDiag==0)) { \
-     if (conjA) a_tmp = lhs.conjugate(); else a_tmp = lhs; \
-     if (IsZeroDiag) \
-       a_tmp.diagonal().setZero(); \
-     else if (IsUnitDiag) \
-       a_tmp.diagonal().setOnes();\
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _lhs; \
-     lda = convert_index<BlasIndex>(lhsStride); \
-   } \
-   /*std::cout << "TRMM_L: A is square! Go to BLAS TRMM implementation! \n";*/ \
-/* call ?trmm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
-\
-/* Add op(a_triangular)*b into res*/ \
-   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
-   res_tmp=res_tmp+b_tmp; \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMM_L(double, double, d, dtrmm)
-EIGEN_BLAS_TRMM_L(dcomplex, MKL_Complex16, cd, ztrmm)
-EIGEN_BLAS_TRMM_L(float, float, f, strmm)
-EIGEN_BLAS_TRMM_L(scomplex, MKL_Complex8, cf, ctrmm)
-#else
-EIGEN_BLAS_TRMM_L(double, double, d, dtrmm_)
-EIGEN_BLAS_TRMM_L(dcomplex, double, cd, ztrmm_)
-EIGEN_BLAS_TRMM_L(float, float, f, strmm_)
-EIGEN_BLAS_TRMM_L(scomplex, float, cf, ctrmm_)
-#endif
-
-// implements col-major += alpha * op(general) * op(triangular)
-#define EIGEN_BLAS_TRMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
-template <typename Index, int Mode, \
-          int LhsStorageOrder, bool ConjugateLhs, \
-          int RhsStorageOrder, bool ConjugateRhs> \
-struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
-         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper, \
-    conjA = ((RhsStorageOrder==ColMajor) && ConjugateRhs) ? 1 : 0 \
-  }; \
-\
-  static void run( \
-    Index _rows, Index _cols, Index _depth, \
-    const EIGTYPE* _lhs, Index lhsStride, \
-    const EIGTYPE* _rhs, Index rhsStride, \
-    EIGTYPE* res,        Index resStride, \
-    EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
-  { \
-   Index diagSize  = (std::min)(_cols,_depth); \
-   Index rows      = _rows; \
-   Index depth     = IsLower ? _depth : diagSize; \
-   Index cols      = IsLower ? diagSize : _cols; \
-\
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
-\
-/* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/ \
-   if (cols != depth) { \
-\
-     int nthr = 1 /*mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS)*/; \
-\
-     if ((nthr==1) && (((std::max)(cols,depth)-diagSize)/(double)diagSize < 0.5)) { \
-     /* Most likely no benefit to call TRMM or GEMM from BLAS*/ \
-       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,false, \
-       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, 1, BuiltIn>::run( \
-           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, 1, resStride, alpha, blocking); \
-       /*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/ \
-     } else { \
-     /* Make sense to call GEMM */ \
-       Map<const MatrixRhs, 0, OuterStride<> > rhsMap(_rhs,depth,cols, OuterStride<>(rhsStride)); \
-       MatrixRhs aa_tmp=rhsMap.template triangularView<Mode>(); \
-       BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride()); \
-       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth, 1, true); \
-       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1>::run( \
-       rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, 1, resStride, alpha, gemm_blocking, 0); \
-\
-     /*std::cout << "TRMM_R: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/ \
-     } \
-     return; \
-   } \
-   char side = 'R', transa, uplo, diag = 'N'; \
-   EIGTYPE *b; \
-   const EIGTYPE *a; \
-   BlasIndex m, n, lda, ldb; \
-\
-/* Set m, n */ \
-   m = convert_index<BlasIndex>(rows); \
-   n = convert_index<BlasIndex>(diagSize); \
-\
-/* Set trans */ \
-   transa = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
-\
-/* Set b, ldb */ \
-   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
-   MatrixX##EIGPREFIX b_tmp; \
-\
-   if (ConjugateLhs) b_tmp = lhs.conjugate(); else b_tmp = lhs; \
-   b = b_tmp.data(); \
-   ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
-\
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (RhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols, OuterStride<>(rhsStride)); \
-   MatrixRhs a_tmp; \
-\
-   if ((conjA!=0) || (SetDiag==0)) { \
-     if (conjA) a_tmp = rhs.conjugate(); else a_tmp = rhs; \
-     if (IsZeroDiag) \
-       a_tmp.diagonal().setZero(); \
-     else if (IsUnitDiag) \
-       a_tmp.diagonal().setOnes();\
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _rhs; \
-     lda = convert_index<BlasIndex>(rhsStride); \
-   } \
-   /*std::cout << "TRMM_R: A is square! Go to BLAS TRMM implementation! \n";*/ \
-/* call ?trmm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
-\
-/* Add op(a_triangular)*b into res*/ \
-   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
-   res_tmp=res_tmp+b_tmp; \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMM_R(double, double, d, dtrmm)
-EIGEN_BLAS_TRMM_R(dcomplex, MKL_Complex16, cd, ztrmm)
-EIGEN_BLAS_TRMM_R(float, float, f, strmm)
-EIGEN_BLAS_TRMM_R(scomplex, MKL_Complex8, cf, ctrmm)
-#else
-EIGEN_BLAS_TRMM_R(double, double, d, dtrmm_)
-EIGEN_BLAS_TRMM_R(dcomplex, double, cd, ztrmm_)
-EIGEN_BLAS_TRMM_R(float, float, f, strmm_)
-EIGEN_BLAS_TRMM_R(scomplex, float, cf, ctrmm_)
-#endif
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
deleted file mode 100644
index 76bfa15..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
+++ /dev/null
@@ -1,350 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULARMATRIXVECTOR_H
-#define EIGEN_TRIANGULARMATRIXVECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder, int Version=Specialized>
-struct triangular_matrix_vector_product;
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
-struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  enum {
-    IsLower = ((Mode&Lower)==Lower),
-    HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
-    HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
-  };
-  static EIGEN_DONT_INLINE  void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-                                     const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha);
-};
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
-EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
-  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha)
-  {
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    Index size = (std::min)(_rows,_cols);
-    Index rows = IsLower ? _rows : (std::min)(_rows,_cols);
-    Index cols = IsLower ? (std::min)(_rows,_cols) : _cols;
-
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
-    typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
-
-    typedef Map<const Matrix<RhsScalar,Dynamic,1>, 0, InnerStride<> > RhsMap;
-    const RhsMap rhs(_rhs,cols,InnerStride<>(rhsIncr));
-    typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
-
-    typedef Map<Matrix<ResScalar,Dynamic,1> > ResMap;
-    ResMap res(_res,rows);
-
-    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
-
-    for (Index pi=0; pi<size; pi+=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(PanelWidth, size-pi);
-      for (Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = pi + k;
-        Index s = IsLower ? ((HasUnitDiag||HasZeroDiag) ? i+1 : i ) : pi;
-        Index r = IsLower ? actualPanelWidth-k : k+1;
-        if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0)
-          res.segment(s,r) += (alpha * cjRhs.coeff(i)) * cjLhs.col(i).segment(s,r);
-        if (HasUnitDiag)
-          res.coeffRef(i) += alpha * cjRhs.coeff(i);
-      }
-      Index r = IsLower ? rows - pi - actualPanelWidth : pi;
-      if (r>0)
-      {
-        Index s = IsLower ? pi+actualPanelWidth : 0;
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run(
-            r, actualPanelWidth,
-            LhsMapper(&lhs.coeffRef(s,pi), lhsStride),
-            RhsMapper(&rhs.coeffRef(pi), rhsIncr),
-            &res.coeffRef(s), resIncr, alpha);
-      }
-    }
-    if((!IsLower) && cols>size)
-    {
-      general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run(
-          rows, cols-size,
-          LhsMapper(&lhs.coeffRef(0,size), lhsStride),
-          RhsMapper(&rhs.coeffRef(size), rhsIncr),
-          _res, resIncr, alpha);
-    }
-  }
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
-struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
-{
-  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  enum {
-    IsLower = ((Mode&Lower)==Lower),
-    HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
-    HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
-  };
-  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-                                    const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha);
-};
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
-EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
-  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
-        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha)
-  {
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    Index diagSize = (std::min)(_rows,_cols);
-    Index rows = IsLower ? _rows : diagSize;
-    Index cols = IsLower ? diagSize : _cols;
-
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
-    typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
-
-    typedef Map<const Matrix<RhsScalar,Dynamic,1> > RhsMap;
-    const RhsMap rhs(_rhs,cols);
-    typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
-
-    typedef Map<Matrix<ResScalar,Dynamic,1>, 0, InnerStride<> > ResMap;
-    ResMap res(_res,rows,InnerStride<>(resIncr));
-
-    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
-
-    for (Index pi=0; pi<diagSize; pi+=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(PanelWidth, diagSize-pi);
-      for (Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = pi + k;
-        Index s = IsLower ? pi  : ((HasUnitDiag||HasZeroDiag) ? i+1 : i);
-        Index r = IsLower ? k+1 : actualPanelWidth-k;
-        if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0)
-          res.coeffRef(i) += alpha * (cjLhs.row(i).segment(s,r).cwiseProduct(cjRhs.segment(s,r).transpose())).sum();
-        if (HasUnitDiag)
-          res.coeffRef(i) += alpha * cjRhs.coeff(i);
-      }
-      Index r = IsLower ? pi : cols - pi - actualPanelWidth;
-      if (r>0)
-      {
-        Index s = IsLower ? 0 : pi + actualPanelWidth;
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run(
-            actualPanelWidth, r,
-            LhsMapper(&lhs.coeffRef(pi,s), lhsStride),
-            RhsMapper(&rhs.coeffRef(s), rhsIncr),
-            &res.coeffRef(pi), resIncr, alpha);
-      }
-    }
-    if(IsLower && rows>diagSize)
-    {
-      general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run(
-            rows-diagSize, cols,
-            LhsMapper(&lhs.coeffRef(diagSize,0), lhsStride),
-            RhsMapper(&rhs.coeffRef(0), rhsIncr),
-            &res.coeffRef(diagSize), resIncr, alpha);
-    }
-  }
-
-/***************************************************************************
-* Wrapper to product_triangular_vector
-***************************************************************************/
-
-template<int Mode,int StorageOrder>
-struct trmv_selector;
-
-} // end namespace internal
-
-namespace internal {
-
-template<int Mode, typename Lhs, typename Rhs>
-struct triangular_product_impl<Mode,true,Lhs,false,Rhs,true>
-{
-  template<typename Dest> static void run(Dest& dst, const Lhs &lhs, const Rhs &rhs, const typename Dest::Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==lhs.rows() && dst.cols()==rhs.cols());
-  
-    internal::trmv_selector<Mode,(int(internal::traits<Lhs>::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(lhs, rhs, dst, alpha);
-  }
-};
-
-template<int Mode, typename Lhs, typename Rhs>
-struct triangular_product_impl<Mode,false,Lhs,true,Rhs,false>
-{
-  template<typename Dest> static void run(Dest& dst, const Lhs &lhs, const Rhs &rhs, const typename Dest::Scalar& alpha)
-  {
-    eigen_assert(dst.rows()==lhs.rows() && dst.cols()==rhs.cols());
-
-    Transpose<Dest> dstT(dst);
-    internal::trmv_selector<(Mode & (UnitDiag|ZeroDiag)) | ((Mode & Lower) ? Upper : Lower),
-                            (int(internal::traits<Rhs>::Flags)&RowMajorBit) ? ColMajor : RowMajor>
-            ::run(rhs.transpose(),lhs.transpose(), dstT, alpha);
-  }
-};
-
-} // end namespace internal
-
-namespace internal {
-
-// TODO: find a way to factorize this piece of code with gemv_selector since the logic is exactly the same.
-  
-template<int Mode> struct trmv_selector<Mode,ColMajor>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar      LhsScalar;
-    typedef typename Rhs::Scalar      RhsScalar;
-    typedef typename Dest::Scalar     ResScalar;
-    typedef typename Dest::RealScalar RealScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    
-    typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
-
-    typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
-    typename internal::add_const_on_value_type<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
-
-    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
-    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
-    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
-
-    enum {
-      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
-      // on, the other hand it is good for the cache to pack the vector anyways...
-      EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1,
-      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
-      MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal
-    };
-
-    gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
-
-    bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
-    bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
-
-    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
-
-    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
-                                                  evalToDest ? dest.data() : static_dest.data());
-
-    if(!evalToDest)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = dest.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      if(!alphaIsCompatible)
-      {
-        MappedDest(actualDestPtr, dest.size()).setZero();
-        compatibleAlpha = RhsScalar(1);
-      }
-      else
-        MappedDest(actualDestPtr, dest.size()) = dest;
-    }
-
-    internal::triangular_matrix_vector_product
-      <Index,Mode,
-       LhsScalar, LhsBlasTraits::NeedToConjugate,
-       RhsScalar, RhsBlasTraits::NeedToConjugate,
-       ColMajor>
-      ::run(actualLhs.rows(),actualLhs.cols(),
-            actualLhs.data(),actualLhs.outerStride(),
-            actualRhs.data(),actualRhs.innerStride(),
-            actualDestPtr,1,compatibleAlpha);
-
-    if (!evalToDest)
-    {
-      if(!alphaIsCompatible)
-        dest += actualAlpha * MappedDest(actualDestPtr, dest.size());
-      else
-        dest = MappedDest(actualDestPtr, dest.size());
-    }
-
-    if ( ((Mode&UnitDiag)==UnitDiag) && (lhs_alpha!=LhsScalar(1)) )
-    {
-      Index diagSize = (std::min)(lhs.rows(),lhs.cols());
-      dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
-    }
-  }
-};
-
-template<int Mode> struct trmv_selector<Mode,RowMajor>
-{
-  template<typename Lhs, typename Rhs, typename Dest>
-  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
-  {
-    typedef typename Lhs::Scalar      LhsScalar;
-    typedef typename Rhs::Scalar      RhsScalar;
-    typedef typename Dest::Scalar     ResScalar;
-    
-    typedef internal::blas_traits<Lhs> LhsBlasTraits;
-    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
-    typedef internal::blas_traits<Rhs> RhsBlasTraits;
-    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
-    typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
-
-    typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
-    typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
-
-    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
-    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
-    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
-
-    enum {
-      DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1
-    };
-
-    gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
-
-    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
-        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
-
-    if(!DirectlyUseRhs)
-    {
-      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      Index size = actualRhs.size();
-      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
-      #endif
-      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
-    }
-
-    internal::triangular_matrix_vector_product
-      <Index,Mode,
-       LhsScalar, LhsBlasTraits::NeedToConjugate,
-       RhsScalar, RhsBlasTraits::NeedToConjugate,
-       RowMajor>
-      ::run(actualLhs.rows(),actualLhs.cols(),
-            actualLhs.data(),actualLhs.outerStride(),
-            actualRhsPtr,1,
-            dest.data(),dest.innerStride(),
-            actualAlpha);
-
-    if ( ((Mode&UnitDiag)==UnitDiag) && (lhs_alpha!=LhsScalar(1)) )
-    {
-      Index diagSize = (std::min)(lhs.rows(),lhs.cols());
-      dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
-    }
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULARMATRIXVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
deleted file mode 100644
index 3d47a2b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
+++ /dev/null
@@ -1,255 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Triangular matrix-vector product functionality based on ?TRMV.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
-#define EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************************************************
-* This file implements triangular matrix-vector multiplication using BLAS
-**********************************************************************/
-
-// trmv/hemv specialization
-
-template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder>
-struct triangular_matrix_vector_product_trmv :
-  triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,StorageOrder,BuiltIn> {};
-
-#define EIGEN_BLAS_TRMV_SPECIALIZE(Scalar) \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor,Specialized> { \
- static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
-                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
-      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor>::run( \
-        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-  } \
-}; \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor,Specialized> { \
- static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
-                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
-      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor>::run( \
-        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-  } \
-};
-
-EIGEN_BLAS_TRMV_SPECIALIZE(double)
-EIGEN_BLAS_TRMV_SPECIALIZE(float)
-EIGEN_BLAS_TRMV_SPECIALIZE(dcomplex)
-EIGEN_BLAS_TRMV_SPECIALIZE(scomplex)
-
-// implements col-major: res += alpha * op(triangular) * vector
-#define EIGEN_BLAS_TRMV_CM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor> { \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper \
-  }; \
- static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
-                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
- { \
-   if (ConjLhs || IsZeroDiag) { \
-     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \
-       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-     return; \
-   }\
-   Index size = (std::min)(_rows,_cols); \
-   Index rows = IsLower ? _rows : size; \
-   Index cols = IsLower ? size : _cols; \
-\
-   typedef VectorX##EIGPREFIX VectorRhs; \
-   EIGTYPE *x, *y;\
-\
-/* Set x*/ \
-   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
-   VectorRhs x_tmp; \
-   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-   x = x_tmp.data(); \
-\
-/* Square part handling */\
-\
-   char trans, uplo, diag; \
-   BlasIndex m, n, lda, incx, incy; \
-   EIGTYPE const *a; \
-   EIGTYPE beta(1); \
-\
-/* Set m, n */ \
-   n = convert_index<BlasIndex>(size); \
-   lda = convert_index<BlasIndex>(lhsStride); \
-   incx = 1; \
-   incy = convert_index<BlasIndex>(resIncr); \
-\
-/* Set uplo, trans and diag*/ \
-   trans = 'N'; \
-   uplo = IsLower ? 'L' : 'U'; \
-   diag = IsUnitDiag ? 'U' : 'N'; \
-\
-/* call ?TRMV*/ \
-   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
-\
-/* Add op(a_tr)rhs into res*/ \
-   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
-/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
-   if (size<(std::max)(rows,cols)) { \
-     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-     x = x_tmp.data(); \
-     if (size<rows) { \
-       y = _res + size*resIncr; \
-       a = _lhs + size; \
-       m = convert_index<BlasIndex>(rows-size); \
-       n = convert_index<BlasIndex>(size); \
-     } \
-     else { \
-       x += size; \
-       y = _res; \
-       a = _lhs + size*lda; \
-       m = convert_index<BlasIndex>(size); \
-       n = convert_index<BlasIndex>(cols-size); \
-     } \
-     BLASPREFIX##gemv##BLASPOSTFIX(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
-   } \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMV_CM(double,   double, d,  d,)
-EIGEN_BLAS_TRMV_CM(dcomplex, MKL_Complex16, cd, z,)
-EIGEN_BLAS_TRMV_CM(float,    float,  f,  s,)
-EIGEN_BLAS_TRMV_CM(scomplex, MKL_Complex8,  cf, c,)
-#else
-EIGEN_BLAS_TRMV_CM(double,   double, d,  d, _)
-EIGEN_BLAS_TRMV_CM(dcomplex, double, cd, z, _)
-EIGEN_BLAS_TRMV_CM(float,    float,  f,  s, _)
-EIGEN_BLAS_TRMV_CM(scomplex, float,  cf, c, _)
-#endif
-
-// implements row-major: res += alpha * op(triangular) * vector
-#define EIGEN_BLAS_TRMV_RM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
-template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
-struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor> { \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    LowUp = IsLower ? Lower : Upper \
-  }; \
- static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
-                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
- { \
-   if (IsZeroDiag) { \
-     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \
-       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
-     return; \
-   }\
-   Index size = (std::min)(_rows,_cols); \
-   Index rows = IsLower ? _rows : size; \
-   Index cols = IsLower ? size : _cols; \
-\
-   typedef VectorX##EIGPREFIX VectorRhs; \
-   EIGTYPE *x, *y;\
-\
-/* Set x*/ \
-   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
-   VectorRhs x_tmp; \
-   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-   x = x_tmp.data(); \
-\
-/* Square part handling */\
-\
-   char trans, uplo, diag; \
-   BlasIndex m, n, lda, incx, incy; \
-   EIGTYPE const *a; \
-   EIGTYPE beta(1); \
-\
-/* Set m, n */ \
-   n = convert_index<BlasIndex>(size); \
-   lda = convert_index<BlasIndex>(lhsStride); \
-   incx = 1; \
-   incy = convert_index<BlasIndex>(resIncr); \
-\
-/* Set uplo, trans and diag*/ \
-   trans = ConjLhs ? 'C' : 'T'; \
-   uplo = IsLower ? 'U' : 'L'; \
-   diag = IsUnitDiag ? 'U' : 'N'; \
-\
-/* call ?TRMV*/ \
-   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
-\
-/* Add op(a_tr)rhs into res*/ \
-   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
-/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
-   if (size<(std::max)(rows,cols)) { \
-     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
-     x = x_tmp.data(); \
-     if (size<rows) { \
-       y = _res + size*resIncr; \
-       a = _lhs + size*lda; \
-       m = convert_index<BlasIndex>(rows-size); \
-       n = convert_index<BlasIndex>(size); \
-     } \
-     else { \
-       x += size; \
-       y = _res; \
-       a = _lhs + size; \
-       m = convert_index<BlasIndex>(size); \
-       n = convert_index<BlasIndex>(cols-size); \
-     } \
-     BLASPREFIX##gemv##BLASPOSTFIX(&trans, &n, &m, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
-   } \
-  } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRMV_RM(double,   double, d,  d,)
-EIGEN_BLAS_TRMV_RM(dcomplex, MKL_Complex16, cd, z,)
-EIGEN_BLAS_TRMV_RM(float,    float,  f,  s,)
-EIGEN_BLAS_TRMV_RM(scomplex, MKL_Complex8,  cf, c,)
-#else
-EIGEN_BLAS_TRMV_RM(double,   double, d,  d,_)
-EIGEN_BLAS_TRMV_RM(dcomplex, double, cd, z,_)
-EIGEN_BLAS_TRMV_RM(float,    float,  f,  s,_)
-EIGEN_BLAS_TRMV_RM(scomplex, float,  cf, c,_)
-#endif
-
-} // end namespase internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h
deleted file mode 100644
index 6d879ba..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h
+++ /dev/null
@@ -1,337 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_H
-#define EIGEN_TRIANGULAR_SOLVER_MATRIX_H
-
-namespace Eigen { 
-
-namespace internal {
-
-// if the rhs is row major, let's transpose the product
-template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-struct triangular_solve_matrix<Scalar,Index,Side,Mode,Conjugate,TriStorageOrder,RowMajor,OtherInnerStride>
-{
-  static void run(
-    Index size, Index cols,
-    const Scalar*  tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking)
-  {
-    triangular_solve_matrix<
-      Scalar, Index, Side==OnTheLeft?OnTheRight:OnTheLeft,
-      (Mode&UnitDiag) | ((Mode&Upper) ? Lower : Upper),
-      NumTraits<Scalar>::IsComplex && Conjugate,
-      TriStorageOrder==RowMajor ? ColMajor : RowMajor, ColMajor, OtherInnerStride>
-      ::run(size, cols, tri, triStride, _other, otherIncr, otherStride, blocking);
-  }
-};
-
-/* Optimized triangular solver with multiple right hand side and the triangular matrix on the left
- */
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder,int OtherInnerStride>
-struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>
-{
-  static EIGEN_DONT_INLINE void run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking);
-};
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>::run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index cols = otherSize;
-
-    typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> TriMapper;
-    typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> OtherMapper;
-    TriMapper tri(_tri, triStride);
-    OtherMapper other(_other, otherStride, otherIncr);
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-
-    enum {
-      SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-      IsLower = (Mode&Lower) == Lower
-    };
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(size,blocking.mc());  // cache block size along the M direction
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*cols;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    conj_if<Conjugate> conj;
-    gebp_kernel<Scalar, Scalar, Index, OtherMapper, Traits::mr, Traits::nr, Conjugate, false> gebp_kernel;
-    gemm_pack_lhs<Scalar, Index, TriMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, TriStorageOrder> pack_lhs;
-    gemm_pack_rhs<Scalar, Index, OtherMapper, Traits::nr, ColMajor, false, true> pack_rhs;
-
-    // the goal here is to subdivise the Rhs panels such that we keep some cache
-    // coherence when accessing the rhs elements
-    std::ptrdiff_t l1, l2, l3;
-    manage_caching_sizes(GetAction, &l1, &l2, &l3);
-    Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * std::max<Index>(otherStride,size)) : 0;
-    subcols = std::max<Index>((subcols/Traits::nr)*Traits::nr, Traits::nr);
-
-    for(Index k2=IsLower ? 0 : size;
-        IsLower ? k2<size : k2>0;
-        IsLower ? k2+=kc : k2-=kc)
-    {
-      const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc);
-
-      // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel,
-      // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into
-      // A11 (the triangular part) and A21 the remaining rectangular part.
-      // Then the high level algorithm is:
-      //  - B = R1                    => general block copy (done during the next step)
-      //  - R1 = A11^-1 B             => tricky part
-      //  - update B from the new R1  => actually this has to be performed continuously during the above step
-      //  - R2 -= A21 * B             => GEPP
-
-      // The tricky part: compute R1 = A11^-1 B while updating B from R1
-      // The idea is to split A11 into multiple small vertical panels.
-      // Each panel can be split into a small triangular part T1k which is processed without optimization,
-      // and the remaining small part T2k which is processed using gebp with appropriate block strides
-      for(Index j2=0; j2<cols; j2+=subcols)
-      {
-        Index actual_cols = (std::min)(cols-j2,subcols);
-        // for each small vertical panels [T1k^T, T2k^T]^T of lhs
-        for (Index k1=0; k1<actual_kc; k1+=SmallPanelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-k1, SmallPanelWidth);
-          // tr solve
-          for (Index k=0; k<actualPanelWidth; ++k)
-          {
-            // TODO write a small kernel handling this (can be shared with trsv)
-            Index i  = IsLower ? k2+k1+k : k2-k1-k-1;
-            Index rs = actualPanelWidth - k - 1; // remaining size
-            Index s  = TriStorageOrder==RowMajor ? (IsLower ? k2+k1 : i+1)
-                                                 :  IsLower ? i+1 : i-rs;
-
-            Scalar a = (Mode & UnitDiag) ? Scalar(1) : Scalar(1)/conj(tri(i,i));
-            for (Index j=j2; j<j2+actual_cols; ++j)
-            {
-              if (TriStorageOrder==RowMajor)
-              {
-                Scalar b(0);
-                const Scalar* l = &tri(i,s);
-                typename OtherMapper::LinearMapper r = other.getLinearMapper(s,j);
-                for (Index i3=0; i3<k; ++i3)
-                  b += conj(l[i3]) * r(i3);
-
-                other(i,j) = (other(i,j) - b)*a;
-              }
-              else
-              {
-                Scalar& otherij = other(i,j);
-                otherij *= a;
-                Scalar b = otherij;
-                typename OtherMapper::LinearMapper r = other.getLinearMapper(s,j);
-                typename TriMapper::LinearMapper l = tri.getLinearMapper(s,i);
-                for (Index i3=0;i3<rs;++i3)
-                  r(i3) -= b * conj(l(i3));
-              }
-            }
-          }
-
-          Index lengthTarget = actual_kc-k1-actualPanelWidth;
-          Index startBlock   = IsLower ? k2+k1 : k2-k1-actualPanelWidth;
-          Index blockBOffset = IsLower ? k1 : lengthTarget;
-
-          // update the respective rows of B from other
-          pack_rhs(blockB+actual_kc*j2, other.getSubMapper(startBlock,j2), actualPanelWidth, actual_cols, actual_kc, blockBOffset);
-
-          // GEBP
-          if (lengthTarget>0)
-          {
-            Index startTarget  = IsLower ? k2+k1+actualPanelWidth : k2-actual_kc;
-
-            pack_lhs(blockA, tri.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget);
-
-            gebp_kernel(other.getSubMapper(startTarget,j2), blockA, blockB+actual_kc*j2, lengthTarget, actualPanelWidth, actual_cols, Scalar(-1),
-                        actualPanelWidth, actual_kc, 0, blockBOffset);
-          }
-        }
-      }
-      
-      // R2 -= A21 * B => GEPP
-      {
-        Index start = IsLower ? k2+kc : 0;
-        Index end   = IsLower ? size : k2-kc;
-        for(Index i2=start; i2<end; i2+=mc)
-        {
-          const Index actual_mc = (std::min)(mc,end-i2);
-          if (actual_mc>0)
-          {
-            pack_lhs(blockA, tri.getSubMapper(i2, IsLower ? k2 : k2-kc), actual_kc, actual_mc);
-
-            gebp_kernel(other.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1), -1, -1, 0, 0);
-          }
-        }
-      }
-    }
-  }
-
-/* Optimized triangular solver with multiple left hand sides and the triangular matrix on the right
- */
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>
-{
-  static EIGEN_DONT_INLINE void run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking);
-};
-template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
-EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>::run(
-    Index size, Index otherSize,
-    const Scalar* _tri, Index triStride,
-    Scalar* _other, Index otherIncr, Index otherStride,
-    level3_blocking<Scalar,Scalar>& blocking)
-  {
-    Index rows = otherSize;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> LhsMapper;
-    typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper;
-    LhsMapper lhs(_other, otherStride, otherIncr);
-    RhsMapper rhs(_tri, triStride);
-
-    typedef gebp_traits<Scalar,Scalar> Traits;
-    enum {
-      RhsStorageOrder   = TriStorageOrder,
-      SmallPanelWidth   = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr),
-      IsLower = (Mode&Lower) == Lower
-    };
-
-    Index kc = blocking.kc();                   // cache block size along the K direction
-    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
-
-    std::size_t sizeA = kc*mc;
-    std::size_t sizeB = kc*size;
-
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
-    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
-
-    conj_if<Conjugate> conj;
-    gebp_kernel<Scalar, Scalar, Index, LhsMapper, Traits::mr, Traits::nr, false, Conjugate> gebp_kernel;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
-    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder,false,true> pack_rhs_panel;
-    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor, false, true> pack_lhs_panel;
-
-    for(Index k2=IsLower ? size : 0;
-        IsLower ? k2>0 : k2<size;
-        IsLower ? k2-=kc : k2+=kc)
-    {
-      const Index actual_kc = (std::min)(IsLower ? k2 : size-k2, kc);
-      Index actual_k2 = IsLower ? k2-actual_kc : k2 ;
-
-      Index startPanel = IsLower ? 0 : k2+actual_kc;
-      Index rs = IsLower ? actual_k2 : size - actual_k2 - actual_kc;
-      Scalar* geb = blockB+actual_kc*actual_kc;
-
-      if (rs>0) pack_rhs(geb, rhs.getSubMapper(actual_k2,startPanel), actual_kc, rs);
-
-      // triangular packing (we only pack the panels off the diagonal,
-      // neglecting the blocks overlapping the diagonal
-      {
-        for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
-        {
-          Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-          Index actual_j2 = actual_k2 + j2;
-          Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
-          Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2;
-
-          if (panelLength>0)
-          pack_rhs_panel(blockB+j2*actual_kc,
-                         rhs.getSubMapper(actual_k2+panelOffset, actual_j2),
-                         panelLength, actualPanelWidth,
-                         actual_kc, panelOffset);
-        }
-      }
-
-      for(Index i2=0; i2<rows; i2+=mc)
-      {
-        const Index actual_mc = (std::min)(mc,rows-i2);
-
-        // triangular solver kernel
-        {
-          // for each small block of the diagonal (=> vertical panels of rhs)
-          for (Index j2 = IsLower
-                      ? (actual_kc - ((actual_kc%SmallPanelWidth) ? Index(actual_kc%SmallPanelWidth)
-                                                                  : Index(SmallPanelWidth)))
-                      : 0;
-               IsLower ? j2>=0 : j2<actual_kc;
-               IsLower ? j2-=SmallPanelWidth : j2+=SmallPanelWidth)
-          {
-            Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
-            Index absolute_j2 = actual_k2 + j2;
-            Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
-            Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2;
-
-            // GEBP
-            if(panelLength>0)
-            {
-              gebp_kernel(lhs.getSubMapper(i2,absolute_j2),
-                          blockA, blockB+j2*actual_kc,
-                          actual_mc, panelLength, actualPanelWidth,
-                          Scalar(-1),
-                          actual_kc, actual_kc, // strides
-                          panelOffset, panelOffset); // offsets
-            }
-
-            // unblocked triangular solve
-            for (Index k=0; k<actualPanelWidth; ++k)
-            {
-              Index j = IsLower ? absolute_j2+actualPanelWidth-k-1 : absolute_j2+k;
-
-              typename LhsMapper::LinearMapper r = lhs.getLinearMapper(i2,j);
-              for (Index k3=0; k3<k; ++k3)
-              {
-                Scalar b = conj(rhs(IsLower ? j+1+k3 : absolute_j2+k3,j));
-                typename LhsMapper::LinearMapper a = lhs.getLinearMapper(i2,IsLower ? j+1+k3 : absolute_j2+k3);
-                for (Index i=0; i<actual_mc; ++i)
-                  r(i) -= a(i) * b;
-              }
-              if((Mode & UnitDiag)==0)
-              {
-                Scalar inv_rjj = RealScalar(1)/conj(rhs(j,j));
-                for (Index i=0; i<actual_mc; ++i)
-                  r(i) *= inv_rjj;
-              }
-            }
-
-            // pack the just computed part of lhs to A
-            pack_lhs_panel(blockA, lhs.getSubMapper(i2,absolute_j2),
-                           actualPanelWidth, actual_mc,
-                           actual_kc, j2);
-          }
-        }
-
-        if (rs>0)
-          gebp_kernel(lhs.getSubMapper(i2, startPanel), blockA, geb,
-                      actual_mc, actual_kc, rs, Scalar(-1),
-                      -1, -1, 0, 0);
-      }
-    }
-  }
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
deleted file mode 100644
index 621194c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
+++ /dev/null
@@ -1,167 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to BLAS F77
- *   Triangular matrix * matrix product functionality based on ?TRMM.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
-#define EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// implements LeftSide op(triangular)^-1 * general
-#define EIGEN_BLAS_TRSM_L(EIGTYPE, BLASTYPE, BLASFUNC) \
-template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
-struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,1> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
-  }; \
-  static void run( \
-      Index size, Index otherSize, \
-      const EIGTYPE* _tri, Index triStride, \
-      EIGTYPE* _other, Index otherIncr, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \
-  { \
-   EIGEN_ONLY_USED_FOR_DEBUG(otherIncr); \
-   eigen_assert(otherIncr == 1); \
-   BlasIndex m = convert_index<BlasIndex>(size), n = convert_index<BlasIndex>(otherSize), lda, ldb; \
-   char side = 'L', uplo, diag='N', transa; \
-   /* Set alpha_ */ \
-   EIGTYPE alpha(1); \
-   ldb = convert_index<BlasIndex>(otherStride);\
-\
-   const EIGTYPE *a; \
-/* Set trans */ \
-   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
-   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
-   MatrixTri a_tmp; \
-\
-   if (conjA) { \
-     a_tmp = tri.conjugate(); \
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _tri; \
-     lda = convert_index<BlasIndex>(triStride); \
-   } \
-   if (IsUnitDiag) diag='U'; \
-/* call ?trsm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
- } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRSM_L(double,   double, dtrsm)
-EIGEN_BLAS_TRSM_L(dcomplex, MKL_Complex16, ztrsm)
-EIGEN_BLAS_TRSM_L(float,    float,  strsm)
-EIGEN_BLAS_TRSM_L(scomplex, MKL_Complex8, ctrsm)
-#else
-EIGEN_BLAS_TRSM_L(double,   double, dtrsm_)
-EIGEN_BLAS_TRSM_L(dcomplex, double, ztrsm_)
-EIGEN_BLAS_TRSM_L(float,    float,  strsm_)
-EIGEN_BLAS_TRSM_L(scomplex, float,  ctrsm_)
-#endif
-
-// implements RightSide general * op(triangular)^-1
-#define EIGEN_BLAS_TRSM_R(EIGTYPE, BLASTYPE, BLASFUNC) \
-template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
-struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,1> \
-{ \
-  enum { \
-    IsLower = (Mode&Lower) == Lower, \
-    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
-    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
-    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
-  }; \
-  static void run( \
-      Index size, Index otherSize, \
-      const EIGTYPE* _tri, Index triStride, \
-      EIGTYPE* _other, Index otherIncr, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \
-  { \
-   EIGEN_ONLY_USED_FOR_DEBUG(otherIncr); \
-   eigen_assert(otherIncr == 1); \
-   BlasIndex m = convert_index<BlasIndex>(otherSize), n = convert_index<BlasIndex>(size), lda, ldb; \
-   char side = 'R', uplo, diag='N', transa; \
-   /* Set alpha_ */ \
-   EIGTYPE alpha(1); \
-   ldb = convert_index<BlasIndex>(otherStride);\
-\
-   const EIGTYPE *a; \
-/* Set trans */ \
-   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
-/* Set uplo */ \
-   uplo = IsLower ? 'L' : 'U'; \
-   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
-/* Set a, lda */ \
-   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
-   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
-   MatrixTri a_tmp; \
-\
-   if (conjA) { \
-     a_tmp = tri.conjugate(); \
-     a = a_tmp.data(); \
-     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
-   } else { \
-     a = _tri; \
-     lda = convert_index<BlasIndex>(triStride); \
-   } \
-   if (IsUnitDiag) diag='U'; \
-/* call ?trsm*/ \
-   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
-   /*std::cout << "TRMS_L specialization!\n";*/ \
- } \
-};
-
-#ifdef EIGEN_USE_MKL
-EIGEN_BLAS_TRSM_R(double,   double, dtrsm)
-EIGEN_BLAS_TRSM_R(dcomplex, MKL_Complex16, ztrsm)
-EIGEN_BLAS_TRSM_R(float,    float,  strsm)
-EIGEN_BLAS_TRSM_R(scomplex, MKL_Complex8,  ctrsm)
-#else
-EIGEN_BLAS_TRSM_R(double,   double, dtrsm_)
-EIGEN_BLAS_TRSM_R(dcomplex, double, ztrsm_)
-EIGEN_BLAS_TRSM_R(float,    float,  strsm_)
-EIGEN_BLAS_TRSM_R(scomplex, float,  ctrsm_)
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h
deleted file mode 100644
index 6473170..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h
+++ /dev/null
@@ -1,148 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIANGULAR_SOLVER_VECTOR_H
-#define EIGEN_TRIANGULAR_SOLVER_VECTOR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate, int StorageOrder>
-struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheRight, Mode, Conjugate, StorageOrder>
-{
-  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
-  {
-    triangular_solve_vector<LhsScalar,RhsScalar,Index,OnTheLeft,
-        ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag),
-        Conjugate,StorageOrder==RowMajor?ColMajor:RowMajor
-      >::run(size, _lhs, lhsStride, rhs);
-  }
-};
-
-// forward and backward substitution, row-major, rhs is a vector
-template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
-struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, RowMajor>
-{
-  enum {
-    IsLower = ((Mode&Lower)==Lower)
-  };
-  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
-  {
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride));
-
-    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
-
-    typename internal::conditional<
-                          Conjugate,
-                          const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>,
-                          const LhsMap&>
-                        ::type cjLhs(lhs);
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    for(Index pi=IsLower ? 0 : size;
-        IsLower ? pi<size : pi>0;
-        IsLower ? pi+=PanelWidth : pi-=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
-
-      Index r = IsLower ? pi : size - pi; // remaining size
-      if (r > 0)
-      {
-        // let's directly call the low level product function because:
-        // 1 - it is faster to compile
-        // 2 - it is slightly faster at runtime
-        Index startRow = IsLower ? pi : pi-actualPanelWidth;
-        Index startCol = IsLower ? 0 : pi;
-
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,Conjugate,RhsScalar,RhsMapper,false>::run(
-          actualPanelWidth, r,
-          LhsMapper(&lhs.coeffRef(startRow,startCol), lhsStride),
-          RhsMapper(rhs + startCol, 1),
-          rhs + startRow, 1,
-          RhsScalar(-1));
-      }
-
-      for(Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = IsLower ? pi+k : pi-k-1;
-        Index s = IsLower ? pi   : i+1;
-        if (k>0)
-          rhs[i] -= (cjLhs.row(i).segment(s,k).transpose().cwiseProduct(Map<const Matrix<RhsScalar,Dynamic,1> >(rhs+s,k))).sum();
-
-        if((!(Mode & UnitDiag)) && numext::not_equal_strict(rhs[i],RhsScalar(0)))
-          rhs[i] /= cjLhs(i,i);
-      }
-    }
-  }
-};
-
-// forward and backward substitution, column-major, rhs is a vector
-template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
-struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, ColMajor>
-{
-  enum {
-    IsLower = ((Mode&Lower)==Lower)
-  };
-  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
-  {
-    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride));
-    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
-    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
-    typename internal::conditional<Conjugate,
-                                   const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>,
-                                   const LhsMap&
-                                  >::type cjLhs(lhs);
-    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-
-    for(Index pi=IsLower ? 0 : size;
-        IsLower ? pi<size : pi>0;
-        IsLower ? pi+=PanelWidth : pi-=PanelWidth)
-    {
-      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
-      Index startBlock = IsLower ? pi : pi-actualPanelWidth;
-      Index endBlock = IsLower ? pi + actualPanelWidth : 0;
-
-      for(Index k=0; k<actualPanelWidth; ++k)
-      {
-        Index i = IsLower ? pi+k : pi-k-1;
-        if(numext::not_equal_strict(rhs[i],RhsScalar(0)))
-        {
-          if(!(Mode & UnitDiag))
-            rhs[i] /= cjLhs.coeff(i,i);
-
-          Index r = actualPanelWidth - k - 1; // remaining size
-          Index s = IsLower ? i+1 : i-r;
-          if (r>0)
-            Map<Matrix<RhsScalar,Dynamic,1> >(rhs+s,r) -= rhs[i] * cjLhs.col(i).segment(s,r);
-        }
-      }
-      Index r = IsLower ? size - endBlock : startBlock; // remaining size
-      if (r > 0)
-      {
-        // let's directly call the low level product function because:
-        // 1 - it is faster to compile
-        // 2 - it is slightly faster at runtime
-        general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,Conjugate,RhsScalar,RhsMapper,false>::run(
-            r, actualPanelWidth,
-            LhsMapper(&lhs.coeffRef(endBlock,startBlock), lhsStride),
-            RhsMapper(rhs+startBlock, 1),
-            rhs+endBlock, 1, RhsScalar(-1));
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIANGULAR_SOLVER_VECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/BlasUtil.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/BlasUtil.h
deleted file mode 100644
index e16a564..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/BlasUtil.h
+++ /dev/null
@@ -1,583 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BLASUTIL_H
-#define EIGEN_BLASUTIL_H
-
-// This file contains many lightweight helper classes used to
-// implement and control fast level 2 and level 3 BLAS-like routines.
-
-namespace Eigen {
-
-namespace internal {
-
-// forward declarations
-template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs=false, bool ConjugateRhs=false>
-struct gebp_kernel;
-
-template<typename Scalar, typename Index, typename DataMapper, int nr, int StorageOrder, bool Conjugate = false, bool PanelMode=false>
-struct gemm_pack_rhs;
-
-template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, int StorageOrder, bool Conjugate = false, bool PanelMode = false>
-struct gemm_pack_lhs;
-
-template<
-  typename Index,
-  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
-  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
-  int ResStorageOrder, int ResInnerStride>
-struct general_matrix_matrix_product;
-
-template<typename Index,
-         typename LhsScalar, typename LhsMapper, int LhsStorageOrder, bool ConjugateLhs,
-         typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version=Specialized>
-struct general_matrix_vector_product;
-
-template<typename From,typename To> struct get_factor {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE To run(const From& x) { return To(x); }
-};
-
-template<typename Scalar> struct get_factor<Scalar,typename NumTraits<Scalar>::Real> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return numext::real(x); }
-};
-
-
-template<typename Scalar, typename Index>
-class BlasVectorMapper {
-  public:
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasVectorMapper(Scalar *data) : m_data(data) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
-    return m_data[i];
-  }
-  template <typename Packet, int AlignmentType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet load(Index i) const {
-    return ploadt<Packet, AlignmentType>(m_data + i);
-  }
-
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC bool aligned(Index i) const {
-    return (UIntPtr(m_data+i)%sizeof(Packet))==0;
-  }
-
-  protected:
-  Scalar* m_data;
-};
-
-template<typename Scalar, typename Index, int AlignmentType, int Incr=1>
-class BlasLinearMapper;
-
-template<typename Scalar, typename Index, int AlignmentType>
-class BlasLinearMapper<Scalar,Index,AlignmentType>
-{
-public:
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar *data, Index incr=1)
-    : m_data(data)
-  {
-    EIGEN_ONLY_USED_FOR_DEBUG(incr);
-    eigen_assert(incr==1);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(int i) const {
-    internal::prefetch(&operator()(i));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const {
-    return m_data[i];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
-    return ploadt<PacketType, AlignmentType>(m_data + i);
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType &p) const {
-    pstoret<Scalar, PacketType, AlignmentType>(m_data + i, p);
-  }
-
-protected:
-  Scalar *m_data;
-};
-
-// Lightweight helper class to access matrix coefficients.
-template<typename Scalar, typename Index, int StorageOrder, int AlignmentType = Unaligned, int Incr = 1>
-class blas_data_mapper;
-
-// TMP to help PacketBlock store implementation.
-// There's currently no known use case for PacketBlock load.
-// The default implementation assumes ColMajor order.
-// It always store each packet sequentially one `stride` apart.
-template<typename Index, typename Scalar, typename Packet, int n, int idx, int StorageOrder>
-struct PacketBlockManagement
-{
-  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, StorageOrder> pbm;
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    pbm.store(to, stride, i, j, block);
-    pstoreu<Scalar>(to + i + (j + idx)*stride, block.packet[idx]);
-  }
-};
-
-// PacketBlockManagement specialization to take care of RowMajor order without ifs.
-template<typename Index, typename Scalar, typename Packet, int n, int idx>
-struct PacketBlockManagement<Index, Scalar, Packet, n, idx, RowMajor>
-{
-  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, RowMajor> pbm;
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    pbm.store(to, stride, i, j, block);
-    pstoreu<Scalar>(to + j + (i + idx)*stride, block.packet[idx]);
-  }
-};
-
-template<typename Index, typename Scalar, typename Packet, int n, int StorageOrder>
-struct PacketBlockManagement<Index, Scalar, Packet, n, -1, StorageOrder>
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    EIGEN_UNUSED_VARIABLE(to);
-    EIGEN_UNUSED_VARIABLE(stride);
-    EIGEN_UNUSED_VARIABLE(i);
-    EIGEN_UNUSED_VARIABLE(j);
-    EIGEN_UNUSED_VARIABLE(block);
-  }
-};
-
-template<typename Index, typename Scalar, typename Packet, int n>
-struct PacketBlockManagement<Index, Scalar, Packet, n, -1, RowMajor>
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
-    EIGEN_UNUSED_VARIABLE(to);
-    EIGEN_UNUSED_VARIABLE(stride);
-    EIGEN_UNUSED_VARIABLE(i);
-    EIGEN_UNUSED_VARIABLE(j);
-    EIGEN_UNUSED_VARIABLE(block);
-  }
-};
-
-template<typename Scalar, typename Index, int StorageOrder, int AlignmentType>
-class blas_data_mapper<Scalar,Index,StorageOrder,AlignmentType,1>
-{
-public:
-  typedef BlasLinearMapper<Scalar, Index, AlignmentType> LinearMapper;
-  typedef BlasVectorMapper<Scalar, Index> VectorMapper;
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr=1)
-   : m_data(data), m_stride(stride)
-  {
-    EIGEN_ONLY_USED_FOR_DEBUG(incr);
-    eigen_assert(incr==1);
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType>
-  getSubMapper(Index i, Index j) const {
-    return blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType>(&operator()(i, j), m_stride);
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
-    return LinearMapper(&operator()(i, j));
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
-    return VectorMapper(&operator()(i, j));
-  }
-
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
-    return m_data[StorageOrder==RowMajor ? j + i*m_stride : i + j*m_stride];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
-    return ploadt<PacketType, AlignmentType>(&operator()(i, j));
-  }
-
-  template <typename PacketT, int AlignmentT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
-    return ploadt<PacketT, AlignmentT>(&operator()(i, j));
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket &p) const {
-    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
-    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
-  }
-
-  EIGEN_DEVICE_FUNC const Index stride() const { return m_stride; }
-  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; }
-
-  EIGEN_DEVICE_FUNC Index firstAligned(Index size) const {
-    if (UIntPtr(m_data)%sizeof(Scalar)) {
-      return -1;
-    }
-    return internal::first_default_aligned(m_data, size);
-  }
-
-  template<typename SubPacket, int n>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j, const PacketBlock<SubPacket, n> &block) const {
-    PacketBlockManagement<Index, Scalar, SubPacket, n, n-1, StorageOrder> pbm;
-    pbm.store(m_data, m_stride, i, j, block);
-  }
-protected:
-  Scalar* EIGEN_RESTRICT m_data;
-  const Index m_stride;
-};
-
-// Implementation of non-natural increment (i.e. inner-stride != 1)
-// The exposed API is not complete yet compared to the Incr==1 case
-// because some features makes less sense in this case.
-template<typename Scalar, typename Index, int AlignmentType, int Incr>
-class BlasLinearMapper
-{
-public:
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar *data,Index incr) : m_data(data), m_incr(incr) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(int i) const {
-    internal::prefetch(&operator()(i));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const {
-    return m_data[i*m_incr.value()];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
-    return pgather<Scalar,PacketType>(m_data + i*m_incr.value(), m_incr.value());
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType &p) const {
-    pscatter<Scalar, PacketType>(m_data + i*m_incr.value(), p, m_incr.value());
-  }
-
-protected:
-  Scalar *m_data;
-  const internal::variable_if_dynamic<Index,Incr> m_incr;
-};
-
-template<typename Scalar, typename Index, int StorageOrder, int AlignmentType,int Incr>
-class blas_data_mapper
-{
-public:
-  typedef BlasLinearMapper<Scalar, Index, AlignmentType,Incr> LinearMapper;
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr) : m_data(data), m_stride(stride), m_incr(incr) {}
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE blas_data_mapper
-  getSubMapper(Index i, Index j) const {
-    return blas_data_mapper(&operator()(i, j), m_stride, m_incr.value());
-  }
-
-  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
-    return LinearMapper(&operator()(i, j), m_incr.value());
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
-    return m_data[StorageOrder==RowMajor ? j*m_incr.value() + i*m_stride : i*m_incr.value() + j*m_stride];
-  }
-
-  template<typename PacketType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
-    return pgather<Scalar,PacketType>(&operator()(i, j),m_incr.value());
-  }
-
-  template <typename PacketT, int AlignmentT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
-    return pgather<Scalar,PacketT>(&operator()(i, j),m_incr.value());
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket &p) const {
-    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
-  }
-
-  template<typename SubPacket>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
-    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
-  }
-
-  // storePacketBlock_helper defines a way to access values inside the PacketBlock, this is essentially required by the Complex types.
-  template<typename SubPacket, typename ScalarT, int n, int idx>
-  struct storePacketBlock_helper
-  {
-    storePacketBlock_helper<SubPacket, ScalarT, n, idx-1> spbh;
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
-      spbh.store(sup, i,j,block);
-      for(int l = 0; l < unpacket_traits<SubPacket>::size; l++)
-      {
-        ScalarT *v = &sup->operator()(i+l, j+idx);
-        *v = block.packet[idx][l];
-      }
-    }
-  };
-
-  template<typename SubPacket, int n, int idx>
-  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, idx>
-  {
-    storePacketBlock_helper<SubPacket, std::complex<float>, n, idx-1> spbh;
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
-      spbh.store(sup,i,j,block);
-      for(int l = 0; l < unpacket_traits<SubPacket>::size; l++)
-      {
-        std::complex<float> *v = &sup->operator()(i+l, j+idx);
-        v->real(block.packet[idx].v[2*l+0]);
-        v->imag(block.packet[idx].v[2*l+1]);
-      }
-    }
-  };
-
-  template<typename SubPacket, int n, int idx>
-  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, idx>
-  {
-    storePacketBlock_helper<SubPacket, std::complex<double>, n, idx-1> spbh;
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
-      spbh.store(sup,i,j,block);
-      for(int l = 0; l < unpacket_traits<SubPacket>::size; l++)
-      {
-        std::complex<double> *v = &sup->operator()(i+l, j+idx);
-        v->real(block.packet[idx].v[2*l+0]);
-        v->imag(block.packet[idx].v[2*l+1]);
-      }
-    }
-  };
-
-  template<typename SubPacket, typename ScalarT, int n>
-  struct storePacketBlock_helper<SubPacket, ScalarT, n, -1>
-  {
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
-    }
-  };
-
-  template<typename SubPacket, int n>
-  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, -1>
-  {
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
-    }
-  };
-
-  template<typename SubPacket, int n>
-  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, -1>
-  {
-    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
-    }
-  };
-  // This function stores a PacketBlock on m_data, this approach is really quite slow compare to Incr=1 and should be avoided when possible.
-  template<typename SubPacket, int n>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j, const PacketBlock<SubPacket, n>&block) const {
-    storePacketBlock_helper<SubPacket, Scalar, n, n-1> spb;
-    spb.store(this, i,j,block);
-  }
-protected:
-  Scalar* EIGEN_RESTRICT m_data;
-  const Index m_stride;
-  const internal::variable_if_dynamic<Index,Incr> m_incr;
-};
-
-// lightweight helper class to access matrix coefficients (const version)
-template<typename Scalar, typename Index, int StorageOrder>
-class const_blas_data_mapper : public blas_data_mapper<const Scalar, Index, StorageOrder> {
-  public:
-  EIGEN_ALWAYS_INLINE const_blas_data_mapper(const Scalar *data, Index stride) : blas_data_mapper<const Scalar, Index, StorageOrder>(data, stride) {}
-
-  EIGEN_ALWAYS_INLINE const_blas_data_mapper<Scalar, Index, StorageOrder> getSubMapper(Index i, Index j) const {
-    return const_blas_data_mapper<Scalar, Index, StorageOrder>(&(this->operator()(i, j)), this->m_stride);
-  }
-};
-
-
-/* Helper class to analyze the factors of a Product expression.
- * In particular it allows to pop out operator-, scalar multiples,
- * and conjugate */
-template<typename XprType> struct blas_traits
-{
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef const XprType& ExtractType;
-  typedef XprType _ExtractType;
-  enum {
-    IsComplex = NumTraits<Scalar>::IsComplex,
-    IsTransposed = false,
-    NeedToConjugate = false,
-    HasUsableDirectAccess = (    (int(XprType::Flags)&DirectAccessBit)
-                              && (   bool(XprType::IsVectorAtCompileTime)
-                                  || int(inner_stride_at_compile_time<XprType>::ret) == 1)
-                             ) ?  1 : 0,
-    HasScalarFactor = false
-  };
-  typedef typename conditional<bool(HasUsableDirectAccess),
-    ExtractType,
-    typename _ExtractType::PlainObject
-    >::type DirectLinearAccessType;
-  static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) { return x; }
-  static inline EIGEN_DEVICE_FUNC const Scalar extractScalarFactor(const XprType&) { return Scalar(1); }
-};
-
-// pop conjugate
-template<typename Scalar, typename NestedXpr>
-struct blas_traits<CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> XprType;
-  typedef typename Base::ExtractType ExtractType;
-
-  enum {
-    IsComplex = NumTraits<Scalar>::IsComplex,
-    NeedToConjugate = Base::NeedToConjugate ? 0 : IsComplex
-  };
-  static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
-  static inline Scalar extractScalarFactor(const XprType& x) { return conj(Base::extractScalarFactor(x.nestedExpression())); }
-};
-
-// pop scalar multiple
-template<typename Scalar, typename NestedXpr, typename Plain>
-struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  enum {
-    HasScalarFactor = true
-  };
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> XprType;
-  typedef typename Base::ExtractType ExtractType;
-  static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) { return Base::extract(x.rhs()); }
-  static inline EIGEN_DEVICE_FUNC Scalar extractScalarFactor(const XprType& x)
-  { return x.lhs().functor().m_other * Base::extractScalarFactor(x.rhs()); }
-};
-template<typename Scalar, typename NestedXpr, typename Plain>
-struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > >
- : blas_traits<NestedXpr>
-{
-  enum {
-    HasScalarFactor = true
-  };
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > XprType;
-  typedef typename Base::ExtractType ExtractType;
-  static inline ExtractType extract(const XprType& x) { return Base::extract(x.lhs()); }
-  static inline Scalar extractScalarFactor(const XprType& x)
-  { return Base::extractScalarFactor(x.lhs()) * x.rhs().functor().m_other; }
-};
-template<typename Scalar, typename Plain1, typename Plain2>
-struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1>,
-                                                            const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain2> > >
- : blas_traits<CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1> >
-{};
-
-// pop opposite
-template<typename Scalar, typename NestedXpr>
-struct blas_traits<CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  enum {
-    HasScalarFactor = true
-  };
-  typedef blas_traits<NestedXpr> Base;
-  typedef CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> XprType;
-  typedef typename Base::ExtractType ExtractType;
-  static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
-  static inline Scalar extractScalarFactor(const XprType& x)
-  { return - Base::extractScalarFactor(x.nestedExpression()); }
-};
-
-// pop/push transpose
-template<typename NestedXpr>
-struct blas_traits<Transpose<NestedXpr> >
- : blas_traits<NestedXpr>
-{
-  typedef typename NestedXpr::Scalar Scalar;
-  typedef blas_traits<NestedXpr> Base;
-  typedef Transpose<NestedXpr> XprType;
-  typedef Transpose<const typename Base::_ExtractType>  ExtractType; // const to get rid of a compile error; anyway blas traits are only used on the RHS
-  typedef Transpose<const typename Base::_ExtractType> _ExtractType;
-  typedef typename conditional<bool(Base::HasUsableDirectAccess),
-    ExtractType,
-    typename ExtractType::PlainObject
-    >::type DirectLinearAccessType;
-  enum {
-    IsTransposed = Base::IsTransposed ? 0 : 1
-  };
-  static inline ExtractType extract(const XprType& x) { return ExtractType(Base::extract(x.nestedExpression())); }
-  static inline Scalar extractScalarFactor(const XprType& x) { return Base::extractScalarFactor(x.nestedExpression()); }
-};
-
-template<typename T>
-struct blas_traits<const T>
-     : blas_traits<T>
-{};
-
-template<typename T, bool HasUsableDirectAccess=blas_traits<T>::HasUsableDirectAccess>
-struct extract_data_selector {
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static const typename T::Scalar* run(const T& m)
-  {
-    return blas_traits<T>::extract(m).data();
-  }
-};
-
-template<typename T>
-struct extract_data_selector<T,false> {
-  static typename T::Scalar* run(const T&) { return 0; }
-};
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename T::Scalar* extract_data(const T& m)
-{
-  return extract_data_selector<T>::run(m);
-}
-
-/**
- * \c combine_scalar_factors extracts and multiplies factors from GEMM and GEMV products.
- * There is a specialization for booleans
- */
-template<typename ResScalar, typename Lhs, typename Rhs>
-struct combine_scalar_factors_impl
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static ResScalar run(const Lhs& lhs, const Rhs& rhs)
-  {
-    return blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static ResScalar run(const ResScalar& alpha, const Lhs& lhs, const Rhs& rhs)
-  {
-    return alpha * blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-};
-template<typename Lhs, typename Rhs>
-struct combine_scalar_factors_impl<bool, Lhs, Rhs>
-{
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static bool run(const Lhs& lhs, const Rhs& rhs)
-  {
-    return blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static bool run(const bool& alpha, const Lhs& lhs, const Rhs& rhs)
-  {
-    return alpha && blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
-  }
-};
-
-template<typename ResScalar, typename Lhs, typename Rhs>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const ResScalar& alpha, const Lhs& lhs, const Rhs& rhs)
-{
-  return combine_scalar_factors_impl<ResScalar,Lhs,Rhs>::run(alpha, lhs, rhs);
-}
-template<typename ResScalar, typename Lhs, typename Rhs>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const Lhs& lhs, const Rhs& rhs)
-{
-  return combine_scalar_factors_impl<ResScalar,Lhs,Rhs>::run(lhs, rhs);
-}
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BLASUTIL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
deleted file mode 100644
index af4e696..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
+++ /dev/null
@@ -1,512 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONFIGURE_VECTORIZATION_H
-#define EIGEN_CONFIGURE_VECTORIZATION_H
-
-//------------------------------------------------------------------------------------------
-// Static and dynamic alignment control
-//
-// The main purpose of this section is to define EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES
-// as the maximal boundary in bytes on which dynamically and statically allocated data may be alignment respectively.
-// The values of EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES can be specified by the user. If not,
-// a default value is automatically computed based on architecture, compiler, and OS.
-//
-// This section also defines macros EIGEN_ALIGN_TO_BOUNDARY(N) and the shortcuts EIGEN_ALIGN{8,16,32,_MAX}
-// to be used to declare statically aligned buffers.
-//------------------------------------------------------------------------------------------
-
-
-/* EIGEN_ALIGN_TO_BOUNDARY(n) forces data to be n-byte aligned. This is used to satisfy SIMD requirements.
- * However, we do that EVEN if vectorization (EIGEN_VECTORIZE) is disabled,
- * so that vectorization doesn't affect binary compatibility.
- *
- * If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link
- * vectorized and non-vectorized code.
- * 
- * FIXME: this code can be cleaned up once we switch to proper C++11 only.
- */
-#if (defined EIGEN_CUDACC)
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __align__(n)
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#elif EIGEN_HAS_ALIGNAS
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) alignas(n)
-  #define EIGEN_ALIGNOF(x) alignof(x)
-#elif EIGEN_COMP_GNUC || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n)))
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#elif EIGEN_COMP_MSVC
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __declspec(align(n))
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#elif EIGEN_COMP_SUNCC
-  // FIXME not sure about this one:
-  #define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n)))
-  #define EIGEN_ALIGNOF(x) __alignof(x)
-#else
-  #error Please tell me what is the equivalent of alignas(n) and alignof(x) for your compiler
-#endif
-
-// If the user explicitly disable vectorization, then we also disable alignment
-#if defined(EIGEN_DONT_VECTORIZE)
-  #if defined(EIGEN_GPUCC)
-    // GPU code is always vectorized and requires memory alignment for
-    // statically allocated buffers.
-    #define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
-  #else
-    #define EIGEN_IDEAL_MAX_ALIGN_BYTES 0
-  #endif
-#elif defined(__AVX512F__)
-  // 64 bytes static alignment is preferred only if really required
-  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 64
-#elif defined(__AVX__)
-  // 32 bytes static alignment is preferred only if really required
-  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 32
-#else
-  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
-#endif
-
-
-// EIGEN_MIN_ALIGN_BYTES defines the minimal value for which the notion of explicit alignment makes sense
-#define EIGEN_MIN_ALIGN_BYTES 16
-
-// Defined the boundary (in bytes) on which the data needs to be aligned. Note
-// that unless EIGEN_ALIGN is defined and not equal to 0, the data may not be
-// aligned at all regardless of the value of this #define.
-
-#if (defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN))  && defined(EIGEN_MAX_STATIC_ALIGN_BYTES) && EIGEN_MAX_STATIC_ALIGN_BYTES>0
-#error EIGEN_MAX_STATIC_ALIGN_BYTES and EIGEN_DONT_ALIGN[_STATICALLY] are both defined with EIGEN_MAX_STATIC_ALIGN_BYTES!=0. Use EIGEN_MAX_STATIC_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN_STATICALLY.
-#endif
-
-// EIGEN_DONT_ALIGN_STATICALLY and EIGEN_DONT_ALIGN are deprecated
-// They imply EIGEN_MAX_STATIC_ALIGN_BYTES=0
-#if defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN)
-  #ifdef EIGEN_MAX_STATIC_ALIGN_BYTES
-    #undef EIGEN_MAX_STATIC_ALIGN_BYTES
-  #endif
-  #define EIGEN_MAX_STATIC_ALIGN_BYTES 0
-#endif
-
-#ifndef EIGEN_MAX_STATIC_ALIGN_BYTES
-
-  // Try to automatically guess what is the best default value for EIGEN_MAX_STATIC_ALIGN_BYTES
-
-  // 16 byte alignment is only useful for vectorization. Since it affects the ABI, we need to enable
-  // 16 byte alignment on all platforms where vectorization might be enabled. In theory we could always
-  // enable alignment, but it can be a cause of problems on some platforms, so we just disable it in
-  // certain common platform (compiler+architecture combinations) to avoid these problems.
-  // Only static alignment is really problematic (relies on nonstandard compiler extensions),
-  // try to keep heap alignment even when we have to disable static alignment.
-  #if EIGEN_COMP_GNUC && !(EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64 || EIGEN_ARCH_PPC || EIGEN_ARCH_IA64 || EIGEN_ARCH_MIPS)
-  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1
-  #elif EIGEN_ARCH_ARM_OR_ARM64 && EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_MOST(4, 6)
-  // Old versions of GCC on ARM, at least 4.4, were once seen to have buggy static alignment support.
-  // Not sure which version fixed it, hopefully it doesn't affect 4.7, which is still somewhat in use.
-  // 4.8 and newer seem definitely unaffected.
-  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1
-  #else
-  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 0
-  #endif
-
-  // static alignment is completely disabled with GCC 3, Sun Studio, and QCC/QNX
-  #if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT \
-  && !EIGEN_GCC3_OR_OLDER \
-  && !EIGEN_COMP_SUNCC \
-  && !EIGEN_OS_QNX
-    #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 1
-  #else
-    #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 0
-  #endif
-
-  #if EIGEN_ARCH_WANTS_STACK_ALIGNMENT
-    #define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
-  #else
-    #define EIGEN_MAX_STATIC_ALIGN_BYTES 0
-  #endif
-
-#endif
-
-// If EIGEN_MAX_ALIGN_BYTES is defined, then it is considered as an upper bound for EIGEN_MAX_STATIC_ALIGN_BYTES
-#if defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES<EIGEN_MAX_STATIC_ALIGN_BYTES
-#undef EIGEN_MAX_STATIC_ALIGN_BYTES
-#define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
-#endif
-
-#if EIGEN_MAX_STATIC_ALIGN_BYTES==0 && !defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
-  #define EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
-#endif
-
-// At this stage, EIGEN_MAX_STATIC_ALIGN_BYTES>0 is the true test whether we want to align arrays on the stack or not.
-// It takes into account both the user choice to explicitly enable/disable alignment (by setting EIGEN_MAX_STATIC_ALIGN_BYTES)
-// and the architecture config (EIGEN_ARCH_WANTS_STACK_ALIGNMENT).
-// Henceforth, only EIGEN_MAX_STATIC_ALIGN_BYTES should be used.
-
-
-// Shortcuts to EIGEN_ALIGN_TO_BOUNDARY
-#define EIGEN_ALIGN8  EIGEN_ALIGN_TO_BOUNDARY(8)
-#define EIGEN_ALIGN16 EIGEN_ALIGN_TO_BOUNDARY(16)
-#define EIGEN_ALIGN32 EIGEN_ALIGN_TO_BOUNDARY(32)
-#define EIGEN_ALIGN64 EIGEN_ALIGN_TO_BOUNDARY(64)
-#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
-#define EIGEN_ALIGN_MAX EIGEN_ALIGN_TO_BOUNDARY(EIGEN_MAX_STATIC_ALIGN_BYTES)
-#else
-#define EIGEN_ALIGN_MAX
-#endif
-
-
-// Dynamic alignment control
-
-#if defined(EIGEN_DONT_ALIGN) && defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES>0
-#error EIGEN_MAX_ALIGN_BYTES and EIGEN_DONT_ALIGN are both defined with EIGEN_MAX_ALIGN_BYTES!=0. Use EIGEN_MAX_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN.
-#endif
-
-#ifdef EIGEN_DONT_ALIGN
-  #ifdef EIGEN_MAX_ALIGN_BYTES
-    #undef EIGEN_MAX_ALIGN_BYTES
-  #endif
-  #define EIGEN_MAX_ALIGN_BYTES 0
-#elif !defined(EIGEN_MAX_ALIGN_BYTES)
-  #define EIGEN_MAX_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
-#endif
-
-#if EIGEN_IDEAL_MAX_ALIGN_BYTES > EIGEN_MAX_ALIGN_BYTES
-#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
-#else
-#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
-#endif
-
-
-#ifndef EIGEN_UNALIGNED_VECTORIZE
-#define EIGEN_UNALIGNED_VECTORIZE 1
-#endif
-
-//----------------------------------------------------------------------
-
-// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
-// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
-#if EIGEN_MAX_ALIGN_BYTES==0
-  #ifndef EIGEN_DONT_VECTORIZE
-    #define EIGEN_DONT_VECTORIZE
-  #endif
-#endif
-
-
-// The following (except #include <malloc.h> and _M_IX86_FP ??) can likely be
-// removed as gcc 4.1 and msvc 2008 are not supported anyways.
-#if EIGEN_COMP_MSVC
-  #include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
-  #if (EIGEN_COMP_MSVC >= 1500) // 2008 or later
-    // a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
-    #if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
-      #define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
-    #endif
-  #endif
-#else
-  #if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )
-    #define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
-  #endif
-#endif
-
-#if !(defined(EIGEN_DONT_VECTORIZE) || defined(EIGEN_GPUCC))
-
-  #if defined (EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
-
-    // Defines symbols for compile-time detection of which instructions are
-    // used.
-    // EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_SSE
-    #define EIGEN_VECTORIZE_SSE2
-
-    // Detect sse3/ssse3/sse4:
-    // gcc and icc defines __SSE3__, ...
-    // there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
-    // want to force the use of those instructions with msvc.
-    #ifdef __SSE3__
-      #define EIGEN_VECTORIZE_SSE3
-    #endif
-    #ifdef __SSSE3__
-      #define EIGEN_VECTORIZE_SSSE3
-    #endif
-    #ifdef __SSE4_1__
-      #define EIGEN_VECTORIZE_SSE4_1
-    #endif
-    #ifdef __SSE4_2__
-      #define EIGEN_VECTORIZE_SSE4_2
-    #endif
-    #ifdef __AVX__
-      #ifndef EIGEN_USE_SYCL 
-        #define EIGEN_VECTORIZE_AVX
-      #endif
-      #define EIGEN_VECTORIZE_SSE3
-      #define EIGEN_VECTORIZE_SSSE3
-      #define EIGEN_VECTORIZE_SSE4_1
-      #define EIGEN_VECTORIZE_SSE4_2
-    #endif
-    #ifdef __AVX2__
-      #ifndef EIGEN_USE_SYCL 
-        #define EIGEN_VECTORIZE_AVX2
-        #define EIGEN_VECTORIZE_AVX
-      #endif
-      #define EIGEN_VECTORIZE_SSE3
-      #define EIGEN_VECTORIZE_SSSE3
-      #define EIGEN_VECTORIZE_SSE4_1
-      #define EIGEN_VECTORIZE_SSE4_2
-    #endif
-    #if defined(__FMA__) || (EIGEN_COMP_MSVC && defined(__AVX2__))
-      // MSVC does not expose a switch dedicated for FMA
-      // For MSVC, AVX2 => FMA
-      #define EIGEN_VECTORIZE_FMA
-    #endif
-    #if defined(__AVX512F__)
-      #ifndef EIGEN_VECTORIZE_FMA
-      #if EIGEN_COMP_GNUC
-      #error Please add -mfma to your compiler flags: compiling with -mavx512f alone without SSE/AVX FMA is not supported (bug 1638).
-      #else
-      #error Please enable FMA in your compiler flags (e.g. -mfma): compiling with AVX512 alone without SSE/AVX FMA is not supported (bug 1638).
-      #endif
-      #endif
-      #ifndef EIGEN_USE_SYCL
-        #define EIGEN_VECTORIZE_AVX512
-        #define EIGEN_VECTORIZE_AVX2
-        #define EIGEN_VECTORIZE_AVX
-      #endif
-      #define EIGEN_VECTORIZE_FMA
-      #define EIGEN_VECTORIZE_SSE3
-      #define EIGEN_VECTORIZE_SSSE3
-      #define EIGEN_VECTORIZE_SSE4_1
-      #define EIGEN_VECTORIZE_SSE4_2
-      #ifndef EIGEN_USE_SYCL
-        #ifdef __AVX512DQ__
-          #define EIGEN_VECTORIZE_AVX512DQ
-        #endif
-        #ifdef __AVX512ER__
-          #define EIGEN_VECTORIZE_AVX512ER
-        #endif
-        #ifdef __AVX512BF16__
-          #define EIGEN_VECTORIZE_AVX512BF16
-        #endif
-      #endif
-    #endif
-
-    // Disable AVX support on broken xcode versions
-    #if defined(__apple_build_version__) && (__apple_build_version__ == 11000033 ) && ( __MAC_OS_X_VERSION_MIN_REQUIRED == 101500 )
-      // A nasty bug in the clang compiler shipped with xcode in a common compilation situation
-      // when XCode 11.0 and Mac deployment target macOS 10.15 is https://trac.macports.org/ticket/58776#no1
-      #ifdef EIGEN_VECTORIZE_AVX
-        #undef EIGEN_VECTORIZE_AVX
-        #warning "Disabling AVX support: clang compiler shipped with XCode 11.[012] generates broken assembly with -macosx-version-min=10.15 and AVX enabled. "
-        #ifdef EIGEN_VECTORIZE_AVX2
-          #undef EIGEN_VECTORIZE_AVX2
-        #endif
-        #ifdef EIGEN_VECTORIZE_FMA
-          #undef EIGEN_VECTORIZE_FMA
-        #endif
-        #ifdef EIGEN_VECTORIZE_AVX512
-          #undef EIGEN_VECTORIZE_AVX512
-        #endif
-        #ifdef EIGEN_VECTORIZE_AVX512DQ
-          #undef EIGEN_VECTORIZE_AVX512DQ
-        #endif
-        #ifdef EIGEN_VECTORIZE_AVX512ER
-          #undef EIGEN_VECTORIZE_AVX512ER
-        #endif
-      #endif
-      // NOTE: Confirmed test failures in XCode 11.0, and XCode 11.2 with  -macosx-version-min=10.15 and AVX
-      // NOTE using -macosx-version-min=10.15 with Xcode 11.0 results in runtime segmentation faults in many tests, 11.2 produce core dumps in 3 tests
-      // NOTE using -macosx-version-min=10.14 produces functioning and passing tests in all cases
-      // NOTE __clang_version__ "11.0.0 (clang-1100.0.33.8)"  XCode 11.0 <- Produces many segfault and core dumping tests
-      //                                                                    with  -macosx-version-min=10.15 and AVX
-      // NOTE __clang_version__ "11.0.0 (clang-1100.0.33.12)" XCode 11.2 <- Produces 3 core dumping tests with  
-      //                                                                    -macosx-version-min=10.15 and AVX
-    #endif
-
-    // include files
-
-    // This extern "C" works around a MINGW-w64 compilation issue
-    // https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
-    // In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
-    // However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
-    // with conflicting linkage.  The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
-    // so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
-    // notice that since these are C headers, the extern "C" is theoretically needed anyways.
-    extern "C" {
-      // In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
-      // Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
-      #if EIGEN_COMP_ICC >= 1110
-        #include <immintrin.h>
-      #else
-        #include <mmintrin.h>
-        #include <emmintrin.h>
-        #include <xmmintrin.h>
-        #ifdef  EIGEN_VECTORIZE_SSE3
-        #include <pmmintrin.h>
-        #endif
-        #ifdef EIGEN_VECTORIZE_SSSE3
-        #include <tmmintrin.h>
-        #endif
-        #ifdef EIGEN_VECTORIZE_SSE4_1
-        #include <smmintrin.h>
-        #endif
-        #ifdef EIGEN_VECTORIZE_SSE4_2
-        #include <nmmintrin.h>
-        #endif
-        #if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)
-        #include <immintrin.h>
-        #endif
-      #endif
-    } // end extern "C"
-
-  #elif defined __VSX__
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_VSX
-    #include <altivec.h>
-    // We need to #undef all these ugly tokens defined in <altivec.h>
-    // => use __vector instead of vector
-    #undef bool
-    #undef vector
-    #undef pixel
-
-  #elif defined __ALTIVEC__
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_ALTIVEC
-    #include <altivec.h>
-    // We need to #undef all these ugly tokens defined in <altivec.h>
-    // => use __vector instead of vector
-    #undef bool
-    #undef vector
-    #undef pixel
-
-  #elif ((defined  __ARM_NEON) || (defined __ARM_NEON__)) && !(defined EIGEN_ARM64_USE_SVE)
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_NEON
-    #include <arm_neon.h>
-
-  // We currently require SVE to be enabled explicitly via EIGEN_ARM64_USE_SVE and
-  // will not select the backend automatically
-  #elif (defined __ARM_FEATURE_SVE) && (defined EIGEN_ARM64_USE_SVE)
-
-    #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_SVE
-    #include <arm_sve.h>
-
-    // Since we depend on knowing SVE vector lengths at compile-time, we need
-    // to ensure a fixed lengths is set
-    #if defined __ARM_FEATURE_SVE_BITS
-      #define EIGEN_ARM64_SVE_VL __ARM_FEATURE_SVE_BITS
-    #else
-#error "Eigen requires a fixed SVE lector length but EIGEN_ARM64_SVE_VL is not set."
-#endif
-
-#elif (defined __s390x__ && defined __VEC__)
-
-#define EIGEN_VECTORIZE
-#define EIGEN_VECTORIZE_ZVECTOR
-#include <vecintrin.h>
-
-#elif defined __mips_msa
-
-// Limit MSA optimizations to little-endian CPUs for now.
-// TODO: Perhaps, eventually support MSA optimizations on big-endian CPUs?
-#if defined(__BYTE_ORDER__) && (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
-#if defined(__LP64__)
-#define EIGEN_MIPS_64
-#else
-#define EIGEN_MIPS_32
-#endif
-#define EIGEN_VECTORIZE
-#define EIGEN_VECTORIZE_MSA
-#include <msa.h>
-#endif
-
-#endif
-#endif
-
-// Following the Arm ACLE arm_neon.h should also include arm_fp16.h but not all
-// compilers seem to follow this. We therefore include it explicitly.
-// See also: https://bugs.llvm.org/show_bug.cgi?id=47955
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
-  #include <arm_fp16.h>
-#endif
-
-#if defined(__F16C__) && (!defined(EIGEN_GPUCC) && (!defined(EIGEN_COMP_CLANG) || EIGEN_COMP_CLANG>=380))
-  // We can use the optimized fp16 to float and float to fp16 conversion routines
-  #define EIGEN_HAS_FP16_C
-
-  #if defined(EIGEN_COMP_CLANG)
-    // Workaround for clang: The FP16C intrinsics for clang are included by
-    // immintrin.h, as opposed to emmintrin.h as suggested by Intel:
-    // https://software.intel.com/sites/landingpage/IntrinsicsGuide/#othertechs=FP16C&expand=1711
-    #include <immintrin.h>
-  #endif
-#endif
-
-#if defined EIGEN_CUDACC
-  #define EIGEN_VECTORIZE_GPU
-  #include <vector_types.h>
-  #if EIGEN_CUDA_SDK_VER >= 70500
-    #define EIGEN_HAS_CUDA_FP16
-  #endif
-#endif
-
-#if defined(EIGEN_HAS_CUDA_FP16)
-  #include <cuda_runtime_api.h>
-  #include <cuda_fp16.h>
-#endif
-
-#if defined(EIGEN_HIPCC)
-  #define EIGEN_VECTORIZE_GPU
-  #include <hip/hip_vector_types.h>
-  #define EIGEN_HAS_HIP_FP16
-  #include <hip/hip_fp16.h>
-#endif
-
-
-/** \brief Namespace containing all symbols from the %Eigen library. */
-namespace Eigen {
-
-inline static const char *SimdInstructionSetsInUse(void) {
-#if defined(EIGEN_VECTORIZE_AVX512)
-  return "AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
-#elif defined(EIGEN_VECTORIZE_AVX)
-  return "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
-#elif defined(EIGEN_VECTORIZE_SSE4_2)
-  return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
-#elif defined(EIGEN_VECTORIZE_SSE4_1)
-  return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
-#elif defined(EIGEN_VECTORIZE_SSSE3)
-  return "SSE, SSE2, SSE3, SSSE3";
-#elif defined(EIGEN_VECTORIZE_SSE3)
-  return "SSE, SSE2, SSE3";
-#elif defined(EIGEN_VECTORIZE_SSE2)
-  return "SSE, SSE2";
-#elif defined(EIGEN_VECTORIZE_ALTIVEC)
-  return "AltiVec";
-#elif defined(EIGEN_VECTORIZE_VSX)
-  return "VSX";
-#elif defined(EIGEN_VECTORIZE_NEON)
-  return "ARM NEON";
-#elif defined(EIGEN_VECTORIZE_SVE)
-  return "ARM SVE";
-#elif defined(EIGEN_VECTORIZE_ZVECTOR)
-  return "S390X ZVECTOR";
-#elif defined(EIGEN_VECTORIZE_MSA)
-  return "MIPS MSA";
-#else
-  return "None";
-#endif
-}
-
-} // end namespace Eigen
-
-
-#endif // EIGEN_CONFIGURE_VECTORIZATION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Constants.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Constants.h
deleted file mode 100644
index 35dcaa7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Constants.h
+++ /dev/null
@@ -1,563 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2020, Arm Limited and Contributors
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONSTANTS_H
-#define EIGEN_CONSTANTS_H
-
-namespace Eigen {
-
-/** This value means that a positive quantity (e.g., a size) is not known at compile-time, and that instead the value is
-  * stored in some runtime variable.
-  *
-  * Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix.
-  */
-const int Dynamic = -1;
-
-/** This value means that a signed quantity (e.g., a signed index) is not known at compile-time, and that instead its value
-  * has to be specified at runtime.
-  */
-const int DynamicIndex = 0xffffff;
-
-/** This value means that the increment to go from one value to another in a sequence is not constant for each step.
-  */
-const int UndefinedIncr = 0xfffffe;
-
-/** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>().
-  * The value Infinity there means the L-infinity norm.
-  */
-const int Infinity = -1;
-
-/** This value means that the cost to evaluate an expression coefficient is either very expensive or
-  * cannot be known at compile time.
-  *
-  * This value has to be positive to (1) simplify cost computation, and (2) allow to distinguish between a very expensive and very very expensive expressions.
-  * It thus must also be large enough to make sure unrolling won't happen and that sub expressions will be evaluated, but not too large to avoid overflow.
-  */
-const int HugeCost = 10000;
-
-/** \defgroup flags Flags
-  * \ingroup Core_Module
-  *
-  * These are the possible bits which can be OR'ed to constitute the flags of a matrix or
-  * expression.
-  *
-  * It is important to note that these flags are a purely compile-time notion. They are a compile-time property of
-  * an expression type, implemented as enum's. They are not stored in memory at runtime, and they do not incur any
-  * runtime overhead.
-  *
-  * \sa MatrixBase::Flags
-  */
-
-/** \ingroup flags
-  *
-  * for a matrix, this means that the storage order is row-major.
-  * If this bit is not set, the storage order is column-major.
-  * For an expression, this determines the storage order of
-  * the matrix created by evaluation of that expression.
-  * \sa \blank  \ref TopicStorageOrders */
-const unsigned int RowMajorBit = 0x1;
-
-/** \ingroup flags
-  * means the expression should be evaluated by the calling expression */
-const unsigned int EvalBeforeNestingBit = 0x2;
-
-/** \ingroup flags
-  * \deprecated
-  * means the expression should be evaluated before any assignment */
-EIGEN_DEPRECATED
-const unsigned int EvalBeforeAssigningBit = 0x4; // FIXME deprecated
-
-/** \ingroup flags
-  *
-  * Short version: means the expression might be vectorized
-  *
-  * Long version: means that the coefficients can be handled by packets
-  * and start at a memory location whose alignment meets the requirements
-  * of the present CPU architecture for optimized packet access. In the fixed-size
-  * case, there is the additional condition that it be possible to access all the
-  * coefficients by packets (this implies the requirement that the size be a multiple of 16 bytes,
-  * and that any nontrivial strides don't break the alignment). In the dynamic-size case,
-  * there is no such condition on the total size and strides, so it might not be possible to access
-  * all coeffs by packets.
-  *
-  * \note This bit can be set regardless of whether vectorization is actually enabled.
-  *       To check for actual vectorizability, see \a ActualPacketAccessBit.
-  */
-const unsigned int PacketAccessBit = 0x8;
-
-#ifdef EIGEN_VECTORIZE
-/** \ingroup flags
-  *
-  * If vectorization is enabled (EIGEN_VECTORIZE is defined) this constant
-  * is set to the value \a PacketAccessBit.
-  *
-  * If vectorization is not enabled (EIGEN_VECTORIZE is not defined) this constant
-  * is set to the value 0.
-  */
-const unsigned int ActualPacketAccessBit = PacketAccessBit;
-#else
-const unsigned int ActualPacketAccessBit = 0x0;
-#endif
-
-/** \ingroup flags
-  *
-  * Short version: means the expression can be seen as 1D vector.
-  *
-  * Long version: means that one can access the coefficients
-  * of this expression by coeff(int), and coeffRef(int) in the case of a lvalue expression. These
-  * index-based access methods are guaranteed
-  * to not have to do any runtime computation of a (row, col)-pair from the index, so that it
-  * is guaranteed that whenever it is available, index-based access is at least as fast as
-  * (row,col)-based access. Expressions for which that isn't possible don't have the LinearAccessBit.
-  *
-  * If both PacketAccessBit and LinearAccessBit are set, then the
-  * packets of this expression can be accessed by packet(int), and writePacket(int) in the case of a
-  * lvalue expression.
-  *
-  * Typically, all vector expressions have the LinearAccessBit, but there is one exception:
-  * Product expressions don't have it, because it would be troublesome for vectorization, even when the
-  * Product is a vector expression. Thus, vector Product expressions allow index-based coefficient access but
-  * not index-based packet access, so they don't have the LinearAccessBit.
-  */
-const unsigned int LinearAccessBit = 0x10;
-
-/** \ingroup flags
-  *
-  * Means the expression has a coeffRef() method, i.e. is writable as its individual coefficients are directly addressable.
-  * This rules out read-only expressions.
-  *
-  * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but note
-  * the other:
-  *   \li writable expressions that don't have a very simple memory layout as a strided array, have LvalueBit but not DirectAccessBit
-  *   \li Map-to-const expressions, for example Map<const Matrix>, have DirectAccessBit but not LvalueBit
-  *
-  * Expressions having LvalueBit also have their coeff() method returning a const reference instead of returning a new value.
-  */
-const unsigned int LvalueBit = 0x20;
-
-/** \ingroup flags
-  *
-  * Means that the underlying array of coefficients can be directly accessed as a plain strided array. The memory layout
-  * of the array of coefficients must be exactly the natural one suggested by rows(), cols(),
-  * outerStride(), innerStride(), and the RowMajorBit. This rules out expressions such as Diagonal, whose coefficients,
-  * though referencable, do not have such a regular memory layout.
-  *
-  * See the comment on LvalueBit for an explanation of how LvalueBit and DirectAccessBit are mutually orthogonal.
-  */
-const unsigned int DirectAccessBit = 0x40;
-
-/** \deprecated \ingroup flags
-  *
-  * means the first coefficient packet is guaranteed to be aligned.
-  * An expression cannot have the AlignedBit without the PacketAccessBit flag.
-  * In other words, this means we are allow to perform an aligned packet access to the first element regardless
-  * of the expression kind:
-  * \code
-  * expression.packet<Aligned>(0);
-  * \endcode
-  */
-EIGEN_DEPRECATED const unsigned int AlignedBit = 0x80;
-
-const unsigned int NestByRefBit = 0x100;
-
-/** \ingroup flags
-  *
-  * for an expression, this means that the storage order
-  * can be either row-major or column-major.
-  * The precise choice will be decided at evaluation time or when
-  * combined with other expressions.
-  * \sa \blank  \ref RowMajorBit, \ref TopicStorageOrders */
-const unsigned int NoPreferredStorageOrderBit = 0x200;
-
-/** \ingroup flags
-  *
-  * Means that the underlying coefficients can be accessed through pointers to the sparse (un)compressed storage format,
-  * that is, the expression provides:
-  * \code
-    inline const Scalar* valuePtr() const;
-    inline const Index* innerIndexPtr() const;
-    inline const Index* outerIndexPtr() const;
-    inline const Index* innerNonZeroPtr() const;
-    \endcode
-  */
-const unsigned int CompressedAccessBit = 0x400;
-
-
-// list of flags that are inherited by default
-const unsigned int HereditaryBits = RowMajorBit
-                                  | EvalBeforeNestingBit;
-
-/** \defgroup enums Enumerations
-  * \ingroup Core_Module
-  *
-  * Various enumerations used in %Eigen. Many of these are used as template parameters.
-  */
-
-/** \ingroup enums
-  * Enum containing possible values for the \c Mode or \c UpLo parameter of
-  * MatrixBase::selfadjointView() and MatrixBase::triangularView(), and selfadjoint solvers. */
-enum UpLoType {
-  /** View matrix as a lower triangular matrix. */
-  Lower=0x1,                      
-  /** View matrix as an upper triangular matrix. */
-  Upper=0x2,                      
-  /** %Matrix has ones on the diagonal; to be used in combination with #Lower or #Upper. */
-  UnitDiag=0x4, 
-  /** %Matrix has zeros on the diagonal; to be used in combination with #Lower or #Upper. */
-  ZeroDiag=0x8,
-  /** View matrix as a lower triangular matrix with ones on the diagonal. */
-  UnitLower=UnitDiag|Lower, 
-  /** View matrix as an upper triangular matrix with ones on the diagonal. */
-  UnitUpper=UnitDiag|Upper,
-  /** View matrix as a lower triangular matrix with zeros on the diagonal. */
-  StrictlyLower=ZeroDiag|Lower, 
-  /** View matrix as an upper triangular matrix with zeros on the diagonal. */
-  StrictlyUpper=ZeroDiag|Upper,
-  /** Used in BandMatrix and SelfAdjointView to indicate that the matrix is self-adjoint. */
-  SelfAdjoint=0x10,
-  /** Used to support symmetric, non-selfadjoint, complex matrices. */
-  Symmetric=0x20
-};
-
-/** \ingroup enums
-  * Enum for indicating whether a buffer is aligned or not. */
-enum AlignmentType {
-  Unaligned=0,        /**< Data pointer has no specific alignment. */
-  Aligned8=8,         /**< Data pointer is aligned on a 8 bytes boundary. */
-  Aligned16=16,       /**< Data pointer is aligned on a 16 bytes boundary. */
-  Aligned32=32,       /**< Data pointer is aligned on a 32 bytes boundary. */
-  Aligned64=64,       /**< Data pointer is aligned on a 64 bytes boundary. */
-  Aligned128=128,     /**< Data pointer is aligned on a 128 bytes boundary. */
-  AlignedMask=255,
-  Aligned=16,         /**< \deprecated Synonym for Aligned16. */
-#if EIGEN_MAX_ALIGN_BYTES==128
-  AlignedMax = Aligned128
-#elif EIGEN_MAX_ALIGN_BYTES==64
-  AlignedMax = Aligned64
-#elif EIGEN_MAX_ALIGN_BYTES==32
-  AlignedMax = Aligned32
-#elif EIGEN_MAX_ALIGN_BYTES==16
-  AlignedMax = Aligned16
-#elif EIGEN_MAX_ALIGN_BYTES==8
-  AlignedMax = Aligned8
-#elif EIGEN_MAX_ALIGN_BYTES==0
-  AlignedMax = Unaligned
-#else
-#error Invalid value for EIGEN_MAX_ALIGN_BYTES
-#endif
-};
-
-/** \ingroup enums
-  * Enum containing possible values for the \p Direction parameter of
-  * Reverse, PartialReduxExpr and VectorwiseOp. */
-enum DirectionType { 
-  /** For Reverse, all columns are reversed; 
-    * for PartialReduxExpr and VectorwiseOp, act on columns. */
-  Vertical, 
-  /** For Reverse, all rows are reversed; 
-    * for PartialReduxExpr and VectorwiseOp, act on rows. */
-  Horizontal, 
-  /** For Reverse, both rows and columns are reversed; 
-    * not used for PartialReduxExpr and VectorwiseOp. */
-  BothDirections 
-};
-
-/** \internal \ingroup enums
-  * Enum to specify how to traverse the entries of a matrix. */
-enum TraversalType {
-  /** \internal Default traversal, no vectorization, no index-based access */
-  DefaultTraversal,
-  /** \internal No vectorization, use index-based access to have only one for loop instead of 2 nested loops */
-  LinearTraversal,
-  /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignment
-    * and good size */
-  InnerVectorizedTraversal,
-  /** \internal Vectorization path using a single loop plus scalar loops for the
-    * unaligned boundaries */
-  LinearVectorizedTraversal,
-  /** \internal Generic vectorization path using one vectorized loop per row/column with some
-    * scalar loops to handle the unaligned boundaries */
-  SliceVectorizedTraversal,
-  /** \internal Special case to properly handle incompatible scalar types or other defecting cases*/
-  InvalidTraversal,
-  /** \internal Evaluate all entries at once */
-  AllAtOnceTraversal
-};
-
-/** \internal \ingroup enums
-  * Enum to specify whether to unroll loops when traversing over the entries of a matrix. */
-enum UnrollingType {
-  /** \internal Do not unroll loops. */
-  NoUnrolling,
-  /** \internal Unroll only the inner loop, but not the outer loop. */
-  InnerUnrolling,
-  /** \internal Unroll both the inner and the outer loop. If there is only one loop, 
-    * because linear traversal is used, then unroll that loop. */
-  CompleteUnrolling
-};
-
-/** \internal \ingroup enums
-  * Enum to specify whether to use the default (built-in) implementation or the specialization. */
-enum SpecializedType {
-  Specialized,
-  BuiltIn
-};
-
-/** \ingroup enums
-  * Enum containing possible values for the \p _Options template parameter of
-  * Matrix, Array and BandMatrix. */
-enum StorageOptions {
-  /** Storage order is column major (see \ref TopicStorageOrders). */
-  ColMajor = 0,
-  /** Storage order is row major (see \ref TopicStorageOrders). */
-  RowMajor = 0x1,  // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that
-  /** Align the matrix itself if it is vectorizable fixed-size */
-  AutoAlign = 0,
-  /** Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
-  DontAlign = 0x2
-};
-
-/** \ingroup enums
-  * Enum for specifying whether to apply or solve on the left or right. */
-enum SideType {
-  /** Apply transformation on the left. */
-  OnTheLeft = 1,
-  /** Apply transformation on the right. */
-  OnTheRight = 2
-};
-
-/** \ingroup enums
- * Enum for specifying NaN-propagation behavior, e.g. for coeff-wise min/max. */
-enum NaNPropagationOptions {
-  /**  Implementation defined behavior if NaNs are present. */
-  PropagateFast = 0,
-  /**  Always propagate NaNs. */
-  PropagateNaN,
-  /**  Always propagate not-NaNs. */
-  PropagateNumbers
-};
-
-/* the following used to be written as:
- *
- *   struct NoChange_t {};
- *   namespace {
- *     EIGEN_UNUSED NoChange_t NoChange;
- *   }
- *
- * on the ground that it feels dangerous to disambiguate overloaded functions on enum/integer types.  
- * However, this leads to "variable declared but never referenced" warnings on Intel Composer XE,
- * and we do not know how to get rid of them (bug 450).
- */
-
-enum NoChange_t   { NoChange };
-enum Sequential_t { Sequential };
-enum Default_t    { Default };
-
-/** \internal \ingroup enums
-  * Used in AmbiVector. */
-enum AmbiVectorMode {
-  IsDense         = 0,
-  IsSparse
-};
-
-/** \ingroup enums
-  * Used as template parameter in DenseCoeffBase and MapBase to indicate 
-  * which accessors should be provided. */
-enum AccessorLevels {
-  /** Read-only access via a member function. */
-  ReadOnlyAccessors, 
-  /** Read/write access via member functions. */
-  WriteAccessors, 
-  /** Direct read-only access to the coefficients. */
-  DirectAccessors, 
-  /** Direct read/write access to the coefficients. */
-  DirectWriteAccessors
-};
-
-/** \ingroup enums
-  * Enum with options to give to various decompositions. */
-enum DecompositionOptions {
-  /** \internal Not used (meant for LDLT?). */
-  Pivoting            = 0x01, 
-  /** \internal Not used (meant for LDLT?). */
-  NoPivoting          = 0x02, 
-  /** Used in JacobiSVD to indicate that the square matrix U is to be computed. */
-  ComputeFullU        = 0x04,
-  /** Used in JacobiSVD to indicate that the thin matrix U is to be computed. */
-  ComputeThinU        = 0x08,
-  /** Used in JacobiSVD to indicate that the square matrix V is to be computed. */
-  ComputeFullV        = 0x10,
-  /** Used in JacobiSVD to indicate that the thin matrix V is to be computed. */
-  ComputeThinV        = 0x20,
-  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
-    * that only the eigenvalues are to be computed and not the eigenvectors. */
-  EigenvaluesOnly     = 0x40,
-  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
-    * that both the eigenvalues and the eigenvectors are to be computed. */
-  ComputeEigenvectors = 0x80,
-  /** \internal */
-  EigVecMask = EigenvaluesOnly | ComputeEigenvectors,
-  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
-    * solve the generalized eigenproblem \f$ Ax = \lambda B x \f$. */
-  Ax_lBx              = 0x100,
-  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
-    * solve the generalized eigenproblem \f$ ABx = \lambda x \f$. */
-  ABx_lx              = 0x200,
-  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
-    * solve the generalized eigenproblem \f$ BAx = \lambda x \f$. */
-  BAx_lx              = 0x400,
-  /** \internal */
-  GenEigMask = Ax_lBx | ABx_lx | BAx_lx
-};
-
-/** \ingroup enums
-  * Possible values for the \p QRPreconditioner template parameter of JacobiSVD. */
-enum QRPreconditioners {
-  /** Do not specify what is to be done if the SVD of a non-square matrix is asked for. */
-  NoQRPreconditioner,
-  /** Use a QR decomposition without pivoting as the first step. */
-  HouseholderQRPreconditioner,
-  /** Use a QR decomposition with column pivoting as the first step. */
-  ColPivHouseholderQRPreconditioner,
-  /** Use a QR decomposition with full pivoting as the first step. */
-  FullPivHouseholderQRPreconditioner
-};
-
-#ifdef Success
-#error The preprocessor symbol 'Success' is defined, possibly by the X11 header file X.h
-#endif
-
-/** \ingroup enums
-  * Enum for reporting the status of a computation. */
-enum ComputationInfo {
-  /** Computation was successful. */
-  Success = 0,        
-  /** The provided data did not satisfy the prerequisites. */
-  NumericalIssue = 1, 
-  /** Iterative procedure did not converge. */
-  NoConvergence = 2,
-  /** The inputs are invalid, or the algorithm has been improperly called.
-    * When assertions are enabled, such errors trigger an assert. */
-  InvalidInput = 3
-};
-
-/** \ingroup enums
-  * Enum used to specify how a particular transformation is stored in a matrix.
-  * \sa Transform, Hyperplane::transform(). */
-enum TransformTraits {
-  /** Transformation is an isometry. */
-  Isometry      = 0x1,
-  /** Transformation is an affine transformation stored as a (Dim+1)^2 matrix whose last row is 
-    * assumed to be [0 ... 0 1]. */
-  Affine        = 0x2,
-  /** Transformation is an affine transformation stored as a (Dim) x (Dim+1) matrix. */
-  AffineCompact = 0x10 | Affine,
-  /** Transformation is a general projective transformation stored as a (Dim+1)^2 matrix. */
-  Projective    = 0x20
-};
-
-/** \internal \ingroup enums
-  * Enum used to choose between implementation depending on the computer architecture. */
-namespace Architecture
-{
-  enum Type {
-    Generic = 0x0,
-    SSE = 0x1,
-    AltiVec = 0x2,
-    VSX = 0x3,
-    NEON = 0x4,
-    MSA = 0x5,
-    SVE = 0x6,
-#if defined EIGEN_VECTORIZE_SSE
-    Target = SSE
-#elif defined EIGEN_VECTORIZE_ALTIVEC
-    Target = AltiVec
-#elif defined EIGEN_VECTORIZE_VSX
-    Target = VSX
-#elif defined EIGEN_VECTORIZE_NEON
-    Target = NEON
-#elif defined EIGEN_VECTORIZE_SVE
-    Target = SVE
-#elif defined EIGEN_VECTORIZE_MSA
-    Target = MSA
-#else
-    Target = Generic
-#endif
-  };
-}
-
-/** \internal \ingroup enums
-  * Enum used as template parameter in Product and product evaluators. */
-enum ProductImplType
-{ DefaultProduct=0, LazyProduct, AliasFreeProduct, CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct };
-
-/** \internal \ingroup enums
-  * Enum used in experimental parallel implementation. */
-enum Action {GetAction, SetAction};
-
-/** The type used to identify a dense storage. */
-struct Dense {};
-
-/** The type used to identify a general sparse storage. */
-struct Sparse {};
-
-/** The type used to identify a general solver (factored) storage. */
-struct SolverStorage {};
-
-/** The type used to identify a permutation storage. */
-struct PermutationStorage {};
-
-/** The type used to identify a permutation storage. */
-struct TranspositionsStorage {};
-
-/** The type used to identify a matrix expression */
-struct MatrixXpr {};
-
-/** The type used to identify an array expression */
-struct ArrayXpr {};
-
-// An evaluator must define its shape. By default, it can be one of the following:
-struct DenseShape             { static std::string debugName() { return "DenseShape"; } };
-struct SolverShape            { static std::string debugName() { return "SolverShape"; } };
-struct HomogeneousShape       { static std::string debugName() { return "HomogeneousShape"; } };
-struct DiagonalShape          { static std::string debugName() { return "DiagonalShape"; } };
-struct BandShape              { static std::string debugName() { return "BandShape"; } };
-struct TriangularShape        { static std::string debugName() { return "TriangularShape"; } };
-struct SelfAdjointShape       { static std::string debugName() { return "SelfAdjointShape"; } };
-struct PermutationShape       { static std::string debugName() { return "PermutationShape"; } };
-struct TranspositionsShape    { static std::string debugName() { return "TranspositionsShape"; } };
-struct SparseShape            { static std::string debugName() { return "SparseShape"; } };
-
-namespace internal {
-
-  // random access iterators based on coeff*() accessors.
-struct IndexBased {};
-
-// evaluator based on iterators to access coefficients. 
-struct IteratorBased {};
-
-/** \internal
- * Constants for comparison functors
- */
-enum ComparisonName {
-  cmp_EQ = 0,
-  cmp_LT = 1,
-  cmp_LE = 2,
-  cmp_UNORD = 3,
-  cmp_NEQ = 4,
-  cmp_GT = 5,
-  cmp_GE = 6
-};
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONSTANTS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
deleted file mode 100644
index fe0cfec..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
+++ /dev/null
@@ -1,106 +0,0 @@
-#ifndef EIGEN_WARNINGS_DISABLED
-#define EIGEN_WARNINGS_DISABLED
-
-#ifdef _MSC_VER
-  // 4100 - unreferenced formal parameter (occurred e.g. in aligned_allocator::destroy(pointer p))
-  // 4101 - unreferenced local variable
-  // 4181 - qualifier applied to reference type ignored
-  // 4211 - nonstandard extension used : redefined extern to static
-  // 4244 - 'argument' : conversion from 'type1' to 'type2', possible loss of data
-  // 4273 - QtAlignedMalloc, inconsistent DLL linkage
-  // 4324 - structure was padded due to declspec(align())
-  // 4503 - decorated name length exceeded, name was truncated
-  // 4512 - assignment operator could not be generated
-  // 4522 - 'class' : multiple assignment operators specified
-  // 4700 - uninitialized local variable 'xyz' used
-  // 4714 - function marked as __forceinline not inlined
-  // 4717 - 'function' : recursive on all control paths, function will cause runtime stack overflow
-  // 4800 - 'type' : forcing value to bool 'true' or 'false' (performance warning)
-  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-    #pragma warning( push )
-  #endif
-  #pragma warning( disable : 4100 4101 4181 4211 4244 4273 4324 4503 4512 4522 4700 4714 4717 4800)
-
-#elif defined __INTEL_COMPILER
-  // 2196 - routine is both "inline" and "noinline" ("noinline" assumed)
-  //        ICC 12 generates this warning even without any inline keyword, when defining class methods 'inline' i.e. inside of class body
-  //        typedef that may be a reference type.
-  // 279  - controlling expression is constant
-  //        ICC 12 generates this warning on assert(constant_expression_depending_on_template_params) and frankly this is a legitimate use case.
-  // 1684 - conversion from pointer to same-sized integral type (potential portability problem)
-  // 2259 - non-pointer conversion from "Eigen::Index={ptrdiff_t={long}}" to "int" may lose significant bits
-  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-    #pragma warning push
-  #endif
-  #pragma warning disable 2196 279 1684 2259
-
-#elif defined __clang__
-  // -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant
-  //     this is really a stupid warning as it warns on compile-time expressions involving enums
-  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-    #pragma clang diagnostic push
-  #endif
-  #pragma clang diagnostic ignored "-Wconstant-logical-operand"
-  #if __clang_major__ >= 3 && __clang_minor__ >= 5
-    #pragma clang diagnostic ignored "-Wabsolute-value"
-  #endif
-  #if __clang_major__ >= 10
-    #pragma clang diagnostic ignored "-Wimplicit-int-float-conversion"
-  #endif
-  #if ( defined(__ALTIVEC__) || defined(__VSX__) ) && __cplusplus < 201103L
-    // warning: generic selections are a C11-specific feature
-    // ignoring warnings thrown at vec_ctf in Altivec/PacketMath.h
-    #pragma clang diagnostic ignored "-Wc11-extensions"
-  #endif
-
-#elif defined __GNUC__
-
-  #if (!defined(EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS)) &&  (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-    #pragma GCC diagnostic push
-  #endif
-  // g++ warns about local variables shadowing member functions, which is too strict
-  #pragma GCC diagnostic ignored "-Wshadow"
-  #if __GNUC__ == 4 && __GNUC_MINOR__ < 8
-    // Until g++-4.7 there are warnings when comparing unsigned int vs 0, even in templated functions:
-    #pragma GCC diagnostic ignored "-Wtype-limits"
-  #endif
-  #if __GNUC__>=6
-    #pragma GCC diagnostic ignored "-Wignored-attributes"
-  #endif
-  #if __GNUC__==7
-    // See: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89325
-    #pragma GCC diagnostic ignored "-Wattributes"
-  #endif
-#endif
-
-#if defined __NVCC__
-  #pragma diag_suppress boolean_controlling_expr_is_constant
-  // Disable the "statement is unreachable" message
-  #pragma diag_suppress code_is_unreachable
-  // Disable the "dynamic initialization in unreachable code" message
-  #pragma diag_suppress initialization_not_reachable
-  // Disable the "invalid error number" message that we get with older versions of nvcc
-  #pragma diag_suppress 1222
-  // Disable the "calling a __host__ function from a __host__ __device__ function is not allowed" messages (yes, there are many of them and they seem to change with every version of the compiler)
-  #pragma diag_suppress 2527
-  #pragma diag_suppress 2529
-  #pragma diag_suppress 2651
-  #pragma diag_suppress 2653
-  #pragma diag_suppress 2668
-  #pragma diag_suppress 2669
-  #pragma diag_suppress 2670
-  #pragma diag_suppress 2671
-  #pragma diag_suppress 2735
-  #pragma diag_suppress 2737
-  #pragma diag_suppress 2739
-#endif
-
-#else
-// warnings already disabled:
-# ifndef EIGEN_WARNINGS_DISABLED_2
-#  define EIGEN_WARNINGS_DISABLED_2
-# elif defined(EIGEN_INTERNAL_DEBUGGING)
-#  error "Do not include \"DisableStupidWarnings.h\" recursively more than twice!"
-# endif
-
-#endif // not EIGEN_WARNINGS_DISABLED
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h
deleted file mode 100644
index 2f9cc44..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h
+++ /dev/null
@@ -1,322 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FORWARDDECLARATIONS_H
-#define EIGEN_FORWARDDECLARATIONS_H
-
-namespace Eigen {
-namespace internal {
-
-template<typename T> struct traits;
-
-// here we say once and for all that traits<const T> == traits<T>
-// When constness must affect traits, it has to be constness on template parameters on which T itself depends.
-// For example, traits<Map<const T> > != traits<Map<T> >, but
-//              traits<const Map<T> > == traits<Map<T> >
-template<typename T> struct traits<const T> : traits<T> {};
-
-template<typename Derived> struct has_direct_access
-{
-  enum { ret = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0 };
-};
-
-template<typename Derived> struct accessors_level
-{
-  enum { has_direct_access = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0,
-         has_write_access = (traits<Derived>::Flags & LvalueBit) ? 1 : 0,
-         value = has_direct_access ? (has_write_access ? DirectWriteAccessors : DirectAccessors)
-                                   : (has_write_access ? WriteAccessors       : ReadOnlyAccessors)
-  };
-};
-
-template<typename T> struct evaluator_traits;
-
-template< typename T> struct evaluator;
-
-} // end namespace internal
-
-template<typename T> struct NumTraits;
-
-template<typename Derived> struct EigenBase;
-template<typename Derived> class DenseBase;
-template<typename Derived> class PlainObjectBase;
-template<typename Derived, int Level> class DenseCoeffsBase;
-
-template<typename _Scalar, int _Rows, int _Cols,
-         int _Options = AutoAlign |
-#if EIGEN_GNUC_AT(3,4)
-    // workaround a bug in at least gcc 3.4.6
-    // the innermost ?: ternary operator is misparsed. We write it slightly
-    // differently and this makes gcc 3.4.6 happy, but it's ugly.
-    // The error would only show up with EIGEN_DEFAULT_TO_ROW_MAJOR is defined
-    // (when EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION is RowMajor)
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : !(_Cols==1 && _Rows!=1) ?  EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION
-                          : Eigen::ColMajor ),
-#else
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : (_Cols==1 && _Rows!=1) ? Eigen::ColMajor
-                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
-#endif
-         int _MaxRows = _Rows,
-         int _MaxCols = _Cols
-> class Matrix;
-
-template<typename Derived> class MatrixBase;
-template<typename Derived> class ArrayBase;
-
-template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged;
-template<typename ExpressionType, template <typename> class StorageBase > class NoAlias;
-template<typename ExpressionType> class NestByValue;
-template<typename ExpressionType> class ForceAlignedAccess;
-template<typename ExpressionType> class SwapWrapper;
-
-template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false> class Block;
-template<typename XprType, typename RowIndices, typename ColIndices> class IndexedView;
-template<typename XprType, int Rows=Dynamic, int Cols=Dynamic, int Order=0> class Reshaped;
-
-template<typename MatrixType, int Size=Dynamic> class VectorBlock;
-template<typename MatrixType> class Transpose;
-template<typename MatrixType> class Conjugate;
-template<typename NullaryOp, typename MatrixType>         class CwiseNullaryOp;
-template<typename UnaryOp,   typename MatrixType>         class CwiseUnaryOp;
-template<typename ViewOp,    typename MatrixType>         class CwiseUnaryView;
-template<typename BinaryOp,  typename Lhs, typename Rhs>  class CwiseBinaryOp;
-template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>  class CwiseTernaryOp;
-template<typename Decomposition, typename Rhstype>        class Solve;
-template<typename XprType>                                class Inverse;
-
-template<typename Lhs, typename Rhs, int Option = DefaultProduct> class Product;
-
-template<typename Derived> class DiagonalBase;
-template<typename _DiagonalVectorType> class DiagonalWrapper;
-template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime=SizeAtCompileTime> class DiagonalMatrix;
-template<typename MatrixType, typename DiagonalType, int ProductOrder> class DiagonalProduct;
-template<typename MatrixType, int Index = 0> class Diagonal;
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class PermutationMatrix;
-template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class Transpositions;
-template<typename Derived> class PermutationBase;
-template<typename Derived> class TranspositionsBase;
-template<typename _IndicesType> class PermutationWrapper;
-template<typename _IndicesType> class TranspositionsWrapper;
-
-template<typename Derived,
-         int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors
-> class MapBase;
-template<int OuterStrideAtCompileTime, int InnerStrideAtCompileTime> class Stride;
-template<int Value = Dynamic> class InnerStride;
-template<int Value = Dynamic> class OuterStride;
-template<typename MatrixType, int MapOptions=Unaligned, typename StrideType = Stride<0,0> > class Map;
-template<typename Derived> class RefBase;
-template<typename PlainObjectType, int Options = 0,
-         typename StrideType = typename internal::conditional<PlainObjectType::IsVectorAtCompileTime,InnerStride<1>,OuterStride<> >::type > class Ref;
-
-template<typename Derived> class TriangularBase;
-template<typename MatrixType, unsigned int Mode> class TriangularView;
-template<typename MatrixType, unsigned int Mode> class SelfAdjointView;
-template<typename MatrixType> class SparseView;
-template<typename ExpressionType> class WithFormat;
-template<typename MatrixType> struct CommaInitializer;
-template<typename Derived> class ReturnByValue;
-template<typename ExpressionType> class ArrayWrapper;
-template<typename ExpressionType> class MatrixWrapper;
-template<typename Derived> class SolverBase;
-template<typename XprType> class InnerIterator;
-
-namespace internal {
-template<typename XprType> class generic_randaccess_stl_iterator;
-template<typename XprType> class pointer_based_stl_iterator;
-template<typename XprType, DirectionType Direction> class subvector_stl_iterator;
-template<typename XprType, DirectionType Direction> class subvector_stl_reverse_iterator;
-template<typename DecompositionType> struct kernel_retval_base;
-template<typename DecompositionType> struct kernel_retval;
-template<typename DecompositionType> struct image_retval_base;
-template<typename DecompositionType> struct image_retval;
-} // end namespace internal
-
-namespace internal {
-template<typename _Scalar, int Rows=Dynamic, int Cols=Dynamic, int Supers=Dynamic, int Subs=Dynamic, int Options=0> class BandMatrix;
-}
-
-namespace internal {
-template<typename Lhs, typename Rhs> struct product_type;
-
-template<bool> struct EnableIf;
-
-/** \internal
-  * \class product_evaluator
-  * Products need their own evaluator with more template arguments allowing for
-  * easier partial template specializations.
-  */
-template< typename T,
-          int ProductTag = internal::product_type<typename T::Lhs,typename T::Rhs>::ret,
-          typename LhsShape = typename evaluator_traits<typename T::Lhs>::Shape,
-          typename RhsShape = typename evaluator_traits<typename T::Rhs>::Shape,
-          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
-          typename RhsScalar = typename traits<typename T::Rhs>::Scalar
-        > struct product_evaluator;
-}
-
-template<typename Lhs, typename Rhs,
-         int ProductType = internal::product_type<Lhs,Rhs>::value>
-struct ProductReturnType;
-
-// this is a workaround for sun CC
-template<typename Lhs, typename Rhs> struct LazyProductReturnType;
-
-namespace internal {
-
-// Provides scalar/packet-wise product and product with accumulation
-// with optional conjugation of the arguments.
-template<typename LhsScalar, typename RhsScalar, bool ConjLhs=false, bool ConjRhs=false> struct conj_helper;
-
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_sum_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_difference_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_conj_product_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar, int NaNPropagation=PropagateFast> struct scalar_min_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar, int NaNPropagation=PropagateFast> struct scalar_max_op;
-template<typename Scalar> struct scalar_opposite_op;
-template<typename Scalar> struct scalar_conjugate_op;
-template<typename Scalar> struct scalar_real_op;
-template<typename Scalar> struct scalar_imag_op;
-template<typename Scalar> struct scalar_abs_op;
-template<typename Scalar> struct scalar_abs2_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_absolute_difference_op;
-template<typename Scalar> struct scalar_sqrt_op;
-template<typename Scalar> struct scalar_rsqrt_op;
-template<typename Scalar> struct scalar_exp_op;
-template<typename Scalar> struct scalar_log_op;
-template<typename Scalar> struct scalar_cos_op;
-template<typename Scalar> struct scalar_sin_op;
-template<typename Scalar> struct scalar_acos_op;
-template<typename Scalar> struct scalar_asin_op;
-template<typename Scalar> struct scalar_tan_op;
-template<typename Scalar> struct scalar_inverse_op;
-template<typename Scalar> struct scalar_square_op;
-template<typename Scalar> struct scalar_cube_op;
-template<typename Scalar, typename NewType> struct scalar_cast_op;
-template<typename Scalar> struct scalar_random_op;
-template<typename Scalar> struct scalar_constant_op;
-template<typename Scalar> struct scalar_identity_op;
-template<typename Scalar,bool is_complex, bool is_integer> struct scalar_sign_op;
-template<typename Scalar,typename ScalarExponent> struct scalar_pow_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_hypot_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op;
-template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op;
-
-// SpecialFunctions module
-template<typename Scalar> struct scalar_lgamma_op;
-template<typename Scalar> struct scalar_digamma_op;
-template<typename Scalar> struct scalar_erf_op;
-template<typename Scalar> struct scalar_erfc_op;
-template<typename Scalar> struct scalar_ndtri_op;
-template<typename Scalar> struct scalar_igamma_op;
-template<typename Scalar> struct scalar_igammac_op;
-template<typename Scalar> struct scalar_zeta_op;
-template<typename Scalar> struct scalar_betainc_op;
-
-// Bessel functions in SpecialFunctions module
-template<typename Scalar> struct scalar_bessel_i0_op;
-template<typename Scalar> struct scalar_bessel_i0e_op;
-template<typename Scalar> struct scalar_bessel_i1_op;
-template<typename Scalar> struct scalar_bessel_i1e_op;
-template<typename Scalar> struct scalar_bessel_j0_op;
-template<typename Scalar> struct scalar_bessel_y0_op;
-template<typename Scalar> struct scalar_bessel_j1_op;
-template<typename Scalar> struct scalar_bessel_y1_op;
-template<typename Scalar> struct scalar_bessel_k0_op;
-template<typename Scalar> struct scalar_bessel_k0e_op;
-template<typename Scalar> struct scalar_bessel_k1_op;
-template<typename Scalar> struct scalar_bessel_k1e_op;
-
-
-} // end namespace internal
-
-struct IOFormat;
-
-// Array module
-template<typename _Scalar, int _Rows, int _Cols,
-         int _Options = AutoAlign |
-#if EIGEN_GNUC_AT(3,4)
-    // workaround a bug in at least gcc 3.4.6
-    // the innermost ?: ternary operator is misparsed. We write it slightly
-    // differently and this makes gcc 3.4.6 happy, but it's ugly.
-    // The error would only show up with EIGEN_DEFAULT_TO_ROW_MAJOR is defined
-    // (when EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION is RowMajor)
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : !(_Cols==1 && _Rows!=1) ?  EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION
-                          : Eigen::ColMajor ),
-#else
-                          ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor
-                          : (_Cols==1 && _Rows!=1) ? Eigen::ColMajor
-                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
-#endif
-         int _MaxRows = _Rows, int _MaxCols = _Cols> class Array;
-template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType> class Select;
-template<typename MatrixType, typename BinaryOp, int Direction> class PartialReduxExpr;
-template<typename ExpressionType, int Direction> class VectorwiseOp;
-template<typename MatrixType,int RowFactor,int ColFactor> class Replicate;
-template<typename MatrixType, int Direction = BothDirections> class Reverse;
-
-template<typename MatrixType> class FullPivLU;
-template<typename MatrixType> class PartialPivLU;
-namespace internal {
-template<typename MatrixType> struct inverse_impl;
-}
-template<typename MatrixType> class HouseholderQR;
-template<typename MatrixType> class ColPivHouseholderQR;
-template<typename MatrixType> class FullPivHouseholderQR;
-template<typename MatrixType> class CompleteOrthogonalDecomposition;
-template<typename MatrixType> class SVDBase;
-template<typename MatrixType, int QRPreconditioner = ColPivHouseholderQRPreconditioner> class JacobiSVD;
-template<typename MatrixType> class BDCSVD;
-template<typename MatrixType, int UpLo = Lower> class LLT;
-template<typename MatrixType, int UpLo = Lower> class LDLT;
-template<typename VectorsType, typename CoeffsType, int Side=OnTheLeft> class HouseholderSequence;
-template<typename Scalar>     class JacobiRotation;
-
-// Geometry module:
-template<typename Derived, int _Dim> class RotationBase;
-template<typename Lhs, typename Rhs> class Cross;
-template<typename Derived> class QuaternionBase;
-template<typename Scalar> class Rotation2D;
-template<typename Scalar> class AngleAxis;
-template<typename Scalar,int Dim> class Translation;
-template<typename Scalar,int Dim> class AlignedBox;
-template<typename Scalar, int Options = AutoAlign> class Quaternion;
-template<typename Scalar,int Dim,int Mode,int _Options=AutoAlign> class Transform;
-template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class ParametrizedLine;
-template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class Hyperplane;
-template<typename Scalar> class UniformScaling;
-template<typename MatrixType,int Direction> class Homogeneous;
-
-// Sparse module:
-template<typename Derived> class SparseMatrixBase;
-
-// MatrixFunctions module
-template<typename Derived> struct MatrixExponentialReturnValue;
-template<typename Derived> class MatrixFunctionReturnValue;
-template<typename Derived> class MatrixSquareRootReturnValue;
-template<typename Derived> class MatrixLogarithmReturnValue;
-template<typename Derived> class MatrixPowerReturnValue;
-template<typename Derived> class MatrixComplexPowerReturnValue;
-
-namespace internal {
-template <typename Scalar>
-struct stem_function
-{
-  typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-  typedef ComplexScalar type(ComplexScalar, int);
-};
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FORWARDDECLARATIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
deleted file mode 100644
index f85de30..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
+++ /dev/null
@@ -1,186 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_INDEXED_VIEW_HELPER_H
-#define EIGEN_INDEXED_VIEW_HELPER_H
-
-namespace Eigen {
-
-namespace internal {
-struct symbolic_last_tag {};
-}
-
-/** \var last
-  * \ingroup Core_Module
-  *
-  * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically reference the last element/row/columns
-  * of the underlying vector or matrix once passed to DenseBase::operator()(const RowIndices&, const ColIndices&).
-  *
-  * This symbolic placeholder supports standard arithmetic operations.
-  *
-  * A typical usage example would be:
-  * \code
-  * using namespace Eigen;
-  * using Eigen::last;
-  * VectorXd v(n);
-  * v(seq(2,last-2)).setOnes();
-  * \endcode
-  *
-  * \sa end
-  */
-static const symbolic::SymbolExpr<internal::symbolic_last_tag> last; // PLEASE use Eigen::last   instead of Eigen::placeholders::last
-
-/** \var lastp1
-  * \ingroup Core_Module
-  *
-  * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically
-  * reference the last+1 element/row/columns of the underlying vector or matrix once
-  * passed to DenseBase::operator()(const RowIndices&, const ColIndices&).
-  *
-  * This symbolic placeholder supports standard arithmetic operations.
-  * It is essentially an alias to last+fix<1>.
-  *
-  * \sa last
-  */
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-static const auto lastp1 = last+fix<1>;
-#else
-// Using a FixedExpr<1> expression is important here to make sure the compiler
-// can fully optimize the computation starting indices with zero overhead.
-static const symbolic::AddExpr<symbolic::SymbolExpr<internal::symbolic_last_tag>,symbolic::ValueExpr<Eigen::internal::FixedInt<1> > > lastp1(last+fix<1>());
-#endif
-
-namespace internal {
-
- // Replace symbolic last/end "keywords" by their true runtime value
-inline Index eval_expr_given_size(Index x, Index /* size */)   { return x; }
-
-template<int N>
-FixedInt<N> eval_expr_given_size(FixedInt<N> x, Index /*size*/)   { return x; }
-
-template<typename Derived>
-Index eval_expr_given_size(const symbolic::BaseExpr<Derived> &x, Index size)
-{
-  return x.derived().eval(last=size-1);
-}
-
-// Extract increment/step at compile time
-template<typename T, typename EnableIf = void> struct get_compile_time_incr {
-  enum { value = UndefinedIncr };
-};
-
-// Analogue of std::get<0>(x), but tailored for our needs.
-template<typename T>
-EIGEN_CONSTEXPR Index first(const T& x) EIGEN_NOEXCEPT { return x.first(); }
-
-// IndexedViewCompatibleType/makeIndexedViewCompatible turn an arbitrary object of type T into something usable by MatrixSlice
-// The generic implementation is a no-op
-template<typename T,int XprSize,typename EnableIf=void>
-struct IndexedViewCompatibleType {
-  typedef T type;
-};
-
-template<typename T,typename Q>
-const T& makeIndexedViewCompatible(const T& x, Index /*size*/, Q) { return x; }
-
-//--------------------------------------------------------------------------------
-// Handling of a single Index
-//--------------------------------------------------------------------------------
-
-struct SingleRange {
-  enum {
-    SizeAtCompileTime = 1
-  };
-  SingleRange(Index val) : m_value(val) {}
-  Index operator[](Index) const { return m_value; }
-  static EIGEN_CONSTEXPR Index size() EIGEN_NOEXCEPT { return 1; }
-  Index first() const EIGEN_NOEXCEPT { return m_value; }
-  Index m_value;
-};
-
-template<> struct get_compile_time_incr<SingleRange> {
-  enum { value = 1 }; // 1 or 0 ??
-};
-
-// Turn a single index into something that looks like an array (i.e., that exposes a .size(), and operator[](int) methods)
-template<typename T, int XprSize>
-struct IndexedViewCompatibleType<T,XprSize,typename internal::enable_if<internal::is_integral<T>::value>::type> {
-  // Here we could simply use Array, but maybe it's less work for the compiler to use
-  // a simpler wrapper as SingleRange
-  //typedef Eigen::Array<Index,1,1> type;
-  typedef SingleRange type;
-};
-
-template<typename T, int XprSize>
-struct IndexedViewCompatibleType<T, XprSize, typename enable_if<symbolic::is_symbolic<T>::value>::type> {
-  typedef SingleRange type;
-};
-
-
-template<typename T>
-typename enable_if<symbolic::is_symbolic<T>::value,SingleRange>::type
-makeIndexedViewCompatible(const T& id, Index size, SpecializedType) {
-  return eval_expr_given_size(id,size);
-}
-
-//--------------------------------------------------------------------------------
-// Handling of all
-//--------------------------------------------------------------------------------
-
-struct all_t { all_t() {} };
-
-// Convert a symbolic 'all' into a usable range type
-template<int XprSize>
-struct AllRange {
-  enum { SizeAtCompileTime = XprSize };
-  AllRange(Index size = XprSize) : m_size(size) {}
-  EIGEN_CONSTEXPR Index operator[](Index i) const EIGEN_NOEXCEPT { return i; }
-  EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_size.value(); }
-  EIGEN_CONSTEXPR Index first() const EIGEN_NOEXCEPT { return 0; }
-  variable_if_dynamic<Index,XprSize> m_size;
-};
-
-template<int XprSize>
-struct IndexedViewCompatibleType<all_t,XprSize> {
-  typedef AllRange<XprSize> type;
-};
-
-template<typename XprSizeType>
-inline AllRange<get_fixed_value<XprSizeType>::value> makeIndexedViewCompatible(all_t , XprSizeType size, SpecializedType) {
-  return AllRange<get_fixed_value<XprSizeType>::value>(size);
-}
-
-template<int Size> struct get_compile_time_incr<AllRange<Size> > {
-  enum { value = 1 };
-};
-
-} // end namespace internal
-
-
-/** \var all
-  * \ingroup Core_Module
-  * Can be used as a parameter to DenseBase::operator()(const RowIndices&, const ColIndices&) to index all rows or columns
-  */
-static const Eigen::internal::all_t all; // PLEASE use Eigen::all instead of Eigen::placeholders::all
-
-
-namespace placeholders {
-  typedef symbolic::SymbolExpr<internal::symbolic_last_tag> last_t;
-  typedef symbolic::AddExpr<symbolic::SymbolExpr<internal::symbolic_last_tag>,symbolic::ValueExpr<Eigen::internal::FixedInt<1> > > end_t;
-  typedef Eigen::internal::all_t all_t;
-
-  EIGEN_DEPRECATED static const all_t  all  = Eigen::all;    // PLEASE use Eigen::all    instead of Eigen::placeholders::all
-  EIGEN_DEPRECATED static const last_t last = Eigen::last;   // PLEASE use Eigen::last   instead of Eigen::placeholders::last
-  EIGEN_DEPRECATED static const end_t  end  = Eigen::lastp1; // PLEASE use Eigen::lastp1 instead of Eigen::placeholders::end
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_INDEXED_VIEW_HELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/IntegralConstant.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
deleted file mode 100644
index 945d426..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
+++ /dev/null
@@ -1,272 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_INTEGRAL_CONSTANT_H
-#define EIGEN_INTEGRAL_CONSTANT_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<int N> class FixedInt;
-template<int N> class VariableAndFixedInt;
-
-/** \internal
-  * \class FixedInt
-  *
-  * This class embeds a compile-time integer \c N.
-  *
-  * It is similar to c++11 std::integral_constant<int,N> but with some additional features
-  * such as:
-  *  - implicit conversion to int
-  *  - arithmetic and some bitwise operators: -, +, *, /, %, &, |
-  *  - c++98/14 compatibility with fix<N> and fix<N>() syntax to define integral constants.
-  *
-  * It is strongly discouraged to directly deal with this class FixedInt. Instances are expcected to
-  * be created by the user using Eigen::fix<N> or Eigen::fix<N>(). In C++98-11, the former syntax does
-  * not create a FixedInt<N> instance but rather a point to function that needs to be \em cleaned-up
-  * using the generic helper:
-  * \code
-  * internal::cleanup_index_type<T>::type
-  * internal::cleanup_index_type<T,DynamicKey>::type
-  * \endcode
-  * where T can a FixedInt<N>, a pointer to function FixedInt<N> (*)(), or numerous other integer-like representations.
-  * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
-  *
-  * For convenience, you can extract the compile-time value \c N in a generic way using the following helper:
-  * \code
-  * internal::get_fixed_value<T,DefaultVal>::value
-  * \endcode
-  * that will give you \c N if T equals FixedInt<N> or FixedInt<N> (*)(), and \c DefaultVal if T does not embed any compile-time value (e.g., T==int).
-  *
-  * \sa fix<N>, class VariableAndFixedInt
-  */
-template<int N> class FixedInt
-{
-public:
-  static const int value = N;
-  EIGEN_CONSTEXPR operator int() const { return value; }
-  FixedInt() {}
-  FixedInt( VariableAndFixedInt<N> other) {
-    #ifndef EIGEN_INTERNAL_DEBUGGING
-    EIGEN_UNUSED_VARIABLE(other);
-    #endif
-    eigen_internal_assert(int(other)==N);
-  }
-
-  FixedInt<-N> operator-() const { return FixedInt<-N>(); }
-  template<int M>
-  FixedInt<N+M> operator+( FixedInt<M>) const { return FixedInt<N+M>(); }
-  template<int M>
-  FixedInt<N-M> operator-( FixedInt<M>) const { return FixedInt<N-M>(); }
-  template<int M>
-  FixedInt<N*M> operator*( FixedInt<M>) const { return FixedInt<N*M>(); }
-  template<int M>
-  FixedInt<N/M> operator/( FixedInt<M>) const { return FixedInt<N/M>(); }
-  template<int M>
-  FixedInt<N%M> operator%( FixedInt<M>) const { return FixedInt<N%M>(); }
-  template<int M>
-  FixedInt<N|M> operator|( FixedInt<M>) const { return FixedInt<N|M>(); }
-  template<int M>
-  FixedInt<N&M> operator&( FixedInt<M>) const { return FixedInt<N&M>(); }
-
-#if EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
-  // Needed in C++14 to allow fix<N>():
-  FixedInt operator() () const { return *this; }
-
-  VariableAndFixedInt<N> operator() (int val) const { return VariableAndFixedInt<N>(val); }
-#else
-  FixedInt ( FixedInt<N> (*)() ) {}
-#endif
-
-#if EIGEN_HAS_CXX11
-  FixedInt(std::integral_constant<int,N>) {}
-#endif
-};
-
-/** \internal
-  * \class VariableAndFixedInt
-  *
-  * This class embeds both a compile-time integer \c N and a runtime integer.
-  * Both values are supposed to be equal unless the compile-time value \c N has a special
-  * value meaning that the runtime-value should be used. Depending on the context, this special
-  * value can be either Eigen::Dynamic (for positive quantities) or Eigen::DynamicIndex (for
-  * quantities that can be negative).
-  *
-  * It is the return-type of the function Eigen::fix<N>(int), and most of the time this is the only
-  * way it is used. It is strongly discouraged to directly deal with instances of VariableAndFixedInt.
-  * Indeed, in order to write generic code, it is the responsibility of the callee to properly convert
-  * it to either a true compile-time quantity (i.e. a FixedInt<N>), or to a runtime quantity (e.g., an Index)
-  * using the following generic helper:
-  * \code
-  * internal::cleanup_index_type<T>::type
-  * internal::cleanup_index_type<T,DynamicKey>::type
-  * \endcode
-  * where T can be a template instantiation of VariableAndFixedInt or numerous other integer-like representations.
-  * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
-  *
-  * For convenience, you can also extract the compile-time value \c N using the following helper:
-  * \code
-  * internal::get_fixed_value<T,DefaultVal>::value
-  * \endcode
-  * that will give you \c N if T equals VariableAndFixedInt<N>, and \c DefaultVal if T does not embed any compile-time value (e.g., T==int).
-  *
-  * \sa fix<N>(int), class FixedInt
-  */
-template<int N> class VariableAndFixedInt
-{
-public:
-  static const int value = N;
-  operator int() const { return m_value; }
-  VariableAndFixedInt(int val) { m_value = val; }
-protected:
-  int m_value;
-};
-
-template<typename T, int Default=Dynamic> struct get_fixed_value {
-  static const int value = Default;
-};
-
-template<int N,int Default> struct get_fixed_value<FixedInt<N>,Default> {
-  static const int value = N;
-};
-
-#if !EIGEN_HAS_CXX14
-template<int N,int Default> struct get_fixed_value<FixedInt<N> (*)(),Default> {
-  static const int value = N;
-};
-#endif
-
-template<int N,int Default> struct get_fixed_value<VariableAndFixedInt<N>,Default> {
-  static const int value = N ;
-};
-
-template<typename T, int N, int Default>
-struct get_fixed_value<variable_if_dynamic<T,N>,Default> {
-  static const int value = N;
-};
-
-template<typename T> EIGEN_DEVICE_FUNC Index get_runtime_value(const T &x) { return x; }
-#if !EIGEN_HAS_CXX14
-template<int N> EIGEN_DEVICE_FUNC Index get_runtime_value(FixedInt<N> (*)()) { return N; }
-#endif
-
-// Cleanup integer/FixedInt/VariableAndFixedInt/etc types:
-
-// By default, no cleanup:
-template<typename T, int DynamicKey=Dynamic, typename EnableIf=void> struct cleanup_index_type { typedef T type; };
-
-// Convert any integral type (e.g., short, int, unsigned int, etc.) to Eigen::Index
-template<typename T, int DynamicKey> struct cleanup_index_type<T,DynamicKey,typename internal::enable_if<internal::is_integral<T>::value>::type> { typedef Index type; };
-
-#if !EIGEN_HAS_CXX14
-// In c++98/c++11, fix<N> is a pointer to function that we better cleanup to a true FixedInt<N>:
-template<int N, int DynamicKey> struct cleanup_index_type<FixedInt<N> (*)(), DynamicKey> { typedef FixedInt<N> type; };
-#endif
-
-// If VariableAndFixedInt does not match DynamicKey, then we turn it to a pure compile-time value:
-template<int N, int DynamicKey> struct cleanup_index_type<VariableAndFixedInt<N>, DynamicKey> { typedef FixedInt<N> type; };
-// If VariableAndFixedInt matches DynamicKey, then we turn it to a pure runtime-value (aka Index):
-template<int DynamicKey> struct cleanup_index_type<VariableAndFixedInt<DynamicKey>, DynamicKey> { typedef Index type; };
-
-#if EIGEN_HAS_CXX11
-template<int N, int DynamicKey> struct cleanup_index_type<std::integral_constant<int,N>, DynamicKey> { typedef FixedInt<N> type; };
-#endif
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-#if EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
-template<int N>
-static const internal::FixedInt<N> fix{};
-#else
-template<int N>
-inline internal::FixedInt<N> fix() { return internal::FixedInt<N>(); }
-
-// The generic typename T is mandatory. Otherwise, a code like fix<N> could refer to either the function above or this next overload.
-// This way a code like fix<N> can only refer to the previous function.
-template<int N,typename T>
-inline internal::VariableAndFixedInt<N> fix(T val) { return internal::VariableAndFixedInt<N>(internal::convert_index<int>(val)); }
-#endif
-
-#else // EIGEN_PARSED_BY_DOXYGEN
-
-/** \var fix<N>()
-  * \ingroup Core_Module
-  *
-  * This \em identifier permits to construct an object embedding a compile-time integer \c N.
-  *
-  * \tparam N the compile-time integer value
-  *
-  * It is typically used in conjunction with the Eigen::seq and Eigen::seqN functions to pass compile-time values to them:
-  * \code
-  * seqN(10,fix<4>,fix<-3>)   // <=> [10 7 4 1]
-  * \endcode
-  *
-  * See also the function fix(int) to pass both a compile-time and runtime value.
-  *
-  * In c++14, it is implemented as:
-  * \code
-  * template<int N> static const internal::FixedInt<N> fix{};
-  * \endcode
-  * where internal::FixedInt<N> is an internal template class similar to
-  * <a href="http://en.cppreference.com/w/cpp/types/integral_constant">\c std::integral_constant </a><tt> <int,N> </tt>
-  * Here, \c fix<N> is thus an object of type \c internal::FixedInt<N>.
-  *
-  * In c++98/11, it is implemented as a function:
-  * \code
-  * template<int N> inline internal::FixedInt<N> fix();
-  * \endcode
-  * Here internal::FixedInt<N> is thus a pointer to function.
-  *
-  * If for some reason you want a true object in c++98 then you can write: \code fix<N>() \endcode which is also valid in c++14.
-  *
-  * \sa fix<N>(int), seq, seqN
-  */
-template<int N>
-static const auto fix();
-
-/** \fn fix<N>(int)
-  * \ingroup Core_Module
-  *
-  * This function returns an object embedding both a compile-time integer \c N, and a fallback runtime value \a val.
-  *
-  * \tparam N the compile-time integer value
-  * \param  val the fallback runtime integer value
-  *
-  * This function is a more general version of the \ref fix identifier/function that can be used in template code
-  * where the compile-time value could turn out to actually mean "undefined at compile-time". For positive integers
-  * such as a size or a dimension, this case is identified by Eigen::Dynamic, whereas runtime signed integers
-  * (e.g., an increment/stride) are identified as Eigen::DynamicIndex. In such a case, the runtime value \a val
-  * will be used as a fallback.
-  *
-  * A typical use case would be:
-  * \code
-  * template<typename Derived> void foo(const MatrixBase<Derived> &mat) {
-  *   const int N = Derived::RowsAtCompileTime==Dynamic ? Dynamic : Derived::RowsAtCompileTime/2;
-  *   const int n = mat.rows()/2;
-  *   ... mat( seqN(0,fix<N>(n) ) ...;
-  * }
-  * \endcode
-  * In this example, the function Eigen::seqN knows that the second argument is expected to be a size.
-  * If the passed compile-time value N equals Eigen::Dynamic, then the proxy object returned by fix will be dissmissed, and converted to an Eigen::Index of value \c n.
-  * Otherwise, the runtime-value \c n will be dissmissed, and the returned ArithmeticSequence will be of the exact same type as <tt> seqN(0,fix<N>) </tt>.
-  *
-  * \sa fix, seqN, class ArithmeticSequence
-  */
-template<int N>
-static const auto fix(int val);
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-} // end namespace Eigen
-
-#endif // EIGEN_INTEGRAL_CONSTANT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/MKL_support.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/MKL_support.h
deleted file mode 100644
index 17963fa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/MKL_support.h
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to Intel(R) MKL
- *   Include file with common MKL declarations
- ********************************************************************************
-*/
-
-#ifndef EIGEN_MKL_SUPPORT_H
-#define EIGEN_MKL_SUPPORT_H
-
-#ifdef EIGEN_USE_MKL_ALL
-  #ifndef EIGEN_USE_BLAS
-    #define EIGEN_USE_BLAS
-  #endif
-  #ifndef EIGEN_USE_LAPACKE
-    #define EIGEN_USE_LAPACKE
-  #endif
-  #ifndef EIGEN_USE_MKL_VML
-    #define EIGEN_USE_MKL_VML
-  #endif
-#endif
-
-#ifdef EIGEN_USE_LAPACKE_STRICT
-  #define EIGEN_USE_LAPACKE
-#endif
-
-#if defined(EIGEN_USE_MKL_VML) && !defined(EIGEN_USE_MKL)
-  #define EIGEN_USE_MKL
-#endif
-
-
-#if defined EIGEN_USE_MKL
-#   if (!defined MKL_DIRECT_CALL) && (!defined EIGEN_MKL_NO_DIRECT_CALL)
-#       define MKL_DIRECT_CALL
-#       define MKL_DIRECT_CALL_JUST_SET
-#   endif
-#   include <mkl.h>
-/*Check IMKL version for compatibility: < 10.3 is not usable with Eigen*/
-#   ifndef INTEL_MKL_VERSION
-#       undef EIGEN_USE_MKL /* INTEL_MKL_VERSION is not even defined on older versions */
-#   elif INTEL_MKL_VERSION < 100305    /* the intel-mkl-103-release-notes say this was when the lapacke.h interface was added*/
-#       undef EIGEN_USE_MKL
-#   endif
-#   ifndef EIGEN_USE_MKL
-    /*If the MKL version is too old, undef everything*/
-#       undef   EIGEN_USE_MKL_ALL
-#       undef   EIGEN_USE_LAPACKE
-#       undef   EIGEN_USE_MKL_VML
-#       undef   EIGEN_USE_LAPACKE_STRICT
-#       undef   EIGEN_USE_LAPACKE
-#       ifdef   MKL_DIRECT_CALL_JUST_SET
-#           undef MKL_DIRECT_CALL
-#       endif
-#   endif
-#endif
-
-#if defined EIGEN_USE_MKL
-
-#define EIGEN_MKL_VML_THRESHOLD 128
-
-/* MKL_DOMAIN_BLAS, etc are defined only in 10.3 update 7 */
-/* MKL_BLAS, etc are not defined in 11.2 */
-#ifdef MKL_DOMAIN_ALL
-#define EIGEN_MKL_DOMAIN_ALL MKL_DOMAIN_ALL
-#else
-#define EIGEN_MKL_DOMAIN_ALL MKL_ALL
-#endif
-
-#ifdef MKL_DOMAIN_BLAS
-#define EIGEN_MKL_DOMAIN_BLAS MKL_DOMAIN_BLAS
-#else
-#define EIGEN_MKL_DOMAIN_BLAS MKL_BLAS
-#endif
-
-#ifdef MKL_DOMAIN_FFT
-#define EIGEN_MKL_DOMAIN_FFT MKL_DOMAIN_FFT
-#else
-#define EIGEN_MKL_DOMAIN_FFT MKL_FFT
-#endif
-
-#ifdef MKL_DOMAIN_VML
-#define EIGEN_MKL_DOMAIN_VML MKL_DOMAIN_VML
-#else
-#define EIGEN_MKL_DOMAIN_VML MKL_VML
-#endif
-
-#ifdef MKL_DOMAIN_PARDISO
-#define EIGEN_MKL_DOMAIN_PARDISO MKL_DOMAIN_PARDISO
-#else
-#define EIGEN_MKL_DOMAIN_PARDISO MKL_PARDISO
-#endif
-#endif
-
-#if defined(EIGEN_USE_BLAS) && !defined(EIGEN_USE_MKL)
-#include "../../misc/blas.h"
-#endif
-
-namespace Eigen {
-
-typedef std::complex<double> dcomplex;
-typedef std::complex<float>  scomplex;
-
-#if defined(EIGEN_USE_MKL)
-typedef MKL_INT BlasIndex;
-#else
-typedef int BlasIndex;
-#endif
-
-} // end namespace Eigen
-
-
-#endif // EIGEN_MKL_SUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Macros.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Macros.h
deleted file mode 100644
index 986c3d4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Macros.h
+++ /dev/null
@@ -1,1464 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MACROS_H
-#define EIGEN_MACROS_H
-
-//------------------------------------------------------------------------------------------
-// Eigen version and basic defaults
-//------------------------------------------------------------------------------------------
-
-#define EIGEN_WORLD_VERSION 3
-#define EIGEN_MAJOR_VERSION 4
-#define EIGEN_MINOR_VERSION 0
-
-#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
-                                      (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
-                                                                 EIGEN_MINOR_VERSION>=z))))
-
-#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
-#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::RowMajor
-#else
-#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::ColMajor
-#endif
-
-#ifndef EIGEN_DEFAULT_DENSE_INDEX_TYPE
-#define EIGEN_DEFAULT_DENSE_INDEX_TYPE std::ptrdiff_t
-#endif
-
-// Upperbound on the C++ version to use.
-// Expected values are 03, 11, 14, 17, etc.
-// By default, let's use an arbitrarily large C++ version.
-#ifndef EIGEN_MAX_CPP_VER
-#define EIGEN_MAX_CPP_VER 99
-#endif
-
-/** Allows to disable some optimizations which might affect the accuracy of the result.
-  * Such optimization are enabled by default, and set EIGEN_FAST_MATH to 0 to disable them.
-  * They currently include:
-  *   - single precision ArrayBase::sin() and ArrayBase::cos() for SSE and AVX vectorization.
-  */
-#ifndef EIGEN_FAST_MATH
-#define EIGEN_FAST_MATH 1
-#endif
-
-#ifndef EIGEN_STACK_ALLOCATION_LIMIT
-// 131072 == 128 KB
-#define EIGEN_STACK_ALLOCATION_LIMIT 131072
-#endif
-
-//------------------------------------------------------------------------------------------
-// Compiler identification, EIGEN_COMP_*
-//------------------------------------------------------------------------------------------
-
-/// \internal EIGEN_COMP_GNUC set to 1 for all compilers compatible with GCC
-#ifdef __GNUC__
-  #define EIGEN_COMP_GNUC (__GNUC__*10+__GNUC_MINOR__)
-#else
-  #define EIGEN_COMP_GNUC 0
-#endif
-
-/// \internal EIGEN_COMP_CLANG set to major+minor version (e.g., 307 for clang 3.7) if the compiler is clang
-#if defined(__clang__)
-  #define EIGEN_COMP_CLANG (__clang_major__*100+__clang_minor__)
-#else
-  #define EIGEN_COMP_CLANG 0
-#endif
-
-/// \internal EIGEN_COMP_CASTXML set to 1 if being preprocessed by CastXML
-#if defined(__castxml__)
-  #define EIGEN_COMP_CASTXML 1
-#else
-  #define EIGEN_COMP_CASTXML 0
-#endif
-
-/// \internal EIGEN_COMP_LLVM set to 1 if the compiler backend is llvm
-#if defined(__llvm__)
-  #define EIGEN_COMP_LLVM 1
-#else
-  #define EIGEN_COMP_LLVM 0
-#endif
-
-/// \internal EIGEN_COMP_ICC set to __INTEL_COMPILER if the compiler is Intel compiler, 0 otherwise
-#if defined(__INTEL_COMPILER)
-  #define EIGEN_COMP_ICC __INTEL_COMPILER
-#else
-  #define EIGEN_COMP_ICC 0
-#endif
-
-/// \internal EIGEN_COMP_MINGW set to 1 if the compiler is mingw
-#if defined(__MINGW32__)
-  #define EIGEN_COMP_MINGW 1
-#else
-  #define EIGEN_COMP_MINGW 0
-#endif
-
-/// \internal EIGEN_COMP_SUNCC set to 1 if the compiler is Solaris Studio
-#if defined(__SUNPRO_CC)
-  #define EIGEN_COMP_SUNCC 1
-#else
-  #define EIGEN_COMP_SUNCC 0
-#endif
-
-/// \internal EIGEN_COMP_MSVC set to _MSC_VER if the compiler is Microsoft Visual C++, 0 otherwise.
-#if defined(_MSC_VER)
-  #define EIGEN_COMP_MSVC _MSC_VER
-#else
-  #define EIGEN_COMP_MSVC 0
-#endif
-
-#if defined(__NVCC__)
-#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
-  #define EIGEN_COMP_NVCC  ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
-#elif defined(__CUDACC_VER__)
-  #define EIGEN_COMP_NVCC __CUDACC_VER__
-#else
-  #error "NVCC did not define compiler version."
-#endif
-#else
-  #define EIGEN_COMP_NVCC 0
-#endif
-
-// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC:
-//  name        ver   MSC_VER
-//  2008         9      1500
-//  2010        10      1600
-//  2012        11      1700
-//  2013        12      1800
-//  2015        14      1900
-//  "15"        15      1900
-//  2017-14.1   15.0    1910
-//  2017-14.11  15.3    1911
-//  2017-14.12  15.5    1912
-//  2017-14.13  15.6    1913
-//  2017-14.14  15.7    1914
-
-/// \internal EIGEN_COMP_MSVC_LANG set to _MSVC_LANG if the compiler is Microsoft Visual C++, 0 otherwise.
-#if defined(_MSVC_LANG)
-  #define EIGEN_COMP_MSVC_LANG _MSVC_LANG
-#else
-  #define EIGEN_COMP_MSVC_LANG 0
-#endif
-
-// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC_LANG:
-// MSVC option                          Standard  MSVC_LANG
-// /std:c++14 (default as of VS 2019)   C++14     201402L
-// /std:c++17                           C++17     201703L
-// /std:c++latest                       >C++17    >201703L
-
-/// \internal EIGEN_COMP_MSVC_STRICT set to 1 if the compiler is really Microsoft Visual C++ and not ,e.g., ICC or clang-cl
-#if EIGEN_COMP_MSVC && !(EIGEN_COMP_ICC || EIGEN_COMP_LLVM || EIGEN_COMP_CLANG)
-  #define EIGEN_COMP_MSVC_STRICT _MSC_VER
-#else
-  #define EIGEN_COMP_MSVC_STRICT 0
-#endif
-
-/// \internal EIGEN_COMP_IBM set to xlc version if the compiler is IBM XL C++
-// XLC   version
-// 3.1   0x0301
-// 4.5   0x0405
-// 5.0   0x0500
-// 12.1  0x0C01
-#if defined(__IBMCPP__) || defined(__xlc__) || defined(__ibmxl__)
-  #define EIGEN_COMP_IBM __xlC__
-#else
-  #define EIGEN_COMP_IBM 0
-#endif
-
-/// \internal EIGEN_COMP_PGI set to PGI version if the compiler is Portland Group Compiler
-#if defined(__PGI)
-  #define EIGEN_COMP_PGI (__PGIC__*100+__PGIC_MINOR__)
-#else
-  #define EIGEN_COMP_PGI 0
-#endif
-
-/// \internal EIGEN_COMP_ARM set to 1 if the compiler is ARM Compiler
-#if defined(__CC_ARM) || defined(__ARMCC_VERSION)
-  #define EIGEN_COMP_ARM 1
-#else
-  #define EIGEN_COMP_ARM 0
-#endif
-
-/// \internal EIGEN_COMP_EMSCRIPTEN set to 1 if the compiler is Emscripten Compiler
-#if defined(__EMSCRIPTEN__)
-  #define EIGEN_COMP_EMSCRIPTEN 1
-#else
-  #define EIGEN_COMP_EMSCRIPTEN 0
-#endif
-
-
-/// \internal EIGEN_GNUC_STRICT set to 1 if the compiler is really GCC and not a compatible compiler (e.g., ICC, clang, mingw, etc.)
-#if EIGEN_COMP_GNUC && !(EIGEN_COMP_CLANG || EIGEN_COMP_ICC || EIGEN_COMP_MINGW || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM || EIGEN_COMP_EMSCRIPTEN)
-  #define EIGEN_COMP_GNUC_STRICT 1
-#else
-  #define EIGEN_COMP_GNUC_STRICT 0
-#endif
-
-
-#if EIGEN_COMP_GNUC
-  #define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__==x && __GNUC_MINOR__>=y) || __GNUC__>x)
-  #define EIGEN_GNUC_AT_MOST(x,y)  ((__GNUC__==x && __GNUC_MINOR__<=y) || __GNUC__<x)
-  #define EIGEN_GNUC_AT(x,y)       ( __GNUC__==x && __GNUC_MINOR__==y )
-#else
-  #define EIGEN_GNUC_AT_LEAST(x,y) 0
-  #define EIGEN_GNUC_AT_MOST(x,y)  0
-  #define EIGEN_GNUC_AT(x,y)       0
-#endif
-
-// FIXME: could probably be removed as we do not support gcc 3.x anymore
-#if EIGEN_COMP_GNUC && (__GNUC__ <= 3)
-#define EIGEN_GCC3_OR_OLDER 1
-#else
-#define EIGEN_GCC3_OR_OLDER 0
-#endif
-
-
-
-//------------------------------------------------------------------------------------------
-// Architecture identification, EIGEN_ARCH_*
-//------------------------------------------------------------------------------------------
-
-
-#if defined(__x86_64__) || (defined(_M_X64) && !defined(_M_ARM64EC)) || defined(__amd64)
-  #define EIGEN_ARCH_x86_64 1
-#else
-  #define EIGEN_ARCH_x86_64 0
-#endif
-
-#if defined(__i386__) || defined(_M_IX86) || defined(_X86_) || defined(__i386)
-  #define EIGEN_ARCH_i386 1
-#else
-  #define EIGEN_ARCH_i386 0
-#endif
-
-#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_i386
-  #define EIGEN_ARCH_i386_OR_x86_64 1
-#else
-  #define EIGEN_ARCH_i386_OR_x86_64 0
-#endif
-
-/// \internal EIGEN_ARCH_ARM set to 1 if the architecture is ARM
-#if defined(__arm__)
-  #define EIGEN_ARCH_ARM 1
-#else
-  #define EIGEN_ARCH_ARM 0
-#endif
-
-/// \internal EIGEN_ARCH_ARM64 set to 1 if the architecture is ARM64
-#if defined(__aarch64__) || defined(_M_ARM64) || defined(_M_ARM64EC)
-  #define EIGEN_ARCH_ARM64 1
-#else
-  #define EIGEN_ARCH_ARM64 0
-#endif
-
-/// \internal EIGEN_ARCH_ARM_OR_ARM64 set to 1 if the architecture is ARM or ARM64
-#if EIGEN_ARCH_ARM || EIGEN_ARCH_ARM64
-  #define EIGEN_ARCH_ARM_OR_ARM64 1
-#else
-  #define EIGEN_ARCH_ARM_OR_ARM64 0
-#endif
-
-/// \internal EIGEN_ARCH_ARMV8 set to 1 if the architecture is armv8 or greater.
-#if EIGEN_ARCH_ARM_OR_ARM64 && defined(__ARM_ARCH) && __ARM_ARCH >= 8
-#define EIGEN_ARCH_ARMV8 1
-#else
-#define EIGEN_ARCH_ARMV8 0
-#endif
-
-
-/// \internal EIGEN_HAS_ARM64_FP16 set to 1 if the architecture provides an IEEE
-/// compliant Arm fp16 type
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16
-    #if defined(__ARM_FP16_FORMAT_IEEE)
-      #define EIGEN_HAS_ARM64_FP16 1
-    #else
-      #define EIGEN_HAS_ARM64_FP16 0
-    #endif
-  #endif
-#endif
-
-/// \internal EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC set to 1 if the architecture
-/// supports Neon vector intrinsics for fp16.
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-    #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 1
-    #else
-      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 0
-    #endif
-  #endif
-#endif
-
-/// \internal EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC set to 1 if the architecture
-/// supports Neon scalar intrinsics for fp16.
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
-    #if defined(__ARM_FEATURE_FP16_SCALAR_ARITHMETIC)
-      #define EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC 1
-    #endif
-  #endif
-#endif
-
-/// \internal EIGEN_ARCH_MIPS set to 1 if the architecture is MIPS
-#if defined(__mips__) || defined(__mips)
-  #define EIGEN_ARCH_MIPS 1
-#else
-  #define EIGEN_ARCH_MIPS 0
-#endif
-
-/// \internal EIGEN_ARCH_SPARC set to 1 if the architecture is SPARC
-#if defined(__sparc__) || defined(__sparc)
-  #define EIGEN_ARCH_SPARC 1
-#else
-  #define EIGEN_ARCH_SPARC 0
-#endif
-
-/// \internal EIGEN_ARCH_IA64 set to 1 if the architecture is Intel Itanium
-#if defined(__ia64__)
-  #define EIGEN_ARCH_IA64 1
-#else
-  #define EIGEN_ARCH_IA64 0
-#endif
-
-/// \internal EIGEN_ARCH_PPC set to 1 if the architecture is PowerPC
-#if defined(__powerpc__) || defined(__ppc__) || defined(_M_PPC)
-  #define EIGEN_ARCH_PPC 1
-#else
-  #define EIGEN_ARCH_PPC 0
-#endif
-
-
-
-//------------------------------------------------------------------------------------------
-// Operating system identification, EIGEN_OS_*
-//------------------------------------------------------------------------------------------
-
-/// \internal EIGEN_OS_UNIX set to 1 if the OS is a unix variant
-#if defined(__unix__) || defined(__unix)
-  #define EIGEN_OS_UNIX 1
-#else
-  #define EIGEN_OS_UNIX 0
-#endif
-
-/// \internal EIGEN_OS_LINUX set to 1 if the OS is based on Linux kernel
-#if defined(__linux__)
-  #define EIGEN_OS_LINUX 1
-#else
-  #define EIGEN_OS_LINUX 0
-#endif
-
-/// \internal EIGEN_OS_ANDROID set to 1 if the OS is Android
-// note: ANDROID is defined when using ndk_build, __ANDROID__ is defined when using a standalone toolchain.
-#if defined(__ANDROID__) || defined(ANDROID)
-  #define EIGEN_OS_ANDROID 1
-#else
-  #define EIGEN_OS_ANDROID 0
-#endif
-
-/// \internal EIGEN_OS_GNULINUX set to 1 if the OS is GNU Linux and not Linux-based OS (e.g., not android)
-#if defined(__gnu_linux__) && !(EIGEN_OS_ANDROID)
-  #define EIGEN_OS_GNULINUX 1
-#else
-  #define EIGEN_OS_GNULINUX 0
-#endif
-
-/// \internal EIGEN_OS_BSD set to 1 if the OS is a BSD variant
-#if defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__) || defined(__bsdi__) || defined(__DragonFly__)
-  #define EIGEN_OS_BSD 1
-#else
-  #define EIGEN_OS_BSD 0
-#endif
-
-/// \internal EIGEN_OS_MAC set to 1 if the OS is MacOS
-#if defined(__APPLE__)
-  #define EIGEN_OS_MAC 1
-#else
-  #define EIGEN_OS_MAC 0
-#endif
-
-/// \internal EIGEN_OS_QNX set to 1 if the OS is QNX
-#if defined(__QNX__)
-  #define EIGEN_OS_QNX 1
-#else
-  #define EIGEN_OS_QNX 0
-#endif
-
-/// \internal EIGEN_OS_WIN set to 1 if the OS is Windows based
-#if defined(_WIN32)
-  #define EIGEN_OS_WIN 1
-#else
-  #define EIGEN_OS_WIN 0
-#endif
-
-/// \internal EIGEN_OS_WIN64 set to 1 if the OS is Windows 64bits
-#if defined(_WIN64)
-  #define EIGEN_OS_WIN64 1
-#else
-  #define EIGEN_OS_WIN64 0
-#endif
-
-/// \internal EIGEN_OS_WINCE set to 1 if the OS is Windows CE
-#if defined(_WIN32_WCE)
-  #define EIGEN_OS_WINCE 1
-#else
-  #define EIGEN_OS_WINCE 0
-#endif
-
-/// \internal EIGEN_OS_CYGWIN set to 1 if the OS is Windows/Cygwin
-#if defined(__CYGWIN__)
-  #define EIGEN_OS_CYGWIN 1
-#else
-  #define EIGEN_OS_CYGWIN 0
-#endif
-
-/// \internal EIGEN_OS_WIN_STRICT set to 1 if the OS is really Windows and not some variants
-#if EIGEN_OS_WIN && !( EIGEN_OS_WINCE || EIGEN_OS_CYGWIN )
-  #define EIGEN_OS_WIN_STRICT 1
-#else
-  #define EIGEN_OS_WIN_STRICT 0
-#endif
-
-/// \internal EIGEN_OS_SUN set to __SUNPRO_C if the OS is SUN
-// compiler  solaris   __SUNPRO_C
-// version   studio
-// 5.7       10        0x570
-// 5.8       11        0x580
-// 5.9       12        0x590
-// 5.10	     12.1      0x5100
-// 5.11	     12.2      0x5110
-// 5.12	     12.3      0x5120
-#if (defined(sun) || defined(__sun)) && !(defined(__SVR4) || defined(__svr4__))
-  #define EIGEN_OS_SUN __SUNPRO_C
-#else
-  #define EIGEN_OS_SUN 0
-#endif
-
-/// \internal EIGEN_OS_SOLARIS set to 1 if the OS is Solaris
-#if (defined(sun) || defined(__sun)) && (defined(__SVR4) || defined(__svr4__))
-  #define EIGEN_OS_SOLARIS 1
-#else
-  #define EIGEN_OS_SOLARIS 0
-#endif
-
-
-//------------------------------------------------------------------------------------------
-// Detect GPU compilers and architectures
-//------------------------------------------------------------------------------------------
-
-// NVCC is not supported as the target platform for HIPCC
-// Note that this also makes EIGEN_CUDACC and EIGEN_HIPCC mutually exclusive
-#if defined(__NVCC__) && defined(__HIPCC__)
-  #error "NVCC as the target platform for HIPCC is currently not supported."
-#endif
-
-#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA)
-  // Means the compiler is either nvcc or clang with CUDA enabled
-  #define EIGEN_CUDACC __CUDACC__
-#endif
-
-#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA)
-  // Means we are generating code for the device
-  #define EIGEN_CUDA_ARCH __CUDA_ARCH__
-#endif
-
-#if defined(EIGEN_CUDACC)
-#include <cuda.h>
-  #define EIGEN_CUDA_SDK_VER (CUDA_VERSION * 10)
-#else
-  #define EIGEN_CUDA_SDK_VER 0
-#endif
-
-#if defined(__HIPCC__) && !defined(EIGEN_NO_HIP)
-  // Means the compiler is HIPCC (analogous to EIGEN_CUDACC, but for HIP)
-  #define EIGEN_HIPCC __HIPCC__
-
-  // We need to include hip_runtime.h here because it pulls in
-  // ++ hip_common.h which contains the define for  __HIP_DEVICE_COMPILE__
-  // ++ host_defines.h which contains the defines for the __host__ and __device__ macros
-  #include <hip/hip_runtime.h>
-
-  #if defined(__HIP_DEVICE_COMPILE__)
-    // analogous to EIGEN_CUDA_ARCH, but for HIP
-    #define EIGEN_HIP_DEVICE_COMPILE __HIP_DEVICE_COMPILE__
-  #endif
-
-  // For HIP (ROCm 3.5 and higher), we need to explicitly set the launch_bounds attribute
-  // value to 1024. The compiler assigns a default value of 256 when the attribute is not
-  // specified. This results in failures on the HIP platform, for cases when a GPU kernel
-  // without an explicit launch_bounds attribute is called with a threads_per_block value
-  // greater than 256.
-  //
-  // This is a regression in functioanlity and is expected to be fixed within the next
-  // couple of ROCm releases (compiler will go back to using 1024 value as the default)
-  //
-  // In the meantime, we will use a "only enabled for HIP" macro to set the launch_bounds
-  // attribute.
-
-  #define EIGEN_HIP_LAUNCH_BOUNDS_1024 __launch_bounds__(1024)
-
-#endif
-
-#if !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
-#define EIGEN_HIP_LAUNCH_BOUNDS_1024
-#endif // !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
-
-// Unify CUDA/HIPCC
-
-#if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
-//
-// If either EIGEN_CUDACC or EIGEN_HIPCC is defined, then define EIGEN_GPUCC
-//
-#define EIGEN_GPUCC
-//
-// EIGEN_HIPCC implies the HIP compiler and is used to tweak Eigen code for use in HIP kernels
-// EIGEN_CUDACC implies the CUDA compiler and is used to tweak Eigen code for use in CUDA kernels
-//
-// In most cases the same tweaks are required to the Eigen code to enable in both the HIP and CUDA kernels.
-// For those cases, the corresponding code should be guarded with
-//      #if defined(EIGEN_GPUCC)
-// instead of
-//      #if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
-//
-// For cases where the tweak is specific to HIP, the code should be guarded with
-//      #if defined(EIGEN_HIPCC)
-//
-// For cases where the tweak is specific to CUDA, the code should be guarded with
-//      #if defined(EIGEN_CUDACC)
-//
-#endif
-
-#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
-//
-// If either EIGEN_CUDA_ARCH or EIGEN_HIP_DEVICE_COMPILE is defined, then define EIGEN_GPU_COMPILE_PHASE
-//
-#define EIGEN_GPU_COMPILE_PHASE
-//
-// GPU compilers (HIPCC, NVCC) typically do two passes over the source code,
-//   + one to compile the source for the "host" (ie CPU)
-//   + another to compile the source for the "device" (ie. GPU)
-//
-// Code that needs to enabled only during the either the "host" or "device" compilation phase
-// needs to be guarded with a macro that indicates the current compilation phase
-//
-// EIGEN_HIP_DEVICE_COMPILE implies the device compilation phase in HIP
-// EIGEN_CUDA_ARCH implies the device compilation phase in CUDA
-//
-// In most cases, the "host" / "device" specific code is the same for both HIP and CUDA
-// For those cases, the code should be guarded with
-//       #if defined(EIGEN_GPU_COMPILE_PHASE)
-// instead of
-//       #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
-//
-// For cases where the tweak is specific to HIP, the code should be guarded with
-//      #if defined(EIGEN_HIP_DEVICE_COMPILE)
-//
-// For cases where the tweak is specific to CUDA, the code should be guarded with
-//      #if defined(EIGEN_CUDA_ARCH)
-//
-#endif
-
-#if defined(EIGEN_USE_SYCL) && defined(__SYCL_DEVICE_ONLY__)
-// EIGEN_USE_SYCL is a user-defined macro while __SYCL_DEVICE_ONLY__ is a compiler-defined macro.
-// In most cases we want to check if both macros are defined which can be done using the define below.
-#define SYCL_DEVICE_ONLY
-#endif
-
-//------------------------------------------------------------------------------------------
-// Detect Compiler/Architecture/OS specific features
-//------------------------------------------------------------------------------------------
-
-#if EIGEN_GNUC_AT_MOST(4,3) && !EIGEN_COMP_CLANG
-  // see bug 89
-  #define EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO 0
-#else
-  #define EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO 1
-#endif
-
-// Cross compiler wrapper around LLVM's __has_builtin
-#ifdef __has_builtin
-#  define EIGEN_HAS_BUILTIN(x) __has_builtin(x)
-#else
-#  define EIGEN_HAS_BUILTIN(x) 0
-#endif
-
-// A Clang feature extension to determine compiler features.
-// We use it to determine 'cxx_rvalue_references'
-#ifndef __has_feature
-# define __has_feature(x) 0
-#endif
-
-// Some old compilers do not support template specializations like:
-// template<typename T,int N> void foo(const T x[N]);
-#if !(   EIGEN_COMP_CLANG && (   (EIGEN_COMP_CLANG<309)                                                       \
-                              || (defined(__apple_build_version__) && (__apple_build_version__ < 9000000)))  \
-      || EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<49)
-#define EIGEN_HAS_STATIC_ARRAY_TEMPLATE 1
-#else
-#define EIGEN_HAS_STATIC_ARRAY_TEMPLATE 0
-#endif
-
-// The macro EIGEN_CPLUSPLUS is a replacement for __cplusplus/_MSVC_LANG that
-// works for both platforms, indicating the C++ standard version number.
-//
-// With MSVC, without defining /Zc:__cplusplus, the __cplusplus macro will
-// report 199711L regardless of the language standard specified via /std.
-// We need to rely on _MSVC_LANG instead, which is only available after
-// VS2015.3.
-#if EIGEN_COMP_MSVC_LANG > 0
-#define EIGEN_CPLUSPLUS EIGEN_COMP_MSVC_LANG
-#elif EIGEN_COMP_MSVC >= 1900
-#define EIGEN_CPLUSPLUS 201103L
-#elif defined(__cplusplus)
-#define EIGEN_CPLUSPLUS __cplusplus
-#else
-#define EIGEN_CPLUSPLUS 0
-#endif
-
-// The macro EIGEN_COMP_CXXVER defines the c++ verson expected by the compiler.
-// For instance, if compiling with gcc and -std=c++17, then EIGEN_COMP_CXXVER
-// is defined to 17.
-#if EIGEN_CPLUSPLUS > 201703L
-  #define EIGEN_COMP_CXXVER 20
-#elif EIGEN_CPLUSPLUS > 201402L
-  #define EIGEN_COMP_CXXVER 17
-#elif EIGEN_CPLUSPLUS > 201103L
-  #define EIGEN_COMP_CXXVER 14
-#elif EIGEN_CPLUSPLUS >= 201103L
-  #define EIGEN_COMP_CXXVER 11
-#else
-  #define EIGEN_COMP_CXXVER 03
-#endif
-
-#ifndef EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
-  #if defined(__cpp_variable_templates) && __cpp_variable_templates >= 201304 && EIGEN_MAX_CPP_VER>=14
-    #define EIGEN_HAS_CXX14_VARIABLE_TEMPLATES 1
-  #else
-    #define EIGEN_HAS_CXX14_VARIABLE_TEMPLATES 0
-  #endif
-#endif
-
-
-// The macros EIGEN_HAS_CXX?? defines a rough estimate of available c++ features
-// but in practice we should not rely on them but rather on the availabilty of
-// individual features as defined later.
-// This is why there is no EIGEN_HAS_CXX17.
-// FIXME: get rid of EIGEN_HAS_CXX14 and maybe even EIGEN_HAS_CXX11.
-#if EIGEN_MAX_CPP_VER>=11 && EIGEN_COMP_CXXVER>=11
-#define EIGEN_HAS_CXX11 1
-#else
-#define EIGEN_HAS_CXX11 0
-#endif
-
-#if EIGEN_MAX_CPP_VER>=14 && EIGEN_COMP_CXXVER>=14
-#define EIGEN_HAS_CXX14 1
-#else
-#define EIGEN_HAS_CXX14 0
-#endif
-
-// Do we support r-value references?
-#ifndef EIGEN_HAS_RVALUE_REFERENCES
-#if EIGEN_MAX_CPP_VER>=11 && \
-    (__has_feature(cxx_rvalue_references) || \
-     (EIGEN_COMP_CXXVER >= 11) || (EIGEN_COMP_MSVC >= 1600))
-  #define EIGEN_HAS_RVALUE_REFERENCES 1
-#else
-  #define EIGEN_HAS_RVALUE_REFERENCES 0
-#endif
-#endif
-
-// Does the compiler support C99?
-// Need to include <cmath> to make sure _GLIBCXX_USE_C99 gets defined
-#include <cmath>
-#ifndef EIGEN_HAS_C99_MATH
-#if EIGEN_MAX_CPP_VER>=11 && \
-    ((defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901))       \
-  || (defined(__GNUC__) && defined(_GLIBCXX_USE_C99)) \
-  || (defined(_LIBCPP_VERSION) && !defined(_MSC_VER)) \
-  || (EIGEN_COMP_MSVC >= 1900) || defined(SYCL_DEVICE_ONLY))
-  #define EIGEN_HAS_C99_MATH 1
-#else
-  #define EIGEN_HAS_C99_MATH 0
-#endif
-#endif
-
-// Does the compiler support result_of?
-// result_of was deprecated in c++17 and removed in c++ 20
-#ifndef EIGEN_HAS_STD_RESULT_OF
-#if EIGEN_HAS_CXX11 && EIGEN_COMP_CXXVER < 17
-#define EIGEN_HAS_STD_RESULT_OF 1
-#else
-#define EIGEN_HAS_STD_RESULT_OF 0
-#endif
-#endif
-
-// Does the compiler support std::hash?
-#ifndef EIGEN_HAS_STD_HASH
-// The std::hash struct is defined in C++11 but is not labelled as a __device__
-// function and is not constexpr, so cannot be used on device.
-#if EIGEN_HAS_CXX11 && !defined(EIGEN_GPU_COMPILE_PHASE)
-#define EIGEN_HAS_STD_HASH 1
-#else
-#define EIGEN_HAS_STD_HASH 0
-#endif
-#endif  // EIGEN_HAS_STD_HASH
-
-#ifndef EIGEN_HAS_STD_INVOKE_RESULT
-#if EIGEN_MAX_CPP_VER >= 17 && EIGEN_COMP_CXXVER >= 17
-#define EIGEN_HAS_STD_INVOKE_RESULT 1
-#else
-#define EIGEN_HAS_STD_INVOKE_RESULT 0
-#endif
-#endif
-
-#ifndef EIGEN_HAS_ALIGNAS
-#if EIGEN_MAX_CPP_VER>=11 && EIGEN_HAS_CXX11 &&   \
-      (     __has_feature(cxx_alignas)            \
-        ||  EIGEN_HAS_CXX14                       \
-        || (EIGEN_COMP_MSVC >= 1800)              \
-        || (EIGEN_GNUC_AT_LEAST(4,8))             \
-        || (EIGEN_COMP_CLANG>=305)                \
-        || (EIGEN_COMP_ICC>=1500)                 \
-        || (EIGEN_COMP_PGI>=1500)                 \
-        || (EIGEN_COMP_SUNCC>=0x5130))
-#define EIGEN_HAS_ALIGNAS 1
-#else
-#define EIGEN_HAS_ALIGNAS 0
-#endif
-#endif
-
-// Does the compiler support type_traits?
-// - full support of type traits was added only to GCC 5.1.0.
-// - 20150626 corresponds to the last release of 4.x libstdc++
-#ifndef EIGEN_HAS_TYPE_TRAITS
-#if EIGEN_MAX_CPP_VER>=11 && (EIGEN_HAS_CXX11 || EIGEN_COMP_MSVC >= 1700) \
-  && ((!EIGEN_COMP_GNUC_STRICT) || EIGEN_GNUC_AT_LEAST(5, 1)) \
-  && ((!defined(__GLIBCXX__))   || __GLIBCXX__ > 20150626)
-#define EIGEN_HAS_TYPE_TRAITS 1
-#define EIGEN_INCLUDE_TYPE_TRAITS
-#else
-#define EIGEN_HAS_TYPE_TRAITS 0
-#endif
-#endif
-
-// Does the compiler support variadic templates?
-#ifndef EIGEN_HAS_VARIADIC_TEMPLATES
-#if EIGEN_MAX_CPP_VER>=11 && (EIGEN_COMP_CXXVER >= 11) \
-  && (!defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64 || (EIGEN_COMP_NVCC >= 80000) )
-    // ^^ Disable the use of variadic templates when compiling with versions of nvcc older than 8.0 on ARM devices:
-    //    this prevents nvcc from crashing when compiling Eigen on Tegra X1
-#define EIGEN_HAS_VARIADIC_TEMPLATES 1
-#elif  EIGEN_MAX_CPP_VER>=11 && (EIGEN_COMP_CXXVER >= 11) && defined(SYCL_DEVICE_ONLY)
-#define EIGEN_HAS_VARIADIC_TEMPLATES 1
-#else
-#define EIGEN_HAS_VARIADIC_TEMPLATES 0
-#endif
-#endif
-
-// Does the compiler fully support const expressions? (as in c++14)
-#ifndef EIGEN_HAS_CONSTEXPR
-  #if defined(EIGEN_CUDACC)
-  // Const expressions are supported provided that c++11 is enabled and we're using either clang or nvcc 7.5 or above
-    #if EIGEN_MAX_CPP_VER>=14 && (EIGEN_COMP_CXXVER >= 11 && (EIGEN_COMP_CLANG || EIGEN_COMP_NVCC >= 70500))
-      #define EIGEN_HAS_CONSTEXPR 1
-    #endif
-  #elif EIGEN_MAX_CPP_VER>=14 && (__has_feature(cxx_relaxed_constexpr) || (EIGEN_COMP_CXXVER >= 14) || \
-    (EIGEN_GNUC_AT_LEAST(4,8) && (EIGEN_COMP_CXXVER >= 11)) || \
-    (EIGEN_COMP_CLANG >= 306 && (EIGEN_COMP_CXXVER >= 11)))
-    #define EIGEN_HAS_CONSTEXPR 1
-  #endif
-
-  #ifndef EIGEN_HAS_CONSTEXPR
-    #define EIGEN_HAS_CONSTEXPR 0
-  #endif
-
-#endif // EIGEN_HAS_CONSTEXPR
-
-#if EIGEN_HAS_CONSTEXPR
-#define EIGEN_CONSTEXPR constexpr
-#else
-#define EIGEN_CONSTEXPR
-#endif
-
-// Does the compiler support C++11 math?
-// Let's be conservative and enable the default C++11 implementation only if we are sure it exists
-#ifndef EIGEN_HAS_CXX11_MATH
-  #if EIGEN_MAX_CPP_VER>=11 && ((EIGEN_COMP_CXXVER > 11) || (EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC)  \
-      && (EIGEN_ARCH_i386_OR_x86_64) && (EIGEN_OS_GNULINUX || EIGEN_OS_WIN_STRICT || EIGEN_OS_MAC))
-    #define EIGEN_HAS_CXX11_MATH 1
-  #else
-    #define EIGEN_HAS_CXX11_MATH 0
-  #endif
-#endif
-
-// Does the compiler support proper C++11 containers?
-#ifndef EIGEN_HAS_CXX11_CONTAINERS
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-         ((EIGEN_COMP_CXXVER > 11) \
-      || ((EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC>=1400)))
-    #define EIGEN_HAS_CXX11_CONTAINERS 1
-  #else
-    #define EIGEN_HAS_CXX11_CONTAINERS 0
-  #endif
-#endif
-
-// Does the compiler support C++11 noexcept?
-#ifndef EIGEN_HAS_CXX11_NOEXCEPT
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-         (__has_feature(cxx_noexcept) \
-      || (EIGEN_COMP_CXXVER > 11) \
-      || ((EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC>=1400)))
-    #define EIGEN_HAS_CXX11_NOEXCEPT 1
-  #else
-    #define EIGEN_HAS_CXX11_NOEXCEPT 0
-  #endif
-#endif
-
-#ifndef EIGEN_HAS_CXX11_ATOMIC
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-         (__has_feature(cxx_atomic) \
-      || (EIGEN_COMP_CXXVER > 11) \
-      || ((EIGEN_COMP_CXXVER == 11) && (EIGEN_COMP_MSVC==0 || EIGEN_COMP_MSVC >= 1700)))
-    #define EIGEN_HAS_CXX11_ATOMIC 1
-  #else
-    #define EIGEN_HAS_CXX11_ATOMIC 0
-  #endif
-#endif
-
-#ifndef EIGEN_HAS_CXX11_OVERRIDE_FINAL
-  #if    EIGEN_MAX_CPP_VER>=11 && \
-       (EIGEN_COMP_CXXVER >= 11 || EIGEN_COMP_MSVC >= 1700)
-    #define EIGEN_HAS_CXX11_OVERRIDE_FINAL 1
-  #else
-    #define EIGEN_HAS_CXX11_OVERRIDE_FINAL 0
-  #endif
-#endif
-
-// NOTE: the required Apple's clang version is very conservative
-//       and it could be that XCode 9 works just fine.
-// NOTE: the MSVC version is based on https://en.cppreference.com/w/cpp/compiler_support
-//       and not tested.
-#ifndef EIGEN_HAS_CXX17_OVERALIGN
-#if EIGEN_MAX_CPP_VER>=17 && EIGEN_COMP_CXXVER>=17 && (                                 \
-           (EIGEN_COMP_MSVC >= 1912)                                                    \
-        || (EIGEN_GNUC_AT_LEAST(7,0))                                                   \
-        || ((!defined(__apple_build_version__)) && (EIGEN_COMP_CLANG>=500))             \
-        || (( defined(__apple_build_version__)) && (__apple_build_version__>=10000000)) \
-      )
-#define EIGEN_HAS_CXX17_OVERALIGN 1
-#else
-#define EIGEN_HAS_CXX17_OVERALIGN 0
-#endif
-#endif
-
-#if defined(EIGEN_CUDACC) && EIGEN_HAS_CONSTEXPR
-  // While available already with c++11, this is useful mostly starting with c++14 and relaxed constexpr rules
-  #if defined(__NVCC__)
-    // nvcc considers constexpr functions as __host__ __device__ with the option --expt-relaxed-constexpr
-    #ifdef __CUDACC_RELAXED_CONSTEXPR__
-      #define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
-    #endif
-  #elif defined(__clang__) && defined(__CUDA__) && __has_feature(cxx_relaxed_constexpr)
-    // clang++ always considers constexpr functions as implicitly __host__ __device__
-    #define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
-  #endif
-#endif
-
-// Does the compiler support the __int128 and __uint128_t extensions for 128-bit
-// integer arithmetic?
-//
-// Clang and GCC define __SIZEOF_INT128__ when these extensions are supported,
-// but we avoid using them in certain cases:
-//
-// * Building using Clang for Windows, where the Clang runtime library has
-//   128-bit support only on LP64 architectures, but Windows is LLP64.
-#ifndef EIGEN_HAS_BUILTIN_INT128
-#if defined(__SIZEOF_INT128__) && !(EIGEN_OS_WIN && EIGEN_COMP_CLANG)
-#define EIGEN_HAS_BUILTIN_INT128 1
-#else
-#define EIGEN_HAS_BUILTIN_INT128 0
-#endif
-#endif
-
-//------------------------------------------------------------------------------------------
-// Preprocessor programming helpers
-//------------------------------------------------------------------------------------------
-
-// This macro can be used to prevent from macro expansion, e.g.:
-//   std::max EIGEN_NOT_A_MACRO(a,b)
-#define EIGEN_NOT_A_MACRO
-
-#define EIGEN_DEBUG_VAR(x) std::cerr << #x << " = " << x << std::endl;
-
-// concatenate two tokens
-#define EIGEN_CAT2(a,b) a ## b
-#define EIGEN_CAT(a,b) EIGEN_CAT2(a,b)
-
-#define EIGEN_COMMA ,
-
-// convert a token to a string
-#define EIGEN_MAKESTRING2(a) #a
-#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
-
-// EIGEN_STRONG_INLINE is a stronger version of the inline, using __forceinline on MSVC,
-// but it still doesn't use GCC's always_inline. This is useful in (common) situations where MSVC needs forceinline
-// but GCC is still doing fine with just inline.
-#ifndef EIGEN_STRONG_INLINE
-#if (EIGEN_COMP_MSVC || EIGEN_COMP_ICC) && !defined(EIGEN_GPUCC)
-#define EIGEN_STRONG_INLINE __forceinline
-#else
-#define EIGEN_STRONG_INLINE inline
-#endif
-#endif
-
-// EIGEN_ALWAYS_INLINE is the stronget, it has the effect of making the function inline and adding every possible
-// attribute to maximize inlining. This should only be used when really necessary: in particular,
-// it uses __attribute__((always_inline)) on GCC, which most of the time is useless and can severely harm compile times.
-// FIXME with the always_inline attribute,
-// gcc 3.4.x and 4.1 reports the following compilation error:
-//   Eval.h:91: sorry, unimplemented: inlining failed in call to 'const Eigen::Eval<Derived> Eigen::MatrixBase<Scalar, Derived>::eval() const'
-//    : function body not available
-//   See also bug 1367
-#if EIGEN_GNUC_AT_LEAST(4,2) && !defined(SYCL_DEVICE_ONLY)
-#define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline
-#else
-#define EIGEN_ALWAYS_INLINE EIGEN_STRONG_INLINE
-#endif
-
-#if EIGEN_COMP_GNUC
-#define EIGEN_DONT_INLINE __attribute__((noinline))
-#elif EIGEN_COMP_MSVC
-#define EIGEN_DONT_INLINE __declspec(noinline)
-#else
-#define EIGEN_DONT_INLINE
-#endif
-
-#if EIGEN_COMP_GNUC
-#define EIGEN_PERMISSIVE_EXPR __extension__
-#else
-#define EIGEN_PERMISSIVE_EXPR
-#endif
-
-// GPU stuff
-
-// Disable some features when compiling with GPU compilers (NVCC/clang-cuda/SYCL/HIPCC)
-#if defined(EIGEN_CUDACC) || defined(SYCL_DEVICE_ONLY) || defined(EIGEN_HIPCC)
-  // Do not try asserts on device code
-  #ifndef EIGEN_NO_DEBUG
-  #define EIGEN_NO_DEBUG
-  #endif
-
-  #ifdef EIGEN_INTERNAL_DEBUGGING
-  #undef EIGEN_INTERNAL_DEBUGGING
-  #endif
-
-  #ifdef EIGEN_EXCEPTIONS
-  #undef EIGEN_EXCEPTIONS
-  #endif
-#endif
-
-#if defined(SYCL_DEVICE_ONLY)
-  #ifndef EIGEN_DONT_VECTORIZE
-    #define EIGEN_DONT_VECTORIZE
-  #endif
-  #define EIGEN_DEVICE_FUNC __attribute__((flatten)) __attribute__((always_inline))
-// All functions callable from CUDA/HIP code must be qualified with __device__
-#elif defined(EIGEN_GPUCC)
-    #define EIGEN_DEVICE_FUNC __host__ __device__
-#else
-  #define EIGEN_DEVICE_FUNC
-#endif
-
-
-// this macro allows to get rid of linking errors about multiply defined functions.
-//  - static is not very good because it prevents definitions from different object files to be merged.
-//           So static causes the resulting linked executable to be bloated with multiple copies of the same function.
-//  - inline is not perfect either as it unwantedly hints the compiler toward inlining the function.
-#define EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
-#define EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC inline
-
-#ifdef NDEBUG
-# ifndef EIGEN_NO_DEBUG
-#  define EIGEN_NO_DEBUG
-# endif
-#endif
-
-// eigen_plain_assert is where we implement the workaround for the assert() bug in GCC <= 4.3, see bug 89
-#ifdef EIGEN_NO_DEBUG
-  #ifdef SYCL_DEVICE_ONLY // used to silence the warning on SYCL device
-    #define eigen_plain_assert(x) EIGEN_UNUSED_VARIABLE(x)
-  #else
-    #define eigen_plain_assert(x)
-  #endif
-#else
-  #if EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO
-    namespace Eigen {
-    namespace internal {
-    inline bool copy_bool(bool b) { return b; }
-    }
-    }
-    #define eigen_plain_assert(x) assert(x)
-  #else
-    // work around bug 89
-    #include <cstdlib>   // for abort
-    #include <iostream>  // for std::cerr
-
-    namespace Eigen {
-    namespace internal {
-    // trivial function copying a bool. Must be EIGEN_DONT_INLINE, so we implement it after including Eigen headers.
-    // see bug 89.
-    namespace {
-    EIGEN_DONT_INLINE bool copy_bool(bool b) { return b; }
-    }
-    inline void assert_fail(const char *condition, const char *function, const char *file, int line)
-    {
-      std::cerr << "assertion failed: " << condition << " in function " << function << " at " << file << ":" << line << std::endl;
-      abort();
-    }
-    }
-    }
-    #define eigen_plain_assert(x) \
-      do { \
-        if(!Eigen::internal::copy_bool(x)) \
-          Eigen::internal::assert_fail(EIGEN_MAKESTRING(x), __PRETTY_FUNCTION__, __FILE__, __LINE__); \
-      } while(false)
-  #endif
-#endif
-
-// eigen_assert can be overridden
-#ifndef eigen_assert
-#define eigen_assert(x) eigen_plain_assert(x)
-#endif
-
-#ifdef EIGEN_INTERNAL_DEBUGGING
-#define eigen_internal_assert(x) eigen_assert(x)
-#else
-#define eigen_internal_assert(x)
-#endif
-
-#ifdef EIGEN_NO_DEBUG
-#define EIGEN_ONLY_USED_FOR_DEBUG(x) EIGEN_UNUSED_VARIABLE(x)
-#else
-#define EIGEN_ONLY_USED_FOR_DEBUG(x)
-#endif
-
-#ifndef EIGEN_NO_DEPRECATED_WARNING
-  #if EIGEN_COMP_GNUC
-    #define EIGEN_DEPRECATED __attribute__((deprecated))
-  #elif EIGEN_COMP_MSVC
-    #define EIGEN_DEPRECATED __declspec(deprecated)
-  #else
-    #define EIGEN_DEPRECATED
-  #endif
-#else
-  #define EIGEN_DEPRECATED
-#endif
-
-#if EIGEN_COMP_GNUC
-#define EIGEN_UNUSED __attribute__((unused))
-#else
-#define EIGEN_UNUSED
-#endif
-
-// Suppresses 'unused variable' warnings.
-namespace Eigen {
-  namespace internal {
-    template<typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ignore_unused_variable(const T&) {}
-  }
-}
-#define EIGEN_UNUSED_VARIABLE(var) Eigen::internal::ignore_unused_variable(var);
-
-#if !defined(EIGEN_ASM_COMMENT)
-  #if EIGEN_COMP_GNUC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64)
-    #define EIGEN_ASM_COMMENT(X)  __asm__("#" X)
-  #else
-    #define EIGEN_ASM_COMMENT(X)
-  #endif
-#endif
-
-
-// Acts as a barrier preventing operations involving `X` from crossing. This
-// occurs, for example, in the fast rounding trick where a magic constant is
-// added then subtracted, which is otherwise compiled away with -ffast-math.
-//
-// See bug 1674
-#if !defined(EIGEN_OPTIMIZATION_BARRIER)
-  #if EIGEN_COMP_GNUC
-    // According to https://gcc.gnu.org/onlinedocs/gcc/Constraints.html:
-    //   X: Any operand whatsoever.
-    //   r: A register operand is allowed provided that it is in a general
-    //      register.
-    //   g: Any register, memory or immediate integer operand is allowed, except
-    //      for registers that are not general registers.
-    //   w: (AArch32/AArch64) Floating point register, Advanced SIMD vector
-    //      register or SVE vector register.
-    //   x: (SSE) Any SSE register.
-    //      (AArch64) Like w, but restricted to registers 0 to 15 inclusive.
-    //   v: (PowerPC) An Altivec vector register.
-    //   wa:(PowerPC) A VSX register.
-    //
-    // "X" (uppercase) should work for all cases, though this seems to fail for
-    // some versions of GCC for arm/aarch64 with
-    //   "error: inconsistent operand constraints in an 'asm'"
-    // Clang x86_64/arm/aarch64 seems to require "g" to support both scalars and
-    // vectors, otherwise
-    //   "error: non-trivial scalar-to-vector conversion, possible invalid
-    //    constraint for vector type"
-    //
-    // GCC for ppc64le generates an internal compiler error with x/X/g.
-    // GCC for AVX generates an internal compiler error with X.
-    //
-    // Tested on icc/gcc/clang for sse, avx, avx2, avx512dq
-    //           gcc for arm, aarch64,
-    //           gcc for ppc64le,
-    // both vectors and scalars.
-    //
-    // Note that this is restricted to plain types - this will not work
-    // directly for std::complex<T>, Eigen::half, Eigen::bfloat16. For these,
-    // you will need to apply to the underlying POD type.
-    #if EIGEN_ARCH_PPC && EIGEN_COMP_GNUC_STRICT
-      // This seems to be broken on clang.  Packet4f is loaded into a single
-      //   register rather than a vector, zeroing out some entries.  Integer
-      //   types also generate a compile error.
-      // General, Altivec, VSX.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,v,wa" (X));
-    #elif EIGEN_ARCH_ARM_OR_ARM64
-      // General, NEON.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,w" (X));
-    #elif EIGEN_ARCH_i386_OR_x86_64
-      // General, SSE.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,x" (X));
-    #else
-      // Not implemented for other architectures.
-      #define EIGEN_OPTIMIZATION_BARRIER(X)
-    #endif
-  #else
-    // Not implemented for other compilers.
-    #define EIGEN_OPTIMIZATION_BARRIER(X)
-  #endif
-#endif
-
-#if EIGEN_COMP_MSVC
-  // NOTE MSVC often gives C4127 warnings with compiletime if statements. See bug 1362.
-  // This workaround is ugly, but it does the job.
-#  define EIGEN_CONST_CONDITIONAL(cond)  (void)0, cond
-#else
-#  define EIGEN_CONST_CONDITIONAL(cond)  cond
-#endif
-
-#ifdef EIGEN_DONT_USE_RESTRICT_KEYWORD
-  #define EIGEN_RESTRICT
-#endif
-#ifndef EIGEN_RESTRICT
-  #define EIGEN_RESTRICT __restrict
-#endif
-
-
-#ifndef EIGEN_DEFAULT_IO_FORMAT
-#ifdef EIGEN_MAKING_DOCS
-// format used in Eigen's documentation
-// needed to define it here as escaping characters in CMake add_definition's argument seems very problematic.
-#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat(3, 0, " ", "\n", "", "")
-#else
-#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat()
-#endif
-#endif
-
-// just an empty macro !
-#define EIGEN_EMPTY
-
-
-// When compiling CUDA/HIP device code with NVCC or HIPCC
-// pull in math functions from the global namespace.
-// In host mode, and when device code is compiled with clang,
-// use the std versions.
-#if (defined(EIGEN_CUDA_ARCH) && defined(__NVCC__)) || defined(EIGEN_HIP_DEVICE_COMPILE)
-  #define EIGEN_USING_STD(FUNC) using ::FUNC;
-#else
-  #define EIGEN_USING_STD(FUNC) using std::FUNC;
-#endif
-
-#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC < 1900 || (EIGEN_COMP_MSVC == 1900 && EIGEN_COMP_NVCC))
-  // For older MSVC versions, as well as 1900 && CUDA 8, using the base operator is necessary,
-  //   otherwise we get duplicate definition errors
-  // For later MSVC versions, we require explicit operator= definition, otherwise we get
-  //   use of implicitly deleted operator errors.
-  // (cf Bugs 920, 1000, 1324, 2291)
-  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    using Base::operator =;
-#elif EIGEN_COMP_CLANG // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
-  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    using Base::operator =; \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \
-    template <typename OtherDerived> \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }
-#else
-  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    using Base::operator =; \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) \
-    { \
-      Base::operator=(other); \
-      return *this; \
-    }
-#endif
-
-
-/**
- * \internal
- * \brief Macro to explicitly define the default copy constructor.
- * This is necessary, because the implicit definition is deprecated if the copy-assignment is overridden.
- */
-#if EIGEN_HAS_CXX11
-#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS) CLASS(const CLASS&) = default;
-#else
-#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS)
-#endif
-
-
-
-/** \internal
- * \brief Macro to manually inherit assignment operators.
- * This is necessary, because the implicitly defined assignment operator gets deleted when a custom operator= is defined.
- * With C++11 or later this also default-implements the copy-constructor
- */
-#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived)  \
-    EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(Derived)
-
-/** \internal
- * \brief Macro to manually define default constructors and destructors.
- * This is necessary when the copy constructor is re-defined.
- * For empty helper classes this should usually be protected, to avoid accidentally creating empty objects.
- *
- * Hiding the default destructor lead to problems in C++03 mode together with boost::multiprecision
- */
-#if EIGEN_HAS_CXX11
-#define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived)  \
-    Derived() = default; \
-    ~Derived() = default;
-#else
-#define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived)  \
-    Derived() {}; \
-    /* ~Derived() {}; */
-#endif
-
-
-
-
-
-/**
-* Just a side note. Commenting within defines works only by documenting
-* behind the object (via '!<'). Comments cannot be multi-line and thus
-* we have these extra long lines. What is confusing doxygen over here is
-* that we use '\' and basically have a bunch of typedefs with their
-* documentation in a single line.
-**/
-
-#define EIGEN_GENERIC_PUBLIC_INTERFACE(Derived) \
-  typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex<float>. */ \
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is e.g. std::complex<T>, T were corresponding to RealScalar. */ \
-  typedef typename Base::CoeffReturnType CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&' or simply 'Scalar' for objects that do not allow direct coefficient access. */ \
-  typedef typename Eigen::internal::ref_selector<Derived>::type Nested; \
-  typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \
-  typedef typename Eigen::internal::traits<Derived>::StorageIndex StorageIndex; \
-  enum CompileTimeTraits \
-      { RowsAtCompileTime = Eigen::internal::traits<Derived>::RowsAtCompileTime, \
-        ColsAtCompileTime = Eigen::internal::traits<Derived>::ColsAtCompileTime, \
-        Flags = Eigen::internal::traits<Derived>::Flags, \
-        SizeAtCompileTime = Base::SizeAtCompileTime, \
-        MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime, \
-        IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \
-  using Base::derived; \
-  using Base::const_cast_derived;
-
-
-// FIXME Maybe the EIGEN_DENSE_PUBLIC_INTERFACE could be removed as importing PacketScalar is rarely needed
-#define EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \
-  EIGEN_GENERIC_PUBLIC_INTERFACE(Derived) \
-  typedef typename Base::PacketScalar PacketScalar;
-
-
-#define EIGEN_PLAIN_ENUM_MIN(a,b) (((int)a <= (int)b) ? (int)a : (int)b)
-#define EIGEN_PLAIN_ENUM_MAX(a,b) (((int)a >= (int)b) ? (int)a : (int)b)
-
-// EIGEN_SIZE_MIN_PREFER_DYNAMIC gives the min between compile-time sizes. 0 has absolute priority, followed by 1,
-// followed by Dynamic, followed by other finite values. The reason for giving Dynamic the priority over
-// finite values is that min(3, Dynamic) should be Dynamic, since that could be anything between 0 and 3.
-#define EIGEN_SIZE_MIN_PREFER_DYNAMIC(a,b) (((int)a == 0 || (int)b == 0) ? 0 \
-                           : ((int)a == 1 || (int)b == 1) ? 1 \
-                           : ((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \
-                           : ((int)a <= (int)b) ? (int)a : (int)b)
-
-// EIGEN_SIZE_MIN_PREFER_FIXED is a variant of EIGEN_SIZE_MIN_PREFER_DYNAMIC comparing MaxSizes. The difference is that finite values
-// now have priority over Dynamic, so that min(3, Dynamic) gives 3. Indeed, whatever the actual value is
-// (between 0 and 3), it is not more than 3.
-#define EIGEN_SIZE_MIN_PREFER_FIXED(a,b)  (((int)a == 0 || (int)b == 0) ? 0 \
-                           : ((int)a == 1 || (int)b == 1) ? 1 \
-                           : ((int)a == Dynamic && (int)b == Dynamic) ? Dynamic \
-                           : ((int)a == Dynamic) ? (int)b \
-                           : ((int)b == Dynamic) ? (int)a \
-                           : ((int)a <= (int)b) ? (int)a : (int)b)
-
-// see EIGEN_SIZE_MIN_PREFER_DYNAMIC. No need for a separate variant for MaxSizes here.
-#define EIGEN_SIZE_MAX(a,b) (((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \
-                           : ((int)a >= (int)b) ? (int)a : (int)b)
-
-#define EIGEN_LOGICAL_XOR(a,b) (((a) || (b)) && !((a) && (b)))
-
-#define EIGEN_IMPLIES(a,b) (!(a) || (b))
-
-#if EIGEN_HAS_BUILTIN(__builtin_expect) || EIGEN_COMP_GNUC
-#define EIGEN_PREDICT_FALSE(x) (__builtin_expect(x, false))
-#define EIGEN_PREDICT_TRUE(x) (__builtin_expect(false || (x), true))
-#else
-#define EIGEN_PREDICT_FALSE(x) (x)
-#define EIGEN_PREDICT_TRUE(x) (x)
-#endif
-
-// the expression type of a standard coefficient wise binary operation
-#define EIGEN_CWISE_BINARY_RETURN_TYPE(LHS,RHS,OPNAME) \
-    CwiseBinaryOp< \
-      EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)< \
-          typename internal::traits<LHS>::Scalar, \
-          typename internal::traits<RHS>::Scalar \
-      >, \
-      const LHS, \
-      const RHS \
-    >
-
-#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,OPNAME) \
-  template<typename OtherDerived> \
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME) \
-  (METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
-  { \
-    return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME)(derived(), other.derived()); \
-  }
-
-#define EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,TYPEA,TYPEB) \
-  (Eigen::internal::has_ReturnType<Eigen::ScalarBinaryOpTraits<TYPEA,TYPEB,EIGEN_CAT(EIGEN_CAT(Eigen::internal::scalar_,OPNAME),_op)<TYPEA,TYPEB>  > >::value)
-
-#define EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(EXPR,SCALAR,OPNAME) \
-  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<typename internal::traits<EXPR>::Scalar,SCALAR>, const EXPR, \
-                const typename internal::plain_constant_type<EXPR,SCALAR>::type>
-
-#define EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(SCALAR,EXPR,OPNAME) \
-  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<SCALAR,typename internal::traits<EXPR>::Scalar>, \
-                const typename internal::plain_constant_type<EXPR,SCALAR>::type, const EXPR>
-
-// Workaround for MSVC 2010 (see ML thread "patch with compile for for MSVC 2010")
-#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC_STRICT<=1600)
-#define EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(X) typename internal::enable_if<true,X>::type
-#else
-#define EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(X) X
-#endif
-
-#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME) \
-  template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type,OPNAME))\
-  (METHOD)(const T& scalar) const { \
-    typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type PromotedT; \
-    return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedT,OPNAME)(derived(), \
-           typename internal::plain_constant_type<Derived,PromotedT>::type(derived().rows(), derived().cols(), internal::scalar_constant_op<PromotedT>(scalar))); \
-  }
-
-#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
-  template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend \
-  EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type,Derived,OPNAME)) \
-  (METHOD)(const T& scalar, const StorageBaseType& matrix) { \
-    typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type PromotedT; \
-    return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedT,Derived,OPNAME)( \
-           typename internal::plain_constant_type<Derived,PromotedT>::type(matrix.derived().rows(), matrix.derived().cols(), internal::scalar_constant_op<PromotedT>(scalar)), matrix.derived()); \
-  }
-
-#define EIGEN_MAKE_SCALAR_BINARY_OP(METHOD,OPNAME) \
-  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
-  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME)
-
-
-#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(EIGEN_CUDA_ARCH) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL) && !defined(EIGEN_HIP_DEVICE_COMPILE)
-  #define EIGEN_EXCEPTIONS
-#endif
-
-
-#ifdef EIGEN_EXCEPTIONS
-#  define EIGEN_THROW_X(X) throw X
-#  define EIGEN_THROW throw
-#  define EIGEN_TRY try
-#  define EIGEN_CATCH(X) catch (X)
-#else
-#  if defined(EIGEN_CUDA_ARCH)
-#    define EIGEN_THROW_X(X) asm("trap;")
-#    define EIGEN_THROW asm("trap;")
-#  elif defined(EIGEN_HIP_DEVICE_COMPILE)
-#    define EIGEN_THROW_X(X) asm("s_trap 0")
-#    define EIGEN_THROW asm("s_trap 0")
-#  else
-#    define EIGEN_THROW_X(X) std::abort()
-#    define EIGEN_THROW std::abort()
-#  endif
-#  define EIGEN_TRY if (true)
-#  define EIGEN_CATCH(X) else
-#endif
-
-
-#if EIGEN_HAS_CXX11_NOEXCEPT
-#   define EIGEN_INCLUDE_TYPE_TRAITS
-#   define EIGEN_NOEXCEPT noexcept
-#   define EIGEN_NOEXCEPT_IF(x) noexcept(x)
-#   define EIGEN_NO_THROW noexcept(true)
-#   define EIGEN_EXCEPTION_SPEC(X) noexcept(false)
-#else
-#   define EIGEN_NOEXCEPT
-#   define EIGEN_NOEXCEPT_IF(x)
-#   define EIGEN_NO_THROW throw()
-#   if EIGEN_COMP_MSVC || EIGEN_COMP_CXXVER>=17
-      // MSVC does not support exception specifications (warning C4290),
-      // and they are deprecated in c++11 anyway. This is even an error in c++17.
-#     define EIGEN_EXCEPTION_SPEC(X) throw()
-#   else
-#     define EIGEN_EXCEPTION_SPEC(X) throw(X)
-#   endif
-#endif
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-// The all function is used to enable a variadic version of eigen_assert which can take a parameter pack as its input.
-namespace Eigen {
-namespace internal {
-
-inline bool all(){ return true; }
-
-template<typename T, typename ...Ts>
-bool all(T t, Ts ... ts){ return t && all(ts...); }
-
-}
-}
-#endif
-
-#if EIGEN_HAS_CXX11_OVERRIDE_FINAL
-// provide override and final specifiers if they are available:
-#   define EIGEN_OVERRIDE override
-#   define EIGEN_FINAL final
-#else
-#   define EIGEN_OVERRIDE
-#   define EIGEN_FINAL
-#endif
-
-// Wrapping #pragma unroll in a macro since it is required for SYCL
-#if defined(SYCL_DEVICE_ONLY)
-  #if defined(_MSC_VER)
-    #define EIGEN_UNROLL_LOOP __pragma(unroll)
-  #else
-    #define EIGEN_UNROLL_LOOP _Pragma("unroll")
-  #endif
-#else
-  #define EIGEN_UNROLL_LOOP
-#endif
-
-#endif // EIGEN_MACROS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Memory.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Memory.h
deleted file mode 100644
index 875318c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Memory.h
+++ /dev/null
@@ -1,1163 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Kenneth Riddile <kfriddile@yahoo.com>
-// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
-// Copyright (C) 2010 Thomas Capricelli <orzel@freehackers.org>
-// Copyright (C) 2013 Pavel Holoborodko <pavel@holoborodko.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-/*****************************************************************************
-*** Platform checks for aligned malloc functions                           ***
-*****************************************************************************/
-
-#ifndef EIGEN_MEMORY_H
-#define EIGEN_MEMORY_H
-
-#ifndef EIGEN_MALLOC_ALREADY_ALIGNED
-
-// Try to determine automatically if malloc is already aligned.
-
-// On 64-bit systems, glibc's malloc returns 16-byte-aligned pointers, see:
-//   http://www.gnu.org/s/libc/manual/html_node/Aligned-Memory-Blocks.html
-// This is true at least since glibc 2.8.
-// This leaves the question how to detect 64-bit. According to this document,
-//   http://gcc.fyxm.net/summit/2003/Porting%20to%2064%20bit.pdf
-// page 114, "[The] LP64 model [...] is used by all 64-bit UNIX ports" so it's indeed
-// quite safe, at least within the context of glibc, to equate 64-bit with LP64.
-#if defined(__GLIBC__) && ((__GLIBC__>=2 && __GLIBC_MINOR__ >= 8) || __GLIBC__>2) \
- && defined(__LP64__) && ! defined( __SANITIZE_ADDRESS__ ) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
-  #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 1
-#else
-  #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 0
-#endif
-
-// FreeBSD 6 seems to have 16-byte aligned malloc
-//   See http://svn.freebsd.org/viewvc/base/stable/6/lib/libc/stdlib/malloc.c?view=markup
-// FreeBSD 7 seems to have 16-byte aligned malloc except on ARM and MIPS architectures
-//   See http://svn.freebsd.org/viewvc/base/stable/7/lib/libc/stdlib/malloc.c?view=markup
-#if defined(__FreeBSD__) && !(EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
-  #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 1
-#else
-  #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 0
-#endif
-
-#if (EIGEN_OS_MAC && (EIGEN_DEFAULT_ALIGN_BYTES == 16))     \
- || (EIGEN_OS_WIN64 && (EIGEN_DEFAULT_ALIGN_BYTES == 16))   \
- || EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED              \
- || EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED
-  #define EIGEN_MALLOC_ALREADY_ALIGNED 1
-#else
-  #define EIGEN_MALLOC_ALREADY_ALIGNED 0
-#endif
-
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-EIGEN_DEVICE_FUNC
-inline void throw_std_bad_alloc()
-{
-  #ifdef EIGEN_EXCEPTIONS
-    throw std::bad_alloc();
-  #else
-    std::size_t huge = static_cast<std::size_t>(-1);
-    #if defined(EIGEN_HIPCC)
-    //
-    // calls to "::operator new" are to be treated as opaque function calls (i.e no inlining),
-    // and as a consequence the code in the #else block triggers the hipcc warning :
-    // "no overloaded function has restriction specifiers that are compatible with the ambient context"
-    //
-    // "throw_std_bad_alloc" has the EIGEN_DEVICE_FUNC attribute, so it seems that hipcc expects
-    // the same on "operator new"
-    // Reverting code back to the old version in this #if block for the hipcc compiler
-    //
-    new int[huge];
-    #else
-    void* unused = ::operator new(huge);
-    EIGEN_UNUSED_VARIABLE(unused);
-    #endif
-  #endif
-}
-
-/*****************************************************************************
-*** Implementation of handmade aligned functions                           ***
-*****************************************************************************/
-
-/* ----- Hand made implementations of aligned malloc/free and realloc ----- */
-
-/** \internal Like malloc, but the returned pointer is guaranteed to be 16-byte aligned.
-  * Fast, but wastes 16 additional bytes of memory. Does not throw any exception.
-  */
-EIGEN_DEVICE_FUNC inline void* handmade_aligned_malloc(std::size_t size, std::size_t alignment = EIGEN_DEFAULT_ALIGN_BYTES)
-{
-  eigen_assert(alignment >= sizeof(void*) && (alignment & (alignment-1)) == 0 && "Alignment must be at least sizeof(void*) and a power of 2");
-
-  EIGEN_USING_STD(malloc)
-  void *original = malloc(size+alignment);
-  
-  if (original == 0) return 0;
-  void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(alignment-1))) + alignment);
-  *(reinterpret_cast<void**>(aligned) - 1) = original;
-  return aligned;
-}
-
-/** \internal Frees memory allocated with handmade_aligned_malloc */
-EIGEN_DEVICE_FUNC inline void handmade_aligned_free(void *ptr)
-{
-  if (ptr) {
-    EIGEN_USING_STD(free)
-    free(*(reinterpret_cast<void**>(ptr) - 1));
-  }
-}
-
-/** \internal
-  * \brief Reallocates aligned memory.
-  * Since we know that our handmade version is based on std::malloc
-  * we can use std::realloc to implement efficient reallocation.
-  */
-inline void* handmade_aligned_realloc(void* ptr, std::size_t size, std::size_t = 0)
-{
-  if (ptr == 0) return handmade_aligned_malloc(size);
-  void *original = *(reinterpret_cast<void**>(ptr) - 1);
-  std::ptrdiff_t previous_offset = static_cast<char *>(ptr)-static_cast<char *>(original);
-  original = std::realloc(original,size+EIGEN_DEFAULT_ALIGN_BYTES);
-  if (original == 0) return 0;
-  void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1))) + EIGEN_DEFAULT_ALIGN_BYTES);
-  void *previous_aligned = static_cast<char *>(original)+previous_offset;
-  if(aligned!=previous_aligned)
-    std::memmove(aligned, previous_aligned, size);
-
-  *(reinterpret_cast<void**>(aligned) - 1) = original;
-  return aligned;
-}
-
-/*****************************************************************************
-*** Implementation of portable aligned versions of malloc/free/realloc     ***
-*****************************************************************************/
-
-#ifdef EIGEN_NO_MALLOC
-EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
-{
-  eigen_assert(false && "heap allocation is forbidden (EIGEN_NO_MALLOC is defined)");
-}
-#elif defined EIGEN_RUNTIME_NO_MALLOC
-EIGEN_DEVICE_FUNC inline bool is_malloc_allowed_impl(bool update, bool new_value = false)
-{
-  static bool value = true;
-  if (update == 1)
-    value = new_value;
-  return value;
-}
-EIGEN_DEVICE_FUNC inline bool is_malloc_allowed() { return is_malloc_allowed_impl(false); }
-EIGEN_DEVICE_FUNC inline bool set_is_malloc_allowed(bool new_value) { return is_malloc_allowed_impl(true, new_value); }
-EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
-{
-  eigen_assert(is_malloc_allowed() && "heap allocation is forbidden (EIGEN_RUNTIME_NO_MALLOC is defined and g_is_malloc_allowed is false)");
-}
-#else
-EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
-{}
-#endif
-
-/** \internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 or 32 bytes alignment depending on the requirements.
-  * On allocation error, the returned pointer is null, and std::bad_alloc is thrown.
-  */
-EIGEN_DEVICE_FUNC inline void* aligned_malloc(std::size_t size)
-{
-  check_that_malloc_is_allowed();
-
-  void *result;
-  #if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
-
-    EIGEN_USING_STD(malloc)
-    result = malloc(size);
-
-    #if EIGEN_DEFAULT_ALIGN_BYTES==16
-    eigen_assert((size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator.");
-    #endif
-  #else
-    result = handmade_aligned_malloc(size);
-  #endif
-
-  if(!result && size)
-    throw_std_bad_alloc();
-
-  return result;
-}
-
-/** \internal Frees memory allocated with aligned_malloc. */
-EIGEN_DEVICE_FUNC inline void aligned_free(void *ptr)
-{
-  #if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
-
-    EIGEN_USING_STD(free)
-    free(ptr);
-
-  #else
-    handmade_aligned_free(ptr);
-  #endif
-}
-
-/**
-  * \internal
-  * \brief Reallocates an aligned block of memory.
-  * \throws std::bad_alloc on allocation failure
-  */
-inline void* aligned_realloc(void *ptr, std::size_t new_size, std::size_t old_size)
-{
-  EIGEN_UNUSED_VARIABLE(old_size)
-
-  void *result;
-#if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
-  result = std::realloc(ptr,new_size);
-#else
-  result = handmade_aligned_realloc(ptr,new_size,old_size);
-#endif
-
-  if (!result && new_size)
-    throw_std_bad_alloc();
-
-  return result;
-}
-
-/*****************************************************************************
-*** Implementation of conditionally aligned functions                      ***
-*****************************************************************************/
-
-/** \internal Allocates \a size bytes. If Align is true, then the returned ptr is 16-byte-aligned.
-  * On allocation error, the returned pointer is null, and a std::bad_alloc is thrown.
-  */
-template<bool Align> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc(std::size_t size)
-{
-  return aligned_malloc(size);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc<false>(std::size_t size)
-{
-  check_that_malloc_is_allowed();
-
-  EIGEN_USING_STD(malloc)
-  void *result = malloc(size);
-
-  if(!result && size)
-    throw_std_bad_alloc();
-  return result;
-}
-
-/** \internal Frees memory allocated with conditional_aligned_malloc */
-template<bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_free(void *ptr)
-{
-  aligned_free(ptr);
-}
-
-template<> EIGEN_DEVICE_FUNC inline void conditional_aligned_free<false>(void *ptr)
-{
-  EIGEN_USING_STD(free)
-  free(ptr);
-}
-
-template<bool Align> inline void* conditional_aligned_realloc(void* ptr, std::size_t new_size, std::size_t old_size)
-{
-  return aligned_realloc(ptr, new_size, old_size);
-}
-
-template<> inline void* conditional_aligned_realloc<false>(void* ptr, std::size_t new_size, std::size_t)
-{
-  return std::realloc(ptr, new_size);
-}
-
-/*****************************************************************************
-*** Construction/destruction of array elements                             ***
-*****************************************************************************/
-
-/** \internal Destructs the elements of an array.
-  * The \a size parameters tells on how many objects to call the destructor of T.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline void destruct_elements_of_array(T *ptr, std::size_t size)
-{
-  // always destruct an array starting from the end.
-  if(ptr)
-    while(size) ptr[--size].~T();
-}
-
-/** \internal Constructs the elements of an array.
-  * The \a size parameter tells on how many objects to call the constructor of T.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline T* construct_elements_of_array(T *ptr, std::size_t size)
-{
-  std::size_t i;
-  EIGEN_TRY
-  {
-      for (i = 0; i < size; ++i) ::new (ptr + i) T;
-      return ptr;
-  }
-  EIGEN_CATCH(...)
-  {
-    destruct_elements_of_array(ptr, i);
-    EIGEN_THROW;
-  }
-  return NULL;
-}
-
-/*****************************************************************************
-*** Implementation of aligned new/delete-like functions                    ***
-*****************************************************************************/
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void check_size_for_overflow(std::size_t size)
-{
-  if(size > std::size_t(-1) / sizeof(T))
-    throw_std_bad_alloc();
-}
-
-/** \internal Allocates \a size objects of type T. The returned pointer is guaranteed to have 16 bytes alignment.
-  * On allocation error, the returned pointer is undefined, but a std::bad_alloc is thrown.
-  * The default constructor of T is called.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline T* aligned_new(std::size_t size)
-{
-  check_size_for_overflow<T>(size);
-  T *result = reinterpret_cast<T*>(aligned_malloc(sizeof(T)*size));
-  EIGEN_TRY
-  {
-    return construct_elements_of_array(result, size);
-  }
-  EIGEN_CATCH(...)
-  {
-    aligned_free(result);
-    EIGEN_THROW;
-  }
-  return result;
-}
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new(std::size_t size)
-{
-  check_size_for_overflow<T>(size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
-  EIGEN_TRY
-  {
-    return construct_elements_of_array(result, size);
-  }
-  EIGEN_CATCH(...)
-  {
-    conditional_aligned_free<Align>(result);
-    EIGEN_THROW;
-  }
-  return result;
-}
-
-/** \internal Deletes objects constructed with aligned_new
-  * The \a size parameters tells on how many objects to call the destructor of T.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline void aligned_delete(T *ptr, std::size_t size)
-{
-  destruct_elements_of_array<T>(ptr, size);
-  Eigen::internal::aligned_free(ptr);
-}
-
-/** \internal Deletes objects constructed with conditional_aligned_new
-  * The \a size parameters tells on how many objects to call the destructor of T.
-  */
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete(T *ptr, std::size_t size)
-{
-  destruct_elements_of_array<T>(ptr, size);
-  conditional_aligned_free<Align>(ptr);
-}
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_realloc_new(T* pts, std::size_t new_size, std::size_t old_size)
-{
-  check_size_for_overflow<T>(new_size);
-  check_size_for_overflow<T>(old_size);
-  if(new_size < old_size)
-    destruct_elements_of_array(pts+new_size, old_size-new_size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
-  if(new_size > old_size)
-  {
-    EIGEN_TRY
-    {
-      construct_elements_of_array(result+old_size, new_size-old_size);
-    }
-    EIGEN_CATCH(...)
-    {
-      conditional_aligned_free<Align>(result);
-      EIGEN_THROW;
-    }
-  }
-  return result;
-}
-
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new_auto(std::size_t size)
-{
-  if(size==0)
-    return 0; // short-cut. Also fixes Bug 884
-  check_size_for_overflow<T>(size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
-  if(NumTraits<T>::RequireInitialization)
-  {
-    EIGEN_TRY
-    {
-      construct_elements_of_array(result, size);
-    }
-    EIGEN_CATCH(...)
-    {
-      conditional_aligned_free<Align>(result);
-      EIGEN_THROW;
-    }
-  }
-  return result;
-}
-
-template<typename T, bool Align> inline T* conditional_aligned_realloc_new_auto(T* pts, std::size_t new_size, std::size_t old_size)
-{
-  check_size_for_overflow<T>(new_size);
-  check_size_for_overflow<T>(old_size);
-  if(NumTraits<T>::RequireInitialization && (new_size < old_size))
-    destruct_elements_of_array(pts+new_size, old_size-new_size);
-  T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
-  if(NumTraits<T>::RequireInitialization && (new_size > old_size))
-  {
-    EIGEN_TRY
-    {
-      construct_elements_of_array(result+old_size, new_size-old_size);
-    }
-    EIGEN_CATCH(...)
-    {
-      conditional_aligned_free<Align>(result);
-      EIGEN_THROW;
-    }
-  }
-  return result;
-}
-
-template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete_auto(T *ptr, std::size_t size)
-{
-  if(NumTraits<T>::RequireInitialization)
-    destruct_elements_of_array<T>(ptr, size);
-  conditional_aligned_free<Align>(ptr);
-}
-
-/****************************************************************************/
-
-/** \internal Returns the index of the first element of the array that is well aligned with respect to the requested \a Alignment.
-  *
-  * \tparam Alignment requested alignment in Bytes.
-  * \param array the address of the start of the array
-  * \param size the size of the array
-  *
-  * \note If no element of the array is well aligned or the requested alignment is not a multiple of a scalar,
-  * the size of the array is returned. For example with SSE, the requested alignment is typically 16-bytes. If
-  * packet size for the given scalar type is 1, then everything is considered well-aligned.
-  *
-  * \note Otherwise, if the Alignment is larger that the scalar size, we rely on the assumptions that sizeof(Scalar) is a
-  * power of 2. On the other hand, we do not assume that the array address is a multiple of sizeof(Scalar), as that fails for
-  * example with Scalar=double on certain 32-bit platforms, see bug #79.
-  *
-  * There is also the variant first_aligned(const MatrixBase&) defined in DenseCoeffsBase.h.
-  * \sa first_default_aligned()
-  */
-template<int Alignment, typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC inline Index first_aligned(const Scalar* array, Index size)
-{
-  const Index ScalarSize = sizeof(Scalar);
-  const Index AlignmentSize = Alignment / ScalarSize;
-  const Index AlignmentMask = AlignmentSize-1;
-
-  if(AlignmentSize<=1)
-  {
-    // Either the requested alignment if smaller than a scalar, or it exactly match a 1 scalar
-    // so that all elements of the array have the same alignment.
-    return 0;
-  }
-  else if( (UIntPtr(array) & (sizeof(Scalar)-1)) || (Alignment%ScalarSize)!=0)
-  {
-    // The array is not aligned to the size of a single scalar, or the requested alignment is not a multiple of the scalar size.
-    // Consequently, no element of the array is well aligned.
-    return size;
-  }
-  else
-  {
-    Index first = (AlignmentSize - (Index((UIntPtr(array)/sizeof(Scalar))) & AlignmentMask)) & AlignmentMask;
-    return (first < size) ? first : size;
-  }
-}
-
-/** \internal Returns the index of the first element of the array that is well aligned with respect the largest packet requirement.
-   * \sa first_aligned(Scalar*,Index) and first_default_aligned(DenseBase<Derived>) */
-template<typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC inline Index first_default_aligned(const Scalar* array, Index size)
-{
-  typedef typename packet_traits<Scalar>::type DefaultPacketType;
-  return first_aligned<unpacket_traits<DefaultPacketType>::alignment>(array, size);
-}
-
-/** \internal Returns the smallest integer multiple of \a base and greater or equal to \a size
-  */
-template<typename Index>
-inline Index first_multiple(Index size, Index base)
-{
-  return ((size+base-1)/base)*base;
-}
-
-// std::copy is much slower than memcpy, so let's introduce a smart_copy which
-// use memcpy on trivial types, i.e., on types that does not require an initialization ctor.
-template<typename T, bool UseMemcpy> struct smart_copy_helper;
-
-template<typename T> EIGEN_DEVICE_FUNC void smart_copy(const T* start, const T* end, T* target)
-{
-  smart_copy_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target);
-}
-
-template<typename T> struct smart_copy_helper<T,true> {
-  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target)
-  {
-    IntPtr size = IntPtr(end)-IntPtr(start);
-    if(size==0) return;
-    eigen_internal_assert(start!=0 && end!=0 && target!=0);
-    EIGEN_USING_STD(memcpy)
-    memcpy(target, start, size);
-  }
-};
-
-template<typename T> struct smart_copy_helper<T,false> {
-  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target)
-  { std::copy(start, end, target); }
-};
-
-// intelligent memmove. falls back to std::memmove for POD types, uses std::copy otherwise.
-template<typename T, bool UseMemmove> struct smart_memmove_helper;
-
-template<typename T> void smart_memmove(const T* start, const T* end, T* target)
-{
-  smart_memmove_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target);
-}
-
-template<typename T> struct smart_memmove_helper<T,true> {
-  static inline void run(const T* start, const T* end, T* target)
-  {
-    IntPtr size = IntPtr(end)-IntPtr(start);
-    if(size==0) return;
-    eigen_internal_assert(start!=0 && end!=0 && target!=0);
-    std::memmove(target, start, size);
-  }
-};
-
-template<typename T> struct smart_memmove_helper<T,false> {
-  static inline void run(const T* start, const T* end, T* target)
-  {
-    if (UIntPtr(target) < UIntPtr(start))
-    {
-      std::copy(start, end, target);
-    }
-    else
-    {
-      std::ptrdiff_t count = (std::ptrdiff_t(end)-std::ptrdiff_t(start)) / sizeof(T);
-      std::copy_backward(start, end, target + count);
-    }
-  }
-};
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-template<typename T> EIGEN_DEVICE_FUNC T* smart_move(T* start, T* end, T* target)
-{
-  return std::move(start, end, target);
-}
-#else
-template<typename T> EIGEN_DEVICE_FUNC T* smart_move(T* start, T* end, T* target)
-{
-  return std::copy(start, end, target);
-}
-#endif
-
-/*****************************************************************************
-*** Implementation of runtime stack allocation (falling back to malloc)    ***
-*****************************************************************************/
-
-// you can overwrite Eigen's default behavior regarding alloca by defining EIGEN_ALLOCA
-// to the appropriate stack allocation function
-#if ! defined EIGEN_ALLOCA && ! defined EIGEN_GPU_COMPILE_PHASE
-  #if EIGEN_OS_LINUX || EIGEN_OS_MAC || (defined alloca)
-    #define EIGEN_ALLOCA alloca
-  #elif EIGEN_COMP_MSVC
-    #define EIGEN_ALLOCA _alloca
-  #endif
-#endif
-
-// With clang -Oz -mthumb, alloca changes the stack pointer in a way that is
-// not allowed in Thumb2. -DEIGEN_STACK_ALLOCATION_LIMIT=0 doesn't work because
-// the compiler still emits bad code because stack allocation checks use "<=".
-// TODO: Eliminate after https://bugs.llvm.org/show_bug.cgi?id=23772
-// is fixed.
-#if defined(__clang__) && defined(__thumb__)
-  #undef EIGEN_ALLOCA
-#endif
-
-// This helper class construct the allocated memory, and takes care of destructing and freeing the handled data
-// at destruction time. In practice this helper class is mainly useful to avoid memory leak in case of exceptions.
-template<typename T> class aligned_stack_memory_handler : noncopyable
-{
-  public:
-    /* Creates a stack_memory_handler responsible for the buffer \a ptr of size \a size.
-     * Note that \a ptr can be 0 regardless of the other parameters.
-     * This constructor takes care of constructing/initializing the elements of the buffer if required by the scalar type T (see NumTraits<T>::RequireInitialization).
-     * In this case, the buffer elements will also be destructed when this handler will be destructed.
-     * Finally, if \a dealloc is true, then the pointer \a ptr is freed.
-     **/
-    EIGEN_DEVICE_FUNC
-    aligned_stack_memory_handler(T* ptr, std::size_t size, bool dealloc)
-      : m_ptr(ptr), m_size(size), m_deallocate(dealloc)
-    {
-      if(NumTraits<T>::RequireInitialization && m_ptr)
-        Eigen::internal::construct_elements_of_array(m_ptr, size);
-    }
-    EIGEN_DEVICE_FUNC
-    ~aligned_stack_memory_handler()
-    {
-      if(NumTraits<T>::RequireInitialization && m_ptr)
-        Eigen::internal::destruct_elements_of_array<T>(m_ptr, m_size);
-      if(m_deallocate)
-        Eigen::internal::aligned_free(m_ptr);
-    }
-  protected:
-    T* m_ptr;
-    std::size_t m_size;
-    bool m_deallocate;
-};
-
-#ifdef EIGEN_ALLOCA
-
-template<typename Xpr, int NbEvaluations,
-         bool MapExternalBuffer = nested_eval<Xpr,NbEvaluations>::Evaluate && Xpr::MaxSizeAtCompileTime==Dynamic
-         >
-struct local_nested_eval_wrapper
-{
-  static const bool NeedExternalBuffer = false;
-  typedef typename Xpr::Scalar Scalar;
-  typedef typename nested_eval<Xpr,NbEvaluations>::type ObjectType;
-  ObjectType object;
-
-  EIGEN_DEVICE_FUNC
-  local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr) : object(xpr)
-  {
-    EIGEN_UNUSED_VARIABLE(ptr);
-    eigen_internal_assert(ptr==0);
-  }
-};
-
-template<typename Xpr, int NbEvaluations>
-struct local_nested_eval_wrapper<Xpr,NbEvaluations,true>
-{
-  static const bool NeedExternalBuffer = true;
-  typedef typename Xpr::Scalar Scalar;
-  typedef typename plain_object_eval<Xpr>::type PlainObject;
-  typedef Map<PlainObject,EIGEN_DEFAULT_ALIGN_BYTES> ObjectType;
-  ObjectType object;
-
-  EIGEN_DEVICE_FUNC
-  local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr)
-    : object(ptr==0 ? reinterpret_cast<Scalar*>(Eigen::internal::aligned_malloc(sizeof(Scalar)*xpr.size())) : ptr, xpr.rows(), xpr.cols()),
-      m_deallocate(ptr==0)
-  {
-    if(NumTraits<Scalar>::RequireInitialization && object.data())
-      Eigen::internal::construct_elements_of_array(object.data(), object.size());
-    object = xpr;
-  }
-
-  EIGEN_DEVICE_FUNC
-  ~local_nested_eval_wrapper()
-  {
-    if(NumTraits<Scalar>::RequireInitialization && object.data())
-      Eigen::internal::destruct_elements_of_array(object.data(), object.size());
-    if(m_deallocate)
-      Eigen::internal::aligned_free(object.data());
-  }
-
-private:
-  bool m_deallocate;
-};
-
-#endif // EIGEN_ALLOCA
-
-template<typename T> class scoped_array : noncopyable
-{
-  T* m_ptr;
-public:
-  explicit scoped_array(std::ptrdiff_t size)
-  {
-    m_ptr = new T[size];
-  }
-  ~scoped_array()
-  {
-    delete[] m_ptr;
-  }
-  T& operator[](std::ptrdiff_t i) { return m_ptr[i]; }
-  const T& operator[](std::ptrdiff_t i) const { return m_ptr[i]; }
-  T* &ptr() { return m_ptr; }
-  const T* ptr() const { return m_ptr; }
-  operator const T*() const { return m_ptr; }
-};
-
-template<typename T> void swap(scoped_array<T> &a,scoped_array<T> &b)
-{
-  std::swap(a.ptr(),b.ptr());
-}
-
-} // end namespace internal
-
-/** \internal
-  *
-  * The macro ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) declares, allocates,
-  * and construct an aligned buffer named NAME of SIZE elements of type TYPE on the stack
-  * if the size in bytes is smaller than EIGEN_STACK_ALLOCATION_LIMIT, and if stack allocation is supported by the platform
-  * (currently, this is Linux, OSX and Visual Studio only). Otherwise the memory is allocated on the heap.
-  * The allocated buffer is automatically deleted when exiting the scope of this declaration.
-  * If BUFFER is non null, then the declared variable is simply an alias for BUFFER, and no allocation/deletion occurs.
-  * Here is an example:
-  * \code
-  * {
-  *   ei_declare_aligned_stack_constructed_variable(float,data,size,0);
-  *   // use data[0] to data[size-1]
-  * }
-  * \endcode
-  * The underlying stack allocation function can controlled with the EIGEN_ALLOCA preprocessor token.
-  *
-  * The macro ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) is analogue to
-  * \code
-  *   typename internal::nested_eval<XPRT_T,N>::type NAME(XPR);
-  * \endcode
-  * with the advantage of using aligned stack allocation even if the maximal size of XPR at compile time is unknown.
-  * This is accomplished through alloca if this later is supported and if the required number of bytes
-  * is below EIGEN_STACK_ALLOCATION_LIMIT.
-  */
-#ifdef EIGEN_ALLOCA
-
-  #if EIGEN_DEFAULT_ALIGN_BYTES>0
-    // We always manually re-align the result of EIGEN_ALLOCA.
-    // If alloca is already aligned, the compiler should be smart enough to optimize away the re-alignment.
-    #define EIGEN_ALIGNED_ALLOCA(SIZE) reinterpret_cast<void*>((internal::UIntPtr(EIGEN_ALLOCA(SIZE+EIGEN_DEFAULT_ALIGN_BYTES-1)) + EIGEN_DEFAULT_ALIGN_BYTES-1) & ~(std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1)))
-  #else
-    #define EIGEN_ALIGNED_ALLOCA(SIZE) EIGEN_ALLOCA(SIZE)
-  #endif
-
-  #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
-    Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
-    TYPE* NAME = (BUFFER)!=0 ? (BUFFER) \
-               : reinterpret_cast<TYPE*>( \
-                      (sizeof(TYPE)*SIZE<=EIGEN_STACK_ALLOCATION_LIMIT) ? EIGEN_ALIGNED_ALLOCA(sizeof(TYPE)*SIZE) \
-                    : Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE) );  \
-    Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,sizeof(TYPE)*SIZE>EIGEN_STACK_ALLOCATION_LIMIT)
-
-
-  #define ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) \
-    Eigen::internal::local_nested_eval_wrapper<XPR_T,N> EIGEN_CAT(NAME,_wrapper)(XPR, reinterpret_cast<typename XPR_T::Scalar*>( \
-      ( (Eigen::internal::local_nested_eval_wrapper<XPR_T,N>::NeedExternalBuffer) && ((sizeof(typename XPR_T::Scalar)*XPR.size())<=EIGEN_STACK_ALLOCATION_LIMIT) ) \
-        ? EIGEN_ALIGNED_ALLOCA( sizeof(typename XPR_T::Scalar)*XPR.size() ) : 0 ) ) ; \
-    typename Eigen::internal::local_nested_eval_wrapper<XPR_T,N>::ObjectType NAME(EIGEN_CAT(NAME,_wrapper).object)
-
-#else
-
-  #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
-    Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
-    TYPE* NAME = (BUFFER)!=0 ? BUFFER : reinterpret_cast<TYPE*>(Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE));    \
-    Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,true)
-
-
-#define ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) typename Eigen::internal::nested_eval<XPR_T,N>::type NAME(XPR)
-
-#endif
-
-
-/*****************************************************************************
-*** Implementation of EIGEN_MAKE_ALIGNED_OPERATOR_NEW [_IF]                ***
-*****************************************************************************/
-
-#if EIGEN_HAS_CXX17_OVERALIGN
-
-// C++17 -> no need to bother about alignment anymore :)
-
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign)
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size)
-
-#else
-
-// HIP does not support new/delete on device.
-#if EIGEN_MAX_ALIGN_BYTES!=0 && !defined(EIGEN_HIP_DEVICE_COMPILE)
-  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
-      EIGEN_DEVICE_FUNC \
-      void* operator new(std::size_t size, const std::nothrow_t&) EIGEN_NO_THROW { \
-        EIGEN_TRY { return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); } \
-        EIGEN_CATCH (...) { return 0; } \
-      }
-  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) \
-      EIGEN_DEVICE_FUNC \
-      void *operator new(std::size_t size) { \
-        return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \
-      } \
-      EIGEN_DEVICE_FUNC \
-      void *operator new[](std::size_t size) { \
-        return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \
-      } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void * ptr) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete[](void * ptr) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void * ptr, std::size_t /* sz */) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete[](void * ptr, std::size_t /* sz */) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
-      /* in-place new and delete. since (at least afaik) there is no actual   */ \
-      /* memory allocated we can safely let the default implementation handle */ \
-      /* this particular case. */ \
-      EIGEN_DEVICE_FUNC \
-      static void *operator new(std::size_t size, void *ptr) { return ::operator new(size,ptr); } \
-      EIGEN_DEVICE_FUNC \
-      static void *operator new[](std::size_t size, void* ptr) { return ::operator new[](size,ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void * memory, void *ptr) EIGEN_NO_THROW { return ::operator delete(memory,ptr); } \
-      EIGEN_DEVICE_FUNC \
-      void operator delete[](void * memory, void *ptr) EIGEN_NO_THROW { return ::operator delete[](memory,ptr); } \
-      /* nothrow-new (returns zero instead of std::bad_alloc) */ \
-      EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
-      EIGEN_DEVICE_FUNC \
-      void operator delete(void *ptr, const std::nothrow_t&) EIGEN_NO_THROW { \
-        Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); \
-      } \
-      typedef void eigen_aligned_operator_new_marker_type;
-#else
-  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
-#endif
-
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(true)
-#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size)                        \
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(                                                             \
-        ((Size)!=Eigen::Dynamic) &&                                                                    \
-        (((EIGEN_MAX_ALIGN_BYTES>=16) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES  )==0)) ||    \
-         ((EIGEN_MAX_ALIGN_BYTES>=32) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES/2)==0)) ||    \
-         ((EIGEN_MAX_ALIGN_BYTES>=64) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES/4)==0))   )))
-
-#endif
-
-/****************************************************************************/
-
-/** \class aligned_allocator
-* \ingroup Core_Module
-*
-* \brief STL compatible allocator to use with types requiring a non standrad alignment.
-*
-* The memory is aligned as for dynamically aligned matrix/array types such as MatrixXd.
-* By default, it will thus provide at least 16 bytes alignment and more in following cases:
-*  - 32 bytes alignment if AVX is enabled.
-*  - 64 bytes alignment if AVX512 is enabled.
-*
-* This can be controlled using the \c EIGEN_MAX_ALIGN_BYTES macro as documented
-* \link TopicPreprocessorDirectivesPerformance there \endlink.
-*
-* Example:
-* \code
-* // Matrix4f requires 16 bytes alignment:
-* std::map< int, Matrix4f, std::less<int>,
-*           aligned_allocator<std::pair<const int, Matrix4f> > > my_map_mat4;
-* // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator:
-* std::map< int, Vector3f > my_map_vec3;
-* \endcode
-*
-* \sa \blank \ref TopicStlContainers.
-*/
-template<class T>
-class aligned_allocator : public std::allocator<T>
-{
-public:
-  typedef std::size_t     size_type;
-  typedef std::ptrdiff_t  difference_type;
-  typedef T*              pointer;
-  typedef const T*        const_pointer;
-  typedef T&              reference;
-  typedef const T&        const_reference;
-  typedef T               value_type;
-
-  template<class U>
-  struct rebind
-  {
-    typedef aligned_allocator<U> other;
-  };
-
-  aligned_allocator() : std::allocator<T>() {}
-
-  aligned_allocator(const aligned_allocator& other) : std::allocator<T>(other) {}
-
-  template<class U>
-  aligned_allocator(const aligned_allocator<U>& other) : std::allocator<T>(other) {}
-
-  ~aligned_allocator() {}
-
-  #if EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_LEAST(7,0)
-  // In gcc std::allocator::max_size() is bugged making gcc triggers a warning:
-  // eigen/Eigen/src/Core/util/Memory.h:189:12: warning: argument 1 value '18446744073709551612' exceeds maximum object size 9223372036854775807
-  // See https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87544
-  size_type max_size() const {
-    return (std::numeric_limits<std::ptrdiff_t>::max)()/sizeof(T);
-  }
-  #endif
-
-  pointer allocate(size_type num, const void* /*hint*/ = 0)
-  {
-    internal::check_size_for_overflow<T>(num);
-    return static_cast<pointer>( internal::aligned_malloc(num * sizeof(T)) );
-  }
-
-  void deallocate(pointer p, size_type /*num*/)
-  {
-    internal::aligned_free(p);
-  }
-};
-
-//---------- Cache sizes ----------
-
-#if !defined(EIGEN_NO_CPUID)
-#  if EIGEN_COMP_GNUC && EIGEN_ARCH_i386_OR_x86_64
-#    if defined(__PIC__) && EIGEN_ARCH_i386
-       // Case for x86 with PIC
-#      define EIGEN_CPUID(abcd,func,id) \
-         __asm__ __volatile__ ("xchgl %%ebx, %k1;cpuid; xchgl %%ebx,%k1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id));
-#    elif defined(__PIC__) && EIGEN_ARCH_x86_64
-       // Case for x64 with PIC. In theory this is only a problem with recent gcc and with medium or large code model, not with the default small code model.
-       // However, we cannot detect which code model is used, and the xchg overhead is negligible anyway.
-#      define EIGEN_CPUID(abcd,func,id) \
-        __asm__ __volatile__ ("xchg{q}\t{%%}rbx, %q1; cpuid; xchg{q}\t{%%}rbx, %q1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id));
-#    else
-       // Case for x86_64 or x86 w/o PIC
-#      define EIGEN_CPUID(abcd,func,id) \
-         __asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id) );
-#    endif
-#  elif EIGEN_COMP_MSVC
-#    if (EIGEN_COMP_MSVC > 1500) && EIGEN_ARCH_i386_OR_x86_64
-#      define EIGEN_CPUID(abcd,func,id) __cpuidex((int*)abcd,func,id)
-#    endif
-#  endif
-#endif
-
-namespace internal {
-
-#ifdef EIGEN_CPUID
-
-inline bool cpuid_is_vendor(int abcd[4], const int vendor[3])
-{
-  return abcd[1]==vendor[0] && abcd[3]==vendor[1] && abcd[2]==vendor[2];
-}
-
-inline void queryCacheSizes_intel_direct(int& l1, int& l2, int& l3)
-{
-  int abcd[4];
-  l1 = l2 = l3 = 0;
-  int cache_id = 0;
-  int cache_type = 0;
-  do {
-    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-    EIGEN_CPUID(abcd,0x4,cache_id);
-    cache_type  = (abcd[0] & 0x0F) >> 0;
-    if(cache_type==1||cache_type==3) // data or unified cache
-    {
-      int cache_level = (abcd[0] & 0xE0) >> 5;  // A[7:5]
-      int ways        = (abcd[1] & 0xFFC00000) >> 22; // B[31:22]
-      int partitions  = (abcd[1] & 0x003FF000) >> 12; // B[21:12]
-      int line_size   = (abcd[1] & 0x00000FFF) >>  0; // B[11:0]
-      int sets        = (abcd[2]);                    // C[31:0]
-
-      int cache_size = (ways+1) * (partitions+1) * (line_size+1) * (sets+1);
-
-      switch(cache_level)
-      {
-        case 1: l1 = cache_size; break;
-        case 2: l2 = cache_size; break;
-        case 3: l3 = cache_size; break;
-        default: break;
-      }
-    }
-    cache_id++;
-  } while(cache_type>0 && cache_id<16);
-}
-
-inline void queryCacheSizes_intel_codes(int& l1, int& l2, int& l3)
-{
-  int abcd[4];
-  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-  l1 = l2 = l3 = 0;
-  EIGEN_CPUID(abcd,0x00000002,0);
-  unsigned char * bytes = reinterpret_cast<unsigned char *>(abcd)+2;
-  bool check_for_p2_core2 = false;
-  for(int i=0; i<14; ++i)
-  {
-    switch(bytes[i])
-    {
-      case 0x0A: l1 = 8; break;   // 0Ah   data L1 cache, 8 KB, 2 ways, 32 byte lines
-      case 0x0C: l1 = 16; break;  // 0Ch   data L1 cache, 16 KB, 4 ways, 32 byte lines
-      case 0x0E: l1 = 24; break;  // 0Eh   data L1 cache, 24 KB, 6 ways, 64 byte lines
-      case 0x10: l1 = 16; break;  // 10h   data L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
-      case 0x15: l1 = 16; break;  // 15h   code L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
-      case 0x2C: l1 = 32; break;  // 2Ch   data L1 cache, 32 KB, 8 ways, 64 byte lines
-      case 0x30: l1 = 32; break;  // 30h   code L1 cache, 32 KB, 8 ways, 64 byte lines
-      case 0x60: l1 = 16; break;  // 60h   data L1 cache, 16 KB, 8 ways, 64 byte lines, sectored
-      case 0x66: l1 = 8; break;   // 66h   data L1 cache, 8 KB, 4 ways, 64 byte lines, sectored
-      case 0x67: l1 = 16; break;  // 67h   data L1 cache, 16 KB, 4 ways, 64 byte lines, sectored
-      case 0x68: l1 = 32; break;  // 68h   data L1 cache, 32 KB, 4 ways, 64 byte lines, sectored
-      case 0x1A: l2 = 96; break;   // code and data L2 cache, 96 KB, 6 ways, 64 byte lines (IA-64)
-      case 0x22: l3 = 512; break;   // code and data L3 cache, 512 KB, 4 ways (!), 64 byte lines, dual-sectored
-      case 0x23: l3 = 1024; break;   // code and data L3 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x25: l3 = 2048; break;   // code and data L3 cache, 2048 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x29: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x39: l2 = 128; break;   // code and data L2 cache, 128 KB, 4 ways, 64 byte lines, sectored
-      case 0x3A: l2 = 192; break;   // code and data L2 cache, 192 KB, 6 ways, 64 byte lines, sectored
-      case 0x3B: l2 = 128; break;   // code and data L2 cache, 128 KB, 2 ways, 64 byte lines, sectored
-      case 0x3C: l2 = 256; break;   // code and data L2 cache, 256 KB, 4 ways, 64 byte lines, sectored
-      case 0x3D: l2 = 384; break;   // code and data L2 cache, 384 KB, 6 ways, 64 byte lines, sectored
-      case 0x3E: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 64 byte lines, sectored
-      case 0x40: l2 = 0; break;   // no integrated L2 cache (P6 core) or L3 cache (P4 core)
-      case 0x41: l2 = 128; break;   // code and data L2 cache, 128 KB, 4 ways, 32 byte lines
-      case 0x42: l2 = 256; break;   // code and data L2 cache, 256 KB, 4 ways, 32 byte lines
-      case 0x43: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 32 byte lines
-      case 0x44: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 4 ways, 32 byte lines
-      case 0x45: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 4 ways, 32 byte lines
-      case 0x46: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines
-      case 0x47: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 8 ways, 64 byte lines
-      case 0x48: l2 = 3072; break;   // code and data L2 cache, 3072 KB, 12 ways, 64 byte lines
-      case 0x49: if(l2!=0) l3 = 4096; else {check_for_p2_core2=true; l3 = l2 = 4096;} break;// code and data L3 cache, 4096 KB, 16 ways, 64 byte lines (P4) or L2 for core2
-      case 0x4A: l3 = 6144; break;   // code and data L3 cache, 6144 KB, 12 ways, 64 byte lines
-      case 0x4B: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 16 ways, 64 byte lines
-      case 0x4C: l3 = 12288; break;   // code and data L3 cache, 12288 KB, 12 ways, 64 byte lines
-      case 0x4D: l3 = 16384; break;   // code and data L3 cache, 16384 KB, 16 ways, 64 byte lines
-      case 0x4E: l2 = 6144; break;   // code and data L2 cache, 6144 KB, 24 ways, 64 byte lines
-      case 0x78: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 4 ways, 64 byte lines
-      case 0x79: l2 = 128; break;   // code and data L2 cache, 128 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7A: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7B: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7C: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
-      case 0x7D: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 8 ways, 64 byte lines
-      case 0x7E: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 128 byte lines, sect. (IA-64)
-      case 0x7F: l2 = 512; break;   // code and data L2 cache, 512 KB, 2 ways, 64 byte lines
-      case 0x80: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 64 byte lines
-      case 0x81: l2 = 128; break;   // code and data L2 cache, 128 KB, 8 ways, 32 byte lines
-      case 0x82: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 32 byte lines
-      case 0x83: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 32 byte lines
-      case 0x84: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 32 byte lines
-      case 0x85: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 8 ways, 32 byte lines
-      case 0x86: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 64 byte lines
-      case 0x87: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines
-      case 0x88: l3 = 2048; break;   // code and data L3 cache, 2048 KB, 4 ways, 64 byte lines (IA-64)
-      case 0x89: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines (IA-64)
-      case 0x8A: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 4 ways, 64 byte lines (IA-64)
-      case 0x8D: l3 = 3072; break;   // code and data L3 cache, 3072 KB, 12 ways, 128 byte lines (IA-64)
-
-      default: break;
-    }
-  }
-  if(check_for_p2_core2 && l2 == l3)
-    l3 = 0;
-  l1 *= 1024;
-  l2 *= 1024;
-  l3 *= 1024;
-}
-
-inline void queryCacheSizes_intel(int& l1, int& l2, int& l3, int max_std_funcs)
-{
-  if(max_std_funcs>=4)
-    queryCacheSizes_intel_direct(l1,l2,l3);
-  else if(max_std_funcs>=2)
-    queryCacheSizes_intel_codes(l1,l2,l3);
-  else
-    l1 = l2 = l3 = 0;
-}
-
-inline void queryCacheSizes_amd(int& l1, int& l2, int& l3)
-{
-  int abcd[4];
-  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-  
-  // First query the max supported function.
-  EIGEN_CPUID(abcd,0x80000000,0);
-  if(static_cast<numext::uint32_t>(abcd[0]) >= static_cast<numext::uint32_t>(0x80000006))
-  {
-    EIGEN_CPUID(abcd,0x80000005,0);
-    l1 = (abcd[2] >> 24) * 1024; // C[31:24] = L1 size in KB
-    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
-    EIGEN_CPUID(abcd,0x80000006,0);
-    l2 = (abcd[2] >> 16) * 1024; // C[31;16] = l2 cache size in KB
-    l3 = ((abcd[3] & 0xFFFC000) >> 18) * 512 * 1024; // D[31;18] = l3 cache size in 512KB
-  }
-  else
-  {
-    l1 = l2 = l3 = 0;
-  }
-}
-#endif
-
-/** \internal
- * Queries and returns the cache sizes in Bytes of the L1, L2, and L3 data caches respectively */
-inline void queryCacheSizes(int& l1, int& l2, int& l3)
-{
-  #ifdef EIGEN_CPUID
-  int abcd[4];
-  const int GenuineIntel[] = {0x756e6547, 0x49656e69, 0x6c65746e};
-  const int AuthenticAMD[] = {0x68747541, 0x69746e65, 0x444d4163};
-  const int AMDisbetter_[] = {0x69444d41, 0x74656273, 0x21726574}; // "AMDisbetter!"
-
-  // identify the CPU vendor
-  EIGEN_CPUID(abcd,0x0,0);
-  int max_std_funcs = abcd[0];
-  if(cpuid_is_vendor(abcd,GenuineIntel))
-    queryCacheSizes_intel(l1,l2,l3,max_std_funcs);
-  else if(cpuid_is_vendor(abcd,AuthenticAMD) || cpuid_is_vendor(abcd,AMDisbetter_))
-    queryCacheSizes_amd(l1,l2,l3);
-  else
-    // by default let's use Intel's API
-    queryCacheSizes_intel(l1,l2,l3,max_std_funcs);
-
-  // here is the list of other vendors:
-//   ||cpuid_is_vendor(abcd,"VIA VIA VIA ")
-//   ||cpuid_is_vendor(abcd,"CyrixInstead")
-//   ||cpuid_is_vendor(abcd,"CentaurHauls")
-//   ||cpuid_is_vendor(abcd,"GenuineTMx86")
-//   ||cpuid_is_vendor(abcd,"TransmetaCPU")
-//   ||cpuid_is_vendor(abcd,"RiseRiseRise")
-//   ||cpuid_is_vendor(abcd,"Geode by NSC")
-//   ||cpuid_is_vendor(abcd,"SiS SiS SiS ")
-//   ||cpuid_is_vendor(abcd,"UMC UMC UMC ")
-//   ||cpuid_is_vendor(abcd,"NexGenDriven")
-  #else
-  l1 = l2 = l3 = -1;
-  #endif
-}
-
-/** \internal
- * \returns the size in Bytes of the L1 data cache */
-inline int queryL1CacheSize()
-{
-  int l1(-1), l2, l3;
-  queryCacheSizes(l1,l2,l3);
-  return l1;
-}
-
-/** \internal
- * \returns the size in Bytes of the L2 or L3 cache if this later is present */
-inline int queryTopLevelCacheSize()
-{
-  int l1, l2(-1), l3(-1);
-  queryCacheSizes(l1,l2,l3);
-  return (std::max)(l2,l3);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MEMORY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Meta.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Meta.h
deleted file mode 100644
index 81ae2a3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/Meta.h
+++ /dev/null
@@ -1,812 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_META_H
-#define EIGEN_META_H
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-
- #include <cfloat>
-
- #if defined(EIGEN_CUDA_ARCH)
-  #include <math_constants.h>
- #endif
-
- #if defined(EIGEN_HIP_DEVICE_COMPILE)
-  #include "Eigen/src/Core/arch/HIP/hcc/math_constants.h"
-  #endif
-
-#endif
-
-// Recent versions of ICC require <cstdint> for pointer types below.
-#define EIGEN_ICC_NEEDS_CSTDINT (EIGEN_COMP_ICC>=1600 && EIGEN_COMP_CXXVER >= 11)
-
-// Define portable (u)int{32,64} types
-#if EIGEN_HAS_CXX11 || EIGEN_ICC_NEEDS_CSTDINT
-#include <cstdint>
-namespace Eigen {
-namespace numext {
-typedef std::uint8_t  uint8_t;
-typedef std::int8_t   int8_t;
-typedef std::uint16_t uint16_t;
-typedef std::int16_t  int16_t;
-typedef std::uint32_t uint32_t;
-typedef std::int32_t  int32_t;
-typedef std::uint64_t uint64_t;
-typedef std::int64_t  int64_t;
-}
-}
-#else
-// Without c++11, all compilers able to compile Eigen also
-// provide the C99 stdint.h header file.
-#include <stdint.h>
-namespace Eigen {
-namespace numext {
-typedef ::uint8_t  uint8_t;
-typedef ::int8_t   int8_t;
-typedef ::uint16_t uint16_t;
-typedef ::int16_t  int16_t;
-typedef ::uint32_t uint32_t;
-typedef ::int32_t  int32_t;
-typedef ::uint64_t uint64_t;
-typedef ::int64_t  int64_t;
-}
-}
-#endif
-
-namespace Eigen {
-
-typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex;
-
-/**
- * \brief The Index type as used for the API.
- * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
- * \sa \blank \ref TopicPreprocessorDirectives, StorageIndex.
- */
-
-typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Index;
-
-namespace internal {
-
-/** \internal
-  * \file Meta.h
-  * This file contains generic metaprogramming classes which are not specifically related to Eigen.
-  * \note In case you wonder, yes we're aware that Boost already provides all these features,
-  * we however don't want to add a dependency to Boost.
-  */
-
-// Only recent versions of ICC complain about using ptrdiff_t to hold pointers,
-// and older versions do not provide *intptr_t types.
-#if EIGEN_ICC_NEEDS_CSTDINT
-typedef std::intptr_t  IntPtr;
-typedef std::uintptr_t UIntPtr;
-#else
-typedef std::ptrdiff_t IntPtr;
-typedef std::size_t UIntPtr;
-#endif
-#undef EIGEN_ICC_NEEDS_CSTDINT
-
-struct true_type {  enum { value = 1 }; };
-struct false_type { enum { value = 0 }; };
-
-template<bool Condition>
-struct bool_constant;
-
-template<>
-struct bool_constant<true> : true_type {};
-
-template<>
-struct bool_constant<false> : false_type {};
-
-template<bool Condition, typename Then, typename Else>
-struct conditional { typedef Then type; };
-
-template<typename Then, typename Else>
-struct conditional <false, Then, Else> { typedef Else type; };
-
-template<typename T> struct remove_reference { typedef T type; };
-template<typename T> struct remove_reference<T&> { typedef T type; };
-
-template<typename T> struct remove_pointer { typedef T type; };
-template<typename T> struct remove_pointer<T*> { typedef T type; };
-template<typename T> struct remove_pointer<T*const> { typedef T type; };
-
-template <class T> struct remove_const { typedef T type; };
-template <class T> struct remove_const<const T> { typedef T type; };
-template <class T> struct remove_const<const T[]> { typedef T type[]; };
-template <class T, unsigned int Size> struct remove_const<const T[Size]> { typedef T type[Size]; };
-
-template<typename T> struct remove_all { typedef T type; };
-template<typename T> struct remove_all<const T>   { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T const&>  { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T&>        { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T const*>  { typedef typename remove_all<T>::type type; };
-template<typename T> struct remove_all<T*>        { typedef typename remove_all<T>::type type; };
-
-template<typename T> struct is_arithmetic      { enum { value = false }; };
-template<> struct is_arithmetic<float>         { enum { value = true }; };
-template<> struct is_arithmetic<double>        { enum { value = true }; };
-template<> struct is_arithmetic<long double>   { enum { value = true }; };
-template<> struct is_arithmetic<bool>          { enum { value = true }; };
-template<> struct is_arithmetic<char>          { enum { value = true }; };
-template<> struct is_arithmetic<signed char>   { enum { value = true }; };
-template<> struct is_arithmetic<unsigned char> { enum { value = true }; };
-template<> struct is_arithmetic<signed short>  { enum { value = true }; };
-template<> struct is_arithmetic<unsigned short>{ enum { value = true }; };
-template<> struct is_arithmetic<signed int>    { enum { value = true }; };
-template<> struct is_arithmetic<unsigned int>  { enum { value = true }; };
-template<> struct is_arithmetic<signed long>   { enum { value = true }; };
-template<> struct is_arithmetic<unsigned long> { enum { value = true }; };
-
-template<typename T, typename U> struct is_same { enum { value = 0 }; };
-template<typename T> struct is_same<T,T> { enum { value = 1 }; };
-
-template< class T >
-struct is_void : is_same<void, typename remove_const<T>::type> {};
-
-#if EIGEN_HAS_CXX11
-template<> struct is_arithmetic<signed long long>   { enum { value = true }; };
-template<> struct is_arithmetic<unsigned long long> { enum { value = true }; };
-using std::is_integral;
-#else
-template<typename T> struct is_integral               { enum { value = false }; };
-template<> struct is_integral<bool>                   { enum { value = true }; };
-template<> struct is_integral<char>                   { enum { value = true }; };
-template<> struct is_integral<signed char>            { enum { value = true }; };
-template<> struct is_integral<unsigned char>          { enum { value = true }; };
-template<> struct is_integral<signed short>           { enum { value = true }; };
-template<> struct is_integral<unsigned short>         { enum { value = true }; };
-template<> struct is_integral<signed int>             { enum { value = true }; };
-template<> struct is_integral<unsigned int>           { enum { value = true }; };
-template<> struct is_integral<signed long>            { enum { value = true }; };
-template<> struct is_integral<unsigned long>          { enum { value = true }; };
-#if EIGEN_COMP_MSVC
-template<> struct is_integral<signed __int64>         { enum { value = true }; };
-template<> struct is_integral<unsigned __int64>       { enum { value = true }; };
-#endif
-#endif
-
-#if EIGEN_HAS_CXX11
-using std::make_unsigned;
-#else
-// TODO: Possibly improve this implementation of make_unsigned.
-// It is currently used only by
-// template<typename Scalar> struct random_default_impl<Scalar, false, true>.
-template<typename> struct make_unsigned;
-template<> struct make_unsigned<char>             { typedef unsigned char type; };
-template<> struct make_unsigned<signed char>      { typedef unsigned char type; };
-template<> struct make_unsigned<unsigned char>    { typedef unsigned char type; };
-template<> struct make_unsigned<signed short>     { typedef unsigned short type; };
-template<> struct make_unsigned<unsigned short>   { typedef unsigned short type; };
-template<> struct make_unsigned<signed int>       { typedef unsigned int type; };
-template<> struct make_unsigned<unsigned int>     { typedef unsigned int type; };
-template<> struct make_unsigned<signed long>      { typedef unsigned long type; };
-template<> struct make_unsigned<unsigned long>    { typedef unsigned long type; };
-#if EIGEN_COMP_MSVC
-template<> struct make_unsigned<signed __int64>   { typedef unsigned __int64 type; };
-template<> struct make_unsigned<unsigned __int64> { typedef unsigned __int64 type; };
-#endif
-
-// Some platforms define int64_t as `long long` even for C++03, where
-// `long long` is not guaranteed by the standard. In this case we are missing
-// the definition for make_unsigned. If we just define it, we run into issues
-// where `long long` doesn't exist in some compilers for C++03. We therefore add
-// the specialization for these platforms only.
-#if EIGEN_OS_MAC || EIGEN_COMP_MINGW
-template<> struct make_unsigned<unsigned long long> { typedef unsigned long long type; };
-template<> struct make_unsigned<long long>          { typedef unsigned long long type; };
-#endif
-#endif
-
-template <typename T> struct add_const { typedef const T type; };
-template <typename T> struct add_const<T&> { typedef T& type; };
-
-template <typename T> struct is_const { enum { value = 0 }; };
-template <typename T> struct is_const<T const> { enum { value = 1 }; };
-
-template<typename T> struct add_const_on_value_type            { typedef const T type;  };
-template<typename T> struct add_const_on_value_type<T&>        { typedef T const& type; };
-template<typename T> struct add_const_on_value_type<T*>        { typedef T const* type; };
-template<typename T> struct add_const_on_value_type<T* const>  { typedef T const* const type; };
-template<typename T> struct add_const_on_value_type<T const* const>  { typedef T const* const type; };
-
-#if EIGEN_HAS_CXX11
-
-using std::is_convertible;
-
-#else
-
-template<typename From, typename To>
-struct is_convertible_impl
-{
-private:
-  struct any_conversion
-  {
-    template <typename T> any_conversion(const volatile T&);
-    template <typename T> any_conversion(T&);
-  };
-  struct yes {int a[1];};
-  struct no  {int a[2];};
-
-  template<typename T>
-  static yes test(T, int);
-
-  template<typename T>
-  static no  test(any_conversion, ...);
-
-public:
-  static typename internal::remove_reference<From>::type* ms_from;
-#ifdef __INTEL_COMPILER
-  #pragma warning push
-  #pragma warning ( disable : 2259 )
-#endif
-  enum { value = sizeof(test<To>(*ms_from, 0))==sizeof(yes) };
-#ifdef __INTEL_COMPILER
-  #pragma warning pop
-#endif
-};
-
-template<typename From, typename To>
-struct is_convertible
-{
-  enum { value = is_convertible_impl<From,To>::value };
-};
-
-template<typename T>
-struct is_convertible<T,T&> { enum { value = false }; };
-
-template<typename T>
-struct is_convertible<const T,const T&> { enum { value = true }; };
-
-#endif
-
-/** \internal Allows to enable/disable an overload
-  * according to a compile time condition.
-  */
-template<bool Condition, typename T=void> struct enable_if;
-
-template<typename T> struct enable_if<true,T>
-{ typedef T type; };
-
-#if defined(EIGEN_GPU_COMPILE_PHASE) && !EIGEN_HAS_CXX11
-#if !defined(__FLT_EPSILON__)
-#define __FLT_EPSILON__ FLT_EPSILON
-#define __DBL_EPSILON__ DBL_EPSILON
-#endif
-
-namespace device {
-
-template<typename T> struct numeric_limits
-{
-  EIGEN_DEVICE_FUNC
-  static EIGEN_CONSTEXPR T epsilon() { return 0; }
-  static T (max)() { assert(false && "Highest not supported for this type"); }
-  static T (min)() { assert(false && "Lowest not supported for this type"); }
-  static T infinity() { assert(false && "Infinity not supported for this type"); }
-  static T quiet_NaN() { assert(false && "quiet_NaN not supported for this type"); }
-};
-template<> struct numeric_limits<float>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static float epsilon() { return __FLT_EPSILON__; }
-  EIGEN_DEVICE_FUNC
-  static float (max)() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_MAX_NORMAL_F;
-  #else
-    return HIPRT_MAX_NORMAL_F;
-  #endif
-  }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static float (min)() { return FLT_MIN; }
-  EIGEN_DEVICE_FUNC
-  static float infinity() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_INF_F;
-  #else
-    return HIPRT_INF_F;
-  #endif
-  }
-  EIGEN_DEVICE_FUNC
-  static float quiet_NaN() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_NAN_F;
-  #else
-    return HIPRT_NAN_F;
-  #endif
-  }
-};
-template<> struct numeric_limits<double>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static double epsilon() { return __DBL_EPSILON__; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static double (max)() { return DBL_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static double (min)() { return DBL_MIN; }
-  EIGEN_DEVICE_FUNC
-  static double infinity() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_INF;
-  #else
-    return HIPRT_INF;
-  #endif
-  }
-  EIGEN_DEVICE_FUNC
-  static double quiet_NaN() {
-  #if defined(EIGEN_CUDA_ARCH)
-    return CUDART_NAN;
-  #else
-    return HIPRT_NAN;
-  #endif
-  }
-};
-template<> struct numeric_limits<int>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int (max)() { return INT_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static int (min)() { return INT_MIN; }
-};
-template<> struct numeric_limits<unsigned int>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned int epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned int (max)() { return UINT_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned int (min)() { return 0; }
-};
-template<> struct numeric_limits<long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long (max)() { return LONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long (min)() { return LONG_MIN; }
-};
-template<> struct numeric_limits<unsigned long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long (max)() { return ULONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long (min)() { return 0; }
-};
-template<> struct numeric_limits<long long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long long (max)() { return LLONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static long long (min)() { return LLONG_MIN; }
-};
-template<> struct numeric_limits<unsigned long long>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long long epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long long (max)() { return ULLONG_MAX; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static unsigned long long (min)() { return 0; }
-};
-template<> struct numeric_limits<bool>
-{
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static bool epsilon() { return false; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static bool (max)() { return true; }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR 
-  static bool (min)() { return false; }
-};
-
-}
-
-#endif // defined(EIGEN_GPU_COMPILE_PHASE) && !EIGEN_HAS_CXX11
-
-/** \internal
-  * A base class do disable default copy ctor and copy assignment operator.
-  */
-class noncopyable
-{
-  EIGEN_DEVICE_FUNC noncopyable(const noncopyable&);
-  EIGEN_DEVICE_FUNC const noncopyable& operator=(const noncopyable&);
-protected:
-  EIGEN_DEVICE_FUNC noncopyable() {}
-  EIGEN_DEVICE_FUNC ~noncopyable() {}
-};
-
-/** \internal
-  * Provides access to the number of elements in the object of as a compile-time constant expression.
-  * It "returns" Eigen::Dynamic if the size cannot be resolved at compile-time (default).
-  *
-  * Similar to std::tuple_size, but more general.
-  *
-  * It currently supports:
-  *  - any types T defining T::SizeAtCompileTime
-  *  - plain C arrays as T[N]
-  *  - std::array (c++11)
-  *  - some internal types such as SingleRange and AllRange
-  *
-  * The second template parameter eases SFINAE-based specializations.
-  */
-template<typename T, typename EnableIf = void> struct array_size {
-  enum { value = Dynamic };
-};
-
-template<typename T> struct array_size<T,typename internal::enable_if<((T::SizeAtCompileTime&0)==0)>::type> {
-  enum { value = T::SizeAtCompileTime };
-};
-
-template<typename T, int N> struct array_size<const T (&)[N]> {
-  enum { value = N };
-};
-template<typename T, int N> struct array_size<T (&)[N]> {
-  enum { value = N };
-};
-
-#if EIGEN_HAS_CXX11
-template<typename T, std::size_t N> struct array_size<const std::array<T,N> > {
-  enum { value = N };
-};
-template<typename T, std::size_t N> struct array_size<std::array<T,N> > {
-  enum { value = N };
-};
-#endif
-
-/** \internal
-  * Analogue of the std::size free function.
-  * It returns the size of the container or view \a x of type \c T
-  *
-  * It currently supports:
-  *  - any types T defining a member T::size() const
-  *  - plain C arrays as T[N]
-  *
-  */
-template<typename T>
-EIGEN_CONSTEXPR Index size(const T& x) { return x.size(); }
-
-template<typename T,std::size_t N>
-EIGEN_CONSTEXPR Index size(const T (&) [N]) { return N; }
-
-/** \internal
-  * Convenient struct to get the result type of a nullary, unary, binary, or
-  * ternary functor.
-  * 
-  * Pre C++11:
-  * Supports both a Func::result_type member and templated
-  * Func::result<Func(ArgTypes...)>::type member.
-  * 
-  * If none of these members is provided, then the type of the first
-  * argument is returned.
-  * 
-  * Post C++11:
-  * This uses std::result_of. However, note the `type` member removes
-  * const and converts references/pointers to their corresponding value type.
-  */
-#if EIGEN_HAS_STD_INVOKE_RESULT
-template<typename T> struct result_of;
-
-template<typename F, typename... ArgTypes>
-struct result_of<F(ArgTypes...)> {
-  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#elif EIGEN_HAS_STD_RESULT_OF
-template<typename T> struct result_of {
-  typedef typename std::result_of<T>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#else
-template<typename T> struct result_of { };
-
-struct has_none {int a[1];};
-struct has_std_result_type {int a[2];};
-struct has_tr1_result {int a[3];};
-
-template<typename Func, int SizeOf>
-struct nullary_result_of_select {};
-
-template<typename Func>
-struct nullary_result_of_select<Func, sizeof(has_std_result_type)> {typedef typename Func::result_type type;};
-
-template<typename Func>
-struct nullary_result_of_select<Func, sizeof(has_tr1_result)> {typedef typename Func::template result<Func()>::type type;};
-
-template<typename Func>
-struct result_of<Func()> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T()>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename nullary_result_of_select<Func, FunctorType>::type type;
-};
-
-template<typename Func, typename ArgType, int SizeOf=sizeof(has_none)>
-struct unary_result_of_select {typedef typename internal::remove_all<ArgType>::type type;};
-
-template<typename Func, typename ArgType>
-struct unary_result_of_select<Func, ArgType, sizeof(has_std_result_type)> {typedef typename Func::result_type type;};
-
-template<typename Func, typename ArgType>
-struct unary_result_of_select<Func, ArgType, sizeof(has_tr1_result)> {typedef typename Func::template result<Func(ArgType)>::type type;};
-
-template<typename Func, typename ArgType>
-struct result_of<Func(ArgType)> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T(ArgType)>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename unary_result_of_select<Func, ArgType, FunctorType>::type type;
-};
-
-template<typename Func, typename ArgType0, typename ArgType1, int SizeOf=sizeof(has_none)>
-struct binary_result_of_select {typedef typename internal::remove_all<ArgType0>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1>
-struct binary_result_of_select<Func, ArgType0, ArgType1, sizeof(has_std_result_type)>
-{typedef typename Func::result_type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1>
-struct binary_result_of_select<Func, ArgType0, ArgType1, sizeof(has_tr1_result)>
-{typedef typename Func::template result<Func(ArgType0,ArgType1)>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1>
-struct result_of<Func(ArgType0,ArgType1)> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T(ArgType0,ArgType1)>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename binary_result_of_select<Func, ArgType0, ArgType1, FunctorType>::type type;
-};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2, int SizeOf=sizeof(has_none)>
-struct ternary_result_of_select {typedef typename internal::remove_all<ArgType0>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
-struct ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, sizeof(has_std_result_type)>
-{typedef typename Func::result_type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
-struct ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, sizeof(has_tr1_result)>
-{typedef typename Func::template result<Func(ArgType0,ArgType1,ArgType2)>::type type;};
-
-template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
-struct result_of<Func(ArgType0,ArgType1,ArgType2)> {
-    template<typename T>
-    static has_std_result_type    testFunctor(T const *, typename T::result_type const * = 0);
-    template<typename T>
-    static has_tr1_result         testFunctor(T const *, typename T::template result<T(ArgType0,ArgType1,ArgType2)>::type const * = 0);
-    static has_none               testFunctor(...);
-
-    // note that the following indirection is needed for gcc-3.3
-    enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
-    typedef typename ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, FunctorType>::type type;
-};
-
-#endif
-
-#if EIGEN_HAS_STD_INVOKE_RESULT
-template<typename F, typename... ArgTypes>
-struct invoke_result {
-  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#elif EIGEN_HAS_CXX11
-template<typename F, typename... ArgTypes>
-struct invoke_result {
-  typedef typename result_of<F(ArgTypes...)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#else
-template<typename F, typename ArgType0 = void, typename ArgType1 = void, typename ArgType2 = void>
-struct invoke_result {
-  typedef typename result_of<F(ArgType0, ArgType1, ArgType2)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-
-template<typename F>
-struct invoke_result<F, void, void, void> {
-  typedef typename result_of<F()>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-
-template<typename F, typename ArgType0>
-struct invoke_result<F, ArgType0, void, void> {
-  typedef typename result_of<F(ArgType0)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-
-template<typename F, typename ArgType0, typename ArgType1>
-struct invoke_result<F, ArgType0, ArgType1, void> {
-  typedef typename result_of<F(ArgType0, ArgType1)>::type type1;
-  typedef typename remove_all<type1>::type type;
-};
-#endif
-
-struct meta_yes { char a[1]; };
-struct meta_no  { char a[2]; };
-
-// Check whether T::ReturnType does exist
-template <typename T>
-struct has_ReturnType
-{
-  template <typename C> static meta_yes testFunctor(C const *, typename C::ReturnType const * = 0);
-  template <typename C> static meta_no  testFunctor(...);
-
-  enum { value = sizeof(testFunctor<T>(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-template<typename T> const T* return_ptr();
-
-template <typename T, typename IndexType=Index>
-struct has_nullary_operator
-{
-  template <typename C> static meta_yes testFunctor(C const *,typename enable_if<(sizeof(return_ptr<C>()->operator()())>0)>::type * = 0);
-  static meta_no testFunctor(...);
-
-  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-template <typename T, typename IndexType=Index>
-struct has_unary_operator
-{
-  template <typename C> static meta_yes testFunctor(C const *,typename enable_if<(sizeof(return_ptr<C>()->operator()(IndexType(0)))>0)>::type * = 0);
-  static meta_no testFunctor(...);
-
-  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-template <typename T, typename IndexType=Index>
-struct has_binary_operator
-{
-  template <typename C> static meta_yes testFunctor(C const *,typename enable_if<(sizeof(return_ptr<C>()->operator()(IndexType(0),IndexType(0)))>0)>::type * = 0);
-  static meta_no testFunctor(...);
-
-  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
-};
-
-/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer.
-  * Usage example: \code meta_sqrt<1023>::ret \endcode
-  */
-template<int Y,
-         int InfX = 0,
-         int SupX = ((Y==1) ? 1 : Y/2),
-         bool Done = ((SupX-InfX)<=1 ? true : ((SupX*SupX <= Y) && ((SupX+1)*(SupX+1) > Y))) >
-                                // use ?: instead of || just to shut up a stupid gcc 4.3 warning
-class meta_sqrt
-{
-    enum {
-      MidX = (InfX+SupX)/2,
-      TakeInf = MidX*MidX > Y ? 1 : 0,
-      NewInf = int(TakeInf) ? InfX : int(MidX),
-      NewSup = int(TakeInf) ? int(MidX) : SupX
-    };
-  public:
-    enum { ret = meta_sqrt<Y,NewInf,NewSup>::ret };
-};
-
-template<int Y, int InfX, int SupX>
-class meta_sqrt<Y, InfX, SupX, true> { public:  enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; };
-
-
-/** \internal Computes the least common multiple of two positive integer A and B
-  * at compile-time. 
-  */
-template<int A, int B, int K=1, bool Done = ((A*K)%B)==0, bool Big=(A>=B)>
-struct meta_least_common_multiple
-{
-  enum { ret = meta_least_common_multiple<A,B,K+1>::ret };
-};
-template<int A, int B, int K, bool Done>
-struct meta_least_common_multiple<A,B,K,Done,false>
-{
-  enum { ret = meta_least_common_multiple<B,A,K>::ret };
-};
-template<int A, int B, int K>
-struct meta_least_common_multiple<A,B,K,true,true>
-{
-  enum { ret = A*K };
-};
-
-
-/** \internal determines whether the product of two numeric types is allowed and what the return type is */
-template<typename T, typename U> struct scalar_product_traits
-{
-  enum { Defined = 0 };
-};
-
-// FIXME quick workaround around current limitation of result_of
-// template<typename Scalar, typename ArgType0, typename ArgType1>
-// struct result_of<scalar_product_op<Scalar>(ArgType0,ArgType1)> {
-// typedef typename scalar_product_traits<typename remove_all<ArgType0>::type, typename remove_all<ArgType1>::type>::ReturnType type;
-// };
-
-/** \internal Obtains a POD type suitable to use as storage for an object of a size
-  * of at most Len bytes, aligned as specified by \c Align.
-  */
-template<unsigned Len, unsigned Align>
-struct aligned_storage {
-  struct type {
-    EIGEN_ALIGN_TO_BOUNDARY(Align) unsigned char data[Len];
-  };
-};
-
-} // end namespace internal
-
-namespace numext {
-
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-template<typename T> EIGEN_DEVICE_FUNC   void swap(T &a, T &b) { T tmp = b; b = a; a = tmp; }
-#else
-template<typename T> EIGEN_STRONG_INLINE void swap(T &a, T &b) { std::swap(a,b); }
-#endif
-
-#if defined(EIGEN_GPU_COMPILE_PHASE) && !EIGEN_HAS_CXX11
-using internal::device::numeric_limits;
-#else
-using std::numeric_limits;
-#endif
-
-// Integer division with rounding up.
-// T is assumed to be an integer type with a>=0, and b>0
-template<typename T>
-EIGEN_DEVICE_FUNC
-T div_ceil(const T &a, const T &b)
-{
-  return (a+b-1) / b;
-}
-
-// The aim of the following functions is to bypass -Wfloat-equal warnings
-// when we really want a strict equality comparison on floating points.
-template<typename X, typename Y> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool equal_strict(const X& x,const Y& y) { return x == y; }
-
-#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool equal_strict(const float& x,const float& y) { return std::equal_to<float>()(x,y); }
-
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool equal_strict(const double& x,const double& y) { return std::equal_to<double>()(x,y); }
-#endif
-
-template<typename X, typename Y> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool not_equal_strict(const X& x,const Y& y) { return x != y; }
-
-#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool not_equal_strict(const float& x,const float& y) { return std::not_equal_to<float>()(x,y); }
-
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
-bool not_equal_strict(const double& x,const double& y) { return std::not_equal_to<double>()(x,y); }
-#endif
-
-} // end namespace numext
-
-} // end namespace Eigen
-
-#endif // EIGEN_META_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/NonMPL2.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/NonMPL2.h
deleted file mode 100644
index 1af67cf..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/NonMPL2.h
+++ /dev/null
@@ -1,3 +0,0 @@
-#ifdef EIGEN_MPL2_ONLY
-#error Including non-MPL2 code in EIGEN_MPL2_ONLY mode
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h
deleted file mode 100644
index 1ce6fd1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h
+++ /dev/null
@@ -1,31 +0,0 @@
-#ifdef EIGEN_WARNINGS_DISABLED_2
-// "DisableStupidWarnings.h" was included twice recursively: Do not reenable warnings yet!
-#  undef EIGEN_WARNINGS_DISABLED_2
-
-#elif defined(EIGEN_WARNINGS_DISABLED)
-#undef EIGEN_WARNINGS_DISABLED
-
-#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
-  #ifdef _MSC_VER
-    #pragma warning( pop )
-  #elif defined __INTEL_COMPILER
-    #pragma warning pop
-  #elif defined __clang__
-    #pragma clang diagnostic pop
-  #elif defined __GNUC__  &&  (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-    #pragma GCC diagnostic pop
-  #endif
-
-  #if defined __NVCC__
-//    Don't reenable the diagnostic messages, as it turns out these messages need
-//    to be disabled at the point of the template instantiation (i.e the user code)
-//    otherwise they'll be triggered by nvcc.
-//    #pragma diag_default code_is_unreachable
-//    #pragma diag_default initialization_not_reachable
-//    #pragma diag_default 2651
-//    #pragma diag_default 2653
-  #endif
-
-#endif
-
-#endif // EIGEN_WARNINGS_DISABLED
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h
deleted file mode 100644
index 4124321..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h
+++ /dev/null
@@ -1,51 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_RESHAPED_HELPER_H
-#define EIGEN_RESHAPED_HELPER_H
-
-namespace Eigen {
-
-enum AutoSize_t   { AutoSize };
-const int AutoOrder = 2;
-
-namespace internal {
-
-template<typename SizeType,typename OtherSize, int TotalSize>
-struct get_compiletime_reshape_size {
-  enum { value = get_fixed_value<SizeType>::value };
-};
-
-template<typename SizeType>
-Index get_runtime_reshape_size(SizeType size, Index /*other*/, Index /*total*/) {
-  return internal::get_runtime_value(size);
-}
-
-template<typename OtherSize, int TotalSize>
-struct get_compiletime_reshape_size<AutoSize_t,OtherSize,TotalSize> {
-  enum {
-    other_size = get_fixed_value<OtherSize>::value,
-    value = (TotalSize==Dynamic || other_size==Dynamic) ? Dynamic : TotalSize / other_size };
-};
-
-inline Index get_runtime_reshape_size(AutoSize_t /*size*/, Index other, Index total) {
-  return total/other;
-}
-
-template<int Flags, int Order>
-struct get_compiletime_reshape_order {
-  enum { value = Order == AutoOrder ? Flags & RowMajorBit : Order };
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_RESHAPED_HELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/StaticAssert.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/StaticAssert.h
deleted file mode 100644
index c45de59..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/StaticAssert.h
+++ /dev/null
@@ -1,221 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STATIC_ASSERT_H
-#define EIGEN_STATIC_ASSERT_H
-
-/* Some notes on Eigen's static assertion mechanism:
- *
- *  - in EIGEN_STATIC_ASSERT(CONDITION,MSG) the parameter CONDITION must be a compile time boolean
- *    expression, and MSG an enum listed in struct internal::static_assertion<true>
- *
- *  - define EIGEN_NO_STATIC_ASSERT to disable them (and save compilation time)
- *    in that case, the static assertion is converted to the following runtime assert:
- *      eigen_assert(CONDITION && "MSG")
- *
- *  - currently EIGEN_STATIC_ASSERT can only be used in function scope
- *
- */
-
-#ifndef EIGEN_STATIC_ASSERT
-#ifndef EIGEN_NO_STATIC_ASSERT
-
-  #if EIGEN_MAX_CPP_VER>=11 && (__has_feature(cxx_static_assert) || (EIGEN_COMP_CXXVER >= 11) || (EIGEN_COMP_MSVC >= 1600))
-
-    // if native static_assert is enabled, let's use it
-    #define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG);
-
-  #else // not CXX0X
-
-    namespace Eigen {
-
-    namespace internal {
-
-    template<bool condition>
-    struct static_assertion {};
-
-    template<>
-    struct static_assertion<true>
-    {
-      enum {
-        YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX=1,
-        YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES=1,
-        YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES=1,
-        THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE=1,
-        THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE=1,
-        THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE=1,
-        OUT_OF_RANGE_ACCESS=1,
-        YOU_MADE_A_PROGRAMMING_MISTAKE=1,
-        EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT=1,
-        EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE=1,
-        YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR=1,
-        YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR=1,
-        UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC=1,
-        THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES=1,
-        FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED=1,
-        NUMERIC_TYPE_MUST_BE_REAL=1,
-        COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED=1,
-        WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED=1,
-        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE=1,
-        INVALID_MATRIX_PRODUCT=1,
-        INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS=1,
-        INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION=1,
-        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY=1,
-        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES=1,
-        THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES=1,
-        INVALID_MATRIX_TEMPLATE_PARAMETERS=1,
-        INVALID_MATRIXBASE_TEMPLATE_PARAMETERS=1,
-        BOTH_MATRICES_MUST_HAVE_THE_SAME_STORAGE_ORDER=1,
-        THIS_METHOD_IS_ONLY_FOR_DIAGONAL_MATRIX=1,
-        THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE=1,
-        THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES=1,
-        YOU_ALREADY_SPECIFIED_THIS_STRIDE=1,
-        INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION=1,
-        THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD=1,
-        PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1=1,
-        THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS=1,
-        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES=1,
-        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION=1,
-        THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY=1,
-        YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT=1,
-        THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS=1,
-        THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS=1,
-        THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL=1,
-        THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES=1,
-        YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED=1,
-        YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED=1,
-        THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE=1,
-        THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH=1,
-        OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG=1,
-        IMPLICIT_CONVERSION_TO_SCALAR_IS_FOR_INNER_PRODUCT_ONLY=1,
-        STORAGE_LAYOUT_DOES_NOT_MATCH=1,
-        EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE=1,
-        THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS=1,
-        MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY=1,
-        THIS_TYPE_IS_NOT_SUPPORTED=1,
-        STORAGE_KIND_MUST_MATCH=1,
-        STORAGE_INDEX_MUST_MATCH=1,
-        CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY=1,
-        SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY=1,
-        INVALID_TEMPLATE_PARAMETER=1,
-        GPU_TENSOR_CONTRACTION_DOES_NOT_SUPPORT_OUTPUT_KERNELS=1,
-        THE_ARRAY_SIZE_SHOULD_EQUAL_WITH_PACKET_SIZE=1
-      };
-    };
-
-    } // end namespace internal
-
-    } // end namespace Eigen
-
-    // Specialized implementation for MSVC to avoid "conditional
-    // expression is constant" warnings.  This implementation doesn't
-    // appear to work under GCC, hence the multiple implementations.
-    #if EIGEN_COMP_MSVC
-
-      #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \
-        {Eigen::internal::static_assertion<bool(CONDITION)>::MSG;}
-
-    #else
-      // In some cases clang interprets bool(CONDITION) as function declaration
-      #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \
-        if (Eigen::internal::static_assertion<static_cast<bool>(CONDITION)>::MSG) {}
-
-    #endif
-
-  #endif // not CXX0X
-
-#else // EIGEN_NO_STATIC_ASSERT
-
-  #define EIGEN_STATIC_ASSERT(CONDITION,MSG) eigen_assert((CONDITION) && #MSG);
-
-#endif // EIGEN_NO_STATIC_ASSERT
-#endif // EIGEN_STATIC_ASSERT
-
-// static assertion failing if the type \a TYPE is not a vector type
-#define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \
-  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime, \
-                      YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX)
-
-// static assertion failing if the type \a TYPE is not fixed-size
-#define EIGEN_STATIC_ASSERT_FIXED_SIZE(TYPE) \
-  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime!=Eigen::Dynamic, \
-                      YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR)
-
-// static assertion failing if the type \a TYPE is not dynamic-size
-#define EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(TYPE) \
-  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime==Eigen::Dynamic, \
-                      YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR)
-
-// static assertion failing if the type \a TYPE is not a vector type of the given size
-#define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE) \
-  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime && TYPE::SizeAtCompileTime==SIZE, \
-                      THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE)
-
-// static assertion failing if the type \a TYPE is not a vector type of the given size
-#define EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(TYPE, ROWS, COLS) \
-  EIGEN_STATIC_ASSERT(TYPE::RowsAtCompileTime==ROWS && TYPE::ColsAtCompileTime==COLS, \
-                      THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE)
-
-// static assertion failing if the two vector expression types are not compatible (same fixed-size or dynamic size)
-#define EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(TYPE0,TYPE1) \
-  EIGEN_STATIC_ASSERT( \
-      (int(TYPE0::SizeAtCompileTime)==Eigen::Dynamic \
-    || int(TYPE1::SizeAtCompileTime)==Eigen::Dynamic \
-    || int(TYPE0::SizeAtCompileTime)==int(TYPE1::SizeAtCompileTime)),\
-    YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES)
-
-#define EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
-     ( \
-        (int(Eigen::internal::size_of_xpr_at_compile_time<TYPE0>::ret)==0 && int(Eigen::internal::size_of_xpr_at_compile_time<TYPE1>::ret)==0) \
-    || (\
-          (int(TYPE0::RowsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE1::RowsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE0::RowsAtCompileTime)==int(TYPE1::RowsAtCompileTime)) \
-      &&  (int(TYPE0::ColsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE1::ColsAtCompileTime)==Eigen::Dynamic \
-        || int(TYPE0::ColsAtCompileTime)==int(TYPE1::ColsAtCompileTime))\
-       ) \
-     )
-
-#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
-    EIGEN_STATIC_ASSERT(!Eigen::NumTraits<TYPE>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
-
-
-// static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes
-#define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
-  EIGEN_STATIC_ASSERT( \
-     EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1),\
-    YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES)
-
-#define EIGEN_STATIC_ASSERT_SIZE_1x1(TYPE) \
-      EIGEN_STATIC_ASSERT((TYPE::RowsAtCompileTime == 1 || TYPE::RowsAtCompileTime == Eigen::Dynamic) && \
-                          (TYPE::ColsAtCompileTime == 1 || TYPE::ColsAtCompileTime == Eigen::Dynamic), \
-                          THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS)
-
-#define EIGEN_STATIC_ASSERT_LVALUE(Derived) \
-      EIGEN_STATIC_ASSERT(Eigen::internal::is_lvalue<Derived>::value, \
-                          THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY)
-
-#define EIGEN_STATIC_ASSERT_ARRAYXPR(Derived) \
-      EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived>::XprKind, ArrayXpr>::value), \
-                          THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES)
-
-#define EIGEN_STATIC_ASSERT_SAME_XPR_KIND(Derived1, Derived2) \
-      EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived1>::XprKind, \
-                                             typename Eigen::internal::traits<Derived2>::XprKind \
-                                            >::value), \
-                          YOU_CANNOT_MIX_ARRAYS_AND_MATRICES)
-
-// Check that a cost value is positive, and that is stay within a reasonable range
-// TODO this check could be enabled for internal debugging only
-#define EIGEN_INTERNAL_CHECK_COST_VALUE(C) \
-      EIGEN_STATIC_ASSERT((C)>=0 && (C)<=HugeCost*HugeCost, EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE);
-
-#endif // EIGEN_STATIC_ASSERT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h
deleted file mode 100644
index 354dd9a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h
+++ /dev/null
@@ -1,293 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SYMBOLIC_INDEX_H
-#define EIGEN_SYMBOLIC_INDEX_H
-
-namespace Eigen {
-
-/** \namespace Eigen::symbolic
-  * \ingroup Core_Module
-  *
-  * This namespace defines a set of classes and functions to build and evaluate symbolic expressions of scalar type Index.
-  * Here is a simple example:
-  *
-  * \code
-  * // First step, defines symbols:
-  * struct x_tag {};  static const symbolic::SymbolExpr<x_tag> x;
-  * struct y_tag {};  static const symbolic::SymbolExpr<y_tag> y;
-  * struct z_tag {};  static const symbolic::SymbolExpr<z_tag> z;
-  *
-  * // Defines an expression:
-  * auto expr = (x+3)/y+z;
-  *
-  * // And evaluate it: (c++14)
-  * std::cout << expr.eval(x=6,y=3,z=-13) << "\n";
-  *
-  * // In c++98/11, only one symbol per expression is supported for now:
-  * auto expr98 = (3-x)/2;
-  * std::cout << expr98.eval(x=6) << "\n";
-  * \endcode
-  *
-  * It is currently only used internally to define and manipulate the Eigen::last and Eigen::lastp1 symbols in Eigen::seq and Eigen::seqN.
-  *
-  */
-namespace symbolic {
-
-template<typename Tag> class Symbol;
-template<typename Arg0> class NegateExpr;
-template<typename Arg1,typename Arg2> class AddExpr;
-template<typename Arg1,typename Arg2> class ProductExpr;
-template<typename Arg1,typename Arg2> class QuotientExpr;
-
-// A simple wrapper around an integral value to provide the eval method.
-// We could also use a free-function symbolic_eval...
-template<typename IndexType=Index>
-class ValueExpr {
-public:
-  ValueExpr(IndexType val) : m_value(val) {}
-  template<typename T>
-  IndexType eval_impl(const T&) const { return m_value; }
-protected:
-  IndexType m_value;
-};
-
-// Specialization for compile-time value,
-// It is similar to ValueExpr(N) but this version helps the compiler to generate better code.
-template<int N>
-class ValueExpr<internal::FixedInt<N> > {
-public:
-  ValueExpr() {}
-  template<typename T>
-  EIGEN_CONSTEXPR Index eval_impl(const T&) const { return N; }
-};
-
-
-/** \class BaseExpr
-  * \ingroup Core_Module
-  * Common base class of any symbolic expressions
-  */
-template<typename Derived>
-class BaseExpr
-{
-public:
-  const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-  /** Evaluate the expression given the \a values of the symbols.
-    *
-    * \param values defines the values of the symbols, it can either be a SymbolValue or a std::tuple of SymbolValue
-    *               as constructed by SymbolExpr::operator= operator.
-    *
-    */
-  template<typename T>
-  Index eval(const T& values) const { return derived().eval_impl(values); }
-
-#if EIGEN_HAS_CXX14
-  template<typename... Types>
-  Index eval(Types&&... values) const { return derived().eval_impl(std::make_tuple(values...)); }
-#endif
-
-  NegateExpr<Derived> operator-() const { return NegateExpr<Derived>(derived()); }
-
-  AddExpr<Derived,ValueExpr<> > operator+(Index b) const
-  { return AddExpr<Derived,ValueExpr<> >(derived(),  b); }
-  AddExpr<Derived,ValueExpr<> > operator-(Index a) const
-  { return AddExpr<Derived,ValueExpr<> >(derived(), -a); }
-  ProductExpr<Derived,ValueExpr<> > operator*(Index a) const
-  { return ProductExpr<Derived,ValueExpr<> >(derived(),a); }
-  QuotientExpr<Derived,ValueExpr<> > operator/(Index a) const
-  { return QuotientExpr<Derived,ValueExpr<> >(derived(),a); }
-
-  friend AddExpr<Derived,ValueExpr<> > operator+(Index a, const BaseExpr& b)
-  { return AddExpr<Derived,ValueExpr<> >(b.derived(), a); }
-  friend AddExpr<NegateExpr<Derived>,ValueExpr<> > operator-(Index a, const BaseExpr& b)
-  { return AddExpr<NegateExpr<Derived>,ValueExpr<> >(-b.derived(), a); }
-  friend ProductExpr<ValueExpr<>,Derived> operator*(Index a, const BaseExpr& b)
-  { return ProductExpr<ValueExpr<>,Derived>(a,b.derived()); }
-  friend QuotientExpr<ValueExpr<>,Derived> operator/(Index a, const BaseExpr& b)
-  { return QuotientExpr<ValueExpr<>,Derived>(a,b.derived()); }
-
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N>) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > > operator-(internal::FixedInt<N>) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > >(derived(), ValueExpr<internal::FixedInt<-N> >()); }
-  template<int N>
-  ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator*(internal::FixedInt<N>) const
-  { return ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator/(internal::FixedInt<N>) const
-  { return QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-
-  template<int N>
-  friend AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N>, const BaseExpr& b)
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > > operator-(internal::FixedInt<N>, const BaseExpr& b)
-  { return AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > >(-b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator*(internal::FixedInt<N>, const BaseExpr& b)
-  { return ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-  template<int N>
-  friend QuotientExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator/(internal::FixedInt<N>, const BaseExpr& b)
-  { return QuotientExpr<ValueExpr<internal::FixedInt<N> > ,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-
-#if (!EIGEN_HAS_CXX14)
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N> (*)()) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > > operator-(internal::FixedInt<N> (*)()) const
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > >(derived(), ValueExpr<internal::FixedInt<-N> >()); }
-  template<int N>
-  ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator*(internal::FixedInt<N> (*)()) const
-  { return ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator/(internal::FixedInt<N> (*)()) const
-  { return QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
-
-  template<int N>
-  friend AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > > operator-(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > >(-b.derived(), ValueExpr<internal::FixedInt<N> >()); }
-  template<int N>
-  friend ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator*(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-  template<int N>
-  friend QuotientExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator/(internal::FixedInt<N> (*)(), const BaseExpr& b)
-  { return QuotientExpr<ValueExpr<internal::FixedInt<N> > ,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
-#endif
-
-
-  template<typename OtherDerived>
-  AddExpr<Derived,OtherDerived> operator+(const BaseExpr<OtherDerived> &b) const
-  { return AddExpr<Derived,OtherDerived>(derived(),  b.derived()); }
-
-  template<typename OtherDerived>
-  AddExpr<Derived,NegateExpr<OtherDerived> > operator-(const BaseExpr<OtherDerived> &b) const
-  { return AddExpr<Derived,NegateExpr<OtherDerived> >(derived(), -b.derived()); }
-
-  template<typename OtherDerived>
-  ProductExpr<Derived,OtherDerived> operator*(const BaseExpr<OtherDerived> &b) const
-  { return ProductExpr<Derived,OtherDerived>(derived(), b.derived()); }
-
-  template<typename OtherDerived>
-  QuotientExpr<Derived,OtherDerived> operator/(const BaseExpr<OtherDerived> &b) const
-  { return QuotientExpr<Derived,OtherDerived>(derived(), b.derived()); }
-};
-
-template<typename T>
-struct is_symbolic {
-  // BaseExpr has no conversion ctor, so we only have to check whether T can be statically cast to its base class BaseExpr<T>.
-  enum { value = internal::is_convertible<T,BaseExpr<T> >::value };
-};
-
-/** Represents the actual value of a symbol identified by its tag
-  *
-  * It is the return type of SymbolValue::operator=, and most of the time this is only way it is used.
-  */
-template<typename Tag>
-class SymbolValue
-{
-public:
-  /** Default constructor from the value \a val */
-  SymbolValue(Index val) : m_value(val) {}
-
-  /** \returns the stored value of the symbol */
-  Index value() const { return m_value; }
-protected:
-  Index m_value;
-};
-
-/** Expression of a symbol uniquely identified by the template parameter type \c tag */
-template<typename tag>
-class SymbolExpr : public BaseExpr<SymbolExpr<tag> >
-{
-public:
-  /** Alias to the template parameter \c tag */
-  typedef tag Tag;
-
-  SymbolExpr() {}
-
-  /** Associate the value \a val to the given symbol \c *this, uniquely identified by its \c Tag.
-    *
-    * The returned object should be passed to ExprBase::eval() to evaluate a given expression with this specified runtime-time value.
-    */
-  SymbolValue<Tag> operator=(Index val) const {
-    return SymbolValue<Tag>(val);
-  }
-
-  Index eval_impl(const SymbolValue<Tag> &values) const { return values.value(); }
-
-#if EIGEN_HAS_CXX14
-  // C++14 versions suitable for multiple symbols
-  template<typename... Types>
-  Index eval_impl(const std::tuple<Types...>& values) const { return std::get<SymbolValue<Tag> >(values).value(); }
-#endif
-};
-
-template<typename Arg0>
-class NegateExpr : public BaseExpr<NegateExpr<Arg0> >
-{
-public:
-  NegateExpr(const Arg0& arg0) : m_arg0(arg0) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return -m_arg0.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-};
-
-template<typename Arg0, typename Arg1>
-class AddExpr : public BaseExpr<AddExpr<Arg0,Arg1> >
-{
-public:
-  AddExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) + m_arg1.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-  Arg1 m_arg1;
-};
-
-template<typename Arg0, typename Arg1>
-class ProductExpr : public BaseExpr<ProductExpr<Arg0,Arg1> >
-{
-public:
-  ProductExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) * m_arg1.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-  Arg1 m_arg1;
-};
-
-template<typename Arg0, typename Arg1>
-class QuotientExpr : public BaseExpr<QuotientExpr<Arg0,Arg1> >
-{
-public:
-  QuotientExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
-
-  template<typename T>
-  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) / m_arg1.eval_impl(values); }
-protected:
-  Arg0 m_arg0;
-  Arg1 m_arg1;
-};
-
-} // end namespace symbolic
-
-} // end namespace Eigen
-
-#endif // EIGEN_SYMBOLIC_INDEX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/XprHelper.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/XprHelper.h
deleted file mode 100644
index 71c32b8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Core/util/XprHelper.h
+++ /dev/null
@@ -1,856 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_XPRHELPER_H
-#define EIGEN_XPRHELPER_H
-
-// just a workaround because GCC seems to not really like empty structs
-// FIXME: gcc 4.3 generates bad code when strict-aliasing is enabled
-// so currently we simply disable this optimization for gcc 4.3
-#if EIGEN_COMP_GNUC && !EIGEN_GNUC_AT(4,3)
-  #define EIGEN_EMPTY_STRUCT_CTOR(X) \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X() {} \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X(const X& ) {}
-#else
-  #define EIGEN_EMPTY_STRUCT_CTOR(X)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename IndexDest, typename IndexSrc>
-EIGEN_DEVICE_FUNC
-inline IndexDest convert_index(const IndexSrc& idx) {
-  // for sizeof(IndexDest)>=sizeof(IndexSrc) compilers should be able to optimize this away:
-  eigen_internal_assert(idx <= NumTraits<IndexDest>::highest() && "Index value to big for target type");
-  return IndexDest(idx);
-}
-
-// true if T can be considered as an integral index (i.e., and integral type or enum)
-template<typename T> struct is_valid_index_type
-{
-  enum { value =
-#if EIGEN_HAS_TYPE_TRAITS
-    internal::is_integral<T>::value || std::is_enum<T>::value
-#elif EIGEN_COMP_MSVC
-    internal::is_integral<T>::value || __is_enum(T)
-#else
-    // without C++11, we use is_convertible to Index instead of is_integral in order to treat enums as Index.
-    internal::is_convertible<T,Index>::value && !internal::is_same<T,float>::value && !is_same<T,double>::value
-#endif
-  };
-};
-
-// true if both types are not valid index types
-template<typename RowIndices, typename ColIndices>
-struct valid_indexed_view_overload {
-  enum { value = !(internal::is_valid_index_type<RowIndices>::value && internal::is_valid_index_type<ColIndices>::value) };
-};
-
-// promote_scalar_arg is an helper used in operation between an expression and a scalar, like:
-//    expression * scalar
-// Its role is to determine how the type T of the scalar operand should be promoted given the scalar type ExprScalar of the given expression.
-// The IsSupported template parameter must be provided by the caller as: internal::has_ReturnType<ScalarBinaryOpTraits<ExprScalar,T,op> >::value using the proper order for ExprScalar and T.
-// Then the logic is as follows:
-//  - if the operation is natively supported as defined by IsSupported, then the scalar type is not promoted, and T is returned.
-//  - otherwise, NumTraits<ExprScalar>::Literal is returned if T is implicitly convertible to NumTraits<ExprScalar>::Literal AND that this does not imply a float to integer conversion.
-//  - otherwise, ExprScalar is returned if T is implicitly convertible to ExprScalar AND that this does not imply a float to integer conversion.
-//  - In all other cases, the promoted type is not defined, and the respective operation is thus invalid and not available (SFINAE).
-template<typename ExprScalar,typename T, bool IsSupported>
-struct promote_scalar_arg;
-
-template<typename S,typename T>
-struct promote_scalar_arg<S,T,true>
-{
-  typedef T type;
-};
-
-// Recursively check safe conversion to PromotedType, and then ExprScalar if they are different.
-template<typename ExprScalar,typename T,typename PromotedType,
-  bool ConvertibleToLiteral = internal::is_convertible<T,PromotedType>::value,
-  bool IsSafe = NumTraits<T>::IsInteger || !NumTraits<PromotedType>::IsInteger>
-struct promote_scalar_arg_unsupported;
-
-// Start recursion with NumTraits<ExprScalar>::Literal
-template<typename S,typename T>
-struct promote_scalar_arg<S,T,false> : promote_scalar_arg_unsupported<S,T,typename NumTraits<S>::Literal> {};
-
-// We found a match!
-template<typename S,typename T, typename PromotedType>
-struct promote_scalar_arg_unsupported<S,T,PromotedType,true,true>
-{
-  typedef PromotedType type;
-};
-
-// No match, but no real-to-integer issues, and ExprScalar and current PromotedType are different,
-// so let's try to promote to ExprScalar
-template<typename ExprScalar,typename T, typename PromotedType>
-struct promote_scalar_arg_unsupported<ExprScalar,T,PromotedType,false,true>
-   : promote_scalar_arg_unsupported<ExprScalar,T,ExprScalar>
-{};
-
-// Unsafe real-to-integer, let's stop.
-template<typename S,typename T, typename PromotedType, bool ConvertibleToLiteral>
-struct promote_scalar_arg_unsupported<S,T,PromotedType,ConvertibleToLiteral,false> {};
-
-// T is not even convertible to ExprScalar, let's stop.
-template<typename S,typename T>
-struct promote_scalar_arg_unsupported<S,T,S,false,true> {};
-
-//classes inheriting no_assignment_operator don't generate a default operator=.
-class no_assignment_operator
-{
-  private:
-    no_assignment_operator& operator=(const no_assignment_operator&);
-  protected:
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(no_assignment_operator)
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(no_assignment_operator)
-};
-
-/** \internal return the index type with the largest number of bits */
-template<typename I1, typename I2>
-struct promote_index_type
-{
-  typedef typename conditional<(sizeof(I1)<sizeof(I2)), I2, I1>::type type;
-};
-
-/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that
-  * can be accessed using value() and setValue().
-  * Otherwise, this class is an empty structure and value() just returns the template parameter Value.
-  */
-template<typename T, int Value> class variable_if_dynamic
-{
-  public:
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(variable_if_dynamic)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
-    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    T value() { return T(Value); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    operator T() const { return T(Value); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void setValue(T v) const { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
-};
-
-template<typename T> class variable_if_dynamic<T, Dynamic>
-{
-    T m_value;
-  public:
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T value = 0) EIGEN_NO_THROW : m_value(value) {}
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T value() const { return m_value; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator T() const { return m_value; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
-};
-
-/** \internal like variable_if_dynamic but for DynamicIndex
-  */
-template<typename T, int Value> class variable_if_dynamicindex
-{
-  public:
-    EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamicindex)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
-    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-    T value() { return T(Value); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    void setValue(T) {}
-};
-
-template<typename T> class variable_if_dynamicindex<T, DynamicIndex>
-{
-    T m_value;
-    EIGEN_DEVICE_FUNC variable_if_dynamicindex() { eigen_assert(false); }
-  public:
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T value) : m_value(value) {}
-    EIGEN_DEVICE_FUNC T EIGEN_STRONG_INLINE value() const { return m_value; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
-};
-
-template<typename T> struct functor_traits
-{
-  enum
-  {
-    Cost = 10,
-    PacketAccess = false,
-    IsRepeatable = false
-  };
-};
-
-template<typename T> struct packet_traits;
-
-template<typename T> struct unpacket_traits;
-
-template<int Size, typename PacketType,
-         bool Stop = Size==Dynamic || (Size%unpacket_traits<PacketType>::size)==0 || is_same<PacketType,typename unpacket_traits<PacketType>::half>::value>
-struct find_best_packet_helper;
-
-template< int Size, typename PacketType>
-struct find_best_packet_helper<Size,PacketType,true>
-{
-  typedef PacketType type;
-};
-
-template<int Size, typename PacketType>
-struct find_best_packet_helper<Size,PacketType,false>
-{
-  typedef typename find_best_packet_helper<Size,typename unpacket_traits<PacketType>::half>::type type;
-};
-
-template<typename T, int Size>
-struct find_best_packet
-{
-  typedef typename find_best_packet_helper<Size,typename packet_traits<T>::type>::type type;
-};
-
-#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
-template<int ArrayBytes, int AlignmentBytes,
-         bool Match     =  bool((ArrayBytes%AlignmentBytes)==0),
-         bool TryHalf   =  bool(EIGEN_MIN_ALIGN_BYTES<AlignmentBytes) >
-struct compute_default_alignment_helper
-{
-  enum { value = 0 };
-};
-
-template<int ArrayBytes, int AlignmentBytes, bool TryHalf>
-struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, true, TryHalf> // Match
-{
-  enum { value = AlignmentBytes };
-};
-
-template<int ArrayBytes, int AlignmentBytes>
-struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, false, true> // Try-half
-{
-  // current packet too large, try with an half-packet
-  enum { value = compute_default_alignment_helper<ArrayBytes, AlignmentBytes/2>::value };
-};
-#else
-// If static alignment is disabled, no need to bother.
-// This also avoids a division by zero in "bool Match =  bool((ArrayBytes%AlignmentBytes)==0)"
-template<int ArrayBytes, int AlignmentBytes>
-struct compute_default_alignment_helper
-{
-  enum { value = 0 };
-};
-#endif
-
-template<typename T, int Size> struct compute_default_alignment {
-  enum { value = compute_default_alignment_helper<Size*sizeof(T),EIGEN_MAX_STATIC_ALIGN_BYTES>::value };
-};
-
-template<typename T> struct compute_default_alignment<T,Dynamic> {
-  enum { value = EIGEN_MAX_ALIGN_BYTES };
-};
-
-template<typename _Scalar, int _Rows, int _Cols,
-         int _Options = AutoAlign |
-                          ( (_Rows==1 && _Cols!=1) ? RowMajor
-                          : (_Cols==1 && _Rows!=1) ? ColMajor
-                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
-         int _MaxRows = _Rows,
-         int _MaxCols = _Cols
-> class make_proper_matrix_type
-{
-    enum {
-      IsColVector = _Cols==1 && _Rows!=1,
-      IsRowVector = _Rows==1 && _Cols!=1,
-      Options = IsColVector ? (_Options | ColMajor) & ~RowMajor
-              : IsRowVector ? (_Options | RowMajor) & ~ColMajor
-              : _Options
-    };
-  public:
-    typedef Matrix<_Scalar, _Rows, _Cols, Options, _MaxRows, _MaxCols> type;
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-class compute_matrix_flags
-{
-    enum { row_major_bit = Options&RowMajor ? RowMajorBit : 0 };
-  public:
-    // FIXME currently we still have to handle DirectAccessBit at the expression level to handle DenseCoeffsBase<>
-    // and then propagate this information to the evaluator's flags.
-    // However, I (Gael) think that DirectAccessBit should only matter at the evaluation stage.
-    enum { ret = DirectAccessBit | LvalueBit | NestByRefBit | row_major_bit };
-};
-
-template<int _Rows, int _Cols> struct size_at_compile_time
-{
-  enum { ret = (_Rows==Dynamic || _Cols==Dynamic) ? Dynamic : _Rows * _Cols };
-};
-
-template<typename XprType> struct size_of_xpr_at_compile_time
-{
-  enum { ret = size_at_compile_time<traits<XprType>::RowsAtCompileTime,traits<XprType>::ColsAtCompileTime>::ret };
-};
-
-/* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type,
- * whereas eval is a const reference in the case of a matrix
- */
-
-template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_matrix_type;
-template<typename T, typename BaseClassType, int Flags> struct plain_matrix_type_dense;
-template<typename T> struct plain_matrix_type<T,Dense>
-{
-  typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, traits<T>::Flags>::type type;
-};
-template<typename T> struct plain_matrix_type<T,DiagonalShape>
-{
-  typedef typename T::PlainObject type;
-};
-
-template<typename T, int Flags> struct plain_matrix_type_dense<T,MatrixXpr,Flags>
-{
-  typedef Matrix<typename traits<T>::Scalar,
-                traits<T>::RowsAtCompileTime,
-                traits<T>::ColsAtCompileTime,
-                AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
-                traits<T>::MaxRowsAtCompileTime,
-                traits<T>::MaxColsAtCompileTime
-          > type;
-};
-
-template<typename T, int Flags> struct plain_matrix_type_dense<T,ArrayXpr,Flags>
-{
-  typedef Array<typename traits<T>::Scalar,
-                traits<T>::RowsAtCompileTime,
-                traits<T>::ColsAtCompileTime,
-                AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
-                traits<T>::MaxRowsAtCompileTime,
-                traits<T>::MaxColsAtCompileTime
-          > type;
-};
-
-/* eval : the return type of eval(). For matrices, this is just a const reference
- * in order to avoid a useless copy
- */
-
-template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct eval;
-
-template<typename T> struct eval<T,Dense>
-{
-  typedef typename plain_matrix_type<T>::type type;
-//   typedef typename T::PlainObject type;
-//   typedef T::Matrix<typename traits<T>::Scalar,
-//                 traits<T>::RowsAtCompileTime,
-//                 traits<T>::ColsAtCompileTime,
-//                 AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
-//                 traits<T>::MaxRowsAtCompileTime,
-//                 traits<T>::MaxColsAtCompileTime
-//           > type;
-};
-
-template<typename T> struct eval<T,DiagonalShape>
-{
-  typedef typename plain_matrix_type<T>::type type;
-};
-
-// for matrices, no need to evaluate, just use a const reference to avoid a useless copy
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct eval<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
-{
-  typedef const Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
-};
-
-template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
-struct eval<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
-{
-  typedef const Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
-};
-
-
-/* similar to plain_matrix_type, but using the evaluator's Flags */
-template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_object_eval;
-
-template<typename T>
-struct plain_object_eval<T,Dense>
-{
-  typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, evaluator<T>::Flags>::type type;
-};
-
-
-/* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major
- */
-template<typename T> struct plain_matrix_type_column_major
-{
-  enum { Rows = traits<T>::RowsAtCompileTime,
-         Cols = traits<T>::ColsAtCompileTime,
-         MaxRows = traits<T>::MaxRowsAtCompileTime,
-         MaxCols = traits<T>::MaxColsAtCompileTime
-  };
-  typedef Matrix<typename traits<T>::Scalar,
-                Rows,
-                Cols,
-                (MaxRows==1&&MaxCols!=1) ? RowMajor : ColMajor,
-                MaxRows,
-                MaxCols
-          > type;
-};
-
-/* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major
- */
-template<typename T> struct plain_matrix_type_row_major
-{
-  enum { Rows = traits<T>::RowsAtCompileTime,
-         Cols = traits<T>::ColsAtCompileTime,
-         MaxRows = traits<T>::MaxRowsAtCompileTime,
-         MaxCols = traits<T>::MaxColsAtCompileTime
-  };
-  typedef Matrix<typename traits<T>::Scalar,
-                Rows,
-                Cols,
-                (MaxCols==1&&MaxRows!=1) ? ColMajor : RowMajor,
-                MaxRows,
-                MaxCols
-          > type;
-};
-
-/** \internal The reference selector for template expressions. The idea is that we don't
-  * need to use references for expressions since they are light weight proxy
-  * objects which should generate no copying overhead. */
-template <typename T>
-struct ref_selector
-{
-  typedef typename conditional<
-    bool(traits<T>::Flags & NestByRefBit),
-    T const&,
-    const T
-  >::type type;
-
-  typedef typename conditional<
-    bool(traits<T>::Flags & NestByRefBit),
-    T &,
-    T
-  >::type non_const_type;
-};
-
-/** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */
-template<typename T1, typename T2>
-struct transfer_constness
-{
-  typedef typename conditional<
-    bool(internal::is_const<T1>::value),
-    typename internal::add_const_on_value_type<T2>::type,
-    T2
-  >::type type;
-};
-
-
-// However, we still need a mechanism to detect whether an expression which is evaluated multiple time
-// has to be evaluated into a temporary.
-// That's the purpose of this new nested_eval helper:
-/** \internal Determines how a given expression should be nested when evaluated multiple times.
-  * For example, when you do a * (b+c), Eigen will determine how the expression b+c should be
-  * evaluated into the bigger product expression. The choice is between nesting the expression b+c as-is, or
-  * evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is
-  * a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes
-  * many coefficient accesses in the nested expressions -- as is the case with matrix product for example.
-  *
-  * \tparam T the type of the expression being nested.
-  * \tparam n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression.
-  * \tparam PlainObject the type of the temporary if needed.
-  */
-template<typename T, int n, typename PlainObject = typename plain_object_eval<T>::type> struct nested_eval
-{
-  enum {
-    ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost,
-    CoeffReadCost = evaluator<T>::CoeffReadCost,  // NOTE What if an evaluator evaluate itself into a temporary?
-                                                  //      Then CoeffReadCost will be small (e.g., 1) but we still have to evaluate, especially if n>1.
-                                                  //      This situation is already taken care by the EvalBeforeNestingBit flag, which is turned ON
-                                                  //      for all evaluator creating a temporary. This flag is then propagated by the parent evaluators.
-                                                  //      Another solution could be to count the number of temps?
-    NAsInteger = n == Dynamic ? HugeCost : n,
-    CostEval   = (NAsInteger+1) * ScalarReadCost + CoeffReadCost,
-    CostNoEval = NAsInteger * CoeffReadCost,
-    Evaluate = (int(evaluator<T>::Flags) & EvalBeforeNestingBit) || (int(CostEval) < int(CostNoEval))
-  };
-
-  typedef typename conditional<Evaluate, PlainObject, typename ref_selector<T>::type>::type type;
-};
-
-template<typename T>
-EIGEN_DEVICE_FUNC
-inline T* const_cast_ptr(const T* ptr)
-{
-  return const_cast<T*>(ptr);
-}
-
-template<typename Derived, typename XprKind = typename traits<Derived>::XprKind>
-struct dense_xpr_base
-{
-  /* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the ArrayXpr cases */
-};
-
-template<typename Derived>
-struct dense_xpr_base<Derived, MatrixXpr>
-{
-  typedef MatrixBase<Derived> type;
-};
-
-template<typename Derived>
-struct dense_xpr_base<Derived, ArrayXpr>
-{
-  typedef ArrayBase<Derived> type;
-};
-
-template<typename Derived, typename XprKind = typename traits<Derived>::XprKind, typename StorageKind = typename traits<Derived>::StorageKind>
-struct generic_xpr_base;
-
-template<typename Derived, typename XprKind>
-struct generic_xpr_base<Derived, XprKind, Dense>
-{
-  typedef typename dense_xpr_base<Derived,XprKind>::type type;
-};
-
-template<typename XprType, typename CastType> struct cast_return_type
-{
-  typedef typename XprType::Scalar CurrentScalarType;
-  typedef typename remove_all<CastType>::type _CastType;
-  typedef typename _CastType::Scalar NewScalarType;
-  typedef typename conditional<is_same<CurrentScalarType,NewScalarType>::value,
-                              const XprType&,CastType>::type type;
-};
-
-template <typename A, typename B> struct promote_storage_type;
-
-template <typename A> struct promote_storage_type<A,A>
-{
-  typedef A ret;
-};
-template <typename A> struct promote_storage_type<A, const A>
-{
-  typedef A ret;
-};
-template <typename A> struct promote_storage_type<const A, A>
-{
-  typedef A ret;
-};
-
-/** \internal Specify the "storage kind" of applying a coefficient-wise
-  * binary operations between two expressions of kinds A and B respectively.
-  * The template parameter Functor permits to specialize the resulting storage kind wrt to
-  * the functor.
-  * The default rules are as follows:
-  * \code
-  * A      op A      -> A
-  * A      op dense  -> dense
-  * dense  op B      -> dense
-  * sparse op dense  -> sparse
-  * dense  op sparse -> sparse
-  * \endcode
-  */
-template <typename A, typename B, typename Functor> struct cwise_promote_storage_type;
-
-template <typename A, typename Functor>                   struct cwise_promote_storage_type<A,A,Functor>                                      { typedef A      ret; };
-template <typename Functor>                               struct cwise_promote_storage_type<Dense,Dense,Functor>                              { typedef Dense  ret; };
-template <typename A, typename Functor>                   struct cwise_promote_storage_type<A,Dense,Functor>                                  { typedef Dense  ret; };
-template <typename B, typename Functor>                   struct cwise_promote_storage_type<Dense,B,Functor>                                  { typedef Dense  ret; };
-template <typename Functor>                               struct cwise_promote_storage_type<Sparse,Dense,Functor>                             { typedef Sparse ret; };
-template <typename Functor>                               struct cwise_promote_storage_type<Dense,Sparse,Functor>                             { typedef Sparse ret; };
-
-template <typename LhsKind, typename RhsKind, int LhsOrder, int RhsOrder> struct cwise_promote_storage_order {
-  enum { value = LhsOrder };
-};
-
-template <typename LhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<LhsKind,Sparse,LhsOrder,RhsOrder>                { enum { value = RhsOrder }; };
-template <typename RhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<Sparse,RhsKind,LhsOrder,RhsOrder>                { enum { value = LhsOrder }; };
-template <int Order>                                      struct cwise_promote_storage_order<Sparse,Sparse,Order,Order>                       { enum { value = Order }; };
-
-
-/** \internal Specify the "storage kind" of multiplying an expression of kind A with kind B.
-  * The template parameter ProductTag permits to specialize the resulting storage kind wrt to
-  * some compile-time properties of the product: GemmProduct, GemvProduct, OuterProduct, InnerProduct.
-  * The default rules are as follows:
-  * \code
-  *  K * K            -> K
-  *  dense * K        -> dense
-  *  K * dense        -> dense
-  *  diag * K         -> K
-  *  K * diag         -> K
-  *  Perm * K         -> K
-  * K * Perm          -> K
-  * \endcode
-  */
-template <typename A, typename B, int ProductTag> struct product_promote_storage_type;
-
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  A,                  ProductTag> { typedef A     ret;};
-template <int ProductTag>             struct product_promote_storage_type<Dense,              Dense,              ProductTag> { typedef Dense ret;};
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  Dense,              ProductTag> { typedef Dense ret; };
-template <typename B, int ProductTag> struct product_promote_storage_type<Dense,              B,                  ProductTag> { typedef Dense ret; };
-
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  DiagonalShape,      ProductTag> { typedef A ret; };
-template <typename B, int ProductTag> struct product_promote_storage_type<DiagonalShape,      B,                  ProductTag> { typedef B ret; };
-template <int ProductTag>             struct product_promote_storage_type<Dense,              DiagonalShape,      ProductTag> { typedef Dense ret; };
-template <int ProductTag>             struct product_promote_storage_type<DiagonalShape,      Dense,              ProductTag> { typedef Dense ret; };
-
-template <typename A, int ProductTag> struct product_promote_storage_type<A,                  PermutationStorage, ProductTag> { typedef A ret; };
-template <typename B, int ProductTag> struct product_promote_storage_type<PermutationStorage, B,                  ProductTag> { typedef B ret; };
-template <int ProductTag>             struct product_promote_storage_type<Dense,              PermutationStorage, ProductTag> { typedef Dense ret; };
-template <int ProductTag>             struct product_promote_storage_type<PermutationStorage, Dense,              ProductTag> { typedef Dense ret; };
-
-/** \internal gives the plain matrix or array type to store a row/column/diagonal of a matrix type.
-  * \tparam Scalar optional parameter allowing to pass a different scalar type than the one of the MatrixType.
-  */
-template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
-struct plain_row_type
-{
-  typedef Matrix<Scalar, 1, ExpressionType::ColsAtCompileTime,
-                 int(ExpressionType::PlainObject::Options) | int(RowMajor), 1, ExpressionType::MaxColsAtCompileTime> MatrixRowType;
-  typedef Array<Scalar, 1, ExpressionType::ColsAtCompileTime,
-                 int(ExpressionType::PlainObject::Options) | int(RowMajor), 1, ExpressionType::MaxColsAtCompileTime> ArrayRowType;
-
-  typedef typename conditional<
-    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
-    MatrixRowType,
-    ArrayRowType
-  >::type type;
-};
-
-template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
-struct plain_col_type
-{
-  typedef Matrix<Scalar, ExpressionType::RowsAtCompileTime, 1,
-                 ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> MatrixColType;
-  typedef Array<Scalar, ExpressionType::RowsAtCompileTime, 1,
-                 ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> ArrayColType;
-
-  typedef typename conditional<
-    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
-    MatrixColType,
-    ArrayColType
-  >::type type;
-};
-
-template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
-struct plain_diag_type
-{
-  enum { diag_size = EIGEN_SIZE_MIN_PREFER_DYNAMIC(ExpressionType::RowsAtCompileTime, ExpressionType::ColsAtCompileTime),
-         max_diag_size = EIGEN_SIZE_MIN_PREFER_FIXED(ExpressionType::MaxRowsAtCompileTime, ExpressionType::MaxColsAtCompileTime)
-  };
-  typedef Matrix<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> MatrixDiagType;
-  typedef Array<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> ArrayDiagType;
-
-  typedef typename conditional<
-    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
-    MatrixDiagType,
-    ArrayDiagType
-  >::type type;
-};
-
-template<typename Expr,typename Scalar = typename Expr::Scalar>
-struct plain_constant_type
-{
-  enum { Options = (traits<Expr>::Flags&RowMajorBit)?RowMajor:0 };
-
-  typedef Array<Scalar,  traits<Expr>::RowsAtCompileTime,   traits<Expr>::ColsAtCompileTime,
-                Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> array_type;
-
-  typedef Matrix<Scalar,  traits<Expr>::RowsAtCompileTime,   traits<Expr>::ColsAtCompileTime,
-                 Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> matrix_type;
-
-  typedef CwiseNullaryOp<scalar_constant_op<Scalar>, const typename conditional<is_same< typename traits<Expr>::XprKind, MatrixXpr >::value, matrix_type, array_type>::type > type;
-};
-
-template<typename ExpressionType>
-struct is_lvalue
-{
-  enum { value = (!bool(is_const<ExpressionType>::value)) &&
-                 bool(traits<ExpressionType>::Flags & LvalueBit) };
-};
-
-template<typename T> struct is_diagonal
-{ enum { ret = false }; };
-
-template<typename T> struct is_diagonal<DiagonalBase<T> >
-{ enum { ret = true }; };
-
-template<typename T> struct is_diagonal<DiagonalWrapper<T> >
-{ enum { ret = true }; };
-
-template<typename T, int S> struct is_diagonal<DiagonalMatrix<T,S> >
-{ enum { ret = true }; };
-
-
-template<typename T> struct is_identity
-{ enum { value = false }; };
-
-template<typename T> struct is_identity<CwiseNullaryOp<internal::scalar_identity_op<typename T::Scalar>, T> >
-{ enum { value = true }; };
-
-
-template<typename S1, typename S2> struct glue_shapes;
-template<> struct glue_shapes<DenseShape,TriangularShape> { typedef TriangularShape type;  };
-
-template<typename T1, typename T2>
-struct possibly_same_dense {
-  enum { value = has_direct_access<T1>::ret && has_direct_access<T2>::ret && is_same<typename T1::Scalar,typename T2::Scalar>::value };
-};
-
-template<typename T1, typename T2>
-EIGEN_DEVICE_FUNC
-bool is_same_dense(const T1 &mat1, const T2 &mat2, typename enable_if<possibly_same_dense<T1,T2>::value>::type * = 0)
-{
-  return (mat1.data()==mat2.data()) && (mat1.innerStride()==mat2.innerStride()) && (mat1.outerStride()==mat2.outerStride());
-}
-
-template<typename T1, typename T2>
-EIGEN_DEVICE_FUNC
-bool is_same_dense(const T1 &, const T2 &, typename enable_if<!possibly_same_dense<T1,T2>::value>::type * = 0)
-{
-  return false;
-}
-
-// Internal helper defining the cost of a scalar division for the type T.
-// The default heuristic can be specialized for each scalar type and architecture.
-template<typename T,bool Vectorized=false,typename EnableIf = void>
-struct scalar_div_cost {
-  enum { value = 8*NumTraits<T>::MulCost };
-};
-
-template<typename T,bool Vectorized>
-struct scalar_div_cost<std::complex<T>, Vectorized> {
-  enum { value = 2*scalar_div_cost<T>::value
-               + 6*NumTraits<T>::MulCost
-               + 3*NumTraits<T>::AddCost
-  };
-};
-
-
-template<bool Vectorized>
-struct scalar_div_cost<signed long,Vectorized,typename conditional<sizeof(long)==8,void,false_type>::type> { enum { value = 24 }; };
-template<bool Vectorized>
-struct scalar_div_cost<unsigned long,Vectorized,typename conditional<sizeof(long)==8,void,false_type>::type> { enum { value = 21 }; };
-
-
-#ifdef EIGEN_DEBUG_ASSIGN
-std::string demangle_traversal(int t)
-{
-  if(t==DefaultTraversal) return "DefaultTraversal";
-  if(t==LinearTraversal) return "LinearTraversal";
-  if(t==InnerVectorizedTraversal) return "InnerVectorizedTraversal";
-  if(t==LinearVectorizedTraversal) return "LinearVectorizedTraversal";
-  if(t==SliceVectorizedTraversal) return "SliceVectorizedTraversal";
-  return "?";
-}
-std::string demangle_unrolling(int t)
-{
-  if(t==NoUnrolling) return "NoUnrolling";
-  if(t==InnerUnrolling) return "InnerUnrolling";
-  if(t==CompleteUnrolling) return "CompleteUnrolling";
-  return "?";
-}
-std::string demangle_flags(int f)
-{
-  std::string res;
-  if(f&RowMajorBit)                 res += " | RowMajor";
-  if(f&PacketAccessBit)             res += " | Packet";
-  if(f&LinearAccessBit)             res += " | Linear";
-  if(f&LvalueBit)                   res += " | Lvalue";
-  if(f&DirectAccessBit)             res += " | Direct";
-  if(f&NestByRefBit)                res += " | NestByRef";
-  if(f&NoPreferredStorageOrderBit)  res += " | NoPreferredStorageOrderBit";
-
-  return res;
-}
-#endif
-
-} // end namespace internal
-
-
-/** \class ScalarBinaryOpTraits
-  * \ingroup Core_Module
-  *
-  * \brief Determines whether the given binary operation of two numeric types is allowed and what the scalar return type is.
-  *
-  * This class permits to control the scalar return type of any binary operation performed on two different scalar types through (partial) template specializations.
-  *
-  * For instance, let \c U1, \c U2 and \c U3 be three user defined scalar types for which most operations between instances of \c U1 and \c U2 returns an \c U3.
-  * You can let %Eigen knows that by defining:
-    \code
-    template<typename BinaryOp>
-    struct ScalarBinaryOpTraits<U1,U2,BinaryOp> { typedef U3 ReturnType;  };
-    template<typename BinaryOp>
-    struct ScalarBinaryOpTraits<U2,U1,BinaryOp> { typedef U3 ReturnType;  };
-    \endcode
-  * You can then explicitly disable some particular operations to get more explicit error messages:
-    \code
-    template<>
-    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_max_op<U1,U2> > {};
-    \endcode
-  * Or customize the return type for individual operation:
-    \code
-    template<>
-    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_sum_op<U1,U2> > { typedef U1 ReturnType; };
-    \endcode
-  *
-  * By default, the following generic combinations are supported:
-  <table class="manual">
-  <tr><th>ScalarA</th><th>ScalarB</th><th>BinaryOp</th><th>ReturnType</th><th>Note</th></tr>
-  <tr            ><td>\c T </td><td>\c T </td><td>\c * </td><td>\c T </td><td></td></tr>
-  <tr class="alt"><td>\c NumTraits<T>::Real </td><td>\c T </td><td>\c * </td><td>\c T </td><td>Only if \c NumTraits<T>::IsComplex </td></tr>
-  <tr            ><td>\c T </td><td>\c NumTraits<T>::Real </td><td>\c * </td><td>\c T </td><td>Only if \c NumTraits<T>::IsComplex </td></tr>
-  </table>
-  *
-  * \sa CwiseBinaryOp
-  */
-template<typename ScalarA, typename ScalarB, typename BinaryOp=internal::scalar_product_op<ScalarA,ScalarB> >
-struct ScalarBinaryOpTraits
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  // for backward compatibility, use the hints given by the (deprecated) internal::scalar_product_traits class.
-  : internal::scalar_product_traits<ScalarA,ScalarB>
-#endif // EIGEN_PARSED_BY_DOXYGEN
-{};
-
-template<typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<T,T,BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-template <typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<T, typename NumTraits<typename internal::enable_if<NumTraits<T>::IsComplex,T>::type>::Real, BinaryOp>
-{
-  typedef T ReturnType;
-};
-template <typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<typename NumTraits<typename internal::enable_if<NumTraits<T>::IsComplex,T>::type>::Real, T, BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-// For Matrix * Permutation
-template<typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<T,void,BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-// For Permutation * Matrix
-template<typename T, typename BinaryOp>
-struct ScalarBinaryOpTraits<void,T,BinaryOp>
-{
-  typedef T ReturnType;
-};
-
-// for Permutation*Permutation
-template<typename BinaryOp>
-struct ScalarBinaryOpTraits<void,void,BinaryOp>
-{
-  typedef void ReturnType;
-};
-
-// We require Lhs and Rhs to have "compatible" scalar types.
-// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
-// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
-// add together a float matrix and a double matrix.
-#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
-  EIGEN_STATIC_ASSERT((Eigen::internal::has_ReturnType<ScalarBinaryOpTraits<LHS, RHS,BINOP> >::value), \
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-} // end namespace Eigen
-
-#endif // EIGEN_XPRHELPER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
deleted file mode 100644
index 081e918..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
+++ /dev/null
@@ -1,346 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Claire Maurice
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_EIGEN_SOLVER_H
-#define EIGEN_COMPLEX_EIGEN_SOLVER_H
-
-#include "./ComplexSchur.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class ComplexEigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of general complex matrices
-  *
-  * \tparam _MatrixType the type of the matrix of which we are
-  * computing the eigendecomposition; this is expected to be an
-  * instantiation of the Matrix class template.
-  *
-  * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
-  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v
-  * \f$.  If \f$ D \f$ is a diagonal matrix with the eigenvalues on
-  * the diagonal, and \f$ V \f$ is a matrix with the eigenvectors as
-  * its columns, then \f$ A V = V D \f$. The matrix \f$ V \f$ is
-  * almost always invertible, in which case we have \f$ A = V D V^{-1}
-  * \f$. This is called the eigendecomposition.
-  *
-  * The main function in this class is compute(), which computes the
-  * eigenvalues and eigenvectors of a given function. The
-  * documentation for that function contains an example showing the
-  * main features of the class.
-  *
-  * \sa class EigenSolver, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class ComplexEigenSolver
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for #MatrixType.
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for vector of eigenvalues as returned by eigenvalues().
-      *
-      * This is a column vector with entries of type #ComplexScalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options&(~RowMajor), MaxColsAtCompileTime, 1> EigenvalueType;
-
-    /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
-      *
-      * This is a square matrix with entries of type #ComplexScalar.
-      * The size is the same as the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorType;
-
-    /** \brief Default constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().
-      */
-    ComplexEigenSolver()
-            : m_eivec(),
-              m_eivalues(),
-              m_schur(),
-              m_isInitialized(false),
-              m_eigenvectorsOk(false),
-              m_matX()
-    {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa ComplexEigenSolver()
-      */
-    explicit ComplexEigenSolver(Index size)
-            : m_eivec(size, size),
-              m_eivalues(size),
-              m_schur(size),
-              m_isInitialized(false),
-              m_eigenvectorsOk(false),
-              m_matX(size, size)
-    {}
-
-    /** \brief Constructor; computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed.
-      *
-      * This constructor calls compute() to compute the eigendecomposition.
-      */
-    template<typename InputType>
-    explicit ComplexEigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
-            : m_eivec(matrix.rows(),matrix.cols()),
-              m_eivalues(matrix.cols()),
-              m_schur(matrix.rows()),
-              m_isInitialized(false),
-              m_eigenvectorsOk(false),
-              m_matX(matrix.rows(),matrix.cols())
-    {
-      compute(matrix.derived(), computeEigenvectors);
-    }
-
-    /** \brief Returns the eigenvectors of given matrix.
-      *
-      * \returns  A const reference to the matrix whose columns are the eigenvectors.
-      *
-      * \pre Either the constructor
-      * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
-      * function compute(const MatrixType& matrix, bool) has been called before
-      * to compute the eigendecomposition of a matrix, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * This function returns a matrix whose columns are the eigenvectors. Column
-      * \f$ k \f$ is an eigenvector corresponding to eigenvalue number \f$ k
-      * \f$ as returned by eigenvalues().  The eigenvectors are normalized to
-      * have (Euclidean) norm equal to one. The matrix returned by this
-      * function is the matrix \f$ V \f$ in the eigendecomposition \f$ A = V D
-      * V^{-1} \f$, if it exists.
-      *
-      * Example: \include ComplexEigenSolver_eigenvectors.cpp
-      * Output: \verbinclude ComplexEigenSolver_eigenvectors.out
-      */
-    const EigenvectorType& eigenvectors() const
-    {
-      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec;
-    }
-
-    /** \brief Returns the eigenvalues of given matrix.
-      *
-      * \returns A const reference to the column vector containing the eigenvalues.
-      *
-      * \pre Either the constructor
-      * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
-      * function compute(const MatrixType& matrix, bool) has been called before
-      * to compute the eigendecomposition of a matrix.
-      *
-      * This function returns a column vector containing the
-      * eigenvalues. Eigenvalues are repeated according to their
-      * algebraic multiplicity, so there are as many eigenvalues as
-      * rows in the matrix. The eigenvalues are not sorted in any particular
-      * order.
-      *
-      * Example: \include ComplexEigenSolver_eigenvalues.cpp
-      * Output: \verbinclude ComplexEigenSolver_eigenvalues.out
-      */
-    const EigenvalueType& eigenvalues() const
-    {
-      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
-      return m_eivalues;
-    }
-
-    /** \brief Computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed.
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of the complex matrix \p matrix.
-      * The eigenvalues() function can be used to retrieve them.  If
-      * \p computeEigenvectors is true, then the eigenvectors are also computed
-      * and can be retrieved by calling eigenvectors().
-      *
-      * The matrix is first reduced to Schur form using the
-      * ComplexSchur class. The Schur decomposition is then used to
-      * compute the eigenvalues and eigenvectors.
-      *
-      * The cost of the computation is dominated by the cost of the
-      * Schur decomposition, which is \f$ O(n^3) \f$ where \f$ n \f$
-      * is the size of the matrix.
-      *
-      * Example: \include ComplexEigenSolver_compute.cpp
-      * Output: \verbinclude ComplexEigenSolver_compute.out
-      */
-    template<typename InputType>
-    ComplexEigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
-      return m_schur.info();
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. */
-    ComplexEigenSolver& setMaxIterations(Index maxIters)
-    {
-      m_schur.setMaxIterations(maxIters);
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_schur.getMaxIterations();
-    }
-
-  protected:
-    
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-    
-    EigenvectorType m_eivec;
-    EigenvalueType m_eivalues;
-    ComplexSchur<MatrixType> m_schur;
-    bool m_isInitialized;
-    bool m_eigenvectorsOk;
-    EigenvectorType m_matX;
-
-  private:
-    void doComputeEigenvectors(RealScalar matrixnorm);
-    void sortEigenvalues(bool computeEigenvectors);
-};
-
-
-template<typename MatrixType>
-template<typename InputType>
-ComplexEigenSolver<MatrixType>& 
-ComplexEigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
-{
-  check_template_parameters();
-  
-  // this code is inspired from Jampack
-  eigen_assert(matrix.cols() == matrix.rows());
-
-  // Do a complex Schur decomposition, A = U T U^*
-  // The eigenvalues are on the diagonal of T.
-  m_schur.compute(matrix.derived(), computeEigenvectors);
-
-  if(m_schur.info() == Success)
-  {
-    m_eivalues = m_schur.matrixT().diagonal();
-    if(computeEigenvectors)
-      doComputeEigenvectors(m_schur.matrixT().norm());
-    sortEigenvalues(computeEigenvectors);
-  }
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-  return *this;
-}
-
-
-template<typename MatrixType>
-void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(RealScalar matrixnorm)
-{
-  const Index n = m_eivalues.size();
-
-  matrixnorm = numext::maxi(matrixnorm,(std::numeric_limits<RealScalar>::min)());
-
-  // Compute X such that T = X D X^(-1), where D is the diagonal of T.
-  // The matrix X is unit triangular.
-  m_matX = EigenvectorType::Zero(n, n);
-  for(Index k=n-1 ; k>=0 ; k--)
-  {
-    m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0);
-    // Compute X(i,k) using the (i,k) entry of the equation X T = D X
-    for(Index i=k-1 ; i>=0 ; i--)
-    {
-      m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
-      if(k-i-1>0)
-        m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.col(k).segment(i+1,k-i-1)).value();
-      ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k);
-      if(z==ComplexScalar(0))
-      {
-        // If the i-th and k-th eigenvalue are equal, then z equals 0.
-        // Use a small value instead, to prevent division by zero.
-        numext::real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
-      }
-      m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
-    }
-  }
-
-  // Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
-  m_eivec.noalias() = m_schur.matrixU() * m_matX;
-  // .. and normalize the eigenvectors
-  for(Index k=0 ; k<n ; k++)
-  {
-    m_eivec.col(k).normalize();
-  }
-}
-
-
-template<typename MatrixType>
-void ComplexEigenSolver<MatrixType>::sortEigenvalues(bool computeEigenvectors)
-{
-  const Index n =  m_eivalues.size();
-  for (Index i=0; i<n; i++)
-  {
-    Index k;
-    m_eivalues.cwiseAbs().tail(n-i).minCoeff(&k);
-    if (k != 0)
-    {
-      k += i;
-      std::swap(m_eivalues[k],m_eivalues[i]);
-      if(computeEigenvectors)
-	m_eivec.col(i).swap(m_eivec.col(k));
-    }
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_EIGEN_SOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h
deleted file mode 100644
index fc71468..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h
+++ /dev/null
@@ -1,462 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Claire Maurice
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLEX_SCHUR_H
-#define EIGEN_COMPLEX_SCHUR_H
-
-#include "./HessenbergDecomposition.h"
-
-namespace Eigen { 
-
-namespace internal {
-template<typename MatrixType, bool IsComplex> struct complex_schur_reduce_to_hessenberg;
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class ComplexSchur
-  *
-  * \brief Performs a complex Schur decomposition of a real or complex square matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are
-  * computing the Schur decomposition; this is expected to be an
-  * instantiation of the Matrix class template.
-  *
-  * Given a real or complex square matrix A, this class computes the
-  * Schur decomposition: \f$ A = U T U^*\f$ where U is a unitary
-  * complex matrix, and T is a complex upper triangular matrix.  The
-  * diagonal of the matrix T corresponds to the eigenvalues of the
-  * matrix A.
-  *
-  * Call the function compute() to compute the Schur decomposition of
-  * a given matrix. Alternatively, you can use the 
-  * ComplexSchur(const MatrixType&, bool) constructor which computes
-  * the Schur decomposition at construction time. Once the
-  * decomposition is computed, you can use the matrixU() and matrixT()
-  * functions to retrieve the matrices U and V in the decomposition.
-  *
-  * \note This code is inspired from Jampack
-  *
-  * \sa class RealSchur, class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType> class ComplexSchur
-{
-  public:
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type \p _MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for \p _MatrixType. 
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for the matrices in the Schur decomposition.
-      *
-      * This is a square matrix with entries of type #ComplexScalar. 
-      * The size is the same as the size of \p _MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> ComplexMatrixType;
-
-    /** \brief Default constructor.
-      *
-      * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user
-      * intends to perform decompositions via compute().  The \p size
-      * parameter is only used as a hint. It is not an error to give a
-      * wrong \p size, but it may impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit ComplexSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime)
-      : m_matT(size,size),
-        m_matU(size,size),
-        m_hess(size),
-        m_isInitialized(false),
-        m_matUisUptodate(false),
-        m_maxIters(-1)
-    {}
-
-    /** \brief Constructor; computes Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-      *
-      * This constructor calls compute() to compute the Schur decomposition.
-      *
-      * \sa matrixT() and matrixU() for examples.
-      */
-    template<typename InputType>
-    explicit ComplexSchur(const EigenBase<InputType>& matrix, bool computeU = true)
-      : m_matT(matrix.rows(),matrix.cols()),
-        m_matU(matrix.rows(),matrix.cols()),
-        m_hess(matrix.rows()),
-        m_isInitialized(false),
-        m_matUisUptodate(false),
-        m_maxIters(-1)
-    {
-      compute(matrix.derived(), computeU);
-    }
-
-    /** \brief Returns the unitary matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix U.
-      *
-      * It is assumed that either the constructor
-      * ComplexSchur(const MatrixType& matrix, bool computeU) or the
-      * member function compute(const MatrixType& matrix, bool computeU)
-      * has been called before to compute the Schur decomposition of a
-      * matrix, and that \p computeU was set to true (the default
-      * value).
-      *
-      * Example: \include ComplexSchur_matrixU.cpp
-      * Output: \verbinclude ComplexSchur_matrixU.out
-      */
-    const ComplexMatrixType& matrixU() const
-    {
-      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
-      eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the ComplexSchur decomposition.");
-      return m_matU;
-    }
-
-    /** \brief Returns the triangular matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix T.
-      *
-      * It is assumed that either the constructor
-      * ComplexSchur(const MatrixType& matrix, bool computeU) or the
-      * member function compute(const MatrixType& matrix, bool computeU)
-      * has been called before to compute the Schur decomposition of a
-      * matrix.
-      *
-      * Note that this function returns a plain square matrix. If you want to reference
-      * only the upper triangular part, use:
-      * \code schur.matrixT().triangularView<Upper>() \endcode 
-      *
-      * Example: \include ComplexSchur_matrixT.cpp
-      * Output: \verbinclude ComplexSchur_matrixT.out
-      */
-    const ComplexMatrixType& matrixT() const
-    {
-      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
-      return m_matT;
-    }
-
-    /** \brief Computes Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix  Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-
-      * \returns    Reference to \c *this
-      *
-      * The Schur decomposition is computed by first reducing the
-      * matrix to Hessenberg form using the class
-      * HessenbergDecomposition. The Hessenberg matrix is then reduced
-      * to triangular form by performing QR iterations with a single
-      * shift. The cost of computing the Schur decomposition depends
-      * on the number of iterations; as a rough guide, it may be taken
-      * on the number of iterations; as a rough guide, it may be taken
-      * to be \f$25n^3\f$ complex flops, or \f$10n^3\f$ complex flops
-      * if \a computeU is false.
-      *
-      * Example: \include ComplexSchur_compute.cpp
-      * Output: \verbinclude ComplexSchur_compute.out
-      *
-      * \sa compute(const MatrixType&, bool, Index)
-      */
-    template<typename InputType>
-    ComplexSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
-    
-    /** \brief Compute Schur decomposition from a given Hessenberg matrix
-     *  \param[in] matrixH Matrix in Hessenberg form H
-     *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
-     *  \param computeU Computes the matriX U of the Schur vectors
-     * \return Reference to \c *this
-     * 
-     *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
-     *  using either the class HessenbergDecomposition or another mean. 
-     *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
-     *  When computeU is true, this routine computes the matrix U such that 
-     *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
-     * 
-     * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
-     * is not available, the user should give an identity matrix (Q.setIdentity())
-     * 
-     * \sa compute(const MatrixType&, bool)
-     */
-    template<typename HessMatrixType, typename OrthMatrixType>
-    ComplexSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU=true);
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. 
-      *
-      * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
-      * of the matrix.
-      */
-    ComplexSchur& setMaxIterations(Index maxIters)
-    {
-      m_maxIters = maxIters;
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_maxIters;
-    }
-
-    /** \brief Maximum number of iterations per row.
-      *
-      * If not otherwise specified, the maximum number of iterations is this number times the size of the
-      * matrix. It is currently set to 30.
-      */
-    static const int m_maxIterationsPerRow = 30;
-
-  protected:
-    ComplexMatrixType m_matT, m_matU;
-    HessenbergDecomposition<MatrixType> m_hess;
-    ComputationInfo m_info;
-    bool m_isInitialized;
-    bool m_matUisUptodate;
-    Index m_maxIters;
-
-  private:  
-    bool subdiagonalEntryIsNeglegible(Index i);
-    ComplexScalar computeShift(Index iu, Index iter);
-    void reduceToTriangularForm(bool computeU);
-    friend struct internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>;
-};
-
-/** If m_matT(i+1,i) is neglegible in floating point arithmetic
-  * compared to m_matT(i,i) and m_matT(j,j), then set it to zero and
-  * return true, else return false. */
-template<typename MatrixType>
-inline bool ComplexSchur<MatrixType>::subdiagonalEntryIsNeglegible(Index i)
-{
-  RealScalar d = numext::norm1(m_matT.coeff(i,i)) + numext::norm1(m_matT.coeff(i+1,i+1));
-  RealScalar sd = numext::norm1(m_matT.coeff(i+1,i));
-  if (internal::isMuchSmallerThan(sd, d, NumTraits<RealScalar>::epsilon()))
-  {
-    m_matT.coeffRef(i+1,i) = ComplexScalar(0);
-    return true;
-  }
-  return false;
-}
-
-
-/** Compute the shift in the current QR iteration. */
-template<typename MatrixType>
-typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::computeShift(Index iu, Index iter)
-{
-  using std::abs;
-  if (iter == 10 || iter == 20) 
-  {
-    // exceptional shift, taken from http://www.netlib.org/eispack/comqr.f
-    return abs(numext::real(m_matT.coeff(iu,iu-1))) + abs(numext::real(m_matT.coeff(iu-1,iu-2)));
-  }
-
-  // compute the shift as one of the eigenvalues of t, the 2x2
-  // diagonal block on the bottom of the active submatrix
-  Matrix<ComplexScalar,2,2> t = m_matT.template block<2,2>(iu-1,iu-1);
-  RealScalar normt = t.cwiseAbs().sum();
-  t /= normt;     // the normalization by sf is to avoid under/overflow
-
-  ComplexScalar b = t.coeff(0,1) * t.coeff(1,0);
-  ComplexScalar c = t.coeff(0,0) - t.coeff(1,1);
-  ComplexScalar disc = sqrt(c*c + RealScalar(4)*b);
-  ComplexScalar det = t.coeff(0,0) * t.coeff(1,1) - b;
-  ComplexScalar trace = t.coeff(0,0) + t.coeff(1,1);
-  ComplexScalar eival1 = (trace + disc) / RealScalar(2);
-  ComplexScalar eival2 = (trace - disc) / RealScalar(2);
-  RealScalar eival1_norm = numext::norm1(eival1);
-  RealScalar eival2_norm = numext::norm1(eival2);
-  // A division by zero can only occur if eival1==eival2==0.
-  // In this case, det==0, and all we have to do is checking that eival2_norm!=0
-  if(eival1_norm > eival2_norm)
-    eival2 = det / eival1;
-  else if(eival2_norm!=RealScalar(0))
-    eival1 = det / eival2;
-
-  // choose the eigenvalue closest to the bottom entry of the diagonal
-  if(numext::norm1(eival1-t.coeff(1,1)) < numext::norm1(eival2-t.coeff(1,1)))
-    return normt * eival1;
-  else
-    return normt * eival2;
-}
-
-
-template<typename MatrixType>
-template<typename InputType>
-ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
-{
-  m_matUisUptodate = false;
-  eigen_assert(matrix.cols() == matrix.rows());
-
-  if(matrix.cols() == 1)
-  {
-    m_matT = matrix.derived().template cast<ComplexScalar>();
-    if(computeU)  m_matU = ComplexMatrixType::Identity(1,1);
-    m_info = Success;
-    m_isInitialized = true;
-    m_matUisUptodate = computeU;
-    return *this;
-  }
-
-  internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>::run(*this, matrix.derived(), computeU);
-  computeFromHessenberg(m_matT, m_matU, computeU);
-  return *this;
-}
-
-template<typename MatrixType>
-template<typename HessMatrixType, typename OrthMatrixType>
-ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU)
-{
-  m_matT = matrixH;
-  if(computeU)
-    m_matU = matrixQ;
-  reduceToTriangularForm(computeU);
-  return *this;
-}
-namespace internal {
-
-/* Reduce given matrix to Hessenberg form */
-template<typename MatrixType, bool IsComplex>
-struct complex_schur_reduce_to_hessenberg
-{
-  // this is the implementation for the case IsComplex = true
-  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU)
-  {
-    _this.m_hess.compute(matrix);
-    _this.m_matT = _this.m_hess.matrixH();
-    if(computeU)  _this.m_matU = _this.m_hess.matrixQ();
-  }
-};
-
-template<typename MatrixType>
-struct complex_schur_reduce_to_hessenberg<MatrixType, false>
-{
-  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU)
-  {
-    typedef typename ComplexSchur<MatrixType>::ComplexScalar ComplexScalar;
-
-    // Note: m_hess is over RealScalar; m_matT and m_matU is over ComplexScalar
-    _this.m_hess.compute(matrix);
-    _this.m_matT = _this.m_hess.matrixH().template cast<ComplexScalar>();
-    if(computeU)  
-    {
-      // This may cause an allocation which seems to be avoidable
-      MatrixType Q = _this.m_hess.matrixQ(); 
-      _this.m_matU = Q.template cast<ComplexScalar>();
-    }
-  }
-};
-
-} // end namespace internal
-
-// Reduce the Hessenberg matrix m_matT to triangular form by QR iteration.
-template<typename MatrixType>
-void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
-{  
-  Index maxIters = m_maxIters;
-  if (maxIters == -1)
-    maxIters = m_maxIterationsPerRow * m_matT.rows();
-
-  // The matrix m_matT is divided in three parts. 
-  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. 
-  // Rows il,...,iu is the part we are working on (the active submatrix).
-  // Rows iu+1,...,end are already brought in triangular form.
-  Index iu = m_matT.cols() - 1;
-  Index il;
-  Index iter = 0; // number of iterations we are working on the (iu,iu) element
-  Index totalIter = 0; // number of iterations for whole matrix
-
-  while(true)
-  {
-    // find iu, the bottom row of the active submatrix
-    while(iu > 0)
-    {
-      if(!subdiagonalEntryIsNeglegible(iu-1)) break;
-      iter = 0;
-      --iu;
-    }
-
-    // if iu is zero then we are done; the whole matrix is triangularized
-    if(iu==0) break;
-
-    // if we spent too many iterations, we give up
-    iter++;
-    totalIter++;
-    if(totalIter > maxIters) break;
-
-    // find il, the top row of the active submatrix
-    il = iu-1;
-    while(il > 0 && !subdiagonalEntryIsNeglegible(il-1))
-    {
-      --il;
-    }
-
-    /* perform the QR step using Givens rotations. The first rotation
-       creates a bulge; the (il+2,il) element becomes nonzero. This
-       bulge is chased down to the bottom of the active submatrix. */
-
-    ComplexScalar shift = computeShift(iu, iter);
-    JacobiRotation<ComplexScalar> rot;
-    rot.makeGivens(m_matT.coeff(il,il) - shift, m_matT.coeff(il+1,il));
-    m_matT.rightCols(m_matT.cols()-il).applyOnTheLeft(il, il+1, rot.adjoint());
-    m_matT.topRows((std::min)(il+2,iu)+1).applyOnTheRight(il, il+1, rot);
-    if(computeU) m_matU.applyOnTheRight(il, il+1, rot);
-
-    for(Index i=il+1 ; i<iu ; i++)
-    {
-      rot.makeGivens(m_matT.coeffRef(i,i-1), m_matT.coeffRef(i+1,i-1), &m_matT.coeffRef(i,i-1));
-      m_matT.coeffRef(i+1,i-1) = ComplexScalar(0);
-      m_matT.rightCols(m_matT.cols()-i).applyOnTheLeft(i, i+1, rot.adjoint());
-      m_matT.topRows((std::min)(i+2,iu)+1).applyOnTheRight(i, i+1, rot);
-      if(computeU) m_matU.applyOnTheRight(i, i+1, rot);
-    }
-  }
-
-  if(totalIter <= maxIters)
-    m_info = Success;
-  else
-    m_info = NoConvergence;
-
-  m_isInitialized = true;
-  m_matUisUptodate = computeU;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_SCHUR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
deleted file mode 100644
index 4980a3e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Complex Schur needed to complex unsymmetrical eigenvalues/eigenvectors.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_COMPLEX_SCHUR_LAPACKE_H
-#define EIGEN_COMPLEX_SCHUR_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_SCHUR_COMPLEX(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW, LAPACKE_COLROW) \
-template<> template<typename InputType> inline \
-ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
-ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, bool computeU) \
-{ \
-  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \
-  typedef MatrixType::RealScalar RealScalar; \
-  typedef std::complex<RealScalar> ComplexScalar; \
-\
-  eigen_assert(matrix.cols() == matrix.rows()); \
-\
-  m_matUisUptodate = false; \
-  if(matrix.cols() == 1) \
-  { \
-    m_matT = matrix.derived().template cast<ComplexScalar>(); \
-    if(computeU)  m_matU = ComplexMatrixType::Identity(1,1); \
-      m_info = Success; \
-      m_isInitialized = true; \
-      m_matUisUptodate = computeU; \
-      return *this; \
-  } \
-  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info; \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  char jobvs, sort='N'; \
-  LAPACK_##LAPACKE_PREFIX_U##_SELECT1 select = 0; \
-  jobvs = (computeU) ? 'V' : 'N'; \
-  m_matU.resize(n, n); \
-  lapack_int ldvs  = internal::convert_index<lapack_int>(m_matU.outerStride()); \
-  m_matT = matrix; \
-  lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride()); \
-  Matrix<EIGTYPE, Dynamic, Dynamic> w; \
-  w.resize(n, 1);\
-  info = LAPACKE_##LAPACKE_PREFIX##gees( matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda, &sdim, (LAPACKE_TYPE*)w.data(), (LAPACKE_TYPE*)m_matU.data(), ldvs ); \
-  if(info == 0) \
-    m_info = Success; \
-  else \
-    m_info = NoConvergence; \
-\
-  m_isInitialized = true; \
-  m_matUisUptodate = computeU; \
-  return *this; \
-\
-}
-
-EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float,  c, C, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float,  c, C, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPLEX_SCHUR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h
deleted file mode 100644
index 572b29e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h
+++ /dev/null
@@ -1,622 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EIGENSOLVER_H
-#define EIGEN_EIGENSOLVER_H
-
-#include "./RealSchur.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class EigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of general matrices
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * eigendecomposition; this is expected to be an instantiation of the Matrix
-  * class template. Currently, only real matrices are supported.
-  *
-  * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
-  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v \f$.  If
-  * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
-  * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
-  * V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
-  * have \f$ A = V D V^{-1} \f$. This is called the eigendecomposition.
-  *
-  * The eigenvalues and eigenvectors of a matrix may be complex, even when the
-  * matrix is real. However, we can choose real matrices \f$ V \f$ and \f$ D
-  * \f$ satisfying \f$ A V = V D \f$, just like the eigendecomposition, if the
-  * matrix \f$ D \f$ is not required to be diagonal, but if it is allowed to
-  * have blocks of the form
-  * \f[ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f]
-  * (where \f$ u \f$ and \f$ v \f$ are real numbers) on the diagonal.  These
-  * blocks correspond to complex eigenvalue pairs \f$ u \pm iv \f$. We call
-  * this variant of the eigendecomposition the pseudo-eigendecomposition.
-  *
-  * Call the function compute() to compute the eigenvalues and eigenvectors of
-  * a given matrix. Alternatively, you can use the 
-  * EigenSolver(const MatrixType&, bool) constructor which computes the
-  * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
-  * eigenvectors are computed, they can be retrieved with the eigenvalues() and
-  * eigenvectors() functions. The pseudoEigenvalueMatrix() and
-  * pseudoEigenvectors() methods allow the construction of the
-  * pseudo-eigendecomposition.
-  *
-  * The documentation for EigenSolver(const MatrixType&, bool) contains an
-  * example of the typical use of this class.
-  *
-  * \note The implementation is adapted from
-  * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
-  * Their code is based on EISPACK.
-  *
-  * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class EigenSolver
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for #MatrixType. 
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for vector of eigenvalues as returned by eigenvalues(). 
-      *
-      * This is a column vector with entries of type #ComplexScalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
-
-    /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). 
-      *
-      * This is a square matrix with entries of type #ComplexScalar. 
-      * The size is the same as the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
-
-    /** \brief Default constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
-      *
-      * \sa compute() for an example.
-      */
-    EigenSolver() : m_eivec(), m_eivalues(), m_isInitialized(false), m_eigenvectorsOk(false), m_realSchur(), m_matT(), m_tmp() {}
-
-    /** \brief Default constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa EigenSolver()
-      */
-    explicit EigenSolver(Index size)
-      : m_eivec(size, size),
-        m_eivalues(size),
-        m_isInitialized(false),
-        m_eigenvectorsOk(false),
-        m_realSchur(size),
-        m_matT(size, size), 
-        m_tmp(size)
-    {}
-
-    /** \brief Constructor; computes eigendecomposition of given matrix. 
-      * 
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed. 
-      *
-      * This constructor calls compute() to compute the eigenvalues
-      * and eigenvectors.
-      *
-      * Example: \include EigenSolver_EigenSolver_MatrixType.cpp
-      * Output: \verbinclude EigenSolver_EigenSolver_MatrixType.out
-      *
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit EigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
-      : m_eivec(matrix.rows(), matrix.cols()),
-        m_eivalues(matrix.cols()),
-        m_isInitialized(false),
-        m_eigenvectorsOk(false),
-        m_realSchur(matrix.cols()),
-        m_matT(matrix.rows(), matrix.cols()), 
-        m_tmp(matrix.cols())
-    {
-      compute(matrix.derived(), computeEigenvectors);
-    }
-
-    /** \brief Returns the eigenvectors of given matrix. 
-      *
-      * \returns  %Matrix whose columns are the (possibly complex) eigenvectors.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
-      * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
-      * eigenvectors are normalized to have (Euclidean) norm equal to one. The
-      * matrix returned by this function is the matrix \f$ V \f$ in the
-      * eigendecomposition \f$ A = V D V^{-1} \f$, if it exists.
-      *
-      * Example: \include EigenSolver_eigenvectors.cpp
-      * Output: \verbinclude EigenSolver_eigenvectors.out
-      *
-      * \sa eigenvalues(), pseudoEigenvectors()
-      */
-    EigenvectorsType eigenvectors() const;
-
-    /** \brief Returns the pseudo-eigenvectors of given matrix. 
-      *
-      * \returns  Const reference to matrix whose columns are the pseudo-eigenvectors.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * The real matrix \f$ V \f$ returned by this function and the
-      * block-diagonal matrix \f$ D \f$ returned by pseudoEigenvalueMatrix()
-      * satisfy \f$ AV = VD \f$.
-      *
-      * Example: \include EigenSolver_pseudoEigenvectors.cpp
-      * Output: \verbinclude EigenSolver_pseudoEigenvectors.out
-      *
-      * \sa pseudoEigenvalueMatrix(), eigenvectors()
-      */
-    const MatrixType& pseudoEigenvectors() const
-    {
-      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec;
-    }
-
-    /** \brief Returns the block-diagonal matrix in the pseudo-eigendecomposition.
-      *
-      * \returns  A block-diagonal matrix.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before.
-      *
-      * The matrix \f$ D \f$ returned by this function is real and
-      * block-diagonal. The blocks on the diagonal are either 1-by-1 or 2-by-2
-      * blocks of the form
-      * \f$ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f$.
-      * These blocks are not sorted in any particular order.
-      * The matrix \f$ D \f$ and the matrix \f$ V \f$ returned by
-      * pseudoEigenvectors() satisfy \f$ AV = VD \f$.
-      *
-      * \sa pseudoEigenvectors() for an example, eigenvalues()
-      */
-    MatrixType pseudoEigenvalueMatrix() const;
-
-    /** \brief Returns the eigenvalues of given matrix. 
-      *
-      * \returns A const reference to the column vector containing the eigenvalues.
-      *
-      * \pre Either the constructor 
-      * EigenSolver(const MatrixType&,bool) or the member function
-      * compute(const MatrixType&, bool) has been called before.
-      *
-      * The eigenvalues are repeated according to their algebraic multiplicity,
-      * so there are as many eigenvalues as rows in the matrix. The eigenvalues 
-      * are not sorted in any particular order.
-      *
-      * Example: \include EigenSolver_eigenvalues.cpp
-      * Output: \verbinclude EigenSolver_eigenvalues.out
-      *
-      * \sa eigenvectors(), pseudoEigenvalueMatrix(),
-      *     MatrixBase::eigenvalues()
-      */
-    const EigenvalueType& eigenvalues() const
-    {
-      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-      return m_eivalues;
-    }
-
-    /** \brief Computes eigendecomposition of given matrix. 
-      * 
-      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed. 
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of the real matrix \p matrix.
-      * The eigenvalues() function can be used to retrieve them.  If 
-      * \p computeEigenvectors is true, then the eigenvectors are also computed
-      * and can be retrieved by calling eigenvectors().
-      *
-      * The matrix is first reduced to real Schur form using the RealSchur
-      * class. The Schur decomposition is then used to compute the eigenvalues
-      * and eigenvectors.
-      *
-      * The cost of the computation is dominated by the cost of the
-      * Schur decomposition, which is very approximately \f$ 25n^3 \f$
-      * (where \f$ n \f$ is the size of the matrix) if \p computeEigenvectors 
-      * is true, and \f$ 10n^3 \f$ if \p computeEigenvectors is false.
-      *
-      * This method reuses of the allocated data in the EigenSolver object.
-      *
-      * Example: \include EigenSolver_compute.cpp
-      * Output: \verbinclude EigenSolver_compute.out
-      */
-    template<typename InputType>
-    EigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
-
-    /** \returns NumericalIssue if the input contains INF or NaN values or overflow occurred. Returns Success otherwise. */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. */
-    EigenSolver& setMaxIterations(Index maxIters)
-    {
-      m_realSchur.setMaxIterations(maxIters);
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_realSchur.getMaxIterations();
-    }
-
-  private:
-    void doComputeEigenvectors();
-
-  protected:
-    
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-      EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL);
-    }
-    
-    MatrixType m_eivec;
-    EigenvalueType m_eivalues;
-    bool m_isInitialized;
-    bool m_eigenvectorsOk;
-    ComputationInfo m_info;
-    RealSchur<MatrixType> m_realSchur;
-    MatrixType m_matT;
-
-    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
-    ColumnVectorType m_tmp;
-};
-
-template<typename MatrixType>
-MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
-{
-  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-  const RealScalar precision = RealScalar(2)*NumTraits<RealScalar>::epsilon();
-  Index n = m_eivalues.rows();
-  MatrixType matD = MatrixType::Zero(n,n);
-  for (Index i=0; i<n; ++i)
-  {
-    if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i)), precision))
-      matD.coeffRef(i,i) = numext::real(m_eivalues.coeff(i));
-    else
-    {
-      matD.template block<2,2>(i,i) <<  numext::real(m_eivalues.coeff(i)), numext::imag(m_eivalues.coeff(i)),
-                                       -numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i));
-      ++i;
-    }
-  }
-  return matD;
-}
-
-template<typename MatrixType>
-typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eigenvectors() const
-{
-  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
-  eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-  const RealScalar precision = RealScalar(2)*NumTraits<RealScalar>::epsilon();
-  Index n = m_eivec.cols();
-  EigenvectorsType matV(n,n);
-  for (Index j=0; j<n; ++j)
-  {
-    if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(j)), numext::real(m_eivalues.coeff(j)), precision) || j+1==n)
-    {
-      // we have a real eigen value
-      matV.col(j) = m_eivec.col(j).template cast<ComplexScalar>();
-      matV.col(j).normalize();
-    }
-    else
-    {
-      // we have a pair of complex eigen values
-      for (Index i=0; i<n; ++i)
-      {
-        matV.coeffRef(i,j)   = ComplexScalar(m_eivec.coeff(i,j),  m_eivec.coeff(i,j+1));
-        matV.coeffRef(i,j+1) = ComplexScalar(m_eivec.coeff(i,j), -m_eivec.coeff(i,j+1));
-      }
-      matV.col(j).normalize();
-      matV.col(j+1).normalize();
-      ++j;
-    }
-  }
-  return matV;
-}
-
-template<typename MatrixType>
-template<typename InputType>
-EigenSolver<MatrixType>& 
-EigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
-{
-  check_template_parameters();
-  
-  using std::sqrt;
-  using std::abs;
-  using numext::isfinite;
-  eigen_assert(matrix.cols() == matrix.rows());
-
-  // Reduce to real Schur form.
-  m_realSchur.compute(matrix.derived(), computeEigenvectors);
-  
-  m_info = m_realSchur.info();
-
-  if (m_info == Success)
-  {
-    m_matT = m_realSchur.matrixT();
-    if (computeEigenvectors)
-      m_eivec = m_realSchur.matrixU();
-  
-    // Compute eigenvalues from matT
-    m_eivalues.resize(matrix.cols());
-    Index i = 0;
-    while (i < matrix.cols()) 
-    {
-      if (i == matrix.cols() - 1 || m_matT.coeff(i+1, i) == Scalar(0)) 
-      {
-        m_eivalues.coeffRef(i) = m_matT.coeff(i, i);
-        if(!(isfinite)(m_eivalues.coeffRef(i)))
-        {
-          m_isInitialized = true;
-          m_eigenvectorsOk = false;
-          m_info = NumericalIssue;
-          return *this;
-        }
-        ++i;
-      }
-      else
-      {
-        Scalar p = Scalar(0.5) * (m_matT.coeff(i, i) - m_matT.coeff(i+1, i+1));
-        Scalar z;
-        // Compute z = sqrt(abs(p * p + m_matT.coeff(i+1, i) * m_matT.coeff(i, i+1)));
-        // without overflow
-        {
-          Scalar t0 = m_matT.coeff(i+1, i);
-          Scalar t1 = m_matT.coeff(i, i+1);
-          Scalar maxval = numext::maxi<Scalar>(abs(p),numext::maxi<Scalar>(abs(t0),abs(t1)));
-          t0 /= maxval;
-          t1 /= maxval;
-          Scalar p0 = p/maxval;
-          z = maxval * sqrt(abs(p0 * p0 + t0 * t1));
-        }
-        
-        m_eivalues.coeffRef(i)   = ComplexScalar(m_matT.coeff(i+1, i+1) + p, z);
-        m_eivalues.coeffRef(i+1) = ComplexScalar(m_matT.coeff(i+1, i+1) + p, -z);
-        if(!((isfinite)(m_eivalues.coeffRef(i)) && (isfinite)(m_eivalues.coeffRef(i+1))))
-        {
-          m_isInitialized = true;
-          m_eigenvectorsOk = false;
-          m_info = NumericalIssue;
-          return *this;
-        }
-        i += 2;
-      }
-    }
-    
-    // Compute eigenvectors.
-    if (computeEigenvectors)
-      doComputeEigenvectors();
-  }
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-
-  return *this;
-}
-
-
-template<typename MatrixType>
-void EigenSolver<MatrixType>::doComputeEigenvectors()
-{
-  using std::abs;
-  const Index size = m_eivec.cols();
-  const Scalar eps = NumTraits<Scalar>::epsilon();
-
-  // inefficient! this is already computed in RealSchur
-  Scalar norm(0);
-  for (Index j = 0; j < size; ++j)
-  {
-    norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum();
-  }
-  
-  // Backsubstitute to find vectors of upper triangular form
-  if (norm == Scalar(0))
-  {
-    return;
-  }
-
-  for (Index n = size-1; n >= 0; n--)
-  {
-    Scalar p = m_eivalues.coeff(n).real();
-    Scalar q = m_eivalues.coeff(n).imag();
-
-    // Scalar vector
-    if (q == Scalar(0))
-    {
-      Scalar lastr(0), lastw(0);
-      Index l = n;
-
-      m_matT.coeffRef(n,n) = Scalar(1);
-      for (Index i = n-1; i >= 0; i--)
-      {
-        Scalar w = m_matT.coeff(i,i) - p;
-        Scalar r = m_matT.row(i).segment(l,n-l+1).dot(m_matT.col(n).segment(l, n-l+1));
-
-        if (m_eivalues.coeff(i).imag() < Scalar(0))
-        {
-          lastw = w;
-          lastr = r;
-        }
-        else
-        {
-          l = i;
-          if (m_eivalues.coeff(i).imag() == Scalar(0))
-          {
-            if (w != Scalar(0))
-              m_matT.coeffRef(i,n) = -r / w;
-            else
-              m_matT.coeffRef(i,n) = -r / (eps * norm);
-          }
-          else // Solve real equations
-          {
-            Scalar x = m_matT.coeff(i,i+1);
-            Scalar y = m_matT.coeff(i+1,i);
-            Scalar denom = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag();
-            Scalar t = (x * lastr - lastw * r) / denom;
-            m_matT.coeffRef(i,n) = t;
-            if (abs(x) > abs(lastw))
-              m_matT.coeffRef(i+1,n) = (-r - w * t) / x;
-            else
-              m_matT.coeffRef(i+1,n) = (-lastr - y * t) / lastw;
-          }
-
-          // Overflow control
-          Scalar t = abs(m_matT.coeff(i,n));
-          if ((eps * t) * t > Scalar(1))
-            m_matT.col(n).tail(size-i) /= t;
-        }
-      }
-    }
-    else if (q < Scalar(0) && n > 0) // Complex vector
-    {
-      Scalar lastra(0), lastsa(0), lastw(0);
-      Index l = n-1;
-
-      // Last vector component imaginary so matrix is triangular
-      if (abs(m_matT.coeff(n,n-1)) > abs(m_matT.coeff(n-1,n)))
-      {
-        m_matT.coeffRef(n-1,n-1) = q / m_matT.coeff(n,n-1);
-        m_matT.coeffRef(n-1,n) = -(m_matT.coeff(n,n) - p) / m_matT.coeff(n,n-1);
-      }
-      else
-      {
-        ComplexScalar cc = ComplexScalar(Scalar(0),-m_matT.coeff(n-1,n)) / ComplexScalar(m_matT.coeff(n-1,n-1)-p,q);
-        m_matT.coeffRef(n-1,n-1) = numext::real(cc);
-        m_matT.coeffRef(n-1,n) = numext::imag(cc);
-      }
-      m_matT.coeffRef(n,n-1) = Scalar(0);
-      m_matT.coeffRef(n,n) = Scalar(1);
-      for (Index i = n-2; i >= 0; i--)
-      {
-        Scalar ra = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n-1).segment(l, n-l+1));
-        Scalar sa = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n).segment(l, n-l+1));
-        Scalar w = m_matT.coeff(i,i) - p;
-
-        if (m_eivalues.coeff(i).imag() < Scalar(0))
-        {
-          lastw = w;
-          lastra = ra;
-          lastsa = sa;
-        }
-        else
-        {
-          l = i;
-          if (m_eivalues.coeff(i).imag() == RealScalar(0))
-          {
-            ComplexScalar cc = ComplexScalar(-ra,-sa) / ComplexScalar(w,q);
-            m_matT.coeffRef(i,n-1) = numext::real(cc);
-            m_matT.coeffRef(i,n) = numext::imag(cc);
-          }
-          else
-          {
-            // Solve complex equations
-            Scalar x = m_matT.coeff(i,i+1);
-            Scalar y = m_matT.coeff(i+1,i);
-            Scalar vr = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag() - q * q;
-            Scalar vi = (m_eivalues.coeff(i).real() - p) * Scalar(2) * q;
-            if ((vr == Scalar(0)) && (vi == Scalar(0)))
-              vr = eps * norm * (abs(w) + abs(q) + abs(x) + abs(y) + abs(lastw));
-
-            ComplexScalar cc = ComplexScalar(x*lastra-lastw*ra+q*sa,x*lastsa-lastw*sa-q*ra) / ComplexScalar(vr,vi);
-            m_matT.coeffRef(i,n-1) = numext::real(cc);
-            m_matT.coeffRef(i,n) = numext::imag(cc);
-            if (abs(x) > (abs(lastw) + abs(q)))
-            {
-              m_matT.coeffRef(i+1,n-1) = (-ra - w * m_matT.coeff(i,n-1) + q * m_matT.coeff(i,n)) / x;
-              m_matT.coeffRef(i+1,n) = (-sa - w * m_matT.coeff(i,n) - q * m_matT.coeff(i,n-1)) / x;
-            }
-            else
-            {
-              cc = ComplexScalar(-lastra-y*m_matT.coeff(i,n-1),-lastsa-y*m_matT.coeff(i,n)) / ComplexScalar(lastw,q);
-              m_matT.coeffRef(i+1,n-1) = numext::real(cc);
-              m_matT.coeffRef(i+1,n) = numext::imag(cc);
-            }
-          }
-
-          // Overflow control
-          Scalar t = numext::maxi<Scalar>(abs(m_matT.coeff(i,n-1)),abs(m_matT.coeff(i,n)));
-          if ((eps * t) * t > Scalar(1))
-            m_matT.block(i, n-1, size-i, 2) /= t;
-
-        }
-      }
-      
-      // We handled a pair of complex conjugate eigenvalues, so need to skip them both
-      n--;
-    }
-    else
-    {
-      eigen_assert(0 && "Internal bug in EigenSolver (INF or NaN has not been detected)"); // this should not happen
-    }
-  }
-
-  // Back transformation to get eigenvectors of original matrix
-  for (Index j = size-1; j >= 0; j--)
-  {
-    m_tmp.noalias() = m_eivec.leftCols(j+1) * m_matT.col(j).segment(0, j+1);
-    m_eivec.col(j) = m_tmp;
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_EIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
deleted file mode 100644
index 87d789b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
+++ /dev/null
@@ -1,418 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-// Copyright (C) 2016 Tobias Wood <tobias@spinicist.org.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERALIZEDEIGENSOLVER_H
-#define EIGEN_GENERALIZEDEIGENSOLVER_H
-
-#include "./RealQZ.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class GeneralizedEigenSolver
-  *
-  * \brief Computes the generalized eigenvalues and eigenvectors of a pair of general matrices
-  *
-  * \tparam _MatrixType the type of the matrices of which we are computing the
-  * eigen-decomposition; this is expected to be an instantiation of the Matrix
-  * class template. Currently, only real matrices are supported.
-  *
-  * The generalized eigenvalues and eigenvectors of a matrix pair \f$ A \f$ and \f$ B \f$ are scalars
-  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda Bv \f$.  If
-  * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
-  * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
-  * B V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
-  * have \f$ A = B V D V^{-1} \f$. This is called the generalized eigen-decomposition.
-  *
-  * The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the
-  * matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is
-  * singular. To workaround this difficulty, the eigenvalues are provided as a pair of complex \f$ \alpha \f$
-  * and real \f$ \beta \f$ such that: \f$ \lambda_i = \alpha_i / \beta_i \f$. If \f$ \beta_i \f$ is (nearly) zero,
-  * then one can consider the well defined left eigenvalue \f$ \mu = \beta_i / \alpha_i\f$ such that:
-  * \f$ \mu_i A v_i = B v_i \f$, or even \f$ \mu_i u_i^T A  = u_i^T B \f$ where \f$ u_i \f$ is
-  * called the left eigenvector.
-  *
-  * Call the function compute() to compute the generalized eigenvalues and eigenvectors of
-  * a given matrix pair. Alternatively, you can use the
-  * GeneralizedEigenSolver(const MatrixType&, const MatrixType&, bool) constructor which computes the
-  * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
-  * eigenvectors are computed, they can be retrieved with the eigenvalues() and
-  * eigenvectors() functions.
-  *
-  * Here is an usage example of this class:
-  * Example: \include GeneralizedEigenSolver.cpp
-  * Output: \verbinclude GeneralizedEigenSolver.out
-  *
-  * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class GeneralizedEigenSolver
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Complex scalar type for #MatrixType. 
-      *
-      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
-      * \c float or \c double) and just \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef std::complex<RealScalar> ComplexScalar;
-
-    /** \brief Type for vector of real scalar values eigenvalues as returned by betas().
-      *
-      * This is a column vector with entries of type #Scalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType;
-
-    /** \brief Type for vector of complex scalar values eigenvalues as returned by alphas().
-      *
-      * This is a column vector with entries of type #ComplexScalar.
-      * The length of the vector is the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType;
-
-    /** \brief Expression type for the eigenvalues as returned by eigenvalues().
-      */
-    typedef CwiseBinaryOp<internal::scalar_quotient_op<ComplexScalar,Scalar>,ComplexVectorType,VectorType> EigenvalueType;
-
-    /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). 
-      *
-      * This is a square matrix with entries of type #ComplexScalar. 
-      * The size is the same as the size of #MatrixType.
-      */
-    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
-
-    /** \brief Default constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
-      *
-      * \sa compute() for an example.
-      */
-    GeneralizedEigenSolver()
-      : m_eivec(),
-        m_alphas(),
-        m_betas(),
-        m_valuesOkay(false),
-        m_vectorsOkay(false),
-        m_realQZ()
-    {}
-
-    /** \brief Default constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa GeneralizedEigenSolver()
-      */
-    explicit GeneralizedEigenSolver(Index size)
-      : m_eivec(size, size),
-        m_alphas(size),
-        m_betas(size),
-        m_valuesOkay(false),
-        m_vectorsOkay(false),
-        m_realQZ(size),
-        m_tmp(size)
-    {}
-
-    /** \brief Constructor; computes the generalized eigendecomposition of given matrix pair.
-      * 
-      * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are computed.
-      *
-      * This constructor calls compute() to compute the generalized eigenvalues
-      * and eigenvectors.
-      *
-      * \sa compute()
-      */
-    GeneralizedEigenSolver(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true)
-      : m_eivec(A.rows(), A.cols()),
-        m_alphas(A.cols()),
-        m_betas(A.cols()),
-        m_valuesOkay(false),
-        m_vectorsOkay(false),
-        m_realQZ(A.cols()),
-        m_tmp(A.cols())
-    {
-      compute(A, B, computeEigenvectors);
-    }
-
-    /* \brief Returns the computed generalized eigenvectors.
-      *
-      * \returns  %Matrix whose columns are the (possibly complex) right eigenvectors.
-      * i.e. the eigenvectors that solve (A - l*B)x = 0. The ordering matches the eigenvalues.
-      *
-      * \pre Either the constructor 
-      * GeneralizedEigenSolver(const MatrixType&,const MatrixType&, bool) or the member function
-      * compute(const MatrixType&, const MatrixType& bool) has been called before, and
-      * \p computeEigenvectors was set to true (the default).
-      *
-      * \sa eigenvalues()
-      */
-    EigenvectorsType eigenvectors() const {
-      eigen_assert(m_vectorsOkay && "Eigenvectors for GeneralizedEigenSolver were not calculated.");
-      return m_eivec;
-    }
-
-    /** \brief Returns an expression of the computed generalized eigenvalues.
-      *
-      * \returns An expression of the column vector containing the eigenvalues.
-      *
-      * It is a shortcut for \code this->alphas().cwiseQuotient(this->betas()); \endcode
-      * Not that betas might contain zeros. It is therefore not recommended to use this function,
-      * but rather directly deal with the alphas and betas vectors.
-      *
-      * \pre Either the constructor 
-      * GeneralizedEigenSolver(const MatrixType&,const MatrixType&,bool) or the member function
-      * compute(const MatrixType&,const MatrixType&,bool) has been called before.
-      *
-      * The eigenvalues are repeated according to their algebraic multiplicity,
-      * so there are as many eigenvalues as rows in the matrix. The eigenvalues 
-      * are not sorted in any particular order.
-      *
-      * \sa alphas(), betas(), eigenvectors()
-      */
-    EigenvalueType eigenvalues() const
-    {
-      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
-      return EigenvalueType(m_alphas,m_betas);
-    }
-
-    /** \returns A const reference to the vectors containing the alpha values
-      *
-      * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
-      *
-      * \sa betas(), eigenvalues() */
-    ComplexVectorType alphas() const
-    {
-      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
-      return m_alphas;
-    }
-
-    /** \returns A const reference to the vectors containing the beta values
-      *
-      * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
-      *
-      * \sa alphas(), eigenvalues() */
-    VectorType betas() const
-    {
-      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
-      return m_betas;
-    }
-
-    /** \brief Computes generalized eigendecomposition of given matrix.
-      * 
-      * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
-      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
-      *    eigenvalues are computed; if false, only the eigenvalues are
-      *    computed. 
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of the real matrix \p matrix.
-      * The eigenvalues() function can be used to retrieve them.  If 
-      * \p computeEigenvectors is true, then the eigenvectors are also computed
-      * and can be retrieved by calling eigenvectors().
-      *
-      * The matrix is first reduced to real generalized Schur form using the RealQZ
-      * class. The generalized Schur decomposition is then used to compute the eigenvalues
-      * and eigenvectors.
-      *
-      * The cost of the computation is dominated by the cost of the
-      * generalized Schur decomposition.
-      *
-      * This method reuses of the allocated data in the GeneralizedEigenSolver object.
-      */
-    GeneralizedEigenSolver& compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true);
-
-    ComputationInfo info() const
-    {
-      eigen_assert(m_valuesOkay && "EigenSolver is not initialized.");
-      return m_realQZ.info();
-    }
-
-    /** Sets the maximal number of iterations allowed.
-    */
-    GeneralizedEigenSolver& setMaxIterations(Index maxIters)
-    {
-      m_realQZ.setMaxIterations(maxIters);
-      return *this;
-    }
-
-  protected:
-    
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-      EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL);
-    }
-    
-    EigenvectorsType m_eivec;
-    ComplexVectorType m_alphas;
-    VectorType m_betas;
-    bool m_valuesOkay, m_vectorsOkay;
-    RealQZ<MatrixType> m_realQZ;
-    ComplexVectorType m_tmp;
-};
-
-template<typename MatrixType>
-GeneralizedEigenSolver<MatrixType>&
-GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors)
-{
-  check_template_parameters();
-  
-  using std::sqrt;
-  using std::abs;
-  eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows());
-  Index size = A.cols();
-  m_valuesOkay = false;
-  m_vectorsOkay = false;
-  // Reduce to generalized real Schur form:
-  // A = Q S Z and B = Q T Z
-  m_realQZ.compute(A, B, computeEigenvectors);
-  if (m_realQZ.info() == Success)
-  {
-    // Resize storage
-    m_alphas.resize(size);
-    m_betas.resize(size);
-    if (computeEigenvectors)
-    {
-      m_eivec.resize(size,size);
-      m_tmp.resize(size);
-    }
-
-    // Aliases:
-    Map<VectorType> v(reinterpret_cast<Scalar*>(m_tmp.data()), size);
-    ComplexVectorType &cv = m_tmp;
-    const MatrixType &mS = m_realQZ.matrixS();
-    const MatrixType &mT = m_realQZ.matrixT();
-
-    Index i = 0;
-    while (i < size)
-    {
-      if (i == size - 1 || mS.coeff(i+1, i) == Scalar(0))
-      {
-        // Real eigenvalue
-        m_alphas.coeffRef(i) = mS.diagonal().coeff(i);
-        m_betas.coeffRef(i)  = mT.diagonal().coeff(i);
-        if (computeEigenvectors)
-        {
-          v.setConstant(Scalar(0.0));
-          v.coeffRef(i) = Scalar(1.0);
-          // For singular eigenvalues do nothing more
-          if(abs(m_betas.coeffRef(i)) >= (std::numeric_limits<RealScalar>::min)())
-          {
-            // Non-singular eigenvalue
-            const Scalar alpha = real(m_alphas.coeffRef(i));
-            const Scalar beta = m_betas.coeffRef(i);
-            for (Index j = i-1; j >= 0; j--)
-            {
-              const Index st = j+1;
-              const Index sz = i-j;
-              if (j > 0 && mS.coeff(j, j-1) != Scalar(0))
-              {
-                // 2x2 block
-                Matrix<Scalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( v.segment(st,sz) );
-                Matrix<Scalar, 2, 2> lhs = beta * mS.template block<2,2>(j-1,j-1) - alpha * mT.template block<2,2>(j-1,j-1);
-                v.template segment<2>(j-1) = lhs.partialPivLu().solve(rhs);
-                j--;
-              }
-              else
-              {
-                v.coeffRef(j) = -v.segment(st,sz).transpose().cwiseProduct(beta*mS.block(j,st,1,sz) - alpha*mT.block(j,st,1,sz)).sum() / (beta*mS.coeffRef(j,j) - alpha*mT.coeffRef(j,j));
-              }
-            }
-          }
-          m_eivec.col(i).real().noalias() = m_realQZ.matrixZ().transpose() * v;
-          m_eivec.col(i).real().normalize();
-          m_eivec.col(i).imag().setConstant(0);
-        }
-        ++i;
-      }
-      else
-      {
-        // We need to extract the generalized eigenvalues of the pair of a general 2x2 block S and a positive diagonal 2x2 block T
-        // Then taking beta=T_00*T_11, we can avoid any division, and alpha is the eigenvalues of A = (U^-1 * S * U) * diag(T_11,T_00):
-
-        // T =  [a 0]
-        //      [0 b]
-        RealScalar a = mT.diagonal().coeff(i),
-                   b = mT.diagonal().coeff(i+1);
-        const RealScalar beta = m_betas.coeffRef(i) = m_betas.coeffRef(i+1) = a*b;
-
-        // ^^ NOTE: using diagonal()(i) instead of coeff(i,i) workarounds a MSVC bug.
-        Matrix<RealScalar,2,2> S2 = mS.template block<2,2>(i,i) * Matrix<Scalar,2,1>(b,a).asDiagonal();
-
-        Scalar p = Scalar(0.5) * (S2.coeff(0,0) - S2.coeff(1,1));
-        Scalar z = sqrt(abs(p * p + S2.coeff(1,0) * S2.coeff(0,1)));
-        const ComplexScalar alpha = ComplexScalar(S2.coeff(1,1) + p, (beta > 0) ? z : -z);
-        m_alphas.coeffRef(i)   = conj(alpha);
-        m_alphas.coeffRef(i+1) = alpha;
-
-        if (computeEigenvectors) {
-          // Compute eigenvector in position (i+1) and then position (i) is just the conjugate
-          cv.setZero();
-          cv.coeffRef(i+1) = Scalar(1.0);
-          // here, the "static_cast" workaound expression template issues.
-          cv.coeffRef(i) = -(static_cast<Scalar>(beta*mS.coeffRef(i,i+1)) - alpha*mT.coeffRef(i,i+1))
-                          / (static_cast<Scalar>(beta*mS.coeffRef(i,i))   - alpha*mT.coeffRef(i,i));
-          for (Index j = i-1; j >= 0; j--)
-          {
-            const Index st = j+1;
-            const Index sz = i+1-j;
-            if (j > 0 && mS.coeff(j, j-1) != Scalar(0))
-            {
-              // 2x2 block
-              Matrix<ComplexScalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( cv.segment(st,sz) );
-              Matrix<ComplexScalar, 2, 2> lhs = beta * mS.template block<2,2>(j-1,j-1) - alpha * mT.template block<2,2>(j-1,j-1);
-              cv.template segment<2>(j-1) = lhs.partialPivLu().solve(rhs);
-              j--;
-            } else {
-              cv.coeffRef(j) =  cv.segment(st,sz).transpose().cwiseProduct(beta*mS.block(j,st,1,sz) - alpha*mT.block(j,st,1,sz)).sum()
-                              / (alpha*mT.coeffRef(j,j) - static_cast<Scalar>(beta*mS.coeffRef(j,j)));
-            }
-          }
-          m_eivec.col(i+1).noalias() = (m_realQZ.matrixZ().transpose() * cv);
-          m_eivec.col(i+1).normalize();
-          m_eivec.col(i) = m_eivec.col(i+1).conjugate();
-        }
-        i += 2;
-      }
-    }
-
-    m_valuesOkay = true;
-    m_vectorsOkay = computeEigenvectors;
-  }
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERALIZEDEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
deleted file mode 100644
index d0f9091..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
-#define EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
-
-#include "./Tridiagonalization.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class GeneralizedSelfAdjointEigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of the generalized selfadjoint eigen problem
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * eigendecomposition; this is expected to be an instantiation of the Matrix
-  * class template.
-  *
-  * This class solves the generalized eigenvalue problem
-  * \f$ Av = \lambda Bv \f$. In this case, the matrix \f$ A \f$ should be
-  * selfadjoint and the matrix \f$ B \f$ should be positive definite.
-  *
-  * Only the \b lower \b triangular \b part of the input matrix is referenced.
-  *
-  * Call the function compute() to compute the eigenvalues and eigenvectors of
-  * a given matrix. Alternatively, you can use the
-  * GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
-  * constructor which computes the eigenvalues and eigenvectors at construction time.
-  * Once the eigenvalue and eigenvectors are computed, they can be retrieved with the eigenvalues()
-  * and eigenvectors() functions.
-  *
-  * The documentation for GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
-  * contains an example of the typical use of this class.
-  *
-  * \sa class SelfAdjointEigenSolver, class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType>
-class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<_MatrixType>
-{
-    typedef SelfAdjointEigenSolver<_MatrixType> Base;
-  public:
-
-    typedef _MatrixType MatrixType;
-
-    /** \brief Default constructor for fixed-size matrices.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute(). This constructor
-      * can only be used if \p _MatrixType is a fixed-size matrix; use
-      * GeneralizedSelfAdjointEigenSolver(Index) for dynamic-size matrices.
-      */
-    GeneralizedSelfAdjointEigenSolver() : Base() {}
-
-    /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
-      *
-      * \param [in]  size  Positive integer, size of the matrix whose
-      * eigenvalues and eigenvectors will be computed.
-      *
-      * This constructor is useful for dynamic-size matrices, when the user
-      * intends to perform decompositions via compute(). The \p size
-      * parameter is only used as a hint. It is not an error to give a wrong
-      * \p size, but it may impair performance.
-      *
-      * \sa compute() for an example
-      */
-    explicit GeneralizedSelfAdjointEigenSolver(Index size)
-        : Base(size)
-    {}
-
-    /** \brief Constructor; computes generalized eigendecomposition of given matrix pencil.
-      *
-      * \param[in]  matA  Selfadjoint matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  matB  Positive-definite matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
-      *                     Default is #ComputeEigenvectors|#Ax_lBx.
-      *
-      * This constructor calls compute(const MatrixType&, const MatrixType&, int)
-      * to compute the eigenvalues and (if requested) the eigenvectors of the
-      * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the
-      * selfadjoint matrix \f$ A \f$ and \a matB the positive definite matrix
-      * \f$ B \f$. Each eigenvector \f$ x \f$ satisfies the property
-      * \f$ x^* B x = 1 \f$. The eigenvectors are computed if
-      * \a options contains ComputeEigenvectors.
-      *
-      * In addition, the two following variants can be solved via \p options:
-      * - \c ABx_lx: \f$ ABx = \lambda x \f$
-      * - \c BAx_lx: \f$ BAx = \lambda x \f$
-      *
-      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.out
-      *
-      * \sa compute(const MatrixType&, const MatrixType&, int)
-      */
-    GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB,
-                                      int options = ComputeEigenvectors|Ax_lBx)
-      : Base(matA.cols())
-    {
-      compute(matA, matB, options);
-    }
-
-    /** \brief Computes generalized eigendecomposition of given matrix pencil.
-      *
-      * \param[in]  matA  Selfadjoint matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  matB  Positive-definite matrix in matrix pencil.
-      *                   Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
-      *                     Default is #ComputeEigenvectors|#Ax_lBx.
-      *
-      * \returns    Reference to \c *this
-      *
-      * According to \p options, this function computes eigenvalues and (if requested)
-      * the eigenvectors of one of the following three generalized eigenproblems:
-      * - \c Ax_lBx: \f$ Ax = \lambda B x \f$
-      * - \c ABx_lx: \f$ ABx = \lambda x \f$
-      * - \c BAx_lx: \f$ BAx = \lambda x \f$
-      * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite
-      * matrix \f$ B \f$.
-      * In addition, each eigenvector \f$ x \f$ satisfies the property \f$ x^* B x = 1 \f$.
-      *
-      * The eigenvalues() function can be used to retrieve
-      * the eigenvalues. If \p options contains ComputeEigenvectors, then the
-      * eigenvectors are also computed and can be retrieved by calling
-      * eigenvectors().
-      *
-      * The implementation uses LLT to compute the Cholesky decomposition
-      * \f$ B = LL^* \f$ and computes the classical eigendecomposition
-      * of the selfadjoint matrix \f$ L^{-1} A (L^*)^{-1} \f$ if \p options contains Ax_lBx
-      * and of \f$ L^{*} A L \f$ otherwise. This solves the
-      * generalized eigenproblem, because any solution of the generalized
-      * eigenproblem \f$ Ax = \lambda B x \f$ corresponds to a solution
-      * \f$ L^{-1} A (L^*)^{-1} (L^* x) = \lambda (L^* x) \f$ of the
-      * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$. Similar statements
-      * can be made for the two other variants.
-      *
-      * Example: \include SelfAdjointEigenSolver_compute_MatrixType2.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType2.out
-      *
-      * \sa GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
-      */
-    GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB,
-                                               int options = ComputeEigenvectors|Ax_lBx);
-
-  protected:
-
-};
-
-
-template<typename MatrixType>
-GeneralizedSelfAdjointEigenSolver<MatrixType>& GeneralizedSelfAdjointEigenSolver<MatrixType>::
-compute(const MatrixType& matA, const MatrixType& matB, int options)
-{
-  eigen_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows());
-  eigen_assert((options&~(EigVecMask|GenEigMask))==0
-          && (options&EigVecMask)!=EigVecMask
-          && ((options&GenEigMask)==0 || (options&GenEigMask)==Ax_lBx
-           || (options&GenEigMask)==ABx_lx || (options&GenEigMask)==BAx_lx)
-          && "invalid option parameter");
-
-  bool computeEigVecs = ((options&EigVecMask)==0) || ((options&EigVecMask)==ComputeEigenvectors);
-
-  // Compute the cholesky decomposition of matB = L L' = U'U
-  LLT<MatrixType> cholB(matB);
-
-  int type = (options&GenEigMask);
-  if(type==0)
-    type = Ax_lBx;
-
-  if(type==Ax_lBx)
-  {
-    // compute C = inv(L) A inv(L')
-    MatrixType matC = matA.template selfadjointView<Lower>();
-    cholB.matrixL().template solveInPlace<OnTheLeft>(matC);
-    cholB.matrixU().template solveInPlace<OnTheRight>(matC);
-
-    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly );
-
-    // transform back the eigen vectors: evecs = inv(U) * evecs
-    if(computeEigVecs)
-      cholB.matrixU().solveInPlace(Base::m_eivec);
-  }
-  else if(type==ABx_lx)
-  {
-    // compute C = L' A L
-    MatrixType matC = matA.template selfadjointView<Lower>();
-    matC = matC * cholB.matrixL();
-    matC = cholB.matrixU() * matC;
-
-    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
-
-    // transform back the eigen vectors: evecs = inv(U) * evecs
-    if(computeEigVecs)
-      cholB.matrixU().solveInPlace(Base::m_eivec);
-  }
-  else if(type==BAx_lx)
-  {
-    // compute C = L' A L
-    MatrixType matC = matA.template selfadjointView<Lower>();
-    matC = matC * cholB.matrixL();
-    matC = cholB.matrixU() * matC;
-
-    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
-
-    // transform back the eigen vectors: evecs = L * evecs
-    if(computeEigVecs)
-      Base::m_eivec = cholB.matrixL() * Base::m_eivec;
-  }
-
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h
deleted file mode 100644
index 1f21139..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h
+++ /dev/null
@@ -1,374 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HESSENBERGDECOMPOSITION_H
-#define EIGEN_HESSENBERGDECOMPOSITION_H
-
-namespace Eigen { 
-
-namespace internal {
-  
-template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType;
-template<typename MatrixType>
-struct traits<HessenbergDecompositionMatrixHReturnType<MatrixType> >
-{
-  typedef MatrixType ReturnType;
-};
-
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class HessenbergDecomposition
-  *
-  * \brief Reduces a square matrix to Hessenberg form by an orthogonal similarity transformation
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the Hessenberg decomposition
-  *
-  * This class performs an Hessenberg decomposition of a matrix \f$ A \f$. In
-  * the real case, the Hessenberg decomposition consists of an orthogonal
-  * matrix \f$ Q \f$ and a Hessenberg matrix \f$ H \f$ such that \f$ A = Q H
-  * Q^T \f$. An orthogonal matrix is a matrix whose inverse equals its
-  * transpose (\f$ Q^{-1} = Q^T \f$). A Hessenberg matrix has zeros below the
-  * subdiagonal, so it is almost upper triangular. The Hessenberg decomposition
-  * of a complex matrix is \f$ A = Q H Q^* \f$ with \f$ Q \f$ unitary (that is,
-  * \f$ Q^{-1} = Q^* \f$).
-  *
-  * Call the function compute() to compute the Hessenberg decomposition of a
-  * given matrix. Alternatively, you can use the
-  * HessenbergDecomposition(const MatrixType&) constructor which computes the
-  * Hessenberg decomposition at construction time. Once the decomposition is
-  * computed, you can use the matrixH() and matrixQ() functions to construct
-  * the matrices H and Q in the decomposition.
-  *
-  * The documentation for matrixH() contains an example of the typical use of
-  * this class.
-  *
-  * \sa class ComplexSchur, class Tridiagonalization, \ref QR_Module "QR Module"
-  */
-template<typename _MatrixType> class HessenbergDecomposition
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    enum {
-      Size = MatrixType::RowsAtCompileTime,
-      SizeMinusOne = Size == Dynamic ? Dynamic : Size - 1,
-      Options = MatrixType::Options,
-      MaxSize = MatrixType::MaxRowsAtCompileTime,
-      MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : MaxSize - 1
-    };
-
-    /** \brief Scalar type for matrices of type #MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    /** \brief Type for vector of Householder coefficients.
-      *
-      * This is column vector with entries of type #Scalar. The length of the
-      * vector is one less than the size of #MatrixType, if it is a fixed-side
-      * type.
-      */
-    typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
-
-    /** \brief Return type of matrixQ() */
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType;
-    
-    typedef internal::HessenbergDecompositionMatrixHReturnType<MatrixType> MatrixHReturnType;
-
-    /** \brief Default constructor; the decomposition will be computed later.
-      *
-      * \param [in] size  The size of the matrix whose Hessenberg decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().  The \p size parameter is only
-      * used as a hint. It is not an error to give a wrong \p size, but it may
-      * impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit HessenbergDecomposition(Index size = Size==Dynamic ? 2 : Size)
-      : m_matrix(size,size),
-        m_temp(size),
-        m_isInitialized(false)
-    {
-      if(size>1)
-        m_hCoeffs.resize(size-1);
-    }
-
-    /** \brief Constructor; computes Hessenberg decomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
-      *
-      * This constructor calls compute() to compute the Hessenberg
-      * decomposition.
-      *
-      * \sa matrixH() for an example.
-      */
-    template<typename InputType>
-    explicit HessenbergDecomposition(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_temp(matrix.rows()),
-        m_isInitialized(false)
-    {
-      if(matrix.rows()<2)
-      {
-        m_isInitialized = true;
-        return;
-      }
-      m_hCoeffs.resize(matrix.rows()-1,1);
-      _compute(m_matrix, m_hCoeffs, m_temp);
-      m_isInitialized = true;
-    }
-
-    /** \brief Computes Hessenberg decomposition of given matrix.
-      *
-      * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
-      * \returns    Reference to \c *this
-      *
-      * The Hessenberg decomposition is computed by bringing the columns of the
-      * matrix successively in the required form using Householder reflections
-      * (see, e.g., Algorithm 7.4.2 in Golub \& Van Loan, <i>%Matrix
-      * Computations</i>). The cost is \f$ 10n^3/3 \f$ flops, where \f$ n \f$
-      * denotes the size of the given matrix.
-      *
-      * This method reuses of the allocated data in the HessenbergDecomposition
-      * object.
-      *
-      * Example: \include HessenbergDecomposition_compute.cpp
-      * Output: \verbinclude HessenbergDecomposition_compute.out
-      */
-    template<typename InputType>
-    HessenbergDecomposition& compute(const EigenBase<InputType>& matrix)
-    {
-      m_matrix = matrix.derived();
-      if(matrix.rows()<2)
-      {
-        m_isInitialized = true;
-        return *this;
-      }
-      m_hCoeffs.resize(matrix.rows()-1,1);
-      _compute(m_matrix, m_hCoeffs, m_temp);
-      m_isInitialized = true;
-      return *this;
-    }
-
-    /** \brief Returns the Householder coefficients.
-      *
-      * \returns a const reference to the vector of Householder coefficients
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * The Householder coefficients allow the reconstruction of the matrix
-      * \f$ Q \f$ in the Hessenberg decomposition from the packed data.
-      *
-      * \sa packedMatrix(), \ref Householder_Module "Householder module"
-      */
-    const CoeffVectorType& householderCoefficients() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return m_hCoeffs;
-    }
-
-    /** \brief Returns the internal representation of the decomposition
-      *
-      *	\returns a const reference to a matrix with the internal representation
-      *	         of the decomposition.
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * The returned matrix contains the following information:
-      *  - the upper part and lower sub-diagonal represent the Hessenberg matrix H
-      *  - the rest of the lower part contains the Householder vectors that, combined with
-      *    Householder coefficients returned by householderCoefficients(),
-      *    allows to reconstruct the matrix Q as
-      *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
-      *    Here, the matrices \f$ H_i \f$ are the Householder transformations
-      *       \f$ H_i = (I - h_i v_i v_i^T) \f$
-      *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
-      *    \f$ v_i \f$ is the Householder vector defined by
-      *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
-      *    with M the matrix returned by this function.
-      *
-      * See LAPACK for further details on this packed storage.
-      *
-      * Example: \include HessenbergDecomposition_packedMatrix.cpp
-      * Output: \verbinclude HessenbergDecomposition_packedMatrix.out
-      *
-      * \sa householderCoefficients()
-      */
-    const MatrixType& packedMatrix() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return m_matrix;
-    }
-
-    /** \brief Reconstructs the orthogonal matrix Q in the decomposition
-      *
-      * \returns object representing the matrix Q
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * This function returns a light-weight object of template class
-      * HouseholderSequence. You can either apply it directly to a matrix or
-      * you can convert it to a matrix of type #MatrixType.
-      *
-      * \sa matrixH() for an example, class HouseholderSequence
-      */
-    HouseholderSequenceType matrixQ() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate())
-             .setLength(m_matrix.rows() - 1)
-             .setShift(1);
-    }
-
-    /** \brief Constructs the Hessenberg matrix H in the decomposition
-      *
-      * \returns expression object representing the matrix H
-      *
-      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
-      * or the member function compute(const MatrixType&) has been called
-      * before to compute the Hessenberg decomposition of a matrix.
-      *
-      * The object returned by this function constructs the Hessenberg matrix H
-      * when it is assigned to a matrix or otherwise evaluated. The matrix H is
-      * constructed from the packed matrix as returned by packedMatrix(): The
-      * upper part (including the subdiagonal) of the packed matrix contains
-      * the matrix H. It may sometimes be better to directly use the packed
-      * matrix instead of constructing the matrix H.
-      *
-      * Example: \include HessenbergDecomposition_matrixH.cpp
-      * Output: \verbinclude HessenbergDecomposition_matrixH.out
-      *
-      * \sa matrixQ(), packedMatrix()
-      */
-    MatrixHReturnType matrixH() const
-    {
-      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
-      return MatrixHReturnType(*this);
-    }
-
-  private:
-
-    typedef Matrix<Scalar, 1, Size, int(Options) | int(RowMajor), 1, MaxSize> VectorType;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp);
-
-  protected:
-    MatrixType m_matrix;
-    CoeffVectorType m_hCoeffs;
-    VectorType m_temp;
-    bool m_isInitialized;
-};
-
-/** \internal
-  * Performs a tridiagonal decomposition of \a matA in place.
-  *
-  * \param matA the input selfadjoint matrix
-  * \param hCoeffs returned Householder coefficients
-  *
-  * The result is written in the lower triangular part of \a matA.
-  *
-  * Implemented from Golub's "%Matrix Computations", algorithm 8.3.1.
-  *
-  * \sa packedMatrix()
-  */
-template<typename MatrixType>
-void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp)
-{
-  eigen_assert(matA.rows()==matA.cols());
-  Index n = matA.rows();
-  temp.resize(n);
-  for (Index i = 0; i<n-1; ++i)
-  {
-    // let's consider the vector v = i-th column starting at position i+1
-    Index remainingSize = n-i-1;
-    RealScalar beta;
-    Scalar h;
-    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
-    matA.col(i).coeffRef(i+1) = beta;
-    hCoeffs.coeffRef(i) = h;
-
-    // Apply similarity transformation to remaining columns,
-    // i.e., compute A = H A H'
-
-    // A = H A
-    matA.bottomRightCorner(remainingSize, remainingSize)
-        .applyHouseholderOnTheLeft(matA.col(i).tail(remainingSize-1), h, &temp.coeffRef(0));
-
-    // A = A H'
-    matA.rightCols(remainingSize)
-        .applyHouseholderOnTheRight(matA.col(i).tail(remainingSize-1), numext::conj(h), &temp.coeffRef(0));
-  }
-}
-
-namespace internal {
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \brief Expression type for return value of HessenbergDecomposition::matrixH()
-  *
-  * \tparam MatrixType type of matrix in the Hessenberg decomposition
-  *
-  * Objects of this type represent the Hessenberg matrix in the Hessenberg
-  * decomposition of some matrix. The object holds a reference to the
-  * HessenbergDecomposition class until the it is assigned or evaluated for
-  * some other reason (the reference should remain valid during the life time
-  * of this object). This class is the return type of
-  * HessenbergDecomposition::matrixH(); there is probably no other use for this
-  * class.
-  */
-template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType
-: public ReturnByValue<HessenbergDecompositionMatrixHReturnType<MatrixType> >
-{
-  public:
-    /** \brief Constructor.
-      *
-      * \param[in] hess  Hessenberg decomposition
-      */
-    HessenbergDecompositionMatrixHReturnType(const HessenbergDecomposition<MatrixType>& hess) : m_hess(hess) { }
-
-    /** \brief Hessenberg matrix in decomposition.
-      *
-      * \param[out] result  Hessenberg matrix in decomposition \p hess which
-      *                     was passed to the constructor
-      */
-    template <typename ResultType>
-    inline void evalTo(ResultType& result) const
-    {
-      result = m_hess.packedMatrix();
-      Index n = result.rows();
-      if (n>2)
-        result.bottomLeftCorner(n-2, n-2).template triangularView<Lower>().setZero();
-    }
-
-    Index rows() const { return m_hess.packedMatrix().rows(); }
-    Index cols() const { return m_hess.packedMatrix().cols(); }
-
-  protected:
-    const HessenbergDecomposition<MatrixType>& m_hess;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_HESSENBERGDECOMPOSITION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
deleted file mode 100644
index 66e5a3d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
+++ /dev/null
@@ -1,158 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIXBASEEIGENVALUES_H
-#define EIGEN_MATRIXBASEEIGENVALUES_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Derived, bool IsComplex>
-struct eigenvalues_selector
-{
-  // this is the implementation for the case IsComplex = true
-  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const
-  run(const MatrixBase<Derived>& m)
-  {
-    typedef typename Derived::PlainObject PlainObject;
-    PlainObject m_eval(m);
-    return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues();
-  }
-};
-
-template<typename Derived>
-struct eigenvalues_selector<Derived, false>
-{
-  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const
-  run(const MatrixBase<Derived>& m)
-  {
-    typedef typename Derived::PlainObject PlainObject;
-    PlainObject m_eval(m);
-    return EigenSolver<PlainObject>(m_eval, false).eigenvalues();
-  }
-};
-
-} // end namespace internal
-
-/** \brief Computes the eigenvalues of a matrix 
-  * \returns Column vector containing the eigenvalues.
-  *
-  * \eigenvalues_module
-  * This function computes the eigenvalues with the help of the EigenSolver
-  * class (for real matrices) or the ComplexEigenSolver class (for complex
-  * matrices). 
-  *
-  * The eigenvalues are repeated according to their algebraic multiplicity,
-  * so there are as many eigenvalues as rows in the matrix.
-  *
-  * The SelfAdjointView class provides a better algorithm for selfadjoint
-  * matrices.
-  *
-  * Example: \include MatrixBase_eigenvalues.cpp
-  * Output: \verbinclude MatrixBase_eigenvalues.out
-  *
-  * \sa EigenSolver::eigenvalues(), ComplexEigenSolver::eigenvalues(),
-  *     SelfAdjointView::eigenvalues()
-  */
-template<typename Derived>
-inline typename MatrixBase<Derived>::EigenvaluesReturnType
-MatrixBase<Derived>::eigenvalues() const
-{
-  return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived());
-}
-
-/** \brief Computes the eigenvalues of a matrix
-  * \returns Column vector containing the eigenvalues.
-  *
-  * \eigenvalues_module
-  * This function computes the eigenvalues with the help of the
-  * SelfAdjointEigenSolver class.  The eigenvalues are repeated according to
-  * their algebraic multiplicity, so there are as many eigenvalues as rows in
-  * the matrix.
-  *
-  * Example: \include SelfAdjointView_eigenvalues.cpp
-  * Output: \verbinclude SelfAdjointView_eigenvalues.out
-  *
-  * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues()
-  */
-template<typename MatrixType, unsigned int UpLo> 
-EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType
-SelfAdjointView<MatrixType, UpLo>::eigenvalues() const
-{
-  PlainObject thisAsMatrix(*this);
-  return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
-}
-
-
-
-/** \brief Computes the L2 operator norm
-  * \returns Operator norm of the matrix.
-  *
-  * \eigenvalues_module
-  * This function computes the L2 operator norm of a matrix, which is also
-  * known as the spectral norm. The norm of a matrix \f$ A \f$ is defined to be
-  * \f[ \|A\|_2 = \max_x \frac{\|Ax\|_2}{\|x\|_2} \f]
-  * where the maximum is over all vectors and the norm on the right is the
-  * Euclidean vector norm. The norm equals the largest singular value, which is
-  * the square root of the largest eigenvalue of the positive semi-definite
-  * matrix \f$ A^*A \f$.
-  *
-  * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed
-  * by SelfAdjointView::eigenvalues(), to compute the operator norm of a
-  * matrix.  The SelfAdjointView class provides a better algorithm for
-  * selfadjoint matrices.
-  *
-  * Example: \include MatrixBase_operatorNorm.cpp
-  * Output: \verbinclude MatrixBase_operatorNorm.out
-  *
-  * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm()
-  */
-template<typename Derived>
-inline typename MatrixBase<Derived>::RealScalar
-MatrixBase<Derived>::operatorNorm() const
-{
-  using std::sqrt;
-  typename Derived::PlainObject m_eval(derived());
-  // FIXME if it is really guaranteed that the eigenvalues are already sorted,
-  // then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
-  return sqrt((m_eval*m_eval.adjoint())
-                 .eval()
-		 .template selfadjointView<Lower>()
-		 .eigenvalues()
-		 .maxCoeff()
-		 );
-}
-
-/** \brief Computes the L2 operator norm
-  * \returns Operator norm of the matrix.
-  *
-  * \eigenvalues_module
-  * This function computes the L2 operator norm of a self-adjoint matrix. For a
-  * self-adjoint matrix, the operator norm is the largest eigenvalue.
-  *
-  * The current implementation uses the eigenvalues of the matrix, as computed
-  * by eigenvalues(), to compute the operator norm of the matrix.
-  *
-  * Example: \include SelfAdjointView_operatorNorm.cpp
-  * Output: \verbinclude SelfAdjointView_operatorNorm.out
-  *
-  * \sa eigenvalues(), MatrixBase::operatorNorm()
-  */
-template<typename MatrixType, unsigned int UpLo>
-EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::RealScalar
-SelfAdjointView<MatrixType, UpLo>::operatorNorm() const
-{
-  return eigenvalues().cwiseAbs().maxCoeff();
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h
deleted file mode 100644
index 5091301..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h
+++ /dev/null
@@ -1,657 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Alexey Korepanov <kaikaikai@yandex.ru>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REAL_QZ_H
-#define EIGEN_REAL_QZ_H
-
-namespace Eigen {
-
-  /** \eigenvalues_module \ingroup Eigenvalues_Module
-   *
-   *
-   * \class RealQZ
-   *
-   * \brief Performs a real QZ decomposition of a pair of square matrices
-   *
-   * \tparam _MatrixType the type of the matrix of which we are computing the
-   * real QZ decomposition; this is expected to be an instantiation of the
-   * Matrix class template.
-   *
-   * Given a real square matrices A and B, this class computes the real QZ
-   * decomposition: \f$ A = Q S Z \f$, \f$ B = Q T Z \f$ where Q and Z are
-   * real orthogonal matrixes, T is upper-triangular matrix, and S is upper
-   * quasi-triangular matrix. An orthogonal matrix is a matrix whose
-   * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
-   * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
-   * blocks and 2-by-2 blocks where further reduction is impossible due to
-   * complex eigenvalues. 
-   *
-   * The eigenvalues of the pencil \f$ A - z B \f$ can be obtained from
-   * 1x1 and 2x2 blocks on the diagonals of S and T.
-   *
-   * Call the function compute() to compute the real QZ decomposition of a
-   * given pair of matrices. Alternatively, you can use the 
-   * RealQZ(const MatrixType& B, const MatrixType& B, bool computeQZ)
-   * constructor which computes the real QZ decomposition at construction
-   * time. Once the decomposition is computed, you can use the matrixS(),
-   * matrixT(), matrixQ() and matrixZ() functions to retrieve the matrices
-   * S, T, Q and Z in the decomposition. If computeQZ==false, some time
-   * is saved by not computing matrices Q and Z.
-   *
-   * Example: \include RealQZ_compute.cpp
-   * Output: \include RealQZ_compute.out
-   *
-   * \note The implementation is based on the algorithm in "Matrix Computations"
-   * by Gene H. Golub and Charles F. Van Loan, and a paper "An algorithm for
-   * generalized eigenvalue problems" by C.B.Moler and G.W.Stewart.
-   *
-   * \sa class RealSchur, class ComplexSchur, class EigenSolver, class ComplexEigenSolver
-   */
-
-  template<typename _MatrixType> class RealQZ
-  {
-    public:
-      typedef _MatrixType MatrixType;
-      enum {
-        RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-        ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-        Options = MatrixType::Options,
-        MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-        MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-      };
-      typedef typename MatrixType::Scalar Scalar;
-      typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-      typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-      typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
-      typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
-
-      /** \brief Default constructor.
-       *
-       * \param [in] size  Positive integer, size of the matrix whose QZ decomposition will be computed.
-       *
-       * The default constructor is useful in cases in which the user intends to
-       * perform decompositions via compute().  The \p size parameter is only
-       * used as a hint. It is not an error to give a wrong \p size, but it may
-       * impair performance.
-       *
-       * \sa compute() for an example.
-       */
-      explicit RealQZ(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime) :
-        m_S(size, size),
-        m_T(size, size),
-        m_Q(size, size),
-        m_Z(size, size),
-        m_workspace(size*2),
-        m_maxIters(400),
-        m_isInitialized(false),
-        m_computeQZ(true)
-      {}
-
-      /** \brief Constructor; computes real QZ decomposition of given matrices
-       * 
-       * \param[in]  A          Matrix A.
-       * \param[in]  B          Matrix B.
-       * \param[in]  computeQZ  If false, A and Z are not computed.
-       *
-       * This constructor calls compute() to compute the QZ decomposition.
-       */
-      RealQZ(const MatrixType& A, const MatrixType& B, bool computeQZ = true) :
-        m_S(A.rows(),A.cols()),
-        m_T(A.rows(),A.cols()),
-        m_Q(A.rows(),A.cols()),
-        m_Z(A.rows(),A.cols()),
-        m_workspace(A.rows()*2),
-        m_maxIters(400),
-        m_isInitialized(false),
-        m_computeQZ(true)
-      {
-        compute(A, B, computeQZ);
-      }
-
-      /** \brief Returns matrix Q in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix Q.
-       */
-      const MatrixType& matrixQ() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
-        return m_Q;
-      }
-
-      /** \brief Returns matrix Z in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix Z.
-       */
-      const MatrixType& matrixZ() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
-        return m_Z;
-      }
-
-      /** \brief Returns matrix S in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix S.
-       */
-      const MatrixType& matrixS() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_S;
-      }
-
-      /** \brief Returns matrix S in the QZ decomposition. 
-       *
-       * \returns A const reference to the matrix S.
-       */
-      const MatrixType& matrixT() const {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_T;
-      }
-
-      /** \brief Computes QZ decomposition of given matrix. 
-       * 
-       * \param[in]  A          Matrix A.
-       * \param[in]  B          Matrix B.
-       * \param[in]  computeQZ  If false, A and Z are not computed.
-       * \returns    Reference to \c *this
-       */
-      RealQZ& compute(const MatrixType& A, const MatrixType& B, bool computeQZ = true);
-
-      /** \brief Reports whether previous computation was successful.
-       *
-       * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-       */
-      ComputationInfo info() const
-      {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_info;
-      }
-
-      /** \brief Returns number of performed QR-like iterations.
-      */
-      Index iterations() const
-      {
-        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
-        return m_global_iter;
-      }
-
-      /** Sets the maximal number of iterations allowed to converge to one eigenvalue
-       * or decouple the problem.
-      */
-      RealQZ& setMaxIterations(Index maxIters)
-      {
-        m_maxIters = maxIters;
-        return *this;
-      }
-
-    private:
-
-      MatrixType m_S, m_T, m_Q, m_Z;
-      Matrix<Scalar,Dynamic,1> m_workspace;
-      ComputationInfo m_info;
-      Index m_maxIters;
-      bool m_isInitialized;
-      bool m_computeQZ;
-      Scalar m_normOfT, m_normOfS;
-      Index m_global_iter;
-
-      typedef Matrix<Scalar,3,1> Vector3s;
-      typedef Matrix<Scalar,2,1> Vector2s;
-      typedef Matrix<Scalar,2,2> Matrix2s;
-      typedef JacobiRotation<Scalar> JRs;
-
-      void hessenbergTriangular();
-      void computeNorms();
-      Index findSmallSubdiagEntry(Index iu);
-      Index findSmallDiagEntry(Index f, Index l);
-      void splitOffTwoRows(Index i);
-      void pushDownZero(Index z, Index f, Index l);
-      void step(Index f, Index l, Index iter);
-
-  }; // RealQZ
-
-  /** \internal Reduces S and T to upper Hessenberg - triangular form */
-  template<typename MatrixType>
-    void RealQZ<MatrixType>::hessenbergTriangular()
-    {
-
-      const Index dim = m_S.cols();
-
-      // perform QR decomposition of T, overwrite T with R, save Q
-      HouseholderQR<MatrixType> qrT(m_T);
-      m_T = qrT.matrixQR();
-      m_T.template triangularView<StrictlyLower>().setZero();
-      m_Q = qrT.householderQ();
-      // overwrite S with Q* S
-      m_S.applyOnTheLeft(m_Q.adjoint());
-      // init Z as Identity
-      if (m_computeQZ)
-        m_Z = MatrixType::Identity(dim,dim);
-      // reduce S to upper Hessenberg with Givens rotations
-      for (Index j=0; j<=dim-3; j++) {
-        for (Index i=dim-1; i>=j+2; i--) {
-          JRs G;
-          // kill S(i,j)
-          if(m_S.coeff(i,j) != 0)
-          {
-            G.makeGivens(m_S.coeff(i-1,j), m_S.coeff(i,j), &m_S.coeffRef(i-1, j));
-            m_S.coeffRef(i,j) = Scalar(0.0);
-            m_S.rightCols(dim-j-1).applyOnTheLeft(i-1,i,G.adjoint());
-            m_T.rightCols(dim-i+1).applyOnTheLeft(i-1,i,G.adjoint());
-            // update Q
-            if (m_computeQZ)
-              m_Q.applyOnTheRight(i-1,i,G);
-          }
-          // kill T(i,i-1)
-          if(m_T.coeff(i,i-1)!=Scalar(0))
-          {
-            G.makeGivens(m_T.coeff(i,i), m_T.coeff(i,i-1), &m_T.coeffRef(i,i));
-            m_T.coeffRef(i,i-1) = Scalar(0.0);
-            m_S.applyOnTheRight(i,i-1,G);
-            m_T.topRows(i).applyOnTheRight(i,i-1,G);
-            // update Z
-            if (m_computeQZ)
-              m_Z.applyOnTheLeft(i,i-1,G.adjoint());
-          }
-        }
-      }
-    }
-
-  /** \internal Computes vector L1 norms of S and T when in Hessenberg-Triangular form already */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::computeNorms()
-    {
-      const Index size = m_S.cols();
-      m_normOfS = Scalar(0.0);
-      m_normOfT = Scalar(0.0);
-      for (Index j = 0; j < size; ++j)
-      {
-        m_normOfS += m_S.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum();
-        m_normOfT += m_T.row(j).segment(j, size - j).cwiseAbs().sum();
-      }
-    }
-
-
-  /** \internal Look for single small sub-diagonal element S(res, res-1) and return res (or 0) */
-  template<typename MatrixType>
-    inline Index RealQZ<MatrixType>::findSmallSubdiagEntry(Index iu)
-    {
-      using std::abs;
-      Index res = iu;
-      while (res > 0)
-      {
-        Scalar s = abs(m_S.coeff(res-1,res-1)) + abs(m_S.coeff(res,res));
-        if (s == Scalar(0.0))
-          s = m_normOfS;
-        if (abs(m_S.coeff(res,res-1)) < NumTraits<Scalar>::epsilon() * s)
-          break;
-        res--;
-      }
-      return res;
-    }
-
-  /** \internal Look for single small diagonal element T(res, res) for res between f and l, and return res (or f-1)  */
-  template<typename MatrixType>
-    inline Index RealQZ<MatrixType>::findSmallDiagEntry(Index f, Index l)
-    {
-      using std::abs;
-      Index res = l;
-      while (res >= f) {
-        if (abs(m_T.coeff(res,res)) <= NumTraits<Scalar>::epsilon() * m_normOfT)
-          break;
-        res--;
-      }
-      return res;
-    }
-
-  /** \internal decouple 2x2 diagonal block in rows i, i+1 if eigenvalues are real */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::splitOffTwoRows(Index i)
-    {
-      using std::abs;
-      using std::sqrt;
-      const Index dim=m_S.cols();
-      if (abs(m_S.coeff(i+1,i))==Scalar(0))
-        return;
-      Index j = findSmallDiagEntry(i,i+1);
-      if (j==i-1)
-      {
-        // block of (S T^{-1})
-        Matrix2s STi = m_T.template block<2,2>(i,i).template triangularView<Upper>().
-          template solve<OnTheRight>(m_S.template block<2,2>(i,i));
-        Scalar p = Scalar(0.5)*(STi(0,0)-STi(1,1));
-        Scalar q = p*p + STi(1,0)*STi(0,1);
-        if (q>=0) {
-          Scalar z = sqrt(q);
-          // one QR-like iteration for ABi - lambda I
-          // is enough - when we know exact eigenvalue in advance,
-          // convergence is immediate
-          JRs G;
-          if (p>=0)
-            G.makeGivens(p + z, STi(1,0));
-          else
-            G.makeGivens(p - z, STi(1,0));
-          m_S.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint());
-          m_T.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint());
-          // update Q
-          if (m_computeQZ)
-            m_Q.applyOnTheRight(i,i+1,G);
-
-          G.makeGivens(m_T.coeff(i+1,i+1), m_T.coeff(i+1,i));
-          m_S.topRows(i+2).applyOnTheRight(i+1,i,G);
-          m_T.topRows(i+2).applyOnTheRight(i+1,i,G);
-          // update Z
-          if (m_computeQZ)
-            m_Z.applyOnTheLeft(i+1,i,G.adjoint());
-
-          m_S.coeffRef(i+1,i) = Scalar(0.0);
-          m_T.coeffRef(i+1,i) = Scalar(0.0);
-        }
-      }
-      else
-      {
-        pushDownZero(j,i,i+1);
-      }
-    }
-
-  /** \internal use zero in T(z,z) to zero S(l,l-1), working in block f..l */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::pushDownZero(Index z, Index f, Index l)
-    {
-      JRs G;
-      const Index dim = m_S.cols();
-      for (Index zz=z; zz<l; zz++)
-      {
-        // push 0 down
-        Index firstColS = zz>f ? (zz-1) : zz;
-        G.makeGivens(m_T.coeff(zz, zz+1), m_T.coeff(zz+1, zz+1));
-        m_S.rightCols(dim-firstColS).applyOnTheLeft(zz,zz+1,G.adjoint());
-        m_T.rightCols(dim-zz).applyOnTheLeft(zz,zz+1,G.adjoint());
-        m_T.coeffRef(zz+1,zz+1) = Scalar(0.0);
-        // update Q
-        if (m_computeQZ)
-          m_Q.applyOnTheRight(zz,zz+1,G);
-        // kill S(zz+1, zz-1)
-        if (zz>f)
-        {
-          G.makeGivens(m_S.coeff(zz+1, zz), m_S.coeff(zz+1,zz-1));
-          m_S.topRows(zz+2).applyOnTheRight(zz, zz-1,G);
-          m_T.topRows(zz+1).applyOnTheRight(zz, zz-1,G);
-          m_S.coeffRef(zz+1,zz-1) = Scalar(0.0);
-          // update Z
-          if (m_computeQZ)
-            m_Z.applyOnTheLeft(zz,zz-1,G.adjoint());
-        }
-      }
-      // finally kill S(l,l-1)
-      G.makeGivens(m_S.coeff(l,l), m_S.coeff(l,l-1));
-      m_S.applyOnTheRight(l,l-1,G);
-      m_T.applyOnTheRight(l,l-1,G);
-      m_S.coeffRef(l,l-1)=Scalar(0.0);
-      // update Z
-      if (m_computeQZ)
-        m_Z.applyOnTheLeft(l,l-1,G.adjoint());
-    }
-
-  /** \internal QR-like iterative step for block f..l */
-  template<typename MatrixType>
-    inline void RealQZ<MatrixType>::step(Index f, Index l, Index iter)
-    {
-      using std::abs;
-      const Index dim = m_S.cols();
-
-      // x, y, z
-      Scalar x, y, z;
-      if (iter==10)
-      {
-        // Wilkinson ad hoc shift
-        const Scalar
-          a11=m_S.coeff(f+0,f+0), a12=m_S.coeff(f+0,f+1),
-          a21=m_S.coeff(f+1,f+0), a22=m_S.coeff(f+1,f+1), a32=m_S.coeff(f+2,f+1),
-          b12=m_T.coeff(f+0,f+1),
-          b11i=Scalar(1.0)/m_T.coeff(f+0,f+0),
-          b22i=Scalar(1.0)/m_T.coeff(f+1,f+1),
-          a87=m_S.coeff(l-1,l-2),
-          a98=m_S.coeff(l-0,l-1),
-          b77i=Scalar(1.0)/m_T.coeff(l-2,l-2),
-          b88i=Scalar(1.0)/m_T.coeff(l-1,l-1);
-        Scalar ss = abs(a87*b77i) + abs(a98*b88i),
-               lpl = Scalar(1.5)*ss,
-               ll = ss*ss;
-        x = ll + a11*a11*b11i*b11i - lpl*a11*b11i + a12*a21*b11i*b22i
-          - a11*a21*b12*b11i*b11i*b22i;
-        y = a11*a21*b11i*b11i - lpl*a21*b11i + a21*a22*b11i*b22i 
-          - a21*a21*b12*b11i*b11i*b22i;
-        z = a21*a32*b11i*b22i;
-      }
-      else if (iter==16)
-      {
-        // another exceptional shift
-        x = m_S.coeff(f,f)/m_T.coeff(f,f)-m_S.coeff(l,l)/m_T.coeff(l,l) + m_S.coeff(l,l-1)*m_T.coeff(l-1,l) /
-          (m_T.coeff(l-1,l-1)*m_T.coeff(l,l));
-        y = m_S.coeff(f+1,f)/m_T.coeff(f,f);
-        z = 0;
-      }
-      else if (iter>23 && !(iter%8))
-      {
-        // extremely exceptional shift
-        x = internal::random<Scalar>(-1.0,1.0);
-        y = internal::random<Scalar>(-1.0,1.0);
-        z = internal::random<Scalar>(-1.0,1.0);
-      }
-      else
-      {
-        // Compute the shifts: (x,y,z,0...) = (AB^-1 - l1 I) (AB^-1 - l2 I) e1
-        // where l1 and l2 are the eigenvalues of the 2x2 matrix C = U V^-1 where
-        // U and V are 2x2 bottom right sub matrices of A and B. Thus:
-        //  = AB^-1AB^-1 + l1 l2 I - (l1+l2)(AB^-1)
-        //  = AB^-1AB^-1 + det(M) - tr(M)(AB^-1)
-        // Since we are only interested in having x, y, z with a correct ratio, we have:
-        const Scalar
-          a11 = m_S.coeff(f,f),     a12 = m_S.coeff(f,f+1),
-          a21 = m_S.coeff(f+1,f),   a22 = m_S.coeff(f+1,f+1),
-                                    a32 = m_S.coeff(f+2,f+1),
-
-          a88 = m_S.coeff(l-1,l-1), a89 = m_S.coeff(l-1,l),
-          a98 = m_S.coeff(l,l-1),   a99 = m_S.coeff(l,l),
-
-          b11 = m_T.coeff(f,f),     b12 = m_T.coeff(f,f+1),
-                                    b22 = m_T.coeff(f+1,f+1),
-
-          b88 = m_T.coeff(l-1,l-1), b89 = m_T.coeff(l-1,l),
-                                    b99 = m_T.coeff(l,l);
-
-        x = ( (a88/b88 - a11/b11)*(a99/b99 - a11/b11) - (a89/b99)*(a98/b88) + (a98/b88)*(b89/b99)*(a11/b11) ) * (b11/a21)
-          + a12/b22 - (a11/b11)*(b12/b22);
-        y = (a22/b22-a11/b11) - (a21/b11)*(b12/b22) - (a88/b88-a11/b11) - (a99/b99-a11/b11) + (a98/b88)*(b89/b99);
-        z = a32/b22;
-      }
-
-      JRs G;
-
-      for (Index k=f; k<=l-2; k++)
-      {
-        // variables for Householder reflections
-        Vector2s essential2;
-        Scalar tau, beta;
-
-        Vector3s hr(x,y,z);
-
-        // Q_k to annihilate S(k+1,k-1) and S(k+2,k-1)
-        hr.makeHouseholderInPlace(tau, beta);
-        essential2 = hr.template bottomRows<2>();
-        Index fc=(std::max)(k-1,Index(0));  // first col to update
-        m_S.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
-        m_T.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
-        if (m_computeQZ)
-          m_Q.template middleCols<3>(k).applyHouseholderOnTheRight(essential2, tau, m_workspace.data());
-        if (k>f)
-          m_S.coeffRef(k+2,k-1) = m_S.coeffRef(k+1,k-1) = Scalar(0.0);
-
-        // Z_{k1} to annihilate T(k+2,k+1) and T(k+2,k)
-        hr << m_T.coeff(k+2,k+2),m_T.coeff(k+2,k),m_T.coeff(k+2,k+1);
-        hr.makeHouseholderInPlace(tau, beta);
-        essential2 = hr.template bottomRows<2>();
-        {
-          Index lr = (std::min)(k+4,dim); // last row to update
-          Map<Matrix<Scalar,Dynamic,1> > tmp(m_workspace.data(),lr);
-          // S
-          tmp = m_S.template middleCols<2>(k).topRows(lr) * essential2;
-          tmp += m_S.col(k+2).head(lr);
-          m_S.col(k+2).head(lr) -= tau*tmp;
-          m_S.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint();
-          // T
-          tmp = m_T.template middleCols<2>(k).topRows(lr) * essential2;
-          tmp += m_T.col(k+2).head(lr);
-          m_T.col(k+2).head(lr) -= tau*tmp;
-          m_T.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint();
-        }
-        if (m_computeQZ)
-        {
-          // Z
-          Map<Matrix<Scalar,1,Dynamic> > tmp(m_workspace.data(),dim);
-          tmp = essential2.adjoint()*(m_Z.template middleRows<2>(k));
-          tmp += m_Z.row(k+2);
-          m_Z.row(k+2) -= tau*tmp;
-          m_Z.template middleRows<2>(k) -= essential2 * (tau*tmp);
-        }
-        m_T.coeffRef(k+2,k) = m_T.coeffRef(k+2,k+1) = Scalar(0.0);
-
-        // Z_{k2} to annihilate T(k+1,k)
-        G.makeGivens(m_T.coeff(k+1,k+1), m_T.coeff(k+1,k));
-        m_S.applyOnTheRight(k+1,k,G);
-        m_T.applyOnTheRight(k+1,k,G);
-        // update Z
-        if (m_computeQZ)
-          m_Z.applyOnTheLeft(k+1,k,G.adjoint());
-        m_T.coeffRef(k+1,k) = Scalar(0.0);
-
-        // update x,y,z
-        x = m_S.coeff(k+1,k);
-        y = m_S.coeff(k+2,k);
-        if (k < l-2)
-          z = m_S.coeff(k+3,k);
-      } // loop over k
-
-      // Q_{n-1} to annihilate y = S(l,l-2)
-      G.makeGivens(x,y);
-      m_S.applyOnTheLeft(l-1,l,G.adjoint());
-      m_T.applyOnTheLeft(l-1,l,G.adjoint());
-      if (m_computeQZ)
-        m_Q.applyOnTheRight(l-1,l,G);
-      m_S.coeffRef(l,l-2) = Scalar(0.0);
-
-      // Z_{n-1} to annihilate T(l,l-1)
-      G.makeGivens(m_T.coeff(l,l),m_T.coeff(l,l-1));
-      m_S.applyOnTheRight(l,l-1,G);
-      m_T.applyOnTheRight(l,l-1,G);
-      if (m_computeQZ)
-        m_Z.applyOnTheLeft(l,l-1,G.adjoint());
-      m_T.coeffRef(l,l-1) = Scalar(0.0);
-    }
-
-  template<typename MatrixType>
-    RealQZ<MatrixType>& RealQZ<MatrixType>::compute(const MatrixType& A_in, const MatrixType& B_in, bool computeQZ)
-    {
-
-      const Index dim = A_in.cols();
-
-      eigen_assert (A_in.rows()==dim && A_in.cols()==dim 
-          && B_in.rows()==dim && B_in.cols()==dim 
-          && "Need square matrices of the same dimension");
-
-      m_isInitialized = true;
-      m_computeQZ = computeQZ;
-      m_S = A_in; m_T = B_in;
-      m_workspace.resize(dim*2);
-      m_global_iter = 0;
-
-      // entrance point: hessenberg triangular decomposition
-      hessenbergTriangular();
-      // compute L1 vector norms of T, S into m_normOfS, m_normOfT
-      computeNorms();
-
-      Index l = dim-1, 
-            f, 
-            local_iter = 0;
-
-      while (l>0 && local_iter<m_maxIters)
-      {
-        f = findSmallSubdiagEntry(l);
-        // now rows and columns f..l (including) decouple from the rest of the problem
-        if (f>0) m_S.coeffRef(f,f-1) = Scalar(0.0);
-        if (f == l) // One root found
-        {
-          l--;
-          local_iter = 0;
-        }
-        else if (f == l-1) // Two roots found
-        {
-          splitOffTwoRows(f);
-          l -= 2;
-          local_iter = 0;
-        }
-        else // No convergence yet
-        {
-          // if there's zero on diagonal of T, we can isolate an eigenvalue with Givens rotations
-          Index z = findSmallDiagEntry(f,l);
-          if (z>=f)
-          {
-            // zero found
-            pushDownZero(z,f,l);
-          }
-          else
-          {
-            // We are sure now that S.block(f,f, l-f+1,l-f+1) is underuced upper-Hessenberg 
-            // and T.block(f,f, l-f+1,l-f+1) is invertible uper-triangular, which allows to
-            // apply a QR-like iteration to rows and columns f..l.
-            step(f,l, local_iter);
-            local_iter++;
-            m_global_iter++;
-          }
-        }
-      }
-      // check if we converged before reaching iterations limit
-      m_info = (local_iter<m_maxIters) ? Success : NoConvergence;
-
-      // For each non triangular 2x2 diagonal block of S,
-      //    reduce the respective 2x2 diagonal block of T to positive diagonal form using 2x2 SVD.
-      // This step is not mandatory for QZ, but it does help further extraction of eigenvalues/eigenvectors,
-      // and is in par with Lapack/Matlab QZ.
-      if(m_info==Success)
-      {
-        for(Index i=0; i<dim-1; ++i)
-        {
-          if(m_S.coeff(i+1, i) != Scalar(0))
-          {
-            JacobiRotation<Scalar> j_left, j_right;
-            internal::real_2x2_jacobi_svd(m_T, i, i+1, &j_left, &j_right);
-
-            // Apply resulting Jacobi rotations
-            m_S.applyOnTheLeft(i,i+1,j_left);
-            m_S.applyOnTheRight(i,i+1,j_right);
-            m_T.applyOnTheLeft(i,i+1,j_left);
-            m_T.applyOnTheRight(i,i+1,j_right);
-            m_T(i+1,i) = m_T(i,i+1) = Scalar(0);
-
-            if(m_computeQZ) {
-              m_Q.applyOnTheRight(i,i+1,j_left.transpose());
-              m_Z.applyOnTheLeft(i,i+1,j_right.transpose());
-            }
-
-            i++;
-          }
-        }
-      }
-
-      return *this;
-    } // end compute
-
-} // end namespace Eigen
-
-#endif //EIGEN_REAL_QZ
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
deleted file mode 100644
index 7304ef3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
+++ /dev/null
@@ -1,558 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REAL_SCHUR_H
-#define EIGEN_REAL_SCHUR_H
-
-#include "./HessenbergDecomposition.h"
-
-namespace Eigen { 
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class RealSchur
-  *
-  * \brief Performs a real Schur decomposition of a square matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * real Schur decomposition; this is expected to be an instantiation of the
-  * Matrix class template.
-  *
-  * Given a real square matrix A, this class computes the real Schur
-  * decomposition: \f$ A = U T U^T \f$ where U is a real orthogonal matrix and
-  * T is a real quasi-triangular matrix. An orthogonal matrix is a matrix whose
-  * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
-  * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
-  * blocks and 2-by-2 blocks with complex eigenvalues. The eigenvalues of the
-  * blocks on the diagonal of T are the same as the eigenvalues of the matrix
-  * A, and thus the real Schur decomposition is used in EigenSolver to compute
-  * the eigendecomposition of a matrix.
-  *
-  * Call the function compute() to compute the real Schur decomposition of a
-  * given matrix. Alternatively, you can use the RealSchur(const MatrixType&, bool)
-  * constructor which computes the real Schur decomposition at construction
-  * time. Once the decomposition is computed, you can use the matrixU() and
-  * matrixT() functions to retrieve the matrices U and T in the decomposition.
-  *
-  * The documentation of RealSchur(const MatrixType&, bool) contains an example
-  * of the typical use of this class.
-  *
-  * \note The implementation is adapted from
-  * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
-  * Their code is based on EISPACK.
-  *
-  * \sa class ComplexSchur, class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType> class RealSchur
-{
-  public:
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
-    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
-
-    /** \brief Default constructor.
-      *
-      * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().  The \p size parameter is only
-      * used as a hint. It is not an error to give a wrong \p size, but it may
-      * impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit RealSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime)
-            : m_matT(size, size),
-              m_matU(size, size),
-              m_workspaceVector(size),
-              m_hess(size),
-              m_isInitialized(false),
-              m_matUisUptodate(false),
-              m_maxIters(-1)
-    { }
-
-    /** \brief Constructor; computes real Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-      *
-      * This constructor calls compute() to compute the Schur decomposition.
-      *
-      * Example: \include RealSchur_RealSchur_MatrixType.cpp
-      * Output: \verbinclude RealSchur_RealSchur_MatrixType.out
-      */
-    template<typename InputType>
-    explicit RealSchur(const EigenBase<InputType>& matrix, bool computeU = true)
-            : m_matT(matrix.rows(),matrix.cols()),
-              m_matU(matrix.rows(),matrix.cols()),
-              m_workspaceVector(matrix.rows()),
-              m_hess(matrix.rows()),
-              m_isInitialized(false),
-              m_matUisUptodate(false),
-              m_maxIters(-1)
-    {
-      compute(matrix.derived(), computeU);
-    }
-
-    /** \brief Returns the orthogonal matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix U.
-      *
-      * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
-      * member function compute(const MatrixType&, bool) has been called before
-      * to compute the Schur decomposition of a matrix, and \p computeU was set
-      * to true (the default value).
-      *
-      * \sa RealSchur(const MatrixType&, bool) for an example
-      */
-    const MatrixType& matrixU() const
-    {
-      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
-      eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the RealSchur decomposition.");
-      return m_matU;
-    }
-
-    /** \brief Returns the quasi-triangular matrix in the Schur decomposition. 
-      *
-      * \returns A const reference to the matrix T.
-      *
-      * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
-      * member function compute(const MatrixType&, bool) has been called before
-      * to compute the Schur decomposition of a matrix.
-      *
-      * \sa RealSchur(const MatrixType&, bool) for an example
-      */
-    const MatrixType& matrixT() const
-    {
-      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
-      return m_matT;
-    }
-  
-    /** \brief Computes Schur decomposition of given matrix. 
-      * 
-      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
-      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
-      * \returns    Reference to \c *this
-      *
-      * The Schur decomposition is computed by first reducing the matrix to
-      * Hessenberg form using the class HessenbergDecomposition. The Hessenberg
-      * matrix is then reduced to triangular form by performing Francis QR
-      * iterations with implicit double shift. The cost of computing the Schur
-      * decomposition depends on the number of iterations; as a rough guide, it
-      * may be taken to be \f$25n^3\f$ flops if \a computeU is true and
-      * \f$10n^3\f$ flops if \a computeU is false.
-      *
-      * Example: \include RealSchur_compute.cpp
-      * Output: \verbinclude RealSchur_compute.out
-      *
-      * \sa compute(const MatrixType&, bool, Index)
-      */
-    template<typename InputType>
-    RealSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
-
-    /** \brief Computes Schur decomposition of a Hessenberg matrix H = Z T Z^T
-     *  \param[in] matrixH Matrix in Hessenberg form H
-     *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
-     *  \param computeU Computes the matriX U of the Schur vectors
-     * \return Reference to \c *this
-     * 
-     *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
-     *  using either the class HessenbergDecomposition or another mean. 
-     *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
-     *  When computeU is true, this routine computes the matrix U such that 
-     *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
-     * 
-     * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
-     * is not available, the user should give an identity matrix (Q.setIdentity())
-     * 
-     * \sa compute(const MatrixType&, bool)
-     */
-    template<typename HessMatrixType, typename OrthMatrixType>
-    RealSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU);
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Sets the maximum number of iterations allowed. 
-      *
-      * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
-      * of the matrix.
-      */
-    RealSchur& setMaxIterations(Index maxIters)
-    {
-      m_maxIters = maxIters;
-      return *this;
-    }
-
-    /** \brief Returns the maximum number of iterations. */
-    Index getMaxIterations()
-    {
-      return m_maxIters;
-    }
-
-    /** \brief Maximum number of iterations per row.
-      *
-      * If not otherwise specified, the maximum number of iterations is this number times the size of the
-      * matrix. It is currently set to 40.
-      */
-    static const int m_maxIterationsPerRow = 40;
-
-  private:
-    
-    MatrixType m_matT;
-    MatrixType m_matU;
-    ColumnVectorType m_workspaceVector;
-    HessenbergDecomposition<MatrixType> m_hess;
-    ComputationInfo m_info;
-    bool m_isInitialized;
-    bool m_matUisUptodate;
-    Index m_maxIters;
-
-    typedef Matrix<Scalar,3,1> Vector3s;
-
-    Scalar computeNormOfT();
-    Index findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero);
-    void splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift);
-    void computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo);
-    void initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector);
-    void performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace);
-};
-
-
-template<typename MatrixType>
-template<typename InputType>
-RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
-{
-  const Scalar considerAsZero = (std::numeric_limits<Scalar>::min)();
-
-  eigen_assert(matrix.cols() == matrix.rows());
-  Index maxIters = m_maxIters;
-  if (maxIters == -1)
-    maxIters = m_maxIterationsPerRow * matrix.rows();
-
-  Scalar scale = matrix.derived().cwiseAbs().maxCoeff();
-  if(scale<considerAsZero)
-  {
-    m_matT.setZero(matrix.rows(),matrix.cols());
-    if(computeU)
-      m_matU.setIdentity(matrix.rows(),matrix.cols());
-    m_info = Success;
-    m_isInitialized = true;
-    m_matUisUptodate = computeU;
-    return *this;
-  }
-
-  // Step 1. Reduce to Hessenberg form
-  m_hess.compute(matrix.derived()/scale);
-
-  // Step 2. Reduce to real Schur form
-  // Note: we copy m_hess.matrixQ() into m_matU here and not in computeFromHessenberg
-  //       to be able to pass our working-space buffer for the Householder to Dense evaluation.
-  m_workspaceVector.resize(matrix.cols());
-  if(computeU)
-    m_hess.matrixQ().evalTo(m_matU, m_workspaceVector);
-  computeFromHessenberg(m_hess.matrixH(), m_matU, computeU);
-
-  m_matT *= scale;
-  
-  return *this;
-}
-template<typename MatrixType>
-template<typename HessMatrixType, typename OrthMatrixType>
-RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU)
-{
-  using std::abs;
-
-  m_matT = matrixH;
-  m_workspaceVector.resize(m_matT.cols());
-  if(computeU && !internal::is_same_dense(m_matU,matrixQ))
-    m_matU = matrixQ;
-  
-  Index maxIters = m_maxIters;
-  if (maxIters == -1)
-    maxIters = m_maxIterationsPerRow * matrixH.rows();
-  Scalar* workspace = &m_workspaceVector.coeffRef(0);
-
-  // The matrix m_matT is divided in three parts. 
-  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. 
-  // Rows il,...,iu is the part we are working on (the active window).
-  // Rows iu+1,...,end are already brought in triangular form.
-  Index iu = m_matT.cols() - 1;
-  Index iter = 0;      // iteration count for current eigenvalue
-  Index totalIter = 0; // iteration count for whole matrix
-  Scalar exshift(0);   // sum of exceptional shifts
-  Scalar norm = computeNormOfT();
-  // sub-diagonal entries smaller than considerAsZero will be treated as zero.
-  // We use eps^2 to enable more precision in small eigenvalues.
-  Scalar considerAsZero = numext::maxi<Scalar>( norm * numext::abs2(NumTraits<Scalar>::epsilon()),
-                                                (std::numeric_limits<Scalar>::min)() );
-
-  if(norm!=Scalar(0))
-  {
-    while (iu >= 0)
-    {
-      Index il = findSmallSubdiagEntry(iu,considerAsZero);
-
-      // Check for convergence
-      if (il == iu) // One root found
-      {
-        m_matT.coeffRef(iu,iu) = m_matT.coeff(iu,iu) + exshift;
-        if (iu > 0)
-          m_matT.coeffRef(iu, iu-1) = Scalar(0);
-        iu--;
-        iter = 0;
-      }
-      else if (il == iu-1) // Two roots found
-      {
-        splitOffTwoRows(iu, computeU, exshift);
-        iu -= 2;
-        iter = 0;
-      }
-      else // No convergence yet
-      {
-        // The firstHouseholderVector vector has to be initialized to something to get rid of a silly GCC warning (-O1 -Wall -DNDEBUG )
-        Vector3s firstHouseholderVector = Vector3s::Zero(), shiftInfo;
-        computeShift(iu, iter, exshift, shiftInfo);
-        iter = iter + 1;
-        totalIter = totalIter + 1;
-        if (totalIter > maxIters) break;
-        Index im;
-        initFrancisQRStep(il, iu, shiftInfo, im, firstHouseholderVector);
-        performFrancisQRStep(il, im, iu, computeU, firstHouseholderVector, workspace);
-      }
-    }
-  }
-  if(totalIter <= maxIters)
-    m_info = Success;
-  else
-    m_info = NoConvergence;
-
-  m_isInitialized = true;
-  m_matUisUptodate = computeU;
-  return *this;
-}
-
-/** \internal Computes and returns vector L1 norm of T */
-template<typename MatrixType>
-inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT()
-{
-  const Index size = m_matT.cols();
-  // FIXME to be efficient the following would requires a triangular reduxion code
-  // Scalar norm = m_matT.upper().cwiseAbs().sum() 
-  //               + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum();
-  Scalar norm(0);
-  for (Index j = 0; j < size; ++j)
-    norm += m_matT.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum();
-  return norm;
-}
-
-/** \internal Look for single small sub-diagonal element and returns its index */
-template<typename MatrixType>
-inline Index RealSchur<MatrixType>::findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero)
-{
-  using std::abs;
-  Index res = iu;
-  while (res > 0)
-  {
-    Scalar s = abs(m_matT.coeff(res-1,res-1)) + abs(m_matT.coeff(res,res));
-
-    s = numext::maxi<Scalar>(s * NumTraits<Scalar>::epsilon(), considerAsZero);
-    
-    if (abs(m_matT.coeff(res,res-1)) <= s)
-      break;
-    res--;
-  }
-  return res;
-}
-
-/** \internal Update T given that rows iu-1 and iu decouple from the rest. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift)
-{
-  using std::sqrt;
-  using std::abs;
-  const Index size = m_matT.cols();
-
-  // The eigenvalues of the 2x2 matrix [a b; c d] are 
-  // trace +/- sqrt(discr/4) where discr = tr^2 - 4*det, tr = a + d, det = ad - bc
-  Scalar p = Scalar(0.5) * (m_matT.coeff(iu-1,iu-1) - m_matT.coeff(iu,iu));
-  Scalar q = p * p + m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu);   // q = tr^2 / 4 - det = discr/4
-  m_matT.coeffRef(iu,iu) += exshift;
-  m_matT.coeffRef(iu-1,iu-1) += exshift;
-
-  if (q >= Scalar(0)) // Two real eigenvalues
-  {
-    Scalar z = sqrt(abs(q));
-    JacobiRotation<Scalar> rot;
-    if (p >= Scalar(0))
-      rot.makeGivens(p + z, m_matT.coeff(iu, iu-1));
-    else
-      rot.makeGivens(p - z, m_matT.coeff(iu, iu-1));
-
-    m_matT.rightCols(size-iu+1).applyOnTheLeft(iu-1, iu, rot.adjoint());
-    m_matT.topRows(iu+1).applyOnTheRight(iu-1, iu, rot);
-    m_matT.coeffRef(iu, iu-1) = Scalar(0); 
-    if (computeU)
-      m_matU.applyOnTheRight(iu-1, iu, rot);
-  }
-
-  if (iu > 1) 
-    m_matT.coeffRef(iu-1, iu-2) = Scalar(0);
-}
-
-/** \internal Form shift in shiftInfo, and update exshift if an exceptional shift is performed. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo)
-{
-  using std::sqrt;
-  using std::abs;
-  shiftInfo.coeffRef(0) = m_matT.coeff(iu,iu);
-  shiftInfo.coeffRef(1) = m_matT.coeff(iu-1,iu-1);
-  shiftInfo.coeffRef(2) = m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu);
-
-  // Wilkinson's original ad hoc shift
-  if (iter == 10)
-  {
-    exshift += shiftInfo.coeff(0);
-    for (Index i = 0; i <= iu; ++i)
-      m_matT.coeffRef(i,i) -= shiftInfo.coeff(0);
-    Scalar s = abs(m_matT.coeff(iu,iu-1)) + abs(m_matT.coeff(iu-1,iu-2));
-    shiftInfo.coeffRef(0) = Scalar(0.75) * s;
-    shiftInfo.coeffRef(1) = Scalar(0.75) * s;
-    shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
-  }
-
-  // MATLAB's new ad hoc shift
-  if (iter == 30)
-  {
-    Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
-    s = s * s + shiftInfo.coeff(2);
-    if (s > Scalar(0))
-    {
-      s = sqrt(s);
-      if (shiftInfo.coeff(1) < shiftInfo.coeff(0))
-        s = -s;
-      s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
-      s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
-      exshift += s;
-      for (Index i = 0; i <= iu; ++i)
-        m_matT.coeffRef(i,i) -= s;
-      shiftInfo.setConstant(Scalar(0.964));
-    }
-  }
-}
-
-/** \internal Compute index im at which Francis QR step starts and the first Householder vector. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector)
-{
-  using std::abs;
-  Vector3s& v = firstHouseholderVector; // alias to save typing
-
-  for (im = iu-2; im >= il; --im)
-  {
-    const Scalar Tmm = m_matT.coeff(im,im);
-    const Scalar r = shiftInfo.coeff(0) - Tmm;
-    const Scalar s = shiftInfo.coeff(1) - Tmm;
-    v.coeffRef(0) = (r * s - shiftInfo.coeff(2)) / m_matT.coeff(im+1,im) + m_matT.coeff(im,im+1);
-    v.coeffRef(1) = m_matT.coeff(im+1,im+1) - Tmm - r - s;
-    v.coeffRef(2) = m_matT.coeff(im+2,im+1);
-    if (im == il) {
-      break;
-    }
-    const Scalar lhs = m_matT.coeff(im,im-1) * (abs(v.coeff(1)) + abs(v.coeff(2)));
-    const Scalar rhs = v.coeff(0) * (abs(m_matT.coeff(im-1,im-1)) + abs(Tmm) + abs(m_matT.coeff(im+1,im+1)));
-    if (abs(lhs) < NumTraits<Scalar>::epsilon() * rhs)
-      break;
-  }
-}
-
-/** \internal Perform a Francis QR step involving rows il:iu and columns im:iu. */
-template<typename MatrixType>
-inline void RealSchur<MatrixType>::performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace)
-{
-  eigen_assert(im >= il);
-  eigen_assert(im <= iu-2);
-
-  const Index size = m_matT.cols();
-
-  for (Index k = im; k <= iu-2; ++k)
-  {
-    bool firstIteration = (k == im);
-
-    Vector3s v;
-    if (firstIteration)
-      v = firstHouseholderVector;
-    else
-      v = m_matT.template block<3,1>(k,k-1);
-
-    Scalar tau, beta;
-    Matrix<Scalar, 2, 1> ess;
-    v.makeHouseholder(ess, tau, beta);
-    
-    if (beta != Scalar(0)) // if v is not zero
-    {
-      if (firstIteration && k > il)
-        m_matT.coeffRef(k,k-1) = -m_matT.coeff(k,k-1);
-      else if (!firstIteration)
-        m_matT.coeffRef(k,k-1) = beta;
-
-      // These Householder transformations form the O(n^3) part of the algorithm
-      m_matT.block(k, k, 3, size-k).applyHouseholderOnTheLeft(ess, tau, workspace);
-      m_matT.block(0, k, (std::min)(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
-      if (computeU)
-        m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, tau, workspace);
-    }
-  }
-
-  Matrix<Scalar, 2, 1> v = m_matT.template block<2,1>(iu-1, iu-2);
-  Scalar tau, beta;
-  Matrix<Scalar, 1, 1> ess;
-  v.makeHouseholder(ess, tau, beta);
-
-  if (beta != Scalar(0)) // if v is not zero
-  {
-    m_matT.coeffRef(iu-1, iu-2) = beta;
-    m_matT.block(iu-1, iu-1, 2, size-iu+1).applyHouseholderOnTheLeft(ess, tau, workspace);
-    m_matT.block(0, iu-1, iu+1, 2).applyHouseholderOnTheRight(ess, tau, workspace);
-    if (computeU)
-      m_matU.block(0, iu-1, size, 2).applyHouseholderOnTheRight(ess, tau, workspace);
-  }
-
-  // clean up pollution due to round-off errors
-  for (Index i = im+2; i <= iu; ++i)
-  {
-    m_matT.coeffRef(i,i-2) = Scalar(0);
-    if (i > im+2)
-      m_matT.coeffRef(i,i-3) = Scalar(0);
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_REAL_SCHUR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
deleted file mode 100644
index 2c22517..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Real Schur needed to real unsymmetrical eigenvalues/eigenvectors.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_REAL_SCHUR_LAPACKE_H
-#define EIGEN_REAL_SCHUR_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_SCHUR_REAL(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW, LAPACKE_COLROW) \
-template<> template<typename InputType> inline \
-RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
-RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, bool computeU) \
-{ \
-  eigen_assert(matrix.cols() == matrix.rows()); \
-\
-  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info; \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  char jobvs, sort='N'; \
-  LAPACK_##LAPACKE_PREFIX_U##_SELECT2 select = 0; \
-  jobvs = (computeU) ? 'V' : 'N'; \
-  m_matU.resize(n, n); \
-  lapack_int ldvs  = internal::convert_index<lapack_int>(m_matU.outerStride()); \
-  m_matT = matrix; \
-  lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride()); \
-  Matrix<EIGTYPE, Dynamic, Dynamic> wr, wi; \
-  wr.resize(n, 1); wi.resize(n, 1); \
-  info = LAPACKE_##LAPACKE_PREFIX##gees( matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda, &sdim, (LAPACKE_TYPE*)wr.data(), (LAPACKE_TYPE*)wi.data(), (LAPACKE_TYPE*)m_matU.data(), ldvs ); \
-  if(info == 0) \
-    m_info = Success; \
-  else \
-    m_info = NoConvergence; \
-\
-  m_isInitialized = true; \
-  m_matUisUptodate = computeU; \
-  return *this; \
-\
-}
-
-EIGEN_LAPACKE_SCHUR_REAL(double,   double, d, D, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_REAL(float,    float,  s, S, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SCHUR_REAL(double,   double, d, D, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SCHUR_REAL(float,    float,  s, S, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_REAL_SCHUR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
deleted file mode 100644
index 1469236..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
+++ /dev/null
@@ -1,904 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SELFADJOINTEIGENSOLVER_H
-#define EIGEN_SELFADJOINTEIGENSOLVER_H
-
-#include "./Tridiagonalization.h"
-
-namespace Eigen { 
-
-template<typename _MatrixType>
-class GeneralizedSelfAdjointEigenSolver;
-
-namespace internal {
-template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues;
-
-template<typename MatrixType, typename DiagType, typename SubDiagType>
-EIGEN_DEVICE_FUNC
-ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const Index maxIterations, bool computeEigenvectors, MatrixType& eivec);
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class SelfAdjointEigenSolver
-  *
-  * \brief Computes eigenvalues and eigenvectors of selfadjoint matrices
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * eigendecomposition; this is expected to be an instantiation of the Matrix
-  * class template.
-  *
-  * A matrix \f$ A \f$ is selfadjoint if it equals its adjoint. For real
-  * matrices, this means that the matrix is symmetric: it equals its
-  * transpose. This class computes the eigenvalues and eigenvectors of a
-  * selfadjoint matrix. These are the scalars \f$ \lambda \f$ and vectors
-  * \f$ v \f$ such that \f$ Av = \lambda v \f$.  The eigenvalues of a
-  * selfadjoint matrix are always real. If \f$ D \f$ is a diagonal matrix with
-  * the eigenvalues on the diagonal, and \f$ V \f$ is a matrix with the
-  * eigenvectors as its columns, then \f$ A = V D V^{-1} \f$. This is called the
-  * eigendecomposition.
-  *
-  * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
-  * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
-  * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
-  * equal to its transpose, \f$ V^{-1} = V^T \f$.
-  *
-  * The algorithm exploits the fact that the matrix is selfadjoint, making it
-  * faster and more accurate than the general purpose eigenvalue algorithms
-  * implemented in EigenSolver and ComplexEigenSolver.
-  *
-  * Only the \b lower \b triangular \b part of the input matrix is referenced.
-  *
-  * Call the function compute() to compute the eigenvalues and eigenvectors of
-  * a given matrix. Alternatively, you can use the
-  * SelfAdjointEigenSolver(const MatrixType&, int) constructor which computes
-  * the eigenvalues and eigenvectors at construction time. Once the eigenvalue
-  * and eigenvectors are computed, they can be retrieved with the eigenvalues()
-  * and eigenvectors() functions.
-  *
-  * The documentation for SelfAdjointEigenSolver(const MatrixType&, int)
-  * contains an example of the typical use of this class.
-  *
-  * To solve the \em generalized eigenvalue problem \f$ Av = \lambda Bv \f$ and
-  * the likes, see the class GeneralizedSelfAdjointEigenSolver.
-  *
-  * \sa MatrixBase::eigenvalues(), class EigenSolver, class ComplexEigenSolver
-  */
-template<typename _MatrixType> class SelfAdjointEigenSolver
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    enum {
-      Size = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-    /** \brief Scalar type for matrices of type \p _MatrixType. */
-    typedef typename MatrixType::Scalar Scalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    
-    typedef Matrix<Scalar,Size,Size,ColMajor,MaxColsAtCompileTime,MaxColsAtCompileTime> EigenvectorsType;
-
-    /** \brief Real scalar type for \p _MatrixType.
-      *
-      * This is just \c Scalar if #Scalar is real (e.g., \c float or
-      * \c double), and the type of the real part of \c Scalar if #Scalar is
-      * complex.
-      */
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    
-    friend struct internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>;
-
-    /** \brief Type for vector of eigenvalues as returned by eigenvalues().
-      *
-      * This is a column vector with entries of type #RealScalar.
-      * The length of the vector is the size of \p _MatrixType.
-      */
-    typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
-    typedef Tridiagonalization<MatrixType> TridiagonalizationType;
-    typedef typename TridiagonalizationType::SubDiagonalType SubDiagonalType;
-
-    /** \brief Default constructor for fixed-size matrices.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute(). This constructor
-      * can only be used if \p _MatrixType is a fixed-size matrix; use
-      * SelfAdjointEigenSolver(Index) for dynamic-size matrices.
-      *
-      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver.out
-      */
-    EIGEN_DEVICE_FUNC
-    SelfAdjointEigenSolver()
-        : m_eivec(),
-          m_eivalues(),
-          m_subdiag(),
-          m_hcoeffs(),
-          m_info(InvalidInput),
-          m_isInitialized(false),
-          m_eigenvectorsOk(false)
-    { }
-
-    /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
-      *
-      * \param [in]  size  Positive integer, size of the matrix whose
-      * eigenvalues and eigenvectors will be computed.
-      *
-      * This constructor is useful for dynamic-size matrices, when the user
-      * intends to perform decompositions via compute(). The \p size
-      * parameter is only used as a hint. It is not an error to give a wrong
-      * \p size, but it may impair performance.
-      *
-      * \sa compute() for an example
-      */
-    EIGEN_DEVICE_FUNC
-    explicit SelfAdjointEigenSolver(Index size)
-        : m_eivec(size, size),
-          m_eivalues(size),
-          m_subdiag(size > 1 ? size - 1 : 1),
-          m_hcoeffs(size > 1 ? size - 1 : 1),
-          m_isInitialized(false),
-          m_eigenvectorsOk(false)
-    {}
-
-    /** \brief Constructor; computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
-      *    be computed. Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-      *
-      * This constructor calls compute(const MatrixType&, int) to compute the
-      * eigenvalues of the matrix \p matrix. The eigenvectors are computed if
-      * \p options equals #ComputeEigenvectors.
-      *
-      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.out
-      *
-      * \sa compute(const MatrixType&, int)
-      */
-    template<typename InputType>
-    EIGEN_DEVICE_FUNC
-    explicit SelfAdjointEigenSolver(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors)
-      : m_eivec(matrix.rows(), matrix.cols()),
-        m_eivalues(matrix.cols()),
-        m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1),
-        m_hcoeffs(matrix.cols() > 1 ? matrix.cols() - 1 : 1),
-        m_isInitialized(false),
-        m_eigenvectorsOk(false)
-    {
-      compute(matrix.derived(), options);
-    }
-
-    /** \brief Computes eigendecomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
-      *    be computed. Only the lower triangular part of the matrix is referenced.
-      * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-      * \returns    Reference to \c *this
-      *
-      * This function computes the eigenvalues of \p matrix.  The eigenvalues()
-      * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
-      * then the eigenvectors are also computed and can be retrieved by
-      * calling eigenvectors().
-      *
-      * This implementation uses a symmetric QR algorithm. The matrix is first
-      * reduced to tridiagonal form using the Tridiagonalization class. The
-      * tridiagonal matrix is then brought to diagonal form with implicit
-      * symmetric QR steps with Wilkinson shift. Details can be found in
-      * Section 8.3 of Golub \& Van Loan, <i>%Matrix Computations</i>.
-      *
-      * The cost of the computation is about \f$ 9n^3 \f$ if the eigenvectors
-      * are required and \f$ 4n^3/3 \f$ if they are not required.
-      *
-      * This method reuses the memory in the SelfAdjointEigenSolver object that
-      * was allocated when the object was constructed, if the size of the
-      * matrix does not change.
-      *
-      * Example: \include SelfAdjointEigenSolver_compute_MatrixType.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType.out
-      *
-      * \sa SelfAdjointEigenSolver(const MatrixType&, int)
-      */
-    template<typename InputType>
-    EIGEN_DEVICE_FUNC
-    SelfAdjointEigenSolver& compute(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors);
-    
-    /** \brief Computes eigendecomposition of given matrix using a closed-form algorithm
-      *
-      * This is a variant of compute(const MatrixType&, int options) which
-      * directly solves the underlying polynomial equation.
-      * 
-      * Currently only 2x2 and 3x3 matrices for which the sizes are known at compile time are supported (e.g., Matrix3d).
-      * 
-      * This method is usually significantly faster than the QR iterative algorithm
-      * but it might also be less accurate. It is also worth noting that
-      * for 3x3 matrices it involves trigonometric operations which are
-      * not necessarily available for all scalar types.
-      * 
-      * For the 3x3 case, we observed the following worst case relative error regarding the eigenvalues:
-      *   - double: 1e-8
-      *   - float:  1e-3
-      *
-      * \sa compute(const MatrixType&, int options)
-      */
-    EIGEN_DEVICE_FUNC
-    SelfAdjointEigenSolver& computeDirect(const MatrixType& matrix, int options = ComputeEigenvectors);
-
-    /**
-      *\brief Computes the eigen decomposition from a tridiagonal symmetric matrix
-      *
-      * \param[in] diag The vector containing the diagonal of the matrix.
-      * \param[in] subdiag The subdiagonal of the matrix.
-      * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-      * \returns Reference to \c *this
-      *
-      * This function assumes that the matrix has been reduced to tridiagonal form.
-      *
-      * \sa compute(const MatrixType&, int) for more information
-      */
-    SelfAdjointEigenSolver& computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options=ComputeEigenvectors);
-
-    /** \brief Returns the eigenvectors of given matrix.
-      *
-      * \returns  A const reference to the matrix whose columns are the eigenvectors.
-      *
-      * \pre The eigenvectors have been computed before.
-      *
-      * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
-      * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
-      * eigenvectors are normalized to have (Euclidean) norm equal to one. If
-      * this object was used to solve the eigenproblem for the selfadjoint
-      * matrix \f$ A \f$, then the matrix returned by this function is the
-      * matrix \f$ V \f$ in the eigendecomposition \f$ A = V D V^{-1} \f$.
-      *
-      * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
-      * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
-      * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
-      * equal to its transpose, \f$ V^{-1} = V^T \f$.
-      *
-      * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out
-      *
-      * \sa eigenvalues()
-      */
-    EIGEN_DEVICE_FUNC
-    const EigenvectorsType& eigenvectors() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec;
-    }
-
-    /** \brief Returns the eigenvalues of given matrix.
-      *
-      * \returns A const reference to the column vector containing the eigenvalues.
-      *
-      * \pre The eigenvalues have been computed before.
-      *
-      * The eigenvalues are repeated according to their algebraic multiplicity,
-      * so there are as many eigenvalues as rows in the matrix. The eigenvalues
-      * are sorted in increasing order.
-      *
-      * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out
-      *
-      * \sa eigenvectors(), MatrixBase::eigenvalues()
-      */
-    EIGEN_DEVICE_FUNC
-    const RealVectorType& eigenvalues() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      return m_eivalues;
-    }
-
-    /** \brief Computes the positive-definite square root of the matrix.
-      *
-      * \returns the positive-definite square root of the matrix
-      *
-      * \pre The eigenvalues and eigenvectors of a positive-definite matrix
-      * have been computed before.
-      *
-      * The square root of a positive-definite matrix \f$ A \f$ is the
-      * positive-definite matrix whose square equals \f$ A \f$. This function
-      * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the
-      * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$.
-      *
-      * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out
-      *
-      * \sa operatorInverseSqrt(), <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
-      */
-    EIGEN_DEVICE_FUNC
-    MatrixType operatorSqrt() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
-    }
-
-    /** \brief Computes the inverse square root of the matrix.
-      *
-      * \returns the inverse positive-definite square root of the matrix
-      *
-      * \pre The eigenvalues and eigenvectors of a positive-definite matrix
-      * have been computed before.
-      *
-      * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to
-      * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is
-      * cheaper than first computing the square root with operatorSqrt() and
-      * then its inverse with MatrixBase::inverse().
-      *
-      * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp
-      * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out
-      *
-      * \sa operatorSqrt(), MatrixBase::inverse(), <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
-      */
-    EIGEN_DEVICE_FUNC
-    MatrixType operatorInverseSqrt() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-      return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
-    }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-      */
-    EIGEN_DEVICE_FUNC
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Maximum number of iterations.
-      *
-      * The algorithm terminates if it does not converge within m_maxIterations * n iterations, where n
-      * denotes the size of the matrix. This value is currently set to 30 (copied from LAPACK).
-      */
-    static const int m_maxIterations = 30;
-
-  protected:
-    static EIGEN_DEVICE_FUNC
-    void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-    
-    EigenvectorsType m_eivec;
-    RealVectorType m_eivalues;
-    typename TridiagonalizationType::SubDiagonalType m_subdiag;
-    typename TridiagonalizationType::CoeffVectorType m_hcoeffs;
-    ComputationInfo m_info;
-    bool m_isInitialized;
-    bool m_eigenvectorsOk;
-};
-
-namespace internal {
-/** \internal
-  *
-  * \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  * Performs a QR step on a tridiagonal symmetric matrix represented as a
-  * pair of two vectors \a diag and \a subdiag.
-  *
-  * \param diag the diagonal part of the input selfadjoint tridiagonal matrix
-  * \param subdiag the sub-diagonal part of the input selfadjoint tridiagonal matrix
-  * \param start starting index of the submatrix to work on
-  * \param end last+1 index of the submatrix to work on
-  * \param matrixQ pointer to the column-major matrix holding the eigenvectors, can be 0
-  * \param n size of the input matrix
-  *
-  * For compilation efficiency reasons, this procedure does not use eigen expression
-  * for its arguments.
-  *
-  * Implemented from Golub's "Matrix Computations", algorithm 8.3.2:
-  * "implicit symmetric QR step with Wilkinson shift"
-  */
-template<int StorageOrder,typename RealScalar, typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC
-static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n);
-}
-
-template<typename MatrixType>
-template<typename InputType>
-EIGEN_DEVICE_FUNC
-SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
-::compute(const EigenBase<InputType>& a_matrix, int options)
-{
-  check_template_parameters();
-  
-  const InputType &matrix(a_matrix.derived());
-  
-  EIGEN_USING_STD(abs);
-  eigen_assert(matrix.cols() == matrix.rows());
-  eigen_assert((options&~(EigVecMask|GenEigMask))==0
-          && (options&EigVecMask)!=EigVecMask
-          && "invalid option parameter");
-  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-  Index n = matrix.cols();
-  m_eivalues.resize(n,1);
-
-  if(n==1)
-  {
-    m_eivec = matrix;
-    m_eivalues.coeffRef(0,0) = numext::real(m_eivec.coeff(0,0));
-    if(computeEigenvectors)
-      m_eivec.setOnes(n,n);
-    m_info = Success;
-    m_isInitialized = true;
-    m_eigenvectorsOk = computeEigenvectors;
-    return *this;
-  }
-
-  // declare some aliases
-  RealVectorType& diag = m_eivalues;
-  EigenvectorsType& mat = m_eivec;
-
-  // map the matrix coefficients to [-1:1] to avoid over- and underflow.
-  mat = matrix.template triangularView<Lower>();
-  RealScalar scale = mat.cwiseAbs().maxCoeff();
-  if(scale==RealScalar(0)) scale = RealScalar(1);
-  mat.template triangularView<Lower>() /= scale;
-  m_subdiag.resize(n-1);
-  m_hcoeffs.resize(n-1);
-  internal::tridiagonalization_inplace(mat, diag, m_subdiag, m_hcoeffs, computeEigenvectors);
-
-  m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
-  
-  // scale back the eigen values
-  m_eivalues *= scale;
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-  return *this;
-}
-
-template<typename MatrixType>
-SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
-::computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options)
-{
-  //TODO : Add an option to scale the values beforehand
-  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-
-  m_eivalues = diag;
-  m_subdiag = subdiag;
-  if (computeEigenvectors)
-  {
-    m_eivec.setIdentity(diag.size(), diag.size());
-  }
-  m_info = internal::computeFromTridiagonal_impl(m_eivalues, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
-
-  m_isInitialized = true;
-  m_eigenvectorsOk = computeEigenvectors;
-  return *this;
-}
-
-namespace internal {
-/**
-  * \internal
-  * \brief Compute the eigendecomposition from a tridiagonal matrix
-  *
-  * \param[in,out] diag : On input, the diagonal of the matrix, on output the eigenvalues
-  * \param[in,out] subdiag : The subdiagonal part of the matrix (entries are modified during the decomposition)
-  * \param[in] maxIterations : the maximum number of iterations
-  * \param[in] computeEigenvectors : whether the eigenvectors have to be computed or not
-  * \param[out] eivec : The matrix to store the eigenvectors if computeEigenvectors==true. Must be allocated on input.
-  * \returns \c Success or \c NoConvergence
-  */
-template<typename MatrixType, typename DiagType, typename SubDiagType>
-EIGEN_DEVICE_FUNC
-ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const Index maxIterations, bool computeEigenvectors, MatrixType& eivec)
-{
-  ComputationInfo info;
-  typedef typename MatrixType::Scalar Scalar;
-
-  Index n = diag.size();
-  Index end = n-1;
-  Index start = 0;
-  Index iter = 0; // total number of iterations
-  
-  typedef typename DiagType::RealScalar RealScalar;
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-  const RealScalar precision_inv = RealScalar(1)/NumTraits<RealScalar>::epsilon();
-  while (end>0)
-  {
-    for (Index i = start; i<end; ++i) {
-      if (numext::abs(subdiag[i]) < considerAsZero) {
-        subdiag[i] = RealScalar(0);
-      } else {
-        // abs(subdiag[i]) <= epsilon * sqrt(abs(diag[i]) + abs(diag[i+1]))
-        // Scaled to prevent underflows.
-        const RealScalar scaled_subdiag = precision_inv * subdiag[i];
-        if (scaled_subdiag * scaled_subdiag <= (numext::abs(diag[i])+numext::abs(diag[i+1]))) {
-          subdiag[i] = RealScalar(0);
-        }
-      }
-    }
-
-    // find the largest unreduced block at the end of the matrix.
-    while (end>0 && subdiag[end-1]==RealScalar(0))
-    {
-      end--;
-    }
-    if (end<=0)
-      break;
-
-    // if we spent too many iterations, we give up
-    iter++;
-    if(iter > maxIterations * n) break;
-
-    start = end - 1;
-    while (start>0 && subdiag[start-1]!=0)
-      start--;
-
-    internal::tridiagonal_qr_step<MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor>(diag.data(), subdiag.data(), start, end, computeEigenvectors ? eivec.data() : (Scalar*)0, n);
-  }
-  if (iter <= maxIterations * n)
-    info = Success;
-  else
-    info = NoConvergence;
-
-  // Sort eigenvalues and corresponding vectors.
-  // TODO make the sort optional ?
-  // TODO use a better sort algorithm !!
-  if (info == Success)
-  {
-    for (Index i = 0; i < n-1; ++i)
-    {
-      Index k;
-      diag.segment(i,n-i).minCoeff(&k);
-      if (k > 0)
-      {
-        numext::swap(diag[i], diag[k+i]);
-        if(computeEigenvectors)
-          eivec.col(i).swap(eivec.col(k+i));
-      }
-    }
-  }
-  return info;
-}
-  
-template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(SolverType& eig, const typename SolverType::MatrixType& A, int options)
-  { eig.compute(A,options); }
-};
-
-template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3,false>
-{
-  typedef typename SolverType::MatrixType MatrixType;
-  typedef typename SolverType::RealVectorType VectorType;
-  typedef typename SolverType::Scalar Scalar;
-  typedef typename SolverType::EigenvectorsType EigenvectorsType;
-  
-
-  /** \internal
-   * Computes the roots of the characteristic polynomial of \a m.
-   * For numerical stability m.trace() should be near zero and to avoid over- or underflow m should be normalized.
-   */
-  EIGEN_DEVICE_FUNC
-  static inline void computeRoots(const MatrixType& m, VectorType& roots)
-  {
-    EIGEN_USING_STD(sqrt)
-    EIGEN_USING_STD(atan2)
-    EIGEN_USING_STD(cos)
-    EIGEN_USING_STD(sin)
-    const Scalar s_inv3 = Scalar(1)/Scalar(3);
-    const Scalar s_sqrt3 = sqrt(Scalar(3));
-
-    // The characteristic equation is x^3 - c2*x^2 + c1*x - c0 = 0.  The
-    // eigenvalues are the roots to this equation, all guaranteed to be
-    // real-valued, because the matrix is symmetric.
-    Scalar c0 = m(0,0)*m(1,1)*m(2,2) + Scalar(2)*m(1,0)*m(2,0)*m(2,1) - m(0,0)*m(2,1)*m(2,1) - m(1,1)*m(2,0)*m(2,0) - m(2,2)*m(1,0)*m(1,0);
-    Scalar c1 = m(0,0)*m(1,1) - m(1,0)*m(1,0) + m(0,0)*m(2,2) - m(2,0)*m(2,0) + m(1,1)*m(2,2) - m(2,1)*m(2,1);
-    Scalar c2 = m(0,0) + m(1,1) + m(2,2);
-
-    // Construct the parameters used in classifying the roots of the equation
-    // and in solving the equation for the roots in closed form.
-    Scalar c2_over_3 = c2*s_inv3;
-    Scalar a_over_3 = (c2*c2_over_3 - c1)*s_inv3;
-    a_over_3 = numext::maxi(a_over_3, Scalar(0));
-
-    Scalar half_b = Scalar(0.5)*(c0 + c2_over_3*(Scalar(2)*c2_over_3*c2_over_3 - c1));
-
-    Scalar q = a_over_3*a_over_3*a_over_3 - half_b*half_b;
-    q = numext::maxi(q, Scalar(0));
-
-    // Compute the eigenvalues by solving for the roots of the polynomial.
-    Scalar rho = sqrt(a_over_3);
-    Scalar theta = atan2(sqrt(q),half_b)*s_inv3;  // since sqrt(q) > 0, atan2 is in [0, pi] and theta is in [0, pi/3]
-    Scalar cos_theta = cos(theta);
-    Scalar sin_theta = sin(theta);
-    // roots are already sorted, since cos is monotonically decreasing on [0, pi]
-    roots(0) = c2_over_3 - rho*(cos_theta + s_sqrt3*sin_theta); // == 2*rho*cos(theta+2pi/3)
-    roots(1) = c2_over_3 - rho*(cos_theta - s_sqrt3*sin_theta); // == 2*rho*cos(theta+ pi/3)
-    roots(2) = c2_over_3 + Scalar(2)*rho*cos_theta;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline bool extract_kernel(MatrixType& mat, Ref<VectorType> res, Ref<VectorType> representative)
-  {
-    EIGEN_USING_STD(abs);
-    EIGEN_USING_STD(sqrt);
-    Index i0;
-    // Find non-zero column i0 (by construction, there must exist a non zero coefficient on the diagonal):
-    mat.diagonal().cwiseAbs().maxCoeff(&i0);
-    // mat.col(i0) is a good candidate for an orthogonal vector to the current eigenvector,
-    // so let's save it:
-    representative = mat.col(i0);
-    Scalar n0, n1;
-    VectorType c0, c1;
-    n0 = (c0 = representative.cross(mat.col((i0+1)%3))).squaredNorm();
-    n1 = (c1 = representative.cross(mat.col((i0+2)%3))).squaredNorm();
-    if(n0>n1) res = c0/sqrt(n0);
-    else      res = c1/sqrt(n1);
-
-    return true;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static inline void run(SolverType& solver, const MatrixType& mat, int options)
-  {
-    eigen_assert(mat.cols() == 3 && mat.cols() == mat.rows());
-    eigen_assert((options&~(EigVecMask|GenEigMask))==0
-            && (options&EigVecMask)!=EigVecMask
-            && "invalid option parameter");
-    bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-    
-    EigenvectorsType& eivecs = solver.m_eivec;
-    VectorType& eivals = solver.m_eivalues;
-  
-    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
-    Scalar shift = mat.trace() / Scalar(3);
-    // TODO Avoid this copy. Currently it is necessary to suppress bogus values when determining maxCoeff and for computing the eigenvectors later
-    MatrixType scaledMat = mat.template selfadjointView<Lower>();
-    scaledMat.diagonal().array() -= shift;
-    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
-    if(scale > 0) scaledMat /= scale;   // TODO for scale==0 we could save the remaining operations
-
-    // compute the eigenvalues
-    computeRoots(scaledMat,eivals);
-
-    // compute the eigenvectors
-    if(computeEigenvectors)
-    {
-      if((eivals(2)-eivals(0))<=Eigen::NumTraits<Scalar>::epsilon())
-      {
-        // All three eigenvalues are numerically the same
-        eivecs.setIdentity();
-      }
-      else
-      {
-        MatrixType tmp;
-        tmp = scaledMat;
-
-        // Compute the eigenvector of the most distinct eigenvalue
-        Scalar d0 = eivals(2) - eivals(1);
-        Scalar d1 = eivals(1) - eivals(0);
-        Index k(0), l(2);
-        if(d0 > d1)
-        {
-          numext::swap(k,l);
-          d0 = d1;
-        }
-
-        // Compute the eigenvector of index k
-        {
-          tmp.diagonal().array () -= eivals(k);
-          // By construction, 'tmp' is of rank 2, and its kernel corresponds to the respective eigenvector.
-          extract_kernel(tmp, eivecs.col(k), eivecs.col(l));
-        }
-
-        // Compute eigenvector of index l
-        if(d0<=2*Eigen::NumTraits<Scalar>::epsilon()*d1)
-        {
-          // If d0 is too small, then the two other eigenvalues are numerically the same,
-          // and thus we only have to ortho-normalize the near orthogonal vector we saved above.
-          eivecs.col(l) -= eivecs.col(k).dot(eivecs.col(l))*eivecs.col(l);
-          eivecs.col(l).normalize();
-        }
-        else
-        {
-          tmp = scaledMat;
-          tmp.diagonal().array () -= eivals(l);
-
-          VectorType dummy;
-          extract_kernel(tmp, eivecs.col(l), dummy);
-        }
-
-        // Compute last eigenvector from the other two
-        eivecs.col(1) = eivecs.col(2).cross(eivecs.col(0)).normalized();
-      }
-    }
-
-    // Rescale back to the original size.
-    eivals *= scale;
-    eivals.array() += shift;
-    
-    solver.m_info = Success;
-    solver.m_isInitialized = true;
-    solver.m_eigenvectorsOk = computeEigenvectors;
-  }
-};
-
-// 2x2 direct eigenvalues decomposition, code from Hauke Heibel
-template<typename SolverType> 
-struct direct_selfadjoint_eigenvalues<SolverType,2,false>
-{
-  typedef typename SolverType::MatrixType MatrixType;
-  typedef typename SolverType::RealVectorType VectorType;
-  typedef typename SolverType::Scalar Scalar;
-  typedef typename SolverType::EigenvectorsType EigenvectorsType;
-  
-  EIGEN_DEVICE_FUNC
-  static inline void computeRoots(const MatrixType& m, VectorType& roots)
-  {
-    EIGEN_USING_STD(sqrt);
-    const Scalar t0 = Scalar(0.5) * sqrt( numext::abs2(m(0,0)-m(1,1)) + Scalar(4)*numext::abs2(m(1,0)));
-    const Scalar t1 = Scalar(0.5) * (m(0,0) + m(1,1));
-    roots(0) = t1 - t0;
-    roots(1) = t1 + t0;
-  }
-  
-  EIGEN_DEVICE_FUNC
-  static inline void run(SolverType& solver, const MatrixType& mat, int options)
-  {
-    EIGEN_USING_STD(sqrt);
-    EIGEN_USING_STD(abs);
-    
-    eigen_assert(mat.cols() == 2 && mat.cols() == mat.rows());
-    eigen_assert((options&~(EigVecMask|GenEigMask))==0
-            && (options&EigVecMask)!=EigVecMask
-            && "invalid option parameter");
-    bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
-    
-    EigenvectorsType& eivecs = solver.m_eivec;
-    VectorType& eivals = solver.m_eivalues;
-  
-    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
-    Scalar shift = mat.trace() / Scalar(2);
-    MatrixType scaledMat = mat;
-    scaledMat.coeffRef(0,1) = mat.coeff(1,0);
-    scaledMat.diagonal().array() -= shift;
-    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
-    if(scale > Scalar(0))
-      scaledMat /= scale;
-
-    // Compute the eigenvalues
-    computeRoots(scaledMat,eivals);
-
-    // compute the eigen vectors
-    if(computeEigenvectors)
-    {
-      if((eivals(1)-eivals(0))<=abs(eivals(1))*Eigen::NumTraits<Scalar>::epsilon())
-      {
-        eivecs.setIdentity();
-      }
-      else
-      {
-        scaledMat.diagonal().array () -= eivals(1);
-        Scalar a2 = numext::abs2(scaledMat(0,0));
-        Scalar c2 = numext::abs2(scaledMat(1,1));
-        Scalar b2 = numext::abs2(scaledMat(1,0));
-        if(a2>c2)
-        {
-          eivecs.col(1) << -scaledMat(1,0), scaledMat(0,0);
-          eivecs.col(1) /= sqrt(a2+b2);
-        }
-        else
-        {
-          eivecs.col(1) << -scaledMat(1,1), scaledMat(1,0);
-          eivecs.col(1) /= sqrt(c2+b2);
-        }
-
-        eivecs.col(0) << eivecs.col(1).unitOrthogonal();
-      }
-    }
-
-    // Rescale back to the original size.
-    eivals *= scale;
-    eivals.array() += shift;
-
-    solver.m_info = Success;
-    solver.m_isInitialized = true;
-    solver.m_eigenvectorsOk = computeEigenvectors;
-  }
-};
-
-}
-
-template<typename MatrixType>
-EIGEN_DEVICE_FUNC
-SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
-::computeDirect(const MatrixType& matrix, int options)
-{
-  internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>::run(*this,matrix,options);
-  return *this;
-}
-
-namespace internal {
-
-// Francis implicit QR step.
-template<int StorageOrder,typename RealScalar, typename Scalar, typename Index>
-EIGEN_DEVICE_FUNC
-static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n)
-{
-  // Wilkinson Shift.
-  RealScalar td = (diag[end-1] - diag[end])*RealScalar(0.5);
-  RealScalar e = subdiag[end-1];
-  // Note that thanks to scaling, e^2 or td^2 cannot overflow, however they can still
-  // underflow thus leading to inf/NaN values when using the following commented code:
-  //   RealScalar e2 = numext::abs2(subdiag[end-1]);
-  //   RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * sqrt(td*td + e2));
-  // This explain the following, somewhat more complicated, version:
-  RealScalar mu = diag[end];
-  if(td==RealScalar(0)) {
-    mu -= numext::abs(e);
-  } else if (e != RealScalar(0)) {
-    const RealScalar e2 = numext::abs2(e);
-    const RealScalar h = numext::hypot(td,e);
-    if(e2 == RealScalar(0)) {
-      mu -= e / ((td + (td>RealScalar(0) ? h : -h)) / e);
-    } else {
-      mu -= e2 / (td + (td>RealScalar(0) ? h : -h)); 
-    }
-  }
-
-  RealScalar x = diag[start] - mu;
-  RealScalar z = subdiag[start];
-  // If z ever becomes zero, the Givens rotation will be the identity and
-  // z will stay zero for all future iterations.
-  for (Index k = start; k < end && z != RealScalar(0); ++k)
-  {
-    JacobiRotation<RealScalar> rot;
-    rot.makeGivens(x, z);
-
-    // do T = G' T G
-    RealScalar sdk = rot.s() * diag[k] + rot.c() * subdiag[k];
-    RealScalar dkp1 = rot.s() * subdiag[k] + rot.c() * diag[k+1];
-
-    diag[k] = rot.c() * (rot.c() * diag[k] - rot.s() * subdiag[k]) - rot.s() * (rot.c() * subdiag[k] - rot.s() * diag[k+1]);
-    diag[k+1] = rot.s() * sdk + rot.c() * dkp1;
-    subdiag[k] = rot.c() * sdk - rot.s() * dkp1;
-    
-    if (k > start)
-      subdiag[k - 1] = rot.c() * subdiag[k-1] - rot.s() * z;
-
-    // "Chasing the bulge" to return to triangular form.
-    x = subdiag[k];
-    if (k < end - 1)
-    {
-      z = -rot.s() * subdiag[k+1];
-      subdiag[k + 1] = rot.c() * subdiag[k+1];
-    }
-    
-    // apply the givens rotation to the unit matrix Q = Q * G
-    if (matrixQ)
-    {
-      // FIXME if StorageOrder == RowMajor this operation is not very efficient
-      Map<Matrix<Scalar,Dynamic,Dynamic,StorageOrder> > q(matrixQ,n,n);
-      q.applyOnTheRight(k,k+1,rot);
-    }
-  }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SELFADJOINTEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
deleted file mode 100644
index b0c947d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Self-adjoint eigenvalues/eigenvectors.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_SAEIGENSOLVER_LAPACKE_H
-#define EIGEN_SAEIGENSOLVER_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, EIGCOLROW ) \
-template<> template<typename InputType> inline \
-SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
-SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, int options) \
-{ \
-  eigen_assert(matrix.cols() == matrix.rows()); \
-  eigen_assert((options&~(EigVecMask|GenEigMask))==0 \
-          && (options&EigVecMask)!=EigVecMask \
-          && "invalid option parameter"); \
-  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \
-  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), lda, info; \
-  m_eivalues.resize(n,1); \
-  m_subdiag.resize(n-1); \
-  m_eivec = matrix; \
-\
-  if(n==1) \
-  { \
-    m_eivalues.coeffRef(0,0) = numext::real(m_eivec.coeff(0,0)); \
-    if(computeEigenvectors) m_eivec.setOnes(n,n); \
-    m_info = Success; \
-    m_isInitialized = true; \
-    m_eigenvectorsOk = computeEigenvectors; \
-    return *this; \
-  } \
-\
-  lda = internal::convert_index<lapack_int>(m_eivec.outerStride()); \
-  char jobz, uplo='L'/*, range='A'*/; \
-  jobz = computeEigenvectors ? 'V' : 'N'; \
-\
-  info = LAPACKE_##LAPACKE_NAME( LAPACK_COL_MAJOR, jobz, uplo, n, (LAPACKE_TYPE*)m_eivec.data(), lda, (LAPACKE_RTYPE*)m_eivalues.data() ); \
-  m_info = (info==0) ? Success : NoConvergence; \
-  m_isInitialized = true; \
-  m_eigenvectorsOk = computeEigenvectors; \
-  return *this; \
-}
-
-#define EIGEN_LAPACKE_EIG_SELFADJ(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME )              \
-        EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, ColMajor )  \
-        EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, RowMajor ) 
-
-EIGEN_LAPACKE_EIG_SELFADJ(double,   double,                double, dsyev)
-EIGEN_LAPACKE_EIG_SELFADJ(float,    float,                 float,  ssyev)
-EIGEN_LAPACKE_EIG_SELFADJ(dcomplex, lapack_complex_double, double, zheev)
-EIGEN_LAPACKE_EIG_SELFADJ(scomplex, lapack_complex_float,  float,  cheev)
-
-} // end namespace Eigen
-
-#endif // EIGEN_SAEIGENSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
deleted file mode 100644
index 674c92a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
+++ /dev/null
@@ -1,561 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRIDIAGONALIZATION_H
-#define EIGEN_TRIDIAGONALIZATION_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType> struct TridiagonalizationMatrixTReturnType;
-template<typename MatrixType>
-struct traits<TridiagonalizationMatrixTReturnType<MatrixType> >
-  : public traits<typename MatrixType::PlainObject>
-{
-  typedef typename MatrixType::PlainObject ReturnType; // FIXME shall it be a BandMatrix?
-  enum { Flags = 0 };
-};
-
-template<typename MatrixType, typename CoeffVectorType>
-EIGEN_DEVICE_FUNC
-void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
-}
-
-/** \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  *
-  * \class Tridiagonalization
-  *
-  * \brief Tridiagonal decomposition of a selfadjoint matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the
-  * tridiagonal decomposition; this is expected to be an instantiation of the
-  * Matrix class template.
-  *
-  * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that:
-  * \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix.
-  *
-  * A tridiagonal matrix is a matrix which has nonzero elements only on the
-  * main diagonal and the first diagonal below and above it. The Hessenberg
-  * decomposition of a selfadjoint matrix is in fact a tridiagonal
-  * decomposition. This class is used in SelfAdjointEigenSolver to compute the
-  * eigenvalues and eigenvectors of a selfadjoint matrix.
-  *
-  * Call the function compute() to compute the tridiagonal decomposition of a
-  * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&)
-  * constructor which computes the tridiagonal Schur decomposition at
-  * construction time. Once the decomposition is computed, you can use the
-  * matrixQ() and matrixT() functions to retrieve the matrices Q and T in the
-  * decomposition.
-  *
-  * The documentation of Tridiagonalization(const MatrixType&) contains an
-  * example of the typical use of this class.
-  *
-  * \sa class HessenbergDecomposition, class SelfAdjointEigenSolver
-  */
-template<typename _MatrixType> class Tridiagonalization
-{
-  public:
-
-    /** \brief Synonym for the template parameter \p _MatrixType. */
-    typedef _MatrixType MatrixType;
-
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-
-    enum {
-      Size = MatrixType::RowsAtCompileTime,
-      SizeMinusOne = Size == Dynamic ? Dynamic : (Size > 1 ? Size - 1 : 1),
-      Options = MatrixType::Options,
-      MaxSize = MatrixType::MaxRowsAtCompileTime,
-      MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : (MaxSize > 1 ? MaxSize - 1 : 1)
-    };
-
-    typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
-    typedef typename internal::plain_col_type<MatrixType, RealScalar>::type DiagonalType;
-    typedef Matrix<RealScalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> SubDiagonalType;
-    typedef typename internal::remove_all<typename MatrixType::RealReturnType>::type MatrixTypeRealView;
-    typedef internal::TridiagonalizationMatrixTReturnType<MatrixTypeRealView> MatrixTReturnType;
-
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-              typename internal::add_const_on_value_type<typename Diagonal<const MatrixType>::RealReturnType>::type,
-              const Diagonal<const MatrixType>
-            >::type DiagonalReturnType;
-
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-              typename internal::add_const_on_value_type<typename Diagonal<const MatrixType, -1>::RealReturnType>::type,
-              const Diagonal<const MatrixType, -1>
-            >::type SubDiagonalReturnType;
-
-    /** \brief Return type of matrixQ() */
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType;
-
-    /** \brief Default constructor.
-      *
-      * \param [in]  size  Positive integer, size of the matrix whose tridiagonal
-      * decomposition will be computed.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via compute().  The \p size parameter is only
-      * used as a hint. It is not an error to give a wrong \p size, but it may
-      * impair performance.
-      *
-      * \sa compute() for an example.
-      */
-    explicit Tridiagonalization(Index size = Size==Dynamic ? 2 : Size)
-      : m_matrix(size,size),
-        m_hCoeffs(size > 1 ? size-1 : 1),
-        m_isInitialized(false)
-    {}
-
-    /** \brief Constructor; computes tridiagonal decomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
-      * is to be computed.
-      *
-      * This constructor calls compute() to compute the tridiagonal decomposition.
-      *
-      * Example: \include Tridiagonalization_Tridiagonalization_MatrixType.cpp
-      * Output: \verbinclude Tridiagonalization_Tridiagonalization_MatrixType.out
-      */
-    template<typename InputType>
-    explicit Tridiagonalization(const EigenBase<InputType>& matrix)
-      : m_matrix(matrix.derived()),
-        m_hCoeffs(matrix.cols() > 1 ? matrix.cols()-1 : 1),
-        m_isInitialized(false)
-    {
-      internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
-      m_isInitialized = true;
-    }
-
-    /** \brief Computes tridiagonal decomposition of given matrix.
-      *
-      * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
-      * is to be computed.
-      * \returns    Reference to \c *this
-      *
-      * The tridiagonal decomposition is computed by bringing the columns of
-      * the matrix successively in the required form using Householder
-      * reflections. The cost is \f$ 4n^3/3 \f$ flops, where \f$ n \f$ denotes
-      * the size of the given matrix.
-      *
-      * This method reuses of the allocated data in the Tridiagonalization
-      * object, if the size of the matrix does not change.
-      *
-      * Example: \include Tridiagonalization_compute.cpp
-      * Output: \verbinclude Tridiagonalization_compute.out
-      */
-    template<typename InputType>
-    Tridiagonalization& compute(const EigenBase<InputType>& matrix)
-    {
-      m_matrix = matrix.derived();
-      m_hCoeffs.resize(matrix.rows()-1, 1);
-      internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
-      m_isInitialized = true;
-      return *this;
-    }
-
-    /** \brief Returns the Householder coefficients.
-      *
-      * \returns a const reference to the vector of Householder coefficients
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * The Householder coefficients allow the reconstruction of the matrix
-      * \f$ Q \f$ in the tridiagonal decomposition from the packed data.
-      *
-      * Example: \include Tridiagonalization_householderCoefficients.cpp
-      * Output: \verbinclude Tridiagonalization_householderCoefficients.out
-      *
-      * \sa packedMatrix(), \ref Householder_Module "Householder module"
-      */
-    inline CoeffVectorType householderCoefficients() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return m_hCoeffs;
-    }
-
-    /** \brief Returns the internal representation of the decomposition
-      *
-      *	\returns a const reference to a matrix with the internal representation
-      *	         of the decomposition.
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * The returned matrix contains the following information:
-      *  - the strict upper triangular part is equal to the input matrix A.
-      *  - the diagonal and lower sub-diagonal represent the real tridiagonal
-      *    symmetric matrix T.
-      *  - the rest of the lower part contains the Householder vectors that,
-      *    combined with Householder coefficients returned by
-      *    householderCoefficients(), allows to reconstruct the matrix Q as
-      *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
-      *    Here, the matrices \f$ H_i \f$ are the Householder transformations
-      *       \f$ H_i = (I - h_i v_i v_i^T) \f$
-      *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
-      *    \f$ v_i \f$ is the Householder vector defined by
-      *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
-      *    with M the matrix returned by this function.
-      *
-      * See LAPACK for further details on this packed storage.
-      *
-      * Example: \include Tridiagonalization_packedMatrix.cpp
-      * Output: \verbinclude Tridiagonalization_packedMatrix.out
-      *
-      * \sa householderCoefficients()
-      */
-    inline const MatrixType& packedMatrix() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return m_matrix;
-    }
-
-    /** \brief Returns the unitary matrix Q in the decomposition
-      *
-      * \returns object representing the matrix Q
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * This function returns a light-weight object of template class
-      * HouseholderSequence. You can either apply it directly to a matrix or
-      * you can convert it to a matrix of type #MatrixType.
-      *
-      * \sa Tridiagonalization(const MatrixType&) for an example,
-      *     matrixT(), class HouseholderSequence
-      */
-    HouseholderSequenceType matrixQ() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate())
-             .setLength(m_matrix.rows() - 1)
-             .setShift(1);
-    }
-
-    /** \brief Returns an expression of the tridiagonal matrix T in the decomposition
-      *
-      * \returns expression object representing the matrix T
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * Currently, this function can be used to extract the matrix T from internal
-      * data and copy it to a dense matrix object. In most cases, it may be
-      * sufficient to directly use the packed matrix or the vector expressions
-      * returned by diagonal() and subDiagonal() instead of creating a new
-      * dense copy matrix with this function.
-      *
-      * \sa Tridiagonalization(const MatrixType&) for an example,
-      * matrixQ(), packedMatrix(), diagonal(), subDiagonal()
-      */
-    MatrixTReturnType matrixT() const
-    {
-      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-      return MatrixTReturnType(m_matrix.real());
-    }
-
-    /** \brief Returns the diagonal of the tridiagonal matrix T in the decomposition.
-      *
-      * \returns expression representing the diagonal of T
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * Example: \include Tridiagonalization_diagonal.cpp
-      * Output: \verbinclude Tridiagonalization_diagonal.out
-      *
-      * \sa matrixT(), subDiagonal()
-      */
-    DiagonalReturnType diagonal() const;
-
-    /** \brief Returns the subdiagonal of the tridiagonal matrix T in the decomposition.
-      *
-      * \returns expression representing the subdiagonal of T
-      *
-      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
-      * the member function compute(const MatrixType&) has been called before
-      * to compute the tridiagonal decomposition of a matrix.
-      *
-      * \sa diagonal() for an example, matrixT()
-      */
-    SubDiagonalReturnType subDiagonal() const;
-
-  protected:
-
-    MatrixType m_matrix;
-    CoeffVectorType m_hCoeffs;
-    bool m_isInitialized;
-};
-
-template<typename MatrixType>
-typename Tridiagonalization<MatrixType>::DiagonalReturnType
-Tridiagonalization<MatrixType>::diagonal() const
-{
-  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-  return m_matrix.diagonal().real();
-}
-
-template<typename MatrixType>
-typename Tridiagonalization<MatrixType>::SubDiagonalReturnType
-Tridiagonalization<MatrixType>::subDiagonal() const
-{
-  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
-  return m_matrix.template diagonal<-1>().real();
-}
-
-namespace internal {
-
-/** \internal
-  * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place.
-  *
-  * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced.
-  *                     On output, the strict upper part is left unchanged, and the lower triangular part
-  *                     represents the T and Q matrices in packed format has detailed below.
-  * \param[out]    hCoeffs returned Householder coefficients (see below)
-  *
-  * On output, the tridiagonal selfadjoint matrix T is stored in the diagonal
-  * and lower sub-diagonal of the matrix \a matA.
-  * The unitary matrix Q is represented in a compact way as a product of
-  * Householder reflectors \f$ H_i \f$ such that:
-  *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
-  * The Householder reflectors are defined as
-  *       \f$ H_i = (I - h_i v_i v_i^T) \f$
-  * where \f$ h_i = hCoeffs[i]\f$ is the \f$ i \f$th Householder coefficient and
-  * \f$ v_i \f$ is the Householder vector defined by
-  *       \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$.
-  *
-  * Implemented from Golub's "Matrix Computations", algorithm 8.3.1.
-  *
-  * \sa Tridiagonalization::packedMatrix()
-  */
-template<typename MatrixType, typename CoeffVectorType>
-EIGEN_DEVICE_FUNC
-void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
-{
-  using numext::conj;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  Index n = matA.rows();
-  eigen_assert(n==matA.cols());
-  eigen_assert(n==hCoeffs.size()+1 || n==1);
-
-  for (Index i = 0; i<n-1; ++i)
-  {
-    Index remainingSize = n-i-1;
-    RealScalar beta;
-    Scalar h;
-    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
-
-    // Apply similarity transformation to remaining columns,
-    // i.e., A = H A H' where H = I - h v v' and v = matA.col(i).tail(n-i-1)
-    matA.col(i).coeffRef(i+1) = 1;
-
-    hCoeffs.tail(n-i-1).noalias() = (matA.bottomRightCorner(remainingSize,remainingSize).template selfadjointView<Lower>()
-                                  * (conj(h) * matA.col(i).tail(remainingSize)));
-
-    hCoeffs.tail(n-i-1) += (conj(h)*RealScalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1);
-
-    matA.bottomRightCorner(remainingSize, remainingSize).template selfadjointView<Lower>()
-      .rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), Scalar(-1));
-
-    matA.col(i).coeffRef(i+1) = beta;
-    hCoeffs.coeffRef(i) = h;
-  }
-}
-
-// forward declaration, implementation at the end of this file
-template<typename MatrixType,
-         int Size=MatrixType::ColsAtCompileTime,
-         bool IsComplex=NumTraits<typename MatrixType::Scalar>::IsComplex>
-struct tridiagonalization_inplace_selector;
-
-/** \brief Performs a full tridiagonalization in place
-  *
-  * \param[in,out]  mat  On input, the selfadjoint matrix whose tridiagonal
-  *    decomposition is to be computed. Only the lower triangular part referenced.
-  *    The rest is left unchanged. On output, the orthogonal matrix Q
-  *    in the decomposition if \p extractQ is true.
-  * \param[out]  diag  The diagonal of the tridiagonal matrix T in the
-  *    decomposition.
-  * \param[out]  subdiag  The subdiagonal of the tridiagonal matrix T in
-  *    the decomposition.
-  * \param[in]  extractQ  If true, the orthogonal matrix Q in the
-  *    decomposition is computed and stored in \p mat.
-  *
-  * Computes the tridiagonal decomposition of the selfadjoint matrix \p mat in place
-  * such that \f$ mat = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real
-  * symmetric tridiagonal matrix.
-  *
-  * The tridiagonal matrix T is passed to the output parameters \p diag and \p subdiag. If
-  * \p extractQ is true, then the orthogonal matrix Q is passed to \p mat. Otherwise the lower
-  * part of the matrix \p mat is destroyed.
-  *
-  * The vectors \p diag and \p subdiag are not resized. The function
-  * assumes that they are already of the correct size. The length of the
-  * vector \p diag should equal the number of rows in \p mat, and the
-  * length of the vector \p subdiag should be one left.
-  *
-  * This implementation contains an optimized path for 3-by-3 matrices
-  * which is especially useful for plane fitting.
-  *
-  * \note Currently, it requires two temporary vectors to hold the intermediate
-  * Householder coefficients, and to reconstruct the matrix Q from the Householder
-  * reflectors.
-  *
-  * Example (this uses the same matrix as the example in
-  *    Tridiagonalization::Tridiagonalization(const MatrixType&)):
-  *    \include Tridiagonalization_decomposeInPlace.cpp
-  * Output: \verbinclude Tridiagonalization_decomposeInPlace.out
-  *
-  * \sa class Tridiagonalization
-  */
-template<typename MatrixType, typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType>
-EIGEN_DEVICE_FUNC
-void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag,
-                                CoeffVectorType& hcoeffs, bool extractQ)
-{
-  eigen_assert(mat.cols()==mat.rows() && diag.size()==mat.rows() && subdiag.size()==mat.rows()-1);
-  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, hcoeffs, extractQ);
-}
-
-/** \internal
-  * General full tridiagonalization
-  */
-template<typename MatrixType, int Size, bool IsComplex>
-struct tridiagonalization_inplace_selector
-{
-  typedef typename Tridiagonalization<MatrixType>::CoeffVectorType CoeffVectorType;
-  typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType;
-  template<typename DiagonalType, typename SubDiagonalType>
-  static EIGEN_DEVICE_FUNC
-      void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType& hCoeffs, bool extractQ)
-  {
-    tridiagonalization_inplace(mat, hCoeffs);
-    diag = mat.diagonal().real();
-    subdiag = mat.template diagonal<-1>().real();
-    if(extractQ)
-      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())
-            .setLength(mat.rows() - 1)
-            .setShift(1);
-  }
-};
-
-/** \internal
-  * Specialization for 3x3 real matrices.
-  * Especially useful for plane fitting.
-  */
-template<typename MatrixType>
-struct tridiagonalization_inplace_selector<MatrixType,3,false>
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-
-  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType>
-  static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType&, bool extractQ)
-  {
-    using std::sqrt;
-    const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
-    diag[0] = mat(0,0);
-    RealScalar v1norm2 = numext::abs2(mat(2,0));
-    if(v1norm2 <= tol)
-    {
-      diag[1] = mat(1,1);
-      diag[2] = mat(2,2);
-      subdiag[0] = mat(1,0);
-      subdiag[1] = mat(2,1);
-      if (extractQ)
-        mat.setIdentity();
-    }
-    else
-    {
-      RealScalar beta = sqrt(numext::abs2(mat(1,0)) + v1norm2);
-      RealScalar invBeta = RealScalar(1)/beta;
-      Scalar m01 = mat(1,0) * invBeta;
-      Scalar m02 = mat(2,0) * invBeta;
-      Scalar q = RealScalar(2)*m01*mat(2,1) + m02*(mat(2,2) - mat(1,1));
-      diag[1] = mat(1,1) + m02*q;
-      diag[2] = mat(2,2) - m02*q;
-      subdiag[0] = beta;
-      subdiag[1] = mat(2,1) - m01 * q;
-      if (extractQ)
-      {
-        mat << 1,   0,    0,
-               0, m01,  m02,
-               0, m02, -m01;
-      }
-    }
-  }
-};
-
-/** \internal
-  * Trivial specialization for 1x1 matrices
-  */
-template<typename MatrixType, bool IsComplex>
-struct tridiagonalization_inplace_selector<MatrixType,1,IsComplex>
-{
-  typedef typename MatrixType::Scalar Scalar;
-
-  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType>
-  static EIGEN_DEVICE_FUNC
-  void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, CoeffVectorType&, bool extractQ)
-  {
-    diag(0,0) = numext::real(mat(0,0));
-    if(extractQ)
-      mat(0,0) = Scalar(1);
-  }
-};
-
-/** \internal
-  * \eigenvalues_module \ingroup Eigenvalues_Module
-  *
-  * \brief Expression type for return value of Tridiagonalization::matrixT()
-  *
-  * \tparam MatrixType type of underlying dense matrix
-  */
-template<typename MatrixType> struct TridiagonalizationMatrixTReturnType
-: public ReturnByValue<TridiagonalizationMatrixTReturnType<MatrixType> >
-{
-  public:
-    /** \brief Constructor.
-      *
-      * \param[in] mat The underlying dense matrix
-      */
-    TridiagonalizationMatrixTReturnType(const MatrixType& mat) : m_matrix(mat) { }
-
-    template <typename ResultType>
-    inline void evalTo(ResultType& result) const
-    {
-      result.setZero();
-      result.template diagonal<1>() = m_matrix.template diagonal<-1>().conjugate();
-      result.diagonal() = m_matrix.diagonal();
-      result.template diagonal<-1>() = m_matrix.template diagonal<-1>();
-    }
-
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRIDIAGONALIZATION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/AlignedBox.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/AlignedBox.h
deleted file mode 100644
index 55a9d0a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/AlignedBox.h
+++ /dev/null
@@ -1,486 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// Function void Eigen::AlignedBox::transform(const Transform& transform)
-// is provided under the following license agreement:
-//
-// Software License Agreement (BSD License)
-//
-// Copyright (c) 2011-2014, Willow Garage, Inc.
-// Copyright (c) 2014-2015, Open Source Robotics Foundation
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions
-// are met:
-//
-//  * Redistributions of source code must retain the above copyright
-//    notice, this list of conditions and the following disclaimer.
-//  * Redistributions in binary form must reproduce the above
-//    copyright notice, this list of conditions and the following
-//    disclaimer in the documentation and/or other materials provided
-//    with the distribution.
-//  * Neither the name of Open Source Robotics Foundation nor the names of its
-//    contributors may be used to endorse or promote products derived
-//    from this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-// FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-// COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-// BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-// LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-// POSSIBILITY OF SUCH DAMAGE.
-
-#ifndef EIGEN_ALIGNEDBOX_H
-#define EIGEN_ALIGNEDBOX_H
-
-namespace Eigen {
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  *
-  * \class AlignedBox
-  *
-  * \brief An axis aligned box
-  *
-  * \tparam _Scalar the type of the scalar coefficients
-  * \tparam _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
-  *
-  * This class represents an axis aligned box as a pair of the minimal and maximal corners.
-  * \warning The result of most methods is undefined when applied to an empty box. You can check for empty boxes using isEmpty().
-  * \sa alignedboxtypedefs
-  */
-template <typename _Scalar, int _AmbientDim>
-class AlignedBox
-{
-public:
-EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
-  enum { AmbientDimAtCompileTime = _AmbientDim };
-  typedef _Scalar                                   Scalar;
-  typedef NumTraits<Scalar>                         ScalarTraits;
-  typedef Eigen::Index                              Index; ///< \deprecated since Eigen 3.3
-  typedef typename ScalarTraits::Real               RealScalar;
-  typedef typename ScalarTraits::NonInteger         NonInteger;
-  typedef Matrix<Scalar,AmbientDimAtCompileTime,1>  VectorType;
-  typedef CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const VectorType, const VectorType> VectorTypeSum;
-
-  /** Define constants to name the corners of a 1D, 2D or 3D axis aligned bounding box */
-  enum CornerType
-  {
-    /** 1D names @{ */
-    Min=0, Max=1,
-    /** @} */
-
-    /** Identifier for 2D corner @{ */
-    BottomLeft=0, BottomRight=1,
-    TopLeft=2, TopRight=3,
-    /** @} */
-
-    /** Identifier for 3D corner  @{ */
-    BottomLeftFloor=0, BottomRightFloor=1,
-    TopLeftFloor=2, TopRightFloor=3,
-    BottomLeftCeil=4, BottomRightCeil=5,
-    TopLeftCeil=6, TopRightCeil=7
-    /** @} */
-  };
-
-
-  /** Default constructor initializing a null box. */
-  EIGEN_DEVICE_FUNC inline AlignedBox()
-  { if (EIGEN_CONST_CONDITIONAL(AmbientDimAtCompileTime!=Dynamic)) setEmpty(); }
-
-  /** Constructs a null box with \a _dim the dimension of the ambient space. */
-  EIGEN_DEVICE_FUNC inline explicit AlignedBox(Index _dim) : m_min(_dim), m_max(_dim)
-  { setEmpty(); }
-
-  /** Constructs a box with extremities \a _min and \a _max.
-   * \warning If either component of \a _min is larger than the same component of \a _max, the constructed box is empty. */
-  template<typename OtherVectorType1, typename OtherVectorType2>
-  EIGEN_DEVICE_FUNC inline AlignedBox(const OtherVectorType1& _min, const OtherVectorType2& _max) : m_min(_min), m_max(_max) {}
-
-  /** Constructs a box containing a single point \a p. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const MatrixBase<Derived>& p) : m_min(p), m_max(m_min)
-  { }
-
-  EIGEN_DEVICE_FUNC ~AlignedBox() {}
-
-  /** \returns the dimension in which the box holds */
-  EIGEN_DEVICE_FUNC inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size() : Index(AmbientDimAtCompileTime); }
-
-  /** \deprecated use isEmpty() */
-  EIGEN_DEVICE_FUNC inline bool isNull() const { return isEmpty(); }
-
-  /** \deprecated use setEmpty() */
-  EIGEN_DEVICE_FUNC inline void setNull() { setEmpty(); }
-
-  /** \returns true if the box is empty.
-   * \sa setEmpty */
-  EIGEN_DEVICE_FUNC inline bool isEmpty() const { return (m_min.array() > m_max.array()).any(); }
-
-  /** Makes \c *this an empty box.
-   * \sa isEmpty */
-  EIGEN_DEVICE_FUNC inline void setEmpty()
-  {
-    m_min.setConstant( ScalarTraits::highest() );
-    m_max.setConstant( ScalarTraits::lowest() );
-  }
-
-  /** \returns the minimal corner */
-  EIGEN_DEVICE_FUNC inline const VectorType& (min)() const { return m_min; }
-  /** \returns a non const reference to the minimal corner */
-  EIGEN_DEVICE_FUNC inline VectorType& (min)() { return m_min; }
-  /** \returns the maximal corner */
-  EIGEN_DEVICE_FUNC inline const VectorType& (max)() const { return m_max; }
-  /** \returns a non const reference to the maximal corner */
-  EIGEN_DEVICE_FUNC inline VectorType& (max)() { return m_max; }
-
-  /** \returns the center of the box */
-  EIGEN_DEVICE_FUNC inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(VectorTypeSum, RealScalar, quotient)
-  center() const
-  { return (m_min+m_max)/RealScalar(2); }
-
-  /** \returns the lengths of the sides of the bounding box.
-    * Note that this function does not get the same
-    * result for integral or floating scalar types: see
-    */
-  EIGEN_DEVICE_FUNC inline const CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> sizes() const
-  { return m_max - m_min; }
-
-  /** \returns the volume of the bounding box */
-  EIGEN_DEVICE_FUNC inline Scalar volume() const
-  { return sizes().prod(); }
-
-  /** \returns an expression for the bounding box diagonal vector
-    * if the length of the diagonal is needed: diagonal().norm()
-    * will provide it.
-    */
-  EIGEN_DEVICE_FUNC inline CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> diagonal() const
-  { return sizes(); }
-
-  /** \returns the vertex of the bounding box at the corner defined by
-    * the corner-id corner. It works only for a 1D, 2D or 3D bounding box.
-    * For 1D bounding boxes corners are named by 2 enum constants:
-    * BottomLeft and BottomRight.
-    * For 2D bounding boxes, corners are named by 4 enum constants:
-    * BottomLeft, BottomRight, TopLeft, TopRight.
-    * For 3D bounding boxes, the following names are added:
-    * BottomLeftCeil, BottomRightCeil, TopLeftCeil, TopRightCeil.
-    */
-  EIGEN_DEVICE_FUNC inline VectorType corner(CornerType corner) const
-  {
-    EIGEN_STATIC_ASSERT(_AmbientDim <= 3, THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE);
-
-    VectorType res;
-
-    Index mult = 1;
-    for(Index d=0; d<dim(); ++d)
-    {
-      if( mult & corner ) res[d] = m_max[d];
-      else                res[d] = m_min[d];
-      mult *= 2;
-    }
-    return res;
-  }
-
-  /** \returns a random point inside the bounding box sampled with
-   * a uniform distribution */
-  EIGEN_DEVICE_FUNC inline VectorType sample() const
-  {
-    VectorType r(dim());
-    for(Index d=0; d<dim(); ++d)
-    {
-      if(!ScalarTraits::IsInteger)
-      {
-        r[d] = m_min[d] + (m_max[d]-m_min[d])
-             * internal::random<Scalar>(Scalar(0), Scalar(1));
-      }
-      else
-        r[d] = internal::random(m_min[d], m_max[d]);
-    }
-    return r;
-  }
-
-  /** \returns true if the point \a p is inside the box \c *this. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline bool contains(const MatrixBase<Derived>& p) const
-  {
-    typename internal::nested_eval<Derived,2>::type p_n(p.derived());
-    return (m_min.array()<=p_n.array()).all() && (p_n.array()<=m_max.array()).all();
-  }
-
-  /** \returns true if the box \a b is entirely inside the box \c *this. */
-  EIGEN_DEVICE_FUNC inline bool contains(const AlignedBox& b) const
-  { return (m_min.array()<=(b.min)().array()).all() && ((b.max)().array()<=m_max.array()).all(); }
-
-  /** \returns true if the box \a b is intersecting the box \c *this.
-   * \sa intersection, clamp */
-  EIGEN_DEVICE_FUNC inline bool intersects(const AlignedBox& b) const
-  { return (m_min.array()<=(b.max)().array()).all() && ((b.min)().array()<=m_max.array()).all(); }
-
-  /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this.
-   * \sa extend(const AlignedBox&) */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const MatrixBase<Derived>& p)
-  {
-    typename internal::nested_eval<Derived,2>::type p_n(p.derived());
-    m_min = m_min.cwiseMin(p_n);
-    m_max = m_max.cwiseMax(p_n);
-    return *this;
-  }
-
-  /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this.
-   * \sa merged, extend(const MatrixBase&) */
-  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const AlignedBox& b)
-  {
-    m_min = m_min.cwiseMin(b.m_min);
-    m_max = m_max.cwiseMax(b.m_max);
-    return *this;
-  }
-
-  /** Clamps \c *this by the box \a b and returns a reference to \c *this.
-   * \note If the boxes don't intersect, the resulting box is empty.
-   * \sa intersection(), intersects() */
-  EIGEN_DEVICE_FUNC inline AlignedBox& clamp(const AlignedBox& b)
-  {
-    m_min = m_min.cwiseMax(b.m_min);
-    m_max = m_max.cwiseMin(b.m_max);
-    return *this;
-  }
-
-  /** Returns an AlignedBox that is the intersection of \a b and \c *this
-   * \note If the boxes don't intersect, the resulting box is empty.
-   * \sa intersects(), clamp, contains()  */
-  EIGEN_DEVICE_FUNC inline AlignedBox intersection(const AlignedBox& b) const
-  {return AlignedBox(m_min.cwiseMax(b.m_min), m_max.cwiseMin(b.m_max)); }
-
-  /** Returns an AlignedBox that is the union of \a b and \c *this.
-   * \note Merging with an empty box may result in a box bigger than \c *this.
-   * \sa extend(const AlignedBox&) */
-  EIGEN_DEVICE_FUNC inline AlignedBox merged(const AlignedBox& b) const
-  { return AlignedBox(m_min.cwiseMin(b.m_min), m_max.cwiseMax(b.m_max)); }
-
-  /** Translate \c *this by the vector \a t and returns a reference to \c *this. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline AlignedBox& translate(const MatrixBase<Derived>& a_t)
-  {
-    const typename internal::nested_eval<Derived,2>::type t(a_t.derived());
-    m_min += t;
-    m_max += t;
-    return *this;
-  }
-
-  /** \returns a copy of \c *this translated by the vector \a t. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline AlignedBox translated(const MatrixBase<Derived>& a_t) const
-  {
-    AlignedBox result(m_min, m_max);
-    result.translate(a_t);
-    return result;
-  }
-
-  /** \returns the squared distance between the point \a p and the box \c *this,
-    * and zero if \a p is inside the box.
-    * \sa exteriorDistance(const MatrixBase&), squaredExteriorDistance(const AlignedBox&)
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const MatrixBase<Derived>& p) const;
-
-  /** \returns the squared distance between the boxes \a b and \c *this,
-    * and zero if the boxes intersect.
-    * \sa exteriorDistance(const AlignedBox&), squaredExteriorDistance(const MatrixBase&)
-    */
-  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const AlignedBox& b) const;
-
-  /** \returns the distance between the point \a p and the box \c *this,
-    * and zero if \a p is inside the box.
-    * \sa squaredExteriorDistance(const MatrixBase&), exteriorDistance(const AlignedBox&)
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const MatrixBase<Derived>& p) const
-  { EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(p))); }
-
-  /** \returns the distance between the boxes \a b and \c *this,
-    * and zero if the boxes intersect.
-    * \sa squaredExteriorDistance(const AlignedBox&), exteriorDistance(const MatrixBase&)
-    */
-  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const AlignedBox& b) const
-  { EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(b))); }
-
-  /**
-   * Specialization of transform for pure translation.
-   */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC inline void transform(
-      const typename Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>::TranslationType& translation)
-  {
-    this->translate(translation);
-  }
-
-  /**
-   * Transforms this box by \a transform and recomputes it to
-   * still be an axis-aligned box.
-   *
-   * \note This method is provided under BSD license (see the top of this file).
-   */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC inline void transform(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform)
-  {
-    // Only Affine and Isometry transforms are currently supported.
-    EIGEN_STATIC_ASSERT(Mode == Affine || Mode == AffineCompact || Mode == Isometry, THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS);
-
-    // Method adapted from FCL src/shape/geometric_shapes_utility.cpp#computeBV<AABB, Box>(...)
-    // https://github.com/flexible-collision-library/fcl/blob/fcl-0.4/src/shape/geometric_shapes_utility.cpp#L292
-    //
-    // Here's a nice explanation why it works: https://zeuxcg.org/2010/10/17/aabb-from-obb-with-component-wise-abs/
-
-    // two times rotated extent
-    const VectorType rotated_extent_2 = transform.linear().cwiseAbs() * sizes();
-    // two times new center
-    const VectorType rotated_center_2 = transform.linear() * (this->m_max + this->m_min) +
-        Scalar(2) * transform.translation();
-
-    this->m_max = (rotated_center_2 + rotated_extent_2) / Scalar(2);
-    this->m_min = (rotated_center_2 - rotated_extent_2) / Scalar(2);
-  }
-
-  /**
-   * \returns a copy of \c *this transformed by \a transform and recomputed to
-   * still be an axis-aligned box.
-   */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC AlignedBox transformed(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform) const
-  {
-    AlignedBox result(m_min, m_max);
-    result.transform(transform);
-    return result;
-  }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<AlignedBox,
-           AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
-  {
-    return typename internal::cast_return_type<AlignedBox,
-                    AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
-  }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
-  {
-    m_min = (other.min)().template cast<Scalar>();
-    m_max = (other.max)().template cast<Scalar>();
-  }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const AlignedBox& other, const RealScalar& prec = ScalarTraits::dummy_precision()) const
-  { return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec); }
-
-protected:
-
-  VectorType m_min, m_max;
-};
-
-
-
-template<typename Scalar,int AmbientDim>
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const MatrixBase<Derived>& a_p) const
-{
-  typename internal::nested_eval<Derived,2*AmbientDim>::type p(a_p.derived());
-  Scalar dist2(0);
-  Scalar aux;
-  for (Index k=0; k<dim(); ++k)
-  {
-    if( m_min[k] > p[k] )
-    {
-      aux = m_min[k] - p[k];
-      dist2 += aux*aux;
-    }
-    else if( p[k] > m_max[k] )
-    {
-      aux = p[k] - m_max[k];
-      dist2 += aux*aux;
-    }
-  }
-  return dist2;
-}
-
-template<typename Scalar,int AmbientDim>
-EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const AlignedBox& b) const
-{
-  Scalar dist2(0);
-  Scalar aux;
-  for (Index k=0; k<dim(); ++k)
-  {
-    if( m_min[k] > b.m_max[k] )
-    {
-      aux = m_min[k] - b.m_max[k];
-      dist2 += aux*aux;
-    }
-    else if( b.m_min[k] > m_max[k] )
-    {
-      aux = b.m_min[k] - m_max[k];
-      dist2 += aux*aux;
-    }
-  }
-  return dist2;
-}
-
-/** \defgroup alignedboxtypedefs Global aligned box typedefs
-  *
-  * \ingroup Geometry_Module
-  *
-  * Eigen defines several typedef shortcuts for most common aligned box types.
-  *
-  * The general patterns are the following:
-  *
-  * \c AlignedBoxSizeType where \c Size can be \c 1, \c 2,\c 3,\c 4 for fixed size boxes or \c X for dynamic size,
-  * and where \c Type can be \c i for integer, \c f for float, \c d for double.
-  *
-  * For example, \c AlignedBox3d is a fixed-size 3x3 aligned box type of doubles, and \c AlignedBoxXf is a dynamic-size aligned box of floats.
-  *
-  * \sa class AlignedBox
-  */
-
-#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)    \
-/** \ingroup alignedboxtypedefs */                                 \
-typedef AlignedBox<Type, Size>   AlignedBox##SizeSuffix##TypeSuffix;
-
-#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 1, 1) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \
-EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X)
-
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int,                  i)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float,                f)
-EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double,               d)
-
-#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
-#undef EIGEN_MAKE_TYPEDEFS
-
-} // end namespace Eigen
-
-#endif // EIGEN_ALIGNEDBOX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/AngleAxis.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/AngleAxis.h
deleted file mode 100644
index 78328b6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/AngleAxis.h
+++ /dev/null
@@ -1,247 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ANGLEAXIS_H
-#define EIGEN_ANGLEAXIS_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class AngleAxis
-  *
-  * \brief Represents a 3D rotation as a rotation angle around an arbitrary 3D axis
-  *
-  * \param _Scalar the scalar type, i.e., the type of the coefficients.
-  *
-  * \warning When setting up an AngleAxis object, the axis vector \b must \b be \b normalized.
-  *
-  * The following two typedefs are provided for convenience:
-  * \li \c AngleAxisf for \c float
-  * \li \c AngleAxisd for \c double
-  *
-  * Combined with MatrixBase::Unit{X,Y,Z}, AngleAxis can be used to easily
-  * mimic Euler-angles. Here is an example:
-  * \include AngleAxis_mimic_euler.cpp
-  * Output: \verbinclude AngleAxis_mimic_euler.out
-  *
-  * \note This class is not aimed to be used to store a rotation transformation,
-  * but rather to make easier the creation of other rotation (Quaternion, rotation Matrix)
-  * and transformation objects.
-  *
-  * \sa class Quaternion, class Transform, MatrixBase::UnitX()
-  */
-
-namespace internal {
-template<typename _Scalar> struct traits<AngleAxis<_Scalar> >
-{
-  typedef _Scalar Scalar;
-};
-}
-
-template<typename _Scalar>
-class AngleAxis : public RotationBase<AngleAxis<_Scalar>,3>
-{
-  typedef RotationBase<AngleAxis<_Scalar>,3> Base;
-
-public:
-
-  using Base::operator*;
-
-  enum { Dim = 3 };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  typedef Matrix<Scalar,3,3> Matrix3;
-  typedef Matrix<Scalar,3,1> Vector3;
-  typedef Quaternion<Scalar> QuaternionType;
-
-protected:
-
-  Vector3 m_axis;
-  Scalar m_angle;
-
-public:
-
-  /** Default constructor without initialization. */
-  EIGEN_DEVICE_FUNC AngleAxis() {}
-  /** Constructs and initialize the angle-axis rotation from an \a angle in radian
-    * and an \a axis which \b must \b be \b normalized.
-    *
-    * \warning If the \a axis vector is not normalized, then the angle-axis object
-    *          represents an invalid rotation. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC 
-  inline AngleAxis(const Scalar& angle, const MatrixBase<Derived>& axis) : m_axis(axis), m_angle(angle) {}
-  /** Constructs and initialize the angle-axis rotation from a quaternion \a q.
-    * This function implicitly normalizes the quaternion \a q.
-    */
-  template<typename QuatDerived> 
-  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const QuaternionBase<QuatDerived>& q) { *this = q; }
-  /** Constructs and initialize the angle-axis rotation from a 3x3 rotation matrix. */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const MatrixBase<Derived>& m) { *this = m; }
-
-  /** \returns the value of the rotation angle in radian */
-  EIGEN_DEVICE_FUNC Scalar angle() const { return m_angle; }
-  /** \returns a read-write reference to the stored angle in radian */
-  EIGEN_DEVICE_FUNC Scalar& angle() { return m_angle; }
-
-  /** \returns the rotation axis */
-  EIGEN_DEVICE_FUNC const Vector3& axis() const { return m_axis; }
-  /** \returns a read-write reference to the stored rotation axis.
-    *
-    * \warning The rotation axis must remain a \b unit vector.
-    */
-  EIGEN_DEVICE_FUNC Vector3& axis() { return m_axis; }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline QuaternionType operator* (const AngleAxis& other) const
-  { return QuaternionType(*this) * QuaternionType(other); }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline QuaternionType operator* (const QuaternionType& other) const
-  { return QuaternionType(*this) * other; }
-
-  /** Concatenates two rotations */
-  friend EIGEN_DEVICE_FUNC inline QuaternionType operator* (const QuaternionType& a, const AngleAxis& b)
-  { return a * QuaternionType(b); }
-
-  /** \returns the inverse rotation, i.e., an angle-axis with opposite rotation angle */
-  EIGEN_DEVICE_FUNC AngleAxis inverse() const
-  { return AngleAxis(-m_angle, m_axis); }
-
-  template<class QuatDerived>
-  EIGEN_DEVICE_FUNC AngleAxis& operator=(const QuaternionBase<QuatDerived>& q);
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC AngleAxis& operator=(const MatrixBase<Derived>& m);
-
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m);
-  EIGEN_DEVICE_FUNC Matrix3 toRotationMatrix(void) const;
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type cast() const
-  { return typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other)
-  {
-    m_axis = other.axis().template cast<Scalar>();
-    m_angle = Scalar(other.angle());
-  }
-
-  EIGEN_DEVICE_FUNC static inline const AngleAxis Identity() { return AngleAxis(Scalar(0), Vector3::UnitX()); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const AngleAxis& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_axis.isApprox(other.m_axis, prec) && internal::isApprox(m_angle,other.m_angle, prec); }
-};
-
-/** \ingroup Geometry_Module
-  * single precision angle-axis type */
-typedef AngleAxis<float> AngleAxisf;
-/** \ingroup Geometry_Module
-  * double precision angle-axis type */
-typedef AngleAxis<double> AngleAxisd;
-
-/** Set \c *this from a \b unit quaternion.
-  *
-  * The resulting axis is normalized, and the computed angle is in the [0,pi] range.
-  * 
-  * This function implicitly normalizes the quaternion \a q.
-  */
-template<typename Scalar>
-template<typename QuatDerived>
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionBase<QuatDerived>& q)
-{
-  EIGEN_USING_STD(atan2)
-  EIGEN_USING_STD(abs)
-  Scalar n = q.vec().norm();
-  if(n<NumTraits<Scalar>::epsilon())
-    n = q.vec().stableNorm();
-
-  if (n != Scalar(0))
-  {
-    m_angle = Scalar(2)*atan2(n, abs(q.w()));
-    if(q.w() < Scalar(0))
-      n = -n;
-    m_axis  = q.vec() / n;
-  }
-  else
-  {
-    m_angle = Scalar(0);
-    m_axis << Scalar(1), Scalar(0), Scalar(0);
-  }
-  return *this;
-}
-
-/** Set \c *this from a 3x3 rotation matrix \a mat.
-  */
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const MatrixBase<Derived>& mat)
-{
-  // Since a direct conversion would not be really faster,
-  // let's use the robust Quaternion implementation:
-  return *this = QuaternionType(mat);
-}
-
-/**
-* \brief Sets \c *this from a 3x3 rotation matrix.
-**/
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
-{
-  return *this = QuaternionType(mat);
-}
-
-/** Constructs and \returns an equivalent 3x3 rotation matrix.
-  */
-template<typename Scalar>
-typename AngleAxis<Scalar>::Matrix3
-EIGEN_DEVICE_FUNC AngleAxis<Scalar>::toRotationMatrix(void) const
-{
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  Matrix3 res;
-  Vector3 sin_axis  = sin(m_angle) * m_axis;
-  Scalar c = cos(m_angle);
-  Vector3 cos1_axis = (Scalar(1)-c) * m_axis;
-
-  Scalar tmp;
-  tmp = cos1_axis.x() * m_axis.y();
-  res.coeffRef(0,1) = tmp - sin_axis.z();
-  res.coeffRef(1,0) = tmp + sin_axis.z();
-
-  tmp = cos1_axis.x() * m_axis.z();
-  res.coeffRef(0,2) = tmp + sin_axis.y();
-  res.coeffRef(2,0) = tmp - sin_axis.y();
-
-  tmp = cos1_axis.y() * m_axis.z();
-  res.coeffRef(1,2) = tmp - sin_axis.x();
-  res.coeffRef(2,1) = tmp + sin_axis.x();
-
-  res.diagonal() = (cos1_axis.cwiseProduct(m_axis)).array() + c;
-
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ANGLEAXIS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/EulerAngles.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/EulerAngles.h
deleted file mode 100644
index 19b734c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/EulerAngles.h
+++ /dev/null
@@ -1,114 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EULERANGLES_H
-#define EIGEN_EULERANGLES_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  *
-  * \returns the Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a a0,\a a1,\a a2)
-  *
-  * Each of the three parameters \a a0,\a a1,\a a2 represents the respective rotation axis as an integer in {0,1,2}.
-  * For instance, in:
-  * \code Vector3f ea = mat.eulerAngles(2, 0, 2); \endcode
-  * "2" represents the z axis and "0" the x axis, etc. The returned angles are such that
-  * we have the following equality:
-  * \code
-  * mat == AngleAxisf(ea[0], Vector3f::UnitZ())
-  *      * AngleAxisf(ea[1], Vector3f::UnitX())
-  *      * AngleAxisf(ea[2], Vector3f::UnitZ()); \endcode
-  * This corresponds to the right-multiply conventions (with right hand side frames).
-  * 
-  * The returned angles are in the ranges [0:pi]x[-pi:pi]x[-pi:pi].
-  * 
-  * \sa class AngleAxis
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Matrix<typename MatrixBase<Derived>::Scalar,3,1>
-MatrixBase<Derived>::eulerAngles(Index a0, Index a1, Index a2) const
-{
-  EIGEN_USING_STD(atan2)
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  /* Implemented from Graphics Gems IV */
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived,3,3)
-
-  Matrix<Scalar,3,1> res;
-  typedef Matrix<typename Derived::Scalar,2,1> Vector2;
-
-  const Index odd = ((a0+1)%3 == a1) ? 0 : 1;
-  const Index i = a0;
-  const Index j = (a0 + 1 + odd)%3;
-  const Index k = (a0 + 2 - odd)%3;
-  
-  if (a0==a2)
-  {
-    res[0] = atan2(coeff(j,i), coeff(k,i));
-    if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0)))
-    {
-      if(res[0] > Scalar(0)) {
-        res[0] -= Scalar(EIGEN_PI);
-      }
-      else {
-        res[0] += Scalar(EIGEN_PI);
-      }
-      Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm();
-      res[1] = -atan2(s2, coeff(i,i));
-    }
-    else
-    {
-      Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm();
-      res[1] = atan2(s2, coeff(i,i));
-    }
-    
-    // With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles,
-    // we can compute their respective rotation, and apply its inverse to M. Since the result must
-    // be a rotation around x, we have:
-    //
-    //  c2  s1.s2 c1.s2                   1  0   0 
-    //  0   c1    -s1       *    M    =   0  c3  s3
-    //  -s2 s1.c2 c1.c2                   0 -s3  c3
-    //
-    //  Thus:  m11.c1 - m21.s1 = c3  &   m12.c1 - m22.s1 = s3
-    
-    Scalar s1 = sin(res[0]);
-    Scalar c1 = cos(res[0]);
-    res[2] = atan2(c1*coeff(j,k)-s1*coeff(k,k), c1*coeff(j,j) - s1 * coeff(k,j));
-  } 
-  else
-  {
-    res[0] = atan2(coeff(j,k), coeff(k,k));
-    Scalar c2 = Vector2(coeff(i,i), coeff(i,j)).norm();
-    if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0))) {
-      if(res[0] > Scalar(0)) {
-        res[0] -= Scalar(EIGEN_PI);
-      }
-      else {
-        res[0] += Scalar(EIGEN_PI);
-      }
-      res[1] = atan2(-coeff(i,k), -c2);
-    }
-    else
-      res[1] = atan2(-coeff(i,k), c2);
-    Scalar s1 = sin(res[0]);
-    Scalar c1 = cos(res[0]);
-    res[2] = atan2(s1*coeff(k,i)-c1*coeff(j,i), c1*coeff(j,j) - s1 * coeff(k,j));
-  }
-  if (!odd)
-    res = -res;
-  
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_EULERANGLES_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Homogeneous.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Homogeneous.h
deleted file mode 100644
index 94083ac..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Homogeneous.h
+++ /dev/null
@@ -1,501 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOMOGENEOUS_H
-#define EIGEN_HOMOGENEOUS_H
-
-namespace Eigen {
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Homogeneous
-  *
-  * \brief Expression of one (or a set of) homogeneous vector(s)
-  *
-  * \param MatrixType the type of the object in which we are making homogeneous
-  *
-  * This class represents an expression of one (or a set of) homogeneous vector(s).
-  * It is the return type of MatrixBase::homogeneous() and most of the time
-  * this is the only way it is used.
-  *
-  * \sa MatrixBase::homogeneous()
-  */
-
-namespace internal {
-
-template<typename MatrixType,int Direction>
-struct traits<Homogeneous<MatrixType,Direction> >
- : traits<MatrixType>
-{
-  typedef typename traits<MatrixType>::StorageKind StorageKind;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-  enum {
-    RowsPlusOne = (MatrixType::RowsAtCompileTime != Dynamic) ?
-                  int(MatrixType::RowsAtCompileTime) + 1 : Dynamic,
-    ColsPlusOne = (MatrixType::ColsAtCompileTime != Dynamic) ?
-                  int(MatrixType::ColsAtCompileTime) + 1 : Dynamic,
-    RowsAtCompileTime = Direction==Vertical  ?  RowsPlusOne : MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = Direction==Horizontal ? ColsPlusOne : MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    TmpFlags = _MatrixTypeNested::Flags & HereditaryBits,
-    Flags = ColsAtCompileTime==1 ? (TmpFlags & ~RowMajorBit)
-          : RowsAtCompileTime==1 ? (TmpFlags | RowMajorBit)
-          : TmpFlags
-  };
-};
-
-template<typename MatrixType,typename Lhs> struct homogeneous_left_product_impl;
-template<typename MatrixType,typename Rhs> struct homogeneous_right_product_impl;
-
-} // end namespace internal
-
-template<typename MatrixType,int _Direction> class Homogeneous
-  : public MatrixBase<Homogeneous<MatrixType,_Direction> >, internal::no_assignment_operator
-{
-  public:
-
-    typedef MatrixType NestedExpression;
-    enum { Direction = _Direction };
-
-    typedef MatrixBase<Homogeneous> Base;
-    EIGEN_DENSE_PUBLIC_INTERFACE(Homogeneous)
-
-    EIGEN_DEVICE_FUNC explicit inline Homogeneous(const MatrixType& matrix)
-      : m_matrix(matrix)
-    {}
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows() + (int(Direction)==Vertical   ? 1 : 0); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols() + (int(Direction)==Horizontal ? 1 : 0); }
-
-    EIGEN_DEVICE_FUNC const NestedExpression& nestedExpression() const { return m_matrix; }
-
-    template<typename Rhs>
-    EIGEN_DEVICE_FUNC inline const Product<Homogeneous,Rhs>
-    operator* (const MatrixBase<Rhs>& rhs) const
-    {
-      eigen_assert(int(Direction)==Horizontal);
-      return Product<Homogeneous,Rhs>(*this,rhs.derived());
-    }
-
-    template<typename Lhs> friend
-    EIGEN_DEVICE_FUNC inline const Product<Lhs,Homogeneous>
-    operator* (const MatrixBase<Lhs>& lhs, const Homogeneous& rhs)
-    {
-      eigen_assert(int(Direction)==Vertical);
-      return Product<Lhs,Homogeneous>(lhs.derived(),rhs);
-    }
-
-    template<typename Scalar, int Dim, int Mode, int Options> friend
-    EIGEN_DEVICE_FUNC inline const Product<Transform<Scalar,Dim,Mode,Options>, Homogeneous >
-    operator* (const Transform<Scalar,Dim,Mode,Options>& lhs, const Homogeneous& rhs)
-    {
-      eigen_assert(int(Direction)==Vertical);
-      return Product<Transform<Scalar,Dim,Mode,Options>, Homogeneous>(lhs,rhs);
-    }
-
-    template<typename Func>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::result_of<Func(Scalar,Scalar)>::type
-    redux(const Func& func) const
-    {
-      return func(m_matrix.redux(func), Scalar(1));
-    }
-
-  protected:
-    typename MatrixType::Nested m_matrix;
-};
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns a vector expression that is one longer than the vector argument, with the value 1 symbolically appended as the last coefficient.
-  *
-  * This can be used to convert affine coordinates to homogeneous coordinates.
-  *
-  * \only_for_vectors
-  *
-  * Example: \include MatrixBase_homogeneous.cpp
-  * Output: \verbinclude MatrixBase_homogeneous.out
-  *
-  * \sa VectorwiseOp::homogeneous(), class Homogeneous
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::HomogeneousReturnType
-MatrixBase<Derived>::homogeneous() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return HomogeneousReturnType(derived());
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns an expression where the value 1 is symbolically appended as the final coefficient to each column (or row) of the matrix.
-  *
-  * This can be used to convert affine coordinates to homogeneous coordinates.
-  *
-  * Example: \include VectorwiseOp_homogeneous.cpp
-  * Output: \verbinclude VectorwiseOp_homogeneous.out
-  *
-  * \sa MatrixBase::homogeneous(), class Homogeneous */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC inline Homogeneous<ExpressionType,Direction>
-VectorwiseOp<ExpressionType,Direction>::homogeneous() const
-{
-  return HomogeneousReturnType(_expression());
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \brief homogeneous normalization
-  *
-  * \returns a vector expression of the N-1 first coefficients of \c *this divided by that last coefficient.
-  *
-  * This can be used to convert homogeneous coordinates to affine coordinates.
-  *
-  * It is essentially a shortcut for:
-  * \code
-    this->head(this->size()-1)/this->coeff(this->size()-1);
-    \endcode
-  *
-  * Example: \include MatrixBase_hnormalized.cpp
-  * Output: \verbinclude MatrixBase_hnormalized.out
-  *
-  * \sa VectorwiseOp::hnormalized() */
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::HNormalizedReturnType
-MatrixBase<Derived>::hnormalized() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-  return ConstStartMinusOne(derived(),0,0,
-    ColsAtCompileTime==1?size()-1:1,
-    ColsAtCompileTime==1?1:size()-1) / coeff(size()-1);
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \brief column or row-wise homogeneous normalization
-  *
-  * \returns an expression of the first N-1 coefficients of each column (or row) of \c *this divided by the last coefficient of each column (or row).
-  *
-  * This can be used to convert homogeneous coordinates to affine coordinates.
-  *
-  * It is conceptually equivalent to calling MatrixBase::hnormalized() to each column (or row) of \c *this.
-  *
-  * Example: \include DirectionWise_hnormalized.cpp
-  * Output: \verbinclude DirectionWise_hnormalized.out
-  *
-  * \sa MatrixBase::hnormalized() */
-template<typename ExpressionType, int Direction>
-EIGEN_DEVICE_FUNC inline const typename VectorwiseOp<ExpressionType,Direction>::HNormalizedReturnType
-VectorwiseOp<ExpressionType,Direction>::hnormalized() const
-{
-  return HNormalized_Block(_expression(),0,0,
-      Direction==Vertical   ? _expression().rows()-1 : _expression().rows(),
-      Direction==Horizontal ? _expression().cols()-1 : _expression().cols()).cwiseQuotient(
-      Replicate<HNormalized_Factors,
-                Direction==Vertical   ? HNormalized_SizeMinusOne : 1,
-                Direction==Horizontal ? HNormalized_SizeMinusOne : 1>
-        (HNormalized_Factors(_expression(),
-          Direction==Vertical    ? _expression().rows()-1:0,
-          Direction==Horizontal  ? _expression().cols()-1:0,
-          Direction==Vertical    ? 1 : _expression().rows(),
-          Direction==Horizontal  ? 1 : _expression().cols()),
-         Direction==Vertical   ? _expression().rows()-1 : 1,
-         Direction==Horizontal ? _expression().cols()-1 : 1));
-}
-
-namespace internal {
-
-template<typename MatrixOrTransformType>
-struct take_matrix_for_product
-{
-  typedef MatrixOrTransformType type;
-  EIGEN_DEVICE_FUNC static const type& run(const type &x) { return x; }
-};
-
-template<typename Scalar, int Dim, int Mode,int Options>
-struct take_matrix_for_product<Transform<Scalar, Dim, Mode, Options> >
-{
-  typedef Transform<Scalar, Dim, Mode, Options> TransformType;
-  typedef typename internal::add_const<typename TransformType::ConstAffinePart>::type type;
-  EIGEN_DEVICE_FUNC static type run (const TransformType& x) { return x.affine(); }
-};
-
-template<typename Scalar, int Dim, int Options>
-struct take_matrix_for_product<Transform<Scalar, Dim, Projective, Options> >
-{
-  typedef Transform<Scalar, Dim, Projective, Options> TransformType;
-  typedef typename TransformType::MatrixType type;
-  EIGEN_DEVICE_FUNC static const type& run (const TransformType& x) { return x.matrix(); }
-};
-
-template<typename MatrixType,typename Lhs>
-struct traits<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> >
-{
-  typedef typename take_matrix_for_product<Lhs>::type LhsMatrixType;
-  typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-  typedef typename remove_all<LhsMatrixType>::type LhsMatrixTypeCleaned;
-  typedef typename make_proper_matrix_type<
-                 typename traits<MatrixTypeCleaned>::Scalar,
-                 LhsMatrixTypeCleaned::RowsAtCompileTime,
-                 MatrixTypeCleaned::ColsAtCompileTime,
-                 MatrixTypeCleaned::PlainObject::Options,
-                 LhsMatrixTypeCleaned::MaxRowsAtCompileTime,
-                 MatrixTypeCleaned::MaxColsAtCompileTime>::type ReturnType;
-};
-
-template<typename MatrixType,typename Lhs>
-struct homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs>
-  : public ReturnByValue<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> >
-{
-  typedef typename traits<homogeneous_left_product_impl>::LhsMatrixType LhsMatrixType;
-  typedef typename remove_all<LhsMatrixType>::type LhsMatrixTypeCleaned;
-  typedef typename remove_all<typename LhsMatrixTypeCleaned::Nested>::type LhsMatrixTypeNested;
-  EIGEN_DEVICE_FUNC homogeneous_left_product_impl(const Lhs& lhs, const MatrixType& rhs)
-    : m_lhs(take_matrix_for_product<Lhs>::run(lhs)),
-      m_rhs(rhs)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  inline Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  template<typename Dest> EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const
-  {
-    // FIXME investigate how to allow lazy evaluation of this product when possible
-    dst = Block<const LhsMatrixTypeNested,
-              LhsMatrixTypeNested::RowsAtCompileTime,
-              LhsMatrixTypeNested::ColsAtCompileTime==Dynamic?Dynamic:LhsMatrixTypeNested::ColsAtCompileTime-1>
-            (m_lhs,0,0,m_lhs.rows(),m_lhs.cols()-1) * m_rhs;
-    dst += m_lhs.col(m_lhs.cols()-1).rowwise()
-            .template replicate<MatrixType::ColsAtCompileTime>(m_rhs.cols());
-  }
-
-  typename LhsMatrixTypeCleaned::Nested m_lhs;
-  typename MatrixType::Nested m_rhs;
-};
-
-template<typename MatrixType,typename Rhs>
-struct traits<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> >
-{
-  typedef typename make_proper_matrix_type<typename traits<MatrixType>::Scalar,
-                 MatrixType::RowsAtCompileTime,
-                 Rhs::ColsAtCompileTime,
-                 MatrixType::PlainObject::Options,
-                 MatrixType::MaxRowsAtCompileTime,
-                 Rhs::MaxColsAtCompileTime>::type ReturnType;
-};
-
-template<typename MatrixType,typename Rhs>
-struct homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs>
-  : public ReturnByValue<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> >
-{
-  typedef typename remove_all<typename Rhs::Nested>::type RhsNested;
-  EIGEN_DEVICE_FUNC homogeneous_right_product_impl(const MatrixType& lhs, const Rhs& rhs)
-    : m_lhs(lhs), m_rhs(rhs)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  template<typename Dest> EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const
-  {
-    // FIXME investigate how to allow lazy evaluation of this product when possible
-    dst = m_lhs * Block<const RhsNested,
-                        RhsNested::RowsAtCompileTime==Dynamic?Dynamic:RhsNested::RowsAtCompileTime-1,
-                        RhsNested::ColsAtCompileTime>
-            (m_rhs,0,0,m_rhs.rows()-1,m_rhs.cols());
-    dst += m_rhs.row(m_rhs.rows()-1).colwise()
-            .template replicate<MatrixType::RowsAtCompileTime>(m_lhs.rows());
-  }
-
-  typename MatrixType::Nested m_lhs;
-  typename Rhs::Nested m_rhs;
-};
-
-template<typename ArgType,int Direction>
-struct evaluator_traits<Homogeneous<ArgType,Direction> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename ArgType::StorageKind>::Kind Kind;
-  typedef HomogeneousShape Shape;
-};
-
-template<> struct AssignmentKind<DenseShape,HomogeneousShape> { typedef Dense2Dense Kind; };
-
-
-template<typename ArgType,int Direction>
-struct unary_evaluator<Homogeneous<ArgType,Direction>, IndexBased>
-  : evaluator<typename Homogeneous<ArgType,Direction>::PlainObject >
-{
-  typedef Homogeneous<ArgType,Direction> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op)
-    : Base(), m_temp(op)
-  {
-    ::new (static_cast<Base*>(this)) Base(m_temp);
-  }
-
-protected:
-  PlainObject m_temp;
-};
-
-// dense = homogeneous
-template< typename DstXprType, typename ArgType, typename Scalar>
-struct Assignment<DstXprType, Homogeneous<ArgType,Vertical>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense>
-{
-  typedef Homogeneous<ArgType,Vertical> SrcXprType;
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst.template topRows<ArgType::RowsAtCompileTime>(src.nestedExpression().rows()) = src.nestedExpression();
-    dst.row(dst.rows()-1).setOnes();
-  }
-};
-
-// dense = homogeneous
-template< typename DstXprType, typename ArgType, typename Scalar>
-struct Assignment<DstXprType, Homogeneous<ArgType,Horizontal>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense>
-{
-  typedef Homogeneous<ArgType,Horizontal> SrcXprType;
-  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst.template leftCols<ArgType::ColsAtCompileTime>(src.nestedExpression().cols()) = src.nestedExpression();
-    dst.col(dst.cols()-1).setOnes();
-  }
-};
-
-template<typename LhsArg, typename Rhs, int ProductTag>
-struct generic_product_impl<Homogeneous<LhsArg,Horizontal>, Rhs, HomogeneousShape, DenseShape, ProductTag>
-{
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Homogeneous<LhsArg,Horizontal>& lhs, const Rhs& rhs)
-  {
-    homogeneous_right_product_impl<Homogeneous<LhsArg,Horizontal>, Rhs>(lhs.nestedExpression(), rhs).evalTo(dst);
-  }
-};
-
-template<typename Lhs,typename Rhs>
-struct homogeneous_right_product_refactoring_helper
-{
-  enum {
-    Dim  = Lhs::ColsAtCompileTime,
-    Rows = Lhs::RowsAtCompileTime
-  };
-  typedef typename Rhs::template ConstNRowsBlockXpr<Dim>::Type          LinearBlockConst;
-  typedef typename remove_const<LinearBlockConst>::type                 LinearBlock;
-  typedef typename Rhs::ConstRowXpr                                     ConstantColumn;
-  typedef Replicate<const ConstantColumn,Rows,1>                        ConstantBlock;
-  typedef Product<Lhs,LinearBlock,LazyProduct>                          LinearProduct;
-  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, HomogeneousShape, DenseShape>
- : public evaluator<typename homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression,Rhs>::Xpr>
-{
-  typedef Product<Lhs, Rhs, LazyProduct> XprType;
-  typedef homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression,Rhs> helper;
-  typedef typename helper::ConstantBlock ConstantBlock;
-  typedef typename helper::Xpr RefactoredXpr;
-  typedef evaluator<RefactoredXpr> Base;
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(  xpr.lhs().nestedExpression() .lazyProduct(  xpr.rhs().template topRows<helper::Dim>(xpr.lhs().nestedExpression().cols()) )
-            + ConstantBlock(xpr.rhs().row(xpr.rhs().rows()-1),xpr.lhs().rows(), 1) )
-  {}
-};
-
-template<typename Lhs, typename RhsArg, int ProductTag>
-struct generic_product_impl<Lhs, Homogeneous<RhsArg,Vertical>, DenseShape, HomogeneousShape, ProductTag>
-{
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
-  {
-    homogeneous_left_product_impl<Homogeneous<RhsArg,Vertical>, Lhs>(lhs, rhs.nestedExpression()).evalTo(dst);
-  }
-};
-
-// TODO: the following specialization is to address a regression from 3.2 to 3.3
-// In the future, this path should be optimized.
-template<typename Lhs, typename RhsArg, int ProductTag>
-struct generic_product_impl<Lhs, Homogeneous<RhsArg,Vertical>, TriangularShape, HomogeneousShape, ProductTag>
-{
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
-  {
-    dst.noalias() = lhs * rhs.eval();
-  }
-};
-
-template<typename Lhs,typename Rhs>
-struct homogeneous_left_product_refactoring_helper
-{
-  enum {
-    Dim = Rhs::RowsAtCompileTime,
-    Cols = Rhs::ColsAtCompileTime
-  };
-  typedef typename Lhs::template ConstNColsBlockXpr<Dim>::Type          LinearBlockConst;
-  typedef typename remove_const<LinearBlockConst>::type                 LinearBlock;
-  typedef typename Lhs::ConstColXpr                                     ConstantColumn;
-  typedef Replicate<const ConstantColumn,1,Cols>                        ConstantBlock;
-  typedef Product<LinearBlock,Rhs,LazyProduct>                          LinearProduct;
-  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, HomogeneousShape>
- : public evaluator<typename homogeneous_left_product_refactoring_helper<Lhs,typename Rhs::NestedExpression>::Xpr>
-{
-  typedef Product<Lhs, Rhs, LazyProduct> XprType;
-  typedef homogeneous_left_product_refactoring_helper<Lhs,typename Rhs::NestedExpression> helper;
-  typedef typename helper::ConstantBlock ConstantBlock;
-  typedef typename helper::Xpr RefactoredXpr;
-  typedef evaluator<RefactoredXpr> Base;
-
-  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
-    : Base(   xpr.lhs().template leftCols<helper::Dim>(xpr.rhs().nestedExpression().rows()) .lazyProduct( xpr.rhs().nestedExpression() )
-            + ConstantBlock(xpr.lhs().col(xpr.lhs().cols()-1),1,xpr.rhs().cols()) )
-  {}
-};
-
-template<typename Scalar, int Dim, int Mode,int Options, typename RhsArg, int ProductTag>
-struct generic_product_impl<Transform<Scalar,Dim,Mode,Options>, Homogeneous<RhsArg,Vertical>, DenseShape, HomogeneousShape, ProductTag>
-{
-  typedef Transform<Scalar,Dim,Mode,Options> TransformType;
-  template<typename Dest>
-  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const TransformType& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
-  {
-    homogeneous_left_product_impl<Homogeneous<RhsArg,Vertical>, TransformType>(lhs, rhs.nestedExpression()).evalTo(dst);
-  }
-};
-
-template<typename ExpressionType, int Side, bool Transposed>
-struct permutation_matrix_product<ExpressionType, Side, Transposed, HomogeneousShape>
-  : public permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
-{};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_HOMOGENEOUS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Hyperplane.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Hyperplane.h
deleted file mode 100644
index cebe035..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Hyperplane.h
+++ /dev/null
@@ -1,282 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HYPERPLANE_H
-#define EIGEN_HYPERPLANE_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Hyperplane
-  *
-  * \brief A hyperplane
-  *
-  * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n.
-  * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane.
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
-  *             Notice that the dimension of the hyperplane is _AmbientDim-1.
-  *
-  * This class represents an hyperplane as the zero set of the implicit equation
-  * \f$ n \cdot x + d = 0 \f$ where \f$ n \f$ is a unit normal vector of the plane (linear part)
-  * and \f$ d \f$ is the distance (offset) to the origin.
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-class Hyperplane
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1)
-  enum {
-    AmbientDimAtCompileTime = _AmbientDim,
-    Options = _Options
-  };
-  typedef _Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
-  typedef Matrix<Scalar,Index(AmbientDimAtCompileTime)==Dynamic
-                        ? Dynamic
-                        : Index(AmbientDimAtCompileTime)+1,1,Options> Coefficients;
-  typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType;
-  typedef const Block<const Coefficients,AmbientDimAtCompileTime,1> ConstNormalReturnType;
-
-  /** Default constructor without initialization */
-  EIGEN_DEVICE_FUNC inline Hyperplane() {}
-  
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC Hyperplane(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)
-   : m_coeffs(other.coeffs())
-  {}
-
-  /** Constructs a dynamic-size hyperplane with \a _dim the dimension
-    * of the ambient space */
-  EIGEN_DEVICE_FUNC inline explicit Hyperplane(Index _dim) : m_coeffs(_dim+1) {}
-
-  /** Construct a plane from its normal \a n and a point \a e onto the plane.
-    * \warning the vector normal is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const VectorType& e)
-    : m_coeffs(n.size()+1)
-  {
-    normal() = n;
-    offset() = -n.dot(e);
-  }
-
-  /** Constructs a plane from its normal \a n and distance to the origin \a d
-    * such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$.
-    * \warning the vector normal is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const Scalar& d)
-    : m_coeffs(n.size()+1)
-  {
-    normal() = n;
-    offset() = d;
-  }
-
-  /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space
-    * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made.
-    */
-  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1)
-  {
-    Hyperplane result(p0.size());
-    result.normal() = (p1 - p0).unitOrthogonal();
-    result.offset() = -p0.dot(result.normal());
-    return result;
-  }
-
-  /** Constructs a hyperplane passing through the three points. The dimension of the ambient space
-    * is required to be exactly 3.
-    */
-  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2)
-  {
-    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3)
-    Hyperplane result(p0.size());
-    VectorType v0(p2 - p0), v1(p1 - p0);
-    result.normal() = v0.cross(v1);
-    RealScalar norm = result.normal().norm();
-    if(norm <= v0.norm() * v1.norm() * NumTraits<RealScalar>::epsilon())
-    {
-      Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
-      JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
-      result.normal() = svd.matrixV().col(2);
-    }
-    else
-      result.normal() /= norm;
-    result.offset() = -p0.dot(result.normal());
-    return result;
-  }
-
-  /** Constructs a hyperplane passing through the parametrized line \a parametrized.
-    * If the dimension of the ambient space is greater than 2, then there isn't uniqueness,
-    * so an arbitrary choice is made.
-    */
-  // FIXME to be consistent with the rest this could be implemented as a static Through function ??
-  EIGEN_DEVICE_FUNC explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized)
-  {
-    normal() = parametrized.direction().unitOrthogonal();
-    offset() = -parametrized.origin().dot(normal());
-  }
-
-  EIGEN_DEVICE_FUNC ~Hyperplane() {}
-
-  /** \returns the dimension in which the plane holds */
-  EIGEN_DEVICE_FUNC inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_coeffs.size()-1 : Index(AmbientDimAtCompileTime); }
-
-  /** normalizes \c *this */
-  EIGEN_DEVICE_FUNC void normalize(void)
-  {
-    m_coeffs /= normal().norm();
-  }
-
-  /** \returns the signed distance between the plane \c *this and a point \a p.
-    * \sa absDistance()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar signedDistance(const VectorType& p) const { return normal().dot(p) + offset(); }
-
-  /** \returns the absolute distance between the plane \c *this and a point \a p.
-    * \sa signedDistance()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar absDistance(const VectorType& p) const { return numext::abs(signedDistance(p)); }
-
-  /** \returns the projection of a point \a p onto the plane \c *this.
-    */
-  EIGEN_DEVICE_FUNC inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); }
-
-  /** \returns a constant reference to the unit normal vector of the plane, which corresponds
-    * to the linear part of the implicit equation.
-    */
-  EIGEN_DEVICE_FUNC inline ConstNormalReturnType normal() const { return ConstNormalReturnType(m_coeffs,0,0,dim(),1); }
-
-  /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds
-    * to the linear part of the implicit equation.
-    */
-  EIGEN_DEVICE_FUNC inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); }
-
-  /** \returns the distance to the origin, which is also the "constant term" of the implicit equation
-    * \warning the vector normal is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC inline const Scalar& offset() const { return m_coeffs.coeff(dim()); }
-
-  /** \returns a non-constant reference to the distance to the origin, which is also the constant part
-    * of the implicit equation */
-  EIGEN_DEVICE_FUNC inline Scalar& offset() { return m_coeffs(dim()); }
-
-  /** \returns a constant reference to the coefficients c_i of the plane equation:
-    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
-    */
-  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
-
-  /** \returns a non-constant reference to the coefficients c_i of the plane equation:
-    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
-    */
-  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
-
-  /** \returns the intersection of *this with \a other.
-    *
-    * \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines.
-    *
-    * \note If \a other is approximately parallel to *this, this method will return any point on *this.
-    */
-  EIGEN_DEVICE_FUNC VectorType intersection(const Hyperplane& other) const
-  {
-    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
-    Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
-    // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests
-    // whether the two lines are approximately parallel.
-    if(internal::isMuchSmallerThan(det, Scalar(1)))
-    {   // special case where the two lines are approximately parallel. Pick any point on the first line.
-        if(numext::abs(coeffs().coeff(1))>numext::abs(coeffs().coeff(0)))
-            return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0));
-        else
-            return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0));
-    }
-    else
-    {   // general case
-        Scalar invdet = Scalar(1) / det;
-        return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)),
-                          invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2)));
-    }
-  }
-
-  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
-    *
-    * \param mat the Dim x Dim transformation matrix
-    * \param traits specifies whether the matrix \a mat represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    */
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
-  {
-    if (traits==Affine)
-    {
-      normal() = mat.inverse().transpose() * normal();
-      m_coeffs /= normal().norm();
-    }
-    else if (traits==Isometry)
-      normal() = mat * normal();
-    else
-    {
-      eigen_assert(0 && "invalid traits value in Hyperplane::transform()");
-    }
-    return *this;
-  }
-
-  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
-    *
-    * \param t the transformation of dimension Dim
-    * \param traits specifies whether the transformation \a t represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    *               Other kind of transformations are not supported.
-    */
-  template<int TrOptions>
-  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime,Affine,TrOptions>& t,
-                                TransformTraits traits = Affine)
-  {
-    transform(t.linear(), traits);
-    offset() -= normal().dot(t.translation());
-    return *this;
-  }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Hyperplane,
-           Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const
-  {
-    return typename internal::cast_return_type<Hyperplane,
-                    Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this);
-  }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline explicit Hyperplane(const Hyperplane<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other)
-  { m_coeffs = other.coeffs().template cast<Scalar>(); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC bool isApprox(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_coeffs.isApprox(other.m_coeffs, prec); }
-
-protected:
-
-  Coefficients m_coeffs;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_HYPERPLANE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/OrthoMethods.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/OrthoMethods.h
deleted file mode 100644
index 524aebe..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/OrthoMethods.h
+++ /dev/null
@@ -1,235 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORTHOMETHODS_H
-#define EIGEN_ORTHOMETHODS_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns the cross product of \c *this and \a other
-  *
-  * Here is a very good explanation of cross-product: http://xkcd.com/199/
-  * 
-  * With complex numbers, the cross product is implemented as
-  * \f$ (\mathbf{a}+i\mathbf{b}) \times (\mathbf{c}+i\mathbf{d}) = (\mathbf{a} \times \mathbf{c} - \mathbf{b} \times \mathbf{d}) - i(\mathbf{a} \times \mathbf{d} - \mathbf{b} \times \mathbf{c})\f$
-  * 
-  * \sa MatrixBase::cross3()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename MatrixBase<Derived>::template cross_product_return_type<OtherDerived>::type
-#else
-typename MatrixBase<Derived>::PlainObject
-#endif
-MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,3)
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
-
-  // Note that there is no need for an expression here since the compiler
-  // optimize such a small temporary very well (even within a complex expression)
-  typename internal::nested_eval<Derived,2>::type lhs(derived());
-  typename internal::nested_eval<OtherDerived,2>::type rhs(other.derived());
-  return typename cross_product_return_type<OtherDerived>::type(
-    numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
-    numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
-    numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0))
-  );
-}
-
-namespace internal {
-
-template< int Arch,typename VectorLhs,typename VectorRhs,
-          typename Scalar = typename VectorLhs::Scalar,
-          bool Vectorizable = bool((VectorLhs::Flags&VectorRhs::Flags)&PacketAccessBit)>
-struct cross3_impl {
-  EIGEN_DEVICE_FUNC static inline typename internal::plain_matrix_type<VectorLhs>::type
-  run(const VectorLhs& lhs, const VectorRhs& rhs)
-  {
-    return typename internal::plain_matrix_type<VectorLhs>::type(
-      numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
-      numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
-      numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)),
-      0
-    );
-  }
-};
-
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns the cross product of \c *this and \a other using only the x, y, and z coefficients
-  *
-  * The size of \c *this and \a other must be four. This function is especially useful
-  * when using 4D vectors instead of 3D ones to get advantage of SSE/AltiVec vectorization.
-  *
-  * \sa MatrixBase::cross()
-  */
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::cross3(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,4)
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,4)
-
-  typedef typename internal::nested_eval<Derived,2>::type DerivedNested;
-  typedef typename internal::nested_eval<OtherDerived,2>::type OtherDerivedNested;
-  DerivedNested lhs(derived());
-  OtherDerivedNested rhs(other.derived());
-
-  return internal::cross3_impl<Architecture::Target,
-                        typename internal::remove_all<DerivedNested>::type,
-                        typename internal::remove_all<OtherDerivedNested>::type>::run(lhs,rhs);
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns a matrix expression of the cross product of each column or row
-  * of the referenced expression with the \a other vector.
-  *
-  * The referenced matrix must have one dimension equal to 3.
-  * The result matrix has the same dimensions than the referenced one.
-  *
-  * \sa MatrixBase::cross() */
-template<typename ExpressionType, int Direction>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC 
-const typename VectorwiseOp<ExpressionType,Direction>::CrossReturnType
-VectorwiseOp<ExpressionType,Direction>::cross(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-  
-  typename internal::nested_eval<ExpressionType,2>::type mat(_expression());
-  typename internal::nested_eval<OtherDerived,2>::type vec(other.derived());
-
-  CrossReturnType res(_expression().rows(),_expression().cols());
-  if(Direction==Vertical)
-  {
-    eigen_assert(CrossReturnType::RowsAtCompileTime==3 && "the matrix must have exactly 3 rows");
-    res.row(0) = (mat.row(1) * vec.coeff(2) - mat.row(2) * vec.coeff(1)).conjugate();
-    res.row(1) = (mat.row(2) * vec.coeff(0) - mat.row(0) * vec.coeff(2)).conjugate();
-    res.row(2) = (mat.row(0) * vec.coeff(1) - mat.row(1) * vec.coeff(0)).conjugate();
-  }
-  else
-  {
-    eigen_assert(CrossReturnType::ColsAtCompileTime==3 && "the matrix must have exactly 3 columns");
-    res.col(0) = (mat.col(1) * vec.coeff(2) - mat.col(2) * vec.coeff(1)).conjugate();
-    res.col(1) = (mat.col(2) * vec.coeff(0) - mat.col(0) * vec.coeff(2)).conjugate();
-    res.col(2) = (mat.col(0) * vec.coeff(1) - mat.col(1) * vec.coeff(0)).conjugate();
-  }
-  return res;
-}
-
-namespace internal {
-
-template<typename Derived, int Size = Derived::SizeAtCompileTime>
-struct unitOrthogonal_selector
-{
-  typedef typename plain_matrix_type<Derived>::type VectorType;
-  typedef typename traits<Derived>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Matrix<Scalar,2,1> Vector2;
-  EIGEN_DEVICE_FUNC
-  static inline VectorType run(const Derived& src)
-  {
-    VectorType perp = VectorType::Zero(src.size());
-    Index maxi = 0;
-    Index sndi = 0;
-    src.cwiseAbs().maxCoeff(&maxi);
-    if (maxi==0)
-      sndi = 1;
-    RealScalar invnm = RealScalar(1)/(Vector2() << src.coeff(sndi),src.coeff(maxi)).finished().norm();
-    perp.coeffRef(maxi) = -numext::conj(src.coeff(sndi)) * invnm;
-    perp.coeffRef(sndi) =  numext::conj(src.coeff(maxi)) * invnm;
-
-    return perp;
-   }
-};
-
-template<typename Derived>
-struct unitOrthogonal_selector<Derived,3>
-{
-  typedef typename plain_matrix_type<Derived>::type VectorType;
-  typedef typename traits<Derived>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  EIGEN_DEVICE_FUNC
-  static inline VectorType run(const Derived& src)
-  {
-    VectorType perp;
-    /* Let us compute the crossed product of *this with a vector
-     * that is not too close to being colinear to *this.
-     */
-
-    /* unless the x and y coords are both close to zero, we can
-     * simply take ( -y, x, 0 ) and normalize it.
-     */
-    if((!isMuchSmallerThan(src.x(), src.z()))
-    || (!isMuchSmallerThan(src.y(), src.z())))
-    {
-      RealScalar invnm = RealScalar(1)/src.template head<2>().norm();
-      perp.coeffRef(0) = -numext::conj(src.y())*invnm;
-      perp.coeffRef(1) = numext::conj(src.x())*invnm;
-      perp.coeffRef(2) = 0;
-    }
-    /* if both x and y are close to zero, then the vector is close
-     * to the z-axis, so it's far from colinear to the x-axis for instance.
-     * So we take the crossed product with (1,0,0) and normalize it.
-     */
-    else
-    {
-      RealScalar invnm = RealScalar(1)/src.template tail<2>().norm();
-      perp.coeffRef(0) = 0;
-      perp.coeffRef(1) = -numext::conj(src.z())*invnm;
-      perp.coeffRef(2) = numext::conj(src.y())*invnm;
-    }
-
-    return perp;
-   }
-};
-
-template<typename Derived>
-struct unitOrthogonal_selector<Derived,2>
-{
-  typedef typename plain_matrix_type<Derived>::type VectorType;
-  EIGEN_DEVICE_FUNC
-  static inline VectorType run(const Derived& src)
-  { return VectorType(-numext::conj(src.y()), numext::conj(src.x())).normalized(); }
-};
-
-} // end namespace internal
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \returns a unit vector which is orthogonal to \c *this
-  *
-  * The size of \c *this must be at least 2. If the size is exactly 2,
-  * then the returned vector is a counter clock wise rotation of \c *this, i.e., (-y,x).normalized().
-  *
-  * \sa cross()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::PlainObject
-MatrixBase<Derived>::unitOrthogonal() const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return internal::unitOrthogonal_selector<Derived>::run(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ORTHOMETHODS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h
deleted file mode 100644
index 584f500..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h
+++ /dev/null
@@ -1,232 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARAMETRIZEDLINE_H
-#define EIGEN_PARAMETRIZEDLINE_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class ParametrizedLine
-  *
-  * \brief A parametrized line
-  *
-  * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit
-  * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to
-  * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ t \in \mathbf{R} \f$.
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic.
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-class ParametrizedLine
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
-  enum {
-    AmbientDimAtCompileTime = _AmbientDim,
-    Options = _Options
-  };
-  typedef _Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  typedef Matrix<Scalar,AmbientDimAtCompileTime,1,Options> VectorType;
-
-  /** Default constructor without initialization */
-  EIGEN_DEVICE_FUNC inline ParametrizedLine() {}
-  
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC ParametrizedLine(const ParametrizedLine<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)
-   : m_origin(other.origin()), m_direction(other.direction())
-  {}
-
-  /** Constructs a dynamic-size line with \a _dim the dimension
-    * of the ambient space */
-  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(Index _dim) : m_origin(_dim), m_direction(_dim) {}
-
-  /** Initializes a parametrized line of direction \a direction and origin \a origin.
-    * \warning the vector direction is assumed to be normalized.
-    */
-  EIGEN_DEVICE_FUNC ParametrizedLine(const VectorType& origin, const VectorType& direction)
-    : m_origin(origin), m_direction(direction) {}
-
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane);
-
-  /** Constructs a parametrized line going from \a p0 to \a p1. */
-  EIGEN_DEVICE_FUNC static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1)
-  { return ParametrizedLine(p0, (p1-p0).normalized()); }
-
-  EIGEN_DEVICE_FUNC ~ParametrizedLine() {}
-
-  /** \returns the dimension in which the line holds */
-  EIGEN_DEVICE_FUNC inline Index dim() const { return m_direction.size(); }
-
-  EIGEN_DEVICE_FUNC const VectorType& origin() const { return m_origin; }
-  EIGEN_DEVICE_FUNC VectorType& origin() { return m_origin; }
-
-  EIGEN_DEVICE_FUNC const VectorType& direction() const { return m_direction; }
-  EIGEN_DEVICE_FUNC VectorType& direction() { return m_direction; }
-
-  /** \returns the squared distance of a point \a p to its projection onto the line \c *this.
-    * \sa distance()
-    */
-  EIGEN_DEVICE_FUNC RealScalar squaredDistance(const VectorType& p) const
-  {
-    VectorType diff = p - origin();
-    return (diff - direction().dot(diff) * direction()).squaredNorm();
-  }
-  /** \returns the distance of a point \a p to its projection onto the line \c *this.
-    * \sa squaredDistance()
-    */
-  EIGEN_DEVICE_FUNC RealScalar distance(const VectorType& p) const { EIGEN_USING_STD(sqrt) return sqrt(squaredDistance(p)); }
-
-  /** \returns the projection of a point \a p onto the line \c *this. */
-  EIGEN_DEVICE_FUNC VectorType projection(const VectorType& p) const
-  { return origin() + direction().dot(p-origin()) * direction(); }
-
-  EIGEN_DEVICE_FUNC VectorType pointAt(const Scalar& t) const;
-  
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC Scalar intersectionParameter(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
- 
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC Scalar intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
-  
-  template <int OtherOptions>
-  EIGEN_DEVICE_FUNC VectorType intersectionPoint(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const;
-
-  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
-    *
-    * \param mat the Dim x Dim transformation matrix
-    * \param traits specifies whether the matrix \a mat represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    */
-  template<typename XprType>
-  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
-  {
-    if (traits==Affine)
-      direction() = (mat * direction()).normalized();
-    else if (traits==Isometry)
-      direction() = mat * direction();
-    else
-    {
-      eigen_assert(0 && "invalid traits value in ParametrizedLine::transform()");
-    }
-    origin() = mat * origin();
-    return *this;
-  }
-
-  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
-    *
-    * \param t the transformation of dimension Dim
-    * \param traits specifies whether the transformation \a t represents an #Isometry
-    *               or a more generic #Affine transformation. The default is #Affine.
-    *               Other kind of transformations are not supported.
-    */
-  template<int TrOptions>
-  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(const Transform<Scalar,AmbientDimAtCompileTime,Affine,TrOptions>& t,
-                                                       TransformTraits traits = Affine)
-  {
-    transform(t.linear(), traits);
-    origin() += t.translation();
-    return *this;
-  }
-
-/** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<ParametrizedLine,
-           ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const
-  {
-    return typename internal::cast_return_type<ParametrizedLine,
-                    ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this);
-  }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(const ParametrizedLine<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other)
-  {
-    m_origin = other.origin().template cast<Scalar>();
-    m_direction = other.direction().template cast<Scalar>();
-  }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const ParametrizedLine& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); }
-
-protected:
-
-  VectorType m_origin, m_direction;
-};
-
-/** Constructs a parametrized line from a 2D hyperplane
-  *
-  * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline ParametrizedLine<_Scalar, _AmbientDim,_Options>::ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim,OtherOptions>& hyperplane)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
-  direction() = hyperplane.normal().unitOrthogonal();
-  origin() = -hyperplane.normal()*hyperplane.offset();
-}
-
-/** \returns the point at \a t along this line
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-EIGEN_DEVICE_FUNC inline typename ParametrizedLine<_Scalar, _AmbientDim,_Options>::VectorType
-ParametrizedLine<_Scalar, _AmbientDim,_Options>::pointAt(const _Scalar& t) const
-{
-  return origin() + (direction()*t); 
-}
-
-/** \returns the parameter value of the intersection between \c *this and the given \a hyperplane
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersectionParameter(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const
-{
-  return -(hyperplane.offset()+hyperplane.normal().dot(origin()))
-          / hyperplane.normal().dot(direction());
-}
-
-
-/** \deprecated use intersectionParameter()
-  * \returns the parameter value of the intersection between \c *this and the given \a hyperplane
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const
-{
-  return intersectionParameter(hyperplane);
-}
-
-/** \returns the point of the intersection between \c *this and the given hyperplane
-  */
-template <typename _Scalar, int _AmbientDim, int _Options>
-template <int OtherOptions>
-EIGEN_DEVICE_FUNC inline typename ParametrizedLine<_Scalar, _AmbientDim,_Options>::VectorType
-ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersectionPoint(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const
-{
-  return pointAt(intersectionParameter(hyperplane));
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARAMETRIZEDLINE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Quaternion.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Quaternion.h
deleted file mode 100644
index 3259e59..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Quaternion.h
+++ /dev/null
@@ -1,870 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Mathieu Gautier <mathieu.gautier@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QUATERNION_H
-#define EIGEN_QUATERNION_H
-namespace Eigen { 
-
-
-/***************************************************************************
-* Definition of QuaternionBase<Derived>
-* The implementation is at the end of the file
-***************************************************************************/
-
-namespace internal {
-template<typename Other,
-         int OtherRows=Other::RowsAtCompileTime,
-         int OtherCols=Other::ColsAtCompileTime>
-struct quaternionbase_assign_impl;
-}
-
-/** \geometry_module \ingroup Geometry_Module
-  * \class QuaternionBase
-  * \brief Base class for quaternion expressions
-  * \tparam Derived derived type (CRTP)
-  * \sa class Quaternion
-  */
-template<class Derived>
-class QuaternionBase : public RotationBase<Derived, 3>
-{
- public:
-  typedef RotationBase<Derived, 3> Base;
-
-  using Base::operator*;
-  using Base::derived;
-
-  typedef typename internal::traits<Derived>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef typename internal::traits<Derived>::Coefficients Coefficients;
-  typedef typename Coefficients::CoeffReturnType CoeffReturnType;
-  typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
-                                        Scalar&, CoeffReturnType>::type NonConstCoeffReturnType;
-
-
-  enum {
-    Flags = Eigen::internal::traits<Derived>::Flags
-  };
-
- // typedef typename Matrix<Scalar,4,1> Coefficients;
-  /** the type of a 3D vector */
-  typedef Matrix<Scalar,3,1> Vector3;
-  /** the equivalent rotation matrix type */
-  typedef Matrix<Scalar,3,3> Matrix3;
-  /** the equivalent angle-axis type */
-  typedef AngleAxis<Scalar> AngleAxisType;
-
-
-
-  /** \returns the \c x coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType x() const { return this->derived().coeffs().coeff(0); }
-  /** \returns the \c y coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType y() const { return this->derived().coeffs().coeff(1); }
-  /** \returns the \c z coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType z() const { return this->derived().coeffs().coeff(2); }
-  /** \returns the \c w coefficient */
-  EIGEN_DEVICE_FUNC inline CoeffReturnType w() const { return this->derived().coeffs().coeff(3); }
-
-  /** \returns a reference to the \c x coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType x() { return this->derived().coeffs().x(); }
-  /** \returns a reference to the \c y coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType y() { return this->derived().coeffs().y(); }
-  /** \returns a reference to the \c z coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType z() { return this->derived().coeffs().z(); }
-  /** \returns a reference to the \c w coefficient (if Derived is a non-const lvalue) */
-  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType w() { return this->derived().coeffs().w(); }
-
-  /** \returns a read-only vector expression of the imaginary part (x,y,z) */
-  EIGEN_DEVICE_FUNC inline const VectorBlock<const Coefficients,3> vec() const { return coeffs().template head<3>(); }
-
-  /** \returns a vector expression of the imaginary part (x,y,z) */
-  EIGEN_DEVICE_FUNC inline VectorBlock<Coefficients,3> vec() { return coeffs().template head<3>(); }
-
-  /** \returns a read-only vector expression of the coefficients (x,y,z,w) */
-  EIGEN_DEVICE_FUNC inline const typename internal::traits<Derived>::Coefficients& coeffs() const { return derived().coeffs(); }
-
-  /** \returns a vector expression of the coefficients (x,y,z,w) */
-  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Coefficients& coeffs() { return derived().coeffs(); }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& operator=(const QuaternionBase<Derived>& other);
-  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const QuaternionBase<OtherDerived>& other);
-
-// disabled this copy operator as it is giving very strange compilation errors when compiling
-// test_stdvector with GCC 4.4.2. This looks like a GCC bug though, so feel free to re-enable it if it's
-// useful; however notice that we already have the templated operator= above and e.g. in MatrixBase
-// we didn't have to add, in addition to templated operator=, such a non-templated copy operator.
-//  Derived& operator=(const QuaternionBase& other)
-//  { return operator=<Derived>(other); }
-
-  EIGEN_DEVICE_FUNC Derived& operator=(const AngleAxisType& aa);
-  template<class OtherDerived> EIGEN_DEVICE_FUNC Derived& operator=(const MatrixBase<OtherDerived>& m);
-
-  /** \returns a quaternion representing an identity rotation
-    * \sa MatrixBase::Identity()
-    */
-  EIGEN_DEVICE_FUNC static inline Quaternion<Scalar> Identity() { return Quaternion<Scalar>(Scalar(1), Scalar(0), Scalar(0), Scalar(0)); }
-
-  /** \sa QuaternionBase::Identity(), MatrixBase::setIdentity()
-    */
-  EIGEN_DEVICE_FUNC inline QuaternionBase& setIdentity() { coeffs() << Scalar(0), Scalar(0), Scalar(0), Scalar(1); return *this; }
-
-  /** \returns the squared norm of the quaternion's coefficients
-    * \sa QuaternionBase::norm(), MatrixBase::squaredNorm()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar squaredNorm() const { return coeffs().squaredNorm(); }
-
-  /** \returns the norm of the quaternion's coefficients
-    * \sa QuaternionBase::squaredNorm(), MatrixBase::norm()
-    */
-  EIGEN_DEVICE_FUNC inline Scalar norm() const { return coeffs().norm(); }
-
-  /** Normalizes the quaternion \c *this
-    * \sa normalized(), MatrixBase::normalize() */
-  EIGEN_DEVICE_FUNC inline void normalize() { coeffs().normalize(); }
-  /** \returns a normalized copy of \c *this
-    * \sa normalize(), MatrixBase::normalized() */
-  EIGEN_DEVICE_FUNC inline Quaternion<Scalar> normalized() const { return Quaternion<Scalar>(coeffs().normalized()); }
-
-    /** \returns the dot product of \c *this and \a other
-    * Geometrically speaking, the dot product of two unit quaternions
-    * corresponds to the cosine of half the angle between the two rotations.
-    * \sa angularDistance()
-    */
-  template<class OtherDerived> EIGEN_DEVICE_FUNC inline Scalar dot(const QuaternionBase<OtherDerived>& other) const { return coeffs().dot(other.coeffs()); }
-
-  template<class OtherDerived> EIGEN_DEVICE_FUNC Scalar angularDistance(const QuaternionBase<OtherDerived>& other) const;
-
-  /** \returns an equivalent 3x3 rotation matrix */
-  EIGEN_DEVICE_FUNC inline Matrix3 toRotationMatrix() const;
-
-  /** \returns the quaternion which transform \a a into \a b through a rotation */
-  template<typename Derived1, typename Derived2>
-  EIGEN_DEVICE_FUNC Derived& setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
-
-  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<Scalar> operator* (const QuaternionBase<OtherDerived>& q) const;
-  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*= (const QuaternionBase<OtherDerived>& q);
-
-  /** \returns the quaternion describing the inverse rotation */
-  EIGEN_DEVICE_FUNC Quaternion<Scalar> inverse() const;
-
-  /** \returns the conjugated quaternion */
-  EIGEN_DEVICE_FUNC Quaternion<Scalar> conjugate() const;
-
-  template<class OtherDerived> EIGEN_DEVICE_FUNC Quaternion<Scalar> slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const;
-
-  /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
-    * \warning When using floating point scalar values you probably should rather use a
-    *          fuzzy comparison such as isApprox()
-    * \sa isApprox(), operator!= */
-  template<class OtherDerived>
-  EIGEN_DEVICE_FUNC inline bool operator==(const QuaternionBase<OtherDerived>& other) const
-  { return coeffs() == other.coeffs(); }
-
-  /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
-    * \warning When using floating point scalar values you probably should rather use a
-    *          fuzzy comparison such as isApprox()
-    * \sa isApprox(), operator== */
-  template<class OtherDerived>
-  EIGEN_DEVICE_FUNC inline bool operator!=(const QuaternionBase<OtherDerived>& other) const
-  { return coeffs() != other.coeffs(); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  template<class OtherDerived>
-  EIGEN_DEVICE_FUNC bool isApprox(const QuaternionBase<OtherDerived>& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return coeffs().isApprox(other.coeffs(), prec); }
-
-  /** return the result vector of \a v through the rotation*/
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Vector3 _transformVector(const Vector3& v) const;
-
-  #ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type cast() const;
-
-  #else
-
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline
-  typename internal::enable_if<internal::is_same<Scalar,NewScalarType>::value,const Derived&>::type cast() const
-  {
-    return derived();
-  }
-
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline
-  typename internal::enable_if<!internal::is_same<Scalar,NewScalarType>::value,Quaternion<NewScalarType> >::type cast() const
-  {
-    return Quaternion<NewScalarType>(coeffs().template cast<NewScalarType>());
-  }
-  #endif
-
-#ifndef EIGEN_NO_IO
-  friend std::ostream& operator<<(std::ostream& s, const QuaternionBase<Derived>& q) {
-    s << q.x() << "i + " << q.y() << "j + " << q.z() << "k" << " + " << q.w();
-    return s;
-  }
-#endif
-
-#ifdef EIGEN_QUATERNIONBASE_PLUGIN
-# include EIGEN_QUATERNIONBASE_PLUGIN
-#endif
-protected:
-  EIGEN_DEFAULT_COPY_CONSTRUCTOR(QuaternionBase)
-  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(QuaternionBase)
-};
-
-/***************************************************************************
-* Definition/implementation of Quaternion<Scalar>
-***************************************************************************/
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Quaternion
-  *
-  * \brief The quaternion class used to represent 3D orientations and rotations
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _Options controls the memory alignment of the coefficients. Can be \# AutoAlign or \# DontAlign. Default is AutoAlign.
-  *
-  * This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of
-  * orientations and rotations of objects in three dimensions. Compared to other representations
-  * like Euler angles or 3x3 matrices, quaternions offer the following advantages:
-  * \li \b compact storage (4 scalars)
-  * \li \b efficient to compose (28 flops),
-  * \li \b stable spherical interpolation
-  *
-  * The following two typedefs are provided for convenience:
-  * \li \c Quaternionf for \c float
-  * \li \c Quaterniond for \c double
-  *
-  * \warning Operations interpreting the quaternion as rotation have undefined behavior if the quaternion is not normalized.
-  *
-  * \sa  class AngleAxis, class Transform
-  */
-
-namespace internal {
-template<typename _Scalar,int _Options>
-struct traits<Quaternion<_Scalar,_Options> >
-{
-  typedef Quaternion<_Scalar,_Options> PlainObject;
-  typedef _Scalar Scalar;
-  typedef Matrix<_Scalar,4,1,_Options> Coefficients;
-  enum{
-    Alignment = internal::traits<Coefficients>::Alignment,
-    Flags = LvalueBit
-  };
-};
-}
-
-template<typename _Scalar, int _Options>
-class Quaternion : public QuaternionBase<Quaternion<_Scalar,_Options> >
-{
-public:
-  typedef QuaternionBase<Quaternion<_Scalar,_Options> > Base;
-  enum { NeedsAlignment = internal::traits<Quaternion>::Alignment>0 };
-
-  typedef _Scalar Scalar;
-
-  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Quaternion)
-  using Base::operator*=;
-
-  typedef typename internal::traits<Quaternion>::Coefficients Coefficients;
-  typedef typename Base::AngleAxisType AngleAxisType;
-
-  /** Default constructor leaving the quaternion uninitialized. */
-  EIGEN_DEVICE_FUNC inline Quaternion() {}
-
-  /** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from
-    * its four coefficients \a w, \a x, \a y and \a z.
-    *
-    * \warning Note the order of the arguments: the real \a w coefficient first,
-    * while internally the coefficients are stored in the following order:
-    * [\c x, \c y, \c z, \c w]
-    */
-  EIGEN_DEVICE_FUNC inline Quaternion(const Scalar& w, const Scalar& x, const Scalar& y, const Scalar& z) : m_coeffs(x, y, z, w){}
-
-  /** Constructs and initialize a quaternion from the array data */
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Scalar* data) : m_coeffs(data) {}
-
-  /** Copy constructor */
-  template<class Derived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion(const QuaternionBase<Derived>& other) { this->Base::operator=(other); }
-
-  /** Constructs and initializes a quaternion from the angle-axis \a aa */
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const AngleAxisType& aa) { *this = aa; }
-
-  /** Constructs and initializes a quaternion from either:
-    *  - a rotation matrix expression,
-    *  - a 4D vector expression representing quaternion coefficients.
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const MatrixBase<Derived>& other) { *this = other; }
-
-  /** Explicit copy constructor with scalar conversion */
-  template<typename OtherScalar, int OtherOptions>
-  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Quaternion<OtherScalar, OtherOptions>& other)
-  { m_coeffs = other.coeffs().template cast<Scalar>(); }
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-  // We define a copy constructor, which means we don't get an implicit move constructor or assignment operator.
-  /** Default move constructor */
-  EIGEN_DEVICE_FUNC inline Quaternion(Quaternion&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
-    : m_coeffs(std::move(other.coeffs()))
-  {}
-
-  /** Default move assignment operator */
-  EIGEN_DEVICE_FUNC Quaternion& operator=(Quaternion&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
-  {
-    m_coeffs = std::move(other.coeffs());
-    return *this;
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC static Quaternion UnitRandom();
-
-  template<typename Derived1, typename Derived2>
-  EIGEN_DEVICE_FUNC static Quaternion FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
-
-  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs;}
-  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs;}
-
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(NeedsAlignment))
-  
-#ifdef EIGEN_QUATERNION_PLUGIN
-# include EIGEN_QUATERNION_PLUGIN
-#endif
-
-protected:
-  Coefficients m_coeffs;
-  
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    static EIGEN_STRONG_INLINE void _check_template_params()
-    {
-      EIGEN_STATIC_ASSERT( (_Options & DontAlign) == _Options,
-        INVALID_MATRIX_TEMPLATE_PARAMETERS)
-    }
-#endif
-};
-
-/** \ingroup Geometry_Module
-  * single precision quaternion type */
-typedef Quaternion<float> Quaternionf;
-/** \ingroup Geometry_Module
-  * double precision quaternion type */
-typedef Quaternion<double> Quaterniond;
-
-/***************************************************************************
-* Specialization of Map<Quaternion<Scalar>>
-***************************************************************************/
-
-namespace internal {
-  template<typename _Scalar, int _Options>
-  struct traits<Map<Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> >
-  {
-    typedef Map<Matrix<_Scalar,4,1>, _Options> Coefficients;
-  };
-}
-
-namespace internal {
-  template<typename _Scalar, int _Options>
-  struct traits<Map<const Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> >
-  {
-    typedef Map<const Matrix<_Scalar,4,1>, _Options> Coefficients;
-    typedef traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> > TraitsBase;
-    enum {
-      Flags = TraitsBase::Flags & ~LvalueBit
-    };
-  };
-}
-
-/** \ingroup Geometry_Module
-  * \brief Quaternion expression mapping a constant memory buffer
-  *
-  * \tparam _Scalar the type of the Quaternion coefficients
-  * \tparam _Options see class Map
-  *
-  * This is a specialization of class Map for Quaternion. This class allows to view
-  * a 4 scalar memory buffer as an Eigen's Quaternion object.
-  *
-  * \sa class Map, class Quaternion, class QuaternionBase
-  */
-template<typename _Scalar, int _Options>
-class Map<const Quaternion<_Scalar>, _Options >
-  : public QuaternionBase<Map<const Quaternion<_Scalar>, _Options> >
-{
-  public:
-    typedef QuaternionBase<Map<const Quaternion<_Scalar>, _Options> > Base;
-
-    typedef _Scalar Scalar;
-    typedef typename internal::traits<Map>::Coefficients Coefficients;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
-    using Base::operator*=;
-
-    /** Constructs a Mapped Quaternion object from the pointer \a coeffs
-      *
-      * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
-      * \code *coeffs == {x, y, z, w} \endcode
-      *
-      * If the template parameter _Options is set to #Aligned, then the pointer coeffs must be aligned. */
-    EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(const Scalar* coeffs) : m_coeffs(coeffs) {}
-
-    EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs;}
-
-  protected:
-    const Coefficients m_coeffs;
-};
-
-/** \ingroup Geometry_Module
-  * \brief Expression of a quaternion from a memory buffer
-  *
-  * \tparam _Scalar the type of the Quaternion coefficients
-  * \tparam _Options see class Map
-  *
-  * This is a specialization of class Map for Quaternion. This class allows to view
-  * a 4 scalar memory buffer as an Eigen's  Quaternion object.
-  *
-  * \sa class Map, class Quaternion, class QuaternionBase
-  */
-template<typename _Scalar, int _Options>
-class Map<Quaternion<_Scalar>, _Options >
-  : public QuaternionBase<Map<Quaternion<_Scalar>, _Options> >
-{
-  public:
-    typedef QuaternionBase<Map<Quaternion<_Scalar>, _Options> > Base;
-
-    typedef _Scalar Scalar;
-    typedef typename internal::traits<Map>::Coefficients Coefficients;
-    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
-    using Base::operator*=;
-
-    /** Constructs a Mapped Quaternion object from the pointer \a coeffs
-      *
-      * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
-      * \code *coeffs == {x, y, z, w} \endcode
-      *
-      * If the template parameter _Options is set to #Aligned, then the pointer coeffs must be aligned. */
-    EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(Scalar* coeffs) : m_coeffs(coeffs) {}
-
-    EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
-    EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
-
-  protected:
-    Coefficients m_coeffs;
-};
-
-/** \ingroup Geometry_Module
-  * Map an unaligned array of single precision scalars as a quaternion */
-typedef Map<Quaternion<float>, 0>         QuaternionMapf;
-/** \ingroup Geometry_Module
-  * Map an unaligned array of double precision scalars as a quaternion */
-typedef Map<Quaternion<double>, 0>        QuaternionMapd;
-/** \ingroup Geometry_Module
-  * Map a 16-byte aligned array of single precision scalars as a quaternion */
-typedef Map<Quaternion<float>, Aligned>   QuaternionMapAlignedf;
-/** \ingroup Geometry_Module
-  * Map a 16-byte aligned array of double precision scalars as a quaternion */
-typedef Map<Quaternion<double>, Aligned>  QuaternionMapAlignedd;
-
-/***************************************************************************
-* Implementation of QuaternionBase methods
-***************************************************************************/
-
-// Generic Quaternion * Quaternion product
-// This product can be specialized for a given architecture via the Arch template argument.
-namespace internal {
-template<int Arch, class Derived1, class Derived2, typename Scalar> struct quat_product
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived1>& a, const QuaternionBase<Derived2>& b){
-    return Quaternion<Scalar>
-    (
-      a.w() * b.w() - a.x() * b.x() - a.y() * b.y() - a.z() * b.z(),
-      a.w() * b.x() + a.x() * b.w() + a.y() * b.z() - a.z() * b.y(),
-      a.w() * b.y() + a.y() * b.w() + a.z() * b.x() - a.x() * b.z(),
-      a.w() * b.z() + a.z() * b.w() + a.x() * b.y() - a.y() * b.x()
-    );
-  }
-};
-}
-
-/** \returns the concatenation of two rotations as a quaternion-quaternion product */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<typename internal::traits<Derived>::Scalar>
-QuaternionBase<Derived>::operator* (const QuaternionBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value),
-   YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-  return internal::quat_product<Architecture::Target, Derived, OtherDerived,
-                         typename internal::traits<Derived>::Scalar>::run(*this, other);
-}
-
-/** \sa operator*(Quaternion) */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator*= (const QuaternionBase<OtherDerived>& other)
-{
-  derived() = derived() * other.derived();
-  return derived();
-}
-
-/** Rotation of a vector by a quaternion.
-  * \remarks If the quaternion is used to rotate several points (>1)
-  * then it is much more efficient to first convert it to a 3x3 Matrix.
-  * Comparison of the operation cost for n transformations:
-  *   - Quaternion2:    30n
-  *   - Via a Matrix3: 24 + 15n
-  */
-template <class Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename QuaternionBase<Derived>::Vector3
-QuaternionBase<Derived>::_transformVector(const Vector3& v) const
-{
-    // Note that this algorithm comes from the optimization by hand
-    // of the conversion to a Matrix followed by a Matrix/Vector product.
-    // It appears to be much faster than the common algorithm found
-    // in the literature (30 versus 39 flops). It also requires two
-    // Vector3 as temporaries.
-    Vector3 uv = this->vec().cross(v);
-    uv += uv;
-    return v + this->w() * uv + this->vec().cross(uv);
-}
-
-template<class Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& QuaternionBase<Derived>::operator=(const QuaternionBase<Derived>& other)
-{
-  coeffs() = other.coeffs();
-  return derived();
-}
-
-template<class Derived>
-template<class OtherDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const QuaternionBase<OtherDerived>& other)
-{
-  coeffs() = other.coeffs();
-  return derived();
-}
-
-/** Set \c *this from an angle-axis \a aa and returns a reference to \c *this
-  */
-template<class Derived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const AngleAxisType& aa)
-{
-  EIGEN_USING_STD(cos)
-  EIGEN_USING_STD(sin)
-  Scalar ha = Scalar(0.5)*aa.angle(); // Scalar(0.5) to suppress precision loss warnings
-  this->w() = cos(ha);
-  this->vec() = sin(ha) * aa.axis();
-  return derived();
-}
-
-/** Set \c *this from the expression \a xpr:
-  *   - if \a xpr is a 4x1 vector, then \a xpr is assumed to be a quaternion
-  *   - if \a xpr is a 3x3 matrix, then \a xpr is assumed to be rotation matrix
-  *     and \a xpr is converted to a quaternion
-  */
-
-template<class Derived>
-template<class MatrixDerived>
-EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::operator=(const MatrixBase<MatrixDerived>& xpr)
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename MatrixDerived::Scalar>::value),
-   YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-  internal::quaternionbase_assign_impl<MatrixDerived>::run(*this, xpr.derived());
-  return derived();
-}
-
-/** Convert the quaternion to a 3x3 rotation matrix. The quaternion is required to
-  * be normalized, otherwise the result is undefined.
-  */
-template<class Derived>
-EIGEN_DEVICE_FUNC inline typename QuaternionBase<Derived>::Matrix3
-QuaternionBase<Derived>::toRotationMatrix(void) const
-{
-  // NOTE if inlined, then gcc 4.2 and 4.4 get rid of the temporary (not gcc 4.3 !!)
-  // if not inlined then the cost of the return by value is huge ~ +35%,
-  // however, not inlining this function is an order of magnitude slower, so
-  // it has to be inlined, and so the return by value is not an issue
-  Matrix3 res;
-
-  const Scalar tx  = Scalar(2)*this->x();
-  const Scalar ty  = Scalar(2)*this->y();
-  const Scalar tz  = Scalar(2)*this->z();
-  const Scalar twx = tx*this->w();
-  const Scalar twy = ty*this->w();
-  const Scalar twz = tz*this->w();
-  const Scalar txx = tx*this->x();
-  const Scalar txy = ty*this->x();
-  const Scalar txz = tz*this->x();
-  const Scalar tyy = ty*this->y();
-  const Scalar tyz = tz*this->y();
-  const Scalar tzz = tz*this->z();
-
-  res.coeffRef(0,0) = Scalar(1)-(tyy+tzz);
-  res.coeffRef(0,1) = txy-twz;
-  res.coeffRef(0,2) = txz+twy;
-  res.coeffRef(1,0) = txy+twz;
-  res.coeffRef(1,1) = Scalar(1)-(txx+tzz);
-  res.coeffRef(1,2) = tyz-twx;
-  res.coeffRef(2,0) = txz-twy;
-  res.coeffRef(2,1) = tyz+twx;
-  res.coeffRef(2,2) = Scalar(1)-(txx+tyy);
-
-  return res;
-}
-
-/** Sets \c *this to be a quaternion representing a rotation between
-  * the two arbitrary vectors \a a and \a b. In other words, the built
-  * rotation represent a rotation sending the line of direction \a a
-  * to the line of direction \a b, both lines passing through the origin.
-  *
-  * \returns a reference to \c *this.
-  *
-  * Note that the two input vectors do \b not have to be normalized, and
-  * do not need to have the same norm.
-  */
-template<class Derived>
-template<typename Derived1, typename Derived2>
-EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b)
-{
-  EIGEN_USING_STD(sqrt)
-  Vector3 v0 = a.normalized();
-  Vector3 v1 = b.normalized();
-  Scalar c = v1.dot(v0);
-
-  // if dot == -1, vectors are nearly opposites
-  // => accurately compute the rotation axis by computing the
-  //    intersection of the two planes. This is done by solving:
-  //       x^T v0 = 0
-  //       x^T v1 = 0
-  //    under the constraint:
-  //       ||x|| = 1
-  //    which yields a singular value problem
-  if (c < Scalar(-1)+NumTraits<Scalar>::dummy_precision())
-  {
-    c = numext::maxi(c,Scalar(-1));
-    Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
-    JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
-    Vector3 axis = svd.matrixV().col(2);
-
-    Scalar w2 = (Scalar(1)+c)*Scalar(0.5);
-    this->w() = sqrt(w2);
-    this->vec() = axis * sqrt(Scalar(1) - w2);
-    return derived();
-  }
-  Vector3 axis = v0.cross(v1);
-  Scalar s = sqrt((Scalar(1)+c)*Scalar(2));
-  Scalar invs = Scalar(1)/s;
-  this->vec() = axis * invs;
-  this->w() = s * Scalar(0.5);
-
-  return derived();
-}
-
-/** \returns a random unit quaternion following a uniform distribution law on SO(3)
-  *
-  * \note The implementation is based on http://planning.cs.uiuc.edu/node198.html
-  */
-template<typename Scalar, int Options>
-EIGEN_DEVICE_FUNC Quaternion<Scalar,Options> Quaternion<Scalar,Options>::UnitRandom()
-{
-  EIGEN_USING_STD(sqrt)
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  const Scalar u1 = internal::random<Scalar>(0, 1),
-               u2 = internal::random<Scalar>(0, 2*EIGEN_PI),
-               u3 = internal::random<Scalar>(0, 2*EIGEN_PI);
-  const Scalar a = sqrt(Scalar(1) - u1),
-               b = sqrt(u1);
-  return Quaternion (a * sin(u2), a * cos(u2), b * sin(u3), b * cos(u3));
-}
-
-
-/** Returns a quaternion representing a rotation between
-  * the two arbitrary vectors \a a and \a b. In other words, the built
-  * rotation represent a rotation sending the line of direction \a a
-  * to the line of direction \a b, both lines passing through the origin.
-  *
-  * \returns resulting quaternion
-  *
-  * Note that the two input vectors do \b not have to be normalized, and
-  * do not need to have the same norm.
-  */
-template<typename Scalar, int Options>
-template<typename Derived1, typename Derived2>
-EIGEN_DEVICE_FUNC Quaternion<Scalar,Options> Quaternion<Scalar,Options>::FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b)
-{
-    Quaternion quat;
-    quat.setFromTwoVectors(a, b);
-    return quat;
-}
-
-
-/** \returns the multiplicative inverse of \c *this
-  * Note that in most cases, i.e., if you simply want the opposite rotation,
-  * and/or the quaternion is normalized, then it is enough to use the conjugate.
-  *
-  * \sa QuaternionBase::conjugate()
-  */
-template <class Derived>
-EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar> QuaternionBase<Derived>::inverse() const
-{
-  // FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite()  ??
-  Scalar n2 = this->squaredNorm();
-  if (n2 > Scalar(0))
-    return Quaternion<Scalar>(conjugate().coeffs() / n2);
-  else
-  {
-    // return an invalid result to flag the error
-    return Quaternion<Scalar>(Coefficients::Zero());
-  }
-}
-
-// Generic conjugate of a Quaternion
-namespace internal {
-template<int Arch, class Derived, typename Scalar> struct quat_conj
-{
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived>& q){
-    return Quaternion<Scalar>(q.w(),-q.x(),-q.y(),-q.z());
-  }
-};
-}
-                         
-/** \returns the conjugate of the \c *this which is equal to the multiplicative inverse
-  * if the quaternion is normalized.
-  * The conjugate of a quaternion represents the opposite rotation.
-  *
-  * \sa Quaternion2::inverse()
-  */
-template <class Derived>
-EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar>
-QuaternionBase<Derived>::conjugate() const
-{
-  return internal::quat_conj<Architecture::Target, Derived,
-                         typename internal::traits<Derived>::Scalar>::run(*this);
-                         
-}
-
-/** \returns the angle (in radian) between two rotations
-  * \sa dot()
-  */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar
-QuaternionBase<Derived>::angularDistance(const QuaternionBase<OtherDerived>& other) const
-{
-  EIGEN_USING_STD(atan2)
-  Quaternion<Scalar> d = (*this) * other.conjugate();
-  return Scalar(2) * atan2( d.vec().norm(), numext::abs(d.w()) );
-}
-
- 
-    
-/** \returns the spherical linear interpolation between the two quaternions
-  * \c *this and \a other at the parameter \a t in [0;1].
-  * 
-  * This represents an interpolation for a constant motion between \c *this and \a other,
-  * see also http://en.wikipedia.org/wiki/Slerp.
-  */
-template <class Derived>
-template <class OtherDerived>
-EIGEN_DEVICE_FUNC Quaternion<typename internal::traits<Derived>::Scalar>
-QuaternionBase<Derived>::slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const
-{
-  EIGEN_USING_STD(acos)
-  EIGEN_USING_STD(sin)
-  const Scalar one = Scalar(1) - NumTraits<Scalar>::epsilon();
-  Scalar d = this->dot(other);
-  Scalar absD = numext::abs(d);
-
-  Scalar scale0;
-  Scalar scale1;
-
-  if(absD>=one)
-  {
-    scale0 = Scalar(1) - t;
-    scale1 = t;
-  }
-  else
-  {
-    // theta is the angle between the 2 quaternions
-    Scalar theta = acos(absD);
-    Scalar sinTheta = sin(theta);
-
-    scale0 = sin( ( Scalar(1) - t ) * theta) / sinTheta;
-    scale1 = sin( ( t * theta) ) / sinTheta;
-  }
-  if(d<Scalar(0)) scale1 = -scale1;
-
-  return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs());
-}
-
-namespace internal {
-
-// set from a rotation matrix
-template<typename Other>
-struct quaternionbase_assign_impl<Other,3,3>
-{
-  typedef typename Other::Scalar Scalar;
-  template<class Derived> EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& a_mat)
-  {
-    const typename internal::nested_eval<Other,2>::type mat(a_mat);
-    EIGEN_USING_STD(sqrt)
-    // This algorithm comes from  "Quaternion Calculus and Fast Animation",
-    // Ken Shoemake, 1987 SIGGRAPH course notes
-    Scalar t = mat.trace();
-    if (t > Scalar(0))
-    {
-      t = sqrt(t + Scalar(1.0));
-      q.w() = Scalar(0.5)*t;
-      t = Scalar(0.5)/t;
-      q.x() = (mat.coeff(2,1) - mat.coeff(1,2)) * t;
-      q.y() = (mat.coeff(0,2) - mat.coeff(2,0)) * t;
-      q.z() = (mat.coeff(1,0) - mat.coeff(0,1)) * t;
-    }
-    else
-    {
-      Index i = 0;
-      if (mat.coeff(1,1) > mat.coeff(0,0))
-        i = 1;
-      if (mat.coeff(2,2) > mat.coeff(i,i))
-        i = 2;
-      Index j = (i+1)%3;
-      Index k = (j+1)%3;
-
-      t = sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + Scalar(1.0));
-      q.coeffs().coeffRef(i) = Scalar(0.5) * t;
-      t = Scalar(0.5)/t;
-      q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t;
-      q.coeffs().coeffRef(j) = (mat.coeff(j,i)+mat.coeff(i,j))*t;
-      q.coeffs().coeffRef(k) = (mat.coeff(k,i)+mat.coeff(i,k))*t;
-    }
-  }
-};
-
-// set from a vector of coefficients assumed to be a quaternion
-template<typename Other>
-struct quaternionbase_assign_impl<Other,4,1>
-{
-  typedef typename Other::Scalar Scalar;
-  template<class Derived> EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& vec)
-  {
-    q.coeffs() = vec;
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_QUATERNION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Rotation2D.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Rotation2D.h
deleted file mode 100644
index d0bd575..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Rotation2D.h
+++ /dev/null
@@ -1,199 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ROTATION2D_H
-#define EIGEN_ROTATION2D_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Rotation2D
-  *
-  * \brief Represents a rotation/orientation in a 2 dimensional space.
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  *
-  * This class is equivalent to a single scalar representing a counter clock wise rotation
-  * as a single angle in radian. It provides some additional features such as the automatic
-  * conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar
-  * interface to Quaternion in order to facilitate the writing of generic algorithms
-  * dealing with rotations.
-  *
-  * \sa class Quaternion, class Transform
-  */
-
-namespace internal {
-
-template<typename _Scalar> struct traits<Rotation2D<_Scalar> >
-{
-  typedef _Scalar Scalar;
-};
-} // end namespace internal
-
-template<typename _Scalar>
-class Rotation2D : public RotationBase<Rotation2D<_Scalar>,2>
-{
-  typedef RotationBase<Rotation2D<_Scalar>,2> Base;
-
-public:
-
-  using Base::operator*;
-
-  enum { Dim = 2 };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  typedef Matrix<Scalar,2,1> Vector2;
-  typedef Matrix<Scalar,2,2> Matrix2;
-
-protected:
-
-  Scalar m_angle;
-
-public:
-
-  /** Construct a 2D counter clock wise rotation from the angle \a a in radian. */
-  EIGEN_DEVICE_FUNC explicit inline Rotation2D(const Scalar& a) : m_angle(a) {}
-  
-  /** Default constructor wihtout initialization. The represented rotation is undefined. */
-  EIGEN_DEVICE_FUNC Rotation2D() {}
-
-  /** Construct a 2D rotation from a 2x2 rotation matrix \a mat.
-    *
-    * \sa fromRotationMatrix()
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC explicit Rotation2D(const MatrixBase<Derived>& m)
-  {
-    fromRotationMatrix(m.derived());
-  }
-
-  /** \returns the rotation angle */
-  EIGEN_DEVICE_FUNC inline Scalar angle() const { return m_angle; }
-
-  /** \returns a read-write reference to the rotation angle */
-  EIGEN_DEVICE_FUNC inline Scalar& angle() { return m_angle; }
-  
-  /** \returns the rotation angle in [0,2pi] */
-  EIGEN_DEVICE_FUNC inline Scalar smallestPositiveAngle() const {
-    Scalar tmp = numext::fmod(m_angle,Scalar(2*EIGEN_PI));
-    return tmp<Scalar(0) ? tmp + Scalar(2*EIGEN_PI) : tmp;
-  }
-  
-  /** \returns the rotation angle in [-pi,pi] */
-  EIGEN_DEVICE_FUNC inline Scalar smallestAngle() const {
-    Scalar tmp = numext::fmod(m_angle,Scalar(2*EIGEN_PI));
-    if(tmp>Scalar(EIGEN_PI))       tmp -= Scalar(2*EIGEN_PI);
-    else if(tmp<-Scalar(EIGEN_PI)) tmp += Scalar(2*EIGEN_PI);
-    return tmp;
-  }
-
-  /** \returns the inverse rotation */
-  EIGEN_DEVICE_FUNC inline Rotation2D inverse() const { return Rotation2D(-m_angle); }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline Rotation2D operator*(const Rotation2D& other) const
-  { return Rotation2D(m_angle + other.m_angle); }
-
-  /** Concatenates two rotations */
-  EIGEN_DEVICE_FUNC inline Rotation2D& operator*=(const Rotation2D& other)
-  { m_angle += other.m_angle; return *this; }
-
-  /** Applies the rotation to a 2D vector */
-  EIGEN_DEVICE_FUNC Vector2 operator* (const Vector2& vec) const
-  { return toRotationMatrix() * vec; }
-  
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m);
-  EIGEN_DEVICE_FUNC Matrix2 toRotationMatrix() const;
-
-  /** Set \c *this from a 2x2 rotation matrix \a mat.
-    * In other words, this function extract the rotation angle from the rotation matrix.
-    *
-    * This method is an alias for fromRotationMatrix()
-    *
-    * \sa fromRotationMatrix()
-    */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC Rotation2D& operator=(const  MatrixBase<Derived>& m)
-  { return fromRotationMatrix(m.derived()); }
-
-  /** \returns the spherical interpolation between \c *this and \a other using
-    * parameter \a t. It is in fact equivalent to a linear interpolation.
-    */
-  EIGEN_DEVICE_FUNC inline Rotation2D slerp(const Scalar& t, const Rotation2D& other) const
-  {
-    Scalar dist = Rotation2D(other.m_angle-m_angle).smallestAngle();
-    return Rotation2D(m_angle + dist*t);
-  }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type cast() const
-  { return typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other)
-  {
-    m_angle = Scalar(other.angle());
-  }
-
-  EIGEN_DEVICE_FUNC static inline Rotation2D Identity() { return Rotation2D(0); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const Rotation2D& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return internal::isApprox(m_angle,other.m_angle, prec); }
-  
-};
-
-/** \ingroup Geometry_Module
-  * single precision 2D rotation type */
-typedef Rotation2D<float> Rotation2Df;
-/** \ingroup Geometry_Module
-  * double precision 2D rotation type */
-typedef Rotation2D<double> Rotation2Dd;
-
-/** Set \c *this from a 2x2 rotation matrix \a mat.
-  * In other words, this function extract the rotation angle
-  * from the rotation matrix.
-  */
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
-{
-  EIGEN_USING_STD(atan2)
-  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
-  m_angle = atan2(mat.coeff(1,0), mat.coeff(0,0));
-  return *this;
-}
-
-/** Constructs and \returns an equivalent 2x2 rotation matrix.
-  */
-template<typename Scalar>
-typename Rotation2D<Scalar>::Matrix2
-EIGEN_DEVICE_FUNC Rotation2D<Scalar>::toRotationMatrix(void) const
-{
-  EIGEN_USING_STD(sin)
-  EIGEN_USING_STD(cos)
-  Scalar sinA = sin(m_angle);
-  Scalar cosA = cos(m_angle);
-  return (Matrix2() << cosA, -sinA, sinA, cosA).finished();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_ROTATION2D_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/RotationBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/RotationBase.h
deleted file mode 100644
index f0ee0bd..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/RotationBase.h
+++ /dev/null
@@ -1,206 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ROTATIONBASE_H
-#define EIGEN_ROTATIONBASE_H
-
-namespace Eigen { 
-
-// forward declaration
-namespace internal {
-template<typename RotationDerived, typename MatrixType, bool IsVector=MatrixType::IsVectorAtCompileTime>
-struct rotation_base_generic_product_selector;
-}
-
-/** \class RotationBase
-  *
-  * \brief Common base class for compact rotation representations
-  *
-  * \tparam Derived is the derived type, i.e., a rotation type
-  * \tparam _Dim the dimension of the space
-  */
-template<typename Derived, int _Dim>
-class RotationBase
-{
-  public:
-    enum { Dim = _Dim };
-    /** the scalar type of the coefficients */
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-
-    /** corresponding linear transformation matrix type */
-    typedef Matrix<Scalar,Dim,Dim> RotationMatrixType;
-    typedef Matrix<Scalar,Dim,1> VectorType;
-
-  public:
-    EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
-
-    /** \returns an equivalent rotation matrix */
-    EIGEN_DEVICE_FUNC inline RotationMatrixType toRotationMatrix() const { return derived().toRotationMatrix(); }
-
-    /** \returns an equivalent rotation matrix 
-      * This function is added to be conform with the Transform class' naming scheme.
-      */
-    EIGEN_DEVICE_FUNC inline RotationMatrixType matrix() const { return derived().toRotationMatrix(); }
-
-    /** \returns the inverse rotation */
-    EIGEN_DEVICE_FUNC inline Derived inverse() const { return derived().inverse(); }
-
-    /** \returns the concatenation of the rotation \c *this with a translation \a t */
-    EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Isometry> operator*(const Translation<Scalar,Dim>& t) const
-    { return Transform<Scalar,Dim,Isometry>(*this) * t; }
-
-    /** \returns the concatenation of the rotation \c *this with a uniform scaling \a s */
-    EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const UniformScaling<Scalar>& s) const
-    { return toRotationMatrix() * s.factor(); }
-
-    /** \returns the concatenation of the rotation \c *this with a generic expression \a e
-      * \a e can be:
-      *  - a DimxDim linear transformation matrix
-      *  - a DimxDim diagonal matrix (axis aligned scaling)
-      *  - a vector of size Dim
-      */
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::rotation_base_generic_product_selector<Derived,OtherDerived,OtherDerived::IsVectorAtCompileTime>::ReturnType
-    operator*(const EigenBase<OtherDerived>& e) const
-    { return internal::rotation_base_generic_product_selector<Derived,OtherDerived>::run(derived(), e.derived()); }
-
-    /** \returns the concatenation of a linear transformation \a l with the rotation \a r */
-    template<typename OtherDerived> friend
-    EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const EigenBase<OtherDerived>& l, const Derived& r)
-    { return l.derived() * r.toRotationMatrix(); }
-
-    /** \returns the concatenation of a scaling \a l with the rotation \a r */
-    EIGEN_DEVICE_FUNC friend inline Transform<Scalar,Dim,Affine> operator*(const DiagonalMatrix<Scalar,Dim>& l, const Derived& r)
-    { 
-      Transform<Scalar,Dim,Affine> res(r);
-      res.linear().applyOnTheLeft(l);
-      return res;
-    }
-
-    /** \returns the concatenation of the rotation \c *this with a transformation \a t */
-    template<int Mode, int Options>
-    EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode> operator*(const Transform<Scalar,Dim,Mode,Options>& t) const
-    { return toRotationMatrix() * t; }
-
-    template<typename OtherVectorType>
-    EIGEN_DEVICE_FUNC inline VectorType _transformVector(const OtherVectorType& v) const
-    { return toRotationMatrix() * v; }
-};
-
-namespace internal {
-
-// implementation of the generic product rotation * matrix
-template<typename RotationDerived, typename MatrixType>
-struct rotation_base_generic_product_selector<RotationDerived,MatrixType,false>
-{
-  enum { Dim = RotationDerived::Dim };
-  typedef Matrix<typename RotationDerived::Scalar,Dim,Dim> ReturnType;
-  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r, const MatrixType& m)
-  { return r.toRotationMatrix() * m; }
-};
-
-template<typename RotationDerived, typename Scalar, int Dim, int MaxDim>
-struct rotation_base_generic_product_selector< RotationDerived, DiagonalMatrix<Scalar,Dim,MaxDim>, false >
-{
-  typedef Transform<Scalar,Dim,Affine> ReturnType;
-  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r, const DiagonalMatrix<Scalar,Dim,MaxDim>& m)
-  {
-    ReturnType res(r);
-    res.linear() *= m;
-    return res;
-  }
-};
-
-template<typename RotationDerived,typename OtherVectorType>
-struct rotation_base_generic_product_selector<RotationDerived,OtherVectorType,true>
-{
-  enum { Dim = RotationDerived::Dim };
-  typedef Matrix<typename RotationDerived::Scalar,Dim,1> ReturnType;
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE ReturnType run(const RotationDerived& r, const OtherVectorType& v)
-  {
-    return r._transformVector(v);
-  }
-};
-
-} // end namespace internal
-
-/** \geometry_module
-  *
-  * \brief Constructs a Dim x Dim rotation matrix from the rotation \a r
-  */
-template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
-::Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
-{
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
-  *this = r.toRotationMatrix();
-}
-
-/** \geometry_module
-  *
-  * \brief Set a Dim x Dim rotation matrix from the rotation \a r
-  */
-template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>&
-Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>
-::operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
-{
-  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
-  return *this = r.toRotationMatrix();
-}
-
-namespace internal {
-
-/** \internal
-  *
-  * Helper function to return an arbitrary rotation object to a rotation matrix.
-  *
-  * \tparam Scalar the numeric type of the matrix coefficients
-  * \tparam Dim the dimension of the current space
-  *
-  * It returns a Dim x Dim fixed size matrix.
-  *
-  * Default specializations are provided for:
-  *   - any scalar type (2D),
-  *   - any matrix expression,
-  *   - any type based on RotationBase (e.g., Quaternion, AngleAxis, Rotation2D)
-  *
-  * Currently toRotationMatrix is only used by Transform.
-  *
-  * \sa class Transform, class Rotation2D, class Quaternion, class AngleAxis
-  */
-template<typename Scalar, int Dim>
-EIGEN_DEVICE_FUNC static inline Matrix<Scalar,2,2> toRotationMatrix(const Scalar& s)
-{
-  EIGEN_STATIC_ASSERT(Dim==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
-  return Rotation2D<Scalar>(s).toRotationMatrix();
-}
-
-template<typename Scalar, int Dim, typename OtherDerived>
-EIGEN_DEVICE_FUNC static inline Matrix<Scalar,Dim,Dim> toRotationMatrix(const RotationBase<OtherDerived,Dim>& r)
-{
-  return r.toRotationMatrix();
-}
-
-template<typename Scalar, int Dim, typename OtherDerived>
-EIGEN_DEVICE_FUNC static inline const MatrixBase<OtherDerived>& toRotationMatrix(const MatrixBase<OtherDerived>& mat)
-{
-  EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime==Dim && OtherDerived::ColsAtCompileTime==Dim,
-    YOU_MADE_A_PROGRAMMING_MISTAKE)
-  return mat;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_ROTATIONBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Scaling.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Scaling.h
deleted file mode 100644
index d352f1f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Scaling.h
+++ /dev/null
@@ -1,188 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SCALING_H
-#define EIGEN_SCALING_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class UniformScaling
-  *
-  * \brief Represents a generic uniform scaling transformation
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients.
-  *
-  * This class represent a uniform scaling transformation. It is the return
-  * type of Scaling(Scalar), and most of the time this is the only way it
-  * is used. In particular, this class is not aimed to be used to store a scaling transformation,
-  * but rather to make easier the constructions and updates of Transform objects.
-  *
-  * To represent an axis aligned scaling, use the DiagonalMatrix class.
-  *
-  * \sa Scaling(), class DiagonalMatrix, MatrixBase::asDiagonal(), class Translation, class Transform
-  */
-
-namespace internal
-{
-  // This helper helps nvcc+MSVC to properly parse this file.
-  // See bug 1412.
-  template <typename Scalar, int Dim, int Mode>
-  struct uniformscaling_times_affine_returntype
-  {
-    enum
-    {
-      NewMode = int(Mode) == int(Isometry) ? Affine : Mode
-    };
-    typedef Transform <Scalar, Dim, NewMode> type;
-  };
-}
-
-template<typename _Scalar>
-class UniformScaling
-{
-public:
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-
-protected:
-
-  Scalar m_factor;
-
-public:
-
-  /** Default constructor without initialization. */
-  UniformScaling() {}
-  /** Constructs and initialize a uniform scaling transformation */
-  explicit inline UniformScaling(const Scalar& s) : m_factor(s) {}
-
-  inline const Scalar& factor() const { return m_factor; }
-  inline Scalar& factor() { return m_factor; }
-
-  /** Concatenates two uniform scaling */
-  inline UniformScaling operator* (const UniformScaling& other) const
-  { return UniformScaling(m_factor * other.factor()); }
-
-  /** Concatenates a uniform scaling and a translation */
-  template<int Dim>
-  inline Transform<Scalar,Dim,Affine> operator* (const Translation<Scalar,Dim>& t) const;
-
-  /** Concatenates a uniform scaling and an affine transformation */
-  template<int Dim, int Mode, int Options>
-  inline typename
-	internal::uniformscaling_times_affine_returntype<Scalar,Dim,Mode>::type
-	operator* (const Transform<Scalar, Dim, Mode, Options>& t) const
-  {
-    typename internal::uniformscaling_times_affine_returntype<Scalar,Dim,Mode>::type res = t;
-    res.prescale(factor());
-    return res;
-  }
-
-  /** Concatenates a uniform scaling and a linear transformation matrix */
-  // TODO returns an expression
-  template<typename Derived>
-  inline typename Eigen::internal::plain_matrix_type<Derived>::type operator* (const MatrixBase<Derived>& other) const
-  { return other * m_factor; }
-
-  template<typename Derived,int Dim>
-  inline Matrix<Scalar,Dim,Dim> operator*(const RotationBase<Derived,Dim>& r) const
-  { return r.toRotationMatrix() * m_factor; }
-
-  /** \returns the inverse scaling */
-  inline UniformScaling inverse() const
-  { return UniformScaling(Scalar(1)/m_factor); }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  inline UniformScaling<NewScalarType> cast() const
-  { return UniformScaling<NewScalarType>(NewScalarType(m_factor)); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  inline explicit UniformScaling(const UniformScaling<OtherScalarType>& other)
-  { m_factor = Scalar(other.factor()); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  bool isApprox(const UniformScaling& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return internal::isApprox(m_factor, other.factor(), prec); }
-
-};
-
-/** \addtogroup Geometry_Module */
-//@{
-
-/** Concatenates a linear transformation matrix and a uniform scaling
-  * \relates UniformScaling
-  */
-// NOTE this operator is defined in MatrixBase and not as a friend function
-// of UniformScaling to fix an internal crash of Intel's ICC
-template<typename Derived,typename Scalar>
-EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,Scalar,product)
-operator*(const MatrixBase<Derived>& matrix, const UniformScaling<Scalar>& s)
-{ return matrix.derived() * s.factor(); }
-
-/** Constructs a uniform scaling from scale factor \a s */
-inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
-/** Constructs a uniform scaling from scale factor \a s */
-inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
-/** Constructs a uniform scaling from scale factor \a s */
-template<typename RealScalar>
-inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s)
-{ return UniformScaling<std::complex<RealScalar> >(s); }
-
-/** Constructs a 2D axis aligned scaling */
-template<typename Scalar>
-inline DiagonalMatrix<Scalar,2> Scaling(const Scalar& sx, const Scalar& sy)
-{ return DiagonalMatrix<Scalar,2>(sx, sy); }
-/** Constructs a 3D axis aligned scaling */
-template<typename Scalar>
-inline DiagonalMatrix<Scalar,3> Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz)
-{ return DiagonalMatrix<Scalar,3>(sx, sy, sz); }
-
-/** Constructs an axis aligned scaling expression from vector expression \a coeffs
-  * This is an alias for coeffs.asDiagonal()
-  */
-template<typename Derived>
-inline const DiagonalWrapper<const Derived> Scaling(const MatrixBase<Derived>& coeffs)
-{ return coeffs.asDiagonal(); }
-
-/** \deprecated */
-typedef DiagonalMatrix<float, 2> AlignedScaling2f;
-/** \deprecated */
-typedef DiagonalMatrix<double,2> AlignedScaling2d;
-/** \deprecated */
-typedef DiagonalMatrix<float, 3> AlignedScaling3f;
-/** \deprecated */
-typedef DiagonalMatrix<double,3> AlignedScaling3d;
-//@}
-
-template<typename Scalar>
-template<int Dim>
-inline Transform<Scalar,Dim,Affine>
-UniformScaling<Scalar>::operator* (const Translation<Scalar,Dim>& t) const
-{
-  Transform<Scalar,Dim,Affine> res;
-  res.matrix().setZero();
-  res.linear().diagonal().fill(factor());
-  res.translation() = factor() * t.vector();
-  res(Dim,Dim) = Scalar(1);
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SCALING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Transform.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Transform.h
deleted file mode 100644
index 52b8c2a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Transform.h
+++ /dev/null
@@ -1,1563 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSFORM_H
-#define EIGEN_TRANSFORM_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Transform>
-struct transform_traits
-{
-  enum
-  {
-    Dim = Transform::Dim,
-    HDim = Transform::HDim,
-    Mode = Transform::Mode,
-    IsProjective = (int(Mode)==int(Projective))
-  };
-};
-
-template< typename TransformType,
-          typename MatrixType,
-          int Case = transform_traits<TransformType>::IsProjective ? 0
-                   : int(MatrixType::RowsAtCompileTime) == int(transform_traits<TransformType>::HDim) ? 1
-                   : 2,
-          int RhsCols = MatrixType::ColsAtCompileTime>
-struct transform_right_product_impl;
-
-template< typename Other,
-          int Mode,
-          int Options,
-          int Dim,
-          int HDim,
-          int OtherRows=Other::RowsAtCompileTime,
-          int OtherCols=Other::ColsAtCompileTime>
-struct transform_left_product_impl;
-
-template< typename Lhs,
-          typename Rhs,
-          bool AnyProjective =
-            transform_traits<Lhs>::IsProjective ||
-            transform_traits<Rhs>::IsProjective>
-struct transform_transform_product_impl;
-
-template< typename Other,
-          int Mode,
-          int Options,
-          int Dim,
-          int HDim,
-          int OtherRows=Other::RowsAtCompileTime,
-          int OtherCols=Other::ColsAtCompileTime>
-struct transform_construct_from_matrix;
-
-template<typename TransformType> struct transform_take_affine_part;
-
-template<typename _Scalar, int _Dim, int _Mode, int _Options>
-struct traits<Transform<_Scalar,_Dim,_Mode,_Options> >
-{
-  typedef _Scalar Scalar;
-  typedef Eigen::Index StorageIndex;
-  typedef Dense StorageKind;
-  enum {
-    Dim1 = _Dim==Dynamic ? _Dim : _Dim + 1,
-    RowsAtCompileTime = _Mode==Projective ? Dim1 : _Dim,
-    ColsAtCompileTime = Dim1,
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    Flags = 0
-  };
-};
-
-template<int Mode> struct transform_make_affine;
-
-} // end namespace internal
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Transform
-  *
-  * \brief Represents an homogeneous transformation in a N dimensional space
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients
-  * \tparam _Dim the dimension of the space
-  * \tparam _Mode the type of the transformation. Can be:
-  *              - #Affine: the transformation is stored as a (Dim+1)^2 matrix,
-  *                         where the last row is assumed to be [0 ... 0 1].
-  *              - #AffineCompact: the transformation is stored as a (Dim)x(Dim+1) matrix.
-  *              - #Projective: the transformation is stored as a (Dim+1)^2 matrix
-  *                             without any assumption.
-  *              - #Isometry: same as #Affine with the additional assumption that
-  *                           the linear part represents a rotation. This assumption is exploited
-  *                           to speed up some functions such as inverse() and rotation().
-  * \tparam _Options has the same meaning as in class Matrix. It allows to specify DontAlign and/or RowMajor.
-  *                  These Options are passed directly to the underlying matrix type.
-  *
-  * The homography is internally represented and stored by a matrix which
-  * is available through the matrix() method. To understand the behavior of
-  * this class you have to think a Transform object as its internal
-  * matrix representation. The chosen convention is right multiply:
-  *
-  * \code v' = T * v \endcode
-  *
-  * Therefore, an affine transformation matrix M is shaped like this:
-  *
-  * \f$ \left( \begin{array}{cc}
-  * linear & translation\\
-  * 0 ... 0 & 1
-  * \end{array} \right) \f$
-  *
-  * Note that for a projective transformation the last row can be anything,
-  * and then the interpretation of different parts might be slightly different.
-  *
-  * However, unlike a plain matrix, the Transform class provides many features
-  * simplifying both its assembly and usage. In particular, it can be composed
-  * with any other transformations (Transform,Translation,RotationBase,DiagonalMatrix)
-  * and can be directly used to transform implicit homogeneous vectors. All these
-  * operations are handled via the operator*. For the composition of transformations,
-  * its principle consists to first convert the right/left hand sides of the product
-  * to a compatible (Dim+1)^2 matrix and then perform a pure matrix product.
-  * Of course, internally, operator* tries to perform the minimal number of operations
-  * according to the nature of each terms. Likewise, when applying the transform
-  * to points, the latters are automatically promoted to homogeneous vectors
-  * before doing the matrix product. The conventions to homogeneous representations
-  * are performed as follow:
-  *
-  * \b Translation t (Dim)x(1):
-  * \f$ \left( \begin{array}{cc}
-  * I & t \\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *
-  * \b Rotation R (Dim)x(Dim):
-  * \f$ \left( \begin{array}{cc}
-  * R & 0\\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *<!--
-  * \b Linear \b Matrix L (Dim)x(Dim):
-  * \f$ \left( \begin{array}{cc}
-  * L & 0\\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *
-  * \b Affine \b Matrix A (Dim)x(Dim+1):
-  * \f$ \left( \begin{array}{c}
-  * A\\
-  * 0\,...\,0\,1
-  * \end{array} \right) \f$
-  *-->
-  * \b Scaling \b DiagonalMatrix S (Dim)x(Dim):
-  * \f$ \left( \begin{array}{cc}
-  * S & 0\\
-  * 0\,...\,0 & 1
-  * \end{array} \right) \f$
-  *
-  * \b Column \b point v (Dim)x(1):
-  * \f$ \left( \begin{array}{c}
-  * v\\
-  * 1
-  * \end{array} \right) \f$
-  *
-  * \b Set \b of \b column \b points V1...Vn (Dim)x(n):
-  * \f$ \left( \begin{array}{ccc}
-  * v_1 & ... & v_n\\
-  * 1 & ... & 1
-  * \end{array} \right) \f$
-  *
-  * The concatenation of a Transform object with any kind of other transformation
-  * always returns a Transform object.
-  *
-  * A little exception to the "as pure matrix product" rule is the case of the
-  * transformation of non homogeneous vectors by an affine transformation. In
-  * that case the last matrix row can be ignored, and the product returns non
-  * homogeneous vectors.
-  *
-  * Since, for instance, a Dim x Dim matrix is interpreted as a linear transformation,
-  * it is not possible to directly transform Dim vectors stored in a Dim x Dim matrix.
-  * The solution is either to use a Dim x Dynamic matrix or explicitly request a
-  * vector transformation by making the vector homogeneous:
-  * \code
-  * m' = T * m.colwise().homogeneous();
-  * \endcode
-  * Note that there is zero overhead.
-  *
-  * Conversion methods from/to Qt's QMatrix and QTransform are available if the
-  * preprocessor token EIGEN_QT_SUPPORT is defined.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TRANSFORM_PLUGIN.
-  *
-  * \sa class Matrix, class Quaternion
-  */
-template<typename _Scalar, int _Dim, int _Mode, int _Options>
-class Transform
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim==Dynamic ? Dynamic : (_Dim+1)*(_Dim+1))
-  enum {
-    Mode = _Mode,
-    Options = _Options,
-    Dim = _Dim,     ///< space dimension in which the transformation holds
-    HDim = _Dim+1,  ///< size of a respective homogeneous vector
-    Rows = int(Mode)==(AffineCompact) ? Dim : HDim
-  };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  typedef Eigen::Index StorageIndex;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  /** type of the matrix used to represent the transformation */
-  typedef typename internal::make_proper_matrix_type<Scalar,Rows,HDim,Options>::type MatrixType;
-  /** constified MatrixType */
-  typedef const MatrixType ConstMatrixType;
-  /** type of the matrix used to represent the linear part of the transformation */
-  typedef Matrix<Scalar,Dim,Dim,Options> LinearMatrixType;
-  /** type of read/write reference to the linear part of the transformation */
-  typedef Block<MatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (int(Options)&RowMajor)==0> LinearPart;
-  /** type of read reference to the linear part of the transformation */
-  typedef const Block<ConstMatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (int(Options)&RowMajor)==0> ConstLinearPart;
-  /** type of read/write reference to the affine part of the transformation */
-  typedef typename internal::conditional<int(Mode)==int(AffineCompact),
-                              MatrixType&,
-                              Block<MatrixType,Dim,HDim> >::type AffinePart;
-  /** type of read reference to the affine part of the transformation */
-  typedef typename internal::conditional<int(Mode)==int(AffineCompact),
-                              const MatrixType&,
-                              const Block<const MatrixType,Dim,HDim> >::type ConstAffinePart;
-  /** type of a vector */
-  typedef Matrix<Scalar,Dim,1> VectorType;
-  /** type of a read/write reference to the translation part of the rotation */
-  typedef Block<MatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> TranslationPart;
-  /** type of a read reference to the translation part of the rotation */
-  typedef const Block<ConstMatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> ConstTranslationPart;
-  /** corresponding translation type */
-  typedef Translation<Scalar,Dim> TranslationType;
-
-  // this intermediate enum is needed to avoid an ICE with gcc 3.4 and 4.0
-  enum { TransformTimeDiagonalMode = ((Mode==int(Isometry))?Affine:int(Mode)) };
-  /** The return type of the product between a diagonal matrix and a transform */
-  typedef Transform<Scalar,Dim,TransformTimeDiagonalMode> TransformTimeDiagonalReturnType;
-
-protected:
-
-  MatrixType m_matrix;
-
-public:
-
-  /** Default constructor without initialization of the meaningful coefficients.
-    * If Mode==Affine or Mode==Isometry, then the last row is set to [0 ... 0 1] */
-  EIGEN_DEVICE_FUNC inline Transform()
-  {
-    check_template_params();
-    internal::transform_make_affine<(int(Mode)==Affine || int(Mode)==Isometry) ? Affine : AffineCompact>::run(m_matrix);
-  }
-
-  EIGEN_DEVICE_FUNC inline explicit Transform(const TranslationType& t)
-  {
-    check_template_params();
-    *this = t;
-  }
-  EIGEN_DEVICE_FUNC inline explicit Transform(const UniformScaling<Scalar>& s)
-  {
-    check_template_params();
-    *this = s;
-  }
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline explicit Transform(const RotationBase<Derived, Dim>& r)
-  {
-    check_template_params();
-    *this = r;
-  }
-
-  typedef internal::transform_take_affine_part<Transform> take_affine_part;
-
-  /** Constructs and initializes a transformation from a Dim^2 or a (Dim+1)^2 matrix. */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline explicit Transform(const EigenBase<OtherDerived>& other)
-  {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value),
-      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
-
-    check_template_params();
-    internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived());
-  }
-
-  /** Set \c *this from a Dim^2 or (Dim+1)^2 matrix. */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline Transform& operator=(const EigenBase<OtherDerived>& other)
-  {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value),
-      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
-
-    internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived());
-    return *this;
-  }
-
-  template<int OtherOptions>
-  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar,Dim,Mode,OtherOptions>& other)
-  {
-    check_template_params();
-    // only the options change, we can directly copy the matrices
-    m_matrix = other.matrix();
-  }
-
-  template<int OtherMode,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar,Dim,OtherMode,OtherOptions>& other)
-  {
-    check_template_params();
-    // prevent conversions as:
-    // Affine | AffineCompact | Isometry = Projective
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(OtherMode==int(Projective), Mode==int(Projective)),
-                        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
-
-    // prevent conversions as:
-    // Isometry = Affine | AffineCompact
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(OtherMode==int(Affine)||OtherMode==int(AffineCompact), Mode!=int(Isometry)),
-                        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
-
-    enum { ModeIsAffineCompact = Mode == int(AffineCompact),
-           OtherModeIsAffineCompact = OtherMode == int(AffineCompact)
-    };
-
-    if(EIGEN_CONST_CONDITIONAL(ModeIsAffineCompact == OtherModeIsAffineCompact))
-    {
-      // We need the block expression because the code is compiled for all
-      // combinations of transformations and will trigger a compile time error
-      // if one tries to assign the matrices directly
-      m_matrix.template block<Dim,Dim+1>(0,0) = other.matrix().template block<Dim,Dim+1>(0,0);
-      makeAffine();
-    }
-    else if(EIGEN_CONST_CONDITIONAL(OtherModeIsAffineCompact))
-    {
-      typedef typename Transform<Scalar,Dim,OtherMode,OtherOptions>::MatrixType OtherMatrixType;
-      internal::transform_construct_from_matrix<OtherMatrixType,Mode,Options,Dim,HDim>::run(this, other.matrix());
-    }
-    else
-    {
-      // here we know that Mode == AffineCompact and OtherMode != AffineCompact.
-      // if OtherMode were Projective, the static assert above would already have caught it.
-      // So the only possibility is that OtherMode == Affine
-      linear() = other.linear();
-      translation() = other.translation();
-    }
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC Transform(const ReturnByValue<OtherDerived>& other)
-  {
-    check_template_params();
-    other.evalTo(*this);
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC Transform& operator=(const ReturnByValue<OtherDerived>& other)
-  {
-    other.evalTo(*this);
-    return *this;
-  }
-
-  #ifdef EIGEN_QT_SUPPORT
-  inline Transform(const QMatrix& other);
-  inline Transform& operator=(const QMatrix& other);
-  inline QMatrix toQMatrix(void) const;
-  inline Transform(const QTransform& other);
-  inline Transform& operator=(const QTransform& other);
-  inline QTransform toQTransform(void) const;
-  #endif
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return int(Mode)==int(Projective) ? m_matrix.cols() : (m_matrix.cols()-1); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
-
-  /** shortcut for m_matrix(row,col);
-    * \sa MatrixBase::operator(Index,Index) const */
-  EIGEN_DEVICE_FUNC inline Scalar operator() (Index row, Index col) const { return m_matrix(row,col); }
-  /** shortcut for m_matrix(row,col);
-    * \sa MatrixBase::operator(Index,Index) */
-  EIGEN_DEVICE_FUNC inline Scalar& operator() (Index row, Index col) { return m_matrix(row,col); }
-
-  /** \returns a read-only expression of the transformation matrix */
-  EIGEN_DEVICE_FUNC inline const MatrixType& matrix() const { return m_matrix; }
-  /** \returns a writable expression of the transformation matrix */
-  EIGEN_DEVICE_FUNC inline MatrixType& matrix() { return m_matrix; }
-
-  /** \returns a read-only expression of the linear part of the transformation */
-  EIGEN_DEVICE_FUNC inline ConstLinearPart linear() const { return ConstLinearPart(m_matrix,0,0); }
-  /** \returns a writable expression of the linear part of the transformation */
-  EIGEN_DEVICE_FUNC inline LinearPart linear() { return LinearPart(m_matrix,0,0); }
-
-  /** \returns a read-only expression of the Dim x HDim affine part of the transformation */
-  EIGEN_DEVICE_FUNC inline ConstAffinePart affine() const { return take_affine_part::run(m_matrix); }
-  /** \returns a writable expression of the Dim x HDim affine part of the transformation */
-  EIGEN_DEVICE_FUNC inline AffinePart affine() { return take_affine_part::run(m_matrix); }
-
-  /** \returns a read-only expression of the translation vector of the transformation */
-  EIGEN_DEVICE_FUNC inline ConstTranslationPart translation() const { return ConstTranslationPart(m_matrix,0,Dim); }
-  /** \returns a writable expression of the translation vector of the transformation */
-  EIGEN_DEVICE_FUNC inline TranslationPart translation() { return TranslationPart(m_matrix,0,Dim); }
-
-  /** \returns an expression of the product between the transform \c *this and a matrix expression \a other.
-    *
-    * The right-hand-side \a other can be either:
-    * \li an homogeneous vector of size Dim+1,
-    * \li a set of homogeneous vectors of size Dim+1 x N,
-    * \li a transformation matrix of size Dim+1 x Dim+1.
-    *
-    * Moreover, if \c *this represents an affine transformation (i.e., Mode!=Projective), then \a other can also be:
-    * \li a point of size Dim (computes: \code this->linear() * other + this->translation()\endcode),
-    * \li a set of N points as a Dim x N matrix (computes: \code (this->linear() * other).colwise() + this->translation()\endcode),
-    *
-    * In all cases, the return type is a matrix or vector of same sizes as the right-hand-side \a other.
-    *
-    * If you want to interpret \a other as a linear or affine transformation, then first convert it to a Transform<> type,
-    * or do your own cooking.
-    *
-    * Finally, if you want to apply Affine transformations to vectors, then explicitly apply the linear part only:
-    * \code
-    * Affine3f A;
-    * Vector3f v1, v2;
-    * v2 = A.linear() * v1;
-    * \endcode
-    *
-    */
-  // note: this function is defined here because some compilers cannot find the respective declaration
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename internal::transform_right_product_impl<Transform, OtherDerived>::ResultType
-  operator * (const EigenBase<OtherDerived> &other) const
-  { return internal::transform_right_product_impl<Transform, OtherDerived>::run(*this,other.derived()); }
-
-  /** \returns the product expression of a transformation matrix \a a times a transform \a b
-    *
-    * The left hand side \a other can be either:
-    * \li a linear transformation matrix of size Dim x Dim,
-    * \li an affine transformation matrix of size Dim x Dim+1,
-    * \li a general transformation matrix of size Dim+1 x Dim+1.
-    */
-  template<typename OtherDerived> friend
-  EIGEN_DEVICE_FUNC inline const typename internal::transform_left_product_impl<OtherDerived,Mode,Options,_Dim,_Dim+1>::ResultType
-    operator * (const EigenBase<OtherDerived> &a, const Transform &b)
-  { return internal::transform_left_product_impl<OtherDerived,Mode,Options,Dim,HDim>::run(a.derived(),b); }
-
-  /** \returns The product expression of a transform \a a times a diagonal matrix \a b
-    *
-    * The rhs diagonal matrix is interpreted as an affine scaling transformation. The
-    * product results in a Transform of the same type (mode) as the lhs only if the lhs
-    * mode is no isometry. In that case, the returned transform is an affinity.
-    */
-  template<typename DiagonalDerived>
-  EIGEN_DEVICE_FUNC inline const TransformTimeDiagonalReturnType
-    operator * (const DiagonalBase<DiagonalDerived> &b) const
-  {
-    TransformTimeDiagonalReturnType res(*this);
-    res.linearExt() *= b;
-    return res;
-  }
-
-  /** \returns The product expression of a diagonal matrix \a a times a transform \a b
-    *
-    * The lhs diagonal matrix is interpreted as an affine scaling transformation. The
-    * product results in a Transform of the same type (mode) as the lhs only if the lhs
-    * mode is no isometry. In that case, the returned transform is an affinity.
-    */
-  template<typename DiagonalDerived>
-  EIGEN_DEVICE_FUNC friend inline TransformTimeDiagonalReturnType
-    operator * (const DiagonalBase<DiagonalDerived> &a, const Transform &b)
-  {
-    TransformTimeDiagonalReturnType res;
-    res.linear().noalias() = a*b.linear();
-    res.translation().noalias() = a*b.translation();
-    if (EIGEN_CONST_CONDITIONAL(Mode!=int(AffineCompact)))
-      res.matrix().row(Dim) = b.matrix().row(Dim);
-    return res;
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline Transform& operator*=(const EigenBase<OtherDerived>& other) { return *this = *this * other; }
-
-  /** Concatenates two transformations */
-  EIGEN_DEVICE_FUNC inline const Transform operator * (const Transform& other) const
-  {
-    return internal::transform_transform_product_impl<Transform,Transform>::run(*this,other);
-  }
-
-  #if EIGEN_COMP_ICC
-private:
-  // this intermediate structure permits to workaround a bug in ICC 11:
-  //   error: template instantiation resulted in unexpected function type of "Eigen::Transform<double, 3, 32, 0>
-  //             (const Eigen::Transform<double, 3, 2, 0> &) const"
-  //  (the meaning of a name may have changed since the template declaration -- the type of the template is:
-  // "Eigen::internal::transform_transform_product_impl<Eigen::Transform<double, 3, 32, 0>,
-  //     Eigen::Transform<double, 3, Mode, Options>, <expression>>::ResultType (const Eigen::Transform<double, 3, Mode, Options> &) const")
-  //
-  template<int OtherMode,int OtherOptions> struct icc_11_workaround
-  {
-    typedef internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> > ProductType;
-    typedef typename ProductType::ResultType ResultType;
-  };
-
-public:
-  /** Concatenates two different transformations */
-  template<int OtherMode,int OtherOptions>
-  inline typename icc_11_workaround<OtherMode,OtherOptions>::ResultType
-    operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const
-  {
-    typedef typename icc_11_workaround<OtherMode,OtherOptions>::ProductType ProductType;
-    return ProductType::run(*this,other);
-  }
-  #else
-  /** Concatenates two different transformations */
-  template<int OtherMode,int OtherOptions>
-  EIGEN_DEVICE_FUNC inline typename internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::ResultType
-    operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const
-  {
-    return internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::run(*this,other);
-  }
-  #endif
-
-  /** \sa MatrixBase::setIdentity() */
-  EIGEN_DEVICE_FUNC void setIdentity() { m_matrix.setIdentity(); }
-
-  /**
-   * \brief Returns an identity transformation.
-   * \todo In the future this function should be returning a Transform expression.
-   */
-  EIGEN_DEVICE_FUNC static const Transform Identity()
-  {
-    return Transform(MatrixType::Identity());
-  }
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& scale(const MatrixBase<OtherDerived> &other);
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& prescale(const MatrixBase<OtherDerived> &other);
-
-  EIGEN_DEVICE_FUNC inline Transform& scale(const Scalar& s);
-  EIGEN_DEVICE_FUNC inline Transform& prescale(const Scalar& s);
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& translate(const MatrixBase<OtherDerived> &other);
-
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC
-  inline Transform& pretranslate(const MatrixBase<OtherDerived> &other);
-
-  template<typename RotationType>
-  EIGEN_DEVICE_FUNC
-  inline Transform& rotate(const RotationType& rotation);
-
-  template<typename RotationType>
-  EIGEN_DEVICE_FUNC
-  inline Transform& prerotate(const RotationType& rotation);
-
-  EIGEN_DEVICE_FUNC Transform& shear(const Scalar& sx, const Scalar& sy);
-  EIGEN_DEVICE_FUNC Transform& preshear(const Scalar& sx, const Scalar& sy);
-
-  EIGEN_DEVICE_FUNC inline Transform& operator=(const TranslationType& t);
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator*=(const TranslationType& t) { return translate(t.vector()); }
-
-  EIGEN_DEVICE_FUNC inline Transform operator*(const TranslationType& t) const;
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator=(const UniformScaling<Scalar>& t);
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator*=(const UniformScaling<Scalar>& s) { return scale(s.factor()); }
-
-  EIGEN_DEVICE_FUNC
-  inline TransformTimeDiagonalReturnType operator*(const UniformScaling<Scalar>& s) const
-  {
-    TransformTimeDiagonalReturnType res = *this;
-    res.scale(s.factor());
-    return res;
-  }
-
-  EIGEN_DEVICE_FUNC
-  inline Transform& operator*=(const DiagonalMatrix<Scalar,Dim>& s) { linearExt() *= s; return *this; }
-
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Transform& operator=(const RotationBase<Derived,Dim>& r);
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Transform& operator*=(const RotationBase<Derived,Dim>& r) { return rotate(r.toRotationMatrix()); }
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline Transform operator*(const RotationBase<Derived,Dim>& r) const;
-
-  typedef typename internal::conditional<int(Mode)==Isometry,ConstLinearPart,const LinearMatrixType>::type RotationReturnType;
-  EIGEN_DEVICE_FUNC RotationReturnType rotation() const;
-
-  template<typename RotationMatrixType, typename ScalingMatrixType>
-  EIGEN_DEVICE_FUNC
-  void computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const;
-  template<typename ScalingMatrixType, typename RotationMatrixType>
-  EIGEN_DEVICE_FUNC
-  void computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const;
-
-  template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
-  EIGEN_DEVICE_FUNC
-  Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
-    const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale);
-
-  EIGEN_DEVICE_FUNC
-  inline Transform inverse(TransformTraits traits = (TransformTraits)Mode) const;
-
-  /** \returns a const pointer to the column major internal matrix */
-  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_matrix.data(); }
-  /** \returns a non-const pointer to the column major internal matrix */
-  EIGEN_DEVICE_FUNC Scalar* data() { return m_matrix.data(); }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type cast() const
-  { return typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit Transform(const Transform<OtherScalarType,Dim,Mode,Options>& other)
-  {
-    check_template_params();
-    m_matrix = other.matrix().template cast<Scalar>();
-  }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const Transform& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_matrix.isApprox(other.m_matrix, prec); }
-
-  /** Sets the last row to [0 ... 0 1]
-    */
-  EIGEN_DEVICE_FUNC void makeAffine()
-  {
-    internal::transform_make_affine<int(Mode)>::run(m_matrix);
-  }
-
-  /** \internal
-    * \returns the Dim x Dim linear part if the transformation is affine,
-    *          and the HDim x Dim part for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt()
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); }
-  /** \internal
-    * \returns the Dim x Dim linear part if the transformation is affine,
-    *          and the HDim x Dim part for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt() const
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); }
-
-  /** \internal
-    * \returns the translation part if the transformation is affine,
-    *          and the last column for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt()
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); }
-  /** \internal
-    * \returns the translation part if the transformation is affine,
-    *          and the last column for projective transformations.
-    */
-  EIGEN_DEVICE_FUNC inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt() const
-  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); }
-
-
-  #ifdef EIGEN_TRANSFORM_PLUGIN
-  #include EIGEN_TRANSFORM_PLUGIN
-  #endif
-
-protected:
-  #ifndef EIGEN_PARSED_BY_DOXYGEN
-    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void check_template_params()
-    {
-      EIGEN_STATIC_ASSERT((Options & (DontAlign|RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS)
-    }
-  #endif
-
-};
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,Isometry> Isometry2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,Isometry> Isometry3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,Isometry> Isometry2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,Isometry> Isometry3d;
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,Affine> Affine2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,Affine> Affine3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,Affine> Affine2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,Affine> Affine3d;
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,AffineCompact> AffineCompact2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,AffineCompact> AffineCompact3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,AffineCompact> AffineCompact2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,AffineCompact> AffineCompact3d;
-
-/** \ingroup Geometry_Module */
-typedef Transform<float,2,Projective> Projective2f;
-/** \ingroup Geometry_Module */
-typedef Transform<float,3,Projective> Projective3f;
-/** \ingroup Geometry_Module */
-typedef Transform<double,2,Projective> Projective2d;
-/** \ingroup Geometry_Module */
-typedef Transform<double,3,Projective> Projective3d;
-
-/**************************
-*** Optional QT support ***
-**************************/
-
-#ifdef EIGEN_QT_SUPPORT
-/** Initializes \c *this from a QMatrix assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode,int Options>
-Transform<Scalar,Dim,Mode,Options>::Transform(const QMatrix& other)
-{
-  check_template_params();
-  *this = other;
-}
-
-/** Set \c *this from a QMatrix assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode,int Options>
-Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QMatrix& other)
-{
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy();
-  else
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy(),
-                0, 0, 1;
-  return *this;
-}
-
-/** \returns a QMatrix from \c *this assuming the dimension is 2.
-  *
-  * \warning this conversion might loss data if \c *this is not affine
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-QMatrix Transform<Scalar,Dim,Mode,Options>::toQMatrix(void) const
-{
-  check_template_params();
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  return QMatrix(m_matrix.coeff(0,0), m_matrix.coeff(1,0),
-                 m_matrix.coeff(0,1), m_matrix.coeff(1,1),
-                 m_matrix.coeff(0,2), m_matrix.coeff(1,2));
-}
-
-/** Initializes \c *this from a QTransform assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode,int Options>
-Transform<Scalar,Dim,Mode,Options>::Transform(const QTransform& other)
-{
-  check_template_params();
-  *this = other;
-}
-
-/** Set \c *this from a QTransform assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QTransform& other)
-{
-  check_template_params();
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy();
-  else
-    m_matrix << other.m11(), other.m21(), other.dx(),
-                other.m12(), other.m22(), other.dy(),
-                other.m13(), other.m23(), other.m33();
-  return *this;
-}
-
-/** \returns a QTransform from \c *this assuming the dimension is 2.
-  *
-  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-QTransform Transform<Scalar,Dim,Mode,Options>::toQTransform(void) const
-{
-  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
-    return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0),
-                      m_matrix.coeff(0,1), m_matrix.coeff(1,1),
-                      m_matrix.coeff(0,2), m_matrix.coeff(1,2));
-  else
-    return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0), m_matrix.coeff(2,0),
-                      m_matrix.coeff(0,1), m_matrix.coeff(1,1), m_matrix.coeff(2,1),
-                      m_matrix.coeff(0,2), m_matrix.coeff(1,2), m_matrix.coeff(2,2));
-}
-#endif
-
-/*********************
-*** Procedural API ***
-*********************/
-
-/** Applies on the right the non uniform scale transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \sa prescale()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::scale(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  linearExt().noalias() = (linearExt() * other.asDiagonal());
-  return *this;
-}
-
-/** Applies on the right a uniform scale of a factor \a c to \c *this
-  * and returns a reference to \c *this.
-  * \sa prescale(Scalar)
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::scale(const Scalar& s)
-{
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  linearExt() *= s;
-  return *this;
-}
-
-/** Applies on the left the non uniform scale transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \sa scale()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::prescale(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  affine().noalias() = (other.asDiagonal() * affine());
-  return *this;
-}
-
-/** Applies on the left a uniform scale of a factor \a c to \c *this
-  * and returns a reference to \c *this.
-  * \sa scale(Scalar)
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::prescale(const Scalar& s)
-{
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  m_matrix.template topRows<Dim>() *= s;
-  return *this;
-}
-
-/** Applies on the right the translation matrix represented by the vector \a other
-  * to \c *this and returns a reference to \c *this.
-  * \sa pretranslate()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::translate(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  translationExt() += linearExt() * other;
-  return *this;
-}
-
-/** Applies on the left the translation matrix represented by the vector \a other
-  * to \c *this and returns a reference to \c *this.
-  * \sa translate()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::pretranslate(const MatrixBase<OtherDerived> &other)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
-  if(EIGEN_CONST_CONDITIONAL(int(Mode)==int(Projective)))
-    affine() += other * m_matrix.row(Dim);
-  else
-    translation() += other;
-  return *this;
-}
-
-/** Applies on the right the rotation represented by the rotation \a rotation
-  * to \c *this and returns a reference to \c *this.
-  *
-  * The template parameter \a RotationType is the type of the rotation which
-  * must be known by internal::toRotationMatrix<>.
-  *
-  * Natively supported types includes:
-  *   - any scalar (2D),
-  *   - a Dim x Dim matrix expression,
-  *   - a Quaternion (3D),
-  *   - a AngleAxis (3D)
-  *
-  * This mechanism is easily extendable to support user types such as Euler angles,
-  * or a pair of Quaternion for 4D rotations.
-  *
-  * \sa rotate(Scalar), class Quaternion, class AngleAxis, prerotate(RotationType)
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename RotationType>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::rotate(const RotationType& rotation)
-{
-  linearExt() *= internal::toRotationMatrix<Scalar,Dim>(rotation);
-  return *this;
-}
-
-/** Applies on the left the rotation represented by the rotation \a rotation
-  * to \c *this and returns a reference to \c *this.
-  *
-  * See rotate() for further details.
-  *
-  * \sa rotate()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename RotationType>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::prerotate(const RotationType& rotation)
-{
-  m_matrix.template block<Dim,HDim>(0,0) = internal::toRotationMatrix<Scalar,Dim>(rotation)
-                                         * m_matrix.template block<Dim,HDim>(0,0);
-  return *this;
-}
-
-/** Applies on the right the shear transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \warning 2D only.
-  * \sa preshear()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::shear(const Scalar& sx, const Scalar& sy)
-{
-  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  VectorType tmp = linear().col(0)*sy + linear().col(1);
-  linear() << linear().col(0) + linear().col(1)*sx, tmp;
-  return *this;
-}
-
-/** Applies on the left the shear transformation represented
-  * by the vector \a other to \c *this and returns a reference to \c *this.
-  * \warning 2D only.
-  * \sa shear()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::preshear(const Scalar& sx, const Scalar& sy)
-{
-  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0);
-  return *this;
-}
-
-/******************************************************
-*** Scaling, Translation and Rotation compatibility ***
-******************************************************/
-
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const TranslationType& t)
-{
-  linear().setIdentity();
-  translation() = t.vector();
-  makeAffine();
-  return *this;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const TranslationType& t) const
-{
-  Transform res = *this;
-  res.translate(t.vector());
-  return res;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const UniformScaling<Scalar>& s)
-{
-  m_matrix.setZero();
-  linear().diagonal().fill(s.factor());
-  makeAffine();
-  return *this;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const RotationBase<Derived,Dim>& r)
-{
-  linear() = internal::toRotationMatrix<Scalar,Dim>(r);
-  translation().setZero();
-  makeAffine();
-  return *this;
-}
-
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename Derived>
-EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const RotationBase<Derived,Dim>& r) const
-{
-  Transform res = *this;
-  res.rotate(r.derived());
-  return res;
-}
-
-/************************
-*** Special functions ***
-************************/
-
-namespace internal {
-template<int Mode> struct transform_rotation_impl {
-  template<typename TransformType>
-  EIGEN_DEVICE_FUNC static inline
-  const typename TransformType::LinearMatrixType run(const TransformType& t)
-  {
-    typedef typename TransformType::LinearMatrixType LinearMatrixType;
-    LinearMatrixType result;
-    t.computeRotationScaling(&result, (LinearMatrixType*)0);
-    return result;
-  }
-};
-template<> struct transform_rotation_impl<Isometry> {
-  template<typename TransformType>
-  EIGEN_DEVICE_FUNC static inline
-  typename TransformType::ConstLinearPart run(const TransformType& t)
-  {
-    return t.linear();
-  }
-};
-}
-/** \returns the rotation part of the transformation
-  *
-  * If Mode==Isometry, then this method is an alias for linear(),
-  * otherwise it calls computeRotationScaling() to extract the rotation
-  * through a SVD decomposition.
-  *
-  * \svd_module
-  *
-  * \sa computeRotationScaling(), computeScalingRotation(), class SVD
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC
-typename Transform<Scalar,Dim,Mode,Options>::RotationReturnType
-Transform<Scalar,Dim,Mode,Options>::rotation() const
-{
-  return internal::transform_rotation_impl<Mode>::run(*this);
-}
-
-
-/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being
-  * not necessarily positive.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  *
-  *
-  * \svd_module
-  *
-  * \sa computeScalingRotation(), rotation(), class SVD
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename RotationMatrixType, typename ScalingMatrixType>
-EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const
-{
-  // Note that JacobiSVD is faster than BDCSVD for small matrices.
-  JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
-
-  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
-  VectorType sv(svd.singularValues());
-  sv.coeffRef(Dim-1) *= x;
-  if(scaling) *scaling = svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint();
-  if(rotation)
-  {
-    LinearMatrixType m(svd.matrixU());
-    m.col(Dim-1) *= x;
-    *rotation = m * svd.matrixV().adjoint();
-  }
-}
-
-/** decomposes the linear part of the transformation as a product scaling x rotation, the scaling being
-  * not necessarily positive.
-  *
-  * If either pointer is zero, the corresponding computation is skipped.
-  *
-  *
-  *
-  * \svd_module
-  *
-  * \sa computeRotationScaling(), rotation(), class SVD
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename ScalingMatrixType, typename RotationMatrixType>
-EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const
-{
-  // Note that JacobiSVD is faster than BDCSVD for small matrices.
-  JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
-
-  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
-  VectorType sv(svd.singularValues());
-  sv.coeffRef(Dim-1) *= x;
-  if(scaling) *scaling = svd.matrixU() * sv.asDiagonal() * svd.matrixU().adjoint();
-  if(rotation)
-  {
-    LinearMatrixType m(svd.matrixU());
-    m.col(Dim-1) *= x;
-    *rotation = m * svd.matrixV().adjoint();
-  }
-}
-
-/** Convenient method to set \c *this from a position, orientation and scale
-  * of a 3D object.
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
-Transform<Scalar,Dim,Mode,Options>::fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
-  const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale)
-{
-  linear() = internal::toRotationMatrix<Scalar,Dim>(orientation);
-  linear() *= scale.asDiagonal();
-  translation() = position;
-  makeAffine();
-  return *this;
-}
-
-namespace internal {
-
-template<int Mode>
-struct transform_make_affine
-{
-  template<typename MatrixType>
-  EIGEN_DEVICE_FUNC static void run(MatrixType &mat)
-  {
-    static const int Dim = MatrixType::ColsAtCompileTime-1;
-    mat.template block<1,Dim>(Dim,0).setZero();
-    mat.coeffRef(Dim,Dim) = typename MatrixType::Scalar(1);
-  }
-};
-
-template<>
-struct transform_make_affine<AffineCompact>
-{
-  template<typename MatrixType> EIGEN_DEVICE_FUNC static void run(MatrixType &) { }
-};
-
-// selector needed to avoid taking the inverse of a 3x4 matrix
-template<typename TransformType, int Mode=TransformType::Mode>
-struct projective_transform_inverse
-{
-  EIGEN_DEVICE_FUNC static inline void run(const TransformType&, TransformType&)
-  {}
-};
-
-template<typename TransformType>
-struct projective_transform_inverse<TransformType, Projective>
-{
-  EIGEN_DEVICE_FUNC static inline void run(const TransformType& m, TransformType& res)
-  {
-    res.matrix() = m.matrix().inverse();
-  }
-};
-
-} // end namespace internal
-
-
-/**
-  *
-  * \returns the inverse transformation according to some given knowledge
-  * on \c *this.
-  *
-  * \param hint allows to optimize the inversion process when the transformation
-  * is known to be not a general transformation (optional). The possible values are:
-  *  - #Projective if the transformation is not necessarily affine, i.e., if the
-  *    last row is not guaranteed to be [0 ... 0 1]
-  *  - #Affine if the last row can be assumed to be [0 ... 0 1]
-  *  - #Isometry if the transformation is only a concatenations of translations
-  *    and rotations.
-  *  The default is the template class parameter \c Mode.
-  *
-  * \warning unless \a traits is always set to NoShear or NoScaling, this function
-  * requires the generic inverse method of MatrixBase defined in the LU module. If
-  * you forget to include this module, then you will get hard to debug linking errors.
-  *
-  * \sa MatrixBase::inverse()
-  */
-template<typename Scalar, int Dim, int Mode, int Options>
-EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>
-Transform<Scalar,Dim,Mode,Options>::inverse(TransformTraits hint) const
-{
-  Transform res;
-  if (hint == Projective)
-  {
-    internal::projective_transform_inverse<Transform>::run(*this, res);
-  }
-  else
-  {
-    if (hint == Isometry)
-    {
-      res.matrix().template topLeftCorner<Dim,Dim>() = linear().transpose();
-    }
-    else if(hint&Affine)
-    {
-      res.matrix().template topLeftCorner<Dim,Dim>() = linear().inverse();
-    }
-    else
-    {
-      eigen_assert(false && "Invalid transform traits in Transform::Inverse");
-    }
-    // translation and remaining parts
-    res.matrix().template topRightCorner<Dim,1>()
-      = - res.matrix().template topLeftCorner<Dim,Dim>() * translation();
-    res.makeAffine(); // we do need this, because in the beginning res is uninitialized
-  }
-  return res;
-}
-
-namespace internal {
-
-/*****************************************************
-*** Specializations of take affine part            ***
-*****************************************************/
-
-template<typename TransformType> struct transform_take_affine_part {
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef typename TransformType::AffinePart AffinePart;
-  typedef typename TransformType::ConstAffinePart ConstAffinePart;
-  static inline AffinePart run(MatrixType& m)
-  { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); }
-  static inline ConstAffinePart run(const MatrixType& m)
-  { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); }
-};
-
-template<typename Scalar, int Dim, int Options>
-struct transform_take_affine_part<Transform<Scalar,Dim,AffineCompact, Options> > {
-  typedef typename Transform<Scalar,Dim,AffineCompact,Options>::MatrixType MatrixType;
-  static inline MatrixType& run(MatrixType& m) { return m; }
-  static inline const MatrixType& run(const MatrixType& m) { return m; }
-};
-
-/*****************************************************
-*** Specializations of construct from matrix       ***
-*****************************************************/
-
-template<typename Other, int Mode, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,Dim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
-  {
-    transform->linear() = other;
-    transform->translation().setZero();
-    transform->makeAffine();
-  }
-};
-
-template<typename Other, int Mode, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,HDim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
-  {
-    transform->affine() = other;
-    transform->makeAffine();
-  }
-};
-
-template<typename Other, int Mode, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, HDim,HDim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
-  { transform->matrix() = other; }
-};
-
-template<typename Other, int Options, int Dim, int HDim>
-struct transform_construct_from_matrix<Other, AffineCompact,Options,Dim,HDim, HDim,HDim>
-{
-  static inline void run(Transform<typename Other::Scalar,Dim,AffineCompact,Options> *transform, const Other& other)
-  { transform->matrix() = other.template block<Dim,HDim>(0,0); }
-};
-
-/**********************************************************
-***   Specializations of operator* with rhs EigenBase   ***
-**********************************************************/
-
-template<int LhsMode,int RhsMode>
-struct transform_product_result
-{
-  enum
-  {
-    Mode =
-      (LhsMode == (int)Projective    || RhsMode == (int)Projective    ) ? Projective :
-      (LhsMode == (int)Affine        || RhsMode == (int)Affine        ) ? Affine :
-      (LhsMode == (int)AffineCompact || RhsMode == (int)AffineCompact ) ? AffineCompact :
-      (LhsMode == (int)Isometry      || RhsMode == (int)Isometry      ) ? Isometry : Projective
-  };
-};
-
-template< typename TransformType, typename MatrixType, int RhsCols>
-struct transform_right_product_impl< TransformType, MatrixType, 0, RhsCols>
-{
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    return T.matrix() * other;
-  }
-};
-
-template< typename TransformType, typename MatrixType, int RhsCols>
-struct transform_right_product_impl< TransformType, MatrixType, 1, RhsCols>
-{
-  enum {
-    Dim = TransformType::Dim,
-    HDim = TransformType::HDim,
-    OtherRows = MatrixType::RowsAtCompileTime,
-    OtherCols = MatrixType::ColsAtCompileTime
-  };
-
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    EIGEN_STATIC_ASSERT(OtherRows==HDim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
-
-    typedef Block<ResultType, Dim, OtherCols, int(MatrixType::RowsAtCompileTime)==Dim> TopLeftLhs;
-
-    ResultType res(other.rows(),other.cols());
-    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() = T.affine() * other;
-    res.row(OtherRows-1) = other.row(OtherRows-1);
-
-    return res;
-  }
-};
-
-template< typename TransformType, typename MatrixType, int RhsCols>
-struct transform_right_product_impl< TransformType, MatrixType, 2, RhsCols>
-{
-  enum {
-    Dim = TransformType::Dim,
-    HDim = TransformType::HDim,
-    OtherRows = MatrixType::RowsAtCompileTime,
-    OtherCols = MatrixType::ColsAtCompileTime
-  };
-
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
-
-    typedef Block<ResultType, Dim, OtherCols, true> TopLeftLhs;
-    ResultType res(Replicate<typename TransformType::ConstTranslationPart, 1, OtherCols>(T.translation(),1,other.cols()));
-    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() += T.linear() * other;
-
-    return res;
-  }
-};
-
-template< typename TransformType, typename MatrixType >
-struct transform_right_product_impl< TransformType, MatrixType, 2, 1> // rhs is a vector of size Dim
-{
-  typedef typename TransformType::MatrixType TransformMatrix;
-  enum {
-    Dim = TransformType::Dim,
-    HDim = TransformType::HDim,
-    OtherRows = MatrixType::RowsAtCompileTime,
-    WorkingRows = EIGEN_PLAIN_ENUM_MIN(TransformMatrix::RowsAtCompileTime,HDim)
-  };
-
-  typedef typename MatrixType::PlainObject ResultType;
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
-  {
-    EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
-
-    Matrix<typename ResultType::Scalar, Dim+1, 1> rhs;
-    rhs.template head<Dim>() = other; rhs[Dim] = typename ResultType::Scalar(1);
-    Matrix<typename ResultType::Scalar, WorkingRows, 1> res(T.matrix() * rhs);
-    return res.template head<Dim>();
-  }
-};
-
-/**********************************************************
-***   Specializations of operator* with lhs EigenBase   ***
-**********************************************************/
-
-// generic HDim x HDim matrix * T => Projective
-template<typename Other,int Mode, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, HDim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  { return ResultType(other * tr.matrix()); }
-};
-
-// generic HDim x HDim matrix * AffineCompact => Projective
-template<typename Other, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, HDim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  {
-    ResultType res;
-    res.matrix().noalias() = other.template block<HDim,Dim>(0,0) * tr.matrix();
-    res.matrix().col(Dim) += other.col(Dim);
-    return res;
-  }
-};
-
-// affine matrix * T
-template<typename Other,int Mode, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef TransformType ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  {
-    ResultType res;
-    res.affine().noalias() = other * tr.matrix();
-    res.matrix().row(Dim) = tr.matrix().row(Dim);
-    return res;
-  }
-};
-
-// affine matrix * AffineCompact
-template<typename Other, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, Dim,HDim>
-{
-  typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef TransformType ResultType;
-  static ResultType run(const Other& other,const TransformType& tr)
-  {
-    ResultType res;
-    res.matrix().noalias() = other.template block<Dim,Dim>(0,0) * tr.matrix();
-    res.translation() += other.col(Dim);
-    return res;
-  }
-};
-
-// linear matrix * T
-template<typename Other,int Mode, int Options, int Dim, int HDim>
-struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,Dim>
-{
-  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
-  typedef typename TransformType::MatrixType MatrixType;
-  typedef TransformType ResultType;
-  static ResultType run(const Other& other, const TransformType& tr)
-  {
-    TransformType res;
-    if(Mode!=int(AffineCompact))
-      res.matrix().row(Dim) = tr.matrix().row(Dim);
-    res.matrix().template topRows<Dim>().noalias()
-      = other * tr.matrix().template topRows<Dim>();
-    return res;
-  }
-};
-
-/**********************************************************
-*** Specializations of operator* with another Transform ***
-**********************************************************/
-
-template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,false >
-{
-  enum { ResultMode = transform_product_result<LhsMode,RhsMode>::Mode };
-  typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,ResultMode,LhsOptions> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    ResultType res;
-    res.linear() = lhs.linear() * rhs.linear();
-    res.translation() = lhs.linear() * rhs.translation() + lhs.translation();
-    res.makeAffine();
-    return res;
-  }
-};
-
-template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,true >
-{
-  typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,Projective> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    return ResultType( lhs.matrix() * rhs.matrix() );
-  }
-};
-
-template<typename Scalar, int Dim, int LhsOptions, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,AffineCompact,LhsOptions>,Transform<Scalar,Dim,Projective,RhsOptions>,true >
-{
-  typedef Transform<Scalar,Dim,AffineCompact,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,Projective,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,Projective> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    ResultType res;
-    res.matrix().template topRows<Dim>() = lhs.matrix() * rhs.matrix();
-    res.matrix().row(Dim) = rhs.matrix().row(Dim);
-    return res;
-  }
-};
-
-template<typename Scalar, int Dim, int LhsOptions, int RhsOptions>
-struct transform_transform_product_impl<Transform<Scalar,Dim,Projective,LhsOptions>,Transform<Scalar,Dim,AffineCompact,RhsOptions>,true >
-{
-  typedef Transform<Scalar,Dim,Projective,LhsOptions> Lhs;
-  typedef Transform<Scalar,Dim,AffineCompact,RhsOptions> Rhs;
-  typedef Transform<Scalar,Dim,Projective> ResultType;
-  static ResultType run(const Lhs& lhs, const Rhs& rhs)
-  {
-    ResultType res(lhs.matrix().template leftCols<Dim>() * rhs.matrix());
-    res.matrix().col(Dim) += lhs.matrix().col(Dim);
-    return res;
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSFORM_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Translation.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Translation.h
deleted file mode 100644
index 8c22901..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Translation.h
+++ /dev/null
@@ -1,202 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_TRANSLATION_H
-#define EIGEN_TRANSLATION_H
-
-namespace Eigen { 
-
-/** \geometry_module \ingroup Geometry_Module
-  *
-  * \class Translation
-  *
-  * \brief Represents a translation transformation
-  *
-  * \tparam _Scalar the scalar type, i.e., the type of the coefficients.
-  * \tparam _Dim the  dimension of the space, can be a compile time value or Dynamic
-  *
-  * \note This class is not aimed to be used to store a translation transformation,
-  * but rather to make easier the constructions and updates of Transform objects.
-  *
-  * \sa class Scaling, class Transform
-  */
-template<typename _Scalar, int _Dim>
-class Translation
-{
-public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim)
-  /** dimension of the space */
-  enum { Dim = _Dim };
-  /** the scalar type of the coefficients */
-  typedef _Scalar Scalar;
-  /** corresponding vector type */
-  typedef Matrix<Scalar,Dim,1> VectorType;
-  /** corresponding linear transformation matrix type */
-  typedef Matrix<Scalar,Dim,Dim> LinearMatrixType;
-  /** corresponding affine transformation type */
-  typedef Transform<Scalar,Dim,Affine> AffineTransformType;
-  /** corresponding isometric transformation type */
-  typedef Transform<Scalar,Dim,Isometry> IsometryTransformType;
-
-protected:
-
-  VectorType m_coeffs;
-
-public:
-
-  /** Default constructor without initialization. */
-  EIGEN_DEVICE_FUNC Translation() {}
-  /**  */
-  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy)
-  {
-    eigen_assert(Dim==2);
-    m_coeffs.x() = sx;
-    m_coeffs.y() = sy;
-  }
-  /**  */
-  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy, const Scalar& sz)
-  {
-    eigen_assert(Dim==3);
-    m_coeffs.x() = sx;
-    m_coeffs.y() = sy;
-    m_coeffs.z() = sz;
-  }
-  /** Constructs and initialize the translation transformation from a vector of translation coefficients */
-  EIGEN_DEVICE_FUNC explicit inline Translation(const VectorType& vector) : m_coeffs(vector) {}
-
-  /** \brief Returns the x-translation by value. **/
-  EIGEN_DEVICE_FUNC inline Scalar x() const { return m_coeffs.x(); }
-  /** \brief Returns the y-translation by value. **/
-  EIGEN_DEVICE_FUNC inline Scalar y() const { return m_coeffs.y(); }
-  /** \brief Returns the z-translation by value. **/
-  EIGEN_DEVICE_FUNC inline Scalar z() const { return m_coeffs.z(); }
-
-  /** \brief Returns the x-translation as a reference. **/
-  EIGEN_DEVICE_FUNC inline Scalar& x() { return m_coeffs.x(); }
-  /** \brief Returns the y-translation as a reference. **/
-  EIGEN_DEVICE_FUNC inline Scalar& y() { return m_coeffs.y(); }
-  /** \brief Returns the z-translation as a reference. **/
-  EIGEN_DEVICE_FUNC inline Scalar& z() { return m_coeffs.z(); }
-
-  EIGEN_DEVICE_FUNC const VectorType& vector() const { return m_coeffs; }
-  EIGEN_DEVICE_FUNC VectorType& vector() { return m_coeffs; }
-
-  EIGEN_DEVICE_FUNC const VectorType& translation() const { return m_coeffs; }
-  EIGEN_DEVICE_FUNC VectorType& translation() { return m_coeffs; }
-
-  /** Concatenates two translation */
-  EIGEN_DEVICE_FUNC inline Translation operator* (const Translation& other) const
-  { return Translation(m_coeffs + other.m_coeffs); }
-
-  /** Concatenates a translation and a uniform scaling */
-  EIGEN_DEVICE_FUNC inline AffineTransformType operator* (const UniformScaling<Scalar>& other) const;
-
-  /** Concatenates a translation and a linear transformation */
-  template<typename OtherDerived>
-  EIGEN_DEVICE_FUNC inline AffineTransformType operator* (const EigenBase<OtherDerived>& linear) const;
-
-  /** Concatenates a translation and a rotation */
-  template<typename Derived>
-  EIGEN_DEVICE_FUNC inline IsometryTransformType operator*(const RotationBase<Derived,Dim>& r) const
-  { return *this * IsometryTransformType(r); }
-
-  /** \returns the concatenation of a linear transformation \a l with the translation \a t */
-  // its a nightmare to define a templated friend function outside its declaration
-  template<typename OtherDerived> friend
-  EIGEN_DEVICE_FUNC inline AffineTransformType operator*(const EigenBase<OtherDerived>& linear, const Translation& t)
-  {
-    AffineTransformType res;
-    res.matrix().setZero();
-    res.linear() = linear.derived();
-    res.translation() = linear.derived() * t.m_coeffs;
-    res.matrix().row(Dim).setZero();
-    res(Dim,Dim) = Scalar(1);
-    return res;
-  }
-
-  /** Concatenates a translation and a transformation */
-  template<int Mode, int Options>
-  EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode> operator* (const Transform<Scalar,Dim,Mode,Options>& t) const
-  {
-    Transform<Scalar,Dim,Mode> res = t;
-    res.pretranslate(m_coeffs);
-    return res;
-  }
-
-  /** Applies translation to vector */
-  template<typename Derived>
-  inline typename internal::enable_if<Derived::IsVectorAtCompileTime,VectorType>::type
-  operator* (const MatrixBase<Derived>& vec) const
-  { return m_coeffs + vec.derived(); }
-
-  /** \returns the inverse translation (opposite) */
-  Translation inverse() const { return Translation(-m_coeffs); }
-
-  static const Translation Identity() { return Translation(VectorType::Zero()); }
-
-  /** \returns \c *this with scalar type casted to \a NewScalarType
-    *
-    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
-    * then this function smartly returns a const reference to \c *this.
-    */
-  template<typename NewScalarType>
-  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type cast() const
-  { return typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type(*this); }
-
-  /** Copy constructor with scalar type conversion */
-  template<typename OtherScalarType>
-  EIGEN_DEVICE_FUNC inline explicit Translation(const Translation<OtherScalarType,Dim>& other)
-  { m_coeffs = other.vector().template cast<Scalar>(); }
-
-  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-    * determined by \a prec.
-    *
-    * \sa MatrixBase::isApprox() */
-  EIGEN_DEVICE_FUNC bool isApprox(const Translation& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
-  { return m_coeffs.isApprox(other.m_coeffs, prec); }
-
-};
-
-/** \addtogroup Geometry_Module */
-//@{
-typedef Translation<float, 2> Translation2f;
-typedef Translation<double,2> Translation2d;
-typedef Translation<float, 3> Translation3f;
-typedef Translation<double,3> Translation3d;
-//@}
-
-template<typename Scalar, int Dim>
-EIGEN_DEVICE_FUNC inline typename Translation<Scalar,Dim>::AffineTransformType
-Translation<Scalar,Dim>::operator* (const UniformScaling<Scalar>& other) const
-{
-  AffineTransformType res;
-  res.matrix().setZero();
-  res.linear().diagonal().fill(other.factor());
-  res.translation() = m_coeffs;
-  res(Dim,Dim) = Scalar(1);
-  return res;
-}
-
-template<typename Scalar, int Dim>
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC inline typename Translation<Scalar,Dim>::AffineTransformType
-Translation<Scalar,Dim>::operator* (const EigenBase<OtherDerived>& linear) const
-{
-  AffineTransformType res;
-  res.matrix().setZero();
-  res.linear() = linear.derived();
-  res.translation() = m_coeffs;
-  res.matrix().row(Dim).setZero();
-  res(Dim,Dim) = Scalar(1);
-  return res;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_TRANSLATION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Umeyama.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Umeyama.h
deleted file mode 100644
index 6b75500..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/Umeyama.h
+++ /dev/null
@@ -1,166 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMEYAMA_H
-#define EIGEN_UMEYAMA_H
-
-// This file requires the user to include 
-// * Eigen/Core
-// * Eigen/LU 
-// * Eigen/SVD
-// * Eigen/Array
-
-namespace Eigen { 
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-// These helpers are required since it allows to use mixed types as parameters
-// for the Umeyama. The problem with mixed parameters is that the return type
-// cannot trivially be deduced when float and double types are mixed.
-namespace internal {
-
-// Compile time return type deduction for different MatrixBase types.
-// Different means here different alignment and parameters but the same underlying
-// real scalar type.
-template<typename MatrixType, typename OtherMatrixType>
-struct umeyama_transform_matrix_type
-{
-  enum {
-    MinRowsAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, OtherMatrixType::RowsAtCompileTime),
-
-    // When possible we want to choose some small fixed size value since the result
-    // is likely to fit on the stack. So here, EIGEN_SIZE_MIN_PREFER_DYNAMIC is not what we want.
-    HomogeneousDimension = int(MinRowsAtCompileTime) == Dynamic ? Dynamic : int(MinRowsAtCompileTime)+1
-  };
-
-  typedef Matrix<typename traits<MatrixType>::Scalar,
-    HomogeneousDimension,
-    HomogeneousDimension,
-    AutoAlign | (traits<MatrixType>::Flags & RowMajorBit ? RowMajor : ColMajor),
-    HomogeneousDimension,
-    HomogeneousDimension
-  > type;
-};
-
-}
-
-#endif
-
-/**
-* \geometry_module \ingroup Geometry_Module
-*
-* \brief Returns the transformation between two point sets.
-*
-* The algorithm is based on:
-* "Least-squares estimation of transformation parameters between two point patterns",
-* Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
-*
-* It estimates parameters \f$ c, \mathbf{R}, \f$ and \f$ \mathbf{t} \f$ such that
-* \f{align*}
-*   \frac{1}{n} \sum_{i=1}^n \vert\vert y_i - (c\mathbf{R}x_i + \mathbf{t}) \vert\vert_2^2
-* \f}
-* is minimized.
-*
-* The algorithm is based on the analysis of the covariance matrix
-* \f$ \Sigma_{\mathbf{x}\mathbf{y}} \in \mathbb{R}^{d \times d} \f$
-* of the input point sets \f$ \mathbf{x} \f$ and \f$ \mathbf{y} \f$ where 
-* \f$d\f$ is corresponding to the dimension (which is typically small).
-* The analysis is involving the SVD having a complexity of \f$O(d^3)\f$
-* though the actual computational effort lies in the covariance
-* matrix computation which has an asymptotic lower bound of \f$O(dm)\f$ when 
-* the input point sets have dimension \f$d \times m\f$.
-*
-* Currently the method is working only for floating point matrices.
-*
-* \todo Should the return type of umeyama() become a Transform?
-*
-* \param src Source points \f$ \mathbf{x} = \left( x_1, \hdots, x_n \right) \f$.
-* \param dst Destination points \f$ \mathbf{y} = \left( y_1, \hdots, y_n \right) \f$.
-* \param with_scaling Sets \f$ c=1 \f$ when <code>false</code> is passed.
-* \return The homogeneous transformation 
-* \f{align*}
-*   T = \begin{bmatrix} c\mathbf{R} & \mathbf{t} \\ \mathbf{0} & 1 \end{bmatrix}
-* \f}
-* minimizing the residual above. This transformation is always returned as an 
-* Eigen::Matrix.
-*/
-template <typename Derived, typename OtherDerived>
-typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type
-umeyama(const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, bool with_scaling = true)
-{
-  typedef typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type TransformationMatrixType;
-  typedef typename internal::traits<TransformationMatrixType>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename internal::traits<OtherDerived>::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  enum { Dimension = EIGEN_SIZE_MIN_PREFER_DYNAMIC(Derived::RowsAtCompileTime, OtherDerived::RowsAtCompileTime) };
-
-  typedef Matrix<Scalar, Dimension, 1> VectorType;
-  typedef Matrix<Scalar, Dimension, Dimension> MatrixType;
-  typedef typename internal::plain_matrix_type_row_major<Derived>::type RowMajorMatrixType;
-
-  const Index m = src.rows(); // dimension
-  const Index n = src.cols(); // number of measurements
-
-  // required for demeaning ...
-  const RealScalar one_over_n = RealScalar(1) / static_cast<RealScalar>(n);
-
-  // computation of mean
-  const VectorType src_mean = src.rowwise().sum() * one_over_n;
-  const VectorType dst_mean = dst.rowwise().sum() * one_over_n;
-
-  // demeaning of src and dst points
-  const RowMajorMatrixType src_demean = src.colwise() - src_mean;
-  const RowMajorMatrixType dst_demean = dst.colwise() - dst_mean;
-
-  // Eq. (36)-(37)
-  const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;
-
-  // Eq. (38)
-  const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose();
-
-  JacobiSVD<MatrixType> svd(sigma, ComputeFullU | ComputeFullV);
-
-  // Initialize the resulting transformation with an identity matrix...
-  TransformationMatrixType Rt = TransformationMatrixType::Identity(m+1,m+1);
-
-  // Eq. (39)
-  VectorType S = VectorType::Ones(m);
-
-  if  ( svd.matrixU().determinant() * svd.matrixV().determinant() < 0 )
-    S(m-1) = -1;
-
-  // Eq. (40) and (43)
-  Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose();
-
-  if (with_scaling)
-  {
-    // Eq. (42)
-    const Scalar c = Scalar(1)/src_var * svd.singularValues().dot(S);
-
-    // Eq. (41)
-    Rt.col(m).head(m) = dst_mean;
-    Rt.col(m).head(m).noalias() -= c*Rt.topLeftCorner(m,m)*src_mean;
-    Rt.block(0,0,m,m) *= c;
-  }
-  else
-  {
-    Rt.col(m).head(m) = dst_mean;
-    Rt.col(m).head(m).noalias() -= Rt.topLeftCorner(m,m)*src_mean;
-  }
-
-  return Rt;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_UMEYAMA_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h
deleted file mode 100644
index 9af6a9a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h
+++ /dev/null
@@ -1,168 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Rohit Garg <rpg.314@gmail.com>
-// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GEOMETRY_SIMD_H
-#define EIGEN_GEOMETRY_SIMD_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<class Derived, class OtherDerived>
-struct quat_product<Architecture::Target, Derived, OtherDerived, float>
-{
-  enum {
-    AAlignment = traits<Derived>::Alignment,
-    BAlignment = traits<OtherDerived>::Alignment,
-    ResAlignment = traits<Quaternion<float> >::Alignment
-  };
-  static inline Quaternion<float> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
-  {
-    evaluator<typename Derived::Coefficients> ae(_a.coeffs());
-    evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
-    Quaternion<float> res;
-    const float neg_zero = numext::bit_cast<float>(0x80000000u);
-    const float arr[4] = {0.f, 0.f, 0.f, neg_zero};
-    const Packet4f mask = ploadu<Packet4f>(arr);
-    Packet4f a = ae.template packet<AAlignment,Packet4f>(0);
-    Packet4f b = be.template packet<BAlignment,Packet4f>(0);
-    Packet4f s1 = pmul(vec4f_swizzle1(a,1,2,0,2),vec4f_swizzle1(b,2,0,1,2));
-    Packet4f s2 = pmul(vec4f_swizzle1(a,3,3,3,1),vec4f_swizzle1(b,0,1,2,1));
-    pstoret<float,Packet4f,ResAlignment>(
-              &res.x(),
-              padd(psub(pmul(a,vec4f_swizzle1(b,3,3,3,3)),
-                                    pmul(vec4f_swizzle1(a,2,0,1,0),
-                                               vec4f_swizzle1(b,1,2,0,0))),
-                         pxor(mask,padd(s1,s2))));
-    
-    return res;
-  }
-};
-
-template<class Derived>
-struct quat_conj<Architecture::Target, Derived, float>
-{
-  enum {
-    ResAlignment = traits<Quaternion<float> >::Alignment
-  };
-  static inline Quaternion<float> run(const QuaternionBase<Derived>& q)
-  {
-    evaluator<typename Derived::Coefficients> qe(q.coeffs());
-    Quaternion<float> res;
-    const float neg_zero = numext::bit_cast<float>(0x80000000u);
-    const float arr[4] = {neg_zero, neg_zero, neg_zero,0.f};
-    const Packet4f mask = ploadu<Packet4f>(arr);
-    pstoret<float,Packet4f,ResAlignment>(&res.x(), pxor(mask, qe.template packet<traits<Derived>::Alignment,Packet4f>(0)));
-    return res;
-  }
-};
-
-
-template<typename VectorLhs,typename VectorRhs>
-struct cross3_impl<Architecture::Target,VectorLhs,VectorRhs,float,true>
-{
-  enum {
-    ResAlignment = traits<typename plain_matrix_type<VectorLhs>::type>::Alignment
-  };
-  static inline typename plain_matrix_type<VectorLhs>::type
-  run(const VectorLhs& lhs, const VectorRhs& rhs)
-  {
-    evaluator<VectorLhs> lhs_eval(lhs);
-    evaluator<VectorRhs> rhs_eval(rhs);
-    Packet4f a = lhs_eval.template packet<traits<VectorLhs>::Alignment,Packet4f>(0);
-    Packet4f b = rhs_eval.template packet<traits<VectorRhs>::Alignment,Packet4f>(0);
-    Packet4f mul1 = pmul(vec4f_swizzle1(a,1,2,0,3),vec4f_swizzle1(b,2,0,1,3));
-    Packet4f mul2 = pmul(vec4f_swizzle1(a,2,0,1,3),vec4f_swizzle1(b,1,2,0,3));
-    typename plain_matrix_type<VectorLhs>::type res;
-    pstoret<float,Packet4f,ResAlignment>(&res.x(),psub(mul1,mul2));
-    return res;
-  }
-};
-
-
-
-#if (defined EIGEN_VECTORIZE_SSE) || (EIGEN_ARCH_ARM64)
-
-template<class Derived, class OtherDerived>
-struct quat_product<Architecture::Target, Derived, OtherDerived, double>
-{
-  enum {
-    BAlignment = traits<OtherDerived>::Alignment,
-    ResAlignment = traits<Quaternion<double> >::Alignment
-  };
-
-  static inline Quaternion<double> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
-  {
-  Quaternion<double> res;
-
-  evaluator<typename Derived::Coefficients> ae(_a.coeffs());
-  evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
-
-  const double* a = _a.coeffs().data();
-  Packet2d b_xy = be.template packet<BAlignment,Packet2d>(0);
-  Packet2d b_zw = be.template packet<BAlignment,Packet2d>(2);
-  Packet2d a_xx = pset1<Packet2d>(a[0]);
-  Packet2d a_yy = pset1<Packet2d>(a[1]);
-  Packet2d a_zz = pset1<Packet2d>(a[2]);
-  Packet2d a_ww = pset1<Packet2d>(a[3]);
-
-  // two temporaries:
-  Packet2d t1, t2;
-
-  /*
-   * t1 = ww*xy + yy*zw
-   * t2 = zz*xy - xx*zw
-   * res.xy = t1 +/- swap(t2)
-   */
-  t1 = padd(pmul(a_ww, b_xy), pmul(a_yy, b_zw));
-  t2 = psub(pmul(a_zz, b_xy), pmul(a_xx, b_zw));
-  pstoret<double,Packet2d,ResAlignment>(&res.x(), paddsub(t1, preverse(t2)));
-  
-  /*
-   * t1 = ww*zw - yy*xy
-   * t2 = zz*zw + xx*xy
-   * res.zw = t1 -/+ swap(t2) = swap( swap(t1) +/- t2)
-   */
-  t1 = psub(pmul(a_ww, b_zw), pmul(a_yy, b_xy));
-  t2 = padd(pmul(a_zz, b_zw), pmul(a_xx, b_xy));
-  pstoret<double,Packet2d,ResAlignment>(&res.z(), preverse(paddsub(preverse(t1), t2)));
-
-  return res;
-}
-};
-
-template<class Derived>
-struct quat_conj<Architecture::Target, Derived, double>
-{
-  enum {
-    ResAlignment = traits<Quaternion<double> >::Alignment
-  };
-  static inline Quaternion<double> run(const QuaternionBase<Derived>& q)
-  {
-    evaluator<typename Derived::Coefficients> qe(q.coeffs());
-    Quaternion<double> res;
-    const double neg_zero = numext::bit_cast<double>(0x8000000000000000ull);
-    const double arr1[2] = {neg_zero, neg_zero};
-    const double arr2[2] = {neg_zero,  0.0};
-    const Packet2d mask0 = ploadu<Packet2d>(arr1);
-    const Packet2d mask2 = ploadu<Packet2d>(arr2);
-    pstoret<double,Packet2d,ResAlignment>(&res.x(), pxor(mask0, qe.template packet<traits<Derived>::Alignment,Packet2d>(0)));
-    pstoret<double,Packet2d,ResAlignment>(&res.z(), pxor(mask2, qe.template packet<traits<Derived>::Alignment,Packet2d>(2)));
-    return res;
-  }
-};
-
-#endif // end EIGEN_VECTORIZE_SSE_OR_EIGEN_ARCH_ARM64
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GEOMETRY_SIMD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/BlockHouseholder.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/BlockHouseholder.h
deleted file mode 100644
index 39ce1c2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/BlockHouseholder.h
+++ /dev/null
@@ -1,110 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Vincent Lejeune
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BLOCK_HOUSEHOLDER_H
-#define EIGEN_BLOCK_HOUSEHOLDER_H
-
-// This file contains some helper function to deal with block householder reflectors
-
-namespace Eigen { 
-
-namespace internal {
-  
-/** \internal */
-// template<typename TriangularFactorType,typename VectorsType,typename CoeffsType>
-// void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs)
-// {
-//   typedef typename VectorsType::Scalar Scalar;
-//   const Index nbVecs = vectors.cols();
-//   eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows()>=nbVecs);
-// 
-//   for(Index i = 0; i < nbVecs; i++)
-//   {
-//     Index rs = vectors.rows() - i;
-//     // Warning, note that hCoeffs may alias with vectors.
-//     // It is then necessary to copy it before modifying vectors(i,i). 
-//     typename CoeffsType::Scalar h = hCoeffs(i);
-//     // This hack permits to pass trough nested Block<> and Transpose<> expressions.
-//     Scalar *Vii_ptr = const_cast<Scalar*>(vectors.data() + vectors.outerStride()*i + vectors.innerStride()*i);
-//     Scalar Vii = *Vii_ptr;
-//     *Vii_ptr = Scalar(1);
-//     triFactor.col(i).head(i).noalias() = -h * vectors.block(i, 0, rs, i).adjoint()
-//                                        * vectors.col(i).tail(rs);
-//     *Vii_ptr = Vii;
-//     // FIXME add .noalias() once the triangular product can work inplace
-//     triFactor.col(i).head(i) = triFactor.block(0,0,i,i).template triangularView<Upper>()
-//                              * triFactor.col(i).head(i);
-//     triFactor(i,i) = hCoeffs(i);
-//   }
-// }
-
-/** \internal */
-// This variant avoid modifications in vectors
-template<typename TriangularFactorType,typename VectorsType,typename CoeffsType>
-void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs)
-{
-  const Index nbVecs = vectors.cols();
-  eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows()>=nbVecs);
-
-  for(Index i = nbVecs-1; i >=0 ; --i)
-  {
-    Index rs = vectors.rows() - i - 1;
-    Index rt = nbVecs-i-1;
-
-    if(rt>0)
-    {
-      triFactor.row(i).tail(rt).noalias() = -hCoeffs(i) * vectors.col(i).tail(rs).adjoint()
-                                                        * vectors.bottomRightCorner(rs, rt).template triangularView<UnitLower>();
-            
-      // FIXME use the following line with .noalias() once the triangular product can work inplace
-      // triFactor.row(i).tail(rt) = triFactor.row(i).tail(rt) * triFactor.bottomRightCorner(rt,rt).template triangularView<Upper>();
-      for(Index j=nbVecs-1; j>i; --j)
-      {
-        typename TriangularFactorType::Scalar z = triFactor(i,j);
-        triFactor(i,j) = z * triFactor(j,j);
-        if(nbVecs-j-1>0)
-          triFactor.row(i).tail(nbVecs-j-1) += z * triFactor.row(j).tail(nbVecs-j-1);
-      }
-      
-    }
-    triFactor(i,i) = hCoeffs(i);
-  }
-}
-
-/** \internal
-  * if forward then perform   mat = H0 * H1 * H2 * mat
-  * otherwise perform         mat = H2 * H1 * H0 * mat
-  */
-template<typename MatrixType,typename VectorsType,typename CoeffsType>
-void apply_block_householder_on_the_left(MatrixType& mat, const VectorsType& vectors, const CoeffsType& hCoeffs, bool forward)
-{
-  enum { TFactorSize = MatrixType::ColsAtCompileTime };
-  Index nbVecs = vectors.cols();
-  Matrix<typename MatrixType::Scalar, TFactorSize, TFactorSize, RowMajor> T(nbVecs,nbVecs);
-  
-  if(forward) make_block_householder_triangular_factor(T, vectors, hCoeffs);
-  else        make_block_householder_triangular_factor(T, vectors, hCoeffs.conjugate());  
-  const TriangularView<const VectorsType, UnitLower> V(vectors);
-
-  // A -= V T V^* A
-  Matrix<typename MatrixType::Scalar,VectorsType::ColsAtCompileTime,MatrixType::ColsAtCompileTime,
-         (VectorsType::MaxColsAtCompileTime==1 && MatrixType::MaxColsAtCompileTime!=1)?RowMajor:ColMajor,
-         VectorsType::MaxColsAtCompileTime,MatrixType::MaxColsAtCompileTime> tmp = V.adjoint() * mat;
-  // FIXME add .noalias() once the triangular product can work inplace
-  if(forward) tmp = T.template triangularView<Upper>()           * tmp;
-  else        tmp = T.template triangularView<Upper>().adjoint() * tmp;
-  mat.noalias() -= V * tmp;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BLOCK_HOUSEHOLDER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/Householder.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/Householder.h
deleted file mode 100644
index 5bc037f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/Householder.h
+++ /dev/null
@@ -1,176 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOUSEHOLDER_H
-#define EIGEN_HOUSEHOLDER_H
-
-namespace Eigen { 
-
-namespace internal {
-template<int n> struct decrement_size
-{
-  enum {
-    ret = n==Dynamic ? n : n-1
-  };
-};
-}
-
-/** Computes the elementary reflector H such that:
-  * \f$ H *this = [ beta 0 ... 0]^T \f$
-  * where the transformation H is:
-  * \f$ H = I - tau v v^*\f$
-  * and the vector v is:
-  * \f$ v^T = [1 essential^T] \f$
-  *
-  * The essential part of the vector \c v is stored in *this.
-  * 
-  * On output:
-  * \param tau the scaling factor of the Householder transformation
-  * \param beta the result of H * \c *this
-  *
-  * \sa MatrixBase::makeHouseholder(), MatrixBase::applyHouseholderOnTheLeft(),
-  *     MatrixBase::applyHouseholderOnTheRight()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::makeHouseholderInPlace(Scalar& tau, RealScalar& beta)
-{
-  VectorBlock<Derived, internal::decrement_size<Base::SizeAtCompileTime>::ret> essentialPart(derived(), 1, size()-1);
-  makeHouseholder(essentialPart, tau, beta);
-}
-
-/** Computes the elementary reflector H such that:
-  * \f$ H *this = [ beta 0 ... 0]^T \f$
-  * where the transformation H is:
-  * \f$ H = I - tau v v^*\f$
-  * and the vector v is:
-  * \f$ v^T = [1 essential^T] \f$
-  *
-  * On output:
-  * \param essential the essential part of the vector \c v
-  * \param tau the scaling factor of the Householder transformation
-  * \param beta the result of H * \c *this
-  *
-  * \sa MatrixBase::makeHouseholderInPlace(), MatrixBase::applyHouseholderOnTheLeft(),
-  *     MatrixBase::applyHouseholderOnTheRight()
-  */
-template<typename Derived>
-template<typename EssentialPart>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::makeHouseholder(
-  EssentialPart& essential,
-  Scalar& tau,
-  RealScalar& beta) const
-{
-  using std::sqrt;
-  using numext::conj;
-  
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(EssentialPart)
-  VectorBlock<const Derived, EssentialPart::SizeAtCompileTime> tail(derived(), 1, size()-1);
-  
-  RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm();
-  Scalar c0 = coeff(0);
-  const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
-
-  if(tailSqNorm <= tol && numext::abs2(numext::imag(c0))<=tol)
-  {
-    tau = RealScalar(0);
-    beta = numext::real(c0);
-    essential.setZero();
-  }
-  else
-  {
-    beta = sqrt(numext::abs2(c0) + tailSqNorm);
-    if (numext::real(c0)>=RealScalar(0))
-      beta = -beta;
-    essential = tail / (c0 - beta);
-    tau = conj((beta - c0) / beta);
-  }
-}
-
-/** Apply the elementary reflector H given by
-  * \f$ H = I - tau v v^*\f$
-  * with
-  * \f$ v^T = [1 essential^T] \f$
-  * from the left to a vector or matrix.
-  *
-  * On input:
-  * \param essential the essential part of the vector \c v
-  * \param tau the scaling factor of the Householder transformation
-  * \param workspace a pointer to working space with at least
-  *                  this->cols() entries
-  *
-  * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), 
-  *     MatrixBase::applyHouseholderOnTheRight()
-  */
-template<typename Derived>
-template<typename EssentialPart>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::applyHouseholderOnTheLeft(
-  const EssentialPart& essential,
-  const Scalar& tau,
-  Scalar* workspace)
-{
-  if(rows() == 1)
-  {
-    *this *= Scalar(1)-tau;
-  }
-  else if(tau!=Scalar(0))
-  {
-    Map<typename internal::plain_row_type<PlainObject>::type> tmp(workspace,cols());
-    Block<Derived, EssentialPart::SizeAtCompileTime, Derived::ColsAtCompileTime> bottom(derived(), 1, 0, rows()-1, cols());
-    tmp.noalias() = essential.adjoint() * bottom;
-    tmp += this->row(0);
-    this->row(0) -= tau * tmp;
-    bottom.noalias() -= tau * essential * tmp;
-  }
-}
-
-/** Apply the elementary reflector H given by
-  * \f$ H = I - tau v v^*\f$
-  * with
-  * \f$ v^T = [1 essential^T] \f$
-  * from the right to a vector or matrix.
-  *
-  * On input:
-  * \param essential the essential part of the vector \c v
-  * \param tau the scaling factor of the Householder transformation
-  * \param workspace a pointer to working space with at least
-  *                  this->rows() entries
-  *
-  * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), 
-  *     MatrixBase::applyHouseholderOnTheLeft()
-  */
-template<typename Derived>
-template<typename EssentialPart>
-EIGEN_DEVICE_FUNC
-void MatrixBase<Derived>::applyHouseholderOnTheRight(
-  const EssentialPart& essential,
-  const Scalar& tau,
-  Scalar* workspace)
-{
-  if(cols() == 1)
-  {
-    *this *= Scalar(1)-tau;
-  }
-  else if(tau!=Scalar(0))
-  {
-    Map<typename internal::plain_col_type<PlainObject>::type> tmp(workspace,rows());
-    Block<Derived, Derived::RowsAtCompileTime, EssentialPart::SizeAtCompileTime> right(derived(), 0, 1, rows(), cols()-1);
-    tmp.noalias() = right * essential;
-    tmp += this->col(0);
-    this->col(0) -= tau * tmp;
-    right.noalias() -= tau * tmp * essential.adjoint();
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_HOUSEHOLDER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/HouseholderSequence.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
deleted file mode 100644
index 022f6c3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
+++ /dev/null
@@ -1,545 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HOUSEHOLDER_SEQUENCE_H
-#define EIGEN_HOUSEHOLDER_SEQUENCE_H
-
-namespace Eigen {
-
-/** \ingroup Householder_Module
-  * \householder_module
-  * \class HouseholderSequence
-  * \brief Sequence of Householder reflections acting on subspaces with decreasing size
-  * \tparam VectorsType type of matrix containing the Householder vectors
-  * \tparam CoeffsType  type of vector containing the Householder coefficients
-  * \tparam Side        either OnTheLeft (the default) or OnTheRight
-  *
-  * This class represents a product sequence of Householder reflections where the first Householder reflection
-  * acts on the whole space, the second Householder reflection leaves the one-dimensional subspace spanned by
-  * the first unit vector invariant, the third Householder reflection leaves the two-dimensional subspace
-  * spanned by the first two unit vectors invariant, and so on up to the last reflection which leaves all but
-  * one dimensions invariant and acts only on the last dimension. Such sequences of Householder reflections
-  * are used in several algorithms to zero out certain parts of a matrix. Indeed, the methods
-  * HessenbergDecomposition::matrixQ(), Tridiagonalization::matrixQ(), HouseholderQR::householderQ(),
-  * and ColPivHouseholderQR::householderQ() all return a %HouseholderSequence.
-  *
-  * More precisely, the class %HouseholderSequence represents an \f$ n \times n \f$ matrix \f$ H \f$ of the
-  * form \f$ H = \prod_{i=0}^{n-1} H_i \f$ where the i-th Householder reflection is \f$ H_i = I - h_i v_i
-  * v_i^* \f$. The i-th Householder coefficient \f$ h_i \f$ is a scalar and the i-th Householder vector \f$
-  * v_i \f$ is a vector of the form
-  * \f[
-  * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
-  * \f]
-  * The last \f$ n-i \f$ entries of \f$ v_i \f$ are called the essential part of the Householder vector.
-  *
-  * Typical usages are listed below, where H is a HouseholderSequence:
-  * \code
-  * A.applyOnTheRight(H);             // A = A * H
-  * A.applyOnTheLeft(H);              // A = H * A
-  * A.applyOnTheRight(H.adjoint());   // A = A * H^*
-  * A.applyOnTheLeft(H.adjoint());    // A = H^* * A
-  * MatrixXd Q = H;                   // conversion to a dense matrix
-  * \endcode
-  * In addition to the adjoint, you can also apply the inverse (=adjoint), the transpose, and the conjugate operators.
-  *
-  * See the documentation for HouseholderSequence(const VectorsType&, const CoeffsType&) for an example.
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-
-namespace internal {
-
-template<typename VectorsType, typename CoeffsType, int Side>
-struct traits<HouseholderSequence<VectorsType,CoeffsType,Side> >
-{
-  typedef typename VectorsType::Scalar Scalar;
-  typedef typename VectorsType::StorageIndex StorageIndex;
-  typedef typename VectorsType::StorageKind StorageKind;
-  enum {
-    RowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::RowsAtCompileTime
-                                        : traits<VectorsType>::ColsAtCompileTime,
-    ColsAtCompileTime = RowsAtCompileTime,
-    MaxRowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::MaxRowsAtCompileTime
-                                           : traits<VectorsType>::MaxColsAtCompileTime,
-    MaxColsAtCompileTime = MaxRowsAtCompileTime,
-    Flags = 0
-  };
-};
-
-struct HouseholderSequenceShape {};
-
-template<typename VectorsType, typename CoeffsType, int Side>
-struct evaluator_traits<HouseholderSequence<VectorsType,CoeffsType,Side> >
-  : public evaluator_traits_base<HouseholderSequence<VectorsType,CoeffsType,Side> >
-{
-  typedef HouseholderSequenceShape Shape;
-};
-
-template<typename VectorsType, typename CoeffsType, int Side>
-struct hseq_side_dependent_impl
-{
-  typedef Block<const VectorsType, Dynamic, 1> EssentialVectorType;
-  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheLeft> HouseholderSequenceType;
-  static EIGEN_DEVICE_FUNC inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k)
-  {
-    Index start = k+1+h.m_shift;
-    return Block<const VectorsType,Dynamic,1>(h.m_vectors, start, k, h.rows()-start, 1);
-  }
-};
-
-template<typename VectorsType, typename CoeffsType>
-struct hseq_side_dependent_impl<VectorsType, CoeffsType, OnTheRight>
-{
-  typedef Transpose<Block<const VectorsType, 1, Dynamic> > EssentialVectorType;
-  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheRight> HouseholderSequenceType;
-  static inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k)
-  {
-    Index start = k+1+h.m_shift;
-    return Block<const VectorsType,1,Dynamic>(h.m_vectors, k, start, 1, h.rows()-start).transpose();
-  }
-};
-
-template<typename OtherScalarType, typename MatrixType> struct matrix_type_times_scalar_type
-{
-  typedef typename ScalarBinaryOpTraits<OtherScalarType, typename MatrixType::Scalar>::ReturnType
-    ResultScalar;
-  typedef Matrix<ResultScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
-                 0, MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime> Type;
-};
-
-} // end namespace internal
-
-template<typename VectorsType, typename CoeffsType, int Side> class HouseholderSequence
-  : public EigenBase<HouseholderSequence<VectorsType,CoeffsType,Side> >
-{
-    typedef typename internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::EssentialVectorType EssentialVectorType;
-
-  public:
-    enum {
-      RowsAtCompileTime = internal::traits<HouseholderSequence>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<HouseholderSequence>::ColsAtCompileTime,
-      MaxRowsAtCompileTime = internal::traits<HouseholderSequence>::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = internal::traits<HouseholderSequence>::MaxColsAtCompileTime
-    };
-    typedef typename internal::traits<HouseholderSequence>::Scalar Scalar;
-
-    typedef HouseholderSequence<
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename VectorsType::ConjugateReturnType>::type,
-        VectorsType>::type,
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename CoeffsType::ConjugateReturnType>::type,
-        CoeffsType>::type,
-      Side
-    > ConjugateReturnType;
-
-    typedef HouseholderSequence<
-      VectorsType,
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename CoeffsType::ConjugateReturnType>::type,
-        CoeffsType>::type,
-      Side
-    > AdjointReturnType;
-
-    typedef HouseholderSequence<
-      typename internal::conditional<NumTraits<Scalar>::IsComplex,
-        typename internal::remove_all<typename VectorsType::ConjugateReturnType>::type,
-        VectorsType>::type,
-      CoeffsType,
-      Side
-    > TransposeReturnType;
-
-    typedef HouseholderSequence<
-      typename internal::add_const<VectorsType>::type,
-      typename internal::add_const<CoeffsType>::type,
-      Side
-    > ConstHouseholderSequence;
-
-    /** \brief Constructor.
-      * \param[in]  v      %Matrix containing the essential parts of the Householder vectors
-      * \param[in]  h      Vector containing the Householder coefficients
-      *
-      * Constructs the Householder sequence with coefficients given by \p h and vectors given by \p v. The
-      * i-th Householder coefficient \f$ h_i \f$ is given by \p h(i) and the essential part of the i-th
-      * Householder vector \f$ v_i \f$ is given by \p v(k,i) with \p k > \p i (the subdiagonal part of the
-      * i-th column). If \p v has fewer columns than rows, then the Householder sequence contains as many
-      * Householder reflections as there are columns.
-      *
-      * \note The %HouseholderSequence object stores \p v and \p h by reference.
-      *
-      * Example: \include HouseholderSequence_HouseholderSequence.cpp
-      * Output: \verbinclude HouseholderSequence_HouseholderSequence.out
-      *
-      * \sa setLength(), setShift()
-      */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence(const VectorsType& v, const CoeffsType& h)
-      : m_vectors(v), m_coeffs(h), m_reverse(false), m_length(v.diagonalSize()),
-        m_shift(0)
-    {
-    }
-
-    /** \brief Copy constructor. */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence(const HouseholderSequence& other)
-      : m_vectors(other.m_vectors),
-        m_coeffs(other.m_coeffs),
-        m_reverse(other.m_reverse),
-        m_length(other.m_length),
-        m_shift(other.m_shift)
-    {
-    }
-
-    /** \brief Number of rows of transformation viewed as a matrix.
-      * \returns Number of rows
-      * \details This equals the dimension of the space that the transformation acts on.
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index rows() const EIGEN_NOEXCEPT { return Side==OnTheLeft ? m_vectors.rows() : m_vectors.cols(); }
-
-    /** \brief Number of columns of transformation viewed as a matrix.
-      * \returns Number of columns
-      * \details This equals the dimension of the space that the transformation acts on.
-      */
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    Index cols() const EIGEN_NOEXCEPT { return rows(); }
-
-    /** \brief Essential part of a Householder vector.
-      * \param[in]  k  Index of Householder reflection
-      * \returns    Vector containing non-trivial entries of k-th Householder vector
-      *
-      * This function returns the essential part of the Householder vector \f$ v_i \f$. This is a vector of
-      * length \f$ n-i \f$ containing the last \f$ n-i \f$ entries of the vector
-      * \f[
-      * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
-      * \f]
-      * The index \f$ i \f$ equals \p k + shift(), corresponding to the k-th column of the matrix \p v
-      * passed to the constructor.
-      *
-      * \sa setShift(), shift()
-      */
-    EIGEN_DEVICE_FUNC
-    const EssentialVectorType essentialVector(Index k) const
-    {
-      eigen_assert(k >= 0 && k < m_length);
-      return internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::essentialVector(*this, k);
-    }
-
-    /** \brief %Transpose of the Householder sequence. */
-    TransposeReturnType transpose() const
-    {
-      return TransposeReturnType(m_vectors.conjugate(), m_coeffs)
-              .setReverseFlag(!m_reverse)
-              .setLength(m_length)
-              .setShift(m_shift);
-    }
-
-    /** \brief Complex conjugate of the Householder sequence. */
-    ConjugateReturnType conjugate() const
-    {
-      return ConjugateReturnType(m_vectors.conjugate(), m_coeffs.conjugate())
-             .setReverseFlag(m_reverse)
-             .setLength(m_length)
-             .setShift(m_shift);
-    }
-
-    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
-     *           returns \c *this otherwise.
-     */
-    template<bool Cond>
-    EIGEN_DEVICE_FUNC
-    inline typename internal::conditional<Cond,ConjugateReturnType,ConstHouseholderSequence>::type
-    conjugateIf() const
-    {
-      typedef typename internal::conditional<Cond,ConjugateReturnType,ConstHouseholderSequence>::type ReturnType;
-      return ReturnType(m_vectors.template conjugateIf<Cond>(), m_coeffs.template conjugateIf<Cond>());
-    }
-
-    /** \brief Adjoint (conjugate transpose) of the Householder sequence. */
-    AdjointReturnType adjoint() const
-    {
-      return AdjointReturnType(m_vectors, m_coeffs.conjugate())
-              .setReverseFlag(!m_reverse)
-              .setLength(m_length)
-              .setShift(m_shift);
-    }
-
-    /** \brief Inverse of the Householder sequence (equals the adjoint). */
-    AdjointReturnType inverse() const { return adjoint(); }
-
-    /** \internal */
-    template<typename DestType>
-    inline EIGEN_DEVICE_FUNC
-    void evalTo(DestType& dst) const
-    {
-      Matrix<Scalar, DestType::RowsAtCompileTime, 1,
-             AutoAlign|ColMajor, DestType::MaxRowsAtCompileTime, 1> workspace(rows());
-      evalTo(dst, workspace);
-    }
-
-    /** \internal */
-    template<typename Dest, typename Workspace>
-    EIGEN_DEVICE_FUNC
-    void evalTo(Dest& dst, Workspace& workspace) const
-    {
-      workspace.resize(rows());
-      Index vecs = m_length;
-      if(internal::is_same_dense(dst,m_vectors))
-      {
-        // in-place
-        dst.diagonal().setOnes();
-        dst.template triangularView<StrictlyUpper>().setZero();
-        for(Index k = vecs-1; k >= 0; --k)
-        {
-          Index cornerSize = rows() - k - m_shift;
-          if(m_reverse)
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-          else
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-
-          // clear the off diagonal vector
-          dst.col(k).tail(rows()-k-1).setZero();
-        }
-        // clear the remaining columns if needed
-        for(Index k = 0; k<cols()-vecs ; ++k)
-          dst.col(k).tail(rows()-k-1).setZero();
-      }
-      else if(m_length>BlockSize)
-      {
-        dst.setIdentity(rows(), rows());
-        if(m_reverse)
-          applyThisOnTheLeft(dst,workspace,true);
-        else
-          applyThisOnTheLeft(dst,workspace,true);
-      }
-      else
-      {
-        dst.setIdentity(rows(), rows());
-        for(Index k = vecs-1; k >= 0; --k)
-        {
-          Index cornerSize = rows() - k - m_shift;
-          if(m_reverse)
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-          else
-            dst.bottomRightCorner(cornerSize, cornerSize)
-               .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
-        }
-      }
-    }
-
-    /** \internal */
-    template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
-    {
-      Matrix<Scalar,1,Dest::RowsAtCompileTime,RowMajor,1,Dest::MaxRowsAtCompileTime> workspace(dst.rows());
-      applyThisOnTheRight(dst, workspace);
-    }
-
-    /** \internal */
-    template<typename Dest, typename Workspace>
-    inline void applyThisOnTheRight(Dest& dst, Workspace& workspace) const
-    {
-      workspace.resize(dst.rows());
-      for(Index k = 0; k < m_length; ++k)
-      {
-        Index actual_k = m_reverse ? m_length-k-1 : k;
-        dst.rightCols(rows()-m_shift-actual_k)
-           .applyHouseholderOnTheRight(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
-      }
-    }
-
-    /** \internal */
-    template<typename Dest> inline void applyThisOnTheLeft(Dest& dst, bool inputIsIdentity = false) const
-    {
-      Matrix<Scalar,1,Dest::ColsAtCompileTime,RowMajor,1,Dest::MaxColsAtCompileTime> workspace;
-      applyThisOnTheLeft(dst, workspace, inputIsIdentity);
-    }
-
-    /** \internal */
-    template<typename Dest, typename Workspace>
-    inline void applyThisOnTheLeft(Dest& dst, Workspace& workspace, bool inputIsIdentity = false) const
-    {
-      if(inputIsIdentity && m_reverse)
-        inputIsIdentity = false;
-      // if the entries are large enough, then apply the reflectors by block
-      if(m_length>=BlockSize && dst.cols()>1)
-      {
-        // Make sure we have at least 2 useful blocks, otherwise it is point-less:
-        Index blockSize = m_length<Index(2*BlockSize) ? (m_length+1)/2 : Index(BlockSize);
-        for(Index i = 0; i < m_length; i+=blockSize)
-        {
-          Index end = m_reverse ? (std::min)(m_length,i+blockSize) : m_length-i;
-          Index k = m_reverse ? i : (std::max)(Index(0),end-blockSize);
-          Index bs = end-k;
-          Index start = k + m_shift;
-
-          typedef Block<typename internal::remove_all<VectorsType>::type,Dynamic,Dynamic> SubVectorsType;
-          SubVectorsType sub_vecs1(m_vectors.const_cast_derived(), Side==OnTheRight ? k : start,
-                                                                   Side==OnTheRight ? start : k,
-                                                                   Side==OnTheRight ? bs : m_vectors.rows()-start,
-                                                                   Side==OnTheRight ? m_vectors.cols()-start : bs);
-          typename internal::conditional<Side==OnTheRight, Transpose<SubVectorsType>, SubVectorsType&>::type sub_vecs(sub_vecs1);
-
-          Index dstStart = dst.rows()-rows()+m_shift+k;
-          Index dstRows  = rows()-m_shift-k;
-          Block<Dest,Dynamic,Dynamic> sub_dst(dst,
-                                              dstStart,
-                                              inputIsIdentity ? dstStart : 0,
-                                              dstRows,
-                                              inputIsIdentity ? dstRows : dst.cols());
-          apply_block_householder_on_the_left(sub_dst, sub_vecs, m_coeffs.segment(k, bs), !m_reverse);
-        }
-      }
-      else
-      {
-        workspace.resize(dst.cols());
-        for(Index k = 0; k < m_length; ++k)
-        {
-          Index actual_k = m_reverse ? k : m_length-k-1;
-          Index dstStart = rows()-m_shift-actual_k;
-          dst.bottomRightCorner(dstStart, inputIsIdentity ? dstStart : dst.cols())
-            .applyHouseholderOnTheLeft(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
-        }
-      }
-    }
-
-    /** \brief Computes the product of a Householder sequence with a matrix.
-      * \param[in]  other  %Matrix being multiplied.
-      * \returns    Expression object representing the product.
-      *
-      * This function computes \f$ HM \f$ where \f$ H \f$ is the Householder sequence represented by \p *this
-      * and \f$ M \f$ is the matrix \p other.
-      */
-    template<typename OtherDerived>
-    typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other) const
-    {
-      typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type
-        res(other.template cast<typename internal::matrix_type_times_scalar_type<Scalar,OtherDerived>::ResultScalar>());
-      applyThisOnTheLeft(res, internal::is_identity<OtherDerived>::value && res.rows()==res.cols());
-      return res;
-    }
-
-    template<typename _VectorsType, typename _CoeffsType, int _Side> friend struct internal::hseq_side_dependent_impl;
-
-    /** \brief Sets the length of the Householder sequence.
-      * \param [in]  length  New value for the length.
-      *
-      * By default, the length \f$ n \f$ of the Householder sequence \f$ H = H_0 H_1 \ldots H_{n-1} \f$ is set
-      * to the number of columns of the matrix \p v passed to the constructor, or the number of rows if that
-      * is smaller. After this function is called, the length equals \p length.
-      *
-      * \sa length()
-      */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence& setLength(Index length)
-    {
-      m_length = length;
-      return *this;
-    }
-
-    /** \brief Sets the shift of the Householder sequence.
-      * \param [in]  shift  New value for the shift.
-      *
-      * By default, a %HouseholderSequence object represents \f$ H = H_0 H_1 \ldots H_{n-1} \f$ and the i-th
-      * column of the matrix \p v passed to the constructor corresponds to the i-th Householder
-      * reflection. After this function is called, the object represents \f$ H = H_{\mathrm{shift}}
-      * H_{\mathrm{shift}+1} \ldots H_{n-1} \f$ and the i-th column of \p v corresponds to the (shift+i)-th
-      * Householder reflection.
-      *
-      * \sa shift()
-      */
-    EIGEN_DEVICE_FUNC
-    HouseholderSequence& setShift(Index shift)
-    {
-      m_shift = shift;
-      return *this;
-    }
-
-    EIGEN_DEVICE_FUNC
-    Index length() const { return m_length; }  /**< \brief Returns the length of the Householder sequence. */
-
-    EIGEN_DEVICE_FUNC
-    Index shift() const { return m_shift; }    /**< \brief Returns the shift of the Householder sequence. */
-
-    /* Necessary for .adjoint() and .conjugate() */
-    template <typename VectorsType2, typename CoeffsType2, int Side2> friend class HouseholderSequence;
-
-  protected:
-
-    /** \internal
-      * \brief Sets the reverse flag.
-      * \param [in]  reverse  New value of the reverse flag.
-      *
-      * By default, the reverse flag is not set. If the reverse flag is set, then this object represents
-      * \f$ H^r = H_{n-1} \ldots H_1 H_0 \f$ instead of \f$ H = H_0 H_1 \ldots H_{n-1} \f$.
-      * \note For real valued HouseholderSequence this is equivalent to transposing \f$ H \f$.
-      *
-      * \sa reverseFlag(), transpose(), adjoint()
-      */
-    HouseholderSequence& setReverseFlag(bool reverse)
-    {
-      m_reverse = reverse;
-      return *this;
-    }
-
-    bool reverseFlag() const { return m_reverse; }     /**< \internal \brief Returns the reverse flag. */
-
-    typename VectorsType::Nested m_vectors;
-    typename CoeffsType::Nested m_coeffs;
-    bool m_reverse;
-    Index m_length;
-    Index m_shift;
-    enum { BlockSize = 48 };
-};
-
-/** \brief Computes the product of a matrix with a Householder sequence.
-  * \param[in]  other  %Matrix being multiplied.
-  * \param[in]  h      %HouseholderSequence being multiplied.
-  * \returns    Expression object representing the product.
-  *
-  * This function computes \f$ MH \f$ where \f$ M \f$ is the matrix \p other and \f$ H \f$ is the
-  * Householder sequence represented by \p h.
-  */
-template<typename OtherDerived, typename VectorsType, typename CoeffsType, int Side>
-typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other, const HouseholderSequence<VectorsType,CoeffsType,Side>& h)
-{
-  typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type
-    res(other.template cast<typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::ResultScalar>());
-  h.applyThisOnTheRight(res);
-  return res;
-}
-
-/** \ingroup Householder_Module \householder_module
-  * \brief Convenience function for constructing a Householder sequence.
-  * \returns A HouseholderSequence constructed from the specified arguments.
-  */
-template<typename VectorsType, typename CoeffsType>
-HouseholderSequence<VectorsType,CoeffsType> householderSequence(const VectorsType& v, const CoeffsType& h)
-{
-  return HouseholderSequence<VectorsType,CoeffsType,OnTheLeft>(v, h);
-}
-
-/** \ingroup Householder_Module \householder_module
-  * \brief Convenience function for constructing a Householder sequence.
-  * \returns A HouseholderSequence constructed from the specified arguments.
-  * \details This function differs from householderSequence() in that the template argument \p OnTheSide of
-  * the constructed HouseholderSequence is set to OnTheRight, instead of the default OnTheLeft.
-  */
-template<typename VectorsType, typename CoeffsType>
-HouseholderSequence<VectorsType,CoeffsType,OnTheRight> rightHouseholderSequence(const VectorsType& v, const CoeffsType& h)
-{
-  return HouseholderSequence<VectorsType,CoeffsType,OnTheRight>(v, h);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_HOUSEHOLDER_SEQUENCE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
deleted file mode 100644
index a117fc1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BASIC_PRECONDITIONERS_H
-#define EIGEN_BASIC_PRECONDITIONERS_H
-
-namespace Eigen {
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A preconditioner based on the digonal entries
-  *
-  * This class allows to approximately solve for A.x = b problems assuming A is a diagonal matrix.
-  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
-    \code
-    A.diagonal().asDiagonal() . x = b
-    \endcode
-  *
-  * \tparam _Scalar the type of the scalar.
-  *
-  * \implsparsesolverconcept
-  *
-  * This preconditioner is suitable for both selfadjoint and general problems.
-  * The diagonal entries are pre-inverted and stored into a dense vector.
-  *
-  * \note A variant that has yet to be implemented would attempt to preserve the norm of each column.
-  *
-  * \sa class LeastSquareDiagonalPreconditioner, class ConjugateGradient
-  */
-template <typename _Scalar>
-class DiagonalPreconditioner
-{
-    typedef _Scalar Scalar;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-  public:
-    typedef typename Vector::StorageIndex StorageIndex;
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-
-    DiagonalPreconditioner() : m_isInitialized(false) {}
-
-    template<typename MatType>
-    explicit DiagonalPreconditioner(const MatType& mat) : m_invdiag(mat.cols())
-    {
-      compute(mat);
-    }
-
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
-
-    template<typename MatType>
-    DiagonalPreconditioner& analyzePattern(const MatType& )
-    {
-      return *this;
-    }
-
-    template<typename MatType>
-    DiagonalPreconditioner& factorize(const MatType& mat)
-    {
-      m_invdiag.resize(mat.cols());
-      for(int j=0; j<mat.outerSize(); ++j)
-      {
-        typename MatType::InnerIterator it(mat,j);
-        while(it && it.index()!=j) ++it;
-        if(it && it.index()==j && it.value()!=Scalar(0))
-          m_invdiag(j) = Scalar(1)/it.value();
-        else
-          m_invdiag(j) = Scalar(1);
-      }
-      m_isInitialized = true;
-      return *this;
-    }
-
-    template<typename MatType>
-    DiagonalPreconditioner& compute(const MatType& mat)
-    {
-      return factorize(mat);
-    }
-
-    /** \internal */
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const Rhs& b, Dest& x) const
-    {
-      x = m_invdiag.array() * b.array() ;
-    }
-
-    template<typename Rhs> inline const Solve<DiagonalPreconditioner, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "DiagonalPreconditioner is not initialized.");
-      eigen_assert(m_invdiag.size()==b.rows()
-                && "DiagonalPreconditioner::solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<DiagonalPreconditioner, Rhs>(*this, b.derived());
-    }
-
-    ComputationInfo info() { return Success; }
-
-  protected:
-    Vector m_invdiag;
-    bool m_isInitialized;
-};
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief Jacobi preconditioner for LeastSquaresConjugateGradient
-  *
-  * This class allows to approximately solve for A' A x  = A' b problems assuming A' A is a diagonal matrix.
-  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
-    \code
-    (A.adjoint() * A).diagonal().asDiagonal() * x = b
-    \endcode
-  *
-  * \tparam _Scalar the type of the scalar.
-  *
-  * \implsparsesolverconcept
-  *
-  * The diagonal entries are pre-inverted and stored into a dense vector.
-  *
-  * \sa class LeastSquaresConjugateGradient, class DiagonalPreconditioner
-  */
-template <typename _Scalar>
-class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<_Scalar>
-{
-    typedef _Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef DiagonalPreconditioner<_Scalar> Base;
-    using Base::m_invdiag;
-  public:
-
-    LeastSquareDiagonalPreconditioner() : Base() {}
-
-    template<typename MatType>
-    explicit LeastSquareDiagonalPreconditioner(const MatType& mat) : Base()
-    {
-      compute(mat);
-    }
-
-    template<typename MatType>
-    LeastSquareDiagonalPreconditioner& analyzePattern(const MatType& )
-    {
-      return *this;
-    }
-
-    template<typename MatType>
-    LeastSquareDiagonalPreconditioner& factorize(const MatType& mat)
-    {
-      // Compute the inverse squared-norm of each column of mat
-      m_invdiag.resize(mat.cols());
-      if(MatType::IsRowMajor)
-      {
-        m_invdiag.setZero();
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          for(typename MatType::InnerIterator it(mat,j); it; ++it)
-            m_invdiag(it.index()) += numext::abs2(it.value());
-        }
-        for(Index j=0; j<mat.cols(); ++j)
-          if(numext::real(m_invdiag(j))>RealScalar(0))
-            m_invdiag(j) = RealScalar(1)/numext::real(m_invdiag(j));
-      }
-      else
-      {
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          RealScalar sum = mat.col(j).squaredNorm();
-          if(sum>RealScalar(0))
-            m_invdiag(j) = RealScalar(1)/sum;
-          else
-            m_invdiag(j) = RealScalar(1);
-        }
-      }
-      Base::m_isInitialized = true;
-      return *this;
-    }
-
-    template<typename MatType>
-    LeastSquareDiagonalPreconditioner& compute(const MatType& mat)
-    {
-      return factorize(mat);
-    }
-
-    ComputationInfo info() { return Success; }
-
-  protected:
-};
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A naive preconditioner which approximates any matrix as the identity matrix
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa class DiagonalPreconditioner
-  */
-class IdentityPreconditioner
-{
-  public:
-
-    IdentityPreconditioner() {}
-
-    template<typename MatrixType>
-    explicit IdentityPreconditioner(const MatrixType& ) {}
-
-    template<typename MatrixType>
-    IdentityPreconditioner& analyzePattern(const MatrixType& ) { return *this; }
-
-    template<typename MatrixType>
-    IdentityPreconditioner& factorize(const MatrixType& ) { return *this; }
-
-    template<typename MatrixType>
-    IdentityPreconditioner& compute(const MatrixType& ) { return *this; }
-
-    template<typename Rhs>
-    inline const Rhs& solve(const Rhs& b) const { return b; }
-
-    ComputationInfo info() { return Success; }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_BASIC_PRECONDITIONERS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
deleted file mode 100644
index 153acef..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
+++ /dev/null
@@ -1,212 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BICGSTAB_H
-#define EIGEN_BICGSTAB_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Low-level bi conjugate gradient stabilized algorithm
-  * \param mat The matrix A
-  * \param rhs The right hand side vector b
-  * \param x On input and initial solution, on output the computed solution.
-  * \param precond A preconditioner being able to efficiently solve for an
-  *                approximation of Ax=b (regardless of b)
-  * \param iters On input the max number of iteration, on output the number of performed iterations.
-  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-  * \return false in the case of numerical issue, for example a break down of BiCGSTAB. 
-  */
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
-              const Preconditioner& precond, Index& iters,
-              typename Dest::RealScalar& tol_error)
-{
-  using std::sqrt;
-  using std::abs;
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,1> VectorType;
-  RealScalar tol = tol_error;
-  Index maxIters = iters;
-
-  Index n = mat.cols();
-  VectorType r  = rhs - mat * x;
-  VectorType r0 = r;
-  
-  RealScalar r0_sqnorm = r0.squaredNorm();
-  RealScalar rhs_sqnorm = rhs.squaredNorm();
-  if(rhs_sqnorm == 0)
-  {
-    x.setZero();
-    return true;
-  }
-  Scalar rho    = 1;
-  Scalar alpha  = 1;
-  Scalar w      = 1;
-  
-  VectorType v = VectorType::Zero(n), p = VectorType::Zero(n);
-  VectorType y(n),  z(n);
-  VectorType kt(n), ks(n);
-
-  VectorType s(n), t(n);
-
-  RealScalar tol2 = tol*tol*rhs_sqnorm;
-  RealScalar eps2 = NumTraits<Scalar>::epsilon()*NumTraits<Scalar>::epsilon();
-  Index i = 0;
-  Index restarts = 0;
-
-  while ( r.squaredNorm() > tol2 && i<maxIters )
-  {
-    Scalar rho_old = rho;
-
-    rho = r0.dot(r);
-    if (abs(rho) < eps2*r0_sqnorm)
-    {
-      // The new residual vector became too orthogonal to the arbitrarily chosen direction r0
-      // Let's restart with a new r0:
-      r  = rhs - mat * x;
-      r0 = r;
-      rho = r0_sqnorm = r.squaredNorm();
-      if(restarts++ == 0)
-        i = 0;
-    }
-    Scalar beta = (rho/rho_old) * (alpha / w);
-    p = r + beta * (p - w * v);
-    
-    y = precond.solve(p);
-    
-    v.noalias() = mat * y;
-
-    alpha = rho / r0.dot(v);
-    s = r - alpha * v;
-
-    z = precond.solve(s);
-    t.noalias() = mat * z;
-
-    RealScalar tmp = t.squaredNorm();
-    if(tmp>RealScalar(0))
-      w = t.dot(s) / tmp;
-    else
-      w = Scalar(0);
-    x += alpha * y + w * z;
-    r = s - w * t;
-    ++i;
-  }
-  tol_error = sqrt(r.squaredNorm()/rhs_sqnorm);
-  iters = i;
-  return true; 
-}
-
-}
-
-template< typename _MatrixType,
-          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-class BiCGSTAB;
-
-namespace internal {
-
-template< typename _MatrixType, typename _Preconditioner>
-struct traits<BiCGSTAB<_MatrixType,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A bi conjugate gradient stabilized solver for sparse square problems
-  *
-  * This class allows to solve for A.x = b sparse linear problems using a bi conjugate gradient
-  * stabilized algorithm. The vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
-  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  *
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  * 
-  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
-  * 
-  * \b Performance: when using sparse matrices, best performance is achied for a row-major sparse matrix format.
-  * Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
-  * See \ref TopicMultiThreading for details.
-  * 
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-  * \include BiCGSTAB_simple.cpp
-  * 
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * BiCGSTAB can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-  *
-  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename _MatrixType, typename _Preconditioner>
-class BiCGSTAB : public IterativeSolverBase<BiCGSTAB<_MatrixType,_Preconditioner> >
-{
-  typedef IterativeSolverBase<BiCGSTAB> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-public:
-
-  /** Default constructor. */
-  BiCGSTAB() : Base() {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit BiCGSTAB(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-
-  ~BiCGSTAB() {}
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {    
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-    
-    bool ret = internal::bicgstab(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
-
-    m_info = (!ret) ? NumericalIssue
-           : m_error <= Base::m_tolerance ? Success
-           : NoConvergence;
-  }
-
-protected:
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_BICGSTAB_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
deleted file mode 100644
index 5d8c6b4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
+++ /dev/null
@@ -1,229 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONJUGATE_GRADIENT_H
-#define EIGEN_CONJUGATE_GRADIENT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Low-level conjugate gradient algorithm
-  * \param mat The matrix A
-  * \param rhs The right hand side vector b
-  * \param x On input and initial solution, on output the computed solution.
-  * \param precond A preconditioner being able to efficiently solve for an
-  *                approximation of Ax=b (regardless of b)
-  * \param iters On input the max number of iteration, on output the number of performed iterations.
-  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-  */
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-EIGEN_DONT_INLINE
-void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
-                        const Preconditioner& precond, Index& iters,
-                        typename Dest::RealScalar& tol_error)
-{
-  using std::sqrt;
-  using std::abs;
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,1> VectorType;
-  
-  RealScalar tol = tol_error;
-  Index maxIters = iters;
-  
-  Index n = mat.cols();
-
-  VectorType residual = rhs - mat * x; //initial residual
-
-  RealScalar rhsNorm2 = rhs.squaredNorm();
-  if(rhsNorm2 == 0) 
-  {
-    x.setZero();
-    iters = 0;
-    tol_error = 0;
-    return;
-  }
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-  RealScalar threshold = numext::maxi(RealScalar(tol*tol*rhsNorm2),considerAsZero);
-  RealScalar residualNorm2 = residual.squaredNorm();
-  if (residualNorm2 < threshold)
-  {
-    iters = 0;
-    tol_error = sqrt(residualNorm2 / rhsNorm2);
-    return;
-  }
-
-  VectorType p(n);
-  p = precond.solve(residual);      // initial search direction
-
-  VectorType z(n), tmp(n);
-  RealScalar absNew = numext::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
-  Index i = 0;
-  while(i < maxIters)
-  {
-    tmp.noalias() = mat * p;                    // the bottleneck of the algorithm
-
-    Scalar alpha = absNew / p.dot(tmp);         // the amount we travel on dir
-    x += alpha * p;                             // update solution
-    residual -= alpha * tmp;                    // update residual
-    
-    residualNorm2 = residual.squaredNorm();
-    if(residualNorm2 < threshold)
-      break;
-    
-    z = precond.solve(residual);                // approximately solve for "A z = residual"
-
-    RealScalar absOld = absNew;
-    absNew = numext::real(residual.dot(z));     // update the absolute value of r
-    RealScalar beta = absNew / absOld;          // calculate the Gram-Schmidt value used to create the new search direction
-    p = z + beta * p;                           // update search direction
-    i++;
-  }
-  tol_error = sqrt(residualNorm2 / rhsNorm2);
-  iters = i;
-}
-
-}
-
-template< typename _MatrixType, int _UpLo=Lower,
-          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-class ConjugateGradient;
-
-namespace internal {
-
-template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A conjugate gradient solver for sparse (or dense) self-adjoint problems
-  *
-  * This class allows to solve for A.x = b linear problems using an iterative conjugate gradient algorithm.
-  * The matrix A must be selfadjoint. The matrix A and the vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix.
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower,
-  *               \c Upper, or \c Lower|Upper in which the full matrix entries will be considered.
-  *               Default is \c Lower, best performance is \c Lower|Upper.
-  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  *
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  * 
-  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
-  * 
-  * \b Performance: Even though the default value of \c _UpLo is \c Lower, significantly higher performance is
-  * achieved when using a complete matrix and \b Lower|Upper as the \a _UpLo template parameter. Moreover, in this
-  * case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
-  * See \ref TopicMultiThreading for details.
-  * 
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-    \code
-    int n = 10000;
-    VectorXd x(n), b(n);
-    SparseMatrix<double> A(n,n);
-    // fill A and b
-    ConjugateGradient<SparseMatrix<double>, Lower|Upper> cg;
-    cg.compute(A);
-    x = cg.solve(b);
-    std::cout << "#iterations:     " << cg.iterations() << std::endl;
-    std::cout << "estimated error: " << cg.error()      << std::endl;
-    // update b, and solve again
-    x = cg.solve(b);
-    \endcode
-  * 
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * ConjugateGradient can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-  *
-  * \sa class LeastSquaresConjugateGradient, class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
-{
-  typedef IterativeSolverBase<ConjugateGradient> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-  enum {
-    UpLo = _UpLo
-  };
-
-public:
-
-  /** Default constructor. */
-  ConjugateGradient() : Base() {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit ConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-
-  ~ConjugateGradient() {}
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {
-    typedef typename Base::MatrixWrapper MatrixWrapper;
-    typedef typename Base::ActualMatrixType ActualMatrixType;
-    enum {
-      TransposeInput  =   (!MatrixWrapper::MatrixFree)
-                      &&  (UpLo==(Lower|Upper))
-                      &&  (!MatrixType::IsRowMajor)
-                      &&  (!NumTraits<Scalar>::IsComplex)
-    };
-    typedef typename internal::conditional<TransposeInput,Transpose<const ActualMatrixType>, ActualMatrixType const&>::type RowMajorWrapper;
-    EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(MatrixWrapper::MatrixFree,UpLo==(Lower|Upper)),MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
-    typedef typename internal::conditional<UpLo==(Lower|Upper),
-                                           RowMajorWrapper,
-                                           typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type
-                                          >::type SelfAdjointWrapper;
-
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-
-    RowMajorWrapper row_mat(matrix());
-    internal::conjugate_gradient(SelfAdjointWrapper(row_mat), b, x, Base::m_preconditioner, m_iterations, m_error);
-    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
-  }
-
-protected:
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONJUGATE_GRADIENT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
deleted file mode 100644
index 7803fd8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
+++ /dev/null
@@ -1,394 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INCOMPLETE_CHOlESKY_H
-#define EIGEN_INCOMPLETE_CHOlESKY_H
-
-#include <vector>
-#include <list>
-
-namespace Eigen {
-/**
-  * \brief Modified Incomplete Cholesky with dual threshold
-  *
-  * References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
-  *              Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
-  *
-  * \tparam Scalar the scalar type of the input matrices
-  * \tparam _UpLo The triangular part that will be used for the computations. It can be Lower
-    *               or Upper. Default is Lower.
-  * \tparam _OrderingType The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<int>,
-  *                       unless EIGEN_MPL2_ONLY is defined, in which case the default is NaturalOrdering<int>.
-  *
-  * \implsparsesolverconcept
-  *
-  * It performs the following incomplete factorization: \f$ S P A P' S \approx L L' \f$
-  * where L is a lower triangular factor, S is a diagonal scaling matrix, and P is a
-  * fill-in reducing permutation as computed by the ordering method.
-  *
-  * \b Shifting \b strategy: Let \f$ B = S P A P' S \f$  be the scaled matrix on which the factorization is carried out,
-  * and \f$ \beta \f$ be the minimum value of the diagonal. If \f$ \beta > 0 \f$ then, the factorization is directly performed
-  * on the matrix B. Otherwise, the factorization is performed on the shifted matrix \f$ B + (\sigma+|\beta| I \f$ where
-  * \f$ \sigma \f$ is the initial shift value as returned and set by setInitialShift() method. The default value is \f$ \sigma = 10^{-3} \f$.
-  * If the factorization fails, then the shift in doubled until it succeed or a maximum of ten attempts. If it still fails, as returned by
-  * the info() method, then you can either increase the initial shift, or better use another preconditioning technique.
-  *
-  */
-template <typename Scalar, int _UpLo = Lower, typename _OrderingType = AMDOrdering<int> >
-class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_UpLo,_OrderingType> >
-{
-  protected:
-    typedef SparseSolverBase<IncompleteCholesky<Scalar,_UpLo,_OrderingType> > Base;
-    using Base::m_isInitialized;
-  public:
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef _OrderingType OrderingType;
-    typedef typename OrderingType::PermutationType PermutationType;
-    typedef typename PermutationType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> FactorType;
-    typedef Matrix<Scalar,Dynamic,1> VectorSx;
-    typedef Matrix<RealScalar,Dynamic,1> VectorRx;
-    typedef Matrix<StorageIndex,Dynamic, 1> VectorIx;
-    typedef std::vector<std::list<StorageIndex> > VectorList;
-    enum { UpLo = _UpLo };
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-  public:
-
-    /** Default constructor leaving the object in a partly non-initialized stage.
-      *
-      * You must call compute() or the pair analyzePattern()/factorize() to make it valid.
-      *
-      * \sa IncompleteCholesky(const MatrixType&)
-      */
-    IncompleteCholesky() : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false) {}
-
-    /** Constructor computing the incomplete factorization for the given matrix \a matrix.
-      */
-    template<typename MatrixType>
-    IncompleteCholesky(const MatrixType& matrix) : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false)
-    {
-      compute(matrix);
-    }
-
-    /** \returns number of rows of the factored matrix */
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_L.rows(); }
-
-    /** \returns number of columns of the factored matrix */
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_L.cols(); }
-
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * It triggers an assertion if \c *this has not been initialized through the respective constructor,
-      * or a call to compute() or analyzePattern().
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
-      return m_info;
-    }
-
-    /** \brief Set the initial shift parameter \f$ \sigma \f$.
-      */
-    void setInitialShift(RealScalar shift) { m_initialShift = shift; }
-
-    /** \brief Computes the fill reducing permutation vector using the sparsity pattern of \a mat
-      */
-    template<typename MatrixType>
-    void analyzePattern(const MatrixType& mat)
-    {
-      OrderingType ord;
-      PermutationType pinv;
-      ord(mat.template selfadjointView<UpLo>(), pinv);
-      if(pinv.size()>0) m_perm = pinv.inverse();
-      else              m_perm.resize(0);
-      m_L.resize(mat.rows(), mat.cols());
-      m_analysisIsOk = true;
-      m_isInitialized = true;
-      m_info = Success;
-    }
-
-    /** \brief Performs the numerical factorization of the input matrix \a mat
-      *
-      * The method analyzePattern() or compute() must have been called beforehand
-      * with a matrix having the same pattern.
-      *
-      * \sa compute(), analyzePattern()
-      */
-    template<typename MatrixType>
-    void factorize(const MatrixType& mat);
-
-    /** Computes or re-computes the incomplete Cholesky factorization of the input matrix \a mat
-      *
-      * It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.
-      *
-      * \sa analyzePattern(), factorize()
-      */
-    template<typename MatrixType>
-    void compute(const MatrixType& mat)
-    {
-      analyzePattern(mat);
-      factorize(mat);
-    }
-
-    // internal
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const Rhs& b, Dest& x) const
-    {
-      eigen_assert(m_factorizationIsOk && "factorize() should be called first");
-      if (m_perm.rows() == b.rows())  x = m_perm * b;
-      else                            x = b;
-      x = m_scale.asDiagonal() * x;
-      x = m_L.template triangularView<Lower>().solve(x);
-      x = m_L.adjoint().template triangularView<Upper>().solve(x);
-      x = m_scale.asDiagonal() * x;
-      if (m_perm.rows() == b.rows())
-        x = m_perm.inverse() * x;
-    }
-
-    /** \returns the sparse lower triangular factor L */
-    const FactorType& matrixL() const { eigen_assert("m_factorizationIsOk"); return m_L; }
-
-    /** \returns a vector representing the scaling factor S */
-    const VectorRx& scalingS() const { eigen_assert("m_factorizationIsOk"); return m_scale; }
-
-    /** \returns the fill-in reducing permutation P (can be empty for a natural ordering) */
-    const PermutationType& permutationP() const { eigen_assert("m_analysisIsOk"); return m_perm; }
-
-  protected:
-    FactorType m_L;              // The lower part stored in CSC
-    VectorRx m_scale;            // The vector for scaling the matrix
-    RealScalar m_initialShift;   // The initial shift parameter
-    bool m_analysisIsOk;
-    bool m_factorizationIsOk;
-    ComputationInfo m_info;
-    PermutationType m_perm;
-
-  private:
-    inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol);
-};
-
-// Based on the following paper:
-//   C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
-//   Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
-//   http://ftp.mcs.anl.gov/pub/tech_reports/reports/P682.pdf
-template<typename Scalar, int _UpLo, typename OrderingType>
-template<typename _MatrixType>
-void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType& mat)
-{
-  using std::sqrt;
-  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
-
-  // Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of the original matrix. Other strategies will be added
-
-  // Apply the fill-reducing permutation computed in analyzePattern()
-  if (m_perm.rows() == mat.rows() ) // To detect the null permutation
-  {
-    // The temporary is needed to make sure that the diagonal entry is properly sorted
-    FactorType tmp(mat.rows(), mat.cols());
-    tmp = mat.template selfadjointView<_UpLo>().twistedBy(m_perm);
-    m_L.template selfadjointView<Lower>() = tmp.template selfadjointView<Lower>();
-  }
-  else
-  {
-    m_L.template selfadjointView<Lower>() = mat.template selfadjointView<_UpLo>();
-  }
-
-  Index n = m_L.cols();
-  Index nnz = m_L.nonZeros();
-  Map<VectorSx> vals(m_L.valuePtr(), nnz);         //values
-  Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz);  //Row indices
-  Map<VectorIx> colPtr( m_L.outerIndexPtr(), n+1); // Pointer to the beginning of each row
-  VectorIx firstElt(n-1); // for each j, points to the next entry in vals that will be used in the factorization
-  VectorList listCol(n);  // listCol(j) is a linked list of columns to update column j
-  VectorSx col_vals(n);   // Store a  nonzero values in each column
-  VectorIx col_irow(n);   // Row indices of nonzero elements in each column
-  VectorIx col_pattern(n);
-  col_pattern.fill(-1);
-  StorageIndex col_nnz;
-
-
-  // Computes the scaling factors
-  m_scale.resize(n);
-  m_scale.setZero();
-  for (Index j = 0; j < n; j++)
-    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
-    {
-      m_scale(j) += numext::abs2(vals(k));
-      if(rowIdx[k]!=j)
-        m_scale(rowIdx[k]) += numext::abs2(vals(k));
-    }
-
-  m_scale = m_scale.cwiseSqrt().cwiseSqrt();
-
-  for (Index j = 0; j < n; ++j)
-    if(m_scale(j)>(std::numeric_limits<RealScalar>::min)())
-      m_scale(j) = RealScalar(1)/m_scale(j);
-    else
-      m_scale(j) = 1;
-
-  // TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
-
-  // Scale and compute the shift for the matrix
-  RealScalar mindiag = NumTraits<RealScalar>::highest();
-  for (Index j = 0; j < n; j++)
-  {
-    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
-      vals[k] *= (m_scale(j)*m_scale(rowIdx[k]));
-    eigen_internal_assert(rowIdx[colPtr[j]]==j && "IncompleteCholesky: only the lower triangular part must be stored");
-    mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
-  }
-
-  FactorType L_save = m_L;
-
-  RealScalar shift = 0;
-  if(mindiag <= RealScalar(0.))
-    shift = m_initialShift - mindiag;
-
-  m_info = NumericalIssue;
-
-  // Try to perform the incomplete factorization using the current shift
-  int iter = 0;
-  do
-  {
-    // Apply the shift to the diagonal elements of the matrix
-    for (Index j = 0; j < n; j++)
-      vals[colPtr[j]] += shift;
-
-    // jki version of the Cholesky factorization
-    Index j=0;
-    for (; j < n; ++j)
-    {
-      // Left-looking factorization of the j-th column
-      // First, load the j-th column into col_vals
-      Scalar diag = vals[colPtr[j]];  // It is assumed that only the lower part is stored
-      col_nnz = 0;
-      for (Index i = colPtr[j] + 1; i < colPtr[j+1]; i++)
-      {
-        StorageIndex l = rowIdx[i];
-        col_vals(col_nnz) = vals[i];
-        col_irow(col_nnz) = l;
-        col_pattern(l) = col_nnz;
-        col_nnz++;
-      }
-      {
-        typename std::list<StorageIndex>::iterator k;
-        // Browse all previous columns that will update column j
-        for(k = listCol[j].begin(); k != listCol[j].end(); k++)
-        {
-          Index jk = firstElt(*k); // First element to use in the column
-          eigen_internal_assert(rowIdx[jk]==j);
-          Scalar v_j_jk = numext::conj(vals[jk]);
-
-          jk += 1;
-          for (Index i = jk; i < colPtr[*k+1]; i++)
-          {
-            StorageIndex l = rowIdx[i];
-            if(col_pattern[l]<0)
-            {
-              col_vals(col_nnz) = vals[i] * v_j_jk;
-              col_irow[col_nnz] = l;
-              col_pattern(l) = col_nnz;
-              col_nnz++;
-            }
-            else
-              col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
-          }
-          updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
-        }
-      }
-
-      // Scale the current column
-      if(numext::real(diag) <= 0)
-      {
-        if(++iter>=10)
-          return;
-
-        // increase shift
-        shift = numext::maxi(m_initialShift,RealScalar(2)*shift);
-        // restore m_L, col_pattern, and listCol
-        vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
-        rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
-        colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n+1);
-        col_pattern.fill(-1);
-        for(Index i=0; i<n; ++i)
-          listCol[i].clear();
-
-        break;
-      }
-
-      RealScalar rdiag = sqrt(numext::real(diag));
-      vals[colPtr[j]] = rdiag;
-      for (Index k = 0; k<col_nnz; ++k)
-      {
-        Index i = col_irow[k];
-        //Scale
-        col_vals(k) /= rdiag;
-        //Update the remaining diagonals with col_vals
-        vals[colPtr[i]] -= numext::abs2(col_vals(k));
-      }
-      // Select the largest p elements
-      // p is the original number of elements in the column (without the diagonal)
-      Index p = colPtr[j+1] - colPtr[j] - 1 ;
-      Ref<VectorSx> cvals = col_vals.head(col_nnz);
-      Ref<VectorIx> cirow = col_irow.head(col_nnz);
-      internal::QuickSplit(cvals,cirow, p);
-      // Insert the largest p elements in the matrix
-      Index cpt = 0;
-      for (Index i = colPtr[j]+1; i < colPtr[j+1]; i++)
-      {
-        vals[i] = col_vals(cpt);
-        rowIdx[i] = col_irow(cpt);
-        // restore col_pattern:
-        col_pattern(col_irow(cpt)) = -1;
-        cpt++;
-      }
-      // Get the first smallest row index and put it after the diagonal element
-      Index jk = colPtr(j)+1;
-      updateList(colPtr,rowIdx,vals,j,jk,firstElt,listCol);
-    }
-
-    if(j==n)
-    {
-      m_factorizationIsOk = true;
-      m_info = Success;
-    }
-  } while(m_info!=Success);
-}
-
-template<typename Scalar, int _UpLo, typename OrderingType>
-inline void IncompleteCholesky<Scalar,_UpLo, OrderingType>::updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol)
-{
-  if (jk < colPtr(col+1) )
-  {
-    Index p = colPtr(col+1) - jk;
-    Index minpos;
-    rowIdx.segment(jk,p).minCoeff(&minpos);
-    minpos += jk;
-    if (rowIdx(minpos) != rowIdx(jk))
-    {
-      //Swap
-      std::swap(rowIdx(jk),rowIdx(minpos));
-      std::swap(vals(jk),vals(minpos));
-    }
-    firstElt(col) = internal::convert_index<StorageIndex,Index>(jk);
-    listCol[rowIdx(jk)].push_back(internal::convert_index<StorageIndex,Index>(col));
-  }
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
deleted file mode 100644
index cdcf709..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
+++ /dev/null
@@ -1,453 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INCOMPLETE_LUT_H
-#define EIGEN_INCOMPLETE_LUT_H
-
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * Compute a quick-sort split of a vector
-  * On output, the vector row is permuted such that its elements satisfy
-  * abs(row(i)) >= abs(row(ncut)) if i<ncut
-  * abs(row(i)) <= abs(row(ncut)) if i>ncut
-  * \param row The vector of values
-  * \param ind The array of index for the elements in @p row
-  * \param ncut  The number of largest elements to keep
-  **/
-template <typename VectorV, typename VectorI>
-Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
-{
-  typedef typename VectorV::RealScalar RealScalar;
-  using std::swap;
-  using std::abs;
-  Index mid;
-  Index n = row.size(); /* length of the vector */
-  Index first, last ;
-
-  ncut--; /* to fit the zero-based indices */
-  first = 0;
-  last = n-1;
-  if (ncut < first || ncut > last ) return 0;
-
-  do {
-    mid = first;
-    RealScalar abskey = abs(row(mid));
-    for (Index j = first + 1; j <= last; j++) {
-      if ( abs(row(j)) > abskey) {
-        ++mid;
-        swap(row(mid), row(j));
-        swap(ind(mid), ind(j));
-      }
-    }
-    /* Interchange for the pivot element */
-    swap(row(mid), row(first));
-    swap(ind(mid), ind(first));
-
-    if (mid > ncut) last = mid - 1;
-    else if (mid < ncut ) first = mid + 1;
-  } while (mid != ncut );
-
-  return 0; /* mid is equal to ncut */
-}
-
-}// end namespace internal
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \class IncompleteLUT
-  * \brief Incomplete LU factorization with dual-threshold strategy
-  *
-  * \implsparsesolverconcept
-  *
-  * During the numerical factorization, two dropping rules are used :
-  *  1) any element whose magnitude is less than some tolerance is dropped.
-  *    This tolerance is obtained by multiplying the input tolerance @p droptol
-  *    by the average magnitude of all the original elements in the current row.
-  *  2) After the elimination of the row, only the @p fill largest elements in
-  *    the L part and the @p fill largest elements in the U part are kept
-  *    (in addition to the diagonal element ). Note that @p fill is computed from
-  *    the input parameter @p fillfactor which is used the ratio to control the fill_in
-  *    relatively to the initial number of nonzero elements.
-  *
-  * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements)
-  * and when @p fill=n/2 with @p droptol being different to zero.
-  *
-  * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization,
-  *              Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
-  *
-  * NOTE : The following implementation is derived from the ILUT implementation
-  * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota
-  *  released under the terms of the GNU LGPL:
-  *    http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README
-  * However, Yousef Saad gave us permission to relicense his ILUT code to MPL2.
-  * See the Eigen mailing list archive, thread: ILUT, date: July 8, 2012:
-  *   http://listengine.tuxfamily.org/lists.tuxfamily.org/eigen/2012/07/msg00064.html
-  * alternatively, on GMANE:
-  *   http://comments.gmane.org/gmane.comp.lib.eigen/3302
-  */
-template <typename _Scalar, typename _StorageIndex = int>
-class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar, _StorageIndex> >
-{
-  protected:
-    typedef SparseSolverBase<IncompleteLUT> Base;
-    using Base::m_isInitialized;
-  public:
-    typedef _Scalar Scalar;
-    typedef _StorageIndex StorageIndex;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> FactorType;
-
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-
-  public:
-
-    IncompleteLUT()
-      : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10),
-        m_analysisIsOk(false), m_factorizationIsOk(false)
-    {}
-
-    template<typename MatrixType>
-    explicit IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
-      : m_droptol(droptol),m_fillfactor(fillfactor),
-        m_analysisIsOk(false),m_factorizationIsOk(false)
-    {
-      eigen_assert(fillfactor != 0);
-      compute(mat);
-    }
-
-    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
-
-    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
-      return m_info;
-    }
-
-    template<typename MatrixType>
-    void analyzePattern(const MatrixType& amat);
-
-    template<typename MatrixType>
-    void factorize(const MatrixType& amat);
-
-    /**
-      * Compute an incomplete LU factorization with dual threshold on the matrix mat
-      * No pivoting is done in this version
-      *
-      **/
-    template<typename MatrixType>
-    IncompleteLUT& compute(const MatrixType& amat)
-    {
-      analyzePattern(amat);
-      factorize(amat);
-      return *this;
-    }
-
-    void setDroptol(const RealScalar& droptol);
-    void setFillfactor(int fillfactor);
-
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const Rhs& b, Dest& x) const
-    {
-      x = m_Pinv * b;
-      x = m_lu.template triangularView<UnitLower>().solve(x);
-      x = m_lu.template triangularView<Upper>().solve(x);
-      x = m_P * x;
-    }
-
-protected:
-
-    /** keeps off-diagonal entries; drops diagonal entries */
-    struct keep_diag {
-      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
-      {
-        return row!=col;
-      }
-    };
-
-protected:
-
-    FactorType m_lu;
-    RealScalar m_droptol;
-    int m_fillfactor;
-    bool m_analysisIsOk;
-    bool m_factorizationIsOk;
-    ComputationInfo m_info;
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_P;     // Fill-reducing permutation
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_Pinv;  // Inverse permutation
-};
-
-/**
- * Set control parameter droptol
- *  \param droptol   Drop any element whose magnitude is less than this tolerance
- **/
-template<typename Scalar, typename StorageIndex>
-void IncompleteLUT<Scalar,StorageIndex>::setDroptol(const RealScalar& droptol)
-{
-  this->m_droptol = droptol;
-}
-
-/**
- * Set control parameter fillfactor
- * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row.
- **/
-template<typename Scalar, typename StorageIndex>
-void IncompleteLUT<Scalar,StorageIndex>::setFillfactor(int fillfactor)
-{
-  this->m_fillfactor = fillfactor;
-}
-
-template <typename Scalar, typename StorageIndex>
-template<typename _MatrixType>
-void IncompleteLUT<Scalar,StorageIndex>::analyzePattern(const _MatrixType& amat)
-{
-  // Compute the Fill-reducing permutation
-  // Since ILUT does not perform any numerical pivoting,
-  // it is highly preferable to keep the diagonal through symmetric permutations.
-  // To this end, let's symmetrize the pattern and perform AMD on it.
-  SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat;
-  SparseMatrix<Scalar,ColMajor, StorageIndex> mat2 = amat.transpose();
-  // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice.
-  //       on the other hand for a really non-symmetric pattern, mat2*mat1 should be preferred...
-  SparseMatrix<Scalar,ColMajor, StorageIndex> AtA = mat2 + mat1;
-  AMDOrdering<StorageIndex> ordering;
-  ordering(AtA,m_P);
-  m_Pinv  = m_P.inverse(); // cache the inverse permutation
-  m_analysisIsOk = true;
-  m_factorizationIsOk = false;
-  m_isInitialized = true;
-}
-
-template <typename Scalar, typename StorageIndex>
-template<typename _MatrixType>
-void IncompleteLUT<Scalar,StorageIndex>::factorize(const _MatrixType& amat)
-{
-  using std::sqrt;
-  using std::swap;
-  using std::abs;
-  using internal::convert_index;
-
-  eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
-  Index n = amat.cols();  // Size of the matrix
-  m_lu.resize(n,n);
-  // Declare Working vectors and variables
-  Vector u(n) ;     // real values of the row -- maximum size is n --
-  VectorI ju(n);   // column position of the values in u -- maximum size  is n
-  VectorI jr(n);   // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
-
-  // Apply the fill-reducing permutation
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  SparseMatrix<Scalar,RowMajor, StorageIndex> mat;
-  mat = amat.twistedBy(m_Pinv);
-
-  // Initialization
-  jr.fill(-1);
-  ju.fill(0);
-  u.fill(0);
-
-  // number of largest elements to keep in each row:
-  Index fill_in = (amat.nonZeros()*m_fillfactor)/n + 1;
-  if (fill_in > n) fill_in = n;
-
-  // number of largest nonzero elements to keep in the L and the U part of the current row:
-  Index nnzL = fill_in/2;
-  Index nnzU = nnzL;
-  m_lu.reserve(n * (nnzL + nnzU + 1));
-
-  // global loop over the rows of the sparse matrix
-  for (Index ii = 0; ii < n; ii++)
-  {
-    // 1 - copy the lower and the upper part of the row i of mat in the working vector u
-
-    Index sizeu = 1; // number of nonzero elements in the upper part of the current row
-    Index sizel = 0; // number of nonzero elements in the lower part of the current row
-    ju(ii)    = convert_index<StorageIndex>(ii);
-    u(ii)     = 0;
-    jr(ii)    = convert_index<StorageIndex>(ii);
-    RealScalar rownorm = 0;
-
-    typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii
-    for (; j_it; ++j_it)
-    {
-      Index k = j_it.index();
-      if (k < ii)
-      {
-        // copy the lower part
-        ju(sizel) = convert_index<StorageIndex>(k);
-        u(sizel) = j_it.value();
-        jr(k) = convert_index<StorageIndex>(sizel);
-        ++sizel;
-      }
-      else if (k == ii)
-      {
-        u(ii) = j_it.value();
-      }
-      else
-      {
-        // copy the upper part
-        Index jpos = ii + sizeu;
-        ju(jpos) = convert_index<StorageIndex>(k);
-        u(jpos) = j_it.value();
-        jr(k) = convert_index<StorageIndex>(jpos);
-        ++sizeu;
-      }
-      rownorm += numext::abs2(j_it.value());
-    }
-
-    // 2 - detect possible zero row
-    if(rownorm==0)
-    {
-      m_info = NumericalIssue;
-      return;
-    }
-    // Take the 2-norm of the current row as a relative tolerance
-    rownorm = sqrt(rownorm);
-
-    // 3 - eliminate the previous nonzero rows
-    Index jj = 0;
-    Index len = 0;
-    while (jj < sizel)
-    {
-      // In order to eliminate in the correct order,
-      // we must select first the smallest column index among  ju(jj:sizel)
-      Index k;
-      Index minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment
-      k += jj;
-      if (minrow != ju(jj))
-      {
-        // swap the two locations
-        Index j = ju(jj);
-        swap(ju(jj), ju(k));
-        jr(minrow) = convert_index<StorageIndex>(jj);
-        jr(j) = convert_index<StorageIndex>(k);
-        swap(u(jj), u(k));
-      }
-      // Reset this location
-      jr(minrow) = -1;
-
-      // Start elimination
-      typename FactorType::InnerIterator ki_it(m_lu, minrow);
-      while (ki_it && ki_it.index() < minrow) ++ki_it;
-      eigen_internal_assert(ki_it && ki_it.col()==minrow);
-      Scalar fact = u(jj) / ki_it.value();
-
-      // drop too small elements
-      if(abs(fact) <= m_droptol)
-      {
-        jj++;
-        continue;
-      }
-
-      // linear combination of the current row ii and the row minrow
-      ++ki_it;
-      for (; ki_it; ++ki_it)
-      {
-        Scalar prod = fact * ki_it.value();
-        Index j     = ki_it.index();
-        Index jpos  = jr(j);
-        if (jpos == -1) // fill-in element
-        {
-          Index newpos;
-          if (j >= ii) // dealing with the upper part
-          {
-            newpos = ii + sizeu;
-            sizeu++;
-            eigen_internal_assert(sizeu<=n);
-          }
-          else // dealing with the lower part
-          {
-            newpos = sizel;
-            sizel++;
-            eigen_internal_assert(sizel<=ii);
-          }
-          ju(newpos) = convert_index<StorageIndex>(j);
-          u(newpos) = -prod;
-          jr(j) = convert_index<StorageIndex>(newpos);
-        }
-        else
-          u(jpos) -= prod;
-      }
-      // store the pivot element
-      u(len)  = fact;
-      ju(len) = convert_index<StorageIndex>(minrow);
-      ++len;
-
-      jj++;
-    } // end of the elimination on the row ii
-
-    // reset the upper part of the pointer jr to zero
-    for(Index k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1;
-
-    // 4 - partially sort and insert the elements in the m_lu matrix
-
-    // sort the L-part of the row
-    sizel = len;
-    len = (std::min)(sizel, nnzL);
-    typename Vector::SegmentReturnType ul(u.segment(0, sizel));
-    typename VectorI::SegmentReturnType jul(ju.segment(0, sizel));
-    internal::QuickSplit(ul, jul, len);
-
-    // store the largest m_fill elements of the L part
-    m_lu.startVec(ii);
-    for(Index k = 0; k < len; k++)
-      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
-
-    // store the diagonal element
-    // apply a shifting rule to avoid zero pivots (we are doing an incomplete factorization)
-    if (u(ii) == Scalar(0))
-      u(ii) = sqrt(m_droptol) * rownorm;
-    m_lu.insertBackByOuterInnerUnordered(ii, ii) = u(ii);
-
-    // sort the U-part of the row
-    // apply the dropping rule first
-    len = 0;
-    for(Index k = 1; k < sizeu; k++)
-    {
-      if(abs(u(ii+k)) > m_droptol * rownorm )
-      {
-        ++len;
-        u(ii + len)  = u(ii + k);
-        ju(ii + len) = ju(ii + k);
-      }
-    }
-    sizeu = len + 1; // +1 to take into account the diagonal element
-    len = (std::min)(sizeu, nnzU);
-    typename Vector::SegmentReturnType uu(u.segment(ii+1, sizeu-1));
-    typename VectorI::SegmentReturnType juu(ju.segment(ii+1, sizeu-1));
-    internal::QuickSplit(uu, juu, len);
-
-    // store the largest elements of the U part
-    for(Index k = ii + 1; k < ii + len; k++)
-      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
-  }
-  m_lu.finalize();
-  m_lu.makeCompressed();
-
-  m_factorizationIsOk = true;
-  m_info = Success;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_INCOMPLETE_LUT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
deleted file mode 100644
index 28a0c51..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
+++ /dev/null
@@ -1,444 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ITERATIVE_SOLVER_BASE_H
-#define EIGEN_ITERATIVE_SOLVER_BASE_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType>
-struct is_ref_compatible_impl
-{
-private:
-  template <typename T0>
-  struct any_conversion
-  {
-    template <typename T> any_conversion(const volatile T&);
-    template <typename T> any_conversion(T&);
-  };
-  struct yes {int a[1];};
-  struct no  {int a[2];};
-
-  template<typename T>
-  static yes test(const Ref<const T>&, int);
-  template<typename T>
-  static no  test(any_conversion<T>, ...);
-
-public:
-  static MatrixType ms_from;
-  enum { value = sizeof(test<MatrixType>(ms_from, 0))==sizeof(yes) };
-};
-
-template<typename MatrixType>
-struct is_ref_compatible
-{
-  enum { value = is_ref_compatible_impl<typename remove_all<MatrixType>::type>::value };
-};
-
-template<typename MatrixType, bool MatrixFree = !internal::is_ref_compatible<MatrixType>::value>
-class generic_matrix_wrapper;
-
-// We have an explicit matrix at hand, compatible with Ref<>
-template<typename MatrixType>
-class generic_matrix_wrapper<MatrixType,false>
-{
-public:
-  typedef Ref<const MatrixType> ActualMatrixType;
-  template<int UpLo> struct ConstSelfAdjointViewReturnType {
-    typedef typename ActualMatrixType::template ConstSelfAdjointViewReturnType<UpLo>::Type Type;
-  };
-
-  enum {
-    MatrixFree = false
-  };
-
-  generic_matrix_wrapper()
-    : m_dummy(0,0), m_matrix(m_dummy)
-  {}
-
-  template<typename InputType>
-  generic_matrix_wrapper(const InputType &mat)
-    : m_matrix(mat)
-  {}
-
-  const ActualMatrixType& matrix() const
-  {
-    return m_matrix;
-  }
-
-  template<typename MatrixDerived>
-  void grab(const EigenBase<MatrixDerived> &mat)
-  {
-    m_matrix.~Ref<const MatrixType>();
-    ::new (&m_matrix) Ref<const MatrixType>(mat.derived());
-  }
-
-  void grab(const Ref<const MatrixType> &mat)
-  {
-    if(&(mat.derived()) != &m_matrix)
-    {
-      m_matrix.~Ref<const MatrixType>();
-      ::new (&m_matrix) Ref<const MatrixType>(mat);
-    }
-  }
-
-protected:
-  MatrixType m_dummy; // used to default initialize the Ref<> object
-  ActualMatrixType m_matrix;
-};
-
-// MatrixType is not compatible with Ref<> -> matrix-free wrapper
-template<typename MatrixType>
-class generic_matrix_wrapper<MatrixType,true>
-{
-public:
-  typedef MatrixType ActualMatrixType;
-  template<int UpLo> struct ConstSelfAdjointViewReturnType
-  {
-    typedef ActualMatrixType Type;
-  };
-
-  enum {
-    MatrixFree = true
-  };
-
-  generic_matrix_wrapper()
-    : mp_matrix(0)
-  {}
-
-  generic_matrix_wrapper(const MatrixType &mat)
-    : mp_matrix(&mat)
-  {}
-
-  const ActualMatrixType& matrix() const
-  {
-    return *mp_matrix;
-  }
-
-  void grab(const MatrixType &mat)
-  {
-    mp_matrix = &mat;
-  }
-
-protected:
-  const ActualMatrixType *mp_matrix;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief Base class for linear iterative solvers
-  *
-  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename Derived>
-class IterativeSolverBase : public SparseSolverBase<Derived>
-{
-protected:
-  typedef SparseSolverBase<Derived> Base;
-  using Base::m_isInitialized;
-
-public:
-  typedef typename internal::traits<Derived>::MatrixType MatrixType;
-  typedef typename internal::traits<Derived>::Preconditioner Preconditioner;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef typename MatrixType::RealScalar RealScalar;
-
-  enum {
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-  };
-
-public:
-
-  using Base::derived;
-
-  /** Default constructor. */
-  IterativeSolverBase()
-  {
-    init();
-  }
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    *
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    *
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit IterativeSolverBase(const EigenBase<MatrixDerived>& A)
-    : m_matrixWrapper(A.derived())
-  {
-    init();
-    compute(matrix());
-  }
-
-  ~IterativeSolverBase() {}
-
-  /** Initializes the iterative solver for the sparsity pattern of the matrix \a A for further solving \c Ax=b problems.
-    *
-    * Currently, this function mostly calls analyzePattern on the preconditioner. In the future
-    * we might, for instance, implement column reordering for faster matrix vector products.
-    */
-  template<typename MatrixDerived>
-  Derived& analyzePattern(const EigenBase<MatrixDerived>& A)
-  {
-    grab(A.derived());
-    m_preconditioner.analyzePattern(matrix());
-    m_isInitialized = true;
-    m_analysisIsOk = true;
-    m_info = m_preconditioner.info();
-    return derived();
-  }
-
-  /** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b problems.
-    *
-    * Currently, this function mostly calls factorize on the preconditioner.
-    *
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  Derived& factorize(const EigenBase<MatrixDerived>& A)
-  {
-    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-    grab(A.derived());
-    m_preconditioner.factorize(matrix());
-    m_factorizationIsOk = true;
-    m_info = m_preconditioner.info();
-    return derived();
-  }
-
-  /** Initializes the iterative solver with the matrix \a A for further solving \c Ax=b problems.
-    *
-    * Currently, this function mostly initializes/computes the preconditioner. In the future
-    * we might, for instance, implement column reordering for faster matrix vector products.
-    *
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  Derived& compute(const EigenBase<MatrixDerived>& A)
-  {
-    grab(A.derived());
-    m_preconditioner.compute(matrix());
-    m_isInitialized = true;
-    m_analysisIsOk = true;
-    m_factorizationIsOk = true;
-    m_info = m_preconditioner.info();
-    return derived();
-  }
-
-  /** \internal */
-  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return matrix().rows(); }
-
-  /** \internal */
-  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return matrix().cols(); }
-
-  /** \returns the tolerance threshold used by the stopping criteria.
-    * \sa setTolerance()
-    */
-  RealScalar tolerance() const { return m_tolerance; }
-
-  /** Sets the tolerance threshold used by the stopping criteria.
-    *
-    * This value is used as an upper bound to the relative residual error: |Ax-b|/|b|.
-    * The default value is the machine precision given by NumTraits<Scalar>::epsilon()
-    */
-  Derived& setTolerance(const RealScalar& tolerance)
-  {
-    m_tolerance = tolerance;
-    return derived();
-  }
-
-  /** \returns a read-write reference to the preconditioner for custom configuration. */
-  Preconditioner& preconditioner() { return m_preconditioner; }
-
-  /** \returns a read-only reference to the preconditioner. */
-  const Preconditioner& preconditioner() const { return m_preconditioner; }
-
-  /** \returns the max number of iterations.
-    * It is either the value set by setMaxIterations or, by default,
-    * twice the number of columns of the matrix.
-    */
-  Index maxIterations() const
-  {
-    return (m_maxIterations<0) ? 2*matrix().cols() : m_maxIterations;
-  }
-
-  /** Sets the max number of iterations.
-    * Default is twice the number of columns of the matrix.
-    */
-  Derived& setMaxIterations(Index maxIters)
-  {
-    m_maxIterations = maxIters;
-    return derived();
-  }
-
-  /** \returns the number of iterations performed during the last solve */
-  Index iterations() const
-  {
-    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
-    return m_iterations;
-  }
-
-  /** \returns the tolerance error reached during the last solve.
-    * It is a close approximation of the true relative residual error |Ax-b|/|b|.
-    */
-  RealScalar error() const
-  {
-    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
-    return m_error;
-  }
-
-  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
-    * and \a x0 as an initial solution.
-    *
-    * \sa solve(), compute()
-    */
-  template<typename Rhs,typename Guess>
-  inline const SolveWithGuess<Derived, Rhs, Guess>
-  solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const
-  {
-    eigen_assert(m_isInitialized && "Solver is not initialized.");
-    eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
-    return SolveWithGuess<Derived, Rhs, Guess>(derived(), b.derived(), x0);
-  }
-
-  /** \returns Success if the iterations converged, and NoConvergence otherwise. */
-  ComputationInfo info() const
-  {
-    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
-    return m_info;
-  }
-
-  /** \internal */
-  template<typename Rhs, typename DestDerived>
-  void _solve_with_guess_impl(const Rhs& b, SparseMatrixBase<DestDerived> &aDest) const
-  {
-    eigen_assert(rows()==b.rows());
-
-    Index rhsCols = b.cols();
-    Index size = b.rows();
-    DestDerived& dest(aDest.derived());
-    typedef typename DestDerived::Scalar DestScalar;
-    Eigen::Matrix<DestScalar,Dynamic,1> tb(size);
-    Eigen::Matrix<DestScalar,Dynamic,1> tx(cols());
-    // We do not directly fill dest because sparse expressions have to be free of aliasing issue.
-    // For non square least-square problems, b and dest might not have the same size whereas they might alias each-other.
-    typename DestDerived::PlainObject tmp(cols(),rhsCols);
-    ComputationInfo global_info = Success;
-    for(Index k=0; k<rhsCols; ++k)
-    {
-      tb = b.col(k);
-      tx = dest.col(k);
-      derived()._solve_vector_with_guess_impl(tb,tx);
-      tmp.col(k) = tx.sparseView(0);
-
-      // The call to _solve_vector_with_guess_impl updates m_info, so if it failed for a previous column
-      // we need to restore it to the worst value.
-      if(m_info==NumericalIssue)
-        global_info = NumericalIssue;
-      else if(m_info==NoConvergence)
-        global_info = NoConvergence;
-    }
-    m_info = global_info;
-    dest.swap(tmp);
-  }
-
-  template<typename Rhs, typename DestDerived>
-  typename internal::enable_if<Rhs::ColsAtCompileTime!=1 && DestDerived::ColsAtCompileTime!=1>::type
-  _solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &aDest) const
-  {
-    eigen_assert(rows()==b.rows());
-
-    Index rhsCols = b.cols();
-    DestDerived& dest(aDest.derived());
-    ComputationInfo global_info = Success;
-    for(Index k=0; k<rhsCols; ++k)
-    {
-      typename DestDerived::ColXpr xk(dest,k);
-      typename Rhs::ConstColXpr bk(b,k);
-      derived()._solve_vector_with_guess_impl(bk,xk);
-
-      // The call to _solve_vector_with_guess updates m_info, so if it failed for a previous column
-      // we need to restore it to the worst value.
-      if(m_info==NumericalIssue)
-        global_info = NumericalIssue;
-      else if(m_info==NoConvergence)
-        global_info = NoConvergence;
-    }
-    m_info = global_info;
-  }
-
-  template<typename Rhs, typename DestDerived>
-  typename internal::enable_if<Rhs::ColsAtCompileTime==1 || DestDerived::ColsAtCompileTime==1>::type
-  _solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &dest) const
-  {
-    derived()._solve_vector_with_guess_impl(b,dest.derived());
-  }
-
-  /** \internal default initial guess = 0 */
-  template<typename Rhs,typename Dest>
-  void _solve_impl(const Rhs& b, Dest& x) const
-  {
-    x.setZero();
-    derived()._solve_with_guess_impl(b,x);
-  }
-
-protected:
-  void init()
-  {
-    m_isInitialized = false;
-    m_analysisIsOk = false;
-    m_factorizationIsOk = false;
-    m_maxIterations = -1;
-    m_tolerance = NumTraits<Scalar>::epsilon();
-  }
-
-  typedef internal::generic_matrix_wrapper<MatrixType> MatrixWrapper;
-  typedef typename MatrixWrapper::ActualMatrixType ActualMatrixType;
-
-  const ActualMatrixType& matrix() const
-  {
-    return m_matrixWrapper.matrix();
-  }
-
-  template<typename InputType>
-  void grab(const InputType &A)
-  {
-    m_matrixWrapper.grab(A);
-  }
-
-  MatrixWrapper m_matrixWrapper;
-  Preconditioner m_preconditioner;
-
-  Index m_maxIterations;
-  RealScalar m_tolerance;
-
-  mutable RealScalar m_error;
-  mutable Index m_iterations;
-  mutable ComputationInfo m_info;
-  mutable bool m_analysisIsOk, m_factorizationIsOk;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_ITERATIVE_SOLVER_BASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
deleted file mode 100644
index 203fd0e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
+++ /dev/null
@@ -1,198 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
-#define EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Low-level conjugate gradient algorithm for least-square problems
-  * \param mat The matrix A
-  * \param rhs The right hand side vector b
-  * \param x On input and initial solution, on output the computed solution.
-  * \param precond A preconditioner being able to efficiently solve for an
-  *                approximation of A'Ax=b (regardless of b)
-  * \param iters On input the max number of iteration, on output the number of performed iterations.
-  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-  */
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-EIGEN_DONT_INLINE
-void least_square_conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
-                                     const Preconditioner& precond, Index& iters,
-                                     typename Dest::RealScalar& tol_error)
-{
-  using std::sqrt;
-  using std::abs;
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,1> VectorType;
-  
-  RealScalar tol = tol_error;
-  Index maxIters = iters;
-  
-  Index m = mat.rows(), n = mat.cols();
-
-  VectorType residual        = rhs - mat * x;
-  VectorType normal_residual = mat.adjoint() * residual;
-
-  RealScalar rhsNorm2 = (mat.adjoint()*rhs).squaredNorm();
-  if(rhsNorm2 == 0) 
-  {
-    x.setZero();
-    iters = 0;
-    tol_error = 0;
-    return;
-  }
-  RealScalar threshold = tol*tol*rhsNorm2;
-  RealScalar residualNorm2 = normal_residual.squaredNorm();
-  if (residualNorm2 < threshold)
-  {
-    iters = 0;
-    tol_error = sqrt(residualNorm2 / rhsNorm2);
-    return;
-  }
-  
-  VectorType p(n);
-  p = precond.solve(normal_residual);                         // initial search direction
-
-  VectorType z(n), tmp(m);
-  RealScalar absNew = numext::real(normal_residual.dot(p));  // the square of the absolute value of r scaled by invM
-  Index i = 0;
-  while(i < maxIters)
-  {
-    tmp.noalias() = mat * p;
-
-    Scalar alpha = absNew / tmp.squaredNorm();      // the amount we travel on dir
-    x += alpha * p;                                 // update solution
-    residual -= alpha * tmp;                        // update residual
-    normal_residual = mat.adjoint() * residual;     // update residual of the normal equation
-    
-    residualNorm2 = normal_residual.squaredNorm();
-    if(residualNorm2 < threshold)
-      break;
-    
-    z = precond.solve(normal_residual);             // approximately solve for "A'A z = normal_residual"
-
-    RealScalar absOld = absNew;
-    absNew = numext::real(normal_residual.dot(z));  // update the absolute value of r
-    RealScalar beta = absNew / absOld;              // calculate the Gram-Schmidt value used to create the new search direction
-    p = z + beta * p;                               // update search direction
-    i++;
-  }
-  tol_error = sqrt(residualNorm2 / rhsNorm2);
-  iters = i;
-}
-
-}
-
-template< typename _MatrixType,
-          typename _Preconditioner = LeastSquareDiagonalPreconditioner<typename _MatrixType::Scalar> >
-class LeastSquaresConjugateGradient;
-
-namespace internal {
-
-template< typename _MatrixType, typename _Preconditioner>
-struct traits<LeastSquaresConjugateGradient<_MatrixType,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A conjugate gradient solver for sparse (or dense) least-square problems
-  *
-  * This class allows to solve for A x = b linear problems using an iterative conjugate gradient algorithm.
-  * The matrix A can be non symmetric and rectangular, but the matrix A' A should be positive-definite to guaranty stability.
-  * Otherwise, the SparseLU or SparseQR classes might be preferable.
-  * The matrix A and the vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix.
-  * \tparam _Preconditioner the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  * 
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  * 
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-    \code
-    int m=1000000, n = 10000;
-    VectorXd x(n), b(m);
-    SparseMatrix<double> A(m,n);
-    // fill A and b
-    LeastSquaresConjugateGradient<SparseMatrix<double> > lscg;
-    lscg.compute(A);
-    x = lscg.solve(b);
-    std::cout << "#iterations:     " << lscg.iterations() << std::endl;
-    std::cout << "estimated error: " << lscg.error()      << std::endl;
-    // update b, and solve again
-    x = lscg.solve(b);
-    \endcode
-  * 
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * \sa class ConjugateGradient, SparseLU, SparseQR
-  */
-template< typename _MatrixType, typename _Preconditioner>
-class LeastSquaresConjugateGradient : public IterativeSolverBase<LeastSquaresConjugateGradient<_MatrixType,_Preconditioner> >
-{
-  typedef IterativeSolverBase<LeastSquaresConjugateGradient> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-public:
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-public:
-
-  /** Default constructor. */
-  LeastSquaresConjugateGradient() : Base() {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit LeastSquaresConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-
-  ~LeastSquaresConjugateGradient() {}
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-
-    internal::least_square_conjugate_gradient(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
-    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
-  }
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
deleted file mode 100644
index 7b89657..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
+++ /dev/null
@@ -1,117 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SOLVEWITHGUESS_H
-#define EIGEN_SOLVEWITHGUESS_H
-
-namespace Eigen {
-
-template<typename Decomposition, typename RhsType, typename GuessType> class SolveWithGuess;
-
-/** \class SolveWithGuess
-  * \ingroup IterativeLinearSolvers_Module
-  *
-  * \brief Pseudo expression representing a solving operation
-  *
-  * \tparam Decomposition the type of the matrix or decomposion object
-  * \tparam Rhstype the type of the right-hand side
-  *
-  * This class represents an expression of A.solve(B)
-  * and most of the time this is the only way it is used.
-  *
-  */
-namespace internal {
-
-
-template<typename Decomposition, typename RhsType, typename GuessType>
-struct traits<SolveWithGuess<Decomposition, RhsType, GuessType> >
-  : traits<Solve<Decomposition,RhsType> >
-{};
-
-}
-
-
-template<typename Decomposition, typename RhsType, typename GuessType>
-class SolveWithGuess : public internal::generic_xpr_base<SolveWithGuess<Decomposition,RhsType,GuessType>, MatrixXpr, typename internal::traits<RhsType>::StorageKind>::type
-{
-public:
-  typedef typename internal::traits<SolveWithGuess>::Scalar Scalar;
-  typedef typename internal::traits<SolveWithGuess>::PlainObject PlainObject;
-  typedef typename internal::generic_xpr_base<SolveWithGuess<Decomposition,RhsType,GuessType>, MatrixXpr, typename internal::traits<RhsType>::StorageKind>::type Base;
-  typedef typename internal::ref_selector<SolveWithGuess>::type Nested;
-
-  SolveWithGuess(const Decomposition &dec, const RhsType &rhs, const GuessType &guess)
-    : m_dec(dec), m_rhs(rhs), m_guess(guess)
-  {}
-
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  Index rows() const EIGEN_NOEXCEPT { return m_dec.cols(); }
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
-
-  EIGEN_DEVICE_FUNC const Decomposition& dec()   const { return m_dec; }
-  EIGEN_DEVICE_FUNC const RhsType&       rhs()   const { return m_rhs; }
-  EIGEN_DEVICE_FUNC const GuessType&     guess() const { return m_guess; }
-
-protected:
-  const Decomposition &m_dec;
-  const RhsType       &m_rhs;
-  const GuessType     &m_guess;
-
-private:
-  Scalar coeff(Index row, Index col) const;
-  Scalar coeff(Index i) const;
-};
-
-namespace internal {
-
-// Evaluator of SolveWithGuess -> eval into a temporary
-template<typename Decomposition, typename RhsType, typename GuessType>
-struct evaluator<SolveWithGuess<Decomposition,RhsType, GuessType> >
-  : public evaluator<typename SolveWithGuess<Decomposition,RhsType,GuessType>::PlainObject>
-{
-  typedef SolveWithGuess<Decomposition,RhsType,GuessType> SolveType;
-  typedef typename SolveType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  evaluator(const SolveType& solve)
-    : m_result(solve.rows(), solve.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    m_result = solve.guess();
-    solve.dec()._solve_with_guess_impl(solve.rhs(), m_result);
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-// Specialization for "dst = dec.solveWithGuess(rhs)"
-// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
-template<typename DstXprType, typename DecType, typename RhsType, typename GuessType, typename Scalar>
-struct Assignment<DstXprType, SolveWithGuess<DecType,RhsType,GuessType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
-{
-  typedef SolveWithGuess<DecType,RhsType,GuessType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    dst = src.guess();
-    src.dec()._solve_with_guess_impl(src.rhs(), dst/*, src.guess()*/);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SOLVEWITHGUESS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Jacobi/Jacobi.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Jacobi/Jacobi.h
deleted file mode 100644
index 76668a5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/Jacobi/Jacobi.h
+++ /dev/null
@@ -1,483 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_JACOBI_H
-#define EIGEN_JACOBI_H
-
-namespace Eigen {
-
-/** \ingroup Jacobi_Module
-  * \jacobi_module
-  * \class JacobiRotation
-  * \brief Rotation given by a cosine-sine pair.
-  *
-  * This class represents a Jacobi or Givens rotation.
-  * This is a 2D rotation in the plane \c J of angle \f$ \theta \f$ defined by
-  * its cosine \c c and sine \c s as follow:
-  * \f$ J = \left ( \begin{array}{cc} c & \overline s \\ -s  & \overline c \end{array} \right ) \f$
-  *
-  * You can apply the respective counter-clockwise rotation to a column vector \c v by
-  * applying its adjoint on the left: \f$ v = J^* v \f$ that translates to the following Eigen code:
-  * \code
-  * v.applyOnTheLeft(J.adjoint());
-  * \endcode
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar> class JacobiRotation
-{
-  public:
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    /** Default constructor without any initialization. */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation() {}
-
-    /** Construct a planar rotation from a cosine-sine pair (\a c, \c s). */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation(const Scalar& c, const Scalar& s) : m_c(c), m_s(s) {}
-
-    EIGEN_DEVICE_FUNC Scalar& c() { return m_c; }
-    EIGEN_DEVICE_FUNC Scalar c() const { return m_c; }
-    EIGEN_DEVICE_FUNC Scalar& s() { return m_s; }
-    EIGEN_DEVICE_FUNC Scalar s() const { return m_s; }
-
-    /** Concatenates two planar rotation */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation operator*(const JacobiRotation& other)
-    {
-      using numext::conj;
-      return JacobiRotation(m_c * other.m_c - conj(m_s) * other.m_s,
-                            conj(m_c * conj(other.m_s) + conj(m_s) * conj(other.m_c)));
-    }
-
-    /** Returns the transposed transformation */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation transpose() const { using numext::conj; return JacobiRotation(m_c, -conj(m_s)); }
-
-    /** Returns the adjoint transformation */
-    EIGEN_DEVICE_FUNC
-    JacobiRotation adjoint() const { using numext::conj; return JacobiRotation(conj(m_c), -m_s); }
-
-    template<typename Derived>
-    EIGEN_DEVICE_FUNC
-    bool makeJacobi(const MatrixBase<Derived>&, Index p, Index q);
-    EIGEN_DEVICE_FUNC
-    bool makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z);
-
-    EIGEN_DEVICE_FUNC
-    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r=0);
-
-  protected:
-    EIGEN_DEVICE_FUNC
-    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type);
-    EIGEN_DEVICE_FUNC
-    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type);
-
-    Scalar m_c, m_s;
-};
-
-/** Makes \c *this as a Jacobi rotation \a J such that applying \a J on both the right and left sides of the selfadjoint 2x2 matrix
-  * \f$ B = \left ( \begin{array}{cc} x & y \\ \overline y & z \end{array} \right )\f$ yields a diagonal matrix \f$ A = J^* B J \f$
-  *
-  * \sa MatrixBase::makeJacobi(const MatrixBase<Derived>&, Index, Index), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z)
-{
-  using std::sqrt;
-  using std::abs;
-
-  RealScalar deno = RealScalar(2)*abs(y);
-  if(deno < (std::numeric_limits<RealScalar>::min)())
-  {
-    m_c = Scalar(1);
-    m_s = Scalar(0);
-    return false;
-  }
-  else
-  {
-    RealScalar tau = (x-z)/deno;
-    RealScalar w = sqrt(numext::abs2(tau) + RealScalar(1));
-    RealScalar t;
-    if(tau>RealScalar(0))
-    {
-      t = RealScalar(1) / (tau + w);
-    }
-    else
-    {
-      t = RealScalar(1) / (tau - w);
-    }
-    RealScalar sign_t = t > RealScalar(0) ? RealScalar(1) : RealScalar(-1);
-    RealScalar n = RealScalar(1) / sqrt(numext::abs2(t)+RealScalar(1));
-    m_s = - sign_t * (numext::conj(y) / abs(y)) * abs(t) * n;
-    m_c = n;
-    return true;
-  }
-}
-
-/** Makes \c *this as a Jacobi rotation \c J such that applying \a J on both the right and left sides of the 2x2 selfadjoint matrix
-  * \f$ B = \left ( \begin{array}{cc} \text{this}_{pp} & \text{this}_{pq} \\ (\text{this}_{pq})^* & \text{this}_{qq} \end{array} \right )\f$ yields
-  * a diagonal matrix \f$ A = J^* B J \f$
-  *
-  * Example: \include Jacobi_makeJacobi.cpp
-  * Output: \verbinclude Jacobi_makeJacobi.out
-  *
-  * \sa JacobiRotation::makeJacobi(RealScalar, Scalar, RealScalar), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar>
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, Index p, Index q)
-{
-  return makeJacobi(numext::real(m.coeff(p,p)), m.coeff(p,q), numext::real(m.coeff(q,q)));
-}
-
-/** Makes \c *this as a Givens rotation \c G such that applying \f$ G^* \f$ to the left of the vector
-  * \f$ V = \left ( \begin{array}{c} p \\ q \end{array} \right )\f$ yields:
-  * \f$ G^* V = \left ( \begin{array}{c} r \\ 0 \end{array} \right )\f$.
-  *
-  * The value of \a r is returned if \a r is not null (the default is null).
-  * Also note that G is built such that the cosine is always real.
-  *
-  * Example: \include Jacobi_makeGivens.cpp
-  * Output: \verbinclude Jacobi_makeGivens.out
-  *
-  * This function implements the continuous Givens rotation generation algorithm
-  * found in Anderson (2000), Discontinuous Plane Rotations and the Symmetric Eigenvalue Problem.
-  * LAPACK Working Note 150, University of Tennessee, UT-CS-00-454, December 4, 2000.
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r)
-{
-  makeGivens(p, q, r, typename internal::conditional<NumTraits<Scalar>::IsComplex, internal::true_type, internal::false_type>::type());
-}
-
-
-// specialization for complexes
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type)
-{
-  using std::sqrt;
-  using std::abs;
-  using numext::conj;
-
-  if(q==Scalar(0))
-  {
-    m_c = numext::real(p)<0 ? Scalar(-1) : Scalar(1);
-    m_s = 0;
-    if(r) *r = m_c * p;
-  }
-  else if(p==Scalar(0))
-  {
-    m_c = 0;
-    m_s = -q/abs(q);
-    if(r) *r = abs(q);
-  }
-  else
-  {
-    RealScalar p1 = numext::norm1(p);
-    RealScalar q1 = numext::norm1(q);
-    if(p1>=q1)
-    {
-      Scalar ps = p / p1;
-      RealScalar p2 = numext::abs2(ps);
-      Scalar qs = q / p1;
-      RealScalar q2 = numext::abs2(qs);
-
-      RealScalar u = sqrt(RealScalar(1) + q2/p2);
-      if(numext::real(p)<RealScalar(0))
-        u = -u;
-
-      m_c = Scalar(1)/u;
-      m_s = -qs*conj(ps)*(m_c/p2);
-      if(r) *r = p * u;
-    }
-    else
-    {
-      Scalar ps = p / q1;
-      RealScalar p2 = numext::abs2(ps);
-      Scalar qs = q / q1;
-      RealScalar q2 = numext::abs2(qs);
-
-      RealScalar u = q1 * sqrt(p2 + q2);
-      if(numext::real(p)<RealScalar(0))
-        u = -u;
-
-      p1 = abs(p);
-      ps = p/p1;
-      m_c = p1/u;
-      m_s = -conj(ps) * (q/u);
-      if(r) *r = ps * u;
-    }
-  }
-}
-
-// specialization for reals
-template<typename Scalar>
-EIGEN_DEVICE_FUNC
-void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type)
-{
-  using std::sqrt;
-  using std::abs;
-  if(q==Scalar(0))
-  {
-    m_c = p<Scalar(0) ? Scalar(-1) : Scalar(1);
-    m_s = Scalar(0);
-    if(r) *r = abs(p);
-  }
-  else if(p==Scalar(0))
-  {
-    m_c = Scalar(0);
-    m_s = q<Scalar(0) ? Scalar(1) : Scalar(-1);
-    if(r) *r = abs(q);
-  }
-  else if(abs(p) > abs(q))
-  {
-    Scalar t = q/p;
-    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
-    if(p<Scalar(0))
-      u = -u;
-    m_c = Scalar(1)/u;
-    m_s = -t * m_c;
-    if(r) *r = p * u;
-  }
-  else
-  {
-    Scalar t = p/q;
-    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
-    if(q<Scalar(0))
-      u = -u;
-    m_s = -Scalar(1)/u;
-    m_c = -t * m_s;
-    if(r) *r = q * u;
-  }
-
-}
-
-/****************************************************************************************
-*   Implementation of MatrixBase methods
-****************************************************************************************/
-
-namespace internal {
-/** \jacobi_module
-  * Applies the clock wise 2D rotation \a j to the set of 2D vectors of coordinates \a x and \a y:
-  * \f$ \left ( \begin{array}{cc} x \\ y \end{array} \right )  =  J \left ( \begin{array}{cc} x \\ y \end{array} \right ) \f$
-  *
-  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
-  */
-template<typename VectorX, typename VectorY, typename OtherScalar>
-EIGEN_DEVICE_FUNC
-void apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j);
-}
-
-/** \jacobi_module
-  * Applies the rotation in the plane \a j to the rows \a p and \a q of \c *this, i.e., it computes B = J * B,
-  * with \f$ B = \left ( \begin{array}{cc} \text{*this.row}(p) \\ \text{*this.row}(q) \end{array} \right ) \f$.
-  *
-  * \sa class JacobiRotation, MatrixBase::applyOnTheRight(), internal::apply_rotation_in_the_plane()
-  */
-template<typename Derived>
-template<typename OtherScalar>
-EIGEN_DEVICE_FUNC
-inline void MatrixBase<Derived>::applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j)
-{
-  RowXpr x(this->row(p));
-  RowXpr y(this->row(q));
-  internal::apply_rotation_in_the_plane(x, y, j);
-}
-
-/** \ingroup Jacobi_Module
-  * Applies the rotation in the plane \a j to the columns \a p and \a q of \c *this, i.e., it computes B = B * J
-  * with \f$ B = \left ( \begin{array}{cc} \text{*this.col}(p) & \text{*this.col}(q) \end{array} \right ) \f$.
-  *
-  * \sa class JacobiRotation, MatrixBase::applyOnTheLeft(), internal::apply_rotation_in_the_plane()
-  */
-template<typename Derived>
-template<typename OtherScalar>
-EIGEN_DEVICE_FUNC
-inline void MatrixBase<Derived>::applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j)
-{
-  ColXpr x(this->col(p));
-  ColXpr y(this->col(q));
-  internal::apply_rotation_in_the_plane(x, y, j.transpose());
-}
-
-namespace internal {
-
-template<typename Scalar, typename OtherScalar,
-         int SizeAtCompileTime, int MinAlignment, bool Vectorizable>
-struct apply_rotation_in_the_plane_selector
-{
-  static EIGEN_DEVICE_FUNC
-  inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
-  {
-    for(Index i=0; i<size; ++i)
-    {
-      Scalar xi = *x;
-      Scalar yi = *y;
-      *x =  c * xi + numext::conj(s) * yi;
-      *y = -s * xi + numext::conj(c) * yi;
-      x += incrx;
-      y += incry;
-    }
-  }
-};
-
-template<typename Scalar, typename OtherScalar,
-         int SizeAtCompileTime, int MinAlignment>
-struct apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,true /* vectorizable */>
-{
-  static inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
-  {
-    enum {
-      PacketSize = packet_traits<Scalar>::size,
-      OtherPacketSize = packet_traits<OtherScalar>::size
-    };
-    typedef typename packet_traits<Scalar>::type Packet;
-    typedef typename packet_traits<OtherScalar>::type OtherPacket;
-
-    /*** dynamic-size vectorized paths ***/
-    if(SizeAtCompileTime == Dynamic && ((incrx==1 && incry==1) || PacketSize == 1))
-    {
-      // both vectors are sequentially stored in memory => vectorization
-      enum { Peeling = 2 };
-
-      Index alignedStart = internal::first_default_aligned(y, size);
-      Index alignedEnd = alignedStart + ((size-alignedStart)/PacketSize)*PacketSize;
-
-      const OtherPacket pc = pset1<OtherPacket>(c);
-      const OtherPacket ps = pset1<OtherPacket>(s);
-      conj_helper<OtherPacket,Packet,NumTraits<OtherScalar>::IsComplex,false> pcj;
-      conj_helper<OtherPacket,Packet,false,false> pm;
-
-      for(Index i=0; i<alignedStart; ++i)
-      {
-        Scalar xi = x[i];
-        Scalar yi = y[i];
-        x[i] =  c * xi + numext::conj(s) * yi;
-        y[i] = -s * xi + numext::conj(c) * yi;
-      }
-
-      Scalar* EIGEN_RESTRICT px = x + alignedStart;
-      Scalar* EIGEN_RESTRICT py = y + alignedStart;
-
-      if(internal::first_default_aligned(x, size)==alignedStart)
-      {
-        for(Index i=alignedStart; i<alignedEnd; i+=PacketSize)
-        {
-          Packet xi = pload<Packet>(px);
-          Packet yi = pload<Packet>(py);
-          pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-          pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-          px += PacketSize;
-          py += PacketSize;
-        }
-      }
-      else
-      {
-        Index peelingEnd = alignedStart + ((size-alignedStart)/(Peeling*PacketSize))*(Peeling*PacketSize);
-        for(Index i=alignedStart; i<peelingEnd; i+=Peeling*PacketSize)
-        {
-          Packet xi   = ploadu<Packet>(px);
-          Packet xi1  = ploadu<Packet>(px+PacketSize);
-          Packet yi   = pload <Packet>(py);
-          Packet yi1  = pload <Packet>(py+PacketSize);
-          pstoreu(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-          pstoreu(px+PacketSize, padd(pm.pmul(pc,xi1),pcj.pmul(ps,yi1)));
-          pstore (py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-          pstore (py+PacketSize, psub(pcj.pmul(pc,yi1),pm.pmul(ps,xi1)));
-          px += Peeling*PacketSize;
-          py += Peeling*PacketSize;
-        }
-        if(alignedEnd!=peelingEnd)
-        {
-          Packet xi = ploadu<Packet>(x+peelingEnd);
-          Packet yi = pload <Packet>(y+peelingEnd);
-          pstoreu(x+peelingEnd, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-          pstore (y+peelingEnd, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-        }
-      }
-
-      for(Index i=alignedEnd; i<size; ++i)
-      {
-        Scalar xi = x[i];
-        Scalar yi = y[i];
-        x[i] =  c * xi + numext::conj(s) * yi;
-        y[i] = -s * xi + numext::conj(c) * yi;
-      }
-    }
-
-    /*** fixed-size vectorized path ***/
-    else if(SizeAtCompileTime != Dynamic && MinAlignment>0) // FIXME should be compared to the required alignment
-    {
-      const OtherPacket pc = pset1<OtherPacket>(c);
-      const OtherPacket ps = pset1<OtherPacket>(s);
-      conj_helper<OtherPacket,Packet,NumTraits<OtherPacket>::IsComplex,false> pcj;
-      conj_helper<OtherPacket,Packet,false,false> pm;
-      Scalar* EIGEN_RESTRICT px = x;
-      Scalar* EIGEN_RESTRICT py = y;
-      for(Index i=0; i<size; i+=PacketSize)
-      {
-        Packet xi = pload<Packet>(px);
-        Packet yi = pload<Packet>(py);
-        pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
-        pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
-        px += PacketSize;
-        py += PacketSize;
-      }
-    }
-
-    /*** non-vectorized path ***/
-    else
-    {
-      apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,false>::run(x,incrx,y,incry,size,c,s);
-    }
-  }
-};
-
-template<typename VectorX, typename VectorY, typename OtherScalar>
-EIGEN_DEVICE_FUNC
-void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j)
-{
-  typedef typename VectorX::Scalar Scalar;
-  const bool Vectorizable =    (int(VectorX::Flags) & int(VectorY::Flags) & PacketAccessBit)
-                            && (int(packet_traits<Scalar>::size) == int(packet_traits<OtherScalar>::size));
-
-  eigen_assert(xpr_x.size() == xpr_y.size());
-  Index size = xpr_x.size();
-  Index incrx = xpr_x.derived().innerStride();
-  Index incry = xpr_y.derived().innerStride();
-
-  Scalar* EIGEN_RESTRICT x = &xpr_x.derived().coeffRef(0);
-  Scalar* EIGEN_RESTRICT y = &xpr_y.derived().coeffRef(0);
-
-  OtherScalar c = j.c();
-  OtherScalar s = j.s();
-  if (c==OtherScalar(1) && s==OtherScalar(0))
-    return;
-
-  apply_rotation_in_the_plane_selector<
-    Scalar,OtherScalar,
-    VectorX::SizeAtCompileTime,
-    EIGEN_PLAIN_ENUM_MIN(evaluator<VectorX>::Alignment, evaluator<VectorY>::Alignment),
-    Vectorizable>::run(x,incrx,y,incry,size,c,s);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_JACOBI_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h
deleted file mode 100644
index 215db35..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h
+++ /dev/null
@@ -1,358 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Kyle Macfarlan <kyle.macfarlan@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_KLUSUPPORT_H
-#define EIGEN_KLUSUPPORT_H
-
-namespace Eigen {
-
-/* TODO extract L, extract U, compute det, etc... */
-
-/** \ingroup KLUSupport_Module
-  * \brief A sparse LU factorization and solver based on KLU
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
-  * using the KLU library. The sparse matrix A must be squared and full rank.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * \warning The input matrix A should be in a \b compressed and \b column-major form.
-  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class UmfPackLU, class SparseLU
-  */
-
-
-inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B [ ], klu_common *Common, double) {
-   return klu_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common);
-}
-
-inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
-   return klu_z_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), &numext::real_ref(B[0]), Common);
-}
-
-inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[], klu_common *Common, double) {
-   return klu_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common);
-}
-
-inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
-   return klu_z_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), &numext::real_ref(B[0]), 0, Common);
-}
-
-inline klu_numeric* klu_factor(int Ap [ ], int Ai [ ], double Ax [ ], klu_symbolic *Symbolic, klu_common *Common, double) {
-   return klu_factor(Ap, Ai, Ax, Symbolic, Common);
-}
-
-inline klu_numeric* klu_factor(int Ap[], int Ai[], std::complex<double> Ax[], klu_symbolic *Symbolic, klu_common *Common, std::complex<double>) {
-   return klu_z_factor(Ap, Ai, &numext::real_ref(Ax[0]), Symbolic, Common);
-}
-
-
-template<typename _MatrixType>
-class KLU : public SparseSolverBase<KLU<_MatrixType> >
-{
-  protected:
-    typedef SparseSolverBase<KLU<_MatrixType> > Base;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
-    typedef SparseMatrix<Scalar> LUMatrixType;
-    typedef SparseMatrix<Scalar,ColMajor,int> KLUMatrixType;
-    typedef Ref<const KLUMatrixType, StandardCompressedFormat> KLUMatrixRef;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    KLU()
-      : m_dummy(0,0), mp_matrix(m_dummy)
-    {
-      init();
-    }
-
-    template<typename InputMatrixType>
-    explicit KLU(const InputMatrixType& matrix)
-      : mp_matrix(matrix)
-    {
-      init();
-      compute(matrix);
-    }
-
-    ~KLU()
-    {
-      if(m_symbolic) klu_free_symbolic(&m_symbolic,&m_common);
-      if(m_numeric)  klu_free_numeric(&m_numeric,&m_common);
-    }
-
-    EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return mp_matrix.rows(); }
-    EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return mp_matrix.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-#if 0 // not implemented yet
-    inline const LUMatrixType& matrixL() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_l;
-    }
-
-    inline const LUMatrixType& matrixU() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_u;
-    }
-
-    inline const IntColVectorType& permutationP() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_p;
-    }
-
-    inline const IntRowVectorType& permutationQ() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_q;
-    }
-#endif
-    /** Computes the sparse Cholesky decomposition of \a matrix
-     *  Note that the matrix should be column-major, and in compressed format for best performance.
-     *  \sa SparseMatrix::makeCompressed().
-     */
-    template<typename InputMatrixType>
-    void compute(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
-      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
-      grab(matrix.derived());
-      analyzePattern_impl();
-      factorize_impl();
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize(), compute()
-      */
-    template<typename InputMatrixType>
-    void analyzePattern(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
-      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
-
-      grab(matrix.derived());
-
-      analyzePattern_impl();
-    }
-
-
-    /** Provides access to the control settings array used by KLU.
-      *
-      * See KLU documentation for details.
-      */
-    inline const klu_common& kluCommon() const
-    {
-      return m_common;
-    }
-
-    /** Provides access to the control settings array used by UmfPack.
-      *
-      * If this array contains NaN's, the default values are used.
-      *
-      * See KLU documentation for details.
-      */
-    inline klu_common& kluCommon()
-    {
-      return m_common;
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
-      *
-      * \sa analyzePattern(), compute()
-      */
-    template<typename InputMatrixType>
-    void factorize(const InputMatrixType& matrix)
-    {
-      eigen_assert(m_analysisIsOk && "KLU: you must first call analyzePattern()");
-      if(m_numeric)
-        klu_free_numeric(&m_numeric,&m_common);
-
-      grab(matrix.derived());
-
-      factorize_impl();
-    }
-
-    /** \internal */
-    template<typename BDerived,typename XDerived>
-    bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
-
-#if 0 // not implemented yet
-    Scalar determinant() const;
-
-    void extractData() const;
-#endif
-
-  protected:
-
-    void init()
-    {
-      m_info                  = InvalidInput;
-      m_isInitialized         = false;
-      m_numeric               = 0;
-      m_symbolic              = 0;
-      m_extractedDataAreDirty = true;
-
-      klu_defaults(&m_common);
-    }
-
-    void analyzePattern_impl()
-    {
-      m_info = InvalidInput;
-      m_analysisIsOk = false;
-      m_factorizationIsOk = false;
-      m_symbolic = klu_analyze(internal::convert_index<int>(mp_matrix.rows()),
-                                     const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()),
-                                     &m_common);
-      if (m_symbolic) {
-         m_isInitialized = true;
-         m_info = Success;
-         m_analysisIsOk = true;
-         m_extractedDataAreDirty = true;
-      }
-    }
-
-    void factorize_impl()
-    {
-
-      m_numeric = klu_factor(const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()), const_cast<Scalar*>(mp_matrix.valuePtr()),
-                                    m_symbolic, &m_common, Scalar());
-
-
-      m_info = m_numeric ? Success : NumericalIssue;
-      m_factorizationIsOk = m_numeric ? 1 : 0;
-      m_extractedDataAreDirty = true;
-    }
-
-    template<typename MatrixDerived>
-    void grab(const EigenBase<MatrixDerived> &A)
-    {
-      mp_matrix.~KLUMatrixRef();
-      ::new (&mp_matrix) KLUMatrixRef(A.derived());
-    }
-
-    void grab(const KLUMatrixRef &A)
-    {
-      if(&(A.derived()) != &mp_matrix)
-      {
-        mp_matrix.~KLUMatrixRef();
-        ::new (&mp_matrix) KLUMatrixRef(A);
-      }
-    }
-
-    // cached data to reduce reallocation, etc.
-#if 0 // not implemented yet
-    mutable LUMatrixType m_l;
-    mutable LUMatrixType m_u;
-    mutable IntColVectorType m_p;
-    mutable IntRowVectorType m_q;
-#endif
-
-    KLUMatrixType m_dummy;
-    KLUMatrixRef mp_matrix;
-
-    klu_numeric* m_numeric;
-    klu_symbolic* m_symbolic;
-    klu_common m_common;
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-    mutable bool m_extractedDataAreDirty;
-
-  private:
-    KLU(const KLU& ) { }
-};
-
-#if 0 // not implemented yet
-template<typename MatrixType>
-void KLU<MatrixType>::extractData() const
-{
-  if (m_extractedDataAreDirty)
-  {
-     eigen_assert(false && "KLU: extractData Not Yet Implemented");
-
-    // get size of the data
-    int lnz, unz, rows, cols, nz_udiag;
-    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
-
-    // allocate data
-    m_l.resize(rows,(std::min)(rows,cols));
-    m_l.resizeNonZeros(lnz);
-
-    m_u.resize((std::min)(rows,cols),cols);
-    m_u.resizeNonZeros(unz);
-
-    m_p.resize(rows);
-    m_q.resize(cols);
-
-    // extract
-    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
-                        m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
-                        m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
-
-    m_extractedDataAreDirty = false;
-  }
-}
-
-template<typename MatrixType>
-typename KLU<MatrixType>::Scalar KLU<MatrixType>::determinant() const
-{
-  eigen_assert(false && "KLU: extractData Not Yet Implemented");
-  return Scalar();
-}
-#endif
-
-template<typename MatrixType>
-template<typename BDerived,typename XDerived>
-bool KLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
-{
-  Index rhsCols = b.cols();
-  EIGEN_STATIC_ASSERT((XDerived::Flags&RowMajorBit)==0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
-
-  x = b;
-  int info = klu_solve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(), const_cast<klu_common*>(&m_common), Scalar());
-
-  m_info = info!=0 ? Success : NumericalIssue;
-  return true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_KLUSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/Determinant.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/Determinant.h
deleted file mode 100644
index 3a41e6f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/Determinant.h
+++ /dev/null
@@ -1,117 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DETERMINANT_H
-#define EIGEN_DETERMINANT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline const typename Derived::Scalar bruteforce_det3_helper
-(const MatrixBase<Derived>& matrix, int a, int b, int c)
-{
-  return matrix.coeff(0,a)
-         * (matrix.coeff(1,b) * matrix.coeff(2,c) - matrix.coeff(1,c) * matrix.coeff(2,b));
-}
-
-template<typename Derived,
-         int DeterminantType = Derived::RowsAtCompileTime
-> struct determinant_impl
-{
-  static inline typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    if(Derived::ColsAtCompileTime==Dynamic && m.rows()==0)
-      return typename traits<Derived>::Scalar(1);
-    return m.partialPivLu().determinant();
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 1>
-{
-  static inline EIGEN_DEVICE_FUNC
-  typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    return m.coeff(0,0);
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 2>
-{
-  static inline EIGEN_DEVICE_FUNC
-  typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    return m.coeff(0,0) * m.coeff(1,1) - m.coeff(1,0) * m.coeff(0,1);
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 3>
-{
-  static inline EIGEN_DEVICE_FUNC
-  typename traits<Derived>::Scalar run(const Derived& m)
-  {
-    return bruteforce_det3_helper(m,0,1,2)
-          - bruteforce_det3_helper(m,1,0,2)
-          + bruteforce_det3_helper(m,2,0,1);
-  }
-};
-
-template<typename Derived> struct determinant_impl<Derived, 4>
-{
-  typedef typename traits<Derived>::Scalar Scalar;
-  static EIGEN_DEVICE_FUNC
-  Scalar run(const Derived& m)
-  {
-    Scalar d2_01 = det2(m, 0, 1);
-    Scalar d2_02 = det2(m, 0, 2);
-    Scalar d2_03 = det2(m, 0, 3);
-    Scalar d2_12 = det2(m, 1, 2);
-    Scalar d2_13 = det2(m, 1, 3);
-    Scalar d2_23 = det2(m, 2, 3);
-    Scalar d3_0 = det3(m, 1,d2_23, 2,d2_13, 3,d2_12);
-    Scalar d3_1 = det3(m, 0,d2_23, 2,d2_03, 3,d2_02);
-    Scalar d3_2 = det3(m, 0,d2_13, 1,d2_03, 3,d2_01);
-    Scalar d3_3 = det3(m, 0,d2_12, 1,d2_02, 2,d2_01);
-    return internal::pmadd(-m(0,3),d3_0, m(1,3)*d3_1) +
-           internal::pmadd(-m(2,3),d3_2, m(3,3)*d3_3);
-  }
-protected:
-  static EIGEN_DEVICE_FUNC
-  Scalar det2(const Derived& m, Index i0, Index i1)
-  {
-    return m(i0,0) * m(i1,1) - m(i1,0) * m(i0,1);
-  }
-
-  static EIGEN_DEVICE_FUNC
-  Scalar det3(const Derived& m, Index i0, const Scalar& d0, Index i1, const Scalar& d1, Index i2, const Scalar& d2)
-  {
-    return internal::pmadd(m(i0,2), d0, internal::pmadd(-m(i1,2), d1, m(i2,2)*d2));
-  }
-};
-
-} // end namespace internal
-
-/** \lu_module
-  *
-  * \returns the determinant of this matrix
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline typename internal::traits<Derived>::Scalar MatrixBase<Derived>::determinant() const
-{
-  eigen_assert(rows() == cols());
-  typedef typename internal::nested_eval<Derived,Base::RowsAtCompileTime>::type Nested;
-  return internal::determinant_impl<typename internal::remove_all<Nested>::type>::run(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DETERMINANT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/FullPivLU.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/FullPivLU.h
deleted file mode 100644
index ba1749f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/FullPivLU.h
+++ /dev/null
@@ -1,877 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LU_H
-#define EIGEN_LU_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _MatrixType> struct traits<FullPivLU<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-} // end namespace internal
-
-/** \ingroup LU_Module
-  *
-  * \class FullPivLU
-  *
-  * \brief LU decomposition of a matrix with complete pivoting, and related features
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the LU decomposition
-  *
-  * This class represents a LU decomposition of any matrix, with complete pivoting: the matrix A is
-  * decomposed as \f$ A = P^{-1} L U Q^{-1} \f$ where L is unit-lower-triangular, U is
-  * upper-triangular, and P and Q are permutation matrices. This is a rank-revealing LU
-  * decomposition. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any
-  * zeros are at the end.
-  *
-  * This decomposition provides the generic approach to solving systems of linear equations, computing
-  * the rank, invertibility, inverse, kernel, and determinant.
-  *
-  * This LU decomposition is very stable and well tested with large matrices. However there are use cases where the SVD
-  * decomposition is inherently more stable and/or flexible. For example, when computing the kernel of a matrix,
-  * working with the SVD allows to select the smallest singular values of the matrix, something that
-  * the LU decomposition doesn't see.
-  *
-  * The data of the LU decomposition can be directly accessed through the methods matrixLU(),
-  * permutationP(), permutationQ().
-  *
-  * As an example, here is how the original matrix can be retrieved:
-  * \include class_FullPivLU.cpp
-  * Output: \verbinclude class_FullPivLU.out
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::fullPivLu(), MatrixBase::determinant(), MatrixBase::inverse()
-  */
-template<typename _MatrixType> class FullPivLU
-  : public SolverBase<FullPivLU<_MatrixType> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<FullPivLU> Base;
-    friend class SolverBase<FullPivLU>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivLU)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename internal::plain_row_type<MatrixType, StorageIndex>::type IntRowVectorType;
-    typedef typename internal::plain_col_type<MatrixType, StorageIndex>::type IntColVectorType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationQType;
-    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationPType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via LU::compute(const MatrixType&).
-      */
-    FullPivLU();
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa FullPivLU()
-      */
-    FullPivLU(Index rows, Index cols);
-
-    /** Constructor.
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *               It is required to be nonzero.
-      */
-    template<typename InputType>
-    explicit FullPivLU(const EigenBase<InputType>& matrix);
-
-    /** \brief Constructs a LU factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa FullPivLU(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit FullPivLU(EigenBase<InputType>& matrix);
-
-    /** Computes the LU decomposition of the given matrix.
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *               It is required to be nonzero.
-      *
-      * \returns a reference to *this
-      */
-    template<typename InputType>
-    FullPivLU& compute(const EigenBase<InputType>& matrix) {
-      m_lu = matrix.derived();
-      computeInPlace();
-      return *this;
-    }
-
-    /** \returns the LU decomposition matrix: the upper-triangular part is U, the
-      * unit-lower-triangular part is L (at least for square matrices; in the non-square
-      * case, special care is needed, see the documentation of class FullPivLU).
-      *
-      * \sa matrixL(), matrixU()
-      */
-    inline const MatrixType& matrixLU() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_lu;
-    }
-
-    /** \returns the number of nonzero pivots in the LU decomposition.
-      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
-      * So that notion isn't really intrinsically interesting, but it is
-      * still useful when implementing algorithms.
-      *
-      * \sa rank()
-      */
-    inline Index nonzeroPivots() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of U.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-    /** \returns the permutation matrix P
-      *
-      * \sa permutationQ()
-      */
-    EIGEN_DEVICE_FUNC inline const PermutationPType& permutationP() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_p;
-    }
-
-    /** \returns the permutation matrix Q
-      *
-      * \sa permutationP()
-      */
-    inline const PermutationQType& permutationQ() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_q;
-    }
-
-    /** \returns the kernel of the matrix, also called its null-space. The columns of the returned matrix
-      * will form a basis of the kernel.
-      *
-      * \note If the kernel has dimension zero, then the returned matrix is a column-vector filled with zeros.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      *
-      * Example: \include FullPivLU_kernel.cpp
-      * Output: \verbinclude FullPivLU_kernel.out
-      *
-      * \sa image()
-      */
-    inline const internal::kernel_retval<FullPivLU> kernel() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return internal::kernel_retval<FullPivLU>(*this);
-    }
-
-    /** \returns the image of the matrix, also called its column-space. The columns of the returned matrix
-      * will form a basis of the image (column-space).
-      *
-      * \param originalMatrix the original matrix, of which *this is the LU decomposition.
-      *                       The reason why it is needed to pass it here, is that this allows
-      *                       a large optimization, as otherwise this method would need to reconstruct it
-      *                       from the LU decomposition.
-      *
-      * \note If the image has dimension zero, then the returned matrix is a column-vector filled with zeros.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      *
-      * Example: \include FullPivLU_image.cpp
-      * Output: \verbinclude FullPivLU_image.out
-      *
-      * \sa kernel()
-      */
-    inline const internal::image_retval<FullPivLU>
-      image(const MatrixType& originalMatrix) const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return internal::image_retval<FullPivLU>(*this, originalMatrix);
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \return a solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the LU decomposition.
-      *
-      * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
-      *          the only requirement in order for the equation to make sense is that
-      *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
-      *
-      * \returns a solution.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      * \note_about_using_kernel_to_study_multiple_solutions
-      *
-      * Example: \include FullPivLU_solve.cpp
-      * Output: \verbinclude FullPivLU_solve.out
-      *
-      * \sa TriangularView::solve(), kernel(), inverse()
-      */
-    template<typename Rhs>
-    inline const Solve<FullPivLU, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
-        the LU decomposition.
-      */
-    inline RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    /** \returns the determinant of the matrix of which
-      * *this is the LU decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the LU decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
-      *       optimized paths.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      *
-      * \sa MatrixBase::determinant()
-      */
-    typename internal::traits<MatrixType>::Scalar determinant() const;
-
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
-      * who need to determine when pivots are to be considered nonzero. This is not used for the
-      * LU decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
-      * uses a formula to automatically determine a reasonable threshold.
-      * Once you have called the present method setThreshold(const RealScalar&),
-      * your value is used instead.
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A pivot will be considered nonzero if its absolute value is strictly greater than
-      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-      * where maxpivot is the biggest pivot.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    FullPivLU& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code lu.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    FullPivLU& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
-
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    RealScalar threshold() const
-    {
-      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-      return m_usePrescribedThreshold ? m_prescribedThreshold
-      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
-      // and turns out to be identical to Higham's formula used already in LDLt.
-          : NumTraits<Scalar>::epsilon() * RealScalar(m_lu.diagonalSize());
-    }
-
-    /** \returns the rank of the matrix of which *this is the LU decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
-      Index result = 0;
-      for(Index i = 0; i < m_nonzero_pivots; ++i)
-        result += (abs(m_lu.coeff(i,i)) > premultiplied_threshold);
-      return result;
-    }
-
-    /** \returns the dimension of the kernel of the matrix of which *this is the LU decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index dimensionOfKernel() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return cols() - rank();
-    }
-
-    /** \returns true if the matrix of which *this is the LU decomposition represents an injective
-      *          linear map, i.e. has trivial kernel; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInjective() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return rank() == cols();
-    }
-
-    /** \returns true if the matrix of which *this is the LU decomposition represents a surjective
-      *          linear map; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isSurjective() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return rank() == rows();
-    }
-
-    /** \returns true if the matrix of which *this is the LU decomposition is invertible.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInvertible() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return isInjective() && (m_lu.rows() == m_lu.cols());
-    }
-
-    /** \returns the inverse of the matrix of which *this is the LU decomposition.
-      *
-      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
-      *       Use isInvertible() to first determine whether this matrix is invertible.
-      *
-      * \sa MatrixBase::inverse()
-      */
-    inline const Inverse<FullPivLU> inverse() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!");
-      return Inverse<FullPivLU>(*this);
-    }
-
-    MatrixType reconstructedMatrix() const;
-
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-    inline Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_lu;
-    PermutationPType m_p;
-    PermutationQType m_q;
-    IntColVectorType m_rowsTranspositions;
-    IntRowVectorType m_colsTranspositions;
-    Index m_nonzero_pivots;
-    RealScalar m_l1_norm;
-    RealScalar m_maxpivot, m_prescribedThreshold;
-    signed char m_det_pq;
-    bool m_isInitialized, m_usePrescribedThreshold;
-};
-
-template<typename MatrixType>
-FullPivLU<MatrixType>::FullPivLU()
-  : m_isInitialized(false), m_usePrescribedThreshold(false)
-{
-}
-
-template<typename MatrixType>
-FullPivLU<MatrixType>::FullPivLU(Index rows, Index cols)
-  : m_lu(rows, cols),
-    m_p(rows),
-    m_q(cols),
-    m_rowsTranspositions(rows),
-    m_colsTranspositions(cols),
-    m_isInitialized(false),
-    m_usePrescribedThreshold(false)
-{
-}
-
-template<typename MatrixType>
-template<typename InputType>
-FullPivLU<MatrixType>::FullPivLU(const EigenBase<InputType>& matrix)
-  : m_lu(matrix.rows(), matrix.cols()),
-    m_p(matrix.rows()),
-    m_q(matrix.cols()),
-    m_rowsTranspositions(matrix.rows()),
-    m_colsTranspositions(matrix.cols()),
-    m_isInitialized(false),
-    m_usePrescribedThreshold(false)
-{
-  compute(matrix.derived());
-}
-
-template<typename MatrixType>
-template<typename InputType>
-FullPivLU<MatrixType>::FullPivLU(EigenBase<InputType>& matrix)
-  : m_lu(matrix.derived()),
-    m_p(matrix.rows()),
-    m_q(matrix.cols()),
-    m_rowsTranspositions(matrix.rows()),
-    m_colsTranspositions(matrix.cols()),
-    m_isInitialized(false),
-    m_usePrescribedThreshold(false)
-{
-  computeInPlace();
-}
-
-template<typename MatrixType>
-void FullPivLU<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  // the permutations are stored as int indices, so just to be sure:
-  eigen_assert(m_lu.rows()<=NumTraits<int>::highest() && m_lu.cols()<=NumTraits<int>::highest());
-
-  m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
-
-  const Index size = m_lu.diagonalSize();
-  const Index rows = m_lu.rows();
-  const Index cols = m_lu.cols();
-
-  // will store the transpositions, before we accumulate them at the end.
-  // can't accumulate on-the-fly because that will be done in reverse order for the rows.
-  m_rowsTranspositions.resize(m_lu.rows());
-  m_colsTranspositions.resize(m_lu.cols());
-  Index number_of_transpositions = 0; // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i
-
-  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
-  m_maxpivot = RealScalar(0);
-
-  for(Index k = 0; k < size; ++k)
-  {
-    // First, we need to find the pivot.
-
-    // biggest coefficient in the remaining bottom-right corner (starting at row k, col k)
-    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
-    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
-    typedef typename Scoring::result_type Score;
-    Score biggest_in_corner;
-    biggest_in_corner = m_lu.bottomRightCorner(rows-k, cols-k)
-                        .unaryExpr(Scoring())
-                        .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
-    row_of_biggest_in_corner += k; // correct the values! since they were computed in the corner,
-    col_of_biggest_in_corner += k; // need to add k to them.
-
-    if(biggest_in_corner==Score(0))
-    {
-      // before exiting, make sure to initialize the still uninitialized transpositions
-      // in a sane state without destroying what we already have.
-      m_nonzero_pivots = k;
-      for(Index i = k; i < size; ++i)
-      {
-        m_rowsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-        m_colsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-      }
-      break;
-    }
-
-    RealScalar abs_pivot = internal::abs_knowing_score<Scalar>()(m_lu(row_of_biggest_in_corner, col_of_biggest_in_corner), biggest_in_corner);
-    if(abs_pivot > m_maxpivot) m_maxpivot = abs_pivot;
-
-    // Now that we've found the pivot, we need to apply the row/col swaps to
-    // bring it to the location (k,k).
-
-    m_rowsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
-    m_colsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
-    if(k != row_of_biggest_in_corner) {
-      m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner));
-      ++number_of_transpositions;
-    }
-    if(k != col_of_biggest_in_corner) {
-      m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner));
-      ++number_of_transpositions;
-    }
-
-    // Now that the pivot is at the right location, we update the remaining
-    // bottom-right corner by Gaussian elimination.
-
-    if(k<rows-1)
-      m_lu.col(k).tail(rows-k-1) /= m_lu.coeff(k,k);
-    if(k<size-1)
-      m_lu.block(k+1,k+1,rows-k-1,cols-k-1).noalias() -= m_lu.col(k).tail(rows-k-1) * m_lu.row(k).tail(cols-k-1);
-  }
-
-  // the main loop is over, we still have to accumulate the transpositions to find the
-  // permutations P and Q
-
-  m_p.setIdentity(rows);
-  for(Index k = size-1; k >= 0; --k)
-    m_p.applyTranspositionOnTheRight(k, m_rowsTranspositions.coeff(k));
-
-  m_q.setIdentity(cols);
-  for(Index k = 0; k < size; ++k)
-    m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-
-  m_isInitialized = true;
-}
-
-template<typename MatrixType>
-typename internal::traits<MatrixType>::Scalar FullPivLU<MatrixType>::determinant() const
-{
-  eigen_assert(m_isInitialized && "LU is not initialized.");
-  eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the determinant of a non-square matrix!");
-  return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod());
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: \f$ P^{-1} L U Q^{-1} \f$.
- * This function is provided for debug purposes. */
-template<typename MatrixType>
-MatrixType FullPivLU<MatrixType>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LU is not initialized.");
-  const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols());
-  // LU
-  MatrixType res(m_lu.rows(),m_lu.cols());
-  // FIXME the .toDenseMatrix() should not be needed...
-  res = m_lu.leftCols(smalldim)
-            .template triangularView<UnitLower>().toDenseMatrix()
-      * m_lu.topRows(smalldim)
-            .template triangularView<Upper>().toDenseMatrix();
-
-  // P^{-1}(LU)
-  res = m_p.inverse() * res;
-
-  // (P^{-1}LU)Q^{-1}
-  res = res * m_q.inverse();
-
-  return res;
-}
-
-/********* Implementation of kernel() **************************************************/
-
-namespace internal {
-template<typename _MatrixType>
-struct kernel_retval<FullPivLU<_MatrixType> >
-  : kernel_retval_base<FullPivLU<_MatrixType> >
-{
-  EIGEN_MAKE_KERNEL_HELPERS(FullPivLU<_MatrixType>)
-
-  enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(
-            MatrixType::MaxColsAtCompileTime,
-            MatrixType::MaxRowsAtCompileTime)
-  };
-
-  template<typename Dest> void evalTo(Dest& dst) const
-  {
-    using std::abs;
-    const Index cols = dec().matrixLU().cols(), dimker = cols - rank();
-    if(dimker == 0)
-    {
-      // The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's
-      // avoid crashing/asserting as that depends on floating point calculations. Let's
-      // just return a single column vector filled with zeros.
-      dst.setZero();
-      return;
-    }
-
-    /* Let us use the following lemma:
-      *
-      * Lemma: If the matrix A has the LU decomposition PAQ = LU,
-      * then Ker A = Q(Ker U).
-      *
-      * Proof: trivial: just keep in mind that P, Q, L are invertible.
-      */
-
-    /* Thus, all we need to do is to compute Ker U, and then apply Q.
-      *
-      * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end.
-      * Thus, the diagonal of U ends with exactly
-      * dimKer zero's. Let us use that to construct dimKer linearly
-      * independent vectors in Ker U.
-      */
-
-    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
-    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
-    Index p = 0;
-    for(Index i = 0; i < dec().nonzeroPivots(); ++i)
-      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
-        pivots.coeffRef(p++) = i;
-    eigen_internal_assert(p == rank());
-
-    // we construct a temporaty trapezoid matrix m, by taking the U matrix and
-    // permuting the rows and cols to bring the nonnegligible pivots to the top of
-    // the main diagonal. We need that to be able to apply our triangular solvers.
-    // FIXME when we get triangularView-for-rectangular-matrices, this can be simplified
-    Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, MatrixType::Options,
-           MaxSmallDimAtCompileTime, MatrixType::MaxColsAtCompileTime>
-      m(dec().matrixLU().block(0, 0, rank(), cols));
-    for(Index i = 0; i < rank(); ++i)
-    {
-      if(i) m.row(i).head(i).setZero();
-      m.row(i).tail(cols-i) = dec().matrixLU().row(pivots.coeff(i)).tail(cols-i);
-    }
-    m.block(0, 0, rank(), rank());
-    m.block(0, 0, rank(), rank()).template triangularView<StrictlyLower>().setZero();
-    for(Index i = 0; i < rank(); ++i)
-      m.col(i).swap(m.col(pivots.coeff(i)));
-
-    // ok, we have our trapezoid matrix, we can apply the triangular solver.
-    // notice that the math behind this suggests that we should apply this to the
-    // negative of the RHS, but for performance we just put the negative sign elsewhere, see below.
-    m.topLeftCorner(rank(), rank())
-     .template triangularView<Upper>().solveInPlace(
-        m.topRightCorner(rank(), dimker)
-      );
-
-    // now we must undo the column permutation that we had applied!
-    for(Index i = rank()-1; i >= 0; --i)
-      m.col(i).swap(m.col(pivots.coeff(i)));
-
-    // see the negative sign in the next line, that's what we were talking about above.
-    for(Index i = 0; i < rank(); ++i) dst.row(dec().permutationQ().indices().coeff(i)) = -m.row(i).tail(dimker);
-    for(Index i = rank(); i < cols; ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero();
-    for(Index k = 0; k < dimker; ++k) dst.coeffRef(dec().permutationQ().indices().coeff(rank()+k), k) = Scalar(1);
-  }
-};
-
-/***** Implementation of image() *****************************************************/
-
-template<typename _MatrixType>
-struct image_retval<FullPivLU<_MatrixType> >
-  : image_retval_base<FullPivLU<_MatrixType> >
-{
-  EIGEN_MAKE_IMAGE_HELPERS(FullPivLU<_MatrixType>)
-
-  enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(
-            MatrixType::MaxColsAtCompileTime,
-            MatrixType::MaxRowsAtCompileTime)
-  };
-
-  template<typename Dest> void evalTo(Dest& dst) const
-  {
-    using std::abs;
-    if(rank() == 0)
-    {
-      // The Image is just {0}, so it doesn't have a basis properly speaking, but let's
-      // avoid crashing/asserting as that depends on floating point calculations. Let's
-      // just return a single column vector filled with zeros.
-      dst.setZero();
-      return;
-    }
-
-    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
-    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
-    Index p = 0;
-    for(Index i = 0; i < dec().nonzeroPivots(); ++i)
-      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
-        pivots.coeffRef(p++) = i;
-    eigen_internal_assert(p == rank());
-
-    for(Index i = 0; i < rank(); ++i)
-      dst.col(i) = originalMatrix().col(dec().permutationQ().indices().coeff(pivots.coeff(i)));
-  }
-};
-
-/***** Implementation of solve() *****************************************************/
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void FullPivLU<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}.
-  * So we proceed as follows:
-  * Step 1: compute c = P * rhs.
-  * Step 2: replace c by the solution x to Lx = c. Exists because L is invertible.
-  * Step 3: replace c by the solution x to Ux = c. May or may not exist.
-  * Step 4: result = Q * c;
-  */
-
-  const Index rows = this->rows(),
-              cols = this->cols(),
-              nonzero_pivots = this->rank();
-  const Index smalldim = (std::min)(rows, cols);
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
-
-  // Step 1
-  c = permutationP() * rhs;
-
-  // Step 2
-  m_lu.topLeftCorner(smalldim,smalldim)
-      .template triangularView<UnitLower>()
-      .solveInPlace(c.topRows(smalldim));
-  if(rows>cols)
-    c.bottomRows(rows-cols) -= m_lu.bottomRows(rows-cols) * c.topRows(cols);
-
-  // Step 3
-  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(nonzero_pivots));
-
-  // Step 4
-  for(Index i = 0; i < nonzero_pivots; ++i)
-    dst.row(permutationQ().indices().coeff(i)) = c.row(i);
-  for(Index i = nonzero_pivots; i < m_lu.cols(); ++i)
-    dst.row(permutationQ().indices().coeff(i)).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void FullPivLU<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1},
-   * and since permutations are real and unitary, we can write this
-   * as   A^T = Q U^T L^T P,
-   * So we proceed as follows:
-   * Step 1: compute c = Q^T rhs.
-   * Step 2: replace c by the solution x to U^T x = c. May or may not exist.
-   * Step 3: replace c by the solution x to L^T x = c.
-   * Step 4: result = P^T c.
-   * If Conjugate is true, replace "^T" by "^*" above.
-   */
-
-  const Index rows = this->rows(), cols = this->cols(),
-    nonzero_pivots = this->rank();
-  const Index smalldim = (std::min)(rows, cols);
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
-
-  // Step 1
-  c = permutationQ().inverse() * rhs;
-
-  // Step 2
-  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
-      .template triangularView<Upper>()
-      .transpose()
-      .template conjugateIf<Conjugate>()
-      .solveInPlace(c.topRows(nonzero_pivots));
-
-  // Step 3
-  m_lu.topLeftCorner(smalldim, smalldim)
-      .template triangularView<UnitLower>()
-      .transpose()
-      .template conjugateIf<Conjugate>()
-      .solveInPlace(c.topRows(smalldim));
-
-  // Step 4
-  PermutationPType invp = permutationP().inverse().eval();
-  for(Index i = 0; i < smalldim; ++i)
-    dst.row(invp.indices().coeff(i)) = c.row(i);
-  for(Index i = smalldim; i < rows; ++i)
-    dst.row(invp.indices().coeff(i)).setZero();
-}
-
-#endif
-
-namespace internal {
-
-
-/***** Implementation of inverse() *****************************************************/
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<FullPivLU<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivLU<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef FullPivLU<MatrixType> LuType;
-  typedef Inverse<LuType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename MatrixType::Scalar> &)
-  {
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-} // end namespace internal
-
-/******* MatrixBase methods *****************************************************************/
-
-/** \lu_module
-  *
-  * \return the full-pivoting LU decomposition of \c *this.
-  *
-  * \sa class FullPivLU
-  */
-template<typename Derived>
-inline const FullPivLU<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::fullPivLu() const
-{
-  return FullPivLU<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/InverseImpl.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/InverseImpl.h
deleted file mode 100644
index a40cefa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/InverseImpl.h
+++ /dev/null
@@ -1,432 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INVERSE_IMPL_H
-#define EIGEN_INVERSE_IMPL_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/**********************************
-*** General case implementation ***
-**********************************/
-
-template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
-struct compute_inverse
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    result = matrix.partialPivLu().inverse();
-  }
-};
-
-template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
-struct compute_inverse_and_det_with_check { /* nothing! general case not supported. */ };
-
-/****************************
-*** Size 1 implementation ***
-****************************/
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    typedef typename MatrixType::Scalar Scalar;
-    internal::evaluator<MatrixType> matrixEval(matrix);
-    result.coeffRef(0,0) = Scalar(1) / matrixEval.coeff(0,0);
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 1>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& result,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    using std::abs;
-    determinant = matrix.coeff(0,0);
-    invertible = abs(determinant) > absDeterminantThreshold;
-    if(invertible) result.coeffRef(0,0) = typename ResultType::Scalar(1) / determinant;
-  }
-};
-
-/****************************
-*** Size 2 implementation ***
-****************************/
-
-template<typename MatrixType, typename ResultType>
-EIGEN_DEVICE_FUNC 
-inline void compute_inverse_size2_helper(
-    const MatrixType& matrix, const typename ResultType::Scalar& invdet,
-    ResultType& result)
-{
-  typename ResultType::Scalar temp = matrix.coeff(0,0);
-  result.coeffRef(0,0) =  matrix.coeff(1,1) * invdet;
-  result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet;
-  result.coeffRef(0,1) = -matrix.coeff(0,1) * invdet;
-  result.coeffRef(1,1) =  temp * invdet;
-}
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 2>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    typedef typename ResultType::Scalar Scalar;
-    const Scalar invdet = typename MatrixType::Scalar(1) / matrix.determinant();
-    compute_inverse_size2_helper(matrix, invdet, result);
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 2>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    using std::abs;
-    typedef typename ResultType::Scalar Scalar;
-    determinant = matrix.determinant();
-    invertible = abs(determinant) > absDeterminantThreshold;
-    if(!invertible) return;
-    const Scalar invdet = Scalar(1) / determinant;
-    compute_inverse_size2_helper(matrix, invdet, inverse);
-  }
-};
-
-/****************************
-*** Size 3 implementation ***
-****************************/
-
-template<typename MatrixType, int i, int j>
-EIGEN_DEVICE_FUNC 
-inline typename MatrixType::Scalar cofactor_3x3(const MatrixType& m)
-{
-  enum {
-    i1 = (i+1) % 3,
-    i2 = (i+2) % 3,
-    j1 = (j+1) % 3,
-    j2 = (j+2) % 3
-  };
-  return m.coeff(i1, j1) * m.coeff(i2, j2)
-       - m.coeff(i1, j2) * m.coeff(i2, j1);
-}
-
-template<typename MatrixType, typename ResultType>
-EIGEN_DEVICE_FUNC
-inline void compute_inverse_size3_helper(
-    const MatrixType& matrix,
-    const typename ResultType::Scalar& invdet,
-    const Matrix<typename ResultType::Scalar,3,1>& cofactors_col0,
-    ResultType& result)
-{
-  // Compute cofactors in a way that avoids aliasing issues.
-  typedef typename ResultType::Scalar Scalar;
-  const Scalar c01 = cofactor_3x3<MatrixType,0,1>(matrix) * invdet;
-  const Scalar c11 = cofactor_3x3<MatrixType,1,1>(matrix) * invdet;
-  const Scalar c02 = cofactor_3x3<MatrixType,0,2>(matrix) * invdet;
-  result.coeffRef(1,2) =  cofactor_3x3<MatrixType,2,1>(matrix) * invdet;
-  result.coeffRef(2,1) =  cofactor_3x3<MatrixType,1,2>(matrix) * invdet;
-  result.coeffRef(2,2) =  cofactor_3x3<MatrixType,2,2>(matrix) * invdet;
-  result.coeffRef(1,0) =  c01;
-  result.coeffRef(1,1) =  c11;
-  result.coeffRef(2,0) =  c02;  
-  result.row(0) = cofactors_col0 * invdet;
-}
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 3>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(const MatrixType& matrix, ResultType& result)
-  {
-    typedef typename ResultType::Scalar Scalar;
-    Matrix<typename MatrixType::Scalar,3,1> cofactors_col0;
-    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
-    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
-    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
-    const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
-    const Scalar invdet = Scalar(1) / det;
-    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result);
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 3>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    typedef typename ResultType::Scalar Scalar;
-    Matrix<Scalar,3,1> cofactors_col0;
-    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
-    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
-    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
-    determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
-    invertible = Eigen::numext::abs(determinant) > absDeterminantThreshold;
-    if(!invertible) return;
-    const Scalar invdet = Scalar(1) / determinant;
-    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, inverse);
-  }
-};
-
-/****************************
-*** Size 4 implementation ***
-****************************/
-
-template<typename Derived>
-EIGEN_DEVICE_FUNC 
-inline const typename Derived::Scalar general_det3_helper
-(const MatrixBase<Derived>& matrix, int i1, int i2, int i3, int j1, int j2, int j3)
-{
-  return matrix.coeff(i1,j1)
-         * (matrix.coeff(i2,j2) * matrix.coeff(i3,j3) - matrix.coeff(i2,j3) * matrix.coeff(i3,j2));
-}
-
-template<typename MatrixType, int i, int j>
-EIGEN_DEVICE_FUNC 
-inline typename MatrixType::Scalar cofactor_4x4(const MatrixType& matrix)
-{
-  enum {
-    i1 = (i+1) % 4,
-    i2 = (i+2) % 4,
-    i3 = (i+3) % 4,
-    j1 = (j+1) % 4,
-    j2 = (j+2) % 4,
-    j3 = (j+3) % 4
-  };
-  return general_det3_helper(matrix, i1, i2, i3, j1, j2, j3)
-       + general_det3_helper(matrix, i2, i3, i1, j1, j2, j3)
-       + general_det3_helper(matrix, i3, i1, i2, j1, j2, j3);
-}
-
-template<int Arch, typename Scalar, typename MatrixType, typename ResultType>
-struct compute_inverse_size4
-{
-  EIGEN_DEVICE_FUNC
-  static void run(const MatrixType& matrix, ResultType& result)
-  {
-    result.coeffRef(0,0) =  cofactor_4x4<MatrixType,0,0>(matrix);
-    result.coeffRef(1,0) = -cofactor_4x4<MatrixType,0,1>(matrix);
-    result.coeffRef(2,0) =  cofactor_4x4<MatrixType,0,2>(matrix);
-    result.coeffRef(3,0) = -cofactor_4x4<MatrixType,0,3>(matrix);
-    result.coeffRef(0,2) =  cofactor_4x4<MatrixType,2,0>(matrix);
-    result.coeffRef(1,2) = -cofactor_4x4<MatrixType,2,1>(matrix);
-    result.coeffRef(2,2) =  cofactor_4x4<MatrixType,2,2>(matrix);
-    result.coeffRef(3,2) = -cofactor_4x4<MatrixType,2,3>(matrix);
-    result.coeffRef(0,1) = -cofactor_4x4<MatrixType,1,0>(matrix);
-    result.coeffRef(1,1) =  cofactor_4x4<MatrixType,1,1>(matrix);
-    result.coeffRef(2,1) = -cofactor_4x4<MatrixType,1,2>(matrix);
-    result.coeffRef(3,1) =  cofactor_4x4<MatrixType,1,3>(matrix);
-    result.coeffRef(0,3) = -cofactor_4x4<MatrixType,3,0>(matrix);
-    result.coeffRef(1,3) =  cofactor_4x4<MatrixType,3,1>(matrix);
-    result.coeffRef(2,3) = -cofactor_4x4<MatrixType,3,2>(matrix);
-    result.coeffRef(3,3) =  cofactor_4x4<MatrixType,3,3>(matrix);
-    result /= (matrix.col(0).cwiseProduct(result.row(0).transpose())).sum();
-  }
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse<MatrixType, ResultType, 4>
- : compute_inverse_size4<Architecture::Target, typename MatrixType::Scalar,
-                            MatrixType, ResultType>
-{
-};
-
-template<typename MatrixType, typename ResultType>
-struct compute_inverse_and_det_with_check<MatrixType, ResultType, 4>
-{
-  EIGEN_DEVICE_FUNC
-  static inline void run(
-    const MatrixType& matrix,
-    const typename MatrixType::RealScalar& absDeterminantThreshold,
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible
-  )
-  {
-    using std::abs;
-    determinant = matrix.determinant();
-    invertible = abs(determinant) > absDeterminantThreshold;
-    if(invertible && extract_data(matrix) != extract_data(inverse)) {
-      compute_inverse<MatrixType, ResultType>::run(matrix, inverse);
-    }
-    else if(invertible) {
-      MatrixType matrix_t = matrix;
-      compute_inverse<MatrixType, ResultType>::run(matrix_t, inverse);
-    }
-  }
-};
-
-/*************************
-*** MatrixBase methods ***
-*************************/
-
-} // end namespace internal
-
-namespace internal {
-
-// Specialization for "dense = dense_xpr.inverse()"
-template<typename DstXprType, typename XprType>
-struct Assignment<DstXprType, Inverse<XprType>, internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar>, Dense2Dense>
-{
-  typedef Inverse<XprType> SrcXprType;
-  EIGEN_DEVICE_FUNC
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    
-    const int Size = EIGEN_PLAIN_ENUM_MIN(XprType::ColsAtCompileTime,DstXprType::ColsAtCompileTime);
-    EIGEN_ONLY_USED_FOR_DEBUG(Size);
-    eigen_assert(( (Size<=1) || (Size>4) || (extract_data(src.nestedExpression())!=extract_data(dst)))
-              && "Aliasing problem detected in inverse(), you need to do inverse().eval() here.");
-
-    typedef typename internal::nested_eval<XprType,XprType::ColsAtCompileTime>::type  ActualXprType;
-    typedef typename internal::remove_all<ActualXprType>::type                        ActualXprTypeCleanded;
-    
-    ActualXprType actual_xpr(src.nestedExpression());
-    
-    compute_inverse<ActualXprTypeCleanded, DstXprType>::run(actual_xpr, dst);
-  }
-};
-
-  
-} // end namespace internal
-
-/** \lu_module
-  *
-  * \returns the matrix inverse of this matrix.
-  *
-  * For small fixed sizes up to 4x4, this method uses cofactors.
-  * In the general case, this method uses class PartialPivLU.
-  *
-  * \note This matrix must be invertible, otherwise the result is undefined. If you need an
-  * invertibility check, do the following:
-  * \li for fixed sizes up to 4x4, use computeInverseAndDetWithCheck().
-  * \li for the general case, use class FullPivLU.
-  *
-  * Example: \include MatrixBase_inverse.cpp
-  * Output: \verbinclude MatrixBase_inverse.out
-  *
-  * \sa computeInverseAndDetWithCheck()
-  */
-template<typename Derived>
-EIGEN_DEVICE_FUNC
-inline const Inverse<Derived> MatrixBase<Derived>::inverse() const
-{
-  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsInteger,THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
-  eigen_assert(rows() == cols());
-  return Inverse<Derived>(derived());
-}
-
-/** \lu_module
-  *
-  * Computation of matrix inverse and determinant, with invertibility check.
-  *
-  * This is only for fixed-size square matrices of size up to 4x4.
-  *
-  * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
-  *
-  * \param inverse Reference to the matrix in which to store the inverse.
-  * \param determinant Reference to the variable in which to store the determinant.
-  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
-  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
-  *                                The matrix will be declared invertible if the absolute value of its
-  *                                determinant is greater than this threshold.
-  *
-  * Example: \include MatrixBase_computeInverseAndDetWithCheck.cpp
-  * Output: \verbinclude MatrixBase_computeInverseAndDetWithCheck.out
-  *
-  * \sa inverse(), computeInverseWithCheck()
-  */
-template<typename Derived>
-template<typename ResultType>
-inline void MatrixBase<Derived>::computeInverseAndDetWithCheck(
-    ResultType& inverse,
-    typename ResultType::Scalar& determinant,
-    bool& invertible,
-    const RealScalar& absDeterminantThreshold
-  ) const
-{
-  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
-  eigen_assert(rows() == cols());
-  // for 2x2, it's worth giving a chance to avoid evaluating.
-  // for larger sizes, evaluating has negligible cost and limits code size.
-  typedef typename internal::conditional<
-    RowsAtCompileTime == 2,
-    typename internal::remove_all<typename internal::nested_eval<Derived, 2>::type>::type,
-    PlainObject
-  >::type MatrixType;
-  internal::compute_inverse_and_det_with_check<MatrixType, ResultType>::run
-    (derived(), absDeterminantThreshold, inverse, determinant, invertible);
-}
-
-/** \lu_module
-  *
-  * Computation of matrix inverse, with invertibility check.
-  *
-  * This is only for fixed-size square matrices of size up to 4x4.
-  *
-  * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
-  *
-  * \param inverse Reference to the matrix in which to store the inverse.
-  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
-  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
-  *                                The matrix will be declared invertible if the absolute value of its
-  *                                determinant is greater than this threshold.
-  *
-  * Example: \include MatrixBase_computeInverseWithCheck.cpp
-  * Output: \verbinclude MatrixBase_computeInverseWithCheck.out
-  *
-  * \sa inverse(), computeInverseAndDetWithCheck()
-  */
-template<typename Derived>
-template<typename ResultType>
-inline void MatrixBase<Derived>::computeInverseWithCheck(
-    ResultType& inverse,
-    bool& invertible,
-    const RealScalar& absDeterminantThreshold
-  ) const
-{
-  Scalar determinant;
-  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
-  eigen_assert(rows() == cols());
-  computeInverseAndDetWithCheck(inverse,determinant,invertible,absDeterminantThreshold);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_INVERSE_IMPL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/PartialPivLU.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/PartialPivLU.h
deleted file mode 100644
index 34aed72..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/PartialPivLU.h
+++ /dev/null
@@ -1,624 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARTIALLU_H
-#define EIGEN_PARTIALLU_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _MatrixType> struct traits<PartialPivLU<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  typedef traits<_MatrixType> BaseTraits;
-  enum {
-    Flags = BaseTraits::Flags & RowMajorBit,
-    CoeffReadCost = Dynamic
-  };
-};
-
-template<typename T,typename Derived>
-struct enable_if_ref;
-// {
-//   typedef Derived type;
-// };
-
-template<typename T,typename Derived>
-struct enable_if_ref<Ref<T>,Derived> {
-  typedef Derived type;
-};
-
-} // end namespace internal
-
-/** \ingroup LU_Module
-  *
-  * \class PartialPivLU
-  *
-  * \brief LU decomposition of a matrix with partial pivoting, and related features
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the LU decomposition
-  *
-  * This class represents a LU decomposition of a \b square \b invertible matrix, with partial pivoting: the matrix A
-  * is decomposed as A = PLU where L is unit-lower-triangular, U is upper-triangular, and P
-  * is a permutation matrix.
-  *
-  * Typically, partial pivoting LU decomposition is only considered numerically stable for square invertible
-  * matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class
-  * does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the
-  * matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices.
-  *
-  * The guaranteed safe alternative, working for all matrices, is the full pivoting LU decomposition, provided
-  * by class FullPivLU.
-  *
-  * This is \b not a rank-revealing LU decomposition. Many features are intentionally absent from this class,
-  * such as rank computation. If you need these features, use class FullPivLU.
-  *
-  * This LU decomposition is suitable to invert invertible matrices. It is what MatrixBase::inverse() uses
-  * in the general case.
-  * On the other hand, it is \b not suitable to determine whether a given matrix is invertible.
-  *
-  * The data of the LU decomposition can be directly accessed through the methods matrixLU(), permutationP().
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::partialPivLu(), MatrixBase::determinant(), MatrixBase::inverse(), MatrixBase::computeInverse(), class FullPivLU
-  */
-template<typename _MatrixType> class PartialPivLU
-  : public SolverBase<PartialPivLU<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<PartialPivLU> Base;
-    friend class SolverBase<PartialPivLU>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(PartialPivLU)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
-    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via PartialPivLU::compute(const MatrixType&).
-      */
-    PartialPivLU();
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa PartialPivLU()
-      */
-    explicit PartialPivLU(Index size);
-
-    /** Constructor.
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *
-      * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
-      * If you need to deal with non-full rank, use class FullPivLU instead.
-      */
-    template<typename InputType>
-    explicit PartialPivLU(const EigenBase<InputType>& matrix);
-
-    /** Constructor for \link InplaceDecomposition inplace decomposition \endlink
-      *
-      * \param matrix the matrix of which to compute the LU decomposition.
-      *
-      * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
-      * If you need to deal with non-full rank, use class FullPivLU instead.
-      */
-    template<typename InputType>
-    explicit PartialPivLU(EigenBase<InputType>& matrix);
-
-    template<typename InputType>
-    PartialPivLU& compute(const EigenBase<InputType>& matrix) {
-      m_lu = matrix.derived();
-      compute();
-      return *this;
-    }
-
-    /** \returns the LU decomposition matrix: the upper-triangular part is U, the
-      * unit-lower-triangular part is L (at least for square matrices; in the non-square
-      * case, special care is needed, see the documentation of class FullPivLU).
-      *
-      * \sa matrixL(), matrixU()
-      */
-    inline const MatrixType& matrixLU() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return m_lu;
-    }
-
-    /** \returns the permutation matrix P.
-      */
-    inline const PermutationType& permutationP() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return m_p;
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method returns the solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the LU decomposition.
-      *
-      * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
-      *          the only requirement in order for the equation to make sense is that
-      *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
-      *
-      * \returns the solution.
-      *
-      * Example: \include PartialPivLU_solve.cpp
-      * Output: \verbinclude PartialPivLU_solve.out
-      *
-      * Since this PartialPivLU class assumes anyway that the matrix A is invertible, the solution
-      * theoretically exists and is unique regardless of b.
-      *
-      * \sa TriangularView::solve(), inverse(), computeInverse()
-      */
-    template<typename Rhs>
-    inline const Solve<PartialPivLU, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
-        the LU decomposition.
-      */
-    inline RealScalar rcond() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return internal::rcond_estimate_helper(m_l1_norm, *this);
-    }
-
-    /** \returns the inverse of the matrix of which *this is the LU decomposition.
-      *
-      * \warning The matrix being decomposed here is assumed to be invertible. If you need to check for
-      *          invertibility, use class FullPivLU instead.
-      *
-      * \sa MatrixBase::inverse(), LU::inverse()
-      */
-    inline const Inverse<PartialPivLU> inverse() const
-    {
-      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-      return Inverse<PartialPivLU>(*this);
-    }
-
-    /** \returns the determinant of the matrix of which
-      * *this is the LU decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the LU decomposition has already been computed.
-      *
-      * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
-      *       optimized paths.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      *
-      * \sa MatrixBase::determinant()
-      */
-    Scalar determinant() const;
-
-    MatrixType reconstructedMatrix() const;
-
-    EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
-    EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    void _solve_impl(const RhsType &rhs, DstType &dst) const {
-     /* The decomposition PA = LU can be rewritten as A = P^{-1} L U.
-      * So we proceed as follows:
-      * Step 1: compute c = Pb.
-      * Step 2: replace c by the solution x to Lx = c.
-      * Step 3: replace c by the solution x to Ux = c.
-      */
-
-      // Step 1
-      dst = permutationP() * rhs;
-
-      // Step 2
-      m_lu.template triangularView<UnitLower>().solveInPlace(dst);
-
-      // Step 3
-      m_lu.template triangularView<Upper>().solveInPlace(dst);
-    }
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const {
-     /* The decomposition PA = LU can be rewritten as A^T = U^T L^T P.
-      * So we proceed as follows:
-      * Step 1: compute c as the solution to L^T c = b
-      * Step 2: replace c by the solution x to U^T x = c.
-      * Step 3: update  c = P^-1 c.
-      */
-
-      eigen_assert(rhs.rows() == m_lu.cols());
-
-      // Step 1
-      dst = m_lu.template triangularView<Upper>().transpose()
-                .template conjugateIf<Conjugate>().solve(rhs);
-      // Step 2
-      m_lu.template triangularView<UnitLower>().transpose()
-          .template conjugateIf<Conjugate>().solveInPlace(dst);
-      // Step 3
-      dst = permutationP().transpose() * dst;
-    }
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void compute();
-
-    MatrixType m_lu;
-    PermutationType m_p;
-    TranspositionType m_rowsTranspositions;
-    RealScalar m_l1_norm;
-    signed char m_det_p;
-    bool m_isInitialized;
-};
-
-template<typename MatrixType>
-PartialPivLU<MatrixType>::PartialPivLU()
-  : m_lu(),
-    m_p(),
-    m_rowsTranspositions(),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-}
-
-template<typename MatrixType>
-PartialPivLU<MatrixType>::PartialPivLU(Index size)
-  : m_lu(size, size),
-    m_p(size),
-    m_rowsTranspositions(size),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-}
-
-template<typename MatrixType>
-template<typename InputType>
-PartialPivLU<MatrixType>::PartialPivLU(const EigenBase<InputType>& matrix)
-  : m_lu(matrix.rows(),matrix.cols()),
-    m_p(matrix.rows()),
-    m_rowsTranspositions(matrix.rows()),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-  compute(matrix.derived());
-}
-
-template<typename MatrixType>
-template<typename InputType>
-PartialPivLU<MatrixType>::PartialPivLU(EigenBase<InputType>& matrix)
-  : m_lu(matrix.derived()),
-    m_p(matrix.rows()),
-    m_rowsTranspositions(matrix.rows()),
-    m_l1_norm(0),
-    m_det_p(0),
-    m_isInitialized(false)
-{
-  compute();
-}
-
-namespace internal {
-
-/** \internal This is the blocked version of fullpivlu_unblocked() */
-template<typename Scalar, int StorageOrder, typename PivIndex, int SizeAtCompileTime=Dynamic>
-struct partial_lu_impl
-{
-  static const int UnBlockedBound = 16;
-  static const bool UnBlockedAtCompileTime = SizeAtCompileTime!=Dynamic && SizeAtCompileTime<=UnBlockedBound;
-  static const int ActualSizeAtCompileTime = UnBlockedAtCompileTime ? SizeAtCompileTime : Dynamic;
-  // Remaining rows and columns at compile-time:
-  static const int RRows = SizeAtCompileTime==2 ? 1 : Dynamic;
-  static const int RCols = SizeAtCompileTime==2 ? 1 : Dynamic;
-  typedef Matrix<Scalar, ActualSizeAtCompileTime, ActualSizeAtCompileTime, StorageOrder> MatrixType;
-  typedef Ref<MatrixType> MatrixTypeRef;
-  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > BlockType;
-  typedef typename MatrixType::RealScalar RealScalar;
-
-  /** \internal performs the LU decomposition in-place of the matrix \a lu
-    * using an unblocked algorithm.
-    *
-    * In addition, this function returns the row transpositions in the
-    * vector \a row_transpositions which must have a size equal to the number
-    * of columns of the matrix \a lu, and an integer \a nb_transpositions
-    * which returns the actual number of transpositions.
-    *
-    * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
-    */
-  static Index unblocked_lu(MatrixTypeRef& lu, PivIndex* row_transpositions, PivIndex& nb_transpositions)
-  {
-    typedef scalar_score_coeff_op<Scalar> Scoring;
-    typedef typename Scoring::result_type Score;
-    const Index rows = lu.rows();
-    const Index cols = lu.cols();
-    const Index size = (std::min)(rows,cols);
-    // For small compile-time matrices it is worth processing the last row separately:
-    //  speedup: +100% for 2x2, +10% for others.
-    const Index endk = UnBlockedAtCompileTime ? size-1 : size;
-    nb_transpositions = 0;
-    Index first_zero_pivot = -1;
-    for(Index k = 0; k < endk; ++k)
-    {
-      int rrows = internal::convert_index<int>(rows-k-1);
-      int rcols = internal::convert_index<int>(cols-k-1);
-
-      Index row_of_biggest_in_col;
-      Score biggest_in_corner
-        = lu.col(k).tail(rows-k).unaryExpr(Scoring()).maxCoeff(&row_of_biggest_in_col);
-      row_of_biggest_in_col += k;
-
-      row_transpositions[k] = PivIndex(row_of_biggest_in_col);
-
-      if(biggest_in_corner != Score(0))
-      {
-        if(k != row_of_biggest_in_col)
-        {
-          lu.row(k).swap(lu.row(row_of_biggest_in_col));
-          ++nb_transpositions;
-        }
-
-        lu.col(k).tail(fix<RRows>(rrows)) /= lu.coeff(k,k);
-      }
-      else if(first_zero_pivot==-1)
-      {
-        // the pivot is exactly zero, we record the index of the first pivot which is exactly 0,
-        // and continue the factorization such we still have A = PLU
-        first_zero_pivot = k;
-      }
-
-      if(k<rows-1)
-        lu.bottomRightCorner(fix<RRows>(rrows),fix<RCols>(rcols)).noalias() -= lu.col(k).tail(fix<RRows>(rrows)) * lu.row(k).tail(fix<RCols>(rcols));
-    }
-
-    // special handling of the last entry
-    if(UnBlockedAtCompileTime)
-    {
-      Index k = endk;
-      row_transpositions[k] = PivIndex(k);
-      if (Scoring()(lu(k, k)) == Score(0) && first_zero_pivot == -1)
-        first_zero_pivot = k;
-    }
-
-    return first_zero_pivot;
-  }
-
-  /** \internal performs the LU decomposition in-place of the matrix represented
-    * by the variables \a rows, \a cols, \a lu_data, and \a lu_stride using a
-    * recursive, blocked algorithm.
-    *
-    * In addition, this function returns the row transpositions in the
-    * vector \a row_transpositions which must have a size equal to the number
-    * of columns of the matrix \a lu, and an integer \a nb_transpositions
-    * which returns the actual number of transpositions.
-    *
-    * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
-    *
-    * \note This very low level interface using pointers, etc. is to:
-    *   1 - reduce the number of instantiations to the strict minimum
-    *   2 - avoid infinite recursion of the instantiations with Block<Block<Block<...> > >
-    */
-  static Index blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, PivIndex* row_transpositions, PivIndex& nb_transpositions, Index maxBlockSize=256)
-  {
-    MatrixTypeRef lu = MatrixType::Map(lu_data,rows, cols, OuterStride<>(luStride));
-
-    const Index size = (std::min)(rows,cols);
-
-    // if the matrix is too small, no blocking:
-    if(UnBlockedAtCompileTime || size<=UnBlockedBound)
-    {
-      return unblocked_lu(lu, row_transpositions, nb_transpositions);
-    }
-
-    // automatically adjust the number of subdivisions to the size
-    // of the matrix so that there is enough sub blocks:
-    Index blockSize;
-    {
-      blockSize = size/8;
-      blockSize = (blockSize/16)*16;
-      blockSize = (std::min)((std::max)(blockSize,Index(8)), maxBlockSize);
-    }
-
-    nb_transpositions = 0;
-    Index first_zero_pivot = -1;
-    for(Index k = 0; k < size; k+=blockSize)
-    {
-      Index bs = (std::min)(size-k,blockSize); // actual size of the block
-      Index trows = rows - k - bs; // trailing rows
-      Index tsize = size - k - bs; // trailing size
-
-      // partition the matrix:
-      //                          A00 | A01 | A02
-      // lu  = A_0 | A_1 | A_2 =  A10 | A11 | A12
-      //                          A20 | A21 | A22
-      BlockType A_0 = lu.block(0,0,rows,k);
-      BlockType A_2 = lu.block(0,k+bs,rows,tsize);
-      BlockType A11 = lu.block(k,k,bs,bs);
-      BlockType A12 = lu.block(k,k+bs,bs,tsize);
-      BlockType A21 = lu.block(k+bs,k,trows,bs);
-      BlockType A22 = lu.block(k+bs,k+bs,trows,tsize);
-
-      PivIndex nb_transpositions_in_panel;
-      // recursively call the blocked LU algorithm on [A11^T A21^T]^T
-      // with a very small blocking size:
-      Index ret = blocked_lu(trows+bs, bs, &lu.coeffRef(k,k), luStride,
-                   row_transpositions+k, nb_transpositions_in_panel, 16);
-      if(ret>=0 && first_zero_pivot==-1)
-        first_zero_pivot = k+ret;
-
-      nb_transpositions += nb_transpositions_in_panel;
-      // update permutations and apply them to A_0
-      for(Index i=k; i<k+bs; ++i)
-      {
-        Index piv = (row_transpositions[i] += internal::convert_index<PivIndex>(k));
-        A_0.row(i).swap(A_0.row(piv));
-      }
-
-      if(trows)
-      {
-        // apply permutations to A_2
-        for(Index i=k;i<k+bs; ++i)
-          A_2.row(i).swap(A_2.row(row_transpositions[i]));
-
-        // A12 = A11^-1 A12
-        A11.template triangularView<UnitLower>().solveInPlace(A12);
-
-        A22.noalias() -= A21 * A12;
-      }
-    }
-    return first_zero_pivot;
-  }
-};
-
-/** \internal performs the LU decomposition with partial pivoting in-place.
-  */
-template<typename MatrixType, typename TranspositionType>
-void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions, typename TranspositionType::StorageIndex& nb_transpositions)
-{
-  // Special-case of zero matrix.
-  if (lu.rows() == 0 || lu.cols() == 0) {
-    nb_transpositions = 0;
-    return;
-  }
-  eigen_assert(lu.cols() == row_transpositions.size());
-  eigen_assert(row_transpositions.size() < 2 || (&row_transpositions.coeffRef(1)-&row_transpositions.coeffRef(0)) == 1);
-
-  partial_lu_impl
-    < typename MatrixType::Scalar, MatrixType::Flags&RowMajorBit?RowMajor:ColMajor,
-      typename TranspositionType::StorageIndex,
-      EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime)>
-    ::blocked_lu(lu.rows(), lu.cols(), &lu.coeffRef(0,0), lu.outerStride(), &row_transpositions.coeffRef(0), nb_transpositions);
-}
-
-} // end namespace internal
-
-template<typename MatrixType>
-void PartialPivLU<MatrixType>::compute()
-{
-  check_template_parameters();
-
-  // the row permutation is stored as int indices, so just to be sure:
-  eigen_assert(m_lu.rows()<NumTraits<int>::highest());
-
-  if(m_lu.cols()>0)
-    m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
-  else
-    m_l1_norm = RealScalar(0);
-
-  eigen_assert(m_lu.rows() == m_lu.cols() && "PartialPivLU is only for square (and moreover invertible) matrices");
-  const Index size = m_lu.rows();
-
-  m_rowsTranspositions.resize(size);
-
-  typename TranspositionType::StorageIndex nb_transpositions;
-  internal::partial_lu_inplace(m_lu, m_rowsTranspositions, nb_transpositions);
-  m_det_p = (nb_transpositions%2) ? -1 : 1;
-
-  m_p = m_rowsTranspositions;
-
-  m_isInitialized = true;
-}
-
-template<typename MatrixType>
-typename PartialPivLU<MatrixType>::Scalar PartialPivLU<MatrixType>::determinant() const
-{
-  eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
-  return Scalar(m_det_p) * m_lu.diagonal().prod();
-}
-
-/** \returns the matrix represented by the decomposition,
- * i.e., it returns the product: P^{-1} L U.
- * This function is provided for debug purpose. */
-template<typename MatrixType>
-MatrixType PartialPivLU<MatrixType>::reconstructedMatrix() const
-{
-  eigen_assert(m_isInitialized && "LU is not initialized.");
-  // LU
-  MatrixType res = m_lu.template triangularView<UnitLower>().toDenseMatrix()
-                 * m_lu.template triangularView<Upper>();
-
-  // P^{-1}(LU)
-  res = m_p.inverse() * res;
-
-  return res;
-}
-
-/***** Implementation details *****************************************************/
-
-namespace internal {
-
-/***** Implementation of inverse() *****************************************************/
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<PartialPivLU<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename PartialPivLU<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef PartialPivLU<MatrixType> LuType;
-  typedef Inverse<LuType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename LuType::Scalar> &)
-  {
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-} // end namespace internal
-
-/******** MatrixBase methods *******/
-
-/** \lu_module
-  *
-  * \return the partial-pivoting LU decomposition of \c *this.
-  *
-  * \sa class PartialPivLU
-  */
-template<typename Derived>
-inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::partialPivLu() const
-{
-  return PartialPivLU<PlainObject>(eval());
-}
-
-/** \lu_module
-  *
-  * Synonym of partialPivLu().
-  *
-  * \return the partial-pivoting LU decomposition of \c *this.
-  *
-  * \sa class PartialPivLU
-  */
-template<typename Derived>
-inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::lu() const
-{
-  return PartialPivLU<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIALLU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h
deleted file mode 100644
index 755168a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *     LU decomposition with partial pivoting based on LAPACKE_?getrf function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_PARTIALLU_LAPACK_H
-#define EIGEN_PARTIALLU_LAPACK_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_LU_PARTPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX) \
-template<int StorageOrder> \
-struct partial_lu_impl<EIGTYPE, StorageOrder, lapack_int> \
-{ \
-  /* \internal performs the LU decomposition in-place of the matrix represented */ \
-  static lapack_int blocked_lu(Index rows, Index cols, EIGTYPE* lu_data, Index luStride, lapack_int* row_transpositions, lapack_int& nb_transpositions, lapack_int maxBlockSize=256) \
-  { \
-    EIGEN_UNUSED_VARIABLE(maxBlockSize);\
-    lapack_int matrix_order, first_zero_pivot; \
-    lapack_int m, n, lda, *ipiv, info; \
-    EIGTYPE* a; \
-/* Set up parameters for ?getrf */ \
-    matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
-    lda = convert_index<lapack_int>(luStride); \
-    a = lu_data; \
-    ipiv = row_transpositions; \
-    m = convert_index<lapack_int>(rows); \
-    n = convert_index<lapack_int>(cols); \
-    nb_transpositions = 0; \
-\
-    info = LAPACKE_##LAPACKE_PREFIX##getrf( matrix_order, m, n, (LAPACKE_TYPE*)a, lda, ipiv ); \
-\
-    for(int i=0;i<m;i++) { ipiv[i]--; if (ipiv[i]!=i) nb_transpositions++; } \
-\
-    eigen_assert(info >= 0); \
-/* something should be done with nb_transpositions */ \
-\
-    first_zero_pivot = info; \
-    return first_zero_pivot; \
-  } \
-};
-
-EIGEN_LAPACKE_LU_PARTPIV(double, double, d)
-EIGEN_LAPACKE_LU_PARTPIV(float, float, s)
-EIGEN_LAPACKE_LU_PARTPIV(dcomplex, lapack_complex_double, z)
-EIGEN_LAPACKE_LU_PARTPIV(scomplex, lapack_complex_float,  c)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARTIALLU_LAPACK_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/arch/InverseSize4.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
deleted file mode 100644
index a232ffc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
+++ /dev/null
@@ -1,351 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2001 Intel Corporation
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-//
-// The algorithm below is a reimplementation of former \src\LU\Inverse_SSE.h using PacketMath.
-// inv(M) = M#/|M|, where inv(M), M# and |M| denote the inverse of M,
-// adjugate of M and determinant of M respectively. M# is computed block-wise
-// using specific formulae. For proof, see:
-// https://lxjk.github.io/2017/09/03/Fast-4x4-Matrix-Inverse-with-SSE-SIMD-Explained.html
-// Variable names are adopted from \src\LU\Inverse_SSE.h.
-//
-// The SSE code for the 4x4 float and double matrix inverse in former (deprecated) \src\LU\Inverse_SSE.h
-// comes from the following Intel's library:
-// http://software.intel.com/en-us/articles/optimized-matrix-library-for-use-with-the-intel-pentiumr-4-processors-sse2-instructions/
-//
-// Here is the respective copyright and license statement:
-//
-//   Copyright (c) 2001 Intel Corporation.
-//
-// Permition is granted to use, copy, distribute and prepare derivative works
-// of this library for any purpose and without fee, provided, that the above
-// copyright notice and this statement appear in all copies.
-// Intel makes no representations about the suitability of this software for
-// any purpose, and specifically disclaims all warranties.
-// See LEGAL.TXT for all the legal information.
-//
-// TODO: Unify implementations of different data types (i.e. float and double).
-#ifndef EIGEN_INVERSE_SIZE_4_H
-#define EIGEN_INVERSE_SIZE_4_H
-
-namespace Eigen
-{
-namespace internal
-{
-template <typename MatrixType, typename ResultType>
-struct compute_inverse_size4<Architecture::Target, float, MatrixType, ResultType>
-{
-  enum
-  {
-    MatrixAlignment = traits<MatrixType>::Alignment,
-    ResultAlignment = traits<ResultType>::Alignment,
-    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
-  };
-  typedef typename conditional<(MatrixType::Flags & LinearAccessBit), MatrixType const &, typename MatrixType::PlainObject>::type ActualMatrixType;
-
-  static void run(const MatrixType &mat, ResultType &result)
-  {
-    ActualMatrixType matrix(mat);
-
-    const float* data = matrix.data();
-    const Index stride = matrix.innerStride();
-    Packet4f _L1 = ploadt<Packet4f,MatrixAlignment>(data);
-    Packet4f _L2 = ploadt<Packet4f,MatrixAlignment>(data + stride*4);
-    Packet4f _L3 = ploadt<Packet4f,MatrixAlignment>(data + stride*8);
-    Packet4f _L4 = ploadt<Packet4f,MatrixAlignment>(data + stride*12);
-
-    // Four 2x2 sub-matrices of the input matrix
-    // input = [[A, B],
-    //          [C, D]]
-    Packet4f A, B, C, D;
-
-    if (!StorageOrdersMatch)
-    {
-      A = vec4f_unpacklo(_L1, _L2);
-      B = vec4f_unpacklo(_L3, _L4);
-      C = vec4f_unpackhi(_L1, _L2);
-      D = vec4f_unpackhi(_L3, _L4);
-    }
-    else
-    {
-      A = vec4f_movelh(_L1, _L2);
-      B = vec4f_movehl(_L2, _L1);
-      C = vec4f_movelh(_L3, _L4);
-      D = vec4f_movehl(_L4, _L3);
-    }
-
-    Packet4f AB, DC;
-
-    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
-    AB = pmul(vec4f_swizzle2(A, A, 3, 3, 0, 0), B);
-    AB = psub(AB, pmul(vec4f_swizzle2(A, A, 1, 1, 2, 2), vec4f_swizzle2(B, B, 2, 3, 0, 1)));
-
-    // DC = D#*C
-    DC = pmul(vec4f_swizzle2(D, D, 3, 3, 0, 0), C);
-    DC = psub(DC, pmul(vec4f_swizzle2(D, D, 1, 1, 2, 2), vec4f_swizzle2(C, C, 2, 3, 0, 1)));
-
-    // determinants of the sub-matrices
-    Packet4f dA, dB, dC, dD;
-
-    dA = pmul(vec4f_swizzle2(A, A, 3, 3, 1, 1), A);
-    dA = psub(dA, vec4f_movehl(dA, dA));
-
-    dB = pmul(vec4f_swizzle2(B, B, 3, 3, 1, 1), B);
-    dB = psub(dB, vec4f_movehl(dB, dB));
-
-    dC = pmul(vec4f_swizzle2(C, C, 3, 3, 1, 1), C);
-    dC = psub(dC, vec4f_movehl(dC, dC));
-
-    dD = pmul(vec4f_swizzle2(D, D, 3, 3, 1, 1), D);
-    dD = psub(dD, vec4f_movehl(dD, dD));
-
-    Packet4f d, d1, d2;
-
-    d = pmul(vec4f_swizzle2(DC, DC, 0, 2, 1, 3), AB);
-    d = padd(d, vec4f_movehl(d, d));
-    d = padd(d, vec4f_swizzle2(d, d, 1, 0, 0, 0));
-    d1 = pmul(dA, dD);
-    d2 = pmul(dB, dC);
-
-    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
-    Packet4f det = vec4f_duplane(psub(padd(d1, d2), d), 0);
-
-    // reciprocal of the determinant of the input matrix, rd = 1/det
-    Packet4f rd = pdiv(pset1<Packet4f>(1.0f), det);
-
-    // Four sub-matrices of the inverse
-    Packet4f iA, iB, iC, iD;
-
-    // iD = D*|A| - C*A#*B
-    iD = pmul(vec4f_swizzle2(C, C, 0, 0, 2, 2), vec4f_movelh(AB, AB));
-    iD = padd(iD, pmul(vec4f_swizzle2(C, C, 1, 1, 3, 3), vec4f_movehl(AB, AB)));
-    iD = psub(pmul(D, vec4f_duplane(dA, 0)), iD);
-
-    // iA = A*|D| - B*D#*C
-    iA = pmul(vec4f_swizzle2(B, B, 0, 0, 2, 2), vec4f_movelh(DC, DC));
-    iA = padd(iA, pmul(vec4f_swizzle2(B, B, 1, 1, 3, 3), vec4f_movehl(DC, DC)));
-    iA = psub(pmul(A, vec4f_duplane(dD, 0)), iA);
-
-    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
-    iB = pmul(D, vec4f_swizzle2(AB, AB, 3, 0, 3, 0));
-    iB = psub(iB, pmul(vec4f_swizzle2(D, D, 1, 0, 3, 2), vec4f_swizzle2(AB, AB, 2, 1, 2, 1)));
-    iB = psub(pmul(C, vec4f_duplane(dB, 0)), iB);
-
-    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
-    iC = pmul(A, vec4f_swizzle2(DC, DC, 3, 0, 3, 0));
-    iC = psub(iC, pmul(vec4f_swizzle2(A, A, 1, 0, 3, 2), vec4f_swizzle2(DC, DC, 2, 1, 2, 1)));
-    iC = psub(pmul(B, vec4f_duplane(dC, 0)), iC);
-
-    const float sign_mask[4] = {0.0f, numext::bit_cast<float>(0x80000000u), numext::bit_cast<float>(0x80000000u), 0.0f};
-    const Packet4f p4f_sign_PNNP = ploadu<Packet4f>(sign_mask);
-    rd = pxor(rd, p4f_sign_PNNP);
-    iA = pmul(iA, rd);
-    iB = pmul(iB, rd);
-    iC = pmul(iC, rd);
-    iD = pmul(iD, rd);
-
-    Index res_stride = result.outerStride();
-    float *res = result.data();
-
-    pstoret<float, Packet4f, ResultAlignment>(res + 0, vec4f_swizzle2(iA, iB, 3, 1, 3, 1));
-    pstoret<float, Packet4f, ResultAlignment>(res + res_stride, vec4f_swizzle2(iA, iB, 2, 0, 2, 0));
-    pstoret<float, Packet4f, ResultAlignment>(res + 2 * res_stride, vec4f_swizzle2(iC, iD, 3, 1, 3, 1));
-    pstoret<float, Packet4f, ResultAlignment>(res + 3 * res_stride, vec4f_swizzle2(iC, iD, 2, 0, 2, 0));
-  }
-};
-
-#if !(defined EIGEN_VECTORIZE_NEON && !(EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG))
-// same algorithm as above, except that each operand is split into
-// halves for two registers to hold.
-template <typename MatrixType, typename ResultType>
-struct compute_inverse_size4<Architecture::Target, double, MatrixType, ResultType>
-{
-  enum
-  {
-    MatrixAlignment = traits<MatrixType>::Alignment,
-    ResultAlignment = traits<ResultType>::Alignment,
-    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
-  };
-  typedef typename conditional<(MatrixType::Flags & LinearAccessBit),
-                               MatrixType const &,
-                               typename MatrixType::PlainObject>::type
-      ActualMatrixType;
-
-  static void run(const MatrixType &mat, ResultType &result)
-  {
-    ActualMatrixType matrix(mat);
-
-    // Four 2x2 sub-matrices of the input matrix, each is further divided into upper and lower
-    // row e.g. A1, upper row of A, A2, lower row of A
-    // input = [[A, B],  =  [[[A1, [B1,
-    //          [C, D]]        A2], B2]],
-    //                       [[C1, [D1,
-    //                         C2], D2]]]
-
-    Packet2d A1, A2, B1, B2, C1, C2, D1, D2;
-
-    const double* data = matrix.data();
-    const Index stride = matrix.innerStride();
-    if (StorageOrdersMatch)
-    {
-      A1 = ploadt<Packet2d,MatrixAlignment>(data + stride*0);
-      B1 = ploadt<Packet2d,MatrixAlignment>(data + stride*2);
-      A2 = ploadt<Packet2d,MatrixAlignment>(data + stride*4);
-      B2 = ploadt<Packet2d,MatrixAlignment>(data + stride*6);
-      C1 = ploadt<Packet2d,MatrixAlignment>(data + stride*8);
-      D1 = ploadt<Packet2d,MatrixAlignment>(data + stride*10);
-      C2 = ploadt<Packet2d,MatrixAlignment>(data + stride*12);
-      D2 = ploadt<Packet2d,MatrixAlignment>(data + stride*14);
-    }
-    else
-    {
-      Packet2d temp;
-      A1 = ploadt<Packet2d,MatrixAlignment>(data + stride*0);
-      C1 = ploadt<Packet2d,MatrixAlignment>(data + stride*2);
-      A2 = ploadt<Packet2d,MatrixAlignment>(data + stride*4);
-      C2 = ploadt<Packet2d,MatrixAlignment>(data + stride*6);
-      temp = A1;
-      A1 = vec2d_unpacklo(A1, A2);
-      A2 = vec2d_unpackhi(temp, A2);
-
-      temp = C1;
-      C1 = vec2d_unpacklo(C1, C2);
-      C2 = vec2d_unpackhi(temp, C2);
-
-      B1 = ploadt<Packet2d,MatrixAlignment>(data + stride*8);
-      D1 = ploadt<Packet2d,MatrixAlignment>(data + stride*10);
-      B2 = ploadt<Packet2d,MatrixAlignment>(data + stride*12);
-      D2 = ploadt<Packet2d,MatrixAlignment>(data + stride*14);
-
-      temp = B1;
-      B1 = vec2d_unpacklo(B1, B2);
-      B2 = vec2d_unpackhi(temp, B2);
-
-      temp = D1;
-      D1 = vec2d_unpacklo(D1, D2);
-      D2 = vec2d_unpackhi(temp, D2);
-    }
-
-    // determinants of the sub-matrices
-    Packet2d dA, dB, dC, dD;
-
-    dA = vec2d_swizzle2(A2, A2, 1);
-    dA = pmul(A1, dA);
-    dA = psub(dA, vec2d_duplane(dA, 1));
-
-    dB = vec2d_swizzle2(B2, B2, 1);
-    dB = pmul(B1, dB);
-    dB = psub(dB, vec2d_duplane(dB, 1));
-
-    dC = vec2d_swizzle2(C2, C2, 1);
-    dC = pmul(C1, dC);
-    dC = psub(dC, vec2d_duplane(dC, 1));
-
-    dD = vec2d_swizzle2(D2, D2, 1);
-    dD = pmul(D1, dD);
-    dD = psub(dD, vec2d_duplane(dD, 1));
-
-    Packet2d DC1, DC2, AB1, AB2;
-
-    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
-    AB1 = pmul(B1, vec2d_duplane(A2, 1));
-    AB2 = pmul(B2, vec2d_duplane(A1, 0));
-    AB1 = psub(AB1, pmul(B2, vec2d_duplane(A1, 1)));
-    AB2 = psub(AB2, pmul(B1, vec2d_duplane(A2, 0)));
-
-    // DC = D#*C
-    DC1 = pmul(C1, vec2d_duplane(D2, 1));
-    DC2 = pmul(C2, vec2d_duplane(D1, 0));
-    DC1 = psub(DC1, pmul(C2, vec2d_duplane(D1, 1)));
-    DC2 = psub(DC2, pmul(C1, vec2d_duplane(D2, 0)));
-
-    Packet2d d1, d2;
-
-    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
-    Packet2d det;
-
-    // reciprocal of the determinant of the input matrix, rd = 1/det
-    Packet2d rd;
-
-    d1 = pmul(AB1, vec2d_swizzle2(DC1, DC2, 0));
-    d2 = pmul(AB2, vec2d_swizzle2(DC1, DC2, 3));
-    rd = padd(d1, d2);
-    rd = padd(rd, vec2d_duplane(rd, 1));
-
-    d1 = pmul(dA, dD);
-    d2 = pmul(dB, dC);
-
-    det = padd(d1, d2);
-    det = psub(det, rd);
-    det = vec2d_duplane(det, 0);
-    rd = pdiv(pset1<Packet2d>(1.0), det);
-
-    // rows of four sub-matrices of the inverse
-    Packet2d iA1, iA2, iB1, iB2, iC1, iC2, iD1, iD2;
-
-    // iD = D*|A| - C*A#*B
-    iD1 = pmul(AB1, vec2d_duplane(C1, 0));
-    iD2 = pmul(AB1, vec2d_duplane(C2, 0));
-    iD1 = padd(iD1, pmul(AB2, vec2d_duplane(C1, 1)));
-    iD2 = padd(iD2, pmul(AB2, vec2d_duplane(C2, 1)));
-    dA = vec2d_duplane(dA, 0);
-    iD1 = psub(pmul(D1, dA), iD1);
-    iD2 = psub(pmul(D2, dA), iD2);
-
-    // iA = A*|D| - B*D#*C
-    iA1 = pmul(DC1, vec2d_duplane(B1, 0));
-    iA2 = pmul(DC1, vec2d_duplane(B2, 0));
-    iA1 = padd(iA1, pmul(DC2, vec2d_duplane(B1, 1)));
-    iA2 = padd(iA2, pmul(DC2, vec2d_duplane(B2, 1)));
-    dD = vec2d_duplane(dD, 0);
-    iA1 = psub(pmul(A1, dD), iA1);
-    iA2 = psub(pmul(A2, dD), iA2);
-
-    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
-    iB1 = pmul(D1, vec2d_swizzle2(AB2, AB1, 1));
-    iB2 = pmul(D2, vec2d_swizzle2(AB2, AB1, 1));
-    iB1 = psub(iB1, pmul(vec2d_swizzle2(D1, D1, 1), vec2d_swizzle2(AB2, AB1, 2)));
-    iB2 = psub(iB2, pmul(vec2d_swizzle2(D2, D2, 1), vec2d_swizzle2(AB2, AB1, 2)));
-    dB = vec2d_duplane(dB, 0);
-    iB1 = psub(pmul(C1, dB), iB1);
-    iB2 = psub(pmul(C2, dB), iB2);
-
-    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
-    iC1 = pmul(A1, vec2d_swizzle2(DC2, DC1, 1));
-    iC2 = pmul(A2, vec2d_swizzle2(DC2, DC1, 1));
-    iC1 = psub(iC1, pmul(vec2d_swizzle2(A1, A1, 1), vec2d_swizzle2(DC2, DC1, 2)));
-    iC2 = psub(iC2, pmul(vec2d_swizzle2(A2, A2, 1), vec2d_swizzle2(DC2, DC1, 2)));
-    dC = vec2d_duplane(dC, 0);
-    iC1 = psub(pmul(B1, dC), iC1);
-    iC2 = psub(pmul(B2, dC), iC2);
-
-    const double sign_mask1[2] = {0.0, numext::bit_cast<double>(0x8000000000000000ull)};
-    const double sign_mask2[2] = {numext::bit_cast<double>(0x8000000000000000ull), 0.0};
-    const Packet2d sign_PN = ploadu<Packet2d>(sign_mask1);
-    const Packet2d sign_NP = ploadu<Packet2d>(sign_mask2);
-    d1 = pxor(rd, sign_PN);
-    d2 = pxor(rd, sign_NP);
-
-    Index res_stride = result.outerStride();
-    double *res = result.data();
-    pstoret<double, Packet2d, ResultAlignment>(res + 0, pmul(vec2d_swizzle2(iA2, iA1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + res_stride, pmul(vec2d_swizzle2(iA2, iA1, 0), d2));
-    pstoret<double, Packet2d, ResultAlignment>(res + 2, pmul(vec2d_swizzle2(iB2, iB1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + res_stride + 2, pmul(vec2d_swizzle2(iB2, iB1, 0), d2));
-    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 0), d2));
-    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 3), d1));
-    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 0), d2));
-  }
-};
-#endif
-} // namespace internal
-} // namespace Eigen
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h
deleted file mode 100644
index 4c15304..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h
+++ /dev/null
@@ -1,137 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef METIS_SUPPORT_H
-#define METIS_SUPPORT_H
-
-namespace Eigen {
-/**
- * Get the fill-reducing ordering from the METIS package
- * 
- * If A is the original matrix and Ap is the permuted matrix, 
- * the fill-reducing permutation is defined as follows :
- * Row (column) i of A is the matperm(i) row (column) of Ap. 
- * WARNING: As computed by METIS, this corresponds to the vector iperm (instead of perm)
- */
-template <typename StorageIndex>
-class MetisOrdering
-{
-public:
-  typedef PermutationMatrix<Dynamic,Dynamic,StorageIndex> PermutationType;
-  typedef Matrix<StorageIndex,Dynamic,1> IndexVector; 
-  
-  template <typename MatrixType>
-  void get_symmetrized_graph(const MatrixType& A)
-  {
-    Index m = A.cols(); 
-    eigen_assert((A.rows() == A.cols()) && "ONLY FOR SQUARED MATRICES");
-    // Get the transpose of the input matrix 
-    MatrixType At = A.transpose(); 
-    // Get the number of nonzeros elements in each row/col of At+A
-    Index TotNz = 0; 
-    IndexVector visited(m); 
-    visited.setConstant(-1); 
-    for (StorageIndex j = 0; j < m; j++)
-    {
-      // Compute the union structure of of A(j,:) and At(j,:)
-      visited(j) = j; // Do not include the diagonal element
-      // Get the nonzeros in row/column j of A
-      for (typename MatrixType::InnerIterator it(A, j); it; ++it)
-      {
-        Index idx = it.index(); // Get the row index (for column major) or column index (for row major)
-        if (visited(idx) != j ) 
-        {
-          visited(idx) = j; 
-          ++TotNz; 
-        }
-      }
-      //Get the nonzeros in row/column j of At
-      for (typename MatrixType::InnerIterator it(At, j); it; ++it)
-      {
-        Index idx = it.index(); 
-        if(visited(idx) != j)
-        {
-          visited(idx) = j; 
-          ++TotNz; 
-        }
-      }
-    }
-    // Reserve place for A + At
-    m_indexPtr.resize(m+1);
-    m_innerIndices.resize(TotNz); 
-
-    // Now compute the real adjacency list of each column/row 
-    visited.setConstant(-1); 
-    StorageIndex CurNz = 0; 
-    for (StorageIndex j = 0; j < m; j++)
-    {
-      m_indexPtr(j) = CurNz; 
-      
-      visited(j) = j; // Do not include the diagonal element
-      // Add the pattern of row/column j of A to A+At
-      for (typename MatrixType::InnerIterator it(A,j); it; ++it)
-      {
-        StorageIndex idx = it.index(); // Get the row index (for column major) or column index (for row major)
-        if (visited(idx) != j ) 
-        {
-          visited(idx) = j; 
-          m_innerIndices(CurNz) = idx; 
-          CurNz++; 
-        }
-      }
-      //Add the pattern of row/column j of At to A+At
-      for (typename MatrixType::InnerIterator it(At, j); it; ++it)
-      {
-        StorageIndex idx = it.index(); 
-        if(visited(idx) != j)
-        {
-          visited(idx) = j; 
-          m_innerIndices(CurNz) = idx; 
-          ++CurNz; 
-        }
-      }
-    }
-    m_indexPtr(m) = CurNz;    
-  }
-  
-  template <typename MatrixType>
-  void operator() (const MatrixType& A, PermutationType& matperm)
-  {
-     StorageIndex m = internal::convert_index<StorageIndex>(A.cols()); // must be StorageIndex, because it is passed by address to METIS
-     IndexVector perm(m),iperm(m); 
-    // First, symmetrize the matrix graph. 
-     get_symmetrized_graph(A); 
-     int output_error;
-     
-     // Call the fill-reducing routine from METIS 
-     output_error = METIS_NodeND(&m, m_indexPtr.data(), m_innerIndices.data(), NULL, NULL, perm.data(), iperm.data());
-     
-    if(output_error != METIS_OK) 
-    {
-      //FIXME The ordering interface should define a class of possible errors 
-     std::cerr << "ERROR WHILE CALLING THE METIS PACKAGE \n"; 
-     return; 
-    }
-    
-    // Get the fill-reducing permutation 
-    //NOTE:  If Ap is the permuted matrix then perm and iperm vectors are defined as follows 
-    // Row (column) i of Ap is the perm(i) row(column) of A, and row (column) i of A is the iperm(i) row(column) of Ap
-    
-     matperm.resize(m);
-     for (int j = 0; j < m; j++)
-       matperm.indices()(iperm(j)) = j;
-   
-  }
-  
-  protected:
-    IndexVector m_indexPtr; // Pointer to the adjacenccy list of each row/column
-    IndexVector m_innerIndices; // Adjacency list 
-};
-
-}// end namespace eigen 
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Amd.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Amd.h
deleted file mode 100644
index 7ca3f33..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Amd.h
+++ /dev/null
@@ -1,435 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*
-NOTE: this routine has been adapted from the CSparse library:
-
-Copyright (c) 2006, Timothy A. Davis.
-http://www.suitesparse.com
-
-The author of CSparse, Timothy A. Davis., has executed a license with Google LLC
-to permit distribution of this code and derivative works as part of Eigen under
-the Mozilla Public License v. 2.0, as stated at the top of this file.
-*/
-
-#ifndef EIGEN_SPARSE_AMD_H
-#define EIGEN_SPARSE_AMD_H
-
-namespace Eigen { 
-
-namespace internal {
-  
-template<typename T> inline T amd_flip(const T& i) { return -i-2; }
-template<typename T> inline T amd_unflip(const T& i) { return i<0 ? amd_flip(i) : i; }
-template<typename T0, typename T1> inline bool amd_marked(const T0* w, const T1& j) { return w[j]<0; }
-template<typename T0, typename T1> inline void amd_mark(const T0* w, const T1& j) { return w[j] = amd_flip(w[j]); }
-
-/* clear w */
-template<typename StorageIndex>
-static StorageIndex cs_wclear (StorageIndex mark, StorageIndex lemax, StorageIndex *w, StorageIndex n)
-{
-  StorageIndex k;
-  if(mark < 2 || (mark + lemax < 0))
-  {
-    for(k = 0; k < n; k++)
-      if(w[k] != 0)
-        w[k] = 1;
-    mark = 2;
-  }
-  return (mark);     /* at this point, w[0..n-1] < mark holds */
-}
-
-/* depth-first search and postorder of a tree rooted at node j */
-template<typename StorageIndex>
-StorageIndex cs_tdfs(StorageIndex j, StorageIndex k, StorageIndex *head, const StorageIndex *next, StorageIndex *post, StorageIndex *stack)
-{
-  StorageIndex i, p, top = 0;
-  if(!head || !next || !post || !stack) return (-1);    /* check inputs */
-  stack[0] = j;                 /* place j on the stack */
-  while (top >= 0)                /* while (stack is not empty) */
-  {
-    p = stack[top];           /* p = top of stack */
-    i = head[p];              /* i = youngest child of p */
-    if(i == -1)
-    {
-      top--;                 /* p has no unordered children left */
-      post[k++] = p;        /* node p is the kth postordered node */
-    }
-    else
-    {
-      head[p] = next[i];   /* remove i from children of p */
-      stack[++top] = i;     /* start dfs on child node i */
-    }
-  }
-  return k;
-}
-
-
-/** \internal
-  * \ingroup OrderingMethods_Module 
-  * Approximate minimum degree ordering algorithm.
-  *
-  * \param[in] C the input selfadjoint matrix stored in compressed column major format.
-  * \param[out] perm the permutation P reducing the fill-in of the input matrix \a C
-  *
-  * Note that the input matrix \a C must be complete, that is both the upper and lower parts have to be stored, as well as the diagonal entries.
-  * On exit the values of C are destroyed */
-template<typename Scalar, typename StorageIndex>
-void minimum_degree_ordering(SparseMatrix<Scalar,ColMajor,StorageIndex>& C, PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm)
-{
-  using std::sqrt;
-  
-  StorageIndex d, dk, dext, lemax = 0, e, elenk, eln, i, j, k, k1,
-                k2, k3, jlast, ln, dense, nzmax, mindeg = 0, nvi, nvj, nvk, mark, wnvi,
-                ok, nel = 0, p, p1, p2, p3, p4, pj, pk, pk1, pk2, pn, q, t, h;
-  
-  StorageIndex n = StorageIndex(C.cols());
-  dense = std::max<StorageIndex> (16, StorageIndex(10 * sqrt(double(n))));   /* find dense threshold */
-  dense = (std::min)(n-2, dense);
-  
-  StorageIndex cnz = StorageIndex(C.nonZeros());
-  perm.resize(n+1);
-  t = cnz + cnz/5 + 2*n;                 /* add elbow room to C */
-  C.resizeNonZeros(t);
-  
-  // get workspace
-  ei_declare_aligned_stack_constructed_variable(StorageIndex,W,8*(n+1),0);
-  StorageIndex* len     = W;
-  StorageIndex* nv      = W +   (n+1);
-  StorageIndex* next    = W + 2*(n+1);
-  StorageIndex* head    = W + 3*(n+1);
-  StorageIndex* elen    = W + 4*(n+1);
-  StorageIndex* degree  = W + 5*(n+1);
-  StorageIndex* w       = W + 6*(n+1);
-  StorageIndex* hhead   = W + 7*(n+1);
-  StorageIndex* last    = perm.indices().data();                              /* use P as workspace for last */
-  
-  /* --- Initialize quotient graph ---------------------------------------- */
-  StorageIndex* Cp = C.outerIndexPtr();
-  StorageIndex* Ci = C.innerIndexPtr();
-  for(k = 0; k < n; k++)
-    len[k] = Cp[k+1] - Cp[k];
-  len[n] = 0;
-  nzmax = t;
-  
-  for(i = 0; i <= n; i++)
-  {
-    head[i]   = -1;                     // degree list i is empty
-    last[i]   = -1;
-    next[i]   = -1;
-    hhead[i]  = -1;                     // hash list i is empty 
-    nv[i]     = 1;                      // node i is just one node
-    w[i]      = 1;                      // node i is alive
-    elen[i]   = 0;                      // Ek of node i is empty
-    degree[i] = len[i];                 // degree of node i
-  }
-  mark = internal::cs_wclear<StorageIndex>(0, 0, w, n);         /* clear w */
-  
-  /* --- Initialize degree lists ------------------------------------------ */
-  for(i = 0; i < n; i++)
-  {
-    bool has_diag = false;
-    for(p = Cp[i]; p<Cp[i+1]; ++p)
-      if(Ci[p]==i)
-      {
-        has_diag = true;
-        break;
-      }
-   
-    d = degree[i];
-    if(d == 1 && has_diag)           /* node i is empty */
-    {
-      elen[i] = -2;                 /* element i is dead */
-      nel++;
-      Cp[i] = -1;                   /* i is a root of assembly tree */
-      w[i] = 0;
-    }
-    else if(d > dense || !has_diag)  /* node i is dense or has no structural diagonal element */
-    {
-      nv[i] = 0;                    /* absorb i into element n */
-      elen[i] = -1;                 /* node i is dead */
-      nel++;
-      Cp[i] = amd_flip (n);
-      nv[n]++;
-    }
-    else
-    {
-      if(head[d] != -1) last[head[d]] = i;
-      next[i] = head[d];           /* put node i in degree list d */
-      head[d] = i;
-    }
-  }
-  
-  elen[n] = -2;                         /* n is a dead element */
-  Cp[n] = -1;                           /* n is a root of assembly tree */
-  w[n] = 0;                             /* n is a dead element */
-  
-  while (nel < n)                         /* while (selecting pivots) do */
-  {
-    /* --- Select node of minimum approximate degree -------------------- */
-    for(k = -1; mindeg < n && (k = head[mindeg]) == -1; mindeg++) {}
-    if(next[k] != -1) last[next[k]] = -1;
-    head[mindeg] = next[k];          /* remove k from degree list */
-    elenk = elen[k];                  /* elenk = |Ek| */
-    nvk = nv[k];                      /* # of nodes k represents */
-    nel += nvk;                        /* nv[k] nodes of A eliminated */
-    
-    /* --- Garbage collection ------------------------------------------- */
-    if(elenk > 0 && cnz + mindeg >= nzmax)
-    {
-      for(j = 0; j < n; j++)
-      {
-        if((p = Cp[j]) >= 0)      /* j is a live node or element */
-        {
-          Cp[j] = Ci[p];          /* save first entry of object */
-          Ci[p] = amd_flip (j);    /* first entry is now amd_flip(j) */
-        }
-      }
-      for(q = 0, p = 0; p < cnz; ) /* scan all of memory */
-      {
-        if((j = amd_flip (Ci[p++])) >= 0)  /* found object j */
-        {
-          Ci[q] = Cp[j];       /* restore first entry of object */
-          Cp[j] = q++;          /* new pointer to object j */
-          for(k3 = 0; k3 < len[j]-1; k3++) Ci[q++] = Ci[p++];
-        }
-      }
-      cnz = q;                       /* Ci[cnz...nzmax-1] now free */
-    }
-    
-    /* --- Construct new element ---------------------------------------- */
-    dk = 0;
-    nv[k] = -nvk;                     /* flag k as in Lk */
-    p = Cp[k];
-    pk1 = (elenk == 0) ? p : cnz;      /* do in place if elen[k] == 0 */
-    pk2 = pk1;
-    for(k1 = 1; k1 <= elenk + 1; k1++)
-    {
-      if(k1 > elenk)
-      {
-        e = k;                     /* search the nodes in k */
-        pj = p;                    /* list of nodes starts at Ci[pj]*/
-        ln = len[k] - elenk;      /* length of list of nodes in k */
-      }
-      else
-      {
-        e = Ci[p++];              /* search the nodes in e */
-        pj = Cp[e];
-        ln = len[e];              /* length of list of nodes in e */
-      }
-      for(k2 = 1; k2 <= ln; k2++)
-      {
-        i = Ci[pj++];
-        if((nvi = nv[i]) <= 0) continue; /* node i dead, or seen */
-        dk += nvi;                 /* degree[Lk] += size of node i */
-        nv[i] = -nvi;             /* negate nv[i] to denote i in Lk*/
-        Ci[pk2++] = i;            /* place i in Lk */
-        if(next[i] != -1) last[next[i]] = last[i];
-        if(last[i] != -1)         /* remove i from degree list */
-        {
-          next[last[i]] = next[i];
-        }
-        else
-        {
-          head[degree[i]] = next[i];
-        }
-      }
-      if(e != k)
-      {
-        Cp[e] = amd_flip (k);      /* absorb e into k */
-        w[e] = 0;                 /* e is now a dead element */
-      }
-    }
-    if(elenk != 0) cnz = pk2;         /* Ci[cnz...nzmax] is free */
-    degree[k] = dk;                   /* external degree of k - |Lk\i| */
-    Cp[k] = pk1;                      /* element k is in Ci[pk1..pk2-1] */
-    len[k] = pk2 - pk1;
-    elen[k] = -2;                     /* k is now an element */
-    
-    /* --- Find set differences ----------------------------------------- */
-    mark = internal::cs_wclear<StorageIndex>(mark, lemax, w, n);  /* clear w if necessary */
-    for(pk = pk1; pk < pk2; pk++)    /* scan 1: find |Le\Lk| */
-    {
-      i = Ci[pk];
-      if((eln = elen[i]) <= 0) continue;/* skip if elen[i] empty */
-      nvi = -nv[i];                      /* nv[i] was negated */
-      wnvi = mark - nvi;
-      for(p = Cp[i]; p <= Cp[i] + eln - 1; p++)  /* scan Ei */
-      {
-        e = Ci[p];
-        if(w[e] >= mark)
-        {
-          w[e] -= nvi;          /* decrement |Le\Lk| */
-        }
-        else if(w[e] != 0)        /* ensure e is a live element */
-        {
-          w[e] = degree[e] + wnvi; /* 1st time e seen in scan 1 */
-        }
-      }
-    }
-    
-    /* --- Degree update ------------------------------------------------ */
-    for(pk = pk1; pk < pk2; pk++)    /* scan2: degree update */
-    {
-      i = Ci[pk];                   /* consider node i in Lk */
-      p1 = Cp[i];
-      p2 = p1 + elen[i] - 1;
-      pn = p1;
-      for(h = 0, d = 0, p = p1; p <= p2; p++)    /* scan Ei */
-      {
-        e = Ci[p];
-        if(w[e] != 0)             /* e is an unabsorbed element */
-        {
-          dext = w[e] - mark;   /* dext = |Le\Lk| */
-          if(dext > 0)
-          {
-            d += dext;         /* sum up the set differences */
-            Ci[pn++] = e;     /* keep e in Ei */
-            h += e;            /* compute the hash of node i */
-          }
-          else
-          {
-            Cp[e] = amd_flip (k);  /* aggressive absorb. e->k */
-            w[e] = 0;             /* e is a dead element */
-          }
-        }
-      }
-      elen[i] = pn - p1 + 1;        /* elen[i] = |Ei| */
-      p3 = pn;
-      p4 = p1 + len[i];
-      for(p = p2 + 1; p < p4; p++) /* prune edges in Ai */
-      {
-        j = Ci[p];
-        if((nvj = nv[j]) <= 0) continue; /* node j dead or in Lk */
-        d += nvj;                  /* degree(i) += |j| */
-        Ci[pn++] = j;             /* place j in node list of i */
-        h += j;                    /* compute hash for node i */
-      }
-      if(d == 0)                     /* check for mass elimination */
-      {
-        Cp[i] = amd_flip (k);      /* absorb i into k */
-        nvi = -nv[i];
-        dk -= nvi;                 /* |Lk| -= |i| */
-        nvk += nvi;                /* |k| += nv[i] */
-        nel += nvi;
-        nv[i] = 0;
-        elen[i] = -1;             /* node i is dead */
-      }
-      else
-      {
-        degree[i] = std::min<StorageIndex> (degree[i], d);   /* update degree(i) */
-        Ci[pn] = Ci[p3];         /* move first node to end */
-        Ci[p3] = Ci[p1];         /* move 1st el. to end of Ei */
-        Ci[p1] = k;               /* add k as 1st element in of Ei */
-        len[i] = pn - p1 + 1;     /* new len of adj. list of node i */
-        h %= n;                    /* finalize hash of i */
-        next[i] = hhead[h];      /* place i in hash bucket */
-        hhead[h] = i;
-        last[i] = h;      /* save hash of i in last[i] */
-      }
-    }                                   /* scan2 is done */
-    degree[k] = dk;                   /* finalize |Lk| */
-    lemax = std::max<StorageIndex>(lemax, dk);
-    mark = internal::cs_wclear<StorageIndex>(mark+lemax, lemax, w, n);    /* clear w */
-    
-    /* --- Supernode detection ------------------------------------------ */
-    for(pk = pk1; pk < pk2; pk++)
-    {
-      i = Ci[pk];
-      if(nv[i] >= 0) continue;         /* skip if i is dead */
-      h = last[i];                      /* scan hash bucket of node i */
-      i = hhead[h];
-      hhead[h] = -1;                    /* hash bucket will be empty */
-      for(; i != -1 && next[i] != -1; i = next[i], mark++)
-      {
-        ln = len[i];
-        eln = elen[i];
-        for(p = Cp[i]+1; p <= Cp[i] + ln-1; p++) w[Ci[p]] = mark;
-        jlast = i;
-        for(j = next[i]; j != -1; ) /* compare i with all j */
-        {
-          ok = (len[j] == ln) && (elen[j] == eln);
-          for(p = Cp[j] + 1; ok && p <= Cp[j] + ln - 1; p++)
-          {
-            if(w[Ci[p]] != mark) ok = 0;    /* compare i and j*/
-          }
-          if(ok)                     /* i and j are identical */
-          {
-            Cp[j] = amd_flip (i);  /* absorb j into i */
-            nv[i] += nv[j];
-            nv[j] = 0;
-            elen[j] = -1;         /* node j is dead */
-            j = next[j];          /* delete j from hash bucket */
-            next[jlast] = j;
-          }
-          else
-          {
-            jlast = j;             /* j and i are different */
-            j = next[j];
-          }
-        }
-      }
-    }
-    
-    /* --- Finalize new element------------------------------------------ */
-    for(p = pk1, pk = pk1; pk < pk2; pk++)   /* finalize Lk */
-    {
-      i = Ci[pk];
-      if((nvi = -nv[i]) <= 0) continue;/* skip if i is dead */
-      nv[i] = nvi;                      /* restore nv[i] */
-      d = degree[i] + dk - nvi;         /* compute external degree(i) */
-      d = std::min<StorageIndex> (d, n - nel - nvi);
-      if(head[d] != -1) last[head[d]] = i;
-      next[i] = head[d];               /* put i back in degree list */
-      last[i] = -1;
-      head[d] = i;
-      mindeg = std::min<StorageIndex> (mindeg, d);       /* find new minimum degree */
-      degree[i] = d;
-      Ci[p++] = i;                      /* place i in Lk */
-    }
-    nv[k] = nvk;                      /* # nodes absorbed into k */
-    if((len[k] = p-pk1) == 0)         /* length of adj list of element k*/
-    {
-      Cp[k] = -1;                   /* k is a root of the tree */
-      w[k] = 0;                     /* k is now a dead element */
-    }
-    if(elenk != 0) cnz = p;           /* free unused space in Lk */
-  }
-  
-  /* --- Postordering ----------------------------------------------------- */
-  for(i = 0; i < n; i++) Cp[i] = amd_flip (Cp[i]);/* fix assembly tree */
-  for(j = 0; j <= n; j++) head[j] = -1;
-  for(j = n; j >= 0; j--)              /* place unordered nodes in lists */
-  {
-    if(nv[j] > 0) continue;          /* skip if j is an element */
-    next[j] = head[Cp[j]];          /* place j in list of its parent */
-    head[Cp[j]] = j;
-  }
-  for(e = n; e >= 0; e--)              /* place elements in lists */
-  {
-    if(nv[e] <= 0) continue;         /* skip unless e is an element */
-    if(Cp[e] != -1)
-    {
-      next[e] = head[Cp[e]];      /* place e in list of its parent */
-      head[Cp[e]] = e;
-    }
-  }
-  for(k = 0, i = 0; i <= n; i++)       /* postorder the assembly tree */
-  {
-    if(Cp[i] == -1) k = internal::cs_tdfs<StorageIndex>(i, k, head, next, perm.indices().data(), w);
-  }
-  
-  perm.indices().conservativeResize(n);
-}
-
-} // namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_AMD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h
deleted file mode 100644
index 8e339a7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h
+++ /dev/null
@@ -1,1863 +0,0 @@
-// // This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is modified from the colamd/symamd library. The copyright is below
-
-//   The authors of the code itself are Stefan I. Larimore and Timothy A.
-//   Davis (davis@cise.ufl.edu), University of Florida.  The algorithm was
-//   developed in collaboration with John Gilbert, Xerox PARC, and Esmond
-//   Ng, Oak Ridge National Laboratory.
-//
-//     Date:
-//
-//   September 8, 2003.  Version 2.3.
-//
-//     Acknowledgements:
-//
-//   This work was supported by the National Science Foundation, under
-//   grants DMS-9504974 and DMS-9803599.
-//
-//     Notice:
-//
-//   Copyright (c) 1998-2003 by the University of Florida.
-//   All Rights Reserved.
-//
-//   THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
-//   EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
-//
-//   Permission is hereby granted to use, copy, modify, and/or distribute
-//   this program, provided that the Copyright, this License, and the
-//   Availability of the original version is retained on all copies and made
-//   accessible to the end-user of any code or package that includes COLAMD
-//   or any modified version of COLAMD.
-//
-//     Availability:
-//
-//   The colamd/symamd library is available at
-//
-//       http://www.suitesparse.com
-
-
-#ifndef EIGEN_COLAMD_H
-#define EIGEN_COLAMD_H
-
-namespace internal {
-
-namespace Colamd {
-
-/* Ensure that debugging is turned off: */
-#ifndef COLAMD_NDEBUG
-#define COLAMD_NDEBUG
-#endif /* NDEBUG */
-
-
-/* ========================================================================== */
-/* === Knob and statistics definitions ====================================== */
-/* ========================================================================== */
-
-/* size of the knobs [ ] array.  Only knobs [0..1] are currently used. */
-const int NKnobs = 20;
-
-/* number of output statistics.  Only stats [0..6] are currently used. */
-const int NStats = 20;
-
-/* Indices into knobs and stats array. */
-enum KnobsStatsIndex {
-  /* knobs [0] and stats [0]: dense row knob and output statistic. */
-  DenseRow = 0,
-
-  /* knobs [1] and stats [1]: dense column knob and output statistic. */
-  DenseCol = 1,
-
-  /* stats [2]: memory defragmentation count output statistic */
-  DefragCount = 2,
-
-  /* stats [3]: colamd status:  zero OK, > 0 warning or notice, < 0 error */
-  Status = 3,
-
-  /* stats [4..6]: error info, or info on jumbled columns */
-  Info1 = 4,
-  Info2 = 5,
-  Info3 = 6
-};
-
-/* error codes returned in stats [3]: */
-enum Status {
-  Ok = 0,
-  OkButJumbled = 1,
-  ErrorANotPresent = -1,
-  ErrorPNotPresent = -2,
-  ErrorNrowNegative = -3,
-  ErrorNcolNegative = -4,
-  ErrorNnzNegative = -5,
-  ErrorP0Nonzero = -6,
-  ErrorATooSmall = -7,
-  ErrorColLengthNegative = -8,
-  ErrorRowIndexOutOfBounds = -9,
-  ErrorOutOfMemory = -10,
-  ErrorInternalError = -999
-};
-/* ========================================================================== */
-/* === Definitions ========================================================== */
-/* ========================================================================== */
-
-template <typename IndexType>
-IndexType ones_complement(const IndexType r) {
-  return (-(r)-1);
-}
-
-/* -------------------------------------------------------------------------- */
-const int Empty = -1;
-
-/* Row and column status */
-enum RowColumnStatus {
-  Alive = 0,
-  Dead = -1
-};
-
-/* Column status */
-enum ColumnStatus {
-  DeadPrincipal = -1,
-  DeadNonPrincipal = -2
-};
-
-/* ========================================================================== */
-/* === Colamd reporting mechanism =========================================== */
-/* ========================================================================== */
-
-// == Row and Column structures ==
-template <typename IndexType>
-struct ColStructure
-{
-  IndexType start ;   /* index for A of first row in this column, or Dead */
-  /* if column is dead */
-  IndexType length ;  /* number of rows in this column */
-  union
-  {
-    IndexType thickness ; /* number of original columns represented by this */
-    /* col, if the column is alive */
-    IndexType parent ;  /* parent in parent tree super-column structure, if */
-    /* the column is dead */
-  } shared1 ;
-  union
-  {
-    IndexType score ; /* the score used to maintain heap, if col is alive */
-    IndexType order ; /* pivot ordering of this column, if col is dead */
-  } shared2 ;
-  union
-  {
-    IndexType headhash ;  /* head of a hash bucket, if col is at the head of */
-    /* a degree list */
-    IndexType hash ;  /* hash value, if col is not in a degree list */
-    IndexType prev ;  /* previous column in degree list, if col is in a */
-    /* degree list (but not at the head of a degree list) */
-  } shared3 ;
-  union
-  {
-    IndexType degree_next ; /* next column, if col is in a degree list */
-    IndexType hash_next ;   /* next column, if col is in a hash list */
-  } shared4 ;
-
-  inline bool is_dead() const { return start < Alive; }
-
-  inline bool is_alive() const { return start >= Alive; }
-
-  inline bool is_dead_principal() const { return start == DeadPrincipal; }
-
-  inline void kill_principal() { start = DeadPrincipal; }
-
-  inline void kill_non_principal() { start = DeadNonPrincipal; }
-
-};
-
-template <typename IndexType>
-struct RowStructure
-{
-  IndexType start ;   /* index for A of first col in this row */
-  IndexType length ;  /* number of principal columns in this row */
-  union
-  {
-    IndexType degree ;  /* number of principal & non-principal columns in row */
-    IndexType p ;   /* used as a row pointer in init_rows_cols () */
-  } shared1 ;
-  union
-  {
-    IndexType mark ;  /* for computing set differences and marking dead rows*/
-    IndexType first_column ;/* first column in row (used in garbage collection) */
-  } shared2 ;
-
-  inline bool is_dead() const { return shared2.mark < Alive; }
-
-  inline bool is_alive() const { return shared2.mark >= Alive; }
-
-  inline void kill() { shared2.mark = Dead; }
-
-};
-
-/* ========================================================================== */
-/* === Colamd recommended memory size ======================================= */
-/* ========================================================================== */
-
-/*
-  The recommended length Alen of the array A passed to colamd is given by
-  the COLAMD_RECOMMENDED (nnz, n_row, n_col) macro.  It returns -1 if any
-  argument is negative.  2*nnz space is required for the row and column
-  indices of the matrix. colamd_c (n_col) + colamd_r (n_row) space is
-  required for the Col and Row arrays, respectively, which are internal to
-  colamd.  An additional n_col space is the minimal amount of "elbow room",
-  and nnz/5 more space is recommended for run time efficiency.
-
-  This macro is not needed when using symamd.
-
-  Explicit typecast to IndexType added Sept. 23, 2002, COLAMD version 2.2, to avoid
-  gcc -pedantic warning messages.
-*/
-template <typename IndexType>
-inline IndexType colamd_c(IndexType n_col)
-{ return IndexType( ((n_col) + 1) * sizeof (ColStructure<IndexType>) / sizeof (IndexType) ) ; }
-
-template <typename IndexType>
-inline IndexType  colamd_r(IndexType n_row)
-{ return IndexType(((n_row) + 1) * sizeof (RowStructure<IndexType>) / sizeof (IndexType)); }
-
-// Prototypes of non-user callable routines
-template <typename IndexType>
-static IndexType init_rows_cols (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> col [], IndexType A [], IndexType p [], IndexType stats[NStats] );
-
-template <typename IndexType>
-static void init_scoring (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType head [], double knobs[NKnobs], IndexType *p_n_row2, IndexType *p_n_col2, IndexType *p_max_deg);
-
-template <typename IndexType>
-static IndexType find_ordering (IndexType n_row, IndexType n_col, IndexType Alen, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType head [], IndexType n_col2, IndexType max_deg, IndexType pfree);
-
-template <typename IndexType>
-static void order_children (IndexType n_col, ColStructure<IndexType> Col [], IndexType p []);
-
-template <typename IndexType>
-static void detect_super_cols (ColStructure<IndexType> Col [], IndexType A [], IndexType head [], IndexType row_start, IndexType row_length ) ;
-
-template <typename IndexType>
-static IndexType garbage_collection (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType *pfree) ;
-
-template <typename IndexType>
-static inline  IndexType clear_mark (IndexType n_row, RowStructure<IndexType> Row [] ) ;
-
-/* === No debugging ========================================================= */
-
-#define COLAMD_DEBUG0(params) ;
-#define COLAMD_DEBUG1(params) ;
-#define COLAMD_DEBUG2(params) ;
-#define COLAMD_DEBUG3(params) ;
-#define COLAMD_DEBUG4(params) ;
-
-#define COLAMD_ASSERT(expression) ((void) 0)
-
-
-/**
- * \brief Returns the recommended value of Alen
- *
- * Returns recommended value of Alen for use by colamd.
- * Returns -1 if any input argument is negative.
- * The use of this routine or macro is optional.
- * Note that the macro uses its arguments   more than once,
- * so be careful for side effects, if you pass expressions as arguments to COLAMD_RECOMMENDED.
- *
- * \param nnz nonzeros in A
- * \param n_row number of rows in A
- * \param n_col number of columns in A
- * \return recommended value of Alen for use by colamd
- */
-template <typename IndexType>
-inline IndexType recommended ( IndexType nnz, IndexType n_row, IndexType n_col)
-{
-  if ((nnz) < 0 || (n_row) < 0 || (n_col) < 0)
-    return (-1);
-  else
-    return (2 * (nnz) + colamd_c (n_col) + colamd_r (n_row) + (n_col) + ((nnz) / 5));
-}
-
-/**
- * \brief set default parameters  The use of this routine is optional.
- *
- * Colamd: rows with more than (knobs [DenseRow] * n_col)
- * entries are removed prior to ordering.  Columns with more than
- * (knobs [DenseCol] * n_row) entries are removed prior to
- * ordering, and placed last in the output column ordering.
- *
- * DenseRow and DenseCol are defined as 0 and 1,
- * respectively, in colamd.h.  Default values of these two knobs
- * are both 0.5.  Currently, only knobs [0] and knobs [1] are
- * used, but future versions may use more knobs.  If so, they will
- * be properly set to their defaults by the future version of
- * colamd_set_defaults, so that the code that calls colamd will
- * not need to change, assuming that you either use
- * colamd_set_defaults, or pass a (double *) NULL pointer as the
- * knobs array to colamd or symamd.
- *
- * \param knobs parameter settings for colamd
- */
-
-static inline void set_defaults(double knobs[NKnobs])
-{
-  /* === Local variables ================================================== */
-
-  int i ;
-
-  if (!knobs)
-  {
-    return ;      /* no knobs to initialize */
-  }
-  for (i = 0 ; i < NKnobs ; i++)
-  {
-    knobs [i] = 0 ;
-  }
-  knobs [Colamd::DenseRow] = 0.5 ;  /* ignore rows over 50% dense */
-  knobs [Colamd::DenseCol] = 0.5 ;  /* ignore columns over 50% dense */
-}
-
-/**
- * \brief  Computes a column ordering using the column approximate minimum degree ordering
- *
- * Computes a column ordering (Q) of A such that P(AQ)=LU or
- * (AQ)'AQ=LL' have less fill-in and require fewer floating point
- * operations than factorizing the unpermuted matrix A or A'A,
- * respectively.
- *
- *
- * \param n_row number of rows in A
- * \param n_col number of columns in A
- * \param Alen, size of the array A
- * \param A row indices of the matrix, of size ALen
- * \param p column pointers of A, of size n_col+1
- * \param knobs parameter settings for colamd
- * \param stats colamd output statistics and error codes
- */
-template <typename IndexType>
-static bool compute_ordering(IndexType n_row, IndexType n_col, IndexType Alen, IndexType *A, IndexType *p, double knobs[NKnobs], IndexType stats[NStats])
-{
-  /* === Local variables ================================================== */
-
-  IndexType i ;     /* loop index */
-  IndexType nnz ;     /* nonzeros in A */
-  IndexType Row_size ;    /* size of Row [], in integers */
-  IndexType Col_size ;    /* size of Col [], in integers */
-  IndexType need ;      /* minimum required length of A */
-  Colamd::RowStructure<IndexType> *Row ;   /* pointer into A of Row [0..n_row] array */
-  Colamd::ColStructure<IndexType> *Col ;   /* pointer into A of Col [0..n_col] array */
-  IndexType n_col2 ;    /* number of non-dense, non-empty columns */
-  IndexType n_row2 ;    /* number of non-dense, non-empty rows */
-  IndexType ngarbage ;    /* number of garbage collections performed */
-  IndexType max_deg ;   /* maximum row degree */
-  double default_knobs [NKnobs] ; /* default knobs array */
-
-
-  /* === Check the input arguments ======================================== */
-
-  if (!stats)
-  {
-    COLAMD_DEBUG0 (("colamd: stats not present\n")) ;
-    return (false) ;
-  }
-  for (i = 0 ; i < NStats ; i++)
-  {
-    stats [i] = 0 ;
-  }
-  stats [Colamd::Status] = Colamd::Ok ;
-  stats [Colamd::Info1] = -1 ;
-  stats [Colamd::Info2] = -1 ;
-
-  if (!A)   /* A is not present */
-  {
-    stats [Colamd::Status] = Colamd::ErrorANotPresent ;
-    COLAMD_DEBUG0 (("colamd: A not present\n")) ;
-    return (false) ;
-  }
-
-  if (!p)   /* p is not present */
-  {
-    stats [Colamd::Status] = Colamd::ErrorPNotPresent ;
-    COLAMD_DEBUG0 (("colamd: p not present\n")) ;
-    return (false) ;
-  }
-
-  if (n_row < 0)  /* n_row must be >= 0 */
-  {
-    stats [Colamd::Status] = Colamd::ErrorNrowNegative ;
-    stats [Colamd::Info1] = n_row ;
-    COLAMD_DEBUG0 (("colamd: nrow negative %d\n", n_row)) ;
-    return (false) ;
-  }
-
-  if (n_col < 0)  /* n_col must be >= 0 */
-  {
-    stats [Colamd::Status] = Colamd::ErrorNcolNegative ;
-    stats [Colamd::Info1] = n_col ;
-    COLAMD_DEBUG0 (("colamd: ncol negative %d\n", n_col)) ;
-    return (false) ;
-  }
-
-  nnz = p [n_col] ;
-  if (nnz < 0)  /* nnz must be >= 0 */
-  {
-    stats [Colamd::Status] = Colamd::ErrorNnzNegative ;
-    stats [Colamd::Info1] = nnz ;
-    COLAMD_DEBUG0 (("colamd: number of entries negative %d\n", nnz)) ;
-    return (false) ;
-  }
-
-  if (p [0] != 0)
-  {
-    stats [Colamd::Status] = Colamd::ErrorP0Nonzero ;
-    stats [Colamd::Info1] = p [0] ;
-    COLAMD_DEBUG0 (("colamd: p[0] not zero %d\n", p [0])) ;
-    return (false) ;
-  }
-
-  /* === If no knobs, set default knobs =================================== */
-
-  if (!knobs)
-  {
-    set_defaults (default_knobs) ;
-    knobs = default_knobs ;
-  }
-
-  /* === Allocate the Row and Col arrays from array A ===================== */
-
-  Col_size = colamd_c (n_col) ;
-  Row_size = colamd_r (n_row) ;
-  need = 2*nnz + n_col + Col_size + Row_size ;
-
-  if (need > Alen)
-  {
-    /* not enough space in array A to perform the ordering */
-    stats [Colamd::Status] = Colamd::ErrorATooSmall ;
-    stats [Colamd::Info1] = need ;
-    stats [Colamd::Info2] = Alen ;
-    COLAMD_DEBUG0 (("colamd: Need Alen >= %d, given only Alen = %d\n", need,Alen));
-    return (false) ;
-  }
-
-  Alen -= Col_size + Row_size ;
-  Col = (ColStructure<IndexType> *) &A [Alen] ;
-  Row = (RowStructure<IndexType> *) &A [Alen + Col_size] ;
-
-  /* === Construct the row and column data structures ===================== */
-
-  if (!Colamd::init_rows_cols (n_row, n_col, Row, Col, A, p, stats))
-  {
-    /* input matrix is invalid */
-    COLAMD_DEBUG0 (("colamd: Matrix invalid\n")) ;
-    return (false) ;
-  }
-
-  /* === Initialize scores, kill dense rows/columns ======================= */
-
-  Colamd::init_scoring (n_row, n_col, Row, Col, A, p, knobs,
-		&n_row2, &n_col2, &max_deg) ;
-
-  /* === Order the supercolumns =========================================== */
-
-  ngarbage = Colamd::find_ordering (n_row, n_col, Alen, Row, Col, A, p,
-			    n_col2, max_deg, 2*nnz) ;
-
-  /* === Order the non-principal columns ================================== */
-
-  Colamd::order_children (n_col, Col, p) ;
-
-  /* === Return statistics in stats ======================================= */
-
-  stats [Colamd::DenseRow] = n_row - n_row2 ;
-  stats [Colamd::DenseCol] = n_col - n_col2 ;
-  stats [Colamd::DefragCount] = ngarbage ;
-  COLAMD_DEBUG0 (("colamd: done.\n")) ;
-  return (true) ;
-}
-
-/* ========================================================================== */
-/* === NON-USER-CALLABLE ROUTINES: ========================================== */
-/* ========================================================================== */
-
-/* There are no user-callable routines beyond this point in the file */
-
-/* ========================================================================== */
-/* === init_rows_cols ======================================================= */
-/* ========================================================================== */
-
-/*
-  Takes the column form of the matrix in A and creates the row form of the
-  matrix.  Also, row and column attributes are stored in the Col and Row
-  structs.  If the columns are un-sorted or contain duplicate row indices,
-  this routine will also sort and remove duplicate row indices from the
-  column form of the matrix.  Returns false if the matrix is invalid,
-  true otherwise.  Not user-callable.
-*/
-template <typename IndexType>
-static IndexType init_rows_cols  /* returns true if OK, or false otherwise */
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows of A */
-    IndexType n_col,      /* number of columns of A */
-    RowStructure<IndexType> Row [],    /* of size n_row+1 */
-    ColStructure<IndexType> Col [],    /* of size n_col+1 */
-    IndexType A [],     /* row indices of A, of size Alen */
-    IndexType p [],     /* pointers to columns in A, of size n_col+1 */
-    IndexType stats [NStats]  /* colamd statistics */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType col ;     /* a column index */
-  IndexType row ;     /* a row index */
-  IndexType *cp ;     /* a column pointer */
-  IndexType *cp_end ;   /* a pointer to the end of a column */
-  IndexType *rp ;     /* a row pointer */
-  IndexType *rp_end ;   /* a pointer to the end of a row */
-  IndexType last_row ;    /* previous row */
-
-  /* === Initialize columns, and check column pointers ==================== */
-
-  for (col = 0 ; col < n_col ; col++)
-  {
-    Col [col].start = p [col] ;
-    Col [col].length = p [col+1] - p [col] ;
-
-    if ((Col [col].length) < 0) // extra parentheses to work-around gcc bug 10200
-    {
-      /* column pointers must be non-decreasing */
-      stats [Colamd::Status] = Colamd::ErrorColLengthNegative ;
-      stats [Colamd::Info1] = col ;
-      stats [Colamd::Info2] = Col [col].length ;
-      COLAMD_DEBUG0 (("colamd: col %d length %d < 0\n", col, Col [col].length)) ;
-      return (false) ;
-    }
-
-    Col [col].shared1.thickness = 1 ;
-    Col [col].shared2.score = 0 ;
-    Col [col].shared3.prev = Empty ;
-    Col [col].shared4.degree_next = Empty ;
-  }
-
-  /* p [0..n_col] no longer needed, used as "head" in subsequent routines */
-
-  /* === Scan columns, compute row degrees, and check row indices ========= */
-
-  stats [Info3] = 0 ;  /* number of duplicate or unsorted row indices*/
-
-  for (row = 0 ; row < n_row ; row++)
-  {
-    Row [row].length = 0 ;
-    Row [row].shared2.mark = -1 ;
-  }
-
-  for (col = 0 ; col < n_col ; col++)
-  {
-    last_row = -1 ;
-
-    cp = &A [p [col]] ;
-    cp_end = &A [p [col+1]] ;
-
-    while (cp < cp_end)
-    {
-      row = *cp++ ;
-
-      /* make sure row indices within range */
-      if (row < 0 || row >= n_row)
-      {
-	stats [Colamd::Status] = Colamd::ErrorRowIndexOutOfBounds ;
-	stats [Colamd::Info1] = col ;
-	stats [Colamd::Info2] = row ;
-	stats [Colamd::Info3] = n_row ;
-	COLAMD_DEBUG0 (("colamd: row %d col %d out of bounds\n", row, col)) ;
-	return (false) ;
-      }
-
-      if (row <= last_row || Row [row].shared2.mark == col)
-      {
-	/* row index are unsorted or repeated (or both), thus col */
-	/* is jumbled.  This is a notice, not an error condition. */
-	stats [Colamd::Status] = Colamd::OkButJumbled ;
-	stats [Colamd::Info1] = col ;
-	stats [Colamd::Info2] = row ;
-	(stats [Colamd::Info3]) ++ ;
-	COLAMD_DEBUG1 (("colamd: row %d col %d unsorted/duplicate\n",row,col));
-      }
-
-      if (Row [row].shared2.mark != col)
-      {
-	Row [row].length++ ;
-      }
-      else
-      {
-	/* this is a repeated entry in the column, */
-	/* it will be removed */
-	Col [col].length-- ;
-      }
-
-      /* mark the row as having been seen in this column */
-      Row [row].shared2.mark = col ;
-
-      last_row = row ;
-    }
-  }
-
-  /* === Compute row pointers ============================================= */
-
-  /* row form of the matrix starts directly after the column */
-  /* form of matrix in A */
-  Row [0].start = p [n_col] ;
-  Row [0].shared1.p = Row [0].start ;
-  Row [0].shared2.mark = -1 ;
-  for (row = 1 ; row < n_row ; row++)
-  {
-    Row [row].start = Row [row-1].start + Row [row-1].length ;
-    Row [row].shared1.p = Row [row].start ;
-    Row [row].shared2.mark = -1 ;
-  }
-
-  /* === Create row form ================================================== */
-
-  if (stats [Status] == OkButJumbled)
-  {
-    /* if cols jumbled, watch for repeated row indices */
-    for (col = 0 ; col < n_col ; col++)
-    {
-      cp = &A [p [col]] ;
-      cp_end = &A [p [col+1]] ;
-      while (cp < cp_end)
-      {
-	row = *cp++ ;
-	if (Row [row].shared2.mark != col)
-	{
-	  A [(Row [row].shared1.p)++] = col ;
-	  Row [row].shared2.mark = col ;
-	}
-      }
-    }
-  }
-  else
-  {
-    /* if cols not jumbled, we don't need the mark (this is faster) */
-    for (col = 0 ; col < n_col ; col++)
-    {
-      cp = &A [p [col]] ;
-      cp_end = &A [p [col+1]] ;
-      while (cp < cp_end)
-      {
-	A [(Row [*cp++].shared1.p)++] = col ;
-      }
-    }
-  }
-
-  /* === Clear the row marks and set row degrees ========================== */
-
-  for (row = 0 ; row < n_row ; row++)
-  {
-    Row [row].shared2.mark = 0 ;
-    Row [row].shared1.degree = Row [row].length ;
-  }
-
-  /* === See if we need to re-create columns ============================== */
-
-  if (stats [Status] == OkButJumbled)
-  {
-    COLAMD_DEBUG0 (("colamd: reconstructing column form, matrix jumbled\n")) ;
-
-
-    /* === Compute col pointers ========================================= */
-
-    /* col form of the matrix starts at A [0]. */
-    /* Note, we may have a gap between the col form and the row */
-    /* form if there were duplicate entries, if so, it will be */
-    /* removed upon the first garbage collection */
-    Col [0].start = 0 ;
-    p [0] = Col [0].start ;
-    for (col = 1 ; col < n_col ; col++)
-    {
-      /* note that the lengths here are for pruned columns, i.e. */
-      /* no duplicate row indices will exist for these columns */
-      Col [col].start = Col [col-1].start + Col [col-1].length ;
-      p [col] = Col [col].start ;
-    }
-
-    /* === Re-create col form =========================================== */
-
-    for (row = 0 ; row < n_row ; row++)
-    {
-      rp = &A [Row [row].start] ;
-      rp_end = rp + Row [row].length ;
-      while (rp < rp_end)
-      {
-	A [(p [*rp++])++] = row ;
-      }
-    }
-  }
-
-  /* === Done.  Matrix is not (or no longer) jumbled ====================== */
-
-  return (true) ;
-}
-
-
-/* ========================================================================== */
-/* === init_scoring ========================================================= */
-/* ========================================================================== */
-
-/*
-  Kills dense or empty columns and rows, calculates an initial score for
-  each column, and places all columns in the degree lists.  Not user-callable.
-*/
-template <typename IndexType>
-static void init_scoring
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows of A */
-    IndexType n_col,      /* number of columns of A */
-    RowStructure<IndexType> Row [],    /* of size n_row+1 */
-    ColStructure<IndexType> Col [],    /* of size n_col+1 */
-    IndexType A [],     /* column form and row form of A */
-    IndexType head [],    /* of size n_col+1 */
-    double knobs [NKnobs],/* parameters */
-    IndexType *p_n_row2,    /* number of non-dense, non-empty rows */
-    IndexType *p_n_col2,    /* number of non-dense, non-empty columns */
-    IndexType *p_max_deg    /* maximum row degree */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType c ;     /* a column index */
-  IndexType r, row ;    /* a row index */
-  IndexType *cp ;     /* a column pointer */
-  IndexType deg ;     /* degree of a row or column */
-  IndexType *cp_end ;   /* a pointer to the end of a column */
-  IndexType *new_cp ;   /* new column pointer */
-  IndexType col_length ;    /* length of pruned column */
-  IndexType score ;     /* current column score */
-  IndexType n_col2 ;    /* number of non-dense, non-empty columns */
-  IndexType n_row2 ;    /* number of non-dense, non-empty rows */
-  IndexType dense_row_count ; /* remove rows with more entries than this */
-  IndexType dense_col_count ; /* remove cols with more entries than this */
-  IndexType min_score ;   /* smallest column score */
-  IndexType max_deg ;   /* maximum row degree */
-  IndexType next_col ;    /* Used to add to degree list.*/
-
-
-  /* === Extract knobs ==================================================== */
-
-  dense_row_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [Colamd::DenseRow] * n_col), n_col)) ;
-  dense_col_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [Colamd::DenseCol] * n_row), n_row)) ;
-  COLAMD_DEBUG1 (("colamd: densecount: %d %d\n", dense_row_count, dense_col_count)) ;
-  max_deg = 0 ;
-  n_col2 = n_col ;
-  n_row2 = n_row ;
-
-  /* === Kill empty columns =============================================== */
-
-  /* Put the empty columns at the end in their natural order, so that LU */
-  /* factorization can proceed as far as possible. */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    deg = Col [c].length ;
-    if (deg == 0)
-    {
-      /* this is a empty column, kill and order it last */
-      Col [c].shared2.order = --n_col2 ;
-      Col[c].kill_principal() ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: null columns killed: %d\n", n_col - n_col2)) ;
-
-  /* === Kill dense columns =============================================== */
-
-  /* Put the dense columns at the end, in their natural order */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    /* skip any dead columns */
-    if (Col[c].is_dead())
-    {
-      continue ;
-    }
-    deg = Col [c].length ;
-    if (deg > dense_col_count)
-    {
-      /* this is a dense column, kill and order it last */
-      Col [c].shared2.order = --n_col2 ;
-      /* decrement the row degrees */
-      cp = &A [Col [c].start] ;
-      cp_end = cp + Col [c].length ;
-      while (cp < cp_end)
-      {
-	Row [*cp++].shared1.degree-- ;
-      }
-      Col[c].kill_principal() ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: Dense and null columns killed: %d\n", n_col - n_col2)) ;
-
-  /* === Kill dense and empty rows ======================================== */
-
-  for (r = 0 ; r < n_row ; r++)
-  {
-    deg = Row [r].shared1.degree ;
-    COLAMD_ASSERT (deg >= 0 && deg <= n_col) ;
-    if (deg > dense_row_count || deg == 0)
-    {
-      /* kill a dense or empty row */
-      Row[r].kill() ;
-      --n_row2 ;
-    }
-    else
-    {
-      /* keep track of max degree of remaining rows */
-      max_deg = numext::maxi(max_deg, deg) ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: Dense and null rows killed: %d\n", n_row - n_row2)) ;
-
-  /* === Compute initial column scores ==================================== */
-
-  /* At this point the row degrees are accurate.  They reflect the number */
-  /* of "live" (non-dense) columns in each row.  No empty rows exist. */
-  /* Some "live" columns may contain only dead rows, however.  These are */
-  /* pruned in the code below. */
-
-  /* now find the initial matlab score for each column */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    /* skip dead column */
-    if (Col[c].is_dead())
-    {
-      continue ;
-    }
-    score = 0 ;
-    cp = &A [Col [c].start] ;
-    new_cp = cp ;
-    cp_end = cp + Col [c].length ;
-    while (cp < cp_end)
-    {
-      /* get a row */
-      row = *cp++ ;
-      /* skip if dead */
-      if (Row[row].is_dead())
-      {
-	continue ;
-      }
-      /* compact the column */
-      *new_cp++ = row ;
-      /* add row's external degree */
-      score += Row [row].shared1.degree - 1 ;
-      /* guard against integer overflow */
-      score = numext::mini(score, n_col) ;
-    }
-    /* determine pruned column length */
-    col_length = (IndexType) (new_cp - &A [Col [c].start]) ;
-    if (col_length == 0)
-    {
-      /* a newly-made null column (all rows in this col are "dense" */
-      /* and have already been killed) */
-      COLAMD_DEBUG2 (("Newly null killed: %d\n", c)) ;
-      Col [c].shared2.order = --n_col2 ;
-      Col[c].kill_principal() ;
-    }
-    else
-    {
-      /* set column length and set score */
-      COLAMD_ASSERT (score >= 0) ;
-      COLAMD_ASSERT (score <= n_col) ;
-      Col [c].length = col_length ;
-      Col [c].shared2.score = score ;
-    }
-  }
-  COLAMD_DEBUG1 (("colamd: Dense, null, and newly-null columns killed: %d\n",
-		  n_col-n_col2)) ;
-
-  /* At this point, all empty rows and columns are dead.  All live columns */
-  /* are "clean" (containing no dead rows) and simplicial (no supercolumns */
-  /* yet).  Rows may contain dead columns, but all live rows contain at */
-  /* least one live column. */
-
-  /* === Initialize degree lists ========================================== */
-
-
-  /* clear the hash buckets */
-  for (c = 0 ; c <= n_col ; c++)
-  {
-    head [c] = Empty ;
-  }
-  min_score = n_col ;
-  /* place in reverse order, so low column indices are at the front */
-  /* of the lists.  This is to encourage natural tie-breaking */
-  for (c = n_col-1 ; c >= 0 ; c--)
-  {
-    /* only add principal columns to degree lists */
-    if (Col[c].is_alive())
-    {
-      COLAMD_DEBUG4 (("place %d score %d minscore %d ncol %d\n",
-		      c, Col [c].shared2.score, min_score, n_col)) ;
-
-      /* === Add columns score to DList =============================== */
-
-      score = Col [c].shared2.score ;
-
-      COLAMD_ASSERT (min_score >= 0) ;
-      COLAMD_ASSERT (min_score <= n_col) ;
-      COLAMD_ASSERT (score >= 0) ;
-      COLAMD_ASSERT (score <= n_col) ;
-      COLAMD_ASSERT (head [score] >= Empty) ;
-
-      /* now add this column to dList at proper score location */
-      next_col = head [score] ;
-      Col [c].shared3.prev = Empty ;
-      Col [c].shared4.degree_next = next_col ;
-
-      /* if there already was a column with the same score, set its */
-      /* previous pointer to this new column */
-      if (next_col != Empty)
-      {
-	Col [next_col].shared3.prev = c ;
-      }
-      head [score] = c ;
-
-      /* see if this score is less than current min */
-      min_score = numext::mini(min_score, score) ;
-
-
-    }
-  }
-
-
-  /* === Return number of remaining columns, and max row degree =========== */
-
-  *p_n_col2 = n_col2 ;
-  *p_n_row2 = n_row2 ;
-  *p_max_deg = max_deg ;
-}
-
-
-/* ========================================================================== */
-/* === find_ordering ======================================================== */
-/* ========================================================================== */
-
-/*
-  Order the principal columns of the supercolumn form of the matrix
-  (no supercolumns on input).  Uses a minimum approximate column minimum
-  degree ordering method.  Not user-callable.
-*/
-template <typename IndexType>
-static IndexType find_ordering /* return the number of garbage collections */
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows of A */
-    IndexType n_col,      /* number of columns of A */
-    IndexType Alen,     /* size of A, 2*nnz + n_col or larger */
-    RowStructure<IndexType> Row [],    /* of size n_row+1 */
-    ColStructure<IndexType> Col [],    /* of size n_col+1 */
-    IndexType A [],     /* column form and row form of A */
-    IndexType head [],    /* of size n_col+1 */
-    IndexType n_col2,     /* Remaining columns to order */
-    IndexType max_deg,    /* Maximum row degree */
-    IndexType pfree     /* index of first free slot (2*nnz on entry) */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType k ;     /* current pivot ordering step */
-  IndexType pivot_col ;   /* current pivot column */
-  IndexType *cp ;     /* a column pointer */
-  IndexType *rp ;     /* a row pointer */
-  IndexType pivot_row ;   /* current pivot row */
-  IndexType *new_cp ;   /* modified column pointer */
-  IndexType *new_rp ;   /* modified row pointer */
-  IndexType pivot_row_start ; /* pointer to start of pivot row */
-  IndexType pivot_row_degree ;  /* number of columns in pivot row */
-  IndexType pivot_row_length ;  /* number of supercolumns in pivot row */
-  IndexType pivot_col_score ; /* score of pivot column */
-  IndexType needed_memory ;   /* free space needed for pivot row */
-  IndexType *cp_end ;   /* pointer to the end of a column */
-  IndexType *rp_end ;   /* pointer to the end of a row */
-  IndexType row ;     /* a row index */
-  IndexType col ;     /* a column index */
-  IndexType max_score ;   /* maximum possible score */
-  IndexType cur_score ;   /* score of current column */
-  unsigned int hash ;   /* hash value for supernode detection */
-  IndexType head_column ;   /* head of hash bucket */
-  IndexType first_col ;   /* first column in hash bucket */
-  IndexType tag_mark ;    /* marker value for mark array */
-  IndexType row_mark ;    /* Row [row].shared2.mark */
-  IndexType set_difference ;  /* set difference size of row with pivot row */
-  IndexType min_score ;   /* smallest column score */
-  IndexType col_thickness ;   /* "thickness" (no. of columns in a supercol) */
-  IndexType max_mark ;    /* maximum value of tag_mark */
-  IndexType pivot_col_thickness ; /* number of columns represented by pivot col */
-  IndexType prev_col ;    /* Used by Dlist operations. */
-  IndexType next_col ;    /* Used by Dlist operations. */
-  IndexType ngarbage ;    /* number of garbage collections performed */
-
-
-  /* === Initialization and clear mark ==================================== */
-
-  max_mark = INT_MAX - n_col ;  /* INT_MAX defined in <limits.h> */
-  tag_mark = Colamd::clear_mark (n_row, Row) ;
-  min_score = 0 ;
-  ngarbage = 0 ;
-  COLAMD_DEBUG1 (("colamd: Ordering, n_col2=%d\n", n_col2)) ;
-
-  /* === Order the columns ================================================ */
-
-  for (k = 0 ; k < n_col2 ; /* 'k' is incremented below */)
-  {
-
-    /* === Select pivot column, and order it ============================ */
-
-    /* make sure degree list isn't empty */
-    COLAMD_ASSERT (min_score >= 0) ;
-    COLAMD_ASSERT (min_score <= n_col) ;
-    COLAMD_ASSERT (head [min_score] >= Empty) ;
-
-    /* get pivot column from head of minimum degree list */
-    while (min_score < n_col && head [min_score] == Empty)
-    {
-      min_score++ ;
-    }
-    pivot_col = head [min_score] ;
-    COLAMD_ASSERT (pivot_col >= 0 && pivot_col <= n_col) ;
-    next_col = Col [pivot_col].shared4.degree_next ;
-    head [min_score] = next_col ;
-    if (next_col != Empty)
-    {
-      Col [next_col].shared3.prev = Empty ;
-    }
-
-    COLAMD_ASSERT (Col[pivot_col].is_alive()) ;
-    COLAMD_DEBUG3 (("Pivot col: %d\n", pivot_col)) ;
-
-    /* remember score for defrag check */
-    pivot_col_score = Col [pivot_col].shared2.score ;
-
-    /* the pivot column is the kth column in the pivot order */
-    Col [pivot_col].shared2.order = k ;
-
-    /* increment order count by column thickness */
-    pivot_col_thickness = Col [pivot_col].shared1.thickness ;
-    k += pivot_col_thickness ;
-    COLAMD_ASSERT (pivot_col_thickness > 0) ;
-
-    /* === Garbage_collection, if necessary ============================= */
-
-    needed_memory = numext::mini(pivot_col_score, n_col - k) ;
-    if (pfree + needed_memory >= Alen)
-    {
-      pfree = Colamd::garbage_collection (n_row, n_col, Row, Col, A, &A [pfree]) ;
-      ngarbage++ ;
-      /* after garbage collection we will have enough */
-      COLAMD_ASSERT (pfree + needed_memory < Alen) ;
-      /* garbage collection has wiped out the Row[].shared2.mark array */
-      tag_mark = Colamd::clear_mark (n_row, Row) ;
-
-    }
-
-    /* === Compute pivot row pattern ==================================== */
-
-    /* get starting location for this new merged row */
-    pivot_row_start = pfree ;
-
-    /* initialize new row counts to zero */
-    pivot_row_degree = 0 ;
-
-    /* tag pivot column as having been visited so it isn't included */
-    /* in merged pivot row */
-    Col [pivot_col].shared1.thickness = -pivot_col_thickness ;
-
-    /* pivot row is the union of all rows in the pivot column pattern */
-    cp = &A [Col [pivot_col].start] ;
-    cp_end = cp + Col [pivot_col].length ;
-    while (cp < cp_end)
-    {
-      /* get a row */
-      row = *cp++ ;
-      COLAMD_DEBUG4 (("Pivot col pattern %d %d\n", Row[row].is_alive(), row)) ;
-      /* skip if row is dead */
-      if (Row[row].is_dead())
-      {
-	continue ;
-      }
-      rp = &A [Row [row].start] ;
-      rp_end = rp + Row [row].length ;
-      while (rp < rp_end)
-      {
-	/* get a column */
-	col = *rp++ ;
-	/* add the column, if alive and untagged */
-	col_thickness = Col [col].shared1.thickness ;
-	if (col_thickness > 0 && Col[col].is_alive())
-	{
-	  /* tag column in pivot row */
-	  Col [col].shared1.thickness = -col_thickness ;
-	  COLAMD_ASSERT (pfree < Alen) ;
-	  /* place column in pivot row */
-	  A [pfree++] = col ;
-	  pivot_row_degree += col_thickness ;
-	}
-      }
-    }
-
-    /* clear tag on pivot column */
-    Col [pivot_col].shared1.thickness = pivot_col_thickness ;
-    max_deg = numext::maxi(max_deg, pivot_row_degree) ;
-
-
-    /* === Kill all rows used to construct pivot row ==================== */
-
-    /* also kill pivot row, temporarily */
-    cp = &A [Col [pivot_col].start] ;
-    cp_end = cp + Col [pivot_col].length ;
-    while (cp < cp_end)
-    {
-      /* may be killing an already dead row */
-      row = *cp++ ;
-      COLAMD_DEBUG3 (("Kill row in pivot col: %d\n", row)) ;
-      Row[row].kill() ;
-    }
-
-    /* === Select a row index to use as the new pivot row =============== */
-
-    pivot_row_length = pfree - pivot_row_start ;
-    if (pivot_row_length > 0)
-    {
-      /* pick the "pivot" row arbitrarily (first row in col) */
-      pivot_row = A [Col [pivot_col].start] ;
-      COLAMD_DEBUG3 (("Pivotal row is %d\n", pivot_row)) ;
-    }
-    else
-    {
-      /* there is no pivot row, since it is of zero length */
-      pivot_row = Empty ;
-      COLAMD_ASSERT (pivot_row_length == 0) ;
-    }
-    COLAMD_ASSERT (Col [pivot_col].length > 0 || pivot_row_length == 0) ;
-
-    /* === Approximate degree computation =============================== */
-
-    /* Here begins the computation of the approximate degree.  The column */
-    /* score is the sum of the pivot row "length", plus the size of the */
-    /* set differences of each row in the column minus the pattern of the */
-    /* pivot row itself.  The column ("thickness") itself is also */
-    /* excluded from the column score (we thus use an approximate */
-    /* external degree). */
-
-    /* The time taken by the following code (compute set differences, and */
-    /* add them up) is proportional to the size of the data structure */
-    /* being scanned - that is, the sum of the sizes of each column in */
-    /* the pivot row.  Thus, the amortized time to compute a column score */
-    /* is proportional to the size of that column (where size, in this */
-    /* context, is the column "length", or the number of row indices */
-    /* in that column).  The number of row indices in a column is */
-    /* monotonically non-decreasing, from the length of the original */
-    /* column on input to colamd. */
-
-    /* === Compute set differences ====================================== */
-
-    COLAMD_DEBUG3 (("** Computing set differences phase. **\n")) ;
-
-    /* pivot row is currently dead - it will be revived later. */
-
-    COLAMD_DEBUG3 (("Pivot row: ")) ;
-    /* for each column in pivot row */
-    rp = &A [pivot_row_start] ;
-    rp_end = rp + pivot_row_length ;
-    while (rp < rp_end)
-    {
-      col = *rp++ ;
-      COLAMD_ASSERT (Col[col].is_alive() && col != pivot_col) ;
-      COLAMD_DEBUG3 (("Col: %d\n", col)) ;
-
-      /* clear tags used to construct pivot row pattern */
-      col_thickness = -Col [col].shared1.thickness ;
-      COLAMD_ASSERT (col_thickness > 0) ;
-      Col [col].shared1.thickness = col_thickness ;
-
-      /* === Remove column from degree list =========================== */
-
-      cur_score = Col [col].shared2.score ;
-      prev_col = Col [col].shared3.prev ;
-      next_col = Col [col].shared4.degree_next ;
-      COLAMD_ASSERT (cur_score >= 0) ;
-      COLAMD_ASSERT (cur_score <= n_col) ;
-      COLAMD_ASSERT (cur_score >= Empty) ;
-      if (prev_col == Empty)
-      {
-	head [cur_score] = next_col ;
-      }
-      else
-      {
-	Col [prev_col].shared4.degree_next = next_col ;
-      }
-      if (next_col != Empty)
-      {
-	Col [next_col].shared3.prev = prev_col ;
-      }
-
-      /* === Scan the column ========================================== */
-
-      cp = &A [Col [col].start] ;
-      cp_end = cp + Col [col].length ;
-      while (cp < cp_end)
-      {
-	/* get a row */
-	row = *cp++ ;
-	/* skip if dead */
-	if (Row[row].is_dead())
-	{
-	  continue ;
-	}
-  row_mark = Row [row].shared2.mark ;
-	COLAMD_ASSERT (row != pivot_row) ;
-	set_difference = row_mark - tag_mark ;
-	/* check if the row has been seen yet */
-	if (set_difference < 0)
-	{
-	  COLAMD_ASSERT (Row [row].shared1.degree <= max_deg) ;
-	  set_difference = Row [row].shared1.degree ;
-	}
-	/* subtract column thickness from this row's set difference */
-	set_difference -= col_thickness ;
-	COLAMD_ASSERT (set_difference >= 0) ;
-	/* absorb this row if the set difference becomes zero */
-	if (set_difference == 0)
-	{
-	  COLAMD_DEBUG3 (("aggressive absorption. Row: %d\n", row)) ;
-	  Row[row].kill() ;
-	}
-	else
-	{
-	  /* save the new mark */
-	  Row [row].shared2.mark = set_difference + tag_mark ;
-	}
-      }
-    }
-
-
-    /* === Add up set differences for each column ======================= */
-
-    COLAMD_DEBUG3 (("** Adding set differences phase. **\n")) ;
-
-    /* for each column in pivot row */
-    rp = &A [pivot_row_start] ;
-    rp_end = rp + pivot_row_length ;
-    while (rp < rp_end)
-    {
-      /* get a column */
-      col = *rp++ ;
-      COLAMD_ASSERT (Col[col].is_alive() && col != pivot_col) ;
-      hash = 0 ;
-      cur_score = 0 ;
-      cp = &A [Col [col].start] ;
-      /* compact the column */
-      new_cp = cp ;
-      cp_end = cp + Col [col].length ;
-
-      COLAMD_DEBUG4 (("Adding set diffs for Col: %d.\n", col)) ;
-
-      while (cp < cp_end)
-      {
-	/* get a row */
-	row = *cp++ ;
-	COLAMD_ASSERT(row >= 0 && row < n_row) ;
-	/* skip if dead */
-	if (Row [row].is_dead())
-	{
-	  continue ;
-	}
-  row_mark = Row [row].shared2.mark ;
-	COLAMD_ASSERT (row_mark > tag_mark) ;
-	/* compact the column */
-	*new_cp++ = row ;
-	/* compute hash function */
-	hash += row ;
-	/* add set difference */
-	cur_score += row_mark - tag_mark ;
-	/* integer overflow... */
-	cur_score = numext::mini(cur_score, n_col) ;
-      }
-
-      /* recompute the column's length */
-      Col [col].length = (IndexType) (new_cp - &A [Col [col].start]) ;
-
-      /* === Further mass elimination ================================= */
-
-      if (Col [col].length == 0)
-      {
-	COLAMD_DEBUG4 (("further mass elimination. Col: %d\n", col)) ;
-	/* nothing left but the pivot row in this column */
-	Col[col].kill_principal() ;
-	pivot_row_degree -= Col [col].shared1.thickness ;
-	COLAMD_ASSERT (pivot_row_degree >= 0) ;
-	/* order it */
-	Col [col].shared2.order = k ;
-	/* increment order count by column thickness */
-	k += Col [col].shared1.thickness ;
-      }
-      else
-      {
-	/* === Prepare for supercolumn detection ==================== */
-
-	COLAMD_DEBUG4 (("Preparing supercol detection for Col: %d.\n", col)) ;
-
-	/* save score so far */
-	Col [col].shared2.score = cur_score ;
-
-	/* add column to hash table, for supercolumn detection */
-	hash %= n_col + 1 ;
-
-	COLAMD_DEBUG4 ((" Hash = %d, n_col = %d.\n", hash, n_col)) ;
-	COLAMD_ASSERT (hash <= n_col) ;
-
-	head_column = head [hash] ;
-	if (head_column > Empty)
-	{
-	  /* degree list "hash" is non-empty, use prev (shared3) of */
-	  /* first column in degree list as head of hash bucket */
-	  first_col = Col [head_column].shared3.headhash ;
-	  Col [head_column].shared3.headhash = col ;
-	}
-	else
-	{
-	  /* degree list "hash" is empty, use head as hash bucket */
-	  first_col = - (head_column + 2) ;
-	  head [hash] = - (col + 2) ;
-	}
-	Col [col].shared4.hash_next = first_col ;
-
-	/* save hash function in Col [col].shared3.hash */
-	Col [col].shared3.hash = (IndexType) hash ;
-	COLAMD_ASSERT (Col[col].is_alive()) ;
-      }
-    }
-
-    /* The approximate external column degree is now computed.  */
-
-    /* === Supercolumn detection ======================================== */
-
-    COLAMD_DEBUG3 (("** Supercolumn detection phase. **\n")) ;
-
-    Colamd::detect_super_cols (Col, A, head, pivot_row_start, pivot_row_length) ;
-
-    /* === Kill the pivotal column ====================================== */
-
-    Col[pivot_col].kill_principal() ;
-
-    /* === Clear mark =================================================== */
-
-    tag_mark += (max_deg + 1) ;
-    if (tag_mark >= max_mark)
-    {
-      COLAMD_DEBUG2 (("clearing tag_mark\n")) ;
-      tag_mark = Colamd::clear_mark (n_row, Row) ;
-    }
-
-    /* === Finalize the new pivot row, and column scores ================ */
-
-    COLAMD_DEBUG3 (("** Finalize scores phase. **\n")) ;
-
-    /* for each column in pivot row */
-    rp = &A [pivot_row_start] ;
-    /* compact the pivot row */
-    new_rp = rp ;
-    rp_end = rp + pivot_row_length ;
-    while (rp < rp_end)
-    {
-      col = *rp++ ;
-      /* skip dead columns */
-      if (Col[col].is_dead())
-      {
-	continue ;
-      }
-      *new_rp++ = col ;
-      /* add new pivot row to column */
-      A [Col [col].start + (Col [col].length++)] = pivot_row ;
-
-      /* retrieve score so far and add on pivot row's degree. */
-      /* (we wait until here for this in case the pivot */
-      /* row's degree was reduced due to mass elimination). */
-      cur_score = Col [col].shared2.score + pivot_row_degree ;
-
-      /* calculate the max possible score as the number of */
-      /* external columns minus the 'k' value minus the */
-      /* columns thickness */
-      max_score = n_col - k - Col [col].shared1.thickness ;
-
-      /* make the score the external degree of the union-of-rows */
-      cur_score -= Col [col].shared1.thickness ;
-
-      /* make sure score is less or equal than the max score */
-      cur_score = numext::mini(cur_score, max_score) ;
-      COLAMD_ASSERT (cur_score >= 0) ;
-
-      /* store updated score */
-      Col [col].shared2.score = cur_score ;
-
-      /* === Place column back in degree list ========================= */
-
-      COLAMD_ASSERT (min_score >= 0) ;
-      COLAMD_ASSERT (min_score <= n_col) ;
-      COLAMD_ASSERT (cur_score >= 0) ;
-      COLAMD_ASSERT (cur_score <= n_col) ;
-      COLAMD_ASSERT (head [cur_score] >= Empty) ;
-      next_col = head [cur_score] ;
-      Col [col].shared4.degree_next = next_col ;
-      Col [col].shared3.prev = Empty ;
-      if (next_col != Empty)
-      {
-	Col [next_col].shared3.prev = col ;
-      }
-      head [cur_score] = col ;
-
-      /* see if this score is less than current min */
-      min_score = numext::mini(min_score, cur_score) ;
-
-    }
-
-    /* === Resurrect the new pivot row ================================== */
-
-    if (pivot_row_degree > 0)
-    {
-      /* update pivot row length to reflect any cols that were killed */
-      /* during super-col detection and mass elimination */
-      Row [pivot_row].start  = pivot_row_start ;
-      Row [pivot_row].length = (IndexType) (new_rp - &A[pivot_row_start]) ;
-      Row [pivot_row].shared1.degree = pivot_row_degree ;
-      Row [pivot_row].shared2.mark = 0 ;
-      /* pivot row is no longer dead */
-    }
-  }
-
-  /* === All principal columns have now been ordered ====================== */
-
-  return (ngarbage) ;
-}
-
-
-/* ========================================================================== */
-/* === order_children ======================================================= */
-/* ========================================================================== */
-
-/*
-  The find_ordering routine has ordered all of the principal columns (the
-  representatives of the supercolumns).  The non-principal columns have not
-  yet been ordered.  This routine orders those columns by walking up the
-  parent tree (a column is a child of the column which absorbed it).  The
-  final permutation vector is then placed in p [0 ... n_col-1], with p [0]
-  being the first column, and p [n_col-1] being the last.  It doesn't look
-  like it at first glance, but be assured that this routine takes time linear
-  in the number of columns.  Although not immediately obvious, the time
-  taken by this routine is O (n_col), that is, linear in the number of
-  columns.  Not user-callable.
-*/
-template <typename IndexType>
-static inline  void order_children
-(
-  /* === Parameters ======================================================= */
-
-  IndexType n_col,      /* number of columns of A */
-  ColStructure<IndexType> Col [],    /* of size n_col+1 */
-  IndexType p []      /* p [0 ... n_col-1] is the column permutation*/
-  )
-{
-  /* === Local variables ================================================== */
-
-  IndexType i ;     /* loop counter for all columns */
-  IndexType c ;     /* column index */
-  IndexType parent ;    /* index of column's parent */
-  IndexType order ;     /* column's order */
-
-  /* === Order each non-principal column ================================== */
-
-  for (i = 0 ; i < n_col ; i++)
-  {
-    /* find an un-ordered non-principal column */
-    COLAMD_ASSERT (col_is_dead(Col, i)) ;
-    if (!Col[i].is_dead_principal() && Col [i].shared2.order == Empty)
-    {
-      parent = i ;
-      /* once found, find its principal parent */
-      do
-      {
-	parent = Col [parent].shared1.parent ;
-      } while (!Col[parent].is_dead_principal()) ;
-
-      /* now, order all un-ordered non-principal columns along path */
-      /* to this parent.  collapse tree at the same time */
-      c = i ;
-      /* get order of parent */
-      order = Col [parent].shared2.order ;
-
-      do
-      {
-	COLAMD_ASSERT (Col [c].shared2.order == Empty) ;
-
-	/* order this column */
-	Col [c].shared2.order = order++ ;
-	/* collaps tree */
-	Col [c].shared1.parent = parent ;
-
-	/* get immediate parent of this column */
-	c = Col [c].shared1.parent ;
-
-	/* continue until we hit an ordered column.  There are */
-	/* guaranteed not to be anymore unordered columns */
-	/* above an ordered column */
-      } while (Col [c].shared2.order == Empty) ;
-
-      /* re-order the super_col parent to largest order for this group */
-      Col [parent].shared2.order = order ;
-    }
-  }
-
-  /* === Generate the permutation ========================================= */
-
-  for (c = 0 ; c < n_col ; c++)
-  {
-    p [Col [c].shared2.order] = c ;
-  }
-}
-
-
-/* ========================================================================== */
-/* === detect_super_cols ==================================================== */
-/* ========================================================================== */
-
-/*
-  Detects supercolumns by finding matches between columns in the hash buckets.
-  Check amongst columns in the set A [row_start ... row_start + row_length-1].
-  The columns under consideration are currently *not* in the degree lists,
-  and have already been placed in the hash buckets.
-
-  The hash bucket for columns whose hash function is equal to h is stored
-  as follows:
-
-  if head [h] is >= 0, then head [h] contains a degree list, so:
-
-  head [h] is the first column in degree bucket h.
-  Col [head [h]].headhash gives the first column in hash bucket h.
-
-  otherwise, the degree list is empty, and:
-
-  -(head [h] + 2) is the first column in hash bucket h.
-
-  For a column c in a hash bucket, Col [c].shared3.prev is NOT a "previous
-  column" pointer.  Col [c].shared3.hash is used instead as the hash number
-  for that column.  The value of Col [c].shared4.hash_next is the next column
-  in the same hash bucket.
-
-  Assuming no, or "few" hash collisions, the time taken by this routine is
-  linear in the sum of the sizes (lengths) of each column whose score has
-  just been computed in the approximate degree computation.
-  Not user-callable.
-*/
-template <typename IndexType>
-static void detect_super_cols
-(
-  /* === Parameters ======================================================= */
-
-  ColStructure<IndexType> Col [],    /* of size n_col+1 */
-  IndexType A [],     /* row indices of A */
-  IndexType head [],    /* head of degree lists and hash buckets */
-  IndexType row_start,    /* pointer to set of columns to check */
-  IndexType row_length    /* number of columns to check */
-)
-{
-  /* === Local variables ================================================== */
-
-  IndexType hash ;      /* hash value for a column */
-  IndexType *rp ;     /* pointer to a row */
-  IndexType c ;     /* a column index */
-  IndexType super_c ;   /* column index of the column to absorb into */
-  IndexType *cp1 ;      /* column pointer for column super_c */
-  IndexType *cp2 ;      /* column pointer for column c */
-  IndexType length ;    /* length of column super_c */
-  IndexType prev_c ;    /* column preceding c in hash bucket */
-  IndexType i ;     /* loop counter */
-  IndexType *rp_end ;   /* pointer to the end of the row */
-  IndexType col ;     /* a column index in the row to check */
-  IndexType head_column ;   /* first column in hash bucket or degree list */
-  IndexType first_col ;   /* first column in hash bucket */
-
-  /* === Consider each column in the row ================================== */
-
-  rp = &A [row_start] ;
-  rp_end = rp + row_length ;
-  while (rp < rp_end)
-  {
-    col = *rp++ ;
-    if (Col[col].is_dead())
-    {
-      continue ;
-    }
-
-    /* get hash number for this column */
-    hash = Col [col].shared3.hash ;
-    COLAMD_ASSERT (hash <= n_col) ;
-
-    /* === Get the first column in this hash bucket ===================== */
-
-    head_column = head [hash] ;
-    if (head_column > Empty)
-    {
-      first_col = Col [head_column].shared3.headhash ;
-    }
-    else
-    {
-      first_col = - (head_column + 2) ;
-    }
-
-    /* === Consider each column in the hash bucket ====================== */
-
-    for (super_c = first_col ; super_c != Empty ;
-	 super_c = Col [super_c].shared4.hash_next)
-    {
-      COLAMD_ASSERT (Col [super_c].is_alive()) ;
-      COLAMD_ASSERT (Col [super_c].shared3.hash == hash) ;
-      length = Col [super_c].length ;
-
-      /* prev_c is the column preceding column c in the hash bucket */
-      prev_c = super_c ;
-
-      /* === Compare super_c with all columns after it ================ */
-
-      for (c = Col [super_c].shared4.hash_next ;
-	   c != Empty ; c = Col [c].shared4.hash_next)
-      {
-	COLAMD_ASSERT (c != super_c) ;
-	COLAMD_ASSERT (Col[c].is_alive()) ;
-	COLAMD_ASSERT (Col [c].shared3.hash == hash) ;
-
-	/* not identical if lengths or scores are different */
-	if (Col [c].length != length ||
-	    Col [c].shared2.score != Col [super_c].shared2.score)
-	{
-	  prev_c = c ;
-	  continue ;
-	}
-
-	/* compare the two columns */
-	cp1 = &A [Col [super_c].start] ;
-	cp2 = &A [Col [c].start] ;
-
-	for (i = 0 ; i < length ; i++)
-	{
-	  /* the columns are "clean" (no dead rows) */
-	  COLAMD_ASSERT ( cp1->is_alive() );
-	  COLAMD_ASSERT ( cp2->is_alive() );
-	  /* row indices will same order for both supercols, */
-	  /* no gather scatter necessary */
-	  if (*cp1++ != *cp2++)
-	  {
-	    break ;
-	  }
-	}
-
-	/* the two columns are different if the for-loop "broke" */
-	if (i != length)
-	{
-	  prev_c = c ;
-	  continue ;
-	}
-
-	/* === Got it!  two columns are identical =================== */
-
-	COLAMD_ASSERT (Col [c].shared2.score == Col [super_c].shared2.score) ;
-
-	Col [super_c].shared1.thickness += Col [c].shared1.thickness ;
-	Col [c].shared1.parent = super_c ;
-	Col[c].kill_non_principal() ;
-	/* order c later, in order_children() */
-	Col [c].shared2.order = Empty ;
-	/* remove c from hash bucket */
-	Col [prev_c].shared4.hash_next = Col [c].shared4.hash_next ;
-      }
-    }
-
-    /* === Empty this hash bucket ======================================= */
-
-    if (head_column > Empty)
-    {
-      /* corresponding degree list "hash" is not empty */
-      Col [head_column].shared3.headhash = Empty ;
-    }
-    else
-    {
-      /* corresponding degree list "hash" is empty */
-      head [hash] = Empty ;
-    }
-  }
-}
-
-
-/* ========================================================================== */
-/* === garbage_collection =================================================== */
-/* ========================================================================== */
-
-/*
-  Defragments and compacts columns and rows in the workspace A.  Used when
-  all available memory has been used while performing row merging.  Returns
-  the index of the first free position in A, after garbage collection.  The
-  time taken by this routine is linear is the size of the array A, which is
-  itself linear in the number of nonzeros in the input matrix.
-  Not user-callable.
-*/
-template <typename IndexType>
-static IndexType garbage_collection  /* returns the new value of pfree */
-  (
-    /* === Parameters ======================================================= */
-
-    IndexType n_row,      /* number of rows */
-    IndexType n_col,      /* number of columns */
-    RowStructure<IndexType> Row [],    /* row info */
-    ColStructure<IndexType> Col [],    /* column info */
-    IndexType A [],     /* A [0 ... Alen-1] holds the matrix */
-    IndexType *pfree      /* &A [0] ... pfree is in use */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType *psrc ;     /* source pointer */
-  IndexType *pdest ;    /* destination pointer */
-  IndexType j ;     /* counter */
-  IndexType r ;     /* a row index */
-  IndexType c ;     /* a column index */
-  IndexType length ;    /* length of a row or column */
-
-  /* === Defragment the columns =========================================== */
-
-  pdest = &A[0] ;
-  for (c = 0 ; c < n_col ; c++)
-  {
-    if (Col[c].is_alive())
-    {
-      psrc = &A [Col [c].start] ;
-
-      /* move and compact the column */
-      COLAMD_ASSERT (pdest <= psrc) ;
-      Col [c].start = (IndexType) (pdest - &A [0]) ;
-      length = Col [c].length ;
-      for (j = 0 ; j < length ; j++)
-      {
-	r = *psrc++ ;
-	if (Row[r].is_alive())
-	{
-	  *pdest++ = r ;
-	}
-      }
-      Col [c].length = (IndexType) (pdest - &A [Col [c].start]) ;
-    }
-  }
-
-  /* === Prepare to defragment the rows =================================== */
-
-  for (r = 0 ; r < n_row ; r++)
-  {
-    if (Row[r].is_alive())
-    {
-      if (Row [r].length == 0)
-      {
-        /* this row is of zero length.  cannot compact it, so kill it */
-        COLAMD_DEBUG3 (("Defrag row kill\n")) ;
-        Row[r].kill() ;
-      }
-      else
-      {
-        /* save first column index in Row [r].shared2.first_column */
-        psrc = &A [Row [r].start] ;
-        Row [r].shared2.first_column = *psrc ;
-        COLAMD_ASSERT (Row[r].is_alive()) ;
-        /* flag the start of the row with the one's complement of row */
-        *psrc = ones_complement(r) ;
-
-      }
-    }
-  }
-
-  /* === Defragment the rows ============================================== */
-
-  psrc = pdest ;
-  while (psrc < pfree)
-  {
-    /* find a negative number ... the start of a row */
-    if (*psrc++ < 0)
-    {
-      psrc-- ;
-      /* get the row index */
-      r = ones_complement(*psrc) ;
-      COLAMD_ASSERT (r >= 0 && r < n_row) ;
-      /* restore first column index */
-      *psrc = Row [r].shared2.first_column ;
-      COLAMD_ASSERT (Row[r].is_alive()) ;
-
-      /* move and compact the row */
-      COLAMD_ASSERT (pdest <= psrc) ;
-      Row [r].start = (IndexType) (pdest - &A [0]) ;
-      length = Row [r].length ;
-      for (j = 0 ; j < length ; j++)
-      {
-	c = *psrc++ ;
-	if (Col[c].is_alive())
-	{
-	  *pdest++ = c ;
-	}
-      }
-      Row [r].length = (IndexType) (pdest - &A [Row [r].start]) ;
-
-    }
-  }
-  /* ensure we found all the rows */
-  COLAMD_ASSERT (debug_rows == 0) ;
-
-  /* === Return the new value of pfree ==================================== */
-
-  return ((IndexType) (pdest - &A [0])) ;
-}
-
-
-/* ========================================================================== */
-/* === clear_mark =========================================================== */
-/* ========================================================================== */
-
-/*
-  Clears the Row [].shared2.mark array, and returns the new tag_mark.
-  Return value is the new tag_mark.  Not user-callable.
-*/
-template <typename IndexType>
-static inline  IndexType clear_mark  /* return the new value for tag_mark */
-  (
-      /* === Parameters ======================================================= */
-
-    IndexType n_row,    /* number of rows in A */
-    RowStructure<IndexType> Row [] /* Row [0 ... n_row-1].shared2.mark is set to zero */
-    )
-{
-  /* === Local variables ================================================== */
-
-  IndexType r ;
-
-  for (r = 0 ; r < n_row ; r++)
-  {
-    if (Row[r].is_alive())
-    {
-      Row [r].shared2.mark = 0 ;
-    }
-  }
-  return (1) ;
-}
-
-} // namespace Colamd
-
-} // namespace internal
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Ordering.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Ordering.h
deleted file mode 100644
index c578970..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/OrderingMethods/Ordering.h
+++ /dev/null
@@ -1,153 +0,0 @@
- 
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012  Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORDERING_H
-#define EIGEN_ORDERING_H
-
-namespace Eigen {
-  
-#include "Eigen_Colamd.h"
-
-namespace internal {
-    
-/** \internal
-  * \ingroup OrderingMethods_Module
-  * \param[in] A the input non-symmetric matrix
-  * \param[out] symmat the symmetric pattern A^T+A from the input matrix \a A.
-  * FIXME: The values should not be considered here
-  */
-template<typename MatrixType> 
-void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat)
-{
-  MatrixType C;
-  C = A.transpose(); // NOTE: Could be  costly
-  for (int i = 0; i < C.rows(); i++) 
-  {
-      for (typename MatrixType::InnerIterator it(C, i); it; ++it)
-        it.valueRef() = typename MatrixType::Scalar(0);
-  }
-  symmat = C + A;
-}
-    
-}
-
-/** \ingroup OrderingMethods_Module
-  * \class AMDOrdering
-  *
-  * Functor computing the \em approximate \em minimum \em degree ordering
-  * If the matrix is not structurally symmetric, an ordering of A^T+A is computed
-  * \tparam  StorageIndex The type of indices of the matrix 
-  * \sa COLAMDOrdering
-  */
-template <typename StorageIndex>
-class AMDOrdering
-{
-  public:
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-    
-    /** Compute the permutation vector from a sparse matrix
-     * This routine is much faster if the input matrix is column-major     
-     */
-    template <typename MatrixType>
-    void operator()(const MatrixType& mat, PermutationType& perm)
-    {
-      // Compute the symmetric pattern
-      SparseMatrix<typename MatrixType::Scalar, ColMajor, StorageIndex> symm;
-      internal::ordering_helper_at_plus_a(mat,symm); 
-    
-      // Call the AMD routine 
-      //m_mat.prune(keep_diag());
-      internal::minimum_degree_ordering(symm, perm);
-    }
-    
-    /** Compute the permutation with a selfadjoint matrix */
-    template <typename SrcType, unsigned int SrcUpLo> 
-    void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm)
-    { 
-      SparseMatrix<typename SrcType::Scalar, ColMajor, StorageIndex> C; C = mat;
-      
-      // Call the AMD routine 
-      // m_mat.prune(keep_diag()); //Remove the diagonal elements 
-      internal::minimum_degree_ordering(C, perm);
-    }
-};
-
-/** \ingroup OrderingMethods_Module
-  * \class NaturalOrdering
-  *
-  * Functor computing the natural ordering (identity)
-  * 
-  * \note Returns an empty permutation matrix
-  * \tparam  StorageIndex The type of indices of the matrix 
-  */
-template <typename StorageIndex>
-class NaturalOrdering
-{
-  public:
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-    
-    /** Compute the permutation vector from a column-major sparse matrix */
-    template <typename MatrixType>
-    void operator()(const MatrixType& /*mat*/, PermutationType& perm)
-    {
-      perm.resize(0); 
-    }
-    
-};
-
-/** \ingroup OrderingMethods_Module
-  * \class COLAMDOrdering
-  *
-  * \tparam  StorageIndex The type of indices of the matrix 
-  * 
-  * Functor computing the \em column \em approximate \em minimum \em degree ordering 
-  * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
-  */
-template<typename StorageIndex>
-class COLAMDOrdering
-{
-  public:
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; 
-    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
-    
-    /** Compute the permutation vector \a perm form the sparse matrix \a mat
-      * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-      */
-    template <typename MatrixType>
-    void operator() (const MatrixType& mat, PermutationType& perm)
-    {
-      eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering");
-      
-      StorageIndex m = StorageIndex(mat.rows());
-      StorageIndex n = StorageIndex(mat.cols());
-      StorageIndex nnz = StorageIndex(mat.nonZeros());
-      // Get the recommended value of Alen to be used by colamd
-      StorageIndex Alen = internal::Colamd::recommended(nnz, m, n); 
-      // Set the default parameters
-      double knobs [internal::Colamd::NKnobs]; 
-      StorageIndex stats [internal::Colamd::NStats];
-      internal::Colamd::set_defaults(knobs);
-      
-      IndexVector p(n+1), A(Alen); 
-      for(StorageIndex i=0; i <= n; i++)   p(i) = mat.outerIndexPtr()[i];
-      for(StorageIndex i=0; i < nnz; i++)  A(i) = mat.innerIndexPtr()[i];
-      // Call Colamd routine to compute the ordering 
-      StorageIndex info = internal::Colamd::compute_ordering(m, n, Alen, A.data(), p.data(), knobs, stats); 
-      EIGEN_UNUSED_VARIABLE(info);
-      eigen_assert( info && "COLAMD failed " );
-      
-      perm.resize(n);
-      for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i;
-    }
-};
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
deleted file mode 100644
index 3742687..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
+++ /dev/null
@@ -1,678 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PASTIXSUPPORT_H
-#define EIGEN_PASTIXSUPPORT_H
-
-namespace Eigen { 
-
-#if defined(DCOMPLEX)
-  #define PASTIX_COMPLEX  COMPLEX
-  #define PASTIX_DCOMPLEX DCOMPLEX
-#else
-  #define PASTIX_COMPLEX  std::complex<float>
-  #define PASTIX_DCOMPLEX std::complex<double>
-#endif
-
-/** \ingroup PaStiXSupport_Module
-  * \brief Interface to the PaStix solver
-  * 
-  * This class is used to solve the linear systems A.X = B via the PaStix library. 
-  * The matrix can be either real or complex, symmetric or not.
-  *
-  * \sa TutorialSparseDirectSolvers
-  */
-template<typename _MatrixType, bool IsStrSym = false> class PastixLU;
-template<typename _MatrixType, int Options> class PastixLLT;
-template<typename _MatrixType, int Options> class PastixLDLT;
-
-namespace internal
-{
-    
-  template<class Pastix> struct pastix_traits;
-
-  template<typename _MatrixType>
-  struct pastix_traits< PastixLU<_MatrixType> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pastix_traits< PastixLLT<_MatrixType,Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pastix_traits< PastixLDLT<_MatrixType,Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_DCOMPLEX*>(x), nbrhs, iparm, dparm); 
-  }
-
-  // Convert the matrix  to Fortran-style Numbering
-  template <typename MatrixType>
-  void c_to_fortran_numbering (MatrixType& mat)
-  {
-    if ( !(mat.outerIndexPtr()[0]) ) 
-    { 
-      int i;
-      for(i = 0; i <= mat.rows(); ++i)
-        ++mat.outerIndexPtr()[i];
-      for(i = 0; i < mat.nonZeros(); ++i)
-        ++mat.innerIndexPtr()[i];
-    }
-  }
-  
-  // Convert to C-style Numbering
-  template <typename MatrixType>
-  void fortran_to_c_numbering (MatrixType& mat)
-  {
-    // Check the Numbering
-    if ( mat.outerIndexPtr()[0] == 1 ) 
-    { // Convert to C-style numbering
-      int i;
-      for(i = 0; i <= mat.rows(); ++i)
-        --mat.outerIndexPtr()[i];
-      for(i = 0; i < mat.nonZeros(); ++i)
-        --mat.innerIndexPtr()[i];
-    }
-  }
-}
-
-// This is the base class to interface with PaStiX functions. 
-// Users should not used this class directly. 
-template <class Derived>
-class PastixBase : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    
-    typedef typename internal::pastix_traits<Derived>::MatrixType _MatrixType;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-  public:
-    
-    PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_pastixdata(0), m_size(0)
-    {
-      init();
-    }
-    
-    ~PastixBase() 
-    {
-      clean();
-    }
-    
-    template<typename Rhs,typename Dest>
-    bool _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const;
-    
-    /** Returns a reference to the integer vector IPARM of PaStiX parameters
-      * to modify the default parameters. 
-      * The statistics related to the different phases of factorization and solve are saved here as well
-      * \sa analyzePattern() factorize()
-      */
-    Array<StorageIndex,IPARM_SIZE,1>& iparm()
-    {
-      return m_iparm; 
-    }
-    
-    /** Return a reference to a particular index parameter of the IPARM vector 
-     * \sa iparm()
-     */
-    
-    int& iparm(int idxparam)
-    {
-      return m_iparm(idxparam);
-    }
-    
-     /** Returns a reference to the double vector DPARM of PaStiX parameters 
-      * The statistics related to the different phases of factorization and solve are saved here as well
-      * \sa analyzePattern() factorize()
-      */
-    Array<double,DPARM_SIZE,1>& dparm()
-    {
-      return m_dparm; 
-    }
-    
-    
-    /** Return a reference to a particular index parameter of the DPARM vector 
-     * \sa dparm()
-     */
-    double& dparm(int idxparam)
-    {
-      return m_dparm(idxparam);
-    }
-    
-    inline Index cols() const { return m_size; }
-    inline Index rows() const { return m_size; }
-    
-     /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the PaStiX reports a problem
-      *          \c InvalidInput if the input matrix is invalid
-      *
-      * \sa iparm()          
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-    
-  protected:
-
-    // Initialize the Pastix data structure, check the matrix
-    void init(); 
-    
-    // Compute the ordering and the symbolic factorization
-    void analyzePattern(ColSpMatrix& mat);
-    
-    // Compute the numerical factorization
-    void factorize(ColSpMatrix& mat);
-    
-    // Free all the data allocated by Pastix
-    void clean()
-    {
-      eigen_assert(m_initisOk && "The Pastix structure should be allocated first"); 
-      m_iparm(IPARM_START_TASK) = API_TASK_CLEAN;
-      m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
-      internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
-                             m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-    }
-    
-    void compute(ColSpMatrix& mat);
-    
-    int m_initisOk; 
-    int m_analysisIsOk;
-    int m_factorizationIsOk;
-    mutable ComputationInfo m_info; 
-    mutable pastix_data_t *m_pastixdata; // Data structure for pastix
-    mutable int m_comm; // The MPI communicator identifier
-    mutable Array<int,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters
-    mutable Array<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters
-    mutable Matrix<StorageIndex,Dynamic,1> m_perm;  // Permutation vector
-    mutable Matrix<StorageIndex,Dynamic,1> m_invp;  // Inverse permutation vector
-    mutable int m_size; // Size of the matrix 
-}; 
-
- /** Initialize the PaStiX data structure. 
-   *A first call to this function fills iparm and dparm with the default PaStiX parameters
-   * \sa iparm() dparm()
-   */
-template <class Derived>
-void PastixBase<Derived>::init()
-{
-  m_size = 0; 
-  m_iparm.setZero(IPARM_SIZE);
-  m_dparm.setZero(DPARM_SIZE);
-  
-  m_iparm(IPARM_MODIFY_PARAMETER) = API_NO;
-  pastix(&m_pastixdata, MPI_COMM_WORLD,
-         0, 0, 0, 0,
-         0, 0, 0, 1, m_iparm.data(), m_dparm.data());
-  
-  m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO;
-  m_iparm[IPARM_VERBOSE]             = API_VERBOSE_NOT;
-  m_iparm[IPARM_ORDERING]            = API_ORDER_SCOTCH;
-  m_iparm[IPARM_INCOMPLETE]          = API_NO;
-  m_iparm[IPARM_OOC_LIMIT]           = 2000;
-  m_iparm[IPARM_RHS_MAKING]          = API_RHS_B;
-  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
-  
-  m_iparm(IPARM_START_TASK) = API_TASK_INIT;
-  m_iparm(IPARM_END_TASK) = API_TASK_INIT;
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
-                         0, 0, 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER)) {
-    m_info = InvalidInput;
-    m_initisOk = false;
-  }
-  else { 
-    m_info = Success;
-    m_initisOk = true;
-  }
-}
-
-template <class Derived>
-void PastixBase<Derived>::compute(ColSpMatrix& mat)
-{
-  eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
-  
-  analyzePattern(mat);  
-  factorize(mat);
-  
-  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
-}
-
-
-template <class Derived>
-void PastixBase<Derived>::analyzePattern(ColSpMatrix& mat)
-{                         
-  eigen_assert(m_initisOk && "The initialization of PaSTiX failed");
-  
-  // clean previous calls
-  if(m_size>0)
-    clean();
-  
-  m_size = internal::convert_index<int>(mat.rows());
-  m_perm.resize(m_size);
-  m_invp.resize(m_size);
-  
-  m_iparm(IPARM_START_TASK) = API_TASK_ORDERING;
-  m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE;
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
-               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER))
-  {
-    m_info = NumericalIssue;
-    m_analysisIsOk = false;
-  }
-  else
-  { 
-    m_info = Success;
-    m_analysisIsOk = true;
-  }
-}
-
-template <class Derived>
-void PastixBase<Derived>::factorize(ColSpMatrix& mat)
-{
-//   if(&m_cpyMat != &mat) m_cpyMat = mat;
-  eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase");
-  m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT;
-  m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT;
-  m_size = internal::convert_index<int>(mat.rows());
-  
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
-               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER))
-  {
-    m_info = NumericalIssue;
-    m_factorizationIsOk = false;
-    m_isInitialized = false;
-  }
-  else
-  {
-    m_info = Success;
-    m_factorizationIsOk = true;
-    m_isInitialized = true;
-  }
-}
-
-/* Solve the system */
-template<typename Base>
-template<typename Rhs,typename Dest>
-bool PastixBase<Base>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const
-{
-  eigen_assert(m_isInitialized && "The matrix should be factorized first");
-  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
-                     THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  int rhs = 1;
-  
-  x = b; /* on return, x is overwritten by the computed solution */
-  
-  for (int i = 0; i < b.cols(); i++){
-    m_iparm[IPARM_START_TASK]          = API_TASK_SOLVE;
-    m_iparm[IPARM_END_TASK]            = API_TASK_REFINE;
-  
-    internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, internal::convert_index<int>(x.rows()), 0, 0, 0,
-                           m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data());
-  }
-  
-  // Check the returned error
-  m_info = m_iparm(IPARM_ERROR_NUMBER)==0 ? Success : NumericalIssue;
-  
-  return m_iparm(IPARM_ERROR_NUMBER)==0;
-}
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLU
-  * \brief Sparse direct LU solver based on PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B with a supernodal LU 
-  * factorization in the PaStiX library. The matrix A should be squared and nonsingular
-  * PaStiX requires that the matrix A has a symmetric structural pattern. 
-  * This interface can symmetrize the input matrix otherwise. 
-  * The vectors or matrices X and B can be either dense or sparse.
-  * 
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false
-  * NOTE : Note that if the analysis and factorization phase are called separately, 
-  * the input matrix will be symmetrized at each call, hence it is advised to 
-  * symmetrize the matrix in a end-user program and set \p IsStrSym to true
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  * 
-  */
-template<typename _MatrixType, bool IsStrSym>
-class PastixLU : public PastixBase< PastixLU<_MatrixType> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLU<MatrixType> > Base;
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    
-  public:
-    PastixLU() : Base()
-    {
-      init();
-    }
-    
-    explicit PastixLU(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-    /** Compute the LU supernodal factorization of \p matrix. 
-      * iparm and dparm can be used to tune the PaStiX parameters. 
-      * see the PaStiX user's manual
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      m_structureIsUptodate = false;
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-    /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern. 
-      * Several ordering methods can be used at this step. See the PaStiX user's manual. 
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      m_structureIsUptodate = false;
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-
-    /** Compute the LU supernodal factorization of \p matrix
-      * WARNING The matrix \p matrix should have the same structural pattern 
-      * as the same used in the analysis phase.
-      * \sa analyzePattern()
-      */ 
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-  protected:
-    
-    void init()
-    {
-      m_structureIsUptodate = false;
-      m_iparm(IPARM_SYM) = API_SYM_NO;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      if(IsStrSym)
-        out = matrix;
-      else
-      {
-        if(!m_structureIsUptodate)
-        {
-          // update the transposed structure
-          m_transposedStructure = matrix.transpose();
-          
-          // Set the elements of the matrix to zero 
-          for (Index j=0; j<m_transposedStructure.outerSize(); ++j) 
-            for(typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it)
-              it.valueRef() = 0.0;
-
-          m_structureIsUptodate = true;
-        }
-        
-        out = m_transposedStructure + matrix;
-      }
-      internal::c_to_fortran_numbering(out);
-    }
-    
-    using Base::m_iparm;
-    using Base::m_dparm;
-    
-    ColSpMatrix m_transposedStructure;
-    bool m_structureIsUptodate;
-};
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLLT
-  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization
-  * available in the PaStiX library. The matrix A should be symmetric and positive definite
-  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
-  * The vectors or matrices X and B can be either dense or sparse
-  * 
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo>
-class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLLT<MatrixType, _UpLo> > Base;
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    
-  public:
-    enum { UpLo = _UpLo };
-    PastixLLT() : Base()
-    {
-      init();
-    }
-    
-    explicit PastixLLT(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-
-    /** Compute the L factor of the LL^T supernodal factorization of \p matrix 
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-
-     /** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-      /** Compute the LL^T supernodal numerical factorization of \p matrix 
-        * \sa analyzePattern()
-        */
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-  protected:
-    using Base::m_iparm;
-    
-    void init()
-    {
-      m_iparm(IPARM_SYM) = API_SYM_YES;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      out.resize(matrix.rows(), matrix.cols());
-      // Pastix supports only lower, column-major matrices 
-      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
-      internal::c_to_fortran_numbering(out);
-    }
-};
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLDLT
-  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization
-  * available in the PaStiX library. The matrix A should be symmetric and positive definite
-  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
-  * The vectors or matrices X and B can be either dense or sparse
-  * 
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
-  */
-template<typename _MatrixType, int _UpLo>
-class PastixLDLT : public PastixBase< PastixLDLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLDLT<MatrixType, _UpLo> > Base; 
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    
-  public:
-    enum { UpLo = _UpLo };
-    PastixLDLT():Base()
-    {
-      init();
-    }
-    
-    explicit PastixLDLT(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-
-    /** Compute the L and D factors of the LDL^T factorization of \p matrix 
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-
-    /** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    { 
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-    /** Compute the LDL^T supernodal numerical factorization of \p matrix 
-      * 
-      */
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-
-  protected:
-    using Base::m_iparm;
-    
-    void init()
-    {
-      m_iparm(IPARM_SYM) = API_SYM_YES;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      // Pastix supports only lower, column-major matrices 
-      out.resize(matrix.rows(), matrix.cols());
-      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
-      internal::c_to_fortran_numbering(out);
-    }
-};
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
deleted file mode 100644
index f89b79b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
+++ /dev/null
@@ -1,545 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to Intel(R) MKL PARDISO
- ********************************************************************************
-*/
-
-#ifndef EIGEN_PARDISOSUPPORT_H
-#define EIGEN_PARDISOSUPPORT_H
-
-namespace Eigen { 
-
-template<typename _MatrixType> class PardisoLU;
-template<typename _MatrixType, int Options=Upper> class PardisoLLT;
-template<typename _MatrixType, int Options=Upper> class PardisoLDLT;
-
-namespace internal
-{
-  template<typename IndexType>
-  struct pardiso_run_selector
-  {
-    static IndexType run( _MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase, IndexType n, void *a,
-                      IndexType *ia, IndexType *ja, IndexType *perm, IndexType nrhs, IndexType *iparm, IndexType msglvl, void *b, void *x)
-    {
-      IndexType error = 0;
-      ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
-      return error;
-    }
-  };
-  template<>
-  struct pardiso_run_selector<long long int>
-  {
-    typedef long long int IndexType;
-    static IndexType run( _MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase, IndexType n, void *a,
-                      IndexType *ia, IndexType *ja, IndexType *perm, IndexType nrhs, IndexType *iparm, IndexType msglvl, void *b, void *x)
-    {
-      IndexType error = 0;
-      ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
-      return error;
-    }
-  };
-
-  template<class Pardiso> struct pardiso_traits;
-
-  template<typename _MatrixType>
-  struct pardiso_traits< PardisoLU<_MatrixType> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pardiso_traits< PardisoLLT<_MatrixType, Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pardiso_traits< PardisoLDLT<_MatrixType, Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;    
-  };
-
-} // end namespace internal
-
-template<class Derived>
-class PardisoImpl : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-    
-    typedef internal::pardiso_traits<Derived> Traits;
-  public:
-    using Base::_solve_impl;
-    
-    typedef typename Traits::MatrixType MatrixType;
-    typedef typename Traits::Scalar Scalar;
-    typedef typename Traits::RealScalar RealScalar;
-    typedef typename Traits::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> SparseMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef Matrix<StorageIndex, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<StorageIndex, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
-    typedef Array<StorageIndex,64,1,DontAlign> ParameterType;
-    enum {
-      ScalarIsComplex = NumTraits<Scalar>::IsComplex,
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-
-    PardisoImpl()
-      : m_analysisIsOk(false), m_factorizationIsOk(false)
-    {
-      eigen_assert((sizeof(StorageIndex) >= sizeof(_INTEGER_t) && sizeof(StorageIndex) <= 8) && "Non-supported index type");
-      m_iparm.setZero();
-      m_msglvl = 0; // No output
-      m_isInitialized = false;
-    }
-
-    ~PardisoImpl()
-    {
-      pardisoRelease();
-    }
-
-    inline Index cols() const { return m_size; }
-    inline Index rows() const { return m_size; }
-  
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    /** \warning for advanced usage only.
-      * \returns a reference to the parameter array controlling PARDISO.
-      * See the PARDISO manual to know how to use it. */
-    ParameterType& pardisoParameterArray()
-    {
-      return m_iparm;
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    Derived& analyzePattern(const MatrixType& matrix);
-    
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    Derived& factorize(const MatrixType& matrix);
-
-    Derived& compute(const MatrixType& matrix);
-
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
-
-  protected:
-    void pardisoRelease()
-    {
-      if(m_isInitialized) // Factorization ran at least once
-      {
-        internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1, internal::convert_index<StorageIndex>(m_size),0, 0, 0, m_perm.data(), 0,
-                                                          m_iparm.data(), m_msglvl, NULL, NULL);
-        m_isInitialized = false;
-      }
-    }
-
-    void pardisoInit(int type)
-    {
-      m_type = type;
-      bool symmetric = std::abs(m_type) < 10;
-      m_iparm[0] = 1;   // No solver default
-      m_iparm[1] = 2;   // use Metis for the ordering
-      m_iparm[2] = 0;   // Reserved. Set to zero. (??Numbers of processors, value of OMP_NUM_THREADS??)
-      m_iparm[3] = 0;   // No iterative-direct algorithm
-      m_iparm[4] = 0;   // No user fill-in reducing permutation
-      m_iparm[5] = 0;   // Write solution into x, b is left unchanged
-      m_iparm[6] = 0;   // Not in use
-      m_iparm[7] = 2;   // Max numbers of iterative refinement steps
-      m_iparm[8] = 0;   // Not in use
-      m_iparm[9] = 13;  // Perturb the pivot elements with 1E-13
-      m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
-      m_iparm[11] = 0;  // Not in use
-      m_iparm[12] = symmetric ? 0 : 1;  // Maximum weighted matching algorithm is switched-off (default for symmetric).
-                                        // Try m_iparm[12] = 1 in case of inappropriate accuracy
-      m_iparm[13] = 0;  // Output: Number of perturbed pivots
-      m_iparm[14] = 0;  // Not in use
-      m_iparm[15] = 0;  // Not in use
-      m_iparm[16] = 0;  // Not in use
-      m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
-      m_iparm[18] = -1; // Output: Mflops for LU factorization
-      m_iparm[19] = 0;  // Output: Numbers of CG Iterations
-      
-      m_iparm[20] = 0;  // 1x1 pivoting
-      m_iparm[26] = 0;  // No matrix checker
-      m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
-      m_iparm[34] = 1;  // C indexing
-      m_iparm[36] = 0;  // CSR
-      m_iparm[59] = 0;  // 0 - In-Core ; 1 - Automatic switch between In-Core and Out-of-Core modes ; 2 - Out-of-Core
-      
-      memset(m_pt, 0, sizeof(m_pt));
-    }
-
-  protected:
-    // cached data to reduce reallocation, etc.
-    
-    void manageErrorCode(Index error) const
-    {
-      switch(error)
-      {
-        case 0:
-          m_info = Success;
-          break;
-        case -4:
-        case -7:
-          m_info = NumericalIssue;
-          break;
-        default:
-          m_info = InvalidInput;
-      }
-    }
-
-    mutable SparseMatrixType m_matrix;
-    mutable ComputationInfo m_info;
-    bool m_analysisIsOk, m_factorizationIsOk;
-    StorageIndex m_type, m_msglvl;
-    mutable void *m_pt[64];
-    mutable ParameterType m_iparm;
-    mutable IntColVectorType m_perm;
-    Index m_size;
-    
-};
-
-template<class Derived>
-Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
-{
-  m_size = a.rows();
-  eigen_assert(a.rows() == a.cols());
-
-  pardisoRelease();
-  m_perm.setZero(m_size);
-  derived().getMatrix(a);
-  
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 12, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
-  manageErrorCode(error);
-  m_analysisIsOk = true;
-  m_factorizationIsOk = true;
-  m_isInitialized = true;
-  return derived();
-}
-
-template<class Derived>
-Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
-{
-  m_size = a.rows();
-  eigen_assert(m_size == a.cols());
-
-  pardisoRelease();
-  m_perm.setZero(m_size);
-  derived().getMatrix(a);
-  
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 11, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
-  
-  manageErrorCode(error);
-  m_analysisIsOk = true;
-  m_factorizationIsOk = false;
-  m_isInitialized = true;
-  return derived();
-}
-
-template<class Derived>
-Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
-{
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  eigen_assert(m_size == a.rows() && m_size == a.cols());
-  
-  derived().getMatrix(a);
-
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 22, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
-  
-  manageErrorCode(error);
-  m_factorizationIsOk = true;
-  return derived();
-}
-
-template<class Derived>
-template<typename BDerived,typename XDerived>
-void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
-{
-  if(m_iparm[0] == 0) // Factorization was not computed
-  {
-    m_info = InvalidInput;
-    return;
-  }
-
-  //Index n = m_matrix.rows();
-  Index nrhs = Index(b.cols());
-  eigen_assert(m_size==b.rows());
-  eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported");
-  eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported");
-  eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));
-
-
-//  switch (transposed) {
-//    case SvNoTrans    : m_iparm[11] = 0 ; break;
-//    case SvTranspose  : m_iparm[11] = 2 ; break;
-//    case SvAdjoint    : m_iparm[11] = 1 ; break;
-//    default:
-//      //std::cerr << "Eigen: transposition  option \"" << transposed << "\" not supported by the PARDISO backend\n";
-//      m_iparm[11] = 0;
-//  }
-
-  Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
-  Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;
-  
-  // Pardiso cannot solve in-place
-  if(rhs_ptr == x.derived().data())
-  {
-    tmp = b;
-    rhs_ptr = tmp.data();
-  }
-  
-  Index error;
-  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 33, internal::convert_index<StorageIndex>(m_size),
-                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
-                                                            m_perm.data(), internal::convert_index<StorageIndex>(nrhs), m_iparm.data(), m_msglvl,
-                                                            rhs_ptr, x.derived().data());
-
-  manageErrorCode(error);
-}
-
-
-/** \ingroup PardisoSupport_Module
-  * \class PardisoLU
-  * \brief A sparse direct LU factorization and solver based on the PARDISO library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
-  * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
-  * \code solver.pardisoParameterArray()[59] = 1; \endcode
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  */
-template<typename MatrixType>
-class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
-{
-  protected:
-    typedef PardisoImpl<PardisoLU> Base;
-    using Base::pardisoInit;
-    using Base::m_matrix;
-    friend class PardisoImpl< PardisoLU<MatrixType> >;
-
-  public:
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-
-    using Base::compute;
-    using Base::solve;
-
-    PardisoLU()
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
-    }
-
-    explicit PardisoLU(const MatrixType& matrix)
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
-      compute(matrix);
-    }
-  protected:
-    void getMatrix(const MatrixType& matrix)
-    {
-      m_matrix = matrix;
-      m_matrix.makeCompressed();
-    }
-};
-
-/** \ingroup PardisoSupport_Module
-  * \class PardisoLLT
-  * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
-  * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
-  * \code solver.pardisoParameterArray()[59] = 1; \endcode
-  *
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
-  *         Upper|Lower can be used to tell both triangular parts can be used as input.
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
-  */
-template<typename MatrixType, int _UpLo>
-class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
-{
-  protected:
-    typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base;
-    using Base::pardisoInit;
-    using Base::m_matrix;
-    friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >;
-
-  public:
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    enum { UpLo = _UpLo };
-    using Base::compute;
-
-    PardisoLLT()
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
-    }
-
-    explicit PardisoLLT(const MatrixType& matrix)
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
-      compute(matrix);
-    }
-    
-  protected:
-    
-    void getMatrix(const MatrixType& matrix)
-    {
-      // PARDISO supports only upper, row-major matrices
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> p_null;
-      m_matrix.resize(matrix.rows(), matrix.cols());
-      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
-      m_matrix.makeCompressed();
-    }
-};
-
-/** \ingroup PardisoSupport_Module
-  * \class PardisoLDLT
-  * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
-  * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
-  * For complex matrices, A can also be symmetric only, see the \a Options template parameter.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
-  * \code solver.pardisoParameterArray()[59] = 1; \endcode
-  *
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
-  *         Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.
-  *         Upper|Lower can be used to tell both triangular parts can be used as input.
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
-  */
-template<typename MatrixType, int Options>
-class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
-{
-  protected:
-    typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
-    using Base::pardisoInit;
-    using Base::m_matrix;
-    friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;
-
-  public:
-
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    using Base::compute;
-    enum { UpLo = Options&(Upper|Lower) };
-
-    PardisoLDLT()
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
-    }
-
-    explicit PardisoLDLT(const MatrixType& matrix)
-      : Base()
-    {
-      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
-      compute(matrix);
-    }
-    
-    void getMatrix(const MatrixType& matrix)
-    {
-      // PARDISO supports only upper, row-major matrices
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> p_null;
-      m_matrix.resize(matrix.rows(), matrix.cols());
-      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
-      m_matrix.makeCompressed();
-    }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_PARDISOSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h
deleted file mode 100644
index 9b677e9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h
+++ /dev/null
@@ -1,674 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
-#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _MatrixType> struct traits<ColPivHouseholderQR<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-} // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  * \class ColPivHouseholderQR
-  *
-  * \brief Householder rank-revealing QR decomposition of a matrix with column-pivoting
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the QR decomposition
-  *
-  * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R
-  * such that
-  * \f[
-  *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b P is a permutation matrix, \b Q a unitary matrix and \b R an
-  * upper triangular matrix.
-  *
-  * This decomposition performs column pivoting in order to be rank-revealing and improve
-  * numerical stability. It is slower than HouseholderQR, and faster than FullPivHouseholderQR.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  * 
-  * \sa MatrixBase::colPivHouseholderQr()
-  */
-template<typename _MatrixType> class ColPivHouseholderQR
-        : public SolverBase<ColPivHouseholderQR<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<ColPivHouseholderQR> Base;
-    friend class SolverBase<ColPivHouseholderQR>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(ColPivHouseholderQR)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
-    typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-  private:
-
-    typedef typename PermutationType::StorageIndex PermIndexType;
-
-  public:
-
-    /**
-    * \brief Default Constructor.
-    *
-    * The default constructor is useful in cases in which the user intends to
-    * perform decompositions via ColPivHouseholderQR::compute(const MatrixType&).
-    */
-    ColPivHouseholderQR()
-      : m_qr(),
-        m_hCoeffs(),
-        m_colsPermutation(),
-        m_colsTranspositions(),
-        m_temp(),
-        m_colNormsUpdated(),
-        m_colNormsDirect(),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa ColPivHouseholderQR()
-      */
-    ColPivHouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_colsPermutation(PermIndexType(cols)),
-        m_colsTranspositions(cols),
-        m_temp(cols),
-        m_colNormsUpdated(cols),
-        m_colNormsDirect(cols),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This constructor computes the QR factorization of the matrix \a matrix by calling
-      * the method compute(). It is a short cut for:
-      *
-      * \code
-      * ColPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      *
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit ColPivHouseholderQR(const EigenBase<InputType>& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_colsPermutation(PermIndexType(matrix.cols())),
-        m_colsTranspositions(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_colNormsUpdated(matrix.cols()),
-        m_colNormsDirect(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa ColPivHouseholderQR(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit ColPivHouseholderQR(EigenBase<InputType>& matrix)
-      : m_qr(matrix.derived()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_colsPermutation(PermIndexType(matrix.cols())),
-        m_colsTranspositions(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_colNormsUpdated(matrix.cols()),
-        m_colNormsDirect(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      computeInPlace();
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the QR decomposition, if any exists.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns a solution.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include ColPivHouseholderQR_solve.cpp
-      * Output: \verbinclude ColPivHouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const Solve<ColPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    HouseholderSequenceType householderQ() const;
-    HouseholderSequenceType matrixQ() const
-    {
-      return householderQ();
-    }
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      */
-    const MatrixType& matrixQR() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    /** \returns a reference to the matrix where the result Householder QR is stored
-     * \warning The strict lower part of this matrix contains internal values.
-     * Only the upper triangular part should be referenced. To get it, use
-     * \code matrixR().template triangularView<Upper>() \endcode
-     * For rank-deficient matrices, use
-     * \code
-     * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>()
-     * \endcode
-     */
-    const MatrixType& matrixR() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    template<typename InputType>
-    ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns a const reference to the column permutation matrix */
-    const PermutationType& colsPermutation() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_colsPermutation;
-    }
-
-    /** \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar absDeterminant() const;
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's inherent
-      * to determinant computation.
-      *
-      * \sa absDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar logAbsDeterminant() const;
-
-    /** \returns the rank of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
-      Index result = 0;
-      for(Index i = 0; i < m_nonzero_pivots; ++i)
-        result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);
-      return result;
-    }
-
-    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index dimensionOfKernel() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return cols() - rank();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents an injective
-      *          linear map, i.e. has trivial kernel; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInjective() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return rank() == cols();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
-      *          linear map; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isSurjective() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return rank() == rows();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInvertible() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return isInjective() && isSurjective();
-    }
-
-    /** \returns the inverse of the matrix of which *this is the QR decomposition.
-      *
-      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
-      *       Use isInvertible() to first determine whether this matrix is invertible.
-      */
-    inline const Inverse<ColPivHouseholderQR> inverse() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return Inverse<ColPivHouseholderQR>(*this);
-    }
-
-    inline Index rows() const { return m_qr.rows(); }
-    inline Index cols() const { return m_qr.cols(); }
-
-    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
-      *
-      * For advanced uses only.
-      */
-    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
-
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
-      * who need to determine when pivots are to be considered nonzero. This is not used for the
-      * QR decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
-      * uses a formula to automatically determine a reasonable threshold.
-      * Once you have called the present method setThreshold(const RealScalar&),
-      * your value is used instead.
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A pivot will be considered nonzero if its absolute value is strictly greater than
-      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-      * where maxpivot is the biggest pivot.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    ColPivHouseholderQR& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code qr.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    ColPivHouseholderQR& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
-
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    RealScalar threshold() const
-    {
-      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-      return m_usePrescribedThreshold ? m_prescribedThreshold
-      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
-      // and turns out to be identical to Higham's formula used already in LDLt.
-                                      : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
-    }
-
-    /** \returns the number of nonzero pivots in the QR decomposition.
-      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
-      * So that notion isn't really intrinsically interesting, but it is
-      * still useful when implementing algorithms.
-      *
-      * \sa rank()
-      */
-    inline Index nonzeroPivots() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of R.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-    /** \brief Reports whether the QR factorization was successful.
-      *
-      * \note This function always returns \c Success. It is provided for compatibility
-      * with other factorization routines.
-      * \returns \c Success
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return Success;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    friend class CompleteOrthogonalDecomposition<MatrixType>;
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    PermutationType m_colsPermutation;
-    IntRowVectorType m_colsTranspositions;
-    RowVectorType m_temp;
-    RealRowVectorType m_colNormsUpdated;
-    RealRowVectorType m_colNormsDirect;
-    bool m_isInitialized, m_usePrescribedThreshold;
-    RealScalar m_prescribedThreshold, m_maxpivot;
-    Index m_nonzero_pivots;
-    Index m_det_pq;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return abs(m_qr.diagonal().prod());
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return m_qr.diagonal().cwiseAbs().array().log().sum();
-}
-
-/** Performs the QR factorization of the given matrix \a matrix. The result of
-  * the factorization is stored into \c *this, and a reference to \c *this
-  * is returned.
-  *
-  * \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-template<typename InputType>
-ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
-{
-  m_qr = matrix.derived();
-  computeInPlace();
-  return *this;
-}
-
-template<typename MatrixType>
-void ColPivHouseholderQR<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  // the column permutation is stored as int indices, so just to be sure:
-  eigen_assert(m_qr.cols()<=NumTraits<int>::highest());
-
-  using std::abs;
-
-  Index rows = m_qr.rows();
-  Index cols = m_qr.cols();
-  Index size = m_qr.diagonalSize();
-
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  m_colsTranspositions.resize(m_qr.cols());
-  Index number_of_transpositions = 0;
-
-  m_colNormsUpdated.resize(cols);
-  m_colNormsDirect.resize(cols);
-  for (Index k = 0; k < cols; ++k) {
-    // colNormsDirect(k) caches the most recent directly computed norm of
-    // column k.
-    m_colNormsDirect.coeffRef(k) = m_qr.col(k).norm();
-    m_colNormsUpdated.coeffRef(k) = m_colNormsDirect.coeffRef(k);
-  }
-
-  RealScalar threshold_helper =  numext::abs2<RealScalar>(m_colNormsUpdated.maxCoeff() * NumTraits<RealScalar>::epsilon()) / RealScalar(rows);
-  RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<RealScalar>::epsilon());
-
-  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
-  m_maxpivot = RealScalar(0);
-
-  for(Index k = 0; k < size; ++k)
-  {
-    // first, we look up in our table m_colNormsUpdated which column has the biggest norm
-    Index biggest_col_index;
-    RealScalar biggest_col_sq_norm = numext::abs2(m_colNormsUpdated.tail(cols-k).maxCoeff(&biggest_col_index));
-    biggest_col_index += k;
-
-    // Track the number of meaningful pivots but do not stop the decomposition to make
-    // sure that the initial matrix is properly reproduced. See bug 941.
-    if(m_nonzero_pivots==size && biggest_col_sq_norm < threshold_helper * RealScalar(rows-k))
-      m_nonzero_pivots = k;
-
-    // apply the transposition to the columns
-    m_colsTranspositions.coeffRef(k) = biggest_col_index;
-    if(k != biggest_col_index) {
-      m_qr.col(k).swap(m_qr.col(biggest_col_index));
-      std::swap(m_colNormsUpdated.coeffRef(k), m_colNormsUpdated.coeffRef(biggest_col_index));
-      std::swap(m_colNormsDirect.coeffRef(k), m_colNormsDirect.coeffRef(biggest_col_index));
-      ++number_of_transpositions;
-    }
-
-    // generate the householder vector, store it below the diagonal
-    RealScalar beta;
-    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
-
-    // apply the householder transformation to the diagonal coefficient
-    m_qr.coeffRef(k,k) = beta;
-
-    // remember the maximum absolute value of diagonal coefficients
-    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
-
-    // apply the householder transformation
-    m_qr.bottomRightCorner(rows-k, cols-k-1)
-        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1));
-
-    // update our table of norms of the columns
-    for (Index j = k + 1; j < cols; ++j) {
-      // The following implements the stable norm downgrade step discussed in
-      // http://www.netlib.org/lapack/lawnspdf/lawn176.pdf
-      // and used in LAPACK routines xGEQPF and xGEQP3.
-      // See lines 278-297 in http://www.netlib.org/lapack/explore-html/dc/df4/sgeqpf_8f_source.html
-      if (m_colNormsUpdated.coeffRef(j) != RealScalar(0)) {
-        RealScalar temp = abs(m_qr.coeffRef(k, j)) / m_colNormsUpdated.coeffRef(j);
-        temp = (RealScalar(1) + temp) * (RealScalar(1) - temp);
-        temp = temp <  RealScalar(0) ? RealScalar(0) : temp;
-        RealScalar temp2 = temp * numext::abs2<RealScalar>(m_colNormsUpdated.coeffRef(j) /
-                                                           m_colNormsDirect.coeffRef(j));
-        if (temp2 <= norm_downdate_threshold) {
-          // The updated norm has become too inaccurate so re-compute the column
-          // norm directly.
-          m_colNormsDirect.coeffRef(j) = m_qr.col(j).tail(rows - k - 1).norm();
-          m_colNormsUpdated.coeffRef(j) = m_colNormsDirect.coeffRef(j);
-        } else {
-          m_colNormsUpdated.coeffRef(j) *= numext::sqrt(temp);
-        }
-      }
-    }
-  }
-
-  m_colsPermutation.setIdentity(PermIndexType(cols));
-  for(PermIndexType k = 0; k < size/*m_nonzero_pivots*/; ++k)
-    m_colsPermutation.applyTranspositionOnTheRight(k, PermIndexType(m_colsTranspositions.coeff(k)));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-  m_isInitialized = true;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void ColPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  const Index nonzero_pivots = nonzeroPivots();
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs);
-
-  c.applyOnTheLeft(householderQ().setLength(nonzero_pivots).adjoint() );
-
-  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(nonzero_pivots));
-
-  for(Index i = 0; i < nonzero_pivots; ++i) dst.row(m_colsPermutation.indices().coeff(i)) = c.row(i);
-  for(Index i = nonzero_pivots; i < cols(); ++i) dst.row(m_colsPermutation.indices().coeff(i)).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void ColPivHouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index nonzero_pivots = nonzeroPivots();
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(m_colsPermutation.transpose()*rhs);
-
-  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
-        .template triangularView<Upper>()
-        .transpose().template conjugateIf<Conjugate>()
-        .solveInPlace(c.topRows(nonzero_pivots));
-
-  dst.topRows(nonzero_pivots) = c.topRows(nonzero_pivots);
-  dst.bottomRows(rows()-nonzero_pivots).setZero();
-
-  dst.applyOnTheLeft(householderQ().setLength(nonzero_pivots).template conjugateIf<!Conjugate>() );
-}
-#endif
-
-namespace internal {
-
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<ColPivHouseholderQR<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename ColPivHouseholderQR<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef ColPivHouseholderQR<MatrixType> QrType;
-  typedef Inverse<QrType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename QrType::Scalar> &)
-  {
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-
-} // end namespace internal
-
-/** \returns the matrix Q as a sequence of householder transformations.
-  * You can extract the meaningful part only by using:
-  * \code qr.householderQ().setLength(qr.nonzeroPivots()) \endcode*/
-template<typename MatrixType>
-typename ColPivHouseholderQR<MatrixType>::HouseholderSequenceType ColPivHouseholderQR<MatrixType>
-  ::householderQ() const
-{
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
-}
-
-/** \return the column-pivoting Householder QR decomposition of \c *this.
-  *
-  * \sa class ColPivHouseholderQR
-  */
-template<typename Derived>
-const ColPivHouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::colPivHouseholderQr() const
-{
-  return ColPivHouseholderQR<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
deleted file mode 100644
index 4e9651f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Householder QR decomposition of a matrix with column pivoting based on
- *    LAPACKE_?geqp3 function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
-#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_QR_COLPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW) \
-template<> template<typename InputType> inline \
-ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >& \
-ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >::compute( \
-              const EigenBase<InputType>& matrix) \
-\
-{ \
-  using std::abs; \
-  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
-  typedef MatrixType::RealScalar RealScalar; \
-  Index rows = matrix.rows();\
-  Index cols = matrix.cols();\
-\
-  m_qr = matrix;\
-  Index size = m_qr.diagonalSize();\
-  m_hCoeffs.resize(size);\
-\
-  m_colsTranspositions.resize(cols);\
-  /*Index number_of_transpositions = 0;*/ \
-\
-  m_nonzero_pivots = 0; \
-  m_maxpivot = RealScalar(0);\
-  m_colsPermutation.resize(cols); \
-  m_colsPermutation.indices().setZero(); \
-\
-  lapack_int lda = internal::convert_index<lapack_int,Index>(m_qr.outerStride()); \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  LAPACKE_##LAPACKE_PREFIX##geqp3( matrix_order, internal::convert_index<lapack_int,Index>(rows), internal::convert_index<lapack_int,Index>(cols), \
-                              (LAPACKE_TYPE*)m_qr.data(), lda, (lapack_int*)m_colsPermutation.indices().data(), (LAPACKE_TYPE*)m_hCoeffs.data()); \
-  m_isInitialized = true; \
-  m_maxpivot=m_qr.diagonal().cwiseAbs().maxCoeff(); \
-  m_hCoeffs.adjointInPlace(); \
-  RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold(); \
-  lapack_int *perm = m_colsPermutation.indices().data(); \
-  for(Index i=0;i<size;i++) { \
-    m_nonzero_pivots += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);\
-  } \
-  for(Index i=0;i<cols;i++) perm[i]--;\
-\
-  /*m_det_pq = (number_of_transpositions%2) ? -1 : 1;  // TODO: It's not needed now; fix upon availability in Eigen */ \
-\
-  return *this; \
-}
-
-EIGEN_LAPACKE_QR_COLPIV(double,   double,        d, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(float,    float,         s, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(dcomplex, lapack_complex_double, z, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(scomplex, lapack_complex_float,  c, ColMajor, LAPACK_COL_MAJOR)
-
-EIGEN_LAPACKE_QR_COLPIV(double,   double,        d, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(float,    float,         s, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(dcomplex, lapack_complex_double, z, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_QR_COLPIV(scomplex, lapack_complex_float,  c, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h
deleted file mode 100644
index 486d337..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h
+++ /dev/null
@@ -1,635 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
-#define EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
-
-namespace Eigen {
-
-namespace internal {
-template <typename _MatrixType>
-struct traits<CompleteOrthogonalDecomposition<_MatrixType> >
-    : traits<_MatrixType> {
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-}  // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  * \class CompleteOrthogonalDecomposition
-  *
-  * \brief Complete orthogonal decomposition (COD) of a matrix.
-  *
-  * \param MatrixType the type of the matrix of which we are computing the COD.
-  *
-  * This class performs a rank-revealing complete orthogonal decomposition of a
-  * matrix  \b A into matrices \b P, \b Q, \b T, and \b Z such that
-  * \f[
-  *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \,
-  *                     \begin{bmatrix} \mathbf{T} &  \mathbf{0} \\
-  *                                     \mathbf{0} & \mathbf{0} \end{bmatrix} \, \mathbf{Z}
-  * \f]
-  * by using Householder transformations. Here, \b P is a permutation matrix,
-  * \b Q and \b Z are unitary matrices and \b T an upper triangular matrix of
-  * size rank-by-rank. \b A may be rank deficient.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  * 
-  * \sa MatrixBase::completeOrthogonalDecomposition()
-  */
-template <typename _MatrixType> class CompleteOrthogonalDecomposition
-          : public SolverBase<CompleteOrthogonalDecomposition<_MatrixType> >
-{
- public:
-  typedef _MatrixType MatrixType;
-  typedef SolverBase<CompleteOrthogonalDecomposition> Base;
-
-  template<typename Derived>
-  friend struct internal::solve_assertion;
-
-  EIGEN_GENERIC_PUBLIC_INTERFACE(CompleteOrthogonalDecomposition)
-  enum {
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-  };
-  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime>
-      PermutationType;
-  typedef typename internal::plain_row_type<MatrixType, Index>::type
-      IntRowVectorType;
-  typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-  typedef typename internal::plain_row_type<MatrixType, RealScalar>::type
-      RealRowVectorType;
-  typedef HouseholderSequence<
-      MatrixType, typename internal::remove_all<
-                      typename HCoeffsType::ConjugateReturnType>::type>
-      HouseholderSequenceType;
-  typedef typename MatrixType::PlainObject PlainObject;
-
- private:
-  typedef typename PermutationType::Index PermIndexType;
-
- public:
-  /**
-   * \brief Default Constructor.
-   *
-   * The default constructor is useful in cases in which the user intends to
-   * perform decompositions via
-   * \c CompleteOrthogonalDecomposition::compute(const* MatrixType&).
-   */
-  CompleteOrthogonalDecomposition() : m_cpqr(), m_zCoeffs(), m_temp() {}
-
-  /** \brief Default Constructor with memory preallocation
-   *
-   * Like the default constructor but with preallocation of the internal data
-   * according to the specified problem \a size.
-   * \sa CompleteOrthogonalDecomposition()
-   */
-  CompleteOrthogonalDecomposition(Index rows, Index cols)
-      : m_cpqr(rows, cols), m_zCoeffs((std::min)(rows, cols)), m_temp(cols) {}
-
-  /** \brief Constructs a complete orthogonal decomposition from a given
-   * matrix.
-   *
-   * This constructor computes the complete orthogonal decomposition of the
-   * matrix \a matrix by calling the method compute(). The default
-   * threshold for rank determination will be used. It is a short cut for:
-   *
-   * \code
-   * CompleteOrthogonalDecomposition<MatrixType> cod(matrix.rows(),
-   *                                                 matrix.cols());
-   * cod.setThreshold(Default);
-   * cod.compute(matrix);
-   * \endcode
-   *
-   * \sa compute()
-   */
-  template <typename InputType>
-  explicit CompleteOrthogonalDecomposition(const EigenBase<InputType>& matrix)
-      : m_cpqr(matrix.rows(), matrix.cols()),
-        m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_temp(matrix.cols())
-  {
-    compute(matrix.derived());
-  }
-
-  /** \brief Constructs a complete orthogonal decomposition from a given matrix
-    *
-    * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-    *
-    * \sa CompleteOrthogonalDecomposition(const EigenBase&)
-    */
-  template<typename InputType>
-  explicit CompleteOrthogonalDecomposition(EigenBase<InputType>& matrix)
-    : m_cpqr(matrix.derived()),
-      m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
-      m_temp(matrix.cols())
-  {
-    computeInPlace();
-  } 
-
-  #ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** This method computes the minimum-norm solution X to a least squares
-   * problem \f[\mathrm{minimize} \|A X - B\|, \f] where \b A is the matrix of
-   * which \c *this is the complete orthogonal decomposition.
-   *
-   * \param b the right-hand sides of the problem to solve.
-   *
-   * \returns a solution.
-   *
-   */
-  template <typename Rhs>
-  inline const Solve<CompleteOrthogonalDecomposition, Rhs> solve(
-      const MatrixBase<Rhs>& b) const;
-  #endif
-
-  HouseholderSequenceType householderQ(void) const;
-  HouseholderSequenceType matrixQ(void) const { return m_cpqr.householderQ(); }
-
-  /** \returns the matrix \b Z.
-   */
-  MatrixType matrixZ() const {
-    MatrixType Z = MatrixType::Identity(m_cpqr.cols(), m_cpqr.cols());
-    applyZOnTheLeftInPlace<false>(Z);
-    return Z;
-  }
-
-  /** \returns a reference to the matrix where the complete orthogonal
-   * decomposition is stored
-   */
-  const MatrixType& matrixQTZ() const { return m_cpqr.matrixQR(); }
-
-  /** \returns a reference to the matrix where the complete orthogonal
-   * decomposition is stored.
-   * \warning The strict lower part and \code cols() - rank() \endcode right
-   * columns of this matrix contains internal values.
-   * Only the upper triangular part should be referenced. To get it, use
-   * \code matrixT().template triangularView<Upper>() \endcode
-   * For rank-deficient matrices, use
-   * \code
-   * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>()
-   * \endcode
-   */
-  const MatrixType& matrixT() const { return m_cpqr.matrixQR(); }
-
-  template <typename InputType>
-  CompleteOrthogonalDecomposition& compute(const EigenBase<InputType>& matrix) {
-    // Compute the column pivoted QR factorization A P = Q R.
-    m_cpqr.compute(matrix);
-    computeInPlace();
-    return *this;
-  }
-
-  /** \returns a const reference to the column permutation matrix */
-  const PermutationType& colsPermutation() const {
-    return m_cpqr.colsPermutation();
-  }
-
-  /** \returns the absolute value of the determinant of the matrix of which
-   * *this is the complete orthogonal decomposition. It has only linear
-   * complexity (that is, O(n) where n is the dimension of the square matrix)
-   * as the complete orthogonal decomposition has already been computed.
-   *
-   * \note This is only for square matrices.
-   *
-   * \warning a determinant can be very big or small, so for matrices
-   * of large enough dimension, there is a risk of overflow/underflow.
-   * One way to work around that is to use logAbsDeterminant() instead.
-   *
-   * \sa logAbsDeterminant(), MatrixBase::determinant()
-   */
-  typename MatrixType::RealScalar absDeterminant() const;
-
-  /** \returns the natural log of the absolute value of the determinant of the
-   * matrix of which *this is the complete orthogonal decomposition. It has
-   * only linear complexity (that is, O(n) where n is the dimension of the
-   * square matrix) as the complete orthogonal decomposition has already been
-   * computed.
-   *
-   * \note This is only for square matrices.
-   *
-   * \note This method is useful to work around the risk of overflow/underflow
-   * that's inherent to determinant computation.
-   *
-   * \sa absDeterminant(), MatrixBase::determinant()
-   */
-  typename MatrixType::RealScalar logAbsDeterminant() const;
-
-  /** \returns the rank of the matrix of which *this is the complete orthogonal
-   * decomposition.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline Index rank() const { return m_cpqr.rank(); }
-
-  /** \returns the dimension of the kernel of the matrix of which *this is the
-   * complete orthogonal decomposition.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline Index dimensionOfKernel() const { return m_cpqr.dimensionOfKernel(); }
-
-  /** \returns true if the matrix of which *this is the decomposition represents
-   * an injective linear map, i.e. has trivial kernel; false otherwise.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline bool isInjective() const { return m_cpqr.isInjective(); }
-
-  /** \returns true if the matrix of which *this is the decomposition represents
-   * a surjective linear map; false otherwise.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline bool isSurjective() const { return m_cpqr.isSurjective(); }
-
-  /** \returns true if the matrix of which *this is the complete orthogonal
-   * decomposition is invertible.
-   *
-   * \note This method has to determine which pivots should be considered
-   * nonzero. For that, it uses the threshold value that you can control by
-   * calling setThreshold(const RealScalar&).
-   */
-  inline bool isInvertible() const { return m_cpqr.isInvertible(); }
-
-  /** \returns the pseudo-inverse of the matrix of which *this is the complete
-   * orthogonal decomposition.
-   * \warning: Do not compute \c this->pseudoInverse()*rhs to solve a linear systems.
-   * It is more efficient and numerically stable to call \c this->solve(rhs).
-   */
-  inline const Inverse<CompleteOrthogonalDecomposition> pseudoInverse() const
-  {
-    eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
-    return Inverse<CompleteOrthogonalDecomposition>(*this);
-  }
-
-  inline Index rows() const { return m_cpqr.rows(); }
-  inline Index cols() const { return m_cpqr.cols(); }
-
-  /** \returns a const reference to the vector of Householder coefficients used
-   * to represent the factor \c Q.
-   *
-   * For advanced uses only.
-   */
-  inline const HCoeffsType& hCoeffs() const { return m_cpqr.hCoeffs(); }
-
-  /** \returns a const reference to the vector of Householder coefficients
-   * used to represent the factor \c Z.
-   *
-   * For advanced uses only.
-   */
-  const HCoeffsType& zCoeffs() const { return m_zCoeffs; }
-
-  /** Allows to prescribe a threshold to be used by certain methods, such as
-   * rank(), who need to determine when pivots are to be considered nonzero.
-   * Most be called before calling compute().
-   *
-   * When it needs to get the threshold value, Eigen calls threshold(). By
-   * default, this uses a formula to automatically determine a reasonable
-   * threshold. Once you have called the present method
-   * setThreshold(const RealScalar&), your value is used instead.
-   *
-   * \param threshold The new value to use as the threshold.
-   *
-   * A pivot will be considered nonzero if its absolute value is strictly
-   * greater than
-   *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-   * where maxpivot is the biggest pivot.
-   *
-   * If you want to come back to the default behavior, call
-   * setThreshold(Default_t)
-   */
-  CompleteOrthogonalDecomposition& setThreshold(const RealScalar& threshold) {
-    m_cpqr.setThreshold(threshold);
-    return *this;
-  }
-
-  /** Allows to come back to the default behavior, letting Eigen use its default
-   * formula for determining the threshold.
-   *
-   * You should pass the special object Eigen::Default as parameter here.
-   * \code qr.setThreshold(Eigen::Default); \endcode
-   *
-   * See the documentation of setThreshold(const RealScalar&).
-   */
-  CompleteOrthogonalDecomposition& setThreshold(Default_t) {
-    m_cpqr.setThreshold(Default);
-    return *this;
-  }
-
-  /** Returns the threshold that will be used by certain methods such as rank().
-   *
-   * See the documentation of setThreshold(const RealScalar&).
-   */
-  RealScalar threshold() const { return m_cpqr.threshold(); }
-
-  /** \returns the number of nonzero pivots in the complete orthogonal
-   * decomposition. Here nonzero is meant in the exact sense, not in a
-   * fuzzy sense. So that notion isn't really intrinsically interesting,
-   * but it is still useful when implementing algorithms.
-   *
-   * \sa rank()
-   */
-  inline Index nonzeroPivots() const { return m_cpqr.nonzeroPivots(); }
-
-  /** \returns the absolute value of the biggest pivot, i.e. the biggest
-   *          diagonal coefficient of R.
-   */
-  inline RealScalar maxPivot() const { return m_cpqr.maxPivot(); }
-
-  /** \brief Reports whether the complete orthogonal decomposition was
-   * successful.
-   *
-   * \note This function always returns \c Success. It is provided for
-   * compatibility
-   * with other factorization routines.
-   * \returns \c Success
-   */
-  ComputationInfo info() const {
-    eigen_assert(m_cpqr.m_isInitialized && "Decomposition is not initialized.");
-    return Success;
-  }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  template <typename RhsType, typename DstType>
-  void _solve_impl(const RhsType& rhs, DstType& dst) const;
-
-  template<bool Conjugate, typename RhsType, typename DstType>
-  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-#endif
-
- protected:
-  static void check_template_parameters() {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-  }
-
-  template<bool Transpose_, typename Rhs>
-  void _check_solve_assertion(const Rhs& b) const {
-      EIGEN_ONLY_USED_FOR_DEBUG(b);
-      eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
-      eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "CompleteOrthogonalDecomposition::solve(): invalid number of rows of the right hand side matrix b");
-  }
-
-  void computeInPlace();
-
-  /** Overwrites \b rhs with \f$ \mathbf{Z} * \mathbf{rhs} \f$ or
-   *  \f$ \mathbf{\overline Z} * \mathbf{rhs} \f$ if \c Conjugate 
-   *  is set to \c true.
-   */
-  template <bool Conjugate, typename Rhs>
-  void applyZOnTheLeftInPlace(Rhs& rhs) const;
-
-  /** Overwrites \b rhs with \f$ \mathbf{Z}^* * \mathbf{rhs} \f$.
-   */
-  template <typename Rhs>
-  void applyZAdjointOnTheLeftInPlace(Rhs& rhs) const;
-
-  ColPivHouseholderQR<MatrixType> m_cpqr;
-  HCoeffsType m_zCoeffs;
-  RowVectorType m_temp;
-};
-
-template <typename MatrixType>
-typename MatrixType::RealScalar
-CompleteOrthogonalDecomposition<MatrixType>::absDeterminant() const {
-  return m_cpqr.absDeterminant();
-}
-
-template <typename MatrixType>
-typename MatrixType::RealScalar
-CompleteOrthogonalDecomposition<MatrixType>::logAbsDeterminant() const {
-  return m_cpqr.logAbsDeterminant();
-}
-
-/** Performs the complete orthogonal decomposition of the given matrix \a
- * matrix. The result of the factorization is stored into \c *this, and a
- * reference to \c *this is returned.
- *
- * \sa class CompleteOrthogonalDecomposition,
- * CompleteOrthogonalDecomposition(const MatrixType&)
- */
-template <typename MatrixType>
-void CompleteOrthogonalDecomposition<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  // the column permutation is stored as int indices, so just to be sure:
-  eigen_assert(m_cpqr.cols() <= NumTraits<int>::highest());
-
-  const Index rank = m_cpqr.rank();
-  const Index cols = m_cpqr.cols();
-  const Index rows = m_cpqr.rows();
-  m_zCoeffs.resize((std::min)(rows, cols));
-  m_temp.resize(cols);
-
-  if (rank < cols) {
-    // We have reduced the (permuted) matrix to the form
-    //   [R11 R12]
-    //   [ 0  R22]
-    // where R11 is r-by-r (r = rank) upper triangular, R12 is
-    // r-by-(n-r), and R22 is empty or the norm of R22 is negligible.
-    // We now compute the complete orthogonal decomposition by applying
-    // Householder transformations from the right to the upper trapezoidal
-    // matrix X = [R11 R12] to zero out R12 and obtain the factorization
-    // [R11 R12] = [T11 0] * Z, where T11 is r-by-r upper triangular and
-    // Z = Z(0) * Z(1) ... Z(r-1) is an n-by-n orthogonal matrix.
-    // We store the data representing Z in R12 and m_zCoeffs.
-    for (Index k = rank - 1; k >= 0; --k) {
-      if (k != rank - 1) {
-        // Given the API for Householder reflectors, it is more convenient if
-        // we swap the leading parts of columns k and r-1 (zero-based) to form
-        // the matrix X_k = [X(0:k, k), X(0:k, r:n)]
-        m_cpqr.m_qr.col(k).head(k + 1).swap(
-            m_cpqr.m_qr.col(rank - 1).head(k + 1));
-      }
-      // Construct Householder reflector Z(k) to zero out the last row of X_k,
-      // i.e. choose Z(k) such that
-      // [X(k, k), X(k, r:n)] * Z(k) = [beta, 0, .., 0].
-      RealScalar beta;
-      m_cpqr.m_qr.row(k)
-          .tail(cols - rank + 1)
-          .makeHouseholderInPlace(m_zCoeffs(k), beta);
-      m_cpqr.m_qr(k, rank - 1) = beta;
-      if (k > 0) {
-        // Apply Z(k) to the first k rows of X_k
-        m_cpqr.m_qr.topRightCorner(k, cols - rank + 1)
-            .applyHouseholderOnTheRight(
-                m_cpqr.m_qr.row(k).tail(cols - rank).adjoint(), m_zCoeffs(k),
-                &m_temp(0));
-      }
-      if (k != rank - 1) {
-        // Swap X(0:k,k) back to its proper location.
-        m_cpqr.m_qr.col(k).head(k + 1).swap(
-            m_cpqr.m_qr.col(rank - 1).head(k + 1));
-      }
-    }
-  }
-}
-
-template <typename MatrixType>
-template <bool Conjugate, typename Rhs>
-void CompleteOrthogonalDecomposition<MatrixType>::applyZOnTheLeftInPlace(
-    Rhs& rhs) const {
-  const Index cols = this->cols();
-  const Index nrhs = rhs.cols();
-  const Index rank = this->rank();
-  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
-  for (Index k = rank-1; k >= 0; --k) {
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-    rhs.middleRows(rank - 1, cols - rank + 1)
-        .applyHouseholderOnTheLeft(
-            matrixQTZ().row(k).tail(cols - rank).transpose().template conjugateIf<!Conjugate>(), zCoeffs().template conjugateIf<Conjugate>()(k),
-            &temp(0));
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-  }
-}
-
-template <typename MatrixType>
-template <typename Rhs>
-void CompleteOrthogonalDecomposition<MatrixType>::applyZAdjointOnTheLeftInPlace(
-    Rhs& rhs) const {
-  const Index cols = this->cols();
-  const Index nrhs = rhs.cols();
-  const Index rank = this->rank();
-  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
-  for (Index k = 0; k < rank; ++k) {
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-    rhs.middleRows(rank - 1, cols - rank + 1)
-        .applyHouseholderOnTheLeft(
-            matrixQTZ().row(k).tail(cols - rank).adjoint(), zCoeffs()(k),
-            &temp(0));
-    if (k != rank - 1) {
-      rhs.row(k).swap(rhs.row(rank - 1));
-    }
-  }
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template <typename _MatrixType>
-template <typename RhsType, typename DstType>
-void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl(
-    const RhsType& rhs, DstType& dst) const {
-  const Index rank = this->rank();
-  if (rank == 0) {
-    dst.setZero();
-    return;
-  }
-
-  // Compute c = Q^* * rhs
-  typename RhsType::PlainObject c(rhs);
-  c.applyOnTheLeft(matrixQ().setLength(rank).adjoint());
-
-  // Solve T z = c(1:rank, :)
-  dst.topRows(rank) = matrixT()
-                          .topLeftCorner(rank, rank)
-                          .template triangularView<Upper>()
-                          .solve(c.topRows(rank));
-
-  const Index cols = this->cols();
-  if (rank < cols) {
-    // Compute y = Z^* * [ z ]
-    //                   [ 0 ]
-    dst.bottomRows(cols - rank).setZero();
-    applyZAdjointOnTheLeftInPlace(dst);
-  }
-
-  // Undo permutation to get x = P^{-1} * y.
-  dst = colsPermutation() * dst;
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index rank = this->rank();
-
-  if (rank == 0) {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(colsPermutation().transpose()*rhs);
-
-  if (rank < cols()) {
-    applyZOnTheLeftInPlace<!Conjugate>(c);
-  }
-
-  matrixT().topLeftCorner(rank, rank)
-           .template triangularView<Upper>()
-           .transpose().template conjugateIf<Conjugate>()
-           .solveInPlace(c.topRows(rank));
-
-  dst.topRows(rank) = c.topRows(rank);
-  dst.bottomRows(rows()-rank).setZero();
-
-  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
-}
-#endif
-
-namespace internal {
-
-template<typename MatrixType>
-struct traits<Inverse<CompleteOrthogonalDecomposition<MatrixType> > >
-  : traits<typename Transpose<typename MatrixType::PlainObject>::PlainObject>
-{
-  enum { Flags = 0 };
-};
-
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<CompleteOrthogonalDecomposition<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename CompleteOrthogonalDecomposition<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef CompleteOrthogonalDecomposition<MatrixType> CodType;
-  typedef Inverse<CodType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename CodType::Scalar> &)
-  {
-    typedef Matrix<typename CodType::Scalar, CodType::RowsAtCompileTime, CodType::RowsAtCompileTime, 0, CodType::MaxRowsAtCompileTime, CodType::MaxRowsAtCompileTime> IdentityMatrixType;
-    dst = src.nestedExpression().solve(IdentityMatrixType::Identity(src.cols(), src.cols()));
-  }
-};
-
-} // end namespace internal
-
-/** \returns the matrix Q as a sequence of householder transformations */
-template <typename MatrixType>
-typename CompleteOrthogonalDecomposition<MatrixType>::HouseholderSequenceType
-CompleteOrthogonalDecomposition<MatrixType>::householderQ() const {
-  return m_cpqr.householderQ();
-}
-
-/** \return the complete orthogonal decomposition of \c *this.
-  *
-  * \sa class CompleteOrthogonalDecomposition
-  */
-template <typename Derived>
-const CompleteOrthogonalDecomposition<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::completeOrthogonalDecomposition() const {
-  return CompleteOrthogonalDecomposition<PlainObject>(eval());
-}
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
deleted file mode 100644
index d0664a1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
+++ /dev/null
@@ -1,713 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
-#define EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename _MatrixType> struct traits<FullPivHouseholderQR<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType;
-
-template<typename MatrixType>
-struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-{
-  typedef typename MatrixType::PlainObject ReturnType;
-};
-
-} // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  * \class FullPivHouseholderQR
-  *
-  * \brief Householder rank-revealing QR decomposition of a matrix with full pivoting
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the QR decomposition
-  *
-  * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b P', \b Q and \b R
-  * such that 
-  * \f[
-  *  \mathbf{P} \, \mathbf{A} \, \mathbf{P}' = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b P and \b P' are permutation matrices, \b Q a unitary matrix 
-  * and \b R an upper triangular matrix.
-  *
-  * This decomposition performs a very prudent full pivoting in order to be rank-revealing and achieve optimal
-  * numerical stability. The trade-off is that it is slower than HouseholderQR and ColPivHouseholderQR.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  * 
-  * \sa MatrixBase::fullPivHouseholderQr()
-  */
-template<typename _MatrixType> class FullPivHouseholderQR
-        : public SolverBase<FullPivHouseholderQR<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<FullPivHouseholderQR> Base;
-    friend class SolverBase<FullPivHouseholderQR>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivHouseholderQR)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType> MatrixQReturnType;
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef Matrix<StorageIndex, 1,
-                   EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime,RowsAtCompileTime), RowMajor, 1,
-                   EIGEN_SIZE_MIN_PREFER_FIXED(MaxColsAtCompileTime,MaxRowsAtCompileTime)> IntDiagSizeVectorType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef typename internal::plain_col_type<MatrixType>::type ColVectorType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via FullPivHouseholderQR::compute(const MatrixType&).
-      */
-    FullPivHouseholderQR()
-      : m_qr(),
-        m_hCoeffs(),
-        m_rows_transpositions(),
-        m_cols_transpositions(),
-        m_cols_permutation(),
-        m_temp(),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa FullPivHouseholderQR()
-      */
-    FullPivHouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_rows_transpositions((std::min)(rows,cols)),
-        m_cols_transpositions((std::min)(rows,cols)),
-        m_cols_permutation(cols),
-        m_temp(cols),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This constructor computes the QR factorization of the matrix \a matrix by calling
-      * the method compute(). It is a short cut for:
-      * 
-      * \code
-      * FullPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      * 
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit FullPivHouseholderQR(const EigenBase<InputType>& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_permutation(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa FullPivHouseholderQR(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit FullPivHouseholderQR(EigenBase<InputType>& matrix)
-      : m_qr(matrix.derived()),
-        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_permutation(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      computeInPlace();
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * \c *this is the QR decomposition.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns the exact or least-square solution if the rank is greater or equal to the number of columns of A,
-      * and an arbitrary solution otherwise.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include FullPivHouseholderQR_solve.cpp
-      * Output: \verbinclude FullPivHouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const Solve<FullPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** \returns Expression object representing the matrix Q
-      */
-    MatrixQReturnType matrixQ(void) const;
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      */
-    const MatrixType& matrixQR() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    template<typename InputType>
-    FullPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns a const reference to the column permutation matrix */
-    const PermutationType& colsPermutation() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_cols_permutation;
-    }
-
-    /** \returns a const reference to the vector of indices representing the rows transpositions */
-    const IntDiagSizeVectorType& rowsTranspositions() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_rows_transpositions;
-    }
-
-    /** \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar absDeterminant() const;
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's inherent
-      * to determinant computation.
-      *
-      * \sa absDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar logAbsDeterminant() const;
-
-    /** \returns the rank of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
-      Index result = 0;
-      for(Index i = 0; i < m_nonzero_pivots; ++i)
-        result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);
-      return result;
-    }
-
-    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index dimensionOfKernel() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return cols() - rank();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents an injective
-      *          linear map, i.e. has trivial kernel; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInjective() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return rank() == cols();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
-      *          linear map; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isSurjective() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return rank() == rows();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInvertible() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return isInjective() && isSurjective();
-    }
-
-    /** \returns the inverse of the matrix of which *this is the QR decomposition.
-      *
-      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
-      *       Use isInvertible() to first determine whether this matrix is invertible.
-      */
-    inline const Inverse<FullPivHouseholderQR> inverse() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return Inverse<FullPivHouseholderQR>(*this);
-    }
-
-    inline Index rows() const { return m_qr.rows(); }
-    inline Index cols() const { return m_qr.cols(); }
-    
-    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
-      * 
-      * For advanced uses only.
-      */
-    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
-
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
-      * who need to determine when pivots are to be considered nonzero. This is not used for the
-      * QR decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
-      * uses a formula to automatically determine a reasonable threshold.
-      * Once you have called the present method setThreshold(const RealScalar&),
-      * your value is used instead.
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A pivot will be considered nonzero if its absolute value is strictly greater than
-      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-      * where maxpivot is the biggest pivot.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    FullPivHouseholderQR& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code qr.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    FullPivHouseholderQR& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
-
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    RealScalar threshold() const
-    {
-      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-      return m_usePrescribedThreshold ? m_prescribedThreshold
-      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
-      // and turns out to be identical to Higham's formula used already in LDLt.
-                                      : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
-    }
-
-    /** \returns the number of nonzero pivots in the QR decomposition.
-      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
-      * So that notion isn't really intrinsically interesting, but it is
-      * still useful when implementing algorithms.
-      *
-      * \sa rank()
-      */
-    inline Index nonzeroPivots() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of U.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    IntDiagSizeVectorType m_rows_transpositions;
-    IntDiagSizeVectorType m_cols_transpositions;
-    PermutationType m_cols_permutation;
-    RowVectorType m_temp;
-    bool m_isInitialized, m_usePrescribedThreshold;
-    RealScalar m_prescribedThreshold, m_maxpivot;
-    Index m_nonzero_pivots;
-    RealScalar m_precision;
-    Index m_det_pq;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return abs(m_qr.diagonal().prod());
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return m_qr.diagonal().cwiseAbs().array().log().sum();
-}
-
-/** Performs the QR factorization of the given matrix \a matrix. The result of
-  * the factorization is stored into \c *this, and a reference to \c *this
-  * is returned.
-  *
-  * \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-template<typename InputType>
-FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
-{
-  m_qr = matrix.derived();
-  computeInPlace();
-  return *this;
-}
-
-template<typename MatrixType>
-void FullPivHouseholderQR<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  using std::abs;
-  Index rows = m_qr.rows();
-  Index cols = m_qr.cols();
-  Index size = (std::min)(rows,cols);
-
-  
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  m_precision = NumTraits<Scalar>::epsilon() * RealScalar(size);
-
-  m_rows_transpositions.resize(size);
-  m_cols_transpositions.resize(size);
-  Index number_of_transpositions = 0;
-
-  RealScalar biggest(0);
-
-  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
-  m_maxpivot = RealScalar(0);
-
-  for (Index k = 0; k < size; ++k)
-  {
-    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
-    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
-    typedef typename Scoring::result_type Score;
-
-    Score score = m_qr.bottomRightCorner(rows-k, cols-k)
-                      .unaryExpr(Scoring())
-                      .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
-    row_of_biggest_in_corner += k;
-    col_of_biggest_in_corner += k;
-    RealScalar biggest_in_corner = internal::abs_knowing_score<Scalar>()(m_qr(row_of_biggest_in_corner, col_of_biggest_in_corner), score);
-    if(k==0) biggest = biggest_in_corner;
-
-    // if the corner is negligible, then we have less than full rank, and we can finish early
-    if(internal::isMuchSmallerThan(biggest_in_corner, biggest, m_precision))
-    {
-      m_nonzero_pivots = k;
-      for(Index i = k; i < size; i++)
-      {
-        m_rows_transpositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-        m_cols_transpositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
-        m_hCoeffs.coeffRef(i) = Scalar(0);
-      }
-      break;
-    }
-
-    m_rows_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
-    m_cols_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
-    if(k != row_of_biggest_in_corner) {
-      m_qr.row(k).tail(cols-k).swap(m_qr.row(row_of_biggest_in_corner).tail(cols-k));
-      ++number_of_transpositions;
-    }
-    if(k != col_of_biggest_in_corner) {
-      m_qr.col(k).swap(m_qr.col(col_of_biggest_in_corner));
-      ++number_of_transpositions;
-    }
-
-    RealScalar beta;
-    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
-    m_qr.coeffRef(k,k) = beta;
-
-    // remember the maximum absolute value of diagonal coefficients
-    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
-
-    m_qr.bottomRightCorner(rows-k, cols-k-1)
-        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1));
-  }
-
-  m_cols_permutation.setIdentity(cols);
-  for(Index k = 0; k < size; ++k)
-    m_cols_permutation.applyTranspositionOnTheRight(k, m_cols_transpositions.coeff(k));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-  m_isInitialized = true;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  const Index l_rank = rank();
-
-  // FIXME introduce nonzeroPivots() and use it here. and more generally,
-  // make the same improvements in this dec as in FullPivLU.
-  if(l_rank==0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs);
-
-  Matrix<typename RhsType::Scalar,1,RhsType::ColsAtCompileTime> temp(rhs.cols());
-  for (Index k = 0; k < l_rank; ++k)
-  {
-    Index remainingSize = rows()-k;
-    c.row(k).swap(c.row(m_rows_transpositions.coeff(k)));
-    c.bottomRightCorner(remainingSize, rhs.cols())
-      .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1),
-                               m_hCoeffs.coeff(k), &temp.coeffRef(0));
-  }
-
-  m_qr.topLeftCorner(l_rank, l_rank)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(l_rank));
-
-  for(Index i = 0; i < l_rank; ++i) dst.row(m_cols_permutation.indices().coeff(i)) = c.row(i);
-  for(Index i = l_rank; i < cols(); ++i) dst.row(m_cols_permutation.indices().coeff(i)).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void FullPivHouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index l_rank = rank();
-
-  if(l_rank == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(m_cols_permutation.transpose()*rhs);
-
-  m_qr.topLeftCorner(l_rank, l_rank)
-         .template triangularView<Upper>()
-         .transpose().template conjugateIf<Conjugate>()
-         .solveInPlace(c.topRows(l_rank));
-
-  dst.topRows(l_rank) = c.topRows(l_rank);
-  dst.bottomRows(rows()-l_rank).setZero();
-
-  Matrix<Scalar, 1, DstType::ColsAtCompileTime> temp(dst.cols());
-  const Index size = (std::min)(rows(), cols());
-  for (Index k = size-1; k >= 0; --k)
-  {
-    Index remainingSize = rows()-k;
-
-    dst.bottomRightCorner(remainingSize, dst.cols())
-       .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1).template conjugateIf<!Conjugate>(),
-                                  m_hCoeffs.template conjugateIf<Conjugate>().coeff(k), &temp.coeffRef(0));
-
-    dst.row(k).swap(dst.row(m_rows_transpositions.coeff(k)));
-  }
-}
-#endif
-
-namespace internal {
-  
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<FullPivHouseholderQR<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivHouseholderQR<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef FullPivHouseholderQR<MatrixType> QrType;
-  typedef Inverse<QrType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename QrType::Scalar> &)
-  {    
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-
-/** \ingroup QR_Module
-  *
-  * \brief Expression type for return value of FullPivHouseholderQR::matrixQ()
-  *
-  * \tparam MatrixType type of underlying dense matrix
-  */
-template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType
-  : public ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-{
-public:
-  typedef typename FullPivHouseholderQR<MatrixType>::IntDiagSizeVectorType IntDiagSizeVectorType;
-  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-  typedef Matrix<typename MatrixType::Scalar, 1, MatrixType::RowsAtCompileTime, RowMajor, 1,
-                 MatrixType::MaxRowsAtCompileTime> WorkVectorType;
-
-  FullPivHouseholderQRMatrixQReturnType(const MatrixType&       qr,
-                                        const HCoeffsType&      hCoeffs,
-                                        const IntDiagSizeVectorType& rowsTranspositions)
-    : m_qr(qr),
-      m_hCoeffs(hCoeffs),
-      m_rowsTranspositions(rowsTranspositions)
-  {}
-
-  template <typename ResultType>
-  void evalTo(ResultType& result) const
-  {
-    const Index rows = m_qr.rows();
-    WorkVectorType workspace(rows);
-    evalTo(result, workspace);
-  }
-
-  template <typename ResultType>
-  void evalTo(ResultType& result, WorkVectorType& workspace) const
-  {
-    using numext::conj;
-    // compute the product H'_0 H'_1 ... H'_n-1,
-    // where H_k is the k-th Householder transformation I - h_k v_k v_k'
-    // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
-    const Index rows = m_qr.rows();
-    const Index cols = m_qr.cols();
-    const Index size = (std::min)(rows, cols);
-    workspace.resize(rows);
-    result.setIdentity(rows, rows);
-    for (Index k = size-1; k >= 0; k--)
-    {
-      result.block(k, k, rows-k, rows-k)
-            .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), conj(m_hCoeffs.coeff(k)), &workspace.coeffRef(k));
-      result.row(k).swap(result.row(m_rowsTranspositions.coeff(k)));
-    }
-  }
-
-  Index rows() const { return m_qr.rows(); }
-  Index cols() const { return m_qr.rows(); }
-
-protected:
-  typename MatrixType::Nested m_qr;
-  typename HCoeffsType::Nested m_hCoeffs;
-  typename IntDiagSizeVectorType::Nested m_rowsTranspositions;
-};
-
-// template<typename MatrixType>
-// struct evaluator<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-//  : public evaluator<ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> > >
-// {};
-
-} // end namespace internal
-
-template<typename MatrixType>
-inline typename FullPivHouseholderQR<MatrixType>::MatrixQReturnType FullPivHouseholderQR<MatrixType>::matrixQ() const
-{
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  return MatrixQReturnType(m_qr, m_hCoeffs, m_rows_transpositions);
-}
-
-/** \return the full-pivoting Householder QR decomposition of \c *this.
-  *
-  * \sa class FullPivHouseholderQR
-  */
-template<typename Derived>
-const FullPivHouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::fullPivHouseholderQr() const
-{
-  return FullPivHouseholderQR<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/HouseholderQR.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/HouseholderQR.h
deleted file mode 100644
index 801739f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/HouseholderQR.h
+++ /dev/null
@@ -1,434 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2010 Vincent Lejeune
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_QR_H
-#define EIGEN_QR_H
-
-namespace Eigen { 
-
-namespace internal {
-template<typename _MatrixType> struct traits<HouseholderQR<_MatrixType> >
- : traits<_MatrixType>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-
-} // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  *
-  * \class HouseholderQR
-  *
-  * \brief Householder QR decomposition of a matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the QR decomposition
-  *
-  * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R
-  * such that 
-  * \f[
-  *  \mathbf{A} = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b Q a unitary matrix and \b R an upper triangular matrix.
-  * The result is stored in a compact way compatible with LAPACK.
-  *
-  * Note that no pivoting is performed. This is \b not a rank-revealing decomposition.
-  * If you want that feature, use FullPivHouseholderQR or ColPivHouseholderQR instead.
-  *
-  * This Householder QR decomposition is faster, but less numerically stable and less feature-full than
-  * FullPivHouseholderQR or ColPivHouseholderQR.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  *
-  * \sa MatrixBase::householderQr()
-  */
-template<typename _MatrixType> class HouseholderQR
-        : public SolverBase<HouseholderQR<_MatrixType> >
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef SolverBase<HouseholderQR> Base;
-    friend class SolverBase<HouseholderQR>;
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(HouseholderQR)
-    enum {
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags&RowMajorBit) ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType;
-
-    /**
-      * \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via HouseholderQR::compute(const MatrixType&).
-      */
-    HouseholderQR() : m_qr(), m_hCoeffs(), m_temp(), m_isInitialized(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa HouseholderQR()
-      */
-    HouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_temp(cols),
-        m_isInitialized(false) {}
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This constructor computes the QR factorization of the matrix \a matrix by calling
-      * the method compute(). It is a short cut for:
-      * 
-      * \code
-      * HouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      * 
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit HouseholderQR(const EigenBase<InputType>& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_temp(matrix.cols()),
-        m_isInitialized(false)
-    {
-      compute(matrix.derived());
-    }
-
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
-      * \c MatrixType is a Eigen::Ref.
-      *
-      * \sa HouseholderQR(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit HouseholderQR(EigenBase<InputType>& matrix)
-      : m_qr(matrix.derived()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_temp(matrix.cols()),
-        m_isInitialized(false)
-    {
-      computeInPlace();
-    }
-
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the QR decomposition, if any exists.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns a solution.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include HouseholderQR_solve.cpp
-      * Output: \verbinclude HouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const Solve<HouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const;
-    #endif
-
-    /** This method returns an expression of the unitary matrix Q as a sequence of Householder transformations.
-      *
-      * The returned expression can directly be used to perform matrix products. It can also be assigned to a dense Matrix object.
-      * Here is an example showing how to recover the full or thin matrix Q, as well as how to perform matrix products using operator*:
-      *
-      * Example: \include HouseholderQR_householderQ.cpp
-      * Output: \verbinclude HouseholderQR_householderQ.out
-      */
-    HouseholderSequenceType householderQ() const
-    {
-      eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-      return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
-    }
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      * in a LAPACK-compatible way.
-      */
-    const MatrixType& matrixQR() const
-    {
-        eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-        return m_qr;
-    }
-
-    template<typename InputType>
-    HouseholderQR& compute(const EigenBase<InputType>& matrix) {
-      m_qr = matrix.derived();
-      computeInPlace();
-      return *this;
-    }
-
-    /** \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar absDeterminant() const;
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's inherent
-      * to determinant computation.
-      *
-      * \sa absDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar logAbsDeterminant() const;
-
-    inline Index rows() const { return m_qr.rows(); }
-    inline Index cols() const { return m_qr.cols(); }
-
-    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
-      * 
-      * For advanced uses only.
-      */
-    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-    template<bool Conjugate, typename RhsType, typename DstType>
-    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    RowVectorType m_temp;
-    bool m_isInitialized;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar HouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return abs(m_qr.diagonal().prod());
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar HouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return m_qr.diagonal().cwiseAbs().array().log().sum();
-}
-
-namespace internal {
-
-/** \internal */
-template<typename MatrixQR, typename HCoeffs>
-void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename MatrixQR::Scalar* tempData = 0)
-{
-  typedef typename MatrixQR::Scalar Scalar;
-  typedef typename MatrixQR::RealScalar RealScalar;
-  Index rows = mat.rows();
-  Index cols = mat.cols();
-  Index size = (std::min)(rows,cols);
-
-  eigen_assert(hCoeffs.size() == size);
-
-  typedef Matrix<Scalar,MatrixQR::ColsAtCompileTime,1> TempType;
-  TempType tempVector;
-  if(tempData==0)
-  {
-    tempVector.resize(cols);
-    tempData = tempVector.data();
-  }
-
-  for(Index k = 0; k < size; ++k)
-  {
-    Index remainingRows = rows - k;
-    Index remainingCols = cols - k - 1;
-
-    RealScalar beta;
-    mat.col(k).tail(remainingRows).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta);
-    mat.coeffRef(k,k) = beta;
-
-    // apply H to remaining part of m_qr from the left
-    mat.bottomRightCorner(remainingRows, remainingCols)
-        .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), hCoeffs.coeffRef(k), tempData+k+1);
-  }
-}
-
-/** \internal */
-template<typename MatrixQR, typename HCoeffs,
-  typename MatrixQRScalar = typename MatrixQR::Scalar,
-  bool InnerStrideIsOne = (MatrixQR::InnerStrideAtCompileTime == 1 && HCoeffs::InnerStrideAtCompileTime == 1)>
-struct householder_qr_inplace_blocked
-{
-  // This is specialized for LAPACK-supported Scalar types in HouseholderQR_LAPACKE.h
-  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index maxBlockSize=32,
-      typename MatrixQR::Scalar* tempData = 0)
-  {
-    typedef typename MatrixQR::Scalar Scalar;
-    typedef Block<MatrixQR,Dynamic,Dynamic> BlockType;
-
-    Index rows = mat.rows();
-    Index cols = mat.cols();
-    Index size = (std::min)(rows, cols);
-
-    typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixQR::MaxColsAtCompileTime,1> TempType;
-    TempType tempVector;
-    if(tempData==0)
-    {
-      tempVector.resize(cols);
-      tempData = tempVector.data();
-    }
-
-    Index blockSize = (std::min)(maxBlockSize,size);
-
-    Index k = 0;
-    for (k = 0; k < size; k += blockSize)
-    {
-      Index bs = (std::min)(size-k,blockSize);  // actual size of the block
-      Index tcols = cols - k - bs;              // trailing columns
-      Index brows = rows-k;                     // rows of the block
-
-      // partition the matrix:
-      //        A00 | A01 | A02
-      // mat  = A10 | A11 | A12
-      //        A20 | A21 | A22
-      // and performs the qr dec of [A11^T A12^T]^T
-      // and update [A21^T A22^T]^T using level 3 operations.
-      // Finally, the algorithm continue on A22
-
-      BlockType A11_21 = mat.block(k,k,brows,bs);
-      Block<HCoeffs,Dynamic,1> hCoeffsSegment = hCoeffs.segment(k,bs);
-
-      householder_qr_inplace_unblocked(A11_21, hCoeffsSegment, tempData);
-
-      if(tcols)
-      {
-        BlockType A21_22 = mat.block(k,k+bs,brows,tcols);
-        apply_block_householder_on_the_left(A21_22,A11_21,hCoeffsSegment, false); // false == backward
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void HouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  const Index rank = (std::min)(rows(), cols());
-
-  typename RhsType::PlainObject c(rhs);
-
-  c.applyOnTheLeft(householderQ().setLength(rank).adjoint() );
-
-  m_qr.topLeftCorner(rank, rank)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(rank));
-
-  dst.topRows(rank) = c.topRows(rank);
-  dst.bottomRows(cols()-rank).setZero();
-}
-
-template<typename _MatrixType>
-template<bool Conjugate, typename RhsType, typename DstType>
-void HouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  const Index rank = (std::min)(rows(), cols());
-
-  typename RhsType::PlainObject c(rhs);
-
-  m_qr.topLeftCorner(rank, rank)
-      .template triangularView<Upper>()
-      .transpose().template conjugateIf<Conjugate>()
-      .solveInPlace(c.topRows(rank));
-
-  dst.topRows(rank) = c.topRows(rank);
-  dst.bottomRows(rows()-rank).setZero();
-
-  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
-}
-#endif
-
-/** Performs the QR factorization of the given matrix \a matrix. The result of
-  * the factorization is stored into \c *this, and a reference to \c *this
-  * is returned.
-  *
-  * \sa class HouseholderQR, HouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-void HouseholderQR<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-  
-  Index rows = m_qr.rows();
-  Index cols = m_qr.cols();
-  Index size = (std::min)(rows,cols);
-
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  internal::householder_qr_inplace_blocked<MatrixType, HCoeffsType>::run(m_qr, m_hCoeffs, 48, m_temp.data());
-
-  m_isInitialized = true;
-}
-
-/** \return the Householder QR decomposition of \c *this.
-  *
-  * \sa class HouseholderQR
-  */
-template<typename Derived>
-const HouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::householderQr() const
-{
-  return HouseholderQR<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_QR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h
deleted file mode 100644
index 1dc7d53..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Householder QR decomposition of a matrix w/o pivoting based on
- *    LAPACKE_?geqrf function.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_QR_LAPACKE_H
-#define EIGEN_QR_LAPACKE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_QR_NOPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX) \
-template<typename MatrixQR, typename HCoeffs> \
-struct householder_qr_inplace_blocked<MatrixQR, HCoeffs, EIGTYPE, true> \
-{ \
-  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index = 32, \
-      typename MatrixQR::Scalar* = 0) \
-  { \
-    lapack_int m = (lapack_int) mat.rows(); \
-    lapack_int n = (lapack_int) mat.cols(); \
-    lapack_int lda = (lapack_int) mat.outerStride(); \
-    lapack_int matrix_order = (MatrixQR::IsRowMajor) ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
-    LAPACKE_##LAPACKE_PREFIX##geqrf( matrix_order, m, n, (LAPACKE_TYPE*)mat.data(), lda, (LAPACKE_TYPE*)hCoeffs.data()); \
-    hCoeffs.adjointInPlace(); \
-  } \
-};
-
-EIGEN_LAPACKE_QR_NOPIV(double, double, d)
-EIGEN_LAPACKE_QR_NOPIV(float, float, s)
-EIGEN_LAPACKE_QR_NOPIV(dcomplex, lapack_complex_double, z)
-EIGEN_LAPACKE_QR_NOPIV(scomplex, lapack_complex_float, c)
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_QR_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
deleted file mode 100644
index 013c7ae..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
+++ /dev/null
@@ -1,335 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SUITESPARSEQRSUPPORT_H
-#define EIGEN_SUITESPARSEQRSUPPORT_H
-
-namespace Eigen {
-  
-  template<typename MatrixType> class SPQR; 
-  template<typename SPQRType> struct SPQRMatrixQReturnType; 
-  template<typename SPQRType> struct SPQRMatrixQTransposeReturnType; 
-  template <typename SPQRType, typename Derived> struct SPQR_QProduct;
-  namespace internal {
-    template <typename SPQRType> struct traits<SPQRMatrixQReturnType<SPQRType> >
-    {
-      typedef typename SPQRType::MatrixType ReturnType;
-    };
-    template <typename SPQRType> struct traits<SPQRMatrixQTransposeReturnType<SPQRType> >
-    {
-      typedef typename SPQRType::MatrixType ReturnType;
-    };
-    template <typename SPQRType, typename Derived> struct traits<SPQR_QProduct<SPQRType, Derived> >
-    {
-      typedef typename Derived::PlainObject ReturnType;
-    };
-  } // End namespace internal
-  
-/**
-  * \ingroup SPQRSupport_Module
-  * \class SPQR
-  * \brief Sparse QR factorization based on SuiteSparseQR library
-  *
-  * This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition
-  * of sparse matrices. The result is then used to solve linear leasts_square systems.
-  * Clearly, a QR factorization is returned such that A*P = Q*R where :
-  *
-  * P is the column permutation. Use colsPermutation() to get it.
-  *
-  * Q is the orthogonal matrix represented as Householder reflectors.
-  * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose.
-  * You can then apply it to a vector.
-  *
-  * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix.
-  * NOTE : The Index type of R is always SuiteSparse_long. You can get it with SPQR::Index
-  *
-  * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  *
-  */
-template<typename _MatrixType>
-class SPQR : public SparseSolverBase<SPQR<_MatrixType> >
-{
-  protected:
-    typedef SparseSolverBase<SPQR<_MatrixType> > Base;
-    using Base::m_isInitialized;
-  public:
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef SuiteSparse_long StorageIndex ;
-    typedef SparseMatrix<Scalar, ColMajor, StorageIndex> MatrixType;
-    typedef Map<PermutationMatrix<Dynamic, Dynamic, StorageIndex> > PermutationType;
-    enum {
-      ColsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic
-    };
-  public:
-    SPQR() 
-      : m_analysisIsOk(false),
-        m_factorizationIsOk(false),
-        m_isRUpToDate(false),
-        m_ordering(SPQR_ORDERING_DEFAULT),
-        m_allow_tol(SPQR_DEFAULT_TOL),
-        m_tolerance (NumTraits<Scalar>::epsilon()),
-        m_cR(0),
-        m_E(0),
-        m_H(0),
-        m_HPinv(0),
-        m_HTau(0),
-        m_useDefaultThreshold(true)
-    { 
-      cholmod_l_start(&m_cc);
-    }
-    
-    explicit SPQR(const _MatrixType& matrix)
-      : m_analysisIsOk(false),
-        m_factorizationIsOk(false),
-        m_isRUpToDate(false),
-        m_ordering(SPQR_ORDERING_DEFAULT),
-        m_allow_tol(SPQR_DEFAULT_TOL),
-        m_tolerance (NumTraits<Scalar>::epsilon()),
-        m_cR(0),
-        m_E(0),
-        m_H(0),
-        m_HPinv(0),
-        m_HTau(0),
-        m_useDefaultThreshold(true)
-    {
-      cholmod_l_start(&m_cc);
-      compute(matrix);
-    }
-    
-    ~SPQR()
-    {
-      SPQR_free();
-      cholmod_l_finish(&m_cc);
-    }
-    void SPQR_free()
-    {
-      cholmod_l_free_sparse(&m_H, &m_cc);
-      cholmod_l_free_sparse(&m_cR, &m_cc);
-      cholmod_l_free_dense(&m_HTau, &m_cc);
-      std::free(m_E);
-      std::free(m_HPinv);
-    }
-
-    void compute(const _MatrixType& matrix)
-    {
-      if(m_isInitialized) SPQR_free();
-
-      MatrixType mat(matrix);
-      
-      /* Compute the default threshold as in MatLab, see:
-       * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
-       * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3 
-       */
-      RealScalar pivotThreshold = m_tolerance;
-      if(m_useDefaultThreshold) 
-      {
-        RealScalar max2Norm = 0.0;
-        for (int j = 0; j < mat.cols(); j++) max2Norm = numext::maxi(max2Norm, mat.col(j).norm());
-        if(max2Norm==RealScalar(0))
-          max2Norm = RealScalar(1);
-        pivotThreshold = 20 * (mat.rows() + mat.cols()) * max2Norm * NumTraits<RealScalar>::epsilon();
-      }
-      cholmod_sparse A; 
-      A = viewAsCholmod(mat);
-      m_rows = matrix.rows();
-      Index col = matrix.cols();
-      m_rank = SuiteSparseQR<Scalar>(m_ordering, pivotThreshold, col, &A, 
-                             &m_cR, &m_E, &m_H, &m_HPinv, &m_HTau, &m_cc);
-
-      if (!m_cR)
-      {
-        m_info = NumericalIssue;
-        m_isInitialized = false;
-        return;
-      }
-      m_info = Success;
-      m_isInitialized = true;
-      m_isRUpToDate = false;
-    }
-    /** 
-     * Get the number of rows of the input matrix and the Q matrix
-     */
-    inline Index rows() const {return m_rows; }
-    
-    /** 
-     * Get the number of columns of the input matrix. 
-     */
-    inline Index cols() const { return m_cR->ncol; }
-    
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
-      eigen_assert(b.cols()==1 && "This method is for vectors only");
-
-      //Compute Q^T * b
-      typename Dest::PlainObject y, y2;
-      y = matrixQ().transpose() * b;
-      
-      // Solves with the triangular matrix R
-      Index rk = this->rank();
-      y2 = y;
-      y.resize((std::max)(cols(),Index(y.rows())),y.cols());
-      y.topRows(rk) = this->matrixR().topLeftCorner(rk, rk).template triangularView<Upper>().solve(y2.topRows(rk));
-
-      // Apply the column permutation 
-      // colsPermutation() performs a copy of the permutation,
-      // so let's apply it manually:
-      for(Index i = 0; i < rk; ++i) dest.row(m_E[i]) = y.row(i);
-      for(Index i = rk; i < cols(); ++i) dest.row(m_E[i]).setZero();
-      
-//       y.bottomRows(y.rows()-rk).setZero();
-//       dest = colsPermutation() * y.topRows(cols());
-      
-      m_info = Success;
-    }
-    
-    /** \returns the sparse triangular factor R. It is a sparse matrix
-     */
-    const MatrixType matrixR() const
-    {
-      eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
-      if(!m_isRUpToDate) {
-        m_R = viewAsEigen<Scalar,ColMajor, typename MatrixType::StorageIndex>(*m_cR);
-        m_isRUpToDate = true;
-      }
-      return m_R;
-    }
-    /// Get an expression of the matrix Q
-    SPQRMatrixQReturnType<SPQR> matrixQ() const
-    {
-      return SPQRMatrixQReturnType<SPQR>(*this);
-    }
-    /// Get the permutation that was applied to columns of A
-    PermutationType colsPermutation() const
-    { 
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return PermutationType(m_E, m_cR->ncol);
-    }
-    /**
-     * Gets the rank of the matrix. 
-     * It should be equal to matrixQR().cols if the matrix is full-rank
-     */
-    Index rank() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_cc.SPQR_istat[4];
-    }
-    /// Set the fill-reducing ordering method to be used
-    void setSPQROrdering(int ord) { m_ordering = ord;}
-    /// Set the tolerance tol to treat columns with 2-norm < =tol as zero
-    void setPivotThreshold(const RealScalar& tol)
-    {
-      m_useDefaultThreshold = false;
-      m_tolerance = tol;
-    }
-    
-    /** \returns a pointer to the SPQR workspace */
-    cholmod_common *cholmodCommon() const { return &m_cc; }
-    
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the sparse QR can not be computed
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-  protected:
-    bool m_analysisIsOk;
-    bool m_factorizationIsOk;
-    mutable bool m_isRUpToDate;
-    mutable ComputationInfo m_info;
-    int m_ordering; // Ordering method to use, see SPQR's manual
-    int m_allow_tol; // Allow to use some tolerance during numerical factorization.
-    RealScalar m_tolerance; // treat columns with 2-norm below this tolerance as zero
-    mutable cholmod_sparse *m_cR; // The sparse R factor in cholmod format
-    mutable MatrixType m_R; // The sparse matrix R in Eigen format
-    mutable StorageIndex *m_E; // The permutation applied to columns
-    mutable cholmod_sparse *m_H;  //The householder vectors
-    mutable StorageIndex *m_HPinv; // The row permutation of H
-    mutable cholmod_dense *m_HTau; // The Householder coefficients
-    mutable Index m_rank; // The rank of the matrix
-    mutable cholmod_common m_cc; // Workspace and parameters
-    bool m_useDefaultThreshold;     // Use default threshold
-    Index m_rows;
-    template<typename ,typename > friend struct SPQR_QProduct;
-};
-
-template <typename SPQRType, typename Derived>
-struct SPQR_QProduct : ReturnByValue<SPQR_QProduct<SPQRType,Derived> >
-{
-  typedef typename SPQRType::Scalar Scalar;
-  typedef typename SPQRType::StorageIndex StorageIndex;
-  //Define the constructor to get reference to argument types
-  SPQR_QProduct(const SPQRType& spqr, const Derived& other, bool transpose) : m_spqr(spqr),m_other(other),m_transpose(transpose) {}
-  
-  inline Index rows() const { return m_transpose ? m_spqr.rows() : m_spqr.cols(); }
-  inline Index cols() const { return m_other.cols(); }
-  // Assign to a vector
-  template<typename ResType>
-  void evalTo(ResType& res) const
-  {
-    cholmod_dense y_cd;
-    cholmod_dense *x_cd; 
-    int method = m_transpose ? SPQR_QTX : SPQR_QX; 
-    cholmod_common *cc = m_spqr.cholmodCommon();
-    y_cd = viewAsCholmod(m_other.const_cast_derived());
-    x_cd = SuiteSparseQR_qmult<Scalar>(method, m_spqr.m_H, m_spqr.m_HTau, m_spqr.m_HPinv, &y_cd, cc);
-    res = Matrix<Scalar,ResType::RowsAtCompileTime,ResType::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x), x_cd->nrow, x_cd->ncol);
-    cholmod_l_free_dense(&x_cd, cc);
-  }
-  const SPQRType& m_spqr; 
-  const Derived& m_other; 
-  bool m_transpose; 
-  
-};
-template<typename SPQRType>
-struct SPQRMatrixQReturnType{
-  
-  SPQRMatrixQReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
-  template<typename Derived>
-  SPQR_QProduct<SPQRType, Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(),false);
-  }
-  SPQRMatrixQTransposeReturnType<SPQRType> adjoint() const
-  {
-    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
-  }
-  // To use for operations with the transpose of Q
-  SPQRMatrixQTransposeReturnType<SPQRType> transpose() const
-  {
-    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
-  }
-  const SPQRType& m_spqr;
-};
-
-template<typename SPQRType>
-struct SPQRMatrixQTransposeReturnType{
-  SPQRMatrixQTransposeReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
-  template<typename Derived>
-  SPQR_QProduct<SPQRType,Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(), true);
-  }
-  const SPQRType& m_spqr;
-};
-
-}// End namespace Eigen
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/BDCSVD.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/BDCSVD.h
deleted file mode 100644
index 17f8e44..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/BDCSVD.h
+++ /dev/null
@@ -1,1366 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-// 
-// We used the "A Divide-And-Conquer Algorithm for the Bidiagonal SVD"
-// research report written by Ming Gu and Stanley C.Eisenstat
-// The code variable names correspond to the names they used in their 
-// report
-//
-// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
-// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
-// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
-// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
-// Copyright (C) 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
-// Copyright (C) 2014-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BDCSVD_H
-#define EIGEN_BDCSVD_H
-// #define EIGEN_BDCSVD_DEBUG_VERBOSE
-// #define EIGEN_BDCSVD_SANITY_CHECKS
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-#undef eigen_internal_assert
-#define eigen_internal_assert(X) assert(X);
-#endif
-
-namespace Eigen {
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-IOFormat bdcsvdfmt(8, 0, ", ", "\n", "  [", "]");
-#endif
-  
-template<typename _MatrixType> class BDCSVD;
-
-namespace internal {
-
-template<typename _MatrixType> 
-struct traits<BDCSVD<_MatrixType> >
-        : traits<_MatrixType>
-{
-  typedef _MatrixType MatrixType;
-};  
-
-} // end namespace internal
-  
-  
-/** \ingroup SVD_Module
- *
- *
- * \class BDCSVD
- *
- * \brief class Bidiagonal Divide and Conquer SVD
- *
- * \tparam _MatrixType the type of the matrix of which we are computing the SVD decomposition
- *
- * This class first reduces the input matrix to bi-diagonal form using class UpperBidiagonalization,
- * and then performs a divide-and-conquer diagonalization. Small blocks are diagonalized using class JacobiSVD.
- * You can control the switching size with the setSwitchSize() method, default is 16.
- * For small matrice (<16), it is thus preferable to directly use JacobiSVD. For larger ones, BDCSVD is highly
- * recommended and can several order of magnitude faster.
- *
- * \warning this algorithm is unlikely to provide accurate result when compiled with unsafe math optimizations.
- * For instance, this concerns Intel's compiler (ICC), which performs such optimization by default unless
- * you compile with the \c -fp-model \c precise option. Likewise, the \c -ffast-math option of GCC or clang will
- * significantly degrade the accuracy.
- *
- * \sa class JacobiSVD
- */
-template<typename _MatrixType> 
-class BDCSVD : public SVDBase<BDCSVD<_MatrixType> >
-{
-  typedef SVDBase<BDCSVD> Base;
-    
-public:
-  using Base::rows;
-  using Base::cols;
-  using Base::computeU;
-  using Base::computeV;
-  
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-  typedef typename NumTraits<RealScalar>::Literal Literal;
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime, 
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime, 
-    DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime), 
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, 
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, 
-    MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime, MaxColsAtCompileTime), 
-    MatrixOptions = MatrixType::Options
-  };
-
-  typedef typename Base::MatrixUType MatrixUType;
-  typedef typename Base::MatrixVType MatrixVType;
-  typedef typename Base::SingularValuesType SingularValuesType;
-  
-  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> MatrixX;
-  typedef Matrix<RealScalar, Dynamic, Dynamic, ColMajor> MatrixXr;
-  typedef Matrix<RealScalar, Dynamic, 1> VectorType;
-  typedef Array<RealScalar, Dynamic, 1> ArrayXr;
-  typedef Array<Index,1,Dynamic> ArrayXi;
-  typedef Ref<ArrayXr> ArrayRef;
-  typedef Ref<ArrayXi> IndicesRef;
-
-  /** \brief Default Constructor.
-   *
-   * The default constructor is useful in cases in which the user intends to
-   * perform decompositions via BDCSVD::compute(const MatrixType&).
-   */
-  BDCSVD() : m_algoswap(16), m_isTranspose(false), m_compU(false), m_compV(false), m_numIters(0)
-  {}
-
-
-  /** \brief Default Constructor with memory preallocation
-   *
-   * Like the default constructor but with preallocation of the internal data
-   * according to the specified problem size.
-   * \sa BDCSVD()
-   */
-  BDCSVD(Index rows, Index cols, unsigned int computationOptions = 0)
-    : m_algoswap(16), m_numIters(0)
-  {
-    allocate(rows, cols, computationOptions);
-  }
-
-  /** \brief Constructor performing the decomposition of given matrix.
-   *
-   * \param matrix the matrix to decompose
-   * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-   *                           By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, 
-   *                           #ComputeFullV, #ComputeThinV.
-   *
-   * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-   * available with the (non - default) FullPivHouseholderQR preconditioner.
-   */
-  BDCSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
-    : m_algoswap(16), m_numIters(0)
-  {
-    compute(matrix, computationOptions);
-  }
-
-  ~BDCSVD() 
-  {
-  }
-  
-  /** \brief Method performing the decomposition of given matrix using custom options.
-   *
-   * \param matrix the matrix to decompose
-   * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-   *                           By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, 
-   *                           #ComputeFullV, #ComputeThinV.
-   *
-   * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-   * available with the (non - default) FullPivHouseholderQR preconditioner.
-   */
-  BDCSVD& compute(const MatrixType& matrix, unsigned int computationOptions);
-
-  /** \brief Method performing the decomposition of given matrix using current options.
-   *
-   * \param matrix the matrix to decompose
-   *
-   * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
-   */
-  BDCSVD& compute(const MatrixType& matrix)
-  {
-    return compute(matrix, this->m_computationOptions);
-  }
-
-  void setSwitchSize(int s) 
-  {
-    eigen_assert(s>3 && "BDCSVD the size of the algo switch has to be greater than 3");
-    m_algoswap = s;
-  }
- 
-private:
-  void allocate(Index rows, Index cols, unsigned int computationOptions);
-  void divide(Index firstCol, Index lastCol, Index firstRowW, Index firstColW, Index shift);
-  void computeSVDofM(Index firstCol, Index n, MatrixXr& U, VectorType& singVals, MatrixXr& V);
-  void computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, VectorType& singVals, ArrayRef shifts, ArrayRef mus);
-  void perturbCol0(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus, ArrayRef zhat);
-  void computeSingVecs(const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus, MatrixXr& U, MatrixXr& V);
-  void deflation43(Index firstCol, Index shift, Index i, Index size);
-  void deflation44(Index firstColu , Index firstColm, Index firstRowW, Index firstColW, Index i, Index j, Index size);
-  void deflation(Index firstCol, Index lastCol, Index k, Index firstRowW, Index firstColW, Index shift);
-  template<typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
-  void copyUV(const HouseholderU &householderU, const HouseholderV &householderV, const NaiveU &naiveU, const NaiveV &naivev);
-  void structured_update(Block<MatrixXr,Dynamic,Dynamic> A, const MatrixXr &B, Index n1);
-  static RealScalar secularEq(RealScalar x, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const ArrayRef& diagShifted, RealScalar shift);
-
-protected:
-  MatrixXr m_naiveU, m_naiveV;
-  MatrixXr m_computed;
-  Index m_nRec;
-  ArrayXr m_workspace;
-  ArrayXi m_workspaceI;
-  int m_algoswap;
-  bool m_isTranspose, m_compU, m_compV;
-  
-  using Base::m_singularValues;
-  using Base::m_diagSize;
-  using Base::m_computeFullU;
-  using Base::m_computeFullV;
-  using Base::m_computeThinU;
-  using Base::m_computeThinV;
-  using Base::m_matrixU;
-  using Base::m_matrixV;
-  using Base::m_info;
-  using Base::m_isInitialized;
-  using Base::m_nonzeroSingularValues;
-
-public:  
-  int m_numIters;
-}; //end class BDCSVD
-
-
-// Method to allocate and initialize matrix and attributes
-template<typename MatrixType>
-void BDCSVD<MatrixType>::allocate(Eigen::Index rows, Eigen::Index cols, unsigned int computationOptions)
-{
-  m_isTranspose = (cols > rows);
-
-  if (Base::allocate(rows, cols, computationOptions))
-    return;
-  
-  m_computed = MatrixXr::Zero(m_diagSize + 1, m_diagSize );
-  m_compU = computeV();
-  m_compV = computeU();
-  if (m_isTranspose)
-    std::swap(m_compU, m_compV);
-  
-  if (m_compU) m_naiveU = MatrixXr::Zero(m_diagSize + 1, m_diagSize + 1 );
-  else         m_naiveU = MatrixXr::Zero(2, m_diagSize + 1 );
-  
-  if (m_compV) m_naiveV = MatrixXr::Zero(m_diagSize, m_diagSize);
-  
-  m_workspace.resize((m_diagSize+1)*(m_diagSize+1)*3);
-  m_workspaceI.resize(3*m_diagSize);
-}// end allocate
-
-template<typename MatrixType>
-BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions) 
-{
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "\n\n\n======================================================================================================================\n\n\n";
-#endif
-  allocate(matrix.rows(), matrix.cols(), computationOptions);
-  using std::abs;
-
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  
-  //**** step -1 - If the problem is too small, directly falls back to JacobiSVD and return
-  if(matrix.cols() < m_algoswap)
-  {
-    // FIXME this line involves temporaries
-    JacobiSVD<MatrixType> jsvd(matrix,computationOptions);
-    m_isInitialized = true;
-    m_info = jsvd.info();
-    if (m_info == Success || m_info == NoConvergence) {
-      if(computeU()) m_matrixU = jsvd.matrixU();
-      if(computeV()) m_matrixV = jsvd.matrixV();
-      m_singularValues = jsvd.singularValues();
-      m_nonzeroSingularValues = jsvd.nonzeroSingularValues();
-    }
-    return *this;
-  }
-  
-  //**** step 0 - Copy the input matrix and apply scaling to reduce over/under-flows
-  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
-  if (!(numext::isfinite)(scale)) {
-    m_isInitialized = true;
-    m_info = InvalidInput;
-    return *this;
-  }
-
-  if(scale==Literal(0)) scale = Literal(1);
-  MatrixX copy;
-  if (m_isTranspose) copy = matrix.adjoint()/scale;
-  else               copy = matrix/scale;
-  
-  //**** step 1 - Bidiagonalization
-  // FIXME this line involves temporaries
-  internal::UpperBidiagonalization<MatrixX> bid(copy);
-
-  //**** step 2 - Divide & Conquer
-  m_naiveU.setZero();
-  m_naiveV.setZero();
-  // FIXME this line involves a temporary matrix
-  m_computed.topRows(m_diagSize) = bid.bidiagonal().toDenseMatrix().transpose();
-  m_computed.template bottomRows<1>().setZero();
-  divide(0, m_diagSize - 1, 0, 0, 0);
-  if (m_info != Success && m_info != NoConvergence) {
-    m_isInitialized = true;
-    return *this;
-  }
-    
-  //**** step 3 - Copy singular values and vectors
-  for (int i=0; i<m_diagSize; i++)
-  {
-    RealScalar a = abs(m_computed.coeff(i, i));
-    m_singularValues.coeffRef(i) = a * scale;
-    if (a<considerZero)
-    {
-      m_nonzeroSingularValues = i;
-      m_singularValues.tail(m_diagSize - i - 1).setZero();
-      break;
-    }
-    else if (i == m_diagSize - 1)
-    {
-      m_nonzeroSingularValues = i + 1;
-      break;
-    }
-  }
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-//   std::cout << "m_naiveU\n" << m_naiveU << "\n\n";
-//   std::cout << "m_naiveV\n" << m_naiveV << "\n\n";
-#endif
-  if(m_isTranspose) copyUV(bid.householderV(), bid.householderU(), m_naiveV, m_naiveU);
-  else              copyUV(bid.householderU(), bid.householderV(), m_naiveU, m_naiveV);
-
-  m_isInitialized = true;
-  return *this;
-}// end compute
-
-
-template<typename MatrixType>
-template<typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
-void BDCSVD<MatrixType>::copyUV(const HouseholderU &householderU, const HouseholderV &householderV, const NaiveU &naiveU, const NaiveV &naiveV)
-{
-  // Note exchange of U and V: m_matrixU is set from m_naiveV and vice versa
-  if (computeU())
-  {
-    Index Ucols = m_computeThinU ? m_diagSize : householderU.cols();
-    m_matrixU = MatrixX::Identity(householderU.cols(), Ucols);
-    m_matrixU.topLeftCorner(m_diagSize, m_diagSize) = naiveV.template cast<Scalar>().topLeftCorner(m_diagSize, m_diagSize);
-    householderU.applyThisOnTheLeft(m_matrixU); // FIXME this line involves a temporary buffer
-  }
-  if (computeV())
-  {
-    Index Vcols = m_computeThinV ? m_diagSize : householderV.cols();
-    m_matrixV = MatrixX::Identity(householderV.cols(), Vcols);
-    m_matrixV.topLeftCorner(m_diagSize, m_diagSize) = naiveU.template cast<Scalar>().topLeftCorner(m_diagSize, m_diagSize);
-    householderV.applyThisOnTheLeft(m_matrixV); // FIXME this line involves a temporary buffer
-  }
-}
-
-/** \internal
-  * Performs A = A * B exploiting the special structure of the matrix A. Splitting A as:
-  *  A = [A1]
-  *      [A2]
-  * such that A1.rows()==n1, then we assume that at least half of the columns of A1 and A2 are zeros.
-  * We can thus pack them prior to the the matrix product. However, this is only worth the effort if the matrix is large
-  * enough.
-  */
-template<typename MatrixType>
-void BDCSVD<MatrixType>::structured_update(Block<MatrixXr,Dynamic,Dynamic> A, const MatrixXr &B, Index n1)
-{
-  Index n = A.rows();
-  if(n>100)
-  {
-    // If the matrices are large enough, let's exploit the sparse structure of A by
-    // splitting it in half (wrt n1), and packing the non-zero columns.
-    Index n2 = n - n1;
-    Map<MatrixXr> A1(m_workspace.data()      , n1, n);
-    Map<MatrixXr> A2(m_workspace.data()+ n1*n, n2, n);
-    Map<MatrixXr> B1(m_workspace.data()+  n*n, n,  n);
-    Map<MatrixXr> B2(m_workspace.data()+2*n*n, n,  n);
-    Index k1=0, k2=0;
-    for(Index j=0; j<n; ++j)
-    {
-      if( (A.col(j).head(n1).array()!=Literal(0)).any() )
-      {
-        A1.col(k1) = A.col(j).head(n1);
-        B1.row(k1) = B.row(j);
-        ++k1;
-      }
-      if( (A.col(j).tail(n2).array()!=Literal(0)).any() )
-      {
-        A2.col(k2) = A.col(j).tail(n2);
-        B2.row(k2) = B.row(j);
-        ++k2;
-      }
-    }
-  
-    A.topRows(n1).noalias()    = A1.leftCols(k1) * B1.topRows(k1);
-    A.bottomRows(n2).noalias() = A2.leftCols(k2) * B2.topRows(k2);
-  }
-  else
-  {
-    Map<MatrixXr,Aligned> tmp(m_workspace.data(),n,n);
-    tmp.noalias() = A*B;
-    A = tmp;
-  }
-}
-
-// The divide algorithm is done "in place", we are always working on subsets of the same matrix. The divide methods takes as argument the 
-// place of the submatrix we are currently working on.
-
-//@param firstCol : The Index of the first column of the submatrix of m_computed and for m_naiveU;
-//@param lastCol : The Index of the last column of the submatrix of m_computed and for m_naiveU; 
-// lastCol + 1 - firstCol is the size of the submatrix.
-//@param firstRowW : The Index of the first row of the matrix W that we are to change. (see the reference paper section 1 for more information on W)
-//@param firstRowW : Same as firstRowW with the column.
-//@param shift : Each time one takes the left submatrix, one must add 1 to the shift. Why? Because! We actually want the last column of the U submatrix 
-// to become the first column (*coeff) and to shift all the other columns to the right. There are more details on the reference paper.
-template<typename MatrixType>
-void BDCSVD<MatrixType>::divide(Eigen::Index firstCol, Eigen::Index lastCol, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index shift)
-{
-  // requires rows = cols + 1;
-  using std::pow;
-  using std::sqrt;
-  using std::abs;
-  const Index n = lastCol - firstCol + 1;
-  const Index k = n/2;
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  RealScalar alphaK;
-  RealScalar betaK; 
-  RealScalar r0; 
-  RealScalar lambda, phi, c0, s0;
-  VectorType l, f;
-  // We use the other algorithm which is more efficient for small 
-  // matrices.
-  if (n < m_algoswap)
-  {
-    // FIXME this line involves temporaries
-    JacobiSVD<MatrixXr> b(m_computed.block(firstCol, firstCol, n + 1, n), ComputeFullU | (m_compV ? ComputeFullV : 0));
-    m_info = b.info();
-    if (m_info != Success && m_info != NoConvergence) return;
-    if (m_compU)
-      m_naiveU.block(firstCol, firstCol, n + 1, n + 1).real() = b.matrixU();
-    else 
-    {
-      m_naiveU.row(0).segment(firstCol, n + 1).real() = b.matrixU().row(0);
-      m_naiveU.row(1).segment(firstCol, n + 1).real() = b.matrixU().row(n);
-    }
-    if (m_compV) m_naiveV.block(firstRowW, firstColW, n, n).real() = b.matrixV();
-    m_computed.block(firstCol + shift, firstCol + shift, n + 1, n).setZero();
-    m_computed.diagonal().segment(firstCol + shift, n) = b.singularValues().head(n);
-    return;
-  }
-  // We use the divide and conquer algorithm
-  alphaK =  m_computed(firstCol + k, firstCol + k);
-  betaK = m_computed(firstCol + k + 1, firstCol + k);
-  // The divide must be done in that order in order to have good results. Divide change the data inside the submatrices
-  // and the divide of the right submatrice reads one column of the left submatrice. That's why we need to treat the 
-  // right submatrix before the left one. 
-  divide(k + 1 + firstCol, lastCol, k + 1 + firstRowW, k + 1 + firstColW, shift);
-  if (m_info != Success && m_info != NoConvergence) return;
-  divide(firstCol, k - 1 + firstCol, firstRowW, firstColW + 1, shift + 1);
-  if (m_info != Success && m_info != NoConvergence) return;
-
-  if (m_compU)
-  {
-    lambda = m_naiveU(firstCol + k, firstCol + k);
-    phi = m_naiveU(firstCol + k + 1, lastCol + 1);
-  } 
-  else 
-  {
-    lambda = m_naiveU(1, firstCol + k);
-    phi = m_naiveU(0, lastCol + 1);
-  }
-  r0 = sqrt((abs(alphaK * lambda) * abs(alphaK * lambda)) + abs(betaK * phi) * abs(betaK * phi));
-  if (m_compU)
-  {
-    l = m_naiveU.row(firstCol + k).segment(firstCol, k);
-    f = m_naiveU.row(firstCol + k + 1).segment(firstCol + k + 1, n - k - 1);
-  } 
-  else 
-  {
-    l = m_naiveU.row(1).segment(firstCol, k);
-    f = m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1);
-  }
-  if (m_compV) m_naiveV(firstRowW+k, firstColW) = Literal(1);
-  if (r0<considerZero)
-  {
-    c0 = Literal(1);
-    s0 = Literal(0);
-  }
-  else
-  {
-    c0 = alphaK * lambda / r0;
-    s0 = betaK * phi / r0;
-  }
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-  
-  if (m_compU)
-  {
-    MatrixXr q1 (m_naiveU.col(firstCol + k).segment(firstCol, k + 1));     
-    // we shiftW Q1 to the right
-    for (Index i = firstCol + k - 1; i >= firstCol; i--) 
-      m_naiveU.col(i + 1).segment(firstCol, k + 1) = m_naiveU.col(i).segment(firstCol, k + 1);
-    // we shift q1 at the left with a factor c0
-    m_naiveU.col(firstCol).segment( firstCol, k + 1) = (q1 * c0);
-    // last column = q1 * - s0
-    m_naiveU.col(lastCol + 1).segment(firstCol, k + 1) = (q1 * ( - s0));
-    // first column = q2 * s0
-    m_naiveU.col(firstCol).segment(firstCol + k + 1, n - k) = m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) * s0; 
-    // q2 *= c0
-    m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) *= c0;
-  } 
-  else 
-  {
-    RealScalar q1 = m_naiveU(0, firstCol + k);
-    // we shift Q1 to the right
-    for (Index i = firstCol + k - 1; i >= firstCol; i--) 
-      m_naiveU(0, i + 1) = m_naiveU(0, i);
-    // we shift q1 at the left with a factor c0
-    m_naiveU(0, firstCol) = (q1 * c0);
-    // last column = q1 * - s0
-    m_naiveU(0, lastCol + 1) = (q1 * ( - s0));
-    // first column = q2 * s0
-    m_naiveU(1, firstCol) = m_naiveU(1, lastCol + 1) *s0; 
-    // q2 *= c0
-    m_naiveU(1, lastCol + 1) *= c0;
-    m_naiveU.row(1).segment(firstCol + 1, k).setZero();
-    m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1).setZero();
-  }
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-  
-  m_computed(firstCol + shift, firstCol + shift) = r0;
-  m_computed.col(firstCol + shift).segment(firstCol + shift + 1, k) = alphaK * l.transpose().real();
-  m_computed.col(firstCol + shift).segment(firstCol + shift + k + 1, n - k - 1) = betaK * f.transpose().real();
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  ArrayXr tmp1 = (m_computed.block(firstCol+shift, firstCol+shift, n, n)).jacobiSvd().singularValues();
-#endif
-  // Second part: try to deflate singular values in combined matrix
-  deflation(firstCol, lastCol, k, firstRowW, firstColW, shift);
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  ArrayXr tmp2 = (m_computed.block(firstCol+shift, firstCol+shift, n, n)).jacobiSvd().singularValues();
-  std::cout << "\n\nj1 = " << tmp1.transpose().format(bdcsvdfmt) << "\n";
-  std::cout << "j2 = " << tmp2.transpose().format(bdcsvdfmt) << "\n\n";
-  std::cout << "err:      " << ((tmp1-tmp2).abs()>1e-12*tmp2.abs()).transpose() << "\n";
-  static int count = 0;
-  std::cout << "# " << ++count << "\n\n";
-  assert((tmp1-tmp2).matrix().norm() < 1e-14*tmp2.matrix().norm());
-//   assert(count<681);
-//   assert(((tmp1-tmp2).abs()<1e-13*tmp2.abs()).all());
-#endif
-  
-  // Third part: compute SVD of combined matrix
-  MatrixXr UofSVD, VofSVD;
-  VectorType singVals;
-  computeSVDofM(firstCol + shift, n, UofSVD, singVals, VofSVD);
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(UofSVD.allFinite());
-  assert(VofSVD.allFinite());
-#endif
-  
-  if (m_compU)
-    structured_update(m_naiveU.block(firstCol, firstCol, n + 1, n + 1), UofSVD, (n+2)/2);
-  else
-  {
-    Map<Matrix<RealScalar,2,Dynamic>,Aligned> tmp(m_workspace.data(),2,n+1);
-    tmp.noalias() = m_naiveU.middleCols(firstCol, n+1) * UofSVD;
-    m_naiveU.middleCols(firstCol, n + 1) = tmp;
-  }
-  
-  if (m_compV)  structured_update(m_naiveV.block(firstRowW, firstColW, n, n), VofSVD, (n+1)/2);
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-  
-  m_computed.block(firstCol + shift, firstCol + shift, n, n).setZero();
-  m_computed.block(firstCol + shift, firstCol + shift, n, n).diagonal() = singVals;
-}// end divide
-
-// Compute SVD of m_computed.block(firstCol, firstCol, n + 1, n); this block only has non-zeros in
-// the first column and on the diagonal and has undergone deflation, so diagonal is in increasing
-// order except for possibly the (0,0) entry. The computed SVD is stored U, singVals and V, except
-// that if m_compV is false, then V is not computed. Singular values are sorted in decreasing order.
-//
-// TODO Opportunities for optimization: better root finding algo, better stopping criterion, better
-// handling of round-off errors, be consistent in ordering
-// For instance, to solve the secular equation using FMM, see http://www.stat.uchicago.edu/~lekheng/courses/302/classics/greengard-rokhlin.pdf
-template <typename MatrixType>
-void BDCSVD<MatrixType>::computeSVDofM(Eigen::Index firstCol, Eigen::Index n, MatrixXr& U, VectorType& singVals, MatrixXr& V)
-{
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  using std::abs;
-  ArrayRef col0 = m_computed.col(firstCol).segment(firstCol, n);
-  m_workspace.head(n) =  m_computed.block(firstCol, firstCol, n, n).diagonal();
-  ArrayRef diag = m_workspace.head(n);
-  diag(0) = Literal(0);
-
-  // Allocate space for singular values and vectors
-  singVals.resize(n);
-  U.resize(n+1, n+1);
-  if (m_compV) V.resize(n, n);
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  if (col0.hasNaN() || diag.hasNaN())
-    std::cout << "\n\nHAS NAN\n\n";
-#endif
-  
-  // Many singular values might have been deflated, the zero ones have been moved to the end,
-  // but others are interleaved and we must ignore them at this stage.
-  // To this end, let's compute a permutation skipping them:
-  Index actual_n = n;
-  while(actual_n>1 && diag(actual_n-1)==Literal(0)) {--actual_n; eigen_internal_assert(col0(actual_n)==Literal(0)); }
-  Index m = 0; // size of the deflated problem
-  for(Index k=0;k<actual_n;++k)
-    if(abs(col0(k))>considerZero)
-      m_workspaceI(m++) = k;
-  Map<ArrayXi> perm(m_workspaceI.data(),m);
-  
-  Map<ArrayXr> shifts(m_workspace.data()+1*n, n);
-  Map<ArrayXr> mus(m_workspace.data()+2*n, n);
-  Map<ArrayXr> zhat(m_workspace.data()+3*n, n);
-
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "computeSVDofM using:\n";
-  std::cout << "  z: " << col0.transpose() << "\n";
-  std::cout << "  d: " << diag.transpose() << "\n";
-#endif
-  
-  // Compute singVals, shifts, and mus
-  computeSingVals(col0, diag, perm, singVals, shifts, mus);
-  
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "  j:        " << (m_computed.block(firstCol, firstCol, n, n)).jacobiSvd().singularValues().transpose().reverse() << "\n\n";
-  std::cout << "  sing-val: " << singVals.transpose() << "\n";
-  std::cout << "  mu:       " << mus.transpose() << "\n";
-  std::cout << "  shift:    " << shifts.transpose() << "\n";
-  
-  {
-    std::cout << "\n\n    mus:    " << mus.head(actual_n).transpose() << "\n\n";
-    std::cout << "    check1 (expect0) : " << ((singVals.array()-(shifts+mus)) / singVals.array()).head(actual_n).transpose() << "\n\n";
-    assert((((singVals.array()-(shifts+mus)) / singVals.array()).head(actual_n) >= 0).all());
-    std::cout << "    check2 (>0)      : " << ((singVals.array()-diag) / singVals.array()).head(actual_n).transpose() << "\n\n";
-    assert((((singVals.array()-diag) / singVals.array()).head(actual_n) >= 0).all());
-  }
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(singVals.allFinite());
-  assert(mus.allFinite());
-  assert(shifts.allFinite());
-#endif
-  
-  // Compute zhat
-  perturbCol0(col0, diag, perm, singVals, shifts, mus, zhat);
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "  zhat: " << zhat.transpose() << "\n";
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(zhat.allFinite());
-#endif
-  
-  computeSingVecs(zhat, diag, perm, singVals, shifts, mus, U, V);
-  
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "U^T U: " << (U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(),U.cols()))).norm() << "\n";
-  std::cout << "V^T V: " << (V.transpose() * V - MatrixXr(MatrixXr::Identity(V.cols(),V.cols()))).norm() << "\n";
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-  assert(U.allFinite());
-  assert(V.allFinite());
-//   assert((U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(),U.cols()))).norm() < 100*NumTraits<RealScalar>::epsilon() * n);
-//   assert((V.transpose() * V - MatrixXr(MatrixXr::Identity(V.cols(),V.cols()))).norm() < 100*NumTraits<RealScalar>::epsilon() * n);
-#endif
-  
-  // Because of deflation, the singular values might not be completely sorted.
-  // Fortunately, reordering them is a O(n) problem
-  for(Index i=0; i<actual_n-1; ++i)
-  {
-    if(singVals(i)>singVals(i+1))
-    {
-      using std::swap;
-      swap(singVals(i),singVals(i+1));
-      U.col(i).swap(U.col(i+1));
-      if(m_compV) V.col(i).swap(V.col(i+1));
-    }
-  }
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  {
-    bool singular_values_sorted = (((singVals.segment(1,actual_n-1)-singVals.head(actual_n-1))).array() >= 0).all();
-    if(!singular_values_sorted)
-      std::cout << "Singular values are not sorted: " << singVals.segment(1,actual_n).transpose() << "\n";
-    assert(singular_values_sorted);
-  }
-#endif
-  
-  // Reverse order so that singular values in increased order
-  // Because of deflation, the zeros singular-values are already at the end
-  singVals.head(actual_n).reverseInPlace();
-  U.leftCols(actual_n).rowwise().reverseInPlace();
-  if (m_compV) V.leftCols(actual_n).rowwise().reverseInPlace();
-  
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  JacobiSVD<MatrixXr> jsvd(m_computed.block(firstCol, firstCol, n, n) );
-  std::cout << "  * j:        " << jsvd.singularValues().transpose() << "\n\n";
-  std::cout << "  * sing-val: " << singVals.transpose() << "\n";
-//   std::cout << "  * err:      " << ((jsvd.singularValues()-singVals)>1e-13*singVals.norm()).transpose() << "\n";
-#endif
-}
-
-template <typename MatrixType>
-typename BDCSVD<MatrixType>::RealScalar BDCSVD<MatrixType>::secularEq(RealScalar mu, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const ArrayRef& diagShifted, RealScalar shift)
-{
-  Index m = perm.size();
-  RealScalar res = Literal(1);
-  for(Index i=0; i<m; ++i)
-  {
-    Index j = perm(i);
-    // The following expression could be rewritten to involve only a single division,
-    // but this would make the expression more sensitive to overflow.
-    res += (col0(j) / (diagShifted(j) - mu)) * (col0(j) / (diag(j) + shift + mu));
-  }
-  return res;
-
-}
-
-template <typename MatrixType>
-void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm,
-                                         VectorType& singVals, ArrayRef shifts, ArrayRef mus)
-{
-  using std::abs;
-  using std::swap;
-  using std::sqrt;
-
-  Index n = col0.size();
-  Index actual_n = n;
-  // Note that here actual_n is computed based on col0(i)==0 instead of diag(i)==0 as above
-  // because 1) we have diag(i)==0 => col0(i)==0 and 2) if col0(i)==0, then diag(i) is already a singular value.
-  while(actual_n>1 && col0(actual_n-1)==Literal(0)) --actual_n;
-
-  for (Index k = 0; k < n; ++k)
-  {
-    if (col0(k) == Literal(0) || actual_n==1)
-    {
-      // if col0(k) == 0, then entry is deflated, so singular value is on diagonal
-      // if actual_n==1, then the deflated problem is already diagonalized
-      singVals(k) = k==0 ? col0(0) : diag(k);
-      mus(k) = Literal(0);
-      shifts(k) = k==0 ? col0(0) : diag(k);
-      continue;
-    } 
-
-    // otherwise, use secular equation to find singular value
-    RealScalar left = diag(k);
-    RealScalar right; // was: = (k != actual_n-1) ? diag(k+1) : (diag(actual_n-1) + col0.matrix().norm());
-    if(k==actual_n-1)
-      right = (diag(actual_n-1) + col0.matrix().norm());
-    else
-    {
-      // Skip deflated singular values,
-      // recall that at this stage we assume that z[j]!=0 and all entries for which z[j]==0 have been put aside.
-      // This should be equivalent to using perm[]
-      Index l = k+1;
-      while(col0(l)==Literal(0)) { ++l; eigen_internal_assert(l<actual_n); }
-      right = diag(l);
-    }
-
-    // first decide whether it's closer to the left end or the right end
-    RealScalar mid = left + (right-left) / Literal(2);
-    RealScalar fMid = secularEq(mid, col0, diag, perm, diag, Literal(0));
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-    std::cout << "right-left = " << right-left << "\n";
-//     std::cout << "fMid = " << fMid << " " << secularEq(mid-left, col0, diag, perm, ArrayXr(diag-left), left)
-//                            << " " << secularEq(mid-right, col0, diag, perm, ArrayXr(diag-right), right)   << "\n";
-    std::cout << "     = " << secularEq(left+RealScalar(0.000001)*(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.1)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.2)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.3)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.4)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.49)    *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.5)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.51)    *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.6)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.7)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.8)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.9)     *(right-left), col0, diag, perm, diag, 0)
-              << " "       << secularEq(left+RealScalar(0.999999)*(right-left), col0, diag, perm, diag, 0) << "\n";
-#endif
-    RealScalar shift = (k == actual_n-1 || fMid > Literal(0)) ? left : right;
-    
-    // measure everything relative to shift
-    Map<ArrayXr> diagShifted(m_workspace.data()+4*n, n);
-    diagShifted = diag - shift;
-
-    if(k!=actual_n-1)
-    {
-      // check that after the shift, f(mid) is still negative:
-      RealScalar midShifted = (right - left) / RealScalar(2);
-      if(shift==right)
-        midShifted = -midShifted;
-      RealScalar fMidShifted = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
-      if(fMidShifted>0)
-      {
-        // fMid was erroneous, fix it:
-        shift =  fMidShifted > Literal(0) ? left : right;
-        diagShifted = diag - shift;
-      }
-    }
-    
-    // initial guess
-    RealScalar muPrev, muCur;
-    if (shift == left)
-    {
-      muPrev = (right - left) * RealScalar(0.1);
-      if (k == actual_n-1) muCur = right - left;
-      else                 muCur = (right - left) * RealScalar(0.5);
-    }
-    else
-    {
-      muPrev = -(right - left) * RealScalar(0.1);
-      muCur = -(right - left) * RealScalar(0.5);
-    }
-
-    RealScalar fPrev = secularEq(muPrev, col0, diag, perm, diagShifted, shift);
-    RealScalar fCur = secularEq(muCur, col0, diag, perm, diagShifted, shift);
-    if (abs(fPrev) < abs(fCur))
-    {
-      swap(fPrev, fCur);
-      swap(muPrev, muCur);
-    }
-
-    // rational interpolation: fit a function of the form a / mu + b through the two previous
-    // iterates and use its zero to compute the next iterate
-    bool useBisection = fPrev*fCur>Literal(0);
-    while (fCur!=Literal(0) && abs(muCur - muPrev) > Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(muCur), abs(muPrev)) && abs(fCur - fPrev)>NumTraits<RealScalar>::epsilon() && !useBisection)
-    {
-      ++m_numIters;
-
-      // Find a and b such that the function f(mu) = a / mu + b matches the current and previous samples.
-      RealScalar a = (fCur - fPrev) / (Literal(1)/muCur - Literal(1)/muPrev);
-      RealScalar b = fCur - a / muCur;
-      // And find mu such that f(mu)==0:
-      RealScalar muZero = -a/b;
-      RealScalar fZero = secularEq(muZero, col0, diag, perm, diagShifted, shift);
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      assert((numext::isfinite)(fZero));
-#endif
-      
-      muPrev = muCur;
-      fPrev = fCur;
-      muCur = muZero;
-      fCur = fZero;
-      
-      if (shift == left  && (muCur < Literal(0) || muCur > right - left)) useBisection = true;
-      if (shift == right && (muCur < -(right - left) || muCur > Literal(0))) useBisection = true;
-      if (abs(fCur)>abs(fPrev)) useBisection = true;
-    }
-
-    // fall back on bisection method if rational interpolation did not work
-    if (useBisection)
-    {
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "useBisection for k = " << k << ", actual_n = " << actual_n << "\n";
-#endif
-      RealScalar leftShifted, rightShifted;
-      if (shift == left)
-      {
-        // to avoid overflow, we must have mu > max(real_min, |z(k)|/sqrt(real_max)),
-        // the factor 2 is to be more conservative
-        leftShifted = numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), Literal(2) * abs(col0(k)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
-
-        // check that we did it right:
-        eigen_internal_assert( (numext::isfinite)( (col0(k)/leftShifted)*(col0(k)/(diag(k)+shift+leftShifted)) ) );
-        // I don't understand why the case k==0 would be special there:
-        // if (k == 0) rightShifted = right - left; else
-        rightShifted = (k==actual_n-1) ? right : ((right - left) * RealScalar(0.51)); // theoretically we can take 0.5, but let's be safe
-      }
-      else
-      {
-        leftShifted = -(right - left) * RealScalar(0.51);
-        if(k+1<n)
-          rightShifted = -numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), abs(col0(k+1)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
-        else
-          rightShifted = -(std::numeric_limits<RealScalar>::min)();
-      }
-
-      RealScalar fLeft = secularEq(leftShifted, col0, diag, perm, diagShifted, shift);
-      eigen_internal_assert(fLeft<Literal(0));
-
-#if defined EIGEN_INTERNAL_DEBUGGING || defined EIGEN_BDCSVD_SANITY_CHECKS
-      RealScalar fRight = secularEq(rightShifted, col0, diag, perm, diagShifted, shift);
-#endif
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      if(!(numext::isfinite)(fLeft))
-        std::cout << "f(" << leftShifted << ") =" << fLeft << " ; " << left << " " << shift << " " << right << "\n";
-      assert((numext::isfinite)(fLeft));
-
-      if(!(numext::isfinite)(fRight))
-        std::cout << "f(" << rightShifted << ") =" << fRight << " ; " << left << " " << shift << " " << right << "\n";
-      // assert((numext::isfinite)(fRight));
-#endif
-    
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      if(!(fLeft * fRight<0))
-      {
-        std::cout << "f(leftShifted) using  leftShifted=" << leftShifted << " ;  diagShifted(1:10):" << diagShifted.head(10).transpose()  << "\n ; "
-                  << "left==shift=" << bool(left==shift) << " ; left-shift = " << (left-shift) << "\n";
-        std::cout << "k=" << k << ", " <<  fLeft << " * " << fRight << " == " << fLeft * fRight << "  ;  "
-                  << "[" << left << " .. " << right << "] -> [" << leftShifted << " " << rightShifted << "], shift=" << shift
-                  << " ,  f(right)=" << secularEq(0,     col0, diag, perm, diagShifted, shift)
-                           << " == " << secularEq(right, col0, diag, perm, diag, 0) << " == " << fRight << "\n";
-      }
-#endif
-      eigen_internal_assert(fLeft * fRight < Literal(0));
-
-      if(fLeft<Literal(0))
-      {
-        while (rightShifted - leftShifted > Literal(2) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(leftShifted), abs(rightShifted)))
-        {
-          RealScalar midShifted = (leftShifted + rightShifted) / Literal(2);
-          fMid = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
-          eigen_internal_assert((numext::isfinite)(fMid));
-
-          if (fLeft * fMid < Literal(0))
-          {
-            rightShifted = midShifted;
-          }
-          else
-          {
-            leftShifted = midShifted;
-            fLeft = fMid;
-          }
-        }
-        muCur = (leftShifted + rightShifted) / Literal(2);
-      }
-      else 
-      {
-        // We have a problem as shifting on the left or right give either a positive or negative value
-        // at the middle of [left,right]...
-        // Instead fo abbording or entering an infinite loop,
-        // let's just use the middle as the estimated zero-crossing:
-        muCur = (right - left) * RealScalar(0.5);
-        if(shift == right)
-          muCur = -muCur;
-      }
-    }
-      
-    singVals[k] = shift + muCur;
-    shifts[k] = shift;
-    mus[k] = muCur;
-
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-    if(k+1<n)
-      std::cout << "found " << singVals[k] << " == " << shift << " + " << muCur << " from " << diag(k) << " .. "  << diag(k+1) << "\n";
-#endif
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-    assert(k==0 || singVals[k]>=singVals[k-1]);
-    assert(singVals[k]>=diag(k));
-#endif
-
-    // perturb singular value slightly if it equals diagonal entry to avoid division by zero later
-    // (deflation is supposed to avoid this from happening)
-    // - this does no seem to be necessary anymore -
-//     if (singVals[k] == left) singVals[k] *= 1 + NumTraits<RealScalar>::epsilon();
-//     if (singVals[k] == right) singVals[k] *= 1 - NumTraits<RealScalar>::epsilon();
-  }
-}
-
-
-// zhat is perturbation of col0 for which singular vectors can be computed stably (see Section 3.1)
-template <typename MatrixType>
-void BDCSVD<MatrixType>::perturbCol0
-   (const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const VectorType& singVals,
-    const ArrayRef& shifts, const ArrayRef& mus, ArrayRef zhat)
-{
-  using std::sqrt;
-  Index n = col0.size();
-  Index m = perm.size();
-  if(m==0)
-  {
-    zhat.setZero();
-    return;
-  }
-  Index lastIdx = perm(m-1);
-  // The offset permits to skip deflated entries while computing zhat
-  for (Index k = 0; k < n; ++k)
-  {
-    if (col0(k) == Literal(0)) // deflated
-      zhat(k) = Literal(0);
-    else
-    {
-      // see equation (3.6)
-      RealScalar dk = diag(k);
-      RealScalar prod = (singVals(lastIdx) + dk) * (mus(lastIdx) + (shifts(lastIdx) - dk));
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      if(prod<0) {
-        std::cout << "k = " << k << " ;  z(k)=" << col0(k) << ", diag(k)=" << dk << "\n";
-        std::cout << "prod = " << "(" << singVals(lastIdx) << " + " << dk << ") * (" << mus(lastIdx) << " + (" << shifts(lastIdx) << " - " << dk << "))" << "\n";
-        std::cout << "     = " << singVals(lastIdx) + dk << " * " << mus(lastIdx) + (shifts(lastIdx) - dk) <<  "\n";
-      }
-      assert(prod>=0);
-#endif
-
-      for(Index l = 0; l<m; ++l)
-      {
-        Index i = perm(l);
-        if(i!=k)
-        {
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-          if(i>=k && (l==0 || l-1>=m))
-          {
-            std::cout << "Error in perturbCol0\n";
-            std::cout << "  " << k << "/" << n << " "  << l << "/" << m << " " << i << "/" << n << " ; " << col0(k) << " " << diag(k) << " "  <<  "\n";
-            std::cout << "  " <<diag(i) << "\n";
-            Index j = (i<k /*|| l==0*/) ? i : perm(l-1);
-            std::cout << "  " << "j=" << j << "\n";
-          }
-#endif
-          Index j = i<k ? i : perm(l-1);
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-          if(!(dk!=Literal(0) || diag(i)!=Literal(0)))
-          {
-            std::cout << "k=" << k << ", i=" << i << ", l=" << l << ", perm.size()=" << perm.size() << "\n";
-          }
-          assert(dk!=Literal(0) || diag(i)!=Literal(0));
-#endif
-          prod *= ((singVals(j)+dk) / ((diag(i)+dk))) * ((mus(j)+(shifts(j)-dk)) / ((diag(i)-dk)));
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-          assert(prod>=0);
-#endif
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-          if(i!=k && numext::abs(((singVals(j)+dk)*(mus(j)+(shifts(j)-dk)))/((diag(i)+dk)*(diag(i)-dk)) - 1) > 0.9 )
-            std::cout << "     " << ((singVals(j)+dk)*(mus(j)+(shifts(j)-dk)))/((diag(i)+dk)*(diag(i)-dk)) << " == (" << (singVals(j)+dk) << " * " << (mus(j)+(shifts(j)-dk))
-                       << ") / (" << (diag(i)+dk) << " * " << (diag(i)-dk) << ")\n";
-#endif
-        }
-      }
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "zhat(" << k << ") =  sqrt( " << prod << ")  ;  " << (singVals(lastIdx) + dk) << " * " << mus(lastIdx) + shifts(lastIdx) << " - " << dk << "\n";
-#endif
-      RealScalar tmp = sqrt(prod);
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-      assert((numext::isfinite)(tmp));
-#endif
-      zhat(k) = col0(k) > Literal(0) ? RealScalar(tmp) : RealScalar(-tmp);
-    }
-  }
-}
-
-// compute singular vectors
-template <typename MatrixType>
-void BDCSVD<MatrixType>::computeSingVecs
-   (const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef &perm, const VectorType& singVals,
-    const ArrayRef& shifts, const ArrayRef& mus, MatrixXr& U, MatrixXr& V)
-{
-  Index n = zhat.size();
-  Index m = perm.size();
-  
-  for (Index k = 0; k < n; ++k)
-  {
-    if (zhat(k) == Literal(0))
-    {
-      U.col(k) = VectorType::Unit(n+1, k);
-      if (m_compV) V.col(k) = VectorType::Unit(n, k);
-    }
-    else
-    {
-      U.col(k).setZero();
-      for(Index l=0;l<m;++l)
-      {
-        Index i = perm(l);
-        U(i,k) = zhat(i)/(((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
-      }
-      U(n,k) = Literal(0);
-      U.col(k).normalize();
-    
-      if (m_compV)
-      {
-        V.col(k).setZero();
-        for(Index l=1;l<m;++l)
-        {
-          Index i = perm(l);
-          V(i,k) = diag(i) * zhat(i) / (((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
-        }
-        V(0,k) = Literal(-1);
-        V.col(k).normalize();
-      }
-    }
-  }
-  U.col(n) = VectorType::Unit(n+1, n);
-}
-
-
-// page 12_13
-// i >= 1, di almost null and zi non null.
-// We use a rotation to zero out zi applied to the left of M
-template <typename MatrixType>
-void BDCSVD<MatrixType>::deflation43(Eigen::Index firstCol, Eigen::Index shift, Eigen::Index i, Eigen::Index size)
-{
-  using std::abs;
-  using std::sqrt;
-  using std::pow;
-  Index start = firstCol + shift;
-  RealScalar c = m_computed(start, start);
-  RealScalar s = m_computed(start+i, start);
-  RealScalar r = numext::hypot(c,s);
-  if (r == Literal(0))
-  {
-    m_computed(start+i, start+i) = Literal(0);
-    return;
-  }
-  m_computed(start,start) = r;  
-  m_computed(start+i, start) = Literal(0);
-  m_computed(start+i, start+i) = Literal(0);
-  
-  JacobiRotation<RealScalar> J(c/r,-s/r);
-  if (m_compU)  m_naiveU.middleRows(firstCol, size+1).applyOnTheRight(firstCol, firstCol+i, J);
-  else          m_naiveU.applyOnTheRight(firstCol, firstCol+i, J);
-}// end deflation 43
-
-
-// page 13
-// i,j >= 1, i!=j and |di - dj| < epsilon * norm2(M)
-// We apply two rotations to have zj = 0;
-// TODO deflation44 is still broken and not properly tested
-template <typename MatrixType>
-void BDCSVD<MatrixType>::deflation44(Eigen::Index firstColu , Eigen::Index firstColm, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index i, Eigen::Index j, Eigen::Index size)
-{
-  using std::abs;
-  using std::sqrt;
-  using std::conj;
-  using std::pow;
-  RealScalar c = m_computed(firstColm+i, firstColm);
-  RealScalar s = m_computed(firstColm+j, firstColm);
-  RealScalar r = sqrt(numext::abs2(c) + numext::abs2(s));
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "deflation 4.4: " << i << "," << j << " -> " << c << " " << s << " " << r << " ; "
-    << m_computed(firstColm + i-1, firstColm)  << " "
-    << m_computed(firstColm + i, firstColm)  << " "
-    << m_computed(firstColm + i+1, firstColm) << " "
-    << m_computed(firstColm + i+2, firstColm) << "\n";
-  std::cout << m_computed(firstColm + i-1, firstColm + i-1)  << " "
-    << m_computed(firstColm + i, firstColm+i)  << " "
-    << m_computed(firstColm + i+1, firstColm+i+1) << " "
-    << m_computed(firstColm + i+2, firstColm+i+2) << "\n";
-#endif
-  if (r==Literal(0))
-  {
-    m_computed(firstColm + i, firstColm + i) = m_computed(firstColm + j, firstColm + j);
-    return;
-  }
-  c/=r;
-  s/=r;
-  m_computed(firstColm + i, firstColm) = r;
-  m_computed(firstColm + j, firstColm + j) = m_computed(firstColm + i, firstColm + i);
-  m_computed(firstColm + j, firstColm) = Literal(0);
-
-  JacobiRotation<RealScalar> J(c,-s);
-  if (m_compU)  m_naiveU.middleRows(firstColu, size+1).applyOnTheRight(firstColu + i, firstColu + j, J);
-  else          m_naiveU.applyOnTheRight(firstColu+i, firstColu+j, J);
-  if (m_compV)  m_naiveV.middleRows(firstRowW, size).applyOnTheRight(firstColW + i, firstColW + j, J);
-}// end deflation 44
-
-
-// acts on block from (firstCol+shift, firstCol+shift) to (lastCol+shift, lastCol+shift) [inclusive]
-template <typename MatrixType>
-void BDCSVD<MatrixType>::deflation(Eigen::Index firstCol, Eigen::Index lastCol, Eigen::Index k, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index shift)
-{
-  using std::sqrt;
-  using std::abs;
-  const Index length = lastCol + 1 - firstCol;
-  
-  Block<MatrixXr,Dynamic,1> col0(m_computed, firstCol+shift, firstCol+shift, length, 1);
-  Diagonal<MatrixXr> fulldiag(m_computed);
-  VectorBlock<Diagonal<MatrixXr>,Dynamic> diag(fulldiag, firstCol+shift, length);
-  
-  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
-  RealScalar maxDiag = diag.tail((std::max)(Index(1),length-1)).cwiseAbs().maxCoeff();
-  RealScalar epsilon_strict = numext::maxi<RealScalar>(considerZero,NumTraits<RealScalar>::epsilon() * maxDiag);
-  RealScalar epsilon_coarse = Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(col0.cwiseAbs().maxCoeff(), maxDiag);
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE  
-  std::cout << "\ndeflate:" << diag.head(k+1).transpose() << "  |  " << diag.segment(k+1,length-k-1).transpose() << "\n";
-#endif
-  
-  //condition 4.1
-  if (diag(0) < epsilon_coarse)
-  { 
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-    std::cout << "deflation 4.1, because " << diag(0) << " < " << epsilon_coarse << "\n";
-#endif
-    diag(0) = epsilon_coarse;
-  }
-
-  //condition 4.2
-  for (Index i=1;i<length;++i)
-    if (abs(col0(i)) < epsilon_strict)
-    {
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "deflation 4.2, set z(" << i << ") to zero because " << abs(col0(i)) << " < " << epsilon_strict << "  (diag(" << i << ")=" << diag(i) << ")\n";
-#endif
-      col0(i) = Literal(0);
-    }
-
-  //condition 4.3
-  for (Index i=1;i<length; i++)
-    if (diag(i) < epsilon_coarse)
-    {
-#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
-      std::cout << "deflation 4.3, cancel z(" << i << ")=" << col0(i) << " because diag(" << i << ")=" << diag(i) << " < " << epsilon_coarse << "\n";
-#endif
-      deflation43(firstCol, shift, i, length);
-    }
-
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "to be sorted: " << diag.transpose() << "\n\n";
-  std::cout << "            : " << col0.transpose() << "\n\n";
-#endif
-  {
-    // Check for total deflation
-    // If we have a total deflation, then we have to consider col0(0)==diag(0) as a singular value during sorting
-    bool total_deflation = (col0.tail(length-1).array()<considerZero).all();
-    
-    // Sort the diagonal entries, since diag(1:k-1) and diag(k:length) are already sorted, let's do a sorted merge.
-    // First, compute the respective permutation.
-    Index *permutation = m_workspaceI.data();
-    {
-      permutation[0] = 0;
-      Index p = 1;
-      
-      // Move deflated diagonal entries at the end.
-      for(Index i=1; i<length; ++i)
-        if(abs(diag(i))<considerZero)
-          permutation[p++] = i;
-        
-      Index i=1, j=k+1;
-      for( ; p < length; ++p)
-      {
-             if (i > k)             permutation[p] = j++;
-        else if (j >= length)       permutation[p] = i++;
-        else if (diag(i) < diag(j)) permutation[p] = j++;
-        else                        permutation[p] = i++;
-      }
-    }
-    
-    // If we have a total deflation, then we have to insert diag(0) at the right place
-    if(total_deflation)
-    {
-      for(Index i=1; i<length; ++i)
-      {
-        Index pi = permutation[i];
-        if(abs(diag(pi))<considerZero || diag(0)<diag(pi))
-          permutation[i-1] = permutation[i];
-        else
-        {
-          permutation[i-1] = 0;
-          break;
-        }
-      }
-    }
-    
-    // Current index of each col, and current column of each index
-    Index *realInd = m_workspaceI.data()+length;
-    Index *realCol = m_workspaceI.data()+2*length;
-    
-    for(int pos = 0; pos< length; pos++)
-    {
-      realCol[pos] = pos;
-      realInd[pos] = pos;
-    }
-    
-    for(Index i = total_deflation?0:1; i < length; i++)
-    {
-      const Index pi = permutation[length - (total_deflation ? i+1 : i)];
-      const Index J = realCol[pi];
-      
-      using std::swap;
-      // swap diagonal and first column entries:
-      swap(diag(i), diag(J));
-      if(i!=0 && J!=0) swap(col0(i), col0(J));
-
-      // change columns
-      if (m_compU) m_naiveU.col(firstCol+i).segment(firstCol, length + 1).swap(m_naiveU.col(firstCol+J).segment(firstCol, length + 1));
-      else         m_naiveU.col(firstCol+i).segment(0, 2)                .swap(m_naiveU.col(firstCol+J).segment(0, 2));
-      if (m_compV) m_naiveV.col(firstColW + i).segment(firstRowW, length).swap(m_naiveV.col(firstColW + J).segment(firstRowW, length));
-
-      //update real pos
-      const Index realI = realInd[i];
-      realCol[realI] = J;
-      realCol[pi] = i;
-      realInd[J] = realI;
-      realInd[i] = pi;
-    }
-  }
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-  std::cout << "sorted: " << diag.transpose().format(bdcsvdfmt) << "\n";
-  std::cout << "      : " << col0.transpose() << "\n\n";
-#endif
-    
-  //condition 4.4
-  {
-    Index i = length-1;
-    while(i>0 && (abs(diag(i))<considerZero || abs(col0(i))<considerZero)) --i;
-    for(; i>1;--i)
-       if( (diag(i) - diag(i-1)) < NumTraits<RealScalar>::epsilon()*maxDiag )
-      {
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-        std::cout << "deflation 4.4 with i = " << i << " because " << diag(i) << " - " << diag(i-1) << " == " << (diag(i) - diag(i-1)) << " < " << NumTraits<RealScalar>::epsilon()*/*diag(i)*/maxDiag << "\n";
-#endif
-        eigen_internal_assert(abs(diag(i) - diag(i-1))<epsilon_coarse && " diagonal entries are not properly sorted");
-        deflation44(firstCol, firstCol + shift, firstRowW, firstColW, i-1, i, length);
-      }
-  }
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  for(Index j=2;j<length;++j)
-    assert(diag(j-1)<=diag(j) || abs(diag(j))<considerZero);
-#endif
-  
-#ifdef EIGEN_BDCSVD_SANITY_CHECKS
-  assert(m_naiveU.allFinite());
-  assert(m_naiveV.allFinite());
-  assert(m_computed.allFinite());
-#endif
-}//end deflation
-
-/** \svd_module
-  *
-  * \return the singular value decomposition of \c *this computed by Divide & Conquer algorithm
-  *
-  * \sa class BDCSVD
-  */
-template<typename Derived>
-BDCSVD<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::bdcSvd(unsigned int computationOptions) const
-{
-  return BDCSVD<PlainObject>(*this, computationOptions);
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/JacobiSVD.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/JacobiSVD.h
deleted file mode 100644
index 9d95acd..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/JacobiSVD.h
+++ /dev/null
@@ -1,812 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_JACOBISVD_H
-#define EIGEN_JACOBISVD_H
-
-namespace Eigen { 
-
-namespace internal {
-// forward declaration (needed by ICC)
-// the empty body is required by MSVC
-template<typename MatrixType, int QRPreconditioner,
-         bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex>
-struct svd_precondition_2x2_block_to_be_real {};
-
-/*** QR preconditioners (R-SVD)
- ***
- *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
- *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
- *** JacobiSVD which by itself is only able to work on square matrices.
- ***/
-
-enum { PreconditionIfMoreColsThanRows, PreconditionIfMoreRowsThanCols };
-
-template<typename MatrixType, int QRPreconditioner, int Case>
-struct qr_preconditioner_should_do_anything
-{
-  enum { a = MatrixType::RowsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime <= MatrixType::RowsAtCompileTime,
-         b = MatrixType::RowsAtCompileTime != Dynamic &&
-             MatrixType::ColsAtCompileTime != Dynamic &&
-             MatrixType::RowsAtCompileTime <= MatrixType::ColsAtCompileTime,
-         ret = !( (QRPreconditioner == NoQRPreconditioner) ||
-                  (Case == PreconditionIfMoreColsThanRows && bool(a)) ||
-                  (Case == PreconditionIfMoreRowsThanCols && bool(b)) )
-  };
-};
-
-template<typename MatrixType, int QRPreconditioner, int Case,
-         bool DoAnything = qr_preconditioner_should_do_anything<MatrixType, QRPreconditioner, Case>::ret
-> struct qr_preconditioner_impl {};
-
-template<typename MatrixType, int QRPreconditioner, int Case>
-class qr_preconditioner_impl<MatrixType, QRPreconditioner, Case, false>
-{
-public:
-  void allocate(const JacobiSVD<MatrixType, QRPreconditioner>&) {}
-  bool run(JacobiSVD<MatrixType, QRPreconditioner>&, const MatrixType&)
-  {
-    return false;
-  }
-};
-
-/*** preconditioner using FullPivHouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
-  };
-  typedef Matrix<Scalar, 1, RowsAtCompileTime, RowMajor, 1, MaxRowsAtCompileTime> WorkspaceType;
-
-  void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.matrixQ().evalTo(svd.m_matrixU, m_workspace);
-      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
-      return true;
-    }
-    return false;
-  }
-private:
-  typedef FullPivHouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  WorkspaceType m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef typename internal::make_proper_matrix_type<
-    Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime
-  >::type TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    m_adjoint.resize(svd.cols(), svd.rows());
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.matrixQ().evalTo(svd.m_matrixV, m_workspace);
-      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
-      return true;
-    }
-    else return false;
-  }
-private:
-  typedef FullPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** preconditioner using ColPivHouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
-      else if(svd.m_computeThinU)
-      {
-        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
-      }
-      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
-      return true;
-    }
-    return false;
-  }
-
-private:
-  typedef ColPivHouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  typename internal::plain_col_type<MatrixType>::type m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef typename internal::make_proper_matrix_type<
-    Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime
-  >::type TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
-    m_adjoint.resize(svd.cols(), svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
-      else if(svd.m_computeThinV)
-      {
-        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
-      }
-      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
-      return true;
-    }
-    else return false;
-  }
-
-private:
-  typedef ColPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** preconditioner using HouseholderQR ***/
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
-{
-public:
-  void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
-  {
-    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.rows(), svd.cols());
-    }
-    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
-    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
-  }
-
-  bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.rows() > matrix.cols())
-    {
-      m_qr.compute(matrix);
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
-      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
-      else if(svd.m_computeThinU)
-      {
-        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
-      }
-      if(svd.computeV()) svd.m_matrixV.setIdentity(matrix.cols(), matrix.cols());
-      return true;
-    }
-    return false;
-  }
-private:
-  typedef HouseholderQR<MatrixType> QRType;
-  QRType m_qr;
-  typename internal::plain_col_type<MatrixType>::type m_workspace;
-};
-
-template<typename MatrixType>
-class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
-{
-public:
-  typedef typename MatrixType::Scalar Scalar;
-  enum
-  {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    Options = MatrixType::Options
-  };
-
-  typedef typename internal::make_proper_matrix_type<
-    Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime
-  >::type TransposeTypeWithSameStorageOrder;
-
-  void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
-  {
-    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
-    {
-      m_qr.~QRType();
-      ::new (&m_qr) QRType(svd.cols(), svd.rows());
-    }
-    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
-    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
-    m_adjoint.resize(svd.cols(), svd.rows());
-  }
-
-  bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
-  {
-    if(matrix.cols() > matrix.rows())
-    {
-      m_adjoint = matrix.adjoint();
-      m_qr.compute(m_adjoint);
-
-      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
-      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
-      else if(svd.m_computeThinV)
-      {
-        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
-        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
-      }
-      if(svd.computeU()) svd.m_matrixU.setIdentity(matrix.rows(), matrix.rows());
-      return true;
-    }
-    else return false;
-  }
-
-private:
-  typedef HouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
-  QRType m_qr;
-  TransposeTypeWithSameStorageOrder m_adjoint;
-  typename internal::plain_row_type<MatrixType>::type m_workspace;
-};
-
-/*** 2x2 SVD implementation
- ***
- *** JacobiSVD consists in performing a series of 2x2 SVD subproblems
- ***/
-
-template<typename MatrixType, int QRPreconditioner>
-struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, false>
-{
-  typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
-  typedef typename MatrixType::RealScalar RealScalar;
-  static bool run(typename SVD::WorkMatrixType&, SVD&, Index, Index, RealScalar&) { return true; }
-};
-
-template<typename MatrixType, int QRPreconditioner>
-struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true>
-{
-  typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  static bool run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q, RealScalar& maxDiagEntry)
-  {
-    using std::sqrt;
-    using std::abs;
-    Scalar z;
-    JacobiRotation<Scalar> rot;
-    RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p,p)) + numext::abs2(work_matrix.coeff(q,p)));
-
-    const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-    const RealScalar precision = NumTraits<Scalar>::epsilon();
-
-    if(n==0)
-    {
-      // make sure first column is zero
-      work_matrix.coeffRef(p,p) = work_matrix.coeffRef(q,p) = Scalar(0);
-
-      if(abs(numext::imag(work_matrix.coeff(p,q)))>considerAsZero)
-      {
-        // work_matrix.coeff(p,q) can be zero if work_matrix.coeff(q,p) is not zero but small enough to underflow when computing n
-        z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
-        work_matrix.row(p) *= z;
-        if(svd.computeU()) svd.m_matrixU.col(p) *= conj(z);
-      }
-      if(abs(numext::imag(work_matrix.coeff(q,q)))>considerAsZero)
-      {
-        z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
-        work_matrix.row(q) *= z;
-        if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
-      }
-      // otherwise the second row is already zero, so we have nothing to do.
-    }
-    else
-    {
-      rot.c() = conj(work_matrix.coeff(p,p)) / n;
-      rot.s() = work_matrix.coeff(q,p) / n;
-      work_matrix.applyOnTheLeft(p,q,rot);
-      if(svd.computeU()) svd.m_matrixU.applyOnTheRight(p,q,rot.adjoint());
-      if(abs(numext::imag(work_matrix.coeff(p,q)))>considerAsZero)
-      {
-        z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
-        work_matrix.col(q) *= z;
-        if(svd.computeV()) svd.m_matrixV.col(q) *= z;
-      }
-      if(abs(numext::imag(work_matrix.coeff(q,q)))>considerAsZero)
-      {
-        z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
-        work_matrix.row(q) *= z;
-        if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
-      }
-    }
-
-    // update largest diagonal entry
-    maxDiagEntry = numext::maxi<RealScalar>(maxDiagEntry,numext::maxi<RealScalar>(abs(work_matrix.coeff(p,p)), abs(work_matrix.coeff(q,q))));
-    // and check whether the 2x2 block is already diagonal
-    RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
-    return abs(work_matrix.coeff(p,q))>threshold || abs(work_matrix.coeff(q,p)) > threshold;
-  }
-};
-
-template<typename _MatrixType, int QRPreconditioner> 
-struct traits<JacobiSVD<_MatrixType,QRPreconditioner> >
-        : traits<_MatrixType>
-{
-  typedef _MatrixType MatrixType;
-};
-
-} // end namespace internal
-
-/** \ingroup SVD_Module
-  *
-  *
-  * \class JacobiSVD
-  *
-  * \brief Two-sided Jacobi SVD decomposition of a rectangular matrix
-  *
-  * \tparam _MatrixType the type of the matrix of which we are computing the SVD decomposition
-  * \tparam QRPreconditioner this optional parameter allows to specify the type of QR decomposition that will be used internally
-  *                        for the R-SVD step for non-square matrices. See discussion of possible values below.
-  *
-  * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
-  *   \f[ A = U S V^* \f]
-  * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
-  * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
-  * and right \em singular \em vectors of \a A respectively.
-  *
-  * Singular values are always sorted in decreasing order.
-  *
-  * This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask for them explicitly.
-  *
-  * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
-  * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
-  * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
-  * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
-  *
-  * Here's an example demonstrating basic usage:
-  * \include JacobiSVD_basic.cpp
-  * Output: \verbinclude JacobiSVD_basic.out
-  *
-  * This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than
-  * bidiagonalizing SVD algorithms for large square matrices; however its complexity is still \f$ O(n^2p) \f$ where \a n is the smaller dimension and
-  * \a p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms.
-  * In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
-  *
-  * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to
-  * terminate in finite (and reasonable) time.
-  *
-  * The possible values for QRPreconditioner are:
-  * \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
-  * \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR.
-  *     Contrary to other QRs, it doesn't allow computing thin unitaries.
-  * \li HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses non-pivoting QR.
-  *     This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing SVD algorithms (since bidiagonalization
-  *     is inherently non-pivoting). However the resulting SVD is still more reliable than bidiagonalizing SVDs because the Jacobi-based iterarive
-  *     process is more reliable than the optimized bidiagonal SVD iterations.
-  * \li NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that you will only be computing
-  *     JacobiSVD decompositions of square matrices. Non-square matrices require a QR preconditioner. Using this option will result in
-  *     faster compilation and smaller executable code. It won't significantly speed up computation, since JacobiSVD is always checking
-  *     if QR preconditioning is needed before applying it anyway.
-  *
-  * \sa MatrixBase::jacobiSvd()
-  */
-template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
- : public SVDBase<JacobiSVD<_MatrixType,QRPreconditioner> >
-{
-    typedef SVDBase<JacobiSVD> Base;
-  public:
-
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-      MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
-      MatrixOptions = MatrixType::Options
-    };
-
-    typedef typename Base::MatrixUType MatrixUType;
-    typedef typename Base::MatrixVType MatrixVType;
-    typedef typename Base::SingularValuesType SingularValuesType;
-    
-    typedef typename internal::plain_row_type<MatrixType>::type RowType;
-    typedef typename internal::plain_col_type<MatrixType>::type ColType;
-    typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
-                   MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime>
-            WorkMatrixType;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via JacobiSVD::compute(const MatrixType&).
-      */
-    JacobiSVD()
-    {}
-
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem size.
-      * \sa JacobiSVD()
-      */
-    JacobiSVD(Index rows, Index cols, unsigned int computationOptions = 0)
-    {
-      allocate(rows, cols, computationOptions);
-    }
-
-    /** \brief Constructor performing the decomposition of given matrix.
-     *
-     * \param matrix the matrix to decompose
-     * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-     *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-     *                           #ComputeFullV, #ComputeThinV.
-     *
-     * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-     * available with the (non-default) FullPivHouseholderQR preconditioner.
-     */
-    explicit JacobiSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
-    {
-      compute(matrix, computationOptions);
-    }
-
-    /** \brief Method performing the decomposition of given matrix using custom options.
-     *
-     * \param matrix the matrix to decompose
-     * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
-     *                           By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
-     *                           #ComputeFullV, #ComputeThinV.
-     *
-     * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
-     * available with the (non-default) FullPivHouseholderQR preconditioner.
-     */
-    JacobiSVD& compute(const MatrixType& matrix, unsigned int computationOptions);
-
-    /** \brief Method performing the decomposition of given matrix using current options.
-     *
-     * \param matrix the matrix to decompose
-     *
-     * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
-     */
-    JacobiSVD& compute(const MatrixType& matrix)
-    {
-      return compute(matrix, m_computationOptions);
-    }
-
-    using Base::computeU;
-    using Base::computeV;
-    using Base::rows;
-    using Base::cols;
-    using Base::rank;
-
-  private:
-    void allocate(Index rows, Index cols, unsigned int computationOptions);
-
-  protected:
-    using Base::m_matrixU;
-    using Base::m_matrixV;
-    using Base::m_singularValues;
-    using Base::m_info;
-    using Base::m_isInitialized;
-    using Base::m_isAllocated;
-    using Base::m_usePrescribedThreshold;
-    using Base::m_computeFullU;
-    using Base::m_computeThinU;
-    using Base::m_computeFullV;
-    using Base::m_computeThinV;
-    using Base::m_computationOptions;
-    using Base::m_nonzeroSingularValues;
-    using Base::m_rows;
-    using Base::m_cols;
-    using Base::m_diagSize;
-    using Base::m_prescribedThreshold;
-    WorkMatrixType m_workMatrix;
-
-    template<typename __MatrixType, int _QRPreconditioner, bool _IsComplex>
-    friend struct internal::svd_precondition_2x2_block_to_be_real;
-    template<typename __MatrixType, int _QRPreconditioner, int _Case, bool _DoAnything>
-    friend struct internal::qr_preconditioner_impl;
-
-    internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreColsThanRows> m_qr_precond_morecols;
-    internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols> m_qr_precond_morerows;
-    MatrixType m_scaledMatrix;
-};
-
-template<typename MatrixType, int QRPreconditioner>
-void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Eigen::Index rows, Eigen::Index cols, unsigned int computationOptions)
-{
-  eigen_assert(rows >= 0 && cols >= 0);
-
-  if (m_isAllocated &&
-      rows == m_rows &&
-      cols == m_cols &&
-      computationOptions == m_computationOptions)
-  {
-    return;
-  }
-
-  m_rows = rows;
-  m_cols = cols;
-  m_info = Success;
-  m_isInitialized = false;
-  m_isAllocated = true;
-  m_computationOptions = computationOptions;
-  m_computeFullU = (computationOptions & ComputeFullU) != 0;
-  m_computeThinU = (computationOptions & ComputeThinU) != 0;
-  m_computeFullV = (computationOptions & ComputeFullV) != 0;
-  m_computeThinV = (computationOptions & ComputeThinV) != 0;
-  eigen_assert(!(m_computeFullU && m_computeThinU) && "JacobiSVD: you can't ask for both full and thin U");
-  eigen_assert(!(m_computeFullV && m_computeThinV) && "JacobiSVD: you can't ask for both full and thin V");
-  eigen_assert(EIGEN_IMPLIES(m_computeThinU || m_computeThinV, MatrixType::ColsAtCompileTime==Dynamic) &&
-              "JacobiSVD: thin U and V are only available when your matrix has a dynamic number of columns.");
-  if (QRPreconditioner == FullPivHouseholderQRPreconditioner)
-  {
-      eigen_assert(!(m_computeThinU || m_computeThinV) &&
-              "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
-              "Use the ColPivHouseholderQR preconditioner instead.");
-  }
-  m_diagSize = (std::min)(m_rows, m_cols);
-  m_singularValues.resize(m_diagSize);
-  if(RowsAtCompileTime==Dynamic)
-    m_matrixU.resize(m_rows, m_computeFullU ? m_rows
-                            : m_computeThinU ? m_diagSize
-                            : 0);
-  if(ColsAtCompileTime==Dynamic)
-    m_matrixV.resize(m_cols, m_computeFullV ? m_cols
-                            : m_computeThinV ? m_diagSize
-                            : 0);
-  m_workMatrix.resize(m_diagSize, m_diagSize);
-  
-  if(m_cols>m_rows)   m_qr_precond_morecols.allocate(*this);
-  if(m_rows>m_cols)   m_qr_precond_morerows.allocate(*this);
-  if(m_rows!=m_cols)  m_scaledMatrix.resize(rows,cols);
-}
-
-template<typename MatrixType, int QRPreconditioner>
-JacobiSVD<MatrixType, QRPreconditioner>&
-JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsigned int computationOptions)
-{
-  using std::abs;
-  allocate(matrix.rows(), matrix.cols(), computationOptions);
-
-  // currently we stop when we reach precision 2*epsilon as the last bit of precision can require an unreasonable number of iterations,
-  // only worsening the precision of U and V as we accumulate more rotations
-  const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();
-
-  // limit for denormal numbers to be considered zero in order to avoid infinite loops (see bug 286)
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-
-  // Scaling factor to reduce over/under-flows
-  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
-  if (!(numext::isfinite)(scale)) {
-    m_isInitialized = true;
-    m_info = InvalidInput;
-    return *this;
-  }
-  if(scale==RealScalar(0)) scale = RealScalar(1);
-  
-  /*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
-
-  if(m_rows!=m_cols)
-  {
-    m_scaledMatrix = matrix / scale;
-    m_qr_precond_morecols.run(*this, m_scaledMatrix);
-    m_qr_precond_morerows.run(*this, m_scaledMatrix);
-  }
-  else
-  {
-    m_workMatrix = matrix.block(0,0,m_diagSize,m_diagSize) / scale;
-    if(m_computeFullU) m_matrixU.setIdentity(m_rows,m_rows);
-    if(m_computeThinU) m_matrixU.setIdentity(m_rows,m_diagSize);
-    if(m_computeFullV) m_matrixV.setIdentity(m_cols,m_cols);
-    if(m_computeThinV) m_matrixV.setIdentity(m_cols, m_diagSize);
-  }
-
-  /*** step 2. The main Jacobi SVD iteration. ***/
-  RealScalar maxDiagEntry = m_workMatrix.cwiseAbs().diagonal().maxCoeff();
-
-  bool finished = false;
-  while(!finished)
-  {
-    finished = true;
-
-    // do a sweep: for all index pairs (p,q), perform SVD of the corresponding 2x2 sub-matrix
-
-    for(Index p = 1; p < m_diagSize; ++p)
-    {
-      for(Index q = 0; q < p; ++q)
-      {
-        // if this 2x2 sub-matrix is not diagonal already...
-        // notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
-        // keep us iterating forever. Similarly, small denormal numbers are considered zero.
-        RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
-        if(abs(m_workMatrix.coeff(p,q))>threshold || abs(m_workMatrix.coeff(q,p)) > threshold)
-        {
-          finished = false;
-          // perform SVD decomposition of 2x2 sub-matrix corresponding to indices p,q to make it diagonal
-          // the complex to real operation returns true if the updated 2x2 block is not already diagonal
-          if(internal::svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner>::run(m_workMatrix, *this, p, q, maxDiagEntry))
-          {
-            JacobiRotation<RealScalar> j_left, j_right;
-            internal::real_2x2_jacobi_svd(m_workMatrix, p, q, &j_left, &j_right);
-
-            // accumulate resulting Jacobi rotations
-            m_workMatrix.applyOnTheLeft(p,q,j_left);
-            if(computeU()) m_matrixU.applyOnTheRight(p,q,j_left.transpose());
-
-            m_workMatrix.applyOnTheRight(p,q,j_right);
-            if(computeV()) m_matrixV.applyOnTheRight(p,q,j_right);
-
-            // keep track of the largest diagonal coefficient
-            maxDiagEntry = numext::maxi<RealScalar>(maxDiagEntry,numext::maxi<RealScalar>(abs(m_workMatrix.coeff(p,p)), abs(m_workMatrix.coeff(q,q))));
-          }
-        }
-      }
-    }
-  }
-
-  /*** step 3. The work matrix is now diagonal, so ensure it's positive so its diagonal entries are the singular values ***/
-
-  for(Index i = 0; i < m_diagSize; ++i)
-  {
-    // For a complex matrix, some diagonal coefficients might note have been
-    // treated by svd_precondition_2x2_block_to_be_real, and the imaginary part
-    // of some diagonal entry might not be null.
-    if(NumTraits<Scalar>::IsComplex && abs(numext::imag(m_workMatrix.coeff(i,i)))>considerAsZero)
-    {
-      RealScalar a = abs(m_workMatrix.coeff(i,i));
-      m_singularValues.coeffRef(i) = abs(a);
-      if(computeU()) m_matrixU.col(i) *= m_workMatrix.coeff(i,i)/a;
-    }
-    else
-    {
-      // m_workMatrix.coeff(i,i) is already real, no difficulty:
-      RealScalar a = numext::real(m_workMatrix.coeff(i,i));
-      m_singularValues.coeffRef(i) = abs(a);
-      if(computeU() && (a<RealScalar(0))) m_matrixU.col(i) = -m_matrixU.col(i);
-    }
-  }
-  
-  m_singularValues *= scale;
-
-  /*** step 4. Sort singular values in descending order and compute the number of nonzero singular values ***/
-
-  m_nonzeroSingularValues = m_diagSize;
-  for(Index i = 0; i < m_diagSize; i++)
-  {
-    Index pos;
-    RealScalar maxRemainingSingularValue = m_singularValues.tail(m_diagSize-i).maxCoeff(&pos);
-    if(maxRemainingSingularValue == RealScalar(0))
-    {
-      m_nonzeroSingularValues = i;
-      break;
-    }
-    if(pos)
-    {
-      pos += i;
-      std::swap(m_singularValues.coeffRef(i), m_singularValues.coeffRef(pos));
-      if(computeU()) m_matrixU.col(pos).swap(m_matrixU.col(i));
-      if(computeV()) m_matrixV.col(pos).swap(m_matrixV.col(i));
-    }
-  }
-
-  m_isInitialized = true;
-  return *this;
-}
-
-/** \svd_module
-  *
-  * \return the singular value decomposition of \c *this computed by two-sided
-  * Jacobi transformations.
-  *
-  * \sa class JacobiSVD
-  */
-template<typename Derived>
-JacobiSVD<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::jacobiSvd(unsigned int computationOptions) const
-{
-  return JacobiSVD<PlainObject>(*this, computationOptions);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_JACOBISVD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h
deleted file mode 100644
index ff0516f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- Copyright (c) 2011, Intel Corporation. All rights reserved.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
- * Neither the name of Intel Corporation nor the names of its contributors may
-   be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
- THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
- ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
- ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
- ********************************************************************************
- *   Content : Eigen bindings to LAPACKe
- *    Singular Value Decomposition - SVD.
- ********************************************************************************
-*/
-
-#ifndef EIGEN_JACOBISVD_LAPACKE_H
-#define EIGEN_JACOBISVD_LAPACKE_H
-
-namespace Eigen { 
-
-/** \internal Specialization for the data types supported by LAPACKe */
-
-#define EIGEN_LAPACKE_SVD(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW) \
-template<> inline \
-JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>& \
-JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) \
-{ \
-  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
-  /*typedef MatrixType::Scalar Scalar;*/ \
-  /*typedef MatrixType::RealScalar RealScalar;*/ \
-  allocate(matrix.rows(), matrix.cols(), computationOptions); \
-\
-  /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
-  m_nonzeroSingularValues = m_diagSize; \
-\
-  lapack_int lda = internal::convert_index<lapack_int>(matrix.outerStride()), ldu, ldvt; \
-  lapack_int matrix_order = LAPACKE_COLROW; \
-  char jobu, jobvt; \
-  LAPACKE_TYPE *u, *vt, dummy; \
-  jobu  = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
-  jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
-  if (computeU()) { \
-    ldu  = internal::convert_index<lapack_int>(m_matrixU.outerStride()); \
-    u    = (LAPACKE_TYPE*)m_matrixU.data(); \
-  } else { ldu=1; u=&dummy; }\
-  MatrixType localV; \
-  lapack_int vt_rows = (m_computeFullV) ? internal::convert_index<lapack_int>(m_cols) : (m_computeThinV) ? internal::convert_index<lapack_int>(m_diagSize) : 1; \
-  if (computeV()) { \
-    localV.resize(vt_rows, m_cols); \
-    ldvt  = internal::convert_index<lapack_int>(localV.outerStride()); \
-    vt   = (LAPACKE_TYPE*)localV.data(); \
-  } else { ldvt=1; vt=&dummy; }\
-  Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \
-  MatrixType m_temp; m_temp = matrix; \
-  LAPACKE_##LAPACKE_PREFIX##gesvd( matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(m_rows), internal::convert_index<lapack_int>(m_cols), (LAPACKE_TYPE*)m_temp.data(), lda, (LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
-  if (computeV()) m_matrixV = localV.adjoint(); \
- /* for(int i=0;i<m_diagSize;i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
-  m_isInitialized = true; \
-  return *this; \
-}
-
-EIGEN_LAPACKE_SVD(double,   double,                double, d, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SVD(float,    float,                 float , s, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, ColMajor, LAPACK_COL_MAJOR)
-EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float,  float , c, ColMajor, LAPACK_COL_MAJOR)
-
-EIGEN_LAPACKE_SVD(double,   double,                double, d, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SVD(float,    float,                 float , s, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, RowMajor, LAPACK_ROW_MAJOR)
-EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float,  float , c, RowMajor, LAPACK_ROW_MAJOR)
-
-} // end namespace Eigen
-
-#endif // EIGEN_JACOBISVD_LAPACKE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/SVDBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/SVDBase.h
deleted file mode 100644
index bc7ab88..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/SVDBase.h
+++ /dev/null
@@ -1,376 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
-// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
-// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
-// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SVDBASE_H
-#define EIGEN_SVDBASE_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename Derived> struct traits<SVDBase<Derived> >
- : traits<Derived>
-{
-  typedef MatrixXpr XprKind;
-  typedef SolverStorage StorageKind;
-  typedef int StorageIndex;
-  enum { Flags = 0 };
-};
-}
-
-/** \ingroup SVD_Module
- *
- *
- * \class SVDBase
- *
- * \brief Base class of SVD algorithms
- *
- * \tparam Derived the type of the actual SVD decomposition
- *
- * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
- *   \f[ A = U S V^* \f]
- * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
- * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
- * and right \em singular \em vectors of \a A respectively.
- *
- * Singular values are always sorted in decreasing order.
- *
- * 
- * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
- * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
- * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
- * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
- * 
- * The status of the computation can be retrived using the \a info() method. Unless \a info() returns \a Success, the results should be not
- * considered well defined.
- *  
- * If the input matrix has inf or nan coefficients, the result of the computation is undefined, and \a info() will return \a InvalidInput, but the computation is guaranteed to
- * terminate in finite (and reasonable) time.
- * \sa class BDCSVD, class JacobiSVD
- */
-template<typename Derived> class SVDBase
- : public SolverBase<SVDBase<Derived> >
-{
-public: 
-   
-  template<typename Derived_>
-  friend struct internal::solve_assertion;
-
-  typedef typename internal::traits<Derived>::MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-  typedef typename Eigen::internal::traits<SVDBase>::StorageIndex StorageIndex;
-  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-  enum {
-    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
-    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
-    MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
-    MatrixOptions = MatrixType::Options
-  };
-
-  typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixUType;
-  typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime, MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime> MatrixVType;
-  typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
-  
-  Derived& derived() { return *static_cast<Derived*>(this); }
-  const Derived& derived() const { return *static_cast<const Derived*>(this); }
-
-  /** \returns the \a U matrix.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-   * the U matrix is n-by-n if you asked for \link Eigen::ComputeFullU ComputeFullU \endlink, and is n-by-m if you asked for \link Eigen::ComputeThinU ComputeThinU \endlink.
-   *
-   * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
-   *
-   * This method asserts that you asked for \a U to be computed.
-   */
-  const MatrixUType& matrixU() const
-  {
-    _check_compute_assertions();
-    eigen_assert(computeU() && "This SVD decomposition didn't compute U. Did you ask for it?");
-    return m_matrixU;
-  }
-
-  /** \returns the \a V matrix.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
-   * the V matrix is p-by-p if you asked for \link Eigen::ComputeFullV ComputeFullV \endlink, and is p-by-m if you asked for \link Eigen::ComputeThinV ComputeThinV \endlink.
-   *
-   * The \a m first columns of \a V are the right singular vectors of the matrix being decomposed.
-   *
-   * This method asserts that you asked for \a V to be computed.
-   */
-  const MatrixVType& matrixV() const
-  {
-    _check_compute_assertions();
-    eigen_assert(computeV() && "This SVD decomposition didn't compute V. Did you ask for it?");
-    return m_matrixV;
-  }
-
-  /** \returns the vector of singular values.
-   *
-   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the
-   * returned vector has size \a m.  Singular values are always sorted in decreasing order.
-   */
-  const SingularValuesType& singularValues() const
-  {
-    _check_compute_assertions();
-    return m_singularValues;
-  }
-
-  /** \returns the number of singular values that are not exactly 0 */
-  Index nonzeroSingularValues() const
-  {
-    _check_compute_assertions();
-    return m_nonzeroSingularValues;
-  }
-  
-  /** \returns the rank of the matrix of which \c *this is the SVD.
-    *
-    * \note This method has to determine which singular values should be considered nonzero.
-    *       For that, it uses the threshold value that you can control by calling
-    *       setThreshold(const RealScalar&).
-    */
-  inline Index rank() const
-  {
-    using std::abs;
-    _check_compute_assertions();
-    if(m_singularValues.size()==0) return 0;
-    RealScalar premultiplied_threshold = numext::maxi<RealScalar>(m_singularValues.coeff(0) * threshold(), (std::numeric_limits<RealScalar>::min)());
-    Index i = m_nonzeroSingularValues-1;
-    while(i>=0 && m_singularValues.coeff(i) < premultiplied_threshold) --i;
-    return i+1;
-  }
-  
-  /** Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(),
-    * which need to determine when singular values are to be considered nonzero.
-    * This is not used for the SVD decomposition itself.
-    *
-    * When it needs to get the threshold value, Eigen calls threshold().
-    * The default is \c NumTraits<Scalar>::epsilon()
-    *
-    * \param threshold The new value to use as the threshold.
-    *
-    * A singular value will be considered nonzero if its value is strictly greater than
-    *  \f$ \vert singular value \vert \leqslant threshold \times \vert max singular value \vert \f$.
-    *
-    * If you want to come back to the default behavior, call setThreshold(Default_t)
-    */
-  Derived& setThreshold(const RealScalar& threshold)
-  {
-    m_usePrescribedThreshold = true;
-    m_prescribedThreshold = threshold;
-    return derived();
-  }
-
-  /** Allows to come back to the default behavior, letting Eigen use its default formula for
-    * determining the threshold.
-    *
-    * You should pass the special object Eigen::Default as parameter here.
-    * \code svd.setThreshold(Eigen::Default); \endcode
-    *
-    * See the documentation of setThreshold(const RealScalar&).
-    */
-  Derived& setThreshold(Default_t)
-  {
-    m_usePrescribedThreshold = false;
-    return derived();
-  }
-
-  /** Returns the threshold that will be used by certain methods such as rank().
-    *
-    * See the documentation of setThreshold(const RealScalar&).
-    */
-  RealScalar threshold() const
-  {
-    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
-    // this temporary is needed to workaround a MSVC issue
-    Index diagSize = (std::max<Index>)(1,m_diagSize);
-    return m_usePrescribedThreshold ? m_prescribedThreshold
-                                    : RealScalar(diagSize)*NumTraits<Scalar>::epsilon();
-  }
-
-  /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
-  inline bool computeU() const { return m_computeFullU || m_computeThinU; }
-  /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
-  inline bool computeV() const { return m_computeFullV || m_computeThinV; }
-
-  inline Index rows() const { return m_rows; }
-  inline Index cols() const { return m_cols; }
-  
-  #ifdef EIGEN_PARSED_BY_DOXYGEN
-  /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
-    *
-    * \param b the right-hand-side of the equation to solve.
-    *
-    * \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V.
-    *
-    * \note SVD solving is implicitly least-squares. Thus, this method serves both purposes of exact solving and least-squares solving.
-    * In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert A x - b \Vert \f$.
-    */
-  template<typename Rhs>
-  inline const Solve<Derived, Rhs>
-  solve(const MatrixBase<Rhs>& b) const;
-  #endif
-
-
-  /** \brief Reports whether previous computation was successful.
-   *
-   * \returns \c Success if computation was successful.
-   */
-  EIGEN_DEVICE_FUNC
-  ComputationInfo info() const
-  {
-    eigen_assert(m_isInitialized && "SVD is not initialized.");
-    return m_info;
-  }
-
-  #ifndef EIGEN_PARSED_BY_DOXYGEN
-  template<typename RhsType, typename DstType>
-  void _solve_impl(const RhsType &rhs, DstType &dst) const;
-
-  template<bool Conjugate, typename RhsType, typename DstType>
-  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
-  #endif
-
-protected:
-
-  static void check_template_parameters()
-  {
-    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-  }
-
-  void _check_compute_assertions() const {
-    eigen_assert(m_isInitialized && "SVD is not initialized.");
-  }
-
-  template<bool Transpose_, typename Rhs>
-  void _check_solve_assertion(const Rhs& b) const {
-      EIGEN_ONLY_USED_FOR_DEBUG(b);
-      _check_compute_assertions();
-      eigen_assert(computeU() && computeV() && "SVDBase::solve(): Both unitaries U and V are required to be computed (thin unitaries suffice).");
-      eigen_assert((Transpose_?cols():rows())==b.rows() && "SVDBase::solve(): invalid number of rows of the right hand side matrix b");
-  }
-
-  // return true if already allocated
-  bool allocate(Index rows, Index cols, unsigned int computationOptions) ;
-
-  MatrixUType m_matrixU;
-  MatrixVType m_matrixV;
-  SingularValuesType m_singularValues;
-  ComputationInfo m_info;
-  bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold;
-  bool m_computeFullU, m_computeThinU;
-  bool m_computeFullV, m_computeThinV;
-  unsigned int m_computationOptions;
-  Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize;
-  RealScalar m_prescribedThreshold;
-
-  /** \brief Default Constructor.
-   *
-   * Default constructor of SVDBase
-   */
-  SVDBase()
-    : m_info(Success),
-      m_isInitialized(false),
-      m_isAllocated(false),
-      m_usePrescribedThreshold(false),
-      m_computeFullU(false),
-      m_computeThinU(false),
-      m_computeFullV(false),
-      m_computeThinV(false),
-      m_computationOptions(0),
-      m_rows(-1), m_cols(-1), m_diagSize(0)
-  {
-    check_template_parameters();
-  }
-
-
-};
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename Derived>
-template<typename RhsType, typename DstType>
-void SVDBase<Derived>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  // A = U S V^*
-  // So A^{-1} = V S^{-1} U^*
-
-  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
-  Index l_rank = rank();
-  tmp.noalias() =  m_matrixU.leftCols(l_rank).adjoint() * rhs;
-  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
-  dst = m_matrixV.leftCols(l_rank) * tmp;
-}
-
-template<typename Derived>
-template<bool Conjugate, typename RhsType, typename DstType>
-void SVDBase<Derived>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
-{
-  // A = U S V^*
-  // So  A^{-*} = U S^{-1} V^*
-  // And A^{-T} = U_conj S^{-1} V^T
-  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
-  Index l_rank = rank();
-
-  tmp.noalias() =  m_matrixV.leftCols(l_rank).transpose().template conjugateIf<Conjugate>() * rhs;
-  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
-  dst = m_matrixU.template conjugateIf<!Conjugate>().leftCols(l_rank) * tmp;
-}
-#endif
-
-template<typename MatrixType>
-bool SVDBase<MatrixType>::allocate(Index rows, Index cols, unsigned int computationOptions)
-{
-  eigen_assert(rows >= 0 && cols >= 0);
-
-  if (m_isAllocated &&
-      rows == m_rows &&
-      cols == m_cols &&
-      computationOptions == m_computationOptions)
-  {
-    return true;
-  }
-
-  m_rows = rows;
-  m_cols = cols;
-  m_info = Success;
-  m_isInitialized = false;
-  m_isAllocated = true;
-  m_computationOptions = computationOptions;
-  m_computeFullU = (computationOptions & ComputeFullU) != 0;
-  m_computeThinU = (computationOptions & ComputeThinU) != 0;
-  m_computeFullV = (computationOptions & ComputeFullV) != 0;
-  m_computeThinV = (computationOptions & ComputeThinV) != 0;
-  eigen_assert(!(m_computeFullU && m_computeThinU) && "SVDBase: you can't ask for both full and thin U");
-  eigen_assert(!(m_computeFullV && m_computeThinV) && "SVDBase: you can't ask for both full and thin V");
-  eigen_assert(EIGEN_IMPLIES(m_computeThinU || m_computeThinV, MatrixType::ColsAtCompileTime==Dynamic) &&
-	       "SVDBase: thin U and V are only available when your matrix has a dynamic number of columns.");
-
-  m_diagSize = (std::min)(m_rows, m_cols);
-  m_singularValues.resize(m_diagSize);
-  if(RowsAtCompileTime==Dynamic)
-    m_matrixU.resize(m_rows, m_computeFullU ? m_rows : m_computeThinU ? m_diagSize : 0);
-  if(ColsAtCompileTime==Dynamic)
-    m_matrixV.resize(m_cols, m_computeFullV ? m_cols : m_computeThinV ? m_diagSize : 0);
-
-  return false;
-}
-
-}// end namespace
-
-#endif // EIGEN_SVDBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
deleted file mode 100644
index 997defc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
+++ /dev/null
@@ -1,414 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BIDIAGONALIZATION_H
-#define EIGEN_BIDIAGONALIZATION_H
-
-namespace Eigen { 
-
-namespace internal {
-// UpperBidiagonalization will probably be replaced by a Bidiagonalization class, don't want to make it stable API.
-// At the same time, it's useful to keep for now as it's about the only thing that is testing the BandMatrix class.
-
-template<typename _MatrixType> class UpperBidiagonalization
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      ColsAtCompileTimeMinusOne = internal::decrement_size<ColsAtCompileTime>::ret
-    };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
-    typedef Matrix<Scalar, 1, ColsAtCompileTime> RowVectorType;
-    typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType;
-    typedef BandMatrix<RealScalar, ColsAtCompileTime, ColsAtCompileTime, 1, 0, RowMajor> BidiagonalType;
-    typedef Matrix<Scalar, ColsAtCompileTime, 1> DiagVectorType;
-    typedef Matrix<Scalar, ColsAtCompileTimeMinusOne, 1> SuperDiagVectorType;
-    typedef HouseholderSequence<
-              const MatrixType,
-              const typename internal::remove_all<typename Diagonal<const MatrixType,0>::ConjugateReturnType>::type
-            > HouseholderUSequenceType;
-    typedef HouseholderSequence<
-              const typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type,
-              Diagonal<const MatrixType,1>,
-              OnTheRight
-            > HouseholderVSequenceType;
-    
-    /**
-    * \brief Default Constructor.
-    *
-    * The default constructor is useful in cases in which the user intends to
-    * perform decompositions via Bidiagonalization::compute(const MatrixType&).
-    */
-    UpperBidiagonalization() : m_householder(), m_bidiagonal(), m_isInitialized(false) {}
-
-    explicit UpperBidiagonalization(const MatrixType& matrix)
-      : m_householder(matrix.rows(), matrix.cols()),
-        m_bidiagonal(matrix.cols(), matrix.cols()),
-        m_isInitialized(false)
-    {
-      compute(matrix);
-    }
-    
-    UpperBidiagonalization& compute(const MatrixType& matrix);
-    UpperBidiagonalization& computeUnblocked(const MatrixType& matrix);
-    
-    const MatrixType& householder() const { return m_householder; }
-    const BidiagonalType& bidiagonal() const { return m_bidiagonal; }
-    
-    const HouseholderUSequenceType householderU() const
-    {
-      eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
-      return HouseholderUSequenceType(m_householder, m_householder.diagonal().conjugate());
-    }
-
-    const HouseholderVSequenceType householderV() // const here gives nasty errors and i'm lazy
-    {
-      eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
-      return HouseholderVSequenceType(m_householder.conjugate(), m_householder.const_derived().template diagonal<1>())
-             .setLength(m_householder.cols()-1)
-             .setShift(1);
-    }
-    
-  protected:
-    MatrixType m_householder;
-    BidiagonalType m_bidiagonal;
-    bool m_isInitialized;
-};
-
-// Standard upper bidiagonalization without fancy optimizations
-// This version should be faster for small matrix size
-template<typename MatrixType>
-void upperbidiagonalization_inplace_unblocked(MatrixType& mat,
-                                              typename MatrixType::RealScalar *diagonal,
-                                              typename MatrixType::RealScalar *upper_diagonal,
-                                              typename MatrixType::Scalar* tempData = 0)
-{
-  typedef typename MatrixType::Scalar Scalar;
-
-  Index rows = mat.rows();
-  Index cols = mat.cols();
-
-  typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixType::MaxRowsAtCompileTime,1> TempType;
-  TempType tempVector;
-  if(tempData==0)
-  {
-    tempVector.resize(rows);
-    tempData = tempVector.data();
-  }
-
-  for (Index k = 0; /* breaks at k==cols-1 below */ ; ++k)
-  {
-    Index remainingRows = rows - k;
-    Index remainingCols = cols - k - 1;
-
-    // construct left householder transform in-place in A
-    mat.col(k).tail(remainingRows)
-       .makeHouseholderInPlace(mat.coeffRef(k,k), diagonal[k]);
-    // apply householder transform to remaining part of A on the left
-    mat.bottomRightCorner(remainingRows, remainingCols)
-       .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), mat.coeff(k,k), tempData);
-
-    if(k == cols-1) break;
-
-    // construct right householder transform in-place in mat
-    mat.row(k).tail(remainingCols)
-       .makeHouseholderInPlace(mat.coeffRef(k,k+1), upper_diagonal[k]);
-    // apply householder transform to remaining part of mat on the left
-    mat.bottomRightCorner(remainingRows-1, remainingCols)
-       .applyHouseholderOnTheRight(mat.row(k).tail(remainingCols-1).adjoint(), mat.coeff(k,k+1), tempData);
-  }
-}
-
-/** \internal
-  * Helper routine for the block reduction to upper bidiagonal form.
-  *
-  * Let's partition the matrix A:
-  * 
-  *      | A00 A01 |
-  *  A = |         |
-  *      | A10 A11 |
-  *
-  * This function reduces to bidiagonal form the left \c rows x \a blockSize vertical panel [A00/A10]
-  * and the \a blockSize x \c cols horizontal panel [A00 A01] of the matrix \a A. The bottom-right block A11
-  * is updated using matrix-matrix products:
-  *   A22 -= V * Y^T - X * U^T
-  * where V and U contains the left and right Householder vectors. U and V are stored in A10, and A01
-  * respectively, and the update matrices X and Y are computed during the reduction.
-  * 
-  */
-template<typename MatrixType>
-void upperbidiagonalization_blocked_helper(MatrixType& A,
-                                           typename MatrixType::RealScalar *diagonal,
-                                           typename MatrixType::RealScalar *upper_diagonal,
-                                           Index bs,
-                                           Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic,
-                                                      traits<MatrixType>::Flags & RowMajorBit> > X,
-                                           Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic,
-                                                      traits<MatrixType>::Flags & RowMajorBit> > Y)
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef typename NumTraits<RealScalar>::Literal Literal;
-  enum { StorageOrder = traits<MatrixType>::Flags & RowMajorBit };
-  typedef InnerStride<int(StorageOrder) == int(ColMajor) ? 1 : Dynamic> ColInnerStride;
-  typedef InnerStride<int(StorageOrder) == int(ColMajor) ? Dynamic : 1> RowInnerStride;
-  typedef Ref<Matrix<Scalar, Dynamic, 1>, 0, ColInnerStride>    SubColumnType;
-  typedef Ref<Matrix<Scalar, 1, Dynamic>, 0, RowInnerStride>    SubRowType;
-  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder > > SubMatType;
-  
-  Index brows = A.rows();
-  Index bcols = A.cols();
-
-  Scalar tau_u, tau_u_prev(0), tau_v;
-
-  for(Index k = 0; k < bs; ++k)
-  {
-    Index remainingRows = brows - k;
-    Index remainingCols = bcols - k - 1;
-
-    SubMatType X_k1( X.block(k,0, remainingRows,k) );
-    SubMatType V_k1( A.block(k,0, remainingRows,k) );
-
-    // 1 - update the k-th column of A
-    SubColumnType v_k = A.col(k).tail(remainingRows);
-          v_k -= V_k1 * Y.row(k).head(k).adjoint();
-    if(k) v_k -= X_k1 * A.col(k).head(k);
-    
-    // 2 - construct left Householder transform in-place
-    v_k.makeHouseholderInPlace(tau_v, diagonal[k]);
-       
-    if(k+1<bcols)
-    {
-      SubMatType Y_k  ( Y.block(k+1,0, remainingCols, k+1) );
-      SubMatType U_k1 ( A.block(0,k+1, k,remainingCols) );
-      
-      // this eases the application of Householder transforAions
-      // A(k,k) will store tau_v later
-      A(k,k) = Scalar(1);
-
-      // 3 - Compute y_k^T = tau_v * ( A^T*v_k - Y_k-1*V_k-1^T*v_k - U_k-1*X_k-1^T*v_k )
-      {
-        SubColumnType y_k( Y.col(k).tail(remainingCols) );
-        
-        // let's use the beginning of column k of Y as a temporary vector
-        SubColumnType tmp( Y.col(k).head(k) );
-        y_k.noalias()  = A.block(k,k+1, remainingRows,remainingCols).adjoint() * v_k; // bottleneck
-        tmp.noalias()  = V_k1.adjoint()  * v_k;
-        y_k.noalias() -= Y_k.leftCols(k) * tmp;
-        tmp.noalias()  = X_k1.adjoint()  * v_k;
-        y_k.noalias() -= U_k1.adjoint()  * tmp;
-        y_k *= numext::conj(tau_v);
-      }
-
-      // 4 - update k-th row of A (it will become u_k)
-      SubRowType u_k( A.row(k).tail(remainingCols) );
-      u_k = u_k.conjugate();
-      {
-        u_k -= Y_k * A.row(k).head(k+1).adjoint();
-        if(k) u_k -= U_k1.adjoint() * X.row(k).head(k).adjoint();
-      }
-
-      // 5 - construct right Householder transform in-place
-      u_k.makeHouseholderInPlace(tau_u, upper_diagonal[k]);
-
-      // this eases the application of Householder transformations
-      // A(k,k+1) will store tau_u later
-      A(k,k+1) = Scalar(1);
-
-      // 6 - Compute x_k = tau_u * ( A*u_k - X_k-1*U_k-1^T*u_k - V_k*Y_k^T*u_k )
-      {
-        SubColumnType x_k ( X.col(k).tail(remainingRows-1) );
-        
-        // let's use the beginning of column k of X as a temporary vectors
-        // note that tmp0 and tmp1 overlaps
-        SubColumnType tmp0 ( X.col(k).head(k) ),
-                      tmp1 ( X.col(k).head(k+1) );
-                    
-        x_k.noalias()   = A.block(k+1,k+1, remainingRows-1,remainingCols) * u_k.transpose(); // bottleneck
-        tmp0.noalias()  = U_k1 * u_k.transpose();
-        x_k.noalias()  -= X_k1.bottomRows(remainingRows-1) * tmp0;
-        tmp1.noalias()  = Y_k.adjoint() * u_k.transpose();
-        x_k.noalias()  -= A.block(k+1,0, remainingRows-1,k+1) * tmp1;
-        x_k *= numext::conj(tau_u);
-        tau_u = numext::conj(tau_u);
-        u_k = u_k.conjugate();
-      }
-
-      if(k>0) A.coeffRef(k-1,k) = tau_u_prev;
-      tau_u_prev = tau_u;
-    }
-    else
-      A.coeffRef(k-1,k) = tau_u_prev;
-
-    A.coeffRef(k,k) = tau_v;
-  }
-  
-  if(bs<bcols)
-    A.coeffRef(bs-1,bs) = tau_u_prev;
-
-  // update A22
-  if(bcols>bs && brows>bs)
-  {
-    SubMatType A11( A.bottomRightCorner(brows-bs,bcols-bs) );
-    SubMatType A10( A.block(bs,0, brows-bs,bs) );
-    SubMatType A01( A.block(0,bs, bs,bcols-bs) );
-    Scalar tmp = A01(bs-1,0);
-    A01(bs-1,0) = Literal(1);
-    A11.noalias() -= A10 * Y.topLeftCorner(bcols,bs).bottomRows(bcols-bs).adjoint();
-    A11.noalias() -= X.topLeftCorner(brows,bs).bottomRows(brows-bs) * A01;
-    A01(bs-1,0) = tmp;
-  }
-}
-
-/** \internal
-  *
-  * Implementation of a block-bidiagonal reduction.
-  * It is based on the following paper:
-  *   The Design of a Parallel Dense Linear Algebra Software Library: Reduction to Hessenberg, Tridiagonal, and Bidiagonal Form.
-  *   by Jaeyoung Choi, Jack J. Dongarra, David W. Walker. (1995)
-  *   section 3.3
-  */
-template<typename MatrixType, typename BidiagType>
-void upperbidiagonalization_inplace_blocked(MatrixType& A, BidiagType& bidiagonal,
-                                            Index maxBlockSize=32,
-                                            typename MatrixType::Scalar* /*tempData*/ = 0)
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef Block<MatrixType,Dynamic,Dynamic> BlockType;
-
-  Index rows = A.rows();
-  Index cols = A.cols();
-  Index size = (std::min)(rows, cols);
-
-  // X and Y are work space
-  enum { StorageOrder = traits<MatrixType>::Flags & RowMajorBit };
-  Matrix<Scalar,
-         MatrixType::RowsAtCompileTime,
-         Dynamic,
-         StorageOrder,
-         MatrixType::MaxRowsAtCompileTime> X(rows,maxBlockSize);
-  Matrix<Scalar,
-         MatrixType::ColsAtCompileTime,
-         Dynamic,
-         StorageOrder,
-         MatrixType::MaxColsAtCompileTime> Y(cols,maxBlockSize);
-  Index blockSize = (std::min)(maxBlockSize,size);
-
-  Index k = 0;
-  for(k = 0; k < size; k += blockSize)
-  {
-    Index bs = (std::min)(size-k,blockSize);  // actual size of the block
-    Index brows = rows - k;                   // rows of the block
-    Index bcols = cols - k;                   // columns of the block
-
-    // partition the matrix A:
-    // 
-    //      | A00 A01 A02 |
-    //      |             |
-    // A  = | A10 A11 A12 |
-    //      |             |
-    //      | A20 A21 A22 |
-    //
-    // where A11 is a bs x bs diagonal block,
-    // and let:
-    //      | A11 A12 |
-    //  B = |         |
-    //      | A21 A22 |
-
-    BlockType B = A.block(k,k,brows,bcols);
-    
-    // This stage performs the bidiagonalization of A11, A21, A12, and updating of A22.
-    // Finally, the algorithm continue on the updated A22.
-    //
-    // However, if B is too small, or A22 empty, then let's use an unblocked strategy
-    if(k+bs==cols || bcols<48) // somewhat arbitrary threshold
-    {
-      upperbidiagonalization_inplace_unblocked(B,
-                                               &(bidiagonal.template diagonal<0>().coeffRef(k)),
-                                               &(bidiagonal.template diagonal<1>().coeffRef(k)),
-                                               X.data()
-                                              );
-      break; // We're done
-    }
-    else
-    {
-      upperbidiagonalization_blocked_helper<BlockType>( B,
-                                                        &(bidiagonal.template diagonal<0>().coeffRef(k)),
-                                                        &(bidiagonal.template diagonal<1>().coeffRef(k)),
-                                                        bs,
-                                                        X.topLeftCorner(brows,bs),
-                                                        Y.topLeftCorner(bcols,bs)
-                                                      );
-    }
-  }
-}
-
-template<typename _MatrixType>
-UpperBidiagonalization<_MatrixType>& UpperBidiagonalization<_MatrixType>::computeUnblocked(const _MatrixType& matrix)
-{
-  Index rows = matrix.rows();
-  Index cols = matrix.cols();
-  EIGEN_ONLY_USED_FOR_DEBUG(cols);
-
-  eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
-
-  m_householder = matrix;
-
-  ColVectorType temp(rows);
-
-  upperbidiagonalization_inplace_unblocked(m_householder,
-                                           &(m_bidiagonal.template diagonal<0>().coeffRef(0)),
-                                           &(m_bidiagonal.template diagonal<1>().coeffRef(0)),
-                                           temp.data());
-
-  m_isInitialized = true;
-  return *this;
-}
-
-template<typename _MatrixType>
-UpperBidiagonalization<_MatrixType>& UpperBidiagonalization<_MatrixType>::compute(const _MatrixType& matrix)
-{
-  Index rows = matrix.rows();
-  Index cols = matrix.cols();
-  EIGEN_ONLY_USED_FOR_DEBUG(rows);
-  EIGEN_ONLY_USED_FOR_DEBUG(cols);
-
-  eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
-
-  m_householder = matrix;
-  upperbidiagonalization_inplace_blocked(m_householder, m_bidiagonal);
-            
-  m_isInitialized = true;
-  return *this;
-}
-
-#if 0
-/** \return the Householder QR decomposition of \c *this.
-  *
-  * \sa class Bidiagonalization
-  */
-template<typename Derived>
-const UpperBidiagonalization<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::bidiagonalization() const
-{
-  return UpperBidiagonalization<PlainObject>(eval());
-}
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BIDIAGONALIZATION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
deleted file mode 100644
index 9f93e32..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
+++ /dev/null
@@ -1,697 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SIMPLICIAL_CHOLESKY_H
-#define EIGEN_SIMPLICIAL_CHOLESKY_H
-
-namespace Eigen { 
-
-enum SimplicialCholeskyMode {
-  SimplicialCholeskyLLT,
-  SimplicialCholeskyLDLT
-};
-
-namespace internal {
-  template<typename CholMatrixType, typename InputMatrixType>
-  struct simplicial_cholesky_grab_input {
-    typedef CholMatrixType const * ConstCholMatrixPtr;
-    static void run(const InputMatrixType& input, ConstCholMatrixPtr &pmat, CholMatrixType &tmp)
-    {
-      tmp = input;
-      pmat = &tmp;
-    }
-  };
-  
-  template<typename MatrixType>
-  struct simplicial_cholesky_grab_input<MatrixType,MatrixType> {
-    typedef MatrixType const * ConstMatrixPtr;
-    static void run(const MatrixType& input, ConstMatrixPtr &pmat, MatrixType &/*tmp*/)
-    {
-      pmat = &input;
-    }
-  };
-} // end namespace internal
-
-/** \ingroup SparseCholesky_Module
-  * \brief A base class for direct sparse Cholesky factorizations
-  *
-  * This is a base class for LL^T and LDL^T Cholesky factorizations of sparse matrices that are
-  * selfadjoint and positive definite. These factorizations allow for solving A.X = B where
-  * X and B can be either dense or sparse.
-  * 
-  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
-  * such that the factorized matrix is P A P^-1.
-  *
-  * \tparam Derived the type of the derived class, that is the actual factorization type.
-  *
-  */
-template<typename Derived>
-class SimplicialCholeskyBase : public SparseSolverBase<Derived>
-{
-    typedef SparseSolverBase<Derived> Base;
-    using Base::m_isInitialized;
-    
-  public:
-    typedef typename internal::traits<Derived>::MatrixType MatrixType;
-    typedef typename internal::traits<Derived>::OrderingType OrderingType;
-    enum { UpLo = internal::traits<Derived>::UpLo };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
-    typedef CholMatrixType const * ConstCholMatrixPtr;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-    
-    using Base::derived;
-
-    /** Default constructor */
-    SimplicialCholeskyBase()
-      : m_info(Success),
-        m_factorizationIsOk(false),
-        m_analysisIsOk(false),
-        m_shiftOffset(0),
-        m_shiftScale(1)
-    {}
-
-    explicit SimplicialCholeskyBase(const MatrixType& matrix)
-      : m_info(Success),
-        m_factorizationIsOk(false),
-        m_analysisIsOk(false),
-        m_shiftOffset(0),
-        m_shiftScale(1)
-    {
-      derived().compute(matrix);
-    }
-
-    ~SimplicialCholeskyBase()
-    {
-    }
-
-    Derived& derived() { return *static_cast<Derived*>(this); }
-    const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    
-    inline Index cols() const { return m_matrix.cols(); }
-    inline Index rows() const { return m_matrix.rows(); }
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-    
-    /** \returns the permutation P
-      * \sa permutationPinv() */
-    const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationP() const
-    { return m_P; }
-    
-    /** \returns the inverse P^-1 of the permutation P
-      * \sa permutationP() */
-    const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationPinv() const
-    { return m_Pinv; }
-
-    /** Sets the shift parameters that will be used to adjust the diagonal coefficients during the numerical factorization.
-      *
-      * During the numerical factorization, the diagonal coefficients are transformed by the following linear model:\n
-      * \c d_ii = \a offset + \a scale * \c d_ii
-      *
-      * The default is the identity transformation with \a offset=0, and \a scale=1.
-      *
-      * \returns a reference to \c *this.
-      */
-    Derived& setShift(const RealScalar& offset, const RealScalar& scale = 1)
-    {
-      m_shiftOffset = offset;
-      m_shiftScale = scale;
-      return derived();
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal */
-    template<typename Stream>
-    void dumpMemory(Stream& s)
-    {
-      int total = 0;
-      s << "  L:        " << ((total+=(m_matrix.cols()+1) * sizeof(int) + m_matrix.nonZeros()*(sizeof(int)+sizeof(Scalar))) >> 20) << "Mb" << "\n";
-      s << "  diag:     " << ((total+=m_diag.size() * sizeof(Scalar)) >> 20) << "Mb" << "\n";
-      s << "  tree:     " << ((total+=m_parent.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  nonzeros: " << ((total+=m_nonZerosPerCol.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  perm:     " << ((total+=m_P.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  perm^-1:  " << ((total+=m_Pinv.size() * sizeof(int)) >> 20) << "Mb" << "\n";
-      s << "  TOTAL:    " << (total>> 20) << "Mb" << "\n";
-    }
-
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      eigen_assert(m_matrix.rows()==b.rows());
-
-      if(m_info!=Success)
-        return;
-
-      if(m_P.size()>0)
-        dest = m_P * b;
-      else
-        dest = b;
-
-      if(m_matrix.nonZeros()>0) // otherwise L==I
-        derived().matrixL().solveInPlace(dest);
-
-      if(m_diag.size()>0)
-        dest = m_diag.asDiagonal().inverse() * dest;
-
-      if (m_matrix.nonZeros()>0) // otherwise U==I
-        derived().matrixU().solveInPlace(dest);
-
-      if(m_P.size()>0)
-        dest = m_Pinv * dest;
-    }
-    
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
-    {
-      internal::solve_sparse_through_dense_panels(derived(), b, dest);
-    }
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-  protected:
-    
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    template<bool DoLDLT>
-    void compute(const MatrixType& matrix)
-    {
-      eigen_assert(matrix.rows()==matrix.cols());
-      Index size = matrix.cols();
-      CholMatrixType tmp(size,size);
-      ConstCholMatrixPtr pmat;
-      ordering(matrix, pmat, tmp);
-      analyzePattern_preordered(*pmat, DoLDLT);
-      factorize_preordered<DoLDLT>(*pmat);
-    }
-    
-    template<bool DoLDLT>
-    void factorize(const MatrixType& a)
-    {
-      eigen_assert(a.rows()==a.cols());
-      Index size = a.cols();
-      CholMatrixType tmp(size,size);
-      ConstCholMatrixPtr pmat;
-      
-      if(m_P.size() == 0 && (int(UpLo) & int(Upper)) == Upper)
-      {
-        // If there is no ordering, try to directly use the input matrix without any copy
-        internal::simplicial_cholesky_grab_input<CholMatrixType,MatrixType>::run(a, pmat, tmp);
-      }
-      else
-      {
-        tmp.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P);
-        pmat = &tmp;
-      }
-      
-      factorize_preordered<DoLDLT>(*pmat);
-    }
-
-    template<bool DoLDLT>
-    void factorize_preordered(const CholMatrixType& a);
-
-    void analyzePattern(const MatrixType& a, bool doLDLT)
-    {
-      eigen_assert(a.rows()==a.cols());
-      Index size = a.cols();
-      CholMatrixType tmp(size,size);
-      ConstCholMatrixPtr pmat;
-      ordering(a, pmat, tmp);
-      analyzePattern_preordered(*pmat,doLDLT);
-    }
-    void analyzePattern_preordered(const CholMatrixType& a, bool doLDLT);
-    
-    void ordering(const MatrixType& a, ConstCholMatrixPtr &pmat, CholMatrixType& ap);
-
-    /** keeps off-diagonal entries; drops diagonal entries */
-    struct keep_diag {
-      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
-      {
-        return row!=col;
-      }
-    };
-
-    mutable ComputationInfo m_info;
-    bool m_factorizationIsOk;
-    bool m_analysisIsOk;
-    
-    CholMatrixType m_matrix;
-    VectorType m_diag;                                // the diagonal coefficients (LDLT mode)
-    VectorI m_parent;                                 // elimination tree
-    VectorI m_nonZerosPerCol;
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_P;     // the permutation
-    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_Pinv;  // the inverse permutation
-
-    RealScalar m_shiftOffset;
-    RealScalar m_shiftScale;
-};
-
-template<typename _MatrixType, int _UpLo = Lower, typename _Ordering = AMDOrdering<typename _MatrixType::StorageIndex> > class SimplicialLLT;
-template<typename _MatrixType, int _UpLo = Lower, typename _Ordering = AMDOrdering<typename _MatrixType::StorageIndex> > class SimplicialLDLT;
-template<typename _MatrixType, int _UpLo = Lower, typename _Ordering = AMDOrdering<typename _MatrixType::StorageIndex> > class SimplicialCholesky;
-
-namespace internal {
-
-template<typename _MatrixType, int _UpLo, typename _Ordering> struct traits<SimplicialLLT<_MatrixType,_UpLo,_Ordering> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Ordering OrderingType;
-  enum { UpLo = _UpLo };
-  typedef typename MatrixType::Scalar                         Scalar;
-  typedef typename MatrixType::StorageIndex                   StorageIndex;
-  typedef SparseMatrix<Scalar, ColMajor, StorageIndex>        CholMatrixType;
-  typedef TriangularView<const CholMatrixType, Eigen::Lower>  MatrixL;
-  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::Upper>   MatrixU;
-  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
-};
-
-template<typename _MatrixType,int _UpLo, typename _Ordering> struct traits<SimplicialLDLT<_MatrixType,_UpLo,_Ordering> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Ordering OrderingType;
-  enum { UpLo = _UpLo };
-  typedef typename MatrixType::Scalar                             Scalar;
-  typedef typename MatrixType::StorageIndex                       StorageIndex;
-  typedef SparseMatrix<Scalar, ColMajor, StorageIndex>            CholMatrixType;
-  typedef TriangularView<const CholMatrixType, Eigen::UnitLower>  MatrixL;
-  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
-  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
-  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
-};
-
-template<typename _MatrixType, int _UpLo, typename _Ordering> struct traits<SimplicialCholesky<_MatrixType,_UpLo,_Ordering> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Ordering OrderingType;
-  enum { UpLo = _UpLo };
-};
-
-}
-
-/** \ingroup SparseCholesky_Module
-  * \class SimplicialLLT
-  * \brief A direct sparse LLT Cholesky factorizations
-  *
-  * This class provides a LL^T Cholesky factorizations of sparse matrices that are
-  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
-  * X and B can be either dense or sparse.
-  * 
-  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
-  * such that the factorized matrix is P A P^-1.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  * \tparam _Ordering The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa class SimplicialLDLT, class AMDOrdering, class NaturalOrdering
-  */
-template<typename _MatrixType, int _UpLo, typename _Ordering>
-    class SimplicialLLT : public SimplicialCholeskyBase<SimplicialLLT<_MatrixType,_UpLo,_Ordering> >
-{
-public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef SimplicialCholeskyBase<SimplicialLLT> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef internal::traits<SimplicialLLT> Traits;
-    typedef typename Traits::MatrixL  MatrixL;
-    typedef typename Traits::MatrixU  MatrixU;
-public:
-    /** Default constructor */
-    SimplicialLLT() : Base() {}
-    /** Constructs and performs the LLT factorization of \a matrix */
-    explicit SimplicialLLT(const MatrixType& matrix)
-        : Base(matrix) {}
-
-    /** \returns an expression of the factor L */
-    inline const MatrixL matrixL() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
-        return Traits::getL(Base::m_matrix);
-    }
-
-    /** \returns an expression of the factor U (= L^*) */
-    inline const MatrixU matrixU() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
-        return Traits::getU(Base::m_matrix);
-    }
-    
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    SimplicialLLT& compute(const MatrixType& matrix)
-    {
-      Base::template compute<false>(matrix);
-      return *this;
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& a)
-    {
-      Base::analyzePattern(a, false);
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& a)
-    {
-      Base::template factorize<false>(a);
-    }
-
-    /** \returns the determinant of the underlying matrix from the current factorization */
-    Scalar determinant() const
-    {
-      Scalar detL = Base::m_matrix.diagonal().prod();
-      return numext::abs2(detL);
-    }
-};
-
-/** \ingroup SparseCholesky_Module
-  * \class SimplicialLDLT
-  * \brief A direct sparse LDLT Cholesky factorizations without square root.
-  *
-  * This class provides a LDL^T Cholesky factorizations without square root of sparse matrices that are
-  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
-  * X and B can be either dense or sparse.
-  * 
-  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
-  * such that the factorized matrix is P A P^-1.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
-  *               or Upper. Default is Lower.
-  * \tparam _Ordering The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa class SimplicialLLT, class AMDOrdering, class NaturalOrdering
-  */
-template<typename _MatrixType, int _UpLo, typename _Ordering>
-    class SimplicialLDLT : public SimplicialCholeskyBase<SimplicialLDLT<_MatrixType,_UpLo,_Ordering> >
-{
-public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef SimplicialCholeskyBase<SimplicialLDLT> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef internal::traits<SimplicialLDLT> Traits;
-    typedef typename Traits::MatrixL  MatrixL;
-    typedef typename Traits::MatrixU  MatrixU;
-public:
-    /** Default constructor */
-    SimplicialLDLT() : Base() {}
-
-    /** Constructs and performs the LLT factorization of \a matrix */
-    explicit SimplicialLDLT(const MatrixType& matrix)
-        : Base(matrix) {}
-
-    /** \returns a vector expression of the diagonal D */
-    inline const VectorType vectorD() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
-        return Base::m_diag;
-    }
-    /** \returns an expression of the factor L */
-    inline const MatrixL matrixL() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
-        return Traits::getL(Base::m_matrix);
-    }
-
-    /** \returns an expression of the factor U (= L^*) */
-    inline const MatrixU matrixU() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
-        return Traits::getU(Base::m_matrix);
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    SimplicialLDLT& compute(const MatrixType& matrix)
-    {
-      Base::template compute<true>(matrix);
-      return *this;
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& a)
-    {
-      Base::analyzePattern(a, true);
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& a)
-    {
-      Base::template factorize<true>(a);
-    }
-
-    /** \returns the determinant of the underlying matrix from the current factorization */
-    Scalar determinant() const
-    {
-      return Base::m_diag.prod();
-    }
-};
-
-/** \deprecated use SimplicialLDLT or class SimplicialLLT
-  * \ingroup SparseCholesky_Module
-  * \class SimplicialCholesky
-  *
-  * \sa class SimplicialLDLT, class SimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo, typename _Ordering>
-    class SimplicialCholesky : public SimplicialCholeskyBase<SimplicialCholesky<_MatrixType,_UpLo,_Ordering> >
-{
-public:
-    typedef _MatrixType MatrixType;
-    enum { UpLo = _UpLo };
-    typedef SimplicialCholeskyBase<SimplicialCholesky> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
-    typedef Matrix<Scalar,Dynamic,1> VectorType;
-    typedef internal::traits<SimplicialCholesky> Traits;
-    typedef internal::traits<SimplicialLDLT<MatrixType,UpLo> > LDLTTraits;
-    typedef internal::traits<SimplicialLLT<MatrixType,UpLo>  > LLTTraits;
-  public:
-    SimplicialCholesky() : Base(), m_LDLT(true) {}
-
-    explicit SimplicialCholesky(const MatrixType& matrix)
-      : Base(), m_LDLT(true)
-    {
-      compute(matrix);
-    }
-
-    SimplicialCholesky& setMode(SimplicialCholeskyMode mode)
-    {
-      switch(mode)
-      {
-      case SimplicialCholeskyLLT:
-        m_LDLT = false;
-        break;
-      case SimplicialCholeskyLDLT:
-        m_LDLT = true;
-        break;
-      default:
-        break;
-      }
-
-      return *this;
-    }
-
-    inline const VectorType vectorD() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
-        return Base::m_diag;
-    }
-    inline const CholMatrixType rawMatrix() const {
-        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
-        return Base::m_matrix;
-    }
-    
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    SimplicialCholesky& compute(const MatrixType& matrix)
-    {
-      if(m_LDLT)
-        Base::template compute<true>(matrix);
-      else
-        Base::template compute<false>(matrix);
-      return *this;
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& a)
-    {
-      Base::analyzePattern(a, m_LDLT);
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& a)
-    {
-      if(m_LDLT)
-        Base::template factorize<true>(a);
-      else
-        Base::template factorize<false>(a);
-    }
-
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(Base::m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
-      eigen_assert(Base::m_matrix.rows()==b.rows());
-
-      if(Base::m_info!=Success)
-        return;
-
-      if(Base::m_P.size()>0)
-        dest = Base::m_P * b;
-      else
-        dest = b;
-
-      if(Base::m_matrix.nonZeros()>0) // otherwise L==I
-      {
-        if(m_LDLT)
-          LDLTTraits::getL(Base::m_matrix).solveInPlace(dest);
-        else
-          LLTTraits::getL(Base::m_matrix).solveInPlace(dest);
-      }
-
-      if(Base::m_diag.size()>0)
-        dest = Base::m_diag.real().asDiagonal().inverse() * dest;
-
-      if (Base::m_matrix.nonZeros()>0) // otherwise I==I
-      {
-        if(m_LDLT)
-          LDLTTraits::getU(Base::m_matrix).solveInPlace(dest);
-        else
-          LLTTraits::getU(Base::m_matrix).solveInPlace(dest);
-      }
-
-      if(Base::m_P.size()>0)
-        dest = Base::m_Pinv * dest;
-    }
-    
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
-    {
-      internal::solve_sparse_through_dense_panels(*this, b, dest);
-    }
-    
-    Scalar determinant() const
-    {
-      if(m_LDLT)
-      {
-        return Base::m_diag.prod();
-      }
-      else
-      {
-        Scalar detL = Diagonal<const CholMatrixType>(Base::m_matrix).prod();
-        return numext::abs2(detL);
-      }
-    }
-    
-  protected:
-    bool m_LDLT;
-};
-
-template<typename Derived>
-void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, ConstCholMatrixPtr &pmat, CholMatrixType& ap)
-{
-  eigen_assert(a.rows()==a.cols());
-  const Index size = a.rows();
-  pmat = &ap;
-  // Note that ordering methods compute the inverse permutation
-  if(!internal::is_same<OrderingType,NaturalOrdering<Index> >::value)
-  {
-    {
-      CholMatrixType C;
-      C = a.template selfadjointView<UpLo>();
-      
-      OrderingType ordering;
-      ordering(C,m_Pinv);
-    }
-
-    if(m_Pinv.size()>0) m_P = m_Pinv.inverse();
-    else                m_P.resize(0);
-    
-    ap.resize(size,size);
-    ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P);
-  }
-  else
-  {
-    m_Pinv.resize(0);
-    m_P.resize(0);
-    if(int(UpLo)==int(Lower) || MatrixType::IsRowMajor)
-    {
-      // we have to transpose the lower part to to the upper one
-      ap.resize(size,size);
-      ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>();
-    }
-    else
-      internal::simplicial_cholesky_grab_input<CholMatrixType,MatrixType>::run(a, pmat, ap);
-  }  
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SIMPLICIAL_CHOLESKY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
deleted file mode 100644
index 72e1740..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
+++ /dev/null
@@ -1,174 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*
-NOTE: these functions have been adapted from the LDL library:
-
-LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved.
-
-The author of LDL, Timothy A. Davis., has executed a license with Google LLC
-to permit distribution of this code and derivative works as part of Eigen under
-the Mozilla Public License v. 2.0, as stated at the top of this file.
- */
-
-#ifndef EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
-#define EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
-
-namespace Eigen {
-
-template<typename Derived>
-void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT)
-{
-  const StorageIndex size = StorageIndex(ap.rows());
-  m_matrix.resize(size, size);
-  m_parent.resize(size);
-  m_nonZerosPerCol.resize(size);
-
-  ei_declare_aligned_stack_constructed_variable(StorageIndex, tags, size, 0);
-
-  for(StorageIndex k = 0; k < size; ++k)
-  {
-    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
-    m_parent[k] = -1;             /* parent of k is not yet known */
-    tags[k] = k;                  /* mark node k as visited */
-    m_nonZerosPerCol[k] = 0;      /* count of nonzeros in column k of L */
-    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if(i < k)
-      {
-        /* follow path from i to root of etree, stop at flagged node */
-        for(; tags[i] != k; i = m_parent[i])
-        {
-          /* find parent of i if not yet determined */
-          if (m_parent[i] == -1)
-            m_parent[i] = k;
-          m_nonZerosPerCol[i]++;        /* L (k,i) is nonzero */
-          tags[i] = k;                  /* mark i as visited */
-        }
-      }
-    }
-  }
-
-  /* construct Lp index array from m_nonZerosPerCol column counts */
-  StorageIndex* Lp = m_matrix.outerIndexPtr();
-  Lp[0] = 0;
-  for(StorageIndex k = 0; k < size; ++k)
-    Lp[k+1] = Lp[k] + m_nonZerosPerCol[k] + (doLDLT ? 0 : 1);
-
-  m_matrix.resizeNonZeros(Lp[size]);
-
-  m_isInitialized     = true;
-  m_info              = Success;
-  m_analysisIsOk      = true;
-  m_factorizationIsOk = false;
-}
-
-
-template<typename Derived>
-template<bool DoLDLT>
-void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap)
-{
-  using std::sqrt;
-
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  eigen_assert(ap.rows()==ap.cols());
-  eigen_assert(m_parent.size()==ap.rows());
-  eigen_assert(m_nonZerosPerCol.size()==ap.rows());
-
-  const StorageIndex size = StorageIndex(ap.rows());
-  const StorageIndex* Lp = m_matrix.outerIndexPtr();
-  StorageIndex* Li = m_matrix.innerIndexPtr();
-  Scalar* Lx = m_matrix.valuePtr();
-
-  ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0);
-  ei_declare_aligned_stack_constructed_variable(StorageIndex,  pattern, size, 0);
-  ei_declare_aligned_stack_constructed_variable(StorageIndex,  tags, size, 0);
-
-  bool ok = true;
-  m_diag.resize(DoLDLT ? size : 0);
-
-  for(StorageIndex k = 0; k < size; ++k)
-  {
-    // compute nonzero pattern of kth row of L, in topological order
-    y[k] = Scalar(0);                     // Y(0:k) is now all zero
-    StorageIndex top = size;               // stack for pattern is empty
-    tags[k] = k;                    // mark node k as visited
-    m_nonZerosPerCol[k] = 0;        // count of nonzeros in column k of L
-    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if(i <= k)
-      {
-        y[i] += numext::conj(it.value());            /* scatter A(i,k) into Y (sum duplicates) */
-        Index len;
-        for(len = 0; tags[i] != k; i = m_parent[i])
-        {
-          pattern[len++] = i;     /* L(k,i) is nonzero */
-          tags[i] = k;            /* mark i as visited */
-        }
-        while(len > 0)
-          pattern[--top] = pattern[--len];
-      }
-    }
-
-    /* compute numerical values kth row of L (a sparse triangular solve) */
-
-    RealScalar d = numext::real(y[k]) * m_shiftScale + m_shiftOffset;    // get D(k,k), apply the shift function, and clear Y(k)
-    y[k] = Scalar(0);
-    for(; top < size; ++top)
-    {
-      Index i = pattern[top];       /* pattern[top:n-1] is pattern of L(:,k) */
-      Scalar yi = y[i];             /* get and clear Y(i) */
-      y[i] = Scalar(0);
-
-      /* the nonzero entry L(k,i) */
-      Scalar l_ki;
-      if(DoLDLT)
-        l_ki = yi / numext::real(m_diag[i]);
-      else
-        yi = l_ki = yi / Lx[Lp[i]];
-
-      Index p2 = Lp[i] + m_nonZerosPerCol[i];
-      Index p;
-      for(p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p)
-        y[Li[p]] -= numext::conj(Lx[p]) * yi;
-      d -= numext::real(l_ki * numext::conj(yi));
-      Li[p] = k;                          /* store L(k,i) in column form of L */
-      Lx[p] = l_ki;
-      ++m_nonZerosPerCol[i];              /* increment count of nonzeros in col i */
-    }
-    if(DoLDLT)
-    {
-      m_diag[k] = d;
-      if(d == RealScalar(0))
-      {
-        ok = false;                         /* failure, D(k,k) is zero */
-        break;
-      }
-    }
-    else
-    {
-      Index p = Lp[k] + m_nonZerosPerCol[k]++;
-      Li[p] = k ;                /* store L(k,k) = sqrt (d) in column k */
-      if(d <= RealScalar(0)) {
-        ok = false;              /* failure, matrix is not positive definite */
-        break;
-      }
-      Lx[p] = sqrt(d) ;
-    }
-  }
-
-  m_info = ok ? Success : NumericalIssue;
-  m_factorizationIsOk = true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/AmbiVector.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/AmbiVector.h
deleted file mode 100644
index 2cb7747..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/AmbiVector.h
+++ /dev/null
@@ -1,378 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_AMBIVECTOR_H
-#define EIGEN_AMBIVECTOR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal
-  * Hybrid sparse/dense vector class designed for intensive read-write operations.
-  *
-  * See BasicSparseLLT and SparseProduct for usage examples.
-  */
-template<typename _Scalar, typename _StorageIndex>
-class AmbiVector
-{
-  public:
-    typedef _Scalar Scalar;
-    typedef _StorageIndex StorageIndex;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    explicit AmbiVector(Index size)
-      : m_buffer(0), m_zero(0), m_size(0), m_end(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
-    {
-      resize(size);
-    }
-
-    void init(double estimatedDensity);
-    void init(int mode);
-
-    Index nonZeros() const;
-
-    /** Specifies a sub-vector to work on */
-    void setBounds(Index start, Index end) { m_start = convert_index(start); m_end = convert_index(end); }
-
-    void setZero();
-
-    void restart();
-    Scalar& coeffRef(Index i);
-    Scalar& coeff(Index i);
-
-    class Iterator;
-
-    ~AmbiVector() { delete[] m_buffer; }
-
-    void resize(Index size)
-    {
-      if (m_allocatedSize < size)
-        reallocate(size);
-      m_size = convert_index(size);
-    }
-
-    StorageIndex size() const { return m_size; }
-
-  protected:
-    StorageIndex convert_index(Index idx)
-    {
-      return internal::convert_index<StorageIndex>(idx);
-    }
-
-    void reallocate(Index size)
-    {
-      // if the size of the matrix is not too large, let's allocate a bit more than needed such
-      // that we can handle dense vector even in sparse mode.
-      delete[] m_buffer;
-      if (size<1000)
-      {
-        Index allocSize = (size * sizeof(ListEl) + sizeof(Scalar) - 1)/sizeof(Scalar);
-        m_allocatedElements = convert_index((allocSize*sizeof(Scalar))/sizeof(ListEl));
-        m_buffer = new Scalar[allocSize];
-      }
-      else
-      {
-        m_allocatedElements = convert_index((size*sizeof(Scalar))/sizeof(ListEl));
-        m_buffer = new Scalar[size];
-      }
-      m_size = convert_index(size);
-      m_start = 0;
-      m_end = m_size;
-    }
-
-    void reallocateSparse()
-    {
-      Index copyElements = m_allocatedElements;
-      m_allocatedElements = (std::min)(StorageIndex(m_allocatedElements*1.5),m_size);
-      Index allocSize = m_allocatedElements * sizeof(ListEl);
-      allocSize = (allocSize + sizeof(Scalar) - 1)/sizeof(Scalar);
-      Scalar* newBuffer = new Scalar[allocSize];
-      std::memcpy(newBuffer,  m_buffer,  copyElements * sizeof(ListEl));
-      delete[] m_buffer;
-      m_buffer = newBuffer;
-    }
-
-  protected:
-    // element type of the linked list
-    struct ListEl
-    {
-      StorageIndex next;
-      StorageIndex index;
-      Scalar value;
-    };
-
-    // used to store data in both mode
-    Scalar* m_buffer;
-    Scalar m_zero;
-    StorageIndex m_size;
-    StorageIndex m_start;
-    StorageIndex m_end;
-    StorageIndex m_allocatedSize;
-    StorageIndex m_allocatedElements;
-    StorageIndex m_mode;
-
-    // linked list mode
-    StorageIndex m_llStart;
-    StorageIndex m_llCurrent;
-    StorageIndex m_llSize;
-};
-
-/** \returns the number of non zeros in the current sub vector */
-template<typename _Scalar,typename _StorageIndex>
-Index AmbiVector<_Scalar,_StorageIndex>::nonZeros() const
-{
-  if (m_mode==IsSparse)
-    return m_llSize;
-  else
-    return m_end - m_start;
-}
-
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::init(double estimatedDensity)
-{
-  if (estimatedDensity>0.1)
-    init(IsDense);
-  else
-    init(IsSparse);
-}
-
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::init(int mode)
-{
-  m_mode = mode;
-  // This is only necessary in sparse mode, but we set these unconditionally to avoid some maybe-uninitialized warnings
-  // if (m_mode==IsSparse)
-  {
-    m_llSize = 0;
-    m_llStart = -1;
-  }
-}
-
-/** Must be called whenever we might perform a write access
-  * with an index smaller than the previous one.
-  *
-  * Don't worry, this function is extremely cheap.
-  */
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::restart()
-{
-  m_llCurrent = m_llStart;
-}
-
-/** Set all coefficients of current subvector to zero */
-template<typename _Scalar,typename _StorageIndex>
-void AmbiVector<_Scalar,_StorageIndex>::setZero()
-{
-  if (m_mode==IsDense)
-  {
-    for (Index i=m_start; i<m_end; ++i)
-      m_buffer[i] = Scalar(0);
-  }
-  else
-  {
-    eigen_assert(m_mode==IsSparse);
-    m_llSize = 0;
-    m_llStart = -1;
-  }
-}
-
-template<typename _Scalar,typename _StorageIndex>
-_Scalar& AmbiVector<_Scalar,_StorageIndex>::coeffRef(Index i)
-{
-  if (m_mode==IsDense)
-    return m_buffer[i];
-  else
-  {
-    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
-    // TODO factorize the following code to reduce code generation
-    eigen_assert(m_mode==IsSparse);
-    if (m_llSize==0)
-    {
-      // this is the first element
-      m_llStart = 0;
-      m_llCurrent = 0;
-      ++m_llSize;
-      llElements[0].value = Scalar(0);
-      llElements[0].index = convert_index(i);
-      llElements[0].next = -1;
-      return llElements[0].value;
-    }
-    else if (i<llElements[m_llStart].index)
-    {
-      // this is going to be the new first element of the list
-      ListEl& el = llElements[m_llSize];
-      el.value = Scalar(0);
-      el.index = convert_index(i);
-      el.next = m_llStart;
-      m_llStart = m_llSize;
-      ++m_llSize;
-      m_llCurrent = m_llStart;
-      return el.value;
-    }
-    else
-    {
-      StorageIndex nextel = llElements[m_llCurrent].next;
-      eigen_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
-      while (nextel >= 0 && llElements[nextel].index<=i)
-      {
-        m_llCurrent = nextel;
-        nextel = llElements[nextel].next;
-      }
-
-      if (llElements[m_llCurrent].index==i)
-      {
-        // the coefficient already exists and we found it !
-        return llElements[m_llCurrent].value;
-      }
-      else
-      {
-        if (m_llSize>=m_allocatedElements)
-        {
-          reallocateSparse();
-          llElements = reinterpret_cast<ListEl*>(m_buffer);
-        }
-        eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
-        // let's insert a new coefficient
-        ListEl& el = llElements[m_llSize];
-        el.value = Scalar(0);
-        el.index = convert_index(i);
-        el.next = llElements[m_llCurrent].next;
-        llElements[m_llCurrent].next = m_llSize;
-        ++m_llSize;
-        return el.value;
-      }
-    }
-  }
-}
-
-template<typename _Scalar,typename _StorageIndex>
-_Scalar& AmbiVector<_Scalar,_StorageIndex>::coeff(Index i)
-{
-  if (m_mode==IsDense)
-    return m_buffer[i];
-  else
-  {
-    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
-    eigen_assert(m_mode==IsSparse);
-    if ((m_llSize==0) || (i<llElements[m_llStart].index))
-    {
-      return m_zero;
-    }
-    else
-    {
-      Index elid = m_llStart;
-      while (elid >= 0 && llElements[elid].index<i)
-        elid = llElements[elid].next;
-
-      if (llElements[elid].index==i)
-        return llElements[m_llCurrent].value;
-      else
-        return m_zero;
-    }
-  }
-}
-
-/** Iterator over the nonzero coefficients */
-template<typename _Scalar,typename _StorageIndex>
-class AmbiVector<_Scalar,_StorageIndex>::Iterator
-{
-  public:
-    typedef _Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    /** Default constructor
-      * \param vec the vector on which we iterate
-      * \param epsilon the minimal value used to prune zero coefficients.
-      * In practice, all coefficients having a magnitude smaller than \a epsilon
-      * are skipped.
-      */
-    explicit Iterator(const AmbiVector& vec, const RealScalar& epsilon = 0)
-      : m_vector(vec)
-    {
-      using std::abs;
-      m_epsilon = epsilon;
-      m_isDense = m_vector.m_mode==IsDense;
-      if (m_isDense)
-      {
-        m_currentEl = 0;   // this is to avoid a compilation warning
-        m_cachedValue = 0; // this is to avoid a compilation warning
-        m_cachedIndex = m_vector.m_start-1;
-        ++(*this);
-      }
-      else
-      {
-        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
-        m_currentEl = m_vector.m_llStart;
-        while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon)
-          m_currentEl = llElements[m_currentEl].next;
-        if (m_currentEl<0)
-        {
-          m_cachedValue = 0; // this is to avoid a compilation warning
-          m_cachedIndex = -1;
-        }
-        else
-        {
-          m_cachedIndex = llElements[m_currentEl].index;
-          m_cachedValue = llElements[m_currentEl].value;
-        }
-      }
-    }
-
-    StorageIndex index() const { return m_cachedIndex; }
-    Scalar value() const { return m_cachedValue; }
-
-    operator bool() const { return m_cachedIndex>=0; }
-
-    Iterator& operator++()
-    {
-      using std::abs;
-      if (m_isDense)
-      {
-        do {
-          ++m_cachedIndex;
-        } while (m_cachedIndex<m_vector.m_end && abs(m_vector.m_buffer[m_cachedIndex])<=m_epsilon);
-        if (m_cachedIndex<m_vector.m_end)
-          m_cachedValue = m_vector.m_buffer[m_cachedIndex];
-        else
-          m_cachedIndex=-1;
-      }
-      else
-      {
-        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
-        do {
-          m_currentEl = llElements[m_currentEl].next;
-        } while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon);
-        if (m_currentEl<0)
-        {
-          m_cachedIndex = -1;
-        }
-        else
-        {
-          m_cachedIndex = llElements[m_currentEl].index;
-          m_cachedValue = llElements[m_currentEl].value;
-        }
-      }
-      return *this;
-    }
-
-  protected:
-    const AmbiVector& m_vector; // the target vector
-    StorageIndex m_currentEl;   // the current element in sparse/linked-list mode
-    RealScalar m_epsilon;       // epsilon used to prune zero coefficients
-    StorageIndex m_cachedIndex; // current coordinate
-    Scalar m_cachedValue;       // current value
-    bool m_isDense;             // mode of the vector
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_AMBIVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h
deleted file mode 100644
index acd986f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h
+++ /dev/null
@@ -1,274 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPRESSED_STORAGE_H
-#define EIGEN_COMPRESSED_STORAGE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal
-  * Stores a sparse set of values as a list of values and a list of indices.
-  *
-  */
-template<typename _Scalar,typename _StorageIndex>
-class CompressedStorage
-{
-  public:
-
-    typedef _Scalar Scalar;
-    typedef _StorageIndex StorageIndex;
-
-  protected:
-
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  public:
-
-    CompressedStorage()
-      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
-    {}
-
-    explicit CompressedStorage(Index size)
-      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
-    {
-      resize(size);
-    }
-
-    CompressedStorage(const CompressedStorage& other)
-      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
-    {
-      *this = other;
-    }
-
-    CompressedStorage& operator=(const CompressedStorage& other)
-    {
-      resize(other.size());
-      if(other.size()>0)
-      {
-        internal::smart_copy(other.m_values,  other.m_values  + m_size, m_values);
-        internal::smart_copy(other.m_indices, other.m_indices + m_size, m_indices);
-      }
-      return *this;
-    }
-
-    void swap(CompressedStorage& other)
-    {
-      std::swap(m_values, other.m_values);
-      std::swap(m_indices, other.m_indices);
-      std::swap(m_size, other.m_size);
-      std::swap(m_allocatedSize, other.m_allocatedSize);
-    }
-
-    ~CompressedStorage()
-    {
-      delete[] m_values;
-      delete[] m_indices;
-    }
-
-    void reserve(Index size)
-    {
-      Index newAllocatedSize = m_size + size;
-      if (newAllocatedSize > m_allocatedSize)
-        reallocate(newAllocatedSize);
-    }
-
-    void squeeze()
-    {
-      if (m_allocatedSize>m_size)
-        reallocate(m_size);
-    }
-
-    void resize(Index size, double reserveSizeFactor = 0)
-    {
-      if (m_allocatedSize<size)
-      {
-        Index realloc_size = (std::min<Index>)(NumTraits<StorageIndex>::highest(),  size + Index(reserveSizeFactor*double(size)));
-        if(realloc_size<size)
-          internal::throw_std_bad_alloc();
-        reallocate(realloc_size);
-      }
-      m_size = size;
-    }
-
-    void append(const Scalar& v, Index i)
-    {
-      Index id = m_size;
-      resize(m_size+1, 1);
-      m_values[id] = v;
-      m_indices[id] = internal::convert_index<StorageIndex>(i);
-    }
-
-    inline Index size() const { return m_size; }
-    inline Index allocatedSize() const { return m_allocatedSize; }
-    inline void clear() { m_size = 0; }
-
-    const Scalar* valuePtr() const { return m_values; }
-    Scalar* valuePtr() { return m_values; }
-    const StorageIndex* indexPtr() const { return m_indices; }
-    StorageIndex* indexPtr() { return m_indices; }
-
-    inline Scalar& value(Index i) { eigen_internal_assert(m_values!=0); return m_values[i]; }
-    inline const Scalar& value(Index i) const { eigen_internal_assert(m_values!=0); return m_values[i]; }
-
-    inline StorageIndex& index(Index i) { eigen_internal_assert(m_indices!=0); return m_indices[i]; }
-    inline const StorageIndex& index(Index i) const { eigen_internal_assert(m_indices!=0); return m_indices[i]; }
-
-    /** \returns the largest \c k such that for all \c j in [0,k) index[\c j]\<\a key */
-    inline Index searchLowerIndex(Index key) const
-    {
-      return searchLowerIndex(0, m_size, key);
-    }
-
-    /** \returns the largest \c k in [start,end) such that for all \c j in [start,k) index[\c j]\<\a key */
-    inline Index searchLowerIndex(Index start, Index end, Index key) const
-    {
-      while(end>start)
-      {
-        Index mid = (end+start)>>1;
-        if (m_indices[mid]<key)
-          start = mid+1;
-        else
-          end = mid;
-      }
-      return start;
-    }
-
-    /** \returns the stored value at index \a key
-      * If the value does not exist, then the value \a defaultValue is returned without any insertion. */
-    inline Scalar at(Index key, const Scalar& defaultValue = Scalar(0)) const
-    {
-      if (m_size==0)
-        return defaultValue;
-      else if (key==m_indices[m_size-1])
-        return m_values[m_size-1];
-      // ^^  optimization: let's first check if it is the last coefficient
-      // (very common in high level algorithms)
-      const Index id = searchLowerIndex(0,m_size-1,key);
-      return ((id<m_size) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
-    }
-
-    /** Like at(), but the search is performed in the range [start,end) */
-    inline Scalar atInRange(Index start, Index end, Index key, const Scalar &defaultValue = Scalar(0)) const
-    {
-      if (start>=end)
-        return defaultValue;
-      else if (end>start && key==m_indices[end-1])
-        return m_values[end-1];
-      // ^^  optimization: let's first check if it is the last coefficient
-      // (very common in high level algorithms)
-      const Index id = searchLowerIndex(start,end-1,key);
-      return ((id<end) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
-    }
-
-    /** \returns a reference to the value at index \a key
-      * If the value does not exist, then the value \a defaultValue is inserted
-      * such that the keys are sorted. */
-    inline Scalar& atWithInsertion(Index key, const Scalar& defaultValue = Scalar(0))
-    {
-      Index id = searchLowerIndex(0,m_size,key);
-      if (id>=m_size || m_indices[id]!=key)
-      {
-        if (m_allocatedSize<m_size+1)
-        {
-          m_allocatedSize = 2*(m_size+1);
-          internal::scoped_array<Scalar> newValues(m_allocatedSize);
-          internal::scoped_array<StorageIndex> newIndices(m_allocatedSize);
-
-          // copy first chunk
-          internal::smart_copy(m_values,  m_values +id, newValues.ptr());
-          internal::smart_copy(m_indices, m_indices+id, newIndices.ptr());
-
-          // copy the rest
-          if(m_size>id)
-          {
-            internal::smart_copy(m_values +id,  m_values +m_size, newValues.ptr() +id+1);
-            internal::smart_copy(m_indices+id,  m_indices+m_size, newIndices.ptr()+id+1);
-          }
-          std::swap(m_values,newValues.ptr());
-          std::swap(m_indices,newIndices.ptr());
-        }
-        else if(m_size>id)
-        {
-          internal::smart_memmove(m_values +id, m_values +m_size, m_values +id+1);
-          internal::smart_memmove(m_indices+id, m_indices+m_size, m_indices+id+1);
-        }
-        m_size++;
-        m_indices[id] = internal::convert_index<StorageIndex>(key);
-        m_values[id] = defaultValue;
-      }
-      return m_values[id];
-    }
-
-    void moveChunk(Index from, Index to, Index chunkSize)
-    {
-      eigen_internal_assert(to+chunkSize <= m_size);
-      if(to>from && from+chunkSize>to)
-      {
-        // move backward
-        internal::smart_memmove(m_values+from,  m_values+from+chunkSize,  m_values+to);
-        internal::smart_memmove(m_indices+from, m_indices+from+chunkSize, m_indices+to);
-      }
-      else
-      {
-        internal::smart_copy(m_values+from,  m_values+from+chunkSize,  m_values+to);
-        internal::smart_copy(m_indices+from, m_indices+from+chunkSize, m_indices+to);
-      }
-    }
-
-    void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
-    {
-      Index k = 0;
-      Index n = size();
-      for (Index i=0; i<n; ++i)
-      {
-        if (!internal::isMuchSmallerThan(value(i), reference, epsilon))
-        {
-          value(k) = value(i);
-          index(k) = index(i);
-          ++k;
-        }
-      }
-      resize(k,0);
-    }
-
-  protected:
-
-    inline void reallocate(Index size)
-    {
-      #ifdef EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
-        EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
-      #endif
-      eigen_internal_assert(size!=m_allocatedSize);
-      internal::scoped_array<Scalar> newValues(size);
-      internal::scoped_array<StorageIndex> newIndices(size);
-      Index copySize = (std::min)(size, m_size);
-      if (copySize>0) {
-        internal::smart_copy(m_values, m_values+copySize, newValues.ptr());
-        internal::smart_copy(m_indices, m_indices+copySize, newIndices.ptr());
-      }
-      std::swap(m_values,newValues.ptr());
-      std::swap(m_indices,newIndices.ptr());
-      m_allocatedSize = size;
-    }
-
-  protected:
-    Scalar* m_values;
-    StorageIndex* m_indices;
-    Index m_size;
-    Index m_allocatedSize;
-
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPRESSED_STORAGE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
deleted file mode 100644
index 9486502..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
+++ /dev/null
@@ -1,352 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
-#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType>
-static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, bool sortedInsertion = false)
-{
-  typedef typename remove_all<Lhs>::type::Scalar LhsScalar;
-  typedef typename remove_all<Rhs>::type::Scalar RhsScalar;
-  typedef typename remove_all<ResultType>::type::Scalar ResScalar;
-
-  // make sure to call innerSize/outerSize since we fake the storage order.
-  Index rows = lhs.innerSize();
-  Index cols = rhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  ei_declare_aligned_stack_constructed_variable(bool,   mask,     rows, 0);
-  ei_declare_aligned_stack_constructed_variable(ResScalar, values,   rows, 0);
-  ei_declare_aligned_stack_constructed_variable(Index,  indices,  rows, 0);
-
-  std::memset(mask,0,sizeof(bool)*rows);
-
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-
-  // estimate the number of non zero entries
-  // given a rhs column containing Y non zeros, we assume that the respective Y columns
-  // of the lhs differs in average of one non zeros, thus the number of non zeros for
-  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
-  // per column of the lhs.
-  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
-  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
-
-  res.setZero();
-  res.reserve(Index(estimated_nnz_prod));
-  // we compute each column of the result, one after the other
-  for (Index j=0; j<cols; ++j)
-  {
-
-    res.startVec(j);
-    Index nnz = 0;
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      RhsScalar y = rhsIt.value();
-      Index k = rhsIt.index();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
-      {
-        Index i = lhsIt.index();
-        LhsScalar x = lhsIt.value();
-        if(!mask[i])
-        {
-          mask[i] = true;
-          values[i] = x * y;
-          indices[nnz] = i;
-          ++nnz;
-        }
-        else
-          values[i] += x * y;
-      }
-    }
-    if(!sortedInsertion)
-    {
-      // unordered insertion
-      for(Index k=0; k<nnz; ++k)
-      {
-        Index i = indices[k];
-        res.insertBackByOuterInnerUnordered(j,i) = values[i];
-        mask[i] = false;
-      }
-    }
-    else
-    {
-      // alternative ordered insertion code:
-      const Index t200 = rows/11; // 11 == (log2(200)*1.39)
-      const Index t = (rows*100)/139;
-
-      // FIXME reserve nnz non zeros
-      // FIXME implement faster sorting algorithms for very small nnz
-      // if the result is sparse enough => use a quick sort
-      // otherwise => loop through the entire vector
-      // In order to avoid to perform an expensive log2 when the
-      // result is clearly very sparse we use a linear bound up to 200.
-      if((nnz<200 && nnz<t200) || nnz * numext::log2(int(nnz)) < t)
-      {
-        if(nnz>1) std::sort(indices,indices+nnz);
-        for(Index k=0; k<nnz; ++k)
-        {
-          Index i = indices[k];
-          res.insertBackByOuterInner(j,i) = values[i];
-          mask[i] = false;
-        }
-      }
-      else
-      {
-        // dense path
-        for(Index i=0; i<rows; ++i)
-        {
-          if(mask[i])
-          {
-            mask[i] = false;
-            res.insertBackByOuterInner(j,i) = values[i];
-          }
-        }
-      }
-    }
-  }
-  res.finalize();
-}
-
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int ResStorageOrder = (traits<ResultType>::Flags&RowMajorBit) ? RowMajor : ColMajor>
-struct conservative_sparse_sparse_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
-{
-  typedef typename remove_all<Lhs>::type LhsCleaned;
-  typedef typename LhsCleaned::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrixAux;
-    typedef typename sparse_eval<ColMajorMatrixAux,ResultType::RowsAtCompileTime,ResultType::ColsAtCompileTime,ColMajorMatrixAux::Flags>::type ColMajorMatrix;
-
-    // If the result is tall and thin (in the extreme case a column vector)
-    // then it is faster to sort the coefficients inplace instead of transposing twice.
-    // FIXME, the following heuristic is probably not very good.
-    if(lhs.rows()>rhs.cols())
-    {
-      ColMajorMatrix resCol(lhs.rows(),rhs.cols());
-      // perform sorted insertion
-      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol, true);
-      res = resCol.markAsRValue();
-    }
-    else
-    {
-      ColMajorMatrixAux resCol(lhs.rows(),rhs.cols());
-      // resort to transpose to sort the entries
-      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrixAux>(lhs, rhs, resCol, false);
-      RowMajorMatrix resRow(resCol);
-      res = resRow.markAsRValue();
-    }
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorRhs;
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorRes;
-    RowMajorRhs rhsRow = rhs;
-    RowMajorRes resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<RowMajorRhs,Lhs,RowMajorRes>(rhsRow, lhs, resRow);
-    res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorLhs;
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorRes;
-    RowMajorLhs lhsRow = lhs;
-    RowMajorRes resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,RowMajorLhs,RowMajorRes>(rhs, lhsRow, resRow);
-    res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    RowMajorMatrix resRow(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
-    res = resRow;
-  }
-};
-
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
-{
-  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorLhs;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRes;
-    ColMajorLhs lhsCol = lhs;
-    ColMajorRes resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<ColMajorLhs,Rhs,ColMajorRes>(lhsCol, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRhs;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRes;
-    ColMajorRhs rhsCol = rhs;
-    ColMajorRes resCol(lhs.rows(), rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Lhs,ColMajorRhs,ColMajorRes>(lhs, rhsCol, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::StorageIndex> RowMajorMatrix;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorMatrix;
-    RowMajorMatrix resRow(lhs.rows(),rhs.cols());
-    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
-    // sort the non zeros:
-    ColMajorMatrix resCol(resRow);
-    res = resCol;
-  }
-};
-
-} // end namespace internal
-
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType>
-static void sparse_sparse_to_dense_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-{
-  typedef typename remove_all<Lhs>::type::Scalar LhsScalar;
-  typedef typename remove_all<Rhs>::type::Scalar RhsScalar;
-  Index cols = rhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-
-  for (Index j=0; j<cols; ++j)
-  {
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      RhsScalar y = rhsIt.value();
-      Index k = rhsIt.index();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
-      {
-        Index i = lhsIt.index();
-        LhsScalar x = lhsIt.value();
-        res.coeffRef(i,j) += x * y;
-      }
-    }
-  }
-}
-
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
-  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor>
-struct sparse_sparse_to_dense_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    internal::sparse_sparse_to_dense_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorLhs;
-    ColMajorLhs lhsCol(lhs);
-    internal::sparse_sparse_to_dense_product_impl<ColMajorLhs,Rhs,ResultType>(lhsCol, rhs, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename ResultType::StorageIndex> ColMajorRhs;
-    ColMajorRhs rhsCol(rhs);
-    internal::sparse_sparse_to_dense_product_impl<Lhs,ColMajorRhs,ResultType>(lhs, rhsCol, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    Transpose<ResultType> trRes(res);
-    internal::sparse_sparse_to_dense_product_impl<Rhs,Lhs,Transpose<ResultType> >(rhs, lhs, trRes);
-  }
-};
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
deleted file mode 100644
index 67718c8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
+++ /dev/null
@@ -1,67 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MAPPED_SPARSEMATRIX_H
-#define EIGEN_MAPPED_SPARSEMATRIX_H
-
-namespace Eigen {
-
-/** \deprecated Use Map<SparseMatrix<> >
-  * \class MappedSparseMatrix
-  *
-  * \brief Sparse matrix
-  *
-  * \param _Scalar the scalar type, i.e. the type of the coefficients
-  *
-  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
-  *
-  */
-namespace internal {
-template<typename _Scalar, int _Flags, typename _StorageIndex>
-struct traits<MappedSparseMatrix<_Scalar, _Flags, _StorageIndex> > : traits<SparseMatrix<_Scalar, _Flags, _StorageIndex> >
-{};
-} // end namespace internal
-
-template<typename _Scalar, int _Flags, typename _StorageIndex>
-class MappedSparseMatrix
-  : public Map<SparseMatrix<_Scalar, _Flags, _StorageIndex> >
-{
-    typedef Map<SparseMatrix<_Scalar, _Flags, _StorageIndex> > Base;
-
-  public:
-    
-    typedef typename Base::StorageIndex StorageIndex;
-    typedef typename Base::Scalar Scalar;
-
-    inline MappedSparseMatrix(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr, Scalar* valuePtr, StorageIndex* innerNonZeroPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZeroPtr)
-    {}
-
-    /** Empty destructor */
-    inline ~MappedSparseMatrix() {}
-};
-
-namespace internal {
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct evaluator<MappedSparseMatrix<_Scalar,_Options,_StorageIndex> >
-  : evaluator<SparseCompressedBase<MappedSparseMatrix<_Scalar,_Options,_StorageIndex> > >
-{
-  typedef MappedSparseMatrix<_Scalar,_Options,_StorageIndex> XprType;
-  typedef evaluator<SparseCompressedBase<XprType> > Base;
-  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MAPPED_SPARSEMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseAssign.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseAssign.h
deleted file mode 100644
index 905485c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseAssign.h
+++ /dev/null
@@ -1,270 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEASSIGN_H
-#define EIGEN_SPARSEASSIGN_H
-
-namespace Eigen { 
-
-template<typename Derived>    
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
-{
-  internal::call_assignment_no_alias(derived(), other.derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
-{
-  // TODO use the evaluator mechanism
-  other.evalTo(derived());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-inline Derived& SparseMatrixBase<Derived>::operator=(const SparseMatrixBase<OtherDerived>& other)
-{
-  // by default sparse evaluation do not alias, so we can safely bypass the generic call_assignment routine
-  internal::Assignment<Derived,OtherDerived,internal::assign_op<Scalar,typename OtherDerived::Scalar> >
-          ::run(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-inline Derived& SparseMatrixBase<Derived>::operator=(const Derived& other)
-{
-  internal::call_assignment_no_alias(derived(), other.derived());
-  return derived();
-}
-
-namespace internal {
-
-template<>
-struct storage_kind_to_evaluator_kind<Sparse> {
-  typedef IteratorBased Kind;
-};
-
-template<>
-struct storage_kind_to_shape<Sparse> {
-  typedef SparseShape Shape;
-};
-
-struct Sparse2Sparse {};
-struct Sparse2Dense  {};
-
-template<> struct AssignmentKind<SparseShape, SparseShape>           { typedef Sparse2Sparse Kind; };
-template<> struct AssignmentKind<SparseShape, SparseTriangularShape> { typedef Sparse2Sparse Kind; };
-template<> struct AssignmentKind<DenseShape,  SparseShape>           { typedef Sparse2Dense  Kind; };
-template<> struct AssignmentKind<DenseShape,  SparseTriangularShape> { typedef Sparse2Dense  Kind; };
-
-
-template<typename DstXprType, typename SrcXprType>
-void assign_sparse_to_sparse(DstXprType &dst, const SrcXprType &src)
-{
-  typedef typename DstXprType::Scalar Scalar;
-  typedef internal::evaluator<DstXprType> DstEvaluatorType;
-  typedef internal::evaluator<SrcXprType> SrcEvaluatorType;
-
-  SrcEvaluatorType srcEvaluator(src);
-
-  const bool transpose = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit);
-  const Index outerEvaluationSize = (SrcEvaluatorType::Flags&RowMajorBit) ? src.rows() : src.cols();
-  if ((!transpose) && src.isRValue())
-  {
-    // eval without temporary
-    dst.resize(src.rows(), src.cols());
-    dst.setZero();
-    dst.reserve((std::min)(src.rows()*src.cols(), (std::max)(src.rows(),src.cols())*2));
-    for (Index j=0; j<outerEvaluationSize; ++j)
-    {
-      dst.startVec(j);
-      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it)
-      {
-        Scalar v = it.value();
-        dst.insertBackByOuterInner(j,it.index()) = v;
-      }
-    }
-    dst.finalize();
-  }
-  else
-  {
-    // eval through a temporary
-    eigen_assert(( ((internal::traits<DstXprType>::SupportedAccessPatterns & OuterRandomAccessPattern)==OuterRandomAccessPattern) ||
-              (!((DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit)))) &&
-              "the transpose operation is supposed to be handled in SparseMatrix::operator=");
-
-    enum { Flip = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit) };
-
-    
-    DstXprType temp(src.rows(), src.cols());
-
-    temp.reserve((std::min)(src.rows()*src.cols(), (std::max)(src.rows(),src.cols())*2));
-    for (Index j=0; j<outerEvaluationSize; ++j)
-    {
-      temp.startVec(j);
-      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it)
-      {
-        Scalar v = it.value();
-        temp.insertBackByOuterInner(Flip?it.index():j,Flip?j:it.index()) = v;
-      }
-    }
-    temp.finalize();
-
-    dst = temp.markAsRValue();
-  }
-}
-
-// Generic Sparse to Sparse assignment
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Sparse>
-{
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  {
-    assign_sparse_to_sparse(dst.derived(), src.derived());
-  }
-};
-
-// Generic Sparse to Dense assignment
-template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
-struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Dense, Weak>
-{
-  static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
-  {
-    if(internal::is_same<Functor,internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> >::value)
-      dst.setZero();
-    
-    internal::evaluator<SrcXprType> srcEval(src);
-    resize_if_allowed(dst, src, func);
-    internal::evaluator<DstXprType> dstEval(dst);
-    
-    const Index outerEvaluationSize = (internal::evaluator<SrcXprType>::Flags&RowMajorBit) ? src.rows() : src.cols();
-    for (Index j=0; j<outerEvaluationSize; ++j)
-      for (typename internal::evaluator<SrcXprType>::InnerIterator i(srcEval,j); i; ++i)
-        func.assignCoeff(dstEval.coeffRef(i.row(),i.col()), i.value());
-  }
-};
-
-// Specialization for dense ?= dense +/- sparse and dense ?= sparse +/- dense
-template<typename DstXprType, typename Func1, typename Func2>
-struct assignment_from_dense_op_sparse
-{
-  template<typename SrcXprType, typename InitialFunc>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
-  {
-    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
-    EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
-    #endif
-
-    call_assignment_no_alias(dst, src.lhs(), Func1());
-    call_assignment_no_alias(dst, src.rhs(), Func2());
-  }
-
-  // Specialization for dense1 = sparse + dense2; -> dense1 = dense2; dense1 += sparse;
-  template<typename Lhs, typename Rhs, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>::type
-  run(DstXprType &dst, const CwiseBinaryOp<internal::scalar_sum_op<Scalar,Scalar>, const Lhs, const Rhs> &src,
-      const internal::assign_op<typename DstXprType::Scalar,Scalar>& /*func*/)
-  {
-    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
-    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
-    #endif
-
-    // Apply the dense matrix first, then the sparse one.
-    call_assignment_no_alias(dst, src.rhs(), Func1());
-    call_assignment_no_alias(dst, src.lhs(), Func2());
-  }
-
-  // Specialization for dense1 = sparse - dense2; -> dense1 = -dense2; dense1 += sparse;
-  template<typename Lhs, typename Rhs, typename Scalar>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>::type
-  run(DstXprType &dst, const CwiseBinaryOp<internal::scalar_difference_op<Scalar,Scalar>, const Lhs, const Rhs> &src,
-      const internal::assign_op<typename DstXprType::Scalar,Scalar>& /*func*/)
-  {
-    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
-    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
-    #endif
-
-    // Apply the dense matrix first, then the sparse one.
-    call_assignment_no_alias(dst, -src.rhs(), Func1());
-    call_assignment_no_alias(dst,  src.lhs(), add_assign_op<typename DstXprType::Scalar,typename Lhs::Scalar>());
-  }
-};
-
-#define EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(ASSIGN_OP,BINOP,ASSIGN_OP2) \
-  template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar> \
-  struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<Scalar,Scalar>, const Lhs, const Rhs>, internal::ASSIGN_OP<typename DstXprType::Scalar,Scalar>, \
-                    Sparse2Dense, \
-                    typename internal::enable_if<   internal::is_same<typename internal::evaluator_traits<Lhs>::Shape,DenseShape>::value \
-                                                 || internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>::type> \
-    : assignment_from_dense_op_sparse<DstXprType, internal::ASSIGN_OP<typename DstXprType::Scalar,typename Lhs::Scalar>, internal::ASSIGN_OP2<typename DstXprType::Scalar,typename Rhs::Scalar> > \
-  {}
-
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op,    scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op,scalar_sum_op,add_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op,scalar_sum_op,sub_assign_op);
-
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op,    scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op,scalar_difference_op,sub_assign_op);
-EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op,scalar_difference_op,add_assign_op);
-
-
-// Specialization for "dst = dec.solve(rhs)"
-// NOTE we need to specialize it for Sparse2Sparse to avoid ambiguous specialization error
-template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
-struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Sparse2Sparse>
-{
-  typedef Solve<DecType,RhsType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-
-    src.dec()._solve_impl(src.rhs(), dst);
-  }
-};
-
-struct Diagonal2Sparse {};
-
-template<> struct AssignmentKind<SparseShape,DiagonalShape> { typedef Diagonal2Sparse Kind; };
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Sparse>
-{
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  typedef typename DstXprType::Scalar Scalar;
-
-  template<int Options, typename AssignFunc>
-  static void run(SparseMatrix<Scalar,Options,StorageIndex> &dst, const SrcXprType &src, const AssignFunc &func)
-  { dst.assignDiagonal(src.diagonal(), func); }
-  
-  template<typename DstDerived>
-  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.derived().diagonal() = src.diagonal(); }
-  
-  template<typename DstDerived>
-  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.derived().diagonal() += src.diagonal(); }
-  
-  template<typename DstDerived>
-  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
-  { dst.derived().diagonal() -= src.diagonal(); }
-};
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEASSIGN_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseBlock.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
deleted file mode 100644
index 5b4f6cc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
+++ /dev/null
@@ -1,571 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_BLOCK_H
-#define EIGEN_SPARSE_BLOCK_H
-
-namespace Eigen {
-
-// Subset of columns or rows
-template<typename XprType, int BlockRows, int BlockCols>
-class BlockImpl<XprType,BlockRows,BlockCols,true,Sparse>
-  : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,true> >
-{
-    typedef typename internal::remove_all<typename XprType::Nested>::type _MatrixTypeNested;
-    typedef Block<XprType, BlockRows, BlockCols, true> BlockType;
-public:
-    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
-protected:
-    enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
-    typedef SparseMatrixBase<BlockType> Base;
-    using Base::convert_index;
-public:
-    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
-
-    inline BlockImpl(XprType& xpr, Index i)
-      : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize)
-    {}
-
-    inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : m_matrix(xpr), m_outerStart(convert_index(IsRowMajor ? startRow : startCol)), m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols))
-    {}
-
-    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-    Index nonZeros() const
-    {
-      typedef internal::evaluator<XprType> EvaluatorType;
-      EvaluatorType matEval(m_matrix);
-      Index nnz = 0;
-      Index end = m_outerStart + m_outerSize.value();
-      for(Index j=m_outerStart; j<end; ++j)
-        for(typename EvaluatorType::InnerIterator it(matEval, j); it; ++it)
-          ++nnz;
-      return nnz;
-    }
-
-    inline const Scalar coeff(Index row, Index col) const
-    {
-      return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
-    }
-
-    inline const Scalar coeff(Index index) const
-    {
-      return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index :  m_outerStart);
-    }
-
-    inline const XprType& nestedExpression() const { return m_matrix; }
-    inline XprType& nestedExpression() { return m_matrix; }
-    Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
-    Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
-    Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-  protected:
-
-    typename internal::ref_selector<XprType>::non_const_type m_matrix;
-    Index m_outerStart;
-    const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
-
-  protected:
-    // Disable assignment with clear error message.
-    // Note that simply removing operator= yields compilation errors with ICC+MSVC
-    template<typename T>
-    BlockImpl& operator=(const T&)
-    {
-      EIGEN_STATIC_ASSERT(sizeof(T)==0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
-      return *this;
-    }
-};
-
-
-/***************************************************************************
-* specialization for SparseMatrix
-***************************************************************************/
-
-namespace internal {
-
-template<typename SparseMatrixType, int BlockRows, int BlockCols>
-class sparse_matrix_block_impl
-  : public SparseCompressedBase<Block<SparseMatrixType,BlockRows,BlockCols,true> >
-{
-    typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _MatrixTypeNested;
-    typedef Block<SparseMatrixType, BlockRows, BlockCols, true> BlockType;
-    typedef SparseCompressedBase<Block<SparseMatrixType,BlockRows,BlockCols,true> > Base;
-    using Base::convert_index;
-public:
-    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
-    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
-protected:
-    typedef typename Base::IndexVector IndexVector;
-    enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
-public:
-
-    inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index i)
-      : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize)
-    {}
-
-    inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : m_matrix(xpr), m_outerStart(convert_index(IsRowMajor ? startRow : startCol)), m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols))
-    {}
-
-    template<typename OtherDerived>
-    inline BlockType& operator=(const SparseMatrixBase<OtherDerived>& other)
-    {
-      typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _NestedMatrixType;
-      _NestedMatrixType& matrix = m_matrix;
-      // This assignment is slow if this vector set is not empty
-      // and/or it is not at the end of the nonzeros of the underlying matrix.
-
-      // 1 - eval to a temporary to avoid transposition and/or aliasing issues
-      Ref<const SparseMatrix<Scalar, IsRowMajor ? RowMajor : ColMajor, StorageIndex> > tmp(other.derived());
-      eigen_internal_assert(tmp.outerSize()==m_outerSize.value());
-
-      // 2 - let's check whether there is enough allocated memory
-      Index nnz           = tmp.nonZeros();
-      Index start         = m_outerStart==0 ? 0 : m_matrix.outerIndexPtr()[m_outerStart]; // starting position of the current block
-      Index end           = m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()]; // ending position of the current block
-      Index block_size    = end - start;                                                // available room in the current block
-      Index tail_size     = m_matrix.outerIndexPtr()[m_matrix.outerSize()] - end;
-
-      Index free_size     = m_matrix.isCompressed()
-                          ? Index(matrix.data().allocatedSize()) + block_size
-                          : block_size;
-
-      Index tmp_start = tmp.outerIndexPtr()[0];
-
-      bool update_trailing_pointers = false;
-      if(nnz>free_size)
-      {
-        // realloc manually to reduce copies
-        typename SparseMatrixType::Storage newdata(m_matrix.data().allocatedSize() - block_size + nnz);
-
-        internal::smart_copy(m_matrix.valuePtr(),       m_matrix.valuePtr() + start,      newdata.valuePtr());
-        internal::smart_copy(m_matrix.innerIndexPtr(),  m_matrix.innerIndexPtr() + start, newdata.indexPtr());
-
-        internal::smart_copy(tmp.valuePtr() + tmp_start,      tmp.valuePtr() + tmp_start + nnz,       newdata.valuePtr() + start);
-        internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,  newdata.indexPtr() + start);
-
-        internal::smart_copy(matrix.valuePtr()+end,       matrix.valuePtr()+end + tail_size,      newdata.valuePtr()+start+nnz);
-        internal::smart_copy(matrix.innerIndexPtr()+end,  matrix.innerIndexPtr()+end + tail_size, newdata.indexPtr()+start+nnz);
-
-        newdata.resize(m_matrix.outerIndexPtr()[m_matrix.outerSize()] - block_size + nnz);
-
-        matrix.data().swap(newdata);
-
-        update_trailing_pointers = true;
-      }
-      else
-      {
-        if(m_matrix.isCompressed() && nnz!=block_size)
-        {
-          // no need to realloc, simply copy the tail at its respective position and insert tmp
-          matrix.data().resize(start + nnz + tail_size);
-
-          internal::smart_memmove(matrix.valuePtr()+end,      matrix.valuePtr() + end+tail_size,      matrix.valuePtr() + start+nnz);
-          internal::smart_memmove(matrix.innerIndexPtr()+end, matrix.innerIndexPtr() + end+tail_size, matrix.innerIndexPtr() + start+nnz);
-
-          update_trailing_pointers = true;
-        }
-
-        internal::smart_copy(tmp.valuePtr() + tmp_start,      tmp.valuePtr() + tmp_start + nnz,       matrix.valuePtr() + start);
-        internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,  matrix.innerIndexPtr() + start);
-      }
-
-      // update outer index pointers and innerNonZeros
-      if(IsVectorAtCompileTime)
-      {
-        if(!m_matrix.isCompressed())
-          matrix.innerNonZeroPtr()[m_outerStart] = StorageIndex(nnz);
-        matrix.outerIndexPtr()[m_outerStart] = StorageIndex(start);
-      }
-      else
-      {
-        StorageIndex p = StorageIndex(start);
-        for(Index k=0; k<m_outerSize.value(); ++k)
-        {
-          StorageIndex nnz_k = internal::convert_index<StorageIndex>(tmp.innerVector(k).nonZeros());
-          if(!m_matrix.isCompressed())
-            matrix.innerNonZeroPtr()[m_outerStart+k] = nnz_k;
-          matrix.outerIndexPtr()[m_outerStart+k] = p;
-          p += nnz_k;
-        }
-      }
-
-      if(update_trailing_pointers)
-      {
-        StorageIndex offset = internal::convert_index<StorageIndex>(nnz - block_size);
-        for(Index k = m_outerStart + m_outerSize.value(); k<=matrix.outerSize(); ++k)
-        {
-          matrix.outerIndexPtr()[k] += offset;
-        }
-      }
-
-      return derived();
-    }
-
-    inline BlockType& operator=(const BlockType& other)
-    {
-      return operator=<BlockType>(other);
-    }
-
-    inline const Scalar* valuePtr() const
-    { return m_matrix.valuePtr(); }
-    inline Scalar* valuePtr()
-    { return m_matrix.valuePtr(); }
-
-    inline const StorageIndex* innerIndexPtr() const
-    { return m_matrix.innerIndexPtr(); }
-    inline StorageIndex* innerIndexPtr()
-    { return m_matrix.innerIndexPtr(); }
-
-    inline const StorageIndex* outerIndexPtr() const
-    { return m_matrix.outerIndexPtr() + m_outerStart; }
-    inline StorageIndex* outerIndexPtr()
-    { return m_matrix.outerIndexPtr() + m_outerStart; }
-
-    inline const StorageIndex* innerNonZeroPtr() const
-    { return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
-    inline StorageIndex* innerNonZeroPtr()
-    { return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
-
-    bool isCompressed() const { return m_matrix.innerNonZeroPtr()==0; }
-
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      return m_matrix.coeffRef(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
-    }
-
-    inline const Scalar coeff(Index row, Index col) const
-    {
-      return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
-    }
-
-    inline const Scalar coeff(Index index) const
-    {
-      return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index :  m_outerStart);
-    }
-
-    const Scalar& lastCoeff() const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(sparse_matrix_block_impl);
-      eigen_assert(Base::nonZeros()>0);
-      if(m_matrix.isCompressed())
-        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart+1]-1];
-      else
-        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart]+m_matrix.innerNonZeroPtr()[m_outerStart]-1];
-    }
-
-    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-    inline const SparseMatrixType& nestedExpression() const { return m_matrix; }
-    inline SparseMatrixType& nestedExpression() { return m_matrix; }
-    Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
-    Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
-    Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-  protected:
-
-    typename internal::ref_selector<SparseMatrixType>::non_const_type m_matrix;
-    Index m_outerStart;
-    const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
-
-};
-
-} // namespace internal
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-class BlockImpl<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true,Sparse>
-  : public internal::sparse_matrix_block_impl<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols>
-{
-public:
-  typedef _StorageIndex StorageIndex;
-  typedef SparseMatrix<_Scalar, _Options, _StorageIndex> SparseMatrixType;
-  typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base;
-  inline BlockImpl(SparseMatrixType& xpr, Index i)
-    : Base(xpr, i)
-  {}
-
-  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-    : Base(xpr, startRow, startCol, blockRows, blockCols)
-  {}
-
-  using Base::operator=;
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-class BlockImpl<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true,Sparse>
-  : public internal::sparse_matrix_block_impl<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols>
-{
-public:
-  typedef _StorageIndex StorageIndex;
-  typedef const SparseMatrix<_Scalar, _Options, _StorageIndex> SparseMatrixType;
-  typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base;
-  inline BlockImpl(SparseMatrixType& xpr, Index i)
-    : Base(xpr, i)
-  {}
-
-  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-    : Base(xpr, startRow, startCol, blockRows, blockCols)
-  {}
-
-  using Base::operator=;
-private:
-  template<typename Derived> BlockImpl(const SparseMatrixBase<Derived>& xpr, Index i);
-  template<typename Derived> BlockImpl(const SparseMatrixBase<Derived>& xpr);
-};
-
-//----------
-
-/** Generic implementation of sparse Block expression.
-  * Real-only.
-  */
-template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
-class BlockImpl<XprType,BlockRows,BlockCols,InnerPanel,Sparse>
-  : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,InnerPanel> >, internal::no_assignment_operator
-{
-    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
-    typedef SparseMatrixBase<BlockType> Base;
-    using Base::convert_index;
-public:
-    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
-    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
-
-    typedef typename internal::remove_all<typename XprType::Nested>::type _MatrixTypeNested;
-
-    /** Column or Row constructor
-      */
-    inline BlockImpl(XprType& xpr, Index i)
-      : m_matrix(xpr),
-        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? convert_index(i) : 0),
-        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? convert_index(i) : 0),
-        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
-        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
-    {}
-
-    /** Dynamic-size constructor
-      */
-    inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
-      : m_matrix(xpr), m_startRow(convert_index(startRow)), m_startCol(convert_index(startCol)), m_blockRows(convert_index(blockRows)), m_blockCols(convert_index(blockCols))
-    {}
-
-    inline Index rows() const { return m_blockRows.value(); }
-    inline Index cols() const { return m_blockCols.value(); }
-
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      return m_matrix.coeffRef(row + m_startRow.value(), col + m_startCol.value());
-    }
-
-    inline const Scalar coeff(Index row, Index col) const
-    {
-      return m_matrix.coeff(row + m_startRow.value(), col + m_startCol.value());
-    }
-
-    inline Scalar& coeffRef(Index index)
-    {
-      return m_matrix.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    inline const Scalar coeff(Index index) const
-    {
-      return m_matrix.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
-                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
-    }
-
-    inline const XprType& nestedExpression() const { return m_matrix; }
-    inline XprType& nestedExpression() { return m_matrix; }
-    Index startRow() const { return m_startRow.value(); }
-    Index startCol() const { return m_startCol.value(); }
-    Index blockRows() const { return m_blockRows.value(); }
-    Index blockCols() const { return m_blockCols.value(); }
-
-  protected:
-//     friend class internal::GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel>;
-    friend struct internal::unary_evaluator<Block<XprType,BlockRows,BlockCols,InnerPanel>, internal::IteratorBased, Scalar >;
-
-    Index nonZeros() const { return Dynamic; }
-
-    typename internal::ref_selector<XprType>::non_const_type m_matrix;
-    const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
-    const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
-    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
-    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
-
-  protected:
-    // Disable assignment with clear error message.
-    // Note that simply removing operator= yields compilation errors with ICC+MSVC
-    template<typename T>
-    BlockImpl& operator=(const T&)
-    {
-      EIGEN_STATIC_ASSERT(sizeof(T)==0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
-      return *this;
-    }
-
-};
-
-namespace internal {
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-struct unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased >
- : public evaluator_base<Block<ArgType,BlockRows,BlockCols,InnerPanel> >
-{
-    class InnerVectorInnerIterator;
-    class OuterVectorInnerIterator;
-  public:
-    typedef Block<ArgType,BlockRows,BlockCols,InnerPanel> XprType;
-    typedef typename XprType::StorageIndex StorageIndex;
-    typedef typename XprType::Scalar Scalar;
-
-    enum {
-      IsRowMajor = XprType::IsRowMajor,
-
-      OuterVector =  (BlockCols==1 && ArgType::IsRowMajor)
-                    | // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
-                      // revert to || as soon as not needed anymore.
-                     (BlockRows==1 && !ArgType::IsRowMajor),
-
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-
-    typedef typename internal::conditional<OuterVector,OuterVectorInnerIterator,InnerVectorInnerIterator>::type InnerIterator;
-
-    explicit unary_evaluator(const XprType& op)
-      : m_argImpl(op.nestedExpression()), m_block(op)
-    {}
-
-    inline Index nonZerosEstimate() const {
-      const Index nnz = m_block.nonZeros();
-      if(nnz < 0) {
-        // Scale the non-zero estimate for the underlying expression linearly with block size.
-        // Return zero if the underlying block is empty.
-        const Index nested_sz = m_block.nestedExpression().size();        
-        return nested_sz == 0 ? 0 : m_argImpl.nonZerosEstimate() * m_block.size() / nested_sz;
-      }
-      return nnz;
-    }
-
-  protected:
-    typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
-
-    evaluator<ArgType> m_argImpl;
-    const XprType &m_block;
-};
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-class unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased>::InnerVectorInnerIterator
- : public EvalIterator
-{
-  // NOTE MSVC fails to compile if we don't explicitely "import" IsRowMajor from unary_evaluator
-  //      because the base class EvalIterator has a private IsRowMajor enum too. (bug #1786)
-  // NOTE We cannot call it IsRowMajor because it would shadow unary_evaluator::IsRowMajor
-  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
-  const XprType& m_block;
-  Index m_end;
-public:
-
-  EIGEN_STRONG_INLINE InnerVectorInnerIterator(const unary_evaluator& aEval, Index outer)
-    : EvalIterator(aEval.m_argImpl, outer + (XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol())),
-      m_block(aEval.m_block),
-      m_end(XprIsRowMajor ? aEval.m_block.startCol()+aEval.m_block.blockCols() : aEval.m_block.startRow()+aEval.m_block.blockRows())
-  {
-    while( (EvalIterator::operator bool()) && (EvalIterator::index() < (XprIsRowMajor ? m_block.startCol() : m_block.startRow())) )
-      EvalIterator::operator++();
-  }
-
-  inline StorageIndex index() const { return EvalIterator::index() - convert_index<StorageIndex>(XprIsRowMajor ? m_block.startCol() : m_block.startRow()); }
-  inline Index outer()  const { return EvalIterator::outer() - (XprIsRowMajor ? m_block.startRow() : m_block.startCol()); }
-  inline Index row()    const { return EvalIterator::row()   - m_block.startRow(); }
-  inline Index col()    const { return EvalIterator::col()   - m_block.startCol(); }
-
-  inline operator bool() const { return EvalIterator::operator bool() && EvalIterator::index() < m_end; }
-};
-
-template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
-class unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased>::OuterVectorInnerIterator
-{
-  // NOTE see above
-  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
-  const unary_evaluator& m_eval;
-  Index m_outerPos;
-  const Index m_innerIndex;
-  Index m_end;
-  EvalIterator m_it;
-public:
-
-  EIGEN_STRONG_INLINE OuterVectorInnerIterator(const unary_evaluator& aEval, Index outer)
-    : m_eval(aEval),
-      m_outerPos( (XprIsRowMajor ? aEval.m_block.startCol() : aEval.m_block.startRow()) ),
-      m_innerIndex(XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol()),
-      m_end(XprIsRowMajor ? aEval.m_block.startCol()+aEval.m_block.blockCols() : aEval.m_block.startRow()+aEval.m_block.blockRows()),
-      m_it(m_eval.m_argImpl, m_outerPos)
-  {
-    EIGEN_UNUSED_VARIABLE(outer);
-    eigen_assert(outer==0);
-
-    while(m_it && m_it.index() < m_innerIndex) ++m_it;
-    if((!m_it) || (m_it.index()!=m_innerIndex))
-      ++(*this);
-  }
-
-  inline StorageIndex index() const { return convert_index<StorageIndex>(m_outerPos - (XprIsRowMajor ? m_eval.m_block.startCol() : m_eval.m_block.startRow())); }
-  inline Index outer()  const { return 0; }
-  inline Index row()    const { return XprIsRowMajor ? 0 : index(); }
-  inline Index col()    const { return XprIsRowMajor ? index() : 0; }
-
-  inline Scalar value() const { return m_it.value(); }
-  inline Scalar& valueRef() { return m_it.valueRef(); }
-
-  inline OuterVectorInnerIterator& operator++()
-  {
-    // search next non-zero entry
-    while(++m_outerPos<m_end)
-    {
-      // Restart iterator at the next inner-vector:
-      m_it.~EvalIterator();
-      ::new (&m_it) EvalIterator(m_eval.m_argImpl, m_outerPos);
-      // search for the key m_innerIndex in the current outer-vector
-      while(m_it && m_it.index() < m_innerIndex) ++m_it;
-      if(m_it && m_it.index()==m_innerIndex) break;
-    }
-    return *this;
-  }
-
-  inline operator bool() const { return m_outerPos < m_end; }
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-struct unary_evaluator<Block<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true>, IteratorBased>
-  : evaluator<SparseCompressedBase<Block<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> > >
-{
-  typedef Block<SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> XprType;
-  typedef evaluator<SparseCompressedBase<XprType> > Base;
-  explicit unary_evaluator(const XprType &xpr) : Base(xpr) {}
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int BlockRows, int BlockCols>
-struct unary_evaluator<Block<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true>, IteratorBased>
-  : evaluator<SparseCompressedBase<Block<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> > >
-{
-  typedef Block<const SparseMatrix<_Scalar, _Options, _StorageIndex>,BlockRows,BlockCols,true> XprType;
-  typedef evaluator<SparseCompressedBase<XprType> > Base;
-  explicit unary_evaluator(const XprType &xpr) : Base(xpr) {}
-};
-
-} // end namespace internal
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_BLOCK_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h
deleted file mode 100644
index ebe02d1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h
+++ /dev/null
@@ -1,206 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-/* 
- 
- * NOTE: This file is the modified version of sp_coletree.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.1) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * August 1, 2008
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSE_COLETREE_H
-#define SPARSE_COLETREE_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** Find the root of the tree/set containing the vertex i : Use Path halving */ 
-template<typename Index, typename IndexVector>
-Index etree_find (Index i, IndexVector& pp)
-{
-  Index p = pp(i); // Parent 
-  Index gp = pp(p); // Grand parent 
-  while (gp != p) 
-  {
-    pp(i) = gp; // Parent pointer on find path is changed to former grand parent
-    i = gp; 
-    p = pp(i);
-    gp = pp(p);
-  }
-  return p; 
-}
-
-/** Compute the column elimination tree of a sparse matrix
-  * \param mat The matrix in column-major format. 
-  * \param parent The elimination tree
-  * \param firstRowElt The column index of the first element in each row
-  * \param perm The permutation to apply to the column of \b mat
-  */
-template <typename MatrixType, typename IndexVector>
-int coletree(const MatrixType& mat, IndexVector& parent, IndexVector& firstRowElt, typename MatrixType::StorageIndex *perm=0)
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  StorageIndex nc = convert_index<StorageIndex>(mat.cols()); // Number of columns
-  StorageIndex m = convert_index<StorageIndex>(mat.rows());
-  StorageIndex diagSize = (std::min)(nc,m);
-  IndexVector root(nc); // root of subtree of etree 
-  root.setZero();
-  IndexVector pp(nc); // disjoint sets 
-  pp.setZero(); // Initialize disjoint sets 
-  parent.resize(mat.cols());
-  //Compute first nonzero column in each row 
-  firstRowElt.resize(m);
-  firstRowElt.setConstant(nc);
-  firstRowElt.segment(0, diagSize).setLinSpaced(diagSize, 0, diagSize-1);
-  bool found_diag;
-  for (StorageIndex col = 0; col < nc; col++)
-  {
-    StorageIndex pcol = col;
-    if(perm) pcol  = perm[col];
-    for (typename MatrixType::InnerIterator it(mat, pcol); it; ++it)
-    { 
-      Index row = it.row();
-      firstRowElt(row) = (std::min)(firstRowElt(row), col);
-    }
-  }
-  /* Compute etree by Liu's algorithm for symmetric matrices,
-          except use (firstRowElt[r],c) in place of an edge (r,c) of A.
-    Thus each row clique in A'*A is replaced by a star
-    centered at its first vertex, which has the same fill. */
-  StorageIndex rset, cset, rroot;
-  for (StorageIndex col = 0; col < nc; col++) 
-  {
-    found_diag = col>=m;
-    pp(col) = col; 
-    cset = col; 
-    root(cset) = col; 
-    parent(col) = nc; 
-    /* The diagonal element is treated here even if it does not exist in the matrix
-     * hence the loop is executed once more */ 
-    StorageIndex pcol = col;
-    if(perm) pcol  = perm[col];
-    for (typename MatrixType::InnerIterator it(mat, pcol); it||!found_diag; ++it)
-    { //  A sequence of interleaved find and union is performed 
-      Index i = col;
-      if(it) i = it.index();
-      if (i == col) found_diag = true;
-      
-      StorageIndex row = firstRowElt(i);
-      if (row >= col) continue; 
-      rset = internal::etree_find(row, pp); // Find the name of the set containing row
-      rroot = root(rset);
-      if (rroot != col) 
-      {
-        parent(rroot) = col; 
-        pp(cset) = rset; 
-        cset = rset; 
-        root(cset) = col; 
-      }
-    }
-  }
-  return 0;  
-}
-
-/** 
-  * Depth-first search from vertex n.  No recursion.
-  * This routine was contributed by Cédric Doucet, CEDRAT Group, Meylan, France.
-*/
-template <typename IndexVector>
-void nr_etdfs (typename IndexVector::Scalar n, IndexVector& parent, IndexVector& first_kid, IndexVector& next_kid, IndexVector& post, typename IndexVector::Scalar postnum)
-{
-  typedef typename IndexVector::Scalar StorageIndex;
-  StorageIndex current = n, first, next;
-  while (postnum != n) 
-  {
-    // No kid for the current node
-    first = first_kid(current);
-    
-    // no kid for the current node
-    if (first == -1) 
-    {
-      // Numbering this node because it has no kid 
-      post(current) = postnum++;
-      
-      // looking for the next kid 
-      next = next_kid(current); 
-      while (next == -1) 
-      {
-        // No more kids : back to the parent node
-        current = parent(current); 
-        // numbering the parent node 
-        post(current) = postnum++;
-        
-        // Get the next kid 
-        next = next_kid(current); 
-      }
-      // stopping criterion 
-      if (postnum == n+1) return; 
-      
-      // Updating current node 
-      current = next; 
-    }
-    else 
-    {
-      current = first; 
-    }
-  }
-}
-
-
-/**
-  * \brief Post order a tree 
-  * \param n the number of nodes
-  * \param parent Input tree
-  * \param post postordered tree
-  */
-template <typename IndexVector>
-void treePostorder(typename IndexVector::Scalar n, IndexVector& parent, IndexVector& post)
-{
-  typedef typename IndexVector::Scalar StorageIndex;
-  IndexVector first_kid, next_kid; // Linked list of children 
-  StorageIndex postnum; 
-  // Allocate storage for working arrays and results 
-  first_kid.resize(n+1); 
-  next_kid.setZero(n+1);
-  post.setZero(n+1);
-  
-  // Set up structure describing children
-  first_kid.setConstant(-1); 
-  for (StorageIndex v = n-1; v >= 0; v--) 
-  {
-    StorageIndex dad = parent(v);
-    next_kid(v) = first_kid(dad); 
-    first_kid(dad) = v; 
-  }
-  
-  // Depth-first search from dummy root vertex #n
-  postnum = 0; 
-  internal::nr_etdfs(n, parent, first_kid, next_kid, post, postnum);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // SPARSE_COLETREE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h
deleted file mode 100644
index 6a2c7a8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h
+++ /dev/null
@@ -1,370 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_COMPRESSED_BASE_H
-#define EIGEN_SPARSE_COMPRESSED_BASE_H
-
-namespace Eigen { 
-
-template<typename Derived> class SparseCompressedBase;
-  
-namespace internal {
-
-template<typename Derived>
-struct traits<SparseCompressedBase<Derived> > : traits<Derived>
-{};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  * \class SparseCompressedBase
-  * \brief Common base class for sparse [compressed]-{row|column}-storage format.
-  *
-  * This class defines the common interface for all derived classes implementing the compressed sparse storage format, such as:
-  *  - SparseMatrix
-  *  - Ref<SparseMatrixType,Options>
-  *  - Map<SparseMatrixType>
-  *
-  */
-template<typename Derived>
-class SparseCompressedBase
-  : public SparseMatrixBase<Derived>
-{
-  public:
-    typedef SparseMatrixBase<Derived> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseCompressedBase)
-    using Base::operator=;
-    using Base::IsRowMajor;
-    
-    class InnerIterator;
-    class ReverseInnerIterator;
-    
-  protected:
-    typedef typename Base::IndexVector IndexVector;
-    Eigen::Map<IndexVector> innerNonZeros() { return Eigen::Map<IndexVector>(innerNonZeroPtr(), isCompressed()?0:derived().outerSize()); }
-    const  Eigen::Map<const IndexVector> innerNonZeros() const { return Eigen::Map<const IndexVector>(innerNonZeroPtr(), isCompressed()?0:derived().outerSize()); }
-        
-  public:
-    
-    /** \returns the number of non zero coefficients */
-    inline Index nonZeros() const
-    {
-      if(Derived::IsVectorAtCompileTime && outerIndexPtr()==0)
-        return derived().nonZeros();
-      else if(isCompressed())
-        return outerIndexPtr()[derived().outerSize()]-outerIndexPtr()[0];
-      else if(derived().outerSize()==0)
-        return 0;
-      else
-        return innerNonZeros().sum();
-    }
-    
-    /** \returns a const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline const Scalar* valuePtr() const { return derived().valuePtr(); }
-    /** \returns a non-const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline Scalar* valuePtr() { return derived().valuePtr(); }
-
-    /** \returns a const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline const StorageIndex* innerIndexPtr() const { return derived().innerIndexPtr(); }
-    /** \returns a non-const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline StorageIndex* innerIndexPtr() { return derived().innerIndexPtr(); }
-
-    /** \returns a const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 for SparseVector
-      * \sa valuePtr(), innerIndexPtr() */
-    inline const StorageIndex* outerIndexPtr() const { return derived().outerIndexPtr(); }
-    /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 for SparseVector
-      * \sa valuePtr(), innerIndexPtr() */
-    inline StorageIndex* outerIndexPtr() { return derived().outerIndexPtr(); }
-
-    /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline const StorageIndex* innerNonZeroPtr() const { return derived().innerNonZeroPtr(); }
-    /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline StorageIndex* innerNonZeroPtr() { return derived().innerNonZeroPtr(); }
-    
-    /** \returns whether \c *this is in compressed form. */
-    inline bool isCompressed() const { return innerNonZeroPtr()==0; }
-
-    /** \returns a read-only view of the stored coefficients as a 1D array expression.
-      *
-      * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
-      *
-      * \sa valuePtr(), isCompressed() */
-    const Map<const Array<Scalar,Dynamic,1> > coeffs() const { eigen_assert(isCompressed()); return Array<Scalar,Dynamic,1>::Map(valuePtr(),nonZeros()); }
-
-    /** \returns a read-write view of the stored coefficients as a 1D array expression
-      *
-      * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
-      *
-      * Here is an example:
-      * \include SparseMatrix_coeffs.cpp
-      * and the output is:
-      * \include SparseMatrix_coeffs.out
-      *
-      * \sa valuePtr(), isCompressed() */
-    Map<Array<Scalar,Dynamic,1> > coeffs() { eigen_assert(isCompressed()); return Array<Scalar,Dynamic,1>::Map(valuePtr(),nonZeros()); }
-
-  protected:
-    /** Default constructor. Do nothing. */
-    SparseCompressedBase() {}
-
-    /** \internal return the index of the coeff at (row,col) or just before if it does not exist.
-      * This is an analogue of std::lower_bound.
-      */
-    internal::LowerBoundIndex lower_bound(Index row, Index col) const
-    {
-      eigen_internal_assert(row>=0 && row<this->rows() && col>=0 && col<this->cols());
-
-      const Index outer = Derived::IsRowMajor ? row : col;
-      const Index inner = Derived::IsRowMajor ? col : row;
-
-      Index start = this->outerIndexPtr()[outer];
-      Index end = this->isCompressed() ? this->outerIndexPtr()[outer+1] : this->outerIndexPtr()[outer] + this->innerNonZeroPtr()[outer];
-      eigen_assert(end>=start && "you are using a non finalized sparse matrix or written coefficient does not exist");
-      internal::LowerBoundIndex p;
-      p.value = std::lower_bound(this->innerIndexPtr()+start, this->innerIndexPtr()+end,inner) - this->innerIndexPtr();
-      p.found = (p.value<end) && (this->innerIndexPtr()[p.value]==inner);
-      return p;
-    }
-
-    friend struct internal::evaluator<SparseCompressedBase<Derived> >;
-
-  private:
-    template<typename OtherDerived> explicit SparseCompressedBase(const SparseCompressedBase<OtherDerived>&);
-};
-
-template<typename Derived>
-class SparseCompressedBase<Derived>::InnerIterator
-{
-  public:
-    InnerIterator()
-      : m_values(0), m_indices(0), m_outer(0), m_id(0), m_end(0)
-    {}
-
-    InnerIterator(const InnerIterator& other)
-      : m_values(other.m_values), m_indices(other.m_indices), m_outer(other.m_outer), m_id(other.m_id), m_end(other.m_end)
-    {}
-
-    InnerIterator& operator=(const InnerIterator& other)
-    {
-      m_values = other.m_values;
-      m_indices = other.m_indices;
-      const_cast<OuterType&>(m_outer).setValue(other.m_outer.value());
-      m_id = other.m_id;
-      m_end = other.m_end;
-      return *this;
-    }
-
-    InnerIterator(const SparseCompressedBase& mat, Index outer)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer)
-    {
-      if(Derived::IsVectorAtCompileTime && mat.outerIndexPtr()==0)
-      {
-        m_id = 0;
-        m_end = mat.nonZeros();
-      }
-      else
-      {
-        m_id = mat.outerIndexPtr()[outer];
-        if(mat.isCompressed())
-          m_end = mat.outerIndexPtr()[outer+1];
-        else
-          m_end = m_id + mat.innerNonZeroPtr()[outer];
-      }
-    }
-
-    explicit InnerIterator(const SparseCompressedBase& mat)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(0), m_id(0), m_end(mat.nonZeros())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    explicit InnerIterator(const internal::CompressedStorage<Scalar,StorageIndex>& data)
-      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_id(0), m_end(data.size())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    inline InnerIterator& operator++() { m_id++; return *this; }
-    inline InnerIterator& operator+=(Index i) { m_id += i ; return *this; }
-
-    inline InnerIterator operator+(Index i) 
-    { 
-        InnerIterator result = *this;
-        result += i;
-        return result;
-    }
-
-    inline const Scalar& value() const { return m_values[m_id]; }
-    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id]); }
-
-    inline StorageIndex index() const { return m_indices[m_id]; }
-    inline Index outer() const { return m_outer.value(); }
-    inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
-    inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
-
-    inline operator bool() const { return (m_id < m_end); }
-
-  protected:
-    const Scalar* m_values;
-    const StorageIndex* m_indices;
-    typedef internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> OuterType;
-    const OuterType m_outer;
-    Index m_id;
-    Index m_end;
-  private:
-    // If you get here, then you're not using the right InnerIterator type, e.g.:
-    //   SparseMatrix<double,RowMajor> A;
-    //   SparseMatrix<double>::InnerIterator it(A,0);
-    template<typename T> InnerIterator(const SparseMatrixBase<T>&, Index outer);
-};
-
-template<typename Derived>
-class SparseCompressedBase<Derived>::ReverseInnerIterator
-{
-  public:
-    ReverseInnerIterator(const SparseCompressedBase& mat, Index outer)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer)
-    {
-      if(Derived::IsVectorAtCompileTime && mat.outerIndexPtr()==0)
-      {
-        m_start = 0;
-        m_id = mat.nonZeros();
-      }
-      else
-      {
-        m_start = mat.outerIndexPtr()[outer];
-        if(mat.isCompressed())
-          m_id = mat.outerIndexPtr()[outer+1];
-        else
-          m_id = m_start + mat.innerNonZeroPtr()[outer];
-      }
-    }
-
-    explicit ReverseInnerIterator(const SparseCompressedBase& mat)
-      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(0), m_start(0), m_id(mat.nonZeros())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    explicit ReverseInnerIterator(const internal::CompressedStorage<Scalar,StorageIndex>& data)
-      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_start(0), m_id(data.size())
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-    }
-
-    inline ReverseInnerIterator& operator--() { --m_id; return *this; }
-    inline ReverseInnerIterator& operator-=(Index i) { m_id -= i; return *this; }
-
-    inline ReverseInnerIterator operator-(Index i) 
-    {
-        ReverseInnerIterator result = *this;
-        result -= i;
-        return result;
-    }
-
-    inline const Scalar& value() const { return m_values[m_id-1]; }
-    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id-1]); }
-
-    inline StorageIndex index() const { return m_indices[m_id-1]; }
-    inline Index outer() const { return m_outer.value(); }
-    inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
-    inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
-
-    inline operator bool() const { return (m_id > m_start); }
-
-  protected:
-    const Scalar* m_values;
-    const StorageIndex* m_indices;
-    typedef internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> OuterType;
-    const OuterType m_outer;
-    Index m_start;
-    Index m_id;
-};
-
-namespace internal {
-
-template<typename Derived>
-struct evaluator<SparseCompressedBase<Derived> >
-  : evaluator_base<Derived>
-{
-  typedef typename Derived::Scalar Scalar;
-  typedef typename Derived::InnerIterator InnerIterator;
-  
-  enum {
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    Flags = Derived::Flags
-  };
-  
-  evaluator() : m_matrix(0), m_zero(0)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  explicit evaluator(const Derived &mat) : m_matrix(&mat), m_zero(0)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_matrix->nonZeros();
-  }
-  
-  operator Derived&() { return m_matrix->const_cast_derived(); }
-  operator const Derived&() const { return *m_matrix; }
-  
-  typedef typename DenseCoeffsBase<Derived,ReadOnlyAccessors>::CoeffReturnType CoeffReturnType;
-  const Scalar& coeff(Index row, Index col) const
-  {
-    Index p = find(row,col);
-
-    if(p==Dynamic)
-      return m_zero;
-    else
-      return m_matrix->const_cast_derived().valuePtr()[p];
-  }
-
-  Scalar& coeffRef(Index row, Index col)
-  {
-    Index p = find(row,col);
-    eigen_assert(p!=Dynamic && "written coefficient does not exist");
-    return m_matrix->const_cast_derived().valuePtr()[p];
-  }
-
-protected:
-
-  Index find(Index row, Index col) const
-  {
-    internal::LowerBoundIndex p = m_matrix->lower_bound(row,col);
-    return p.found ? p.value : Dynamic;
-  }
-
-  const Derived *m_matrix;
-  const Scalar m_zero;
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_COMPRESSED_BASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
deleted file mode 100644
index 9b0d3f9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
+++ /dev/null
@@ -1,722 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_CWISE_BINARY_OP_H
-#define EIGEN_SPARSE_CWISE_BINARY_OP_H
-
-namespace Eigen { 
-
-// Here we have to handle 3 cases:
-//  1 - sparse op dense
-//  2 - dense op sparse
-//  3 - sparse op sparse
-// We also need to implement a 4th iterator for:
-//  4 - dense op dense
-// Finally, we also need to distinguish between the product and other operations :
-//                configuration      returned mode
-//  1 - sparse op dense    product      sparse
-//                         generic      dense
-//  2 - dense op sparse    product      sparse
-//                         generic      dense
-//  3 - sparse op sparse   product      sparse
-//                         generic      sparse
-//  4 - dense op dense     product      dense
-//                         generic      dense
-//
-// TODO to ease compiler job, we could specialize product/quotient with a scalar
-//      and fallback to cwise-unary evaluator using bind1st_op and bind2nd_op.
-
-template<typename BinaryOp, typename Lhs, typename Rhs>
-class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
-  : public SparseMatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-  public:
-    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
-    typedef SparseMatrixBase<Derived> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
-    CwiseBinaryOpImpl()
-    {
-      EIGEN_STATIC_ASSERT((
-                (!internal::is_same<typename internal::traits<Lhs>::StorageKind,
-                                    typename internal::traits<Rhs>::StorageKind>::value)
-            ||  ((internal::evaluator<Lhs>::Flags&RowMajorBit) == (internal::evaluator<Rhs>::Flags&RowMajorBit))),
-            THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH);
-    }
-};
-
-namespace internal {
-
-  
-// Generic "sparse OP sparse"
-template<typename XprType> struct binary_sparse_evaluator;
-
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IteratorBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-protected:
-  typedef typename evaluator<Lhs>::InnerIterator  LhsIterator;
-  typedef typename evaluator<Rhs>::InnerIterator  RhsIterator;
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-public:
-
-  class InnerIterator
-  {
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor)
-    {
-      this->operator++();
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index()))
-      {
-        m_id = m_lhsIter.index();
-        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
-        ++m_lhsIter;
-        ++m_rhsIter;
-      }
-      else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index())))
-      {
-        m_id = m_lhsIter.index();
-        m_value = m_functor(m_lhsIter.value(), Scalar(0));
-        ++m_lhsIter;
-      }
-      else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index())))
-      {
-        m_id = m_rhsIter.index();
-        m_value = m_functor(Scalar(0), m_rhsIter.value());
-        ++m_rhsIter;
-      }
-      else
-      {
-        m_value = Scalar(0); // this is to avoid a compilation warning
-        m_id = -1;
-      }
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); }
-    EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_id>=0; }
-
-  protected:
-    LhsIterator m_lhsIter;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-    Scalar m_value;
-    StorageIndex m_id;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit binary_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_lhsImpl.nonZerosEstimate() + m_rhsImpl.nonZerosEstimate();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<Lhs> m_lhsImpl;
-  evaluator<Rhs> m_rhsImpl;
-};
-
-// dense op sparse
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IteratorBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-protected:
-  typedef typename evaluator<Rhs>::InnerIterator  RhsIterator;
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(Rhs::Flags)&RowMajorBit)==RowMajorBit };
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
-      : m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_value(0), m_id(-1), m_innerSize(aEval.m_expr.rhs().innerSize())
-    {
-      this->operator++();
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_id;
-      if(m_id<m_innerSize)
-      {
-        Scalar lhsVal = m_lhsEval.coeff(IsRowMajor?m_rhsIter.outer():m_id,
-                                        IsRowMajor?m_id:m_rhsIter.outer());
-        if(m_rhsIter && m_rhsIter.index()==m_id)
-        {
-          m_value = m_functor(lhsVal, m_rhsIter.value());
-          ++m_rhsIter;
-        }
-        else
-          m_value = m_functor(lhsVal, Scalar(0));
-      }
-
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
-    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
-    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
-
-  protected:
-    const evaluator<Lhs> &m_lhsEval;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-    Scalar m_value;
-    StorageIndex m_id;
-    StorageIndex m_innerSize;
-  };
-
-
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-
-  explicit binary_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()),
-      m_rhsImpl(xpr.rhs()),
-      m_expr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  inline Index nonZerosEstimate() const {
-    return m_expr.size();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<Lhs> m_lhsImpl;
-  evaluator<Rhs> m_rhsImpl;
-  const XprType &m_expr;
-};
-
-// sparse op dense
-template<typename BinaryOp, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IndexBased>
-  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
-{
-protected:
-  typedef typename evaluator<Lhs>::InnerIterator  LhsIterator;
-  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
-  typedef typename traits<XprType>::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(Lhs::Flags)&RowMajorBit)==RowMajorBit };
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_value(0), m_id(-1), m_innerSize(aEval.m_expr.lhs().innerSize())
-    {
-      this->operator++();
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_id;
-      if(m_id<m_innerSize)
-      {
-        Scalar rhsVal = m_rhsEval.coeff(IsRowMajor?m_lhsIter.outer():m_id,
-                                        IsRowMajor?m_id:m_lhsIter.outer());
-        if(m_lhsIter && m_lhsIter.index()==m_id)
-        {
-          m_value = m_functor(m_lhsIter.value(), rhsVal);
-          ++m_lhsIter;
-        }
-        else
-          m_value = m_functor(Scalar(0),rhsVal);
-      }
-
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
-    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
-
-  protected:
-    LhsIterator m_lhsIter;
-    const evaluator<Rhs> &m_rhsEval;
-    const BinaryOp& m_functor;
-    Scalar m_value;
-    StorageIndex m_id;
-    StorageIndex m_innerSize;
-  };
-
-
-  enum {
-    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-
-  explicit binary_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()),
-      m_rhsImpl(xpr.rhs()),
-      m_expr(xpr)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-
-  inline Index nonZerosEstimate() const {
-    return m_expr.size();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<Lhs> m_lhsImpl;
-  evaluator<Rhs> m_rhsImpl;
-  const XprType &m_expr;
-};
-
-template<typename T,
-         typename LhsKind   = typename evaluator_traits<typename T::Lhs>::Kind,
-         typename RhsKind   = typename evaluator_traits<typename T::Rhs>::Kind,
-         typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
-         typename RhsScalar = typename traits<typename T::Rhs>::Scalar> struct sparse_conjunction_evaluator;
-
-// "sparse .* sparse"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "dense .* sparse"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IndexBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "sparse .* dense"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IndexBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// "sparse ./ dense"
-template<typename T1, typename T2, typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs>, IteratorBased, IndexBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// "sparse && sparse"
-template<typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs>, IteratorBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "dense && sparse"
-template<typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs>, IndexBased, IteratorBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-// "sparse && dense"
-template<typename Lhs, typename Rhs>
-struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs>, IteratorBased, IndexBased>
-  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> >
-{
-  typedef CwiseBinaryOp<scalar_boolean_and_op, Lhs, Rhs> XprType;
-  typedef sparse_conjunction_evaluator<XprType> Base;
-  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
-};
-
-// "sparse ^ sparse"
-template<typename XprType>
-struct sparse_conjunction_evaluator<XprType, IteratorBased, IteratorBased>
-  : evaluator_base<XprType>
-{
-protected:
-  typedef typename XprType::Functor BinaryOp;
-  typedef typename XprType::Lhs LhsArg;
-  typedef typename XprType::Rhs RhsArg;
-  typedef typename evaluator<LhsArg>::InnerIterator  LhsIterator;
-  typedef typename evaluator<RhsArg>::InnerIterator  RhsIterator;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename traits<XprType>::Scalar Scalar;
-public:
-
-  class InnerIterator
-  {
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor)
-    {
-      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
-      {
-        if (m_lhsIter.index() < m_rhsIter.index())
-          ++m_lhsIter;
-        else
-          ++m_rhsIter;
-      }
-    }
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_lhsIter;
-      ++m_rhsIter;
-      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
-      {
-        if (m_lhsIter.index() < m_rhsIter.index())
-          ++m_lhsIter;
-        else
-          ++m_rhsIter;
-      }
-      return *this;
-    }
-    
-    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
-    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return (m_lhsIter && m_rhsIter); }
-
-  protected:
-    LhsIterator m_lhsIter;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit sparse_conjunction_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return (std::min)(m_lhsImpl.nonZerosEstimate(), m_rhsImpl.nonZerosEstimate());
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<LhsArg> m_lhsImpl;
-  evaluator<RhsArg> m_rhsImpl;
-};
-
-// "dense ^ sparse"
-template<typename XprType>
-struct sparse_conjunction_evaluator<XprType, IndexBased, IteratorBased>
-  : evaluator_base<XprType>
-{
-protected:
-  typedef typename XprType::Functor BinaryOp;
-  typedef typename XprType::Lhs LhsArg;
-  typedef typename XprType::Rhs RhsArg;
-  typedef evaluator<LhsArg> LhsEvaluator;
-  typedef typename evaluator<RhsArg>::InnerIterator  RhsIterator;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename traits<XprType>::Scalar Scalar;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(RhsArg::Flags)&RowMajorBit)==RowMajorBit };
-
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
-      : m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_outer(outer)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_rhsIter;
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const
-    { return m_functor(m_lhsEval.coeff(IsRowMajor?m_outer:m_rhsIter.index(),IsRowMajor?m_rhsIter.index():m_outer), m_rhsIter.value()); }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_rhsIter.index(); }
-    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); }
-    EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_rhsIter; }
-    
-  protected:
-    const LhsEvaluator &m_lhsEval;
-    RhsIterator m_rhsIter;
-    const BinaryOp& m_functor;
-    const Index m_outer;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit sparse_conjunction_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_rhsImpl.nonZerosEstimate();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<LhsArg> m_lhsImpl;
-  evaluator<RhsArg> m_rhsImpl;
-};
-
-// "sparse ^ dense"
-template<typename XprType>
-struct sparse_conjunction_evaluator<XprType, IteratorBased, IndexBased>
-  : evaluator_base<XprType>
-{
-protected:
-  typedef typename XprType::Functor BinaryOp;
-  typedef typename XprType::Lhs LhsArg;
-  typedef typename XprType::Rhs RhsArg;
-  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
-  typedef evaluator<RhsArg> RhsEvaluator;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename traits<XprType>::Scalar Scalar;
-public:
-
-  class InnerIterator
-  {
-    enum { IsRowMajor = (int(LhsArg::Flags)&RowMajorBit)==RowMajorBit };
-
-  public:
-    
-    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
-      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_outer(outer)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    {
-      ++m_lhsIter;
-      return *this;
-    }
-
-    EIGEN_STRONG_INLINE Scalar value() const
-    { return m_functor(m_lhsIter.value(),
-                       m_rhsEval.coeff(IsRowMajor?m_outer:m_lhsIter.index(),IsRowMajor?m_lhsIter.index():m_outer)); }
-
-    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
-    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
-    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
-    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
-
-    EIGEN_STRONG_INLINE operator bool() const { return m_lhsIter; }
-    
-  protected:
-    LhsIterator m_lhsIter;
-    const evaluator<RhsArg> &m_rhsEval;
-    const BinaryOp& m_functor;
-    const Index m_outer;
-  };
-  
-  
-  enum {
-    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
-    Flags = XprType::Flags
-  };
-  
-  explicit sparse_conjunction_evaluator(const XprType& xpr)
-    : m_functor(xpr.functor()),
-      m_lhsImpl(xpr.lhs()), 
-      m_rhsImpl(xpr.rhs())  
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_lhsImpl.nonZerosEstimate();
-  }
-
-protected:
-  const BinaryOp m_functor;
-  evaluator<LhsArg> m_lhsImpl;
-  evaluator<RhsArg> m_rhsImpl;
-};
-
-}
-
-/***************************************************************************
-* Implementation of SparseMatrixBase and SparseCwise functions/operators
-***************************************************************************/
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
-{
-  call_assignment(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE Derived &
-SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived> &other)
-{
-  return derived() = derived() - other.derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE Derived &
-SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other)
-{
-  return derived() = derived() + other.derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator+=(const DiagonalBase<OtherDerived>& other)
-{
-  call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-Derived& SparseMatrixBase<Derived>::operator-=(const DiagonalBase<OtherDerived>& other)
-{
-  call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
-  return derived();
-}
-    
-template<typename Derived>
-template<typename OtherDerived>
-EIGEN_STRONG_INLINE const typename SparseMatrixBase<Derived>::template CwiseProductDenseReturnType<OtherDerived>::Type
-SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) const
-{
-  return typename CwiseProductDenseReturnType<OtherDerived>::Type(derived(), other.derived());
-}
-
-template<typename DenseDerived, typename SparseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
-operator+(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
-}
-
-template<typename SparseDerived, typename DenseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
-operator+(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
-}
-
-template<typename DenseDerived, typename SparseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
-operator-(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
-}
-
-template<typename SparseDerived, typename DenseDerived>
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
-operator-(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
-{
-  return CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_CWISE_BINARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
deleted file mode 100644
index 32dac0f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
+++ /dev/null
@@ -1,150 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_CWISE_UNARY_OP_H
-#define EIGEN_SPARSE_CWISE_UNARY_OP_H
-
-namespace Eigen { 
-
-namespace internal {
-  
-template<typename UnaryOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>
-  : public evaluator_base<CwiseUnaryOp<UnaryOp,ArgType> >
-{
-  public:
-    typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
-
-    class InnerIterator;
-    
-    enum {
-      CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression())
-    {
-      EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
-      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-    }
-    
-    inline Index nonZerosEstimate() const {
-      return m_argImpl.nonZerosEstimate();
-    }
-
-  protected:
-    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
-    
-    const UnaryOp m_functor;
-    evaluator<ArgType> m_argImpl;
-};
-
-template<typename UnaryOp, typename ArgType>
-class unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::InnerIterator
-    : public unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator
-{
-  protected:
-    typedef typename XprType::Scalar Scalar;
-    typedef typename unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator Base;
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
-      : Base(unaryOp.m_argImpl,outer), m_functor(unaryOp.m_functor)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    { Base::operator++(); return *this; }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
-
-  protected:
-    const UnaryOp m_functor;
-  private:
-    Scalar& valueRef();
-};
-
-template<typename ViewOp, typename ArgType>
-struct unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>
-  : public evaluator_base<CwiseUnaryView<ViewOp,ArgType> >
-{
-  public:
-    typedef CwiseUnaryView<ViewOp, ArgType> XprType;
-
-    class InnerIterator;
-    
-    enum {
-      CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<ViewOp>::Cost),
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression())
-    {
-      EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<ViewOp>::Cost);
-      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-    }
-
-  protected:
-    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
-    
-    const ViewOp m_functor;
-    evaluator<ArgType> m_argImpl;
-};
-
-template<typename ViewOp, typename ArgType>
-class unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::InnerIterator
-    : public unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator
-{
-  protected:
-    typedef typename XprType::Scalar Scalar;
-    typedef typename unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator Base;
-  public:
-
-    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
-      : Base(unaryOp.m_argImpl,outer), m_functor(unaryOp.m_functor)
-    {}
-
-    EIGEN_STRONG_INLINE InnerIterator& operator++()
-    { Base::operator++(); return *this; }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
-    EIGEN_STRONG_INLINE Scalar& valueRef() { return m_functor(Base::valueRef()); }
-
-  protected:
-    const ViewOp m_functor;
-};
-
-} // end namespace internal
-
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-SparseMatrixBase<Derived>::operator*=(const Scalar& other)
-{
-  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
-  internal::evaluator<Derived> thisEval(derived());
-  for (Index j=0; j<outerSize(); ++j)
-    for (EvalIterator i(thisEval,j); i; ++i)
-      i.valueRef() *= other;
-  return derived();
-}
-
-template<typename Derived>
-EIGEN_STRONG_INLINE Derived&
-SparseMatrixBase<Derived>::operator/=(const Scalar& other)
-{
-  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
-  internal::evaluator<Derived> thisEval(derived());
-  for (Index j=0; j<outerSize(); ++j)
-    for (EvalIterator i(thisEval,j); i; ++i)
-      i.valueRef() /= other;
-  return derived();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_CWISE_UNARY_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
deleted file mode 100644
index f005a18..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
+++ /dev/null
@@ -1,342 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEDENSEPRODUCT_H
-#define EIGEN_SPARSEDENSEPRODUCT_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template <> struct product_promote_storage_type<Sparse,Dense, OuterProduct> { typedef Sparse ret; };
-template <> struct product_promote_storage_type<Dense,Sparse, OuterProduct> { typedef Sparse ret; };
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,
-         typename AlphaType,
-         int LhsStorageOrder = ((SparseLhsType::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor,
-         bool ColPerCol = ((DenseRhsType::Flags&RowMajorBit)==0) || DenseRhsType::ColsAtCompileTime==1>
-struct sparse_time_dense_product_impl;
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, true>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
-  typedef evaluator<Lhs> LhsEval;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
-  {
-    LhsEval lhsEval(lhs);
-    
-    Index n = lhs.outerSize();
-#ifdef EIGEN_HAS_OPENMP
-    Eigen::initParallel();
-    Index threads = Eigen::nbThreads();
-#endif
-    
-    for(Index c=0; c<rhs.cols(); ++c)
-    {
-#ifdef EIGEN_HAS_OPENMP
-      // This 20000 threshold has been found experimentally on 2D and 3D Poisson problems.
-      // It basically represents the minimal amount of work to be done to be worth it.
-      if(threads>1 && lhsEval.nonZerosEstimate() > 20000)
-      {
-        #pragma omp parallel for schedule(dynamic,(n+threads*4-1)/(threads*4)) num_threads(threads)
-        for(Index i=0; i<n; ++i)
-          processRow(lhsEval,rhs,res,alpha,i,c);
-      }
-      else
-#endif
-      {
-        for(Index i=0; i<n; ++i)
-          processRow(lhsEval,rhs,res,alpha,i,c);
-      }
-    }
-  }
-  
-  static void processRow(const LhsEval& lhsEval, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha, Index i, Index col)
-  {
-    typename Res::Scalar tmp(0);
-    for(LhsInnerIterator it(lhsEval,i); it ;++it)
-      tmp += it.value() * rhs.coeff(it.index(),col);
-    res.coeffRef(i,col) += alpha * tmp;
-  }
-  
-};
-
-// FIXME: what is the purpose of the following specialization? Is it for the BlockedSparse format?
-// -> let's disable it for now as it is conflicting with generic scalar*matrix and matrix*scalar operators
-// template<typename T1, typename T2/*, int _Options, typename _StrideType*/>
-// struct ScalarBinaryOpTraits<T1, Ref<T2/*, _Options, _StrideType*/> >
-// {
-//   enum {
-//     Defined = 1
-//   };
-//   typedef typename CwiseUnaryOp<scalar_multiple2_op<T1, typename T2::Scalar>, T2>::PlainObject ReturnType;
-// };
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType, ColMajor, true>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename LhsEval::InnerIterator LhsInnerIterator;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index c=0; c<rhs.cols(); ++c)
-    {
-      for(Index j=0; j<lhs.outerSize(); ++j)
-      {
-//        typename Res::Scalar rhs_j = alpha * rhs.coeff(j,c);
-        typename ScalarBinaryOpTraits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j,c));
-        for(LhsInnerIterator it(lhsEval,j); it ;++it)
-          res.coeffRef(it.index(),c) += it.value() * rhs_j;
-      }
-    }
-  }
-};
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, false>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename LhsEval::InnerIterator LhsInnerIterator;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
-  {
-    Index n = lhs.rows();
-    LhsEval lhsEval(lhs);
-
-#ifdef EIGEN_HAS_OPENMP
-    Eigen::initParallel();
-    Index threads = Eigen::nbThreads();
-    // This 20000 threshold has been found experimentally on 2D and 3D Poisson problems.
-    // It basically represents the minimal amount of work to be done to be worth it.
-    if(threads>1 && lhsEval.nonZerosEstimate()*rhs.cols() > 20000)
-    {
-      #pragma omp parallel for schedule(dynamic,(n+threads*4-1)/(threads*4)) num_threads(threads)
-      for(Index i=0; i<n; ++i)
-        processRow(lhsEval,rhs,res,alpha,i);
-    }
-    else
-#endif
-    {
-      for(Index i=0; i<n; ++i)
-        processRow(lhsEval, rhs, res, alpha, i);
-    }
-  }
-
-  static void processRow(const LhsEval& lhsEval, const DenseRhsType& rhs, Res& res, const typename Res::Scalar& alpha, Index i)
-  {
-    typename Res::RowXpr res_i(res.row(i));
-    for(LhsInnerIterator it(lhsEval,i); it ;++it)
-      res_i += (alpha*it.value()) * rhs.row(it.index());
-  }
-};
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
-struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, ColMajor, false>
-{
-  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
-  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
-  typedef typename internal::remove_all<DenseResType>::type Res;
-  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
-  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
-  {
-    evaluator<Lhs> lhsEval(lhs);
-    for(Index j=0; j<lhs.outerSize(); ++j)
-    {
-      typename Rhs::ConstRowXpr rhs_j(rhs.row(j));
-      for(LhsInnerIterator it(lhsEval,j); it ;++it)
-        res.row(it.index()) += (alpha*it.value()) * rhs_j;
-    }
-  }
-};
-
-template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,typename AlphaType>
-inline void sparse_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
-{
-  sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType>::run(lhs, rhs, res, alpha);
-}
-
-} // end namespace internal
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType>
- : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SparseShape,DenseShape,ProductType> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    typedef typename nested_eval<Lhs,((Rhs::Flags&RowMajorBit)==0) ? 1 : Rhs::ColsAtCompileTime>::type LhsNested;
-    typedef typename nested_eval<Rhs,((Lhs::Flags&RowMajorBit)==0) ? 1 : Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    internal::sparse_time_dense_product(lhsNested, rhsNested, dst, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, DenseShape, ProductType>
-  : generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType>
-{};
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType>
-  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SparseShape,ProductType> >
-{
-  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
-  
-  template<typename Dst>
-  static void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
-  {
-    typedef typename nested_eval<Lhs,((Rhs::Flags&RowMajorBit)==0) ? Dynamic : 1>::type LhsNested;
-    typedef typename nested_eval<Rhs,((Lhs::Flags&RowMajorBit)==RowMajorBit) ? 1 : Lhs::RowsAtCompileTime>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    
-    // transpose everything
-    Transpose<Dst> dstT(dst);
-    internal::sparse_time_dense_product(rhsNested.transpose(), lhsNested.transpose(), dstT, alpha);
-  }
-};
-
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, DenseShape, SparseTriangularShape, ProductType>
-  : generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType>
-{};
-
-template<typename LhsT, typename RhsT, bool NeedToTranspose>
-struct sparse_dense_outer_product_evaluator
-{
-protected:
-  typedef typename conditional<NeedToTranspose,RhsT,LhsT>::type Lhs1;
-  typedef typename conditional<NeedToTranspose,LhsT,RhsT>::type ActualRhs;
-  typedef Product<LhsT,RhsT,DefaultProduct> ProdXprType;
-  
-  // if the actual left-hand side is a dense vector,
-  // then build a sparse-view so that we can seamlessly iterate over it.
-  typedef typename conditional<is_same<typename internal::traits<Lhs1>::StorageKind,Sparse>::value,
-            Lhs1, SparseView<Lhs1> >::type ActualLhs;
-  typedef typename conditional<is_same<typename internal::traits<Lhs1>::StorageKind,Sparse>::value,
-            Lhs1 const&, SparseView<Lhs1> >::type LhsArg;
-            
-  typedef evaluator<ActualLhs> LhsEval;
-  typedef evaluator<ActualRhs> RhsEval;
-  typedef typename evaluator<ActualLhs>::InnerIterator LhsIterator;
-  typedef typename ProdXprType::Scalar Scalar;
-  
-public:
-  enum {
-    Flags = NeedToTranspose ? RowMajorBit : 0,
-    CoeffReadCost = HugeCost
-  };
-  
-  class InnerIterator : public LhsIterator
-  {
-  public:
-    InnerIterator(const sparse_dense_outer_product_evaluator &xprEval, Index outer)
-      : LhsIterator(xprEval.m_lhsXprImpl, 0),
-        m_outer(outer),
-        m_empty(false),
-        m_factor(get(xprEval.m_rhsXprImpl, outer, typename internal::traits<ActualRhs>::StorageKind() ))
-    {}
-    
-    EIGEN_STRONG_INLINE Index outer() const { return m_outer; }
-    EIGEN_STRONG_INLINE Index row()   const { return NeedToTranspose ? m_outer : LhsIterator::index(); }
-    EIGEN_STRONG_INLINE Index col()   const { return NeedToTranspose ? LhsIterator::index() : m_outer; }
-
-    EIGEN_STRONG_INLINE Scalar value() const { return LhsIterator::value() * m_factor; }
-    EIGEN_STRONG_INLINE operator bool() const { return LhsIterator::operator bool() && (!m_empty); }
-    
-  protected:
-    Scalar get(const RhsEval &rhs, Index outer, Dense = Dense()) const
-    {
-      return rhs.coeff(outer);
-    }
-    
-    Scalar get(const RhsEval &rhs, Index outer, Sparse = Sparse())
-    {
-      typename RhsEval::InnerIterator it(rhs, outer);
-      if (it && it.index()==0 && it.value()!=Scalar(0))
-        return it.value();
-      m_empty = true;
-      return Scalar(0);
-    }
-    
-    Index m_outer;
-    bool m_empty;
-    Scalar m_factor;
-  };
-  
-  sparse_dense_outer_product_evaluator(const Lhs1 &lhs, const ActualRhs &rhs)
-     : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  // transpose case
-  sparse_dense_outer_product_evaluator(const ActualRhs &rhs, const Lhs1 &lhs)
-     : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-    
-protected:
-  const LhsArg m_lhs;
-  evaluator<ActualLhs> m_lhsXprImpl;
-  evaluator<ActualRhs> m_rhsXprImpl;
-};
-
-// sparse * dense outer product
-template<typename Lhs, typename Rhs>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, SparseShape, DenseShape>
-  : sparse_dense_outer_product_evaluator<Lhs,Rhs, Lhs::IsRowMajor>
-{
-  typedef sparse_dense_outer_product_evaluator<Lhs,Rhs, Lhs::IsRowMajor> Base;
-  
-  typedef Product<Lhs, Rhs> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-
-  explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.lhs(), xpr.rhs())
-  {}
-  
-};
-
-template<typename Lhs, typename Rhs>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, DenseShape, SparseShape>
-  : sparse_dense_outer_product_evaluator<Lhs,Rhs, Rhs::IsRowMajor>
-{
-  typedef sparse_dense_outer_product_evaluator<Lhs,Rhs, Rhs::IsRowMajor> Base;
-  
-  typedef Product<Lhs, Rhs> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-
-  explicit product_evaluator(const XprType& xpr)
-    : Base(xpr.lhs(), xpr.rhs())
-  {}
-  
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEDENSEPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
deleted file mode 100644
index 941c03b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
+++ /dev/null
@@ -1,138 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_DIAGONAL_PRODUCT_H
-#define EIGEN_SPARSE_DIAGONAL_PRODUCT_H
-
-namespace Eigen { 
-
-// The product of a diagonal matrix with a sparse matrix can be easily
-// implemented using expression template.
-// We have two consider very different cases:
-// 1 - diag * row-major sparse
-//     => each inner vector <=> scalar * sparse vector product
-//     => so we can reuse CwiseUnaryOp::InnerIterator
-// 2 - diag * col-major sparse
-//     => each inner vector <=> densevector * sparse vector cwise product
-//     => again, we can reuse specialization of CwiseBinaryOp::InnerIterator
-//        for that particular case
-// The two other cases are symmetric.
-
-namespace internal {
-
-enum {
-  SDP_AsScalarProduct,
-  SDP_AsCwiseProduct
-};
-  
-template<typename SparseXprType, typename DiagonalCoeffType, int SDP_Tag>
-struct sparse_diagonal_product_evaluator;
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, DiagonalShape, SparseShape>
-  : public sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType, Rhs::Flags&RowMajorBit?SDP_AsScalarProduct:SDP_AsCwiseProduct>
-{
-  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
-  enum { CoeffReadCost = HugeCost, Flags = Rhs::Flags&RowMajorBit, Alignment = 0 }; // FIXME CoeffReadCost & Flags
-  
-  typedef sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType, Rhs::Flags&RowMajorBit?SDP_AsScalarProduct:SDP_AsCwiseProduct> Base;
-  explicit product_evaluator(const XprType& xpr) : Base(xpr.rhs(), xpr.lhs().diagonal()) {}
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, SparseShape, DiagonalShape>
-  : public sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>, Lhs::Flags&RowMajorBit?SDP_AsCwiseProduct:SDP_AsScalarProduct>
-{
-  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
-  enum { CoeffReadCost = HugeCost, Flags = Lhs::Flags&RowMajorBit, Alignment = 0 }; // FIXME CoeffReadCost & Flags
-  
-  typedef sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>, Lhs::Flags&RowMajorBit?SDP_AsCwiseProduct:SDP_AsScalarProduct> Base;
-  explicit product_evaluator(const XprType& xpr) : Base(xpr.lhs(), xpr.rhs().diagonal().transpose()) {}
-};
-
-template<typename SparseXprType, typename DiagonalCoeffType>
-struct sparse_diagonal_product_evaluator<SparseXprType, DiagonalCoeffType, SDP_AsScalarProduct>
-{
-protected:
-  typedef typename evaluator<SparseXprType>::InnerIterator SparseXprInnerIterator;
-  typedef typename SparseXprType::Scalar Scalar;
-  
-public:
-  class InnerIterator : public SparseXprInnerIterator
-  {
-  public:
-    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
-      : SparseXprInnerIterator(xprEval.m_sparseXprImpl, outer),
-        m_coeff(xprEval.m_diagCoeffImpl.coeff(outer))
-    {}
-    
-    EIGEN_STRONG_INLINE Scalar value() const { return m_coeff * SparseXprInnerIterator::value(); }
-  protected:
-    typename DiagonalCoeffType::Scalar m_coeff;
-  };
-  
-  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagonalCoeffType &diagCoeff)
-    : m_sparseXprImpl(sparseXpr), m_diagCoeffImpl(diagCoeff)
-  {}
-
-  Index nonZerosEstimate() const { return m_sparseXprImpl.nonZerosEstimate(); }
-    
-protected:
-  evaluator<SparseXprType> m_sparseXprImpl;
-  evaluator<DiagonalCoeffType> m_diagCoeffImpl;
-};
-
-
-template<typename SparseXprType, typename DiagCoeffType>
-struct sparse_diagonal_product_evaluator<SparseXprType, DiagCoeffType, SDP_AsCwiseProduct>
-{
-  typedef typename SparseXprType::Scalar Scalar;
-  typedef typename SparseXprType::StorageIndex StorageIndex;
-  
-  typedef typename nested_eval<DiagCoeffType,SparseXprType::IsRowMajor ? SparseXprType::RowsAtCompileTime
-                                                                       : SparseXprType::ColsAtCompileTime>::type DiagCoeffNested;
-  
-  class InnerIterator
-  {
-    typedef typename evaluator<SparseXprType>::InnerIterator SparseXprIter;
-  public:
-    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
-      : m_sparseIter(xprEval.m_sparseXprEval, outer), m_diagCoeffNested(xprEval.m_diagCoeffNested)
-    {}
-    
-    inline Scalar value() const { return m_sparseIter.value() * m_diagCoeffNested.coeff(index()); }
-    inline StorageIndex index() const  { return m_sparseIter.index(); }
-    inline Index outer() const  { return m_sparseIter.outer(); }
-    inline Index col() const    { return SparseXprType::IsRowMajor ? m_sparseIter.index() : m_sparseIter.outer(); }
-    inline Index row() const    { return SparseXprType::IsRowMajor ? m_sparseIter.outer() : m_sparseIter.index(); }
-    
-    EIGEN_STRONG_INLINE InnerIterator& operator++() { ++m_sparseIter; return *this; }
-    inline operator bool() const  { return m_sparseIter; }
-    
-  protected:
-    SparseXprIter m_sparseIter;
-    DiagCoeffNested m_diagCoeffNested;
-  };
-  
-  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagCoeffType &diagCoeff)
-    : m_sparseXprEval(sparseXpr), m_diagCoeffNested(diagCoeff)
-  {}
-
-  Index nonZerosEstimate() const { return m_sparseXprEval.nonZerosEstimate(); }
-    
-protected:
-  evaluator<SparseXprType> m_sparseXprEval;
-  DiagCoeffNested m_diagCoeffNested;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_DIAGONAL_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDot.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDot.h
deleted file mode 100644
index 38bc4aa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseDot.h
+++ /dev/null
@@ -1,98 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_DOT_H
-#define EIGEN_SPARSE_DOT_H
-
-namespace Eigen { 
-
-template<typename Derived>
-template<typename OtherDerived>
-typename internal::traits<Derived>::Scalar
-SparseMatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  eigen_assert(size() == other.size());
-  eigen_assert(other.size()>0 && "you are using a non initialized vector");
-
-  internal::evaluator<Derived> thisEval(derived());
-  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
-  Scalar res(0);
-  while (i)
-  {
-    res += numext::conj(i.value()) * other.coeff(i.index());
-    ++i;
-  }
-  return res;
-}
-
-template<typename Derived>
-template<typename OtherDerived>
-typename internal::traits<Derived>::Scalar
-SparseMatrixBase<Derived>::dot(const SparseMatrixBase<OtherDerived>& other) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
-  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  eigen_assert(size() == other.size());
-
-  internal::evaluator<Derived> thisEval(derived());
-  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
-  
-  internal::evaluator<OtherDerived>  otherEval(other.derived());
-  typename internal::evaluator<OtherDerived>::InnerIterator j(otherEval, 0);
-
-  Scalar res(0);
-  while (i && j)
-  {
-    if (i.index()==j.index())
-    {
-      res += numext::conj(i.value()) * j.value();
-      ++i; ++j;
-    }
-    else if (i.index()<j.index())
-      ++i;
-    else
-      ++j;
-  }
-  return res;
-}
-
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-SparseMatrixBase<Derived>::squaredNorm() const
-{
-  return numext::real((*this).cwiseAbs2().sum());
-}
-
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-SparseMatrixBase<Derived>::norm() const
-{
-  using std::sqrt;
-  return sqrt(squaredNorm());
-}
-
-template<typename Derived>
-inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
-SparseMatrixBase<Derived>::blueNorm() const
-{
-  return internal::blueNorm_impl(*this);
-}
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_DOT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h
deleted file mode 100644
index 7d47eb9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h
+++ /dev/null
@@ -1,29 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_FUZZY_H
-#define EIGEN_SPARSE_FUZZY_H
-
-namespace Eigen {
-  
-template<typename Derived>
-template<typename OtherDerived>
-bool SparseMatrixBase<Derived>::isApprox(const SparseMatrixBase<OtherDerived>& other, const RealScalar &prec) const
-{
-  const typename internal::nested_eval<Derived,2,PlainObject>::type actualA(derived());
-  typename internal::conditional<bool(IsRowMajor)==bool(OtherDerived::IsRowMajor),
-    const typename internal::nested_eval<OtherDerived,2,PlainObject>::type,
-    const PlainObject>::type actualB(other.derived());
-
-  return (actualA - actualB).squaredNorm() <= prec * prec * numext::mini(actualA.squaredNorm(), actualB.squaredNorm());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_FUZZY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMap.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMap.h
deleted file mode 100644
index f99be33..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMap.h
+++ /dev/null
@@ -1,305 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_MAP_H
-#define EIGEN_SPARSE_MAP_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct traits<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  typedef traits<PlainObjectType> TraitsBase;
-  enum {
-    Flags = TraitsBase::Flags & (~NestByRefBit)
-  };
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct traits<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  typedef traits<PlainObjectType> TraitsBase;
-  enum {
-    Flags = TraitsBase::Flags & (~ (NestByRefBit | LvalueBit))
-  };
-};
-
-} // end namespace internal
-
-template<typename Derived,
-         int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors
-> class SparseMapBase;
-
-/** \ingroup SparseCore_Module
-  * class SparseMapBase
-  * \brief Common base class for Map and Ref instance of sparse matrix and vector.
-  */
-template<typename Derived>
-class SparseMapBase<Derived,ReadOnlyAccessors>
-  : public SparseCompressedBase<Derived>
-{
-  public:
-    typedef SparseCompressedBase<Derived> Base;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    enum { IsRowMajor = Base::IsRowMajor };
-    using Base::operator=;
-  protected:
-    
-    typedef typename internal::conditional<
-                         bool(internal::is_lvalue<Derived>::value),
-                         Scalar *, const Scalar *>::type ScalarPointer;
-    typedef typename internal::conditional<
-                         bool(internal::is_lvalue<Derived>::value),
-                         StorageIndex *, const StorageIndex *>::type IndexPointer;
-
-    Index   m_outerSize;
-    Index   m_innerSize;
-    Array<StorageIndex,2,1>  m_zero_nnz;
-    IndexPointer  m_outerIndex;
-    IndexPointer  m_innerIndices;
-    ScalarPointer m_values;
-    IndexPointer  m_innerNonZeros;
-
-  public:
-
-    /** \copydoc SparseMatrixBase::rows() */
-    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
-    /** \copydoc SparseMatrixBase::cols() */
-    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
-    /** \copydoc SparseMatrixBase::innerSize() */
-    inline Index innerSize() const { return m_innerSize; }
-    /** \copydoc SparseMatrixBase::outerSize() */
-    inline Index outerSize() const { return m_outerSize; }
-    /** \copydoc SparseCompressedBase::nonZeros */
-    inline Index nonZeros() const { return m_zero_nnz[1]; }
-    
-    /** \copydoc SparseCompressedBase::isCompressed */
-    bool isCompressed() const { return m_innerNonZeros==0; }
-
-    //----------------------------------------
-    // direct access interface
-    /** \copydoc SparseMatrix::valuePtr */
-    inline const Scalar* valuePtr() const { return m_values; }
-    /** \copydoc SparseMatrix::innerIndexPtr */
-    inline const StorageIndex* innerIndexPtr() const { return m_innerIndices; }
-    /** \copydoc SparseMatrix::outerIndexPtr */
-    inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
-    /** \copydoc SparseMatrix::innerNonZeroPtr */
-    inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
-    //----------------------------------------
-
-    /** \copydoc SparseMatrix::coeff */
-    inline Scalar coeff(Index row, Index col) const
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      Index start = m_outerIndex[outer];
-      Index end = isCompressed() ? m_outerIndex[outer+1] : start + m_innerNonZeros[outer];
-      if (start==end)
-        return Scalar(0);
-      else if (end>0 && inner==m_innerIndices[end-1])
-        return m_values[end-1];
-      // ^^  optimization: let's first check if it is the last coefficient
-      // (very common in high level algorithms)
-
-      const StorageIndex* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end-1],inner);
-      const Index id = r-&m_innerIndices[0];
-      return ((*r==inner) && (id<end)) ? m_values[id] : Scalar(0);
-    }
-
-    inline SparseMapBase(Index rows, Index cols, Index nnz, IndexPointer outerIndexPtr, IndexPointer innerIndexPtr,
-                              ScalarPointer valuePtr, IndexPointer innerNonZerosPtr = 0)
-      : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(outerIndexPtr),
-        m_innerIndices(innerIndexPtr), m_values(valuePtr), m_innerNonZeros(innerNonZerosPtr)
-    {}
-
-    // for vectors
-    inline SparseMapBase(Index size, Index nnz, IndexPointer innerIndexPtr, ScalarPointer valuePtr)
-      : m_outerSize(1), m_innerSize(size), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(m_zero_nnz.data()),
-        m_innerIndices(innerIndexPtr), m_values(valuePtr), m_innerNonZeros(0)
-    {}
-
-    /** Empty destructor */
-    inline ~SparseMapBase() {}
-
-  protected:
-    inline SparseMapBase() {}
-};
-
-/** \ingroup SparseCore_Module
-  * class SparseMapBase
-  * \brief Common base class for writable Map and Ref instance of sparse matrix and vector.
-  */
-template<typename Derived>
-class SparseMapBase<Derived,WriteAccessors>
-  : public SparseMapBase<Derived,ReadOnlyAccessors>
-{
-    typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
-    
-  public:
-    typedef SparseMapBase<Derived, ReadOnlyAccessors> Base;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    enum { IsRowMajor = Base::IsRowMajor };
-    
-    using Base::operator=;
-
-  public:
-    
-    //----------------------------------------
-    // direct access interface
-    using Base::valuePtr;
-    using Base::innerIndexPtr;
-    using Base::outerIndexPtr;
-    using Base::innerNonZeroPtr;
-    /** \copydoc SparseMatrix::valuePtr */
-    inline Scalar* valuePtr()              { return Base::m_values; }
-    /** \copydoc SparseMatrix::innerIndexPtr */
-    inline StorageIndex* innerIndexPtr()   { return Base::m_innerIndices; }
-    /** \copydoc SparseMatrix::outerIndexPtr */
-    inline StorageIndex* outerIndexPtr()   { return Base::m_outerIndex; }
-    /** \copydoc SparseMatrix::innerNonZeroPtr */
-    inline StorageIndex* innerNonZeroPtr() { return Base::m_innerNonZeros; }
-    //----------------------------------------
-
-    /** \copydoc SparseMatrix::coeffRef */
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      Index start = Base::m_outerIndex[outer];
-      Index end = Base::isCompressed() ? Base::m_outerIndex[outer+1] : start + Base::m_innerNonZeros[outer];
-      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
-      eigen_assert(end>start && "coeffRef cannot be called on a zero coefficient");
-      StorageIndex* r = std::lower_bound(&Base::m_innerIndices[start],&Base::m_innerIndices[end],inner);
-      const Index id = r - &Base::m_innerIndices[0];
-      eigen_assert((*r==inner) && (id<end) && "coeffRef cannot be called on a zero coefficient");
-      return const_cast<Scalar*>(Base::m_values)[id];
-    }
-    
-    inline SparseMapBase(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr,
-                         Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
-    {}
-
-    // for vectors
-    inline SparseMapBase(Index size, Index nnz, StorageIndex* innerIndexPtr, Scalar* valuePtr)
-      : Base(size, nnz, innerIndexPtr, valuePtr)
-    {}
-
-    /** Empty destructor */
-    inline ~SparseMapBase() {}
-
-  protected:
-    inline SparseMapBase() {}
-};
-
-/** \ingroup SparseCore_Module
-  *
-  * \brief Specialization of class Map for SparseMatrix-like storage.
-  *
-  * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of class SparseMatrix.
-  *
-  * \sa class Map, class SparseMatrix, class Ref<SparseMatrixType,Options>
-  */
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public SparseMapBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-#else
-template<typename SparseMatrixType>
-class Map<SparseMatrixType>
-  : public SparseMapBase<Derived,WriteAccessors>
-#endif
-{
-  public:
-    typedef SparseMapBase<Map> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
-    enum { IsRowMajor = Base::IsRowMajor };
-
-  public:
-
-    /** Constructs a read-write Map to a sparse matrix of size \a rows x \a cols, containing \a nnz non-zero coefficients,
-      * stored as a sparse format as defined by the pointers \a outerIndexPtr, \a innerIndexPtr, and \a valuePtr.
-      * If the optional parameter \a innerNonZerosPtr is the null pointer, then a standard compressed format is assumed.
-      *
-      * This constructor is available only if \c SparseMatrixType is non-const.
-      *
-      * More details on the expected storage schemes are given in the \ref TutorialSparse "manual pages".
-      */
-    inline Map(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr,
-               StorageIndex* innerIndexPtr, Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
-    {}
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** Empty destructor */
-    inline ~Map() {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public SparseMapBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-{
-  public:
-    typedef SparseMapBase<Map> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
-    enum { IsRowMajor = Base::IsRowMajor };
-
-  public:
-#endif
-    /** This is the const version of the above constructor.
-      *
-      * This constructor is available only if \c SparseMatrixType is const, e.g.:
-      * \code Map<const SparseMatrix<double> >  \endcode
-      */
-    inline Map(Index rows, Index cols, Index nnz, const StorageIndex* outerIndexPtr,
-               const StorageIndex* innerIndexPtr, const Scalar* valuePtr, const StorageIndex* innerNonZerosPtr = 0)
-      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
-    {}
-
-    /** Empty destructor */
-    inline ~Map() {}
-};
-
-namespace internal {
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_MAP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
deleted file mode 100644
index 616b4a0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
+++ /dev/null
@@ -1,1518 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEMATRIX_H
-#define EIGEN_SPARSEMATRIX_H
-
-namespace Eigen { 
-
-/** \ingroup SparseCore_Module
-  *
-  * \class SparseMatrix
-  *
-  * \brief A versatible sparse matrix representation
-  *
-  * This class implements a more versatile variants of the common \em compressed row/column storage format.
-  * Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index.
-  * All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra
-  * space in between the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero
-  * can be done with limited memory reallocation and copies.
-  *
-  * A call to the function makeCompressed() turns the matrix into the standard \em compressed format
-  * compatible with many library.
-  *
-  * More details on this storage sceheme are given in the \ref TutorialSparse "manual pages".
-  *
-  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
-  * \tparam _Options Union of bit flags controlling the storage scheme. Currently the only possibility
-  *                 is ColMajor or RowMajor. The default is 0 which means column-major.
-  * \tparam _StorageIndex the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int.
-  *
-  * \warning In %Eigen 3.2, the undocumented type \c SparseMatrix::Index was improperly defined as the storage index type (e.g., int),
-  *          whereas it is now (starting from %Eigen 3.3) deprecated and always defined as Eigen::Index.
-  *          Codes making use of \c SparseMatrix::Index, might thus likely have to be changed to use \c SparseMatrix::StorageIndex instead.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN.
-  */
-
-namespace internal {
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct traits<SparseMatrix<_Scalar, _Options, _StorageIndex> >
-{
-  typedef _Scalar Scalar;
-  typedef _StorageIndex StorageIndex;
-  typedef Sparse StorageKind;
-  typedef MatrixXpr XprKind;
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = Dynamic,
-    MaxRowsAtCompileTime = Dynamic,
-    MaxColsAtCompileTime = Dynamic,
-    Flags = _Options | NestByRefBit | LvalueBit | CompressedAccessBit,
-    SupportedAccessPatterns = InnerRandomAccessPattern
-  };
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int DiagIndex>
-struct traits<Diagonal<SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
-{
-  typedef SparseMatrix<_Scalar, _Options, _StorageIndex> MatrixType;
-  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
-  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
-
-  typedef _Scalar Scalar;
-  typedef Dense StorageKind;
-  typedef _StorageIndex StorageIndex;
-  typedef MatrixXpr XprKind;
-
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = 1,
-    MaxRowsAtCompileTime = Dynamic,
-    MaxColsAtCompileTime = 1,
-    Flags = LvalueBit
-  };
-};
-
-template<typename _Scalar, int _Options, typename _StorageIndex, int DiagIndex>
-struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
- : public traits<Diagonal<SparseMatrix<_Scalar, _Options, _StorageIndex>, DiagIndex> >
-{
-  enum {
-    Flags = 0
-  };
-};
-
-} // end namespace internal
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-class SparseMatrix
-  : public SparseCompressedBase<SparseMatrix<_Scalar, _Options, _StorageIndex> >
-{
-    typedef SparseCompressedBase<SparseMatrix> Base;
-    using Base::convert_index;
-    friend class SparseVector<_Scalar,0,_StorageIndex>;
-    template<typename, typename, typename, typename, typename>
-    friend struct internal::Assignment;
-  public:
-    using Base::isCompressed;
-    using Base::nonZeros;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix)
-    using Base::operator+=;
-    using Base::operator-=;
-
-    typedef MappedSparseMatrix<Scalar,Flags> Map;
-    typedef Diagonal<SparseMatrix> DiagonalReturnType;
-    typedef Diagonal<const SparseMatrix> ConstDiagonalReturnType;
-    typedef typename Base::InnerIterator InnerIterator;
-    typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
-    
-
-    using Base::IsRowMajor;
-    typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
-    enum {
-      Options = _Options
-    };
-
-    typedef typename Base::IndexVector IndexVector;
-    typedef typename Base::ScalarVector ScalarVector;
-  protected:
-    typedef SparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0)> TransposedSparseMatrix;
-
-    Index m_outerSize;
-    Index m_innerSize;
-    StorageIndex* m_outerIndex;
-    StorageIndex* m_innerNonZeros;     // optional, if null then the data is compressed
-    Storage m_data;
-
-  public:
-    
-    /** \returns the number of rows of the matrix */
-    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
-    /** \returns the number of columns of the matrix */
-    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
-
-    /** \returns the number of rows (resp. columns) of the matrix if the storage order column major (resp. row major) */
-    inline Index innerSize() const { return m_innerSize; }
-    /** \returns the number of columns (resp. rows) of the matrix if the storage order column major (resp. row major) */
-    inline Index outerSize() const { return m_outerSize; }
-    
-    /** \returns a const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline const Scalar* valuePtr() const { return m_data.valuePtr(); }
-    /** \returns a non-const pointer to the array of values.
-      * This function is aimed at interoperability with other libraries.
-      * \sa innerIndexPtr(), outerIndexPtr() */
-    inline Scalar* valuePtr() { return m_data.valuePtr(); }
-
-    /** \returns a const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
-    /** \returns a non-const pointer to the array of inner indices.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), outerIndexPtr() */
-    inline StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
-
-    /** \returns a const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), innerIndexPtr() */
-    inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
-    /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \sa valuePtr(), innerIndexPtr() */
-    inline StorageIndex* outerIndexPtr() { return m_outerIndex; }
-
-    /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
-    /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
-      * This function is aimed at interoperability with other libraries.
-      * \warning it returns the null pointer 0 in compressed mode */
-    inline StorageIndex* innerNonZeroPtr() { return m_innerNonZeros; }
-
-    /** \internal */
-    inline Storage& data() { return m_data; }
-    /** \internal */
-    inline const Storage& data() const { return m_data; }
-
-    /** \returns the value of the matrix at position \a i, \a j
-      * This function returns Scalar(0) if the element is an explicit \em zero */
-    inline Scalar coeff(Index row, Index col) const
-    {
-      eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
-      
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-      Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
-      return m_data.atInRange(m_outerIndex[outer], end, StorageIndex(inner));
-    }
-
-    /** \returns a non-const reference to the value of the matrix at position \a i, \a j
-      *
-      * If the element does not exist then it is inserted via the insert(Index,Index) function
-      * which itself turns the matrix into a non compressed form if that was not the case.
-      *
-      * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index)
-      * function if the element does not already exist.
-      */
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
-      
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      Index start = m_outerIndex[outer];
-      Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
-      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
-      if(end<=start)
-        return insert(row,col);
-      const Index p = m_data.searchLowerIndex(start,end-1,StorageIndex(inner));
-      if((p<end) && (m_data.index(p)==inner))
-        return m_data.value(p);
-      else
-        return insert(row,col);
-    }
-
-    /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
-      * The non zero coefficient must \b not already exist.
-      *
-      * If the matrix \c *this is in compressed mode, then \c *this is turned into uncompressed
-      * mode while reserving room for 2 x this->innerSize() non zeros if reserve(Index) has not been called earlier.
-      * In this case, the insertion procedure is optimized for a \e sequential insertion mode where elements are assumed to be
-      * inserted by increasing outer-indices.
-      * 
-      * If that's not the case, then it is strongly recommended to either use a triplet-list to assemble the matrix, or to first
-      * call reserve(const SizesType &) to reserve the appropriate number of non-zero elements per inner vector.
-      *
-      * Assuming memory has been appropriately reserved, this function performs a sorted insertion in O(1)
-      * if the elements of each inner vector are inserted in increasing inner index order, and in O(nnz_j) for a random insertion.
-      *
-      */
-    Scalar& insert(Index row, Index col);
-
-  public:
-
-    /** Removes all non zeros but keep allocated memory
-      *
-      * This function does not free the currently allocated memory. To release as much as memory as possible,
-      * call \code mat.data().squeeze(); \endcode after resizing it.
-      * 
-      * \sa resize(Index,Index), data()
-      */
-    inline void setZero()
-    {
-      m_data.clear();
-      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(StorageIndex));
-      if(m_innerNonZeros)
-        memset(m_innerNonZeros, 0, (m_outerSize)*sizeof(StorageIndex));
-    }
-
-    /** Preallocates \a reserveSize non zeros.
-      *
-      * Precondition: the matrix must be in compressed mode. */
-    inline void reserve(Index reserveSize)
-    {
-      eigen_assert(isCompressed() && "This function does not make sense in non compressed mode.");
-      m_data.reserve(reserveSize);
-    }
-    
-    #ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** Preallocates \a reserveSize[\c j] non zeros for each column (resp. row) \c j.
-      *
-      * This function turns the matrix in non-compressed mode.
-      * 
-      * The type \c SizesType must expose the following interface:
-        \code
-        typedef value_type;
-        const value_type& operator[](i) const;
-        \endcode
-      * for \c i in the [0,this->outerSize()[ range.
-      * Typical choices include std::vector<int>, Eigen::VectorXi, Eigen::VectorXi::Constant, etc.
-      */
-    template<class SizesType>
-    inline void reserve(const SizesType& reserveSizes);
-    #else
-    template<class SizesType>
-    inline void reserve(const SizesType& reserveSizes, const typename SizesType::value_type& enableif =
-    #if (!EIGEN_COMP_MSVC) || (EIGEN_COMP_MSVC>=1500) // MSVC 2005 fails to compile with this typename
-        typename
-    #endif
-        SizesType::value_type())
-    {
-      EIGEN_UNUSED_VARIABLE(enableif);
-      reserveInnerVectors(reserveSizes);
-    }
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-  protected:
-    template<class SizesType>
-    inline void reserveInnerVectors(const SizesType& reserveSizes)
-    {
-      if(isCompressed())
-      {
-        Index totalReserveSize = 0;
-        // turn the matrix into non-compressed mode
-        m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-        if (!m_innerNonZeros) internal::throw_std_bad_alloc();
-        
-        // temporarily use m_innerSizes to hold the new starting points.
-        StorageIndex* newOuterIndex = m_innerNonZeros;
-        
-        StorageIndex count = 0;
-        for(Index j=0; j<m_outerSize; ++j)
-        {
-          newOuterIndex[j] = count;
-          count += reserveSizes[j] + (m_outerIndex[j+1]-m_outerIndex[j]);
-          totalReserveSize += reserveSizes[j];
-        }
-        m_data.reserve(totalReserveSize);
-        StorageIndex previousOuterIndex = m_outerIndex[m_outerSize];
-        for(Index j=m_outerSize-1; j>=0; --j)
-        {
-          StorageIndex innerNNZ = previousOuterIndex - m_outerIndex[j];
-          for(Index i=innerNNZ-1; i>=0; --i)
-          {
-            m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i);
-            m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i);
-          }
-          previousOuterIndex = m_outerIndex[j];
-          m_outerIndex[j] = newOuterIndex[j];
-          m_innerNonZeros[j] = innerNNZ;
-        }
-        if(m_outerSize>0)
-          m_outerIndex[m_outerSize] = m_outerIndex[m_outerSize-1] + m_innerNonZeros[m_outerSize-1] + reserveSizes[m_outerSize-1];
-        
-        m_data.resize(m_outerIndex[m_outerSize]);
-      }
-      else
-      {
-        StorageIndex* newOuterIndex = static_cast<StorageIndex*>(std::malloc((m_outerSize+1)*sizeof(StorageIndex)));
-        if (!newOuterIndex) internal::throw_std_bad_alloc();
-        
-        StorageIndex count = 0;
-        for(Index j=0; j<m_outerSize; ++j)
-        {
-          newOuterIndex[j] = count;
-          StorageIndex alreadyReserved = (m_outerIndex[j+1]-m_outerIndex[j]) - m_innerNonZeros[j];
-          StorageIndex toReserve = std::max<StorageIndex>(reserveSizes[j], alreadyReserved);
-          count += toReserve + m_innerNonZeros[j];
-        }
-        newOuterIndex[m_outerSize] = count;
-        
-        m_data.resize(count);
-        for(Index j=m_outerSize-1; j>=0; --j)
-        {
-          Index offset = newOuterIndex[j] - m_outerIndex[j];
-          if(offset>0)
-          {
-            StorageIndex innerNNZ = m_innerNonZeros[j];
-            for(Index i=innerNNZ-1; i>=0; --i)
-            {
-              m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i);
-              m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i);
-            }
-          }
-        }
-        
-        std::swap(m_outerIndex, newOuterIndex);
-        std::free(newOuterIndex);
-      }
-      
-    }
-  public:
-
-    //--- low level purely coherent filling ---
-
-    /** \internal
-      * \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
-      * - the nonzero does not already exist
-      * - the new coefficient is the last one according to the storage order
-      *
-      * Before filling a given inner vector you must call the statVec(Index) function.
-      *
-      * After an insertion session, you should call the finalize() function.
-      *
-      * \sa insert, insertBackByOuterInner, startVec */
-    inline Scalar& insertBack(Index row, Index col)
-    {
-      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
-    }
-
-    /** \internal
-      * \sa insertBack, startVec */
-    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
-    {
-      eigen_assert(Index(m_outerIndex[outer+1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)");
-      eigen_assert( (m_outerIndex[outer+1]-m_outerIndex[outer]==0 || m_data.index(m_data.size()-1)<inner) && "Invalid ordered insertion (invalid inner index)");
-      Index p = m_outerIndex[outer+1];
-      ++m_outerIndex[outer+1];
-      m_data.append(Scalar(0), inner);
-      return m_data.value(p);
-    }
-
-    /** \internal
-      * \warning use it only if you know what you are doing */
-    inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
-    {
-      Index p = m_outerIndex[outer+1];
-      ++m_outerIndex[outer+1];
-      m_data.append(Scalar(0), inner);
-      return m_data.value(p);
-    }
-
-    /** \internal
-      * \sa insertBack, insertBackByOuterInner */
-    inline void startVec(Index outer)
-    {
-      eigen_assert(m_outerIndex[outer]==Index(m_data.size()) && "You must call startVec for each inner vector sequentially");
-      eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially");
-      m_outerIndex[outer+1] = m_outerIndex[outer];
-    }
-
-    /** \internal
-      * Must be called after inserting a set of non zero entries using the low level compressed API.
-      */
-    inline void finalize()
-    {
-      if(isCompressed())
-      {
-        StorageIndex size = internal::convert_index<StorageIndex>(m_data.size());
-        Index i = m_outerSize;
-        // find the last filled column
-        while (i>=0 && m_outerIndex[i]==0)
-          --i;
-        ++i;
-        while (i<=m_outerSize)
-        {
-          m_outerIndex[i] = size;
-          ++i;
-        }
-      }
-    }
-
-    //---
-
-    template<typename InputIterators>
-    void setFromTriplets(const InputIterators& begin, const InputIterators& end);
-
-    template<typename InputIterators,typename DupFunctor>
-    void setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
-
-    void sumupDuplicates() { collapseDuplicates(internal::scalar_sum_op<Scalar,Scalar>()); }
-
-    template<typename DupFunctor>
-    void collapseDuplicates(DupFunctor dup_func = DupFunctor());
-
-    //---
-    
-    /** \internal
-      * same as insert(Index,Index) except that the indices are given relative to the storage order */
-    Scalar& insertByOuterInner(Index j, Index i)
-    {
-      return insert(IsRowMajor ? j : i, IsRowMajor ? i : j);
-    }
-
-    /** Turns the matrix into the \em compressed format.
-      */
-    void makeCompressed()
-    {
-      if(isCompressed())
-        return;
-      
-      eigen_internal_assert(m_outerIndex!=0 && m_outerSize>0);
-      
-      Index oldStart = m_outerIndex[1];
-      m_outerIndex[1] = m_innerNonZeros[0];
-      for(Index j=1; j<m_outerSize; ++j)
-      {
-        Index nextOldStart = m_outerIndex[j+1];
-        Index offset = oldStart - m_outerIndex[j];
-        if(offset>0)
-        {
-          for(Index k=0; k<m_innerNonZeros[j]; ++k)
-          {
-            m_data.index(m_outerIndex[j]+k) = m_data.index(oldStart+k);
-            m_data.value(m_outerIndex[j]+k) = m_data.value(oldStart+k);
-          }
-        }
-        m_outerIndex[j+1] = m_outerIndex[j] + m_innerNonZeros[j];
-        oldStart = nextOldStart;
-      }
-      std::free(m_innerNonZeros);
-      m_innerNonZeros = 0;
-      m_data.resize(m_outerIndex[m_outerSize]);
-      m_data.squeeze();
-    }
-
-    /** Turns the matrix into the uncompressed mode */
-    void uncompress()
-    {
-      if(m_innerNonZeros != 0)
-        return; 
-      m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-      for (Index i = 0; i < m_outerSize; i++)
-      {
-        m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i]; 
-      }
-    }
-
-    /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerance \a epsilon */
-    void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
-    {
-      prune(default_prunning_func(reference,epsilon));
-    }
-    
-    /** Turns the matrix into compressed format, and suppresses all nonzeros which do not satisfy the predicate \a keep.
-      * The functor type \a KeepFunc must implement the following function:
-      * \code
-      * bool operator() (const Index& row, const Index& col, const Scalar& value) const;
-      * \endcode
-      * \sa prune(Scalar,RealScalar)
-      */
-    template<typename KeepFunc>
-    void prune(const KeepFunc& keep = KeepFunc())
-    {
-      // TODO optimize the uncompressed mode to avoid moving and allocating the data twice
-      makeCompressed();
-
-      StorageIndex k = 0;
-      for(Index j=0; j<m_outerSize; ++j)
-      {
-        Index previousStart = m_outerIndex[j];
-        m_outerIndex[j] = k;
-        Index end = m_outerIndex[j+1];
-        for(Index i=previousStart; i<end; ++i)
-        {
-          if(keep(IsRowMajor?j:m_data.index(i), IsRowMajor?m_data.index(i):j, m_data.value(i)))
-          {
-            m_data.value(k) = m_data.value(i);
-            m_data.index(k) = m_data.index(i);
-            ++k;
-          }
-        }
-      }
-      m_outerIndex[m_outerSize] = k;
-      m_data.resize(k,0);
-    }
-
-    /** Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched.
-      *
-      * If the sizes of the matrix are decreased, then the matrix is turned to \b uncompressed-mode
-      * and the storage of the out of bounds coefficients is kept and reserved.
-      * Call makeCompressed() to pack the entries and squeeze extra memory.
-      *
-      * \sa reserve(), setZero(), makeCompressed()
-      */
-    void conservativeResize(Index rows, Index cols) 
-    {
-      // No change
-      if (this->rows() == rows && this->cols() == cols) return;
-      
-      // If one dimension is null, then there is nothing to be preserved
-      if(rows==0 || cols==0) return resize(rows,cols);
-
-      Index innerChange = IsRowMajor ? cols - this->cols() : rows - this->rows();
-      Index outerChange = IsRowMajor ? rows - this->rows() : cols - this->cols();
-      StorageIndex newInnerSize = convert_index(IsRowMajor ? cols : rows);
-
-      // Deals with inner non zeros
-      if (m_innerNonZeros)
-      {
-        // Resize m_innerNonZeros
-        StorageIndex *newInnerNonZeros = static_cast<StorageIndex*>(std::realloc(m_innerNonZeros, (m_outerSize + outerChange) * sizeof(StorageIndex)));
-        if (!newInnerNonZeros) internal::throw_std_bad_alloc();
-        m_innerNonZeros = newInnerNonZeros;
-        
-        for(Index i=m_outerSize; i<m_outerSize+outerChange; i++)          
-          m_innerNonZeros[i] = 0;
-      } 
-      else if (innerChange < 0) 
-      {
-        // Inner size decreased: allocate a new m_innerNonZeros
-        m_innerNonZeros = static_cast<StorageIndex*>(std::malloc((m_outerSize + outerChange) * sizeof(StorageIndex)));
-        if (!m_innerNonZeros) internal::throw_std_bad_alloc();
-        for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++)
-          m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i];
-        for(Index i = m_outerSize; i < m_outerSize + outerChange; i++)
-          m_innerNonZeros[i] = 0;
-      }
-      
-      // Change the m_innerNonZeros in case of a decrease of inner size
-      if (m_innerNonZeros && innerChange < 0)
-      {
-        for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++)
-        {
-          StorageIndex &n = m_innerNonZeros[i];
-          StorageIndex start = m_outerIndex[i];
-          while (n > 0 && m_data.index(start+n-1) >= newInnerSize) --n; 
-        }
-      }
-      
-      m_innerSize = newInnerSize;
-
-      // Re-allocate outer index structure if necessary
-      if (outerChange == 0)
-        return;
-          
-      StorageIndex *newOuterIndex = static_cast<StorageIndex*>(std::realloc(m_outerIndex, (m_outerSize + outerChange + 1) * sizeof(StorageIndex)));
-      if (!newOuterIndex) internal::throw_std_bad_alloc();
-      m_outerIndex = newOuterIndex;
-      if (outerChange > 0)
-      {
-        StorageIndex lastIdx = m_outerSize == 0 ? 0 : m_outerIndex[m_outerSize];
-        for(Index i=m_outerSize; i<m_outerSize+outerChange+1; i++)          
-          m_outerIndex[i] = lastIdx; 
-      }
-      m_outerSize += outerChange;
-    }
-    
-    /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero.
-      * 
-      * This function does not free the currently allocated memory. To release as much as memory as possible,
-      * call \code mat.data().squeeze(); \endcode after resizing it.
-      * 
-      * \sa reserve(), setZero()
-      */
-    void resize(Index rows, Index cols)
-    {
-      const Index outerSize = IsRowMajor ? rows : cols;
-      m_innerSize = IsRowMajor ? cols : rows;
-      m_data.clear();
-      if (m_outerSize != outerSize || m_outerSize==0)
-      {
-        std::free(m_outerIndex);
-        m_outerIndex = static_cast<StorageIndex*>(std::malloc((outerSize + 1) * sizeof(StorageIndex)));
-        if (!m_outerIndex) internal::throw_std_bad_alloc();
-        
-        m_outerSize = outerSize;
-      }
-      if(m_innerNonZeros)
-      {
-        std::free(m_innerNonZeros);
-        m_innerNonZeros = 0;
-      }
-      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(StorageIndex));
-    }
-
-    /** \internal
-      * Resize the nonzero vector to \a size */
-    void resizeNonZeros(Index size)
-    {
-      m_data.resize(size);
-    }
-
-    /** \returns a const expression of the diagonal coefficients. */
-    const ConstDiagonalReturnType diagonal() const { return ConstDiagonalReturnType(*this); }
-    
-    /** \returns a read-write expression of the diagonal coefficients.
-      * \warning If the diagonal entries are written, then all diagonal
-      * entries \b must already exist, otherwise an assertion will be raised.
-      */
-    DiagonalReturnType diagonal() { return DiagonalReturnType(*this); }
-
-    /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */
-    inline SparseMatrix()
-      : m_outerSize(-1), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      resize(0, 0);
-    }
-
-    /** Constructs a \a rows \c x \a cols empty matrix */
-    inline SparseMatrix(Index rows, Index cols)
-      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      resize(rows, cols);
-    }
-
-    /** Constructs a sparse matrix from the sparse expression \a other */
-    template<typename OtherDerived>
-    inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other)
-      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-      check_template_parameters();
-      const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
-      if (needToTranspose)
-        *this = other.derived();
-      else
-      {
-        #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-          EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        #endif
-        internal::call_assignment_no_alias(*this, other.derived());
-      }
-    }
-    
-    /** Constructs a sparse matrix from the sparse selfadjoint view \a other */
-    template<typename OtherDerived, unsigned int UpLo>
-    inline SparseMatrix(const SparseSelfAdjointView<OtherDerived, UpLo>& other)
-      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      Base::operator=(other);
-    }
-
-    /** Copy constructor (it performs a deep copy) */
-    inline SparseMatrix(const SparseMatrix& other)
-      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    /** \brief Copy constructor with in-place evaluation */
-    template<typename OtherDerived>
-    SparseMatrix(const ReturnByValue<OtherDerived>& other)
-      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      initAssignment(other);
-      other.evalTo(*this);
-    }
-    
-    /** \brief Copy constructor with in-place evaluation */
-    template<typename OtherDerived>
-    explicit SparseMatrix(const DiagonalBase<OtherDerived>& other)
-      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
-    {
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    /** Swaps the content of two sparse matrices of the same type.
-      * This is a fast operation that simply swaps the underlying pointers and parameters. */
-    inline void swap(SparseMatrix& other)
-    {
-      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
-      std::swap(m_outerIndex, other.m_outerIndex);
-      std::swap(m_innerSize, other.m_innerSize);
-      std::swap(m_outerSize, other.m_outerSize);
-      std::swap(m_innerNonZeros, other.m_innerNonZeros);
-      m_data.swap(other.m_data);
-    }
-
-    /** Sets *this to the identity matrix.
-      * This function also turns the matrix into compressed mode, and drop any reserved memory. */
-    inline void setIdentity()
-    {
-      eigen_assert(rows() == cols() && "ONLY FOR SQUARED MATRICES");
-      this->m_data.resize(rows());
-      Eigen::Map<IndexVector>(this->m_data.indexPtr(), rows()).setLinSpaced(0, StorageIndex(rows()-1));
-      Eigen::Map<ScalarVector>(this->m_data.valuePtr(), rows()).setOnes();
-      Eigen::Map<IndexVector>(this->m_outerIndex, rows()+1).setLinSpaced(0, StorageIndex(rows()));
-      std::free(m_innerNonZeros);
-      m_innerNonZeros = 0;
-    }
-    inline SparseMatrix& operator=(const SparseMatrix& other)
-    {
-      if (other.isRValue())
-      {
-        swap(other.const_cast_derived());
-      }
-      else if(this!=&other)
-      {
-        #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-          EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        #endif
-        initAssignment(other);
-        if(other.isCompressed())
-        {
-          internal::smart_copy(other.m_outerIndex, other.m_outerIndex + m_outerSize + 1, m_outerIndex);
-          m_data = other.m_data;
-        }
-        else
-        {
-          Base::operator=(other);
-        }
-      }
-      return *this;
-    }
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename OtherDerived>
-    inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other)
-    { return Base::operator=(other.derived()); }
-
-    template<typename Lhs, typename Rhs>
-    inline SparseMatrix& operator=(const Product<Lhs,Rhs,AliasFreeProduct>& other);
-#endif // EIGEN_PARSED_BY_DOXYGEN
-
-    template<typename OtherDerived>
-    EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other);
-
-    friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m)
-    {
-      EIGEN_DBG_SPARSE(
-        s << "Nonzero entries:\n";
-        if(m.isCompressed())
-        {
-          for (Index i=0; i<m.nonZeros(); ++i)
-            s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
-        }
-        else
-        {
-          for (Index i=0; i<m.outerSize(); ++i)
-          {
-            Index p = m.m_outerIndex[i];
-            Index pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
-            Index k=p;
-            for (; k<pe; ++k) {
-              s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") ";
-            }
-            for (; k<m.m_outerIndex[i+1]; ++k) {
-              s << "(_,_) ";
-            }
-          }
-        }
-        s << std::endl;
-        s << std::endl;
-        s << "Outer pointers:\n";
-        for (Index i=0; i<m.outerSize(); ++i) {
-          s << m.m_outerIndex[i] << " ";
-        }
-        s << " $" << std::endl;
-        if(!m.isCompressed())
-        {
-          s << "Inner non zeros:\n";
-          for (Index i=0; i<m.outerSize(); ++i) {
-            s << m.m_innerNonZeros[i] << " ";
-          }
-          s << " $" << std::endl;
-        }
-        s << std::endl;
-      );
-      s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m);
-      return s;
-    }
-
-    /** Destructor */
-    inline ~SparseMatrix()
-    {
-      std::free(m_outerIndex);
-      std::free(m_innerNonZeros);
-    }
-
-    /** Overloaded for performance */
-    Scalar sum() const;
-    
-#   ifdef EIGEN_SPARSEMATRIX_PLUGIN
-#     include EIGEN_SPARSEMATRIX_PLUGIN
-#   endif
-
-protected:
-
-    template<typename Other>
-    void initAssignment(const Other& other)
-    {
-      resize(other.rows(), other.cols());
-      if(m_innerNonZeros)
-      {
-        std::free(m_innerNonZeros);
-        m_innerNonZeros = 0;
-      }
-    }
-
-    /** \internal
-      * \sa insert(Index,Index) */
-    EIGEN_DONT_INLINE Scalar& insertCompressed(Index row, Index col);
-
-    /** \internal
-      * A vector object that is equal to 0 everywhere but v at the position i */
-    class SingletonVector
-    {
-        StorageIndex m_index;
-        StorageIndex m_value;
-      public:
-        typedef StorageIndex value_type;
-        SingletonVector(Index i, Index v)
-          : m_index(convert_index(i)), m_value(convert_index(v))
-        {}
-
-        StorageIndex operator[](Index i) const { return i==m_index ? m_value : 0; }
-    };
-
-    /** \internal
-      * \sa insert(Index,Index) */
-    EIGEN_DONT_INLINE Scalar& insertUncompressed(Index row, Index col);
-
-public:
-    /** \internal
-      * \sa insert(Index,Index) */
-    EIGEN_STRONG_INLINE Scalar& insertBackUncompressed(Index row, Index col)
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      eigen_assert(!isCompressed());
-      eigen_assert(m_innerNonZeros[outer]<=(m_outerIndex[outer+1] - m_outerIndex[outer]));
-
-      Index p = m_outerIndex[outer] + m_innerNonZeros[outer]++;
-      m_data.index(p) = convert_index(inner);
-      return (m_data.value(p) = Scalar(0));
-    }
-protected:
-    struct IndexPosPair {
-      IndexPosPair(Index a_i, Index a_p) : i(a_i), p(a_p) {}
-      Index i;
-      Index p;
-    };
-
-    /** \internal assign \a diagXpr to the diagonal of \c *this
-      * There are different strategies:
-      *   1 - if *this is overwritten (Func==assign_op) or *this is empty, then we can work treat *this as a dense vector expression.
-      *   2 - otherwise, for each diagonal coeff,
-      *     2.a - if it already exists, then we update it,
-      *     2.b - otherwise, if *this is uncompressed and that the current inner-vector has empty room for at least 1 element, then we perform an in-place insertion.
-      *     2.c - otherwise, we'll have to reallocate and copy everything, so instead of doing so for each new element, it is recorded in a std::vector.
-      *   3 - at the end, if some entries failed to be inserted in-place, then we alloc a new buffer, copy each chunk at the right position, and insert the new elements.
-      * 
-      * TODO: some piece of code could be isolated and reused for a general in-place update strategy.
-      * TODO: if we start to defer the insertion of some elements (i.e., case 2.c executed once),
-      *       then it *might* be better to disable case 2.b since they will have to be copied anyway.
-      */
-    template<typename DiagXpr, typename Func>
-    void assignDiagonal(const DiagXpr diagXpr, const Func& assignFunc)
-    {
-      Index n = diagXpr.size();
-
-      const bool overwrite = internal::is_same<Func, internal::assign_op<Scalar,Scalar> >::value;
-      if(overwrite)
-      {
-        if((this->rows()!=n) || (this->cols()!=n))
-          this->resize(n, n);
-      }
-
-      if(m_data.size()==0 || overwrite)
-      {
-        typedef Array<StorageIndex,Dynamic,1> ArrayXI;  
-        this->makeCompressed();
-        this->resizeNonZeros(n);
-        Eigen::Map<ArrayXI>(this->innerIndexPtr(), n).setLinSpaced(0,StorageIndex(n)-1);
-        Eigen::Map<ArrayXI>(this->outerIndexPtr(), n+1).setLinSpaced(0,StorageIndex(n));
-        Eigen::Map<Array<Scalar,Dynamic,1> > values = this->coeffs();
-        values.setZero();
-        internal::call_assignment_no_alias(values, diagXpr, assignFunc);
-      }
-      else
-      {
-        bool isComp = isCompressed();
-        internal::evaluator<DiagXpr> diaEval(diagXpr);
-        std::vector<IndexPosPair> newEntries;
-
-        // 1 - try in-place update and record insertion failures
-        for(Index i = 0; i<n; ++i)
-        {
-          internal::LowerBoundIndex lb = this->lower_bound(i,i);
-          Index p = lb.value;
-          if(lb.found)
-          {
-            // the coeff already exists
-            assignFunc.assignCoeff(m_data.value(p), diaEval.coeff(i));
-          }
-          else if((!isComp) && m_innerNonZeros[i] < (m_outerIndex[i+1]-m_outerIndex[i]))
-          {
-            // non compressed mode with local room for inserting one element
-            m_data.moveChunk(p, p+1, m_outerIndex[i]+m_innerNonZeros[i]-p);
-            m_innerNonZeros[i]++;
-            m_data.value(p) = Scalar(0);
-            m_data.index(p) = StorageIndex(i);
-            assignFunc.assignCoeff(m_data.value(p), diaEval.coeff(i));
-          }
-          else
-          {
-            // defer insertion
-            newEntries.push_back(IndexPosPair(i,p));
-          }
-        }
-        // 2 - insert deferred entries
-        Index n_entries = Index(newEntries.size());
-        if(n_entries>0)
-        {
-          Storage newData(m_data.size()+n_entries);
-          Index prev_p = 0;
-          Index prev_i = 0;
-          for(Index k=0; k<n_entries;++k)
-          {
-            Index i = newEntries[k].i;
-            Index p = newEntries[k].p;
-            internal::smart_copy(m_data.valuePtr()+prev_p, m_data.valuePtr()+p, newData.valuePtr()+prev_p+k);
-            internal::smart_copy(m_data.indexPtr()+prev_p, m_data.indexPtr()+p, newData.indexPtr()+prev_p+k);
-            for(Index j=prev_i;j<i;++j)
-              m_outerIndex[j+1] += k;
-            if(!isComp)
-              m_innerNonZeros[i]++;
-            prev_p = p;
-            prev_i = i;
-            newData.value(p+k) = Scalar(0);
-            newData.index(p+k) = StorageIndex(i);
-            assignFunc.assignCoeff(newData.value(p+k), diaEval.coeff(i));
-          }
-          {
-            internal::smart_copy(m_data.valuePtr()+prev_p, m_data.valuePtr()+m_data.size(), newData.valuePtr()+prev_p+n_entries);
-            internal::smart_copy(m_data.indexPtr()+prev_p, m_data.indexPtr()+m_data.size(), newData.indexPtr()+prev_p+n_entries);
-            for(Index j=prev_i+1;j<=m_outerSize;++j)
-              m_outerIndex[j] += n_entries;
-          }
-          m_data.swap(newData);
-        }
-      }
-    }
-
-private:
-  static void check_template_parameters()
-  {
-    EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
-    EIGEN_STATIC_ASSERT((Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS);
-  }
-
-  struct default_prunning_func {
-    default_prunning_func(const Scalar& ref, const RealScalar& eps) : reference(ref), epsilon(eps) {}
-    inline bool operator() (const Index&, const Index&, const Scalar& value) const
-    {
-      return !internal::isMuchSmallerThan(value, reference, epsilon);
-    }
-    Scalar reference;
-    RealScalar epsilon;
-  };
-};
-
-namespace internal {
-
-template<typename InputIterator, typename SparseMatrixType, typename DupFunctor>
-void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat, DupFunctor dup_func)
-{
-  enum { IsRowMajor = SparseMatrixType::IsRowMajor };
-  typedef typename SparseMatrixType::Scalar Scalar;
-  typedef typename SparseMatrixType::StorageIndex StorageIndex;
-  SparseMatrix<Scalar,IsRowMajor?ColMajor:RowMajor,StorageIndex> trMat(mat.rows(),mat.cols());
-
-  if(begin!=end)
-  {
-    // pass 1: count the nnz per inner-vector
-    typename SparseMatrixType::IndexVector wi(trMat.outerSize());
-    wi.setZero();
-    for(InputIterator it(begin); it!=end; ++it)
-    {
-      eigen_assert(it->row()>=0 && it->row()<mat.rows() && it->col()>=0 && it->col()<mat.cols());
-      wi(IsRowMajor ? it->col() : it->row())++;
-    }
-
-    // pass 2: insert all the elements into trMat
-    trMat.reserve(wi);
-    for(InputIterator it(begin); it!=end; ++it)
-      trMat.insertBackUncompressed(it->row(),it->col()) = it->value();
-
-    // pass 3:
-    trMat.collapseDuplicates(dup_func);
-  }
-
-  // pass 4: transposed copy -> implicit sorting
-  mat = trMat;
-}
-
-}
-
-
-/** Fill the matrix \c *this with the list of \em triplets defined by the iterator range \a begin - \a end.
-  *
-  * A \em triplet is a tuple (i,j,value) defining a non-zero element.
-  * The input list of triplets does not have to be sorted, and can contains duplicated elements.
-  * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up.
-  * This is a \em O(n) operation, with \em n the number of triplet elements.
-  * The initial contents of \c *this is destroyed.
-  * The matrix \c *this must be properly resized beforehand using the SparseMatrix(Index,Index) constructor,
-  * or the resize(Index,Index) method. The sizes are not extracted from the triplet list.
-  *
-  * The \a InputIterators value_type must provide the following interface:
-  * \code
-  * Scalar value() const; // the value
-  * Scalar row() const;   // the row index i
-  * Scalar col() const;   // the column index j
-  * \endcode
-  * See for instance the Eigen::Triplet template class.
-  *
-  * Here is a typical usage example:
-  * \code
-    typedef Triplet<double> T;
-    std::vector<T> tripletList;
-    tripletList.reserve(estimation_of_entries);
-    for(...)
-    {
-      // ...
-      tripletList.push_back(T(i,j,v_ij));
-    }
-    SparseMatrixType m(rows,cols);
-    m.setFromTriplets(tripletList.begin(), tripletList.end());
-    // m is ready to go!
-  * \endcode
-  *
-  * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
-  * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
-  * be explicitly stored into a std::vector for instance.
-  */
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename InputIterators>
-void SparseMatrix<Scalar,_Options,_StorageIndex>::setFromTriplets(const InputIterators& begin, const InputIterators& end)
-{
-  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_StorageIndex> >(begin, end, *this, internal::scalar_sum_op<Scalar,Scalar>());
-}
-
-/** The same as setFromTriplets but when duplicates are met the functor \a dup_func is applied:
-  * \code
-  * value = dup_func(OldValue, NewValue)
-  * \endcode 
-  * Here is a C++11 example keeping the latest entry only:
-  * \code
-  * mat.setFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
-  * \endcode
-  */
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename InputIterators,typename DupFunctor>
-void SparseMatrix<Scalar,_Options,_StorageIndex>::setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
-{
-  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_StorageIndex>, DupFunctor>(begin, end, *this, dup_func);
-}
-
-/** \internal */
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename DupFunctor>
-void SparseMatrix<Scalar,_Options,_StorageIndex>::collapseDuplicates(DupFunctor dup_func)
-{
-  eigen_assert(!isCompressed());
-  // TODO, in practice we should be able to use m_innerNonZeros for that task
-  IndexVector wi(innerSize());
-  wi.fill(-1);
-  StorageIndex count = 0;
-  // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
-  for(Index j=0; j<outerSize(); ++j)
-  {
-    StorageIndex start   = count;
-    Index oldEnd  = m_outerIndex[j]+m_innerNonZeros[j];
-    for(Index k=m_outerIndex[j]; k<oldEnd; ++k)
-    {
-      Index i = m_data.index(k);
-      if(wi(i)>=start)
-      {
-        // we already meet this entry => accumulate it
-        m_data.value(wi(i)) = dup_func(m_data.value(wi(i)), m_data.value(k));
-      }
-      else
-      {
-        m_data.value(count) = m_data.value(k);
-        m_data.index(count) = m_data.index(k);
-        wi(i) = count;
-        ++count;
-      }
-    }
-    m_outerIndex[j] = start;
-  }
-  m_outerIndex[m_outerSize] = count;
-
-  // turn the matrix into compressed form
-  std::free(m_innerNonZeros);
-  m_innerNonZeros = 0;
-  m_data.resize(m_outerIndex[m_outerSize]);
-}
-
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename OtherDerived>
-EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_StorageIndex>& SparseMatrix<Scalar,_Options,_StorageIndex>::operator=(const SparseMatrixBase<OtherDerived>& other)
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
-        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-    EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-  #endif
-      
-  const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
-  if (needToTranspose)
-  {
-    #ifdef EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
-      EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
-    #endif
-    // two passes algorithm:
-    //  1 - compute the number of coeffs per dest inner vector
-    //  2 - do the actual copy/eval
-    // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed
-    typedef typename internal::nested_eval<OtherDerived,2,typename internal::plain_matrix_type<OtherDerived>::type >::type OtherCopy;
-    typedef typename internal::remove_all<OtherCopy>::type _OtherCopy;
-    typedef internal::evaluator<_OtherCopy> OtherCopyEval;
-    OtherCopy otherCopy(other.derived());
-    OtherCopyEval otherCopyEval(otherCopy);
-
-    SparseMatrix dest(other.rows(),other.cols());
-    Eigen::Map<IndexVector> (dest.m_outerIndex,dest.outerSize()).setZero();
-
-    // pass 1
-    // FIXME the above copy could be merged with that pass
-    for (Index j=0; j<otherCopy.outerSize(); ++j)
-      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it)
-        ++dest.m_outerIndex[it.index()];
-
-    // prefix sum
-    StorageIndex count = 0;
-    IndexVector positions(dest.outerSize());
-    for (Index j=0; j<dest.outerSize(); ++j)
-    {
-      StorageIndex tmp = dest.m_outerIndex[j];
-      dest.m_outerIndex[j] = count;
-      positions[j] = count;
-      count += tmp;
-    }
-    dest.m_outerIndex[dest.outerSize()] = count;
-    // alloc
-    dest.m_data.resize(count);
-    // pass 2
-    for (StorageIndex j=0; j<otherCopy.outerSize(); ++j)
-    {
-      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it)
-      {
-        Index pos = positions[it.index()]++;
-        dest.m_data.index(pos) = j;
-        dest.m_data.value(pos) = it.value();
-      }
-    }
-    this->swap(dest);
-    return *this;
-  }
-  else
-  {
-    if(other.isRValue())
-    {
-      initAssignment(other.derived());
-    }
-    // there is no special optimization
-    return Base::operator=(other.derived());
-  }
-}
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insert(Index row, Index col)
-{
-  eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
-  
-  const Index outer = IsRowMajor ? row : col;
-  const Index inner = IsRowMajor ? col : row;
-  
-  if(isCompressed())
-  {
-    if(nonZeros()==0)
-    {
-      // reserve space if not already done
-      if(m_data.allocatedSize()==0)
-        m_data.reserve(2*m_innerSize);
-      
-      // turn the matrix into non-compressed mode
-      m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-      if(!m_innerNonZeros) internal::throw_std_bad_alloc();
-      
-      memset(m_innerNonZeros, 0, (m_outerSize)*sizeof(StorageIndex));
-      
-      // pack all inner-vectors to the end of the pre-allocated space
-      // and allocate the entire free-space to the first inner-vector
-      StorageIndex end = convert_index(m_data.allocatedSize());
-      for(Index j=1; j<=m_outerSize; ++j)
-        m_outerIndex[j] = end;
-    }
-    else
-    {
-      // turn the matrix into non-compressed mode
-      m_innerNonZeros = static_cast<StorageIndex*>(std::malloc(m_outerSize * sizeof(StorageIndex)));
-      if(!m_innerNonZeros) internal::throw_std_bad_alloc();
-      for(Index j=0; j<m_outerSize; ++j)
-        m_innerNonZeros[j] = m_outerIndex[j+1]-m_outerIndex[j];
-    }
-  }
-  
-  // check whether we can do a fast "push back" insertion
-  Index data_end = m_data.allocatedSize();
-  
-  // First case: we are filling a new inner vector which is packed at the end.
-  // We assume that all remaining inner-vectors are also empty and packed to the end.
-  if(m_outerIndex[outer]==data_end)
-  {
-    eigen_internal_assert(m_innerNonZeros[outer]==0);
-    
-    // pack previous empty inner-vectors to end of the used-space
-    // and allocate the entire free-space to the current inner-vector.
-    StorageIndex p = convert_index(m_data.size());
-    Index j = outer;
-    while(j>=0 && m_innerNonZeros[j]==0)
-      m_outerIndex[j--] = p;
-    
-    // push back the new element
-    ++m_innerNonZeros[outer];
-    m_data.append(Scalar(0), inner);
-    
-    // check for reallocation
-    if(data_end != m_data.allocatedSize())
-    {
-      // m_data has been reallocated
-      //  -> move remaining inner-vectors back to the end of the free-space
-      //     so that the entire free-space is allocated to the current inner-vector.
-      eigen_internal_assert(data_end < m_data.allocatedSize());
-      StorageIndex new_end = convert_index(m_data.allocatedSize());
-      for(Index k=outer+1; k<=m_outerSize; ++k)
-        if(m_outerIndex[k]==data_end)
-          m_outerIndex[k] = new_end;
-    }
-    return m_data.value(p);
-  }
-  
-  // Second case: the next inner-vector is packed to the end
-  // and the current inner-vector end match the used-space.
-  if(m_outerIndex[outer+1]==data_end && m_outerIndex[outer]+m_innerNonZeros[outer]==m_data.size())
-  {
-    eigen_internal_assert(outer+1==m_outerSize || m_innerNonZeros[outer+1]==0);
-    
-    // add space for the new element
-    ++m_innerNonZeros[outer];
-    m_data.resize(m_data.size()+1);
-    
-    // check for reallocation
-    if(data_end != m_data.allocatedSize())
-    {
-      // m_data has been reallocated
-      //  -> move remaining inner-vectors back to the end of the free-space
-      //     so that the entire free-space is allocated to the current inner-vector.
-      eigen_internal_assert(data_end < m_data.allocatedSize());
-      StorageIndex new_end = convert_index(m_data.allocatedSize());
-      for(Index k=outer+1; k<=m_outerSize; ++k)
-        if(m_outerIndex[k]==data_end)
-          m_outerIndex[k] = new_end;
-    }
-    
-    // and insert it at the right position (sorted insertion)
-    Index startId = m_outerIndex[outer];
-    Index p = m_outerIndex[outer]+m_innerNonZeros[outer]-1;
-    while ( (p > startId) && (m_data.index(p-1) > inner) )
-    {
-      m_data.index(p) = m_data.index(p-1);
-      m_data.value(p) = m_data.value(p-1);
-      --p;
-    }
-    
-    m_data.index(p) = convert_index(inner);
-    return (m_data.value(p) = Scalar(0));
-  }
-  
-  if(m_data.size() != m_data.allocatedSize())
-  {
-    // make sure the matrix is compatible to random un-compressed insertion:
-    m_data.resize(m_data.allocatedSize());
-    this->reserveInnerVectors(Array<StorageIndex,Dynamic,1>::Constant(m_outerSize, 2));
-  }
-  
-  return insertUncompressed(row,col);
-}
-    
-template<typename _Scalar, int _Options, typename _StorageIndex>
-EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insertUncompressed(Index row, Index col)
-{
-  eigen_assert(!isCompressed());
-
-  const Index outer = IsRowMajor ? row : col;
-  const StorageIndex inner = convert_index(IsRowMajor ? col : row);
-
-  Index room = m_outerIndex[outer+1] - m_outerIndex[outer];
-  StorageIndex innerNNZ = m_innerNonZeros[outer];
-  if(innerNNZ>=room)
-  {
-    // this inner vector is full, we need to reallocate the whole buffer :(
-    reserve(SingletonVector(outer,std::max<StorageIndex>(2,innerNNZ)));
-  }
-
-  Index startId = m_outerIndex[outer];
-  Index p = startId + m_innerNonZeros[outer];
-  while ( (p > startId) && (m_data.index(p-1) > inner) )
-  {
-    m_data.index(p) = m_data.index(p-1);
-    m_data.value(p) = m_data.value(p-1);
-    --p;
-  }
-  eigen_assert((p<=startId || m_data.index(p-1)!=inner) && "you cannot insert an element that already exists, you must call coeffRef to this end");
-
-  m_innerNonZeros[outer]++;
-
-  m_data.index(p) = inner;
-  return (m_data.value(p) = Scalar(0));
-}
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar& SparseMatrix<_Scalar,_Options,_StorageIndex>::insertCompressed(Index row, Index col)
-{
-  eigen_assert(isCompressed());
-
-  const Index outer = IsRowMajor ? row : col;
-  const Index inner = IsRowMajor ? col : row;
-
-  Index previousOuter = outer;
-  if (m_outerIndex[outer+1]==0)
-  {
-    // we start a new inner vector
-    while (previousOuter>=0 && m_outerIndex[previousOuter]==0)
-    {
-      m_outerIndex[previousOuter] = convert_index(m_data.size());
-      --previousOuter;
-    }
-    m_outerIndex[outer+1] = m_outerIndex[outer];
-  }
-
-  // here we have to handle the tricky case where the outerIndex array
-  // starts with: [ 0 0 0 0 0 1 ...] and we are inserted in, e.g.,
-  // the 2nd inner vector...
-  bool isLastVec = (!(previousOuter==-1 && m_data.size()!=0))
-                && (std::size_t(m_outerIndex[outer+1]) == m_data.size());
-
-  std::size_t startId = m_outerIndex[outer];
-  // FIXME let's make sure sizeof(long int) == sizeof(std::size_t)
-  std::size_t p = m_outerIndex[outer+1];
-  ++m_outerIndex[outer+1];
-
-  double reallocRatio = 1;
-  if (m_data.allocatedSize()<=m_data.size())
-  {
-    // if there is no preallocated memory, let's reserve a minimum of 32 elements
-    if (m_data.size()==0)
-    {
-      m_data.reserve(32);
-    }
-    else
-    {
-      // we need to reallocate the data, to reduce multiple reallocations
-      // we use a smart resize algorithm based on the current filling ratio
-      // in addition, we use double to avoid integers overflows
-      double nnzEstimate = double(m_outerIndex[outer])*double(m_outerSize)/double(outer+1);
-      reallocRatio = (nnzEstimate-double(m_data.size()))/double(m_data.size());
-      // furthermore we bound the realloc ratio to:
-      //   1) reduce multiple minor realloc when the matrix is almost filled
-      //   2) avoid to allocate too much memory when the matrix is almost empty
-      reallocRatio = (std::min)((std::max)(reallocRatio,1.5),8.);
-    }
-  }
-  m_data.resize(m_data.size()+1,reallocRatio);
-
-  if (!isLastVec)
-  {
-    if (previousOuter==-1)
-    {
-      // oops wrong guess.
-      // let's correct the outer offsets
-      for (Index k=0; k<=(outer+1); ++k)
-        m_outerIndex[k] = 0;
-      Index k=outer+1;
-      while(m_outerIndex[k]==0)
-        m_outerIndex[k++] = 1;
-      while (k<=m_outerSize && m_outerIndex[k]!=0)
-        m_outerIndex[k++]++;
-      p = 0;
-      --k;
-      k = m_outerIndex[k]-1;
-      while (k>0)
-      {
-        m_data.index(k) = m_data.index(k-1);
-        m_data.value(k) = m_data.value(k-1);
-        k--;
-      }
-    }
-    else
-    {
-      // we are not inserting into the last inner vec
-      // update outer indices:
-      Index j = outer+2;
-      while (j<=m_outerSize && m_outerIndex[j]!=0)
-        m_outerIndex[j++]++;
-      --j;
-      // shift data of last vecs:
-      Index k = m_outerIndex[j]-1;
-      while (k>=Index(p))
-      {
-        m_data.index(k) = m_data.index(k-1);
-        m_data.value(k) = m_data.value(k-1);
-        k--;
-      }
-    }
-  }
-
-  while ( (p > startId) && (m_data.index(p-1) > inner) )
-  {
-    m_data.index(p) = m_data.index(p-1);
-    m_data.value(p) = m_data.value(p-1);
-    --p;
-  }
-
-  m_data.index(p) = inner;
-  return (m_data.value(p) = Scalar(0));
-}
-
-namespace internal {
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct evaluator<SparseMatrix<_Scalar,_Options,_StorageIndex> >
-  : evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_StorageIndex> > >
-{
-  typedef evaluator<SparseCompressedBase<SparseMatrix<_Scalar,_Options,_StorageIndex> > > Base;
-  typedef SparseMatrix<_Scalar,_Options,_StorageIndex> SparseMatrixType;
-  evaluator() : Base() {}
-  explicit evaluator(const SparseMatrixType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
deleted file mode 100644
index 229449f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
+++ /dev/null
@@ -1,398 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEMATRIXBASE_H
-#define EIGEN_SPARSEMATRIXBASE_H
-
-namespace Eigen { 
-
-/** \ingroup SparseCore_Module
-  *
-  * \class SparseMatrixBase
-  *
-  * \brief Base class of any sparse matrices or sparse expressions
-  *
-  * \tparam Derived is the derived type, e.g. a sparse matrix type, or an expression, etc.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN.
-  */
-template<typename Derived> class SparseMatrixBase
-  : public EigenBase<Derived>
-{
-  public:
-
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    
-    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
-      *
-      * It is an alias for the Scalar type */
-    typedef Scalar value_type;
-    
-    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-
-    /** The integer type used to \b store indices within a SparseMatrix.
-      * For a \c SparseMatrix<Scalar,Options,IndexType> it an alias of the third template parameter \c IndexType. */
-    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
-
-    typedef typename internal::add_const_on_value_type_if_arithmetic<
-                         typename internal::packet_traits<Scalar>::type
-                     >::type PacketReturnType;
-
-    typedef SparseMatrixBase StorageBaseType;
-
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-    
-    template<typename OtherDerived>
-    Derived& operator=(const EigenBase<OtherDerived> &other);
-
-    enum {
-
-      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-        /**< The number of rows at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
-
-      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-        /**< The number of columns at compile-time. This is just a copy of the value provided
-          * by the \a Derived type. If a value is not known at compile-time,
-          * it is set to the \a Dynamic constant.
-          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
-
-
-      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
-        /**< This is equal to the number of coefficients, i.e. the number of
-          * rows times the number of columns, or to \a Dynamic if this is not
-          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
-
-      MaxRowsAtCompileTime = RowsAtCompileTime,
-      MaxColsAtCompileTime = ColsAtCompileTime,
-
-      MaxSizeAtCompileTime = (internal::size_at_compile_time<MaxRowsAtCompileTime,
-                                                      MaxColsAtCompileTime>::ret),
-
-      IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
-        /**< This is set to true if either the number of rows or the number of
-          * columns is known at compile-time to be equal to 1. Indeed, in that case,
-          * we are dealing with a column-vector (if there is only one column) or with
-          * a row-vector (if there is only one row). */
-
-      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2,
-        /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
-         * and 2 for matrices.
-         */
-
-      Flags = internal::traits<Derived>::Flags,
-        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
-          * constructed from this one. See the \ref flags "list of flags".
-          */
-
-      IsRowMajor = Flags&RowMajorBit ? 1 : 0,
-      
-      InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
-                             : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
-
-      #ifndef EIGEN_PARSED_BY_DOXYGEN
-      _HasDirectAccess = (int(Flags)&DirectAccessBit) ? 1 : 0 // workaround sunCC
-      #endif
-    };
-
-    /** \internal the return type of MatrixBase::adjoint() */
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >,
-                        Transpose<const Derived>
-                     >::type AdjointReturnType;
-    typedef Transpose<Derived> TransposeReturnType;
-    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
-
-    // FIXME storage order do not match evaluator storage order
-    typedef SparseMatrix<Scalar, Flags&RowMajorBit ? RowMajor : ColMajor, StorageIndex> PlainObject;
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is the "real scalar" type; if the \a Scalar type is already real numbers
-      * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
-      * \a Scalar is \a std::complex<T> then RealScalar is \a T.
-      *
-      * \sa class NumTraits
-      */
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    /** \internal the return type of coeff()
-      */
-    typedef typename internal::conditional<_HasDirectAccess, const Scalar&, Scalar>::type CoeffReturnType;
-
-    /** \internal Represents a matrix with all coefficients equal to one another*/
-    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Matrix<Scalar,Dynamic,Dynamic> > ConstantReturnType;
-
-    /** type of the equivalent dense matrix */
-    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
-    /** type of the equivalent square matrix */
-    typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
-                          EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
-
-    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    inline Derived& derived() { return *static_cast<Derived*>(this); }
-    inline Derived& const_cast_derived() const
-    { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
-
-    typedef EigenBase<Derived> Base;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-#define EIGEN_DOC_UNARY_ADDONS(METHOD,OP)           /** <p>This method does not change the sparsity of \c *this: the OP is applied to explicitly stored coefficients only. \sa SparseCompressedBase::coeffs() </p> */
-#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL      /** <p> \warning This method returns a read-only expression for any sparse matrices. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
-#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND) /** <p> \warning This method returns a read-write expression for COND sparse matrices only. Otherwise, the returned expression is read-only. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
-#else
-#define EIGEN_DOC_UNARY_ADDONS(X,Y)
-#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
-#endif
-#   include "../plugins/CommonCwiseUnaryOps.h"
-#   include "../plugins/CommonCwiseBinaryOps.h"
-#   include "../plugins/MatrixCwiseUnaryOps.h"
-#   include "../plugins/MatrixCwiseBinaryOps.h"
-#   include "../plugins/BlockMethods.h"
-#   ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN
-#     include EIGEN_SPARSEMATRIXBASE_PLUGIN
-#   endif
-#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
-#undef EIGEN_DOC_UNARY_ADDONS
-#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
-
-    /** \returns the number of rows. \sa cols() */
-    inline Index rows() const { return derived().rows(); }
-    /** \returns the number of columns. \sa rows() */
-    inline Index cols() const { return derived().cols(); }
-    /** \returns the number of coefficients, which is \a rows()*cols().
-      * \sa rows(), cols(). */
-    inline Index size() const { return rows() * cols(); }
-    /** \returns true if either the number of rows or the number of columns is equal to 1.
-      * In other words, this function returns
-      * \code rows()==1 || cols()==1 \endcode
-      * \sa rows(), cols(), IsVectorAtCompileTime. */
-    inline bool isVector() const { return rows()==1 || cols()==1; }
-    /** \returns the size of the storage major dimension,
-      * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
-    Index outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); }
-    /** \returns the size of the inner dimension according to the storage order,
-      * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
-    Index innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
-
-    bool isRValue() const { return m_isRValue; }
-    Derived& markAsRValue() { m_isRValue = true; return derived(); }
-
-    SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */ }
-
-    
-    template<typename OtherDerived>
-    Derived& operator=(const ReturnByValue<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other);
-
-    inline Derived& operator=(const Derived& other);
-
-  protected:
-
-    template<typename OtherDerived>
-    inline Derived& assign(const OtherDerived& other);
-
-    template<typename OtherDerived>
-    inline void assignGeneric(const OtherDerived& other);
-
-  public:
-
-    friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
-    {
-      typedef typename Derived::Nested Nested;
-      typedef typename internal::remove_all<Nested>::type NestedCleaned;
-
-      if (Flags&RowMajorBit)
-      {
-        Nested nm(m.derived());
-        internal::evaluator<NestedCleaned> thisEval(nm);
-        for (Index row=0; row<nm.outerSize(); ++row)
-        {
-          Index col = 0;
-          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, row); it; ++it)
-          {
-            for ( ; col<it.index(); ++col)
-              s << "0 ";
-            s << it.value() << " ";
-            ++col;
-          }
-          for ( ; col<m.cols(); ++col)
-            s << "0 ";
-          s << std::endl;
-        }
-      }
-      else
-      {
-        Nested nm(m.derived());
-        internal::evaluator<NestedCleaned> thisEval(nm);
-        if (m.cols() == 1) {
-          Index row = 0;
-          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, 0); it; ++it)
-          {
-            for ( ; row<it.index(); ++row)
-              s << "0" << std::endl;
-            s << it.value() << std::endl;
-            ++row;
-          }
-          for ( ; row<m.rows(); ++row)
-            s << "0" << std::endl;
-        }
-        else
-        {
-          SparseMatrix<Scalar, RowMajorBit, StorageIndex> trans = m;
-          s << static_cast<const SparseMatrixBase<SparseMatrix<Scalar, RowMajorBit, StorageIndex> >&>(trans);
-        }
-      }
-      return s;
-    }
-
-    template<typename OtherDerived>
-    Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
-    
-    template<typename OtherDerived>
-    Derived& operator+=(const DiagonalBase<OtherDerived>& other);
-    template<typename OtherDerived>
-    Derived& operator-=(const DiagonalBase<OtherDerived>& other);
-
-    template<typename OtherDerived>
-    Derived& operator+=(const EigenBase<OtherDerived> &other);
-    template<typename OtherDerived>
-    Derived& operator-=(const EigenBase<OtherDerived> &other);
-
-    Derived& operator*=(const Scalar& other);
-    Derived& operator/=(const Scalar& other);
-
-    template<typename OtherDerived> struct CwiseProductDenseReturnType {
-      typedef CwiseBinaryOp<internal::scalar_product_op<typename ScalarBinaryOpTraits<
-                                                          typename internal::traits<Derived>::Scalar,
-                                                          typename internal::traits<OtherDerived>::Scalar
-                                                        >::ReturnType>,
-                            const Derived,
-                            const OtherDerived
-                          > Type;
-    };
-
-    template<typename OtherDerived>
-    EIGEN_STRONG_INLINE const typename CwiseProductDenseReturnType<OtherDerived>::Type
-    cwiseProduct(const MatrixBase<OtherDerived> &other) const;
-
-    // sparse * diagonal
-    template<typename OtherDerived>
-    const Product<Derived,OtherDerived>
-    operator*(const DiagonalBase<OtherDerived> &other) const
-    { return Product<Derived,OtherDerived>(derived(), other.derived()); }
-
-    // diagonal * sparse
-    template<typename OtherDerived> friend
-    const Product<OtherDerived,Derived>
-    operator*(const DiagonalBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
-    { return Product<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
-    
-    // sparse * sparse
-    template<typename OtherDerived>
-    const Product<Derived,OtherDerived,AliasFreeProduct>
-    operator*(const SparseMatrixBase<OtherDerived> &other) const;
-    
-    // sparse * dense
-    template<typename OtherDerived>
-    const Product<Derived,OtherDerived>
-    operator*(const MatrixBase<OtherDerived> &other) const
-    { return Product<Derived,OtherDerived>(derived(), other.derived()); }
-    
-    // dense * sparse
-    template<typename OtherDerived> friend
-    const Product<OtherDerived,Derived>
-    operator*(const MatrixBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
-    { return Product<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
-    
-     /** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */
-    SparseSymmetricPermutationProduct<Derived,Upper|Lower> twistedBy(const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm) const
-    {
-      return SparseSymmetricPermutationProduct<Derived,Upper|Lower>(derived(), perm);
-    }
-
-    template<typename OtherDerived>
-    Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
-
-    template<int Mode>
-    inline const TriangularView<const Derived, Mode> triangularView() const;
-    
-    template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SparseSelfAdjointView<Derived, UpLo> Type; };
-    template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SparseSelfAdjointView<const Derived, UpLo> Type; };
-
-    template<unsigned int UpLo> inline 
-    typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
-    template<unsigned int UpLo> inline
-    typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
-
-    template<typename OtherDerived> Scalar dot(const MatrixBase<OtherDerived>& other) const;
-    template<typename OtherDerived> Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
-    RealScalar squaredNorm() const;
-    RealScalar norm()  const;
-    RealScalar blueNorm() const;
-
-    TransposeReturnType transpose() { return TransposeReturnType(derived()); }
-    const ConstTransposeReturnType transpose() const { return ConstTransposeReturnType(derived()); }
-    const AdjointReturnType adjoint() const { return AdjointReturnType(transpose()); }
-
-    DenseMatrixType toDense() const
-    {
-      return DenseMatrixType(derived());
-    }
-
-    template<typename OtherDerived>
-    bool isApprox(const SparseMatrixBase<OtherDerived>& other,
-                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
-
-    template<typename OtherDerived>
-    bool isApprox(const MatrixBase<OtherDerived>& other,
-                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
-    { return toDense().isApprox(other,prec); }
-
-    /** \returns the matrix or vector obtained by evaluating this expression.
-      *
-      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
-      * a const reference, in order to avoid a useless copy.
-      */
-    inline const typename internal::eval<Derived>::type eval() const
-    { return typename internal::eval<Derived>::type(derived()); }
-
-    Scalar sum() const;
-    
-    inline const SparseView<Derived>
-    pruned(const Scalar& reference = Scalar(0), const RealScalar& epsilon = NumTraits<Scalar>::dummy_precision()) const;
-
-  protected:
-
-    bool m_isRValue;
-
-    static inline StorageIndex convert_index(const Index idx) {
-      return internal::convert_index<StorageIndex>(idx);
-    }
-  private:
-    template<typename Dest> void evalTo(Dest &) const;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEMATRIXBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h
deleted file mode 100644
index ef38357..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h
+++ /dev/null
@@ -1,178 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_PERMUTATION_H
-#define EIGEN_SPARSE_PERMUTATION_H
-
-// This file implements sparse * permutation products
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename ExpressionType, int Side, bool Transposed>
-struct permutation_matrix_product<ExpressionType, Side, Transposed, SparseShape>
-{
-    typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
-    typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
-
-    typedef typename MatrixTypeCleaned::Scalar Scalar;
-    typedef typename MatrixTypeCleaned::StorageIndex StorageIndex;
-
-    enum {
-      SrcStorageOrder = MatrixTypeCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
-      MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
-    };
-    
-    typedef typename internal::conditional<MoveOuter,
-        SparseMatrix<Scalar,SrcStorageOrder,StorageIndex>,
-        SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,StorageIndex> >::type ReturnType;
-
-    template<typename Dest,typename PermutationType>
-    static inline void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
-    {
-      MatrixType mat(xpr);
-      if(MoveOuter)
-      {
-        SparseMatrix<Scalar,SrcStorageOrder,StorageIndex> tmp(mat.rows(), mat.cols());
-        Matrix<StorageIndex,Dynamic,1> sizes(mat.outerSize());
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          Index jp = perm.indices().coeff(j);
-          sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = StorageIndex(mat.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).nonZeros());
-        }
-        tmp.reserve(sizes);
-        for(Index j=0; j<mat.outerSize(); ++j)
-        {
-          Index jp = perm.indices().coeff(j);
-          Index jsrc = ((Side==OnTheRight) ^ Transposed) ? jp : j;
-          Index jdst = ((Side==OnTheLeft) ^ Transposed) ? jp : j;
-          for(typename MatrixTypeCleaned::InnerIterator it(mat,jsrc); it; ++it)
-            tmp.insertByOuterInner(jdst,it.index()) = it.value();
-        }
-        dst = tmp;
-      }
-      else
-      {
-        SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,StorageIndex> tmp(mat.rows(), mat.cols());
-        Matrix<StorageIndex,Dynamic,1> sizes(tmp.outerSize());
-        sizes.setZero();
-        PermutationMatrix<Dynamic,Dynamic,StorageIndex> perm_cpy;
-        if((Side==OnTheLeft) ^ Transposed)
-          perm_cpy = perm;
-        else
-          perm_cpy = perm.transpose();
-
-        for(Index j=0; j<mat.outerSize(); ++j)
-          for(typename MatrixTypeCleaned::InnerIterator it(mat,j); it; ++it)
-            sizes[perm_cpy.indices().coeff(it.index())]++;
-        tmp.reserve(sizes);
-        for(Index j=0; j<mat.outerSize(); ++j)
-          for(typename MatrixTypeCleaned::InnerIterator it(mat,j); it; ++it)
-            tmp.insertByOuterInner(perm_cpy.indices().coeff(it.index()),j) = it.value();
-        dst = tmp;
-      }
-    }
-};
-
-}
-
-namespace internal {
-
-template <int ProductTag> struct product_promote_storage_type<Sparse,             PermutationStorage, ProductTag> { typedef Sparse ret; };
-template <int ProductTag> struct product_promote_storage_type<PermutationStorage, Sparse,             ProductTag> { typedef Sparse ret; };
-
-// TODO, the following two overloads are only needed to define the right temporary type through 
-// typename traits<permutation_sparse_matrix_product<Rhs,Lhs,OnTheRight,false> >::ReturnType
-// whereas it should be correctly handled by traits<Product<> >::PlainObject
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, PermutationShape, SparseShape>
-  : public evaluator<typename permutation_matrix_product<Rhs,OnTheLeft,false,SparseShape>::ReturnType>
-{
-  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
-  typedef typename permutation_matrix_product<Rhs,OnTheLeft,false,SparseShape>::ReturnType PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-
-  explicit product_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<Lhs, Rhs, PermutationShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-template<typename Lhs, typename Rhs, int ProductTag>
-struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, SparseShape, PermutationShape >
-  : public evaluator<typename permutation_matrix_product<Lhs,OnTheRight,false,SparseShape>::ReturnType>
-{
-  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
-  typedef typename permutation_matrix_product<Lhs,OnTheRight,false,SparseShape>::ReturnType PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  enum {
-    Flags = Base::Flags | EvalBeforeNestingBit
-  };
-
-  explicit product_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<Lhs, Rhs, SparseShape, PermutationShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-/** \returns the matrix with the permutation applied to the columns
-  */
-template<typename SparseDerived, typename PermDerived>
-inline const Product<SparseDerived, PermDerived, AliasFreeProduct>
-operator*(const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm)
-{ return Product<SparseDerived, PermDerived, AliasFreeProduct>(matrix.derived(), perm.derived()); }
-
-/** \returns the matrix with the permutation applied to the rows
-  */
-template<typename SparseDerived, typename PermDerived>
-inline const Product<PermDerived, SparseDerived, AliasFreeProduct>
-operator*( const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix)
-{ return  Product<PermDerived, SparseDerived, AliasFreeProduct>(perm.derived(), matrix.derived()); }
-
-
-/** \returns the matrix with the inverse permutation applied to the columns.
-  */
-template<typename SparseDerived, typename PermutationType>
-inline const Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct>
-operator*(const SparseMatrixBase<SparseDerived>& matrix, const InverseImpl<PermutationType, PermutationStorage>& tperm)
-{
-  return Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct>(matrix.derived(), tperm.derived());
-}
-
-/** \returns the matrix with the inverse permutation applied to the rows.
-  */
-template<typename SparseDerived, typename PermutationType>
-inline const Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct>
-operator*(const InverseImpl<PermutationType,PermutationStorage>& tperm, const SparseMatrixBase<SparseDerived>& matrix)
-{
-  return Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct>(tperm.derived(), matrix.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
deleted file mode 100644
index af8a774..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
+++ /dev/null
@@ -1,181 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEPRODUCT_H
-#define EIGEN_SPARSEPRODUCT_H
-
-namespace Eigen { 
-
-/** \returns an expression of the product of two sparse matrices.
-  * By default a conservative product preserving the symbolic non zeros is performed.
-  * The automatic pruning of the small values can be achieved by calling the pruned() function
-  * in which case a totally different product algorithm is employed:
-  * \code
-  * C = (A*B).pruned();             // suppress numerical zeros (exact)
-  * C = (A*B).pruned(ref);
-  * C = (A*B).pruned(ref,epsilon);
-  * \endcode
-  * where \c ref is a meaningful non zero reference value.
-  * */
-template<typename Derived>
-template<typename OtherDerived>
-inline const Product<Derived,OtherDerived,AliasFreeProduct>
-SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
-{
-  return Product<Derived,OtherDerived,AliasFreeProduct>(derived(), other.derived());
-}
-
-namespace internal {
-
-// sparse * sparse
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
-{
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
-  {
-    evalTo(dst, lhs, rhs, typename evaluator_traits<Dest>::Shape());
-  }
-
-  // dense += sparse * sparse
-  template<typename Dest,typename ActualLhs>
-  static void addTo(Dest& dst, const ActualLhs& lhs, const Rhs& rhs, typename enable_if<is_same<typename evaluator_traits<Dest>::Shape,DenseShape>::value,int*>::type* = 0)
-  {
-    typedef typename nested_eval<ActualLhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    internal::sparse_sparse_to_dense_product_selector<typename remove_all<LhsNested>::type,
-                                                      typename remove_all<RhsNested>::type, Dest>::run(lhsNested,rhsNested,dst);
-  }
-
-  // dense -= sparse * sparse
-  template<typename Dest>
-  static void subTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, typename enable_if<is_same<typename evaluator_traits<Dest>::Shape,DenseShape>::value,int*>::type* = 0)
-  {
-    addTo(dst, -lhs, rhs);
-  }
-
-protected:
-
-  // sparse = sparse * sparse
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, SparseShape)
-  {
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhs);
-    internal::conservative_sparse_sparse_product_selector<typename remove_all<LhsNested>::type,
-                                                          typename remove_all<RhsNested>::type, Dest>::run(lhsNested,rhsNested,dst);
-  }
-
-  // dense = sparse * sparse
-  template<typename Dest>
-  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, DenseShape)
-  {
-    dst.setZero();
-    addTo(dst, lhs, rhs);
-  }
-};
-
-// sparse * sparse-triangular
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseShape, SparseTriangularShape, ProductType>
- : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
-{};
-
-// sparse-triangular * sparse
-template<typename Lhs, typename Rhs, int ProductType>
-struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, SparseShape, ProductType>
- : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
-{};
-
-// dense = sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template< typename DstXprType, typename Lhs, typename Rhs>
-struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
-{
-  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
-  {
-    Index dstRows = src.rows();
-    Index dstCols = src.cols();
-    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
-      dst.resize(dstRows, dstCols);
-    
-    generic_product_impl<Lhs, Rhs>::evalTo(dst,src.lhs(),src.rhs());
-  }
-};
-
-// dense += sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template< typename DstXprType, typename Lhs, typename Rhs>
-struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::add_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
-{
-  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
-  {
-    generic_product_impl<Lhs, Rhs>::addTo(dst,src.lhs(),src.rhs());
-  }
-};
-
-// dense -= sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template< typename DstXprType, typename Lhs, typename Rhs>
-struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::sub_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
-{
-  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
-  {
-    generic_product_impl<Lhs, Rhs>::subTo(dst,src.lhs(),src.rhs());
-  }
-};
-
-template<typename Lhs, typename Rhs, int Options>
-struct unary_evaluator<SparseView<Product<Lhs, Rhs, Options> >, IteratorBased>
- : public evaluator<typename Product<Lhs, Rhs, DefaultProduct>::PlainObject>
-{
-  typedef SparseView<Product<Lhs, Rhs, Options> > XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  explicit unary_evaluator(const XprType& xpr)
-    : m_result(xpr.rows(), xpr.cols())
-  {
-    using std::abs;
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(xpr.nestedExpression().lhs());
-    RhsNested rhsNested(xpr.nestedExpression().rhs());
-
-    internal::sparse_sparse_product_with_pruning_selector<typename remove_all<LhsNested>::type,
-                                                          typename remove_all<RhsNested>::type, PlainObject>::run(lhsNested,rhsNested,m_result,
-                                                                                                                  abs(xpr.reference())*xpr.epsilon());
-  }
-
-protected:
-  PlainObject m_result;
-};
-
-} // end namespace internal
-
-// sparse matrix = sparse-product (can be sparse*sparse, sparse*perm, etc.)
-template<typename Scalar, int _Options, typename _StorageIndex>
-template<typename Lhs, typename Rhs>
-SparseMatrix<Scalar,_Options,_StorageIndex>& SparseMatrix<Scalar,_Options,_StorageIndex>::operator=(const Product<Lhs,Rhs,AliasFreeProduct>& src)
-{
-  // std::cout << "in Assignment : " << DstOptions << "\n";
-  SparseMatrix dst(src.rows(),src.cols());
-  internal::generic_product_impl<Lhs, Rhs>::evalTo(dst,src.lhs(),src.rhs());
-  this->swap(dst);
-  return *this;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseRedux.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseRedux.h
deleted file mode 100644
index 4587749..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseRedux.h
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEREDUX_H
-#define EIGEN_SPARSEREDUX_H
-
-namespace Eigen { 
-
-template<typename Derived>
-typename internal::traits<Derived>::Scalar
-SparseMatrixBase<Derived>::sum() const
-{
-  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
-  Scalar res(0);
-  internal::evaluator<Derived> thisEval(derived());
-  for (Index j=0; j<outerSize(); ++j)
-    for (typename internal::evaluator<Derived>::InnerIterator iter(thisEval,j); iter; ++iter)
-      res += iter.value();
-  return res;
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-typename internal::traits<SparseMatrix<_Scalar,_Options,_Index> >::Scalar
-SparseMatrix<_Scalar,_Options,_Index>::sum() const
-{
-  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
-  if(this->isCompressed())
-    return Matrix<Scalar,1,Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
-  else
-    return Base::sum();
-}
-
-template<typename _Scalar, int _Options, typename _Index>
-typename internal::traits<SparseVector<_Scalar,_Options, _Index> >::Scalar
-SparseVector<_Scalar,_Options,_Index>::sum() const
-{
-  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
-  return Matrix<Scalar,1,Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEREDUX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseRef.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseRef.h
deleted file mode 100644
index 748f87d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseRef.h
+++ /dev/null
@@ -1,397 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_REF_H
-#define EIGEN_SPARSE_REF_H
-
-namespace Eigen {
-
-enum {
-  StandardCompressedFormat = 2 /**< used by Ref<SparseMatrix> to specify whether the input storage must be in standard compressed form */
-};
-  
-namespace internal {
-
-template<typename Derived> class SparseRefBase;
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  enum {
-    Options = _Options,
-    Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit
-  };
-
-  template<typename Derived> struct match {
-    enum {
-      StorageOrderMatch = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
-      MatchAtCompileTime = (Derived::Flags&CompressedAccessBit) && StorageOrderMatch
-    };
-    typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
-  };
-  
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-{
-  enum {
-    Flags = (traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
-  };
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<SparseVector<MatScalar,MatOptions,MatIndex> >
-{
-  typedef SparseVector<MatScalar,MatOptions,MatIndex> PlainObjectType;
-  enum {
-    Options = _Options,
-    Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit
-  };
-
-  template<typename Derived> struct match {
-    enum {
-      MatchAtCompileTime = (Derived::Flags&CompressedAccessBit) && Derived::IsVectorAtCompileTime
-    };
-    typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
-  };
-
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int _Options, typename _StrideType>
-struct traits<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-  : public traits<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, _Options, _StrideType> >
-{
-  enum {
-    Flags = (traits<SparseVector<MatScalar,MatOptions,MatIndex> >::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
-  };
-};
-
-template<typename Derived>
-struct traits<SparseRefBase<Derived> > : public traits<Derived> {};
-
-template<typename Derived> class SparseRefBase
-  : public SparseMapBase<Derived>
-{
-public:
-
-  typedef SparseMapBase<Derived> Base;
-  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseRefBase)
-
-  SparseRefBase()
-    : Base(RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime, 0, 0, 0, 0, 0)
-  {}
-  
-protected:
-
-  template<typename Expression>
-  void construct(Expression& expr)
-  {
-    if(expr.outerIndexPtr()==0)
-      ::new (static_cast<Base*>(this)) Base(expr.size(), expr.nonZeros(), expr.innerIndexPtr(), expr.valuePtr());
-    else
-      ::new (static_cast<Base*>(this)) Base(expr.rows(), expr.cols(), expr.nonZeros(), expr.outerIndexPtr(), expr.innerIndexPtr(), expr.valuePtr(), expr.innerNonZeroPtr());
-  }
-};
-
-} // namespace internal
-
-
-/** 
-  * \ingroup SparseCore_Module
-  *
-  * \brief A sparse matrix expression referencing an existing sparse expression
-  *
-  * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of class SparseMatrix.
-  * \tparam Options specifies whether the a standard compressed format is required \c Options is  \c #StandardCompressedFormat, or \c 0.
-  *                The default is \c 0.
-  *
-  * \sa class Ref
-  */
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType >
-  : public internal::SparseRefBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType > >
-#else
-template<typename SparseMatrixType, int Options>
-class Ref<SparseMatrixType, Options>
-  : public SparseMapBase<Derived,WriteAccessors> // yes, that's weird to use Derived here, but that works!
-#endif
-{
-    typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
-    typedef internal::traits<Ref> Traits;
-    template<int OtherOptions>
-    inline Ref(const SparseMatrix<MatScalar,OtherOptions,MatIndex>& expr);
-    template<int OtherOptions>
-    inline Ref(const MappedSparseMatrix<MatScalar,OtherOptions,MatIndex>& expr);
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<int OtherOptions>
-    inline Ref(SparseMatrix<MatScalar,OtherOptions,MatIndex>& expr)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseMatrix<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
-      Base::construct(expr.derived());
-    }
-    
-    template<int OtherOptions>
-    inline Ref(MappedSparseMatrix<MatScalar,OtherOptions,MatIndex>& expr)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseMatrix<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
-      Base::construct(expr.derived());
-    }
-    
-    template<typename Derived>
-    inline Ref(const SparseCompressedBase<Derived>& expr)
-    #else
-    /** Implicit constructor from any sparse expression (2D matrix or 1D vector) */
-    template<typename Derived>
-    inline Ref(SparseCompressedBase<Derived>& expr)
-    #endif
-    {
-      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
-      Base::construct(expr.const_cast_derived());
-    }
-};
-
-// this is the const ref version
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public internal::SparseRefBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-{
-    typedef SparseMatrix<MatScalar,MatOptions,MatIndex> TPlainObjectType;
-    typedef internal::traits<Ref> Traits;
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-    template<typename Derived>
-    inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false)
-    {
-      construct(expr.derived(), typename Traits::template match<Derived>::type());
-    }
-
-    inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
-      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
-    }
-
-    template<typename OtherRef>
-    inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
-      construct(other.derived(), typename Traits::template match<OtherRef>::type());
-    }
-
-    ~Ref() {
-      if(m_hasCopy) {
-        TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-        obj->~TPlainObjectType();
-      }
-    }
-
-  protected:
-
-    template<typename Expression>
-    void construct(const Expression& expr,internal::true_type)
-    {
-      if((Options & int(StandardCompressedFormat)) && (!expr.isCompressed()))
-      {
-        TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-        ::new (obj) TPlainObjectType(expr);
-        m_hasCopy = true;
-        Base::construct(*obj);
-      }
-      else
-      {
-        Base::construct(expr);
-      }
-    }
-
-    template<typename Expression>
-    void construct(const Expression& expr, internal::false_type)
-    {
-      TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-      ::new (obj) TPlainObjectType(expr);
-      m_hasCopy = true;
-      Base::construct(*obj);
-    }
-
-  protected:
-    typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
-    bool m_hasCopy;
-};
-
-
-
-/**
-  * \ingroup SparseCore_Module
-  *
-  * \brief A sparse vector expression referencing an existing sparse vector expression
-  *
-  * \tparam SparseVectorType the equivalent sparse vector type of the referenced data, it must be a template instance of class SparseVector.
-  *
-  * \sa class Ref
-  */
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType >
-  : public internal::SparseRefBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType > >
-#else
-template<typename SparseVectorType>
-class Ref<SparseVectorType>
-  : public SparseMapBase<Derived,WriteAccessors>
-#endif
-{
-    typedef SparseVector<MatScalar,MatOptions,MatIndex> PlainObjectType;
-    typedef internal::traits<Ref> Traits;
-    template<int OtherOptions>
-    inline Ref(const SparseVector<MatScalar,OtherOptions,MatIndex>& expr);
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<int OtherOptions>
-    inline Ref(SparseVector<MatScalar,OtherOptions,MatIndex>& expr)
-    {
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseVector<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      Base::construct(expr.derived());
-    }
-
-    template<typename Derived>
-    inline Ref(const SparseCompressedBase<Derived>& expr)
-    #else
-    /** Implicit constructor from any 1D sparse vector expression */
-    template<typename Derived>
-    inline Ref(SparseCompressedBase<Derived>& expr)
-    #endif
-    {
-      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
-      Base::construct(expr.const_cast_derived());
-    }
-};
-
-// this is the const ref version
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-class Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType>
-  : public internal::SparseRefBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-{
-    typedef SparseVector<MatScalar,MatOptions,MatIndex> TPlainObjectType;
-    typedef internal::traits<Ref> Traits;
-  public:
-
-    typedef internal::SparseRefBase<Ref> Base;
-    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
-
-    template<typename Derived>
-    inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false)
-    {
-      construct(expr.derived(), typename Traits::template match<Derived>::type());
-    }
-
-    inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
-      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
-    }
-
-    template<typename OtherRef>
-    inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
-      construct(other.derived(), typename Traits::template match<OtherRef>::type());
-    }
-
-    ~Ref() {
-      if(m_hasCopy) {
-        TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-        obj->~TPlainObjectType();
-      }
-    }
-
-  protected:
-
-    template<typename Expression>
-    void construct(const Expression& expr,internal::true_type)
-    {
-      Base::construct(expr);
-    }
-
-    template<typename Expression>
-    void construct(const Expression& expr, internal::false_type)
-    {
-      TPlainObjectType* obj = reinterpret_cast<TPlainObjectType*>(&m_storage);
-      ::new (obj) TPlainObjectType(expr);
-      m_hasCopy = true;
-      Base::construct(*obj);
-    }
-
-  protected:
-    typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
-    bool m_hasCopy;
-};
-
-namespace internal {
-
-// FIXME shall we introduce a general evaluatior_ref that we can specialize for any sparse object once, and thus remove this copy-pasta thing...
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
-struct evaluator<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
-  : evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
-{
-  typedef evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
-  typedef Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
-  evaluator() : Base() {}
-  explicit evaluator(const XprType &mat) : Base(mat) {}
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_REF_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
deleted file mode 100644
index 85b00e1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
+++ /dev/null
@@ -1,659 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_SELFADJOINTVIEW_H
-#define EIGEN_SPARSE_SELFADJOINTVIEW_H
-
-namespace Eigen { 
-  
-/** \ingroup SparseCore_Module
-  * \class SparseSelfAdjointView
-  *
-  * \brief Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix.
-  *
-  * \param MatrixType the type of the dense matrix storing the coefficients
-  * \param Mode can be either \c #Lower or \c #Upper
-  *
-  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
-  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
-  * and most of the time this is the only way that it is used.
-  *
-  * \sa SparseMatrixBase::selfadjointView()
-  */
-namespace internal {
-  
-template<typename MatrixType, unsigned int Mode>
-struct traits<SparseSelfAdjointView<MatrixType,Mode> > : traits<MatrixType> {
-};
-
-template<int SrcMode,int DstMode,typename MatrixType,int DestOrder>
-void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm = 0);
-
-template<int Mode,typename MatrixType,int DestOrder>
-void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm = 0);
-
-}
-
-template<typename MatrixType, unsigned int _Mode> class SparseSelfAdjointView
-  : public EigenBase<SparseSelfAdjointView<MatrixType,_Mode> >
-{
-  public:
-    
-    enum {
-      Mode = _Mode,
-      TransposeMode = ((Mode & Upper) ? Lower : 0) | ((Mode & Lower) ? Upper : 0),
-      RowsAtCompileTime = internal::traits<SparseSelfAdjointView>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<SparseSelfAdjointView>::ColsAtCompileTime
-    };
-
-    typedef EigenBase<SparseSelfAdjointView> Base;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
-    typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
-    
-    explicit inline SparseSelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
-    {
-      eigen_assert(rows()==cols() && "SelfAdjointView is only for squared matrices");
-    }
-
-    inline Index rows() const { return m_matrix.rows(); }
-    inline Index cols() const { return m_matrix.cols(); }
-
-    /** \internal \returns a reference to the nested matrix */
-    const _MatrixTypeNested& matrix() const { return m_matrix; }
-    typename internal::remove_reference<MatrixTypeNested>::type& matrix() { return m_matrix; }
-
-    /** \returns an expression of the matrix product between a sparse self-adjoint matrix \c *this and a sparse matrix \a rhs.
-      *
-      * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
-      * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
-      */
-    template<typename OtherDerived>
-    Product<SparseSelfAdjointView, OtherDerived>
-    operator*(const SparseMatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<SparseSelfAdjointView, OtherDerived>(*this, rhs.derived());
-    }
-
-    /** \returns an expression of the matrix product between a sparse matrix \a lhs and a sparse self-adjoint matrix \a rhs.
-      *
-      * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
-      * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
-      */
-    template<typename OtherDerived> friend
-    Product<OtherDerived, SparseSelfAdjointView>
-    operator*(const SparseMatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
-    {
-      return Product<OtherDerived, SparseSelfAdjointView>(lhs.derived(), rhs);
-    }
-    
-    /** Efficient sparse self-adjoint matrix times dense vector/matrix product */
-    template<typename OtherDerived>
-    Product<SparseSelfAdjointView,OtherDerived>
-    operator*(const MatrixBase<OtherDerived>& rhs) const
-    {
-      return Product<SparseSelfAdjointView,OtherDerived>(*this, rhs.derived());
-    }
-
-    /** Efficient dense vector/matrix times sparse self-adjoint matrix product */
-    template<typename OtherDerived> friend
-    Product<OtherDerived,SparseSelfAdjointView>
-    operator*(const MatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
-    {
-      return Product<OtherDerived,SparseSelfAdjointView>(lhs.derived(), rhs);
-    }
-
-    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
-      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
-      *
-      * \returns a reference to \c *this
-      *
-      * To perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
-      * call this function with u.adjoint().
-      */
-    template<typename DerivedU>
-    SparseSelfAdjointView& rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
-    
-    /** \returns an expression of P H P^-1 */
-    // TODO implement twists in a more evaluator friendly fashion
-    SparseSymmetricPermutationProduct<_MatrixTypeNested,Mode> twistedBy(const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm) const
-    {
-      return SparseSymmetricPermutationProduct<_MatrixTypeNested,Mode>(m_matrix, perm);
-    }
-
-    template<typename SrcMatrixType,int SrcMode>
-    SparseSelfAdjointView& operator=(const SparseSymmetricPermutationProduct<SrcMatrixType,SrcMode>& permutedMatrix)
-    {
-      internal::call_assignment_no_alias_no_transpose(*this, permutedMatrix);
-      return *this;
-    }
-
-    SparseSelfAdjointView& operator=(const SparseSelfAdjointView& src)
-    {
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> pnull;
-      return *this = src.twistedBy(pnull);
-    }
-
-    // Since we override the copy-assignment operator, we need to explicitly re-declare the copy-constructor
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(SparseSelfAdjointView)
-
-    template<typename SrcMatrixType,unsigned int SrcMode>
-    SparseSelfAdjointView& operator=(const SparseSelfAdjointView<SrcMatrixType,SrcMode>& src)
-    {
-      PermutationMatrix<Dynamic,Dynamic,StorageIndex> pnull;
-      return *this = src.twistedBy(pnull);
-    }
-    
-    void resize(Index rows, Index cols)
-    {
-      EIGEN_ONLY_USED_FOR_DEBUG(rows);
-      EIGEN_ONLY_USED_FOR_DEBUG(cols);
-      eigen_assert(rows == this->rows() && cols == this->cols()
-                && "SparseSelfadjointView::resize() does not actually allow to resize.");
-    }
-    
-  protected:
-
-    MatrixTypeNested m_matrix;
-    //mutable VectorI m_countPerRow;
-    //mutable VectorI m_countPerCol;
-  private:
-    template<typename Dest> void evalTo(Dest &) const;
-};
-
-/***************************************************************************
-* Implementation of SparseMatrixBase methods
-***************************************************************************/
-
-template<typename Derived>
-template<unsigned int UpLo>
-typename SparseMatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type SparseMatrixBase<Derived>::selfadjointView() const
-{
-  return SparseSelfAdjointView<const Derived, UpLo>(derived());
-}
-
-template<typename Derived>
-template<unsigned int UpLo>
-typename SparseMatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type SparseMatrixBase<Derived>::selfadjointView()
-{
-  return SparseSelfAdjointView<Derived, UpLo>(derived());
-}
-
-/***************************************************************************
-* Implementation of SparseSelfAdjointView methods
-***************************************************************************/
-
-template<typename MatrixType, unsigned int Mode>
-template<typename DerivedU>
-SparseSelfAdjointView<MatrixType,Mode>&
-SparseSelfAdjointView<MatrixType,Mode>::rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha)
-{
-  SparseMatrix<Scalar,(MatrixType::Flags&RowMajorBit)?RowMajor:ColMajor> tmp = u * u.adjoint();
-  if(alpha==Scalar(0))
-    m_matrix = tmp.template triangularView<Mode>();
-  else
-    m_matrix += alpha * tmp.template triangularView<Mode>();
-
-  return *this;
-}
-
-namespace internal {
-  
-// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
-//      in the future selfadjoint-ness should be defined by the expression traits
-//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
-template<typename MatrixType, unsigned int Mode>
-struct evaluator_traits<SparseSelfAdjointView<MatrixType,Mode> >
-{
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef SparseSelfAdjointShape Shape;
-};
-
-struct SparseSelfAdjoint2Sparse {};
-
-template<> struct AssignmentKind<SparseShape,SparseSelfAdjointShape> { typedef SparseSelfAdjoint2Sparse Kind; };
-template<> struct AssignmentKind<SparseSelfAdjointShape,SparseShape> { typedef Sparse2Sparse Kind; };
-
-template< typename DstXprType, typename SrcXprType, typename Functor>
-struct Assignment<DstXprType, SrcXprType, Functor, SparseSelfAdjoint2Sparse>
-{
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  typedef internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> AssignOpType;
-
-  template<typename DestScalar,int StorageOrder>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignOpType&/*func*/)
-  {
-    internal::permute_symm_to_fullsymm<SrcXprType::Mode>(src.matrix(), dst);
-  }
-
-  // FIXME: the handling of += and -= in sparse matrices should be cleanup so that next two overloads could be reduced to:
-  template<typename DestScalar,int StorageOrder,typename AssignFunc>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignFunc& func)
-  {
-    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
-    run(tmp, src, AssignOpType());
-    call_assignment_no_alias_no_transpose(dst, tmp, func);
-  }
-
-  template<typename DestScalar,int StorageOrder>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
-                  const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
-  {
-    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
-    run(tmp, src, AssignOpType());
-    dst += tmp;
-  }
-
-  template<typename DestScalar,int StorageOrder>
-  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
-                  const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
-  {
-    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
-    run(tmp, src, AssignOpType());
-    dst -= tmp;
-  }
-  
-  template<typename DestScalar>
-  static void run(DynamicSparseMatrix<DestScalar,ColMajor,StorageIndex>& dst, const SrcXprType &src, const AssignOpType&/*func*/)
-  {
-    // TODO directly evaluate into dst;
-    SparseMatrix<DestScalar,ColMajor,StorageIndex> tmp(dst.rows(),dst.cols());
-    internal::permute_symm_to_fullsymm<SrcXprType::Mode>(src.matrix(), tmp);
-    dst = tmp;
-  }
-};
-
-} // end namespace internal
-
-/***************************************************************************
-* Implementation of sparse self-adjoint time dense matrix
-***************************************************************************/
-
-namespace internal {
-
-template<int Mode, typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
-inline void sparse_selfadjoint_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
-{
-  EIGEN_ONLY_USED_FOR_DEBUG(alpha);
-  
-  typedef typename internal::nested_eval<SparseLhsType,DenseRhsType::MaxColsAtCompileTime>::type SparseLhsTypeNested;
-  typedef typename internal::remove_all<SparseLhsTypeNested>::type SparseLhsTypeNestedCleaned;
-  typedef evaluator<SparseLhsTypeNestedCleaned> LhsEval;
-  typedef typename LhsEval::InnerIterator LhsIterator;
-  typedef typename SparseLhsType::Scalar LhsScalar;
-  
-  enum {
-    LhsIsRowMajor = (LhsEval::Flags&RowMajorBit)==RowMajorBit,
-    ProcessFirstHalf =
-              ((Mode&(Upper|Lower))==(Upper|Lower))
-          || ( (Mode&Upper) && !LhsIsRowMajor)
-          || ( (Mode&Lower) && LhsIsRowMajor),
-    ProcessSecondHalf = !ProcessFirstHalf
-  };
-  
-  SparseLhsTypeNested lhs_nested(lhs);
-  LhsEval lhsEval(lhs_nested);
-
-  // work on one column at once
-  for (Index k=0; k<rhs.cols(); ++k)
-  {
-    for (Index j=0; j<lhs.outerSize(); ++j)
-    {
-      LhsIterator i(lhsEval,j);
-      // handle diagonal coeff
-      if (ProcessSecondHalf)
-      {
-        while (i && i.index()<j) ++i;
-        if(i && i.index()==j)
-        {
-          res.coeffRef(j,k) += alpha * i.value() * rhs.coeff(j,k);
-          ++i;
-        }
-      }
-
-      // premultiplied rhs for scatters
-      typename ScalarBinaryOpTraits<AlphaType, typename DenseRhsType::Scalar>::ReturnType rhs_j(alpha*rhs(j,k));
-      // accumulator for partial scalar product
-      typename DenseResType::Scalar res_j(0);
-      for(; (ProcessFirstHalf ? i && i.index() < j : i) ; ++i)
-      {
-        LhsScalar lhs_ij = i.value();
-        if(!LhsIsRowMajor) lhs_ij = numext::conj(lhs_ij);
-        res_j += lhs_ij * rhs.coeff(i.index(),k);
-        res(i.index(),k) += numext::conj(lhs_ij) * rhs_j;
-      }
-      res.coeffRef(j,k) += alpha * res_j;
-
-      // handle diagonal coeff
-      if (ProcessFirstHalf && i && (i.index()==j))
-        res.coeffRef(j,k) += alpha * i.value() * rhs.coeff(j,k);
-    }
-  }
-}
-
-
-template<typename LhsView, typename Rhs, int ProductType>
-struct generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType>
-: generic_product_impl_base<LhsView, Rhs, generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType> >
-{
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const LhsView& lhsView, const Rhs& rhs, const typename Dest::Scalar& alpha)
-  {
-    typedef typename LhsView::_MatrixTypeNested Lhs;
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhsView.matrix());
-    RhsNested rhsNested(rhs);
-    
-    internal::sparse_selfadjoint_time_dense_product<LhsView::Mode>(lhsNested, rhsNested, dst, alpha);
-  }
-};
-
-template<typename Lhs, typename RhsView, int ProductType>
-struct generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType>
-: generic_product_impl_base<Lhs, RhsView, generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType> >
-{
-  template<typename Dest>
-  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const RhsView& rhsView, const typename Dest::Scalar& alpha)
-  {
-    typedef typename RhsView::_MatrixTypeNested Rhs;
-    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
-    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
-    LhsNested lhsNested(lhs);
-    RhsNested rhsNested(rhsView.matrix());
-    
-    // transpose everything
-    Transpose<Dest> dstT(dst);
-    internal::sparse_selfadjoint_time_dense_product<RhsView::TransposeMode>(rhsNested.transpose(), lhsNested.transpose(), dstT, alpha);
-  }
-};
-
-// NOTE: these two overloads are needed to evaluate the sparse selfadjoint view into a full sparse matrix
-// TODO: maybe the copy could be handled by generic_product_impl so that these overloads would not be needed anymore
-
-template<typename LhsView, typename Rhs, int ProductTag>
-struct product_evaluator<Product<LhsView, Rhs, DefaultProduct>, ProductTag, SparseSelfAdjointShape, SparseShape>
-  : public evaluator<typename Product<typename Rhs::PlainObject, Rhs, DefaultProduct>::PlainObject>
-{
-  typedef Product<LhsView, Rhs, DefaultProduct> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  product_evaluator(const XprType& xpr)
-    : m_lhs(xpr.lhs()), m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<typename Rhs::PlainObject, Rhs, SparseShape, SparseShape, ProductTag>::evalTo(m_result, m_lhs, xpr.rhs());
-  }
-  
-protected:
-  typename Rhs::PlainObject m_lhs;
-  PlainObject m_result;
-};
-
-template<typename Lhs, typename RhsView, int ProductTag>
-struct product_evaluator<Product<Lhs, RhsView, DefaultProduct>, ProductTag, SparseShape, SparseSelfAdjointShape>
-  : public evaluator<typename Product<Lhs, typename Lhs::PlainObject, DefaultProduct>::PlainObject>
-{
-  typedef Product<Lhs, RhsView, DefaultProduct> XprType;
-  typedef typename XprType::PlainObject PlainObject;
-  typedef evaluator<PlainObject> Base;
-
-  product_evaluator(const XprType& xpr)
-    : m_rhs(xpr.rhs()), m_result(xpr.rows(), xpr.cols())
-  {
-    ::new (static_cast<Base*>(this)) Base(m_result);
-    generic_product_impl<Lhs, typename Lhs::PlainObject, SparseShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), m_rhs);
-  }
-  
-protected:
-  typename Lhs::PlainObject m_rhs;
-  PlainObject m_result;
-};
-
-} // namespace internal
-
-/***************************************************************************
-* Implementation of symmetric copies and permutations
-***************************************************************************/
-namespace internal {
-
-template<int Mode,typename MatrixType,int DestOrder>
-void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm)
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef SparseMatrix<Scalar,DestOrder,StorageIndex> Dest;
-  typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-  typedef evaluator<MatrixType> MatEval;
-  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
-  
-  MatEval matEval(mat);
-  Dest& dest(_dest.derived());
-  enum {
-    StorageOrderMatch = int(Dest::IsRowMajor) == int(MatrixType::IsRowMajor)
-  };
-  
-  Index size = mat.rows();
-  VectorI count;
-  count.resize(size);
-  count.setZero();
-  dest.resize(size,size);
-  for(Index j = 0; j<size; ++j)
-  {
-    Index jp = perm ? perm[j] : j;
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      Index i = it.index();
-      Index r = it.row();
-      Index c = it.col();
-      Index ip = perm ? perm[i] : i;
-      if(Mode==int(Upper|Lower))
-        count[StorageOrderMatch ? jp : ip]++;
-      else if(r==c)
-        count[ip]++;
-      else if(( Mode==Lower && r>c) || ( Mode==Upper && r<c))
-      {
-        count[ip]++;
-        count[jp]++;
-      }
-    }
-  }
-  Index nnz = count.sum();
-  
-  // reserve space
-  dest.resizeNonZeros(nnz);
-  dest.outerIndexPtr()[0] = 0;
-  for(Index j=0; j<size; ++j)
-    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
-  for(Index j=0; j<size; ++j)
-    count[j] = dest.outerIndexPtr()[j];
-  
-  // copy data
-  for(StorageIndex j = 0; j<size; ++j)
-  {
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      StorageIndex i = internal::convert_index<StorageIndex>(it.index());
-      Index r = it.row();
-      Index c = it.col();
-      
-      StorageIndex jp = perm ? perm[j] : j;
-      StorageIndex ip = perm ? perm[i] : i;
-      
-      if(Mode==int(Upper|Lower))
-      {
-        Index k = count[StorageOrderMatch ? jp : ip]++;
-        dest.innerIndexPtr()[k] = StorageOrderMatch ? ip : jp;
-        dest.valuePtr()[k] = it.value();
-      }
-      else if(r==c)
-      {
-        Index k = count[ip]++;
-        dest.innerIndexPtr()[k] = ip;
-        dest.valuePtr()[k] = it.value();
-      }
-      else if(( (Mode&Lower)==Lower && r>c) || ( (Mode&Upper)==Upper && r<c))
-      {
-        if(!StorageOrderMatch)
-          std::swap(ip,jp);
-        Index k = count[jp]++;
-        dest.innerIndexPtr()[k] = ip;
-        dest.valuePtr()[k] = it.value();
-        k = count[ip]++;
-        dest.innerIndexPtr()[k] = jp;
-        dest.valuePtr()[k] = numext::conj(it.value());
-      }
-    }
-  }
-}
-
-template<int _SrcMode,int _DstMode,typename MatrixType,int DstOrder>
-void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DstOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm)
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef typename MatrixType::Scalar Scalar;
-  SparseMatrix<Scalar,DstOrder,StorageIndex>& dest(_dest.derived());
-  typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-  typedef evaluator<MatrixType> MatEval;
-  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
-
-  enum {
-    SrcOrder = MatrixType::IsRowMajor ? RowMajor : ColMajor,
-    StorageOrderMatch = int(SrcOrder) == int(DstOrder),
-    DstMode = DstOrder==RowMajor ? (_DstMode==Upper ? Lower : Upper) : _DstMode,
-    SrcMode = SrcOrder==RowMajor ? (_SrcMode==Upper ? Lower : Upper) : _SrcMode
-  };
-
-  MatEval matEval(mat);
-  
-  Index size = mat.rows();
-  VectorI count(size);
-  count.setZero();
-  dest.resize(size,size);
-  for(StorageIndex j = 0; j<size; ++j)
-  {
-    StorageIndex jp = perm ? perm[j] : j;
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if((int(SrcMode)==int(Lower) && i<j) || (int(SrcMode)==int(Upper) && i>j))
-        continue;
-                  
-      StorageIndex ip = perm ? perm[i] : i;
-      count[int(DstMode)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
-    }
-  }
-  dest.outerIndexPtr()[0] = 0;
-  for(Index j=0; j<size; ++j)
-    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
-  dest.resizeNonZeros(dest.outerIndexPtr()[size]);
-  for(Index j=0; j<size; ++j)
-    count[j] = dest.outerIndexPtr()[j];
-  
-  for(StorageIndex j = 0; j<size; ++j)
-  {
-    
-    for(MatIterator it(matEval,j); it; ++it)
-    {
-      StorageIndex i = it.index();
-      if((int(SrcMode)==int(Lower) && i<j) || (int(SrcMode)==int(Upper) && i>j))
-        continue;
-                  
-      StorageIndex jp = perm ? perm[j] : j;
-      StorageIndex ip = perm? perm[i] : i;
-      
-      Index k = count[int(DstMode)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
-      dest.innerIndexPtr()[k] = int(DstMode)==int(Lower) ? (std::max)(ip,jp) : (std::min)(ip,jp);
-      
-      if(!StorageOrderMatch) std::swap(ip,jp);
-      if( ((int(DstMode)==int(Lower) && ip<jp) || (int(DstMode)==int(Upper) && ip>jp)))
-        dest.valuePtr()[k] = numext::conj(it.value());
-      else
-        dest.valuePtr()[k] = it.value();
-    }
-  }
-}
-
-}
-
-// TODO implement twists in a more evaluator friendly fashion
-
-namespace internal {
-
-template<typename MatrixType, int Mode>
-struct traits<SparseSymmetricPermutationProduct<MatrixType,Mode> > : traits<MatrixType> {
-};
-
-}
-
-template<typename MatrixType,int Mode>
-class SparseSymmetricPermutationProduct
-  : public EigenBase<SparseSymmetricPermutationProduct<MatrixType,Mode> >
-{
-  public:
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    enum {
-      RowsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::RowsAtCompileTime,
-      ColsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::ColsAtCompileTime
-    };
-  protected:
-    typedef PermutationMatrix<Dynamic,Dynamic,StorageIndex> Perm;
-  public:
-    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
-    typedef typename MatrixType::Nested MatrixTypeNested;
-    typedef typename internal::remove_all<MatrixTypeNested>::type NestedExpression;
-    
-    SparseSymmetricPermutationProduct(const MatrixType& mat, const Perm& perm)
-      : m_matrix(mat), m_perm(perm)
-    {}
-    
-    inline Index rows() const { return m_matrix.rows(); }
-    inline Index cols() const { return m_matrix.cols(); }
-        
-    const NestedExpression& matrix() const { return m_matrix; }
-    const Perm& perm() const { return m_perm; }
-    
-  protected:
-    MatrixTypeNested m_matrix;
-    const Perm& m_perm;
-
-};
-
-namespace internal {
-  
-template<typename DstXprType, typename MatrixType, int Mode, typename Scalar>
-struct Assignment<DstXprType, SparseSymmetricPermutationProduct<MatrixType,Mode>, internal::assign_op<Scalar,typename MatrixType::Scalar>, Sparse2Sparse>
-{
-  typedef SparseSymmetricPermutationProduct<MatrixType,Mode> SrcXprType;
-  typedef typename DstXprType::StorageIndex DstIndex;
-  template<int Options>
-  static void run(SparseMatrix<Scalar,Options,DstIndex> &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
-  {
-    // internal::permute_symm_to_fullsymm<Mode>(m_matrix,_dest,m_perm.indices().data());
-    SparseMatrix<Scalar,(Options&RowMajor)==RowMajor ? ColMajor : RowMajor, DstIndex> tmp;
-    internal::permute_symm_to_fullsymm<Mode>(src.matrix(),tmp,src.perm().indices().data());
-    dst = tmp;
-  }
-  
-  template<typename DestType,unsigned int DestMode>
-  static void run(SparseSelfAdjointView<DestType,DestMode>& dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
-  {
-    internal::permute_symm_to_symm<Mode,DestMode>(src.matrix(),dst.matrix(),src.perm().indices().data());
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h
deleted file mode 100644
index b4c9a42..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h
+++ /dev/null
@@ -1,124 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSESOLVERBASE_H
-#define EIGEN_SPARSESOLVERBASE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-  /** \internal
-  * Helper functions to solve with a sparse right-hand-side and result.
-  * The rhs is decomposed into small vertical panels which are solved through dense temporaries.
-  */
-template<typename Decomposition, typename Rhs, typename Dest>
-typename enable_if<Rhs::ColsAtCompileTime!=1 && Dest::ColsAtCompileTime!=1>::type
-solve_sparse_through_dense_panels(const Decomposition &dec, const Rhs& rhs, Dest &dest)
-{
-  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  typedef typename Dest::Scalar DestScalar;
-  // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
-  static const Index NbColsAtOnce = 4;
-  Index rhsCols = rhs.cols();
-  Index size = rhs.rows();
-  // the temporary matrices do not need more columns than NbColsAtOnce:
-  Index tmpCols = (std::min)(rhsCols, NbColsAtOnce); 
-  Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmp(size,tmpCols);
-  Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmpX(size,tmpCols);
-  for(Index k=0; k<rhsCols; k+=NbColsAtOnce)
-  {
-    Index actualCols = std::min<Index>(rhsCols-k, NbColsAtOnce);
-    tmp.leftCols(actualCols) = rhs.middleCols(k,actualCols);
-    tmpX.leftCols(actualCols) = dec.solve(tmp.leftCols(actualCols));
-    dest.middleCols(k,actualCols) = tmpX.leftCols(actualCols).sparseView();
-  }
-}
-
-// Overload for vector as rhs
-template<typename Decomposition, typename Rhs, typename Dest>
-typename enable_if<Rhs::ColsAtCompileTime==1 || Dest::ColsAtCompileTime==1>::type
-solve_sparse_through_dense_panels(const Decomposition &dec, const Rhs& rhs, Dest &dest)
-{
-  typedef typename Dest::Scalar DestScalar;
-  Index size = rhs.rows();
-  Eigen::Matrix<DestScalar,Dynamic,1> rhs_dense(rhs);
-  Eigen::Matrix<DestScalar,Dynamic,1> dest_dense(size);
-  dest_dense = dec.solve(rhs_dense);
-  dest = dest_dense.sparseView();
-}
-
-} // end namespace internal
-
-/** \class SparseSolverBase
-  * \ingroup SparseCore_Module
-  * \brief A base class for sparse solvers
-  *
-  * \tparam Derived the actual type of the solver.
-  *
-  */
-template<typename Derived>
-class SparseSolverBase : internal::noncopyable
-{
-  public:
-
-    /** Default constructor */
-    SparseSolverBase()
-      : m_isInitialized(false)
-    {}
-
-    ~SparseSolverBase()
-    {}
-
-    Derived& derived() { return *static_cast<Derived*>(this); }
-    const Derived& derived() const { return *static_cast<const Derived*>(this); }
-    
-    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<Derived, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "Solver is not initialized.");
-      eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<Derived, Rhs>(derived(), b.derived());
-    }
-    
-    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<Derived, Rhs>
-    solve(const SparseMatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "Solver is not initialized.");
-      eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
-      return Solve<Derived, Rhs>(derived(), b.derived());
-    }
-    
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal default implementation of solving with a sparse rhs */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
-    {
-      internal::solve_sparse_through_dense_panels(derived(), b.derived(), dest.derived());
-    }
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-
-  protected:
-    
-    mutable bool m_isInitialized;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSESOLVERBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
deleted file mode 100644
index 88820a4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
+++ /dev/null
@@ -1,198 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
-#define EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
-
-namespace Eigen { 
-
-namespace internal {
-
-
-// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
-template<typename Lhs, typename Rhs, typename ResultType>
-static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, const typename ResultType::RealScalar& tolerance)
-{
-  // return sparse_sparse_product_with_pruning_impl2(lhs,rhs,res);
-
-  typedef typename remove_all<Rhs>::type::Scalar RhsScalar;
-  typedef typename remove_all<ResultType>::type::Scalar ResScalar;
-  typedef typename remove_all<Lhs>::type::StorageIndex StorageIndex;
-
-  // make sure to call innerSize/outerSize since we fake the storage order.
-  Index rows = lhs.innerSize();
-  Index cols = rhs.outerSize();
-  //Index size = lhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  // allocate a temporary buffer
-  AmbiVector<ResScalar,StorageIndex> tempVector(rows);
-
-  // mimics a resizeByInnerOuter:
-  if(ResultType::IsRowMajor)
-    res.resize(cols, rows);
-  else
-    res.resize(rows, cols);
-  
-  evaluator<Lhs> lhsEval(lhs);
-  evaluator<Rhs> rhsEval(rhs);
-  
-  // estimate the number of non zero entries
-  // given a rhs column containing Y non zeros, we assume that the respective Y columns
-  // of the lhs differs in average of one non zeros, thus the number of non zeros for
-  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
-  // per column of the lhs.
-  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
-  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
-
-  res.reserve(estimated_nnz_prod);
-  double ratioColRes = double(estimated_nnz_prod)/(double(lhs.rows())*double(rhs.cols()));
-  for (Index j=0; j<cols; ++j)
-  {
-    // FIXME:
-    //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows());
-    // let's do a more accurate determination of the nnz ratio for the current column j of res
-    tempVector.init(ratioColRes);
-    tempVector.setZero();
-    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
-    {
-      // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
-      tempVector.restart();
-      RhsScalar x = rhsIt.value();
-      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, rhsIt.index()); lhsIt; ++lhsIt)
-      {
-        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
-      }
-    }
-    res.startVec(j);
-    for (typename AmbiVector<ResScalar,StorageIndex>::Iterator it(tempVector,tolerance); it; ++it)
-      res.insertBackByOuterInner(j,it.index()) = it.value();
-  }
-  res.finalize();
-}
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
-  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
-  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
-struct sparse_sparse_product_with_pruning_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance);
-    res.swap(_res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    // we need a col-major matrix to hold the result
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> SparseTemporaryType;
-    SparseTemporaryType _res(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance);
-    res = _res;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    // let's transpose the product to get a column x column product
-    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance);
-    res.swap(_res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
-    ColMajorMatrixLhs colLhs(lhs);
-    ColMajorMatrixRhs colRhs(rhs);
-    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,ColMajorMatrixRhs,ResultType>(colLhs, colRhs, res, tolerance);
-
-    // let's transpose the product to get a column x column product
-//     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
-//     SparseTemporaryType _res(res.cols(), res.rows());
-//     sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
-//     res = _res.transpose();
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixLhs;
-    RowMajorMatrixLhs rowLhs(lhs);
-    sparse_sparse_product_with_pruning_selector<RowMajorMatrixLhs,Rhs,ResultType,RowMajor,RowMajor>(rowLhs,rhs,res,tolerance);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixRhs;
-    RowMajorMatrixRhs rowRhs(rhs);
-    sparse_sparse_product_with_pruning_selector<Lhs,RowMajorMatrixRhs,ResultType,RowMajor,RowMajor,RowMajor>(lhs,rowRhs,res,tolerance);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
-    ColMajorMatrixRhs colRhs(rhs);
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,ColMajorMatrixRhs,ResultType>(lhs, colRhs, res, tolerance);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
-{
-  typedef typename ResultType::RealScalar RealScalar;
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
-  {
-    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
-    ColMajorMatrixLhs colLhs(lhs);
-    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,Rhs,ResultType>(colLhs, rhs, res, tolerance);
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h
deleted file mode 100644
index 3757d4c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h
+++ /dev/null
@@ -1,92 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSETRANSPOSE_H
-#define EIGEN_SPARSETRANSPOSE_H
-
-namespace Eigen { 
-
-namespace internal {
-  template<typename MatrixType,int CompressedAccess=int(MatrixType::Flags&CompressedAccessBit)>
-  class SparseTransposeImpl
-    : public SparseMatrixBase<Transpose<MatrixType> >
-  {};
-  
-  template<typename MatrixType>
-  class SparseTransposeImpl<MatrixType,CompressedAccessBit>
-    : public SparseCompressedBase<Transpose<MatrixType> >
-  {
-    typedef SparseCompressedBase<Transpose<MatrixType> > Base;
-  public:
-    using Base::derived;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::StorageIndex StorageIndex;
-
-    inline Index nonZeros() const { return derived().nestedExpression().nonZeros(); }
-    
-    inline const Scalar* valuePtr() const { return derived().nestedExpression().valuePtr(); }
-    inline const StorageIndex* innerIndexPtr() const { return derived().nestedExpression().innerIndexPtr(); }
-    inline const StorageIndex* outerIndexPtr() const { return derived().nestedExpression().outerIndexPtr(); }
-    inline const StorageIndex* innerNonZeroPtr() const { return derived().nestedExpression().innerNonZeroPtr(); }
-
-    inline Scalar* valuePtr() { return derived().nestedExpression().valuePtr(); }
-    inline StorageIndex* innerIndexPtr() { return derived().nestedExpression().innerIndexPtr(); }
-    inline StorageIndex* outerIndexPtr() { return derived().nestedExpression().outerIndexPtr(); }
-    inline StorageIndex* innerNonZeroPtr() { return derived().nestedExpression().innerNonZeroPtr(); }
-  };
-}
-  
-template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>
-  : public internal::SparseTransposeImpl<MatrixType>
-{
-  protected:
-    typedef internal::SparseTransposeImpl<MatrixType> Base;
-};
-
-namespace internal {
-  
-template<typename ArgType>
-struct unary_evaluator<Transpose<ArgType>, IteratorBased>
-  : public evaluator_base<Transpose<ArgType> >
-{
-    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
-  public:
-    typedef Transpose<ArgType> XprType;
-    
-    inline Index nonZerosEstimate() const {
-      return m_argImpl.nonZerosEstimate();
-    }
-
-    class InnerIterator : public EvalIterator
-    {
-    public:
-      EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
-        : EvalIterator(unaryOp.m_argImpl,outer)
-      {}
-      
-      Index row() const { return EvalIterator::col(); }
-      Index col() const { return EvalIterator::row(); }
-    };
-    
-    enum {
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& op) :m_argImpl(op.nestedExpression()) {}
-
-  protected:
-    evaluator<ArgType> m_argImpl;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSETRANSPOSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h
deleted file mode 100644
index 9ac1202..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h
+++ /dev/null
@@ -1,189 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_TRIANGULARVIEW_H
-#define EIGEN_SPARSE_TRIANGULARVIEW_H
-
-namespace Eigen {
-
-/** \ingroup SparseCore_Module
-  *
-  * \brief Base class for a triangular part in a \b sparse matrix
-  *
-  * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be instantiated.
-  * It extends class TriangularView with additional methods which are available for sparse expressions only.
-  *
-  * \sa class TriangularView, SparseMatrixBase::triangularView()
-  */
-template<typename MatrixType, unsigned int Mode> class TriangularViewImpl<MatrixType,Mode,Sparse>
-  : public SparseMatrixBase<TriangularView<MatrixType,Mode> >
-{
-    enum { SkipFirst = ((Mode&Lower) && !(MatrixType::Flags&RowMajorBit))
-                    || ((Mode&Upper) &&  (MatrixType::Flags&RowMajorBit)),
-           SkipLast = !SkipFirst,
-           SkipDiag = (Mode&ZeroDiag) ? 1 : 0,
-           HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
-    };
-    
-    typedef TriangularView<MatrixType,Mode> TriangularViewType;
-    
-  protected:
-    // dummy solve function to make TriangularView happy.
-    void solve() const;
-
-    typedef SparseMatrixBase<TriangularViewType> Base;
-  public:
-    
-    EIGEN_SPARSE_PUBLIC_INTERFACE(TriangularViewType)
-    
-    typedef typename MatrixType::Nested MatrixTypeNested;
-    typedef typename internal::remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef;
-    typedef typename internal::remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
-
-    template<typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE void _solve_impl(const RhsType &rhs, DstType &dst) const {
-      if(!(internal::is_same<RhsType,DstType>::value && internal::extract_data(dst) == internal::extract_data(rhs)))
-        dst = rhs;
-      this->solveInPlace(dst);
-    }
-
-    /** Applies the inverse of \c *this to the dense vector or matrix \a other, "in-place" */
-    template<typename OtherDerived> void solveInPlace(MatrixBase<OtherDerived>& other) const;
-
-    /** Applies the inverse of \c *this to the sparse vector or matrix \a other, "in-place" */
-    template<typename OtherDerived> void solveInPlace(SparseMatrixBase<OtherDerived>& other) const;
-  
-};
-
-namespace internal {
-
-template<typename ArgType, unsigned int Mode>
-struct unary_evaluator<TriangularView<ArgType,Mode>, IteratorBased>
- : evaluator_base<TriangularView<ArgType,Mode> >
-{
-  typedef TriangularView<ArgType,Mode> XprType;
-  
-protected:
-  
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::StorageIndex StorageIndex;
-  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
-  
-  enum { SkipFirst = ((Mode&Lower) && !(ArgType::Flags&RowMajorBit))
-                    || ((Mode&Upper) &&  (ArgType::Flags&RowMajorBit)),
-         SkipLast = !SkipFirst,
-         SkipDiag = (Mode&ZeroDiag) ? 1 : 0,
-         HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
-  };
-  
-public:
-  
-  enum {
-    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-    Flags = XprType::Flags
-  };
-    
-  explicit unary_evaluator(const XprType &xpr) : m_argImpl(xpr.nestedExpression()), m_arg(xpr.nestedExpression()) {}
-  
-  inline Index nonZerosEstimate() const {
-    return m_argImpl.nonZerosEstimate();
-  }
-  
-  class InnerIterator : public EvalIterator
-  {
-      typedef EvalIterator Base;
-    public:
-
-      EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& xprEval, Index outer)
-        : Base(xprEval.m_argImpl,outer), m_returnOne(false), m_containsDiag(Base::outer()<xprEval.m_arg.innerSize())
-      {
-        if(SkipFirst)
-        {
-          while((*this) && ((HasUnitDiag||SkipDiag)  ? this->index()<=outer : this->index()<outer))
-            Base::operator++();
-          if(HasUnitDiag)
-            m_returnOne = m_containsDiag;
-        }
-        else if(HasUnitDiag && ((!Base::operator bool()) || Base::index()>=Base::outer()))
-        {
-          if((!SkipFirst) && Base::operator bool())
-            Base::operator++();
-          m_returnOne = m_containsDiag;
-        }
-      }
-
-      EIGEN_STRONG_INLINE InnerIterator& operator++()
-      {
-        if(HasUnitDiag && m_returnOne)
-          m_returnOne = false;
-        else
-        {
-          Base::operator++();
-          if(HasUnitDiag && (!SkipFirst) && ((!Base::operator bool()) || Base::index()>=Base::outer()))
-          {
-            if((!SkipFirst) && Base::operator bool())
-              Base::operator++();
-            m_returnOne = m_containsDiag;
-          }
-        }
-        return *this;
-      }
-      
-      EIGEN_STRONG_INLINE operator bool() const
-      {
-        if(HasUnitDiag && m_returnOne)
-          return true;
-        if(SkipFirst) return  Base::operator bool();
-        else
-        {
-          if (SkipDiag) return (Base::operator bool() && this->index() < this->outer());
-          else return (Base::operator bool() && this->index() <= this->outer());
-        }
-      }
-
-//       inline Index row() const { return (ArgType::Flags&RowMajorBit ? Base::outer() : this->index()); }
-//       inline Index col() const { return (ArgType::Flags&RowMajorBit ? this->index() : Base::outer()); }
-      inline StorageIndex index() const
-      {
-        if(HasUnitDiag && m_returnOne)  return internal::convert_index<StorageIndex>(Base::outer());
-        else                            return Base::index();
-      }
-      inline Scalar value() const
-      {
-        if(HasUnitDiag && m_returnOne)  return Scalar(1);
-        else                            return Base::value();
-      }
-
-    protected:
-      bool m_returnOne;
-      bool m_containsDiag;
-    private:
-      Scalar& valueRef();
-  };
-  
-protected:
-  evaluator<ArgType> m_argImpl;
-  const ArgType& m_arg;
-};
-
-} // end namespace internal
-
-template<typename Derived>
-template<int Mode>
-inline const TriangularView<const Derived, Mode>
-SparseMatrixBase<Derived>::triangularView() const
-{
-  return TriangularView<const Derived, Mode>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_TRIANGULARVIEW_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseUtil.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseUtil.h
deleted file mode 100644
index ceb9368..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseUtil.h
+++ /dev/null
@@ -1,186 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEUTIL_H
-#define EIGEN_SPARSEUTIL_H
-
-namespace Eigen { 
-
-#ifdef NDEBUG
-#define EIGEN_DBG_SPARSE(X)
-#else
-#define EIGEN_DBG_SPARSE(X) X
-#endif
-
-#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
-template<typename OtherDerived> \
-EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SparseMatrixBase<OtherDerived>& other) \
-{ \
-  return Base::operator Op(other.derived()); \
-} \
-EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
-{ \
-  return Base::operator Op(other); \
-}
-
-#define EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
-template<typename Other> \
-EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
-{ \
-  return Base::operator Op(scalar); \
-}
-
-#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
-EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =)
-
-
-#define EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) \
-  EIGEN_GENERIC_PUBLIC_INTERFACE(Derived)
-
-  
-const int CoherentAccessPattern     = 0x1;
-const int InnerRandomAccessPattern  = 0x2 | CoherentAccessPattern;
-const int OuterRandomAccessPattern  = 0x4 | CoherentAccessPattern;
-const int RandomAccessPattern       = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern;
-
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class SparseMatrix;
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class DynamicSparseMatrix;
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class SparseVector;
-template<typename _Scalar, int _Flags = 0, typename _StorageIndex = int>  class MappedSparseMatrix;
-
-template<typename MatrixType, unsigned int UpLo>  class SparseSelfAdjointView;
-template<typename Lhs, typename Rhs>              class SparseDiagonalProduct;
-template<typename MatrixType> class SparseView;
-
-template<typename Lhs, typename Rhs>        class SparseSparseProduct;
-template<typename Lhs, typename Rhs>        class SparseTimeDenseProduct;
-template<typename Lhs, typename Rhs>        class DenseTimeSparseProduct;
-template<typename Lhs, typename Rhs, bool Transpose> class SparseDenseOuterProduct;
-
-template<typename Lhs, typename Rhs> struct SparseSparseProductReturnType;
-template<typename Lhs, typename Rhs,
-         int InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(internal::traits<Lhs>::ColsAtCompileTime,internal::traits<Rhs>::RowsAtCompileTime)> struct DenseSparseProductReturnType;
-         
-template<typename Lhs, typename Rhs,
-         int InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(internal::traits<Lhs>::ColsAtCompileTime,internal::traits<Rhs>::RowsAtCompileTime)> struct SparseDenseProductReturnType;
-template<typename MatrixType,int UpLo> class SparseSymmetricPermutationProduct;
-
-namespace internal {
-
-template<typename T,int Rows,int Cols,int Flags> struct sparse_eval;
-
-template<typename T> struct eval<T,Sparse>
-  : sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime,traits<T>::Flags>
-{};
-
-template<typename T,int Cols,int Flags> struct sparse_eval<T,1,Cols,Flags> {
-    typedef typename traits<T>::Scalar _Scalar;
-    typedef typename traits<T>::StorageIndex _StorageIndex;
-  public:
-    typedef SparseVector<_Scalar, RowMajor, _StorageIndex> type;
-};
-
-template<typename T,int Rows,int Flags> struct sparse_eval<T,Rows,1,Flags> {
-    typedef typename traits<T>::Scalar _Scalar;
-    typedef typename traits<T>::StorageIndex _StorageIndex;
-  public:
-    typedef SparseVector<_Scalar, ColMajor, _StorageIndex> type;
-};
-
-// TODO this seems almost identical to plain_matrix_type<T, Sparse>
-template<typename T,int Rows,int Cols,int Flags> struct sparse_eval {
-    typedef typename traits<T>::Scalar _Scalar;
-    typedef typename traits<T>::StorageIndex _StorageIndex;
-    enum { _Options = ((Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor };
-  public:
-    typedef SparseMatrix<_Scalar, _Options, _StorageIndex> type;
-};
-
-template<typename T,int Flags> struct sparse_eval<T,1,1,Flags> {
-    typedef typename traits<T>::Scalar _Scalar;
-  public:
-    typedef Matrix<_Scalar, 1, 1> type;
-};
-
-template<typename T> struct plain_matrix_type<T,Sparse>
-{
-  typedef typename traits<T>::Scalar _Scalar;
-  typedef typename traits<T>::StorageIndex _StorageIndex;
-  enum { _Options = ((evaluator<T>::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor };
-  public:
-    typedef SparseMatrix<_Scalar, _Options, _StorageIndex> type;
-};
-
-template<typename T>
-struct plain_object_eval<T,Sparse>
-  : sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime, evaluator<T>::Flags>
-{};
-
-template<typename Decomposition, typename RhsType>
-struct solve_traits<Decomposition,RhsType,Sparse>
-{
-  typedef typename sparse_eval<RhsType, RhsType::RowsAtCompileTime, RhsType::ColsAtCompileTime,traits<RhsType>::Flags>::type PlainObject;
-};
-
-template<typename Derived>
-struct generic_xpr_base<Derived, MatrixXpr, Sparse>
-{
-  typedef SparseMatrixBase<Derived> type;
-};
-
-struct SparseTriangularShape  { static std::string debugName() { return "SparseTriangularShape"; } };
-struct SparseSelfAdjointShape { static std::string debugName() { return "SparseSelfAdjointShape"; } };
-
-template<> struct glue_shapes<SparseShape,SelfAdjointShape> { typedef SparseSelfAdjointShape type;  };
-template<> struct glue_shapes<SparseShape,TriangularShape > { typedef SparseTriangularShape  type;  };
-
-// return type of SparseCompressedBase::lower_bound;
-struct LowerBoundIndex {
-  LowerBoundIndex() : value(-1), found(false) {}
-  LowerBoundIndex(Index val, bool ok) : value(val), found(ok) {}
-  Index value;
-  bool found;
-};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  *
-  * \class Triplet
-  *
-  * \brief A small structure to hold a non zero as a triplet (i,j,value).
-  *
-  * \sa SparseMatrix::setFromTriplets()
-  */
-template<typename Scalar, typename StorageIndex=typename SparseMatrix<Scalar>::StorageIndex >
-class Triplet
-{
-public:
-  Triplet() : m_row(0), m_col(0), m_value(0) {}
-
-  Triplet(const StorageIndex& i, const StorageIndex& j, const Scalar& v = Scalar(0))
-    : m_row(i), m_col(j), m_value(v)
-  {}
-
-  /** \returns the row index of the element */
-  const StorageIndex& row() const { return m_row; }
-
-  /** \returns the column index of the element */
-  const StorageIndex& col() const { return m_col; }
-
-  /** \returns the value of the element */
-  const Scalar& value() const { return m_value; }
-protected:
-  StorageIndex m_row, m_col;
-  Scalar m_value;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEUTIL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseVector.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseVector.h
deleted file mode 100644
index 05779be..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseVector.h
+++ /dev/null
@@ -1,478 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEVECTOR_H
-#define EIGEN_SPARSEVECTOR_H
-
-namespace Eigen { 
-
-/** \ingroup SparseCore_Module
-  * \class SparseVector
-  *
-  * \brief a sparse vector class
-  *
-  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
-  *
-  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
-  */
-
-namespace internal {
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct traits<SparseVector<_Scalar, _Options, _StorageIndex> >
-{
-  typedef _Scalar Scalar;
-  typedef _StorageIndex StorageIndex;
-  typedef Sparse StorageKind;
-  typedef MatrixXpr XprKind;
-  enum {
-    IsColVector = (_Options & RowMajorBit) ? 0 : 1,
-
-    RowsAtCompileTime = IsColVector ? Dynamic : 1,
-    ColsAtCompileTime = IsColVector ? 1 : Dynamic,
-    MaxRowsAtCompileTime = RowsAtCompileTime,
-    MaxColsAtCompileTime = ColsAtCompileTime,
-    Flags = _Options | NestByRefBit | LvalueBit | (IsColVector ? 0 : RowMajorBit) | CompressedAccessBit,
-    SupportedAccessPatterns = InnerRandomAccessPattern
-  };
-};
-
-// Sparse-Vector-Assignment kinds:
-enum {
-  SVA_RuntimeSwitch,
-  SVA_Inner,
-  SVA_Outer
-};
-
-template< typename Dest, typename Src,
-          int AssignmentKind = !bool(Src::IsVectorAtCompileTime) ? SVA_RuntimeSwitch
-                             : Src::InnerSizeAtCompileTime==1 ? SVA_Outer
-                             : SVA_Inner>
-struct sparse_vector_assign_selector;
-
-}
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-class SparseVector
-  : public SparseCompressedBase<SparseVector<_Scalar, _Options, _StorageIndex> >
-{
-    typedef SparseCompressedBase<SparseVector> Base;
-    using Base::convert_index;
-  public:
-    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
-    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
-    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)
-    
-    typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
-    enum { IsColVector = internal::traits<SparseVector>::IsColVector };
-    
-    enum {
-      Options = _Options
-    };
-    
-    EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; }
-    EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; }
-    EIGEN_STRONG_INLINE Index innerSize() const { return m_size; }
-    EIGEN_STRONG_INLINE Index outerSize() const { return 1; }
-
-    EIGEN_STRONG_INLINE const Scalar* valuePtr() const { return m_data.valuePtr(); }
-    EIGEN_STRONG_INLINE Scalar* valuePtr() { return m_data.valuePtr(); }
-
-    EIGEN_STRONG_INLINE const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
-    EIGEN_STRONG_INLINE StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
-
-    inline const StorageIndex* outerIndexPtr() const { return 0; }
-    inline StorageIndex* outerIndexPtr() { return 0; }
-    inline const StorageIndex* innerNonZeroPtr() const { return 0; }
-    inline StorageIndex* innerNonZeroPtr() { return 0; }
-    
-    /** \internal */
-    inline Storage& data() { return m_data; }
-    /** \internal */
-    inline const Storage& data() const { return m_data; }
-
-    inline Scalar coeff(Index row, Index col) const
-    {
-      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
-      return coeff(IsColVector ? row : col);
-    }
-    inline Scalar coeff(Index i) const
-    {
-      eigen_assert(i>=0 && i<m_size);
-      return m_data.at(StorageIndex(i));
-    }
-
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
-      return coeffRef(IsColVector ? row : col);
-    }
-
-    /** \returns a reference to the coefficient value at given index \a i
-      * This operation involes a log(rho*size) binary search. If the coefficient does not
-      * exist yet, then a sorted insertion into a sequential buffer is performed.
-      *
-      * This insertion might be very costly if the number of nonzeros above \a i is large.
-      */
-    inline Scalar& coeffRef(Index i)
-    {
-      eigen_assert(i>=0 && i<m_size);
-
-      return m_data.atWithInsertion(StorageIndex(i));
-    }
-
-  public:
-
-    typedef typename Base::InnerIterator InnerIterator;
-    typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
-
-    inline void setZero() { m_data.clear(); }
-
-    /** \returns the number of non zero coefficients */
-    inline Index nonZeros() const  { return m_data.size(); }
-
-    inline void startVec(Index outer)
-    {
-      EIGEN_UNUSED_VARIABLE(outer);
-      eigen_assert(outer==0);
-    }
-
-    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
-    {
-      EIGEN_UNUSED_VARIABLE(outer);
-      eigen_assert(outer==0);
-      return insertBack(inner);
-    }
-    inline Scalar& insertBack(Index i)
-    {
-      m_data.append(0, i);
-      return m_data.value(m_data.size()-1);
-    }
-    
-    Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
-    {
-      EIGEN_UNUSED_VARIABLE(outer);
-      eigen_assert(outer==0);
-      return insertBackUnordered(inner);
-    }
-    inline Scalar& insertBackUnordered(Index i)
-    {
-      m_data.append(0, i);
-      return m_data.value(m_data.size()-1);
-    }
-
-    inline Scalar& insert(Index row, Index col)
-    {
-      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
-      
-      Index inner = IsColVector ? row : col;
-      Index outer = IsColVector ? col : row;
-      EIGEN_ONLY_USED_FOR_DEBUG(outer);
-      eigen_assert(outer==0);
-      return insert(inner);
-    }
-    Scalar& insert(Index i)
-    {
-      eigen_assert(i>=0 && i<m_size);
-      
-      Index startId = 0;
-      Index p = Index(m_data.size()) - 1;
-      // TODO smart realloc
-      m_data.resize(p+2,1);
-
-      while ( (p >= startId) && (m_data.index(p) > i) )
-      {
-        m_data.index(p+1) = m_data.index(p);
-        m_data.value(p+1) = m_data.value(p);
-        --p;
-      }
-      m_data.index(p+1) = convert_index(i);
-      m_data.value(p+1) = 0;
-      return m_data.value(p+1);
-    }
-
-    /**
-      */
-    inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }
-
-
-    inline void finalize() {}
-
-    /** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
-    void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
-    {
-      m_data.prune(reference,epsilon);
-    }
-
-    /** Resizes the sparse vector to \a rows x \a cols
-      *
-      * This method is provided for compatibility with matrices.
-      * For a column vector, \a cols must be equal to 1.
-      * For a row vector, \a rows must be equal to 1.
-      *
-      * \sa resize(Index)
-      */
-    void resize(Index rows, Index cols)
-    {
-      eigen_assert((IsColVector ? cols : rows)==1 && "Outer dimension must equal 1");
-      resize(IsColVector ? rows : cols);
-    }
-
-    /** Resizes the sparse vector to \a newSize
-      * This method deletes all entries, thus leaving an empty sparse vector
-      *
-      * \sa  conservativeResize(), setZero() */
-    void resize(Index newSize)
-    {
-      m_size = newSize;
-      m_data.clear();
-    }
-
-    /** Resizes the sparse vector to \a newSize, while leaving old values untouched.
-      *
-      * If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
-      * Call .data().squeeze() to free extra memory.
-      *
-      * \sa reserve(), setZero()
-      */
-    void conservativeResize(Index newSize)
-    {
-      if (newSize < m_size)
-      {
-        Index i = 0;
-        while (i<m_data.size() && m_data.index(i)<newSize) ++i;
-        m_data.resize(i);
-      }
-      m_size = newSize;
-    }
-
-    void resizeNonZeros(Index size) { m_data.resize(size); }
-
-    inline SparseVector() : m_size(0) { check_template_parameters(); resize(0); }
-
-    explicit inline SparseVector(Index size) : m_size(0) { check_template_parameters(); resize(size); }
-
-    inline SparseVector(Index rows, Index cols) : m_size(0) { check_template_parameters(); resize(rows,cols); }
-
-    template<typename OtherDerived>
-    inline SparseVector(const SparseMatrixBase<OtherDerived>& other)
-      : m_size(0)
-    {
-      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-      #endif
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    inline SparseVector(const SparseVector& other)
-      : Base(other), m_size(0)
-    {
-      check_template_parameters();
-      *this = other.derived();
-    }
-
-    /** Swaps the values of \c *this and \a other.
-      * Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only.
-      * \sa SparseMatrixBase::swap()
-      */
-    inline void swap(SparseVector& other)
-    {
-      std::swap(m_size, other.m_size);
-      m_data.swap(other.m_data);
-    }
-
-    template<int OtherOptions>
-    inline void swap(SparseMatrix<Scalar,OtherOptions,StorageIndex>& other)
-    {
-      eigen_assert(other.outerSize()==1);
-      std::swap(m_size, other.m_innerSize);
-      m_data.swap(other.m_data);
-    }
-
-    inline SparseVector& operator=(const SparseVector& other)
-    {
-      if (other.isRValue())
-      {
-        swap(other.const_cast_derived());
-      }
-      else
-      {
-        resize(other.size());
-        m_data = other.m_data;
-      }
-      return *this;
-    }
-
-    template<typename OtherDerived>
-    inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
-    {
-      SparseVector tmp(other.size());
-      internal::sparse_vector_assign_selector<SparseVector,OtherDerived>::run(tmp,other.derived());
-      this->swap(tmp);
-      return *this;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename Lhs, typename Rhs>
-    inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
-    {
-      return Base::operator=(product);
-    }
-    #endif
-
-    friend std::ostream & operator << (std::ostream & s, const SparseVector& m)
-    {
-      for (Index i=0; i<m.nonZeros(); ++i)
-        s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
-      s << std::endl;
-      return s;
-    }
-
-    /** Destructor */
-    inline ~SparseVector() {}
-
-    /** Overloaded for performance */
-    Scalar sum() const;
-
-  public:
-
-    /** \internal \deprecated use setZero() and reserve() */
-    EIGEN_DEPRECATED void startFill(Index reserve)
-    {
-      setZero();
-      m_data.reserve(reserve);
-    }
-
-    /** \internal \deprecated use insertBack(Index,Index) */
-    EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
-    {
-      eigen_assert(r==0 || c==0);
-      return fill(IsColVector ? r : c);
-    }
-
-    /** \internal \deprecated use insertBack(Index) */
-    EIGEN_DEPRECATED Scalar& fill(Index i)
-    {
-      m_data.append(0, i);
-      return m_data.value(m_data.size()-1);
-    }
-
-    /** \internal \deprecated use insert(Index,Index) */
-    EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
-    {
-      eigen_assert(r==0 || c==0);
-      return fillrand(IsColVector ? r : c);
-    }
-
-    /** \internal \deprecated use insert(Index) */
-    EIGEN_DEPRECATED Scalar& fillrand(Index i)
-    {
-      return insert(i);
-    }
-
-    /** \internal \deprecated use finalize() */
-    EIGEN_DEPRECATED void endFill() {}
-    
-    // These two functions were here in the 3.1 release, so let's keep them in case some code rely on them.
-    /** \internal \deprecated use data() */
-    EIGEN_DEPRECATED Storage& _data() { return m_data; }
-    /** \internal \deprecated use data() */
-    EIGEN_DEPRECATED const Storage& _data() const { return m_data; }
-    
-#   ifdef EIGEN_SPARSEVECTOR_PLUGIN
-#     include EIGEN_SPARSEVECTOR_PLUGIN
-#   endif
-
-protected:
-  
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
-      EIGEN_STATIC_ASSERT((_Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS);
-    }
-    
-    Storage m_data;
-    Index m_size;
-};
-
-namespace internal {
-
-template<typename _Scalar, int _Options, typename _Index>
-struct evaluator<SparseVector<_Scalar,_Options,_Index> >
-  : evaluator_base<SparseVector<_Scalar,_Options,_Index> >
-{
-  typedef SparseVector<_Scalar,_Options,_Index> SparseVectorType;
-  typedef evaluator_base<SparseVectorType> Base;
-  typedef typename SparseVectorType::InnerIterator InnerIterator;
-  typedef typename SparseVectorType::ReverseInnerIterator ReverseInnerIterator;
-  
-  enum {
-    CoeffReadCost = NumTraits<_Scalar>::ReadCost,
-    Flags = SparseVectorType::Flags
-  };
-
-  evaluator() : Base() {}
-  
-  explicit evaluator(const SparseVectorType &mat) : m_matrix(&mat)
-  {
-    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
-  }
-  
-  inline Index nonZerosEstimate() const {
-    return m_matrix->nonZeros();
-  }
-  
-  operator SparseVectorType&() { return m_matrix->const_cast_derived(); }
-  operator const SparseVectorType&() const { return *m_matrix; }
-  
-  const SparseVectorType *m_matrix;
-};
-
-template< typename Dest, typename Src>
-struct sparse_vector_assign_selector<Dest,Src,SVA_Inner> {
-  static void run(Dest& dst, const Src& src) {
-    eigen_internal_assert(src.innerSize()==src.size());
-    typedef internal::evaluator<Src> SrcEvaluatorType;
-    SrcEvaluatorType srcEval(src);
-    for(typename SrcEvaluatorType::InnerIterator it(srcEval, 0); it; ++it)
-      dst.insert(it.index()) = it.value();
-  }
-};
-
-template< typename Dest, typename Src>
-struct sparse_vector_assign_selector<Dest,Src,SVA_Outer> {
-  static void run(Dest& dst, const Src& src) {
-    eigen_internal_assert(src.outerSize()==src.size());
-    typedef internal::evaluator<Src> SrcEvaluatorType;
-    SrcEvaluatorType srcEval(src);
-    for(Index i=0; i<src.size(); ++i)
-    {
-      typename SrcEvaluatorType::InnerIterator it(srcEval, i);
-      if(it)
-        dst.insert(i) = it.value();
-    }
-  }
-};
-
-template< typename Dest, typename Src>
-struct sparse_vector_assign_selector<Dest,Src,SVA_RuntimeSwitch> {
-  static void run(Dest& dst, const Src& src) {
-    if(src.outerSize()==1)  sparse_vector_assign_selector<Dest,Src,SVA_Inner>::run(dst, src);
-    else                    sparse_vector_assign_selector<Dest,Src,SVA_Outer>::run(dst, src);
-  }
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseView.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseView.h
deleted file mode 100644
index 92b3d1f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/SparseView.h
+++ /dev/null
@@ -1,254 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEVIEW_H
-#define EIGEN_SPARSEVIEW_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename MatrixType>
-struct traits<SparseView<MatrixType> > : traits<MatrixType>
-{
-  typedef typename MatrixType::StorageIndex StorageIndex;
-  typedef Sparse StorageKind;
-  enum {
-    Flags = int(traits<MatrixType>::Flags) & (RowMajorBit)
-  };
-};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  * \class SparseView
-  *
-  * \brief Expression of a dense or sparse matrix with zero or too small values removed
-  *
-  * \tparam MatrixType the type of the object of which we are removing the small entries
-  *
-  * This class represents an expression of a given dense or sparse matrix with
-  * entries smaller than \c reference * \c epsilon are removed.
-  * It is the return type of MatrixBase::sparseView() and SparseMatrixBase::pruned()
-  * and most of the time this is the only way it is used.
-  *
-  * \sa MatrixBase::sparseView(), SparseMatrixBase::pruned()
-  */
-template<typename MatrixType>
-class SparseView : public SparseMatrixBase<SparseView<MatrixType> >
-{
-  typedef typename MatrixType::Nested MatrixTypeNested;
-  typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
-  typedef SparseMatrixBase<SparseView > Base;
-public:
-  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView)
-  typedef typename internal::remove_all<MatrixType>::type NestedExpression;
-
-  explicit SparseView(const MatrixType& mat, const Scalar& reference = Scalar(0),
-                      const RealScalar &epsilon = NumTraits<Scalar>::dummy_precision())
-    : m_matrix(mat), m_reference(reference), m_epsilon(epsilon) {}
-
-  inline Index rows() const { return m_matrix.rows(); }
-  inline Index cols() const { return m_matrix.cols(); }
-
-  inline Index innerSize() const { return m_matrix.innerSize(); }
-  inline Index outerSize() const { return m_matrix.outerSize(); }
-  
-  /** \returns the nested expression */
-  const typename internal::remove_all<MatrixTypeNested>::type&
-  nestedExpression() const { return m_matrix; }
-  
-  Scalar reference() const { return m_reference; }
-  RealScalar epsilon() const { return m_epsilon; }
-  
-protected:
-  MatrixTypeNested m_matrix;
-  Scalar m_reference;
-  RealScalar m_epsilon;
-};
-
-namespace internal {
-
-// TODO find a way to unify the two following variants
-// This is tricky because implementing an inner iterator on top of an IndexBased evaluator is
-// not easy because the evaluators do not expose the sizes of the underlying expression.
-  
-template<typename ArgType>
-struct unary_evaluator<SparseView<ArgType>, IteratorBased>
-  : public evaluator_base<SparseView<ArgType> >
-{
-    typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
-  public:
-    typedef SparseView<ArgType> XprType;
-    
-    class InnerIterator : public EvalIterator
-    {
-      protected:
-        typedef typename XprType::Scalar Scalar;
-      public:
-
-        EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
-          : EvalIterator(sve.m_argImpl,outer), m_view(sve.m_view)
-        {
-          incrementToNonZero();
-        }
-
-        EIGEN_STRONG_INLINE InnerIterator& operator++()
-        {
-          EvalIterator::operator++();
-          incrementToNonZero();
-          return *this;
-        }
-
-        using EvalIterator::value;
-
-      protected:
-        const XprType &m_view;
-
-      private:
-        void incrementToNonZero()
-        {
-          while((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.reference(), m_view.epsilon()))
-          {
-            EvalIterator::operator++();
-          }
-        }
-    };
-    
-    enum {
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
-
-  protected:
-    evaluator<ArgType> m_argImpl;
-    const XprType &m_view;
-};
-
-template<typename ArgType>
-struct unary_evaluator<SparseView<ArgType>, IndexBased>
-  : public evaluator_base<SparseView<ArgType> >
-{
-  public:
-    typedef SparseView<ArgType> XprType;
-  protected:
-    enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
-    typedef typename XprType::Scalar Scalar;
-    typedef typename XprType::StorageIndex StorageIndex;
-  public:
-    
-    class InnerIterator
-    {
-      public:
-
-        EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
-          : m_sve(sve), m_inner(0), m_outer(outer), m_end(sve.m_view.innerSize())
-        {
-          incrementToNonZero();
-        }
-
-        EIGEN_STRONG_INLINE InnerIterator& operator++()
-        {
-          m_inner++;
-          incrementToNonZero();
-          return *this;
-        }
-
-        EIGEN_STRONG_INLINE Scalar value() const
-        {
-          return (IsRowMajor) ? m_sve.m_argImpl.coeff(m_outer, m_inner)
-                              : m_sve.m_argImpl.coeff(m_inner, m_outer);
-        }
-
-        EIGEN_STRONG_INLINE StorageIndex index() const { return m_inner; }
-        inline Index row() const { return IsRowMajor ? m_outer : index(); }
-        inline Index col() const { return IsRowMajor ? index() : m_outer; }
-
-        EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
-
-      protected:
-        const unary_evaluator &m_sve;
-        Index m_inner;
-        const Index m_outer;
-        const Index m_end;
-
-      private:
-        void incrementToNonZero()
-        {
-          while((bool(*this)) && internal::isMuchSmallerThan(value(), m_sve.m_view.reference(), m_sve.m_view.epsilon()))
-          {
-            m_inner++;
-          }
-        }
-    };
-    
-    enum {
-      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
-      Flags = XprType::Flags
-    };
-    
-    explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
-
-  protected:
-    evaluator<ArgType> m_argImpl;
-    const XprType &m_view;
-};
-
-} // end namespace internal
-
-/** \ingroup SparseCore_Module
-  *
-  * \returns a sparse expression of the dense expression \c *this with values smaller than
-  * \a reference * \a epsilon removed.
-  *
-  * This method is typically used when prototyping to convert a quickly assembled dense Matrix \c D to a SparseMatrix \c S:
-  * \code
-  * MatrixXd D(n,m);
-  * SparseMatrix<double> S;
-  * S = D.sparseView();             // suppress numerical zeros (exact)
-  * S = D.sparseView(reference);
-  * S = D.sparseView(reference,epsilon);
-  * \endcode
-  * where \a reference is a meaningful non zero reference value,
-  * and \a epsilon is a tolerance factor defaulting to NumTraits<Scalar>::dummy_precision().
-  *
-  * \sa SparseMatrixBase::pruned(), class SparseView */
-template<typename Derived>
-const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& reference,
-                                                          const typename NumTraits<Scalar>::Real& epsilon) const
-{
-  return SparseView<Derived>(derived(), reference, epsilon);
-}
-
-/** \returns an expression of \c *this with values smaller than
-  * \a reference * \a epsilon removed.
-  *
-  * This method is typically used in conjunction with the product of two sparse matrices
-  * to automatically prune the smallest values as follows:
-  * \code
-  * C = (A*B).pruned();             // suppress numerical zeros (exact)
-  * C = (A*B).pruned(ref);
-  * C = (A*B).pruned(ref,epsilon);
-  * \endcode
-  * where \c ref is a meaningful non zero reference value.
-  * */
-template<typename Derived>
-const SparseView<Derived>
-SparseMatrixBase<Derived>::pruned(const Scalar& reference,
-                                  const RealScalar& epsilon) const
-{
-  return SparseView<Derived>(derived(), reference, epsilon);
-}
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
deleted file mode 100644
index f9c56ba..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
+++ /dev/null
@@ -1,315 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSETRIANGULARSOLVER_H
-#define EIGEN_SPARSETRIANGULARSOLVER_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Mode,
-  int UpLo = (Mode & Lower)
-           ? Lower
-           : (Mode & Upper)
-           ? Upper
-           : -1,
-  int StorageOrder = int(traits<Lhs>::Flags) & RowMajorBit>
-struct sparse_solve_triangular_selector;
-
-// forward substitution, row-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,RowMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=0; i<lhs.rows(); ++i)
-      {
-        Scalar tmp = other.coeff(i,col);
-        Scalar lastVal(0);
-        Index lastIndex = 0;
-        for(LhsIterator it(lhsEval, i); it; ++it)
-        {
-          lastVal = it.value();
-          lastIndex = it.index();
-          if(lastIndex==i)
-            break;
-          tmp -= lastVal * other.coeff(lastIndex,col);
-        }
-        if (Mode & UnitDiag)
-          other.coeffRef(i,col) = tmp;
-        else
-        {
-          eigen_assert(lastIndex==i);
-          other.coeffRef(i,col) = tmp/lastVal;
-        }
-      }
-    }
-  }
-};
-
-// backward substitution, row-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,RowMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=lhs.rows()-1 ; i>=0 ; --i)
-      {
-        Scalar tmp = other.coeff(i,col);
-        Scalar l_ii(0);
-        LhsIterator it(lhsEval, i);
-        while(it && it.index()<i)
-          ++it;
-        if(!(Mode & UnitDiag))
-        {
-          eigen_assert(it && it.index()==i);
-          l_ii = it.value();
-          ++it;
-        }
-        else if (it && it.index() == i)
-          ++it;
-        for(; it; ++it)
-        {
-          tmp -= it.value() * other.coeff(it.index(),col);
-        }
-
-        if (Mode & UnitDiag)  other.coeffRef(i,col) = tmp;
-        else                  other.coeffRef(i,col) = tmp/l_ii;
-      }
-    }
-  }
-};
-
-// forward substitution, col-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,ColMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=0; i<lhs.cols(); ++i)
-      {
-        Scalar& tmp = other.coeffRef(i,col);
-        if (tmp!=Scalar(0)) // optimization when other is actually sparse
-        {
-          LhsIterator it(lhsEval, i);
-          while(it && it.index()<i)
-            ++it;
-          if(!(Mode & UnitDiag))
-          {
-            eigen_assert(it && it.index()==i);
-            tmp /= it.value();
-          }
-          if (it && it.index()==i)
-            ++it;
-          for(; it; ++it)
-            other.coeffRef(it.index(), col) -= tmp * it.value();
-        }
-      }
-    }
-  }
-};
-
-// backward substitution, col-major
-template<typename Lhs, typename Rhs, int Mode>
-struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,ColMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef evaluator<Lhs> LhsEval;
-  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    LhsEval lhsEval(lhs);
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      for(Index i=lhs.cols()-1; i>=0; --i)
-      {
-        Scalar& tmp = other.coeffRef(i,col);
-        if (tmp!=Scalar(0)) // optimization when other is actually sparse
-        {
-          if(!(Mode & UnitDiag))
-          {
-            // TODO replace this by a binary search. make sure the binary search is safe for partially sorted elements
-            LhsIterator it(lhsEval, i);
-            while(it && it.index()!=i)
-              ++it;
-            eigen_assert(it && it.index()==i);
-            other.coeffRef(i,col) /= it.value();
-          }
-          LhsIterator it(lhsEval, i);
-          for(; it && it.index()<i; ++it)
-            other.coeffRef(it.index(), col) -= tmp * it.value();
-        }
-      }
-    }
-  }
-};
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-template<typename ExpressionType,unsigned int Mode>
-template<typename OtherDerived>
-void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(MatrixBase<OtherDerived>& other) const
-{
-  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
-  eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
-
-  enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
-
-  typedef typename internal::conditional<copy,
-    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
-  OtherCopy otherCopy(other.derived());
-
-  internal::sparse_solve_triangular_selector<ExpressionType, typename internal::remove_reference<OtherCopy>::type, Mode>::run(derived().nestedExpression(), otherCopy);
-
-  if (copy)
-    other = otherCopy;
-}
-#endif
-
-// pure sparse path
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, int Mode,
-  int UpLo = (Mode & Lower)
-           ? Lower
-           : (Mode & Upper)
-           ? Upper
-           : -1,
-  int StorageOrder = int(Lhs::Flags) & (RowMajorBit)>
-struct sparse_solve_triangular_sparse_selector;
-
-// forward substitution, col-major
-template<typename Lhs, typename Rhs, int Mode, int UpLo>
-struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor>
-{
-  typedef typename Rhs::Scalar Scalar;
-  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
-                                      typename traits<Rhs>::StorageIndex>::type StorageIndex;
-  static void run(const Lhs& lhs, Rhs& other)
-  {
-    const bool IsLower = (UpLo==Lower);
-    AmbiVector<Scalar,StorageIndex> tempVector(other.rows()*2);
-    tempVector.setBounds(0,other.rows());
-
-    Rhs res(other.rows(), other.cols());
-    res.reserve(other.nonZeros());
-
-    for(Index col=0 ; col<other.cols() ; ++col)
-    {
-      // FIXME estimate number of non zeros
-      tempVector.init(.99/*float(other.col(col).nonZeros())/float(other.rows())*/);
-      tempVector.setZero();
-      tempVector.restart();
-      for (typename Rhs::InnerIterator rhsIt(other, col); rhsIt; ++rhsIt)
-      {
-        tempVector.coeffRef(rhsIt.index()) = rhsIt.value();
-      }
-
-      for(Index i=IsLower?0:lhs.cols()-1;
-          IsLower?i<lhs.cols():i>=0;
-          i+=IsLower?1:-1)
-      {
-        tempVector.restart();
-        Scalar& ci = tempVector.coeffRef(i);
-        if (ci!=Scalar(0))
-        {
-          // find
-          typename Lhs::InnerIterator it(lhs, i);
-          if(!(Mode & UnitDiag))
-          {
-            if (IsLower)
-            {
-              eigen_assert(it.index()==i);
-              ci /= it.value();
-            }
-            else
-              ci /= lhs.coeff(i,i);
-          }
-          tempVector.restart();
-          if (IsLower)
-          {
-            if (it.index()==i)
-              ++it;
-            for(; it; ++it)
-              tempVector.coeffRef(it.index()) -= ci * it.value();
-          }
-          else
-          {
-            for(; it && it.index()<i; ++it)
-              tempVector.coeffRef(it.index()) -= ci * it.value();
-          }
-        }
-      }
-
-
-      Index count = 0;
-      // FIXME compute a reference value to filter zeros
-      for (typename AmbiVector<Scalar,StorageIndex>::Iterator it(tempVector/*,1e-12*/); it; ++it)
-      {
-        ++ count;
-//         std::cerr << "fill " << it.index() << ", " << col << "\n";
-//         std::cout << it.value() << "  ";
-        // FIXME use insertBack
-        res.insert(it.index(), col) = it.value();
-      }
-//       std::cout << "tempVector.nonZeros() == " << int(count) << " / " << (other.rows()) << "\n";
-    }
-    res.finalize();
-    other = res.markAsRValue();
-  }
-};
-
-} // end namespace internal
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename ExpressionType,unsigned int Mode>
-template<typename OtherDerived>
-void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const
-{
-  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
-  eigen_assert( (!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
-
-//   enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
-
-//   typedef typename internal::conditional<copy,
-//     typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
-//   OtherCopy otherCopy(other.derived());
-
-  internal::sparse_solve_triangular_sparse_selector<ExpressionType, OtherDerived, Mode>::run(derived().nestedExpression(), other.derived());
-
-//   if (copy)
-//     other = otherCopy;
-}
-#endif
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSETRIANGULARSOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU.h
deleted file mode 100644
index 0c8d893..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU.h
+++ /dev/null
@@ -1,923 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_SPARSE_LU_H
-#define EIGEN_SPARSE_LU_H
-
-namespace Eigen {
-
-template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::StorageIndex> > class SparseLU;
-template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
-template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType;
-
-template <bool Conjugate,class SparseLUType>
-class SparseLUTransposeView : public SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> >
-{
-protected:
-  typedef SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> > APIBase;
-  using APIBase::m_isInitialized;
-public:
-  typedef typename SparseLUType::Scalar Scalar;
-  typedef typename SparseLUType::StorageIndex StorageIndex;
-  typedef typename SparseLUType::MatrixType MatrixType;
-  typedef typename SparseLUType::OrderingType OrderingType;
-
-  enum {
-    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-  };
-
-  SparseLUTransposeView() : m_sparseLU(NULL) {}
-  SparseLUTransposeView(const SparseLUTransposeView& view) {
-    this->m_sparseLU = view.m_sparseLU;
-  }
-  void setIsInitialized(const bool isInitialized) {this->m_isInitialized = isInitialized;}
-  void setSparseLU(SparseLUType* sparseLU) {m_sparseLU = sparseLU;}
-  using APIBase::_solve_impl;
-  template<typename Rhs, typename Dest>
-  bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const
-  {
-    Dest& X(X_base.derived());
-    eigen_assert(m_sparseLU->info() == Success && "The matrix should be factorized first");
-    EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
-                        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-
-
-    // this ugly const_cast_derived() helps to detect aliasing when applying the permutations
-    for(Index j = 0; j < B.cols(); ++j){
-      X.col(j) = m_sparseLU->colsPermutation() * B.const_cast_derived().col(j);
-    }
-    //Forward substitution with transposed or adjoint of U
-    m_sparseLU->matrixU().template solveTransposedInPlace<Conjugate>(X);
-
-    //Backward substitution with transposed or adjoint of L
-    m_sparseLU->matrixL().template solveTransposedInPlace<Conjugate>(X);
-
-    // Permute back the solution
-    for (Index j = 0; j < B.cols(); ++j)
-      X.col(j) = m_sparseLU->rowsPermutation().transpose() * X.col(j);
-    return true;
-  }
-  inline Index rows() const { return m_sparseLU->rows(); }
-  inline Index cols() const { return m_sparseLU->cols(); }
-
-private:
-  SparseLUType *m_sparseLU;
-  SparseLUTransposeView& operator=(const SparseLUTransposeView&);
-};
-
-
-/** \ingroup SparseLU_Module
-  * \class SparseLU
-  * 
-  * \brief Sparse supernodal LU factorization for general matrices
-  * 
-  * This class implements the supernodal LU factorization for general matrices.
-  * It uses the main techniques from the sequential SuperLU package 
-  * (http://crd-legacy.lbl.gov/~xiaoye/SuperLU/). It handles transparently real 
-  * and complex arithmetic with single and double precision, depending on the 
-  * scalar type of your input matrix. 
-  * The code has been optimized to provide BLAS-3 operations during supernode-panel updates. 
-  * It benefits directly from the built-in high-performant Eigen BLAS routines. 
-  * Moreover, when the size of a supernode is very small, the BLAS calls are avoided to 
-  * enable a better optimization from the compiler. For best performance, 
-  * you should compile it with NDEBUG flag to avoid the numerous bounds checking on vectors. 
-  * 
-  * An important parameter of this class is the ordering method. It is used to reorder the columns 
-  * (and eventually the rows) of the matrix to reduce the number of new elements that are created during 
-  * numerical factorization. The cheapest method available is COLAMD. 
-  * See  \link OrderingMethods_Module the OrderingMethods module \endlink for the list of 
-  * built-in and external ordering methods. 
-  *
-  * Simple example with key steps 
-  * \code
-  * VectorXd x(n), b(n);
-  * SparseMatrix<double> A;
-  * SparseLU<SparseMatrix<double>, COLAMDOrdering<int> >   solver;
-  * // fill A and b;
-  * // Compute the ordering permutation vector from the structural pattern of A
-  * solver.analyzePattern(A); 
-  * // Compute the numerical factorization 
-  * solver.factorize(A); 
-  * //Use the factors to solve the linear system 
-  * x = solver.solve(b); 
-  * \endcode
-  * 
-  * \warning The input matrix A should be in a \b compressed and \b column-major form.
-  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
-  * 
-  * \note Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix. 
-  * For badly scaled matrices, this step can be useful to reduce the pivoting during factorization. 
-  * If this is the case for your matrices, you can try the basic scaling method at
-  *  "unsupported/Eigen/src/IterativeSolvers/Scaling.h"
-  * 
-  * \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<>
-  * \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD
-  *
-  * \implsparsesolverconcept
-  * 
-  * \sa \ref TutorialSparseSolverConcept
-  * \sa \ref OrderingMethods_Module
-  */
-template <typename _MatrixType, typename _OrderingType>
-class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >, public internal::SparseLUImpl<typename _MatrixType::Scalar, typename _MatrixType::StorageIndex>
-{
-  protected:
-    typedef SparseSolverBase<SparseLU<_MatrixType,_OrderingType> > APIBase;
-    using APIBase::m_isInitialized;
-  public:
-    using APIBase::_solve_impl;
-    
-    typedef _MatrixType MatrixType; 
-    typedef _OrderingType OrderingType;
-    typedef typename MatrixType::Scalar Scalar; 
-    typedef typename MatrixType::RealScalar RealScalar; 
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> NCMatrix;
-    typedef internal::MappedSuperNodalMatrix<Scalar, StorageIndex> SCMatrix;
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-    typedef internal::SparseLUImpl<Scalar, StorageIndex> Base;
-
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-  public:
-
-    SparseLU():m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
-    {
-      initperfvalues(); 
-    }
-    explicit SparseLU(const MatrixType& matrix)
-      : m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
-    {
-      initperfvalues(); 
-      compute(matrix);
-    }
-    
-    ~SparseLU()
-    {
-      // Free all explicit dynamic pointers 
-    }
-    
-    void analyzePattern (const MatrixType& matrix);
-    void factorize (const MatrixType& matrix);
-    void simplicialfactorize(const MatrixType& matrix);
-    
-    /**
-      * Compute the symbolic and numeric factorization of the input sparse matrix.
-      * The input matrix should be in column-major storage. 
-      */
-    void compute (const MatrixType& matrix)
-    {
-      // Analyze 
-      analyzePattern(matrix); 
-      //Factorize
-      factorize(matrix);
-    } 
-
-    /** \returns an expression of the transposed of the factored matrix.
-      *
-      * A typical usage is to solve for the transposed problem A^T x = b:
-      * \code
-      * solver.compute(A);
-      * x = solver.transpose().solve(b);
-      * \endcode
-      *
-      * \sa adjoint(), solve()
-      */
-    const SparseLUTransposeView<false,SparseLU<_MatrixType,_OrderingType> > transpose()
-    {
-      SparseLUTransposeView<false,  SparseLU<_MatrixType,_OrderingType> > transposeView;
-      transposeView.setSparseLU(this);
-      transposeView.setIsInitialized(this->m_isInitialized);
-      return transposeView;
-    }
-
-
-    /** \returns an expression of the adjoint of the factored matrix
-      *
-      * A typical usage is to solve for the adjoint problem A' x = b:
-      * \code
-      * solver.compute(A);
-      * x = solver.adjoint().solve(b);
-      * \endcode
-      *
-      * For real scalar types, this function is equivalent to transpose().
-      *
-      * \sa transpose(), solve()
-      */
-    const SparseLUTransposeView<true, SparseLU<_MatrixType,_OrderingType> > adjoint()
-    {
-      SparseLUTransposeView<true,  SparseLU<_MatrixType,_OrderingType> > adjointView;
-      adjointView.setSparseLU(this);
-      adjointView.setIsInitialized(this->m_isInitialized);
-      return adjointView;
-    }
-    
-    inline Index rows() const { return m_mat.rows(); }
-    inline Index cols() const { return m_mat.cols(); }
-    /** Indicate that the pattern of the input matrix is symmetric */
-    void isSymmetric(bool sym)
-    {
-      m_symmetricmode = sym;
-    }
-    
-    /** \returns an expression of the matrix L, internally stored as supernodes
-      * The only operation available with this expression is the triangular solve
-      * \code
-      * y = b; matrixL().solveInPlace(y);
-      * \endcode
-      */
-    SparseLUMatrixLReturnType<SCMatrix> matrixL() const
-    {
-      return SparseLUMatrixLReturnType<SCMatrix>(m_Lstore);
-    }
-    /** \returns an expression of the matrix U,
-      * The only operation available with this expression is the triangular solve
-      * \code
-      * y = b; matrixU().solveInPlace(y);
-      * \endcode
-      */
-    SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,StorageIndex> > matrixU() const
-    {
-      return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,StorageIndex> >(m_Lstore, m_Ustore);
-    }
-
-    /**
-      * \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$
-      * \sa colsPermutation()
-      */
-    inline const PermutationType& rowsPermutation() const
-    {
-      return m_perm_r;
-    }
-    /**
-      * \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$
-      * \sa rowsPermutation()
-      */
-    inline const PermutationType& colsPermutation() const
-    {
-      return m_perm_c;
-    }
-    /** Set the threshold used for a diagonal entry to be an acceptable pivot. */
-    void setPivotThreshold(const RealScalar& thresh)
-    {
-      m_diagpivotthresh = thresh; 
-    }
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-    /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
-      *
-      * \warning the destination matrix X in X = this->solve(B) must be colmun-major.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const;
-#endif // EIGEN_PARSED_BY_DOXYGEN
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance
-      *          \c InvalidInput if the input matrix is invalid
-      *
-      * \sa iparm()          
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-    
-    /**
-      * \returns A string describing the type of error
-      */
-    std::string lastErrorMessage() const
-    {
-      return m_lastError; 
-    }
-
-    template<typename Rhs, typename Dest>
-    bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const
-    {
-      Dest& X(X_base.derived());
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first");
-      EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
-                        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-      
-      // Permute the right hand side to form X = Pr*B
-      // on return, X is overwritten by the computed solution
-      X.resize(B.rows(),B.cols());
-
-      // this ugly const_cast_derived() helps to detect aliasing when applying the permutations
-      for(Index j = 0; j < B.cols(); ++j)
-        X.col(j) = rowsPermutation() * B.const_cast_derived().col(j);
-      
-      //Forward substitution with L
-      this->matrixL().solveInPlace(X);
-      this->matrixU().solveInPlace(X);
-      
-      // Permute back the solution 
-      for (Index j = 0; j < B.cols(); ++j)
-        X.col(j) = colsPermutation().inverse() * X.col(j);
-      
-      return true; 
-    }
-    
-    /**
-      * \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), signDeterminant()
-      */
-    Scalar absDeterminant()
-    {
-      using std::abs;
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      // Initialize with the determinant of the row matrix
-      Scalar det = Scalar(1.);
-      // Note that the diagonal blocks of U are stored in supernodes,
-      // which are available in the  L part :)
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.index() == j)
-          {
-            det *= abs(it.value());
-            break;
-          }
-        }
-      }
-      return det;
-    }
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix
-      * of which **this is the QR decomposition
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's
-      * inherent to the determinant computation.
-      *
-      * \sa absDeterminant(), signDeterminant()
-      */
-    Scalar logAbsDeterminant() const
-    {
-      using std::log;
-      using std::abs;
-
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      Scalar det = Scalar(0.);
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.row() < j) continue;
-          if(it.row() == j)
-          {
-            det += log(abs(it.value()));
-            break;
-          }
-        }
-      }
-      return det;
-    }
-
-    /** \returns A number representing the sign of the determinant
-      *
-      * \sa absDeterminant(), logAbsDeterminant()
-      */
-    Scalar signDeterminant()
-    {
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      // Initialize with the determinant of the row matrix
-      Index det = 1;
-      // Note that the diagonal blocks of U are stored in supernodes,
-      // which are available in the  L part :)
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.index() == j)
-          {
-            if(it.value()<0)
-              det = -det;
-            else if(it.value()==0)
-              return 0;
-            break;
-          }
-        }
-      }
-      return det * m_detPermR * m_detPermC;
-    }
-    
-    /** \returns The determinant of the matrix.
-      *
-      * \sa absDeterminant(), logAbsDeterminant()
-      */
-    Scalar determinant()
-    {
-      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
-      // Initialize with the determinant of the row matrix
-      Scalar det = Scalar(1.);
-      // Note that the diagonal blocks of U are stored in supernodes,
-      // which are available in the  L part :)
-      for (Index j = 0; j < this->cols(); ++j)
-      {
-        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
-        {
-          if(it.index() == j)
-          {
-            det *= it.value();
-            break;
-          }
-        }
-      }
-      return (m_detPermR * m_detPermC) > 0 ? det : -det;
-    }
-
-    Index nnzL() const { return m_nnzL; };
-    Index nnzU() const { return m_nnzU; };
-
-  protected:
-    // Functions 
-    void initperfvalues()
-    {
-      m_perfv.panel_size = 16;
-      m_perfv.relax = 1; 
-      m_perfv.maxsuper = 128; 
-      m_perfv.rowblk = 16; 
-      m_perfv.colblk = 8; 
-      m_perfv.fillfactor = 20;  
-    }
-      
-    // Variables 
-    mutable ComputationInfo m_info;
-    bool m_factorizationIsOk;
-    bool m_analysisIsOk;
-    std::string m_lastError;
-    NCMatrix m_mat; // The input (permuted ) matrix 
-    SCMatrix m_Lstore; // The lower triangular matrix (supernodal)
-    MappedSparseMatrix<Scalar,ColMajor,StorageIndex> m_Ustore; // The upper triangular matrix
-    PermutationType m_perm_c; // Column permutation 
-    PermutationType m_perm_r ; // Row permutation
-    IndexVector m_etree; // Column elimination tree 
-    
-    typename Base::GlobalLU_t m_glu; 
-                               
-    // SparseLU options 
-    bool m_symmetricmode;
-    // values for performance 
-    internal::perfvalues m_perfv;
-    RealScalar m_diagpivotthresh; // Specifies the threshold used for a diagonal entry to be an acceptable pivot
-    Index m_nnzL, m_nnzU; // Nonzeros in L and U factors
-    Index m_detPermR, m_detPermC; // Determinants of the permutation matrices
-  private:
-    // Disable copy constructor 
-    SparseLU (const SparseLU& );
-}; // End class SparseLU
-
-
-
-// Functions needed by the anaysis phase
-/** 
-  * Compute the column permutation to minimize the fill-in
-  * 
-  *  - Apply this permutation to the input matrix - 
-  * 
-  *  - Compute the column elimination tree on the permuted matrix 
-  * 
-  *  - Postorder the elimination tree and the column permutation
-  * 
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
-{
-  
-  //TODO  It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat.
-  
-  // Firstly, copy the whole input matrix. 
-  m_mat = mat;
-  
-  // Compute fill-in ordering
-  OrderingType ord; 
-  ord(m_mat,m_perm_c);
-  
-  // Apply the permutation to the column of the input  matrix
-  if (m_perm_c.size())
-  {
-    m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used.  
-    // Then, permute only the column pointers
-    ei_declare_aligned_stack_constructed_variable(StorageIndex,outerIndexPtr,mat.cols()+1,mat.isCompressed()?const_cast<StorageIndex*>(mat.outerIndexPtr()):0);
-    
-    // If the input matrix 'mat' is uncompressed, then the outer-indices do not match the ones of m_mat, and a copy is thus needed.
-    if(!mat.isCompressed()) 
-      IndexVector::Map(outerIndexPtr, mat.cols()+1) = IndexVector::Map(m_mat.outerIndexPtr(),mat.cols()+1);
-    
-    // Apply the permutation and compute the nnz per column.
-    for (Index i = 0; i < mat.cols(); i++)
-    {
-      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
-      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
-    }
-  }
-  
-  // Compute the column elimination tree of the permuted matrix 
-  IndexVector firstRowElt;
-  internal::coletree(m_mat, m_etree,firstRowElt); 
-     
-  // In symmetric mode, do not do postorder here
-  if (!m_symmetricmode) {
-    IndexVector post, iwork; 
-    // Post order etree
-    internal::treePostorder(StorageIndex(m_mat.cols()), m_etree, post); 
-      
-   
-    // Renumber etree in postorder 
-    Index m = m_mat.cols(); 
-    iwork.resize(m+1);
-    for (Index i = 0; i < m; ++i) iwork(post(i)) = post(m_etree(i));
-    m_etree = iwork;
-    
-    // Postmultiply A*Pc by post, i.e reorder the matrix according to the postorder of the etree
-    PermutationType post_perm(m); 
-    for (Index i = 0; i < m; i++) 
-      post_perm.indices()(i) = post(i); 
-        
-    // Combine the two permutations : postorder the permutation for future use
-    if(m_perm_c.size()) {
-      m_perm_c = post_perm * m_perm_c;
-    }
-    
-  } // end postordering 
-  
-  m_analysisIsOk = true; 
-}
-
-// Functions needed by the numerical factorization phase
-
-
-/** 
-  *  - Numerical factorization 
-  *  - Interleaved with the symbolic factorization 
-  * On exit,  info is 
-  * 
-  *    = 0: successful factorization
-  * 
-  *    > 0: if info = i, and i is
-  * 
-  *       <= A->ncol: U(i,i) is exactly zero. The factorization has
-  *          been completed, but the factor U is exactly singular,
-  *          and division by zero will occur if it is used to solve a
-  *          system of equations.
-  * 
-  *       > A->ncol: number of bytes allocated when memory allocation
-  *         failure occurred, plus A->ncol. If lwork = -1, it is
-  *         the estimated amount of space needed, plus A->ncol.  
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
-{
-  using internal::emptyIdxLU;
-  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); 
-  eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices");
-  
-  m_isInitialized = true;
-  
-  // Apply the column permutation computed in analyzepattern()
-  //   m_mat = matrix * m_perm_c.inverse(); 
-  m_mat = matrix;
-  if (m_perm_c.size()) 
-  {
-    m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers.
-    //Then, permute only the column pointers
-    const StorageIndex * outerIndexPtr;
-    if (matrix.isCompressed()) outerIndexPtr = matrix.outerIndexPtr();
-    else
-    {
-      StorageIndex* outerIndexPtr_t = new StorageIndex[matrix.cols()+1];
-      for(Index i = 0; i <= matrix.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i];
-      outerIndexPtr = outerIndexPtr_t;
-    }
-    for (Index i = 0; i < matrix.cols(); i++)
-    {
-      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
-      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
-    }
-    if(!matrix.isCompressed()) delete[] outerIndexPtr;
-  } 
-  else 
-  { //FIXME This should not be needed if the empty permutation is handled transparently
-    m_perm_c.resize(matrix.cols());
-    for(StorageIndex i = 0; i < matrix.cols(); ++i) m_perm_c.indices()(i) = i;
-  }
-  
-  Index m = m_mat.rows();
-  Index n = m_mat.cols();
-  Index nnz = m_mat.nonZeros();
-  Index maxpanel = m_perfv.panel_size * m;
-  // Allocate working storage common to the factor routines
-  Index lwork = 0;
-  Index info = Base::memInit(m, n, nnz, lwork, m_perfv.fillfactor, m_perfv.panel_size, m_glu); 
-  if (info) 
-  {
-    m_lastError = "UNABLE TO ALLOCATE WORKING MEMORY\n\n" ;
-    m_factorizationIsOk = false;
-    return ; 
-  }
-  
-  // Set up pointers for integer working arrays 
-  IndexVector segrep(m); segrep.setZero();
-  IndexVector parent(m); parent.setZero();
-  IndexVector xplore(m); xplore.setZero();
-  IndexVector repfnz(maxpanel);
-  IndexVector panel_lsub(maxpanel);
-  IndexVector xprune(n); xprune.setZero();
-  IndexVector marker(m*internal::LUNoMarker); marker.setZero();
-  
-  repfnz.setConstant(-1); 
-  panel_lsub.setConstant(-1);
-  
-  // Set up pointers for scalar working arrays 
-  ScalarVector dense; 
-  dense.setZero(maxpanel);
-  ScalarVector tempv; 
-  tempv.setZero(internal::LUnumTempV(m, m_perfv.panel_size, m_perfv.maxsuper, /*m_perfv.rowblk*/m) );
-  
-  // Compute the inverse of perm_c
-  PermutationType iperm_c(m_perm_c.inverse()); 
-  
-  // Identify initial relaxed snodes
-  IndexVector relax_end(n);
-  if ( m_symmetricmode == true ) 
-    Base::heap_relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
-  else
-    Base::relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
-  
-  
-  m_perm_r.resize(m); 
-  m_perm_r.indices().setConstant(-1);
-  marker.setConstant(-1);
-  m_detPermR = 1; // Record the determinant of the row permutation
-  
-  m_glu.supno(0) = emptyIdxLU; m_glu.xsup.setConstant(0);
-  m_glu.xsup(0) = m_glu.xlsub(0) = m_glu.xusub(0) = m_glu.xlusup(0) = Index(0);
-  
-  // Work on one 'panel' at a time. A panel is one of the following :
-  //  (a) a relaxed supernode at the bottom of the etree, or
-  //  (b) panel_size contiguous columns, <panel_size> defined by the user
-  Index jcol; 
-  Index pivrow; // Pivotal row number in the original row matrix
-  Index nseg1; // Number of segments in U-column above panel row jcol
-  Index nseg; // Number of segments in each U-column 
-  Index irep; 
-  Index i, k, jj; 
-  for (jcol = 0; jcol < n; )
-  {
-    // Adjust panel size so that a panel won't overlap with the next relaxed snode. 
-    Index panel_size = m_perfv.panel_size; // upper bound on panel width
-    for (k = jcol + 1; k < (std::min)(jcol+panel_size, n); k++)
-    {
-      if (relax_end(k) != emptyIdxLU) 
-      {
-        panel_size = k - jcol; 
-        break; 
-      }
-    }
-    if (k == n) 
-      panel_size = n - jcol; 
-      
-    // Symbolic outer factorization on a panel of columns 
-    Base::panel_dfs(m, panel_size, jcol, m_mat, m_perm_r.indices(), nseg1, dense, panel_lsub, segrep, repfnz, xprune, marker, parent, xplore, m_glu); 
-    
-    // Numeric sup-panel updates in topological order 
-    Base::panel_bmod(m, panel_size, jcol, nseg1, dense, tempv, segrep, repfnz, m_glu); 
-    
-    // Sparse LU within the panel, and below the panel diagonal 
-    for ( jj = jcol; jj< jcol + panel_size; jj++) 
-    {
-      k = (jj - jcol) * m; // Column index for w-wide arrays 
-      
-      nseg = nseg1; // begin after all the panel segments
-      //Depth-first-search for the current column
-      VectorBlock<IndexVector> panel_lsubk(panel_lsub, k, m);
-      VectorBlock<IndexVector> repfnz_k(repfnz, k, m); 
-      info = Base::column_dfs(m, jj, m_perm_r.indices(), m_perfv.maxsuper, nseg, panel_lsubk, segrep, repfnz_k, xprune, marker, parent, xplore, m_glu); 
-      if ( info ) 
-      {
-        m_lastError =  "UNABLE TO EXPAND MEMORY IN COLUMN_DFS() ";
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      // Numeric updates to this column 
-      VectorBlock<ScalarVector> dense_k(dense, k, m); 
-      VectorBlock<IndexVector> segrep_k(segrep, nseg1, m-nseg1); 
-      info = Base::column_bmod(jj, (nseg - nseg1), dense_k, tempv, segrep_k, repfnz_k, jcol, m_glu); 
-      if ( info ) 
-      {
-        m_lastError = "UNABLE TO EXPAND MEMORY IN COLUMN_BMOD() ";
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      
-      // Copy the U-segments to ucol(*)
-      info = Base::copy_to_ucol(jj, nseg, segrep, repfnz_k ,m_perm_r.indices(), dense_k, m_glu); 
-      if ( info ) 
-      {
-        m_lastError = "UNABLE TO EXPAND MEMORY IN COPY_TO_UCOL() ";
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      
-      // Form the L-segment 
-      info = Base::pivotL(jj, m_diagpivotthresh, m_perm_r.indices(), iperm_c.indices(), pivrow, m_glu);
-      if ( info ) 
-      {
-        m_lastError = "THE MATRIX IS STRUCTURALLY SINGULAR ... ZERO COLUMN AT ";
-        std::ostringstream returnInfo;
-        returnInfo << info; 
-        m_lastError += returnInfo.str();
-        m_info = NumericalIssue; 
-        m_factorizationIsOk = false; 
-        return; 
-      }
-      
-      // Update the determinant of the row permutation matrix
-      // FIXME: the following test is not correct, we should probably take iperm_c into account and pivrow is not directly the row pivot.
-      if (pivrow != jj) m_detPermR = -m_detPermR;
-
-      // Prune columns (0:jj-1) using column jj
-      Base::pruneL(jj, m_perm_r.indices(), pivrow, nseg, segrep, repfnz_k, xprune, m_glu); 
-      
-      // Reset repfnz for this column 
-      for (i = 0; i < nseg; i++)
-      {
-        irep = segrep(i); 
-        repfnz_k(irep) = emptyIdxLU; 
-      }
-    } // end SparseLU within the panel  
-    jcol += panel_size;  // Move to the next panel
-  } // end for -- end elimination 
-  
-  m_detPermR = m_perm_r.determinant();
-  m_detPermC = m_perm_c.determinant();
-  
-  // Count the number of nonzeros in factors 
-  Base::countnz(n, m_nnzL, m_nnzU, m_glu); 
-  // Apply permutation  to the L subscripts 
-  Base::fixupL(n, m_perm_r.indices(), m_glu);
-  
-  // Create supernode matrix L 
-  m_Lstore.setInfos(m, n, m_glu.lusup, m_glu.xlusup, m_glu.lsub, m_glu.xlsub, m_glu.supno, m_glu.xsup); 
-  // Create the column major upper sparse matrix  U; 
-  new (&m_Ustore) MappedSparseMatrix<Scalar, ColMajor, StorageIndex> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() );
-  
-  m_info = Success;
-  m_factorizationIsOk = true;
-}
-
-template<typename MappedSupernodalType>
-struct SparseLUMatrixLReturnType : internal::no_assignment_operator
-{
-  typedef typename MappedSupernodalType::Scalar Scalar;
-  explicit SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL)
-  { }
-  Index rows() const { return m_mapL.rows(); }
-  Index cols() const { return m_mapL.cols(); }
-  template<typename Dest>
-  void solveInPlace( MatrixBase<Dest> &X) const
-  {
-    m_mapL.solveInPlace(X);
-  }
-  template<bool Conjugate, typename Dest>
-  void solveTransposedInPlace( MatrixBase<Dest> &X) const
-  {
-    m_mapL.template solveTransposedInPlace<Conjugate>(X);
-  }
-
-  const MappedSupernodalType& m_mapL;
-};
-
-template<typename MatrixLType, typename MatrixUType>
-struct SparseLUMatrixUReturnType : internal::no_assignment_operator
-{
-  typedef typename MatrixLType::Scalar Scalar;
-  SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU)
-  : m_mapL(mapL),m_mapU(mapU)
-  { }
-  Index rows() const { return m_mapL.rows(); }
-  Index cols() const { return m_mapL.cols(); }
-
-  template<typename Dest>   void solveInPlace(MatrixBase<Dest> &X) const
-  {
-    Index nrhs = X.cols();
-    Index n    = X.rows();
-    // Backward solve with U
-    for (Index k = m_mapL.nsuper(); k >= 0; k--)
-    {
-      Index fsupc = m_mapL.supToCol()[k];
-      Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
-      Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
-      Index luptr = m_mapL.colIndexPtr()[fsupc];
-
-      if (nsupc == 1)
-      {
-        for (Index j = 0; j < nrhs; j++)
-        {
-          X(fsupc, j) /= m_mapL.valuePtr()[luptr];
-        }
-      }
-      else
-      {
-        // FIXME: the following lines should use Block expressions and not Map!
-        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X.coeffRef(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-        U = A.template triangularView<Upper>().solve(U);
-      }
-
-      for (Index j = 0; j < nrhs; ++j)
-      {
-        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
-        {
-          typename MatrixUType::InnerIterator it(m_mapU, jcol);
-          for ( ; it; ++it)
-          {
-            Index irow = it.index();
-            X(irow, j) -= X(jcol, j) * it.value();
-          }
-        }
-      }
-    } // End For U-solve
-  }
-
-  template<bool Conjugate, typename Dest>   void solveTransposedInPlace(MatrixBase<Dest> &X) const
-  {
-    using numext::conj;
-    Index nrhs = X.cols();
-    Index n    = X.rows();
-    // Forward solve with U
-    for (Index k = 0; k <=  m_mapL.nsuper(); k++)
-    {
-      Index fsupc = m_mapL.supToCol()[k];
-      Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
-      Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
-      Index luptr = m_mapL.colIndexPtr()[fsupc];
-
-      for (Index j = 0; j < nrhs; ++j)
-      {
-        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
-        {
-          typename MatrixUType::InnerIterator it(m_mapU, jcol);
-          for ( ; it; ++it)
-          {
-            Index irow = it.index();
-            X(jcol, j) -= X(irow, j) * (Conjugate? conj(it.value()): it.value());
-          }
-        }
-      }
-      if (nsupc == 1)
-      {
-        for (Index j = 0; j < nrhs; j++)
-        {
-          X(fsupc, j) /= (Conjugate? conj(m_mapL.valuePtr()[luptr]) : m_mapL.valuePtr()[luptr]);
-        }
-      }
-      else
-      {
-        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-        if(Conjugate)
-          U = A.adjoint().template triangularView<Lower>().solve(U);
-        else
-          U = A.transpose().template triangularView<Lower>().solve(U);
-      }
-    }// End For U-solve
-  }
-
-
-  const MatrixLType& m_mapL;
-  const MatrixUType& m_mapU;
-};
-
-} // End namespace Eigen 
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h
deleted file mode 100644
index fc0cfc4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h
+++ /dev/null
@@ -1,66 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef SPARSELU_IMPL_H
-#define SPARSELU_IMPL_H
-
-namespace Eigen {
-namespace internal {
-  
-/** \ingroup SparseLU_Module
-  * \class SparseLUImpl
-  * Base class for sparseLU
-  */
-template <typename Scalar, typename StorageIndex>
-class SparseLUImpl
-{
-  public:
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector; 
-    typedef Matrix<Scalar,Dynamic,Dynamic,ColMajor> ScalarMatrix;
-    typedef Map<ScalarMatrix, 0,  OuterStride<> > MappedMatrixBlock;
-    typedef typename ScalarVector::RealScalar RealScalar; 
-    typedef Ref<Matrix<Scalar,Dynamic,1> > BlockScalarVector;
-    typedef Ref<Matrix<StorageIndex,Dynamic,1> > BlockIndexVector;
-    typedef LU_GlobalLU_t<IndexVector, ScalarVector> GlobalLU_t; 
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> MatrixType; 
-    
-  protected:
-     template <typename VectorType>
-     Index expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions);
-     Index memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size,  GlobalLU_t& glu); 
-     template <typename VectorType>
-     Index memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions);
-     void heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); 
-     void relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); 
-     Index snode_dfs(const Index jcol, const Index kcol,const MatrixType& mat,  IndexVector& xprune, IndexVector& marker, GlobalLU_t& glu); 
-     Index snode_bmod (const Index jcol, const Index fsupc, ScalarVector& dense, GlobalLU_t& glu);
-     Index pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu);
-     template <typename Traits>
-     void dfs_kernel(const StorageIndex jj, IndexVector& perm_r,
-                    Index& nseg, IndexVector& panel_lsub, IndexVector& segrep,
-                    Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent,
-                    IndexVector& xplore, GlobalLU_t& glu, Index& nextl_col, Index krow, Traits& traits);
-     void panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
-    
-     void panel_bmod(const Index m, const Index w, const Index jcol, const Index nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu);
-     Index column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg,  BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
-     Index column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv, BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu); 
-     Index copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep, BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu); 
-     void pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg, const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu);
-     void countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu); 
-     void fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu); 
-     
-     template<typename , typename >
-     friend struct column_dfs_traits;
-}; 
-
-} // end namespace internal
-} // namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h
deleted file mode 100644
index 349bfd5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h
+++ /dev/null
@@ -1,226 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]memory.c files in SuperLU 
- 
- * -- SuperLU routine (version 3.1) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * August 1, 2008
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-
-#ifndef EIGEN_SPARSELU_MEMORY
-#define EIGEN_SPARSELU_MEMORY
-
-namespace Eigen {
-namespace internal {
-  
-enum { LUNoMarker = 3 };
-enum {emptyIdxLU = -1};
-inline Index LUnumTempV(Index& m, Index& w, Index& t, Index& b)
-{
-  return (std::max)(m, (t+b)*w);
-}
-
-template< typename Scalar>
-inline Index LUTempSpace(Index&m, Index& w)
-{
-  return (2*w + 4 + LUNoMarker) * m * sizeof(Index) + (w + 1) * m * sizeof(Scalar);
-}
-
-
-
-
-/** 
-  * Expand the existing storage to accommodate more fill-ins
-  * \param vec Valid pointer to the vector to allocate or expand
-  * \param[in,out] length  At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector
-  * \param[in] nbElts Current number of elements in the factors
-  * \param keep_prev  1: use length  and do not expand the vector; 0: compute new_len and expand
-  * \param[in,out] num_expansions Number of times the memory has been expanded
-  */
-template <typename Scalar, typename StorageIndex>
-template <typename VectorType>
-Index  SparseLUImpl<Scalar,StorageIndex>::expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions) 
-{
-  
-  float alpha = 1.5; // Ratio of the memory increase 
-  Index new_len; // New size of the allocated memory
-  
-  if(num_expansions == 0 || keep_prev) 
-    new_len = length ; // First time allocate requested
-  else 
-    new_len = (std::max)(length+1,Index(alpha * length));
-  
-  VectorType old_vec; // Temporary vector to hold the previous values   
-  if (nbElts > 0 )
-    old_vec = vec.segment(0,nbElts); 
-  
-  //Allocate or expand the current vector
-#ifdef EIGEN_EXCEPTIONS
-  try
-#endif
-  {
-    vec.resize(new_len); 
-  }
-#ifdef EIGEN_EXCEPTIONS
-  catch(std::bad_alloc& )
-#else
-  if(!vec.size())
-#endif
-  {
-    if (!num_expansions)
-    {
-      // First time to allocate from LUMemInit()
-      // Let LUMemInit() deals with it.
-      return -1;
-    }
-    if (keep_prev)
-    {
-      // In this case, the memory length should not not be reduced
-      return new_len;
-    }
-    else 
-    {
-      // Reduce the size and increase again 
-      Index tries = 0; // Number of attempts
-      do 
-      {
-        alpha = (alpha + 1)/2;
-        new_len = (std::max)(length+1,Index(alpha * length));
-#ifdef EIGEN_EXCEPTIONS
-        try
-#endif
-        {
-          vec.resize(new_len); 
-        }
-#ifdef EIGEN_EXCEPTIONS
-        catch(std::bad_alloc& )
-#else
-        if (!vec.size())
-#endif
-        {
-          tries += 1; 
-          if ( tries > 10) return new_len; 
-        }
-      } while (!vec.size());
-    }
-  }
-  //Copy the previous values to the newly allocated space 
-  if (nbElts > 0)
-    vec.segment(0, nbElts) = old_vec;   
-   
-  
-  length  = new_len;
-  if(num_expansions) ++num_expansions;
-  return 0; 
-}
-
-/**
- * \brief  Allocate various working space for the numerical factorization phase.
- * \param m number of rows of the input matrix 
- * \param n number of columns 
- * \param annz number of initial nonzeros in the matrix 
- * \param lwork  if lwork=-1, this routine returns an estimated size of the required memory
- * \param glu persistent data to facilitate multiple factors : will be deleted later ??
- * \param fillratio estimated ratio of fill in the factors
- * \param panel_size Size of a panel
- * \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated memory when allocation failed, and 0 on success
- * \note Unlike SuperLU, this routine does not support successive factorization with the same pattern and the same row permutation
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size,  GlobalLU_t& glu)
-{
-  Index& num_expansions = glu.num_expansions; //No memory expansions so far
-  num_expansions = 0;
-  glu.nzumax = glu.nzlumax = (std::min)(fillratio * (annz+1) / n, m) * n; // estimated number of nonzeros in U 
-  glu.nzlmax = (std::max)(Index(4), fillratio) * (annz+1) / 4; // estimated  nnz in L factor
-  // Return the estimated size to the user if necessary
-  Index tempSpace;
-  tempSpace = (2*panel_size + 4 + LUNoMarker) * m * sizeof(Index) + (panel_size + 1) * m * sizeof(Scalar);
-  if (lwork == emptyIdxLU) 
-  {
-    Index estimated_size;
-    estimated_size = (5 * n + 5) * sizeof(Index)  + tempSpace
-                    + (glu.nzlmax + glu.nzumax) * sizeof(Index) + (glu.nzlumax+glu.nzumax) *  sizeof(Scalar) + n; 
-    return estimated_size;
-  }
-  
-  // Setup the required space 
-  
-  // First allocate Integer pointers for L\U factors
-  glu.xsup.resize(n+1);
-  glu.supno.resize(n+1);
-  glu.xlsub.resize(n+1);
-  glu.xlusup.resize(n+1);
-  glu.xusub.resize(n+1);
-
-  // Reserve memory for L/U factors
-  do 
-  {
-    if(     (expand<ScalarVector>(glu.lusup, glu.nzlumax, 0, 0, num_expansions)<0)
-        ||  (expand<ScalarVector>(glu.ucol,  glu.nzumax,  0, 0, num_expansions)<0)
-        ||  (expand<IndexVector> (glu.lsub,  glu.nzlmax,  0, 0, num_expansions)<0)
-        ||  (expand<IndexVector> (glu.usub,  glu.nzumax,  0, 1, num_expansions)<0) )
-    {
-      //Reduce the estimated size and retry
-      glu.nzlumax /= 2;
-      glu.nzumax /= 2;
-      glu.nzlmax /= 2;
-      if (glu.nzlumax < annz ) return glu.nzlumax; 
-    }
-  } while (!glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size());
-  
-  ++num_expansions;
-  return 0;
-  
-} // end LuMemInit
-
-/** 
- * \brief Expand the existing storage 
- * \param vec vector to expand 
- * \param[in,out] maxlen On input, previous size of vec (Number of elements to copy ). on output, new size
- * \param nbElts current number of elements in the vector.
- * \param memtype Type of the element to expand
- * \param num_expansions Number of expansions 
- * \return 0 on success, > 0 size of the memory allocated so far
- */
-template <typename Scalar, typename StorageIndex>
-template <typename VectorType>
-Index SparseLUImpl<Scalar,StorageIndex>::memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions)
-{
-  Index failed_size; 
-  if (memtype == USUB)
-     failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 1, num_expansions);
-  else
-    failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 0, num_expansions);
-
-  if (failed_size)
-    return failed_size; 
-  
-  return 0 ;  
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // EIGEN_SPARSELU_MEMORY
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
deleted file mode 100644
index cf5ec44..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
+++ /dev/null
@@ -1,110 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- * NOTE: This file comes from a partly modified version of files slu_[s,d,c,z]defs.h
- * -- SuperLU routine (version 4.1) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November, 2010
- * 
- * Global data structures used in LU factorization -
- * 
- *   nsuper: #supernodes = nsuper + 1, numbered [0, nsuper].
- *   (xsup,supno): supno[i] is the supernode no to which i belongs;
- *  xsup(s) points to the beginning of the s-th supernode.
- *  e.g.   supno 0 1 2 2 3 3 3 4 4 4 4 4   (n=12)
- *          xsup 0 1 2 4 7 12
- *  Note: dfs will be performed on supernode rep. relative to the new 
- *        row pivoting ordering
- *
- *   (xlsub,lsub): lsub[*] contains the compressed subscript of
- *  rectangular supernodes; xlsub[j] points to the starting
- *  location of the j-th column in lsub[*]. Note that xlsub 
- *  is indexed by column.
- *  Storage: original row subscripts
- *
- *      During the course of sparse LU factorization, we also use
- *  (xlsub,lsub) for the purpose of symmetric pruning. For each
- *  supernode {s,s+1,...,t=s+r} with first column s and last
- *  column t, the subscript set
- *    lsub[j], j=xlsub[s], .., xlsub[s+1]-1
- *  is the structure of column s (i.e. structure of this supernode).
- *  It is used for the storage of numerical values.
- *  Furthermore,
- *    lsub[j], j=xlsub[t], .., xlsub[t+1]-1
- *  is the structure of the last column t of this supernode.
- *  It is for the purpose of symmetric pruning. Therefore, the
- *  structural subscripts can be rearranged without making physical
- *  interchanges among the numerical values.
- *
- *  However, if the supernode has only one column, then we
- *  only keep one set of subscripts. For any subscript interchange
- *  performed, similar interchange must be done on the numerical
- *  values.
- *
- *  The last column structures (for pruning) will be removed
- *  after the numercial LU factorization phase.
- *
- *   (xlusup,lusup): lusup[*] contains the numerical values of the
- *  rectangular supernodes; xlusup[j] points to the starting
- *  location of the j-th column in storage vector lusup[*]
- *  Note: xlusup is indexed by column.
- *  Each rectangular supernode is stored by column-major
- *  scheme, consistent with Fortran 2-dim array storage.
- *
- *   (xusub,ucol,usub): ucol[*] stores the numerical values of
- *  U-columns outside the rectangular supernodes. The row
- *  subscript of nonzero ucol[k] is stored in usub[k].
- *  xusub[i] points to the starting location of column i in ucol.
- *  Storage: new row subscripts; that is subscripts of PA.
- */
-
-#ifndef EIGEN_LU_STRUCTS
-#define EIGEN_LU_STRUCTS
-namespace Eigen {
-namespace internal {
-  
-typedef enum {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL} MemType; 
-
-template <typename IndexVector, typename ScalarVector>
-struct LU_GlobalLU_t {
-  typedef typename IndexVector::Scalar StorageIndex; 
-  IndexVector xsup; //First supernode column ... xsup(s) points to the beginning of the s-th supernode
-  IndexVector supno; // Supernode number corresponding to this column (column to supernode mapping)
-  ScalarVector  lusup; // nonzero values of L ordered by columns 
-  IndexVector lsub; // Compressed row indices of L rectangular supernodes. 
-  IndexVector xlusup; // pointers to the beginning of each column in lusup
-  IndexVector xlsub; // pointers to the beginning of each column in lsub
-  Index   nzlmax; // Current max size of lsub
-  Index   nzlumax; // Current max size of lusup
-  ScalarVector  ucol; // nonzero values of U ordered by columns 
-  IndexVector usub; // row indices of U columns in ucol
-  IndexVector xusub; // Pointers to the beginning of each column of U in ucol 
-  Index   nzumax; // Current max size of ucol
-  Index   n; // Number of columns in the matrix  
-  Index   num_expansions; 
-};
-
-// Values to set for performance
-struct perfvalues {
-  Index panel_size; // a panel consists of at most <panel_size> consecutive columns
-  Index relax; // To control degree of relaxing supernodes. If the number of nodes (columns) 
-                // in a subtree of the elimination tree is less than relax, this subtree is considered 
-                // as one supernode regardless of the row structures of those columns
-  Index maxsuper; // The maximum size for a supernode in complete LU
-  Index rowblk; // The minimum row dimension for 2-D blocking to be used;
-  Index colblk; // The minimum column dimension for 2-D blocking to be used;
-  Index fillfactor; // The estimated fills factors for L and U, compared with A
-}; 
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // EIGEN_LU_STRUCTS
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
deleted file mode 100644
index 0be293d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
+++ /dev/null
@@ -1,375 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
-#define EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
-
-namespace Eigen {
-namespace internal {
-
-/** \ingroup SparseLU_Module
- * \brief a class to manipulate the L supernodal factor from the SparseLU factorization
- * 
- * This class  contain the data to easily store 
- * and manipulate the supernodes during the factorization and solution phase of Sparse LU. 
- * Only the lower triangular matrix has supernodes.
- * 
- * NOTE : This class corresponds to the SCformat structure in SuperLU
- * 
- */
-/* TODO
- * InnerIterator as for sparsematrix 
- * SuperInnerIterator to iterate through all supernodes 
- * Function for triangular solve
- */
-template <typename _Scalar, typename _StorageIndex>
-class MappedSuperNodalMatrix
-{
-  public:
-    typedef _Scalar Scalar; 
-    typedef _StorageIndex StorageIndex;
-    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
-    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
-  public:
-    MappedSuperNodalMatrix()
-    {
-      
-    }
-    MappedSuperNodalMatrix(Index m, Index n,  ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
-             IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col )
-    {
-      setInfos(m, n, nzval, nzval_colptr, rowind, rowind_colptr, col_to_sup, sup_to_col);
-    }
-    
-    ~MappedSuperNodalMatrix()
-    {
-      
-    }
-    /**
-     * Set appropriate pointers for the lower triangular supernodal matrix
-     * These infos are available at the end of the numerical factorization
-     * FIXME This class will be modified such that it can be use in the course 
-     * of the factorization.
-     */
-    void setInfos(Index m, Index n, ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
-             IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col )
-    {
-      m_row = m;
-      m_col = n; 
-      m_nzval = nzval.data(); 
-      m_nzval_colptr = nzval_colptr.data(); 
-      m_rowind = rowind.data(); 
-      m_rowind_colptr = rowind_colptr.data(); 
-      m_nsuper = col_to_sup(n); 
-      m_col_to_sup = col_to_sup.data(); 
-      m_sup_to_col = sup_to_col.data(); 
-    }
-    
-    /**
-     * Number of rows
-     */
-    Index rows() const { return m_row; }
-    
-    /**
-     * Number of columns
-     */
-    Index cols() const { return m_col; }
-    
-    /**
-     * Return the array of nonzero values packed by column
-     * 
-     * The size is nnz
-     */
-    Scalar* valuePtr() {  return m_nzval; }
-    
-    const Scalar* valuePtr() const 
-    {
-      return m_nzval; 
-    }
-    /**
-     * Return the pointers to the beginning of each column in \ref valuePtr()
-     */
-    StorageIndex* colIndexPtr()
-    {
-      return m_nzval_colptr; 
-    }
-    
-    const StorageIndex* colIndexPtr() const
-    {
-      return m_nzval_colptr; 
-    }
-    
-    /**
-     * Return the array of compressed row indices of all supernodes
-     */
-    StorageIndex* rowIndex()  { return m_rowind; }
-    
-    const StorageIndex* rowIndex() const
-    {
-      return m_rowind; 
-    }
-    
-    /**
-     * Return the location in \em rowvaluePtr() which starts each column
-     */
-    StorageIndex* rowIndexPtr() { return m_rowind_colptr; }
-    
-    const StorageIndex* rowIndexPtr() const
-    {
-      return m_rowind_colptr; 
-    }
-    
-    /** 
-     * Return the array of column-to-supernode mapping 
-     */
-    StorageIndex* colToSup()  { return m_col_to_sup; }
-    
-    const StorageIndex* colToSup() const
-    {
-      return m_col_to_sup;       
-    }
-    /**
-     * Return the array of supernode-to-column mapping
-     */
-    StorageIndex* supToCol() { return m_sup_to_col; }
-    
-    const StorageIndex* supToCol() const
-    {
-      return m_sup_to_col;
-    }
-    
-    /**
-     * Return the number of supernodes
-     */
-    Index nsuper() const
-    {
-      return m_nsuper; 
-    }
-    
-    class InnerIterator; 
-    template<typename Dest>
-    void solveInPlace( MatrixBase<Dest>&X) const;
-    template<bool Conjugate, typename Dest>
-    void solveTransposedInPlace( MatrixBase<Dest>&X) const;
-
-    
-      
-      
-    
-  protected:
-    Index m_row; // Number of rows
-    Index m_col; // Number of columns
-    Index m_nsuper; // Number of supernodes
-    Scalar* m_nzval; //array of nonzero values packed by column
-    StorageIndex* m_nzval_colptr; //nzval_colptr[j] Stores the location in nzval[] which starts column j
-    StorageIndex* m_rowind; // Array of compressed row indices of rectangular supernodes
-    StorageIndex* m_rowind_colptr; //rowind_colptr[j] stores the location in rowind[] which starts column j
-    StorageIndex* m_col_to_sup; // col_to_sup[j] is the supernode number to which column j belongs
-    StorageIndex* m_sup_to_col; //sup_to_col[s] points to the starting column of the s-th supernode
-    
-  private :
-};
-
-/**
-  * \brief InnerIterator class to iterate over nonzero values of the current column in the supernodal matrix L
-  * 
-  */
-template<typename Scalar, typename StorageIndex>
-class MappedSuperNodalMatrix<Scalar,StorageIndex>::InnerIterator
-{
-  public:
-     InnerIterator(const MappedSuperNodalMatrix& mat, Index outer)
-      : m_matrix(mat),
-        m_outer(outer),
-        m_supno(mat.colToSup()[outer]),
-        m_idval(mat.colIndexPtr()[outer]),
-        m_startidval(m_idval),
-        m_endidval(mat.colIndexPtr()[outer+1]),
-        m_idrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]]),
-        m_endidrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]+1])
-    {}
-    inline InnerIterator& operator++()
-    { 
-      m_idval++; 
-      m_idrow++;
-      return *this;
-    }
-    inline Scalar value() const { return m_matrix.valuePtr()[m_idval]; }
-    
-    inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_idval]); }
-    
-    inline Index index() const { return m_matrix.rowIndex()[m_idrow]; }
-    inline Index row() const { return index(); }
-    inline Index col() const { return m_outer; }
-    
-    inline Index supIndex() const { return m_supno; }
-    
-    inline operator bool() const 
-    { 
-      return ( (m_idval < m_endidval) && (m_idval >= m_startidval)
-                && (m_idrow < m_endidrow) );
-    }
-    
-  protected:
-    const MappedSuperNodalMatrix& m_matrix; // Supernodal lower triangular matrix 
-    const Index m_outer;                    // Current column 
-    const Index m_supno;                    // Current SuperNode number
-    Index m_idval;                          // Index to browse the values in the current column
-    const Index m_startidval;               // Start of the column value
-    const Index m_endidval;                 // End of the column value
-    Index m_idrow;                          // Index to browse the row indices 
-    Index m_endidrow;                       // End index of row indices of the current column
-};
-
-/**
- * \brief Solve with the supernode triangular matrix
- * 
- */
-template<typename Scalar, typename Index_>
-template<typename Dest>
-void MappedSuperNodalMatrix<Scalar,Index_>::solveInPlace( MatrixBase<Dest>&X) const
-{
-    /* Explicit type conversion as the Index type of MatrixBase<Dest> may be wider than Index */
-//    eigen_assert(X.rows() <= NumTraits<Index>::highest());
-//    eigen_assert(X.cols() <= NumTraits<Index>::highest());
-    Index n    = int(X.rows());
-    Index nrhs = Index(X.cols());
-    const Scalar * Lval = valuePtr();                 // Nonzero values 
-    Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
-    work.setZero();
-    for (Index k = 0; k <= nsuper(); k ++)
-    {
-      Index fsupc = supToCol()[k];                    // First column of the current supernode 
-      Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
-      Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
-      Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
-      Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
-      Index irow;                                     //Current index row
-      
-      if (nsupc == 1 )
-      {
-        for (Index j = 0; j < nrhs; j++)
-        {
-          InnerIterator it(*this, fsupc);
-          ++it; // Skip the diagonal element
-          for (; it; ++it)
-          {
-            irow = it.row();
-            X(irow, j) -= X(fsupc, j) * it.value();
-          }
-        }
-      }
-      else
-      {
-        // The supernode has more than one column 
-        Index luptr = colIndexPtr()[fsupc]; 
-        Index lda = colIndexPtr()[fsupc+1] - luptr;
-        
-        // Triangular solve 
-        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-        U = A.template triangularView<UnitLower>().solve(U); 
-        
-        // Matrix-vector product 
-        new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-        work.topRows(nrow).noalias() = A * U;
-        
-        //Begin Scatter 
-        for (Index j = 0; j < nrhs; j++)
-        {
-          Index iptr = istart + nsupc; 
-          for (Index i = 0; i < nrow; i++)
-          {
-            irow = rowIndex()[iptr]; 
-            X(irow, j) -= work(i, j); // Scatter operation
-            work(i, j) = Scalar(0); 
-            iptr++;
-          }
-        }
-      }
-    } 
-}
-
-template<typename Scalar, typename Index_>
-template<bool Conjugate, typename Dest>
-void MappedSuperNodalMatrix<Scalar,Index_>::solveTransposedInPlace( MatrixBase<Dest>&X) const
-{
-    using numext::conj;
-  Index n    = int(X.rows());
-  Index nrhs = Index(X.cols());
-  const Scalar * Lval = valuePtr();                 // Nonzero values
-  Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
-  work.setZero();
-  for (Index k = nsuper(); k >= 0; k--)
-  {
-    Index fsupc = supToCol()[k];                    // First column of the current supernode
-    Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
-    Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
-    Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
-    Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
-    Index irow;                                     //Current index row
-
-    if (nsupc == 1 )
-    {
-      for (Index j = 0; j < nrhs; j++)
-      {
-        InnerIterator it(*this, fsupc);
-        ++it; // Skip the diagonal element
-        for (; it; ++it)
-        {
-          irow = it.row();
-          X(fsupc,j) -= X(irow, j) * (Conjugate?conj(it.value()):it.value());
-        }
-      }
-    }
-    else
-    {
-      // The supernode has more than one column
-      Index luptr = colIndexPtr()[fsupc];
-      Index lda = colIndexPtr()[fsupc+1] - luptr;
-
-      //Begin Gather
-      for (Index j = 0; j < nrhs; j++)
-      {
-        Index iptr = istart + nsupc;
-        for (Index i = 0; i < nrow; i++)
-        {
-          irow = rowIndex()[iptr];
-          work.topRows(nrow)(i,j)= X(irow,j); // Gather operation
-          iptr++;
-        }
-      }
-
-      // Matrix-vector product with transposed submatrix
-      Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-      Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
-      if(Conjugate)
-        U = U - A.adjoint() * work.topRows(nrow);
-      else
-        U = U - A.transpose() * work.topRows(nrow);
-
-      // Triangular solve (of transposed diagonal block)
-      new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-      if(Conjugate)
-        U = A.adjoint().template triangularView<UnitUpper>().solve(U);
-      else
-        U = A.transpose().template triangularView<UnitUpper>().solve(U);
-
-    }
-
-  }
-}
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSELU_MATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h
deleted file mode 100644
index 9e3dab4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h
+++ /dev/null
@@ -1,80 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_SPARSELU_UTILS_H
-#define EIGEN_SPARSELU_UTILS_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Count Nonzero elements in the factors
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu)
-{
- nnzL = 0; 
- nnzU = (glu.xusub)(n); 
- Index nsuper = (glu.supno)(n); 
- Index jlen; 
- Index i, j, fsupc;
- if (n <= 0 ) return; 
- // For each supernode
- for (i = 0; i <= nsuper; i++)
- {
-   fsupc = glu.xsup(i); 
-   jlen = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
-   
-   for (j = fsupc; j < glu.xsup(i+1); j++)
-   {
-     nnzL += jlen; 
-     nnzU += j - fsupc + 1; 
-     jlen--; 
-   }
- }
-}
-
-/**
- * \brief Fix up the data storage lsub for L-subscripts. 
- * 
- * It removes the subscripts sets for structural pruning, 
- * and applies permutation to the remaining subscripts
- * 
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu)
-{
-  Index fsupc, i, j, k, jstart; 
-  
-  StorageIndex nextl = 0; 
-  Index nsuper = (glu.supno)(n); 
-  
-  // For each supernode 
-  for (i = 0; i <= nsuper; i++)
-  {
-    fsupc = glu.xsup(i); 
-    jstart = glu.xlsub(fsupc); 
-    glu.xlsub(fsupc) = nextl; 
-    for (j = jstart; j < glu.xlsub(fsupc + 1); j++)
-    {
-      glu.lsub(nextl) = perm_r(glu.lsub(j)); // Now indexed into P*A
-      nextl++;
-    }
-    for (k = fsupc+1; k < glu.xsup(i+1); k++)
-      glu.xlsub(k) = nextl; // other columns in supernode i
-  }
-  
-  glu.xlsub(n) = nextl; 
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // EIGEN_SPARSELU_UTILS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h
deleted file mode 100644
index b57f068..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h
+++ /dev/null
@@ -1,181 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of xcolumn_bmod.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_COLUMN_BMOD_H
-#define SPARSELU_COLUMN_BMOD_H
-
-namespace Eigen {
-
-namespace internal {
-/**
- * \brief Performs numeric block updates (sup-col) in topological order
- * 
- * \param jcol current column to update
- * \param nseg Number of segments in the U part
- * \param dense Store the full representation of the column
- * \param tempv working array 
- * \param segrep segment representative ...
- * \param repfnz ??? First nonzero column in each row ???  ...
- * \param fpanelc First column in the current panel
- * \param glu Global LU data. 
- * \return 0 - successful return 
- *         > 0 - number of bytes allocated when run out of space
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv,
-                                                     BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu)
-{
-  Index  jsupno, k, ksub, krep, ksupno; 
-  Index lptr, nrow, isub, irow, nextlu, new_next, ufirst; 
-  Index fsupc, nsupc, nsupr, luptr, kfnz, no_zeros; 
-  /* krep = representative of current k-th supernode
-    * fsupc =  first supernodal column
-    * nsupc = number of columns in a supernode
-    * nsupr = number of rows in a supernode
-    * luptr = location of supernodal LU-block in storage
-    * kfnz = first nonz in the k-th supernodal segment
-    * no_zeros = no lf leading zeros in a supernodal U-segment
-    */
-  
-  jsupno = glu.supno(jcol);
-  // For each nonzero supernode segment of U[*,j] in topological order 
-  k = nseg - 1; 
-  Index d_fsupc; // distance between the first column of the current panel and the 
-               // first column of the current snode
-  Index fst_col; // First column within small LU update
-  Index segsize; 
-  for (ksub = 0; ksub < nseg; ksub++)
-  {
-    krep = segrep(k); k--; 
-    ksupno = glu.supno(krep); 
-    if (jsupno != ksupno )
-    {
-      // outside the rectangular supernode 
-      fsupc = glu.xsup(ksupno); 
-      fst_col = (std::max)(fsupc, fpanelc); 
-      
-      // Distance from the current supernode to the current panel; 
-      // d_fsupc = 0 if fsupc > fpanelc
-      d_fsupc = fst_col - fsupc; 
-      
-      luptr = glu.xlusup(fst_col) + d_fsupc; 
-      lptr = glu.xlsub(fsupc) + d_fsupc; 
-      
-      kfnz = repfnz(krep); 
-      kfnz = (std::max)(kfnz, fpanelc); 
-      
-      segsize = krep - kfnz + 1; 
-      nsupc = krep - fst_col + 1; 
-      nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
-      nrow = nsupr - d_fsupc - nsupc;
-      Index lda = glu.xlusup(fst_col+1) - glu.xlusup(fst_col);
-      
-      
-      // Perform a triangular solver and block update, 
-      // then scatter the result of sup-col update to dense
-      no_zeros = kfnz - fst_col; 
-      if(segsize==1)
-        LU_kernel_bmod<1>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-      else
-        LU_kernel_bmod<Dynamic>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-    } // end if jsupno 
-  } // end for each segment
-  
-  // Process the supernodal portion of  L\U[*,j]
-  nextlu = glu.xlusup(jcol); 
-  fsupc = glu.xsup(jsupno);
-  
-  // copy the SPA dense into L\U[*,j]
-  Index mem; 
-  new_next = nextlu + glu.xlsub(fsupc + 1) - glu.xlsub(fsupc); 
-  Index offset = internal::first_multiple<Index>(new_next, internal::packet_traits<Scalar>::size) - new_next;
-  if(offset)
-    new_next += offset;
-  while (new_next > glu.nzlumax )
-  {
-    mem = memXpand<ScalarVector>(glu.lusup, glu.nzlumax, nextlu, LUSUP, glu.num_expansions);  
-    if (mem) return mem; 
-  }
-  
-  for (isub = glu.xlsub(fsupc); isub < glu.xlsub(fsupc+1); isub++)
-  {
-    irow = glu.lsub(isub);
-    glu.lusup(nextlu) = dense(irow);
-    dense(irow) = Scalar(0.0); 
-    ++nextlu; 
-  }
-  
-  if(offset)
-  {
-    glu.lusup.segment(nextlu,offset).setZero();
-    nextlu += offset;
-  }
-  glu.xlusup(jcol + 1) = StorageIndex(nextlu);  // close L\U(*,jcol); 
-  
-  /* For more updates within the panel (also within the current supernode),
-   * should start from the first column of the panel, or the first column
-   * of the supernode, whichever is bigger. There are two cases:
-   *  1) fsupc < fpanelc, then fst_col <-- fpanelc
-   *  2) fsupc >= fpanelc, then fst_col <-- fsupc
-   */
-  fst_col = (std::max)(fsupc, fpanelc); 
-  
-  if (fst_col  < jcol)
-  {
-    // Distance between the current supernode and the current panel
-    // d_fsupc = 0 if fsupc >= fpanelc
-    d_fsupc = fst_col - fsupc; 
-    
-    lptr = glu.xlsub(fsupc) + d_fsupc; 
-    luptr = glu.xlusup(fst_col) + d_fsupc; 
-    nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); // leading dimension
-    nsupc = jcol - fst_col; // excluding jcol 
-    nrow = nsupr - d_fsupc - nsupc; 
-    
-    // points to the beginning of jcol in snode L\U(jsupno) 
-    ufirst = glu.xlusup(jcol) + d_fsupc; 
-    Index lda = glu.xlusup(jcol+1) - glu.xlusup(jcol);
-    MappedMatrixBlock A( &(glu.lusup.data()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-    VectorBlock<ScalarVector> u(glu.lusup, ufirst, nsupc); 
-    u = A.template triangularView<UnitLower>().solve(u); 
-    
-    new (&A) MappedMatrixBlock ( &(glu.lusup.data()[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-    VectorBlock<ScalarVector> l(glu.lusup, ufirst+nsupc, nrow); 
-    l.noalias() -= A * u;
-    
-  } // End if fst_col
-  return 0; 
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_COLUMN_BMOD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h
deleted file mode 100644
index 5a2c941..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h
+++ /dev/null
@@ -1,179 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]column_dfs.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_COLUMN_DFS_H
-#define SPARSELU_COLUMN_DFS_H
-
-template <typename Scalar, typename StorageIndex> class SparseLUImpl;
-namespace Eigen {
-
-namespace internal {
-
-template<typename IndexVector, typename ScalarVector>
-struct column_dfs_traits : no_assignment_operator
-{
-  typedef typename ScalarVector::Scalar Scalar;
-  typedef typename IndexVector::Scalar StorageIndex;
-  column_dfs_traits(Index jcol, Index& jsuper, typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& glu, SparseLUImpl<Scalar, StorageIndex>& luImpl)
-   : m_jcol(jcol), m_jsuper_ref(jsuper), m_glu(glu), m_luImpl(luImpl)
- {}
-  bool update_segrep(Index /*krep*/, Index /*jj*/)
-  {
-    return true;
-  }
-  void mem_expand(IndexVector& lsub, Index& nextl, Index chmark)
-  {
-    if (nextl >= m_glu.nzlmax)
-      m_luImpl.memXpand(lsub, m_glu.nzlmax, nextl, LSUB, m_glu.num_expansions); 
-    if (chmark != (m_jcol-1)) m_jsuper_ref = emptyIdxLU;
-  }
-  enum { ExpandMem = true };
-  
-  Index m_jcol;
-  Index& m_jsuper_ref;
-  typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& m_glu;
-  SparseLUImpl<Scalar, StorageIndex>& m_luImpl;
-};
-
-
-/**
- * \brief Performs a symbolic factorization on column jcol and decide the supernode boundary
- * 
- * A supernode representative is the last column of a supernode.
- * The nonzeros in U[*,j] are segments that end at supernodes representatives. 
- * The routine returns a list of the supernodal representatives 
- * in topological order of the dfs that generates them. 
- * The location of the first nonzero in each supernodal segment 
- * (supernodal entry location) is also returned. 
- * 
- * \param m number of rows in the matrix
- * \param jcol Current column 
- * \param perm_r Row permutation
- * \param maxsuper  Maximum number of column allowed in a supernode
- * \param [in,out] nseg Number of segments in current U[*,j] - new segments appended
- * \param lsub_col defines the rhs vector to start the dfs
- * \param [in,out] segrep Segment representatives - new segments appended 
- * \param repfnz  First nonzero location in each row
- * \param xprune 
- * \param marker  marker[i] == jj, if i was visited during dfs of current column jj;
- * \param parent
- * \param xplore working array
- * \param glu global LU data 
- * \return 0 success
- *         > 0 number of bytes allocated when run out of space
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg,
-                                                    BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune,
-                                                    IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu)
-{
-  
-  Index jsuper = glu.supno(jcol); 
-  Index nextl = glu.xlsub(jcol); 
-  VectorBlock<IndexVector> marker2(marker, 2*m, m); 
-  
-  
-  column_dfs_traits<IndexVector, ScalarVector> traits(jcol, jsuper, glu, *this);
-  
-  // For each nonzero in A(*,jcol) do dfs 
-  for (Index k = 0; ((k < m) ? lsub_col[k] != emptyIdxLU : false) ; k++)
-  {
-    Index krow = lsub_col(k); 
-    lsub_col(k) = emptyIdxLU; 
-    Index kmark = marker2(krow); 
-    
-    // krow was visited before, go to the next nonz; 
-    if (kmark == jcol) continue;
-    
-    dfs_kernel(StorageIndex(jcol), perm_r, nseg, glu.lsub, segrep, repfnz, xprune, marker2, parent,
-                   xplore, glu, nextl, krow, traits);
-  } // for each nonzero ... 
-  
-  Index fsupc;
-  StorageIndex nsuper = glu.supno(jcol);
-  StorageIndex jcolp1 = StorageIndex(jcol) + 1;
-  Index jcolm1 = jcol - 1;
-  
-  // check to see if j belongs in the same supernode as j-1
-  if ( jcol == 0 )
-  { // Do nothing for column 0 
-    nsuper = glu.supno(0) = 0 ;
-  }
-  else 
-  {
-    fsupc = glu.xsup(nsuper); 
-    StorageIndex jptr = glu.xlsub(jcol); // Not yet compressed
-    StorageIndex jm1ptr = glu.xlsub(jcolm1); 
-    
-    // Use supernodes of type T2 : see SuperLU paper
-    if ( (nextl-jptr != jptr-jm1ptr-1) ) jsuper = emptyIdxLU;
-    
-    // Make sure the number of columns in a supernode doesn't
-    // exceed threshold
-    if ( (jcol - fsupc) >= maxsuper) jsuper = emptyIdxLU; 
-    
-    /* If jcol starts a new supernode, reclaim storage space in
-     * glu.lsub from previous supernode. Note we only store 
-     * the subscript set of the first and last columns of 
-     * a supernode. (first for num values, last for pruning)
-     */
-    if (jsuper == emptyIdxLU)
-    { // starts a new supernode 
-      if ( (fsupc < jcolm1-1) ) 
-      { // >= 3 columns in nsuper
-        StorageIndex ito = glu.xlsub(fsupc+1);
-        glu.xlsub(jcolm1) = ito; 
-        StorageIndex istop = ito + jptr - jm1ptr; 
-        xprune(jcolm1) = istop; // initialize xprune(jcol-1)
-        glu.xlsub(jcol) = istop; 
-        
-        for (StorageIndex ifrom = jm1ptr; ifrom < nextl; ++ifrom, ++ito)
-          glu.lsub(ito) = glu.lsub(ifrom); 
-        nextl = ito;  // = istop + length(jcol)
-      }
-      nsuper++; 
-      glu.supno(jcol) = nsuper; 
-    } // if a new supernode 
-  } // end else:  jcol > 0
-  
-  // Tidy up the pointers before exit
-  glu.xsup(nsuper+1) = jcolp1; 
-  glu.supno(jcolp1) = nsuper; 
-  xprune(jcol) = StorageIndex(nextl);  // Initialize upper bound for pruning
-  glu.xlsub(jcolp1) = StorageIndex(nextl); 
-  
-  return 0; 
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
deleted file mode 100644
index c32d8d8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
+++ /dev/null
@@ -1,107 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]copy_to_ucol.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_COPY_TO_UCOL_H
-#define SPARSELU_COPY_TO_UCOL_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Performs numeric block updates (sup-col) in topological order
- * 
- * \param jcol current column to update
- * \param nseg Number of segments in the U part
- * \param segrep segment representative ...
- * \param repfnz First nonzero column in each row  ...
- * \param perm_r Row permutation 
- * \param dense Store the full representation of the column
- * \param glu Global LU data. 
- * \return 0 - successful return 
- *         > 0 - number of bytes allocated when run out of space
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep,
-                                                      BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu)
-{  
-  Index ksub, krep, ksupno; 
-    
-  Index jsupno = glu.supno(jcol);
-  
-  // For each nonzero supernode segment of U[*,j] in topological order 
-  Index k = nseg - 1, i; 
-  StorageIndex nextu = glu.xusub(jcol); 
-  Index kfnz, isub, segsize; 
-  Index new_next,irow; 
-  Index fsupc, mem; 
-  for (ksub = 0; ksub < nseg; ksub++)
-  {
-    krep = segrep(k); k--; 
-    ksupno = glu.supno(krep); 
-    if (jsupno != ksupno ) // should go into ucol(); 
-    {
-      kfnz = repfnz(krep); 
-      if (kfnz != emptyIdxLU)
-      { // Nonzero U-segment 
-        fsupc = glu.xsup(ksupno); 
-        isub = glu.xlsub(fsupc) + kfnz - fsupc; 
-        segsize = krep - kfnz + 1; 
-        new_next = nextu + segsize; 
-        while (new_next > glu.nzumax) 
-        {
-          mem = memXpand<ScalarVector>(glu.ucol, glu.nzumax, nextu, UCOL, glu.num_expansions); 
-          if (mem) return mem; 
-          mem = memXpand<IndexVector>(glu.usub, glu.nzumax, nextu, USUB, glu.num_expansions); 
-          if (mem) return mem; 
-          
-        }
-        
-        for (i = 0; i < segsize; i++)
-        {
-          irow = glu.lsub(isub); 
-          glu.usub(nextu) = perm_r(irow); // Unlike the L part, the U part is stored in its final order
-          glu.ucol(nextu) = dense(irow); 
-          dense(irow) = Scalar(0.0); 
-          nextu++;
-          isub++;
-        }
-        
-      } // end nonzero U-segment 
-      
-    } // end if jsupno 
-    
-  } // end for each segment
-  glu.xusub(jcol + 1) = nextu; // close U(*,jcol)
-  return 0; 
-}
-
-} // namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_COPY_TO_UCOL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
deleted file mode 100644
index e37c2fe..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
+++ /dev/null
@@ -1,280 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSELU_GEMM_KERNEL_H
-#define EIGEN_SPARSELU_GEMM_KERNEL_H
-
-namespace Eigen {
-
-namespace internal {
-
-
-/** \internal
-  * A general matrix-matrix product kernel optimized for the SparseLU factorization.
-  *  - A, B, and C must be column major
-  *  - lda and ldc must be multiples of the respective packet size
-  *  - C must have the same alignment as A
-  */
-template<typename Scalar>
-EIGEN_DONT_INLINE
-void sparselu_gemm(Index m, Index n, Index d, const Scalar* A, Index lda, const Scalar* B, Index ldb, Scalar* C, Index ldc)
-{
-  using namespace Eigen::internal;
-  
-  typedef typename packet_traits<Scalar>::type Packet;
-  enum {
-    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
-    PacketSize = packet_traits<Scalar>::size,
-    PM = 8,                             // peeling in M
-    RN = 2,                             // register blocking
-    RK = NumberOfRegisters>=16 ? 4 : 2, // register blocking
-    BM = 4096/sizeof(Scalar),           // number of rows of A-C per chunk
-    SM = PM*PacketSize                  // step along M
-  };
-  Index d_end = (d/RK)*RK;    // number of columns of A (rows of B) suitable for full register blocking
-  Index n_end = (n/RN)*RN;    // number of columns of B-C suitable for processing RN columns at once
-  Index i0 = internal::first_default_aligned(A,m);
-  
-  eigen_internal_assert(((lda%PacketSize)==0) && ((ldc%PacketSize)==0) && (i0==internal::first_default_aligned(C,m)));
-  
-  // handle the non aligned rows of A and C without any optimization:
-  for(Index i=0; i<i0; ++i)
-  {
-    for(Index j=0; j<n; ++j)
-    {
-      Scalar c = C[i+j*ldc];
-      for(Index k=0; k<d; ++k)
-        c += B[k+j*ldb] * A[i+k*lda];
-      C[i+j*ldc] = c;
-    }
-  }
-  // process the remaining rows per chunk of BM rows
-  for(Index ib=i0; ib<m; ib+=BM)
-  {
-    Index actual_b = std::min<Index>(BM, m-ib);                 // actual number of rows
-    Index actual_b_end1 = (actual_b/SM)*SM;                   // actual number of rows suitable for peeling
-    Index actual_b_end2 = (actual_b/PacketSize)*PacketSize;   // actual number of rows suitable for vectorization
-    
-    // Let's process two columns of B-C at once
-    for(Index j=0; j<n_end; j+=RN)
-    {
-      const Scalar* Bc0 = B+(j+0)*ldb;
-      const Scalar* Bc1 = B+(j+1)*ldb;
-      
-      for(Index k=0; k<d_end; k+=RK)
-      {
-        
-        // load and expand a RN x RK block of B
-        Packet b00, b10, b20, b30, b01, b11, b21, b31;
-                  { b00 = pset1<Packet>(Bc0[0]); }
-                  { b10 = pset1<Packet>(Bc0[1]); }
-        if(RK==4) { b20 = pset1<Packet>(Bc0[2]); }
-        if(RK==4) { b30 = pset1<Packet>(Bc0[3]); }
-                  { b01 = pset1<Packet>(Bc1[0]); }
-                  { b11 = pset1<Packet>(Bc1[1]); }
-        if(RK==4) { b21 = pset1<Packet>(Bc1[2]); }
-        if(RK==4) { b31 = pset1<Packet>(Bc1[3]); }
-        
-        Packet a0, a1, a2, a3, c0, c1, t0, t1;
-        
-        const Scalar* A0 = A+ib+(k+0)*lda;
-        const Scalar* A1 = A+ib+(k+1)*lda;
-        const Scalar* A2 = A+ib+(k+2)*lda;
-        const Scalar* A3 = A+ib+(k+3)*lda;
-        
-        Scalar* C0 = C+ib+(j+0)*ldc;
-        Scalar* C1 = C+ib+(j+1)*ldc;
-        
-                  a0 = pload<Packet>(A0);
-                  a1 = pload<Packet>(A1);
-        if(RK==4)
-        {
-          a2 = pload<Packet>(A2);
-          a3 = pload<Packet>(A3);
-        }
-        else
-        {
-          // workaround "may be used uninitialized in this function" warning
-          a2 = a3 = a0;
-        }
-        
-#define KMADD(c, a, b, tmp) {tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);}
-#define WORK(I)  \
-                     c0 = pload<Packet>(C0+i+(I)*PacketSize);    \
-                     c1 = pload<Packet>(C1+i+(I)*PacketSize);    \
-                     KMADD(c0, a0, b00, t0)                      \
-                     KMADD(c1, a0, b01, t1)                      \
-                     a0 = pload<Packet>(A0+i+(I+1)*PacketSize);  \
-                     KMADD(c0, a1, b10, t0)                      \
-                     KMADD(c1, a1, b11, t1)                      \
-                     a1 = pload<Packet>(A1+i+(I+1)*PacketSize);  \
-          if(RK==4){ KMADD(c0, a2, b20, t0)                     }\
-          if(RK==4){ KMADD(c1, a2, b21, t1)                     }\
-          if(RK==4){ a2 = pload<Packet>(A2+i+(I+1)*PacketSize); }\
-          if(RK==4){ KMADD(c0, a3, b30, t0)                     }\
-          if(RK==4){ KMADD(c1, a3, b31, t1)                     }\
-          if(RK==4){ a3 = pload<Packet>(A3+i+(I+1)*PacketSize); }\
-                     pstore(C0+i+(I)*PacketSize, c0);            \
-                     pstore(C1+i+(I)*PacketSize, c1)
-        
-        // process rows of A' - C' with aggressive vectorization and peeling 
-        for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
-        {
-          EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL1");
-                    prefetch((A0+i+(5)*PacketSize));
-                    prefetch((A1+i+(5)*PacketSize));
-          if(RK==4) prefetch((A2+i+(5)*PacketSize));
-          if(RK==4) prefetch((A3+i+(5)*PacketSize));
-
-          WORK(0);
-          WORK(1);
-          WORK(2);
-          WORK(3);
-          WORK(4);
-          WORK(5);
-          WORK(6);
-          WORK(7);
-        }
-        // process the remaining rows with vectorization only
-        for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
-        {
-          WORK(0);
-        }
-#undef WORK
-        // process the remaining rows without vectorization
-        for(Index i=actual_b_end2; i<actual_b; ++i)
-        {
-          if(RK==4)
-          {
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
-            C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1]+A2[i]*Bc1[2]+A3[i]*Bc1[3];
-          }
-          else
-          {
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
-            C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1];
-          }
-        }
-        
-        Bc0 += RK;
-        Bc1 += RK;
-      } // peeled loop on k
-    } // peeled loop on the columns j
-    // process the last column (we now perform a matrix-vector product)
-    if((n-n_end)>0)
-    {
-      const Scalar* Bc0 = B+(n-1)*ldb;
-      
-      for(Index k=0; k<d_end; k+=RK)
-      {
-        
-        // load and expand a 1 x RK block of B
-        Packet b00, b10, b20, b30;
-                  b00 = pset1<Packet>(Bc0[0]);
-                  b10 = pset1<Packet>(Bc0[1]);
-        if(RK==4) b20 = pset1<Packet>(Bc0[2]);
-        if(RK==4) b30 = pset1<Packet>(Bc0[3]);
-        
-        Packet a0, a1, a2, a3, c0, t0/*, t1*/;
-        
-        const Scalar* A0 = A+ib+(k+0)*lda;
-        const Scalar* A1 = A+ib+(k+1)*lda;
-        const Scalar* A2 = A+ib+(k+2)*lda;
-        const Scalar* A3 = A+ib+(k+3)*lda;
-        
-        Scalar* C0 = C+ib+(n_end)*ldc;
-        
-                  a0 = pload<Packet>(A0);
-                  a1 = pload<Packet>(A1);
-        if(RK==4)
-        {
-          a2 = pload<Packet>(A2);
-          a3 = pload<Packet>(A3);
-        }
-        else
-        {
-          // workaround "may be used uninitialized in this function" warning
-          a2 = a3 = a0;
-        }
-        
-#define WORK(I) \
-                   c0 = pload<Packet>(C0+i+(I)*PacketSize);     \
-                   KMADD(c0, a0, b00, t0)                       \
-                   a0 = pload<Packet>(A0+i+(I+1)*PacketSize);   \
-                   KMADD(c0, a1, b10, t0)                       \
-                   a1 = pload<Packet>(A1+i+(I+1)*PacketSize);   \
-        if(RK==4){ KMADD(c0, a2, b20, t0)                      }\
-        if(RK==4){ a2 = pload<Packet>(A2+i+(I+1)*PacketSize);  }\
-        if(RK==4){ KMADD(c0, a3, b30, t0)                      }\
-        if(RK==4){ a3 = pload<Packet>(A3+i+(I+1)*PacketSize);  }\
-                   pstore(C0+i+(I)*PacketSize, c0);
-        
-        // aggressive vectorization and peeling
-        for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
-        {
-          EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL2");
-          WORK(0);
-          WORK(1);
-          WORK(2);
-          WORK(3);
-          WORK(4);
-          WORK(5);
-          WORK(6);
-          WORK(7);
-        }
-        // vectorization only
-        for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
-        {
-          WORK(0);
-        }
-        // remaining scalars
-        for(Index i=actual_b_end2; i<actual_b; ++i)
-        {
-          if(RK==4) 
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
-          else
-            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
-        }
-        
-        Bc0 += RK;
-#undef WORK
-      }
-    }
-    
-    // process the last columns of A, corresponding to the last rows of B
-    Index rd = d-d_end;
-    if(rd>0)
-    {
-      for(Index j=0; j<n; ++j)
-      {
-        enum {
-          Alignment = PacketSize>1 ? Aligned : 0
-        };
-        typedef Map<Matrix<Scalar,Dynamic,1>, Alignment > MapVector;
-        typedef Map<const Matrix<Scalar,Dynamic,1>, Alignment > ConstMapVector;
-        if(rd==1)       MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b);
-        
-        else if(rd==2)  MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
-                                                        + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b);
-        
-        else            MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
-                                                        + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b)
-                                                        + B[2+d_end+j*ldb] * ConstMapVector(A+(d_end+2)*lda+ib, actual_b);
-      }
-    }
-  
-  } // blocking on the rows of A and C
-}
-#undef KMADD
-
-} // namespace internal
-
-} // namespace Eigen
-
-#endif // EIGEN_SPARSELU_GEMM_KERNEL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
deleted file mode 100644
index 6f75d50..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
+++ /dev/null
@@ -1,126 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* This file is a modified version of heap_relax_snode.c file in SuperLU
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-
-#ifndef SPARSELU_HEAP_RELAX_SNODE_H
-#define SPARSELU_HEAP_RELAX_SNODE_H
-
-namespace Eigen {
-namespace internal {
-
-/** 
- * \brief Identify the initial relaxed supernodes
- * 
- * This routine applied to a symmetric elimination tree. 
- * It assumes that the matrix has been reordered according to the postorder of the etree
- * \param n The number of columns
- * \param et elimination tree 
- * \param relax_columns Maximum number of columns allowed in a relaxed snode 
- * \param descendants Number of descendants of each node in the etree
- * \param relax_end last column in a supernode
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end)
-{
-  
-  // The etree may not be postordered, but its heap ordered  
-  IndexVector post;
-  internal::treePostorder(StorageIndex(n), et, post); // Post order etree
-  IndexVector inv_post(n+1); 
-  for (StorageIndex i = 0; i < n+1; ++i) inv_post(post(i)) = i; // inv_post = post.inverse()???
-  
-  // Renumber etree in postorder 
-  IndexVector iwork(n);
-  IndexVector et_save(n+1);
-  for (Index i = 0; i < n; ++i)
-  {
-    iwork(post(i)) = post(et(i));
-  }
-  et_save = et; // Save the original etree
-  et = iwork; 
-  
-  // compute the number of descendants of each node in the etree
-  relax_end.setConstant(emptyIdxLU);
-  Index j, parent; 
-  descendants.setZero();
-  for (j = 0; j < n; j++) 
-  {
-    parent = et(j);
-    if (parent != n) // not the dummy root
-      descendants(parent) += descendants(j) + 1;
-  }
-  // Identify the relaxed supernodes by postorder traversal of the etree
-  Index snode_start; // beginning of a snode 
-  StorageIndex k;
-  Index nsuper_et_post = 0; // Number of relaxed snodes in postordered etree 
-  Index nsuper_et = 0; // Number of relaxed snodes in the original etree 
-  StorageIndex l; 
-  for (j = 0; j < n; )
-  {
-    parent = et(j);
-    snode_start = j; 
-    while ( parent != n && descendants(parent) < relax_columns ) 
-    {
-      j = parent; 
-      parent = et(j);
-    }
-    // Found a supernode in postordered etree, j is the last column 
-    ++nsuper_et_post;
-    k = StorageIndex(n);
-    for (Index i = snode_start; i <= j; ++i)
-      k = (std::min)(k, inv_post(i));
-    l = inv_post(j);
-    if ( (l - k) == (j - snode_start) )  // Same number of columns in the snode
-    {
-      // This is also a supernode in the original etree
-      relax_end(k) = l; // Record last column 
-      ++nsuper_et; 
-    }
-    else 
-    {
-      for (Index i = snode_start; i <= j; ++i) 
-      {
-        l = inv_post(i);
-        if (descendants(i) == 0) 
-        {
-          relax_end(l) = l;
-          ++nsuper_et;
-        }
-      }
-    }
-    j++;
-    // Search for a new leaf
-    while (descendants(j) != 0 && j < n) j++;
-  } // End postorder traversal of the etree
-  
-  // Recover the original etree
-  et = et_save; 
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // SPARSELU_HEAP_RELAX_SNODE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
deleted file mode 100644
index 8c1b3e8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
+++ /dev/null
@@ -1,130 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef SPARSELU_KERNEL_BMOD_H
-#define SPARSELU_KERNEL_BMOD_H
-
-namespace Eigen {
-namespace internal {
-  
-template <int SegSizeAtCompileTime> struct LU_kernel_bmod
-{
-  /** \internal
-    * \brief Performs numeric block updates from a given supernode to a single column
-    *
-    * \param segsize Size of the segment (and blocks ) to use for updates
-    * \param[in,out] dense Packed values of the original matrix
-    * \param tempv temporary vector to use for updates
-    * \param lusup array containing the supernodes
-    * \param lda Leading dimension in the supernode
-    * \param nrow Number of rows in the rectangular part of the supernode
-    * \param lsub compressed row subscripts of supernodes
-    * \param lptr pointer to the first column of the current supernode in lsub
-    * \param no_zeros Number of nonzeros elements before the diagonal part of the supernode
-    */
-  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-  static EIGEN_DONT_INLINE void run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda,
-                                    const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros);
-};
-
-template <int SegSizeAtCompileTime>
-template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-EIGEN_DONT_INLINE void LU_kernel_bmod<SegSizeAtCompileTime>::run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda,
-                                                                  const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros)
-{
-  typedef typename ScalarVector::Scalar Scalar;
-  // First, copy U[*,j] segment from dense(*) to tempv(*)
-  // The result of triangular solve is in tempv[*]; 
-    // The result of matric-vector update is in dense[*]
-  Index isub = lptr + no_zeros; 
-  Index i;
-  Index irow;
-  for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++)
-  {
-    irow = lsub(isub); 
-    tempv(i) = dense(irow); 
-    ++isub; 
-  }
-  // Dense triangular solve -- start effective triangle
-  luptr += lda * no_zeros + no_zeros; 
-  // Form Eigen matrix and vector 
-  Map<Matrix<Scalar,SegSizeAtCompileTime,SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > A( &(lusup.data()[luptr]), segsize, segsize, OuterStride<>(lda) );
-  Map<Matrix<Scalar,SegSizeAtCompileTime,1> > u(tempv.data(), segsize);
-  
-  u = A.template triangularView<UnitLower>().solve(u); 
-  
-  // Dense matrix-vector product y <-- B*x 
-  luptr += segsize;
-  const Index PacketSize = internal::packet_traits<Scalar>::size;
-  Index ldl = internal::first_multiple(nrow, PacketSize);
-  Map<Matrix<Scalar,Dynamic,SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > B( &(lusup.data()[luptr]), nrow, segsize, OuterStride<>(lda) );
-  Index aligned_offset = internal::first_default_aligned(tempv.data()+segsize, PacketSize);
-  Index aligned_with_B_offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize))%PacketSize;
-  Map<Matrix<Scalar,Dynamic,1>, 0, OuterStride<> > l(tempv.data()+segsize+aligned_offset+aligned_with_B_offset, nrow, OuterStride<>(ldl) );
-  
-  l.setZero();
-  internal::sparselu_gemm<Scalar>(l.rows(), l.cols(), B.cols(), B.data(), B.outerStride(), u.data(), u.outerStride(), l.data(), l.outerStride());
-  
-  // Scatter tempv[] into SPA dense[] as a temporary storage 
-  isub = lptr + no_zeros;
-  for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++)
-  {
-    irow = lsub(isub++); 
-    dense(irow) = tempv(i);
-  }
-  
-  // Scatter l into SPA dense[]
-  for (i = 0; i < nrow; i++)
-  {
-    irow = lsub(isub++); 
-    dense(irow) -= l(i);
-  } 
-}
-
-template <> struct LU_kernel_bmod<1>
-{
-  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-  static EIGEN_DONT_INLINE void run(const Index /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr,
-                                    const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros);
-};
-
-
-template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
-EIGEN_DONT_INLINE void LU_kernel_bmod<1>::run(const Index /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr,
-                                              const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros)
-{
-  typedef typename ScalarVector::Scalar Scalar;
-  typedef typename IndexVector::Scalar StorageIndex;
-  Scalar f = dense(lsub(lptr + no_zeros));
-  luptr += lda * no_zeros + no_zeros + 1;
-  const Scalar* a(lusup.data() + luptr);
-  const StorageIndex*  irow(lsub.data()+lptr + no_zeros + 1);
-  Index i = 0;
-  for (; i+1 < nrow; i+=2)
-  {
-    Index i0 = *(irow++);
-    Index i1 = *(irow++);
-    Scalar a0 = *(a++);
-    Scalar a1 = *(a++);
-    Scalar d0 = dense.coeff(i0);
-    Scalar d1 = dense.coeff(i1);
-    d0 -= f*a0;
-    d1 -= f*a1;
-    dense.coeffRef(i0) = d0;
-    dense.coeffRef(i1) = d1;
-  }
-  if(i<nrow)
-    dense.coeffRef(*(irow++)) -= f * *(a++);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif // SPARSELU_KERNEL_BMOD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
deleted file mode 100644
index f052001..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
+++ /dev/null
@@ -1,223 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]panel_bmod.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PANEL_BMOD_H
-#define SPARSELU_PANEL_BMOD_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Performs numeric block updates (sup-panel) in topological order.
- * 
- * Before entering this routine, the original nonzeros in the panel
- * were already copied into the spa[m,w]
- * 
- * \param m number of rows in the matrix
- * \param w Panel size
- * \param jcol Starting  column of the panel
- * \param nseg Number of segments in the U part
- * \param dense Store the full representation of the panel 
- * \param tempv working array 
- * \param segrep segment representative... first row in the segment
- * \param repfnz First nonzero rows
- * \param glu Global LU data. 
- * 
- * 
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::panel_bmod(const Index m, const Index w, const Index jcol, 
-                                            const Index nseg, ScalarVector& dense, ScalarVector& tempv,
-                                            IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu)
-{
-  
-  Index ksub,jj,nextl_col; 
-  Index fsupc, nsupc, nsupr, nrow; 
-  Index krep, kfnz; 
-  Index lptr; // points to the row subscripts of a supernode 
-  Index luptr; // ...
-  Index segsize,no_zeros ; 
-  // For each nonz supernode segment of U[*,j] in topological order
-  Index k = nseg - 1; 
-  const Index PacketSize = internal::packet_traits<Scalar>::size;
-  
-  for (ksub = 0; ksub < nseg; ksub++)
-  { // For each updating supernode
-    /* krep = representative of current k-th supernode
-     * fsupc =  first supernodal column
-     * nsupc = number of columns in a supernode
-     * nsupr = number of rows in a supernode
-     */
-    krep = segrep(k); k--; 
-    fsupc = glu.xsup(glu.supno(krep)); 
-    nsupc = krep - fsupc + 1; 
-    nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
-    nrow = nsupr - nsupc; 
-    lptr = glu.xlsub(fsupc); 
-    
-    // loop over the panel columns to detect the actual number of columns and rows
-    Index u_rows = 0;
-    Index u_cols = 0;
-    for (jj = jcol; jj < jcol + w; jj++)
-    {
-      nextl_col = (jj-jcol) * m; 
-      VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-      
-      kfnz = repfnz_col(krep); 
-      if ( kfnz == emptyIdxLU ) 
-        continue; // skip any zero segment
-      
-      segsize = krep - kfnz + 1;
-      u_cols++;
-      u_rows = (std::max)(segsize,u_rows);
-    }
-    
-    if(nsupc >= 2)
-    { 
-      Index ldu = internal::first_multiple<Index>(u_rows, PacketSize);
-      Map<ScalarMatrix, Aligned,  OuterStride<> > U(tempv.data(), u_rows, u_cols, OuterStride<>(ldu));
-      
-      // gather U
-      Index u_col = 0;
-      for (jj = jcol; jj < jcol + w; jj++)
-      {
-        nextl_col = (jj-jcol) * m; 
-        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
-        
-        kfnz = repfnz_col(krep); 
-        if ( kfnz == emptyIdxLU ) 
-          continue; // skip any zero segment
-        
-        segsize = krep - kfnz + 1;
-        luptr = glu.xlusup(fsupc);    
-        no_zeros = kfnz - fsupc; 
-        
-        Index isub = lptr + no_zeros;
-        Index off = u_rows-segsize;
-        for (Index i = 0; i < off; i++) U(i,u_col) = 0;
-        for (Index i = 0; i < segsize; i++)
-        {
-          Index irow = glu.lsub(isub); 
-          U(i+off,u_col) = dense_col(irow); 
-          ++isub; 
-        }
-        u_col++;
-      }
-      // solve U = A^-1 U
-      luptr = glu.xlusup(fsupc);
-      Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc);
-      no_zeros = (krep - u_rows + 1) - fsupc;
-      luptr += lda * no_zeros + no_zeros;
-      MappedMatrixBlock A(glu.lusup.data()+luptr, u_rows, u_rows, OuterStride<>(lda) );
-      U = A.template triangularView<UnitLower>().solve(U);
-      
-      // update
-      luptr += u_rows;
-      MappedMatrixBlock B(glu.lusup.data()+luptr, nrow, u_rows, OuterStride<>(lda) );
-      eigen_assert(tempv.size()>w*ldu + nrow*w + 1);
-      
-      Index ldl = internal::first_multiple<Index>(nrow, PacketSize);
-      Index offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize)) % PacketSize;
-      MappedMatrixBlock L(tempv.data()+w*ldu+offset, nrow, u_cols, OuterStride<>(ldl));
-      
-      L.setZero();
-      internal::sparselu_gemm<Scalar>(L.rows(), L.cols(), B.cols(), B.data(), B.outerStride(), U.data(), U.outerStride(), L.data(), L.outerStride());
-      
-      // scatter U and L
-      u_col = 0;
-      for (jj = jcol; jj < jcol + w; jj++)
-      {
-        nextl_col = (jj-jcol) * m; 
-        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
-        
-        kfnz = repfnz_col(krep); 
-        if ( kfnz == emptyIdxLU ) 
-          continue; // skip any zero segment
-        
-        segsize = krep - kfnz + 1;
-        no_zeros = kfnz - fsupc; 
-        Index isub = lptr + no_zeros;
-        
-        Index off = u_rows-segsize;
-        for (Index i = 0; i < segsize; i++)
-        {
-          Index irow = glu.lsub(isub++); 
-          dense_col(irow) = U.coeff(i+off,u_col);
-          U.coeffRef(i+off,u_col) = 0;
-        }
-        
-        // Scatter l into SPA dense[]
-        for (Index i = 0; i < nrow; i++)
-        {
-          Index irow = glu.lsub(isub++); 
-          dense_col(irow) -= L.coeff(i,u_col);
-          L.coeffRef(i,u_col) = 0;
-        }
-        u_col++;
-      }
-    }
-    else // level 2 only
-    {
-      // Sequence through each column in the panel
-      for (jj = jcol; jj < jcol + w; jj++)
-      {
-        nextl_col = (jj-jcol) * m; 
-        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
-        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
-        
-        kfnz = repfnz_col(krep); 
-        if ( kfnz == emptyIdxLU ) 
-          continue; // skip any zero segment
-        
-        segsize = krep - kfnz + 1;
-        luptr = glu.xlusup(fsupc);
-        
-        Index lda = glu.xlusup(fsupc+1)-glu.xlusup(fsupc);// nsupr
-        
-        // Perform a trianglar solve and block update, 
-        // then scatter the result of sup-col update to dense[]
-        no_zeros = kfnz - fsupc; 
-              if(segsize==1)  LU_kernel_bmod<1>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-        else  if(segsize==2)  LU_kernel_bmod<2>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-        else  if(segsize==3)  LU_kernel_bmod<3>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
-        else                  LU_kernel_bmod<Dynamic>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); 
-      } // End for each column in the panel 
-    }
-    
-  } // End for each updating supernode
-} // end panel bmod
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // SPARSELU_PANEL_BMOD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h
deleted file mode 100644
index 155df73..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h
+++ /dev/null
@@ -1,258 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]panel_dfs.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PANEL_DFS_H
-#define SPARSELU_PANEL_DFS_H
-
-namespace Eigen {
-
-namespace internal {
-  
-template<typename IndexVector>
-struct panel_dfs_traits
-{
-  typedef typename IndexVector::Scalar StorageIndex;
-  panel_dfs_traits(Index jcol, StorageIndex* marker)
-    : m_jcol(jcol), m_marker(marker)
-  {}
-  bool update_segrep(Index krep, StorageIndex jj)
-  {
-    if(m_marker[krep]<m_jcol)
-    {
-      m_marker[krep] = jj; 
-      return true;
-    }
-    return false;
-  }
-  void mem_expand(IndexVector& /*glu.lsub*/, Index /*nextl*/, Index /*chmark*/) {}
-  enum { ExpandMem = false };
-  Index m_jcol;
-  StorageIndex* m_marker;
-};
-
-
-template <typename Scalar, typename StorageIndex>
-template <typename Traits>
-void SparseLUImpl<Scalar,StorageIndex>::dfs_kernel(const StorageIndex jj, IndexVector& perm_r,
-                   Index& nseg, IndexVector& panel_lsub, IndexVector& segrep,
-                   Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent,
-                   IndexVector& xplore, GlobalLU_t& glu,
-                   Index& nextl_col, Index krow, Traits& traits
-                  )
-{
-  
-  StorageIndex kmark = marker(krow);
-      
-  // For each unmarked krow of jj
-  marker(krow) = jj; 
-  StorageIndex kperm = perm_r(krow); 
-  if (kperm == emptyIdxLU ) {
-    // krow is in L : place it in structure of L(*, jj)
-    panel_lsub(nextl_col++) = StorageIndex(krow);  // krow is indexed into A
-    
-    traits.mem_expand(panel_lsub, nextl_col, kmark);
-  }
-  else 
-  {
-    // krow is in U : if its supernode-representative krep
-    // has been explored, update repfnz(*)
-    // krep = supernode representative of the current row
-    StorageIndex krep = glu.xsup(glu.supno(kperm)+1) - 1; 
-    // First nonzero element in the current column:
-    StorageIndex myfnz = repfnz_col(krep); 
-    
-    if (myfnz != emptyIdxLU )
-    {
-      // Representative visited before
-      if (myfnz > kperm ) repfnz_col(krep) = kperm; 
-      
-    }
-    else 
-    {
-      // Otherwise, perform dfs starting at krep
-      StorageIndex oldrep = emptyIdxLU; 
-      parent(krep) = oldrep; 
-      repfnz_col(krep) = kperm; 
-      StorageIndex xdfs =  glu.xlsub(krep); 
-      Index maxdfs = xprune(krep); 
-      
-      StorageIndex kpar;
-      do 
-      {
-        // For each unmarked kchild of krep
-        while (xdfs < maxdfs) 
-        {
-          StorageIndex kchild = glu.lsub(xdfs); 
-          xdfs++; 
-          StorageIndex chmark = marker(kchild); 
-          
-          if (chmark != jj ) 
-          {
-            marker(kchild) = jj; 
-            StorageIndex chperm = perm_r(kchild); 
-            
-            if (chperm == emptyIdxLU) 
-            {
-              // case kchild is in L: place it in L(*, j)
-              panel_lsub(nextl_col++) = kchild;
-              traits.mem_expand(panel_lsub, nextl_col, chmark);
-            }
-            else
-            {
-              // case kchild is in U :
-              // chrep = its supernode-rep. If its rep has been explored, 
-              // update its repfnz(*)
-              StorageIndex chrep = glu.xsup(glu.supno(chperm)+1) - 1; 
-              myfnz = repfnz_col(chrep); 
-              
-              if (myfnz != emptyIdxLU) 
-              { // Visited before 
-                if (myfnz > chperm) 
-                  repfnz_col(chrep) = chperm; 
-              }
-              else 
-              { // Cont. dfs at snode-rep of kchild
-                xplore(krep) = xdfs; 
-                oldrep = krep; 
-                krep = chrep; // Go deeper down G(L)
-                parent(krep) = oldrep; 
-                repfnz_col(krep) = chperm; 
-                xdfs = glu.xlsub(krep); 
-                maxdfs = xprune(krep); 
-                
-              } // end if myfnz != -1
-            } // end if chperm == -1 
-                
-          } // end if chmark !=jj
-        } // end while xdfs < maxdfs
-        
-        // krow has no more unexplored nbrs :
-        //    Place snode-rep krep in postorder DFS, if this 
-        //    segment is seen for the first time. (Note that 
-        //    "repfnz(krep)" may change later.)
-        //    Baktrack dfs to its parent
-        if(traits.update_segrep(krep,jj))
-        //if (marker1(krep) < jcol )
-        {
-          segrep(nseg) = krep; 
-          ++nseg; 
-          //marker1(krep) = jj; 
-        }
-        
-        kpar = parent(krep); // Pop recursion, mimic recursion 
-        if (kpar == emptyIdxLU) 
-          break; // dfs done 
-        krep = kpar; 
-        xdfs = xplore(krep); 
-        maxdfs = xprune(krep); 
-
-      } while (kpar != emptyIdxLU); // Do until empty stack 
-      
-    } // end if (myfnz = -1)
-
-  } // end if (kperm == -1)   
-}
-
-/**
- * \brief Performs a symbolic factorization on a panel of columns [jcol, jcol+w)
- * 
- * A supernode representative is the last column of a supernode.
- * The nonzeros in U[*,j] are segments that end at supernodes representatives
- * 
- * The routine returns a list of the supernodal representatives 
- * in topological order of the dfs that generates them. This list is 
- * a superset of the topological order of each individual column within 
- * the panel.
- * The location of the first nonzero in each supernodal segment 
- * (supernodal entry location) is also returned. Each column has 
- * a separate list for this purpose. 
- * 
- * Two markers arrays are used for dfs :
- *    marker[i] == jj, if i was visited during dfs of current column jj;
- *    marker1[i] >= jcol, if i was visited by earlier columns in this panel; 
- * 
- * \param[in] m number of rows in the matrix
- * \param[in] w Panel size
- * \param[in] jcol Starting  column of the panel
- * \param[in] A Input matrix in column-major storage
- * \param[in] perm_r Row permutation
- * \param[out] nseg Number of U segments
- * \param[out] dense Accumulate the column vectors of the panel
- * \param[out] panel_lsub Subscripts of the row in the panel 
- * \param[out] segrep Segment representative i.e first nonzero row of each segment
- * \param[out] repfnz First nonzero location in each row
- * \param[out] xprune The pruned elimination tree
- * \param[out] marker work vector
- * \param  parent The elimination tree
- * \param xplore work vector
- * \param glu The global data structure
- * 
- */
-
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu)
-{
-  Index nextl_col; // Next available position in panel_lsub[*,jj] 
-  
-  // Initialize pointers 
-  VectorBlock<IndexVector> marker1(marker, m, m); 
-  nseg = 0; 
-  
-  panel_dfs_traits<IndexVector> traits(jcol, marker1.data());
-  
-  // For each column in the panel 
-  for (StorageIndex jj = StorageIndex(jcol); jj < jcol + w; jj++) 
-  {
-    nextl_col = (jj - jcol) * m; 
-    
-    VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero location in each row
-    VectorBlock<ScalarVector> dense_col(dense,nextl_col, m); // Accumulate a column vector here
-    
-    
-    // For each nnz in A[*, jj] do depth first search
-    for (typename MatrixType::InnerIterator it(A, jj); it; ++it)
-    {
-      Index krow = it.row(); 
-      dense_col(krow) = it.value();
-      
-      StorageIndex kmark = marker(krow); 
-      if (kmark == jj) 
-        continue; // krow visited before, go to the next nonzero
-      
-      dfs_kernel(jj, perm_r, nseg, panel_lsub, segrep, repfnz_col, xprune, marker, parent,
-                   xplore, glu, nextl_col, krow, traits);
-    }// end for nonzeros in column jj
-    
-  } // end for column jj
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_PANEL_DFS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h
deleted file mode 100644
index a86dac9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h
+++ /dev/null
@@ -1,137 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of xpivotL.c file in SuperLU 
- 
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PIVOTL_H
-#define SPARSELU_PIVOTL_H
-
-namespace Eigen {
-namespace internal {
-  
-/**
- * \brief Performs the numerical pivotin on the current column of L, and the CDIV operation.
- * 
- * Pivot policy :
- * (1) Compute thresh = u * max_(i>=j) abs(A_ij);
- * (2) IF user specifies pivot row k and abs(A_kj) >= thresh THEN
- *           pivot row = k;
- *       ELSE IF abs(A_jj) >= thresh THEN
- *           pivot row = j;
- *       ELSE
- *           pivot row = m;
- * 
- *   Note: If you absolutely want to use a given pivot order, then set u=0.0.
- * 
- * \param jcol The current column of L
- * \param diagpivotthresh diagonal pivoting threshold
- * \param[in,out] perm_r Row permutation (threshold pivoting)
- * \param[in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc'
- * \param[out] pivrow  The pivot row
- * \param glu Global LU data
- * \return 0 if success, i > 0 if U(i,i) is exactly zero 
- * 
- */
-template <typename Scalar, typename StorageIndex>
-Index SparseLUImpl<Scalar,StorageIndex>::pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu)
-{
-  
-  Index fsupc = (glu.xsup)((glu.supno)(jcol)); // First column in the supernode containing the column jcol
-  Index nsupc = jcol - fsupc; // Number of columns in the supernode portion, excluding jcol; nsupc >=0
-  Index lptr = glu.xlsub(fsupc); // pointer to the starting location of the row subscripts for this supernode portion
-  Index nsupr = glu.xlsub(fsupc+1) - lptr; // Number of rows in the supernode
-  Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc); // leading dimension
-  Scalar* lu_sup_ptr = &(glu.lusup.data()[glu.xlusup(fsupc)]); // Start of the current supernode
-  Scalar* lu_col_ptr = &(glu.lusup.data()[glu.xlusup(jcol)]); // Start of jcol in the supernode
-  StorageIndex* lsub_ptr = &(glu.lsub.data()[lptr]); // Start of row indices of the supernode
-  
-  // Determine the largest abs numerical value for partial pivoting 
-  Index diagind = iperm_c(jcol); // diagonal index 
-  RealScalar pivmax(-1.0);
-  Index pivptr = nsupc; 
-  Index diag = emptyIdxLU; 
-  RealScalar rtemp;
-  Index isub, icol, itemp, k; 
-  for (isub = nsupc; isub < nsupr; ++isub) {
-    using std::abs;
-    rtemp = abs(lu_col_ptr[isub]);
-    if (rtemp > pivmax) {
-      pivmax = rtemp; 
-      pivptr = isub;
-    } 
-    if (lsub_ptr[isub] == diagind) diag = isub;
-  }
-  
-  // Test for singularity
-  if ( pivmax <= RealScalar(0.0) ) {
-    // if pivmax == -1, the column is structurally empty, otherwise it is only numerically zero
-    pivrow = pivmax < RealScalar(0.0) ? diagind : lsub_ptr[pivptr];
-    perm_r(pivrow) = StorageIndex(jcol);
-    return (jcol+1);
-  }
-  
-  RealScalar thresh = diagpivotthresh * pivmax; 
-  
-  // Choose appropriate pivotal element 
-  
-  {
-    // Test if the diagonal element can be used as a pivot (given the threshold value)
-    if (diag >= 0 ) 
-    {
-      // Diagonal element exists
-      using std::abs;
-      rtemp = abs(lu_col_ptr[diag]);
-      if (rtemp != RealScalar(0.0) && rtemp >= thresh) pivptr = diag;
-    }
-    pivrow = lsub_ptr[pivptr];
-  }
-  
-  // Record pivot row
-  perm_r(pivrow) = StorageIndex(jcol);
-  // Interchange row subscripts
-  if (pivptr != nsupc )
-  {
-    std::swap( lsub_ptr[pivptr], lsub_ptr[nsupc] );
-    // Interchange numerical values as well, for the two rows in the whole snode
-    // such that L is indexed the same way as A
-    for (icol = 0; icol <= nsupc; icol++)
-    {
-      itemp = pivptr + icol * lda; 
-      std::swap(lu_sup_ptr[itemp], lu_sup_ptr[nsupc + icol * lda]);
-    }
-  }
-  // cdiv operations
-  Scalar temp = Scalar(1.0) / lu_col_ptr[nsupc];
-  for (k = nsupc+1; k < nsupr; k++)
-    lu_col_ptr[k] *= temp; 
-  return 0;
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_PIVOTL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h
deleted file mode 100644
index ad32fed..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h
+++ /dev/null
@@ -1,136 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* 
- 
- * NOTE: This file is the modified version of [s,d,c,z]pruneL.c file in SuperLU 
- 
- * -- SuperLU routine (version 2.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * November 15, 1997
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-#ifndef SPARSELU_PRUNEL_H
-#define SPARSELU_PRUNEL_H
-
-namespace Eigen {
-namespace internal {
-
-/**
- * \brief Prunes the L-structure.
- *
- * It prunes the L-structure  of supernodes whose L-structure contains the current pivot row "pivrow"
- * 
- * 
- * \param jcol The current column of L
- * \param[in] perm_r Row permutation
- * \param[out] pivrow  The pivot row
- * \param nseg Number of segments
- * \param segrep 
- * \param repfnz
- * \param[out] xprune 
- * \param glu Global LU data
- * 
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg,
-                                               const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu)
-{
-  // For each supernode-rep irep in U(*,j]
-  Index jsupno = glu.supno(jcol); 
-  Index i,irep,irep1; 
-  bool movnum, do_prune = false; 
-  Index kmin = 0, kmax = 0, minloc, maxloc,krow; 
-  for (i = 0; i < nseg; i++)
-  {
-    irep = segrep(i); 
-    irep1 = irep + 1; 
-    do_prune = false; 
-    
-    // Don't prune with a zero U-segment 
-    if (repfnz(irep) == emptyIdxLU) continue; 
-    
-    // If a snode overlaps with the next panel, then the U-segment
-    // is fragmented into two parts -- irep and irep1. We should let 
-    // pruning occur at the rep-column in irep1s snode. 
-    if (glu.supno(irep) == glu.supno(irep1) ) continue; // don't prune 
-    
-    // If it has not been pruned & it has a nonz in row L(pivrow,i)
-    if (glu.supno(irep) != jsupno )
-    {
-      if ( xprune (irep) >= glu.xlsub(irep1) )
-      {
-        kmin = glu.xlsub(irep);
-        kmax = glu.xlsub(irep1) - 1; 
-        for (krow = kmin; krow <= kmax; krow++)
-        {
-          if (glu.lsub(krow) == pivrow) 
-          {
-            do_prune = true; 
-            break; 
-          }
-        }
-      }
-      
-      if (do_prune) 
-      {
-        // do a quicksort-type partition
-        // movnum=true means that the num values have to be exchanged
-        movnum = false; 
-        if (irep == glu.xsup(glu.supno(irep)) ) // Snode of size 1 
-          movnum = true; 
-        
-        while (kmin <= kmax)
-        {
-          if (perm_r(glu.lsub(kmax)) == emptyIdxLU)
-            kmax--; 
-          else if ( perm_r(glu.lsub(kmin)) != emptyIdxLU)
-            kmin++;
-          else 
-          {
-            // kmin below pivrow (not yet pivoted), and kmax
-            // above pivrow: interchange the two suscripts
-            std::swap(glu.lsub(kmin), glu.lsub(kmax)); 
-            
-            // If the supernode has only one column, then we 
-            // only keep one set of subscripts. For any subscript
-            // intercnahge performed, similar interchange must be 
-            // done on the numerical values. 
-            if (movnum) 
-            {
-              minloc = glu.xlusup(irep) + ( kmin - glu.xlsub(irep) ); 
-              maxloc = glu.xlusup(irep) + ( kmax - glu.xlsub(irep) ); 
-              std::swap(glu.lusup(minloc), glu.lusup(maxloc)); 
-            }
-            kmin++;
-            kmax--;
-          }
-        } // end while 
-        
-        xprune(irep) = StorageIndex(kmin);  //Pruning 
-      } // end if do_prune 
-    } // end pruning 
-  } // End for each U-segment
-}
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // SPARSELU_PRUNEL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h
deleted file mode 100644
index c408d01..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h
+++ /dev/null
@@ -1,83 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/* This file is a modified version of heap_relax_snode.c file in SuperLU
- * -- SuperLU routine (version 3.0) --
- * Univ. of California Berkeley, Xerox Palo Alto Research Center,
- * and Lawrence Berkeley National Lab.
- * October 15, 2003
- *
- * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
- *
- * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
- * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
- *
- * Permission is hereby granted to use or copy this program for any
- * purpose, provided the above notices are retained on all copies.
- * Permission to modify the code and to distribute modified code is
- * granted, provided the above notices are retained, and a notice that
- * the code was modified is included with the above copyright notice.
- */
-
-#ifndef SPARSELU_RELAX_SNODE_H
-#define SPARSELU_RELAX_SNODE_H
-
-namespace Eigen {
-
-namespace internal {
- 
-/** 
- * \brief Identify the initial relaxed supernodes
- * 
- * This routine is applied to a column elimination tree. 
- * It assumes that the matrix has been reordered according to the postorder of the etree
- * \param n  the number of columns
- * \param et elimination tree 
- * \param relax_columns Maximum number of columns allowed in a relaxed snode 
- * \param descendants Number of descendants of each node in the etree
- * \param relax_end last column in a supernode
- */
-template <typename Scalar, typename StorageIndex>
-void SparseLUImpl<Scalar,StorageIndex>::relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end)
-{
-  
-  // compute the number of descendants of each node in the etree
-  Index parent; 
-  relax_end.setConstant(emptyIdxLU);
-  descendants.setZero();
-  for (Index j = 0; j < n; j++) 
-  {
-    parent = et(j);
-    if (parent != n) // not the dummy root
-      descendants(parent) += descendants(j) + 1;
-  }
-  // Identify the relaxed supernodes by postorder traversal of the etree
-  Index snode_start; // beginning of a snode 
-  for (Index j = 0; j < n; )
-  {
-    parent = et(j);
-    snode_start = j; 
-    while ( parent != n && descendants(parent) < relax_columns ) 
-    {
-      j = parent; 
-      parent = et(j);
-    }
-    // Found a supernode in postordered etree, j is the last column 
-    relax_end(snode_start) = StorageIndex(j); // Record last column
-    j++;
-    // Search for a new leaf
-    while (descendants(j) != 0 && j < n) j++;
-  } // End postorder traversal of the etree
-  
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseQR/SparseQR.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseQR/SparseQR.h
deleted file mode 100644
index d1fb96f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SparseQR/SparseQR.h
+++ /dev/null
@@ -1,758 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012-2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_QR_H
-#define EIGEN_SPARSE_QR_H
-
-namespace Eigen {
-
-template<typename MatrixType, typename OrderingType> class SparseQR;
-template<typename SparseQRType> struct SparseQRMatrixQReturnType;
-template<typename SparseQRType> struct SparseQRMatrixQTransposeReturnType;
-template<typename SparseQRType, typename Derived> struct SparseQR_QProduct;
-namespace internal {
-  template <typename SparseQRType> struct traits<SparseQRMatrixQReturnType<SparseQRType> >
-  {
-    typedef typename SparseQRType::MatrixType ReturnType;
-    typedef typename ReturnType::StorageIndex StorageIndex;
-    typedef typename ReturnType::StorageKind StorageKind;
-    enum {
-      RowsAtCompileTime = Dynamic,
-      ColsAtCompileTime = Dynamic
-    };
-  };
-  template <typename SparseQRType> struct traits<SparseQRMatrixQTransposeReturnType<SparseQRType> >
-  {
-    typedef typename SparseQRType::MatrixType ReturnType;
-  };
-  template <typename SparseQRType, typename Derived> struct traits<SparseQR_QProduct<SparseQRType, Derived> >
-  {
-    typedef typename Derived::PlainObject ReturnType;
-  };
-} // End namespace internal
-
-/**
-  * \ingroup SparseQR_Module
-  * \class SparseQR
-  * \brief Sparse left-looking QR factorization with numerical column pivoting
-  * 
-  * This class implements a left-looking QR decomposition of sparse matrices
-  * with numerical column pivoting.
-  * When a column has a norm less than a given tolerance
-  * it is implicitly permuted to the end. The QR factorization thus obtained is 
-  * given by A*P = Q*R where R is upper triangular or trapezoidal. 
-  * 
-  * P is the column permutation which is the product of the fill-reducing and the
-  * numerical permutations. Use colsPermutation() to get it.
-  * 
-  * Q is the orthogonal matrix represented as products of Householder reflectors. 
-  * Use matrixQ() to get an expression and matrixQ().adjoint() to get the adjoint.
-  * You can then apply it to a vector.
-  * 
-  * R is the sparse triangular or trapezoidal matrix. The later occurs when A is rank-deficient.
-  * matrixR().topLeftCorner(rank(), rank()) always returns a triangular factor of full rank.
-  * 
-  * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<>
-  * \tparam _OrderingType The fill-reducing ordering method. See the \link OrderingMethods_Module 
-  *  OrderingMethods \endlink module for the list of built-in and external ordering methods.
-  * 
-  * \implsparsesolverconcept
-  *
-  * The numerical pivoting strategy and default threshold are the same as in SuiteSparse QR, and
-  * detailed in the following paper:
-  * <i>
-  * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
-  * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011.
-  * </i>
-  * Even though it is qualified as "rank-revealing", this strategy might fail for some 
-  * rank deficient problems. When this class is used to solve linear or least-square problems
-  * it is thus strongly recommended to check the accuracy of the computed solution. If it
-  * failed, it usually helps to increase the threshold with setPivotThreshold.
-  * 
-  * \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()).
-  * \warning For complex matrices matrixQ().transpose() will actually return the adjoint matrix.
-  * 
-  */
-template<typename _MatrixType, typename _OrderingType>
-class SparseQR : public SparseSolverBase<SparseQR<_MatrixType,_OrderingType> >
-{
-  protected:
-    typedef SparseSolverBase<SparseQR<_MatrixType,_OrderingType> > Base;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    typedef _MatrixType MatrixType;
-    typedef _OrderingType OrderingType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> QRMatrixType;
-    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
-    typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
-    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
-
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-  public:
-    SparseQR () :  m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
-    { }
-    
-    /** Construct a QR factorization of the matrix \a mat.
-      * 
-      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-      * 
-      * \sa compute()
-      */
-    explicit SparseQR(const MatrixType& mat) : m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
-    {
-      compute(mat);
-    }
-    
-    /** Computes the QR factorization of the sparse matrix \a mat.
-      * 
-      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-      * 
-      * \sa analyzePattern(), factorize()
-      */
-    void compute(const MatrixType& mat)
-    {
-      analyzePattern(mat);
-      factorize(mat);
-    }
-    void analyzePattern(const MatrixType& mat);
-    void factorize(const MatrixType& mat);
-    
-    /** \returns the number of rows of the represented matrix. 
-      */
-    inline Index rows() const { return m_pmat.rows(); }
-    
-    /** \returns the number of columns of the represented matrix. 
-      */
-    inline Index cols() const { return m_pmat.cols();}
-    
-    /** \returns a const reference to the \b sparse upper triangular matrix R of the QR factorization.
-      * \warning The entries of the returned matrix are not sorted. This means that using it in algorithms
-      *          expecting sorted entries will fail. This include random coefficient accesses (SpaseMatrix::coeff()),
-      *          and coefficient-wise operations. Matrix products and triangular solves are fine though.
-      *
-      * To sort the entries, you can assign it to a row-major matrix, and if a column-major matrix
-      * is required, you can copy it again:
-      * \code
-      * SparseMatrix<double>          R  = qr.matrixR();  // column-major, not sorted!
-      * SparseMatrix<double,RowMajor> Rr = qr.matrixR();  // row-major, sorted
-      * SparseMatrix<double>          Rc = Rr;            // column-major, sorted
-      * \endcode
-      */
-    const QRMatrixType& matrixR() const { return m_R; }
-    
-    /** \returns the number of non linearly dependent columns as determined by the pivoting threshold.
-      *
-      * \sa setPivotThreshold()
-      */
-    Index rank() const
-    {
-      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-      return m_nonzeropivots; 
-    }
-    
-    /** \returns an expression of the matrix Q as products of sparse Householder reflectors.
-    * The common usage of this function is to apply it to a dense matrix or vector
-    * \code
-    * VectorXd B1, B2;
-    * // Initialize B1
-    * B2 = matrixQ() * B1;
-    * \endcode
-    *
-    * To get a plain SparseMatrix representation of Q:
-    * \code
-    * SparseMatrix<double> Q;
-    * Q = SparseQR<SparseMatrix<double> >(A).matrixQ();
-    * \endcode
-    * Internally, this call simply performs a sparse product between the matrix Q
-    * and a sparse identity matrix. However, due to the fact that the sparse
-    * reflectors are stored unsorted, two transpositions are needed to sort
-    * them before performing the product.
-    */
-    SparseQRMatrixQReturnType<SparseQR> matrixQ() const 
-    { return SparseQRMatrixQReturnType<SparseQR>(*this); }
-    
-    /** \returns a const reference to the column permutation P that was applied to A such that A*P = Q*R
-      * It is the combination of the fill-in reducing permutation and numerical column pivoting.
-      */
-    const PermutationType& colsPermutation() const
-    { 
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_outputPerm_c;
-    }
-    
-    /** \returns A string describing the type of error.
-      * This method is provided to ease debugging, not to handle errors.
-      */
-    std::string lastErrorMessage() const { return m_lastError; }
-    
-    /** \internal */
-    template<typename Rhs, typename Dest>
-    bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &dest) const
-    {
-      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-      eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
-
-      Index rank = this->rank();
-      
-      // Compute Q^* * b;
-      typename Dest::PlainObject y, b;
-      y = this->matrixQ().adjoint() * B;
-      b = y;
-      
-      // Solve with the triangular matrix R
-      y.resize((std::max<Index>)(cols(),y.rows()),y.cols());
-      y.topRows(rank) = this->matrixR().topLeftCorner(rank, rank).template triangularView<Upper>().solve(b.topRows(rank));
-      y.bottomRows(y.rows()-rank).setZero();
-      
-      // Apply the column permutation
-      if (m_perm_c.size())  dest = colsPermutation() * y.topRows(cols());
-      else                  dest = y.topRows(cols());
-      
-      m_info = Success;
-      return true;
-    }
-
-    /** Sets the threshold that is used to determine linearly dependent columns during the factorization.
-      *
-      * In practice, if during the factorization the norm of the column that has to be eliminated is below
-      * this threshold, then the entire column is treated as zero, and it is moved at the end.
-      */
-    void setPivotThreshold(const RealScalar& threshold)
-    {
-      m_useDefaultThreshold = false;
-      m_threshold = threshold;
-    }
-    
-    /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
-      *
-      * \sa compute()
-      */
-    template<typename Rhs>
-    inline const Solve<SparseQR, Rhs> solve(const MatrixBase<Rhs>& B) const 
-    {
-      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-      eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
-      return Solve<SparseQR, Rhs>(*this, B.derived());
-    }
-    template<typename Rhs>
-    inline const Solve<SparseQR, Rhs> solve(const SparseMatrixBase<Rhs>& B) const
-    {
-          eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
-          eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
-          return Solve<SparseQR, Rhs>(*this, B.derived());
-    }
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the QR factorization reports a numerical problem
-      *          \c InvalidInput if the input matrix is invalid
-      *
-      * \sa iparm()          
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-
-    /** \internal */
-    inline void _sort_matrix_Q()
-    {
-      if(this->m_isQSorted) return;
-      // The matrix Q is sorted during the transposition
-      SparseMatrix<Scalar, RowMajor, Index> mQrm(this->m_Q);
-      this->m_Q = mQrm;
-      this->m_isQSorted = true;
-    }
-
-    
-  protected:
-    bool m_analysisIsok;
-    bool m_factorizationIsok;
-    mutable ComputationInfo m_info;
-    std::string m_lastError;
-    QRMatrixType m_pmat;            // Temporary matrix
-    QRMatrixType m_R;               // The triangular factor matrix
-    QRMatrixType m_Q;               // The orthogonal reflectors
-    ScalarVector m_hcoeffs;         // The Householder coefficients
-    PermutationType m_perm_c;       // Fill-reducing  Column  permutation
-    PermutationType m_pivotperm;    // The permutation for rank revealing
-    PermutationType m_outputPerm_c; // The final column permutation
-    RealScalar m_threshold;         // Threshold to determine null Householder reflections
-    bool m_useDefaultThreshold;     // Use default threshold
-    Index m_nonzeropivots;          // Number of non zero pivots found
-    IndexVector m_etree;            // Column elimination tree
-    IndexVector m_firstRowElt;      // First element in each row
-    bool m_isQSorted;               // whether Q is sorted or not
-    bool m_isEtreeOk;               // whether the elimination tree match the initial input matrix
-    
-    template <typename, typename > friend struct SparseQR_QProduct;
-    
-};
-
-/** \brief Preprocessing step of a QR factorization 
-  * 
-  * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-  * 
-  * In this step, the fill-reducing permutation is computed and applied to the columns of A
-  * and the column elimination tree is computed as well. Only the sparsity pattern of \a mat is exploited.
-  * 
-  * \note In this step it is assumed that there is no empty row in the matrix \a mat.
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
-{
-  eigen_assert(mat.isCompressed() && "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR");
-  // Copy to a column major matrix if the input is rowmajor
-  typename internal::conditional<MatrixType::IsRowMajor,QRMatrixType,const MatrixType&>::type matCpy(mat);
-  // Compute the column fill reducing ordering
-  OrderingType ord; 
-  ord(matCpy, m_perm_c); 
-  Index n = mat.cols();
-  Index m = mat.rows();
-  Index diagSize = (std::min)(m,n);
-  
-  if (!m_perm_c.size())
-  {
-    m_perm_c.resize(n);
-    m_perm_c.indices().setLinSpaced(n, 0,StorageIndex(n-1));
-  }
-  
-  // Compute the column elimination tree of the permuted matrix
-  m_outputPerm_c = m_perm_c.inverse();
-  internal::coletree(matCpy, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
-  m_isEtreeOk = true;
-  
-  m_R.resize(m, n);
-  m_Q.resize(m, diagSize);
-  
-  // Allocate space for nonzero elements: rough estimation
-  m_R.reserve(2*mat.nonZeros()); //FIXME Get a more accurate estimation through symbolic factorization with the etree
-  m_Q.reserve(2*mat.nonZeros());
-  m_hcoeffs.resize(diagSize);
-  m_analysisIsok = true;
-}
-
-/** \brief Performs the numerical QR factorization of the input matrix
-  * 
-  * The function SparseQR::analyzePattern(const MatrixType&) must have been called beforehand with
-  * a matrix having the same sparsity pattern than \a mat.
-  * 
-  * \param mat The sparse column-major matrix
-  */
-template <typename MatrixType, typename OrderingType>
-void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
-{
-  using std::abs;
-  
-  eigen_assert(m_analysisIsok && "analyzePattern() should be called before this step");
-  StorageIndex m = StorageIndex(mat.rows());
-  StorageIndex n = StorageIndex(mat.cols());
-  StorageIndex diagSize = (std::min)(m,n);
-  IndexVector mark((std::max)(m,n)); mark.setConstant(-1);  // Record the visited nodes
-  IndexVector Ridx(n), Qidx(m);                             // Store temporarily the row indexes for the current column of R and Q
-  Index nzcolR, nzcolQ;                                     // Number of nonzero for the current column of R and Q
-  ScalarVector tval(m);                                     // The dense vector used to compute the current column
-  RealScalar pivotThreshold = m_threshold;
-  
-  m_R.setZero();
-  m_Q.setZero();
-  m_pmat = mat;
-  if(!m_isEtreeOk)
-  {
-    m_outputPerm_c = m_perm_c.inverse();
-    internal::coletree(m_pmat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
-    m_isEtreeOk = true;
-  }
-
-  m_pmat.uncompress(); // To have the innerNonZeroPtr allocated
-  
-  // Apply the fill-in reducing permutation lazily:
-  {
-    // If the input is row major, copy the original column indices,
-    // otherwise directly use the input matrix
-    // 
-    IndexVector originalOuterIndicesCpy;
-    const StorageIndex *originalOuterIndices = mat.outerIndexPtr();
-    if(MatrixType::IsRowMajor)
-    {
-      originalOuterIndicesCpy = IndexVector::Map(m_pmat.outerIndexPtr(),n+1);
-      originalOuterIndices = originalOuterIndicesCpy.data();
-    }
-    
-    for (int i = 0; i < n; i++)
-    {
-      Index p = m_perm_c.size() ? m_perm_c.indices()(i) : i;
-      m_pmat.outerIndexPtr()[p] = originalOuterIndices[i]; 
-      m_pmat.innerNonZeroPtr()[p] = originalOuterIndices[i+1] - originalOuterIndices[i]; 
-    }
-  }
-  
-  /* Compute the default threshold as in MatLab, see:
-   * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
-   * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3 
-   */
-  if(m_useDefaultThreshold) 
-  {
-    RealScalar max2Norm = 0.0;
-    for (int j = 0; j < n; j++) max2Norm = numext::maxi(max2Norm, m_pmat.col(j).norm());
-    if(max2Norm==RealScalar(0))
-      max2Norm = RealScalar(1);
-    pivotThreshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon();
-  }
-  
-  // Initialize the numerical permutation
-  m_pivotperm.setIdentity(n);
-  
-  StorageIndex nonzeroCol = 0; // Record the number of valid pivots
-  m_Q.startVec(0);
-
-  // Left looking rank-revealing QR factorization: compute a column of R and Q at a time
-  for (StorageIndex col = 0; col < n; ++col)
-  {
-    mark.setConstant(-1);
-    m_R.startVec(col);
-    mark(nonzeroCol) = col;
-    Qidx(0) = nonzeroCol;
-    nzcolR = 0; nzcolQ = 1;
-    bool found_diag = nonzeroCol>=m;
-    tval.setZero(); 
-    
-    // Symbolic factorization: find the nonzero locations of the column k of the factors R and Q, i.e.,
-    // all the nodes (with indexes lower than rank) reachable through the column elimination tree (etree) rooted at node k.
-    // Note: if the diagonal entry does not exist, then its contribution must be explicitly added,
-    // thus the trick with found_diag that permits to do one more iteration on the diagonal element if this one has not been found.
-    for (typename QRMatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp)
-    {
-      StorageIndex curIdx = nonzeroCol;
-      if(itp) curIdx = StorageIndex(itp.row());
-      if(curIdx == nonzeroCol) found_diag = true;
-      
-      // Get the nonzeros indexes of the current column of R
-      StorageIndex st = m_firstRowElt(curIdx); // The traversal of the etree starts here
-      if (st < 0 )
-      {
-        m_lastError = "Empty row found during numerical factorization";
-        m_info = InvalidInput;
-        return;
-      }
-
-      // Traverse the etree 
-      Index bi = nzcolR;
-      for (; mark(st) != col; st = m_etree(st))
-      {
-        Ridx(nzcolR) = st;  // Add this row to the list,
-        mark(st) = col;     // and mark this row as visited
-        nzcolR++;
-      }
-
-      // Reverse the list to get the topological ordering
-      Index nt = nzcolR-bi;
-      for(Index i = 0; i < nt/2; i++) std::swap(Ridx(bi+i), Ridx(nzcolR-i-1));
-       
-      // Copy the current (curIdx,pcol) value of the input matrix
-      if(itp) tval(curIdx) = itp.value();
-      else    tval(curIdx) = Scalar(0);
-      
-      // Compute the pattern of Q(:,k)
-      if(curIdx > nonzeroCol && mark(curIdx) != col ) 
-      {
-        Qidx(nzcolQ) = curIdx;  // Add this row to the pattern of Q,
-        mark(curIdx) = col;     // and mark it as visited
-        nzcolQ++;
-      }
-    }
-
-    // Browse all the indexes of R(:,col) in reverse order
-    for (Index i = nzcolR-1; i >= 0; i--)
-    {
-      Index curIdx = Ridx(i);
-      
-      // Apply the curIdx-th householder vector to the current column (temporarily stored into tval)
-      Scalar tdot(0);
-      
-      // First compute q' * tval
-      tdot = m_Q.col(curIdx).dot(tval);
-
-      tdot *= m_hcoeffs(curIdx);
-      
-      // Then update tval = tval - q * tau
-      // FIXME: tval -= tdot * m_Q.col(curIdx) should amount to the same (need to check/add support for efficient "dense ?= sparse")
-      for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq)
-        tval(itq.row()) -= itq.value() * tdot;
-
-      // Detect fill-in for the current column of Q
-      if(m_etree(Ridx(i)) == nonzeroCol)
-      {
-        for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq)
-        {
-          StorageIndex iQ = StorageIndex(itq.row());
-          if (mark(iQ) != col)
-          {
-            Qidx(nzcolQ++) = iQ;  // Add this row to the pattern of Q,
-            mark(iQ) = col;       // and mark it as visited
-          }
-        }
-      }
-    } // End update current column
-    
-    Scalar tau = RealScalar(0);
-    RealScalar beta = 0;
-    
-    if(nonzeroCol < diagSize)
-    {
-      // Compute the Householder reflection that eliminate the current column
-      // FIXME this step should call the Householder module.
-      Scalar c0 = nzcolQ ? tval(Qidx(0)) : Scalar(0);
-      
-      // First, the squared norm of Q((col+1):m, col)
-      RealScalar sqrNorm = 0.;
-      for (Index itq = 1; itq < nzcolQ; ++itq) sqrNorm += numext::abs2(tval(Qidx(itq)));
-      if(sqrNorm == RealScalar(0) && numext::imag(c0) == RealScalar(0))
-      {
-        beta = numext::real(c0);
-        tval(Qidx(0)) = 1;
-      }
-      else
-      {
-        using std::sqrt;
-        beta = sqrt(numext::abs2(c0) + sqrNorm);
-        if(numext::real(c0) >= RealScalar(0))
-          beta = -beta;
-        tval(Qidx(0)) = 1;
-        for (Index itq = 1; itq < nzcolQ; ++itq)
-          tval(Qidx(itq)) /= (c0 - beta);
-        tau = numext::conj((beta-c0) / beta);
-          
-      }
-    }
-
-    // Insert values in R
-    for (Index  i = nzcolR-1; i >= 0; i--)
-    {
-      Index curIdx = Ridx(i);
-      if(curIdx < nonzeroCol) 
-      {
-        m_R.insertBackByOuterInnerUnordered(col, curIdx) = tval(curIdx);
-        tval(curIdx) = Scalar(0.);
-      }
-    }
-
-    if(nonzeroCol < diagSize && abs(beta) >= pivotThreshold)
-    {
-      m_R.insertBackByOuterInner(col, nonzeroCol) = beta;
-      // The householder coefficient
-      m_hcoeffs(nonzeroCol) = tau;
-      // Record the householder reflections
-      for (Index itq = 0; itq < nzcolQ; ++itq)
-      {
-        Index iQ = Qidx(itq);
-        m_Q.insertBackByOuterInnerUnordered(nonzeroCol,iQ) = tval(iQ);
-        tval(iQ) = Scalar(0.);
-      }
-      nonzeroCol++;
-      if(nonzeroCol<diagSize)
-        m_Q.startVec(nonzeroCol);
-    }
-    else
-    {
-      // Zero pivot found: move implicitly this column to the end
-      for (Index j = nonzeroCol; j < n-1; j++) 
-        std::swap(m_pivotperm.indices()(j), m_pivotperm.indices()[j+1]);
-      
-      // Recompute the column elimination tree
-      internal::coletree(m_pmat, m_etree, m_firstRowElt, m_pivotperm.indices().data());
-      m_isEtreeOk = false;
-    }
-  }
-  
-  m_hcoeffs.tail(diagSize-nonzeroCol).setZero();
-  
-  // Finalize the column pointers of the sparse matrices R and Q
-  m_Q.finalize();
-  m_Q.makeCompressed();
-  m_R.finalize();
-  m_R.makeCompressed();
-  m_isQSorted = false;
-
-  m_nonzeropivots = nonzeroCol;
-  
-  if(nonzeroCol<n)
-  {
-    // Permute the triangular factor to put the 'dead' columns to the end
-    QRMatrixType tempR(m_R);
-    m_R = tempR * m_pivotperm;
-    
-    // Update the column permutation
-    m_outputPerm_c = m_outputPerm_c * m_pivotperm;
-  }
-  
-  m_isInitialized = true; 
-  m_factorizationIsok = true;
-  m_info = Success;
-}
-
-template <typename SparseQRType, typename Derived>
-struct SparseQR_QProduct : ReturnByValue<SparseQR_QProduct<SparseQRType, Derived> >
-{
-  typedef typename SparseQRType::QRMatrixType MatrixType;
-  typedef typename SparseQRType::Scalar Scalar;
-  // Get the references 
-  SparseQR_QProduct(const SparseQRType& qr, const Derived& other, bool transpose) : 
-  m_qr(qr),m_other(other),m_transpose(transpose) {}
-  inline Index rows() const { return m_qr.matrixQ().rows(); }
-  inline Index cols() const { return m_other.cols(); }
-  
-  // Assign to a vector
-  template<typename DesType>
-  void evalTo(DesType& res) const
-  {
-    Index m = m_qr.rows();
-    Index n = m_qr.cols();
-    Index diagSize = (std::min)(m,n);
-    res = m_other;
-    if (m_transpose)
-    {
-      eigen_assert(m_qr.m_Q.rows() == m_other.rows() && "Non conforming object sizes");
-      //Compute res = Q' * other column by column
-      for(Index j = 0; j < res.cols(); j++){
-        for (Index k = 0; k < diagSize; k++)
-        {
-          Scalar tau = Scalar(0);
-          tau = m_qr.m_Q.col(k).dot(res.col(j));
-          if(tau==Scalar(0)) continue;
-          tau = tau * m_qr.m_hcoeffs(k);
-          res.col(j) -= tau * m_qr.m_Q.col(k);
-        }
-      }
-    }
-    else
-    {
-      eigen_assert(m_qr.matrixQ().cols() == m_other.rows() && "Non conforming object sizes");
-
-      res.conservativeResize(rows(), cols());
-
-      // Compute res = Q * other column by column
-      for(Index j = 0; j < res.cols(); j++)
-      {
-        Index start_k = internal::is_identity<Derived>::value ? numext::mini(j,diagSize-1) : diagSize-1;
-        for (Index k = start_k; k >=0; k--)
-        {
-          Scalar tau = Scalar(0);
-          tau = m_qr.m_Q.col(k).dot(res.col(j));
-          if(tau==Scalar(0)) continue;
-          tau = tau * numext::conj(m_qr.m_hcoeffs(k));
-          res.col(j) -= tau * m_qr.m_Q.col(k);
-        }
-      }
-    }
-  }
-  
-  const SparseQRType& m_qr;
-  const Derived& m_other;
-  bool m_transpose; // TODO this actually means adjoint
-};
-
-template<typename SparseQRType>
-struct SparseQRMatrixQReturnType : public EigenBase<SparseQRMatrixQReturnType<SparseQRType> >
-{  
-  typedef typename SparseQRType::Scalar Scalar;
-  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = Dynamic
-  };
-  explicit SparseQRMatrixQReturnType(const SparseQRType& qr) : m_qr(qr) {}
-  template<typename Derived>
-  SparseQR_QProduct<SparseQRType, Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(),false);
-  }
-  // To use for operations with the adjoint of Q
-  SparseQRMatrixQTransposeReturnType<SparseQRType> adjoint() const
-  {
-    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
-  }
-  inline Index rows() const { return m_qr.rows(); }
-  inline Index cols() const { return m_qr.rows(); }
-  // To use for operations with the transpose of Q FIXME this is the same as adjoint at the moment
-  SparseQRMatrixQTransposeReturnType<SparseQRType> transpose() const
-  {
-    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
-  }
-  const SparseQRType& m_qr;
-};
-
-// TODO this actually represents the adjoint of Q
-template<typename SparseQRType>
-struct SparseQRMatrixQTransposeReturnType
-{
-  explicit SparseQRMatrixQTransposeReturnType(const SparseQRType& qr) : m_qr(qr) {}
-  template<typename Derived>
-  SparseQR_QProduct<SparseQRType,Derived> operator*(const MatrixBase<Derived>& other)
-  {
-    return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(), true);
-  }
-  const SparseQRType& m_qr;
-};
-
-namespace internal {
-  
-template<typename SparseQRType>
-struct evaluator_traits<SparseQRMatrixQReturnType<SparseQRType> >
-{
-  typedef typename SparseQRType::MatrixType MatrixType;
-  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
-  typedef SparseShape Shape;
-};
-
-template< typename DstXprType, typename SparseQRType>
-struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Sparse>
-{
-  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
-  typedef typename DstXprType::Scalar Scalar;
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
-  {
-    typename DstXprType::PlainObject idMat(src.rows(), src.cols());
-    idMat.setIdentity();
-    // Sort the sparse householder reflectors if needed
-    const_cast<SparseQRType *>(&src.m_qr)->_sort_matrix_Q();
-    dst = SparseQR_QProduct<SparseQRType, DstXprType>(src.m_qr, idMat, false);
-  }
-};
-
-template< typename DstXprType, typename SparseQRType>
-struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Dense>
-{
-  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
-  typedef typename DstXprType::Scalar Scalar;
-  typedef typename DstXprType::StorageIndex StorageIndex;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
-  {
-    dst = src.m_qr.matrixQ() * DstXprType::Identity(src.m_qr.rows(), src.m_qr.rows());
-  }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdDeque.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdDeque.h
deleted file mode 100644
index 6d47e75..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdDeque.h
+++ /dev/null
@@ -1,116 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDDEQUE_H
-#define EIGEN_STDDEQUE_H
-
-#include "details.h"
-
-/**
- * This section contains a convenience MACRO which allows an easy specialization of
- * std::deque such that for data types with alignment issues the correct allocator
- * is used automatically.
- */
-#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) \
-namespace std \
-{ \
-  template<> \
-  class deque<__VA_ARGS__, std::allocator<__VA_ARGS__> >           \
-    : public deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
-  { \
-    typedef deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > deque_base; \
-  public: \
-    typedef __VA_ARGS__ value_type; \
-    typedef deque_base::allocator_type allocator_type; \
-    typedef deque_base::size_type size_type;  \
-    typedef deque_base::iterator iterator;  \
-    explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {}  \
-    template<typename InputIterator> \
-    deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : deque_base(first, last, a) {} \
-    deque(const deque& c) : deque_base(c) {}  \
-    explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \
-    deque(iterator start_, iterator end_) : deque_base(start_, end_) {}  \
-    deque& operator=(const deque& x) {  \
-      deque_base::operator=(x);  \
-      return *this;  \
-    } \
-  }; \
-}
-
-// check whether we really need the std::deque specialization
-#if !EIGEN_HAS_CXX11_CONTAINERS && !(defined(_GLIBCXX_DEQUE) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::deque::resize(size_type,const T&). */
-
-namespace std {
-
-#define EIGEN_STD_DEQUE_SPECIALIZATION_BODY \
-  public:  \
-    typedef T value_type; \
-    typedef typename deque_base::allocator_type allocator_type; \
-    typedef typename deque_base::size_type size_type;  \
-    typedef typename deque_base::iterator iterator;  \
-    typedef typename deque_base::const_iterator const_iterator;  \
-    explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {}  \
-    template<typename InputIterator> \
-    deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \
-    : deque_base(first, last, a) {} \
-    deque(const deque& c) : deque_base(c) {}  \
-    explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \
-    deque(iterator start_, iterator end_) : deque_base(start_, end_) {}  \
-    deque& operator=(const deque& x) {  \
-      deque_base::operator=(x);  \
-      return *this;  \
-    }
-
-  template<typename T>
-  class deque<T,EIGEN_ALIGNED_ALLOCATOR<T> >
-    : public deque<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                   Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> >
-{
-  typedef deque<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > deque_base;
-  EIGEN_STD_DEQUE_SPECIALIZATION_BODY
-
-  void resize(size_type new_size)
-  { resize(new_size, T()); }
-
-#if defined(_DEQUE_)
-  // workaround MSVC std::deque implementation
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (deque_base::size() < new_size)
-      deque_base::_Insert_n(deque_base::end(), new_size - deque_base::size(), x);
-    else if (new_size < deque_base::size())
-      deque_base::erase(deque_base::begin() + new_size, deque_base::end());
-  }
-  void push_back(const value_type& x)
-  { deque_base::push_back(x); } 
-  void push_front(const value_type& x)
-  { deque_base::push_front(x); }
-  using deque_base::insert;  
-  iterator insert(const_iterator position, const value_type& x)
-  { return deque_base::insert(position,x); }
-  void insert(const_iterator position, size_type new_size, const value_type& x)
-  { deque_base::insert(position, new_size, x); }
-#else
-  // default implementation which should always work.
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (new_size < deque_base::size())
-      deque_base::erase(deque_base::begin() + new_size, deque_base::end());
-    else if (new_size > deque_base::size())
-      deque_base::insert(deque_base::end(), new_size - deque_base::size(), x);
-  }
-#endif
-  };
-}
-
-#endif // check whether specialization is actually required
-
-#endif // EIGEN_STDDEQUE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdList.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdList.h
deleted file mode 100644
index 8ba3fad..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdList.h
+++ /dev/null
@@ -1,106 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDLIST_H
-#define EIGEN_STDLIST_H
-
-#include "details.h"
-
-/**
- * This section contains a convenience MACRO which allows an easy specialization of
- * std::list such that for data types with alignment issues the correct allocator
- * is used automatically.
- */
-#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) \
-namespace std \
-{ \
-  template<> \
-  class list<__VA_ARGS__, std::allocator<__VA_ARGS__> >           \
-    : public list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
-  { \
-    typedef list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > list_base; \
-  public: \
-    typedef __VA_ARGS__ value_type; \
-    typedef list_base::allocator_type allocator_type; \
-    typedef list_base::size_type size_type;  \
-    typedef list_base::iterator iterator;  \
-    explicit list(const allocator_type& a = allocator_type()) : list_base(a) {}  \
-    template<typename InputIterator> \
-    list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : list_base(first, last, a) {} \
-    list(const list& c) : list_base(c) {}  \
-    explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \
-    list(iterator start_, iterator end_) : list_base(start_, end_) {}  \
-    list& operator=(const list& x) {  \
-      list_base::operator=(x);  \
-      return *this;  \
-    } \
-  }; \
-}
-
-// check whether we really need the std::list specialization
-#if !EIGEN_HAS_CXX11_CONTAINERS && !(defined(_GLIBCXX_LIST) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::list::resize(size_type,const T&). */
-
-namespace std
-{
-
-#define EIGEN_STD_LIST_SPECIALIZATION_BODY \
-  public:  \
-    typedef T value_type; \
-    typedef typename list_base::allocator_type allocator_type; \
-    typedef typename list_base::size_type size_type;  \
-    typedef typename list_base::iterator iterator;  \
-    typedef typename list_base::const_iterator const_iterator;  \
-    explicit list(const allocator_type& a = allocator_type()) : list_base(a) {}  \
-    template<typename InputIterator> \
-    list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \
-    : list_base(first, last, a) {} \
-    list(const list& c) : list_base(c) {}  \
-    explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \
-    list(iterator start_, iterator end_) : list_base(start_, end_) {}  \
-    list& operator=(const list& x) {  \
-    list_base::operator=(x);  \
-    return *this; \
-  }
-
-  template<typename T>
-  class list<T,EIGEN_ALIGNED_ALLOCATOR<T> >
-    : public list<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                  Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> >
-  {
-    typedef list<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                 Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > list_base;
-    EIGEN_STD_LIST_SPECIALIZATION_BODY
-
-    void resize(size_type new_size)
-    { resize(new_size, T()); }
-
-    void resize(size_type new_size, const value_type& x)
-    {
-      if (list_base::size() < new_size)
-        list_base::insert(list_base::end(), new_size - list_base::size(), x);
-      else
-        while (new_size < list_base::size()) list_base::pop_back();
-    }
-
-#if defined(_LIST_)
-    // workaround MSVC std::list implementation
-    void push_back(const value_type& x)
-    { list_base::push_back(x); } 
-    using list_base::insert;  
-    iterator insert(const_iterator position, const value_type& x)
-    { return list_base::insert(position,x); }
-    void insert(const_iterator position, size_type new_size, const value_type& x)
-    { list_base::insert(position, new_size, x); }
-#endif
-  };
-}
-
-#endif // check whether specialization is actually required
-
-#endif // EIGEN_STDLIST_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdVector.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdVector.h
deleted file mode 100644
index 9fcf19b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/StdVector.h
+++ /dev/null
@@ -1,131 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STDVECTOR_H
-#define EIGEN_STDVECTOR_H
-
-#include "details.h"
-
-/**
- * This section contains a convenience MACRO which allows an easy specialization of
- * std::vector such that for data types with alignment issues the correct allocator
- * is used automatically.
- */
-#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) \
-namespace std \
-{ \
-  template<> \
-  class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> >  \
-    : public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
-  { \
-    typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base; \
-  public: \
-    typedef __VA_ARGS__ value_type; \
-    typedef vector_base::allocator_type allocator_type; \
-    typedef vector_base::size_type size_type;  \
-    typedef vector_base::iterator iterator;  \
-    explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {}  \
-    template<typename InputIterator> \
-    vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : vector_base(first, last, a) {} \
-    vector(const vector& c) : vector_base(c) {}  \
-    explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
-    vector(iterator start_, iterator end_) : vector_base(start_, end_) {}  \
-    vector& operator=(const vector& x) {  \
-      vector_base::operator=(x);  \
-      return *this;  \
-    } \
-  }; \
-}
-
-// Don't specialize if containers are implemented according to C++11
-#if !EIGEN_HAS_CXX11_CONTAINERS
-
-namespace std {
-
-#define EIGEN_STD_VECTOR_SPECIALIZATION_BODY \
-  public:  \
-    typedef T value_type; \
-    typedef typename vector_base::allocator_type allocator_type; \
-    typedef typename vector_base::size_type size_type;  \
-    typedef typename vector_base::iterator iterator;  \
-    typedef typename vector_base::const_iterator const_iterator;  \
-    explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {}  \
-    template<typename InputIterator> \
-    vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \
-    : vector_base(first, last, a) {} \
-    vector(const vector& c) : vector_base(c) {}  \
-    explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
-    vector(iterator start_, iterator end_) : vector_base(start_, end_) {}  \
-    vector& operator=(const vector& x) {  \
-      vector_base::operator=(x);  \
-      return *this;  \
-    }
-
-  template<typename T>
-  class vector<T,EIGEN_ALIGNED_ALLOCATOR<T> >
-    : public vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                    Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> >
-{
-  typedef vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T),
-                 Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > vector_base;
-  EIGEN_STD_VECTOR_SPECIALIZATION_BODY
-
-  void resize(size_type new_size)
-  { resize(new_size, T()); }
-
-#if defined(_VECTOR_)
-  // workaround MSVC std::vector implementation
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (vector_base::size() < new_size)
-      vector_base::_Insert_n(vector_base::end(), new_size - vector_base::size(), x);
-    else if (new_size < vector_base::size())
-      vector_base::erase(vector_base::begin() + new_size, vector_base::end());
-  }
-  void push_back(const value_type& x)
-  { vector_base::push_back(x); } 
-  using vector_base::insert;  
-  iterator insert(const_iterator position, const value_type& x)
-  { return vector_base::insert(position,x); }
-  void insert(const_iterator position, size_type new_size, const value_type& x)
-  { vector_base::insert(position, new_size, x); }
-#elif defined(_GLIBCXX_VECTOR) && (!(EIGEN_GNUC_AT_LEAST(4,1)))
-  /* Note that before gcc-4.1 we already have: std::vector::resize(size_type,const T&).
-   * However, this specialization is still needed to make the above EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION trick to work. */
-  void resize(size_type new_size, const value_type& x)
-  {
-    vector_base::resize(new_size,x);
-  }
-#elif defined(_GLIBCXX_VECTOR) && EIGEN_GNUC_AT_LEAST(4,2)
-  // workaround GCC std::vector implementation
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (new_size < vector_base::size())
-      vector_base::_M_erase_at_end(this->_M_impl._M_start + new_size);
-    else
-      vector_base::insert(vector_base::end(), new_size - vector_base::size(), x);
-  }
-#else
-  // either GCC 4.1 or non-GCC
-  // default implementation which should always work.
-  void resize(size_type new_size, const value_type& x)
-  {
-    if (new_size < vector_base::size())
-      vector_base::erase(vector_base::begin() + new_size, vector_base::end());
-    else if (new_size > vector_base::size())
-      vector_base::insert(vector_base::end(), new_size - vector_base::size(), x);
-  }
-#endif
-  };
-}
-#endif // !EIGEN_HAS_CXX11_CONTAINERS
-
-
-#endif // EIGEN_STDVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/details.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/details.h
deleted file mode 100644
index 2cfd13e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/StlSupport/details.h
+++ /dev/null
@@ -1,84 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STL_DETAILS_H
-#define EIGEN_STL_DETAILS_H
-
-#ifndef EIGEN_ALIGNED_ALLOCATOR
-  #define EIGEN_ALIGNED_ALLOCATOR Eigen::aligned_allocator
-#endif
-
-namespace Eigen {
-
-  // This one is needed to prevent reimplementing the whole std::vector.
-  template <class T>
-  class aligned_allocator_indirection : public EIGEN_ALIGNED_ALLOCATOR<T>
-  {
-  public:
-    typedef std::size_t     size_type;
-    typedef std::ptrdiff_t  difference_type;
-    typedef T*              pointer;
-    typedef const T*        const_pointer;
-    typedef T&              reference;
-    typedef const T&        const_reference;
-    typedef T               value_type;
-
-    template<class U>
-    struct rebind
-    {
-      typedef aligned_allocator_indirection<U> other;
-    };
-
-    aligned_allocator_indirection() {}
-    aligned_allocator_indirection(const aligned_allocator_indirection& ) : EIGEN_ALIGNED_ALLOCATOR<T>() {}
-    aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<T>& ) {}
-    template<class U>
-    aligned_allocator_indirection(const aligned_allocator_indirection<U>& ) {}
-    template<class U>
-    aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<U>& ) {}
-    ~aligned_allocator_indirection() {}
-  };
-
-#if EIGEN_COMP_MSVC
-
-  // sometimes, MSVC detects, at compile time, that the argument x
-  // in std::vector::resize(size_t s,T x) won't be aligned and generate an error
-  // even if this function is never called. Whence this little wrapper.
-#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) \
-  typename Eigen::internal::conditional< \
-    Eigen::internal::is_arithmetic<T>::value, \
-    T, \
-    Eigen::internal::workaround_msvc_stl_support<T> \
-  >::type
-
-  namespace internal {
-  template<typename T> struct workaround_msvc_stl_support : public T
-  {
-    inline workaround_msvc_stl_support() : T() {}
-    inline workaround_msvc_stl_support(const T& other) : T(other) {}
-    inline operator T& () { return *static_cast<T*>(this); }
-    inline operator const T& () const { return *static_cast<const T*>(this); }
-    template<typename OtherT>
-    inline T& operator=(const OtherT& other)
-    { T::operator=(other); return *this; }
-    inline workaround_msvc_stl_support& operator=(const workaround_msvc_stl_support& other)
-    { T::operator=(other); return *this; }
-  };
-  }
-
-#else
-
-#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) T
-
-#endif
-
-}
-
-#endif // EIGEN_STL_DETAILS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
deleted file mode 100644
index d1d3ad7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
+++ /dev/null
@@ -1,1025 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SUPERLUSUPPORT_H
-#define EIGEN_SUPERLUSUPPORT_H
-
-namespace Eigen {
-
-#if defined(SUPERLU_MAJOR_VERSION) && (SUPERLU_MAJOR_VERSION >= 5)
-#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE)		\
-    extern "C" {                                                                                          \
-      extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *,                  \
-                                char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,           \
-                                void *, int, SuperMatrix *, SuperMatrix *,                                \
-                                FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *,                       \
-                                GlobalLU_t *, mem_usage_t *, SuperLUStat_t *, int *);                     \
-    }                                                                                                     \
-    inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A,                                \
-         int *perm_c, int *perm_r, int *etree, char *equed,                                               \
-         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                                      \
-         SuperMatrix *U, void *work, int lwork,                                                           \
-         SuperMatrix *B, SuperMatrix *X,                                                                  \
-         FLOATTYPE *recip_pivot_growth,                                                                   \
-         FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr,                                              \
-         SuperLUStat_t *stats, int *info, KEYTYPE) {                                                      \
-    mem_usage_t mem_usage;                                                                                \
-    GlobalLU_t gLU;                                                                                       \
-    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L,                                      \
-         U, work, lwork, B, X, recip_pivot_growth, rcond,                                                 \
-         ferr, berr, &gLU, &mem_usage, stats, info);                                                      \
-    return mem_usage.for_lu; /* bytes used by the factor storage */                                       \
-  }
-#else // version < 5.0
-#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE)		\
-    extern "C" {                                                                                          \
-      extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *,                  \
-                                char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,           \
-                                void *, int, SuperMatrix *, SuperMatrix *,                                \
-                                FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *,                       \
-                                mem_usage_t *, SuperLUStat_t *, int *);                                   \
-    }                                                                                                     \
-    inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A,                                \
-         int *perm_c, int *perm_r, int *etree, char *equed,                                               \
-         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                                      \
-         SuperMatrix *U, void *work, int lwork,                                                           \
-         SuperMatrix *B, SuperMatrix *X,                                                                  \
-         FLOATTYPE *recip_pivot_growth,                                                                   \
-         FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr,                                              \
-         SuperLUStat_t *stats, int *info, KEYTYPE) {                                                      \
-    mem_usage_t mem_usage;                                                                                \
-    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L,                                      \
-         U, work, lwork, B, X, recip_pivot_growth, rcond,                                                 \
-         ferr, berr, &mem_usage, stats, info);                                                            \
-    return mem_usage.for_lu; /* bytes used by the factor storage */                                       \
-  }
-#endif
-
-DECL_GSSVX(s,float,float)
-DECL_GSSVX(c,float,std::complex<float>)
-DECL_GSSVX(d,double,double)
-DECL_GSSVX(z,double,std::complex<double>)
-
-#ifdef MILU_ALPHA
-#define EIGEN_SUPERLU_HAS_ILU
-#endif
-
-#ifdef EIGEN_SUPERLU_HAS_ILU
-
-// similarly for the incomplete factorization using gsisx
-#define DECL_GSISX(PREFIX,FLOATTYPE,KEYTYPE)                                                    \
-    extern "C" {                                                                                \
-      extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *,        \
-                         char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,        \
-                         void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *, FLOATTYPE *,   \
-                         mem_usage_t *, SuperLUStat_t *, int *);                        \
-    }                                                                                           \
-    inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A,                      \
-         int *perm_c, int *perm_r, int *etree, char *equed,                                     \
-         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                            \
-         SuperMatrix *U, void *work, int lwork,                                                 \
-         SuperMatrix *B, SuperMatrix *X,                                                        \
-         FLOATTYPE *recip_pivot_growth,                                                         \
-         FLOATTYPE *rcond,                                                                      \
-         SuperLUStat_t *stats, int *info, KEYTYPE) {                                            \
-    mem_usage_t mem_usage;                                                              \
-    PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L,                            \
-         U, work, lwork, B, X, recip_pivot_growth, rcond,                                       \
-         &mem_usage, stats, info);                                                              \
-    return mem_usage.for_lu; /* bytes used by the factor storage */                             \
-  }
-
-DECL_GSISX(s,float,float)
-DECL_GSISX(c,float,std::complex<float>)
-DECL_GSISX(d,double,double)
-DECL_GSISX(z,double,std::complex<double>)
-
-#endif
-
-template<typename MatrixType>
-struct SluMatrixMapHelper;
-
-/** \internal
-  *
-  * A wrapper class for SuperLU matrices. It supports only compressed sparse matrices
-  * and dense matrices. Supernodal and other fancy format are not supported by this wrapper.
-  *
-  * This wrapper class mainly aims to avoids the need of dynamic allocation of the storage structure.
-  */
-struct SluMatrix : SuperMatrix
-{
-  SluMatrix()
-  {
-    Store = &storage;
-  }
-
-  SluMatrix(const SluMatrix& other)
-    : SuperMatrix(other)
-  {
-    Store = &storage;
-    storage = other.storage;
-  }
-
-  SluMatrix& operator=(const SluMatrix& other)
-  {
-    SuperMatrix::operator=(static_cast<const SuperMatrix&>(other));
-    Store = &storage;
-    storage = other.storage;
-    return *this;
-  }
-
-  struct
-  {
-    union {int nnz;int lda;};
-    void *values;
-    int *innerInd;
-    int *outerInd;
-  } storage;
-
-  void setStorageType(Stype_t t)
-  {
-    Stype = t;
-    if (t==SLU_NC || t==SLU_NR || t==SLU_DN)
-      Store = &storage;
-    else
-    {
-      eigen_assert(false && "storage type not supported");
-      Store = 0;
-    }
-  }
-
-  template<typename Scalar>
-  void setScalarType()
-  {
-    if (internal::is_same<Scalar,float>::value)
-      Dtype = SLU_S;
-    else if (internal::is_same<Scalar,double>::value)
-      Dtype = SLU_D;
-    else if (internal::is_same<Scalar,std::complex<float> >::value)
-      Dtype = SLU_C;
-    else if (internal::is_same<Scalar,std::complex<double> >::value)
-      Dtype = SLU_Z;
-    else
-    {
-      eigen_assert(false && "Scalar type not supported by SuperLU");
-    }
-  }
-
-  template<typename MatrixType>
-  static SluMatrix Map(MatrixBase<MatrixType>& _mat)
-  {
-    MatrixType& mat(_mat.derived());
-    eigen_assert( ((MatrixType::Flags&RowMajorBit)!=RowMajorBit) && "row-major dense matrices are not supported by SuperLU");
-    SluMatrix res;
-    res.setStorageType(SLU_DN);
-    res.setScalarType<typename MatrixType::Scalar>();
-    res.Mtype     = SLU_GE;
-
-    res.nrow      = internal::convert_index<int>(mat.rows());
-    res.ncol      = internal::convert_index<int>(mat.cols());
-
-    res.storage.lda       = internal::convert_index<int>(MatrixType::IsVectorAtCompileTime ? mat.size() : mat.outerStride());
-    res.storage.values    = (void*)(mat.data());
-    return res;
-  }
-
-  template<typename MatrixType>
-  static SluMatrix Map(SparseMatrixBase<MatrixType>& a_mat)
-  {
-    MatrixType &mat(a_mat.derived());
-    SluMatrix res;
-    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
-    {
-      res.setStorageType(SLU_NR);
-      res.nrow      = internal::convert_index<int>(mat.cols());
-      res.ncol      = internal::convert_index<int>(mat.rows());
-    }
-    else
-    {
-      res.setStorageType(SLU_NC);
-      res.nrow      = internal::convert_index<int>(mat.rows());
-      res.ncol      = internal::convert_index<int>(mat.cols());
-    }
-
-    res.Mtype       = SLU_GE;
-
-    res.storage.nnz       = internal::convert_index<int>(mat.nonZeros());
-    res.storage.values    = mat.valuePtr();
-    res.storage.innerInd  = mat.innerIndexPtr();
-    res.storage.outerInd  = mat.outerIndexPtr();
-
-    res.setScalarType<typename MatrixType::Scalar>();
-
-    // FIXME the following is not very accurate
-    if (int(MatrixType::Flags) & int(Upper))
-      res.Mtype = SLU_TRU;
-    if (int(MatrixType::Flags) & int(Lower))
-      res.Mtype = SLU_TRL;
-
-    eigen_assert(((int(MatrixType::Flags) & int(SelfAdjoint))==0) && "SelfAdjoint matrix shape not supported by SuperLU");
-
-    return res;
-  }
-};
-
-template<typename Scalar, int Rows, int Cols, int Options, int MRows, int MCols>
-struct SluMatrixMapHelper<Matrix<Scalar,Rows,Cols,Options,MRows,MCols> >
-{
-  typedef Matrix<Scalar,Rows,Cols,Options,MRows,MCols> MatrixType;
-  static void run(MatrixType& mat, SluMatrix& res)
-  {
-    eigen_assert( ((Options&RowMajor)!=RowMajor) && "row-major dense matrices is not supported by SuperLU");
-    res.setStorageType(SLU_DN);
-    res.setScalarType<Scalar>();
-    res.Mtype     = SLU_GE;
-
-    res.nrow      = mat.rows();
-    res.ncol      = mat.cols();
-
-    res.storage.lda       = mat.outerStride();
-    res.storage.values    = mat.data();
-  }
-};
-
-template<typename Derived>
-struct SluMatrixMapHelper<SparseMatrixBase<Derived> >
-{
-  typedef Derived MatrixType;
-  static void run(MatrixType& mat, SluMatrix& res)
-  {
-    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
-    {
-      res.setStorageType(SLU_NR);
-      res.nrow      = mat.cols();
-      res.ncol      = mat.rows();
-    }
-    else
-    {
-      res.setStorageType(SLU_NC);
-      res.nrow      = mat.rows();
-      res.ncol      = mat.cols();
-    }
-
-    res.Mtype       = SLU_GE;
-
-    res.storage.nnz       = mat.nonZeros();
-    res.storage.values    = mat.valuePtr();
-    res.storage.innerInd  = mat.innerIndexPtr();
-    res.storage.outerInd  = mat.outerIndexPtr();
-
-    res.setScalarType<typename MatrixType::Scalar>();
-
-    // FIXME the following is not very accurate
-    if (MatrixType::Flags & Upper)
-      res.Mtype = SLU_TRU;
-    if (MatrixType::Flags & Lower)
-      res.Mtype = SLU_TRL;
-
-    eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU");
-  }
-};
-
-namespace internal {
-
-template<typename MatrixType>
-SluMatrix asSluMatrix(MatrixType& mat)
-{
-  return SluMatrix::Map(mat);
-}
-
-/** View a Super LU matrix as an Eigen expression */
-template<typename Scalar, int Flags, typename Index>
-MappedSparseMatrix<Scalar,Flags,Index> map_superlu(SluMatrix& sluMat)
-{
-  eigen_assert(((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR)
-         || ((Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC));
-
-  Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow;
-
-  return MappedSparseMatrix<Scalar,Flags,Index>(
-    sluMat.nrow, sluMat.ncol, sluMat.storage.outerInd[outerSize],
-    sluMat.storage.outerInd, sluMat.storage.innerInd, reinterpret_cast<Scalar*>(sluMat.storage.values) );
-}
-
-} // end namespace internal
-
-/** \ingroup SuperLUSupport_Module
-  * \class SuperLUBase
-  * \brief The base class for the direct and incomplete LU factorization of SuperLU
-  */
-template<typename _MatrixType, typename Derived>
-class SuperLUBase : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-  public:
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;    
-    typedef Map<PermutationMatrix<Dynamic,Dynamic,int> > PermutationMap;
-    typedef SparseMatrix<Scalar> LUMatrixType;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    SuperLUBase() {}
-
-    ~SuperLUBase()
-    {
-      clearFactors();
-    }
-    
-    inline Index rows() const { return m_matrix.rows(); }
-    inline Index cols() const { return m_matrix.cols(); }
-    
-    /** \returns a reference to the Super LU option object to configure the  Super LU algorithms. */
-    inline superlu_options_t& options() { return m_sluOptions; }
-    
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix */
-    void compute(const MatrixType& matrix)
-    {
-      derived().analyzePattern(matrix);
-      derived().factorize(matrix);
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& /*matrix*/)
-    {
-      m_isInitialized = true;
-      m_info = Success;
-      m_analysisIsOk = true;
-      m_factorizationIsOk = false;
-    }
-    
-    template<typename Stream>
-    void dumpMemory(Stream& /*s*/)
-    {}
-    
-  protected:
-    
-    void initFactorization(const MatrixType& a)
-    {
-      set_default_options(&this->m_sluOptions);
-      
-      const Index size = a.rows();
-      m_matrix = a;
-
-      m_sluA = internal::asSluMatrix(m_matrix);
-      clearFactors();
-
-      m_p.resize(size);
-      m_q.resize(size);
-      m_sluRscale.resize(size);
-      m_sluCscale.resize(size);
-      m_sluEtree.resize(size);
-
-      // set empty B and X
-      m_sluB.setStorageType(SLU_DN);
-      m_sluB.setScalarType<Scalar>();
-      m_sluB.Mtype          = SLU_GE;
-      m_sluB.storage.values = 0;
-      m_sluB.nrow           = 0;
-      m_sluB.ncol           = 0;
-      m_sluB.storage.lda    = internal::convert_index<int>(size);
-      m_sluX                = m_sluB;
-      
-      m_extractedDataAreDirty = true;
-    }
-    
-    void init()
-    {
-      m_info = InvalidInput;
-      m_isInitialized = false;
-      m_sluL.Store = 0;
-      m_sluU.Store = 0;
-    }
-    
-    void extractData() const;
-
-    void clearFactors()
-    {
-      if(m_sluL.Store)
-        Destroy_SuperNode_Matrix(&m_sluL);
-      if(m_sluU.Store)
-        Destroy_CompCol_Matrix(&m_sluU);
-
-      m_sluL.Store = 0;
-      m_sluU.Store = 0;
-
-      memset(&m_sluL,0,sizeof m_sluL);
-      memset(&m_sluU,0,sizeof m_sluU);
-    }
-
-    // cached data to reduce reallocation, etc.
-    mutable LUMatrixType m_l;
-    mutable LUMatrixType m_u;
-    mutable IntColVectorType m_p;
-    mutable IntRowVectorType m_q;
-
-    mutable LUMatrixType m_matrix;  // copy of the factorized matrix
-    mutable SluMatrix m_sluA;
-    mutable SuperMatrix m_sluL, m_sluU;
-    mutable SluMatrix m_sluB, m_sluX;
-    mutable SuperLUStat_t m_sluStat;
-    mutable superlu_options_t m_sluOptions;
-    mutable std::vector<int> m_sluEtree;
-    mutable Matrix<RealScalar,Dynamic,1> m_sluRscale, m_sluCscale;
-    mutable Matrix<RealScalar,Dynamic,1> m_sluFerr, m_sluBerr;
-    mutable char m_sluEqued;
-
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-    mutable bool m_extractedDataAreDirty;
-    
-  private:
-    SuperLUBase(SuperLUBase& ) { }
-};
-
-
-/** \ingroup SuperLUSupport_Module
-  * \class SuperLU
-  * \brief A sparse direct LU factorization and solver based on the SuperLU library
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
-  * using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices
-  * X and B can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  */
-template<typename _MatrixType>
-class SuperLU : public SuperLUBase<_MatrixType,SuperLU<_MatrixType> >
-{
-  public:
-    typedef SuperLUBase<_MatrixType,SuperLU> Base;
-    typedef _MatrixType MatrixType;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-    typedef typename Base::StorageIndex StorageIndex;
-    typedef typename Base::IntRowVectorType IntRowVectorType;
-    typedef typename Base::IntColVectorType IntColVectorType;   
-    typedef typename Base::PermutationMap PermutationMap;
-    typedef typename Base::LUMatrixType LUMatrixType;
-    typedef TriangularView<LUMatrixType, Lower|UnitDiag>  LMatrixType;
-    typedef TriangularView<LUMatrixType,  Upper>          UMatrixType;
-
-  public:
-    using Base::_solve_impl;
-
-    SuperLU() : Base() { init(); }
-
-    explicit SuperLU(const MatrixType& matrix) : Base()
-    {
-      init();
-      Base::compute(matrix);
-    }
-
-    ~SuperLU()
-    {
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      m_info = InvalidInput;
-      m_isInitialized = false;
-      Base::analyzePattern(matrix);
-    }
-    
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& matrix);
-    
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
-    
-    inline const LMatrixType& matrixL() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_l;
-    }
-
-    inline const UMatrixType& matrixU() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_u;
-    }
-
-    inline const IntColVectorType& permutationP() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_p;
-    }
-
-    inline const IntRowVectorType& permutationQ() const
-    {
-      if (m_extractedDataAreDirty) this->extractData();
-      return m_q;
-    }
-    
-    Scalar determinant() const;
-    
-  protected:
-    
-    using Base::m_matrix;
-    using Base::m_sluOptions;
-    using Base::m_sluA;
-    using Base::m_sluB;
-    using Base::m_sluX;
-    using Base::m_p;
-    using Base::m_q;
-    using Base::m_sluEtree;
-    using Base::m_sluEqued;
-    using Base::m_sluRscale;
-    using Base::m_sluCscale;
-    using Base::m_sluL;
-    using Base::m_sluU;
-    using Base::m_sluStat;
-    using Base::m_sluFerr;
-    using Base::m_sluBerr;
-    using Base::m_l;
-    using Base::m_u;
-    
-    using Base::m_analysisIsOk;
-    using Base::m_factorizationIsOk;
-    using Base::m_extractedDataAreDirty;
-    using Base::m_isInitialized;
-    using Base::m_info;
-    
-    void init()
-    {
-      Base::init();
-      
-      set_default_options(&this->m_sluOptions);
-      m_sluOptions.PrintStat        = NO;
-      m_sluOptions.ConditionNumber  = NO;
-      m_sluOptions.Trans            = NOTRANS;
-      m_sluOptions.ColPerm          = COLAMD;
-    }
-    
-    
-  private:
-    SuperLU(SuperLU& ) { }
-};
-
-template<typename MatrixType>
-void SuperLU<MatrixType>::factorize(const MatrixType& a)
-{
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  if(!m_analysisIsOk)
-  {
-    m_info = InvalidInput;
-    return;
-  }
-  
-  this->initFactorization(a);
-  
-  m_sluOptions.ColPerm = COLAMD;
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-  RealScalar ferr, berr;
-
-  StatInit(&m_sluStat);
-  SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
-                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &ferr, &berr,
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-
-  m_extractedDataAreDirty = true;
-
-  // FIXME how to better check for errors ???
-  m_info = info == 0 ? Success : NumericalIssue;
-  m_factorizationIsOk = true;
-}
-
-template<typename MatrixType>
-template<typename Rhs,typename Dest>
-void SuperLU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
-
-  const Index rhsCols = b.cols();
-  eigen_assert(m_matrix.rows()==b.rows());
-
-  m_sluOptions.Trans = NOTRANS;
-  m_sluOptions.Fact = FACTORED;
-  m_sluOptions.IterRefine = NOREFINE;
-  
-
-  m_sluFerr.resize(rhsCols);
-  m_sluBerr.resize(rhsCols);
-  
-  Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b);
-  Ref<const Matrix<typename Dest::Scalar,Dynamic,Dynamic,ColMajor> > x_ref(x);
-  
-  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
-  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
-  
-  typename Rhs::PlainObject b_cpy;
-  if(m_sluEqued!='N')
-  {
-    b_cpy = b;
-    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
-  }
-
-  StatInit(&m_sluStat);
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-  SuperLU_gssvx(&m_sluOptions, &m_sluA,
-                m_q.data(), m_p.data(),
-                &m_sluEtree[0], &m_sluEqued,
-                &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &m_sluFerr[0], &m_sluBerr[0],
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-  
-  if(x.derived().data() != x_ref.data())
-    x = x_ref;
-  
-  m_info = info==0 ? Success : NumericalIssue;
-}
-
-// the code of this extractData() function has been adapted from the SuperLU's Matlab support code,
-//
-//  Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
-//
-//  THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
-//  EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
-//
-template<typename MatrixType, typename Derived>
-void SuperLUBase<MatrixType,Derived>::extractData() const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for extracting factors, you must first call either compute() or analyzePattern()/factorize()");
-  if (m_extractedDataAreDirty)
-  {
-    int         upper;
-    int         fsupc, istart, nsupr;
-    int         lastl = 0, lastu = 0;
-    SCformat    *Lstore = static_cast<SCformat*>(m_sluL.Store);
-    NCformat    *Ustore = static_cast<NCformat*>(m_sluU.Store);
-    Scalar      *SNptr;
-
-    const Index size = m_matrix.rows();
-    m_l.resize(size,size);
-    m_l.resizeNonZeros(Lstore->nnz);
-    m_u.resize(size,size);
-    m_u.resizeNonZeros(Ustore->nnz);
-
-    int* Lcol = m_l.outerIndexPtr();
-    int* Lrow = m_l.innerIndexPtr();
-    Scalar* Lval = m_l.valuePtr();
-
-    int* Ucol = m_u.outerIndexPtr();
-    int* Urow = m_u.innerIndexPtr();
-    Scalar* Uval = m_u.valuePtr();
-
-    Ucol[0] = 0;
-    Ucol[0] = 0;
-
-    /* for each supernode */
-    for (int k = 0; k <= Lstore->nsuper; ++k)
-    {
-      fsupc   = L_FST_SUPC(k);
-      istart  = L_SUB_START(fsupc);
-      nsupr   = L_SUB_START(fsupc+1) - istart;
-      upper   = 1;
-
-      /* for each column in the supernode */
-      for (int j = fsupc; j < L_FST_SUPC(k+1); ++j)
-      {
-        SNptr = &((Scalar*)Lstore->nzval)[L_NZ_START(j)];
-
-        /* Extract U */
-        for (int i = U_NZ_START(j); i < U_NZ_START(j+1); ++i)
-        {
-          Uval[lastu] = ((Scalar*)Ustore->nzval)[i];
-          /* Matlab doesn't like explicit zero. */
-          if (Uval[lastu] != 0.0)
-            Urow[lastu++] = U_SUB(i);
-        }
-        for (int i = 0; i < upper; ++i)
-        {
-          /* upper triangle in the supernode */
-          Uval[lastu] = SNptr[i];
-          /* Matlab doesn't like explicit zero. */
-          if (Uval[lastu] != 0.0)
-            Urow[lastu++] = L_SUB(istart+i);
-        }
-        Ucol[j+1] = lastu;
-
-        /* Extract L */
-        Lval[lastl] = 1.0; /* unit diagonal */
-        Lrow[lastl++] = L_SUB(istart + upper - 1);
-        for (int i = upper; i < nsupr; ++i)
-        {
-          Lval[lastl] = SNptr[i];
-          /* Matlab doesn't like explicit zero. */
-          if (Lval[lastl] != 0.0)
-            Lrow[lastl++] = L_SUB(istart+i);
-        }
-        Lcol[j+1] = lastl;
-
-        ++upper;
-      } /* for j ... */
-
-    } /* for k ... */
-
-    // squeeze the matrices :
-    m_l.resizeNonZeros(lastl);
-    m_u.resizeNonZeros(lastu);
-
-    m_extractedDataAreDirty = false;
-  }
-}
-
-template<typename MatrixType>
-typename SuperLU<MatrixType>::Scalar SuperLU<MatrixType>::determinant() const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for computing the determinant, you must first call either compute() or analyzePattern()/factorize()");
-  
-  if (m_extractedDataAreDirty)
-    this->extractData();
-
-  Scalar det = Scalar(1);
-  for (int j=0; j<m_u.cols(); ++j)
-  {
-    if (m_u.outerIndexPtr()[j+1]-m_u.outerIndexPtr()[j] > 0)
-    {
-      int lastId = m_u.outerIndexPtr()[j+1]-1;
-      eigen_assert(m_u.innerIndexPtr()[lastId]<=j);
-      if (m_u.innerIndexPtr()[lastId]==j)
-        det *= m_u.valuePtr()[lastId];
-    }
-  }
-  if(PermutationMap(m_p.data(),m_p.size()).determinant()*PermutationMap(m_q.data(),m_q.size()).determinant()<0)
-    det = -det;
-  if(m_sluEqued!='N')
-    return det/m_sluRscale.prod()/m_sluCscale.prod();
-  else
-    return det;
-}
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-#define EIGEN_SUPERLU_HAS_ILU
-#endif
-
-#ifdef EIGEN_SUPERLU_HAS_ILU
-
-/** \ingroup SuperLUSupport_Module
-  * \class SuperILU
-  * \brief A sparse direct \b incomplete LU factorization and solver based on the SuperLU library
-  *
-  * This class allows to solve for an approximate solution of A.X = B sparse linear problems via an incomplete LU factorization
-  * using the SuperLU library. This class is aimed to be used as a preconditioner of the iterative linear solvers.
-  *
-  * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class IncompleteLUT, class ConjugateGradient, class BiCGSTAB
-  */
-
-template<typename _MatrixType>
-class SuperILU : public SuperLUBase<_MatrixType,SuperILU<_MatrixType> >
-{
-  public:
-    typedef SuperLUBase<_MatrixType,SuperILU> Base;
-    typedef _MatrixType MatrixType;
-    typedef typename Base::Scalar Scalar;
-    typedef typename Base::RealScalar RealScalar;
-
-  public:
-    using Base::_solve_impl;
-
-    SuperILU() : Base() { init(); }
-
-    SuperILU(const MatrixType& matrix) : Base()
-    {
-      init();
-      Base::compute(matrix);
-    }
-
-    ~SuperILU()
-    {
-    }
-    
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      * 
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      Base::analyzePattern(matrix);
-    }
-    
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
-      *
-      * \sa analyzePattern()
-      */
-    void factorize(const MatrixType& matrix);
-    
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** \internal */
-    template<typename Rhs,typename Dest>
-    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
-    #endif // EIGEN_PARSED_BY_DOXYGEN
-    
-  protected:
-    
-    using Base::m_matrix;
-    using Base::m_sluOptions;
-    using Base::m_sluA;
-    using Base::m_sluB;
-    using Base::m_sluX;
-    using Base::m_p;
-    using Base::m_q;
-    using Base::m_sluEtree;
-    using Base::m_sluEqued;
-    using Base::m_sluRscale;
-    using Base::m_sluCscale;
-    using Base::m_sluL;
-    using Base::m_sluU;
-    using Base::m_sluStat;
-    using Base::m_sluFerr;
-    using Base::m_sluBerr;
-    using Base::m_l;
-    using Base::m_u;
-    
-    using Base::m_analysisIsOk;
-    using Base::m_factorizationIsOk;
-    using Base::m_extractedDataAreDirty;
-    using Base::m_isInitialized;
-    using Base::m_info;
-
-    void init()
-    {
-      Base::init();
-      
-      ilu_set_default_options(&m_sluOptions);
-      m_sluOptions.PrintStat        = NO;
-      m_sluOptions.ConditionNumber  = NO;
-      m_sluOptions.Trans            = NOTRANS;
-      m_sluOptions.ColPerm          = MMD_AT_PLUS_A;
-      
-      // no attempt to preserve column sum
-      m_sluOptions.ILU_MILU = SILU;
-      // only basic ILU(k) support -- no direct control over memory consumption
-      // better to use ILU_DropRule = DROP_BASIC | DROP_AREA
-      // and set ILU_FillFactor to max memory growth
-      m_sluOptions.ILU_DropRule = DROP_BASIC;
-      m_sluOptions.ILU_DropTol = NumTraits<Scalar>::dummy_precision()*10;
-    }
-    
-  private:
-    SuperILU(SuperILU& ) { }
-};
-
-template<typename MatrixType>
-void SuperILU<MatrixType>::factorize(const MatrixType& a)
-{
-  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
-  if(!m_analysisIsOk)
-  {
-    m_info = InvalidInput;
-    return;
-  }
-  
-  this->initFactorization(a);
-
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-
-  StatInit(&m_sluStat);
-  SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
-                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-
-  // FIXME how to better check for errors ???
-  m_info = info == 0 ? Success : NumericalIssue;
-  m_factorizationIsOk = true;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename MatrixType>
-template<typename Rhs,typename Dest>
-void SuperILU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
-{
-  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
-
-  const int rhsCols = b.cols();
-  eigen_assert(m_matrix.rows()==b.rows());
-
-  m_sluOptions.Trans = NOTRANS;
-  m_sluOptions.Fact = FACTORED;
-  m_sluOptions.IterRefine = NOREFINE;
-
-  m_sluFerr.resize(rhsCols);
-  m_sluBerr.resize(rhsCols);
-  
-  Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b);
-  Ref<const Matrix<typename Dest::Scalar,Dynamic,Dynamic,ColMajor> > x_ref(x);
-  
-  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
-  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
-
-  typename Rhs::PlainObject b_cpy;
-  if(m_sluEqued!='N')
-  {
-    b_cpy = b;
-    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
-  }
-  
-  int info = 0;
-  RealScalar recip_pivot_growth, rcond;
-
-  StatInit(&m_sluStat);
-  SuperLU_gsisx(&m_sluOptions, &m_sluA,
-                m_q.data(), m_p.data(),
-                &m_sluEtree[0], &m_sluEqued,
-                &m_sluRscale[0], &m_sluCscale[0],
-                &m_sluL, &m_sluU,
-                NULL, 0,
-                &m_sluB, &m_sluX,
-                &recip_pivot_growth, &rcond,
-                &m_sluStat, &info, Scalar());
-  StatFree(&m_sluStat);
-  
-  if(x.derived().data() != x_ref.data())
-    x = x_ref;
-
-  m_info = info==0 ? Success : NumericalIssue;
-}
-#endif
-
-#endif
-
-} // end namespace Eigen
-
-#endif // EIGEN_SUPERLUSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
deleted file mode 100644
index e3a333f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
+++ /dev/null
@@ -1,642 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMFPACKSUPPORT_H
-#define EIGEN_UMFPACKSUPPORT_H
-
-// for compatibility with super old version of umfpack,
-// not sure this is really needed, but this is harmless.
-#ifndef SuiteSparse_long
-#ifdef UF_long
-#define SuiteSparse_long UF_long
-#else
-#error neither SuiteSparse_long nor UF_long are defined
-#endif
-#endif
-
-namespace Eigen {
-
-/* TODO extract L, extract U, compute det, etc... */
-
-// generic double/complex<double> wrapper functions:
-
-
- // Defaults
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, int)
-{ umfpack_di_defaults(control); }
-
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, int)
-{ umfpack_zi_defaults(control); }
-
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, SuiteSparse_long)
-{ umfpack_dl_defaults(control); }
-
-inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_defaults(control); }
-
-// Report info
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, int)
-{ umfpack_di_report_info(control, info);}
-
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>, int)
-{ umfpack_zi_report_info(control, info);}
-
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, SuiteSparse_long)
-{ umfpack_dl_report_info(control, info);}
-
-inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_report_info(control, info);}
-
-// Report status
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, int)
-{ umfpack_di_report_status(control, status);}
-
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, int)
-{ umfpack_zi_report_status(control, status);}
-
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, SuiteSparse_long)
-{ umfpack_dl_report_status(control, status);}
-
-inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_report_status(control, status);}
-
-// report control
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, int)
-{ umfpack_di_report_control(control);}
-
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, int)
-{ umfpack_zi_report_control(control);}
-
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, SuiteSparse_long)
-{ umfpack_dl_report_control(control);}
-
-inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_report_control(control);}
-
-// Free numeric
-inline void umfpack_free_numeric(void **Numeric, double, int)
-{ umfpack_di_free_numeric(Numeric); *Numeric = 0; }
-
-inline void umfpack_free_numeric(void **Numeric, std::complex<double>, int)
-{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; }
-
-inline void umfpack_free_numeric(void **Numeric, double, SuiteSparse_long)
-{ umfpack_dl_free_numeric(Numeric); *Numeric = 0; }
-
-inline void umfpack_free_numeric(void **Numeric, std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_free_numeric(Numeric); *Numeric = 0; }
-
-// Free symbolic
-inline void umfpack_free_symbolic(void **Symbolic, double, int)
-{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; }
-
-inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, int)
-{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; }
-
-inline void umfpack_free_symbolic(void **Symbolic, double, SuiteSparse_long)
-{ umfpack_dl_free_symbolic(Symbolic); *Symbolic = 0; }
-
-inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, SuiteSparse_long)
-{ umfpack_zl_free_symbolic(Symbolic); *Symbolic = 0; }
-
-// Symbolic
-inline int umfpack_symbolic(int n_row,int n_col,
-                            const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
-                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
-}
-
-inline int umfpack_symbolic(int n_row,int n_col,
-                            const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
-                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
-}
-inline SuiteSparse_long umfpack_symbolic( SuiteSparse_long n_row,SuiteSparse_long n_col,
-                                          const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[], void **Symbolic,
-                                          const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_dl_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_symbolic( SuiteSparse_long n_row,SuiteSparse_long n_col,
-                                          const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[], void **Symbolic,
-                                          const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
-{
-  return umfpack_zl_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
-}
-
-// Numeric
-inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
-                            void *Symbolic, void **Numeric,
-                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
-}
-
-inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
-                            void *Symbolic, void **Numeric,
-                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
-}
-inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[],
-                                        void *Symbolic, void **Numeric,
-                                        const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_dl_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[],
-                                        void *Symbolic, void **Numeric,
-                                        const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
-{
-  return umfpack_zl_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
-}
-
-// solve
-inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
-                          double X[], const double B[], void *Numeric,
-                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
-}
-
-inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
-                          std::complex<double> X[], const std::complex<double> B[], void *Numeric,
-                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[],
-                                      double X[], const double B[], void *Numeric,
-                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_dl_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
-}
-
-inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[],
-                                      std::complex<double> X[], const std::complex<double> B[], void *Numeric,
-                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
-{
-  return umfpack_zl_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
-}
-
-// Get Lunz
-inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double)
-{
-  return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>)
-{
-  return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-inline SuiteSparse_long umfpack_get_lunz( SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row, SuiteSparse_long *n_col,
-                                          SuiteSparse_long *nz_udiag, void *Numeric, double)
-{
-  return umfpack_dl_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-inline SuiteSparse_long umfpack_get_lunz( SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row, SuiteSparse_long *n_col,
-                                          SuiteSparse_long *nz_udiag, void *Numeric, std::complex<double>)
-{
-  return umfpack_zl_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
-}
-
-// Get Numeric
-inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[],
-                               int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric)
-{
-  return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
-}
-
-inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[],
-                               int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric)
-{
-  double& lx0_real = numext::real_ref(Lx[0]);
-  double& ux0_real = numext::real_ref(Ux[0]);
-  double& dx0_real = numext::real_ref(Dx[0]);
-  return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
-                                Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
-}
-inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], double Lx[], SuiteSparse_long Up[], SuiteSparse_long Ui[], double Ux[],
-                                            SuiteSparse_long P[], SuiteSparse_long Q[], double Dx[], SuiteSparse_long *do_recip, double Rs[], void *Numeric)
-{
-  return umfpack_dl_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
-}
-
-inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], std::complex<double> Lx[], SuiteSparse_long Up[], SuiteSparse_long Ui[], std::complex<double> Ux[],
-                                            SuiteSparse_long P[], SuiteSparse_long Q[], std::complex<double> Dx[], SuiteSparse_long *do_recip, double Rs[], void *Numeric)
-{
-  double& lx0_real = numext::real_ref(Lx[0]);
-  double& ux0_real = numext::real_ref(Ux[0]);
-  double& dx0_real = numext::real_ref(Dx[0]);
-  return umfpack_zl_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
-                                Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
-}
-
-// Get Determinant
-inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], int)
-{
-  return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info);
-}
-
-inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], int)
-{
-  double& mx_real = numext::real_ref(*Mx);
-  return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
-}
-
-inline SuiteSparse_long umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], SuiteSparse_long)
-{
-  return umfpack_dl_get_determinant(Mx,Ex,NumericHandle,User_Info);
-}
-
-inline SuiteSparse_long umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], SuiteSparse_long)
-{
-  double& mx_real = numext::real_ref(*Mx);
-  return umfpack_zl_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
-}
-
-
-/** \ingroup UmfPackSupport_Module
-  * \brief A sparse LU factorization and solver based on UmfPack
-  *
-  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
-  * using the UmfPack library. The sparse matrix A must be squared and full rank.
-  * The vectors or matrices X and B can be either dense or sparse.
-  *
-  * \warning The input matrix A should be in a \b compressed and \b column-major form.
-  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  */
-template<typename _MatrixType>
-class UmfPackLU : public SparseSolverBase<UmfPackLU<_MatrixType> >
-{
-  protected:
-    typedef SparseSolverBase<UmfPackLU<_MatrixType> > Base;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
-    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
-    typedef SparseMatrix<Scalar> LUMatrixType;
-    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> UmfpackMatrixType;
-    typedef Ref<const UmfpackMatrixType, StandardCompressedFormat> UmfpackMatrixRef;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-
-  public:
-
-    typedef Array<double, UMFPACK_CONTROL, 1> UmfpackControl;
-    typedef Array<double, UMFPACK_INFO, 1> UmfpackInfo;
-
-    UmfPackLU()
-      : m_dummy(0,0), mp_matrix(m_dummy)
-    {
-      init();
-    }
-
-    template<typename InputMatrixType>
-    explicit UmfPackLU(const InputMatrixType& matrix)
-      : mp_matrix(matrix)
-    {
-      init();
-      compute(matrix);
-    }
-
-    ~UmfPackLU()
-    {
-      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(), StorageIndex());
-      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(), StorageIndex());
-    }
-
-    inline Index rows() const { return mp_matrix.rows(); }
-    inline Index cols() const { return mp_matrix.cols(); }
-
-    /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-
-    inline const LUMatrixType& matrixL() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_l;
-    }
-
-    inline const LUMatrixType& matrixU() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_u;
-    }
-
-    inline const IntColVectorType& permutationP() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_p;
-    }
-
-    inline const IntRowVectorType& permutationQ() const
-    {
-      if (m_extractedDataAreDirty) extractData();
-      return m_q;
-    }
-
-    /** Computes the sparse Cholesky decomposition of \a matrix
-     *  Note that the matrix should be column-major, and in compressed format for best performance.
-     *  \sa SparseMatrix::makeCompressed().
-     */
-    template<typename InputMatrixType>
-    void compute(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(),StorageIndex());
-      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
-      grab(matrix.derived());
-      analyzePattern_impl();
-      factorize_impl();
-    }
-
-    /** Performs a symbolic decomposition on the sparcity of \a matrix.
-      *
-      * This function is particularly useful when solving for several problems having the same structure.
-      *
-      * \sa factorize(), compute()
-      */
-    template<typename InputMatrixType>
-    void analyzePattern(const InputMatrixType& matrix)
-    {
-      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(),StorageIndex());
-      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
-
-      grab(matrix.derived());
-
-      analyzePattern_impl();
-    }
-
-    /** Provides the return status code returned by UmfPack during the numeric
-      * factorization.
-      *
-      * \sa factorize(), compute()
-      */
-    inline int umfpackFactorizeReturncode() const
-    {
-      eigen_assert(m_numeric && "UmfPackLU: you must first call factorize()");
-      return m_fact_errorCode;
-    }
-
-    /** Provides access to the control settings array used by UmfPack.
-      *
-      * If this array contains NaN's, the default values are used.
-      *
-      * See UMFPACK documentation for details.
-      */
-    inline const UmfpackControl& umfpackControl() const
-    {
-      return m_control;
-    }
-
-    /** Provides access to the control settings array used by UmfPack.
-      *
-      * If this array contains NaN's, the default values are used.
-      *
-      * See UMFPACK documentation for details.
-      */
-    inline UmfpackControl& umfpackControl()
-    {
-      return m_control;
-    }
-
-    /** Performs a numeric decomposition of \a matrix
-      *
-      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
-      *
-      * \sa analyzePattern(), compute()
-      */
-    template<typename InputMatrixType>
-    void factorize(const InputMatrixType& matrix)
-    {
-      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
-      if(m_numeric)
-        umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
-
-      grab(matrix.derived());
-
-      factorize_impl();
-    }
-
-    /** Prints the current UmfPack control settings.
-      *
-      * \sa umfpackControl()
-      */
-    void printUmfpackControl()
-    {
-      umfpack_report_control(m_control.data(), Scalar(),StorageIndex());
-    }
-
-    /** Prints statistics collected by UmfPack.
-      *
-      * \sa analyzePattern(), compute()
-      */
-    void printUmfpackInfo()
-    {
-      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
-      umfpack_report_info(m_control.data(), m_umfpackInfo.data(), Scalar(),StorageIndex());
-    }
-
-    /** Prints the status of the previous factorization operation performed by UmfPack (symbolic or numerical factorization).
-      *
-      * \sa analyzePattern(), compute()
-      */
-    void printUmfpackStatus() {
-      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
-      umfpack_report_status(m_control.data(), m_fact_errorCode, Scalar(),StorageIndex());
-    }
-
-    /** \internal */
-    template<typename BDerived,typename XDerived>
-    bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
-
-    Scalar determinant() const;
-
-    void extractData() const;
-
-  protected:
-
-    void init()
-    {
-      m_info                  = InvalidInput;
-      m_isInitialized         = false;
-      m_numeric               = 0;
-      m_symbolic              = 0;
-      m_extractedDataAreDirty = true;
-
-      umfpack_defaults(m_control.data(), Scalar(),StorageIndex());
-    }
-
-    void analyzePattern_impl()
-    {
-      m_fact_errorCode = umfpack_symbolic(internal::convert_index<StorageIndex>(mp_matrix.rows()),
-                                          internal::convert_index<StorageIndex>(mp_matrix.cols()),
-                                          mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
-                                          &m_symbolic, m_control.data(), m_umfpackInfo.data());
-
-      m_isInitialized = true;
-      m_info = m_fact_errorCode ? InvalidInput : Success;
-      m_analysisIsOk = true;
-      m_factorizationIsOk = false;
-      m_extractedDataAreDirty = true;
-    }
-
-    void factorize_impl()
-    {
-
-      m_fact_errorCode = umfpack_numeric(mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
-                                         m_symbolic, &m_numeric, m_control.data(), m_umfpackInfo.data());
-
-      m_info = m_fact_errorCode == UMFPACK_OK ? Success : NumericalIssue;
-      m_factorizationIsOk = true;
-      m_extractedDataAreDirty = true;
-    }
-
-    template<typename MatrixDerived>
-    void grab(const EigenBase<MatrixDerived> &A)
-    {
-      mp_matrix.~UmfpackMatrixRef();
-      ::new (&mp_matrix) UmfpackMatrixRef(A.derived());
-    }
-
-    void grab(const UmfpackMatrixRef &A)
-    {
-      if(&(A.derived()) != &mp_matrix)
-      {
-        mp_matrix.~UmfpackMatrixRef();
-        ::new (&mp_matrix) UmfpackMatrixRef(A);
-      }
-    }
-
-    // cached data to reduce reallocation, etc.
-    mutable LUMatrixType m_l;
-    StorageIndex m_fact_errorCode;
-    UmfpackControl m_control;
-    mutable UmfpackInfo m_umfpackInfo;
-
-    mutable LUMatrixType m_u;
-    mutable IntColVectorType m_p;
-    mutable IntRowVectorType m_q;
-
-    UmfpackMatrixType m_dummy;
-    UmfpackMatrixRef mp_matrix;
-
-    void* m_numeric;
-    void* m_symbolic;
-
-    mutable ComputationInfo m_info;
-    int m_factorizationIsOk;
-    int m_analysisIsOk;
-    mutable bool m_extractedDataAreDirty;
-
-  private:
-    UmfPackLU(const UmfPackLU& ) { }
-};
-
-
-template<typename MatrixType>
-void UmfPackLU<MatrixType>::extractData() const
-{
-  if (m_extractedDataAreDirty)
-  {
-    // get size of the data
-    StorageIndex lnz, unz, rows, cols, nz_udiag;
-    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
-
-    // allocate data
-    m_l.resize(rows,(std::min)(rows,cols));
-    m_l.resizeNonZeros(lnz);
-
-    m_u.resize((std::min)(rows,cols),cols);
-    m_u.resizeNonZeros(unz);
-
-    m_p.resize(rows);
-    m_q.resize(cols);
-
-    // extract
-    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
-                        m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
-                        m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
-
-    m_extractedDataAreDirty = false;
-  }
-}
-
-template<typename MatrixType>
-typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const
-{
-  Scalar det;
-  umfpack_get_determinant(&det, 0, m_numeric, 0, StorageIndex());
-  return det;
-}
-
-template<typename MatrixType>
-template<typename BDerived,typename XDerived>
-bool UmfPackLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
-{
-  Index rhsCols = b.cols();
-  eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet");
-  eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet");
-  eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve");
-
-  Scalar* x_ptr = 0;
-  Matrix<Scalar,Dynamic,1> x_tmp;
-  if(x.innerStride()!=1)
-  {
-    x_tmp.resize(x.rows());
-    x_ptr = x_tmp.data();
-  }
-  for (int j=0; j<rhsCols; ++j)
-  {
-    if(x.innerStride()==1)
-      x_ptr = &x.col(j).coeffRef(0);
-    StorageIndex errorCode = umfpack_solve(UMFPACK_A,
-                                mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
-                                x_ptr, &b.const_cast_derived().col(j).coeffRef(0),
-                                m_numeric, m_control.data(), m_umfpackInfo.data());
-    if(x.innerStride()!=1)
-      x.col(j) = x_tmp;
-    if (errorCode!=0)
-      return false;
-  }
-
-  return true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_UMFPACKSUPPORT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/Image.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/Image.h
deleted file mode 100644
index b8b8a04..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/Image.h
+++ /dev/null
@@ -1,82 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MISC_IMAGE_H
-#define EIGEN_MISC_IMAGE_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \class image_retval_base
-  *
-  */
-template<typename DecompositionType>
-struct traits<image_retval_base<DecompositionType> >
-{
-  typedef typename DecompositionType::MatrixType MatrixType;
-  typedef Matrix<
-    typename MatrixType::Scalar,
-    MatrixType::RowsAtCompileTime, // the image is a subspace of the destination space, whose
-                                   // dimension is the number of rows of the original matrix
-    Dynamic,                       // we don't know at compile time the dimension of the image (the rank)
-    MatrixType::Options,
-    MatrixType::MaxRowsAtCompileTime, // the image matrix will consist of columns from the original matrix,
-    MatrixType::MaxColsAtCompileTime  // so it has the same number of rows and at most as many columns.
-  > ReturnType;
-};
-
-template<typename _DecompositionType> struct image_retval_base
- : public ReturnByValue<image_retval_base<_DecompositionType> >
-{
-  typedef _DecompositionType DecompositionType;
-  typedef typename DecompositionType::MatrixType MatrixType;
-  typedef ReturnByValue<image_retval_base> Base;
-
-  image_retval_base(const DecompositionType& dec, const MatrixType& originalMatrix)
-    : m_dec(dec), m_rank(dec.rank()),
-      m_cols(m_rank == 0 ? 1 : m_rank),
-      m_originalMatrix(originalMatrix)
-  {}
-
-  inline Index rows() const { return m_dec.rows(); }
-  inline Index cols() const { return m_cols; }
-  inline Index rank() const { return m_rank; }
-  inline const DecompositionType& dec() const { return m_dec; }
-  inline const MatrixType& originalMatrix() const { return m_originalMatrix; }
-
-  template<typename Dest> inline void evalTo(Dest& dst) const
-  {
-    static_cast<const image_retval<DecompositionType>*>(this)->evalTo(dst);
-  }
-
-  protected:
-    const DecompositionType& m_dec;
-    Index m_rank, m_cols;
-    const MatrixType& m_originalMatrix;
-};
-
-} // end namespace internal
-
-#define EIGEN_MAKE_IMAGE_HELPERS(DecompositionType) \
-  typedef typename DecompositionType::MatrixType MatrixType; \
-  typedef typename MatrixType::Scalar Scalar; \
-  typedef typename MatrixType::RealScalar RealScalar; \
-  typedef Eigen::internal::image_retval_base<DecompositionType> Base; \
-  using Base::dec; \
-  using Base::originalMatrix; \
-  using Base::rank; \
-  using Base::rows; \
-  using Base::cols; \
-  image_retval(const DecompositionType& dec, const MatrixType& originalMatrix) \
-    : Base(dec, originalMatrix) {}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MISC_IMAGE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/Kernel.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/Kernel.h
deleted file mode 100644
index bef5d6f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/Kernel.h
+++ /dev/null
@@ -1,79 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MISC_KERNEL_H
-#define EIGEN_MISC_KERNEL_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \class kernel_retval_base
-  *
-  */
-template<typename DecompositionType>
-struct traits<kernel_retval_base<DecompositionType> >
-{
-  typedef typename DecompositionType::MatrixType MatrixType;
-  typedef Matrix<
-    typename MatrixType::Scalar,
-    MatrixType::ColsAtCompileTime, // the number of rows in the "kernel matrix"
-                                   // is the number of cols of the original matrix
-                                   // so that the product "matrix * kernel = zero" makes sense
-    Dynamic,                       // we don't know at compile-time the dimension of the kernel
-    MatrixType::Options,
-    MatrixType::MaxColsAtCompileTime, // see explanation for 2nd template parameter
-    MatrixType::MaxColsAtCompileTime // the kernel is a subspace of the domain space,
-                                     // whose dimension is the number of columns of the original matrix
-  > ReturnType;
-};
-
-template<typename _DecompositionType> struct kernel_retval_base
- : public ReturnByValue<kernel_retval_base<_DecompositionType> >
-{
-  typedef _DecompositionType DecompositionType;
-  typedef ReturnByValue<kernel_retval_base> Base;
-
-  explicit kernel_retval_base(const DecompositionType& dec)
-    : m_dec(dec),
-      m_rank(dec.rank()),
-      m_cols(m_rank==dec.cols() ? 1 : dec.cols() - m_rank)
-  {}
-
-  inline Index rows() const { return m_dec.cols(); }
-  inline Index cols() const { return m_cols; }
-  inline Index rank() const { return m_rank; }
-  inline const DecompositionType& dec() const { return m_dec; }
-
-  template<typename Dest> inline void evalTo(Dest& dst) const
-  {
-    static_cast<const kernel_retval<DecompositionType>*>(this)->evalTo(dst);
-  }
-
-  protected:
-    const DecompositionType& m_dec;
-    Index m_rank, m_cols;
-};
-
-} // end namespace internal
-
-#define EIGEN_MAKE_KERNEL_HELPERS(DecompositionType) \
-  typedef typename DecompositionType::MatrixType MatrixType; \
-  typedef typename MatrixType::Scalar Scalar; \
-  typedef typename MatrixType::RealScalar RealScalar; \
-  typedef Eigen::internal::kernel_retval_base<DecompositionType> Base; \
-  using Base::dec; \
-  using Base::rank; \
-  using Base::rows; \
-  using Base::cols; \
-  kernel_retval(const DecompositionType& dec) : Base(dec) {}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MISC_KERNEL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/RealSvd2x2.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/RealSvd2x2.h
deleted file mode 100644
index abb4d3c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/RealSvd2x2.h
+++ /dev/null
@@ -1,55 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-// Copyright (C) 2013-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_REALSVD2X2_H
-#define EIGEN_REALSVD2X2_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename MatrixType, typename RealScalar, typename Index>
-void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
-                         JacobiRotation<RealScalar> *j_left,
-                         JacobiRotation<RealScalar> *j_right)
-{
-  using std::sqrt;
-  using std::abs;
-  Matrix<RealScalar,2,2> m;
-  m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
-       numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
-  JacobiRotation<RealScalar> rot1;
-  RealScalar t = m.coeff(0,0) + m.coeff(1,1);
-  RealScalar d = m.coeff(1,0) - m.coeff(0,1);
-
-  if(abs(d) < (std::numeric_limits<RealScalar>::min)())
-  {
-    rot1.s() = RealScalar(0);
-    rot1.c() = RealScalar(1);
-  }
-  else
-  {
-    // If d!=0, then t/d cannot overflow because the magnitude of the
-    // entries forming d are not too small compared to the ones forming t.
-    RealScalar u = t / d;
-    RealScalar tmp = sqrt(RealScalar(1) + numext::abs2(u));
-    rot1.s() = RealScalar(1) / tmp;
-    rot1.c() = u / tmp;
-  }
-  m.applyOnTheLeft(0,1,rot1);
-  j_right->makeJacobi(m,0,1);
-  *j_left = rot1 * j_right->transpose();
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_REALSVD2X2_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/blas.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/blas.h
deleted file mode 100644
index 25215b1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/blas.h
+++ /dev/null
@@ -1,440 +0,0 @@
-#ifndef BLAS_H
-#define BLAS_H
-
-#ifdef __cplusplus
-extern "C"
-{
-#endif
-
-#define BLASFUNC(FUNC) FUNC##_
-
-#ifdef __WIN64__
-typedef long long BLASLONG;
-typedef unsigned long long BLASULONG;
-#else
-typedef long BLASLONG;
-typedef unsigned long BLASULONG;
-#endif
-
-int    BLASFUNC(xerbla)(const char *, int *info, int);
-
-float  BLASFUNC(sdot)  (int *, float  *, int *, float  *, int *);
-float  BLASFUNC(sdsdot)(int *, float  *,        float  *, int *, float  *, int *);
-
-double BLASFUNC(dsdot) (int *, float  *, int *, float  *, int *);
-double BLASFUNC(ddot)  (int *, double *, int *, double *, int *);
-double BLASFUNC(qdot)  (int *, double *, int *, double *, int *);
-
-int  BLASFUNC(cdotuw)  (int *, float  *, int *, float  *, int *, float*);
-int  BLASFUNC(cdotcw)  (int *, float  *, int *, float  *, int *, float*);
-int  BLASFUNC(zdotuw)  (int *, double  *, int *, double  *, int *, double*);
-int  BLASFUNC(zdotcw)  (int *, double  *, int *, double  *, int *, double*);
-
-int    BLASFUNC(saxpy) (const int *, const float  *, const float  *, const int *, float  *, const int *);
-int    BLASFUNC(daxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(qaxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(caxpy) (const int *, const float  *, const float  *, const int *, float  *, const int *);
-int    BLASFUNC(zaxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(xaxpy) (const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(caxpyc)(const int *, const float  *, const float  *, const int *, float  *, const int *);
-int    BLASFUNC(zaxpyc)(const int *, const double *, const double *, const int *, double *, const int *);
-int    BLASFUNC(xaxpyc)(const int *, const double *, const double *, const int *, double *, const int *);
-
-int    BLASFUNC(scopy) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(dcopy) (int *, double *, int *, double *, int *);
-int    BLASFUNC(qcopy) (int *, double *, int *, double *, int *);
-int    BLASFUNC(ccopy) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(zcopy) (int *, double *, int *, double *, int *);
-int    BLASFUNC(xcopy) (int *, double *, int *, double *, int *);
-
-int    BLASFUNC(sswap) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(dswap) (int *, double *, int *, double *, int *);
-int    BLASFUNC(qswap) (int *, double *, int *, double *, int *);
-int    BLASFUNC(cswap) (int *, float  *, int *, float  *, int *);
-int    BLASFUNC(zswap) (int *, double *, int *, double *, int *);
-int    BLASFUNC(xswap) (int *, double *, int *, double *, int *);
-
-float  BLASFUNC(sasum) (int *, float  *, int *);
-float  BLASFUNC(scasum)(int *, float  *, int *);
-double BLASFUNC(dasum) (int *, double *, int *);
-double BLASFUNC(qasum) (int *, double *, int *);
-double BLASFUNC(dzasum)(int *, double *, int *);
-double BLASFUNC(qxasum)(int *, double *, int *);
-
-int    BLASFUNC(isamax)(int *, float  *, int *);
-int    BLASFUNC(idamax)(int *, double *, int *);
-int    BLASFUNC(iqamax)(int *, double *, int *);
-int    BLASFUNC(icamax)(int *, float  *, int *);
-int    BLASFUNC(izamax)(int *, double *, int *);
-int    BLASFUNC(ixamax)(int *, double *, int *);
-
-int    BLASFUNC(ismax) (int *, float  *, int *);
-int    BLASFUNC(idmax) (int *, double *, int *);
-int    BLASFUNC(iqmax) (int *, double *, int *);
-int    BLASFUNC(icmax) (int *, float  *, int *);
-int    BLASFUNC(izmax) (int *, double *, int *);
-int    BLASFUNC(ixmax) (int *, double *, int *);
-
-int    BLASFUNC(isamin)(int *, float  *, int *);
-int    BLASFUNC(idamin)(int *, double *, int *);
-int    BLASFUNC(iqamin)(int *, double *, int *);
-int    BLASFUNC(icamin)(int *, float  *, int *);
-int    BLASFUNC(izamin)(int *, double *, int *);
-int    BLASFUNC(ixamin)(int *, double *, int *);
-
-int    BLASFUNC(ismin)(int *, float  *, int *);
-int    BLASFUNC(idmin)(int *, double *, int *);
-int    BLASFUNC(iqmin)(int *, double *, int *);
-int    BLASFUNC(icmin)(int *, float  *, int *);
-int    BLASFUNC(izmin)(int *, double *, int *);
-int    BLASFUNC(ixmin)(int *, double *, int *);
-
-float  BLASFUNC(samax) (int *, float  *, int *);
-double BLASFUNC(damax) (int *, double *, int *);
-double BLASFUNC(qamax) (int *, double *, int *);
-float  BLASFUNC(scamax)(int *, float  *, int *);
-double BLASFUNC(dzamax)(int *, double *, int *);
-double BLASFUNC(qxamax)(int *, double *, int *);
-
-float  BLASFUNC(samin) (int *, float  *, int *);
-double BLASFUNC(damin) (int *, double *, int *);
-double BLASFUNC(qamin) (int *, double *, int *);
-float  BLASFUNC(scamin)(int *, float  *, int *);
-double BLASFUNC(dzamin)(int *, double *, int *);
-double BLASFUNC(qxamin)(int *, double *, int *);
-
-float  BLASFUNC(smax)  (int *, float  *, int *);
-double BLASFUNC(dmax)  (int *, double *, int *);
-double BLASFUNC(qmax)  (int *, double *, int *);
-float  BLASFUNC(scmax) (int *, float  *, int *);
-double BLASFUNC(dzmax) (int *, double *, int *);
-double BLASFUNC(qxmax) (int *, double *, int *);
-
-float  BLASFUNC(smin)  (int *, float  *, int *);
-double BLASFUNC(dmin)  (int *, double *, int *);
-double BLASFUNC(qmin)  (int *, double *, int *);
-float  BLASFUNC(scmin) (int *, float  *, int *);
-double BLASFUNC(dzmin) (int *, double *, int *);
-double BLASFUNC(qxmin) (int *, double *, int *);
-
-int    BLASFUNC(sscal) (int *,  float  *, float  *, int *);
-int    BLASFUNC(dscal) (int *,  double *, double *, int *);
-int    BLASFUNC(qscal) (int *,  double *, double *, int *);
-int    BLASFUNC(cscal) (int *,  float  *, float  *, int *);
-int    BLASFUNC(zscal) (int *,  double *, double *, int *);
-int    BLASFUNC(xscal) (int *,  double *, double *, int *);
-int    BLASFUNC(csscal)(int *,  float  *, float  *, int *);
-int    BLASFUNC(zdscal)(int *,  double *, double *, int *);
-int    BLASFUNC(xqscal)(int *,  double *, double *, int *);
-
-float  BLASFUNC(snrm2) (int *, float  *, int *);
-float  BLASFUNC(scnrm2)(int *, float  *, int *);
-
-double BLASFUNC(dnrm2) (int *, double *, int *);
-double BLASFUNC(qnrm2) (int *, double *, int *);
-double BLASFUNC(dznrm2)(int *, double *, int *);
-double BLASFUNC(qxnrm2)(int *, double *, int *);
-
-int    BLASFUNC(srot)  (int *, float  *, int *, float  *, int *, float  *, float  *);
-int    BLASFUNC(drot)  (int *, double *, int *, double *, int *, double *, double *);
-int    BLASFUNC(qrot)  (int *, double *, int *, double *, int *, double *, double *);
-int    BLASFUNC(csrot) (int *, float  *, int *, float  *, int *, float  *, float  *);
-int    BLASFUNC(zdrot) (int *, double *, int *, double *, int *, double *, double *);
-int    BLASFUNC(xqrot) (int *, double *, int *, double *, int *, double *, double *);
-
-int    BLASFUNC(srotg) (float  *, float  *, float  *, float  *);
-int    BLASFUNC(drotg) (double *, double *, double *, double *);
-int    BLASFUNC(qrotg) (double *, double *, double *, double *);
-int    BLASFUNC(crotg) (float  *, float  *, float  *, float  *);
-int    BLASFUNC(zrotg) (double *, double *, double *, double *);
-int    BLASFUNC(xrotg) (double *, double *, double *, double *);
-
-int    BLASFUNC(srotmg)(float  *, float  *, float  *, float  *, float  *);
-int    BLASFUNC(drotmg)(double *, double *, double *, double *, double *);
-
-int    BLASFUNC(srotm) (int *, float  *, int *, float  *, int *, float  *);
-int    BLASFUNC(drotm) (int *, double *, int *, double *, int *, double *);
-int    BLASFUNC(qrotm) (int *, double *, int *, double *, int *, double *);
-
-/* Level 2 routines */
-
-int BLASFUNC(sger)(int *,    int *, float *,  float *, int *,
-		   float *,  int *, float *,  int *);
-int BLASFUNC(dger)(int *,    int *, double *, double *, int *,
-		   double *, int *, double *, int *);
-int BLASFUNC(qger)(int *,    int *, double *, double *, int *,
-		   double *, int *, double *, int *);
-int BLASFUNC(cgeru)(int *,    int *, float *,  float *, int *,
-		    float *,  int *, float *,  int *);
-int BLASFUNC(cgerc)(int *,    int *, float *,  float *, int *,
-		    float *,  int *, float *,  int *);
-int BLASFUNC(zgeru)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-int BLASFUNC(zgerc)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-int BLASFUNC(xgeru)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-int BLASFUNC(xgerc)(int *,    int *, double *, double *, int *,
-		    double *, int *, double *, int *);
-
-int BLASFUNC(sgemv)(const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(cgemv)(const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(strsv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dtrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrsv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(ztrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrsv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(stpsv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(dtpsv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(qtpsv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(ctpsv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(ztpsv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(xtpsv) (char *, char *, char *, int *, double *, double *, int *);
-
-int BLASFUNC(strmv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dtrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrmv) (const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(ztrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrmv) (const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(stpmv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(dtpmv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(qtpmv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(ctpmv) (char *, char *, char *, int *, float  *, float  *, int *);
-int BLASFUNC(ztpmv) (char *, char *, char *, int *, double *, double *, int *);
-int BLASFUNC(xtpmv) (char *, char *, char *, int *, double *, double *, int *);
-
-int BLASFUNC(stbmv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(dtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(qtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(ctbmv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(ztbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(xtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-
-int BLASFUNC(stbsv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(dtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(qtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(ctbsv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(ztbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-int BLASFUNC(xtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
-
-int BLASFUNC(ssymv) (const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(sspmv) (char *, int *, float  *, float *,
-		     float  *, int *, float *, float *, int *);
-int BLASFUNC(dspmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-int BLASFUNC(qspmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-
-int BLASFUNC(ssyr) (const char *, const int *, const float   *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dsyr) (const char *, const int *, const double  *, const double *, const int *, double *, const int *);
-int BLASFUNC(qsyr) (const char *, const int *, const double  *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(ssyr2) (const char *, const int *, const float   *, const float  *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(dsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(qsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(csyr2) (const char *, const int *, const float   *, const float  *, const int *, const float  *, const int *, float  *, const int *);
-int BLASFUNC(zsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-int BLASFUNC(xsyr2) (const char *, const int *, const double  *, const double *, const int *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(sspr) (char *, int *, float   *, float  *, int *,
-		    float  *);
-int BLASFUNC(dspr) (char *, int *, double  *, double *, int *,
-		    double *);
-int BLASFUNC(qspr) (char *, int *, double  *, double *, int *,
-		    double *);
-
-int BLASFUNC(sspr2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *);
-int BLASFUNC(dspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(qspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(cspr2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *);
-int BLASFUNC(zspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(xspr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-
-int BLASFUNC(cher) (char *, int *, float   *, float  *, int *,
-		    float  *, int *);
-int BLASFUNC(zher) (char *, int *, double  *, double *, int *,
-		    double *, int *);
-int BLASFUNC(xher) (char *, int *, double  *, double *, int *,
-		    double *, int *);
-
-int BLASFUNC(chpr) (char *, int *, float   *, float  *, int *, float  *);
-int BLASFUNC(zhpr) (char *, int *, double  *, double *, int *, double *);
-int BLASFUNC(xhpr) (char *, int *, double  *, double *, int *, double *);
-
-int BLASFUNC(cher2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *, int *);
-int BLASFUNC(zher2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *, int *);
-int BLASFUNC(xher2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *, int *);
-
-int BLASFUNC(chpr2) (char *, int *, float   *,
-		     float  *, int *, float  *, int *, float  *);
-int BLASFUNC(zhpr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-int BLASFUNC(xhpr2) (char *, int *, double  *,
-		     double *, int *, double *, int *, double *);
-
-int BLASFUNC(chemv) (const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zhemv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xhemv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(chpmv) (char *, int *, float  *, float *,
-		     float  *, int *, float *, float *, int *);
-int BLASFUNC(zhpmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-int BLASFUNC(xhpmv) (char *, int *, double  *, double *,
-		     double  *, int *, double *, double *, int *);
-
-int BLASFUNC(snorm)(char *, int *, int *, float  *, int *);
-int BLASFUNC(dnorm)(char *, int *, int *, double *, int *);
-int BLASFUNC(cnorm)(char *, int *, int *, float  *, int *);
-int BLASFUNC(znorm)(char *, int *, int *, double *, int *);
-
-int BLASFUNC(sgbmv)(char *, int *, int *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(dgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(qgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(cgbmv)(char *, int *, int *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(xgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-
-int BLASFUNC(ssbmv)(char *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(dsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(qsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(csbmv)(char *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(xsbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-
-int BLASFUNC(chbmv)(char *, int *, int *, float  *, float  *, int *,
-		    float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zhbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-int BLASFUNC(xhbmv)(char *, int *, int *, double *, double *, int *,
-		    double *, int *, double *, double *, int *);
-
-/* Level 3 routines */
-
-int BLASFUNC(sgemm)(const char *, const char *, const int *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(cgemm)(const char *, const char *, const int *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(cgemm3m)(char *, char *, int *, int *, int *, float *,
-	   float  *, int *, float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zgemm3m)(char *, char *, int *, int *, int *, double *,
-	   double *, int *, double *, int *, double *, double *, int *);
-int BLASFUNC(xgemm3m)(char *, char *, int *, int *, int *, double *,
-	   double *, int *, double *, int *, double *, double *, int *);
-
-int BLASFUNC(sge2mm)(char *, char *, char *, int *, int *,
-		     float *, float  *, int *, float  *, int *,
-		     float *, float  *, int *);
-int BLASFUNC(dge2mm)(char *, char *, char *, int *, int *,
-		     double *, double  *, int *, double  *, int *,
-		     double *, double  *, int *);
-int BLASFUNC(cge2mm)(char *, char *, char *, int *, int *,
-		     float *, float  *, int *, float  *, int *,
-		     float *, float  *, int *);
-int BLASFUNC(zge2mm)(char *, char *, char *, int *, int *,
-		     double *, double  *, int *, double  *, int *,
-		     double *, double  *, int *);
-
-int BLASFUNC(strsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(dtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(ztrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(strmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(dtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(qtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(ctrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
-int BLASFUNC(ztrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-int BLASFUNC(xtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
-
-int BLASFUNC(ssymm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(csymm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(csymm3m)(char *, char *, int *, int *, float  *, float  *, int *, float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zsymm3m)(char *, char *, int *, int *, double *, double *, int *, double *, int *, double *, double *, int *);
-int BLASFUNC(xsymm3m)(char *, char *, int *, int *, double *, double *, int *, double *, int *, double *, double *, int *);
-
-int BLASFUNC(ssyrk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(qsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(csyrk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(ssyr2k)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(dsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(qsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(csyr2k)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(xsyr2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-
-int BLASFUNC(chemm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zhemm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xhemm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(chemm3m)(char *, char *, int *, int *, float  *, float  *, int *,
-	   float  *, int *, float  *, float  *, int *);
-int BLASFUNC(zhemm3m)(char *, char *, int *, int *, double *, double *, int *,
-	   double *, int *, double *, double *, int *);
-int BLASFUNC(xhemm3m)(char *, char *, int *, int *, double *, double *, int *,
-	   double *, int *, double *, double *, int *);
-
-int BLASFUNC(cherk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zherk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xherk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
-
-int BLASFUNC(cher2k)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zher2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xher2k)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(cher2m)(const char *, const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zher2m)(const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-int BLASFUNC(xher2m)(const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double*, const int *, const double *, double *, const int *);
-
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapack.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapack.h
deleted file mode 100644
index 249f357..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapack.h
+++ /dev/null
@@ -1,152 +0,0 @@
-#ifndef LAPACK_H
-#define LAPACK_H
-
-#include "blas.h"
-
-#ifdef __cplusplus
-extern "C"
-{
-#endif
-
-int BLASFUNC(csymv) (const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
-int BLASFUNC(zsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-int BLASFUNC(xsymv) (const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
-
-
-int BLASFUNC(cspmv) (char *, int *, float  *, float *,
-         float  *, int *, float *, float *, int *);
-int BLASFUNC(zspmv) (char *, int *, double  *, double *,
-         double  *, int *, double *, double *, int *);
-int BLASFUNC(xspmv) (char *, int *, double  *, double *,
-         double  *, int *, double *, double *, int *);
-
-int BLASFUNC(csyr) (char *, int *, float   *, float  *, int *,
-        float  *, int *);
-int BLASFUNC(zsyr) (char *, int *, double  *, double *, int *,
-        double *, int *);
-int BLASFUNC(xsyr) (char *, int *, double  *, double *, int *,
-        double *, int *);
-
-int BLASFUNC(cspr) (char *, int *, float   *, float  *, int *,
-        float  *);
-int BLASFUNC(zspr) (char *, int *, double  *, double *, int *,
-        double *);
-int BLASFUNC(xspr) (char *, int *, double  *, double *, int *,
-        double *);
-
-int BLASFUNC(sgemt)(char *, int *, int *, float  *, float  *, int *,
-        float  *, int *);
-int BLASFUNC(dgemt)(char *, int *, int *, double *, double *, int *,
-        double *, int *);
-int BLASFUNC(cgemt)(char *, int *, int *, float  *, float  *, int *,
-        float  *, int *);
-int BLASFUNC(zgemt)(char *, int *, int *, double *, double *, int *,
-        double *, int *);
-
-int BLASFUNC(sgema)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(dgema)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-int BLASFUNC(cgema)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(zgema)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-
-int BLASFUNC(sgems)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(dgems)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-int BLASFUNC(cgems)(char *, char *, int *, int *, float  *,
-        float  *, int *, float *, float  *, int *, float *, int *);
-int BLASFUNC(zgems)(char *, char *, int *, int *, double *,
-        double *, int *, double*, double *, int *, double*, int *);
-
-int BLASFUNC(sgetf2)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(dgetf2)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(qgetf2)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(cgetf2)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(zgetf2)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(xgetf2)(int *, int *, double *, int *, int *, int *);
-
-int BLASFUNC(sgetrf)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(dgetrf)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(qgetrf)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(cgetrf)(int *, int *, float  *, int *, int *, int *);
-int BLASFUNC(zgetrf)(int *, int *, double *, int *, int *, int *);
-int BLASFUNC(xgetrf)(int *, int *, double *, int *, int *, int *);
-
-int BLASFUNC(slaswp)(int *, float  *, int *, int *, int *, int *, int *);
-int BLASFUNC(dlaswp)(int *, double *, int *, int *, int *, int *, int *);
-int BLASFUNC(qlaswp)(int *, double *, int *, int *, int *, int *, int *);
-int BLASFUNC(claswp)(int *, float  *, int *, int *, int *, int *, int *);
-int BLASFUNC(zlaswp)(int *, double *, int *, int *, int *, int *, int *);
-int BLASFUNC(xlaswp)(int *, double *, int *, int *, int *, int *, int *);
-
-int BLASFUNC(sgetrs)(char *, int *, int *, float  *, int *, int *, float  *, int *, int *);
-int BLASFUNC(dgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-int BLASFUNC(qgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-int BLASFUNC(cgetrs)(char *, int *, int *, float  *, int *, int *, float  *, int *, int *);
-int BLASFUNC(zgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-int BLASFUNC(xgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
-
-int BLASFUNC(sgesv)(int *, int *, float  *, int *, int *, float *, int *, int *);
-int BLASFUNC(dgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-int BLASFUNC(qgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-int BLASFUNC(cgesv)(int *, int *, float  *, int *, int *, float *, int *, int *);
-int BLASFUNC(zgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-int BLASFUNC(xgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
-
-int BLASFUNC(spotf2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dpotf2)(char *, int *, double *, int *, int *);
-int BLASFUNC(qpotf2)(char *, int *, double *, int *, int *);
-int BLASFUNC(cpotf2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zpotf2)(char *, int *, double *, int *, int *);
-int BLASFUNC(xpotf2)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(spotrf)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dpotrf)(char *, int *, double *, int *, int *);
-int BLASFUNC(qpotrf)(char *, int *, double *, int *, int *);
-int BLASFUNC(cpotrf)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zpotrf)(char *, int *, double *, int *, int *);
-int BLASFUNC(xpotrf)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(slauu2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dlauu2)(char *, int *, double *, int *, int *);
-int BLASFUNC(qlauu2)(char *, int *, double *, int *, int *);
-int BLASFUNC(clauu2)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zlauu2)(char *, int *, double *, int *, int *);
-int BLASFUNC(xlauu2)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(slauum)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dlauum)(char *, int *, double *, int *, int *);
-int BLASFUNC(qlauum)(char *, int *, double *, int *, int *);
-int BLASFUNC(clauum)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zlauum)(char *, int *, double *, int *, int *);
-int BLASFUNC(xlauum)(char *, int *, double *, int *, int *);
-
-int BLASFUNC(strti2)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(dtrti2)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(qtrti2)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(ctrti2)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(ztrti2)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(xtrti2)(char *, char *, int *, double *, int *, int *);
-
-int BLASFUNC(strtri)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(dtrtri)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(qtrtri)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(ctrtri)(char *, char *, int *, float  *, int *, int *);
-int BLASFUNC(ztrtri)(char *, char *, int *, double *, int *, int *);
-int BLASFUNC(xtrtri)(char *, char *, int *, double *, int *, int *);
-
-int BLASFUNC(spotri)(char *, int *, float  *, int *, int *);
-int BLASFUNC(dpotri)(char *, int *, double *, int *, int *);
-int BLASFUNC(qpotri)(char *, int *, double *, int *, int *);
-int BLASFUNC(cpotri)(char *, int *, float  *, int *, int *);
-int BLASFUNC(zpotri)(char *, int *, double *, int *, int *);
-int BLASFUNC(xpotri)(char *, int *, double *, int *, int *);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapacke.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapacke.h
deleted file mode 100644
index 3d8e24f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapacke.h
+++ /dev/null
@@ -1,16292 +0,0 @@
-/*****************************************************************************
-  Copyright (c) 2010, Intel Corp.
-  All rights reserved.
-
-  Redistribution and use in source and binary forms, with or without
-  modification, are permitted provided that the following conditions are met:
-
-    * Redistributions of source code must retain the above copyright notice,
-      this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in the
-      documentation and/or other materials provided with the distribution.
-    * Neither the name of Intel Corporation nor the names of its contributors
-      may be used to endorse or promote products derived from this software
-      without specific prior written permission.
-
-  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
-  THE POSSIBILITY OF SUCH DAMAGE.
-******************************************************************************
-* Contents: Native C interface to LAPACK
-* Author: Intel Corporation
-* Generated November, 2011
-*****************************************************************************/
-
-#ifndef _MKL_LAPACKE_H_
-
-#ifndef _LAPACKE_H_
-#define _LAPACKE_H_
-
-/*
-*  Turn on HAVE_LAPACK_CONFIG_H to redefine C-LAPACK datatypes
-*/
-#ifdef HAVE_LAPACK_CONFIG_H
-#include "lapacke_config.h"
-#endif
-
-#include <stdlib.h>
-
-#ifndef lapack_int
-#define lapack_int     int
-#endif
-
-#ifndef lapack_logical
-#define lapack_logical lapack_int
-#endif
-
-/* Complex types are structures equivalent to the
-* Fortran complex types COMPLEX(4) and COMPLEX(8).
-*
-* One can also redefine the types with his own types
-* for example by including in the code definitions like
-*
-* #define lapack_complex_float std::complex<float>
-* #define lapack_complex_double std::complex<double>
-*
-* or define these types in the command line:
-*
-* -Dlapack_complex_float="std::complex<float>"
-* -Dlapack_complex_double="std::complex<double>"
-*/
-
-#ifndef LAPACK_COMPLEX_CUSTOM
-
-/* Complex type (single precision) */
-#ifndef lapack_complex_float
-#include <complex.h>
-#define lapack_complex_float    float _Complex
-#endif
-
-#ifndef lapack_complex_float_real
-#define lapack_complex_float_real(z)       (creal(z))
-#endif
-
-#ifndef lapack_complex_float_imag
-#define lapack_complex_float_imag(z)       (cimag(z))
-#endif
-
-lapack_complex_float lapack_make_complex_float( float re, float im );
-
-/* Complex type (double precision) */
-#ifndef lapack_complex_double
-#include <complex.h>
-#define lapack_complex_double   double _Complex
-#endif
-
-#ifndef lapack_complex_double_real
-#define lapack_complex_double_real(z)      (creal(z))
-#endif
-
-#ifndef lapack_complex_double_imag
-#define lapack_complex_double_imag(z)       (cimag(z))
-#endif
-
-lapack_complex_double lapack_make_complex_double( double re, double im );
-
-#endif
-
-
-#ifdef __cplusplus
-extern "C" {
-#endif /* __cplusplus */
-
-#ifndef LAPACKE_malloc
-#define LAPACKE_malloc( size ) malloc( size )
-#endif
-#ifndef LAPACKE_free
-#define LAPACKE_free( p )      free( p )
-#endif
-
-#define LAPACK_C2INT( x ) (lapack_int)(*((float*)&x ))
-#define LAPACK_Z2INT( x ) (lapack_int)(*((double*)&x ))
-
-#define LAPACK_ROW_MAJOR               101
-#define LAPACK_COL_MAJOR               102
-
-#define LAPACK_WORK_MEMORY_ERROR       -1010
-#define LAPACK_TRANSPOSE_MEMORY_ERROR  -1011
-
-/* Callback logical functions of one, two, or three arguments are used
-*  to select eigenvalues to sort to the top left of the Schur form.
-*  The value is selected if function returns TRUE (non-zero). */
-
-typedef lapack_logical (*LAPACK_S_SELECT2) ( const float*, const float* );
-typedef lapack_logical (*LAPACK_S_SELECT3)
-    ( const float*, const float*, const float* );
-typedef lapack_logical (*LAPACK_D_SELECT2) ( const double*, const double* );
-typedef lapack_logical (*LAPACK_D_SELECT3)
-    ( const double*, const double*, const double* );
-
-typedef lapack_logical (*LAPACK_C_SELECT1) ( const lapack_complex_float* );
-typedef lapack_logical (*LAPACK_C_SELECT2)
-    ( const lapack_complex_float*, const lapack_complex_float* );
-typedef lapack_logical (*LAPACK_Z_SELECT1) ( const lapack_complex_double* );
-typedef lapack_logical (*LAPACK_Z_SELECT2)
-    ( const lapack_complex_double*, const lapack_complex_double* );
-
-#include "lapacke_mangling.h"
-
-#define LAPACK_lsame LAPACK_GLOBAL(lsame,LSAME)
-lapack_logical LAPACK_lsame( char* ca,  char* cb,
-                              lapack_int lca, lapack_int lcb );
-
-/* C-LAPACK function prototypes */
-
-lapack_int LAPACKE_sbdsdc( int matrix_order, char uplo, char compq,
-                           lapack_int n, float* d, float* e, float* u,
-                           lapack_int ldu, float* vt, lapack_int ldvt, float* q,
-                           lapack_int* iq );
-lapack_int LAPACKE_dbdsdc( int matrix_order, char uplo, char compq,
-                           lapack_int n, double* d, double* e, double* u,
-                           lapack_int ldu, double* vt, lapack_int ldvt,
-                           double* q, lapack_int* iq );
-
-lapack_int LAPACKE_sbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           float* d, float* e, float* vt, lapack_int ldvt,
-                           float* u, lapack_int ldu, float* c, lapack_int ldc );
-lapack_int LAPACKE_dbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           double* d, double* e, double* vt, lapack_int ldvt,
-                           double* u, lapack_int ldu, double* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_cbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           float* d, float* e, lapack_complex_float* vt,
-                           lapack_int ldvt, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_zbdsqr( int matrix_order, char uplo, lapack_int n,
-                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                           double* d, double* e, lapack_complex_double* vt,
-                           lapack_int ldvt, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* c,
-                           lapack_int ldc );
-
-lapack_int LAPACKE_sdisna( char job, lapack_int m, lapack_int n, const float* d,
-                           float* sep );
-lapack_int LAPACKE_ddisna( char job, lapack_int m, lapack_int n,
-                           const double* d, double* sep );
-
-lapack_int LAPACKE_sgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, float* ab, lapack_int ldab, float* d,
-                           float* e, float* q, lapack_int ldq, float* pt,
-                           lapack_int ldpt, float* c, lapack_int ldc );
-lapack_int LAPACKE_dgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, double* ab, lapack_int ldab,
-                           double* d, double* e, double* q, lapack_int ldq,
-                           double* pt, lapack_int ldpt, double* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_cgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, lapack_complex_float* ab,
-                           lapack_int ldab, float* d, float* e,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* pt, lapack_int ldpt,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zgbbrd( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int ncc, lapack_int kl,
-                           lapack_int ku, lapack_complex_double* ab,
-                           lapack_int ldab, double* d, double* e,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* pt, lapack_int ldpt,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* ab,
-                           lapack_int ldab, const lapack_int* ipiv, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* ab,
-                           lapack_int ldab, const lapack_int* ipiv,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zgbcon( int matrix_order, char norm, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* ab,
-                           lapack_int ldab, float* r, float* c, float* rowcnd,
-                           float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* ab,
-                           lapack_int ldab, double* r, double* c,
-                           double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           float* r, float* c, float* rowcnd, float* colcnd,
-                           float* amax );
-lapack_int LAPACKE_zgbequ( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           double* r, double* c, double* rowcnd, double* colcnd,
-                           double* amax );
-
-lapack_int LAPACKE_sgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku, const float* ab,
-                            lapack_int ldab, float* r, float* c, float* rowcnd,
-                            float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku, const double* ab,
-                            lapack_int ldab, double* r, double* c,
-                            double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku,
-                            const lapack_complex_float* ab, lapack_int ldab,
-                            float* r, float* c, float* rowcnd, float* colcnd,
-                            float* amax );
-lapack_int LAPACKE_zgbequb( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_int kl, lapack_int ku,
-                            const lapack_complex_double* ab, lapack_int ldab,
-                            double* r, double* c, double* rowcnd,
-                            double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const float* ab, lapack_int ldab, const float* afb,
-                           lapack_int ldafb, const lapack_int* ipiv,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const double* ab, lapack_int ldab, const double* afb,
-                           lapack_int ldafb, const lapack_int* ipiv,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* afb, lapack_int ldafb,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgbrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* afb, lapack_int ldafb,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const float* ab, lapack_int ldab,
-                            const float* afb, lapack_int ldafb,
-                            const lapack_int* ipiv, const float* r,
-                            const float* c, const float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const double* ab, lapack_int ldab,
-                            const double* afb, lapack_int ldafb,
-                            const lapack_int* ipiv, const double* r,
-                            const double* c, const double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const lapack_complex_float* ab,
-                            lapack_int ldab, const lapack_complex_float* afb,
-                            lapack_int ldafb, const lapack_int* ipiv,
-                            const float* r, const float* c,
-                            const lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zgbrfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, const lapack_complex_double* ab,
-                            lapack_int ldab, const lapack_complex_double* afb,
-                            lapack_int ldafb, const lapack_int* ipiv,
-                            const double* r, const double* c,
-                            const lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_sgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs, float* ab,
-                          lapack_int ldab, lapack_int* ipiv, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs, double* ab,
-                          lapack_int ldab, lapack_int* ipiv, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs,
-                          lapack_complex_float* ab, lapack_int ldab,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zgbsv( int matrix_order, lapack_int n, lapack_int kl,
-                          lapack_int ku, lapack_int nrhs,
-                          lapack_complex_double* ab, lapack_int ldab,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_sgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, float* ab, lapack_int ldab,
-                           float* afb, lapack_int ldafb, lapack_int* ipiv,
-                           char* equed, float* r, float* c, float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr,
-                           float* rpivot );
-lapack_int LAPACKE_dgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, double* ab, lapack_int ldab,
-                           double* afb, lapack_int ldafb, lapack_int* ipiv,
-                           char* equed, double* r, double* c, double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr,
-                           double* rpivot );
-lapack_int LAPACKE_cgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, lapack_complex_float* ab,
-                           lapack_int ldab, lapack_complex_float* afb,
-                           lapack_int ldafb, lapack_int* ipiv, char* equed,
-                           float* r, float* c, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr, float* rpivot );
-lapack_int LAPACKE_zgbsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int kl, lapack_int ku,
-                           lapack_int nrhs, lapack_complex_double* ab,
-                           lapack_int ldab, lapack_complex_double* afb,
-                           lapack_int ldafb, lapack_int* ipiv, char* equed,
-                           double* r, double* c, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, double* rcond, double* ferr,
-                           double* berr, double* rpivot );
-
-lapack_int LAPACKE_sgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, float* ab, lapack_int ldab,
-                            float* afb, lapack_int ldafb, lapack_int* ipiv,
-                            char* equed, float* r, float* c, float* b,
-                            lapack_int ldb, float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, double* ab, lapack_int ldab,
-                            double* afb, lapack_int ldafb, lapack_int* ipiv,
-                            char* equed, double* r, double* c, double* b,
-                            lapack_int ldb, double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-lapack_int LAPACKE_cgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, lapack_complex_float* ab,
-                            lapack_int ldab, lapack_complex_float* afb,
-                            lapack_int ldafb, lapack_int* ipiv, char* equed,
-                            float* r, float* c, lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* rpvgrw,
-                            float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zgbsvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int kl, lapack_int ku,
-                            lapack_int nrhs, lapack_complex_double* ab,
-                            lapack_int ldab, lapack_complex_double* afb,
-                            lapack_int ldafb, lapack_int* ipiv, char* equed,
-                            double* r, double* c, lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* rpvgrw,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_sgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, float* ab,
-                           lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_dgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, double* ab,
-                           lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_cgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zgbtrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_sgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const float* ab, lapack_int ldab,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const double* ab, lapack_int ldab,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zgbtrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int kl, lapack_int ku, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_sgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* scale,
-                           lapack_int m, float* v, lapack_int ldv );
-lapack_int LAPACKE_dgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* scale,
-                           lapack_int m, double* v, lapack_int ldv );
-lapack_int LAPACKE_cgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* scale,
-                           lapack_int m, lapack_complex_float* v,
-                           lapack_int ldv );
-lapack_int LAPACKE_zgebak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* scale,
-                           lapack_int m, lapack_complex_double* v,
-                           lapack_int ldv );
-
-lapack_int LAPACKE_sgebal( int matrix_order, char job, lapack_int n, float* a,
-                           lapack_int lda, lapack_int* ilo, lapack_int* ihi,
-                           float* scale );
-lapack_int LAPACKE_dgebal( int matrix_order, char job, lapack_int n, double* a,
-                           lapack_int lda, lapack_int* ilo, lapack_int* ihi,
-                           double* scale );
-lapack_int LAPACKE_cgebal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ilo, lapack_int* ihi, float* scale );
-lapack_int LAPACKE_zgebal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ilo, lapack_int* ihi, double* scale );
-
-lapack_int LAPACKE_sgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* d, float* e,
-                           float* tauq, float* taup );
-lapack_int LAPACKE_dgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* d, double* e,
-                           double* tauq, double* taup );
-lapack_int LAPACKE_cgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda, float* d,
-                           float* e, lapack_complex_float* tauq,
-                           lapack_complex_float* taup );
-lapack_int LAPACKE_zgebrd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda, double* d,
-                           double* e, lapack_complex_double* tauq,
-                           lapack_complex_double* taup );
-
-lapack_int LAPACKE_sgecon( int matrix_order, char norm, lapack_int n,
-                           const float* a, lapack_int lda, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dgecon( int matrix_order, char norm, lapack_int n,
-                           const double* a, lapack_int lda, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_cgecon( int matrix_order, char norm, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_zgecon( int matrix_order, char norm, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double anorm, double* rcond );
-
-lapack_int LAPACKE_sgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const float* a, lapack_int lda, float* r, float* c,
-                           float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const double* a, lapack_int lda, double* r,
-                           double* c, double* rowcnd, double* colcnd,
-                           double* amax );
-lapack_int LAPACKE_cgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float* r, float* c, float* rowcnd, float* colcnd,
-                           float* amax );
-lapack_int LAPACKE_zgeequ( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double* r, double* c, double* rowcnd, double* colcnd,
-                           double* amax );
-
-lapack_int LAPACKE_sgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const float* a, lapack_int lda, float* r, float* c,
-                            float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const double* a, lapack_int lda, double* r,
-                            double* c, double* rowcnd, double* colcnd,
-                            double* amax );
-lapack_int LAPACKE_cgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* r, float* c, float* rowcnd, float* colcnd,
-                            float* amax );
-lapack_int LAPACKE_zgeequb( int matrix_order, lapack_int m, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* r, double* c, double* rowcnd,
-                            double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_S_SELECT2 select, lapack_int n, float* a,
-                          lapack_int lda, lapack_int* sdim, float* wr,
-                          float* wi, float* vs, lapack_int ldvs );
-lapack_int LAPACKE_dgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_D_SELECT2 select, lapack_int n, double* a,
-                          lapack_int lda, lapack_int* sdim, double* wr,
-                          double* wi, double* vs, lapack_int ldvs );
-lapack_int LAPACKE_cgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_C_SELECT1 select, lapack_int n,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_int* sdim, lapack_complex_float* w,
-                          lapack_complex_float* vs, lapack_int ldvs );
-lapack_int LAPACKE_zgees( int matrix_order, char jobvs, char sort,
-                          LAPACK_Z_SELECT1 select, lapack_int n,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_int* sdim, lapack_complex_double* w,
-                          lapack_complex_double* vs, lapack_int ldvs );
-
-lapack_int LAPACKE_sgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_S_SELECT2 select, char sense, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* sdim,
-                           float* wr, float* wi, float* vs, lapack_int ldvs,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_dgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_D_SELECT2 select, char sense, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* sdim,
-                           double* wr, double* wi, double* vs, lapack_int ldvs,
-                           double* rconde, double* rcondv );
-lapack_int LAPACKE_cgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_C_SELECT1 select, char sense, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* sdim, lapack_complex_float* w,
-                           lapack_complex_float* vs, lapack_int ldvs,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_zgeesx( int matrix_order, char jobvs, char sort,
-                           LAPACK_Z_SELECT1 select, char sense, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* sdim, lapack_complex_double* w,
-                           lapack_complex_double* vs, lapack_int ldvs,
-                           double* rconde, double* rcondv );
-
-lapack_int LAPACKE_sgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, float* a, lapack_int lda, float* wr,
-                          float* wi, float* vl, lapack_int ldvl, float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_dgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, double* a, lapack_int lda, double* wr,
-                          double* wi, double* vl, lapack_int ldvl, double* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_cgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* w, lapack_complex_float* vl,
-                          lapack_int ldvl, lapack_complex_float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_zgeev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* w,
-                          lapack_complex_double* vl, lapack_int ldvl,
-                          lapack_complex_double* vr, lapack_int ldvr );
-
-lapack_int LAPACKE_sgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, float* a,
-                           lapack_int lda, float* wr, float* wi, float* vl,
-                           lapack_int ldvl, float* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, float* scale,
-                           float* abnrm, float* rconde, float* rcondv );
-lapack_int LAPACKE_dgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, double* a,
-                           lapack_int lda, double* wr, double* wi, double* vl,
-                           lapack_int ldvl, double* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, double* scale,
-                           double* abnrm, double* rconde, double* rcondv );
-lapack_int LAPACKE_cgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* w, lapack_complex_float* vl,
-                           lapack_int ldvl, lapack_complex_float* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           float* scale, float* abnrm, float* rconde,
-                           float* rcondv );
-lapack_int LAPACKE_zgeevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* w, lapack_complex_double* vl,
-                           lapack_int ldvl, lapack_complex_double* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           double* scale, double* abnrm, double* rconde,
-                           double* rcondv );
-
-lapack_int LAPACKE_sgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, float* a, lapack_int lda,
-                           float* tau );
-lapack_int LAPACKE_dgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, double* a, lapack_int lda,
-                           double* tau );
-lapack_int LAPACKE_cgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* tau );
-lapack_int LAPACKE_zgehrd( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgejsv( int matrix_order, char joba, char jobu, char jobv,
-                           char jobr, char jobt, char jobp, lapack_int m,
-                           lapack_int n, float* a, lapack_int lda, float* sva,
-                           float* u, lapack_int ldu, float* v, lapack_int ldv,
-                           float* stat, lapack_int* istat );
-lapack_int LAPACKE_dgejsv( int matrix_order, char joba, char jobu, char jobv,
-                           char jobr, char jobt, char jobp, lapack_int m,
-                           lapack_int n, double* a, lapack_int lda, double* sva,
-                           double* u, lapack_int ldu, double* v, lapack_int ldv,
-                           double* stat, lapack_int* istat );
-
-lapack_int LAPACKE_sgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgelq2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgelqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs, float* a,
-                          lapack_int lda, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs, double* a,
-                          lapack_int lda, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgels( int matrix_order, char trans, lapack_int m,
-                          lapack_int n, lapack_int nrhs,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_dgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_cgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_zgelsd( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-
-lapack_int LAPACKE_sgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_dgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_cgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float* s, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_zgelss( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double* s, double rcond,
-                           lapack_int* rank );
-
-lapack_int LAPACKE_sgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, lapack_int* jpvt, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_dgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, lapack_int* jpvt,
-                           double rcond, lapack_int* rank );
-lapack_int LAPACKE_cgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_int* jpvt, float rcond,
-                           lapack_int* rank );
-lapack_int LAPACKE_zgelsy( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_int* jpvt, double rcond,
-                           lapack_int* rank );
-
-lapack_int LAPACKE_sgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqlf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* jpvt,
-                           float* tau );
-lapack_int LAPACKE_dgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* jpvt,
-                           double* tau );
-lapack_int LAPACKE_cgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqp3( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* jpvt,
-                           float* tau );
-lapack_int LAPACKE_dgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* jpvt,
-                           double* tau );
-lapack_int LAPACKE_cgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqpf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* jpvt, lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqr2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* tau );
-lapack_int LAPACKE_zgeqrfp( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const float* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const double* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgerfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs, const float* a,
-                            lapack_int lda, const float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* r,
-                            const float* c, const float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs, const double* a,
-                            lapack_int lda, const double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* r,
-                            const double* c, const double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* r,
-                            const float* c, const lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zgerfsx( int matrix_order, char trans, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* r,
-                            const double* c, const lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_sgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_cgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zgerqf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_sgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, float* a, lapack_int lda, float* s,
-                           float* u, lapack_int ldu, float* vt,
-                           lapack_int ldvt );
-lapack_int LAPACKE_dgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, double* a, lapack_int lda, double* s,
-                           double* u, lapack_int ldu, double* vt,
-                           lapack_int ldvt );
-lapack_int LAPACKE_cgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float* s, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* vt,
-                           lapack_int ldvt );
-lapack_int LAPACKE_zgesdd( int matrix_order, char jobz, lapack_int m,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double* s, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* vt,
-                           lapack_int ldvt );
-
-lapack_int LAPACKE_sgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* a, lapack_int lda, lapack_int* ipiv, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* a, lapack_int lda, lapack_int* ipiv,
-                          double* b, lapack_int ldb );
-lapack_int LAPACKE_cgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dsgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                           double* a, lapack_int lda, lapack_int* ipiv,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           lapack_int* iter );
-lapack_int LAPACKE_zcgesv( int matrix_order, lapack_int n, lapack_int nrhs,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, lapack_int* iter );
-
-lapack_int LAPACKE_sgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, float* a, lapack_int lda,
-                           float* s, float* u, lapack_int ldu, float* vt,
-                           lapack_int ldvt, float* superb );
-lapack_int LAPACKE_dgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, double* a,
-                           lapack_int lda, double* s, double* u, lapack_int ldu,
-                           double* vt, lapack_int ldvt, double* superb );
-lapack_int LAPACKE_cgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float* s, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* vt,
-                           lapack_int ldvt, float* superb );
-lapack_int LAPACKE_zgesvd( int matrix_order, char jobu, char jobvt,
-                           lapack_int m, lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double* s, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* vt,
-                           lapack_int ldvt, double* superb );
-
-lapack_int LAPACKE_sgesvj( int matrix_order, char joba, char jobu, char jobv,
-                           lapack_int m, lapack_int n, float* a, lapack_int lda,
-                           float* sva, lapack_int mv, float* v, lapack_int ldv,
-                           float* stat );
-lapack_int LAPACKE_dgesvj( int matrix_order, char joba, char jobu, char jobv,
-                           lapack_int m, lapack_int n, double* a,
-                           lapack_int lda, double* sva, lapack_int mv,
-                           double* v, lapack_int ldv, double* stat );
-
-lapack_int LAPACKE_sgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, float* a,
-                           lapack_int lda, float* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, float* r, float* c,
-                           float* b, lapack_int ldb, float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr,
-                           float* rpivot );
-lapack_int LAPACKE_dgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, double* a,
-                           lapack_int lda, double* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, double* r, double* c,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr,
-                           double* rpivot );
-lapack_int LAPACKE_cgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, float* r, float* c,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr,
-                           float* rpivot );
-lapack_int LAPACKE_zgesvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* af, lapack_int ldaf,
-                           lapack_int* ipiv, char* equed, double* r, double* c,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr,
-                           double* rpivot );
-
-lapack_int LAPACKE_sgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs, float* a,
-                            lapack_int lda, float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* r, float* c,
-                            float* b, lapack_int ldb, float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs, double* a,
-                            lapack_int lda, double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* r, double* c,
-                            double* b, lapack_int ldb, double* x,
-                            lapack_int ldx, double* rcond, double* rpvgrw,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* r, float* c,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zgesvxx( int matrix_order, char fact, char trans,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* r, double* c,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_sgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zgetf2( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zgetrf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetri( int matrix_order, lapack_int n, float* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dgetri( int matrix_order, lapack_int n, double* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_cgetri( int matrix_order, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_zgetri( int matrix_order, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgetrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* lscale,
-                           const float* rscale, lapack_int m, float* v,
-                           lapack_int ldv );
-lapack_int LAPACKE_dggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* lscale,
-                           const double* rscale, lapack_int m, double* v,
-                           lapack_int ldv );
-lapack_int LAPACKE_cggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const float* lscale,
-                           const float* rscale, lapack_int m,
-                           lapack_complex_float* v, lapack_int ldv );
-lapack_int LAPACKE_zggbak( int matrix_order, char job, char side, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, const double* lscale,
-                           const double* rscale, lapack_int m,
-                           lapack_complex_double* v, lapack_int ldv );
-
-lapack_int LAPACKE_sggbal( int matrix_order, char job, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, float* lscale,
-                           float* rscale );
-lapack_int LAPACKE_dggbal( int matrix_order, char job, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, double* lscale,
-                           double* rscale );
-lapack_int LAPACKE_cggbal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, float* lscale,
-                           float* rscale );
-lapack_int LAPACKE_zggbal( int matrix_order, char job, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_int* ilo, lapack_int* ihi, double* lscale,
-                           double* rscale );
-
-lapack_int LAPACKE_sgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_S_SELECT3 selctg, lapack_int n, float* a,
-                          lapack_int lda, float* b, lapack_int ldb,
-                          lapack_int* sdim, float* alphar, float* alphai,
-                          float* beta, float* vsl, lapack_int ldvsl, float* vsr,
-                          lapack_int ldvsr );
-lapack_int LAPACKE_dgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_D_SELECT3 selctg, lapack_int n, double* a,
-                          lapack_int lda, double* b, lapack_int ldb,
-                          lapack_int* sdim, double* alphar, double* alphai,
-                          double* beta, double* vsl, lapack_int ldvsl,
-                          double* vsr, lapack_int ldvsr );
-lapack_int LAPACKE_cgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_C_SELECT2 selctg, lapack_int n,
-                          lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* b, lapack_int ldb,
-                          lapack_int* sdim, lapack_complex_float* alpha,
-                          lapack_complex_float* beta, lapack_complex_float* vsl,
-                          lapack_int ldvsl, lapack_complex_float* vsr,
-                          lapack_int ldvsr );
-lapack_int LAPACKE_zgges( int matrix_order, char jobvsl, char jobvsr, char sort,
-                          LAPACK_Z_SELECT2 selctg, lapack_int n,
-                          lapack_complex_double* a, lapack_int lda,
-                          lapack_complex_double* b, lapack_int ldb,
-                          lapack_int* sdim, lapack_complex_double* alpha,
-                          lapack_complex_double* beta,
-                          lapack_complex_double* vsl, lapack_int ldvsl,
-                          lapack_complex_double* vsr, lapack_int ldvsr );
-
-lapack_int LAPACKE_sggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_S_SELECT3 selctg, char sense,
-                           lapack_int n, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, lapack_int* sdim, float* alphar,
-                           float* alphai, float* beta, float* vsl,
-                           lapack_int ldvsl, float* vsr, lapack_int ldvsr,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_dggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_D_SELECT3 selctg, char sense,
-                           lapack_int n, double* a, lapack_int lda, double* b,
-                           lapack_int ldb, lapack_int* sdim, double* alphar,
-                           double* alphai, double* beta, double* vsl,
-                           lapack_int ldvsl, double* vsr, lapack_int ldvsr,
-                           double* rconde, double* rcondv );
-lapack_int LAPACKE_cggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_C_SELECT2 selctg, char sense,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_int* sdim,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta,
-                           lapack_complex_float* vsl, lapack_int ldvsl,
-                           lapack_complex_float* vsr, lapack_int ldvsr,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_zggesx( int matrix_order, char jobvsl, char jobvsr,
-                           char sort, LAPACK_Z_SELECT2 selctg, char sense,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_int* sdim,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* vsl, lapack_int ldvsl,
-                           lapack_complex_double* vsr, lapack_int ldvsr,
-                           double* rconde, double* rcondv );
-
-lapack_int LAPACKE_sggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, float* a, lapack_int lda, float* b,
-                          lapack_int ldb, float* alphar, float* alphai,
-                          float* beta, float* vl, lapack_int ldvl, float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_dggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, double* a, lapack_int lda, double* b,
-                          lapack_int ldb, double* alphar, double* alphai,
-                          double* beta, double* vl, lapack_int ldvl, double* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_cggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_float* a, lapack_int lda,
-                          lapack_complex_float* b, lapack_int ldb,
-                          lapack_complex_float* alpha,
-                          lapack_complex_float* beta, lapack_complex_float* vl,
-                          lapack_int ldvl, lapack_complex_float* vr,
-                          lapack_int ldvr );
-lapack_int LAPACKE_zggev( int matrix_order, char jobvl, char jobvr,
-                          lapack_int n, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* b,
-                          lapack_int ldb, lapack_complex_double* alpha,
-                          lapack_complex_double* beta,
-                          lapack_complex_double* vl, lapack_int ldvl,
-                          lapack_complex_double* vr, lapack_int ldvr );
-
-lapack_int LAPACKE_sggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           float* alphar, float* alphai, float* beta, float* vl,
-                           lapack_int ldvl, float* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, float* lscale,
-                           float* rscale, float* abnrm, float* bbnrm,
-                           float* rconde, float* rcondv );
-lapack_int LAPACKE_dggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double* alphar, double* alphai, double* beta,
-                           double* vl, lapack_int ldvl, double* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           double* lscale, double* rscale, double* abnrm,
-                           double* bbnrm, double* rconde, double* rcondv );
-lapack_int LAPACKE_cggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta, lapack_complex_float* vl,
-                           lapack_int ldvl, lapack_complex_float* vr,
-                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
-                           float* lscale, float* rscale, float* abnrm,
-                           float* bbnrm, float* rconde, float* rcondv );
-lapack_int LAPACKE_zggevx( int matrix_order, char balanc, char jobvl,
-                           char jobvr, char sense, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* vl, lapack_int ldvl,
-                           lapack_complex_double* vr, lapack_int ldvr,
-                           lapack_int* ilo, lapack_int* ihi, double* lscale,
-                           double* rscale, double* abnrm, double* bbnrm,
-                           double* rconde, double* rcondv );
-
-lapack_int LAPACKE_sggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* d, float* x, float* y );
-lapack_int LAPACKE_dggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, double* a, lapack_int lda, double* b,
-                           lapack_int ldb, double* d, double* x, double* y );
-lapack_int LAPACKE_cggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* d,
-                           lapack_complex_float* x, lapack_complex_float* y );
-lapack_int LAPACKE_zggglm( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* d,
-                           lapack_complex_double* x, lapack_complex_double* y );
-
-lapack_int LAPACKE_sgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           float* a, lapack_int lda, float* b, lapack_int ldb,
-                           float* q, lapack_int ldq, float* z, lapack_int ldz );
-lapack_int LAPACKE_dgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           double* a, lapack_int lda, double* b, lapack_int ldb,
-                           double* q, lapack_int ldq, double* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_cgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zgghrd( int matrix_order, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, float* a, lapack_int lda, float* b,
-                           lapack_int ldb, float* c, float* d, float* x );
-lapack_int LAPACKE_dgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, double* a, lapack_int lda, double* b,
-                           lapack_int ldb, double* c, double* d, double* x );
-lapack_int LAPACKE_cgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* c,
-                           lapack_complex_float* d, lapack_complex_float* x );
-lapack_int LAPACKE_zgglse( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int p, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* c,
-                           lapack_complex_double* d, lapack_complex_double* x );
-
-lapack_int LAPACKE_sggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, float* a, lapack_int lda, float* taua,
-                           float* b, lapack_int ldb, float* taub );
-lapack_int LAPACKE_dggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, double* a, lapack_int lda,
-                           double* taua, double* b, lapack_int ldb,
-                           double* taub );
-lapack_int LAPACKE_cggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* taua,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* taub );
-lapack_int LAPACKE_zggqrf( int matrix_order, lapack_int n, lapack_int m,
-                           lapack_int p, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* taua,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* taub );
-
-lapack_int LAPACKE_sggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, float* a, lapack_int lda, float* taua,
-                           float* b, lapack_int ldb, float* taub );
-lapack_int LAPACKE_dggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, double* a, lapack_int lda,
-                           double* taua, double* b, lapack_int ldb,
-                           double* taub );
-lapack_int LAPACKE_cggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* taua,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* taub );
-lapack_int LAPACKE_zggrqf( int matrix_order, lapack_int m, lapack_int p,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* taua,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* taub );
-
-lapack_int LAPACKE_sggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           float* alpha, float* beta, float* u, lapack_int ldu,
-                           float* v, lapack_int ldv, float* q, lapack_int ldq,
-                           lapack_int* iwork );
-lapack_int LAPACKE_dggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double* alpha, double* beta, double* u,
-                           lapack_int ldu, double* v, lapack_int ldv, double* q,
-                           lapack_int ldq, lapack_int* iwork );
-lapack_int LAPACKE_cggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           float* alpha, float* beta, lapack_complex_float* u,
-                           lapack_int ldu, lapack_complex_float* v,
-                           lapack_int ldv, lapack_complex_float* q,
-                           lapack_int ldq, lapack_int* iwork );
-lapack_int LAPACKE_zggsvd( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int n, lapack_int p,
-                           lapack_int* k, lapack_int* l,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           double* alpha, double* beta,
-                           lapack_complex_double* u, lapack_int ldu,
-                           lapack_complex_double* v, lapack_int ldv,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_int* iwork );
-
-lapack_int LAPACKE_sggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb, float tola,
-                           float tolb, lapack_int* k, lapack_int* l, float* u,
-                           lapack_int ldu, float* v, lapack_int ldv, float* q,
-                           lapack_int ldq );
-lapack_int LAPACKE_dggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double tola, double tolb, lapack_int* k,
-                           lapack_int* l, double* u, lapack_int ldu, double* v,
-                           lapack_int ldv, double* q, lapack_int ldq );
-lapack_int LAPACKE_cggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb, float tola,
-                           float tolb, lapack_int* k, lapack_int* l,
-                           lapack_complex_float* u, lapack_int ldu,
-                           lapack_complex_float* v, lapack_int ldv,
-                           lapack_complex_float* q, lapack_int ldq );
-lapack_int LAPACKE_zggsvp( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           double tola, double tolb, lapack_int* k,
-                           lapack_int* l, lapack_complex_double* u,
-                           lapack_int ldu, lapack_complex_double* v,
-                           lapack_int ldv, lapack_complex_double* q,
-                           lapack_int ldq );
-
-lapack_int LAPACKE_sgtcon( char norm, lapack_int n, const float* dl,
-                           const float* d, const float* du, const float* du2,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_dgtcon( char norm, lapack_int n, const double* dl,
-                           const double* d, const double* du, const double* du2,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_cgtcon( char norm, lapack_int n,
-                           const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           const lapack_complex_float* du2,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zgtcon( char norm, lapack_int n,
-                           const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           const lapack_complex_double* du2,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* dl, const float* d,
-                           const float* du, const float* dlf, const float* df,
-                           const float* duf, const float* du2,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* dl, const double* d,
-                           const double* du, const double* dlf,
-                           const double* df, const double* duf,
-                           const double* du2, const lapack_int* ipiv,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           const lapack_complex_float* dlf,
-                           const lapack_complex_float* df,
-                           const lapack_complex_float* duf,
-                           const lapack_complex_float* du2,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgtrfs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           const lapack_complex_double* dlf,
-                           const lapack_complex_double* df,
-                           const lapack_complex_double* duf,
-                           const lapack_complex_double* du2,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* dl, float* d, float* du, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* dl, double* d, double* du, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_float* dl, lapack_complex_float* d,
-                          lapack_complex_float* du, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          lapack_complex_double* dl, lapack_complex_double* d,
-                          lapack_complex_double* du, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_sgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, const float* dl,
-                           const float* d, const float* du, float* dlf,
-                           float* df, float* duf, float* du2, lapack_int* ipiv,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs, const double* dl,
-                           const double* d, const double* du, double* dlf,
-                           double* df, double* duf, double* du2,
-                           lapack_int* ipiv, const double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           lapack_complex_float* dlf, lapack_complex_float* df,
-                           lapack_complex_float* duf, lapack_complex_float* du2,
-                           lapack_int* ipiv, const lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zgtsvx( int matrix_order, char fact, char trans,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           lapack_complex_double* dlf,
-                           lapack_complex_double* df,
-                           lapack_complex_double* duf,
-                           lapack_complex_double* du2, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_sgttrf( lapack_int n, float* dl, float* d, float* du,
-                           float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_dgttrf( lapack_int n, double* dl, double* d, double* du,
-                           double* du2, lapack_int* ipiv );
-lapack_int LAPACKE_cgttrf( lapack_int n, lapack_complex_float* dl,
-                           lapack_complex_float* d, lapack_complex_float* du,
-                           lapack_complex_float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_zgttrf( lapack_int n, lapack_complex_double* dl,
-                           lapack_complex_double* d, lapack_complex_double* du,
-                           lapack_complex_double* du2, lapack_int* ipiv );
-
-lapack_int LAPACKE_sgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const float* dl, const float* d,
-                           const float* du, const float* du2,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const double* dl, const double* d,
-                           const double* du, const double* du2,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_cgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* dl,
-                           const lapack_complex_float* d,
-                           const lapack_complex_float* du,
-                           const lapack_complex_float* du2,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zgttrs( int matrix_order, char trans, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* dl,
-                           const lapack_complex_double* d,
-                           const lapack_complex_double* du,
-                           const lapack_complex_double* du2,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_chbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, lapack_complex_float* ab,
-                          lapack_int ldab, float* w, lapack_complex_float* z,
-                          lapack_int ldz );
-lapack_int LAPACKE_zhbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, lapack_complex_double* ab,
-                          lapack_int ldab, double* w, lapack_complex_double* z,
-                          lapack_int ldz );
-
-lapack_int LAPACKE_chbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_float* ab,
-                           lapack_int ldab, float* w, lapack_complex_float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_zhbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_double* ab,
-                           lapack_int ldab, double* w, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_chbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* q, lapack_int ldq, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* bb, lapack_int ldbb,
-                           lapack_complex_float* x, lapack_int ldx );
-lapack_int LAPACKE_zhbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* bb, lapack_int ldbb,
-                           lapack_complex_double* x, lapack_int ldx );
-
-lapack_int LAPACKE_chbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb,
-                          lapack_complex_float* ab, lapack_int ldab,
-                          lapack_complex_float* bb, lapack_int ldbb, float* w,
-                          lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb,
-                          lapack_complex_double* ab, lapack_int ldab,
-                          lapack_complex_double* bb, lapack_int ldbb, double* w,
-                          lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* bb, lapack_int ldbb, float* w,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* bb, lapack_int ldbb,
-                           double* w, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_chbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* bb, lapack_int ldbb,
-                           lapack_complex_float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* bb, lapack_int ldbb,
-                           lapack_complex_double* q, lapack_int ldq, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_float* ab,
-                           lapack_int ldab, float* d, float* e,
-                           lapack_complex_float* q, lapack_int ldq );
-lapack_int LAPACKE_zhbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_double* ab,
-                           lapack_int ldab, double* d, double* e,
-                           lapack_complex_double* q, lapack_int ldq );
-
-lapack_int LAPACKE_checon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zhecon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_cheequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* s, float* scond, float* amax );
-lapack_int LAPACKE_zheequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_cheev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_zheev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_double* a, lapack_int lda, double* w );
-
-lapack_int LAPACKE_cheevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_zheevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           double* w );
-
-lapack_int LAPACKE_cheevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float vl, float vu, lapack_int il,
-                           lapack_int iu, float abstol, lapack_int* m, float* w,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_zheevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double vl, double vu, lapack_int il,
-                           lapack_int iu, double abstol, lapack_int* m,
-                           double* w, lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* isuppz );
-
-lapack_int LAPACKE_cheevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, float vl, float vu, lapack_int il,
-                           lapack_int iu, float abstol, lapack_int* m, float* w,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_zheevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, double vl, double vu, lapack_int il,
-                           lapack_int iu, double abstol, lapack_int* m,
-                           double* w, lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chegst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zhegst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_chegv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_float* a,
-                          lapack_int lda, lapack_complex_float* b,
-                          lapack_int ldb, float* w );
-lapack_int LAPACKE_zhegv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* b,
-                          lapack_int ldb, double* w );
-
-lapack_int LAPACKE_chegvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float* w );
-lapack_int LAPACKE_zhegvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double* w );
-
-lapack_int LAPACKE_chegvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhegvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_cherfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zherfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_cherfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* s,
-                            const lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zherfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* s,
-                            const lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_chesv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zhesv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chesvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zhesvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_chesvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* s,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zhesvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* s,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_chetrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda, float* d,
-                           float* e, lapack_complex_float* tau );
-lapack_int LAPACKE_zhetrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda, double* d,
-                           double* e, lapack_complex_double* tau );
-
-lapack_int LAPACKE_chetrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zhetrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_chetri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_zhetri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv );
-
-lapack_int LAPACKE_chetrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zhetrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, float alpha,
-                          const lapack_complex_float* a, lapack_int lda,
-                          float beta, lapack_complex_float* c );
-lapack_int LAPACKE_zhfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, double alpha,
-                          const lapack_complex_double* a, lapack_int lda,
-                          double beta, lapack_complex_double* c );
-
-lapack_int LAPACKE_shgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           float* h, lapack_int ldh, float* t, lapack_int ldt,
-                           float* alphar, float* alphai, float* beta, float* q,
-                           lapack_int ldq, float* z, lapack_int ldz );
-lapack_int LAPACKE_dhgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           double* h, lapack_int ldh, double* t, lapack_int ldt,
-                           double* alphar, double* alphai, double* beta,
-                           double* q, lapack_int ldq, double* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_chgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_float* h, lapack_int ldh,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta, lapack_complex_float* q,
-                           lapack_int ldq, lapack_complex_float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_zhgeqz( int matrix_order, char job, char compq, char compz,
-                           lapack_int n, lapack_int ilo, lapack_int ihi,
-                           lapack_complex_double* h, lapack_int ldh,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zhpcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_chpev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_float* ap, float* w,
-                          lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_complex_double* ap, double* w,
-                          lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_float* ap, float* w,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_complex_double* ap, double* w,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_float* ap, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_zhpevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_complex_double* ap, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chpgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_float* ap,
-                           const lapack_complex_float* bp );
-lapack_int LAPACKE_zhpgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, lapack_complex_double* ap,
-                           const lapack_complex_double* bp );
-
-lapack_int LAPACKE_chpgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_float* ap,
-                          lapack_complex_float* bp, float* w,
-                          lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, lapack_complex_double* ap,
-                          lapack_complex_double* bp, double* w,
-                          lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_float* ap,
-                           lapack_complex_float* bp, float* w,
-                           lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zhpgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, lapack_complex_double* ap,
-                           lapack_complex_double* bp, double* w,
-                           lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_chpgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_float* ap, lapack_complex_float* bp,
-                           float vl, float vu, lapack_int il, lapack_int iu,
-                           float abstol, lapack_int* m, float* w,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_zhpgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n,
-                           lapack_complex_double* ap, lapack_complex_double* bp,
-                           double vl, double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_chprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_complex_float* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zhprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_complex_double* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_chpsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* ap,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zhpsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* ap,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_chpsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           lapack_complex_float* afp, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zhpsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           lapack_complex_double* afp, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_chptrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, float* d, float* e,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zhptrd( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, double* d, double* e,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_chptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zhptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_chptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, const lapack_int* ipiv );
-lapack_int LAPACKE_zhptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, const lapack_int* ipiv );
-
-lapack_int LAPACKE_chptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zhptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_shsein( int matrix_order, char job, char eigsrc, char initv,
-                           lapack_logical* select, lapack_int n, const float* h,
-                           lapack_int ldh, float* wr, const float* wi,
-                           float* vl, lapack_int ldvl, float* vr,
-                           lapack_int ldvr, lapack_int mm, lapack_int* m,
-                           lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_dhsein( int matrix_order, char job, char eigsrc, char initv,
-                           lapack_logical* select, lapack_int n,
-                           const double* h, lapack_int ldh, double* wr,
-                           const double* wi, double* vl, lapack_int ldvl,
-                           double* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m, lapack_int* ifaill,
-                           lapack_int* ifailr );
-lapack_int LAPACKE_chsein( int matrix_order, char job, char eigsrc, char initv,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* h, lapack_int ldh,
-                           lapack_complex_float* w, lapack_complex_float* vl,
-                           lapack_int ldvl, lapack_complex_float* vr,
-                           lapack_int ldvr, lapack_int mm, lapack_int* m,
-                           lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_zhsein( int matrix_order, char job, char eigsrc, char initv,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* h, lapack_int ldh,
-                           lapack_complex_double* w, lapack_complex_double* vl,
-                           lapack_int ldvl, lapack_complex_double* vr,
-                           lapack_int ldvr, lapack_int mm, lapack_int* m,
-                           lapack_int* ifaill, lapack_int* ifailr );
-
-lapack_int LAPACKE_shseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, float* h,
-                           lapack_int ldh, float* wr, float* wi, float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_dhseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi, double* h,
-                           lapack_int ldh, double* wr, double* wi, double* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_chseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi,
-                           lapack_complex_float* h, lapack_int ldh,
-                           lapack_complex_float* w, lapack_complex_float* z,
-                           lapack_int ldz );
-lapack_int LAPACKE_zhseqr( int matrix_order, char job, char compz, lapack_int n,
-                           lapack_int ilo, lapack_int ihi,
-                           lapack_complex_double* h, lapack_int ldh,
-                           lapack_complex_double* w, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_clacgv( lapack_int n, lapack_complex_float* x,
-                           lapack_int incx );
-lapack_int LAPACKE_zlacgv( lapack_int n, lapack_complex_double* x,
-                           lapack_int incx );
-
-lapack_int LAPACKE_slacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const float* a, lapack_int lda, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dlacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const double* a, lapack_int lda, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_clacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zlacpy( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_zlag2c( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_float* sa, lapack_int ldsa );
-
-lapack_int LAPACKE_slag2d( int matrix_order, lapack_int m, lapack_int n,
-                           const float* sa, lapack_int ldsa, double* a,
-                           lapack_int lda );
-
-lapack_int LAPACKE_dlag2s( int matrix_order, lapack_int m, lapack_int n,
-                           const double* a, lapack_int lda, float* sa,
-                           lapack_int ldsa );
-
-lapack_int LAPACKE_clag2z( int matrix_order, lapack_int m, lapack_int n,
-                           const lapack_complex_float* sa, lapack_int ldsa,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* d,
-                           float* a, lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_dlagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* d,
-                           double* a, lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_clagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const float* d,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* iseed );
-lapack_int LAPACKE_zlagge( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int kl, lapack_int ku, const double* d,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* iseed );
-
-float LAPACKE_slamch( char cmach );
-double LAPACKE_dlamch( char cmach );
-
-float LAPACKE_slange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const float* a, lapack_int lda );
-double LAPACKE_dlange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const double* a, lapack_int lda );
-float LAPACKE_clange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda );
-double LAPACKE_zlange( int matrix_order, char norm, lapack_int m,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda );
-
-float LAPACKE_clanhe( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda );
-double LAPACKE_zlanhe( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda );
-
-float LAPACKE_slansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const float* a, lapack_int lda );
-double LAPACKE_dlansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const double* a, lapack_int lda );
-float LAPACKE_clansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda );
-double LAPACKE_zlansy( int matrix_order, char norm, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda );
-
-float LAPACKE_slantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const float* a,
-                           lapack_int lda );
-double LAPACKE_dlantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const double* a,
-                           lapack_int lda );
-float LAPACKE_clantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda );
-double LAPACKE_zlantr( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int m, lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda );
-
-
-lapack_int LAPACKE_slarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const float* v, lapack_int ldv,
-                           const float* t, lapack_int ldt, float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_dlarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const double* v, lapack_int ldv,
-                           const double* t, lapack_int ldt, double* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_clarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const lapack_complex_float* v,
-                           lapack_int ldv, const lapack_complex_float* t,
-                           lapack_int ldt, lapack_complex_float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_zlarfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, const lapack_complex_double* v,
-                           lapack_int ldv, const lapack_complex_double* t,
-                           lapack_int ldt, lapack_complex_double* c,
-                           lapack_int ldc );
-
-lapack_int LAPACKE_slarfg( lapack_int n, float* alpha, float* x,
-                           lapack_int incx, float* tau );
-lapack_int LAPACKE_dlarfg( lapack_int n, double* alpha, double* x,
-                           lapack_int incx, double* tau );
-lapack_int LAPACKE_clarfg( lapack_int n, lapack_complex_float* alpha,
-                           lapack_complex_float* x, lapack_int incx,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_zlarfg( lapack_int n, lapack_complex_double* alpha,
-                           lapack_complex_double* x, lapack_int incx,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_slarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k, const float* v,
-                           lapack_int ldv, const float* tau, float* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_dlarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k, const double* v,
-                           lapack_int ldv, const double* tau, double* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_clarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k,
-                           const lapack_complex_float* v, lapack_int ldv,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zlarft( int matrix_order, char direct, char storev,
-                           lapack_int n, lapack_int k,
-                           const lapack_complex_double* v, lapack_int ldv,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_slarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const float* v, float tau, float* c,
-                           lapack_int ldc, float* work );
-lapack_int LAPACKE_dlarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const double* v, double tau, double* c,
-                           lapack_int ldc, double* work );
-lapack_int LAPACKE_clarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const lapack_complex_float* v,
-                           lapack_complex_float tau, lapack_complex_float* c,
-                           lapack_int ldc, lapack_complex_float* work );
-lapack_int LAPACKE_zlarfx( int matrix_order, char side, lapack_int m,
-                           lapack_int n, const lapack_complex_double* v,
-                           lapack_complex_double tau, lapack_complex_double* c,
-                           lapack_int ldc, lapack_complex_double* work );
-
-lapack_int LAPACKE_slarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           float* x );
-lapack_int LAPACKE_dlarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           double* x );
-lapack_int LAPACKE_clarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           lapack_complex_float* x );
-lapack_int LAPACKE_zlarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
-                           lapack_complex_double* x );
-
-lapack_int LAPACKE_slaset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, float alpha, float beta, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dlaset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, double alpha, double beta, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_claset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, lapack_complex_float alpha,
-                           lapack_complex_float beta, lapack_complex_float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_zlaset( int matrix_order, char uplo, lapack_int m,
-                           lapack_int n, lapack_complex_double alpha,
-                           lapack_complex_double beta, lapack_complex_double* a,
-                           lapack_int lda );
-
-lapack_int LAPACKE_slasrt( char id, lapack_int n, float* d );
-lapack_int LAPACKE_dlasrt( char id, lapack_int n, double* d );
-
-lapack_int LAPACKE_slaswp( int matrix_order, lapack_int n, float* a,
-                           lapack_int lda, lapack_int k1, lapack_int k2,
-                           const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_dlaswp( int matrix_order, lapack_int n, double* a,
-                           lapack_int lda, lapack_int k1, lapack_int k2,
-                           const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_claswp( int matrix_order, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int k1, lapack_int k2, const lapack_int* ipiv,
-                           lapack_int incx );
-lapack_int LAPACKE_zlaswp( int matrix_order, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int k1, lapack_int k2, const lapack_int* ipiv,
-                           lapack_int incx );
-
-lapack_int LAPACKE_slatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, float* d,
-                           lapack_int mode, float cond, float dmax,
-                           lapack_int kl, lapack_int ku, char pack, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dlatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, double* d,
-                           lapack_int mode, double cond, double dmax,
-                           lapack_int kl, lapack_int ku, char pack, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_clatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, float* d,
-                           lapack_int mode, float cond, float dmax,
-                           lapack_int kl, lapack_int ku, char pack,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlatms( int matrix_order, lapack_int m, lapack_int n,
-                           char dist, lapack_int* iseed, char sym, double* d,
-                           lapack_int mode, double cond, double dmax,
-                           lapack_int kl, lapack_int ku, char pack,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slauum( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dlauum( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_clauum( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlauum( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_sopgtr( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, const float* tau, float* q,
-                           lapack_int ldq );
-lapack_int LAPACKE_dopgtr( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, const double* tau, double* q,
-                           lapack_int ldq );
-
-lapack_int LAPACKE_sopmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const float* ap,
-                           const float* tau, float* c, lapack_int ldc );
-lapack_int LAPACKE_dopmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const double* ap,
-                           const double* tau, double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sorgbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, double* a,
-                           lapack_int lda, const double* tau );
-
-lapack_int LAPACKE_sorghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, float* a, lapack_int lda,
-                           const float* tau );
-lapack_int LAPACKE_dorgrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, double* a, lapack_int lda,
-                           const double* tau );
-
-lapack_int LAPACKE_sorgtr( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, const float* tau );
-lapack_int LAPACKE_dorgtr( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, const double* tau );
-
-lapack_int LAPACKE_sormbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const float* a, lapack_int lda,
-                           const float* tau, float* c, lapack_int ldc );
-lapack_int LAPACKE_dormhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const double* a, lapack_int lda,
-                           const double* tau, double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const float* a, lapack_int lda, const float* tau,
-                           float* c, lapack_int ldc );
-lapack_int LAPACKE_dormrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const double* a, lapack_int lda, const double* tau,
-                           double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const float* a, lapack_int lda,
-                           const float* tau, float* c, lapack_int ldc );
-lapack_int LAPACKE_dormrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const double* a, lapack_int lda,
-                           const double* tau, double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sormtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const float* a,
-                           lapack_int lda, const float* tau, float* c,
-                           lapack_int ldc );
-lapack_int LAPACKE_dormtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n, const double* a,
-                           lapack_int lda, const double* tau, double* c,
-                           lapack_int ldc );
-
-lapack_int LAPACKE_spbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const float* ab, lapack_int ldab,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_dpbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const double* ab, lapack_int ldab,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cpbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_float* ab,
-                           lapack_int ldab, float anorm, float* rcond );
-lapack_int LAPACKE_zpbcon( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_double* ab,
-                           lapack_int ldab, double anorm, double* rcond );
-
-lapack_int LAPACKE_spbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const float* ab, lapack_int ldab,
-                           float* s, float* scond, float* amax );
-lapack_int LAPACKE_dpbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const double* ab, lapack_int ldab,
-                           double* s, double* scond, double* amax );
-lapack_int LAPACKE_cpbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_float* ab,
-                           lapack_int ldab, float* s, float* scond,
-                           float* amax );
-lapack_int LAPACKE_zpbequ( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, const lapack_complex_double* ab,
-                           lapack_int ldab, double* s, double* scond,
-                           double* amax );
-
-lapack_int LAPACKE_spbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const float* ab,
-                           lapack_int ldab, const float* afb, lapack_int ldafb,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dpbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const double* ab,
-                           lapack_int ldab, const double* afb, lapack_int ldafb,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cpbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* afb, lapack_int ldafb,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zpbrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* afb, lapack_int ldafb,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_spbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, float* bb, lapack_int ldbb );
-lapack_int LAPACKE_dpbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, double* bb, lapack_int ldbb );
-lapack_int LAPACKE_cpbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, lapack_complex_float* bb,
-                           lapack_int ldbb );
-lapack_int LAPACKE_zpbstf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kb, lapack_complex_double* bb,
-                           lapack_int ldbb );
-
-lapack_int LAPACKE_spbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs, float* ab,
-                          lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs, double* ab,
-                          lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs,
-                          lapack_complex_float* ab, lapack_int ldab,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int kd, lapack_int nrhs,
-                          lapack_complex_double* ab, lapack_int ldab,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, float* ab,
-                           lapack_int ldab, float* afb, lapack_int ldafb,
-                           char* equed, float* s, float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, double* ab,
-                           lapack_int ldab, double* afb, lapack_int ldafb,
-                           char* equed, double* s, double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* afb, lapack_int ldafb,
-                           char* equed, float* s, lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* afb, lapack_int ldafb,
-                           char* equed, double* s, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, double* rcond, double* ferr,
-                           double* berr );
-
-lapack_int LAPACKE_spbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, float* ab, lapack_int ldab );
-lapack_int LAPACKE_dpbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, double* ab, lapack_int ldab );
-lapack_int LAPACKE_cpbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_float* ab,
-                           lapack_int ldab );
-lapack_int LAPACKE_zpbtrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_complex_double* ab,
-                           lapack_int ldab );
-
-lapack_int LAPACKE_spbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const float* ab,
-                           lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs, const double* ab,
-                           lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbtrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, float* a );
-lapack_int LAPACKE_dpftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, double* a );
-lapack_int LAPACKE_cpftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftrf( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, float* a );
-lapack_int LAPACKE_dpftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, double* a );
-lapack_int LAPACKE_cpftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftri( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs, const float* a,
-                           float* b, lapack_int ldb );
-lapack_int LAPACKE_dpftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs, const double* a,
-                           double* b, lapack_int ldb );
-lapack_int LAPACKE_cpftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* a,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpftrs( int matrix_order, char transr, char uplo,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* a,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spocon( int matrix_order, char uplo, lapack_int n,
-                           const float* a, lapack_int lda, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dpocon( int matrix_order, char uplo, lapack_int n,
-                           const double* a, lapack_int lda, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_cpocon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_zpocon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double anorm, double* rcond );
-
-lapack_int LAPACKE_spoequ( int matrix_order, lapack_int n, const float* a,
-                           lapack_int lda, float* s, float* scond,
-                           float* amax );
-lapack_int LAPACKE_dpoequ( int matrix_order, lapack_int n, const double* a,
-                           lapack_int lda, double* s, double* scond,
-                           double* amax );
-lapack_int LAPACKE_cpoequ( int matrix_order, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequ( int matrix_order, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_spoequb( int matrix_order, lapack_int n, const float* a,
-                            lapack_int lda, float* s, float* scond,
-                            float* amax );
-lapack_int LAPACKE_dpoequb( int matrix_order, lapack_int n, const double* a,
-                            lapack_int lda, double* s, double* scond,
-                            double* amax );
-lapack_int LAPACKE_cpoequb( int matrix_order, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequb( int matrix_order, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_sporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const float* af, lapack_int ldaf, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const double* af, lapack_int ldaf, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_complex_float* b,
-                           lapack_int ldb, lapack_complex_float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_zporfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-
-lapack_int LAPACKE_sporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const float* a,
-                            lapack_int lda, const float* af, lapack_int ldaf,
-                            const float* s, const float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const double* a,
-                            lapack_int lda, const double* af, lapack_int ldaf,
-                            const double* s, const double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const float* s, const lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zporfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const double* s, const lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_sposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* a, lapack_int lda, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* a, lapack_int lda, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* a,
-                          lapack_int lda, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zposv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* a,
-                          lapack_int lda, lapack_complex_double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dsposv( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           lapack_int* iter );
-lapack_int LAPACKE_zcposv( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, lapack_complex_double* x,
-                           lapack_int ldx, lapack_int* iter );
-
-lapack_int LAPACKE_sposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, float* a, lapack_int lda, float* af,
-                           lapack_int ldaf, char* equed, float* s, float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_dposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, double* a, lapack_int lda,
-                           double* af, lapack_int ldaf, char* equed, double* s,
-                           double* b, lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-lapack_int LAPACKE_cposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* af,
-                           lapack_int ldaf, char* equed, float* s,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zposvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* af,
-                           lapack_int ldaf, char* equed, double* s,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_sposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, float* a,
-                            lapack_int lda, float* af, lapack_int ldaf,
-                            char* equed, float* s, float* b, lapack_int ldb,
-                            float* x, lapack_int ldx, float* rcond,
-                            float* rpvgrw, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_dposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, double* a,
-                            lapack_int lda, double* af, lapack_int ldaf,
-                            char* equed, double* s, double* b, lapack_int ldb,
-                            double* x, lapack_int ldx, double* rcond,
-                            double* rpvgrw, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-lapack_int LAPACKE_cposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            char* equed, float* s, lapack_complex_float* b,
-                            lapack_int ldb, lapack_complex_float* x,
-                            lapack_int ldx, float* rcond, float* rpvgrw,
-                            float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zposvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            char* equed, double* s, lapack_complex_double* b,
-                            lapack_int ldb, lapack_complex_double* x,
-                            lapack_int ldx, double* rcond, double* rpvgrw,
-                            double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_spotrf( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dpotrf( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_cpotrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotri( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dpotri( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_cpotri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           float* b, lapack_int ldb );
-lapack_int LAPACKE_dpotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           double* b, lapack_int ldb );
-lapack_int LAPACKE_cpotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zpotrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_sppcon( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, float anorm, float* rcond );
-lapack_int LAPACKE_dppcon( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, double anorm, double* rcond );
-lapack_int LAPACKE_cppcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_zppcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sppequ( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, float* s, float* scond,
-                           float* amax );
-lapack_int LAPACKE_dppequ( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, double* s, double* scond,
-                           double* amax );
-lapack_int LAPACKE_cppequ( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap, float* s,
-                           float* scond, float* amax );
-lapack_int LAPACKE_zppequ( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap, double* s,
-                           double* scond, double* amax );
-
-lapack_int LAPACKE_spprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, const float* afp,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dpprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, const double* afp,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_cpprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_complex_float* afp,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zpprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_complex_double* afp,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* ap, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* ap, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_cppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* ap,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zppsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* ap,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, float* ap, float* afp, char* equed,
-                           float* s, float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, double* ap, double* afp,
-                           char* equed, double* s, double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_float* ap,
-                           lapack_complex_float* afp, char* equed, float* s,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zppsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, lapack_complex_double* ap,
-                           lapack_complex_double* afp, char* equed, double* s,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_spptrf( int matrix_order, char uplo, lapack_int n,
-                           float* ap );
-lapack_int LAPACKE_dpptrf( int matrix_order, char uplo, lapack_int n,
-                           double* ap );
-lapack_int LAPACKE_cpptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_zpptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptri( int matrix_order, char uplo, lapack_int n,
-                           float* ap );
-lapack_int LAPACKE_dpptri( int matrix_order, char uplo, lapack_int n,
-                           double* ap );
-lapack_int LAPACKE_cpptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_zpptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dpptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_cpptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spstrf( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, lapack_int* piv, lapack_int* rank,
-                           float tol );
-lapack_int LAPACKE_dpstrf( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, lapack_int* piv, lapack_int* rank,
-                           double tol );
-lapack_int LAPACKE_cpstrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* piv, lapack_int* rank, float tol );
-lapack_int LAPACKE_zpstrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* piv, lapack_int* rank, double tol );
-
-lapack_int LAPACKE_sptcon( lapack_int n, const float* d, const float* e,
-                           float anorm, float* rcond );
-lapack_int LAPACKE_dptcon( lapack_int n, const double* d, const double* e,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cptcon( lapack_int n, const float* d,
-                           const lapack_complex_float* e, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_zptcon( lapack_int n, const double* d,
-                           const lapack_complex_double* e, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_spteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dpteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, double* z, lapack_int ldz );
-lapack_int LAPACKE_cpteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zpteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_sptrfs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const float* d, const float* e, const float* df,
-                           const float* ef, const float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dptrfs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const double* d, const double* e, const double* df,
-                           const double* ef, const double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* ferr,
-                           double* berr );
-lapack_int LAPACKE_cptrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* d,
-                           const lapack_complex_float* e, const float* df,
-                           const lapack_complex_float* ef,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zptrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* d,
-                           const lapack_complex_double* e, const double* df,
-                           const lapack_complex_double* ef,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* d, float* e, float* b, lapack_int ldb );
-lapack_int LAPACKE_dptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* d, double* e, double* b, lapack_int ldb );
-lapack_int LAPACKE_cptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          float* d, lapack_complex_float* e,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zptsv( int matrix_order, lapack_int n, lapack_int nrhs,
-                          double* d, lapack_complex_double* e,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const float* d, const float* e,
-                           float* df, float* ef, const float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const double* d, const double* e,
-                           double* df, double* ef, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-lapack_int LAPACKE_cptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const float* d,
-                           const lapack_complex_float* e, float* df,
-                           lapack_complex_float* ef,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zptsvx( int matrix_order, char fact, lapack_int n,
-                           lapack_int nrhs, const double* d,
-                           const lapack_complex_double* e, double* df,
-                           lapack_complex_double* ef,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_spttrf( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dpttrf( lapack_int n, double* d, double* e );
-lapack_int LAPACKE_cpttrf( lapack_int n, float* d, lapack_complex_float* e );
-lapack_int LAPACKE_zpttrf( lapack_int n, double* d, lapack_complex_double* e );
-
-lapack_int LAPACKE_spttrs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const float* d, const float* e, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dpttrs( int matrix_order, lapack_int n, lapack_int nrhs,
-                           const double* d, const double* e, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_cpttrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* d,
-                           const lapack_complex_float* e,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpttrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* d,
-                           const lapack_complex_double* e,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, float* ab, lapack_int ldab, float* w,
-                          float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int kd, double* ab, lapack_int ldab, double* w,
-                          double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, float* ab, lapack_int ldab, float* w,
-                           float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int kd, double* ab, lapack_int ldab,
-                           double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd, float* ab,
-                           lapack_int ldab, float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsbevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int kd, double* ab,
-                           lapack_int ldab, double* q, lapack_int ldq,
-                           double vl, double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, float* ab,
-                           lapack_int ldab, const float* bb, lapack_int ldbb,
-                           float* x, lapack_int ldx );
-lapack_int LAPACKE_dsbgst( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, double* ab,
-                           lapack_int ldab, const double* bb, lapack_int ldbb,
-                           double* x, lapack_int ldx );
-
-lapack_int LAPACKE_ssbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb, float* ab,
-                          lapack_int ldab, float* bb, lapack_int ldbb, float* w,
-                          float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbgv( int matrix_order, char jobz, char uplo, lapack_int n,
-                          lapack_int ka, lapack_int kb, double* ab,
-                          lapack_int ldab, double* bb, lapack_int ldbb,
-                          double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, float* ab,
-                           lapack_int ldab, float* bb, lapack_int ldbb,
-                           float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dsbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           lapack_int ka, lapack_int kb, double* ab,
-                           lapack_int ldab, double* bb, lapack_int ldbb,
-                           double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_ssbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           float* ab, lapack_int ldab, float* bb,
-                           lapack_int ldbb, float* q, lapack_int ldq, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsbgvx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, lapack_int ka, lapack_int kb,
-                           double* ab, lapack_int ldab, double* bb,
-                           lapack_int ldbb, double* q, lapack_int ldq,
-                           double vl, double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, float* ab, lapack_int ldab, float* d,
-                           float* e, float* q, lapack_int ldq );
-lapack_int LAPACKE_dsbtrd( int matrix_order, char vect, char uplo, lapack_int n,
-                           lapack_int kd, double* ab, lapack_int ldab,
-                           double* d, double* e, double* q, lapack_int ldq );
-
-lapack_int LAPACKE_ssfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, float alpha,
-                          const float* a, lapack_int lda, float beta,
-                          float* c );
-lapack_int LAPACKE_dsfrk( int matrix_order, char transr, char uplo, char trans,
-                          lapack_int n, lapack_int k, double alpha,
-                          const double* a, lapack_int lda, double beta,
-                          double* c );
-
-lapack_int LAPACKE_sspcon( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, const lapack_int* ipiv, float anorm,
-                           float* rcond );
-lapack_int LAPACKE_dspcon( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, const lapack_int* ipiv,
-                           double anorm, double* rcond );
-lapack_int LAPACKE_cspcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zspcon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_sspev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          float* ap, float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          double* ap, double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           float* ap, float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           double* ap, double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, float* ap, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dspevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, double* ap, double vl, double vu,
-                           lapack_int il, lapack_int iu, double abstol,
-                           lapack_int* m, double* w, double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_sspgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, float* ap, const float* bp );
-lapack_int LAPACKE_dspgst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, double* ap, const double* bp );
-
-lapack_int LAPACKE_sspgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, float* ap, float* bp,
-                          float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspgv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, double* ap, double* bp,
-                          double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, float* ap, float* bp,
-                           float* w, float* z, lapack_int ldz );
-lapack_int LAPACKE_dspgvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, double* ap, double* bp,
-                           double* w, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sspgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, float* ap,
-                           float* bp, float vl, float vu, lapack_int il,
-                           lapack_int iu, float abstol, lapack_int* m, float* w,
-                           float* z, lapack_int ldz, lapack_int* ifail );
-lapack_int LAPACKE_dspgvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, double* ap,
-                           double* bp, double vl, double vu, lapack_int il,
-                           lapack_int iu, double abstol, lapack_int* m,
-                           double* w, double* z, lapack_int ldz,
-                           lapack_int* ifail );
-
-lapack_int LAPACKE_ssprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, const float* afp,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dsprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, const double* afp,
-                           const lapack_int* ipiv, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_csprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_complex_float* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zsprfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_complex_double* afp,
-                           const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_sspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* ap, lapack_int* ipiv,
-                          float* b, lapack_int ldb );
-lapack_int LAPACKE_dspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* ap, lapack_int* ipiv,
-                          double* b, lapack_int ldb );
-lapack_int LAPACKE_cspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* ap,
-                          lapack_int* ipiv, lapack_complex_float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_zspsv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* ap,
-                          lapack_int* ipiv, lapack_complex_double* b,
-                          lapack_int ldb );
-
-lapack_int LAPACKE_sspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap, float* afp,
-                           lapack_int* ipiv, const float* b, lapack_int ldb,
-                           float* x, lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap, double* afp,
-                           lapack_int* ipiv, const double* b, lapack_int ldb,
-                           double* x, lapack_int ldx, double* rcond,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_cspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           lapack_complex_float* afp, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zspsvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           lapack_complex_double* afp, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_ssptrd( int matrix_order, char uplo, lapack_int n, float* ap,
-                           float* d, float* e, float* tau );
-lapack_int LAPACKE_dsptrd( int matrix_order, char uplo, lapack_int n,
-                           double* ap, double* d, double* e, double* tau );
-
-lapack_int LAPACKE_ssptrf( int matrix_order, char uplo, lapack_int n, float* ap,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_dsptrf( int matrix_order, char uplo, lapack_int n,
-                           double* ap, lapack_int* ipiv );
-lapack_int LAPACKE_csptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zsptrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_ssptri( int matrix_order, char uplo, lapack_int n, float* ap,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_dsptri( int matrix_order, char uplo, lapack_int n,
-                           double* ap, const lapack_int* ipiv );
-lapack_int LAPACKE_csptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* ap, const lapack_int* ipiv );
-lapack_int LAPACKE_zsptri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* ap, const lapack_int* ipiv );
-
-lapack_int LAPACKE_ssptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* ap,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dsptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* ap,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_csptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* ap,
-                           const lapack_int* ipiv, lapack_complex_float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_zsptrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* ap,
-                           const lapack_int* ipiv, lapack_complex_double* b,
-                           lapack_int ldb );
-
-lapack_int LAPACKE_sstebz( char range, char order, lapack_int n, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           const float* d, const float* e, lapack_int* m,
-                           lapack_int* nsplit, float* w, lapack_int* iblock,
-                           lapack_int* isplit );
-lapack_int LAPACKE_dstebz( char range, char order, lapack_int n, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, const double* d, const double* e,
-                           lapack_int* m, lapack_int* nsplit, double* w,
-                           lapack_int* iblock, lapack_int* isplit );
-
-lapack_int LAPACKE_sstedc( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dstedc( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, double* z, lapack_int ldz );
-lapack_int LAPACKE_cstedc( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zstedc( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_sstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_dstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* isuppz );
-lapack_int LAPACKE_cstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* isuppz );
-lapack_int LAPACKE_zstegr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* isuppz );
-
-lapack_int LAPACKE_sstein( int matrix_order, lapack_int n, const float* d,
-                           const float* e, lapack_int m, const float* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           float* z, lapack_int ldz, lapack_int* ifailv );
-lapack_int LAPACKE_dstein( int matrix_order, lapack_int n, const double* d,
-                           const double* e, lapack_int m, const double* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           double* z, lapack_int ldz, lapack_int* ifailv );
-lapack_int LAPACKE_cstein( int matrix_order, lapack_int n, const float* d,
-                           const float* e, lapack_int m, const float* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int* ifailv );
-lapack_int LAPACKE_zstein( int matrix_order, lapack_int n, const double* d,
-                           const double* e, lapack_int m, const double* w,
-                           const lapack_int* iblock, const lapack_int* isplit,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* ifailv );
-
-lapack_int LAPACKE_sstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, lapack_int* m,
-                           float* w, float* z, lapack_int ldz, lapack_int nzc,
-                           lapack_int* isuppz, lapack_logical* tryrac );
-lapack_int LAPACKE_dstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           lapack_int* m, double* w, double* z, lapack_int ldz,
-                           lapack_int nzc, lapack_int* isuppz,
-                           lapack_logical* tryrac );
-lapack_int LAPACKE_cstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, lapack_int* m,
-                           float* w, lapack_complex_float* z, lapack_int ldz,
-                           lapack_int nzc, lapack_int* isuppz,
-                           lapack_logical* tryrac );
-lapack_int LAPACKE_zstemr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           lapack_int* m, double* w, lapack_complex_double* z,
-                           lapack_int ldz, lapack_int nzc, lapack_int* isuppz,
-                           lapack_logical* tryrac );
-
-lapack_int LAPACKE_ssteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dsteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, double* z, lapack_int ldz );
-lapack_int LAPACKE_csteqr( int matrix_order, char compz, lapack_int n, float* d,
-                           float* e, lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zsteqr( int matrix_order, char compz, lapack_int n,
-                           double* d, double* e, lapack_complex_double* z,
-                           lapack_int ldz );
-
-lapack_int LAPACKE_ssterf( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dsterf( lapack_int n, double* d, double* e );
-
-lapack_int LAPACKE_sstev( int matrix_order, char jobz, lapack_int n, float* d,
-                          float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dstev( int matrix_order, char jobz, lapack_int n, double* d,
-                          double* e, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sstevd( int matrix_order, char jobz, lapack_int n, float* d,
-                           float* e, float* z, lapack_int ldz );
-lapack_int LAPACKE_dstevd( int matrix_order, char jobz, lapack_int n, double* d,
-                           double* e, double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sstevr( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_dstevr( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* isuppz );
-
-lapack_int LAPACKE_sstevx( int matrix_order, char jobz, char range,
-                           lapack_int n, float* d, float* e, float vl, float vu,
-                           lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dstevx( int matrix_order, char jobz, char range,
-                           lapack_int n, double* d, double* e, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssycon( int matrix_order, char uplo, lapack_int n,
-                           const float* a, lapack_int lda,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_dsycon( int matrix_order, char uplo, lapack_int n,
-                           const double* a, lapack_int lda,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-lapack_int LAPACKE_csycon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv, float anorm, float* rcond );
-lapack_int LAPACKE_zsycon( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv, double anorm,
-                           double* rcond );
-
-lapack_int LAPACKE_ssyequb( int matrix_order, char uplo, lapack_int n,
-                            const float* a, lapack_int lda, float* s,
-                            float* scond, float* amax );
-lapack_int LAPACKE_dsyequb( int matrix_order, char uplo, lapack_int n,
-                            const double* a, lapack_int lda, double* s,
-                            double* scond, double* amax );
-lapack_int LAPACKE_csyequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_float* a, lapack_int lda,
-                            float* s, float* scond, float* amax );
-lapack_int LAPACKE_zsyequb( int matrix_order, char uplo, lapack_int n,
-                            const lapack_complex_double* a, lapack_int lda,
-                            double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_ssyev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_dsyev( int matrix_order, char jobz, char uplo, lapack_int n,
-                          double* a, lapack_int lda, double* w );
-
-lapack_int LAPACKE_ssyevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           float* a, lapack_int lda, float* w );
-lapack_int LAPACKE_dsyevd( int matrix_order, char jobz, char uplo, lapack_int n,
-                           double* a, lapack_int lda, double* w );
-
-lapack_int LAPACKE_ssyevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, float* a, lapack_int lda, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* isuppz );
-lapack_int LAPACKE_dsyevr( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, double* a, lapack_int lda, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* isuppz );
-
-lapack_int LAPACKE_ssyevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, float* a, lapack_int lda, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsyevx( int matrix_order, char jobz, char range, char uplo,
-                           lapack_int n, double* a, lapack_int lda, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssygst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, float* a, lapack_int lda,
-                           const float* b, lapack_int ldb );
-lapack_int LAPACKE_dsygst( int matrix_order, lapack_int itype, char uplo,
-                           lapack_int n, double* a, lapack_int lda,
-                           const double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssygv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, float* a, lapack_int lda,
-                          float* b, lapack_int ldb, float* w );
-lapack_int LAPACKE_dsygv( int matrix_order, lapack_int itype, char jobz,
-                          char uplo, lapack_int n, double* a, lapack_int lda,
-                          double* b, lapack_int ldb, double* w );
-
-lapack_int LAPACKE_ssygvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, float* a, lapack_int lda,
-                           float* b, lapack_int ldb, float* w );
-lapack_int LAPACKE_dsygvd( int matrix_order, lapack_int itype, char jobz,
-                           char uplo, lapack_int n, double* a, lapack_int lda,
-                           double* b, lapack_int ldb, double* w );
-
-lapack_int LAPACKE_ssygvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb, float vl,
-                           float vu, lapack_int il, lapack_int iu, float abstol,
-                           lapack_int* m, float* w, float* z, lapack_int ldz,
-                           lapack_int* ifail );
-lapack_int LAPACKE_dsygvx( int matrix_order, lapack_int itype, char jobz,
-                           char range, char uplo, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb, double vl,
-                           double vu, lapack_int il, lapack_int iu,
-                           double abstol, lapack_int* m, double* w, double* z,
-                           lapack_int ldz, lapack_int* ifail );
-
-lapack_int LAPACKE_ssyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const float* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const float* b,
-                           lapack_int ldb, float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dsyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const double* af, lapack_int ldaf,
-                           const lapack_int* ipiv, const double* b,
-                           lapack_int ldb, double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_csyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_zsyrfs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* af,
-                           lapack_int ldaf, const lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_ssyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const float* a,
-                            lapack_int lda, const float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* s,
-                            const float* b, lapack_int ldb, float* x,
-                            lapack_int ldx, float* rcond, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dsyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs, const double* a,
-                            lapack_int lda, const double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* s,
-                            const double* b, lapack_int ldb, double* x,
-                            lapack_int ldx, double* rcond, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-lapack_int LAPACKE_csyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_float* a, lapack_int lda,
-                            const lapack_complex_float* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const float* s,
-                            const lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* berr, lapack_int n_err_bnds,
-                            float* err_bnds_norm, float* err_bnds_comp,
-                            lapack_int nparams, float* params );
-lapack_int LAPACKE_zsyrfsx( int matrix_order, char uplo, char equed,
-                            lapack_int n, lapack_int nrhs,
-                            const lapack_complex_double* a, lapack_int lda,
-                            const lapack_complex_double* af, lapack_int ldaf,
-                            const lapack_int* ipiv, const double* s,
-                            const lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* berr, lapack_int n_err_bnds,
-                            double* err_bnds_norm, double* err_bnds_comp,
-                            lapack_int nparams, double* params );
-
-lapack_int LAPACKE_ssysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, float* a, lapack_int lda,
-                          lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dsysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, double* a, lapack_int lda,
-                          lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_csysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_float* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zsysv( int matrix_order, char uplo, lapack_int n,
-                          lapack_int nrhs, lapack_complex_double* a,
-                          lapack_int lda, lapack_int* ipiv,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           float* af, lapack_int ldaf, lapack_int* ipiv,
-                           const float* b, lapack_int ldb, float* x,
-                           lapack_int ldx, float* rcond, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dsysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           double* af, lapack_int ldaf, lapack_int* ipiv,
-                           const double* b, lapack_int ldb, double* x,
-                           lapack_int ldx, double* rcond, double* ferr,
-                           double* berr );
-lapack_int LAPACKE_csysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* x, lapack_int ldx,
-                           float* rcond, float* ferr, float* berr );
-lapack_int LAPACKE_zsysvx( int matrix_order, char fact, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* af,
-                           lapack_int ldaf, lapack_int* ipiv,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* x, lapack_int ldx,
-                           double* rcond, double* ferr, double* berr );
-
-lapack_int LAPACKE_ssysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, float* a,
-                            lapack_int lda, float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* s, float* b,
-                            lapack_int ldb, float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_dsysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs, double* a,
-                            lapack_int lda, double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* s, double* b,
-                            lapack_int ldb, double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-lapack_int LAPACKE_csysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, float* s,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* x, lapack_int ldx,
-                            float* rcond, float* rpvgrw, float* berr,
-                            lapack_int n_err_bnds, float* err_bnds_norm,
-                            float* err_bnds_comp, lapack_int nparams,
-                            float* params );
-lapack_int LAPACKE_zsysvxx( int matrix_order, char fact, char uplo,
-                            lapack_int n, lapack_int nrhs,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* af, lapack_int ldaf,
-                            lapack_int* ipiv, char* equed, double* s,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* x, lapack_int ldx,
-                            double* rcond, double* rpvgrw, double* berr,
-                            lapack_int n_err_bnds, double* err_bnds_norm,
-                            double* err_bnds_comp, lapack_int nparams,
-                            double* params );
-
-lapack_int LAPACKE_ssytrd( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, float* d, float* e, float* tau );
-lapack_int LAPACKE_dsytrd( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, double* d, double* e, double* tau );
-
-lapack_int LAPACKE_ssytrf( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dsytrf( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_csytrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_int* ipiv );
-lapack_int LAPACKE_zsytrf( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_int* ipiv );
-
-lapack_int LAPACKE_ssytri( int matrix_order, char uplo, lapack_int n, float* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dsytri( int matrix_order, char uplo, lapack_int n, double* a,
-                           lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_csytri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_int* ipiv );
-lapack_int LAPACKE_zsytri( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_int* ipiv );
-
-lapack_int LAPACKE_ssytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const float* a, lapack_int lda,
-                           const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_dsytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const double* a, lapack_int lda,
-                           const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_csytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zsytrs( int matrix_order, char uplo, lapack_int n,
-                           lapack_int nrhs, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_int* ipiv,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd, const float* ab,
-                           lapack_int ldab, float* rcond );
-lapack_int LAPACKE_dtbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd, const double* ab,
-                           lapack_int ldab, double* rcond );
-lapack_int LAPACKE_ctbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           float* rcond );
-lapack_int LAPACKE_ztbcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, lapack_int kd,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           double* rcond );
-
-lapack_int LAPACKE_stbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const float* ab, lapack_int ldab, const float* b,
-                           lapack_int ldb, const float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_dtbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const double* ab, lapack_int ldab, const double* b,
-                           lapack_int ldb, const double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-lapack_int LAPACKE_ctbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_ztbrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_stbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const float* ab, lapack_int ldab, float* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_dtbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const double* ab, lapack_int ldab, double* b,
-                           lapack_int ldb );
-lapack_int LAPACKE_ctbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_float* ab, lapack_int ldab,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztbtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int kd, lapack_int nrhs,
-                           const lapack_complex_double* ab, lapack_int ldab,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          float alpha, const float* a, float* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_dtfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          double alpha, const double* a, double* b,
-                          lapack_int ldb );
-lapack_int LAPACKE_ctfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          lapack_complex_float alpha,
-                          const lapack_complex_float* a,
-                          lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztfsm( int matrix_order, char transr, char side, char uplo,
-                          char trans, char diag, lapack_int m, lapack_int n,
-                          lapack_complex_double alpha,
-                          const lapack_complex_double* a,
-                          lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, float* a );
-lapack_int LAPACKE_dtftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, double* a );
-lapack_int LAPACKE_ctftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_ztftri( int matrix_order, char transr, char uplo, char diag,
-                           lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_stfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* arf, float* ap );
-lapack_int LAPACKE_dtfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* arf, double* ap );
-lapack_int LAPACKE_ctfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* arf,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_ztfttp( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* arf,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_stfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* arf, float* a,
-                           lapack_int lda );
-lapack_int LAPACKE_dtfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* arf, double* a,
-                           lapack_int lda );
-lapack_int LAPACKE_ctfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* arf,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztfttr( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* arf,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_stgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const float* s, lapack_int lds, const float* p,
-                           lapack_int ldp, float* vl, lapack_int ldvl,
-                           float* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_dtgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const double* s, lapack_int lds, const double* p,
-                           lapack_int ldp, double* vl, lapack_int ldvl,
-                           double* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_ctgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* s, lapack_int lds,
-                           const lapack_complex_float* p, lapack_int ldp,
-                           lapack_complex_float* vl, lapack_int ldvl,
-                           lapack_complex_float* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztgevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* s, lapack_int lds,
-                           const lapack_complex_double* p, lapack_int ldp,
-                           lapack_complex_double* vl, lapack_int ldvl,
-                           lapack_complex_double* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-
-lapack_int LAPACKE_stgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb, float* q,
-                           lapack_int ldq, float* z, lapack_int ldz,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_dtgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n, double* a,
-                           lapack_int lda, double* b, lapack_int ldb, double* q,
-                           lapack_int ldq, double* z, lapack_int ldz,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_ctgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* z, lapack_int ldz,
-                           lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztgexc( int matrix_order, lapack_logical wantq,
-                           lapack_logical wantz, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_stgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n, float* a,
-                           lapack_int lda, float* b, lapack_int ldb,
-                           float* alphar, float* alphai, float* beta, float* q,
-                           lapack_int ldq, float* z, lapack_int ldz,
-                           lapack_int* m, float* pl, float* pr, float* dif );
-lapack_int LAPACKE_dtgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n,
-                           double* a, lapack_int lda, double* b, lapack_int ldb,
-                           double* alphar, double* alphai, double* beta,
-                           double* q, lapack_int ldq, double* z, lapack_int ldz,
-                           lapack_int* m, double* pl, double* pr, double* dif );
-lapack_int LAPACKE_ctgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* alpha,
-                           lapack_complex_float* beta, lapack_complex_float* q,
-                           lapack_int ldq, lapack_complex_float* z,
-                           lapack_int ldz, lapack_int* m, float* pl, float* pr,
-                           float* dif );
-lapack_int LAPACKE_ztgsen( int matrix_order, lapack_int ijob,
-                           lapack_logical wantq, lapack_logical wantz,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* alpha,
-                           lapack_complex_double* beta,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* z, lapack_int ldz,
-                           lapack_int* m, double* pl, double* pr, double* dif );
-
-lapack_int LAPACKE_stgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, float* a, lapack_int lda,
-                           float* b, lapack_int ldb, float tola, float tolb,
-                           float* alpha, float* beta, float* u, lapack_int ldu,
-                           float* v, lapack_int ldv, float* q, lapack_int ldq,
-                           lapack_int* ncycle );
-lapack_int LAPACKE_dtgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, double* a,
-                           lapack_int lda, double* b, lapack_int ldb,
-                           double tola, double tolb, double* alpha,
-                           double* beta, double* u, lapack_int ldu, double* v,
-                           lapack_int ldv, double* q, lapack_int ldq,
-                           lapack_int* ncycle );
-lapack_int LAPACKE_ctgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* b,
-                           lapack_int ldb, float tola, float tolb, float* alpha,
-                           float* beta, lapack_complex_float* u, lapack_int ldu,
-                           lapack_complex_float* v, lapack_int ldv,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_int* ncycle );
-lapack_int LAPACKE_ztgsja( int matrix_order, char jobu, char jobv, char jobq,
-                           lapack_int m, lapack_int p, lapack_int n,
-                           lapack_int k, lapack_int l, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* b,
-                           lapack_int ldb, double tola, double tolb,
-                           double* alpha, double* beta,
-                           lapack_complex_double* u, lapack_int ldu,
-                           lapack_complex_double* v, lapack_int ldv,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_int* ncycle );
-
-lapack_int LAPACKE_stgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const float* a, lapack_int lda, const float* b,
-                           lapack_int ldb, const float* vl, lapack_int ldvl,
-                           const float* vr, lapack_int ldvr, float* s,
-                           float* dif, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_dtgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const double* a, lapack_int lda, const double* b,
-                           lapack_int ldb, const double* vl, lapack_int ldvl,
-                           const double* vr, lapack_int ldvr, double* s,
-                           double* dif, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ctgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* vl, lapack_int ldvl,
-                           const lapack_complex_float* vr, lapack_int ldvr,
-                           float* s, float* dif, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztgsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* vl, lapack_int ldvl,
-                           const lapack_complex_double* vr, lapack_int ldvr,
-                           double* s, double* dif, lapack_int mm,
-                           lapack_int* m );
-
-lapack_int LAPACKE_stgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n, const float* a,
-                           lapack_int lda, const float* b, lapack_int ldb,
-                           float* c, lapack_int ldc, const float* d,
-                           lapack_int ldd, const float* e, lapack_int lde,
-                           float* f, lapack_int ldf, float* scale, float* dif );
-lapack_int LAPACKE_dtgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n, const double* a,
-                           lapack_int lda, const double* b, lapack_int ldb,
-                           double* c, lapack_int ldc, const double* d,
-                           lapack_int ldd, const double* e, lapack_int lde,
-                           double* f, lapack_int ldf, double* scale,
-                           double* dif );
-lapack_int LAPACKE_ctgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* c, lapack_int ldc,
-                           const lapack_complex_float* d, lapack_int ldd,
-                           const lapack_complex_float* e, lapack_int lde,
-                           lapack_complex_float* f, lapack_int ldf,
-                           float* scale, float* dif );
-lapack_int LAPACKE_ztgsyl( int matrix_order, char trans, lapack_int ijob,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* c, lapack_int ldc,
-                           const lapack_complex_double* d, lapack_int ldd,
-                           const lapack_complex_double* e, lapack_int lde,
-                           lapack_complex_double* f, lapack_int ldf,
-                           double* scale, double* dif );
-
-lapack_int LAPACKE_stpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const float* ap, float* rcond );
-lapack_int LAPACKE_dtpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const double* ap, double* rcond );
-lapack_int LAPACKE_ctpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_float* ap,
-                           float* rcond );
-lapack_int LAPACKE_ztpcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_double* ap,
-                           double* rcond );
-
-lapack_int LAPACKE_stprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* ap,
-                           const float* b, lapack_int ldb, const float* x,
-                           lapack_int ldx, float* ferr, float* berr );
-lapack_int LAPACKE_dtprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* ap,
-                           const double* b, lapack_int ldb, const double* x,
-                           lapack_int ldx, double* ferr, double* berr );
-lapack_int LAPACKE_ctprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* ap,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_ztprfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* ap,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_stptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           float* ap );
-lapack_int LAPACKE_dtptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           double* ap );
-lapack_int LAPACKE_ctptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_ztptri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_stptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* ap,
-                           float* b, lapack_int ldb );
-lapack_int LAPACKE_dtptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* ap,
-                           double* b, lapack_int ldb );
-lapack_int LAPACKE_ctptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* ap,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztptrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* ap,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* ap, float* arf );
-lapack_int LAPACKE_dtpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* ap, double* arf );
-lapack_int LAPACKE_ctpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* ap,
-                           lapack_complex_float* arf );
-lapack_int LAPACKE_ztpttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* ap,
-                           lapack_complex_double* arf );
-
-lapack_int LAPACKE_stpttr( int matrix_order, char uplo, lapack_int n,
-                           const float* ap, float* a, lapack_int lda );
-lapack_int LAPACKE_dtpttr( int matrix_order, char uplo, lapack_int n,
-                           const double* ap, double* a, lapack_int lda );
-lapack_int LAPACKE_ctpttr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztpttr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_strcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const float* a, lapack_int lda,
-                           float* rcond );
-lapack_int LAPACKE_dtrcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const double* a, lapack_int lda,
-                           double* rcond );
-lapack_int LAPACKE_ctrcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda, float* rcond );
-lapack_int LAPACKE_ztrcon( int matrix_order, char norm, char uplo, char diag,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda, double* rcond );
-
-lapack_int LAPACKE_strevc( int matrix_order, char side, char howmny,
-                           lapack_logical* select, lapack_int n, const float* t,
-                           lapack_int ldt, float* vl, lapack_int ldvl,
-                           float* vr, lapack_int ldvr, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_dtrevc( int matrix_order, char side, char howmny,
-                           lapack_logical* select, lapack_int n,
-                           const double* t, lapack_int ldt, double* vl,
-                           lapack_int ldvl, double* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ctrevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* vl, lapack_int ldvl,
-                           lapack_complex_float* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztrevc( int matrix_order, char side, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* vl, lapack_int ldvl,
-                           lapack_complex_double* vr, lapack_int ldvr,
-                           lapack_int mm, lapack_int* m );
-
-lapack_int LAPACKE_strexc( int matrix_order, char compq, lapack_int n, float* t,
-                           lapack_int ldt, float* q, lapack_int ldq,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_dtrexc( int matrix_order, char compq, lapack_int n,
-                           double* t, lapack_int ldt, double* q, lapack_int ldq,
-                           lapack_int* ifst, lapack_int* ilst );
-lapack_int LAPACKE_ctrexc( int matrix_order, char compq, lapack_int n,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztrexc( int matrix_order, char compq, lapack_int n,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_strrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* a,
-                           lapack_int lda, const float* b, lapack_int ldb,
-                           const float* x, lapack_int ldx, float* ferr,
-                           float* berr );
-lapack_int LAPACKE_dtrrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* a,
-                           lapack_int lda, const double* b, lapack_int ldb,
-                           const double* x, lapack_int ldx, double* ferr,
-                           double* berr );
-lapack_int LAPACKE_ctrrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           const lapack_complex_float* x, lapack_int ldx,
-                           float* ferr, float* berr );
-lapack_int LAPACKE_ztrrfs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           const lapack_complex_double* x, lapack_int ldx,
-                           double* ferr, double* berr );
-
-lapack_int LAPACKE_strsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n, float* t,
-                           lapack_int ldt, float* q, lapack_int ldq, float* wr,
-                           float* wi, lapack_int* m, float* s, float* sep );
-lapack_int LAPACKE_dtrsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n,
-                           double* t, lapack_int ldt, double* q, lapack_int ldq,
-                           double* wr, double* wi, lapack_int* m, double* s,
-                           double* sep );
-lapack_int LAPACKE_ctrsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* q, lapack_int ldq,
-                           lapack_complex_float* w, lapack_int* m, float* s,
-                           float* sep );
-lapack_int LAPACKE_ztrsen( int matrix_order, char job, char compq,
-                           const lapack_logical* select, lapack_int n,
-                           lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* q, lapack_int ldq,
-                           lapack_complex_double* w, lapack_int* m, double* s,
-                           double* sep );
-
-lapack_int LAPACKE_strsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const float* t, lapack_int ldt, const float* vl,
-                           lapack_int ldvl, const float* vr, lapack_int ldvr,
-                           float* s, float* sep, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_dtrsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const double* t, lapack_int ldt, const double* vl,
-                           lapack_int ldvl, const double* vr, lapack_int ldvr,
-                           double* s, double* sep, lapack_int mm,
-                           lapack_int* m );
-lapack_int LAPACKE_ctrsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_float* t, lapack_int ldt,
-                           const lapack_complex_float* vl, lapack_int ldvl,
-                           const lapack_complex_float* vr, lapack_int ldvr,
-                           float* s, float* sep, lapack_int mm, lapack_int* m );
-lapack_int LAPACKE_ztrsna( int matrix_order, char job, char howmny,
-                           const lapack_logical* select, lapack_int n,
-                           const lapack_complex_double* t, lapack_int ldt,
-                           const lapack_complex_double* vl, lapack_int ldvl,
-                           const lapack_complex_double* vr, lapack_int ldvr,
-                           double* s, double* sep, lapack_int mm,
-                           lapack_int* m );
-
-lapack_int LAPACKE_strsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const float* a, lapack_int lda, const float* b,
-                           lapack_int ldb, float* c, lapack_int ldc,
-                           float* scale );
-lapack_int LAPACKE_dtrsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const double* a, lapack_int lda, const double* b,
-                           lapack_int ldb, double* c, lapack_int ldc,
-                           double* scale );
-lapack_int LAPACKE_ctrsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* b, lapack_int ldb,
-                           lapack_complex_float* c, lapack_int ldc,
-                           float* scale );
-lapack_int LAPACKE_ztrsyl( int matrix_order, char trana, char tranb,
-                           lapack_int isgn, lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* c, lapack_int ldc,
-                           double* scale );
-
-lapack_int LAPACKE_strtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           float* a, lapack_int lda );
-lapack_int LAPACKE_dtrtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           double* a, lapack_int lda );
-lapack_int LAPACKE_ctrtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztrtri( int matrix_order, char uplo, char diag, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_strtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const float* a,
-                           lapack_int lda, float* b, lapack_int ldb );
-lapack_int LAPACKE_dtrtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs, const double* a,
-                           lapack_int lda, double* b, lapack_int ldb );
-lapack_int LAPACKE_ctrtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztrtrs( int matrix_order, char uplo, char trans, char diag,
-                           lapack_int n, lapack_int nrhs,
-                           const lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_strttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const float* a, lapack_int lda,
-                           float* arf );
-lapack_int LAPACKE_dtrttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const double* a, lapack_int lda,
-                           double* arf );
-lapack_int LAPACKE_ctrttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* arf );
-lapack_int LAPACKE_ztrttf( int matrix_order, char transr, char uplo,
-                           lapack_int n, const lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* arf );
-
-lapack_int LAPACKE_strttp( int matrix_order, char uplo, lapack_int n,
-                           const float* a, lapack_int lda, float* ap );
-lapack_int LAPACKE_dtrttp( int matrix_order, char uplo, lapack_int n,
-                           const double* a, lapack_int lda, double* ap );
-lapack_int LAPACKE_ctrttp( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* ap );
-lapack_int LAPACKE_ztrttp( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* ap );
-
-lapack_int LAPACKE_stzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           float* a, lapack_int lda, float* tau );
-lapack_int LAPACKE_dtzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           double* a, lapack_int lda, double* tau );
-lapack_int LAPACKE_ctzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* tau );
-lapack_int LAPACKE_ztzrzf( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungbr( int matrix_order, char vect, lapack_int m,
-                           lapack_int n, lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cunghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zunghr( int matrix_order, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cunglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zunglq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungql( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungqr( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau );
-lapack_int LAPACKE_zungrq( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cungtr( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau );
-lapack_int LAPACKE_zungtr( int matrix_order, char uplo, lapack_int n,
-                           lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau );
-
-lapack_int LAPACKE_cunmbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmbr( int matrix_order, char vect, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmhr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int ilo,
-                           lapack_int ihi, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmlq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmql( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmqr( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmrq( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const lapack_complex_float* a,
-                           lapack_int lda, const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmrz( int matrix_order, char side, char trans,
-                           lapack_int m, lapack_int n, lapack_int k,
-                           lapack_int l, const lapack_complex_double* a,
-                           lapack_int lda, const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cunmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_float* a, lapack_int lda,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zunmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_double* a, lapack_int lda,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_cupgtr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* q, lapack_int ldq );
-lapack_int LAPACKE_zupgtr( int matrix_order, char uplo, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* q, lapack_int ldq );
-
-lapack_int LAPACKE_cupmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_float* ap,
-                           const lapack_complex_float* tau,
-                           lapack_complex_float* c, lapack_int ldc );
-lapack_int LAPACKE_zupmtr( int matrix_order, char side, char uplo, char trans,
-                           lapack_int m, lapack_int n,
-                           const lapack_complex_double* ap,
-                           const lapack_complex_double* tau,
-                           lapack_complex_double* c, lapack_int ldc );
-
-lapack_int LAPACKE_sbdsdc_work( int matrix_order, char uplo, char compq,
-                                lapack_int n, float* d, float* e, float* u,
-                                lapack_int ldu, float* vt, lapack_int ldvt,
-                                float* q, lapack_int* iq, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dbdsdc_work( int matrix_order, char uplo, char compq,
-                                lapack_int n, double* d, double* e, double* u,
-                                lapack_int ldu, double* vt, lapack_int ldvt,
-                                double* q, lapack_int* iq, double* work,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                float* d, float* e, float* vt, lapack_int ldvt,
-                                float* u, lapack_int ldu, float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_dbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                double* d, double* e, double* vt,
-                                lapack_int ldvt, double* u, lapack_int ldu,
-                                double* c, lapack_int ldc, double* work );
-lapack_int LAPACKE_cbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                float* d, float* e, lapack_complex_float* vt,
-                                lapack_int ldvt, lapack_complex_float* u,
-                                lapack_int ldu, lapack_complex_float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_zbdsqr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
-                                double* d, double* e, lapack_complex_double* vt,
-                                lapack_int ldvt, lapack_complex_double* u,
-                                lapack_int ldu, lapack_complex_double* c,
-                                lapack_int ldc, double* work );
-
-lapack_int LAPACKE_sdisna_work( char job, lapack_int m, lapack_int n,
-                                const float* d, float* sep );
-lapack_int LAPACKE_ddisna_work( char job, lapack_int m, lapack_int n,
-                                const double* d, double* sep );
-
-lapack_int LAPACKE_sgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, float* ab, lapack_int ldab,
-                                float* d, float* e, float* q, lapack_int ldq,
-                                float* pt, lapack_int ldpt, float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_dgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, double* ab, lapack_int ldab,
-                                double* d, double* e, double* q, lapack_int ldq,
-                                double* pt, lapack_int ldpt, double* c,
-                                lapack_int ldc, double* work );
-lapack_int LAPACKE_cgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, lapack_complex_float* ab,
-                                lapack_int ldab, float* d, float* e,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* pt, lapack_int ldpt,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgbbrd_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int ncc, lapack_int kl,
-                                lapack_int ku, lapack_complex_double* ab,
-                                lapack_int ldab, double* d, double* e,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* pt, lapack_int ldpt,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                double anorm, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zgbcon_work( int matrix_order, char norm, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* ab,
-                                lapack_int ldab, float* r, float* c,
-                                float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* ab,
-                                lapack_int ldab, double* r, double* c,
-                                double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                float* r, float* c, float* rowcnd,
-                                float* colcnd, float* amax );
-lapack_int LAPACKE_zgbequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, double* r, double* c,
-                                double* rowcnd, double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku, const float* ab,
-                                 lapack_int ldab, float* r, float* c,
-                                 float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_dgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku, const double* ab,
-                                 lapack_int ldab, double* r, double* c,
-                                 double* rowcnd, double* colcnd, double* amax );
-lapack_int LAPACKE_cgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku,
-                                 const lapack_complex_float* ab,
-                                 lapack_int ldab, float* r, float* c,
-                                 float* rowcnd, float* colcnd, float* amax );
-lapack_int LAPACKE_zgbequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_int kl, lapack_int ku,
-                                 const lapack_complex_double* ab,
-                                 lapack_int ldab, double* r, double* c,
-                                 double* rowcnd, double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const float* ab, lapack_int ldab,
-                                const float* afb, lapack_int ldafb,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const double* ab, lapack_int ldab,
-                                const double* afb, lapack_int ldafb,
-                                const lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_complex_float* afb,
-                                lapack_int ldafb, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgbrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab,
-                                const lapack_complex_double* afb,
-                                lapack_int ldafb, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, const float* ab,
-                                 lapack_int ldab, const float* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const float* r, const float* c, const float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, const double* ab,
-                                 lapack_int ldab, const double* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs,
-                                 const lapack_complex_float* ab,
-                                 lapack_int ldab,
-                                 const lapack_complex_float* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const float* r, const float* c,
-                                 const lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zgbrfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs,
-                                 const lapack_complex_double* ab,
-                                 lapack_int ldab,
-                                 const lapack_complex_double* afb,
-                                 lapack_int ldafb, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs, float* ab,
-                               lapack_int ldab, lapack_int* ipiv, float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs, double* ab,
-                               lapack_int ldab, lapack_int* ipiv, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
-                               lapack_int ku, lapack_int nrhs,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-
-lapack_int LAPACKE_sgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, float* ab, lapack_int ldab,
-                                float* afb, lapack_int ldafb, lapack_int* ipiv,
-                                char* equed, float* r, float* c, float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, double* ab, lapack_int ldab,
-                                double* afb, lapack_int ldafb, lapack_int* ipiv,
-                                char* equed, double* r, double* c, double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, lapack_complex_float* ab,
-                                lapack_int ldab, lapack_complex_float* afb,
-                                lapack_int ldafb, lapack_int* ipiv, char* equed,
-                                float* r, float* c, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zgbsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int kl, lapack_int ku,
-                                lapack_int nrhs, lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* afb,
-                                lapack_int ldafb, lapack_int* ipiv, char* equed,
-                                double* r, double* c, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_sgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, float* ab, lapack_int ldab,
-                                 float* afb, lapack_int ldafb, lapack_int* ipiv,
-                                 char* equed, float* r, float* c, float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, double* ab, lapack_int ldab,
-                                 double* afb, lapack_int ldafb,
-                                 lapack_int* ipiv, char* equed, double* r,
-                                 double* c, double* b, lapack_int ldb,
-                                 double* x, lapack_int ldx, double* rcond,
-                                 double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, lapack_complex_float* ab,
-                                 lapack_int ldab, lapack_complex_float* afb,
-                                 lapack_int ldafb, lapack_int* ipiv,
-                                 char* equed, float* r, float* c,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zgbsvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int kl, lapack_int ku,
-                                 lapack_int nrhs, lapack_complex_double* ab,
-                                 lapack_int ldab, lapack_complex_double* afb,
-                                 lapack_int ldafb, lapack_int* ipiv,
-                                 char* equed, double* r, double* c,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, float* ab,
-                                lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_dgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, double* ab,
-                                lapack_int ldab, lapack_int* ipiv );
-lapack_int LAPACKE_cgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_int* ipiv );
-lapack_int LAPACKE_zgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_int* ipiv );
-
-lapack_int LAPACKE_sgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const float* ab, lapack_int ldab,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const double* ab, lapack_int ldab,
-                                const lapack_int* ipiv, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zgbtrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int kl, lapack_int ku, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* scale, lapack_int m, float* v,
-                                lapack_int ldv );
-lapack_int LAPACKE_dgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* scale, lapack_int m, double* v,
-                                lapack_int ldv );
-lapack_int LAPACKE_cgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* scale, lapack_int m,
-                                lapack_complex_float* v, lapack_int ldv );
-lapack_int LAPACKE_zgebak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* scale, lapack_int m,
-                                lapack_complex_double* v, lapack_int ldv );
-
-lapack_int LAPACKE_sgebal_work( int matrix_order, char job, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ilo,
-                                lapack_int* ihi, float* scale );
-lapack_int LAPACKE_dgebal_work( int matrix_order, char job, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ilo,
-                                lapack_int* ihi, double* scale );
-lapack_int LAPACKE_cgebal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ilo, lapack_int* ihi,
-                                float* scale );
-lapack_int LAPACKE_zgebal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ilo, lapack_int* ihi,
-                                double* scale );
-
-lapack_int LAPACKE_sgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* d, float* e,
-                                float* tauq, float* taup, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* d, double* e,
-                                double* tauq, double* taup, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_cgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float* d, float* e, lapack_complex_float* tauq,
-                                lapack_complex_float* taup,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgebrd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double* d, double* e,
-                                lapack_complex_double* tauq,
-                                lapack_complex_double* taup,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgecon_work( int matrix_order, char norm, lapack_int n,
-                                const float* a, lapack_int lda, float anorm,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgecon_work( int matrix_order, char norm, lapack_int n,
-                                const double* a, lapack_int lda, double anorm,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgecon_work( int matrix_order, char norm, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float anorm, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgecon_work( int matrix_order, char norm, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const float* a, lapack_int lda, float* r,
-                                float* c, float* rowcnd, float* colcnd,
-                                float* amax );
-lapack_int LAPACKE_dgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda, double* r,
-                                double* c, double* rowcnd, double* colcnd,
-                                double* amax );
-lapack_int LAPACKE_cgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* r, float* c, float* rowcnd,
-                                float* colcnd, float* amax );
-lapack_int LAPACKE_zgeequ_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* r, double* c, double* rowcnd,
-                                double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const float* a, lapack_int lda, float* r,
-                                 float* c, float* rowcnd, float* colcnd,
-                                 float* amax );
-lapack_int LAPACKE_dgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const double* a, lapack_int lda, double* r,
-                                 double* c, double* rowcnd, double* colcnd,
-                                 double* amax );
-lapack_int LAPACKE_cgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* r, float* c, float* rowcnd,
-                                 float* colcnd, float* amax );
-lapack_int LAPACKE_zgeequb_work( int matrix_order, lapack_int m, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* r, double* c, double* rowcnd,
-                                 double* colcnd, double* amax );
-
-lapack_int LAPACKE_sgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_S_SELECT2 select, lapack_int n, float* a,
-                               lapack_int lda, lapack_int* sdim, float* wr,
-                               float* wi, float* vs, lapack_int ldvs,
-                               float* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_dgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_D_SELECT2 select, lapack_int n, double* a,
-                               lapack_int lda, lapack_int* sdim, double* wr,
-                               double* wi, double* vs, lapack_int ldvs,
-                               double* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_cgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_C_SELECT1 select, lapack_int n,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_int* sdim, lapack_complex_float* w,
-                               lapack_complex_float* vs, lapack_int ldvs,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork, lapack_logical* bwork );
-lapack_int LAPACKE_zgees_work( int matrix_order, char jobvs, char sort,
-                               LAPACK_Z_SELECT1 select, lapack_int n,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_int* sdim, lapack_complex_double* w,
-                               lapack_complex_double* vs, lapack_int ldvs,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_S_SELECT2 select, char sense,
-                                lapack_int n, float* a, lapack_int lda,
-                                lapack_int* sdim, float* wr, float* wi,
-                                float* vs, lapack_int ldvs, float* rconde,
-                                float* rcondv, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_dgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_D_SELECT2 select, char sense,
-                                lapack_int n, double* a, lapack_int lda,
-                                lapack_int* sdim, double* wr, double* wi,
-                                double* vs, lapack_int ldvs, double* rconde,
-                                double* rcondv, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_cgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_C_SELECT1 select, char sense,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_int* sdim,
-                                lapack_complex_float* w,
-                                lapack_complex_float* vs, lapack_int ldvs,
-                                float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_logical* bwork );
-lapack_int LAPACKE_zgeesx_work( int matrix_order, char jobvs, char sort,
-                                LAPACK_Z_SELECT1 select, char sense,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_int* sdim,
-                                lapack_complex_double* w,
-                                lapack_complex_double* vs, lapack_int ldvs,
-                                double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, float* a, lapack_int lda,
-                               float* wr, float* wi, float* vl, lapack_int ldvl,
-                               float* vr, lapack_int ldvr, float* work,
-                               lapack_int lwork );
-lapack_int LAPACKE_dgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, double* a, lapack_int lda,
-                               double* wr, double* wi, double* vl,
-                               lapack_int ldvl, double* vr, lapack_int ldvr,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* w,
-                               lapack_complex_float* vl, lapack_int ldvl,
-                               lapack_complex_float* vr, lapack_int ldvr,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zgeev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_double* a,
-                               lapack_int lda, lapack_complex_double* w,
-                               lapack_complex_double* vl, lapack_int ldvl,
-                               lapack_complex_double* vr, lapack_int ldvr,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork );
-
-lapack_int LAPACKE_sgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, float* a,
-                                lapack_int lda, float* wr, float* wi, float* vl,
-                                lapack_int ldvl, float* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, float* scale,
-                                float* abnrm, float* rconde, float* rcondv,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, double* a,
-                                lapack_int lda, double* wr, double* wi,
-                                double* vl, lapack_int ldvl, double* vr,
-                                lapack_int ldvr, lapack_int* ilo,
-                                lapack_int* ihi, double* scale, double* abnrm,
-                                double* rconde, double* rcondv, double* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_cgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* w,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, float* scale,
-                                float* abnrm, float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork );
-lapack_int LAPACKE_zgeevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* w,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, double* scale,
-                                double* abnrm, double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_sgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, float* a, lapack_int lda,
-                                float* tau, float* work, lapack_int lwork );
-lapack_int LAPACKE_dgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, double* a, lapack_int lda,
-                                double* tau, double* work, lapack_int lwork );
-lapack_int LAPACKE_cgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgejsv_work( int matrix_order, char joba, char jobu,
-                                char jobv, char jobr, char jobt, char jobp,
-                                lapack_int m, lapack_int n, float* a,
-                                lapack_int lda, float* sva, float* u,
-                                lapack_int ldu, float* v, lapack_int ldv,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgejsv_work( int matrix_order, char joba, char jobu,
-                                char jobv, char jobr, char jobt, char jobp,
-                                lapack_int m, lapack_int n, double* a,
-                                lapack_int lda, double* sva, double* u,
-                                lapack_int ldu, double* v, lapack_int ldv,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work );
-lapack_int LAPACKE_dgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work );
-lapack_int LAPACKE_cgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zgelq2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_sgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgelqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs, float* a,
-                               lapack_int lda, float* b, lapack_int ldb,
-                               float* work, lapack_int lwork );
-lapack_int LAPACKE_dgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs, double* a,
-                               lapack_int lda, double* b, lapack_int ldb,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_cgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgels_work( int matrix_order, char trans, lapack_int m,
-                               lapack_int n, lapack_int nrhs,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* s,
-                                double rcond, lapack_int* rank, double* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_cgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_zgelsd_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, double* s, double rcond,
-                                lapack_int* rank, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* s,
-                                double rcond, lapack_int* rank, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_cgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, float* s, float rcond,
-                                lapack_int* rank, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zgelss_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, double* s, double rcond,
-                                lapack_int* rank, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_sgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, lapack_int* jpvt,
-                                float rcond, lapack_int* rank, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, lapack_int* jpvt,
-                                double rcond, lapack_int* rank, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_cgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_int* jpvt, float rcond,
-                                lapack_int* rank, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zgelsy_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_int* jpvt, double rcond,
-                                lapack_int* rank, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_sgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* jpvt,
-                                float* tau, float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* jpvt,
-                                double* tau, double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork );
-lapack_int LAPACKE_zgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_sgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* jpvt,
-                                float* tau, float* work );
-lapack_int LAPACKE_dgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* jpvt,
-                                double* tau, double* work );
-lapack_int LAPACKE_cgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_float* tau,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* jpvt, lapack_complex_double* tau,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work );
-lapack_int LAPACKE_dgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work );
-lapack_int LAPACKE_cgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_sgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* tau,
-                                 float* work, lapack_int lwork );
-lapack_int LAPACKE_dgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* tau,
-                                 double* work, lapack_int lwork );
-lapack_int LAPACKE_cgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* tau,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* tau,
-                                 lapack_complex_double* work,
-                                 lapack_int lwork );
-
-lapack_int LAPACKE_sgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const float* af, lapack_int ldaf,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgerfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs, const float* a,
-                                 lapack_int lda, const float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* r, const float* c, const float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs, const double* a,
-                                 lapack_int lda, const double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* r, const float* c,
-                                 const lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zgerfsx_work( int matrix_order, char trans, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* r, const double* c,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgerqf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* s, float* u, lapack_int ldu, float* vt,
-                                lapack_int ldvt, float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* s, double* u, lapack_int ldu,
-                                double* vt, lapack_int ldvt, double* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_cgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, float* s,
-                                lapack_complex_float* u, lapack_int ldu,
-                                lapack_complex_float* vt, lapack_int ldvt,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int* iwork );
-lapack_int LAPACKE_zgesdd_work( int matrix_order, char jobz, lapack_int m,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, double* s,
-                                lapack_complex_double* u, lapack_int ldu,
-                                lapack_complex_double* vt, lapack_int ldvt,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int* iwork );
-
-lapack_int LAPACKE_sgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* a, lapack_int lda, lapack_int* ipiv,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* a, lapack_int lda, lapack_int* ipiv,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_cgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dsgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                double* a, lapack_int lda, lapack_int* ipiv,
-                                double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* work, float* swork,
-                                lapack_int* iter );
-lapack_int LAPACKE_zcgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, lapack_complex_double* work,
-                                lapack_complex_float* swork, double* rwork,
-                                lapack_int* iter );
-
-lapack_int LAPACKE_sgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n, float* a,
-                                lapack_int lda, float* s, float* u,
-                                lapack_int ldu, float* vt, lapack_int ldvt,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n, double* a,
-                                lapack_int lda, double* s, double* u,
-                                lapack_int ldu, double* vt, lapack_int ldvt,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float* s, lapack_complex_float* u,
-                                lapack_int ldu, lapack_complex_float* vt,
-                                lapack_int ldvt, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zgesvd_work( int matrix_order, char jobu, char jobvt,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double* s, lapack_complex_double* u,
-                                lapack_int ldu, lapack_complex_double* vt,
-                                lapack_int ldvt, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_sgesvj_work( int matrix_order, char joba, char jobu,
-                                char jobv, lapack_int m, lapack_int n, float* a,
-                                lapack_int lda, float* sva, lapack_int mv,
-                                float* v, lapack_int ldv, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dgesvj_work( int matrix_order, char joba, char jobu,
-                                char jobv, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* sva,
-                                lapack_int mv, double* v, lapack_int ldv,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, float* a,
-                                lapack_int lda, float* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, float* r,
-                                float* c, float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, double* a,
-                                lapack_int lda, double* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, double* r,
-                                double* c, double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, float* r,
-                                float* c, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zgesvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                lapack_int* ipiv, char* equed, double* r,
-                                double* c, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_sgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs, float* a,
-                                 lapack_int lda, float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* r,
-                                 float* c, float* b, lapack_int ldb, float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs, double* a,
-                                 lapack_int lda, double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* r,
-                                 double* c, double* b, lapack_int ldb,
-                                 double* x, lapack_int ldx, double* rcond,
-                                 double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* r,
-                                 float* c, lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params,
-                                 lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgesvxx_work( int matrix_order, char fact, char trans,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* r,
-                                 double* c, lapack_complex_double* b,
-                                 lapack_int ldb, lapack_complex_double* x,
-                                 lapack_int ldx, double* rcond, double* rpvgrw,
-                                 double* berr, lapack_int n_err_bnds,
-                                 double* err_bnds_norm, double* err_bnds_comp,
-                                 lapack_int nparams, double* params,
-                                 lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv );
-lapack_int LAPACKE_zgetf2_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_dgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ipiv );
-lapack_int LAPACKE_cgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv );
-lapack_int LAPACKE_zgetrf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv );
-
-lapack_int LAPACKE_sgetri_work( int matrix_order, lapack_int n, float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dgetri_work( int matrix_order, lapack_int n, double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_cgetri_work( int matrix_order, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgetri_work( int matrix_order, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_cgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgetrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* lscale, const float* rscale,
-                                lapack_int m, float* v, lapack_int ldv );
-lapack_int LAPACKE_dggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* lscale, const double* rscale,
-                                lapack_int m, double* v, lapack_int ldv );
-lapack_int LAPACKE_cggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const float* lscale, const float* rscale,
-                                lapack_int m, lapack_complex_float* v,
-                                lapack_int ldv );
-lapack_int LAPACKE_zggbak_work( int matrix_order, char job, char side,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                const double* lscale, const double* rscale,
-                                lapack_int m, lapack_complex_double* v,
-                                lapack_int ldv );
-
-lapack_int LAPACKE_sggbal_work( int matrix_order, char job, lapack_int n,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, lapack_int* ilo,
-                                lapack_int* ihi, float* lscale, float* rscale,
-                                float* work );
-lapack_int LAPACKE_dggbal_work( int matrix_order, char job, lapack_int n,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, lapack_int* ilo,
-                                lapack_int* ihi, double* lscale, double* rscale,
-                                double* work );
-lapack_int LAPACKE_cggbal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_int* ilo, lapack_int* ihi, float* lscale,
-                                float* rscale, float* work );
-lapack_int LAPACKE_zggbal_work( int matrix_order, char job, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_int* ilo, lapack_int* ihi,
-                                double* lscale, double* rscale, double* work );
-
-lapack_int LAPACKE_sgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_S_SELECT3 selctg, lapack_int n,
-                               float* a, lapack_int lda, float* b,
-                               lapack_int ldb, lapack_int* sdim, float* alphar,
-                               float* alphai, float* beta, float* vsl,
-                               lapack_int ldvsl, float* vsr, lapack_int ldvsr,
-                               float* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_dgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_D_SELECT3 selctg, lapack_int n,
-                               double* a, lapack_int lda, double* b,
-                               lapack_int ldb, lapack_int* sdim, double* alphar,
-                               double* alphai, double* beta, double* vsl,
-                               lapack_int ldvsl, double* vsr, lapack_int ldvsr,
-                               double* work, lapack_int lwork,
-                               lapack_logical* bwork );
-lapack_int LAPACKE_cgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_C_SELECT2 selctg, lapack_int n,
-                               lapack_complex_float* a, lapack_int lda,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_int* sdim, lapack_complex_float* alpha,
-                               lapack_complex_float* beta,
-                               lapack_complex_float* vsl, lapack_int ldvsl,
-                               lapack_complex_float* vsr, lapack_int ldvsr,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork, lapack_logical* bwork );
-lapack_int LAPACKE_zgges_work( int matrix_order, char jobvsl, char jobvsr,
-                               char sort, LAPACK_Z_SELECT2 selctg, lapack_int n,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_int* sdim, lapack_complex_double* alpha,
-                               lapack_complex_double* beta,
-                               lapack_complex_double* vsl, lapack_int ldvsl,
-                               lapack_complex_double* vsr, lapack_int ldvsr,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_S_SELECT3 selctg, char sense,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, lapack_int* sdim,
-                                float* alphar, float* alphai, float* beta,
-                                float* vsl, lapack_int ldvsl, float* vsr,
-                                lapack_int ldvsr, float* rconde, float* rcondv,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_dggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_D_SELECT3 selctg, char sense,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, lapack_int* sdim,
-                                double* alphar, double* alphai, double* beta,
-                                double* vsl, lapack_int ldvsl, double* vsr,
-                                lapack_int ldvsr, double* rconde,
-                                double* rcondv, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_cggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_C_SELECT2 selctg, char sense,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_int* sdim,
-                                lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* vsl, lapack_int ldvsl,
-                                lapack_complex_float* vsr, lapack_int ldvsr,
-                                float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int* iwork,
-                                lapack_int liwork, lapack_logical* bwork );
-lapack_int LAPACKE_zggesx_work( int matrix_order, char jobvsl, char jobvsr,
-                                char sort, LAPACK_Z_SELECT2 selctg, char sense,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_int* sdim,
-                                lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* vsl, lapack_int ldvsl,
-                                lapack_complex_double* vsr, lapack_int ldvsr,
-                                double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int* iwork,
-                                lapack_int liwork, lapack_logical* bwork );
-
-lapack_int LAPACKE_sggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, float* a, lapack_int lda, float* b,
-                               lapack_int ldb, float* alphar, float* alphai,
-                               float* beta, float* vl, lapack_int ldvl,
-                               float* vr, lapack_int ldvr, float* work,
-                               lapack_int lwork );
-lapack_int LAPACKE_dggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, double* a, lapack_int lda,
-                               double* b, lapack_int ldb, double* alphar,
-                               double* alphai, double* beta, double* vl,
-                               lapack_int ldvl, double* vr, lapack_int ldvr,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_cggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* b,
-                               lapack_int ldb, lapack_complex_float* alpha,
-                               lapack_complex_float* beta,
-                               lapack_complex_float* vl, lapack_int ldvl,
-                               lapack_complex_float* vr, lapack_int ldvr,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zggev_work( int matrix_order, char jobvl, char jobvr,
-                               lapack_int n, lapack_complex_double* a,
-                               lapack_int lda, lapack_complex_double* b,
-                               lapack_int ldb, lapack_complex_double* alpha,
-                               lapack_complex_double* beta,
-                               lapack_complex_double* vl, lapack_int ldvl,
-                               lapack_complex_double* vr, lapack_int ldvr,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork );
-
-lapack_int LAPACKE_sggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float* alphar, float* alphai, float* beta,
-                                float* vl, lapack_int ldvl, float* vr,
-                                lapack_int ldvr, lapack_int* ilo,
-                                lapack_int* ihi, float* lscale, float* rscale,
-                                float* abnrm, float* bbnrm, float* rconde,
-                                float* rcondv, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_logical* bwork );
-lapack_int LAPACKE_dggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* alphar, double* alphai, double* beta,
-                                double* vl, lapack_int ldvl, double* vr,
-                                lapack_int ldvr, lapack_int* ilo,
-                                lapack_int* ihi, double* lscale, double* rscale,
-                                double* abnrm, double* bbnrm, double* rconde,
-                                double* rcondv, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_logical* bwork );
-lapack_int LAPACKE_cggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi, float* lscale,
-                                float* rscale, float* abnrm, float* bbnrm,
-                                float* rconde, float* rcondv,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int* iwork,
-                                lapack_logical* bwork );
-lapack_int LAPACKE_zggevx_work( int matrix_order, char balanc, char jobvl,
-                                char jobvr, char sense, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int* ilo, lapack_int* ihi,
-                                double* lscale, double* rscale, double* abnrm,
-                                double* bbnrm, double* rconde, double* rcondv,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int* iwork,
-                                lapack_logical* bwork );
-
-lapack_int LAPACKE_sggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* d, float* x,
-                                float* y, float* work, lapack_int lwork );
-lapack_int LAPACKE_dggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* d, double* x,
-                                double* y, double* work, lapack_int lwork );
-lapack_int LAPACKE_cggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* d,
-                                lapack_complex_float* x,
-                                lapack_complex_float* y,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zggglm_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* d,
-                                lapack_complex_double* x,
-                                lapack_complex_double* y,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float* q, lapack_int ldq,
-                                float* z, lapack_int ldz );
-lapack_int LAPACKE_dgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double* q, lapack_int ldq,
-                                double* z, lapack_int ldz );
-lapack_int LAPACKE_cgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz );
-lapack_int LAPACKE_zgghrd_work( int matrix_order, char compq, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz );
-
-lapack_int LAPACKE_sgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float* c, float* d,
-                                float* x, float* work, lapack_int lwork );
-lapack_int LAPACKE_dgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* c, double* d,
-                                double* x, double* work, lapack_int lwork );
-lapack_int LAPACKE_cgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* c,
-                                lapack_complex_float* d,
-                                lapack_complex_float* x,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zgglse_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* c,
-                                lapack_complex_double* d,
-                                lapack_complex_double* x,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, float* a, lapack_int lda,
-                                float* taua, float* b, lapack_int ldb,
-                                float* taub, float* work, lapack_int lwork );
-lapack_int LAPACKE_dggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, double* a, lapack_int lda,
-                                double* taua, double* b, lapack_int ldb,
-                                double* taub, double* work, lapack_int lwork );
-lapack_int LAPACKE_cggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* taua,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* taub,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zggqrf_work( int matrix_order, lapack_int n, lapack_int m,
-                                lapack_int p, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* taua,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* taub,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* taua, float* b, lapack_int ldb,
-                                float* taub, float* work, lapack_int lwork );
-lapack_int LAPACKE_dggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* taua, double* b, lapack_int ldb,
-                                double* taub, double* work, lapack_int lwork );
-lapack_int LAPACKE_cggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* taua,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* taub,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zggrqf_work( int matrix_order, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* taua,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* taub,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float* alpha, float* beta,
-                                float* u, lapack_int ldu, float* v,
-                                lapack_int ldv, float* q, lapack_int ldq,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double* alpha, double* beta,
-                                double* u, lapack_int ldu, double* v,
-                                lapack_int ldv, double* q, lapack_int ldq,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float* alpha, float* beta,
-                                lapack_complex_float* u, lapack_int ldu,
-                                lapack_complex_float* v, lapack_int ldv,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_zggsvd_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int n,
-                                lapack_int p, lapack_int* k, lapack_int* l,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double* alpha, double* beta,
-                                lapack_complex_double* u, lapack_int ldu,
-                                lapack_complex_double* v, lapack_int ldv,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_sggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, float tola,
-                                float tolb, lapack_int* k, lapack_int* l,
-                                float* u, lapack_int ldu, float* v,
-                                lapack_int ldv, float* q, lapack_int ldq,
-                                lapack_int* iwork, float* tau, float* work );
-lapack_int LAPACKE_dggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double tola,
-                                double tolb, lapack_int* k, lapack_int* l,
-                                double* u, lapack_int ldu, double* v,
-                                lapack_int ldv, double* q, lapack_int ldq,
-                                lapack_int* iwork, double* tau, double* work );
-lapack_int LAPACKE_cggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb, float tola, float tolb,
-                                lapack_int* k, lapack_int* l,
-                                lapack_complex_float* u, lapack_int ldu,
-                                lapack_complex_float* v, lapack_int ldv,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_int* iwork, float* rwork,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zggsvp_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, double tola, double tolb,
-                                lapack_int* k, lapack_int* l,
-                                lapack_complex_double* u, lapack_int ldu,
-                                lapack_complex_double* v, lapack_int ldv,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_int* iwork, double* rwork,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_sgtcon_work( char norm, lapack_int n, const float* dl,
-                                const float* d, const float* du,
-                                const float* du2, const lapack_int* ipiv,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgtcon_work( char norm, lapack_int n, const double* dl,
-                                const double* d, const double* du,
-                                const double* du2, const lapack_int* ipiv,
-                                double anorm, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgtcon_work( char norm, lapack_int n,
-                                const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                const lapack_complex_float* du2,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zgtcon_work( char norm, lapack_int n,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                const lapack_complex_double* du2,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_sgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* dl,
-                                const float* d, const float* du,
-                                const float* dlf, const float* df,
-                                const float* duf, const float* du2,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* dl,
-                                const double* d, const double* du,
-                                const double* dlf, const double* df,
-                                const double* duf, const double* du2,
-                                const lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                const lapack_complex_float* dlf,
-                                const lapack_complex_float* df,
-                                const lapack_complex_float* duf,
-                                const lapack_complex_float* du2,
-                                const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgtrfs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                const lapack_complex_double* dlf,
-                                const lapack_complex_double* df,
-                                const lapack_complex_double* duf,
-                                const lapack_complex_double* du2,
-                                const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* dl, float* d, float* du, float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* dl, double* d, double* du, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_float* dl,
-                               lapack_complex_float* d,
-                               lapack_complex_float* du,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               lapack_complex_double* dl,
-                               lapack_complex_double* d,
-                               lapack_complex_double* du,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, const float* dl,
-                                const float* d, const float* du, float* dlf,
-                                float* df, float* duf, float* du2,
-                                lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs, const double* dl,
-                                const double* d, const double* du, double* dlf,
-                                double* df, double* duf, double* du2,
-                                lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                lapack_complex_float* dlf,
-                                lapack_complex_float* df,
-                                lapack_complex_float* duf,
-                                lapack_complex_float* du2, lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zgtsvx_work( int matrix_order, char fact, char trans,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                lapack_complex_double* dlf,
-                                lapack_complex_double* df,
-                                lapack_complex_double* duf,
-                                lapack_complex_double* du2, lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sgttrf_work( lapack_int n, float* dl, float* d, float* du,
-                                float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_dgttrf_work( lapack_int n, double* dl, double* d, double* du,
-                                double* du2, lapack_int* ipiv );
-lapack_int LAPACKE_cgttrf_work( lapack_int n, lapack_complex_float* dl,
-                                lapack_complex_float* d,
-                                lapack_complex_float* du,
-                                lapack_complex_float* du2, lapack_int* ipiv );
-lapack_int LAPACKE_zgttrf_work( lapack_int n, lapack_complex_double* dl,
-                                lapack_complex_double* d,
-                                lapack_complex_double* du,
-                                lapack_complex_double* du2, lapack_int* ipiv );
-
-lapack_int LAPACKE_sgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const float* dl,
-                                const float* d, const float* du,
-                                const float* du2, const lapack_int* ipiv,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const double* dl,
-                                const double* d, const double* du,
-                                const double* du2, const lapack_int* ipiv,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_cgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* dl,
-                                const lapack_complex_float* d,
-                                const lapack_complex_float* du,
-                                const lapack_complex_float* du2,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zgttrs_work( int matrix_order, char trans, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* dl,
-                                const lapack_complex_double* d,
-                                const lapack_complex_double* du,
-                                const lapack_complex_double* du2,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               float* w, lapack_complex_float* z,
-                               lapack_int ldz, lapack_complex_float* work,
-                               float* rwork );
-lapack_int LAPACKE_zhbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               double* w, lapack_complex_double* z,
-                               lapack_int ldz, lapack_complex_double* work,
-                               double* rwork );
-
-lapack_int LAPACKE_chbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* q, lapack_int ldq,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                float* rwork, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_zhbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_complex_double* q, lapack_int ldq,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                double* rwork, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_chbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_complex_float* bb, lapack_int ldbb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                const lapack_complex_double* bb,
-                                lapack_int ldbb, lapack_complex_double* x,
-                                lapack_int ldx, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_chbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               lapack_complex_float* bb, lapack_int ldbb,
-                               float* w, lapack_complex_float* z,
-                               lapack_int ldz, lapack_complex_float* work,
-                               float* rwork );
-lapack_int LAPACKE_zhbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               lapack_complex_double* bb, lapack_int ldbb,
-                               double* w, lapack_complex_double* z,
-                               lapack_int ldz, lapack_complex_double* work,
-                               double* rwork );
-
-lapack_int LAPACKE_chbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* bb, lapack_int ldbb,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_complex_double* bb, lapack_int ldbb,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, lapack_complex_float* ab,
-                                lapack_int ldab, lapack_complex_float* bb,
-                                lapack_int ldbb, lapack_complex_float* q,
-                                lapack_int ldq, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* bb,
-                                lapack_int ldbb, lapack_complex_double* q,
-                                lapack_int ldq, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                float* d, float* e, lapack_complex_float* q,
-                                lapack_int ldq, lapack_complex_float* work );
-lapack_int LAPACKE_zhbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                double* d, double* e, lapack_complex_double* q,
-                                lapack_int ldq, lapack_complex_double* work );
-
-lapack_int LAPACKE_checon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zhecon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_cheequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* s, float* scond, float* amax,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_zheequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* s, double* scond, double* amax,
-                                 lapack_complex_double* work );
-
-lapack_int LAPACKE_cheev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, float* w,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zheev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_double* a,
-                               lapack_int lda, double* w,
-                               lapack_complex_double* work, lapack_int lwork,
-                               double* rwork );
-
-lapack_int LAPACKE_cheevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, float* w,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_zheevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, double* w,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_cheevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_int* isuppz,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_zheevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_int* isuppz,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_cheevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zheevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chegst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zhegst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_chegv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* b,
-                               lapack_int ldb, float* w,
-                               lapack_complex_float* work, lapack_int lwork,
-                               float* rwork );
-lapack_int LAPACKE_zhegv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n,
-                               lapack_complex_double* a, lapack_int lda,
-                               lapack_complex_double* b, lapack_int ldb,
-                               double* w, lapack_complex_double* work,
-                               lapack_int lwork, double* rwork );
-
-lapack_int LAPACKE_chegvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float* w, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhegvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double* w, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chegvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhegvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_cherfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zherfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_cherfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* s, const lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zherfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* s,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_chesv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zhesv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_chesvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                lapack_int* ipiv, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zhesvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_chesvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* s,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zhesvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* s,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_chetrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                float* d, float* e, lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zhetrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                double* d, double* e,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_chetrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_zhetrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_chetri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zhetri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_chetrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zhetrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_chfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               float alpha, const lapack_complex_float* a,
-                               lapack_int lda, float beta,
-                               lapack_complex_float* c );
-lapack_int LAPACKE_zhfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               double alpha, const lapack_complex_double* a,
-                               lapack_int lda, double beta,
-                               lapack_complex_double* c );
-
-lapack_int LAPACKE_shgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, float* h, lapack_int ldh,
-                                float* t, lapack_int ldt, float* alphar,
-                                float* alphai, float* beta, float* q,
-                                lapack_int ldq, float* z, lapack_int ldz,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dhgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, double* h, lapack_int ldh,
-                                double* t, lapack_int ldt, double* alphar,
-                                double* alphai, double* beta, double* q,
-                                lapack_int ldq, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_chgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_float* h,
-                                lapack_int ldh, lapack_complex_float* t,
-                                lapack_int ldt, lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork );
-lapack_int LAPACKE_zhgeqz_work( int matrix_order, char job, char compq,
-                                char compz, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_double* h,
-                                lapack_int ldh, lapack_complex_double* t,
-                                lapack_int ldt, lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_chpcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zhpcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_chpev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_float* ap, float* w,
-                               lapack_complex_float* z, lapack_int ldz,
-                               lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhpev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_complex_double* ap,
-                               double* w, lapack_complex_double* z,
-                               lapack_int ldz, lapack_complex_double* work,
-                               double* rwork );
-
-lapack_int LAPACKE_chpevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_float* ap,
-                                float* w, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zhpevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_complex_double* ap,
-                                double* w, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_chpevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* ap, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhpevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* ap, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chpgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_float* ap,
-                                const lapack_complex_float* bp );
-lapack_int LAPACKE_zhpgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, lapack_complex_double* ap,
-                                const lapack_complex_double* bp );
-
-lapack_int LAPACKE_chpgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n,
-                               lapack_complex_float* ap,
-                               lapack_complex_float* bp, float* w,
-                               lapack_complex_float* z, lapack_int ldz,
-                               lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhpgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n,
-                               lapack_complex_double* ap,
-                               lapack_complex_double* bp, double* w,
-                               lapack_complex_double* z, lapack_int ldz,
-                               lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_chpgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                lapack_complex_float* bp, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_zhpgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                lapack_complex_double* bp, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_chpgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                lapack_complex_float* bp, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_zhpgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                lapack_complex_double* bp, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_chprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_complex_float* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_chpsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* ap,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zhpsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* ap,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-
-lapack_int LAPACKE_chpsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* afp, lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zhpsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* afp, lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_chptrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap, float* d, float* e,
-                                lapack_complex_float* tau );
-lapack_int LAPACKE_zhptrd_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap, double* d, double* e,
-                                lapack_complex_double* tau );
-
-lapack_int LAPACKE_chptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zhptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_chptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zhptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_chptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zhptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_shsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, lapack_logical* select,
-                                lapack_int n, const float* h, lapack_int ldh,
-                                float* wr, const float* wi, float* vl,
-                                lapack_int ldvl, float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, float* work,
-                                lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_dhsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, lapack_logical* select,
-                                lapack_int n, const double* h, lapack_int ldh,
-                                double* wr, const double* wi, double* vl,
-                                lapack_int ldvl, double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, double* work,
-                                lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_chsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, const lapack_logical* select,
-                                lapack_int n, const lapack_complex_float* h,
-                                lapack_int ldh, lapack_complex_float* w,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_float* work, float* rwork,
-                                lapack_int* ifaill, lapack_int* ifailr );
-lapack_int LAPACKE_zhsein_work( int matrix_order, char job, char eigsrc,
-                                char initv, const lapack_logical* select,
-                                lapack_int n, const lapack_complex_double* h,
-                                lapack_int ldh, lapack_complex_double* w,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, double* rwork,
-                                lapack_int* ifaill, lapack_int* ifailr );
-
-lapack_int LAPACKE_shseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                float* h, lapack_int ldh, float* wr, float* wi,
-                                float* z, lapack_int ldz, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dhseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                double* h, lapack_int ldh, double* wr,
-                                double* wi, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_chseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_float* h, lapack_int ldh,
-                                lapack_complex_float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zhseqr_work( int matrix_order, char job, char compz,
-                                lapack_int n, lapack_int ilo, lapack_int ihi,
-                                lapack_complex_double* h, lapack_int ldh,
-                                lapack_complex_double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_clacgv_work( lapack_int n, lapack_complex_float* x,
-                                lapack_int incx );
-lapack_int LAPACKE_zlacgv_work( lapack_int n, lapack_complex_double* x,
-                                lapack_int incx );
-
-lapack_int LAPACKE_slacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dlacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_clacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zlacpy_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_zlag2c_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_float* sa, lapack_int ldsa );
-
-lapack_int LAPACKE_slag2d_work( int matrix_order, lapack_int m, lapack_int n,
-                                const float* sa, lapack_int ldsa, double* a,
-                                lapack_int lda );
-
-lapack_int LAPACKE_dlag2s_work( int matrix_order, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda, float* sa,
-                                lapack_int ldsa );
-
-lapack_int LAPACKE_clag2z_work( int matrix_order, lapack_int m, lapack_int n,
-                                const lapack_complex_float* sa, lapack_int ldsa,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* d,
-                                float* a, lapack_int lda, lapack_int* iseed,
-                                float* work );
-lapack_int LAPACKE_dlagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* d,
-                                double* a, lapack_int lda, lapack_int* iseed,
-                                double* work );
-lapack_int LAPACKE_clagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const float* d,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* iseed, lapack_complex_float* work );
-lapack_int LAPACKE_zlagge_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int kl, lapack_int ku, const double* d,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* iseed,
-                                lapack_complex_double* work );
-                                
-lapack_int LAPACKE_claghe_work( int matrix_order, lapack_int n, lapack_int k,
-                                const float* d, lapack_complex_float* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zlaghe_work( int matrix_order, lapack_int n, lapack_int k,
-                                const double* d, lapack_complex_double* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_slagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const float* d, float* a, lapack_int lda,
-                                lapack_int* iseed, float* work );
-lapack_int LAPACKE_dlagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const double* d, double* a, lapack_int lda,
-                                lapack_int* iseed, double* work );
-lapack_int LAPACKE_clagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const float* d, lapack_complex_float* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zlagsy_work( int matrix_order, lapack_int n, lapack_int k,
-                                const double* d, lapack_complex_double* a,
-                                lapack_int lda, lapack_int* iseed,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_slapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n, float* x,
-                                lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_dlapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n, double* x,
-                                lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_clapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_float* x, lapack_int ldx,
-                                lapack_int* k );
-lapack_int LAPACKE_zlapmr_work( int matrix_order, lapack_logical forwrd,
-                                lapack_int m, lapack_int n,
-                                lapack_complex_double* x, lapack_int ldx,
-                                lapack_int* k );
-
-lapack_int LAPACKE_slartgp_work( float f, float g, float* cs, float* sn,
-                                 float* r );
-lapack_int LAPACKE_dlartgp_work( double f, double g, double* cs, double* sn,
-                                 double* r );
-
-lapack_int LAPACKE_slartgs_work( float x, float y, float sigma, float* cs,
-                                 float* sn );
-lapack_int LAPACKE_dlartgs_work( double x, double y, double sigma, double* cs,
-                                 double* sn );
-                                
-float LAPACKE_slapy2_work( float x, float y );
-double LAPACKE_dlapy2_work( double x, double y );
-
-float LAPACKE_slapy3_work( float x, float y, float z );
-double LAPACKE_dlapy3_work( double x, double y, double z );
-
-float LAPACKE_slamch_work( char cmach );
-double LAPACKE_dlamch_work( char cmach );
-
-float LAPACKE_slange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* work );
-double LAPACKE_dlange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* work );
-float LAPACKE_clange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_zlange_work( int matrix_order, char norm, lapack_int m,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, double* work );
-
-float LAPACKE_clanhe_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_zlanhe_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, double* work );
-
-float LAPACKE_slansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* work );
-double LAPACKE_dlansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* work );
-float LAPACKE_clansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_zlansy_work( int matrix_order, char norm, char uplo,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, double* work );
-
-float LAPACKE_slantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n, const float* a,
-                                lapack_int lda, float* work );
-double LAPACKE_dlantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda, double* work );
-float LAPACKE_clantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* work );
-double LAPACKE_zlantr_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* work );
-
-lapack_int LAPACKE_slarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, const float* v,
-                                lapack_int ldv, const float* t, lapack_int ldt,
-                                float* c, lapack_int ldc, float* work,
-                                lapack_int ldwork );
-lapack_int LAPACKE_dlarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, const double* v,
-                                lapack_int ldv, const double* t, lapack_int ldt,
-                                double* c, lapack_int ldc, double* work,
-                                lapack_int ldwork );
-lapack_int LAPACKE_clarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_float* v, lapack_int ldv,
-                                const lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int ldwork );
-lapack_int LAPACKE_zlarfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_double* v, lapack_int ldv,
-                                const lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work,
-                                lapack_int ldwork );
-
-lapack_int LAPACKE_slarfg_work( lapack_int n, float* alpha, float* x,
-                                lapack_int incx, float* tau );
-lapack_int LAPACKE_dlarfg_work( lapack_int n, double* alpha, double* x,
-                                lapack_int incx, double* tau );
-lapack_int LAPACKE_clarfg_work( lapack_int n, lapack_complex_float* alpha,
-                                lapack_complex_float* x, lapack_int incx,
-                                lapack_complex_float* tau );
-lapack_int LAPACKE_zlarfg_work( lapack_int n, lapack_complex_double* alpha,
-                                lapack_complex_double* x, lapack_int incx,
-                                lapack_complex_double* tau );
-
-lapack_int LAPACKE_slarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k, const float* v,
-                                lapack_int ldv, const float* tau, float* t,
-                                lapack_int ldt );
-lapack_int LAPACKE_dlarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k, const double* v,
-                                lapack_int ldv, const double* tau, double* t,
-                                lapack_int ldt );
-lapack_int LAPACKE_clarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_float* v, lapack_int ldv,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zlarft_work( int matrix_order, char direct, char storev,
-                                lapack_int n, lapack_int k,
-                                const lapack_complex_double* v, lapack_int ldv,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_slarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const float* v, float tau,
-                                float* c, lapack_int ldc, float* work );
-lapack_int LAPACKE_dlarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const double* v, double tau,
-                                double* c, lapack_int ldc, double* work );
-lapack_int LAPACKE_clarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const lapack_complex_float* v,
-                                lapack_complex_float tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zlarfx_work( int matrix_order, char side, lapack_int m,
-                                lapack_int n, const lapack_complex_double* v,
-                                lapack_complex_double tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_slarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, float* x );
-lapack_int LAPACKE_dlarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, double* x );
-lapack_int LAPACKE_clarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, lapack_complex_float* x );
-lapack_int LAPACKE_zlarnv_work( lapack_int idist, lapack_int* iseed,
-                                lapack_int n, lapack_complex_double* x );
-
-lapack_int LAPACKE_slaset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, float alpha, float beta, float* a,
-                                lapack_int lda );
-lapack_int LAPACKE_dlaset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, double alpha, double beta,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_claset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, lapack_complex_float alpha,
-                                lapack_complex_float beta,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlaset_work( int matrix_order, char uplo, lapack_int m,
-                                lapack_int n, lapack_complex_double alpha,
-                                lapack_complex_double beta,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_slasrt_work( char id, lapack_int n, float* d );
-lapack_int LAPACKE_dlasrt_work( char id, lapack_int n, double* d );
-
-lapack_int LAPACKE_slaswp_work( int matrix_order, lapack_int n, float* a,
-                                lapack_int lda, lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_dlaswp_work( int matrix_order, lapack_int n, double* a,
-                                lapack_int lda, lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_claswp_work( int matrix_order, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-lapack_int LAPACKE_zlaswp_work( int matrix_order, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int k1, lapack_int k2,
-                                const lapack_int* ipiv, lapack_int incx );
-
-lapack_int LAPACKE_slatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                float* d, lapack_int mode, float cond,
-                                float dmax, lapack_int kl, lapack_int ku,
-                                char pack, float* a, lapack_int lda,
-                                float* work );
-lapack_int LAPACKE_dlatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                double* d, lapack_int mode, double cond,
-                                double dmax, lapack_int kl, lapack_int ku,
-                                char pack, double* a, lapack_int lda,
-                                double* work );
-lapack_int LAPACKE_clatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                float* d, lapack_int mode, float cond,
-                                float dmax, lapack_int kl, lapack_int ku,
-                                char pack, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* work );
-lapack_int LAPACKE_zlatms_work( int matrix_order, lapack_int m, lapack_int n,
-                                char dist, lapack_int* iseed, char sym,
-                                double* d, lapack_int mode, double cond,
-                                double dmax, lapack_int kl, lapack_int ku,
-                                char pack, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* work );
-
-lapack_int LAPACKE_slauum_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda );
-lapack_int LAPACKE_dlauum_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_clauum_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zlauum_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_sopgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, const float* tau, float* q,
-                                lapack_int ldq, float* work );
-lapack_int LAPACKE_dopgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, const double* tau, double* q,
-                                lapack_int ldq, double* work );
-
-lapack_int LAPACKE_sopmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const float* ap, const float* tau, float* c,
-                                lapack_int ldc, float* work );
-lapack_int LAPACKE_dopmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const double* ap, const double* tau, double* c,
-                                lapack_int ldc, double* work );
-
-lapack_int LAPACKE_sorgbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k, float* a,
-                                lapack_int lda, const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k, double* a,
-                                lapack_int lda, const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, float* a, lapack_int lda,
-                                const float* tau, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorgrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, double* a, lapack_int lda,
-                                const double* tau, double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_sorgtr_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, const float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dorgtr_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, const double* tau,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_sormtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const float* a, lapack_int lda,
-                                const float* tau, float* c, lapack_int ldc,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dormtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const double* tau, double* c, lapack_int ldc,
-                                double* work, lapack_int lwork );
-
-lapack_int LAPACKE_spbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const float* ab, lapack_int ldab,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dpbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const double* ab,
-                                lapack_int ldab, double anorm, double* rcond,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cpbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_float* ab,
-                                lapack_int ldab, float anorm, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpbcon_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_double* ab,
-                                lapack_int ldab, double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const float* ab, lapack_int ldab,
-                                float* s, float* scond, float* amax );
-lapack_int LAPACKE_dpbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const double* ab,
-                                lapack_int ldab, double* s, double* scond,
-                                double* amax );
-lapack_int LAPACKE_cpbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_float* ab,
-                                lapack_int ldab, float* s, float* scond,
-                                float* amax );
-lapack_int LAPACKE_zpbequ_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, const lapack_complex_double* ab,
-                                lapack_int ldab, double* s, double* scond,
-                                double* amax );
-
-lapack_int LAPACKE_spbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs, const float* ab,
-                                lapack_int ldab, const float* afb,
-                                lapack_int ldafb, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dpbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const double* ab, lapack_int ldab,
-                                const double* afb, lapack_int ldafb,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cpbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                const lapack_complex_float* afb,
-                                lapack_int ldafb, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpbrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab,
-                                const lapack_complex_double* afb,
-                                lapack_int ldafb,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, float* bb, lapack_int ldbb );
-lapack_int LAPACKE_dpbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, double* bb, lapack_int ldbb );
-lapack_int LAPACKE_cpbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, lapack_complex_float* bb,
-                                lapack_int ldbb );
-lapack_int LAPACKE_zpbstf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kb, lapack_complex_double* bb,
-                                lapack_int ldbb );
-
-lapack_int LAPACKE_spbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs, float* ab,
-                               lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs, double* ab,
-                               lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs,
-                               lapack_complex_float* ab, lapack_int ldab,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int kd, lapack_int nrhs,
-                               lapack_complex_double* ab, lapack_int ldab,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                float* ab, lapack_int ldab, float* afb,
-                                lapack_int ldafb, char* equed, float* s,
-                                float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dpbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                double* ab, lapack_int ldab, double* afb,
-                                lapack_int ldafb, char* equed, double* s,
-                                double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, double* work, lapack_int* iwork );
-lapack_int LAPACKE_cpbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* afb, lapack_int ldafb,
-                                char* equed, float* s, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zpbsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int kd, lapack_int nrhs,
-                                lapack_complex_double* ab, lapack_int ldab,
-                                lapack_complex_double* afb, lapack_int ldafb,
-                                char* equed, double* s,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, float* ab, lapack_int ldab );
-lapack_int LAPACKE_dpbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, double* ab, lapack_int ldab );
-lapack_int LAPACKE_cpbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_complex_float* ab,
-                                lapack_int ldab );
-lapack_int LAPACKE_zpbtrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_complex_double* ab,
-                                lapack_int ldab );
-
-lapack_int LAPACKE_spbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs, const float* ab,
-                                lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dpbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const double* ab, lapack_int ldab, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cpbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpbtrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int kd, lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_spftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, float* a );
-lapack_int LAPACKE_dpftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, double* a );
-lapack_int LAPACKE_cpftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftrf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, float* a );
-lapack_int LAPACKE_dpftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, double* a );
-lapack_int LAPACKE_cpftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_float* a );
-lapack_int LAPACKE_zpftri_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_complex_double* a );
-
-lapack_int LAPACKE_spftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs, const float* a,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dpftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs, const double* a,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_cpftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpftrs_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spocon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* a, lapack_int lda, float anorm,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dpocon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* a, lapack_int lda, double anorm,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cpocon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float anorm, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpocon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double anorm, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spoequ_work( int matrix_order, lapack_int n, const float* a,
-                                lapack_int lda, float* s, float* scond,
-                                float* amax );
-lapack_int LAPACKE_dpoequ_work( int matrix_order, lapack_int n, const double* a,
-                                lapack_int lda, double* s, double* scond,
-                                double* amax );
-lapack_int LAPACKE_cpoequ_work( int matrix_order, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequ_work( int matrix_order, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_spoequb_work( int matrix_order, lapack_int n, const float* a,
-                                 lapack_int lda, float* s, float* scond,
-                                 float* amax );
-lapack_int LAPACKE_dpoequb_work( int matrix_order, lapack_int n,
-                                 const double* a, lapack_int lda, double* s,
-                                 double* scond, double* amax );
-lapack_int LAPACKE_cpoequb_work( int matrix_order, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* s, float* scond, float* amax );
-lapack_int LAPACKE_zpoequb_work( int matrix_order, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* s, double* scond, double* amax );
-
-lapack_int LAPACKE_sporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const float* af, lapack_int ldaf,
-                                const float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const double* af,
-                                lapack_int ldaf, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zporfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const float* a,
-                                 lapack_int lda, const float* af,
-                                 lapack_int ldaf, const float* s,
-                                 const float* b, lapack_int ldb, float* x,
-                                 lapack_int ldx, float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const double* a,
-                                 lapack_int lda, const double* af,
-                                 lapack_int ldaf, const double* s,
-                                 const double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const float* s,
-                                 const lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zporfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const double* s,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_sposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* a, lapack_int lda,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* a, lapack_int lda,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_cposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* a,
-                               lapack_int lda, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zposv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* a,
-                               lapack_int lda, lapack_complex_double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dsposv_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, double* a, lapack_int lda,
-                                double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* work, float* swork,
-                                lapack_int* iter );
-lapack_int LAPACKE_zcposv_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, lapack_complex_double* work,
-                                lapack_complex_float* swork, double* rwork,
-                                lapack_int* iter );
-
-lapack_int LAPACKE_sposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, float* a,
-                                lapack_int lda, float* af, lapack_int ldaf,
-                                char* equed, float* s, float* b, lapack_int ldb,
-                                float* x, lapack_int ldx, float* rcond,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, double* a,
-                                lapack_int lda, double* af, lapack_int ldaf,
-                                char* equed, double* s, double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                char* equed, float* s, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zposvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                char* equed, double* s,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, float* a,
-                                 lapack_int lda, float* af, lapack_int ldaf,
-                                 char* equed, float* s, float* b,
-                                 lapack_int ldb, float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, double* a,
-                                 lapack_int lda, double* af, lapack_int ldaf,
-                                 char* equed, double* s, double* b,
-                                 lapack_int ldb, double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_cposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 char* equed, float* s, lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params,
-                                 lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zposvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 char* equed, double* s,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_spotrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda );
-lapack_int LAPACKE_dpotrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_cpotrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotri_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda );
-lapack_int LAPACKE_dpotri_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda );
-lapack_int LAPACKE_cpotri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zpotri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_spotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                float* b, lapack_int ldb );
-lapack_int LAPACKE_dpotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, double* b, lapack_int ldb );
-lapack_int LAPACKE_cpotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zpotrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_sppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, float anorm, float* rcond,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, double anorm, double* rcond,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap, float anorm,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zppcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap, double anorm,
-                                double* rcond, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_sppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, float* s, float* scond,
-                                float* amax );
-lapack_int LAPACKE_dppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, double* s, double* scond,
-                                double* amax );
-lapack_int LAPACKE_cppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap, float* s,
-                                float* scond, float* amax );
-lapack_int LAPACKE_zppequ_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap, double* s,
-                                double* scond, double* amax );
-
-lapack_int LAPACKE_spprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap,
-                                const float* afp, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dpprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap,
-                                const double* afp, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cpprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_complex_float* afp,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zpprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* afp,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* ap, float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_dppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* ap, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* ap,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zppsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* ap,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, float* ap,
-                                float* afp, char* equed, float* s, float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, double* ap,
-                                double* afp, char* equed, double* s, double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_float* ap,
-                                lapack_complex_float* afp, char* equed,
-                                float* s, lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_zppsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                lapack_complex_double* ap,
-                                lapack_complex_double* afp, char* equed,
-                                double* s, lapack_complex_double* b,
-                                lapack_int ldb, lapack_complex_double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_spptrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap );
-lapack_int LAPACKE_dpptrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap );
-lapack_int LAPACKE_cpptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_zpptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptri_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap );
-lapack_int LAPACKE_dpptri_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap );
-lapack_int LAPACKE_cpptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_zpptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_spptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dpptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cpptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_spstrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* piv,
-                                lapack_int* rank, float tol, float* work );
-lapack_int LAPACKE_dpstrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* piv,
-                                lapack_int* rank, double tol, double* work );
-lapack_int LAPACKE_cpstrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* piv, lapack_int* rank, float tol,
-                                float* work );
-lapack_int LAPACKE_zpstrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* piv, lapack_int* rank, double tol,
-                                double* work );
-
-lapack_int LAPACKE_sptcon_work( lapack_int n, const float* d, const float* e,
-                                float anorm, float* rcond, float* work );
-lapack_int LAPACKE_dptcon_work( lapack_int n, const double* d, const double* e,
-                                double anorm, double* rcond, double* work );
-lapack_int LAPACKE_cptcon_work( lapack_int n, const float* d,
-                                const lapack_complex_float* e, float anorm,
-                                float* rcond, float* work );
-lapack_int LAPACKE_zptcon_work( lapack_int n, const double* d,
-                                const lapack_complex_double* e, double anorm,
-                                double* rcond, double* work );
-
-lapack_int LAPACKE_spteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work );
-lapack_int LAPACKE_dpteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work );
-lapack_int LAPACKE_cpteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, lapack_complex_float* z,
-                                lapack_int ldz, float* work );
-lapack_int LAPACKE_zpteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, lapack_complex_double* z,
-                                lapack_int ldz, double* work );
-
-lapack_int LAPACKE_sptrfs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const float* d, const float* e, const float* df,
-                                const float* ef, const float* b, lapack_int ldb,
-                                float* x, lapack_int ldx, float* ferr,
-                                float* berr, float* work );
-lapack_int LAPACKE_dptrfs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const double* d, const double* e,
-                                const double* df, const double* ef,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work );
-lapack_int LAPACKE_cptrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* d,
-                                const lapack_complex_float* e, const float* df,
-                                const lapack_complex_float* ef,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zptrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const lapack_complex_double* e,
-                                const double* df,
-                                const lapack_complex_double* ef,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* d, float* e, float* b, lapack_int ldb );
-lapack_int LAPACKE_dptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* d, double* e, double* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_cptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               float* d, lapack_complex_float* e,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                               double* d, lapack_complex_double* e,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const float* d, const float* e,
-                                float* df, float* ef, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work );
-lapack_int LAPACKE_dptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const double* e, double* df, double* ef,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* rcond, double* ferr,
-                                double* berr, double* work );
-lapack_int LAPACKE_cptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const float* d,
-                                const lapack_complex_float* e, float* df,
-                                lapack_complex_float* ef,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zptsvx_work( int matrix_order, char fact, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const lapack_complex_double* e, double* df,
-                                lapack_complex_double* ef,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_spttrf_work( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dpttrf_work( lapack_int n, double* d, double* e );
-lapack_int LAPACKE_cpttrf_work( lapack_int n, float* d,
-                                lapack_complex_float* e );
-lapack_int LAPACKE_zpttrf_work( lapack_int n, double* d,
-                                lapack_complex_double* e );
-
-lapack_int LAPACKE_spttrs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const float* d, const float* e, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dpttrs_work( int matrix_order, lapack_int n, lapack_int nrhs,
-                                const double* d, const double* e, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_cpttrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* d,
-                                const lapack_complex_float* e,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zpttrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* d,
-                                const lapack_complex_double* e,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd, float* ab,
-                               lapack_int ldab, float* w, float* z,
-                               lapack_int ldz, float* work );
-lapack_int LAPACKE_dsbev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int kd, double* ab,
-                               lapack_int ldab, double* w, double* z,
-                               lapack_int ldz, double* work );
-
-lapack_int LAPACKE_ssbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd, float* ab,
-                                lapack_int ldab, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsbevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int kd, double* ab,
-                                lapack_int ldab, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                float* ab, lapack_int ldab, float* q,
-                                lapack_int ldq, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dsbevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int kd,
-                                double* ab, lapack_int ldab, double* q,
-                                lapack_int ldq, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_ssbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                float* ab, lapack_int ldab, const float* bb,
-                                lapack_int ldbb, float* x, lapack_int ldx,
-                                float* work );
-lapack_int LAPACKE_dsbgst_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                double* ab, lapack_int ldab, const double* bb,
-                                lapack_int ldbb, double* x, lapack_int ldx,
-                                double* work );
-
-lapack_int LAPACKE_ssbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               float* ab, lapack_int ldab, float* bb,
-                               lapack_int ldbb, float* w, float* z,
-                               lapack_int ldz, float* work );
-lapack_int LAPACKE_dsbgv_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, lapack_int ka, lapack_int kb,
-                               double* ab, lapack_int ldab, double* bb,
-                               lapack_int ldbb, double* w, double* z,
-                               lapack_int ldz, double* work );
-
-lapack_int LAPACKE_ssbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                float* ab, lapack_int ldab, float* bb,
-                                lapack_int ldbb, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsbgvd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, lapack_int ka, lapack_int kb,
-                                double* ab, lapack_int ldab, double* bb,
-                                lapack_int ldbb, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, float* ab, lapack_int ldab,
-                                float* bb, lapack_int ldbb, float* q,
-                                lapack_int ldq, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dsbgvx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, lapack_int ka,
-                                lapack_int kb, double* ab, lapack_int ldab,
-                                double* bb, lapack_int ldbb, double* q,
-                                lapack_int ldq, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_ssbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd, float* ab,
-                                lapack_int ldab, float* d, float* e, float* q,
-                                lapack_int ldq, float* work );
-lapack_int LAPACKE_dsbtrd_work( int matrix_order, char vect, char uplo,
-                                lapack_int n, lapack_int kd, double* ab,
-                                lapack_int ldab, double* d, double* e,
-                                double* q, lapack_int ldq, double* work );
-
-lapack_int LAPACKE_ssfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               float alpha, const float* a, lapack_int lda,
-                               float beta, float* c );
-lapack_int LAPACKE_dsfrk_work( int matrix_order, char transr, char uplo,
-                               char trans, lapack_int n, lapack_int k,
-                               double alpha, const double* a, lapack_int lda,
-                               double beta, double* c );
-
-lapack_int LAPACKE_sspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, const lapack_int* ipiv,
-                                float anorm, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, const lapack_int* ipiv,
-                                double anorm, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_cspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zspcon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_sspev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, float* ap, float* w, float* z,
-                               lapack_int ldz, float* work );
-lapack_int LAPACKE_dspev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, double* ap, double* w, double* z,
-                               lapack_int ldz, double* work );
-
-lapack_int LAPACKE_sspevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, float* ap, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dspevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, double* ap, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sspevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, float* ap, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dspevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, double* ap, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, double* work,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_sspgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, float* ap, const float* bp );
-lapack_int LAPACKE_dspgst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, double* ap, const double* bp );
-
-lapack_int LAPACKE_sspgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, float* ap, float* bp,
-                               float* w, float* z, lapack_int ldz,
-                               float* work );
-lapack_int LAPACKE_dspgv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, double* ap, double* bp,
-                               double* w, double* z, lapack_int ldz,
-                               double* work );
-
-lapack_int LAPACKE_sspgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, float* ap, float* bp,
-                                float* w, float* z, lapack_int ldz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dspgvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, double* ap, double* bp,
-                                double* w, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sspgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, float* ap,
-                                float* bp, float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, float* z, lapack_int ldz, float* work,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_dspgvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, double* ap,
-                                double* bp, double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, double* z, lapack_int ldz,
-                                double* work, lapack_int* iwork,
-                                lapack_int* ifail );
-
-lapack_int LAPACKE_ssprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap,
-                                const float* afp, const lapack_int* ipiv,
-                                const float* b, lapack_int ldb, float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dsprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap,
-                                const double* afp, const lapack_int* ipiv,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_csprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_complex_float* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zsprfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* afp,
-                                const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_sspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* ap, lapack_int* ipiv,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* ap, lapack_int* ipiv,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_cspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* ap,
-                               lapack_int* ipiv, lapack_complex_float* b,
-                               lapack_int ldb );
-lapack_int LAPACKE_zspsv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* ap,
-                               lapack_int* ipiv, lapack_complex_double* b,
-                               lapack_int ldb );
-
-lapack_int LAPACKE_sspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const float* ap,
-                                float* afp, lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const double* ap,
-                                double* afp, lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_cspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* afp, lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zspsvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* afp, lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_ssptrd_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap, float* d, float* e, float* tau );
-lapack_int LAPACKE_dsptrd_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap, double* d, double* e, double* tau );
-
-lapack_int LAPACKE_ssptrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_dsptrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap, lapack_int* ipiv );
-lapack_int LAPACKE_csptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap, lapack_int* ipiv );
-lapack_int LAPACKE_zsptrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap, lapack_int* ipiv );
-
-lapack_int LAPACKE_ssptri_work( int matrix_order, char uplo, lapack_int n,
-                                float* ap, const lapack_int* ipiv,
-                                float* work );
-lapack_int LAPACKE_dsptri_work( int matrix_order, char uplo, lapack_int n,
-                                double* ap, const lapack_int* ipiv,
-                                double* work );
-lapack_int LAPACKE_csptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zsptri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_ssptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* ap,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dsptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* ap,
-                                const lapack_int* ipiv, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_csptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* ap,
-                                const lapack_int* ipiv, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_zsptrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_sstebz_work( char range, char order, lapack_int n, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, const float* d, const float* e,
-                                lapack_int* m, lapack_int* nsplit, float* w,
-                                lapack_int* iblock, lapack_int* isplit,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dstebz_work( char range, char order, lapack_int n, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, const double* d, const double* e,
-                                lapack_int* m, lapack_int* nsplit, double* w,
-                                lapack_int* iblock, lapack_int* isplit,
-                                double* work, lapack_int* iwork );
-
-lapack_int LAPACKE_sstedc_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dstedc_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_cstedc_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, lapack_complex_float* z,
-                                lapack_int ldz, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zstedc_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, lapack_complex_double* z,
-                                lapack_int ldz, lapack_complex_double* work,
-                                lapack_int lwork, double* rwork,
-                                lapack_int lrwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_sstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, lapack_int* isuppz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_cstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zstegr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int* isuppz, double* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_sstein_work( int matrix_order, lapack_int n, const float* d,
-                                const float* e, lapack_int m, const float* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-lapack_int LAPACKE_dstein_work( int matrix_order, lapack_int n, const double* d,
-                                const double* e, lapack_int m, const double* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit, double* z,
-                                lapack_int ldz, double* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-lapack_int LAPACKE_cstein_work( int matrix_order, lapack_int n, const float* d,
-                                const float* e, lapack_int m, const float* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit,
-                                lapack_complex_float* z, lapack_int ldz,
-                                float* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-lapack_int LAPACKE_zstein_work( int matrix_order, lapack_int n, const double* d,
-                                const double* e, lapack_int m, const double* w,
-                                const lapack_int* iblock,
-                                const lapack_int* isplit,
-                                lapack_complex_double* z, lapack_int ldz,
-                                double* work, lapack_int* iwork,
-                                lapack_int* ifailv );
-
-lapack_int LAPACKE_sstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int nzc,
-                                lapack_int* isuppz, lapack_logical* tryrac,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, lapack_int nzc,
-                                lapack_int* isuppz, lapack_logical* tryrac,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_cstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, float* w,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int nzc, lapack_int* isuppz,
-                                lapack_logical* tryrac, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_zstemr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                lapack_int* m, double* w,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int nzc, lapack_int* isuppz,
-                                lapack_logical* tryrac, double* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_ssteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work );
-lapack_int LAPACKE_dsteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work );
-lapack_int LAPACKE_csteqr_work( int matrix_order, char compz, lapack_int n,
-                                float* d, float* e, lapack_complex_float* z,
-                                lapack_int ldz, float* work );
-lapack_int LAPACKE_zsteqr_work( int matrix_order, char compz, lapack_int n,
-                                double* d, double* e, lapack_complex_double* z,
-                                lapack_int ldz, double* work );
-
-lapack_int LAPACKE_ssterf_work( lapack_int n, float* d, float* e );
-lapack_int LAPACKE_dsterf_work( lapack_int n, double* d, double* e );
-
-lapack_int LAPACKE_sstev_work( int matrix_order, char jobz, lapack_int n,
-                               float* d, float* e, float* z, lapack_int ldz,
-                               float* work );
-lapack_int LAPACKE_dstev_work( int matrix_order, char jobz, lapack_int n,
-                               double* d, double* e, double* z, lapack_int ldz,
-                               double* work );
-
-lapack_int LAPACKE_sstevd_work( int matrix_order, char jobz, lapack_int n,
-                                float* d, float* e, float* z, lapack_int ldz,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dstevd_work( int matrix_order, char jobz, lapack_int n,
-                                double* d, double* e, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sstevr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dstevr_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, lapack_int* isuppz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_sstevx_work( int matrix_order, char jobz, char range,
-                                lapack_int n, float* d, float* e, float vl,
-                                float vu, lapack_int il, lapack_int iu,
-                                float abstol, lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dstevx_work( int matrix_order, char jobz, char range,
-                                lapack_int n, double* d, double* e, double vl,
-                                double vu, lapack_int il, lapack_int iu,
-                                double abstol, lapack_int* m, double* w,
-                                double* z, lapack_int ldz, double* work,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_ssycon_work( int matrix_order, char uplo, lapack_int n,
-                                const float* a, lapack_int lda,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dsycon_work( int matrix_order, char uplo, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_csycon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv, float anorm,
-                                float* rcond, lapack_complex_float* work );
-lapack_int LAPACKE_zsycon_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv, double anorm,
-                                double* rcond, lapack_complex_double* work );
-
-lapack_int LAPACKE_ssyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const float* a, lapack_int lda, float* s,
-                                 float* scond, float* amax, float* work );
-lapack_int LAPACKE_dsyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const double* a, lapack_int lda, double* s,
-                                 double* scond, double* amax, double* work );
-lapack_int LAPACKE_csyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 float* s, float* scond, float* amax,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_zsyequb_work( int matrix_order, char uplo, lapack_int n,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 double* s, double* scond, double* amax,
-                                 lapack_complex_double* work );
-
-lapack_int LAPACKE_ssyev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, float* a, lapack_int lda, float* w,
-                               float* work, lapack_int lwork );
-lapack_int LAPACKE_dsyev_work( int matrix_order, char jobz, char uplo,
-                               lapack_int n, double* a, lapack_int lda,
-                               double* w, double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssyevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, float* a, lapack_int lda,
-                                float* w, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsyevd_work( int matrix_order, char jobz, char uplo,
-                                lapack_int n, double* a, lapack_int lda,
-                                double* w, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssyevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, float* a,
-                                lapack_int lda, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, lapack_int* isuppz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dsyevr_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, double* a,
-                                lapack_int lda, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, lapack_int* isuppz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssyevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, float* a,
-                                lapack_int lda, float vl, float vu,
-                                lapack_int il, lapack_int iu, float abstol,
-                                lapack_int* m, float* w, float* z,
-                                lapack_int ldz, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int* ifail );
-lapack_int LAPACKE_dsyevx_work( int matrix_order, char jobz, char range,
-                                char uplo, lapack_int n, double* a,
-                                lapack_int lda, double vl, double vu,
-                                lapack_int il, lapack_int iu, double abstol,
-                                lapack_int* m, double* w, double* z,
-                                lapack_int ldz, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_ssygst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, float* a, lapack_int lda,
-                                const float* b, lapack_int ldb );
-lapack_int LAPACKE_dsygst_work( int matrix_order, lapack_int itype, char uplo,
-                                lapack_int n, double* a, lapack_int lda,
-                                const double* b, lapack_int ldb );
-
-lapack_int LAPACKE_ssygv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, float* a,
-                               lapack_int lda, float* b, lapack_int ldb,
-                               float* w, float* work, lapack_int lwork );
-lapack_int LAPACKE_dsygv_work( int matrix_order, lapack_int itype, char jobz,
-                               char uplo, lapack_int n, double* a,
-                               lapack_int lda, double* b, lapack_int ldb,
-                               double* w, double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssygvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float* w, float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dsygvd_work( int matrix_order, lapack_int itype, char jobz,
-                                char uplo, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* w, double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-
-lapack_int LAPACKE_ssygvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float vl, float vu, lapack_int il,
-                                lapack_int iu, float abstol, lapack_int* m,
-                                float* w, float* z, lapack_int ldz, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int* ifail );
-lapack_int LAPACKE_dsygvx_work( int matrix_order, lapack_int itype, char jobz,
-                                char range, char uplo, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double vl, double vu, lapack_int il,
-                                lapack_int iu, double abstol, lapack_int* m,
-                                double* w, double* z, lapack_int ldz,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int* ifail );
-
-lapack_int LAPACKE_ssyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const float* af, lapack_int ldaf,
-                                const lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dsyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const double* b, lapack_int ldb, double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                double* work, lapack_int* iwork );
-lapack_int LAPACKE_csyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_zsyrfs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_complex_double* af,
-                                lapack_int ldaf, const lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_ssyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const float* a,
-                                 lapack_int lda, const float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* s, const float* b, lapack_int ldb,
-                                 float* x, lapack_int ldx, float* rcond,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dsyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs, const double* a,
-                                 lapack_int lda, const double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* s, const double* b,
-                                 lapack_int ldb, double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, double* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_csyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_float* a, lapack_int lda,
-                                 const lapack_complex_float* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const float* s, const lapack_complex_float* b,
-                                 lapack_int ldb, lapack_complex_float* x,
-                                 lapack_int ldx, float* rcond, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zsyrfsx_work( int matrix_order, char uplo, char equed,
-                                 lapack_int n, lapack_int nrhs,
-                                 const lapack_complex_double* a, lapack_int lda,
-                                 const lapack_complex_double* af,
-                                 lapack_int ldaf, const lapack_int* ipiv,
-                                 const double* s,
-                                 const lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_ssysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, float* a, lapack_int lda,
-                               lapack_int* ipiv, float* b, lapack_int ldb,
-                               float* work, lapack_int lwork );
-lapack_int LAPACKE_dsysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, double* a, lapack_int lda,
-                               lapack_int* ipiv, double* b, lapack_int ldb,
-                               double* work, lapack_int lwork );
-lapack_int LAPACKE_csysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_float* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_float* b, lapack_int ldb,
-                               lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zsysv_work( int matrix_order, char uplo, lapack_int n,
-                               lapack_int nrhs, lapack_complex_double* a,
-                               lapack_int lda, lapack_int* ipiv,
-                               lapack_complex_double* b, lapack_int ldb,
-                               lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const float* a,
-                                lapack_int lda, float* af, lapack_int ldaf,
-                                lapack_int* ipiv, const float* b,
-                                lapack_int ldb, float* x, lapack_int ldx,
-                                float* rcond, float* ferr, float* berr,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dsysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs, const double* a,
-                                lapack_int lda, double* af, lapack_int ldaf,
-                                lapack_int* ipiv, const double* b,
-                                lapack_int ldb, double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_csysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* af, lapack_int ldaf,
-                                lapack_int* ipiv, const lapack_complex_float* b,
-                                lapack_int ldb, lapack_complex_float* x,
-                                lapack_int ldx, float* rcond, float* ferr,
-                                float* berr, lapack_complex_float* work,
-                                lapack_int lwork, float* rwork );
-lapack_int LAPACKE_zsysvx_work( int matrix_order, char fact, char uplo,
-                                lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* af, lapack_int ldaf,
-                                lapack_int* ipiv,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* x, lapack_int ldx,
-                                double* rcond, double* ferr, double* berr,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork );
-
-lapack_int LAPACKE_ssysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, float* a,
-                                 lapack_int lda, float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* s,
-                                 float* b, lapack_int ldb, float* x,
-                                 lapack_int ldx, float* rcond, float* rpvgrw,
-                                 float* berr, lapack_int n_err_bnds,
-                                 float* err_bnds_norm, float* err_bnds_comp,
-                                 lapack_int nparams, float* params, float* work,
-                                 lapack_int* iwork );
-lapack_int LAPACKE_dsysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs, double* a,
-                                 lapack_int lda, double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* s,
-                                 double* b, lapack_int ldb, double* x,
-                                 lapack_int ldx, double* rcond, double* rpvgrw,
-                                 double* berr, lapack_int n_err_bnds,
-                                 double* err_bnds_norm, double* err_bnds_comp,
-                                 lapack_int nparams, double* params,
-                                 double* work, lapack_int* iwork );
-lapack_int LAPACKE_csysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, float* s,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* x, lapack_int ldx,
-                                 float* rcond, float* rpvgrw, float* berr,
-                                 lapack_int n_err_bnds, float* err_bnds_norm,
-                                 float* err_bnds_comp, lapack_int nparams,
-                                 float* params, lapack_complex_float* work,
-                                 float* rwork );
-lapack_int LAPACKE_zsysvxx_work( int matrix_order, char fact, char uplo,
-                                 lapack_int n, lapack_int nrhs,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* af, lapack_int ldaf,
-                                 lapack_int* ipiv, char* equed, double* s,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* x, lapack_int ldx,
-                                 double* rcond, double* rpvgrw, double* berr,
-                                 lapack_int n_err_bnds, double* err_bnds_norm,
-                                 double* err_bnds_comp, lapack_int nparams,
-                                 double* params, lapack_complex_double* work,
-                                 double* rwork );
-
-lapack_int LAPACKE_ssytrd_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, float* d, float* e,
-                                float* tau, float* work, lapack_int lwork );
-lapack_int LAPACKE_dsytrd_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, double* d, double* e,
-                                double* tau, double* work, lapack_int lwork );
-
-lapack_int LAPACKE_ssytrf_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda, lapack_int* ipiv,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dsytrf_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda, lapack_int* ipiv,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_csytrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_zsytrf_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_int* ipiv, lapack_complex_double* work,
-                                lapack_int lwork );
-
-lapack_int LAPACKE_ssytri_work( int matrix_order, char uplo, lapack_int n,
-                                float* a, lapack_int lda,
-                                const lapack_int* ipiv, float* work );
-lapack_int LAPACKE_dsytri_work( int matrix_order, char uplo, lapack_int n,
-                                double* a, lapack_int lda,
-                                const lapack_int* ipiv, double* work );
-lapack_int LAPACKE_csytri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zsytri_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_int* ipiv,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_ssytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const float* a, lapack_int lda,
-                                const lapack_int* ipiv, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dsytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                double* b, lapack_int ldb );
-lapack_int LAPACKE_csytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_zsytrs_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_int nrhs, const lapack_complex_double* a,
-                                lapack_int lda, const lapack_int* ipiv,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const float* ab, lapack_int ldab, float* rcond,
-                                float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const double* ab, lapack_int ldab,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const lapack_complex_float* ab, lapack_int ldab,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_ztbcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, lapack_int kd,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const float* ab,
-                                lapack_int ldab, const float* b, lapack_int ldb,
-                                const float* x, lapack_int ldx, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const double* ab,
-                                lapack_int ldab, const double* b,
-                                lapack_int ldb, const double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const lapack_complex_float* ab,
-                                lapack_int ldab, const lapack_complex_float* b,
-                                lapack_int ldb, const lapack_complex_float* x,
-                                lapack_int ldx, float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztbrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, const lapack_complex_double* b,
-                                lapack_int ldb, const lapack_complex_double* x,
-                                lapack_int ldx, double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const float* ab,
-                                lapack_int ldab, float* b, lapack_int ldb );
-lapack_int LAPACKE_dtbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const double* ab,
-                                lapack_int ldab, double* b, lapack_int ldb );
-lapack_int LAPACKE_ctbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs, const lapack_complex_float* ab,
-                                lapack_int ldab, lapack_complex_float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_ztbtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int kd,
-                                lapack_int nrhs,
-                                const lapack_complex_double* ab,
-                                lapack_int ldab, lapack_complex_double* b,
-                                lapack_int ldb );
-
-lapack_int LAPACKE_stfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, float alpha, const float* a,
-                               float* b, lapack_int ldb );
-lapack_int LAPACKE_dtfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, double alpha, const double* a,
-                               double* b, lapack_int ldb );
-lapack_int LAPACKE_ctfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, lapack_complex_float alpha,
-                               const lapack_complex_float* a,
-                               lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztfsm_work( int matrix_order, char transr, char side,
-                               char uplo, char trans, char diag, lapack_int m,
-                               lapack_int n, lapack_complex_double alpha,
-                               const lapack_complex_double* a,
-                               lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n, float* a );
-lapack_int LAPACKE_dtftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n, double* a );
-lapack_int LAPACKE_ctftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n,
-                                lapack_complex_float* a );
-lapack_int LAPACKE_ztftri_work( int matrix_order, char transr, char uplo,
-                                char diag, lapack_int n,
-                                lapack_complex_double* a );
-
-lapack_int LAPACKE_stfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* arf, float* ap );
-lapack_int LAPACKE_dtfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* arf, double* ap );
-lapack_int LAPACKE_ctfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* arf,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_ztfttp_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* arf,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_stfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* arf, float* a,
-                                lapack_int lda );
-lapack_int LAPACKE_dtfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* arf, double* a,
-                                lapack_int lda );
-lapack_int LAPACKE_ctfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* arf,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztfttr_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* arf,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_stgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const float* s, lapack_int lds, const float* p,
-                                lapack_int ldp, float* vl, lapack_int ldvl,
-                                float* vr, lapack_int ldvr, lapack_int mm,
-                                lapack_int* m, float* work );
-lapack_int LAPACKE_dtgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const double* s, lapack_int lds,
-                                const double* p, lapack_int ldp, double* vl,
-                                lapack_int ldvl, double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, double* work );
-lapack_int LAPACKE_ctgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_float* s, lapack_int lds,
-                                const lapack_complex_float* p, lapack_int ldp,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztgevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_double* s, lapack_int lds,
-                                const lapack_complex_double* p, lapack_int ldp,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n, float* a,
-                                lapack_int lda, float* b, lapack_int ldb,
-                                float* q, lapack_int ldq, float* z,
-                                lapack_int ldz, lapack_int* ifst,
-                                lapack_int* ilst, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dtgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* q, lapack_int ldq, double* z,
-                                lapack_int ldz, lapack_int* ifst,
-                                lapack_int* ilst, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_ctgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztgexc_work( int matrix_order, lapack_logical wantq,
-                                lapack_logical wantz, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_stgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float* alphar, float* alphai,
-                                float* beta, float* q, lapack_int ldq, float* z,
-                                lapack_int ldz, lapack_int* m, float* pl,
-                                float* pr, float* dif, float* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-lapack_int LAPACKE_dtgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double* alphar, double* alphai,
-                                double* beta, double* q, lapack_int ldq,
-                                double* z, lapack_int ldz, lapack_int* m,
-                                double* pl, double* pr, double* dif,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_ctgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* alpha,
-                                lapack_complex_float* beta,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* z, lapack_int ldz,
-                                lapack_int* m, float* pl, float* pr, float* dif,
-                                lapack_complex_float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_ztgsen_work( int matrix_order, lapack_int ijob,
-                                lapack_logical wantq, lapack_logical wantz,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* alpha,
-                                lapack_complex_double* beta,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* z, lapack_int ldz,
-                                lapack_int* m, double* pl, double* pr,
-                                double* dif, lapack_complex_double* work,
-                                lapack_int lwork, lapack_int* iwork,
-                                lapack_int liwork );
-
-lapack_int LAPACKE_stgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                float* a, lapack_int lda, float* b,
-                                lapack_int ldb, float tola, float tolb,
-                                float* alpha, float* beta, float* u,
-                                lapack_int ldu, float* v, lapack_int ldv,
-                                float* q, lapack_int ldq, float* work,
-                                lapack_int* ncycle );
-lapack_int LAPACKE_dtgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                double* a, lapack_int lda, double* b,
-                                lapack_int ldb, double tola, double tolb,
-                                double* alpha, double* beta, double* u,
-                                lapack_int ldu, double* v, lapack_int ldv,
-                                double* q, lapack_int ldq, double* work,
-                                lapack_int* ncycle );
-lapack_int LAPACKE_ctgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                float tola, float tolb, float* alpha,
-                                float* beta, lapack_complex_float* u,
-                                lapack_int ldu, lapack_complex_float* v,
-                                lapack_int ldv, lapack_complex_float* q,
-                                lapack_int ldq, lapack_complex_float* work,
-                                lapack_int* ncycle );
-lapack_int LAPACKE_ztgsja_work( int matrix_order, char jobu, char jobv,
-                                char jobq, lapack_int m, lapack_int p,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                double tola, double tolb, double* alpha,
-                                double* beta, lapack_complex_double* u,
-                                lapack_int ldu, lapack_complex_double* v,
-                                lapack_int ldv, lapack_complex_double* q,
-                                lapack_int ldq, lapack_complex_double* work,
-                                lapack_int* ncycle );
-
-lapack_int LAPACKE_stgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const float* a, lapack_int lda, const float* b,
-                                lapack_int ldb, const float* vl,
-                                lapack_int ldvl, const float* vr,
-                                lapack_int ldvr, float* s, float* dif,
-                                lapack_int mm, lapack_int* m, float* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_dtgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const double* b, lapack_int ldb,
-                                const double* vl, lapack_int ldvl,
-                                const double* vr, lapack_int ldvr, double* s,
-                                double* dif, lapack_int mm, lapack_int* m,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                const lapack_complex_float* vl, lapack_int ldvl,
-                                const lapack_complex_float* vr, lapack_int ldvr,
-                                float* s, float* dif, lapack_int mm,
-                                lapack_int* m, lapack_complex_float* work,
-                                lapack_int lwork, lapack_int* iwork );
-lapack_int LAPACKE_ztgsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                const lapack_complex_double* vl,
-                                lapack_int ldvl,
-                                const lapack_complex_double* vr,
-                                lapack_int ldvr, double* s, double* dif,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, lapack_int lwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_stgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n, const float* a,
-                                lapack_int lda, const float* b, lapack_int ldb,
-                                float* c, lapack_int ldc, const float* d,
-                                lapack_int ldd, const float* e, lapack_int lde,
-                                float* f, lapack_int ldf, float* scale,
-                                float* dif, float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dtgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n, const double* a,
-                                lapack_int lda, const double* b, lapack_int ldb,
-                                double* c, lapack_int ldc, const double* d,
-                                lapack_int ldd, const double* e, lapack_int lde,
-                                double* f, lapack_int ldf, double* scale,
-                                double* dif, double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* c, lapack_int ldc,
-                                const lapack_complex_float* d, lapack_int ldd,
-                                const lapack_complex_float* e, lapack_int lde,
-                                lapack_complex_float* f, lapack_int ldf,
-                                float* scale, float* dif,
-                                lapack_complex_float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ztgsyl_work( int matrix_order, char trans, lapack_int ijob,
-                                lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* c, lapack_int ldc,
-                                const lapack_complex_double* d, lapack_int ldd,
-                                const lapack_complex_double* e, lapack_int lde,
-                                lapack_complex_double* f, lapack_int ldf,
-                                double* scale, double* dif,
-                                lapack_complex_double* work, lapack_int lwork,
-                                lapack_int* iwork );
-
-lapack_int LAPACKE_stpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const float* ap,
-                                float* rcond, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const double* ap,
-                                double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_float* ap, float* rcond,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztpcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_double* ap, double* rcond,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* ap, const float* b, lapack_int ldb,
-                                const float* x, lapack_int ldx, float* ferr,
-                                float* berr, float* work, lapack_int* iwork );
-lapack_int LAPACKE_dtprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* ap, const double* b,
-                                lapack_int ldb, const double* x, lapack_int ldx,
-                                double* ferr, double* berr, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                const lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztprfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                const lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_stptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, float* ap );
-lapack_int LAPACKE_dtptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, double* ap );
-lapack_int LAPACKE_ctptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_float* ap );
-lapack_int LAPACKE_ztptri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_double* ap );
-
-lapack_int LAPACKE_stptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* ap, float* b, lapack_int ldb );
-lapack_int LAPACKE_dtptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* ap, double* b, lapack_int ldb );
-lapack_int LAPACKE_ctptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztptrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_stpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* ap, float* arf );
-lapack_int LAPACKE_dtpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* ap, double* arf );
-lapack_int LAPACKE_ctpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* ap,
-                                lapack_complex_float* arf );
-lapack_int LAPACKE_ztpttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* ap,
-                                lapack_complex_double* arf );
-
-lapack_int LAPACKE_stpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const float* ap, float* a, lapack_int lda );
-lapack_int LAPACKE_dtpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const double* ap, double* a, lapack_int lda );
-lapack_int LAPACKE_ctpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_ztpttr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_strcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const float* a,
-                                lapack_int lda, float* rcond, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dtrcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n, const double* a,
-                                lapack_int lda, double* rcond, double* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctrcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                float* rcond, lapack_complex_float* work,
-                                float* rwork );
-lapack_int LAPACKE_ztrcon_work( int matrix_order, char norm, char uplo,
-                                char diag, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                double* rcond, lapack_complex_double* work,
-                                double* rwork );
-
-lapack_int LAPACKE_strevc_work( int matrix_order, char side, char howmny,
-                                lapack_logical* select, lapack_int n,
-                                const float* t, lapack_int ldt, float* vl,
-                                lapack_int ldvl, float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, float* work );
-lapack_int LAPACKE_dtrevc_work( int matrix_order, char side, char howmny,
-                                lapack_logical* select, lapack_int n,
-                                const double* t, lapack_int ldt, double* vl,
-                                lapack_int ldvl, double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m, double* work );
-lapack_int LAPACKE_ctrevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* vl, lapack_int ldvl,
-                                lapack_complex_float* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztrevc_work( int matrix_order, char side, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* vl, lapack_int ldvl,
-                                lapack_complex_double* vr, lapack_int ldvr,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_strexc_work( int matrix_order, char compq, lapack_int n,
-                                float* t, lapack_int ldt, float* q,
-                                lapack_int ldq, lapack_int* ifst,
-                                lapack_int* ilst, float* work );
-lapack_int LAPACKE_dtrexc_work( int matrix_order, char compq, lapack_int n,
-                                double* t, lapack_int ldt, double* q,
-                                lapack_int ldq, lapack_int* ifst,
-                                lapack_int* ilst, double* work );
-lapack_int LAPACKE_ctrexc_work( int matrix_order, char compq, lapack_int n,
-                                lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_int ifst, lapack_int ilst );
-lapack_int LAPACKE_ztrexc_work( int matrix_order, char compq, lapack_int n,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_int ifst, lapack_int ilst );
-
-lapack_int LAPACKE_strrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* a, lapack_int lda, const float* b,
-                                lapack_int ldb, const float* x, lapack_int ldx,
-                                float* ferr, float* berr, float* work,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dtrrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* a, lapack_int lda,
-                                const double* b, lapack_int ldb,
-                                const double* x, lapack_int ldx, double* ferr,
-                                double* berr, double* work, lapack_int* iwork );
-lapack_int LAPACKE_ctrrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                const lapack_complex_float* x, lapack_int ldx,
-                                float* ferr, float* berr,
-                                lapack_complex_float* work, float* rwork );
-lapack_int LAPACKE_ztrrfs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                const lapack_complex_double* x, lapack_int ldx,
-                                double* ferr, double* berr,
-                                lapack_complex_double* work, double* rwork );
-
-lapack_int LAPACKE_strsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                float* t, lapack_int ldt, float* q,
-                                lapack_int ldq, float* wr, float* wi,
-                                lapack_int* m, float* s, float* sep,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_dtrsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                double* t, lapack_int ldt, double* q,
-                                lapack_int ldq, double* wr, double* wi,
-                                lapack_int* m, double* s, double* sep,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork, lapack_int liwork );
-lapack_int LAPACKE_ctrsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* w, lapack_int* m,
-                                float* s, float* sep,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_ztrsen_work( int matrix_order, char job, char compq,
-                                const lapack_logical* select, lapack_int n,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* w, lapack_int* m,
-                                double* s, double* sep,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_strsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const float* t, lapack_int ldt, const float* vl,
-                                lapack_int ldvl, const float* vr,
-                                lapack_int ldvr, float* s, float* sep,
-                                lapack_int mm, lapack_int* m, float* work,
-                                lapack_int ldwork, lapack_int* iwork );
-lapack_int LAPACKE_dtrsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const double* t, lapack_int ldt,
-                                const double* vl, lapack_int ldvl,
-                                const double* vr, lapack_int ldvr, double* s,
-                                double* sep, lapack_int mm, lapack_int* m,
-                                double* work, lapack_int ldwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ctrsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_float* t, lapack_int ldt,
-                                const lapack_complex_float* vl, lapack_int ldvl,
-                                const lapack_complex_float* vr, lapack_int ldvr,
-                                float* s, float* sep, lapack_int mm,
-                                lapack_int* m, lapack_complex_float* work,
-                                lapack_int ldwork, float* rwork );
-lapack_int LAPACKE_ztrsna_work( int matrix_order, char job, char howmny,
-                                const lapack_logical* select, lapack_int n,
-                                const lapack_complex_double* t, lapack_int ldt,
-                                const lapack_complex_double* vl,
-                                lapack_int ldvl,
-                                const lapack_complex_double* vr,
-                                lapack_int ldvr, double* s, double* sep,
-                                lapack_int mm, lapack_int* m,
-                                lapack_complex_double* work, lapack_int ldwork,
-                                double* rwork );
-
-lapack_int LAPACKE_strsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const float* a, lapack_int lda, const float* b,
-                                lapack_int ldb, float* c, lapack_int ldc,
-                                float* scale );
-lapack_int LAPACKE_dtrsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const double* a, lapack_int lda,
-                                const double* b, lapack_int ldb, double* c,
-                                lapack_int ldc, double* scale );
-lapack_int LAPACKE_ctrsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* b, lapack_int ldb,
-                                lapack_complex_float* c, lapack_int ldc,
-                                float* scale );
-lapack_int LAPACKE_ztrsyl_work( int matrix_order, char trana, char tranb,
-                                lapack_int isgn, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* c, lapack_int ldc,
-                                double* scale );
-
-lapack_int LAPACKE_strtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, float* a, lapack_int lda );
-lapack_int LAPACKE_dtrtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, double* a, lapack_int lda );
-lapack_int LAPACKE_ctrtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_float* a,
-                                lapack_int lda );
-lapack_int LAPACKE_ztrtri_work( int matrix_order, char uplo, char diag,
-                                lapack_int n, lapack_complex_double* a,
-                                lapack_int lda );
-
-lapack_int LAPACKE_strtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const float* a, lapack_int lda, float* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_dtrtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const double* a, lapack_int lda, double* b,
-                                lapack_int ldb );
-lapack_int LAPACKE_ctrtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztrtrs_work( int matrix_order, char uplo, char trans,
-                                char diag, lapack_int n, lapack_int nrhs,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_strttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const float* a, lapack_int lda,
-                                float* arf );
-lapack_int LAPACKE_dtrttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const double* a, lapack_int lda,
-                                double* arf );
-lapack_int LAPACKE_ctrttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* arf );
-lapack_int LAPACKE_ztrttf_work( int matrix_order, char transr, char uplo,
-                                lapack_int n, const lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* arf );
-
-lapack_int LAPACKE_strttp_work( int matrix_order, char uplo, lapack_int n,
-                                const float* a, lapack_int lda, float* ap );
-lapack_int LAPACKE_dtrttp_work( int matrix_order, char uplo, lapack_int n,
-                                const double* a, lapack_int lda, double* ap );
-lapack_int LAPACKE_ctrttp_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* ap );
-lapack_int LAPACKE_ztrttp_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* ap );
-
-lapack_int LAPACKE_stzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                float* a, lapack_int lda, float* tau,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_dtzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                double* a, lapack_int lda, double* tau,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_ctzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_ztzrzf_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungbr_work( int matrix_order, char vect, lapack_int m,
-                                lapack_int n, lapack_int k,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunghr_work( int matrix_order, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunglq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungql_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungqr_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungrq_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int k, lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cungtr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zungtr_work( int matrix_order, char uplo, lapack_int n,
-                                lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmbr_work( int matrix_order, char vect, char side,
-                                char trans, lapack_int m, lapack_int n,
-                                lapack_int k, const lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmhr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int ilo,
-                                lapack_int ihi, const lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmlq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmql_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmqr_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmrq_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const lapack_complex_float* a,
-                                lapack_int lda, const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmrz_work( int matrix_order, char side, char trans,
-                                lapack_int m, lapack_int n, lapack_int k,
-                                lapack_int l, const lapack_complex_double* a,
-                                lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cunmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_float* a, lapack_int lda,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_zunmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_double* a, lapack_int lda,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work, lapack_int lwork );
-
-lapack_int LAPACKE_cupgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* q, lapack_int ldq,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zupgtr_work( int matrix_order, char uplo, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* q, lapack_int ldq,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_cupmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_float* ap,
-                                const lapack_complex_float* tau,
-                                lapack_complex_float* c, lapack_int ldc,
-                                lapack_complex_float* work );
-lapack_int LAPACKE_zupmtr_work( int matrix_order, char side, char uplo,
-                                char trans, lapack_int m, lapack_int n,
-                                const lapack_complex_double* ap,
-                                const lapack_complex_double* tau,
-                                lapack_complex_double* c, lapack_int ldc,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_claghe( int matrix_order, lapack_int n, lapack_int k,
-                           const float* d, lapack_complex_float* a,
-                           lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_zlaghe( int matrix_order, lapack_int n, lapack_int k,
-                           const double* d, lapack_complex_double* a,
-                           lapack_int lda, lapack_int* iseed );
-
-lapack_int LAPACKE_slagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const float* d, float* a, lapack_int lda,
-                           lapack_int* iseed );
-lapack_int LAPACKE_dlagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const double* d, double* a, lapack_int lda,
-                           lapack_int* iseed );
-lapack_int LAPACKE_clagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const float* d, lapack_complex_float* a,
-                           lapack_int lda, lapack_int* iseed );
-lapack_int LAPACKE_zlagsy( int matrix_order, lapack_int n, lapack_int k,
-                           const double* d, lapack_complex_double* a,
-                           lapack_int lda, lapack_int* iseed );
-
-lapack_int LAPACKE_slapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, float* x, lapack_int ldx,
-                           lapack_int* k );
-lapack_int LAPACKE_dlapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, double* x,
-                           lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_clapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, lapack_complex_float* x,
-                           lapack_int ldx, lapack_int* k );
-lapack_int LAPACKE_zlapmr( int matrix_order, lapack_logical forwrd,
-                           lapack_int m, lapack_int n, lapack_complex_double* x,
-                           lapack_int ldx, lapack_int* k );
-
-
-float LAPACKE_slapy2( float x, float y );
-double LAPACKE_dlapy2( double x, double y );
-
-float LAPACKE_slapy3( float x, float y, float z );
-double LAPACKE_dlapy3( double x, double y, double z );
-
-lapack_int LAPACKE_slartgp( float f, float g, float* cs, float* sn, float* r );
-lapack_int LAPACKE_dlartgp( double f, double g, double* cs, double* sn,
-                            double* r );
-
-lapack_int LAPACKE_slartgs( float x, float y, float sigma, float* cs,
-                            float* sn );
-lapack_int LAPACKE_dlartgs( double x, double y, double sigma, double* cs,
-                            double* sn );
-
-
-//LAPACK 3.3.0
-lapack_int LAPACKE_cbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, float* theta, float* phi,
-                           lapack_complex_float* u1, lapack_int ldu1,
-                           lapack_complex_float* u2, lapack_int ldu2,
-                           lapack_complex_float* v1t, lapack_int ldv1t,
-                           lapack_complex_float* v2t, lapack_int ldv2t,
-                           float* b11d, float* b11e, float* b12d, float* b12e,
-                           float* b21d, float* b21e, float* b22d, float* b22e );
-lapack_int LAPACKE_cbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                float* theta, float* phi,
-                                lapack_complex_float* u1, lapack_int ldu1,
-                                lapack_complex_float* u2, lapack_int ldu2,
-                                lapack_complex_float* v1t, lapack_int ldv1t,
-                                lapack_complex_float* v2t, lapack_int ldv2t,
-                                float* b11d, float* b11e, float* b12d,
-                                float* b12e, float* b21d, float* b21e,
-                                float* b22d, float* b22e, float* rwork,
-                                lapack_int lrwork );
-lapack_int LAPACKE_cheswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_cheswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_chetri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_chetri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_chetri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_chetri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_float* work, lapack_int nb );
-lapack_int LAPACKE_chetrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_float* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_chetrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_csyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_csyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, lapack_complex_float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_csyswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_csyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_csytri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_csytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_csytri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_csytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_float* work, lapack_int nb );
-lapack_int LAPACKE_csytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_float* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_csytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_cunbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_float* x11, lapack_int ldx11,
-                           lapack_complex_float* x12, lapack_int ldx12,
-                           lapack_complex_float* x21, lapack_int ldx21,
-                           lapack_complex_float* x22, lapack_int ldx22,
-                           float* theta, float* phi,
-                           lapack_complex_float* taup1,
-                           lapack_complex_float* taup2,
-                           lapack_complex_float* tauq1,
-                           lapack_complex_float* tauq2 );
-lapack_int LAPACKE_cunbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                lapack_complex_float* x11, lapack_int ldx11,
-                                lapack_complex_float* x12, lapack_int ldx12,
-                                lapack_complex_float* x21, lapack_int ldx21,
-                                lapack_complex_float* x22, lapack_int ldx22,
-                                float* theta, float* phi,
-                                lapack_complex_float* taup1,
-                                lapack_complex_float* taup2,
-                                lapack_complex_float* tauq1,
-                                lapack_complex_float* tauq2,
-                                lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_cuncsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_float* x11, lapack_int ldx11,
-                           lapack_complex_float* x12, lapack_int ldx12,
-                           lapack_complex_float* x21, lapack_int ldx21,
-                           lapack_complex_float* x22, lapack_int ldx22,
-                           float* theta, lapack_complex_float* u1,
-                           lapack_int ldu1, lapack_complex_float* u2,
-                           lapack_int ldu2, lapack_complex_float* v1t,
-                           lapack_int ldv1t, lapack_complex_float* v2t,
-                           lapack_int ldv2t );
-lapack_int LAPACKE_cuncsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, lapack_complex_float* x11,
-                                lapack_int ldx11, lapack_complex_float* x12,
-                                lapack_int ldx12, lapack_complex_float* x21,
-                                lapack_int ldx21, lapack_complex_float* x22,
-                                lapack_int ldx22, float* theta,
-                                lapack_complex_float* u1, lapack_int ldu1,
-                                lapack_complex_float* u2, lapack_int ldu2,
-                                lapack_complex_float* v1t, lapack_int ldv1t,
-                                lapack_complex_float* v2t, lapack_int ldv2t,
-                                lapack_complex_float* work, lapack_int lwork,
-                                float* rwork, lapack_int lrwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, double* theta,
-                           double* phi, double* u1, lapack_int ldu1, double* u2,
-                           lapack_int ldu2, double* v1t, lapack_int ldv1t,
-                           double* v2t, lapack_int ldv2t, double* b11d,
-                           double* b11e, double* b12d, double* b12e,
-                           double* b21d, double* b21e, double* b22d,
-                           double* b22e );
-lapack_int LAPACKE_dbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                double* theta, double* phi, double* u1,
-                                lapack_int ldu1, double* u2, lapack_int ldu2,
-                                double* v1t, lapack_int ldv1t, double* v2t,
-                                lapack_int ldv2t, double* b11d, double* b11e,
-                                double* b12d, double* b12e, double* b21d,
-                                double* b21e, double* b22d, double* b22e,
-                                double* work, lapack_int lwork );
-lapack_int LAPACKE_dorbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           double* x11, lapack_int ldx11, double* x12,
-                           lapack_int ldx12, double* x21, lapack_int ldx21,
-                           double* x22, lapack_int ldx22, double* theta,
-                           double* phi, double* taup1, double* taup2,
-                           double* tauq1, double* tauq2 );
-lapack_int LAPACKE_dorbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                double* x11, lapack_int ldx11, double* x12,
-                                lapack_int ldx12, double* x21, lapack_int ldx21,
-                                double* x22, lapack_int ldx22, double* theta,
-                                double* phi, double* taup1, double* taup2,
-                                double* tauq1, double* tauq2, double* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_dorcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           double* x11, lapack_int ldx11, double* x12,
-                           lapack_int ldx12, double* x21, lapack_int ldx21,
-                           double* x22, lapack_int ldx22, double* theta,
-                           double* u1, lapack_int ldu1, double* u2,
-                           lapack_int ldu2, double* v1t, lapack_int ldv1t,
-                           double* v2t, lapack_int ldv2t );
-lapack_int LAPACKE_dorcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, double* x11, lapack_int ldx11,
-                                double* x12, lapack_int ldx12, double* x21,
-                                lapack_int ldx21, double* x22, lapack_int ldx22,
-                                double* theta, double* u1, lapack_int ldu1,
-                                double* u2, lapack_int ldu2, double* v1t,
-                                lapack_int ldv1t, double* v2t, lapack_int ldv2t,
-                                double* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_dsyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            double* a, lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dsyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, double* a, lapack_int lda,
-                                 const lapack_int* ipiv, double* work );
-lapack_int LAPACKE_dsyswapr( int matrix_order, char uplo, lapack_int n,
-                             double* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_dsyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  double* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_dsytri2( int matrix_order, char uplo, lapack_int n,
-                            double* a, lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_dsytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 double* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_dsytri2x( int matrix_order, char uplo, lapack_int n,
-                             double* a, lapack_int lda, const lapack_int* ipiv,
-                             lapack_int nb );
-lapack_int LAPACKE_dsytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  double* a, lapack_int lda,
-                                  const lapack_int* ipiv, double* work,
-                                  lapack_int nb );
-lapack_int LAPACKE_dsytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const double* a, lapack_int lda,
-                            const lapack_int* ipiv, double* b, lapack_int ldb );
-lapack_int LAPACKE_dsytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 double* b, lapack_int ldb, double* work );
-lapack_int LAPACKE_sbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, float* theta, float* phi,
-                           float* u1, lapack_int ldu1, float* u2,
-                           lapack_int ldu2, float* v1t, lapack_int ldv1t,
-                           float* v2t, lapack_int ldv2t, float* b11d,
-                           float* b11e, float* b12d, float* b12e, float* b21d,
-                           float* b21e, float* b22d, float* b22e );
-lapack_int LAPACKE_sbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                float* theta, float* phi, float* u1,
-                                lapack_int ldu1, float* u2, lapack_int ldu2,
-                                float* v1t, lapack_int ldv1t, float* v2t,
-                                lapack_int ldv2t, float* b11d, float* b11e,
-                                float* b12d, float* b12e, float* b21d,
-                                float* b21e, float* b22d, float* b22e,
-                                float* work, lapack_int lwork );
-lapack_int LAPACKE_sorbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q, float* x11,
-                           lapack_int ldx11, float* x12, lapack_int ldx12,
-                           float* x21, lapack_int ldx21, float* x22,
-                           lapack_int ldx22, float* theta, float* phi,
-                           float* taup1, float* taup2, float* tauq1,
-                           float* tauq2 );
-lapack_int LAPACKE_sorbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                float* x11, lapack_int ldx11, float* x12,
-                                lapack_int ldx12, float* x21, lapack_int ldx21,
-                                float* x22, lapack_int ldx22, float* theta,
-                                float* phi, float* taup1, float* taup2,
-                                float* tauq1, float* tauq2, float* work,
-                                lapack_int lwork );
-lapack_int LAPACKE_sorcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q, float* x11,
-                           lapack_int ldx11, float* x12, lapack_int ldx12,
-                           float* x21, lapack_int ldx21, float* x22,
-                           lapack_int ldx22, float* theta, float* u1,
-                           lapack_int ldu1, float* u2, lapack_int ldu2,
-                           float* v1t, lapack_int ldv1t, float* v2t,
-                           lapack_int ldv2t );
-lapack_int LAPACKE_sorcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, float* x11, lapack_int ldx11,
-                                float* x12, lapack_int ldx12, float* x21,
-                                lapack_int ldx21, float* x22, lapack_int ldx22,
-                                float* theta, float* u1, lapack_int ldu1,
-                                float* u2, lapack_int ldu2, float* v1t,
-                                lapack_int ldv1t, float* v2t, lapack_int ldv2t,
-                                float* work, lapack_int lwork,
-                                lapack_int* iwork );
-lapack_int LAPACKE_ssyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            float* a, lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_ssyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, float* a, lapack_int lda,
-                                 const lapack_int* ipiv, float* work );
-lapack_int LAPACKE_ssyswapr( int matrix_order, char uplo, lapack_int n,
-                             float* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_ssyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  float* a, lapack_int i1, lapack_int i2 );
-lapack_int LAPACKE_ssytri2( int matrix_order, char uplo, lapack_int n, float* a,
-                            lapack_int lda, const lapack_int* ipiv );
-lapack_int LAPACKE_ssytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 float* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_float* work, lapack_int lwork );
-lapack_int LAPACKE_ssytri2x( int matrix_order, char uplo, lapack_int n,
-                             float* a, lapack_int lda, const lapack_int* ipiv,
-                             lapack_int nb );
-lapack_int LAPACKE_ssytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  float* a, lapack_int lda,
-                                  const lapack_int* ipiv, float* work,
-                                  lapack_int nb );
-lapack_int LAPACKE_ssytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const float* a, lapack_int lda,
-                            const lapack_int* ipiv, float* b, lapack_int ldb );
-lapack_int LAPACKE_ssytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const float* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 float* b, lapack_int ldb, float* work );
-lapack_int LAPACKE_zbbcsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, lapack_int m,
-                           lapack_int p, lapack_int q, double* theta,
-                           double* phi, lapack_complex_double* u1,
-                           lapack_int ldu1, lapack_complex_double* u2,
-                           lapack_int ldu2, lapack_complex_double* v1t,
-                           lapack_int ldv1t, lapack_complex_double* v2t,
-                           lapack_int ldv2t, double* b11d, double* b11e,
-                           double* b12d, double* b12e, double* b21d,
-                           double* b21e, double* b22d, double* b22e );
-lapack_int LAPACKE_zbbcsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                double* theta, double* phi,
-                                lapack_complex_double* u1, lapack_int ldu1,
-                                lapack_complex_double* u2, lapack_int ldu2,
-                                lapack_complex_double* v1t, lapack_int ldv1t,
-                                lapack_complex_double* v2t, lapack_int ldv2t,
-                                double* b11d, double* b11e, double* b12d,
-                                double* b12e, double* b21d, double* b21e,
-                                double* b22d, double* b22e, double* rwork,
-                                lapack_int lrwork );
-lapack_int LAPACKE_zheswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_zheswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_zhetri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_zhetri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_zhetri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_zhetri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_double* work, lapack_int nb );
-lapack_int LAPACKE_zhetrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_double* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_double* b, lapack_int ldb );
-lapack_int LAPACKE_zhetrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* work );
-lapack_int LAPACKE_zsyconv( int matrix_order, char uplo, char way, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_zsyconv_work( int matrix_order, char uplo, char way,
-                                 lapack_int n, lapack_complex_double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_double* work );
-lapack_int LAPACKE_zsyswapr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int i1,
-                             lapack_int i2 );
-lapack_int LAPACKE_zsyswapr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int i1,
-                                  lapack_int i2 );
-lapack_int LAPACKE_zsytri2( int matrix_order, char uplo, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            const lapack_int* ipiv );
-lapack_int LAPACKE_zsytri2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 const lapack_int* ipiv,
-                                 lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_zsytri2x( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double* a, lapack_int lda,
-                             const lapack_int* ipiv, lapack_int nb );
-lapack_int LAPACKE_zsytri2x_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double* a, lapack_int lda,
-                                  const lapack_int* ipiv,
-                                  lapack_complex_double* work, lapack_int nb );
-lapack_int LAPACKE_zsytrs2( int matrix_order, char uplo, lapack_int n,
-                            lapack_int nrhs, const lapack_complex_double* a,
-                            lapack_int lda, const lapack_int* ipiv,
-                            lapack_complex_double* b, lapack_int ldb );
-lapack_int LAPACKE_zsytrs2_work( int matrix_order, char uplo, lapack_int n,
-                                 lapack_int nrhs, const lapack_complex_double* a,
-                                 lapack_int lda, const lapack_int* ipiv,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* work );
-lapack_int LAPACKE_zunbdb( int matrix_order, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_double* x11, lapack_int ldx11,
-                           lapack_complex_double* x12, lapack_int ldx12,
-                           lapack_complex_double* x21, lapack_int ldx21,
-                           lapack_complex_double* x22, lapack_int ldx22,
-                           double* theta, double* phi,
-                           lapack_complex_double* taup1,
-                           lapack_complex_double* taup2,
-                           lapack_complex_double* tauq1,
-                           lapack_complex_double* tauq2 );
-lapack_int LAPACKE_zunbdb_work( int matrix_order, char trans, char signs,
-                                lapack_int m, lapack_int p, lapack_int q,
-                                lapack_complex_double* x11, lapack_int ldx11,
-                                lapack_complex_double* x12, lapack_int ldx12,
-                                lapack_complex_double* x21, lapack_int ldx21,
-                                lapack_complex_double* x22, lapack_int ldx22,
-                                double* theta, double* phi,
-                                lapack_complex_double* taup1,
-                                lapack_complex_double* taup2,
-                                lapack_complex_double* tauq1,
-                                lapack_complex_double* tauq2,
-                                lapack_complex_double* work, lapack_int lwork );
-lapack_int LAPACKE_zuncsd( int matrix_order, char jobu1, char jobu2,
-                           char jobv1t, char jobv2t, char trans, char signs,
-                           lapack_int m, lapack_int p, lapack_int q,
-                           lapack_complex_double* x11, lapack_int ldx11,
-                           lapack_complex_double* x12, lapack_int ldx12,
-                           lapack_complex_double* x21, lapack_int ldx21,
-                           lapack_complex_double* x22, lapack_int ldx22,
-                           double* theta, lapack_complex_double* u1,
-                           lapack_int ldu1, lapack_complex_double* u2,
-                           lapack_int ldu2, lapack_complex_double* v1t,
-                           lapack_int ldv1t, lapack_complex_double* v2t,
-                           lapack_int ldv2t );
-lapack_int LAPACKE_zuncsd_work( int matrix_order, char jobu1, char jobu2,
-                                char jobv1t, char jobv2t, char trans,
-                                char signs, lapack_int m, lapack_int p,
-                                lapack_int q, lapack_complex_double* x11,
-                                lapack_int ldx11, lapack_complex_double* x12,
-                                lapack_int ldx12, lapack_complex_double* x21,
-                                lapack_int ldx21, lapack_complex_double* x22,
-                                lapack_int ldx22, double* theta,
-                                lapack_complex_double* u1, lapack_int ldu1,
-                                lapack_complex_double* u2, lapack_int ldu2,
-                                lapack_complex_double* v1t, lapack_int ldv1t,
-                                lapack_complex_double* v2t, lapack_int ldv2t,
-                                lapack_complex_double* work, lapack_int lwork,
-                                double* rwork, lapack_int lrwork,
-                                lapack_int* iwork );
-//LAPACK 3.4.0
-lapack_int LAPACKE_sgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const float* v, lapack_int ldv,
-                            const float* t, lapack_int ldt, float* c,
-                            lapack_int ldc );
-lapack_int LAPACKE_dgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const double* v, lapack_int ldv,
-                            const double* t, lapack_int ldt, double* c,
-                            lapack_int ldc );
-lapack_int LAPACKE_cgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const lapack_complex_float* v,
-                            lapack_int ldv, const lapack_complex_float* t,
-                            lapack_int ldt, lapack_complex_float* c,
-                            lapack_int ldc );
-lapack_int LAPACKE_zgemqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int nb, const lapack_complex_double* v,
-                            lapack_int ldv, const lapack_complex_double* t,
-                            lapack_int ldt, lapack_complex_double* c,
-                            lapack_int ldc );
-
-lapack_int LAPACKE_sgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, float* a, lapack_int lda, float* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_dgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, double* a, lapack_int lda, double* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_cgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_zgeqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int nb, lapack_complex_double* a,
-                           lapack_int lda, lapack_complex_double* t,
-                           lapack_int ldt );
-
-lapack_int LAPACKE_sgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_dgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_cgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_sgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_dgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* t,
-                            lapack_int ldt );
-lapack_int LAPACKE_cgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt3( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb, const float* v,
-                            lapack_int ldv, const float* t, lapack_int ldt,
-                            float* a, lapack_int lda, float* b,
-                            lapack_int ldb );
-lapack_int LAPACKE_dtpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb, const double* v,
-                            lapack_int ldv, const double* t, lapack_int ldt,
-                            double* a, lapack_int lda, double* b,
-                            lapack_int ldb );
-lapack_int LAPACKE_ctpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb,
-                            const lapack_complex_float* v, lapack_int ldv,
-                            const lapack_complex_float* t, lapack_int ldt,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* b, lapack_int ldb );
-lapack_int LAPACKE_ztpmqrt( int matrix_order, char side, char trans,
-                            lapack_int m, lapack_int n, lapack_int k,
-                            lapack_int l, lapack_int nb,
-                            const lapack_complex_double* v, lapack_int ldv,
-                            const lapack_complex_double* t, lapack_int ldt,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* b, lapack_int ldb );
-
-lapack_int LAPACKE_dtpqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int l, lapack_int nb, double* a,
-                           lapack_int lda, double* b, lapack_int ldb, double* t,
-                           lapack_int ldt );
-lapack_int LAPACKE_ctpqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int l, lapack_int nb, lapack_complex_float* a,
-                           lapack_int lda, lapack_complex_float* t,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_int ldt );
-lapack_int LAPACKE_ztpqrt( int matrix_order, lapack_int m, lapack_int n,
-                           lapack_int l, lapack_int nb,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            float* a, lapack_int lda, float* b, lapack_int ldb,
-                            float* t, lapack_int ldt );
-lapack_int LAPACKE_dtpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            double* a, lapack_int lda, double* b,
-                            lapack_int ldb, double* t, lapack_int ldt );
-lapack_int LAPACKE_ctpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_float* a, lapack_int lda,
-                            lapack_complex_float* b, lapack_int ldb,
-                            lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_ztpqrt2( int matrix_order, lapack_int m, lapack_int n,
-                            lapack_complex_double* a, lapack_int lda,
-                            lapack_complex_double* b, lapack_int ldb,
-                            lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l, const float* v,
-                           lapack_int ldv, const float* t, lapack_int ldt,
-                           float* a, lapack_int lda, float* b, lapack_int ldb,
-                           lapack_int myldwork );
-lapack_int LAPACKE_dtprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l, const double* v,
-                           lapack_int ldv, const double* t, lapack_int ldt,
-                           double* a, lapack_int lda, double* b, lapack_int ldb,
-                           lapack_int myldwork );
-lapack_int LAPACKE_ctprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l,
-                           const lapack_complex_float* v, lapack_int ldv,
-                           const lapack_complex_float* t, lapack_int ldt,
-                           lapack_complex_float* a, lapack_int lda,
-                           lapack_complex_float* b, lapack_int ldb,
-                           lapack_int myldwork );
-lapack_int LAPACKE_ztprfb( int matrix_order, char side, char trans, char direct,
-                           char storev, lapack_int m, lapack_int n,
-                           lapack_int k, lapack_int l,
-                           const lapack_complex_double* v, lapack_int ldv,
-                           const lapack_complex_double* t, lapack_int ldt,
-                           lapack_complex_double* a, lapack_int lda,
-                           lapack_complex_double* b, lapack_int ldb,
-                           lapack_int myldwork );
-
-lapack_int LAPACKE_sgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const float* v, lapack_int ldv,
-                                 const float* t, lapack_int ldt, float* c,
-                                 lapack_int ldc, float* work );
-lapack_int LAPACKE_dgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const double* v, lapack_int ldv,
-                                 const double* t, lapack_int ldt, double* c,
-                                 lapack_int ldc, double* work );
-lapack_int LAPACKE_cgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const lapack_complex_float* v,
-                                 lapack_int ldv, const lapack_complex_float* t,
-                                 lapack_int ldt, lapack_complex_float* c,
-                                 lapack_int ldc, lapack_complex_float* work );
-lapack_int LAPACKE_zgemqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int nb, const lapack_complex_double* v,
-                                 lapack_int ldv, const lapack_complex_double* t,
-                                 lapack_int ldt, lapack_complex_double* c,
-                                 lapack_int ldc, lapack_complex_double* work );
-
-lapack_int LAPACKE_sgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, float* a, lapack_int lda,
-                                float* t, lapack_int ldt, float* work );
-lapack_int LAPACKE_dgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, double* a, lapack_int lda,
-                                double* t, lapack_int ldt, double* work );
-lapack_int LAPACKE_cgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, lapack_complex_float* a,
-                                lapack_int lda, lapack_complex_float* t,
-                                lapack_int ldt, lapack_complex_float* work );
-lapack_int LAPACKE_zgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int nb, lapack_complex_double* a,
-                                lapack_int lda, lapack_complex_double* t,
-                                lapack_int ldt, lapack_complex_double* work );
-
-lapack_int LAPACKE_sgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_dgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_cgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_sgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_dgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* t,
-                                 lapack_int ldt );
-lapack_int LAPACKE_cgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_zgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb, const float* v,
-                                 lapack_int ldv, const float* t, lapack_int ldt,
-                                 float* a, lapack_int lda, float* b,
-                                 lapack_int ldb, float* work );
-lapack_int LAPACKE_dtpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb, const double* v,
-                                 lapack_int ldv, const double* t,
-                                 lapack_int ldt, double* a, lapack_int lda,
-                                 double* b, lapack_int ldb, double* work );
-lapack_int LAPACKE_ctpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb,
-                                 const lapack_complex_float* v, lapack_int ldv,
-                                 const lapack_complex_float* t, lapack_int ldt,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* work );
-lapack_int LAPACKE_ztpmqrt_work( int matrix_order, char side, char trans,
-                                 lapack_int m, lapack_int n, lapack_int k,
-                                 lapack_int l, lapack_int nb,
-                                 const lapack_complex_double* v, lapack_int ldv,
-                                 const lapack_complex_double* t, lapack_int ldt,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* work );
-
-lapack_int LAPACKE_dtpqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int l, lapack_int nb, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                double* t, lapack_int ldt, double* work );
-lapack_int LAPACKE_ctpqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int l, lapack_int nb,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* t,
-                                lapack_complex_float* b, lapack_int ldb,
-                                lapack_int ldt, lapack_complex_float* work );
-lapack_int LAPACKE_ztpqrt_work( int matrix_order, lapack_int m, lapack_int n,
-                                lapack_int l, lapack_int nb,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* work );
-
-lapack_int LAPACKE_stpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 float* a, lapack_int lda, float* b,
-                                 lapack_int ldb, float* t, lapack_int ldt );
-lapack_int LAPACKE_dtpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 double* a, lapack_int lda, double* b,
-                                 lapack_int ldb, double* t, lapack_int ldt );
-lapack_int LAPACKE_ctpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_float* a, lapack_int lda,
-                                 lapack_complex_float* b, lapack_int ldb,
-                                 lapack_complex_float* t, lapack_int ldt );
-lapack_int LAPACKE_ztpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
-                                 lapack_complex_double* a, lapack_int lda,
-                                 lapack_complex_double* b, lapack_int ldb,
-                                 lapack_complex_double* t, lapack_int ldt );
-
-lapack_int LAPACKE_stprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const float* v, lapack_int ldv, const float* t,
-                                lapack_int ldt, float* a, lapack_int lda,
-                                float* b, lapack_int ldb, const float* mywork,
-                                lapack_int myldwork );
-lapack_int LAPACKE_dtprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const double* v, lapack_int ldv,
-                                const double* t, lapack_int ldt, double* a,
-                                lapack_int lda, double* b, lapack_int ldb,
-                                const double* mywork, lapack_int myldwork );
-lapack_int LAPACKE_ctprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const lapack_complex_float* v, lapack_int ldv,
-                                const lapack_complex_float* t, lapack_int ldt,
-                                lapack_complex_float* a, lapack_int lda,
-                                lapack_complex_float* b, lapack_int ldb,
-                                const float* mywork, lapack_int myldwork );
-lapack_int LAPACKE_ztprfb_work( int matrix_order, char side, char trans,
-                                char direct, char storev, lapack_int m,
-                                lapack_int n, lapack_int k, lapack_int l,
-                                const lapack_complex_double* v, lapack_int ldv,
-                                const lapack_complex_double* t, lapack_int ldt,
-                                lapack_complex_double* a, lapack_int lda,
-                                lapack_complex_double* b, lapack_int ldb,
-                                const double* mywork, lapack_int myldwork );
-//LAPACK 3.X.X
-lapack_int LAPACKE_csyr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_float alpha,
-                             const lapack_complex_float* x, lapack_int incx,
-                             lapack_complex_float* a, lapack_int lda );
-lapack_int LAPACKE_zsyr( int matrix_order, char uplo, lapack_int n,
-                             lapack_complex_double alpha,
-                             const lapack_complex_double* x, lapack_int incx,
-                             lapack_complex_double* a, lapack_int lda );
-
-lapack_int LAPACKE_csyr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_float alpha,
-                                  const lapack_complex_float* x,
-                                  lapack_int incx, lapack_complex_float* a,
-                                  lapack_int lda );
-lapack_int LAPACKE_zsyr_work( int matrix_order, char uplo, lapack_int n,
-                                  lapack_complex_double alpha,
-                                  const lapack_complex_double* x,
-                                  lapack_int incx, lapack_complex_double* a,
-                                  lapack_int lda );
-
-
-
-#define LAPACK_sgetrf LAPACK_GLOBAL(sgetrf,SGETRF)
-#define LAPACK_dgetrf LAPACK_GLOBAL(dgetrf,DGETRF)
-#define LAPACK_cgetrf LAPACK_GLOBAL(cgetrf,CGETRF)
-#define LAPACK_zgetrf LAPACK_GLOBAL(zgetrf,ZGETRF)
-#define LAPACK_sgbtrf LAPACK_GLOBAL(sgbtrf,SGBTRF)
-#define LAPACK_dgbtrf LAPACK_GLOBAL(dgbtrf,DGBTRF)
-#define LAPACK_cgbtrf LAPACK_GLOBAL(cgbtrf,CGBTRF)
-#define LAPACK_zgbtrf LAPACK_GLOBAL(zgbtrf,ZGBTRF)
-#define LAPACK_sgttrf LAPACK_GLOBAL(sgttrf,SGTTRF)
-#define LAPACK_dgttrf LAPACK_GLOBAL(dgttrf,DGTTRF)
-#define LAPACK_cgttrf LAPACK_GLOBAL(cgttrf,CGTTRF)
-#define LAPACK_zgttrf LAPACK_GLOBAL(zgttrf,ZGTTRF)
-#define LAPACK_spotrf LAPACK_GLOBAL(spotrf,SPOTRF)
-#define LAPACK_dpotrf LAPACK_GLOBAL(dpotrf,DPOTRF)
-#define LAPACK_cpotrf LAPACK_GLOBAL(cpotrf,CPOTRF)
-#define LAPACK_zpotrf LAPACK_GLOBAL(zpotrf,ZPOTRF)
-#define LAPACK_dpstrf LAPACK_GLOBAL(dpstrf,DPSTRF)
-#define LAPACK_spstrf LAPACK_GLOBAL(spstrf,SPSTRF)
-#define LAPACK_zpstrf LAPACK_GLOBAL(zpstrf,ZPSTRF)
-#define LAPACK_cpstrf LAPACK_GLOBAL(cpstrf,CPSTRF)
-#define LAPACK_dpftrf LAPACK_GLOBAL(dpftrf,DPFTRF)
-#define LAPACK_spftrf LAPACK_GLOBAL(spftrf,SPFTRF)
-#define LAPACK_zpftrf LAPACK_GLOBAL(zpftrf,ZPFTRF)
-#define LAPACK_cpftrf LAPACK_GLOBAL(cpftrf,CPFTRF)
-#define LAPACK_spptrf LAPACK_GLOBAL(spptrf,SPPTRF)
-#define LAPACK_dpptrf LAPACK_GLOBAL(dpptrf,DPPTRF)
-#define LAPACK_cpptrf LAPACK_GLOBAL(cpptrf,CPPTRF)
-#define LAPACK_zpptrf LAPACK_GLOBAL(zpptrf,ZPPTRF)
-#define LAPACK_spbtrf LAPACK_GLOBAL(spbtrf,SPBTRF)
-#define LAPACK_dpbtrf LAPACK_GLOBAL(dpbtrf,DPBTRF)
-#define LAPACK_cpbtrf LAPACK_GLOBAL(cpbtrf,CPBTRF)
-#define LAPACK_zpbtrf LAPACK_GLOBAL(zpbtrf,ZPBTRF)
-#define LAPACK_spttrf LAPACK_GLOBAL(spttrf,SPTTRF)
-#define LAPACK_dpttrf LAPACK_GLOBAL(dpttrf,DPTTRF)
-#define LAPACK_cpttrf LAPACK_GLOBAL(cpttrf,CPTTRF)
-#define LAPACK_zpttrf LAPACK_GLOBAL(zpttrf,ZPTTRF)
-#define LAPACK_ssytrf LAPACK_GLOBAL(ssytrf,SSYTRF)
-#define LAPACK_dsytrf LAPACK_GLOBAL(dsytrf,DSYTRF)
-#define LAPACK_csytrf LAPACK_GLOBAL(csytrf,CSYTRF)
-#define LAPACK_zsytrf LAPACK_GLOBAL(zsytrf,ZSYTRF)
-#define LAPACK_chetrf LAPACK_GLOBAL(chetrf,CHETRF)
-#define LAPACK_zhetrf LAPACK_GLOBAL(zhetrf,ZHETRF)
-#define LAPACK_ssptrf LAPACK_GLOBAL(ssptrf,SSPTRF)
-#define LAPACK_dsptrf LAPACK_GLOBAL(dsptrf,DSPTRF)
-#define LAPACK_csptrf LAPACK_GLOBAL(csptrf,CSPTRF)
-#define LAPACK_zsptrf LAPACK_GLOBAL(zsptrf,ZSPTRF)
-#define LAPACK_chptrf LAPACK_GLOBAL(chptrf,CHPTRF)
-#define LAPACK_zhptrf LAPACK_GLOBAL(zhptrf,ZHPTRF)
-#define LAPACK_sgetrs LAPACK_GLOBAL(sgetrs,SGETRS)
-#define LAPACK_dgetrs LAPACK_GLOBAL(dgetrs,DGETRS)
-#define LAPACK_cgetrs LAPACK_GLOBAL(cgetrs,CGETRS)
-#define LAPACK_zgetrs LAPACK_GLOBAL(zgetrs,ZGETRS)
-#define LAPACK_sgbtrs LAPACK_GLOBAL(sgbtrs,SGBTRS)
-#define LAPACK_dgbtrs LAPACK_GLOBAL(dgbtrs,DGBTRS)
-#define LAPACK_cgbtrs LAPACK_GLOBAL(cgbtrs,CGBTRS)
-#define LAPACK_zgbtrs LAPACK_GLOBAL(zgbtrs,ZGBTRS)
-#define LAPACK_sgttrs LAPACK_GLOBAL(sgttrs,SGTTRS)
-#define LAPACK_dgttrs LAPACK_GLOBAL(dgttrs,DGTTRS)
-#define LAPACK_cgttrs LAPACK_GLOBAL(cgttrs,CGTTRS)
-#define LAPACK_zgttrs LAPACK_GLOBAL(zgttrs,ZGTTRS)
-#define LAPACK_spotrs LAPACK_GLOBAL(spotrs,SPOTRS)
-#define LAPACK_dpotrs LAPACK_GLOBAL(dpotrs,DPOTRS)
-#define LAPACK_cpotrs LAPACK_GLOBAL(cpotrs,CPOTRS)
-#define LAPACK_zpotrs LAPACK_GLOBAL(zpotrs,ZPOTRS)
-#define LAPACK_dpftrs LAPACK_GLOBAL(dpftrs,DPFTRS)
-#define LAPACK_spftrs LAPACK_GLOBAL(spftrs,SPFTRS)
-#define LAPACK_zpftrs LAPACK_GLOBAL(zpftrs,ZPFTRS)
-#define LAPACK_cpftrs LAPACK_GLOBAL(cpftrs,CPFTRS)
-#define LAPACK_spptrs LAPACK_GLOBAL(spptrs,SPPTRS)
-#define LAPACK_dpptrs LAPACK_GLOBAL(dpptrs,DPPTRS)
-#define LAPACK_cpptrs LAPACK_GLOBAL(cpptrs,CPPTRS)
-#define LAPACK_zpptrs LAPACK_GLOBAL(zpptrs,ZPPTRS)
-#define LAPACK_spbtrs LAPACK_GLOBAL(spbtrs,SPBTRS)
-#define LAPACK_dpbtrs LAPACK_GLOBAL(dpbtrs,DPBTRS)
-#define LAPACK_cpbtrs LAPACK_GLOBAL(cpbtrs,CPBTRS)
-#define LAPACK_zpbtrs LAPACK_GLOBAL(zpbtrs,ZPBTRS)
-#define LAPACK_spttrs LAPACK_GLOBAL(spttrs,SPTTRS)
-#define LAPACK_dpttrs LAPACK_GLOBAL(dpttrs,DPTTRS)
-#define LAPACK_cpttrs LAPACK_GLOBAL(cpttrs,CPTTRS)
-#define LAPACK_zpttrs LAPACK_GLOBAL(zpttrs,ZPTTRS)
-#define LAPACK_ssytrs LAPACK_GLOBAL(ssytrs,SSYTRS)
-#define LAPACK_dsytrs LAPACK_GLOBAL(dsytrs,DSYTRS)
-#define LAPACK_csytrs LAPACK_GLOBAL(csytrs,CSYTRS)
-#define LAPACK_zsytrs LAPACK_GLOBAL(zsytrs,ZSYTRS)
-#define LAPACK_chetrs LAPACK_GLOBAL(chetrs,CHETRS)
-#define LAPACK_zhetrs LAPACK_GLOBAL(zhetrs,ZHETRS)
-#define LAPACK_ssptrs LAPACK_GLOBAL(ssptrs,SSPTRS)
-#define LAPACK_dsptrs LAPACK_GLOBAL(dsptrs,DSPTRS)
-#define LAPACK_csptrs LAPACK_GLOBAL(csptrs,CSPTRS)
-#define LAPACK_zsptrs LAPACK_GLOBAL(zsptrs,ZSPTRS)
-#define LAPACK_chptrs LAPACK_GLOBAL(chptrs,CHPTRS)
-#define LAPACK_zhptrs LAPACK_GLOBAL(zhptrs,ZHPTRS)
-#define LAPACK_strtrs LAPACK_GLOBAL(strtrs,STRTRS)
-#define LAPACK_dtrtrs LAPACK_GLOBAL(dtrtrs,DTRTRS)
-#define LAPACK_ctrtrs LAPACK_GLOBAL(ctrtrs,CTRTRS)
-#define LAPACK_ztrtrs LAPACK_GLOBAL(ztrtrs,ZTRTRS)
-#define LAPACK_stptrs LAPACK_GLOBAL(stptrs,STPTRS)
-#define LAPACK_dtptrs LAPACK_GLOBAL(dtptrs,DTPTRS)
-#define LAPACK_ctptrs LAPACK_GLOBAL(ctptrs,CTPTRS)
-#define LAPACK_ztptrs LAPACK_GLOBAL(ztptrs,ZTPTRS)
-#define LAPACK_stbtrs LAPACK_GLOBAL(stbtrs,STBTRS)
-#define LAPACK_dtbtrs LAPACK_GLOBAL(dtbtrs,DTBTRS)
-#define LAPACK_ctbtrs LAPACK_GLOBAL(ctbtrs,CTBTRS)
-#define LAPACK_ztbtrs LAPACK_GLOBAL(ztbtrs,ZTBTRS)
-#define LAPACK_sgecon LAPACK_GLOBAL(sgecon,SGECON)
-#define LAPACK_dgecon LAPACK_GLOBAL(dgecon,DGECON)
-#define LAPACK_cgecon LAPACK_GLOBAL(cgecon,CGECON)
-#define LAPACK_zgecon LAPACK_GLOBAL(zgecon,ZGECON)
-#define LAPACK_sgbcon LAPACK_GLOBAL(sgbcon,SGBCON)
-#define LAPACK_dgbcon LAPACK_GLOBAL(dgbcon,DGBCON)
-#define LAPACK_cgbcon LAPACK_GLOBAL(cgbcon,CGBCON)
-#define LAPACK_zgbcon LAPACK_GLOBAL(zgbcon,ZGBCON)
-#define LAPACK_sgtcon LAPACK_GLOBAL(sgtcon,SGTCON)
-#define LAPACK_dgtcon LAPACK_GLOBAL(dgtcon,DGTCON)
-#define LAPACK_cgtcon LAPACK_GLOBAL(cgtcon,CGTCON)
-#define LAPACK_zgtcon LAPACK_GLOBAL(zgtcon,ZGTCON)
-#define LAPACK_spocon LAPACK_GLOBAL(spocon,SPOCON)
-#define LAPACK_dpocon LAPACK_GLOBAL(dpocon,DPOCON)
-#define LAPACK_cpocon LAPACK_GLOBAL(cpocon,CPOCON)
-#define LAPACK_zpocon LAPACK_GLOBAL(zpocon,ZPOCON)
-#define LAPACK_sppcon LAPACK_GLOBAL(sppcon,SPPCON)
-#define LAPACK_dppcon LAPACK_GLOBAL(dppcon,DPPCON)
-#define LAPACK_cppcon LAPACK_GLOBAL(cppcon,CPPCON)
-#define LAPACK_zppcon LAPACK_GLOBAL(zppcon,ZPPCON)
-#define LAPACK_spbcon LAPACK_GLOBAL(spbcon,SPBCON)
-#define LAPACK_dpbcon LAPACK_GLOBAL(dpbcon,DPBCON)
-#define LAPACK_cpbcon LAPACK_GLOBAL(cpbcon,CPBCON)
-#define LAPACK_zpbcon LAPACK_GLOBAL(zpbcon,ZPBCON)
-#define LAPACK_sptcon LAPACK_GLOBAL(sptcon,SPTCON)
-#define LAPACK_dptcon LAPACK_GLOBAL(dptcon,DPTCON)
-#define LAPACK_cptcon LAPACK_GLOBAL(cptcon,CPTCON)
-#define LAPACK_zptcon LAPACK_GLOBAL(zptcon,ZPTCON)
-#define LAPACK_ssycon LAPACK_GLOBAL(ssycon,SSYCON)
-#define LAPACK_dsycon LAPACK_GLOBAL(dsycon,DSYCON)
-#define LAPACK_csycon LAPACK_GLOBAL(csycon,CSYCON)
-#define LAPACK_zsycon LAPACK_GLOBAL(zsycon,ZSYCON)
-#define LAPACK_checon LAPACK_GLOBAL(checon,CHECON)
-#define LAPACK_zhecon LAPACK_GLOBAL(zhecon,ZHECON)
-#define LAPACK_sspcon LAPACK_GLOBAL(sspcon,SSPCON)
-#define LAPACK_dspcon LAPACK_GLOBAL(dspcon,DSPCON)
-#define LAPACK_cspcon LAPACK_GLOBAL(cspcon,CSPCON)
-#define LAPACK_zspcon LAPACK_GLOBAL(zspcon,ZSPCON)
-#define LAPACK_chpcon LAPACK_GLOBAL(chpcon,CHPCON)
-#define LAPACK_zhpcon LAPACK_GLOBAL(zhpcon,ZHPCON)
-#define LAPACK_strcon LAPACK_GLOBAL(strcon,STRCON)
-#define LAPACK_dtrcon LAPACK_GLOBAL(dtrcon,DTRCON)
-#define LAPACK_ctrcon LAPACK_GLOBAL(ctrcon,CTRCON)
-#define LAPACK_ztrcon LAPACK_GLOBAL(ztrcon,ZTRCON)
-#define LAPACK_stpcon LAPACK_GLOBAL(stpcon,STPCON)
-#define LAPACK_dtpcon LAPACK_GLOBAL(dtpcon,DTPCON)
-#define LAPACK_ctpcon LAPACK_GLOBAL(ctpcon,CTPCON)
-#define LAPACK_ztpcon LAPACK_GLOBAL(ztpcon,ZTPCON)
-#define LAPACK_stbcon LAPACK_GLOBAL(stbcon,STBCON)
-#define LAPACK_dtbcon LAPACK_GLOBAL(dtbcon,DTBCON)
-#define LAPACK_ctbcon LAPACK_GLOBAL(ctbcon,CTBCON)
-#define LAPACK_ztbcon LAPACK_GLOBAL(ztbcon,ZTBCON)
-#define LAPACK_sgerfs LAPACK_GLOBAL(sgerfs,SGERFS)
-#define LAPACK_dgerfs LAPACK_GLOBAL(dgerfs,DGERFS)
-#define LAPACK_cgerfs LAPACK_GLOBAL(cgerfs,CGERFS)
-#define LAPACK_zgerfs LAPACK_GLOBAL(zgerfs,ZGERFS)
-#define LAPACK_dgerfsx LAPACK_GLOBAL(dgerfsx,DGERFSX)
-#define LAPACK_sgerfsx LAPACK_GLOBAL(sgerfsx,SGERFSX)
-#define LAPACK_zgerfsx LAPACK_GLOBAL(zgerfsx,ZGERFSX)
-#define LAPACK_cgerfsx LAPACK_GLOBAL(cgerfsx,CGERFSX)
-#define LAPACK_sgbrfs LAPACK_GLOBAL(sgbrfs,SGBRFS)
-#define LAPACK_dgbrfs LAPACK_GLOBAL(dgbrfs,DGBRFS)
-#define LAPACK_cgbrfs LAPACK_GLOBAL(cgbrfs,CGBRFS)
-#define LAPACK_zgbrfs LAPACK_GLOBAL(zgbrfs,ZGBRFS)
-#define LAPACK_dgbrfsx LAPACK_GLOBAL(dgbrfsx,DGBRFSX)
-#define LAPACK_sgbrfsx LAPACK_GLOBAL(sgbrfsx,SGBRFSX)
-#define LAPACK_zgbrfsx LAPACK_GLOBAL(zgbrfsx,ZGBRFSX)
-#define LAPACK_cgbrfsx LAPACK_GLOBAL(cgbrfsx,CGBRFSX)
-#define LAPACK_sgtrfs LAPACK_GLOBAL(sgtrfs,SGTRFS)
-#define LAPACK_dgtrfs LAPACK_GLOBAL(dgtrfs,DGTRFS)
-#define LAPACK_cgtrfs LAPACK_GLOBAL(cgtrfs,CGTRFS)
-#define LAPACK_zgtrfs LAPACK_GLOBAL(zgtrfs,ZGTRFS)
-#define LAPACK_sporfs LAPACK_GLOBAL(sporfs,SPORFS)
-#define LAPACK_dporfs LAPACK_GLOBAL(dporfs,DPORFS)
-#define LAPACK_cporfs LAPACK_GLOBAL(cporfs,CPORFS)
-#define LAPACK_zporfs LAPACK_GLOBAL(zporfs,ZPORFS)
-#define LAPACK_dporfsx LAPACK_GLOBAL(dporfsx,DPORFSX)
-#define LAPACK_sporfsx LAPACK_GLOBAL(sporfsx,SPORFSX)
-#define LAPACK_zporfsx LAPACK_GLOBAL(zporfsx,ZPORFSX)
-#define LAPACK_cporfsx LAPACK_GLOBAL(cporfsx,CPORFSX)
-#define LAPACK_spprfs LAPACK_GLOBAL(spprfs,SPPRFS)
-#define LAPACK_dpprfs LAPACK_GLOBAL(dpprfs,DPPRFS)
-#define LAPACK_cpprfs LAPACK_GLOBAL(cpprfs,CPPRFS)
-#define LAPACK_zpprfs LAPACK_GLOBAL(zpprfs,ZPPRFS)
-#define LAPACK_spbrfs LAPACK_GLOBAL(spbrfs,SPBRFS)
-#define LAPACK_dpbrfs LAPACK_GLOBAL(dpbrfs,DPBRFS)
-#define LAPACK_cpbrfs LAPACK_GLOBAL(cpbrfs,CPBRFS)
-#define LAPACK_zpbrfs LAPACK_GLOBAL(zpbrfs,ZPBRFS)
-#define LAPACK_sptrfs LAPACK_GLOBAL(sptrfs,SPTRFS)
-#define LAPACK_dptrfs LAPACK_GLOBAL(dptrfs,DPTRFS)
-#define LAPACK_cptrfs LAPACK_GLOBAL(cptrfs,CPTRFS)
-#define LAPACK_zptrfs LAPACK_GLOBAL(zptrfs,ZPTRFS)
-#define LAPACK_ssyrfs LAPACK_GLOBAL(ssyrfs,SSYRFS)
-#define LAPACK_dsyrfs LAPACK_GLOBAL(dsyrfs,DSYRFS)
-#define LAPACK_csyrfs LAPACK_GLOBAL(csyrfs,CSYRFS)
-#define LAPACK_zsyrfs LAPACK_GLOBAL(zsyrfs,ZSYRFS)
-#define LAPACK_dsyrfsx LAPACK_GLOBAL(dsyrfsx,DSYRFSX)
-#define LAPACK_ssyrfsx LAPACK_GLOBAL(ssyrfsx,SSYRFSX)
-#define LAPACK_zsyrfsx LAPACK_GLOBAL(zsyrfsx,ZSYRFSX)
-#define LAPACK_csyrfsx LAPACK_GLOBAL(csyrfsx,CSYRFSX)
-#define LAPACK_cherfs LAPACK_GLOBAL(cherfs,CHERFS)
-#define LAPACK_zherfs LAPACK_GLOBAL(zherfs,ZHERFS)
-#define LAPACK_zherfsx LAPACK_GLOBAL(zherfsx,ZHERFSX)
-#define LAPACK_cherfsx LAPACK_GLOBAL(cherfsx,CHERFSX)
-#define LAPACK_ssprfs LAPACK_GLOBAL(ssprfs,SSPRFS)
-#define LAPACK_dsprfs LAPACK_GLOBAL(dsprfs,DSPRFS)
-#define LAPACK_csprfs LAPACK_GLOBAL(csprfs,CSPRFS)
-#define LAPACK_zsprfs LAPACK_GLOBAL(zsprfs,ZSPRFS)
-#define LAPACK_chprfs LAPACK_GLOBAL(chprfs,CHPRFS)
-#define LAPACK_zhprfs LAPACK_GLOBAL(zhprfs,ZHPRFS)
-#define LAPACK_strrfs LAPACK_GLOBAL(strrfs,STRRFS)
-#define LAPACK_dtrrfs LAPACK_GLOBAL(dtrrfs,DTRRFS)
-#define LAPACK_ctrrfs LAPACK_GLOBAL(ctrrfs,CTRRFS)
-#define LAPACK_ztrrfs LAPACK_GLOBAL(ztrrfs,ZTRRFS)
-#define LAPACK_stprfs LAPACK_GLOBAL(stprfs,STPRFS)
-#define LAPACK_dtprfs LAPACK_GLOBAL(dtprfs,DTPRFS)
-#define LAPACK_ctprfs LAPACK_GLOBAL(ctprfs,CTPRFS)
-#define LAPACK_ztprfs LAPACK_GLOBAL(ztprfs,ZTPRFS)
-#define LAPACK_stbrfs LAPACK_GLOBAL(stbrfs,STBRFS)
-#define LAPACK_dtbrfs LAPACK_GLOBAL(dtbrfs,DTBRFS)
-#define LAPACK_ctbrfs LAPACK_GLOBAL(ctbrfs,CTBRFS)
-#define LAPACK_ztbrfs LAPACK_GLOBAL(ztbrfs,ZTBRFS)
-#define LAPACK_sgetri LAPACK_GLOBAL(sgetri,SGETRI)
-#define LAPACK_dgetri LAPACK_GLOBAL(dgetri,DGETRI)
-#define LAPACK_cgetri LAPACK_GLOBAL(cgetri,CGETRI)
-#define LAPACK_zgetri LAPACK_GLOBAL(zgetri,ZGETRI)
-#define LAPACK_spotri LAPACK_GLOBAL(spotri,SPOTRI)
-#define LAPACK_dpotri LAPACK_GLOBAL(dpotri,DPOTRI)
-#define LAPACK_cpotri LAPACK_GLOBAL(cpotri,CPOTRI)
-#define LAPACK_zpotri LAPACK_GLOBAL(zpotri,ZPOTRI)
-#define LAPACK_dpftri LAPACK_GLOBAL(dpftri,DPFTRI)
-#define LAPACK_spftri LAPACK_GLOBAL(spftri,SPFTRI)
-#define LAPACK_zpftri LAPACK_GLOBAL(zpftri,ZPFTRI)
-#define LAPACK_cpftri LAPACK_GLOBAL(cpftri,CPFTRI)
-#define LAPACK_spptri LAPACK_GLOBAL(spptri,SPPTRI)
-#define LAPACK_dpptri LAPACK_GLOBAL(dpptri,DPPTRI)
-#define LAPACK_cpptri LAPACK_GLOBAL(cpptri,CPPTRI)
-#define LAPACK_zpptri LAPACK_GLOBAL(zpptri,ZPPTRI)
-#define LAPACK_ssytri LAPACK_GLOBAL(ssytri,SSYTRI)
-#define LAPACK_dsytri LAPACK_GLOBAL(dsytri,DSYTRI)
-#define LAPACK_csytri LAPACK_GLOBAL(csytri,CSYTRI)
-#define LAPACK_zsytri LAPACK_GLOBAL(zsytri,ZSYTRI)
-#define LAPACK_chetri LAPACK_GLOBAL(chetri,CHETRI)
-#define LAPACK_zhetri LAPACK_GLOBAL(zhetri,ZHETRI)
-#define LAPACK_ssptri LAPACK_GLOBAL(ssptri,SSPTRI)
-#define LAPACK_dsptri LAPACK_GLOBAL(dsptri,DSPTRI)
-#define LAPACK_csptri LAPACK_GLOBAL(csptri,CSPTRI)
-#define LAPACK_zsptri LAPACK_GLOBAL(zsptri,ZSPTRI)
-#define LAPACK_chptri LAPACK_GLOBAL(chptri,CHPTRI)
-#define LAPACK_zhptri LAPACK_GLOBAL(zhptri,ZHPTRI)
-#define LAPACK_strtri LAPACK_GLOBAL(strtri,STRTRI)
-#define LAPACK_dtrtri LAPACK_GLOBAL(dtrtri,DTRTRI)
-#define LAPACK_ctrtri LAPACK_GLOBAL(ctrtri,CTRTRI)
-#define LAPACK_ztrtri LAPACK_GLOBAL(ztrtri,ZTRTRI)
-#define LAPACK_dtftri LAPACK_GLOBAL(dtftri,DTFTRI)
-#define LAPACK_stftri LAPACK_GLOBAL(stftri,STFTRI)
-#define LAPACK_ztftri LAPACK_GLOBAL(ztftri,ZTFTRI)
-#define LAPACK_ctftri LAPACK_GLOBAL(ctftri,CTFTRI)
-#define LAPACK_stptri LAPACK_GLOBAL(stptri,STPTRI)
-#define LAPACK_dtptri LAPACK_GLOBAL(dtptri,DTPTRI)
-#define LAPACK_ctptri LAPACK_GLOBAL(ctptri,CTPTRI)
-#define LAPACK_ztptri LAPACK_GLOBAL(ztptri,ZTPTRI)
-#define LAPACK_sgeequ LAPACK_GLOBAL(sgeequ,SGEEQU)
-#define LAPACK_dgeequ LAPACK_GLOBAL(dgeequ,DGEEQU)
-#define LAPACK_cgeequ LAPACK_GLOBAL(cgeequ,CGEEQU)
-#define LAPACK_zgeequ LAPACK_GLOBAL(zgeequ,ZGEEQU)
-#define LAPACK_dgeequb LAPACK_GLOBAL(dgeequb,DGEEQUB)
-#define LAPACK_sgeequb LAPACK_GLOBAL(sgeequb,SGEEQUB)
-#define LAPACK_zgeequb LAPACK_GLOBAL(zgeequb,ZGEEQUB)
-#define LAPACK_cgeequb LAPACK_GLOBAL(cgeequb,CGEEQUB)
-#define LAPACK_sgbequ LAPACK_GLOBAL(sgbequ,SGBEQU)
-#define LAPACK_dgbequ LAPACK_GLOBAL(dgbequ,DGBEQU)
-#define LAPACK_cgbequ LAPACK_GLOBAL(cgbequ,CGBEQU)
-#define LAPACK_zgbequ LAPACK_GLOBAL(zgbequ,ZGBEQU)
-#define LAPACK_dgbequb LAPACK_GLOBAL(dgbequb,DGBEQUB)
-#define LAPACK_sgbequb LAPACK_GLOBAL(sgbequb,SGBEQUB)
-#define LAPACK_zgbequb LAPACK_GLOBAL(zgbequb,ZGBEQUB)
-#define LAPACK_cgbequb LAPACK_GLOBAL(cgbequb,CGBEQUB)
-#define LAPACK_spoequ LAPACK_GLOBAL(spoequ,SPOEQU)
-#define LAPACK_dpoequ LAPACK_GLOBAL(dpoequ,DPOEQU)
-#define LAPACK_cpoequ LAPACK_GLOBAL(cpoequ,CPOEQU)
-#define LAPACK_zpoequ LAPACK_GLOBAL(zpoequ,ZPOEQU)
-#define LAPACK_dpoequb LAPACK_GLOBAL(dpoequb,DPOEQUB)
-#define LAPACK_spoequb LAPACK_GLOBAL(spoequb,SPOEQUB)
-#define LAPACK_zpoequb LAPACK_GLOBAL(zpoequb,ZPOEQUB)
-#define LAPACK_cpoequb LAPACK_GLOBAL(cpoequb,CPOEQUB)
-#define LAPACK_sppequ LAPACK_GLOBAL(sppequ,SPPEQU)
-#define LAPACK_dppequ LAPACK_GLOBAL(dppequ,DPPEQU)
-#define LAPACK_cppequ LAPACK_GLOBAL(cppequ,CPPEQU)
-#define LAPACK_zppequ LAPACK_GLOBAL(zppequ,ZPPEQU)
-#define LAPACK_spbequ LAPACK_GLOBAL(spbequ,SPBEQU)
-#define LAPACK_dpbequ LAPACK_GLOBAL(dpbequ,DPBEQU)
-#define LAPACK_cpbequ LAPACK_GLOBAL(cpbequ,CPBEQU)
-#define LAPACK_zpbequ LAPACK_GLOBAL(zpbequ,ZPBEQU)
-#define LAPACK_dsyequb LAPACK_GLOBAL(dsyequb,DSYEQUB)
-#define LAPACK_ssyequb LAPACK_GLOBAL(ssyequb,SSYEQUB)
-#define LAPACK_zsyequb LAPACK_GLOBAL(zsyequb,ZSYEQUB)
-#define LAPACK_csyequb LAPACK_GLOBAL(csyequb,CSYEQUB)
-#define LAPACK_zheequb LAPACK_GLOBAL(zheequb,ZHEEQUB)
-#define LAPACK_cheequb LAPACK_GLOBAL(cheequb,CHEEQUB)
-#define LAPACK_sgesv LAPACK_GLOBAL(sgesv,SGESV)
-#define LAPACK_dgesv LAPACK_GLOBAL(dgesv,DGESV)
-#define LAPACK_cgesv LAPACK_GLOBAL(cgesv,CGESV)
-#define LAPACK_zgesv LAPACK_GLOBAL(zgesv,ZGESV)
-#define LAPACK_dsgesv LAPACK_GLOBAL(dsgesv,DSGESV)
-#define LAPACK_zcgesv LAPACK_GLOBAL(zcgesv,ZCGESV)
-#define LAPACK_sgesvx LAPACK_GLOBAL(sgesvx,SGESVX)
-#define LAPACK_dgesvx LAPACK_GLOBAL(dgesvx,DGESVX)
-#define LAPACK_cgesvx LAPACK_GLOBAL(cgesvx,CGESVX)
-#define LAPACK_zgesvx LAPACK_GLOBAL(zgesvx,ZGESVX)
-#define LAPACK_dgesvxx LAPACK_GLOBAL(dgesvxx,DGESVXX)
-#define LAPACK_sgesvxx LAPACK_GLOBAL(sgesvxx,SGESVXX)
-#define LAPACK_zgesvxx LAPACK_GLOBAL(zgesvxx,ZGESVXX)
-#define LAPACK_cgesvxx LAPACK_GLOBAL(cgesvxx,CGESVXX)
-#define LAPACK_sgbsv LAPACK_GLOBAL(sgbsv,SGBSV)
-#define LAPACK_dgbsv LAPACK_GLOBAL(dgbsv,DGBSV)
-#define LAPACK_cgbsv LAPACK_GLOBAL(cgbsv,CGBSV)
-#define LAPACK_zgbsv LAPACK_GLOBAL(zgbsv,ZGBSV)
-#define LAPACK_sgbsvx LAPACK_GLOBAL(sgbsvx,SGBSVX)
-#define LAPACK_dgbsvx LAPACK_GLOBAL(dgbsvx,DGBSVX)
-#define LAPACK_cgbsvx LAPACK_GLOBAL(cgbsvx,CGBSVX)
-#define LAPACK_zgbsvx LAPACK_GLOBAL(zgbsvx,ZGBSVX)
-#define LAPACK_dgbsvxx LAPACK_GLOBAL(dgbsvxx,DGBSVXX)
-#define LAPACK_sgbsvxx LAPACK_GLOBAL(sgbsvxx,SGBSVXX)
-#define LAPACK_zgbsvxx LAPACK_GLOBAL(zgbsvxx,ZGBSVXX)
-#define LAPACK_cgbsvxx LAPACK_GLOBAL(cgbsvxx,CGBSVXX)
-#define LAPACK_sgtsv LAPACK_GLOBAL(sgtsv,SGTSV)
-#define LAPACK_dgtsv LAPACK_GLOBAL(dgtsv,DGTSV)
-#define LAPACK_cgtsv LAPACK_GLOBAL(cgtsv,CGTSV)
-#define LAPACK_zgtsv LAPACK_GLOBAL(zgtsv,ZGTSV)
-#define LAPACK_sgtsvx LAPACK_GLOBAL(sgtsvx,SGTSVX)
-#define LAPACK_dgtsvx LAPACK_GLOBAL(dgtsvx,DGTSVX)
-#define LAPACK_cgtsvx LAPACK_GLOBAL(cgtsvx,CGTSVX)
-#define LAPACK_zgtsvx LAPACK_GLOBAL(zgtsvx,ZGTSVX)
-#define LAPACK_sposv LAPACK_GLOBAL(sposv,SPOSV)
-#define LAPACK_dposv LAPACK_GLOBAL(dposv,DPOSV)
-#define LAPACK_cposv LAPACK_GLOBAL(cposv,CPOSV)
-#define LAPACK_zposv LAPACK_GLOBAL(zposv,ZPOSV)
-#define LAPACK_dsposv LAPACK_GLOBAL(dsposv,DSPOSV)
-#define LAPACK_zcposv LAPACK_GLOBAL(zcposv,ZCPOSV)
-#define LAPACK_sposvx LAPACK_GLOBAL(sposvx,SPOSVX)
-#define LAPACK_dposvx LAPACK_GLOBAL(dposvx,DPOSVX)
-#define LAPACK_cposvx LAPACK_GLOBAL(cposvx,CPOSVX)
-#define LAPACK_zposvx LAPACK_GLOBAL(zposvx,ZPOSVX)
-#define LAPACK_dposvxx LAPACK_GLOBAL(dposvxx,DPOSVXX)
-#define LAPACK_sposvxx LAPACK_GLOBAL(sposvxx,SPOSVXX)
-#define LAPACK_zposvxx LAPACK_GLOBAL(zposvxx,ZPOSVXX)
-#define LAPACK_cposvxx LAPACK_GLOBAL(cposvxx,CPOSVXX)
-#define LAPACK_sppsv LAPACK_GLOBAL(sppsv,SPPSV)
-#define LAPACK_dppsv LAPACK_GLOBAL(dppsv,DPPSV)
-#define LAPACK_cppsv LAPACK_GLOBAL(cppsv,CPPSV)
-#define LAPACK_zppsv LAPACK_GLOBAL(zppsv,ZPPSV)
-#define LAPACK_sppsvx LAPACK_GLOBAL(sppsvx,SPPSVX)
-#define LAPACK_dppsvx LAPACK_GLOBAL(dppsvx,DPPSVX)
-#define LAPACK_cppsvx LAPACK_GLOBAL(cppsvx,CPPSVX)
-#define LAPACK_zppsvx LAPACK_GLOBAL(zppsvx,ZPPSVX)
-#define LAPACK_spbsv LAPACK_GLOBAL(spbsv,SPBSV)
-#define LAPACK_dpbsv LAPACK_GLOBAL(dpbsv,DPBSV)
-#define LAPACK_cpbsv LAPACK_GLOBAL(cpbsv,CPBSV)
-#define LAPACK_zpbsv LAPACK_GLOBAL(zpbsv,ZPBSV)
-#define LAPACK_spbsvx LAPACK_GLOBAL(spbsvx,SPBSVX)
-#define LAPACK_dpbsvx LAPACK_GLOBAL(dpbsvx,DPBSVX)
-#define LAPACK_cpbsvx LAPACK_GLOBAL(cpbsvx,CPBSVX)
-#define LAPACK_zpbsvx LAPACK_GLOBAL(zpbsvx,ZPBSVX)
-#define LAPACK_sptsv LAPACK_GLOBAL(sptsv,SPTSV)
-#define LAPACK_dptsv LAPACK_GLOBAL(dptsv,DPTSV)
-#define LAPACK_cptsv LAPACK_GLOBAL(cptsv,CPTSV)
-#define LAPACK_zptsv LAPACK_GLOBAL(zptsv,ZPTSV)
-#define LAPACK_sptsvx LAPACK_GLOBAL(sptsvx,SPTSVX)
-#define LAPACK_dptsvx LAPACK_GLOBAL(dptsvx,DPTSVX)
-#define LAPACK_cptsvx LAPACK_GLOBAL(cptsvx,CPTSVX)
-#define LAPACK_zptsvx LAPACK_GLOBAL(zptsvx,ZPTSVX)
-#define LAPACK_ssysv LAPACK_GLOBAL(ssysv,SSYSV)
-#define LAPACK_dsysv LAPACK_GLOBAL(dsysv,DSYSV)
-#define LAPACK_csysv LAPACK_GLOBAL(csysv,CSYSV)
-#define LAPACK_zsysv LAPACK_GLOBAL(zsysv,ZSYSV)
-#define LAPACK_ssysvx LAPACK_GLOBAL(ssysvx,SSYSVX)
-#define LAPACK_dsysvx LAPACK_GLOBAL(dsysvx,DSYSVX)
-#define LAPACK_csysvx LAPACK_GLOBAL(csysvx,CSYSVX)
-#define LAPACK_zsysvx LAPACK_GLOBAL(zsysvx,ZSYSVX)
-#define LAPACK_dsysvxx LAPACK_GLOBAL(dsysvxx,DSYSVXX)
-#define LAPACK_ssysvxx LAPACK_GLOBAL(ssysvxx,SSYSVXX)
-#define LAPACK_zsysvxx LAPACK_GLOBAL(zsysvxx,ZSYSVXX)
-#define LAPACK_csysvxx LAPACK_GLOBAL(csysvxx,CSYSVXX)
-#define LAPACK_chesv LAPACK_GLOBAL(chesv,CHESV)
-#define LAPACK_zhesv LAPACK_GLOBAL(zhesv,ZHESV)
-#define LAPACK_chesvx LAPACK_GLOBAL(chesvx,CHESVX)
-#define LAPACK_zhesvx LAPACK_GLOBAL(zhesvx,ZHESVX)
-#define LAPACK_zhesvxx LAPACK_GLOBAL(zhesvxx,ZHESVXX)
-#define LAPACK_chesvxx LAPACK_GLOBAL(chesvxx,CHESVXX)
-#define LAPACK_sspsv LAPACK_GLOBAL(sspsv,SSPSV)
-#define LAPACK_dspsv LAPACK_GLOBAL(dspsv,DSPSV)
-#define LAPACK_cspsv LAPACK_GLOBAL(cspsv,CSPSV)
-#define LAPACK_zspsv LAPACK_GLOBAL(zspsv,ZSPSV)
-#define LAPACK_sspsvx LAPACK_GLOBAL(sspsvx,SSPSVX)
-#define LAPACK_dspsvx LAPACK_GLOBAL(dspsvx,DSPSVX)
-#define LAPACK_cspsvx LAPACK_GLOBAL(cspsvx,CSPSVX)
-#define LAPACK_zspsvx LAPACK_GLOBAL(zspsvx,ZSPSVX)
-#define LAPACK_chpsv LAPACK_GLOBAL(chpsv,CHPSV)
-#define LAPACK_zhpsv LAPACK_GLOBAL(zhpsv,ZHPSV)
-#define LAPACK_chpsvx LAPACK_GLOBAL(chpsvx,CHPSVX)
-#define LAPACK_zhpsvx LAPACK_GLOBAL(zhpsvx,ZHPSVX)
-#define LAPACK_sgeqrf LAPACK_GLOBAL(sgeqrf,SGEQRF)
-#define LAPACK_dgeqrf LAPACK_GLOBAL(dgeqrf,DGEQRF)
-#define LAPACK_cgeqrf LAPACK_GLOBAL(cgeqrf,CGEQRF)
-#define LAPACK_zgeqrf LAPACK_GLOBAL(zgeqrf,ZGEQRF)
-#define LAPACK_sgeqpf LAPACK_GLOBAL(sgeqpf,SGEQPF)
-#define LAPACK_dgeqpf LAPACK_GLOBAL(dgeqpf,DGEQPF)
-#define LAPACK_cgeqpf LAPACK_GLOBAL(cgeqpf,CGEQPF)
-#define LAPACK_zgeqpf LAPACK_GLOBAL(zgeqpf,ZGEQPF)
-#define LAPACK_sgeqp3 LAPACK_GLOBAL(sgeqp3,SGEQP3)
-#define LAPACK_dgeqp3 LAPACK_GLOBAL(dgeqp3,DGEQP3)
-#define LAPACK_cgeqp3 LAPACK_GLOBAL(cgeqp3,CGEQP3)
-#define LAPACK_zgeqp3 LAPACK_GLOBAL(zgeqp3,ZGEQP3)
-#define LAPACK_sorgqr LAPACK_GLOBAL(sorgqr,SORGQR)
-#define LAPACK_dorgqr LAPACK_GLOBAL(dorgqr,DORGQR)
-#define LAPACK_sormqr LAPACK_GLOBAL(sormqr,SORMQR)
-#define LAPACK_dormqr LAPACK_GLOBAL(dormqr,DORMQR)
-#define LAPACK_cungqr LAPACK_GLOBAL(cungqr,CUNGQR)
-#define LAPACK_zungqr LAPACK_GLOBAL(zungqr,ZUNGQR)
-#define LAPACK_cunmqr LAPACK_GLOBAL(cunmqr,CUNMQR)
-#define LAPACK_zunmqr LAPACK_GLOBAL(zunmqr,ZUNMQR)
-#define LAPACK_sgelqf LAPACK_GLOBAL(sgelqf,SGELQF)
-#define LAPACK_dgelqf LAPACK_GLOBAL(dgelqf,DGELQF)
-#define LAPACK_cgelqf LAPACK_GLOBAL(cgelqf,CGELQF)
-#define LAPACK_zgelqf LAPACK_GLOBAL(zgelqf,ZGELQF)
-#define LAPACK_sorglq LAPACK_GLOBAL(sorglq,SORGLQ)
-#define LAPACK_dorglq LAPACK_GLOBAL(dorglq,DORGLQ)
-#define LAPACK_sormlq LAPACK_GLOBAL(sormlq,SORMLQ)
-#define LAPACK_dormlq LAPACK_GLOBAL(dormlq,DORMLQ)
-#define LAPACK_cunglq LAPACK_GLOBAL(cunglq,CUNGLQ)
-#define LAPACK_zunglq LAPACK_GLOBAL(zunglq,ZUNGLQ)
-#define LAPACK_cunmlq LAPACK_GLOBAL(cunmlq,CUNMLQ)
-#define LAPACK_zunmlq LAPACK_GLOBAL(zunmlq,ZUNMLQ)
-#define LAPACK_sgeqlf LAPACK_GLOBAL(sgeqlf,SGEQLF)
-#define LAPACK_dgeqlf LAPACK_GLOBAL(dgeqlf,DGEQLF)
-#define LAPACK_cgeqlf LAPACK_GLOBAL(cgeqlf,CGEQLF)
-#define LAPACK_zgeqlf LAPACK_GLOBAL(zgeqlf,ZGEQLF)
-#define LAPACK_sorgql LAPACK_GLOBAL(sorgql,SORGQL)
-#define LAPACK_dorgql LAPACK_GLOBAL(dorgql,DORGQL)
-#define LAPACK_cungql LAPACK_GLOBAL(cungql,CUNGQL)
-#define LAPACK_zungql LAPACK_GLOBAL(zungql,ZUNGQL)
-#define LAPACK_sormql LAPACK_GLOBAL(sormql,SORMQL)
-#define LAPACK_dormql LAPACK_GLOBAL(dormql,DORMQL)
-#define LAPACK_cunmql LAPACK_GLOBAL(cunmql,CUNMQL)
-#define LAPACK_zunmql LAPACK_GLOBAL(zunmql,ZUNMQL)
-#define LAPACK_sgerqf LAPACK_GLOBAL(sgerqf,SGERQF)
-#define LAPACK_dgerqf LAPACK_GLOBAL(dgerqf,DGERQF)
-#define LAPACK_cgerqf LAPACK_GLOBAL(cgerqf,CGERQF)
-#define LAPACK_zgerqf LAPACK_GLOBAL(zgerqf,ZGERQF)
-#define LAPACK_sorgrq LAPACK_GLOBAL(sorgrq,SORGRQ)
-#define LAPACK_dorgrq LAPACK_GLOBAL(dorgrq,DORGRQ)
-#define LAPACK_cungrq LAPACK_GLOBAL(cungrq,CUNGRQ)
-#define LAPACK_zungrq LAPACK_GLOBAL(zungrq,ZUNGRQ)
-#define LAPACK_sormrq LAPACK_GLOBAL(sormrq,SORMRQ)
-#define LAPACK_dormrq LAPACK_GLOBAL(dormrq,DORMRQ)
-#define LAPACK_cunmrq LAPACK_GLOBAL(cunmrq,CUNMRQ)
-#define LAPACK_zunmrq LAPACK_GLOBAL(zunmrq,ZUNMRQ)
-#define LAPACK_stzrzf LAPACK_GLOBAL(stzrzf,STZRZF)
-#define LAPACK_dtzrzf LAPACK_GLOBAL(dtzrzf,DTZRZF)
-#define LAPACK_ctzrzf LAPACK_GLOBAL(ctzrzf,CTZRZF)
-#define LAPACK_ztzrzf LAPACK_GLOBAL(ztzrzf,ZTZRZF)
-#define LAPACK_sormrz LAPACK_GLOBAL(sormrz,SORMRZ)
-#define LAPACK_dormrz LAPACK_GLOBAL(dormrz,DORMRZ)
-#define LAPACK_cunmrz LAPACK_GLOBAL(cunmrz,CUNMRZ)
-#define LAPACK_zunmrz LAPACK_GLOBAL(zunmrz,ZUNMRZ)
-#define LAPACK_sggqrf LAPACK_GLOBAL(sggqrf,SGGQRF)
-#define LAPACK_dggqrf LAPACK_GLOBAL(dggqrf,DGGQRF)
-#define LAPACK_cggqrf LAPACK_GLOBAL(cggqrf,CGGQRF)
-#define LAPACK_zggqrf LAPACK_GLOBAL(zggqrf,ZGGQRF)
-#define LAPACK_sggrqf LAPACK_GLOBAL(sggrqf,SGGRQF)
-#define LAPACK_dggrqf LAPACK_GLOBAL(dggrqf,DGGRQF)
-#define LAPACK_cggrqf LAPACK_GLOBAL(cggrqf,CGGRQF)
-#define LAPACK_zggrqf LAPACK_GLOBAL(zggrqf,ZGGRQF)
-#define LAPACK_sgebrd LAPACK_GLOBAL(sgebrd,SGEBRD)
-#define LAPACK_dgebrd LAPACK_GLOBAL(dgebrd,DGEBRD)
-#define LAPACK_cgebrd LAPACK_GLOBAL(cgebrd,CGEBRD)
-#define LAPACK_zgebrd LAPACK_GLOBAL(zgebrd,ZGEBRD)
-#define LAPACK_sgbbrd LAPACK_GLOBAL(sgbbrd,SGBBRD)
-#define LAPACK_dgbbrd LAPACK_GLOBAL(dgbbrd,DGBBRD)
-#define LAPACK_cgbbrd LAPACK_GLOBAL(cgbbrd,CGBBRD)
-#define LAPACK_zgbbrd LAPACK_GLOBAL(zgbbrd,ZGBBRD)
-#define LAPACK_sorgbr LAPACK_GLOBAL(sorgbr,SORGBR)
-#define LAPACK_dorgbr LAPACK_GLOBAL(dorgbr,DORGBR)
-#define LAPACK_sormbr LAPACK_GLOBAL(sormbr,SORMBR)
-#define LAPACK_dormbr LAPACK_GLOBAL(dormbr,DORMBR)
-#define LAPACK_cungbr LAPACK_GLOBAL(cungbr,CUNGBR)
-#define LAPACK_zungbr LAPACK_GLOBAL(zungbr,ZUNGBR)
-#define LAPACK_cunmbr LAPACK_GLOBAL(cunmbr,CUNMBR)
-#define LAPACK_zunmbr LAPACK_GLOBAL(zunmbr,ZUNMBR)
-#define LAPACK_sbdsqr LAPACK_GLOBAL(sbdsqr,SBDSQR)
-#define LAPACK_dbdsqr LAPACK_GLOBAL(dbdsqr,DBDSQR)
-#define LAPACK_cbdsqr LAPACK_GLOBAL(cbdsqr,CBDSQR)
-#define LAPACK_zbdsqr LAPACK_GLOBAL(zbdsqr,ZBDSQR)
-#define LAPACK_sbdsdc LAPACK_GLOBAL(sbdsdc,SBDSDC)
-#define LAPACK_dbdsdc LAPACK_GLOBAL(dbdsdc,DBDSDC)
-#define LAPACK_ssytrd LAPACK_GLOBAL(ssytrd,SSYTRD)
-#define LAPACK_dsytrd LAPACK_GLOBAL(dsytrd,DSYTRD)
-#define LAPACK_sorgtr LAPACK_GLOBAL(sorgtr,SORGTR)
-#define LAPACK_dorgtr LAPACK_GLOBAL(dorgtr,DORGTR)
-#define LAPACK_sormtr LAPACK_GLOBAL(sormtr,SORMTR)
-#define LAPACK_dormtr LAPACK_GLOBAL(dormtr,DORMTR)
-#define LAPACK_chetrd LAPACK_GLOBAL(chetrd,CHETRD)
-#define LAPACK_zhetrd LAPACK_GLOBAL(zhetrd,ZHETRD)
-#define LAPACK_cungtr LAPACK_GLOBAL(cungtr,CUNGTR)
-#define LAPACK_zungtr LAPACK_GLOBAL(zungtr,ZUNGTR)
-#define LAPACK_cunmtr LAPACK_GLOBAL(cunmtr,CUNMTR)
-#define LAPACK_zunmtr LAPACK_GLOBAL(zunmtr,ZUNMTR)
-#define LAPACK_ssptrd LAPACK_GLOBAL(ssptrd,SSPTRD)
-#define LAPACK_dsptrd LAPACK_GLOBAL(dsptrd,DSPTRD)
-#define LAPACK_sopgtr LAPACK_GLOBAL(sopgtr,SOPGTR)
-#define LAPACK_dopgtr LAPACK_GLOBAL(dopgtr,DOPGTR)
-#define LAPACK_sopmtr LAPACK_GLOBAL(sopmtr,SOPMTR)
-#define LAPACK_dopmtr LAPACK_GLOBAL(dopmtr,DOPMTR)
-#define LAPACK_chptrd LAPACK_GLOBAL(chptrd,CHPTRD)
-#define LAPACK_zhptrd LAPACK_GLOBAL(zhptrd,ZHPTRD)
-#define LAPACK_cupgtr LAPACK_GLOBAL(cupgtr,CUPGTR)
-#define LAPACK_zupgtr LAPACK_GLOBAL(zupgtr,ZUPGTR)
-#define LAPACK_cupmtr LAPACK_GLOBAL(cupmtr,CUPMTR)
-#define LAPACK_zupmtr LAPACK_GLOBAL(zupmtr,ZUPMTR)
-#define LAPACK_ssbtrd LAPACK_GLOBAL(ssbtrd,SSBTRD)
-#define LAPACK_dsbtrd LAPACK_GLOBAL(dsbtrd,DSBTRD)
-#define LAPACK_chbtrd LAPACK_GLOBAL(chbtrd,CHBTRD)
-#define LAPACK_zhbtrd LAPACK_GLOBAL(zhbtrd,ZHBTRD)
-#define LAPACK_ssterf LAPACK_GLOBAL(ssterf,SSTERF)
-#define LAPACK_dsterf LAPACK_GLOBAL(dsterf,DSTERF)
-#define LAPACK_ssteqr LAPACK_GLOBAL(ssteqr,SSTEQR)
-#define LAPACK_dsteqr LAPACK_GLOBAL(dsteqr,DSTEQR)
-#define LAPACK_csteqr LAPACK_GLOBAL(csteqr,CSTEQR)
-#define LAPACK_zsteqr LAPACK_GLOBAL(zsteqr,ZSTEQR)
-#define LAPACK_sstemr LAPACK_GLOBAL(sstemr,SSTEMR)
-#define LAPACK_dstemr LAPACK_GLOBAL(dstemr,DSTEMR)
-#define LAPACK_cstemr LAPACK_GLOBAL(cstemr,CSTEMR)
-#define LAPACK_zstemr LAPACK_GLOBAL(zstemr,ZSTEMR)
-#define LAPACK_sstedc LAPACK_GLOBAL(sstedc,SSTEDC)
-#define LAPACK_dstedc LAPACK_GLOBAL(dstedc,DSTEDC)
-#define LAPACK_cstedc LAPACK_GLOBAL(cstedc,CSTEDC)
-#define LAPACK_zstedc LAPACK_GLOBAL(zstedc,ZSTEDC)
-#define LAPACK_sstegr LAPACK_GLOBAL(sstegr,SSTEGR)
-#define LAPACK_dstegr LAPACK_GLOBAL(dstegr,DSTEGR)
-#define LAPACK_cstegr LAPACK_GLOBAL(cstegr,CSTEGR)
-#define LAPACK_zstegr LAPACK_GLOBAL(zstegr,ZSTEGR)
-#define LAPACK_spteqr LAPACK_GLOBAL(spteqr,SPTEQR)
-#define LAPACK_dpteqr LAPACK_GLOBAL(dpteqr,DPTEQR)
-#define LAPACK_cpteqr LAPACK_GLOBAL(cpteqr,CPTEQR)
-#define LAPACK_zpteqr LAPACK_GLOBAL(zpteqr,ZPTEQR)
-#define LAPACK_sstebz LAPACK_GLOBAL(sstebz,SSTEBZ)
-#define LAPACK_dstebz LAPACK_GLOBAL(dstebz,DSTEBZ)
-#define LAPACK_sstein LAPACK_GLOBAL(sstein,SSTEIN)
-#define LAPACK_dstein LAPACK_GLOBAL(dstein,DSTEIN)
-#define LAPACK_cstein LAPACK_GLOBAL(cstein,CSTEIN)
-#define LAPACK_zstein LAPACK_GLOBAL(zstein,ZSTEIN)
-#define LAPACK_sdisna LAPACK_GLOBAL(sdisna,SDISNA)
-#define LAPACK_ddisna LAPACK_GLOBAL(ddisna,DDISNA)
-#define LAPACK_ssygst LAPACK_GLOBAL(ssygst,SSYGST)
-#define LAPACK_dsygst LAPACK_GLOBAL(dsygst,DSYGST)
-#define LAPACK_chegst LAPACK_GLOBAL(chegst,CHEGST)
-#define LAPACK_zhegst LAPACK_GLOBAL(zhegst,ZHEGST)
-#define LAPACK_sspgst LAPACK_GLOBAL(sspgst,SSPGST)
-#define LAPACK_dspgst LAPACK_GLOBAL(dspgst,DSPGST)
-#define LAPACK_chpgst LAPACK_GLOBAL(chpgst,CHPGST)
-#define LAPACK_zhpgst LAPACK_GLOBAL(zhpgst,ZHPGST)
-#define LAPACK_ssbgst LAPACK_GLOBAL(ssbgst,SSBGST)
-#define LAPACK_dsbgst LAPACK_GLOBAL(dsbgst,DSBGST)
-#define LAPACK_chbgst LAPACK_GLOBAL(chbgst,CHBGST)
-#define LAPACK_zhbgst LAPACK_GLOBAL(zhbgst,ZHBGST)
-#define LAPACK_spbstf LAPACK_GLOBAL(spbstf,SPBSTF)
-#define LAPACK_dpbstf LAPACK_GLOBAL(dpbstf,DPBSTF)
-#define LAPACK_cpbstf LAPACK_GLOBAL(cpbstf,CPBSTF)
-#define LAPACK_zpbstf LAPACK_GLOBAL(zpbstf,ZPBSTF)
-#define LAPACK_sgehrd LAPACK_GLOBAL(sgehrd,SGEHRD)
-#define LAPACK_dgehrd LAPACK_GLOBAL(dgehrd,DGEHRD)
-#define LAPACK_cgehrd LAPACK_GLOBAL(cgehrd,CGEHRD)
-#define LAPACK_zgehrd LAPACK_GLOBAL(zgehrd,ZGEHRD)
-#define LAPACK_sorghr LAPACK_GLOBAL(sorghr,SORGHR)
-#define LAPACK_dorghr LAPACK_GLOBAL(dorghr,DORGHR)
-#define LAPACK_sormhr LAPACK_GLOBAL(sormhr,SORMHR)
-#define LAPACK_dormhr LAPACK_GLOBAL(dormhr,DORMHR)
-#define LAPACK_cunghr LAPACK_GLOBAL(cunghr,CUNGHR)
-#define LAPACK_zunghr LAPACK_GLOBAL(zunghr,ZUNGHR)
-#define LAPACK_cunmhr LAPACK_GLOBAL(cunmhr,CUNMHR)
-#define LAPACK_zunmhr LAPACK_GLOBAL(zunmhr,ZUNMHR)
-#define LAPACK_sgebal LAPACK_GLOBAL(sgebal,SGEBAL)
-#define LAPACK_dgebal LAPACK_GLOBAL(dgebal,DGEBAL)
-#define LAPACK_cgebal LAPACK_GLOBAL(cgebal,CGEBAL)
-#define LAPACK_zgebal LAPACK_GLOBAL(zgebal,ZGEBAL)
-#define LAPACK_sgebak LAPACK_GLOBAL(sgebak,SGEBAK)
-#define LAPACK_dgebak LAPACK_GLOBAL(dgebak,DGEBAK)
-#define LAPACK_cgebak LAPACK_GLOBAL(cgebak,CGEBAK)
-#define LAPACK_zgebak LAPACK_GLOBAL(zgebak,ZGEBAK)
-#define LAPACK_shseqr LAPACK_GLOBAL(shseqr,SHSEQR)
-#define LAPACK_dhseqr LAPACK_GLOBAL(dhseqr,DHSEQR)
-#define LAPACK_chseqr LAPACK_GLOBAL(chseqr,CHSEQR)
-#define LAPACK_zhseqr LAPACK_GLOBAL(zhseqr,ZHSEQR)
-#define LAPACK_shsein LAPACK_GLOBAL(shsein,SHSEIN)
-#define LAPACK_dhsein LAPACK_GLOBAL(dhsein,DHSEIN)
-#define LAPACK_chsein LAPACK_GLOBAL(chsein,CHSEIN)
-#define LAPACK_zhsein LAPACK_GLOBAL(zhsein,ZHSEIN)
-#define LAPACK_strevc LAPACK_GLOBAL(strevc,STREVC)
-#define LAPACK_dtrevc LAPACK_GLOBAL(dtrevc,DTREVC)
-#define LAPACK_ctrevc LAPACK_GLOBAL(ctrevc,CTREVC)
-#define LAPACK_ztrevc LAPACK_GLOBAL(ztrevc,ZTREVC)
-#define LAPACK_strsna LAPACK_GLOBAL(strsna,STRSNA)
-#define LAPACK_dtrsna LAPACK_GLOBAL(dtrsna,DTRSNA)
-#define LAPACK_ctrsna LAPACK_GLOBAL(ctrsna,CTRSNA)
-#define LAPACK_ztrsna LAPACK_GLOBAL(ztrsna,ZTRSNA)
-#define LAPACK_strexc LAPACK_GLOBAL(strexc,STREXC)
-#define LAPACK_dtrexc LAPACK_GLOBAL(dtrexc,DTREXC)
-#define LAPACK_ctrexc LAPACK_GLOBAL(ctrexc,CTREXC)
-#define LAPACK_ztrexc LAPACK_GLOBAL(ztrexc,ZTREXC)
-#define LAPACK_strsen LAPACK_GLOBAL(strsen,STRSEN)
-#define LAPACK_dtrsen LAPACK_GLOBAL(dtrsen,DTRSEN)
-#define LAPACK_ctrsen LAPACK_GLOBAL(ctrsen,CTRSEN)
-#define LAPACK_ztrsen LAPACK_GLOBAL(ztrsen,ZTRSEN)
-#define LAPACK_strsyl LAPACK_GLOBAL(strsyl,STRSYL)
-#define LAPACK_dtrsyl LAPACK_GLOBAL(dtrsyl,DTRSYL)
-#define LAPACK_ctrsyl LAPACK_GLOBAL(ctrsyl,CTRSYL)
-#define LAPACK_ztrsyl LAPACK_GLOBAL(ztrsyl,ZTRSYL)
-#define LAPACK_sgghrd LAPACK_GLOBAL(sgghrd,SGGHRD)
-#define LAPACK_dgghrd LAPACK_GLOBAL(dgghrd,DGGHRD)
-#define LAPACK_cgghrd LAPACK_GLOBAL(cgghrd,CGGHRD)
-#define LAPACK_zgghrd LAPACK_GLOBAL(zgghrd,ZGGHRD)
-#define LAPACK_sggbal LAPACK_GLOBAL(sggbal,SGGBAL)
-#define LAPACK_dggbal LAPACK_GLOBAL(dggbal,DGGBAL)
-#define LAPACK_cggbal LAPACK_GLOBAL(cggbal,CGGBAL)
-#define LAPACK_zggbal LAPACK_GLOBAL(zggbal,ZGGBAL)
-#define LAPACK_sggbak LAPACK_GLOBAL(sggbak,SGGBAK)
-#define LAPACK_dggbak LAPACK_GLOBAL(dggbak,DGGBAK)
-#define LAPACK_cggbak LAPACK_GLOBAL(cggbak,CGGBAK)
-#define LAPACK_zggbak LAPACK_GLOBAL(zggbak,ZGGBAK)
-#define LAPACK_shgeqz LAPACK_GLOBAL(shgeqz,SHGEQZ)
-#define LAPACK_dhgeqz LAPACK_GLOBAL(dhgeqz,DHGEQZ)
-#define LAPACK_chgeqz LAPACK_GLOBAL(chgeqz,CHGEQZ)
-#define LAPACK_zhgeqz LAPACK_GLOBAL(zhgeqz,ZHGEQZ)
-#define LAPACK_stgevc LAPACK_GLOBAL(stgevc,STGEVC)
-#define LAPACK_dtgevc LAPACK_GLOBAL(dtgevc,DTGEVC)
-#define LAPACK_ctgevc LAPACK_GLOBAL(ctgevc,CTGEVC)
-#define LAPACK_ztgevc LAPACK_GLOBAL(ztgevc,ZTGEVC)
-#define LAPACK_stgexc LAPACK_GLOBAL(stgexc,STGEXC)
-#define LAPACK_dtgexc LAPACK_GLOBAL(dtgexc,DTGEXC)
-#define LAPACK_ctgexc LAPACK_GLOBAL(ctgexc,CTGEXC)
-#define LAPACK_ztgexc LAPACK_GLOBAL(ztgexc,ZTGEXC)
-#define LAPACK_stgsen LAPACK_GLOBAL(stgsen,STGSEN)
-#define LAPACK_dtgsen LAPACK_GLOBAL(dtgsen,DTGSEN)
-#define LAPACK_ctgsen LAPACK_GLOBAL(ctgsen,CTGSEN)
-#define LAPACK_ztgsen LAPACK_GLOBAL(ztgsen,ZTGSEN)
-#define LAPACK_stgsyl LAPACK_GLOBAL(stgsyl,STGSYL)
-#define LAPACK_dtgsyl LAPACK_GLOBAL(dtgsyl,DTGSYL)
-#define LAPACK_ctgsyl LAPACK_GLOBAL(ctgsyl,CTGSYL)
-#define LAPACK_ztgsyl LAPACK_GLOBAL(ztgsyl,ZTGSYL)
-#define LAPACK_stgsna LAPACK_GLOBAL(stgsna,STGSNA)
-#define LAPACK_dtgsna LAPACK_GLOBAL(dtgsna,DTGSNA)
-#define LAPACK_ctgsna LAPACK_GLOBAL(ctgsna,CTGSNA)
-#define LAPACK_ztgsna LAPACK_GLOBAL(ztgsna,ZTGSNA)
-#define LAPACK_sggsvp LAPACK_GLOBAL(sggsvp,SGGSVP)
-#define LAPACK_dggsvp LAPACK_GLOBAL(dggsvp,DGGSVP)
-#define LAPACK_cggsvp LAPACK_GLOBAL(cggsvp,CGGSVP)
-#define LAPACK_zggsvp LAPACK_GLOBAL(zggsvp,ZGGSVP)
-#define LAPACK_stgsja LAPACK_GLOBAL(stgsja,STGSJA)
-#define LAPACK_dtgsja LAPACK_GLOBAL(dtgsja,DTGSJA)
-#define LAPACK_ctgsja LAPACK_GLOBAL(ctgsja,CTGSJA)
-#define LAPACK_ztgsja LAPACK_GLOBAL(ztgsja,ZTGSJA)
-#define LAPACK_sgels LAPACK_GLOBAL(sgels,SGELS)
-#define LAPACK_dgels LAPACK_GLOBAL(dgels,DGELS)
-#define LAPACK_cgels LAPACK_GLOBAL(cgels,CGELS)
-#define LAPACK_zgels LAPACK_GLOBAL(zgels,ZGELS)
-#define LAPACK_sgelsy LAPACK_GLOBAL(sgelsy,SGELSY)
-#define LAPACK_dgelsy LAPACK_GLOBAL(dgelsy,DGELSY)
-#define LAPACK_cgelsy LAPACK_GLOBAL(cgelsy,CGELSY)
-#define LAPACK_zgelsy LAPACK_GLOBAL(zgelsy,ZGELSY)
-#define LAPACK_sgelss LAPACK_GLOBAL(sgelss,SGELSS)
-#define LAPACK_dgelss LAPACK_GLOBAL(dgelss,DGELSS)
-#define LAPACK_cgelss LAPACK_GLOBAL(cgelss,CGELSS)
-#define LAPACK_zgelss LAPACK_GLOBAL(zgelss,ZGELSS)
-#define LAPACK_sgelsd LAPACK_GLOBAL(sgelsd,SGELSD)
-#define LAPACK_dgelsd LAPACK_GLOBAL(dgelsd,DGELSD)
-#define LAPACK_cgelsd LAPACK_GLOBAL(cgelsd,CGELSD)
-#define LAPACK_zgelsd LAPACK_GLOBAL(zgelsd,ZGELSD)
-#define LAPACK_sgglse LAPACK_GLOBAL(sgglse,SGGLSE)
-#define LAPACK_dgglse LAPACK_GLOBAL(dgglse,DGGLSE)
-#define LAPACK_cgglse LAPACK_GLOBAL(cgglse,CGGLSE)
-#define LAPACK_zgglse LAPACK_GLOBAL(zgglse,ZGGLSE)
-#define LAPACK_sggglm LAPACK_GLOBAL(sggglm,SGGGLM)
-#define LAPACK_dggglm LAPACK_GLOBAL(dggglm,DGGGLM)
-#define LAPACK_cggglm LAPACK_GLOBAL(cggglm,CGGGLM)
-#define LAPACK_zggglm LAPACK_GLOBAL(zggglm,ZGGGLM)
-#define LAPACK_ssyev LAPACK_GLOBAL(ssyev,SSYEV)
-#define LAPACK_dsyev LAPACK_GLOBAL(dsyev,DSYEV)
-#define LAPACK_cheev LAPACK_GLOBAL(cheev,CHEEV)
-#define LAPACK_zheev LAPACK_GLOBAL(zheev,ZHEEV)
-#define LAPACK_ssyevd LAPACK_GLOBAL(ssyevd,SSYEVD)
-#define LAPACK_dsyevd LAPACK_GLOBAL(dsyevd,DSYEVD)
-#define LAPACK_cheevd LAPACK_GLOBAL(cheevd,CHEEVD)
-#define LAPACK_zheevd LAPACK_GLOBAL(zheevd,ZHEEVD)
-#define LAPACK_ssyevx LAPACK_GLOBAL(ssyevx,SSYEVX)
-#define LAPACK_dsyevx LAPACK_GLOBAL(dsyevx,DSYEVX)
-#define LAPACK_cheevx LAPACK_GLOBAL(cheevx,CHEEVX)
-#define LAPACK_zheevx LAPACK_GLOBAL(zheevx,ZHEEVX)
-#define LAPACK_ssyevr LAPACK_GLOBAL(ssyevr,SSYEVR)
-#define LAPACK_dsyevr LAPACK_GLOBAL(dsyevr,DSYEVR)
-#define LAPACK_cheevr LAPACK_GLOBAL(cheevr,CHEEVR)
-#define LAPACK_zheevr LAPACK_GLOBAL(zheevr,ZHEEVR)
-#define LAPACK_sspev LAPACK_GLOBAL(sspev,SSPEV)
-#define LAPACK_dspev LAPACK_GLOBAL(dspev,DSPEV)
-#define LAPACK_chpev LAPACK_GLOBAL(chpev,CHPEV)
-#define LAPACK_zhpev LAPACK_GLOBAL(zhpev,ZHPEV)
-#define LAPACK_sspevd LAPACK_GLOBAL(sspevd,SSPEVD)
-#define LAPACK_dspevd LAPACK_GLOBAL(dspevd,DSPEVD)
-#define LAPACK_chpevd LAPACK_GLOBAL(chpevd,CHPEVD)
-#define LAPACK_zhpevd LAPACK_GLOBAL(zhpevd,ZHPEVD)
-#define LAPACK_sspevx LAPACK_GLOBAL(sspevx,SSPEVX)
-#define LAPACK_dspevx LAPACK_GLOBAL(dspevx,DSPEVX)
-#define LAPACK_chpevx LAPACK_GLOBAL(chpevx,CHPEVX)
-#define LAPACK_zhpevx LAPACK_GLOBAL(zhpevx,ZHPEVX)
-#define LAPACK_ssbev LAPACK_GLOBAL(ssbev,SSBEV)
-#define LAPACK_dsbev LAPACK_GLOBAL(dsbev,DSBEV)
-#define LAPACK_chbev LAPACK_GLOBAL(chbev,CHBEV)
-#define LAPACK_zhbev LAPACK_GLOBAL(zhbev,ZHBEV)
-#define LAPACK_ssbevd LAPACK_GLOBAL(ssbevd,SSBEVD)
-#define LAPACK_dsbevd LAPACK_GLOBAL(dsbevd,DSBEVD)
-#define LAPACK_chbevd LAPACK_GLOBAL(chbevd,CHBEVD)
-#define LAPACK_zhbevd LAPACK_GLOBAL(zhbevd,ZHBEVD)
-#define LAPACK_ssbevx LAPACK_GLOBAL(ssbevx,SSBEVX)
-#define LAPACK_dsbevx LAPACK_GLOBAL(dsbevx,DSBEVX)
-#define LAPACK_chbevx LAPACK_GLOBAL(chbevx,CHBEVX)
-#define LAPACK_zhbevx LAPACK_GLOBAL(zhbevx,ZHBEVX)
-#define LAPACK_sstev LAPACK_GLOBAL(sstev,SSTEV)
-#define LAPACK_dstev LAPACK_GLOBAL(dstev,DSTEV)
-#define LAPACK_sstevd LAPACK_GLOBAL(sstevd,SSTEVD)
-#define LAPACK_dstevd LAPACK_GLOBAL(dstevd,DSTEVD)
-#define LAPACK_sstevx LAPACK_GLOBAL(sstevx,SSTEVX)
-#define LAPACK_dstevx LAPACK_GLOBAL(dstevx,DSTEVX)
-#define LAPACK_sstevr LAPACK_GLOBAL(sstevr,SSTEVR)
-#define LAPACK_dstevr LAPACK_GLOBAL(dstevr,DSTEVR)
-#define LAPACK_sgees LAPACK_GLOBAL(sgees,SGEES)
-#define LAPACK_dgees LAPACK_GLOBAL(dgees,DGEES)
-#define LAPACK_cgees LAPACK_GLOBAL(cgees,CGEES)
-#define LAPACK_zgees LAPACK_GLOBAL(zgees,ZGEES)
-#define LAPACK_sgeesx LAPACK_GLOBAL(sgeesx,SGEESX)
-#define LAPACK_dgeesx LAPACK_GLOBAL(dgeesx,DGEESX)
-#define LAPACK_cgeesx LAPACK_GLOBAL(cgeesx,CGEESX)
-#define LAPACK_zgeesx LAPACK_GLOBAL(zgeesx,ZGEESX)
-#define LAPACK_sgeev LAPACK_GLOBAL(sgeev,SGEEV)
-#define LAPACK_dgeev LAPACK_GLOBAL(dgeev,DGEEV)
-#define LAPACK_cgeev LAPACK_GLOBAL(cgeev,CGEEV)
-#define LAPACK_zgeev LAPACK_GLOBAL(zgeev,ZGEEV)
-#define LAPACK_sgeevx LAPACK_GLOBAL(sgeevx,SGEEVX)
-#define LAPACK_dgeevx LAPACK_GLOBAL(dgeevx,DGEEVX)
-#define LAPACK_cgeevx LAPACK_GLOBAL(cgeevx,CGEEVX)
-#define LAPACK_zgeevx LAPACK_GLOBAL(zgeevx,ZGEEVX)
-#define LAPACK_sgesvd LAPACK_GLOBAL(sgesvd,SGESVD)
-#define LAPACK_dgesvd LAPACK_GLOBAL(dgesvd,DGESVD)
-#define LAPACK_cgesvd LAPACK_GLOBAL(cgesvd,CGESVD)
-#define LAPACK_zgesvd LAPACK_GLOBAL(zgesvd,ZGESVD)
-#define LAPACK_sgesdd LAPACK_GLOBAL(sgesdd,SGESDD)
-#define LAPACK_dgesdd LAPACK_GLOBAL(dgesdd,DGESDD)
-#define LAPACK_cgesdd LAPACK_GLOBAL(cgesdd,CGESDD)
-#define LAPACK_zgesdd LAPACK_GLOBAL(zgesdd,ZGESDD)
-#define LAPACK_dgejsv LAPACK_GLOBAL(dgejsv,DGEJSV)
-#define LAPACK_sgejsv LAPACK_GLOBAL(sgejsv,SGEJSV)
-#define LAPACK_dgesvj LAPACK_GLOBAL(dgesvj,DGESVJ)
-#define LAPACK_sgesvj LAPACK_GLOBAL(sgesvj,SGESVJ)
-#define LAPACK_sggsvd LAPACK_GLOBAL(sggsvd,SGGSVD)
-#define LAPACK_dggsvd LAPACK_GLOBAL(dggsvd,DGGSVD)
-#define LAPACK_cggsvd LAPACK_GLOBAL(cggsvd,CGGSVD)
-#define LAPACK_zggsvd LAPACK_GLOBAL(zggsvd,ZGGSVD)
-#define LAPACK_ssygv LAPACK_GLOBAL(ssygv,SSYGV)
-#define LAPACK_dsygv LAPACK_GLOBAL(dsygv,DSYGV)
-#define LAPACK_chegv LAPACK_GLOBAL(chegv,CHEGV)
-#define LAPACK_zhegv LAPACK_GLOBAL(zhegv,ZHEGV)
-#define LAPACK_ssygvd LAPACK_GLOBAL(ssygvd,SSYGVD)
-#define LAPACK_dsygvd LAPACK_GLOBAL(dsygvd,DSYGVD)
-#define LAPACK_chegvd LAPACK_GLOBAL(chegvd,CHEGVD)
-#define LAPACK_zhegvd LAPACK_GLOBAL(zhegvd,ZHEGVD)
-#define LAPACK_ssygvx LAPACK_GLOBAL(ssygvx,SSYGVX)
-#define LAPACK_dsygvx LAPACK_GLOBAL(dsygvx,DSYGVX)
-#define LAPACK_chegvx LAPACK_GLOBAL(chegvx,CHEGVX)
-#define LAPACK_zhegvx LAPACK_GLOBAL(zhegvx,ZHEGVX)
-#define LAPACK_sspgv LAPACK_GLOBAL(sspgv,SSPGV)
-#define LAPACK_dspgv LAPACK_GLOBAL(dspgv,DSPGV)
-#define LAPACK_chpgv LAPACK_GLOBAL(chpgv,CHPGV)
-#define LAPACK_zhpgv LAPACK_GLOBAL(zhpgv,ZHPGV)
-#define LAPACK_sspgvd LAPACK_GLOBAL(sspgvd,SSPGVD)
-#define LAPACK_dspgvd LAPACK_GLOBAL(dspgvd,DSPGVD)
-#define LAPACK_chpgvd LAPACK_GLOBAL(chpgvd,CHPGVD)
-#define LAPACK_zhpgvd LAPACK_GLOBAL(zhpgvd,ZHPGVD)
-#define LAPACK_sspgvx LAPACK_GLOBAL(sspgvx,SSPGVX)
-#define LAPACK_dspgvx LAPACK_GLOBAL(dspgvx,DSPGVX)
-#define LAPACK_chpgvx LAPACK_GLOBAL(chpgvx,CHPGVX)
-#define LAPACK_zhpgvx LAPACK_GLOBAL(zhpgvx,ZHPGVX)
-#define LAPACK_ssbgv LAPACK_GLOBAL(ssbgv,SSBGV)
-#define LAPACK_dsbgv LAPACK_GLOBAL(dsbgv,DSBGV)
-#define LAPACK_chbgv LAPACK_GLOBAL(chbgv,CHBGV)
-#define LAPACK_zhbgv LAPACK_GLOBAL(zhbgv,ZHBGV)
-#define LAPACK_ssbgvd LAPACK_GLOBAL(ssbgvd,SSBGVD)
-#define LAPACK_dsbgvd LAPACK_GLOBAL(dsbgvd,DSBGVD)
-#define LAPACK_chbgvd LAPACK_GLOBAL(chbgvd,CHBGVD)
-#define LAPACK_zhbgvd LAPACK_GLOBAL(zhbgvd,ZHBGVD)
-#define LAPACK_ssbgvx LAPACK_GLOBAL(ssbgvx,SSBGVX)
-#define LAPACK_dsbgvx LAPACK_GLOBAL(dsbgvx,DSBGVX)
-#define LAPACK_chbgvx LAPACK_GLOBAL(chbgvx,CHBGVX)
-#define LAPACK_zhbgvx LAPACK_GLOBAL(zhbgvx,ZHBGVX)
-#define LAPACK_sgges LAPACK_GLOBAL(sgges,SGGES)
-#define LAPACK_dgges LAPACK_GLOBAL(dgges,DGGES)
-#define LAPACK_cgges LAPACK_GLOBAL(cgges,CGGES)
-#define LAPACK_zgges LAPACK_GLOBAL(zgges,ZGGES)
-#define LAPACK_sggesx LAPACK_GLOBAL(sggesx,SGGESX)
-#define LAPACK_dggesx LAPACK_GLOBAL(dggesx,DGGESX)
-#define LAPACK_cggesx LAPACK_GLOBAL(cggesx,CGGESX)
-#define LAPACK_zggesx LAPACK_GLOBAL(zggesx,ZGGESX)
-#define LAPACK_sggev LAPACK_GLOBAL(sggev,SGGEV)
-#define LAPACK_dggev LAPACK_GLOBAL(dggev,DGGEV)
-#define LAPACK_cggev LAPACK_GLOBAL(cggev,CGGEV)
-#define LAPACK_zggev LAPACK_GLOBAL(zggev,ZGGEV)
-#define LAPACK_sggevx LAPACK_GLOBAL(sggevx,SGGEVX)
-#define LAPACK_dggevx LAPACK_GLOBAL(dggevx,DGGEVX)
-#define LAPACK_cggevx LAPACK_GLOBAL(cggevx,CGGEVX)
-#define LAPACK_zggevx LAPACK_GLOBAL(zggevx,ZGGEVX)
-#define LAPACK_dsfrk LAPACK_GLOBAL(dsfrk,DSFRK)
-#define LAPACK_ssfrk LAPACK_GLOBAL(ssfrk,SSFRK)
-#define LAPACK_zhfrk LAPACK_GLOBAL(zhfrk,ZHFRK)
-#define LAPACK_chfrk LAPACK_GLOBAL(chfrk,CHFRK)
-#define LAPACK_dtfsm LAPACK_GLOBAL(dtfsm,DTFSM)
-#define LAPACK_stfsm LAPACK_GLOBAL(stfsm,STFSM)
-#define LAPACK_ztfsm LAPACK_GLOBAL(ztfsm,ZTFSM)
-#define LAPACK_ctfsm LAPACK_GLOBAL(ctfsm,CTFSM)
-#define LAPACK_dtfttp LAPACK_GLOBAL(dtfttp,DTFTTP)
-#define LAPACK_stfttp LAPACK_GLOBAL(stfttp,STFTTP)
-#define LAPACK_ztfttp LAPACK_GLOBAL(ztfttp,ZTFTTP)
-#define LAPACK_ctfttp LAPACK_GLOBAL(ctfttp,CTFTTP)
-#define LAPACK_dtfttr LAPACK_GLOBAL(dtfttr,DTFTTR)
-#define LAPACK_stfttr LAPACK_GLOBAL(stfttr,STFTTR)
-#define LAPACK_ztfttr LAPACK_GLOBAL(ztfttr,ZTFTTR)
-#define LAPACK_ctfttr LAPACK_GLOBAL(ctfttr,CTFTTR)
-#define LAPACK_dtpttf LAPACK_GLOBAL(dtpttf,DTPTTF)
-#define LAPACK_stpttf LAPACK_GLOBAL(stpttf,STPTTF)
-#define LAPACK_ztpttf LAPACK_GLOBAL(ztpttf,ZTPTTF)
-#define LAPACK_ctpttf LAPACK_GLOBAL(ctpttf,CTPTTF)
-#define LAPACK_dtpttr LAPACK_GLOBAL(dtpttr,DTPTTR)
-#define LAPACK_stpttr LAPACK_GLOBAL(stpttr,STPTTR)
-#define LAPACK_ztpttr LAPACK_GLOBAL(ztpttr,ZTPTTR)
-#define LAPACK_ctpttr LAPACK_GLOBAL(ctpttr,CTPTTR)
-#define LAPACK_dtrttf LAPACK_GLOBAL(dtrttf,DTRTTF)
-#define LAPACK_strttf LAPACK_GLOBAL(strttf,STRTTF)
-#define LAPACK_ztrttf LAPACK_GLOBAL(ztrttf,ZTRTTF)
-#define LAPACK_ctrttf LAPACK_GLOBAL(ctrttf,CTRTTF)
-#define LAPACK_dtrttp LAPACK_GLOBAL(dtrttp,DTRTTP)
-#define LAPACK_strttp LAPACK_GLOBAL(strttp,STRTTP)
-#define LAPACK_ztrttp LAPACK_GLOBAL(ztrttp,ZTRTTP)
-#define LAPACK_ctrttp LAPACK_GLOBAL(ctrttp,CTRTTP)
-#define LAPACK_sgeqrfp LAPACK_GLOBAL(sgeqrfp,SGEQRFP)
-#define LAPACK_dgeqrfp LAPACK_GLOBAL(dgeqrfp,DGEQRFP)
-#define LAPACK_cgeqrfp LAPACK_GLOBAL(cgeqrfp,CGEQRFP)
-#define LAPACK_zgeqrfp LAPACK_GLOBAL(zgeqrfp,ZGEQRFP)
-#define LAPACK_clacgv LAPACK_GLOBAL(clacgv,CLACGV)
-#define LAPACK_zlacgv LAPACK_GLOBAL(zlacgv,ZLACGV)
-#define LAPACK_slarnv LAPACK_GLOBAL(slarnv,SLARNV)
-#define LAPACK_dlarnv LAPACK_GLOBAL(dlarnv,DLARNV)
-#define LAPACK_clarnv LAPACK_GLOBAL(clarnv,CLARNV)
-#define LAPACK_zlarnv LAPACK_GLOBAL(zlarnv,ZLARNV)
-#define LAPACK_sgeqr2 LAPACK_GLOBAL(sgeqr2,SGEQR2)
-#define LAPACK_dgeqr2 LAPACK_GLOBAL(dgeqr2,DGEQR2)
-#define LAPACK_cgeqr2 LAPACK_GLOBAL(cgeqr2,CGEQR2)
-#define LAPACK_zgeqr2 LAPACK_GLOBAL(zgeqr2,ZGEQR2)
-#define LAPACK_slacpy LAPACK_GLOBAL(slacpy,SLACPY)
-#define LAPACK_dlacpy LAPACK_GLOBAL(dlacpy,DLACPY)
-#define LAPACK_clacpy LAPACK_GLOBAL(clacpy,CLACPY)
-#define LAPACK_zlacpy LAPACK_GLOBAL(zlacpy,ZLACPY)
-#define LAPACK_sgetf2 LAPACK_GLOBAL(sgetf2,SGETF2)
-#define LAPACK_dgetf2 LAPACK_GLOBAL(dgetf2,DGETF2)
-#define LAPACK_cgetf2 LAPACK_GLOBAL(cgetf2,CGETF2)
-#define LAPACK_zgetf2 LAPACK_GLOBAL(zgetf2,ZGETF2)
-#define LAPACK_slaswp LAPACK_GLOBAL(slaswp,SLASWP)
-#define LAPACK_dlaswp LAPACK_GLOBAL(dlaswp,DLASWP)
-#define LAPACK_claswp LAPACK_GLOBAL(claswp,CLASWP)
-#define LAPACK_zlaswp LAPACK_GLOBAL(zlaswp,ZLASWP)
-#define LAPACK_slange LAPACK_GLOBAL(slange,SLANGE)
-#define LAPACK_dlange LAPACK_GLOBAL(dlange,DLANGE)
-#define LAPACK_clange LAPACK_GLOBAL(clange,CLANGE)
-#define LAPACK_zlange LAPACK_GLOBAL(zlange,ZLANGE)
-#define LAPACK_clanhe LAPACK_GLOBAL(clanhe,CLANHE)
-#define LAPACK_zlanhe LAPACK_GLOBAL(zlanhe,ZLANHE)
-#define LAPACK_slansy LAPACK_GLOBAL(slansy,SLANSY)
-#define LAPACK_dlansy LAPACK_GLOBAL(dlansy,DLANSY)
-#define LAPACK_clansy LAPACK_GLOBAL(clansy,CLANSY)
-#define LAPACK_zlansy LAPACK_GLOBAL(zlansy,ZLANSY)
-#define LAPACK_slantr LAPACK_GLOBAL(slantr,SLANTR)
-#define LAPACK_dlantr LAPACK_GLOBAL(dlantr,DLANTR)
-#define LAPACK_clantr LAPACK_GLOBAL(clantr,CLANTR)
-#define LAPACK_zlantr LAPACK_GLOBAL(zlantr,ZLANTR)
-#define LAPACK_slamch LAPACK_GLOBAL(slamch,SLAMCH)
-#define LAPACK_dlamch LAPACK_GLOBAL(dlamch,DLAMCH)
-#define LAPACK_sgelq2 LAPACK_GLOBAL(sgelq2,SGELQ2)
-#define LAPACK_dgelq2 LAPACK_GLOBAL(dgelq2,DGELQ2)
-#define LAPACK_cgelq2 LAPACK_GLOBAL(cgelq2,CGELQ2)
-#define LAPACK_zgelq2 LAPACK_GLOBAL(zgelq2,ZGELQ2)
-#define LAPACK_slarfb LAPACK_GLOBAL(slarfb,SLARFB)
-#define LAPACK_dlarfb LAPACK_GLOBAL(dlarfb,DLARFB)
-#define LAPACK_clarfb LAPACK_GLOBAL(clarfb,CLARFB)
-#define LAPACK_zlarfb LAPACK_GLOBAL(zlarfb,ZLARFB)
-#define LAPACK_slarfg LAPACK_GLOBAL(slarfg,SLARFG)
-#define LAPACK_dlarfg LAPACK_GLOBAL(dlarfg,DLARFG)
-#define LAPACK_clarfg LAPACK_GLOBAL(clarfg,CLARFG)
-#define LAPACK_zlarfg LAPACK_GLOBAL(zlarfg,ZLARFG)
-#define LAPACK_slarft LAPACK_GLOBAL(slarft,SLARFT)
-#define LAPACK_dlarft LAPACK_GLOBAL(dlarft,DLARFT)
-#define LAPACK_clarft LAPACK_GLOBAL(clarft,CLARFT)
-#define LAPACK_zlarft LAPACK_GLOBAL(zlarft,ZLARFT)
-#define LAPACK_slarfx LAPACK_GLOBAL(slarfx,SLARFX)
-#define LAPACK_dlarfx LAPACK_GLOBAL(dlarfx,DLARFX)
-#define LAPACK_clarfx LAPACK_GLOBAL(clarfx,CLARFX)
-#define LAPACK_zlarfx LAPACK_GLOBAL(zlarfx,ZLARFX)
-#define LAPACK_slatms LAPACK_GLOBAL(slatms,SLATMS)
-#define LAPACK_dlatms LAPACK_GLOBAL(dlatms,DLATMS)
-#define LAPACK_clatms LAPACK_GLOBAL(clatms,CLATMS)
-#define LAPACK_zlatms LAPACK_GLOBAL(zlatms,ZLATMS)
-#define LAPACK_slag2d LAPACK_GLOBAL(slag2d,SLAG2D)
-#define LAPACK_dlag2s LAPACK_GLOBAL(dlag2s,DLAG2S)
-#define LAPACK_clag2z LAPACK_GLOBAL(clag2z,CLAG2Z)
-#define LAPACK_zlag2c LAPACK_GLOBAL(zlag2c,ZLAG2C)
-#define LAPACK_slauum LAPACK_GLOBAL(slauum,SLAUUM)
-#define LAPACK_dlauum LAPACK_GLOBAL(dlauum,DLAUUM)
-#define LAPACK_clauum LAPACK_GLOBAL(clauum,CLAUUM)
-#define LAPACK_zlauum LAPACK_GLOBAL(zlauum,ZLAUUM)
-#define LAPACK_slagge LAPACK_GLOBAL(slagge,SLAGGE)
-#define LAPACK_dlagge LAPACK_GLOBAL(dlagge,DLAGGE)
-#define LAPACK_clagge LAPACK_GLOBAL(clagge,CLAGGE)
-#define LAPACK_zlagge LAPACK_GLOBAL(zlagge,ZLAGGE)
-#define LAPACK_slaset LAPACK_GLOBAL(slaset,SLASET)
-#define LAPACK_dlaset LAPACK_GLOBAL(dlaset,DLASET)
-#define LAPACK_claset LAPACK_GLOBAL(claset,CLASET)
-#define LAPACK_zlaset LAPACK_GLOBAL(zlaset,ZLASET)
-#define LAPACK_slasrt LAPACK_GLOBAL(slasrt,SLASRT)
-#define LAPACK_dlasrt LAPACK_GLOBAL(dlasrt,DLASRT)
-#define LAPACK_slagsy LAPACK_GLOBAL(slagsy,SLAGSY)
-#define LAPACK_dlagsy LAPACK_GLOBAL(dlagsy,DLAGSY)
-#define LAPACK_clagsy LAPACK_GLOBAL(clagsy,CLAGSY)
-#define LAPACK_zlagsy LAPACK_GLOBAL(zlagsy,ZLAGSY)
-#define LAPACK_claghe LAPACK_GLOBAL(claghe,CLAGHE)
-#define LAPACK_zlaghe LAPACK_GLOBAL(zlaghe,ZLAGHE)
-#define LAPACK_slapmr LAPACK_GLOBAL(slapmr,SLAPMR)
-#define LAPACK_dlapmr LAPACK_GLOBAL(dlapmr,DLAPMR)
-#define LAPACK_clapmr LAPACK_GLOBAL(clapmr,CLAPMR)
-#define LAPACK_zlapmr LAPACK_GLOBAL(zlapmr,ZLAPMR)
-#define LAPACK_slapy2 LAPACK_GLOBAL(slapy2,SLAPY2)
-#define LAPACK_dlapy2 LAPACK_GLOBAL(dlapy2,DLAPY2)
-#define LAPACK_slapy3 LAPACK_GLOBAL(slapy3,SLAPY3)
-#define LAPACK_dlapy3 LAPACK_GLOBAL(dlapy3,DLAPY3)
-#define LAPACK_slartgp LAPACK_GLOBAL(slartgp,SLARTGP)
-#define LAPACK_dlartgp LAPACK_GLOBAL(dlartgp,DLARTGP)
-#define LAPACK_slartgs LAPACK_GLOBAL(slartgs,SLARTGS)
-#define LAPACK_dlartgs LAPACK_GLOBAL(dlartgs,DLARTGS)
-// LAPACK 3.3.0
-#define LAPACK_cbbcsd LAPACK_GLOBAL(cbbcsd,CBBCSD)
-#define LAPACK_cheswapr LAPACK_GLOBAL(cheswapr,CHESWAPR)
-#define LAPACK_chetri2 LAPACK_GLOBAL(chetri2,CHETRI2)
-#define LAPACK_chetri2x LAPACK_GLOBAL(chetri2x,CHETRI2X)
-#define LAPACK_chetrs2 LAPACK_GLOBAL(chetrs2,CHETRS2)
-#define LAPACK_csyconv LAPACK_GLOBAL(csyconv,CSYCONV)
-#define LAPACK_csyswapr LAPACK_GLOBAL(csyswapr,CSYSWAPR)
-#define LAPACK_csytri2 LAPACK_GLOBAL(csytri2,CSYTRI2)
-#define LAPACK_csytri2x LAPACK_GLOBAL(csytri2x,CSYTRI2X)
-#define LAPACK_csytrs2 LAPACK_GLOBAL(csytrs2,CSYTRS2)
-#define LAPACK_cunbdb LAPACK_GLOBAL(cunbdb,CUNBDB)
-#define LAPACK_cuncsd LAPACK_GLOBAL(cuncsd,CUNCSD)
-#define LAPACK_dbbcsd LAPACK_GLOBAL(dbbcsd,DBBCSD)
-#define LAPACK_dorbdb LAPACK_GLOBAL(dorbdb,DORBDB)
-#define LAPACK_dorcsd LAPACK_GLOBAL(dorcsd,DORCSD)
-#define LAPACK_dsyconv LAPACK_GLOBAL(dsyconv,DSYCONV)
-#define LAPACK_dsyswapr LAPACK_GLOBAL(dsyswapr,DSYSWAPR)
-#define LAPACK_dsytri2 LAPACK_GLOBAL(dsytri2,DSYTRI2)
-#define LAPACK_dsytri2x LAPACK_GLOBAL(dsytri2x,DSYTRI2X)
-#define LAPACK_dsytrs2 LAPACK_GLOBAL(dsytrs2,DSYTRS2)
-#define LAPACK_sbbcsd LAPACK_GLOBAL(sbbcsd,SBBCSD)
-#define LAPACK_sorbdb LAPACK_GLOBAL(sorbdb,SORBDB)
-#define LAPACK_sorcsd LAPACK_GLOBAL(sorcsd,SORCSD)
-#define LAPACK_ssyconv LAPACK_GLOBAL(ssyconv,SSYCONV)
-#define LAPACK_ssyswapr LAPACK_GLOBAL(ssyswapr,SSYSWAPR)
-#define LAPACK_ssytri2 LAPACK_GLOBAL(ssytri2,SSYTRI2)
-#define LAPACK_ssytri2x LAPACK_GLOBAL(ssytri2x,SSYTRI2X)
-#define LAPACK_ssytrs2 LAPACK_GLOBAL(ssytrs2,SSYTRS2)
-#define LAPACK_zbbcsd LAPACK_GLOBAL(zbbcsd,ZBBCSD)
-#define LAPACK_zheswapr LAPACK_GLOBAL(zheswapr,ZHESWAPR)
-#define LAPACK_zhetri2 LAPACK_GLOBAL(zhetri2,ZHETRI2)
-#define LAPACK_zhetri2x LAPACK_GLOBAL(zhetri2x,ZHETRI2X)
-#define LAPACK_zhetrs2 LAPACK_GLOBAL(zhetrs2,ZHETRS2)
-#define LAPACK_zsyconv LAPACK_GLOBAL(zsyconv,ZSYCONV)
-#define LAPACK_zsyswapr LAPACK_GLOBAL(zsyswapr,ZSYSWAPR)
-#define LAPACK_zsytri2 LAPACK_GLOBAL(zsytri2,ZSYTRI2)
-#define LAPACK_zsytri2x LAPACK_GLOBAL(zsytri2x,ZSYTRI2X)
-#define LAPACK_zsytrs2 LAPACK_GLOBAL(zsytrs2,ZSYTRS2)
-#define LAPACK_zunbdb LAPACK_GLOBAL(zunbdb,ZUNBDB)
-#define LAPACK_zuncsd LAPACK_GLOBAL(zuncsd,ZUNCSD)
-// LAPACK 3.4.0
-#define LAPACK_sgemqrt LAPACK_GLOBAL(sgemqrt,SGEMQRT)
-#define LAPACK_dgemqrt LAPACK_GLOBAL(dgemqrt,DGEMQRT)
-#define LAPACK_cgemqrt LAPACK_GLOBAL(cgemqrt,CGEMQRT)
-#define LAPACK_zgemqrt LAPACK_GLOBAL(zgemqrt,ZGEMQRT)
-#define LAPACK_sgeqrt LAPACK_GLOBAL(sgeqrt,SGEQRT)
-#define LAPACK_dgeqrt LAPACK_GLOBAL(dgeqrt,DGEQRT)
-#define LAPACK_cgeqrt LAPACK_GLOBAL(cgeqrt,CGEQRT)
-#define LAPACK_zgeqrt LAPACK_GLOBAL(zgeqrt,ZGEQRT)
-#define LAPACK_sgeqrt2 LAPACK_GLOBAL(sgeqrt2,SGEQRT2)
-#define LAPACK_dgeqrt2 LAPACK_GLOBAL(dgeqrt2,DGEQRT2)
-#define LAPACK_cgeqrt2 LAPACK_GLOBAL(cgeqrt2,CGEQRT2)
-#define LAPACK_zgeqrt2 LAPACK_GLOBAL(zgeqrt2,ZGEQRT2)
-#define LAPACK_sgeqrt3 LAPACK_GLOBAL(sgeqrt3,SGEQRT3)
-#define LAPACK_dgeqrt3 LAPACK_GLOBAL(dgeqrt3,DGEQRT3)
-#define LAPACK_cgeqrt3 LAPACK_GLOBAL(cgeqrt3,CGEQRT3)
-#define LAPACK_zgeqrt3 LAPACK_GLOBAL(zgeqrt3,ZGEQRT3)
-#define LAPACK_stpmqrt LAPACK_GLOBAL(stpmqrt,STPMQRT)
-#define LAPACK_dtpmqrt LAPACK_GLOBAL(dtpmqrt,DTPMQRT)
-#define LAPACK_ctpmqrt LAPACK_GLOBAL(ctpmqrt,CTPMQRT)
-#define LAPACK_ztpmqrt LAPACK_GLOBAL(ztpmqrt,ZTPMQRT)
-#define LAPACK_dtpqrt LAPACK_GLOBAL(dtpqrt,DTPQRT)
-#define LAPACK_ctpqrt LAPACK_GLOBAL(ctpqrt,CTPQRT)
-#define LAPACK_ztpqrt LAPACK_GLOBAL(ztpqrt,ZTPQRT)
-#define LAPACK_stpqrt2 LAPACK_GLOBAL(stpqrt2,STPQRT2)
-#define LAPACK_dtpqrt2 LAPACK_GLOBAL(dtpqrt2,DTPQRT2)
-#define LAPACK_ctpqrt2 LAPACK_GLOBAL(ctpqrt2,CTPQRT2)
-#define LAPACK_ztpqrt2 LAPACK_GLOBAL(ztpqrt2,ZTPQRT2)
-#define LAPACK_stprfb LAPACK_GLOBAL(stprfb,STPRFB)
-#define LAPACK_dtprfb LAPACK_GLOBAL(dtprfb,DTPRFB)
-#define LAPACK_ctprfb LAPACK_GLOBAL(ctprfb,CTPRFB)
-#define LAPACK_ztprfb LAPACK_GLOBAL(ztprfb,ZTPRFB)
-// LAPACK 3.X.X
-#define LAPACK_csyr LAPACK_GLOBAL(csyr,CSYR)
-#define LAPACK_zsyr LAPACK_GLOBAL(zsyr,ZSYR)
-
-
-void LAPACK_sgetrf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgetrf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgetrf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_zgetrf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_sgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, float* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, double* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_zgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_sgttrf( lapack_int* n, float* dl, float* d, float* du, float* du2,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgttrf( lapack_int* n, double* dl, double* d, double* du,
-                    double* du2, lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgttrf( lapack_int* n, lapack_complex_float* dl,
-                    lapack_complex_float* d, lapack_complex_float* du,
-                    lapack_complex_float* du2, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_zgttrf( lapack_int* n, lapack_complex_double* dl,
-                    lapack_complex_double* d, lapack_complex_double* du,
-                    lapack_complex_double* du2, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_spotrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dpotrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_cpotrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_zpotrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dpstrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* piv, lapack_int* rank, double* tol,
-                    double* work, lapack_int *info );
-void LAPACK_spstrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* piv, lapack_int* rank, float* tol, float* work,
-                    lapack_int *info );
-void LAPACK_zpstrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* piv, lapack_int* rank,
-                    double* tol, double* work, lapack_int *info );
-void LAPACK_cpstrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* piv, lapack_int* rank,
-                    float* tol, float* work, lapack_int *info );
-void LAPACK_dpftrf( char* transr, char* uplo, lapack_int* n, double* a,
-                    lapack_int *info );
-void LAPACK_spftrf( char* transr, char* uplo, lapack_int* n, float* a,
-                    lapack_int *info );
-void LAPACK_zpftrf( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int *info );
-void LAPACK_cpftrf( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int *info );
-void LAPACK_spptrf( char* uplo, lapack_int* n, float* ap, lapack_int *info );
-void LAPACK_dpptrf( char* uplo, lapack_int* n, double* ap, lapack_int *info );
-void LAPACK_cpptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_zpptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_spbtrf( char* uplo, lapack_int* n, lapack_int* kd, float* ab,
-                    lapack_int* ldab, lapack_int *info );
-void LAPACK_dpbtrf( char* uplo, lapack_int* n, lapack_int* kd, double* ab,
-                    lapack_int* ldab, lapack_int *info );
-void LAPACK_cpbtrf( char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_int *info );
-void LAPACK_zpbtrf( char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_int *info );
-void LAPACK_spttrf( lapack_int* n, float* d, float* e, lapack_int *info );
-void LAPACK_dpttrf( lapack_int* n, double* d, double* e, lapack_int *info );
-void LAPACK_cpttrf( lapack_int* n, float* d, lapack_complex_float* e,
-                    lapack_int *info );
-void LAPACK_zpttrf( lapack_int* n, double* d, lapack_complex_double* e,
-                    lapack_int *info );
-void LAPACK_ssytrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ipiv, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dsytrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ipiv, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_csytrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zsytrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_chetrf( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zhetrf( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ssptrf( char* uplo, lapack_int* n, float* ap, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_dsptrf( char* uplo, lapack_int* n, double* ap, lapack_int* ipiv,
-                    lapack_int *info );
-void LAPACK_csptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_zsptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_chptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_zhptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_sgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* a, lapack_int* lda, const lapack_int* ipiv,
-                    float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const lapack_int* ipiv,
-                    double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_sgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const float* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const double* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_cgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_float* ab,
-                    lapack_int* ldab, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_double* ab,
-                    lapack_int* ldab, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_sgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* dl, const float* d, const float* du,
-                    const float* du2, const lapack_int* ipiv, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* dl, const double* d, const double* du,
-                    const double* du2, const lapack_int* ipiv, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du,
-                    const lapack_complex_float* du2, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du,
-                    const lapack_complex_double* du2, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_spotrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_spftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* a, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_spptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_cpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_spbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const float* ab, lapack_int* ldab, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const double* ab, lapack_int* ldab, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_cpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_spttrs( lapack_int* n, lapack_int* nrhs, const float* d,
-                    const float* e, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dpttrs( lapack_int* n, lapack_int* nrhs, const double* d,
-                    const double* e, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_cpttrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const lapack_complex_float* e, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zpttrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const lapack_complex_double* e,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ssytrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, const lapack_int* ipiv, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dsytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const lapack_int* ipiv,
-                    double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_csytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zsytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_chetrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_zhetrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_ssptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, const lapack_int* ipiv, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dsptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, const lapack_int* ipiv, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_csptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zsptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_chptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, const lapack_int* ipiv,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zhptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, const lapack_int* ipiv,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_strtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dtrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* a, lapack_int* lda,
-                    double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_ctrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ztrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_stptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* ap, float* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_dtptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* ap, double* b,
-                    lapack_int* ldb, lapack_int *info );
-void LAPACK_ctptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* ap,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ztptrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* ap,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_stbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const float* ab,
-                    lapack_int* ldab, float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dtbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const double* ab,
-                    lapack_int* ldab, double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ctbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_ztbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_sgecon( char* norm, lapack_int* n, const float* a, lapack_int* lda,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgecon( char* norm, lapack_int* n, const double* a, lapack_int* lda,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgecon( char* norm, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* anorm, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgecon( char* norm, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, double* anorm, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const float* ab, lapack_int* ldab, const lapack_int* ipiv,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const double* ab, lapack_int* ldab, const lapack_int* ipiv,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgtcon( char* norm, lapack_int* n, const float* dl, const float* d,
-                    const float* du, const float* du2, const lapack_int* ipiv,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgtcon( char* norm, lapack_int* n, const double* dl,
-                    const double* d, const double* du, const double* du2,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgtcon( char* norm, lapack_int* n, const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du,
-                    const lapack_complex_float* du2, const lapack_int* ipiv,
-                    float* anorm, float* rcond, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zgtcon( char* norm, lapack_int* n, const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du,
-                    const lapack_complex_double* du2, const lapack_int* ipiv,
-                    double* anorm, double* rcond, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_spocon( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                    float* anorm, float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dpocon( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cpocon( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* anorm, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zpocon( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, double* anorm, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sppcon( char* uplo, lapack_int* n, const float* ap, float* anorm,
-                    float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dppcon( char* uplo, lapack_int* n, const double* ap, double* anorm,
-                    double* rcond, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cppcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    float* anorm, float* rcond, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zppcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    double* anorm, double* rcond, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_spbcon( char* uplo, lapack_int* n, lapack_int* kd, const float* ab,
-                    lapack_int* ldab, float* anorm, float* rcond, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dpbcon( char* uplo, lapack_int* n, lapack_int* kd, const double* ab,
-                    lapack_int* ldab, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cpbcon( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    float* anorm, float* rcond, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zpbcon( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    double* anorm, double* rcond, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sptcon( lapack_int* n, const float* d, const float* e, float* anorm,
-                    float* rcond, float* work, lapack_int *info );
-void LAPACK_dptcon( lapack_int* n, const double* d, const double* e,
-                    double* anorm, double* rcond, double* work,
-                    lapack_int *info );
-void LAPACK_cptcon( lapack_int* n, const float* d,
-                    const lapack_complex_float* e, float* anorm, float* rcond,
-                    float* work, lapack_int *info );
-void LAPACK_zptcon( lapack_int* n, const double* d,
-                    const lapack_complex_double* e, double* anorm,
-                    double* rcond, double* work, lapack_int *info );
-void LAPACK_ssycon( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dsycon( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_csycon( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv, float* anorm,
-                    float* rcond, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zsycon( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv, double* anorm,
-                    double* rcond, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_checon( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv, float* anorm,
-                    float* rcond, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zhecon( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv, double* anorm,
-                    double* rcond, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_sspcon( char* uplo, lapack_int* n, const float* ap,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dspcon( char* uplo, lapack_int* n, const double* ap,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cspcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zspcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_chpcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_int* ipiv, float* anorm, float* rcond,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zhpcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_int* ipiv, double* anorm, double* rcond,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_strcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const float* a, lapack_int* lda, float* rcond, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtrcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const double* a, lapack_int* lda, double* rcond,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_ctrcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    float* rcond, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztrcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    double* rcond, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const float* ap, float* rcond, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const double* ap, double* rcond, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_float* ap, float* rcond,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztpcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    const lapack_complex_double* ap, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const float* ab, lapack_int* ldab,
-                    float* rcond, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dtbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const double* ab, lapack_int* ldab,
-                    double* rcond, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const lapack_complex_float* ab,
-                    lapack_int* ldab, float* rcond, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_ztbcon( char* norm, char* uplo, char* diag, lapack_int* n,
-                    lapack_int* kd, const lapack_complex_double* ab,
-                    lapack_int* ldab, double* rcond,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* a, lapack_int* lda, const float* af,
-                    lapack_int* ldaf, const lapack_int* ipiv, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
-                    float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const double* af,
-                    lapack_int* ldaf, const lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const double* a, lapack_int* lda, const double* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const double* r,
-                     const double* c, const double* b, lapack_int* ldb,
-                     double* x, lapack_int* ldx, double* rcond, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const float* a, lapack_int* lda, const float* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const float* r,
-                     const float* c, const float* b, lapack_int* ldb, float* x,
-                     lapack_int* ldx, float* rcond, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const double* r, const double* c,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const float* r, const float* c,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const float* ab, lapack_int* ldab,
-                    const float* afb, lapack_int* ldafb, const lapack_int* ipiv,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const double* ab, lapack_int* ldab,
-                    const double* afb, lapack_int* ldafb,
-                    const lapack_int* ipiv, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_float* ab,
-                    lapack_int* ldab, const lapack_complex_float* afb,
-                    lapack_int* ldafb, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
-                    lapack_int* nrhs, const lapack_complex_double* ab,
-                    lapack_int* ldab, const lapack_complex_double* afb,
-                    lapack_int* ldafb, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_dgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, const double* ab,
-                     lapack_int* ldab, const double* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const double* r, const double* c,
-                     const double* b, lapack_int* ldb, double* x,
-                     lapack_int* ldx, double* rcond, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, const float* ab,
-                     lapack_int* ldab, const float* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const float* r, const float* c,
-                     const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params, float* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_zgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs,
-                     const lapack_complex_double* ab, lapack_int* ldab,
-                     const lapack_complex_double* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const double* r, const double* c,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs,
-                     const lapack_complex_float* ab, lapack_int* ldab,
-                     const lapack_complex_float* afb, lapack_int* ldafb,
-                     const lapack_int* ipiv, const float* r, const float* c,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* dl, const float* d, const float* du,
-                    const float* dlf, const float* df, const float* duf,
-                    const float* du2, const lapack_int* ipiv, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
-                    float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* dl, const double* d, const double* du,
-                    const double* dlf, const double* df, const double* duf,
-                    const double* du2, const lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du,
-                    const lapack_complex_float* dlf,
-                    const lapack_complex_float* df,
-                    const lapack_complex_float* duf,
-                    const lapack_complex_float* du2, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du,
-                    const lapack_complex_double* dlf,
-                    const lapack_complex_double* df,
-                    const lapack_complex_double* duf,
-                    const lapack_complex_double* du2, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sporfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, const float* af, lapack_int* ldaf,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const double* af,
-                    lapack_int* ldaf, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_dporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const double* a, lapack_int* lda, const double* af,
-                     lapack_int* ldaf, const double* s, const double* b,
-                     lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params, double* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_sporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const float* a, lapack_int* lda, const float* af,
-                     lapack_int* ldaf, const float* s, const float* b,
-                     lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const double* s, const lapack_complex_double* b,
-                     lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                     double* rcond, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const float* s, const lapack_complex_float* b,
-                     lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                     float* rcond, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_spprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, const float* afp, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
-                    float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, const double* afp, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap,
-                    const lapack_complex_float* afp,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap,
-                    const lapack_complex_double* afp,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_spbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const float* ab, lapack_int* ldab, const float* afb,
-                    lapack_int* ldafb, const float* b, lapack_int* ldb,
-                    float* x, lapack_int* ldx, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const double* ab, lapack_int* ldab, const double* afb,
-                    lapack_int* ldafb, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_complex_float* afb, lapack_int* ldafb,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_complex_double* afb, lapack_int* ldafb,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sptrfs( lapack_int* n, lapack_int* nrhs, const float* d,
-                    const float* e, const float* df, const float* ef,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int *info );
-void LAPACK_dptrfs( lapack_int* n, lapack_int* nrhs, const double* d,
-                    const double* e, const double* df, const double* ef,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* ferr, double* berr, double* work,
-                    lapack_int *info );
-void LAPACK_cptrfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const lapack_complex_float* e, const float* df,
-                    const lapack_complex_float* ef,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zptrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const lapack_complex_double* e,
-                    const double* df, const lapack_complex_double* ef,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_ssyrfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
-                    lapack_int* lda, const float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const float* b, lapack_int* ldb,
-                    float* x, lapack_int* ldx, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dsyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, const double* af,
-                    lapack_int* ldaf, const lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_csyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zsyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dsyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const double* a, lapack_int* lda, const double* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const double* s,
-                     const double* b, lapack_int* ldb, double* x,
-                     lapack_int* ldx, double* rcond, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_ssyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const float* a, lapack_int* lda, const float* af,
-                     lapack_int* ldaf, const lapack_int* ipiv, const float* s,
-                     const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params, float* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_zsyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const double* s,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_csyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const float* s,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_cherfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zherfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* af, lapack_int* ldaf,
-                    const lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_zherfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_complex_double* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const double* s,
-                     const lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cherfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
-                     const lapack_complex_float* a, lapack_int* lda,
-                     const lapack_complex_float* af, lapack_int* ldaf,
-                     const lapack_int* ipiv, const float* s,
-                     const lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_ssprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, const float* afp, const lapack_int* ipiv,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dsprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, const double* afp, const lapack_int* ipiv,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_csprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap,
-                    const lapack_complex_float* afp, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zsprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap,
-                    const lapack_complex_double* afp, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_chprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap,
-                    const lapack_complex_float* afp, const lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap,
-                    const lapack_complex_double* afp, const lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
-                    double* berr, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_strrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* a, lapack_int* lda,
-                    const float* b, lapack_int* ldb, const float* x,
-                    lapack_int* ldx, float* ferr, float* berr, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* a, lapack_int* lda,
-                    const double* b, lapack_int* ldb, const double* x,
-                    lapack_int* ldx, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* b,
-                    lapack_int* ldb, const lapack_complex_float* x,
-                    lapack_int* ldx, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* b,
-                    lapack_int* ldb, const lapack_complex_double* x,
-                    lapack_int* ldx, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const float* ap, const float* b,
-                    lapack_int* ldb, const float* x, lapack_int* ldx,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dtprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const double* ap, const double* b,
-                    lapack_int* ldb, const double* x, lapack_int* ldx,
-                    double* ferr, double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_float* ap,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    const lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztprfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* nrhs, const lapack_complex_double* ap,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    const lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_stbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const float* ab,
-                    lapack_int* ldab, const float* b, lapack_int* ldb,
-                    const float* x, lapack_int* ldx, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dtbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs, const double* ab,
-                    lapack_int* ldab, const double* b, lapack_int* ldb,
-                    const double* x, lapack_int* ldx, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    const lapack_complex_float* x, lapack_int* ldx, float* ferr,
-                    float* berr, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
-                    lapack_int* kd, lapack_int* nrhs,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    const lapack_complex_double* x, lapack_int* ldx,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgetri( lapack_int* n, float* a, lapack_int* lda,
-                    const lapack_int* ipiv, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgetri( lapack_int* n, double* a, lapack_int* lda,
-                    const lapack_int* ipiv, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgetri( lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zgetri( lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    const lapack_int* ipiv, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_spotri( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dpotri( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_cpotri( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_zpotri( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dpftri( char* transr, char* uplo, lapack_int* n, double* a,
-                    lapack_int *info );
-void LAPACK_spftri( char* transr, char* uplo, lapack_int* n, float* a,
-                    lapack_int *info );
-void LAPACK_zpftri( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int *info );
-void LAPACK_cpftri( char* transr, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int *info );
-void LAPACK_spptri( char* uplo, lapack_int* n, float* ap, lapack_int *info );
-void LAPACK_dpptri( char* uplo, lapack_int* n, double* ap, lapack_int *info );
-void LAPACK_cpptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_zpptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_ssytri( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    const lapack_int* ipiv, float* work, lapack_int *info );
-void LAPACK_dsytri( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    const lapack_int* ipiv, double* work, lapack_int *info );
-void LAPACK_csytri( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zsytri( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_chetri( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zhetri( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, const lapack_int* ipiv,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_ssptri( char* uplo, lapack_int* n, float* ap,
-                    const lapack_int* ipiv, float* work, lapack_int *info );
-void LAPACK_dsptri( char* uplo, lapack_int* n, double* ap,
-                    const lapack_int* ipiv, double* work, lapack_int *info );
-void LAPACK_csptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    const lapack_int* ipiv, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zsptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    const lapack_int* ipiv, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_chptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    const lapack_int* ipiv, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zhptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    const lapack_int* ipiv, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_strtri( char* uplo, char* diag, lapack_int* n, float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dtrtri( char* uplo, char* diag, lapack_int* n, double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_ctrtri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_ztrtri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dtftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    double* a, lapack_int *info );
-void LAPACK_stftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    float* a, lapack_int *info );
-void LAPACK_ztftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_double* a, lapack_int *info );
-void LAPACK_ctftri( char* transr, char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_float* a, lapack_int *info );
-void LAPACK_stptri( char* uplo, char* diag, lapack_int* n, float* ap,
-                    lapack_int *info );
-void LAPACK_dtptri( char* uplo, char* diag, lapack_int* n, double* ap,
-                    lapack_int *info );
-void LAPACK_ctptri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_float* ap, lapack_int *info );
-void LAPACK_ztptri( char* uplo, char* diag, lapack_int* n,
-                    lapack_complex_double* ap, lapack_int *info );
-void LAPACK_sgeequ( lapack_int* m, lapack_int* n, const float* a,
-                    lapack_int* lda, float* r, float* c, float* rowcnd,
-                    float* colcnd, float* amax, lapack_int *info );
-void LAPACK_dgeequ( lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, double* r, double* c, double* rowcnd,
-                    double* colcnd, double* amax, lapack_int *info );
-void LAPACK_cgeequ( lapack_int* m, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* r, float* c, float* rowcnd,
-                    float* colcnd, float* amax, lapack_int *info );
-void LAPACK_zgeequ( lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* r,
-                    double* c, double* rowcnd, double* colcnd, double* amax,
-                    lapack_int *info );
-void LAPACK_dgeequb( lapack_int* m, lapack_int* n, const double* a,
-                     lapack_int* lda, double* r, double* c, double* rowcnd,
-                     double* colcnd, double* amax, lapack_int *info );
-void LAPACK_sgeequb( lapack_int* m, lapack_int* n, const float* a,
-                     lapack_int* lda, float* r, float* c, float* rowcnd,
-                     float* colcnd, float* amax, lapack_int *info );
-void LAPACK_zgeequb( lapack_int* m, lapack_int* n,
-                     const lapack_complex_double* a, lapack_int* lda, double* r,
-                     double* c, double* rowcnd, double* colcnd, double* amax,
-                     lapack_int *info );
-void LAPACK_cgeequb( lapack_int* m, lapack_int* n,
-                     const lapack_complex_float* a, lapack_int* lda, float* r,
-                     float* c, float* rowcnd, float* colcnd, float* amax,
-                     lapack_int *info );
-void LAPACK_sgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const float* ab, lapack_int* ldab, float* r,
-                    float* c, float* rowcnd, float* colcnd, float* amax,
-                    lapack_int *info );
-void LAPACK_dgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const double* ab, lapack_int* ldab,
-                    double* r, double* c, double* rowcnd, double* colcnd,
-                    double* amax, lapack_int *info );
-void LAPACK_cgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const lapack_complex_float* ab,
-                    lapack_int* ldab, float* r, float* c, float* rowcnd,
-                    float* colcnd, float* amax, lapack_int *info );
-void LAPACK_zgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const lapack_complex_double* ab,
-                    lapack_int* ldab, double* r, double* c, double* rowcnd,
-                    double* colcnd, double* amax, lapack_int *info );
-void LAPACK_dgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const double* ab, lapack_int* ldab,
-                     double* r, double* c, double* rowcnd, double* colcnd,
-                     double* amax, lapack_int *info );
-void LAPACK_sgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const float* ab, lapack_int* ldab,
-                     float* r, float* c, float* rowcnd, float* colcnd,
-                     float* amax, lapack_int *info );
-void LAPACK_zgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const lapack_complex_double* ab,
-                     lapack_int* ldab, double* r, double* c, double* rowcnd,
-                     double* colcnd, double* amax, lapack_int *info );
-void LAPACK_cgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, const lapack_complex_float* ab,
-                     lapack_int* ldab, float* r, float* c, float* rowcnd,
-                     float* colcnd, float* amax, lapack_int *info );
-void LAPACK_spoequ( lapack_int* n, const float* a, lapack_int* lda, float* s,
-                    float* scond, float* amax, lapack_int *info );
-void LAPACK_dpoequ( lapack_int* n, const double* a, lapack_int* lda, double* s,
-                    double* scond, double* amax, lapack_int *info );
-void LAPACK_cpoequ( lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, float* s, float* scond, float* amax,
-                    lapack_int *info );
-void LAPACK_zpoequ( lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, double* s, double* scond, double* amax,
-                    lapack_int *info );
-void LAPACK_dpoequb( lapack_int* n, const double* a, lapack_int* lda, double* s,
-                     double* scond, double* amax, lapack_int *info );
-void LAPACK_spoequb( lapack_int* n, const float* a, lapack_int* lda, float* s,
-                     float* scond, float* amax, lapack_int *info );
-void LAPACK_zpoequb( lapack_int* n, const lapack_complex_double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     lapack_int *info );
-void LAPACK_cpoequb( lapack_int* n, const lapack_complex_float* a,
-                     lapack_int* lda, float* s, float* scond, float* amax,
-                     lapack_int *info );
-void LAPACK_sppequ( char* uplo, lapack_int* n, const float* ap, float* s,
-                    float* scond, float* amax, lapack_int *info );
-void LAPACK_dppequ( char* uplo, lapack_int* n, const double* ap, double* s,
-                    double* scond, double* amax, lapack_int *info );
-void LAPACK_cppequ( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    float* s, float* scond, float* amax, lapack_int *info );
-void LAPACK_zppequ( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    double* s, double* scond, double* amax, lapack_int *info );
-void LAPACK_spbequ( char* uplo, lapack_int* n, lapack_int* kd, const float* ab,
-                    lapack_int* ldab, float* s, float* scond, float* amax,
-                    lapack_int *info );
-void LAPACK_dpbequ( char* uplo, lapack_int* n, lapack_int* kd, const double* ab,
-                    lapack_int* ldab, double* s, double* scond, double* amax,
-                    lapack_int *info );
-void LAPACK_cpbequ( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_float* ab, lapack_int* ldab, float* s,
-                    float* scond, float* amax, lapack_int *info );
-void LAPACK_zpbequ( char* uplo, lapack_int* n, lapack_int* kd,
-                    const lapack_complex_double* ab, lapack_int* ldab,
-                    double* s, double* scond, double* amax, lapack_int *info );
-void LAPACK_dsyequb( char* uplo, lapack_int* n, const double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     double* work, lapack_int *info );
-void LAPACK_ssyequb( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                     float* s, float* scond, float* amax, float* work,
-                     lapack_int *info );
-void LAPACK_zsyequb( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_csyequb( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                     lapack_int* lda, float* s, float* scond, float* amax,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_zheequb( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                     lapack_int* lda, double* s, double* scond, double* amax,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_cheequb( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                     lapack_int* lda, float* s, float* scond, float* amax,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_sgesv( lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda,
-                   lapack_int* ipiv, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dgesv( lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda,
-                   lapack_int* ipiv, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_float* a,
-                   lapack_int* lda, lapack_int* ipiv, lapack_complex_float* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_zgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* a,
-                   lapack_int* lda, lapack_int* ipiv, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_dsgesv( lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda,
-                    lapack_int* ipiv, double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* work, float* swork,
-                    lapack_int* iter, lapack_int *info );
-void LAPACK_zcgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    lapack_complex_double* work, lapack_complex_float* swork,
-                    double* rwork, lapack_int* iter, lapack_int *info );
-void LAPACK_sgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, float* r, float* c, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, double* r, double* c,
-                    double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, float* r, float* c,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, char* equed, double* r, double* c,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                     double* rcond, double* rpvgrw, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* r, float* c,
-                     float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* rpvgrw, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* r, float* c,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, float* ab, lapack_int* ldab,
-                   lapack_int* ipiv, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, double* ab, lapack_int* ldab,
-                   lapack_int* ipiv, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, lapack_complex_float* ab, lapack_int* ldab,
-                   lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_zgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
-                   lapack_int* nrhs, lapack_complex_double* ab,
-                   lapack_int* ldab, lapack_int* ipiv, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_sgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, float* ab,
-                    lapack_int* ldab, float* afb, lapack_int* ldafb,
-                    lapack_int* ipiv, char* equed, float* r, float* c, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, double* ab,
-                    lapack_int* ldab, double* afb, lapack_int* ldafb,
-                    lapack_int* ipiv, char* equed, double* r, double* c,
-                    double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, lapack_complex_float* ab,
-                    lapack_int* ldab, lapack_complex_float* afb,
-                    lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r,
-                    float* c, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, lapack_int* nrhs, lapack_complex_double* ab,
-                    lapack_int* ldab, lapack_complex_double* afb,
-                    lapack_int* ldafb, lapack_int* ipiv, char* equed, double* r,
-                    double* c, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, double* ab,
-                     lapack_int* ldab, double* afb, lapack_int* ldafb,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     double* b, lapack_int* ldb, double* x, lapack_int* ldx,
-                     double* rcond, double* rpvgrw, double* berr,
-                     lapack_int* n_err_bnds, double* err_bnds_norm,
-                     double* err_bnds_comp, lapack_int* nparams, double* params,
-                     double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, float* ab,
-                     lapack_int* ldab, float* afb, lapack_int* ldafb,
-                     lapack_int* ipiv, char* equed, float* r, float* c,
-                     float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                     float* rcond, float* rpvgrw, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs,
-                     lapack_complex_double* ab, lapack_int* ldab,
-                     lapack_complex_double* afb, lapack_int* ldafb,
-                     lapack_int* ipiv, char* equed, double* r, double* c,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
-                     lapack_int* ku, lapack_int* nrhs, lapack_complex_float* ab,
-                     lapack_int* ldab, lapack_complex_float* afb,
-                     lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r,
-                     float* c, lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sgtsv( lapack_int* n, lapack_int* nrhs, float* dl, float* d,
-                   float* du, float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dgtsv( lapack_int* n, lapack_int* nrhs, double* dl, double* d,
-                   double* du, double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cgtsv( lapack_int* n, lapack_int* nrhs, lapack_complex_float* dl,
-                   lapack_complex_float* d, lapack_complex_float* du,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zgtsv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* dl,
-                   lapack_complex_double* d, lapack_complex_double* du,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_sgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const float* dl, const float* d, const float* du,
-                    float* dlf, float* df, float* duf, float* du2,
-                    lapack_int* ipiv, const float* b, lapack_int* ldb, float* x,
-                    lapack_int* ldx, float* rcond, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const double* dl, const double* d, const double* du,
-                    double* dlf, double* df, double* duf, double* du2,
-                    lapack_int* ipiv, const double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* rcond, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* dl,
-                    const lapack_complex_float* d,
-                    const lapack_complex_float* du, lapack_complex_float* dlf,
-                    lapack_complex_float* df, lapack_complex_float* duf,
-                    lapack_complex_float* du2, lapack_int* ipiv,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* dl,
-                    const lapack_complex_double* d,
-                    const lapack_complex_double* du, lapack_complex_double* dlf,
-                    lapack_complex_double* df, lapack_complex_double* duf,
-                    lapack_complex_double* du2, lapack_int* ipiv,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sposv( char* uplo, lapack_int* n, lapack_int* nrhs, float* a,
-                   lapack_int* lda, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dposv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
-                   lapack_int* lda, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cposv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zposv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dsposv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* work, float* swork,
-                    lapack_int* iter, lapack_int *info );
-void LAPACK_zcposv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx,
-                    lapack_complex_double* work, lapack_complex_float* swork,
-                    double* rwork, lapack_int* iter, lapack_int *info );
-void LAPACK_sposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                    char* equed, float* s, float* b, lapack_int* ldb, float* x,
-                    lapack_int* ldx, float* rcond, float* ferr, float* berr,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                    char* equed, double* s, double* b, lapack_int* ldb,
-                    double* x, lapack_int* ldx, double* rcond, double* ferr,
-                    double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf, char* equed,
-                    float* s, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf, char* equed,
-                    double* s, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_dposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                     char* equed, double* s, double* b, lapack_int* ldb,
-                     double* x, lapack_int* ldx, double* rcond, double* rpvgrw,
-                     double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params, double* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_sposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                     char* equed, float* s, float* b, lapack_int* ldb, float* x,
-                     lapack_int* ldx, float* rcond, float* rpvgrw, float* berr,
-                     lapack_int* n_err_bnds, float* err_bnds_norm,
-                     float* err_bnds_comp, lapack_int* nparams, float* params,
-                     float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_zposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf, char* equed,
-                     double* s, lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_cposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf, char* equed,
-                     float* s, lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sppsv( char* uplo, lapack_int* n, lapack_int* nrhs, float* ap,
-                   float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dppsv( char* uplo, lapack_int* n, lapack_int* nrhs, double* ap,
-                   double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cppsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* ap, lapack_complex_float* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_zppsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* ap, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_sppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    float* ap, float* afp, char* equed, float* s, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    double* ap, double* afp, char* equed, double* s, double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* ap, lapack_complex_float* afp,
-                    char* equed, float* s, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* ap, lapack_complex_double* afp,
-                    char* equed, double* s, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_spbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   float* ab, lapack_int* ldab, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   double* ab, lapack_int* ldab, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   lapack_complex_float* ab, lapack_int* ldab,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
-                   lapack_complex_double* ab, lapack_int* ldab,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_spbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, float* ab, lapack_int* ldab, float* afb,
-                    lapack_int* ldafb, char* equed, float* s, float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, double* ab, lapack_int* ldab, double* afb,
-                    lapack_int* ldafb, char* equed, double* s, double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_cpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, lapack_complex_float* ab,
-                    lapack_int* ldab, lapack_complex_float* afb,
-                    lapack_int* ldafb, char* equed, float* s,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_int* nrhs, lapack_complex_double* ab,
-                    lapack_int* ldab, lapack_complex_double* afb,
-                    lapack_int* ldafb, char* equed, double* s,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sptsv( lapack_int* n, lapack_int* nrhs, float* d, float* e,
-                   float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_dptsv( lapack_int* n, lapack_int* nrhs, double* d, double* e,
-                   double* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_cptsv( lapack_int* n, lapack_int* nrhs, float* d,
-                   lapack_complex_float* e, lapack_complex_float* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_zptsv( lapack_int* n, lapack_int* nrhs, double* d,
-                   lapack_complex_double* e, lapack_complex_double* b,
-                   lapack_int* ldb, lapack_int *info );
-void LAPACK_sptsvx( char* fact, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const float* e, float* df, float* ef, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int *info );
-void LAPACK_dptsvx( char* fact, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const double* e, double* df, double* ef,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* rcond, double* ferr, double* berr,
-                    double* work, lapack_int *info );
-void LAPACK_cptsvx( char* fact, lapack_int* n, lapack_int* nrhs, const float* d,
-                    const lapack_complex_float* e, float* df,
-                    lapack_complex_float* ef, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zptsvx( char* fact, lapack_int* n, lapack_int* nrhs,
-                    const double* d, const lapack_complex_double* e, double* df,
-                    lapack_complex_double* ef, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_ssysv( char* uplo, lapack_int* n, lapack_int* nrhs, float* a,
-                   lapack_int* lda, lapack_int* ipiv, float* b, lapack_int* ldb,
-                   float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dsysv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
-                   lapack_int* lda, lapack_int* ipiv, double* b,
-                   lapack_int* ldb, double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_csysv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_zsysv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_ssysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* a, lapack_int* lda, float* af,
-                    lapack_int* ldaf, lapack_int* ipiv, const float* b,
-                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                    float* ferr, float* berr, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dsysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* a, lapack_int* lda, double* af,
-                    lapack_int* ldaf, lapack_int* ipiv, const double* b,
-                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                    double* ferr, double* berr, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_csysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zsysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_dsysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* s, double* b,
-                     lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params, double* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_ssysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* s, float* b,
-                     lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params, float* work,
-                     lapack_int* iwork, lapack_int *info );
-void LAPACK_zsysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* s,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_csysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* s,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_chesv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_zhesv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_chesvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhesvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* af, lapack_int* ldaf,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_zhesvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, double* s,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
-                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
-                     double* err_bnds_norm, double* err_bnds_comp,
-                     lapack_int* nparams, double* params,
-                     lapack_complex_double* work, double* rwork,
-                     lapack_int *info );
-void LAPACK_chesvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* af, lapack_int* ldaf,
-                     lapack_int* ipiv, char* equed, float* s,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
-                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
-                     float* err_bnds_norm, float* err_bnds_comp,
-                     lapack_int* nparams, float* params,
-                     lapack_complex_float* work, float* rwork,
-                     lapack_int *info );
-void LAPACK_sspsv( char* uplo, lapack_int* n, lapack_int* nrhs, float* ap,
-                   lapack_int* ipiv, float* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_dspsv( char* uplo, lapack_int* n, lapack_int* nrhs, double* ap,
-                   lapack_int* ipiv, double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_cspsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* ap, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zspsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* ap, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_sspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const float* ap, float* afp, lapack_int* ipiv,
-                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr, float* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const double* ap, double* afp, lapack_int* ipiv,
-                    const double* b, lapack_int* ldb, double* x,
-                    lapack_int* ldx, double* rcond, double* ferr, double* berr,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, lapack_complex_float* afp,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, lapack_complex_double* afp,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_chpsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* ap, lapack_int* ipiv,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
-void LAPACK_zhpsv( char* uplo, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* ap, lapack_int* ipiv,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_int *info );
-void LAPACK_chpsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_float* ap, lapack_complex_float* afp,
-                    lapack_int* ipiv, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
-                    float* rcond, float* ferr, float* berr,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhpsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
-                    const lapack_complex_double* ap, lapack_complex_double* afp,
-                    lapack_int* ipiv, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
-                    double* rcond, double* ferr, double* berr,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sgeqrf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgeqrf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgeqrf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgeqrf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgeqpf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* jpvt, float* tau, float* work,
-                    lapack_int *info );
-void LAPACK_dgeqpf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* jpvt, double* tau, double* work,
-                    lapack_int *info );
-void LAPACK_cgeqpf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    float* rwork, lapack_int *info );
-void LAPACK_zgeqpf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgeqp3( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* jpvt, float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgeqp3( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* jpvt, double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgeqp3( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int *info );
-void LAPACK_zgeqp3( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* jpvt,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int *info );
-void LAPACK_sorgqr( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgqr( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungqr( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungqr( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmqr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgelqf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgelqf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgelqf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgelqf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sorglq( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorglq( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cunglq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zunglq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmlq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgeqlf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgeqlf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgeqlf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgeqlf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sorgql( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgql( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungql( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungql( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cunmql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmql( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgerqf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgerqf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgerqf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgerqf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sorgrq( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgrq( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungrq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungrq( lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cunmrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmrq( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_stzrzf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dtzrzf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ctzrzf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ztzrzf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l, const float* a,
-                    lapack_int* lda, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dormrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l, const double* a,
-                    lapack_int* lda, const double* tau, double* c,
-                    lapack_int* ldc, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmrz( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* k, lapack_int* l,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sggqrf( lapack_int* n, lapack_int* m, lapack_int* p, float* a,
-                    lapack_int* lda, float* taua, float* b, lapack_int* ldb,
-                    float* taub, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dggqrf( lapack_int* n, lapack_int* m, lapack_int* p, double* a,
-                    lapack_int* lda, double* taua, double* b, lapack_int* ldb,
-                    double* taub, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cggqrf( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* taua, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* taub,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zggqrf( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* taua, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* taub,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sggrqf( lapack_int* m, lapack_int* p, lapack_int* n, float* a,
-                    lapack_int* lda, float* taua, float* b, lapack_int* ldb,
-                    float* taub, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dggrqf( lapack_int* m, lapack_int* p, lapack_int* n, double* a,
-                    lapack_int* lda, double* taua, double* b, lapack_int* ldb,
-                    double* taub, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cggrqf( lapack_int* m, lapack_int* p, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* taua, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* taub,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zggrqf( lapack_int* m, lapack_int* p, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* taua, lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* taub,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgebrd( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* d, float* e, float* tauq, float* taup, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgebrd( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* d, double* e, double* tauq, double* taup,
-                    double* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_cgebrd( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, float* d, float* e,
-                    lapack_complex_float* tauq, lapack_complex_float* taup,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zgebrd( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, double* d, double* e,
-                    lapack_complex_double* tauq, lapack_complex_double* taup,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, float* ab, lapack_int* ldab,
-                    float* d, float* e, float* q, lapack_int* ldq, float* pt,
-                    lapack_int* ldpt, float* c, lapack_int* ldc, float* work,
-                    lapack_int *info );
-void LAPACK_dgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, double* ab,
-                    lapack_int* ldab, double* d, double* e, double* q,
-                    lapack_int* ldq, double* pt, lapack_int* ldpt, double* c,
-                    lapack_int* ldc, double* work, lapack_int *info );
-void LAPACK_cgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, lapack_complex_float* ab,
-                    lapack_int* ldab, float* d, float* e,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* pt, lapack_int* ldpt,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
-                    lapack_int* kl, lapack_int* ku, lapack_complex_double* ab,
-                    lapack_int* ldab, double* d, double* e,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* pt, lapack_int* ldpt,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_sorgbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    float* a, lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorgbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    double* a, lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k, const float* a,
-                    lapack_int* lda, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dormbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k, const double* a,
-                    lapack_int* lda, const double* tau, double* c,
-                    lapack_int* ldc, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cungbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zungbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmbr( char* vect, char* side, char* trans, lapack_int* m,
-                    lapack_int* n, lapack_int* k,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, float* d, float* e,
-                    float* vt, lapack_int* ldvt, float* u, lapack_int* ldu,
-                    float* c, lapack_int* ldc, float* work, lapack_int *info );
-void LAPACK_dbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, double* d, double* e,
-                    double* vt, lapack_int* ldvt, double* u, lapack_int* ldu,
-                    double* c, lapack_int* ldc, double* work,
-                    lapack_int *info );
-void LAPACK_cbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, float* d, float* e,
-                    lapack_complex_float* vt, lapack_int* ldvt,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* c, lapack_int* ldc, float* work,
-                    lapack_int *info );
-void LAPACK_zbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
-                    lapack_int* nru, lapack_int* ncc, double* d, double* e,
-                    lapack_complex_double* vt, lapack_int* ldvt,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* c, lapack_int* ldc, double* work,
-                    lapack_int *info );
-void LAPACK_sbdsdc( char* uplo, char* compq, lapack_int* n, float* d, float* e,
-                    float* u, lapack_int* ldu, float* vt, lapack_int* ldvt,
-                    float* q, lapack_int* iq, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dbdsdc( char* uplo, char* compq, lapack_int* n, double* d,
-                    double* e, double* u, lapack_int* ldu, double* vt,
-                    lapack_int* ldvt, double* q, lapack_int* iq, double* work,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ssytrd( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    float* d, float* e, float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dsytrd( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    double* d, double* e, double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sorgtr( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    const float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dorgtr( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    const double* tau, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_sormtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const float* a, lapack_int* lda,
-                    const float* tau, float* c, lapack_int* ldc, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dormtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const double* a, lapack_int* lda,
-                    const double* tau, double* c, lapack_int* ldc, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_chetrd( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, float* d, float* e,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zhetrd( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, double* d, double* e,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cungtr( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zungtr( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_zunmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ssptrd( char* uplo, lapack_int* n, float* ap, float* d, float* e,
-                    float* tau, lapack_int *info );
-void LAPACK_dsptrd( char* uplo, lapack_int* n, double* ap, double* d, double* e,
-                    double* tau, lapack_int *info );
-void LAPACK_sopgtr( char* uplo, lapack_int* n, const float* ap,
-                    const float* tau, float* q, lapack_int* ldq, float* work,
-                    lapack_int *info );
-void LAPACK_dopgtr( char* uplo, lapack_int* n, const double* ap,
-                    const double* tau, double* q, lapack_int* ldq, double* work,
-                    lapack_int *info );
-void LAPACK_sopmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const float* ap, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int *info );
-void LAPACK_dopmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const double* ap, const double* tau,
-                    double* c, lapack_int* ldc, double* work,
-                    lapack_int *info );
-void LAPACK_chptrd( char* uplo, lapack_int* n, lapack_complex_float* ap,
-                    float* d, float* e, lapack_complex_float* tau,
-                    lapack_int *info );
-void LAPACK_zhptrd( char* uplo, lapack_int* n, lapack_complex_double* ap,
-                    double* d, double* e, lapack_complex_double* tau,
-                    lapack_int *info );
-void LAPACK_cupgtr( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_complex_float* tau, lapack_complex_float* q,
-                    lapack_int* ldq, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zupgtr( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_complex_double* tau, lapack_complex_double* q,
-                    lapack_int* ldq, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_cupmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* ap,
-                    const lapack_complex_float* tau, lapack_complex_float* c,
-                    lapack_int* ldc, lapack_complex_float* work,
-                    lapack_int *info );
-void LAPACK_zupmtr( char* side, char* uplo, char* trans, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* ap,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_ssbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    float* ab, lapack_int* ldab, float* d, float* e, float* q,
-                    lapack_int* ldq, float* work, lapack_int *info );
-void LAPACK_dsbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    double* ab, lapack_int* ldab, double* d, double* e,
-                    double* q, lapack_int* ldq, double* work,
-                    lapack_int *info );
-void LAPACK_chbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_float* ab, lapack_int* ldab, float* d,
-                    float* e, lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zhbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_double* ab, lapack_int* ldab, double* d,
-                    double* e, lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_ssterf( lapack_int* n, float* d, float* e, lapack_int *info );
-void LAPACK_dsterf( lapack_int* n, double* d, double* e, lapack_int *info );
-void LAPACK_ssteqr( char* compz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dsteqr( char* compz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_csteqr( char* compz, lapack_int* n, float* d, float* e,
-                    lapack_complex_float* z, lapack_int* ldz, float* work,
-                    lapack_int *info );
-void LAPACK_zsteqr( char* compz, lapack_int* n, double* d, double* e,
-                    lapack_complex_double* z, lapack_int* ldz, double* work,
-                    lapack_int *info );
-void LAPACK_sstemr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    lapack_int* nzc, lapack_int* isuppz, lapack_logical* tryrac,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dstemr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, lapack_int* m, double* w, double* z,
-                    lapack_int* ldz, lapack_int* nzc, lapack_int* isuppz,
-                    lapack_logical* tryrac, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cstemr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_int* nzc, lapack_int* isuppz,
-                    lapack_logical* tryrac, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_zstemr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, lapack_int* m, double* w,
-                    lapack_complex_double* z, lapack_int* ldz, lapack_int* nzc,
-                    lapack_int* isuppz, lapack_logical* tryrac, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_sstedc( char* compz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dstedc( char* compz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cstedc( char* compz, lapack_int* n, float* d, float* e,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zstedc( char* compz, lapack_int* n, double* d, double* e,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sstegr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, lapack_int* isuppz, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_dstegr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, lapack_int* isuppz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_cstegr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_int* isuppz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_zstegr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_int* isuppz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_spteqr( char* compz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dpteqr( char* compz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_cpteqr( char* compz, lapack_int* n, float* d, float* e,
-                    lapack_complex_float* z, lapack_int* ldz, float* work,
-                    lapack_int *info );
-void LAPACK_zpteqr( char* compz, lapack_int* n, double* d, double* e,
-                    lapack_complex_double* z, lapack_int* ldz, double* work,
-                    lapack_int *info );
-void LAPACK_sstebz( char* range, char* order, lapack_int* n, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    const float* d, const float* e, lapack_int* m,
-                    lapack_int* nsplit, float* w, lapack_int* iblock,
-                    lapack_int* isplit, float* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dstebz( char* range, char* order, lapack_int* n, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    const double* d, const double* e, lapack_int* m,
-                    lapack_int* nsplit, double* w, lapack_int* iblock,
-                    lapack_int* isplit, double* work, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_sstein( lapack_int* n, const float* d, const float* e,
-                    lapack_int* m, const float* w, const lapack_int* iblock,
-                    const lapack_int* isplit, float* z, lapack_int* ldz,
-                    float* work, lapack_int* iwork, lapack_int* ifailv,
-                    lapack_int *info );
-void LAPACK_dstein( lapack_int* n, const double* d, const double* e,
-                    lapack_int* m, const double* w, const lapack_int* iblock,
-                    const lapack_int* isplit, double* z, lapack_int* ldz,
-                    double* work, lapack_int* iwork, lapack_int* ifailv,
-                    lapack_int *info );
-void LAPACK_cstein( lapack_int* n, const float* d, const float* e,
-                    lapack_int* m, const float* w, const lapack_int* iblock,
-                    const lapack_int* isplit, lapack_complex_float* z,
-                    lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifailv, lapack_int *info );
-void LAPACK_zstein( lapack_int* n, const double* d, const double* e,
-                    lapack_int* m, const double* w, const lapack_int* iblock,
-                    const lapack_int* isplit, lapack_complex_double* z,
-                    lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifailv, lapack_int *info );
-void LAPACK_sdisna( char* job, lapack_int* m, lapack_int* n, const float* d,
-                    float* sep, lapack_int *info );
-void LAPACK_ddisna( char* job, lapack_int* m, lapack_int* n, const double* d,
-                    double* sep, lapack_int *info );
-void LAPACK_ssygst( lapack_int* itype, char* uplo, lapack_int* n, float* a,
-                    lapack_int* lda, const float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_dsygst( lapack_int* itype, char* uplo, lapack_int* n, double* a,
-                    lapack_int* lda, const double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_chegst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_zhegst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int *info );
-void LAPACK_sspgst( lapack_int* itype, char* uplo, lapack_int* n, float* ap,
-                    const float* bp, lapack_int *info );
-void LAPACK_dspgst( lapack_int* itype, char* uplo, lapack_int* n, double* ap,
-                    const double* bp, lapack_int *info );
-void LAPACK_chpgst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, const lapack_complex_float* bp,
-                    lapack_int *info );
-void LAPACK_zhpgst( lapack_int* itype, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, const lapack_complex_double* bp,
-                    lapack_int *info );
-void LAPACK_ssbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, float* ab, lapack_int* ldab,
-                    const float* bb, lapack_int* ldbb, float* x,
-                    lapack_int* ldx, float* work, lapack_int *info );
-void LAPACK_dsbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, double* ab, lapack_int* ldab,
-                    const double* bb, lapack_int* ldbb, double* x,
-                    lapack_int* ldx, double* work, lapack_int *info );
-void LAPACK_chbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
-                    const lapack_complex_float* bb, lapack_int* ldbb,
-                    lapack_complex_float* x, lapack_int* ldx,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
-                    const lapack_complex_double* bb, lapack_int* ldbb,
-                    lapack_complex_double* x, lapack_int* ldx,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_spbstf( char* uplo, lapack_int* n, lapack_int* kb, float* bb,
-                    lapack_int* ldbb, lapack_int *info );
-void LAPACK_dpbstf( char* uplo, lapack_int* n, lapack_int* kb, double* bb,
-                    lapack_int* ldbb, lapack_int *info );
-void LAPACK_cpbstf( char* uplo, lapack_int* n, lapack_int* kb,
-                    lapack_complex_float* bb, lapack_int* ldbb,
-                    lapack_int *info );
-void LAPACK_zpbstf( char* uplo, lapack_int* n, lapack_int* kb,
-                    lapack_complex_double* bb, lapack_int* ldbb,
-                    lapack_int *info );
-void LAPACK_sgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a,
-                    lapack_int* lda, float* tau, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a,
-                    lapack_int* lda, double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sorghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a,
-                    lapack_int* lda, const float* tau, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dorghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a,
-                    lapack_int* lda, const double* tau, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sormhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, const float* a,
-                    lapack_int* lda, const float* tau, float* c,
-                    lapack_int* ldc, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dormhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, const double* a,
-                    lapack_int* lda, const double* tau, double* c,
-                    lapack_int* ldc, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zunghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
-                    lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cunmhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* tau, lapack_complex_float* c,
-                    lapack_int* ldc, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zunmhr( char* side, char* trans, lapack_int* m, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* tau, lapack_complex_double* c,
-                    lapack_int* ldc, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sgebal( char* job, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ilo, lapack_int* ihi, float* scale,
-                    lapack_int *info );
-void LAPACK_dgebal( char* job, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ilo, lapack_int* ihi, double* scale,
-                    lapack_int *info );
-void LAPACK_cgebal( char* job, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
-                    float* scale, lapack_int *info );
-void LAPACK_zgebal( char* job, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
-                    double* scale, lapack_int *info );
-void LAPACK_sgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* scale, lapack_int* m,
-                    float* v, lapack_int* ldv, lapack_int *info );
-void LAPACK_dgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* scale, lapack_int* m,
-                    double* v, lapack_int* ldv, lapack_int *info );
-void LAPACK_cgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* scale, lapack_int* m,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_zgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* scale, lapack_int* m,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_shseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, float* h, lapack_int* ldh, float* wr,
-                    float* wi, float* z, lapack_int* ldz, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dhseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, double* h, lapack_int* ldh, double* wr,
-                    double* wi, double* z, lapack_int* ldz, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_chseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_float* h, lapack_int* ldh,
-                    lapack_complex_float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zhseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_double* h, lapack_int* ldh,
-                    lapack_complex_double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_shsein( char* job, char* eigsrc, char* initv,
-                    lapack_logical* select, lapack_int* n, const float* h,
-                    lapack_int* ldh, float* wr, const float* wi, float* vl,
-                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, float* work,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_dhsein( char* job, char* eigsrc, char* initv,
-                    lapack_logical* select, lapack_int* n, const double* h,
-                    lapack_int* ldh, double* wr, const double* wi, double* vl,
-                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, double* work,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_chsein( char* job, char* eigsrc, char* initv,
-                    const lapack_logical* select, lapack_int* n,
-                    const lapack_complex_float* h, lapack_int* ldh,
-                    lapack_complex_float* w, lapack_complex_float* vl,
-                    lapack_int* ldvl, lapack_complex_float* vr,
-                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
-                    lapack_complex_float* work, float* rwork,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_zhsein( char* job, char* eigsrc, char* initv,
-                    const lapack_logical* select, lapack_int* n,
-                    const lapack_complex_double* h, lapack_int* ldh,
-                    lapack_complex_double* w, lapack_complex_double* vl,
-                    lapack_int* ldvl, lapack_complex_double* vr,
-                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
-void LAPACK_strevc( char* side, char* howmny, lapack_logical* select,
-                    lapack_int* n, const float* t, lapack_int* ldt, float* vl,
-                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, float* work,
-                    lapack_int *info );
-void LAPACK_dtrevc( char* side, char* howmny, lapack_logical* select,
-                    lapack_int* n, const double* t, lapack_int* ldt, double* vl,
-                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, double* work,
-                    lapack_int *info );
-void LAPACK_ctrevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* vl, lapack_int* ldvl,
-                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztrevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* vl, lapack_int* ldvl,
-                    lapack_complex_double* vr, lapack_int* ldvr, lapack_int* mm,
-                    lapack_int* m, lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_strsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const float* t, lapack_int* ldt,
-                    const float* vl, lapack_int* ldvl, const float* vr,
-                    lapack_int* ldvr, float* s, float* sep, lapack_int* mm,
-                    lapack_int* m, float* work, lapack_int* ldwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dtrsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const double* t, lapack_int* ldt,
-                    const double* vl, lapack_int* ldvl, const double* vr,
-                    lapack_int* ldvr, double* s, double* sep, lapack_int* mm,
-                    lapack_int* m, double* work, lapack_int* ldwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctrsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_float* t,
-                    lapack_int* ldt, const lapack_complex_float* vl,
-                    lapack_int* ldvl, const lapack_complex_float* vr,
-                    lapack_int* ldvr, float* s, float* sep, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work,
-                    lapack_int* ldwork, float* rwork, lapack_int *info );
-void LAPACK_ztrsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_double* t,
-                    lapack_int* ldt, const lapack_complex_double* vl,
-                    lapack_int* ldvl, const lapack_complex_double* vr,
-                    lapack_int* ldvr, double* s, double* sep, lapack_int* mm,
-                    lapack_int* m, lapack_complex_double* work,
-                    lapack_int* ldwork, double* rwork, lapack_int *info );
-void LAPACK_strexc( char* compq, lapack_int* n, float* t, lapack_int* ldt,
-                    float* q, lapack_int* ldq, lapack_int* ifst,
-                    lapack_int* ilst, float* work, lapack_int *info );
-void LAPACK_dtrexc( char* compq, lapack_int* n, double* t, lapack_int* ldt,
-                    double* q, lapack_int* ldq, lapack_int* ifst,
-                    lapack_int* ilst, double* work, lapack_int *info );
-void LAPACK_ctrexc( char* compq, lapack_int* n, lapack_complex_float* t,
-                    lapack_int* ldt, lapack_complex_float* q, lapack_int* ldq,
-                    lapack_int* ifst, lapack_int* ilst, lapack_int *info );
-void LAPACK_ztrexc( char* compq, lapack_int* n, lapack_complex_double* t,
-                    lapack_int* ldt, lapack_complex_double* q, lapack_int* ldq,
-                    lapack_int* ifst, lapack_int* ilst, lapack_int *info );
-void LAPACK_strsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, float* t, lapack_int* ldt, float* q,
-                    lapack_int* ldq, float* wr, float* wi, lapack_int* m,
-                    float* s, float* sep, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dtrsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, double* t, lapack_int* ldt, double* q,
-                    lapack_int* ldq, double* wr, double* wi, lapack_int* m,
-                    double* s, double* sep, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_ctrsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* w, lapack_int* m, float* s,
-                    float* sep, lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_ztrsen( char* job, char* compq, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* w, lapack_int* m, double* s,
-                    double* sep, lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_strsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const float* a, lapack_int* lda,
-                    const float* b, lapack_int* ldb, float* c, lapack_int* ldc,
-                    float* scale, lapack_int *info );
-void LAPACK_dtrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const double* a, lapack_int* lda,
-                    const double* b, lapack_int* ldb, double* c,
-                    lapack_int* ldc, double* scale, lapack_int *info );
-void LAPACK_ctrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* b,
-                    lapack_int* ldb, lapack_complex_float* c, lapack_int* ldc,
-                    float* scale, lapack_int *info );
-void LAPACK_ztrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* b,
-                    lapack_int* ldb, lapack_complex_double* c, lapack_int* ldc,
-                    double* scale, lapack_int *info );
-void LAPACK_sgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* q, lapack_int* ldq, float* z,
-                    lapack_int* ldz, lapack_int *info );
-void LAPACK_dgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* q, lapack_int* ldq, double* z,
-                    lapack_int* ldz, lapack_int *info );
-void LAPACK_cgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_int *info );
-void LAPACK_zgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_int *info );
-void LAPACK_sggbal( char* job, lapack_int* n, float* a, lapack_int* lda,
-                    float* b, lapack_int* ldb, lapack_int* ilo, lapack_int* ihi,
-                    float* lscale, float* rscale, float* work,
-                    lapack_int *info );
-void LAPACK_dggbal( char* job, lapack_int* n, double* a, lapack_int* lda,
-                    double* b, lapack_int* ldb, lapack_int* ilo,
-                    lapack_int* ihi, double* lscale, double* rscale,
-                    double* work, lapack_int *info );
-void LAPACK_cggbal( char* job, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                    lapack_int* ilo, lapack_int* ihi, float* lscale,
-                    float* rscale, float* work, lapack_int *info );
-void LAPACK_zggbal( char* job, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                    lapack_int* ilo, lapack_int* ihi, double* lscale,
-                    double* rscale, double* work, lapack_int *info );
-void LAPACK_sggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* lscale, const float* rscale,
-                    lapack_int* m, float* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_dggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* lscale, const double* rscale,
-                    lapack_int* m, double* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_cggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const float* lscale, const float* rscale,
-                    lapack_int* m, lapack_complex_float* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_zggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
-                    lapack_int* ihi, const double* lscale, const double* rscale,
-                    lapack_int* m, lapack_complex_double* v, lapack_int* ldv,
-                    lapack_int *info );
-void LAPACK_shgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, float* h, lapack_int* ldh,
-                    float* t, lapack_int* ldt, float* alphar, float* alphai,
-                    float* beta, float* q, lapack_int* ldq, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dhgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, double* h,
-                    lapack_int* ldh, double* t, lapack_int* ldt, double* alphar,
-                    double* alphai, double* beta, double* q, lapack_int* ldq,
-                    double* z, lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_chgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, lapack_complex_float* h,
-                    lapack_int* ldh, lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zhgeqz( char* job, char* compq, char* compz, lapack_int* n,
-                    lapack_int* ilo, lapack_int* ihi, lapack_complex_double* h,
-                    lapack_int* ldh, lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_stgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const float* s, lapack_int* lds,
-                    const float* p, lapack_int* ldp, float* vl,
-                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, float* work,
-                    lapack_int *info );
-void LAPACK_dtgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const double* s, lapack_int* lds,
-                    const double* p, lapack_int* ldp, double* vl,
-                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
-                    lapack_int* mm, lapack_int* m, double* work,
-                    lapack_int *info );
-void LAPACK_ctgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_float* s,
-                    lapack_int* lds, const lapack_complex_float* p,
-                    lapack_int* ldp, lapack_complex_float* vl, lapack_int* ldvl,
-                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work, float* rwork,
-                    lapack_int *info );
-void LAPACK_ztgevc( char* side, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_double* s,
-                    lapack_int* lds, const lapack_complex_double* p,
-                    lapack_int* ldp, lapack_complex_double* vl,
-                    lapack_int* ldvl, lapack_complex_double* vr,
-                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int *info );
-void LAPACK_stgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* q, lapack_int* ldq, float* z, lapack_int* ldz,
-                    lapack_int* ifst, lapack_int* ilst, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dtgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* q, lapack_int* ldq, double* z, lapack_int* ldz,
-                    lapack_int* ifst, lapack_int* ilst, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_ctgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz, lapack_int* ifst,
-                    lapack_int* ilst, lapack_int *info );
-void LAPACK_ztgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz, lapack_int* ifst,
-                    lapack_int* ilst, lapack_int *info );
-void LAPACK_stgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* alphar, float* alphai, float* beta,
-                    float* q, lapack_int* ldq, float* z, lapack_int* ldz,
-                    lapack_int* m, float* pl, float* pr, float* dif,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dtgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* alphar, double* alphai,
-                    double* beta, double* q, lapack_int* ldq, double* z,
-                    lapack_int* ldz, lapack_int* m, double* pl, double* pr,
-                    double* dif, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_ctgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* z, lapack_int* ldz, lapack_int* m,
-                    float* pl, float* pr, float* dif,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_ztgsen( lapack_int* ijob, lapack_logical* wantq,
-                    lapack_logical* wantz, const lapack_logical* select,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* z, lapack_int* ldz, lapack_int* m,
-                    double* pl, double* pr, double* dif,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_stgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const float* a, lapack_int* lda, const float* b,
-                    lapack_int* ldb, float* c, lapack_int* ldc, const float* d,
-                    lapack_int* ldd, const float* e, lapack_int* lde, float* f,
-                    lapack_int* ldf, float* scale, float* dif, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_dtgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const double* a, lapack_int* lda, const double* b,
-                    lapack_int* ldb, double* c, lapack_int* ldc,
-                    const double* d, lapack_int* ldd, const double* e,
-                    lapack_int* lde, double* f, lapack_int* ldf, double* scale,
-                    double* dif, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ctgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    const lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    const lapack_complex_float* d, lapack_int* ldd,
-                    const lapack_complex_float* e, lapack_int* lde,
-                    lapack_complex_float* f, lapack_int* ldf, float* scale,
-                    float* dif, lapack_complex_float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ztgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    const lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    const lapack_complex_double* d, lapack_int* ldd,
-                    const lapack_complex_double* e, lapack_int* lde,
-                    lapack_complex_double* f, lapack_int* ldf, double* scale,
-                    double* dif, lapack_complex_double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_stgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const float* a, lapack_int* lda,
-                    const float* b, lapack_int* ldb, const float* vl,
-                    lapack_int* ldvl, const float* vr, lapack_int* ldvr,
-                    float* s, float* dif, lapack_int* mm, lapack_int* m,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dtgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const double* a, lapack_int* lda,
-                    const double* b, lapack_int* ldb, const double* vl,
-                    lapack_int* ldvl, const double* vr, lapack_int* ldvr,
-                    double* s, double* dif, lapack_int* mm, lapack_int* m,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_ctgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, const lapack_complex_float* b,
-                    lapack_int* ldb, const lapack_complex_float* vl,
-                    lapack_int* ldvl, const lapack_complex_float* vr,
-                    lapack_int* ldvr, float* s, float* dif, lapack_int* mm,
-                    lapack_int* m, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_ztgsna( char* job, char* howmny, const lapack_logical* select,
-                    lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, const lapack_complex_double* b,
-                    lapack_int* ldb, const lapack_complex_double* vl,
-                    lapack_int* ldvl, const lapack_complex_double* vr,
-                    lapack_int* ldvr, double* s, double* dif, lapack_int* mm,
-                    lapack_int* m, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_sggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, float* a, lapack_int* lda,
-                    float* b, lapack_int* ldb, float* tola, float* tolb,
-                    lapack_int* k, lapack_int* l, float* u, lapack_int* ldu,
-                    float* v, lapack_int* ldv, float* q, lapack_int* ldq,
-                    lapack_int* iwork, float* tau, float* work,
-                    lapack_int *info );
-void LAPACK_dggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, double* a, lapack_int* lda,
-                    double* b, lapack_int* ldb, double* tola, double* tolb,
-                    lapack_int* k, lapack_int* l, double* u, lapack_int* ldu,
-                    double* v, lapack_int* ldv, double* q, lapack_int* ldq,
-                    lapack_int* iwork, double* tau, double* work,
-                    lapack_int *info );
-void LAPACK_cggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                    float* tola, float* tolb, lapack_int* k, lapack_int* l,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_complex_float* q, lapack_int* ldq, lapack_int* iwork,
-                    float* rwork, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                    double* tola, double* tolb, lapack_int* k, lapack_int* l,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_int* iwork, double* rwork,
-                    lapack_complex_double* tau, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_stgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* tola, float* tolb, float* alpha, float* beta,
-                    float* u, lapack_int* ldu, float* v, lapack_int* ldv,
-                    float* q, lapack_int* ldq, float* work, lapack_int* ncycle,
-                    lapack_int *info );
-void LAPACK_dtgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* tola, double* tolb, double* alpha, double* beta,
-                    double* u, lapack_int* ldu, double* v, lapack_int* ldv,
-                    double* q, lapack_int* ldq, double* work,
-                    lapack_int* ncycle, lapack_int *info );
-void LAPACK_ctgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* tola,
-                    float* tolb, float* alpha, float* beta,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* work, lapack_int* ncycle,
-                    lapack_int *info );
-void LAPACK_ztgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* tola,
-                    double* tolb, double* alpha, double* beta,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* work, lapack_int* ncycle,
-                    lapack_int *info );
-void LAPACK_sgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                   float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                   double* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_cgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_zgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_sgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb,
-                    lapack_int* jpvt, float* rcond, lapack_int* rank,
-                    float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb,
-                    lapack_int* jpvt, double* rcond, lapack_int* rank,
-                    double* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_cgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, lapack_int* jpvt,
-                    float* rcond, lapack_int* rank, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int *info );
-void LAPACK_zgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, lapack_int* jpvt,
-                    double* rcond, lapack_int* rank,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int *info );
-void LAPACK_zgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_dgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* s,
-                    float* rcond, lapack_int* rank, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_zgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* s,
-                    double* rcond, lapack_int* rank,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_sgglse( lapack_int* m, lapack_int* n, lapack_int* p, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* c,
-                    float* d, float* x, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dgglse( lapack_int* m, lapack_int* n, lapack_int* p, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* c,
-                    double* d, double* x, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cgglse( lapack_int* m, lapack_int* n, lapack_int* p,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* c, lapack_complex_float* d,
-                    lapack_complex_float* x, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zgglse( lapack_int* m, lapack_int* n, lapack_int* p,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* c, lapack_complex_double* d,
-                    lapack_complex_double* x, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sggglm( lapack_int* n, lapack_int* m, lapack_int* p, float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb, float* d,
-                    float* x, float* y, float* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_dggglm( lapack_int* n, lapack_int* m, lapack_int* p, double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb, double* d,
-                    double* x, double* y, double* work, lapack_int* lwork,
-                    lapack_int *info );
-void LAPACK_cggglm( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* d, lapack_complex_float* x,
-                    lapack_complex_float* y, lapack_complex_float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_zggglm( lapack_int* n, lapack_int* m, lapack_int* p,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* d, lapack_complex_double* x,
-                    lapack_complex_double* y, lapack_complex_double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_ssyev( char* jobz, char* uplo, lapack_int* n, float* a,
-                   lapack_int* lda, float* w, float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_dsyev( char* jobz, char* uplo, lapack_int* n, double* a,
-                   lapack_int* lda, double* w, double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_cheev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda, float* w,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zheev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda, double* w,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_int *info );
-void LAPACK_ssyevd( char* jobz, char* uplo, lapack_int* n, float* a,
-                    lapack_int* lda, float* w, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dsyevd( char* jobz, char* uplo, lapack_int* n, double* a,
-                    lapack_int* lda, double* w, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cheevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* w,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zheevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* w,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssyevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    float* a, lapack_int* lda, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dsyevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    double* a, lapack_int* lda, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, double* z, lapack_int* ldz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_cheevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_zheevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_ssyevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    float* a, lapack_int* lda, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    lapack_int* isuppz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dsyevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    double* a, lapack_int* lda, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, double* z, lapack_int* ldz,
-                    lapack_int* isuppz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_cheevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_int* isuppz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zheevr( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_int* isuppz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sspev( char* jobz, char* uplo, lapack_int* n, float* ap, float* w,
-                   float* z, lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dspev( char* jobz, char* uplo, lapack_int* n, double* ap, double* w,
-                   double* z, lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_chpev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* ap, float* w, lapack_complex_float* z,
-                   lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                   lapack_int *info );
-void LAPACK_zhpev( char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* ap, double* w,
-                   lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_sspevd( char* jobz, char* uplo, lapack_int* n, float* ap, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dspevd( char* jobz, char* uplo, lapack_int* n, double* ap,
-                    double* w, double* z, lapack_int* ldz, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_chpevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* lrwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_zhpevd( char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sspevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    float* ap, float* vl, float* vu, lapack_int* il,
-                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dspevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    double* ap, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_chpevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_zhpevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_ssbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   float* ab, lapack_int* ldab, float* w, float* z,
-                   lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dsbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   double* ab, lapack_int* ldab, double* w, double* z,
-                   lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_chbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   lapack_complex_float* ab, lapack_int* ldab, float* w,
-                   lapack_complex_float* z, lapack_int* ldz,
-                   lapack_complex_float* work, float* rwork, lapack_int *info );
-void LAPACK_zhbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                   lapack_complex_double* ab, lapack_int* ldab, double* w,
-                   lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_ssbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    float* ab, lapack_int* ldab, float* w, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dsbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    double* ab, lapack_int* ldab, double* w, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_chbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_float* ab, lapack_int* ldab, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
-                    lapack_complex_double* ab, lapack_int* ldab, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, float* ab, lapack_int* ldab, float* q,
-                    lapack_int* ldq, float* vl, float* vu, lapack_int* il,
-                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dsbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, double* ab, lapack_int* ldab, double* q,
-                    lapack_int* ldq, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_chbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* q, lapack_int* ldq, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_zhbevx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* kd, lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* q, lapack_int* ldq, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_sstev( char* jobz, lapack_int* n, float* d, float* e, float* z,
-                   lapack_int* ldz, float* work, lapack_int *info );
-void LAPACK_dstev( char* jobz, lapack_int* n, double* d, double* e, double* z,
-                   lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_sstevd( char* jobz, lapack_int* n, float* d, float* e, float* z,
-                    lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_dstevd( char* jobz, lapack_int* n, double* d, double* e, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_sstevx( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dstevx( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_sstevr( char* jobz, char* range, lapack_int* n, float* d, float* e,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, lapack_int* isuppz, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_dstevr( char* jobz, char* range, lapack_int* n, double* d,
-                    double* e, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, lapack_int* isuppz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sgees( char* jobvs, char* sort, LAPACK_S_SELECT2 select,
-                   lapack_int* n, float* a, lapack_int* lda, lapack_int* sdim,
-                   float* wr, float* wi, float* vs, lapack_int* ldvs,
-                   float* work, lapack_int* lwork, lapack_logical* bwork,
-                   lapack_int *info );
-void LAPACK_dgees( char* jobvs, char* sort, LAPACK_D_SELECT2 select,
-                   lapack_int* n, double* a, lapack_int* lda, lapack_int* sdim,
-                   double* wr, double* wi, double* vs, lapack_int* ldvs,
-                   double* work, lapack_int* lwork, lapack_logical* bwork,
-                   lapack_int *info );
-void LAPACK_cgees( char* jobvs, char* sort, LAPACK_C_SELECT1 select,
-                   lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                   lapack_int* sdim, lapack_complex_float* w,
-                   lapack_complex_float* vs, lapack_int* ldvs,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_logical* bwork, lapack_int *info );
-void LAPACK_zgees( char* jobvs, char* sort, LAPACK_Z_SELECT1 select,
-                   lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                   lapack_int* sdim, lapack_complex_double* w,
-                   lapack_complex_double* vs, lapack_int* ldvs,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_logical* bwork, lapack_int *info );
-void LAPACK_sgeesx( char* jobvs, char* sort, LAPACK_S_SELECT2 select,
-                    char* sense, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* sdim, float* wr, float* wi, float* vs,
-                    lapack_int* ldvs, float* rconde, float* rcondv, float* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_logical* bwork, lapack_int *info );
-void LAPACK_dgeesx( char* jobvs, char* sort, LAPACK_D_SELECT2 select,
-                    char* sense, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* sdim, double* wr, double* wi, double* vs,
-                    lapack_int* ldvs, double* rconde, double* rcondv,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_cgeesx( char* jobvs, char* sort, LAPACK_C_SELECT1 select,
-                    char* sense, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* sdim, lapack_complex_float* w,
-                    lapack_complex_float* vs, lapack_int* ldvs, float* rconde,
-                    float* rcondv, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_zgeesx( char* jobvs, char* sort, LAPACK_Z_SELECT1 select,
-                    char* sense, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* sdim, lapack_complex_double* w,
-                    lapack_complex_double* vs, lapack_int* ldvs, double* rconde,
-                    double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_sgeev( char* jobvl, char* jobvr, lapack_int* n, float* a,
-                   lapack_int* lda, float* wr, float* wi, float* vl,
-                   lapack_int* ldvl, float* vr, lapack_int* ldvr, float* work,
-                   lapack_int* lwork, lapack_int *info );
-void LAPACK_dgeev( char* jobvl, char* jobvr, lapack_int* n, double* a,
-                   lapack_int* lda, double* wr, double* wi, double* vl,
-                   lapack_int* ldvl, double* vr, lapack_int* ldvr, double* work,
-                   lapack_int* lwork, lapack_int *info );
-void LAPACK_cgeev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* w, lapack_complex_float* vl,
-                   lapack_int* ldvl, lapack_complex_float* vr, lapack_int* ldvr,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zgeev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* w, lapack_complex_double* vl,
-                   lapack_int* ldvl, lapack_complex_double* vr,
-                   lapack_int* ldvr, lapack_complex_double* work,
-                   lapack_int* lwork, double* rwork, lapack_int *info );
-void LAPACK_sgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, float* a, lapack_int* lda, float* wr,
-                    float* wi, float* vl, lapack_int* ldvl, float* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    float* scale, float* abnrm, float* rconde, float* rcondv,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_dgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, double* a, lapack_int* lda, double* wr,
-                    double* wi, double* vl, lapack_int* ldvl, double* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    double* scale, double* abnrm, double* rconde,
-                    double* rcondv, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_cgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* w, lapack_complex_float* vl,
-                    lapack_int* ldvl, lapack_complex_float* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    float* scale, float* abnrm, float* rconde, float* rcondv,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* w, lapack_complex_double* vl,
-                    lapack_int* ldvl, lapack_complex_double* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    double* scale, double* abnrm, double* rconde,
-                    double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int *info );
-void LAPACK_sgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    float* a, lapack_int* lda, float* s, float* u,
-                    lapack_int* ldu, float* vt, lapack_int* ldvt, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_dgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    double* a, lapack_int* lda, double* s, double* u,
-                    lapack_int* ldu, double* vt, lapack_int* ldvt, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_cgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* s,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* vt, lapack_int* ldvt,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int *info );
-void LAPACK_zgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* s,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* vt, lapack_int* ldvt,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int *info );
-void LAPACK_sgesdd( char* jobz, lapack_int* m, lapack_int* n, float* a,
-                    lapack_int* lda, float* s, float* u, lapack_int* ldu,
-                    float* vt, lapack_int* ldvt, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dgesdd( char* jobz, lapack_int* m, lapack_int* n, double* a,
-                    lapack_int* lda, double* s, double* u, lapack_int* ldu,
-                    double* vt, lapack_int* ldvt, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_cgesdd( char* jobz, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda, float* s,
-                    lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* vt, lapack_int* ldvt,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_zgesdd( char* jobz, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda, double* s,
-                    lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* vt, lapack_int* ldvt,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* iwork, lapack_int *info );
-void LAPACK_dgejsv( char* joba, char* jobu, char* jobv, char* jobr, char* jobt,
-                    char* jobp, lapack_int* m, lapack_int* n, double* a,
-                    lapack_int* lda, double* sva, double* u, lapack_int* ldu,
-                    double* v, lapack_int* ldv, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_sgejsv( char* joba, char* jobu, char* jobv, char* jobr, char* jobt,
-                    char* jobp, lapack_int* m, lapack_int* n, float* a,
-                    lapack_int* lda, float* sva, float* u, lapack_int* ldu,
-                    float* v, lapack_int* ldv, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_dgesvj( char* joba, char* jobu, char* jobv, lapack_int* m,
-                    lapack_int* n, double* a, lapack_int* lda, double* sva,
-                    lapack_int* mv, double* v, lapack_int* ldv, double* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sgesvj( char* joba, char* jobu, char* jobv, lapack_int* m,
-                    lapack_int* n, float* a, lapack_int* lda, float* sva,
-                    lapack_int* mv, float* v, lapack_int* ldv, float* work,
-                    lapack_int* lwork, lapack_int *info );
-void LAPACK_sggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* alpha, float* beta, float* u, lapack_int* ldu,
-                    float* v, lapack_int* ldv, float* q, lapack_int* ldq,
-                    float* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_dggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* alpha, double* beta, double* u, lapack_int* ldu,
-                    double* v, lapack_int* ldv, double* q, lapack_int* ldq,
-                    double* work, lapack_int* iwork, lapack_int *info );
-void LAPACK_cggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* alpha,
-                    float* beta, lapack_complex_float* u, lapack_int* ldu,
-                    lapack_complex_float* v, lapack_int* ldv,
-                    lapack_complex_float* q, lapack_int* ldq,
-                    lapack_complex_float* work, float* rwork, lapack_int* iwork,
-                    lapack_int *info );
-void LAPACK_zggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
-                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* alpha,
-                    double* beta, lapack_complex_double* u, lapack_int* ldu,
-                    lapack_complex_double* v, lapack_int* ldv,
-                    lapack_complex_double* q, lapack_int* ldq,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int *info );
-void LAPACK_ssygv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                   float* w, float* work, lapack_int* lwork, lapack_int *info );
-void LAPACK_dsygv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                   double* w, double* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_chegv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb, float* w,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zhegv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb, double* w,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_int *info );
-void LAPACK_ssygvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    float* w, float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dsygvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* w, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_chegvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* w,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhegvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* w,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssygvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* vl, float* vu, lapack_int* il,
-                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
-                    float* z, lapack_int* ldz, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_dsygvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_chegvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, float* vl,
-                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_zhegvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_sspgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   float* ap, float* bp, float* w, float* z, lapack_int* ldz,
-                   float* work, lapack_int *info );
-void LAPACK_dspgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   double* ap, double* bp, double* w, double* z,
-                   lapack_int* ldz, double* work, lapack_int *info );
-void LAPACK_chpgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_float* ap, lapack_complex_float* bp, float* w,
-                   lapack_complex_float* z, lapack_int* ldz,
-                   lapack_complex_float* work, float* rwork, lapack_int *info );
-void LAPACK_zhpgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                   lapack_complex_double* ap, lapack_complex_double* bp,
-                   double* w, lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_sspgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    float* ap, float* bp, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dspgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    double* ap, double* bp, double* w, double* z,
-                    lapack_int* ldz, double* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
-void LAPACK_chpgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_float* ap, lapack_complex_float* bp,
-                    float* w, lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhpgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
-                    lapack_complex_double* ap, lapack_complex_double* bp,
-                    double* w, lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_sspgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, float* ap, float* bp, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* iwork, lapack_int* ifail,
-                    lapack_int *info );
-void LAPACK_dspgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, double* ap, double* bp, double* vl,
-                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, double* z, lapack_int* ldz,
-                    double* work, lapack_int* iwork, lapack_int* ifail,
-                    lapack_int *info );
-void LAPACK_chpgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_float* ap,
-                    lapack_complex_float* bp, float* vl, float* vu,
-                    lapack_int* il, lapack_int* iu, float* abstol,
-                    lapack_int* m, float* w, lapack_complex_float* z,
-                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_zhpgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
-                    lapack_int* n, lapack_complex_double* ap,
-                    lapack_complex_double* bp, double* vl, double* vu,
-                    lapack_int* il, lapack_int* iu, double* abstol,
-                    lapack_int* m, double* w, lapack_complex_double* z,
-                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_ssbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, float* ab, lapack_int* ldab, float* bb,
-                   lapack_int* ldbb, float* w, float* z, lapack_int* ldz,
-                   float* work, lapack_int *info );
-void LAPACK_dsbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, double* ab, lapack_int* ldab, double* bb,
-                   lapack_int* ldbb, double* w, double* z, lapack_int* ldz,
-                   double* work, lapack_int *info );
-void LAPACK_chbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
-                   lapack_complex_float* bb, lapack_int* ldbb, float* w,
-                   lapack_complex_float* z, lapack_int* ldz,
-                   lapack_complex_float* work, float* rwork, lapack_int *info );
-void LAPACK_zhbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                   lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
-                   lapack_complex_double* bb, lapack_int* ldbb, double* w,
-                   lapack_complex_double* z, lapack_int* ldz,
-                   lapack_complex_double* work, double* rwork,
-                   lapack_int *info );
-void LAPACK_ssbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, float* ab, lapack_int* ldab, float* bb,
-                    lapack_int* ldbb, float* w, float* z, lapack_int* ldz,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_dsbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, double* ab, lapack_int* ldab, double* bb,
-                    lapack_int* ldbb, double* w, double* z, lapack_int* ldz,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_chbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
-                    lapack_complex_float* bb, lapack_int* ldbb, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
-                    lapack_int *info );
-void LAPACK_zhbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
-                    lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
-                    lapack_complex_double* bb, lapack_int* ldbb, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_int *info );
-void LAPACK_ssbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, float* ab, lapack_int* ldab,
-                    float* bb, lapack_int* ldbb, float* q, lapack_int* ldq,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w, float* z,
-                    lapack_int* ldz, float* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_dsbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, double* ab,
-                    lapack_int* ldab, double* bb, lapack_int* ldbb, double* q,
-                    lapack_int* ldq, double* vl, double* vu, lapack_int* il,
-                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
-                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_chbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, lapack_complex_float* ab,
-                    lapack_int* ldab, lapack_complex_float* bb,
-                    lapack_int* ldbb, lapack_complex_float* q, lapack_int* ldq,
-                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
-                    float* abstol, lapack_int* m, float* w,
-                    lapack_complex_float* z, lapack_int* ldz,
-                    lapack_complex_float* work, float* rwork, lapack_int* iwork,
-                    lapack_int* ifail, lapack_int *info );
-void LAPACK_zhbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
-                    lapack_int* ka, lapack_int* kb, lapack_complex_double* ab,
-                    lapack_int* ldab, lapack_complex_double* bb,
-                    lapack_int* ldbb, lapack_complex_double* q, lapack_int* ldq,
-                    double* vl, double* vu, lapack_int* il, lapack_int* iu,
-                    double* abstol, lapack_int* m, double* w,
-                    lapack_complex_double* z, lapack_int* ldz,
-                    lapack_complex_double* work, double* rwork,
-                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
-void LAPACK_sgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_S_SELECT3 selctg, lapack_int* n, float* a,
-                   lapack_int* lda, float* b, lapack_int* ldb, lapack_int* sdim,
-                   float* alphar, float* alphai, float* beta, float* vsl,
-                   lapack_int* ldvsl, float* vsr, lapack_int* ldvsr,
-                   float* work, lapack_int* lwork, lapack_logical* bwork,
-                   lapack_int *info );
-void LAPACK_dgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_D_SELECT3 selctg, lapack_int* n, double* a,
-                   lapack_int* lda, double* b, lapack_int* ldb,
-                   lapack_int* sdim, double* alphar, double* alphai,
-                   double* beta, double* vsl, lapack_int* ldvsl, double* vsr,
-                   lapack_int* ldvsr, double* work, lapack_int* lwork,
-                   lapack_logical* bwork, lapack_int *info );
-void LAPACK_cgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_C_SELECT2 selctg, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
-                   lapack_complex_float* alpha, lapack_complex_float* beta,
-                   lapack_complex_float* vsl, lapack_int* ldvsl,
-                   lapack_complex_float* vsr, lapack_int* ldvsr,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_logical* bwork, lapack_int *info );
-void LAPACK_zgges( char* jobvsl, char* jobvsr, char* sort,
-                   LAPACK_Z_SELECT2 selctg, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb, lapack_int* sdim,
-                   lapack_complex_double* alpha, lapack_complex_double* beta,
-                   lapack_complex_double* vsl, lapack_int* ldvsl,
-                   lapack_complex_double* vsr, lapack_int* ldvsr,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_logical* bwork, lapack_int *info );
-void LAPACK_sggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_S_SELECT3 selctg, char* sense, lapack_int* n,
-                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
-                    lapack_int* sdim, float* alphar, float* alphai, float* beta,
-                    float* vsl, lapack_int* ldvsl, float* vsr,
-                    lapack_int* ldvsr, float* rconde, float* rcondv,
-                    float* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_dggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_D_SELECT3 selctg, char* sense, lapack_int* n,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    lapack_int* sdim, double* alphar, double* alphai,
-                    double* beta, double* vsl, lapack_int* ldvsl, double* vsr,
-                    lapack_int* ldvsr, double* rconde, double* rcondv,
-                    double* work, lapack_int* lwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_cggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_C_SELECT2 selctg, char* sense, lapack_int* n,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* vsl, lapack_int* ldvsl,
-                    lapack_complex_float* vsr, lapack_int* ldvsr, float* rconde,
-                    float* rcondv, lapack_complex_float* work,
-                    lapack_int* lwork, float* rwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_zggesx( char* jobvsl, char* jobvsr, char* sort,
-                    LAPACK_Z_SELECT2 selctg, char* sense, lapack_int* n,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb, lapack_int* sdim,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* vsl, lapack_int* ldvsl,
-                    lapack_complex_double* vsr, lapack_int* ldvsr,
-                    double* rconde, double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_int* liwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_sggev( char* jobvl, char* jobvr, lapack_int* n, float* a,
-                   lapack_int* lda, float* b, lapack_int* ldb, float* alphar,
-                   float* alphai, float* beta, float* vl, lapack_int* ldvl,
-                   float* vr, lapack_int* ldvr, float* work, lapack_int* lwork,
-                   lapack_int *info );
-void LAPACK_dggev( char* jobvl, char* jobvr, lapack_int* n, double* a,
-                   lapack_int* lda, double* b, lapack_int* ldb, double* alphar,
-                   double* alphai, double* beta, double* vl, lapack_int* ldvl,
-                   double* vr, lapack_int* ldvr, double* work,
-                   lapack_int* lwork, lapack_int *info );
-void LAPACK_cggev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_float* a, lapack_int* lda,
-                   lapack_complex_float* b, lapack_int* ldb,
-                   lapack_complex_float* alpha, lapack_complex_float* beta,
-                   lapack_complex_float* vl, lapack_int* ldvl,
-                   lapack_complex_float* vr, lapack_int* ldvr,
-                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                   lapack_int *info );
-void LAPACK_zggev( char* jobvl, char* jobvr, lapack_int* n,
-                   lapack_complex_double* a, lapack_int* lda,
-                   lapack_complex_double* b, lapack_int* ldb,
-                   lapack_complex_double* alpha, lapack_complex_double* beta,
-                   lapack_complex_double* vl, lapack_int* ldvl,
-                   lapack_complex_double* vr, lapack_int* ldvr,
-                   lapack_complex_double* work, lapack_int* lwork,
-                   double* rwork, lapack_int *info );
-void LAPACK_sggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, float* alphar, float* alphai, float* beta,
-                    float* vl, lapack_int* ldvl, float* vr, lapack_int* ldvr,
-                    lapack_int* ilo, lapack_int* ihi, float* lscale,
-                    float* rscale, float* abnrm, float* bbnrm, float* rconde,
-                    float* rcondv, float* work, lapack_int* lwork,
-                    lapack_int* iwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_dggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, double* alphar, double* alphai,
-                    double* beta, double* vl, lapack_int* ldvl, double* vr,
-                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
-                    double* lscale, double* rscale, double* abnrm,
-                    double* bbnrm, double* rconde, double* rcondv, double* work,
-                    lapack_int* lwork, lapack_int* iwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_cggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* vl, lapack_int* ldvl,
-                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* ilo,
-                    lapack_int* ihi, float* lscale, float* rscale, float* abnrm,
-                    float* bbnrm, float* rconde, float* rcondv,
-                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
-                    lapack_int* iwork, lapack_logical* bwork,
-                    lapack_int *info );
-void LAPACK_zggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
-                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* vl, lapack_int* ldvl,
-                    lapack_complex_double* vr, lapack_int* ldvr,
-                    lapack_int* ilo, lapack_int* ihi, double* lscale,
-                    double* rscale, double* abnrm, double* bbnrm,
-                    double* rconde, double* rcondv, lapack_complex_double* work,
-                    lapack_int* lwork, double* rwork, lapack_int* iwork,
-                    lapack_logical* bwork, lapack_int *info );
-void LAPACK_dsfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, double* alpha, const double* a,
-                   lapack_int* lda, double* beta, double* c );
-void LAPACK_ssfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, float* alpha, const float* a, lapack_int* lda,
-                   float* beta, float* c );
-void LAPACK_zhfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, double* alpha, const lapack_complex_double* a,
-                   lapack_int* lda, double* beta, lapack_complex_double* c );
-void LAPACK_chfrk( char* transr, char* uplo, char* trans, lapack_int* n,
-                   lapack_int* k, float* alpha, const lapack_complex_float* a,
-                   lapack_int* lda, float* beta, lapack_complex_float* c );
-void LAPACK_dtfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n, double* alpha,
-                   const double* a, double* b, lapack_int* ldb );
-void LAPACK_stfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n, float* alpha,
-                   const float* a, float* b, lapack_int* ldb );
-void LAPACK_ztfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n,
-                   lapack_complex_double* alpha, const lapack_complex_double* a,
-                   lapack_complex_double* b, lapack_int* ldb );
-void LAPACK_ctfsm( char* transr, char* side, char* uplo, char* trans,
-                   char* diag, lapack_int* m, lapack_int* n,
-                   lapack_complex_float* alpha, const lapack_complex_float* a,
-                   lapack_complex_float* b, lapack_int* ldb );
-void LAPACK_dtfttp( char* transr, char* uplo, lapack_int* n, const double* arf,
-                    double* ap, lapack_int *info );
-void LAPACK_stfttp( char* transr, char* uplo, lapack_int* n, const float* arf,
-                    float* ap, lapack_int *info );
-void LAPACK_ztfttp( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* arf, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_ctfttp( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* arf, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_dtfttr( char* transr, char* uplo, lapack_int* n, const double* arf,
-                    double* a, lapack_int* lda, lapack_int *info );
-void LAPACK_stfttr( char* transr, char* uplo, lapack_int* n, const float* arf,
-                    float* a, lapack_int* lda, lapack_int *info );
-void LAPACK_ztfttr( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* arf, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_ctfttr( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* arf, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_dtpttf( char* transr, char* uplo, lapack_int* n, const double* ap,
-                    double* arf, lapack_int *info );
-void LAPACK_stpttf( char* transr, char* uplo, lapack_int* n, const float* ap,
-                    float* arf, lapack_int *info );
-void LAPACK_ztpttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* ap, lapack_complex_double* arf,
-                    lapack_int *info );
-void LAPACK_ctpttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* ap, lapack_complex_float* arf,
-                    lapack_int *info );
-void LAPACK_dtpttr( char* uplo, lapack_int* n, const double* ap, double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_stpttr( char* uplo, lapack_int* n, const float* ap, float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_ztpttr( char* uplo, lapack_int* n, const lapack_complex_double* ap,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_ctpttr( char* uplo, lapack_int* n, const lapack_complex_float* ap,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dtrttf( char* transr, char* uplo, lapack_int* n, const double* a,
-                    lapack_int* lda, double* arf, lapack_int *info );
-void LAPACK_strttf( char* transr, char* uplo, lapack_int* n, const float* a,
-                    lapack_int* lda, float* arf, lapack_int *info );
-void LAPACK_ztrttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* arf, lapack_int *info );
-void LAPACK_ctrttf( char* transr, char* uplo, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* arf, lapack_int *info );
-void LAPACK_dtrttp( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
-                    double* ap, lapack_int *info );
-void LAPACK_strttp( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
-                    float* ap, lapack_int *info );
-void LAPACK_ztrttp( char* uplo, lapack_int* n, const lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* ap,
-                    lapack_int *info );
-void LAPACK_ctrttp( char* uplo, lapack_int* n, const lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* ap,
-                    lapack_int *info );
-void LAPACK_sgeqrfp( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* tau, float* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_dgeqrfp( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* tau, double* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_cgeqrfp( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* tau,
-                     lapack_complex_float* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_zgeqrfp( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* tau,
-                     lapack_complex_double* work, lapack_int* lwork,
-                     lapack_int *info );
-void LAPACK_clacgv( lapack_int* n, lapack_complex_float* x, lapack_int* incx );
-void LAPACK_zlacgv( lapack_int* n, lapack_complex_double* x, lapack_int* incx );
-void LAPACK_slarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    float* x );
-void LAPACK_dlarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    double* x );
-void LAPACK_clarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    lapack_complex_float* x );
-void LAPACK_zlarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
-                    lapack_complex_double* x );
-void LAPACK_sgeqr2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int *info );
-void LAPACK_dgeqr2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int *info );
-void LAPACK_cgeqr2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgeqr2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slacpy( char* uplo, lapack_int* m, lapack_int* n, const float* a,
-                    lapack_int* lda, float* b, lapack_int* ldb );
-void LAPACK_dlacpy( char* uplo, lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, double* b, lapack_int* ldb );
-void LAPACK_clacpy( char* uplo, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb );
-void LAPACK_zlacpy( char* uplo, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb );
-void LAPACK_sgetf2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_dgetf2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int* ipiv, lapack_int *info );
-void LAPACK_cgetf2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_zgetf2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
-void LAPACK_slaswp( lapack_int* n, float* a, lapack_int* lda, lapack_int* k1,
-                    lapack_int* k2, const lapack_int* ipiv, lapack_int* incx );
-void LAPACK_dlaswp( lapack_int* n, double* a, lapack_int* lda, lapack_int* k1,
-                    lapack_int* k2, const lapack_int* ipiv, lapack_int* incx );
-void LAPACK_claswp( lapack_int* n, lapack_complex_float* a, lapack_int* lda,
-                    lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
-                    lapack_int* incx );
-void LAPACK_zlaswp( lapack_int* n, lapack_complex_double* a, lapack_int* lda,
-                    lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
-                    lapack_int* incx );
-float LAPACK_slange( char* norm, lapack_int* m, lapack_int* n, const float* a,
-                    lapack_int* lda, float* work );
-double LAPACK_dlange( char* norm, lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, double* work );
-float LAPACK_clange( char* norm, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda, float* work );
-double LAPACK_zlange( char* norm, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* work );
-float LAPACK_clanhe( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda, float* work );
-double LAPACK_zlanhe( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* work );
-float LAPACK_slansy( char* norm, char* uplo, lapack_int* n, const float* a,
-                    lapack_int* lda, float* work );
-double LAPACK_dlansy( char* norm, char* uplo, lapack_int* n, const double* a,
-                    lapack_int* lda, double* work );
-float LAPACK_clansy( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_float* a, lapack_int* lda, float* work );
-double LAPACK_zlansy( char* norm, char* uplo, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda, double* work );
-float LAPACK_slantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const float* a, lapack_int* lda, float* work );
-double LAPACK_dlantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const double* a, lapack_int* lda, double* work );
-float LAPACK_clantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const lapack_complex_float* a, lapack_int* lda,
-                    float* work );
-double LAPACK_zlantr( char* norm, char* uplo, char* diag, lapack_int* m,
-                    lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
-                    double* work );
-float LAPACK_slamch( char* cmach );
-double LAPACK_dlamch( char* cmach );
-void LAPACK_sgelq2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                    float* tau, float* work, lapack_int *info );
-void LAPACK_dgelq2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                    double* tau, double* work, lapack_int *info );
-void LAPACK_cgelq2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_complex_float* tau,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgelq2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_complex_double* tau,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, const float* v,
-                    lapack_int* ldv, const float* t, lapack_int* ldt, float* c,
-                    lapack_int* ldc, float* work, lapack_int* ldwork );
-void LAPACK_dlarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k,
-                    const double* v, lapack_int* ldv, const double* t,
-                    lapack_int* ldt, double* c, lapack_int* ldc, double* work,
-                    lapack_int* ldwork );
-void LAPACK_clarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k,
-                    const lapack_complex_float* v, lapack_int* ldv,
-                    const lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work, lapack_int* ldwork );
-void LAPACK_zlarfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k,
-                    const lapack_complex_double* v, lapack_int* ldv,
-                    const lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work, lapack_int* ldwork );
-void LAPACK_slarfg( lapack_int* n, float* alpha, float* x, lapack_int* incx,
-                    float* tau );
-void LAPACK_dlarfg( lapack_int* n, double* alpha, double* x, lapack_int* incx,
-                    double* tau );
-void LAPACK_clarfg( lapack_int* n, lapack_complex_float* alpha,
-                    lapack_complex_float* x, lapack_int* incx,
-                    lapack_complex_float* tau );
-void LAPACK_zlarfg( lapack_int* n, lapack_complex_double* alpha,
-                    lapack_complex_double* x, lapack_int* incx,
-                    lapack_complex_double* tau );
-void LAPACK_slarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const float* v, lapack_int* ldv, const float* tau, float* t,
-                    lapack_int* ldt );
-void LAPACK_dlarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const double* v, lapack_int* ldv, const double* tau,
-                    double* t, lapack_int* ldt );
-void LAPACK_clarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const lapack_complex_float* v, lapack_int* ldv,
-                    const lapack_complex_float* tau, lapack_complex_float* t,
-                    lapack_int* ldt );
-void LAPACK_zlarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
-                    const lapack_complex_double* v, lapack_int* ldv,
-                    const lapack_complex_double* tau, lapack_complex_double* t,
-                    lapack_int* ldt );
-void LAPACK_slarfx( char* side, lapack_int* m, lapack_int* n, const float* v,
-                    float* tau, float* c, lapack_int* ldc, float* work );
-void LAPACK_dlarfx( char* side, lapack_int* m, lapack_int* n, const double* v,
-                    double* tau, double* c, lapack_int* ldc, double* work );
-void LAPACK_clarfx( char* side, lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* v, lapack_complex_float* tau,
-                    lapack_complex_float* c, lapack_int* ldc,
-                    lapack_complex_float* work );
-void LAPACK_zlarfx( char* side, lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* v, lapack_complex_double* tau,
-                    lapack_complex_double* c, lapack_int* ldc,
-                    lapack_complex_double* work );
-void LAPACK_slatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, float* d, lapack_int* mode, float* cond,
-                    float* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    float* a, lapack_int* lda, float* work, lapack_int *info );
-void LAPACK_dlatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, double* d, lapack_int* mode, double* cond,
-                    double* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    double* a, lapack_int* lda, double* work,
-                    lapack_int *info );
-void LAPACK_clatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, float* d, lapack_int* mode, float* cond,
-                    float* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
-                    char* sym, double* d, lapack_int* mode, double* cond,
-                    double* dmax, lapack_int* kl, lapack_int* ku, char* pack,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slag2d( lapack_int* m, lapack_int* n, const float* sa,
-                    lapack_int* ldsa, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dlag2s( lapack_int* m, lapack_int* n, const double* a,
-                    lapack_int* lda, float* sa, lapack_int* ldsa,
-                    lapack_int *info );
-void LAPACK_clag2z( lapack_int* m, lapack_int* n,
-                    const lapack_complex_float* sa, lapack_int* ldsa,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_zlag2c( lapack_int* m, lapack_int* n,
-                    const lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_float* sa, lapack_int* ldsa,
-                    lapack_int *info );
-void LAPACK_slauum( char* uplo, lapack_int* n, float* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_dlauum( char* uplo, lapack_int* n, double* a, lapack_int* lda,
-                    lapack_int *info );
-void LAPACK_clauum( char* uplo, lapack_int* n, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_zlauum( char* uplo, lapack_int* n, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int *info );
-void LAPACK_slagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const float* d, float* a, lapack_int* lda,
-                    lapack_int* iseed, float* work, lapack_int *info );
-void LAPACK_dlagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const double* d, double* a, lapack_int* lda,
-                    lapack_int* iseed, double* work, lapack_int *info );
-void LAPACK_clagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const float* d, lapack_complex_float* a,
-                    lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlagge( lapack_int* m, lapack_int* n, lapack_int* kl,
-                    lapack_int* ku, const double* d, lapack_complex_double* a,
-                    lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_slaset( char* uplo, lapack_int* m, lapack_int* n, float* alpha,
-                    float* beta, float* a, lapack_int* lda );
-void LAPACK_dlaset( char* uplo, lapack_int* m, lapack_int* n, double* alpha,
-                    double* beta, double* a, lapack_int* lda );
-void LAPACK_claset( char* uplo, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* alpha, lapack_complex_float* beta,
-                    lapack_complex_float* a, lapack_int* lda );
-void LAPACK_zlaset( char* uplo, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* alpha, lapack_complex_double* beta,
-                    lapack_complex_double* a, lapack_int* lda );
-void LAPACK_slasrt( char* id, lapack_int* n, float* d, lapack_int *info );
-void LAPACK_dlasrt( char* id, lapack_int* n, double* d, lapack_int *info );
-void LAPACK_claghe( lapack_int* n, lapack_int* k, const float* d,
-                    lapack_complex_float* a, lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlaghe( lapack_int* n, lapack_int* k, const double* d,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int* iseed, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_slagsy( lapack_int* n, lapack_int* k, const float* d, float* a,
-                    lapack_int* lda, lapack_int* iseed, float* work,
-                    lapack_int *info );
-void LAPACK_dlagsy( lapack_int* n, lapack_int* k, const double* d, double* a,
-                    lapack_int* lda, lapack_int* iseed, double* work,
-                    lapack_int *info );
-void LAPACK_clagsy( lapack_int* n, lapack_int* k, const float* d,
-                    lapack_complex_float* a, lapack_int* lda, lapack_int* iseed,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zlagsy( lapack_int* n, lapack_int* k, const double* d,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_int* iseed, lapack_complex_double* work,
-                    lapack_int *info );
-void LAPACK_slapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    float* x, lapack_int* ldx, lapack_int* k );
-void LAPACK_dlapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    double* x, lapack_int* ldx, lapack_int* k );
-void LAPACK_clapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    lapack_complex_float* x, lapack_int* ldx, lapack_int* k );
-void LAPACK_zlapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
-                    lapack_complex_double* x, lapack_int* ldx, lapack_int* k );
-float LAPACK_slapy2( float* x, float* y );
-double LAPACK_dlapy2( double* x, double* y );
-float LAPACK_slapy3( float* x, float* y, float* z );
-double LAPACK_dlapy3( double* x, double* y, double* z );
-void LAPACK_slartgp( float* f, float* g, float* cs, float* sn, float* r );
-void LAPACK_dlartgp( double* f, double* g, double* cs, double* sn, double* r );
-void LAPACK_slartgs( float* x, float* y, float* sigma, float* cs, float* sn );
-void LAPACK_dlartgs( double* x, double* y, double* sigma, double* cs,
-                     double* sn );
-// LAPACK 3.3.0
-void LAPACK_cbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    float* theta, float* phi,
-                    lapack_complex_float* u1, lapack_int* ldu1,
-                    lapack_complex_float* u2, lapack_int* ldu2,
-                    lapack_complex_float* v1t, lapack_int* ldv1t,
-                    lapack_complex_float* v2t, lapack_int* ldv2t,
-                    float* b11d, float* b11e, float* b12d,
-                    float* b12e, float* b21d, float* b21e,
-                    float* b22d, float* b22e, float* rwork,
-                    lapack_int* lrwork , lapack_int *info );
-void LAPACK_cheswapr( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_chetri2( char* uplo, lapack_int* n,
-                     lapack_complex_float* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_chetri2x( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_float* work, lapack_int* nb , lapack_int *info );
-void LAPACK_chetrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const lapack_complex_float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* work , lapack_int *info );
-void LAPACK_csyconv( char* uplo, char* way,
-                     lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_float* work , lapack_int *info );
-void LAPACK_csyswapr( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_csytri2( char* uplo, lapack_int* n,
-                     lapack_complex_float* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_csytri2x( char* uplo, lapack_int* n,
-                      lapack_complex_float* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_float* work, lapack_int* nb , lapack_int *info );
-void LAPACK_csytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const lapack_complex_float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* work , lapack_int *info );
-void LAPACK_cunbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    lapack_complex_float* x11, lapack_int* ldx11,
-                    lapack_complex_float* x12, lapack_int* ldx12,
-                    lapack_complex_float* x21, lapack_int* ldx21,
-                    lapack_complex_float* x22, lapack_int* ldx22,
-                    float* theta, float* phi,
-                    lapack_complex_float* taup1,
-                    lapack_complex_float* taup2,
-                    lapack_complex_float* tauq1,
-                    lapack_complex_float* tauq2,
-                    lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_cuncsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, lapack_complex_float* x11,
-                    lapack_int* ldx11, lapack_complex_float* x12,
-                    lapack_int* ldx12, lapack_complex_float* x21,
-                    lapack_int* ldx21, lapack_complex_float* x22,
-                    lapack_int* ldx22, float* theta,
-                    lapack_complex_float* u1, lapack_int* ldu1,
-                    lapack_complex_float* u2, lapack_int* ldu2,
-                    lapack_complex_float* v1t, lapack_int* ldv1t,
-                    lapack_complex_float* v2t, lapack_int* ldv2t,
-                    lapack_complex_float* work, lapack_int* lwork,
-                    float* rwork, lapack_int* lrwork,
-                    lapack_int* iwork , lapack_int *info );
-void LAPACK_dbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    double* theta, double* phi, double* u1,
-                    lapack_int* ldu1, double* u2, lapack_int* ldu2,
-                    double* v1t, lapack_int* ldv1t, double* v2t,
-                    lapack_int* ldv2t, double* b11d, double* b11e,
-                    double* b12d, double* b12e, double* b21d,
-                    double* b21e, double* b22d, double* b22e,
-                    double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_dorbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    double* x11, lapack_int* ldx11, double* x12,
-                    lapack_int* ldx12, double* x21, lapack_int* ldx21,
-                    double* x22, lapack_int* ldx22, double* theta,
-                    double* phi, double* taup1, double* taup2,
-                    double* tauq1, double* tauq2, double* work,
-                    lapack_int* lwork , lapack_int *info );
-void LAPACK_dorcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, double* x11, lapack_int* ldx11,
-                    double* x12, lapack_int* ldx12, double* x21,
-                    lapack_int* ldx21, double* x22, lapack_int* ldx22,
-                    double* theta, double* u1, lapack_int* ldu1,
-                    double* u2, lapack_int* ldu2, double* v1t,
-                    lapack_int* ldv1t, double* v2t, lapack_int* ldv2t,
-                    double* work, lapack_int* lwork,
-                    lapack_int* iwork , lapack_int *info );
-void LAPACK_dsyconv( char* uplo, char* way,
-                     lapack_int* n, double* a, lapack_int* lda,
-                     const lapack_int* ipiv, double* work , lapack_int *info );
-void LAPACK_dsyswapr( char* uplo, lapack_int* n,
-                      double* a, lapack_int* i1, lapack_int* i2 );
-void LAPACK_dsytri2( char* uplo, lapack_int* n,
-                     double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_dsytri2x( char* uplo, lapack_int* n,
-                      double* a, lapack_int* lda,
-                      const lapack_int* ipiv, double* work,
-                      lapack_int* nb , lapack_int *info );
-void LAPACK_dsytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const double* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     double* b, lapack_int* ldb, double* work , lapack_int *info );
-void LAPACK_sbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    float* theta, float* phi, float* u1,
-                    lapack_int* ldu1, float* u2, lapack_int* ldu2,
-                    float* v1t, lapack_int* ldv1t, float* v2t,
-                    lapack_int* ldv2t, float* b11d, float* b11e,
-                    float* b12d, float* b12e, float* b21d,
-                    float* b21e, float* b22d, float* b22e,
-                    float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_sorbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    float* x11, lapack_int* ldx11, float* x12,
-                    lapack_int* ldx12, float* x21, lapack_int* ldx21,
-                    float* x22, lapack_int* ldx22, float* theta,
-                    float* phi, float* taup1, float* taup2,
-                    float* tauq1, float* tauq2, float* work,
-                    lapack_int* lwork , lapack_int *info );
-void LAPACK_sorcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, float* x11, lapack_int* ldx11,
-                    float* x12, lapack_int* ldx12, float* x21,
-                    lapack_int* ldx21, float* x22, lapack_int* ldx22,
-                    float* theta, float* u1, lapack_int* ldu1,
-                    float* u2, lapack_int* ldu2, float* v1t,
-                    lapack_int* ldv1t, float* v2t, lapack_int* ldv2t,
-                    float* work, lapack_int* lwork,
-                    lapack_int* iwork , lapack_int *info );
-void LAPACK_ssyconv( char* uplo, char* way,
-                     lapack_int* n, float* a, lapack_int* lda,
-                     const lapack_int* ipiv, float* work , lapack_int *info );
-void LAPACK_ssyswapr( char* uplo, lapack_int* n,
-                      float* a, lapack_int* i1, lapack_int* i2 );
-void LAPACK_ssytri2( char* uplo, lapack_int* n,
-                     float* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_ssytri2x( char* uplo, lapack_int* n,
-                      float* a, lapack_int* lda,
-                      const lapack_int* ipiv, float* work,
-                      lapack_int* nb , lapack_int *info );
-void LAPACK_ssytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs, const float* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     float* b, lapack_int* ldb, float* work , lapack_int *info );
-void LAPACK_zbbcsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    double* theta, double* phi,
-                    lapack_complex_double* u1, lapack_int* ldu1,
-                    lapack_complex_double* u2, lapack_int* ldu2,
-                    lapack_complex_double* v1t, lapack_int* ldv1t,
-                    lapack_complex_double* v2t, lapack_int* ldv2t,
-                    double* b11d, double* b11e, double* b12d,
-                    double* b12e, double* b21d, double* b21e,
-                    double* b22d, double* b22e, double* rwork,
-                    lapack_int* lrwork , lapack_int *info );
-void LAPACK_zheswapr( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_zhetri2( char* uplo, lapack_int* n,
-                     lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_zhetri2x( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_double* work, lapack_int* nb , lapack_int *info );
-void LAPACK_zhetrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* work , lapack_int *info );
-void LAPACK_zsyconv( char* uplo, char* way,
-                     lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, const lapack_int* ipiv,
-                     lapack_complex_double* work , lapack_int *info );
-void LAPACK_zsyswapr( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* i1,
-                      lapack_int* i2 );
-void LAPACK_zsytri2( char* uplo, lapack_int* n,
-                     lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_zsytri2x( char* uplo, lapack_int* n,
-                      lapack_complex_double* a, lapack_int* lda,
-                      const lapack_int* ipiv,
-                      lapack_complex_double* work, lapack_int* nb , lapack_int *info );
-void LAPACK_zsytrs2( char* uplo, lapack_int* n,
-                     lapack_int* nrhs,
-                     const lapack_complex_double* a, lapack_int* lda,
-                     const lapack_int* ipiv,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* work , lapack_int *info );
-void LAPACK_zunbdb( char* trans, char* signs,
-                    lapack_int* m, lapack_int* p, lapack_int* q,
-                    lapack_complex_double* x11, lapack_int* ldx11,
-                    lapack_complex_double* x12, lapack_int* ldx12,
-                    lapack_complex_double* x21, lapack_int* ldx21,
-                    lapack_complex_double* x22, lapack_int* ldx22,
-                    double* theta, double* phi,
-                    lapack_complex_double* taup1,
-                    lapack_complex_double* taup2,
-                    lapack_complex_double* tauq1,
-                    lapack_complex_double* tauq2,
-                    lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
-void LAPACK_zuncsd( char* jobu1, char* jobu2,
-                    char* jobv1t, char* jobv2t, char* trans,
-                    char* signs, lapack_int* m, lapack_int* p,
-                    lapack_int* q, lapack_complex_double* x11,
-                    lapack_int* ldx11, lapack_complex_double* x12,
-                    lapack_int* ldx12, lapack_complex_double* x21,
-                    lapack_int* ldx21, lapack_complex_double* x22,
-                    lapack_int* ldx22, double* theta,
-                    lapack_complex_double* u1, lapack_int* ldu1,
-                    lapack_complex_double* u2, lapack_int* ldu2,
-                    lapack_complex_double* v1t, lapack_int* ldv1t,
-                    lapack_complex_double* v2t, lapack_int* ldv2t,
-                    lapack_complex_double* work, lapack_int* lwork,
-                    double* rwork, lapack_int* lrwork,
-                    lapack_int* iwork , lapack_int *info );
-// LAPACK 3.4.0
-void LAPACK_sgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb, const float* v,
-                     lapack_int* ldv, const float* t, lapack_int* ldt, float* c,
-                     lapack_int* ldc, float* work, lapack_int *info );
-void LAPACK_dgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb, const double* v,
-                     lapack_int* ldv, const double* t, lapack_int* ldt,
-                     double* c, lapack_int* ldc, double* work,
-                     lapack_int *info );
-void LAPACK_cgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb,
-                     const lapack_complex_float* v, lapack_int* ldv,
-                     const lapack_complex_float* t, lapack_int* ldt,
-                     lapack_complex_float* c, lapack_int* ldc,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* nb,
-                     const lapack_complex_double* v, lapack_int* ldv,
-                     const lapack_complex_double* t, lapack_int* ldt,
-                     lapack_complex_double* c, lapack_int* ldc,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_sgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb, float* a,
-                    lapack_int* lda, float* t, lapack_int* ldt, float* work,
-                    lapack_int *info );
-void LAPACK_dgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb, double* a,
-                    lapack_int* lda, double* t, lapack_int* ldt, double* work,
-                    lapack_int *info );
-void LAPACK_cgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_zgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_sgeqrt2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_dgeqrt2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_cgeqrt2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_zgeqrt2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_sgeqrt3( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_dgeqrt3( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* t, lapack_int* ldt, lapack_int *info );
-void LAPACK_cgeqrt3( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_zgeqrt3( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_stpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const float* v, lapack_int* ldv, const float* t,
-                     lapack_int* ldt, float* a, lapack_int* lda, float* b,
-                     lapack_int* ldb, float* work, lapack_int *info );
-void LAPACK_dtpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const double* v, lapack_int* ldv, const double* t,
-                     lapack_int* ldt, double* a, lapack_int* lda, double* b,
-                     lapack_int* ldb, double* work, lapack_int *info );
-void LAPACK_ctpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const lapack_complex_float* v, lapack_int* ldv,
-                     const lapack_complex_float* t, lapack_int* ldt,
-                     lapack_complex_float* a, lapack_int* lda,
-                     lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* work, lapack_int *info );
-void LAPACK_ztpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
-                     lapack_int* k, lapack_int* l, lapack_int* nb,
-                     const lapack_complex_double* v, lapack_int* ldv,
-                     const lapack_complex_double* t, lapack_int* ldt,
-                     lapack_complex_double* a, lapack_int* lda,
-                     lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* work, lapack_int *info );
-void LAPACK_dtpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
-                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
-                    double* t, lapack_int* ldt, double* work,
-                    lapack_int *info );
-void LAPACK_ctpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* t, lapack_complex_float* b,
-                    lapack_int* ldb, lapack_int* ldt,
-                    lapack_complex_float* work, lapack_int *info );
-void LAPACK_ztpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* work, lapack_int *info );
-void LAPACK_stpqrt2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
-                     float* b, lapack_int* ldb, float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_dtpqrt2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
-                     double* b, lapack_int* ldb, double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_ctpqrt2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
-                     lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
-                     lapack_complex_float* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_ztpqrt2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
-                     lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
-                     lapack_complex_double* t, lapack_int* ldt,
-                     lapack_int *info );
-void LAPACK_stprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const float* v, lapack_int* ldv, const float* t,
-                    lapack_int* ldt, float* a, lapack_int* lda, float* b,
-                    lapack_int* ldb, const float* mywork,
-                    lapack_int* myldwork );
-void LAPACK_dtprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const double* v, lapack_int* ldv, const double* t,
-                    lapack_int* ldt, double* a, lapack_int* lda, double* b,
-                    lapack_int* ldb, const double* mywork,
-                    lapack_int* myldwork );
-void LAPACK_ctprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const lapack_complex_float* v, lapack_int* ldv,
-                    const lapack_complex_float* t, lapack_int* ldt,
-                    lapack_complex_float* a, lapack_int* lda,
-                    lapack_complex_float* b, lapack_int* ldb,
-                    const float* mywork, lapack_int* myldwork );
-void LAPACK_ztprfb( char* side, char* trans, char* direct, char* storev,
-                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
-                    const lapack_complex_double* v, lapack_int* ldv,
-                    const lapack_complex_double* t, lapack_int* ldt,
-                    lapack_complex_double* a, lapack_int* lda,
-                    lapack_complex_double* b, lapack_int* ldb,
-                    const double* mywork, lapack_int* myldwork );
-// LAPACK 3.X.X
-void LAPACK_csyr( char* uplo, lapack_int* n, lapack_complex_float* alpha,
-                      const lapack_complex_float* x, lapack_int* incx,
-                      lapack_complex_float* a, lapack_int* lda );
-void LAPACK_zsyr( char* uplo, lapack_int* n, lapack_complex_double* alpha,
-                      const lapack_complex_double* x, lapack_int* incx,
-                      lapack_complex_double* a, lapack_int* lda );
-
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-
-#endif /* _LAPACKE_H_ */
-
-#endif /* _MKL_LAPACKE_H_ */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapacke_mangling.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapacke_mangling.h
deleted file mode 100644
index 6211fd1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/misc/lapacke_mangling.h
+++ /dev/null
@@ -1,17 +0,0 @@
-#ifndef LAPACK_HEADER_INCLUDED
-#define LAPACK_HEADER_INCLUDED
-
-#ifndef LAPACK_GLOBAL
-#if defined(LAPACK_GLOBAL_PATTERN_LC) || defined(ADD_)
-#define LAPACK_GLOBAL(lcname,UCNAME)  lcname##_
-#elif defined(LAPACK_GLOBAL_PATTERN_UC) || defined(UPPER)
-#define LAPACK_GLOBAL(lcname,UCNAME)  UCNAME
-#elif defined(LAPACK_GLOBAL_PATTERN_MC) || defined(NOCHANGE)
-#define LAPACK_GLOBAL(lcname,UCNAME)  lcname
-#else
-#define LAPACK_GLOBAL(lcname,UCNAME)  lcname##_
-#endif
-#endif
-
-#endif
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
deleted file mode 100644
index 0e5d544..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
+++ /dev/null
@@ -1,358 +0,0 @@
-
-/** \returns an expression of the coefficient wise product of \c *this and \a other
-  *
-  * \sa MatrixBase::cwiseProduct
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
-operator*(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient wise quotient of \c *this and \a other
-  *
-  * \sa MatrixBase::cwiseQuotient
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>
-operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise min of \c *this and \a other
-  *
-  * Example: \include Cwise_min.cpp
-  * Output: \verbinclude Cwise_min.out
-  *
-  * \sa max()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(min,min)
-
-/** \returns an expression of the coefficient-wise min of \c *this and scalar \a other
-  *
-  * \sa max()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived,
-                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-min
-#else
-(min)
-#endif
-(const Scalar &other) const
-{
-  return (min)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise max of \c *this and \a other
-  *
-  * Example: \include Cwise_max.cpp
-  * Output: \verbinclude Cwise_max.out
-  *
-  * \sa min()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(max,max)
-
-/** \returns an expression of the coefficient-wise max of \c *this and scalar \a other
-  *
-  * \sa min()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived,
-                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-max
-#else
-(max)
-#endif
-(const Scalar &other) const
-{
-  return (max)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise absdiff of \c *this and \a other
-  *
-  * Example: \include Cwise_absolute_difference.cpp
-  * Output: \verbinclude Cwise_absolute_difference.out
-  *
-  * \sa absolute_difference()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(absolute_difference,absolute_difference)
-
-/** \returns an expression of the coefficient-wise absolute_difference of \c *this and scalar \a other
-  *
-  * \sa absolute_difference()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_absolute_difference_op<Scalar,Scalar>, const Derived,
-                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-absolute_difference
-#else
-(absolute_difference)
-#endif
-(const Scalar &other) const
-{
-  return (absolute_difference)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise power of \c *this to the given array of \a exponents.
-  *
-  * This function computes the coefficient-wise power.
-  *
-  * Example: \include Cwise_array_power_array.cpp
-  * Output: \verbinclude Cwise_array_power_array.out
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(pow,pow)
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(pow,pow)
-#else
-/** \returns an expression of the coefficients of \c *this rasied to the constant power \a exponent
-  *
-  * \tparam T is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression.
-  *
-  * This function computes the coefficient-wise power. The function MatrixBase::pow() in the
-  * unsupported module MatrixFunctions computes the matrix power.
-  *
-  * Example: \include Cwise_pow.cpp
-  * Output: \verbinclude Cwise_pow.out
-  *
-  * \sa ArrayBase::pow(ArrayBase), square(), cube(), exp(), log()
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_pow_op<Scalar,T>,Derived,Constant<T> > pow(const T& exponent) const;
-#endif
-
-
-// TODO code generating macros could be moved to Macros.h and could include generation of documentation
-#define EIGEN_MAKE_CWISE_COMP_OP(OP, COMPARATOR) \
-template<typename OtherDerived> \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived> \
-OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
-{ \
-  return CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived>(derived(), other.derived()); \
-}\
-typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> > Cmp ## COMPARATOR ## ReturnType; \
-typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject>, const Derived > RCmp ## COMPARATOR ## ReturnType; \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Cmp ## COMPARATOR ## ReturnType \
-OP(const Scalar& s) const { \
-  return this->OP(Derived::PlainObject::Constant(rows(), cols(), s)); \
-} \
-EIGEN_DEVICE_FUNC friend EIGEN_STRONG_INLINE const RCmp ## COMPARATOR ## ReturnType \
-OP(const Scalar& s, const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived>& d) { \
-  return Derived::PlainObject::Constant(d.rows(), d.cols(), s).OP(d); \
-}
-
-#define EIGEN_MAKE_CWISE_COMP_R_OP(OP, R_OP, RCOMPARATOR) \
-template<typename OtherDerived> \
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived> \
-OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
-{ \
-  return CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived>(other.derived(), derived()); \
-} \
-EIGEN_DEVICE_FUNC \
-inline const RCmp ## RCOMPARATOR ## ReturnType \
-OP(const Scalar& s) const { \
-  return Derived::PlainObject::Constant(rows(), cols(), s).R_OP(*this); \
-} \
-friend inline const Cmp ## RCOMPARATOR ## ReturnType \
-OP(const Scalar& s, const Derived& d) { \
-  return d.R_OP(Derived::PlainObject::Constant(d.rows(), d.cols(), s)); \
-}
-
-
-
-/** \returns an expression of the coefficient-wise \< operator of *this and \a other
-  *
-  * Example: \include Cwise_less.cpp
-  * Output: \verbinclude Cwise_less.out
-  *
-  * \sa all(), any(), operator>(), operator<=()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator<, LT)
-
-/** \returns an expression of the coefficient-wise \<= operator of *this and \a other
-  *
-  * Example: \include Cwise_less_equal.cpp
-  * Output: \verbinclude Cwise_less_equal.out
-  *
-  * \sa all(), any(), operator>=(), operator<()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator<=, LE)
-
-/** \returns an expression of the coefficient-wise \> operator of *this and \a other
-  *
-  * Example: \include Cwise_greater.cpp
-  * Output: \verbinclude Cwise_greater.out
-  *
-  * \sa all(), any(), operator>=(), operator<()
-  */
-EIGEN_MAKE_CWISE_COMP_R_OP(operator>, operator<, LT)
-
-/** \returns an expression of the coefficient-wise \>= operator of *this and \a other
-  *
-  * Example: \include Cwise_greater_equal.cpp
-  * Output: \verbinclude Cwise_greater_equal.out
-  *
-  * \sa all(), any(), operator>(), operator<=()
-  */
-EIGEN_MAKE_CWISE_COMP_R_OP(operator>=, operator<=, LE)
-
-/** \returns an expression of the coefficient-wise == operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include Cwise_equal_equal.cpp
-  * Output: \verbinclude Cwise_equal_equal.out
-  *
-  * \sa all(), any(), isApprox(), isMuchSmallerThan()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator==, EQ)
-
-/** \returns an expression of the coefficient-wise != operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include Cwise_not_equal.cpp
-  * Output: \verbinclude Cwise_not_equal.out
-  *
-  * \sa all(), any(), isApprox(), isMuchSmallerThan()
-  */
-EIGEN_MAKE_CWISE_COMP_OP(operator!=, NEQ)
-
-
-#undef EIGEN_MAKE_CWISE_COMP_OP
-#undef EIGEN_MAKE_CWISE_COMP_R_OP
-
-// scalar addition
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP(operator+,sum)
-#else
-/** \returns an expression of \c *this with each coeff incremented by the constant \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  *
-  * Example: \include Cwise_plus.cpp
-  * Output: \verbinclude Cwise_plus.out
-  *
-  * \sa operator+=(), operator-()
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_sum_op<Scalar,T>,Derived,Constant<T> > operator+(const T& scalar) const;
-/** \returns an expression of \a expr with each coeff incremented by the constant \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T> friend
-const CwiseBinaryOp<internal::scalar_sum_op<T,Scalar>,Constant<T>,Derived> operator+(const T& scalar, const StorageBaseType& expr);
-#endif
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP(operator-,difference)
-#else
-/** \returns an expression of \c *this with each coeff decremented by the constant \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  *
-  * Example: \include Cwise_minus.cpp
-  * Output: \verbinclude Cwise_minus.out
-  *
-  * \sa operator+=(), operator-()
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_difference_op<Scalar,T>,Derived,Constant<T> > operator-(const T& scalar) const;
-/** \returns an expression of the constant matrix of value \a scalar decremented by the coefficients of \a expr
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T> friend
-const CwiseBinaryOp<internal::scalar_difference_op<T,Scalar>,Constant<T>,Derived> operator-(const T& scalar, const StorageBaseType& expr);
-#endif
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(operator/,quotient)
-#else
-  /**
-    * \brief Component-wise division of the scalar \a s by array elements of \a a.
-    *
-    * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
-    */
-  template<typename T> friend
-  inline const CwiseBinaryOp<internal::scalar_quotient_op<T,Scalar>,Constant<T>,Derived>
-  operator/(const T& s,const StorageBaseType& a);
-#endif
-
-/** \returns an expression of the coefficient-wise ^ operator of *this and \a other
- *
- * \warning this operator is for expression of bool only.
- *
- * Example: \include Cwise_boolean_xor.cpp
- * Output: \verbinclude Cwise_boolean_xor.out
- *
- * \sa operator&&(), select()
- */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived>
-operator^(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return CwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived>(derived(),other.derived());
-}
-
-// NOTE disabled until we agree on argument order
-#if 0
-/** \cpp11 \returns an expression of the coefficient-wise polygamma function.
-  *
-  * \specialfunctions_module
-  *
-  * It returns the \a n -th derivative of the digamma(psi) evaluated at \c *this.
-  *
-  * \warning Be careful with the order of the parameters: x.polygamma(n) is equivalent to polygamma(n,x)
-  *
-  * \sa Eigen::polygamma()
-  */
-template<typename DerivedN>
-inline const CwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const DerivedN, const Derived>
-polygamma(const EIGEN_CURRENT_STORAGE_BASE_CLASS<DerivedN> &n) const
-{
-  return CwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const DerivedN, const Derived>(n.derived(), this->derived());
-}
-#endif
-
-/** \returns an expression of the coefficient-wise zeta function.
-  *
-  * \specialfunctions_module
-  *
-  * It returns the Riemann zeta function of two arguments \c *this and \a q:
-  *
-  * \param q is the shift, it must be > 0
-  *
-  * \note *this is the exponent, it must be > 1.
-  * \note This function supports only float and double scalar types. To support other scalar types, the user has
-  * to provide implementations of zeta(T,T) for any scalar type T to be supported.
-  *
-  * This method is an alias for zeta(*this,q);
-  *
-  * \sa Eigen::zeta()
-  */
-template<typename DerivedQ>
-inline const CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ>
-zeta(const EIGEN_CURRENT_STORAGE_BASE_CLASS<DerivedQ> &q) const
-{
-  return CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ>(this->derived(), q.derived());
-}
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h
deleted file mode 100644
index 13c55f4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h
+++ /dev/null
@@ -1,696 +0,0 @@
-
-
-typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> AbsReturnType;
-typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> ArgReturnType;
-typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> Abs2ReturnType;
-typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> SqrtReturnType;
-typedef CwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived> RsqrtReturnType;
-typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> SignReturnType;
-typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> InverseReturnType;
-typedef CwiseUnaryOp<internal::scalar_boolean_not_op<Scalar>, const Derived> BooleanNotReturnType;
-
-typedef CwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived> ExpReturnType;
-typedef CwiseUnaryOp<internal::scalar_expm1_op<Scalar>, const Derived> Expm1ReturnType;
-typedef CwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived> LogReturnType;
-typedef CwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived> Log1pReturnType;
-typedef CwiseUnaryOp<internal::scalar_log10_op<Scalar>, const Derived> Log10ReturnType;
-typedef CwiseUnaryOp<internal::scalar_log2_op<Scalar>, const Derived> Log2ReturnType;
-typedef CwiseUnaryOp<internal::scalar_cos_op<Scalar>, const Derived> CosReturnType;
-typedef CwiseUnaryOp<internal::scalar_sin_op<Scalar>, const Derived> SinReturnType;
-typedef CwiseUnaryOp<internal::scalar_tan_op<Scalar>, const Derived> TanReturnType;
-typedef CwiseUnaryOp<internal::scalar_acos_op<Scalar>, const Derived> AcosReturnType;
-typedef CwiseUnaryOp<internal::scalar_asin_op<Scalar>, const Derived> AsinReturnType;
-typedef CwiseUnaryOp<internal::scalar_atan_op<Scalar>, const Derived> AtanReturnType;
-typedef CwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived> TanhReturnType;
-typedef CwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived> LogisticReturnType;
-typedef CwiseUnaryOp<internal::scalar_sinh_op<Scalar>, const Derived> SinhReturnType;
-#if EIGEN_HAS_CXX11_MATH
-typedef CwiseUnaryOp<internal::scalar_atanh_op<Scalar>, const Derived> AtanhReturnType;
-typedef CwiseUnaryOp<internal::scalar_asinh_op<Scalar>, const Derived> AsinhReturnType;
-typedef CwiseUnaryOp<internal::scalar_acosh_op<Scalar>, const Derived> AcoshReturnType;
-#endif
-typedef CwiseUnaryOp<internal::scalar_cosh_op<Scalar>, const Derived> CoshReturnType;
-typedef CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> SquareReturnType;
-typedef CwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived> CubeReturnType;
-typedef CwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived> RoundReturnType;
-typedef CwiseUnaryOp<internal::scalar_rint_op<Scalar>, const Derived> RintReturnType;
-typedef CwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived> FloorReturnType;
-typedef CwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived> CeilReturnType;
-typedef CwiseUnaryOp<internal::scalar_isnan_op<Scalar>, const Derived> IsNaNReturnType;
-typedef CwiseUnaryOp<internal::scalar_isinf_op<Scalar>, const Derived> IsInfReturnType;
-typedef CwiseUnaryOp<internal::scalar_isfinite_op<Scalar>, const Derived> IsFiniteReturnType;
-
-/** \returns an expression of the coefficient-wise absolute value of \c *this
-  *
-  * Example: \include Cwise_abs.cpp
-  * Output: \verbinclude Cwise_abs.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs">Math functions</a>, abs2()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const AbsReturnType
-abs() const
-{
-  return AbsReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise phase angle of \c *this
-  *
-  * Example: \include Cwise_arg.cpp
-  * Output: \verbinclude Cwise_arg.out
-  *
-  * \sa abs()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const ArgReturnType
-arg() const
-{
-  return ArgReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise squared absolute value of \c *this
-  *
-  * Example: \include Cwise_abs2.cpp
-  * Output: \verbinclude Cwise_abs2.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs2">Math functions</a>, abs(), square()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const Abs2ReturnType
-abs2() const
-{
-  return Abs2ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise exponential of *this.
-  *
-  * This function computes the coefficient-wise exponential. The function MatrixBase::exp() in the
-  * unsupported module MatrixFunctions computes the matrix exponential.
-  *
-  * Example: \include Cwise_exp.cpp
-  * Output: \verbinclude Cwise_exp.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_exp">Math functions</a>, pow(), log(), sin(), cos()
-  */
-EIGEN_DEVICE_FUNC
-inline const ExpReturnType
-exp() const
-{
-  return ExpReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise exponential of *this minus 1.
-  *
-  * In exact arithmetic, \c x.expm1() is equivalent to \c x.exp() - 1,
-  * however, with finite precision, this function is much more accurate when \c x is close to zero.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_expm1">Math functions</a>, exp()
-  */
-EIGEN_DEVICE_FUNC
-inline const Expm1ReturnType
-expm1() const
-{
-  return Expm1ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise logarithm of *this.
-  *
-  * This function computes the coefficient-wise logarithm. The function MatrixBase::log() in the
-  * unsupported module MatrixFunctions computes the matrix logarithm.
-  *
-  * Example: \include Cwise_log.cpp
-  * Output: \verbinclude Cwise_log.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log">Math functions</a>, log()
-  */
-EIGEN_DEVICE_FUNC
-inline const LogReturnType
-log() const
-{
-  return LogReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise logarithm of 1 plus \c *this.
-  *
-  * In exact arithmetic, \c x.log() is equivalent to \c (x+1).log(),
-  * however, with finite precision, this function is much more accurate when \c x is close to zero.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log1p">Math functions</a>, log()
-  */
-EIGEN_DEVICE_FUNC
-inline const Log1pReturnType
-log1p() const
-{
-  return Log1pReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise base-10 logarithm of *this.
-  *
-  * This function computes the coefficient-wise base-10 logarithm.
-  *
-  * Example: \include Cwise_log10.cpp
-  * Output: \verbinclude Cwise_log10.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log10">Math functions</a>, log()
-  */
-EIGEN_DEVICE_FUNC
-inline const Log10ReturnType
-log10() const
-{
-  return Log10ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise base-2 logarithm of *this.
-  *
-  * This function computes the coefficient-wise base-2 logarithm.
-  *
-  */
-EIGEN_DEVICE_FUNC
-inline const Log2ReturnType
-log2() const
-{
-  return Log2ReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise square root of *this.
-  *
-  * This function computes the coefficient-wise square root. The function MatrixBase::sqrt() in the
-  * unsupported module MatrixFunctions computes the matrix square root.
-  *
-  * Example: \include Cwise_sqrt.cpp
-  * Output: \verbinclude Cwise_sqrt.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sqrt">Math functions</a>, pow(), square()
-  */
-EIGEN_DEVICE_FUNC
-inline const SqrtReturnType
-sqrt() const
-{
-  return SqrtReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse square root of *this.
-  *
-  * This function computes the coefficient-wise inverse square root.
-  *
-  * Example: \include Cwise_sqrt.cpp
-  * Output: \verbinclude Cwise_sqrt.out
-  *
-  * \sa pow(), square()
-  */
-EIGEN_DEVICE_FUNC
-inline const RsqrtReturnType
-rsqrt() const
-{
-  return RsqrtReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise signum of *this.
-  *
-  * This function computes the coefficient-wise signum.
-  *
-  * Example: \include Cwise_sign.cpp
-  * Output: \verbinclude Cwise_sign.out
-  *
-  * \sa pow(), square()
-  */
-EIGEN_DEVICE_FUNC
-inline const SignReturnType
-sign() const
-{
-  return SignReturnType(derived());
-}
-
-
-/** \returns an expression of the coefficient-wise cosine of *this.
-  *
-  * This function computes the coefficient-wise cosine. The function MatrixBase::cos() in the
-  * unsupported module MatrixFunctions computes the matrix cosine.
-  *
-  * Example: \include Cwise_cos.cpp
-  * Output: \verbinclude Cwise_cos.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cos">Math functions</a>, sin(), acos()
-  */
-EIGEN_DEVICE_FUNC
-inline const CosReturnType
-cos() const
-{
-  return CosReturnType(derived());
-}
-
-
-/** \returns an expression of the coefficient-wise sine of *this.
-  *
-  * This function computes the coefficient-wise sine. The function MatrixBase::sin() in the
-  * unsupported module MatrixFunctions computes the matrix sine.
-  *
-  * Example: \include Cwise_sin.cpp
-  * Output: \verbinclude Cwise_sin.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sin">Math functions</a>, cos(), asin()
-  */
-EIGEN_DEVICE_FUNC
-inline const SinReturnType
-sin() const
-{
-  return SinReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise tan of *this.
-  *
-  * Example: \include Cwise_tan.cpp
-  * Output: \verbinclude Cwise_tan.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tan">Math functions</a>, cos(), sin()
-  */
-EIGEN_DEVICE_FUNC
-inline const TanReturnType
-tan() const
-{
-  return TanReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise arc tan of *this.
-  *
-  * Example: \include Cwise_atan.cpp
-  * Output: \verbinclude Cwise_atan.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atan">Math functions</a>, tan(), asin(), acos()
-  */
-EIGEN_DEVICE_FUNC
-inline const AtanReturnType
-atan() const
-{
-  return AtanReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise arc cosine of *this.
-  *
-  * Example: \include Cwise_acos.cpp
-  * Output: \verbinclude Cwise_acos.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acos">Math functions</a>, cos(), asin()
-  */
-EIGEN_DEVICE_FUNC
-inline const AcosReturnType
-acos() const
-{
-  return AcosReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise arc sine of *this.
-  *
-  * Example: \include Cwise_asin.cpp
-  * Output: \verbinclude Cwise_asin.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asin">Math functions</a>, sin(), acos()
-  */
-EIGEN_DEVICE_FUNC
-inline const AsinReturnType
-asin() const
-{
-  return AsinReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise hyperbolic tan of *this.
-  *
-  * Example: \include Cwise_tanh.cpp
-  * Output: \verbinclude Cwise_tanh.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tanh">Math functions</a>, tan(), sinh(), cosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const TanhReturnType
-tanh() const
-{
-  return TanhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise hyperbolic sin of *this.
-  *
-  * Example: \include Cwise_sinh.cpp
-  * Output: \verbinclude Cwise_sinh.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sinh">Math functions</a>, sin(), tanh(), cosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const SinhReturnType
-sinh() const
-{
-  return SinhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise hyperbolic cos of *this.
-  *
-  * Example: \include Cwise_cosh.cpp
-  * Output: \verbinclude Cwise_cosh.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cosh">Math functions</a>, tanh(), sinh(), cosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const CoshReturnType
-cosh() const
-{
-  return CoshReturnType(derived());
-}
-
-#if EIGEN_HAS_CXX11_MATH
-/** \returns an expression of the coefficient-wise inverse hyperbolic tan of *this.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atanh">Math functions</a>, atanh(), asinh(), acosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const AtanhReturnType
-atanh() const
-{
-  return AtanhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse hyperbolic sin of *this.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asinh">Math functions</a>, atanh(), asinh(), acosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const AsinhReturnType
-asinh() const
-{
-  return AsinhReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse hyperbolic cos of *this.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acosh">Math functions</a>, atanh(), asinh(), acosh()
-  */
-EIGEN_DEVICE_FUNC
-inline const AcoshReturnType
-acosh() const
-{
-  return AcoshReturnType(derived());
-}
-#endif
-
-/** \returns an expression of the coefficient-wise logistic of *this.
-  */
-EIGEN_DEVICE_FUNC
-inline const LogisticReturnType
-logistic() const
-{
-  return LogisticReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse of *this.
-  *
-  * Example: \include Cwise_inverse.cpp
-  * Output: \verbinclude Cwise_inverse.out
-  *
-  * \sa operator/(), operator*()
-  */
-EIGEN_DEVICE_FUNC
-inline const InverseReturnType
-inverse() const
-{
-  return InverseReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise square of *this.
-  *
-  * Example: \include Cwise_square.cpp
-  * Output: \verbinclude Cwise_square.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_squareE">Math functions</a>, abs2(), cube(), pow()
-  */
-EIGEN_DEVICE_FUNC
-inline const SquareReturnType
-square() const
-{
-  return SquareReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise cube of *this.
-  *
-  * Example: \include Cwise_cube.cpp
-  * Output: \verbinclude Cwise_cube.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cube">Math functions</a>, square(), pow()
-  */
-EIGEN_DEVICE_FUNC
-inline const CubeReturnType
-cube() const
-{
-  return CubeReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise rint of *this.
-  *
-  * Example: \include Cwise_rint.cpp
-  * Output: \verbinclude Cwise_rint.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_rint">Math functions</a>, ceil(), floor()
-  */
-EIGEN_DEVICE_FUNC
-inline const RintReturnType
-rint() const
-{
-  return RintReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise round of *this.
-  *
-  * Example: \include Cwise_round.cpp
-  * Output: \verbinclude Cwise_round.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_round">Math functions</a>, ceil(), floor()
-  */
-EIGEN_DEVICE_FUNC
-inline const RoundReturnType
-round() const
-{
-  return RoundReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise floor of *this.
-  *
-  * Example: \include Cwise_floor.cpp
-  * Output: \verbinclude Cwise_floor.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_floor">Math functions</a>, ceil(), round()
-  */
-EIGEN_DEVICE_FUNC
-inline const FloorReturnType
-floor() const
-{
-  return FloorReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise ceil of *this.
-  *
-  * Example: \include Cwise_ceil.cpp
-  * Output: \verbinclude Cwise_ceil.out
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ceil">Math functions</a>, floor(), round()
-  */
-EIGEN_DEVICE_FUNC
-inline const CeilReturnType
-ceil() const
-{
-  return CeilReturnType(derived());
-}
-
-template<int N> struct ShiftRightXpr {
-  typedef CwiseUnaryOp<internal::scalar_shift_right_op<Scalar, N>, const Derived> Type;
-};
-
-/** \returns an expression of \c *this with the \a Scalar type arithmetically
-  * shifted right by \a N bit positions.
-  *
-  * The template parameter \a N specifies the number of bit positions to shift.
-  * 
-  * \sa shiftLeft()
-  */
-template<int N>
-EIGEN_DEVICE_FUNC
-typename ShiftRightXpr<N>::Type
-shiftRight() const
-{
-  return typename ShiftRightXpr<N>::Type(derived());
-}
-
-
-template<int N> struct ShiftLeftXpr {
-  typedef CwiseUnaryOp<internal::scalar_shift_left_op<Scalar, N>, const Derived> Type;
-};
-
-/** \returns an expression of \c *this with the \a Scalar type logically
-  * shifted left by \a N bit positions.
-  *
-  * The template parameter \a N specifies the number of bit positions to shift.
-  *
-  * \sa shiftRight()
-  */
-template<int N>
-EIGEN_DEVICE_FUNC
-typename ShiftLeftXpr<N>::Type
-shiftLeft() const
-{
-  return typename ShiftLeftXpr<N>::Type(derived());
-}
-
-/** \returns an expression of the coefficient-wise isnan of *this.
-  *
-  * Example: \include Cwise_isNaN.cpp
-  * Output: \verbinclude Cwise_isNaN.out
-  *
-  * \sa isfinite(), isinf()
-  */
-EIGEN_DEVICE_FUNC
-inline const IsNaNReturnType
-isNaN() const
-{
-  return IsNaNReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise isinf of *this.
-  *
-  * Example: \include Cwise_isInf.cpp
-  * Output: \verbinclude Cwise_isInf.out
-  *
-  * \sa isnan(), isfinite()
-  */
-EIGEN_DEVICE_FUNC
-inline const IsInfReturnType
-isInf() const
-{
-  return IsInfReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise isfinite of *this.
-  *
-  * Example: \include Cwise_isFinite.cpp
-  * Output: \verbinclude Cwise_isFinite.out
-  *
-  * \sa isnan(), isinf()
-  */
-EIGEN_DEVICE_FUNC
-inline const IsFiniteReturnType
-isFinite() const
-{
-  return IsFiniteReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise ! operator of *this
-  *
-  * \warning this operator is for expression of bool only.
-  *
-  * Example: \include Cwise_boolean_not.cpp
-  * Output: \verbinclude Cwise_boolean_not.out
-  *
-  * \sa operator!=()
-  */
-EIGEN_DEVICE_FUNC
-inline const BooleanNotReturnType
-operator!() const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return BooleanNotReturnType(derived());
-}
-
-
-// --- SpecialFunctions module ---
-
-typedef CwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived> LgammaReturnType;
-typedef CwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived> DigammaReturnType;
-typedef CwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived> ErfReturnType;
-typedef CwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived> ErfcReturnType;
-typedef CwiseUnaryOp<internal::scalar_ndtri_op<Scalar>, const Derived> NdtriReturnType;
-
-/** \cpp11 \returns an expression of the coefficient-wise ln(|gamma(*this)|).
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of lgamma(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_lgamma">Math functions</a>, digamma()
-  */
-EIGEN_DEVICE_FUNC
-inline const LgammaReturnType
-lgamma() const
-{
-  return LgammaReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise digamma (psi, derivative of lgamma).
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types. To support other scalar types,
-  * the user has to provide implementations of digamma(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_digamma">Math functions</a>, Eigen::digamma(), Eigen::polygamma(), lgamma()
-  */
-EIGEN_DEVICE_FUNC
-inline const DigammaReturnType
-digamma() const
-{
-  return DigammaReturnType(derived());
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise Gauss error
-  * function of *this.
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of erf(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erf">Math functions</a>, erfc()
-  */
-EIGEN_DEVICE_FUNC
-inline const ErfReturnType
-erf() const
-{
-  return ErfReturnType(derived());
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise Complementary error
-  * function of *this.
-  *
-  * \specialfunctions_module
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of erfc(T) for any scalar
-  * type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erfc">Math functions</a>, erf()
-  */
-EIGEN_DEVICE_FUNC
-inline const ErfcReturnType
-erfc() const
-{
-  return ErfcReturnType(derived());
-}
-
-/** \returns an expression of the coefficient-wise inverse of the CDF of the Normal distribution function
-  * function of *this.
-  *
-  * \specialfunctions_module
-  * 
-  * In other words, considering `x = ndtri(y)`, it returns the argument, x, for which the area under the
-  * Gaussian probability density function (integrated from minus infinity to x) is equal to y.
-  *
-  * \note This function supports only float and double scalar types. To support other scalar types,
-  * the user has to provide implementations of ndtri(T) for any scalar type T to be supported.
-  *
-  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ndtri">Math functions</a>
-  */
-EIGEN_DEVICE_FUNC
-inline const NdtriReturnType
-ndtri() const
-{
-  return NdtriReturnType(derived());
-}
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/BlockMethods.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/BlockMethods.h
deleted file mode 100644
index 63a52a6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/BlockMethods.h
+++ /dev/null
@@ -1,1442 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-/// \internal expression type of a column */
-typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ColXpr;
-typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ConstColXpr;
-/// \internal expression type of a row */
-typedef Block<Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowXpr;
-typedef const Block<const Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowXpr;
-/// \internal expression type of a block of whole columns */
-typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ColsBlockXpr;
-typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ConstColsBlockXpr;
-/// \internal expression type of a block of whole rows */
-typedef Block<Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowsBlockXpr;
-typedef const Block<const Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowsBlockXpr;
-/// \internal expression type of a block of whole columns */
-template<int N> struct NColsBlockXpr { typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; };
-template<int N> struct ConstNColsBlockXpr { typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; };
-/// \internal expression type of a block of whole rows */
-template<int N> struct NRowsBlockXpr { typedef Block<Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; };
-template<int N> struct ConstNRowsBlockXpr { typedef const Block<const Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; };
-/// \internal expression of a block */
-typedef Block<Derived> BlockXpr;
-typedef const Block<const Derived> ConstBlockXpr;
-/// \internal expression of a block of fixed sizes */
-template<int Rows, int Cols> struct FixedBlockXpr { typedef Block<Derived,Rows,Cols> Type; };
-template<int Rows, int Cols> struct ConstFixedBlockXpr { typedef Block<const Derived,Rows,Cols> Type; };
-
-typedef VectorBlock<Derived> SegmentReturnType;
-typedef const VectorBlock<const Derived> ConstSegmentReturnType;
-template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
-template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
-
-/// \internal inner-vector
-typedef Block<Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true>       InnerVectorReturnType;
-typedef Block<const Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true> ConstInnerVectorReturnType;
-
-/// \internal set of inner-vectors
-typedef Block<Derived,Dynamic,Dynamic,true> InnerVectorsReturnType;
-typedef Block<const Derived,Dynamic,Dynamic,true> ConstInnerVectorsReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-/// \returns an expression of a block in \c *this with either dynamic or fixed sizes.
-///
-/// \param  startRow  the first row in the block
-/// \param  startCol  the first column in the block
-/// \param  blockRows number of rows in the block, specified at either run-time or compile-time
-/// \param  blockCols number of columns in the block, specified at either run-time or compile-time
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example using runtime (aka dynamic) sizes: \include MatrixBase_block_int_int_int_int.cpp
-/// Output: \verbinclude MatrixBase_block_int_int_int_int.out
-///
-/// \newin{3.4}:
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value in case \c N equals Eigen::Dynamic.
-/// Here is an example with a fixed number of rows \c NRows and dynamic number of columns \c cols:
-/// \code
-/// mat.block(i,j,fix<NRows>,cols)
-/// \endcode
-///
-/// This function thus fully covers the features offered by the following overloads block<NRows,NCols>(Index, Index),
-/// and block<NRows,NCols>(Index, Index, Index, Index) that are thus obsolete. Indeed, this generic version avoids
-/// redundancy, it preserves the argument order, and prevents the need to rely on the template keyword in templated code.
-///
-/// but with less redundancy and more consistency as it does not modify the argument order
-/// and seamlessly enable hybrid fixed/dynamic sizes.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size matrix, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block, fix, fix<N>(int)
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type(
-            derived(), startRow, startCol, internal::get_runtime_value(blockRows), internal::get_runtime_value(blockCols));
-}
-
-/// This is the const version of block(Index,Index,NRowsType,NColsType)
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type(
-            derived(), startRow, startCol, internal::get_runtime_value(blockRows), internal::get_runtime_value(blockCols));
-}
-
-
-
-/// \returns a expression of a top-right corner of \c *this with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example with dynamic sizes: \include MatrixBase_topRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_topRightCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-topRightCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(cCols), internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of topRightCorner(NRowsType, NColsType).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-topRightCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(cCols), internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size top-right corner of \c *this.
-///
-/// \tparam CRows the number of rows in the corner
-/// \tparam CCols the number of columns in the corner
-///
-/// Example: \include MatrixBase_template_int_int_topRightCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block, block<int,int>(Index,Index)
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topRightCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - CCols);
-}
-
-/// This is the const version of topRightCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topRightCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - CCols);
-}
-
-/// \returns an expression of a top-right corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_topRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topRightCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
-}
-
-/// This is the const version of topRightCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topRightCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
-}
-
-
-
-/// \returns an expression of a top-left corner of \c *this  with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_topLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_topLeftCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-topLeftCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of topLeftCorner(Index, Index).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-topLeftCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size top-left corner of \c *this.
-///
-/// The template parameters CRows and CCols are the number of rows and columns in the corner.
-///
-/// Example: \include MatrixBase_template_int_int_topLeftCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topLeftCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0);
-}
-
-/// This is the const version of topLeftCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topLeftCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0);
-}
-
-/// \returns an expression of a top-left corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_topLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type topLeftCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0, cRows, cCols);
-}
-
-/// This is the const version of topLeftCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type topLeftCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0, cRows, cCols);
-}
-
-
-
-/// \returns an expression of a bottom-right corner of \c *this  with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_bottomRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_bottomRightCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-bottomRightCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), cols() - internal::get_runtime_value(cCols),
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of bottomRightCorner(NRowsType, NColsType).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstFixedBlockXpr<...,...>::Type
-#endif
-bottomRightCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), cols() - internal::get_runtime_value(cCols),
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size bottom-right corner of \c *this.
-///
-/// The template parameters CRows and CCols are the number of rows and columns in the corner.
-///
-/// Example: \include MatrixBase_template_int_int_bottomRightCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomRightCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, cols() - CCols);
-}
-
-/// This is the const version of bottomRightCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomRightCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, cols() - CCols);
-}
-
-/// \returns an expression of a bottom-right corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_bottomRightCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomRightCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
-}
-
-/// This is the const version of bottomRightCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomRightCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
-}
-
-
-
-/// \returns an expression of a bottom-left corner of \c *this  with either dynamic or fixed sizes.
-///
-/// \param cRows the number of rows in the corner
-/// \param cCols the number of columns in the corner
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_bottomLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_bottomLeftCorner_int_int.out
-///
-/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename FixedBlockXpr<...,...>::Type
-#endif
-bottomLeftCorner(NRowsType cRows, NColsType cCols)
-{
-  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), 0,
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// This is the const version of bottomLeftCorner(NRowsType, NColsType).
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename ConstFixedBlockXpr<...,...>::Type
-#endif
-bottomLeftCorner(NRowsType cRows, NColsType cCols) const
-{
-  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(cRows), 0,
-                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
-}
-
-/// \returns an expression of a fixed-size bottom-left corner of \c *this.
-///
-/// The template parameters CRows and CCols are the number of rows and columns in the corner.
-///
-/// Example: \include MatrixBase_template_int_int_bottomLeftCorner.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomLeftCorner()
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, 0);
-}
-
-/// This is the const version of bottomLeftCorner<int, int>().
-template<int CRows, int CCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomLeftCorner() const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, 0);
-}
-
-/// \returns an expression of a bottom-left corner of \c *this.
-///
-/// \tparam CRows number of rows in corner as specified at compile-time
-/// \tparam CCols number of columns in corner as specified at compile-time
-/// \param  cRows number of rows in corner as specified at run-time
-/// \param  cCols number of columns in corner as specified at run-time
-///
-/// This function is mainly useful for corners where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a cRows should equal \a CRows unless
-/// \a CRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_bottomLeftCorner_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner_int_int.out
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa class Block
-///
-template<int CRows, int CCols>
-EIGEN_STRONG_INLINE
-typename FixedBlockXpr<CRows,CCols>::Type bottomLeftCorner(Index cRows, Index cCols)
-{
-  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
-}
-
-/// This is the const version of bottomLeftCorner<int, int>(Index, Index).
-template<int CRows, int CCols>
-EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomLeftCorner(Index cRows, Index cCols) const
-{
-  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
-}
-
-
-
-/// \returns a block consisting of the top rows of \c *this.
-///
-/// \param n the number of rows in the block
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-///
-/// Example: \include MatrixBase_topRows_int.cpp
-/// Output: \verbinclude MatrixBase_topRows_int.out
-///
-/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-typename NRowsBlockXpr<...>::Type
-#endif
-topRows(NRowsType n)
-{
-  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(n), cols());
-}
-
-/// This is the const version of topRows(NRowsType).
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-const typename ConstNRowsBlockXpr<...>::Type
-#endif
-topRows(NRowsType n) const
-{
-  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), 0, 0, internal::get_runtime_value(n), cols());
-}
-
-/// \returns a block consisting of the top rows of \c *this.
-///
-/// \tparam N the number of rows in the block as specified at compile-time
-/// \param n the number of rows in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_topRows.cpp
-/// Output: \verbinclude MatrixBase_template_int_topRows.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NRowsBlockXpr<N>::Type topRows(Index n = N)
-{
-  return typename NRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
-}
-
-/// This is the const version of topRows<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNRowsBlockXpr<N>::Type topRows(Index n = N) const
-{
-  return typename ConstNRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
-}
-
-
-
-/// \returns a block consisting of the bottom rows of \c *this.
-///
-/// \param n the number of rows in the block
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-///
-/// Example: \include MatrixBase_bottomRows_int.cpp
-/// Output: \verbinclude MatrixBase_bottomRows_int.out
-///
-/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-typename NRowsBlockXpr<...>::Type
-#endif
-bottomRows(NRowsType n)
-{
-  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
-}
-
-/// This is the const version of bottomRows(NRowsType).
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-const typename ConstNRowsBlockXpr<...>::Type
-#endif
-bottomRows(NRowsType n) const
-{
-  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
-}
-
-/// \returns a block consisting of the bottom rows of \c *this.
-///
-/// \tparam N the number of rows in the block as specified at compile-time
-/// \param n the number of rows in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_bottomRows.cpp
-/// Output: \verbinclude MatrixBase_template_int_bottomRows.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NRowsBlockXpr<N>::Type bottomRows(Index n = N)
-{
-  return typename NRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
-}
-
-/// This is the const version of bottomRows<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNRowsBlockXpr<N>::Type bottomRows(Index n = N) const
-{
-  return typename ConstNRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
-}
-
-
-
-/// \returns a block consisting of a range of rows of \c *this.
-///
-/// \param startRow the index of the first row in the block
-/// \param n the number of rows in the block
-/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
-///
-/// Example: \include DenseBase_middleRows_int.cpp
-/// Output: \verbinclude DenseBase_middleRows_int.out
-///
-/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-typename NRowsBlockXpr<...>::Type
-#endif
-middleRows(Index startRow, NRowsType n)
-{
-  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), startRow, 0, internal::get_runtime_value(n), cols());
-}
-
-/// This is the const version of middleRows(Index,NRowsType).
-template<typename NRowsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-#else
-const typename ConstNRowsBlockXpr<...>::Type
-#endif
-middleRows(Index startRow, NRowsType n) const
-{
-  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
-            (derived(), startRow, 0, internal::get_runtime_value(n), cols());
-}
-
-/// \returns a block consisting of a range of rows of \c *this.
-///
-/// \tparam N the number of rows in the block as specified at compile-time
-/// \param startRow the index of the first row in the block
-/// \param n the number of rows in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include DenseBase_template_int_middleRows.cpp
-/// Output: \verbinclude DenseBase_template_int_middleRows.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N)
-{
-  return typename NRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
-}
-
-/// This is the const version of middleRows<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N) const
-{
-  return typename ConstNRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
-}
-
-
-
-/// \returns a block consisting of the left columns of \c *this.
-///
-/// \param n the number of columns in the block
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_leftCols_int.cpp
-/// Output: \verbinclude MatrixBase_leftCols_int.out
-///
-/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename NColsBlockXpr<...>::Type
-#endif
-leftCols(NColsType n)
-{
-  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, rows(), internal::get_runtime_value(n));
-}
-
-/// This is the const version of leftCols(NColsType).
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstNColsBlockXpr<...>::Type
-#endif
-leftCols(NColsType n) const
-{
-  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, 0, rows(), internal::get_runtime_value(n));
-}
-
-/// \returns a block consisting of the left columns of \c *this.
-///
-/// \tparam N the number of columns in the block as specified at compile-time
-/// \param n the number of columns in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_leftCols.cpp
-/// Output: \verbinclude MatrixBase_template_int_leftCols.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NColsBlockXpr<N>::Type leftCols(Index n = N)
-{
-  return typename NColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
-}
-
-/// This is the const version of leftCols<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNColsBlockXpr<N>::Type leftCols(Index n = N) const
-{
-  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
-}
-
-
-
-/// \returns a block consisting of the right columns of \c *this.
-///
-/// \param n the number of columns in the block
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include MatrixBase_rightCols_int.cpp
-/// Output: \verbinclude MatrixBase_rightCols_int.out
-///
-/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename NColsBlockXpr<...>::Type
-#endif
-rightCols(NColsType n)
-{
-  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
-}
-
-/// This is the const version of rightCols(NColsType).
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstNColsBlockXpr<...>::Type
-#endif
-rightCols(NColsType n) const
-{
-  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
-}
-
-/// \returns a block consisting of the right columns of \c *this.
-///
-/// \tparam N the number of columns in the block as specified at compile-time
-/// \param n the number of columns in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_rightCols.cpp
-/// Output: \verbinclude MatrixBase_template_int_rightCols.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NColsBlockXpr<N>::Type rightCols(Index n = N)
-{
-  return typename NColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
-}
-
-/// This is the const version of rightCols<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNColsBlockXpr<N>::Type rightCols(Index n = N) const
-{
-  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
-}
-
-
-
-/// \returns a block consisting of a range of columns of \c *this.
-///
-/// \param startCol the index of the first column in the block
-/// \param numCols the number of columns in the block
-/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
-///
-/// Example: \include DenseBase_middleCols_int.cpp
-/// Output: \verbinclude DenseBase_middleCols_int.out
-///
-/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-typename NColsBlockXpr<...>::Type
-#endif
-middleCols(Index startCol, NColsType numCols)
-{
-  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
-}
-
-/// This is the const version of middleCols(Index,NColsType).
-template<typename NColsType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-#else
-const typename ConstNColsBlockXpr<...>::Type
-#endif
-middleCols(Index startCol, NColsType numCols) const
-{
-  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
-            (derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
-}
-
-/// \returns a block consisting of a range of columns of \c *this.
-///
-/// \tparam N the number of columns in the block as specified at compile-time
-/// \param startCol the index of the first column in the block
-/// \param n the number of columns in the block as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include DenseBase_template_int_middleCols.cpp
-/// Output: \verbinclude DenseBase_template_int_middleCols.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename NColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N)
-{
-  return typename NColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
-}
-
-/// This is the const version of middleCols<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstNColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N) const
-{
-  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
-}
-
-
-
-/// \returns a fixed-size expression of a block of \c *this.
-///
-/// The template parameters \a NRows and \a NCols are the number of
-/// rows and columns in the block.
-///
-/// \param startRow the first row in the block
-/// \param startCol the first column in the block
-///
-/// Example: \include MatrixBase_block_int_int.cpp
-/// Output: \verbinclude MatrixBase_block_int_int.out
-///
-/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
-/// block(Index,Index,NRowsType,NColsType), here is the one-to-one equivalence:
-/// \code
-/// mat.template block<NRows,NCols>(i,j)  <-->  mat.block(i,j,fix<NRows>,fix<NCols>)
-/// \endcode
-///
-/// \note since block is a templated member, the keyword template has to be used
-/// if the matrix type is also a template parameter: \code m.template block<3,3>(1,1); \endcode
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol)
-{
-  return typename FixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol);
-}
-
-/// This is the const version of block<>(Index, Index). */
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol) const
-{
-  return typename ConstFixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol);
-}
-
-/// \returns an expression of a block of \c *this.
-///
-/// \tparam NRows number of rows in block as specified at compile-time
-/// \tparam NCols number of columns in block as specified at compile-time
-/// \param  startRow  the first row in the block
-/// \param  startCol  the first column in the block
-/// \param  blockRows number of rows in block as specified at run-time
-/// \param  blockCols number of columns in block as specified at run-time
-///
-/// This function is mainly useful for blocks where the number of rows is specified at compile-time
-/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
-/// information should not contradict. In other words, \a blockRows should equal \a NRows unless
-/// \a NRows is \a Dynamic, and the same for the number of columns.
-///
-/// Example: \include MatrixBase_template_int_int_block_int_int_int_int.cpp
-/// Output: \verbinclude MatrixBase_template_int_int_block_int_int_int_int.out
-///
-/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
-/// block(Index,Index,NRowsType,NColsType), here is the one-to-one complete equivalence:
-/// \code
-/// mat.template block<NRows,NCols>(i,j,rows,cols)     <-->  mat.block(i,j,fix<NRows>(rows),fix<NCols>(cols))
-/// \endcode
-/// If we known that, e.g., NRows==Dynamic and NCols!=Dynamic, then the equivalence becomes:
-/// \code
-/// mat.template block<Dynamic,NCols>(i,j,rows,NCols)  <-->  mat.block(i,j,rows,fix<NCols>)
-/// \endcode
-///
-EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
-///
-/// \sa block(Index,Index,NRowsType,NColsType), class Block
-///
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol,
-                                                  Index blockRows, Index blockCols)
-{
-  return typename FixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
-}
-
-/// This is the const version of block<>(Index, Index, Index, Index).
-template<int NRows, int NCols>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol,
-                                                              Index blockRows, Index blockCols) const
-{
-  return typename ConstFixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
-}
-
-/// \returns an expression of the \a i-th column of \c *this. Note that the numbering starts at 0.
-///
-/// Example: \include MatrixBase_col.cpp
-/// Output: \verbinclude MatrixBase_col.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
-/**
-  * \sa row(), class Block */
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-ColXpr col(Index i)
-{
-  return ColXpr(derived(), i);
-}
-
-/// This is the const version of col().
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-ConstColXpr col(Index i) const
-{
-  return ConstColXpr(derived(), i);
-}
-
-/// \returns an expression of the \a i-th row of \c *this. Note that the numbering starts at 0.
-///
-/// Example: \include MatrixBase_row.cpp
-/// Output: \verbinclude MatrixBase_row.out
-///
-EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
-/**
-  * \sa col(), class Block */
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-RowXpr row(Index i)
-{
-  return RowXpr(derived(), i);
-}
-
-/// This is the const version of row(). */
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-ConstRowXpr row(Index i) const
-{
-  return ConstRowXpr(derived(), i);
-}
-
-/// \returns an expression of a segment (i.e. a vector block) in \c *this with either dynamic or fixed sizes.
-///
-/// \only_for_vectors
-///
-/// \param start the first coefficient in the segment
-/// \param n the number of coefficients in the segment
-/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
-///
-/// Example: \include MatrixBase_segment_int_int.cpp
-/// Output: \verbinclude MatrixBase_segment_int_int.out
-///
-/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-/// \sa block(Index,Index,NRowsType,NColsType), fix<N>, fix<N>(int), class Block
-///
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-typename FixedSegmentReturnType<...>::Type
-#endif
-segment(Index start, NType n)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), start, internal::get_runtime_value(n));
-}
-
-
-/// This is the const version of segment(Index,NType).
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-const typename ConstFixedSegmentReturnType<...>::Type
-#endif
-segment(Index start, NType n) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), start, internal::get_runtime_value(n));
-}
-
-/// \returns an expression of the first coefficients of \c *this with either dynamic or fixed sizes.
-///
-/// \only_for_vectors
-///
-/// \param n the number of coefficients in the segment
-/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
-///
-/// Example: \include MatrixBase_start_int.cpp
-/// Output: \verbinclude MatrixBase_start_int.out
-///
-/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-/// \sa class Block, block(Index,Index)
-///
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-typename FixedSegmentReturnType<...>::Type
-#endif
-head(NType n)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-              (derived(), 0, internal::get_runtime_value(n));
-}
-
-/// This is the const version of head(NType).
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-const typename ConstFixedSegmentReturnType<...>::Type
-#endif
-head(NType n) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), 0, internal::get_runtime_value(n));
-}
-
-/// \returns an expression of a last coefficients of \c *this with either dynamic or fixed sizes.
-///
-/// \only_for_vectors
-///
-/// \param n the number of coefficients in the segment
-/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
-///
-/// Example: \include MatrixBase_end_int.cpp
-/// Output: \verbinclude MatrixBase_end_int.out
-///
-/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments.
-/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
-///
-/// \note Even in the case that the returned expression has dynamic size, in the case
-/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
-/// which means that evaluating it does not cause a dynamic memory allocation.
-///
-/// \sa class Block, block(Index,Index)
-///
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-typename FixedSegmentReturnType<...>::Type
-#endif
-tail(NType n)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
-}
-
-/// This is the const version of tail(Index).
-template<typename NType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-#else
-const typename ConstFixedSegmentReturnType<...>::Type
-#endif
-tail(NType n) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
-            (derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
-}
-
-/// \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this
-///
-/// \only_for_vectors
-///
-/// \tparam N the number of coefficients in the segment as specified at compile-time
-/// \param start the index of the first element in the segment
-/// \param n the number of coefficients in the segment as specified at compile-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_segment.cpp
-/// Output: \verbinclude MatrixBase_template_int_segment.out
-///
-/// \sa segment(Index,NType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedSegmentReturnType<N>::Type segment(Index start, Index n = N)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<N>::Type(derived(), start, n);
-}
-
-/// This is the const version of segment<int>(Index).
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstFixedSegmentReturnType<N>::Type segment(Index start, Index n = N) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<N>::Type(derived(), start, n);
-}
-
-/// \returns a fixed-size expression of the first coefficients of \c *this.
-///
-/// \only_for_vectors
-///
-/// \tparam N the number of coefficients in the segment as specified at compile-time
-/// \param  n the number of coefficients in the segment as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_start.cpp
-/// Output: \verbinclude MatrixBase_template_int_start.out
-///
-/// \sa head(NType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedSegmentReturnType<N>::Type head(Index n = N)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<N>::Type(derived(), 0, n);
-}
-
-/// This is the const version of head<int>().
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstFixedSegmentReturnType<N>::Type head(Index n = N) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<N>::Type(derived(), 0, n);
-}
-
-/// \returns a fixed-size expression of the last coefficients of \c *this.
-///
-/// \only_for_vectors
-///
-/// \tparam N the number of coefficients in the segment as specified at compile-time
-/// \param  n the number of coefficients in the segment as specified at run-time
-///
-/// The compile-time and run-time information should not contradict. In other words,
-/// \a n should equal \a N unless \a N is \a Dynamic.
-///
-/// Example: \include MatrixBase_template_int_end.cpp
-/// Output: \verbinclude MatrixBase_template_int_end.out
-///
-/// \sa tail(NType), class Block
-///
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename FixedSegmentReturnType<N>::Type tail(Index n = N)
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename FixedSegmentReturnType<N>::Type(derived(), size() - n);
-}
-
-/// This is the const version of tail<int>.
-template<int N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename ConstFixedSegmentReturnType<N>::Type tail(Index n = N) const
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return typename ConstFixedSegmentReturnType<N>::Type(derived(), size() - n);
-}
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major).
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-InnerVectorReturnType innerVector(Index outer)
-{ return InnerVectorReturnType(derived(), outer); }
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major). Read-only.
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const ConstInnerVectorReturnType innerVector(Index outer) const
-{ return ConstInnerVectorReturnType(derived(), outer); }
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major).
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-InnerVectorsReturnType
-innerVectors(Index outerStart, Index outerSize)
-{
-  return Block<Derived,Dynamic,Dynamic,true>(derived(),
-                                             IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart,
-                                             IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize);
-
-}
-
-/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
-/// is col-major (resp. row-major). Read-only.
-///
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const ConstInnerVectorsReturnType
-innerVectors(Index outerStart, Index outerSize) const
-{
-  return Block<const Derived,Dynamic,Dynamic,true>(derived(),
-                                                  IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart,
-                                                  IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize);
-
-}
-
-/** \returns the i-th subvector (column or vector) according to the \c Direction
-  * \sa subVectors()
-  */
-template<DirectionType Direction>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename internal::conditional<Direction==Vertical,ColXpr,RowXpr>::type
-subVector(Index i)
-{
-  return typename internal::conditional<Direction==Vertical,ColXpr,RowXpr>::type(derived(),i);
-}
-
-/** This is the const version of subVector(Index) */
-template<DirectionType Direction>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename internal::conditional<Direction==Vertical,ConstColXpr,ConstRowXpr>::type
-subVector(Index i) const
-{
-  return typename internal::conditional<Direction==Vertical,ConstColXpr,ConstRowXpr>::type(derived(),i);
-}
-
-/** \returns the number of subvectors (rows or columns) in the direction \c Direction
-  * \sa subVector(Index)
-  */
-template<DirectionType Direction>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
-Index subVectors() const
-{ return (Direction==Vertical)?cols():rows(); }
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h
deleted file mode 100644
index 8b6730e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h
+++ /dev/null
@@ -1,115 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is a base class plugin containing common coefficient wise functions.
-
-/** \returns an expression of the difference of \c *this and \a other
-  *
-  * \note If you want to substract a given scalar from all coefficients, see Cwise::operator-().
-  *
-  * \sa class CwiseBinaryOp, operator-=()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(operator-,difference)
-
-/** \returns an expression of the sum of \c *this and \a other
-  *
-  * \note If you want to add a given scalar to all coefficients, see Cwise::operator+().
-  *
-  * \sa class CwiseBinaryOp, operator+=()
-  */
-EIGEN_MAKE_CWISE_BINARY_OP(operator+,sum)
-
-/** \returns an expression of a custom coefficient-wise operator \a func of *this and \a other
-  *
-  * The template parameter \a CustomBinaryOp is the type of the functor
-  * of the custom operator (see class CwiseBinaryOp for an example)
-  *
-  * Here is an example illustrating the use of custom functors:
-  * \include class_CwiseBinaryOp.cpp
-  * Output: \verbinclude class_CwiseBinaryOp.out
-  *
-  * \sa class CwiseBinaryOp, operator+(), operator-(), cwiseProduct()
-  */
-template<typename CustomBinaryOp, typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
-binaryExpr(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other, const CustomBinaryOp& func = CustomBinaryOp()) const
-{
-  return CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other.derived(), func);
-}
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP(operator*,product)
-#else
-/** \returns an expression of \c *this scaled by the scalar factor \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_product_op<Scalar,T>,Derived,Constant<T> > operator*(const T& scalar) const;
-/** \returns an expression of \a expr scaled by the scalar factor \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T> friend
-const CwiseBinaryOp<internal::scalar_product_op<T,Scalar>,Constant<T>,Derived> operator*(const T& scalar, const StorageBaseType& expr);
-#endif
-
-
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(operator/,quotient)
-#else
-/** \returns an expression of \c *this divided by the scalar value \a scalar
-  *
-  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
-  */
-template<typename T>
-const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,T>,Derived,Constant<T> > operator/(const T& scalar) const;
-#endif
-
-/** \returns an expression of the coefficient-wise boolean \b and operator of \c *this and \a other
-  *
-  * \warning this operator is for expression of bool only.
-  *
-  * Example: \include Cwise_boolean_and.cpp
-  * Output: \verbinclude Cwise_boolean_and.out
-  *
-  * \sa operator||(), select()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>
-operator&&(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return CwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>(derived(),other.derived());
-}
-
-/** \returns an expression of the coefficient-wise boolean \b or operator of \c *this and \a other
-  *
-  * \warning this operator is for expression of bool only.
-  *
-  * Example: \include Cwise_boolean_or.cpp
-  * Output: \verbinclude Cwise_boolean_or.out
-  *
-  * \sa operator&&(), select()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>
-operator||(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value),
-                      THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL);
-  return CwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>(derived(),other.derived());
-}
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h
deleted file mode 100644
index 5418dc4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h
+++ /dev/null
@@ -1,177 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is a base class plugin containing common coefficient wise functions.
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-/** \internal the return type of conjugate() */
-typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                    const CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>,
-                    const Derived&
-                  >::type ConjugateReturnType;
-/** \internal the return type of real() const */
-typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                    const CwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>,
-                    const Derived&
-                  >::type RealReturnType;
-/** \internal the return type of real() */
-typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                    CwiseUnaryView<internal::scalar_real_ref_op<Scalar>, Derived>,
-                    Derived&
-                  >::type NonConstRealReturnType;
-/** \internal the return type of imag() const */
-typedef CwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived> ImagReturnType;
-/** \internal the return type of imag() */
-typedef CwiseUnaryView<internal::scalar_imag_ref_op<Scalar>, Derived> NonConstImagReturnType;
-
-typedef CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived> NegativeReturnType;
-
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-/// \returns an expression of the opposite of \c *this
-///
-EIGEN_DOC_UNARY_ADDONS(operator-,opposite)
-///
-EIGEN_DEVICE_FUNC
-inline const NegativeReturnType
-operator-() const { return NegativeReturnType(derived()); }
-
-
-template<class NewType> struct CastXpr { typedef typename internal::cast_return_type<Derived,const CwiseUnaryOp<internal::scalar_cast_op<Scalar, NewType>, const Derived> >::type Type; };
-
-/// \returns an expression of \c *this with the \a Scalar type casted to
-/// \a NewScalar.
-///
-/// The template parameter \a NewScalar is the type we are casting the scalars to.
-///
-EIGEN_DOC_UNARY_ADDONS(cast,conversion function)
-///
-/// \sa class CwiseUnaryOp
-///
-template<typename NewType>
-EIGEN_DEVICE_FUNC
-typename CastXpr<NewType>::Type
-cast() const
-{
-  return typename CastXpr<NewType>::Type(derived());
-}
-
-/// \returns an expression of the complex conjugate of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(conjugate,complex conjugate)
-///
-/// \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_conj">Math functions</a>, MatrixBase::adjoint()
-EIGEN_DEVICE_FUNC
-inline ConjugateReturnType
-conjugate() const
-{
-  return ConjugateReturnType(derived());
-}
-
-/// \returns an expression of the complex conjugate of \c *this if Cond==true, returns derived() otherwise.
-///
-EIGEN_DOC_UNARY_ADDONS(conjugate,complex conjugate)
-///
-/// \sa conjugate()
-template<bool Cond>
-EIGEN_DEVICE_FUNC
-inline typename internal::conditional<Cond,ConjugateReturnType,const Derived&>::type
-conjugateIf() const
-{
-  typedef typename internal::conditional<Cond,ConjugateReturnType,const Derived&>::type ReturnType;
-  return ReturnType(derived());
-}
-
-/// \returns a read-only expression of the real part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(real,real part function)
-///
-/// \sa imag()
-EIGEN_DEVICE_FUNC
-inline RealReturnType
-real() const { return RealReturnType(derived()); }
-
-/// \returns an read-only expression of the imaginary part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(imag,imaginary part function)
-///
-/// \sa real()
-EIGEN_DEVICE_FUNC
-inline const ImagReturnType
-imag() const { return ImagReturnType(derived()); }
-
-/// \brief Apply a unary operator coefficient-wise
-/// \param[in]  func  Functor implementing the unary operator
-/// \tparam  CustomUnaryOp Type of \a func
-/// \returns An expression of a custom coefficient-wise unary operator \a func of *this
-///
-/// The function \c ptr_fun() from the C++ standard library can be used to make functors out of normal functions.
-///
-/// Example:
-/// \include class_CwiseUnaryOp_ptrfun.cpp
-/// Output: \verbinclude class_CwiseUnaryOp_ptrfun.out
-///
-/// Genuine functors allow for more possibilities, for instance it may contain a state.
-///
-/// Example:
-/// \include class_CwiseUnaryOp.cpp
-/// Output: \verbinclude class_CwiseUnaryOp.out
-///
-EIGEN_DOC_UNARY_ADDONS(unaryExpr,unary function)
-///
-/// \sa unaryViewExpr, binaryExpr, class CwiseUnaryOp
-///
-template<typename CustomUnaryOp>
-EIGEN_DEVICE_FUNC
-inline const CwiseUnaryOp<CustomUnaryOp, const Derived>
-unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const
-{
-  return CwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
-}
-
-/// \returns an expression of a custom coefficient-wise unary operator \a func of *this
-///
-/// The template parameter \a CustomUnaryOp is the type of the functor
-/// of the custom unary operator.
-///
-/// Example:
-/// \include class_CwiseUnaryOp.cpp
-/// Output: \verbinclude class_CwiseUnaryOp.out
-///
-EIGEN_DOC_UNARY_ADDONS(unaryViewExpr,unary function)
-///
-/// \sa unaryExpr, binaryExpr class CwiseUnaryOp
-///
-template<typename CustomViewOp>
-EIGEN_DEVICE_FUNC
-inline const CwiseUnaryView<CustomViewOp, const Derived>
-unaryViewExpr(const CustomViewOp& func = CustomViewOp()) const
-{
-  return CwiseUnaryView<CustomViewOp, const Derived>(derived(), func);
-}
-
-/// \returns a non const expression of the real part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(real,real part function)
-///
-/// \sa imag()
-EIGEN_DEVICE_FUNC
-inline NonConstRealReturnType
-real() { return NonConstRealReturnType(derived()); }
-
-/// \returns a non const expression of the imaginary part of \c *this.
-///
-EIGEN_DOC_UNARY_ADDONS(imag,imaginary part function)
-///
-/// \sa real()
-EIGEN_DEVICE_FUNC
-inline NonConstImagReturnType
-imag() { return NonConstImagReturnType(derived()); }
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
deleted file mode 100644
index 5bfb19a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
+++ /dev/null
@@ -1,262 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#if !defined(EIGEN_PARSED_BY_DOXYGEN)
-
-// This file is automatically included twice to generate const and non-const versions
-
-#ifndef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#define EIGEN_INDEXED_VIEW_METHOD_CONST const
-#define EIGEN_INDEXED_VIEW_METHOD_TYPE  ConstIndexedViewType
-#else
-#define EIGEN_INDEXED_VIEW_METHOD_CONST
-#define EIGEN_INDEXED_VIEW_METHOD_TYPE IndexedViewType
-#endif
-
-#ifndef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-protected:
-
-// define some aliases to ease readability
-
-template<typename Indices>
-struct IvcRowType : public internal::IndexedViewCompatibleType<Indices,RowsAtCompileTime> {};
-
-template<typename Indices>
-struct IvcColType : public internal::IndexedViewCompatibleType<Indices,ColsAtCompileTime> {};
-
-template<typename Indices>
-struct IvcType : public internal::IndexedViewCompatibleType<Indices,SizeAtCompileTime> {};
-
-typedef typename internal::IndexedViewCompatibleType<Index,1>::type IvcIndex;
-
-template<typename Indices>
-typename IvcRowType<Indices>::type
-ivcRow(const Indices& indices) const {
-  return internal::makeIndexedViewCompatible(indices, internal::variable_if_dynamic<Index,RowsAtCompileTime>(derived().rows()),Specialized);
-}
-
-template<typename Indices>
-typename IvcColType<Indices>::type
-ivcCol(const Indices& indices) const {
-  return internal::makeIndexedViewCompatible(indices, internal::variable_if_dynamic<Index,ColsAtCompileTime>(derived().cols()),Specialized);
-}
-
-template<typename Indices>
-typename IvcColType<Indices>::type
-ivcSize(const Indices& indices) const {
-  return internal::makeIndexedViewCompatible(indices, internal::variable_if_dynamic<Index,SizeAtCompileTime>(derived().size()),Specialized);
-}
-
-public:
-
-#endif
-
-template<typename RowIndices, typename ColIndices>
-struct EIGEN_INDEXED_VIEW_METHOD_TYPE {
-  typedef IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,
-                      typename IvcRowType<RowIndices>::type,
-                      typename IvcColType<ColIndices>::type> type;
-};
-
-// This is the generic version
-
-template<typename RowIndices, typename ColIndices>
-typename internal::enable_if<internal::valid_indexed_view_overload<RowIndices,ColIndices>::value
-  && internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::ReturnAsIndexedView,
-  typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type >::type
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type
-            (derived(), ivcRow(rowIndices), ivcCol(colIndices));
-}
-
-// The following overload returns a Block<> object
-
-template<typename RowIndices, typename ColIndices>
-typename internal::enable_if<internal::valid_indexed_view_overload<RowIndices,ColIndices>::value
-  && internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::ReturnAsBlock,
-  typename internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::BlockType>::type
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  typedef typename internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::BlockType BlockType;
-  typename IvcRowType<RowIndices>::type actualRowIndices = ivcRow(rowIndices);
-  typename IvcColType<ColIndices>::type actualColIndices = ivcCol(colIndices);
-  return BlockType(derived(),
-                   internal::first(actualRowIndices),
-                   internal::first(actualColIndices),
-                   internal::size(actualRowIndices),
-                   internal::size(actualColIndices));
-}
-
-// The following overload returns a Scalar
-
-template<typename RowIndices, typename ColIndices>
-typename internal::enable_if<internal::valid_indexed_view_overload<RowIndices,ColIndices>::value
-  && internal::traits<typename EIGEN_INDEXED_VIEW_METHOD_TYPE<RowIndices,ColIndices>::type>::ReturnAsScalar,
-  CoeffReturnType >::type
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return Base::operator()(internal::eval_expr_given_size(rowIndices,rows()),internal::eval_expr_given_size(colIndices,cols()));
-}
-
-#if EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-// The following three overloads are needed to handle raw Index[N] arrays.
-
-template<typename RowIndicesT, std::size_t RowIndicesN, typename ColIndices>
-IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN],typename IvcColType<ColIndices>::type>
-operator()(const RowIndicesT (&rowIndices)[RowIndicesN], const ColIndices& colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN],typename IvcColType<ColIndices>::type>
-                    (derived(), rowIndices, ivcCol(colIndices));
-}
-
-template<typename RowIndices, typename ColIndicesT, std::size_t ColIndicesN>
-IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcRowType<RowIndices>::type, const ColIndicesT (&)[ColIndicesN]>
-operator()(const RowIndices& rowIndices, const ColIndicesT (&colIndices)[ColIndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcRowType<RowIndices>::type,const ColIndicesT (&)[ColIndicesN]>
-                    (derived(), ivcRow(rowIndices), colIndices);
-}
-
-template<typename RowIndicesT, std::size_t RowIndicesN, typename ColIndicesT, std::size_t ColIndicesN>
-IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN], const ColIndicesT (&)[ColIndicesN]>
-operator()(const RowIndicesT (&rowIndices)[RowIndicesN], const ColIndicesT (&colIndices)[ColIndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const RowIndicesT (&)[RowIndicesN],const ColIndicesT (&)[ColIndicesN]>
-                    (derived(), rowIndices, colIndices);
-}
-
-#endif // EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-// Overloads for 1D vectors/arrays
-
-template<typename Indices>
-typename internal::enable_if<
-  IsRowMajor && (!(internal::get_compile_time_incr<typename IvcType<Indices>::type>::value==1 || internal::is_valid_index_type<Indices>::value)),
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,typename IvcType<Indices>::type> >::type
-operator()(const Indices& indices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,typename IvcType<Indices>::type>
-            (derived(), IvcIndex(0), ivcCol(indices));
-}
-
-template<typename Indices>
-typename internal::enable_if<
-  (!IsRowMajor) && (!(internal::get_compile_time_incr<typename IvcType<Indices>::type>::value==1 || internal::is_valid_index_type<Indices>::value)),
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcType<Indices>::type,IvcIndex> >::type
-operator()(const Indices& indices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,typename IvcType<Indices>::type,IvcIndex>
-            (derived(), ivcRow(indices), IvcIndex(0));
-}
-
-template<typename Indices>
-typename internal::enable_if<
-  (internal::get_compile_time_incr<typename IvcType<Indices>::type>::value==1) && (!internal::is_valid_index_type<Indices>::value) && (!symbolic::is_symbolic<Indices>::value),
-  VectorBlock<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,internal::array_size<Indices>::value> >::type
-operator()(const Indices& indices) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  typename IvcType<Indices>::type actualIndices = ivcSize(indices);
-  return VectorBlock<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,internal::array_size<Indices>::value>
-            (derived(), internal::first(actualIndices), internal::size(actualIndices));
-}
-
-template<typename IndexType>
-typename internal::enable_if<symbolic::is_symbolic<IndexType>::value, CoeffReturnType >::type
-operator()(const IndexType& id) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  return Base::operator()(internal::eval_expr_given_size(id,size()));
-}
-
-#if EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-template<typename IndicesT, std::size_t IndicesN>
-typename internal::enable_if<IsRowMajor,
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,const IndicesT (&)[IndicesN]> >::type
-operator()(const IndicesT (&indices)[IndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,IvcIndex,const IndicesT (&)[IndicesN]>
-            (derived(), IvcIndex(0), indices);
-}
-
-template<typename IndicesT, std::size_t IndicesN>
-typename internal::enable_if<!IsRowMajor,
-  IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const IndicesT (&)[IndicesN],IvcIndex> >::type
-operator()(const IndicesT (&indices)[IndicesN]) EIGEN_INDEXED_VIEW_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
-  return IndexedView<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,const IndicesT (&)[IndicesN],IvcIndex>
-            (derived(), indices, IvcIndex(0));
-}
-
-#endif // EIGEN_HAS_STATIC_ARRAY_TEMPLATE
-
-#undef EIGEN_INDEXED_VIEW_METHOD_CONST
-#undef EIGEN_INDEXED_VIEW_METHOD_TYPE
-
-#ifndef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#define EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#include "IndexedViewMethods.h"
-#undef EIGEN_INDEXED_VIEW_METHOD_2ND_PASS
-#endif
-
-#else // EIGEN_PARSED_BY_DOXYGEN
-
-/**
-  * \returns a generic submatrix view defined by the rows and columns indexed \a rowIndices and \a colIndices respectively.
-  *
-  * Each parameter must either be:
-  *  - An integer indexing a single row or column
-  *  - Eigen::all indexing the full set of respective rows or columns in increasing order
-  *  - An ArithmeticSequence as returned by the Eigen::seq and Eigen::seqN functions
-  *  - Any %Eigen's vector/array of integers or expressions
-  *  - Plain C arrays: \c int[N]
-  *  - And more generally any type exposing the following two member functions:
-  * \code
-  * <integral type> operator[](<integral type>) const;
-  * <integral type> size() const;
-  * \endcode
-  * where \c <integral \c type>  stands for any integer type compatible with Eigen::Index (i.e. \c std::ptrdiff_t).
-  *
-  * The last statement implies compatibility with \c std::vector, \c std::valarray, \c std::array, many of the Range-v3's ranges, etc.
-  *
-  * If the submatrix can be represented using a starting position \c (i,j) and positive sizes \c (rows,columns), then this
-  * method will returns a Block object after extraction of the relevant information from the passed arguments. This is the case
-  * when all arguments are either:
-  *  - An integer
-  *  - Eigen::all
-  *  - An ArithmeticSequence with compile-time increment strictly equal to 1, as returned by Eigen::seq(a,b), and Eigen::seqN(a,N).
-  *
-  * Otherwise a more general IndexedView<Derived,RowIndices',ColIndices'> object will be returned, after conversion of the inputs
-  * to more suitable types \c RowIndices' and \c ColIndices'.
-  *
-  * For 1D vectors and arrays, you better use the operator()(const Indices&) overload, which behave the same way but taking a single parameter.
-  *
-  * See also this <a href="https://stackoverflow.com/questions/46110917/eigen-replicate-items-along-one-dimension-without-useless-allocations">question</a> and its answer for an example of how to duplicate coefficients.
-  *
-  * \sa operator()(const Indices&), class Block, class IndexedView, DenseBase::block(Index,Index,Index,Index)
-  */
-template<typename RowIndices, typename ColIndices>
-IndexedView_or_Block
-operator()(const RowIndices& rowIndices, const ColIndices& colIndices);
-
-/** This is an overload of operator()(const RowIndices&, const ColIndices&) for 1D vectors or arrays
-  *
-  * \only_for_vectors
-  */
-template<typename Indices>
-IndexedView_or_VectorBlock
-operator()(const Indices& indices);
-
-#endif  // EIGEN_PARSED_BY_DOXYGEN
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
deleted file mode 100644
index a0feef8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
+++ /dev/null
@@ -1,152 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is a base class plugin containing matrix specifics coefficient wise functions.
-
-/** \returns an expression of the Schur product (coefficient wise product) of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseProduct.cpp
-  * Output: \verbinclude MatrixBase_cwiseProduct.out
-  *
-  * \sa class CwiseBinaryOp, cwiseAbs2
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
-cwiseProduct(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise == operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include MatrixBase_cwiseEqual.cpp
-  * Output: \verbinclude MatrixBase_cwiseEqual.out
-  *
-  * \sa cwiseNotEqual(), isApprox(), isMuchSmallerThan()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<numext::equal_to<Scalar>, const Derived, const OtherDerived>
-cwiseEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<numext::equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise != operator of *this and \a other
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * Example: \include MatrixBase_cwiseNotEqual.cpp
-  * Output: \verbinclude MatrixBase_cwiseNotEqual.out
-  *
-  * \sa cwiseEqual(), isApprox(), isMuchSmallerThan()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-inline const CwiseBinaryOp<numext::not_equal_to<Scalar>, const Derived, const OtherDerived>
-cwiseNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<numext::not_equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise min of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseMin.cpp
-  * Output: \verbinclude MatrixBase_cwiseMin.out
-  *
-  * \sa class CwiseBinaryOp, max()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>
-cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise min of *this and scalar \a other
-  *
-  * \sa class CwiseBinaryOp, min()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
-cwiseMin(const Scalar &other) const
-{
-  return cwiseMin(Derived::Constant(rows(), cols(), other));
-}
-
-/** \returns an expression of the coefficient-wise max of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseMax.cpp
-  * Output: \verbinclude MatrixBase_cwiseMax.out
-  *
-  * \sa class CwiseBinaryOp, min()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>
-cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-/** \returns an expression of the coefficient-wise max of *this and scalar \a other
-  *
-  * \sa class CwiseBinaryOp, min()
-  */
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
-cwiseMax(const Scalar &other) const
-{
-  return cwiseMax(Derived::Constant(rows(), cols(), other));
-}
-
-
-/** \returns an expression of the coefficient-wise quotient of *this and \a other
-  *
-  * Example: \include MatrixBase_cwiseQuotient.cpp
-  * Output: \verbinclude MatrixBase_cwiseQuotient.out
-  *
-  * \sa class CwiseBinaryOp, cwiseProduct(), cwiseInverse()
-  */
-template<typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
-cwiseQuotient(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar,internal::cmp_EQ>, const Derived, const ConstantReturnType> CwiseScalarEqualReturnType;
-
-/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s
-  *
-  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
-  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
-  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
-  * isMuchSmallerThan().
-  *
-  * \sa cwiseEqual(const MatrixBase<OtherDerived> &) const
-  */
-EIGEN_DEVICE_FUNC
-inline const CwiseScalarEqualReturnType
-cwiseEqual(const Scalar& s) const
-{
-  return CwiseScalarEqualReturnType(derived(), Derived::Constant(rows(), cols(), s), internal::scalar_cmp_op<Scalar,Scalar,internal::cmp_EQ>());
-}
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h
deleted file mode 100644
index 0514d8f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h
+++ /dev/null
@@ -1,95 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// This file is included into the body of the base classes supporting matrix specific coefficient-wise functions.
-// This include MatrixBase and SparseMatrixBase.
-
-
-typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> CwiseAbsReturnType;
-typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> CwiseAbs2ReturnType;
-typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> CwiseArgReturnType;
-typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> CwiseSqrtReturnType;
-typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> CwiseSignReturnType;
-typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> CwiseInverseReturnType;
-
-/// \returns an expression of the coefficient-wise absolute value of \c *this
-///
-/// Example: \include MatrixBase_cwiseAbs.cpp
-/// Output: \verbinclude MatrixBase_cwiseAbs.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseAbs,absolute value)
-///
-/// \sa cwiseAbs2()
-///
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseAbsReturnType
-cwiseAbs() const { return CwiseAbsReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise squared absolute value of \c *this
-///
-/// Example: \include MatrixBase_cwiseAbs2.cpp
-/// Output: \verbinclude MatrixBase_cwiseAbs2.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseAbs2,squared absolute value)
-///
-/// \sa cwiseAbs()
-///
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseAbs2ReturnType
-cwiseAbs2() const { return CwiseAbs2ReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise square root of *this.
-///
-/// Example: \include MatrixBase_cwiseSqrt.cpp
-/// Output: \verbinclude MatrixBase_cwiseSqrt.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseSqrt,square-root)
-///
-/// \sa cwisePow(), cwiseSquare()
-///
-EIGEN_DEVICE_FUNC
-inline const CwiseSqrtReturnType
-cwiseSqrt() const { return CwiseSqrtReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise signum of *this.
-///
-/// Example: \include MatrixBase_cwiseSign.cpp
-/// Output: \verbinclude MatrixBase_cwiseSign.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseSign,sign function)
-///
-EIGEN_DEVICE_FUNC
-inline const CwiseSignReturnType
-cwiseSign() const { return CwiseSignReturnType(derived()); }
-
-
-/// \returns an expression of the coefficient-wise inverse of *this.
-///
-/// Example: \include MatrixBase_cwiseInverse.cpp
-/// Output: \verbinclude MatrixBase_cwiseInverse.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseInverse,inverse)
-///
-/// \sa cwiseProduct()
-///
-EIGEN_DEVICE_FUNC
-inline const CwiseInverseReturnType
-cwiseInverse() const { return CwiseInverseReturnType(derived()); }
-
-/// \returns an expression of the coefficient-wise phase angle of \c *this
-///
-/// Example: \include MatrixBase_cwiseArg.cpp
-/// Output: \verbinclude MatrixBase_cwiseArg.out
-///
-EIGEN_DOC_UNARY_ADDONS(cwiseArg,arg)
-
-EIGEN_DEVICE_FUNC
-inline const CwiseArgReturnType
-cwiseArg() const { return CwiseArgReturnType(derived()); }
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ReshapedMethods.h b/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ReshapedMethods.h
deleted file mode 100644
index 482a6b0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/Eigen/src/plugins/ReshapedMethods.h
+++ /dev/null
@@ -1,149 +0,0 @@
-
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-
-/// \returns an expression of \c *this with reshaped sizes.
-///
-/// \param nRows the number of rows in the reshaped expression, specified at either run-time or compile-time, or AutoSize
-/// \param nCols the number of columns in the reshaped expression, specified at either run-time or compile-time, or AutoSize
-/// \tparam Order specifies whether the coefficients should be processed in column-major-order (ColMajor), in row-major-order (RowMajor),
-///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
-/// \tparam NRowsType the type of the value handling the number of rows, typically Index.
-/// \tparam NColsType the type of the value handling the number of columns, typically Index.
-///
-/// Dynamic size example: \include MatrixBase_reshaped_int_int.cpp
-/// Output: \verbinclude MatrixBase_reshaped_int_int.out
-///
-/// The number of rows \a nRows and columns \a nCols can also be specified at compile-time by passing Eigen::fix<N>,
-/// or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value in case \c N equals Eigen::Dynamic.
-/// Here is an example with a fixed number of rows and columns:
-/// \include MatrixBase_reshaped_fixed.cpp
-/// Output: \verbinclude MatrixBase_reshaped_fixed.out
-///
-/// Finally, one of the sizes parameter can be automatically deduced from the other one by passing AutoSize as in the following example:
-/// \include MatrixBase_reshaped_auto.cpp
-/// Output: \verbinclude MatrixBase_reshaped_auto.out
-/// AutoSize does preserve compile-time sizes when possible, i.e., when the sizes of the input are known at compile time \b and
-/// that the other size is passed at compile-time using Eigen::fix<N> as above.
-///
-/// \sa class Reshaped, fix, fix<N>(int)
-///
-template<int Order = ColMajor, typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline Reshaped<Derived,...>
-reshaped(NRowsType nRows, NColsType nCols);
-
-/// This is the const version of reshaped(NRowsType,NColsType).
-template<int Order = ColMajor, typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline const Reshaped<const Derived,...>
-reshaped(NRowsType nRows, NColsType nCols) const;
-
-/// \returns an expression of \c *this with columns (or rows) stacked to a linear column vector
-///
-/// \tparam Order specifies whether the coefficients should be processed in column-major-order (ColMajor), in row-major-order (RowMajor),
-///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
-///
-/// This overloads is essentially a shortcut for `A.reshaped<Order>(AutoSize,fix<1>)`.
-///
-/// - If `Order==ColMajor` (the default), then it returns a column-vector from the stacked columns of \c *this.
-/// - If `Order==RowMajor`, then it returns a column-vector from the stacked rows of \c *this.
-/// - If `Order==AutoOrder`, then it returns a column-vector with elements stacked following the storage order of \c *this.
-///   This mode is the recommended one when the particular ordering of the element is not relevant.
-///
-/// Example:
-/// \include MatrixBase_reshaped_to_vector.cpp
-/// Output: \verbinclude MatrixBase_reshaped_to_vector.out
-///
-/// If you want more control, you can still fall back to reshaped(NRowsType,NColsType).
-///
-/// \sa reshaped(NRowsType,NColsType), class Reshaped
-///
-template<int Order = ColMajor>
-EIGEN_DEVICE_FUNC
-inline Reshaped<Derived,...>
-reshaped();
-
-/// This is the const version of reshaped().
-template<int Order = ColMajor>
-EIGEN_DEVICE_FUNC
-inline const Reshaped<const Derived,...>
-reshaped() const;
-
-#else
-
-// This file is automatically included twice to generate const and non-const versions
-
-#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
-#define EIGEN_RESHAPED_METHOD_CONST const
-#else
-#define EIGEN_RESHAPED_METHOD_CONST
-#endif
-
-#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
-
-// This part is included once
-
-#endif
-
-template<typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value>
-reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST
-{
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                  internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                  internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value>
-                (derived(),
-                 internal::get_runtime_reshape_size(nRows,internal::get_runtime_value(nCols),size()),
-                 internal::get_runtime_reshape_size(nCols,internal::get_runtime_value(nRows),size()));
-}
-
-template<int Order, typename NRowsType, typename NColsType>
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value,
-                internal::get_compiletime_reshape_order<Flags,Order>::value>
-reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST
-{
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
-                  internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
-                  internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value,
-                  internal::get_compiletime_reshape_order<Flags,Order>::value>
-                (derived(),
-                 internal::get_runtime_reshape_size(nRows,internal::get_runtime_value(nCols),size()),
-                 internal::get_runtime_reshape_size(nCols,internal::get_runtime_value(nRows),size()));
-}
-
-// Views as linear vectors
-
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,SizeAtCompileTime,1>
-reshaped() EIGEN_RESHAPED_METHOD_CONST
-{
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,SizeAtCompileTime,1>(derived(),size(),1);
-}
-
-template<int Order>
-EIGEN_DEVICE_FUNC
-inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
-                internal::get_compiletime_reshape_order<Flags,Order>::value>
-reshaped() EIGEN_RESHAPED_METHOD_CONST
-{
-  EIGEN_STATIC_ASSERT(Order==RowMajor || Order==ColMajor || Order==AutoOrder, INVALID_TEMPLATE_PARAMETER);
-  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
-                  internal::get_compiletime_reshape_order<Flags,Order>::value>
-                (derived(), size(), 1);
-}
-
-#undef EIGEN_RESHAPED_METHOD_CONST
-
-#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
-#define EIGEN_RESHAPED_METHOD_2ND_PASS
-#include "ReshapedMethods.h"
-#undef EIGEN_RESHAPED_METHOD_2ND_PASS
-#endif
-
-#endif // EIGEN_PARSED_BY_DOXYGEN
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/signature_of_eigen3_matrix_library b/3rdParty/my_ceres_vc17/include/eigen3/signature_of_eigen3_matrix_library
deleted file mode 100644
index 80aaf46..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/signature_of_eigen3_matrix_library
+++ /dev/null
@@ -1 +0,0 @@
-This file is just there as a signature to help identify directories containing Eigen3. When writing a script looking for Eigen3, just look for this file. This is especially useful to help disambiguate with Eigen2...
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AdolcForward b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AdolcForward
deleted file mode 100644
index 56caeae..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AdolcForward
+++ /dev/null
@@ -1,159 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ADLOC_FORWARD
-#define EIGEN_ADLOC_FORWARD
-
-//--------------------------------------------------------------------------------
-//
-// This file provides support for adolc's adouble type in forward mode.
-// ADOL-C is a C++ automatic differentiation library,
-// see https://projects.coin-or.org/ADOL-C for more information.
-//
-// Note that the maximal number of directions is controlled by
-// the preprocessor token NUMBER_DIRECTIONS. The default is 2.
-//
-//--------------------------------------------------------------------------------
-
-#define ADOLC_TAPELESS
-#ifndef NUMBER_DIRECTIONS
-# define NUMBER_DIRECTIONS 2
-#endif
-#include <adolc/adtl.h>
-
-// adolc defines some very stupid macros:
-#if defined(malloc)
-# undef malloc
-#endif
-
-#if defined(calloc)
-# undef calloc
-#endif
-
-#if defined(realloc)
-# undef realloc
-#endif
-
-#include "../../Eigen/Core"
-
-namespace Eigen {
-
-/**
-  * \defgroup AdolcForward_Module Adolc forward module
-  * This module provides support for adolc's adouble type in forward mode.
-  * ADOL-C is a C++ automatic differentiation library,
-  * see https://projects.coin-or.org/ADOL-C for more information.
-  * It mainly consists in:
-  *  - a struct Eigen::NumTraits<adtl::adouble> specialization
-  *  - overloads of internal::* math function for adtl::adouble type.
-  *
-  * Note that the maximal number of directions is controlled by
-  * the preprocessor token NUMBER_DIRECTIONS. The default is 2.
-  *
-  * \code
-  * #include <unsupported/Eigen/AdolcSupport>
-  * \endcode
-  */
-  //@{
-
-} // namespace Eigen
-
-// Eigen's require a few additional functions which must be defined in the same namespace
-// than the custom scalar type own namespace
-namespace adtl {
-
-inline const adouble& conj(const adouble& x)  { return x; }
-inline const adouble& real(const adouble& x)  { return x; }
-inline adouble imag(const adouble&)    { return 0.; }
-inline adouble abs(const adouble&  x)  { return fabs(x); }
-inline adouble abs2(const adouble& x)  { return x*x; }
-
-inline bool (isinf)(const adouble& x) { return (Eigen::numext::isinf)(x.getValue()); }
-inline bool (isnan)(const adouble& x) { return (Eigen::numext::isnan)(x.getValue()); }
-
-}
-
-namespace Eigen {
-
-template<> struct NumTraits<adtl::adouble>
-    : NumTraits<double>
-{
-  typedef adtl::adouble Real;
-  typedef adtl::adouble NonInteger;
-  typedef adtl::adouble Nested;
-  enum {
-    IsComplex = 0,
-    IsInteger = 0,
-    IsSigned = 1,
-    RequireInitialization = 1,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-};
-
-template<typename Functor> class AdolcForwardJacobian : public Functor
-{
-  typedef adtl::adouble ActiveScalar;
-public:
-
-  AdolcForwardJacobian() : Functor() {}
-  AdolcForwardJacobian(const Functor& f) : Functor(f) {}
-
-  // forward constructors
-  template<typename T0>
-  AdolcForwardJacobian(const T0& a0) : Functor(a0) {}
-  template<typename T0, typename T1>
-  AdolcForwardJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
-  template<typename T0, typename T1, typename T2>
-  AdolcForwardJacobian(const T0& a0, const T1& a1, const T1& a2) : Functor(a0, a1, a2) {}
-
-  typedef typename Functor::InputType InputType;
-  typedef typename Functor::ValueType ValueType;
-  typedef typename Functor::JacobianType JacobianType;
-
-  typedef Matrix<ActiveScalar, InputType::SizeAtCompileTime, 1> ActiveInput;
-  typedef Matrix<ActiveScalar, ValueType::SizeAtCompileTime, 1> ActiveValue;
-
-  void operator() (const InputType& x, ValueType* v, JacobianType* _jac) const
-  {
-    eigen_assert(v!=0);
-    if (!_jac)
-    {
-      Functor::operator()(x, v);
-      return;
-    }
-
-    JacobianType& jac = *_jac;
-
-    ActiveInput ax = x.template cast<ActiveScalar>();
-    ActiveValue av(jac.rows());
-
-    for (int j=0; j<jac.cols(); j++)
-      for (int i=0; i<jac.cols(); i++)
-        ax[i].setADValue(j, i==j ? 1 : 0);
-
-    Functor::operator()(ax, &av);
-
-    for (int i=0; i<jac.rows(); i++)
-    {
-      (*v)[i] = av[i].getValue();
-      for (int j=0; j<jac.cols(); j++)
-        jac.coeffRef(i,j) = av[i].getADValue(j);
-    }
-  }
-protected:
-
-};
-
-//@}
-
-}
-
-#endif // EIGEN_ADLOC_FORWARD
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AlignedVector3 b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AlignedVector3
deleted file mode 100644
index 4fa1842..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AlignedVector3
+++ /dev/null
@@ -1,234 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ALIGNED_VECTOR3
-#define EIGEN_ALIGNED_VECTOR3
-
-#include "../../Eigen/Geometry"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-namespace Eigen {
-
-/**
-  * \defgroup AlignedVector3_Module Aligned vector3 module
-  *
-  * \code
-  * #include <unsupported/Eigen/AlignedVector3>
-  * \endcode
-  */
-  //@{
-
-
-/** \class AlignedVector3
-  *
-  * \brief A vectorization friendly 3D vector
-  *
-  * This class represents a 3D vector internally using a 4D vector
-  * such that vectorization can be seamlessly enabled. Of course,
-  * the same result can be achieved by directly using a 4D vector.
-  * This class makes this process simpler.
-  *
-  */
-// TODO specialize Cwise
-template<typename _Scalar> class AlignedVector3;
-
-namespace internal {
-template<typename _Scalar> struct traits<AlignedVector3<_Scalar> >
-  : traits<Matrix<_Scalar,3,1,0,4,1> >
-{
-};
-}
-
-template<typename _Scalar> class AlignedVector3
-  : public MatrixBase<AlignedVector3<_Scalar> >
-{
-    typedef Matrix<_Scalar,4,1> CoeffType;
-    CoeffType m_coeffs;
-  public:
-
-    typedef MatrixBase<AlignedVector3<_Scalar> > Base;	
-    EIGEN_DENSE_PUBLIC_INTERFACE(AlignedVector3)
-    using Base::operator*;
-
-    inline Index rows() const { return 3; }
-    inline Index cols() const { return 1; }
-    
-    Scalar* data() { return m_coeffs.data(); }
-    const Scalar* data() const { return m_coeffs.data(); }
-    Index innerStride() const { return 1; }
-    Index outerStride() const { return 3; }
-
-    inline const Scalar& coeff(Index row, Index col) const
-    { return m_coeffs.coeff(row, col); }
-
-    inline Scalar& coeffRef(Index row, Index col)
-    { return m_coeffs.coeffRef(row, col); }
-
-    inline const Scalar& coeff(Index index) const
-    { return m_coeffs.coeff(index); }
-
-    inline Scalar& coeffRef(Index index)
-    { return m_coeffs.coeffRef(index);}
-
-
-    inline AlignedVector3()
-    {}
-
-    inline AlignedVector3(const Scalar& x, const Scalar& y, const Scalar& z)
-      : m_coeffs(x, y, z, Scalar(0))
-    {}
-
-    inline AlignedVector3(const AlignedVector3& other)
-      : Base(), m_coeffs(other.m_coeffs)
-    {}
-
-    template<typename XprType, int Size=XprType::SizeAtCompileTime>
-    struct generic_assign_selector {};
-
-    template<typename XprType> struct generic_assign_selector<XprType,4>
-    {
-      inline static void run(AlignedVector3& dest, const XprType& src)
-      {
-        dest.m_coeffs = src;
-      }
-    };
-
-    template<typename XprType> struct generic_assign_selector<XprType,3>
-    {
-      inline static void run(AlignedVector3& dest, const XprType& src)
-      {
-        dest.m_coeffs.template head<3>() = src;
-        dest.m_coeffs.w() = Scalar(0);
-      }
-    };
-
-    template<typename Derived>
-    inline AlignedVector3(const MatrixBase<Derived>& other)
-    {
-      generic_assign_selector<Derived>::run(*this,other.derived());
-    }
-
-    inline AlignedVector3& operator=(const AlignedVector3& other)
-    { m_coeffs = other.m_coeffs; return *this; }
-
-    template <typename Derived>
-    inline AlignedVector3& operator=(const MatrixBase<Derived>& other)
-    {
-      generic_assign_selector<Derived>::run(*this,other.derived());
-      return *this;
-    }
-
-    inline AlignedVector3 operator+(const AlignedVector3& other) const
-    { return AlignedVector3(m_coeffs + other.m_coeffs); }
-
-    inline AlignedVector3& operator+=(const AlignedVector3& other)
-    { m_coeffs += other.m_coeffs; return *this; }
-
-    inline AlignedVector3 operator-(const AlignedVector3& other) const
-    { return AlignedVector3(m_coeffs - other.m_coeffs); }
-
-    inline AlignedVector3 operator-() const
-    { return AlignedVector3(-m_coeffs); }
-
-    inline AlignedVector3 operator-=(const AlignedVector3& other)
-    { m_coeffs -= other.m_coeffs; return *this; }
-
-    inline AlignedVector3 operator*(const Scalar& s) const
-    { return AlignedVector3(m_coeffs * s); }
-
-    inline friend AlignedVector3 operator*(const Scalar& s,const AlignedVector3& vec)
-    { return AlignedVector3(s * vec.m_coeffs); }
-
-    inline AlignedVector3& operator*=(const Scalar& s)
-    { m_coeffs *= s; return *this; }
-
-    inline AlignedVector3 operator/(const Scalar& s) const
-    { return AlignedVector3(m_coeffs / s); }
-
-    inline AlignedVector3& operator/=(const Scalar& s)
-    { m_coeffs /= s; return *this; }
-
-    inline Scalar dot(const AlignedVector3& other) const
-    {
-      eigen_assert(m_coeffs.w()==Scalar(0));
-      eigen_assert(other.m_coeffs.w()==Scalar(0));
-      return m_coeffs.dot(other.m_coeffs);
-    }
-
-    inline void normalize()
-    {
-      m_coeffs /= norm();
-    }
-
-    inline AlignedVector3 normalized() const
-    {
-      return AlignedVector3(m_coeffs / norm());
-    }
-
-    inline Scalar sum() const
-    {
-      eigen_assert(m_coeffs.w()==Scalar(0));
-      return m_coeffs.sum();
-    }
-
-    inline Scalar squaredNorm() const
-    {
-      eigen_assert(m_coeffs.w()==Scalar(0));
-      return m_coeffs.squaredNorm();
-    }
-
-    inline Scalar norm() const
-    {
-      using std::sqrt;
-      return sqrt(squaredNorm());
-    }
-
-    inline AlignedVector3 cross(const AlignedVector3& other) const
-    {
-      return AlignedVector3(m_coeffs.cross3(other.m_coeffs));
-    }
-
-    template<typename Derived>
-    inline bool isApprox(const MatrixBase<Derived>& other, const RealScalar& eps=NumTraits<Scalar>::dummy_precision()) const
-    {
-      return m_coeffs.template head<3>().isApprox(other,eps);
-    }
-    
-    CoeffType& coeffs() { return m_coeffs; }
-    const CoeffType& coeffs() const { return m_coeffs; }
-};
-
-namespace internal {
-
-template<typename _Scalar>
-struct eval<AlignedVector3<_Scalar>, Dense>
-{
- typedef const AlignedVector3<_Scalar>& type;
-};
-
-template<typename Scalar>
-struct evaluator<AlignedVector3<Scalar> >
-  : evaluator<Matrix<Scalar,4,1> >
-{
-  typedef AlignedVector3<Scalar> XprType;
-  typedef evaluator<Matrix<Scalar,4,1> > Base;
-  
-  evaluator(const XprType &m) : Base(m.coeffs()) {}  
-};
-
-}
-
-//@}
-
-}
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_ALIGNED_VECTOR3
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/ArpackSupport b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/ArpackSupport
deleted file mode 100644
index 67c4ac8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/ArpackSupport
+++ /dev/null
@@ -1,30 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARPACKSUPPORT_MODULE_H
-#define EIGEN_ARPACKSUPPORT_MODULE_H
-
-#include "../../Eigen/Core"
-
-/** \defgroup ArpackSupport_Module Arpack support module
-  *
-  * This module provides a wrapper to Arpack, a library for sparse eigenvalue decomposition.
-  *
-  * \code
-  * #include <Eigen/ArpackSupport>
-  * \endcode
-  */
-
-#include "../../Eigen/SparseCholesky"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-#include "src/Eigenvalues/ArpackSelfAdjointEigenSolver.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_ARPACKSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AutoDiff b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AutoDiff
deleted file mode 100644
index 7a4ff46..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/AutoDiff
+++ /dev/null
@@ -1,46 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_AUTODIFF_MODULE
-#define EIGEN_AUTODIFF_MODULE
-
-namespace Eigen {
-
-/**
-  * \defgroup AutoDiff_Module Auto Diff module
-  *
-  * This module features forward automatic differentation via a simple
-  * templated scalar type wrapper AutoDiffScalar.
-  *
-  * Warning : this should NOT be confused with numerical differentiation, which
-  * is a different method and has its own module in Eigen : \ref NumericalDiff_Module.
-  *
-  * \code
-  * #include <unsupported/Eigen/AutoDiff>
-  * \endcode
-  */
-//@{
-
-}
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-
-#include "src/AutoDiff/AutoDiffScalar.h"
-// #include "src/AutoDiff/AutoDiffVector.h"
-#include "src/AutoDiff/AutoDiffJacobian.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-
-
-namespace Eigen {
-//@}
-}
-
-#endif // EIGEN_AUTODIFF_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/BVH b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/BVH
deleted file mode 100644
index 666c983..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/BVH
+++ /dev/null
@@ -1,95 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BVH_MODULE_H
-#define EIGEN_BVH_MODULE_H
-
-#include "../../Eigen/Core"
-#include "../../Eigen/Geometry"
-#include "../../Eigen/StdVector"
-#include <algorithm>
-#include <queue>
-
-namespace Eigen {
-
-/**
-  * \defgroup BVH_Module BVH module
-  * \brief This module provides generic bounding volume hierarchy algorithms
-  * and reference tree implementations.
-  *
-  *
-  * \code
-  * #include <unsupported/Eigen/BVH>
-  * \endcode
-  *
-  * A bounding volume hierarchy (BVH) can accelerate many geometric queries.  This module provides a generic implementation
-  * of the two basic algorithms over a BVH: intersection of a query object against all objects in the hierarchy and minimization
-  * of a function over the objects in the hierarchy.  It also provides intersection and minimization over a cartesian product of
-  * two BVH's.  A BVH accelerates intersection by using the fact that if a query object does not intersect a volume, then it cannot
-  * intersect any object contained in that volume.  Similarly, a BVH accelerates minimization because the minimum of a function
-  * over a volume is no greater than the minimum of a function over any object contained in it.
-  *
-  * Some sample queries that can be written in terms of intersection are:
-  *   - Determine all points where a ray intersects a triangle mesh
-  *   - Given a set of points, determine which are contained in a query sphere
-  *   - Given a set of spheres, determine which contain the query point
-  *   - Given a set of disks, determine if any is completely contained in a query rectangle (represent each 2D disk as a point \f$(x,y,r)\f$
-  *     in 3D and represent the rectangle as a pyramid based on the original rectangle and shrinking in the \f$r\f$ direction)
-  *   - Given a set of points, count how many pairs are \f$d\pm\epsilon\f$ apart (done by looking at the cartesian product of the set
-  *     of points with itself)
-  *
-  * Some sample queries that can be written in terms of function minimization over a set of objects are:
-  *   - Find the intersection between a ray and a triangle mesh closest to the ray origin (function is infinite off the ray)
-  *   - Given a polyline and a query point, determine the closest point on the polyline to the query
-  *   - Find the diameter of a point cloud (done by looking at the cartesian product and using negative distance as the function)
-  *   - Determine how far two meshes are from colliding (this is also a cartesian product query)
-  *
-  * This implementation decouples the basic algorithms both from the type of hierarchy (and the types of the bounding volumes) and
-  * from the particulars of the query.  To enable abstraction from the BVH, the BVH is required to implement a generic mechanism
-  * for traversal.  To abstract from the query, the query is responsible for keeping track of results.
-  *
-  * To be used in the algorithms, a hierarchy must implement the following traversal mechanism (see KdBVH for a sample implementation): \code
-      typedef Volume  //the type of bounding volume
-      typedef Object  //the type of object in the hierarchy
-      typedef Index   //a reference to a node in the hierarchy--typically an int or a pointer
-      typedef VolumeIterator //an iterator type over node children--returns Index
-      typedef ObjectIterator //an iterator over object (leaf) children--returns const Object &
-      Index getRootIndex() const //returns the index of the hierarchy root
-      const Volume &getVolume(Index index) const //returns the bounding volume of the node at given index
-      void getChildren(Index index, VolumeIterator &outVBegin, VolumeIterator &outVEnd,
-                      ObjectIterator &outOBegin, ObjectIterator &outOEnd) const
-      //getChildren takes a node index and makes [outVBegin, outVEnd) range over its node children
-      //and [outOBegin, outOEnd) range over its object children
-    \endcode
-  *
-  * To use the hierarchy, call BVIntersect or BVMinimize, passing it a BVH (or two, for cartesian product) and a minimizer or intersector.
-  * For an intersection query on a single BVH, the intersector encapsulates the query and must provide two functions:
-  * \code
-      bool intersectVolume(const Volume &volume) //returns true if the query intersects the volume
-      bool intersectObject(const Object &object) //returns true if the intersection search should terminate immediately
-    \endcode
-  * The guarantee that BVIntersect provides is that intersectObject will be called on every object whose bounding volume
-  * intersects the query (but possibly on other objects too) unless the search is terminated prematurely.  It is the
-  * responsibility of the intersectObject function to keep track of the results in whatever manner is appropriate.
-  * The cartesian product intersection and the BVMinimize queries are similar--see their individual documentation.
-  *
-  * The following is a simple but complete example for how to use the BVH to accelerate the search for a closest red-blue point pair:
-  * \include BVH_Example.cpp
-  * Output: \verbinclude BVH_Example.out
-  */
-}
-
-//@{
-
-#include "src/BVH/BVAlgorithms.h"
-#include "src/BVH/KdBVH.h"
-
-//@}
-
-#endif // EIGEN_BVH_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/Tensor b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/Tensor
deleted file mode 100644
index 0938bb5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/Tensor
+++ /dev/null
@@ -1,137 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-//#ifndef EIGEN_CXX11_TENSOR_MODULE
-//#define EIGEN_CXX11_TENSOR_MODULE
-
-#include "../../../Eigen/Core"
-
-#if EIGEN_HAS_CXX11
-
-#include "../SpecialFunctions"
-
-#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
-#include "src/util/CXX11Meta.h"
-#include "src/util/MaxSizeVector.h"
-
-/** \defgroup CXX11_Tensor_Module Tensor Module
-  *
-  * This module provides a Tensor class for storing arbitrarily indexed
-  * objects.
-  *
-  * \code
-  * #include <Eigen/CXX11/Tensor>
-  * \endcode
-  *
-  * Much of the documentation can be found \ref eigen_tensors "here".
-  */
-
-#include <atomic>
-#include <chrono>
-#include <cmath>
-#include <cstddef>
-#include <cstring>
-#include <random>
-#include <thread>
-
-#if defined(EIGEN_USE_THREADS) || defined(EIGEN_USE_SYCL)
-#include "ThreadPool"
-#endif
-
-#ifdef EIGEN_USE_GPU
-  #include <iostream>
-  #if defined(EIGEN_USE_HIP)
-    #include <hip/hip_runtime.h>
-  #else
-    #include <cuda_runtime.h>
-  #endif
-#endif
-
-#include "src/Tensor/TensorMacros.h"
-#include "src/Tensor/TensorForwardDeclarations.h"
-#include "src/Tensor/TensorMeta.h"
-#include "src/Tensor/TensorFunctors.h"
-#include "src/Tensor/TensorCostModel.h"
-#include "src/Tensor/TensorDeviceDefault.h"
-#include "src/Tensor/TensorDeviceThreadPool.h"
-#include "src/Tensor/TensorDeviceGpu.h"
-#ifndef gpu_assert
-#define gpu_assert(x)
-#endif
-#include "src/Tensor/TensorDeviceSycl.h"
-#include "src/Tensor/TensorIndexList.h"
-#include "src/Tensor/TensorDimensionList.h"
-#include "src/Tensor/TensorDimensions.h"
-#include "src/Tensor/TensorInitializer.h"
-#include "src/Tensor/TensorTraits.h"
-#include "src/Tensor/TensorRandom.h"
-#include "src/Tensor/TensorUInt128.h"
-#include "src/Tensor/TensorIntDiv.h"
-#include "src/Tensor/TensorGlobalFunctions.h"
-
-#include "src/Tensor/TensorBase.h"
-#include "src/Tensor/TensorBlock.h"
-
-#include "src/Tensor/TensorEvaluator.h"
-#include "src/Tensor/TensorExpr.h"
-#include "src/Tensor/TensorReduction.h"
-#include "src/Tensor/TensorReductionGpu.h"
-#include "src/Tensor/TensorArgMax.h"
-#include "src/Tensor/TensorConcatenation.h"
-#include "src/Tensor/TensorContractionMapper.h"
-#include "src/Tensor/TensorContractionBlocking.h"
-#include "src/Tensor/TensorContraction.h"
-#include "src/Tensor/TensorContractionThreadPool.h"
-#include "src/Tensor/TensorContractionGpu.h"
-#include "src/Tensor/TensorConversion.h"
-#include "src/Tensor/TensorConvolution.h"
-#include "src/Tensor/TensorFFT.h"
-#include "src/Tensor/TensorPatch.h"
-#include "src/Tensor/TensorImagePatch.h"
-#include "src/Tensor/TensorVolumePatch.h"
-#include "src/Tensor/TensorBroadcasting.h"
-#include "src/Tensor/TensorChipping.h"
-#include "src/Tensor/TensorInflation.h"
-#include "src/Tensor/TensorLayoutSwap.h"
-#include "src/Tensor/TensorMorphing.h"
-#include "src/Tensor/TensorPadding.h"
-#include "src/Tensor/TensorReverse.h"
-#include "src/Tensor/TensorShuffling.h"
-#include "src/Tensor/TensorStriding.h"
-#include "src/Tensor/TensorCustomOp.h"
-#include "src/Tensor/TensorEvalTo.h"
-#include "src/Tensor/TensorForcedEval.h"
-#include "src/Tensor/TensorGenerator.h"
-#include "src/Tensor/TensorAssign.h"
-#include "src/Tensor/TensorScan.h"
-#include "src/Tensor/TensorTrace.h"
-
-#ifdef EIGEN_USE_SYCL
-#include "src/Tensor/TensorReductionSycl.h"
-#include "src/Tensor/TensorConvolutionSycl.h"
-#include "src/Tensor/TensorContractionSycl.h"
-#include "src/Tensor/TensorScanSycl.h"
-#endif
-
-#include "src/Tensor/TensorExecutor.h"
-#include "src/Tensor/TensorDevice.h"
-
-#include "src/Tensor/TensorStorage.h"
-#include "src/Tensor/Tensor.h"
-#include "src/Tensor/TensorFixedSize.h"
-#include "src/Tensor/TensorMap.h"
-#include "src/Tensor/TensorRef.h"
-
-#include "src/Tensor/TensorIO.h"
-
-#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif  // EIGEN_HAS_CXX11
-//#endif // EIGEN_CXX11_TENSOR_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry
deleted file mode 100644
index b09c5e4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry
+++ /dev/null
@@ -1,42 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSORSYMMETRY_MODULE
-#define EIGEN_CXX11_TENSORSYMMETRY_MODULE
-
-#include "Tensor"
-
-#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#include "src/util/CXX11Meta.h"
-
-/** \defgroup CXX11_TensorSymmetry_Module Tensor Symmetry Module
-  *
-  * This module provides a classes that allow for the definition of
-  * symmetries w.r.t. tensor indices.
-  *
-  * Including this module will implicitly include the Tensor module.
-  *
-  * \code
-  * #include <Eigen/TensorSymmetry>
-  * \endcode
-  */
-
-#include "src/TensorSymmetry/util/TemplateGroupTheory.h"
-#include "src/TensorSymmetry/Symmetry.h"
-#include "src/TensorSymmetry/StaticSymmetry.h"
-#include "src/TensorSymmetry/DynamicSymmetry.h"
-
-#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CXX11_TENSORSYMMETRY_MODULE
-
-/*
- * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
- */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/ThreadPool b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/ThreadPool
deleted file mode 100644
index c5cafb2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/ThreadPool
+++ /dev/null
@@ -1,74 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_MODULE
-#define EIGEN_CXX11_THREADPOOL_MODULE
-
-#include "../../../Eigen/Core"
-
-#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-/** \defgroup CXX11_ThreadPool_Module C++11 ThreadPool Module
-  *
-  * This module provides 2 threadpool implementations
-  *  - a simple reference implementation
-  *  - a faster non blocking implementation
-  *
-  * This module requires C++11.
-  *
-  * \code
-  * #include <Eigen/CXX11/ThreadPool>
-  * \endcode
-  */
-
-
-// The code depends on CXX11, so only include the module if the
-// compiler supports it.
-#if (EIGEN_COMP_CXXVER >= 11)
-#include <cstddef>
-#include <cstring>
-#include <time.h>
-
-#include <vector>
-#include <atomic>
-#include <condition_variable>
-#include <deque>
-#include <mutex>
-#include <thread>
-#include <functional>
-#include <memory>
-#include <utility>
-
-// There are non-parenthesized calls to "max" in the  <unordered_map> header,
-// which trigger a check in test/main.h causing compilation to fail.
-// We work around the check here by removing the check for max in
-// the case where we have to emulate thread_local.
-#ifdef max
-#undef max
-#endif
-#include <unordered_map>
-
-#include "src/util/CXX11Meta.h"
-#include "src/util/MaxSizeVector.h"
-
-#include "src/ThreadPool/ThreadLocal.h"
-#include "src/ThreadPool/ThreadYield.h"
-#include "src/ThreadPool/ThreadCancel.h"
-#include "src/ThreadPool/EventCount.h"
-#include "src/ThreadPool/RunQueue.h"
-#include "src/ThreadPool/ThreadPoolInterface.h"
-#include "src/ThreadPool/ThreadEnvironment.h"
-#include "src/ThreadPool/Barrier.h"
-#include "src/ThreadPool/NonBlockingThreadPool.h"
-
-#endif
-
-#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_CXX11_THREADPOOL_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
deleted file mode 100644
index 8cac2bb..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
+++ /dev/null
@@ -1,554 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_H
-#define EIGEN_CXX11_TENSOR_TENSOR_H
-
-namespace Eigen {
-
-/** \class Tensor
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief The tensor class.
-  *
-  * The %Tensor class is the work-horse for all \em dense tensors within Eigen.
-  *
-  * The %Tensor class encompasses only dynamic-size objects so far.
-  *
-  * The first two template parameters are required:
-  * \tparam Scalar_  Numeric type, e.g. float, double, int or `std::complex<float>`.
-  *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
-  * \tparam NumIndices_ Number of indices (i.e. rank of the tensor)
-  *
-  * The remaining template parameters are optional -- in most cases you don't have to worry about them.
-  * \tparam Options_  A combination of either \b #RowMajor or \b #ColMajor, and of either
-  *                 \b #AutoAlign or \b #DontAlign.
-  *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
-  *                 for vectorization. It defaults to aligning tensors. Note that tensors currently do not support any operations that profit from vectorization.
-  *                 Support for such operations (i.e. adding two tensors etc.) is planned.
-  *
-  * You can access elements of tensors using normal subscripting:
-  *
-  * \code
-  * Eigen::Tensor<double, 4> t(10, 10, 10, 10);
-  * t(0, 1, 2, 3) = 42.0;
-  * \endcode
-  *
-  * This class can be extended with the help of the plugin mechanism described on the page
-  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN.
-  *
-  * <i><b>Some notes:</b></i>
-  *
-  * <dl>
-  * <dt><b>Relation to other parts of Eigen:</b></dt>
-  * <dd>The midterm development goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that
-  * taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code
-  * by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the %Tensor
-  * class does not provide any of these features and is only available as a stand-alone class that just allows for
-  * coefficient access. Also, when fixed-size tensors are implemented, the number of template arguments is likely to
-  * change dramatically.</dd>
-  * </dl>
-  *
-  * \ref TopicStorageOrders
-  */
-
-template<typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
-class Tensor : public TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
-{
-  public:
-    typedef Tensor<Scalar_, NumIndices_, Options_, IndexType_> Self;
-    typedef TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > Base;
-    typedef typename Eigen::internal::nested<Self>::type Nested;
-    typedef typename internal::traits<Self>::StorageKind StorageKind;
-    typedef typename internal::traits<Self>::Index Index;
-    typedef Scalar_ Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-    enum {
-      IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0) & !(Options_&DontAlign),
-      Layout = Options_ & RowMajor ? RowMajor : ColMajor,
-      CoordAccess = true,
-      RawAccess = true
-    };
-
-    static const int Options = Options_;
-    static const int NumIndices = NumIndices_;
-    typedef DSizes<Index, NumIndices_> Dimensions;
-
-  protected:
-    TensorStorage<Scalar, Dimensions, Options> m_storage;
-
-#ifdef EIGEN_HAS_SFINAE
-    template<typename CustomIndices>
-    struct isOfNormalIndex{
-      static const bool is_array = internal::is_base_of<array<Index, NumIndices>, CustomIndices>::value;
-      static const bool is_int = NumTraits<CustomIndices>::IsInteger;
-      static const bool value = is_array | is_int;
-    };
-#endif
-
-  public:
-    // Metadata
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                         rank()                   const { return NumIndices; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                         dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions&             dimensions()             const { return m_storage.dimensions(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                         size()                   const { return m_storage.size(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar                        *data()                        { return m_storage.data(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar                  *data()                  const { return m_storage.data(); }
-
-    // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    // work, because that uses base().coeffRef() - and we don't yet
-    // implement a similar class hierarchy
-    inline Self& base()             { return *this; }
-    inline const Self& base() const { return *this; }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return coeff(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
-    }
-#endif
-
-    // normal indices
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const
-    {
-      eigen_internal_assert(checkIndexRange(indices));
-      return m_storage.data()[linearizedIndex(indices)];
-    }
-
-    // custom indices
-#ifdef EIGEN_HAS_SFINAE
-    template<typename CustomIndices,
-             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
-    >
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(CustomIndices& indices) const
-    {
-        return coeff(internal::customIndices2Array<Index,NumIndices>(indices));
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff() const
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return m_storage.data()[0];
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return m_storage.data()[index];
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    inline Scalar& coeffRef(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return coeffRef(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
-    }
-#endif
-
-    // normal indices
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
-    {
-      eigen_internal_assert(checkIndexRange(indices));
-      return m_storage.data()[linearizedIndex(indices)];
-    }
-
-    // custom indices
-#ifdef EIGEN_HAS_SFINAE
-    template<typename CustomIndices,
-             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
-             >
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(CustomIndices& indices)
-    {
-        return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices));
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef()
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return m_storage.data()[0];
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return m_storage.data()[index];
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    inline const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return this->operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
-    }
-#else
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
-    {
-      return coeff(array<Index, 2>(i0, i1));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
-    {
-      return coeff(array<Index, 3>(i0, i1, i2));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
-    {
-      return coeff(array<Index, 4>(i0, i1, i2, i3));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
-    {
-      return coeff(array<Index, 5>(i0, i1, i2, i3, i4));
-    }
-#endif
-
-    // custom indices
-#ifdef EIGEN_HAS_SFINAE
-    template<typename CustomIndices,
-             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
-    >
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(CustomIndices& indices) const
-    {
-        return coeff(internal::customIndices2Array<Index,NumIndices>(indices));
-    }
-#endif
-
-    // normal indices
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
-    {
-      return coeff(indices);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return coeff(index);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()() const
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return coeff();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const
-    {
-      // The bracket operator is only for vectors, use the parenthesis operator instead.
-      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return coeff(index);
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    inline Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
-    }
-#else
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
-    {
-      return coeffRef(array<Index, 2>(i0, i1));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
-    {
-      return coeffRef(array<Index, 3>(i0, i1, i2));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
-    {
-      return coeffRef(array<Index, 4>(i0, i1, i2, i3));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
-    {
-      return coeffRef(array<Index, 5>(i0, i1, i2, i3, i4));
-    }
-#endif
-
-    // normal indices
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
-    {
-      return coeffRef(indices);
-    }
-
-    // custom indices
-#ifdef EIGEN_HAS_SFINAE
-    template<typename CustomIndices,
-             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
-    >
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(CustomIndices& indices)
-    {
-      return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices));
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index index)
-    {
-      eigen_assert(index >= 0 && index < size());
-      return coeffRef(index);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()()
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return coeffRef();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator[](Index index)
-    {
-      // The bracket operator is only for vectors, use the parenthesis operator instead
-      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return coeffRef(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor()
-      : m_storage()
-    {
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor(const Self& other)
-      : m_storage(other.m_storage)
-    {
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index firstDimension, IndexTypes... otherDimensions)
-        : m_storage(firstDimension, otherDimensions...)
-    {
-      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-#else
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(Index dim1)
-      : m_storage(dim1, array<Index, 1>(dim1))
-    {
-      EIGEN_STATIC_ASSERT(1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2)
-      : m_storage(dim1*dim2, array<Index, 2>(dim1, dim2))
-    {
-      EIGEN_STATIC_ASSERT(2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3)
-      : m_storage(dim1*dim2*dim3, array<Index, 3>(dim1, dim2, dim3))
-    {
-      EIGEN_STATIC_ASSERT(3 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4)
-      : m_storage(dim1*dim2*dim3*dim4, array<Index, 4>(dim1, dim2, dim3, dim4))
-    {
-      EIGEN_STATIC_ASSERT(4 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4, Index dim5)
-      : m_storage(dim1*dim2*dim3*dim4*dim5, array<Index, 5>(dim1, dim2, dim3, dim4, dim5))
-    {
-      EIGEN_STATIC_ASSERT(5 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-#endif
-
-    /** Normal Dimension */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(const array<Index, NumIndices>& dimensions)
-        : m_storage(internal::array_prod(dimensions), dimensions)
-    {
-      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
-    {
-      typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
-      Assign assign(*this, other.derived());
-      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
-      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
-    }
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, WriteAccessors>& other)
-    {
-      typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
-      Assign assign(*this, other.derived());
-      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
-      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
-    }
-
-    #if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor(Self&& other)
-      : m_storage(std::move(other.m_storage))
-    {
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor& operator=(Self&& other)
-    {
-      m_storage = std::move(other.m_storage);
-      return *this;
-    }
-    #endif
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor& operator=(const Tensor& other)
-    {
-      typedef TensorAssignOp<Tensor, const Tensor> Assign;
-      Assign assign(*this, other);
-      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
-      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
-      return *this;
-    }
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Tensor& operator=(const OtherDerived& other)
-    {
-      typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
-      Assign assign(*this, other);
-      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
-      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
-      return *this;
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
-    void resize(Index firstDimension, IndexTypes... otherDimensions)
-    {
-      // The number of dimensions used to resize a tensor must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      resize(array<Index, NumIndices>{{firstDimension, otherDimensions...}});
-    }
-#endif
-
-    /** Normal Dimension */
-    EIGEN_DEVICE_FUNC void resize(const array<Index, NumIndices>& dimensions)
-    {
-      int i;
-      Index size = Index(1);
-      for (i = 0; i < NumIndices; i++) {
-        internal::check_rows_cols_for_overflow<Dynamic>::run(size, dimensions[i]);
-        size *= dimensions[i];
-      }
-      #ifdef EIGEN_INITIALIZE_COEFFS
-        bool size_changed = size != this->size();
-        m_storage.resize(size, dimensions);
-        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-      #else
-        m_storage.resize(size, dimensions);
-      #endif
-    }
-
-    // Why this overload, DSizes is derived from array ??? //
-    EIGEN_DEVICE_FUNC void resize(const DSizes<Index, NumIndices>& dimensions) {
-      array<Index, NumIndices> dims;
-      for (int i = 0; i < NumIndices; ++i) {
-        dims[i] = dimensions[i];
-      }
-      resize(dims);
-    }
-
-    EIGEN_DEVICE_FUNC
-    void resize()
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      // Nothing to do: rank 0 tensors have fixed size
-    }
-
-#ifdef EIGEN_HAS_INDEX_LIST
-    template <typename FirstType, typename... OtherTypes>
-    EIGEN_DEVICE_FUNC
-    void resize(const Eigen::IndexList<FirstType, OtherTypes...>& dimensions) {
-      array<Index, NumIndices> dims;
-      for (int i = 0; i < NumIndices; ++i) {
-        dims[i] = static_cast<Index>(dimensions[i]);
-      }
-      resize(dims);
-    }
-#endif
-
-    /** Custom Dimension */
-#ifdef EIGEN_HAS_SFINAE
-    template<typename CustomDimension,
-             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomDimension>::value) )
-    >
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(CustomDimension& dimensions)
-    {
-      resize(internal::customIndices2Array<Index,NumIndices>(dimensions));
-    }
-#endif
-
-#ifndef EIGEN_EMULATE_CXX11_META_H
-    template <typename std::ptrdiff_t... Indices>
-    EIGEN_DEVICE_FUNC
-    void resize(const Sizes<Indices...>& dimensions) {
-      array<Index, NumIndices> dims;
-      for (int i = 0; i < NumIndices; ++i) {
-        dims[i] = static_cast<Index>(dimensions[i]);
-      }
-      resize(dims);
-    }
-#else
-    template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5>
-    EIGEN_DEVICE_FUNC
-    void resize(const Sizes<V1, V2, V3, V4, V5>& dimensions) {
-      array<Index, NumIndices> dims;
-      for (int i = 0; i < NumIndices; ++i) {
-        dims[i] = static_cast<Index>(dimensions[i]);
-      }
-      resize(dims);
-    }
-#endif
-
-  protected:
-
-    bool checkIndexRange(const array<Index, NumIndices>& indices) const
-    {
-      using internal::array_apply_and_reduce;
-      using internal::array_zip_and_reduce;
-      using internal::greater_equal_zero_op;
-      using internal::logical_and_op;
-      using internal::lesser_op;
-
-      return
-        // check whether the indices are all >= 0
-        array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
-        // check whether the indices fit in the dimensions
-        array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const
-    {
-      if (Options&RowMajor) {
-        return m_storage.dimensions().IndexOfRowMajor(indices);
-      } else {
-        return m_storage.dimensions().IndexOfColMajor(indices);
-      }
-    }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
deleted file mode 100644
index 8b8fb92..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
+++ /dev/null
@@ -1,329 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
-//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
-#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
-
-namespace Eigen {
-namespace internal {
-
-/** \class TensorIndexTuple
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor + Index Tuple class.
-  *
-  *
-  */
-template<typename XprType>
-struct traits<TensorIndexTupleOp<XprType> > : public traits<XprType>
-{
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef Tuple<Index, typename XprTraits::Scalar> Scalar;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-};
-
-template<typename XprType>
-struct eval<TensorIndexTupleOp<XprType>, Eigen::Dense>
-{
-  typedef const TensorIndexTupleOp<XprType>EIGEN_DEVICE_REF type;
-};
-
-template<typename XprType>
-struct nested<TensorIndexTupleOp<XprType>, 1,
-              typename eval<TensorIndexTupleOp<XprType> >::type>
-{
-  typedef TensorIndexTupleOp<XprType> type;
-};
-
-}  // end namespace internal
-
-template<typename XprType>
-class TensorIndexTupleOp : public TensorBase<TensorIndexTupleOp<XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename Eigen::internal::nested<TensorIndexTupleOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Index Index;
-  typedef Tuple<Index, typename XprType::CoeffReturnType> CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexTupleOp(const XprType& expr)
-      : m_xpr(expr) {}
-
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename XprType::Nested>::type&
-  expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-};
-
-// Eval as rvalue
-template<typename ArgType, typename Device>
-struct TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device>
-{
-  typedef TensorIndexTupleOp<ArgType> XprType;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
-  static const int NumDims = internal::array_size<Dimensions>::value;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
-    PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device) { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
-    return m_impl.dimensions();
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return CoeffReturnType(index, m_impl.coeff(index));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, 1);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- protected:
-  TensorEvaluator<ArgType, Device> m_impl;
-};
-
-namespace internal {
-
-/** \class TensorTupleIndex
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Converts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>.
-  *
-  */
-template<typename ReduceOp, typename Dims, typename XprType>
-struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType>
-{
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef Index Scalar;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
-  static const int Layout = XprTraits::Layout;
-};
-
-template<typename ReduceOp, typename Dims, typename XprType>
-struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense>
-{
-  typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>EIGEN_DEVICE_REF type;
-};
-
-template<typename ReduceOp, typename Dims, typename XprType>
-struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1,
-              typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type>
-{
-  typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type;
-};
-
-}  // end namespace internal
-
-template<typename ReduceOp, typename Dims, typename XprType>
-class TensorTupleReducerOp : public TensorBase<TensorTupleReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename Eigen::internal::nested<TensorTupleReducerOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Index Index;
-  typedef Index CoeffReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTupleReducerOp(const XprType& expr,
-                                                          const ReduceOp& reduce_op,
-                                                          const Index return_dim,
-                                                          const Dims& reduce_dims)
-      : m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_dims) {}
-
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename XprType::Nested>::type&
-  expression() const { return m_xpr; }
-
-  EIGEN_DEVICE_FUNC
-  const ReduceOp& reduce_op() const { return m_reduce_op; }
-
-  EIGEN_DEVICE_FUNC
-  const Dims& reduce_dims() const { return m_reduce_dims; }
-
-  EIGEN_DEVICE_FUNC
-  Index return_dim() const { return m_return_dim; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const ReduceOp m_reduce_op;
-    const Index m_return_dim;
-    const Dims m_reduce_dims;
-};
-
-// Eval as rvalue
-template<typename ReduceOp, typename Dims, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorTupleReducerOp<ReduceOp, Dims, ArgType>, Device>
-{
-  typedef TensorTupleReducerOp<ReduceOp, Dims, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename TensorIndexTupleOp<ArgType>::CoeffReturnType TupleType;
-  typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Dimensions Dimensions;
-  typedef typename TensorEvaluator<const TensorIndexTupleOp<ArgType> , Device>::Dimensions InputDimensions;
-  static const int NumDims = internal::array_size<InputDimensions>::value;
-  typedef array<Index, NumDims> StrideDims;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  typedef StorageMemory<TupleType, Device> TupleStorageMem;
-
-  enum {
-    IsAligned         = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
-    PacketAccess      = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
-    BlockAccess       = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout            = TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_orig_impl(op.expression(), device),
-        m_impl(op.expression().index_tuples().reduce(op.reduce_dims(), op.reduce_op()), device),
-        m_return_dim(op.return_dim())
-  {
-    gen_strides(m_orig_impl.dimensions(), m_strides);
-    if (Layout == static_cast<int>(ColMajor)) {
-      const Index total_size = internal::array_prod(m_orig_impl.dimensions());
-      m_stride_mod = (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : total_size;
-    } else {
-      const Index total_size = internal::array_prod(m_orig_impl.dimensions());
-      m_stride_mod = (m_return_dim > 0) ? m_strides[m_return_dim - 1] : total_size;
-    }    
-    // If m_return_dim is not a valid index, returns 1 or this can crash on Windows.
-    m_stride_div = ((m_return_dim >= 0) &&
-                    (m_return_dim < static_cast<Index>(m_strides.size())))
-                   ? m_strides[m_return_dim] : 1;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
-    return m_impl.dimensions();
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    const TupleType v = m_impl.coeff(index);
-    return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div;
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-#ifdef EIGEN_USE_SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-    m_orig_impl.bind(cgh);
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    const double compute_cost = 1.0 +
-        (m_return_dim < 0 ? 0.0 : (TensorOpCost::ModCost<Index>() + TensorOpCost::DivCost<Index>()));
-    return m_orig_impl.costPerCoeff(vectorized) +
-           m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost);
-  }
-
- private:
-  EIGEN_DEVICE_FUNC void gen_strides(const InputDimensions& dims, StrideDims& strides) {
-    if (m_return_dim < 0) {
-      return;  // Won't be using the strides.
-    }
-    eigen_assert(m_return_dim < NumDims &&
-                 "Asking to convert index to a dimension outside of the rank");
-
-    // Calculate m_stride_div and m_stride_mod, which are used to
-    // calculate the value of an index w.r.t. the m_return_dim.
-    if (Layout == static_cast<int>(ColMajor)) {
-      strides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        strides[i] = strides[i-1] * dims[i-1];
-      }
-    } else {
-      strides[NumDims-1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        strides[i] = strides[i+1] * dims[i+1];
-      }
-    }
-  }
-
- protected:
-  TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> m_orig_impl;
-  TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device> m_impl;
-  const Index m_return_dim;
-  StrideDims m_strides;
-  Index m_stride_mod;
-  Index m_stride_div;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
deleted file mode 100644
index e5811d6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
+++ /dev/null
@@ -1,247 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
-#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
-
-namespace Eigen {
-
-/** \class TensorAssign
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief The tensor assignment class.
-  *
-  * This class is represents the assignment of the values resulting from the evaluation of
-  * the rhs expression to the memory locations denoted by the lhs expression.
-  */
-namespace internal {
-template<typename LhsXprType, typename RhsXprType>
-struct traits<TensorAssignOp<LhsXprType, RhsXprType> >
-{
-  typedef typename LhsXprType::Scalar Scalar;
-  typedef typename traits<LhsXprType>::StorageKind StorageKind;
-  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
-                                      typename traits<RhsXprType>::Index>::type Index;
-  typedef typename LhsXprType::Nested LhsNested;
-  typedef typename RhsXprType::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  static const std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions;
-  static const int Layout = internal::traits<LhsXprType>::Layout;
-  typedef typename traits<LhsXprType>::PointerType PointerType;
-
-  enum {
-    Flags = 0
-  };
-};
-
-template<typename LhsXprType, typename RhsXprType>
-struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense>
-{
-  typedef const TensorAssignOp<LhsXprType, RhsXprType>& type;
-};
-
-template<typename LhsXprType, typename RhsXprType>
-struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type>
-{
-  typedef TensorAssignOp<LhsXprType, RhsXprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename LhsXprType, typename RhsXprType>
-class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> >
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
-
-  static const int NumDims = Eigen::internal::traits<TensorAssignOp>::NumDimensions;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs)
-      : m_lhs_xpr(lhs), m_rhs_xpr(rhs) {}
-
-    /** \returns the nested expressions */
-    EIGEN_DEVICE_FUNC
-    typename internal::remove_all<typename LhsXprType::Nested>::type&
-    lhsExpression() const { return *((typename internal::remove_all<typename LhsXprType::Nested>::type*)&m_lhs_xpr); }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename RhsXprType::Nested>::type&
-    rhsExpression() const { return m_rhs_xpr; }
-
-  protected:
-    typename internal::remove_all<typename LhsXprType::Nested>::type& m_lhs_xpr;
-    const typename internal::remove_all<typename RhsXprType::Nested>::type& m_rhs_xpr;
-};
-
-
-template<typename LeftArgType, typename RightArgType, typename Device>
-struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device>
-{
-  typedef TensorAssignOp<LeftArgType, RightArgType> XprType;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  static const int NumDims = XprType::NumDims;
-
-  enum {
-    IsAligned         = int(TensorEvaluator<LeftArgType, Device>::IsAligned) &
-                        int(TensorEvaluator<RightArgType, Device>::IsAligned),
-    PacketAccess      = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
-                        int(TensorEvaluator<RightArgType, Device>::PacketAccess),
-    BlockAccess       = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
-                        int(TensorEvaluator<RightArgType, Device>::BlockAccess),
-    PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
-                        int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
-    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
-    RawAccess         = TensorEvaluator<LeftArgType, Device>::RawAccess
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock
-      RightTensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  TensorEvaluator(const XprType& op, const Device& device) :
-      m_leftImpl(op.lhsExpression(), device),
-      m_rightImpl(op.rhsExpression(), device)
-  {
-    EIGEN_STATIC_ASSERT(
-        (static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
-         static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
-        YOU_MADE_A_PROGRAMMING_MISTAKE);
-  }
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
-  {
-    // The dimensions of the lhs and the rhs tensors should be equal to prevent
-    // overflows and ensure the result is fully initialized.
-    // TODO: use left impl instead if right impl dimensions are known at compile time.
-    return m_rightImpl.dimensions();
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
-    m_leftImpl.evalSubExprsIfNeeded(NULL);
-    // If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non
-    // null value), attempt to evaluate the rhs expression in place. Returns true iff in place
-    // evaluation isn't supported and the caller still needs to manually assign the values generated
-    // by the rhs to the lhs.
-    return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
-      m_rightImpl.evalSubExprsIfNeededAsync(
-          m_leftImpl.data(), [done](bool need_assign) { done(need_assign); });
-    });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_leftImpl.cleanup();
-    m_rightImpl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
-    m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
-
-    const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned;
-    const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned;
-    m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i));
-  }
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
-  {
-    return m_leftImpl.coeff(index);
-  }
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
-  {
-    return m_leftImpl.template packet<LoadMode>(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    // We assume that evalPacket or evalScalar is called to perform the
-    // assignment and account for the cost of the write here, but reduce left
-    // cost by one load because we are using m_leftImpl.coeffRef.
-    TensorOpCost left = m_leftImpl.costPerCoeff(vectorized);
-    return m_rightImpl.costPerCoeff(vectorized) +
-           TensorOpCost(
-               numext::maxi(0.0, left.bytes_loaded() - sizeof(CoeffReturnType)),
-               left.bytes_stored(), left.compute_cycles()) +
-           TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    return internal::TensorBlockResourceRequirements::merge(
-        m_leftImpl.getResourceRequirements(),
-        m_rightImpl.getResourceRequirements());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(
-      TensorBlockDesc& desc, TensorBlockScratch& scratch) {
-    if (TensorEvaluator<LeftArgType, Device>::RawAccess &&
-        m_leftImpl.data() != NULL) {
-      // If destination has raw data access, we pass it as a potential
-      // destination for a block descriptor evaluation.
-      desc.template AddDestinationBuffer<Layout>(
-          /*dst_base=*/m_leftImpl.data() + desc.offset(),
-          /*dst_strides=*/internal::strides<Layout>(m_leftImpl.dimensions()));
-    }
-
-    RightTensorBlock block = m_rightImpl.block(desc, scratch, /*root_of_expr_ast=*/true);
-    // If block was evaluated into a destination, there is no need to do assignment.
-    if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
-      m_leftImpl.writeBlock(desc, block);
-    }
-    block.cleanup();
-  }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_leftImpl.bind(cgh);
-    m_rightImpl.bind(cgh);
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_leftImpl.data(); }
-
- private:
-  TensorEvaluator<LeftArgType, Device> m_leftImpl;
-  TensorEvaluator<RightArgType, Device> m_rightImpl;
-};
-
-}
-
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
deleted file mode 100644
index 35b6458..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
+++ /dev/null
@@ -1,1176 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_BASE_H
-#define EIGEN_CXX11_TENSOR_TENSOR_BASE_H
-
-// clang-format off
-
-namespace Eigen {
-
-/** \class TensorBase
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief The tensor base class.
-  *
-  * This class is the common parent of the Tensor and TensorMap class, thus
-  * making it possible to use either class interchangeably in expressions.
-  */
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-// FIXME Doxygen does not like the inheritance with different template parameters
-// Since there is no doxygen documentation inside, we disable it for now
-template<typename Derived>
-class TensorBase<Derived, ReadOnlyAccessors>
-{
-  public:
-    typedef internal::traits<Derived> DerivedTraits;
-    typedef typename DerivedTraits::Scalar Scalar;
-    typedef typename DerivedTraits::Index Index;
-    typedef typename internal::remove_const<Scalar>::type CoeffReturnType;
-    static const int NumDimensions = DerivedTraits::NumDimensions;
-
-    // Generic nullary operation support.
-    template <typename CustomNullaryOp> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<CustomNullaryOp, const Derived>
-    nullaryExpr(const CustomNullaryOp& func) const {
-      return TensorCwiseNullaryOp<CustomNullaryOp, const Derived>(derived(), func);
-    }
-
-    // Coefficient-wise nullary operators
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived>
-    constant(const Scalar& value) const {
-      return nullaryExpr(internal::scalar_constant_op<Scalar>(value));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::UniformRandomGenerator<Scalar>, const Derived>
-    random() const {
-      return nullaryExpr(internal::UniformRandomGenerator<Scalar>());
-    }
-    template <typename RandomGenerator> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<RandomGenerator, const Derived>
-    random(const RandomGenerator& gen = RandomGenerator()) const {
-      return nullaryExpr(gen);
-    }
-
-    // Tensor generation
-    template <typename Generator> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorGeneratorOp<Generator, const Derived>
-    generate(const Generator& generator) const {
-      return TensorGeneratorOp<Generator, const Derived>(derived(), generator);
-    }
-
-    // Generic unary operation support.
-    template <typename CustomUnaryOp> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<CustomUnaryOp, const Derived>
-    unaryExpr(const CustomUnaryOp& func) const {
-      return TensorCwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
-    }
-
-    // Coefficient-wise unary operators
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived>
-    operator-() const {
-      return unaryExpr(internal::scalar_opposite_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived>
-    sqrt() const {
-      return unaryExpr(internal::scalar_sqrt_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived>
-    sign() const {
-      return unaryExpr(internal::scalar_sign_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived>
-    rsqrt() const {
-      return unaryExpr(internal::scalar_rsqrt_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived>
-    square() const {
-      return unaryExpr(internal::scalar_square_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived>
-    cube() const {
-      return unaryExpr(internal::scalar_cube_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived>
-    inverse() const {
-      return unaryExpr(internal::scalar_inverse_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived>
-    tanh() const {
-      return unaryExpr(internal::scalar_tanh_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived>
-    lgamma() const {
-      return unaryExpr(internal::scalar_lgamma_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived>
-    digamma() const {
-      return unaryExpr(internal::scalar_digamma_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i0_op<Scalar>, const Derived>
-    bessel_i0() const {
-      return unaryExpr(internal::scalar_bessel_i0_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i0e_op<Scalar>, const Derived>
-    bessel_i0e() const {
-      return unaryExpr(internal::scalar_bessel_i0e_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i1_op<Scalar>, const Derived>
-    bessel_i1() const {
-      return unaryExpr(internal::scalar_bessel_i1_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i1e_op<Scalar>, const Derived>
-    bessel_i1e() const {
-      return unaryExpr(internal::scalar_bessel_i1e_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_j0_op<Scalar>, const Derived>
-    bessel_j0() const {
-      return unaryExpr(internal::scalar_bessel_j0_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_y0_op<Scalar>, const Derived>
-    bessel_y0() const {
-      return unaryExpr(internal::scalar_bessel_y0_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_j1_op<Scalar>, const Derived>
-    bessel_j1() const {
-      return unaryExpr(internal::scalar_bessel_j1_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_y1_op<Scalar>, const Derived>
-    bessel_y1() const {
-      return unaryExpr(internal::scalar_bessel_y1_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k0_op<Scalar>, const Derived>
-    bessel_k0() const {
-      return unaryExpr(internal::scalar_bessel_k0_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k0e_op<Scalar>, const Derived>
-    bessel_k0e() const {
-      return unaryExpr(internal::scalar_bessel_k0e_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k1_op<Scalar>, const Derived>
-    bessel_k1() const {
-      return unaryExpr(internal::scalar_bessel_k1_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k1e_op<Scalar>, const Derived>
-    bessel_k1e() const {
-      return unaryExpr(internal::scalar_bessel_k1e_op<Scalar>());
-    }
-
-    // igamma(a = this, x = other)
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_igamma_op<Scalar>, const Derived, const OtherDerived>
-    igamma(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_igamma_op<Scalar>());
-    }
-
-    // igamma_der_a(a = this, x = other)
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_igamma_der_a_op<Scalar>, const Derived, const OtherDerived>
-    igamma_der_a(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_igamma_der_a_op<Scalar>());
-    }
-
-    // gamma_sample_der_alpha(alpha = this, sample = other)
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_gamma_sample_der_alpha_op<Scalar>, const Derived, const OtherDerived>
-    gamma_sample_der_alpha(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_gamma_sample_der_alpha_op<Scalar>());
-    }
-
-    // igammac(a = this, x = other)
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_igammac_op<Scalar>, const Derived, const OtherDerived>
-    igammac(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_igammac_op<Scalar>());
-    }
-
-    // zeta(x = this, q = other)
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const OtherDerived>
-    zeta(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_zeta_op<Scalar>());
-    }
-
-    // polygamma(n = this, x = other)
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const Derived, const OtherDerived>
-    polygamma(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_polygamma_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived>
-    erf() const {
-      return unaryExpr(internal::scalar_erf_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived>
-    erfc() const {
-      return unaryExpr(internal::scalar_erfc_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_ndtri_op<Scalar>, const Derived>
-    ndtri() const {
-      return unaryExpr(internal::scalar_ndtri_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived>
-    sigmoid() const {
-      return unaryExpr(internal::scalar_logistic_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived>
-    exp() const {
-      return unaryExpr(internal::scalar_exp_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_expm1_op<Scalar>, const Derived>
-    expm1() const {
-      return unaryExpr(internal::scalar_expm1_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived>
-    log() const {
-      return unaryExpr(internal::scalar_log_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived>
-    log1p() const {
-      return unaryExpr(internal::scalar_log1p_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log2_op<Scalar>, const Derived>
-    log2() const {
-      return unaryExpr(internal::scalar_log2_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived>
-    abs() const {
-      return unaryExpr(internal::scalar_abs_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_clamp_op<Scalar>, const Derived>
-    clip(Scalar min, Scalar max) const {
-      return unaryExpr(internal::scalar_clamp_op<Scalar>(min, max));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const typename internal::conditional<NumTraits<CoeffReturnType>::IsComplex,
-                                                             TensorCwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>,
-                                                             Derived>::type
-    conjugate() const {
-      return choose(Cond<NumTraits<CoeffReturnType>::IsComplex>(), unaryExpr(internal::scalar_conjugate_op<Scalar>()), derived());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >, const Derived>
-    pow(Scalar exponent) const {
-      return unaryExpr(internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >(exponent));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>
-    real() const {
-      return unaryExpr(internal::scalar_real_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived>
-    imag() const {
-      return unaryExpr(internal::scalar_imag_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >, const Derived>
-    operator+ (Scalar rhs) const {
-      return unaryExpr(internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >(rhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE friend
-    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_sum_op<Scalar> >, const Derived>
-    operator+ (Scalar lhs, const Derived& rhs) {
-      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_sum_op<Scalar> >(lhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >, const Derived>
-    operator- (Scalar rhs) const {
-      EIGEN_STATIC_ASSERT((NumTraits<Scalar>::IsSigned || internal::is_same<Scalar, const std::complex<float> >::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return unaryExpr(internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >(rhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE friend
-    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_difference_op<Scalar> >, const Derived>
-    operator- (Scalar lhs, const Derived& rhs) {
-      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_difference_op<Scalar> >(lhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >, const Derived>
-    operator* (Scalar rhs) const {
-      return unaryExpr(internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >(rhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE friend
-    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_product_op<Scalar> >, const Derived>
-    operator* (Scalar lhs, const Derived& rhs) {
-      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_product_op<Scalar> >(lhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >, const Derived>
-    operator/ (Scalar rhs) const {
-      return unaryExpr(internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >(rhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE friend
-    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_quotient_op<Scalar> >, const Derived>
-    operator/ (Scalar lhs, const Derived& rhs) {
-      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_quotient_op<Scalar> >(lhs));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_mod_op<Scalar>, const Derived>
-    operator% (Scalar rhs) const {
-      EIGEN_STATIC_ASSERT(NumTraits<Scalar>::IsInteger, YOU_MADE_A_PROGRAMMING_MISTAKE_TRY_MOD);
-      return unaryExpr(internal::scalar_mod_op<Scalar>(rhs));
-    }
-
-    template <int NanPropagation=PropagateFast>
-    EIGEN_DEVICE_FUNC
-        EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NanPropagation>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    cwiseMax(Scalar threshold) const {
-      return cwiseMax<NanPropagation>(constant(threshold));
-    }
-
-    template <int NanPropagation=PropagateFast>
-    EIGEN_DEVICE_FUNC
-        EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NanPropagation>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    cwiseMin(Scalar threshold) const {
-      return cwiseMin<NanPropagation>(constant(threshold));
-    }
-
-    template<typename NewType>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const typename internal::conditional<internal::is_same<NewType, CoeffReturnType>::value,
-                                                             Derived,
-                                                             TensorConversionOp<NewType, const Derived> >::type
-    cast() const {
-      return choose(Cond<internal::is_same<NewType, CoeffReturnType>::value>(), derived(), TensorConversionOp<NewType, const Derived>(derived()));
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived>
-    round() const {
-      return unaryExpr(internal::scalar_round_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_rint_op<Scalar>, const Derived>
-    rint() const {
-      return unaryExpr(internal::scalar_rint_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived>
-    ceil() const {
-      return unaryExpr(internal::scalar_ceil_op<Scalar>());
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived>
-    floor() const {
-      return unaryExpr(internal::scalar_floor_op<Scalar>());
-    }
-
-    // Generic binary operation support.
-    template <typename CustomBinaryOp, typename OtherDerived> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
-    binaryExpr(const OtherDerived& other, const CustomBinaryOp& func) const {
-      return TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other, func);
-    }
-
-    // Coefficient-wise binary operators.
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const OtherDerived>
-    operator+(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_sum_op<Scalar>());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const Derived, const OtherDerived>
-    operator-(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_difference_op<Scalar>());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_product_op<Scalar>, const Derived, const OtherDerived>
-    operator*(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_product_op<Scalar>());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
-    operator/(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_quotient_op<Scalar>());
-    }
-
-  template<int NaNPropagation=PropagateFast, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar, NaNPropagation>, const Derived, const OtherDerived>
-    cwiseMax(const OtherDerived& other) const {
-    return binaryExpr(other.derived(), internal::scalar_max_op<Scalar,Scalar, NaNPropagation>());
-    }
-
-  template<int NaNPropagation=PropagateFast, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar, NaNPropagation>, const Derived, const OtherDerived>
-    cwiseMin(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_min_op<Scalar,Scalar, NaNPropagation>());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>
-    operator&&(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_boolean_and_op());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>
-    operator||(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_boolean_or_op());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived>
-    operator^(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_boolean_xor_op());
-    }
-
-    // Comparisons and tests.
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const OtherDerived>
-    operator<(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>());
-    }
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const OtherDerived>
-    operator<=(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>());
-    }
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const OtherDerived>
-    operator>(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>());
-    }
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const OtherDerived>
-    operator>=(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const OtherDerived>
-    operator==(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>());
-    }
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived>
-    operator!=(const OtherDerived& other) const {
-      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>());
-    }
-
-    // comparisons and tests for Scalars
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    operator<(Scalar threshold) const {
-      return operator<(constant(threshold));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    operator<=(Scalar threshold) const {
-      return operator<=(constant(threshold));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    operator>(Scalar threshold) const {
-      return operator>(constant(threshold));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    operator>=(Scalar threshold) const {
-      return operator>=(constant(threshold));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    operator==(Scalar threshold) const {
-      return operator==(constant(threshold));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
-    operator!=(Scalar threshold) const {
-      return operator!=(constant(threshold));
-    }
-
-    // Checks
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isnan_op<Scalar>, const Derived>
-    (isnan)() const {
-      return unaryExpr(internal::scalar_isnan_op<Scalar>());
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isinf_op<Scalar>, const Derived>
-    (isinf)() const {
-      return unaryExpr(internal::scalar_isinf_op<Scalar>());
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isfinite_op<Scalar>, const Derived>
-    (isfinite)() const {
-      return unaryExpr(internal::scalar_isfinite_op<Scalar>());
-    }
-
-    // Coefficient-wise ternary operators.
-    template<typename ThenDerived, typename ElseDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
-    select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) const {
-      return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived());
-    }
-
-    // Contractions.
-    typedef Eigen::IndexPair<Index> DimensionPair;
-
-    template<typename OtherDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>
-    contract(const OtherDerived& other, const Dimensions& dims) const {
-      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>(derived(), other.derived(), dims);
-    }
-
-    template<typename OtherDerived, typename Dimensions, typename OutputKernel> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>
-    contract(const OtherDerived& other, const Dimensions& dims, const OutputKernel& output_kernel) const {
-      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>(derived(), other.derived(), dims, output_kernel);
-    }
-
-    // Convolutions.
-    template<typename KernelDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>
-    convolve(const KernelDerived& kernel, const Dimensions& dims) const {
-      return TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>(derived(), kernel.derived(), dims);
-    }
-
-    // Fourier transforms
-    template <int FFTDataType, int FFTDirection, typename FFT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection>
-    fft(const FFT& dims) const {
-      return TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection>(derived(), dims);
-    }
-
-    // Scan.
-    typedef TensorScanOp<internal::SumReducer<CoeffReturnType>, const Derived> TensorScanSumOp;
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorScanSumOp
-    cumsum(const Index& axis, bool exclusive = false) const {
-      return TensorScanSumOp(derived(), axis, exclusive);
-    }
-
-    typedef TensorScanOp<internal::ProdReducer<CoeffReturnType>, const Derived> TensorScanProdOp;
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorScanProdOp
-    cumprod(const Index& axis, bool exclusive = false) const {
-      return TensorScanProdOp(derived(), axis, exclusive);
-    }
-
-    template <typename Reducer>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorScanOp<Reducer, const Derived>
-    scan(const Index& axis, const Reducer& reducer, bool exclusive = false) const {
-      return TensorScanOp<Reducer, const Derived>(derived(), axis, exclusive, reducer);
-    }
-
-    // Reductions.
-    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>
-    sum(const Dims& dims) const {
-      return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::SumReducer<CoeffReturnType>());
-    }
-
-    const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
-    sum() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::SumReducer<CoeffReturnType>());
-    }
-
-    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>
-    mean(const Dims& dims) const {
-      return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MeanReducer<CoeffReturnType>());
-    }
-
-    const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
-    mean() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MeanReducer<CoeffReturnType>());
-    }
-
-    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>
-    prod(const Dims& dims) const {
-      return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::ProdReducer<CoeffReturnType>());
-    }
-
-    const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
-    prod() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::ProdReducer<CoeffReturnType>());
-    }
-
-    template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
-    maximum(const Dims& dims) const {
-      return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
-    }
-
-    template <int NanPropagation=PropagateFast>
-    const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
-    maximum() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
-    }
-
-    template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
-    minimum(const Dims& dims) const {
-      return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
-    }
-
-    template <int NanPropagation=PropagateFast>
-    const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
-    minimum() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
-    }
-
-    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::AndReducer, const Dims, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
-    all(const Dims& dims) const {
-      return cast<bool>().reduce(dims, internal::AndReducer());
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::AndReducer, const DimensionList<Index, NumDimensions>, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
-    all() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return cast<bool>().reduce(in_dims, internal::AndReducer());
-    }
-
-    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::OrReducer, const Dims, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
-    any(const Dims& dims) const {
-      return cast<bool>().reduce(dims, internal::OrReducer());
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<internal::OrReducer, const DimensionList<Index, NumDimensions>, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
-    any() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return cast<bool>().reduce(in_dims, internal::OrReducer());
-    }
-
-   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorTupleReducerOp<
-      internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
-      const array<Index, NumDimensions>, const Derived>
-    argmax() const {
-      array<Index, NumDimensions> in_dims;
-      for (Index d = 0; d < NumDimensions; ++d) in_dims[d] = d;
-      return TensorTupleReducerOp<
-        internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
-        const array<Index, NumDimensions>,
-        const Derived>(derived(), internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >(), -1, in_dims);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorTupleReducerOp<
-      internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
-      const array<Index, NumDimensions>, const Derived>
-    argmin() const {
-      array<Index, NumDimensions> in_dims;
-      for (Index d = 0; d < NumDimensions; ++d) in_dims[d] = d;
-      return TensorTupleReducerOp<
-        internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
-        const array<Index, NumDimensions>,
-        const Derived>(derived(), internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >(), -1, in_dims);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorTupleReducerOp<
-      internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
-      const array<Index, 1>, const Derived>
-    argmax(const Index return_dim) const {
-      array<Index, 1> in_dims;
-      in_dims[0] = return_dim;
-      return TensorTupleReducerOp<
-        internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
-        const array<Index, 1>,
-        const Derived>(derived(), internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >(), return_dim, in_dims);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorTupleReducerOp<
-      internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
-      const array<Index, 1>, const Derived>
-    argmin(const Index return_dim) const {
-      array<Index, 1> in_dims;
-      in_dims[0] = return_dim;
-      return TensorTupleReducerOp<
-        internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
-        const array<Index, 1>,
-        const Derived>(derived(), internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >(), return_dim, in_dims);
-    }
-
-    template <typename Reducer, typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReductionOp<Reducer, const Dims, const Derived>
-    reduce(const Dims& dims, const Reducer& reducer) const {
-      return TensorReductionOp<Reducer, const Dims, const Derived>(derived(), dims, reducer);
-    }
-
-    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorTraceOp<const Dims, const Derived>
-    trace(const Dims& dims) const {
-      return TensorTraceOp<const Dims, const Derived>(derived(), dims);
-    }
-
-    const TensorTraceOp<const DimensionList<Index, NumDimensions>, const Derived>
-    trace() const {
-      DimensionList<Index, NumDimensions> in_dims;
-      return TensorTraceOp<const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims);
-    }
-
-    template <typename Broadcast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorBroadcastingOp<const Broadcast, const Derived>
-    broadcast(const Broadcast& bcast) const {
-      return TensorBroadcastingOp<const Broadcast, const Derived>(derived(), bcast);
-    }
-
-    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorConcatenationOp<Axis, const Derived, const OtherDerived>
-    concatenate(const OtherDerived& other, Axis axis) const {
-      return TensorConcatenationOp<Axis, const Derived, const OtherDerived>(derived(), other.derived(), axis);
-    }
-
-    template <typename PatchDims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorPatchOp<const PatchDims, const Derived>
-    extract_patches(const PatchDims& patch_dims) const {
-      return TensorPatchOp<const PatchDims, const Derived>(derived(), patch_dims);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
-    extract_image_patches(const Index patch_rows = 1, const Index patch_cols = 1,
-                          const Index row_stride = 1, const Index col_stride = 1,
-                          const Index in_row_stride = 1, const Index in_col_stride = 1,
-                          const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const {
-      return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
-                                                                 in_row_stride, in_col_stride, 1, 1, padding_type, padding_value);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
-    extract_image_patches(const Index patch_rows, const Index patch_cols,
-                          const Index row_stride, const Index col_stride,
-                          const Index in_row_stride, const Index in_col_stride,
-                          const Index row_inflate_stride, const Index col_inflate_stride,
-                          const Index padding_top, const Index padding_bottom,
-                          const Index padding_left,const Index padding_right,
-                          const Scalar padding_value) const {
-      return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
-                                                                 in_row_stride, in_col_stride, row_inflate_stride, col_inflate_stride,
-                                                                 padding_top, padding_bottom, padding_left, padding_right, padding_value);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>
-    extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols,
-                           const Index plane_stride = 1, const Index row_stride = 1, const Index col_stride = 1,
-                           const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const {
-      return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, 1, 1, 1, padding_type, padding_value);
-    }
-
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>
-    extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols,
-                           const Index plane_stride, const Index row_stride, const Index col_stride,
-                           const Index plane_inflate_stride, const Index row_inflate_stride, const Index col_inflate_stride,
-                           const Index padding_top_z, const Index padding_bottom_z,
-                           const Index padding_top, const Index padding_bottom,
-                           const Index padding_left, const Index padding_right, const Scalar padding_value = Scalar(0)) const {
-      return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, plane_inflate_stride, row_inflate_stride, col_inflate_stride, padding_top_z, padding_bottom_z, padding_top, padding_bottom, padding_left, padding_right, padding_value);
-    }
-
-    // Morphing operators.
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorLayoutSwapOp<const Derived>
-    swap_layout() const {
-      return TensorLayoutSwapOp<const Derived>(derived());
-    }
-    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReshapingOp<const NewDimensions, const Derived>
-    reshape(const NewDimensions& newDimensions) const {
-      return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions);
-    }
-    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorSlicingOp<const StartIndices, const Sizes, const Derived>
-    slice(const StartIndices& startIndices, const Sizes& sizes) const {
-      return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
-    }
-    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived>
-    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const {
-      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
-                                const Derived>(derived(), startIndices, stopIndices, strides);
-    }
-    template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorChippingOp<DimId, const Derived>
-    chip(const Index offset) const {
-      return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId);
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorChippingOp<Dynamic, const Derived>
-    chip(const Index offset, const Index dim) const {
-      return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim);
-    }
-    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReverseOp<const ReverseDimensions, const Derived>
-    reverse(const ReverseDimensions& rev) const {
-      return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev);
-    }
-    template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorPaddingOp<const PaddingDimensions, const Derived>
-    pad(const PaddingDimensions& padding) const {
-      return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, internal::scalar_cast_op<int, Scalar>()(0));
-    }
-    template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorPaddingOp<const PaddingDimensions, const Derived>
-    pad(const PaddingDimensions& padding, const Scalar padding_value) const {
-      return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, padding_value);
-    }
-    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorShufflingOp<const Shuffle, const Derived>
-    shuffle(const Shuffle& shfl) const {
-      return TensorShufflingOp<const Shuffle, const Derived>(derived(), shfl);
-    }
-    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorStridingOp<const Strides, const Derived>
-    stride(const Strides& strides) const {
-      return TensorStridingOp<const Strides, const Derived>(derived(), strides);
-    }
-    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorInflationOp<const Strides, const Derived>
-    inflate(const Strides& strides) const {
-      return TensorInflationOp<const Strides, const Derived>(derived(), strides);
-    }
-
-    // Returns a tensor containing index/value tuples
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorIndexTupleOp<const Derived>
-    index_tuples() const {
-      return TensorIndexTupleOp<const Derived>(derived());
-    }
-
-    // Support for custom unary and binary operations
-    template <typename CustomUnaryFunc>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCustomUnaryOp<const CustomUnaryFunc, const Derived> customOp(const CustomUnaryFunc& op) const {
-      return TensorCustomUnaryOp<const CustomUnaryFunc, const Derived>(derived(), op);
-    }
-    template <typename OtherDerived, typename CustomBinaryFunc>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived> customOp(const OtherDerived& other, const CustomBinaryFunc& op) const {
-      return TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived>(derived(), other, op);
-    }
-
-    // Force the evaluation of the expression.
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorForcedEvalOp<const Derived> eval() const {
-      return TensorForcedEvalOp<const Derived>(derived());
-    }
-
-  protected:
-    template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
-    template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
-    // the Eigen:: prefix is required to workaround a compilation issue with nvcc 9.0
-    template <typename OtherDerived, int AccessLevel> friend class Eigen::TensorBase;
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
-};
-
-template<typename Derived, int AccessLevel = internal::accessors_level<Derived>::value>
-class TensorBase : public TensorBase<Derived, ReadOnlyAccessors> {
- public:
-    typedef TensorBase<Derived, ReadOnlyAccessors> Base;
-    typedef internal::traits<Derived> DerivedTraits;
-    typedef typename DerivedTraits::Scalar Scalar;
-    typedef typename DerivedTraits::Index Index;
-    typedef Scalar CoeffReturnType;
-    static const int NumDimensions = DerivedTraits::NumDimensions;
-
-    template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
-    template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
-    // the Eigen:: prefix is required to workaround a compilation issue with nvcc 9.0
-    template <typename OtherDerived, int OtherAccessLevel> friend class Eigen::TensorBase;
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& setZero() {
-      return setConstant(Scalar(0));
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& setConstant(const Scalar& val) {
-      return derived() = this->constant(val);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& setRandom() {
-      return derived() = this->random();
-    }
-    template <typename RandomGenerator> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& setRandom() {
-      return derived() = this->template random<RandomGenerator>();
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& setValues(
-        const typename internal::Initializer<Derived, NumDimensions>::InitList& vals) {
-      TensorEvaluator<Derived, DefaultDevice> eval(derived(), DefaultDevice());
-      internal::initialize_tensor<Derived, NumDimensions>(eval, vals);
-      return derived();
-    }
-#endif  // EIGEN_HAS_VARIADIC_TEMPLATES
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator+=(const OtherDerived& other) {
-      return derived() = derived() + other.derived();
-    }
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator-=(const OtherDerived& other) {
-      return derived() = derived() - other.derived();
-    }
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator*=(const OtherDerived& other) {
-      return derived() = derived() * other.derived();
-    }
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    Derived& operator/=(const OtherDerived& other) {
-      return derived() = derived() / other.derived();
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorLayoutSwapOp<const Derived>
-    swap_layout() const {
-      return TensorLayoutSwapOp<const Derived>(derived());
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorLayoutSwapOp<Derived>
-    swap_layout() {
-      return TensorLayoutSwapOp<Derived>(derived());
-    }
-
-    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorConcatenationOp<const Axis, const Derived, const OtherDerived>
-    concatenate(const OtherDerived& other, const Axis& axis) const {
-      return TensorConcatenationOp<const Axis, const Derived, const OtherDerived>(derived(), other, axis);
-    }
-    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorConcatenationOp<const Axis, Derived, OtherDerived>
-    concatenate(const OtherDerived& other, const Axis& axis) {
-      return TensorConcatenationOp<const Axis, Derived, OtherDerived>(derived(), other, axis);
-    }
-
-    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReshapingOp<const NewDimensions, const Derived>
-    reshape(const NewDimensions& newDimensions) const {
-      return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions);
-    }
-    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorReshapingOp<const NewDimensions, Derived>
-    reshape(const NewDimensions& newDimensions) {
-      return TensorReshapingOp<const NewDimensions, Derived>(derived(), newDimensions);
-    }
-
-    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorSlicingOp<const StartIndices, const Sizes, const Derived>
-    slice(const StartIndices& startIndices, const Sizes& sizes) const {
-      return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
-    }
-    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorSlicingOp<const StartIndices, const Sizes, Derived>
-    slice(const StartIndices& startIndices, const Sizes& sizes) {
-      return TensorSlicingOp<const StartIndices, const Sizes, Derived>(derived(), startIndices, sizes);
-    }
-
-    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived>
-    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const {
-      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
-                                const Derived>(derived(), startIndices, stopIndices, strides);
-    }
-    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, Derived>
-    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) {
-      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
-                                Derived>(derived(), startIndices, stopIndices, strides);
-    }
-
-    template <DenseIndex DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorChippingOp<DimId, const Derived>
-    chip(const Index offset) const {
-      return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId);
-    }
-    template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorChippingOp<DimId, Derived>
-    chip(const Index offset) {
-      return TensorChippingOp<DimId, Derived>(derived(), offset, DimId);
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorChippingOp<Dynamic, const Derived>
-    chip(const Index offset, const Index dim) const {
-      return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim);
-    }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorChippingOp<Dynamic, Derived>
-    chip(const Index offset, const Index dim) {
-      return TensorChippingOp<Dynamic, Derived>(derived(), offset, dim);
-    }
-
-    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorReverseOp<const ReverseDimensions, const Derived>
-    reverse(const ReverseDimensions& rev) const {
-      return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev);
-    }
-    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorReverseOp<const ReverseDimensions, Derived>
-    reverse(const ReverseDimensions& rev) {
-      return TensorReverseOp<const ReverseDimensions, Derived>(derived(), rev);
-    }
-
-    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorShufflingOp<const Shuffle, const Derived>
-    shuffle(const Shuffle& shfl) const {
-      return TensorShufflingOp<const Shuffle, const Derived>(derived(), shfl);
-    }
-    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorShufflingOp<const Shuffle, Derived>
-    shuffle(const Shuffle& shfl) {
-      return TensorShufflingOp<const Shuffle, Derived>(derived(), shfl);
-    }
-
-    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorStridingOp<const Strides, const Derived>
-    stride(const Strides& strides) const {
-      return TensorStridingOp<const Strides, const Derived>(derived(), strides);
-    }
-    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorStridingOp<const Strides, Derived>
-    stride(const Strides& strides) {
-      return TensorStridingOp<const Strides, Derived>(derived(), strides);
-    }
-
-    // Select the device on which to evaluate the expression.
-    template <typename DeviceType>
-    TensorDevice<Derived, DeviceType> device(const DeviceType& dev) {
-      return TensorDevice<Derived, DeviceType>(dev, derived());
-    }
-
-    // Select the async device on which to evaluate the expression.
-    template <typename DeviceType, typename DoneCallback>
-    TensorAsyncDevice<Derived, DeviceType, DoneCallback> device(const DeviceType& dev, DoneCallback done) {
-      return TensorAsyncDevice<Derived, DeviceType, DoneCallback>(dev, derived(), std::move(done));
-    }
-
- protected:
-    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TensorBase)
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TensorBase)
-
-    template<typename OtherDerived> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& operator=(const OtherDerived& other)
-    {
-      typedef TensorAssignOp<Derived, const OtherDerived> Assign;
-      Assign assign(derived(), other.derived());
-      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
-      return derived();
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Derived& derived() { return *static_cast<Derived*>(this); }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
-};
-#endif // EIGEN_PARSED_BY_DOXYGEN
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_BASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
deleted file mode 100644
index 1e55d12..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
+++ /dev/null
@@ -1,1559 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
-#define EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
-
-namespace Eigen {
-namespace internal {
-
-// -------------------------------------------------------------------------- //
-// Forward declarations for templates defined below.
-template <typename Scalar, typename IndexType, int NumDims, int Layout>
-class TensorBlockIO;
-
-// -------------------------------------------------------------------------- //
-// Helper function to compute strides for densely stored buffer of given
-// dimensions.
-
-// TODO(ezhulenev): We compute strides 1000 times in different evaluators, use
-// this function instead everywhere.
-template <int Layout, typename IndexType, int NumDims>
-EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(
-    const DSizes<IndexType, NumDims>& dimensions) {
-  DSizes<IndexType, NumDims> strides;
-  if (NumDims == 0) return strides;
-
-  // TODO(ezhulenev): Use templates to unroll this loop (similar to
-  // h_array_reduce in CXX11meta.h)? Benchmark it.
-  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-    strides[0] = 1;
-    for (int i = 1; i < NumDims; ++i) {
-      strides[i] = strides[i - 1] * dimensions[i - 1];
-    }
-  } else {
-    strides[NumDims - 1] = 1;
-    for (int i = NumDims - 2; i >= 0; --i) {
-      strides[i] = strides[i + 1] * dimensions[i + 1];
-    }
-  }
-
-  return strides;
-}
-
-template <int Layout, typename IndexType, size_t NumDims>
-EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(
-    const Eigen::array<IndexType, NumDims>& dimensions) {
-  return strides<Layout>(DSizes<IndexType, NumDims>(dimensions));
-}
-
-template <int Layout, std::ptrdiff_t... Indices>
-EIGEN_STRONG_INLINE DSizes<std::ptrdiff_t, sizeof...(Indices)> strides(
-    const Sizes<Indices...>& sizes) {
-  return strides<Layout>(DSizes<std::ptrdiff_t, sizeof...(Indices)>(sizes));
-}
-
-// -------------------------------------------------------------------------- //
-
-// Tensor block shape type defines what are the shape preference for the blocks
-// extracted from the larger tensor.
-//
-// Example: blocks of 100 elements from the large 100x100 tensor:
-// - tensor: 100x100
-// - target_block_size: 100
-//
-// TensorBlockShapeType:
-//  - kUniformAllDims: 100 blocks of size 10x10
-//  - kSkewedInnerDims: 100 blocks of size 100x1 (or 1x100 depending on a column
-//                      or row major layout)
-enum class TensorBlockShapeType { kUniformAllDims, kSkewedInnerDims };
-
-struct TensorBlockResourceRequirements {
-  TensorBlockShapeType shape_type;  // target block shape
-  size_t size;                      // target block size
-  TensorOpCost cost_per_coeff;      // cost of computing a single block element
-
-#ifdef EIGEN_HIPCC
-  // For HIPCC, we need to explicitly declare as a "device fun", the constructor
-  // which is implicitly invoked in the "merge" / "any" routines. else HIPCC
-  // errors out complaining about the lack of a matching constructor
-  EIGEN_DEVICE_FUNC
-  TensorBlockResourceRequirements(TensorBlockShapeType shape_type_, size_t size_,
-				  TensorOpCost cost_)
-    : shape_type(shape_type_), size(size_), cost_per_coeff(cost_)
-  {}
-#endif
-
-  template <typename Scalar>
-  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
-      TensorBlockShapeType shape_type, size_t size_in_bytes,
-      TensorOpCost cost) {
-    const size_t size = numext::maxi(size_t(1), size_in_bytes / sizeof(Scalar));
-    return {shape_type, size, cost};
-  }
-
-  template <typename Scalar>
-  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
-      TensorBlockShapeType shape_type, size_t size_in_bytes) {
-    // This default cost per coefficient is valid for most materialized tensor
-    // block evaluation implementations, because they typically just read
-    // coefficients from the underlying tensor storage, and write to the tensor
-    // block buffer (scratch or destination memory, reads and writes have linear
-    // access pattern). We ignore the fixed cost of block evaluation, because in
-    // practice it should negligible.
-    //
-    // Lazy block evaluation adds the cost of calling a functor for each
-    // coefficient.
-    //
-    // All non-trivial block evaluation implementations must provide their own
-    // cost approximation (e.g. shuffling inner dimension has a much higher cost
-    // because it reads memory randomly, although the total number of moved
-    // bytes is the same).
-    return withShapeAndSize<Scalar>(shape_type, size_in_bytes,
-                                    {/*bytes_loaded=*/sizeof(Scalar),
-                                     /*bytes_stored=*/sizeof(Scalar),
-                                     /*compute_cycles=*/0});
-  }
-
-  template <typename Scalar>
-  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements skewed(
-      size_t size_in_bytes) {
-    return withShapeAndSize<Scalar>(TensorBlockShapeType::kSkewedInnerDims,
-                                    size_in_bytes);
-  }
-
-  template <typename Scalar>
-  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements uniform(
-      size_t size_in_bytes) {
-    return withShapeAndSize<Scalar>(TensorBlockShapeType::kUniformAllDims,
-                                    size_in_bytes);
-  }
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE TensorBlockResourceRequirements
-  merge(const TensorBlockResourceRequirements& lhs,
-        const TensorBlockResourceRequirements& rhs) {
-    return {merge(lhs.shape_type, rhs.shape_type),           // shape_type
-            merge(lhs.size, rhs.size),                       // size
-            merge(lhs.cost_per_coeff, rhs.cost_per_coeff)};  // cost_per_coeff
-  }
-
-  EIGEN_DEVICE_FUNC TensorBlockResourceRequirements& addCostPerCoeff(
-      TensorOpCost cost) {
-    cost_per_coeff += cost;
-    return *this;
-  }
-
-  // This is a resource requirement that should be returned from expressions
-  // that do not have any block evaluation preference (e.g. default tensor
-  // expression with raw buffer access).
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE TensorBlockResourceRequirements any() {
-    return {TensorBlockShapeType::kUniformAllDims, 1, {0, 0, 0}};
-  }
-
- private:
-  using Requirements = TensorBlockResourceRequirements;
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE size_t merge(size_t lhs_size, size_t rhs_size) {
-    return numext::maxi(lhs_size, rhs_size);
-  }
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE TensorBlockShapeType
-  merge(TensorBlockShapeType lhs, TensorBlockShapeType rhs) {
-    return (lhs == TensorBlockShapeType::kSkewedInnerDims ||
-            rhs == TensorBlockShapeType::kSkewedInnerDims)
-               ? TensorBlockShapeType::kSkewedInnerDims
-               : TensorBlockShapeType::kUniformAllDims;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE TensorOpCost merge(TensorOpCost lhs_cost,
-                                                TensorOpCost rhs_cost) {
-    return lhs_cost + rhs_cost;
-  }
-};
-
-// -------------------------------------------------------------------------- //
-// TensorBlockDescriptor specifies a block offset within a tensor and the block
-// sizes along each of the tensor dimensions.
-
-template <int NumDims, typename IndexType = Eigen::Index>
-class TensorBlockDescriptor {
- public:
-  typedef DSizes<IndexType, NumDims> Dimensions;
-
-  // If we evaluate a Tensor assignment, and expression on the left, already has
-  // a memory buffer, then we might do performance optimization, and evaluate
-  // the root expression directly into the final output memory. Some time it's
-  // possible to reuse it for materializing subexpressions inside an expression
-  // tree, to to avoid dynamic memory allocation.
-  //
-  // The pointer type of the underlying storage is erased, because passing
-  // Scalar type through all the expression evaluation layers is way too many
-  // templates. In practice destination buffer type should always match the
-  // evaluated expression scalar type.
-  class DestinationBuffer {
-   public:
-    enum DestinationBufferKind : int {
-      // The above explicit specification of "int" as the enum basetype is
-      // needed to get around a HIPCC link error ("the field type is not
-      // amp-compatible")
-      // which is issued for class members with the enum type.
-      // TODO(rocm):
-      // remove the "int" basetype once HIPCC has been fixed to not error out
-      // in the above scenario.
-
-      // Destination buffer is not defined (`m_data` == nullptr).
-      kEmpty,
-
-      // Tensor block defined by an owning tensor block descriptor can fit
-      // contiguously into the destination buffer. In this case it's safe to
-      // materialize tensor block in the destination buffer, wrap it in a
-      // TensorMap, and use to build Eigen expression on top of it.
-      kContiguous,
-
-      // Destination buffer strides do not match strides of the contiguously
-      // stored block, and it's impossible to define a TensorMap over this
-      // buffer. However if we are evaluating a root of an expression tree, we
-      // still can materialize an output into this destination, because we can
-      // guarantee that no one will ever access it through block API.
-      //
-      // In theory it is possible to build valid TensorStriding<TensorMap>
-      // expression on top of this destination buffer, however it has
-      // inefficient coeff/packet access, and defeats the purpose of fast block
-      // evaluation API.
-      kStrided
-    };
-
-    template <typename Scalar>
-    Scalar* data() const {
-      eigen_assert(m_data_type_size == sizeof(Scalar));
-      return static_cast<Scalar*>(m_data);
-    }
-
-    const Dimensions& strides() const { return m_strides; }
-    const DestinationBufferKind& kind() const { return m_kind; }
-
-   private:
-    friend class TensorBlockDescriptor;
-
-    DestinationBuffer() : m_data(NULL), m_data_type_size(0), m_kind(kEmpty) {}
-
-    template <typename Scalar>
-    DestinationBuffer(Scalar* data, const Dimensions& strides,
-                      DestinationBufferKind kind)
-        : m_data(static_cast<void*>(data)),
-          m_data_type_size(sizeof(Scalar)),
-          m_strides(strides),
-          m_kind(kind) {}
-
-    template <int Layout, typename Scalar>
-    static DestinationBuffer make(const TensorBlockDescriptor& desc,
-                                  Scalar* data, const Dimensions& strides) {
-      return DestinationBuffer(data, strides, kind<Layout>(desc, strides));
-    }
-
-    template <int Layout>
-    static DestinationBufferKind kind(const TensorBlockDescriptor& desc,
-                                      const Dimensions& strides) {
-      const Dimensions& desc_dims = desc.dimensions();
-      const Dimensions& desc_strides = internal::strides<Layout>(desc_dims);
-      for (int i = 0; i < NumDims; ++i) {
-        if (desc_dims[i] == 1) continue;
-        if (desc_strides[i] != strides[i]) return kStrided;
-      }
-      return kContiguous;
-    }
-
-    // Storage pointer is type erased, to reduce template bloat, but we still
-    // keep the size of the underlying element type for error checking.
-    void* m_data;
-    size_t m_data_type_size;
-
-    // Destination buffer dimensions always match the dimensions of a tensor
-    // block descriptor it belongs to, however strides might be different.
-    Dimensions m_strides;
-
-    DestinationBufferKind m_kind;
-  };
-
-  TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions,
-                        const DestinationBuffer& destination)
-      : m_offset(offset),
-        m_dimensions(dimensions),
-        m_destination(destination) {}
-
-  TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions)
-      : m_offset(offset),
-        m_dimensions(dimensions),
-        m_destination(DestinationBuffer()) {}
-
-  IndexType offset() const { return m_offset; }
-  const Dimensions& dimensions() const { return m_dimensions; }
-  IndexType dimension(int index) const { return m_dimensions[index]; }
-  IndexType size() const { return array_prod<IndexType>(m_dimensions); }
-
-  const DestinationBuffer& destination() const { return m_destination; }
-
-  template <int Layout, typename Scalar>
-  void AddDestinationBuffer(Scalar* dst_base, const Dimensions& dst_strides) {
-    eigen_assert(dst_base != NULL);
-    m_destination =
-        DestinationBuffer::template make<Layout>(*this, dst_base, dst_strides);
-  }
-
-  template <int Layout, typename Scalar, typename DstStridesIndexType>
-  void AddDestinationBuffer(
-      Scalar* dst_base,
-      const DSizes<DstStridesIndexType, NumDims>& dst_strides) {
-    // DSizes constructor will do index type promotion if it's safe.
-    AddDestinationBuffer<Layout>(dst_base, Dimensions(dst_strides));
-  }
-
-  TensorBlockDescriptor& DropDestinationBuffer() {
-    m_destination.m_data = NULL;
-    m_destination.m_kind = DestinationBuffer::kEmpty;
-    return *this;
-  }
-
-  bool HasDestinationBuffer() const {
-    return m_destination.kind() != DestinationBuffer::kEmpty;
-  }
-
-  // Returns a copy of `*this` with updated offset.
-  TensorBlockDescriptor WithOffset(IndexType offset) const {
-    return TensorBlockDescriptor(offset, m_dimensions, m_destination);
-  }
-
- private:
-  // Offset and dimensions are immutable after construction. Block descriptor
-  // can only be mutated by adding or dropping destination.
-  const IndexType m_offset;
-  const Dimensions m_dimensions;
-  DestinationBuffer m_destination;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorBlockMapper is responsible for iterating over the blocks of a tensor.
-
-template <int NumDims, int Layout, typename IndexType = Eigen::Index>
-class TensorBlockMapper {
-  typedef TensorBlockDescriptor<NumDims, IndexType> BlockDescriptor;
-
- public:
-  typedef DSizes<IndexType, NumDims> Dimensions;
-
-  TensorBlockMapper() = default;
-  TensorBlockMapper(const DSizes<IndexType, NumDims>& dimensions,
-                    const TensorBlockResourceRequirements& requirements)
-      : m_tensor_dimensions(dimensions), m_requirements(requirements) {
-    // Compute block dimensions and the total number of blocks.
-    InitializeBlockDimensions();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockCount() const {
-    return m_total_block_count;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockTotalSize() const {
-    return m_block_dimensions.TotalSize();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DSizes<IndexType, NumDims>&
-  blockDimensions() const {
-    return m_block_dimensions;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockDescriptor
-  blockDescriptor(IndexType block_index) const {
-    static const bool isColMajor = Layout == static_cast<int>(ColMajor);
-
-    IndexType offset = 0;
-    DSizes<IndexType, NumDims> dimensions;
-
-    if (NumDims == 0) return BlockDescriptor(offset, dimensions);
-
-    // Iterate outer -> inner dimensions.
-    for (int i = NumDims - 1; i >= 0; --i) {
-      const int dim = isColMajor ? i : NumDims - i - 1;
-
-      const IndexType idx = block_index / m_block_strides[dim];
-      block_index -= idx * m_block_strides[dim];
-
-      const IndexType coord = idx * m_block_dimensions[dim];
-      dimensions[dim] = numext::mini(m_tensor_dimensions[dim] - coord,
-                                     m_block_dimensions[dim]);
-      offset += coord * m_tensor_strides[dim];
-    }
-
-    return {offset, dimensions};
-  }
-
- private:
-  void InitializeBlockDimensions() {
-    // Requested block shape and size.
-    const TensorBlockShapeType shape_type = m_requirements.shape_type;
-    IndexType target_block_size =
-        numext::maxi<IndexType>(1, static_cast<IndexType>(m_requirements.size));
-
-    IndexType tensor_size = m_tensor_dimensions.TotalSize();
-
-    // Corner case: one of the dimensions is zero. Logic below is too complex
-    // to handle this case on a general basis, just use unit block size.
-    // Note: we must not yield blocks with zero dimensions (recipe for
-    // overflows/underflows, divisions by zero and NaNs later).
-    if (tensor_size == 0) {
-      for (int i = 0; i < NumDims; ++i) {
-        m_block_dimensions[i] = 1;
-      }
-      m_total_block_count = 0;
-      return;
-    }
-
-    // If tensor fits into a target block size, evaluate it as a single block.
-    if (tensor_size <= target_block_size) {
-      m_block_dimensions = m_tensor_dimensions;
-      m_total_block_count = 1;
-      // The only valid block index is `0`, and in this case we do not need
-      // to compute real strides for tensor or blocks (see blockDescriptor).
-      for (int i = 0; i < NumDims; ++i) {
-        m_tensor_strides[i] = 0;
-        m_block_strides[i] = 1;
-      }
-      return;
-    }
-
-    static const bool isColMajor = Layout == static_cast<int>(ColMajor);
-
-    // Block shape skewed towards inner dimension.
-    if (shape_type == TensorBlockShapeType::kSkewedInnerDims) {
-      IndexType coeff_to_allocate = target_block_size;
-
-      for (int i = 0; i < NumDims; ++i) {
-        const int dim = isColMajor ? i : NumDims - i - 1;
-        m_block_dimensions[dim] =
-            numext::mini(coeff_to_allocate, m_tensor_dimensions[dim]);
-        coeff_to_allocate = divup(
-            coeff_to_allocate,
-            numext::maxi(static_cast<IndexType>(1), m_block_dimensions[dim]));
-      }
-      eigen_assert(coeff_to_allocate == 1);
-
-    } else if (shape_type == TensorBlockShapeType::kUniformAllDims) {
-      // Tensor will not fit within 'target_block_size' budget: calculate tensor
-      // block dimension sizes based on "square" dimension size target.
-      const IndexType dim_size_target = convert_index<IndexType>(
-          std::pow(static_cast<float>(target_block_size),
-                   1.0f / static_cast<float>(m_block_dimensions.rank())));
-
-      for (int i = 0; i < NumDims; ++i) {
-        // TODO(andydavis) Adjust the inner most 'block_dim_size' to make it
-        // a multiple of the packet size. Note that reducing
-        // 'block_dim_size' in this manner can increase the number of
-        // blocks, and so will amplify any per-block overhead.
-        m_block_dimensions[i] =
-            numext::mini(dim_size_target, m_tensor_dimensions[i]);
-      }
-
-      // Add any un-allocated coefficients to inner dimension(s).
-      IndexType total_size = m_block_dimensions.TotalSize();
-      for (int i = 0; i < NumDims; ++i) {
-        const int dim = isColMajor ? i : NumDims - i - 1;
-
-        if (m_block_dimensions[dim] < m_tensor_dimensions[dim]) {
-          const IndexType total_size_other_dims =
-              total_size / m_block_dimensions[dim];
-          const IndexType alloc_avail =
-              divup<IndexType>(target_block_size, total_size_other_dims);
-          if (alloc_avail == m_block_dimensions[dim]) {
-            // Insufficient excess coefficients to allocate.
-            break;
-          }
-          m_block_dimensions[dim] =
-              numext::mini(m_tensor_dimensions[dim], alloc_avail);
-          total_size = total_size_other_dims * m_block_dimensions[dim];
-        }
-      }
-
-    } else {
-      eigen_assert(false);  // unknown block shape
-    }
-
-    eigen_assert(m_block_dimensions.TotalSize() >=
-                 numext::mini<IndexType>(target_block_size,
-                                         m_tensor_dimensions.TotalSize()));
-
-    // Calculate block counts by dimension and total block count.
-    DSizes<IndexType, NumDims> block_count;
-    for (int i = 0; i < NumDims; ++i) {
-      block_count[i] = divup(m_tensor_dimensions[i], m_block_dimensions[i]);
-    }
-    m_total_block_count = array_prod(block_count);
-
-    // Calculate block strides (used for enumerating blocks).
-    m_tensor_strides = strides<Layout>(m_tensor_dimensions);
-    m_block_strides = strides<Layout>(block_count);
-  }
-
-  DSizes<IndexType, NumDims> m_tensor_dimensions;
-  TensorBlockResourceRequirements m_requirements;
-
-  DSizes<IndexType, NumDims> m_block_dimensions;
-  IndexType m_total_block_count;
-
-  DSizes<IndexType, NumDims> m_tensor_strides;
-  DSizes<IndexType, NumDims> m_block_strides;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorBlockScratchAllocator is responsible for allocating temporary buffers
-// for block evaluation (output or input block materialization). Given that
-// Eigen expression traversal order is deterministic, all temporary allocations
-// are happening in the same order, and usually have exactly the same size.
-// Scratch allocator keeps a trace of all dynamic allocations, and after the
-// first block evaluation is completed, we should be able to reuse all the
-// temporary buffers for the next block evaluation.
-
-template <typename Device>
-class TensorBlockScratchAllocator {
- public:
-  explicit TensorBlockScratchAllocator(const Device& device)
-      : m_device(device), m_allocation_index(0) {}
-
-  ~TensorBlockScratchAllocator() {
-    for (size_t i = 0; i < m_allocations.size(); ++i) {
-      m_device.deallocate(m_allocations[i].ptr);
-    }
-  }
-
-  void* allocate(size_t size) {
-    // TODO(ezhulenev): Remove when replaced with inlined vector.
-    if (m_allocations.capacity() == 0) m_allocations.reserve(8);
-
-    // Check if we already have an existing allocation att current index.
-    const int num_allocations = static_cast<int>(m_allocations.size());
-    const bool has_allocation = m_allocation_index < num_allocations;
-
-    // Allocation index can't be larger than the number of allocations.
-    eigen_assert(m_allocation_index <= num_allocations);
-
-    // If we have existing allocation, and its size is larger or equal to
-    // requested size, we do nothing.
-
-    // If current allocation can't fit requested size, we deallocate it, and
-    // replace with a larger allocation.
-    if (has_allocation && m_allocations[m_allocation_index].size < size) {
-      m_device.deallocate(m_allocations[m_allocation_index].ptr);
-      m_allocations[m_allocation_index].ptr = m_device.allocate(size);
-      m_allocations[m_allocation_index].size = size;
-    }
-
-    // Make a new allocation if we don't have and existing one.
-    if (!has_allocation) {
-      Allocation allocation;
-      allocation.ptr = m_device.allocate(size);
-      allocation.size = size;
-      m_allocations.push_back(allocation);
-    }
-
-    eigen_assert(m_allocations[m_allocation_index].ptr != NULL);
-    eigen_assert(m_allocations[m_allocation_index].size >= size);
-
-    return m_allocations[m_allocation_index++].ptr;
-  }
-
-  void reset() { m_allocation_index = 0; }
-
- private:
-  struct Allocation {
-    void* ptr;
-    size_t size;
-  };
-
-  const Device& m_device;
-  int m_allocation_index;
-  // TODO(ezhulenev): This should be an inlined vector.
-  std::vector<Allocation> m_allocations;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorBlockKind represents all possible block kinds, that can be produced by
-// TensorEvaluator::evalBlock function.
-enum TensorBlockKind {
-  // Tensor block that is a lazy expression that must be assigned to a
-  // destination using TensorBlockAssign.
-  kExpr,
-
-  // Tensor block that is a view into a memory buffer owned by an underlying
-  // Tensor expression (e.g. it can be a view into a Tensor buffer).
-  kView,
-
-  // Tensor block that was materialized in a scratch memory buffer, allocated
-  // with TensorBlockScratchAllocator. This block must be copied to a
-  // destination, similar to a block of `kExpr` type.
-  kMaterializedInScratch,
-
-  // Tensor block that was materialized directly into the final output memory
-  // buffer. For example if the left side of an assignment is a Tensor, we can
-  // directly materialize the block in the destination memory.
-  //
-  // If strides in the output buffer do not match tensor block strides, the
-  // Tensor expression will be invalid, and should not be used by
-  // TensorBlockAssign or for constructing another block expression.
-  kMaterializedInOutput
-};
-
-// -------------------------------------------------------------------------- //
-// TensorBlockNotImplemented should be used to defined TensorBlock typedef in
-// TensorEvaluators that do not support block evaluation.
-
-class TensorBlockNotImplemented {
- public:
-  typedef void XprType;
-};
-
-// -------------------------------------------------------------------------- //
-// XprScalar extracts Scalar type from the Eigen expressions (if expression type
-// is not void). It's required to be able to define lazy block expression for
-// argument types, that do not support block evaluation.
-
-template <typename XprType>
-struct XprScalar {
-  typedef typename XprType::Scalar type;
-};
-template <>
-struct XprScalar<void> {
-  typedef void type;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorMaterializedBlock is a fully evaluated block of the original tensor,
-// and XprType is just a TensorMap over the data. This block type is typically
-// used to materialize blocks of tensor expressions, that can't be efficiently
-// represented as lazy Tensor expressions with fast coeff/packet operations,
-// e.g. we materialize all broadcasts into evaluated blocks.
-//
-// TensorMaterializedBlock does not own its memory buffer, it's either a memory
-// buffer that backs the original expression (e.g. block is just a view into a
-// Tensor), or a memory buffer allocated with scratch allocator, and in this
-// case the scratch allocator will deallocate it at the end of block based
-// expression execution.
-//
-// If the block was evaluated directly into the output buffer, and strides in
-// the output buffer do not match block strides, the TensorMap expression will
-// be invalid, and should never be used in block assignment or any other tensor
-// expression.
-
-template <typename Scalar, int NumDims, int Layout,
-          typename IndexType = Eigen::Index>
-class TensorMaterializedBlock {
- public:
-  typedef DSizes<IndexType, NumDims> Dimensions;
-  typedef TensorMap<const Tensor<Scalar, NumDims, Layout> > XprType;
-
-  TensorMaterializedBlock(TensorBlockKind kind, const Scalar* data,
-                          const Dimensions& dimensions, bool valid_expr = true)
-      : m_kind(kind),
-        m_data(data),
-        m_dimensions(dimensions),
-        m_expr(m_data, m_dimensions),
-        m_valid_expr(valid_expr) {
-    eigen_assert(m_kind == internal::TensorBlockKind::kView ||
-                 m_kind == internal::TensorBlockKind::kMaterializedInScratch ||
-                 m_kind == internal::TensorBlockKind::kMaterializedInOutput);
-  }
-
-  TensorBlockKind kind() const { return m_kind; }
-  // NOTE(ezhulenev): Returning XprType by value like in other block types
-  // causes asan failures. The theory is that XprType::Nested doesn't work
-  // properly for TensorMap.
-  const XprType& expr() const {
-    eigen_assert(m_valid_expr);
-    return m_expr;
-  }
-  const Scalar* data() const { return m_data; }
-  void cleanup() {}
-
-  typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
-
-  // TensorMaterializedBlock can be backed by different types of storage:
-  //
-  //   (1) Contiguous block of memory allocated with scratch allocator.
-  //   (2) Contiguous block of memory reused from tensor block descriptor
-  //       destination buffer.
-  //   (3) Strided block of memory reused from tensor block descriptor
-  //       destination buffer.
-  //
-  class Storage {
-   public:
-    Scalar* data() const { return m_data; }
-    const Dimensions& dimensions() const { return m_dimensions; }
-    const Dimensions& strides() const { return m_strides; }
-
-    TensorMaterializedBlock AsTensorMaterializedBlock() const {
-      return TensorMaterializedBlock(
-          m_materialized_in_output
-              ? internal::TensorBlockKind::kMaterializedInOutput
-              : internal::TensorBlockKind::kMaterializedInScratch,
-          m_data, m_dimensions, !m_strided_storage);
-    }
-
-   private:
-    friend class TensorMaterializedBlock;
-
-    Storage(Scalar* data, const Dimensions& dimensions,
-            const Dimensions& strides, bool materialized_in_output,
-            bool strided_storage)
-        : m_data(data),
-          m_dimensions(dimensions),
-          m_strides(strides),
-          m_materialized_in_output(materialized_in_output),
-          m_strided_storage(strided_storage) {}
-
-    Scalar* m_data;
-    Dimensions m_dimensions;
-    Dimensions m_strides;
-    bool m_materialized_in_output;
-    bool m_strided_storage;
-  };
-
-  // Creates a storage for materialized block either from the block descriptor
-  // destination buffer, or allocates a new buffer with scratch allocator.
-  template <typename TensorBlockScratch>
-  EIGEN_STRONG_INLINE static Storage prepareStorage(
-      TensorBlockDesc& desc, TensorBlockScratch& scratch,
-      bool allow_strided_storage = false) {
-    // Try to reuse destination as an output block buffer.
-    typedef typename TensorBlockDesc::DestinationBuffer DestinationBuffer;
-
-    if (desc.destination().kind() == DestinationBuffer::kContiguous) {
-      Scalar* buffer = desc.destination().template data<Scalar>();
-      desc.DropDestinationBuffer();
-      return Storage(buffer, desc.dimensions(),
-                     internal::strides<Layout>(desc.dimensions()),
-                     /*materialized_in_output=*/true,
-                     /*strided_storage=*/false);
-
-    } else if (desc.destination().kind() == DestinationBuffer::kStrided &&
-               allow_strided_storage) {
-      Scalar* buffer = desc.destination().template data<Scalar>();
-      desc.DropDestinationBuffer();
-      return Storage(buffer, desc.dimensions(), desc.destination().strides(),
-                     /*materialized_in_output=*/true, /*strided_storage=*/true);
-
-    } else {
-      void* mem = scratch.allocate(desc.size() * sizeof(Scalar));
-      return Storage(static_cast<Scalar*>(mem), desc.dimensions(),
-                     internal::strides<Layout>(desc.dimensions()),
-                     /*materialized_in_output=*/false,
-                     /*strided_storage=*/false);
-    }
-  }
-
-  // Creates a materialized block for the given descriptor from a memory buffer.
-  template <typename DataDimensions, typename TensorBlockScratch>
-  EIGEN_STRONG_INLINE static TensorMaterializedBlock materialize(
-      const Scalar* data, const DataDimensions& data_dims,
-      TensorBlockDesc& desc, TensorBlockScratch& scratch) {
-    eigen_assert(array_size<DataDimensions>::value == desc.dimensions().size());
-
-    // If a tensor block dimensions covers a contiguous block of the underlying
-    // memory, we can skip block buffer memory allocation, and construct a block
-    // from existing `data` memory buffer.
-    //
-    // Example: (RowMajor layout)
-    //   data_dims:          [11, 12, 13, 14]
-    //   desc.dimensions():  [1,   1,  3, 14]
-    //
-    // In this case we can construct a TensorBlock starting at
-    // `data + desc.offset()`, with a `desc.dimensions()` block sizes.
-    static const bool is_col_major = Layout == ColMajor;
-
-    // Find out how many inner dimensions have a matching size.
-    int num_matching_inner_dims = 0;
-    for (int i = 0; i < NumDims; ++i) {
-      int dim = is_col_major ? i : NumDims - i - 1;
-      if (data_dims[dim] != desc.dimensions()[dim]) break;
-      ++num_matching_inner_dims;
-    }
-
-    // All the outer dimensions must be of size `1`, except a single dimension
-    // before the matching inner dimension (`3` in the example above).
-    bool can_use_direct_access = true;
-    for (int i = num_matching_inner_dims + 1; i < NumDims; ++i) {
-      int dim = is_col_major ? i : NumDims - i - 1;
-      if (desc.dimension(dim) != 1) {
-        can_use_direct_access = false;
-        break;
-      }
-    }
-
-    if (can_use_direct_access) {
-      const Scalar* block_start = data + desc.offset();
-      return TensorMaterializedBlock(internal::TensorBlockKind::kView,
-                                     block_start, desc.dimensions());
-
-    } else {
-      // Reuse destination buffer or allocate new buffer with scratch allocator.
-      const Storage storage = prepareStorage(desc, scratch);
-
-      typedef internal::TensorBlockIO<Scalar, IndexType, NumDims, Layout>
-          TensorBlockIO;
-      typedef typename TensorBlockIO::Dst TensorBlockIODst;
-      typedef typename TensorBlockIO::Src TensorBlockIOSrc;
-
-      TensorBlockIOSrc src(internal::strides<Layout>(Dimensions(data_dims)),
-                           data, desc.offset());
-      TensorBlockIODst dst(storage.dimensions(), storage.strides(),
-                           storage.data());
-
-      TensorBlockIO::Copy(dst, src);
-      return storage.AsTensorMaterializedBlock();
-    }
-  }
-
- private:
-  TensorBlockKind m_kind;
-  const Scalar* m_data;
-  Dimensions m_dimensions;
-  XprType m_expr;
-  bool m_valid_expr;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorCwiseUnaryBlock is a lazy tensor expression block that applies UnaryOp
-// functor to the blocks produced by the underlying Tensor expression.
-
-template <typename UnaryOp, typename ArgTensorBlock>
-class TensorCwiseUnaryBlock {
-  static const bool NoArgBlockAccess =
-      internal::is_void<typename ArgTensorBlock::XprType>::value;
-
- public:
-  typedef typename conditional<
-      NoArgBlockAccess, void,
-      TensorCwiseUnaryOp<UnaryOp, const typename ArgTensorBlock::XprType> >::
-      type XprType;
-
-  typedef typename XprScalar<XprType>::type Scalar;
-
-  TensorCwiseUnaryBlock(const ArgTensorBlock& arg_block, const UnaryOp& functor)
-      : m_arg_block(arg_block), m_functor(functor) {}
-
-  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
-
-  XprType expr() const { return XprType(m_arg_block.expr(), m_functor); }
-  const Scalar* data() const { return NULL; }
-  void cleanup() { m_arg_block.cleanup(); }
-
- private:
-  ArgTensorBlock m_arg_block;
-  UnaryOp m_functor;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorCwiseUnaryBlock is a lazy tensor expression block that applies BinaryOp
-// functor to the blocks produced by the underlying Tensor expression.
-
-template <typename BinaryOp, typename LhsTensorBlock, typename RhsTensorBlock>
-class TensorCwiseBinaryBlock {
-  static const bool NoArgBlockAccess =
-      internal::is_void<typename LhsTensorBlock::XprType>::value ||
-      internal::is_void<typename RhsTensorBlock::XprType>::value;
-
- public:
-  typedef typename conditional<
-      NoArgBlockAccess, void,
-      TensorCwiseBinaryOp<BinaryOp, const typename LhsTensorBlock::XprType,
-                          const typename RhsTensorBlock::XprType> >::type
-      XprType;
-
-  typedef typename XprScalar<XprType>::type Scalar;
-
-  TensorCwiseBinaryBlock(const LhsTensorBlock& left_block,
-                         const RhsTensorBlock& right_block,
-                         const BinaryOp& functor)
-      : m_left_block(left_block),
-        m_right_block(right_block),
-        m_functor(functor) {}
-
-  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
-
-  XprType expr() const {
-    return XprType(m_left_block.expr(), m_right_block.expr(), m_functor);
-  }
-
-  const Scalar* data() const { return NULL; }
-
-  void cleanup() {
-    m_left_block.cleanup();
-    m_right_block.cleanup();
-  }
-
- private:
-  LhsTensorBlock m_left_block;
-  RhsTensorBlock m_right_block;
-  BinaryOp m_functor;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorUnaryExprBlock is a lazy tensor expression block that can construct
-// an arbitrary tensor expression from a block of the underlying type (this is a
-// generalization of the TensorCwiseUnaryBlock for arbitrary expressions).
-
-template <typename BlockFactory, typename ArgTensorBlock>
-class TensorUnaryExprBlock {
-  typedef typename ArgTensorBlock::XprType ArgXprType;
-  static const bool NoArgBlockAccess = internal::is_void<ArgXprType>::value;
-
- public:
-  typedef typename conditional<
-      NoArgBlockAccess, void,
-      typename BlockFactory::template XprType<ArgXprType>::type>::type XprType;
-
-  typedef typename XprScalar<XprType>::type Scalar;
-
-  TensorUnaryExprBlock(const ArgTensorBlock& arg_block,
-                       const BlockFactory& factory)
-      : m_arg_block(arg_block), m_factory(factory) {}
-
-  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
-  XprType expr() const { return m_factory.expr(m_arg_block.expr()); }
-  const Scalar* data() const { return NULL; }
-  void cleanup() { m_arg_block.cleanup(); }
-
- private:
-  ArgTensorBlock m_arg_block;
-  BlockFactory m_factory;
-};
-
-// -------------------------------------------------------------------------- //
-// TensorTernaryExprBlock is a lazy tensor expression block that can construct
-// an arbitrary tensor expression from three blocks of the underlying type.
-
-template <typename BlockFactory, typename Arg1TensorBlock,
-          typename Arg2TensorBlock, typename Arg3TensorBlock>
-class TensorTernaryExprBlock {
-  typedef typename Arg1TensorBlock::XprType Arg1XprType;
-  typedef typename Arg2TensorBlock::XprType Arg2XprType;
-  typedef typename Arg3TensorBlock::XprType Arg3XprType;
-
-  static const bool NoArgBlockAccess = internal::is_void<Arg1XprType>::value ||
-                                       internal::is_void<Arg2XprType>::value ||
-                                       internal::is_void<Arg3XprType>::value;
-
- public:
-  typedef typename conditional<
-      NoArgBlockAccess, void,
-      typename BlockFactory::template XprType<Arg1XprType, Arg2XprType,
-                                              Arg3XprType>::type>::type XprType;
-
-  typedef typename XprScalar<XprType>::type Scalar;
-
-  TensorTernaryExprBlock(const Arg1TensorBlock& arg1_block,
-                         const Arg2TensorBlock& arg2_block,
-                         const Arg3TensorBlock& arg3_block,
-                         const BlockFactory& factory)
-      : m_arg1_block(arg1_block),
-        m_arg2_block(arg2_block),
-        m_arg3_block(arg3_block),
-        m_factory(factory) {}
-
-  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
-  XprType expr() const {
-    return m_factory.expr(m_arg1_block.expr(), m_arg2_block.expr(),
-                          m_arg3_block.expr());
-  }
-  const Scalar* data() const { return NULL; }
-  void cleanup() {
-    m_arg1_block.cleanup();
-    m_arg2_block.cleanup();
-    m_arg3_block.cleanup();
-  }
-
- private:
-  Arg1TensorBlock m_arg1_block;
-  Arg2TensorBlock m_arg2_block;
-  Arg3TensorBlock m_arg3_block;
-  BlockFactory m_factory;
-};
-
-// -------------------------------------------------------------------------- //
-// StridedLinearBufferCopy provides a method to copy data between two linear
-// buffers with different strides, with optimized paths for scatter/gather.
-
-template <typename Scalar, typename IndexType>
-class StridedLinearBufferCopy {
-  typedef typename packet_traits<Scalar>::type Packet;
-  enum {
-    Vectorizable = packet_traits<Scalar>::Vectorizable,
-    PacketSize = packet_traits<Scalar>::size
-  };
-
- public:
-  // Specifying linear copy kind statically gives ~30% speedup for small sizes.
-  enum class Kind {
-    Linear = 0,       // src_stride == 1 && dst_stride == 1
-    Scatter = 1,      // src_stride == 1 && dst_stride != 1
-    FillLinear = 2,   // src_stride == 0 && dst_stride == 1
-    FillScatter = 3,  // src_stride == 0 && dst_stride != 1
-    Gather = 4,       // dst_stride == 1
-    Random = 5        // everything else
-  };
-
-  struct Dst {
-    Dst(IndexType o, IndexType s, Scalar* d) : offset(o), stride(s), data(d) {}
-
-    IndexType offset;
-    IndexType stride;
-    Scalar* data;
-  };
-
-  struct Src {
-    Src(IndexType o, IndexType s, const Scalar* d)
-        : offset(o), stride(s), data(d) {}
-
-    IndexType offset;
-    IndexType stride;
-    const Scalar* data;
-  };
-
-  template <typename StridedLinearBufferCopy::Kind kind>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Dst& dst,
-                                                        const Src& src,
-                                                        const size_t count) {
-    Run<kind>(count, dst.offset, dst.stride, dst.data, src.offset, src.stride,
-              src.data);
-  }
-
- private:
-  template <typename StridedLinearBufferCopy::Kind kind>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      const IndexType count, const IndexType dst_offset,
-      const IndexType dst_stride, Scalar* EIGEN_RESTRICT dst_data,
-      const IndexType src_offset, const IndexType src_stride,
-      const Scalar* EIGEN_RESTRICT src_data) {
-    const Scalar* src = &src_data[src_offset];
-    Scalar* dst = &dst_data[dst_offset];
-
-    if (!Vectorizable) {
-      for (Index i = 0; i < count; ++i) {
-        dst[i * dst_stride] = src[i * src_stride];
-      }
-      return;
-    }
-
-    const IndexType vectorized_size = count - PacketSize;
-    IndexType i = 0;
-
-    if (kind == StridedLinearBufferCopy::Kind::Linear) {
-      // ******************************************************************** //
-      // Linear copy from `src` to `dst`.
-      const IndexType unrolled_size = count - 4 * PacketSize;
-      eigen_assert(src_stride == 1 && dst_stride == 1);
-      for (; i <= unrolled_size; i += 4 * PacketSize) {
-        for (int j = 0; j < 4; ++j) {
-          Packet p = ploadu<Packet>(src + i + j * PacketSize);
-          pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
-        }
-      }
-      for (; i <= vectorized_size; i += PacketSize) {
-        Packet p = ploadu<Packet>(src + i);
-        pstoreu<Scalar, Packet>(dst + i, p);
-      }
-      for (; i < count; ++i) {
-        dst[i] = src[i];
-      }
-      // ******************************************************************** //
-    } else if (kind == StridedLinearBufferCopy::Kind::Scatter) {
-      // Scatter from `src` to `dst`.
-      eigen_assert(src_stride == 1 && dst_stride != 1);
-      for (; i <= vectorized_size; i += PacketSize) {
-        Packet p = ploadu<Packet>(src + i);
-        pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
-      }
-      for (; i < count; ++i) {
-        dst[i * dst_stride] = src[i];
-      }
-      // ******************************************************************** //
-    } else if (kind == StridedLinearBufferCopy::Kind::FillLinear) {
-      // Fill `dst` with value at `*src`.
-      eigen_assert(src_stride == 0 && dst_stride == 1);
-      const IndexType unrolled_size = count - 4 * PacketSize;
-      Packet p = pload1<Packet>(src);
-      for (; i <= unrolled_size; i += 4 * PacketSize) {
-        for (int j = 0; j < 4; ++j) {
-          pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
-        }
-      }
-      for (; i <= vectorized_size; i += PacketSize) {
-        pstoreu<Scalar, Packet>(dst + i, p);
-      }
-      for (; i < count; ++i) {
-        dst[i] = *src;
-      }
-      // ******************************************************************** //
-    } else if (kind == StridedLinearBufferCopy::Kind::FillScatter) {
-      // Scatter `*src` into `dst`.
-      eigen_assert(src_stride == 0 && dst_stride != 1);
-      Packet p = pload1<Packet>(src);
-      for (; i <= vectorized_size; i += PacketSize) {
-        pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
-      }
-      for (; i < count; ++i) {
-        dst[i * dst_stride] = *src;
-      }
-      // ******************************************************************** //
-    } else if (kind == StridedLinearBufferCopy::Kind::Gather) {
-      // Gather from `src` into `dst`.
-      eigen_assert(dst_stride == 1);
-      for (; i <= vectorized_size; i += PacketSize) {
-        Packet p = pgather<Scalar, Packet>(src + i * src_stride, src_stride);
-        pstoreu<Scalar, Packet>(dst + i, p);
-      }
-      for (; i < count; ++i) {
-        dst[i] = src[i * src_stride];
-      }
-      // ******************************************************************** //
-    } else if (kind == StridedLinearBufferCopy::Kind::Random) {
-      // Random.
-      for (; i < count; ++i) {
-        dst[i * dst_stride] = src[i * src_stride];
-      }
-    } else {
-      eigen_assert(false);
-    }
-  }
-};
-
-// -------------------------------------------------------------------------- //
-// TensorBlockIO copies data from `src` tensor block, to the `dst` tensor block.
-// It's possible to specify src->dst dimension mapping for the copy operation.
-// Dimensions of `dst` specify how many elements have to be copied, for the
-// `src` we need to know only stride to navigate through source memory buffer.
-
-template <typename Scalar, typename IndexType, int NumDims, int Layout>
-class TensorBlockIO {
-  static const bool IsColMajor = (Layout == ColMajor);
-
-  typedef StridedLinearBufferCopy<Scalar, IndexType> LinCopy;
-
- public:
-  typedef DSizes<IndexType, NumDims> Dimensions;
-  typedef DSizes<int, NumDims> DimensionsMap;
-
-  struct Dst {
-    Dst(const Dimensions& dst_dims, const Dimensions& dst_strides, Scalar* dst,
-        IndexType dst_offset = 0)
-        : dims(dst_dims), strides(dst_strides), data(dst), offset(dst_offset) {}
-
-    Dimensions dims;
-    Dimensions strides;
-    Scalar* data;
-    IndexType offset;
-  };
-
-  struct Src {
-    Src(const Dimensions& src_strides, const Scalar* src,
-        IndexType src_offset = 0)
-        : strides(src_strides), data(src), offset(src_offset) {}
-
-    Dimensions strides;
-    const Scalar* data;
-    IndexType offset;
-  };
-
-  // Copies data to `dst` from `src`, using provided dimensions mapping:
-  //
-  //   src_dimension_index = dst_to_src_dim_map[dst_dimension_index]
-  //
-  // Returns the number of copied elements.
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType Copy(
-      const Dst& dst, const Src& src, const DimensionsMap& dst_to_src_dim_map) {
-    // Copy single scalar value from `src` to `dst`.
-    if (NumDims == 0) {
-      *(dst.data + dst.offset) = *(src.data + src.offset);
-      return 1;
-    }
-
-    // Both `dst` and `src` must have contiguous innermost dimension. We also
-    // accept the special case with stride '0', because it's used as a trick to
-    // implement broadcasting.
-    {
-      int inner_dim = IsColMajor ? 0 : NumDims - 1;
-      EIGEN_UNUSED_VARIABLE(inner_dim);
-      eigen_assert(dst.strides[inner_dim] == 1 || dst.strides[inner_dim] == 0);
-      eigen_assert(src.strides[inner_dim] == 1 || src.strides[inner_dim] == 0);
-    }
-
-    // Give a shorter name to `dst_to_src_dim_map`.
-    const DimensionsMap& dim_map = dst_to_src_dim_map;
-
-    // Do not squeeze reordered inner dimensions.
-    int num_squeezable_dims = NumSqueezableInnerDims(dim_map);
-
-    // NOTE: We find the innermost dimension (contiguous in memory) in the dst
-    // block, and we write data linearly into that dimension, reading it from
-    // the src. If dimensions are reordered, we might end up reading data from
-    // the src with `stride != 1`.
-    //
-    // NOTE: Random-Read/Linear-Write can be up to ~2X faster than
-    // Linear-Read/Random-Write: https://stackoverflow.com/a/54935680
-
-    // Find the innermost dimension in the dst whose size is not 1. This is the
-    // effective inner dim.
-    int num_size_one_inner_dims = 0;
-    for (int i = 0; i < num_squeezable_dims; ++i) {
-      const int dst_dim = IsColMajor ? i : NumDims - i - 1;
-      if (dst.dims[dst_dim] != 1) break;
-      num_size_one_inner_dims++;
-    }
-
-    // If all dimensions are of size 1, just copy a scalar from `src` to `dst`.
-    if (num_size_one_inner_dims == NumDims) {
-      *(dst.data + dst.offset) = *(src.data + src.offset);
-      return 1;
-    }
-
-    // Outermost dimension in the dst with `stride == 1` (contiguous in memory).
-    const int dst_stride1_dim = IsColMajor
-                                    ? num_size_one_inner_dims
-                                    : NumDims - num_size_one_inner_dims - 1;
-
-    // Dimension in the src that corresponds to the dst innermost dimension.
-    const int src_dim_for_dst_stride1_dim =
-        NumDims == 0 ? 1 : dim_map[dst_stride1_dim];
-
-    // Size of the innermost dimension (length of contiguous blocks of memory).
-    IndexType dst_inner_dim_size = NumDims == 0 ? 1 : dst.dims[dst_stride1_dim];
-
-    // Squeeze multiple inner dims into one if they are contiguous in `dst` and
-    // `src` memory, so we can do less linear copy calls.
-    for (int i = num_size_one_inner_dims + 1; i < num_squeezable_dims; ++i) {
-      const int dst_dim = IsColMajor ? i : NumDims - i - 1;
-      const IndexType dst_stride = dst.strides[dst_dim];
-      const IndexType src_stride = src.strides[dim_map[dst_dim]];
-      if (dst_inner_dim_size == dst_stride && dst_stride == src_stride) {
-        dst_inner_dim_size *= dst.dims[dst_dim];
-        ++num_size_one_inner_dims;
-      } else {
-        break;
-      }
-    }
-
-    // Setup strides to read data from `src` and write to `dst`.
-    IndexType input_offset = src.offset;
-    IndexType output_offset = dst.offset;
-    IndexType input_stride =
-        NumDims == 0 ? 1 : src.strides[src_dim_for_dst_stride1_dim];
-    IndexType output_stride = NumDims == 0 ? 1 : dst.strides[dst_stride1_dim];
-
-    const int at_least_1_dim = NumDims <= 1 ? 1 : NumDims - 1;
-    array<BlockIteratorState, at_least_1_dim> it;
-
-    // Initialize block iterator state. Squeeze away any dimension of size 1.
-    int idx = 0;  // currently initialized iterator state index
-    for (int i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
-      const int dst_dim = IsColMajor ? i + 1 : NumDims - i - 2;
-      if (dst.dims[dst_dim] == 1) continue;
-
-      it[idx].size = dst.dims[dst_dim];
-      it[idx].input_stride = src.strides[dim_map[dst_dim]];
-      it[idx].output_stride = dst.strides[dst_dim];
-
-      it[idx].input_span = it[idx].input_stride * (it[idx].size - 1);
-      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
-
-      idx++;
-    }
-
-    // Iterate copying data from src to dst.
-    const IndexType block_total_size = NumDims == 0 ? 1 : dst.dims.TotalSize();
-
-#define COPY_INNER_DIM(KIND)                                           \
-  IndexType num_copied = 0;                                            \
-  for (num_copied = 0; num_copied < block_total_size;                  \
-       num_copied += dst_inner_dim_size) {                             \
-    LinCopy::template Run<KIND>(                                       \
-        typename LinCopy::Dst(output_offset, output_stride, dst.data), \
-        typename LinCopy::Src(input_offset, input_stride, src.data),   \
-        dst_inner_dim_size);                                           \
-                                                                       \
-    for (int j = 0; j < idx; ++j) {                                    \
-      if (++it[j].count < it[j].size) {                                \
-        input_offset += it[j].input_stride;                            \
-        output_offset += it[j].output_stride;                          \
-        break;                                                         \
-      }                                                                \
-      it[j].count = 0;                                                 \
-      input_offset -= it[j].input_span;                                \
-      output_offset -= it[j].output_span;                              \
-    }                                                                  \
-  }                                                                    \
-  return num_copied;
-
-    if (input_stride == 1 && output_stride == 1) {
-      COPY_INNER_DIM(LinCopy::Kind::Linear);
-    } else if (input_stride == 1 && output_stride != 1) {
-      COPY_INNER_DIM(LinCopy::Kind::Scatter);
-    } else if (input_stride == 0 && output_stride == 1) {
-      COPY_INNER_DIM(LinCopy::Kind::FillLinear);
-    } else if (input_stride == 0 && output_stride != 1) {
-      COPY_INNER_DIM(LinCopy::Kind::FillScatter);
-    } else if (output_stride == 1) {
-      COPY_INNER_DIM(LinCopy::Kind::Gather);
-    } else {
-      COPY_INNER_DIM(LinCopy::Kind::Random);
-    }
-
-#undef COPY_INNER_DIM
-  }
-
-  // Copy from `src` to `dst` with an identity src->dst dimension map. Returns
-  // the number of copied elements.
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexType Copy(const Dst& dst,
-                                                              const Src& src) {
-    DimensionsMap dst_to_src_map;
-    for (int i = 0; i < NumDims; ++i) dst_to_src_map[i] = i;
-    return Copy(dst, src, dst_to_src_map);
-  }
-
- private:
-  struct BlockIteratorState {
-    BlockIteratorState()
-        : size(0),
-          count(0),
-          input_stride(0),
-          output_stride(0),
-          input_span(0),
-          output_span(0) {}
-
-    IndexType size;
-    IndexType count;
-    IndexType input_stride;
-    IndexType output_stride;
-    IndexType input_span;
-    IndexType output_span;
-  };
-
-  // Compute how many inner dimensions it's allowed to squeeze when doing IO
-  // between two tensor blocks. It's safe to squeeze inner dimensions, only
-  // if they are not reordered.
-  static int NumSqueezableInnerDims(const DimensionsMap& dim_map) {
-    int num_squeezable_dims = 0;
-    for (int i = 0; i < NumDims; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-      if (dim_map[dim] != dim) break;
-      num_squeezable_dims++;
-    }
-    return num_squeezable_dims;
-  }
-};
-
-// -------------------------------------------------------------------------- //
-// TensorBlockAssignment assigns a block expression of type `TensorBlockExpr` to
-// a Tensor block defined by `desc`, backed by a memory buffer at `target`.
-//
-// Currently there is no way to write from a Tensor expression to a block of
-// memory, if dimensions are reordered. If you need to do that, you should
-// materialize a Tensor block expression into a memory buffer, and then use
-// TensorBlockIO to copy data between two memory buffers with a custom
-// `target->src` dimension map (see definition above).
-//
-// Also currently the innermost dimension of `target` must have a stride '1'
-// (contiguous in memory). This restriction could be lifted with a `pscatter`,
-// but in practice it's never needed, and there is a similar TensorBlockIO
-// workaround for that.
-//
-// TODO(ezhulenev): TensorBlockAssignment is a special case of TensorBlockIO
-// where `src` is a tensor expression. Explore if it is possible to rewrite IO
-// to use expressions instead of pointers, and after that TensorBlockAssignment
-// will become an alias to IO.
-template <typename Scalar, int NumDims, typename TensorBlockExpr,
-          typename IndexType = Eigen::Index>
-class TensorBlockAssignment {
-  // We will use coeff/packet path to evaluate block expressions.
-  typedef TensorEvaluator<const TensorBlockExpr, DefaultDevice>
-      TensorBlockEvaluator;
-
-  typedef DSizes<IndexType, NumDims> Dimensions;
-
-  enum {
-    Vectorizable = packet_traits<Scalar>::Vectorizable,
-    PacketSize = packet_traits<Scalar>::size
-  };
-
-  template <bool Vectorizable, typename Evaluator>
-  struct InnerDimAssign {
-    EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count,
-                                        const Evaluator& eval,
-                                        IndexType eval_offset) {
-      for (IndexType i = 0; i < count; ++i) {
-        target[i] = eval.coeff(eval_offset + i);
-      }
-    }
-  };
-
-  template <typename Evaluator>
-  struct InnerDimAssign<true, Evaluator> {
-    EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count,
-                                        const Evaluator& eval,
-                                        IndexType eval_offset) {
-      typedef typename packet_traits<Scalar>::type Packet;
-
-      const IndexType unrolled_size = count - 4 * PacketSize;
-      const IndexType vectorized_size = count - PacketSize;
-      IndexType i = 0;
-
-      for (; i <= unrolled_size; i += 4 * PacketSize) {
-        for (int j = 0; j < 4; ++j) {
-          const IndexType idx = eval_offset + i + j * PacketSize;
-          Packet p = eval.template packet<Unaligned>(idx);
-          pstoreu<Scalar>(target + i + j * PacketSize, p);
-        }
-      }
-
-      for (; i <= vectorized_size; i += PacketSize) {
-        Packet p = eval.template packet<Unaligned>(eval_offset + i);
-        pstoreu<Scalar>(target + i, p);
-      }
-
-      for (; i < count; ++i) {
-        target[i] = eval.coeff(eval_offset + i);
-      }
-    }
-  };
-
- public:
-  struct Target {
-    Target(const Dimensions& target_dims, const Dimensions& target_strides,
-           Scalar* target_data, IndexType target_offset = 0)
-        : dims(target_dims),
-          strides(target_strides),
-          data(target_data),
-          offset(target_offset) {}
-
-    Dimensions dims;
-    Dimensions strides;
-    Scalar* data;
-    IndexType offset;
-  };
-
-  static Target target(const Dimensions& target_dims,
-                       const Dimensions& target_strides, Scalar* target_data,
-                       IndexType target_offset = 0) {
-    return Target(target_dims, target_strides, target_data, target_offset);
-  }
-
-  template <typename TargetDimsIndexType, typename TargetStridesIndexType>
-  static Target target(
-      const DSizes<TargetDimsIndexType, NumDims>& target_dims,
-      const DSizes<TargetStridesIndexType, NumDims>& target_strides,
-      Scalar* target_data, IndexType target_offset = 0) {
-    // DSizes constructor will do index type promotion if it's safe.
-    return Target(Dimensions(target_dims), Dimensions(target_strides),
-                  target_data, target_offset);
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      const Target& target, const TensorBlockExpr& expr) {
-    // Prepare evaluator for block expression.
-    DefaultDevice default_device;
-    TensorBlockEvaluator eval(expr, default_device);
-
-    // Tensor block expression dimension should match destination dimensions.
-    eigen_assert(dimensions_match(target.dims, eval.dimensions()));
-
-    static const int Layout = TensorBlockEvaluator::Layout;
-    static const bool is_col_major = Layout == ColMajor;
-
-    // Initialize output inner dimension size based on a layout.
-    const IndexType output_size = NumDims == 0 ? 1 : target.dims.TotalSize();
-    const int inner_dim_idx = is_col_major ? 0 : NumDims - 1;
-    IndexType output_inner_dim_size = target.dims[inner_dim_idx];
-
-    // Target inner dimension stride must be '1'.
-    eigen_assert(target.strides[inner_dim_idx] == 1);
-
-    // Squeeze multiple inner dims into one if they are contiguous in `target`.
-    IndexType num_squeezed_dims = 0;
-    for (Index i = 1; i < NumDims; ++i) {
-      const Index dim = is_col_major ? i : NumDims - i - 1;
-      const IndexType target_stride = target.strides[dim];
-
-      if (output_inner_dim_size == target_stride) {
-        output_inner_dim_size *= target.dims[dim];
-        num_squeezed_dims++;
-      } else {
-        break;
-      }
-    }
-
-    // Initialize output block iterator state. Dimension in this array are
-    // always in inner_most -> outer_most order (col major layout).
-    array<BlockIteratorState, NumDims> it;
-
-    int idx = 0;  // currently initialized iterator state index
-    for (Index i = num_squeezed_dims; i < NumDims - 1; ++i) {
-      const Index dim = is_col_major ? i + 1 : NumDims - i - 2;
-
-      it[idx].count = 0;
-      it[idx].size = target.dims[dim];
-      it[idx].output_stride = target.strides[dim];
-      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
-      idx++;
-    }
-
-    // We read block expression from the beginning, and start writing data to
-    // `target` at given offset.
-    IndexType input_offset = 0;
-    IndexType output_offset = target.offset;
-
-    // Iterate copying data from `eval` to `target`.
-    for (IndexType i = 0; i < output_size; i += output_inner_dim_size) {
-      // Assign to `target` at current offset.
-      InnerDimAssign<Vectorizable && TensorBlockEvaluator::PacketAccess,
-                     TensorBlockEvaluator>::Run(target.data + output_offset,
-                                                output_inner_dim_size, eval,
-                                                input_offset);
-
-      // Move input offset forward by the number of assigned coefficients.
-      input_offset += output_inner_dim_size;
-
-      // Update index.
-      for (int j = 0; j < idx; ++j) {
-        if (++it[j].count < it[j].size) {
-          output_offset += it[j].output_stride;
-          break;
-        }
-        it[j].count = 0;
-        output_offset -= it[j].output_span;
-      }
-    }
-  }
-
- private:
-  struct BlockIteratorState {
-    BlockIteratorState()
-        : count(0), size(0), output_stride(0), output_span(0) {}
-
-    IndexType count;
-    IndexType size;
-    IndexType output_stride;
-    IndexType output_span;
-  };
-};
-
-// -------------------------------------------------------------------------- //
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
deleted file mode 100644
index a354132..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
+++ /dev/null
@@ -1,1093 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
-#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
-
-namespace Eigen {
-
-/** \class TensorBroadcasting
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor broadcasting class.
-  *
-  *
-  */
-namespace internal {
-template<typename Broadcast, typename XprType>
-struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename Broadcast, typename XprType>
-struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense>
-{
-  typedef const TensorBroadcastingOp<Broadcast, XprType> EIGEN_DEVICE_REF type;
-};
-
-template<typename Broadcast, typename XprType>
-struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type>
-{
-  typedef TensorBroadcastingOp<Broadcast, XprType> type;
-};
-
-template <typename Dims>
-struct is_input_scalar {
-  static const bool value = false;
-};
-template <>
-struct is_input_scalar<Sizes<> > {
-  static const bool value = true;
-};
-#ifndef EIGEN_EMULATE_CXX11_META_H
-template <typename std::ptrdiff_t... Indices>
-struct is_input_scalar<Sizes<Indices...> > {
-  static const bool value = (Sizes<Indices...>::total_size == 1);
-};
-#endif
-
-}  // end namespace internal
-
-
-
-template<typename Broadcast, typename XprType>
-class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast)
-      : m_xpr(expr), m_broadcast(broadcast) {}
-
-    EIGEN_DEVICE_FUNC
-    const Broadcast& broadcast() const { return m_broadcast; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const Broadcast m_broadcast;
-};
-
-
-// Eval as rvalue
-template<typename Broadcast, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
-{
-  typedef TensorBroadcastingOp<Broadcast, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  protected: //  all the non-static fields must have the same access control, otherwise the TensorEvaluator wont be standard layout;
-  bool isCopy, nByOne, oneByN;
-  public:
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    RawAccess         = false
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  // We do block based broadcasting using a trick with 2x tensor rank and 0
-  // strides. See block method implementation for details.
-  typedef DSizes<Index, 2 * NumDims> BroadcastDimensions;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
- typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
-      ArgTensorBlock;
-
-  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : isCopy(false), nByOne(false), oneByN(false),
-        m_device(device), m_broadcast(op.broadcast()), m_impl(op.expression(), device)
-  {
-
-    // The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar
-    // and store the result in a scalar. Instead one should reshape the scalar into a a N-D
-    // tensor with N >= 1 of 1 element first and then broadcast.
-    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    const InputDimensions& input_dims = m_impl.dimensions();
-    isCopy = true;
-    for (int i = 0; i < NumDims; ++i) {
-      eigen_assert(input_dims[i] > 0);
-      m_dimensions[i] = input_dims[i] * m_broadcast[i];
-      if (m_broadcast[i] != 1) {
-        isCopy = false;
-      }
-    }
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputStrides[0] = 1;
-      m_outputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
-        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
-      }
-    } else {
-      m_inputStrides[NumDims-1] = 1;
-      m_outputStrides[NumDims-1] = 1;
-      for (int i = NumDims-2; i >= 0; --i) {
-        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
-        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
-      }
-    }
-
-    if (input_dims[0] == 1) {
-      oneByN = true;
-      for (int i = 1; i < NumDims; ++i) {
-        if (m_broadcast[i] != 1) {
-          oneByN = false;
-          break;
-        }
-      }
-    } else if (input_dims[NumDims-1] == 1) {
-      nByOne = true;
-      for (int i = 0; i < NumDims-1; ++i) {
-        if (m_broadcast[i] != 1) {
-          nByOne = false;
-          break;
-        }
-      }
-    }
-
-    // Handle special format like NCHW, its input shape is '[1, N..., 1]' and
-    // broadcast shape is '[N, 1..., N]'
-    if (!oneByN && !nByOne) {
-      if (input_dims[0] == 1 && input_dims[NumDims-1] == 1 && NumDims > 2) {
-        nByOne = true;
-        oneByN = true;
-        for (int i = 1; i < NumDims-1; ++i) {
-          if (m_broadcast[i] != 1) {
-            nByOne = false;
-            oneByN = false;
-            break;
-          }
-        }
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const
-  {
-    if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
-      return m_impl.coeff(0);
-    }
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      if (isCopy) {
-        return m_impl.coeff(index);
-      } else {
-        return coeffColMajor(index);
-      }
-    } else {
-      if (isCopy) {
-        return m_impl.coeff(index);
-      } else {
-        return coeffRowMajor(index);
-      }
-    }
-  }
-
-  // TODO: attempt to speed this up. The integer divisions and modulo are slow
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexColMajor(Index index) const {
-    Index inputIndex = 0;
-    EIGEN_UNROLL_LOOP
-    for (int i = NumDims - 1; i > 0; --i) {
-      const Index idx = index / m_outputStrides[i];
-      if (internal::index_statically_eq<Broadcast>(i, 1)) {
-        eigen_assert(idx < m_impl.dimensions()[i]);
-        inputIndex += idx * m_inputStrides[i];
-      } else {
-        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
-          eigen_assert(idx % m_impl.dimensions()[i] == 0);
-        } else {
-          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
-        }
-      }
-      index -= idx * m_outputStrides[i];
-    }
-    if (internal::index_statically_eq<Broadcast>(0, 1)) {
-      eigen_assert(index < m_impl.dimensions()[0]);
-      inputIndex += index;
-    } else {
-      if (internal::index_statically_eq<InputDimensions>(0, 1)) {
-        eigen_assert(index % m_impl.dimensions()[0] == 0);
-      } else {
-        inputIndex += (index % m_impl.dimensions()[0]);
-      }
-    }
-    return inputIndex;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const
-  {
-    return m_impl.coeff(indexColMajor(index));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexRowMajor(Index index) const {
-    Index inputIndex = 0;
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < NumDims - 1; ++i) {
-      const Index idx = index / m_outputStrides[i];
-      if (internal::index_statically_eq<Broadcast>(i, 1)) {
-        eigen_assert(idx < m_impl.dimensions()[i]);
-        inputIndex += idx * m_inputStrides[i];
-      } else {
-        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
-          eigen_assert(idx % m_impl.dimensions()[i] == 0);
-        } else {
-          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
-        }
-      }
-      index -= idx * m_outputStrides[i];
-    }
-    if (internal::index_statically_eq<Broadcast>(NumDims - 1, 1)) {
-      eigen_assert(index < m_impl.dimensions()[NumDims - 1]);
-      inputIndex += index;
-    } else {
-      if (internal::index_statically_eq<InputDimensions>(NumDims - 1, 1)) {
-        eigen_assert(index % m_impl.dimensions()[NumDims - 1] == 0);
-      } else {
-        inputIndex += (index % m_impl.dimensions()[NumDims - 1]);
-      }
-    }
-    return inputIndex;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const
-  {
-    return m_impl.coeff(indexRowMajor(index));
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const
-  {
-    if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
-      return internal::pset1<PacketReturnType>(m_impl.coeff(0));
-    }
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      if (isCopy) {
-        #ifdef EIGEN_GPU_COMPILE_PHASE
-        // See PR 437: on NVIDIA P100 and K20m we observed a x3-4 speed up by enforcing
-        // unaligned loads here. The reason is unclear though.
-        return m_impl.template packet<Unaligned>(index);
-        #else
-        return m_impl.template packet<LoadMode>(index);
-        #endif
-      } else if (oneByN && !nByOne) {
-        return packetNByOne<LoadMode>(index);
-      } else if (!oneByN && nByOne) {
-        return packetOneByN<LoadMode>(index);
-      } else if (oneByN && nByOne) {
-        return packetOneByNByOne<LoadMode>(index);
-      } else {
-        return packetColMajor<LoadMode>(index);
-      }
-    } else {
-      if (isCopy) {
-        #ifdef EIGEN_GPU_COMPILE_PHASE
-        // See above.
-        return m_impl.template packet<Unaligned>(index);
-        #else
-        return m_impl.template packet<LoadMode>(index);
-        #endif
-      } else if (oneByN && !nByOne) {
-        return packetOneByN<LoadMode>(index);
-      } else if (!oneByN && nByOne) {
-        return packetNByOne<LoadMode>(index);
-      } else if (oneByN && nByOne) {
-        return packetOneByNByOne<LoadMode>(index);
-      } else {
-        return packetRowMajor<LoadMode>(index);
-      }
-    }
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByNByOne
-  (Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    Index startDim, endDim;
-    Index inputIndex, outputOffset, batchedIndex;
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      startDim = NumDims - 1;
-      endDim = 1;
-    } else {
-      startDim = 0;
-      endDim = NumDims - 2;
-    }
-
-    batchedIndex = index % m_outputStrides[startDim];
-    inputIndex   = batchedIndex / m_outputStrides[endDim];
-    outputOffset = batchedIndex % m_outputStrides[endDim];
-
-    if (outputOffset + PacketSize <= m_outputStrides[endDim]) {
-      values[0] = m_impl.coeff(inputIndex);
-      return internal::pload1<PacketReturnType>(values);
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
-        if (outputOffset + cur < m_outputStrides[endDim]) {
-          values[i] = m_impl.coeff(inputIndex);
-        } else {
-          ++inputIndex;
-          inputIndex = (inputIndex == m_inputStrides[startDim] ? 0 : inputIndex);
-          values[i] = m_impl.coeff(inputIndex);
-          outputOffset = 0;
-          cur = 0;
-        }
-      }
-      return internal::pload<PacketReturnType>(values);
-    }
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByN(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    Index dim, inputIndex;
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      dim = NumDims - 1;
-    } else {
-      dim = 0;
-    }
-
-    inputIndex = index % m_inputStrides[dim];
-    if (inputIndex + PacketSize <= m_inputStrides[dim]) {
-      return m_impl.template packet<Unaligned>(inputIndex);
-    } else {
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < PacketSize; ++i) {
-        if (inputIndex > m_inputStrides[dim]-1) {
-          inputIndex = 0;
-        }
-        values[i] = m_impl.coeff(inputIndex++);
-      }
-      return internal::pload<PacketReturnType>(values);
-    }
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetNByOne(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    Index dim, inputIndex, outputOffset;
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      dim = 1;
-    } else {
-      dim = NumDims - 2;
-    }
-
-    inputIndex   = index / m_outputStrides[dim];
-    outputOffset = index % m_outputStrides[dim];
-    if (outputOffset + PacketSize <= m_outputStrides[dim]) {
-      values[0] = m_impl.coeff(inputIndex);
-      return internal::pload1<PacketReturnType>(values);
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
-        if (outputOffset + cur < m_outputStrides[dim]) {
-          values[i] = m_impl.coeff(inputIndex);
-        } else {
-          values[i] = m_impl.coeff(++inputIndex);
-          outputOffset = 0;
-          cur = 0;
-        }
-      }
-      return internal::pload<PacketReturnType>(values);
-    }
-  }
-
-  // Ignore the LoadMode and always use unaligned loads since we can't guarantee
-  // the alignment at compile time.
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    const Index originalIndex = index;
-
-    Index inputIndex = 0;
-    EIGEN_UNROLL_LOOP
-    for (int i = NumDims - 1; i > 0; --i) {
-      const Index idx = index / m_outputStrides[i];
-      if (internal::index_statically_eq<Broadcast>(i, 1)) {
-        eigen_assert(idx < m_impl.dimensions()[i]);
-        inputIndex += idx * m_inputStrides[i];
-      } else {
-        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
-          eigen_assert(idx % m_impl.dimensions()[i] == 0);
-        } else {
-          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
-        }
-      }
-      index -= idx * m_outputStrides[i];
-    }
-    Index innermostLoc;
-    if (internal::index_statically_eq<Broadcast>(0, 1)) {
-      eigen_assert(index < m_impl.dimensions()[0]);
-      innermostLoc = index;
-    } else {
-      if (internal::index_statically_eq<InputDimensions>(0, 1)) {
-        eigen_assert(index % m_impl.dimensions()[0] == 0);
-        innermostLoc = 0;
-      } else {
-        innermostLoc = index % m_impl.dimensions()[0];
-      }
-    }
-    inputIndex += innermostLoc;
-
-    // Todo: this could be extended to the second dimension if we're not
-    // broadcasting alongside the first dimension, and so on.
-    if (innermostLoc + PacketSize <= m_impl.dimensions()[0]) {
-      return m_impl.template packet<Unaligned>(inputIndex);
-    } else {
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-      values[0] = m_impl.coeff(inputIndex);
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < PacketSize; ++i) {
-        if (innermostLoc + i < m_impl.dimensions()[0]) {
-          values[i] = m_impl.coeff(inputIndex+i);
-        } else {
-          values[i] = coeffColMajor(originalIndex+i);
-        }
-      }
-      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-      return rslt;
-    }
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    const Index originalIndex = index;
-
-    Index inputIndex = 0;
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < NumDims - 1; ++i) {
-      const Index idx = index / m_outputStrides[i];
-      if (internal::index_statically_eq<Broadcast>(i, 1)) {
-        eigen_assert(idx < m_impl.dimensions()[i]);
-        inputIndex += idx * m_inputStrides[i];
-      } else {
-        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
-          eigen_assert(idx % m_impl.dimensions()[i] == 0);
-        } else {
-          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
-        }
-      }
-      index -= idx * m_outputStrides[i];
-    }
-    Index innermostLoc;
-    if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) {
-      eigen_assert(index < m_impl.dimensions()[NumDims-1]);
-      innermostLoc = index;
-    } else {
-      if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) {
-        eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
-        innermostLoc = 0;
-      } else {
-        innermostLoc = index % m_impl.dimensions()[NumDims-1];
-      }
-    }
-    inputIndex += innermostLoc;
-
-    // Todo: this could be extended to the second dimension if we're not
-    // broadcasting alongside the first dimension, and so on.
-    if (innermostLoc + PacketSize <= m_impl.dimensions()[NumDims-1]) {
-      return m_impl.template packet<Unaligned>(inputIndex);
-    } else {
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-      values[0] = m_impl.coeff(inputIndex);
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < PacketSize; ++i) {
-        if (innermostLoc + i < m_impl.dimensions()[NumDims-1]) {
-          values[i] = m_impl.coeff(inputIndex+i);
-        } else {
-          values[i] = coeffRowMajor(originalIndex+i);
-        }
-      }
-      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-      return rslt;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    double compute_cost = TensorOpCost::AddCost<Index>();
-    if (!isCopy && NumDims > 0) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        compute_cost += TensorOpCost::DivCost<Index>();
-        if (internal::index_statically_eq<Broadcast>(i, 1)) {
-          compute_cost +=
-              TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
-        } else {
-          if (!internal::index_statically_eq<InputDimensions>(i, 1)) {
-            compute_cost += TensorOpCost::MulCost<Index>() +
-                            TensorOpCost::ModCost<Index>() +
-                            TensorOpCost::AddCost<Index>();
-          }
-        }
-        compute_cost +=
-            TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
-      }
-    }
-    return m_impl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    // TODO(wuke): Targeting L1 size is 30% faster than targeting L{-1} on large
-    // tensors. But this might need further tuning.
-    const size_t target_size = m_device.firstLevelCacheSize();
-    return internal::TensorBlockResourceRequirements::merge(
-        m_impl.getResourceRequirements(),
-        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    BlockBroadcastingParams params = blockBroadcastingParams(desc);
-
-    if (params.inner_dim_size == 0 || params.bcast_dim_size == 0) {
-      return emptyBlock();
-    }
-
-    // Prepare storage for the materialized broadcasting result.
-    const typename TensorBlock::Storage block_storage =
-        TensorBlock::prepareStorage(desc, scratch);
-    ScalarNoConst* materialized_output = block_storage.data();
-
-    // We potentially will need to materialize input blocks.
-    size_t materialized_input_size = 0;
-    ScalarNoConst* materialized_input = NULL;
-
-    // Initialize block broadcating iterator state for outer dimensions (outer
-    // with regard to bcast dimension). Dimension in this array are always in
-    // inner_most -> outer_most order (col major layout).
-    array<BlockBroadcastingIteratorState, NumDims> it;
-    int idx = 0;
-
-    for (int i = params.inner_dim_count + 1; i < NumDims; ++i) {
-      const Index dim = IsColMajor ? i : NumDims - 1 - i;
-      it[idx].size = params.output_dims[dim];
-      it[idx].count = 0;
-      it[idx].output_stride = m_outputStrides[dim];
-      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
-      idx++;
-    }
-
-    // Write output into the beginning of `materialized_output`.
-    Index output_offset = 0;
-
-    // We will fill output block by broadcasting along the bcast dim, and
-    // iterating over outer dimension.
-    const Index output_size = NumDims == 0 ? 1 : params.output_dims.TotalSize();
-
-    for (Index num_output_coeffs = 0; num_output_coeffs < output_size;) {
-      ScalarNoConst* bcast_output = materialized_output + num_output_coeffs;
-      Index bcast_offset = desc.offset() + output_offset;
-
-      // Broadcast along the bcast dimension.
-      num_output_coeffs += BroadcastBlockAlongBcastDim(
-          params, bcast_offset, scratch, bcast_output, &materialized_input,
-          &materialized_input_size);
-
-      // Switch to the next outer dimension.
-      for (int j = 0; j < idx; ++j) {
-        if (++it[j].count < it[j].size) {
-          output_offset += it[j].output_stride;
-          break;
-        }
-        it[j].count = 0;
-        output_offset -= it[j].output_span;
-      }
-    }
-
-    return block_storage.AsTensorMaterializedBlock();
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
-
-  Broadcast functor() const { return m_broadcast; }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(
-      cl::sycl::handler& cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
- private:
-  static const bool IsColMajor =
-      static_cast<int>(Layout) == static_cast<int>(ColMajor);
-
-  // We will build a general case block broadcasting on top of broadcasting
-  // primitive that will do broadcasting only for the inner dimension(s) along
-  // the first dimension smaller than the input size (it's called `bcast_dim`).
-  //
-  // Example:
-  //           dim:  0  1  2   (ColMajor)
-  //    input size: [9, 3, 6]
-  //    block size: [9, 2, 6]
-  //
-  // We will compute broadcasted block by iterating over the outer dimensions
-  // before `bcast_dim` (only dimension `2` in this example) and computing
-  // broadcasts along the `bcast_dim` (dimension `1` in this example).
-
-  // BlockBroadcastingParams holds precomputed parameters for broadcasting a
-  // single block along the broadcasting dimension. Sizes and strides along the
-  // `bcast_dim` might be invalid, they will be adjusted later in
-  // `BroadcastBlockAlongBcastDim`.
-  struct BlockBroadcastingParams {
-    Dimensions input_dims;      // input expression dimensions
-    Dimensions output_dims;     // output block sizes
-    Dimensions output_strides;  // output block strides
-
-    int inner_dim_count;   // count inner dimensions matching in size
-    int bcast_dim;         // broadcasting dimension index
-    Index bcast_dim_size;  // broadcasting dimension size
-    Index inner_dim_size;  // inner dimensions size
-
-    // Block sizes and strides for the input block where all dimensions before
-    // `bcast_dim` are equal to `1`.
-    Dimensions input_block_sizes;
-    Dimensions input_block_strides;
-
-    // Block sizes and strides for blocks with extra dimensions and strides `0`.
-    BroadcastDimensions bcast_block_sizes;
-    BroadcastDimensions bcast_block_strides;
-    BroadcastDimensions bcast_input_strides;
-  };
-
-  struct BlockBroadcastingIteratorState {
-    Index size;
-    Index count;
-    Index output_stride;
-    Index output_span;
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlockBroadcastingParams
-  blockBroadcastingParams(TensorBlockDesc& desc) const {
-    BlockBroadcastingParams params;
-
-    params.input_dims = Dimensions(m_impl.dimensions());
-
-    // Output block sizes and strides.
-    params.output_dims = desc.dimensions();
-    params.output_strides = internal::strides<Layout>(params.output_dims);
-
-    // Find the broadcasting dimension (first dimension with output size smaller
-    // that the input size).
-    params.bcast_dim = 0;
-    params.bcast_dim_size = 1;
-    params.inner_dim_size = 1;
-
-    // Count the number of inner dimensions that have the same size in the block
-    // and in the broadcast expression.
-    params.inner_dim_count = 0;
-
-    for (int i = 0; i < NumDims; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-
-      if (params.output_dims[dim] == m_dimensions[dim]) {
-        params.inner_dim_size *= params.output_dims[dim];
-        ++params.inner_dim_count;
-        continue;
-      }
-
-      // First non-matching dimension is the broadcasting dimension.
-      eigen_assert(params.output_dims[dim] < m_dimensions[dim]);
-      params.bcast_dim = dim;
-      params.bcast_dim_size = params.output_dims[dim];
-      break;
-    }
-
-    // Calculate the input block size for looking into the input.
-    for (int i = 0; i < params.inner_dim_count; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-      params.input_block_sizes[dim] = params.input_dims[dim];
-    }
-    for (int i = params.inner_dim_count; i < NumDims; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-      params.input_block_sizes[dim] = 1;
-    }
-    params.input_block_strides =
-        internal::strides<Layout>(params.input_block_sizes);
-
-    // Broadcast with the 0-stride trick: Create 1 extra dim for each
-    // broadcast, set the input stride to 0.
-    //
-    // When ColMajor:
-    //
-    // - bcast_block_sizes:
-    //   [d_0, b_0, d_1, b_1, ...]
-    //
-    // - bcast_block_strides:
-    //   [output_block_strides[0], output_block_strides[0] * d_0,
-    //    output_block_strides[1], output_block_strides[1] * d_1,
-    //   ...]
-    //
-    // - bcast_input_strides:
-    //   [input_block_strides[0], 0,
-    //    input_block_strides[1], 0,
-    //   ...].
-    //
-    for (int i = 0; i < params.inner_dim_count; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-
-      const int copy_dim = IsColMajor ? 2 * i : 2 * NumDims - 2 * i - 1;
-      const int broadcast_dim = IsColMajor ? copy_dim + 1 : copy_dim - 1;
-
-      params.bcast_block_sizes[copy_dim] = params.input_dims[dim];
-      params.bcast_block_sizes[broadcast_dim] = m_broadcast[dim];
-      params.bcast_block_strides[copy_dim] = params.output_strides[dim];
-      params.bcast_block_strides[broadcast_dim] =
-          params.output_strides[dim] * params.input_dims[dim];
-      params.bcast_input_strides[copy_dim] = params.input_block_strides[dim];
-      params.bcast_input_strides[broadcast_dim] = 0;
-    }
-
-    for (int i = 2 * params.inner_dim_count; i < 2 * NumDims; ++i) {
-      const int dim = IsColMajor ? i : 2 * NumDims - i - 1;
-      params.bcast_block_sizes[dim] = 1;
-      params.bcast_block_strides[dim] = 0;
-      params.bcast_input_strides[dim] = 0;
-    }
-
-    return params;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock emptyBlock() const {
-    DSizes<Index, NumDims> dimensions;
-    for (int i = 0; i < NumDims; ++i) dimensions[i] = 0;
-    return TensorBlock(internal::TensorBlockKind::kView, NULL, dimensions);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlockAlongBcastDim(
-      BlockBroadcastingParams params, Index bcast_offset,
-      TensorBlockScratch& scratch, ScalarNoConst* materialized_output,
-      ScalarNoConst** materialized_input,
-      size_t* materialized_input_size) const {
-    if (params.bcast_dim_size == 1) {
-      // We just need one block read using the ready-set values above.
-      return BroadcastBlock(
-          params.input_block_sizes, params.input_block_strides,
-          params.bcast_block_sizes, params.bcast_block_strides,
-          params.bcast_input_strides, bcast_offset, 0, scratch,
-          materialized_output, materialized_input, materialized_input_size);
-
-    } else if (params.input_dims[params.bcast_dim] == 1) {
-      // Broadcast bcast dimension (< NumDims) by bcast_dim_size.
-      const int broadcast_bcast_dim =
-          IsColMajor ? 2 * params.inner_dim_count + 1
-                     : 2 * NumDims - 2 * params.inner_dim_count - 2;
-
-      params.bcast_block_sizes[broadcast_bcast_dim] = params.bcast_dim_size;
-      params.bcast_input_strides[broadcast_bcast_dim] = 0;
-      params.bcast_block_strides[broadcast_bcast_dim] =
-          params.output_strides[params.bcast_dim];
-
-      return BroadcastBlock(
-          params.input_block_sizes, params.input_block_strides,
-          params.bcast_block_sizes, params.bcast_block_strides,
-          params.bcast_input_strides, bcast_offset, 0, scratch,
-          materialized_output, materialized_input, materialized_input_size);
-
-    } else {
-      // Keep track of the total number of the coefficients written to the
-      // output block.
-      Index num_output_coeffs = 0;
-
-      // The general case. Let's denote the output block as
-      //
-      //   x[..., a:a+bcast_dim_size, :, ..., :]
-      //
-      // where a:a+bcast_dim_size is a slice on the bcast_dim dimension
-      // (< NumDims). We need to split the a:a+bcast_dim_size into possibly 3
-      // sub-blocks:
-      //
-      // (1) a:b, where b is the smallest multiple of
-      //     input_dims[bcast_dim_start] in [a, a+bcast_dim_size].
-      //
-      // (2) b:c, where c is the largest multiple of input_dims[bcast_dim_start]
-      //     in [a, a+bcast_dim_size].
-      //
-      // (3) c:a+bcast_dim_size .
-      //
-      // Or, when b and c do not exist, we just need to process the whole block
-      // together.
-
-      // Find a.
-      const Index bcast_dim_left_index =
-          bcast_offset / m_outputStrides[params.bcast_dim];
-
-      // Find b and c.
-      const Index input_bcast_dim_size = params.input_dims[params.bcast_dim];
-
-      // First multiple after a. This is b when <= bcast_dim_left_index +
-      // bcast_dim_size.
-      const Index first_multiple =
-          divup<Index>(bcast_dim_left_index, input_bcast_dim_size) *
-          input_bcast_dim_size;
-
-      if (first_multiple <= bcast_dim_left_index + params.bcast_dim_size) {
-        // b exists, so does c. Find it.
-        const Index last_multiple =
-            (bcast_dim_left_index + params.bcast_dim_size) /
-            input_bcast_dim_size * input_bcast_dim_size;
-        const int copy_bcast_dim =
-            IsColMajor ? 2 * params.inner_dim_count
-                       : 2 * NumDims - 2 * params.inner_dim_count - 1;
-        const int broadcast_bcast_dim =
-            IsColMajor ? 2 * params.inner_dim_count + 1
-                       : 2 * NumDims - 2 * params.inner_dim_count - 2;
-
-        if (first_multiple > bcast_dim_left_index) {
-          const Index head_size = first_multiple - bcast_dim_left_index;
-          params.input_block_sizes[params.bcast_dim] = head_size;
-          params.bcast_block_sizes[copy_bcast_dim] = head_size;
-          params.bcast_input_strides[copy_bcast_dim] =
-              params.input_block_strides[params.bcast_dim];
-          params.bcast_block_strides[copy_bcast_dim] =
-              params.output_strides[params.bcast_dim];
-          params.bcast_block_sizes[broadcast_bcast_dim] = 1;
-          params.bcast_input_strides[broadcast_bcast_dim] = 0;
-          params.bcast_block_strides[broadcast_bcast_dim] =
-              params.output_strides[params.bcast_dim] *
-              params.input_dims[params.bcast_dim];
-
-          num_output_coeffs += BroadcastBlock(
-              params.input_block_sizes, params.input_block_strides,
-              params.bcast_block_sizes, params.bcast_block_strides,
-              params.bcast_input_strides, bcast_offset, 0, scratch,
-              materialized_output, materialized_input, materialized_input_size);
-        }
-        if (first_multiple < last_multiple) {
-          params.input_block_sizes[params.bcast_dim] = input_bcast_dim_size;
-          params.bcast_block_sizes[copy_bcast_dim] = input_bcast_dim_size;
-          params.bcast_input_strides[copy_bcast_dim] =
-              params.input_block_strides[params.bcast_dim];
-          params.bcast_block_strides[copy_bcast_dim] =
-              params.output_strides[params.bcast_dim];
-          params.bcast_block_sizes[broadcast_bcast_dim] =
-              (last_multiple - first_multiple) / input_bcast_dim_size;
-          params.bcast_input_strides[broadcast_bcast_dim] = 0;
-          params.bcast_block_strides[broadcast_bcast_dim] =
-              params.output_strides[params.bcast_dim] *
-              params.input_dims[params.bcast_dim];
-          const Index offset = (first_multiple - bcast_dim_left_index) *
-                               m_outputStrides[params.bcast_dim];
-
-          num_output_coeffs += BroadcastBlock(
-              params.input_block_sizes, params.input_block_strides,
-              params.bcast_block_sizes, params.bcast_block_strides,
-              params.bcast_input_strides, bcast_offset, offset, scratch,
-              materialized_output, materialized_input, materialized_input_size);
-        }
-        if (last_multiple < bcast_dim_left_index + params.bcast_dim_size) {
-          const Index tail_size =
-              bcast_dim_left_index + params.bcast_dim_size - last_multiple;
-          params.input_block_sizes[params.bcast_dim] = tail_size;
-          params.bcast_block_sizes[copy_bcast_dim] = tail_size;
-          params.bcast_input_strides[copy_bcast_dim] =
-              params.input_block_strides[params.bcast_dim];
-          params.bcast_block_strides[copy_bcast_dim] =
-              params.output_strides[params.bcast_dim];
-          params.bcast_block_sizes[broadcast_bcast_dim] = 1;
-          params.bcast_input_strides[broadcast_bcast_dim] = 0;
-          params.bcast_block_strides[broadcast_bcast_dim] =
-              params.output_strides[params.bcast_dim] *
-              params.input_dims[params.bcast_dim];
-          const Index offset = (last_multiple - bcast_dim_left_index) *
-                               m_outputStrides[params.bcast_dim];
-
-          num_output_coeffs += BroadcastBlock(
-              params.input_block_sizes, params.input_block_strides,
-              params.bcast_block_sizes, params.bcast_block_strides,
-              params.bcast_input_strides, bcast_offset, offset, scratch,
-              materialized_output, materialized_input, materialized_input_size);
-        }
-      } else {
-        // b and c do not exist.
-        const int copy_bcast_dim =
-            IsColMajor ? 2 * params.inner_dim_count
-                       : 2 * NumDims - 2 * params.inner_dim_count - 1;
-        params.input_block_sizes[params.bcast_dim] = params.bcast_dim_size;
-        params.bcast_block_sizes[copy_bcast_dim] = params.bcast_dim_size;
-        params.bcast_input_strides[copy_bcast_dim] =
-            params.input_block_strides[params.bcast_dim];
-        params.bcast_block_strides[copy_bcast_dim] =
-            params.output_strides[params.bcast_dim];
-
-        num_output_coeffs += BroadcastBlock(
-            params.input_block_sizes, params.input_block_strides,
-            params.bcast_block_sizes, params.bcast_block_strides,
-            params.bcast_input_strides, bcast_offset, 0, scratch,
-            materialized_output, materialized_input, materialized_input_size);
-      }
-
-      return num_output_coeffs;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlock(
-      const Dimensions& input_block_sizes,
-      const Dimensions& input_block_strides,
-      const BroadcastDimensions& bcast_block_sizes,
-      const BroadcastDimensions& bcast_block_strides,
-      const BroadcastDimensions& bcast_input_strides, Index bcast_offset,
-      Index offset, TensorBlockScratch& scratch,
-      ScalarNoConst* materialized_output, ScalarNoConst** materialized_input,
-      size_t* materialized_input_size) const {
-    // ---------------------------------------------------------------------- //
-    // Tensor block descriptor for reading block from the input.
-    const Index input_offset = bcast_offset + offset;
-    TensorBlockDesc input_desc(
-        IsColMajor ? indexColMajor(input_offset) : indexRowMajor(input_offset),
-        input_block_sizes);
-
-    ArgTensorBlock input_block = m_impl.block(input_desc, scratch);
-
-    // ---------------------------------------------------------------------- //
-    // Materialize input block into a temporary memory buffer only if it's not
-    // already available in the arg block.
-    const ScalarNoConst* input_buffer = NULL;
-
-    if (input_block.data() != NULL) {
-      // Input block already has raw data, there is no need to materialize it.
-      input_buffer = input_block.data();
-
-    } else {
-      // Otherwise we have to do block assignment into a temporary buffer.
-
-      // Maybe reuse previously allocated buffer, or allocate a new one with a
-      // scratch allocator.
-      const size_t input_total_size = input_block_sizes.TotalSize();
-      if (*materialized_input == NULL ||
-          *materialized_input_size < input_total_size) {
-        *materialized_input_size = input_total_size;
-        void* mem = scratch.allocate(*materialized_input_size * sizeof(Scalar));
-        *materialized_input = static_cast<ScalarNoConst*>(mem);
-      }
-
-      typedef internal::TensorBlockAssignment<
-          ScalarNoConst, NumDims, typename ArgTensorBlock::XprType, Index>
-          TensorBlockAssignment;
-
-      TensorBlockAssignment::Run(
-          TensorBlockAssignment::target(input_block_sizes, input_block_strides,
-                                        *materialized_input),
-          input_block.expr());
-
-      input_buffer = *materialized_input;
-    }
-
-    // ---------------------------------------------------------------------- //
-    // Copy data from materialized input block to the materialized output, using
-    // given broadcast strides (strides with zeroes).
-    typedef internal::TensorBlockIO<ScalarNoConst, Index, 2 * NumDims, Layout>
-        TensorBlockIO;
-
-    typename TensorBlockIO::Src src(bcast_input_strides, input_buffer);
-    typename TensorBlockIO::Dst dst(bcast_block_sizes, bcast_block_strides,
-                                      materialized_output + offset);
-
-    return TensorBlockIO::Copy(dst, src);
-  }
-
-protected:
-  const Device EIGEN_DEVICE_REF m_device;
-  const typename internal::remove_reference<Broadcast>::type m_broadcast;
-  Dimensions m_dimensions;
-  array<Index, NumDims> m_outputStrides;
-  array<Index, NumDims> m_inputStrides;
-  TensorEvaluator<ArgType, Device> m_impl;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
deleted file mode 100644
index 3764573..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
+++ /dev/null
@@ -1,518 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
-
-namespace Eigen {
-
-/** \class TensorKChippingReshaping
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
-  *
-  *
-  */
-
-namespace internal {
-template<DenseIndex DimId, typename XprType>
-struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions - 1;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<DenseIndex DimId, typename XprType>
-struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
-{
-  typedef const TensorChippingOp<DimId, XprType> EIGEN_DEVICE_REF type;
-};
-
-template<DenseIndex DimId, typename XprType>
-struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
-{
-  typedef TensorChippingOp<DimId, XprType> type;
-};
-
-template <DenseIndex DimId>
-struct DimensionId
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) {
-    EIGEN_UNUSED_VARIABLE(dim);
-    eigen_assert(dim == DimId);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
-    return DimId;
-  }
-};
-template <>
-struct DimensionId<Dynamic>
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) : actual_dim(dim) {
-    eigen_assert(dim >= 0);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
-    return actual_dim;
-  }
- private:
-  const DenseIndex actual_dim;
-};
-
-
-}  // end namespace internal
-
-
-
-template<DenseIndex DimId, typename XprType>
-class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
-{
-  public:
-    typedef TensorBase<TensorChippingOp<DimId, XprType> > Base;
-    typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename XprType::CoeffReturnType CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorChippingOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim)
-        : m_xpr(expr), m_offset(offset), m_dim(dim) {
-    }
-
-    EIGEN_DEVICE_FUNC
-    const Index offset() const { return m_offset; }
-    EIGEN_DEVICE_FUNC
-    const Index dim() const { return m_dim.actualDim(); }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorChippingOp)
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const Index m_offset;
-    const internal::DimensionId<DimId> m_dim;
-};
-
-
-// Eval as rvalue
-template<DenseIndex DimId, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
-{
-  typedef TensorChippingOp<DimId, ArgType> XprType;
-  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  static const int NumDims = NumInputDims-1;
-  typedef typename XprType::Index Index;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    // Alignment can't be guaranteed at compile time since it depends on the
-    // slice offsets.
-    IsAligned         = false,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
-    // Chipping of outer-most dimension is a trivial operation, because we can
-    // read and write directly from the underlying tensor using single offset.
-    IsOuterChipping   = (static_cast<int>(Layout) == ColMajor && DimId == NumInputDims - 1) ||
-                        (static_cast<int>(Layout) == RowMajor && DimId == 0),
-    // Chipping inner-most dimension.
-    IsInnerChipping   = (static_cast<int>(Layout) == ColMajor && DimId == 0) ||
-                        (static_cast<int>(Layout) == RowMajor && DimId == NumInputDims - 1),
-    // Prefer block access if the underlying expression prefers it, otherwise
-    // only if chipping is not trivial.
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess ||
-                        !IsOuterChipping,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef internal::TensorBlockDescriptor<NumInputDims, Index>
-      ArgTensorBlockDesc;
-  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
-      ArgTensorBlock;
-
-  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_dim(op.dim()), m_device(device)
-  {
-    EIGEN_STATIC_ASSERT((NumInputDims >= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    eigen_assert(NumInputDims > m_dim.actualDim());
-
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    eigen_assert(op.offset() < input_dims[m_dim.actualDim()]);
-
-    int j = 0;
-    for (int i = 0; i < NumInputDims; ++i) {
-      if (i != m_dim.actualDim()) {
-        m_dimensions[j] = input_dims[i];
-        ++j;
-      }
-    }
-
-    m_stride = 1;
-    m_inputStride = 1;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = 0; i < m_dim.actualDim(); ++i) {
-        m_stride *= input_dims[i];
-        m_inputStride *= input_dims[i];
-      }
-    } else {
-      for (int i = NumInputDims-1; i > m_dim.actualDim(); --i) {
-        m_stride *= input_dims[i];
-        m_inputStride *= input_dims[i];
-      }
-    }
-    m_inputStride *= input_dims[m_dim.actualDim()];
-    m_inputOffset = m_stride * op.offset();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return m_impl.coeff(srcCoeff(index));
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    if (isInnerChipping()) {
-      // m_stride is equal to 1, so let's avoid the integer division.
-      eigen_assert(m_stride == 1);
-      Index inputIndex = index * m_inputStride + m_inputOffset;
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < PacketSize; ++i) {
-        values[i] = m_impl.coeff(inputIndex);
-        inputIndex += m_inputStride;
-      }
-      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-      return rslt;
-    } else if (isOuterChipping()) {
-      // m_stride is always greater than index, so let's avoid the integer division.
-      eigen_assert(m_stride > index);
-      return m_impl.template packet<LoadMode>(index + m_inputOffset);
-    } else {
-      const Index idx = index / m_stride;
-      const Index rem = index - idx * m_stride;
-      if (rem + PacketSize <= m_stride) {
-        Index inputIndex = idx * m_inputStride + m_inputOffset + rem;
-        return m_impl.template packet<LoadMode>(inputIndex);
-      } else {
-        // Cross the stride boundary. Fallback to slow path.
-        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-       EIGEN_UNROLL_LOOP
-        for (int i = 0; i < PacketSize; ++i) {
-          values[i] = coeff(index);
-          ++index;
-        }
-        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-        return rslt;
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    double cost = 0;
-    if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) &&
-         m_dim.actualDim() == 0) ||
-        (static_cast<int>(Layout) == static_cast<int>(RowMajor) &&
-         m_dim.actualDim() == NumInputDims - 1)) {
-      cost += TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
-    } else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) &&
-                m_dim.actualDim() == NumInputDims - 1) ||
-               (static_cast<int>(Layout) == static_cast<int>(RowMajor) &&
-                m_dim.actualDim() == 0)) {
-      cost += TensorOpCost::AddCost<Index>();
-    } else {
-      cost += 3 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>() +
-              3 * TensorOpCost::AddCost<Index>();
-    }
-
-    return m_impl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    const size_t target_size = m_device.lastLevelCacheSize();
-    return internal::TensorBlockResourceRequirements::merge(
-        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
-        m_impl.getResourceRequirements());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool root_of_expr_ast = false) const {
-    const Index chip_dim = m_dim.actualDim();
-
-    DSizes<Index, NumInputDims> input_block_dims;
-    for (int i = 0; i < NumInputDims; ++i) {
-      input_block_dims[i]
-            = i < chip_dim ? desc.dimension(i)
-            : i > chip_dim ? desc.dimension(i - 1)
-            : 1;
-    }
-
-    ArgTensorBlockDesc arg_desc(srcCoeff(desc.offset()), input_block_dims);
-
-    // Try to reuse destination buffer for materializing argument block.
-    if (desc.HasDestinationBuffer()) {
-      DSizes<Index, NumInputDims> arg_destination_strides;
-      for (int i = 0; i < NumInputDims; ++i) {
-      arg_destination_strides[i]
-            = i < chip_dim ? desc.destination().strides()[i]
-            : i > chip_dim ? desc.destination().strides()[i - 1]
-            : 0; // for dimensions of size `1` stride should never be used.
-      }
-
-      arg_desc.template AddDestinationBuffer<Layout>(
-          desc.destination().template data<ScalarNoConst>(),
-          arg_destination_strides);
-    }
-
-    ArgTensorBlock arg_block = m_impl.block(arg_desc, scratch, root_of_expr_ast);
-    if (!arg_desc.HasDestinationBuffer()) desc.DropDestinationBuffer();
-
-    if (arg_block.data() != NULL) {
-      // Forward argument block buffer if possible.
-      return TensorBlock(arg_block.kind(), arg_block.data(),
-                           desc.dimensions());
-
-    } else {
-      // Assign argument block expression to a buffer.
-
-      // Prepare storage for the materialized chipping result.
-      const typename TensorBlock::Storage block_storage =
-          TensorBlock::prepareStorage(desc, scratch);
-
-      typedef internal::TensorBlockAssignment<
-          ScalarNoConst, NumInputDims, typename ArgTensorBlock::XprType, Index>
-          TensorBlockAssignment;
-
-      TensorBlockAssignment::Run(
-          TensorBlockAssignment::target(
-              arg_desc.dimensions(),
-              internal::strides<Layout>(arg_desc.dimensions()),
-              block_storage.data()),
-          arg_block.expr());
-
-      return block_storage.AsTensorMaterializedBlock();
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
-    typename Storage::Type result = constCast(m_impl.data());
-    if (isOuterChipping() && result) {
-      return result + m_inputOffset;
-    } else {
-      return NULL;
-    }
-  }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
-  {
-    Index inputIndex;
-    if (isInnerChipping()) {
-      // m_stride is equal to 1, so let's avoid the integer division.
-      eigen_assert(m_stride == 1);
-      inputIndex = index * m_inputStride + m_inputOffset;
-    } else if (isOuterChipping()) {
-      // m_stride is always greater than index, so let's avoid the integer
-      // division.
-      eigen_assert(m_stride > index);
-      inputIndex = index + m_inputOffset;
-    } else {
-      const Index idx = index / m_stride;
-      inputIndex = idx * m_inputStride + m_inputOffset;
-      index -= idx * m_stride;
-      inputIndex += index;
-    }
-    return inputIndex;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool isInnerChipping() const {
-    return IsInnerChipping ||
-           (static_cast<int>(Layout) == ColMajor && m_dim.actualDim() == 0) ||
-           (static_cast<int>(Layout) == RowMajor && m_dim.actualDim() == NumInputDims - 1);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool isOuterChipping() const {
-    return IsOuterChipping ||
-           (static_cast<int>(Layout) == ColMajor && m_dim.actualDim() == NumInputDims-1) ||
-           (static_cast<int>(Layout) == RowMajor && m_dim.actualDim() == 0);
-  }
-
-  Dimensions m_dimensions;
-  Index m_stride;
-  Index m_inputOffset;
-  Index m_inputStride;
-  TensorEvaluator<ArgType, Device> m_impl;
-  const internal::DimensionId<DimId> m_dim;
-  const Device EIGEN_DEVICE_REF m_device;
-};
-
-
-// Eval as lvalue
-template<DenseIndex DimId, typename ArgType, typename Device>
-struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
-  : public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
-{
-  typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
-  typedef TensorChippingOp<DimId, ArgType> XprType;
-  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  static const int NumDims = NumInputDims-1;
-  typedef typename XprType::Index Index;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-
-  enum {
-    IsAligned     = false,
-    PacketAccess  = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess   = TensorEvaluator<ArgType, Device>::RawAccess,
-    Layout        = TensorEvaluator<ArgType, Device>::Layout,
-    RawAccess     = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : Base(op, device)
-    { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
-  {
-    return this->m_impl.coeffRef(this->srcCoeff(index));
-  }
-
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-
-    if (this->isInnerChipping()) {
-      // m_stride is equal to 1, so let's avoid the integer division.
-      eigen_assert(this->m_stride == 1);
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-      internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
-      Index inputIndex = index * this->m_inputStride + this->m_inputOffset;
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < PacketSize; ++i) {
-        this->m_impl.coeffRef(inputIndex) = values[i];
-        inputIndex += this->m_inputStride;
-      }
-    } else if (this->isOuterChipping()) {
-      // m_stride is always greater than index, so let's avoid the integer division.
-      eigen_assert(this->m_stride > index);
-      this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
-    } else {
-      const Index idx = index / this->m_stride;
-      const Index rem = index - idx * this->m_stride;
-      if (rem + PacketSize <= this->m_stride) {
-        const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem;
-        this->m_impl.template writePacket<StoreMode>(inputIndex, x);
-      } else {
-        // Cross stride boundary. Fallback to slow path.
-        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-        internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
-        EIGEN_UNROLL_LOOP
-        for (int i = 0; i < PacketSize; ++i) {
-          this->coeffRef(index) = values[i];
-          ++index;
-        }
-      }
-    }
-  }
-
-  template <typename TensorBlock>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
-      const TensorBlockDesc& desc, const TensorBlock& block) {
-    assert(this->m_impl.data() != NULL);
-
-    const Index chip_dim = this->m_dim.actualDim();
-
-    DSizes<Index, NumInputDims> input_block_dims;
-    for (int i = 0; i < NumInputDims; ++i) {
-      input_block_dims[i] = i < chip_dim ? desc.dimension(i)
-                          : i > chip_dim ? desc.dimension(i - 1)
-                          : 1;
-    }
-
-    typedef TensorReshapingOp<const DSizes<Index, NumInputDims>,
-                              const typename TensorBlock::XprType>
-        TensorBlockExpr;
-
-    typedef internal::TensorBlockAssignment<Scalar, NumInputDims,
-                                            TensorBlockExpr, Index>
-        TensorBlockAssign;
-
-    TensorBlockAssign::Run(
-        TensorBlockAssign::target(
-            input_block_dims,
-            internal::strides<Layout>(this->m_impl.dimensions()),
-            this->m_impl.data(), this->srcCoeff(desc.offset())),
-        block.expr().reshape(input_block_dims));
-  }
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
deleted file mode 100644
index 5235a8e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
+++ /dev/null
@@ -1,377 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
-
-namespace Eigen {
-
-/** \class TensorConcatenationOp
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor concatenation class.
-  *
-  *
-  */
-namespace internal {
-template<typename Axis, typename LhsXprType, typename RhsXprType>
-struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef typename promote_storage_type<typename LhsXprType::Scalar,
-                                        typename RhsXprType::Scalar>::ret Scalar;
-  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
-                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
-                                      typename traits<RhsXprType>::Index>::type Index;
-  typedef typename LhsXprType::Nested LhsNested;
-  typedef typename RhsXprType::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  static const int NumDimensions = traits<LhsXprType>::NumDimensions;
-  static const int Layout = traits<LhsXprType>::Layout;
-  enum { Flags = 0 };
-  typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
-                               typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType>::type PointerType;
-};
-
-template<typename Axis, typename LhsXprType, typename RhsXprType>
-struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
-{
-  typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
-};
-
-template<typename Axis, typename LhsXprType, typename RhsXprType>
-struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
-{
-  typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
-};
-
-}  // end namespace internal
-
-
-template<typename Axis, typename LhsXprType, typename RhsXprType>
-class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
-{
-  public:
-    typedef TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> Base;
-    typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
-    typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
-    typedef typename internal::traits<TensorConcatenationOp>::Index Index;
-    typedef typename internal::nested<TensorConcatenationOp>::type Nested;
-    typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
-                                                    typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
-        : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename LhsXprType::Nested>::type&
-    lhsExpression() const { return m_lhs_xpr; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename RhsXprType::Nested>::type&
-    rhsExpression() const { return m_rhs_xpr; }
-
-    EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorConcatenationOp)
-  protected:
-    typename LhsXprType::Nested m_lhs_xpr;
-    typename RhsXprType::Nested m_rhs_xpr;
-    const Axis m_axis;
-};
-
-
-// Eval as rvalue
-template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
-struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
-{
-  typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
-  static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  enum {
-    IsAligned         = false,
-    PacketAccess      = TensorEvaluator<LeftArgType, Device>::PacketAccess &&
-                        TensorEvaluator<RightArgType, Device>::PacketAccess,
-    BlockAccess       = false,
-    PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess ||
-                        TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
-    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
-    RawAccess         = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    eigen_assert(0 <= m_axis && m_axis < NumDims);
-    const Dimensions& lhs_dims = m_leftImpl.dimensions();
-    const Dimensions& rhs_dims = m_rightImpl.dimensions();
-    {
-      int i = 0;
-      for (; i < m_axis; ++i) {
-        eigen_assert(lhs_dims[i] > 0);
-        eigen_assert(lhs_dims[i] == rhs_dims[i]);
-        m_dimensions[i] = lhs_dims[i];
-      }
-      eigen_assert(lhs_dims[i] > 0);  // Now i == m_axis.
-      eigen_assert(rhs_dims[i] > 0);
-      m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
-      for (++i; i < NumDims; ++i) {
-        eigen_assert(lhs_dims[i] > 0);
-        eigen_assert(lhs_dims[i] == rhs_dims[i]);
-        m_dimensions[i] = lhs_dims[i];
-      }
-    }
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_leftStrides[0] = 1;
-      m_rightStrides[0] = 1;
-      m_outputStrides[0] = 1;
-
-      for (int j = 1; j < NumDims; ++j) {
-        m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1];
-        m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1];
-        m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1];
-      }
-    } else {
-      m_leftStrides[NumDims - 1] = 1;
-      m_rightStrides[NumDims - 1] = 1;
-      m_outputStrides[NumDims - 1] = 1;
-
-      for (int j = NumDims - 2; j >= 0; --j) {
-        m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1];
-        m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1];
-        m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
-  {
-    m_leftImpl.evalSubExprsIfNeeded(NULL);
-    m_rightImpl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-  EIGEN_STRONG_INLINE void cleanup()
-  {
-    m_leftImpl.cleanup();
-    m_rightImpl.cleanup();
-  }
-
-  // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
-  // See CL/76180724 comments for more ideas.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    // Collect dimension-wise indices (subs).
-    array<Index, NumDims> subs;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumDims - 1; i > 0; --i) {
-        subs[i] = index / m_outputStrides[i];
-        index -= subs[i] * m_outputStrides[i];
-      }
-      subs[0] = index;
-    } else {
-      for (int i = 0; i < NumDims - 1; ++i) {
-        subs[i] = index / m_outputStrides[i];
-        index -= subs[i] * m_outputStrides[i];
-      }
-      subs[NumDims - 1] = index;
-    }
-
-    const Dimensions& left_dims = m_leftImpl.dimensions();
-    if (subs[m_axis] < left_dims[m_axis]) {
-      Index left_index;
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        left_index = subs[0];
-        EIGEN_UNROLL_LOOP
-        for (int i = 1; i < NumDims; ++i) {
-          left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
-        }
-      } else {
-        left_index = subs[NumDims - 1];
-        EIGEN_UNROLL_LOOP
-        for (int i = NumDims - 2; i >= 0; --i) {
-          left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
-        }
-      }
-      return m_leftImpl.coeff(left_index);
-    } else {
-      subs[m_axis] -= left_dims[m_axis];
-      const Dimensions& right_dims = m_rightImpl.dimensions();
-      Index right_index;
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        right_index = subs[0];
-        EIGEN_UNROLL_LOOP
-        for (int i = 1; i < NumDims; ++i) {
-          right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
-        }
-      } else {
-        right_index = subs[NumDims - 1];
-        EIGEN_UNROLL_LOOP
-        for (int i = NumDims - 2; i >= 0; --i) {
-          right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
-        }
-      }
-      return m_rightImpl.coeff(right_index);
-    }
-  }
-
-  // TODO(phli): Add a real vectorization.
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    const int packetSize = PacketType<CoeffReturnType, Device>::size;
-    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
-
-    EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < packetSize; ++i) {
-      values[i] = coeff(index+i);
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
-                                           2 * TensorOpCost::MulCost<Index>() +
-                                           TensorOpCost::DivCost<Index>() +
-                                           TensorOpCost::ModCost<Index>());
-    const double lhs_size = m_leftImpl.dimensions().TotalSize();
-    const double rhs_size = m_rightImpl.dimensions().TotalSize();
-    return (lhs_size / (lhs_size + rhs_size)) *
-               m_leftImpl.costPerCoeff(vectorized) +
-           (rhs_size / (lhs_size + rhs_size)) *
-               m_rightImpl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, compute_cost);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-  #ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_leftImpl.bind(cgh);
-    m_rightImpl.bind(cgh);
-  }
-  #endif
-
-  protected:
-    Dimensions m_dimensions;
-    array<Index, NumDims> m_outputStrides;
-    array<Index, NumDims> m_leftStrides;
-    array<Index, NumDims> m_rightStrides;
-    TensorEvaluator<LeftArgType, Device> m_leftImpl;
-    TensorEvaluator<RightArgType, Device> m_rightImpl;
-    const Axis m_axis;
-};
-
-// Eval as lvalue
-template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
-  struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
-  : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
-{
-  typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base;
-  typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
-  typedef typename Base::Dimensions Dimensions;
-  enum {
-    IsAligned         = false,
-    PacketAccess      = TensorEvaluator<LeftArgType, Device>::PacketAccess &&
-                        TensorEvaluator<RightArgType, Device>::PacketAccess,
-    BlockAccess       = false,
-    PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess ||
-                        TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
-    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
-    RawAccess         = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device)
-    : Base(op, device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-  }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
-  {
-    // Collect dimension-wise indices (subs).
-    array<Index, Base::NumDims> subs;
-    for (int i = Base::NumDims - 1; i > 0; --i) {
-      subs[i] = index / this->m_outputStrides[i];
-      index -= subs[i] * this->m_outputStrides[i];
-    }
-    subs[0] = index;
-
-    const Dimensions& left_dims = this->m_leftImpl.dimensions();
-    if (subs[this->m_axis] < left_dims[this->m_axis]) {
-      Index left_index = subs[0];
-      for (int i = 1; i < Base::NumDims; ++i) {
-        left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i];
-      }
-      return this->m_leftImpl.coeffRef(left_index);
-    } else {
-      subs[this->m_axis] -= left_dims[this->m_axis];
-      const Dimensions& right_dims = this->m_rightImpl.dimensions();
-      Index right_index = subs[0];
-      for (int i = 1; i < Base::NumDims; ++i) {
-        right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i];
-      }
-      return this->m_rightImpl.coeffRef(right_index);
-    }
-  }
-
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    const int packetSize = PacketType<CoeffReturnType, Device>::size;
-    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());
-
-    EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
-    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
-    for (int i = 0; i < packetSize; ++i) {
-      coeffRef(index+i) = values[i];
-    }
-  }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
deleted file mode 100644
index 8b35f79..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
+++ /dev/null
@@ -1,1023 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
-
-namespace Eigen {
-
-/** \class TensorContraction
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor contraction class.
-  *
-  *
-  */
-namespace internal {
-
-template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
-struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef typename gebp_traits<typename remove_const<typename LhsXprType::Scalar>::type,
-                               typename remove_const<typename RhsXprType::Scalar>::type>::ResScalar Scalar;
-
-  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
-                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
-                                      typename traits<RhsXprType>::Index>::type Index;
-  typedef typename LhsXprType::Nested LhsNested;
-  typedef typename RhsXprType::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-
-  // From NumDims below.
-  static const int NumDimensions = traits<LhsXprType>::NumDimensions + traits<RhsXprType>::NumDimensions - 2 * array_size<Dimensions>::value;
-  static const int Layout = traits<LhsXprType>::Layout;
-  typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
-                               typename traits<LhsXprType>::PointerType,
-                               typename traits<RhsXprType>::PointerType>::type
-      PointerType;
-
-  enum {
-    Flags = 0
-  };
-};
-
-template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
-struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, Eigen::Dense>
-{
-  typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>& type;
-};
-
-template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
-struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >::type>
-{
-  typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> type;
-};
-
-template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename OutputKernelType_, typename Device_>
-struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_, OutputKernelType_>, Device_> > {
-  typedef Indices_ Indices;
-  typedef LeftArgType_ LeftArgType;
-  typedef RightArgType_ RightArgType;
-  typedef OutputKernelType_ OutputKernelType;
-  typedef Device_ Device;
-
-  // From NumDims below.
-  static const int NumDimensions = traits<LeftArgType_>::NumDimensions + traits<RightArgType_>::NumDimensions - 2 * array_size<Indices_>::value;
-};
-
-// Helper class to allocate and deallocate temporary memory for packed buffers.
-template <typename LhsScalar, typename RhsScalar>
-struct TensorContractionBlockMemAllocator {
-  typedef void* BlockMemHandle;
-
-  template <typename Device>
-  EIGEN_DEVICE_FUNC static BlockMemHandle allocate(Device& d, const Index bm,
-                                                   const Index bk,
-                                                   const Index bn,
-                                                   LhsScalar** lhs_block,
-                                                   RhsScalar** rhs_block) {
-    eigen_assert(lhs_block);
-    eigen_assert(rhs_block);
-    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
-    char* block_mem = static_cast<char*>(d.allocate(sz.lhs_size + sz.rhs_size));
-    eigen_assert(block_mem);
-    *lhs_block = reinterpret_cast<LhsScalar*>(block_mem);
-    *rhs_block = reinterpret_cast<RhsScalar*>(block_mem + sz.lhs_size);
-    return block_mem;
-  }
-
-  template <typename Device>
-  EIGEN_DEVICE_FUNC static BlockMemHandle allocateSlices(
-      Device& d, const Index bm, const Index bk, const Index bn,
-      const Index num_lhs, const Index num_rhs, const Index num_slices,
-      std::vector<LhsScalar*>* lhs_blocks,
-      std::vector<RhsScalar*>* rhs_blocks) {
-    eigen_assert(num_slices > 0);
-    eigen_assert(num_lhs >= 0 && num_rhs >= 0);
-    eigen_assert(num_lhs == 0 || lhs_blocks);
-    eigen_assert(num_rhs == 0 || rhs_blocks);
-    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
-    void* block_mem = d.allocate(
-        (num_lhs * sz.lhs_size + num_rhs * sz.rhs_size) * num_slices);
-    eigen_assert(block_mem);
-    char* mem = static_cast<char*>(block_mem);
-
-    for (Index x = 0; x < num_slices; x++) {
-      if (num_lhs > 0) lhs_blocks[x].resize(num_lhs);
-      for (Index m = 0; m < num_lhs; m++) {
-        lhs_blocks[x][m] = reinterpret_cast<LhsScalar*>(mem);
-        mem += sz.lhs_size;
-      }
-      if (num_rhs > 0) rhs_blocks[x].resize(num_rhs);
-      for (Index n = 0; n < num_rhs; n++) {
-        rhs_blocks[x][n] = reinterpret_cast<RhsScalar*>(mem);
-        mem += sz.rhs_size;
-      }
-    }
-
-    return block_mem;
-  }
-
-  template <typename Device>
-  EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
-    d.deallocate(handle);
-  }
-
- private:
-  struct BlockSizes {
-    Index lhs_size;
-    Index rhs_size;
-  };
-  EIGEN_DEVICE_FUNC static BlockSizes ComputeLhsRhsBlockSizes(const Index bm,
-                                                              const Index bk,
-                                                              const Index bn) {
-    Index align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);
-    BlockSizes sz;
-    sz.lhs_size = divup<Index>(bm * bk * sizeof(LhsScalar), align) * align;
-    sz.rhs_size = divup<Index>(bn * bk * sizeof(RhsScalar), align) * align;
-    return sz;
-  }
-};
-
-// WARNING: In this code we assume that Lhs and Rhs tensor expressions are in
-// ColMajor storage order. This property is guaranteed by the
-// TensorContractionOp evaluator. TensorContractionKernel specifies how we pack
-// blocks of Lhs and Rhs tensor expressions, and how we invoke matrix
-// multiplication for these blocks. Default tensor contraction uses
-// gemm_pack_rhs, gemm_pack_lhs and gebp_kernel from Eigen Core (see
-// GeneralBlocPanelKernel.h for details).
-//
-// By specializing contraction kernels we can use other low level libraries to
-// perform matrix multiplication, and still rely on Eigen contraction evaluator.
-// This also includes full support in TensorContractionThreadPool, assuming that
-// underlying gemm do not use it's own threading.
-//
-// - ResScalar/LhsScalar/RhsScalar - scalar type for the result of
-//   multiplication, lhs tensor and rhs tensor respectively.
-//
-// - StorageIndex - index type for the tensor expressions. In practice almost
-//   always is Eigen::Index.
-//
-// - OutputMapper provides access to the memory of the output matrix. In
-//   practice it's always column major blas_data_mapper (it must be of ResScalar
-//   type).
-//
-// - LhsMapper/RhsMapper similarly to blas_data_mapper provide a two dimensional
-//   view into the Lhs/Rhs tensor expressions. In practice it's
-//   TensorContractionInputMapper, or some specialization of it based on the
-//   type of tensor expression (e.g. TensorImagePatchOp has optimized input
-//   mapper).
-template <typename ResScalar, typename LhsScalar, typename RhsScalar,
-    typename StorageIndex, typename OutputMapper, typename LhsMapper,
-    typename RhsMapper>
-struct TensorContractionKernel {
-  // True if `invoke()` supports `beta` in `C <- alpha * A * B + beta * C`
-  // (otherwise beta should be always equal to 1).
-  enum { HasBeta = false };
-
-  EIGEN_DEVICE_FUNC
-  TensorContractionKernel(StorageIndex m_, StorageIndex k_, StorageIndex n_,
-                          StorageIndex bm_, StorageIndex bk_, StorageIndex bn_)
-      : m(m_), k(k_), n(n_), bm(bm_), bk(bk_), bn(bn_) {}
-
-  // Pack blocks of Lhs and Rhs into contiguous blocks in memory.
-  typedef LhsScalar* LhsBlock;
-  typedef RhsScalar* RhsBlock;
-
-  // Packed Lhs/Rhs block memory allocator.
-  typedef TensorContractionBlockMemAllocator<LhsScalar, RhsScalar>
-      BlockMemAllocator;
-  typedef typename BlockMemAllocator::BlockMemHandle BlockMemHandle;
-
-  typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
-
-  typedef internal::gemm_pack_lhs<
-      LhsScalar, StorageIndex, typename LhsMapper::SubMapper, Traits::mr,
-      Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor>
-      LhsPacker;
-
-  typedef internal::gemm_pack_rhs<RhsScalar, StorageIndex,
-                                  typename RhsMapper::SubMapper, Traits::nr,
-                                  ColMajor>
-      RhsPacker;
-
-  typedef internal::gebp_kernel<LhsScalar, RhsScalar, StorageIndex,
-                                OutputMapper, Traits::mr, Traits::nr,
-      /*ConjugateLhs*/ false, /*ConjugateRhs*/ false>
-      GebpKernel;
-
-  template <typename Device>
-  EIGEN_DEVICE_FUNC BlockMemHandle allocate(Device& d, LhsBlock* lhs_block,
-                                            RhsBlock* rhs_block) {
-    return BlockMemAllocator::allocate(d, bm, bk, bn, lhs_block, rhs_block);
-  }
-
-  template <typename Device>
-  EIGEN_DEVICE_FUNC BlockMemHandle allocateSlices(
-      Device& d, const StorageIndex num_lhs, const StorageIndex num_rhs,
-      const StorageIndex num_slices, std::vector<LhsBlock>* lhs_blocks,
-      std::vector<RhsBlock>* rhs_blocks) {
-    return BlockMemAllocator::allocateSlices(
-        d, bm, bk, bn, num_lhs, num_rhs, num_slices, lhs_blocks, rhs_blocks);
-  }
-
-  template <typename Device>
-  EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
-    BlockMemAllocator::deallocate(d, handle);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packLhs(
-      LhsBlock* lhsBlock, const typename LhsMapper::SubMapper& data_mapper,
-      const StorageIndex depth, const StorageIndex rows) {
-    LhsPacker()(*lhsBlock, data_mapper, depth, rows, /*stride*/ 0,
-        /*offset*/ 0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packRhs(
-      RhsBlock* rhsBlock, const typename RhsMapper::SubMapper& data_mapper,
-      const StorageIndex depth, const StorageIndex cols) {
-    RhsPacker()(*rhsBlock, data_mapper, depth, cols);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void invoke(
-      const OutputMapper& output_mapper, const LhsBlock& lhsBlock,
-      const RhsBlock& rhsBlock, const StorageIndex rows,
-      const StorageIndex depth, const StorageIndex cols,
-      const ResScalar alpha, const ResScalar beta) {
-    // Default GEBP kernel does not support beta.
-    eigen_assert(beta == ResScalar(1));
-    static const int kComputeStrideFromBlockDimensions = -1;
-    GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,
-        /*strideA*/ kComputeStrideFromBlockDimensions,
-        /*strideB*/ kComputeStrideFromBlockDimensions,
-        /*offsetA*/ 0, /*offsetB*/ 0);
-  }
-
- private:
-  // These are dimensions of the original Tensors, and selected block sizes. The
-  // actual block sizes passed to all function above might be smaller because of
-  // the partial blocks at the end.
-  const StorageIndex m;
-  const StorageIndex k;
-  const StorageIndex n;
-  const StorageIndex bm;
-  const StorageIndex bk;
-  const StorageIndex bn;
-};
-
-}  // end namespace internal
-
-// Tensor contraction params that should enable to get from output matrix
-// 2-dimensional coordinates to the output tensor dimensions.
-struct TensorContractionParams {
-  // TensorContraction evaluator assumes that both tensors are in ColMajor
-  // layout, if tensors are in RowMajor evaluator swap lhs with rhs.
-  bool swapped_arguments;
-};
-
-// Output kernel allows to fuse operations into the tensor contraction.
-//
-// Examples:
-//   1. Elementwise Relu transformation following Conv2D.
-//   2. AddBias to the Conv2D output channels dimension.
-//
-// The NoOpOutputKernel implements an output kernel that does absolutely nothing.
-struct NoOpOutputKernel {
-  /**
-   * Tensor contraction evaluator calls this kernel after finishing each block
-   * of output matrix. Output blocks belong to the 2-dimensional output tensor.
-   *
-   * TensorContractionParams contains contraction dimensions information
-   * required to map output 2-d space into the expected output tensor space
-   * (potentially higher dimensional).
-   *
-   * \param[in] output_mapper Access to output tensor memory
-   * \param[in] params   Tensor contraction parameters
-   * \param[in] i        Index of a first row available through output_mapper
-   * \param[in] j        Index of a first column available through output_mapper
-   * \param[in] num_rows Number of available rows
-   * \param[in] num_cols Number of available columns
-   */
-  template <typename Index, typename Scalar>
-  EIGEN_ALWAYS_INLINE void operator()(
-      const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
-      const TensorContractionParams& params, Index i,
-      Index j, Index num_rows, Index num_cols) const {
-    EIGEN_UNUSED_VARIABLE(output_mapper);
-    EIGEN_UNUSED_VARIABLE(params);
-    EIGEN_UNUSED_VARIABLE(i);
-    EIGEN_UNUSED_VARIABLE(j);
-    EIGEN_UNUSED_VARIABLE(num_rows);
-    EIGEN_UNUSED_VARIABLE(num_cols);
-  }
-};
-
-template<typename Indices, typename LhsXprType, typename RhsXprType, typename OutputKernelType = const NoOpOutputKernel>
-class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType, OutputKernelType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorContractionOp>::Scalar Scalar;
-  typedef typename internal::gebp_traits<typename LhsXprType::CoeffReturnType,
-                                         typename RhsXprType::CoeffReturnType>::ResScalar CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorContractionOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorContractionOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorContractionOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp(
-      const LhsXprType& lhs, const RhsXprType& rhs, const Indices& dims,
-      const OutputKernelType& output_kernel = OutputKernelType())
-      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims),
-        m_output_kernel(output_kernel) {}
-
-  EIGEN_DEVICE_FUNC
-  const Indices& indices() const { return m_indices; }
-
-  /** \returns the nested expressions */
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename LhsXprType::Nested>::type&
-  lhsExpression() const { return m_lhs_xpr; }
-
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename RhsXprType::Nested>::type&
-  rhsExpression() const { return m_rhs_xpr; }
-
-  EIGEN_DEVICE_FUNC
-  const OutputKernelType& outputKernel() const { return m_output_kernel; }
-
-  protected:
-    typename LhsXprType::Nested m_lhs_xpr;
-    typename RhsXprType::Nested m_rhs_xpr;
-    const Indices m_indices;
-    const OutputKernelType m_output_kernel;
-};
-
-
-template<typename Derived>
-struct TensorContractionEvaluatorBase : internal::no_assignment_operator
-{
-  typedef typename internal::traits<Derived>::Indices Indices;
-  typedef typename internal::traits<Derived>::LeftArgType LeftArgType;
-  typedef typename internal::traits<Derived>::RightArgType RightArgType;
-  typedef typename internal::traits<Derived>::OutputKernelType OutputKernelType;
-  typedef typename internal::traits<Derived>::Device Device;
-
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef StorageMemory<Scalar, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = true,
-    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess       = false,
-    PreferBlockAccess = false,
-    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = true
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  // Most of the code is assuming that both input tensors are ColMajor. If the
-  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
-  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
-  // will pretend B is LHS and A is RHS.
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
-
-  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluatorType;
-  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluatorType;
-
-  static const int LDims =
-      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
-  static const int RDims =
-      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
-  static const int ContractDims = internal::array_size<Indices>::value;
-  static const int NumDims = LDims + RDims - 2 * ContractDims;
-
-  typedef array<Index, ContractDims> contract_t;
-  typedef array<Index, LDims - ContractDims> left_nocontract_t;
-  typedef array<Index, RDims - ContractDims> right_nocontract_t;
-
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  EIGEN_STRONG_INLINE
-  TensorContractionEvaluatorBase(const XprType& op, const Device& device)
-      : m_leftImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
-                          op.lhsExpression(), op.rhsExpression()), device),
-        m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
-                           op.rhsExpression(), op.lhsExpression()), device),
-        m_device(device),
-        m_output_kernel(op.outputKernel()),
-        m_result(NULL) {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
-         static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
-                        YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-
-    DSizes<Index, LDims> eval_left_dims;
-    DSizes<Index, RDims> eval_right_dims;
-    array<IndexPair<Index>, ContractDims> eval_op_indices;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      // For ColMajor, we keep using the existing dimensions
-      for (int i = 0; i < LDims; i++) {
-        eval_left_dims[i] = m_leftImpl.dimensions()[i];
-      }
-      for (int i = 0; i < RDims; i++) {
-        eval_right_dims[i] = m_rightImpl.dimensions()[i];
-      }
-      // We keep the pairs of contracting indices.
-      for (int i = 0; i < ContractDims; i++) {
-        eval_op_indices[i].first = op.indices()[i].first;
-        eval_op_indices[i].second = op.indices()[i].second;
-      }
-    } else {
-      // For RowMajor, we need to reverse the existing dimensions
-      for (int i = 0; i < LDims; i++) {
-        eval_left_dims[i] = m_leftImpl.dimensions()[LDims - i - 1];
-      }
-      for (int i = 0; i < RDims; i++) {
-        eval_right_dims[i] = m_rightImpl.dimensions()[RDims - i - 1];
-      }
-      // We need to flip all the pairs of contracting indices as well as
-      // reversing the dimensions.
-      for (int i = 0; i < ContractDims; i++) {
-        eval_op_indices[i].first = LDims - 1 - op.indices()[ContractDims - 1 - i].second;
-        eval_op_indices[i].second = RDims - 1 - op.indices()[ContractDims - 1 - i].first;
-      }
-    }
-
-    // Check for duplicate axes and make sure the first index in eval_op_indices
-    // is increasing. Using O(n^2) sorting is OK since ContractDims is small
-    for (int i = 0; i < ContractDims; i++) {
-      for (int j = i + 1; j < ContractDims; j++) {
-        eigen_assert(eval_op_indices[j].first != eval_op_indices[i].first &&
-                     eval_op_indices[j].second != eval_op_indices[i].second &&
-                     "contraction axes should be unique");
-        if (eval_op_indices[j].first < eval_op_indices[i].first) {
-          numext::swap(eval_op_indices[j], eval_op_indices[i]);
-        }
-      }
-    }
-
-    array<Index, LDims> lhs_strides;
-    lhs_strides[0] = 1;
-    for (int i = 0; i < LDims-1; ++i) {
-      lhs_strides[i+1] = lhs_strides[i] * eval_left_dims[i];
-    }
-
-    array<Index, RDims> rhs_strides;
-    rhs_strides[0] = 1;
-    for (int i = 0; i < RDims-1; ++i) {
-      rhs_strides[i+1] = rhs_strides[i] * eval_right_dims[i];
-    }
-
-    if (m_i_strides.size() > 0) m_i_strides[0] = 1;
-    if (m_j_strides.size() > 0) m_j_strides[0] = 1;
-    if (m_k_strides.size() > 0) m_k_strides[0] = 1;
-
-    m_i_size = 1;
-    m_j_size = 1;
-    m_k_size = 1;
-
-    // To compute the dimension, we simply concatenate the non-contracting
-    // dimensions of the left and then the right tensor. Additionally, we also
-    // compute the strides corresponding to the left non-contracting
-    // dimensions and right non-contracting dimensions.
-    m_lhs_inner_dim_contiguous = true;
-    int dim_idx = 0;
-    Index nocontract_idx = 0;
-
-    for (int i = 0; i < LDims; i++) {
-      // find if we are contracting on index i of left tensor
-      bool contracting = false;
-      for (int j = 0; j < ContractDims; j++) {
-        if (eval_op_indices[j].first == i) {
-          contracting = true;
-          break;
-        }
-      }
-      if (!contracting) {
-        // add dimension size to output dimensions
-        m_dimensions[dim_idx] = eval_left_dims[i];
-        m_left_nocontract_strides[nocontract_idx] = lhs_strides[i];
-        if (dim_idx != i) {
-          m_lhs_inner_dim_contiguous = false;
-        }
-        if (nocontract_idx+1 < internal::array_size<left_nocontract_t>::value) {
-          m_i_strides[nocontract_idx+1] =
-              m_i_strides[nocontract_idx] * eval_left_dims[i];
-        } else {
-          m_i_size = m_i_strides[nocontract_idx] * eval_left_dims[i];
-        }
-        dim_idx++;
-        nocontract_idx++;
-      }
-    }
-
-    nocontract_idx = 0;
-    for (int i = 0; i < RDims; i++) {
-      bool contracting = false;
-      // find if we are contracting on index i of right tensor
-      for (int j = 0; j < ContractDims; j++) {
-        if (eval_op_indices[j].second == i) {
-          contracting = true;
-          break;
-        }
-      }
-      if (!contracting) {
-        m_dimensions[dim_idx] = eval_right_dims[i];
-        if (nocontract_idx+1 < internal::array_size<right_nocontract_t>::value) {
-          m_j_strides[nocontract_idx+1] =
-              m_j_strides[nocontract_idx] * eval_right_dims[i];
-        } else {
-          m_j_size = m_j_strides[nocontract_idx] * eval_right_dims[i];
-        }
-        m_right_nocontract_strides[nocontract_idx] = rhs_strides[i];
-        dim_idx++;
-        nocontract_idx++;
-      }
-    }
-
-    // Now compute the strides corresponding to the contracting dimensions. We
-    // assumed above that non-contracting axes are represented in the same order
-    // in the matrix as they are in the tensor. This is not the case for
-    // contracting axes. As the contracting axes must be of the same size in
-    // each tensor, we'll only look at the first tensor here.
-    m_rhs_inner_dim_contiguous = true;
-    m_rhs_inner_dim_reordered = false;
-    for (int i = 0; i < ContractDims; i++) {
-      Index left = eval_op_indices[i].first;
-      Index right = eval_op_indices[i].second;
-
-      Index size = eval_left_dims[left];
-      eigen_assert(size == eval_right_dims[right] &&
-                   "Contraction axes must be same size");
-
-      if (i+1 < static_cast<int>(internal::array_size<contract_t>::value)) {
-        m_k_strides[i+1] = m_k_strides[i] * size;
-      } else {
-        m_k_size = m_k_strides[i] * size;
-      }
-      m_left_contracting_strides[i] = lhs_strides[left];
-      m_right_contracting_strides[i] = rhs_strides[right];
-
-      if (i > 0 && right < eval_op_indices[i-1].second) {
-        m_rhs_inner_dim_reordered = true;
-      }
-      if (right != i) {
-        m_rhs_inner_dim_contiguous = false;
-      }
-    }
-
-    // If the layout is RowMajor, we need to reverse the m_dimensions
-    if (static_cast<int>(Layout) == static_cast<int>(RowMajor)) {
-      for (int i = 0, j = NumDims - 1; i < j; i++, j--) {
-        numext::swap(m_dimensions[i], m_dimensions[j]);
-      }
-    }
-
-    // A set of parameters that will allow output kernel to get from output
-    // tensor dimensions (i, j) into the original tensor dimensions.
-    // TODO(ezhulenev): Add parameters required to infer output tensor index for
-    // more complex contractions than 2x2 on internal dimension.
-    m_tensor_contraction_params.swapped_arguments = static_cast<int>(Layout) == RowMajor;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    m_leftImpl.evalSubExprsIfNeeded(NULL);
-    m_rightImpl.evalSubExprsIfNeeded(NULL);
-    if (data) {
-      evalTo(data);
-      return false;
-    } else {
-      m_result = static_cast<EvaluatorPointerType>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
-      evalTo(m_result);
-      return true;
-    }
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType dest, EvalSubExprsCallback done) {
-    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
-      m_rightImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
-        if (dest) {
-          evalToAsync(dest, [done]() { done(false); });
-        } else {
-          m_result = static_cast<EvaluatorPointerType>(
-              m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
-          evalToAsync(m_result, [done]() { done(true); });
-        }
-      });
-    });
-  }
-#endif  // EIGEN_USE_THREADS
-
-#ifndef TENSOR_CONTRACTION_DISPATCH
-#define TENSOR_CONTRACTION_DISPATCH(METHOD, ALIGNMENT, ARGS) \
-  if (this->m_lhs_inner_dim_contiguous) {                    \
-    if (this->m_rhs_inner_dim_contiguous) {                  \
-      if (this->m_rhs_inner_dim_reordered) {                 \
-        METHOD<true, true, true, ALIGNMENT> ARGS;            \
-      } else {                                               \
-        METHOD<true, true, false, ALIGNMENT> ARGS;           \
-      }                                                      \
-    } else {                                                 \
-      if (this->m_rhs_inner_dim_reordered) {                 \
-        METHOD<true, false, true, ALIGNMENT> ARGS;           \
-      } else {                                               \
-        METHOD<true, false, false, ALIGNMENT> ARGS;          \
-      }                                                      \
-    }                                                        \
-  } else {                                                   \
-    if (this->m_rhs_inner_dim_contiguous) {                  \
-      if (this->m_rhs_inner_dim_reordered) {                 \
-        METHOD<false, true, true, ALIGNMENT> ARGS;           \
-      } else {                                               \
-        METHOD<false, true, false, ALIGNMENT> ARGS;          \
-      }                                                      \
-    } else {                                                 \
-      if (this->m_rhs_inner_dim_reordered) {                 \
-        METHOD<false, false, true, ALIGNMENT> ARGS;          \
-      } else {                                               \
-        METHOD<false, false, false, ALIGNMENT> ARGS;         \
-      }                                                      \
-    }                                                        \
-  }
-#endif
-
-#ifndef TENSOR_CONTRACTION_ASYNC_DISPATCH
-#define TENSOR_CONTRACTION_ASYNC_DISPATCH(METHOD, DONE, ALIGNMENT, ARGS, FN) \
-  if (this->m_lhs_inner_dim_contiguous) {                                    \
-    if (this->m_rhs_inner_dim_contiguous) {                                  \
-      if (this->m_rhs_inner_dim_reordered) {                                 \
-        (new METHOD<DONE, true, true, true, ALIGNMENT> ARGS)->FN;            \
-      } else {                                                               \
-        (new METHOD<DONE, true, true, false, ALIGNMENT> ARGS)->FN;           \
-      }                                                                      \
-    } else {                                                                 \
-      if (this->m_rhs_inner_dim_reordered) {                                 \
-        (new METHOD<DONE, true, false, true, ALIGNMENT> ARGS)->FN;           \
-      } else {                                                               \
-        (new METHOD<DONE, true, false, false, ALIGNMENT> ARGS)->FN;          \
-      }                                                                      \
-    }                                                                        \
-  } else {                                                                   \
-    if (this->m_rhs_inner_dim_contiguous) {                                  \
-      if (this->m_rhs_inner_dim_reordered) {                                 \
-        (new METHOD<DONE, false, true, true, ALIGNMENT> ARGS)->FN;           \
-      } else {                                                               \
-        (new METHOD<DONE, false, true, false, ALIGNMENT> ARGS)->FN;          \
-      }                                                                      \
-    } else {                                                                 \
-      if (this->m_rhs_inner_dim_reordered) {                                 \
-        (new METHOD<DONE, false, false, true, ALIGNMENT> ARGS)->FN;          \
-      } else {                                                               \
-        (new METHOD<DONE, false, false, false, ALIGNMENT> ARGS)->FN;         \
-      }                                                                      \
-    }                                                                        \
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC void evalTo(Scalar* buffer) const {
-   static_cast<const Derived*>(this)->template evalProduct<Unaligned>(buffer);
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalToCallback>
-  void evalToAsync(Scalar* buffer, EvalToCallback done) const {
-    static_cast<const Derived*>(this)
-        ->template evalProductAsync<EvalToCallback, Unaligned>(buffer,
-                                                               std::move(done));
-  }
-#endif  // EIGEN_USE_THREADS
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
-            bool rhs_inner_dim_reordered, int Alignment>
-  void evalProductSequential(Scalar* buffer) const {
-    if (this->m_j_size == 1) {
-      this->template evalGemv<lhs_inner_dim_contiguous,
-                              rhs_inner_dim_contiguous, rhs_inner_dim_reordered,
-                              Alignment>(buffer);
-    } else {
-      this->template evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
-                              rhs_inner_dim_reordered, Alignment>(buffer);
-    }
-  }
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
-  #if !defined(EIGEN_HIPCC)
-  EIGEN_DEVICE_FUNC
-  #endif
-  void evalGemv(Scalar* buffer) const {
-    const Index rows = m_i_size;
-    const Index cols = m_k_size;
-
-    typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
-    typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
-    typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
-    typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
-    const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
-    const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
-    const int lhs_alignment = LeftEvaluator::IsAligned ? Aligned : Unaligned;
-    const int rhs_alignment = RightEvaluator::IsAligned ? Aligned : Unaligned;
-    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
-                                                   LeftEvaluator, left_nocontract_t,
-                                                   contract_t, lhs_packet_size,
-                                                   lhs_inner_dim_contiguous,
-                                                   false, lhs_alignment> LhsMapper;
-
-    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
-                                                   RightEvaluator, right_nocontract_t,
-                                                   contract_t, rhs_packet_size,
-                                                   rhs_inner_dim_contiguous,
-                                                   rhs_inner_dim_reordered, rhs_alignment> RhsMapper;
-
-    LhsMapper lhs(m_leftImpl, m_left_nocontract_strides, m_i_strides,
-                  m_left_contracting_strides, m_k_strides);
-    RhsMapper rhs(m_rightImpl, m_right_nocontract_strides, m_j_strides,
-                  m_right_contracting_strides, m_k_strides);
-
-    const Scalar alpha(1);
-    const Index resIncr(1);
-
-    // zero out the result buffer (which must be of size at least rows * sizeof(Scalar)
-    m_device.memset(buffer, 0, rows * sizeof(Scalar));
-
-    internal::general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,false,RhsScalar,RhsMapper,false>::run(
-        rows, cols, lhs, rhs,
-        buffer, resIncr, alpha);
-
-    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
-    m_output_kernel(OutputMapper(buffer, rows), m_tensor_contraction_params,
-                    static_cast<Index>(0), static_cast<Index>(0), rows,
-                    static_cast<Index>(1));
-  }
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
-  #if !defined(EIGEN_HIPCC)
-  EIGEN_DEVICE_FUNC
-  #endif
-  void evalGemm(Scalar* buffer) const {
-    // columns in left side, rows in right side
-    const Index k = this->m_k_size;
-    this->template evalGemmPartial<lhs_inner_dim_contiguous,
-                                   rhs_inner_dim_contiguous,
-                                   rhs_inner_dim_reordered,
-                                   Alignment, true>(buffer, 0, k, 1);
-  }
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
-      bool rhs_inner_dim_reordered, int Alignment>
-  EIGEN_DEVICE_FUNC void evalGemmPartialWithoutOutputKernel(
-      Scalar* buffer, Index k_start, Index k_end, int num_threads) const {
-    evalGemmPartial<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
-                    rhs_inner_dim_reordered, Alignment,
-        /*use_output_kernel*/ false>(buffer, k_start, k_end,
-                                     num_threads);
-  }
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment, bool use_output_kernel>
-  EIGEN_DEVICE_FUNC void evalGemmPartial(Scalar* buffer, Index k_start, Index k_end, int num_threads) const {
-    eigen_assert(k_end >= k_start && k_start >= 0 && k_end <= this->m_k_size);
-    // columns in slice on left side, rows on right side
-    const Index k_slice = k_end - k_start;
-
-    // rows in left side
-    const Index m = this->m_i_size;
-
-    // columns in right side
-    const Index n = this->m_j_size;
-
-    // define data mappers for Lhs and Rhs
-    typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
-    typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
-
-    typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
-    typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
-
-    const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
-    const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
-
-    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
-                                                   LeftEvaluator, left_nocontract_t,
-                                                   contract_t, lhs_packet_size,
-                                                   lhs_inner_dim_contiguous,
-                                                   false, Unaligned> LhsMapper;
-
-    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
-                                                   RightEvaluator, right_nocontract_t,
-                                                   contract_t, rhs_packet_size,
-                                                   rhs_inner_dim_contiguous,
-                                                   rhs_inner_dim_reordered, Unaligned> RhsMapper;
-
-    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
-
-    typedef internal::TensorContractionKernel<
-        Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
-        TensorContractionKernel;
-
-    // initialize data mappers
-    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
-                  this->m_left_contracting_strides, this->m_k_strides);
-
-    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
-                  this->m_right_contracting_strides, this->m_k_strides);
-
-    OutputMapper output(buffer, m);
-
-    // Sizes of the blocks to load in cache. See the Goto paper for details.
-    internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar,
-                                        Index, internal::ShardByCol>
-        blocking(k_slice, m, n, num_threads);
-    const Index kc = blocking.kc();
-    const Index mc = numext::mini(m, blocking.mc());
-    const Index nc = numext::mini(n, blocking.nc());
-
-    typedef typename TensorContractionKernel::LhsBlock LhsBlock;
-    typedef typename TensorContractionKernel::RhsBlock RhsBlock;
-
-    LhsBlock blockA;
-    RhsBlock blockB;
-
-    TensorContractionKernel kernel(m, k_slice, n, mc, kc, nc);
-
-    typedef typename TensorContractionKernel::BlockMemHandle BlockMemHandle;
-    const BlockMemHandle packed_mem =
-        kernel.allocate(this->m_device, &blockA, &blockB);
-
-    // If a contraction kernel does not support beta, explicitly initialize
-    // output buffer with zeroes.
-    if (!TensorContractionKernel::HasBeta) {
-      this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
-    }
-
-    for(Index i2=0; i2<m; i2+=mc)
-    {
-      const Index actual_mc = numext::mini(i2+mc,m)-i2;
-      for (Index k2 = k_start; k2 < k_end; k2 += kc) {
-        // make sure we don't overshoot right edge of left matrix, then pack vertical panel
-        const Index actual_kc = numext::mini(k2 + kc, k_end) - k2;
-        kernel.packLhs(&blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
-
-        // If kernel supports beta, there is no need to initialize output
-        // buffer with zeroes.
-        const Scalar alpha = Scalar(1);
-        const Scalar beta = (TensorContractionKernel::HasBeta && k2 == k_start)
-                                ? Scalar(0)
-                                : Scalar(1);
-
-        // series of horizontal blocks
-        for (Index j2 = 0; j2 < n; j2 += nc) {
-          // make sure we don't overshoot right edge of right matrix, then pack block
-          const Index actual_nc = numext::mini(j2 + nc, n) - j2;
-          kernel.packRhs(&blockB, rhs.getSubMapper(k2, j2), actual_kc,
-                         actual_nc);
-
-          // call gebp (matrix kernel)
-          // The parameters here are copied from Eigen's GEMM implementation
-          const OutputMapper output_mapper = output.getSubMapper(i2, j2);
-          kernel.invoke(output_mapper, blockA, blockB, actual_mc, actual_kc,
-                        actual_nc, alpha, beta);
-
-          // We are done with this [i2, j2] output block.
-          if (use_output_kernel && k2 + kc >= k_end) {
-            m_output_kernel(output_mapper, m_tensor_contraction_params, i2, j2,
-                            actual_mc, actual_nc);
-          }
-        }
-      }
-    }
-
-    kernel.deallocate(this->m_device, packed_mem);
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_leftImpl.cleanup();
-    m_rightImpl.cleanup();
-
-    if (m_result != NULL) {
-      m_device.deallocate(m_result);
-      m_result = NULL;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    return m_result[index];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_result; }
-
-protected:
-  Dimensions m_dimensions;
-
-  contract_t m_k_strides;
-  contract_t m_left_contracting_strides;
-  contract_t m_right_contracting_strides;
-
-  bool m_lhs_inner_dim_contiguous;
-  bool m_rhs_inner_dim_contiguous;
-  bool m_rhs_inner_dim_reordered;
-
-  left_nocontract_t m_i_strides;
-  right_nocontract_t m_j_strides;
-  left_nocontract_t m_left_nocontract_strides;
-  right_nocontract_t m_right_nocontract_strides;
-
-  Index m_i_size;
-  Index m_j_size;
-  Index m_k_size;
-
-  TensorContractionParams m_tensor_contraction_params;
-
-  TensorEvaluator<EvalLeftArgType, Device> m_leftImpl;
-  TensorEvaluator<EvalRightArgType, Device> m_rightImpl;
-  const Device EIGEN_DEVICE_REF m_device;
-  OutputKernelType m_output_kernel;
-  EvaluatorPointerType m_result;
-};
-
-
-// evaluator for default device
-template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType, typename Device>
-struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> :
-    public TensorContractionEvaluatorBase<
-      TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> > {
-  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
-  typedef TensorContractionEvaluatorBase<Self> Base;
-
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-
-  enum {
-    Layout = TensorEvaluator<LeftArgType, Device>::Layout
-  };
-
-  // Most of the code is assuming that both input tensors are ColMajor. If the
-  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
-  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
-  // will pretend B is LHS and A is RHS.
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
-
-  static const int LDims =
-      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
-  static const int RDims =
-      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
-  static const int ContractDims = internal::array_size<Indices>::value;
-
-  typedef array<Index, ContractDims> contract_t;
-  typedef array<Index, LDims - ContractDims> left_nocontract_t;
-  typedef array<Index, RDims - ContractDims> right_nocontract_t;
-
-  static const int NumDims = LDims + RDims - 2 * ContractDims;
-
-  // Could we use NumDimensions here?
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  TensorEvaluator(const XprType& op, const Device& device) :
-      Base(op, device) { }
-
-  template <int Alignment>
-  void evalProduct(Scalar* buffer) const {
-    TENSOR_CONTRACTION_DISPATCH(this->template evalProductSequential, Alignment, (buffer));
-  }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
deleted file mode 100644
index 974feb0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
+++ /dev/null
@@ -1,73 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
-
-
-namespace Eigen {
-namespace internal {
-
-enum {
-  ShardByRow = 0,
-  ShardByCol = 1
-};
-
-
-// Default Blocking Strategy
-template<typename ResScalar, typename LhsScalar, typename RhsScalar, typename StorageIndex, int ShardingType = ShardByCol>
-class TensorContractionBlocking {
- public:
-
- /*
-   adding EIGEN_DEVICE_FUNC unconditionally to 'TensorContractionBlocking' constructor in `TensorContractionBlocking.h`
-     requires adding EIGEN_DEVICE_FUNC to `computeProductBlockingSizes` in `GeneralBlockPanelKernel.h`
-     which in turn, requires adding EIGEN_DEVICE_FUNC to `evaluateProductBlockingSizesHeuristic` in `GeneralBlockPanelKernel.h`
-     which in turn, requires adding EIGEN_DEVICE_FUNC to `manage_caching_sizes` in `GeneralBlockPanelKernel.h`
-     (else HIPCC will error out)
-
-   However adding EIGEN_DEVICE_FUNC to `manage_caching_sizes` in `GeneralBlockPanelKernel.h`
-   results in NVCC erroring out with the following error
-
-   ../Eigen/src/Core/products/GeneralBlockPanelKernel.h(57): error #2901:
-      dynamic initialization is not supported for function-scope static variables within a __device__/__global__ function
- */
-
-  #if !defined(EIGEN_HIPCC)
-  EIGEN_DEVICE_FUNC
-  #endif
- TensorContractionBlocking(StorageIndex k, StorageIndex m, StorageIndex n, StorageIndex num_threads = 1) :
-      kc_(k), mc_(m), nc_(n)
-  {
-    if (ShardingType == ShardByCol) {
-      computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, mc_, nc_, num_threads);
-    }
-    else {
-      computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, nc_, mc_, num_threads);
-    }
-
-    const int rhs_packet_size = internal::packet_traits<RhsScalar>::size;
-    kc_ = (rhs_packet_size <= 8 || kc_ <= rhs_packet_size) ?
-      kc_ : (kc_ / rhs_packet_size) * rhs_packet_size;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex kc() const { return kc_; }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex mc() const { return mc_; }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex nc() const { return nc_; }
-
- private:
-  StorageIndex kc_;
-  StorageIndex mc_;
-  StorageIndex nc_;
-};
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
deleted file mode 100644
index 3f315fe..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
+++ /dev/null
@@ -1,6 +0,0 @@
-
-#if defined(__clang__) || defined(__GNUC__)
-#warning "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorContractionGpu.h file"
-#endif
-
-#include "TensorContractionGpu.h"
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
deleted file mode 100644
index c818038..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
+++ /dev/null
@@ -1,1413 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2015 Navdeep Jaitly <ndjaitly@google.com>
-// Copyright (C) 2014 Eric Martin <eric@ericmart.in>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
-
-#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
-
-namespace Eigen {
-
-template<typename Scalar, typename Index, typename LhsMapper,
-         typename RhsMapper, typename OutputMapper, bool needs_edge_check>
-__device__ EIGEN_STRONG_INLINE void
-EigenContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
-                               const OutputMapper output, Scalar* lhs_shmem, Scalar* rhs_shmem,
-                       const Index m_size, const Index n_size, const Index k_size) {
-
-  const Index m_block_idx = blockIdx.x;
-  const Index n_block_idx = blockIdx.y;
-
-  const Index base_m = 64 * m_block_idx;
-  const Index base_n = 64 * n_block_idx;
-
-  // declare and initialize 64 registers for output 8x8 block
-
-  // prefetch registers
-  Scalar lhs_pf0;
-  Scalar lhs_pf1;
-  Scalar lhs_pf2;
-  Scalar lhs_pf3;
-  Scalar lhs_pf4;
-  Scalar lhs_pf5;
-  Scalar lhs_pf6;
-  Scalar lhs_pf7;
-
-  Scalar rhs_pf0;
-  Scalar rhs_pf1;
-  Scalar rhs_pf2;
-  Scalar rhs_pf3;
-  Scalar rhs_pf4;
-  Scalar rhs_pf5;
-  Scalar rhs_pf6;
-  Scalar rhs_pf7;
-
-  // shared memory is formatted
-  // (contract idx in block, nocontract idx in block, block idx)
-  // where block idx is column major. This transposition limits the number of
-  // bank conflicts when reading the LHS. The core idea is that since the contracting
-  // index is shared by both sides, then the contracting index should be in threadIdx.x.
-
-  // On the LHS, we pad each row inside of each block with an extra element. This makes
-  // each block 8 rows of 9 elements, which is 72 elements. This gives no bank conflicts
-  // on writes and very few 2-way conflicts on reads. There is an 8x8 grid of these blocks.
-
-  // On the RHS we just add 8 padding elements to the end of each block. This gives no bank
-  // conflicts on writes and also none on reads.
-
-  // storage indices
-  const Index lhs_store_idx_base = threadIdx.y * 72 + threadIdx.x * 9 + threadIdx.z;
-  const Index rhs_store_idx_base = threadIdx.y * 72 + threadIdx.z * 8 + threadIdx.x;
-
-  const Index lhs_store_idx_0 = lhs_store_idx_base + 576 * 0;
-  const Index lhs_store_idx_1 = lhs_store_idx_base + 576 * 1;
-  const Index lhs_store_idx_2 = lhs_store_idx_base + 576 * 2;
-  const Index lhs_store_idx_3 = lhs_store_idx_base + 576 * 3;
-  const Index lhs_store_idx_4 = lhs_store_idx_base + 576 * 4;
-  const Index lhs_store_idx_5 = lhs_store_idx_base + 576 * 5;
-  const Index lhs_store_idx_6 = lhs_store_idx_base + 576 * 6;
-  const Index lhs_store_idx_7 = lhs_store_idx_base + 576 * 7;
-
-  const Index rhs_store_idx_0 = rhs_store_idx_base + 576 * 0;
-  const Index rhs_store_idx_1 = rhs_store_idx_base + 576 * 1;
-  const Index rhs_store_idx_2 = rhs_store_idx_base + 576 * 2;
-  const Index rhs_store_idx_3 = rhs_store_idx_base + 576 * 3;
-  const Index rhs_store_idx_4 = rhs_store_idx_base + 576 * 4;
-  const Index rhs_store_idx_5 = rhs_store_idx_base + 576 * 5;
-  const Index rhs_store_idx_6 = rhs_store_idx_base + 576 * 6;
-  const Index rhs_store_idx_7 = rhs_store_idx_base + 576 * 7;
-
-  // in the loading code, the following variables are important:
-  // threadIdx.x: the vertical position in an 8x8 block
-  // threadIdx.y: the vertical index of the 8x8 block in the grid
-  // threadIdx.z: the horizontal position in an 8x8 block
-  // k: the horizontal index of the 8x8 block in the grid
-  //
-  // The k parameter is implicit (it was the loop counter for a loop that went
-  // from 0 to <8, but now that loop is unrolled in the below code.
-
-  const Index load_idx_vert = threadIdx.x + 8 * threadIdx.y;
-  const Index lhs_vert = base_m + load_idx_vert;
-
-#define prefetchIntoRegisters(base_k)                           \
-  {                                                             \
-    lhs_pf0 = conv(0);                                          \
-    lhs_pf1 = conv(0);                                          \
-    lhs_pf2 = conv(0);                                          \
-    lhs_pf3 = conv(0);                                          \
-    lhs_pf4 = conv(0);                                          \
-    lhs_pf5 = conv(0);                                          \
-    lhs_pf6 = conv(0);                                          \
-    lhs_pf7 = conv(0);                                          \
-                                                                \
-    rhs_pf0 = conv(0);                                          \
-    rhs_pf1 = conv(0);                                          \
-    rhs_pf2 = conv(0);                                          \
-    rhs_pf3 = conv(0);                                          \
-    rhs_pf4 = conv(0);                                          \
-    rhs_pf5 = conv(0);                                          \
-    rhs_pf6 = conv(0);                                          \
-    rhs_pf7 = conv(0);                                          \
-                                                                \
-    if (!needs_edge_check || lhs_vert < m_size) {               \
-      const Index lhs_horiz_0 = base_k + threadIdx.z + 0 * 8;   \
-      const Index lhs_horiz_1 = base_k + threadIdx.z + 1 * 8;   \
-      const Index lhs_horiz_2 = base_k + threadIdx.z + 2 * 8;   \
-      const Index lhs_horiz_3 = base_k + threadIdx.z + 3 * 8;   \
-      const Index lhs_horiz_4 = base_k + threadIdx.z + 4 * 8;   \
-      const Index lhs_horiz_5 = base_k + threadIdx.z + 5 * 8;   \
-      const Index lhs_horiz_6 = base_k + threadIdx.z + 6 * 8;   \
-      const Index lhs_horiz_7 = base_k + threadIdx.z + 7 * 8;   \
-                                                                \
-      if (!needs_edge_check || lhs_horiz_7 < k_size) {          \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
-        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
-        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
-        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
-        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                   \
-        lhs_pf6 = lhs(lhs_vert, lhs_horiz_6);                   \
-        lhs_pf7 = lhs(lhs_vert, lhs_horiz_7);                   \
-      } else if (lhs_horiz_6 < k_size) {                        \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
-        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
-        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
-        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
-        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                   \
-        lhs_pf6 = lhs(lhs_vert, lhs_horiz_6);                   \
-      } else if (lhs_horiz_5 < k_size) {                        \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
-        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
-        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
-        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
-        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                   \
-      } else if (lhs_horiz_4 < k_size) {                        \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
-        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
-        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
-        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
-      } else if (lhs_horiz_3 < k_size) {                        \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
-        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
-        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
-      } else if (lhs_horiz_2 < k_size) {                        \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
-        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
-      } else if (lhs_horiz_1 < k_size) {                        \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
-      } else if (lhs_horiz_0 < k_size) {                        \
-        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
-      }                                                         \
-    }                                                           \
-                                                                \
-    const Index rhs_vert = base_k + load_idx_vert;              \
-    if (!needs_edge_check || rhs_vert < k_size) {               \
-      const Index rhs_horiz_0 = base_n + threadIdx.z + 0 * 8;   \
-      const Index rhs_horiz_1 = base_n + threadIdx.z + 1 * 8;   \
-      const Index rhs_horiz_2 = base_n + threadIdx.z + 2 * 8;   \
-      const Index rhs_horiz_3 = base_n + threadIdx.z + 3 * 8;   \
-      const Index rhs_horiz_4 = base_n + threadIdx.z + 4 * 8;   \
-      const Index rhs_horiz_5 = base_n + threadIdx.z + 5 * 8;   \
-      const Index rhs_horiz_6 = base_n + threadIdx.z + 6 * 8;   \
-      const Index rhs_horiz_7 = base_n + threadIdx.z + 7 * 8;   \
-                                                                \
-      if (rhs_horiz_7 < n_size) {                               \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
-        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
-        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
-        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
-        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                   \
-        rhs_pf6 = rhs(rhs_vert, rhs_horiz_6);                   \
-        rhs_pf7 = rhs(rhs_vert, rhs_horiz_7);                   \
-      } else if (rhs_horiz_6 < n_size) {                        \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
-        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
-        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
-        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
-        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                   \
-        rhs_pf6 = rhs(rhs_vert, rhs_horiz_6);                   \
-      } else if (rhs_horiz_5 < n_size) {                        \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
-        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
-        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
-        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
-        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                   \
-      } else if (rhs_horiz_4 < n_size) {                        \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
-        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
-        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
-        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
-      } else if (rhs_horiz_3 < n_size) {                        \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
-        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
-        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
-      } else if (rhs_horiz_2 < n_size) {                        \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
-        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
-      } else if (rhs_horiz_1 < n_size) {                        \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
-      } else if (rhs_horiz_0 < n_size) {                        \
-        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
-      }                                                         \
-    }                                                           \
-  }                                                             \
-
-#define writeRegToShmem(_)                      \
-  lhs_shmem[lhs_store_idx_0] = lhs_pf0;         \
-  rhs_shmem[rhs_store_idx_0] = rhs_pf0;         \
-                                                \
-  lhs_shmem[lhs_store_idx_1] = lhs_pf1;         \
-  rhs_shmem[rhs_store_idx_1] = rhs_pf1;         \
-                                                \
-  lhs_shmem[lhs_store_idx_2] = lhs_pf2;         \
-  rhs_shmem[rhs_store_idx_2] = rhs_pf2;         \
-                                                \
-  lhs_shmem[lhs_store_idx_3] = lhs_pf3;         \
-  rhs_shmem[rhs_store_idx_3] = rhs_pf3;         \
-                                                \
-  lhs_shmem[lhs_store_idx_4] = lhs_pf4;         \
-  rhs_shmem[rhs_store_idx_4] = rhs_pf4;         \
-                                                \
-  lhs_shmem[lhs_store_idx_5] = lhs_pf5;         \
-  rhs_shmem[rhs_store_idx_5] = rhs_pf5;         \
-                                                \
-  lhs_shmem[lhs_store_idx_6] = lhs_pf6;         \
-  rhs_shmem[rhs_store_idx_6] = rhs_pf6;         \
-                                                \
-  lhs_shmem[lhs_store_idx_7] = lhs_pf7;         \
-  rhs_shmem[rhs_store_idx_7] = rhs_pf7;         \
-
-  // declare and initialize result array
-#define res(i, j) _res_##i##j
-#define initResultRow(i)                        \
-  Scalar res(i, 0) = conv(0);                   \
-  Scalar res(i, 1) = conv(0);                   \
-  Scalar res(i, 2) = conv(0);                   \
-  Scalar res(i, 3) = conv(0);                   \
-  Scalar res(i, 4) = conv(0);                   \
-  Scalar res(i, 5) = conv(0);                   \
-  Scalar res(i, 6) = conv(0);                   \
-  Scalar res(i, 7) = conv(0);                   \
-
-  internal::scalar_cast_op<int, Scalar> conv;
-  initResultRow(0);
-  initResultRow(1);
-  initResultRow(2);
-  initResultRow(3);
-  initResultRow(4);
-  initResultRow(5);
-  initResultRow(6);
-  initResultRow(7);
-#undef initResultRow
-
-  for (Index base_k = 0; base_k < k_size; base_k += 64) {
-    // wait for previous iteration to finish with shmem. Despite common sense,
-    // the code is a bit faster with this here then at bottom of loop
-    __syncthreads();
-
-    prefetchIntoRegisters(base_k);
-    writeRegToShmem();
-
-    #undef prefetchIntoRegisters
-    #undef writeRegToShmem
-
-    // wait for shared mem packing to be done before starting computation
-    __syncthreads();
-
-    // compute 8x8 matrix product by outer product. This involves packing one column
-    // of LHS and one row of RHS into registers (takes 16 registers).
-
-#define lcol(i) _lcol##i
-    Scalar lcol(0);
-    Scalar lcol(1);
-    Scalar lcol(2);
-    Scalar lcol(3);
-    Scalar lcol(4);
-    Scalar lcol(5);
-    Scalar lcol(6);
-    Scalar lcol(7);
-
-#define rrow(j) _rrow##j
-    Scalar rrow(0);
-    Scalar rrow(1);
-    Scalar rrow(2);
-    Scalar rrow(3);
-    Scalar rrow(4);
-    Scalar rrow(5);
-    Scalar rrow(6);
-    Scalar rrow(7);
-
-    // Now x corresponds to k, y to m, and z to n
-    const Scalar* lhs_block = &lhs_shmem[threadIdx.x + 9 * threadIdx.y];
-    const Scalar* rhs_block = &rhs_shmem[threadIdx.x + 8 * threadIdx.z];
-
-#define lhs_element(i, j) lhs_block[72 * ((i) + 8 * (j))]
-#define rhs_element(i, j) rhs_block[72 * ((i) + 8 * (j))]
-
-#define loadData(i, j)                          \
-    lcol(0) = lhs_element(0, j);               \
-    rrow(0) = rhs_element(i, 0);               \
-    lcol(1) = lhs_element(1, j);               \
-    rrow(1) = rhs_element(i, 1);               \
-    lcol(2) = lhs_element(2, j);               \
-    rrow(2) = rhs_element(i, 2);               \
-    lcol(3) = lhs_element(3, j);               \
-    rrow(3) = rhs_element(i, 3);               \
-    lcol(4) = lhs_element(4, j);               \
-    rrow(4) = rhs_element(i, 4);               \
-    lcol(5) = lhs_element(5, j);               \
-    rrow(5) = rhs_element(i, 5);               \
-    lcol(6) = lhs_element(6, j);               \
-    rrow(6) = rhs_element(i, 6);               \
-    lcol(7) = lhs_element(7, j);               \
-    rrow(7) = rhs_element(i, 7);               \
-
-#define computeCol(j)                           \
-    res(0, j) += lcol(0) * rrow(j);             \
-    res(1, j) += lcol(1) * rrow(j);             \
-    res(2, j) += lcol(2) * rrow(j);             \
-    res(3, j) += lcol(3) * rrow(j);             \
-    res(4, j) += lcol(4) * rrow(j);             \
-    res(5, j) += lcol(5) * rrow(j);             \
-    res(6, j) += lcol(6) * rrow(j);             \
-    res(7, j) += lcol(7) * rrow(j);             \
-
-#define computePass(i)                          \
-    loadData(i, i);                             \
-                                                \
-    computeCol(0);                              \
-    computeCol(1);                              \
-    computeCol(2);                              \
-    computeCol(3);                              \
-    computeCol(4);                              \
-    computeCol(5);                              \
-    computeCol(6);                              \
-    computeCol(7);                              \
-
-    computePass(0);
-    computePass(1);
-    computePass(2);
-    computePass(3);
-    computePass(4);
-    computePass(5);
-    computePass(6);
-    computePass(7);
-
-#undef lcol
-#undef rrow
-#undef lhs_element
-#undef rhs_element
-#undef loadData
-#undef computeCol
-#undef computePass
-  } // end loop over k
-
-  // we've now iterated over all of the large (ie width 64) k blocks and
-  // accumulated results in registers. At this point thread (x, y, z) contains
-  // the sum across all big k blocks of the product of little k block of index (x, y)
-  // with block of index (y, z). To compute the final output, we need to reduce
-  // the 8 threads over y by summation.
-#if defined(EIGEN_HIPCC) || (defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000)
-#define shuffleInc(i, j, mask) res(i, j) += __shfl_xor(res(i, j), mask)
-#else
-#define shuffleInc(i, j, mask) res(i, j) += __shfl_xor_sync(0xFFFFFFFF, res(i, j), mask)
-#endif
-
-#define reduceRow(i, mask)                      \
-  shuffleInc(i, 0, mask);                       \
-  shuffleInc(i, 1, mask);                       \
-  shuffleInc(i, 2, mask);                       \
-  shuffleInc(i, 3, mask);                       \
-  shuffleInc(i, 4, mask);                       \
-  shuffleInc(i, 5, mask);                       \
-  shuffleInc(i, 6, mask);                       \
-  shuffleInc(i, 7, mask);                       \
-
-#define reduceMatrix(mask)                      \
-  reduceRow(0, mask);                           \
-  reduceRow(1, mask);                           \
-  reduceRow(2, mask);                           \
-  reduceRow(3, mask);                           \
-  reduceRow(4, mask);                           \
-  reduceRow(5, mask);                           \
-  reduceRow(6, mask);                           \
-  reduceRow(7, mask);                           \
-
-  // actually perform the reduction, now each thread of index (_, y, z)
-  // contains the correct values in its registers that belong in the output
-  // block
-  reduceMatrix(1);
-  reduceMatrix(2);
-  reduceMatrix(4);
-
-#undef shuffleInc
-#undef reduceRow
-#undef reduceMatrix
-
-  // now we need to copy the 64 values into main memory. We can't split work
-  // among threads because all variables are in registers. There's 2 ways
-  // to do this:
-  // (1) have 1 thread do 64 writes from registers into global memory
-  // (2) have 1 thread do 64 writes into shared memory, and then 8 threads
-  //     each do 8 writes into global memory. We can just overwrite the shared
-  //     memory from the problem we just solved.
-  // (2) is slightly faster than (1) due to less branching and more ILP
-
-  // TODO: won't yield much gain, but could just use currently unused shared mem
-  //       and then we won't have to sync
-  // wait for shared mem to be out of use
-  __syncthreads();
-
-#define writeResultShmem(i, j)                                          \
-  lhs_shmem[i + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j] = res(i, j); \
-
-#define writeRow(i)                             \
-  writeResultShmem(i, 0);                       \
-  writeResultShmem(i, 1);                       \
-  writeResultShmem(i, 2);                       \
-  writeResultShmem(i, 3);                       \
-  writeResultShmem(i, 4);                       \
-  writeResultShmem(i, 5);                       \
-  writeResultShmem(i, 6);                       \
-  writeResultShmem(i, 7);                       \
-
-  if (threadIdx.x == 0) {
-    writeRow(0);
-    writeRow(1);
-    writeRow(2);
-    writeRow(3);
-    writeRow(4);
-    writeRow(5);
-    writeRow(6);
-    writeRow(7);
-  }
-#undef writeResultShmem
-#undef writeRow
-
-  const int max_i_write = numext::mini((int)((m_size - base_m - threadIdx.y + 7) / 8), 8);
-  const int max_j_write = numext::mini((int)((n_size - base_n - threadIdx.z + 7) / 8), 8);
-
-  if (threadIdx.x < max_i_write) {
-    if (max_j_write == 8) {
-      // TODO: can i trade bank conflicts for coalesced writes?
-      Scalar val0 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 0];
-      Scalar val1 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 1];
-      Scalar val2 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 2];
-      Scalar val3 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 3];
-      Scalar val4 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 4];
-      Scalar val5 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 5];
-      Scalar val6 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 6];
-      Scalar val7 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 7];
-
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 0) = val0;
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 1) = val1;
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 2) = val2;
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 3) = val3;
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 4) = val4;
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 5) = val5;
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 6) = val6;
-      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 7) = val7;
-    } else {
-#pragma unroll 7
-      for (int j = 0; j < max_j_write; j++) {
-        Scalar val = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j];
-        output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * j) = val;
-      }
-    }
-  }
-#undef res
-}
-
-
-template<typename Scalar, typename Index, typename LhsMapper,
-         typename RhsMapper, typename OutputMapper>
-__global__ void
-#if defined(EIGEN_HIPCC)
-__launch_bounds__(512, 1)
-#else
-__launch_bounds__(512)
-#endif
-EigenContractionKernel(const LhsMapper lhs, const RhsMapper rhs,
-                       const OutputMapper output,
-                       const Index m_size, const Index n_size, const Index k_size) {
-  __shared__ Scalar lhs_shmem[72 * 64];
-  __shared__ Scalar rhs_shmem[72 * 64];
-
-  const Index m_block_idx = blockIdx.x;
-  const Index n_block_idx = blockIdx.y;
-
-  const Index base_m = 64 * m_block_idx;
-  const Index base_n = 64 * n_block_idx;
-
-  if (base_m + 63 < m_size && base_n + 63 < n_size) {
-    EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
-  } else {
-    EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
-  }
-}
-
-
-template<typename Index, typename LhsMapper,
-         typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
-         bool CHECK_RHS_BOUNDARY>
-__device__ __forceinline__ void
-EigenFloatContractionKernelInternal16x16(const LhsMapper lhs, const RhsMapper rhs,
-                       const OutputMapper output, float2 lhs_shmem2[][16],
-                       float2 rhs_shmem2[][8], const Index m_size,
-                       const Index n_size, const Index k_size,
-                       const Index base_m, const Index base_n) {
-
-  // prefetch registers
-  float4 lhs_pf0, rhs_pf0;
-
-  float4 results[4];
-  for (int i=0; i < 4; i++) {
-    results[i].x = results[i].y = results[i].z = results[i].w = 0;
-  }
-
-#define prefetch_lhs(reg, row, col)                            \
-    if (!CHECK_LHS_BOUNDARY) {                                 \
-      if (col < k_size) {                                      \
-        reg =lhs.template loadPacket<float4,Unaligned>(row, col);     \
-      }                                                        \
-    } else {                                                   \
-      if (col < k_size) {                                      \
-        if (row + 3 < m_size) {                                \
-          reg =lhs.template loadPacket<float4,Unaligned>(row, col);   \
-        } else if (row + 2 < m_size) {                         \
-          reg.x =lhs(row + 0, col);                            \
-          reg.y =lhs(row + 1, col);                            \
-          reg.z =lhs(row + 2, col);                            \
-        } else if (row + 1 < m_size) {                         \
-          reg.x =lhs(row + 0, col);                            \
-          reg.y =lhs(row + 1, col);                            \
-        } else if (row  < m_size) {                            \
-          reg.x =lhs(row + 0, col);                            \
-        }                                                      \
-      }                                                        \
-    }							       \
-
-  Index lhs_vert = base_m+threadIdx.x*4;
-
-  for (Index k = 0; k < k_size; k += 16) {
-
-    lhs_pf0 = internal::pset1<float4>(0);
-    rhs_pf0 = internal::pset1<float4>(0);
-
-    Index lhs_horiz = threadIdx.y+k;
-    prefetch_lhs(lhs_pf0, lhs_vert, lhs_horiz)
-
-    Index rhs_vert = k+(threadIdx.x%4)*4;
-    Index rhs_horiz0 = (threadIdx.x>>2)+threadIdx.y*4+base_n;
-
-    if (!CHECK_RHS_BOUNDARY) {
-      if ((rhs_vert + 3) < k_size) {
-        // just CHECK_RHS_BOUNDARY
-        rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
-      } else if (rhs_vert + 2 < k_size) {
-        // just CHECK_RHS_BOUNDARY
-        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-        rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
-      } else if (rhs_vert + 1 < k_size) {
-        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-      } else if (rhs_vert  < k_size) {
-        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-      }
-    } else {
-      if (rhs_horiz0 < n_size) {
-        if ((rhs_vert + 3) < k_size) {
-          rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
-        } else if ((rhs_vert + 2) < k_size) {
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
-        } else if ((rhs_vert + 1) < k_size) {
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-        } else if (rhs_vert  < k_size) {
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-        }
-      }
-    }
-    float x1, x2 ;
-    // the following can be a bitwise operation..... some day.
-    if((threadIdx.x%8) < 4) {
-      x1 = rhs_pf0.y;
-      x2 = rhs_pf0.w;
-    } else {
-      x1 = rhs_pf0.x;
-      x2 = rhs_pf0.z;
-    }
-    #if defined(EIGEN_HIPCC) || (defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000)
-    x1 = __shfl_xor(x1, 4);
-    x2 = __shfl_xor(x2, 4);
-    #else
-    x1 = __shfl_xor_sync(0xFFFFFFFF, x1, 4);
-    x2 = __shfl_xor_sync(0xFFFFFFFF, x2, 4);
-    #endif
-    if((threadIdx.x%8) < 4) {
-      rhs_pf0.y = x1;
-      rhs_pf0.w = x2;
-    } else {
-      rhs_pf0.x = x1;
-      rhs_pf0.z = x2;
-    }
-
-    // We have 64 features.
-    // Row 0 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 0, 1.
-    // Row 1 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 2, 3.
-    // ...
-    // Row 31 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 62, 63
-    // Row 32 -> times (2, 6, 10, 14, 3, 7, 11, 15) for features 0, 1
-    // ...
-    rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2][threadIdx.x%8] = make_float2(rhs_pf0.x, rhs_pf0.y);
-    rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2+32][threadIdx.x%8] = make_float2(rhs_pf0.z, rhs_pf0.w);
-
-    // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
-    // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
-    // ...
-    // Row 15 (time 15) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
-    // Row 16 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63)
-    // ...
-
-    lhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(lhs_pf0.x, lhs_pf0.y);
-    lhs_shmem2[threadIdx.y+16][threadIdx.x] = make_float2(lhs_pf0.z, lhs_pf0.w);
-
-
-#define add_vals(fl1, fl2, fr1, fr2)\
-    results[0].x += fl1.x * fr1.x;\
-    results[0].y += fl1.y * fr1.x;\
-    results[0].z += fl2.x * fr1.x;\
-    results[0].w += fl2.y * fr1.x;\
-\
-    results[1].x += fl1.x * fr1.y;\
-    results[1].y += fl1.y * fr1.y;\
-    results[1].z += fl2.x * fr1.y;\
-    results[1].w += fl2.y * fr1.y;\
-\
-    results[2].x += fl1.x * fr2.x;\
-    results[2].y += fl1.y * fr2.x;\
-    results[2].z += fl2.x * fr2.x;\
-    results[2].w += fl2.y * fr2.x;\
-\
-    results[3].x += fl1.x * fr2.y;\
-    results[3].y += fl1.y * fr2.y;\
-    results[3].z += fl2.x * fr2.y;\
-    results[3].w += fl2.y * fr2.y;\
-
-    __syncthreads();
-
-    // Do the multiplies.
-    #pragma unroll
-    for (int koff = 0; koff < 16; koff ++) {
-      // 32 x threads.
-      float2 fl1 = lhs_shmem2[koff][threadIdx.x];
-      float2 fl2 = lhs_shmem2[koff + 16][threadIdx.x];
-
-      int start_feature = threadIdx.y * 4;
-      float2 fr1 = rhs_shmem2[(start_feature>>1) + 32*((koff%4)/2)][koff/4 + (koff%2)*4];
-      float2 fr2 = rhs_shmem2[(start_feature>>1) + 1 + 32*((koff%4)/2)][koff/4 + (koff%2)*4];
-
-      add_vals(fl1, fl2, fr1, fr2)
-    }
-    __syncthreads();
-  }
-
-#undef prefetch_lhs
-#undef add_vals
-
-  Index horiz_base = threadIdx.y*4+base_n;
-  if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
-    for (int i = 0; i < 4; i++) {
-      output(lhs_vert, horiz_base + i) = results[i].x;
-      output(lhs_vert + 1, horiz_base + i) = results[i].y;
-      output(lhs_vert + 2, horiz_base + i) = results[i].z;
-      output(lhs_vert + 3, horiz_base + i) = results[i].w;
-    }
-  } else if (!CHECK_RHS_BOUNDARY) {
-    // CHECK LHS
-    if (lhs_vert + 3 < m_size) {
-      for (int i = 0; i < 4; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        output(lhs_vert + 2, horiz_base + i) = results[i].z;
-        output(lhs_vert + 3, horiz_base + i) = results[i].w;
-      }
-    } else if (lhs_vert + 2 < m_size) {
-      for (int i = 0; i < 4; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        output(lhs_vert + 2, horiz_base + i) = results[i].z;
-      }
-    } else if (lhs_vert + 1 < m_size) {
-      for (int i = 0; i < 4; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-      }
-    } else if (lhs_vert  < m_size) {
-      for (int i = 0; i < 4; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-      }
-    }
-  } else if (!CHECK_LHS_BOUNDARY) {
-    // CHECK RHS
-    /*
-    int ncols_rem = fminf(n_size- horiz_base, 4);
-    for (int i = 0; i < ncols_rem; i++) {
-      output(lhs_vert, horiz_base + i) = results[i].x;
-      output(lhs_vert + 1, horiz_base + i) = results[i].y;
-      output(lhs_vert + 2, horiz_base + i) = results[i].z;
-      output(lhs_vert + 3, horiz_base + i) = results[i].w;
-    }*/
-    for (int i = 0; i < 4; i++) {
-      if (horiz_base+i < n_size) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        output(lhs_vert + 2, horiz_base + i) = results[i].z;
-        output(lhs_vert + 3, horiz_base + i) = results[i].w;
-       }
-    }
-  } else {
-    // CHECK both boundaries.
-    for (int i = 0; i < 4; i++) {
-      if (horiz_base+i < n_size) {
-        if (lhs_vert < m_size)
-          output(lhs_vert, horiz_base + i) = results[i].x;
-        if (lhs_vert + 1 < m_size)
-          output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        if (lhs_vert + 2 < m_size)
-          output(lhs_vert + 2, horiz_base + i) = results[i].z;
-        if (lhs_vert + 3 < m_size)
-          output(lhs_vert + 3, horiz_base + i) = results[i].w;
-      }
-    }
-  }
-}
-
-
-template<typename Index, typename LhsMapper,
-         typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
-         bool CHECK_RHS_BOUNDARY>
-__device__ __forceinline__ void
-EigenFloatContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
-                       const OutputMapper output, float2 lhs_shmem2[][32],
-                       float2 rhs_shmem2[][8], const Index m_size,
-                       const Index n_size, const Index k_size,
-                       const Index base_m, const Index base_n) {
-
-  // prefetch registers
-  float4 lhs_pf0, lhs_pf1, lhs_pf2, lhs_pf3;
-  float4 rhs_pf0, rhs_pf1;
-
-  float4 results[8];
-  for (int i=0; i < 8; i++) {
-    results[i].x = results[i].y = results[i].z = results[i].w = 0;
-  }
-
-  Index lhs_vert = base_m+threadIdx.x*4+(threadIdx.y%4)*32;
-  for (Index k = 0; k < k_size; k += 32) {
-    lhs_pf0 = internal::pset1<float4>(0);
-    lhs_pf1 = internal::pset1<float4>(0);
-    lhs_pf2 = internal::pset1<float4>(0);
-    lhs_pf3 = internal::pset1<float4>(0);
-
-    rhs_pf0 = internal::pset1<float4>(0);
-    rhs_pf1 = internal::pset1<float4>(0);
-
-     if (!CHECK_LHS_BOUNDARY) {
-      if ((threadIdx.y/4+k+24) < k_size) {
-        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-        lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
-        lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
-        lhs_pf3 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+24));
-      } else if ((threadIdx.y/4+k+16) < k_size) {
-        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-        lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
-        lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
-      } else if ((threadIdx.y/4+k+8) < k_size) {
-        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-        lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
-      } else if ((threadIdx.y/4+k) < k_size) {
-        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-      }
-    } else {
-      // just CHECK_LHS_BOUNDARY
-      if (lhs_vert + 3 < m_size) {
-        if ((threadIdx.y/4+k+24) < k_size) {
-          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-          lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
-          lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
-          lhs_pf3 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+24));
-        } else if ((threadIdx.y/4+k+16) < k_size) {
-          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-          lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
-          lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
-        } else if ((threadIdx.y/4+k+8) < k_size) {
-          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-          lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
-        } else if ((threadIdx.y/4+k) < k_size) {
-          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
-        }
-      } else if (lhs_vert + 2 < m_size) {
-        if ((threadIdx.y/4+k+24) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
-          lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
-          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
-          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
-          lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16));
-          lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
-          lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24));
-          lhs_pf3.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+24));
-        } else if ((threadIdx.y/4+k+16) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
-          lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
-          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
-          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
-          lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16));
-        } else if ((threadIdx.y/4+k+8) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
-          lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
-        } else if ((threadIdx.y/4+k) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
-        }
-      } else if (lhs_vert + 1 < m_size) {
-        if ((threadIdx.y/4+k+24) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
-          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
-          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
-          lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
-          lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24));
-        } else if ((threadIdx.y/4+k+16) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
-          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
-          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
-        } else if ((threadIdx.y/4+k+8) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
-        } else if ((threadIdx.y/4+k) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
-        }
-      } else if (lhs_vert < m_size) {
-        if ((threadIdx.y/4+k+24) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
-          lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
-        } else if ((threadIdx.y/4+k+16) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
-        } else if ((threadIdx.y/4+k+8) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
-        } else if ((threadIdx.y/4+k) < k_size) {
-          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
-        }
-      }
-    }
-    __syncthreads();
-    Index rhs_vert = k+threadIdx.x*4;
-    Index rhs_horiz0 = threadIdx.y*2+base_n;
-    Index rhs_horiz1 = threadIdx.y*2+1+base_n;
-    if (!CHECK_RHS_BOUNDARY) {
-      if ((rhs_vert + 3) < k_size) {
-        // just CHECK_RHS_BOUNDARY
-        rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
-        rhs_pf1 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz1);
-      } else if (rhs_vert + 2 < k_size) {
-        // just CHECK_RHS_BOUNDARY
-        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-        rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
-        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
-        rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
-        rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
-      } else if (rhs_vert + 1 < k_size) {
-        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
-        rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
-      } else if (rhs_vert  < k_size) {
-        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
-      }
-    } else {
-      if (rhs_horiz1 < n_size) {
-        if ((rhs_vert + 3) < k_size) {
-          // just CHECK_RHS_BOUNDARY
-          rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
-          rhs_pf1 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz1);
-        } else if (rhs_vert + 2 < k_size) {
-          // just CHECK_RHS_BOUNDARY
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
-          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
-          rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
-          rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
-        } else if (k+threadIdx.x*4 + 1 < k_size) {
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
-          rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
-        } else if (k+threadIdx.x*4  < k_size) {
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
-        }
-      } else if (rhs_horiz0 < n_size) {
-        if ((rhs_vert + 3) < k_size) {
-          // just CHECK_RHS_BOUNDARY
-          rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
-        } else if ((rhs_vert + 2) < k_size) {
-          // just CHECK_RHS_BOUNDARY
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
-        } else if ((rhs_vert + 1) < k_size) {
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
-        } else if (rhs_vert  < k_size) {
-          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
-        }
-      }
-    }
-    __syncthreads();
-    // Loaded. Do computation
-    // Row 0 -> times (0, 4, 8, .. 28) for features 0, 1.
-    // Row 1 -> times (0, 4, 8, .. 28) for features 2, 3.
-    // ..
-    // Row 31 -> times (0, 4, 8, .. 28) for features 62, 63
-    rhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(rhs_pf0.x, rhs_pf1.x);
-    // Row 32 -> times (1, 5, 9, .. 29) for features 0, 1.
-    // Row 33 -> times (1, 5, 9, .. 29) for features 2, 3.
-    // ..
-    rhs_shmem2[threadIdx.y+32][threadIdx.x] = make_float2(rhs_pf0.y, rhs_pf1.y);
-    // Row 64 -> times (2, 6, 10, .. 30) for features 0, 1.
-    // Row 65 -> times (2, 6, 10, .. 30) for features 2, 3.
-    rhs_shmem2[threadIdx.y+64][threadIdx.x] = make_float2(rhs_pf0.z, rhs_pf1.z);
-    // Row 96 -> times (3, 7, 11, .. 31) for features 0, 1.
-    // Row 97 -> times (3, 7, 11, .. 31) for features 2, 3.
-    rhs_shmem2[threadIdx.y+96][threadIdx.x] = make_float2(rhs_pf0.w, rhs_pf1.w);
-
-    // LHS.
-    // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61) .. (124, 125)
-    // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61) .. (124, 125)
-    // ...
-    // Row 8 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63) .. (126, 127)
-    // Row 15 (time 7) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63) .. (126, 127)
-
-
-#define add_vals(a_feat1, a_feat2, f1, f2, f3, f4)\
-      results[0].x += a_feat1.x * f1.x;\
-      results[1].x += a_feat1.x * f1.y;\
-      results[2].x += a_feat1.x * f2.x;\
-      results[3].x += a_feat1.x * f2.y;\
-      results[4].x += a_feat1.x * f3.x;\
-      results[5].x += a_feat1.x * f3.y;\
-      results[6].x += a_feat1.x * f4.x;\
-      results[7].x += a_feat1.x * f4.y;\
-\
-      results[0].y += a_feat1.y * f1.x;\
-      results[1].y += a_feat1.y * f1.y;\
-      results[2].y += a_feat1.y * f2.x;\
-      results[3].y += a_feat1.y * f2.y;\
-      results[4].y += a_feat1.y * f3.x;\
-      results[5].y += a_feat1.y * f3.y;\
-      results[6].y += a_feat1.y * f4.x;\
-      results[7].y += a_feat1.y * f4.y;\
-\
-      results[0].z += a_feat2.x * f1.x;\
-      results[1].z += a_feat2.x * f1.y;\
-      results[2].z += a_feat2.x * f2.x;\
-      results[3].z += a_feat2.x * f2.y;\
-      results[4].z += a_feat2.x * f3.x;\
-      results[5].z += a_feat2.x * f3.y;\
-      results[6].z += a_feat2.x * f4.x;\
-      results[7].z += a_feat2.x * f4.y;\
-\
-      results[0].w += a_feat2.y * f1.x;\
-      results[1].w += a_feat2.y * f1.y;\
-      results[2].w += a_feat2.y * f2.x;\
-      results[3].w += a_feat2.y * f2.y;\
-      results[4].w += a_feat2.y * f3.x;\
-      results[5].w += a_feat2.y * f3.y;\
-      results[6].w += a_feat2.y * f4.x;\
-      results[7].w += a_feat2.y * f4.y;\
-
-    lhs_shmem2[threadIdx.y/4][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.x, lhs_pf0.y);
-    lhs_shmem2[threadIdx.y/4+8][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.x, lhs_pf1.y);
-    lhs_shmem2[threadIdx.y/4+16][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.x, lhs_pf2.y);
-    lhs_shmem2[threadIdx.y/4+24][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.x, lhs_pf3.y);
-
-    lhs_shmem2[threadIdx.y/4 + 32][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.z, lhs_pf0.w);
-    lhs_shmem2[threadIdx.y/4 + 40][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.z, lhs_pf1.w);
-    lhs_shmem2[threadIdx.y/4 + 48][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.z, lhs_pf2.w);
-    lhs_shmem2[threadIdx.y/4 + 56][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.z, lhs_pf3.w);
-
-    __syncthreads();
-
-    // Do the multiplies.
-    #pragma unroll
-    for (int koff = 0; koff < 32; koff ++) {
-      float2 a3 = lhs_shmem2[koff][threadIdx.x + (threadIdx.y % 4) * 8];
-      float2 a4 = lhs_shmem2[koff + 32][threadIdx.x + (threadIdx.y % 4) * 8];
-
-      // first feature is at (threadIdx.y/4) * 8 last is at start + 8.
-      int start_feature = (threadIdx.y / 4) * 8;
-
-      float2 br1 = rhs_shmem2[start_feature/2 +     (koff % 4) * 32][koff/4];
-      float2 br2 = rhs_shmem2[start_feature/2 + 1 + (koff % 4) * 32][koff/4];
-      float2 br3 = rhs_shmem2[start_feature/2 + 2 + (koff % 4) * 32][koff/4];
-      float2 br4 = rhs_shmem2[start_feature/2 + 3 + (koff % 4) * 32][koff/4];
-
-      add_vals(a3, a4, br1, br2, br3, br4)
-    }
-    __syncthreads();
-  } // end loop over k
-
-  __syncthreads();
-  Index horiz_base = (threadIdx.y/4)*8+base_n;
-  if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
-    for (int i = 0; i < 8; i++) {
-      output(lhs_vert, horiz_base + i) = results[i].x;
-      output(lhs_vert + 1, horiz_base + i) = results[i].y;
-      output(lhs_vert + 2, horiz_base + i) = results[i].z;
-      output(lhs_vert + 3, horiz_base + i) = results[i].w;
-    }
-  } else if (!CHECK_RHS_BOUNDARY) {
-    if (lhs_vert + 3 < m_size) {
-      for (int i = 0; i < 8; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        output(lhs_vert + 2, horiz_base + i) = results[i].z;
-        output(lhs_vert + 3, horiz_base + i) = results[i].w;
-      }
-    } else if (lhs_vert + 2 < m_size) {
-      for (int i = 0; i < 8; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        output(lhs_vert + 2, horiz_base + i) = results[i].z;
-      }
-    } else if (lhs_vert + 1 < m_size) {
-      for (int i = 0; i < 8; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-      }
-    } else if (lhs_vert  < m_size) {
-      for (int i = 0; i < 8; i++) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-      }
-    }
-  } else if (!CHECK_LHS_BOUNDARY) {
-    // CHECK BOUNDARY_B
-    for (int i = 0; i < 8; i++) {
-      if (horiz_base + i < n_size) {
-        output(lhs_vert, horiz_base + i) = results[i].x;
-        output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        output(lhs_vert + 2, horiz_base + i) = results[i].z;
-        output(lhs_vert + 3, horiz_base + i) = results[i].w;
-      }
-    }
-  } else {
-    // CHECK both boundaries.
-    for (int i = 0; i < 8; i++) {
-      if (horiz_base + i < n_size) {
-        if (lhs_vert < m_size)
-          output(lhs_vert, horiz_base + i) = results[i].x;
-        if (lhs_vert + 1 < m_size)
-          output(lhs_vert + 1, horiz_base + i) = results[i].y;
-        if (lhs_vert + 2 < m_size)
-          output(lhs_vert + 2, horiz_base + i) = results[i].z;
-        if (lhs_vert + 3 < m_size)
-          output(lhs_vert + 3, horiz_base + i) = results[i].w;
-      }
-    }
-  }
-}
-
-
-template<typename Index, typename LhsMapper,
-         typename RhsMapper, typename OutputMapper>
-__global__ void
-#if defined(EIGEN_HIPCC)
-__launch_bounds__(256, 1)
-#else
-__launch_bounds__(256)
-#endif
-EigenFloatContractionKernel(const LhsMapper lhs, const RhsMapper rhs,
-                       const OutputMapper output,
-                       const Index m_size, const Index n_size, const Index k_size) {
-  __shared__ float2 lhs_shmem[64*32];
-  __shared__ float2 rhs_shmem[128*8];
-
-  typedef float2 LHS_MEM[64][32];
-  typedef float2 RHS_MEM[128][8];
-
-  const Index m_block_idx = blockIdx.x;
-  const Index n_block_idx = blockIdx.y;
-
-  const Index base_m = 128 * m_block_idx;
-  const Index base_n = 64 * n_block_idx;
-
-  bool check_rhs = (base_n + 63) >= n_size;
-  bool check_lhs128 = (base_m + 127) >= m_size;
-
-  if (!check_rhs) {
-    if (!check_lhs128) {
-      // >= 128 rows left
-      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(
-                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
-    } else {
-      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(
-                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
-    }
-  } else {
-    if (!check_lhs128) {
-      // >= 128 rows left
-      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(
-                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
-    } else {
-      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(
-                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
-    }
-  }
-}
-
-template<typename Index, typename LhsMapper,
-         typename RhsMapper, typename OutputMapper>
-__global__ void
-#if defined(EIGEN_HIPCC)
-__launch_bounds__(256, 1)
-#else
-__launch_bounds__(256)
-#endif
-EigenFloatContractionKernel16x16(const LhsMapper lhs, const RhsMapper rhs,
-                       const OutputMapper output,
-                       const Index m_size, const Index n_size, const Index k_size) {
-  __shared__ float2 lhs_shmem[32][16];
-  __shared__ float2 rhs_shmem[64][8];
-
-  const Index m_block_idx = blockIdx.x;
-  const Index n_block_idx = blockIdx.y;
-
-  const Index base_m = 64 * m_block_idx;
-  const Index base_n = 64 * n_block_idx;
-
-  if (base_m + 63 < m_size) {
-    if (base_n + 63 < n_size) {
-      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
-    } else {
-      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
-    }
-  } else {
-    if (base_n + 63 < n_size) {
-      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
-    } else {
-      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
-    }
-  }
-}
-
-
-template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
-struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, GpuDevice> :
-    public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, GpuDevice> > {
-
-  typedef GpuDevice Device;
-
-  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
-  typedef TensorContractionEvaluatorBase<Self> Base;
-
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType;
-
-  enum {
-    Layout = TensorEvaluator<LeftArgType, Device>::Layout,
-  };
-
-  // Most of the code is assuming that both input tensors are ColMajor. If the
-  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
-  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
-  // will pretend B is LHS and A is RHS.
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
-
-  static const int LDims =
-      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
-  static const int RDims =
-      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
-  static const int ContractDims = internal::array_size<Indices>::value;
-
-  typedef array<Index, LDims> left_dim_mapper_t;
-  typedef array<Index, RDims> right_dim_mapper_t;
-
-  typedef array<Index, ContractDims> contract_t;
-  typedef array<Index, LDims - ContractDims> left_nocontract_t;
-  typedef array<Index, RDims - ContractDims> right_nocontract_t;
-
-  static const int NumDims = LDims + RDims - 2 * ContractDims;
-
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  // typedefs needed in evalTo
-  typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
-  typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
-
-  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
-  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
-
-  typedef typename LeftEvaluator::Dimensions LeftDimensions;
-  typedef typename RightEvaluator::Dimensions RightDimensions;
-
-  TensorEvaluator(const XprType& op, const Device& device) :
-      Base(op, device)
-  {
-    EIGEN_STATIC_ASSERT( (internal::is_same<OutputKernelType, const NoOpOutputKernel>::value),
-                          GPU_TENSOR_CONTRACTION_DOES_NOT_SUPPORT_OUTPUT_KERNELS);
-  }
-
-  // We need to redefine this method to make nvcc happy
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
-    this->m_leftImpl.evalSubExprsIfNeeded(NULL);
-    this->m_rightImpl.evalSubExprsIfNeeded(NULL);
-    if (data) {
-      evalTo(data);
-      return false;
-    } else {
-      this->m_result = static_cast<Scalar *>(this->m_device.allocate(this->dimensions().TotalSize() * sizeof(Scalar)));
-      evalTo(this->m_result);
-      return true;
-    }
-  }
-
-  void evalTo(Scalar* buffer) const {
-    if (this->m_lhs_inner_dim_contiguous) {
-      if (this->m_rhs_inner_dim_contiguous) {
-        if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<true, true, true, Unaligned>(buffer);
-        }
-        else {
-          evalTyped<true, true, false, Unaligned>(buffer);
-        }
-      }
-      else {
-       if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<true, false, true, Unaligned>(buffer);
-        }
-        else {
-          evalTyped<true, false, false, Unaligned>(buffer);
-        }
-      }
-    }
-    else {
-      if (this->m_rhs_inner_dim_contiguous) {
-        if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<false, true, true, Unaligned>(buffer);
-        }
-        else {
-          evalTyped<false, true, false, Unaligned>(buffer);
-        }
-      }
-      else {
-       if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<false, false, true, Unaligned>(buffer);
-        }
-        else {
-          evalTyped<false, false, false, Unaligned>(buffer);
-        }
-      }
-    }
-  }
-
-  template <typename LhsScalar, typename RhsScalar, typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels {
-    static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) {
-    const Index m_blocks = (m + 63) / 64;
-    const Index n_blocks = (n + 63) / 64;
-    const dim3 num_blocks(m_blocks, n_blocks, 1);
-    const dim3 block_size(8, 8, 8);
-    LAUNCH_GPU_KERNEL((EigenContractionKernel<Scalar, Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
-    }
-  };
-
-  template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels<float, float, Index, LhsMapper, RhsMapper, OutputMapper> {
-    static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) {
-      if (m < 768 || n < 768) {
-        const Index m_blocks = (m + 63) / 64;
-        const Index n_blocks = (n + 63) / 64;
-        const dim3 num_blocks(m_blocks, n_blocks, 1);
-        const dim3 block_size(16, 16, 1);
-        LAUNCH_GPU_KERNEL((EigenFloatContractionKernel16x16<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
-      } else {
-        const Index m_blocks = (m + 127) / 128;
-        const Index n_blocks = (n + 63) / 64;
-        const dim3 num_blocks(m_blocks, n_blocks, 1);
-        const dim3 block_size(8, 32, 1);
-        LAUNCH_GPU_KERNEL((EigenFloatContractionKernel<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
-      }
-    }
-  };
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
-  void evalTyped(Scalar* buffer) const {
-    // columns in left side, rows in right side
-    const Index k = this->m_k_size;
-    EIGEN_UNUSED_VARIABLE(k)
-
-    // rows in left side
-    const Index m = this->m_i_size;
-
-    // columns in right side
-    const Index n = this->m_j_size;
-
-    // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
-    this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
-
-    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
-                                                   LeftEvaluator, left_nocontract_t,
-                                                   contract_t, 4,
-                                                   lhs_inner_dim_contiguous,
-                                                   false, Unaligned> LhsMapper;
-
-    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
-                                                   RightEvaluator, right_nocontract_t,
-                                                   contract_t, 4,
-                                                   rhs_inner_dim_contiguous,
-                                                   rhs_inner_dim_reordered, Unaligned> RhsMapper;
-
-    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
-
-
-    // initialize data mappers
-    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
-                  this->m_left_contracting_strides, this->m_k_strides);
-
-    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
-                  this->m_right_contracting_strides, this->m_k_strides);
-
-    OutputMapper output(buffer, m);
-
-#if defined(EIGEN_USE_HIP)
-    setGpuSharedMemConfig(hipSharedMemBankSizeEightByte);
-#else
-    setGpuSharedMemConfig(cudaSharedMemBankSizeEightByte);
-#endif
-
-    LaunchKernels<LhsScalar, RhsScalar, Index, LhsMapper, RhsMapper, OutputMapper>::Run(lhs, rhs, output,  m, n, k, this->m_device);
-  }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_USE_GPU and EIGEN_GPUCC
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
deleted file mode 100644
index 9ab900b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
+++ /dev/null
@@ -1,575 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
-
-namespace Eigen {
-
-namespace internal {
-
-enum {
-  Rhs = 0,
-  Lhs = 1
-};
-
-/*
- * Implementation of the Eigen blas_data_mapper class for tensors.
- */
-/// The make pointer class is used by sycl in order to build the mapper class on the device. For other platform the default make pointer is used which
-/// is scalar * for CoeffLoader.
-template <typename Tensor, bool HasRawAccess, template <class> class MakePointer_ = MakePointer>
-struct CoeffLoader;
-
-template <typename Scalar, typename Index, int side, typename Tensor,
-          typename nocontract_t, typename contract_t, int packet_size,
-          bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,
-          template <class> class MakePointer_ = MakePointer>
-class BaseTensorContractionMapper;
-
-template <typename Tensor, bool HasRawAccess, template <class> class MakePointer_>
-struct CoeffLoader {
-  enum {
-    DirectOffsets = false
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_tensor(tensor) { }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index) {
-    eigen_assert(false && "unsupported");
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename MakePointer_<const typename Tensor::Scalar>::Type
-  data() const {
-    eigen_assert(false && "unsupported");
-    return NULL;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const { return m_tensor.coeff(index); }
-
- template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
- typename Tensor::PacketReturnType packet(typename Tensor::Index index) const
-  {
-    return m_tensor.template packet<LoadMode>(index);
-  }
-
-  #ifdef EIGEN_USE_SYCL
-  // The placeholder accessors require to be bound to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_tensor.bind(cgh);
-  }
-  #endif
-
- private:
-  const Tensor m_tensor;
-};
-
-template <typename Tensor, template <class> class MakePointer_>
-struct CoeffLoader<Tensor, true, MakePointer_> {
-  enum {
-    DirectOffsets = true
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_data(tensor.data()) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) {
-    m_data += offset;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename MakePointer_<const typename Tensor::Scalar>::Type
-  data() const {
-    return m_data;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const { return loadConstant(m_data+index); }
-
- template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
- typename Tensor::PacketReturnType packet(typename Tensor::Index index) const
-  {
-    return internal::ploadt_ro<typename Tensor::PacketReturnType, LoadMode>(m_data + index);
-  }
-
-  #ifdef EIGEN_USE_SYCL
-  // The placeholder accessors require to be bound to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_data.bind(cgh);
-  }
-  #endif
- private:
-  typedef typename Tensor::Scalar Scalar;
-
-  typename MakePointer_<const Scalar>::Type m_data;
-};
-
-template<typename Scalar, typename Index, int side,
-         typename Tensor,
-         typename nocontract_t, typename contract_t,
-         int packet_size, bool inner_dim_contiguous, int Alignment, template <class> class MakePointer_ = MakePointer>
-class SimpleTensorContractionMapper {
-  public:
-  EIGEN_DEVICE_FUNC
-  SimpleTensorContractionMapper(const Tensor& tensor,
-                                const nocontract_t& nocontract_strides,
-                                const nocontract_t& ij_strides,
-                                const contract_t& contract_strides,
-                                const contract_t& k_strides) :
-      m_tensor(tensor),
-      m_nocontract_strides(nocontract_strides),
-      m_ij_strides(ij_strides),
-      m_contract_strides(contract_strides),
-      m_k_strides(k_strides) { }
-
-  enum {
-    DirectOffsets = CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>::DirectOffsets
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) {
-    m_tensor.offsetBuffer(offset);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE void prefetch(Index /*i*/) { }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Scalar operator()(Index row) const {
-    // column major assumption
-    return operator()(row, 0);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Scalar operator()(Index row, Index col) const {
-    return m_tensor.coeff(computeIndex(row, col));
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Index computeIndex(Index row, Index col) const {
-    const bool left = (side == Lhs);
-    EIGEN_UNUSED_VARIABLE(left); // annoying bug in g++8.1: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=85963
-    Index nocontract_val = left ? row : col;
-    Index linidx = 0;
-    EIGEN_UNROLL_LOOP
-    for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) {
-      const Index idx = nocontract_val / m_ij_strides[i];
-      linidx += idx * m_nocontract_strides[i];
-      nocontract_val -= idx * m_ij_strides[i];
-    }
-    if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
-      if (side == Lhs && inner_dim_contiguous) {
-        eigen_assert(m_nocontract_strides[0] == 1);
-        linidx += nocontract_val;
-      } else {
-        linidx += nocontract_val * m_nocontract_strides[0];
-      }
-    }
-
-    Index contract_val = left ? col : row;
-    if(array_size<contract_t>::value > 0) {
-      EIGEN_UNROLL_LOOP
-      for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) {
-        const Index idx = contract_val / m_k_strides[i];
-        linidx += idx * m_contract_strides[i];
-        contract_val -= idx * m_k_strides[i];
-      }
-
-      if (side == Rhs && inner_dim_contiguous) {
-        eigen_assert(m_contract_strides[0] == 1);
-        linidx += contract_val;
-      } else {
-        linidx += contract_val * m_contract_strides[0];
-      }
-    }
-
-    return linidx;
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE IndexPair<Index> computeIndexPair(Index row, Index col, const Index distance) const {
-    const bool left = (side == Lhs);
-    EIGEN_UNUSED_VARIABLE(left); // annoying bug in g++8.1: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=85963
-    Index nocontract_val[2] = {left ? row : col, left ? row + distance : col};
-    Index linidx[2] = {0, 0};
-    if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
-      EIGEN_UNROLL_LOOP
-      for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) {
-        const Index idx0 = nocontract_val[0] / m_ij_strides[i];
-        const Index idx1 = nocontract_val[1] / m_ij_strides[i];
-        linidx[0] += idx0 * m_nocontract_strides[i];
-        linidx[1] += idx1 * m_nocontract_strides[i];
-        nocontract_val[0] -= idx0 * m_ij_strides[i];
-        nocontract_val[1] -= idx1 * m_ij_strides[i];
-      }
-      if (side == Lhs && inner_dim_contiguous) {
-        eigen_assert(m_nocontract_strides[0] == 1);
-        linidx[0] += nocontract_val[0];
-        linidx[1] += nocontract_val[1];
-      } else {
-        linidx[0] += nocontract_val[0] * m_nocontract_strides[0];
-        linidx[1] += nocontract_val[1] * m_nocontract_strides[0];
-      }
-    }
-
-    Index contract_val[2] = {left ? col : row, left ? col : row + distance};
-    if (array_size<contract_t>::value> 0) {
-      EIGEN_UNROLL_LOOP
-      for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) {
-        const Index idx0 = contract_val[0] / m_k_strides[i];
-        const Index idx1 = contract_val[1] / m_k_strides[i];
-        linidx[0] += idx0 * m_contract_strides[i];
-        linidx[1] += idx1 * m_contract_strides[i];
-        contract_val[0] -= idx0 * m_k_strides[i];
-        contract_val[1] -= idx1 * m_k_strides[i];
-      }
-
-      if (side == Rhs && inner_dim_contiguous) {
-        eigen_assert(m_contract_strides[0] == 1);
-        linidx[0] += contract_val[0];
-        linidx[1] += contract_val[1];
-      } else {
-        linidx[0] += contract_val[0] * m_contract_strides[0];
-        linidx[1] += contract_val[1] * m_contract_strides[0];
-      }
-    }
-    return IndexPair<Index>(linidx[0], linidx[1]);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index firstAligned(Index size) const {
-    // Only claim alignment when we can compute the actual stride (ie when we're
-    // dealing with the lhs with inner_dim_contiguous. This is because the
-    // matrix-vector product relies on the stride when dealing with aligned inputs.
-    return (Alignment == Aligned) && (side == Lhs) && inner_dim_contiguous ? 0 : size;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index stride() const {
-    return ((side == Lhs) && inner_dim_contiguous && array_size<contract_t>::value > 0) ? m_contract_strides[0] : 1;
-  }
-
-  #ifdef EIGEN_USE_SYCL
-  // The placeholder accessors require to be bound to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_tensor.bind(cgh);
-  }
-  #endif
-
-  const CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>& tensor() const {
-    return m_tensor;
-  }
-
-  const nocontract_t& nocontract_strides() const {
-    return m_nocontract_strides;
-  }
-  const nocontract_t& ij_strides() const { return m_ij_strides; }
-  const contract_t& contract_strides() const { return m_contract_strides; }
-  const contract_t& k_strides() const { return m_k_strides; }
-
- protected:
-  CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_> m_tensor;
-  const nocontract_t m_nocontract_strides;
-  const nocontract_t m_ij_strides;
-  const contract_t m_contract_strides;
-  const contract_t m_k_strides;
-};
-
-template<typename Scalar, typename Index, int side,
-         typename Tensor,
-         typename nocontract_t, typename contract_t,
-         int packet_size, bool inner_dim_contiguous,
-         bool inner_dim_reordered, int Alignment, template <class> class MakePointer_>
-class BaseTensorContractionMapper : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, Alignment, MakePointer_>
-{
- public:
-  typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, Alignment, MakePointer_> ParentMapper;
-
-  EIGEN_DEVICE_FUNC
-  BaseTensorContractionMapper(const Tensor& tensor,
-                              const nocontract_t& nocontract_strides,
-                              const nocontract_t& ij_strides,
-                              const contract_t& contract_strides,
-                              const contract_t& k_strides) :
-  ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
-
-  template <typename PacketT,int AlignmentType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::unpacket_traits<PacketT>::size==packet_size,PacketT>::type
-  load(Index i, Index j) const
-  {
-    // whole method makes column major assumption
-
-    // don't need to add offsets for now (because operator handles that)
-    // current code assumes packet size must be a multiple of 2
-    EIGEN_STATIC_ASSERT(packet_size % 2 == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    if (Tensor::PacketAccess && inner_dim_contiguous && !inner_dim_reordered) {
-      const Index index = this->computeIndex(i, j);
-      eigen_assert(this->computeIndex(i+packet_size-1, j) == index + packet_size-1);
-      return this->m_tensor.template packet<AlignmentType>(index);
-    }
-
-    const IndexPair<Index> indexPair = this->computeIndexPair(i, j, packet_size - 1);
-    const Index first = indexPair.first;
-    const Index lastIdx = indexPair.second;
-
-    // We can always do optimized packet reads from left hand side right now, because
-    // the vertical matrix dimension on the left hand side is never contracting.
-    // On the right hand side we need to check if the contracting dimensions may have
-    // been shuffled first.
-    if (Tensor::PacketAccess &&
-        (side == Lhs || internal::array_size<contract_t>::value <= 1 || !inner_dim_reordered) &&
-        (lastIdx - first) == (packet_size - 1)) {
-
-      return this->m_tensor.template packet<AlignmentType>(first);
-    }
-
-    EIGEN_ALIGN_MAX Scalar data[packet_size];
-
-    data[0] = this->m_tensor.coeff(first);
-    EIGEN_UNROLL_LOOP
-    for (Index k = 1; k < packet_size - 1; k += 2) {
-      const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1);
-      data[k] = this->m_tensor.coeff(internal_pair.first);
-      data[k + 1] = this->m_tensor.coeff(internal_pair.second);
-    }
-    data[packet_size - 1] = this->m_tensor.coeff(lastIdx);
-
-    return pload<PacketT>(data);
-  }
-
-  template <typename PacketT,int AlignmentType>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::unpacket_traits<PacketT>::size!=packet_size,PacketT>::type
-  load(Index i, Index j) const
-  {
-    const Index requested_packet_size = internal::unpacket_traits<PacketT>::size;
-    EIGEN_ALIGN_MAX Scalar data[requested_packet_size];
-
-    const IndexPair<Index> indexPair = this->computeIndexPair(i, j, requested_packet_size - 1);
-    const Index first = indexPair.first;
-    const Index lastIdx = indexPair.second;
-
-    data[0] = this->m_tensor.coeff(first);
-    for (Index k = 1; k < requested_packet_size - 1; k += 2) {
-      const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1);
-      data[k] = this->m_tensor.coeff(internal_pair.first);
-      data[k + 1] = this->m_tensor.coeff(internal_pair.second);
-    }
-    data[requested_packet_size - 1] = this->m_tensor.coeff(lastIdx);
-
-    return pload<PacketT>(data);
-  }
-
-  template <typename PacketT,int AlignmentType>
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE PacketT loadPacket(Index i, Index j) const {
-    return this->load<PacketT,AlignmentType>(i,j);
-  }
-};
-
-
-template<typename Scalar, typename Index, int side,
-         typename Tensor,
-         typename nocontract_t, typename contract_t,
-         bool inner_dim_contiguous,
-         bool inner_dim_reordered, int Alignment, template <class> class MakePointer_>
-class BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_>
-  : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, Alignment, MakePointer_>
-{
- public:
-  typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, Alignment, MakePointer_> ParentMapper;
-
-  EIGEN_DEVICE_FUNC
-  BaseTensorContractionMapper(const Tensor& tensor,
-                              const nocontract_t& nocontract_strides,
-                              const nocontract_t& ij_strides,
-                              const contract_t& contract_strides,
-                              const contract_t& k_strides) :
-  ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
-
-  template <typename PacketT,int> EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE PacketT loadPacket(Index i, Index j) const {
-    EIGEN_ALIGN_MAX Scalar data[1];
-    data[0] = this->m_tensor.coeff(this->computeIndex(i, j));
-    return pload<PacketT>(data);
-  }
-  template <typename PacketT,int> EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE PacketT load(Index i, Index j) const {
-    EIGEN_ALIGN_MAX Scalar data[1];
-    data[0] = this->m_tensor.coeff(this->computeIndex(i, j));
-    return pload<PacketT>(data);
-  }
-};
-
-
-template<typename Scalar, typename Index, int side,
-         typename Tensor,
-         typename nocontract_t, typename contract_t,
-         int packet_size,
-         bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment, template <class> class MakePointer_=MakePointer>
-class TensorContractionSubMapper {
- public:
-
-  typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> ParentMapper;
-  typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> Self;
-  typedef Self LinearMapper;
-
-  enum {
-    // We can use direct offsets iff the parent mapper supports then and we can compute the strides.
-    // TODO: we should also enable direct offsets for the Rhs case.
-    UseDirectOffsets = ParentMapper::DirectOffsets && (side == Lhs) && inner_dim_contiguous && (array_size<contract_t>::value > 0)
-  };
-
-  EIGEN_DEVICE_FUNC TensorContractionSubMapper(const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset)
-      : m_base_mapper(base_mapper), m_vert_offset(vert_offset), m_horiz_offset(horiz_offset) {
-    // Bake the offsets into the buffer used by the base mapper whenever possible. This avoids the need to recompute
-    // this offset every time we attempt to access a coefficient.
-    if (UseDirectOffsets) {
-      Index stride = m_base_mapper.stride();
-      m_base_mapper.offsetBuffer(vert_offset + horiz_offset * stride);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
-    if (UseDirectOffsets) {
-      return m_base_mapper(i, 0);
-    }
-    return m_base_mapper(i + m_vert_offset, m_horiz_offset);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, Index j) const {
-    if (UseDirectOffsets) {
-      return m_base_mapper(i, j);
-    }
-    return m_base_mapper(i + m_vert_offset, j + m_horiz_offset);
-  }
-
-  template <typename PacketT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i) const {
-    if (UseDirectOffsets) {
-      return m_base_mapper.template loadPacket<PacketT,Alignment>(i, 0);
-    }
-    return m_base_mapper.template loadPacket<PacketT,Alignment>(i + m_vert_offset, m_horiz_offset);
-  }
-
-  template <typename PacketT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i, Index j) const {
-    if (UseDirectOffsets) {
-      return m_base_mapper.template loadPacket<PacketT,Alignment>(i, j);
-    }
-    return m_base_mapper.template loadPacket<PacketT,Alignment>(i + m_vert_offset, j + m_horiz_offset);
-  }
-
-  template <typename PacketT, int AlignmentType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i, Index j) const {
-    if (UseDirectOffsets) {
-      return m_base_mapper.template load<PacketT,AlignmentType>(i, j);
-    }
-    return m_base_mapper.template loadPacket<PacketT,AlignmentType>(i + m_vert_offset, j + m_horiz_offset);
-  }
-
-  template <typename PacketT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketT& p) const {
-    if (UseDirectOffsets) {
-      m_base_mapper.storePacket(i, 0, p);
-    }
-    m_base_mapper.storePacket(i + m_vert_offset, m_horiz_offset, p);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
-    if (UseDirectOffsets) {
-      return LinearMapper(m_base_mapper, i, j);
-    }
-    return LinearMapper(m_base_mapper, i + m_vert_offset, j + m_horiz_offset);
-  }
-
-  template <typename PacketT, int AlignmentType>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i) const {
-    EIGEN_STATIC_ASSERT((internal::is_same<PacketT, PacketT>::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    const int ActualAlignment = (AlignmentType == Aligned) && (Alignment == Aligned) ? Aligned : Unaligned;
-    if (UseDirectOffsets) {
-     return m_base_mapper.template loadPacket<PacketT,ActualAlignment>(i, 0);
-    }
-    return m_base_mapper.template loadPacket<PacketT,ActualAlignment>(i + m_vert_offset, m_horiz_offset);
-  }
-
-  template <typename PacketT>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool aligned(Index) const {
-    return false;
-  }
-
-  #ifdef EIGEN_USE_SYCL
-  // The placeholder accessors require to be bound to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_base_mapper.bind(cgh);
-  }
-  #endif
-
-  const ParentMapper& base_mapper() const { return m_base_mapper; }
-  Index vert_offset() const { return m_vert_offset; }
-  Index horiz_offset() const { return m_horiz_offset; }
-
- private:
-  ParentMapper m_base_mapper;
-  const Index m_vert_offset;
-  const Index m_horiz_offset;
-};
-
-
-template<typename Scalar_, typename Index, int side,
-         typename Tensor,
-         typename nocontract_t, typename contract_t,
-         int packet_size,
-         bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,  template <class> class MakePointer_=MakePointer>
-class TensorContractionInputMapper
-  : public BaseTensorContractionMapper<Scalar_, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> {
-
- public:
-  typedef Scalar_ Scalar;
-  typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> Base;
-  typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> SubMapper;
-  typedef SubMapper VectorMapper;
-
-  EIGEN_DEVICE_FUNC TensorContractionInputMapper(const Tensor& tensor,
-                               const nocontract_t& nocontract_strides,
-                               const nocontract_t& ij_strides,
-                               const contract_t& contract_strides,
-                               const contract_t& k_strides)
-      : Base(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const {
-    return SubMapper(*this, i, j);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
-    return VectorMapper(*this, i, j);
-  }
-  
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>& get_tensor() const {
-    return Base::m_tensor;
-  }
-};
-
-
-template <typename T> struct TensorContractionInputMapperTrait;
-
-template<typename Scalar_, typename Index_, int side_,
-         typename Tensor_,
-         typename nocontract_t_, typename contract_t_,
-         int packet_size_,
-         bool inner_dim_contiguous_, bool inner_dim_reordered_, int Alignment_,  template <class> class MakePointer_>
-struct TensorContractionInputMapperTrait<TensorContractionInputMapper<Scalar_, Index_, side_, Tensor_, 
-                                                    nocontract_t_, contract_t_, packet_size_, inner_dim_contiguous_, 
-                                                    inner_dim_reordered_, Alignment_, MakePointer_> > {
-
-      typedef Tensor_ XprType;
-      static const bool  inner_dim_contiguous = inner_dim_contiguous_;
-      static const bool  inner_dim_reordered = inner_dim_reordered_;
-  };  
-
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
deleted file mode 100644
index 473c228..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
+++ /dev/null
@@ -1,1650 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla Public License v. 2.0. If a copy of the MPL was not
-// distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * TensorContractionSycl.h
- *
- * \brief:
- *  TensorContractionSycl.h, provides various tensor contraction kernel for SYCL backend
- *
- *****************************************************************/
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
-
-namespace Eigen {
-
-namespace TensorSycl {
-namespace internal {
-
-#ifndef EIGEN_SYCL_DISABLE_GEMV
-/*!
- * \brief TVPanelSize, a template class used for setting the panel size required for launching General TensorVector
- * contraction kernel on various hardware devices.
- *
- * \tparam Scalar: determines the element type of the tensor/vector
- *
- * \tparam StorageIndex  determines the Index type.
- *
- * \tparam NCWindow: determines the number of non-contracting element to be process by each work-group
- *
- * \tparam CFactor: determines the number of contracting element to be process by each thread
- *
- * \tparam NCFactor: determines the number of non-contracting element to be process by each thread
- */
-template <typename Scalar, typename StorageIndex, StorageIndex NCWindow, StorageIndex CFactor, StorageIndex NCFactor>
-struct TVPanelSize {
-  // LocalThreadSizeC: determines total number of thread per workgroup for the contracting dimension
-  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeC = EIGEN_SYCL_LOCAL_THREAD_DIM0;
-  // LocalThreadSizeNC: determines total number of thread per workgroup for the non-contracting dimension
-  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeNC = EIGEN_SYCL_LOCAL_THREAD_DIM1;
-  // TileSizeDimNC: determines the tile size for the non-contracting dimension
-  static EIGEN_CONSTEXPR StorageIndex TileSizeDimNC = NCWindow / NCFactor;
-  // TileSizeDimC: determines the tile size for the contracting dimension
-  static EIGEN_CONSTEXPR StorageIndex TileSizeDimC = CFactor * LocalThreadSizeNC * LocalThreadSizeC;
-  // WorkLoadPerThreadNC : determines workload per thread for loading the non-contracting dimension
-  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadNC = TileSizeDimNC / LocalThreadSizeNC;
-  // WorkLoadPerThreadC: determines workload per thread for loading the non-contracting dimension
-  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadC = TileSizeDimC / LocalThreadSizeC;
-  // BC : determines if supporting bank conflict is required
-  static EIGEN_CONSTEXPR bool BC = false;
-};
-#endif
-
-/*!
- * \brief TTPanelSize, a template class used for setting the panel size required for launching General Tensor Tensor
- contraction kernel on various hardware devices.
- *
- * \tparam Scalar: determines the element type of the tensor
- *
- * \tparam StorageIndex: determines the Index type.
- *
- * \tparam REG_SIZE_M: determines workload per thread for loading the M dimension This can be varied based on the
- available register on a chosen device(can be controlled by EIGEN_SYCL_REG_M macro).
- *
- * \tparam REG_SIZE_N: determines workload per thread for loading the N dimension This can be varied based on the
- available register on a chosen device(can be controlled by EIGEN_SYCL_REG_N macro).
- *
- * \tparam TSDK: determines Tile size for dimension K. The packet size is assumed to be considered
- */
-
-template <typename Scalar, typename StorageIndex, StorageIndex REG_SIZE_M, StorageIndex REG_SIZE_N, StorageIndex TSDK>
-struct TTPanelSize {
-  // TileSizeDimK: determines Tile size for dimension K. The packet size is assumed to be considered
-  static EIGEN_CONSTEXPR StorageIndex TileSizeDimK = TSDK;
-  // WorkLoadPerThreadM : determines workload per thread for loading the M dimension This can be varied based on the
-  // available register on a chosen device(can be controlled by EIGEN_SYCL_REG_M macro//
-#ifndef EIGEN_SYCL_REG_M
-  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadM = REG_SIZE_M;
-#else
-  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadM = EIGEN_SYCL_REG_M;
-#endif
-// WorkLoadPerThreadN : determines workload per thread for loading the N dimension This can be varied based on the
-// available register on a chosen device(can be controlled by EIGEN_SYCL_REG_N macro
-#ifndef EIGEN_SYCL_REG_N
-  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadN = REG_SIZE_N;
-#else
-  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadN = EIGEN_SYCL_REG_N;
-#endif
-  // LocalThreadSizeM: determines total number of thread per workgroup for the m dimension
-  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeM = EIGEN_SYCL_LOCAL_THREAD_DIM0;
-  // LocalThreadSizeN: determines total number of thread per workgroup for the n dimension
-  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeN = EIGEN_SYCL_LOCAL_THREAD_DIM1;
-  // TileSizeDimM: determines the tile size for the m dimension
-  static EIGEN_CONSTEXPR StorageIndex TileSizeDimM = LocalThreadSizeM * WorkLoadPerThreadM;
-  // TileSizeDimN: determines the tile size for the n dimension
-  static EIGEN_CONSTEXPR StorageIndex TileSizeDimN = LocalThreadSizeN * WorkLoadPerThreadN;
-  // LoadPerThreadLhs: determines workload per thread for loading Lhs Tensor. This must be divisable by packetsize
-  static EIGEN_CONSTEXPR StorageIndex LoadPerThreadLhs =
-      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimN));
-  // LoadPerThreadRhs: determines workload per thread for loading Rhs Tensor. This must be divisable by packetsize
-  static EIGEN_CONSTEXPR StorageIndex LoadPerThreadRhs =
-      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimM));
-  // BC : determines if supporting bank conflict is required
-  static EIGEN_CONSTEXPR bool BC = true;
-  // DoubleBuffer: determines if double buffering technique should be used (This can be disabled by
-  // EIGEN_SYCL_DISABLE_DOUBLE_BUFFER macro when the device doesnot have sufficient  local memory)
-  static EIGEN_CONSTEXPR bool DoubleBuffer =
-#ifdef EIGEN_SYCL_DISABLE_DOUBLE_BUFFER
-      false;
-#else
-      true;
-#endif
-};
-
-/* !
- * \brief contraction_type: an enum class representing the Tensor Contraction implementation algorithm. This is used to
- * specialize the contraction algorithm based on device support for dedicated local memory.
- */
-enum class contraction_type { local, no_local };
-/* !
- * \brief data_source an enum class determining the location of the data in a memory hierarchy (global, local, private).
- */
-enum class data_source { global_mem, local_mem, private_mem };
-
-/*!
- * \brief read, a template function used for loading the data from global
- memory. This function is used to guarantee coalesced and vectorized load whenever possible
- *
- * \tparam PacketLoad: determines if the each element of this tensor block should be loaded in a packet mode
- *
- * \param is_coalesced_layout: determines whether or not the Tensor data in a memory can be access coalesced and
- vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
- contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
- when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
- *
- * \tparam PacketType:  determines the type of packet
- *
- * \tparam TensorMapper: determines the input tensor mapper type
- *
- * \tparam StorageIndex: determines the Index type
-
- * \param tensorMapper: is the input tensor
- *
- * \param NCIndex: is the non-contracting dim index
- *
- * \param CIndex is the contracting dim index
- *
- * \param ld: is the leading dimension of the flattened tensor
- */
-template <bool PacketLoad, bool is_coalesced_layout, bool, typename PacketType, typename TensorMapper,
-          typename StorageIndex>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<PacketLoad, PacketType>::type read(
-    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &ld) {
-  const StorageIndex row = (is_coalesced_layout) ? NCIndex : CIndex;
-  const StorageIndex col = (is_coalesced_layout) ? CIndex : NCIndex;
-  return tensorMapper.get_tensor().template packet<Unaligned>(row + (col * ld));
-}
-
-/*!
- * \brief read, special overload of read function, when the read access is not vectorized
- *
- * \tparam PacketLoad: determines if the each element of this tensor block should be loaded in a packet mode
- *
- * \param is_coalesced_layout: determines whether or not the Tensor data in a memory can be access coalesced and
-  vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
-  contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
-  when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
- *
- * \tparam PacketType: determines the type of packet
- *
- * \tparam TensorMapper: determines the input tensor mapper type
- *
- * \tparam StorageIndex: determines the Index type
-
- * \param tensorMapper: is the input tensor
- *
- * \param NCIndex: is the non-contracting dim index
- *
- * \param CIndex: is the contracting dim index
- */
-template <bool PacketLoad, bool, bool IsRhs, typename PacketType, typename TensorMapper, typename StorageIndex>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<!PacketLoad, PacketType>::type read(
-    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &) {
-  const StorageIndex row = (IsRhs) ? CIndex : NCIndex;
-  const StorageIndex col = (IsRhs) ? NCIndex : CIndex;
-  return tensorMapper(row, col);
-}
-
-/*!
- * \brief write, a template function used for storing the data to local memory. This function is used to guarantee
- * coalesced and vectorized store whenever possible.
- *
- * \tparam StorageIndex: determines the Index type
- *
- * \param ld is the leading dimension of the local memory. ld is a compile time value for the local memory
- *
- * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
- *
- * \tparam PacketType:  determines the type of packet
- *
- * \tparam DataScalar: determines the output data type
- *
- * \param packet_data: the data to be written in the local memory
- *
- * \param ptr: a pointer to the local memory
- *
- * \param CIndex is the contracting dim index
- */
-
-template <typename StorageIndex, StorageIndex ld, data_source dt, typename PacketType, typename DataScalar>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    typename ::Eigen::internal::enable_if<dt != data_source::global_mem, void>::type
-    write(PacketType &packet_data, DataScalar ptr) {
-  EIGEN_CONSTEXPR int PacketSize = Eigen::internal::unpacket_traits<PacketType>::size;
-  EIGEN_UNROLL_LOOP
-  for (int i = 0; i < PacketSize; i++) {
-    *ptr = PacketWrapper<PacketType, PacketSize>::scalarize(i, packet_data);
-    ptr += ld;
-  }
-}
-
-/*!
- * \brief Overloading the write function for storing the data to global memory, when vectorization enabled This function
- * is used to guarantee coalesced and vectorized store whenever possible.
- *
- * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
- *
- * \tparam PacketType:  determines the type of packet
- *
- * \tparam DataScalar: determines the output data type
- *
- * \param packet_data: the data to be written in the local memory
- *
- * \param ptr: a pointer to the local memory
- */
-
-template <data_source dt, typename PacketType, typename DataScalar>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<
-    Eigen::internal::unpacket_traits<PacketType>::size != 1 && dt == data_source::global_mem, void>::type
-write(PacketType &packet_data, DataScalar *ptr) {
-  ::Eigen::internal::pstoreu<DataScalar, PacketType>(ptr, packet_data);
-}
-
-/*!
- * \brief Overloading the write function for storing the data to global memory, when vectorization is disabled.
- *
- * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
- *
- * \tparam PacketType:  determines the type of packet
- *
- * \tparam DataScalar: determines the output data type
- *
- * \param packet_data: the data to be written in the local memory
- *
- * \param ptr: a pointer to the local memory
- */
-template <data_source dt, typename PacketType, typename DataScalar>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<
-    Eigen::internal::unpacket_traits<PacketType>::size == 1 && dt == data_source::global_mem, void>::type
-write(PacketType &packet_data, DataScalar *ptr) {
-  *ptr = packet_data;
-}
-
-/*!
- * \brief check_boundary: is used to check the edge condition for non-internal blocks.
- *
- * \tparam is_internal: determines if the block is internal
- */
-template <bool is_internal>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary(bool) {
-  return true;
-}
-
-/*!
- * \brief check_boundary: specialization of the check_boundary for non-internal blocks.
- *
- * \param cond: true when the data is in range. Otherwise false
- */
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary<false>(bool cond) {
-  return cond;
-}
-
-/*!
- * \brief BlockProperties is a template class that provides different characteristic of a block of each Tensor processed
- * by each workgroup.
- *
- * \tparam is_transposed: iff true, determines whether or not the block of the Tensor is transposed
- *
- * \tparam packet_load_: determines if the each element of this tensor block should be loaded in a packet mode
- *
- * \tparam PacketType:  determines the type of packet
- *
- * \tparam OutType: determines the type of each element for this block of tensor. If packet load is true, it will be
- * packetType; Otherwise it will be scalar Type
- *
- * \param elements_per_access determines the size of each element based on OutType
- *
- * \param is_coalesced_layout  determines whether or not the Tensor data in a memory can be access coalesced and
- * vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
- * contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
- * when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
- *
- * \param nc_stride determines the stride of non-contracting dimension to access the next adjustment element within the
- * Tensor Block for each workgroup
- *
- * \param c_stride  determines the stride of contracting dimension to access the next adjustment element within the
- * Tensor Block for each workgroup
- */
-template <bool is_transposed, bool is_rhs_, bool packet_load_, typename PacketType>
-struct BlockProperties {
-  static EIGEN_CONSTEXPR bool packet_load = packet_load_;
-  typedef typename Eigen::internal::unpacket_traits<PacketType>::type OutScalar;
-  static EIGEN_CONSTEXPR bool is_rhs = is_rhs_;
-  typedef typename Eigen::internal::conditional<packet_load, PacketType, OutScalar>::type OutType;
-  static EIGEN_CONSTEXPR int elements_per_access = Eigen::internal::unpacket_traits<OutType>::size;
-  static EIGEN_CONSTEXPR bool is_coalesced_layout = !(is_transposed ^ is_rhs);
-  static EIGEN_CONSTEXPR int nc_stride = (is_coalesced_layout ? elements_per_access : 1);
-  static EIGEN_CONSTEXPR int c_stride = (is_coalesced_layout ? 1 : elements_per_access);
-};
-
-/*!
- * \brief ThreadProperties is a template class that provides each thread's properties within a workgroup.  Please see
- * the sycl-1.2.1 specification (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for the workgroup,
- * work-items
- *
- * \tparam StorageIndex: determines the StorageIndex Type
- *
- * \param linearLocalThreadId: determines the linearized location of a thread within a work-group
- *
- * \param kGroupId: determines the logical group id in a k dimension of the flattened tensor. It will be > 1 when
- * tall/skinny algorithm is used
- *
- * \param mGroupOffset: determines the logical start position of all thread within a workgroup for the m dimension of
- * the flattened tensor.
- *
- * \param kGroupOffset determines the logical start position of all thread within a workgroup for the k dimension of the
- * flattened tensor. It will be > 1 when tall/skinny algorithm is used.
- *
- * \param mLocalOffset: determines the logical start position of each thread within a workgroup for the m dimension of a
- * flattened tensor. The position determines the distance of each thread within the workgroup from each other
- * independent from their global position.
- *
- * \param nLocalOffset: determines the logical start position of each thread within a workgroup for the n dimension of a
- * flattened tensor. The position determines the distance of each thread within the workgroup from each other
- * independent from their global position.
- *
- * \param mGlobalOffset: determines the logical start position of each thread a thread for the m dimension on a
- * flattened tensor
- *
- * \param nGlobalOffset: determines the logical start position of each thread a thread for the n dimension on a
- * flattened tensor
- *
- * \param kSize : determine the number of the k elements of the flattened Tensor to be processed by each thread for the
- * given tensor block. This is !=K dimension of Flattened Tensor when Tall/Skinny matrix is used.
- *
- * \param is_internal : this will determined if the thread within the work-group computes an internal block of tensor or
- * the edge blocks. When it is internal, there is no need to check the boundaries and all the if stantement can be
- * resolve by compiler.
- */
-template <typename StorageIndex>
-struct ThreadProperties {
-  const StorageIndex linearLocalThreadId;
-  const StorageIndex kGroupId;
-  const StorageIndex mGroupOffset;
-  const StorageIndex nGroupOffset;
-  const StorageIndex kGroupOffset;
-  const StorageIndex mLocalOffset;
-  const StorageIndex nLocalOffset;
-  const StorageIndex mGlobalOffset;
-  const StorageIndex nGlobalOffset;
-  StorageIndex kSize;
-  const bool is_internal;
-  // this is used to adjust the last block
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ThreadProperties(
-      const StorageIndex linearLocalThreadId_, const StorageIndex kGroupId_, const StorageIndex mGroupOffset_,
-      const StorageIndex nGroupOffset_, const StorageIndex kGroupOffset_, const StorageIndex mLocalOffset_,
-      const StorageIndex nLocalOffset_, const StorageIndex mGlobalOffset_, const StorageIndex nGlobalOffset_,
-      StorageIndex kSize_, const bool is_internal_)
-      : linearLocalThreadId(linearLocalThreadId_),
-        kGroupId(kGroupId_),
-        mGroupOffset(mGroupOffset_),
-        nGroupOffset(nGroupOffset_),
-        kGroupOffset(kGroupOffset_),
-        mLocalOffset(mLocalOffset_),
-        nLocalOffset(nLocalOffset_),
-        mGlobalOffset(mGlobalOffset_),
-        nGlobalOffset(nGlobalOffset_),
-        kSize(kSize_),
-        is_internal(is_internal_) {}
-};
-
-/*!
- * \brief TensorContractionKernel is a template class that provides Tensor -Tensor contraction operation.
- *
- * \tparam OutScalar: determines the output scalar type
- *
- * \tparam LhsScalar: determines the left-hand-side scalar type
- *
- * \tparam RhsScalar: determines the right-hand-side scalar type
- *
- * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
- (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
- *
- * \tparam LhsMapper determines the tensor contraction mapper type for left-hand-side matrix
- *
- * \tparam RhsMapper determines the tensor contraction mapper type for right-hand-side matrix
- *
- * \tparam StorageIndex: determines the StorageIndex Type
- *
- * \tparam Properties: determines the Contraction Panel properties
- *
- * \tparam TripleDim: determines the M, K, N dimensions for the flatten tensors in order to treat them as a matrix
- *
- * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
- *
- * \tparam input_mapper_properties : determine if the input tensors are matrix. If they are matrix, special memory
- access is used to guarantee that always the memory access are coalesced.
- *
- * \tptaram IsFinal : determine if this is the final kernel. If so, the result will be written in a final output.
- Otherwise, the result of contraction will be written iin a temporary buffer. This is the case when Tall/Skinny
- contraction is used. So in this case, a final reduction step is required to compute final output.
-
- * \tparam contraction_tp: it is an enum value representing whether the local memroy/no local memory implementation of
- the algorithm to be used
- *
- * \param scratch: local memory containing tiles of LHS and RHS tensors for each work-group
- *
- * \param lhs: determines the left-hand-side flattened tensor (tensor mapper)
- *
- * \param rhs: determines the right-hand-side flattened tensor (tensor mapper)
- *
- * \param out_res: determines the output tensor containing the contraction result
- *
- * \param groupSizeM: a logical number determining the number of work-group for m dimension
- *
- * \param groupSizeN: a logical number determining the number of work-group for n dimension
- *
- * \param numTiles: determines total number of tiles on the k dimension
- *
- * \param TripleDim: determines the M, K, N dimensions for the flatten tensors in order to treat them as a matrix
- */
-template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
-          typename RhsMapper, typename StorageIndex, typename Properties, typename TripleDim, bool Vectorizable,
-          typename input_mapper_properties, bool IsFinal, contraction_type contraction_tp>
-class TensorContractionKernel {
- public:
-  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
-      PacketReturnType;
-  static EIGEN_CONSTEXPR int PacketSize =
-      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
-  static EIGEN_CONSTEXPR bool is_lhs_transposed =
-      !::Eigen::internal::TensorContractionInputMapperTrait<LhsMapper>::inner_dim_contiguous;
-  static EIGEN_CONSTEXPR bool is_rhs_transposed =
-      !::Eigen::internal::TensorContractionInputMapperTrait<RhsMapper>::inner_dim_contiguous;
-
-  typedef BlockProperties<is_lhs_transposed, false, input_mapper_properties::is_lhs_matrix && Vectorizable,
-                          PacketReturnType>
-      LHSBlockProperties;
-
-  typedef BlockProperties<is_rhs_transposed, true, input_mapper_properties::is_rhs_matrix && Vectorizable,
-                          PacketReturnType>
-      RHSBlockProperties;
-
-  static EIGEN_CONSTEXPR StorageIndex NStride =
-      contraction_tp == contraction_type::local ? Properties::WorkLoadPerThreadN : RHSBlockProperties::nc_stride;
-
-  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
-  typedef cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::local_space> local_ptr;
-  typedef OutScalar * /*cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::private_space>*/ private_ptr;
-  typedef
-      typename ::Eigen::internal::conditional<contraction_tp == contraction_type::local, local_ptr, private_ptr>::type
-          tile_ptr;
-  static EIGEN_CONSTEXPR StorageIndex LSDL = contraction_tp == contraction_type::local
-                                                 ? Properties::TileSizeDimM + Properties::BC
-                                                 : Properties::WorkLoadPerThreadM;
-  static EIGEN_CONSTEXPR StorageIndex LSDR = contraction_tp == contraction_type::local
-                                                 ? Properties::TileSizeDimN + Properties::BC
-                                                 : Properties::WorkLoadPerThreadN;
-  static EIGEN_CONSTEXPR StorageIndex LocalOffset = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;
-
-  /**
-   * \brief MemHolder this is a place holder struct for creating memory hierarchy in SYCL. Inside SYCL kernel it is not
-   * allowed to have dynamic memory allocation. While the local memory is created outside of the kernel and passed to
-   * the kernel as an accessor, the private memory can only allowed to be allocated statically. Since we are abstracting
-   * the TiledMemory for both local and private memory, the MemHolder structs is used as a helper to abstract out
-   * different type of memory needed when local/no_local memory computation is called.
-   *
-   * \tparam contraction_type: it is an enum value representing whether the local memroy/no local memory implementation
-   of the algorithm to be used
-   * \tparam the private memory size
-   * \param ptr the tile memory pointer type
-   */
-  template <contraction_type, StorageIndex>
-  struct MemHolder {
-    tile_ptr ptr;
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MemHolder(local_ptr block_start_ptr) : ptr(block_start_ptr) {}
-  };
-  /**
-   * \brief specialization of memHolder class when no local memory kernel is used.
-   */
-  template <StorageIndex MemSize>
-  struct MemHolder<contraction_type::no_local, MemSize> {
-    OutScalar ptr[MemSize] = {OutScalar{0}};
-  };
-  /**
-   * \brief TiledMemory: contains required memory pointer for loading  each tile of the TensorContraction panel from
-   * global memory to local/private memory when local/no_local algorithm used.
-   *
-   * \param lhs_scratch_extract : determines the LHS tile memory. It is either private or local memory based on the
-   * selected contraction_type.
-   *
-   * \param rhs_scratch_extract : determines the RHS tile memory. It is either private or local memory based on the
-   * selected contraction_type.
-   *
-   * \param lhs_extract_index: determins the position of each thread on a local memory for lhs input. When private
-   * memory is used this is set to zero as this is not applicable in case of private memory.
-   *
-   * \param rhs_extract_index: determins the position of each thread on a local memory for rhs input. When private
-   * memory is used this is set to zero as this is not applicable in case of private memory.
-   *
-   * \param lhs_scratch_compute : determines the  location to load for computation for lhs_local memory. This is the
-   * same as lhs_scratch_extract for private memory.
-   *
-   * \param rhs_scratch_compute : determines the  location to load for computation for rhs_local memory. This is the
-   * same as rhs_scratch_extract for private memory.
-   */
-  struct TiledMemory {
-    MemHolder<contraction_tp, Properties::WorkLoadPerThreadM * Properties::TileSizeDimK> lhs_scratch_extract;
-    MemHolder<contraction_tp, Properties::WorkLoadPerThreadN * Properties::TileSizeDimK> rhs_scratch_extract;
-    tile_ptr lhs_scratch_ptr_compute;
-    tile_ptr rhs_scratch_ptr_compute;
-    const std::pair<StorageIndex, StorageIndex> lhs_extract_index;
-    const std::pair<StorageIndex, StorageIndex> rhs_extract_index;
-    template <contraction_type tp = contraction_tp>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TiledMemory(const ThreadProperties<StorageIndex> &, local_ptr,
-                typename ::Eigen::internal::enable_if<tp == contraction_type::no_local>::type * = 0)
-        : lhs_scratch_extract{},
-          rhs_scratch_extract{},
-          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr),
-          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr),
-          lhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})),
-          rhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})) {}
-
-    template <contraction_type tp = contraction_tp>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TiledMemory(const ThreadProperties<StorageIndex> &thread_properties, local_ptr block_start_ptr,
-                typename ::Eigen::internal::enable_if<tp == contraction_type::local>::type * = 0)
-        : lhs_scratch_extract{block_start_ptr},
-          rhs_scratch_extract{lhs_scratch_extract.ptr +
-                              ((Properties::DoubleBuffer + 1) * LSDL * Properties::TileSizeDimK)},
-          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr + thread_properties.mLocalOffset),
-          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr + thread_properties.nLocalOffset),
-          lhs_extract_index(
-              local_id_extract<LHSBlockProperties, Properties::TileSizeDimM>(thread_properties.linearLocalThreadId)),
-          rhs_extract_index(
-              local_id_extract<RHSBlockProperties, Properties::TileSizeDimN>(thread_properties.linearLocalThreadId)) {}
-  };
-
-  Scratch scratch;
-  const LhsMapper lhs;
-  const RhsMapper rhs;
-  OutAccessor out_res;
-  const StorageIndex groupSizeM;
-  const StorageIndex groupSizeN;
-  const StorageIndex numTiles;
-  const TripleDim triple_dim;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
-                                                                const RhsMapper rhs_, OutAccessor out_res_,
-                                                                const StorageIndex groupSizeM_,
-                                                                const StorageIndex groupSizeN_,
-                                                                const StorageIndex numTiles_,
-                                                                const TripleDim triple_dim_)
-      : scratch(scratch_),
-        lhs(lhs_),
-        rhs(rhs_),
-        out_res(out_res_),
-        groupSizeM(groupSizeM_),
-        groupSizeN(groupSizeN_),
-        numTiles(numTiles_),
-        triple_dim(triple_dim_) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
-                                                                const RhsMapper rhs_, OutAccessor out_res_,
-                                                                const StorageIndex groupSizeM_,
-                                                                const StorageIndex numTiles_,
-                                                                const TripleDim triple_dim_)
-      : TensorContractionKernel(scratch_, lhs_, rhs_, out_res_, groupSizeM_, 1, numTiles_, triple_dim_) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
-    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
-    const StorageIndex nLocalThreadId = linearLocalThreadId / Properties::LocalThreadSizeM;
-    const StorageIndex mLocalThreadId = linearLocalThreadId % Properties::LocalThreadSizeM;
-    const StorageIndex mGroupId = itemID.get_group(0) % groupSizeM;
-    const StorageIndex tmp = itemID.get_group(0) / groupSizeM;
-    const StorageIndex nGroupId = IsFinal ? tmp : tmp % groupSizeN;
-    const StorageIndex kGroupId = IsFinal ? 0 : tmp / groupSizeN;
-    const StorageIndex mGroupOffset = mGroupId * Properties::TileSizeDimM;
-    const StorageIndex nGroupOffset = nGroupId * Properties::TileSizeDimN;
-    const StorageIndex mLocalOffset = PacketSize * mLocalThreadId;
-    const StorageIndex nLocalOffset = NStride * nLocalThreadId;
-    const StorageIndex mGlobalOffset = mGroupOffset + mLocalOffset;
-    const StorageIndex nGlobalOffset = nGroupOffset + nLocalOffset;
-
-    const StorageIndex kSizePerWG = IsFinal ? triple_dim.K : numTiles * Properties::TileSizeDimK;
-    StorageIndex kGroupOffset = kGroupId * kSizePerWG;
-    const bool is_internal = triple_dim.M - mGroupOffset >= Properties::TileSizeDimM &&
-                             triple_dim.N - nGroupOffset >= Properties::TileSizeDimN &&
-                             triple_dim.K - kGroupOffset >= kSizePerWG;
-    // this is used to adjust the last block
-    StorageIndex kSize = IsFinal ? triple_dim.K : std::min(kSizePerWG, triple_dim.K - kGroupOffset);
-    // This is used to find out the lats K offset so that kGroupOffset -kSize can compute the coffset for loading to
-    // tile
-    kGroupOffset += kSize;
-
-    auto thread_properties =
-        ThreadProperties<StorageIndex>(linearLocalThreadId, kGroupId, mGroupOffset, nGroupOffset, kGroupOffset,
-                                       mLocalOffset, nLocalOffset, mGlobalOffset, nGlobalOffset, kSize, is_internal);
-
-    auto out_ptr = out_res.get_pointer() + (IsFinal ? 0 : thread_properties.kGroupId * triple_dim.M * triple_dim.N);
-
-    (thread_properties.is_internal) ? compute_panel<true>(itemID, thread_properties, out_ptr)
-                                    : compute_panel<false>(itemID, thread_properties, out_ptr);
-  }
-  // The compute block computes the contraction operation private block for each thread and store the resutl in the
-  // privateRes memory of Each computation the compute block function is independent of local and no local concepts as
-  // it only compute the block on each thread's private memory space
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_block_per_tile(OutScalar *lhs_block_ptr, OutScalar *rhs_block_ptr,
-                                                                    PacketReturnType *privateRes) {
-    StorageIndex idx = 0;
-    EIGEN_CONSTEXPR StorageIndex lhs_stride =
-        contraction_tp == contraction_type::local ? (PacketSize * Properties::LocalThreadSizeM) : 1;
-    EIGEN_UNROLL_LOOP
-    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN; wLPTN++) {
-      auto rhsPacket = PacketReturnType{*(rhs_block_ptr + wLPTN)};
-      StorageIndex lhs_index = 0;
-      EIGEN_UNROLL_LOOP
-      for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
-        PacketReturnType lhsPack{};
-        Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::set_packet(lhsPack,
-                                                                                             lhs_block_ptr + lhs_index);
-        privateRes[idx] = ::Eigen::internal::pmadd(lhsPack, rhsPacket, privateRes[idx]);
-
-        lhs_index += lhs_stride;
-        idx++;
-      }
-    }
-  }
-  // The store function write the computed contraction operation in the private memory of each thread to the global
-  // memory. The store function is independent of local and no local concepts s that it can be abstract out in the base
-  // class.
-  template <bool is_internal_block, StorageIndex PrivateNStride, typename OutPtr>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void store(OutPtr *out_ptr, PacketReturnType *privateRes,
-                                                   StorageIndex mGlobalOffset, StorageIndex nGlobalOffset) {
-    auto chk_bound = [&](const StorageIndex &mIndex, const StorageIndex &nIndex) EIGEN_DEVICE_FUNC {
-      return (mIndex + PacketSize - 1 < triple_dim.M && nGlobalOffset + nIndex < triple_dim.N);
-    };
-    // when local memory is not used M and N are both accessed in a coalesced way. However, when local memory is
-    // available the k*N is transposed in the local to N*K therefore, each blocks operates on blockId*
-    // WorkLoadPerThreadN slice of N
-    EIGEN_CONSTEXPR StorageIndex GlobalNStride =
-        contraction_tp == contraction_type::local ? 1 : Properties::LocalThreadSizeN;
-    EIGEN_UNROLL_LOOP
-    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN / PrivateNStride; wLPTN++) {
-      // output leading dimension
-      StorageIndex outputLD = 0;
-      // When local memory is used the PrivateNstride is always 1 because the coalesed access on N is loaded into Local
-      // memory and extracting from local to global is the same as no transposed version. However, when local memory is
-      // not used and RHS is transposed we packetize the load for RHS.
-      EIGEN_UNROLL_LOOP
-      for (StorageIndex nId = 0; nId < PrivateNStride; nId++) {
-        StorageIndex globalRow = mGlobalOffset;
-        EIGEN_UNROLL_LOOP
-        for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
-          PacketReturnType privetOut = privateRes[wLPTM];
-          if (check_boundary<is_internal_block>(chk_bound(globalRow, nId))) {
-            // Store the final results in C. The C matrix has always M as a first StorageIndex and N as a second
-            // StorageIndex Therefore it is always coalesced layout
-            write<data_source::global_mem>(privetOut, out_ptr + outputLD + globalRow);
-          } else {
-            EIGEN_UNROLL_LOOP
-            for (StorageIndex mId = 0; mId < PacketSize; mId++) {
-              StorageIndex mOffset = globalRow + mId;
-              if (mOffset < triple_dim.M && (nGlobalOffset + nId < triple_dim.N)) {
-                out_ptr[mOffset + outputLD] =
-                    Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::scalarize(mId, privetOut);
-              }
-            }
-          }
-          globalRow += (PacketSize * Properties::LocalThreadSizeM);
-        }
-        outputLD += triple_dim.M;
-        privateRes += Properties::WorkLoadPerThreadM / PacketSize;
-      }
-      out_ptr += (GlobalNStride * outputLD);
-
-      nGlobalOffset += (PrivateNStride * GlobalNStride);
-    }
-  }
-  // when no local memory is used the following extract_block will be enabled
-  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename PrivateReg,
-            contraction_type contract_tp = contraction_tp>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      typename ::Eigen::internal::enable_if<contract_tp == contraction_type::no_local>::type
-      extract_block(const Input &inpt, PrivateReg private_ptr, const std::pair<StorageIndex, StorageIndex> &,
-                    const StorageIndex &ncOffset, const StorageIndex cOffset) {
-    EIGEN_CONSTEXPR StorageIndex LocalThreadSizeNC =
-        InputBlockProperties::is_rhs ? Properties::LocalThreadSizeN : Properties::LocalThreadSizeM;
-    EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadNC =
-        InputBlockProperties::is_rhs ? Properties::WorkLoadPerThreadN : Properties::WorkLoadPerThreadM;
-    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;
-
-    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
-      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
-              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
-    };
-    const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
-    StorageIndex cIndex = cOffset;
-
-    EIGEN_UNROLL_LOOP
-    for (StorageIndex cId = 0; cId < Properties::TileSizeDimK / InputBlockProperties::c_stride; cId++) {
-      StorageIndex ncIndex = ncOffset;
-      EIGEN_UNROLL_LOOP
-      for (StorageIndex ncId = 0; ncId < WorkLoadPerThreadNC / InputBlockProperties::nc_stride; ncId++) {
-        if (check_boundary<is_internal_block>(chk_bound(cIndex, ncIndex))) {
-          auto val =
-              read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
-                   InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, ncIndex, cIndex, ld);
-
-          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
-                data_source::private_mem>(val, private_ptr);
-        } else {
-          EIGEN_UNROLL_LOOP
-          for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
-            const StorageIndex ncInd = ncIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
-            const StorageIndex cInd = cIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
-            OutScalar val =
-                (ncInd < NC && cInd < triple_dim.K)
-                    ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
-                          inpt, ncInd, cInd, ld)
-                    : OutScalar(0);
-            write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
-                  data_source::private_mem>(
-                val, private_ptr + (InputBlockProperties::is_coalesced_layout ? i : 0) +
-                         ((InputBlockProperties::is_coalesced_layout ? 0 : i) * WorkLoadPerThreadNC));
-          }
-        }
-
-        // if it is lhs we have to load it packetised when the packet size is > 1, because the output is coalesced. So
-        // even if M is not accessed in a coalesced mode, we have to load packet_size number of m per thread.
-        ncIndex = (!InputBlockProperties::is_rhs && InputBlockProperties::nc_stride == 1 && PacketSize != 1)
-                      ? ncOffset + (ncId + 1) % PacketSize + ((ncId + 1) / PacketSize) * LocalThreadSizeNC
-                      : (ncIndex + InputBlockProperties::nc_stride * LocalThreadSizeNC);
-        private_ptr += InputBlockProperties::nc_stride;
-      }
-      // the previous for loop ( private_ptr += (ncId * nc_stride)) has already moved ptr with one WorkLoadPerThreadNC
-      private_ptr += (InputBlockProperties::c_stride - 1) * WorkLoadPerThreadNC;
-      cIndex += InputBlockProperties::c_stride;
-    }
-  }
-  template <typename InputBlockProperties, StorageIndex TileSizeDimNC>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::pair<StorageIndex, StorageIndex> local_id_extract(
-      const StorageIndex &linearLocalThreadId) {
-    const StorageIndex localThreadNC =
-        (InputBlockProperties::is_coalesced_layout)
-            ? linearLocalThreadId % (TileSizeDimNC / InputBlockProperties::nc_stride)
-            : linearLocalThreadId / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
-    const StorageIndex localThreadC =
-        (InputBlockProperties::is_coalesced_layout)
-            ? linearLocalThreadId / (TileSizeDimNC / InputBlockProperties::nc_stride)
-            : linearLocalThreadId % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
-    return std::pair<StorageIndex, StorageIndex>(localThreadNC, localThreadC);
-  }
-
-  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      typename ::Eigen::internal::enable_if<db && ctp == contraction_type::local>::type
-      sync_mem(const cl::sycl::nd_item<1> &, bool &db_offset) noexcept {
-    db_offset = !db_offset;
-  }
-
-  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      typename ::Eigen::internal::enable_if<!db && ctp == contraction_type::local>::type
-      sync_mem(const cl::sycl::nd_item<1> &itemID, bool &) noexcept {
-    itemID.barrier(cl::sycl::access::fence_space::local_space);
-  }
-
-  template <contraction_type ctp = contraction_tp>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      typename ::Eigen::internal::enable_if<ctp == contraction_type::no_local>::type
-      sync_mem(const cl::sycl::nd_item<1> &, bool &) noexcept {
-    return;
-  }
-
-  template <bool need_sync, contraction_type ctp = contraction_tp>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      typename ::Eigen::internal::enable_if<need_sync && ctp == contraction_type::no_local>::type
-      sync_thread(const cl::sycl::nd_item<1> &
-#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
-                      itemID
-#endif
-                  ) noexcept {
-#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
-    itemID.barrier(cl::sycl::access::fence_spacce::local_space);
-#else
-    return;
-#endif
-  }
-  template <bool need_sync, contraction_type ctp = contraction_tp>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      typename ::Eigen::internal::enable_if<need_sync && ctp == contraction_type::local>::type
-      sync_thread(const cl::sycl::nd_item<1> &itemID) {
-    itemID.barrier(cl::sycl::access::fence_space::local_space);
-  }
-  template <bool need_sync>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<!need_sync>::type sync_thread(
-      const cl::sycl::nd_item<1> &) {
-    return;
-  }
-
-  template <bool is_internal_block>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_tile_per_panel(const cl::sycl::nd_item<1> &itemID,
-                                                                    ThreadProperties<StorageIndex> &thread_properties,
-                                                                    TiledMemory &tiled_input_block,
-                                                                    PacketReturnType *privateRes, bool &db_offset) {
-    // Tiling the Rhs block from global to local memory
-    extract_block<RHSBlockProperties, is_internal_block>(
-        rhs, tiled_input_block.rhs_scratch_extract.ptr + (db_offset * Properties::TileSizeDimK * LSDR),
-        tiled_input_block.rhs_extract_index,
-        contraction_tp == contraction_type::local ? thread_properties.nGroupOffset : thread_properties.nGlobalOffset,
-        thread_properties.kGroupOffset - thread_properties.kSize);
-
-    sync_thread<contraction_tp == contraction_type::no_local>(itemID);
-
-    // Tiling the Lhs block from global to local memory
-    extract_block<LHSBlockProperties, is_internal_block>(
-        lhs, tiled_input_block.lhs_scratch_extract.ptr + (db_offset * LSDL * Properties::TileSizeDimK),
-        tiled_input_block.lhs_extract_index,
-        contraction_tp == contraction_type::local ? thread_properties.mGroupOffset : thread_properties.mGlobalOffset,
-        thread_properties.kGroupOffset - thread_properties.kSize);
-
-    // itemID.barrier(cl::sycl::access::fence_space::local_space);
-    sync_thread<contraction_tp == contraction_type::local>(itemID);
-    // switch to compute mede
-    StorageIndex lhs_offset = (db_offset * LSDL * Properties::TileSizeDimK);
-    StorageIndex rhs_offset = (db_offset * Properties::TileSizeDimK * LSDR);
-    // Loop over the values of a single tile
-    for (StorageIndex k = 0; k < Properties::TileSizeDimK; k++) {
-      compute_block_per_tile(tiled_input_block.lhs_scratch_ptr_compute + lhs_offset,
-                             tiled_input_block.rhs_scratch_ptr_compute + rhs_offset, privateRes);
-      lhs_offset += LSDL;
-      rhs_offset += LSDR;
-    }
-    // computing the K index for the next tile
-    thread_properties.kSize -= Properties::TileSizeDimK;
-    sync_mem(itemID, db_offset);
-  }
-
-  // when local memory is available the following compute_panel will be enabled
-  template <bool is_internal_block, typename OutPtr>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(const cl::sycl::nd_item<1> &itemID,
-                                                           ThreadProperties<StorageIndex> &thread_properties,
-                                                           OutPtr out_ptr) {
-    auto tiled_input_block = TiledMemory{thread_properties, scratch.get_pointer()};
-    // Allocate register space
-    PacketReturnType privateRes[Properties::WorkLoadPerThreadM * Properties::WorkLoadPerThreadN / PacketSize] = {
-        PacketReturnType{0}};
-    bool db_offset = 0;
-
-    while (thread_properties.kSize >= Properties::TileSizeDimK) {
-      compute_tile_per_panel<is_internal_block>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
-    }
-    if (thread_properties.kSize > 0) {
-      compute_tile_per_panel<false>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
-    }
-
-    // Storing the final results in the output
-    store<is_internal_block,
-          contraction_tp == contraction_type::local ? static_cast<StorageIndex>(1) : RHSBlockProperties::nc_stride>(
-        out_ptr + thread_properties.nGlobalOffset * triple_dim.M, privateRes, thread_properties.mGlobalOffset,
-        thread_properties.nGlobalOffset);
-  }
-  // When local memory is available the following extract_block will be enabled
-  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename Local,
-            contraction_type contract_tp = contraction_tp>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-      typename ::Eigen::internal::enable_if<contract_tp == contraction_type::local>::type
-      extract_block(const Input &inpt, Local local_ptr, const std::pair<StorageIndex, StorageIndex>& local_index,
-                    const StorageIndex &ncOffset, const StorageIndex cOffset) {
-    EIGEN_CONSTEXPR StorageIndex TileSizeDimNC =
-        InputBlockProperties::is_rhs ? Properties::TileSizeDimN : Properties::TileSizeDimM;
-    EIGEN_CONSTEXPR StorageIndex LoadPerThread =
-        InputBlockProperties::is_rhs ? Properties::LoadPerThreadRhs : Properties::LoadPerThreadLhs;
-    EIGEN_CONSTEXPR StorageIndex LSD = InputBlockProperties::is_rhs ? LSDR : LSDL;
-    static_assert(((LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride) == 0) &&
-                   (LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride) == 0)),
-                  " LocalOffset must be divisable by stride");
-    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;
-    StorageIndex localThreadNC = local_index.first;
-    StorageIndex localThreadC = local_index.second;
-    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
-      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
-              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
-    };
-    EIGEN_UNROLL_LOOP
-    for (StorageIndex lPT = 0; lPT < LoadPerThread / InputBlockProperties::elements_per_access; lPT++) {
-      const StorageIndex CIndex = cOffset + (InputBlockProperties::c_stride * localThreadC);
-      const StorageIndex NCIndex = ncOffset + (InputBlockProperties::nc_stride * localThreadNC);
-      const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
-      if (check_boundary<is_internal_block>(chk_bound(CIndex, NCIndex))) {
-        auto val =
-            read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
-                 InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, NCIndex, CIndex, ld);
-        write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
-            val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
-                     (InputBlockProperties::c_stride * localThreadC * LSD));
-      } else {
-        EIGEN_UNROLL_LOOP
-        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
-          const StorageIndex nCInd = NCIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
-          const StorageIndex cInd = CIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
-          OutScalar val =
-              (nCInd < NC && cInd < triple_dim.K)
-                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
-                        inpt, nCInd, cInd, ld)
-                  : OutScalar(0);
-
-          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
-              val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
-                       (InputBlockProperties::is_coalesced_layout ? i : 0) +
-                       ((InputBlockProperties::c_stride * localThreadC +
-                         (InputBlockProperties::is_coalesced_layout ? 0 : i)) *
-                        LSD));
-        }
-      }
-      localThreadNC += (InputBlockProperties::is_coalesced_layout)
-                           ? LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride)
-                           : LocalOffset / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
-      localThreadC += (InputBlockProperties::is_coalesced_layout)
-                          ? LocalOffset / (TileSizeDimNC / InputBlockProperties::nc_stride)
-                          : LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
-    }
-  }
-};
-
-#ifndef EIGEN_SYCL_DISABLE_GEMV
-
-/*!
- * \brief GeneralVectorTensor is a template class that provides Tensor -vector contraction operation, which is a special
- * case of Tensor Tensor contraction.
- *
- * \tparam OutScalar: determines the output scalar type
- *
- * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
- * (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
- *
- * \tparam VectorMapper: determines the tensor contraction mapper for the vector input (can be lhs or rhs)
- *
- * \tparam TensorMapper: determines the tensor contraction mapper for the tensor input (can be lhs or rhs)
- *
- * \tparam StorageIndex: determines the StorageIndex Type
- *
- * \tparam Properties: determines the Contraction Panel properties
- *
- * \tparam KFactor: determines the number of elements in K dimension in a Tile
- *
- * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
- *
- * \tparam is_lhs_vec: determines whether lhs is a vector or rhs is a vector
- *
- * \tparam IsFinal: determine if this is the final kernel. If so, the result will be written in a final output.
- * Otherwise, the result of contraction will be written iin a temporary buffer.
- *
- * \param scratch: determines the local memory containing the vector block for each work-group
- *
- * \param vec: determines the vector input (tensor mapper)
- *
- * \param mat: determines the tensor input (tensor mapper)
- *
- * \param out_res: determines the output vector containing the contraction result
- *
- * \param nonContractGroupSize: a logical number determining the number of work-group for non-contracting dimension
- *
- * \param nonContractDim: determines the size of non contracting dimension for the flattened tensor
- *
- * \param contractDim: determines the size of non contracting dimension for the flattened tensor
- *
- */
-template <typename OutScalar, typename OutAccessor, typename VectorMapper, typename TensorMapper, typename StorageIndex,
-          typename Properties, StorageIndex KFactor, bool Vectorizable, bool is_lhs_vec, bool IsFinal>
-struct GeneralVectorTensor {
-  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
-      PacketReturnType;
-  static EIGEN_CONSTEXPR int PacketSize =
-      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
-  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
-
-  static EIGEN_CONSTEXPR StorageIndex OutScratchOffset =
-      KFactor * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;
-
-  // Since the access layout for a vector can always be coalesced, when LHS is a vector, we pass false and false to make
-  // sure that the !^ is true When RHS is a vector, we pass true and true to make sure that the !^ is true.
-  typedef BlockProperties<is_lhs_vec ? false : true, is_lhs_vec ? false : true, Vectorizable, PacketReturnType>
-      VecBlockProperties;
-
-  Scratch scratch;
-  const VectorMapper vec;
-  const TensorMapper mat;
-  OutAccessor out_res;
-  const StorageIndex nonContractGroupSize;
-  const StorageIndex nonContractDim;
-  const StorageIndex contractDim;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE GeneralVectorTensor(Scratch scratch_, const VectorMapper vec_,
-                                                            const TensorMapper mat_, OutAccessor out_res_,
-                                                            const StorageIndex nonContractGroupSize_,
-                                                            const StorageIndex nonContractDim_,
-                                                            const StorageIndex contractDim_)
-      : scratch(scratch_),
-        vec(vec_),
-        mat(mat_),
-        out_res(out_res_),
-        nonContractGroupSize(nonContractGroupSize_),
-        nonContractDim(nonContractDim_),
-        contractDim(contractDim_) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
-    auto scratch_ptr = scratch.get_pointer();
-    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
-    StorageIndex nonContractId = is_lhs_vec ? linearLocalThreadId / Properties::LocalThreadSizeC
-                                            : linearLocalThreadId % Properties::LocalThreadSizeNC;
-    StorageIndex contractId = is_lhs_vec ? linearLocalThreadId % Properties::LocalThreadSizeC
-                                         : linearLocalThreadId / Properties::LocalThreadSizeNC;
-    const StorageIndex cGroupSize = itemID.get_group_range(0) / nonContractGroupSize;
-    const StorageIndex nonContractGroupId =
-        is_lhs_vec ? itemID.get_group(0) / cGroupSize : itemID.get_group(0) % nonContractGroupSize;
-    const StorageIndex contractGroupId =
-        is_lhs_vec ? itemID.get_group(0) % cGroupSize : itemID.get_group(0) / nonContractGroupSize;
-    auto out_ptr = out_res.get_pointer() + (IsFinal ? 0 : contractGroupId * nonContractDim);
-
-    const StorageIndex nonContractGroupOffset = nonContractGroupId * Properties::TileSizeDimNC;
-    const StorageIndex contractGroupOffset = contractGroupId * Properties::TileSizeDimC;
-    auto outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
-    const StorageIndex globalNonContractDimOffset = nonContractGroupOffset + nonContractId;
-    const StorageIndex globalContractDimOffset = contractGroupOffset + contractId;
-    auto local_output = scratch_ptr + OutScratchOffset;
-    const bool is_internal = nonContractDim - nonContractGroupOffset >= Properties::TileSizeDimNC &&
-                             contractDim - contractGroupOffset >= Properties::TileSizeDimC;
-    is_internal
-        ? compute_panel<true>(itemID, vec, mat, local_output, out_ptr,
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
-                              scratch_ptr, contractGroupOffset,
-#endif
-                              nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
-                              nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex)
-        : compute_panel<false>(itemID, vec, mat, local_output, out_ptr,
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
-                               scratch_ptr, contractGroupOffset,
-#endif
-                               nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
-                               nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex);
-  }
-  template <bool is_internal_block, typename OutPtr>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(
-      const cl::sycl::nd_item<1> &itemID, const VectorMapper &vec, const TensorMapper &mat, OutScalar *local_output,
-      OutPtr out_ptr,
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
-      OutScalar *scratch_ptr, const StorageIndex contractGroupOffset,
-#endif
-      const StorageIndex nonContractGroupOffset, const StorageIndex linearLocalThreadId, StorageIndex contractDim,
-      StorageIndex nonContractDim, StorageIndex contractId, StorageIndex nonContractId,
-      StorageIndex globalContractDimOffset, StorageIndex globalNonContractDimOffset, StorageIndex outScratchIndex) {
-    OutScalar outScalar[Properties::WorkLoadPerThreadNC] = {OutScalar(0)};
-    // Reading the vector
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
-    const StorageIndex vectorOffset = contractGroupOffset + linearLocalThreadId;
-    extract_block<VecBlockProperties, is_internal_block, KFactor,
-                  Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC>(vec, scratch_ptr, linearLocalThreadId,
-                                                                                vectorOffset, contractDim);
-
-    itemID.barrier(cl::sycl::access::fence_space::local_space);
-    auto in_scratch_ptr = scratch_ptr + contractId;
-#endif
-
-    StorageIndex privateOffsetC = 0;
-    EIGEN_UNROLL_LOOP
-    for (StorageIndex i = 0; i < Properties::WorkLoadPerThreadC; i++) {
-      StorageIndex privateOffsetNC = 0;
-      bool contract_conds = ((globalContractDimOffset + privateOffsetC) < contractDim);
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
-      auto vecScalar = *in_scratch_ptr;
-#else
-      auto vecScalar = (check_boundary<is_internal_block>(contract_conds))
-                           ? vec(is_lhs_vec ? StorageIndex(0) : globalContractDimOffset + privateOffsetC,
-                                 is_lhs_vec ? globalContractDimOffset + privateOffsetC : StorageIndex(0))
-                           : OutScalar(0);
-#endif
-      EIGEN_UNROLL_LOOP
-      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
-        auto matScalar = (check_boundary<is_internal_block>(
-                             contract_conds && ((globalNonContractDimOffset + privateOffsetNC) < nonContractDim)))
-                             ? mat(is_lhs_vec ? globalContractDimOffset + privateOffsetC
-                                              : globalNonContractDimOffset + privateOffsetNC,
-                                   is_lhs_vec ? globalNonContractDimOffset + privateOffsetNC
-                                              : globalContractDimOffset + privateOffsetC)
-                             : OutScalar(0);
-
-        outScalar[j] = cl::sycl::mad(matScalar, vecScalar, outScalar[j]);
-        privateOffsetNC += Properties::LocalThreadSizeNC;
-      }
-      privateOffsetC += Properties::LocalThreadSizeC;
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
-      in_scratch_ptr += Properties::LocalThreadSizeC;
-#endif
-    }
-
-    auto out_scratch_ptr = local_output + outScratchIndex;
-    // Each block of 16*16 element in shared memory should reduce to 16*1
-    EIGEN_UNROLL_LOOP
-    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
-      *out_scratch_ptr = outScalar[j];
-
-      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
-    }
-    if (is_lhs_vec) {
-      nonContractId = linearLocalThreadId % Properties::LocalThreadSizeNC;
-      contractId = linearLocalThreadId / Properties::LocalThreadSizeNC;
-      outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
-    }
-
-    out_scratch_ptr = local_output + outScratchIndex;
-    EIGEN_UNROLL_LOOP
-    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
-      EIGEN_UNROLL_LOOP
-      for (StorageIndex offset = Properties::LocalThreadSizeC >> 1; offset > 0; offset >>= 1) {
-        itemID.barrier(cl::sycl::access::fence_space::local_space);
-        if (contractId < offset) {
-          StorageIndex myNeigbourId = (Properties::LocalThreadSizeNC * offset);
-          *out_scratch_ptr += out_scratch_ptr[myNeigbourId];
-        }
-      }
-      // moving to next 16 by 16 block
-      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
-    }
-
-    if (contractId == 0) {
-      out_scratch_ptr = local_output + nonContractId;
-      StorageIndex global_final_offset = nonContractGroupOffset + nonContractId;
-      out_ptr += global_final_offset;
-      EIGEN_UNROLL_LOOP
-      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
-        if (check_boundary<is_internal_block>(global_final_offset < nonContractDim)) {
-          auto res = *out_scratch_ptr;
-
-          *out_ptr = res;
-          out_ptr += Properties::LocalThreadSizeNC;
-        }
-        // moving to next 16 by 16 block to ge the next 16 reduced elements
-        out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
-        if (!(is_internal_block)) global_final_offset += Properties::LocalThreadSizeNC;
-      }
-    }
-  }
-
-  template <typename InputBlockProperties, bool is_internal_block, int CFactor, int GroupSize, typename Input,
-            typename Local>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_block(const Input &inpt, Local *local_ptr,
-                                                                  const StorageIndex &linearLocalThreadId,
-                                                                  const StorageIndex &cOffset, const StorageIndex &C) {
-    local_ptr += InputBlockProperties::c_stride * linearLocalThreadId;
-    StorageIndex cIndex = cOffset;
-    for (StorageIndex cId = 0; cId < CFactor / InputBlockProperties::c_stride; cId++) {
-      if (check_boundary<is_internal_block>(cIndex + InputBlockProperties::c_stride - 1 < C)) {
-        auto val = read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
-                        InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, StorageIndex(0),
-                                                                                              cIndex, StorageIndex(1));
-        write<StorageIndex, 1, data_source::local_mem>(val, local_ptr);
-      } else {
-        EIGEN_UNROLL_LOOP
-        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
-          OutScalar val =
-              (cIndex + i < C)
-                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
-                        inpt, StorageIndex(0), cIndex + i, StorageIndex(1))
-                  : OutScalar(0);
-          write<StorageIndex, 1, data_source::local_mem>(val, local_ptr + i);
-        }
-      }
-      local_ptr += InputBlockProperties::c_stride * GroupSize;
-      cIndex += InputBlockProperties::c_stride * GroupSize;
-    }
-  }
-};
-#endif
-
-#ifndef EIGEN_SYCL_DISABLE_SCALAR
-
-/*!
- * \brief GeneralScalarContraction is a template class that provides the scalar value of Tensor -Tensor contraction
- * operation, when all the dimensions are contracting dimensions. This Kernel reduces two tensors to an scalar
- *
- * \tparam OutScalar: determines the output scalar type
- *
- * \tparam LhsScalar: determines the left-hand-side scalar type
- *
- * \tparam RhsScalar: determines the right-hand-side scalar type
- *
- * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
- * (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
- *
- * \tparam LhsMapper: determines the tensor contraction mapper type for left-hand-side matrix
- *
- * \tparam RhsMapper: determines the tensor contraction mapper type for right-hand-side matrix
- *
- * \tparam StorageIndex: determines the StorageIndex Type
- *
- * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
- *
- * \param scratch: local memory containing tiles of LHS and RHS tensors for each work-group
- *
- * \param lhs: determines the left-hand-side flattened tensor (tensor mapper)
- *
- * \param rhs: determines the right-hand-side flattened tensor (tensor mapper)
- *
- * \param out_res: determines the output tensor containing the contraction result
- *
- * \param rng: determins the total input data size
- */
-template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
-          typename RhsMapper, typename StorageIndex, bool Vectorizable>
-struct GeneralScalarContraction {
-  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
-  Scratch scratch;
-  const LhsMapper lhs;
-  const RhsMapper rhs;
-  OutAccessor out_res;
-  const StorageIndex rng;
-
-  EIGEN_DEVICE_FUNC
-  GeneralScalarContraction(Scratch scratch_, const LhsMapper lhs_, const RhsMapper rhs_, OutAccessor out_res_,
-                           const StorageIndex rng_)
-      : scratch(scratch_), lhs(lhs_), rhs(rhs_), out_res(out_res_), rng(rng_) {}
-
-  EIGEN_DEVICE_FUNC void operator()(cl::sycl::nd_item<1> itemID) {
-    auto out_ptr = out_res.get_pointer();
-    auto scratch_ptr = scratch.get_pointer().get();
-
-    StorageIndex globalid = itemID.get_global_id(0);
-    StorageIndex localid = itemID.get_local_id(0);
-    OutScalar accumulator = OutScalar(0);
-    for (StorageIndex i = globalid; i < rng; i += itemID.get_global_range(0)) {
-      accumulator = cl::sycl::mad(lhs(0, i), rhs(i, 0), accumulator);
-    }
-    auto out_scratch_ptr = scratch_ptr + localid;
-    *out_scratch_ptr = accumulator;
-    for (StorageIndex offset = itemID.get_local_range(0) >> 1; offset > 0; offset >>= 1) {
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-      if (localid < offset) {
-        *out_scratch_ptr = (accumulator += out_scratch_ptr[offset]);
-      }
-    }
-    if (localid == 0) {
-      out_ptr[itemID.get_group(0)] = accumulator;
-    }
-  }
-};
-#endif
-
-}  // namespace internal
-}  // namespace TensorSycl
-
-template <typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
-struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>,
-                       Eigen::SyclDevice>
-    : public TensorContractionEvaluatorBase<TensorEvaluator<
-          const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Eigen::SyclDevice>> {
-  static_assert(std::is_same<OutputKernelType, const NoOpOutputKernel>::value,
-                "SYCL tensor contraction does not support output kernels.");
-
-  typedef Eigen::SyclDevice Device;
-
-  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
-  typedef TensorContractionEvaluatorBase<Self> Base;
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef typename XprType::Index StorageIndex;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef typename Base::Storage Storage;
-  typedef typename Base::EvaluatorPointerType EvaluatorPointerType;
-  struct TripleDim {
-    const StorageIndex M;
-    const StorageIndex N;
-    const StorageIndex K;
-    TripleDim(const StorageIndex M_, const StorageIndex N_, const StorageIndex K_) : M(M_), N(N_), K(K_) {}
-  };
-  enum {
-    Layout = TensorEvaluator<LeftArgType, Device>::Layout,
-    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess = false,
-  };
-
-  static EIGEN_CONSTEXPR int LDims = Base::LDims;
-  static EIGEN_CONSTEXPR int RDims = Base::RDims;
-  static EIGEN_CONSTEXPR int ContractDims = Base::ContractDims;
-
-  typedef array<StorageIndex, LDims> left_dim_mapper_t;
-  typedef array<StorageIndex, RDims> right_dim_mapper_t;
-
-  typedef array<StorageIndex, ContractDims> contract_t;
-  typedef array<StorageIndex, LDims - ContractDims> left_nocontract_t;
-  typedef array<StorageIndex, RDims - ContractDims> right_nocontract_t;
-
-  static const int NumDims = LDims + RDims - 2 * ContractDims;
-
-  typedef DSizes<StorageIndex, NumDims> Dimensions;
-
-  typedef TensorEvaluator<typename Base::EvalLeftArgType, Device> LeftEvaluator;
-  typedef TensorEvaluator<typename Base::EvalRightArgType, Device> RightEvaluator;
-  typedef typename Eigen::internal::remove_const<typename LeftEvaluator::CoeffReturnType>::type LhsScalar;
-  typedef typename Eigen::internal::remove_const<typename RightEvaluator::CoeffReturnType>::type RhsScalar;
-
-  typedef typename LeftEvaluator::Dimensions LeftDimensions;
-  typedef typename RightEvaluator::Dimensions RightDimensions;
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered>
-  struct input_mapper_propertis {
-    static EIGEN_CONSTEXPR bool is_lhs_matrix = (LDims == 2 && ContractDims == 1) || lhs_inner_dim_contiguous;
-    static EIGEN_CONSTEXPR bool is_rhs_matrix =
-        (RDims == 2 && ContractDims == 1) || (rhs_inner_dim_contiguous && !rhs_inner_dim_reordered);
-  };
-
-  TensorEvaluator(const XprType &op, const Device &device) : Base(op, device) {}
-
-  // We need to redefine this method to make nvcc happy
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(typename Base::EvaluatorPointerType data) {
-    this->m_leftImpl.evalSubExprsIfNeeded(NULL);
-    this->m_rightImpl.evalSubExprsIfNeeded(NULL);
-    if (!data) {
-      this->m_result = this->m_device.get(
-          static_cast<Scalar *>(this->m_device.allocate_temp(this->dimensions().TotalSize() * sizeof(Scalar))));
-      data = this->m_result;
-    }
-    evalToSycl(data);
-    return (this->m_result != NULL);
-  }
-  const Eigen::SyclDevice &device() const { return this->m_device; }
-  void evalToSycl(typename Base::EvaluatorPointerType buffer) const {
-    if (this->m_lhs_inner_dim_contiguous) {
-      if (this->m_rhs_inner_dim_contiguous) {
-        if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<true, true, true, Unaligned>(buffer);
-        } else {
-          evalTyped<true, true, false, Unaligned>(buffer);
-        }
-      } else {
-        if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<true, false, true, Unaligned>(buffer);
-        } else {
-          evalTyped<true, false, false, Unaligned>(buffer);
-        }
-      }
-    } else {
-      if (this->m_rhs_inner_dim_contiguous) {
-        if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<false, true, true, Unaligned>(buffer);
-        } else {
-          evalTyped<false, true, false, Unaligned>(buffer);
-        }
-      } else {
-        if (this->m_rhs_inner_dim_reordered) {
-          evalTyped<false, false, true, Unaligned>(buffer);
-        } else {
-          evalTyped<false, false, false, Unaligned>(buffer);
-        }
-      }
-    }
-  }
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
-  void evalTyped(typename Base::EvaluatorPointerType buffer) const {
-    const auto triple_dim = TripleDim{this->m_i_size, this->m_j_size, this->m_k_size};
-    typedef internal::TensorContractionInputMapper<
-        LhsScalar, StorageIndex, internal::Lhs, LeftEvaluator, left_nocontract_t, contract_t,
-        PacketType<CoeffReturnType, Device>::size, lhs_inner_dim_contiguous, false, Unaligned, MakeSYCLPointer>
-        LhsMapper;
-
-    typedef internal::TensorContractionInputMapper<RhsScalar, StorageIndex, internal::Rhs, RightEvaluator,
-                                                   right_nocontract_t, contract_t,
-                                                   PacketType<CoeffReturnType, Device>::size, rhs_inner_dim_contiguous,
-                                                   rhs_inner_dim_reordered, Unaligned, MakeSYCLPointer>
-        RhsMapper;
-
-    // initialize data mappers
-    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
-                  this->m_left_contracting_strides, this->m_k_strides);
-
-    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
-                  this->m_right_contracting_strides, this->m_k_strides);
-
-#ifndef EIGEN_SYCL_DISABLE_SCALAR
-    if (triple_dim.M == 1 && triple_dim.N == 1) {
-      launchSC(buffer, lhs, rhs, triple_dim.K);
-    } else
-#endif
-#ifndef EIGEN_SYCL_DISABLE_GEMV
-        if (triple_dim.M != 1 && triple_dim.N == 1) {
-      LaunchVT<false>(buffer, rhs, lhs, triple_dim.M, triple_dim.K);
-    } else if (triple_dim.M == 1 && triple_dim.N != 1) {
-      LaunchVT<true>(buffer, lhs, rhs, triple_dim.N, triple_dim.K);
-    } else  // This is equivalent of if (m!=1 && n!=1)
-#endif
-    {
-      typedef input_mapper_propertis<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered>
-          inpt_mapper_properties;
-#ifndef EIGEN_SYCL_DISABLE_SKINNY
-      bool skinny = false;
-      auto platform_name = this->device().getPlatformName();
-      // This is based on empirical calculation for AMD r9-nano and Fiji
-      if (platform_name.find("AMD") == 0) {
-        skinny = (triple_dim.M < triple_dim.K || triple_dim.N < triple_dim.K) &&
-                 ((triple_dim.M < 1024 && triple_dim.N < 1024) ||
-                  (uint64_t(triple_dim.M * triple_dim.N) < uint64_t(triple_dim.K)));
-      } else {
-        skinny = (((std::max(triple_dim.K, triple_dim.N) / std::min(triple_dim.K, triple_dim.N)) > 100) ||
-                  ((std::max(triple_dim.K, triple_dim.M) / std::min(triple_dim.K, triple_dim.M)) > 100) ||
-                  ((std::max(triple_dim.N, triple_dim.M) / std::min(triple_dim.N, triple_dim.M)) > 100));
-      }
-      if (skinny)
-        adjustTT<true, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
-      else
-#endif  // EIGEN_SYCL_DISABLE_SKINNY
-        adjustTT<false, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
-    }
-  }
-
-  template <bool skinny, typename input_mapper_properties, typename LhsMapper, typename RhsMapper>
-  void EIGEN_ALWAYS_INLINE adjustTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
-                                    const TripleDim &triple_dim) const {
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
-    if (device().has_local_memory()) {
-      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 16> PanelParameters;
-      launchTT<TensorSycl::internal::contraction_type::local, skinny, input_mapper_properties, PanelParameters>(
-          buffer, lhs, rhs, triple_dim);
-    }
-#endif
-#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF
-    if (!(device().has_local_memory())) {
-      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 4> PanelParameters;
-      launchTT<TensorSycl::internal::contraction_type::no_local, skinny, input_mapper_properties, PanelParameters>(
-          buffer, lhs, rhs, triple_dim);
-    }
-#endif
-  }
-
-  template <TensorSycl::internal::contraction_type ct, bool skinny, typename input_mapper_properties,
-            typename Properties, typename LhsMapper, typename RhsMapper>
-  void launchTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
-                const TripleDim &triple_dim) const {
-    const StorageIndex roundUpM = Eigen::TensorSycl::internal::roundUp(triple_dim.M, Properties::TileSizeDimM);
-    const StorageIndex roundUpN = Eigen::TensorSycl::internal::roundUp(triple_dim.N, Properties::TileSizeDimN);
-    const StorageIndex groupSizeM = roundUpM / Properties::TileSizeDimM;
-    const StorageIndex groupSizeN = roundUpN / Properties::TileSizeDimN;
-
-    const StorageIndex roundUpK = Eigen::TensorSycl::internal::roundUp(triple_dim.K, Properties::TileSizeDimK);
-    StorageIndex totalTilesK = roundUpK / Properties::TileSizeDimK;
-    StorageIndex groupSizeK =
-        skinny
-            ? std::max(std::min(totalTilesK,
-                                (StorageIndex)(device().getPowerOfTwo(device().getNumSyclMultiProcessors(), true) * 4) /
-                                    (groupSizeM * groupSizeN)),
-                       StorageIndex(1))
-            : StorageIndex(1);
-
-    const StorageIndex numTilesPerGroup = Eigen::TensorSycl::internal::roundUp(totalTilesK, groupSizeK) / groupSizeK;
-
-    const StorageIndex totalGroupSize = groupSizeM * groupSizeN * groupSizeK;
-
-    const StorageIndex localRange = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;
-    const StorageIndex globalRange = totalGroupSize * localRange;
-
-    const StorageIndex scratchSize = (ct == TensorSycl::internal::contraction_type::local)
-                                         ? ((Properties::DoubleBuffer + 1) *
-                                            (Properties::TileSizeDimM + Properties::BC) * (Properties::TileSizeDimK)) +
-                                               ((Properties::DoubleBuffer + 1) * (Properties::TileSizeDimK) *
-                                                (Properties::TileSizeDimN + Properties::BC))
-                                         : StorageIndex(1);
-
-    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
-    if (groupSizeK == 1) {
-      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
-                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
-                                                            PacketAccess, input_mapper_properties, true, ct>
-          ContractKernelName;
-      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
-          lhs, rhs, buffer, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup, triple_dim);
-    } else {
-      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
-                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
-                                                            PacketAccess, input_mapper_properties, false, ct>
-          ContractKernelName;
-      CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
-          device().allocate_temp(triple_dim.M * triple_dim.N * groupSizeK * sizeof(CoeffReturnType)));
-      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
-
-      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
-          lhs, rhs, tmp_global_accessor, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup,
-          triple_dim);
-
-      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
-      auto op = Op();
-      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
-                                                               EvaluatorPointerType, Op>
-          ReductionKernel;
-
-      device().template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
-          tmp_global_accessor, buffer,
-          cl::sycl::nd_range<1>(cl::sycl::range<1>(StorageIndex(
-                                    Eigen::TensorSycl::internal::roundUp(triple_dim.M * triple_dim.N, localRange))),
-                                cl::sycl::range<1>(localRange)),
-          StorageIndex(1), op, StorageIndex(triple_dim.M * triple_dim.N), groupSizeK);
-
-      device().deallocate_temp(temp_pointer);
-    }
-  }
-
-#ifndef EIGEN_SYCL_DISABLE_GEMV
-  template <bool is_lhs_vec, typename VectorMapper, typename TensorMapper, typename StorageIndex>
-  void EIGEN_ALWAYS_INLINE LaunchVT(EvaluatorPointerType buffer, const VectorMapper &vec, const TensorMapper &mat,
-                                    StorageIndex NC, StorageIndex C) const {
-    const StorageIndex nonContractDim = NC;
-    EIGEN_CONSTEXPR StorageIndex NCFactor = 1;
-    EIGEN_CONSTEXPR StorageIndex CFactor = 1;
-    EIGEN_CONSTEXPR StorageIndex NCWindow = 16;
-    typedef Eigen::TensorSycl::internal::TVPanelSize<CoeffReturnType, StorageIndex, NCWindow, CFactor, NCFactor>
-        Properties;
-    const StorageIndex roundUpC = Eigen::TensorSycl::internal::roundUp(C, Properties::TileSizeDimC);
-    const StorageIndex cNumGroups = roundUpC / (Properties::LocalThreadSizeC * Properties::WorkLoadPerThreadC);
-    const StorageIndex roundUpNC = Eigen::TensorSycl::internal::roundUp(nonContractDim, Properties::TileSizeDimNC);
-    const StorageIndex nCNumGroups = roundUpNC / (Properties::LocalThreadSizeNC * Properties::WorkLoadPerThreadNC);
-    const StorageIndex globalRange =
-        (roundUpNC / (Properties::WorkLoadPerThreadNC)) * (roundUpC / (Properties::WorkLoadPerThreadC));
-    const StorageIndex localRange = Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC;
-    const StorageIndex scratchSize =
-        (Properties::WorkLoadPerThreadNC + CFactor) * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;
-    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
-    if (cNumGroups > 1) {
-      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
-                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
-                                                               is_lhs_vec, false>
-          ContractKernelName;
-      CoeffReturnType *temp_pointer =
-          static_cast<CoeffReturnType *>(device().allocate_temp(nonContractDim * cNumGroups * sizeof(CoeffReturnType)));
-      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
-
-      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
-          vec, mat, tmp_global_accessor, thread_range, scratchSize, nCNumGroups, nonContractDim, C);
-
-      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
-      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
-                                                               EvaluatorPointerType, Op>
-          ReductionKernel;
-
-      device().template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
-          tmp_global_accessor, buffer,
-          cl::sycl::nd_range<1>(cl::sycl::range<1>(Eigen::TensorSycl::internal::roundUp(nonContractDim, localRange)),
-                                cl::sycl::range<1>(localRange)),
-          StorageIndex(1), Op(), nonContractDim, cNumGroups);
-
-      device().deallocate_temp(temp_pointer);
-    } else {
-      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
-                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
-                                                               is_lhs_vec, true>
-          ContractKernelName;
-      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
-          vec, mat, buffer, thread_range, scratchSize, nCNumGroups, nonContractDim, C);
-    }
-  }
-#endif
-
-#ifndef EIGEN_SYCL_DISABLE_SCALAR
-  template <typename LhsMapper, typename RhsMapper>
-  EIGEN_ALWAYS_INLINE void launchSC(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
-                                    StorageIndex K) const {
-    EIGEN_STATIC_ASSERT(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
-                          (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
-                        "The Local thread size must be a power of 2 for the reduction "
-                        "operation");
-    EIGEN_CONSTEXPR StorageIndex local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;
-
-    // Here we force the code not to be more than 2-step reduction: Our empirical research shows that if each thread
-    // reduces at least 512 elementss individually, we get better performance.
-    const StorageIndex num_work_group = ((K + (512 * local_range - 1)) / (512 * local_range) > 1 ? local_range : 1);
-    const StorageIndex global_range = num_work_group * local_range;
-
-    typedef Eigen::TensorSycl::internal::GeneralScalarContraction<
-        CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType, LhsMapper, RhsMapper, StorageIndex, false>
-        ContractKernelName;
-    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
-    if (num_work_group > 1) {
-      CoeffReturnType *temp_pointer =
-          static_cast<CoeffReturnType *>(device().allocate_temp(num_work_group * sizeof(CoeffReturnType)));
-      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
-      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, tmp_global_accessor,
-                                                                                    thread_range, local_range, K);
-      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
-      typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
-                                                          EvaluatorPointerType, StorageIndex, local_range>
-          GenericRKernel;
-      device().template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
-          tmp_global_accessor, buffer,
-          cl::sycl::nd_range<1>(cl::sycl::range<1>(local_range), cl::sycl::range<1>(local_range)), local_range, Op());
-
-      device().deallocate_temp(temp_pointer);
-    } else {
-      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, buffer, thread_range,
-                                                                                    local_range, K);
-    }
-  }
-#endif
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    this->m_leftImpl.cleanup();
-    this->m_rightImpl.cleanup();
-
-    if (this->m_result) {
-      this->m_device.deallocate_temp(this->m_result);
-      this->m_result = NULL;
-    }
-  }
-  // The placeholder accessors must bound to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    this->m_leftImpl.bind(cgh);
-    this->m_rightImpl.bind(cgh);
-    this->m_result.bind(cgh);
-  }
-};
-}  // namespace Eigen
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
deleted file mode 100644
index 21be6ea..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
+++ /dev/null
@@ -1,1679 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
-
-// evaluator for thread pool device
-#ifdef EIGEN_USE_THREADS
-
-namespace Eigen {
-
-template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
-struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> :
-    public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> > {
-
-  typedef ThreadPoolDevice Device;
-
-  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
-  typedef TensorContractionEvaluatorBase<Self> Base;
-
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-
-  enum {
-    Layout = TensorEvaluator<LeftArgType, Device>::Layout,
-  };
-
-  // Most of the code is assuming that both input tensors are ColMajor. If the
-  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
-  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
-  // will pretend B is LHS and A is RHS.
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
-  typedef typename internal::conditional<
-    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
-
-  static const int LDims =
-      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
-  static const int RDims =
-      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
-  static const int ContractDims = internal::array_size<Indices>::value;
-
-  typedef array<Index, LDims> left_dim_mapper_t;
-  typedef array<Index, RDims> right_dim_mapper_t;
-
-  typedef array<Index, ContractDims> contract_t;
-  typedef array<Index, LDims - ContractDims> left_nocontract_t;
-  typedef array<Index, RDims - ContractDims> right_nocontract_t;
-
-  static const int NumDims = LDims + RDims - 2 * ContractDims;
-
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  // typedefs needed in evalTo
-  typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
-  typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
-  typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
-
-  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
-  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
-
-  TensorEvaluator(const XprType& op, const Device& device) :
-      Base(op, device) {}
-
-  template <int Alignment>
-  void evalProduct(Scalar* buffer) const {
-    evalProductImpl<NoCallback, Alignment>(buffer, NoCallback());
-  }
-
-  template <typename EvalToCallback, int Alignment>
-  void evalProductAsync(Scalar* buffer, EvalToCallback done) const {
-    evalProductImpl<EvalToCallback, Alignment>(buffer, std::move(done));
-  }
-
-  template <typename DoneCallback, int Alignment>
-  void evalProductImpl(Scalar* buffer, DoneCallback done) const {
-    // This function computes a lot of heuristics in multiple steps, and it
-    // also has multiple exit points. To keep it sane, readable and all in one
-    // place, sync/async execution decision is made at runtime at the very end.
-    //
-    // (1) In sync mode we allocate Context on the stack, submit computations
-    //     to the device thread pool, and block on a barrier until it is
-    //     completed.
-    //
-    // (2) In async mode we allocate Context on the heap, and after all tasks
-    //     are finished, we call provided the done callback, and delete a
-    //     context from the heap.
-    //
-    // (*) EvalParallelContext & EvalShardedByInnerDimContext owns all the state
-    // and temporary buffers, requried for executing the tensor contraction.
-    // They are responsible for cleaning it up after contraction is done.
-    static const bool IsEvalInSyncMode =
-        std::is_same<DoneCallback, NoCallback>::value;
-
-    const Index m = this->m_i_size;
-    const Index n = this->m_j_size;
-    const Index k = this->m_k_size;
-    if (m == 0 || n == 0 || k == 0) return;
-
-    // Compute a set of algorithm parameters:
-    // - kernel block sizes (bm, bn, bk)
-    // - task grain sizes (number of kernels executed per task: gm, gn)
-    // - number of threads
-    // - sharding by row/column
-    // - parallel packing or first lhs then rhs
-    // and some derived parameters:
-    // - number of tasks (nm, nn, nk)
-    // - number of kernels (nm0, nn0)
-    // Unfortunately, all these parameters are tightly interdependent.
-    // So in some cases we first compute approximate values, then compute other
-    // values based on these approximations and then refine the approximations.
-
-    // There are lots of heuristics here. There is some reasoning behind them,
-    // but ultimately they are just tuned on contraction benchmarks for
-    // different input configurations, thread counts and instruction sets.
-    // So feel free to question any of them.
-
-    // Compute whether we want to shard by row or by column.
-    // This is a first approximation, it will be refined later. Since we don't
-    // know number of threads yet we use 2, because what's we are most
-    // interested in at this point is whether it makes sense to use
-    // parallelization at all or not.
-    bool shard_by_col = shardByCol(m, n, 2);
-
-    // First approximation of kernel blocking sizes.
-    // Again, we don't know number of threads yet, so we use 2.
-    Index bm, bn, bk;
-    if (shard_by_col) {
-      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
-                                          internal::ShardByCol>
-          blocking(k, m, n, 2);
-      bm = blocking.mc();
-      bn = blocking.nc();
-      bk = blocking.kc();
-    } else {
-      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
-                                          internal::ShardByRow>
-          blocking(k, m, n, 2);
-      bm = blocking.mc();
-      bn = blocking.nc();
-      bk = blocking.kc();
-    }
-
-    // Compute optimal number of threads.
-    // Note: we use bk instead of k here because we are interested in amount of
-    // _parallelizable_ computations, and computations are not parallelizable
-    // across k dimension.
-    const TensorOpCost cost =
-        contractionCost(m, n, bm, bn, bk, shard_by_col, false);
-    int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(
-        static_cast<double>(n) * m, cost, this->m_device.numThreads());
-    int num_threads_by_k = numThreadsInnerDim(m, n, k);
-    if (shardByInnerDim(m, n, k, num_threads, num_threads_by_k)) {
-      // We are in the scenario where it is more effective to shard by the
-      // inner dimension.
-      if (IsEvalInSyncMode) {
-        EvalShardedByInnerDimContext<DoneCallback> ctx(
-            this, num_threads_by_k, buffer, m, n, k, std::move(done));
-        ctx.template run<Alignment>();
-      } else {
-        auto* ctx = new EvalShardedByInnerDimContext<DoneCallback>(
-            this, num_threads_by_k, buffer, m, n, k, std::move(done));
-        ctx->template runAsync<Alignment>();
-      }
-
-      return;
-    }
-
-    // TODO(dvyukov): this is a stop-gap to prevent regressions while the cost
-    // model is not tuned. Remove this when the cost model is tuned.
-    if (n == 1) num_threads = 1;
-
-    if (num_threads == 1) {
-      TENSOR_CONTRACTION_DISPATCH(this->template evalProductSequential,
-                                  Unaligned, (buffer));
-      if (!IsEvalInSyncMode) done();
-      return;
-    }
-
-    // Now that we know number of threads, recalculate sharding and blocking.
-    shard_by_col = shardByCol(m, n, num_threads);
-    if (shard_by_col) {
-      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
-                                          internal::ShardByCol>
-          blocking(k, m, n, num_threads);
-      bm = blocking.mc();
-      bn = blocking.nc();
-      bk = blocking.kc();
-    } else {
-      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
-                                          internal::ShardByRow>
-          blocking(k, m, n, num_threads);
-      bm = blocking.mc();
-      bn = blocking.nc();
-      bk = blocking.kc();
-    }
-
-    // Number of kernels for each dimension.
-    Index nm0 = divup(m, bm);
-    Index nn0 = divup(n, bn);
-    Index nk = divup(k, bk);
-
-    // Calculate task grain size (number of kernels executed per task).
-    // This task size coarsening serves two purposes:
-    // 1. It reduces per-task overheads including synchronization overheads.
-    // 2. It allows to use caches better (reuse the same packed rhs in several
-    // consecutive kernels).
-    Index gm = 1;
-    Index gn = 1;
-    // If we are sharding by column, then we prefer to reduce rows first.
-    if (shard_by_col) {
-      gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col);
-      gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col);
-    } else {
-      gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col);
-      gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col);
-    }
-    // Number of tasks in each dimension.
-    Index nm = divup(nm0, gm);
-    Index nn = divup(nn0, gn);
-
-    // If there is enough concurrency in the sharding dimension, we choose not
-    // to paralellize by the other dimension, and execute all kernels in sync
-    // mode. This reduces parallelism from the nm x nn down to nn
-    // (shard_by_col==true) or nm (shard_by_col==false).
-    const Index sharding_dim_tasks = shard_by_col ? nn : nm;
-    const int num_worker_threads = this->m_device.numThreadsInPool();
-
-    // With small number of threads we want to make sure that we do not reduce
-    // parallelism too much. With large number of threads we trade maximum
-    // parallelism for better memory locality.
-    const float oversharding_factor =
-        num_worker_threads <= 4  ? 8.0 :
-        num_worker_threads <= 8  ? 4.0 :
-        num_worker_threads <= 16 ? 2.0 :
-        num_worker_threads <= 32 ? 1.0 :
-        num_worker_threads <= 64 ? 0.8 : /* num_worker_threads > 64 */ 0.6;
-
-    const bool parallelize_by_sharding_dim_only =
-        sharding_dim_tasks >= oversharding_factor * num_worker_threads;
-
-    // Last by not least, decide whether we want to issue both lhs and rhs
-    // packing in parallel; or issue lhs packing first, and then issue rhs
-    // packing when lhs packing completes (for !shard_by_col lhs and rhs are
-    // swapped). Parallel packing allows more parallelism (for both packing and
-    // kernels), while sequential packing provides better locality (once
-    // a thread finishes rhs packing it proceed to kernels with that rhs).
-    // First, we are interested in parallel packing if there are few tasks.
-    bool parallel_pack = num_threads >= nm * nn;
-    // Also do parallel packing if all data fits into L2$.
-    if (m * bk * Index(sizeof(LhsScalar)) + n * bk * Index(sizeof(RhsScalar)) <=
-        l2CacheSize() * num_threads)
-      parallel_pack = true;
-    // But don't do it if we will use each rhs only once. Locality seems to be
-    // more important in this case.
-    if ((shard_by_col ? nm : nn) == 1) parallel_pack = false;
-    // Also don't get in the way of parallelize_by_sharding_dim_only
-    // optimization.
-    if (parallelize_by_sharding_dim_only) parallel_pack = false;
-
-    // TODO(ezhulnev): With if contexpr we don't need SyncEvalParallelContext.
-    if (IsEvalInSyncMode) {
-#define CONTEXT_ARGS                                                        \
-  (this, num_threads, buffer, m, n, k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, \
-   nn0, shard_by_col, parallel_pack, parallelize_by_sharding_dim_only,      \
-   NoCallback())                                                            \
-      .run()
-      TENSOR_CONTRACTION_DISPATCH(SyncEvalParallelContext, Alignment,
-                                  CONTEXT_ARGS);
-#undef CONTEXT_ARGS
-
-    } else {
-#define CONTEXT_ARGS                                                        \
-  (this, num_threads, buffer, m, n, k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, \
-   nn0, shard_by_col, parallel_pack, parallelize_by_sharding_dim_only,      \
-   std::move(done))
-      TENSOR_CONTRACTION_ASYNC_DISPATCH(EvalParallelContext, DoneCallback,
-                                        Alignment, CONTEXT_ARGS, run());
-#undef CONTEXT_ARGS
-    }
-  }
-
-  // ------------------------------------------------------------------------ //
-
-  // Dummy struct to represent an empty DoneCallback.
-
-  struct NoCallback {
-    void operator()() {
-      eigen_assert(false && "NoCallback should never be called");
-    }
-  };
-
-  // ------------------------------------------------------------------------ //
-
-  template <typename DoneCallback, typename Context>
-  class EvalParallelNotification;
-
-  // Synchronous evaluation notification that blocks caller thread in Wait().
-  template <typename Context>
-  class EvalParallelNotification<NoCallback, Context> {
-   public:
-    EvalParallelNotification(Context*, NoCallback) {}
-    void Notify() { done_.Notify(); }
-    void Wait() { done_.Wait(); }
-   private:
-    Eigen::Notification done_;
-  };
-
-  // Asynchronous evaluation notification that does not block in Wait().
-  template <typename DoneCallback, typename Context>
-  class EvalParallelNotification {
-   public:
-    EvalParallelNotification(Context* ctx, DoneCallback done)
-        : ctx_(ctx), done_(std::move(done)) {}
-
-    void Notify() {
-      // Make a copy of done callback, because it will be destructed when we
-      // will delete context in the next line (EvalParallelNotification is a
-      // data member of EvalParallelContext class).
-      DoneCallback done_copy = std::move(done_);
-
-      // Delete parallel evaluation context.
-      delete ctx_;
-
-      // Now safely call the done callback.
-      done_copy();
-    }
-
-    void Wait() {}
-
-   private:
-    Context* ctx_;
-    DoneCallback done_;
-  };
-
-  // Context orchestrates sync/async parallel contraction evaluation. When it is
-  // executed in asynchronous mode, it owns all the shared state that might be
-  // accessible by block packing and kernel tasks.
-
-  template <typename DoneCallback, bool lhs_inner_dim_contiguous,
-            bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered,
-            int Alignment>
-  class EvalParallelContext {
-   public:
-    typedef internal::TensorContractionInputMapper<
-        LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t,
-        contract_t, internal::packet_traits<LhsScalar>::size,
-        lhs_inner_dim_contiguous, false, Unaligned>
-        LhsMapper;
-    typedef internal::TensorContractionInputMapper<
-        RhsScalar, Index, internal::Rhs, RightEvaluator, right_nocontract_t,
-        contract_t, internal::packet_traits<RhsScalar>::size,
-        rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Unaligned>
-        RhsMapper;
-
-    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
-
-    typedef internal::TensorContractionKernel<
-        Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
-        TensorContractionKernel;
-
-    typedef typename TensorContractionKernel::LhsBlock LhsBlock;
-    typedef typename TensorContractionKernel::RhsBlock RhsBlock;
-    typedef typename TensorContractionKernel::BlockMemHandle BlockMemHandle;
-
-    EvalParallelContext(const Self* self, int num_threads, Scalar* buffer,
-                        Index tm, Index tn, Index tk, Index bm, Index bn,
-                        Index bk, Index nm, Index nn, Index nk, Index gm,
-                        Index gn, Index nm0, Index nn0, bool shard_by_col,
-                        bool parallel_pack,
-                        bool parallelize_by_sharding_dim_only,
-                        DoneCallback done)
-        : created_by_thread_id_(std::this_thread::get_id()),
-          done_(this, std::move(done)),
-          device_(self->m_device),
-          lhs_(self->m_leftImpl, self->m_left_nocontract_strides,
-               self->m_i_strides, self->m_left_contracting_strides,
-               self->m_k_strides),
-          rhs_(self->m_rightImpl, self->m_right_nocontract_strides,
-               self->m_j_strides, self->m_right_contracting_strides,
-               self->m_k_strides),
-          buffer_(buffer),
-          output_(buffer, tm),
-          output_kernel_(self->m_output_kernel),
-          tensor_contraction_params_(self->m_tensor_contraction_params),
-          num_threads_(num_threads),
-          shard_by_col_(shard_by_col),
-          parallel_pack_(parallel_pack),
-          parallelize_by_sharding_dim_only_(parallelize_by_sharding_dim_only),
-          m_(tm),
-          n_(tn),
-          k_(tk),
-          bm_(bm),
-          bn_(bn),
-          bk_(bk),
-          nm_(nm),
-          nn_(nn),
-          nk_(nk),
-          gm_(gm),
-          gn_(gn),
-          nm0_(nm0),
-          nn0_(nn0),
-          kernel_(m_, k_, n_, bm_, bk_, bn_),
-          num_thread_local_allocations_(0),
-          // We reserve 2X more capacity for a thread local values, than the
-          // number of threads in the pool to efficiently handle task stealing
-          // by threads that are not managed by the pool.
-          thread_local_capacity(2 * (parallelize_by_sharding_dim_only_
-                                         ? device_.numThreadsInPool()
-                                         : 0)),
-          // We will use only one of the Lhs/Rhs thread local storage depending
-          // on the shard_by_col value and we parallelize by sharding dim ONLY.
-          lhs_thread_local_blocks_(shard_by_col_ ? 0 : thread_local_capacity,
-                                   {*this}, {*this}),
-          rhs_thread_local_blocks_(shard_by_col_ ? thread_local_capacity : 0,
-                                   {*this}, {*this}) {
-      // These two options are mutually exclusive.
-      eigen_assert(!(parallel_pack && parallelize_by_sharding_dim_only));
-
-      for (Index x = 0; x < P; x++) {
-        // Normal number of notifications for k slice switch is
-        // nm_ + nn_ + nm_ * nn_. However, first P - 1 slices will receive only
-        // nm_ + nn_ notifications, because they will not receive notifications
-        // from preceding kernels.
-        state_switch_[x] =
-            x == 0
-                ? 1
-                : (parallel_pack_ ? nn_ + nm_ : (shard_by_col_ ? nn_ : nm_)) +
-                      (x == P - 1 ? nm_ * nn_ : 0);
-        state_packing_ready_[x] =
-            parallel_pack_ ? 0 : (shard_by_col_ ? nm_ : nn_);
-        state_kernel_[x] = new std::atomic<uint8_t>*[nm_];
-        for (Index m = 0; m < nm_; m++) {
-          state_kernel_[x][m] = new std::atomic<uint8_t>[nn_];
-          // Kernels generally receive 3 notifications (previous kernel + 2
-          // packing), but the first slice won't get notifications from previous
-          // kernels.
-          for (Index n = 0; n < nn_; n++)
-            state_kernel_[x][m][n].store(
-                (x == 0 ? 0 : 1) + (parallel_pack_ ? 2 : 1),
-                std::memory_order_relaxed);
-        }
-      }
-
-      // Allocate memory for packed rhs/lhs matrices.
-      packed_mem_ = kernel_.allocateSlices(            //
-          device_,                                     //
-          /*num_lhs=*/nm0_,                            //
-          /*num_rhs=*/nn0_,                            //
-          /*num_slices=*/std::min<Index>(nk_, P - 1),  //
-          packed_lhs_, packed_rhs_);
-
-      if (parallelize_by_sharding_dim_only_) {
-        const int num_worker_threads = device_.numThreadsInPool();
-
-        if (shard_by_col) {
-          can_use_thread_local_packed_ = new std::atomic<bool>[nn_];
-          for (int i = 0; i < nn_; ++i)
-            can_use_thread_local_packed_[i].store(true,
-                                                  std::memory_order_relaxed);
-
-          Index num_blocks = num_worker_threads * gn_;
-          thread_local_pre_alocated_mem_ = kernel_.allocateSlices(  //
-              device_,                                              //
-              /*num_lhs=*/0,                                        //
-              /*num_rhs=*/num_blocks,                               //
-              /*num_slices=*/1,                                     //
-              /*lhs_blocks=*/nullptr, &rhs_thread_local_pre_allocated_);
-
-        } else {
-          can_use_thread_local_packed_ = new std::atomic<bool>[nm_];
-          for (int i = 0; i < nm_; ++i)
-            can_use_thread_local_packed_[i].store(true,
-                                                  std::memory_order_relaxed);
-
-          Index num_blocks = num_worker_threads * gm_;
-          thread_local_pre_alocated_mem_ = kernel_.allocateSlices(  //
-              device_,                                              //
-              /*num_lhs=*/num_blocks,                               //
-              /*num_rhs=*/0,                                        //
-              /*num_slices=*/1, &lhs_thread_local_pre_allocated_,   //
-              /*rhs_blocks=*/nullptr);
-        }
-      }
-    }
-
-    ~EvalParallelContext() {
-      for (Index x = 0; x < P; x++) {
-        for (Index m = 0; m < nm_; m++) delete[] state_kernel_[x][m];
-        delete[] state_kernel_[x];
-      }
-      kernel_.deallocate(device_, packed_mem_);
-      if (parallelize_by_sharding_dim_only_) {
-        kernel_.deallocate(device_, thread_local_pre_alocated_mem_);
-        delete[] can_use_thread_local_packed_;
-      }
-    }
-
-    void run() {
-      // Kick off packing of the first slice.
-      signal_switch(0, 1);
-
-      // Wait for overall completion.
-      //
-      // If parallel evaluation is executed in async mode, this is a no-op, and
-      // Wait() will return immediately. In synchronous mode it will block the
-      // caller thread until it will receive notification from last task.
-      //
-      // In async mode, last task when completed will call done callback from
-      // the same thread, and will delete this context.
-      //
-      // TODO(dvyukov): This wait can lead to deadlock if contraction is
-      // evaluated in synchronous mode. If nthreads contractions are
-      // concurrently submitted from worker threads, this wait will block all
-      // worker threads and the system will deadlock.
-      done_.Wait();
-    }
-
-   private:
-    std::thread::id created_by_thread_id_;
-
-    // This notification is specialized on the type of DoneCallback and can be
-    // blocking or non-blocking.
-    EvalParallelNotification<DoneCallback, EvalParallelContext> done_;
-
-    const Device& device_;
-    LhsMapper lhs_;
-    RhsMapper rhs_;
-    Scalar* const buffer_;
-    OutputMapper output_;
-    OutputKernelType output_kernel_;
-    TensorContractionParams tensor_contraction_params_;
-    const int num_threads_;
-    const bool shard_by_col_;
-    const bool parallel_pack_;
-    const bool parallelize_by_sharding_dim_only_;
-    // Matrix sizes.
-    const Index m_;
-    const Index n_;
-    const Index k_;
-    // Block sizes.
-    const Index bm_;
-    const Index bn_;
-    const Index bk_;
-    // Number of tasks.
-    const Index nm_;
-    const Index nn_;
-    const Index nk_;
-    // Task grain sizes (number of kernels executed per task).
-    const Index gm_;
-    const Index gn_;
-    // Number of blocks (this is different from ni_/nn_ because of task size
-    // coarsening).
-    const Index nm0_;
-    const Index nn0_;
-    // Tensor contraction kernel.
-    TensorContractionKernel kernel_;
-
-    // Parallelization strategy.
-    //
-    // Blocks related to the same k block can run in parallel because they write
-    // to different output blocks. So we parallelize within k slices, this
-    // gives us parallelism level of m x n. Before we can start any kernels
-    // related to k-th slice, we need to issue m lhs packing tasks and n rhs
-    // packing tasks.
-    //
-    // However, there is a bottleneck when we are finishing kernels for k-th
-    // slice (at the very end there is only 1 runnable kernel). To mitigate this
-    // bottleneck we allow kernels from k-th and k+1-th slices to run in
-    // parallel. Note that (m, n, k) and (m, n, k+1) kernels write to the same
-    // output block, so they must not run in parallel.
-    //
-    // This gives us the following dependency graph.
-    // On each k slice we have m x n kernel tasks, m lhs paking tasks and n rhs
-    // packing tasks.
-    // Kernel (m, n, k) can start when:
-    //  - kernel (m, n, k-1) has finished
-    //  - lhs packing (m, k) has finished
-    //  - rhs packing (n, k) has finished
-    // Lhs/rhs packing can start when:
-    //  - all k-1 packing has finished (artificially imposed to limit amount of
-    //  parallel packing)
-    //
-    // On top of that we limit runnable tasks to two consecutive k slices.
-    // This is done to limit amount of memory we need for packed lhs/rhs
-    // (for each k slice we need m*bk + n*bk memory in packed_lhs_/packed_rhs_).
-    //
-    // state_switch_ tracks when we are ready to switch to the next k slice.
-    // state_kernel_[m][n] tracks when we are ready to kick off kernel (m, n).
-    // These variable are rolling over 3 consecutive k slices: first two we are
-    // actively executing + one to track completion of kernels in the second
-    // slice.
-    static const Index P = 3;
-
-    // Handle to the allocated temporary storage for Lhs/Rhs blocks.
-    BlockMemHandle packed_mem_;
-    std::vector<LhsBlock> packed_lhs_[P - 1];
-    std::vector<RhsBlock> packed_rhs_[P - 1];
-
-    // If we choose to parallelize only by the sharding dimension, each thread
-    // will have it's own "thead local" (not a c++ thread local storage) memory
-    // for packed_lhs or packed_rhs (shard_by_col = false of true). This memory
-    // can't be passed to a kernel that might execute on a different thread.
-    //
-    // In practice when we are ready to pack memory for the sharding dimension
-    // (rhs if shard_by_col==true) of the K-th slice, all kernels for K-1 slice
-    // already computed (99% of the time), and we can pack data into the thread
-    // local storage, and guarantee that all the kernels will be executed
-    // immediately in the same thread. This significantly increases L1 cache hit
-    // ratio and reduces pressure on the memory bus.
-    //
-    // It's still possible that kernel for the K-th slice will be ready before
-    // completion of the K-1 kernel, so we have to allocate "global" packed_lhs_
-    // and packed_rhs_ to allow kernels to be executed later on a thread
-    // different from the thread that was used for packing.
-
-    // Handle for pre-allocated thread local memory buffers.
-    BlockMemHandle thread_local_pre_alocated_mem_;
-
-    // Only one of these will be initialized depending on shard_by_col value
-    // (the size will be `num_worker_threads * num_grains_in_the_sharding_dim`).
-    std::vector<LhsBlock> lhs_thread_local_pre_allocated_;
-    std::vector<RhsBlock> rhs_thread_local_pre_allocated_;
-
-    // How many thread local blocks were already allocated.
-    std::atomic<int> num_thread_local_allocations_;
-    const int thread_local_capacity;
-
-    // We will use pre-allocated Lhs/Rhs blocks defined above, if the number of
-    // unique threads in a system is below or equal to the number of threads in
-    // a thread pool. We will fallback on dynamic memory allocation after that.
-
-    // ThreadLocalBlocks is a container for Lhs or Rhs thread local buffers. Its
-    // size is equal to the grain size in Lhs/Rhs sharding dimension.
-    template <typename BlockType>
-    class ThreadLocalBlocks {
-     public:
-      ThreadLocalBlocks() = default;
-
-      ThreadLocalBlocks(BlockType* base, size_t grain_size)
-          : is_pre_allocated_(true),
-            thread_local_pre_allocated_base_(base),
-            grain_size_(grain_size) {}
-
-      ThreadLocalBlocks(BlockMemHandle mem_handle,
-                        std::vector<BlockType> blocks)
-          : is_pre_allocated_(false),
-            mem_handle_(std::move(mem_handle)),
-            blocks_(std::move(blocks)) {}
-
-      BlockType& block(int grain_index) {
-        eigen_assert(grain_index >= 0);
-        eigen_assert(static_cast<size_t>(grain_index) < size());
-        return is_pre_allocated_ ? thread_local_pre_allocated_base_[grain_index]
-                                 : blocks_[grain_index];
-      }
-
-      void Release(EvalParallelContext& ctx) const {
-        if (!is_pre_allocated_) {
-          ctx.kernel_.deallocate(ctx.device_, mem_handle_);
-        }
-      }
-
-      size_t size() const {
-        return is_pre_allocated_ ? grain_size_ : blocks_.size();
-      }
-
-     private:
-      bool is_pre_allocated_;
-
-      // Reuse pre-allocated thread local buffers.
-      BlockType* thread_local_pre_allocated_base_ = nullptr;
-      size_t grain_size_ = 0;
-
-      // These will be initialized only if `is_pre_allocated == false`.
-      BlockMemHandle mem_handle_{};
-      std::vector<BlockType> blocks_;
-    };
-
-    // ThreadLocalBlocksInitialize callable does custom thread local blocks
-    // initialization, and will reuse pre-allocated buffers if possible, or will
-    // dynamically allocate new memory.
-    //
-    // Lhs/Rhs blocks might be of the same type, so we have to pass explicitly
-    // for what side do we plan to do block allocation.
-    template <typename BlockType, bool is_rhs>
-    class ThreadLocalBlocksInitialize {
-      static constexpr bool kIsLhs =
-          !is_rhs && std::is_same<BlockType, LhsBlock>::value;
-      static const bool kIsRhs =
-          is_rhs && std::is_same<BlockType, RhsBlock>::value;
-      static_assert(kIsLhs || kIsRhs, "Unkown block type");
-
-      using Blocks = ThreadLocalBlocks<BlockType>;
-
-     public:
-      ThreadLocalBlocksInitialize(EvalParallelContext& ctx)
-          : ctx_(ctx),
-            num_worker_threads_(ctx_.device_.numThreadsInPool()) {}
-
-      void operator()(Blocks& blocks) {
-        const int n = ctx_.num_thread_local_allocations_.fetch_add(
-            1, std::memory_order_relaxed);
-
-        if (n >= num_worker_threads_) {
-          ThreadLocalBlocksAllocator<is_rhs>::allocate(ctx_, blocks);
-        } else {
-          ThreadLocalBlocksAllocator<is_rhs>::reuse(ctx_, n, blocks);
-        }
-      }
-
-     private:
-      // NOTE(ezhulenev): Without 'if constexpr' we have to put calls to
-      // TensorContractionKernel::allocateSlices into template specializations.
-      // Also explicit specializations are not allowed at class scope in C++03,
-      // EvalCtx type parameter is just a workaround for that limitation.
-      template <bool pack_rhs, typename EvalCtx = EvalParallelContext>
-      struct ThreadLocalBlocksAllocator;
-
-      template <typename EvalCtx>
-      struct ThreadLocalBlocksAllocator</*pack_rhs=*/true, EvalCtx> {
-        static void allocate(EvalCtx& ctx, Blocks& blocks) {
-          std::vector<RhsBlock> rhs_blocks;
-          BlockMemHandle mem_handle = ctx.kernel_.allocateSlices(
-              ctx.device_,
-              /*num_lhs=*/0,
-              /*num_rhs=*/ctx.gn_,
-              /*num_slices=*/1,
-              /*lhs_blocks=*/nullptr, /*rhs_blocks=*/&rhs_blocks);
-
-          blocks = ThreadLocalBlocks<RhsBlock>(std::move(mem_handle),
-                                               std::move(rhs_blocks));
-        }
-
-        static void reuse(EvalCtx& ctx, int index, Blocks& blocks) {
-          RhsBlock* ptr = &ctx.rhs_thread_local_pre_allocated_[ctx.gn_ * index];
-          blocks = ThreadLocalBlocks<RhsBlock>(ptr, ctx.gn_);
-        }
-      };
-
-      template <typename EvalCtx>
-      struct ThreadLocalBlocksAllocator</*pack_rhs=*/false, EvalCtx> {
-        static void allocate(EvalCtx& ctx, Blocks& blocks) {
-          std::vector<LhsBlock> lhs_blocks;
-          BlockMemHandle mem_handle = ctx.kernel_.allocateSlices(
-              ctx.device_,
-              /*num_lhs=*/ctx.gm_,
-              /*num_rhs=*/0,
-              /*num_slices=*/1,
-              /*lhs_blocks=*/&lhs_blocks, /*rhs_blocks=*/nullptr);
-
-          blocks = ThreadLocalBlocks<LhsBlock>(std::move(mem_handle),
-                                               std::move(lhs_blocks));
-        }
-
-        static void reuse(EvalCtx& ctx, int index, Blocks& blocks) {
-          LhsBlock* ptr = &ctx.lhs_thread_local_pre_allocated_[ctx.gm_ * index];
-          blocks = ThreadLocalBlocks<LhsBlock>(ptr, ctx.gm_);
-        }
-      };
-
-      EvalParallelContext& ctx_;
-      const int num_worker_threads_;
-    };
-
-    template <typename BlockType>
-    class ThreadLocalBlocksRelease {
-     public:
-      using Blocks = ThreadLocalBlocks<BlockType>;
-      ThreadLocalBlocksRelease(EvalParallelContext& ctx) : ctx_(ctx) {}
-      void operator()(Blocks& blocks) { blocks.Release(ctx_); }
-
-     private:
-      EvalParallelContext& ctx_;
-    };
-
-    // ThreadLocalBlocks initialization callables.
-    using ThreadLocalLhsInit =
-        ThreadLocalBlocksInitialize<LhsBlock, /*is_rhs=*/false>;
-    using ThreadLocalRhsInit =
-        ThreadLocalBlocksInitialize<RhsBlock, /*is_rhs=*/true>;
-
-    // ThreadLocalBlocks release callables.
-    using ThreadLocalLhsRelease = ThreadLocalBlocksRelease<LhsBlock>;
-    using ThreadLocalRhsRelease = ThreadLocalBlocksRelease<RhsBlock>;
-
-    // Thread local containers for Lhs/Rhs block packs. In practice only one of
-    // them will be used, depending on the shard_by_col value.
-    Eigen::ThreadLocal<ThreadLocalBlocks<LhsBlock>, ThreadLocalLhsInit,
-                       ThreadLocalLhsRelease>
-        lhs_thread_local_blocks_;
-    Eigen::ThreadLocal<ThreadLocalBlocks<RhsBlock>, ThreadLocalRhsInit,
-                       ThreadLocalRhsRelease>
-        rhs_thread_local_blocks_;
-
-    // After a particular shard for Kth slice missed thread local execution
-    // opportunity (K-1 slice didn't complete kernels execution), we can no
-    // longer schedule K+1 and following slices in thread local mode, because
-    // there is no more guarantee that previous kernels were executed
-    // sequentially in the same thread (size is nn_ or nm_).
-    std::atomic<bool>* can_use_thread_local_packed_;
-
-    std::atomic<uint8_t>** state_kernel_[P];
-    // state_switch_ is frequently modified by worker threads, while other
-    // fields are read-only after constructor. Let's move it to a separate cache
-    // line to reduce cache-coherency traffic.
-    char pad_[128];
-    std::atomic<Index> state_packing_ready_[P];
-    std::atomic<Index> state_switch_[P];
-
-    LhsBlock& packed_lhs(Index m, Index k, Index m1, bool use_thread_local) {
-      if (use_thread_local) {
-        eigen_assert(!shard_by_col_);
-        ThreadLocalBlocks<LhsBlock>& blocks = lhs_thread_local_blocks_.local();
-
-        Index grain_index = m1 - m * gm_;
-        return blocks.block(internal::convert_index<int>(grain_index)); // FIXME better make ThreadLocalBlocks use Eigen::Index?
-      } else {
-        return packed_lhs_[k % (P - 1)][m1];
-      }
-    }
-
-    RhsBlock& packed_rhs(Index n, Index k, Index n1, bool use_thread_local) {
-      if (use_thread_local) {
-        eigen_assert(shard_by_col_);
-        ThreadLocalBlocks<RhsBlock>& blocks = rhs_thread_local_blocks_.local();
-
-        Index grain_index = n1 - n * gn_;
-        return blocks.block(internal::convert_index<int>(grain_index)); // FIXME better make ThreadLocalBlocks use Eigen::Index?
-      } else {
-        return packed_rhs_[k % (P - 1)][n1];
-      }
-    }
-
-    // In following two methods (pack_lhs and pack_rhs), if we know for sure
-    // that we'll be able to immediately call a kernel with packed data, and do
-    // not submit it to the thread pool, we can use thread local memory for
-    // packed data.
-    //
-    // We can only reliably check it if we are running all kernels in sync mode
-    // (parallelize only by sharding dim). If kernel for m==0 (n==0) is ready to
-    // run, it's guaranteed that all kernels with larger values of m (n) are
-    // also ready, because we execute them in the same order for all K slices.
-
-    void pack_lhs(Index m, Index k) {
-      bool use_thread_local = false;
-
-      if (parallelize_by_sharding_dim_only_ && !shard_by_col_ &&
-          can_use_thread_local_packed_[m].load(std::memory_order_relaxed)) {
-        if (state_kernel_[k % P][m][0].load(std::memory_order_relaxed) == 1) {
-          use_thread_local = true;
-        } else {
-          // If we can't guarantee that all kernels in `k` slice will be
-          // executed sequentially in current thread, it's no longer safe to use
-          // thread local memory in following slices along the k dimensions.
-          eigen_assert(k > 0);
-          can_use_thread_local_packed_[m].store(false,
-                                                std::memory_order_relaxed);
-        }
-      }
-
-      const Index mend = m * gm_ + gm(m);
-      for (Index m1 = m * gm_; m1 < mend; m1++)
-        kernel_.packLhs(&packed_lhs(m, k, m1, use_thread_local),
-                        lhs_.getSubMapper(m1 * bm_, k * bk_), bk(k), bm(m1));
-
-      if (!parallel_pack_ && shard_by_col_) {
-        assert(!use_thread_local);
-        signal_packing(k);
-      } else {
-        signal_switch(k + 1);
-        for (Index n = nn_ - 1; n >= 0; n--) {
-          bool sync = parallelize_by_sharding_dim_only_ || n == 0;
-          signal_kernel(m, n, k, sync, use_thread_local);
-        }
-      }
-    }
-
-    void pack_rhs(Index n, Index k) {
-      bool use_thread_local = false;
-
-      if (parallelize_by_sharding_dim_only_ && shard_by_col_ &&
-          can_use_thread_local_packed_[n].load(std::memory_order_relaxed)) {
-        if (state_kernel_[k % P][0][n].load(std::memory_order_relaxed) == 1) {
-          use_thread_local = true;
-        } else {
-          // If we can't guarantee that all kernels in `k` slice will be
-          // executed sequentially in current thread, it's no longer safe to use
-          // thread local memory in followig slices along the k dimensions.
-          eigen_assert(k > 0);
-          can_use_thread_local_packed_[n].store(false,
-                                                std::memory_order_relaxed);
-        }
-      }
-
-      const Index nend = n * gn_ + gn(n);
-      for (Index n1 = n * gn_; n1 < nend; n1++) {
-        if (!TensorContractionKernel::HasBeta && k == 0) {
-          // Zero the output memory in parallel, only if contraction kernel does
-          // not support `beta`. Otherwise we will pass beta 0.0 to the first
-          // call to the `TensorContractionKernel::invoke()`.
-          //
-          // On 10000x2x10000 mm zeroing can easily take half of time. Zero (bn
-          // x m) row. Safe to do here because all kernels that will write to
-          // this memory depend on completion of this task. Note: don't call
-          // device_.memset() here. device_.memset() blocks on thread pool
-          // worker thread, which can lead to underutilization and deadlocks.
-          memset(buffer_ + n1 * bn_ * m_, 0, bn(n1) * m_ * sizeof(Scalar));
-        }
-        kernel_.packRhs(&packed_rhs(n, k, n1, use_thread_local),
-                        rhs_.getSubMapper(k * bk_, n1 * bn_), bk(k), bn(n1));
-      }
-
-      if (parallel_pack_ || shard_by_col_) {
-        signal_switch(k + 1);
-        for (Index m = nm_ - 1; m >= 0; m--) {
-          bool sync = parallelize_by_sharding_dim_only_ || m == 0;
-          signal_kernel(m, n, k, sync, use_thread_local);
-        }
-      } else {
-        assert(!use_thread_local);
-        signal_packing(k);
-      }
-    }
-
-    void kernel(Index m, Index n, Index k, bool use_thread_local) {
-      // Note: order of iteration matters here. Iteration over m is innermost
-      // because we want to reuse the same packed rhs in consecutive tasks
-      // (rhs fits into L2$ while lhs only into L3$).
-      const Index nend = n * gn_ + gn(n);
-      const Index mend = m * gm_ + gm(m);
-
-      // NOTE: output = alpha * LHS * RHS + beta * output.
-      const Scalar alpha = Scalar(1);
-      const Scalar beta =
-          (TensorContractionKernel::HasBeta && k == 0) ? Scalar(0) : Scalar(1);
-
-      if (shard_by_col_) {
-        for (Index n1 = n * gn_; n1 < nend; n1++) {
-          for (Index m1 = m * gm_; m1 < mend; m1++) {
-            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
-            kernel_.invoke(
-                output_mapper,
-                packed_lhs(m, k, m1, !shard_by_col_ && use_thread_local),
-                packed_rhs(n, k, n1, shard_by_col_ && use_thread_local), bm(m1),
-                bk(k), bn(n1), alpha, beta);
-
-            // We are done with the last task for the [m1, n1] block.
-            if (k + 1 == nk_) {
-              output_kernel_(output_mapper, tensor_contraction_params_,
-                             m1 * bm_, n1 * bn_, bm(m1), bn(n1));
-            }
-          }
-        }
-      } else {
-        for (Index m1 = m * gm_; m1 < mend; m1++)
-          for (Index n1 = n * gn_; n1 < nend; n1++) {
-            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
-            kernel_.invoke(
-                output_mapper,
-                packed_lhs(m, k, m1, !shard_by_col_ && use_thread_local),
-                packed_rhs(n, k, n1, shard_by_col_ && use_thread_local), bm(m1),
-                bk(k), bn(n1), alpha, beta);
-
-            // We are done with the last task for the [m1, n1] block.
-            if (k + 1 == nk_) {
-              output_kernel_(output_mapper, tensor_contraction_params_,
-                             m1 * bm_, n1 * bn_, bm(m1), bn(n1));
-            }
-          }
-      }
-      signal_kernel(m, n, k + 1, /*sync=*/false, /*use_thread_local=*/false);
-      signal_switch(k + 2);
-    }
-
-    void signal_packing(Index k) {
-      eigen_assert(!parallel_pack_);
-      Index s = state_packing_ready_[k % P].fetch_sub(1);
-      eigen_assert(s > 0);
-      if (s != 1) return;
-      state_packing_ready_[k % P] = shard_by_col_ ? nm_ : nn_;
-      enqueue_packing(k, shard_by_col_);
-    }
-
-    void signal_kernel(Index m, Index n, Index k, bool sync,
-                       bool use_thread_local) {
-      std::atomic<uint8_t>* state = &state_kernel_[k % P][m][n];
-      Index s = state->load();
-      eigen_assert(s > 0);
-      if (s != 1 && state->fetch_sub(1) != 1) {
-        eigen_assert(!use_thread_local);
-        return;
-      }
-      state->store(parallel_pack_ ? 3 : 2, std::memory_order_relaxed);
-      if (sync) {
-        kernel(m, n, k, use_thread_local);
-      } else {
-        eigen_assert(!use_thread_local);
-        device_.enqueueNoNotification(
-            [=]() { kernel(m, n, k, use_thread_local); });
-      }
-    }
-
-    void signal_switch(Index k, Index v = 1) {
-      Index s = state_switch_[k % P].fetch_sub(v);
-      eigen_assert(s >= v);
-      if (s != v) return;
-
-      // Ready to switch to the next k slice.
-      // Reset counter for the next iteration.
-      state_switch_[k % P] =
-          (parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_)) +
-          nm_ * nn_;
-      if (k < nk_) {
-        // Issue lhs/rhs packing. Their completion will in turn kick off
-        // kernels.
-        if (parallel_pack_) {
-          enqueue_packing(k, !shard_by_col_);
-          enqueue_packing(k, shard_by_col_);
-        } else if (shard_by_col_) {
-          enqueue_packing(k, false);
-        } else {
-          enqueue_packing(k, true);
-        }
-
-        // Termination handling.
-        // Because kernel completion signals k + 2 switch, we need to finish nk
-        // + 2 slices without issuing any tasks on nk + 1 slice. So here we
-        // pretend that all nk + 1 packing tasks just finish instantly; so that
-        // nk + 2 switch only waits for completion of nk kernels.
-      } else if (k == nk_) {
-        signal_switch(k + 1,
-                      parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_));
-      } else {
-        done_.Notify();
-      }
-    }
-
-    // Enqueue all rhs/lhs packing for k-th slice.
-    void enqueue_packing(Index k, bool rhs) {
-      enqueue_packing_helper(0, rhs ? nn_ : nm_, k, rhs);
-    }
-
-    void enqueue_packing_helper(Index start, Index end, Index k, bool rhs) {
-      if (end - start == 1) {
-        if (rhs)
-          pack_rhs(start, k);
-        else
-          pack_lhs(start, k);
-      } else {
-        while (end - start > 1) {
-          Index mid = (start + end) / 2;
-          device_.enqueueNoNotification(
-              [=]() { enqueue_packing_helper(mid, end, k, rhs); });
-          end = mid;
-        }
-
-        // Decide if we want to run first packing task (start == 0) in
-        // async mode if we parallelize only by sharding dim:
-        // (1) pack_lhs and pack_rhs call signal_switch before completing
-        //     all calls to signal_kernel, which in sync mode might lead
-        //     to the execution of the first kernel of the k+1 slice, before
-        //     completing a call to the last kernel of the k slice.
-        // (2) all pack tasks for sharded dim must be executed in a thread
-        //     pool to get pre-allocated thead local buffers.
-        bool pack_async =
-          (start == 0) &&
-          (parallelize_by_sharding_dim_only_&& shard_by_col_ == rhs) &&
-          (k > 0 || std::this_thread::get_id() == created_by_thread_id_);
-
-        if (pack_async) {
-          device_.enqueueNoNotification(
-              [=]() { enqueue_packing_helper(start, end, k, rhs); });
-        } else {
-          enqueue_packing_helper(start, end, k, rhs);
-        }
-      }
-    }
-
-    // Block sizes with accounting for potentially incomplete last block.
-    Index bm(Index m) const { return m + 1 < nm0_ ? bm_ : m_ + bm_ - bm_ * nm0_; }
-    Index bn(Index n) const { return n + 1 < nn0_ ? bn_ : n_ + bn_ - bn_ * nn0_; }
-    Index bk(Index k) const { return k + 1 < nk_ ? bk_ : k_ + bk_ - bk_ * nk_; }
-    // Task grain sizes accounting for potentially incomplete last task.
-    Index gm(Index m) const { return m + 1 < nm_ ? gm_ : nm0_ + gm_ - gm_ * nm_; }
-    Index gn(Index n) const { return n + 1 < nn_ ? gn_ : nn0_ + gn_ - gn_ * nn_; }
-
-    EvalParallelContext(const EvalParallelContext&) = delete;
-    void operator=(const EvalParallelContext&) = delete;
-  };
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
-            bool rhs_inner_dim_reordered, int Alignment>
-  using SyncEvalParallelContext =
-      EvalParallelContext<NoCallback, lhs_inner_dim_contiguous,
-                          rhs_inner_dim_contiguous, rhs_inner_dim_reordered,
-                          Alignment>;
-
-  // ------------------------------------------------------------------------ //
-
-  // EvalShardedByInnerDimContext orchestrates sync/async contraction
-  // evaluation, when we shard by inner dimension. When it is executed in
-  // asynchronous mode, it owns all the shared state that might be accessible by
-  // block processing tasks.
-
-  template <typename DoneCallback>
-  struct EvalShardedByInnerDimContext {
-    EvalShardedByInnerDimContext(const Self* self, int num_threads,
-                                 Scalar* result_buffer,
-                                 Index m_size, Index n_size, Index k_size,
-                                 DoneCallback done_callback)
-        : evaluator(self),
-          m_lhs_inner_dim_contiguous(evaluator->m_lhs_inner_dim_contiguous),
-          m_rhs_inner_dim_contiguous(evaluator->m_rhs_inner_dim_contiguous),
-          m_rhs_inner_dim_reordered(evaluator->m_rhs_inner_dim_reordered),
-          result(result_buffer),
-          m(m_size),
-          n(n_size),
-          k(k_size),
-          done(std::move(done_callback)),
-          buffer_size_bytes(m * n * sizeof(Scalar)),
-          block_size(blockSize(k, num_threads)),
-          num_blocks(divup<Index>(k, block_size)),
-          num_pending_blocks(internal::convert_index<int>(num_blocks)),
-          l0_ranges(divup<Index>(num_blocks, l0_size)),
-          l0_state(l0_ranges),
-          block_buffers(num_blocks) {
-      // Keep count of pending gemm tasks for each l0 range.
-      for (int i = 0; i < l0_ranges; ++i) {
-        const Index num_pending_tasks = actualRangeSize(l0_ranges, l0_size, i);
-        l0_state.emplace_back(internal::convert_index<int>(num_pending_tasks));
-      }
-
-      // Allocate temporary buffers for each block.
-      for (Index block_idx = 0; block_idx < num_blocks; ++block_idx) {
-        Scalar* buf = block_idx == 0
-                          ? result
-                          : static_cast<Scalar*>(evaluator->m_device.allocate(
-                                buffer_size_bytes));
-        block_buffers.emplace_back(buf);
-      }
-    }
-
-    ~EvalShardedByInnerDimContext() {
-      for (Index i = 1; i < num_blocks; ++i) {
-        evaluator->m_device.deallocate(block_buffers[i]);
-      }
-    }
-
-    template <int Alignment>
-    void run() {
-      Barrier barrier(internal::convert_index<int>(num_blocks));
-      eval<Alignment>(barrier, 0, num_blocks);
-      barrier.Wait();
-
-      // Aggregate partial sums from l0 ranges.
-      aggregateL0Blocks<Alignment>();
-
-      // Apply output kernel.
-      applyOutputKernel();
-    }
-
-    template <int Alignment>
-    void runAsync() {
-      evalAsync<Alignment>(0, num_blocks);
-    }
-
-   private:
-    // The underlying GEMM kernel assumes that k is a multiple of
-    // the packet size and subtle breakage occurs if this is violated.
-    static const Index packet_size = internal::packet_traits<RhsScalar>::size;
-
-    const Self* evaluator;  // TensorContraction evaluator
-
-    // These fields required fromTENSOR_CONTRACTION_DISPATCH macro.
-    bool m_lhs_inner_dim_contiguous;
-    bool m_rhs_inner_dim_contiguous;
-    bool m_rhs_inner_dim_reordered;
-
-    Scalar* result;
-
-    Index m;
-    Index n;
-    Index k;
-
-    DoneCallback done;
-
-    // ----------------------------------------------------------------------//
-    // Algorithm parameters.
-
-    // We will compute partial results into the buffers of this size.
-    Index buffer_size_bytes;
-
-    Index block_size;
-    Index num_blocks;
-
-    // Keep track of pending tasks when evaluate in async mode.
-    std::atomic<int> num_pending_blocks;
-
-    // We compute partial gemm results in parallel, and to get the final result
-    // we need to add them all together. For the large number of threads (>= 48)
-    // this adds a very expensive sequential step at the end.
-    //
-    // We split the [0, num_blocks) into small ranges, and when a task for the
-    // block finishes its partial gemm computation, it checks if it was the last
-    // gemm in the range, and if so, it will add all blocks of the range.
-    //
-    // After all tasks done, we need to add only these pre-aggregated blocks.
-
-    // For now we use just a single level of ranges to compute pre-aggregated
-    // partial sums, but in general we can use more layers to compute tree
-    // aggregation in parallel and reduce the size of the sequential step.
-    //
-    // TODO(ezhulenev): Add multilevel tree aggregation? Probably will make
-    // sense only if number of threads >= ~128?
-    static const Index l0_size = 4;
-    Index l0_ranges;
-
-    // Keep count of pending gemm tasks for each l0 range.
-    MaxSizeVector<std::atomic<int>> l0_state;  // [0, l0_ranges)
-
-    // Buffers allocated for each temporary block computation.
-    MaxSizeVector<Scalar*> block_buffers;  // [0, num_blocks)
-
-    template <int Alignment>
-    void processBlock(Index block_idx, Index begin, Index end) {
-      Scalar* buf = block_buffers[block_idx];
-
-      TENSOR_CONTRACTION_DISPATCH(
-          evaluator->template evalGemmPartialWithoutOutputKernel, Alignment,
-          (buf, begin, end,
-           /*num_threads=*/internal::convert_index<int>(num_blocks)));
-
-      // Check if it was the last task in l0 range.
-      const Index l0_index = block_idx / l0_size;
-      const int v = l0_state[l0_index].fetch_sub(1);
-      eigen_assert(v >= 1);
-
-      // If we processed the last block of the range, we can aggregate all
-      // partial results into the first block of the range.
-      if (v == 1) {
-        const Index rng_size = actualRangeSize(l0_ranges, l0_size, l0_index);
-        const Index dst_block_idx = l0_index * l0_size;
-
-        if (rng_size == l0_size) {
-          addAllToBuffer<Alignment>(
-              m * n,
-              /*src_buf0=*/block_buffers[dst_block_idx + 1],
-              /*src_buf1=*/block_buffers[dst_block_idx + 2],
-              /*src_buf2=*/block_buffers[dst_block_idx + 3],
-              /*dst_buf= */ block_buffers[dst_block_idx]);
-        } else {
-          // Aggregate blocks of potentially incomplete last range.
-          for (int i = 1; i < rng_size; ++i) {
-            addToBuffer<Alignment>(m * n,
-                                   /*src_buf=*/block_buffers[dst_block_idx + i],
-                                   /*dst_buf=*/block_buffers[dst_block_idx]);
-          }
-        }
-      }
-    }
-
-    // Aggregate partial sums from l0 ranges.
-    template <int Alignment>
-    void aggregateL0Blocks() const {
-      Index l0_index = 1;
-
-      for (; l0_index + 2 < l0_ranges; l0_index += 3) {
-        addAllToBuffer<Alignment>(
-            m * n,
-            /*src_buf0=*/block_buffers[(l0_index + 0) * l0_size],
-            /*src_buf1=*/block_buffers[(l0_index + 1) * l0_size],
-            /*src_buf2=*/block_buffers[(l0_index + 2) * l0_size],
-            /*dst_buf= */ block_buffers[0]);
-      }
-
-      for (; l0_index < l0_ranges; ++l0_index) {
-        addToBuffer<Alignment>(m * n, block_buffers[l0_index * l0_size],
-                               block_buffers[0]);
-      }
-    }
-
-    void applyOutputKernel() const {
-      typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
-      evaluator->m_output_kernel(
-          OutputMapper(result, m), evaluator->m_tensor_contraction_params,
-          static_cast<Eigen::Index>(0), static_cast<Eigen::Index>(0), m, n);
-    }
-
-    // Compute block size with accounting for potentially incomplete last block.
-    Index actualBlockSize(Index block_idx) const {
-      return block_idx + 1 < num_blocks
-                 ? block_size
-                 : k + block_size - block_size * num_blocks;
-    };
-
-    // Compute range size with accounting for potentially incomplete last range.
-    Index actualRangeSize(Index num_ranges, Index range_size,
-                          Index range_idx) const {
-      eigen_assert(range_idx < num_ranges);
-      return range_idx + 1 < num_ranges
-                 ? range_size
-                 : num_blocks + range_size - range_size * num_ranges;
-    };
-
-    template <int Alignment>
-    EIGEN_STRONG_INLINE static void addToBuffer(size_t n, const Scalar* src_buf,
-                                                Scalar* tgt_buf) {
-      const int output_packet_size =
-          internal::unpacket_traits<PacketReturnType>::size;
-      size_t i = 0;
-      const size_t num_packets = n / output_packet_size;
-      for (; i < output_packet_size * num_packets; i += output_packet_size) {
-        const PacketReturnType src_val =
-            internal::pload<PacketReturnType>(src_buf + i);
-        const PacketReturnType tgt_val =
-            internal::ploadt<PacketReturnType, Alignment>(tgt_buf + i);
-        const PacketReturnType sum = internal::padd(src_val, tgt_val);
-        internal::pstoret<Scalar, PacketReturnType, Alignment>(tgt_buf + i,
-                                                               sum);
-      }
-      for (; i < n; ++i) {
-        tgt_buf[i] += src_buf[i];
-      }
-    }
-
-    template <int Alignment>
-    EIGEN_STRONG_INLINE static void addAllToBuffer(size_t n,
-                                                   const Scalar* src_buf0,
-                                                   const Scalar* src_buf1,
-                                                   const Scalar* src_buf2,
-                                                   Scalar* dst_buf) {
-      using ::Eigen::internal::padd;
-      using ::Eigen::internal::pload;
-      using ::Eigen::internal::ploadt;
-      using ::Eigen::internal::pstoret;
-
-      const int output_packet_size =
-          internal::unpacket_traits<PacketReturnType>::size;
-
-      size_t i = 0;
-      const size_t num_packets = n / output_packet_size;
-      for (; i < output_packet_size * num_packets; i += output_packet_size) {
-        const auto src_val0 = pload<PacketReturnType>(src_buf0 + i);
-        const auto src_val1 = pload<PacketReturnType>(src_buf1 + i);
-        const auto src_val2 = pload<PacketReturnType>(src_buf2 + i);
-
-        const auto dst_val = ploadt<PacketReturnType, Alignment>(dst_buf + i);
-        const auto sum =
-            padd(padd(dst_val, src_val0), padd(src_val1, src_val2));
-
-        pstoret<Scalar, PacketReturnType, Alignment>(dst_buf + i, sum);
-      }
-      for (; i < n; ++i) {
-        dst_buf[i] += src_buf0[i] + src_buf1[i] + src_buf2[i];
-      }
-    }
-
-    template <int Alignment>
-    void eval(Barrier& barrier, Index start_block_idx, Index end_block_idx) {
-      while (end_block_idx - start_block_idx > 1) {
-        Index mid_block_idx = (start_block_idx + end_block_idx) / 2;
-        evaluator->m_device.enqueueNoNotification(
-            [this, &barrier, mid_block_idx, end_block_idx]() {
-              eval<Alignment>(barrier, mid_block_idx, end_block_idx);
-            });
-        end_block_idx = mid_block_idx;
-      }
-
-      Index block_idx = start_block_idx;
-      Index block_start = block_idx * block_size;
-      Index block_end = block_start + actualBlockSize(block_idx);
-
-      processBlock<Alignment>(block_idx, block_start, block_end);
-      barrier.Notify();
-    }
-
-    template <int Alignment>
-    void evalAsync(Index start_block_idx, Index end_block_idx) {
-      while (end_block_idx - start_block_idx > 1) {
-        Index mid_block_idx = (start_block_idx + end_block_idx) / 2;
-        evaluator->m_device.enqueueNoNotification(
-            [this, mid_block_idx, end_block_idx]() {
-              evalAsync<Alignment>(mid_block_idx, end_block_idx);
-            });
-        end_block_idx = mid_block_idx;
-      }
-
-      Index block_idx = start_block_idx;
-
-      Index block_start = block_idx * block_size;
-      Index block_end = block_start + actualBlockSize(block_idx);
-
-      processBlock<Alignment>(block_idx, block_start, block_end);
-
-      int v = num_pending_blocks.fetch_sub(1);
-      eigen_assert(v >= 1);
-
-      if (v == 1) {
-        // Aggregate partial sums from l0 ranges.
-        aggregateL0Blocks<Alignment>();
-
-        // Apply output kernel.
-        applyOutputKernel();
-
-        // NOTE: If we call `done` callback before deleting this (context),
-        // it might deallocate Self* pointer captured by context, and we'll
-        // fail in destructor trying to deallocate temporary buffers.
-
-        // Move done call back from context before it will be destructed.
-        DoneCallback done_copy = std::move(done);
-
-        // We are confident that we are the last one who touches context.
-        delete this;
-
-        // Now safely call the done callback.
-        done_copy();
-      }
-    }
-
-    // Cost model doesn't capture well the cost associated with constructing
-    // tensor contraction mappers and computing loop bounds in gemm_pack_lhs
-    // and gemm_pack_rhs, so we specify minimum desired block size.
-    static Index blockSize(Index k, int num_threads) {
-      const auto round_up = [=](Index index) -> Index {
-        const Index kmultiple = packet_size <= 8 ? 8 : packet_size;
-        return divup<Index>(index, kmultiple) * kmultiple;
-      };
-
-      const Index target_block_size = round_up(divup<Index>(k, num_threads));
-      const Index desired_min_block_size = 12 * packet_size;
-
-      return numext::mini<Index>(
-          k, numext::maxi<Index>(desired_min_block_size, target_block_size));
-    }
-
-    EvalShardedByInnerDimContext(const EvalShardedByInnerDimContext&) = delete;
-    void operator=(const EvalShardedByInnerDimContext&) = delete;
-  };
-
-  // ------------------------------------------------------------------------ //
-
-  // Below are the function used by evalProductImpl heuristics, trying to select
-  // optimcal parameters for parallelization algorithm.
-
-  // Decide whether we want to shard m x n contraction by columns or by rows.
-  static bool shardByCol(Index m, Index n, Index num_threads) {
-    // Note: we are comparing both n and m against Traits::nr, it is not
-    // a mistake. We are trying to figure out how both n and m will fit into
-    // the main sharding dimension.
-
-    // Sharding by column is the default
-    // ... unless there is enough data for vectorization over rows
-    if (m / num_threads >= Traits::nr &&
-        // and not enough data for vectorization over columns
-        (n / num_threads < Traits::nr ||
-         // ... or barely enough data for vectorization over columns,
-         // but it is not evenly dividable across threads
-         (n / num_threads < 4 * Traits::nr &&
-          (n % (num_threads * Traits::nr)) != 0 &&
-          // ... and it is evenly dividable across threads for rows
-          ((m % (num_threads * Traits::nr)) == 0 ||
-           // .. or it is not evenly dividable for both dimensions but
-           // there is much more data over rows so that corner effects are
-           // mitigated.
-           (m / n >= 6)))))
-      return false;
-    // Wait, or if matrices are just substantially prolonged over the other
-    // dimension.
-    if (n / num_threads < 16 * Traits::nr && m > n * 32) return false;
-    return true;
-  }
-
-  Index coarsenM(Index m, Index n, Index bm, Index bn, Index bk, Index gn,
-                 int num_threads, bool shard_by_col) const {
-    Index gm = 1;
-    Index gm1 = 1;
-    Index nm0 = divup(m, bm);
-    Index nm1 = nm0;
-    for (;;) {
-      // Find the next candidate for m grain size. It needs to result in
-      // different number of blocks. E.g. if we have 10 kernels, we want to try
-      // 5 and 10, but not 6, 7, 8 and 9.
-      while (gm1 <= nm0 && nm1 == divup(nm0, gm1)) gm1++;
-      if (gm1 > nm0) break;
-      // Check the candidate.
-      int res = checkGrain(m, n, bm, bn, bk, gm1, gn, gm, gn, num_threads,
-                           shard_by_col);
-      if (res < 0) break;
-      nm1 = divup(nm0, gm1);
-      if (res == 0) continue;
-      // Commit new grain size.
-      gm = gm1;
-    }
-    return gm;
-  }
-
-  Index coarsenN(Index m, Index n, Index bm, Index bn, Index bk, Index gm,
-                 int num_threads, bool shard_by_col) const {
-    Index gn = 1;
-    Index gn1 = 1;
-    Index nn0 = divup(n, bn);
-    Index nn1 = nn0;
-    for (;;) {
-      while (gn1 <= nn0 && nn1 == divup(nn0, gn1)) gn1++;
-      if (gn1 > nn0) break;
-      int res = checkGrain(m, n, bm, bn, bk, gm, gn1, gm, gn, num_threads,
-                           shard_by_col);
-      if (res < 0) break;
-      nn1 = divup(nn0, gn1);
-      if (res == 0) continue;
-      gn = gn1;
-    }
-    return gn;
-  }
-
-  // checkGrain checks whether grain (gm, gn) is suitable and is better than
-  // (oldgm, oldgn).
-  int checkGrain(Index m, Index n, Index bm, Index bn, Index bk, Index gm,
-                 Index gn, Index oldgm, Index oldgn, int num_threads,
-                 bool shard_by_col) const {
-    const TensorOpCost cost =
-        contractionCost(bm * gm, bn * gn, bm, bn, bk, shard_by_col, true);
-    double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(
-        static_cast<double>(bm) * gm * bn * gn, cost);
-    // If the task is too small, then we agree on it regardless of anything
-    // else. Otherwise synchronization overheads will dominate.
-    if (taskSize < 1) return 1;
-    // If it is too large, then we reject it and all larger tasks.
-    if (taskSize > 2) return -1;
-    // Now we are in presumably good task size range.
-    // The main deciding factor here is parallelism. Consider that we have 12
-    // kernels and 4 threads. Grains of 2, 3 and 4 all yield good task sizes.
-    // But 2/4 yield 6/3 tasks, which gives us parallelism of 0.75 (at most 3/4
-    // of cores will be busy). While grain size 3 gives us 4 tasks, which gives
-    // us parallelism of 1 (we can load all cores).
-    Index nm0 = divup(m, bm);
-    Index nn0 = divup(n, bn);
-    Index new_tasks = divup(nm0, gm) * divup(nn0, gn);
-    double new_parallelism = static_cast<double>(new_tasks) /
-                             (divup<int>(new_tasks, num_threads) * num_threads);
-    Index old_tasks = divup(nm0, oldgm) * divup(nn0, oldgn);
-    double old_parallelism = static_cast<double>(old_tasks) /
-                             (divup<int>(old_tasks, num_threads) * num_threads);
-    if (new_parallelism > old_parallelism || new_parallelism == 1) return 1;
-    return 0;
-  }
-
-  TensorOpCost contractionCost(Index m, Index n, Index bm, Index bn, Index bk,
-                               bool shard_by_col, bool prepacked) const {
-    const int packed_size = std::min<int>(PacketType<LhsScalar, Device>::size,
-                                          PacketType<RhsScalar, Device>::size);
-    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
-    const double kd = static_cast<double>(bk);
-    double compute_bandwidth = computeBandwidth(false, bm, bn, bk);
-    // Computations.
-    TensorOpCost cost = TensorOpCost(0, 0, kd * compute_bandwidth, true, packed_size);
-    // Output stores.
-    cost += TensorOpCost(0, sizeof(CoeffReturnType), 0, true, output_packet_size);
-    if (prepacked) {
-      // Packing and kernels are executed in different tasks. When we calculate
-      // task grain size we look only at kernel cost assuming that kernel
-      // is more expensive than packing.
-      return cost;
-    }
-    // Lhs/rhs loads + computations.
-    TensorOpCost lhsCost = this->m_leftImpl.costPerCoeff(true) * (kd / n);
-    TensorOpCost rhsCost = this->m_rightImpl.costPerCoeff(true) * (kd / m);
-    // Lhs packing memory cost does not contribute considerably to overall
-    // execution time because lhs is prefetched early and accessed sequentially.
-    if (shard_by_col)
-      lhsCost.dropMemoryCost();
-    else
-      rhsCost.dropMemoryCost();
-    return cost + lhsCost + rhsCost;
-  }
-
-  // Decide whether we want to shard m x k x n contraction over the inner
-  // (contraction) dimension (k).
-  static bool shardByInnerDim(Index m, Index n, Index k, int num_threads,
-                              int num_threads_by_k) {
-    std::ptrdiff_t bufsize = m * n * sizeof(Scalar);
-    bool shard_by_k = false;
-    if (n == 1 ||                // If mat*vec or...
-        num_threads_by_k < 2 ||  // running single threaded or...
-        num_threads_by_k <
-            num_threads ||  // sharding by k gives less parallelism or...
-        bufsize > l3CacheSize() / num_threads_by_k ||  // need more buffer space
-        // than L3 cache or...
-        k / num_threads_by_k < 2 * Traits::nr) {  // k per thread is tiny.
-      shard_by_k = false;
-    } else if (numext::maxi(m, n) / num_threads <
-                   Traits::nr ||  // both other dimensions are tiny or...
-               // k per thread is not small and...
-               (k / num_threads_by_k > 8 * Traits::nr &&
-                // one of the outer dimensions is tiny or sharding by k offers
-                // more parallelism.
-                (numext::mini(m, n) < 2 * Traits::nr ||
-                 num_threads_by_k > num_threads))) {
-      shard_by_k = true;
-    }
-    return shard_by_k;
-  }
-
-  TensorOpCost contractionCostPerInnerDim(Index m, Index n, Index k) const {
-    // Compute cost.
-    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
-    TensorOpCost cost(0, 0, (computeBandwidth(true, m, n, k) * m) * n, true, output_packet_size);
-    // Output stores.
-    cost += TensorOpCost(0, sizeof(CoeffReturnType), 0, true, output_packet_size);
-    TensorOpCost lhsCost = this->m_leftImpl.costPerCoeff(true) * m;
-    TensorOpCost rhsCost = this->m_rightImpl.costPerCoeff(true) * n;
-    // Since the inner gemm kernel is always sharded by column, the lhs
-    // load cost is negligible.
-    lhsCost.dropMemoryCost();
-    return cost + lhsCost + rhsCost;
-  }
-
-  int numThreadsInnerDim(Index m, Index n, Index k) const {
-    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
-    TensorOpCost cost = contractionCostPerInnerDim(m, n, k);
-    double total_parallel_cost =
-        TensorCostModel<ThreadPoolDevice>::totalCost(k, cost);
-    // Cost of reduction step accumulating the m*n per-thread buffers into the
-    // result.
-    double reduction_cost = TensorCostModel<ThreadPoolDevice>::totalCost(
-        m * n, TensorOpCost(2, 1, 1, true, output_packet_size));
-    int num_threads = 1;
-    double min_cost = total_parallel_cost;
-    double kPerThreadOverHead = 3000;
-    double kFixedOverHead = 100000;
-    for (int nt = 2; nt <= this->m_device.numThreads(); nt += 2) {
-      double sequential_cost =
-          kFixedOverHead + nt * (reduction_cost + kPerThreadOverHead);
-      double parallel_cost = total_parallel_cost / nt + sequential_cost;
-      if (parallel_cost < min_cost) {
-        num_threads = nt;
-        min_cost = parallel_cost;
-      }
-    }
-    return num_threads;
-  }
-
-  double computeBandwidth(bool shard_by_col, Index bm, Index bn,
-                          Index bk) const {
-    // Peak VFMA bandwidth is 0.5. However if we have not enough data for
-    // vectorization bandwidth drops. The 4.0 and 2.0 bandwidth is determined
-    // experimentally.
-    double computeBandwidth =
-        bk == 1 ? 4.0
-                : (shard_by_col ? bn : bm) < Traits::nr ||
-                          (shard_by_col ? bm : bn) < Traits::mr
-                      ? 2.0
-                      : 0.5;
-#ifndef EIGEN_VECTORIZE_FMA
-    // Bandwidth of all of VFMA/MULPS/ADDPS is 0.5 on latest Intel processors.
-    // However for MULPS/ADDPS we have dependent sequence of 2 such
-    // instructions,
-    // so overall bandwidth is 1.0.
-    if (computeBandwidth == 0.5) computeBandwidth = 1.0;
-#endif
-    return computeBandwidth;
-  }
-
-};
-
-} // end namespace Eigen
-
-#endif  // EIGEN_USE_THREADS
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
deleted file mode 100644
index 09d2da9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
+++ /dev/null
@@ -1,456 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
-
-namespace Eigen {
-
-/** \class TensorConversionOp
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor conversion class. This class makes it possible to vectorize
-  * type casting operations when the number of scalars per packet in the source
-  * and the destination type differ
-  */
-namespace internal {
-template<typename TargetType, typename XprType>
-struct traits<TensorConversionOp<TargetType, XprType> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef TargetType Scalar;
-  typedef typename traits<XprType>::StorageKind StorageKind;
-  typedef typename traits<XprType>::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = traits<XprType>::NumDimensions;
-  static const int Layout = traits<XprType>::Layout;
-  enum { Flags = 0 };
-  typedef typename TypeConversion<Scalar, typename traits<XprType>::PointerType>::type PointerType;
-};
-
-template<typename TargetType, typename XprType>
-struct eval<TensorConversionOp<TargetType, XprType>, Eigen::Dense>
-{
-  typedef const TensorConversionOp<TargetType, XprType>& type;
-};
-
-template<typename TargetType, typename XprType>
-struct nested<TensorConversionOp<TargetType, XprType>, 1, typename eval<TensorConversionOp<TargetType, XprType> >::type>
-{
-  typedef TensorConversionOp<TargetType, XprType> type;
-};
-
-}  // end namespace internal
-
-
-template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int SrcCoeffRatio, int TgtCoeffRatio>
-struct PacketConverter;
-
-template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
-struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, 1> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketConverter(const TensorEvaluator& impl)
-      : m_impl(impl) {}
-
-  template<int LoadMode, typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
-    return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<LoadMode>(index));
-  }
-
- private:
-  const TensorEvaluator& m_impl;
-};
-
-
-template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
-struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 2, 1> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketConverter(const TensorEvaluator& impl)
-      : m_impl(impl) {}
-
-  template<int LoadMode, typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
-    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
-
-    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
-    SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
-    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2);
-    return result;
-  }
-
- private:
-  const TensorEvaluator& m_impl;
-};
-
-template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
-struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 4, 1> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketConverter(const TensorEvaluator& impl)
-      : m_impl(impl) {}
-
-  template<int LoadMode, typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
-    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
-
-    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
-    SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
-    SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
-    SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
-    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4);
-    return result;
-  }
-
- private:
-  const TensorEvaluator& m_impl;
-};
-
-template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
-struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 8, 1> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketConverter(const TensorEvaluator& impl)
-      : m_impl(impl) {}
-
-  template<int LoadMode, typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
-    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
-
-    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
-    SrcPacket src2 = m_impl.template packet<LoadMode>(index + 1 * SrcPacketSize);
-    SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
-    SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
-    SrcPacket src5 = m_impl.template packet<LoadMode>(index + 4 * SrcPacketSize);
-    SrcPacket src6 = m_impl.template packet<LoadMode>(index + 5 * SrcPacketSize);
-    SrcPacket src7 = m_impl.template packet<LoadMode>(index + 6 * SrcPacketSize);
-    SrcPacket src8 = m_impl.template packet<LoadMode>(index + 7 * SrcPacketSize);
-    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4, src5, src6, src7, src8);
-    return result;
-  }
-
- private:
-  const TensorEvaluator& m_impl;
-};
-
-template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int TgtCoeffRatio>
-struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, TgtCoeffRatio> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketConverter(const TensorEvaluator& impl)
-      : m_impl(impl), m_maxIndex(impl.dimensions().TotalSize()) {}
-
-  template<int LoadMode, typename Index>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
-    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
-    // Only call m_impl.packet() when we have direct access to the underlying data. This
-    // ensures that we don't compute the subexpression twice. We may however load some
-    // coefficients twice, but in practice this doesn't negatively impact performance.
-    if (m_impl.data() && (index + SrcPacketSize < m_maxIndex)) {
-      // Force unaligned memory loads since we can't ensure alignment anymore
-      return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<Unaligned>(index));
-    } else {
-      const int TgtPacketSize = internal::unpacket_traits<TgtPacket>::size;
-      typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
-      typedef typename internal::unpacket_traits<TgtPacket>::type TgtType;
-      internal::scalar_cast_op<SrcType, TgtType> converter;
-      EIGEN_ALIGN_MAX typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize];
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < TgtPacketSize; ++i) {
-        values[i] = converter(m_impl.coeff(index+i));
-      }
-      TgtPacket rslt = internal::pload<TgtPacket>(values);
-      return rslt;
-    }
-  }
-
- private:
-  const TensorEvaluator& m_impl;
-  const typename TensorEvaluator::Index m_maxIndex;
-};
-
-template<typename TargetType, typename XprType>
-class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprType>, ReadOnlyAccessors>
-{
-  public:
-    typedef typename internal::traits<TensorConversionOp>::Scalar Scalar;
-    typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind;
-    typedef typename internal::traits<TensorConversionOp>::Index Index;
-    typedef typename internal::nested<TensorConversionOp>::type Nested;
-    typedef Scalar CoeffReturnType;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConversionOp(const XprType& xpr)
-        : m_xpr(xpr) {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-};
-
-template <bool SameType, typename Eval, typename EvalPointerType> struct ConversionSubExprEval {
-  static EIGEN_STRONG_INLINE bool run(Eval& impl, EvalPointerType) {
-    impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-};
-
-template <typename Eval, typename EvalPointerType> struct ConversionSubExprEval<true, Eval, EvalPointerType> {
-  static EIGEN_STRONG_INLINE bool run(Eval& impl, EvalPointerType data) {
-    return impl.evalSubExprsIfNeeded(data);
-  }
-};
-
-#ifdef EIGEN_USE_THREADS
-template <bool SameType, typename Eval, typename EvalPointerType,
-          typename EvalSubExprsCallback>
-struct ConversionSubExprEvalAsync {
-  static EIGEN_STRONG_INLINE void run(Eval& impl, EvalPointerType, EvalSubExprsCallback done) {
-    impl.evalSubExprsIfNeededAsync(nullptr, std::move(done));
-  }
-};
-
-template <typename Eval, typename EvalPointerType,
-          typename EvalSubExprsCallback>
-struct ConversionSubExprEvalAsync<true, Eval, EvalPointerType,
-                                  EvalSubExprsCallback> {
-  static EIGEN_STRONG_INLINE void run(Eval& impl, EvalPointerType data, EvalSubExprsCallback done) {
-    impl.evalSubExprsIfNeededAsync(data, std::move(done));
-  }
-};
-#endif
-
-namespace internal {
-
-template <typename SrcType, typename TargetType, bool IsSameT>
-struct CoeffConv {
-  template <typename ArgType, typename Device>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
-    internal::scalar_cast_op<SrcType, TargetType> converter;
-    return converter(impl.coeff(index));
-  }
-};
-
-template <typename SrcType, typename TargetType>
-struct CoeffConv<SrcType, TargetType, true> {
-  template <typename ArgType, typename Device>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
-    return impl.coeff(index);
-  }
-};
-
-template <typename SrcPacket, typename TargetPacket, int LoadMode, bool ActuallyVectorize, bool IsSameT>
-struct PacketConv {
-  typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
-  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
-
-  static const int PacketSize = internal::unpacket_traits<TargetPacket>::size;
-
-  template <typename ArgType, typename Device>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
-    internal::scalar_cast_op<SrcType, TargetType> converter;
-    EIGEN_ALIGN_MAX typename internal::remove_const<TargetType>::type values[PacketSize];
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      values[i] = converter(impl.coeff(index+i));
-    }
-    TargetPacket rslt = internal::pload<TargetPacket>(values);
-    return rslt;
-  }
-};
-
-template <typename SrcPacket, typename TargetPacket, int LoadMode, bool IsSameT>
-struct PacketConv<SrcPacket, TargetPacket, LoadMode, true, IsSameT> {
-  typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
-  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
-
-  template <typename ArgType, typename Device>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
-    const int SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
-    const int TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
-    PacketConverter<TensorEvaluator<ArgType, Device>, SrcPacket, TargetPacket,
-                    SrcCoeffRatio, TgtCoeffRatio> converter(impl);
-    return converter.template packet<LoadMode>(index);
-  }
-};
-
-template <typename SrcPacket, typename TargetPacket, int LoadMode>
-struct PacketConv<SrcPacket, TargetPacket, LoadMode, /*ActuallyVectorize=*/false, /*IsSameT=*/true> {
-  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
-  static const int PacketSize = internal::unpacket_traits<TargetPacket>::size;
-
-  template <typename ArgType, typename Device>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
-    EIGEN_ALIGN_MAX typename internal::remove_const<TargetType>::type values[PacketSize];
-    for (int i = 0; i < PacketSize; ++i) values[i] = impl.coeff(index+i);
-    return internal::pload<TargetPacket>(values);
-  }
-};
-
-template <typename SrcPacket, typename TargetPacket, int LoadMode>
-struct PacketConv<SrcPacket, TargetPacket, LoadMode, /*ActuallyVectorize=*/true, /*IsSameT=*/true> {
-  template <typename ArgType, typename Device>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
-    return impl.template packet<LoadMode>(index);
-  }
-};
-
-}  // namespace internal
-
-// Eval as rvalue
-template<typename TargetType, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device>
-{
-  typedef TensorConversionOp<TargetType, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
-  typedef TargetType Scalar;
-  typedef TargetType CoeffReturnType;
-  typedef typename internal::remove_all<typename internal::traits<ArgType>::Scalar>::type SrcType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef typename PacketType<SrcType, Device>::type PacketSourceType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  static const bool IsSameType = internal::is_same<TargetType, SrcType>::value;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = false,
-    PacketAccess      =
-    #ifndef EIGEN_USE_SYCL
-                        true,
-    #else
-                        TensorEvaluator<ArgType, Device>::PacketAccess &
-                        internal::type_casting_traits<SrcType, TargetType>::VectorizedCast,
-    #endif
-    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    RawAccess         = false
-  };
-
-  static const int NumDims = internal::array_size<Dimensions>::value;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
-      ArgTensorBlock;
-
-  struct TensorConversionOpBlockFactory {
-    template <typename ArgXprType>
-    struct XprType {
-      typedef TensorConversionOp<TargetType, const ArgXprType> type;
-    };
-
-    template <typename ArgXprType>
-    typename XprType<ArgXprType>::type expr(const ArgXprType& expr) const {
-      return typename XprType<ArgXprType>::type(expr);
-    }
-  };
-
-  typedef internal::TensorUnaryExprBlock<TensorConversionOpBlockFactory,
-                                         ArgTensorBlock>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : m_impl(op.expression(), device)
-  {
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
-  {
-    return ConversionSubExprEval<IsSameType, TensorEvaluator<ArgType, Device>, EvaluatorPointerType>::run(m_impl, data);
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType data, EvalSubExprsCallback done) {
-    ConversionSubExprEvalAsync<IsSameType, TensorEvaluator<ArgType, Device>,
-                               EvaluatorPointerType,
-        EvalSubExprsCallback>::run(m_impl, data, std::move(done));
-  }
-#endif
-
-  EIGEN_STRONG_INLINE void cleanup()
-  {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return internal::CoeffConv<SrcType, TargetType, IsSameType>::run(m_impl,index);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType
-  packet(Index index) const {
-    // If we are not going to do the cast, we just need to check that base
-    // TensorEvaluator has packet access. Otherwise we also need to make sure,
-    // that we have an implementation of vectorized cast.
-    const bool Vectorizable =
-        IsSameType
-        ? TensorEvaluator<ArgType, Device>::PacketAccess
-        : int(TensorEvaluator<ArgType, Device>::PacketAccess) &
-          int(internal::type_casting_traits<SrcType, TargetType>::VectorizedCast);
-
-    return internal::PacketConv<PacketSourceType, PacketReturnType, LoadMode,
-                                Vectorizable, IsSameType>::run(m_impl, index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    const double cast_cost = TensorOpCost::CastCost<SrcType, TargetType>();
-    if (vectorized) {
-      const double SrcCoeffRatio =
-          internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
-      const double TgtCoeffRatio =
-          internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
-      return m_impl.costPerCoeff(vectorized) * (SrcCoeffRatio / PacketSize) +
-          TensorOpCost(0, 0, TgtCoeffRatio * (cast_cost / PacketSize));
-    } else {
-      return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cast_cost);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    return m_impl.getResourceRequirements();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    return TensorBlock(m_impl.block(desc, scratch),
-                         TensorConversionOpBlockFactory());
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-  /// required by sycl in order to extract the sycl accessor
-  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- protected:
-  TensorEvaluator<ArgType, Device> m_impl;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
deleted file mode 100644
index b20f80b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
+++ /dev/null
@@ -1,1132 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
-
-namespace Eigen {
-
-/** \class TensorConvolution
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor convolution class.
-  *
-  *
-  */
-namespace internal {
-
-template <typename Index, typename InputDims, int NumKernelDims, int Layout>
-class IndexMapper {
- public:
-  IndexMapper(const InputDims& input_dims, const array<Index, NumKernelDims>& kernel_dims,
-              const array<Index, NumKernelDims>& indices) {
-
-    array<Index, NumDims> dimensions = input_dims;
-    for (int i = 0; i < NumKernelDims; ++i) {
-      const Index index = indices[i];
-      const Index input_dim = input_dims[index];
-      const Index kernel_dim = kernel_dims[i];
-      const Index result_dim = input_dim - kernel_dim + 1;
-      dimensions[index] = result_dim;
-    }
-
-    array<Index, NumDims> inputStrides;
-    array<Index, NumDims> outputStrides;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      inputStrides[0] = 1;
-      outputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        inputStrides[i] = inputStrides[i-1] * input_dims[i-1];
-        outputStrides[i] = outputStrides[i-1] * dimensions[i-1];
-      }
-    } else {
-      inputStrides[NumDims - 1] = 1;
-      outputStrides[NumDims - 1] = 1;
-      for (int i = static_cast<int>(NumDims) - 2; i >= 0; --i) {
-        inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1];
-        outputStrides[i] = outputStrides[i + 1] * dimensions[i + 1];
-      }
-    }
-
-    array<Index, NumDims> gpuInputDimensions;
-    array<Index, NumDims> gpuOutputDimensions;
-    array<Index, NumDims> tmp = dimensions;
-    array<Index, NumDims> ordering;
-    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                              ? 0
-                              : NumDims - NumKernelDims;
-    for (int i = 0; i < NumKernelDims; ++i) {
-      const Index index = i + offset;
-      ordering[index] = indices[i];
-      tmp[indices[i]] = -1;
-      gpuInputDimensions[index] = input_dims[indices[i]];
-      gpuOutputDimensions[index] = dimensions[indices[i]];
-    }
-
-    int written = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                      ? NumKernelDims
-                      : 0;
-    for (int i = 0; i < NumDims; ++i) {
-      if (tmp[i] >= 0) {
-        ordering[written] = i;
-        gpuInputDimensions[written] = input_dims[i];
-        gpuOutputDimensions[written] = dimensions[i];
-        ++written;
-      }
-    }
-
-    for (int i = 0; i < NumDims; ++i) {
-      m_inputStrides[i] = inputStrides[ordering[i]];
-      m_outputStrides[i] = outputStrides[ordering[i]];
-    }
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = 0; i < NumDims; ++i) {
-        if (i > NumKernelDims) {
-          m_gpuInputStrides[i] =
-              m_gpuInputStrides[i - 1] * gpuInputDimensions[i - 1];
-          m_gpuOutputStrides[i] =
-              m_gpuOutputStrides[i - 1] * gpuOutputDimensions[i - 1];
-        } else {
-          m_gpuInputStrides[i] = 1;
-          m_gpuOutputStrides[i] = 1;
-        }
-      }
-    } else {
-      for (int i = NumDims - 1; i >= 0; --i) {
-        if (static_cast<size_t>(i + 1) < offset) {
-          m_gpuInputStrides[i] =
-              m_gpuInputStrides[i + 1] * gpuInputDimensions[i + 1];
-          m_gpuOutputStrides[i] =
-              m_gpuOutputStrides[i + 1] * gpuOutputDimensions[i + 1];
-        } else {
-          m_gpuInputStrides[i] = 1;
-          m_gpuOutputStrides[i] = 1;
-        }
-      }
-    }
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputPlaneToTensorInputOffset(Index p) const {
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int d = NumDims - 1; d > NumKernelDims; --d) {
-        const Index idx = p / m_gpuInputStrides[d];
-        inputIndex += idx * m_inputStrides[d];
-        p -= idx * m_gpuInputStrides[d];
-      }
-      inputIndex += p * m_inputStrides[NumKernelDims];
-    } else {
-      std::ptrdiff_t limit = 0;
-      if (NumKernelDims < NumDims) {
-        limit = NumDims - NumKernelDims - 1;
-      }
-      for (int d = 0; d < limit; ++d) {
-        const Index idx = p / m_gpuInputStrides[d];
-        inputIndex += idx * m_inputStrides[d];
-        p -= idx * m_gpuInputStrides[d];
-      }
-      inputIndex += p * m_inputStrides[limit];
-    }
-    return inputIndex;
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputPlaneToTensorOutputOffset(Index p) const {
-    Index outputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int d = NumDims - 1; d > NumKernelDims; --d) {
-        const Index idx = p / m_gpuOutputStrides[d];
-        outputIndex += idx * m_outputStrides[d];
-        p -= idx * m_gpuOutputStrides[d];
-      }
-      outputIndex += p * m_outputStrides[NumKernelDims];
-    } else {
-      std::ptrdiff_t limit = 0;
-      if (NumKernelDims < NumDims) {
-        limit = NumDims - NumKernelDims - 1;
-      }
-      for (int d = 0; d < limit; ++d) {
-        const Index idx = p / m_gpuOutputStrides[d];
-        outputIndex += idx * m_outputStrides[d];
-        p -= idx * m_gpuOutputStrides[d];
-      }
-      outputIndex += p * m_outputStrides[limit];
-    }
-    return outputIndex;
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i) const {
-    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                              ? 0
-                              : NumDims - NumKernelDims;
-    return i * m_inputStrides[offset];
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i) const {
-    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                              ? 0
-                              : NumDims - NumKernelDims;
-    return i * m_outputStrides[offset];
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i, Index j) const {
-    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                              ? 0
-                              : NumDims - NumKernelDims;
-    return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1];
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i, Index j) const {
-    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                              ? 0
-                              : NumDims - NumKernelDims;
-    return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1];
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i, Index j, Index k) const {
-    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                              ? 0
-                              : NumDims - NumKernelDims;
-    return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1] +
-           k * m_inputStrides[offset + 2];
-  }
-
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i, Index j, Index k) const {
-    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                              ? 0
-                              : NumDims - NumKernelDims;
-    return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1] +
-           k * m_outputStrides[offset + 2];
-  }
-
- private:
-  static const int NumDims = internal::array_size<InputDims>::value;
-  array<Index, NumDims> m_inputStrides;
-  array<Index, NumDims> m_outputStrides;
-  array<Index, NumDims> m_gpuInputStrides;
-  array<Index, NumDims> m_gpuOutputStrides;
-};
-
-
-
-template<typename Dimensions, typename InputXprType, typename KernelXprType>
-struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef typename promote_storage_type<typename InputXprType::Scalar,
-                                        typename KernelXprType::Scalar>::ret Scalar;
-  typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind,
-                                        typename traits<KernelXprType>::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<InputXprType>::Index,
-                                      typename traits<KernelXprType>::Index>::type Index;
-  typedef typename InputXprType::Nested LhsNested;
-  typedef typename KernelXprType::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  static const int NumDimensions = traits<InputXprType>::NumDimensions;
-  static const int Layout = traits<InputXprType>::Layout;
-  typedef typename conditional<Pointer_type_promotion<typename InputXprType::Scalar, Scalar>::val,
-  typename traits<InputXprType>::PointerType, typename traits<KernelXprType>::PointerType>::type PointerType;
-
-  enum {
-    Flags = 0
-  };
-};
-
-template<typename Dimensions, typename InputXprType, typename KernelXprType>
-struct eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense>
-{
-  typedef const TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>& type;
-};
-
-template<typename Dimensions, typename InputXprType, typename KernelXprType>
-struct nested<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >::type>
-{
-  typedef TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename Indices, typename InputXprType, typename KernelXprType>
-class TensorConvolutionOp : public TensorBase<TensorConvolutionOp<Indices, InputXprType, KernelXprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType,
-                                                  typename KernelXprType::CoeffReturnType>::ret CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorConvolutionOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorConvolutionOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
-      : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Indices& indices() const { return m_indices; }
-
-    /** \returns the nested expressions */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<typename InputXprType::Nested>::type&
-    inputExpression() const { return m_input_xpr; }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<typename KernelXprType::Nested>::type&
-    kernelExpression() const { return m_kernel_xpr; }
-
-  protected:
-    typename InputXprType::Nested m_input_xpr;
-    typename KernelXprType::Nested m_kernel_xpr;
-    const Indices m_indices;
-};
-
-
-template<typename Indices, typename InputArgType, typename KernelArgType, typename Device>
-struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Device>
-{
-  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
-
-  static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, Device>::Dimensions>::value;
-  static const int NumKernelDims = internal::array_size<Indices>::value;
-  typedef typename XprType::Index Index;
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<Scalar, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = int(TensorEvaluator<InputArgType, Device>::IsAligned) & int(TensorEvaluator<KernelArgType, Device>::IsAligned),
-    PacketAccess = int(TensorEvaluator<InputArgType, Device>::PacketAccess) & int(TensorEvaluator<KernelArgType, Device>::PacketAccess),
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<InputArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
-    const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputStride[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1];
-      }
-    } else {
-      m_inputStride[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1];
-      }
-    }
-
-    m_dimensions = m_inputImpl.dimensions();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = 0; i < NumKernelDims; ++i) {
-        const Index index = op.indices()[i];
-        const Index input_dim = input_dims[index];
-        const Index kernel_dim = kernel_dims[i];
-        const Index result_dim = input_dim - kernel_dim + 1;
-        m_dimensions[index] = result_dim;
-        if (i > 0) {
-          m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1];
-        } else {
-          m_kernelStride[0] = 1;
-        }
-        m_indexStride[i] = m_inputStride[index];
-      }
-
-      m_outputStride[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1];
-      }
-    } else {
-      for (int i = NumKernelDims - 1; i >= 0; --i) {
-        const Index index = op.indices()[i];
-        const Index input_dim = input_dims[index];
-        const Index kernel_dim = kernel_dims[i];
-        const Index result_dim = input_dim - kernel_dim + 1;
-        m_dimensions[index] = result_dim;
-        if (i < NumKernelDims - 1) {
-          m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1];
-        } else {
-          m_kernelStride[NumKernelDims - 1] = 1;
-        }
-        m_indexStride[i] = m_inputStride[index];
-      }
-
-      m_outputStride[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_outputStride[i] = m_outputStride[i + 1] * m_dimensions[i + 1];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
-    m_inputImpl.evalSubExprsIfNeeded(NULL);
-    preloadKernel();
-    return true;
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_inputImpl.cleanup();
-    if (m_local_kernel) {
-      m_device.deallocate((void*)m_kernel);
-      m_local_kernel = false;
-    }
-    m_kernel = NULL;
-  }
-
-  void evalTo(typename XprType::Scalar* buffer) {
-    evalSubExprsIfNeeded(NULL);
-    for (int i = 0; i < dimensions().TotalSize(); ++i) {
-      buffer[i] += coeff(i);
-    }
-    cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    CoeffReturnType result = CoeffReturnType(0);
-    convolve(firstInput(index), 0, NumKernelDims-1, result);
-    return result;
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC PacketReturnType packet(const Index index) const
-  {
-    Index indices[2] = {index, index+PacketSize-1};
-    Index startInputs[2] = {0, 0};
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx0 = indices[0] / m_outputStride[i];
-        const Index idx1 = indices[1] / m_outputStride[i];
-        startInputs[0] += idx0 * m_inputStride[i];
-        startInputs[1] += idx1 * m_inputStride[i];
-        indices[0] -= idx0 * m_outputStride[i];
-        indices[1] -= idx1 * m_outputStride[i];
-      }
-    } else {
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx0 = indices[0] / m_outputStride[i];
-        const Index idx1 = indices[1] / m_outputStride[i];
-        startInputs[0] += idx0 * m_inputStride[i];
-        startInputs[1] += idx1 * m_inputStride[i];
-        indices[0] -= idx0 * m_outputStride[i];
-        indices[1] -= idx1 * m_outputStride[i];
-      }
-    }
-    startInputs[0] += indices[0];
-    startInputs[1] += indices[1];
-
-    if (startInputs[1]-startInputs[0] == PacketSize-1) {
-      PacketReturnType result = internal::pset1<PacketReturnType>(0);
-      convolvePacket(startInputs[0], 0, NumKernelDims-1, result);
-      return result;
-    } else {
-      EIGEN_ALIGN_MAX Scalar data[PacketSize];
-      data[0] = Scalar(0);
-      convolve(startInputs[0], 0, NumKernelDims-1, data[0]);
-      for (int i = 1; i < PacketSize-1; ++i) {
-        data[i] = Scalar(0);
-        convolve(firstInput(index+i), 0, NumKernelDims-1, data[i]);
-      }
-      data[PacketSize-1] = Scalar(0);
-      convolve(startInputs[1], 0, NumKernelDims-1, data[PacketSize-1]);
-      return internal::pload<PacketReturnType>(data);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
-    // We ignore the use of fused multiply-add.
-    const double convolve_compute_cost =
-        TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
-    const double firstIndex_compute_cost =
-        NumDims *
-        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
-         TensorOpCost::DivCost<Index>());
-    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
-           kernel_size * (m_inputImpl.costPerCoeff(vectorized) +
-                          m_kernelImpl.costPerCoeff(vectorized) +
-                          TensorOpCost(0, 0, convolve_compute_cost, vectorized,
-                                       PacketSize));
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
- private:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
-    Index startInput = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = index / m_outputStride[i];
-        startInput += idx * m_inputStride[i];
-        index -= idx * m_outputStride[i];
-      }
-    } else {
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = index / m_outputStride[i];
-        startInput += idx * m_inputStride[i];
-        index -= idx * m_outputStride[i];
-      }
-    }
-    startInput += index;
-    return startInput;
-  }
-
-  EIGEN_DEVICE_FUNC void convolve(Index firstIndex, Index firstKernel, int DimIndex, CoeffReturnType& accum) const {
-    for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
-      const Index input = firstIndex + j * m_indexStride[DimIndex];
-      const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
-      if (DimIndex > 0) {
-        convolve(input, kernel, DimIndex-1, accum);
-      } else {
-        accum += m_inputImpl.coeff(input) * m_kernel[kernel];
-      }
-    }
-  }
-
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC void convolvePacket(Index firstIndex, Index firstKernel, int DimIndex, Packet& accum) const {
-    for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
-      const Index input = firstIndex + j * m_indexStride[DimIndex];
-      const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
-      if (DimIndex > 0) {
-        convolvePacket(input, kernel, DimIndex-1, accum);
-      } else {
-        accum = internal::pmadd<Packet>(m_inputImpl.template packet<Unaligned>(input), internal::pset1<Packet>(m_kernel[kernel]), accum);
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() {
-    // Don't make a local copy of the kernel unless we have to (i.e. it's an
-    // expression that needs to be evaluated)
-    const Scalar* in_place = m_kernelImpl.data();
-    if (in_place) {
-      m_kernel = in_place;
-      m_local_kernel = false;
-    } else {
-      size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
-      Scalar* local = (Scalar*)m_device.allocate_temp(kernel_sz);
-      typedef TensorEvalToOp<const KernelArgType> EvalTo;
-      EvalTo evalToTmp(local, m_kernelArg);
-      const bool Vectorize = internal::IsVectorizable<Device, KernelArgType>::value;
-      internal::TensorExecutor<const EvalTo, Device, Vectorize>::run(evalToTmp, m_device);
-
-      m_kernel = local;
-      m_local_kernel = true;
-    }
-  }
-
-  array<Index, NumDims> m_inputStride;
-  array<Index, NumDims> m_outputStride;
-
-  array<Index, NumKernelDims> m_indexStride;
-  array<Index, NumKernelDims> m_kernelStride;
-  TensorEvaluator<InputArgType, Device> m_inputImpl;
-  TensorEvaluator<KernelArgType, Device> m_kernelImpl;
-  Dimensions m_dimensions;
-
-  KernelArgType m_kernelArg;
-  const Scalar* m_kernel;
-  bool m_local_kernel;
-  const Device EIGEN_DEVICE_REF m_device;
-};
-
-
-
-
-// Use an optimized implementation of the evaluation code for GPUs whenever possible.
-#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
-
-template <int StaticKernelSize>
-struct GetKernelSize {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int /*kernelSize*/) const {
-    return StaticKernelSize;
-  }
-};
-template <>
-struct GetKernelSize<Dynamic> {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int kernelSize) const {
-    return kernelSize;
-  }
-};
-
-template <typename InputEvaluator, typename Index, typename InputDims,
-          int StaticKernelSize>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel1D(
-    InputEvaluator eval,
-    const internal::IndexMapper<Index, InputDims, 1, InputEvaluator::Layout>
-        indexMapper,
-    const float* __restrict kernel, const int numPlanes, const int numX,
-    const int maxX, const int kernelSize, float* buffer) {
-#if defined(EIGEN_HIPCC)
-  HIP_DYNAMIC_SHARED(float, s)
-#else
-  extern __shared__ float s[];
-#endif
-
-  const int first_x = blockIdx.x * maxX;
-  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
-  const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSize>()(kernelSize);
-  const int num_x_output = last_x - first_x + 1;
-
-  const int first_plane = blockIdx.y * blockDim.y;
-  const int plane_stride = blockDim.y * gridDim.y;
-
-  for (int p = first_plane + threadIdx.y; p < numPlanes; p += plane_stride) {
-    // Load inputs to shared memory
-    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
-    const int plane_kernel_offset = threadIdx.y * num_x_input;
-    #pragma unroll
-    for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
-      const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i+first_x);
-      s[i + plane_kernel_offset] = eval.coeff(tensor_index);
-    }
-
-    __syncthreads();
-
-    // Compute the convolution
-    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
-
-    #pragma unroll
-    for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
-      const int kernel_offset = plane_kernel_offset + i;
-      float result = 0.0f;
-      #pragma unroll
-      for (int k = 0; k < GetKernelSize<StaticKernelSize>()(kernelSize); ++k) {
-        result += s[k + kernel_offset] * kernel[k];
-      }
-      const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i+first_x);
-      buffer[tensor_index] = result;
-    }
-    __syncthreads();
-  }
-};
-
-template <typename InputEvaluator, typename Index, typename InputDims,
-          int StaticKernelSizeX, int StaticKernelSizeY>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel2D(
-    InputEvaluator eval,
-    const internal::IndexMapper<Index, InputDims, 2, InputEvaluator::Layout>
-        indexMapper,
-    const float* __restrict kernel, const int numPlanes, const int numX,
-    const int maxX, const int numY, const int maxY, const int kernelSizeX,
-    const int kernelSizeY, float* buffer) {
-#if defined(EIGEN_HIPCC)
-  HIP_DYNAMIC_SHARED(float, s)
-#else
-  extern __shared__ float s[];
-#endif
-
-  const int first_x = blockIdx.x * maxX;
-  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
-  const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSizeX>()(kernelSizeX);
-  const int num_x_output = last_x - first_x + 1;
-
-  const int first_y = blockIdx.y * maxY;
-  const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
-  const int num_y_input = last_y - first_y + GetKernelSize<StaticKernelSizeY>()(kernelSizeY);
-  const int num_y_output = last_y - first_y + 1;
-
-  const int first_plane = blockIdx.z * blockDim.z;
-  const int plane_stride = blockDim.z * gridDim.z;
-
-  for (int p = first_plane + threadIdx.z; p < numPlanes; p += plane_stride) {
-
-    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
-    const int plane_kernel_offset = threadIdx.z * num_y_input;
-
-    // Load inputs to shared memory
-    #pragma unroll
-    for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
-      const int input_offset = num_x_input * (j + plane_kernel_offset);
-      #pragma unroll
-      for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
-        const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i+first_x, j+first_y);
-        s[i + input_offset] = eval.coeff(tensor_index);
-      }
-    }
-
-    __syncthreads();
-
-    // Convolution
-    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
-
-    #pragma unroll
-    for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
-      #pragma unroll
-      for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
-        float result = 0.0f;
-        #pragma unroll
-        for (int l = 0; l < GetKernelSize<StaticKernelSizeY>()(kernelSizeY); ++l) {
-          const int kernel_offset = kernelSizeX * l;
-          const int input_offset = i + num_x_input * (j + l + plane_kernel_offset);
-          #pragma unroll
-          for (int k = 0; k < GetKernelSize<StaticKernelSizeX>()(kernelSizeX); ++k) {
-            result += s[k + input_offset] * kernel[k + kernel_offset];
-          }
-        }
-        const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i+first_x, j+first_y);
-        buffer[tensor_index] = result;
-      }
-    }
-
-    __syncthreads();
-  }
-};
-
-template <typename InputEvaluator, typename Index, typename InputDims>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel3D(
-    InputEvaluator eval,
-    const internal::IndexMapper<Index, InputDims, 3, InputEvaluator::Layout>
-        indexMapper,
-    const float* __restrict kernel, const size_t numPlanes, const size_t numX,
-    const size_t maxX, const size_t numY, const size_t maxY, const size_t numZ,
-    const size_t maxZ, const size_t kernelSizeX, const size_t kernelSizeY,
-    const size_t kernelSizeZ, float* buffer) {
-#if defined(EIGEN_HIPCC)
-  HIP_DYNAMIC_SHARED(float, s)
-#else
-  extern __shared__ float s[];
-#endif
-
-  // Load inputs to shared memory
-  const int first_x = blockIdx.x * maxX;
-  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
-  const int num_x_input = last_x - first_x + kernelSizeX;
-
-  const int first_y = blockIdx.y * maxY;
-  const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
-  const int num_y_input = last_y - first_y + kernelSizeY;
-
-  const int first_z = blockIdx.z * maxZ;
-  const int last_z = (first_z + maxZ < numZ ? first_z + maxZ : numZ) - 1;
-  const int num_z_input = last_z - first_z + kernelSizeZ;
-
-  for (int p = 0; p < numPlanes; ++p) {
-
-    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
-    const int plane_kernel_offset = 0;
-
-    for (int k = threadIdx.z; k < num_z_input; k += blockDim.z) {
-      for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
-        for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
-          const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i+first_x, j+first_y, k+first_z);
-          s[i + num_x_input * (j + num_y_input * (k + plane_kernel_offset))] = eval.coeff(tensor_index);
-        }
-      }
-    }
-
-    __syncthreads();
-
-    // Convolution
-    const int num_z_output = last_z - first_z + 1;
-    const int num_y_output = last_y - first_y + 1;
-    const int num_x_output = last_x - first_x + 1;
-    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
-
-    for (int k = threadIdx.z; k < num_z_output; k += blockDim.z) {
-      for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
-        for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
-          float result = 0.0f;
-          for (int n = 0; n < kernelSizeZ; ++n) {
-            for (int m = 0; m < kernelSizeY; ++m) {
-              for (int l = 0; l < kernelSizeX; ++l) {
-                result += s[i + l + num_x_input * (j + m + num_y_input * (k + n + plane_kernel_offset))] * kernel[l + kernelSizeX * (m + kernelSizeY * n)];
-              }
-            }
-          }
-          const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i+first_x, j+first_y, k+first_z);
-          buffer[tensor_index] = result;
-        }
-      }
-    }
-    __syncthreads();
-  }
-};
-
-
-
-template<typename Indices, typename InputArgType, typename KernelArgType>
-struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, GpuDevice>
-{
-  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
-
-  static const int NumDims =  internal::array_size<typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions>::value;
-  static const int NumKernelDims = internal::array_size<Indices>::value;
-  typedef typename XprType::Index Index;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions KernelDimensions;
-
-  enum {
-    IsAligned = TensorEvaluator<InputArgType, GpuDevice>::IsAligned & TensorEvaluator<KernelArgType, GpuDevice>::IsAligned,
-    PacketAccess = false,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<InputArgType, GpuDevice>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  TensorEvaluator(const XprType& op, const GpuDevice& device)
-      : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_indices(op.indices()), m_buf(NULL), m_kernel(NULL), m_local_kernel(false), m_device(device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, GpuDevice>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, GpuDevice>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    const typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions& input_dims = m_inputImpl.dimensions();
-    const typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
-
-    m_dimensions = m_inputImpl.dimensions();
-    for (int i = 0; i < NumKernelDims; ++i) {
-      const Index index = op.indices()[i];
-      const Index input_dim = input_dims[index];
-      const Index kernel_dim = kernel_dims[i];
-      const Index result_dim = input_dim - kernel_dim + 1;
-      m_dimensions[index] = result_dim;
-    }
-  }
-
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType;
-  typedef typename InputArgType::Scalar Scalar;
-  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
-    preloadKernel();
-    m_inputImpl.evalSubExprsIfNeeded(NULL);
-    if (data) {
-      executeEval(data);
-      return false;
-    } else {
-      m_buf = (Scalar*)m_device.allocate(dimensions().TotalSize() * sizeof(Scalar));
-      executeEval(m_buf);
-      return true;
-    }
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_inputImpl.cleanup();
-    if (m_buf) {
-      m_device.deallocate(m_buf);
-      m_buf = NULL;
-    }
-    if (m_local_kernel) {
-      m_device.deallocate((void*)m_kernel);
-      m_local_kernel = false;
-    }
-    m_kernel = NULL;
-  }
-
-  EIGEN_STRONG_INLINE void preloadKernel() {
-    // Don't make a local copy of the kernel unless we have to (i.e. it's an
-    // expression that needs to be evaluated)
-    const Scalar* in_place = m_kernelImpl.data();
-    if (in_place) {
-      m_kernel = in_place;
-      m_local_kernel = false;
-    } else {
-      size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
-      Scalar* local = (Scalar*)m_device.allocate(kernel_sz);
-      typedef TensorEvalToOp<const KernelArgType> EvalTo;
-      EvalTo evalToTmp(local, m_kernelArg);
-      const bool PacketAccess = internal::IsVectorizable<GpuDevice, KernelArgType>::value;
-      internal::TensorExecutor<const EvalTo, GpuDevice, PacketAccess>::run(evalToTmp, m_device);
-
-      m_kernel = local;
-      m_local_kernel = true;
-    }
-  }
-
-  static unsigned int ceil(unsigned int num, unsigned int denom) {
-    const unsigned int rounded_toward_zero = num / denom;
-    if (num > rounded_toward_zero * denom) {
-      return rounded_toward_zero + 1;
-    }
-    return rounded_toward_zero;
-  }
-
-  void executeEval(Scalar* data) const {
-    typedef typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions InputDims;
-
-    const int maxSharedMem = m_device.sharedMemPerBlock();
-    const int maxThreadsPerBlock = m_device.maxGpuThreadsPerBlock();
-    const int maxBlocksPerProcessor = m_device.maxGpuThreadsPerMultiProcessor() / maxThreadsPerBlock;
-    const int numMultiProcessors = m_device.getNumGpuMultiProcessors();
-    const int warpSize = 32;
-
-    switch (NumKernelDims) {
-      case 1: {
-        const int kernel_size = m_kernelImpl.dimensions().TotalSize();
-
-        const int numX = dimensions()[m_indices[0]];
-        const int numP = dimensions().TotalSize() / numX;
-        int maxX;
-        dim3 block_size;
-
-        const int single_stride_dim =
-            static_cast<int>(Layout) == static_cast<int>(ColMajor)
-                ? 0
-                : m_inputImpl.dimensions().rank() - 1;
-        if (m_indices[0] == single_stride_dim) {
-          // Maximum the reuse
-          const int inner_dim = ((maxSharedMem / (sizeof(Scalar)) - kernel_size + 1 + 31) / 32) * 32;
-          maxX = numext::mini<int>(inner_dim, numX);
-          const int maxP = numext::mini<int>(maxSharedMem / ((kernel_size - 1 + maxX) * sizeof(Scalar)), numP);
-          block_size.x = numext::mini(maxThreadsPerBlock, maxX);
-          block_size.y = numext::mini<int>(maxThreadsPerBlock / block_size.x, maxP);
-        }
-        else {
-          // Read as much as possible alongside the inner most dimension, that is the plane
-          const int inner_dim = maxSharedMem / ((warpSize + kernel_size) * sizeof(Scalar));
-          const int maxP = numext::mini<int>(inner_dim, numP);
-          maxX = numext::mini<int>(maxSharedMem / (inner_dim * sizeof(Scalar)) - kernel_size + 1, numX);
-
-          block_size.x = numext::mini(warpSize, maxX);
-          block_size.y = numext::mini<int>(maxThreadsPerBlock/block_size.x, maxP);
-        }
-
-        const int shared_mem = block_size.y * (maxX + kernel_size - 1) * sizeof(Scalar);
-        gpu_assert(shared_mem <= maxSharedMem);
-
-        const int num_x_blocks = ceil(numX, maxX);
-        const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem);
-        const int num_y_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks);
-
-        dim3 num_blocks(num_x_blocks, numext::mini<int>(num_y_blocks, ceil(numP, block_size.y)));
-
-
-        //cout << "launching 1D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " maxX: " << maxX << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
-
-        const array<Index, 1> indices(m_indices[0]);
-        const array<Index, 1> kernel_dims(m_kernelImpl.dimensions()[0]);
-        internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(
-            m_inputImpl.dimensions(), kernel_dims, indices);
-        switch(kernel_size) {
-          case 4: {
-            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 4, data);
-            break;
-          }
-          case 7: {
-            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 7, data);
-            break;
-          }
-          default: {
-            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, kernel_size, data);
-          }
-        }
-        break;
-      }
-
-      case 2: {
-        const int idxX =
-            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1;
-        const int idxY =
-            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0;
-        const int kernel_size_x = m_kernelImpl.dimensions()[idxX];
-        const int kernel_size_y = m_kernelImpl.dimensions()[idxY];
-
-        const int numX = dimensions()[m_indices[idxX]];
-        const int numY = dimensions()[m_indices[idxY]];
-        const int numP = dimensions().TotalSize() / (numX*numY);
-
-        const float scaling_factor = sqrtf(static_cast<float>(maxSharedMem) / (sizeof(Scalar) * kernel_size_y * kernel_size_x));
-
-        // Snap maxX to warp size
-        int inner_dim = ((static_cast<int>(scaling_factor * kernel_size_x) - kernel_size_x + 1 + 32) / 32) * 32;
-        const int maxX = numext::mini<int>(inner_dim, numX);
-        const int maxY = numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1)) - kernel_size_y + 1, numY);
-        const int maxP = numext::mini<int>(maxSharedMem / ((kernel_size_x - 1 + maxX) * (kernel_size_y - 1 + maxY) * sizeof(Scalar)), numP);
-
-        dim3 block_size;
-        block_size.x = numext::mini(1024, maxX);
-        block_size.y = numext::mini<int>(1024/block_size.x, maxY);
-        block_size.z = numext::mini<int>(1024/(block_size.x*block_size.y), maxP);
-
-        const int shared_mem = block_size.z * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * sizeof(Scalar);
-        gpu_assert(shared_mem <= maxSharedMem);
-
-        const int num_x_blocks = ceil(numX, maxX);
-        const int num_y_blocks = ceil(numY, maxY);
-        const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem);
-        const int num_z_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks * num_y_blocks);
-
-        dim3 num_blocks(num_x_blocks, num_y_blocks, numext::mini<int>(num_z_blocks, ceil(numP, block_size.z)));
-
-
-        //cout << "launching 2D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y  << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z << " maxX: " << maxX << " maxY: " << maxY << " maxP: " << maxP << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
-
-        const array<Index, 2> indices(m_indices[idxX], m_indices[idxY]);
-        const array<Index, 2> kernel_dims(m_kernelImpl.dimensions()[idxX],
-                                          m_kernelImpl.dimensions()[idxY]);
-        internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(
-            m_inputImpl.dimensions(), kernel_dims, indices);
-        switch (kernel_size_x) {
-          case 4: {
-            switch (kernel_size_y) {
-              case 7: {
-                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, 7, data);
-                break;
-              }
-              default: {
-                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, kernel_size_y, data);
-                break;
-              }
-            }
-            break;
-          }
-          case 7: {
-            switch (kernel_size_y) {
-              case 4: {
-                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, 4, data);
-                break;
-              }
-              default: {
-                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, kernel_size_y, data);
-                break;
-              }
-            }
-            break;
-          }
-          default: {
-            LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, kernel_size_x, kernel_size_y, data);
-            break;
-          }
-        }
-        break;
-      }
-
-      case 3: {
-        const int idxX =
-            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2;
-        const int idxY =
-            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1;
-        const int idxZ =
-            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0;
-
-        const int kernel_size_x = m_kernelImpl.dimensions()[idxX];
-        const int kernel_size_y = m_kernelImpl.dimensions()[idxY];
-        const int kernel_size_z = m_kernelImpl.dimensions()[idxZ];
-
-        const int numX = dimensions()[m_indices[idxX]];
-        const int numY = dimensions()[m_indices[idxY]];
-        const int numZ = dimensions()[m_indices[idxZ]];
-        const int numP = dimensions().TotalSize() / (numX*numY*numZ);
-
-        const int maxX = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * kernel_size_y * kernel_size_z) - kernel_size_x + 1, numX));
-        const int maxY = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * kernel_size_z) - kernel_size_y + 1, numY));
-        const int maxZ = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1)) - kernel_size_z + 1, numZ));
-
-        dim3 block_size;
-        block_size.x = numext::mini(32, maxX);
-        block_size.y = numext::mini(32, maxY);
-        block_size.z = numext::mini<int>(1024/(block_size.x*block_size.y), maxZ);
-        dim3 num_blocks(ceil(numX, maxX), ceil(numY, maxY), ceil(numZ, maxZ));
-
-        const int shared_mem = (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * (maxZ + kernel_size_z - 1) * sizeof(Scalar);
-        gpu_assert(shared_mem <= maxSharedMem);
-
-        //cout << "launching 3D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y  << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z  << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
-        const array<Index, 3> indices(m_indices[idxX], m_indices[idxY],
-                                      m_indices[idxZ]);
-        const array<Index, 3> kernel_dims(m_kernelImpl.dimensions()[idxX],
-                                          m_kernelImpl.dimensions()[idxY],
-                                          m_kernelImpl.dimensions()[idxZ]);
-        internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(
-            m_inputImpl.dimensions(), kernel_dims, indices);
-
-        LAUNCH_GPU_KERNEL((EigenConvolutionKernel3D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, numZ, maxZ, kernel_size_x, kernel_size_y, kernel_size_z, data);
-        break;
-      }
-
-      default: {
-        EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3), THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    eigen_assert(m_buf);
-    eigen_assert(index < m_dimensions.TotalSize());
-    return m_buf[index];
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const
-  {
-    eigen_assert(m_buf);
-    eigen_assert(index < m_dimensions.TotalSize());
-    return internal::ploadt<PacketReturnType, LoadMode>(m_buf+index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    // TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost
-    // model.
-    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
-    // We ignore the use of fused multiply-add.
-    const double convolve_compute_cost =
-        TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
-    const double firstIndex_compute_cost =
-        NumDims *
-        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
-         TensorOpCost::DivCost<Index>());
-    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
-           kernel_size * (m_inputImpl.costPerCoeff(vectorized) +
-                          m_kernelImpl.costPerCoeff(vectorized) +
-                          TensorOpCost(0, 0, convolve_compute_cost, vectorized,
-                                       PacketSize));
-  }
-
- private:
-  // No assignment (copies are needed by the kernels)
-  TensorEvaluator& operator = (const TensorEvaluator&);
-
-  TensorEvaluator<InputArgType, GpuDevice> m_inputImpl;
-  TensorEvaluator<KernelArgType, GpuDevice> m_kernelImpl;
-  KernelArgType m_kernelArg;
-  Indices m_indices;
-  Dimensions m_dimensions;
-  Scalar* m_buf;
-  const Scalar* m_kernel;
-  bool m_local_kernel;
-
-  const GpuDevice& m_device;
-};
-#endif
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
deleted file mode 100644
index 033318f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
+++ /dev/null
@@ -1,544 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
-
-namespace Eigen {
-
-/** \class TensorConvolution
- * \ingroup CXX11_Tensor_Module
- *
- * \brief Tensor convolution class.
- *
- *
- */
-
-enum class convolution_type { CONV1D, CONV2D, CONV3D };
-template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
-          typename Kernel_accessor, typename Buffer_accessor, convolution_type Conv_Dim>
-struct EigenConvolutionKernel;
-template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
-          typename Kernel_accessor, typename Buffer_accessor>
-struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
-                              Buffer_accessor, convolution_type::CONV1D> {
-  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      Local_accessor;
-  Local_accessor local_acc;
-  Evaluator device_evaluator;
-  Kernel_accessor kernel_filter;
-  Buffer_accessor buffer_acc;
-  internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper;
-  const size_t kernelSize;
-  const cl::sycl::range<2> input_range;
-  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
-                         Buffer_accessor buffer_acc_,
-                         internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper_,
-                         const size_t kernelSize_, const cl::sycl::range<2> input_range_)
-      : local_acc(local_acc_),
-        device_evaluator(device_evaluator_),
-        kernel_filter(kernel_filter_),
-        buffer_acc(buffer_acc_),
-        indexMapper(indexMapper_),
-        kernelSize(kernelSize_),
-        input_range(input_range_) {}
-
-  template <typename BooleanDim2>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim2 boolean_check) {
-    return (boolean_check[0] && boolean_check[1]);
-  }
-  void operator()(cl::sycl::nd_item<2> itemID) {
-    auto buffer_ptr = buffer_acc.get_pointer();
-    auto kernel_ptr = kernel_filter.get_pointer();
-    // the required row to be calculated for the for each plane in shered memory
-    const size_t num_input = (itemID.get_local_range()[0] + kernelSize - 1);
-    const size_t plane_kernel_offset = itemID.get_local_id(1) * num_input;
-    const size_t input_offset = itemID.get_group(0) * itemID.get_local_range()[0];
-    const size_t plane_tensor_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(1));
-    /// fill the shared memory
-    for (size_t i = itemID.get_local_id(0); i < num_input; i += itemID.get_local_range()[0]) {
-      const size_t local_index = i + plane_kernel_offset;
-      const size_t tensor_index =
-          plane_tensor_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i + input_offset);
-
-      local_acc[local_index] =
-          (((i + input_offset) < (input_range[0] + kernelSize - 1)) && itemID.get_global_id(1) < input_range[1])
-              ? device_evaluator.coeff(tensor_index)
-              : CoeffReturnType(0);
-    }
-
-    itemID.barrier(cl::sycl::access::fence_space::local_space);
-
-    // calculate the convolution // output start x
-    const size_t first_output_start = itemID.get_group(0) * (itemID.get_local_range()[0]);
-    if (boundary_check(itemID.get_global_id() < input_range)) {
-      CoeffReturnType result = static_cast<CoeffReturnType>(0);
-      const size_t index = plane_kernel_offset + itemID.get_local_id(0);
-      for (size_t k = 0; k < kernelSize; ++k) {
-        result += (local_acc[k + index] * kernel_ptr[k]);
-      }
-      const size_t tensor_index =
-          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(1)) +
-          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + first_output_start);
-      buffer_ptr[tensor_index] = result;
-    }
-  }
-};
-
-template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
-          typename Kernel_accessor, typename Buffer_accessor>
-struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
-                              Buffer_accessor, convolution_type::CONV2D> {
-  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      Local_accessor;
-  Local_accessor local_acc;
-  Evaluator device_evaluator;
-  Kernel_accessor kernel_filter;
-  Buffer_accessor buffer_acc;
-  internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper;
-  const cl::sycl::range<2> kernel_size;
-  const cl::sycl::range<3> input_range;
-  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
-                         Buffer_accessor buffer_acc_,
-                         internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper_,
-                         const cl::sycl::range<2> kernel_size_, const cl::sycl::range<3> input_range_)
-      : local_acc(local_acc_),
-        device_evaluator(device_evaluator_),
-        kernel_filter(kernel_filter_),
-        buffer_acc(buffer_acc_),
-        indexMapper(indexMapper_),
-        kernel_size(kernel_size_),
-        input_range(input_range_) {}
-  template <typename BooleanDim3>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) {
-    return (boolean_check[0] && boolean_check[1] && boolean_check[2]);
-  }
-
-  void operator()(cl::sycl::nd_item<3> itemID) {
-    auto buffer_ptr = buffer_acc.get_pointer();
-    auto kernel_ptr = kernel_filter.get_pointer();
-    // the required row to be calculated for the for each plane in shered memory
-    const auto num_input = cl::sycl::range<2>{
-        (cl::sycl::range<2>(itemID.get_local_range()[0], itemID.get_local_range()[1]) + kernel_size - 1)};
-
-    const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(2));
-    const size_t plane_kernel_offset = itemID.get_local_id(2) * num_input[1];
-
-    const auto input_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0],
-                                                 itemID.get_group(1) * itemID.get_local_range()[1]};
-      
-    // fill the local memory
-    bool in_range_dim2 = itemID.get_global_id(2) < input_range[2];
-    for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
-      const size_t local_input_offset = num_input[0] * (j + plane_kernel_offset);
-      bool in_range_dim1 = ((j + input_offset[1]) < (input_range[1] + kernel_size[1] - 1)); 
-      for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
-        const size_t local_index = i + local_input_offset;
-        const size_t tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
-                                                             i + input_offset[0], j + input_offset[1]);
-        local_acc[local_index] = (((i + input_offset[0]) < (input_range[0] + kernel_size[0] - 1)) &&
-                                  in_range_dim1 && in_range_dim2)
-                                     ? device_evaluator.coeff(tensor_index)
-                                     : CoeffReturnType(0);
-      }
-    }
-
-    itemID.barrier(cl::sycl::access::fence_space::local_space);
-
-    // output offset start for each thread
-    const auto output_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0],
-                                                  itemID.get_group(1) * itemID.get_local_range()[1]};
-
-    if (boundary_check(itemID.get_global_id() < input_range)) {
-      CoeffReturnType result = static_cast<CoeffReturnType>(0);
-
-      for (size_t j = 0; j < kernel_size[1]; j++) {
-        size_t kernel_offset = kernel_size[0] * j;
-        const size_t index =
-            (num_input[0] * (plane_kernel_offset + j + itemID.get_local_id(1))) + itemID.get_local_id(0);
-        for (size_t i = 0; i < kernel_size[0]; i++) {
-          result += (local_acc[i + index] * kernel_ptr[i + kernel_offset]);
-        }
-      }
-      const size_t tensor_index =
-          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(2)) +
-          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + output_offset[0],
-                                                             itemID.get_local_id(1) + output_offset[1]);
-
-      buffer_ptr[tensor_index] = result;
-    }
-  }
-};
-
-template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
-          typename Kernel_accessor, typename Buffer_accessor>
-struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
-                              Buffer_accessor, convolution_type::CONV3D> {
-  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      Local_accessor;
-  Local_accessor local_acc;
-  Evaluator device_evaluator;
-  Kernel_accessor kernel_filter;
-  Buffer_accessor buffer_acc;
-  internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper;
-  const cl::sycl::range<3> kernel_size;
-  const cl::sycl::range<3> input_range;
-  const size_t numP;
-
-  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
-                         Buffer_accessor buffer_acc_,
-                         internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper_,
-                         const cl::sycl::range<3> kernel_size_, const cl::sycl::range<3> input_range_,
-                         const size_t numP_)
-      : local_acc(local_acc_),
-        device_evaluator(device_evaluator_),
-        kernel_filter(kernel_filter_),
-        buffer_acc(buffer_acc_),
-        indexMapper(indexMapper_),
-        kernel_size(kernel_size_),
-        input_range(input_range_),
-        numP(numP_) {}
-  template <typename BooleanDim3>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) {
-    return (boolean_check[0] && boolean_check[1] && boolean_check[2]);
-  }
-  void operator()(cl::sycl::nd_item<3> itemID) {
-    auto buffer_ptr = buffer_acc.get_pointer();
-    auto kernel_ptr = kernel_filter.get_pointer();
-    const auto num_input = cl::sycl::range<3>{itemID.get_local_range() + kernel_size - 1};
-
-    const auto input_offset = cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range()};
-
-    const auto output_offset =
-          cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range() + itemID.get_local_id()};
-
-    for (size_t p = 0; p < numP; p++) {
-      /// fill the shared memory
-      const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
-      for (size_t k = itemID.get_local_id(2); k < num_input[2]; k += itemID.get_local_range()[2]) {
-        size_t local_index_dim2 = num_input[0] * num_input[1] * k;
-        bool cond_k_dim = (k + input_offset[2] < (input_range[2] + kernel_size[2] - 1));
-        for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
-          bool cond_j_dim = cond_k_dim && (j + input_offset[1] < (input_range[1] + kernel_size[1] - 1));
-          size_t local_index_dim1 = (num_input[0] * j)  + local_index_dim2;
-          for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
-            bool conds = cond_j_dim && (i + input_offset[0] < (input_range[0] + kernel_size[0] - 1));
-            const size_t local_index = local_index_dim1 + i;
-            const size_t tensor_index =
-                plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
-                                         i + input_offset[0], j + input_offset[1], k + input_offset[2]);
-            local_acc[local_index] = conds ? device_evaluator.coeff(tensor_index) : CoeffReturnType(0);
-          }
-        }
-      }
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-
-      // calculate the convolution
-
-      if (boundary_check(itemID.get_global_id() < input_range)) {
-        CoeffReturnType result = static_cast<CoeffReturnType>(0);
-        for (size_t k = 0; k < kernel_size[2]; k++) {
-          for (size_t j = 0; j < kernel_size[1]; j++) {
-            for (size_t i = 0; i < kernel_size[0]; i++) {
-              const size_t kernel_index = i + kernel_size[0] * (j + kernel_size[1] * k);
-              const size_t local_index =
-                  ((i + itemID.get_local_id(0)) +
-                   num_input[0] * ((j + itemID.get_local_id(1)) + num_input[1] * (k + itemID.get_local_id(2))));
-
-              result += (local_acc[local_index] * kernel_ptr[kernel_index]);
-            }
-          }
-        }
-        const size_t tensor_index =
-            indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p) +
-            indexMapper.mapGpuOutputKernelToTensorOutputOffset(output_offset[0], output_offset[1], output_offset[2]);
-        buffer_ptr[tensor_index] = result;
-      }
-
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-    }
-  }
-};
-
-template <typename Indices, typename InputArgType, typename KernelArgType>
-struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Eigen::SyclDevice> {
-  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
-
-  static const int NumDims =
-      internal::array_size<typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions>::value;
-  static const int NumKernelDims = internal::array_size<Indices>::value;
-  typedef typename XprType::Index Index;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions KernelDimensions;
-  typedef const Eigen::SyclDevice Device;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Eigen::SyclDevice>::type PacketReturnType;
-  typedef typename InputArgType::Scalar Scalar;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Eigen::SyclDevice> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  typedef StorageMemory<const CoeffReturnType, Eigen::SyclDevice> KernelStorage;
-
-  enum {
-    IsAligned = TensorEvaluator<InputArgType, Eigen::SyclDevice>::IsAligned &
-                TensorEvaluator<KernelArgType, Eigen::SyclDevice>::IsAligned,
-    PacketAccess = false,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  TensorEvaluator(const XprType &op, const Eigen::SyclDevice &device)
-      : m_inputImpl(op.inputExpression(), device),
-        m_kernelArg(op.kernelExpression()),
-        m_kernelImpl(op.kernelExpression(), device),
-        m_indices(op.indices()),
-        m_buf(NULL),
-        m_kernel(NULL),
-        m_local_kernel(false),
-        m_device(device) {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout) ==
-                         static_cast<int>(TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Layout)),
-                        YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    const typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions &input_dims = m_inputImpl.dimensions();
-    const typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions &kernel_dims =
-        m_kernelImpl.dimensions();
-
-    m_dimensions = m_inputImpl.dimensions();
-    for (int i = 0; i < NumKernelDims; ++i) {
-      const Index index = op.indices()[i];
-      const Index input_dim = input_dims[index];
-      const Index kernel_dim = kernel_dims[i];
-      const Index result_dim = input_dim - kernel_dim + 1;
-      m_dimensions[index] = result_dim;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC const Dimensions &dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    preloadKernel();
-    m_inputImpl.evalSubExprsIfNeeded(NULL);
-    if (data) {
-      executeEval(data);
-      return false;
-    } else {
-      m_buf = (EvaluatorPointerType)m_device.get(
-          (Scalar *)m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar)));
-      executeEval(m_buf);
-      return true;
-    }
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_inputImpl.cleanup();
-    if (m_buf) {
-      m_device.deallocate_temp(m_buf);
-      m_buf = NULL;
-    }
-    if (m_local_kernel) {
-      m_device.deallocate_temp(m_kernel);
-      m_local_kernel = false;
-    }
-    m_kernel = NULL;
-  }
-  /// used by sycl in order to build the sycl buffer
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device &device() const { return m_device; }
-  /// used by sycl in order to build the sycl buffer
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_buf; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() {
-    // Don't make a local copy of the kernel unless we have to (i.e. it's an
-    // expression that needs to be evaluated)
-    typename KernelStorage::Type in_place = m_kernelImpl.data();
-    if (in_place) {
-      m_kernel = in_place;
-      m_local_kernel = false;
-    } else {
-      ptrdiff_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
-      EvaluatorPointerType local = (EvaluatorPointerType)m_device.get((Scalar *)m_device.allocate_temp(kernel_sz));
-      typedef TensorEvalToOp<const KernelArgType> EvalTo;
-      EvalTo evalToTmp(m_device.get(local), m_kernelArg);
-      const bool PacketAccess = internal::IsVectorizable<Eigen::SyclDevice, KernelArgType>::value;
-      internal::TensorExecutor<const EvalTo, Eigen::SyclDevice, PacketAccess>::run(evalToTmp, m_device);
-      m_kernel = local;
-      m_local_kernel = true;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void executeEval(EvaluatorPointerType data) const {
-    typedef TensorEvaluator<InputArgType, Eigen::SyclDevice> InputEvaluator;
-    typedef typename InputEvaluator::Dimensions InputDims;
-    switch (NumKernelDims) {
-      case 1: {
-        const size_t numX = dimensions()[m_indices[0]];
-        const size_t numP = dimensions().TotalSize() / numX;
-        const auto input_dim = std::array<size_t, 2>{numX, numP};
-        auto global_range = cl::sycl::range<2>{};
-        auto local_range = cl::sycl::range<2>{};
-        const size_t kernel_size = m_kernelImpl.dimensions().TotalSize();
-
-        m_device.parallel_for_setup(input_dim, global_range, local_range);
-        const size_t local_memory_size = (local_range[0] + kernel_size - 1) * (local_range[1]);
-        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
-        const array<Index, 1> indices{{m_indices[0]}};
-        const array<Index, 1> kernel_dims{{m_kernelImpl.dimensions()[0]}};
-        internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
-
-        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
-                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV1D>
-            ConvKernel;
-
-        m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
-            m_inputImpl, m_kernel, data, cl::sycl::nd_range<2>(global_range, local_range), local_memory_size,
-            indexMapper, kernel_size, cl::sycl::range<2>(input_dim[0], input_dim[1]));
-        break;
-      }
-
-      case 2: {
-        auto kernel_index = std::array<size_t, 2>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1,
-                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0};
-        auto kernel_size = cl::sycl::range<2>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],
-                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]]};
-        const size_t numX = dimensions()[m_indices[kernel_index[0]]];
-        const size_t numY = dimensions()[m_indices[kernel_index[1]]];
-        const size_t numP = dimensions().TotalSize() / (numX * numY);
-        auto input_dim = std::array<size_t, 3>{numX, numY, numP};
-
-        auto global_range = cl::sycl::range<3>{};
-        auto local_range = cl::sycl::range<3>{};
-
-        m_device.parallel_for_setup(input_dim, global_range, local_range);
-
-        const size_t local_memory_size =
-            (local_range[0] + kernel_size[0] - 1) * (local_range[1] + kernel_size[1] - 1) * local_range[2];
-        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
-        const array<Index, 2> indices{{m_indices[kernel_index[0]], m_indices[kernel_index[1]]}};
-        const array<Index, 2> kernel_dims{
-            {m_kernelImpl.dimensions()[kernel_index[0]], m_kernelImpl.dimensions()[kernel_index[1]]}};
-        internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
-        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
-                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV2D>
-            ConvKernel;
-        m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
-            m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
-            indexMapper, kernel_size, cl::sycl::range<3>{input_dim[0], input_dim[1], input_dim[2]});
-        break;
-      }
-
-      case 3: {
-        auto kernel_index = std::array<size_t, 3>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2,
-                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1,
-                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0};
-
-        auto kernel_size = cl::sycl::range<3>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],
-                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]],
-                                              (size_t)m_kernelImpl.dimensions()[kernel_index[2]]};
-
-        const size_t numX = dimensions()[m_indices[kernel_index[0]]];
-        const size_t numY = dimensions()[m_indices[kernel_index[1]]];
-        const size_t numZ = dimensions()[m_indices[kernel_index[2]]];
-        auto input_dim = std::array<size_t, 3>{numX, numY, numZ};
-        const size_t numP = dimensions().TotalSize() / (numX * numY * numZ);
-
-        const array<Index, 3> indices{
-            {m_indices[kernel_index[0]], m_indices[kernel_index[1]], m_indices[kernel_index[2]]}};
-        const array<Index, 3> kernel_dims{{m_kernelImpl.dimensions()[kernel_index[0]],
-                                           m_kernelImpl.dimensions()[kernel_index[1]],
-                                           m_kernelImpl.dimensions()[kernel_index[2]]}};
-
-        internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
-
-        auto global_range = cl::sycl::range<3>{};
-        auto local_range = cl::sycl::range<3>{};
-
-        m_device.parallel_for_setup(input_dim, global_range, local_range);
-        auto local_memory_range = (local_range + kernel_size - 1);
-        const size_t local_memory_size = local_memory_range[0] * local_memory_range[1] * local_memory_range[2];
-
-        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
-        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
-                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV3D>
-            ConvKernel;
-        m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
-            m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
-            indexMapper, kernel_size, cl::sycl::range<3>(input_dim[0], input_dim[1], input_dim[2]), numP);
-        break;
-      }
-
-      default: {
-        EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3),
-                            THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    eigen_assert(m_buf != NULL);
-    eigen_assert(index < m_dimensions.TotalSize());
-    return m_buf[index];
-  }
-
-  template <int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const {
-    eigen_assert(m_buf != NULL);
-    eigen_assert(index < m_dimensions.TotalSize());
-    return internal::ploadt<PacketReturnType, LoadMode>(m_buf + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    // TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost
-    // model.
-    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
-    // We ignore the use of fused multiply-add.
-    const double convolve_compute_cost = TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
-    const double firstIndex_compute_cost =
-        NumDims *
-        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>());
-    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
-           kernel_size * (m_inputImpl.costPerCoeff(vectorized) + m_kernelImpl.costPerCoeff(vectorized) +
-                          TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize));
-  }
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_kernelImpl.bind(cgh);
-    m_inputImpl.bind(cgh);
-    m_buf.bind(cgh);
-    m_kernel.bind(cgh);
-  }
-
- private:
-  // No assignment (copies are needed by the kernels)
-  TensorEvaluator &operator=(const TensorEvaluator &);
-  TensorEvaluator<InputArgType, Eigen::SyclDevice> m_inputImpl;
-  KernelArgType m_kernelArg;
-  TensorEvaluator<KernelArgType, Eigen::SyclDevice> m_kernelImpl;
-  Indices m_indices;
-  Dimensions m_dimensions;
-  EvaluatorPointerType m_buf;
-  typename KernelStorage::Type m_kernel;
-  bool m_local_kernel;
-  const Eigen::SyclDevice EIGEN_DEVICE_REF m_device;
-};  // namespace Eigen
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
deleted file mode 100644
index 195267c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
+++ /dev/null
@@ -1,214 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
-#define EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
-
-namespace Eigen {
-
-/** \class TensorEvaluator
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief A cost model used to limit the number of threads used for evaluating
-  * tensor expression.
-  *
-  */
-
-// Class storing the cost of evaluating a tensor expression in terms of the
-// estimated number of operand bytes loads, bytes stored, and compute cycles.
-class TensorOpCost {
- public:
-  // TODO(rmlarsen): Fix the scalar op costs in Eigen proper. Even a simple
-  // model based on minimal reciprocal throughput numbers from Intel or
-  // Agner Fog's tables would be better than what is there now.
-  template <typename ArgType>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int MulCost() {
-    return internal::functor_traits<
-        internal::scalar_product_op<ArgType, ArgType> >::Cost;
-  }
-  template <typename ArgType>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int AddCost() {
-    return internal::functor_traits<internal::scalar_sum_op<ArgType> >::Cost;
-  }
-  template <typename ArgType>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int DivCost() {
-    return internal::functor_traits<
-        internal::scalar_quotient_op<ArgType, ArgType> >::Cost;
-  }
-  template <typename ArgType>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int ModCost() {
-    return internal::functor_traits<internal::scalar_mod_op<ArgType> >::Cost;
-  }
-  template <typename SrcType, typename TargetType>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int CastCost() {
-    return internal::functor_traits<
-        internal::scalar_cast_op<SrcType, TargetType> >::Cost;
-  }
-
-  EIGEN_DEVICE_FUNC
-  TensorOpCost() : bytes_loaded_(0), bytes_stored_(0), compute_cycles_(0) {}
-  EIGEN_DEVICE_FUNC
-  TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles)
-      : bytes_loaded_(bytes_loaded),
-        bytes_stored_(bytes_stored),
-        compute_cycles_(compute_cycles) {}
-
-  EIGEN_DEVICE_FUNC
-  TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles,
-               bool vectorized, double packet_size)
-      : bytes_loaded_(bytes_loaded),
-        bytes_stored_(bytes_stored),
-        compute_cycles_(vectorized ? compute_cycles / packet_size
-                                   : compute_cycles) {
-    eigen_assert(bytes_loaded >= 0 && (numext::isfinite)(bytes_loaded));
-    eigen_assert(bytes_stored >= 0 && (numext::isfinite)(bytes_stored));
-    eigen_assert(compute_cycles >= 0 && (numext::isfinite)(compute_cycles));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_loaded() const {
-    return bytes_loaded_;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_stored() const {
-    return bytes_stored_;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double compute_cycles() const {
-    return compute_cycles_;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double total_cost(
-      double load_cost, double store_cost, double compute_cost) const {
-    return load_cost * bytes_loaded_ + store_cost * bytes_stored_ +
-           compute_cost * compute_cycles_;
-  }
-
-  // Drop memory access component. Intended for cases when memory accesses are
-  // sequential or are completely masked by computations.
-  EIGEN_DEVICE_FUNC void dropMemoryCost() {
-    bytes_loaded_ = 0;
-    bytes_stored_ = 0;
-  }
-
-  // TODO(rmlarsen): Define min in terms of total cost, not elementwise.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMin(
-      const TensorOpCost& rhs) const {
-    double bytes_loaded = numext::mini(bytes_loaded_, rhs.bytes_loaded());
-    double bytes_stored = numext::mini(bytes_stored_, rhs.bytes_stored());
-    double compute_cycles = numext::mini(compute_cycles_, rhs.compute_cycles());
-    return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
-  }
-
-  // TODO(rmlarsen): Define max in terms of total cost, not elementwise.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMax(
-      const TensorOpCost& rhs) const {
-    double bytes_loaded = numext::maxi(bytes_loaded_, rhs.bytes_loaded());
-    double bytes_stored = numext::maxi(bytes_stored_, rhs.bytes_stored());
-    double compute_cycles = numext::maxi(compute_cycles_, rhs.compute_cycles());
-    return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator+=(
-      const TensorOpCost& rhs) {
-    bytes_loaded_ += rhs.bytes_loaded();
-    bytes_stored_ += rhs.bytes_stored();
-    compute_cycles_ += rhs.compute_cycles();
-    return *this;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator*=(double rhs) {
-    bytes_loaded_ *= rhs;
-    bytes_stored_ *= rhs;
-    compute_cycles_ *= rhs;
-    return *this;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator+(
-      TensorOpCost lhs, const TensorOpCost& rhs) {
-    lhs += rhs;
-    return lhs;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(
-      TensorOpCost lhs, double rhs) {
-    lhs *= rhs;
-    return lhs;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(
-      double lhs, TensorOpCost rhs) {
-    rhs *= lhs;
-    return rhs;
-  }
-
-  friend std::ostream& operator<<(std::ostream& os, const TensorOpCost& tc) {
-    return os << "[bytes_loaded = " << tc.bytes_loaded()
-              << ", bytes_stored = " << tc.bytes_stored()
-              << ", compute_cycles = " << tc.compute_cycles() << "]";
-  }
-
- private:
-  double bytes_loaded_;
-  double bytes_stored_;
-  double compute_cycles_;
-};
-
-// TODO(rmlarsen): Implement a policy that chooses an "optimal" number of theads
-// in [1:max_threads] instead of just switching multi-threading off for small
-// work units.
-template <typename Device>
-class TensorCostModel {
- public:
-  // Scaling from Eigen compute cost to device cycles.
-  static const int kDeviceCyclesPerComputeCycle = 1;
-
- // Costs in device cycles.
-  static const int kStartupCycles = 100000;
-  static const int kPerThreadCycles = 100000;
-  static const int kTaskSize = 40000;
-
-  // Returns the number of threads in [1:max_threads] to use for
-  // evaluating an expression with the given output size and cost per
-  // coefficient.
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int numThreads(
-      double output_size, const TensorOpCost& cost_per_coeff, int max_threads) {
-    double cost = totalCost(output_size, cost_per_coeff);
-    double threads = (cost - kStartupCycles) / kPerThreadCycles + 0.9;
-    // Make sure we don't invoke undefined behavior when we convert to an int.
-    threads = numext::mini<double>(threads, GenericNumTraits<int>::highest());
-    return numext::mini(max_threads,
-                        numext::maxi<int>(1, static_cast<int>(threads)));
-  }
-
-  // taskSize assesses parallel task size.
-  // Value of 1.0 means ideal parallel task size. Values < 1.0 mean that task
-  // granularity needs to be increased to mitigate parallelization overheads.
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double taskSize(
-      double output_size, const TensorOpCost& cost_per_coeff) {
-    return totalCost(output_size, cost_per_coeff) / kTaskSize;
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double totalCost(
-      double output_size, const TensorOpCost& cost_per_coeff) {
-    // Cost of memory fetches from L2 cache. 64 is typical cache line size.
-    // 11 is L2 cache latency on Haswell.
-    // We don't know whether data is in L1, L2 or L3. But we are most interested
-    // in single-threaded computational time around 100us-10ms (smaller time
-    // is too small for parallelization, larger time is not interesting
-    // either because we are probably using all available threads already).
-    // And for the target time range, L2 seems to be what matters. Data set
-    // fitting into L1 is too small to take noticeable time. Data set fitting
-    // only into L3 presumably will take more than 10ms to load and process.
-    const double kLoadCycles = 1.0 / 64 * 11;
-    const double kStoreCycles = 1.0 / 64 * 11;
-    // Scaling from Eigen compute cost to device cycles.
-    return output_size *
-        cost_per_coeff.total_cost(kLoadCycles, kStoreCycles,
-                                  kDeviceCyclesPerComputeCycle);
-  }
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
deleted file mode 100644
index 95a8a84..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
+++ /dev/null
@@ -1,347 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
-
-namespace Eigen {
-
-/** \class TensorCustomUnaryOp
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor custom class.
-  *
-  *
-  */
-namespace internal {
-template<typename CustomUnaryFunc, typename XprType>
-struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::StorageKind StorageKind;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = traits<XprType>::NumDimensions;
-  static const int Layout = traits<XprType>::Layout;
-  typedef typename traits<XprType>::PointerType PointerType;
-};
-
-template<typename CustomUnaryFunc, typename XprType>
-struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense>
-{
-  typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType>EIGEN_DEVICE_REF type;
-};
-
-template<typename CustomUnaryFunc, typename XprType>
-struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
-{
-  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename CustomUnaryFunc, typename XprType>
-class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename internal::nested<TensorCustomUnaryOp>::type Nested;
-  typedef typename internal::traits<TensorCustomUnaryOp>::StorageKind StorageKind;
-  typedef typename internal::traits<TensorCustomUnaryOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomUnaryOp(const XprType& expr, const CustomUnaryFunc& func)
-      : m_expr(expr), m_func(func) {}
-
-  EIGEN_DEVICE_FUNC
-  const CustomUnaryFunc& func() const { return m_func; }
-
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename XprType::Nested>::type&
-  expression() const { return m_expr; }
-
-  protected:
-    typename XprType::Nested m_expr;
-    const CustomUnaryFunc m_func;
-};
-
-
-// Eval as rvalue
-template<typename CustomUnaryFunc, typename XprType, typename Device>
-struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device>
-{
-  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType;
-  typedef typename internal::traits<ArgType>::Index Index;
-  static const int NumDims = internal::traits<ArgType>::NumDimensions;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename internal::remove_const<typename ArgType::Scalar>::type Scalar;
-  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<XprType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const ArgType& op, const Device& device)
-      : m_op(op), m_device(device), m_result(NULL)
-  {
-    m_dimensions = op.func().dimensions(op.expression());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    if (data) {
-      evalTo(data);
-      return false;
-    } else {
-      m_result = static_cast<EvaluatorPointerType>(m_device.get( (CoeffReturnType*)
-          m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar))));
-      evalTo(m_result);
-      return true;
-    }
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    if (m_result) {
-      m_device.deallocate_temp(m_result);
-      m_result = NULL;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    return m_result[index];
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_result.bind(cgh);
-  }
-#endif
-
- protected:
-  void evalTo(EvaluatorPointerType data) {
-    TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(m_device.get(data), m_dimensions);
-    m_op.func().eval(m_op.expression(), result, m_device);
-  }
-
-  Dimensions m_dimensions;
-  const ArgType m_op;
-  const Device EIGEN_DEVICE_REF m_device;
-  EvaluatorPointerType m_result;
-};
-
-
-
-/** \class TensorCustomBinaryOp
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor custom class.
-  *
-  *
-  */
-namespace internal {
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
-struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
-{
-  typedef typename internal::promote_storage_type<typename LhsXprType::Scalar,
-                                                  typename RhsXprType::Scalar>::ret Scalar;
-  typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
-                                                  typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
-  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
-                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
-                                      typename traits<RhsXprType>::Index>::type Index;
-  typedef typename LhsXprType::Nested LhsNested;
-  typedef typename RhsXprType::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  static const int NumDimensions = traits<LhsXprType>::NumDimensions;
-  static const int Layout = traits<LhsXprType>::Layout;
-  typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
-                                typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType>::type PointerType;
-};
-
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
-struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense>
-{
-  typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type;
-};
-
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
-struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
-{
-  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
-class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename internal::traits<TensorCustomBinaryOp>::CoeffReturnType CoeffReturnType;
-  typedef typename internal::nested<TensorCustomBinaryOp>::type Nested;
-  typedef typename internal::traits<TensorCustomBinaryOp>::StorageKind StorageKind;
-  typedef typename internal::traits<TensorCustomBinaryOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const CustomBinaryFunc& func)
-
-      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_func(func) {}
-
-  EIGEN_DEVICE_FUNC
-  const CustomBinaryFunc& func() const { return m_func; }
-
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename LhsXprType::Nested>::type&
-  lhsExpression() const { return m_lhs_xpr; }
-
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename RhsXprType::Nested>::type&
-  rhsExpression() const { return m_rhs_xpr; }
-
-  protected:
-    typename LhsXprType::Nested m_lhs_xpr;
-    typename RhsXprType::Nested m_rhs_xpr;
-    const CustomBinaryFunc m_func;
-};
-
-
-// Eval as rvalue
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device>
-struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device>
-{
-  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType;
-  typedef typename internal::traits<XprType>::Index Index;
-  static const int NumDims = internal::traits<XprType>::NumDimensions;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-
-  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<LhsXprType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_op(op), m_device(device), m_result(NULL)
-  {
-    m_dimensions = op.func().dimensions(op.lhsExpression(), op.rhsExpression());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    if (data) {
-      evalTo(data);
-      return false;
-    } else {
-      m_result = static_cast<EvaluatorPointerType>(m_device.get( (CoeffReturnType*)
-        m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType))));
-      evalTo(m_result);
-      return true;
-    }
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    if (m_result != NULL) {
-      m_device.deallocate_temp(m_result);
-      m_result = NULL;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    return m_result[index];
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_result.bind(cgh);
-  }
-#endif
-
- protected:
-  void evalTo(EvaluatorPointerType data) {
-    TensorMap<Tensor<CoeffReturnType, NumDims, Layout> > result(m_device.get(data), m_dimensions);
-    m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device);
-  }
-
-  Dimensions m_dimensions;
-  const XprType m_op;
-  const Device EIGEN_DEVICE_REF m_device;
-  EvaluatorPointerType m_result;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
deleted file mode 100644
index 96fa46c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
+++ /dev/null
@@ -1,137 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
-#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
-
-namespace Eigen {
-
-/** \class TensorDevice
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Pseudo expression providing an operator = that will evaluate its argument
-  * on the specified computing 'device' (GPU, thread pool, ...)
-  *
-  * Example:
-  *    C.device(EIGEN_GPU) = A + B;
-  *
-  * Todo: operator *= and /=.
-  */
-
-template <typename ExpressionType, typename DeviceType> class TensorDevice {
-  public:
-    TensorDevice(const DeviceType& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
-
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TensorDevice)
-
-    template<typename OtherDerived>
-    EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
-      typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
-      Assign assign(m_expression, other);
-      internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
-      return *this;
-    }
-
-    template<typename OtherDerived>
-    EIGEN_STRONG_INLINE TensorDevice& operator+=(const OtherDerived& other) {
-      typedef typename OtherDerived::Scalar Scalar;
-      typedef TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ExpressionType, const OtherDerived> Sum;
-      Sum sum(m_expression, other);
-      typedef TensorAssignOp<ExpressionType, const Sum> Assign;
-      Assign assign(m_expression, sum);
-      internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
-      return *this;
-    }
-
-    template<typename OtherDerived>
-    EIGEN_STRONG_INLINE TensorDevice& operator-=(const OtherDerived& other) {
-      typedef typename OtherDerived::Scalar Scalar;
-      typedef TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const ExpressionType, const OtherDerived> Difference;
-      Difference difference(m_expression, other);
-      typedef TensorAssignOp<ExpressionType, const Difference> Assign;
-      Assign assign(m_expression, difference);
-      internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
-      return *this;
-    }
-
-  protected:
-    const DeviceType& m_device;
-    ExpressionType& m_expression;
-};
-
-/** \class TensorAsyncDevice
- * \ingroup CXX11_Tensor_Module
- *
- * \brief Pseudo expression providing an operator = that will evaluate its
- * argument asynchronously on the specified device. Currently only
- * ThreadPoolDevice implements proper asynchronous execution, while the default
- * and GPU devices just run the expression synchronously and call m_done() on
- * completion..
- *
- * Example:
- *    auto done = []() { ... expression evaluation done ... };
- *    C.device(thread_pool_device, std::move(done)) = A + B;
- */
-
-template <typename ExpressionType, typename DeviceType, typename DoneCallback>
-class TensorAsyncDevice {
- public:
-  TensorAsyncDevice(const DeviceType& device, ExpressionType& expression,
-                    DoneCallback done)
-      : m_device(device), m_expression(expression), m_done(std::move(done)) {}
-
-  template <typename OtherDerived>
-  EIGEN_STRONG_INLINE TensorAsyncDevice& operator=(const OtherDerived& other) {
-    typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
-    typedef internal::TensorExecutor<const Assign, DeviceType> Executor;
-
-    Assign assign(m_expression, other);
-    Executor::run(assign, m_device);
-    m_done();
-
-    return *this;
-  }
-
- protected:
-  const DeviceType& m_device;
-  ExpressionType& m_expression;
-  DoneCallback m_done;
-};
-
-
-#ifdef EIGEN_USE_THREADS
-template <typename ExpressionType, typename DoneCallback>
-class TensorAsyncDevice<ExpressionType, ThreadPoolDevice, DoneCallback> {
- public:
-  TensorAsyncDevice(const ThreadPoolDevice& device, ExpressionType& expression,
-                    DoneCallback done)
-      : m_device(device), m_expression(expression), m_done(std::move(done)) {}
-
-  template <typename OtherDerived>
-  EIGEN_STRONG_INLINE TensorAsyncDevice& operator=(const OtherDerived& other) {
-    typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
-    typedef internal::TensorAsyncExecutor<const Assign, ThreadPoolDevice, DoneCallback> Executor;
-
-    // WARNING: After assignment 'm_done' callback will be in undefined state.
-    Assign assign(m_expression, other);
-    Executor::runAsync(assign, m_device, std::move(m_done));
-
-    return *this;
-  }
-
- protected:
-  const ThreadPoolDevice& m_device;
-  ExpressionType& m_expression;
-  DoneCallback m_done;
-};
-#endif
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
deleted file mode 100644
index f779239..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
+++ /dev/null
@@ -1,6 +0,0 @@
-
-#if defined(__clang__) || defined(__GNUC__)
-#warning "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorDeviceGpu.h file"
-#endif
-
-#include "TensorDeviceGpu.h"
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
deleted file mode 100644
index 46b9d3a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
+++ /dev/null
@@ -1,104 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
-#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
-
-
-namespace Eigen {
-
-// Default device for the machine (typically a single cpu core)
-struct DefaultDevice {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
-    return internal::aligned_malloc(num_bytes);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
-    internal::aligned_free(buffer);
-  }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const {
-    return allocate(num_bytes);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const {
-    deallocate(buffer);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
-    ::memcpy(dst, src, n);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
-    memcpy(dst, src, n);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
-    memcpy(dst, src, n);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
-    ::memset(buffer, c, n);
-  }
-  template<typename Type>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const { 
-    return data;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const {
-#if !defined(EIGEN_GPU_COMPILE_PHASE)
-    // Running on the host CPU
-    return 1;
-#elif defined(EIGEN_HIP_DEVICE_COMPILE)
-    // Running on a HIP device
-    return 64;
-#else
-    // Running on a CUDA device
-    return 32;
-#endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
-#if !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
-    // Running on the host CPU
-    return l1CacheSize();
-#elif defined(EIGEN_HIP_DEVICE_COMPILE)
-    // Running on a HIP device
-    return 48*1024; // FIXME : update this number for HIP
-#else
-    // Running on a CUDA device, return the amount of shared memory available.
-    return 48*1024;
-#endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
-#if !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
-    // Running single threaded on the host CPU
-    return l3CacheSize();
-#elif defined(EIGEN_HIP_DEVICE_COMPILE)
-    // Running on a HIP device
-    return firstLevelCacheSize(); // FIXME : update this number for HIP
-#else
-    // Running on a CUDA device
-    return firstLevelCacheSize();
-#endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
-#if !defined(EIGEN_GPU_COMPILE_PHASE)
-    // Running single threaded on the host CPU
-    // Should return an enum that encodes the ISA supported by the CPU
-    return 1;
-#elif defined(EIGEN_HIP_DEVICE_COMPILE)
-    // Running on a HIP device
-    // return 1 as major for HIP
-    return 1;
-#else
-    // Running on a CUDA device
-    return EIGEN_CUDA_ARCH / 100;
-#endif
-  }
-};
-
-}  // namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
deleted file mode 100644
index ec2e3cb..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
+++ /dev/null
@@ -1,389 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H)
-#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H
-
-// This header file container defines fo gpu* macros which will resolve to
-// their equivalent hip* or cuda* versions depending on the compiler in use
-// A separate header (included at the end of this file) will undefine all 
-#include "TensorGpuHipCudaDefines.h"
-
-namespace Eigen {
-
-static const int kGpuScratchSize = 1024;
-
-// This defines an interface that GPUDevice can take to use
-// HIP / CUDA streams underneath.
-class StreamInterface {
- public:
-  virtual ~StreamInterface() {}
-
-  virtual const gpuStream_t& stream() const = 0;
-  virtual const gpuDeviceProp_t& deviceProperties() const = 0;
-
-  // Allocate memory on the actual device where the computation will run
-  virtual void* allocate(size_t num_bytes) const = 0;
-  virtual void deallocate(void* buffer) const = 0;
-
-  // Return a scratchpad buffer of size 1k
-  virtual void* scratchpad() const = 0;
-
-  // Return a semaphore. The semaphore is initially initialized to 0, and
-  // each kernel using it is responsible for resetting to 0 upon completion
-  // to maintain the invariant that the semaphore is always equal to 0 upon
-  // each kernel start.
-  virtual unsigned int* semaphore() const = 0;
-};
-
-class GpuDeviceProperties {
- public:
-  GpuDeviceProperties() : 
-      initialized_(false), first_(true), device_properties_(nullptr) {}
- 
-  ~GpuDeviceProperties() {
-    if (device_properties_) {
-      delete[] device_properties_;
-    }
-  }
-  
-  EIGEN_STRONG_INLINE const gpuDeviceProp_t& get(int device) const {
-    return device_properties_[device];
-  }
-
-  EIGEN_STRONG_INLINE bool isInitialized() const {
-    return initialized_;
-  }
-
-  void initialize() {
-    if (!initialized_) {
-      // Attempts to ensure proper behavior in the case of multiple threads
-      // calling this function simultaneously. This would be trivial to
-      // implement if we could use std::mutex, but unfortunately mutex don't
-      // compile with nvcc, so we resort to atomics and thread fences instead.
-      // Note that if the caller uses a compiler that doesn't support c++11 we
-      // can't ensure that the initialization is thread safe.
-      if (first_.exchange(false)) {
-        // We're the first thread to reach this point.
-        int num_devices;
-        gpuError_t status = gpuGetDeviceCount(&num_devices);
-        if (status != gpuSuccess) {
-          std::cerr << "Failed to get the number of GPU devices: "
-                    << gpuGetErrorString(status)
-                    << std::endl;
-          gpu_assert(status == gpuSuccess);
-        }
-        device_properties_ = new gpuDeviceProp_t[num_devices];
-        for (int i = 0; i < num_devices; ++i) {
-          status = gpuGetDeviceProperties(&device_properties_[i], i);
-          if (status != gpuSuccess) {
-            std::cerr << "Failed to initialize GPU device #"
-                      << i
-                      << ": "
-                      << gpuGetErrorString(status)
-                      << std::endl;
-            gpu_assert(status == gpuSuccess);
-          }
-        }
-
-        std::atomic_thread_fence(std::memory_order_release);
-        initialized_ = true;
-      } else {
-        // Wait for the other thread to inititialize the properties.
-        while (!initialized_) {
-          std::atomic_thread_fence(std::memory_order_acquire);
-          std::this_thread::sleep_for(std::chrono::milliseconds(1000));
-        }
-      }
-    }
-  }
-
- private:
-  volatile bool initialized_;
-  std::atomic<bool> first_;
-  gpuDeviceProp_t* device_properties_;
-};
-
-EIGEN_ALWAYS_INLINE const GpuDeviceProperties& GetGpuDeviceProperties() {
-  static GpuDeviceProperties* deviceProperties = new GpuDeviceProperties();
-  if (!deviceProperties->isInitialized()) {
-    deviceProperties->initialize();
-  }
-  return *deviceProperties;
-}
-
-EIGEN_ALWAYS_INLINE const gpuDeviceProp_t& GetGpuDeviceProperties(int device) {
-  return GetGpuDeviceProperties().get(device);
-}
-
-static const gpuStream_t default_stream = gpuStreamDefault;
-
-class GpuStreamDevice : public StreamInterface {
- public:
-  // Use the default stream on the current device
-  GpuStreamDevice() : stream_(&default_stream), scratch_(NULL), semaphore_(NULL) {
-    gpuGetDevice(&device_);
-  }
-  // Use the default stream on the specified device
-  GpuStreamDevice(int device) : stream_(&default_stream), device_(device), scratch_(NULL), semaphore_(NULL) {}
-  // Use the specified stream. Note that it's the
-  // caller responsibility to ensure that the stream can run on
-  // the specified device. If no device is specified the code
-  // assumes that the stream is associated to the current gpu device.
-  GpuStreamDevice(const gpuStream_t* stream, int device = -1)
-      : stream_(stream), device_(device), scratch_(NULL), semaphore_(NULL) {
-    if (device < 0) {
-      gpuGetDevice(&device_);
-    } else {
-      int num_devices;
-      gpuError_t err = gpuGetDeviceCount(&num_devices);
-      EIGEN_UNUSED_VARIABLE(err)
-      gpu_assert(err == gpuSuccess);
-      gpu_assert(device < num_devices);
-      device_ = device;
-    }
-  }
-
-  virtual ~GpuStreamDevice() {
-    if (scratch_) {
-      deallocate(scratch_);
-    }
-  }
-
-  const gpuStream_t& stream() const { return *stream_; }
-  const gpuDeviceProp_t& deviceProperties() const {
-    return GetGpuDeviceProperties(device_);
-  }
-  virtual void* allocate(size_t num_bytes) const {
-    gpuError_t err = gpuSetDevice(device_);
-    EIGEN_UNUSED_VARIABLE(err)
-    gpu_assert(err == gpuSuccess);
-    void* result;
-    err = gpuMalloc(&result, num_bytes);
-    gpu_assert(err == gpuSuccess);
-    gpu_assert(result != NULL);
-    return result;
-  }
-  virtual void deallocate(void* buffer) const {
-    gpuError_t err = gpuSetDevice(device_);
-    EIGEN_UNUSED_VARIABLE(err)
-    gpu_assert(err == gpuSuccess);
-    gpu_assert(buffer != NULL);
-    err = gpuFree(buffer);
-    gpu_assert(err == gpuSuccess);
-  }
-
-  virtual void* scratchpad() const {
-    if (scratch_ == NULL) {
-      scratch_ = allocate(kGpuScratchSize + sizeof(unsigned int));
-    }
-    return scratch_;
-  }
-
-  virtual unsigned int* semaphore() const {
-    if (semaphore_ == NULL) {
-      char* scratch = static_cast<char*>(scratchpad()) + kGpuScratchSize;
-      semaphore_ = reinterpret_cast<unsigned int*>(scratch);
-      gpuError_t err = gpuMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_);
-      EIGEN_UNUSED_VARIABLE(err)
-      gpu_assert(err == gpuSuccess);
-    }
-    return semaphore_;
-  }
-
- private:
-  const gpuStream_t* stream_;
-  int device_;
-  mutable void* scratch_;
-  mutable unsigned int* semaphore_;
-};
-
-struct GpuDevice {
-  // The StreamInterface is not owned: the caller is
-  // responsible for its initialization and eventual destruction.
-  explicit GpuDevice(const StreamInterface* stream) : stream_(stream), max_blocks_(INT_MAX) {
-    eigen_assert(stream);
-  }
-  explicit GpuDevice(const StreamInterface* stream, int num_blocks) : stream_(stream), max_blocks_(num_blocks) {
-    eigen_assert(stream);
-  }
-  // TODO(bsteiner): This is an internal API, we should not expose it.
-  EIGEN_STRONG_INLINE const gpuStream_t& stream() const {
-    return stream_->stream();
-  }
-
-  EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
-    return stream_->allocate(num_bytes);
-  }
-
-  EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
-    stream_->deallocate(buffer);
-  }
-
-  EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const {
-    return stream_->allocate(num_bytes);
-  }
-
-  EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const {
-    stream_->deallocate(buffer);
-  }
-
-  template<typename Type>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const { 
-    return data;
-  }
-
-  EIGEN_STRONG_INLINE void* scratchpad() const {
-    return stream_->scratchpad();
-  }
-
-  EIGEN_STRONG_INLINE unsigned int* semaphore() const {
-    return stream_->semaphore();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
-#ifndef EIGEN_GPU_COMPILE_PHASE
-    gpuError_t err = gpuMemcpyAsync(dst, src, n, gpuMemcpyDeviceToDevice,
-                                      stream_->stream());
-    EIGEN_UNUSED_VARIABLE(err)
-    gpu_assert(err == gpuSuccess);
-#else
-    EIGEN_UNUSED_VARIABLE(dst);
-    EIGEN_UNUSED_VARIABLE(src);
-    EIGEN_UNUSED_VARIABLE(n);
-    eigen_assert(false && "The default device should be used instead to generate kernel code");
-#endif
-  }
-
-  EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
-    gpuError_t err =
-        gpuMemcpyAsync(dst, src, n, gpuMemcpyHostToDevice, stream_->stream());
-    EIGEN_UNUSED_VARIABLE(err)
-    gpu_assert(err == gpuSuccess);
-  }
-
-  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
-    gpuError_t err =
-        gpuMemcpyAsync(dst, src, n, gpuMemcpyDeviceToHost, stream_->stream());
-    EIGEN_UNUSED_VARIABLE(err)
-    gpu_assert(err == gpuSuccess);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
-#ifndef EIGEN_GPU_COMPILE_PHASE
-    gpuError_t err = gpuMemsetAsync(buffer, c, n, stream_->stream());
-    EIGEN_UNUSED_VARIABLE(err)
-    gpu_assert(err == gpuSuccess);
-#else
-  eigen_assert(false && "The default device should be used instead to generate kernel code");
-#endif
-  }
-
-  EIGEN_STRONG_INLINE size_t numThreads() const {
-    // FIXME
-    return 32;
-  }
-
-  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
-    // FIXME
-    return 48*1024;
-  }
-
-  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
-    // We won't try to take advantage of the l2 cache for the time being, and
-    // there is no l3 cache on hip/cuda devices.
-    return firstLevelCacheSize();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
-#ifndef EIGEN_GPU_COMPILE_PHASE
-    gpuError_t err = gpuStreamSynchronize(stream_->stream());
-    if (err != gpuSuccess) {
-      std::cerr << "Error detected in GPU stream: "
-                << gpuGetErrorString(err)
-                << std::endl;
-      gpu_assert(err == gpuSuccess);
-    }
-#else
-    gpu_assert(false && "The default device should be used instead to generate kernel code");
-#endif
-  }
-
-  EIGEN_STRONG_INLINE int getNumGpuMultiProcessors() const {
-    return stream_->deviceProperties().multiProcessorCount;
-  }
-  EIGEN_STRONG_INLINE int maxGpuThreadsPerBlock() const {
-    return stream_->deviceProperties().maxThreadsPerBlock;
-  }
-  EIGEN_STRONG_INLINE int maxGpuThreadsPerMultiProcessor() const {
-    return stream_->deviceProperties().maxThreadsPerMultiProcessor;
-  }
-  EIGEN_STRONG_INLINE int sharedMemPerBlock() const {
-    return stream_->deviceProperties().sharedMemPerBlock;
-  }
-  EIGEN_STRONG_INLINE int majorDeviceVersion() const {
-    return stream_->deviceProperties().major;
-  }
-  EIGEN_STRONG_INLINE int minorDeviceVersion() const {
-    return stream_->deviceProperties().minor;
-  }
-
-  EIGEN_STRONG_INLINE int maxBlocks() const {
-    return max_blocks_;
-  }
-
-  // This function checks if the GPU runtime recorded an error for the
-  // underlying stream device.
-  inline bool ok() const {
-#ifdef EIGEN_GPUCC
-    gpuError_t error = gpuStreamQuery(stream_->stream());
-    return (error == gpuSuccess) || (error == gpuErrorNotReady);
-#else
-    return false;
-#endif
-  }
-
- private:
-  const StreamInterface* stream_;
-  int max_blocks_;
-};
-
-#if defined(EIGEN_HIPCC)
-
-#define LAUNCH_GPU_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)             \
-  hipLaunchKernelGGL(kernel, dim3(gridsize), dim3(blocksize), (sharedmem), (device).stream(), __VA_ARGS__); \
-  gpu_assert(hipGetLastError() == hipSuccess);
-
-#else
- 
-#define LAUNCH_GPU_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)             \
-  (kernel) <<< (gridsize), (blocksize), (sharedmem), (device).stream() >>> (__VA_ARGS__);   \
-  gpu_assert(cudaGetLastError() == cudaSuccess);
-
-#endif
- 
-// FIXME: Should be device and kernel specific.
-#ifdef EIGEN_GPUCC
-static EIGEN_DEVICE_FUNC inline void setGpuSharedMemConfig(gpuSharedMemConfig config) {
-#ifndef EIGEN_GPU_COMPILE_PHASE
-  gpuError_t status = gpuDeviceSetSharedMemConfig(config);
-  EIGEN_UNUSED_VARIABLE(status)
-  gpu_assert(status == gpuSuccess);
-#else
-  EIGEN_UNUSED_VARIABLE(config)
-#endif
-}
-#endif
-
-}  // end namespace Eigen
-
-// undefine all the gpu* macros we defined at the beginning of the file
-#include "TensorGpuHipCudaUndefines.h"
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
deleted file mode 100644
index df591c2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
+++ /dev/null
@@ -1,1048 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#if defined(EIGEN_USE_SYCL) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H)
-#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
-#include <unordered_set>
-
-namespace Eigen {
-
-namespace TensorSycl {
-namespace internal {
-
-/// Cache all the device information needed
-struct SyclDeviceInfo {
-  SyclDeviceInfo(cl::sycl::queue queue)
-      : local_mem_type(
-            queue.get_device()
-                .template get_info<cl::sycl::info::device::local_mem_type>()),
-        max_work_item_sizes(
-            queue.get_device()
-                .template get_info<
-                    cl::sycl::info::device::max_work_item_sizes>()),
-        max_mem_alloc_size(
-            queue.get_device()
-                .template get_info<
-                    cl::sycl::info::device::max_mem_alloc_size>()),
-        max_compute_units(queue.get_device()
-                              .template get_info<
-                                  cl::sycl::info::device::max_compute_units>()),
-        max_work_group_size(
-            queue.get_device()
-                .template get_info<
-                    cl::sycl::info::device::max_work_group_size>()),
-        local_mem_size(
-            queue.get_device()
-                .template get_info<cl::sycl::info::device::local_mem_size>()),
-        platform_name(queue.get_device()
-                          .get_platform()
-                          .template get_info<cl::sycl::info::platform::name>()),
-        device_name(queue.get_device()
-                        .template get_info<cl::sycl::info::device::name>()),
-        device_vendor(
-            queue.get_device()
-                .template get_info<cl::sycl::info::device::vendor>()) {}
-
-  cl::sycl::info::local_mem_type local_mem_type;
-  cl::sycl::id<3> max_work_item_sizes;
-  unsigned long max_mem_alloc_size;
-  unsigned long max_compute_units;
-  unsigned long max_work_group_size;
-  size_t local_mem_size;
-  std::string platform_name;
-  std::string device_name;
-  std::string device_vendor;
-};
-
-}  // end namespace internal
-}  // end namespace TensorSycl
-
-typedef TensorSycl::internal::buffer_data_type_t buffer_scalar_t;
-// All devices (even AMD CPU with intel OpenCL runtime) that support OpenCL and
-// can consume SPIR or SPIRV can use the Eigen SYCL backend and consequently
-// TensorFlow via the Eigen SYCL Backend.
-EIGEN_STRONG_INLINE auto get_sycl_supported_devices()
-    -> decltype(cl::sycl::device::get_devices()) {
-#ifdef EIGEN_SYCL_USE_DEFAULT_SELECTOR
-  return {cl::sycl::device(cl::sycl::default_selector())};
-#else
-  std::vector<cl::sycl::device> supported_devices;
-  auto platform_list = cl::sycl::platform::get_platforms();
-  for (const auto &platform : platform_list) {
-    auto device_list = platform.get_devices();
-    auto platform_name =
-        platform.template get_info<cl::sycl::info::platform::name>();
-    std::transform(platform_name.begin(), platform_name.end(),
-                   platform_name.begin(), ::tolower);
-    for (const auto &device : device_list) {
-      auto vendor = device.template get_info<cl::sycl::info::device::vendor>();
-      std::transform(vendor.begin(), vendor.end(), vendor.begin(), ::tolower);
-      bool unsupported_condition =
-          (device.is_cpu() && platform_name.find("amd") != std::string::npos &&
-           vendor.find("apu") == std::string::npos) ||
-          (platform_name.find("experimental") != std::string::npos) ||
-          device.is_host();
-      if (!unsupported_condition) {
-        supported_devices.push_back(device);
-      }
-    }
-  }
-  return supported_devices;
-#endif
-}
-
-class QueueInterface {
- public:
-  /// Creating device by using cl::sycl::selector or cl::sycl::device.
-  template <typename DeviceOrSelector>
-  explicit QueueInterface(
-      const DeviceOrSelector &dev_or_sel, cl::sycl::async_handler handler,
-      unsigned num_threads = std::thread::hardware_concurrency())
-      : m_queue(dev_or_sel, handler),
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-        m_prog(m_queue.get_context(), get_sycl_supported_devices()),
-#endif
-        m_thread_pool(num_threads),
-        m_device_info(m_queue) {
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-    m_prog.build_with_kernel_type<DeviceOrSelector>();
-    auto f = [&](cl::sycl::handler &cgh) {
-      cgh.single_task<DeviceOrSelector>(m_prog.get_kernel<DeviceOrSelector>(),
-                                        [=]() {})
-    };
-    EIGEN_SYCL_TRY_CATCH(m_queue.submit(f));
-#endif
-  }
-
-  template <typename DeviceOrSelector>
-  explicit QueueInterface(
-      const DeviceOrSelector &dev_or_sel,
-      unsigned num_threads = std::thread::hardware_concurrency())
-      : QueueInterface(dev_or_sel,
-                       [this](cl::sycl::exception_list l) {
-                         this->exception_caught_ = this->sycl_async_handler(l);
-                       },
-                       num_threads) {}
-
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-  EIGEN_STRONG_INLINE cl::sycl::program &program() const { return m_prog; }
-#endif
-
-  /// Attach an existing buffer to the pointer map, Eigen will not reuse it
-  EIGEN_STRONG_INLINE void *attach_buffer(
-      cl::sycl::buffer<buffer_scalar_t, 1> &buf) const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    return static_cast<void *>(pMapper.add_pointer(buf));
-  }
-
-  /// Detach previously attached buffer
-  EIGEN_STRONG_INLINE void detach_buffer(void *p) const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    TensorSycl::internal::SYCLfree<false>(p, pMapper);
-  }
-
-  /// Allocating device pointer. This pointer is actually an 8 bytes host
-  /// pointer used as key to access the sycl device buffer. The reason is that
-  /// we cannot use device buffer as a pointer as a m_data in Eigen leafNode
-  /// expressions. So we create a key pointer to be used in Eigen expression
-  /// construction. When we convert the Eigen construction into the sycl
-  /// construction we use this pointer as a key in our buffer_map and we make
-  /// sure that we dedicate only one buffer only for this pointer. The device
-  /// pointer would be deleted by calling deallocate function.
-  EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const {
-#if EIGEN_MAX_ALIGN_BYTES > 0
-    size_t align = num_bytes % EIGEN_MAX_ALIGN_BYTES;
-    if (align > 0) {
-      num_bytes += EIGEN_MAX_ALIGN_BYTES - align;
-    }
-#endif
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
-  }
-
-  EIGEN_STRONG_INLINE void *allocate_temp(size_t num_bytes) const {
-#if EIGEN_MAX_ALIGN_BYTES > 0
-    size_t align = num_bytes % EIGEN_MAX_ALIGN_BYTES;
-    if (align > 0) {
-      num_bytes += EIGEN_MAX_ALIGN_BYTES - align;
-    }
-#endif
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
-    if (scratch_buffers.empty()) {
-      return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
-      ;
-    } else {
-      for (auto it = scratch_buffers.begin(); it != scratch_buffers.end();) {
-        auto buff = pMapper.get_buffer(*it);
-        if (buff.get_size() >= num_bytes) {
-          auto ptr = *it;
-          scratch_buffers.erase(it);
-          return ptr;
-        } else {
-          ++it;
-        }
-      }
-      return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
-    }
-#else
-    return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
-#endif
-  }
-  template <typename data_t>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<
-      cl::sycl::access::mode::read_write, data_t>
-  get(data_t *data) const {
-    return get_range_accessor<cl::sycl::access::mode::read_write, data_t>(data);
-  }
-  template <typename data_t>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE data_t *get(
-      TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write,
-                                        data_t>
-          data) const {
-    return static_cast<data_t *>(data.get_virtual_pointer());
-  }
-
-  EIGEN_STRONG_INLINE void deallocate_temp(void *p) const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
-    scratch_buffers.insert(p);
-#else
-    TensorSycl::internal::SYCLfree(p, pMapper);
-#endif
-  }
-  template <cl::sycl::access::mode AcMd, typename T>
-  EIGEN_STRONG_INLINE void deallocate_temp(
-      const TensorSycl::internal::RangeAccess<AcMd, T> &p) const {
-    deallocate_temp(p.get_virtual_pointer());
-  }
-
-  /// This is used to deallocate the device pointer. p is used as a key inside
-  /// the map to find the device buffer and delete it.
-  EIGEN_STRONG_INLINE void deallocate(void *p) const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    TensorSycl::internal::SYCLfree(p, pMapper);
-  }
-
-  EIGEN_STRONG_INLINE void deallocate_all() const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    TensorSycl::internal::SYCLfreeAll(pMapper);
-#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
-    scratch_buffers.clear();
-#endif
-  }
-
-  /// The memcpyHostToDevice is used to copy the data from host to device
-  /// The destination pointer could be deleted before the copy happend which is
-  /// why a callback function is needed. By default if none is provided, the
-  /// function is blocking.
-  EIGEN_STRONG_INLINE void memcpyHostToDevice(
-      void *dst, const void *src, size_t n,
-      std::function<void()> callback) const {
-    static const auto write_mode = cl::sycl::access::mode::discard_write;
-    static const auto global_access = cl::sycl::access::target::global_buffer;
-    typedef cl::sycl::accessor<buffer_scalar_t, 1, write_mode, global_access>
-        write_accessor;
-    if (n == 0) {
-      if (callback) callback();
-      return;
-    }
-    n /= sizeof(buffer_scalar_t);
-    auto f = [&](cl::sycl::handler &cgh) {
-      write_accessor dst_acc = get_range_accessor<write_mode>(cgh, dst, n);
-      buffer_scalar_t const *ptr = static_cast<buffer_scalar_t const *>(src);
-      auto non_deleter = [](buffer_scalar_t const *) {};
-      std::shared_ptr<const buffer_scalar_t> s_ptr(ptr, non_deleter);
-      cgh.copy(s_ptr, dst_acc);
-    };
-    cl::sycl::event e;
-    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
-    synchronize_and_callback(e, callback);
-  }
-
-  /// The memcpyDeviceToHost is used to copy the data from device to host.
-  /// The source pointer could be deleted before the copy happend which is
-  /// why a callback function is needed. By default if none is provided, the
-  /// function is blocking.
-  EIGEN_STRONG_INLINE void memcpyDeviceToHost(
-      void *dst, const void *src, size_t n,
-      std::function<void()> callback) const {
-    static const auto read_mode = cl::sycl::access::mode::read;
-    static const auto global_access = cl::sycl::access::target::global_buffer;
-    typedef cl::sycl::accessor<buffer_scalar_t, 1, read_mode, global_access>
-        read_accessor;
-    if (n == 0) {
-      if (callback) callback();
-      return;
-    }
-    n /= sizeof(buffer_scalar_t);
-    auto f = [&](cl::sycl::handler &cgh) {
-      read_accessor src_acc = get_range_accessor<read_mode>(cgh, src, n);
-      buffer_scalar_t *ptr = static_cast<buffer_scalar_t *>(dst);
-      auto non_deleter = [](buffer_scalar_t *) {};
-      std::shared_ptr<buffer_scalar_t> s_ptr(ptr, non_deleter);
-      cgh.copy(src_acc, s_ptr);
-    };
-    cl::sycl::event e;
-    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
-    synchronize_and_callback(e, callback);
-  }
-
-  /// The memcpy function.
-  /// No callback is required here as both arguments are on the device
-  /// and SYCL can handle the dependency.
-  EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src, size_t n) const {
-    static const auto read_mode = cl::sycl::access::mode::read;
-    static const auto write_mode = cl::sycl::access::mode::discard_write;
-    if (n == 0) {
-      return;
-    }
-    n /= sizeof(buffer_scalar_t);
-    auto f = [&](cl::sycl::handler &cgh) {
-      auto src_acc = get_range_accessor<read_mode>(cgh, src, n);
-      auto dst_acc = get_range_accessor<write_mode>(cgh, dst, n);
-      cgh.copy(src_acc, dst_acc);
-    };
-    cl::sycl::event e;
-    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
-    async_synchronize(e);
-  }
-
-  /// the memset function.
-  /// No callback is required here as both arguments are on the device
-  /// and SYCL can handle the dependency.
-  EIGEN_STRONG_INLINE void memset(void *data, int c, size_t n) const {
-    static const auto write_mode = cl::sycl::access::mode::discard_write;
-    if (n == 0) {
-      return;
-    }
-    n /= sizeof(buffer_scalar_t);
-    auto f = [&](cl::sycl::handler &cgh) {
-      auto dst_acc = get_range_accessor<write_mode>(cgh, data, n);
-      // The cast to uint8_t is here to match the behaviour of the standard
-      // memset. The cast to buffer_scalar_t is needed to match the type of the
-      // accessor (in case buffer_scalar_t is not uint8_t)
-      cgh.fill(dst_acc, static_cast<buffer_scalar_t>(static_cast<uint8_t>(c)));
-    };
-    cl::sycl::event e;
-    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
-    async_synchronize(e);
-  }
-
-  /// Get a range accessor to the virtual pointer's device memory. This range
-  /// accessor will allow access to the memory from the pointer to the end of
-  /// the buffer.
-  ///
-  /// NOTE: Inside a kernel the range accessor will always be indexed from the
-  /// start of the buffer, so the offset in the accessor is only used by
-  /// methods like handler::copy and will not be available inside a kernel.
-  template <cl::sycl::access::mode AcMd, typename T>
-  EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<AcMd, T>
-  get_range_accessor(const void *ptr) const {
-    static const auto global_access = cl::sycl::access::target::global_buffer;
-    static const auto is_place_holder = cl::sycl::access::placeholder::true_t;
-    typedef TensorSycl::internal::RangeAccess<AcMd, T> ret_type;
-    typedef const TensorSycl::internal::buffer_data_type_t *internal_ptr_t;
-
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-
-    auto original_buffer = pMapper.get_buffer(ptr);
-    const ptrdiff_t offset = pMapper.get_offset(ptr);
-    const ptrdiff_t typed_offset = offset / sizeof(T);
-    eigen_assert(typed_offset >= 0);
-    const auto typed_size = original_buffer.get_size() / sizeof(T);
-    auto buffer = original_buffer.template reinterpret<
-        typename Eigen::internal::remove_const<T>::type>(
-        cl::sycl::range<1>(typed_size));
-    const ptrdiff_t size = buffer.get_count() - typed_offset;
-    eigen_assert(size >= 0);
-    typedef cl::sycl::accessor<typename Eigen::internal::remove_const<T>::type,
-                               1, AcMd, global_access, is_place_holder>
-        placeholder_accessor_t;
-    const auto start_ptr = static_cast<internal_ptr_t>(ptr) - offset;
-    return ret_type(placeholder_accessor_t(buffer, cl::sycl::range<1>(size),
-                                           cl::sycl::id<1>(typed_offset)),
-                    static_cast<size_t>(typed_offset),
-                    reinterpret_cast<std::intptr_t>(start_ptr));
-  }
-
-  /// Get a range accessor to the virtual pointer's device memory with a
-  /// specified size.
-  template <cl::sycl::access::mode AcMd, typename Index>
-  EIGEN_STRONG_INLINE cl::sycl::accessor<
-      buffer_scalar_t, 1, AcMd, cl::sycl::access::target::global_buffer>
-  get_range_accessor(cl::sycl::handler &cgh, const void *ptr,
-                     const Index n_bytes) const {
-    static const auto global_access = cl::sycl::access::target::global_buffer;
-    eigen_assert(n_bytes >= 0);
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    auto buffer = pMapper.get_buffer(ptr);
-    const ptrdiff_t offset = pMapper.get_offset(ptr);
-    eigen_assert(offset >= 0);
-    eigen_assert(offset + n_bytes <= buffer.get_size());
-    return buffer.template get_access<AcMd, global_access>(
-        cgh, cl::sycl::range<1>(n_bytes), cl::sycl::id<1>(offset));
-  }
-
-  /// Creation of sycl accessor for a buffer. This function first tries to find
-  /// the buffer in the buffer_map. If found it gets the accessor from it, if
-  /// not, the function then adds an entry by creating a sycl buffer for that
-  /// particular pointer.
-  template <cl::sycl::access::mode AcMd>
-  EIGEN_STRONG_INLINE cl::sycl::accessor<
-      buffer_scalar_t, 1, AcMd, cl::sycl::access::target::global_buffer>
-  get_sycl_accessor(cl::sycl::handler &cgh, const void *ptr) const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    return pMapper.get_buffer(ptr)
-        .template get_access<AcMd, cl::sycl::access::target::global_buffer>(
-            cgh);
-  }
-
-  EIGEN_STRONG_INLINE cl::sycl::buffer<buffer_scalar_t, 1> get_sycl_buffer(
-      const void *ptr) const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    return pMapper.get_buffer(ptr);
-  }
-
-  EIGEN_STRONG_INLINE ptrdiff_t get_offset(const void *ptr) const {
-    std::lock_guard<std::mutex> lock(pmapper_mutex_);
-    return pMapper.get_offset(ptr);
-  }
-
-  template <typename OutScalar, typename sycl_kernel, typename Lhs,
-            typename Rhs, typename OutPtr, typename Range, typename Index,
-            typename... T>
-  EIGEN_ALWAYS_INLINE void binary_kernel_launcher(const Lhs &lhs,
-                                                  const Rhs &rhs, OutPtr outptr,
-                                                  Range thread_range,
-                                                  Index scratchSize,
-                                                  T... var) const {
-    auto kernel_functor = [=](cl::sycl::handler &cgh) {
-      // binding the placeholder accessors to a commandgroup handler
-      lhs.bind(cgh);
-      rhs.bind(cgh);
-      outptr.bind(cgh);
-      typedef cl::sycl::accessor<OutScalar, 1,
-                                 cl::sycl::access::mode::read_write,
-                                 cl::sycl::access::target::local>
-          LocalAccessor;
-
-      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
-      cgh.parallel_for(
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-          program().template get_kernel<sycl_kernel>(),
-#endif
-          thread_range, sycl_kernel(scratch, lhs, rhs, outptr, var...));
-    };
-    cl::sycl::event e;
-    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(kernel_functor));
-    async_synchronize(e);
-  }
-
-  template <typename OutScalar, typename sycl_kernel, typename InPtr,
-            typename OutPtr, typename Range, typename Index, typename... T>
-  EIGEN_ALWAYS_INLINE void unary_kernel_launcher(const InPtr &inptr,
-                                                 OutPtr &outptr,
-                                                 Range thread_range,
-                                                 Index scratchSize,
-                                                 T... var) const {
-    auto kernel_functor = [=](cl::sycl::handler &cgh) {
-      // binding the placeholder accessors to a commandgroup handler
-      inptr.bind(cgh);
-      outptr.bind(cgh);
-      typedef cl::sycl::accessor<OutScalar, 1,
-                                 cl::sycl::access::mode::read_write,
-                                 cl::sycl::access::target::local>
-          LocalAccessor;
-
-      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
-      cgh.parallel_for(
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-          program().template get_kernel<sycl_kernel>(),
-#endif
-          thread_range, sycl_kernel(scratch, inptr, outptr, var...));
-    };
-    cl::sycl::event e;
-    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(kernel_functor));
-    async_synchronize(e);
-  }
-
-    template <typename OutScalar, typename sycl_kernel, typename InPtr,
-           typename Range, typename Index, typename... T>
-  EIGEN_ALWAYS_INLINE void nullary_kernel_launcher(const InPtr &inptr,
-                                                 Range thread_range,
-                                                 Index scratchSize,
-                                                 T... var) const {
-    auto kernel_functor = [=](cl::sycl::handler &cgh) {
-      // binding the placeholder accessors to a commandgroup handler
-      inptr.bind(cgh);
-      typedef cl::sycl::accessor<OutScalar, 1,
-                                 cl::sycl::access::mode::read_write,
-                                 cl::sycl::access::target::local>
-          LocalAccessor;
-
-      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
-      cgh.parallel_for(
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-          program().template get_kernel<sycl_kernel>(),
-#endif
-          thread_range, sycl_kernel(scratch, inptr, var...));
-    };
-    cl::sycl::event e;
-    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(kernel_functor));
-    async_synchronize(e);
-  }
-
-
-  EIGEN_STRONG_INLINE void synchronize() const {
-#ifdef EIGEN_EXCEPTIONS
-    m_queue.wait_and_throw();
-#else
-    m_queue.wait();
-#endif
-  }
-
-
-  EIGEN_STRONG_INLINE void async_synchronize(cl::sycl::event e) const {
-    set_latest_event(e);
-#ifndef EIGEN_SYCL_ASYNC_EXECUTION
-    synchronize();
-#endif
-  }
-
-  template <typename Index>
-  EIGEN_STRONG_INLINE void parallel_for_setup(Index n, Index &tileSize,
-                                              Index &rng, Index &GRange) const {
-    tileSize = static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
-    tileSize = std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 *
-                                           EIGEN_SYCL_LOCAL_THREAD_DIM1),
-                        static_cast<Index>(tileSize));
-    rng = n;
-    if (rng == 0) rng = static_cast<Index>(1);
-    GRange = rng;
-    if (tileSize > GRange)
-      tileSize = GRange;
-    else if (GRange > tileSize) {
-      Index xMode = static_cast<Index>(GRange % tileSize);
-      if (xMode != 0) GRange += static_cast<Index>(tileSize - xMode);
-    }
-  }
-
-  /// This is used to prepare the number of threads and also the number of
-  /// threads per block for sycl kernels
-  template <typename Index>
-  EIGEN_STRONG_INLINE void parallel_for_setup(
-      const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range,
-      cl::sycl::range<2> &local_range) const {
-    std::array<Index, 2> input_range = input_dim;
-    Index max_workgroup_Size =
-        static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
-    max_workgroup_Size =
-        std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 *
-                                    EIGEN_SYCL_LOCAL_THREAD_DIM1),
-                 static_cast<Index>(max_workgroup_Size));
-    Index pow_of_2 = static_cast<Index>(std::log2(max_workgroup_Size));
-    local_range[1] =
-        static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 2)));
-    input_range[1] = input_dim[1];
-    if (input_range[1] == 0) input_range[1] = static_cast<Index>(1);
-    global_range[1] = input_range[1];
-    if (local_range[1] > global_range[1])
-      local_range[1] = global_range[1];
-    else if (global_range[1] > local_range[1]) {
-      Index xMode = static_cast<Index>(global_range[1] % local_range[1]);
-      if (xMode != 0)
-        global_range[1] += static_cast<Index>(local_range[1] - xMode);
-    }
-    local_range[0] = static_cast<Index>(max_workgroup_Size / local_range[1]);
-    input_range[0] = input_dim[0];
-    if (input_range[0] == 0) input_range[0] = static_cast<Index>(1);
-    global_range[0] = input_range[0];
-    if (local_range[0] > global_range[0])
-      local_range[0] = global_range[0];
-    else if (global_range[0] > local_range[0]) {
-      Index xMode = static_cast<Index>(global_range[0] % local_range[0]);
-      if (xMode != 0)
-        global_range[0] += static_cast<Index>(local_range[0] - xMode);
-    }
-  }
-
-  /// This is used to prepare the number of threads and also the number of
-  /// threads per block for sycl kernels
-  template <typename Index>
-  EIGEN_STRONG_INLINE void parallel_for_setup(
-      const std::array<Index, 3> &input_dim, cl::sycl::range<3> &global_range,
-      cl::sycl::range<3> &local_range) const {
-    std::array<Index, 3> input_range = input_dim;
-    Index max_workgroup_Size =
-        static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
-    max_workgroup_Size =
-        std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 *
-                                    EIGEN_SYCL_LOCAL_THREAD_DIM1),
-                 static_cast<Index>(max_workgroup_Size));
-    Index pow_of_2 = static_cast<Index>(std::log2(max_workgroup_Size));
-    local_range[2] =
-        static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 3)));
-    input_range[2] = input_dim[2];
-    if (input_range[2] == 0) input_range[1] = static_cast<Index>(1);
-    global_range[2] = input_range[2];
-    if (local_range[2] > global_range[2])
-      local_range[2] = global_range[2];
-    else if (global_range[2] > local_range[2]) {
-      Index xMode = static_cast<Index>(global_range[2] % local_range[2]);
-      if (xMode != 0)
-        global_range[2] += static_cast<Index>(local_range[2] - xMode);
-    }
-    pow_of_2 = static_cast<Index>(
-        std::log2(static_cast<Index>(max_workgroup_Size / local_range[2])));
-    local_range[1] =
-        static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 2)));
-    input_range[1] = input_dim[1];
-    if (input_range[1] == 0) input_range[1] = static_cast<Index>(1);
-    global_range[1] = input_range[1];
-    if (local_range[1] > global_range[1])
-      local_range[1] = global_range[1];
-    else if (global_range[1] > local_range[1]) {
-      Index xMode = static_cast<Index>(global_range[1] % local_range[1]);
-      if (xMode != 0)
-        global_range[1] += static_cast<Index>(local_range[1] - xMode);
-    }
-    local_range[0] = static_cast<Index>(max_workgroup_Size /
-                                        (local_range[1] * local_range[2]));
-    input_range[0] = input_dim[0];
-    if (input_range[0] == 0) input_range[0] = static_cast<Index>(1);
-    global_range[0] = input_range[0];
-    if (local_range[0] > global_range[0])
-      local_range[0] = global_range[0];
-    else if (global_range[0] > local_range[0]) {
-      Index xMode = static_cast<Index>(global_range[0] % local_range[0]);
-      if (xMode != 0)
-        global_range[0] += static_cast<Index>(local_range[0] - xMode);
-    }
-  }
-
-  EIGEN_STRONG_INLINE bool has_local_memory() const {
-#if !defined(EIGEN_SYCL_LOCAL_MEM) && defined(EIGEN_SYCL_NO_LOCAL_MEM)
-    return false;
-#elif defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)
-    return true;
-#else
-    return m_device_info.local_mem_type ==
-           cl::sycl::info::local_mem_type::local;
-#endif
-  }
-
-  EIGEN_STRONG_INLINE unsigned long max_buffer_size() const {
-    return m_device_info.max_mem_alloc_size;
-  }
-
-  EIGEN_STRONG_INLINE unsigned long getNumSyclMultiProcessors() const {
-    return m_device_info.max_compute_units;
-  }
-
-  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerBlock() const {
-    return m_device_info.max_work_group_size;
-  }
-
-  EIGEN_STRONG_INLINE cl::sycl::id<3> maxWorkItemSizes() const {
-    return m_device_info.max_work_item_sizes;
-  }
-
-  /// No need for sycl it should act the same as CPU version
-  EIGEN_STRONG_INLINE int majorDeviceVersion() const { return 1; }
-
-  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerMultiProcessor() const {
-    // OpenCL doesnot have such concept
-    return 2;
-  }
-
-  EIGEN_STRONG_INLINE size_t sharedMemPerBlock() const {
-    return m_device_info.local_mem_size;
-  }
-
-  // This function returns the nearest power of 2 Work-group size which is <=
-  // maximum device workgroup size.
-  EIGEN_STRONG_INLINE size_t getNearestPowerOfTwoWorkGroupSize() const {
-    return getPowerOfTwo(m_device_info.max_work_group_size, false);
-  }
-
-  EIGEN_STRONG_INLINE std::string getPlatformName() const {
-    return m_device_info.platform_name;
-  }
-
-  EIGEN_STRONG_INLINE std::string getDeviceName() const {
-    return m_device_info.device_name;
-  }
-
-  EIGEN_STRONG_INLINE std::string getDeviceVendor() const {
-    return m_device_info.device_vendor;
-  }
-
-  // This function returns the nearest power of 2
-  // if roundup is true returns result>=wgsize
-  // else it return result <= wgsize
-  EIGEN_STRONG_INLINE size_t getPowerOfTwo(size_t wGSize, bool roundUp) const {
-    if (roundUp) --wGSize;
-    wGSize |= (wGSize >> 1);
-    wGSize |= (wGSize >> 2);
-    wGSize |= (wGSize >> 4);
-    wGSize |= (wGSize >> 8);
-    wGSize |= (wGSize >> 16);
-#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_ARM64 || EIGEN_OS_WIN64
-    wGSize |= (wGSize >> 32);
-#endif
-    return ((!roundUp) ? (wGSize - (wGSize >> 1)) : ++wGSize);
-  }
-
-  EIGEN_STRONG_INLINE cl::sycl::queue &sycl_queue() const { return m_queue; }
-
-  // This function checks if the runtime recorded an error for the
-  // underlying stream device.
-  EIGEN_STRONG_INLINE bool ok() const {
-    if (!exception_caught_) {
-      synchronize();
-    }
-    return !exception_caught_;
-  }
-
-  EIGEN_STRONG_INLINE cl::sycl::event get_latest_event() const {
-#ifdef EIGEN_SYCL_STORE_LATEST_EVENT
-    std::lock_guard<std::mutex> lock(event_mutex_);
-    return latest_events_[std::this_thread::get_id()];
-#else
-    eigen_assert(false);
-    return cl::sycl::event();
-#endif
-  }
-
-  // destructor
-  ~QueueInterface() {
-    pMapper.clear();
-#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
-    scratch_buffers.clear();
-#endif
-  }
-
- protected:
-  EIGEN_STRONG_INLINE void set_latest_event(cl::sycl::event e) const {
-#ifdef EIGEN_SYCL_STORE_LATEST_EVENT
-    std::lock_guard<std::mutex> lock(event_mutex_);
-    latest_events_[std::this_thread::get_id()] = e;
-#else
-    EIGEN_UNUSED_VARIABLE(e);
-#endif
-  }
-
-  void synchronize_and_callback(cl::sycl::event e,
-                                const std::function<void()> &callback) const {
-    set_latest_event(e);
-    if (callback) {
-      auto callback_ = [=]() {
-#ifdef EIGEN_EXCEPTIONS
-        cl::sycl::event(e).wait_and_throw();
-#else
-        cl::sycl::event(e).wait();
-#endif
-        callback();
-      };
-      m_thread_pool.Schedule(std::move(callback_));
-    } else {
-#ifdef EIGEN_EXCEPTIONS
-      m_queue.wait_and_throw();
-#else
-      m_queue.wait();
-#endif
-    }
-  }
-
-  bool sycl_async_handler(cl::sycl::exception_list exceptions) const {
-    bool exception_caught = false;
-    for (const auto &e : exceptions) {
-      if (e) {
-        exception_caught = true;
-        EIGEN_THROW_X(e);
-      }
-    }
-    return exception_caught;
-  }
-
-  /// class members:
-  bool exception_caught_ = false;
-
-  mutable std::mutex pmapper_mutex_;
-
-#ifdef EIGEN_SYCL_STORE_LATEST_EVENT
-  mutable std::mutex event_mutex_;
-  mutable std::unordered_map<std::thread::id, cl::sycl::event> latest_events_;
-#endif
-
-  /// std::map is the container used to make sure that we create only one buffer
-  /// per pointer. The lifespan of the buffer now depends on the lifespan of
-  /// SyclDevice. If a non-read-only pointer is needed to be accessed on the
-  /// host we should manually deallocate it.
-  mutable TensorSycl::internal::PointerMapper pMapper;
-#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
-  mutable std::unordered_set<void *> scratch_buffers;
-#endif
-  /// sycl queue
-  mutable cl::sycl::queue m_queue;
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-  mutable cl::sycl::program m_prog;
-#endif
-
-  /// The thread pool is used to wait on events and call callbacks
-  /// asynchronously
-  mutable Eigen::ThreadPool m_thread_pool;
-
-  const TensorSycl::internal::SyclDeviceInfo m_device_info;
-};
-
-struct SyclDeviceBase {
-  /// QueueInterface is not owned. it is the caller's responsibility to destroy
-  /// it
-  const QueueInterface *m_queue_stream;
-  explicit SyclDeviceBase(const QueueInterface *queue_stream)
-      : m_queue_stream(queue_stream) {}
-  EIGEN_STRONG_INLINE const QueueInterface *queue_stream() const {
-    return m_queue_stream;
-  }
-};
-
-// Here is a sycl device struct which accept the sycl queue interface
-// as an input
-struct SyclDevice : public SyclDeviceBase {
-  explicit SyclDevice(const QueueInterface *queue_stream)
-      : SyclDeviceBase(queue_stream) {}
-
-  // this is the accessor used to construct the evaluator
-  template <cl::sycl::access::mode AcMd, typename T>
-  EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<AcMd, T>
-  get_range_accessor(const void *ptr) const {
-    return queue_stream()->template get_range_accessor<AcMd, T>(ptr);
-  }
-
-  // get sycl accessor
-  template <cl::sycl::access::mode AcMd>
-  EIGEN_STRONG_INLINE cl::sycl::accessor<
-      buffer_scalar_t, 1, AcMd, cl::sycl::access::target::global_buffer>
-  get_sycl_accessor(cl::sycl::handler &cgh, const void *ptr) const {
-    return queue_stream()->template get_sycl_accessor<AcMd>(cgh, ptr);
-  }
-
-  /// Accessing the created sycl device buffer for the device pointer
-  EIGEN_STRONG_INLINE cl::sycl::buffer<buffer_scalar_t, 1> get_sycl_buffer(
-      const void *ptr) const {
-    return queue_stream()->get_sycl_buffer(ptr);
-  }
-
-  /// This is used to prepare the number of threads and also the number of
-  /// threads per block for sycl kernels
-  template <typename Index>
-  EIGEN_STRONG_INLINE void parallel_for_setup(Index n, Index &tileSize,
-                                              Index &rng, Index &GRange) const {
-    queue_stream()->parallel_for_setup(n, tileSize, rng, GRange);
-  }
-
-  /// This is used to prepare the number of threads and also the number of
-  /// threads per block for sycl kernels
-  template <typename Index>
-  EIGEN_STRONG_INLINE void parallel_for_setup(
-      const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range,
-      cl::sycl::range<2> &local_range) const {
-    queue_stream()->parallel_for_setup(input_dim, global_range, local_range);
-  }
-
-  /// This is used to prepare the number of threads and also the number of
-  /// threads per block for sycl kernels
-  template <typename Index>
-  EIGEN_STRONG_INLINE void parallel_for_setup(
-      const std::array<Index, 3> &input_dim, cl::sycl::range<3> &global_range,
-      cl::sycl::range<3> &local_range) const {
-    queue_stream()->parallel_for_setup(input_dim, global_range, local_range);
-  }
-
-  /// allocate device memory
-  EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const {
-    return queue_stream()->allocate(num_bytes);
-  }
-
-  EIGEN_STRONG_INLINE void *allocate_temp(size_t num_bytes) const {
-    return queue_stream()->allocate_temp(num_bytes);
-  }
-
-  /// deallocate device memory
-  EIGEN_STRONG_INLINE void deallocate(void *p) const {
-    queue_stream()->deallocate(p);
-  }
-
-  EIGEN_STRONG_INLINE void deallocate_temp(void *buffer) const {
-    queue_stream()->deallocate_temp(buffer);
-  }
-  template <cl::sycl::access::mode AcMd, typename T>
-  EIGEN_STRONG_INLINE void deallocate_temp(
-      const TensorSycl::internal::RangeAccess<AcMd, T> &buffer) const {
-    queue_stream()->deallocate_temp(buffer);
-  }
-  EIGEN_STRONG_INLINE void deallocate_all() const {
-    queue_stream()->deallocate_all();
-  }
-
-  template <typename data_t>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<
-      cl::sycl::access::mode::read_write, data_t>
-  get(data_t *data) const {
-    return queue_stream()->get(data);
-  }
-  template <typename data_t>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE data_t *get(
-      TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write,
-                                        data_t>
-          data) const {
-    return queue_stream()->get(data);
-  }
-
-  /// attach existing buffer
-  EIGEN_STRONG_INLINE void *attach_buffer(
-      cl::sycl::buffer<buffer_scalar_t, 1> &buf) const {
-    return queue_stream()->attach_buffer(buf);
-  }
-  /// detach buffer
-  EIGEN_STRONG_INLINE void detach_buffer(void *p) const {
-    queue_stream()->detach_buffer(p);
-  }
-  EIGEN_STRONG_INLINE ptrdiff_t get_offset(const void *ptr) const {
-    return queue_stream()->get_offset(ptr);
-  }
-
-  // some runtime conditions that can be applied here
-  EIGEN_STRONG_INLINE bool isDeviceSuitable() const { return true; }
-
-  /// memcpyHostToDevice
-  template <typename Index>
-  EIGEN_STRONG_INLINE void memcpyHostToDevice(
-      Index *dst, const Index *src, size_t n,
-      std::function<void()> callback = {}) const {
-    queue_stream()->memcpyHostToDevice(dst, src, n, callback);
-  }
-  /// memcpyDeviceToHost
-  template <typename Index>
-  EIGEN_STRONG_INLINE void memcpyDeviceToHost(
-      void *dst, const Index *src, size_t n,
-      std::function<void()> callback = {}) const {
-    queue_stream()->memcpyDeviceToHost(dst, src, n, callback);
-  }
-  /// the memcpy function
-  template <typename Index>
-  EIGEN_STRONG_INLINE void memcpy(void *dst, const Index *src, size_t n) const {
-    queue_stream()->memcpy(dst, src, n);
-  }
-  /// the memset function
-  EIGEN_STRONG_INLINE void memset(void *data, int c, size_t n) const {
-    queue_stream()->memset(data, c, n);
-  }
-  /// returning the sycl queue
-  EIGEN_STRONG_INLINE cl::sycl::queue &sycl_queue() const {
-    return queue_stream()->sycl_queue();
-  }
-#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
-  EIGEN_STRONG_INLINE cl::sycl::program &program() const {
-    return queue_stream()->program();
-  }
-#endif
-
-  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const { return 48 * 1024; }
-
-  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
-    // We won't try to take advantage of the l2 cache for the time being, and
-    // there is no l3 cache on sycl devices.
-    return firstLevelCacheSize();
-  }
-  EIGEN_STRONG_INLINE unsigned long getNumSyclMultiProcessors() const {
-    return queue_stream()->getNumSyclMultiProcessors();
-  }
-  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerBlock() const {
-    return queue_stream()->maxSyclThreadsPerBlock();
-  }
-  EIGEN_STRONG_INLINE cl::sycl::id<3> maxWorkItemSizes() const {
-    return queue_stream()->maxWorkItemSizes();
-  }
-  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerMultiProcessor() const {
-    // OpenCL doesnot have such concept
-    return queue_stream()->maxSyclThreadsPerMultiProcessor();
-  }
-  EIGEN_STRONG_INLINE size_t sharedMemPerBlock() const {
-    return queue_stream()->sharedMemPerBlock();
-  }
-  EIGEN_STRONG_INLINE size_t getNearestPowerOfTwoWorkGroupSize() const {
-    return queue_stream()->getNearestPowerOfTwoWorkGroupSize();
-  }
-
-  EIGEN_STRONG_INLINE size_t getPowerOfTwo(size_t val, bool roundUp) const {
-    return queue_stream()->getPowerOfTwo(val, roundUp);
-  }
-  /// No need for sycl it should act the same as CPU version
-  EIGEN_STRONG_INLINE int majorDeviceVersion() const {
-    return queue_stream()->majorDeviceVersion();
-  }
-
-  EIGEN_STRONG_INLINE void synchronize() const {
-    queue_stream()->synchronize();
-  }
-  EIGEN_STRONG_INLINE void async_synchronize(
-      cl::sycl::event e = cl::sycl::event()) const {
-    queue_stream()->async_synchronize(e);
-  }
-  EIGEN_STRONG_INLINE cl::sycl::event get_latest_event() const {
-    return queue_stream()->get_latest_event();
-  }
-
-  // This function checks if the runtime recorded an error for the
-  // underlying stream device.
-  EIGEN_STRONG_INLINE bool ok() const { return queue_stream()->ok(); }
-
-  EIGEN_STRONG_INLINE bool has_local_memory() const {
-    return queue_stream()->has_local_memory();
-  }
-  EIGEN_STRONG_INLINE long max_buffer_size() const {
-    return queue_stream()->max_buffer_size();
-  }
-  EIGEN_STRONG_INLINE std::string getPlatformName() const {
-    return queue_stream()->getPlatformName();
-  }
-  EIGEN_STRONG_INLINE std::string getDeviceName() const {
-    return queue_stream()->getDeviceName();
-  }
-  EIGEN_STRONG_INLINE std::string getDeviceVendor() const {
-    return queue_stream()->getDeviceVendor();
-  }
-  template <typename OutScalar, typename KernelType, typename... T>
-  EIGEN_ALWAYS_INLINE void binary_kernel_launcher(T... var) const {
-    queue_stream()->template binary_kernel_launcher<OutScalar, KernelType>(
-        var...);
-  }
-  template <typename OutScalar, typename KernelType, typename... T>
-  EIGEN_ALWAYS_INLINE void unary_kernel_launcher(T... var) const {
-    queue_stream()->template unary_kernel_launcher<OutScalar, KernelType>(
-        var...);
-  }
-
-  template <typename OutScalar, typename KernelType, typename... T>
-  EIGEN_ALWAYS_INLINE void nullary_kernel_launcher(T... var) const {
-    queue_stream()->template nullary_kernel_launcher<OutScalar, KernelType>(
-        var...);
-  }
-};
-}  // end namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
deleted file mode 100644
index e524b53..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
+++ /dev/null
@@ -1,409 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#if defined(EIGEN_USE_THREADS) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H)
-#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H
-
-namespace Eigen {
-
-// Runs an arbitrary function and then calls Notify() on the passed in
-// Notification.
-template <typename Function, typename... Args> struct FunctionWrapperWithNotification
-{
-  static void run(Notification* n, Function f, Args... args) {
-    f(args...);
-    if (n) {
-      n->Notify();
-    }
-  }
-};
-
-template <typename Function, typename... Args> struct FunctionWrapperWithBarrier
-{
-  static void run(Barrier* b, Function f, Args... args) {
-    f(args...);
-    if (b) {
-      b->Notify();
-    }
-  }
-};
-
-template <typename SyncType>
-static EIGEN_STRONG_INLINE void wait_until_ready(SyncType* n) {
-  if (n) {
-    n->Wait();
-  }
-}
-
-// An abstract interface to a device specific memory allocator.
-class Allocator {
- public:
-  virtual ~Allocator() {}
-  virtual void* allocate(size_t num_bytes) const = 0;
-  virtual void deallocate(void* buffer) const = 0;
-};
-
-// Build a thread pool device on top the an existing pool of threads.
-struct ThreadPoolDevice {
-  // The ownership of the thread pool remains with the caller.
-  ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores, Allocator* allocator = nullptr)
-      : pool_(pool), num_threads_(num_cores), allocator_(allocator) { }
-
-  EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
-    return allocator_ ? allocator_->allocate(num_bytes)
-        : internal::aligned_malloc(num_bytes);
-  }
-
-  EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
-    if (allocator_) {
-      allocator_->deallocate(buffer);
-    } else {
-      internal::aligned_free(buffer);
-    }
-  }
-
-    EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const {
-    return allocate(num_bytes);
-  }
-
-  EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const {
-    deallocate(buffer);
-  }
-
-  template<typename Type>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const {
-    return data;
-  }
-
-  EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
-#ifdef __ANDROID__
-    ::memcpy(dst, src, n);
-#else
-    // TODO(rmlarsen): Align blocks on cache lines.
-    // We have observed that going beyond 4 threads usually just wastes
-    // CPU cycles due to the threads competing for memory bandwidth, so we
-    // statically schedule at most 4 block copies here.
-    const size_t kMinBlockSize = 32768;
-    const size_t num_threads = CostModel::numThreads(n, TensorOpCost(1.0, 1.0, 0), 4);
-    if (n <= kMinBlockSize || num_threads < 2) {
-      ::memcpy(dst, src, n);
-    } else {
-      const char* src_ptr = static_cast<const char*>(src);
-      char* dst_ptr = static_cast<char*>(dst);
-      const size_t blocksize = (n + (num_threads - 1)) / num_threads;
-      Barrier barrier(static_cast<int>(num_threads - 1));
-      // Launch the last 3 blocks on worker threads.
-      for (size_t i = 1; i < num_threads; ++i) {
-        enqueue_with_barrier(&barrier, [n, i, src_ptr, dst_ptr, blocksize] {
-          ::memcpy(dst_ptr + i * blocksize, src_ptr + i * blocksize,
-                   numext::mini(blocksize, n - (i * blocksize)));
-        });
-      }
-      // Launch the first block on the main thread.
-      ::memcpy(dst_ptr, src_ptr, blocksize);
-      barrier.Wait();
-    }
-#endif
-  }
-  EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
-    memcpy(dst, src, n);
-  }
-  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
-    memcpy(dst, src, n);
-  }
-
-  EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
-    ::memset(buffer, c, n);
-  }
-
-  EIGEN_STRONG_INLINE int numThreads() const {
-    return num_threads_;
-  }
-
-  // Number of theads available in the underlying thread pool. This number can
-  // be different from the value returned by numThreads().
-  EIGEN_STRONG_INLINE int numThreadsInPool() const {
-    return pool_->NumThreads();
-  }
-
-  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
-    return l1CacheSize();
-  }
-
-  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
-    // The l3 cache size is shared between all the cores.
-    return l3CacheSize() / num_threads_;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
-    // Should return an enum that encodes the ISA supported by the CPU
-    return 1;
-  }
-
-  template <class Function, class... Args>
-  EIGEN_STRONG_INLINE Notification* enqueue(Function&& f,
-                                            Args&&... args) const {
-    Notification* n = new Notification();
-    pool_->Schedule(
-        std::bind(&FunctionWrapperWithNotification<Function, Args...>::run, n,
-                  std::move(f), args...));
-    return n;
-  }
-
-  template <class Function, class... Args>
-  EIGEN_STRONG_INLINE void enqueue_with_barrier(Barrier* b, Function&& f,
-                                                Args&&... args) const {
-    pool_->Schedule(
-        std::bind(&FunctionWrapperWithBarrier<Function, Args...>::run, b,
-                  std::move(f), args...));
-  }
-
-  template <class Function, class... Args>
-  EIGEN_STRONG_INLINE void enqueueNoNotification(Function&& f,
-                                                 Args&&... args) const {
-    if (sizeof...(args) > 0) {
-      pool_->Schedule(std::bind(std::move(f), args...));
-    } else {
-      pool_->Schedule(std::move(f));
-    }
-  }
-
-  // Returns a logical thread index between 0 and pool_->NumThreads() - 1 if
-  // called from one of the threads in pool_. Returns -1 otherwise.
-  EIGEN_STRONG_INLINE int currentThreadId() const {
-    return pool_->CurrentThreadId();
-  }
-
-  // WARNING: This function is synchronous and will block the calling thread.
-  //
-  // Synchronous parallelFor executes f with [0, n) arguments in parallel and
-  // waits for completion. F accepts a half-open interval [first, last). Block
-  // size is chosen based on the iteration cost and resulting parallel
-  // efficiency. If block_align is not nullptr, it is called to round up the
-  // block size.
-  void parallelFor(Index n, const TensorOpCost& cost,
-                   std::function<Index(Index)> block_align,
-                   std::function<void(Index, Index)> f) const {
-    if (EIGEN_PREDICT_FALSE(n <= 0)){
-      return;
-    // Compute small problems directly in the caller thread.
-    } else if (n == 1 || numThreads() == 1 ||
-               CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
-      f(0, n);
-      return;
-    }
-
-    // Compute block size and total count of blocks.
-    ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
-
-    // Recursively divide size into halves until we reach block_size.
-    // Division code rounds mid to block_size, so we are guaranteed to get
-    // block_count leaves that do actual computations.
-    Barrier barrier(static_cast<unsigned int>(block.count));
-    std::function<void(Index, Index)> handleRange;
-    handleRange = [=, &handleRange, &barrier, &f](Index firstIdx,
-                                                  Index lastIdx) {
-      while (lastIdx - firstIdx > block.size) {
-        // Split into halves and schedule the second half on a different thread.
-        const Index midIdx = firstIdx + divup((lastIdx - firstIdx) / 2, block.size) * block.size;
-        pool_->Schedule([=, &handleRange]() { handleRange(midIdx, lastIdx); });
-        lastIdx = midIdx;
-      }
-      // Single block or less, execute directly.
-      f(firstIdx, lastIdx);
-      barrier.Notify();
-    };
-
-    if (block.count <= numThreads()) {
-      // Avoid a thread hop by running the root of the tree and one block on the
-      // main thread.
-      handleRange(0, n);
-    } else {
-      // Execute the root in the thread pool to avoid running work on more than
-      // numThreads() threads.
-      pool_->Schedule([=, &handleRange]() { handleRange(0, n); });
-    }
-
-    barrier.Wait();
-  }
-
-  // Convenience wrapper for parallelFor that does not align blocks.
-  void parallelFor(Index n, const TensorOpCost& cost,
-                   std::function<void(Index, Index)> f) const {
-    parallelFor(n, cost, nullptr, std::move(f));
-  }
-
-  // WARNING: This function is asynchronous and will not block the calling thread.
-  //
-  // Asynchronous parallelFor executes f with [0, n) arguments in parallel
-  // without waiting for completion. When the last block finished, it will call
-  // 'done' callback. F accepts a half-open interval [first, last). Block size
-  // is chosen based on the iteration cost and resulting parallel efficiency. If
-  // block_align is not nullptr, it is called to round up the block size.
-  void parallelForAsync(Index n, const TensorOpCost& cost,
-                        std::function<Index(Index)> block_align,
-                        std::function<void(Index, Index)> f,
-                        std::function<void()> done) const {
-    // Compute small problems directly in the caller thread.
-    if (n <= 1 || numThreads() == 1 ||
-        CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
-      f(0, n);
-      done();
-      return;
-    }
-
-    // Compute block size and total count of blocks.
-    ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
-
-    ParallelForAsyncContext* const ctx =
-        new ParallelForAsyncContext(block.count, std::move(f), std::move(done));
-
-    // Recursively divide size into halves until we reach block_size.
-    // Division code rounds mid to block_size, so we are guaranteed to get
-    // block_count leaves that do actual computations.
-    ctx->handle_range = [this, ctx, block](Index firstIdx, Index lastIdx) {
-      while (lastIdx - firstIdx > block.size) {
-        // Split into halves and schedule the second half on a different thread.
-        const Index midIdx = firstIdx + divup((lastIdx - firstIdx) / 2, block.size) * block.size;
-        pool_->Schedule(
-            [ctx, midIdx, lastIdx]() { ctx->handle_range(midIdx, lastIdx); });
-        lastIdx = midIdx;
-      }
-
-      // Single block or less, execute directly.
-      ctx->f(firstIdx, lastIdx);
-
-      // Delete async context if it was the last block.
-      if (ctx->count.fetch_sub(1) == 1) delete ctx;
-    };
-
-    if (block.count <= numThreads()) {
-      // Avoid a thread hop by running the root of the tree and one block on the
-      // main thread.
-      ctx->handle_range(0, n);
-    } else {
-      // Execute the root in the thread pool to avoid running work on more than
-      // numThreads() threads.
-      pool_->Schedule([ctx, n]() { ctx->handle_range(0, n); });
-    }
-  }
-
-  // Convenience wrapper for parallelForAsync that does not align blocks.
-  void parallelForAsync(Index n, const TensorOpCost& cost,
-                        std::function<void(Index, Index)> f,
-                        std::function<void()> done) const {
-    parallelForAsync(n, cost, nullptr, std::move(f), std::move(done));
-  }
-
-  // Thread pool accessor.
-  ThreadPoolInterface* getPool() const { return pool_; }
-
-  // Allocator accessor.
-  Allocator* allocator() const { return allocator_; }
-
- private:
-  typedef TensorCostModel<ThreadPoolDevice> CostModel;
-
-  // For parallelForAsync we must keep passed in closures on the heap, and
-  // delete them only after `done` callback finished.
-  struct ParallelForAsyncContext {
-    ParallelForAsyncContext(Index block_count,
-                            std::function<void(Index, Index)> block_f,
-                            std::function<void()> done_callback)
-        : count(block_count),
-          f(std::move(block_f)),
-          done(std::move(done_callback)) {}
-    ~ParallelForAsyncContext() { done(); }
-
-    std::atomic<Index> count;
-    std::function<void(Index, Index)> f;
-    std::function<void()> done;
-
-    std::function<void(Index, Index)> handle_range;
-  };
-
-  struct ParallelForBlock {
-    Index size;   // block size
-    Index count;  // number of blocks
-  };
-
-  // Calculates block size based on (1) the iteration cost and (2) parallel
-  // efficiency. We want blocks to be not too small to mitigate parallelization
-  // overheads; not too large to mitigate tail effect and potential load
-  // imbalance and we also want number of blocks to be evenly dividable across
-  // threads.
-  ParallelForBlock CalculateParallelForBlock(
-      const Index n, const TensorOpCost& cost,
-      std::function<Index(Index)> block_align) const {
-    const double block_size_f = 1.0 / CostModel::taskSize(1, cost);
-    const Index max_oversharding_factor = 4;
-    Index block_size = numext::mini(
-        n, numext::maxi<Index>(
-               divup<Index>(n, max_oversharding_factor * numThreads()),
-               block_size_f));
-    const Index max_block_size = numext::mini(n, 2 * block_size);
-
-    if (block_align) {
-      Index new_block_size = block_align(block_size);
-      eigen_assert(new_block_size >= block_size);
-      block_size = numext::mini(n, new_block_size);
-    }
-
-    Index block_count = divup(n, block_size);
-
-    // Calculate parallel efficiency as fraction of total CPU time used for
-    // computations:
-    double max_efficiency =
-        static_cast<double>(block_count) /
-        (divup<int>(block_count, numThreads()) * numThreads());
-
-    // Now try to increase block size up to max_block_size as long as it
-    // doesn't decrease parallel efficiency.
-    for (Index prev_block_count = block_count;
-         max_efficiency < 1.0 && prev_block_count > 1;) {
-      // This is the next block size that divides size into a smaller number
-      // of blocks than the current block_size.
-      Index coarser_block_size = divup(n, prev_block_count - 1);
-      if (block_align) {
-        Index new_block_size = block_align(coarser_block_size);
-        eigen_assert(new_block_size >= coarser_block_size);
-        coarser_block_size = numext::mini(n, new_block_size);
-      }
-      if (coarser_block_size > max_block_size) {
-        break;  // Reached max block size. Stop.
-      }
-      // Recalculate parallel efficiency.
-      const Index coarser_block_count = divup(n, coarser_block_size);
-      eigen_assert(coarser_block_count < prev_block_count);
-      prev_block_count = coarser_block_count;
-      const double coarser_efficiency =
-          static_cast<double>(coarser_block_count) /
-          (divup<int>(coarser_block_count, numThreads()) * numThreads());
-      if (coarser_efficiency + 0.01 >= max_efficiency) {
-        // Taking it.
-        block_size = coarser_block_size;
-        block_count = coarser_block_count;
-        if (max_efficiency < coarser_efficiency) {
-          max_efficiency = coarser_efficiency;
-        }
-      }
-    }
-
-    return {block_size, block_count};
-  }
-
-  ThreadPoolInterface* pool_;
-  int num_threads_;
-  Allocator* allocator_;
-};
-
-
-}  // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h
deleted file mode 100644
index 1a30e45..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h
+++ /dev/null
@@ -1,236 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
-#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
-
-namespace Eigen {
-
-/** \internal
-  *
-  * \class TensorDimensionList
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Special case of tensor index list used to list all the dimensions of a tensor of rank n.
-  *
-  * \sa Tensor
-  */
-
-template <typename Index, std::size_t Rank> struct DimensionList {
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  const Index operator[] (const Index i) const { return i; }
-};
-
-namespace internal {
-
-template<typename Index, std::size_t Rank> struct array_size<DimensionList<Index, Rank> > {
-  static const size_t value = Rank;
-};
-template<typename Index, std::size_t Rank> struct array_size<const DimensionList<Index, Rank> > {
-  static const size_t value = Rank;
-};
-
-template<DenseIndex n, typename Index, std::size_t Rank> const Index array_get(DimensionList<Index, Rank>&) {
-  return n;
-}
-template<DenseIndex n, typename Index, std::size_t Rank> const Index array_get(const DimensionList<Index, Rank>&) {
-  return n;
-}
-
-
-#if EIGEN_HAS_CONSTEXPR
-template <typename Index, std::size_t Rank>
-struct index_known_statically_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) {
-    return true;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_known_statically_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) {
-    return true;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct all_indices_known_statically_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return true;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct all_indices_known_statically_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return true;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct indices_statically_known_to_increase_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return true;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct indices_statically_known_to_increase_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return true;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_eq_impl<DimensionList<Index, Rank> > {
-  static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i == value;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_eq_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i == value;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_ne_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i != value;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_ne_impl<const DimensionList<Index, Rank> > {
-  static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i != value;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_gt_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i > value;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_gt_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i > value;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_lt_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i < value;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_lt_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
-    return i < value;
-  }
-};
-
-#else
-template <typename Index, std::size_t Rank>
-struct index_known_statically_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run(const DenseIndex) {
-    return true;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_known_statically_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run(const DenseIndex) {
-    return true;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct all_indices_known_statically_impl<DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run() {
-    return true;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct all_indices_known_statically_impl<const DimensionList<Index, Rank> > {
-  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run() {
-    return true;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct indices_statically_known_to_increase_impl<DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
-    return true;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct indices_statically_known_to_increase_impl<const DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
-    return true;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_eq_impl<DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
-    return false;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_eq_impl<const DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
-    return false;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_ne_impl<DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex){
-    return false;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_ne_impl<const DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
-    return false;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_gt_impl<DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
-    return false;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_gt_impl<const DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
-    return false;
-  }
-};
-
-template <typename Index, std::size_t Rank>
-struct index_statically_lt_impl<DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
-    return false;
-  }
-};
-template <typename Index, std::size_t Rank>
-struct index_statically_lt_impl<const DimensionList<Index, Rank> > {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
-    return false;
-  }
-};
-#endif
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
deleted file mode 100644
index f0f1e83..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
+++ /dev/null
@@ -1,490 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
-#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
-
-
-namespace Eigen {
-
-/** \internal
-  *
-  * \class TensorDimensions
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Set of classes used to encode and store the dimensions of a Tensor.
-  *
-  * The Sizes class encodes as part of the type the number of dimensions and the
-  * sizes corresponding to each dimension. It uses no storage space since it is
-  * entirely known at compile time.
-  * The DSizes class is its dynamic sibling: the number of dimensions is known
-  * at compile time but the sizes are set during execution.
-  *
-  * \sa Tensor
-  */
-
-// Boilerplate code
-namespace internal {
-
-template<std::ptrdiff_t n, typename Dimension> struct dget {
-  static const std::ptrdiff_t value = get<n, Dimension>::value;
-};
-
-
-template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
-struct fixed_size_tensor_index_linearization_helper
-{
-  template <typename Dimensions> EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const& indices,
-                          const Dimensions& dimensions)
-  {
-    return array_get<RowMajor ? n - 1 : (NumIndices - n)>(indices) +
-        dget<RowMajor ? n - 1 : (NumIndices - n), Dimensions>::value *
-        fixed_size_tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
-  }
-};
-
-template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
-struct fixed_size_tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
-{
-  template <typename Dimensions> EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const&, const Dimensions&)
-  {
-    return 0;
-  }
-};
-
-template<typename Index, std::ptrdiff_t n>
-struct fixed_size_tensor_index_extraction_helper
-{
-  template <typename Dimensions> EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Index run(const Index index,
-                          const Dimensions& dimensions)
-  {
-    const Index mult = (index == n-1) ? 1 : 0;
-    return array_get<n-1>(dimensions) * mult +
-        fixed_size_tensor_index_extraction_helper<Index, n - 1>::run(index, dimensions);
-  }
-};
-
-template<typename Index>
-struct fixed_size_tensor_index_extraction_helper<Index, 0>
-{
-  template <typename Dimensions> EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Index run(const Index,
-                          const Dimensions&)
-  {
-    return 0;
-  }
-  };
-
-}  // end namespace internal
-
-
-// Fixed size
-#ifndef EIGEN_EMULATE_CXX11_META_H
-template <typename std::ptrdiff_t... Indices>
-struct Sizes {
-  typedef internal::numeric_list<std::ptrdiff_t, Indices...> Base;
-  const Base t = Base();
-  static const std::ptrdiff_t total_size = internal::arg_prod(Indices...);
-  static const ptrdiff_t count = Base::count;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t rank() const {
-    return Base::count;
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t TotalSize() {
-    return internal::arg_prod(Indices...);
-  }
-
-  EIGEN_DEVICE_FUNC Sizes() { }
-  template <typename DenseIndex>
-  explicit EIGEN_DEVICE_FUNC Sizes(const array<DenseIndex, Base::count>& /*indices*/) {
-    // todo: add assertion
-  }
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  template <typename... DenseIndex> EIGEN_DEVICE_FUNC Sizes(DenseIndex...) { }
-  explicit EIGEN_DEVICE_FUNC Sizes(std::initializer_list<std::ptrdiff_t> /*l*/) {
-    // todo: add assertion
-  }
-#endif
-
-  template <typename T> Sizes& operator = (const T& /*other*/) {
-    // add assertion failure if the size of other is different
-    return *this;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t operator[] (const std::ptrdiff_t index) const {
-    return internal::fixed_size_tensor_index_extraction_helper<std::ptrdiff_t, Base::count>::run(index, t);
-  }
-
-  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  ptrdiff_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
-    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, t);
-  }
-  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  ptrdiff_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
-    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, t);
-  }
-};
-
-namespace internal {
-template <typename std::ptrdiff_t... Indices>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<Indices...>&) {
-  return Sizes<Indices...>::total_size;
-}
-}
-
-#else
-
-template <std::ptrdiff_t n>
-struct non_zero_size {
-  typedef internal::type2val<std::ptrdiff_t, n> type;
-};
-template <>
-struct non_zero_size<0> {
-  typedef internal::null_type type;
-};
-
-template <std::ptrdiff_t V1=0, std::ptrdiff_t V2=0, std::ptrdiff_t V3=0, std::ptrdiff_t V4=0, std::ptrdiff_t V5=0> struct Sizes {
-  typedef typename internal::make_type_list<typename non_zero_size<V1>::type, typename non_zero_size<V2>::type, typename non_zero_size<V3>::type, typename non_zero_size<V4>::type, typename non_zero_size<V5>::type >::type Base;
-  static const std::ptrdiff_t count = Base::count;
-  static const std::ptrdiff_t total_size = internal::arg_prod<Base>::value;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t rank() const {
-    return count;
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t TotalSize() {
-    return internal::arg_prod<Base>::value;
-  }
-
-  Sizes() { }
-  template <typename DenseIndex>
-  explicit Sizes(const array<DenseIndex, Base::count>& /*indices*/) {
-    // todo: add assertion
-  }
-  template <typename T> Sizes& operator = (const T& /*other*/) {
-    // add assertion failure if the size of other is different
-    return *this;
-  }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  template <typename... DenseIndex> Sizes(DenseIndex... /*indices*/) { }
-  explicit Sizes(std::initializer_list<std::ptrdiff_t>) {
-    // todo: add assertion
-  }
-#else
-  EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex) {
-  }
-  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex) {
-  }
-  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex) {
-  }
-  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex) {
-  }
-  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex) {
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index operator[] (const Index index) const {
-    switch (index) {
-      case 0:
-        return internal::get<0, Base>::value;
-      case 1:
-        return internal::get<1, Base>::value;
-      case 2:
-        return internal::get<2, Base>::value;
-      case 3:
-        return internal::get<3, Base>::value;
-      case 4:
-        return internal::get<4, Base>::value;
-      default:
-        eigen_assert(false && "index overflow");
-        return static_cast<Index>(-1);
-    }
-  }
-
-  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  ptrdiff_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
-    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, *reinterpret_cast<const Base*>(this));
-  }
-  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  ptrdiff_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
-    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, *reinterpret_cast<const Base*>(this));
-  }
-};
-
-namespace internal {
-template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<V1, V2, V3, V4, V5>&) {
-  return Sizes<V1, V2, V3, V4, V5>::total_size;
-}
-}
-
-#endif
-
-// Boilerplate
-namespace internal {
-template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
-struct tensor_index_linearization_helper
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const& dimensions)
-  {
-    return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
-      array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
-        tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
-  }
-};
-
-template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
-struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const&)
-  {
-    return array_get<RowMajor ? 0 : NumIndices - 1>(indices);
-  }
-};
-}  // end namespace internal
-
-
-
-// Dynamic size
-template <typename DenseIndex, int NumDims>
-struct DSizes : array<DenseIndex, NumDims> {
-  typedef array<DenseIndex, NumDims> Base;
-  static const int count = NumDims;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const {
-    return NumDims;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex TotalSize() const {
-    return (NumDims == 0) ? 1 : internal::array_prod(*static_cast<const Base*>(this));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DSizes() {
-    for (int i = 0 ; i < NumDims; ++i) {
-      (*this)[i] = 0;
-    }
-  }
-  EIGEN_DEVICE_FUNC explicit DSizes(const array<DenseIndex, NumDims>& a) : Base(a) { }
-
-  EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0) {
-    eigen_assert(NumDims == 1);
-    (*this)[0] = i0;
-  }
-
-  EIGEN_DEVICE_FUNC DSizes(const DimensionList<DenseIndex, NumDims>& a) {
-    for (int i = 0 ; i < NumDims; ++i) {
-      (*this)[i] = a[i];
-    }
-  }
-
-  // Enable DSizes index type promotion only if we are promoting to the
-  // larger type, e.g. allow to promote dimensions of type int to long.
-  template<typename OtherIndex>
-  EIGEN_DEVICE_FUNC
-  explicit DSizes(const array<OtherIndex, NumDims>& other,
-                  // Default template parameters require c++11.
-                  typename internal::enable_if<
-                     internal::is_same<
-                         DenseIndex,
-                         typename internal::promote_index_type<
-                             DenseIndex,
-                             OtherIndex
-                         >::type
-                     >::value, void*>::type = 0) {
-    for (int i = 0; i < NumDims; ++i) {
-      (*this)[i] = static_cast<DenseIndex>(other[i]);
-    }
-  }
-
-#ifdef EIGEN_HAS_INDEX_LIST
-  template <typename FirstType, typename... OtherTypes>
-  EIGEN_DEVICE_FUNC
-  explicit DSizes(const Eigen::IndexList<FirstType, OtherTypes...>& dimensions) {
-    for (int i = 0; i < dimensions.count; ++i) {
-      (*this)[i] = dimensions[i];
-    }
-  }
-#endif
-
-#ifndef EIGEN_EMULATE_CXX11_META_H
-  template <typename std::ptrdiff_t... Indices>
-  EIGEN_DEVICE_FUNC DSizes(const Sizes<Indices...>& a) {
-    for (int i = 0 ; i < NumDims; ++i) {
-      (*this)[i] = a[i];
-    }
-  }
-#else
-  template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5>
-  EIGEN_DEVICE_FUNC DSizes(const Sizes<V1, V2, V3, V4, V5>& a) {
-    for (int i = 0 ; i < NumDims; ++i) {
-      (*this)[i] = a[i];
-    }
-  }
-#endif
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  template<typename... IndexTypes> EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE explicit DSizes(DenseIndex firstDimension, DenseIndex secondDimension, IndexTypes... otherDimensions) : Base({{firstDimension, secondDimension, otherDimensions...}}) {
-    EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 2 == NumDims, YOU_MADE_A_PROGRAMMING_MISTAKE)
-  }
-#else
-  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1) {
-    eigen_assert(NumDims == 2);
-    (*this)[0] = i0;
-    (*this)[1] = i1;
-  }
-  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2) {
-    eigen_assert(NumDims == 3);
-    (*this)[0] = i0;
-    (*this)[1] = i1;
-    (*this)[2] = i2;
-  }
-  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3) {
-    eigen_assert(NumDims == 4);
-    (*this)[0] = i0;
-    (*this)[1] = i1;
-    (*this)[2] = i2;
-    (*this)[3] = i3;
-  }
-  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3, const DenseIndex i4) {
-    eigen_assert(NumDims == 5);
-    (*this)[0] = i0;
-    (*this)[1] = i1;
-    (*this)[2] = i2;
-    (*this)[3] = i3;
-    (*this)[4] = i4;
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC DSizes& operator = (const array<DenseIndex, NumDims>& other) {
-    *static_cast<Base*>(this) = other;
-    return *this;
-  }
-
-  // A constexpr would be so much better here
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfColMajor(const array<DenseIndex, NumDims>& indices) const {
-    return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, false>::run(indices, *static_cast<const Base*>(this));
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfRowMajor(const array<DenseIndex, NumDims>& indices) const {
-    return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, true>::run(indices, *static_cast<const Base*>(this));
-  }
-};
-
-template <typename IndexType, int NumDims>
-std::ostream& operator<<(std::ostream& os,
-                         const DSizes<IndexType, NumDims>& dims) {
-  os << "[";
-  for (int i = 0; i < NumDims; ++i) {
-    if (i > 0) os << ", ";
-    os << dims[i];
-  }
-  os << "]";
-  return os;
-}
-
-// Boilerplate
-namespace internal {
-template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
-struct tensor_vsize_index_linearization_helper
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const& dimensions)
-  {
-    return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
-      array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
-        tensor_vsize_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
-  }
-};
-
-template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
-struct tensor_vsize_index_linearization_helper<Index, NumIndices, 0, RowMajor>
-{
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const&)
-  {
-    return array_get<RowMajor ? 0 : NumIndices - 1>(indices);
-  }
-};
-}  // end namespace internal
-
-
-namespace internal {
-
-template <typename DenseIndex, int NumDims> struct array_size<const DSizes<DenseIndex, NumDims> > {
-  static const ptrdiff_t value = NumDims;
-};
-template <typename DenseIndex, int NumDims> struct array_size<DSizes<DenseIndex, NumDims> > {
-  static const ptrdiff_t value = NumDims;
-};
-#ifndef EIGEN_EMULATE_CXX11_META_H
-template <typename std::ptrdiff_t... Indices> struct array_size<const Sizes<Indices...> > {
-static const std::ptrdiff_t value = Sizes<Indices...>::count;
-};
-template <typename std::ptrdiff_t... Indices> struct array_size<Sizes<Indices...> > {
-static const std::ptrdiff_t value = Sizes<Indices...>::count;
-};
-template <std::ptrdiff_t n, typename std::ptrdiff_t... Indices> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<Indices...>&) {
-  return get<n, internal::numeric_list<std::ptrdiff_t, Indices...> >::value;
-}
-template <std::ptrdiff_t n> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<>&) {
-  eigen_assert(false && "should never be called");
-  return -1;
-}
-#else
-template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> struct array_size<const Sizes<V1,V2,V3,V4,V5> > {
-  static const ptrdiff_t value = Sizes<V1,V2,V3,V4,V5>::count;
-};
-template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> struct array_size<Sizes<V1,V2,V3,V4,V5> > {
-  static const ptrdiff_t value = Sizes<V1,V2,V3,V4,V5>::count;
-};
-template <std::ptrdiff_t n, std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<V1,V2,V3,V4,V5>&) {
-  return get<n, typename Sizes<V1,V2,V3,V4,V5>::Base>::value;
-}
-
-#endif
-
-
-template <typename Dims1, typename Dims2, ptrdiff_t n, ptrdiff_t m>
-struct sizes_match_below_dim {
-  static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1&, Dims2&) {
-    return false;
-  }
-};
-template <typename Dims1, typename Dims2, ptrdiff_t n>
-struct sizes_match_below_dim<Dims1, Dims2, n, n> {
-  static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1& dims1, Dims2& dims2) {
-    return (array_get<n-1>(dims1) == array_get<n-1>(dims2)) &&
-        sizes_match_below_dim<Dims1, Dims2, n-1, n-1>::run(dims1, dims2);
-  }
-};
-template <typename Dims1, typename Dims2>
-struct sizes_match_below_dim<Dims1, Dims2, 0, 0> {
-  static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1&, Dims2&) {
-    return true;
-  }
-};
-
-} // end namespace internal
-
-
-template <typename Dims1, typename Dims2>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool dimensions_match(Dims1 dims1, Dims2 dims2) {
-  return internal::sizes_match_below_dim<Dims1, Dims2, internal::array_size<Dims1>::value, internal::array_size<Dims2>::value>::run(dims1, dims2);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
deleted file mode 100644
index a48d035..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
+++ /dev/null
@@ -1,236 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
-#define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
-
-namespace Eigen {
-
-/** \class TensorForcedEval
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor reshaping class.
-  *
-  *
-  */
-namespace internal {
-template<typename XprType, template <class> class MakePointer_>
-struct traits<TensorEvalToOp<XprType, MakePointer_> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename MakePointer_<Scalar>::Type PointerType;
-
-  enum {
-    Flags = 0
-  };
-  template <class T>
-  struct MakePointer {
-    // Intermediate typedef to workaround MSVC issue.
-    typedef MakePointer_<T> MakePointerT;
-    typedef typename MakePointerT::Type Type;
-
-
-  };
-};
-
-template<typename XprType, template <class> class MakePointer_>
-struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense>
-{
-  typedef const TensorEvalToOp<XprType, MakePointer_>& type;
-};
-
-template<typename XprType, template <class> class MakePointer_>
-struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type>
-{
-  typedef TensorEvalToOp<XprType, MakePointer_> type;
-};
-
-}  // end namespace internal
-
-
-
-
-template<typename XprType, template <class> class MakePointer_>
-class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType, MakePointer_>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename MakePointer_<CoeffReturnType>::Type PointerType;
-  typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index;
-
-  static const int NumDims = Eigen::internal::traits<TensorEvalToOp>::NumDimensions;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType& expr)
-      : m_xpr(expr), m_buffer(buffer) {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_DEVICE_FUNC PointerType buffer() const { return m_buffer; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    PointerType m_buffer;
-};
-
-
-
-template<typename ArgType, typename Device, template <class> class MakePointer_>
-struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
-{
-  typedef TensorEvalToOp<ArgType, MakePointer_> XprType;
-  typedef typename ArgType::Scalar Scalar;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
-  typedef typename XprType::Index Index;
-  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  enum {
-    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = true,
-    PreferBlockAccess = false,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = true
-  };
-
-  static const int NumDims = internal::traits<ArgType>::NumDimensions;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
-      ArgTensorBlock;
-
-  typedef internal::TensorBlockAssignment<
-      CoeffReturnType, NumDims, typename ArgTensorBlock::XprType, Index>
-      TensorBlockAssignment;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_buffer(device.get(op.buffer())), m_expression(op.expression()){}
-
-
-  EIGEN_STRONG_INLINE ~TensorEvaluator() {
-  }
-
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType scalar) {
-    EIGEN_UNUSED_VARIABLE(scalar);
-    eigen_assert(scalar == NULL);
-    return m_impl.evalSubExprsIfNeeded(m_buffer);
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType scalar, EvalSubExprsCallback done) {
-    EIGEN_UNUSED_VARIABLE(scalar);
-    eigen_assert(scalar == NULL);
-    m_impl.evalSubExprsIfNeededAsync(m_buffer, std::move(done));
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
-    m_buffer[i] = m_impl.coeff(i);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
-    internal::pstoret<CoeffReturnType, PacketReturnType, Aligned>(m_buffer + i, m_impl.template packet<TensorEvaluator<ArgType, Device>::IsAligned ? Aligned : Unaligned>(i));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    return m_impl.getResourceRequirements();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(
-      TensorBlockDesc& desc, TensorBlockScratch& scratch) {
-    // Add `m_buffer` as destination buffer to the block descriptor.
-    desc.template AddDestinationBuffer<Layout>(
-        /*dst_base=*/m_buffer + desc.offset(),
-        /*dst_strides=*/internal::strides<Layout>(m_impl.dimensions()));
-
-    ArgTensorBlock block =
-        m_impl.block(desc, scratch, /*root_of_expr_ast=*/true);
-
-    // If block was evaluated into a destination buffer, there is no need to do
-    // an assignment.
-    if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
-      TensorBlockAssignment::Run(
-          TensorBlockAssignment::target(
-              desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()),
-              m_buffer, desc.offset()),
-          block.expr());
-    }
-    block.cleanup();
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return m_buffer[index];
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    // We assume that evalPacket or evalScalar is called to perform the
-    // assignment and account for the cost of the write here.
-    return m_impl.costPerCoeff(vectorized) +
-        TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_buffer; }
-  ArgType expression() const { return m_expression; }
-  #ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-    m_buffer.bind(cgh);
-  }
-  #endif
-
-
- private:
-  TensorEvaluator<ArgType, Device> m_impl;
-  EvaluatorPointerType m_buffer;
-  const ArgType m_expression;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
deleted file mode 100644
index 3aff7fa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
+++ /dev/null
@@ -1,983 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
-#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
-
-namespace Eigen {
-
-/** \class TensorEvaluator
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief The tensor evaluator classes.
-  *
-  * These classes are responsible for the evaluation of the tensor expression.
-  *
-  * TODO: add support for more types of expressions, in particular expressions
-  * leading to lvalues (slicing, reshaping, etc...)
-  */
-
-// Generic evaluator
-template<typename Derived, typename Device>
-struct TensorEvaluator
-{
-  typedef typename Derived::Index Index;
-  typedef typename Derived::Scalar Scalar;
-  typedef typename Derived::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef typename Derived::Dimensions Dimensions;
-  typedef Derived XprType;
-  static const int PacketSize =  PacketType<CoeffReturnType, Device>::size;
-  typedef typename internal::traits<Derived>::template MakePointer<Scalar>::Type TensorPointerType;
-  typedef StorageMemory<Scalar, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  // NumDimensions is -1 for variable dim tensors
-  static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
-                               internal::traits<Derived>::NumDimensions : 0;
-
-  enum {
-    IsAligned          = Derived::IsAligned,
-    PacketAccess       = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess        = internal::is_arithmetic<typename internal::remove_const<Scalar>::type>::value,
-    PreferBlockAccess  = false,
-    Layout             = Derived::Layout,
-    CoordAccess        = NumCoords > 0,
-    RawAccess          = true
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
-      : m_data(device.get((const_cast<TensorPointerType>(m.data())))),
-        m_dims(m.dimensions()),
-        m_device(device)
-  { }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType dest) {
-    if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && dest) {
-      m_device.memcpy((void*)(m_device.get(dest)), m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
-      return false;
-    }
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType dest, EvalSubExprsCallback done) {
-    // TODO(ezhulenev): ThreadPoolDevice memcpy is blockign operation.
-    done(evalSubExprsIfNeeded(dest));
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    eigen_assert(m_data != NULL);
-    return m_data[index];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) {
-    eigen_assert(m_data != NULL);
-    return m_data[index];
-  }
-
-  template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketReturnType packet(Index index) const
-  {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
-  }
-
-  // Return a packet starting at `index` where `umask` specifies which elements
-  // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
-  // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
-  // float element will be loaded, otherwise 0 will be loaded.
-  // Function has been templatized to enable Sfinae.
-  template <typename PacketReturnTypeT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>::type
-  partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
-  {
-    return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
-  }
-
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    return internal::pstoret<Scalar, PacketReturnType, StoreMode>(m_data + index, x);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
-    eigen_assert(m_data != NULL);
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      return m_data[m_dims.IndexOfColMajor(coords)];
-    } else {
-      return m_data[m_dims.IndexOfRowMajor(coords)];
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType&
-  coeffRef(const array<DenseIndex, NumCoords>& coords) {
-    eigen_assert(m_data != NULL);
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      return m_data[m_dims.IndexOfColMajor(coords)];
-    } else {
-      return m_data[m_dims.IndexOfRowMajor(coords)];
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
-                        PacketType<CoeffReturnType, Device>::size);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    return internal::TensorBlockResourceRequirements::any();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    assert(m_data != NULL);
-    return TensorBlock::materialize(m_data, m_dims, desc, scratch);
-  }
-
-  template<typename TensorBlock>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
-      const TensorBlockDesc& desc, const TensorBlock& block) {
-    assert(m_data != NULL);
-
-    typedef typename TensorBlock::XprType TensorBlockExpr;
-    typedef internal::TensorBlockAssignment<Scalar, NumCoords, TensorBlockExpr,
-                                            Index>
-        TensorBlockAssign;
-
-    TensorBlockAssign::Run(
-        TensorBlockAssign::target(desc.dimensions(),
-                                  internal::strides<Layout>(m_dims), m_data,
-                                  desc.offset()),
-        block.expr());
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_data.bind(cgh);
-  }
-#endif
- protected:
-  EvaluatorPointerType m_data;
-  Dimensions m_dims;
-  const Device EIGEN_DEVICE_REF m_device;
-};
-
-namespace {
-template <typename T> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T loadConstant(const T* address) {
-  return *address;
-}
-// Use the texture cache on CUDA devices whenever possible
-#if defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350
-template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-float loadConstant(const float* address) {
-  return __ldg(address);
-}
-template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-double loadConstant(const double* address) {
-  return __ldg(address);
-}
-template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-Eigen::half loadConstant(const Eigen::half* address) {
-  return Eigen::half(half_impl::raw_uint16_to_half(__ldg(&address->x)));
-}
-#endif
-#ifdef EIGEN_USE_SYCL
-// overload of load constant should be implemented here based on range access
-template <cl::sycl::access::mode AcMd, typename T>
-T &loadConstant(const Eigen::TensorSycl::internal::RangeAccess<AcMd, T> &address) {
-  return *address;
-}
-#endif
-}
-
-
-// Default evaluator for rvalues
-template<typename Derived, typename Device>
-struct TensorEvaluator<const Derived, Device>
-{
-  typedef typename Derived::Index Index;
-  typedef typename Derived::Scalar Scalar;
-  typedef typename Derived::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef typename Derived::Dimensions Dimensions;
-  typedef const Derived XprType;
-  typedef typename internal::traits<Derived>::template MakePointer<const Scalar>::Type TensorPointerType;
-  typedef StorageMemory<const Scalar, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  // NumDimensions is -1 for variable dim tensors
-  static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
-                               internal::traits<Derived>::NumDimensions : 0;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-
-  enum {
-    IsAligned         = Derived::IsAligned,
-    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess       = internal::is_arithmetic<ScalarNoConst>::value,
-    PreferBlockAccess = false,
-    Layout            = Derived::Layout,
-    CoordAccess       = NumCoords > 0,
-    RawAccess         = true
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
-      : m_data(device.get(m.data())), m_dims(m.dimensions()), m_device(device)
-  { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && data) {
-      m_device.memcpy((void*)(m_device.get(data)),m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
-      return false;
-    }
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType dest, EvalSubExprsCallback done) {
-    // TODO(ezhulenev): ThreadPoolDevice memcpy is a blockign operation.
-    done(evalSubExprsIfNeeded(dest));
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    eigen_assert(m_data != NULL);
-    return loadConstant(m_data+index);
-  }
-
-  template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketReturnType packet(Index index) const
-  {
-    return internal::ploadt_ro<PacketReturnType, LoadMode>(m_data + index);
-  }
-
-  // Return a packet starting at `index` where `umask` specifies which elements
-  // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
-  // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
-  // float element will be loaded, otherwise 0 will be loaded.
-  // Function has been templatized to enable Sfinae.
-  template <typename PacketReturnTypeT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  typename internal::enable_if<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>::type
-  partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
-  {
-    return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
-    eigen_assert(m_data != NULL);
-    const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords)
-                        : m_dims.IndexOfRowMajor(coords);
-    return loadConstant(m_data+index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
-                        PacketType<CoeffReturnType, Device>::size);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    return internal::TensorBlockResourceRequirements::any();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    assert(m_data != NULL);
-    return TensorBlock::materialize(m_data, m_dims, desc, scratch);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_data.bind(cgh);
-  }
-#endif
- protected:
-  EvaluatorPointerType m_data;
-  Dimensions m_dims;
-  const Device EIGEN_DEVICE_REF m_device;
-};
-
-
-
-
-// -------------------- CwiseNullaryOp --------------------
-
-template<typename NullaryOp, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device>
-{
-  typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType;
-
-  TensorEvaluator(const XprType& op, const Device& device)
-      : m_functor(op.functor()), m_argImpl(op.nestedExpression(), device), m_wrapper()
-  { }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = true,
-    PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess
-    #ifdef EIGEN_USE_SYCL
-    &&  (PacketType<CoeffReturnType, Device>::size >1)
-    #endif
-    ,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { return true; }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    done(true);
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() { }
-
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
-  {
-    return m_wrapper(m_functor, index);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return m_wrapper.template packetOp<PacketReturnType, Index>(m_functor, index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
-                        PacketType<CoeffReturnType, Device>::size);
-  }
-
-  EIGEN_DEVICE_FUNC  EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-   // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_argImpl.bind(cgh);
-  }
-#endif
-
- private:
-  const NullaryOp m_functor;
-  TensorEvaluator<ArgType, Device> m_argImpl;
-  const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper;
-};
-
-
-
-// -------------------- CwiseUnaryOp --------------------
-
-template<typename UnaryOp, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
-{
-  typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType;
-
-  enum {
-    IsAligned          = TensorEvaluator<ArgType, Device>::IsAligned,
-    PacketAccess       = int(TensorEvaluator<ArgType, Device>::PacketAccess) &
-                         int(internal::functor_traits<UnaryOp>::PacketAccess),
-    BlockAccess        = TensorEvaluator<ArgType, Device>::BlockAccess,
-    PreferBlockAccess  = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout             = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess        = false,  // to be implemented
-    RawAccess          = false
-  };
-
-  TensorEvaluator(const XprType& op, const Device& device)
-    : m_device(device),
-      m_functor(op.functor()),
-      m_argImpl(op.nestedExpression(), device)
-  { }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  static const int NumDims = internal::array_size<Dimensions>::value;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
-      ArgTensorBlock;
-
-  typedef internal::TensorCwiseUnaryBlock<UnaryOp, ArgTensorBlock>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_argImpl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_argImpl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_argImpl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
-  {
-    return m_functor(m_argImpl.coeff(index));
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
-    return m_argImpl.costPerCoeff(vectorized) +
-        TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    static const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
-    return m_argImpl.getResourceRequirements().addCostPerCoeff(
-        {0, 0, functor_cost / PacketSize});
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    return TensorBlock(m_argImpl.block(desc, scratch), m_functor);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const{
-    m_argImpl.bind(cgh);
-  }
-#endif
-
-
- private:
-  const Device EIGEN_DEVICE_REF m_device;
-  const UnaryOp m_functor;
-  TensorEvaluator<ArgType, Device> m_argImpl;
-};
-
-
-// -------------------- CwiseBinaryOp --------------------
-
-template<typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device>
-struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device>
-{
-  typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType;
-
-  enum {
-    IsAligned         = int(TensorEvaluator<LeftArgType, Device>::IsAligned) &
-                        int(TensorEvaluator<RightArgType, Device>::IsAligned),
-    PacketAccess      = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
-                        int(TensorEvaluator<RightArgType, Device>::PacketAccess) &
-                        int(internal::functor_traits<BinaryOp>::PacketAccess),
-    BlockAccess       = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
-                        int(TensorEvaluator<RightArgType, Device>::BlockAccess),
-    PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
-                        int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
-    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  TensorEvaluator(const XprType& op, const Device& device)
-    : m_device(device),
-      m_functor(op.functor()),
-      m_leftImpl(op.lhsExpression(), device),
-      m_rightImpl(op.rhsExpression(), device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
-  }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  static const int NumDims = internal::array_size<
-      typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const LeftArgType, Device>::TensorBlock
-      LeftTensorBlock;
-  typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock
-      RightTensorBlock;
-
-  typedef internal::TensorCwiseBinaryBlock<BinaryOp, LeftTensorBlock,
-                                           RightTensorBlock>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
-  {
-    // TODO: use right impl instead if right impl dimensions are known at compile time.
-    return m_leftImpl.dimensions();
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_leftImpl.evalSubExprsIfNeeded(NULL);
-    m_rightImpl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    // TODO(ezhulenev): Evaluate two expression in parallel?
-    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
-      m_rightImpl.evalSubExprsIfNeededAsync(nullptr,
-                                            [done](bool) { done(true); });
-    });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_leftImpl.cleanup();
-    m_rightImpl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
-  {
-    return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index));
-  }
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
-    return m_leftImpl.costPerCoeff(vectorized) +
-           m_rightImpl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    static const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
-    return internal::TensorBlockResourceRequirements::merge(
-               m_leftImpl.getResourceRequirements(),
-               m_rightImpl.getResourceRequirements())
-        .addCostPerCoeff({0, 0, functor_cost / PacketSize});
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    desc.DropDestinationBuffer();
-    return TensorBlock(m_leftImpl.block(desc, scratch),
-                         m_rightImpl.block(desc, scratch), m_functor);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-  #ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_leftImpl.bind(cgh);
-    m_rightImpl.bind(cgh);
-  }
-  #endif
- private:
-  const Device EIGEN_DEVICE_REF m_device;
-  const BinaryOp m_functor;
-  TensorEvaluator<LeftArgType, Device> m_leftImpl;
-  TensorEvaluator<RightArgType, Device> m_rightImpl;
-};
-
-// -------------------- CwiseTernaryOp --------------------
-
-template<typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type, typename Device>
-struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type>, Device>
-{
-  typedef TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type> XprType;
-
-  enum {
-    IsAligned = TensorEvaluator<Arg1Type, Device>::IsAligned & TensorEvaluator<Arg2Type, Device>::IsAligned & TensorEvaluator<Arg3Type, Device>::IsAligned,
-    PacketAccess      = TensorEvaluator<Arg1Type, Device>::PacketAccess &&
-                        TensorEvaluator<Arg2Type, Device>::PacketAccess &&
-                        TensorEvaluator<Arg3Type, Device>::PacketAccess &&
-                        internal::functor_traits<TernaryOp>::PacketAccess,
-    BlockAccess       = false,
-    PreferBlockAccess = TensorEvaluator<Arg1Type, Device>::PreferBlockAccess ||
-                        TensorEvaluator<Arg2Type, Device>::PreferBlockAccess ||
-                        TensorEvaluator<Arg3Type, Device>::PreferBlockAccess,
-    Layout            = TensorEvaluator<Arg1Type, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  TensorEvaluator(const XprType& op, const Device& device)
-    : m_functor(op.functor()),
-      m_arg1Impl(op.arg1Expression(), device),
-      m_arg2Impl(op.arg2Expression(), device),
-      m_arg3Impl(op.arg3Expression(), device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<Arg1Type, Device>::Layout) == static_cast<int>(TensorEvaluator<Arg3Type, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
-                         typename internal::traits<Arg2Type>::StorageKind>::value),
-                        STORAGE_KIND_MUST_MATCH)
-    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
-                         typename internal::traits<Arg3Type>::StorageKind>::value),
-                        STORAGE_KIND_MUST_MATCH)
-    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
-                         typename internal::traits<Arg2Type>::Index>::value),
-                        STORAGE_INDEX_MUST_MATCH)
-    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
-                         typename internal::traits<Arg3Type>::Index>::value),
-                        STORAGE_INDEX_MUST_MATCH)
-
-    eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) && dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions()));
-  }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename TensorEvaluator<Arg1Type, Device>::Dimensions Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
-  {
-    // TODO: use arg2 or arg3 dimensions if they are known at compile time.
-    return m_arg1Impl.dimensions();
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_arg1Impl.evalSubExprsIfNeeded(NULL);
-    m_arg2Impl.evalSubExprsIfNeeded(NULL);
-    m_arg3Impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_arg1Impl.cleanup();
-    m_arg2Impl.cleanup();
-    m_arg3Impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
-  {
-    return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index));
-  }
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return m_functor.packetOp(m_arg1Impl.template packet<LoadMode>(index),
-                              m_arg2Impl.template packet<LoadMode>(index),
-                              m_arg3Impl.template packet<LoadMode>(index));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    const double functor_cost = internal::functor_traits<TernaryOp>::Cost;
-    return m_arg1Impl.costPerCoeff(vectorized) +
-           m_arg2Impl.costPerCoeff(vectorized) +
-           m_arg3Impl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-   // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_arg1Impl.bind(cgh);
-    m_arg2Impl.bind(cgh);
-    m_arg3Impl.bind(cgh);
-  }
-#endif
-
- private:
-  const TernaryOp m_functor;
-  TensorEvaluator<Arg1Type, Device> m_arg1Impl;
-  TensorEvaluator<Arg2Type, Device> m_arg2Impl;
-  TensorEvaluator<Arg3Type, Device> m_arg3Impl;
-};
-
-
-// -------------------- SelectOp --------------------
-
-template<typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device>
-struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device>
-{
-  typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType;
-  typedef typename XprType::Scalar Scalar;
-
-  enum {
-    IsAligned         = TensorEvaluator<ThenArgType, Device>::IsAligned &
-                        TensorEvaluator<ElseArgType, Device>::IsAligned,
-    PacketAccess      = TensorEvaluator<ThenArgType, Device>::PacketAccess &
-                        TensorEvaluator<ElseArgType, Device>::PacketAccess &
-                        PacketType<Scalar, Device>::HasBlend,
-    BlockAccess       = TensorEvaluator<IfArgType, Device>::BlockAccess &&
-                        TensorEvaluator<ThenArgType, Device>::BlockAccess &&
-                        TensorEvaluator<ElseArgType, Device>::BlockAccess,
-    PreferBlockAccess = TensorEvaluator<IfArgType, Device>::PreferBlockAccess ||
-                        TensorEvaluator<ThenArgType, Device>::PreferBlockAccess ||
-                        TensorEvaluator<ElseArgType, Device>::PreferBlockAccess,
-    Layout            = TensorEvaluator<IfArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  TensorEvaluator(const XprType& op, const Device& device)
-    : m_condImpl(op.ifExpression(), device),
-      m_thenImpl(op.thenExpression(), device),
-      m_elseImpl(op.elseExpression(), device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions()));
-    eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions()));
-  }
-
-  typedef typename XprType::Index Index;
-  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  static const int NumDims = internal::array_size<Dimensions>::value;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-    typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const IfArgType, Device>::TensorBlock
-      IfArgTensorBlock;
-  typedef typename TensorEvaluator<const ThenArgType, Device>::TensorBlock
-      ThenArgTensorBlock;
-  typedef typename TensorEvaluator<const ElseArgType, Device>::TensorBlock
-      ElseArgTensorBlock;
-
-  struct TensorSelectOpBlockFactory {
-    template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
-    struct XprType {
-      typedef TensorSelectOp<const IfArgXprType, const ThenArgXprType, const ElseArgXprType> type;
-    };
-
-    template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
-    typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type expr(
-        const IfArgXprType& if_expr, const ThenArgXprType& then_expr, const ElseArgXprType& else_expr) const {
-      return typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type(if_expr, then_expr, else_expr);
-    }
-  };
-
-  typedef internal::TensorTernaryExprBlock<TensorSelectOpBlockFactory,
-                                           IfArgTensorBlock, ThenArgTensorBlock,
-                                           ElseArgTensorBlock>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
-  {
-    // TODO: use then or else impl instead if they happen to be known at compile time.
-    return m_condImpl.dimensions();
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_condImpl.evalSubExprsIfNeeded(NULL);
-    m_thenImpl.evalSubExprsIfNeeded(NULL);
-    m_elseImpl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_condImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
-      m_thenImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
-        m_elseImpl.evalSubExprsIfNeeded(nullptr, [done](bool) { done(true); });
-      });
-    });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_condImpl.cleanup();
-    m_thenImpl.cleanup();
-    m_elseImpl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
-  {
-    return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index);
-  }
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
-  {
-     internal::Selector<PacketSize> select;
-     EIGEN_UNROLL_LOOP
-     for (Index i = 0; i < PacketSize; ++i) {
-       select.select[i] = m_condImpl.coeff(index+i);
-     }
-     return internal::pblend(select,
-                             m_thenImpl.template packet<LoadMode>(index),
-                             m_elseImpl.template packet<LoadMode>(index));
-
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    return m_condImpl.costPerCoeff(vectorized) +
-           m_thenImpl.costPerCoeff(vectorized)
-        .cwiseMax(m_elseImpl.costPerCoeff(vectorized));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    auto then_req = m_thenImpl.getResourceRequirements();
-    auto else_req = m_elseImpl.getResourceRequirements();
-
-    auto merged_req =
-        internal::TensorBlockResourceRequirements::merge(then_req, else_req);
-    merged_req.cost_per_coeff =
-        then_req.cost_per_coeff.cwiseMax(else_req.cost_per_coeff);
-
-    return internal::TensorBlockResourceRequirements::merge(
-        m_condImpl.getResourceRequirements(), merged_req);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    // It's unsafe to pass destination buffer to underlying expressions, because
-    // output might be aliased with one of the inputs.
-    desc.DropDestinationBuffer();
-
-    return TensorBlock(
-        m_condImpl.block(desc, scratch), m_thenImpl.block(desc, scratch),
-        m_elseImpl.block(desc, scratch), TensorSelectOpBlockFactory());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
- // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_condImpl.bind(cgh);
-    m_thenImpl.bind(cgh);
-    m_elseImpl.bind(cgh);
-  }
-#endif
- private:
-  TensorEvaluator<IfArgType, Device> m_condImpl;
-  TensorEvaluator<ThenArgType, Device> m_thenImpl;
-  TensorEvaluator<ElseArgType, Device> m_elseImpl;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
deleted file mode 100644
index c52fb77..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
+++ /dev/null
@@ -1,703 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
-#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
-
-namespace Eigen {
-
-/**
- * \class TensorExecutor
- * \ingroup CXX11_Tensor_Module
- *
- * \brief The tensor executor class.
- *
- * This class is responsible for launch the evaluation of the expression on
- * the specified computing device.
- *
- * @tparam Vectorizable can use packet math (SSE/AVX/etc... registers and
- *                      instructions)
- * @tparam Tiling       can use block based tensor evaluation
- *                      (see TensorBlock.h)
- */
-namespace internal {
-
-/**
- * Evaluating TensorBroadcastingOp via coefficient of packet path is extremely
- * expensive. If expression has at least one broadcast op in it, and it supports
- * block based evaluation, we always prefer it, even for the small tensors. For
- * all other tileable ops, block evaluation overhead for small tensors (fits
- * into L1) is too large, and we fallback on vectorized evaluation.
- */
-
-// TODO(ezhulenev): Add specializations for all other types of Tensor ops.
-
-template<typename Expression>
-struct ExpressionHasTensorBroadcastingOp {
-  enum { value = false };
-};
-
-template<typename LhsXprType, typename RhsXprType>
-struct ExpressionHasTensorBroadcastingOp<
-    const TensorAssignOp<LhsXprType, RhsXprType> > {
-  enum { value = ExpressionHasTensorBroadcastingOp<RhsXprType>::value };
-};
-
-template<typename UnaryOp, typename XprType>
-struct ExpressionHasTensorBroadcastingOp<
-    const TensorCwiseUnaryOp<UnaryOp, XprType> > {
-  enum { value = ExpressionHasTensorBroadcastingOp<XprType>::value };
-};
-
-template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
-struct ExpressionHasTensorBroadcastingOp<
-    const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> > {
-  enum {
-    value = ExpressionHasTensorBroadcastingOp<LhsXprType>::value ||
-        ExpressionHasTensorBroadcastingOp<RhsXprType>::value
-  };
-};
-
-template<typename Broadcast, typename XprType>
-struct ExpressionHasTensorBroadcastingOp<
-    const TensorBroadcastingOp<Broadcast, XprType> > {
-  enum { value = true };
-};
-
-// -------------------------------------------------------------------------- //
-
-/**
- * Default strategy: the expression is evaluated sequentially with a single cpu
- * thread, without vectorization and block evaluation.
- */
-template <typename Expression, typename Device, bool Vectorizable,
-          TiledEvaluation Tiling>
-class TensorExecutor {
- public:
-  typedef typename Expression::Index StorageIndex;
-
-  // Including `unsupported/Eigen/CXX11/Tensor` in different translation units
-  // with/without `EIGEN_USE_THREADS` or `EIGEN_USE_GPU` is a potential ODR
-  // violation. If this template is instantiated with a non-default device, it
-  // means that this header file was included without defining
-  // `EIGEN_USE_THREADS`, `EIGEN_USE_GPU` or `EIGEN_USE_SYCL`.
-  static_assert(std::is_same<Device, DefaultDevice>::value,
-                "Default executor instantiated with non-default device. "
-                "You must #define EIGEN_USE_THREADS, EIGEN_USE_GPU or "
-                "EIGEN_USE_SYCL before including Eigen headers.");
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(const Expression& expr,
-                                      const Device& device = Device()) {
-    TensorEvaluator<Expression, Device> evaluator(expr, device);
-    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
-    if (needs_assign) {
-      const StorageIndex size = array_prod(evaluator.dimensions());
-      for (StorageIndex i = 0; i < size; ++i) {
-        evaluator.evalScalar(i);
-      }
-    }
-    evaluator.cleanup();
-  }
-};
-
-/**
- * Default async execution strategy is not implemented. Currently it's only
- * available for ThreadPoolDevice (see definition below).
- */
-template <typename Expression, typename Device, typename DoneCallback,
-          bool Vectorizable, TiledEvaluation Tiling>
-class TensorAsyncExecutor {};
-
-/**
- * Process all the data with a single cpu thread, using vectorized instructions.
- */
-template <typename Expression>
-class TensorExecutor<Expression, DefaultDevice, /*Vectorizable=*/true,
-                     /*Tiling=*/TiledEvaluation::Off> {
- public:
-  typedef typename Expression::Index StorageIndex;
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(
-      const Expression& expr, const DefaultDevice& device = DefaultDevice()) {
-    TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
-    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
-    if (needs_assign) {
-      const StorageIndex size = array_prod(evaluator.dimensions());
-      const int PacketSize = unpacket_traits<typename TensorEvaluator<
-          Expression, DefaultDevice>::PacketReturnType>::size;
-
-      // Give compiler a strong possibility to unroll the loop. But don't insist
-      // on unrolling, because if the function is expensive compiler should not
-      // unroll the loop at the expense of inlining.
-      const StorageIndex UnrolledSize =
-          (size / (4 * PacketSize)) * 4 * PacketSize;
-      for (StorageIndex i = 0; i < UnrolledSize; i += 4 * PacketSize) {
-        for (StorageIndex j = 0; j < 4; j++) {
-          evaluator.evalPacket(i + j * PacketSize);
-        }
-      }
-      const StorageIndex VectorizedSize = (size / PacketSize) * PacketSize;
-      for (StorageIndex i = UnrolledSize; i < VectorizedSize; i += PacketSize) {
-        evaluator.evalPacket(i);
-      }
-      for (StorageIndex i = VectorizedSize; i < size; ++i) {
-        evaluator.evalScalar(i);
-      }
-    }
-    evaluator.cleanup();
-  }
-};
-
-/**
- * Process all the data with a single cpu thread, using blocks of data. By
- * sizing a block to fit L1 cache we get better cache performance.
- */
-template <typename Expression, bool Vectorizable>
-class TensorExecutor<Expression, DefaultDevice, Vectorizable,
-                     /*Tiling=*/TiledEvaluation::On> {
- public:
-  typedef typename traits<Expression>::Scalar Scalar;
-  typedef typename remove_const<Scalar>::type ScalarNoConst;
-
-  typedef TensorEvaluator<Expression, DefaultDevice> Evaluator;
-  typedef typename traits<Expression>::Index StorageIndex;
-
-  static const int NumDims = traits<Expression>::NumDimensions;
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE void run(const Expression& expr,
-                         const DefaultDevice& device = DefaultDevice()) {
-    typedef TensorBlockMapper<NumDims, Evaluator::Layout, StorageIndex>
-        TensorBlockMapper;
-
-    typedef internal::TensorBlockDescriptor<NumDims, StorageIndex>
-        TensorBlockDesc;
-    typedef internal::TensorBlockScratchAllocator<DefaultDevice>
-        TensorBlockScratch;
-
-    Evaluator evaluator(expr, device);
-
-    // TODO(ezhulenev): Do not use tiling for small tensors?
-    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
-
-    if (needs_assign) {
-      // Query expression tree for desired block size/shape.
-      const TensorBlockResourceRequirements requirements =
-          evaluator.getResourceRequirements();
-
-      const TensorBlockMapper block_mapper(
-          typename TensorBlockDesc::Dimensions(evaluator.dimensions()),
-          requirements);
-
-      // Share scratch memory allocator between all blocks.
-      TensorBlockScratch scratch(device);
-
-      const StorageIndex total_block_count = block_mapper.blockCount();
-      for (StorageIndex i = 0; i < total_block_count; ++i) {
-        TensorBlockDesc desc = block_mapper.blockDescriptor(i);
-        evaluator.evalBlock(desc, scratch);
-        scratch.reset();
-      }
-    }
-    evaluator.cleanup();
-  }
-};
-
-/**
- * Multicore strategy: the index space is partitioned and each partition is
- * executed on a single core.
- *
- * (1) TensorExecutor will submit work to the ThreadPoolDevice managed thread
- *     pool, and will block the caller thread until all tasks are finished.
- *
- * (2) TensorAsyncExecutor is a non-blocking version, that will submit work to
- *     the ThreadPoolDevice managed thread pool, and will return immediately.
- *     It will call 'done' callback after all tasks are finished.
- */
-#ifdef EIGEN_USE_THREADS
-
-template <typename TensorBlockMapper>
-struct TensorExecutorTilingContext {
-  TensorExecutorTilingContext() = default;
-  TensorExecutorTilingContext(const TensorBlockMapper& b_mapper,
-                              const TensorOpCost& b_cost, size_t b_aligned_size)
-      : block_mapper(b_mapper),
-        cost(b_cost),
-        aligned_blocksize(b_aligned_size) {}
-
-  TensorBlockMapper block_mapper;  // navigate through blocks
-  TensorOpCost cost;               // cost of computing a single block
-  size_t aligned_blocksize;        // block size after memory alignment
-};
-
-// Computes a block evaluation parameters, and allocates temporary memory buffer
-// for blocks. See TensorExecutor/TensorAsyncExecutor (Tiling=On) below.
-template <typename Evaluator, typename TensorBlockMapper, bool Vectorizable>
-TensorExecutorTilingContext<TensorBlockMapper> GetTensorExecutorTilingContext(
-    const Evaluator& evaluator) {
-  // Query expression tree for desired block size/shape.
-  TensorBlockResourceRequirements requirements =
-      evaluator.getResourceRequirements();
-
-  // Update target block size based on cost model.
-  double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(
-      1, requirements.cost_per_coeff);
-  requirements.size = static_cast<size_t>(1.0 / taskSize);
-
-  TensorBlockMapper block_mapper(
-      typename TensorBlockMapper::Dimensions(evaluator.dimensions()),
-      requirements);
-
-  size_t block_size = block_mapper.blockTotalSize();
-  const size_t align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);
-  const size_t aligned_blocksize =
-      align *
-      divup<size_t>(block_size * sizeof(typename Evaluator::Scalar), align);
-
-  return {block_mapper, requirements.cost_per_coeff * block_size,
-          aligned_blocksize};
-}
-
-template <typename Evaluator, typename StorageIndex, bool Vectorizable>
-struct EvalRange {
-  static void run(Evaluator* evaluator_in, const StorageIndex firstIdx,
-                  const StorageIndex lastIdx) {
-    Evaluator evaluator = *evaluator_in;
-    eigen_assert(lastIdx >= firstIdx);
-    for (StorageIndex i = firstIdx; i < lastIdx; ++i) {
-      evaluator.evalScalar(i);
-    }
-  }
-
-  static StorageIndex alignBlockSize(StorageIndex size) { return size; }
-};
-
-template <typename Evaluator, typename StorageIndex>
-struct EvalRange<Evaluator, StorageIndex, /*Vectorizable*/ true> {
-  static const int PacketSize =
-      unpacket_traits<typename Evaluator::PacketReturnType>::size;
-
-  static void run(Evaluator* evaluator_in, const StorageIndex firstIdx,
-                  const StorageIndex lastIdx) {
-    Evaluator evaluator = *evaluator_in;
-    eigen_assert(lastIdx >= firstIdx);
-    StorageIndex i = firstIdx;
-    if (lastIdx - firstIdx >= PacketSize) {
-      eigen_assert(firstIdx % PacketSize == 0);
-      StorageIndex last_chunk_offset = lastIdx - 4 * PacketSize;
-      // Give compiler a strong possibility to unroll the loop. But don't insist
-      // on unrolling, because if the function is expensive compiler should not
-      // unroll the loop at the expense of inlining.
-      for (; i <= last_chunk_offset; i += 4 * PacketSize) {
-        for (StorageIndex j = 0; j < 4; j++) {
-          evaluator.evalPacket(i + j * PacketSize);
-        }
-      }
-      last_chunk_offset = lastIdx - PacketSize;
-      for (; i <= last_chunk_offset; i += PacketSize) {
-        evaluator.evalPacket(i);
-      }
-    }
-    for (; i < lastIdx; ++i) {
-      evaluator.evalScalar(i);
-    }
-  }
-
-  static StorageIndex alignBlockSize(StorageIndex size) {
-    // Align block size to packet size and account for unrolling in run above.
-    if (size >= 16 * PacketSize) {
-      return (size + 4 * PacketSize - 1) & ~(4 * PacketSize - 1);
-    }
-    // Aligning to 4 * PacketSize would increase block size by more than 25%.
-    return (size + PacketSize - 1) & ~(PacketSize - 1);
-  }
-};
-
-template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
-class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tiling> {
- public:
-  typedef typename Expression::Index StorageIndex;
-
-  static EIGEN_STRONG_INLINE void run(const Expression& expr,
-                         const ThreadPoolDevice& device) {
-    typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
-    typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
-
-    Evaluator evaluator(expr, device);
-    const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
-    if (needs_assign) {
-      const StorageIndex size = array_prod(evaluator.dimensions());
-      device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
-                         EvalRange::alignBlockSize,
-                         [&evaluator](StorageIndex firstIdx, StorageIndex lastIdx) {
-                           EvalRange::run(&evaluator, firstIdx, lastIdx);
-                         });
-    }
-    evaluator.cleanup();
-  }
-};
-
-template <typename Expression, bool Vectorizable>
-class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable,
-                     /*Tiling=*/TiledEvaluation::On> {
- public:
-  typedef typename traits<Expression>::Index IndexType;
-  typedef typename traits<Expression>::Scalar Scalar;
-  typedef typename remove_const<Scalar>::type ScalarNoConst;
-
-  static const int NumDims = traits<Expression>::NumDimensions;
-
-  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
-  typedef TensorBlockMapper<NumDims, Evaluator::Layout, IndexType> BlockMapper;
-  typedef TensorExecutorTilingContext<BlockMapper> TilingContext;
-
-  typedef internal::TensorBlockDescriptor<NumDims, IndexType>
-      TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<ThreadPoolDevice>
-      TensorBlockScratch;
-
-  static EIGEN_STRONG_INLINE void run(const Expression& expr,
-                                      const ThreadPoolDevice& device) {
-    Evaluator evaluator(expr, device);
-
-    const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
-    if (needs_assign) {
-      const TilingContext tiling =
-          internal::GetTensorExecutorTilingContext<Evaluator, BlockMapper,
-                                                   Vectorizable>(evaluator);
-
-      auto eval_block = [&device, &evaluator, &tiling](IndexType firstBlockIdx,
-                                                       IndexType lastBlockIdx) {
-        TensorBlockScratch scratch(device);
-
-        for (IndexType block_idx = firstBlockIdx; block_idx < lastBlockIdx;
-             ++block_idx) {
-          TensorBlockDesc desc = tiling.block_mapper.blockDescriptor(block_idx);
-          evaluator.evalBlock(desc, scratch);
-          scratch.reset();
-        }
-      };
-
-      // Evaluate small expressions directly as a single block.
-      if (tiling.block_mapper.blockCount() == 1) {
-        TensorBlockScratch scratch(device);
-        TensorBlockDesc desc(0, tiling.block_mapper.blockDimensions());
-        evaluator.evalBlock(desc, scratch);
-      } else {
-        device.parallelFor(tiling.block_mapper.blockCount(), tiling.cost,
-                           eval_block);
-      }
-    }
-    evaluator.cleanup();
-  }
-};
-
-template <typename Expression, typename DoneCallback, bool Vectorizable,
-          TiledEvaluation Tiling>
-class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback,
-                          Vectorizable, Tiling> {
- public:
-  typedef typename Expression::Index StorageIndex;
-  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
-
-  static EIGEN_STRONG_INLINE void runAsync(const Expression& expr,
-                                           const ThreadPoolDevice& device,
-                                           DoneCallback done) {
-    TensorAsyncExecutorContext* const ctx =
-        new TensorAsyncExecutorContext(expr, device, std::move(done));
-
-    const auto on_eval_subexprs = [ctx, &device](bool need_assign) -> void {
-      if (!need_assign) {
-        delete ctx;
-        return;
-      }
-
-      typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
-      const StorageIndex size = array_prod(ctx->evaluator.dimensions());
-      device.parallelForAsync(
-          size, ctx->evaluator.costPerCoeff(Vectorizable),
-          EvalRange::alignBlockSize,
-          [ctx](StorageIndex firstIdx, StorageIndex lastIdx) {
-            EvalRange::run(&ctx->evaluator, firstIdx, lastIdx);
-          },
-          [ctx]() { delete ctx; });
-    };
-
-    ctx->evaluator.evalSubExprsIfNeededAsync(nullptr, on_eval_subexprs);
-  }
-
- private:
-  struct TensorAsyncExecutorContext {
-    TensorAsyncExecutorContext(const Expression& expr,
-                               const ThreadPoolDevice& thread_pool,
-                               DoneCallback done)
-        : evaluator(expr, thread_pool), on_done(std::move(done)) {}
-
-    ~TensorAsyncExecutorContext() {
-      evaluator.cleanup();
-      on_done();
-    }
-
-    Evaluator evaluator;
-
-   private:
-    DoneCallback on_done;
-  };
-};
-
-template <typename Expression, typename DoneCallback, bool Vectorizable>
-class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback,
-                          Vectorizable, /*Tileable*/ TiledEvaluation::On> {
- public:
-  typedef typename traits<Expression>::Index IndexType;
-  typedef typename traits<Expression>::Scalar Scalar;
-  typedef typename remove_const<Scalar>::type ScalarNoConst;
-
-  static const int NumDims = traits<Expression>::NumDimensions;
-
-  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
-  typedef TensorBlockMapper<NumDims, Evaluator::Layout, IndexType> BlockMapper;
-  typedef TensorExecutorTilingContext<BlockMapper> TilingContext;
-
-  typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<ThreadPoolDevice>
-      TensorBlockScratch;
-
-  static EIGEN_STRONG_INLINE void runAsync(const Expression& expr,
-                                           const ThreadPoolDevice& device,
-                                           DoneCallback done) {
-
-    TensorAsyncExecutorContext* const ctx =
-        new TensorAsyncExecutorContext(expr, device, std::move(done));
-
-    const auto on_eval_subexprs = [ctx](bool need_assign) -> void {
-      if (!need_assign) {
-        delete ctx;
-        return;
-      }
-
-      ctx->tiling = internal::GetTensorExecutorTilingContext<
-          Evaluator, BlockMapper, Vectorizable>(ctx->evaluator);
-
-      auto eval_block = [ctx](IndexType firstBlockIdx, IndexType lastBlockIdx) {
-        TensorBlockScratch scratch(ctx->device);
-
-        for (IndexType block_idx = firstBlockIdx; block_idx < lastBlockIdx;
-             ++block_idx) {
-          TensorBlockDesc desc =
-              ctx->tiling.block_mapper.blockDescriptor(block_idx);
-          ctx->evaluator.evalBlock(desc, scratch);
-          scratch.reset();
-        }
-      };
-
-      // Evaluate small expressions directly as a single block.
-      if (ctx->tiling.block_mapper.blockCount() == 1) {
-        TensorBlockScratch scratch(ctx->device);
-        TensorBlockDesc desc(0, ctx->tiling.block_mapper.blockDimensions());
-        ctx->evaluator.evalBlock(desc, scratch);
-        delete ctx;
-      } else {
-        ctx->device.parallelForAsync(ctx->tiling.block_mapper.blockCount(),
-                                     ctx->tiling.cost, eval_block,
-                                     [ctx]() { delete ctx; });
-      }
-    };
-
-    ctx->evaluator.evalSubExprsIfNeededAsync(nullptr, on_eval_subexprs);
-  }
-
- private:
-  struct TensorAsyncExecutorContext {
-    TensorAsyncExecutorContext(const Expression& expr,
-                               const ThreadPoolDevice& thread_pool,
-                               DoneCallback done)
-        : device(thread_pool),
-          evaluator(expr, thread_pool),
-          on_done(std::move(done)) {}
-
-    ~TensorAsyncExecutorContext() {
-      evaluator.cleanup();
-      on_done();
-    }
-
-    const ThreadPoolDevice& device;
-    Evaluator evaluator;
-    TilingContext tiling;
-
-   private:
-    DoneCallback on_done;
-  };
-};
-
-#endif  // EIGEN_USE_THREADS
-
-// GPU: the evaluation of the expression is offloaded to a GPU.
-#if defined(EIGEN_USE_GPU)
-
-template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
-class TensorExecutor<Expression, GpuDevice, Vectorizable, Tiling> {
- public:
-  typedef typename Expression::Index StorageIndex;
-  static void run(const Expression& expr, const GpuDevice& device);
-};
-
-#if defined(EIGEN_GPUCC)
-template <typename Evaluator, typename StorageIndex, bool Vectorizable>
-struct EigenMetaKernelEval {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  void run(Evaluator& eval, StorageIndex firstIdx, StorageIndex lastIdx, StorageIndex step_size) {
-    for (StorageIndex i = firstIdx; i < lastIdx; i += step_size) {
-      eval.evalScalar(i);
-    }
-  }
-};
-
-template <typename Evaluator, typename StorageIndex>
-struct EigenMetaKernelEval<Evaluator, StorageIndex, true> {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  void run(Evaluator& eval, StorageIndex firstIdx, StorageIndex lastIdx, StorageIndex step_size) {
-    const StorageIndex PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
-    const StorageIndex vectorized_size = (lastIdx / PacketSize) * PacketSize;
-    const StorageIndex vectorized_step_size = step_size * PacketSize;
-
-    // Use the vector path
-    for (StorageIndex i = firstIdx * PacketSize; i < vectorized_size;
-         i += vectorized_step_size) {
-      eval.evalPacket(i);
-    }
-    for (StorageIndex i = vectorized_size + firstIdx; i < lastIdx; i += step_size) {
-      eval.evalScalar(i);
-    }
-  }
-};
-
-template <typename Evaluator, typename StorageIndex>
-__global__ void
-__launch_bounds__(1024)
-EigenMetaKernel(Evaluator eval, StorageIndex size) {
-
-  const StorageIndex first_index = blockIdx.x * blockDim.x + threadIdx.x;
-  const StorageIndex step_size = blockDim.x * gridDim.x;
-
-  const bool vectorizable = Evaluator::PacketAccess & Evaluator::IsAligned;
-  EigenMetaKernelEval<Evaluator, StorageIndex, vectorizable>::run(eval, first_index, size, step_size);
-}
-
-/*static*/
-template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
-EIGEN_STRONG_INLINE void TensorExecutor<Expression, GpuDevice, Vectorizable, Tiling>::run(
-    const Expression& expr, const GpuDevice& device) {
-  TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
-  const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
-  if (needs_assign) {
-
-    const int block_size = device.maxGpuThreadsPerBlock();
-    const int max_blocks = device.getNumGpuMultiProcessors() *
-                           device.maxGpuThreadsPerMultiProcessor() / block_size;
-    const StorageIndex size = array_prod(evaluator.dimensions());
-    // Create a least one block to ensure we won't crash when tensorflow calls with tensors of size 0.
-    const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, divup<int>(size, block_size)), 1);
-
-    LAUNCH_GPU_KERNEL(
-        (EigenMetaKernel<TensorEvaluator<Expression, GpuDevice>, StorageIndex>),
-        num_blocks, block_size, 0, device, evaluator, size);
-  }
-  evaluator.cleanup();
-}
-
-#endif  // EIGEN_GPUCC
-#endif  // EIGEN_USE_GPU
-
-// SYCL Executor policy
-#ifdef EIGEN_USE_SYCL
-
-template <typename Evaluator>
-struct ExecExprFunctorKernel {
-  typedef typename Evaluator::Index Index;
-  Evaluator evaluator;
-  const Index range;
-  template <typename Scratch>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ExecExprFunctorKernel(
-      const Scratch, Evaluator evaluator_, const Index range_)
-      : evaluator(evaluator_), range(range_) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void operator()(
-      cl::sycl::nd_item<1> itemID) {
-    compute(itemID);
-  }
-  template <bool is_vec = Evaluator::PacketAccess>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename std::enable_if<!is_vec>::type
-  compute(const cl::sycl::nd_item<1>& itemID) {
-    Index gId = static_cast<Index>(itemID.get_global_linear_id());
-    Index total_threads = itemID.get_global_range(0);
-
-    for (Index i = gId; i < range; i += total_threads) {
-      evaluator.evalScalar(i);
-    }
-  }
-  template <bool is_vec = Evaluator::PacketAccess>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename std::enable_if<is_vec>::type
-  compute(const cl::sycl::nd_item<1>& itemID) {
-    const Index vectorizedRange =
-        (range / Evaluator::PacketSize) * Evaluator::PacketSize;
-    Index gId = static_cast<Index>(itemID.get_global_linear_id());
-    const Index step = Evaluator::PacketSize * itemID.get_global_range(0);
-    const Index start = Evaluator::PacketSize * gId;
-    for (Index i = start; i < vectorizedRange; i += step) {
-      evaluator.evalPacket(i);
-    }
-    gId += vectorizedRange;
-    for (Index i = gId; i < range; i += itemID.get_global_range(0)) {
-      evaluator.evalScalar(i);
-    }
-  }
-};
-
-template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
-class TensorExecutor<Expression, Eigen::SyclDevice, Vectorizable, Tiling> {
- public:
-  typedef typename Expression::Index Index;
-  static EIGEN_STRONG_INLINE void run(const Expression& expr,
-                                      const Eigen::SyclDevice& dev) {
-    typedef Eigen::TensorEvaluator<Expression, Eigen::SyclDevice> Evaluator;
-    Evaluator evaluator(expr, dev);
-    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
-    if (needs_assign) {
-      Index range, GRange, tileSize;
-      Index total_size = ::Eigen::internal::array_prod(evaluator.dimensions());
-      total_size = (total_size == 0) ? 1 : total_size;
-      const int PacketSize =
-          Eigen::PacketType<typename Evaluator::CoeffReturnType,
-                            Eigen::SyclDevice>::size;
-      Index vectorizable_threads = static_cast<Index>(total_size / PacketSize);
-      dev.parallel_for_setup(vectorizable_threads, tileSize, range, GRange);
-      range = total_size;
-
-      dev.template nullary_kernel_launcher<
-          typename Evaluator::CoeffReturnType,
-          ExecExprFunctorKernel<Evaluator> >(
-          evaluator,
-          cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange),
-                                cl::sycl::range<1>(tileSize)),
-          Index(1), range);
-    }
-    evaluator.cleanup();
-  }
-};
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
deleted file mode 100644
index c9bccfc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
+++ /dev/null
@@ -1,388 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
-#define EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
-
-namespace Eigen {
-
-/** \class TensorExpr
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor expression classes.
-  *
-  * The TensorCwiseNullaryOp class applies a nullary operators to an expression.
-  * This is typically used to generate constants.
-  *
-  * The TensorCwiseUnaryOp class represents an expression where a unary operator
-  * (e.g. cwiseSqrt) is applied to an expression.
-  *
-  * The TensorCwiseBinaryOp class represents an expression where a binary
-  * operator (e.g. addition) is applied to a lhs and a rhs expression.
-  *
-  */
-namespace internal {
-template<typename NullaryOp, typename XprType>
-struct traits<TensorCwiseNullaryOp<NullaryOp, XprType> >
-    : traits<XprType>
-{
-  typedef traits<XprType> XprTraits;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::Nested XprTypeNested;
-  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-  enum {
-    Flags = 0
-  };
-};
-
-}  // end namespace internal
-
-
-
-template<typename NullaryOp, typename XprType>
-class TensorCwiseNullaryOp : public TensorBase<TensorCwiseNullaryOp<NullaryOp, XprType>, ReadOnlyAccessors>
-{
-  public:
-    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename XprType::CoeffReturnType CoeffReturnType;
-    typedef TensorCwiseNullaryOp<NullaryOp, XprType> Nested;
-    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseNullaryOp(const XprType& xpr, const NullaryOp& func = NullaryOp())
-        : m_xpr(xpr), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    nestedExpression() const { return m_xpr; }
-
-    EIGEN_DEVICE_FUNC
-    const NullaryOp& functor() const { return m_functor; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const NullaryOp m_functor;
-};
-
-
-
-namespace internal {
-template<typename UnaryOp, typename XprType>
-struct traits<TensorCwiseUnaryOp<UnaryOp, XprType> >
-    : traits<XprType>
-{
-  // TODO(phli): Add InputScalar, InputPacket.  Check references to
-  // current Scalar/Packet to see if the intent is Input or Output.
-  typedef typename result_of<UnaryOp(typename XprType::Scalar)>::type Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprType::Nested XprTypeNested;
-  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename TypeConversion<Scalar, 
-                                  typename XprTraits::PointerType
-                                  >::type 
-                                  PointerType;
-};
-
-template<typename UnaryOp, typename XprType>
-struct eval<TensorCwiseUnaryOp<UnaryOp, XprType>, Eigen::Dense>
-{
-  typedef const TensorCwiseUnaryOp<UnaryOp, XprType>& type;
-};
-
-template<typename UnaryOp, typename XprType>
-struct nested<TensorCwiseUnaryOp<UnaryOp, XprType>, 1, typename eval<TensorCwiseUnaryOp<UnaryOp, XprType> >::type>
-{
-  typedef TensorCwiseUnaryOp<UnaryOp, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename UnaryOp, typename XprType>
-class TensorCwiseUnaryOp : public TensorBase<TensorCwiseUnaryOp<UnaryOp, XprType>, ReadOnlyAccessors>
-{
-  public:
-    // TODO(phli): Add InputScalar, InputPacket.  Check references to
-    // current Scalar/Packet to see if the intent is Input or Output.
-    typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef Scalar CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorCwiseUnaryOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
-      : m_xpr(xpr), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC
-    const UnaryOp& functor() const { return m_functor; }
-
-    /** \returns the nested expression */
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    nestedExpression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const UnaryOp m_functor;
-};
-
-
-namespace internal {
-template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
-struct traits<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs
-  // are different.
-  // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket.  Check references to
-  // current Scalar/Packet to see if the intent is Inputs or Output.
-  typedef typename result_of<
-      BinaryOp(typename LhsXprType::Scalar,
-               typename RhsXprType::Scalar)>::type Scalar;
-  typedef traits<LhsXprType> XprTraits;
-  typedef typename promote_storage_type<
-      typename traits<LhsXprType>::StorageKind,
-      typename traits<RhsXprType>::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type<
-      typename traits<LhsXprType>::Index,
-      typename traits<RhsXprType>::Index>::type Index;
-  typedef typename LhsXprType::Nested LhsNested;
-  typedef typename RhsXprType::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename TypeConversion<Scalar,
-                                  typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
-                                                      typename traits<LhsXprType>::PointerType,
-                                                      typename traits<RhsXprType>::PointerType>::type
-                                  >::type 
-                                  PointerType;
-  enum {
-    Flags = 0
-  };
-};
-
-template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
-struct eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, Eigen::Dense>
-{
-  typedef const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>& type;
-};
-
-template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
-struct nested<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, 1, typename eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >::type>
-{
-  typedef TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
-class TensorCwiseBinaryOp : public TensorBase<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, ReadOnlyAccessors>
-{
-  public:
-    // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket.  Check references to
-    // current Scalar/Packet to see if the intent is Inputs or Output.
-    typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef Scalar CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorCwiseBinaryOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const BinaryOp& func = BinaryOp())
-        : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC
-    const BinaryOp& functor() const { return m_functor; }
-
-    /** \returns the nested expressions */
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename LhsXprType::Nested>::type&
-    lhsExpression() const { return m_lhs_xpr; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename RhsXprType::Nested>::type&
-    rhsExpression() const { return m_rhs_xpr; }
-
-  protected:
-    typename LhsXprType::Nested m_lhs_xpr;
-    typename RhsXprType::Nested m_rhs_xpr;
-    const BinaryOp m_functor;
-};
-
-
-namespace internal {
-template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
-struct traits<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> >
-{
-  // Type promotion to handle the case where the types of the args are different.
-  typedef typename result_of<
-      TernaryOp(typename Arg1XprType::Scalar,
-                typename Arg2XprType::Scalar,
-                typename Arg3XprType::Scalar)>::type Scalar;
-  typedef traits<Arg1XprType> XprTraits;
-  typedef typename traits<Arg1XprType>::StorageKind StorageKind;
-  typedef typename traits<Arg1XprType>::Index Index;
-  typedef typename Arg1XprType::Nested Arg1Nested;
-  typedef typename Arg2XprType::Nested Arg2Nested;
-  typedef typename Arg3XprType::Nested Arg3Nested;
-  typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
-  typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
-  typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename TypeConversion<Scalar,
-                                  typename conditional<Pointer_type_promotion<typename Arg2XprType::Scalar, Scalar>::val,
-                                                      typename traits<Arg2XprType>::PointerType,
-                                                      typename traits<Arg3XprType>::PointerType>::type
-                                  >::type 
-                                  PointerType;
-  enum {
-    Flags = 0
-  };
-};
-
-template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
-struct eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, Eigen::Dense>
-{
-  typedef const TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>& type;
-};
-
-template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
-struct nested<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, 1, typename eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> >::type>
-{
-  typedef TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
-class TensorCwiseTernaryOp : public TensorBase<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, ReadOnlyAccessors>
-{
-  public:
-    typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef Scalar CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorCwiseTernaryOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseTernaryOp(const Arg1XprType& arg1, const Arg2XprType& arg2, const Arg3XprType& arg3, const TernaryOp& func = TernaryOp())
-        : m_arg1_xpr(arg1), m_arg2_xpr(arg2), m_arg3_xpr(arg3), m_functor(func) {}
-
-    EIGEN_DEVICE_FUNC
-    const TernaryOp& functor() const { return m_functor; }
-
-    /** \returns the nested expressions */
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename Arg1XprType::Nested>::type&
-    arg1Expression() const { return m_arg1_xpr; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename Arg2XprType::Nested>::type&
-    arg2Expression() const { return m_arg2_xpr; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename Arg3XprType::Nested>::type&
-    arg3Expression() const { return m_arg3_xpr; }
-
-  protected:
-    typename Arg1XprType::Nested m_arg1_xpr;
-    typename Arg2XprType::Nested m_arg2_xpr;
-    typename Arg3XprType::Nested m_arg3_xpr;
-    const TernaryOp m_functor;
-};
-
-
-namespace internal {
-template<typename IfXprType, typename ThenXprType, typename ElseXprType>
-struct traits<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >
-    : traits<ThenXprType>
-{
-  typedef typename traits<ThenXprType>::Scalar Scalar;
-  typedef traits<ThenXprType> XprTraits;
-  typedef typename promote_storage_type<typename traits<ThenXprType>::StorageKind,
-                                        typename traits<ElseXprType>::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<ElseXprType>::Index,
-                                      typename traits<ThenXprType>::Index>::type Index;
-  typedef typename IfXprType::Nested IfNested;
-  typedef typename ThenXprType::Nested ThenNested;
-  typedef typename ElseXprType::Nested ElseNested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename conditional<Pointer_type_promotion<typename ThenXprType::Scalar, Scalar>::val,
-                               typename traits<ThenXprType>::PointerType,
-                               typename traits<ElseXprType>::PointerType>::type PointerType;
-};
-
-template<typename IfXprType, typename ThenXprType, typename ElseXprType>
-struct eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, Eigen::Dense>
-{
-  typedef const TensorSelectOp<IfXprType, ThenXprType, ElseXprType>& type;
-};
-
-template<typename IfXprType, typename ThenXprType, typename ElseXprType>
-struct nested<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, 1, typename eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >::type>
-{
-  typedef TensorSelectOp<IfXprType, ThenXprType, ElseXprType> type;
-};
-
-}  // end namespace internal
-
-
-template<typename IfXprType, typename ThenXprType, typename ElseXprType>
-class TensorSelectOp : public TensorBase<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, ReadOnlyAccessors>
-{
-  public:
-    typedef typename Eigen::internal::traits<TensorSelectOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename internal::promote_storage_type<typename ThenXprType::CoeffReturnType,
-                                                    typename ElseXprType::CoeffReturnType>::ret CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorSelectOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorSelectOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorSelectOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC
-    TensorSelectOp(const IfXprType& a_condition,
-                   const ThenXprType& a_then,
-                   const ElseXprType& a_else)
-      : m_condition(a_condition), m_then(a_then), m_else(a_else)
-    { }
-
-    EIGEN_DEVICE_FUNC
-    const IfXprType& ifExpression() const { return m_condition; }
-
-    EIGEN_DEVICE_FUNC
-    const ThenXprType& thenExpression() const { return m_then; }
-
-    EIGEN_DEVICE_FUNC
-    const ElseXprType& elseExpression() const { return m_else; }
-
-  protected:
-    typename IfXprType::Nested m_condition;
-    typename ThenXprType::Nested m_then;
-    typename ElseXprType::Nested m_else;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
deleted file mode 100644
index 4a1a068..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
+++ /dev/null
@@ -1,669 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Jianwei Cui <thucjw@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_FFT_H
-#define EIGEN_CXX11_TENSOR_TENSOR_FFT_H
-
-namespace Eigen {
-
-/** \class TensorFFT
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor FFT class.
-  *
-  * TODO:
-  * Vectorize the Cooley Tukey and the Bluestein algorithm
-  * Add support for multithreaded evaluation
-  * Improve the performance on GPU
-  */
-
-template <bool NeedUprade> struct MakeComplex {
-  template <typename T>
-  EIGEN_DEVICE_FUNC
-  T operator() (const T& val) const { return val; }
-};
-
-template <> struct MakeComplex<true> {
-  template <typename T>
-  EIGEN_DEVICE_FUNC
-  std::complex<T> operator() (const T& val) const { return std::complex<T>(val, 0); }
-};
-
-template <> struct MakeComplex<false> {
-  template <typename T>
-  EIGEN_DEVICE_FUNC
-  std::complex<T> operator() (const std::complex<T>& val) const { return val; }
-};
-
-template <int ResultType> struct PartOf {
-  template <typename T> T operator() (const T& val) const { return val; }
-};
-
-template <> struct PartOf<RealPart> {
-  template <typename T> T operator() (const std::complex<T>& val) const { return val.real(); }
-};
-
-template <> struct PartOf<ImagPart> {
-  template <typename T> T operator() (const std::complex<T>& val) const { return val.imag(); }
-};
-
-namespace internal {
-template <typename FFT, typename XprType, int FFTResultType, int FFTDir>
-struct traits<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir> > : public traits<XprType> {
-  typedef traits<XprType> XprTraits;
-  typedef typename NumTraits<typename XprTraits::Scalar>::Real RealScalar;
-  typedef typename std::complex<RealScalar> ComplexScalar;
-  typedef typename XprTraits::Scalar InputScalar;
-  typedef typename conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename traits<XprType>::PointerType PointerType;
-};
-
-template <typename FFT, typename XprType, int FFTResultType, int FFTDirection>
-struct eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, Eigen::Dense> {
-  typedef const TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>& type;
-};
-
-template <typename FFT, typename XprType, int FFTResultType, int FFTDirection>
-struct nested<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, 1, typename eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection> >::type> {
-  typedef TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection> type;
-};
-
-}  // end namespace internal
-
-template <typename FFT, typename XprType, int FFTResultType, int FFTDir>
-class TensorFFTOp : public TensorBase<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir>, ReadOnlyAccessors> {
- public:
-  typedef typename Eigen::internal::traits<TensorFFTOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename std::complex<RealScalar> ComplexScalar;
-  typedef typename internal::conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar;
-  typedef OutputScalar CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorFFTOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorFFTOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorFFTOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFFTOp(const XprType& expr, const FFT& fft)
-      : m_xpr(expr), m_fft(fft) {}
-
-  EIGEN_DEVICE_FUNC
-  const FFT& fft() const { return m_fft; }
-
-  EIGEN_DEVICE_FUNC
-  const typename internal::remove_all<typename XprType::Nested>::type& expression() const {
-    return m_xpr;
-  }
-
- protected:
-  typename XprType::Nested m_xpr;
-  const FFT m_fft;
-};
-
-// Eval as rvalue
-template <typename FFT, typename ArgType, typename Device, int FFTResultType, int FFTDir>
-struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, Device> {
-  typedef TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename std::complex<RealScalar> ComplexScalar;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
-  typedef internal::traits<XprType> XprTraits;
-  typedef typename XprTraits::Scalar InputScalar;
-  typedef typename internal::conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar;
-  typedef OutputScalar CoeffReturnType;
-  typedef typename PacketType<OutputScalar, Device>::type PacketReturnType;
-  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
-    typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = true,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_fft(op.fft()), m_impl(op.expression(), device), m_data(NULL), m_device(device) {
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    for (int i = 0; i < NumDims; ++i) {
-      eigen_assert(input_dims[i] > 0);
-      m_dimensions[i] = input_dims[i];
-    }
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_strides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
-      }
-    } else {
-      m_strides[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
-      }
-    }
-    m_size = m_dimensions.TotalSize();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
-    return m_dimensions;
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    if (data) {
-      evalToBuf(data);
-      return false;
-    } else {
-      m_data = (EvaluatorPointerType)m_device.get((CoeffReturnType*)(m_device.allocate_temp(sizeof(CoeffReturnType) * m_size)));
-      evalToBuf(m_data);
-      return true;
-    }
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    if (m_data) {
-      m_device.deallocate(m_data);
-      m_data = NULL;
-    }
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const {
-    return m_data[index];
-  }
-
-  template <int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType
-  packet(Index index) const {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_data.bind(cgh);
-  }
-#endif
-
- private:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalToBuf(EvaluatorPointerType data) {
-    const bool write_to_out = internal::is_same<OutputScalar, ComplexScalar>::value;
-    ComplexScalar* buf = write_to_out ? (ComplexScalar*)data : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * m_size);
-
-    for (Index i = 0; i < m_size; ++i) {
-      buf[i] = MakeComplex<internal::is_same<InputScalar, RealScalar>::value>()(m_impl.coeff(i));
-    }
-
-    for (size_t i = 0; i < m_fft.size(); ++i) {
-      Index dim = m_fft[i];
-      eigen_assert(dim >= 0 && dim < NumDims);
-      Index line_len = m_dimensions[dim];
-      eigen_assert(line_len >= 1);
-      ComplexScalar* line_buf = (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * line_len);
-      const bool is_power_of_two = isPowerOfTwo(line_len);
-      const Index good_composite = is_power_of_two ? 0 : findGoodComposite(line_len);
-      const Index log_len = is_power_of_two ? getLog2(line_len) : getLog2(good_composite);
-
-      ComplexScalar* a = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite);
-      ComplexScalar* b = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite);
-      ComplexScalar* pos_j_base_powered = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * (line_len + 1));
-      if (!is_power_of_two) {
-        // Compute twiddle factors
-        //   t_n = exp(sqrt(-1) * pi * n^2 / line_len)
-        // for n = 0, 1,..., line_len-1.
-        // For n > 2 we use the recurrence t_n = t_{n-1}^2 / t_{n-2} * t_1^2
-
-        // The recurrence is correct in exact arithmetic, but causes
-        // numerical issues for large transforms, especially in
-        // single-precision floating point.
-        //
-        // pos_j_base_powered[0] = ComplexScalar(1, 0);
-        // if (line_len > 1) {
-        //   const ComplexScalar pos_j_base = ComplexScalar(
-        //       numext::cos(M_PI / line_len), numext::sin(M_PI / line_len));
-        //   pos_j_base_powered[1] = pos_j_base;
-        //   if (line_len > 2) {
-        //     const ComplexScalar pos_j_base_sq = pos_j_base * pos_j_base;
-        //     for (int i = 2; i < line_len + 1; ++i) {
-        //       pos_j_base_powered[i] = pos_j_base_powered[i - 1] *
-        //           pos_j_base_powered[i - 1] /
-        //           pos_j_base_powered[i - 2] *
-        //           pos_j_base_sq;
-        //     }
-        //   }
-        // }
-        // TODO(rmlarsen): Find a way to use Eigen's vectorized sin
-        // and cosine functions here.
-        for (int j = 0; j < line_len + 1; ++j) {
-          double arg = ((EIGEN_PI * j) * j) / line_len;
-          std::complex<double> tmp(numext::cos(arg), numext::sin(arg));
-          pos_j_base_powered[j] = static_cast<ComplexScalar>(tmp);
-        }
-      }
-
-      for (Index partial_index = 0; partial_index < m_size / line_len; ++partial_index) {
-        const Index base_offset = getBaseOffsetFromIndex(partial_index, dim);
-
-        // get data into line_buf
-        const Index stride = m_strides[dim];
-        if (stride == 1) {
-          m_device.memcpy(line_buf, &buf[base_offset], line_len*sizeof(ComplexScalar));
-        } else {
-          Index offset = base_offset;
-          for (int j = 0; j < line_len; ++j, offset += stride) {
-            line_buf[j] = buf[offset];
-          }
-        }
-
-        // process the line
-        if (is_power_of_two) {
-          processDataLineCooleyTukey(line_buf, line_len, log_len);
-        }
-        else {
-          processDataLineBluestein(line_buf, line_len, good_composite, log_len, a, b, pos_j_base_powered);
-        }
-
-        // write back
-        if (FFTDir == FFT_FORWARD && stride == 1) {
-          m_device.memcpy(&buf[base_offset], line_buf, line_len*sizeof(ComplexScalar));
-        } else {
-          Index offset = base_offset;
-          const ComplexScalar div_factor =  ComplexScalar(1.0 / line_len, 0);
-          for (int j = 0; j < line_len; ++j, offset += stride) {
-             buf[offset] = (FFTDir == FFT_FORWARD) ? line_buf[j] : line_buf[j] * div_factor;
-          }
-        }
-      }
-      m_device.deallocate(line_buf);
-      if (!is_power_of_two) {
-        m_device.deallocate(a);
-        m_device.deallocate(b);
-        m_device.deallocate(pos_j_base_powered);
-      }
-    }
-
-    if(!write_to_out) {
-      for (Index i = 0; i < m_size; ++i) {
-        data[i] = PartOf<FFTResultType>()(buf[i]);
-      }
-      m_device.deallocate(buf);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static bool isPowerOfTwo(Index x) {
-    eigen_assert(x > 0);
-    return !(x & (x - 1));
-  }
-
-  // The composite number for padding, used in Bluestein's FFT algorithm
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index findGoodComposite(Index n) {
-    Index i = 2;
-    while (i < 2 * n - 1) i *= 2;
-    return i;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index getLog2(Index m) {
-    Index log2m = 0;
-    while (m >>= 1) log2m++;
-    return log2m;
-  }
-
-  // Call Cooley Tukey algorithm directly, data length must be power of 2
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineCooleyTukey(ComplexScalar* line_buf, Index line_len, Index log_len) {
-    eigen_assert(isPowerOfTwo(line_len));
-    scramble_FFT(line_buf, line_len);
-    compute_1D_Butterfly<FFTDir>(line_buf, line_len, log_len);
-  }
-
-  // Call Bluestein's FFT algorithm, m is a good composite number greater than (2 * n - 1), used as the padding length
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineBluestein(ComplexScalar* line_buf, Index line_len, Index good_composite, Index log_len, ComplexScalar* a, ComplexScalar* b, const ComplexScalar* pos_j_base_powered) {
-    Index n = line_len;
-    Index m = good_composite;
-    ComplexScalar* data = line_buf;
-
-    for (Index i = 0; i < n; ++i) {
-      if(FFTDir == FFT_FORWARD) {
-        a[i] = data[i] * numext::conj(pos_j_base_powered[i]);
-      }
-      else {
-        a[i] = data[i] * pos_j_base_powered[i];
-      }
-    }
-    for (Index i = n; i < m; ++i) {
-      a[i] = ComplexScalar(0, 0);
-    }
-
-    for (Index i = 0; i < n; ++i) {
-      if(FFTDir == FFT_FORWARD) {
-        b[i] = pos_j_base_powered[i];
-      }
-      else {
-        b[i] = numext::conj(pos_j_base_powered[i]);
-      }
-    }
-    for (Index i = n; i < m - n; ++i) {
-      b[i] = ComplexScalar(0, 0);
-    }
-    for (Index i = m - n; i < m; ++i) {
-      if(FFTDir == FFT_FORWARD) {
-        b[i] = pos_j_base_powered[m-i];
-      }
-      else {
-        b[i] = numext::conj(pos_j_base_powered[m-i]);
-      }
-    }
-
-    scramble_FFT(a, m);
-    compute_1D_Butterfly<FFT_FORWARD>(a, m, log_len);
-
-    scramble_FFT(b, m);
-    compute_1D_Butterfly<FFT_FORWARD>(b, m, log_len);
-
-    for (Index i = 0; i < m; ++i) {
-      a[i] *= b[i];
-    }
-
-    scramble_FFT(a, m);
-    compute_1D_Butterfly<FFT_REVERSE>(a, m, log_len);
-
-    //Do the scaling after ifft
-    for (Index i = 0; i < m; ++i) {
-      a[i] /= m;
-    }
-
-    for (Index i = 0; i < n; ++i) {
-      if(FFTDir == FFT_FORWARD) {
-        data[i] = a[i] * numext::conj(pos_j_base_powered[i]);
-      }
-      else {
-        data[i] = a[i] * pos_j_base_powered[i];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void scramble_FFT(ComplexScalar* data, Index n) {
-    eigen_assert(isPowerOfTwo(n));
-    Index j = 1;
-    for (Index i = 1; i < n; ++i){
-      if (j > i) {
-        std::swap(data[j-1], data[i-1]);
-      }
-      Index m = n >> 1;
-      while (m >= 2 && j > m) {
-        j -= m;
-        m >>= 1;
-      }
-      j += m;
-    }
-  }
-
-  template <int Dir>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_2(ComplexScalar* data) {
-    ComplexScalar tmp = data[1];
-    data[1] = data[0] - data[1];
-    data[0] += tmp;
-  }
-
-  template <int Dir>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_4(ComplexScalar* data) {
-    ComplexScalar tmp[4];
-    tmp[0] = data[0] + data[1];
-    tmp[1] = data[0] - data[1];
-    tmp[2] = data[2] + data[3];
-    if (Dir == FFT_FORWARD) {
-      tmp[3] = ComplexScalar(0.0, -1.0) * (data[2] - data[3]);
-    } else {
-      tmp[3] = ComplexScalar(0.0, 1.0) * (data[2] - data[3]);
-    }
-    data[0] = tmp[0] + tmp[2];
-    data[1] = tmp[1] + tmp[3];
-    data[2] = tmp[0] - tmp[2];
-    data[3] = tmp[1] - tmp[3];
-  }
-
-  template <int Dir>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_8(ComplexScalar* data) {
-    ComplexScalar tmp_1[8];
-    ComplexScalar tmp_2[8];
-
-    tmp_1[0] = data[0] + data[1];
-    tmp_1[1] = data[0] - data[1];
-    tmp_1[2] = data[2] + data[3];
-    if (Dir == FFT_FORWARD) {
-      tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, -1);
-    } else {
-      tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, 1);
-    }
-    tmp_1[4] = data[4] + data[5];
-    tmp_1[5] = data[4] - data[5];
-    tmp_1[6] = data[6] + data[7];
-    if (Dir == FFT_FORWARD) {
-      tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, -1);
-    } else {
-      tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, 1);
-    }
-    tmp_2[0] = tmp_1[0] + tmp_1[2];
-    tmp_2[1] = tmp_1[1] + tmp_1[3];
-    tmp_2[2] = tmp_1[0] - tmp_1[2];
-    tmp_2[3] = tmp_1[1] - tmp_1[3];
-    tmp_2[4] = tmp_1[4] + tmp_1[6];
-// SQRT2DIV2 = sqrt(2)/2
-#define SQRT2DIV2 0.7071067811865476
-    if (Dir == FFT_FORWARD) {
-      tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, -SQRT2DIV2);
-      tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, -1);
-      tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, -SQRT2DIV2);
-    } else {
-      tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, SQRT2DIV2);
-      tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, 1);
-      tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, SQRT2DIV2);
-    }
-    data[0] = tmp_2[0] + tmp_2[4];
-    data[1] = tmp_2[1] + tmp_2[5];
-    data[2] = tmp_2[2] + tmp_2[6];
-    data[3] = tmp_2[3] + tmp_2[7];
-    data[4] = tmp_2[0] - tmp_2[4];
-    data[5] = tmp_2[1] - tmp_2[5];
-    data[6] = tmp_2[2] - tmp_2[6];
-    data[7] = tmp_2[3] - tmp_2[7];
-  }
-
-  template <int Dir>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_1D_merge(
-      ComplexScalar* data, Index n, Index n_power_of_2) {
-    // Original code:
-    // RealScalar wtemp = std::sin(M_PI/n);
-    // RealScalar wpi =  -std::sin(2 * M_PI/n);
-    const RealScalar wtemp = m_sin_PI_div_n_LUT[n_power_of_2];
-    const RealScalar wpi = (Dir == FFT_FORWARD)
-                               ? m_minus_sin_2_PI_div_n_LUT[n_power_of_2]
-                               : -m_minus_sin_2_PI_div_n_LUT[n_power_of_2];
-
-    const ComplexScalar wp(wtemp, wpi);
-    const ComplexScalar wp_one = wp + ComplexScalar(1, 0);
-    const ComplexScalar wp_one_2 = wp_one * wp_one;
-    const ComplexScalar wp_one_3 = wp_one_2 * wp_one;
-    const ComplexScalar wp_one_4 = wp_one_3 * wp_one;
-    const Index n2 = n / 2;
-    ComplexScalar w(1.0, 0.0);
-    for (Index i = 0; i < n2; i += 4) {
-       ComplexScalar temp0(data[i + n2] * w);
-       ComplexScalar temp1(data[i + 1 + n2] * w * wp_one);
-       ComplexScalar temp2(data[i + 2 + n2] * w * wp_one_2);
-       ComplexScalar temp3(data[i + 3 + n2] * w * wp_one_3);
-       w = w * wp_one_4;
-
-       data[i + n2] = data[i] - temp0;
-       data[i] += temp0;
-
-       data[i + 1 + n2] = data[i + 1] - temp1;
-       data[i + 1] += temp1;
-
-       data[i + 2 + n2] = data[i + 2] - temp2;
-       data[i + 2] += temp2;
-
-       data[i + 3 + n2] = data[i + 3] - temp3;
-       data[i + 3] += temp3;
-    }
-  }
-
- template <int Dir>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_1D_Butterfly(
-      ComplexScalar* data, Index n, Index n_power_of_2) {
-    eigen_assert(isPowerOfTwo(n));
-    if (n > 8) {
-      compute_1D_Butterfly<Dir>(data, n / 2, n_power_of_2 - 1);
-      compute_1D_Butterfly<Dir>(data + n / 2, n / 2, n_power_of_2 - 1);
-      butterfly_1D_merge<Dir>(data, n, n_power_of_2);
-    } else if (n == 8) {
-      butterfly_8<Dir>(data);
-    } else if (n == 4) {
-      butterfly_4<Dir>(data);
-    } else if (n == 2) {
-      butterfly_2<Dir>(data);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getBaseOffsetFromIndex(Index index, Index omitted_dim) const {
-    Index result = 0;
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumDims - 1; i > omitted_dim; --i) {
-        const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim];
-        const Index idx = index / partial_m_stride;
-        index -= idx * partial_m_stride;
-        result += idx * m_strides[i];
-      }
-      result += index;
-    }
-    else {
-      for (Index i = 0; i < omitted_dim; ++i) {
-        const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim];
-        const Index idx = index / partial_m_stride;
-        index -= idx * partial_m_stride;
-        result += idx * m_strides[i];
-      }
-      result += index;
-    }
-    // Value of index_coords[omitted_dim] is not determined to this step
-    return result;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getIndexFromOffset(Index base, Index omitted_dim, Index offset) const {
-    Index result = base + offset * m_strides[omitted_dim] ;
-    return result;
-  }
-
- protected:
-  Index m_size;
-  const FFT EIGEN_DEVICE_REF m_fft;
-  Dimensions m_dimensions;
-  array<Index, NumDims> m_strides;
-  TensorEvaluator<ArgType, Device> m_impl;
-  EvaluatorPointerType m_data;
-  const Device EIGEN_DEVICE_REF m_device;
-
-  // This will support a maximum FFT size of 2^32 for each dimension
-  // m_sin_PI_div_n_LUT[i] = (-2) * std::sin(M_PI / std::pow(2,i)) ^ 2;
-  const RealScalar m_sin_PI_div_n_LUT[32] = {
-    RealScalar(0.0),
-    RealScalar(-2),
-    RealScalar(-0.999999999999999),
-    RealScalar(-0.292893218813453),
-    RealScalar(-0.0761204674887130),
-    RealScalar(-0.0192147195967696),
-    RealScalar(-0.00481527332780311),
-    RealScalar(-0.00120454379482761),
-    RealScalar(-3.01181303795779e-04),
-    RealScalar(-7.52981608554592e-05),
-    RealScalar(-1.88247173988574e-05),
-    RealScalar(-4.70619042382852e-06),
-    RealScalar(-1.17654829809007e-06),
-    RealScalar(-2.94137117780840e-07),
-    RealScalar(-7.35342821488550e-08),
-    RealScalar(-1.83835707061916e-08),
-    RealScalar(-4.59589268710903e-09),
-    RealScalar(-1.14897317243732e-09),
-    RealScalar(-2.87243293150586e-10),
-    RealScalar( -7.18108232902250e-11),
-    RealScalar(-1.79527058227174e-11),
-    RealScalar(-4.48817645568941e-12),
-    RealScalar(-1.12204411392298e-12),
-    RealScalar(-2.80511028480785e-13),
-    RealScalar(-7.01277571201985e-14),
-    RealScalar(-1.75319392800498e-14),
-    RealScalar(-4.38298482001247e-15),
-    RealScalar(-1.09574620500312e-15),
-    RealScalar(-2.73936551250781e-16),
-    RealScalar(-6.84841378126949e-17),
-    RealScalar(-1.71210344531737e-17),
-    RealScalar(-4.28025861329343e-18)
-  };
-
-  // m_minus_sin_2_PI_div_n_LUT[i] = -std::sin(2 * M_PI / std::pow(2,i));
-  const RealScalar m_minus_sin_2_PI_div_n_LUT[32] = {
-    RealScalar(0.0),
-    RealScalar(0.0),
-    RealScalar(-1.00000000000000e+00),
-    RealScalar(-7.07106781186547e-01),
-    RealScalar(-3.82683432365090e-01),
-    RealScalar(-1.95090322016128e-01),
-    RealScalar(-9.80171403295606e-02),
-    RealScalar(-4.90676743274180e-02),
-    RealScalar(-2.45412285229123e-02),
-    RealScalar(-1.22715382857199e-02),
-    RealScalar(-6.13588464915448e-03),
-    RealScalar(-3.06795676296598e-03),
-    RealScalar(-1.53398018628477e-03),
-    RealScalar(-7.66990318742704e-04),
-    RealScalar(-3.83495187571396e-04),
-    RealScalar(-1.91747597310703e-04),
-    RealScalar(-9.58737990959773e-05),
-    RealScalar(-4.79368996030669e-05),
-    RealScalar(-2.39684498084182e-05),
-    RealScalar(-1.19842249050697e-05),
-    RealScalar(-5.99211245264243e-06),
-    RealScalar(-2.99605622633466e-06),
-    RealScalar(-1.49802811316901e-06),
-    RealScalar(-7.49014056584716e-07),
-    RealScalar(-3.74507028292384e-07),
-    RealScalar(-1.87253514146195e-07),
-    RealScalar(-9.36267570730981e-08),
-    RealScalar(-4.68133785365491e-08),
-    RealScalar(-2.34066892682746e-08),
-    RealScalar(-1.17033446341373e-08),
-    RealScalar(-5.85167231706864e-09),
-    RealScalar(-2.92583615853432e-09)
-  };
-};
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_FFT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
deleted file mode 100644
index ca39bb8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
+++ /dev/null
@@ -1,379 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
-#define EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
-
-namespace Eigen {
-
-/** \class TensorFixedSize
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief The fixed sized version of the tensor class.
-  *
-  * The fixed sized equivalent of
-  * Eigen::Tensor<float, 3> t(3, 5, 7);
-  * is
-  * Eigen::TensorFixedSize<float, Sizes<3,5,7>> t;
-  */
-
-template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType>
-class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> >
-{
-  public:
-    typedef TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> Self;
-    typedef TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> > Base;
-    typedef typename Eigen::internal::nested<Self>::type Nested;
-    typedef typename internal::traits<Self>::StorageKind StorageKind;
-    typedef typename internal::traits<Self>::Index Index;
-    typedef Scalar_ Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-
-    static const int Options = Options_;
-
-    enum {
-      IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0),
-      PacketAccess = (internal::packet_traits<Scalar>::size > 1),
-      BlockAccess = false,
-      PreferBlockAccess = false,
-      Layout = Options_ & RowMajor ? RowMajor : ColMajor,
-      CoordAccess = true,
-      RawAccess = true
-    };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  typedef Dimensions_ Dimensions;
-  static const std::size_t NumIndices = Dimensions::count;
-
-  protected:
-  TensorStorage<Scalar, Dimensions, Options> m_storage;
-
-  public:
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                    rank()                   const { return NumIndices; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                    dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions&        dimensions()             const { return m_storage.dimensions(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                    size()                   const { return m_storage.size(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar                   *data()                        { return m_storage.data(); }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar             *data()                  const { return m_storage.data(); }
-
-    // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
-    // work, because that uses base().coeffRef() - and we don't yet
-    // implement a similar class hierarchy
-    inline Self& base()             { return *this; }
-    inline const Self& base() const { return *this; }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, IndexTypes... otherIndices) const
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return coeff(array<Index, NumIndices>{{firstIndex, otherIndices...}});
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const
-    {
-      eigen_internal_assert(checkIndexRange(indices));
-      return m_storage.data()[linearizedIndex(indices)];
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return m_storage.data()[index];
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& coeff() const
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return m_storage.data()[0];
-    }
-
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return coeffRef(array<Index, NumIndices>{{firstIndex, otherIndices...}});
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
-    {
-      eigen_internal_assert(checkIndexRange(indices));
-      return m_storage.data()[linearizedIndex(indices)];
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return m_storage.data()[index];
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef()
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return m_storage.data()[0];
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return this->operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
-    }
-#else
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
-    {
-      if (Options&RowMajor) {
-        const Index index = i1 + i0 * m_storage.dimensions()[1];
-        return m_storage.data()[index];
-      } else {
-        const Index index = i0 + i1 * m_storage.dimensions()[0];
-        return m_storage.data()[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
-    {
-      if (Options&RowMajor) {
-         const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0);
-         return m_storage.data()[index];
-      } else {
-         const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2);
-        return m_storage.data()[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
-    {
-      if (Options&RowMajor) {
-        const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0));
-        return m_storage.data()[index];
-      } else {
-        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3));
-        return m_storage.data()[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
-    {
-      if (Options&RowMajor) {
-        const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)));
-        return m_storage.data()[index];
-      } else {
-        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4)));
-        return m_storage.data()[index];
-      }
-    }
-#endif
-
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
-    {
-      eigen_assert(checkIndexRange(indices));
-      return coeff(indices);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return coeff(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()() const
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return coeff();
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const
-    {
-      // The bracket operator is only for vectors, use the parenthesis operator instead.
-      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return coeff(index);
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
-    {
-      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
-    }
-#else
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
-    {
-       if (Options&RowMajor) {
-         const Index index = i1 + i0 * m_storage.dimensions()[1];
-        return m_storage.data()[index];
-      } else {
-        const Index index = i0 + i1 * m_storage.dimensions()[0];
-        return m_storage.data()[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
-    {
-       if (Options&RowMajor) {
-         const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0);
-        return m_storage.data()[index];
-      } else {
-         const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2);
-        return m_storage.data()[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
-    {
-      if (Options&RowMajor) {
-        const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0));
-        return m_storage.data()[index];
-      } else {
-        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3));
-        return m_storage.data()[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
-    {
-      if (Options&RowMajor) {
-        const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)));
-        return m_storage.data()[index];
-      } else {
-        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4)));
-        return m_storage.data()[index];
-      }
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
-    {
-      eigen_assert(checkIndexRange(indices));
-      return coeffRef(indices);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index index)
-    {
-      eigen_assert(index >= 0 && index < size());
-      return coeffRef(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()()
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return coeffRef();
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator[](Index index)
-    {
-      // The bracket operator is only for vectors, use the parenthesis operator instead
-      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return coeffRef(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorFixedSize()
-      : m_storage()
-    {
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorFixedSize(const Self& other)
-      : m_storage(other.m_storage)
-    {
-    }
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(Self&& other)
-      : m_storage(other.m_storage)
-    {
-    }
-#endif
-
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
-    {
-      typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
-      Assign assign(*this, other.derived());
-      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
-    }
-    template<typename OtherDerived>
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, WriteAccessors>& other)
-    {
-      typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
-      Assign assign(*this, other.derived());
-      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
-    }
-
-    // FIXME: check that the dimensions of other match the dimensions of *this.
-    // Unfortunately this isn't possible yet when the rhs is an expression.
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(TensorFixedSize)
-
-
-  protected:
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE bool checkIndexRange(const array<Index, NumIndices>& /*indices*/) const
-    {
-      using internal::array_apply_and_reduce;
-      using internal::array_zip_and_reduce;
-      using internal::greater_equal_zero_op;
-      using internal::logical_and_op;
-      using internal::lesser_op;
-
-      return true;
-        // check whether the indices are all >= 0
-          /*       array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
-        // check whether the indices fit in the dimensions
-        array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());*/
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const
-    {
-      if (Options&RowMajor) {
-        return m_storage.dimensions().IndexOfRowMajor(indices);
-      } else {
-        return m_storage.dimensions().IndexOfColMajor(indices);
-      }
-    }
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
deleted file mode 100644
index e800ded..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
+++ /dev/null
@@ -1,237 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
-#define EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
-
-namespace Eigen {
-
-/** \class TensorForcedEval
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor reshaping class.
-  *
-  *
-  */
-namespace internal {
-template<typename XprType>
-struct traits<TensorForcedEvalOp<XprType> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename traits<XprType>::StorageKind StorageKind;
-  typedef typename traits<XprType>::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-
-  enum {
-    Flags = 0
-  };
-};
-
-template<typename XprType>
-struct eval<TensorForcedEvalOp<XprType>, Eigen::Dense>
-{
-  typedef const TensorForcedEvalOp<XprType>& type;
-};
-
-template<typename XprType>
-struct nested<TensorForcedEvalOp<XprType>, 1, typename eval<TensorForcedEvalOp<XprType> >::type>
-{
-  typedef TensorForcedEvalOp<XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename XprType>
-class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorForcedEvalOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorForcedEvalOp(const XprType& expr)
-      : m_xpr(expr) {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-};
-
-namespace internal {
-template <typename Device, typename CoeffReturnType>
-struct non_integral_type_placement_new{
-  template <typename StorageType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index numValues, StorageType m_buffer) {
-   // Initialize non-trivially constructible types.
-    if (!internal::is_arithmetic<CoeffReturnType>::value) {
-      for (Index i = 0; i < numValues; ++i) new (m_buffer + i) CoeffReturnType();
-    }
-}
-};
-
-// SYCL does not support non-integral types 
-// having new (m_buffer + i) CoeffReturnType() causes the following compiler error for SYCL Devices 
-// no matching function for call to 'operator new'
-template <typename CoeffReturnType>
-struct non_integral_type_placement_new<Eigen::SyclDevice, CoeffReturnType> {
-  template <typename StorageType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index, StorageType) {
-}
-};
-} // end namespace internal
-
-template<typename ArgType_, typename Device>
-struct TensorEvaluator<const TensorForcedEvalOp<ArgType_>, Device>
-{
-  typedef const typename internal::remove_all<ArgType_>::type ArgType;
-  typedef TensorForcedEvalOp<ArgType> XprType;
-  typedef typename ArgType::Scalar Scalar;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = true,
-    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess       = internal::is_arithmetic<CoeffReturnType>::value,
-    PreferBlockAccess = false,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    RawAccess         = true
-  };
-
-  static const int NumDims = internal::traits<ArgType>::NumDimensions;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_op(op.expression()),
-      m_device(device), m_buffer(NULL)
-  { }
-
-  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    const Index numValues =  internal::array_prod(m_impl.dimensions());
-    m_buffer = m_device.get((CoeffReturnType*)m_device.allocate_temp(numValues * sizeof(CoeffReturnType)));
-
-   internal::non_integral_type_placement_new<Device, CoeffReturnType>()(numValues, m_buffer);
-
-    typedef TensorEvalToOp< const typename internal::remove_const<ArgType>::type > EvalTo;
-    EvalTo evalToTmp(m_device.get(m_buffer), m_op);
-
-    internal::TensorExecutor<
-        const EvalTo, typename internal::remove_const<Device>::type,
-        /*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
-        /*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::
-        run(evalToTmp, m_device);
-
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    const Index numValues = internal::array_prod(m_impl.dimensions());
-    m_buffer = m_device.get((CoeffReturnType*)m_device.allocate_temp(
-        numValues * sizeof(CoeffReturnType)));
-    typedef TensorEvalToOp<const typename internal::remove_const<ArgType>::type>
-        EvalTo;
-    EvalTo evalToTmp(m_device.get(m_buffer), m_op);
-
-    auto on_done = std::bind([](EvalSubExprsCallback done_) { done_(true); },
-                             std::move(done));
-    internal::TensorAsyncExecutor<
-        const EvalTo, typename internal::remove_const<Device>::type,
-        decltype(on_done),
-        /*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
-        /*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::
-        runAsync(evalToTmp, m_device, std::move(on_done));
-  }
-#endif
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_device.deallocate_temp(m_buffer);
-    m_buffer = NULL;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return m_buffer[index];
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    return internal::TensorBlockResourceRequirements::any();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    assert(m_buffer != NULL);
-    return TensorBlock::materialize(m_buffer, m_impl.dimensions(), desc, scratch);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  EvaluatorPointerType data() const { return m_buffer; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_buffer.bind(cgh);
-    m_impl.bind(cgh);
-  }
-#endif
- private:
-  TensorEvaluator<ArgType, Device> m_impl;
-  const ArgType m_op;
-  const Device EIGEN_DEVICE_REF m_device;
-  EvaluatorPointerType m_buffer;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
deleted file mode 100644
index 246ebe4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
+++ /dev/null
@@ -1,191 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
-#define EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
-
-namespace Eigen {
-
-// MakePointer class is used as a container of the address space of the pointer
-// on the host and on the device. From the host side it generates the T* pointer
-// and when EIGEN_USE_SYCL is used it construct a buffer with a map_allocator to
-// T* m_data on the host. It is always called on the device.
-// Specialisation of MakePointer class for creating the sycl buffer with
-// map_allocator.
-template<typename T> struct MakePointer {
-  typedef T* Type;
-  typedef const T* ConstType;
-};
-
-template <typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T* constCast(const T* data) {
-  return const_cast<T*>(data);
-}
-
-// The StorageMemory class is a container of the device specific pointer
-// used for refering to a Pointer on TensorEvaluator class. While the TensorExpression
-// is a device-agnostic type and need MakePointer class for type conversion,
-// the TensorEvaluator class can be specialized for a device, hence it is possible
-// to construct different types of temproray storage memory in TensorEvaluator
-// for different devices by specializing the following StorageMemory class.
-template<typename T, typename device> struct StorageMemory: MakePointer <T> {};
-
-namespace internal{
-template<typename A, typename B> struct Pointer_type_promotion {
-  static const bool val=false;
-};
-template<typename A> struct Pointer_type_promotion<A, A> {
-  static const bool val = true;
-};
-template<typename A, typename B> struct TypeConversion {
-  typedef A* type;
-};
-}
-
-
-template<typename PlainObjectType, int Options_ = Unaligned, template <class> class MakePointer_ = MakePointer> class TensorMap;
-template<typename Scalar_, int NumIndices_, int Options_ = 0, typename IndexType = DenseIndex> class Tensor;
-template<typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex> class TensorFixedSize;
-template<typename PlainObjectType> class TensorRef;
-template<typename Derived, int AccessLevel> class TensorBase;
-
-template<typename NullaryOp, typename PlainObjectType> class TensorCwiseNullaryOp;
-template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp;
-template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
-template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> class TensorCwiseTernaryOp;
-template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
-template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_ = MakePointer > class TensorReductionOp;
-template<typename XprType> class TensorIndexTupleOp;
-template<typename ReduceOp, typename Dims, typename XprType> class TensorTupleReducerOp;
-template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
-template<typename Dimensions, typename LeftXprType, typename RightXprType, typename OutputKernelType> class TensorContractionOp;
-template<typename TargetType, typename XprType> class TensorConversionOp;
-template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
-template<typename FFT, typename XprType, int FFTDataType, int FFTDirection> class TensorFFTOp;
-template<typename PatchDim, typename XprType> class TensorPatchOp;
-template<DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorImagePatchOp;
-template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorVolumePatchOp;
-template<typename Broadcast, typename XprType> class TensorBroadcastingOp;
-template<DenseIndex DimId, typename XprType> class TensorChippingOp;
-template<typename NewDimensions, typename XprType> class TensorReshapingOp;
-template<typename XprType> class TensorLayoutSwapOp;
-template<typename StartIndices, typename Sizes, typename XprType> class TensorSlicingOp;
-template<typename ReverseDimensions, typename XprType> class TensorReverseOp;
-template<typename PaddingDimensions, typename XprType> class TensorPaddingOp;
-template<typename Shuffle, typename XprType> class TensorShufflingOp;
-template<typename Strides, typename XprType> class TensorStridingOp;
-template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> class TensorStridingSlicingOp;
-template<typename Strides, typename XprType> class TensorInflationOp;
-template<typename Generator, typename XprType> class TensorGeneratorOp;
-template<typename LeftXprType, typename RightXprType> class TensorAssignOp;
-template<typename Op, typename XprType> class TensorScanOp;
-template<typename Dims, typename XprType> class TensorTraceOp;
-
-template<typename CustomUnaryFunc, typename XprType> class TensorCustomUnaryOp;
-template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> class TensorCustomBinaryOp;
-
-template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorEvalToOp;
-template<typename XprType> class TensorForcedEvalOp;
-
-template<typename ExpressionType, typename DeviceType> class TensorDevice;
-template<typename ExpressionType, typename DeviceType, typename DoneCallback> class TensorAsyncDevice;
-template<typename Derived, typename Device> struct TensorEvaluator;
-
-struct NoOpOutputKernel;
-
-struct DefaultDevice;
-struct ThreadPoolDevice;
-struct GpuDevice;
-struct SyclDevice;
-
-#ifdef EIGEN_USE_SYCL
-
-template <typename T> struct MakeSYCLPointer {
-  typedef Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T> Type;
-};
-
-template <typename T>
-EIGEN_STRONG_INLINE const Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T>&
-constCast(const Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T>& data) {
-  return data;
-}
-
-template <typename T>
-struct StorageMemory<T, SyclDevice> : MakeSYCLPointer<T> {};
-template <typename T>
-struct StorageMemory<T, const SyclDevice> : StorageMemory<T, SyclDevice> {};
-
-namespace TensorSycl {
-namespace internal{
-template <typename Evaluator, typename Op> class GenericNondeterministicReducer;
-}
-}
-#endif
-
-
-enum FFTResultType {
-  RealPart = 0,
-  ImagPart = 1,
-  BothParts = 2
-};
-
-enum FFTDirection {
-    FFT_FORWARD = 0,
-    FFT_REVERSE = 1
-};
-
-
-namespace internal {
-
-template <typename Device, typename Expression>
-struct IsVectorizable {
-  static const bool value = TensorEvaluator<Expression, Device>::PacketAccess;
-};
-
-template <typename Expression>
-struct IsVectorizable<GpuDevice, Expression> {
-  static const bool value = TensorEvaluator<Expression, GpuDevice>::PacketAccess &&
-                            TensorEvaluator<Expression, GpuDevice>::IsAligned;
-};
-
-// Tiled evaluation strategy.
-enum TiledEvaluation {
-  Off = 0,    // tiled evaluation is not supported
-  On = 1,     // still work in progress (see TensorBlock.h)
-};
-
-template <typename Device, typename Expression>
-struct IsTileable {
-  // Check that block evaluation is supported and it's a preferred option (at
-  // least one sub-expression has much faster block evaluation, e.g.
-  // broadcasting).
-  static const bool BlockAccess =
-      TensorEvaluator<Expression, Device>::BlockAccess &&
-      TensorEvaluator<Expression, Device>::PreferBlockAccess;
-
-  static const TiledEvaluation value =
-      BlockAccess ? TiledEvaluation::On : TiledEvaluation::Off;
-};
-
-template <typename Expression, typename Device,
-          bool Vectorizable      = IsVectorizable<Device, Expression>::value,
-          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
-class TensorExecutor;
-
-template <typename Expression, typename Device, typename DoneCallback,
-          bool Vectorizable = IsVectorizable<Device, Expression>::value,
-          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
-class TensorAsyncExecutor;
-
-
-}  // end namespace internal
-
-}  // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
deleted file mode 100644
index d963032..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
+++ /dev/null
@@ -1,488 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
-#define EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
-
-namespace Eigen {
-namespace internal {
-
-
-/** \internal
- * \brief Template functor to compute the modulo between an array and a scalar.
- */
-template <typename Scalar>
-struct scalar_mod_op {
-  EIGEN_DEVICE_FUNC scalar_mod_op(const Scalar& divisor) : m_divisor(divisor) {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a % m_divisor; }
-  const Scalar m_divisor;
-};
-template <typename Scalar>
-struct functor_traits<scalar_mod_op<Scalar> >
-{ enum { Cost = scalar_div_cost<Scalar,false>::value, PacketAccess = false }; };
-
-
-/** \internal
- * \brief Template functor to compute the modulo between 2 arrays.
- */
-template <typename Scalar>
-struct scalar_mod2_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_mod2_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a, const Scalar& b) const { return a % b; }
-};
-template <typename Scalar>
-struct functor_traits<scalar_mod2_op<Scalar> >
-{ enum { Cost = scalar_div_cost<Scalar,false>::value, PacketAccess = false }; };
-
-template <typename Scalar>
-struct scalar_fmod_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar
-  operator()(const Scalar& a, const Scalar& b) const {
-    return numext::fmod(a, b);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_fmod_op<Scalar> > {
-  enum { Cost = 13,  // Reciprocal throughput of FPREM on Haswell.
-         PacketAccess = false };
-};
-
-template<typename Reducer, typename Device>
-struct reducer_traits {
-  enum {
-    Cost = 1,
-    PacketAccess = false,
-    IsStateful = false,
-    IsExactlyAssociative = true
-  };
-};
-
-// Standard reduction functors
-template <typename T> struct SumReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
-    internal::scalar_sum_op<T> sum_op;
-    *accum = sum_op(*accum, t);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
-    (*accum) = padd<Packet>(*accum, p);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    internal::scalar_cast_op<int, T> conv;
-    return conv(0);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
-    return pset1<Packet>(initialize());
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
-    return accum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
-    return vaccum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
-    internal::scalar_sum_op<T> sum_op;
-    return sum_op(saccum, predux(vaccum));
-  }
-};
-
-template <typename T, typename Device>
-struct reducer_traits<SumReducer<T>, Device> {
-  enum {
-    Cost = NumTraits<T>::AddCost,
-    PacketAccess = PacketType<T, Device>::HasAdd,
-    IsStateful = false,
-    IsExactlyAssociative = NumTraits<T>::IsInteger
-  };
-};
-
-template <typename T> struct MeanReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  MeanReducer() : scalarCount_(0), packetCount_(0) { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) {
-    internal::scalar_sum_op<T> sum_op;
-    *accum = sum_op(*accum, t);
-    scalarCount_++;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) {
-    (*accum) = padd<Packet>(*accum, p);
-    packetCount_++;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    internal::scalar_cast_op<int, T> conv;
-    return conv(0);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
-    return pset1<Packet>(initialize());
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
-    internal::scalar_quotient_op<T> quotient_op;
-    return quotient_op(accum, T(scalarCount_));
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
-    return pdiv(vaccum, pset1<Packet>(T(packetCount_)));
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
-    internal::scalar_sum_op<T> sum_op;
-    internal::scalar_quotient_op<T> quotient_op;
-    return quotient_op(
-        sum_op(saccum, predux(vaccum)),
-        T(scalarCount_ + packetCount_ * unpacket_traits<Packet>::size));
-  }
-
-  protected:
-    DenseIndex scalarCount_;
-    DenseIndex packetCount_;
-};
-
-template <typename T, typename Device>
-struct reducer_traits<MeanReducer<T>, Device> {
-  enum {
-    Cost = NumTraits<T>::AddCost,
-    PacketAccess = PacketType<T, Device>::HasAdd &&
-                   PacketType<T, Device>::HasDiv && !NumTraits<T>::IsInteger,
-    IsStateful = true,
-    IsExactlyAssociative = NumTraits<T>::IsInteger
-  };
-};
-
-
-template <typename T, bool IsMax = true, bool IsInteger = true>
-struct MinMaxBottomValue {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
-    return Eigen::NumTraits<T>::lowest();
-  }
-};
-template <typename T>
-struct MinMaxBottomValue<T, true, false> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
-    return -Eigen::NumTraits<T>::infinity();
-  }
-};
-template <typename T>
-struct MinMaxBottomValue<T, false, true> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
-    return Eigen::NumTraits<T>::highest();
-  }
-};
-template <typename T>
-struct MinMaxBottomValue<T, false, false> {
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
-    return Eigen::NumTraits<T>::infinity();
-  }
-};
-
-
-template <typename T, int NaNPropagation=PropagateFast> struct MaxReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
-    scalar_max_op<T, T, NaNPropagation> op;
-    *accum = op(t, *accum);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
-    scalar_max_op<T, T, NaNPropagation> op;
-    (*accum) = op.packetOp(*accum, p);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    return MinMaxBottomValue<T, /*IsMax=*/true, Eigen::NumTraits<T>::IsInteger>::bottom_value();
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
-    return pset1<Packet>(initialize());
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
-    return accum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
-    return vaccum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
-    scalar_max_op<T, T, NaNPropagation> op;
-    return op(saccum, op.predux(vaccum));
-  }
-};
-
-template <typename T, typename Device, int NaNPropagation>
-    struct reducer_traits<MaxReducer<T, NaNPropagation>, Device> {
-  enum {
-    Cost = NumTraits<T>::AddCost,
-    PacketAccess = PacketType<T, Device>::HasMax,
-    IsStateful = false,
-    IsExactlyAssociative = (NaNPropagation!=PropagateFast)
-  };
-};
-
-template <typename T, int NaNPropagation=PropagateFast> struct MinReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
-    scalar_min_op<T, T, NaNPropagation> op;
-    *accum = op(t, *accum);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
-    scalar_min_op<T, T, NaNPropagation> op;
-    (*accum) = op.packetOp(*accum, p);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    return MinMaxBottomValue<T, /*IsMax=*/false, Eigen::NumTraits<T>::IsInteger>::bottom_value();
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
-    return pset1<Packet>(initialize());
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
-    return accum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
-    return vaccum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
-    scalar_min_op<T, T, NaNPropagation> op;
-    return op(saccum, op.predux(vaccum));
-  }
-};
-
-template <typename T, typename Device, int NaNPropagation>
-    struct reducer_traits<MinReducer<T, NaNPropagation>, Device> {
-  enum {
-    Cost = NumTraits<T>::AddCost,
-    PacketAccess = PacketType<T, Device>::HasMin,
-    IsStateful = false,
-    IsExactlyAssociative = (NaNPropagation!=PropagateFast)
-  };
-};
-
-template <typename T> struct ProdReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
-    internal::scalar_product_op<T> prod_op;
-    (*accum) = prod_op(*accum, t);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
-    (*accum) = pmul<Packet>(*accum, p);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    internal::scalar_cast_op<int, T> conv;
-    return conv(1);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
-    return pset1<Packet>(initialize());
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
-    return accum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
-    return vaccum;
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
-    internal::scalar_product_op<T> prod_op;
-    return prod_op(saccum, predux_mul(vaccum));
-  }
-};
-
-template <typename T, typename Device>
-struct reducer_traits<ProdReducer<T>, Device> {
-  enum {
-    Cost = NumTraits<T>::MulCost,
-    PacketAccess = PacketType<T, Device>::HasMul,
-    IsStateful = false,
-    IsExactlyAssociative = true
-  };
-};
-
-
-struct AndReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const {
-    *accum = *accum && t;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const {
-    return true;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const {
-    return accum;
-  }
-};
-
-template <typename Device>
-struct reducer_traits<AndReducer, Device> {
-  enum {
-    Cost = 1,
-    PacketAccess = false,
-    IsStateful = false,
-    IsExactlyAssociative = true
-  };
-};
-
-
-struct OrReducer {
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const {
-    *accum = *accum || t;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const {
-    return false;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const {
-    return accum;
-  }
-};
-
-template <typename Device>
-struct reducer_traits<OrReducer, Device> {
-  enum {
-    Cost = 1,
-    PacketAccess = false,
-    IsStateful = false,
-    IsExactlyAssociative = true
-  };
-};
-
-// Argmin/Argmax reducers.  Returns the first occurrence if multiple locations
-// contain the same min/max value.
-template <typename T> struct ArgMaxTupleReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
-    if (t.second < accum->second) {
-      return;
-    } else if (t.second > accum->second || accum->first > t.first ) {
-      *accum = t;
-    }
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    return T(0, NumTraits<typename T::second_type>::lowest());
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const {
-    return accum;
-  }
-};
-
-template <typename T, typename Device>
-struct reducer_traits<ArgMaxTupleReducer<T>, Device> {
-  enum {
-    Cost = NumTraits<T>::AddCost,
-    PacketAccess = false,
-    IsStateful = false,
-    IsExactlyAssociative = true
-  };
-};
-
-
-template <typename T> struct ArgMinTupleReducer
-{
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T& t, T* accum) const {
-    if (t.second > accum->second) {
-      return;
-    } else if (t.second < accum->second || accum->first > t.first) {
-      *accum = t;
-    }
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    return T(0, NumTraits<typename T::second_type>::highest());
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const {
-    return accum;
-  }
-};
-
-template <typename T, typename Device>
-struct reducer_traits<ArgMinTupleReducer<T>, Device> {
-  enum {
-    Cost = NumTraits<T>::AddCost,
-    PacketAccess = false,
-    IsStateful = false,
-    IsExactlyAssociative = true
-  };
-};
-
-
-template <typename T, typename Index, size_t NumDims>
-class GaussianGenerator {
- public:
-  static const bool PacketAccess = false;
-
-  EIGEN_DEVICE_FUNC GaussianGenerator(const array<T, NumDims>& means,
-                                      const array<T, NumDims>& std_devs)
-      : m_means(means)
-  {
-    EIGEN_UNROLL_LOOP
-    for (size_t i = 0; i < NumDims; ++i) {
-      m_two_sigmas[i] = std_devs[i] * std_devs[i] * 2;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC T operator()(const array<Index, NumDims>& coordinates) const {
-    T tmp = T(0);
-    EIGEN_UNROLL_LOOP
-    for (size_t i = 0; i < NumDims; ++i) {
-      T offset = coordinates[i] - m_means[i];
-      tmp += offset * offset / m_two_sigmas[i];
-    }
-    return numext::exp(-tmp);
-  }
-
- private:
-  array<T, NumDims> m_means;
-  array<T, NumDims> m_two_sigmas;
-};
-
-template <typename T, typename Index, size_t NumDims>
-struct functor_traits<GaussianGenerator<T, Index, NumDims> > {
-  enum {
-    Cost = NumDims * (2 * NumTraits<T>::AddCost + NumTraits<T>::MulCost +
-                      functor_traits<scalar_quotient_op<T, T> >::Cost) +
-           functor_traits<scalar_exp_op<T> >::Cost,
-    PacketAccess = GaussianGenerator<T, Index, NumDims>::PacketAccess
-  };
-};
-
-template <typename Scalar>
-struct scalar_clamp_op {
-  EIGEN_DEVICE_FUNC inline scalar_clamp_op(const Scalar& _min, const Scalar& _max) : m_min(_min), m_max(_max) {}
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar
-  operator()(const Scalar& x) const {
-    return numext::mini(numext::maxi(x, m_min), m_max);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
-  packetOp(const Packet& x) const {
-    return internal::pmin(internal::pmax(x, pset1<Packet>(m_min)), pset1<Packet>(m_max));
-  }
-  const Scalar m_min;
-  const Scalar m_max;
-};
-template<typename Scalar>
-struct functor_traits<scalar_clamp_op<Scalar> >
-{ enum { Cost = 2 * NumTraits<Scalar>::AddCost, PacketAccess = (packet_traits<Scalar>::HasMin && packet_traits<Scalar>::HasMax)}; };
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
deleted file mode 100644
index 174bf06..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
+++ /dev/null
@@ -1,302 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
-#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
-
-namespace Eigen {
-
-/** \class TensorGeneratorOp
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor generator class.
-  *
-  *
-  */
-namespace internal {
-template<typename Generator, typename XprType>
-struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename Generator, typename XprType>
-struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
-{
-  typedef const TensorGeneratorOp<Generator, XprType>& type;
-};
-
-template<typename Generator, typename XprType>
-struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type>
-{
-  typedef TensorGeneratorOp<Generator, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename Generator, typename XprType>
-class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
-      : m_xpr(expr), m_generator(generator) {}
-
-    EIGEN_DEVICE_FUNC
-    const Generator& generator() const { return m_generator; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const Generator m_generator;
-};
-
-
-// Eval as rvalue
-template<typename Generator, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
-{
-  typedef TensorGeneratorOp<Generator, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
-  static const int NumDims = internal::array_size<Dimensions>::value;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  enum {
-    IsAligned         = false,
-    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess       = true,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  typedef internal::TensorIntDivisor<Index> IndexDivisor;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      :  m_device(device), m_generator(op.generator())
-  {
-    TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
-    m_dimensions = argImpl.dimensions();
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_strides[0] = 1;
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < NumDims; ++i) {
-        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
-        if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
-      }
-    } else {
-      m_strides[NumDims - 1] = 1;
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
-        if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    return true;
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    array<Index, NumDims> coords;
-    extract_coordinates(index, coords);
-    return m_generator(coords);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    const int packetSize = PacketType<CoeffReturnType, Device>::size;
-    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+packetSize-1 < dimensions().TotalSize());
-
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
-    for (int i = 0; i < packetSize; ++i) {
-      values[i] = coeff(index+i);
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    const size_t target_size = m_device.firstLevelCacheSize();
-    // TODO(ezhulenev): Generator should have a cost.
-    return internal::TensorBlockResourceRequirements::skewed<Scalar>(
-        target_size);
-  }
-
-  struct BlockIteratorState {
-    Index stride;
-    Index span;
-    Index size;
-    Index count;
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    static const bool is_col_major =
-        static_cast<int>(Layout) == static_cast<int>(ColMajor);
-
-    // Compute spatial coordinates for the first block element.
-    array<Index, NumDims> coords;
-    extract_coordinates(desc.offset(), coords);
-    array<Index, NumDims> initial_coords = coords;
-
-    // Offset in the output block buffer.
-    Index offset = 0;
-
-    // Initialize output block iterator state. Dimension in this array are
-    // always in inner_most -> outer_most order (col major layout).
-    array<BlockIteratorState, NumDims> it;
-    for (int i = 0; i < NumDims; ++i) {
-      const int dim = is_col_major ? i : NumDims - 1 - i;
-      it[i].size = desc.dimension(dim);
-      it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
-      it[i].span = it[i].stride * (it[i].size - 1);
-      it[i].count = 0;
-    }
-    eigen_assert(it[0].stride == 1);
-
-    // Prepare storage for the materialized generator result.
-    const typename TensorBlock::Storage block_storage =
-        TensorBlock::prepareStorage(desc, scratch);
-
-    CoeffReturnType* block_buffer = block_storage.data();
-
-    static const int packet_size = PacketType<CoeffReturnType, Device>::size;
-
-    static const int inner_dim = is_col_major ? 0 : NumDims - 1;
-    const Index inner_dim_size = it[0].size;
-    const Index inner_dim_vectorized = inner_dim_size - packet_size;
-
-    while (it[NumDims - 1].count < it[NumDims - 1].size) {
-      Index i = 0;
-      // Generate data for the vectorized part of the inner-most dimension.
-      for (; i <= inner_dim_vectorized; i += packet_size) {
-        for (Index j = 0; j < packet_size; ++j) {
-          array<Index, NumDims> j_coords = coords;  // Break loop dependence.
-          j_coords[inner_dim] += j;
-          *(block_buffer + offset + i + j) = m_generator(j_coords);
-        }
-        coords[inner_dim] += packet_size;
-      }
-      // Finalize non-vectorized part of the inner-most dimension.
-      for (; i < inner_dim_size; ++i) {
-        *(block_buffer + offset + i) = m_generator(coords);
-        coords[inner_dim]++;
-      }
-      coords[inner_dim] = initial_coords[inner_dim];
-
-      // For the 1d tensor we need to generate only one inner-most dimension.
-      if (NumDims == 1) break;
-
-      // Update offset.
-      for (i = 1; i < NumDims; ++i) {
-        if (++it[i].count < it[i].size) {
-          offset += it[i].stride;
-          coords[is_col_major ? i : NumDims - 1 - i]++;
-          break;
-        }
-        if (i != NumDims - 1) it[i].count = 0;
-        coords[is_col_major ? i : NumDims - 1 - i] =
-            initial_coords[is_col_major ? i : NumDims - 1 - i];
-        offset -= it[i].span;
-      }
-    }
-
-    return block_storage.AsTensorMaterializedBlock();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool) const {
-    // TODO(rmlarsen): This is just a placeholder. Define interface to make
-    // generators return their cost.
-    return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() +
-                                  TensorOpCost::MulCost<Scalar>());
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType  data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler&) const {}
-#endif
-
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = index / m_fast_strides[i];
-        index -= idx * m_strides[i];
-        coords[i] = idx;
-      }
-      coords[0] = index;
-    } else {
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = index / m_fast_strides[i];
-        index -= idx * m_strides[i];
-        coords[i] = idx;
-      }
-      coords[NumDims-1] = index;
-    }
-  }
-
-  const Device EIGEN_DEVICE_REF m_device;
-  Dimensions m_dimensions;
-  array<Index, NumDims> m_strides;
-  array<IndexDivisor, NumDims> m_fast_strides;
-  Generator m_generator;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h
deleted file mode 100644
index 665b861..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h
+++ /dev/null
@@ -1,33 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
-#define EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
-
-namespace Eigen {
-
-/** \cpp11 \returns an expression of the coefficient-wise betainc(\a x, \a a, \a b) to the given tensors.
- *
- * This function computes the regularized incomplete beta function (integral).
- *
- */
-template <typename ADerived, typename BDerived, typename XDerived>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const
-    TensorCwiseTernaryOp<internal::scalar_betainc_op<typename XDerived::Scalar>,
-                         const ADerived, const BDerived, const XDerived>
-    betainc(const ADerived& a, const BDerived& b, const XDerived& x) {
-  return TensorCwiseTernaryOp<
-      internal::scalar_betainc_op<typename XDerived::Scalar>, const ADerived,
-      const BDerived, const XDerived>(
-      a, b, x, internal::scalar_betainc_op<typename XDerived::Scalar>());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h
deleted file mode 100644
index cb53ce2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h
+++ /dev/null
@@ -1,99 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2018 Deven Desai <deven.desai.amd@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H)
-#define EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
-
-// Note that we are using EIGEN_USE_HIP here instead of EIGEN_HIPCC...this is by design
-// There is code in the Tensorflow codebase that will define EIGEN_USE_GPU,  but
-// for some reason gets sent to the gcc/host compiler instead of the gpu/nvcc/hipcc compiler
-// When compiling such files, gcc will end up trying to pick up the CUDA headers by 
-// default (see the code within "unsupported/Eigen/CXX11/Tensor" that is guarded by EIGEN_USE_GPU)
-// This will obviously not work when trying to compile tensorflow on a system with no CUDA
-// To work around this issue for HIP systems (and leave the default behaviour intact), the
-// HIP tensorflow build defines EIGEN_USE_HIP when compiling all source files, and 
-// "unsupported/Eigen/CXX11/Tensor" has been updated to use HIP header when EIGEN_USE_HIP is
-// defined. In continuation of that requirement, the guard here needs to be EIGEN_USE_HIP as well
-
-#if defined(EIGEN_USE_HIP)
-
-#define gpuStream_t hipStream_t
-#define gpuDeviceProp_t hipDeviceProp_t
-#define gpuError_t hipError_t
-#define gpuSuccess hipSuccess
-#define gpuErrorNotReady hipErrorNotReady
-#define gpuGetDeviceCount hipGetDeviceCount
-#define gpuGetLastError hipGetLastError
-#define gpuPeekAtLastError hipPeekAtLastError
-#define gpuGetErrorName hipGetErrorName
-#define gpuGetErrorString hipGetErrorString
-#define gpuGetDeviceProperties hipGetDeviceProperties
-#define gpuStreamDefault hipStreamDefault
-#define gpuGetDevice hipGetDevice
-#define gpuSetDevice hipSetDevice
-#define gpuMalloc hipMalloc
-#define gpuFree hipFree
-#define gpuMemsetAsync hipMemsetAsync
-#define gpuMemcpyAsync hipMemcpyAsync
-#define gpuMemcpyDeviceToDevice hipMemcpyDeviceToDevice
-#define gpuMemcpyDeviceToHost hipMemcpyDeviceToHost
-#define gpuMemcpyHostToDevice hipMemcpyHostToDevice
-#define gpuStreamQuery hipStreamQuery
-#define gpuSharedMemConfig hipSharedMemConfig
-#define gpuDeviceSetSharedMemConfig hipDeviceSetSharedMemConfig
-#define gpuStreamSynchronize hipStreamSynchronize
-#define gpuDeviceSynchronize hipDeviceSynchronize
-#define gpuMemcpy hipMemcpy
-
-#else
-
-#define gpuStream_t cudaStream_t
-#define gpuDeviceProp_t cudaDeviceProp
-#define gpuError_t cudaError_t
-#define gpuSuccess cudaSuccess
-#define gpuErrorNotReady cudaErrorNotReady
-#define gpuGetDeviceCount cudaGetDeviceCount
-#define gpuGetLastError cudaGetLastError
-#define gpuPeekAtLastError cudaPeekAtLastError
-#define gpuGetErrorName cudaGetErrorName
-#define gpuGetErrorString cudaGetErrorString
-#define gpuGetDeviceProperties cudaGetDeviceProperties
-#define gpuStreamDefault cudaStreamDefault
-#define gpuGetDevice cudaGetDevice
-#define gpuSetDevice cudaSetDevice
-#define gpuMalloc cudaMalloc
-#define gpuFree cudaFree
-#define gpuMemsetAsync cudaMemsetAsync
-#define gpuMemcpyAsync cudaMemcpyAsync
-#define gpuMemcpyDeviceToDevice cudaMemcpyDeviceToDevice
-#define gpuMemcpyDeviceToHost cudaMemcpyDeviceToHost
-#define gpuMemcpyHostToDevice cudaMemcpyHostToDevice
-#define gpuStreamQuery cudaStreamQuery
-#define gpuSharedMemConfig cudaSharedMemConfig
-#define gpuDeviceSetSharedMemConfig cudaDeviceSetSharedMemConfig
-#define gpuStreamSynchronize cudaStreamSynchronize
-#define gpuDeviceSynchronize cudaDeviceSynchronize
-#define gpuMemcpy cudaMemcpy
-
-#endif
-
-// gpu_assert can be overridden
-#ifndef gpu_assert
-
-#if defined(EIGEN_HIP_DEVICE_COMPILE)
-// HIPCC do not support the use of assert on the GPU side.
-#define gpu_assert(COND)
-#else
-#define gpu_assert(COND) assert(COND)
-#endif
-
-#endif // gpu_assert
-
-#endif  // EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h
deleted file mode 100644
index 1d142f2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h
+++ /dev/null
@@ -1,44 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2018 Deven Desai <deven.desai.amd@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#if defined(EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H)
-
-#ifndef EIGEN_PERMANENTLY_ENABLE_GPU_HIP_CUDA_DEFINES
-
-#undef gpuStream_t
-#undef gpuDeviceProp_t 
-#undef gpuError_t
-#undef gpuSuccess
-#undef gpuErrorNotReady
-#undef gpuGetDeviceCount
-#undef gpuGetErrorString
-#undef gpuGetDeviceProperties
-#undef gpuStreamDefault
-#undef gpuGetDevice
-#undef gpuSetDevice
-#undef gpuMalloc
-#undef gpuFree
-#undef gpuMemsetAsync
-#undef gpuMemcpyAsync
-#undef gpuMemcpyDeviceToDevice
-#undef gpuMemcpyDeviceToHost
-#undef gpuMemcpyHostToDevice
-#undef gpuStreamQuery
-#undef gpuSharedMemConfig
-#undef gpuDeviceSetSharedMemConfig
-#undef gpuStreamSynchronize
-#undef gpuDeviceSynchronize
-#undef gpuMemcpy
-
-#endif // EIGEN_PERMANENTLY_ENABLE_GPU_HIP_CUDA_DEFINES
-
-#undef EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
-
-#endif // EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
deleted file mode 100644
index a901c5d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
+++ /dev/null
@@ -1,79 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_IO_H
-#define EIGEN_CXX11_TENSOR_TENSOR_IO_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Print the tensor as a 2d matrix
-template <typename Tensor, int Rank>
-struct TensorPrinter {
-  static void run (std::ostream& os, const Tensor& tensor) {
-    typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar;
-    typedef typename Tensor::Index Index;
-    const Index total_size = internal::array_prod(tensor.dimensions());
-    if (total_size > 0) {
-      const Index first_dim = Eigen::internal::array_get<0>(tensor.dimensions());
-      static const int layout = Tensor::Layout;
-      Map<const Array<Scalar, Dynamic, Dynamic, layout> > matrix(const_cast<Scalar*>(tensor.data()), first_dim, total_size/first_dim);
-      os << matrix;
-    }
-  }
-};
-
-
-// Print the tensor as a vector
-template <typename Tensor>
-struct TensorPrinter<Tensor, 1> {
-  static void run (std::ostream& os, const Tensor& tensor) {
-    typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar;
-    typedef typename Tensor::Index Index;
-    const Index total_size = internal::array_prod(tensor.dimensions());
-    if (total_size > 0) {
-      Map<const Array<Scalar, Dynamic, 1> > array(const_cast<Scalar*>(tensor.data()), total_size);
-      os << array;
-    }
-  }
-};
-
-
-// Print the tensor as a scalar
-template <typename Tensor>
-struct TensorPrinter<Tensor, 0> {
-  static void run (std::ostream& os, const Tensor& tensor) {
-    os << tensor.coeff(0);
-  }
-};
-}
-
-template <typename T>
-std::ostream& operator << (std::ostream& os, const TensorBase<T, ReadOnlyAccessors>& expr) {
-  typedef TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> Evaluator;
-  typedef typename Evaluator::Dimensions Dimensions;
-
-  // Evaluate the expression if needed
-  TensorForcedEvalOp<const T> eval = expr.eval();
-  Evaluator tensor(eval, DefaultDevice());
-  tensor.evalSubExprsIfNeeded(NULL);
-
-  // Print the result
-  static const int rank = internal::array_size<Dimensions>::value;
-  internal::TensorPrinter<Evaluator, rank>::run(os, tensor);
-
-  // Cleanup.
-  tensor.cleanup();
-  return os;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_IO_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
deleted file mode 100644
index dd51850..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
+++ /dev/null
@@ -1,603 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
-#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
-
-namespace Eigen {
-
-/** \class TensorImagePatch
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Patch extraction specialized for image processing.
-  * This assumes that the input has a least 3 dimensions ordered as follow:
-  *  1st dimension: channels (of size d)
-  *  2nd dimension: rows (of size r)
-  *  3rd dimension: columns (of size c)
-  *  There can be additional dimensions such as time (for video) or batch (for
-  * bulk processing after the first 3.
-  * Calling the image patch code with patch_rows and patch_cols is equivalent
-  * to calling the regular patch extraction code with parameters d, patch_rows,
-  * patch_cols, and 1 for all the additional dimensions.
-  */
-namespace internal {
-
-template<DenseIndex Rows, DenseIndex Cols, typename XprType>
-struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
-{
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions + 1;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<DenseIndex Rows, DenseIndex Cols, typename XprType>
-struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
-{
-  typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
-};
-
-template<DenseIndex Rows, DenseIndex Cols, typename XprType>
-struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type>
-{
-  typedef TensorImagePatchOp<Rows, Cols, XprType> type;
-};
-
-template <typename Self, bool Vectorizable>
-struct ImagePatchCopyOp {
-  typedef typename Self::Index Index;
-  typedef typename Self::Scalar Scalar;
-  typedef typename Self::Impl Impl;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      const Self& self, const Index num_coeff_to_copy, const Index dst_index,
-      Scalar* dst_data, const Index src_index) {
-    const Impl& impl = self.impl();
-    for (Index i = 0; i < num_coeff_to_copy; ++i) {
-      dst_data[dst_index + i] = impl.coeff(src_index + i);
-    }
-  }
-};
-
-template <typename Self>
-struct ImagePatchCopyOp<Self, true> {
-  typedef typename Self::Index Index;
-  typedef typename Self::Scalar Scalar;
-  typedef typename Self::Impl Impl;
-  typedef typename packet_traits<Scalar>::type Packet;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      const Self& self, const Index num_coeff_to_copy, const Index dst_index,
-      Scalar* dst_data, const Index src_index) {
-    const Impl& impl = self.impl();
-    const Index packet_size = internal::unpacket_traits<Packet>::size;
-    const Index vectorized_size =
-        (num_coeff_to_copy / packet_size) * packet_size;
-    for (Index i = 0; i < vectorized_size; i += packet_size) {
-      Packet p = impl.template packet<Unaligned>(src_index + i);
-      internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, p);
-    }
-    for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
-      dst_data[dst_index + i] = impl.coeff(src_index + i);
-    }
-  }
-};
-
-template <typename Self>
-struct ImagePatchPaddingOp {
-  typedef typename Self::Index Index;
-  typedef typename Self::Scalar Scalar;
-  typedef typename packet_traits<Scalar>::type Packet;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
-      const Index num_coeff_to_pad, const Scalar padding_value,
-      const Index dst_index, Scalar* dst_data) {
-    const Index packet_size = internal::unpacket_traits<Packet>::size;
-    const Packet padded_packet = internal::pset1<Packet>(padding_value);
-    const Index vectorized_size =
-        (num_coeff_to_pad / packet_size) * packet_size;
-    for (Index i = 0; i < vectorized_size; i += packet_size) {
-      internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i,
-                                                   padded_packet);
-    }
-    for (Index i = vectorized_size; i < num_coeff_to_pad; ++i) {
-      dst_data[dst_index + i] = padding_value;
-    }
-  }
-};
-
-}  // end namespace internal
-
-template<DenseIndex Rows, DenseIndex Cols, typename XprType>
-class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
-                                                           DenseIndex row_strides, DenseIndex col_strides,
-                                                           DenseIndex in_row_strides, DenseIndex in_col_strides,
-                                                           DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
-                                                           PaddingType padding_type, Scalar padding_value)
-                                                           : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
-                                                           m_row_strides(row_strides), m_col_strides(col_strides),
-                                                           m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
-                                                           m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
-                                                           m_padding_explicit(false), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
-                                                           m_padding_type(padding_type), m_padding_value(padding_value) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
-                                                           DenseIndex row_strides, DenseIndex col_strides,
-                                                           DenseIndex in_row_strides, DenseIndex in_col_strides,
-                                                           DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
-                                                           DenseIndex padding_top, DenseIndex padding_bottom,
-                                                           DenseIndex padding_left, DenseIndex padding_right,
-                                                           Scalar padding_value)
-                                                           : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
-                                                           m_row_strides(row_strides), m_col_strides(col_strides),
-                                                           m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
-                                                           m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
-                                                           m_padding_explicit(true), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
-                                                           m_padding_left(padding_left), m_padding_right(padding_right),
-                                                           m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
-
-
-    EIGEN_DEVICE_FUNC
-    DenseIndex patch_rows() const { return m_patch_rows; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex patch_cols() const { return m_patch_cols; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex row_strides() const { return m_row_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex col_strides() const { return m_col_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex in_row_strides() const { return m_in_row_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex in_col_strides() const { return m_in_col_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
-    EIGEN_DEVICE_FUNC
-    bool padding_explicit() const { return m_padding_explicit; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_top() const { return m_padding_top; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_bottom() const { return m_padding_bottom; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_left() const { return m_padding_left; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_right() const { return m_padding_right; }
-    EIGEN_DEVICE_FUNC
-    PaddingType padding_type() const { return m_padding_type; }
-    EIGEN_DEVICE_FUNC
-    Scalar padding_value() const { return m_padding_value; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const DenseIndex m_patch_rows;
-    const DenseIndex m_patch_cols;
-    const DenseIndex m_row_strides;
-    const DenseIndex m_col_strides;
-    const DenseIndex m_in_row_strides;
-    const DenseIndex m_in_col_strides;
-    const DenseIndex m_row_inflate_strides;
-    const DenseIndex m_col_inflate_strides;
-    const bool m_padding_explicit;
-    const DenseIndex m_padding_top;
-    const DenseIndex m_padding_bottom;
-    const DenseIndex m_padding_left;
-    const DenseIndex m_padding_right;
-    const PaddingType m_padding_type;
-    const Scalar m_padding_value;
-};
-
-// Eval as rvalue
-template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
-{
-  typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  static const int NumDims = NumInputDims + 1;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>,
-                          Device> Self;
-  typedef TensorEvaluator<ArgType, Device> Impl;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = false,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = false,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,
-    RawAccess         = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator( const XprType& op, const Device& device)
-      : m_device(device), m_impl(op.expression(), device)
-  {
-    EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    m_paddingValue = op.padding_value();
-
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-
-    // Caches a few variables.
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputDepth = input_dims[0];
-      m_inputRows = input_dims[1];
-      m_inputCols = input_dims[2];
-    } else {
-      m_inputDepth = input_dims[NumInputDims-1];
-      m_inputRows = input_dims[NumInputDims-2];
-      m_inputCols = input_dims[NumInputDims-3];
-    }
-
-    m_row_strides = op.row_strides();
-    m_col_strides = op.col_strides();
-
-    // Input strides and effective input/patch size
-    m_in_row_strides = op.in_row_strides();
-    m_in_col_strides = op.in_col_strides();
-    m_row_inflate_strides = op.row_inflate_strides();
-    m_col_inflate_strides = op.col_inflate_strides();
-    // The "effective" input rows and input cols are the input rows and cols
-    // after inflating them with zeros.
-    // For examples, a 2x3 matrix with row_inflate_strides and
-    // col_inflate_strides of 2 comes from:
-    //   A B C
-    //   D E F
-    //
-    // to a matrix is 3 x 5:
-    //
-    //   A . B . C
-    //   . . . . .
-    //   D . E . F
-
-    m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
-    m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
-    m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
-    m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
-
-    if (op.padding_explicit()) {
-      m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
-      m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
-      m_rowPaddingTop = op.padding_top();
-      m_colPaddingLeft = op.padding_left();
-    } else {
-      // Computing padding from the type
-      switch (op.padding_type()) {
-        case PADDING_VALID:
-          m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
-          m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
-          // Calculate the padding
-          m_rowPaddingTop = numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
-          m_colPaddingLeft = numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
-          break;
-        case PADDING_SAME:
-          m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
-          m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
-          // Calculate the padding
-          m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
-          m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
-          // The padding size calculation for PADDING_SAME has been updated to
-          // be consistent with how TensorFlow extracts its paddings.
-          m_rowPaddingTop = numext::maxi<Index>(0, m_rowPaddingTop);
-          m_colPaddingLeft = numext::maxi<Index>(0, m_colPaddingLeft);
-          break;
-        default:
-          eigen_assert(false && "unexpected padding");
-          m_outputCols=0; // silence the uninitialised warning;
-          m_outputRows=0; //// silence the uninitialised warning;
-      }
-    }
-    eigen_assert(m_outputRows > 0);
-    eigen_assert(m_outputCols > 0);
-
-    // Dimensions for result of extraction.
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      // ColMajor
-      // 0: depth
-      // 1: patch_rows
-      // 2: patch_cols
-      // 3: number of patches
-      // 4 and beyond: anything else (such as batch).
-      m_dimensions[0] = input_dims[0];
-      m_dimensions[1] = op.patch_rows();
-      m_dimensions[2] = op.patch_cols();
-      m_dimensions[3] = m_outputRows * m_outputCols;
-      for (int i = 4; i < NumDims; ++i) {
-        m_dimensions[i] = input_dims[i-1];
-      }
-    } else {
-      // RowMajor
-      // NumDims-1: depth
-      // NumDims-2: patch_rows
-      // NumDims-3: patch_cols
-      // NumDims-4: number of patches
-      // NumDims-5 and beyond: anything else (such as batch).
-      m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
-      m_dimensions[NumDims-2] = op.patch_rows();
-      m_dimensions[NumDims-3] = op.patch_cols();
-      m_dimensions[NumDims-4] = m_outputRows * m_outputCols;
-      for (int i = NumDims-5; i >= 0; --i) {
-        m_dimensions[i] = input_dims[i];
-      }
-    }
-
-    // Strides for moving the patch in various dimensions.
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_colStride = m_dimensions[1];
-      m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
-      m_otherStride = m_patchStride * m_dimensions[3];
-    } else {
-      m_colStride = m_dimensions[NumDims-2];
-      m_patchStride = m_colStride * m_dimensions[NumDims-3] * m_dimensions[NumDims-1];
-      m_otherStride = m_patchStride * m_dimensions[NumDims-4];
-    }
-
-    // Strides for navigating through the input tensor.
-    m_rowInputStride = m_inputDepth;
-    m_colInputStride = m_inputDepth * m_inputRows;
-    m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols;
-
-    // Fast representations of different variables.
-    m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
-    m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
-    m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
-    m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
-    m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
-    m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
-
-    // Number of patches in the width dimension.
-    m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
-    } else {
-      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    // Patch index corresponding to the passed in index.
-    const Index patchIndex = index / m_fastPatchStride;
-    // Find the offset of the element wrt the location of the first element.
-    const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
-
-    // Other ways to index this element.
-    const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
-    const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
-
-    // Calculate col index in the input original tensor.
-    const Index colIndex = patch2DIndex / m_fastOutputRows;
-    const Index colOffset = patchOffset / m_fastColStride;
-    const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
-    const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0);
-    if (inputCol < 0 || inputCol >= m_input_cols_eff ||
-        ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
-      return Scalar(m_paddingValue);
-    }
-
-    // Calculate row index in the original input tensor.
-    const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
-    const Index rowOffset = patchOffset - colOffset * m_colStride;
-    const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
-    const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0);
-    if (inputRow < 0 || inputRow >= m_input_rows_eff ||
-        ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
-      return Scalar(m_paddingValue);
-    }
-
-    const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
-    const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
-
-    const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
-    return m_impl.coeff(inputIndex);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) {
-      return packetWithPossibleZero(index);
-    }
-
-    const Index indices[2] = {index, index + PacketSize - 1};
-    const Index patchIndex = indices[0] / m_fastPatchStride;
-    if (patchIndex != indices[1] / m_fastPatchStride) {
-      return packetWithPossibleZero(index);
-    }
-    const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
-    eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
-
-    // Find the offset of the element wrt the location of the first element.
-    const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
-                                   (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
-
-    const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
-    eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
-
-    const Index colIndex = patch2DIndex / m_fastOutputRows;
-    const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
-
-    // Calculate col indices in the original input tensor.
-    const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] -
-      m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
-    if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
-      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
-    }
-
-    if (inputCols[0] == inputCols[1]) {
-      const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
-      const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
-      eigen_assert(rowOffsets[0] <= rowOffsets[1]);
-      // Calculate col indices in the original input tensor.
-      const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] -
-        m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
-
-      if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
-        return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
-      }
-
-      if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
-        // no padding
-        const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
-        const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
-        const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
-        return m_impl.template packet<Unaligned>(inputIndex);
-      }
-    }
-
-    return packetWithPossibleZero(index);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    // We conservatively estimate the cost for the code path where the computed
-    // index is inside the original image and
-    // TensorEvaluator<ArgType, Device>::CoordAccess is false.
-    const double compute_cost = 3 * TensorOpCost::DivCost<Index>() +
-                                6 * TensorOpCost::MulCost<Index>() +
-                                8 * TensorOpCost::MulCost<Index>();
-    return m_impl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
-  }
-
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
-  {
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      values[i] = coeff(index+i);
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  Dimensions m_dimensions;
-
-  Index m_otherStride;
-  Index m_patchStride;
-  Index m_colStride;
-  Index m_row_strides;
-  Index m_col_strides;
-
-  Index m_in_row_strides;
-  Index m_in_col_strides;
-  Index m_row_inflate_strides;
-  Index m_col_inflate_strides;
-
-  Index m_input_rows_eff;
-  Index m_input_cols_eff;
-  Index m_patch_rows_eff;
-  Index m_patch_cols_eff;
-
-  internal::TensorIntDivisor<Index> m_fastOtherStride;
-  internal::TensorIntDivisor<Index> m_fastPatchStride;
-  internal::TensorIntDivisor<Index> m_fastColStride;
-  internal::TensorIntDivisor<Index> m_fastInflateRowStride;
-  internal::TensorIntDivisor<Index> m_fastInflateColStride;
-  internal::TensorIntDivisor<Index> m_fastInputColsEff;
-
-  Index m_rowInputStride;
-  Index m_colInputStride;
-  Index m_patchInputStride;
-
-  Index m_inputDepth;
-  Index m_inputRows;
-  Index m_inputCols;
-
-  Index m_outputRows;
-  Index m_outputCols;
-
-  Index m_rowPaddingTop;
-  Index m_colPaddingLeft;
-
-  internal::TensorIntDivisor<Index> m_fastOutputRows;
-  internal::TensorIntDivisor<Index> m_fastOutputDepth;
-
-  Scalar m_paddingValue;
-
-  const Device EIGEN_DEVICE_REF m_device;
-  TensorEvaluator<ArgType, Device> m_impl;
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
deleted file mode 100644
index 2d8c7b9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
+++ /dev/null
@@ -1,738 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
-#define EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
-
-
-#if EIGEN_HAS_CONSTEXPR && EIGEN_HAS_VARIADIC_TEMPLATES
-
-#define EIGEN_HAS_INDEX_LIST
-
-namespace Eigen {
-
-/** \internal
-  *
-  * \class TensorIndexList
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Set of classes used to encode a set of Tensor dimensions/indices.
-  *
-  * The indices in the list can be known at compile time or at runtime. A mix
-  * of static and dynamic indices can also be provided if needed. The tensor
-  * code will attempt to take advantage of the indices that are known at
-  * compile time to optimize the code it generates.
-  *
-  * This functionality requires a c++11 compliant compiler. If your compiler
-  * is older you need to use arrays of indices instead.
-  *
-  * Several examples are provided in the cxx11_tensor_index_list.cpp file.
-  *
-  * \sa Tensor
-  */
-
-template <Index n>
-struct type2index {
-  static const Index value = n;
-  EIGEN_DEVICE_FUNC constexpr operator Index() const { return n; }
-  EIGEN_DEVICE_FUNC void set(Index val) {
-    eigen_assert(val == n);
-  }
-};
-
-// This can be used with IndexPairList to get compile-time constant pairs,
-// such as IndexPairList<type2indexpair<1,2>, type2indexpair<3,4>>().
-template <Index f, Index s>
-struct type2indexpair {
-  static const Index first = f;
-  static const Index second = s;
-
-  constexpr EIGEN_DEVICE_FUNC operator IndexPair<Index>() const {
-    return IndexPair<Index>(f, s);
-  }
-
-  EIGEN_DEVICE_FUNC void set(const IndexPair<Index>& val) {
-    eigen_assert(val.first == f);
-    eigen_assert(val.second == s);
-  }
-};
-
-
-template<Index n> struct NumTraits<type2index<n> >
-{
-  typedef Index Real;
-  enum {
-    IsComplex = 0,
-    RequireInitialization = false,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-
-  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real epsilon() { return 0; }
-  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real dummy_precision() { return 0; }
-  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real highest() { return n; }
-  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real lowest() { return n; }
-};
-
-namespace internal {
-template <typename T>
-EIGEN_DEVICE_FUNC void update_value(T& val, Index new_val) {
-  val = internal::convert_index<T>(new_val);
-}
-template <Index n>
-EIGEN_DEVICE_FUNC void update_value(type2index<n>& val, Index new_val) {
-  val.set(new_val);
-}
-
-template <typename T>
-EIGEN_DEVICE_FUNC void update_value(T& val, IndexPair<Index> new_val) {
-  val = new_val;
-}
-template <Index f, Index s>
-EIGEN_DEVICE_FUNC void update_value(type2indexpair<f, s>& val, IndexPair<Index> new_val) {
-  val.set(new_val);
-}
-
-
-template <typename T>
-struct is_compile_time_constant {
-  static constexpr bool value = false;
-};
-
-template <Index idx>
-struct is_compile_time_constant<type2index<idx> > {
-  static constexpr bool value = true;
-};
-template <Index idx>
-struct is_compile_time_constant<const type2index<idx> > {
-  static constexpr bool value = true;
-};
-template <Index idx>
-struct is_compile_time_constant<type2index<idx>& > {
-  static constexpr bool value = true;
-};
-template <Index idx>
-struct is_compile_time_constant<const type2index<idx>& > {
-  static constexpr bool value = true;
-};
-
-template <Index f, Index s>
-struct is_compile_time_constant<type2indexpair<f, s> > {
-  static constexpr bool value = true;
-};
-template <Index f, Index s>
-struct is_compile_time_constant<const type2indexpair<f, s> > {
-  static constexpr bool value = true;
-};
-template <Index f, Index s>
-struct is_compile_time_constant<type2indexpair<f, s>& > {
-  static constexpr bool value = true;
-};
-template <Index f, Index s>
-struct is_compile_time_constant<const type2indexpair<f, s>& > {
-  static constexpr bool value = true;
-};
-
-
-template<typename... T>
-struct IndexTuple;
-
-template<typename T, typename... O>
-struct IndexTuple<T, O...> {
-  EIGEN_DEVICE_FUNC constexpr IndexTuple() : head(), others() { }
-  EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v, const O... o) : head(v), others(o...) { }
-
-  constexpr static int count = 1 + sizeof...(O);
-  T head;
-  IndexTuple<O...> others;
-  typedef T Head;
-  typedef IndexTuple<O...> Other;
-};
-
-template<typename T>
-  struct IndexTuple<T> {
-  EIGEN_DEVICE_FUNC constexpr IndexTuple() : head() { }
-  EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v) : head(v) { }
-
-  constexpr static int count = 1;
-  T head;
-  typedef T Head;
-};
-
-
-template<int N, typename... T>
-struct IndexTupleExtractor;
-
-template<int N, typename T, typename... O>
-struct IndexTupleExtractor<N, T, O...> {
-
-  typedef typename IndexTupleExtractor<N-1, O...>::ValType ValType;
-
-  EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) {
-    return IndexTupleExtractor<N-1, O...>::get_val(val.others);
-  }
-
-  EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) {
-    return IndexTupleExtractor<N-1, O...>::get_val(val.others);
-  }
-  template <typename V>
-  EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) {
-    IndexTupleExtractor<N-1, O...>::set_val(val.others, new_val);
-  }
-
-};
-
-template<typename T, typename... O>
-  struct IndexTupleExtractor<0, T, O...> {
-
-  typedef T ValType;
-
-  EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) {
-    return val.head;
-  }
-  EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) {
-    return val.head;
-  }
-  template <typename V>
-  EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) {
-    val.head = new_val;
-  }
-};
-
-
-
-template <int N, typename T, typename... O>
-EIGEN_DEVICE_FUNC constexpr typename IndexTupleExtractor<N, T, O...>::ValType& array_get(IndexTuple<T, O...>& tuple) {
-  return IndexTupleExtractor<N, T, O...>::get_val(tuple);
-}
-template <int N, typename T, typename... O>
-EIGEN_DEVICE_FUNC constexpr const typename IndexTupleExtractor<N, T, O...>::ValType& array_get(const IndexTuple<T, O...>& tuple) {
-  return IndexTupleExtractor<N, T, O...>::get_val(tuple);
-}
-template <typename T, typename... O>
-  struct array_size<IndexTuple<T, O...> > {
-  static const size_t value = IndexTuple<T, O...>::count;
-};
-template <typename T, typename... O>
-  struct array_size<const IndexTuple<T, O...> > {
-  static const size_t value = IndexTuple<T, O...>::count;
-};
-
-
-
-
-template <Index Idx, typename ValueT>
-struct tuple_coeff {
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr ValueT get(const Index i, const IndexTuple<T...>& t) {
-    //    return array_get<Idx>(t) * (i == Idx) + tuple_coeff<Idx-1>::get(i, t) * (i != Idx);
-    return (i == Idx ? array_get<Idx>(t) : tuple_coeff<Idx-1, ValueT>::get(i, t));
-  }
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static void set(const Index i, IndexTuple<T...>& t, const ValueT& value) {
-    if (i == Idx) {
-      update_value(array_get<Idx>(t), value);
-    } else {
-      tuple_coeff<Idx-1, ValueT>::set(i, t, value);
-    }
-  }
-
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const Index i, const IndexTuple<T...>& t) {
-    return ((i == Idx) & is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value) ||
-        tuple_coeff<Idx-1, ValueT>::value_known_statically(i, t);
-  }
-
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>& t) {
-    return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
-        tuple_coeff<Idx-1, ValueT>::values_up_to_known_statically(t);
-  }
-
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>& t) {
-    return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
-           is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
-           array_get<Idx>(t) > array_get<Idx-1>(t) &&
-           tuple_coeff<Idx-1, ValueT>::values_up_to_statically_known_to_increase(t);
-  }
-};
-
-template <typename ValueT>
-struct tuple_coeff<0, ValueT> {
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr ValueT get(const Index /*i*/, const IndexTuple<T...>& t) {
-    //  eigen_assert (i == 0);  // gcc fails to compile assertions in constexpr
-    return array_get<0>(t)/* * (i == 0)*/;
-  }
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static void set(const Index i, IndexTuple<T...>& t, const ValueT value) {
-    eigen_assert (i == 0);
-    update_value(array_get<0>(t), value);
-  }
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const Index i, const IndexTuple<T...>&) {
-    return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value && (i == 0);
-  }
-
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>&) {
-    return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value;
-  }
-
-  template <typename... T>
-  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>&) {
-    return true;
-  }
-};
-}  // namespace internal
-
-
-
-template<typename FirstType, typename... OtherTypes>
-struct IndexList : internal::IndexTuple<FirstType, OtherTypes...> {
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Index operator[] (const Index i) const {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::get(i, *this);
-  }
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Index get(const Index i) const {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::get(i, *this);
-  }
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const Index i, const Index value) {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::set(i, *this, value);
-  }
-
-  EIGEN_DEVICE_FUNC constexpr IndexList(const internal::IndexTuple<FirstType, OtherTypes...>& other) : internal::IndexTuple<FirstType, OtherTypes...>(other) { }
-  EIGEN_DEVICE_FUNC constexpr IndexList(FirstType& first, OtherTypes... other) : internal::IndexTuple<FirstType, OtherTypes...>(first, other...) { }
-  EIGEN_DEVICE_FUNC constexpr IndexList() : internal::IndexTuple<FirstType, OtherTypes...>() { }
-
-  EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const Index i) const {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::value_known_statically(i, *this);
-  }
-  EIGEN_DEVICE_FUNC constexpr bool all_values_known_statically() const {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::values_up_to_known_statically(*this);
-  }
-
-  EIGEN_DEVICE_FUNC constexpr bool values_statically_known_to_increase() const {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::values_up_to_statically_known_to_increase(*this);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-std::ostream& operator<<(std::ostream& os,
-                         const IndexList<FirstType, OtherTypes...>& dims) {
-  os << "[";
-  for (size_t i = 0; i < 1 + sizeof...(OtherTypes); ++i) {
-    if (i > 0) os << ", ";
-    os << dims[i];
-  }
-  os << "]";
-  return os;
-}
-
-template<typename FirstType, typename... OtherTypes>
-constexpr IndexList<FirstType, OtherTypes...> make_index_list(FirstType val1, OtherTypes... other_vals) {
-  return IndexList<FirstType, OtherTypes...>(val1, other_vals...);
-}
-
-
-template<typename FirstType, typename... OtherTypes>
-struct IndexPairList : internal::IndexTuple<FirstType, OtherTypes...> {
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr IndexPair<Index> operator[] (const Index i) const {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, IndexPair<Index>>::get(i, *this);
-  }
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const Index i, const IndexPair<Index> value) {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value-1, IndexPair<Index> >::set(i, *this, value);
-  }
-
-  EIGEN_DEVICE_FUNC  constexpr IndexPairList(const internal::IndexTuple<FirstType, OtherTypes...>& other) : internal::IndexTuple<FirstType, OtherTypes...>(other) { }
-  EIGEN_DEVICE_FUNC  constexpr IndexPairList() : internal::IndexTuple<FirstType, OtherTypes...>() { }
-
-  EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const Index i) const {
-    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::value_known_statically(i, *this);
-  }
-};
-
-namespace internal {
-
-template<typename FirstType, typename... OtherTypes>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index array_prod(const IndexList<FirstType, OtherTypes...>& sizes) {
-  Index result = 1;
-  EIGEN_UNROLL_LOOP
-  for (size_t i = 0; i < array_size<IndexList<FirstType, OtherTypes...> >::value; ++i) {
-    result *= sizes[i];
-  }
-  return result;
-}
-
-template<typename FirstType, typename... OtherTypes> struct array_size<IndexList<FirstType, OtherTypes...> > {
-  static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...> >::value;
-};
-template<typename FirstType, typename... OtherTypes> struct array_size<const IndexList<FirstType, OtherTypes...> > {
-  static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...> >::value;
-};
-
-template<typename FirstType, typename... OtherTypes> struct array_size<IndexPairList<FirstType, OtherTypes...> > {
-  static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value;
-};
-template<typename FirstType, typename... OtherTypes> struct array_size<const IndexPairList<FirstType, OtherTypes...> > {
-  static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value;
-};
-
-template<Index N, typename FirstType, typename... OtherTypes> EIGEN_DEVICE_FUNC constexpr Index array_get(IndexList<FirstType, OtherTypes...>& a) {
-  return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a);
-}
-template<Index N, typename FirstType, typename... OtherTypes> EIGEN_DEVICE_FUNC constexpr Index array_get(const IndexList<FirstType, OtherTypes...>& a) {
-  return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a);
-}
-
-template <typename T>
-struct index_known_statically_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index) {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_known_statically_impl<IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_known_statically_impl<const IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
-  }
-};
-
-
-template <typename T>
-struct all_indices_known_statically_impl {
-  static constexpr bool run() {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct all_indices_known_statically_impl<IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct all_indices_known_statically_impl<const IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
-  }
-};
-
-
-template <typename T>
-struct indices_statically_known_to_increase_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-  struct indices_statically_known_to_increase_impl<IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-  struct indices_statically_known_to_increase_impl<const IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run() {
-    return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
-  }
-};
-
-
-template <typename Tx>
-struct index_statically_eq_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_eq_impl<IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) == value);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_eq_impl<const IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) == value);
-  }
-};
-
-
-template <typename T>
-struct index_statically_ne_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_ne_impl<IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) != value);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_ne_impl<const IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) != value);
-  }
-};
-
-
-template <typename T>
-struct index_statically_gt_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_gt_impl<IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) > value);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_gt_impl<const IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) > value);
-  }
-};
-
-
-
-template <typename T>
-struct index_statically_lt_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_lt_impl<IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) < value);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_statically_lt_impl<const IndexList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexList<FirstType, OtherTypes...>().get(i) < value);
-  }
-};
-
-
-
-template <typename Tx>
-struct index_pair_first_statically_eq_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_pair_first_statically_eq_impl<IndexPairList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_pair_first_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value);
-  }
-};
-
-
-
-template <typename Tx>
-struct index_pair_second_statically_eq_impl {
-  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_pair_second_statically_eq_impl<IndexPairList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value);
-  }
-};
-
-template <typename FirstType, typename... OtherTypes>
-struct index_pair_second_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...> > {
-  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
-    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
-        (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value);
-  }
-};
-
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-#else
-
-namespace Eigen {
-namespace internal {
-
-template <typename T>
-struct index_known_statically_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const Index) {
-    return false;
-  }
-};
-
-template <typename T>
-struct all_indices_known_statically_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
-    return false;
-  }
-};
-
-template <typename T>
-struct indices_statically_known_to_increase_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
-    return false;
-  }
-};
-
-template <typename T>
-struct index_statically_eq_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename T>
-struct index_statically_ne_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename T>
-struct index_statically_gt_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename T>
-struct index_statically_lt_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename Tx>
-struct index_pair_first_statically_eq_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
-    return false;
-  }
-};
-
-template <typename Tx>
-struct index_pair_second_statically_eq_impl {
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
-    return false;
-  }
-};
-
-
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-#endif
-
-
-namespace Eigen {
-namespace internal {
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_known_statically(Index i) {
-  return index_known_statically_impl<T>::run(i);
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool all_indices_known_statically() {
-  return all_indices_known_statically_impl<T>::run();
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool indices_statically_known_to_increase() {
-  return indices_statically_known_to_increase_impl<T>::run();
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_eq(Index i, Index value) {
-  return index_statically_eq_impl<T>::run(i, value);
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_ne(Index i, Index value) {
-  return index_statically_ne_impl<T>::run(i, value);
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_gt(Index i, Index value) {
-  return index_statically_gt_impl<T>::run(i, value);
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_lt(Index i, Index value) {
-  return index_statically_lt_impl<T>::run(i, value);
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_pair_first_statically_eq(Index i, Index value) {
-  return index_pair_first_statically_eq_impl<T>::run(i, value);
-}
-
-template <typename T>
-static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_pair_second_statically_eq(Index i, Index value) {
-  return index_pair_second_statically_eq_impl<T>::run(i, value);
-}
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h
deleted file mode 100644
index c5cb61a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h
+++ /dev/null
@@ -1,247 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Ke Yang <yangke@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
-#define EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
-
-namespace Eigen {
-
-/** \class TensorInflation
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor inflation class.
-  *
-  *
-  */
-namespace internal {
-template<typename Strides, typename XprType>
-struct traits<TensorInflationOp<Strides, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename Strides, typename XprType>
-struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense>
-{
-  typedef const TensorInflationOp<Strides, XprType>& type;
-};
-
-template<typename Strides, typename XprType>
-struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type>
-{
-  typedef TensorInflationOp<Strides, XprType> type;
-};
-
-}  // end namespace internal
-
-template<typename Strides, typename XprType>
-class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides)
-      : m_xpr(expr), m_strides(strides) {}
-
-    EIGEN_DEVICE_FUNC
-    const Strides& strides() const { return m_strides; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const Strides m_strides;
-};
-
-// Eval as rvalue
-template<typename Strides, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
-{
-  typedef TensorInflationOp<Strides, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_strides(op.strides())
-  {
-    m_dimensions = m_impl.dimensions();
-    // Expand each dimension to the inflated dimension.
-    for (int i = 0; i < NumDims; ++i) {
-      m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1;
-    }
-
-    // Remember the strides for fast division.
-    for (int i = 0; i < NumDims; ++i) {
-      m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]);
-    }
-
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_outputStrides[0] = 1;
-      m_inputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
-        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
-      }
-    } else {  // RowMajor
-      m_outputStrides[NumDims-1] = 1;
-      m_inputStrides[NumDims-1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
-        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  // Computes the input index given the output index. Returns true if the output
-  // index doesn't fall into a hole.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const
-  {
-    eigen_assert(index < dimensions().TotalSize());
-    *inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = index / m_outputStrides[i];
-        if (idx != idx / m_fastStrides[i] * m_strides[i]) {
-          return false;
-        }
-        *inputIndex += idx / m_strides[i] * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      if (index != index / m_fastStrides[0] * m_strides[0]) {
-        return false;
-      }
-      *inputIndex += index / m_strides[0];
-      return true;
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = index / m_outputStrides[i];
-        if (idx != idx / m_fastStrides[i] * m_strides[i]) {
-          return false;
-        }
-        *inputIndex += idx / m_strides[i] * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      if (index != index / m_fastStrides[NumDims-1] * m_strides[NumDims-1]) {
-        return false;
-      }
-      *inputIndex += index / m_strides[NumDims - 1];
-    }
-    return true;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    Index inputIndex = 0;
-    if (getInputIndex(index, &inputIndex)) {
-     return m_impl.coeff(inputIndex);
-    } else {
-     return Scalar(0);
-    }
-  }
-
-  // TODO(yangke): optimize this function so that we can detect and produce
-  // all-zero packets
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      values[i] = coeff(index+i);
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() +
-                                           3 * TensorOpCost::MulCost<Index>() +
-                                           2 * TensorOpCost::AddCost<Index>());
-    const double input_size = m_impl.dimensions().TotalSize();
-    const double output_size = m_dimensions.TotalSize();
-    if (output_size == 0)
-      return TensorOpCost();
-    return m_impl.costPerCoeff(vectorized) +
-           TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0,
-                        compute_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- protected:
-  Dimensions m_dimensions;
-  array<Index, NumDims> m_outputStrides;
-  array<Index, NumDims> m_inputStrides;
-  TensorEvaluator<ArgType, Device> m_impl;
-  const Strides m_strides;
-  array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h
deleted file mode 100644
index 26a3818..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h
+++ /dev/null
@@ -1,82 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
-#define EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-
-#include <initializer_list>
-
-namespace Eigen {
-
-/** \class TensorInitializer
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Helper template to initialize Tensors from std::initializer_lists.
-  */
-namespace internal {
-
-template <typename Derived, int N>
-struct Initializer {
-  typedef std::initializer_list<
-    typename Initializer<Derived, N - 1>::InitList> InitList;
-
-  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
-                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
-                  const InitList& vals) {
-    int i = 0;
-    for (const auto& v : vals) {
-      (*indices)[traits<Derived>::NumDimensions - N] = i++;
-      Initializer<Derived, N - 1>::run(tensor, indices, v);
-    }
-  }
-};
-
-template <typename Derived>
-struct Initializer<Derived, 1> {
-  typedef std::initializer_list<typename traits<Derived>::Scalar> InitList;
-
-  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
-                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
-                  const InitList& vals) {
-    int i = 0;
-    // There is likely a faster way to do that than iterating.
-    for (const auto& v : vals) {
-      (*indices)[traits<Derived>::NumDimensions - 1] = i++;
-      tensor.coeffRef(*indices) = v;
-    }
-  }
-};
-
-template <typename Derived>
-struct Initializer<Derived, 0> {
-  typedef typename traits<Derived>::Scalar InitList;
-
-  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
-                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>*,
-                  const InitList& v) {
-    tensor.coeffRef(0) = v;
-  }
-};
-
-
-template <typename Derived, int N>
-void initialize_tensor(TensorEvaluator<Derived, DefaultDevice>& tensor,
-                       const typename Initializer<Derived, traits<Derived>::NumDimensions>::InitList& vals) {
-  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions> indices;
-  Initializer<Derived, traits<Derived>::NumDimensions>::run(tensor, &indices, vals);
-}
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_HAS_VARIADIC_TEMPLATES
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h
deleted file mode 100644
index 6d5cce4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h
+++ /dev/null
@@ -1,263 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
-#define EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
-
-
-namespace Eigen {
-
-/** \internal
-  *
-  * \class TensorIntDiv
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Fast integer division by a constant.
-  *
-  * See the paper from Granlund and Montgomery for explanation.
-  *   (at https://doi.org/10.1145/773473.178249)
-  *
-  * \sa Tensor
-  */
-
-namespace internal {
-
-namespace {
-
-  // Note: result is undefined if val == 0
-  template <typename T>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  typename internal::enable_if<sizeof(T)==4,int>::type count_leading_zeros(const T val)
-  {
-#ifdef EIGEN_GPU_COMPILE_PHASE
-    return __clz(val);
-#elif defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::clz(val);
-#elif EIGEN_COMP_MSVC
-    unsigned long index;
-    _BitScanReverse(&index, val);
-    return 31 - index;
-#else
-    EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE);
-    return __builtin_clz(static_cast<uint32_t>(val));
-#endif
-  }
-
-  template <typename T>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  typename internal::enable_if<sizeof(T)==8,int>::type count_leading_zeros(const T val)
-  {
-#ifdef EIGEN_GPU_COMPILE_PHASE
-    return __clzll(val);
-#elif defined(SYCL_DEVICE_ONLY)
-    return static_cast<int>(cl::sycl::clz(val));
-#elif EIGEN_COMP_MSVC && EIGEN_ARCH_x86_64
-    unsigned long index;
-    _BitScanReverse64(&index, val);
-    return 63 - index;
-#elif EIGEN_COMP_MSVC
-    // MSVC's _BitScanReverse64 is not available for 32bits builds.
-    unsigned int lo = (unsigned int)(val&0xffffffff);
-    unsigned int hi = (unsigned int)((val>>32)&0xffffffff);
-    int n;
-    if(hi==0)
-      n = 32 + count_leading_zeros<unsigned int>(lo);
-    else
-      n = count_leading_zeros<unsigned int>(hi);
-    return n;
-#else
-    EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE);
-    return __builtin_clzll(static_cast<uint64_t>(val));
-#endif
-  }
-
-  template <typename T>
-  struct UnsignedTraits {
-    typedef typename conditional<sizeof(T) == 8, uint64_t, uint32_t>::type type;
-  };
-
-  template <typename T>
-  struct DividerTraits {
-    typedef typename UnsignedTraits<T>::type type;
-    static const int N = sizeof(T) * 8;
-  };
-
-  template <typename T>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t muluh(const uint32_t a, const T b) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-    return __umulhi(a, b);
-#elif defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::mul_hi(a, static_cast<uint32_t>(b));
-#else
-    return (static_cast<uint64_t>(a) * b) >> 32;
-#endif
-  }
-
-  template <typename T>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t muluh(const uint64_t a, const T b) {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-    return __umul64hi(a, b);
-#elif defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::mul_hi(a, static_cast<uint64_t>(b));
-#elif EIGEN_HAS_BUILTIN_INT128
-    __uint128_t v = static_cast<__uint128_t>(a) * static_cast<__uint128_t>(b);
-    return static_cast<uint64_t>(v >> 64);
-#else
-    return (TensorUInt128<static_val<0>, uint64_t>(a) * TensorUInt128<static_val<0>, uint64_t>(b)).upper();
-#endif
-  }
-
-  template <int N, typename T>
-  struct DividerHelper {
-    static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t computeMultiplier(const int log_div, const T divider) {
-      EIGEN_STATIC_ASSERT(N == 32, YOU_MADE_A_PROGRAMMING_MISTAKE);
-      return static_cast<uint32_t>((static_cast<uint64_t>(1) << (N+log_div)) / divider - (static_cast<uint64_t>(1) << N) + 1);
-    }
-  };
-
-  template <typename T>
-  struct DividerHelper<64, T> {
-    static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t computeMultiplier(const int log_div, const T divider) {
-#if EIGEN_HAS_BUILTIN_INT128 && !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
-      return static_cast<uint64_t>((static_cast<__uint128_t>(1) << (64+log_div)) / static_cast<__uint128_t>(divider) - (static_cast<__uint128_t>(1) << 64) + 1);
-#else
-      const uint64_t shift = 1ULL << log_div;
-      TensorUInt128<uint64_t, uint64_t> result = TensorUInt128<uint64_t, static_val<0> >(shift, 0) / TensorUInt128<static_val<0>, uint64_t>(divider)
-                                               - TensorUInt128<static_val<1>, static_val<0> >(1, 0)
-                                               + TensorUInt128<static_val<0>, static_val<1> >(1);
-      return static_cast<uint64_t>(result);
-#endif
-    }
-  };
-}
-
-
-template <typename T, bool div_gt_one = false>
-struct TensorIntDivisor {
- public:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() {
-    multiplier = 0;
-    shift1 = 0;
-    shift2 = 0;
-  }
-
-  // Must have 0 < divider < 2^31. This is relaxed to
-  // 0 < divider < 2^63 when using 64-bit indices on platforms that support
-  // the __uint128_t type.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor(const T divider) {
-    const int N = DividerTraits<T>::N;
-    eigen_assert(static_cast<typename UnsignedTraits<T>::type>(divider) < NumTraits<UnsignedType>::highest()/2);
-    eigen_assert(divider > 0);
-
-    // fast ln2
-    const int leading_zeros = count_leading_zeros(static_cast<UnsignedType>(divider));
-    int log_div = N - leading_zeros;
-    // if divider is a power of two then log_div is 1 more than it should be.
-    if ((static_cast<typename UnsignedTraits<T>::type>(1) << (log_div-1)) == static_cast<typename UnsignedTraits<T>::type>(divider))
-      log_div--;
-
-    multiplier = DividerHelper<N, T>::computeMultiplier(log_div, divider);
-    shift1 = log_div > 1 ? 1 : log_div;
-    shift2 = log_div > 1 ? log_div-1 : 0;
-  }
-
-  // Must have 0 <= numerator. On platforms that don't support the __uint128_t
-  // type numerator should also be less than 2^32-1.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T divide(const T numerator) const {
-    eigen_assert(static_cast<typename UnsignedTraits<T>::type>(numerator) < NumTraits<UnsignedType>::highest()/2);
-    //eigen_assert(numerator >= 0); // this is implicitly asserted by the line above
-
-    UnsignedType t1 = muluh(multiplier, numerator);
-    UnsignedType t = (static_cast<UnsignedType>(numerator) - t1) >> shift1;
-    return (t1 + t) >> shift2;
-  }
-
- private:
-  typedef typename DividerTraits<T>::type UnsignedType;
-  UnsignedType multiplier;
-  int32_t shift1;
-  int32_t shift2;
-};
-
-
-// Optimized version for signed 32 bit integers.
-// Derived from Hacker's Delight.
-// Only works for divisors strictly greater than one
-template <>
-class TensorIntDivisor<int32_t, true> {
- public:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() {
-    magic = 0;
-    shift = 0;
-  }
-  // Must have 2 <= divider
-  EIGEN_DEVICE_FUNC TensorIntDivisor(int32_t divider)  {
-    eigen_assert(divider >= 2);
-    calcMagic(divider);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE int divide(const int32_t n) const {
-#ifdef EIGEN_GPU_COMPILE_PHASE
-    return (__umulhi(magic, n) >> shift);
-#elif defined(SYCL_DEVICE_ONLY)
-    return (cl::sycl::mul_hi(magic, static_cast<uint32_t>(n)) >> shift);
-#else
-    uint64_t v = static_cast<uint64_t>(magic) * static_cast<uint64_t>(n);
-    return (static_cast<uint32_t>(v >> 32) >> shift);
-#endif
-  }
-
-private:
-  // Compute the magic numbers. See Hacker's Delight section 10 for an in
-  // depth explanation.
-  EIGEN_DEVICE_FUNC void calcMagic(int32_t d) {
-   const unsigned two31 = 0x80000000;     // 2**31.
-   unsigned ad = d;
-   unsigned t = two31 + (ad >> 31);
-   unsigned anc = t - 1 - t%ad;     // Absolute value of nc.
-   int p = 31;                      // Init. p.
-   unsigned q1 = two31/anc;         // Init. q1 = 2**p/|nc|.
-   unsigned r1 = two31 - q1*anc;    // Init. r1 = rem(2**p, |nc|).
-   unsigned q2 = two31/ad;          // Init. q2 = 2**p/|d|.
-   unsigned r2 = two31 - q2*ad;     // Init. r2 = rem(2**p, |d|).
-   unsigned delta = 0;
-   do {
-      p = p + 1;
-      q1 = 2*q1;           // Update q1 = 2**p/|nc|.
-      r1 = 2*r1;           // Update r1 = rem(2**p, |nc|).
-      if (r1 >= anc) {     // (Must be an unsigned
-         q1 = q1 + 1;      // comparison here).
-         r1 = r1 - anc;}
-      q2 = 2*q2;           // Update q2 = 2**p/|d|.
-      r2 = 2*r2;           // Update r2 = rem(2**p, |d|).
-      if (r2 >= ad) {      // (Must be an unsigned
-         q2 = q2 + 1;      // comparison here).
-         r2 = r2 - ad;}
-      delta = ad - r2;
-   } while (q1 < delta || (q1 == delta && r1 == 0));
-
-   magic = (unsigned)(q2 + 1);
-   shift = p - 32;
-  }
-
-  uint32_t magic;
-  int32_t shift;
-};
-
-
-template <typename T, bool div_gt_one>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator / (const T& numerator, const TensorIntDivisor<T, div_gt_one>& divisor) {
-  return divisor.divide(numerator);
-}
-
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
deleted file mode 100644
index 80106c1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
+++ /dev/null
@@ -1,216 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
-#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
-
-namespace Eigen {
-
-/** \class TensorLayoutSwap
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Swap the layout from col-major to row-major, or row-major
-  * to col-major, and invert the order of the dimensions.
-  *
-  * Beware: the dimensions are reversed by this operation. If you want to
-  * preserve the ordering of the dimensions, you need to combine this
-  * operation with a shuffle.
-  *
-  * \example:
-  * Tensor<float, 2, ColMajor> input(2, 4);
-  * Tensor<float, 2, RowMajor> output = input.swap_layout();
-  * eigen_assert(output.dimension(0) == 4);
-  * eigen_assert(output.dimension(1) == 2);
-  *
-  * array<int, 2> shuffle(1, 0);
-  * output = input.swap_layout().shuffle(shuffle);
-  * eigen_assert(output.dimension(0) == 2);
-  * eigen_assert(output.dimension(1) == 4);
-  *
-  */
-namespace internal {
-template<typename XprType>
-struct traits<TensorLayoutSwapOp<XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = traits<XprType>::NumDimensions;
-  static const int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename XprType>
-struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense>
-{
-  typedef const TensorLayoutSwapOp<XprType>& type;
-};
-
-template<typename XprType>
-struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType> >::type>
-{
-  typedef TensorLayoutSwapOp<XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename XprType>
-class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors>
-{
-  public:
-    typedef TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> Base;
-    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr)
-        : m_xpr(expr) {}
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorLayoutSwapOp)
-  protected:
-    typename XprType::Nested m_xpr;
-};
-
-
-// Eval as rvalue
-template<typename ArgType, typename Device>
-struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
-{
-  typedef TensorLayoutSwapOp<ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  enum {
-    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
-    CoordAccess = false,  // to be implemented
-    RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device)
-  {
-    for(int i = 0; i < NumDims; ++i) {
-      m_dimensions[i] = m_impl.dimensions()[NumDims-1-i];
-    }
-  }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    return m_impl.evalSubExprsIfNeeded(data);
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return m_impl.coeff(index);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return m_impl.template packet<LoadMode>(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    return m_impl.costPerCoeff(vectorized);
-  }
-
-  EIGEN_DEVICE_FUNC typename Storage::Type data() const {
-    return constCast(m_impl.data());
-  }
-
-  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
-
- protected:
-  TensorEvaluator<ArgType, Device> m_impl;
-  Dimensions m_dimensions;
-};
-
-
-// Eval as lvalue
-template<typename ArgType, typename Device>
-  struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device>
-  : public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
-{
-  typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
-  typedef TensorLayoutSwapOp<ArgType> XprType;
-
-  enum {
-    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
-    CoordAccess = false  // to be implemented
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : Base(op, device)
-  { }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
-  {
-    return this->m_impl.coeffRef(index);
-  }
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    this->m_impl.template writePacket<StoreMode>(index, x);
-  }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h
deleted file mode 100644
index 73ff3d2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h
+++ /dev/null
@@ -1,98 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H
-#define EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H
-
-
-/** use this macro in sfinae selection in templated functions
- *
- *   template<typename T,
- *            typename std::enable_if< isBanana<T>::value , int >::type = 0
- *   >
- *   void foo(){}
- *
- *   becomes =>
- *
- *   template<typename TopoType,
- *           SFINAE_ENABLE_IF( isBanana<T>::value )
- *   >
- *   void foo(){}
- */
-
-// SFINAE requires variadic templates
-#if !defined(EIGEN_GPUCC)
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  // SFINAE doesn't work for gcc <= 4.7
-  #ifdef EIGEN_COMP_GNUC
-    #if EIGEN_GNUC_AT_LEAST(4,8)
-      #define EIGEN_HAS_SFINAE
-    #endif
-  #else
-    #define EIGEN_HAS_SFINAE
-  #endif
-#endif
-#endif
-
-#define EIGEN_SFINAE_ENABLE_IF( __condition__ ) \
-    typename internal::enable_if< ( __condition__ ) , int >::type = 0
-
-// Define a macro to use a reference on the host but a value on the device
-#if defined(SYCL_DEVICE_ONLY)
-  #define EIGEN_DEVICE_REF
-#else
-  #define EIGEN_DEVICE_REF &
-#endif
-
-// Define a macro for catching SYCL exceptions if exceptions are enabled
-#define EIGEN_SYCL_TRY_CATCH(X) \
-  do { \
-    EIGEN_TRY {X;} \
-    EIGEN_CATCH(const cl::sycl::exception& e) { \
-      EIGEN_THROW_X(std::runtime_error("SYCL exception at " + \
-                                       std::string(__FILE__) + ":" + \
-                                       std::to_string(__LINE__) + "\n" + \
-                                       e.what())); \
-    } \
-  } while (false)
-
-// Define a macro if local memory flags are unset or one of them is set
-// Setting both flags is the same as unsetting them
-#if (!defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)) || \
-     (defined(EIGEN_SYCL_LOCAL_MEM) &&  defined(EIGEN_SYCL_NO_LOCAL_MEM))
-  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON 1
-  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF 1
-#elif defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)
-  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON 1
-#elif !defined(EIGEN_SYCL_LOCAL_MEM) && defined(EIGEN_SYCL_NO_LOCAL_MEM)
-  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF 1
-#endif
-
-#if EIGEN_COMP_CLANG // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
-  #define EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    using Base::operator =; \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \
-    template <typename OtherDerived> \
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const OtherDerived& other) { Base::operator=(other); return *this; }
-#else
-  #define EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)
-#endif
-
-/** \internal
- * \brief Macro to manually inherit assignment operators.
- * This is necessary, because the implicitly defined assignment operator gets deleted when a custom operator= is defined.
- * This also inherits template<OtherDerived> operator=(const OtherDerived&) assignments.
- * With C++11 or later this also default-implements the copy-constructor
- */
-#define EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(Derived)  \
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
-    EIGEN_DEFAULT_COPY_CONSTRUCTOR(Derived)
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
deleted file mode 100644
index 6834c97..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
+++ /dev/null
@@ -1,327 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_MAP_H
-#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
-
-namespace Eigen {
-
-// FIXME use proper doxygen documentation (e.g. \tparam MakePointer_)
-
-/** \class TensorMap
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief A tensor expression mapping an existing array of data.
-  *
-  */
-/// `template <class> class MakePointer_` is added to convert the host pointer to the device pointer.
-/// It is added due to the fact that for our device compiler `T*` is not allowed.
-/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer `T`.
-/// This is done through our `MakePointer_` class. By default the Type in the `MakePointer_<T>` is `T*` .
-/// Therefore, by adding the default value, we managed to convert the type and it does not break any
-/// existing code as its default value is `T*`.
-template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> >
-{
-  public:
-    typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self;
-    typedef TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > Base;
-  #ifdef EIGEN_USE_SYCL
-    typedef  typename Eigen::internal::remove_reference<typename Eigen::internal::nested<Self>::type>::type Nested;
-  #else
-     typedef typename Eigen::internal::nested<Self>::type Nested;
-  #endif
-   typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
-    typedef typename internal::traits<PlainObjectType>::Index Index;
-    typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef typename PlainObjectType::Base::CoeffReturnType CoeffReturnType;
-
-    typedef typename MakePointer_<Scalar>::Type PointerType;
-    typedef typename MakePointer_<Scalar>::ConstType PointerConstType;
-
-    // WARN: PointerType still can be a pointer to const (const Scalar*), for
-    // example in TensorMap<Tensor<const Scalar, ...>> expression. This type of
-    // expression should be illegal, but adding this restriction is not possible
-    // in practice (see https://bitbucket.org/eigen/eigen/pull-requests/488).
-    typedef typename internal::conditional<
-        bool(internal::is_lvalue<PlainObjectType>::value),
-        PointerType,      // use simple pointer in lvalue expressions
-        PointerConstType  // use const pointer in rvalue expressions
-        >::type StoragePointerType;
-
-    // If TensorMap was constructed over rvalue expression (e.g. const Tensor),
-    // we should return a reference to const from operator() (and others), even
-    // if TensorMap itself is not const.
-    typedef typename internal::conditional<
-        bool(internal::is_lvalue<PlainObjectType>::value),
-        Scalar&,
-        const Scalar&
-        >::type StorageRefType;
-
-    static const int Options = Options_;
-
-    static const Index NumIndices = PlainObjectType::NumIndices;
-    typedef typename PlainObjectType::Dimensions Dimensions;
-
-    enum {
-      IsAligned = ((int(Options_)&Aligned)==Aligned),
-      Layout = PlainObjectType::Layout,
-      CoordAccess = true,
-      RawAccess = true
-    };
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr) : m_data(dataPtr), m_dimensions() {
-      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT((0 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) {
-      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-#else
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(firstDimension) {
-      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
-      EIGEN_STATIC_ASSERT((1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2) : m_data(dataPtr), m_dimensions(dim1, dim2) {
-      EIGEN_STATIC_ASSERT(2 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3) {
-      EIGEN_STATIC_ASSERT(3 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4) {
-      EIGEN_STATIC_ASSERT(4 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4, dim5) {
-      EIGEN_STATIC_ASSERT(5 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    }
-#endif
-
-   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const array<Index, NumIndices>& dimensions)
-      : m_data(dataPtr), m_dimensions(dimensions)
-    { }
-
-    template <typename Dimensions>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const Dimensions& dimensions)
-      : m_data(dataPtr), m_dimensions(dimensions)
-    { }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PlainObjectType& tensor)
-      : m_data(tensor.data()), m_dimensions(tensor.dimensions())
-    { }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StoragePointerType data() { return m_data; }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StoragePointerType data() const { return m_data; }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices) const
-    {
-      //      eigen_assert(checkIndexRange(indices));
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = m_dimensions.IndexOfRowMajor(indices);
-        return m_data[index];
-      } else {
-        const Index index = m_dimensions.IndexOfColMajor(indices);
-        return m_data[index];
-      }
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()() const
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return m_data[0];
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index index) const
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return m_data[index];
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
-    {
-      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
-        return m_data[index];
-      } else {
-        const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
-        return m_data[index];
-      }
-    }
-#else
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1) const
-    {
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = i1 + i0 * m_dimensions[1];
-        return m_data[index];
-      } else {
-        const Index index = i0 + i1 * m_dimensions[0];
-        return m_data[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2) const
-    {
-      if (PlainObjectType::Options&RowMajor) {
-         const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
-         return m_data[index];
-      } else {
-         const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
-        return m_data[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3) const
-    {
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
-        return m_data[index];
-      } else {
-        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
-        return m_data[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
-    {
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
-        return m_data[index];
-      } else {
-        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
-        return m_data[index];
-      }
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices)
-    {
-      //      eigen_assert(checkIndexRange(indices));
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = m_dimensions.IndexOfRowMajor(indices);
-        return m_data[index];
-      } else {
-        const Index index = m_dimensions.IndexOfColMajor(indices);
-        return m_data[index];
-      }
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()()
-    {
-      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
-      return m_data[0];
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index index)
-    {
-      eigen_internal_assert(index >= 0 && index < size());
-      return m_data[index];
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
-    {
-      static_assert(sizeof...(otherIndices) + 2 == NumIndices || NumIndices == Dynamic, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
-       eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
-      const std::size_t NumDims = sizeof...(otherIndices) + 2;
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
-        return m_data[index];
-      } else {
-        const Index index = m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
-        return m_data[index];
-      }
-    }
-#else
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1)
-    {
-       if (PlainObjectType::Options&RowMajor) {
-         const Index index = i1 + i0 * m_dimensions[1];
-        return m_data[index];
-      } else {
-        const Index index = i0 + i1 * m_dimensions[0];
-        return m_data[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2)
-    {
-       if (PlainObjectType::Options&RowMajor) {
-         const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
-        return m_data[index];
-      } else {
-         const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
-        return m_data[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3)
-    {
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
-        return m_data[index];
-      } else {
-        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
-        return m_data[index];
-      }
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
-    {
-      if (PlainObjectType::Options&RowMajor) {
-        const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
-        return m_data[index];
-      } else {
-        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
-        return m_data[index];
-      }
-    }
-#endif
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorMap)
-
-  private:
-    StoragePointerType m_data;
-    Dimensions m_dimensions;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
deleted file mode 100644
index a6181d3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
+++ /dev/null
@@ -1,311 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_H
-#define EIGEN_CXX11_TENSOR_TENSOR_META_H
-
-namespace Eigen {
-
-template<bool cond> struct Cond {};
-
-template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-const T1& choose(Cond<true>, const T1& first, const T2&) {
-  return first;
-}
-
-template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-const T2& choose(Cond<false>, const T1&, const T2& second) {
-  return second;
-}
-
-
-template <typename T, typename X, typename Y>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T divup(const X x, const Y y) {
-  return static_cast<T>((x + y - 1) / y);
-}
-
-template <typename T>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T divup(const T x, const T y) {
-  return static_cast<T>((x + y - 1) / y);
-}
-
-template <size_t n> struct max_n_1 {
-  static const size_t size = n;
-};
-template <> struct max_n_1<0> {
-  static const size_t size = 1;
-};
-
-
-// Default packet types
-template <typename Scalar, typename Device>
-struct PacketType : internal::packet_traits<Scalar> {
-  typedef typename internal::packet_traits<Scalar>::type type;
-};
-
-// For CUDA packet types when using a GpuDevice
-#if defined(EIGEN_USE_GPU) && defined(EIGEN_HAS_GPU_FP16)
-
-typedef ulonglong2 Packet4h2;
-template<>
-struct PacketType<half, GpuDevice> {
-  typedef Packet4h2 type;
-  static const int size = 8;
-  enum {
-    HasAdd    = 1,
-    HasSub    = 1,
-    HasMul    = 1,
-    HasNegate = 1,
-    HasAbs    = 1,
-    HasArg    = 0,
-    HasAbs2   = 0,
-    HasMin    = 1,
-    HasMax    = 1,
-    HasConj   = 0,
-    HasSetLinear = 0,
-    HasBlend  = 0,
-
-    HasDiv    = 1,
-    HasSqrt   = 1,
-    HasRsqrt  = 1,
-    HasExp    = 1,
-    HasExpm1  = 0,
-    HasLog    = 1,
-    HasLog1p  = 0,
-    HasLog10  = 0,
-    HasPow    = 1,
-  };
-};
-#endif
-
-#if defined(EIGEN_USE_SYCL)
-
-namespace TensorSycl {
-namespace internal {
-
-template <typename Index, Index A, Index B> struct PlusOp {
-  static constexpr Index Value = A + B;
-};
-
-template <typename Index, Index A, Index B> struct DivOp {
-  static constexpr Index Value = A / B;
-};
-
-template <typename Index, Index start, Index end, Index step,
-          template <class Indx, Indx...> class StepOp>
-struct static_for {
-  template <typename UnaryOperator>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void loop(UnaryOperator op) {
-    op(start);
-    static_for<Index, StepOp<Index, start, step>::Value, end, step,
-               StepOp>::loop(op);
-  }
-};
-template <typename Index, Index end, Index step,
-          template <class Indx, Indx...> class StepOp>
-struct static_for<Index, end, end, step, StepOp> {
-  template <typename UnaryOperator>
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void loop(UnaryOperator) {}
-};
-
-template <typename OutScalar, typename Device, bool Vectorizable>
-struct Vectorise {
-  static const int PacketSize = 1;
-  typedef OutScalar PacketReturnType;
-};
-
-template <typename OutScalar, typename Device>
-struct Vectorise<OutScalar, Device, true> {
-  static const int PacketSize = Eigen::PacketType<OutScalar, Device>::size;
-  typedef typename Eigen::PacketType<OutScalar, Device>::type PacketReturnType;
-};
-
-static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index roundUp(Index x, Index y) {
-  return ((((x) + (y)-1) / (y)) * (y));
-}
-
-} // namespace internal
-} // namespace TensorSycl
-
-template <>
-  struct PacketType<half, SyclDevice> {
-  typedef half type;
-  static const int size = 1;
-  enum {
-    HasAdd    = 0,
-    HasSub    = 0,
-    HasMul    = 0,
-    HasNegate = 0,
-    HasAbs    = 0,
-    HasArg    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasConj   = 0,
-    HasSetLinear = 0,
-    HasBlend  = 0
-  };
-};
-template <typename Scalar>
-struct PacketType<Scalar, SyclDevice> : internal::default_packet_traits {
-  typedef Scalar type;
-  typedef Scalar half;
-  enum {
-    Vectorizable = 0,
-    size = 1,
-    AlignedOnScalar = 0,
-    HasHalfPacket = 0
-  };
-  enum {
-    HasAdd    = 0,
-    HasSub    = 0,
-    HasMul    = 0,
-    HasNegate = 0,
-    HasAbs    = 0,
-    HasAbs2   = 0,
-    HasMin    = 0,
-    HasMax    = 0,
-    HasConj   = 0,
-    HasSetLinear = 0
-  };
-
-};
-
-template <typename Scalar>
-struct PacketType<Scalar, const SyclDevice> : PacketType<Scalar, SyclDevice>{};
-
-#ifndef EIGEN_DONT_VECTORIZE_SYCL
-#define PACKET_TYPE(CVQual, Type, val, lengths, DEV)\
-template<> struct PacketType<CVQual Type, DEV> : internal::sycl_packet_traits<val, lengths> \
-{\
-  typedef typename internal::packet_traits<Type>::type type;\
-  typedef typename internal::packet_traits<Type>::half half;\
-};
-
-
-PACKET_TYPE(const, float, 1, 4, SyclDevice)
-PACKET_TYPE(, float, 1, 4, SyclDevice)
-PACKET_TYPE(const, float, 1, 4, const SyclDevice)
-PACKET_TYPE(, float, 1, 4, const SyclDevice)
-
-PACKET_TYPE(const, double, 0, 2, SyclDevice)
-PACKET_TYPE(, double, 0, 2, SyclDevice)
-PACKET_TYPE(const, double, 0, 2, const SyclDevice)
-PACKET_TYPE(, double, 0, 2, const SyclDevice)
-#undef PACKET_TYPE
-
-template<> struct PacketType<half, const SyclDevice>: PacketType<half, SyclDevice>{};
-template<> struct PacketType<const half, const SyclDevice>: PacketType<half, SyclDevice>{};
-#endif
-#endif
-
-// Tuple mimics std::pair but works on e.g. nvcc.
-template <typename U, typename V> struct Tuple {
- public:
-  U first;
-  V second;
-
-  typedef U first_type;
-  typedef V second_type;
-
-  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Tuple() : first(), second() {}
-
-  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Tuple(const U& f, const V& s) : first(f), second(s) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void swap(Tuple& rhs) {
-    using numext::swap;
-    swap(first, rhs.first);
-    swap(second, rhs.second);
-  }
-};
-
-template <typename U, typename V>
-EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-bool operator==(const Tuple<U, V>& x, const Tuple<U, V>& y) {
-  return (x.first == y.first && x.second == y.second);
-}
-
-template <typename U, typename V>
-EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-bool operator!=(const Tuple<U, V>& x, const Tuple<U, V>& y) {
-  return !(x == y);
-}
-
-
-// Can't use std::pairs on cuda devices
-template <typename Idx> struct IndexPair {
-  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair() : first(0), second(0) {}
-  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair(Idx f, Idx s) : first(f), second(s) {}
-
-  EIGEN_DEVICE_FUNC void set(IndexPair<Idx> val) {
-    first = val.first;
-    second = val.second;
-  }
-
-  Idx first;
-  Idx second;
-};
-
-
-#ifdef EIGEN_HAS_SFINAE
-namespace internal {
-
-  template<typename IndexType, typename Index, Index... Is>
-  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  array<Index, sizeof...(Is)> customIndices2Array(IndexType& idx, numeric_list<Index, Is...>) {
-    return { idx[Is]... };
-  }
-  template<typename IndexType, typename Index>
-  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  array<Index, 0> customIndices2Array(IndexType&, numeric_list<Index>) {
-    return array<Index, 0>();
-  }
-
-  /** Make an array (for index/dimensions) out of a custom index */
-  template<typename Index, std::size_t NumIndices, typename IndexType>
-  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  array<Index, NumIndices> customIndices2Array(IndexType& idx) {
-    return customIndices2Array(idx, typename gen_numeric_list<Index, NumIndices>::type{});
-  }
-
-
-  template <typename B, typename D>
-  struct is_base_of
-  {
-
-    typedef char (&yes)[1];
-    typedef char (&no)[2];
-
-    template <typename BB, typename DD>
-    struct Host
-    {
-      operator BB*() const;
-      operator DD*();
-    };
-
-    template<typename T>
-    static yes check(D*, T);
-    static no check(B*, int);
-
-    static const bool value = sizeof(check(Host<B,D>(), int())) == sizeof(yes);
-  };
-
-}
-#endif
-
-
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_META_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
deleted file mode 100644
index b3f00f7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
+++ /dev/null
@@ -1,1102 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
-#define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
-
-namespace Eigen {
-
-/** \class TensorReshaping
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor reshaping class.
-  *
-  *
-  */
-namespace internal {
-template<typename NewDimensions, typename XprType>
-struct traits<TensorReshapingOp<NewDimensions, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = array_size<NewDimensions>::value;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename NewDimensions, typename XprType>
-struct eval<TensorReshapingOp<NewDimensions, XprType>, Eigen::Dense>
-{
-  typedef const TensorReshapingOp<NewDimensions, XprType>EIGEN_DEVICE_REF type;
-};
-
-template<typename NewDimensions, typename XprType>
-struct nested<TensorReshapingOp<NewDimensions, XprType>, 1, typename eval<TensorReshapingOp<NewDimensions, XprType> >::type>
-{
-  typedef TensorReshapingOp<NewDimensions, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename NewDimensions, typename XprType>
-class TensorReshapingOp : public TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors>
-{
-  public:
-  typedef TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors> Base;
-  typedef typename Eigen::internal::traits<TensorReshapingOp>::Scalar Scalar;
-  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorReshapingOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorReshapingOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorReshapingOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType& expr, const NewDimensions& dims)
-      : m_xpr(expr), m_dims(dims) {}
-
-    EIGEN_DEVICE_FUNC
-    const NewDimensions& dimensions() const { return m_dims; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorReshapingOp)
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const NewDimensions m_dims;
-};
-
-
-// Eval as rvalue
-template<typename NewDimensions, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
-{
-  typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
-  typedef NewDimensions Dimensions;
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  typedef StorageMemory<typename internal::remove_const<CoeffReturnType>::type, Device> ConstCastStorage;
-
-  static const int NumOutputDims = internal::array_size<Dimensions>::value;
-  static const int NumInputDims  = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-
-  enum ReshapingKind {
-    // We do not use layout information to determine reshaping kind.
-    // Depending on the layout `N` can be inner or outer dimension.
-    OneByN = 0,  // expr.reshape(1, N)
-    NByOne = 1,  // expr.reshape(N, 1)
-    Runtime = 2  // Reshape dimensions are dynamic (specified at runtime).
-  };
-
-  // clang-format off
-  static const ReshapingKind kind =
-#if defined(EIGEN_HAS_INDEX_LIST)
-        (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(/*index=*/0, /*value=*/1)) ? OneByN
-      : (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(/*index=*/1, /*value=*/1)) ? NByOne
-      : Runtime;
-#else
-        Runtime;
-#endif
-  // clang-format on
-
-  enum {
-    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    // For trivial reshapes with raw access to underlying data we will provide
-    // zero overhead block access.
-    // TODO(ezhulenev): Consider adding block access without raw access?
-    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess &&
-                        NumInputDims > 0 && NumOutputDims > 0,
-    PreferBlockAccess = false,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = TensorEvaluator<ArgType, Device>::RawAccess
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumOutputDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef
-      typename internal::TensorMaterializedBlock<ScalarNoConst, NumOutputDims,
-                                                 Layout, Index>
-          TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_dimensions(op.dimensions())
-  {
-    // The total size of the reshaped tensor must be equal to the total size
-    // of the input tensor.
-    eigen_assert(internal::array_prod(m_impl.dimensions()) == internal::array_prod(op.dimensions()));
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType data, EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(data, std::move(done));
-  }
-#endif
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    return m_impl.evalSubExprsIfNeeded(data);
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return m_impl.coeff(index);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    return m_impl.template packet<LoadMode>(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    return m_impl.costPerCoeff(vectorized);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    return internal::TensorBlockResourceRequirements::any();
-  }
-
-  // required in block(OutputTensorBlock* output_block) const
-  // For C++03 compatibility this must be defined outside the method
-  struct BlockIteratorState {
-    Index stride;
-    Index span;
-    Index size;
-    Index count;
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    eigen_assert(m_impl.data() != NULL);
-    eigen_assert((kind == Runtime) ||
-                 (kind == OneByN && desc.dimensions()[0] == 1) ||
-                 (kind == NByOne && desc.dimensions()[1] == 1));
-
-    if (kind == OneByN || kind == NByOne) {
-      // We can guarantee at compile time that block is just a contiguous slice
-      // of the underlying expression memory buffer.
-      return TensorBlock(internal::TensorBlockKind::kView,
-                           m_impl.data() + desc.offset(), desc.dimensions());
-    } else {
-      // This will do additional runtime checks, and in the end it might be also
-      // a view, or it might be a block materialized in the temporary buffer.
-      return TensorBlock::materialize(m_impl.data(), m_dimensions, desc,
-                                        scratch);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC typename Storage::Type data() const {
-    return constCast(m_impl.data());
-  }
-
-  EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
-
-  #ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-  #endif
- protected:
-  TensorEvaluator<ArgType, Device> m_impl;
-  NewDimensions m_dimensions;
-};
-
-
-// Eval as lvalue
-template<typename NewDimensions, typename ArgType, typename Device>
-  struct TensorEvaluator<TensorReshapingOp<NewDimensions, ArgType>, Device>
-  : public TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
-
-{
-  typedef TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> Base;
-  typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
-  typedef NewDimensions Dimensions;
-
-  enum {
-    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
-    PreferBlockAccess = false,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = TensorEvaluator<ArgType, Device>::RawAccess
-  };
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : Base(op, device)
-  { }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<TensorEvaluator::NumOutputDims, Index>
-      TensorBlockDesc;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
-  {
-    return this->m_impl.coeffRef(index);
-  }
-
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    this->m_impl.template writePacket<StoreMode>(index, x);
-  }
-
-  template <typename TensorBlock>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
-      const TensorBlockDesc& desc, const TensorBlock& block) {
-    assert(this->m_impl.data() != NULL);
-
-    typedef typename TensorBlock::XprType TensorBlockExpr;
-    typedef internal::TensorBlockAssignment<
-        Scalar, TensorEvaluator::NumOutputDims, TensorBlockExpr, Index>
-        TensorBlockAssign;
-
-    TensorBlockAssign::Run(
-        TensorBlockAssign::target(desc.dimensions(),
-                                  internal::strides<Layout>(this->dimensions()),
-                                  this->m_impl.data(), desc.offset()),
-        block.expr());
-  }
-};
-
-
-/** \class TensorSlicing
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor slicing class.
-  *
-  *
-  */
-namespace internal {
-template<typename StartIndices, typename Sizes, typename XprType>
-struct traits<TensorSlicingOp<StartIndices, Sizes, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = array_size<StartIndices>::value;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename StartIndices, typename Sizes, typename XprType>
-struct eval<TensorSlicingOp<StartIndices, Sizes, XprType>, Eigen::Dense>
-{
-  typedef const TensorSlicingOp<StartIndices, Sizes, XprType>EIGEN_DEVICE_REF type;
-};
-
-template<typename StartIndices, typename Sizes, typename XprType>
-struct nested<TensorSlicingOp<StartIndices, Sizes, XprType>, 1, typename eval<TensorSlicingOp<StartIndices, Sizes, XprType> >::type>
-{
-  typedef TensorSlicingOp<StartIndices, Sizes, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename StartIndices, typename Sizes, typename XprType>
-class TensorSlicingOp : public TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType> >
-{
-  public:
-  typedef TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType> > Base;
-  typedef typename Eigen::internal::traits<TensorSlicingOp>::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorSlicingOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorSlicingOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorSlicingOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSlicingOp(const XprType& expr, const StartIndices& indices, const Sizes& sizes)
-      : m_xpr(expr), m_indices(indices), m_sizes(sizes) {}
-
-    EIGEN_DEVICE_FUNC
-    const StartIndices& startIndices() const { return m_indices; }
-    EIGEN_DEVICE_FUNC
-    const Sizes& sizes() const { return m_sizes; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorSlicingOp)
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const StartIndices m_indices;
-    const Sizes m_sizes;
-};
-
-
-// Fixme: figure out the exact threshold
-namespace {
-template <typename Index, typename Device, bool BlockAccess> struct MemcpyTriggerForSlicing {
-  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const Device& device) : threshold_(2 * device.numThreads()) { }
-  EIGEN_DEVICE_FUNC bool operator ()(Index total, Index contiguous) const {
-    const bool prefer_block_evaluation = BlockAccess && total > 32*1024;
-    return !prefer_block_evaluation && contiguous > threshold_;
-  }
-
- private:
-  Index threshold_;
-};
-
-// It is very expensive to start the memcpy kernel on GPU: we therefore only
-// use it for large copies.
-#ifdef EIGEN_USE_GPU
-template <typename Index, bool BlockAccess> struct MemcpyTriggerForSlicing<Index, GpuDevice, BlockAccess>  {
-  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const GpuDevice&) { }
-  EIGEN_DEVICE_FUNC bool operator ()(Index, Index contiguous) const { return contiguous > 4*1024*1024; }
-};
-#endif
-
-// It is very expensive to start the memcpy kernel on GPU: we therefore only
-// use it for large copies.
-#ifdef EIGEN_USE_SYCL
-template <typename Index, bool BlockAccess> struct MemcpyTriggerForSlicing<Index, Eigen::SyclDevice, BlockAccess>  {
-  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const SyclDevice&) { }
-  EIGEN_DEVICE_FUNC bool operator ()(Index, Index contiguous) const { return contiguous > 4*1024*1024; }
-};
-#endif
-
-}
-
-// Eval as rvalue
-template<typename StartIndices, typename Sizes, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
-{
-  typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
-  static const int NumDims = internal::array_size<Sizes>::value;
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef Sizes Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef StorageMemory<typename internal::remove_const<CoeffReturnType>::type, Device> ConstCastStorage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    // Alignment can't be guaranteed at compile time since it depends on the
-    // slice offsets and sizes.
-    IsAligned         = false,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess &&
-                        // FIXME: Temporary workaround for bug in slicing of bool tensors.
-                        !internal::is_same<typename internal::remove_const<Scalar>::type, bool>::value,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,
-    RawAccess         = false
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  // Tensor slicing does not change the block type.
-  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_device(device), m_dimensions(op.sizes()), m_offsets(op.startIndices())
-  {
-    m_is_identity = true;
-    for (int i = 0; i < internal::array_size<Dimensions>::value; ++i) {
-      eigen_assert(m_impl.dimensions()[i] >=
-                   op.sizes()[i] + op.startIndices()[i]);
-      if (m_impl.dimensions()[i] != op.sizes()[i] ||
-          op.startIndices()[i] != 0) {
-        m_is_identity = false;
-      }
-    }
-
-    // No strides for scalars.
-    if (NumDims == 0) return;
-
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    const Sizes& output_dims = op.sizes();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
-      }
-
-     // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed.
-      m_outputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1];
-        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
-      }
-    } else {
-      m_inputStrides[NumDims-1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
-      }
-
-     // Don't initialize m_fastOutputStrides[NumDims-1] since it won't ever be accessed.
-      m_outputStrides[NumDims-1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1];
-        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization
-        && data && m_impl.data()) {
-      Index contiguous_values = 1;
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        for (int i = 0; i < NumDims; ++i) {
-          contiguous_values *= dimensions()[i];
-          if (dimensions()[i] != m_impl.dimensions()[i]) {
-            break;
-          }
-        }
-      } else {
-        for (int i = NumDims-1; i >= 0; --i) {
-          contiguous_values *= dimensions()[i];
-          if (dimensions()[i] != m_impl.dimensions()[i]) {
-            break;
-          }
-        }
-      }
-      // Use memcpy if it's going to be faster than using the regular evaluation.
-      const MemcpyTriggerForSlicing<Index, Device, BlockAccess> trigger(m_device);
-      if (trigger(internal::array_prod(dimensions()), contiguous_values)) {
-        EvaluatorPointerType src = (EvaluatorPointerType)m_impl.data();
-        for (Index i = 0; i < internal::array_prod(dimensions()); i += contiguous_values) {
-          Index offset = srcCoeff(i);
-          m_device.memcpy((void*)(m_device.get(data + i)), m_device.get(src+offset), contiguous_values * sizeof(Scalar));
-        }
-        return false;
-      }
-    }
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType /*data*/, EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    if (m_is_identity) {
-      return m_impl.coeff(index);
-    } else {
-      return m_impl.coeff(srcCoeff(index));
-    }
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    const int packetSize = PacketType<CoeffReturnType, Device>::size;
-    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+packetSize-1 < internal::array_prod(dimensions()));
-
-    if (m_is_identity) {
-      return m_impl.template packet<LoadMode>(index);
-    }
-
-    Index inputIndices[] = {0, 0};
-    Index indices[] = {index, index + packetSize - 1};
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx0 = indices[0] / m_fastOutputStrides[i];
-        const Index idx1 = indices[1] / m_fastOutputStrides[i];
-        inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
-        inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
-        indices[0] -= idx0 * m_outputStrides[i];
-        indices[1] -= idx1 * m_outputStrides[i];
-      }
-      inputIndices[0] += (indices[0] + m_offsets[0]);
-      inputIndices[1] += (indices[1] + m_offsets[0]);
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx0 = indices[0] / m_fastOutputStrides[i];
-        const Index idx1 = indices[1] / m_fastOutputStrides[i];
-        inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
-        inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
-        indices[0] -= idx0 * m_outputStrides[i];
-        indices[1] -= idx1 * m_outputStrides[i];
-      }
-      inputIndices[0] += (indices[0] + m_offsets[NumDims-1]);
-      inputIndices[1] += (indices[1] + m_offsets[NumDims-1]);
-    }
-    if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
-      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
-      return rslt;
-    }
-    else {
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
-      values[0] = m_impl.coeff(inputIndices[0]);
-      values[packetSize-1] = m_impl.coeff(inputIndices[1]);
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < packetSize-1; ++i) {
-        values[i] = coeff(index+i);
-      }
-      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-      return rslt;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    const size_t target_size = m_device.lastLevelCacheSize();
-    return internal::TensorBlockResourceRequirements::merge(
-        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
-        m_impl.getResourceRequirements());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    TensorBlockDesc arg_desc = desc.WithOffset(srcCoeff(desc.offset()));
-    TensorBlock block = m_impl.block(arg_desc, scratch);
-    if (!arg_desc.HasDestinationBuffer()) desc.DropDestinationBuffer();
-    return block;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
-    typename Storage::Type result = constCast(m_impl.data());
-    if (result) {
-      Index offset = 0;
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        for (int i = 0; i < NumDims; ++i) {
-          if (m_dimensions[i] != m_impl.dimensions()[i]) {
-            offset += m_offsets[i] * m_inputStrides[i];
-            for (int j = i+1; j < NumDims; ++j) {
-              if (m_dimensions[j] > 1) {
-                return NULL;
-              }
-              offset += m_offsets[j] * m_inputStrides[j];
-            }
-            break;
-          }
-        }
-      } else {
-        for (int i = NumDims - 1; i >= 0; --i) {
-          if (m_dimensions[i] != m_impl.dimensions()[i]) {
-            offset += m_offsets[i] * m_inputStrides[i];
-            for (int j = i-1; j >= 0; --j) {
-              if (m_dimensions[j] > 1) {
-                return NULL;
-              }
-              offset += m_offsets[j] * m_inputStrides[j];
-            }
-            break;
-          }
-        }
-      }
-      return result + offset;
-    }
-    return NULL;
-  }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
-  {
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = index / m_fastOutputStrides[i];
-        inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      inputIndex += (index + m_offsets[0]);
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = index / m_fastOutputStrides[i];
-        inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      inputIndex += (index + m_offsets[NumDims-1]);
-    }
-    return inputIndex;
-  }
-
-  array<Index, NumDims> m_outputStrides;
-  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
-  array<Index, NumDims> m_inputStrides;
-  TensorEvaluator<ArgType, Device> m_impl;
-  const Device EIGEN_DEVICE_REF m_device;
-  Dimensions m_dimensions;
-  bool m_is_identity;
-  const StartIndices m_offsets;
-};
-
-
-// Eval as lvalue
-template<typename StartIndices, typename Sizes, typename ArgType, typename Device>
-struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
-  : public TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
-{
-  typedef TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> Base;
-  typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
-  static const int NumDims = internal::array_size<Sizes>::value;
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef Sizes Dimensions;
-
-  enum {
-    IsAligned         = false,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,
-    RawAccess         = (NumDims == 1) & TensorEvaluator<ArgType, Device>::RawAccess
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : Base(op, device)
-    { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
-  {
-    if (this->m_is_identity) {
-      return this->m_impl.coeffRef(index);
-    } else {
-      return this->m_impl.coeffRef(this->srcCoeff(index));
-    }
-  }
-
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    if (this->m_is_identity) {
-      this->m_impl.template writePacket<StoreMode>(index, x);
-      return;
-    }
-
-    const int packetSize = PacketType<CoeffReturnType, Device>::size;
-    Index inputIndices[] = {0, 0};
-    Index indices[] = {index, index + packetSize - 1};
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
-        const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
-        inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
-        inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
-        indices[0] -= idx0 * this->m_outputStrides[i];
-        indices[1] -= idx1 * this->m_outputStrides[i];
-      }
-      inputIndices[0] += (indices[0] + this->m_offsets[0]);
-      inputIndices[1] += (indices[1] + this->m_offsets[0]);
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
-        const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
-        inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
-        inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
-        indices[0] -= idx0 * this->m_outputStrides[i];
-        indices[1] -= idx1 * this->m_outputStrides[i];
-      }
-      inputIndices[0] += (indices[0] + this->m_offsets[NumDims-1]);
-      inputIndices[1] += (indices[1] + this->m_offsets[NumDims-1]);
-    }
-    if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
-      this->m_impl.template writePacket<StoreMode>(inputIndices[0], x);
-    }
-    else {
-      EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
-      internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
-      this->m_impl.coeffRef(inputIndices[0]) = values[0];
-      this->m_impl.coeffRef(inputIndices[1]) = values[packetSize-1];
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < packetSize-1; ++i) {
-        this->coeffRef(index+i) = values[i];
-      }
-    }
-  }
-
-  template<typename TensorBlock>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
-      const TensorBlockDesc& desc, const TensorBlock& block) {
-    TensorBlockDesc arg_desc = desc.WithOffset(this->srcCoeff(desc.offset()));
-    this->m_impl.writeBlock(arg_desc, block);
-  }
-};
-
-namespace internal {
-template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
-struct traits<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = array_size<StartIndices>::value;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
-struct eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, Eigen::Dense>
-{
-  typedef const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>EIGEN_DEVICE_REF type;
-};
-
-template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
-struct nested<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, 1, typename eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >::type>
-{
-  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> type;
-};
-
-}  // end namespace internal
-
-
-template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
-class TensorStridingSlicingOp : public TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >
-{
-  public:
-  typedef TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > Base;
-  typedef typename internal::traits<TensorStridingSlicingOp>::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename internal::nested<TensorStridingSlicingOp>::type Nested;
-  typedef typename internal::traits<TensorStridingSlicingOp>::StorageKind StorageKind;
-  typedef typename internal::traits<TensorStridingSlicingOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingSlicingOp(
-    const XprType& expr, const StartIndices& startIndices,
-    const StopIndices& stopIndices, const Strides& strides)
-      : m_xpr(expr), m_startIndices(startIndices), m_stopIndices(stopIndices),
-        m_strides(strides) {}
-
-    EIGEN_DEVICE_FUNC
-    const StartIndices& startIndices() const { return m_startIndices; }
-    EIGEN_DEVICE_FUNC
-    const StartIndices& stopIndices() const { return m_stopIndices; }
-    EIGEN_DEVICE_FUNC
-    const StartIndices& strides() const { return m_strides; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorStridingSlicingOp)
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const StartIndices m_startIndices;
-    const StopIndices m_stopIndices;
-    const Strides m_strides;
-};
-
-// Eval as rvalue
-template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
-{
-  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType;
-  static const int NumDims = internal::array_size<Strides>::value;
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  typedef Strides Dimensions;
-
-  enum {
-    // Alignment can't be guaranteed at compile time since it depends on the
-    // slice offsets and sizes.
-    IsAligned = false,
-    PacketAccess = false,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device),
-        m_device(device),
-        m_strides(op.strides())
-  {
-    // Handle degenerate intervals by gracefully clamping and allowing m_dimensions to be zero
-    DSizes<Index, NumDims> startIndicesClamped, stopIndicesClamped;
-    for (ptrdiff_t i = 0; i < internal::array_size<Dimensions>::value; ++i) {
-      eigen_assert(m_strides[i] != 0 && "0 stride is invalid");
-      if (m_strides[i] > 0) {
-        startIndicesClamped[i] =
-            clamp(op.startIndices()[i], 0, m_impl.dimensions()[i]);
-        stopIndicesClamped[i] =
-            clamp(op.stopIndices()[i], 0, m_impl.dimensions()[i]);
-      } else {
-        /* implies m_strides[i] < 0 by assert */
-        startIndicesClamped[i] =
-            clamp(op.startIndices()[i], -1, m_impl.dimensions()[i] - 1);
-        stopIndicesClamped[i] =
-            clamp(op.stopIndices()[i], -1, m_impl.dimensions()[i] - 1);
-      }
-      m_startIndices[i] = startIndicesClamped[i];
-    }
-
-    typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
-    const InputDimensions& input_dims = m_impl.dimensions();
-
-    // compute output tensor shape
-    m_is_identity = true;
-    for (int i = 0; i < NumDims; i++) {
-      Index interval = stopIndicesClamped[i] - startIndicesClamped[i];
-      if (interval == 0 || ((interval < 0) != (m_strides[i] < 0))) {
-        m_dimensions[i] = 0;
-      } else {
-        m_dimensions[i] =
-            (interval / m_strides[i]) + (interval % m_strides[i] != 0 ? 1 : 0);
-        eigen_assert(m_dimensions[i] >= 0);
-      }
-      if (m_strides[i] != 1 || interval != m_impl.dimensions()[i]) {
-        m_is_identity = false;
-      }
-    }
-
-    Strides output_dims = m_dimensions;
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputStrides[0] = m_strides[0];
-      m_offsets[0] = startIndicesClamped[0];
-      Index previousDimProduct = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        previousDimProduct *= input_dims[i-1];
-        m_inputStrides[i] = previousDimProduct * m_strides[i];
-        m_offsets[i] = startIndicesClamped[i] * previousDimProduct;
-      }
-
-      // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed.
-      m_outputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1];
-        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
-      }
-    } else {
-      m_inputStrides[NumDims-1] = m_strides[NumDims-1];
-      m_offsets[NumDims-1] = startIndicesClamped[NumDims-1];
-      Index previousDimProduct = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        previousDimProduct *= input_dims[i+1];
-        m_inputStrides[i] = previousDimProduct * m_strides[i];
-        m_offsets[i] = startIndicesClamped[i] * previousDimProduct;
-      }
-
-      m_outputStrides[NumDims-1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1];
-        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    if (m_is_identity) {
-      return m_impl.coeff(index);
-    } else {
-      return m_impl.coeff(srcCoeff(index));
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
-    return NULL;
-  }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
-  {
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i >= 0; --i) {
-        const Index idx = index / m_fastOutputStrides[i];
-        inputIndex += idx * m_inputStrides[i] + m_offsets[i];
-        index -= idx * m_outputStrides[i];
-      }
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims; ++i) {
-        const Index idx = index / m_fastOutputStrides[i];
-        inputIndex += idx * m_inputStrides[i] + m_offsets[i];
-        index -= idx * m_outputStrides[i];
-      }
-    }
-    return inputIndex;
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index clamp(Index value, Index min, Index max) {
-#ifndef SYCL_DEVICE_ONLY
-    return numext::maxi(min, numext::mini(max,value));
-#else
-    return cl::sycl::clamp(value, min, max);
-#endif
-  }
-
-  array<Index, NumDims> m_outputStrides;
-  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
-  array<Index, NumDims> m_inputStrides;
-  bool m_is_identity;
-  TensorEvaluator<ArgType, Device> m_impl;
-  const Device EIGEN_DEVICE_REF m_device;
-  DSizes<Index, NumDims> m_startIndices; // clamped startIndices
-  DSizes<Index, NumDims> m_dimensions;
-  DSizes<Index, NumDims> m_offsets; // offset in a flattened shape
-  const Strides m_strides;
-};
-
-// Eval as lvalue
-template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device>
-struct TensorEvaluator<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
-  : public TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
-{
-  typedef TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> Base;
-  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType;
-  static const int NumDims = internal::array_size<Strides>::value;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = false,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess,
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : Base(op, device)
-    { }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef Strides Dimensions;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
-  {
-    if (this->m_is_identity) {
-      return this->m_impl.coeffRef(index);
-    } else {
-      return this->m_impl.coeffRef(this->srcCoeff(index));
-    }
-  }
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
deleted file mode 100644
index ee44382..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
+++ /dev/null
@@ -1,708 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
-#define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
-
-namespace Eigen {
-
-/** \class TensorPadding
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor padding class.
-  * At the moment only padding with a constant value is supported.
-  *
-  */
-namespace internal {
-template<typename PaddingDimensions, typename XprType>
-struct traits<TensorPaddingOp<PaddingDimensions, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename PaddingDimensions, typename XprType>
-struct eval<TensorPaddingOp<PaddingDimensions, XprType>, Eigen::Dense>
-{
-  typedef const TensorPaddingOp<PaddingDimensions, XprType>& type;
-};
-
-template<typename PaddingDimensions, typename XprType>
-struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1, typename eval<TensorPaddingOp<PaddingDimensions, XprType> >::type>
-{
-  typedef TensorPaddingOp<PaddingDimensions, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename PaddingDimensions, typename XprType>
-class TensorPaddingOp : public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorPaddingOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(const XprType& expr, const PaddingDimensions& padding_dims, const Scalar padding_value)
-      : m_xpr(expr), m_padding_dims(padding_dims), m_padding_value(padding_value) {}
-
-    EIGEN_DEVICE_FUNC
-    const PaddingDimensions& padding() const { return m_padding_dims; }
-    EIGEN_DEVICE_FUNC
-    Scalar padding_value() const { return m_padding_value; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const PaddingDimensions m_padding_dims;
-    const Scalar m_padding_value;
-};
-
-
-// Eval as rvalue
-template<typename PaddingDimensions, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device>
-{
-  typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<PaddingDimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = true,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = true,
-    RawAccess         = false
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_padding(op.padding()), m_paddingValue(op.padding_value()), m_device(device)
-  {
-    // The padding op doesn't change the rank of the tensor. Directly padding a scalar would lead
-    // to a vector, which doesn't make sense. Instead one should reshape the scalar into a vector
-    // of 1 element first and then pad.
-    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    // Compute dimensions
-    m_dimensions = m_impl.dimensions();
-    for (int i = 0; i < NumDims; ++i) {
-      m_dimensions[i] += m_padding[i].first + m_padding[i].second;
-    }
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputStrides[0] = 1;
-      m_outputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
-        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
-      }
-      m_outputStrides[NumDims] = m_outputStrides[NumDims-1] * m_dimensions[NumDims-1];
-    } else {
-      m_inputStrides[NumDims - 1] = 1;
-      m_outputStrides[NumDims] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
-        m_outputStrides[i+1] = m_outputStrides[i+2] * m_dimensions[i+1];
-      }
-      m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0];
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    eigen_assert(index < dimensions().TotalSize());
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = index / m_outputStrides[i];
-        if (isPaddingAtIndexForDim(idx, i)) {
-          return m_paddingValue;
-        }
-        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      if (isPaddingAtIndexForDim(index, 0)) {
-        return m_paddingValue;
-      }
-      inputIndex += (index - m_padding[0].first);
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = index / m_outputStrides[i+1];
-        if (isPaddingAtIndexForDim(idx, i)) {
-          return m_paddingValue;
-        }
-        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
-        index -= idx * m_outputStrides[i+1];
-      }
-      if (isPaddingAtIndexForDim(index, NumDims-1)) {
-        return m_paddingValue;
-      }
-      inputIndex += (index - m_padding[NumDims-1].first);
-    }
-    return m_impl.coeff(inputIndex);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      return packetColMajor(index);
-    }
-    return packetRowMajor(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    TensorOpCost cost = m_impl.costPerCoeff(vectorized);
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims; ++i)
-        updateCostPerDimension(cost, i, i == 0);
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i >= 0; --i)
-        updateCostPerDimension(cost, i, i == NumDims - 1);
-    }
-    return cost;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    const size_t target_size = m_device.lastLevelCacheSize();
-    return internal::TensorBlockResourceRequirements::merge(
-        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
-        m_impl.getResourceRequirements());
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    // If one of the dimensions is zero, return empty block view.
-    if (desc.size() == 0) {
-      return TensorBlock(internal::TensorBlockKind::kView, NULL,
-                           desc.dimensions());
-    }
-
-    static const bool IsColMajor = Layout == static_cast<int>(ColMajor);
-    const int inner_dim_idx = IsColMajor ? 0 : NumDims - 1;
-
-    Index offset = desc.offset();
-
-    // Compute offsets in the output tensor corresponding to the desc.offset().
-    DSizes<Index, NumDims> output_offsets;
-    for (int i = NumDims - 1; i > 0; --i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-      const int stride_dim = IsColMajor ? dim : dim + 1;
-      output_offsets[dim] = offset / m_outputStrides[stride_dim];
-      offset -= output_offsets[dim] * m_outputStrides[stride_dim];
-    }
-    output_offsets[inner_dim_idx] = offset;
-
-    // Offsets in the input corresponding to output offsets.
-    DSizes<Index, NumDims> input_offsets = output_offsets;
-    for (int i = 0; i < NumDims; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-      input_offsets[dim] = input_offsets[dim] - m_padding[dim].first;
-    }
-
-    // Compute offset in the input buffer (at this point it might be illegal and
-    // point outside of the input buffer, because we don't check for negative
-    // offsets, it will be autocorrected in the block iteration loop below).
-    Index input_offset = 0;
-    for (int i = 0; i < NumDims; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-      input_offset += input_offsets[dim] * m_inputStrides[dim];
-    }
-
-    // Destination buffer and scratch buffer both indexed from 0 and have the
-    // same dimensions as the requested block (for destination buffer this
-    // property is guaranteed by `desc.destination()`).
-    Index output_offset = 0;
-    const DSizes<Index, NumDims> output_strides =
-        internal::strides<Layout>(desc.dimensions());
-
-    // NOTE(ezhulenev): We initialize bock iteration state for `NumDims - 1`
-    // dimensions, skipping innermost dimension. In theory it should be possible
-    // to squeeze matching innermost dimensions, however in practice that did
-    // not show any improvements in benchmarks. Also in practice first outer
-    // dimension usually has padding, and will prevent squeezing.
-
-    // Initialize output block iterator state. Dimension in this array are
-    // always in inner_most -> outer_most order (col major layout).
-    array<BlockIteratorState, NumDims - 1> it;
-    for (int i = 0; i < NumDims - 1; ++i) {
-      const int dim = IsColMajor ? i + 1 : NumDims - i - 2;
-      it[i].count = 0;
-      it[i].size = desc.dimension(dim);
-
-      it[i].input_stride = m_inputStrides[dim];
-      it[i].input_span = it[i].input_stride * (it[i].size - 1);
-
-      it[i].output_stride = output_strides[dim];
-      it[i].output_span = it[i].output_stride * (it[i].size - 1);
-    }
-
-    const Index input_inner_dim_size =
-        static_cast<Index>(m_impl.dimensions()[inner_dim_idx]);
-
-    // Total output size.
-    const Index output_size = desc.size();
-
-    // We will fill inner dimension of this size in the output. It might be
-    // larger than the inner dimension in the input, so we might have to pad
-    // before/after we copy values from the input inner dimension.
-    const Index output_inner_dim_size = desc.dimension(inner_dim_idx);
-
-    // How many values to fill with padding BEFORE reading from the input inner
-    // dimension.
-    const Index output_inner_pad_before_size =
-        input_offsets[inner_dim_idx] < 0
-            ? numext::mini(numext::abs(input_offsets[inner_dim_idx]),
-                           output_inner_dim_size)
-            : 0;
-
-    // How many values we can actually copy from the input inner dimension.
-    const Index output_inner_copy_size = numext::mini(
-        // Want to copy from input.
-        (output_inner_dim_size - output_inner_pad_before_size),
-        // Can copy from input.
-        numext::maxi(input_inner_dim_size - (input_offsets[inner_dim_idx] +
-                                             output_inner_pad_before_size),
-                     Index(0)));
-
-    eigen_assert(output_inner_copy_size >= 0);
-
-    // How many values to fill with padding AFTER reading from the input inner
-    // dimension.
-    const Index output_inner_pad_after_size =
-        (output_inner_dim_size - output_inner_copy_size -
-         output_inner_pad_before_size);
-
-    // Sanity check, sum of all sizes must be equal to the output size.
-    eigen_assert(output_inner_dim_size ==
-                 (output_inner_pad_before_size + output_inner_copy_size +
-                  output_inner_pad_after_size));
-
-    // Keep track of current coordinates and padding in the output.
-    DSizes<Index, NumDims> output_coord = output_offsets;
-    DSizes<Index, NumDims> output_padded;
-    for (int i = 0; i < NumDims; ++i) {
-      const int dim = IsColMajor ? i : NumDims - i - 1;
-      output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
-    }
-
-    typedef internal::StridedLinearBufferCopy<ScalarNoConst, Index> LinCopy;
-
-    // Prepare storage for the materialized padding result.
-    const typename TensorBlock::Storage block_storage =
-        TensorBlock::prepareStorage(desc, scratch);
-
-    // TODO(ezhulenev): Squeeze multiple non-padded inner dimensions into a
-    // single logical inner dimension.
-
-    // When possible we squeeze writes for the innermost (only if non-padded)
-    // dimension with the first padded dimension. This allows to reduce the
-    // number of calls to LinCopy and better utilize vector instructions.
-    const bool squeeze_writes =
-        NumDims > 1 &&
-        // inner dimension is not padded
-        (input_inner_dim_size == m_dimensions[inner_dim_idx]) &&
-        // and equal to the block inner dimension
-        (input_inner_dim_size == output_inner_dim_size);
-
-    const int squeeze_dim = IsColMajor ? inner_dim_idx + 1 : inner_dim_idx - 1;
-
-    // Maximum coordinate on a squeeze dimension that we can write to.
-    const Index squeeze_max_coord =
-        squeeze_writes ? numext::mini(
-                             // max non-padded element in the input
-                             static_cast<Index>(m_dimensions[squeeze_dim] -
-                                                m_padding[squeeze_dim].second),
-                             // max element in the output buffer
-                             static_cast<Index>(output_offsets[squeeze_dim] +
-                                                desc.dimension(squeeze_dim)))
-                       : static_cast<Index>(0);
-
-    // Iterate copying data from `m_impl.data()` to the output buffer.
-    for (Index size = 0; size < output_size;) {
-      // Detect if we are in the padded region (exclude innermost dimension).
-      bool is_padded = false;
-      for (int j = 1; j < NumDims; ++j) {
-        const int dim = IsColMajor ? j : NumDims - j - 1;
-        is_padded = output_padded[dim];
-        if (is_padded) break;
-      }
-
-      if (is_padded) {
-        // Fill single innermost dimension with padding value.
-        size += output_inner_dim_size;
-
-        LinCopy::template Run<LinCopy::Kind::FillLinear>(
-            typename LinCopy::Dst(output_offset, 1, block_storage.data()),
-            typename LinCopy::Src(0, 0, &m_paddingValue),
-            output_inner_dim_size);
-
-
-      } else if (squeeze_writes) {
-        // Squeeze multiple reads from innermost dimensions.
-        const Index squeeze_num = squeeze_max_coord - output_coord[squeeze_dim];
-        size += output_inner_dim_size * squeeze_num;
-
-        // Copy `squeeze_num` inner dimensions from input to output.
-        LinCopy::template Run<LinCopy::Kind::Linear>(
-            typename LinCopy::Dst(output_offset, 1, block_storage.data()),
-            typename LinCopy::Src(input_offset, 1, m_impl.data()),
-            output_inner_dim_size * squeeze_num);
-
-        // Update iteration state for only `squeeze_num - 1` processed inner
-        // dimensions, because we have another iteration state update at the end
-        // of the loop that will update iteration state for the last inner
-        // processed dimension.
-        it[0].count += (squeeze_num - 1);
-        input_offset += it[0].input_stride * (squeeze_num - 1);
-        output_offset += it[0].output_stride * (squeeze_num - 1);
-        output_coord[squeeze_dim] += (squeeze_num - 1);
-
-      } else {
-        // Single read from innermost dimension.
-        size += output_inner_dim_size;
-
-        {  // Fill with padding before copying from input inner dimension.
-          const Index out = output_offset;
-
-          LinCopy::template Run<LinCopy::Kind::FillLinear>(
-              typename LinCopy::Dst(out, 1, block_storage.data()),
-              typename LinCopy::Src(0, 0, &m_paddingValue),
-              output_inner_pad_before_size);
-        }
-
-        {  // Copy data from input inner dimension.
-          const Index out = output_offset + output_inner_pad_before_size;
-          const Index in = input_offset + output_inner_pad_before_size;
-
-          eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL);
-
-          LinCopy::template Run<LinCopy::Kind::Linear>(
-              typename LinCopy::Dst(out, 1, block_storage.data()),
-              typename LinCopy::Src(in, 1, m_impl.data()),
-              output_inner_copy_size);
-        }
-
-        {  // Fill with padding after copying from input inner dimension.
-          const Index out = output_offset + output_inner_pad_before_size +
-                            output_inner_copy_size;
-
-          LinCopy::template Run<LinCopy::Kind::FillLinear>(
-              typename LinCopy::Dst(out, 1, block_storage.data()),
-              typename LinCopy::Src(0, 0, &m_paddingValue),
-              output_inner_pad_after_size);
-        }
-      }
-
-      for (int j = 0; j < NumDims - 1; ++j) {
-        const int dim = IsColMajor ? j + 1 : NumDims - j - 2;
-
-        if (++it[j].count < it[j].size) {
-          input_offset += it[j].input_stride;
-          output_offset += it[j].output_stride;
-          output_coord[dim] += 1;
-          output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
-          break;
-        }
-        it[j].count = 0;
-        input_offset -= it[j].input_span;
-        output_offset -= it[j].output_span;
-        output_coord[dim] -= it[j].size - 1;
-        output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
-      }
-    }
-
-    return block_storage.AsTensorMaterializedBlock();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- private:
-  struct BlockIteratorState {
-    BlockIteratorState()
-        : count(0),
-          size(0),
-          input_stride(0),
-          input_span(0),
-          output_stride(0),
-          output_span(0) {}
-
-    Index count;
-    Index size;
-    Index input_stride;
-    Index input_span;
-    Index output_stride;
-    Index output_span;
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isPaddingAtIndexForDim(
-      Index index, int dim_index) const {
-#if defined(EIGEN_HAS_INDEX_LIST)
-    return (!internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0) &&
-            index < m_padding[dim_index].first) ||
-        (!internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0) &&
-         index >= m_dimensions[dim_index] - m_padding[dim_index].second);
-#else
-    return (index < m_padding[dim_index].first) ||
-           (index >= m_dimensions[dim_index] - m_padding[dim_index].second);
-#endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isLeftPaddingCompileTimeZero(
-      int dim_index) const {
-#if defined(EIGEN_HAS_INDEX_LIST)
-    return internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0);
-#else
-    EIGEN_UNUSED_VARIABLE(dim_index);
-    return false;
-#endif
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isRightPaddingCompileTimeZero(
-      int dim_index) const {
-#if defined(EIGEN_HAS_INDEX_LIST)
-    return internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0);
-#else
-    EIGEN_UNUSED_VARIABLE(dim_index);
-    return false;
-#endif
-  }
-
-
-  void updateCostPerDimension(TensorOpCost& cost, int i, bool first) const {
-    const double in = static_cast<double>(m_impl.dimensions()[i]);
-    const double out = in + m_padding[i].first + m_padding[i].second;
-    if (out == 0)
-      return;
-    const double reduction = in / out;
-    cost *= reduction;
-    if (first) {
-      cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
-                    reduction * (1 * TensorOpCost::AddCost<Index>()));
-    } else {
-      cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
-                                 2 * TensorOpCost::MulCost<Index>() +
-                    reduction * (2 * TensorOpCost::MulCost<Index>() +
-                                 1 * TensorOpCost::DivCost<Index>()));
-    }
-  }
-
- protected:
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    const Index initialIndex = index;
-    Index inputIndex = 0;
-    EIGEN_UNROLL_LOOP
-    for (int i = NumDims - 1; i > 0; --i) {
-      const Index firstIdx = index;
-      const Index lastIdx = index + PacketSize - 1;
-      const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i];
-      const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i];
-      const Index lastPaddedRight = m_outputStrides[i+1];
-
-      if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
-        // all the coefficient are in the padding zone.
-        return internal::pset1<PacketReturnType>(m_paddingValue);
-      }
-      else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
-        // all the coefficient are in the padding zone.
-        return internal::pset1<PacketReturnType>(m_paddingValue);
-      }
-      else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
-        // all the coefficient are between the 2 padding zones.
-        const Index idx = index / m_outputStrides[i];
-        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      else {
-        // Every other case
-        return packetWithPossibleZero(initialIndex);
-      }
-    }
-
-    const Index lastIdx = index + PacketSize - 1;
-    const Index firstIdx = index;
-    const Index lastPaddedLeft = m_padding[0].first;
-    const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second);
-    const Index lastPaddedRight = m_outputStrides[1];
-
-    if (!isLeftPaddingCompileTimeZero(0) && lastIdx < lastPaddedLeft) {
-      // all the coefficient are in the padding zone.
-      return internal::pset1<PacketReturnType>(m_paddingValue);
-    }
-    else if (!isRightPaddingCompileTimeZero(0) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
-      // all the coefficient are in the padding zone.
-      return internal::pset1<PacketReturnType>(m_paddingValue);
-    }
-    else if ((isLeftPaddingCompileTimeZero(0) && isRightPaddingCompileTimeZero(0)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
-      // all the coefficient are between the 2 padding zones.
-      inputIndex += (index - m_padding[0].first);
-      return m_impl.template packet<Unaligned>(inputIndex);
-    }
-    // Every other case
-    return packetWithPossibleZero(initialIndex);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    const Index initialIndex = index;
-    Index inputIndex = 0;
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < NumDims - 1; ++i) {
-      const Index firstIdx = index;
-      const Index lastIdx = index + PacketSize - 1;
-      const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i+1];
-      const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i+1];
-      const Index lastPaddedRight = m_outputStrides[i];
-
-      if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
-        // all the coefficient are in the padding zone.
-        return internal::pset1<PacketReturnType>(m_paddingValue);
-      }
-      else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
-        // all the coefficient are in the padding zone.
-        return internal::pset1<PacketReturnType>(m_paddingValue);
-      }
-      else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
-        // all the coefficient are between the 2 padding zones.
-        const Index idx = index / m_outputStrides[i+1];
-        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
-        index -= idx * m_outputStrides[i+1];
-      }
-      else {
-        // Every other case
-        return packetWithPossibleZero(initialIndex);
-      }
-    }
-
-    const Index lastIdx = index + PacketSize - 1;
-    const Index firstIdx = index;
-    const Index lastPaddedLeft = m_padding[NumDims-1].first;
-    const Index firstPaddedRight = (m_dimensions[NumDims-1] - m_padding[NumDims-1].second);
-    const Index lastPaddedRight = m_outputStrides[NumDims-1];
-
-    if (!isLeftPaddingCompileTimeZero(NumDims-1) && lastIdx < lastPaddedLeft) {
-      // all the coefficient are in the padding zone.
-      return internal::pset1<PacketReturnType>(m_paddingValue);
-    }
-    else if (!isRightPaddingCompileTimeZero(NumDims-1) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
-      // all the coefficient are in the padding zone.
-      return internal::pset1<PacketReturnType>(m_paddingValue);
-    }
-    else if ((isLeftPaddingCompileTimeZero(NumDims-1) && isRightPaddingCompileTimeZero(NumDims-1)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
-      // all the coefficient are between the 2 padding zones.
-      inputIndex += (index - m_padding[NumDims-1].first);
-      return m_impl.template packet<Unaligned>(inputIndex);
-    }
-    // Every other case
-    return packetWithPossibleZero(initialIndex);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
-  {
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      values[i] = coeff(index+i);
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  Dimensions m_dimensions;
-  array<Index, NumDims+1> m_outputStrides;
-  array<Index, NumDims> m_inputStrides;
-  TensorEvaluator<ArgType, Device> m_impl;
-  PaddingDimensions m_padding;
-
-  Scalar m_paddingValue;
-
-  const Device EIGEN_DEVICE_REF m_device;
-};
-
-
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
deleted file mode 100644
index 413d25d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
+++ /dev/null
@@ -1,291 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
-#define EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
-
-namespace Eigen {
-
-/** \class TensorPatch
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor patch class.
-  *
-  *
-  */
-namespace internal {
-template<typename PatchDim, typename XprType>
-struct traits<TensorPatchOp<PatchDim, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions + 1;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename PatchDim, typename XprType>
-struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense>
-{
-  typedef const TensorPatchOp<PatchDim, XprType>& type;
-};
-
-template<typename PatchDim, typename XprType>
-struct nested<TensorPatchOp<PatchDim, XprType>, 1, typename eval<TensorPatchOp<PatchDim, XprType> >::type>
-{
-  typedef TensorPatchOp<PatchDim, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename PatchDim, typename XprType>
-class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorPatchOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims)
-      : m_xpr(expr), m_patch_dims(patch_dims) {}
-
-    EIGEN_DEVICE_FUNC
-    const PatchDim& patch_dims() const { return m_patch_dims; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const PatchDim m_patch_dims;
-};
-
-
-// Eval as rvalue
-template<typename PatchDim, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
-{
-  typedef TensorPatchOp<PatchDim, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-
-  enum {
-    IsAligned = false,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,
-    RawAccess = false
- };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device)
-  {
-    Index num_patches = 1;
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    const PatchDim& patch_dims = op.patch_dims();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = 0; i < NumDims-1; ++i) {
-        m_dimensions[i] = patch_dims[i];
-        num_patches *= (input_dims[i] - patch_dims[i] + 1);
-      }
-      m_dimensions[NumDims-1] = num_patches;
-
-      m_inputStrides[0] = 1;
-      m_patchStrides[0] = 1;
-      for (int i = 1; i < NumDims-1; ++i) {
-        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
-        m_patchStrides[i] = m_patchStrides[i-1] * (input_dims[i-1] - patch_dims[i-1] + 1);
-      }
-      m_outputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
-      }
-    } else {
-      for (int i = 0; i < NumDims-1; ++i) {
-        m_dimensions[i+1] = patch_dims[i];
-        num_patches *= (input_dims[i] - patch_dims[i] + 1);
-      }
-      m_dimensions[0] = num_patches;
-
-      m_inputStrides[NumDims-2] = 1;
-      m_patchStrides[NumDims-2] = 1;
-      for (int i = NumDims-3; i >= 0; --i) {
-        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
-        m_patchStrides[i] = m_patchStrides[i+1] * (input_dims[i+1] - patch_dims[i+1] + 1);
-      }
-      m_outputStrides[NumDims-1] = 1;
-      for (int i = NumDims-2; i >= 0; --i) {
-        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
-    // Find the location of the first element of the patch.
-    Index patchIndex = index / m_outputStrides[output_stride_index];
-    // Find the offset of the element wrt the location of the first element.
-    Index patchOffset = index - patchIndex * m_outputStrides[output_stride_index];
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 2; i > 0; --i) {
-        const Index patchIdx = patchIndex / m_patchStrides[i];
-        patchIndex -= patchIdx * m_patchStrides[i];
-        const Index offsetIdx = patchOffset / m_outputStrides[i];
-        patchOffset -= offsetIdx * m_outputStrides[i];
-        inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
-      }
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 2; ++i) {
-        const Index patchIdx = patchIndex / m_patchStrides[i];
-        patchIndex -= patchIdx * m_patchStrides[i];
-        const Index offsetIdx = patchOffset / m_outputStrides[i+1];
-        patchOffset -= offsetIdx * m_outputStrides[i+1];
-        inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
-      }
-    }
-    inputIndex += (patchIndex + patchOffset);
-    return m_impl.coeff(inputIndex);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
-    Index indices[2] = {index, index + PacketSize - 1};
-    Index patchIndices[2] = {indices[0] / m_outputStrides[output_stride_index],
-                             indices[1] / m_outputStrides[output_stride_index]};
-    Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[output_stride_index],
-                             indices[1] - patchIndices[1] * m_outputStrides[output_stride_index]};
-
-    Index inputIndices[2] = {0, 0};
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 2; i > 0; --i) {
-        const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i],
-                                   patchIndices[1] / m_patchStrides[i]};
-        patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
-        patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
-
-        const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i],
-                                    patchOffsets[1] / m_outputStrides[i]};
-        patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i];
-        patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i];
-
-        inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
-        inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
-      }
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 2; ++i) {
-        const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i],
-                                   patchIndices[1] / m_patchStrides[i]};
-        patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
-        patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
-
-        const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i+1],
-                                    patchOffsets[1] / m_outputStrides[i+1]};
-        patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i+1];
-        patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i+1];
-
-        inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
-        inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
-      }
-    }
-    inputIndices[0] += (patchIndices[0] + patchOffsets[0]);
-    inputIndices[1] += (patchIndices[1] + patchOffsets[1]);
-
-    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
-      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
-      return rslt;
-    }
-    else {
-      EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
-      values[0] = m_impl.coeff(inputIndices[0]);
-      values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < PacketSize-1; ++i) {
-        values[i] = coeff(index+i);
-      }
-      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-      return rslt;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() +
-                                           TensorOpCost::MulCost<Index>() +
-                                           2 * TensorOpCost::AddCost<Index>());
-    return m_impl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh); 
-  }
-#endif
-
- protected:
-  Dimensions m_dimensions;
-  array<Index, NumDims> m_outputStrides;
-  array<Index, NumDims-1> m_inputStrides;
-  array<Index, NumDims-1> m_patchStrides;
-
-  TensorEvaluator<ArgType, Device> m_impl;
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h
deleted file mode 100644
index 37c1d1c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h
+++ /dev/null
@@ -1,322 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2018 Mehdi Goli <eigen@codeplay.com> Codeplay Software Ltd.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
-#define EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
-
-namespace Eigen {
-namespace internal {
-
-namespace {
-
-EIGEN_DEVICE_FUNC uint64_t get_random_seed() {
-#if defined(EIGEN_GPU_COMPILE_PHASE)
-  // We don't support 3d kernels since we currently only use 1 and
-  // 2d kernels.
-  gpu_assert(threadIdx.z == 0);
-  return blockIdx.x * blockDim.x + threadIdx.x 
-         + gridDim.x * blockDim.x * (blockIdx.y * blockDim.y + threadIdx.y);
-#else
-  // Rely on Eigen's random implementation.
-  return random<uint64_t>();
-#endif
-}
-
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE unsigned PCG_XSH_RS_generator(uint64_t* state, uint64_t stream) {
-  // TODO: Unify with the implementation in the non blocking thread pool.
-  uint64_t current = *state;
-  // Update the internal state
-  *state = current * 6364136223846793005ULL + (stream << 1 | 1);
-  // Generate the random output (using the PCG-XSH-RS scheme)
-  return static_cast<unsigned>((current ^ (current >> 22)) >> (22 + (current >> 61)));
-}
-
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint64_t PCG_XSH_RS_state(uint64_t seed) {
-  seed = seed ? seed : get_random_seed();
-  return seed * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
-}
-
-}  // namespace
-
-
-template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-T RandomToTypeUniform(uint64_t* state, uint64_t stream) {
-  unsigned rnd = PCG_XSH_RS_generator(state, stream);
-  return static_cast<T>(rnd);
-}
-
-
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-Eigen::half RandomToTypeUniform<Eigen::half>(uint64_t* state, uint64_t stream) {
-  // Generate 10 random bits for the mantissa, merge with exponent.
-  unsigned rnd = PCG_XSH_RS_generator(state, stream);
-  const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x3ffu) | (static_cast<uint16_t>(15) << 10);
-  Eigen::half result = Eigen::numext::bit_cast<Eigen::half>(half_bits);
-  // Return the final result
-  return result - Eigen::half(1.0f);
-}
-
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-Eigen::bfloat16 RandomToTypeUniform<Eigen::bfloat16>(uint64_t* state, uint64_t stream) {
-
-  // Generate 7 random bits for the mantissa, merge with exponent.
-  unsigned rnd = PCG_XSH_RS_generator(state, stream);
-  const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x7fu) | (static_cast<uint16_t>(127) << 7);
-  Eigen::bfloat16 result = Eigen::numext::bit_cast<Eigen::bfloat16>(half_bits);
-  // Return the final result
-  return result - Eigen::bfloat16(1.0f);
-}
-
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float RandomToTypeUniform<float>(uint64_t* state, uint64_t stream) {
-  typedef union {
-    uint32_t raw;
-    float fp;
-  } internal;
-  internal result;
-  // Generate 23 random bits for the mantissa mantissa
-  const unsigned rnd = PCG_XSH_RS_generator(state, stream);
-  result.raw = rnd & 0x7fffffu;
-  // Set the exponent
-  result.raw |= (static_cast<uint32_t>(127) << 23);
-  // Return the final result
-  return result.fp - 1.0f;
-}
-
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double RandomToTypeUniform<double>(uint64_t* state, uint64_t stream) {
-  typedef union {
-    uint64_t raw;
-    double dp;
-  } internal;
-  internal result;
-  result.raw = 0;
-  // Generate 52 random bits for the mantissa
-  // First generate the upper 20 bits
-  unsigned rnd1 = PCG_XSH_RS_generator(state, stream) & 0xfffffu;
-  // The generate the lower 32 bits
-  unsigned rnd2 = PCG_XSH_RS_generator(state, stream);
-  result.raw = (static_cast<uint64_t>(rnd1) << 32) | rnd2;
-  // Set the exponent
-  result.raw |= (static_cast<uint64_t>(1023) << 52);
-  // Return the final result
-  return result.dp - 1.0;
-}
-
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<float> RandomToTypeUniform<std::complex<float> >(uint64_t* state, uint64_t stream) {
-  return std::complex<float>(RandomToTypeUniform<float>(state, stream),
-                             RandomToTypeUniform<float>(state, stream));
-}
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<double> RandomToTypeUniform<std::complex<double> >(uint64_t* state, uint64_t stream) {
-  return std::complex<double>(RandomToTypeUniform<double>(state, stream),
-                              RandomToTypeUniform<double>(state, stream));
-}
-
-template <typename T> class UniformRandomGenerator {
- public:
-  static const bool PacketAccess = true;
-
-  // Uses the given "seed" if non-zero, otherwise uses a random seed.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(
-      uint64_t seed = 0) {
-    m_state = PCG_XSH_RS_state(seed);
-    #ifdef EIGEN_USE_SYCL
-    // In SYCL it is not possible to build PCG_XSH_RS_state in one step.
-    // Therefor, we need two step to initializate the m_state.
-    // IN SYCL, the constructor of the functor is s called on the CPU
-    // and we get the clock seed here from the CPU. However, This seed is
-    //the same for all the thread. As unlike CUDA, the thread.ID, BlockID, etc is not a global function.
-    // and only  available on the Operator() function (which is called on the GPU).
-    // Thus for CUDA (((CLOCK  + global_thread_id)* 6364136223846793005ULL) + 0xda3e39cb94b95bdbULL) is passed to each thread
-    // but for SYCL ((CLOCK * 6364136223846793005ULL) + 0xda3e39cb94b95bdbULL) is passed to each thread and each thread adds
-    // the  (global_thread_id* 6364136223846793005ULL) for itself only once, in order to complete the construction
-    // similar to CUDA Therefore, the thread Id injection is not available at this stage.
-    //However when the operator() is called the thread ID will be avilable. So inside the opeator,
-    // we add the thrreadID, BlockId,... (which is equivalent of i)
-    //to the seed and construct the unique m_state per thead similar to cuda.
-    m_exec_once =false;
-   #endif
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(
-      const UniformRandomGenerator& other) {
-    m_state = other.m_state;
-    #ifdef EIGEN_USE_SYCL
-     m_exec_once =other.m_exec_once;
-    #endif
-  }
-
-  template<typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  T operator()(Index i) const {
-    #ifdef EIGEN_USE_SYCL
-      if(!m_exec_once) {
-      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
-      // The (i * 6364136223846793005ULL) is the remaining part of the PCG_XSH_RS_state on the GPU side
-       m_state += (i * 6364136223846793005ULL);
-       m_exec_once =true;
-      }
-    #endif
-    T result = RandomToTypeUniform<T>(&m_state, i);
-    return result;
-  }
-
-  template<typename Packet, typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Packet packetOp(Index i) const {
-    const int packetSize = internal::unpacket_traits<Packet>::size;
-    EIGEN_ALIGN_MAX T values[packetSize];
-      #ifdef EIGEN_USE_SYCL
-      if(!m_exec_once) {
-      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
-       m_state += (i * 6364136223846793005ULL);
-       m_exec_once =true;
-      }
-    #endif
-    EIGEN_UNROLL_LOOP
-    for (int j = 0; j < packetSize; ++j) {
-      values[j] = RandomToTypeUniform<T>(&m_state, i);
-    }
-    return internal::pload<Packet>(values);
-  }
-
- private:
-  mutable uint64_t m_state;
-  #ifdef EIGEN_USE_SYCL
-  mutable bool m_exec_once;
-  #endif
-};
-
-template <typename Scalar>
-struct functor_traits<UniformRandomGenerator<Scalar> > {
-  enum {
-    // Rough estimate for floating point, multiplied by ceil(sizeof(T) / sizeof(float)).
-    Cost = 12 * NumTraits<Scalar>::AddCost *
-           ((sizeof(Scalar) + sizeof(float) - 1) / sizeof(float)),
-    PacketAccess = UniformRandomGenerator<Scalar>::PacketAccess
-  };
-};
-
-
-
-template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-T RandomToTypeNormal(uint64_t* state, uint64_t stream) {
-  // Use the ratio of uniform method to generate numbers following a normal
-  // distribution. See for example Numerical Recipes chapter 7.3.9 for the
-  // details.
-  T u, v, q;
-  do {
-    u = RandomToTypeUniform<T>(state, stream);
-    v = T(1.7156) * (RandomToTypeUniform<T>(state, stream) - T(0.5));
-    const T x = u - T(0.449871);
-    const T y = numext::abs(v) + T(0.386595);
-    q = x*x + y * (T(0.196)*y - T(0.25472)*x);
-  } while (q > T(0.27597) &&
-           (q > T(0.27846) || v*v > T(-4) * numext::log(u) * u*u));
-
-  return v/u;
-}
-
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<float> RandomToTypeNormal<std::complex<float> >(uint64_t* state, uint64_t stream) {
-  return std::complex<float>(RandomToTypeNormal<float>(state, stream),
-                             RandomToTypeNormal<float>(state, stream));
-}
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-std::complex<double> RandomToTypeNormal<std::complex<double> >(uint64_t* state, uint64_t stream) {
-  return std::complex<double>(RandomToTypeNormal<double>(state, stream),
-                              RandomToTypeNormal<double>(state, stream));
-}
-
-
-template <typename T> class NormalRandomGenerator {
- public:
-  static const bool PacketAccess = true;
-
-  // Uses the given "seed" if non-zero, otherwise uses a random seed.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(uint64_t seed = 0) {
-    m_state = PCG_XSH_RS_state(seed);
-    #ifdef EIGEN_USE_SYCL
-    // In SYCL it is not possible to build PCG_XSH_RS_state in one step.
-    // Therefor, we need two steps to initializate the m_state.
-    // IN SYCL, the constructor of the functor is s called on the CPU
-    // and we get the clock seed here from the CPU. However, This seed is
-    //the same for all the thread. As unlike CUDA, the thread.ID, BlockID, etc is not a global function.
-    // and only  available on the Operator() function (which is called on the GPU).
-    // Therefore, the thread Id injection is not available at this stage. However when the operator()
-    //is called the thread ID will be avilable. So inside the opeator,
-    // we add the thrreadID, BlockId,... (which is equivalent of i)
-    //to the seed and construct the unique m_state per thead similar to cuda.
-    m_exec_once =false;
-   #endif
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(
-      const NormalRandomGenerator& other) {
-    m_state = other.m_state;
-#ifdef EIGEN_USE_SYCL
-    m_exec_once=other.m_exec_once;
-#endif
-  }
-
- template<typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  T operator()(Index i) const {
-    #ifdef EIGEN_USE_SYCL
-    if(!m_exec_once) {
-      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
-      m_state += (i * 6364136223846793005ULL);
-      m_exec_once =true;
-    }
-    #endif
-    T result = RandomToTypeNormal<T>(&m_state, i);
-    return result;
-  }
-
-  template<typename Packet, typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Packet packetOp(Index i) const {
-    const int packetSize = internal::unpacket_traits<Packet>::size;
-    EIGEN_ALIGN_MAX T values[packetSize];
-    #ifdef EIGEN_USE_SYCL
-    if(!m_exec_once) {
-      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
-      m_state += (i * 6364136223846793005ULL);
-      m_exec_once =true;
-    }
-    #endif
-    EIGEN_UNROLL_LOOP
-    for (int j = 0; j < packetSize; ++j) {
-      values[j] = RandomToTypeNormal<T>(&m_state, i);
-    }
-    return internal::pload<Packet>(values);
-  }
-
- private:
-  mutable uint64_t m_state;
-   #ifdef EIGEN_USE_SYCL
-  mutable bool m_exec_once;
-  #endif
-};
-
-
-template <typename Scalar>
-struct functor_traits<NormalRandomGenerator<Scalar> > {
-  enum {
-    // On average, we need to generate about 3 random numbers
-    // 15 mul, 8 add, 1.5 logs
-    Cost = 3 * functor_traits<UniformRandomGenerator<Scalar> >::Cost +
-           15 * NumTraits<Scalar>::AddCost + 8 * NumTraits<Scalar>::AddCost +
-           3 * functor_traits<scalar_log_op<Scalar> >::Cost / 2,
-    PacketAccess = NormalRandomGenerator<Scalar>::PacketAccess
-  };
-};
-
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
deleted file mode 100644
index 583f462..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
+++ /dev/null
@@ -1,998 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2016 Mehdi Goli, Codeplay Software Ltd <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
-#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
-
-// clang is incompatible with the CUDA syntax wrt making a kernel a class friend,
-// so we'll use a macro to make clang happy.
-#ifndef KERNEL_FRIEND
-#if defined(__clang__) && (defined(__CUDA__) || defined(__HIP__))
-#define KERNEL_FRIEND friend __global__ EIGEN_HIP_LAUNCH_BOUNDS_1024
-#else
-#define KERNEL_FRIEND friend
-#endif
-#endif
-
-
-namespace Eigen {
-
-
-/** \class TensorReduction
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor reduction class.
-  *
-  */
-
-namespace internal {
-  template<typename Op, typename Dims, typename XprType,template <class> class MakePointer_ >
-  struct traits<TensorReductionOp<Op, Dims, XprType, MakePointer_> >
- : traits<XprType>
-{
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::Scalar Scalar;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-
-  template <class T> struct MakePointer {
-    // Intermediate typedef to workaround MSVC issue.
-    typedef MakePointer_<T> MakePointerT;
-    typedef typename MakePointerT::Type Type;
-  };
-};
-
-template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
-struct eval<TensorReductionOp<Op, Dims, XprType, MakePointer_>, Eigen::Dense>
-{
-  typedef const TensorReductionOp<Op, Dims, XprType, MakePointer_>& type;
-};
-
-template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
-struct nested<TensorReductionOp<Op, Dims, XprType, MakePointer_>, 1, typename eval<TensorReductionOp<Op, Dims, XprType, MakePointer_> >::type>
-{
-  typedef TensorReductionOp<Op, Dims, XprType, MakePointer_> type;
-};
-
-
-template <typename OutputDims> struct DimInitializer {
-  template <typename InputDims, typename ReducedDims> EIGEN_DEVICE_FUNC
-  static void run(const InputDims& input_dims,
-                  const array<bool, internal::array_size<InputDims>::value>& reduced,
-                  OutputDims* output_dims, ReducedDims* reduced_dims) {
-    const int NumInputDims = internal::array_size<InputDims>::value;
-    int outputIndex = 0;
-    int reduceIndex = 0;
-    for (int i = 0; i < NumInputDims; ++i) {
-      if (reduced[i]) {
-        (*reduced_dims)[reduceIndex] = input_dims[i];
-        ++reduceIndex;
-      } else {
-        (*output_dims)[outputIndex] = input_dims[i];
-        ++outputIndex;
-      }
-    }
-  }
-};
-
-template <> struct DimInitializer<Sizes<> > {
-  template <typename InputDims, typename Index, size_t Rank> EIGEN_DEVICE_FUNC
-  static void run(const InputDims& input_dims, const array<bool, Rank>&,
-                  Sizes<>*, array<Index, Rank>* reduced_dims) {
-    const int NumInputDims = internal::array_size<InputDims>::value;
-    for (int i = 0; i < NumInputDims; ++i) {
-      (*reduced_dims)[i] = input_dims[i];
-    }
-  }
-};
-
-
-template <typename ReducedDims, int NumTensorDims, int Layout>
-struct are_inner_most_dims {
-  static const bool value = false;
-};
-template <typename ReducedDims, int NumTensorDims, int Layout>
-struct preserve_inner_most_dims {
-  static const bool value = false;
-};
-
-#if EIGEN_HAS_CONSTEXPR && EIGEN_HAS_VARIADIC_TEMPLATES
-template <typename ReducedDims, int NumTensorDims>
-struct are_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{
-  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
-  static const bool tmp2 = index_statically_eq<ReducedDims>(0, 0);
-  static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value-1, array_size<ReducedDims>::value-1);
-  static const bool value = tmp1 & tmp2 & tmp3;
-};
-template <typename ReducedDims, int NumTensorDims>
-struct are_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{
-  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
-  static const bool tmp2 = index_statically_eq<ReducedDims>(0, NumTensorDims - array_size<ReducedDims>::value);
-  static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
-  static const bool value = tmp1 & tmp2 & tmp3;
-
-};
-template <typename ReducedDims, int NumTensorDims>
-struct preserve_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{
-  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
-  static const bool tmp2 = index_statically_gt<ReducedDims>(0, 0);
-  static const bool value = tmp1 & tmp2;
-
-};
-template <typename ReducedDims, int NumTensorDims>
-struct preserve_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{
-  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
-  static const bool tmp2 = index_statically_lt<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
-  static const bool value = tmp1 & tmp2;
-};
-#endif
-
-
-template <int DimIndex, typename Self, typename Op>
-struct GenericDimReducer {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) {
-    EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    for (int j = 0; j < self.m_reducedDims[DimIndex]; ++j) {
-      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex];
-      GenericDimReducer<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum);
-    }
-  }
-};
-template <typename Self, typename Op>
-struct GenericDimReducer<0, Self, Op> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) {
-    for (int j = 0; j < self.m_reducedDims[0]; ++j) {
-      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0];
-      reducer.reduce(self.m_impl.coeff(input), accum);
-    }
-  }
-};
-template <typename Self, typename Op>
-struct GenericDimReducer<-1, Self, Op> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index index, Op& reducer, typename Self::CoeffReturnType* accum) {
-    reducer.reduce(self.m_impl.coeff(index), accum);
-  }
-};
-
-template <typename Self, typename Op, bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess),
-          bool UseTreeReduction = (!Self::ReducerTraits::IsStateful &&
-                                   !Self::ReducerTraits::IsExactlyAssociative)>
-struct InnerMostDimReducer {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) {
-    typename Self::CoeffReturnType accum = reducer.initialize();
-    for (typename Self::Index j = 0; j < numValuesToReduce; ++j) {
-      reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
-    }
-    return reducer.finalize(accum);
-  }
-};
-
-template <typename Self, typename Op>
-struct InnerMostDimReducer<Self, Op, true, false> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) {
-    const typename Self::Index packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size;
-    const typename Self::Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize;
-    typename Self::PacketReturnType paccum = reducer.template initializePacket<typename Self::PacketReturnType>();
-    for (typename Self::Index j = 0; j < VectorizedSize; j += packetSize) {
-      reducer.reducePacket(self.m_impl.template packet<Unaligned>(firstIndex + j), &paccum);
-    }
-    typename Self::CoeffReturnType accum = reducer.initialize();
-    for (typename Self::Index j = VectorizedSize; j < numValuesToReduce; ++j) {
-      reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
-    }
-    return reducer.finalizeBoth(accum, paccum);
-  }
-};
-
-#if !defined(EIGEN_HIPCC) 
-static const int kLeafSize = 1024;
-
-template <typename Self, typename Op>
-struct InnerMostDimReducer<Self, Op, false, true> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType
-  reduce(const Self& self, typename Self::Index firstIndex,
-         typename Self::Index numValuesToReduce, Op& reducer) {
-    typename Self::CoeffReturnType accum = reducer.initialize();
-    if (numValuesToReduce > kLeafSize) {
-      const typename Self::Index half = numValuesToReduce / 2;
-      reducer.reduce(reduce(self, firstIndex, half, reducer), &accum);
-      reducer.reduce(
-          reduce(self, firstIndex + half, numValuesToReduce - half, reducer),
-          &accum);
-    } else {
-      for (typename Self::Index j = 0; j < numValuesToReduce; ++j) {
-        reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
-      }
-    }
-    return reducer.finalize(accum);
-  }
-};
-
-template <typename Self, typename Op>
-struct InnerMostDimReducer<Self, Op, true, true> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType
-  reduce(const Self& self, typename Self::Index firstIndex,
-         typename Self::Index numValuesToReduce, Op& reducer) {
-    const typename Self::Index packetSize =
-        internal::unpacket_traits<typename Self::PacketReturnType>::size;
-    typename Self::CoeffReturnType accum = reducer.initialize();
-    if (numValuesToReduce > packetSize * kLeafSize) {
-      // Make sure the split point is aligned on a packet boundary.
-      const typename Self::Index split =
-          packetSize *
-          divup(firstIndex + divup(numValuesToReduce, typename Self::Index(2)),
-                packetSize);
-      const typename Self::Index num_left =
-          numext::mini(split - firstIndex, numValuesToReduce);
-      reducer.reduce(reduce(self, firstIndex, num_left, reducer), &accum);
-      if (num_left < numValuesToReduce) {
-        reducer.reduce(
-            reduce(self, split, numValuesToReduce - num_left, reducer), &accum);
-      }
-      return reducer.finalize(accum);
-    } else {
-      const typename Self::Index UnrollSize =
-          (numValuesToReduce / (2*packetSize)) * 2*packetSize;
-      const typename Self::Index VectorizedSize =
-          (numValuesToReduce / packetSize) * packetSize;
-      typename Self::PacketReturnType paccum =
-          reducer.template initializePacket<typename Self::PacketReturnType>();
-      typename Self::PacketReturnType paccum2 =
-          reducer.template initializePacket<typename Self::PacketReturnType>();
-      for (typename Self::Index j = 0; j < UnrollSize; j += packetSize * 2) {
-        reducer.reducePacket(
-            self.m_impl.template packet<Unaligned>(firstIndex + j), &paccum);
-        reducer.reducePacket(
-            self.m_impl.template packet<Unaligned>(firstIndex + j + packetSize),
-            &paccum2);
-      }
-      for (typename Self::Index j = UnrollSize; j < VectorizedSize; j+= packetSize) {
-        reducer.reducePacket(self.m_impl.template packet<Unaligned>(
-                                 firstIndex + j), &paccum);
-      }
-      reducer.reducePacket(paccum2, &paccum);
-      for (typename Self::Index j = VectorizedSize; j < numValuesToReduce;
-           ++j) {
-        reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
-      }
-      return reducer.finalizeBoth(accum, paccum);
-    }
-  }
-};
-#endif
- 
-template <int DimIndex, typename Self, typename Op, bool vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
-struct InnerMostDimPreserver {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) {
-    eigen_assert(false && "should never be called");
-  }
-};
-
-template <int DimIndex, typename Self, typename Op>
-struct InnerMostDimPreserver<DimIndex, Self, Op, true> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) {
-    EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    for (typename Self::Index j = 0; j < self.m_reducedDims[DimIndex]; ++j) {
-      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex];
-      InnerMostDimPreserver<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum);
-    }
-  }
-};
-
-template <typename Self, typename Op>
-struct InnerMostDimPreserver<0, Self, Op, true> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) {
-    for (typename Self::Index j = 0; j < self.m_reducedDims[0]; ++j) {
-      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0];
-      reducer.reducePacket(self.m_impl.template packet<Unaligned>(input), accum);
-    }
-  }
-};
-template <typename Self, typename Op>
-struct InnerMostDimPreserver<-1, Self, Op, true> {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) {
-    eigen_assert(false && "should never be called");
-  }
-};
-
-// Default full reducer
-template <typename Self, typename Op, typename Device, bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
-struct FullReducer {
-  static const bool HasOptimizedImplementation = false;
-
-  static EIGEN_DEVICE_FUNC void run(const Self& self, Op& reducer, const Device&, typename Self::EvaluatorPointerType output) {
-    const typename Self::Index num_coeffs = array_prod(self.m_impl.dimensions());
-    *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer);
-  }
-};
-
-
-#ifdef EIGEN_USE_THREADS
-// Multithreaded full reducers
-template <typename Self, typename Op,
-          bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
-struct FullReducerShard {
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Self& self, typename Self::Index firstIndex,
-                  typename Self::Index numValuesToReduce, Op& reducer,
-                  typename Self::CoeffReturnType* output) {
-    *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(
-        self, firstIndex, numValuesToReduce, reducer);
-  }
-};
-
-// Multithreaded full reducer
-template <typename Self, typename Op, bool Vectorizable>
-struct FullReducer<Self, Op, ThreadPoolDevice, Vectorizable> {
-  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful;
-  static const Index PacketSize =
-      unpacket_traits<typename Self::PacketReturnType>::size;
-
-  // launch one reducer per thread and accumulate the result.
-  static void run(const Self& self, Op& reducer, const ThreadPoolDevice& device,
-                  typename Self::CoeffReturnType* output) {
-    typedef typename Self::Index Index;
-    const Index num_coeffs = array_prod(self.m_impl.dimensions());
-    if (num_coeffs == 0) {
-      *output = reducer.finalize(reducer.initialize());
-      return;
-    }
-    const TensorOpCost cost =
-        self.m_impl.costPerCoeff(Vectorizable) +
-        TensorOpCost(0, 0, internal::functor_traits<Op>::Cost, Vectorizable,
-                     PacketSize);
-    const int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(
-        num_coeffs, cost, device.numThreads());
-    if (num_threads == 1) {
-      *output =
-          InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer);
-      return;
-    }
-    const Index blocksize =
-        std::floor<Index>(static_cast<float>(num_coeffs) / num_threads);
-    const Index numblocks = blocksize > 0 ? num_coeffs / blocksize : 0;
-    eigen_assert(num_coeffs >= numblocks * blocksize);
-
-    Barrier barrier(internal::convert_index<unsigned int>(numblocks));
-    MaxSizeVector<typename Self::CoeffReturnType> shards(numblocks, reducer.initialize());
-    for (Index i = 0; i < numblocks; ++i) {
-      device.enqueue_with_barrier(&barrier, &FullReducerShard<Self, Op, Vectorizable>::run,
-                                  self, i * blocksize, blocksize, reducer,
-                                  &shards[i]);
-    }
-    typename Self::CoeffReturnType finalShard;
-    if (numblocks * blocksize < num_coeffs) {
-      finalShard = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(
-          self, numblocks * blocksize, num_coeffs - numblocks * blocksize,
-          reducer);
-    } else {
-      finalShard = reducer.initialize();
-    }
-    barrier.Wait();
-
-    for (Index i = 0; i < numblocks; ++i) {
-      reducer.reduce(shards[i], &finalShard);
-    }
-    *output = reducer.finalize(finalShard);
-  }
-};
-
-#endif
-
-
-// Default inner reducer
-template <typename Self, typename Op, typename Device>
-struct InnerReducer {
-  static const bool HasOptimizedImplementation = false;
-
-  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) {
-    eigen_assert(false && "Not implemented");
-    return true;
-  }
-};
-
-// Default outer reducer
-template <typename Self, typename Op, typename Device>
-struct OuterReducer {
-  static const bool HasOptimizedImplementation = false;
-
-  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) {
-    eigen_assert(false && "Not implemented");
-    return true;
-  }
-};
-
-#ifdef EIGEN_USE_SYCL
-// Default Generic reducer
-template <typename Self, typename Op, typename Device>
-struct GenericReducer {
-  static const bool HasOptimizedImplementation = false;
-
-  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) {
-    eigen_assert(false && "Not implemented");
-    return true;
-  }
-};
-#endif
-
-#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
-template <int B, int N, typename S, typename R, typename I_>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*, unsigned int*);
-
-
-#if defined(EIGEN_HAS_GPU_FP16)
-template <typename S, typename R, typename I_>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(R, const S, I_, internal::packet_traits<half>::type*);
-template <int B, int N, typename S, typename R, typename I_>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(R, const S, I_, half*, internal::packet_traits<half>::type*);
-template <int NPT, typename S, typename R, typename I_>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(R, const S, I_, I_, half*);
-
-#endif
-
-template <int NPT, typename S, typename R, typename I_>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
-
-template <int NPT, typename S, typename R, typename I_>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void OuterReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
-#endif
-
-/**
- * For SYCL, the return type of the reduction is deduced from the initialize method of the given Op.
- * This allows the reduction to have a different type for the accumulator than the input data type.
- * If this is the case, the functor needs to have two reduce method: one for reducing an element of the input
- * with the accumulator and the other for reducing two accumulators.
- * Such a reducer can be useful for instance when the accumulator is a boolean or a bitset that checks for
- * some properties of the input.
- */
-template <typename Op, typename CoeffReturnType>
-struct ReductionReturnType {
-#if defined(EIGEN_USE_SYCL)
-  typedef typename remove_const<decltype(std::declval<Op>().initialize())>::type type;
-#else
-  typedef typename remove_const<CoeffReturnType>::type type;
-#endif
-};
-
-}  // end namespace internal
-
-
-template <typename Op, typename Dims, typename XprType,  template <class> class MakePointer_>
-class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType, MakePointer_>, ReadOnlyAccessors> {
-  public:
-    typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorReductionOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorReductionOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorReductionOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorReductionOp(const XprType& expr, const Dims& dims) : m_expr(expr), m_dims(dims)
-    { }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    TensorReductionOp(const XprType& expr, const Dims& dims, const Op& reducer) : m_expr(expr), m_dims(dims), m_reducer(reducer)
-    { }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const XprType& expression() const { return m_expr; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Dims& dims() const { return m_dims; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Op& reducer() const { return m_reducer; }
-
-  protected:
-    typename XprType::Nested m_expr;
-    const Dims m_dims;
-    const Op m_reducer;
-};
-
-template<typename ArgType, typename Device>
-struct TensorReductionEvaluatorBase;
-
-// Eval as rvalue
-template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
-struct TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>
-{
-  typedef internal::reducer_traits<Op, Device> ReducerTraits;
-  typedef Dims ReducedDims;
-  typedef TensorReductionOp<Op, Dims, ArgType, MakePointer_> XprType;
-  typedef typename XprType::Index Index;
-  typedef ArgType ChildType;
-  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
-  static const int NumInputDims = internal::array_size<InputDimensions>::value;
-  static const int NumReducedDims = internal::array_size<Dims>::value;
-  static const int NumOutputDims = NumInputDims - NumReducedDims;
-  typedef typename internal::conditional<NumOutputDims==0, Sizes<>, DSizes<Index, NumOutputDims> >::type Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self;
-  static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess;
-  typedef typename internal::ReductionReturnType<Op, typename XprType::CoeffReturnType>::type CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const Index PacketSize = PacketType<CoeffReturnType, Device>::size;
-
-  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-    // Subset of strides of the input tensor for the non-reduced dimensions.
-  // Indexed by output dimensions.
-  static const int NumPreservedStrides = max_n_1<NumOutputDims>::size;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = Self::InputPacketAccess && ReducerTraits::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = true,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  static const bool ReducingInnerMostDims = internal::are_inner_most_dims<Dims, NumInputDims, Layout>::value;
-  static const bool PreservingInnerMostDims = internal::preserve_inner_most_dims<Dims, NumInputDims, Layout>::value;
-  static const bool RunningFullReduction = (NumOutputDims==0);
-
-  EIGEN_STRONG_INLINE TensorReductionEvaluatorBase(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device)
-  {
-    EIGEN_STATIC_ASSERT((NumInputDims >= NumReducedDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)),
-                        YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    // Build the bitmap indicating if an input dimension is reduced or not.
-    for (int i = 0; i < NumInputDims; ++i) {
-      m_reduced[i] = false;
-    }
-    for (int i = 0; i < NumReducedDims; ++i) {
-      eigen_assert(op.dims()[i] >= 0);
-      eigen_assert(op.dims()[i] < NumInputDims);
-      m_reduced[op.dims()[i]] = true;
-    }
-
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    internal::DimInitializer<Dimensions>::run(input_dims, m_reduced, &m_dimensions, &m_reducedDims);
-
-    // Precompute output strides.
-    if (NumOutputDims > 0) {
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        m_outputStrides[0] = 1;
-        for (int i = 1; i < NumOutputDims; ++i) {
-          m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
-          m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
-        }
-      } else {
-        m_outputStrides[NumOutputDims - 1] = 1;
-        for (int i = NumOutputDims - 2; i >= 0; --i) {
-          m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
-          m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
-        }
-      }
-    }
-
-    // Precompute input strides.
-    if (NumInputDims > 0) {
-      array<Index, NumInputDims> input_strides;
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        input_strides[0] = 1;
-        for (int i = 1; i < NumInputDims; ++i) {
-          input_strides[i] = input_strides[i-1] * input_dims[i-1];
-        }
-      } else {
-        input_strides.back() = 1;
-        for (int i = NumInputDims - 2; i >= 0; --i) {
-          input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
-        }
-      }
-
-      int outputIndex = 0;
-      int reduceIndex = 0;
-      for (int i = 0; i < NumInputDims; ++i) {
-        if (m_reduced[i]) {
-          m_reducedStrides[reduceIndex] = input_strides[i];
-          ++reduceIndex;
-        } else {
-          m_preservedStrides[outputIndex] = input_strides[i];
-          m_output_to_input_dim_map[outputIndex] = i;
-          ++outputIndex;
-        }
-      }
-    }
-
-    // Special case for full reductions
-    if (NumOutputDims == 0) {
-      m_preservedStrides[0] = internal::array_prod(input_dims);
-    }
-
-    m_numValuesToReduce =
-        NumOutputDims == 0
-            ? internal::array_prod(input_dims)
-            : (static_cast<int>(Layout) == static_cast<int>(ColMajor))
-                  ? m_preservedStrides[0]
-                  : m_preservedStrides[NumOutputDims - 1];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE
-  bool evalSubExprsIfNeededCommon(EvaluatorPointerType data) {
-    // Use the FullReducer if possible.
-    if ((RunningFullReduction && RunningOnSycl) ||(RunningFullReduction &&
-        internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation &&
-        ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) ||
-         !RunningOnGPU))) {
-      bool need_assign = false;
-      if (!data) {
-        m_result = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType))));
-        data = m_result;
-        need_assign = true;
-      }
-      Op reducer(m_reducer);
-      internal::FullReducer<Self, Op, Device>::run(*this, reducer, m_device, data);
-      return need_assign;
-    }
-
-    // Attempt to use an optimized reduction.
-    else if ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || (RunningOnSycl)) {
-      bool reducing_inner_dims = true;
-      for (int i = 0; i < NumReducedDims; ++i) {
-        if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-          reducing_inner_dims &= m_reduced[i];
-        } else {
-          reducing_inner_dims &= m_reduced[NumInputDims - 1 - i];
-        }
-      }
-      if (internal::InnerReducer<Self, Op, Device>::HasOptimizedImplementation &&
-          (reducing_inner_dims || ReducingInnerMostDims)) {
-        const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
-        const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
-        if (!data) {
-          if ((num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 128) || (RunningOnSycl)) {
-            data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
-            m_result = data;
-          }
-          else {
-            return true;
-          }
-        }
-        Op reducer(m_reducer);
-        // For SYCL this if always return false
-        if (internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) {
-          if (m_result) {
-            m_device.deallocate_temp(m_result);
-            m_result = NULL;
-          }
-          return true;
-        } else {
-          return (m_result != NULL);
-        }
-      }
-
-      bool preserving_inner_dims = true;
-      for (int i = 0; i < NumReducedDims; ++i) {
-        if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-          preserving_inner_dims &= m_reduced[NumInputDims - 1 - i];
-        } else {
-          preserving_inner_dims &= m_reduced[i];
-        }
-      }
-      if (internal::OuterReducer<Self, Op, Device>::HasOptimizedImplementation &&
-          preserving_inner_dims) {
-        const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
-        const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
-        if (!data) {
-          if ((num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 32) || (RunningOnSycl)) {
-            data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
-            m_result = data;
-          }
-          else {
-            return true;
-          }
-        }
-        Op reducer(m_reducer);
-        // For SYCL this if always return false
-        if (internal::OuterReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) {
-          if (m_result) {
-            m_device.deallocate_temp(m_result);
-            m_result = NULL;
-          }
-          return true;
-        } else {
-          return (m_result != NULL);
-        }
-      }
-      #if defined(EIGEN_USE_SYCL)
-      // If there is no Optimised version for SYCL, the reduction expression 
-      // must break into two subexpression and use the SYCL generic Reducer on the device.
-      if(RunningOnSycl) {
-         const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
-         const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
-         if (!data) {
-           data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
-           m_result = data;
-         }
-         Op reducer(m_reducer);
-         internal::GenericReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve);
-         return (m_result != NULL);
-       }
-      #endif
-    }
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE
-      void
-      evalSubExprsIfNeededAsync(EvaluatorPointerType data,
-                                EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(NULL, [this, data, done](bool) {
-      done(evalSubExprsIfNeededCommon(data));
-    });
-  }
-#endif
-
-  EIGEN_STRONG_INLINE
-  bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return evalSubExprsIfNeededCommon(data);
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-    if (m_result) {
-      m_device.deallocate_temp(m_result);
-      m_result = NULL;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    if (( RunningFullReduction || RunningOnGPU) && m_result ) {
-      return *(m_result + index);
-    }
-    Op reducer(m_reducer);
-    if (ReducingInnerMostDims || RunningFullReduction) {
-      const Index num_values_to_reduce =
-        (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
-      return internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstInput(index),
-                                                             num_values_to_reduce, reducer);
-    } else {
-      typename Self::CoeffReturnType accum = reducer.initialize();
-      internal::GenericDimReducer<NumReducedDims-1, Self, Op>::reduce(*this, firstInput(index), reducer, &accum);
-      return reducer.finalize(accum);
-    }
-  }
-
-  // TODO(bsteiner): provide a more efficient implementation.
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index + PacketSize - 1 < Index(internal::array_prod(dimensions())));
-
-    if (RunningOnGPU && m_result) {
-      return internal::pload<PacketReturnType>(m_result + index);
-    }
-
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    if (ReducingInnerMostDims) {
-      const Index num_values_to_reduce =
-        (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
-      const Index firstIndex = firstInput(index);
-      for (Index i = 0; i < PacketSize; ++i) {
-        Op reducer(m_reducer);
-        values[i] = internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstIndex + i * num_values_to_reduce,
-                                                                    num_values_to_reduce, reducer);
-      }
-    } else if (PreservingInnerMostDims) {
-      const Index firstIndex = firstInput(index);
-      const int innermost_dim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : NumOutputDims - 1;
-      // TBD: extend this the the n innermost dimensions that we preserve.
-      if (((firstIndex % m_dimensions[innermost_dim]) + PacketSize - 1) < m_dimensions[innermost_dim]) {
-        Op reducer(m_reducer);
-        typename Self::PacketReturnType accum = reducer.template initializePacket<typename Self::PacketReturnType>();
-        internal::InnerMostDimPreserver<NumReducedDims-1, Self, Op>::reduce(*this, firstIndex, reducer, &accum);
-        return reducer.finalizePacket(accum);
-      } else {
-        for (int i = 0; i < PacketSize; ++i) {
-          values[i] = coeff(index + i);
-        }
-      }
-    } else {
-      for (int i = 0; i < PacketSize; ++i) {
-        values[i] = coeff(index + i);
-      }
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  // Must be called after evalSubExprsIfNeeded().
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    if (RunningFullReduction && m_result) {
-      return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
-    } else {
-      const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
-      const double compute_cost = num_values_to_reduce * internal::functor_traits<Op>::Cost;
-      return m_impl.costPerCoeff(vectorized) * num_values_to_reduce +
-          TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
-  EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
-  EIGEN_DEVICE_FUNC const Device& device() const { return m_device; }
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-    m_result.bind(cgh);
-  }
-#endif
-
-  private:
-  template <int, typename, typename> friend struct internal::GenericDimReducer;
-  template <typename, typename, bool, bool> friend struct internal::InnerMostDimReducer;
-  template <int, typename, typename, bool> friend struct internal::InnerMostDimPreserver;
-  template <typename S, typename O, typename D, bool V> friend struct internal::FullReducer;
-#ifdef EIGEN_USE_THREADS
-  template <typename S, typename O, bool V> friend struct internal::FullReducerShard;
-#endif
-#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
-  template <int B, int N, typename S, typename R, typename I_> KERNEL_FRIEND void internal::FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*, unsigned int*);
-#if defined(EIGEN_HAS_GPU_FP16)
-  template <typename S, typename R, typename I_> KERNEL_FRIEND void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I_, internal::packet_traits<Eigen::half>::type*);
-  template <int B, int N, typename S, typename R, typename I_> KERNEL_FRIEND void internal::FullReductionKernelHalfFloat(R, const S, I_, half*, internal::packet_traits<Eigen::half>::type*);
-  template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::InnerReductionKernelHalfFloat(R, const S, I_, I_, half*);
-#endif
-  template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
-
-  template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::OuterReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
-#endif
-
-#if defined(EIGEN_USE_SYCL)
- template < typename Evaluator_, typename Op__> friend class TensorSycl::internal::GenericNondeterministicReducer;
- // SYCL need the Generic reducer for the case the recution algorithm is neither inner, outer, and full reducer
- template <typename, typename, typename> friend struct internal::GenericReducer;
-#endif
-
-
-  template <typename S, typename O, typename D> friend struct internal::InnerReducer;
-
-  struct BlockIteratorState {
-    Index input_dim;
-    Index output_size;
-    Index output_count;
-  };
-
-  // Returns the Index in the input tensor of the first value that needs to be
-  // used to compute the reduction at output index "index".
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
-    if (ReducingInnerMostDims) {
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        return index * m_preservedStrides[0];
-      } else {
-        return index * m_preservedStrides[NumPreservedStrides - 1];
-      }
-    }
-    // TBD: optimize the case where we preserve the innermost dimensions.
-    Index startInput = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumOutputDims - 1; i > 0; --i) {
-        // This is index_i in the output tensor.
-        const Index idx = index / m_outputStrides[i];
-        startInput += idx * m_preservedStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      if (PreservingInnerMostDims) {
-        eigen_assert(m_preservedStrides[0] == 1);
-        startInput += index;
-      } else {
-        startInput += index * m_preservedStrides[0];
-      }
-    } else {
-      for (int i = 0; i < NumOutputDims - 1; ++i) {
-        // This is index_i in the output tensor.
-        const Index idx = index / m_outputStrides[i];
-        startInput += idx * m_preservedStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      if (PreservingInnerMostDims) {
-        eigen_assert(m_preservedStrides[NumPreservedStrides - 1] == 1);
-        startInput += index;
-      } else {
-        startInput += index * m_preservedStrides[NumPreservedStrides - 1];
-      }
-    }
-    return startInput;
-  }
-
-  // Bitmap indicating if an input dimension is reduced or not.
-  array<bool, NumInputDims> m_reduced;
-  // Dimensions of the output of the operation.
-  Dimensions m_dimensions;
-  // Precomputed strides for the output tensor.
-  array<Index, NumOutputDims> m_outputStrides;
-  array<internal::TensorIntDivisor<Index>, NumOutputDims> m_fastOutputStrides;
-  array<Index, NumPreservedStrides> m_preservedStrides;
-  // Map from output to input dimension index.
-  array<Index, NumOutputDims> m_output_to_input_dim_map;
-  // How many values go into each reduction
-  Index m_numValuesToReduce;
-
-  // Subset of strides of the input tensor for the reduced dimensions.
-  // Indexed by reduced dimensions.
-  array<Index, NumReducedDims> m_reducedStrides;
-  // Size of the input dimensions that are reduced.
-  // Indexed by reduced dimensions.
-  array<Index, NumReducedDims> m_reducedDims;
-
-  // Evaluator for the input expression.
-  TensorEvaluator<ArgType, Device> m_impl;
-
-  // Operation to apply for computing the reduction.
-  Op m_reducer;
-
-  // For full reductions
-#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
-  static const bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value;
-  static const bool RunningOnSycl = false;
-#elif defined(EIGEN_USE_SYCL)
-static const bool RunningOnSycl = internal::is_same<typename internal::remove_all<Device>::type, Eigen::SyclDevice>::value;
-static const bool RunningOnGPU = false;
-#else
-  static const bool RunningOnGPU = false;
-  static const bool RunningOnSycl = false;
-#endif
-  EvaluatorPointerType m_result;
-
-  const Device EIGEN_DEVICE_REF m_device;
-};
-
-template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
-struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>
-: public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> {
-  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Base;
-  EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Device& device) : Base(op, device){}
-};
-
-
-template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_>
-struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice>
-: public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> {
-
-  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> Base;
-  EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Eigen::SyclDevice& device) : Base(op, device){}
-  // The coeff function in the base the recursive method which is not an standard layout and cannot be used in the SYCL kernel
-  //Therefore the coeff function should be overridden by for SYCL kernel
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::CoeffReturnType coeff(typename Base::Index index) const {
-    return *(this->data() + index);
-  }
-  // The packet function in the base the recursive method which is not an standard layout and cannot be used in the SYCL kernel
-  //Therefore the packet function should be overridden by for SYCL kernel
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::PacketReturnType packet(typename Base::Index index) const {
-    return internal::pload<typename Base::PacketReturnType>(this->data() + index);
-  }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
deleted file mode 100644
index 68780cd..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
+++ /dev/null
@@ -1,6 +0,0 @@
-
-#if defined(__clang__) || defined(__GNUC__)
-#warning "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorReductionGpu.h file"
-#endif
-
-#include "TensorReductionGpu.h"
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
deleted file mode 100644
index db4e8d8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
+++ /dev/null
@@ -1,966 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
-#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
-
-namespace Eigen {
-namespace internal {
-
-
-#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
-// Full reducers for GPU, don't vectorize for now
-
-// Reducer function that enables multiple gpu thread to safely accumulate at the same
-// output address. It basically reads the current value of the output variable, and
-// attempts to update it with the new value. If in the meantime another gpu thread
-// updated the content of the output address it will try again.
-template <typename T, typename R>
-__device__ EIGEN_ALWAYS_INLINE void atomicReduce(T* output, T accum, R& reducer) {
-#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
-  if (sizeof(T) == 4)
-  {
-    unsigned int oldval = *reinterpret_cast<unsigned int*>(output);
-    unsigned int newval = oldval;
-    reducer.reduce(accum, reinterpret_cast<T*>(&newval));
-    if (newval == oldval) {
-      return;
-    }
-    unsigned int readback;
-    while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) {
-      oldval = readback;
-      newval = oldval;
-      reducer.reduce(accum, reinterpret_cast<T*>(&newval));
-      if (newval == oldval) {
-        return;
-      }
-    }
-  }
-  else if (sizeof(T) == 8) {
-    unsigned long long oldval = *reinterpret_cast<unsigned long long*>(output);
-    unsigned long long newval = oldval;
-    reducer.reduce(accum, reinterpret_cast<T*>(&newval));
-    if (newval == oldval) {
-      return;
-    }
-    unsigned long long readback;
-    while ((readback = atomicCAS((unsigned long long*)output, oldval, newval)) != oldval) {
-      oldval = readback;
-      newval = oldval;
-      reducer.reduce(accum, reinterpret_cast<T*>(&newval));
-      if (newval == oldval) {
-        return;
-      }
-    }
-  }
-  else {
-    gpu_assert(0 && "Wordsize not supported");
-  }
-#else // EIGEN_CUDA_ARCH >= 300
-  gpu_assert(0 && "Shouldn't be called on unsupported device");
-#endif // EIGEN_CUDA_ARCH >= 300
-}
-
-// We extend atomicExch to support extra data types
-template <typename Type>
-__device__ inline Type atomicExchCustom(Type* address, Type val) {
-  return atomicExch(address, val);
-}
-
-template <>
-__device__ inline double atomicExchCustom(double* address, double val) {
-  unsigned long long int* address_as_ull = reinterpret_cast<unsigned long long int*>(address);
-  return __longlong_as_double(atomicExch(address_as_ull, __double_as_longlong(val)));
-}
-
-#ifdef EIGEN_HAS_GPU_FP16
-template <typename R>
-__device__ inline void atomicReduce(half2* output, half2 accum, R& reducer) {
-  unsigned int oldval = *reinterpret_cast<unsigned int*>(output);
-  unsigned int newval = oldval;
-  reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval));
-  if (newval == oldval) {
-    return;
-  }
-  unsigned int readback;
-  while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) {
-    oldval = readback;
-    newval = oldval;
-    reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval));
-    if (newval == oldval) {
-      return;
-    }
-  }
-}
-// reduction should be associative since reduction is not atomic in wide vector but atomic in half2 operations
-template <typename R>
-__device__ inline void atomicReduce(Packet4h2* output, Packet4h2 accum, R& reducer) {
-  half2* houtput=reinterpret_cast<half2*>(output);
-  half2* haccum=reinterpret_cast<half2*>(&accum);
-  for(int i=0;i<4;++i){
-    atomicReduce(houtput+i,*(haccum+i),reducer);
-  }
-}
-#endif  // EIGEN_HAS_GPU_FP16
-
-template <>
-__device__ inline void atomicReduce(float* output, float accum, SumReducer<float>&) {
-#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
-  atomicAdd(output, accum);
-#else // EIGEN_CUDA_ARCH >= 300
-  gpu_assert(0 && "Shouldn't be called on unsupported device");
-#endif // EIGEN_CUDA_ARCH >= 300
-}
-
-
-template <typename CoeffType, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernel(const CoeffType val, Index num_preserved_coeffs, CoeffType* output) {
-  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
-  const Index num_threads = blockDim.x * gridDim.x;
-  for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
-    output[i] = val;
-  }
-}
-
-
-template <int BlockSize, int NumPerThread, typename Self,
-          typename Reducer, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernel(Reducer reducer, const Self input, Index num_coeffs,
-                                    typename Self::CoeffReturnType* output, unsigned int* semaphore) {
-#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
-  // Initialize the output value
-  const Index first_index = blockIdx.x * BlockSize * NumPerThread + threadIdx.x;
-  if (gridDim.x == 1) {
-    if (first_index == 0) {
-      *output = reducer.initialize();
-    }
-  }
-  else {
-    if (threadIdx.x == 0) {
-      unsigned int block = atomicCAS(semaphore, 0u, 1u);
-      if (block == 0) {
-        // We're the first block to run, initialize the output value
-        atomicExchCustom(output, reducer.initialize());
-        __threadfence();
-        atomicExch(semaphore, 2u);
-      }
-      else {
-        // Wait for the first block to initialize the output value.
-        // Use atomicCAS here to ensure that the reads aren't cached
-        unsigned int val;
-        do {
-          val = atomicCAS(semaphore, 2u, 2u);
-        }
-        while (val < 2u);
-      }
-    }
-  }
-
-  __syncthreads();
-
-  eigen_assert(gridDim.x == 1 || *semaphore >= 2u);
-
-  typename Self::CoeffReturnType accum = reducer.initialize();
-  Index max_iter = numext::mini<Index>(num_coeffs - first_index, NumPerThread*BlockSize);
-  for (Index i = 0; i < max_iter; i+=BlockSize) {
-    const Index index = first_index + i;
-    eigen_assert(index < num_coeffs);
-    typename Self::CoeffReturnType val = input.m_impl.coeff(index);
-    reducer.reduce(val, &accum);
-  }
-
-#pragma unroll
-  for (int offset = warpSize/2; offset > 0; offset /= 2) {
-  #if defined(EIGEN_HIPCC)
-    // use std::is_floating_point to determine the type of reduced_val 
-    // This is needed because when Type == double, hipcc will give a "call to __shfl_down is ambguous" error 
-    // and list the float and int versions of __shfl_down as the candidate functions. 
-    if (std::is_floating_point<typename Self::CoeffReturnType>::value) {
-      reducer.reduce(__shfl_down(static_cast<float>(accum), offset, warpSize), &accum);
-    } else {
-      reducer.reduce(__shfl_down(static_cast<int>(accum), offset, warpSize), &accum);
-    }
-  #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
-    reducer.reduce(__shfl_down(accum, offset, warpSize), &accum);
-  #else
-    reducer.reduce(__shfl_down_sync(0xFFFFFFFF, accum, offset, warpSize), &accum);
-  #endif
-  }
-
-  if ((threadIdx.x & (warpSize - 1)) == 0) {
-    atomicReduce(output, accum, reducer);
-  }
-
-  if (gridDim.x > 1 && threadIdx.x == 0) {
-    // Let the last block reset the semaphore
-    atomicInc(semaphore, gridDim.x + 1);
-#if defined(EIGEN_HIPCC)
-    __threadfence_system();
-#endif
-  }
-#else // EIGEN_CUDA_ARCH >= 300
-  gpu_assert(0 && "Shouldn't be called on unsupported device");
-#endif // EIGEN_CUDA_ARCH >= 300
-}
-
-
-#ifdef EIGEN_HAS_GPU_FP16
-template <typename Self,
-          typename Reducer, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
-                                                      packet_traits<Eigen::half>::type* scratch) {
-  eigen_assert(blockDim.x == 1);
-  eigen_assert(gridDim.x == 1);
-  typedef packet_traits<Eigen::half>::type packet_type;
-  Index packet_remainder =
-      num_coeffs % Index(unpacket_traits<packet_type>::size);
-  if (packet_remainder != 0) {
-    half2* h2scratch = reinterpret_cast<half2*>(scratch);
-    for (Index i = num_coeffs - packet_remainder; i + 2 <= num_coeffs; i += 2) {
-      *h2scratch =
-          __halves2half2(input.m_impl.coeff(i), input.m_impl.coeff(i + 1));
-      h2scratch++;
-    }
-    if ((num_coeffs & 1) != 0) {
-      half lastCoeff = input.m_impl.coeff(num_coeffs - 1);
-      *h2scratch = __halves2half2(lastCoeff, reducer.initialize());
-    }
-  } else {
-    *scratch = reducer.template initializePacket<packet_type>();
-  }
-}
-
-template <typename Self,
-          typename Reducer, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs, half* output) {
-  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
-  const Index num_threads = blockDim.x * gridDim.x;
-  typedef typename packet_traits<Eigen::half>::type PacketType;
-
-  const Index num_packets =
-      num_coeffs / Index(unpacket_traits<PacketType>::size);
-  PacketType* p_output = reinterpret_cast<PacketType*>(output);
-  for (Index i = thread_id; i < num_packets; i += num_threads) {
-    p_output[i] = reducer.template initializePacket<PacketType>();
-  }
-  Index packet_remainder =
-      num_coeffs % Index(unpacket_traits<PacketType>::size);
-  if (thread_id < packet_remainder) {
-    output[num_coeffs - packet_remainder + thread_id] = reducer.initialize();
-  }
-}
-
-template <int BlockSize, int NumPerThread, typename Self,
-          typename Reducer, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
-                                    half* output, packet_traits<Eigen::half>::type* scratch) {
-  typedef typename packet_traits<Eigen::half>::type PacketType;
-  const int packet_width = unpacket_traits<PacketType>::size;
-  eigen_assert(NumPerThread % packet_width == 0);
-  const Index first_index =
-      blockIdx.x * BlockSize * NumPerThread + packet_width * threadIdx.x;
-
-  // Initialize the output value if it wasn't initialized by the ReductionInitKernel
-
-  if (gridDim.x == 1) {
-    if (first_index == 0) {
-      int rem = num_coeffs % packet_width;
-      if (rem != 0) {
-        half2* p_scratch = reinterpret_cast<half2*>(scratch);
-        *scratch = reducer.template initializePacket<PacketType>();
-        for (int i = 0; i < rem / 2; i++) {
-          *p_scratch = __halves2half2(
-              input.m_impl.coeff(num_coeffs - packet_width + 2 * i),
-              input.m_impl.coeff(num_coeffs - packet_width + 2 * i + 1));
-          p_scratch++;
-        }
-        if ((num_coeffs & 1) != 0) {
-          half last = input.m_impl.coeff(num_coeffs - 1);
-          *p_scratch = __halves2half2(last, reducer.initialize());
-        }
-      } else {
-        *scratch = reducer.template initializePacket<PacketType>();
-      }
-    }
-    __syncthreads();
-  }
-
-  PacketType accum = reducer.template initializePacket<PacketType>();
-  const Index max_iter =
-      numext::mini<Index>((num_coeffs - first_index) / packet_width,
-                          NumPerThread * BlockSize / packet_width);
-  for (Index i = 0; i < max_iter; i += BlockSize) {
-    const Index index = first_index + packet_width * i;
-    eigen_assert(index + packet_width < num_coeffs);
-    PacketType val = input.m_impl.template packet<Unaligned>(index);
-    reducer.reducePacket(val, &accum);
-  }
-
-#pragma unroll
-  for (int offset = warpSize/2; offset > 0; offset /= 2) {
-  #if defined(EIGEN_HIPCC)
-    PacketType r1;
-    half2* hr = reinterpret_cast<half2*>(&r1);
-    half2* hacc = reinterpret_cast<half2*>(&accum);
-    for (int i = 0; i < packet_width / 2; i++) {
-      // FIXME : remove this workaround once we have native half/half2 support for __shfl_down
-      union { int i; half2 h; } wka_in, wka_out;
-      wka_in.h = hacc[i];
-      wka_out.i = __shfl_down(wka_in.i, offset, warpSize);
-      hr[i] = wka_out.h;
-    }
-    reducer.reducePacket(r1, &accum);
-  #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
-    PacketType r1;
-    half2* hr = reinterpret_cast<half2*>(&r1);
-    half2* hacc = reinterpret_cast<half2*>(&accum);
-    for (int i = 0; i < packet_width / 2; i++) {
-      hr[i] = __shfl_down(hacc[i], offset, warpSize);
-    }
-    reducer.reducePacket(r1, &accum);
-  #else
-    PacketType r1;
-    half2* hr = reinterpret_cast<half2*>(&r1);
-    half2* hacc = reinterpret_cast<half2*>(&accum);
-    for (int i = 0; i < packet_width / 2; i++) {
-      hr[i] = __shfl_down_sync(0xFFFFFFFF, hacc[i], (unsigned)offset, warpSize);
-    }
-    reducer.reducePacket(r1, &accum);
-
-  #endif
-  }
-
-  if ((threadIdx.x & (warpSize - 1)) == 0) {
-    atomicReduce(scratch, accum, reducer);
-  }
-
-  __syncthreads();
-  half2* rv1 = reinterpret_cast<half2*>(scratch);
-  if (packet_width > 2) {
-    reducer.reducePacket(rv1[2], rv1);
-    reducer.reducePacket(rv1[3], rv1 + 1);
-    reducer.reducePacket(rv1[1], rv1);
-  }
-  if (gridDim.x == 1) {
-    if (first_index == 0) {
-      half tmp = __low2half(*rv1);
-      reducer.reduce(__high2half(*rv1), &tmp);
-      *output = tmp;
-    }
-  }
-}
-
-template <typename Op>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionCleanupKernelHalfFloat(Op reducer, half* output, packet_traits<Eigen::half>::type* scratch) {
-  eigen_assert(threadIdx.x == 1);
-  half2* pscratch = reinterpret_cast<half2*>(scratch);
-  half tmp = __float2half(0.f);
-  typedef packet_traits<Eigen::half>::type packet_type;
-  for (int i = 0; i < unpacket_traits<packet_type>::size; i += 2) {
-    reducer.reduce(__low2half(*pscratch), &tmp);
-    reducer.reduce(__high2half(*pscratch), &tmp);
-    pscratch++;
-  }
-  *output = tmp;
-}
-
-#endif // EIGEN_HAS_GPU_FP16
-
-template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void>
-struct FullReductionLauncher {
-  static void run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index) {
-    gpu_assert(false && "Should only be called on doubles, floats and half floats");
-  }
-};
-
-// Specialization for float and double
-template <typename Self, typename Op, typename OutputType, bool PacketAccess>
-struct FullReductionLauncher<
-    Self, Op, OutputType, PacketAccess,
-    typename internal::enable_if<
-      internal::is_same<float, OutputType>::value ||
-      internal::is_same<double, OutputType>::value,
-    void>::type> {
-  static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs) {
-
-    typedef typename Self::Index Index;
-    const int block_size = 256;
-    const int num_per_thread = 128;
-    const int num_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
-
-    unsigned int* semaphore = NULL;
-    if (num_blocks > 1) {
-      semaphore = device.semaphore();
-    }
-
-    LAUNCH_GPU_KERNEL((FullReductionKernel<block_size, num_per_thread, Self, Op, Index>),
-                       num_blocks, block_size, 0, device, reducer, self, num_coeffs, output, semaphore);
-  }
-};
-
-#ifdef EIGEN_HAS_GPU_FP16
-template <typename Self, typename Op>
-struct FullReductionLauncher<Self, Op, Eigen::half, false> {
-  static void run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index) {
-    gpu_assert(false && "Should not be called since there is no packet accessor");
-  }
-};
-
-template <typename Self, typename Op>
-struct FullReductionLauncher<Self, Op, Eigen::half, true> {
-  static void run(const Self& self, Op& reducer, const GpuDevice& device, half* output, typename Self::Index num_coeffs) {
-    typedef typename Self::Index Index;
-    typedef typename packet_traits<Eigen::half>::type PacketType;
-
-    const int block_size = 256;
-    const int num_per_thread = 128;
-    const int num_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
-    PacketType* scratch = static_cast<PacketType*>(device.scratchpad());
-    // half2* scratch = static_cast<half2*>(device.scratchpad());
-
-    if (num_blocks > 1) {
-      // We initialize the output and the scrathpad outside the reduction kernel when we can't be sure that there
-      // won't be a race conditions between multiple thread blocks.
-      LAUNCH_GPU_KERNEL((ReductionInitFullReduxKernelHalfFloat<Self, Op, Index>),
-                         1, 1, 0, device, reducer, self, num_coeffs, scratch);
-    }
-
-    LAUNCH_GPU_KERNEL((FullReductionKernelHalfFloat<block_size, num_per_thread, Self, Op, Index>),
-                       num_blocks, block_size, 0, device, reducer, self, num_coeffs, output, scratch);
-
-    if (num_blocks > 1) {
-      LAUNCH_GPU_KERNEL((ReductionCleanupKernelHalfFloat<Op>),
-                         1, 1, 0, device, reducer, output, scratch);
-    }
-  }
-};
-#endif // EIGEN_HAS_GPU_FP16
-
-
-template <typename Self, typename Op, bool Vectorizable>
-struct FullReducer<Self, Op, GpuDevice, Vectorizable> {
-  // Unfortunately nvidia doesn't support well exotic types such as complex,
-  // so reduce the scope of the optimized version of the code to the simple cases
-  // of doubles, floats and half floats
-#ifdef EIGEN_HAS_GPU_FP16
-  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
-      (internal::is_same<typename Self::CoeffReturnType, float>::value ||
-       internal::is_same<typename Self::CoeffReturnType, double>::value ||
-       (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess));
-#else // EIGEN_HAS_GPU_FP16
-  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
-                                                (internal::is_same<typename Self::CoeffReturnType, float>::value ||
-                                                 internal::is_same<typename Self::CoeffReturnType, double>::value);
-#endif // EIGEN_HAS_GPU_FP16
-
-  template <typename OutputType>
-  static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output) {
-    gpu_assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats");
-    const Index num_coeffs = array_prod(self.m_impl.dimensions());
-    // Don't crash when we're called with an input tensor of size 0.
-    if (num_coeffs == 0) {
-      return;
-    }
-
-    FullReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs);
-  }
-};
-
-
-template <int NumPerThread, typename Self,
-          typename Reducer, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
-                                         typename Self::CoeffReturnType* output) {
-#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
-  typedef typename Self::CoeffReturnType Type;
-  eigen_assert(blockDim.y == 1);
-  eigen_assert(blockDim.z == 1);
-  eigen_assert(gridDim.y == 1);
-  eigen_assert(gridDim.z == 1);
-
-  const int unroll_times = 16;
-  eigen_assert(NumPerThread % unroll_times == 0);
-
-  const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread);
-  const Index num_input_blocks = input_col_blocks * num_preserved_coeffs;
-
-  const Index num_threads = blockDim.x * gridDim.x;
-  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
-
-  // Initialize the output values if they weren't initialized by the ReductionInitKernel
-  if (gridDim.x == 1) {
-    for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
-      output[i] = reducer.initialize();
-    }
-    __syncthreads();
-  }
-
-  for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) {
-    const Index row = i / input_col_blocks;
-
-    if (row < num_preserved_coeffs) {
-      const Index col_block = i % input_col_blocks;
-      const Index col_begin = col_block * blockDim.x * NumPerThread + threadIdx.x;
-
-      Type reduced_val = reducer.initialize();
-
-      for (Index j = 0; j < NumPerThread; j += unroll_times) {
-        const Index last_col = col_begin + blockDim.x * (j + unroll_times - 1);
-        if (last_col >= num_coeffs_to_reduce) {
-          for (Index col = col_begin + blockDim.x * j; col < num_coeffs_to_reduce; col += blockDim.x) {
-            const Type val = input.m_impl.coeff(row * num_coeffs_to_reduce + col);
-            reducer.reduce(val, &reduced_val);
-          }
-          break;
-        } else {
-          // Faster version of the loop with no branches after unrolling.
-#pragma unroll
-          for (int k = 0; k < unroll_times; ++k) {
-            const Index col = col_begin + blockDim.x * (j + k);
-            reducer.reduce(input.m_impl.coeff(row * num_coeffs_to_reduce + col), &reduced_val);
-          }
-        }
-      }
-
-#pragma unroll
-      for (int offset = warpSize/2; offset > 0; offset /= 2) {
-      #if defined(EIGEN_HIPCC)
-        // use std::is_floating_point to determine the type of reduced_val 
-       // This is needed because when Type == double, hipcc will give a "call to __shfl_down is ambguous" error 
-       // and list the float and int versions of __shfl_down as the candidate functions. 
-        if (std::is_floating_point<Type>::value) {
-          reducer.reduce(__shfl_down(static_cast<float>(reduced_val), offset), &reduced_val);
-        } else {
-          reducer.reduce(__shfl_down(static_cast<int>(reduced_val), offset), &reduced_val);
-        }
-      #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
-        reducer.reduce(__shfl_down(reduced_val, offset), &reduced_val);
-      #else
-        reducer.reduce(__shfl_down_sync(0xFFFFFFFF, reduced_val, offset), &reduced_val);
-      #endif
-      }
-
-      if ((threadIdx.x & (warpSize - 1)) == 0) {
-        atomicReduce(&(output[row]), reduced_val, reducer);
-      }
-    }
-  }
-#else // EIGEN_CUDA_ARCH >= 300
-  gpu_assert(0 && "Shouldn't be called on unsupported device");
-#endif // EIGEN_CUDA_ARCH >= 300
-}
-
-#ifdef EIGEN_HAS_GPU_FP16
-
-template <int NumPerThread, typename Self,
-          typename Reducer, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
-                                              half* output) {
-  eigen_assert(blockDim.y == 1);
-  eigen_assert(blockDim.z == 1);
-  eigen_assert(gridDim.y == 1);
-  eigen_assert(gridDim.z == 1);
-
-  typedef typename packet_traits<Eigen::half>::type PacketType;
-  const int packet_width = unpacket_traits<PacketType>::size;
-  const int unroll_times = 16 / packet_width;
-  eigen_assert(NumPerThread % unroll_times == 0);
-  eigen_assert(unroll_times % 2 == 0);
-
-  const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread * 2);
-  const Index num_input_blocks = divup<Index>(input_col_blocks * num_preserved_coeffs, 2);
-
-  const Index num_threads = blockDim.x * gridDim.x;
-  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
-
-  // Initialize the output values if they weren't initialized by the ReductionInitKernel
-  if (gridDim.x == 1) {
-    Index i = packet_width * thread_id;
-    for (; i + packet_width <= num_preserved_coeffs;
-         i += packet_width * num_threads) {
-      PacketType* poutput = reinterpret_cast<PacketType*>(output + i);
-      *poutput = reducer.template initializePacket<PacketType>();
-    }
-    if (i < num_preserved_coeffs) {
-      output[i] = reducer.initialize();
-    }
-    __syncthreads();
-  }
-
-  for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) {
-    const Index row = 2 * (i / input_col_blocks);  // everybody takes 2 rows
-
-    if (row + 1 < num_preserved_coeffs) {
-      const Index col_block = i % input_col_blocks;
-      const Index col_begin =
-          packet_width * (col_block * blockDim.x * NumPerThread + threadIdx.x);
-
-      PacketType reduced_val1 = reducer.template initializePacket<PacketType>();
-      PacketType reduced_val2 = reducer.template initializePacket<PacketType>();
-
-      for (Index j = 0; j < NumPerThread; j += unroll_times) {
-        const Index last_col =
-            col_begin + blockDim.x * (j + unroll_times - 1) * packet_width;
-        if (last_col >= num_coeffs_to_reduce) {
-          Index col = col_begin + blockDim.x * j;
-          for (; col + packet_width <= num_coeffs_to_reduce;
-               col += blockDim.x) {
-            const PacketType val1 = input.m_impl.template packet<Unaligned>(
-                row * num_coeffs_to_reduce + col);
-            reducer.reducePacket(val1, &reduced_val1);
-            const PacketType val2 = input.m_impl.template packet<Unaligned>(
-                (row + 1) * num_coeffs_to_reduce + col);
-            reducer.reducePacket(val2, &reduced_val2);
-          }
-          if (col < num_coeffs_to_reduce) {
-            PacketType r1 = reducer.template initializePacket<PacketType>();
-            PacketType r2 = reducer.template initializePacket<PacketType>();
-            half2* hr1 = reinterpret_cast<half2*>(&r1);
-            half2* hr2 = reinterpret_cast<half2*>(&r2);
-            while (col + 1 < num_coeffs_to_reduce) {
-              *hr1 = __halves2half2(
-                  input.m_impl.coeff(row * num_coeffs_to_reduce + col),
-                  input.m_impl.coeff(row * num_coeffs_to_reduce + col + 1));
-              *hr2 = __halves2half2(
-                  input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col),
-                  input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col +
-                                     1));
-              hr1++;
-              hr2++;
-              col += 2;
-            }
-            if (col < num_coeffs_to_reduce) {
-              // Peel;
-              const half last1 =
-                  input.m_impl.coeff(row * num_coeffs_to_reduce + col);
-              *hr1 = __halves2half2(last1, reducer.initialize());
-              const half last2 =
-                  input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col);
-              *hr2 = __halves2half2(last2, reducer.initialize());
-            }
-            reducer.reducePacket(r1, &reduced_val1);
-            reducer.reducePacket(r2, &reduced_val2);
-          }
-          break;
-        } else {
-          // Faster version of the loop with no branches after unrolling.
-#pragma unroll
-          for (int k = 0; k < unroll_times; ++k) {
-            const Index col = col_begin + blockDim.x * (j + k) * packet_width;
-            reducer.reducePacket(input.m_impl.template packet<Unaligned>(
-                                     row * num_coeffs_to_reduce + col),
-                                 &reduced_val1);
-            reducer.reducePacket(input.m_impl.template packet<Unaligned>(
-                                     (row + 1) * num_coeffs_to_reduce + col),
-                                 &reduced_val2);
-          }
-        }
-      }
-
-#pragma unroll
-      for (int offset = warpSize/2; offset > 0; offset /= 2) {
-      #if defined(EIGEN_HIPCC)
-        PacketType r1;
-        PacketType r2;
-        half2* hr1 = reinterpret_cast<half2*>(&r1);
-        half2* hr2 = reinterpret_cast<half2*>(&r2);
-        half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
-        half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
-        for (int i = 0; i < packet_width / 2; i++) {
-	  // FIXME : remove this workaround once we have native half/half2 support for __shfl_down
-	  union { int i; half2 h; } wka_in1, wka_out1;
-	  wka_in1.h = rv1[i];
-	  wka_out1.i = __shfl_down(wka_in1.i, offset, warpSize);
-	  hr1[i] = wka_out1.h;
-
-	  union { int i; half2 h; } wka_in2, wka_out2;
-	  wka_in2.h = rv2[i];
-	  wka_out2.i = __shfl_down(wka_in2.i, offset, warpSize);
-	  hr2[i] = wka_out2.h;
-        }
-        reducer.reducePacket(r1, &reduced_val1);
-        reducer.reducePacket(r2, &reduced_val2);
-      #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
-        PacketType r1;
-        PacketType r2;
-        half2* hr1 = reinterpret_cast<half2*>(&r1);
-        half2* hr2 = reinterpret_cast<half2*>(&r2);
-        half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
-        half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
-        for (int i = 0; i < packet_width / 2; i++) {
-          hr1[i] = __shfl_down(rv1[i], offset, warpSize);
-          hr2[i] = __shfl_down(rv2[i], offset, warpSize);
-        }
-        reducer.reducePacket(r1, &reduced_val1);
-        reducer.reducePacket(r2, &reduced_val2);
-      #else
-        PacketType r1;
-        PacketType r2;
-        half2* hr1 = reinterpret_cast<half2*>(&r1);
-        half2* hr2 = reinterpret_cast<half2*>(&r2);
-        half2* rr1 = reinterpret_cast<half2*>(&reduced_val1);
-        half2* rr2 = reinterpret_cast<half2*>(&reduced_val2);
-        for (int i = 0; i < packet_width / 2; i++) {
-          hr1[i] =
-              __shfl_down_sync(0xFFFFFFFF, rr1[i], (unsigned)offset, warpSize);
-          hr2[i] =
-              __shfl_down_sync(0xFFFFFFFF, rr2[i], (unsigned)offset, warpSize);
-        }
-        reducer.reducePacket(r1, &reduced_val1);
-        reducer.reducePacket(r2, &reduced_val2);
-
-      #endif
-      }
-      half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
-      half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
-      half2 val;
-      if (packet_width > 2) {
-        reducer.reducePacket(rv1[2], rv1);
-        reducer.reducePacket(rv1[3], rv1 + 1);
-        reducer.reducePacket(rv1[1], rv1);
-        reducer.reducePacket(rv2[2], rv2);
-        reducer.reducePacket(rv2[3], rv2 + 1);
-        reducer.reducePacket(rv2[1], rv2);
-      }
-      half val1 = __low2half(*rv1);
-      reducer.reduce(__high2half(*rv1), &val1);
-      half val2 = __low2half(*rv2);
-      reducer.reduce(__high2half(*rv2), &val2);
-      val = __halves2half2(val1, val2);
-      if ((threadIdx.x & (warpSize - 1)) == 0) {
-        half* loc = output + row;
-        atomicReduce((half2*)loc, val, reducer);
-      }
-    }
-  }
-}
-
-#endif // EIGEN_HAS_GPU_FP16
-
-template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void>
-struct InnerReductionLauncher {
-  static EIGEN_DEVICE_FUNC bool run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index, typename Self::Index) {
-    gpu_assert(false && "Should only be called to reduce doubles, floats and half floats on a gpu device");
-    return true;
-  }
-};
-
-// Specialization for float and double
-template <typename Self, typename Op, typename OutputType, bool PacketAccess>
-struct InnerReductionLauncher<
-  Self, Op, OutputType, PacketAccess,
-  typename internal::enable_if<
-    internal::is_same<float, OutputType>::value ||
-    internal::is_same<double, OutputType>::value,
-  void>::type> {
-  static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
-    typedef typename Self::Index Index;
-
-    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
-    const int block_size = 256;
-    const int num_per_thread = 128;
-    const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
-    const int max_blocks = device.getNumGpuMultiProcessors() *
-                           device.maxGpuThreadsPerMultiProcessor() / block_size;
-    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
-
-    if (num_blocks > 1) {
-      // We initialize the outputs outside the reduction kernel when we can't be sure that there
-      // won't be a race conditions between multiple thread blocks.
-      const int dyn_blocks = divup<int>(num_preserved_vals, 1024);
-      const int max_blocks = device.getNumGpuMultiProcessors() *
-                           device.maxGpuThreadsPerMultiProcessor() / 1024;
-      const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
-      LAUNCH_GPU_KERNEL((ReductionInitKernel<OutputType, Index>),
-                         num_blocks, 1024, 0, device, reducer.initialize(),
-                         num_preserved_vals, output);
-    }
-
-    LAUNCH_GPU_KERNEL((InnerReductionKernel<num_per_thread, Self, Op, Index>),
-                       num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
-
-    return false;
-  }
-};
-
-#ifdef EIGEN_HAS_GPU_FP16
-template <typename Self, typename Op>
-struct InnerReductionLauncher<Self, Op, Eigen::half, false> {
-  static bool run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index, typename Self::Index) {
-    gpu_assert(false && "Should not be called since there is no packet accessor");
-    return true;
-  }
-};
-
-template <typename Self, typename Op>
-struct InnerReductionLauncher<Self, Op, Eigen::half, true> {
-  static bool run(const Self& self, Op& reducer, const GpuDevice& device, half* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
-    typedef typename Self::Index Index;
-
-    if (num_preserved_vals % 2 != 0) {
-      // Not supported yet, revert to the slower code path
-      return true;
-    }
-
-    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
-    const int block_size = /*256*/128;
-    const int num_per_thread = /*128*/64;
-    const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
-    const int max_blocks = device.getNumGpuMultiProcessors() *
-                           device.maxGpuThreadsPerMultiProcessor() / block_size;
-    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
-
-    if (num_blocks > 1) {
-      // We initialize the outputs outside the reduction kernel when we can't be sure that there
-      // won't be a race conditions between multiple thread blocks.
-      LAUNCH_GPU_KERNEL((ReductionInitKernelHalfFloat<Self, Op, Index>),
-                         1, 1, 0, device, reducer, self, num_preserved_vals, output);
-    }
-
-    LAUNCH_GPU_KERNEL((InnerReductionKernelHalfFloat<num_per_thread, Self, Op, Index>),
-                       num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
-
-    return false;
-  }
-};
-#endif // EIGEN_HAS_GPU_FP16
-
-
-template <typename Self, typename Op>
-struct InnerReducer<Self, Op, GpuDevice> {
-  // Unfortunately nvidia doesn't support well exotic types such as complex,
-  // so reduce the scope of the optimized version of the code to the simple case
-  // of floats and half floats.
-#ifdef EIGEN_HAS_GPU_FP16
-  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
-      (internal::is_same<typename Self::CoeffReturnType, float>::value ||
-       internal::is_same<typename Self::CoeffReturnType, double>::value ||
-       (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess));
-#else // EIGEN_HAS_GPU_FP16
-  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
-                                                 (internal::is_same<typename Self::CoeffReturnType, float>::value ||
-                                                  internal::is_same<typename Self::CoeffReturnType, double>::value);
-#endif // EIGEN_HAS_GPU_FP16
-
-  template <typename OutputType>
-  static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
-    gpu_assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats");
-    const Index num_coeffs = array_prod(self.m_impl.dimensions());
-    // Don't crash when we're called with an input tensor of size 0.
-    if (num_coeffs == 0) {
-      return true;
-    }
-    // It's faster to use the usual code.
-    if (num_coeffs_to_reduce <= 128) {
-      return true;
-    }
-
-    return InnerReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs_to_reduce, num_preserved_vals);
-  }
-};
-
-template <int NumPerThread, typename Self,
-          typename Reducer, typename Index>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void OuterReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
-                                     typename Self::CoeffReturnType* output) {
-  const Index num_threads = blockDim.x * gridDim.x;
-  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
-  // Initialize the output values if they weren't initialized by the ReductionInitKernel
-  if (gridDim.x == 1) {
-    for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
-      output[i] = reducer.initialize();
-    }
-    __syncthreads();
-  }
-
-  // Do the reduction.
-  const Index max_iter = num_preserved_coeffs * divup<Index>(num_coeffs_to_reduce, NumPerThread);
-  for (Index i = thread_id; i < max_iter; i += num_threads) {
-    const Index input_col = i % num_preserved_coeffs;
-    const Index input_row = (i / num_preserved_coeffs) * NumPerThread;
-    typename Self::CoeffReturnType reduced_val = reducer.initialize();
-    const Index max_row = numext::mini(input_row + NumPerThread, num_coeffs_to_reduce);
-    for (Index j = input_row; j < max_row; j++) {
-      typename Self::CoeffReturnType val = input.m_impl.coeff(j * num_preserved_coeffs + input_col);
-      reducer.reduce(val, &reduced_val);
-    }
-    atomicReduce(&(output[input_col]), reduced_val, reducer);
-  }
-}
-
-
-template <typename Self, typename Op>
-struct OuterReducer<Self, Op, GpuDevice> {
-  // Unfortunately nvidia doesn't support well exotic types such as complex,
-  // so reduce the scope of the optimized version of the code to the simple case
-  // of floats.
-  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
-                                                 (internal::is_same<typename Self::CoeffReturnType, float>::value ||
-                                                  internal::is_same<typename Self::CoeffReturnType, double>::value);
-  template <typename Device, typename OutputType>
-  static
-    #if !defined(EIGEN_HIPCC)
-    // FIXME :  leaving this EIGEN_DEVICE_FUNC in, results in the following runtime error
-    //          (in the cxx11_tensor_reduction_gpu test)
-    //
-    // terminate called after throwing an instance of 'std::runtime_error'
-    //   what():  No device code available for function: _ZN5Eigen8internal20OuterReductionKernelIL...
-    //
-    // don't know why this happens (and why is it a runtime error instead of a compile time error)
-    //
-    // this will be fixed by HIP PR#457
-    EIGEN_DEVICE_FUNC
-    #endif
-    bool run(const Self&, Op&, const Device&, OutputType*, typename Self::Index, typename Self::Index) {
-    gpu_assert(false && "Should only be called to reduce doubles or floats on a gpu device");
-    return true;
-  }
-
-  static bool run(const Self& self, Op& reducer, const GpuDevice& device, float* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
-    typedef typename Self::Index Index;
-
-    // It's faster to use the usual code.
-    if (num_coeffs_to_reduce <= 32) {
-      return true;
-    }
-
-    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
-    const int block_size = 256;
-    const int num_per_thread = 16;
-    const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
-    const int max_blocks = device.getNumGpuMultiProcessors() *
-                           device.maxGpuThreadsPerMultiProcessor() / block_size;
-    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
-
-    if (num_blocks > 1) {
-      // We initialize the outputs in the reduction kernel itself when we don't have to worry
-      // about race conditions between multiple thread blocks.
-      const int dyn_blocks = divup<int>(num_preserved_vals, 1024);
-      const int max_blocks = device.getNumGpuMultiProcessors() *
-                             device.maxGpuThreadsPerMultiProcessor() / 1024;
-      const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
-      LAUNCH_GPU_KERNEL((ReductionInitKernel<float, Index>),
-                         num_blocks, 1024, 0, device, reducer.initialize(),
-                         num_preserved_vals, output);
-    }
-
-    LAUNCH_GPU_KERNEL((OuterReductionKernel<num_per_thread, Self, Op, Index>),
-                       num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
-
-    return false;
-  }
-};
-
-#endif // defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
-
-
-} // end namespace internal
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
deleted file mode 100644
index 474eba0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
+++ /dev/null
@@ -1,582 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * TensorReductionSycl.h
- *
- * \brief:
- *  This is the specialization of the reduction operation. Two phase reduction approach 
- * is used since the GPU does not have Global Synchronization for global memory among 
- * different work-group/thread block. To solve the problem, we need to create two kernels 
- * to reduce the data, where the first kernel reduce the data locally and each local 
- * workgroup/thread-block save the input data into global memory. In the second phase (global reduction)
- * one work-group uses one work-group/thread-block to reduces the intermediate data into one single element. 
- * Here is an NVIDIA presentation explaining the optimized two phase reduction algorithm on GPU:
- * https://developer.download.nvidia.com/assets/cuda/files/reduction.pdf
- *
- *****************************************************************/
-
-#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
-#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
-namespace Eigen {
-namespace TensorSycl {
-namespace internal {
-
-template <typename Op, typename CoeffReturnType, typename Index, bool Vectorizable>
-struct OpDefiner {
-  typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, Vectorizable>::PacketReturnType PacketReturnType;
-  typedef Op type;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Op &op) { return op; }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
-                                                                            const Index &) {
-    return accumulator;
-  }
-};
-
-template <typename CoeffReturnType, typename Index>
-struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, false> {
-  typedef Eigen::internal::SumReducer<CoeffReturnType> type;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
-    return type();
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType finalise_op(const CoeffReturnType &accumulator,
-                                                                           const Index &scale) {
-    ::Eigen::internal::scalar_quotient_op<CoeffReturnType> quotient_op;
-    return quotient_op(accumulator, CoeffReturnType(scale));
-  }
-};
-
-template <typename CoeffReturnType, typename Index>
-struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, true> {
-  typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, true>::PacketReturnType PacketReturnType;
-  typedef Eigen::internal::SumReducer<CoeffReturnType> type;
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
-    return type();
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
-                                                                            const Index &scale) {
-    return ::Eigen::internal::pdiv(accumulator, ::Eigen::internal::pset1<PacketReturnType>(CoeffReturnType(scale)));
-  }
-};
-
-template <typename CoeffReturnType, typename OpType, typename InputAccessor, typename OutputAccessor, typename Index,
-          Index local_range>
-struct SecondStepFullReducer {
-  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      LocalAccessor;
-  typedef OpDefiner<OpType, CoeffReturnType, Index, true> OpDef;
-  typedef typename OpDef::type Op;
-  LocalAccessor scratch;
-  InputAccessor aI;
-  OutputAccessor outAcc;
-  Op op;
-  SecondStepFullReducer(LocalAccessor scratch_, InputAccessor aI_, OutputAccessor outAcc_, OpType op_)
-      : scratch(scratch_), aI(aI_), outAcc(outAcc_), op(OpDef::get_op(op_)) {}
-
-  void operator()(cl::sycl::nd_item<1> itemID) {
-    // Our empirical research shows that the best performance will be achieved
-    // when there is only one element per thread to reduce in the second step.
-    // in this step the second step reduction time is almost negligible.
-    // Hence, in the second step of reduction the input size is fixed to the
-    // local size, thus, there is only one element read per thread. The
-    // algorithm must be changed if the number of reduce per thread in the
-    // second step is greater than 1. Otherwise, the result will be wrong.
-    const Index localid = itemID.get_local_id(0);
-    auto aInPtr = aI.get_pointer() + localid;
-    auto aOutPtr = outAcc.get_pointer();
-    CoeffReturnType *scratchptr = scratch.get_pointer();
-    CoeffReturnType accumulator = *aInPtr;
-
-    scratchptr[localid] = op.finalize(accumulator);
-    for (Index offset = itemID.get_local_range(0) / 2; offset > 0; offset /= 2) {
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-      if (localid < offset) {
-        op.reduce(scratchptr[localid + offset], &accumulator);
-        scratchptr[localid] = op.finalize(accumulator);
-      }
-    }
-    if (localid == 0) *aOutPtr = op.finalize(accumulator);
-  }
-};
-
-// Full reduction first phase. In this version the vectorization is true and the reduction accept 
-// any generic reducerOp  e.g( max, min, sum, mean, iamax, iamin, etc ). 
-template <typename Evaluator, typename OpType, typename Evaluator::Index local_range>
-class FullReductionKernelFunctor {
- public:
-  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
-  typedef typename Evaluator::Index Index;
-  typedef OpDefiner<OpType, typename Evaluator::CoeffReturnType, Index,
-                    (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
-      OpDef;
-
-  typedef typename OpDef::type Op;
-  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
-  typedef typename Evaluator::PacketReturnType PacketReturnType;
-  typedef
-      typename ::Eigen::internal::conditional<(Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess),
-                                              PacketReturnType, CoeffReturnType>::type OutType;
-  typedef cl::sycl::accessor<OutType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      LocalAccessor;
-  LocalAccessor scratch;
-  Evaluator evaluator;
-  EvaluatorPointerType final_output;
-  Index rng;
-  Op op;
-
-  FullReductionKernelFunctor(LocalAccessor scratch_, Evaluator evaluator_, EvaluatorPointerType final_output_,
-                             Index rng_, OpType op_)
-      : scratch(scratch_), evaluator(evaluator_), final_output(final_output_), rng(rng_), op(OpDef::get_op(op_)) {}
-
-  void operator()(cl::sycl::nd_item<1> itemID) { compute_reduction(itemID); }
-
-  template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<Vect>::type compute_reduction(
-      const cl::sycl::nd_item<1> &itemID) {
-    auto output_ptr = final_output.get_pointer();
-    Index VectorizedRange = (rng / Evaluator::PacketSize) * Evaluator::PacketSize;
-    Index globalid = itemID.get_global_id(0);
-    Index localid = itemID.get_local_id(0);
-    Index step = Evaluator::PacketSize * itemID.get_global_range(0);
-    Index start = Evaluator::PacketSize * globalid;
-    // vectorizable parts
-    PacketReturnType packetAccumulator = op.template initializePacket<PacketReturnType>();
-    for (Index i = start; i < VectorizedRange; i += step) {
-      op.template reducePacket<PacketReturnType>(evaluator.impl().template packet<Unaligned>(i), &packetAccumulator);
-    }
-    globalid += VectorizedRange;
-    // non vectorizable parts
-    for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
-      op.template reducePacket<PacketReturnType>(
-          ::Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, Evaluator::PacketSize>::convert_to_packet_type(
-              evaluator.impl().coeff(i), op.initialize()),
-          &packetAccumulator);
-    }
-    scratch[localid] = packetAccumulator =
-        OpDef::finalise_op(op.template finalizePacket<PacketReturnType>(packetAccumulator), rng);
-    // reduction parts // Local size is always power of 2
-    EIGEN_UNROLL_LOOP
-    for (Index offset = local_range / 2; offset > 0; offset /= 2) {
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-      if (localid < offset) {
-        op.template reducePacket<PacketReturnType>(scratch[localid + offset], &packetAccumulator);
-        scratch[localid] = op.template finalizePacket<PacketReturnType>(packetAccumulator);
-      }
-    }
-    if (localid == 0) {
-      output_ptr[itemID.get_group(0)] =
-          op.finalizeBoth(op.initialize(), op.template finalizePacket<PacketReturnType>(packetAccumulator));
-    }
-  }
-
-  template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<!Vect>::type compute_reduction(
-      const cl::sycl::nd_item<1> &itemID) {
-    auto output_ptr = final_output.get_pointer();
-    Index globalid = itemID.get_global_id(0);
-    Index localid = itemID.get_local_id(0);
-    // vectorizable parts
-    CoeffReturnType accumulator = op.initialize();
-    // non vectorizable parts
-    for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
-      op.reduce(evaluator.impl().coeff(i), &accumulator);
-    }
-    scratch[localid] = accumulator = OpDef::finalise_op(op.finalize(accumulator), rng);
-
-    // reduction parts. the local size is always power of 2
-    EIGEN_UNROLL_LOOP
-    for (Index offset = local_range / 2; offset > 0; offset /= 2) {
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-      if (localid < offset) {
-        op.reduce(scratch[localid + offset], &accumulator);
-        scratch[localid] = op.finalize(accumulator);
-      }
-    }
-    if (localid == 0) {
-      output_ptr[itemID.get_group(0)] = op.finalize(accumulator);
-    }
-  }
-};
-
-template <typename Evaluator, typename OpType>
-class GenericNondeterministicReducer {
- public:
-  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
-  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
-  typedef typename Evaluator::Index Index;
-  typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
-  typedef typename OpDef::type Op;
-  template <typename Scratch>
-  GenericNondeterministicReducer(Scratch, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType functor_,
-                       Index range_, Index num_values_to_reduce_)
-      : evaluator(evaluator_),
-        output_accessor(output_accessor_),
-        functor(OpDef::get_op(functor_)),
-        range(range_),
-        num_values_to_reduce(num_values_to_reduce_) {}
-
-  void operator()(cl::sycl::nd_item<1> itemID) {
-    auto output_accessor_ptr = output_accessor.get_pointer();
-    /// const cast added as a naive solution to solve the qualifier drop error
-    Index globalid = static_cast<Index>(itemID.get_global_linear_id());
-    if (globalid < range) {
-      CoeffReturnType accum = functor.initialize();
-      Eigen::internal::GenericDimReducer<Evaluator::NumReducedDims - 1, Evaluator, Op>::reduce(
-          evaluator, evaluator.firstInput(globalid), functor, &accum);
-      output_accessor_ptr[globalid] = OpDef::finalise_op(functor.finalize(accum), num_values_to_reduce);
-    }
-  }
-
- private:
-  Evaluator evaluator;
-  EvaluatorPointerType output_accessor;
-  Op functor;
-  Index range;
-  Index num_values_to_reduce;
-};
-
-enum class reduction_dim { inner_most, outer_most };
-// default is preserver
-template <typename Evaluator, typename OpType, typename PannelParameters, reduction_dim rt>
-struct PartialReductionKernel {
-  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
-  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
-  typedef typename Evaluator::Index Index;
-  typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
-  typedef typename OpDef::type Op;
-  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      ScratchAcc;
-  ScratchAcc scratch;
-  Evaluator evaluator;
-  EvaluatorPointerType output_accessor;
-  Op op;
-  const Index preserve_elements_num_groups;
-  const Index reduce_elements_num_groups;
-  const Index num_coeffs_to_preserve;
-  const Index num_coeffs_to_reduce;
-
-  PartialReductionKernel(ScratchAcc scratch_, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType op_,
-                         const Index preserve_elements_num_groups_, const Index reduce_elements_num_groups_,
-                         const Index num_coeffs_to_preserve_, const Index num_coeffs_to_reduce_)
-      : scratch(scratch_),
-        evaluator(evaluator_),
-        output_accessor(output_accessor_),
-        op(OpDef::get_op(op_)),
-        preserve_elements_num_groups(preserve_elements_num_groups_),
-        reduce_elements_num_groups(reduce_elements_num_groups_),
-        num_coeffs_to_preserve(num_coeffs_to_preserve_),
-        num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void element_wise_reduce(Index globalRId, Index globalPId,
-                                                                 CoeffReturnType &accumulator) {
-    if (globalPId >= num_coeffs_to_preserve) {
-      return;
-    }
-    Index global_offset = rt == reduction_dim::outer_most ? globalPId + (globalRId * num_coeffs_to_preserve)
-                                                          : globalRId + (globalPId * num_coeffs_to_reduce);
-    Index localOffset = globalRId;
-
-    const Index per_thread_local_stride = PannelParameters::LocalThreadSizeR * reduce_elements_num_groups;
-    const Index per_thread_global_stride =
-        rt == reduction_dim::outer_most ? num_coeffs_to_preserve * per_thread_local_stride : per_thread_local_stride;
-    for (Index i = globalRId; i < num_coeffs_to_reduce; i += per_thread_local_stride) {
-      op.reduce(evaluator.impl().coeff(global_offset), &accumulator);
-      localOffset += per_thread_local_stride;
-      global_offset += per_thread_global_stride;
-    }
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
-    const Index linearLocalThreadId = itemID.get_local_id(0);
-    Index pLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId % PannelParameters::LocalThreadSizeP
-                                                           : linearLocalThreadId / PannelParameters::LocalThreadSizeR;
-    Index rLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId / PannelParameters::LocalThreadSizeP
-                                                           : linearLocalThreadId % PannelParameters::LocalThreadSizeR;
-    const Index pGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) % preserve_elements_num_groups
-                                                           : itemID.get_group(0) / reduce_elements_num_groups;
-    const Index rGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) / preserve_elements_num_groups
-                                                           : itemID.get_group(0) % reduce_elements_num_groups;
-
-    Index globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
-    const Index globalRId = rGroupId * PannelParameters::LocalThreadSizeR + rLocalThreadId;
-    auto scratchPtr = scratch.get_pointer().get();
-    auto outPtr =
-        output_accessor.get_pointer() + (reduce_elements_num_groups > 1 ? rGroupId * num_coeffs_to_preserve : 0);
-    CoeffReturnType accumulator = op.initialize();
-
-    element_wise_reduce(globalRId, globalPId, accumulator);
-
-    accumulator = OpDef::finalise_op(op.finalize(accumulator), num_coeffs_to_reduce);
-    scratchPtr[pLocalThreadId + rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)] =
-        accumulator;
-    if (rt == reduction_dim::inner_most) {
-      pLocalThreadId = linearLocalThreadId % PannelParameters::LocalThreadSizeP;
-      rLocalThreadId = linearLocalThreadId / PannelParameters::LocalThreadSizeP;
-      globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
-    }
-
-    /* Apply the reduction operation between the current local
-     * id and the one on the other half of the vector. */
-    auto out_scratch_ptr =
-        scratchPtr + (pLocalThreadId + (rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)));
-    itemID.barrier(cl::sycl::access::fence_space::local_space);
-    if (rt == reduction_dim::inner_most) {
-      accumulator = *out_scratch_ptr;
-    }
-    // The Local LocalThreadSizeR is always power of 2
-    EIGEN_UNROLL_LOOP
-    for (Index offset = PannelParameters::LocalThreadSizeR >> 1; offset > 0; offset >>= 1) {
-      if (rLocalThreadId < offset) {
-        op.reduce(out_scratch_ptr[(PannelParameters::LocalThreadSizeP + PannelParameters::BC) * offset], &accumulator);
-        // The result has already been divided for mean reducer in the
-        // previous reduction so no need to divide furthermore
-        *out_scratch_ptr = op.finalize(accumulator);
-      }
-      /* All threads collectively read from global memory into local.
-       * The barrier ensures all threads' IO is resolved before
-       * execution continues (strictly speaking, all threads within
-       * a single work-group - there is no co-ordination between
-       * work-groups, only work-items). */
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-    }
-
-    if (rLocalThreadId == 0 && (globalPId < num_coeffs_to_preserve)) {
-      outPtr[globalPId] = op.finalize(accumulator);
-    }
-  }
-};
-
-template <typename OutScalar, typename Index, typename InputAccessor, typename OutputAccessor, typename OpType>
-struct SecondStepPartialReduction {
-  typedef OpDefiner<OpType, OutScalar, Index, false> OpDef;
-  typedef typename OpDef::type Op;
-  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      ScratchAccessor;
-  InputAccessor input_accessor;
-  OutputAccessor output_accessor;
-  Op op;
-  const Index num_coeffs_to_preserve;
-  const Index num_coeffs_to_reduce;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SecondStepPartialReduction(ScratchAccessor, InputAccessor input_accessor_,
-                                                                   OutputAccessor output_accessor_, OpType op_,
-                                                                   const Index num_coeffs_to_preserve_,
-                                                                   const Index num_coeffs_to_reduce_)
-      : input_accessor(input_accessor_),
-        output_accessor(output_accessor_),
-        op(OpDef::get_op(op_)),
-        num_coeffs_to_preserve(num_coeffs_to_preserve_),
-        num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
-    const Index globalId = itemID.get_global_id(0);
-
-    if (globalId >= num_coeffs_to_preserve) return;
-
-    auto in_ptr = input_accessor.get_pointer() + globalId;
-
-    OutScalar accumulator = op.initialize();
-// num_coeffs_to_reduce is not bigger that 256
-    for (Index i = 0; i < num_coeffs_to_reduce; i++) {
-      op.reduce(*in_ptr, &accumulator);
-      in_ptr += num_coeffs_to_preserve;
-    }
-    output_accessor.get_pointer()[globalId] = op.finalize(accumulator);
-  }
-};  // namespace internal
-
-template <typename Index, Index LTP, Index LTR, bool BC_>
-struct ReductionPannel {
-  static EIGEN_CONSTEXPR Index LocalThreadSizeP = LTP;
-  static EIGEN_CONSTEXPR Index LocalThreadSizeR = LTR;
-  static EIGEN_CONSTEXPR bool BC = BC_;
-};
-
-template <typename Self, typename Op, TensorSycl::internal::reduction_dim rt>
-struct PartialReducerLauncher {
-  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
-  typedef typename Self::CoeffReturnType CoeffReturnType;
-  typedef typename Self::Storage Storage;
-  typedef typename Self::Index Index;
-  typedef ReductionPannel<typename Self::Index, EIGEN_SYCL_LOCAL_THREAD_DIM0, EIGEN_SYCL_LOCAL_THREAD_DIM1, true>
-      PannelParameters;
-
-  typedef PartialReductionKernel<Self, Op, PannelParameters, rt> SyclReducerKerneType;
-
-  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType output,
-                  Index num_coeffs_to_reduce, Index num_coeffs_to_preserve) {
-    Index roundUpP = roundUp(num_coeffs_to_preserve, PannelParameters::LocalThreadSizeP);
-
-    // getPowerOfTwo makes sure local range is power of 2 and <=
-    // maxSyclThreadPerBlock this will help us to avoid extra check on the
-    // kernel
-    static_assert(!((PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR) &
-                    (PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR - 1)),
-                  "The Local thread size must be a power of 2 for the reduction "
-                  "operation");
-
-    EIGEN_CONSTEXPR Index localRange = PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR;
-    // In this step, we force the code not to be more than 2-step reduction:
-    // Our empirical research shows that if each thread reduces at least 64
-    // elemnts individually, we get better performance. However, this can change
-    // on different platforms. In this step we force the code not to be
-    // morthan step reduction: Our empirical research shows that for inner_most
-    // dim reducer, it is better to have 8 group in a reduce dimension for sizes
-    // > 1024 to achieve the best performance.
-    const Index reductionPerThread = 64;
-    Index cu = dev.getPowerOfTwo(dev.getNumSyclMultiProcessors(), true);
-    const Index pNumGroups = roundUpP / PannelParameters::LocalThreadSizeP;
-    Index rGroups = (cu + pNumGroups - 1) / pNumGroups;
-    const Index rNumGroups = num_coeffs_to_reduce > reductionPerThread * localRange ? std::min(rGroups, localRange) : 1;
-    const Index globalRange = pNumGroups * rNumGroups * localRange;
-
-    EIGEN_CONSTEXPR Index scratchSize =
-        PannelParameters::LocalThreadSizeR * (PannelParameters::LocalThreadSizeP + PannelParameters::BC);
-    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
-    if (rNumGroups > 1) {
-      CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
-          dev.allocate_temp(num_coeffs_to_preserve * rNumGroups * sizeof(CoeffReturnType)));
-      EvaluatorPointerType temp_accessor = dev.get(temp_pointer);
-      dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
-          self, temp_accessor, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
-          num_coeffs_to_reduce);
-
-      typedef SecondStepPartialReduction<CoeffReturnType, Index, EvaluatorPointerType, EvaluatorPointerType, Op>
-          SecondStepPartialReductionKernel;
-
-      dev.template unary_kernel_launcher<CoeffReturnType, SecondStepPartialReductionKernel>(
-          temp_accessor, output,
-          cl::sycl::nd_range<1>(cl::sycl::range<1>(pNumGroups * localRange), cl::sycl::range<1>(localRange)), Index(1),
-          reducer, num_coeffs_to_preserve, rNumGroups);
-
-      self.device().deallocate_temp(temp_pointer);
-    } else {
-      dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
-          self, output, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
-          num_coeffs_to_reduce);
-    }
-    return false;
-  }
-};
-}  // namespace internal
-}  // namespace TensorSycl
-
-namespace internal {
-
-template <typename Self, typename Op, bool Vectorizable>
-struct FullReducer<Self, Op, Eigen::SyclDevice, Vectorizable> {
-  typedef typename Self::CoeffReturnType CoeffReturnType;
-  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
-  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
-  static EIGEN_CONSTEXPR int PacketSize = Self::PacketAccess ? Self::PacketSize : 1;
-  static void run(const Self &self, Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType data) {
-    typedef typename conditional<Self::PacketAccess, typename Self::PacketReturnType, CoeffReturnType>::type OutType;
-    static_assert(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
-                    (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
-                  "The Local thread size must be a power of 2 for the reduction "
-                  "operation");
-    EIGEN_CONSTEXPR Index local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;
-
-    typename Self::Index inputSize = self.impl().dimensions().TotalSize();
-    // In this step we force the code not to be more than 2-step reduction:
-    // Our empirical research shows that if each thread reduces at least 512
-    // elemnts individually, we get better performance.
-    const Index reductionPerThread = 2048;
-    // const Index num_work_group =
-    Index reductionGroup = dev.getPowerOfTwo(
-        (inputSize + (reductionPerThread * local_range - 1)) / (reductionPerThread * local_range), true);
-    const Index num_work_group = std::min(reductionGroup, local_range);
-    // 1
-    // ? local_range
-    // : 1);
-    const Index global_range = num_work_group * local_range;
-
-    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
-    typedef TensorSycl::internal::FullReductionKernelFunctor<Self, Op, local_range> reduction_kernel_t;
-    if (num_work_group > 1) {
-      CoeffReturnType *temp_pointer =
-          static_cast<CoeffReturnType *>(dev.allocate_temp(num_work_group * sizeof(CoeffReturnType)));
-      typename Self::EvaluatorPointerType tmp_global_accessor = dev.get(temp_pointer);
-      dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, tmp_global_accessor, thread_range,
-                                                                      local_range, inputSize, reducer);
-
-      typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
-                                                          EvaluatorPointerType, Index, local_range>
-          GenericRKernel;
-      dev.template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
-          tmp_global_accessor, data,
-          cl::sycl::nd_range<1>(cl::sycl::range<1>(num_work_group), cl::sycl::range<1>(num_work_group)), num_work_group,
-          reducer);
-
-      dev.deallocate_temp(temp_pointer);
-    } else {
-      dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, data, thread_range, local_range, inputSize,
-                                                                      reducer);
-    }
-  }
-};
-// vectorizable inner_most most dim preserver
-// col reduction
-template <typename Self, typename Op>
-struct OuterReducer<Self, Op, Eigen::SyclDevice> {
-  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
-
-  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
-                  typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
-                  typename Self::Index num_coeffs_to_preserve) {
-    return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
-        Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::outer_most>::run(self, reducer, dev, output,
-                                                                                 num_coeffs_to_reduce,
-                                                                                 num_coeffs_to_preserve);
-  }
-};
-// row reduction
-template <typename Self, typename Op>
-struct InnerReducer<Self, Op, Eigen::SyclDevice> {
-  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
-
-  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
-                  typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
-                  typename Self::Index num_coeffs_to_preserve) {
-    return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
-        Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::inner_most>::run(self, reducer, dev, output,
-                                                                                 num_coeffs_to_reduce,
-                                                                                 num_coeffs_to_preserve);
-  }
-};
-
-// ArmgMax uses this kernel for partial reduction//
-// TODO(@mehdi.goli) come up with a better kernel
-// generic partial reduction
-template <typename Self, typename Op>
-struct GenericReducer<Self, Op, Eigen::SyclDevice> {
-  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = false;
-  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
-                  typename Self::EvaluatorPointerType output, typename Self::Index num_values_to_reduce,
-                  typename Self::Index num_coeffs_to_preserve) {
-    typename Self::Index range, GRange, tileSize;
-    dev.parallel_for_setup(num_coeffs_to_preserve, tileSize, range, GRange);
-
-    dev.template unary_kernel_launcher<typename Self::CoeffReturnType,
-                                       TensorSycl::internal::GenericNondeterministicReducer<Self, Op>>(
-        self, output, cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), Index(1),
-        reducer, range, (num_values_to_reduce != 0) ? num_values_to_reduce : static_cast<Index>(1));
-    return false;
-  }
-};
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
deleted file mode 100644
index a27d364..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
+++ /dev/null
@@ -1,454 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_REF_H
-#define EIGEN_CXX11_TENSOR_TENSOR_REF_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Dimensions, typename Scalar>
-class TensorLazyBaseEvaluator {
- public:
-  TensorLazyBaseEvaluator() : m_refcount(0) { }
-  virtual ~TensorLazyBaseEvaluator() { }
-
-  EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const = 0;
-  EIGEN_DEVICE_FUNC virtual const Scalar* data() const = 0;
-
-  EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const = 0;
-  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) = 0;
-
-  void incrRefCount() { ++m_refcount; }
-  void decrRefCount() { --m_refcount; }
-  int refCount() const { return m_refcount; }
-
- private:
-  // No copy, no assignment;
-  TensorLazyBaseEvaluator(const TensorLazyBaseEvaluator& other);
-  TensorLazyBaseEvaluator& operator = (const TensorLazyBaseEvaluator& other);
-
-  int m_refcount;
-};
-
-
-template <typename Dimensions, typename Expr, typename Device>
-class TensorLazyEvaluatorReadOnly : public TensorLazyBaseEvaluator<Dimensions, typename TensorEvaluator<Expr, Device>::Scalar> {
- public:
-  //  typedef typename TensorEvaluator<Expr, Device>::Dimensions Dimensions;
-  typedef typename TensorEvaluator<Expr, Device>::Scalar Scalar;
-  typedef StorageMemory<Scalar, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-  typedef  TensorEvaluator<Expr, Device> EvalType;
-
-  TensorLazyEvaluatorReadOnly(const Expr& expr, const Device& device) : m_impl(expr, device), m_dummy(Scalar(0)) {
-    m_dims = m_impl.dimensions();
-    m_impl.evalSubExprsIfNeeded(NULL);
-  }
-  virtual ~TensorLazyEvaluatorReadOnly() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const {
-    return m_dims;
-  }
-  EIGEN_DEVICE_FUNC virtual const Scalar* data() const {
-    return m_impl.data();
-  }
-
-  EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const {
-    return m_impl.coeff(index);
-  }
-  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex /*index*/) {
-    eigen_assert(false && "can't reference the coefficient of a rvalue");
-    return m_dummy;
-  };
-
- protected:
-  TensorEvaluator<Expr, Device> m_impl;
-  Dimensions m_dims;
-  Scalar m_dummy;
-};
-
-template <typename Dimensions, typename Expr, typename Device>
-class TensorLazyEvaluatorWritable : public TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> {
- public:
-  typedef TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> Base;
-  typedef typename Base::Scalar Scalar;
-  typedef StorageMemory<Scalar, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  TensorLazyEvaluatorWritable(const Expr& expr, const Device& device) : Base(expr, device) {
-  }
-  virtual ~TensorLazyEvaluatorWritable() {
-  }
-
-  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) {
-    return this->m_impl.coeffRef(index);
-  }
-};
-
-template <typename Dimensions, typename Expr, typename Device>
-class TensorLazyEvaluator : public internal::conditional<bool(internal::is_lvalue<Expr>::value),
-                            TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
-                            TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type {
- public:
-  typedef typename internal::conditional<bool(internal::is_lvalue<Expr>::value),
-                                         TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
-                                         TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type Base;
-  typedef typename Base::Scalar Scalar;
-
-  TensorLazyEvaluator(const Expr& expr, const Device& device) : Base(expr, device) {
-  }
-  virtual ~TensorLazyEvaluator() {
-  }
-};
-
-}  // namespace internal
-
-
-/** \class TensorRef
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief A reference to a tensor expression
-  * The expression will be evaluated lazily (as much as possible).
-  *
-  */
-template<typename PlainObjectType> class TensorRef : public TensorBase<TensorRef<PlainObjectType> >
-{
-  public:
-    typedef TensorRef<PlainObjectType> Self;
-    typedef typename PlainObjectType::Base Base;
-    typedef typename Eigen::internal::nested<Self>::type Nested;
-    typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
-    typedef typename internal::traits<PlainObjectType>::Index Index;
-    typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef typename Base::CoeffReturnType CoeffReturnType;
-    typedef Scalar* PointerType;
-    typedef PointerType PointerArgType;
-
-    static const Index NumIndices = PlainObjectType::NumIndices;
-    typedef typename PlainObjectType::Dimensions Dimensions;
-
-    enum {
-      IsAligned = false,
-      PacketAccess = false,
-      BlockAccess = false,
-      PreferBlockAccess = false,
-      Layout = PlainObjectType::Layout,
-      CoordAccess = false,  // to be implemented
-      RawAccess = false
-    };
-
-    //===- Tensor block evaluation strategy (see TensorBlock.h) -----------===//
-    typedef internal::TensorBlockNotImplemented TensorBlock;
-    //===------------------------------------------------------------------===//
-
-    EIGEN_STRONG_INLINE TensorRef() : m_evaluator(NULL) {
-    }
-
-    template <typename Expression>
-    EIGEN_STRONG_INLINE TensorRef(const Expression& expr) : m_evaluator(new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice())) {
-      m_evaluator->incrRefCount();
-    }
-
-    template <typename Expression>
-    EIGEN_STRONG_INLINE TensorRef& operator = (const Expression& expr) {
-      unrefEvaluator();
-      m_evaluator = new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice());
-      m_evaluator->incrRefCount();
-      return *this;
-    }
-
-    ~TensorRef() {
-      unrefEvaluator();
-    }
-
-    TensorRef(const TensorRef& other) : m_evaluator(other.m_evaluator) {
-      eigen_assert(m_evaluator->refCount() > 0);
-      m_evaluator->incrRefCount();
-    }
-
-    TensorRef& operator = (const TensorRef& other) {
-      if (this != &other) {
-        unrefEvaluator();
-        m_evaluator = other.m_evaluator;
-        eigen_assert(m_evaluator->refCount() > 0);
-        m_evaluator->incrRefCount();
-      }
-      return *this;
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index rank() const { return m_evaluator->dimensions().size(); }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_evaluator->dimensions()[n]; }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_evaluator->dimensions(); }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Index size() const { return m_evaluator->dimensions().TotalSize(); }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar* data() const { return m_evaluator->data(); }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar operator()(Index index) const
-    {
-      return m_evaluator->coeff(index);
-    }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar operator()(Index firstIndex, IndexTypes... otherIndices) const
-    {
-      const std::size_t num_indices = (sizeof...(otherIndices) + 1);
-      const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
-      return coeff(indices);
-    }
-    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
-    {
-      const std::size_t num_indices = (sizeof...(otherIndices) + 1);
-      const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
-      return coeffRef(indices);
-    }
-#else
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1) const
-    {
-      array<Index, 2> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      return coeff(indices);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2) const
-    {
-      array<Index, 3> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      indices[2] = i2;
-      return coeff(indices);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3) const
-    {
-      array<Index, 4> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      indices[2] = i2;
-      indices[3] = i3;
-      return coeff(indices);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
-    {
-      array<Index, 5> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      indices[2] = i2;
-      indices[3] = i3;
-      indices[4] = i4;
-      return coeff(indices);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1)
-    {
-      array<Index, 2> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      return coeffRef(indices);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2)
-    {
-      array<Index, 3> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      indices[2] = i2;
-      return coeffRef(indices);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
-    {
-      array<Index, 4> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      indices[2] = i2;
-      indices[3] = i3;
-      return coeffRef(indices);
-    }
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2, Index i3, Index i4)
-    {
-      array<Index, 5> indices;
-      indices[0] = i0;
-      indices[1] = i1;
-      indices[2] = i2;
-      indices[3] = i3;
-      indices[4] = i4;
-      return coeffRef(indices);
-    }
-#endif
-
-    template <std::size_t NumIndices> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar coeff(const array<Index, NumIndices>& indices) const
-    {
-      const Dimensions& dims = this->dimensions();
-      Index index = 0;
-      if (PlainObjectType::Options & RowMajor) {
-        index += indices[0];
-        for (size_t i = 1; i < NumIndices; ++i) {
-          index = index * dims[i] + indices[i];
-        }
-      } else {
-        index += indices[NumIndices-1];
-        for (int i = NumIndices-2; i >= 0; --i) {
-          index = index * dims[i] + indices[i];
-        }
-      }
-      return m_evaluator->coeff(index);
-    }
-    template <std::size_t NumIndices> EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
-    {
-      const Dimensions& dims = this->dimensions();
-      Index index = 0;
-      if (PlainObjectType::Options & RowMajor) {
-        index += indices[0];
-        for (size_t i = 1; i < NumIndices; ++i) {
-          index = index * dims[i] + indices[i];
-        }
-      } else {
-        index += indices[NumIndices-1];
-        for (int i = NumIndices-2; i >= 0; --i) {
-          index = index * dims[i] + indices[i];
-        }
-      }
-      return m_evaluator->coeffRef(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
-    {
-      return m_evaluator->coeff(index);
-    }
-
-    EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
-    {
-      return m_evaluator->coeffRef(index);
-    }
-
-  private:
-    EIGEN_STRONG_INLINE void unrefEvaluator() {
-      if (m_evaluator) {
-        m_evaluator->decrRefCount();
-        if (m_evaluator->refCount() == 0) {
-          delete m_evaluator;
-        }
-      }
-    }
-
-  internal::TensorLazyBaseEvaluator<Dimensions, Scalar>* m_evaluator;
-};
-
-
-// evaluator for rvalues
-template<typename Derived, typename Device>
-struct TensorEvaluator<const TensorRef<Derived>, Device>
-{
-  typedef typename Derived::Index Index;
-  typedef typename Derived::Scalar Scalar;
-  typedef typename Derived::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef typename Derived::Dimensions Dimensions;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = false,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorRef<Derived>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const TensorRef<Derived>& m, const Device&)
-      : m_ref(m)
-  { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_ref.dimensions(); }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    return true;
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
-    return m_ref.coeff(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
-    return m_ref.coeffRef(index);
-  }
-
-  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_ref.data(); }
-
- protected:
-  TensorRef<Derived> m_ref;
-};
-
-
-// evaluator for lvalues
-template<typename Derived, typename Device>
-struct TensorEvaluator<TensorRef<Derived>, Device> : public TensorEvaluator<const TensorRef<Derived>, Device>
-{
-  typedef typename Derived::Index Index;
-  typedef typename Derived::Scalar Scalar;
-  typedef typename Derived::Scalar CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef typename Derived::Dimensions Dimensions;
-
-  typedef TensorEvaluator<const TensorRef<Derived>, Device> Base;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = false,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(TensorRef<Derived>& m, const Device& d) : Base(m, d)
-  { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
-    return this->m_ref.coeffRef(index);
-  }
-};
-
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_REF_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
deleted file mode 100644
index 586ce68..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
+++ /dev/null
@@ -1,465 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
-//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
-#define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
-namespace Eigen {
-
-/** \class TensorReverse
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor reverse elements class.
-  *
-  */
-namespace internal {
-template<typename ReverseDimensions, typename XprType>
-struct traits<TensorReverseOp<ReverseDimensions,
-                              XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename ReverseDimensions, typename XprType>
-struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense>
-{
-  typedef const TensorReverseOp<ReverseDimensions, XprType>& type;
-};
-
-template<typename ReverseDimensions, typename XprType>
-struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1,
-            typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type>
-{
-  typedef TensorReverseOp<ReverseDimensions, XprType> type;
-};
-
-}  // end namespace internal
-
-template<typename ReverseDimensions, typename XprType>
-class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions,
-                                          XprType>, WriteAccessors>
-{
-  public:
-    typedef TensorBase<TensorReverseOp<ReverseDimensions, XprType>, WriteAccessors>Base;
-    typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename XprType::CoeffReturnType CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind
-                                                                      StorageKind;
-    typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(
-      const XprType& expr, const ReverseDimensions& reverse_dims)
-      : m_xpr(expr), m_reverse_dims(reverse_dims) { }
-
-    EIGEN_DEVICE_FUNC
-    const ReverseDimensions& reverse() const { return m_reverse_dims; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorReverseOp)
-
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const ReverseDimensions m_reverse_dims;
-};
-
-// Eval as rvalue
-template<typename ReverseDimensions, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device>
-{
-  typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<ReverseDimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = false,
-    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess       = NumDims > 0,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  typedef internal::TensorIntDivisor<Index> IndexDivisor;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
-      ArgTensorBlock;
-
-  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device),
-        m_reverse(op.reverse()),
-        m_device(device)
-  {
-    // Reversing a scalar isn't supported yet. It would be a no-op anyway.
-    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    // Compute strides
-    m_dimensions = m_impl.dimensions();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_strides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_strides[i] = m_strides[i-1] * m_dimensions[i-1];
-        if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]);
-      }
-    } else {
-      m_strides[NumDims-1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_strides[i] = m_strides[i+1] * m_dimensions[i+1];
-        if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]);
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index reverseIndex(
-      Index index) const {
-    eigen_assert(index < dimensions().TotalSize());
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        Index idx = index / m_fastStrides[i];
-        index -= idx * m_strides[i];
-        if (m_reverse[i]) {
-          idx = m_dimensions[i] - idx - 1;
-        }
-        inputIndex += idx * m_strides[i] ;
-      }
-      if (m_reverse[0]) {
-        inputIndex += (m_dimensions[0] - index - 1);
-      } else {
-        inputIndex += index;
-      }
-    } else {
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        Index idx = index / m_fastStrides[i];
-        index -= idx * m_strides[i];
-        if (m_reverse[i]) {
-          idx = m_dimensions[i] - idx - 1;
-        }
-        inputIndex += idx * m_strides[i] ;
-      }
-      if (m_reverse[NumDims-1]) {
-        inputIndex += (m_dimensions[NumDims-1] - index - 1);
-      } else {
-        inputIndex += index;
-      }
-    }
-    return inputIndex;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(
-      Index index) const  {
-    return m_impl.coeff(reverseIndex(index));
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    // TODO(ndjaitly): write a better packing routine that uses
-    // local structure.
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type
-                                                            values[PacketSize];
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      values[i] = coeff(index+i);
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    const size_t target_size = m_device.lastLevelCacheSize();
-    // Block evaluation reads underlying memory in reverse order, and default
-    // cost model does not properly catch this in bytes stored/loaded.
-    return internal::TensorBlockResourceRequirements::skewed<Scalar>(
-               target_size)
-        .addCostPerCoeff({0, 0, 24});
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool /*root_of_expr_ast*/ = false) const {
-    // TODO(ezhulenev): If underlying tensor expression supports and prefers
-    // block evaluation we must use it. Currently we use coeff and packet
-    // access into the underlying tensor expression.
-    // static const bool useBlockAccessForArgType =
-    //     TensorEvaluator<ArgType, Device>::BlockAccess &&
-    //     TensorEvaluator<ArgType, Device>::PreferBlockAccess;
-
-    static const bool isColMajor =
-        static_cast<int>(Layout) == static_cast<int>(ColMajor);
-
-    static const Index inner_dim_idx = isColMajor ? 0 : NumDims - 1;
-    const bool inner_dim_reversed = m_reverse[inner_dim_idx];
-
-    // Offset in the output block.
-    Index block_offset = 0;
-
-    // Offset in the input Tensor.
-    Index input_offset = reverseIndex(desc.offset());
-
-    // Initialize output block iterator state. Dimension in this array are
-    // always in inner_most -> outer_most order (col major layout).
-    array<BlockIteratorState, NumDims> it;
-    for (int i = 0; i < NumDims; ++i) {
-      const int dim = isColMajor ? i : NumDims - 1 - i;
-      it[i].size = desc.dimension(dim);
-      it[i].count = 0;
-      it[i].reverse = m_reverse[dim];
-
-      it[i].block_stride =
-          i == 0 ? 1 : (it[i - 1].size * it[i - 1].block_stride);
-      it[i].block_span = it[i].block_stride * (it[i].size - 1);
-
-      it[i].input_stride = m_strides[dim];
-      it[i].input_span = it[i].input_stride * (it[i].size - 1);
-
-      if (it[i].reverse) {
-        it[i].input_stride = -1 * it[i].input_stride;
-        it[i].input_span = -1 * it[i].input_span;
-      }
-    }
-
-    // If multiple inner dimensions have the same reverse flag, check if we can
-    // merge them into a single virtual inner dimension.
-    int effective_inner_dim = 0;
-    for (int i = 1; i < NumDims; ++i) {
-      if (it[i].reverse != it[effective_inner_dim].reverse) break;
-      if (it[i].block_stride != it[effective_inner_dim].size) break;
-      if (it[i].block_stride != numext::abs(it[i].input_stride)) break;
-
-      it[i].size = it[effective_inner_dim].size * it[i].size;
-
-      it[i].block_stride = 1;
-      it[i].input_stride = (inner_dim_reversed ? -1 : 1);
-
-      it[i].block_span = it[i].block_stride * (it[i].size - 1);
-      it[i].input_span = it[i].input_stride * (it[i].size - 1);
-
-      effective_inner_dim = i;
-    }
-
-    eigen_assert(it[effective_inner_dim].block_stride == 1);
-    eigen_assert(it[effective_inner_dim].input_stride ==
-                 (inner_dim_reversed ? -1 : 1));
-
-    const Index inner_dim_size = it[effective_inner_dim].size;
-
-    // Prepare storage for the materialized reverse result.
-    const typename TensorBlock::Storage block_storage =
-        TensorBlock::prepareStorage(desc, scratch);
-    CoeffReturnType* block_buffer = block_storage.data();
-
-    while (it[NumDims - 1].count < it[NumDims - 1].size) {
-      // Copy inner-most dimension data from reversed location in input.
-      Index dst = block_offset;
-      Index src = input_offset;
-
-      // NOTE(ezhulenev): Adding vectorized path with internal::preverse showed
-      // worse results in benchmarks than a simple coefficient loop.
-      if (inner_dim_reversed) {
-        for (Index i = 0; i < inner_dim_size; ++i) {
-          block_buffer[dst] = m_impl.coeff(src);
-          ++dst;
-          --src;
-        }
-      } else {
-        for (Index i = 0; i < inner_dim_size; ++i) {
-          block_buffer[dst] = m_impl.coeff(src);
-          ++dst;
-          ++src;
-        }
-      }
-
-      // For the 1d tensor we need to generate only one inner-most dimension.
-      if ((NumDims - effective_inner_dim) == 1) break;
-
-      // Update offset.
-      for (Index i = effective_inner_dim + 1; i < NumDims; ++i) {
-        if (++it[i].count < it[i].size) {
-          block_offset += it[i].block_stride;
-          input_offset += it[i].input_stride;
-          break;
-        }
-        if (i != NumDims - 1) it[i].count = 0;
-        block_offset -= it[i].block_span;
-        input_offset -= it[i].input_span;
-      }
-    }
-
-    return block_storage.AsTensorMaterializedBlock();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
-                                     2 * TensorOpCost::MulCost<Index>() +
-                                     TensorOpCost::DivCost<Index>());
-    for (int i = 0; i < NumDims; ++i) {
-      if (m_reverse[i]) {
-        compute_cost += 2 * TensorOpCost::AddCost<Index>();
-      }
-    }
-    return m_impl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- protected:
-  Dimensions m_dimensions;
-  array<Index, NumDims> m_strides;
-  array<IndexDivisor, NumDims> m_fastStrides;
-  TensorEvaluator<ArgType, Device> m_impl;
-  ReverseDimensions m_reverse;
-  const Device EIGEN_DEVICE_REF m_device;
-
- private:
-  struct BlockIteratorState {
-    BlockIteratorState()
-        : size(0),
-          count(0),
-          reverse(false),
-          block_stride(0),
-          block_span(0),
-          input_stride(0),
-          input_span(0) {}
-
-    Index size;
-    Index count;
-    bool reverse;
-    Index block_stride;
-    Index block_span;
-    Index input_stride;
-    Index input_span;
-  };
-};
-
-// Eval as lvalue
-
-template <typename ReverseDimensions, typename ArgType, typename Device>
-struct TensorEvaluator<TensorReverseOp<ReverseDimensions, ArgType>, Device>
-    : public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
-                             Device> {
-  typedef TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
-                          Device> Base;
-  typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<ReverseDimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : Base(op, device) {}
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Dimensions& dimensions() const { return this->m_dimensions; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
-    return this->m_impl.coeffRef(this->reverseIndex(index));
-  }
-
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x) {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    // This code is pilfered from TensorMorphing.h
-    EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
-    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      this->coeffRef(index+i) = values[i];
-    }
-  }
-};
-
-
-}  // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
deleted file mode 100644
index beae854..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
+++ /dev/null
@@ -1,528 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
-#define EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
-
-namespace Eigen {
-
-namespace internal {
-
-template <typename Op, typename XprType>
-struct traits<TensorScanOp<Op, XprType> >
-    : public traits<XprType> {
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename Op, typename XprType>
-struct eval<TensorScanOp<Op, XprType>, Eigen::Dense>
-{
-  typedef const TensorScanOp<Op, XprType>& type;
-};
-
-template<typename Op, typename XprType>
-struct nested<TensorScanOp<Op, XprType>, 1,
-            typename eval<TensorScanOp<Op, XprType> >::type>
-{
-  typedef TensorScanOp<Op, XprType> type;
-};
-} // end namespace internal
-
-/** \class TensorScan
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor scan class.
-  */
-template <typename Op, typename XprType>
-class TensorScanOp
-    : public TensorBase<TensorScanOp<Op, XprType>, ReadOnlyAccessors> {
-public:
-  typedef typename Eigen::internal::traits<TensorScanOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorScanOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorScanOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorScanOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorScanOp(
-      const XprType& expr, const Index& axis, bool exclusive = false, const Op& op = Op())
-      : m_expr(expr), m_axis(axis), m_accumulator(op), m_exclusive(exclusive) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Index axis() const { return m_axis; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const XprType& expression() const { return m_expr; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const Op accumulator() const { return m_accumulator; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  bool exclusive() const { return m_exclusive; }
-
-protected:
-  typename XprType::Nested m_expr;
-  const Index m_axis;
-  const Op m_accumulator;
-  const bool m_exclusive;
-};
-
-
-namespace internal {
-
-template <typename Self>
-EIGEN_STRONG_INLINE void ReduceScalar(Self& self, Index offset,
-                                      typename Self::CoeffReturnType* data) {
-  // Compute the scan along the axis, starting at the given offset
-  typename Self::CoeffReturnType accum = self.accumulator().initialize();
-  if (self.stride() == 1) {
-    if (self.exclusive()) {
-      for (Index curr = offset; curr < offset + self.size(); ++curr) {
-        data[curr] = self.accumulator().finalize(accum);
-        self.accumulator().reduce(self.inner().coeff(curr), &accum);
-      }
-    } else {
-      for (Index curr = offset; curr < offset + self.size(); ++curr) {
-        self.accumulator().reduce(self.inner().coeff(curr), &accum);
-        data[curr] = self.accumulator().finalize(accum);
-      }
-    }
-  } else {
-    if (self.exclusive()) {
-      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
-        Index curr = offset + idx3 * self.stride();
-        data[curr] = self.accumulator().finalize(accum);
-        self.accumulator().reduce(self.inner().coeff(curr), &accum);
-      }
-    } else {
-      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
-        Index curr = offset + idx3 * self.stride();
-        self.accumulator().reduce(self.inner().coeff(curr), &accum);
-        data[curr] = self.accumulator().finalize(accum);
-      }
-    }
-  }
-}
-
-template <typename Self>
-EIGEN_STRONG_INLINE void ReducePacket(Self& self, Index offset,
-                                      typename Self::CoeffReturnType* data) {
-  using Scalar = typename Self::CoeffReturnType;
-  using Packet = typename Self::PacketReturnType;
-  // Compute the scan along the axis, starting at the calculated offset
-  Packet accum = self.accumulator().template initializePacket<Packet>();
-  if (self.stride() == 1) {
-    if (self.exclusive()) {
-      for (Index curr = offset; curr < offset + self.size(); ++curr) {
-        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
-        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
-      }
-    } else {
-      for (Index curr = offset; curr < offset + self.size(); ++curr) {
-        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
-        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
-      }
-    }
-  } else {
-    if (self.exclusive()) {
-      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
-        const Index curr = offset + idx3 * self.stride();
-        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
-        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
-      }
-    } else {
-      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
-        const Index curr = offset + idx3 * self.stride();
-        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
-        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
-      }
-    }
-  }
-}
-
-template <typename Self, bool Vectorize, bool Parallel>
-struct ReduceBlock {
-  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
-                                      typename Self::CoeffReturnType* data) {
-    for (Index idx2 = 0; idx2 < self.stride(); idx2++) {
-      // Calculate the starting offset for the scan
-      Index offset = idx1 + idx2;
-      ReduceScalar(self, offset, data);
-    }
-  }
-};
-
-// Specialization for vectorized reduction.
-template <typename Self>
-struct ReduceBlock<Self, /*Vectorize=*/true, /*Parallel=*/false> {
-  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
-                                      typename Self::CoeffReturnType* data) {
-    using Packet = typename Self::PacketReturnType;
-    const int PacketSize = internal::unpacket_traits<Packet>::size;
-    Index idx2 = 0;
-    for (; idx2 + PacketSize <= self.stride(); idx2 += PacketSize) {
-      // Calculate the starting offset for the packet scan
-      Index offset = idx1 + idx2;
-      ReducePacket(self, offset, data);
-    }
-    for (; idx2 < self.stride(); idx2++) {
-      // Calculate the starting offset for the scan
-      Index offset = idx1 + idx2;
-      ReduceScalar(self, offset, data);
-    }
-  }
-};
-
-// Single-threaded CPU implementation of scan
-template <typename Self, typename Reducer, typename Device,
-          bool Vectorize =
-              (TensorEvaluator<typename Self::ChildTypeNoConst, Device>::PacketAccess &&
-               internal::reducer_traits<Reducer, Device>::PacketAccess)>
-struct ScanLauncher {
-  void operator()(Self& self, typename Self::CoeffReturnType* data) {
-    Index total_size = internal::array_prod(self.dimensions());
-
-    // We fix the index along the scan axis to 0 and perform a
-    // scan per remaining entry. The iteration is split into two nested
-    // loops to avoid an integer division by keeping track of each idx1 and
-    // idx2.
-    for (Index idx1 = 0; idx1 < total_size; idx1 += self.stride() * self.size()) {
-      ReduceBlock<Self, Vectorize, /*Parallel=*/false> block_reducer;
-      block_reducer(self, idx1, data);
-    }
-  }
-};
-
-#ifdef EIGEN_USE_THREADS
-
-// Adjust block_size to avoid false sharing of cachelines among
-// threads. Currently set to twice the cache line size on Intel and ARM
-// processors.
-EIGEN_STRONG_INLINE Index AdjustBlockSize(Index item_size, Index block_size) {
-  EIGEN_CONSTEXPR Index kBlockAlignment = 128;
-  const Index items_per_cacheline =
-      numext::maxi<Index>(1, kBlockAlignment / item_size);
-  return items_per_cacheline * divup(block_size, items_per_cacheline);
-}
-
-template <typename Self>
-struct ReduceBlock<Self, /*Vectorize=*/true, /*Parallel=*/true> {
-  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
-                                      typename Self::CoeffReturnType* data) {
-    using Scalar = typename Self::CoeffReturnType;
-    using Packet = typename Self::PacketReturnType;
-    const int PacketSize = internal::unpacket_traits<Packet>::size;
-    Index num_scalars = self.stride();
-    Index num_packets = 0;
-    if (self.stride() >= PacketSize) {
-      num_packets = self.stride() / PacketSize;
-      self.device().parallelFor(
-          num_packets,
-        TensorOpCost(PacketSize * self.size(), PacketSize * self.size(),
-                     16 * PacketSize * self.size(), true, PacketSize),
-        // Make the shard size large enough that two neighboring threads
-        // won't write to the same cacheline of `data`.
-        [=](Index blk_size) {
-          return AdjustBlockSize(PacketSize * sizeof(Scalar), blk_size);
-        },
-        [&](Index first, Index last) {
-          for (Index packet = first; packet < last; ++packet) {
-            const Index idx2 = packet * PacketSize;
-            ReducePacket(self, idx1 + idx2, data);
-          }
-        });
-      num_scalars -= num_packets * PacketSize;
-    }
-    self.device().parallelFor(
-        num_scalars, TensorOpCost(self.size(), self.size(), 16 * self.size()),
-        // Make the shard size large enough that two neighboring threads
-        // won't write to the same cacheline of `data`.
-        [=](Index blk_size) {
-          return AdjustBlockSize(sizeof(Scalar), blk_size);
-        },
-        [&](Index first, Index last) {
-          for (Index scalar = first; scalar < last; ++scalar) {
-            const Index idx2 = num_packets * PacketSize + scalar;
-            ReduceScalar(self, idx1 + idx2, data);
-          }
-        });
-  }
-};
-
-template <typename Self>
-struct ReduceBlock<Self, /*Vectorize=*/false, /*Parallel=*/true> {
-  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
-                                      typename Self::CoeffReturnType* data) {
-    using Scalar = typename Self::CoeffReturnType;
-    self.device().parallelFor(
-        self.stride(), TensorOpCost(self.size(), self.size(), 16 * self.size()),
-        // Make the shard size large enough that two neighboring threads
-        // won't write to the same cacheline of `data`.
-        [=](Index blk_size) {
-          return AdjustBlockSize(sizeof(Scalar), blk_size);
-        },
-        [&](Index first, Index last) {
-          for (Index idx2 = first; idx2 < last; ++idx2) {
-            ReduceScalar(self, idx1 + idx2, data);
-          }
-        });
-  }
-};
-
-// Specialization for multi-threaded execution.
-template <typename Self, typename Reducer, bool Vectorize>
-struct ScanLauncher<Self, Reducer, ThreadPoolDevice, Vectorize> {
-  void operator()(Self& self, typename Self::CoeffReturnType* data) {
-    using Scalar = typename Self::CoeffReturnType;
-    using Packet = typename Self::PacketReturnType;
-    const int PacketSize = internal::unpacket_traits<Packet>::size;
-    const Index total_size = internal::array_prod(self.dimensions());
-    const Index inner_block_size = self.stride() * self.size();
-    bool parallelize_by_outer_blocks = (total_size >= (self.stride() * inner_block_size));
-
-    if ((parallelize_by_outer_blocks && total_size <= 4096) ||
-        (!parallelize_by_outer_blocks && self.stride() < PacketSize)) {
-      ScanLauncher<Self, Reducer, DefaultDevice, Vectorize> launcher;
-      launcher(self, data);
-      return;
-    }
-
-    if (parallelize_by_outer_blocks) {
-      // Parallelize over outer blocks.
-      const Index num_outer_blocks = total_size / inner_block_size;
-      self.device().parallelFor(
-          num_outer_blocks,
-          TensorOpCost(inner_block_size, inner_block_size,
-                       16 * PacketSize * inner_block_size, Vectorize,
-                       PacketSize),
-          [=](Index blk_size) {
-            return AdjustBlockSize(inner_block_size * sizeof(Scalar), blk_size);
-          },
-          [&](Index first, Index last) {
-            for (Index idx1 = first; idx1 < last; ++idx1) {
-              ReduceBlock<Self, Vectorize, /*Parallelize=*/false> block_reducer;
-              block_reducer(self, idx1 * inner_block_size, data);
-            }
-          });
-    } else {
-      // Parallelize over inner packets/scalars dimensions when the reduction
-      // axis is not an inner dimension.
-      ReduceBlock<Self, Vectorize, /*Parallelize=*/true> block_reducer;
-      for (Index idx1 = 0; idx1 < total_size;
-           idx1 += self.stride() * self.size()) {
-        block_reducer(self, idx1, data);
-      }
-    }
-  }
-};
-#endif  // EIGEN_USE_THREADS
-
-#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
-
-// GPU implementation of scan
-// TODO(ibab) This placeholder implementation performs multiple scans in
-// parallel, but it would be better to use a parallel scan algorithm and
-// optimize memory access.
-template <typename Self, typename Reducer>
-__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ScanKernel(Self self, Index total_size, typename Self::CoeffReturnType* data) {
-  // Compute offset as in the CPU version
-  Index val = threadIdx.x + blockIdx.x * blockDim.x;
-  Index offset = (val / self.stride()) * self.stride() * self.size() + val % self.stride();
-
-  if (offset + (self.size() - 1) * self.stride() < total_size) {
-    // Compute the scan along the axis, starting at the calculated offset
-    typename Self::CoeffReturnType accum = self.accumulator().initialize();
-    for (Index idx = 0; idx < self.size(); idx++) {
-      Index curr = offset + idx * self.stride();
-      if (self.exclusive()) {
-        data[curr] = self.accumulator().finalize(accum);
-        self.accumulator().reduce(self.inner().coeff(curr), &accum);
-      } else {
-        self.accumulator().reduce(self.inner().coeff(curr), &accum);
-        data[curr] = self.accumulator().finalize(accum);
-      }
-    }
-  }
-  __syncthreads();
-
-}
-
-template <typename Self, typename Reducer, bool Vectorize>
-struct ScanLauncher<Self, Reducer, GpuDevice, Vectorize> {
-  void operator()(const Self& self, typename Self::CoeffReturnType* data) {
-     Index total_size = internal::array_prod(self.dimensions());
-     Index num_blocks = (total_size / self.size() + 63) / 64;
-     Index block_size = 64;
-
-     LAUNCH_GPU_KERNEL((ScanKernel<Self, Reducer>), num_blocks, block_size, 0, self.device(), self, total_size, data);
-  }
-};
-#endif  // EIGEN_USE_GPU && (EIGEN_GPUCC)
-
-}  // namespace internal
-
-// Eval as rvalue
-template <typename Op, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
-
-  typedef TensorScanOp<Op, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  typedef const ArgType ChildTypeNoConst;
-  typedef const ArgType ChildType;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  typedef TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> Self;
-  typedef StorageMemory<Scalar, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess = false,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,
-    RawAccess = true
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device),
-        m_device(device),
-        m_exclusive(op.exclusive()),
-        m_accumulator(op.accumulator()),
-        m_size(m_impl.dimensions()[op.axis()]),
-        m_stride(1), m_consume_dim(op.axis()),
-        m_output(NULL) {
-
-    // Accumulating a scalar isn't supported.
-    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    eigen_assert(op.axis() >= 0 && op.axis() < NumDims);
-
-    // Compute stride of scan axis
-    const Dimensions& dims = m_impl.dimensions();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = 0; i < op.axis(); ++i) {
-        m_stride = m_stride * dims[i];
-      }
-    } else {
-      // dims can only be indexed through unsigned integers,
-      // so let's use an unsigned type to let the compiler knows.
-      // This prevents stupid warnings: ""'*((void*)(& evaluator)+64)[18446744073709551615]' may be used uninitialized in this function"
-      unsigned int axis = internal::convert_index<unsigned int>(op.axis());
-      for (unsigned int i = NumDims - 1; i > axis; --i) {
-        m_stride = m_stride * dims[i];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
-    return m_impl.dimensions();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& stride() const {
-    return m_stride;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& consume_dim() const {
-    return m_consume_dim;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& size() const {
-    return m_size;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Op& accumulator() const {
-    return m_accumulator;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool exclusive() const {
-    return m_exclusive;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& inner() const {
-    return m_impl;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const {
-    return m_device;
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    internal::ScanLauncher<Self, Op, Device> launcher;
-    if (data) {
-      launcher(*this, data);
-      return false;
-    }
-
-    const Index total_size = internal::array_prod(dimensions());
-    m_output = static_cast<EvaluatorPointerType>(m_device.get((Scalar*) m_device.allocate_temp(total_size * sizeof(Scalar))));
-    launcher(*this, m_output);
-    return true;
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
-    return internal::ploadt<PacketReturnType, LoadMode>(m_output + index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const
-  {
-    return m_output;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return m_output[index];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
-    return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    if (m_output) {
-      m_device.deallocate_temp(m_output);
-      m_output = NULL;
-    }
-    m_impl.cleanup();
-  }
-
-#ifdef EIGEN_USE_SYCL
- // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-    m_output.bind(cgh);
-  }
-#endif
-protected:
-  TensorEvaluator<ArgType, Device> m_impl;
-  const Device EIGEN_DEVICE_REF m_device;
-  const bool m_exclusive;
-  Op m_accumulator;
-  const Index m_size;
-  Index m_stride;
-  Index m_consume_dim;
-  EvaluatorPointerType m_output;
-};
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h
deleted file mode 100644
index 7f68ecb..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h
+++ /dev/null
@@ -1,513 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * TensorScanSycl.h
- *
- * \brief:
- *  Tensor Scan Sycl implement the extend  version of
- * "Efficient parallel scan algorithms for GPUs." .for Tensor operations.
- * The algorithm requires up to 3 stage (consequently 3 kernels) depending on
- * the size of the tensor. In the first kernel (ScanKernelFunctor), each
- * threads within the work-group individually reduces the allocated elements per
- * thread in order to reduces the total number of blocks. In the next step all
- * thread within the work-group will reduce the associated blocks into the
- * temporary buffers. In the next kernel(ScanBlockKernelFunctor), the temporary
- * buffer is given as an input and all the threads within a work-group scan and
- * reduces the boundaries between the blocks (generated from the previous
- * kernel). and write the data on the temporary buffer. If the second kernel is
- * required, the third and final kerenl (ScanAdjustmentKernelFunctor) will
- * adjust the final result into the output buffer.
- * The original algorithm for the parallel prefix sum can be found here:
- *
- * Sengupta, Shubhabrata, Mark Harris, and Michael Garland. "Efficient parallel
- * scan algorithms for GPUs." NVIDIA, Santa Clara, CA, Tech. Rep. NVR-2008-003
- *1, no. 1 (2008): 1-17.
- *****************************************************************/
-
-#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
-#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
-
-namespace Eigen {
-namespace TensorSycl {
-namespace internal {
-
-#ifndef EIGEN_SYCL_MAX_GLOBAL_RANGE
-#define EIGEN_SYCL_MAX_GLOBAL_RANGE (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 * 4)
-#endif
-
-template <typename index_t>
-struct ScanParameters {
-  // must be power of 2
-  static EIGEN_CONSTEXPR index_t ScanPerThread = 8;
-  const index_t total_size;
-  const index_t non_scan_size;
-  const index_t scan_size;
-  const index_t non_scan_stride;
-  const index_t scan_stride;
-  const index_t panel_threads;
-  const index_t group_threads;
-  const index_t block_threads;
-  const index_t elements_per_group;
-  const index_t elements_per_block;
-  const index_t loop_range;
-
-  ScanParameters(index_t total_size_, index_t non_scan_size_, index_t scan_size_, index_t non_scan_stride_,
-                 index_t scan_stride_, index_t panel_threads_, index_t group_threads_, index_t block_threads_,
-                 index_t elements_per_group_, index_t elements_per_block_, index_t loop_range_)
-      : total_size(total_size_),
-        non_scan_size(non_scan_size_),
-        scan_size(scan_size_),
-        non_scan_stride(non_scan_stride_),
-        scan_stride(scan_stride_),
-        panel_threads(panel_threads_),
-        group_threads(group_threads_),
-        block_threads(block_threads_),
-        elements_per_group(elements_per_group_),
-        elements_per_block(elements_per_block_),
-        loop_range(loop_range_) {}
-};
-
-enum class scan_step { first, second };
-template <typename Evaluator, typename CoeffReturnType, typename OutAccessor, typename Op, typename Index,
-          scan_step stp>
-struct ScanKernelFunctor {
-  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      LocalAccessor;
-  static EIGEN_CONSTEXPR int PacketSize = ScanParameters<Index>::ScanPerThread / 2;
-
-  LocalAccessor scratch;
-  Evaluator dev_eval;
-  OutAccessor out_accessor;
-  OutAccessor temp_accessor;
-  const ScanParameters<Index> scanParameters;
-  Op accumulator;
-  const bool inclusive;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScanKernelFunctor(LocalAccessor scratch_, const Evaluator dev_eval_,
-                                                          OutAccessor out_accessor_, OutAccessor temp_accessor_,
-                                                          const ScanParameters<Index> scanParameters_, Op accumulator_,
-                                                          const bool inclusive_)
-      : scratch(scratch_),
-        dev_eval(dev_eval_),
-        out_accessor(out_accessor_),
-        temp_accessor(temp_accessor_),
-        scanParameters(scanParameters_),
-        accumulator(accumulator_),
-        inclusive(inclusive_) {}
-
-  template <scan_step sst = stp, typename Input>
-  typename ::Eigen::internal::enable_if<sst == scan_step::first, CoeffReturnType>::type EIGEN_DEVICE_FUNC
-      EIGEN_STRONG_INLINE
-      read(const Input &inpt, Index global_id) {
-    return inpt.coeff(global_id);
-  }
-
-  template <scan_step sst = stp, typename Input>
-  typename ::Eigen::internal::enable_if<sst != scan_step::first, CoeffReturnType>::type EIGEN_DEVICE_FUNC
-      EIGEN_STRONG_INLINE
-      read(const Input &inpt, Index global_id) {
-    return inpt[global_id];
-  }
-
-  template <scan_step sst = stp, typename InclusiveOp>
-  typename ::Eigen::internal::enable_if<sst == scan_step::first>::type EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  first_step_inclusive_Operation(InclusiveOp inclusive_op) {
-    inclusive_op();
-  }
-
-  template <scan_step sst = stp, typename InclusiveOp>
-  typename ::Eigen::internal::enable_if<sst != scan_step::first>::type EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  first_step_inclusive_Operation(InclusiveOp) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
-    auto out_ptr = out_accessor.get_pointer();
-    auto tmp_ptr = temp_accessor.get_pointer();
-    auto scratch_ptr = scratch.get_pointer().get();
-
-    for (Index loop_offset = 0; loop_offset < scanParameters.loop_range; loop_offset++) {
-      Index data_offset = (itemID.get_global_id(0) + (itemID.get_global_range(0) * loop_offset));
-      Index tmp = data_offset % scanParameters.panel_threads;
-      const Index panel_id = data_offset / scanParameters.panel_threads;
-      const Index group_id = tmp / scanParameters.group_threads;
-      tmp = tmp % scanParameters.group_threads;
-      const Index block_id = tmp / scanParameters.block_threads;
-      const Index local_id = tmp % scanParameters.block_threads;
-      // we put one element per packet in scratch_mem
-      const Index scratch_stride = scanParameters.elements_per_block / PacketSize;
-      const Index scratch_offset = (itemID.get_local_id(0) / scanParameters.block_threads) * scratch_stride;
-      CoeffReturnType private_scan[ScanParameters<Index>::ScanPerThread];
-      CoeffReturnType inclusive_scan;
-      // the actual panel size is scan_size * non_scan_size.
-      // elements_per_panel is roundup to power of 2 for binary tree
-      const Index panel_offset = panel_id * scanParameters.scan_size * scanParameters.non_scan_size;
-      const Index group_offset = group_id * scanParameters.non_scan_stride;
-      // This will be effective when the size is bigger than elements_per_block
-      const Index block_offset = block_id * scanParameters.elements_per_block * scanParameters.scan_stride;
-      const Index thread_offset = (ScanParameters<Index>::ScanPerThread * local_id * scanParameters.scan_stride);
-      const Index global_offset = panel_offset + group_offset + block_offset + thread_offset;
-      Index next_elements = 0;
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
-        Index global_id = global_offset + next_elements;
-        private_scan[i] = ((((block_id * scanParameters.elements_per_block) +
-                             (ScanParameters<Index>::ScanPerThread * local_id) + i) < scanParameters.scan_size) &&
-                           (global_id < scanParameters.total_size))
-                              ? read(dev_eval, global_id)
-                              : accumulator.initialize();
-        next_elements += scanParameters.scan_stride;
-      }
-      first_step_inclusive_Operation([&]() EIGEN_DEVICE_FUNC {
-        if (inclusive) {
-          inclusive_scan = private_scan[ScanParameters<Index>::ScanPerThread - 1];
-        }
-      });
-      // This for loop must be 2
-      EIGEN_UNROLL_LOOP
-      for (int packetIndex = 0; packetIndex < ScanParameters<Index>::ScanPerThread; packetIndex += PacketSize) {
-        Index private_offset = 1;
-        // build sum in place up the tree
-        EIGEN_UNROLL_LOOP
-        for (Index d = PacketSize >> 1; d > 0; d >>= 1) {
-          EIGEN_UNROLL_LOOP
-          for (Index l = 0; l < d; l++) {
-            Index ai = private_offset * (2 * l + 1) - 1 + packetIndex;
-            Index bi = private_offset * (2 * l + 2) - 1 + packetIndex;
-            CoeffReturnType accum = accumulator.initialize();
-            accumulator.reduce(private_scan[ai], &accum);
-            accumulator.reduce(private_scan[bi], &accum);
-            private_scan[bi] = accumulator.finalize(accum);
-          }
-          private_offset *= 2;
-        }
-        scratch_ptr[2 * local_id + (packetIndex / PacketSize) + scratch_offset] =
-            private_scan[PacketSize - 1 + packetIndex];
-        private_scan[PacketSize - 1 + packetIndex] = accumulator.initialize();
-        // traverse down tree & build scan
-        EIGEN_UNROLL_LOOP
-        for (Index d = 1; d < PacketSize; d *= 2) {
-          private_offset >>= 1;
-          EIGEN_UNROLL_LOOP
-          for (Index l = 0; l < d; l++) {
-            Index ai = private_offset * (2 * l + 1) - 1 + packetIndex;
-            Index bi = private_offset * (2 * l + 2) - 1 + packetIndex;
-            CoeffReturnType accum = accumulator.initialize();
-            accumulator.reduce(private_scan[ai], &accum);
-            accumulator.reduce(private_scan[bi], &accum);
-            private_scan[ai] = private_scan[bi];
-            private_scan[bi] = accumulator.finalize(accum);
-          }
-        }
-      }
-
-      Index offset = 1;
-      // build sum in place up the tree
-      for (Index d = scratch_stride >> 1; d > 0; d >>= 1) {
-        // Synchronise
-        itemID.barrier(cl::sycl::access::fence_space::local_space);
-        if (local_id < d) {
-          Index ai = offset * (2 * local_id + 1) - 1 + scratch_offset;
-          Index bi = offset * (2 * local_id + 2) - 1 + scratch_offset;
-          CoeffReturnType accum = accumulator.initialize();
-          accumulator.reduce(scratch_ptr[ai], &accum);
-          accumulator.reduce(scratch_ptr[bi], &accum);
-          scratch_ptr[bi] = accumulator.finalize(accum);
-        }
-        offset *= 2;
-      }
-      // Synchronise
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-      // next step optimisation
-      if (local_id == 0) {
-        if (((scanParameters.elements_per_group / scanParameters.elements_per_block) > 1)) {
-          const Index temp_id = panel_id * (scanParameters.elements_per_group / scanParameters.elements_per_block) *
-                                    scanParameters.non_scan_size +
-                                group_id * (scanParameters.elements_per_group / scanParameters.elements_per_block) +
-                                block_id;
-          tmp_ptr[temp_id] = scratch_ptr[scratch_stride - 1 + scratch_offset];
-        }
-        // clear the last element
-        scratch_ptr[scratch_stride - 1 + scratch_offset] = accumulator.initialize();
-      }
-      // traverse down tree & build scan
-      for (Index d = 1; d < scratch_stride; d *= 2) {
-        offset >>= 1;
-        // Synchronise
-        itemID.barrier(cl::sycl::access::fence_space::local_space);
-        if (local_id < d) {
-          Index ai = offset * (2 * local_id + 1) - 1 + scratch_offset;
-          Index bi = offset * (2 * local_id + 2) - 1 + scratch_offset;
-          CoeffReturnType accum = accumulator.initialize();
-          accumulator.reduce(scratch_ptr[ai], &accum);
-          accumulator.reduce(scratch_ptr[bi], &accum);
-          scratch_ptr[ai] = scratch_ptr[bi];
-          scratch_ptr[bi] = accumulator.finalize(accum);
-        }
-      }
-      // Synchronise
-      itemID.barrier(cl::sycl::access::fence_space::local_space);
-      // This for loop must be 2
-      EIGEN_UNROLL_LOOP
-      for (int packetIndex = 0; packetIndex < ScanParameters<Index>::ScanPerThread; packetIndex += PacketSize) {
-        EIGEN_UNROLL_LOOP
-        for (Index i = 0; i < PacketSize; i++) {
-          CoeffReturnType accum = private_scan[packetIndex + i];
-          accumulator.reduce(scratch_ptr[2 * local_id + (packetIndex / PacketSize) + scratch_offset], &accum);
-          private_scan[packetIndex + i] = accumulator.finalize(accum);
-        }
-      }
-      first_step_inclusive_Operation([&]() EIGEN_DEVICE_FUNC {
-        if (inclusive) {
-          accumulator.reduce(private_scan[ScanParameters<Index>::ScanPerThread - 1], &inclusive_scan);
-          private_scan[0] = accumulator.finalize(inclusive_scan);
-        }
-      });
-      next_elements = 0;
-      // right the first set of private param
-      EIGEN_UNROLL_LOOP
-      for (Index i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
-        Index global_id = global_offset + next_elements;
-        if ((((block_id * scanParameters.elements_per_block) + (ScanParameters<Index>::ScanPerThread * local_id) + i) <
-             scanParameters.scan_size) &&
-            (global_id < scanParameters.total_size)) {
-          Index private_id = (i * !inclusive) + (((i + 1) % ScanParameters<Index>::ScanPerThread) * (inclusive));
-          out_ptr[global_id] = private_scan[private_id];
-        }
-        next_elements += scanParameters.scan_stride;
-      }
-    }  // end for loop
-  }
-};
-
-template <typename CoeffReturnType, typename InAccessor, typename OutAccessor, typename Op, typename Index>
-struct ScanAdjustmentKernelFunctor {
-  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
-      LocalAccessor;
-  static EIGEN_CONSTEXPR int PacketSize = ScanParameters<Index>::ScanPerThread / 2;
-  InAccessor in_accessor;
-  OutAccessor out_accessor;
-  const ScanParameters<Index> scanParameters;
-  Op accumulator;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScanAdjustmentKernelFunctor(LocalAccessor, InAccessor in_accessor_,
-                                                                    OutAccessor out_accessor_,
-                                                                    const ScanParameters<Index> scanParameters_,
-                                                                    Op accumulator_)
-      : in_accessor(in_accessor_),
-        out_accessor(out_accessor_),
-        scanParameters(scanParameters_),
-        accumulator(accumulator_) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
-    auto in_ptr = in_accessor.get_pointer();
-    auto out_ptr = out_accessor.get_pointer();
-
-    for (Index loop_offset = 0; loop_offset < scanParameters.loop_range; loop_offset++) {
-      Index data_offset = (itemID.get_global_id(0) + (itemID.get_global_range(0) * loop_offset));
-      Index tmp = data_offset % scanParameters.panel_threads;
-      const Index panel_id = data_offset / scanParameters.panel_threads;
-      const Index group_id = tmp / scanParameters.group_threads;
-      tmp = tmp % scanParameters.group_threads;
-      const Index block_id = tmp / scanParameters.block_threads;
-      const Index local_id = tmp % scanParameters.block_threads;
-
-      // the actual panel size is scan_size * non_scan_size.
-      // elements_per_panel is roundup to power of 2 for binary tree
-      const Index panel_offset = panel_id * scanParameters.scan_size * scanParameters.non_scan_size;
-      const Index group_offset = group_id * scanParameters.non_scan_stride;
-      // This will be effective when the size is bigger than elements_per_block
-      const Index block_offset = block_id * scanParameters.elements_per_block * scanParameters.scan_stride;
-      const Index thread_offset = ScanParameters<Index>::ScanPerThread * local_id * scanParameters.scan_stride;
-
-      const Index global_offset = panel_offset + group_offset + block_offset + thread_offset;
-      const Index block_size = scanParameters.elements_per_group / scanParameters.elements_per_block;
-      const Index in_id = (panel_id * block_size * scanParameters.non_scan_size) + (group_id * block_size) + block_id;
-      CoeffReturnType adjust_val = in_ptr[in_id];
-
-      Index next_elements = 0;
-      EIGEN_UNROLL_LOOP
-      for (Index i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
-        Index global_id = global_offset + next_elements;
-        if ((((block_id * scanParameters.elements_per_block) + (ScanParameters<Index>::ScanPerThread * local_id) + i) <
-             scanParameters.scan_size) &&
-            (global_id < scanParameters.total_size)) {
-          CoeffReturnType accum = adjust_val;
-          accumulator.reduce(out_ptr[global_id], &accum);
-          out_ptr[global_id] = accumulator.finalize(accum);
-        }
-        next_elements += scanParameters.scan_stride;
-      }
-    }
-  }
-};
-
-template <typename Index>
-struct ScanInfo {
-  const Index &total_size;
-  const Index &scan_size;
-  const Index &panel_size;
-  const Index &non_scan_size;
-  const Index &scan_stride;
-  const Index &non_scan_stride;
-
-  Index max_elements_per_block;
-  Index block_size;
-  Index panel_threads;
-  Index group_threads;
-  Index block_threads;
-  Index elements_per_group;
-  Index elements_per_block;
-  Index loop_range;
-  Index global_range;
-  Index local_range;
-  const Eigen::SyclDevice &dev;
-  EIGEN_STRONG_INLINE ScanInfo(const Index &total_size_, const Index &scan_size_, const Index &panel_size_,
-                               const Index &non_scan_size_, const Index &scan_stride_, const Index &non_scan_stride_,
-                               const Eigen::SyclDevice &dev_)
-      : total_size(total_size_),
-        scan_size(scan_size_),
-        panel_size(panel_size_),
-        non_scan_size(non_scan_size_),
-        scan_stride(scan_stride_),
-        non_scan_stride(non_scan_stride_),
-        dev(dev_) {
-    // must be power of 2
-    local_range = std::min(Index(dev.getNearestPowerOfTwoWorkGroupSize()),
-                           Index(EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1));
-
-    max_elements_per_block = local_range * ScanParameters<Index>::ScanPerThread;
-
-    elements_per_group =
-        dev.getPowerOfTwo(Index(roundUp(Index(scan_size), ScanParameters<Index>::ScanPerThread)), true);
-    const Index elements_per_panel = elements_per_group * non_scan_size;
-    elements_per_block = std::min(Index(elements_per_group), Index(max_elements_per_block));
-    panel_threads = elements_per_panel / ScanParameters<Index>::ScanPerThread;
-    group_threads = elements_per_group / ScanParameters<Index>::ScanPerThread;
-    block_threads = elements_per_block / ScanParameters<Index>::ScanPerThread;
-    block_size = elements_per_group / elements_per_block;
-#ifdef EIGEN_SYCL_MAX_GLOBAL_RANGE
-    const Index max_threads = std::min(Index(panel_threads * panel_size), Index(EIGEN_SYCL_MAX_GLOBAL_RANGE));
-#else
-    const Index max_threads = panel_threads * panel_size;
-#endif
-    global_range = roundUp(max_threads, local_range);
-    loop_range = Index(
-        std::ceil(double(elements_per_panel * panel_size) / (global_range * ScanParameters<Index>::ScanPerThread)));
-  }
-  inline ScanParameters<Index> get_scan_parameter() {
-    return ScanParameters<Index>(total_size, non_scan_size, scan_size, non_scan_stride, scan_stride, panel_threads,
-                                 group_threads, block_threads, elements_per_group, elements_per_block, loop_range);
-  }
-  inline cl::sycl::nd_range<1> get_thread_range() {
-    return cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
-  }
-};
-
-template <typename EvaluatorPointerType, typename CoeffReturnType, typename Reducer, typename Index>
-struct SYCLAdjustBlockOffset {
-  EIGEN_STRONG_INLINE static void adjust_scan_block_offset(EvaluatorPointerType in_ptr, EvaluatorPointerType out_ptr,
-                                                           Reducer &accumulator, const Index total_size,
-                                                           const Index scan_size, const Index panel_size,
-                                                           const Index non_scan_size, const Index scan_stride,
-                                                           const Index non_scan_stride, const Eigen::SyclDevice &dev) {
-    auto scan_info =
-        ScanInfo<Index>(total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride, dev);
-
-    typedef ScanAdjustmentKernelFunctor<CoeffReturnType, EvaluatorPointerType, EvaluatorPointerType, Reducer, Index>
-        AdjustFuctor;
-    dev.template unary_kernel_launcher<CoeffReturnType, AdjustFuctor>(in_ptr, out_ptr, scan_info.get_thread_range(),
-                                                                      scan_info.max_elements_per_block,
-                                                                      scan_info.get_scan_parameter(), accumulator);
-  }
-};
-
-template <typename CoeffReturnType, scan_step stp>
-struct ScanLauncher_impl {
-  template <typename Input, typename EvaluatorPointerType, typename Reducer, typename Index>
-  EIGEN_STRONG_INLINE static void scan_block(Input in_ptr, EvaluatorPointerType out_ptr, Reducer &accumulator,
-                                             const Index total_size, const Index scan_size, const Index panel_size,
-                                             const Index non_scan_size, const Index scan_stride,
-                                             const Index non_scan_stride, const bool inclusive,
-                                             const Eigen::SyclDevice &dev) {
-    auto scan_info =
-        ScanInfo<Index>(total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride, dev);
-    const Index temp_pointer_size = scan_info.block_size * non_scan_size * panel_size;
-    const Index scratch_size = scan_info.max_elements_per_block / (ScanParameters<Index>::ScanPerThread / 2);
-    CoeffReturnType *temp_pointer =
-        static_cast<CoeffReturnType *>(dev.allocate_temp(temp_pointer_size * sizeof(CoeffReturnType)));
-    EvaluatorPointerType tmp_global_accessor = dev.get(temp_pointer);
-
-    typedef ScanKernelFunctor<Input, CoeffReturnType, EvaluatorPointerType, Reducer, Index, stp> ScanFunctor;
-    dev.template binary_kernel_launcher<CoeffReturnType, ScanFunctor>(
-        in_ptr, out_ptr, tmp_global_accessor, scan_info.get_thread_range(), scratch_size,
-        scan_info.get_scan_parameter(), accumulator, inclusive);
-
-    if (scan_info.block_size > 1) {
-      ScanLauncher_impl<CoeffReturnType, scan_step::second>::scan_block(
-          tmp_global_accessor, tmp_global_accessor, accumulator, temp_pointer_size, scan_info.block_size, panel_size,
-          non_scan_size, Index(1), scan_info.block_size, false, dev);
-
-      SYCLAdjustBlockOffset<EvaluatorPointerType, CoeffReturnType, Reducer, Index>::adjust_scan_block_offset(
-          tmp_global_accessor, out_ptr, accumulator, total_size, scan_size, panel_size, non_scan_size, scan_stride,
-          non_scan_stride, dev);
-    }
-    dev.deallocate_temp(temp_pointer);
-  }
-};
-
-}  // namespace internal
-}  // namespace TensorSycl
-namespace internal {
-template <typename Self, typename Reducer, bool vectorize>
-struct ScanLauncher<Self, Reducer, Eigen::SyclDevice, vectorize> {
-  typedef typename Self::Index Index;
-  typedef typename Self::CoeffReturnType CoeffReturnType;
-  typedef typename Self::Storage Storage;
-  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
-  void operator()(Self &self, EvaluatorPointerType data) {
-    const Index total_size = internal::array_prod(self.dimensions());
-    const Index scan_size = self.size();
-    const Index scan_stride = self.stride();
-    // this is the scan op (can be sum or ...)
-    auto accumulator = self.accumulator();
-    auto inclusive = !self.exclusive();
-    auto consume_dim = self.consume_dim();
-    auto dev = self.device();
-
-    auto dims = self.inner().dimensions();
-
-    Index non_scan_size = 1;
-    Index panel_size = 1;
-    if (static_cast<int>(Self::Layout) == static_cast<int>(ColMajor)) {
-      for (int i = 0; i < consume_dim; i++) {
-        non_scan_size *= dims[i];
-      }
-      for (int i = consume_dim + 1; i < Self::NumDims; i++) {
-        panel_size *= dims[i];
-      }
-    } else {
-      for (int i = Self::NumDims - 1; i > consume_dim; i--) {
-        non_scan_size *= dims[i];
-      }
-      for (int i = consume_dim - 1; i >= 0; i--) {
-        panel_size *= dims[i];
-      }
-    }
-    const Index non_scan_stride = (scan_stride > 1) ? 1 : scan_size;
-    auto eval_impl = self.inner();
-    TensorSycl::internal::ScanLauncher_impl<CoeffReturnType, TensorSycl::internal::scan_step::first>::scan_block(
-        eval_impl, data, accumulator, total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride,
-        inclusive, dev);
-  }
-};
-} // namespace internal
-}  // namespace Eigen
-
-#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
deleted file mode 100644
index e5e5efd..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
+++ /dev/null
@@ -1,471 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
-#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
-
-namespace Eigen {
-
-/** \class TensorShuffling
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor shuffling class.
-  *
-  *
-  */
-namespace internal {
-template<typename Shuffle, typename XprType>
-struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename Shuffle, typename XprType>
-struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense>
-{
-  typedef const TensorShufflingOp<Shuffle, XprType>& type;
-};
-
-template<typename Shuffle, typename XprType>
-struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type>
-{
-  typedef TensorShufflingOp<Shuffle, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename Shuffle, typename XprType>
-class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> >
-{
-  public:
-    typedef TensorBase<TensorShufflingOp<Shuffle, XprType> > Base;
-    typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename XprType::CoeffReturnType CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shfl)
-      : m_xpr(expr), m_shuffle(shfl) {}
-
-    EIGEN_DEVICE_FUNC
-    const Shuffle& shufflePermutation() const { return m_shuffle; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorShufflingOp)
-
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const Shuffle m_shuffle;
-};
-
-
-// Eval as rvalue
-template<typename Shuffle, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
-{
-  typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Self;
-  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned         = false,
-    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess       = false,  // to be implemented
-    RawAccess         = false
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
-
-  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
-                                                     Layout, Index>
-      TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_device(device),
-        m_impl(op.expression(), device)
-  {
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    const Shuffle& shuffle = op.shufflePermutation();
-    m_is_identity = true;
-    for (int i = 0; i < NumDims; ++i) {
-      m_shuffle[i] = static_cast<int>(shuffle[i]);
-      m_dimensions[i] = input_dims[shuffle[i]];
-      m_inverseShuffle[shuffle[i]] = i;
-      if (m_is_identity && shuffle[i] != i) {
-        m_is_identity = false;
-      }
-    }
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_unshuffledInputStrides[0] = 1;
-      m_outputStrides[0] = 1;
-
-      for (int i = 1; i < NumDims; ++i) {
-        m_unshuffledInputStrides[i] =
-            m_unshuffledInputStrides[i - 1] * input_dims[i - 1];
-        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
-        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(
-                  m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
-      }
-    } else {
-      m_unshuffledInputStrides[NumDims - 1] = 1;
-      m_outputStrides[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_unshuffledInputStrides[i] =
-            m_unshuffledInputStrides[i + 1] * input_dims[i + 1];
-        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
-        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(
-                  m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
-      }
-    }
-
-    for (int i = 0; i < NumDims; ++i) {
-      m_inputStrides[i] = m_unshuffledInputStrides[shuffle[i]];
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-#ifdef EIGEN_USE_THREADS
-  template <typename EvalSubExprsCallback>
-  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
-      EvaluatorPointerType, EvalSubExprsCallback done) {
-    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
-  }
-#endif  // EIGEN_USE_THREADS
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    if (m_is_identity) {
-      return m_impl.coeff(index);
-    } else {
-      return m_impl.coeff(srcCoeff(index));
-    }
-  }
-
-  template <int LoadMode, typename Self, bool ImplPacketAccess>
-  struct PacketLoader {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    static PacketReturnType Run(const Self& self, Index index) {
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < PacketSize; ++i) {
-        values[i] = self.coeff(index + i);
-      }
-      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-      return rslt;
-    }
-  };
-
-  template<int LoadMode, typename Self>
-  struct PacketLoader<LoadMode, Self, true> {
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    static PacketReturnType Run(const Self& self, Index index) {
-      if (self.m_is_identity) {
-        return self.m_impl.template packet<LoadMode>(index);
-      } else {
-        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-        EIGEN_UNROLL_LOOP
-        for (int i = 0; i < PacketSize; ++i) {
-          values[i] = self.coeff(index + i);
-        }
-        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-        return rslt;
-      }
-    }
-  };
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-        eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
-    return PacketLoader<LoadMode, Self, TensorEvaluator<ArgType, Device>::PacketAccess>::Run(*this, index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  internal::TensorBlockResourceRequirements getResourceRequirements() const {
-    static const int inner_dim =
-        Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
-
-    const size_t target_size = m_device.firstLevelCacheSize();
-    const bool inner_dim_shuffled = m_shuffle[inner_dim] != inner_dim;
-
-    // Shuffled inner dimensions leads to a random memory access, which is not
-    // captured by default cost model bytes loaded/stored. We add this cost
-    // explicitly. The number of cycles picked based on the benchmarks.
-    // TODO(ezhulenev): This number was picked based on a very questionable
-    // benchmarks, add benchmarks that are representative of real workloads.
-    using BlockRequirements = internal::TensorBlockResourceRequirements;
-    if (inner_dim_shuffled) {
-      return BlockRequirements::uniform<Scalar>(target_size)
-          .addCostPerCoeff({0, 0, NumDims * 28});
-    } else {
-      return BlockRequirements::skewed<Scalar>(target_size);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
-  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
-          bool root_of_expr_ast = false) const {
-    assert(m_impl.data() != NULL);
-
-    typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout>
-        TensorBlockIO;
-    typedef typename TensorBlockIO::Dst TensorBlockIODst;
-    typedef typename TensorBlockIO::Src TensorBlockIOSrc;
-
-    const typename TensorBlock::Storage block_storage =
-        TensorBlock::prepareStorage(
-            desc, scratch, /*allow_strided_storage=*/root_of_expr_ast);
-
-    typename TensorBlockIO::Dimensions input_strides(m_unshuffledInputStrides);
-    TensorBlockIOSrc src(input_strides, m_impl.data(), srcCoeff(desc.offset()));
-
-    TensorBlockIODst dst(block_storage.dimensions(), block_storage.strides(),
-                         block_storage.data());
-
-    typename TensorBlockIO::DimensionsMap dst_to_src_dim_map(m_shuffle);
-    TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
-
-    return block_storage.AsTensorMaterializedBlock();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    const double compute_cost = m_is_identity ? TensorOpCost::AddCost<Index>() :
-                                NumDims * (2 * TensorOpCost::AddCost<Index>() +
-                                           2 * TensorOpCost::MulCost<Index>() +
-                                           TensorOpCost::DivCost<Index>());
-    return m_impl.costPerCoeff(vectorized) +
-           TensorOpCost(0, 0, compute_cost, m_is_identity /* vectorized */, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-   // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index GetBlockOutputIndex(
-      Index input_index,
-      const DSizes<Index, NumDims>& input_block_strides,
-      const DSizes<Index, NumDims>& output_block_strides,
-      const DSizes<internal::TensorIntDivisor<Index>, NumDims>& fast_input_block_strides) const {
-    Index output_index = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = input_index / fast_input_block_strides[i];
-        output_index += idx * output_block_strides[m_inverseShuffle[i]];
-        input_index -= idx * input_block_strides[i];
-      }
-      return output_index + input_index *
-          output_block_strides[m_inverseShuffle[0]];
-    } else {
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = input_index / fast_input_block_strides[i];
-        output_index += idx * output_block_strides[m_inverseShuffle[i]];
-        input_index -= idx * input_block_strides[i];
-      }
-      return output_index + input_index *
-          output_block_strides[m_inverseShuffle[NumDims - 1]];
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = index / m_fastOutputStrides[i];
-        inputIndex += idx * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      return inputIndex + index * m_inputStrides[0];
-    } else {
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = index / m_fastOutputStrides[i];
-        inputIndex += idx * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      return inputIndex + index * m_inputStrides[NumDims - 1];
-    }
-  }
-
-  Dimensions m_dimensions;
-  bool m_is_identity;
-  array<int, NumDims> m_shuffle;
-  array<Index, NumDims> m_inverseShuffle;  // TODO(ezhulenev): Make it int type.
-  array<Index, NumDims> m_outputStrides;
-  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
-  array<Index, NumDims> m_inputStrides;
-  array<Index, NumDims> m_unshuffledInputStrides;
-
-  const Device EIGEN_DEVICE_REF m_device;
-  TensorEvaluator<ArgType, Device> m_impl;
-};
-
-
-// Eval as lvalue
-template<typename Shuffle, typename ArgType, typename Device>
-struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
-    : public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
-{
-  typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
-
-  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-
-  enum {
-    IsAligned         = false,
-    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
-    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
-    PreferBlockAccess = true,
-    Layout            = TensorEvaluator<ArgType, Device>::Layout,
-    RawAccess         = false
-  };
-
-  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : Base(op, device)
-  { }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
-  {
-    return this->m_impl.coeffRef(this->srcCoeff(index));
-  }
-
-  template <int StoreMode> EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      this->coeffRef(index+i) = values[i];
-    }
-  }
-
-  template <typename TensorBlock>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
-      const TensorBlockDesc& desc, const TensorBlock& block) {
-    eigen_assert(this->m_impl.data() != NULL);
-
-    typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout>
-        TensorBlockIO;
-    typedef typename TensorBlockIO::Dst TensorBlockIODst;
-    typedef typename TensorBlockIO::Src TensorBlockIOSrc;
-
-    const Scalar* block_buffer = block.data();
-
-    // TODO(ezhulenev): TensorBlockIO should be able to read from any Eigen
-    // expression with coefficient and packet access as `src`.
-    void* mem = NULL;
-    if (block_buffer == NULL) {
-      mem = this->m_device.allocate(desc.size() * sizeof(Scalar));
-      ScalarNoConst* buf = static_cast<ScalarNoConst*>(mem);
-
-      typedef internal::TensorBlockAssignment<
-          ScalarNoConst, NumDims, typename TensorBlock::XprType, Index>
-          TensorBlockAssignment;
-
-      TensorBlockAssignment::Run(
-          TensorBlockAssignment::target(
-              desc.dimensions(), internal::strides<Layout>(desc.dimensions()),
-              buf),
-          block.expr());
-
-      block_buffer = buf;
-    }
-
-    // Read from block.
-    TensorBlockIOSrc src(internal::strides<Layout>(desc.dimensions()),
-                         block_buffer);
-
-    // Write to the output buffer.
-    typename TensorBlockIO::Dimensions output_strides(
-        this->m_unshuffledInputStrides);
-    typename TensorBlockIO::Dimensions output_dimensions;
-    for (int i = 0; i < NumDims; ++i) {
-      output_dimensions[this->m_shuffle[i]] = desc.dimension(i);
-    }
-    TensorBlockIODst dst(output_dimensions, output_strides, this->m_impl.data(),
-                         this->srcCoeff(desc.offset()));
-
-    // Reorder dimensions according to the shuffle.
-    typename TensorBlockIO::DimensionsMap dst_to_src_dim_map;
-    for (int i = 0; i < NumDims; ++i) {
-      dst_to_src_dim_map[i] = static_cast<int>(this->m_inverseShuffle[i]);
-    }
-    TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
-
-    // Deallocate temporary buffer used for the block materialization.
-    if (mem != NULL) this->m_device.deallocate(mem);
-  }
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
deleted file mode 100644
index 5ff0880..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
+++ /dev/null
@@ -1,161 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
-#define EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
-
-#ifdef EIGEN_TENSOR_STORAGE_CTOR_PLUGIN
-  #define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN EIGEN_TENSOR_STORAGE_CTOR_PLUGIN;
-#else
-  #define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN
-#endif
-
-namespace Eigen {
-
-/** \internal
-  *
-  * \class TensorStorage
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Stores the data of a tensor
-  *
-  * This class stores the data of fixed-size, dynamic-size or mixed tensors
-  * in a way as compact as possible.
-  *
-  * \sa Tensor
-  */
-template<typename T, typename Dimensions, int Options> class TensorStorage;
-
-
-// Pure fixed-size storage
-template<typename T, typename FixedDimensions, int Options_>
-class TensorStorage
-{
- private:
-  static const std::size_t Size = FixedDimensions::total_size;
-
-  // Allocate an array of size at least one to prevent compiler warnings.
-  static const std::size_t MinSize = max_n_1<Size>::size;
-  EIGEN_ALIGN_MAX T m_data[MinSize];
-
- public:
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE TensorStorage() {
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T *data() { return m_data; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T *data() const { return m_data; }
-
-  static EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const FixedDimensions& dimensions()
-  {
-    static const FixedDimensions* singleton_dimensions = new FixedDimensions();
-    return *singleton_dimensions;
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE DenseIndex size() const { return Size; }
-};
-
-// pure dynamic
-template<typename T, typename IndexType, int NumIndices_, int Options_>
-class TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_>
-{
-  public:
-    typedef IndexType Index;
-    typedef DSizes<IndexType, NumIndices_> Dimensions;
-    typedef TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_> Self;
-
-    EIGEN_DEVICE_FUNC TensorStorage() : m_data(0), m_dimensions() {
-      if (NumIndices_ == 0) {
-	m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(1);
-      }
-    }
-    EIGEN_DEVICE_FUNC TensorStorage(internal::constructor_without_unaligned_array_assert)
-      : m_data(0), m_dimensions(internal::template repeat<NumIndices_, Index>(0)) {}
-    EIGEN_DEVICE_FUNC TensorStorage(Index size, const array<Index, NumIndices_>& dimensions)
-        : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size)), m_dimensions(dimensions)
-      { EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    template <typename... DenseIndex>
-    EIGEN_DEVICE_FUNC TensorStorage(DenseIndex... indices) : m_dimensions(indices...) {
-      m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(internal::array_prod(m_dimensions));
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC TensorStorage(const Self& other)
-      : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(internal::array_prod(other.m_dimensions)))
-      , m_dimensions(other.m_dimensions)
-    {
-      internal::smart_copy(other.m_data, other.m_data+internal::array_prod(other.m_dimensions), m_data);
-    }
-    EIGEN_DEVICE_FUNC Self& operator=(const Self& other)
-    {
-      if (this != &other) {
-        Self tmp(other);
-        this->swap(tmp);
-      }
-      return *this;
-    }
-
-#if EIGEN_HAS_RVALUE_REFERENCES
-    EIGEN_DEVICE_FUNC TensorStorage(Self&& other) : TensorStorage()
-    {
-      *this = std::move(other);
-    }
-    
-    EIGEN_DEVICE_FUNC Self& operator=(Self&& other)
-    {
-      numext::swap(m_data, other.m_data);
-      numext::swap(m_dimensions, other.m_dimensions);
-      return *this;
-    }
-#endif
-
-    EIGEN_DEVICE_FUNC  ~TensorStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, internal::array_prod(m_dimensions)); }
-    EIGEN_DEVICE_FUNC  void swap(Self& other)
-    { numext::swap(m_data,other.m_data); numext::swap(m_dimensions,other.m_dimensions); }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {return m_dimensions;}
-
-    EIGEN_DEVICE_FUNC void resize(Index size, const array<Index, NumIndices_>& nbDimensions)
-    {
-      const Index currentSz = internal::array_prod(m_dimensions);
-      if(size != currentSz)
-      {
-        internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, currentSz);
-        if (size)
-          m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
-        else if (NumIndices_ == 0) {
-	  m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(1);
-	}
-	else 
-          m_data = 0;
-        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
-      }
-      m_dimensions = nbDimensions;
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T *data() { return m_data; }
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T *data() const { return m_data; }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
-
- private:
-  T *m_data;
-  Dimensions m_dimensions;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
deleted file mode 100644
index 2f62a66..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
+++ /dev/null
@@ -1,346 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
-#define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
-
-namespace Eigen {
-
-/** \class TensorStriding
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor striding class.
-  *
-  *
-  */
-namespace internal {
-template<typename Strides, typename XprType>
-struct traits<TensorStridingOp<Strides, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-};
-
-template<typename Strides, typename XprType>
-struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense>
-{
-  typedef const TensorStridingOp<Strides, XprType>EIGEN_DEVICE_REF type;
-};
-
-template<typename Strides, typename XprType>
-struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type>
-{
-  typedef TensorStridingOp<Strides, XprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename Strides, typename XprType>
-class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> >
-{
-  public:
-    typedef TensorBase<TensorStridingOp<Strides, XprType> > Base;
-    typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename XprType::CoeffReturnType CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims)
-      : m_xpr(expr), m_dims(dims) {}
-
-    EIGEN_DEVICE_FUNC
-    const Strides& strides() const { return m_dims; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorStridingOp)
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const Strides m_dims;
-};
-
-
-// Eval as rvalue
-template<typename Strides, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
-{
-  typedef TensorStridingOp<Strides, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device)
-  {
-    m_dimensions = m_impl.dimensions();
-    for (int i = 0; i < NumDims; ++i) {
-      m_dimensions[i] =Eigen::numext::ceil(static_cast<float>(m_dimensions[i]) / op.strides()[i]);
-    }
-
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_outputStrides[0] = 1;
-      m_inputStrides[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
-        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
-        m_inputStrides[i-1] *= op.strides()[i-1];
-      }
-      m_inputStrides[NumDims-1] *= op.strides()[NumDims-1];
-    } else {  // RowMajor
-      m_outputStrides[NumDims-1] = 1;
-      m_inputStrides[NumDims-1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
-        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
-        m_inputStrides[i+1] *= op.strides()[i+1];
-      }
-      m_inputStrides[0] *= op.strides()[0];
-    }
-  }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType/*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    return m_impl.coeff(srcCoeff(index));
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    Index inputIndices[] = {0, 0};
-    Index indices[] = {index, index + PacketSize - 1};
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx0 = indices[0] / m_outputStrides[i];
-        const Index idx1 = indices[1] / m_outputStrides[i];
-        inputIndices[0] += idx0 * m_inputStrides[i];
-        inputIndices[1] += idx1 * m_inputStrides[i];
-        indices[0] -= idx0 * m_outputStrides[i];
-        indices[1] -= idx1 * m_outputStrides[i];
-      }
-      inputIndices[0] += indices[0] * m_inputStrides[0];
-      inputIndices[1] += indices[1] * m_inputStrides[0];
-    } else {  // RowMajor
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx0 = indices[0] / m_outputStrides[i];
-        const Index idx1 = indices[1] / m_outputStrides[i];
-        inputIndices[0] += idx0 * m_inputStrides[i];
-        inputIndices[1] += idx1 * m_inputStrides[i];
-        indices[0] -= idx0 * m_outputStrides[i];
-        indices[1] -= idx1 * m_outputStrides[i];
-      }
-      inputIndices[0] += indices[0] * m_inputStrides[NumDims-1];
-      inputIndices[1] += indices[1] * m_inputStrides[NumDims-1];
-    }
-    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
-      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
-      return rslt;
-    }
-    else {
-      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-      values[0] = m_impl.coeff(inputIndices[0]);
-      values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < PacketSize-1; ++i) {
-        values[i] = coeff(index+i);
-      }
-      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-      return rslt;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
-    double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() +
-                                           TensorOpCost::MulCost<Index>() +
-                                           TensorOpCost::DivCost<Index>()) +
-        TensorOpCost::MulCost<Index>();
-    if (vectorized) {
-      compute_cost *= 2;  // packet() computes two indices
-    }
-    const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1);
-    return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) +
-        // Computation is not vectorized per se, but it is done once per packet.
-        TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
-  {
-    Index inputIndex = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx = index / m_outputStrides[i];
-        inputIndex += idx * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      inputIndex += index * m_inputStrides[0];
-    } else {  // RowMajor
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx = index / m_outputStrides[i];
-        inputIndex += idx * m_inputStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      inputIndex += index * m_inputStrides[NumDims-1];
-    }
-    return inputIndex;
-  }
-
-  Dimensions m_dimensions;
-  array<Index, NumDims> m_outputStrides;
-  array<Index, NumDims> m_inputStrides;
-  TensorEvaluator<ArgType, Device> m_impl;
-};
-
-// Eval as lvalue
-template<typename Strides, typename ArgType, typename Device>
-struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device>
-    : public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
-{
-  typedef TensorStridingOp<Strides, ArgType> XprType;
-  typedef TensorEvaluator<const XprType, Device> Base;
-  //  typedef typename XprType::Index Index;
-  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  //  typedef DSizes<Index, NumDims> Dimensions;
-
-  enum {
-    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    PreferBlockAccess = false,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-    RawAccess = false
-  };
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : Base(op, device) { }
-
-  typedef typename XprType::Index Index;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
-  {
-    return this->m_impl.coeffRef(this->srcCoeff(index));
-  }
-
-  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void writePacket(Index index, const PacketReturnType& x)
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < this->dimensions().TotalSize());
-
-    Index inputIndices[] = {0, 0};
-    Index indices[] = {index, index + PacketSize - 1};
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      EIGEN_UNROLL_LOOP
-      for (int i = NumDims - 1; i > 0; --i) {
-        const Index idx0 = indices[0] / this->m_outputStrides[i];
-        const Index idx1 = indices[1] / this->m_outputStrides[i];
-        inputIndices[0] += idx0 * this->m_inputStrides[i];
-        inputIndices[1] += idx1 * this->m_inputStrides[i];
-        indices[0] -= idx0 * this->m_outputStrides[i];
-        indices[1] -= idx1 * this->m_outputStrides[i];
-      }
-      inputIndices[0] += indices[0] * this->m_inputStrides[0];
-      inputIndices[1] += indices[1] * this->m_inputStrides[0];
-    } else {  // RowMajor
-      EIGEN_UNROLL_LOOP
-      for (int i = 0; i < NumDims - 1; ++i) {
-        const Index idx0 = indices[0] / this->m_outputStrides[i];
-        const Index idx1 = indices[1] / this->m_outputStrides[i];
-        inputIndices[0] += idx0 * this->m_inputStrides[i];
-        inputIndices[1] += idx1 * this->m_inputStrides[i];
-        indices[0] -= idx0 * this->m_outputStrides[i];
-        indices[1] -= idx1 * this->m_outputStrides[i];
-      }
-      inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1];
-      inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1];
-    }
-    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
-      this->m_impl.template writePacket<Unaligned>(inputIndices[0], x);
-    }
-    else {
-      EIGEN_ALIGN_MAX Scalar values[PacketSize];
-      internal::pstore<Scalar, PacketReturnType>(values, x);
-      this->m_impl.coeffRef(inputIndices[0]) = values[0];
-      this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize-1];
-      EIGEN_UNROLL_LOOP
-      for (int i = 1; i < PacketSize-1; ++i) {
-        this->coeffRef(index+i) = values[i];
-      }
-    }
-  }
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
deleted file mode 100644
index 926ecdd..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
+++ /dev/null
@@ -1,303 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2017 Gagan Goel <gagan.nith@gmail.com>
-// Copyright (C) 2017 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
-#define EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
-
-namespace Eigen {
-
-/** \class TensorTrace
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor Trace class.
-  *
-  *
-  */
-
-namespace internal {
-template<typename Dims, typename XprType>
-struct traits<TensorTraceOp<Dims, XprType> > : public traits<XprType>
-{
-  typedef typename XprType::Scalar Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
-  static const int Layout = XprTraits::Layout;
-};
-
-template<typename Dims, typename XprType>
-struct eval<TensorTraceOp<Dims, XprType>, Eigen::Dense>
-{
-  typedef const TensorTraceOp<Dims, XprType>& type;
-};
-
-template<typename Dims, typename XprType>
-struct nested<TensorTraceOp<Dims, XprType>, 1, typename eval<TensorTraceOp<Dims, XprType> >::type>
-{
-  typedef TensorTraceOp<Dims, XprType> type;
-};
-
-} // end namespace internal
-
-
-template<typename Dims, typename XprType>
-class TensorTraceOp : public TensorBase<TensorTraceOp<Dims, XprType> >
-{
-  public:
-    typedef typename Eigen::internal::traits<TensorTraceOp>::Scalar Scalar;
-    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-    typedef typename XprType::CoeffReturnType CoeffReturnType;
-    typedef typename Eigen::internal::nested<TensorTraceOp>::type Nested;
-    typedef typename Eigen::internal::traits<TensorTraceOp>::StorageKind StorageKind;
-    typedef typename Eigen::internal::traits<TensorTraceOp>::Index Index;
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTraceOp(const XprType& expr, const Dims& dims)
-      : m_xpr(expr), m_dims(dims) {
-    }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Dims& dims() const { return m_dims; }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const Dims m_dims;
-};
-
-
-// Eval as rvalue
-template<typename Dims, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorTraceOp<Dims, ArgType>, Device>
-{
-  typedef TensorTraceOp<Dims, ArgType> XprType;
-  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  static const int NumReducedDims = internal::array_size<Dims>::value;
-  static const int NumOutputDims = NumInputDims - NumReducedDims;
-  typedef typename XprType::Index Index;
-  typedef DSizes<Index, NumOutputDims> Dimensions;
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-    : m_impl(op.expression(), device), m_traceDim(1), m_device(device)
-  {
-
-    EIGEN_STATIC_ASSERT((NumOutputDims >= 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    EIGEN_STATIC_ASSERT((NumReducedDims >= 2) || ((NumReducedDims == 0) && (NumInputDims == 0)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    for (int i = 0; i < NumInputDims; ++i) {
-      m_reduced[i] = false;
-    }
-
-    const Dims& op_dims = op.dims();
-    for (int i = 0; i < NumReducedDims; ++i) {
-      eigen_assert(op_dims[i] >= 0);
-      eigen_assert(op_dims[i] < NumInputDims);
-      m_reduced[op_dims[i]] = true;
-    }
-
-    // All the dimensions should be distinct to compute the trace
-    int num_distinct_reduce_dims = 0;
-    for (int i = 0; i < NumInputDims; ++i) {
-      if (m_reduced[i]) {
-        ++num_distinct_reduce_dims;
-      }
-    }
-
-    eigen_assert(num_distinct_reduce_dims == NumReducedDims);
-
-    // Compute the dimensions of the result.
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-
-    int output_index = 0;
-    int reduced_index = 0;
-    for (int i = 0; i < NumInputDims; ++i) {
-      if (m_reduced[i]) {
-        m_reducedDims[reduced_index] = input_dims[i];
-        if (reduced_index > 0) {
-          // All the trace dimensions must have the same size
-          eigen_assert(m_reducedDims[0] == m_reducedDims[reduced_index]);
-        }
-        ++reduced_index;
-      }
-      else {
-        m_dimensions[output_index] = input_dims[i];
-        ++output_index;
-      }
-    }
-
-    if (NumReducedDims != 0) {
-      m_traceDim = m_reducedDims[0];
-    }
-
-    // Compute the output strides
-    if (NumOutputDims > 0) {
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        m_outputStrides[0] = 1;
-        for (int i = 1; i < NumOutputDims; ++i) {
-          m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
-        }
-      }
-      else {
-        m_outputStrides.back() = 1;
-        for (int i = NumOutputDims - 2; i >= 0; --i) {
-          m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
-        }
-      }
-    }
-
-    // Compute the input strides
-    if (NumInputDims > 0) {
-      array<Index, NumInputDims> input_strides;
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        input_strides[0] = 1;
-        for (int i = 1; i < NumInputDims; ++i) {
-          input_strides[i] = input_strides[i - 1] * input_dims[i - 1];
-        }
-      }
-      else {
-        input_strides.back() = 1;
-        for (int i = NumInputDims - 2; i >= 0; --i) {
-          input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
-        }
-      }
-
-      output_index = 0;
-      reduced_index = 0;
-      for (int i = 0; i < NumInputDims; ++i) {
-        if(m_reduced[i]) {
-          m_reducedStrides[reduced_index] = input_strides[i];
-          ++reduced_index;
-        }
-        else {
-          m_preservedStrides[output_index] = input_strides[i];
-          ++output_index;
-        }
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
-    return m_dimensions;
-  }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    // Initialize the result
-    CoeffReturnType result = internal::cast<int, CoeffReturnType>(0);
-    Index index_stride = 0;
-    for (int i = 0; i < NumReducedDims; ++i) {
-      index_stride += m_reducedStrides[i];
-    }
-
-    // If trace is requested along all dimensions, starting index would be 0
-    Index cur_index = 0;
-    if (NumOutputDims != 0)
-      cur_index = firstInput(index);
-    for (Index i = 0; i < m_traceDim; ++i) {
-        result += m_impl.coeff(cur_index);
-        cur_index += index_stride;
-    }
-
-    return result;
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
-
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
-
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    for (int i = 0; i < PacketSize; ++i) {
-        values[i] = coeff(index + i);
-    }
-    PacketReturnType result = internal::ploadt<PacketReturnType, LoadMode>(values);
-    return result;
-  }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
-
- protected:
-  // Given the output index, finds the first index in the input tensor used to compute the trace
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
-    Index startInput = 0;
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = NumOutputDims - 1; i > 0; --i) {
-        const Index idx = index / m_outputStrides[i];
-        startInput += idx * m_preservedStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      startInput += index * m_preservedStrides[0];
-    }
-    else {
-      for (int i = 0; i < NumOutputDims - 1; ++i) {
-        const Index idx = index / m_outputStrides[i];
-        startInput += idx * m_preservedStrides[i];
-        index -= idx * m_outputStrides[i];
-      }
-      startInput += index * m_preservedStrides[NumOutputDims - 1];
-    }
-    return startInput;
-  }
-
-  Dimensions m_dimensions;
-  TensorEvaluator<ArgType, Device> m_impl;
-  // Initialize the size of the trace dimension
-  Index m_traceDim;
-  const Device EIGEN_DEVICE_REF m_device;
-  array<bool, NumInputDims> m_reduced;
-  array<Index, NumReducedDims> m_reducedDims;
-  array<Index, NumOutputDims> m_outputStrides;
-  array<Index, NumReducedDims> m_reducedStrides;
-  array<Index, NumOutputDims> m_preservedStrides;
-};
-
-
-} // End namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
deleted file mode 100644
index 4f7fd34..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
+++ /dev/null
@@ -1,264 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
-#define EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
-
-namespace Eigen {
-namespace internal {
-
-
-template<typename Scalar, int Options>
-class compute_tensor_flags
-{
-  enum {
-    is_dynamic_size_storage = 1,
-
-    is_aligned =
-    (
-        ((Options&DontAlign)==0) && (
-#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
-            (!is_dynamic_size_storage)
-#else
-            0
-#endif
-            |
-#if EIGEN_MAX_ALIGN_BYTES>0
-            is_dynamic_size_storage
-#else
-            0
-#endif
-      )
-     ),
-    packet_access_bit = packet_traits<Scalar>::Vectorizable && is_aligned ? PacketAccessBit : 0
-  };
-
-  public:
-    enum { ret = packet_access_bit };
-};
-
-
-template<typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
-struct traits<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
-{
-  typedef Scalar_ Scalar;
-  typedef Dense StorageKind;
-  typedef IndexType_ Index;
-  static const int NumDimensions = NumIndices_;
-  static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
-  enum {
-    Options = Options_,
-    Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0 : LvalueBit)
-  };
-  template <typename T> struct MakePointer {
-    typedef T* Type;
-  };
-  typedef typename MakePointer<Scalar>::Type PointerType;
-};
-
-
-template<typename Scalar_, typename Dimensions, int Options_, typename IndexType_>
-struct traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_> >
-{
-  typedef Scalar_ Scalar;
-  typedef Dense StorageKind;
-  typedef IndexType_ Index;
-  static const int NumDimensions = array_size<Dimensions>::value;
-  static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
-  enum {
-    Options = Options_,
-    Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0: LvalueBit)
-  };
-  template <typename T> struct MakePointer {
-    typedef T* Type;
-  };
-  typedef typename MakePointer<Scalar>::Type PointerType;
-};
-
-
-template<typename PlainObjectType, int Options_, template <class> class MakePointer_>
-struct traits<TensorMap<PlainObjectType, Options_, MakePointer_> >
-  : public traits<PlainObjectType>
-{
-  typedef traits<PlainObjectType> BaseTraits;
-  typedef typename BaseTraits::Scalar Scalar;
-  typedef typename BaseTraits::StorageKind StorageKind;
-  typedef typename BaseTraits::Index Index;
-  static const int NumDimensions = BaseTraits::NumDimensions;
-  static const int Layout = BaseTraits::Layout;
-  enum {
-    Options = Options_,
-    Flags = BaseTraits::Flags
-  };
-  template <class T> struct MakePointer {
-    // Intermediate typedef to workaround MSVC issue.
-    typedef MakePointer_<T> MakePointerT;
-    typedef typename MakePointerT::Type Type;
-  };
-  typedef typename MakePointer<Scalar>::Type PointerType;
-};
-
-template<typename PlainObjectType>
-struct traits<TensorRef<PlainObjectType> >
-  : public traits<PlainObjectType>
-{
-  typedef traits<PlainObjectType> BaseTraits;
-  typedef typename BaseTraits::Scalar Scalar;
-  typedef typename BaseTraits::StorageKind StorageKind;
-  typedef typename BaseTraits::Index Index;
-  static const int NumDimensions = BaseTraits::NumDimensions;
-  static const int Layout = BaseTraits::Layout;
-  enum {
-    Options = BaseTraits::Options,
-    Flags = BaseTraits::Flags
-  };
-  typedef typename BaseTraits::PointerType PointerType;
-};
-
-
-template<typename _Scalar, int NumIndices_, int Options, typename IndexType_>
-struct eval<Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense>
-{
-  typedef const Tensor<_Scalar, NumIndices_, Options, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-template<typename _Scalar, int NumIndices_, int Options, typename IndexType_>
-struct eval<const Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense>
-{
-  typedef const Tensor<_Scalar, NumIndices_, Options, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-template<typename Scalar_, typename Dimensions, int Options, typename IndexType_>
-struct eval<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense>
-{
-  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-template<typename Scalar_, typename Dimensions, int Options, typename IndexType_>
-struct eval<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense>
-{
-  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-template<typename PlainObjectType, int Options, template <class> class MakePointer>
-struct eval<TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense>
-{
-  typedef const TensorMap<PlainObjectType, Options, MakePointer>EIGEN_DEVICE_REF type;
-};
-
-template<typename PlainObjectType, int Options, template <class> class MakePointer>
-struct eval<const TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense>
-{
-  typedef const TensorMap<PlainObjectType, Options, MakePointer>EIGEN_DEVICE_REF type;
-};
-
-template<typename PlainObjectType>
-struct eval<TensorRef<PlainObjectType>, Eigen::Dense>
-{
-  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
-};
-
-template<typename PlainObjectType>
-struct eval<const TensorRef<PlainObjectType>, Eigen::Dense>
-{
-  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
-};
-
-// TODO nested<> does not exist anymore in Eigen/Core, and it thus has to be removed in favor of ref_selector.
-template<typename T, int n=1, typename PlainObject = void> struct nested
-{
-  typedef typename ref_selector<T>::type type;
-};
-
-template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
-struct nested<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
-{
-  typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
-struct nested<const Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
-{
-  typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
-struct nested<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> >
-{
-  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
-struct nested<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> >
-{
-  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
-};
-
-
-template <typename PlainObjectType>
-struct nested<TensorRef<PlainObjectType> >
-{
-  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
-};
-
-template <typename PlainObjectType>
-struct nested<const TensorRef<PlainObjectType> >
-{
-  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
-};
-
-}  // end namespace internal
-
-// Convolutional layers take in an input tensor of shape (D, R, C, B), or (D, C,
-// R, B), and convolve it with a set of filters, which can also be presented as
-// a tensor (D, K, K, M), where M is the number of filters, K is the filter
-// size, and each 3-dimensional tensor of size (D, K, K) is a filter. For
-// simplicity we assume that we always use square filters (which is usually the
-// case in images), hence the two Ks in the tensor dimension.  It also takes in
-// a few additional parameters:
-// Stride (S): The convolution stride is the offset between locations where we
-//             apply the filters.  A larger stride means that the output will be
-//             spatially smaller.
-// Padding (P): The padding we apply to the input tensor along the R and C
-//              dimensions.  This is usually used to make sure that the spatial
-//              dimensions of the output matches our intention.
-//
-// Two types of padding are often used:
-//   SAME: The pad value is computed so that the output will have size
-//         R/S and C/S.
-//   VALID: no padding is carried out.
-// When we do padding, the padded values at the padded locations are usually
-// zero.
-//
-// The output dimensions for convolution, when given all the parameters above,
-// are as follows:
-// When Padding = SAME: the output size is (B, R', C', M), where
-//   R' = ceil(float(R) / float(S))
-//   C' = ceil(float(C) / float(S))
-// where ceil is the ceiling function.  The input tensor is padded with 0 as
-// needed.  The number of padded rows and columns are computed as:
-//   Pr = ((R' - 1) * S + K - R) / 2
-//   Pc = ((C' - 1) * S + K - C) / 2
-// when the stride is 1, we have the simplified case R'=R, C'=C, Pr=Pc=(K-1)/2.
-// This is where SAME comes from - the output has the same size as the input has.
-// When Padding = VALID: the output size is computed as
-//   R' = ceil(float(R - K + 1) / float(S))
-//   C' = ceil(float(C - K + 1) / float(S))
-// and the number of padded rows and columns are computed in the same way as in
-// the SAME case.
-// When the stride is 1, we have the simplified case R'=R-K+1, C'=C-K+1, Pr=0,
-// Pc=0.
-typedef enum {
-  PADDING_VALID = 1,
-  PADDING_SAME = 2
-} PaddingType;
-
-}  // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h
deleted file mode 100644
index d23f2e4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h
+++ /dev/null
@@ -1,249 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
-#define EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
-
-namespace Eigen {
-namespace internal {
-
-
-template <uint64_t n>
-struct static_val {
-  static const uint64_t value = n;
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator uint64_t() const { return n; }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val() { }
-
-  template <typename T>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val(const T& v) {
-    EIGEN_UNUSED_VARIABLE(v);
-    eigen_assert(v == n);
-  }
-};
-
-
-template <typename HIGH = uint64_t, typename LOW = uint64_t>
-struct TensorUInt128
-{
-  HIGH high;
-  LOW low;
-
-  template<typename OTHER_HIGH, typename OTHER_LOW>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  TensorUInt128(const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other) : high(other.high), low(other.low) {
-    EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE);
-  }
-
-  template<typename OTHER_HIGH, typename OTHER_LOW>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  TensorUInt128& operator = (const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other) {
-    EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    high = other.high;
-    low = other.low;
-    return *this;
-  }
-
-  template<typename T>
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  explicit TensorUInt128(const T& x) : high(0), low(x) {
-    eigen_assert((static_cast<typename conditional<sizeof(T) == 8, uint64_t, uint32_t>::type>(x) <= NumTraits<uint64_t>::highest()));
-    eigen_assert(x >= 0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  TensorUInt128(HIGH y, LOW x) : high(y), low(x) { }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator LOW() const {
-    return low;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LOW lower() const {
-    return low;
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE HIGH upper() const {
-    return high;
-  }
-};
-
-
-template <typename HL, typename LL, typename HR, typename LR>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool operator == (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  return (lhs.high == rhs.high) & (lhs.low == rhs.low);
-}
-
-template <typename HL, typename LL, typename HR, typename LR>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool operator != (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  return (lhs.high != rhs.high) | (lhs.low != rhs.low);
-}
-
-template <typename HL, typename LL, typename HR, typename LR>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool operator >= (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  if (lhs.high != rhs.high) {
-    return lhs.high > rhs.high;
-  }
-  return lhs.low >= rhs.low;
-}
-
-template <typename HL, typename LL, typename HR, typename LR>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-bool operator < (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  if (lhs.high != rhs.high) {
-    return lhs.high < rhs.high;
-  }
-  return lhs.low < rhs.low;
-}
-
-template <typename HL, typename LL, typename HR, typename LR>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-TensorUInt128<uint64_t, uint64_t> operator + (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  TensorUInt128<uint64_t, uint64_t> result(lhs.high + rhs.high, lhs.low + rhs.low);
-  if (result.low < rhs.low) {
-    result.high += 1;
-  }
-  return result;
-}
-
-template <typename HL, typename LL, typename HR, typename LR>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-TensorUInt128<uint64_t, uint64_t> operator - (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  TensorUInt128<uint64_t, uint64_t> result(lhs.high - rhs.high, lhs.low - rhs.low);
-  if (result.low > lhs.low) {
-    result.high -= 1;
-  }
-  return result;
-}
-
-
-template <typename HL, typename LL, typename HR, typename LR>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-TensorUInt128<uint64_t, uint64_t> operator * (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  // Split each 128-bit integer into 4 32-bit integers, and then do the
-  // multiplications by hand as follow:
-  //   lhs      a  b  c  d
-  //   rhs      e  f  g  h
-  //           -----------
-  //           ah bh ch dh
-  //           bg cg dg
-  //           cf df
-  //           de
-  // The result is stored in 2 64bit integers, high and low.
-
-  const uint64_t LOW = 0x00000000FFFFFFFFLL;
-  const uint64_t HIGH = 0xFFFFFFFF00000000LL;
-
-  uint64_t d = lhs.low & LOW;
-  uint64_t c = (lhs.low & HIGH) >> 32LL;
-  uint64_t b = lhs.high & LOW;
-  uint64_t a = (lhs.high & HIGH) >> 32LL;
-
-  uint64_t h = rhs.low & LOW;
-  uint64_t g = (rhs.low & HIGH) >> 32LL;
-  uint64_t f = rhs.high & LOW;
-  uint64_t e = (rhs.high & HIGH) >> 32LL;
-
-  // Compute the low 32 bits of low
-  uint64_t acc = d * h;
-  uint64_t low = acc & LOW;
-  //  Compute the high 32 bits of low. Add a carry every time we wrap around
-  acc >>= 32LL;
-  uint64_t carry = 0;
-  uint64_t acc2 = acc + c * h;
-  if (acc2 < acc) {
-    carry++;
-  }
-  acc = acc2 + d * g;
-  if (acc < acc2) {
-    carry++;
-  }
-  low |= (acc << 32LL);
-
-  // Carry forward the high bits of acc to initiate the computation of the
-  // low 32 bits of high
-  acc2 = (acc >> 32LL) | (carry << 32LL);
-  carry = 0;
-
-  acc = acc2 + b * h;
-  if (acc < acc2) {
-    carry++;
-  }
-  acc2 = acc + c * g;
-  if (acc2 < acc) {
-    carry++;
-  }
-  acc = acc2 + d * f;
-  if (acc < acc2) {
-    carry++;
-  }
-  uint64_t high = acc & LOW;
-
-  // Start to compute the high 32 bits of high.
-  acc2 = (acc >> 32LL) | (carry << 32LL);
-
-  acc = acc2 + a * h;
-  acc2 = acc + b * g;
-  acc = acc2 + c * f;
-  acc2 = acc + d * e;
-  high |= (acc2 << 32LL);
-
-  return TensorUInt128<uint64_t, uint64_t>(high, low);
-}
-
-template <typename HL, typename LL, typename HR, typename LR>
-static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-TensorUInt128<uint64_t, uint64_t> operator / (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
-{
-  if (rhs == TensorUInt128<static_val<0>, static_val<1> >(1)) {
-    return TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low);
-  } else if (lhs < rhs) {
-    return TensorUInt128<uint64_t, uint64_t>(0);
-  } else {
-    // calculate the biggest power of 2 times rhs that's less than or equal to lhs
-    TensorUInt128<uint64_t, uint64_t> power2(1);
-    TensorUInt128<uint64_t, uint64_t> d(rhs);
-    TensorUInt128<uint64_t, uint64_t> tmp(lhs - d);
-    while (lhs >= d) {
-      tmp = tmp - d;
-      d = d + d;
-      power2 = power2 + power2;
-    }
-
-    tmp = TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low);
-    TensorUInt128<uint64_t, uint64_t> result(0);
-    while (power2 != TensorUInt128<static_val<0>, static_val<0> >(0)) {
-      if (tmp >= d) {
-        tmp = tmp - d;
-        result = result + power2;
-      }
-      // Shift right
-      power2 = TensorUInt128<uint64_t, uint64_t>(power2.high >> 1, (power2.low >> 1) | (power2.high << 63));
-      d = TensorUInt128<uint64_t, uint64_t>(d.high >> 1, (d.low >> 1) | (d.high << 63));
-    }
-
-    return result;
-  }
-}
-
-
-}  // namespace internal
-}  // namespace Eigen
-
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
deleted file mode 100644
index 0beb9ff..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
+++ /dev/null
@@ -1,629 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
-#define EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
-
-namespace Eigen {
-
-/** \class TensorVolumePatch
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Patch extraction specialized for processing of volumetric data.
-  * This assumes that the input has a least 4 dimensions ordered as follows:
-  *  - channels
-  *  - planes
-  *  - rows
-  *  - columns
-  *  - (optional) additional dimensions such as time or batch size.
-  * Calling the volume patch code with patch_planes, patch_rows, and patch_cols
-  * is equivalent to calling the regular patch extraction code with parameters
-  * d, patch_planes, patch_rows, patch_cols, and 1 for all the additional
-  * dimensions.
-  */
-namespace internal {
-
-template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
-struct traits<TensorVolumePatchOp<Planes, Rows, Cols, XprType> > : public traits<XprType>
-{
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef traits<XprType> XprTraits;
-  typedef typename XprTraits::StorageKind StorageKind;
-  typedef typename XprTraits::Index Index;
-  typedef typename XprType::Nested Nested;
-  typedef typename remove_reference<Nested>::type _Nested;
-  static const int NumDimensions = XprTraits::NumDimensions + 1;
-  static const int Layout = XprTraits::Layout;
-  typedef typename XprTraits::PointerType PointerType;
-
-};
-
-template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
-struct eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Eigen::Dense>
-{
-  typedef const TensorVolumePatchOp<Planes, Rows, Cols, XprType>& type;
-};
-
-template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
-struct nested<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, 1, typename eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType> >::type>
-{
-  typedef TensorVolumePatchOp<Planes, Rows, Cols, XprType> type;
-};
-
-}  // end namespace internal
-
-template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
-class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, ReadOnlyAccessors>
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Scalar Scalar;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename Eigen::internal::nested<TensorVolumePatchOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
-                                                            DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
-                                                            DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
-                                                            DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
-                                                            PaddingType padding_type, Scalar padding_value)
-                                                            : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
-                                                            m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
-                                                            m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
-                                                            m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
-                                                            m_padding_explicit(false), m_padding_top_z(0), m_padding_bottom_z(0), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
-                                                            m_padding_type(padding_type), m_padding_value(padding_value) {}
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
-                                                           DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
-                                                           DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
-                                                           DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
-                                                           DenseIndex padding_top_z, DenseIndex padding_bottom_z,
-                                                           DenseIndex padding_top, DenseIndex padding_bottom,
-                                                           DenseIndex padding_left, DenseIndex padding_right,
-                                                           Scalar padding_value)
-                                                           : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
-                                                           m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
-                                                           m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
-                                                           m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
-                                                           m_padding_explicit(true), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
-                                                           m_padding_left(padding_left), m_padding_right(padding_right),
-                                                           m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
-
-    EIGEN_DEVICE_FUNC
-    DenseIndex patch_planes() const { return m_patch_planes; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex patch_rows() const { return m_patch_rows; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex patch_cols() const { return m_patch_cols; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex plane_strides() const { return m_plane_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex row_strides() const { return m_row_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex col_strides() const { return m_col_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex in_plane_strides() const { return m_in_plane_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex in_row_strides() const { return m_in_row_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex in_col_strides() const { return m_in_col_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex plane_inflate_strides() const { return m_plane_inflate_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
-    EIGEN_DEVICE_FUNC
-    bool padding_explicit() const { return m_padding_explicit; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_top_z() const { return m_padding_top_z; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_bottom_z() const { return m_padding_bottom_z; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_top() const { return m_padding_top; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_bottom() const { return m_padding_bottom; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_left() const { return m_padding_left; }
-    EIGEN_DEVICE_FUNC
-    DenseIndex padding_right() const { return m_padding_right; }
-    EIGEN_DEVICE_FUNC
-    PaddingType padding_type() const { return m_padding_type; }
-    EIGEN_DEVICE_FUNC
-    Scalar padding_value() const { return m_padding_value; }
-
-    EIGEN_DEVICE_FUNC
-    const typename internal::remove_all<typename XprType::Nested>::type&
-    expression() const { return m_xpr; }
-
-  protected:
-    typename XprType::Nested m_xpr;
-    const DenseIndex m_patch_planes;
-    const DenseIndex m_patch_rows;
-    const DenseIndex m_patch_cols;
-    const DenseIndex m_plane_strides;
-    const DenseIndex m_row_strides;
-    const DenseIndex m_col_strides;
-    const DenseIndex m_in_plane_strides;
-    const DenseIndex m_in_row_strides;
-    const DenseIndex m_in_col_strides;
-    const DenseIndex m_plane_inflate_strides;
-    const DenseIndex m_row_inflate_strides;
-    const DenseIndex m_col_inflate_strides;
-    const bool m_padding_explicit;
-    const DenseIndex m_padding_top_z;
-    const DenseIndex m_padding_bottom_z;
-    const DenseIndex m_padding_top;
-    const DenseIndex m_padding_bottom;
-    const DenseIndex m_padding_left;
-    const DenseIndex m_padding_right;
-    const PaddingType m_padding_type;
-    const Scalar m_padding_value;
-};
-
-
-// Eval as rvalue
-template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, Device>
-{
-  typedef TensorVolumePatchOp<Planes, Rows, Cols, ArgType> XprType;
-  typedef typename XprType::Index Index;
-  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
-  static const int NumDims = NumInputDims + 1;
-  typedef DSizes<Index, NumDims> Dimensions;
-  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
-  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
-  typedef StorageMemory<CoeffReturnType, Device> Storage;
-  typedef typename Storage::Type EvaluatorPointerType;
-
-  enum {
-    IsAligned = false,
-    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
-    BlockAccess = false,
-    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
-    Layout = TensorEvaluator<ArgType, Device>::Layout,
-    CoordAccess = false,
-    RawAccess = false
-  };
-
-  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
-  typedef internal::TensorBlockNotImplemented TensorBlock;
-  //===--------------------------------------------------------------------===//
-
-  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) :
- m_impl(op.expression(), device)
-  {
-    EIGEN_STATIC_ASSERT((NumDims >= 5), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    m_paddingValue = op.padding_value();
-
-    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
-
-    // Cache a few variables.
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputDepth = input_dims[0];
-      m_inputPlanes = input_dims[1];
-      m_inputRows = input_dims[2];
-      m_inputCols = input_dims[3];
-    } else {
-      m_inputDepth = input_dims[NumInputDims-1];
-      m_inputPlanes = input_dims[NumInputDims-2];
-      m_inputRows = input_dims[NumInputDims-3];
-      m_inputCols = input_dims[NumInputDims-4];
-    }
-
-    m_plane_strides = op.plane_strides();
-    m_row_strides = op.row_strides();
-    m_col_strides = op.col_strides();
-
-    // Input strides and effective input/patch size
-    m_in_plane_strides = op.in_plane_strides();
-    m_in_row_strides = op.in_row_strides();
-    m_in_col_strides = op.in_col_strides();
-    m_plane_inflate_strides = op.plane_inflate_strides();
-    m_row_inflate_strides = op.row_inflate_strides();
-    m_col_inflate_strides = op.col_inflate_strides();
-
-    // The "effective" spatial size after inflating data with zeros.
-    m_input_planes_eff = (m_inputPlanes - 1) * m_plane_inflate_strides + 1;
-    m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
-    m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
-    m_patch_planes_eff = op.patch_planes() + (op.patch_planes() - 1) * (m_in_plane_strides - 1);
-    m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
-    m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
-
-    if (op.padding_explicit()) {
-      m_outputPlanes = numext::ceil((m_input_planes_eff + op.padding_top_z() + op.padding_bottom_z() - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides));
-      m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
-      m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
-      m_planePaddingTop = op.padding_top_z();
-      m_rowPaddingTop = op.padding_top();
-      m_colPaddingLeft = op.padding_left();
-    } else {
-      // Computing padding from the type
-      switch (op.padding_type()) {
-        case PADDING_VALID:
-          m_outputPlanes = numext::ceil((m_input_planes_eff - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides));
-          m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
-          m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
-          m_planePaddingTop = 0;
-          m_rowPaddingTop = 0;
-          m_colPaddingLeft = 0;
-          break;
-        case PADDING_SAME: {
-          m_outputPlanes = numext::ceil(m_input_planes_eff / static_cast<float>(m_plane_strides));
-          m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
-          m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
-          const Index dz = (m_outputPlanes - 1) * m_plane_strides + m_patch_planes_eff - m_input_planes_eff;
-          const Index dy = (m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff;
-          const Index dx = (m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff;
-          m_planePaddingTop = dz / 2;
-          m_rowPaddingTop = dy / 2;
-          m_colPaddingLeft = dx / 2;
-          break;
-        }
-        default:
-          eigen_assert(false && "unexpected padding");
-      }
-    }
-    eigen_assert(m_outputRows > 0);
-    eigen_assert(m_outputCols > 0);
-    eigen_assert(m_outputPlanes > 0);
-
-    // Dimensions for result of extraction.
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      // ColMajor
-      // 0: depth
-      // 1: patch_planes
-      // 2: patch_rows
-      // 3: patch_cols
-      // 4: number of patches
-      // 5 and beyond: anything else (such as batch).
-      m_dimensions[0] = input_dims[0];
-      m_dimensions[1] = op.patch_planes();
-      m_dimensions[2] = op.patch_rows();
-      m_dimensions[3] = op.patch_cols();
-      m_dimensions[4] = m_outputPlanes * m_outputRows * m_outputCols;
-      for (int i = 5; i < NumDims; ++i) {
-        m_dimensions[i] = input_dims[i-1];
-      }
-    } else {
-      // RowMajor
-      // NumDims-1: depth
-      // NumDims-2: patch_planes
-      // NumDims-3: patch_rows
-      // NumDims-4: patch_cols
-      // NumDims-5: number of patches
-      // NumDims-6 and beyond: anything else (such as batch).
-      m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
-      m_dimensions[NumDims-2] = op.patch_planes();
-      m_dimensions[NumDims-3] = op.patch_rows();
-      m_dimensions[NumDims-4] = op.patch_cols();
-      m_dimensions[NumDims-5] = m_outputPlanes * m_outputRows * m_outputCols;
-      for (int i = NumDims-6; i >= 0; --i) {
-        m_dimensions[i] = input_dims[i];
-      }
-    }
-
-    // Strides for the output tensor.
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_rowStride = m_dimensions[1];
-      m_colStride = m_dimensions[2] * m_rowStride;
-      m_patchStride = m_colStride * m_dimensions[3] * m_dimensions[0];
-      m_otherStride = m_patchStride * m_dimensions[4];
-    } else {
-      m_rowStride = m_dimensions[NumDims-2];
-      m_colStride = m_dimensions[NumDims-3] * m_rowStride;
-      m_patchStride = m_colStride * m_dimensions[NumDims-4] * m_dimensions[NumDims-1];
-      m_otherStride = m_patchStride * m_dimensions[NumDims-5];
-    }
-
-    // Strides for navigating through the input tensor.
-    m_planeInputStride = m_inputDepth;
-    m_rowInputStride = m_inputDepth * m_inputPlanes;
-    m_colInputStride = m_inputDepth * m_inputRows * m_inputPlanes;
-    m_otherInputStride = m_inputDepth * m_inputRows * m_inputCols * m_inputPlanes;
-
-    m_outputPlanesRows = m_outputPlanes * m_outputRows;
-
-    // Fast representations of different variables.
-    m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
-
-    m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
-    m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
-    m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride);
-    m_fastInputRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
-    m_fastInputColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
-    m_fastInputPlaneStride = internal::TensorIntDivisor<Index>(m_plane_inflate_strides);
-    m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
-    m_fastOutputPlanes = internal::TensorIntDivisor<Index>(m_outputPlanes);
-    m_fastOutputPlanesRows = internal::TensorIntDivisor<Index>(m_outputPlanesRows);
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
-    } else {
-      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
-    m_impl.evalSubExprsIfNeeded(NULL);
-    return true;
-  }
-
-  EIGEN_STRONG_INLINE void cleanup() {
-    m_impl.cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    // Patch index corresponding to the passed in index.
-    const Index patchIndex = index / m_fastPatchStride;
-
-    // Spatial offset within the patch. This has to be translated into 3D
-    // coordinates within the patch.
-    const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
-
-    // Batch, etc.
-    const Index otherIndex = (NumDims == 5) ? 0 : index / m_fastOtherStride;
-    const Index patch3DIndex = (NumDims == 5) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
-
-    // Calculate column index in the input original tensor.
-    const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
-    const Index colOffset = patchOffset / m_fastColStride;
-    const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
-    const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
-    if (inputCol < 0 || inputCol >= m_input_cols_eff ||
-        ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
-      return Scalar(m_paddingValue);
-    }
-
-    // Calculate row index in the original input tensor.
-    const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
-    const Index rowOffset = (patchOffset - colOffset * m_colStride) / m_fastRowStride;
-    const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
-    const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
-    if (inputRow < 0 || inputRow >= m_input_rows_eff ||
-        ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
-      return Scalar(m_paddingValue);
-    }
-
-    // Calculate plane index in the original input tensor.
-    const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
-    const Index planeOffset = patchOffset - colOffset * m_colStride - rowOffset * m_rowStride;
-    const Index inputPlane = planeIndex * m_plane_strides + planeOffset * m_in_plane_strides - m_planePaddingTop;
-    const Index origInputPlane = (m_plane_inflate_strides == 1) ? inputPlane : ((inputPlane >= 0) ? (inputPlane / m_fastInputPlaneStride) : 0);
-    if (inputPlane < 0 || inputPlane >= m_input_planes_eff ||
-        ((m_plane_inflate_strides != 1) && (inputPlane != origInputPlane * m_plane_inflate_strides))) {
-      return Scalar(m_paddingValue);
-    }
-
-    const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
-    const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
-
-    const Index inputIndex = depth +
-        origInputRow * m_rowInputStride +
-        origInputCol * m_colInputStride +
-        origInputPlane * m_planeInputStride +
-        otherIndex * m_otherInputStride;
-
-    return m_impl.coeff(inputIndex);
-  }
-
-  template<int LoadMode>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
-  {
-    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
-
-    if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1 ||
-        m_in_plane_strides != 1 || m_plane_inflate_strides != 1) {
-      return packetWithPossibleZero(index);
-    }
-
-    const Index indices[2] = {index, index + PacketSize - 1};
-    const Index patchIndex = indices[0] / m_fastPatchStride;
-    if (patchIndex != indices[1] / m_fastPatchStride) {
-      return packetWithPossibleZero(index);
-    }
-    const Index otherIndex = (NumDims == 5) ? 0 : indices[0] / m_fastOtherStride;
-    eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
-
-    // Find the offset of the element wrt the location of the first element.
-    const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
-                                   (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
-
-    const Index patch3DIndex = (NumDims == 5) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
-    eigen_assert(patch3DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
-
-    const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
-    const Index colOffsets[2] = {
-      patchOffsets[0] / m_fastColStride,
-      patchOffsets[1] / m_fastColStride};
-
-    // Calculate col indices in the original input tensor.
-    const Index inputCols[2] = {
-      colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft,
-      colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
-    if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
-      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
-    }
-
-    if (inputCols[0] != inputCols[1]) {
-      return packetWithPossibleZero(index);
-    }
-
-    const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
-    const Index rowOffsets[2] = {
-      (patchOffsets[0] - colOffsets[0] * m_colStride) / m_fastRowStride,
-      (patchOffsets[1] - colOffsets[1] * m_colStride) / m_fastRowStride};
-    eigen_assert(rowOffsets[0] <= rowOffsets[1]);
-    // Calculate col indices in the original input tensor.
-    const Index inputRows[2] = {
-      rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop,
-      rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
-
-    if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
-      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
-    }
-
-    if (inputRows[0] != inputRows[1]) {
-      return packetWithPossibleZero(index);
-    }
-
-    const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
-    const Index planeOffsets[2] = {
-      patchOffsets[0] - colOffsets[0] * m_colStride - rowOffsets[0] * m_rowStride,
-      patchOffsets[1] - colOffsets[1] * m_colStride - rowOffsets[1] * m_rowStride};
-    eigen_assert(planeOffsets[0] <= planeOffsets[1]);
-    const Index inputPlanes[2] = {
-      planeIndex * m_plane_strides + planeOffsets[0] - m_planePaddingTop,
-      planeIndex * m_plane_strides + planeOffsets[1] - m_planePaddingTop};
-
-    if (inputPlanes[1] < 0 || inputPlanes[0] >= m_inputPlanes) {
-      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
-    }
-
-    if (inputPlanes[0] >= 0 && inputPlanes[1] < m_inputPlanes) {
-      // no padding
-      const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
-      const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
-      const Index inputIndex = depth +
-          inputRows[0] * m_rowInputStride +
-          inputCols[0] * m_colInputStride +
-          m_planeInputStride * inputPlanes[0] +
-          otherIndex * m_otherInputStride;
-      return m_impl.template packet<Unaligned>(inputIndex);
-    }
-
-    return packetWithPossibleZero(index);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
-  costPerCoeff(bool vectorized) const {
-    const double compute_cost =
-        10 * TensorOpCost::DivCost<Index>() + 21 * TensorOpCost::MulCost<Index>() +
-        8 * TensorOpCost::AddCost<Index>();
-    return TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
-  }
-
-  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
-
-  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index planePaddingTop() const { return m_planePaddingTop; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputPlanes() const { return m_outputPlanes; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userPlaneStride() const { return m_plane_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInPlaneStride() const { return m_in_plane_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index planeInflateStride() const { return m_plane_inflate_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
-
-#ifdef EIGEN_USE_SYCL
-  // binding placeholder accessors to a command group handler for SYCL
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
-    m_impl.bind(cgh);
-  }
-#endif
- protected:
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
-  {
-    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
-    EIGEN_UNROLL_LOOP
-    for (int i = 0; i < PacketSize; ++i) {
-      values[i] = coeff(index+i);
-    }
-    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
-    return rslt;
-  }
-
-  Dimensions m_dimensions;
-
-  // Parameters passed to the constructor.
-  Index m_plane_strides;
-  Index m_row_strides;
-  Index m_col_strides;
-
-  Index m_outputPlanes;
-  Index m_outputRows;
-  Index m_outputCols;
-
-  Index m_planePaddingTop;
-  Index m_rowPaddingTop;
-  Index m_colPaddingLeft;
-
-  Index m_in_plane_strides;
-  Index m_in_row_strides;
-  Index m_in_col_strides;
-
-  Index m_plane_inflate_strides;
-  Index m_row_inflate_strides;
-  Index m_col_inflate_strides;
-
-  // Cached input size.
-  Index m_inputDepth;
-  Index m_inputPlanes;
-  Index m_inputRows;
-  Index m_inputCols;
-
-  // Other cached variables.
-  Index m_outputPlanesRows;
-
-  // Effective input/patch post-inflation size.
-  Index m_input_planes_eff;
-  Index m_input_rows_eff;
-  Index m_input_cols_eff;
-  Index m_patch_planes_eff;
-  Index m_patch_rows_eff;
-  Index m_patch_cols_eff;
-
-  // Strides for the output tensor.
-  Index m_otherStride;
-  Index m_patchStride;
-  Index m_rowStride;
-  Index m_colStride;
-
-  // Strides for the input tensor.
-  Index m_planeInputStride;
-  Index m_rowInputStride;
-  Index m_colInputStride;
-  Index m_otherInputStride;
-
-  internal::TensorIntDivisor<Index> m_fastOtherStride;
-  internal::TensorIntDivisor<Index> m_fastPatchStride;
-  internal::TensorIntDivisor<Index> m_fastColStride;
-  internal::TensorIntDivisor<Index> m_fastRowStride;
-  internal::TensorIntDivisor<Index> m_fastInputPlaneStride;
-  internal::TensorIntDivisor<Index> m_fastInputRowStride;
-  internal::TensorIntDivisor<Index> m_fastInputColStride;
-  internal::TensorIntDivisor<Index> m_fastInputColsEff;
-  internal::TensorIntDivisor<Index> m_fastOutputPlanesRows;
-  internal::TensorIntDivisor<Index> m_fastOutputPlanes;
-  internal::TensorIntDivisor<Index> m_fastOutputDepth;
-
-  Scalar m_paddingValue;
-
-  TensorEvaluator<ArgType, Device> m_impl;
-
-
-};
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
deleted file mode 100644
index bc4f202..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
+++ /dev/null
@@ -1,293 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
-#define EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
-
-namespace Eigen {
-
-class DynamicSGroup
-{
-  public:
-    inline explicit DynamicSGroup() : m_numIndices(1), m_elements(), m_generators(), m_globalFlags(0) { m_elements.push_back(ge(Generator(0, 0, 0))); }
-    inline DynamicSGroup(const DynamicSGroup& o) : m_numIndices(o.m_numIndices), m_elements(o.m_elements), m_generators(o.m_generators), m_globalFlags(o.m_globalFlags) { }
-    inline DynamicSGroup(DynamicSGroup&& o) : m_numIndices(o.m_numIndices), m_elements(), m_generators(o.m_generators), m_globalFlags(o.m_globalFlags) { std::swap(m_elements, o.m_elements); }
-    inline DynamicSGroup& operator=(const DynamicSGroup& o) { m_numIndices = o.m_numIndices; m_elements = o.m_elements; m_generators = o.m_generators; m_globalFlags = o.m_globalFlags; return *this; }
-    inline DynamicSGroup& operator=(DynamicSGroup&& o) { m_numIndices = o.m_numIndices; std::swap(m_elements, o.m_elements); m_generators = o.m_generators; m_globalFlags = o.m_globalFlags; return *this; }
-
-    void add(int one, int two, int flags = 0);
-
-    template<typename Gen_>
-    inline void add(Gen_) { add(Gen_::One, Gen_::Two, Gen_::Flags); }
-    inline void addSymmetry(int one, int two) { add(one, two, 0); }
-    inline void addAntiSymmetry(int one, int two) { add(one, two, NegationFlag); }
-    inline void addHermiticity(int one, int two) { add(one, two, ConjugationFlag); }
-    inline void addAntiHermiticity(int one, int two) { add(one, two, NegationFlag | ConjugationFlag); }
-
-    template<typename Op, typename RV, typename Index, std::size_t N, typename... Args>
-    inline RV apply(const std::array<Index, N>& idx, RV initial, Args&&... args) const
-    {
-      eigen_assert(N >= m_numIndices && "Can only apply symmetry group to objects that have at least the required amount of indices.");
-      for (std::size_t i = 0; i < size(); i++)
-        initial = Op::run(h_permute(i, idx, typename internal::gen_numeric_list<int, N>::type()), m_elements[i].flags, initial, std::forward<Args>(args)...);
-      return initial;
-    }
-
-    template<typename Op, typename RV, typename Index, typename... Args>
-    inline RV apply(const std::vector<Index>& idx, RV initial, Args&&... args) const
-    {
-      eigen_assert(idx.size() >= m_numIndices && "Can only apply symmetry group to objects that have at least the required amount of indices.");
-      for (std::size_t i = 0; i < size(); i++)
-        initial = Op::run(h_permute(i, idx), m_elements[i].flags, initial, std::forward<Args>(args)...);
-      return initial;
-    }
-
-    inline int globalFlags() const { return m_globalFlags; }
-    inline std::size_t size() const { return m_elements.size(); }
-
-    template<typename Tensor_, typename... IndexTypes>
-    inline internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup> operator()(Tensor_& tensor, typename Tensor_::Index firstIndex, IndexTypes... otherIndices) const
-    {
-      static_assert(sizeof...(otherIndices) + 1 == Tensor_::NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
-      return operator()(tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices>{{firstIndex, otherIndices...}});
-    }
-
-    template<typename Tensor_>
-    inline internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup> operator()(Tensor_& tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices> const& indices) const
-    {
-      return internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup>(tensor, *this, indices);
-    }
-  private:
-    struct GroupElement {
-      std::vector<int> representation;
-      int flags;
-      bool isId() const
-      {
-        for (std::size_t i = 0; i < representation.size(); i++)
-          if (i != (size_t)representation[i])
-            return false;
-        return true;
-      }
-    };
-    struct Generator {
-      int one;
-      int two;
-      int flags;
-      constexpr inline Generator(int one_, int two_, int flags_) : one(one_), two(two_), flags(flags_) {}
-    };
-
-    std::size_t m_numIndices;
-    std::vector<GroupElement> m_elements;
-    std::vector<Generator> m_generators;
-    int m_globalFlags;
-
-    template<typename Index, std::size_t N, int... n>
-    inline std::array<Index, N> h_permute(std::size_t which, const std::array<Index, N>& idx, internal::numeric_list<int, n...>) const
-    {
-      return std::array<Index, N>{{ idx[n >= m_numIndices ? n : m_elements[which].representation[n]]... }};
-    }
-
-    template<typename Index>
-    inline std::vector<Index> h_permute(std::size_t which, std::vector<Index> idx) const
-    {
-      std::vector<Index> result;
-      result.reserve(idx.size());
-      for (auto k : m_elements[which].representation)
-        result.push_back(idx[k]);
-      for (std::size_t i = m_numIndices; i < idx.size(); i++)
-        result.push_back(idx[i]);
-      return result;
-    }
-
-    inline GroupElement ge(Generator const& g) const
-    {
-      GroupElement result;
-      result.representation.reserve(m_numIndices);
-      result.flags = g.flags;
-      for (std::size_t k = 0; k < m_numIndices; k++) {
-        if (k == (std::size_t)g.one)
-          result.representation.push_back(g.two);
-        else if (k == (std::size_t)g.two)
-          result.representation.push_back(g.one);
-        else
-          result.representation.push_back(int(k));
-      }
-      return result;
-    }
-
-    GroupElement mul(GroupElement, GroupElement) const;
-    inline GroupElement mul(Generator g1, GroupElement g2) const
-    {
-      return mul(ge(g1), g2);
-    }
-
-    inline GroupElement mul(GroupElement g1, Generator g2) const
-    {
-      return mul(g1, ge(g2));
-    }
-
-    inline GroupElement mul(Generator g1, Generator g2) const
-    {
-      return mul(ge(g1), ge(g2));
-    }
-
-    inline int findElement(GroupElement e) const
-    {
-      for (auto ee : m_elements) {
-        if (ee.representation == e.representation)
-          return ee.flags ^ e.flags;
-      }
-      return -1;
-    }
-
-    void updateGlobalFlags(int flagDiffOfSameGenerator);
-};
-
-// dynamic symmetry group that auto-adds the template parameters in the constructor
-template<typename... Gen>
-class DynamicSGroupFromTemplateArgs : public DynamicSGroup
-{
-  public:
-    inline DynamicSGroupFromTemplateArgs() : DynamicSGroup()
-    {
-      add_all(internal::type_list<Gen...>());
-    }
-    inline DynamicSGroupFromTemplateArgs(DynamicSGroupFromTemplateArgs const& other) : DynamicSGroup(other) { }
-    inline DynamicSGroupFromTemplateArgs(DynamicSGroupFromTemplateArgs&& other) : DynamicSGroup(other) { }
-    inline DynamicSGroupFromTemplateArgs<Gen...>& operator=(const DynamicSGroupFromTemplateArgs<Gen...>& o) { DynamicSGroup::operator=(o); return *this; }
-    inline DynamicSGroupFromTemplateArgs<Gen...>& operator=(DynamicSGroupFromTemplateArgs<Gen...>&& o) { DynamicSGroup::operator=(o); return *this; }
-  
-  private:
-    template<typename Gen1, typename... GenNext>
-    inline void add_all(internal::type_list<Gen1, GenNext...>)
-    {
-      add(Gen1());
-      add_all(internal::type_list<GenNext...>());
-    }
-
-    inline void add_all(internal::type_list<>)
-    {
-    }
-};
-
-inline DynamicSGroup::GroupElement DynamicSGroup::mul(GroupElement g1, GroupElement g2) const
-{
-  eigen_internal_assert(g1.representation.size() == m_numIndices);
-  eigen_internal_assert(g2.representation.size() == m_numIndices);
-
-  GroupElement result;
-  result.representation.reserve(m_numIndices);
-  for (std::size_t i = 0; i < m_numIndices; i++) {
-    int v = g2.representation[g1.representation[i]];
-    eigen_assert(v >= 0);
-    result.representation.push_back(v);
-  }
-  result.flags = g1.flags ^ g2.flags;
-  return result;
-}
-
-inline void DynamicSGroup::add(int one, int two, int flags)
-{
-  eigen_assert(one >= 0);
-  eigen_assert(two >= 0);
-  eigen_assert(one != two);
-
-  if ((std::size_t)one >= m_numIndices || (std::size_t)two >= m_numIndices) {
-    std::size_t newNumIndices = (one > two) ? one : two + 1;
-    for (auto& gelem : m_elements) {
-      gelem.representation.reserve(newNumIndices);
-      for (std::size_t i = m_numIndices; i < newNumIndices; i++)
-        gelem.representation.push_back(i);
-    }
-    m_numIndices = newNumIndices;
-  }
-
-  Generator g{one, two, flags};
-  GroupElement e = ge(g);
-
-  /* special case for first generator */
-  if (m_elements.size() == 1) {
-    while (!e.isId()) {
-      m_elements.push_back(e);
-      e = mul(e, g);
-    }
-
-    if (e.flags > 0)
-      updateGlobalFlags(e.flags);
-
-    // only add in case we didn't have identity
-    if (m_elements.size() > 1)
-      m_generators.push_back(g);
-    return;
-  }
-
-  int p = findElement(e);
-  if (p >= 0) {
-    updateGlobalFlags(p);
-    return;
-  }
-
-  std::size_t coset_order = m_elements.size();
-  m_elements.push_back(e);
-  for (std::size_t i = 1; i < coset_order; i++)
-    m_elements.push_back(mul(m_elements[i], e));
-  m_generators.push_back(g);
-
-  std::size_t coset_rep = coset_order;
-  do {
-    for (auto g : m_generators) {
-      e = mul(m_elements[coset_rep], g);
-      p = findElement(e);
-      if (p < 0) {
-        // element not yet in group
-        m_elements.push_back(e);
-        for (std::size_t i = 1; i < coset_order; i++)
-          m_elements.push_back(mul(m_elements[i], e));
-      } else if (p > 0) {
-        updateGlobalFlags(p);
-      }
-    }
-    coset_rep += coset_order;
-  } while (coset_rep < m_elements.size());
-}
-
-inline void DynamicSGroup::updateGlobalFlags(int flagDiffOfSameGenerator)
-{
-    switch (flagDiffOfSameGenerator) {
-      case 0:
-      default:
-        // nothing happened
-        break;
-      case NegationFlag:
-        // every element is it's own negative => whole tensor is zero
-        m_globalFlags |= GlobalZeroFlag;
-        break;
-      case ConjugationFlag:
-        // every element is it's own conjugate => whole tensor is real
-        m_globalFlags |= GlobalRealFlag;
-        break;
-      case (NegationFlag | ConjugationFlag):
-        // every element is it's own negative conjugate => whole tensor is imaginary
-        m_globalFlags |= GlobalImagFlag;
-        break;
-      /* NOTE:
-       *   since GlobalZeroFlag == GlobalRealFlag | GlobalImagFlag, if one generator
-       *   causes the tensor to be real and the next one to be imaginary, this will
-       *   trivially give the correct result
-       */
-    }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
-
-/*
- * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
- */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h
deleted file mode 100644
index 942293b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h
+++ /dev/null
@@ -1,236 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
-#define EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename list> struct tensor_static_symgroup_permutate;
-
-template<int... nn>
-struct tensor_static_symgroup_permutate<numeric_list<int, nn...>>
-{
-  constexpr static std::size_t N = sizeof...(nn);
-
-  template<typename T>
-  constexpr static inline std::array<T, N> run(const std::array<T, N>& indices)
-  {
-    return {{indices[nn]...}};
-  }
-};
-
-template<typename indices_, int flags_>
-struct tensor_static_symgroup_element
-{
-  typedef indices_ indices;
-  constexpr static int flags = flags_;
-};
-
-template<typename Gen, int N>
-struct tensor_static_symgroup_element_ctor
-{
-  typedef tensor_static_symgroup_element<
-    typename gen_numeric_list_swapped_pair<int, N, Gen::One, Gen::Two>::type,
-    Gen::Flags
-  > type;
-};
-
-template<int N>
-struct tensor_static_symgroup_identity_ctor
-{
-  typedef tensor_static_symgroup_element<
-    typename gen_numeric_list<int, N>::type,
-    0
-  > type;
-};
-
-template<typename iib>
-struct tensor_static_symgroup_multiply_helper
-{
-  template<int... iia>
-  constexpr static inline numeric_list<int, get<iia, iib>::value...> helper(numeric_list<int, iia...>) {
-    return numeric_list<int, get<iia, iib>::value...>();
-  }
-};
-
-template<typename A, typename B>
-struct tensor_static_symgroup_multiply
-{
-  private:
-    typedef typename A::indices iia;
-    typedef typename B::indices iib;
-    constexpr static int ffa = A::flags;
-    constexpr static int ffb = B::flags;
-  
-  public:
-    static_assert(iia::count == iib::count, "Cannot multiply symmetry elements with different number of indices.");
-
-    typedef tensor_static_symgroup_element<
-      decltype(tensor_static_symgroup_multiply_helper<iib>::helper(iia())),
-      ffa ^ ffb
-    > type;
-};
-
-template<typename A, typename B>
-struct tensor_static_symgroup_equality
-{
-    typedef typename A::indices iia;
-    typedef typename B::indices iib;
-    constexpr static int ffa = A::flags;
-    constexpr static int ffb = B::flags;
-    static_assert(iia::count == iib::count, "Cannot compare symmetry elements with different number of indices.");
-
-    constexpr static bool value = is_same<iia, iib>::value;
-
-  private:
-    /* this should be zero if they are identical, or else the tensor
-     * will be forced to be pure real, pure imaginary or even pure zero
-     */
-    constexpr static int flags_cmp_ = ffa ^ ffb;
-
-    /* either they are not equal, then we don't care whether the flags
-     * match, or they are equal, and then we have to check
-     */
-    constexpr static bool is_zero      = value && flags_cmp_ == NegationFlag;
-    constexpr static bool is_real      = value && flags_cmp_ == ConjugationFlag;
-    constexpr static bool is_imag      = value && flags_cmp_ == (NegationFlag | ConjugationFlag);
-
-  public:
-    constexpr static int global_flags = 
-      (is_real ? GlobalRealFlag : 0) |
-      (is_imag ? GlobalImagFlag : 0) |
-      (is_zero ? GlobalZeroFlag : 0);
-};
-
-template<std::size_t NumIndices, typename... Gen>
-struct tensor_static_symgroup
-{
-  typedef StaticSGroup<Gen...> type;
-  constexpr static std::size_t size = type::static_size;
-};
-
-template<typename Index, std::size_t N, int... ii, int... jj>
-constexpr static inline std::array<Index, N> tensor_static_symgroup_index_permute(std::array<Index, N> idx, internal::numeric_list<int, ii...>, internal::numeric_list<int, jj...>)
-{
-  return {{ idx[ii]..., idx[jj]... }};
-}
-
-template<typename Index, int... ii>
-static inline std::vector<Index> tensor_static_symgroup_index_permute(std::vector<Index> idx, internal::numeric_list<int, ii...>)
-{
-  std::vector<Index> result{{ idx[ii]... }};
-  std::size_t target_size = idx.size();
-  for (std::size_t i = result.size(); i < target_size; i++)
-    result.push_back(idx[i]);
-  return result;
-}
-
-template<typename T> struct tensor_static_symgroup_do_apply;
-
-template<typename first, typename... next>
-struct tensor_static_symgroup_do_apply<internal::type_list<first, next...>>
-{
-  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, std::size_t NumIndices, typename... Args>
-  static inline RV run(const std::array<Index, NumIndices>& idx, RV initial, Args&&... args)
-  {
-    static_assert(NumIndices >= SGNumIndices, "Can only apply symmetry group to objects that have at least the required amount of indices.");
-    typedef typename internal::gen_numeric_list<int, NumIndices - SGNumIndices, SGNumIndices>::type remaining_indices;
-    initial = Op::run(tensor_static_symgroup_index_permute(idx, typename first::indices(), remaining_indices()), first::flags, initial, std::forward<Args>(args)...);
-    return tensor_static_symgroup_do_apply<internal::type_list<next...>>::template run<Op, RV, SGNumIndices>(idx, initial, args...);
-  }
-
-  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, typename... Args>
-  static inline RV run(const std::vector<Index>& idx, RV initial, Args&&... args)
-  {
-    eigen_assert(idx.size() >= SGNumIndices && "Can only apply symmetry group to objects that have at least the required amount of indices.");
-    initial = Op::run(tensor_static_symgroup_index_permute(idx, typename first::indices()), first::flags, initial, std::forward<Args>(args)...);
-    return tensor_static_symgroup_do_apply<internal::type_list<next...>>::template run<Op, RV, SGNumIndices>(idx, initial, args...);
-  }
-};
-
-template<EIGEN_TPL_PP_SPEC_HACK_DEF(typename, empty)>
-struct tensor_static_symgroup_do_apply<internal::type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>>
-{
-  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, std::size_t NumIndices, typename... Args>
-  static inline RV run(const std::array<Index, NumIndices>&, RV initial, Args&&...)
-  {
-    // do nothing
-    return initial;
-  }
-
-  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, typename... Args>
-  static inline RV run(const std::vector<Index>&, RV initial, Args&&...)
-  {
-    // do nothing
-    return initial;
-  }
-};
-
-} // end namespace internal
-
-template<typename... Gen>
-class StaticSGroup
-{
-    constexpr static std::size_t NumIndices = internal::tensor_symmetry_num_indices<Gen...>::value;
-    typedef internal::group_theory::enumerate_group_elements<
-      internal::tensor_static_symgroup_multiply,
-      internal::tensor_static_symgroup_equality,
-      typename internal::tensor_static_symgroup_identity_ctor<NumIndices>::type,
-      internal::type_list<typename internal::tensor_static_symgroup_element_ctor<Gen, NumIndices>::type...>
-    > group_elements;
-    typedef typename group_elements::type ge;
-  public:
-    constexpr inline StaticSGroup() {}
-    constexpr inline StaticSGroup(const StaticSGroup<Gen...>&) {}
-    constexpr inline StaticSGroup(StaticSGroup<Gen...>&&) {}
-
-    template<typename Op, typename RV, typename Index, std::size_t N, typename... Args>
-    static inline RV apply(const std::array<Index, N>& idx, RV initial, Args&&... args)
-    {
-      return internal::tensor_static_symgroup_do_apply<ge>::template run<Op, RV, NumIndices>(idx, initial, args...);
-    }
-
-    template<typename Op, typename RV, typename Index, typename... Args>
-    static inline RV apply(const std::vector<Index>& idx, RV initial, Args&&... args)
-    {
-      eigen_assert(idx.size() == NumIndices);
-      return internal::tensor_static_symgroup_do_apply<ge>::template run<Op, RV, NumIndices>(idx, initial, args...);
-    }
-
-    constexpr static std::size_t static_size = ge::count;
-
-    constexpr static inline std::size_t size() {
-      return ge::count;
-    }
-    constexpr static inline int globalFlags() { return group_elements::global_flags; }
-
-    template<typename Tensor_, typename... IndexTypes>
-    inline internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>> operator()(Tensor_& tensor, typename Tensor_::Index firstIndex, IndexTypes... otherIndices) const
-    {
-      static_assert(sizeof...(otherIndices) + 1 == Tensor_::NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
-      return operator()(tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices>{{firstIndex, otherIndices...}});
-    }
-
-    template<typename Tensor_>
-    inline internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>> operator()(Tensor_& tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices> const& indices) const
-    {
-      return internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>>(tensor, *this, indices);
-    }
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
-
-/*
- * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
- */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h
deleted file mode 100644
index 879d6cd..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h
+++ /dev/null
@@ -1,338 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
-#define EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
-
-namespace Eigen {
-
-enum {
-  NegationFlag           = 0x01,
-  ConjugationFlag        = 0x02
-};
-
-enum {
-  GlobalRealFlag         = 0x01,
-  GlobalImagFlag         = 0x02,
-  GlobalZeroFlag         = 0x03
-};
-
-namespace internal {
-
-template<std::size_t NumIndices, typename... Sym>                   struct tensor_symmetry_pre_analysis;
-template<std::size_t NumIndices, typename... Sym>                   struct tensor_static_symgroup;
-template<bool instantiate, std::size_t NumIndices, typename... Sym> struct tensor_static_symgroup_if;
-template<typename Tensor_> struct tensor_symmetry_calculate_flags;
-template<typename Tensor_> struct tensor_symmetry_assign_value;
-template<typename... Sym> struct tensor_symmetry_num_indices;
-
-} // end namespace internal
-
-template<int One_, int Two_>
-struct Symmetry
-{
-  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
-  constexpr static int One = One_;
-  constexpr static int Two = Two_;
-  constexpr static int Flags = 0;
-};
-
-template<int One_, int Two_>
-struct AntiSymmetry
-{
-  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
-  constexpr static int One = One_;
-  constexpr static int Two = Two_;
-  constexpr static int Flags = NegationFlag;
-};
-
-template<int One_, int Two_>
-struct Hermiticity
-{
-  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
-  constexpr static int One = One_;
-  constexpr static int Two = Two_;
-  constexpr static int Flags = ConjugationFlag;
-};
-
-template<int One_, int Two_>
-struct AntiHermiticity
-{
-  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
-  constexpr static int One = One_;
-  constexpr static int Two = Two_;
-  constexpr static int Flags = ConjugationFlag | NegationFlag;
-};
-
-/** \class DynamicSGroup
-  * \ingroup TensorSymmetry_Module
-  *
-  * \brief Dynamic symmetry group
-  *
-  * The %DynamicSGroup class represents a symmetry group that need not be known at
-  * compile time. It is useful if one wants to support arbitrary run-time defineable
-  * symmetries for tensors, but it is also instantiated if a symmetry group is defined
-  * at compile time that would be either too large for the compiler to reasonably
-  * generate (using templates to calculate this at compile time is very inefficient)
-  * or that the compiler could generate the group but that it wouldn't make sense to
-  * unroll the loop for setting coefficients anymore.
-  */
-class DynamicSGroup;
-
-/** \internal
-  *
-  * \class DynamicSGroupFromTemplateArgs
-  * \ingroup TensorSymmetry_Module
-  *
-  * \brief Dynamic symmetry group, initialized from template arguments
-  *
-  * This class is a child class of DynamicSGroup. It uses the template arguments
-  * specified to initialize itself.
-  */
-template<typename... Gen>
-class DynamicSGroupFromTemplateArgs;
-
-/** \class StaticSGroup
-  * \ingroup TensorSymmetry_Module
-  *
-  * \brief Static symmetry group
-  *
-  * This class represents a symmetry group that is known and resolved completely
-  * at compile time. Ideally, no run-time penalty is incurred compared to the
-  * manual unrolling of the symmetry.
-  *
-  * <b><i>CAUTION:</i></b>
-  *
-  * Do not use this class directly for large symmetry groups. The compiler
-  * may run into a limit, or segfault or in the very least will take a very,
-  * very, very long time to compile the code. Use the SGroup class instead
-  * if you want a static group. That class contains logic that will
-  * automatically select the DynamicSGroup class instead if the symmetry
-  * group becomes too large. (In that case, unrolling may not even be
-  * beneficial.)
-  */
-template<typename... Gen>
-class StaticSGroup;
-
-/** \class SGroup
-  * \ingroup TensorSymmetry_Module
-  *
-  * \brief Symmetry group, initialized from template arguments
-  *
-  * This class represents a symmetry group whose generators are already
-  * known at compile time. It may or may not be resolved at compile time,
-  * depending on the estimated size of the group.
-  *
-  * \sa StaticSGroup
-  * \sa DynamicSGroup
-  */
-template<typename... Gen>
-class SGroup : public internal::tensor_symmetry_pre_analysis<internal::tensor_symmetry_num_indices<Gen...>::value, Gen...>::root_type
-{
-  public:
-    constexpr static std::size_t NumIndices = internal::tensor_symmetry_num_indices<Gen...>::value;
-    typedef typename internal::tensor_symmetry_pre_analysis<NumIndices, Gen...>::root_type Base;
-
-    // make standard constructors + assignment operators public
-    inline SGroup() : Base() { }
-    inline SGroup(const SGroup<Gen...>& other) : Base(other) { }
-    inline SGroup(SGroup<Gen...>&& other) : Base(other) { }
-    inline SGroup<Gen...>& operator=(const SGroup<Gen...>& other) { Base::operator=(other); return *this; }
-    inline SGroup<Gen...>& operator=(SGroup<Gen...>&& other) { Base::operator=(other); return *this; }
-
-    // all else is defined in the base class
-};
-
-namespace internal {
-
-template<typename... Sym> struct tensor_symmetry_num_indices
-{
-  constexpr static std::size_t value = 1;
-};
-
-template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...>
-{
-private:
-  constexpr static std::size_t One = static_cast<std::size_t>(One_);
-  constexpr static std::size_t Two = static_cast<std::size_t>(Two_);
-  constexpr static std::size_t Three = tensor_symmetry_num_indices<Sym...>::value;
-
-  // don't use std::max, since it's not constexpr until C++14...
-  constexpr static std::size_t maxOneTwoPlusOne = ((One > Two) ? One : Two) + 1;
-public:
-  constexpr static std::size_t value = (maxOneTwoPlusOne > Three) ? maxOneTwoPlusOne : Three;
-};
-
-template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<AntiSymmetry<One_, Two_>, Sym...>
-  : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
-template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<Hermiticity<One_, Two_>, Sym...>
-  : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
-template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<AntiHermiticity<One_, Two_>, Sym...>
-  : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
-
-/** \internal
-  *
-  * \class tensor_symmetry_pre_analysis
-  * \ingroup TensorSymmetry_Module
-  *
-  * \brief Pre-select whether to use a static or dynamic symmetry group
-  *
-  * When a symmetry group could in principle be determined at compile time,
-  * this template implements the logic whether to actually do that or whether
-  * to rather defer that to runtime.
-  *
-  * The logic is as follows:
-  * <dl>
-  * <dt><b>No generators (trivial symmetry):</b></dt>
-  * <dd>Use a trivial static group. Ideally, this has no performance impact
-  *     compared to not using symmetry at all. In practice, this might not
-  *     be the case.</dd>
-  * <dt><b>More than 4 generators:</b></dt>
-  * <dd>Calculate the group at run time, it is likely far too large for the
-  *     compiler to be able to properly generate it in a realistic time.</dd>
-  * <dt><b>Up to and including 4 generators:</b></dt>
-  * <dd>Actually enumerate all group elements, but then check how many there
-  *     are. If there are more than 16, it is unlikely that unrolling the
-  *     loop (as is done in the static compile-time case) is sensible, so
-  *     use a dynamic group instead. If there are at most 16 elements, actually
-  *     use that static group. Note that the largest group with 4 generators
-  *     still compiles with reasonable resources.</dd>
-  * </dl>
-  *
-  * Note: Example compile time performance with g++-4.6 on an Intenl Core i5-3470
-  *       with 16 GiB RAM (all generators non-redundant and the subgroups don't
-  *       factorize):
-  *
-  *          # Generators          -O0 -ggdb               -O2
-  *          -------------------------------------------------------------------
-  *          1                 0.5 s  /   250 MiB     0.45s /   230 MiB
-  *          2                 0.5 s  /   260 MiB     0.5 s /   250 MiB
-  *          3                 0.65s  /   310 MiB     0.62s /   310 MiB
-  *          4                 2.2 s  /   860 MiB     1.7 s /   770 MiB
-  *          5               130   s  / 13000 MiB   120   s / 11000 MiB
-  *
-  * It is clear that everything is still very efficient up to 4 generators, then
-  * the memory and CPU requirements become unreasonable. Thus we only instantiate
-  * the template group theory logic if the number of generators supplied is 4 or
-  * lower, otherwise this will be forced to be done during runtime, where the
-  * algorithm is reasonably fast.
-  */
-template<std::size_t NumIndices>
-struct tensor_symmetry_pre_analysis<NumIndices>
-{
-  typedef StaticSGroup<> root_type;
-};
-
-template<std::size_t NumIndices, typename Gen_, typename... Gens_>
-struct tensor_symmetry_pre_analysis<NumIndices, Gen_, Gens_...>
-{
-  constexpr static std::size_t max_static_generators = 4;
-  constexpr static std::size_t max_static_elements = 16;
-  typedef tensor_static_symgroup_if<(sizeof...(Gens_) + 1 <= max_static_generators), NumIndices, Gen_, Gens_...> helper;
-  constexpr static std::size_t possible_size = helper::size;
-
-  typedef typename conditional<
-    possible_size == 0 || possible_size >= max_static_elements,
-    DynamicSGroupFromTemplateArgs<Gen_, Gens_...>,
-    typename helper::type
-  >::type root_type;
-};
-
-template<bool instantiate, std::size_t NumIndices, typename... Gens>
-struct tensor_static_symgroup_if
-{
-  constexpr static std::size_t size = 0;
-  typedef void type;
-};
-
-template<std::size_t NumIndices, typename... Gens>
-struct tensor_static_symgroup_if<true, NumIndices, Gens...> : tensor_static_symgroup<NumIndices, Gens...> {};
-
-template<typename Tensor_>
-struct tensor_symmetry_assign_value
-{
-  typedef typename Tensor_::Index Index;
-  typedef typename Tensor_::Scalar Scalar;
-  constexpr static std::size_t NumIndices = Tensor_::NumIndices;
-
-  static inline int run(const std::array<Index, NumIndices>& transformed_indices, int transformation_flags, int dummy, Tensor_& tensor, const Scalar& value_)
-  {
-    Scalar value(value_);
-    if (transformation_flags & ConjugationFlag)
-      value = numext::conj(value);
-    if (transformation_flags & NegationFlag)
-      value = -value;
-    tensor.coeffRef(transformed_indices) = value;
-    return dummy;
-  }
-};
-
-template<typename Tensor_>
-struct tensor_symmetry_calculate_flags
-{
-  typedef typename Tensor_::Index Index;
-  constexpr static std::size_t NumIndices = Tensor_::NumIndices;
-
-  static inline int run(const std::array<Index, NumIndices>& transformed_indices, int transform_flags, int current_flags, const std::array<Index, NumIndices>& orig_indices)
-  {
-    if (transformed_indices == orig_indices) {
-      if (transform_flags & (ConjugationFlag | NegationFlag))
-        return current_flags | GlobalImagFlag; // anti-hermitian diagonal
-      else if (transform_flags & ConjugationFlag)
-        return current_flags | GlobalRealFlag; // hermitian diagonal
-      else if (transform_flags & NegationFlag)
-        return current_flags | GlobalZeroFlag; // anti-symmetric diagonal
-    }
-    return current_flags;
-  }
-};
-
-template<typename Tensor_, typename Symmetry_, int Flags = 0>
-class tensor_symmetry_value_setter
-{
-  public:
-    typedef typename Tensor_::Index Index;
-    typedef typename Tensor_::Scalar Scalar;
-    constexpr static std::size_t NumIndices = Tensor_::NumIndices;
-
-    inline tensor_symmetry_value_setter(Tensor_& tensor, Symmetry_ const& symmetry, std::array<Index, NumIndices> const& indices)
-      : m_tensor(tensor), m_symmetry(symmetry), m_indices(indices) { }
-
-    inline tensor_symmetry_value_setter<Tensor_, Symmetry_, Flags>& operator=(Scalar const& value)
-    {
-      doAssign(value);
-      return *this;
-    }
-  private:
-    Tensor_& m_tensor;
-    Symmetry_ m_symmetry;
-    std::array<Index, NumIndices> m_indices;
-
-    inline void doAssign(Scalar const& value)
-    {
-      #ifdef EIGEN_TENSOR_SYMMETRY_CHECK_VALUES
-        int value_flags = m_symmetry.template apply<internal::tensor_symmetry_calculate_flags<Tensor_>, int>(m_indices, m_symmetry.globalFlags(), m_indices);
-        if (value_flags & GlobalRealFlag)
-          eigen_assert(numext::imag(value) == 0);
-        if (value_flags & GlobalImagFlag)
-          eigen_assert(numext::real(value) == 0);
-      #endif
-      m_symmetry.template apply<internal::tensor_symmetry_assign_value<Tensor_>, int>(m_indices, 0, m_tensor, value);
-    }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
-
-/*
- * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
- */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
deleted file mode 100644
index 54bf9db..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
+++ /dev/null
@@ -1,669 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
-#define EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
-
-namespace Eigen {
-
-namespace internal {
-
-namespace group_theory {
-
-/** \internal
-  * \file CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
-  * This file contains C++ templates that implement group theory algorithms.
-  *
-  * The algorithms allow for a compile-time analysis of finite groups.
-  *
-  * Currently only Dimino's algorithm is implemented, which returns a list
-  * of all elements in a group given a set of (possibly redundant) generators.
-  * (One could also do that with the so-called orbital algorithm, but that
-  * is much more expensive and usually has no advantages.)
-  */
-
-/**********************************************************************
- *                "Ok kid, here is where it gets complicated."
- *                         - Amelia Pond in the "Doctor Who" episode
- *                           "The Big Bang"
- *
- * Dimino's algorithm
- * ==================
- *
- * The following is Dimino's algorithm in sequential form:
- *
- * Input: identity element, list of generators, equality check,
- *        multiplication operation
- * Output: list of group elements
- *
- * 1. add identity element
- * 2. remove identities from list of generators
- * 3. add all powers of first generator that aren't the
- *    identity element
- * 4. go through all remaining generators:
- *        a. if generator is already in the list of elements
- *                -> do nothing
- *        b. otherwise
- *                i.   remember current # of elements
- *                     (i.e. the size of the current subgroup)
- *                ii.  add all current elements (which includes
- *                     the identity) each multiplied from right
- *                     with the current generator to the group
- *                iii. add all remaining cosets that are generated
- *                     by products of the new generator with itself
- *                     and all other generators seen so far
- *
- * In functional form, this is implemented as a long set of recursive
- * templates that have a complicated relationship.
- *
- * The main interface for Dimino's algorithm is the template
- * enumerate_group_elements. All lists are implemented as variadic
- * type_list<typename...> and numeric_list<typename = int, int...>
- * templates.
- *
- * 'Calling' templates is usually done via typedefs.
- *
- * This algorithm is an extended version of the basic version. The
- * extension consists in the fact that each group element has a set
- * of flags associated with it. Multiplication of two group elements
- * with each other results in a group element whose flags are the
- * XOR of the flags of the previous elements. Each time the algorithm
- * notices that a group element it just calculated is already in the
- * list of current elements, the flags of both will be compared and
- * added to the so-called 'global flags' of the group.
- *
- * The rationale behind this extension is that this allows not only
- * for the description of symmetries between tensor indices, but
- * also allows for the description of hermiticity, antisymmetry and
- * antihermiticity. Negation and conjugation each are specific bit
- * in the flags value and if two different ways to reach a group
- * element lead to two different flags, this poses a constraint on
- * the allowed values of the resulting tensor. For example, if a
- * group element is reach both with and without the conjugation
- * flags, it is clear that the resulting tensor has to be real.
- *
- * Note that this flag mechanism is quite generic and may have other
- * uses beyond tensor properties.
- *
- * IMPORTANT: 
- *     This algorithm assumes the group to be finite. If you try to
- *     run it with a group that's infinite, the algorithm will only
- *     terminate once you hit a compiler limit (max template depth).
- *     Also note that trying to use this implementation to create a
- *     very large group will probably either make you hit the same
- *     limit, cause the compiler to segfault or at the very least
- *     take a *really* long time (hours, days, weeks - sic!) to
- *     compile. It is not recommended to plug in more than 4
- *     generators, unless they are independent of each other.
- */
-
-/** \internal
-  *
-  * \class strip_identities
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Cleanse a list of group elements of the identity element
-  *
-  * This template is used to make a first pass through all initial
-  * generators of Dimino's algorithm and remove the identity
-  * elements.
-  *
-  * \sa enumerate_group_elements
-  */
-template<template<typename, typename> class Equality, typename id, typename L> struct strip_identities;
-
-template<
-  template<typename, typename> class Equality,
-  typename id,
-  typename t,
-  typename... ts
->
-struct strip_identities<Equality, id, type_list<t, ts...>>
-{
-  typedef typename conditional<
-    Equality<id, t>::value,
-    typename strip_identities<Equality, id, type_list<ts...>>::type,
-    typename concat<type_list<t>, typename strip_identities<Equality, id, type_list<ts...>>::type>::type
-  >::type type;
-  constexpr static int global_flags = Equality<id, t>::global_flags | strip_identities<Equality, id, type_list<ts...>>::global_flags;
-};
-
-template<
-  template<typename, typename> class Equality,
-  typename id
-  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, ts)
->
-struct strip_identities<Equality, id, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(ts)>>
-{
-  typedef type_list<> type;
-  constexpr static int global_flags = 0;
-};
-
-/** \internal
-  *
-  * \class dimino_first_step_elements_helper 
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Recursive template that adds powers of the first generator to the list of group elements
-  *
-  * This template calls itself recursively to add powers of the first
-  * generator to the list of group elements. It stops if it reaches
-  * the identity element again.
-  *
-  * \sa enumerate_group_elements, dimino_first_step_elements
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename g,
-  typename current_element,
-  typename elements,
-  bool dont_add_current_element   // = false
->
-struct dimino_first_step_elements_helper
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-  : // recursive inheritance is too difficult for Doxygen
-  public dimino_first_step_elements_helper<
-    Multiply,
-    Equality,
-    id,
-    g,
-    typename Multiply<current_element, g>::type,
-    typename concat<elements, type_list<current_element>>::type,
-    Equality<typename Multiply<current_element, g>::type, id>::value
-  > {};
-
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename g,
-  typename current_element,
-  typename elements
->
-struct dimino_first_step_elements_helper<Multiply, Equality, id, g, current_element, elements, true>
-#endif // EIGEN_PARSED_BY_DOXYGEN
-{
-  typedef elements type;
-  constexpr static int global_flags = Equality<current_element, id>::global_flags;
-};
-
-/** \internal
-  *
-  * \class dimino_first_step_elements
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Add all powers of the first generator to the list of group elements
-  *
-  * This template takes the first non-identity generator and generates the initial
-  * list of elements which consists of all powers of that generator. For a group
-  * with just one generated, it would be enumerated after this.
-  *
-  * \sa enumerate_group_elements
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename generators
->
-struct dimino_first_step_elements
-{
-  typedef typename get<0, generators>::type first_generator;
-  typedef typename skip<1, generators>::type next_generators;
-  typedef type_list<first_generator> generators_done;
-
-  typedef dimino_first_step_elements_helper<
-    Multiply,
-    Equality,
-    id,
-    first_generator,
-    first_generator,
-    type_list<id>,
-    false
-  > helper;
-  typedef typename helper::type type;
-  constexpr static int global_flags = helper::global_flags;
-};
-
-/** \internal
-  *
-  * \class dimino_get_coset_elements
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Generate all elements of a specific coset
-  *
-  * This template generates all the elements of a specific coset by
-  * multiplying all elements in the given subgroup with the new
-  * coset representative. Note that the first element of the
-  * subgroup is always the identity element, so the first element of
-  * the result of this template is going to be the coset
-  * representative itself.
-  *
-  * Note that this template accepts an additional boolean parameter
-  * that specifies whether to actually generate the coset (true) or
-  * just return an empty list (false).
-  *
-  * \sa enumerate_group_elements, dimino_add_cosets_for_rep
-  */
-template<
-  template<typename, typename> class Multiply,
-  typename sub_group_elements,
-  typename new_coset_rep,
-  bool generate_coset      // = true
->
-struct dimino_get_coset_elements
-{
-  typedef typename apply_op_from_right<Multiply, new_coset_rep, sub_group_elements>::type type;
-};
-
-template<
-  template<typename, typename> class Multiply,
-  typename sub_group_elements,
-  typename new_coset_rep
->
-struct dimino_get_coset_elements<Multiply, sub_group_elements, new_coset_rep, false>
-{
-  typedef type_list<> type;
-};
-
-/** \internal
-  *
-  * \class dimino_add_cosets_for_rep
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Recursive template for adding coset spaces
-  *
-  * This template multiplies the coset representative with a generator
-  * from the list of previous generators. If the new element is not in
-  * the group already, it adds the corresponding coset. Finally it
-  * proceeds to call itself with the next generator from the list.
-  *
-  * \sa enumerate_group_elements, dimino_add_all_coset_spaces
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename sub_group_elements,
-  typename elements,
-  typename generators,
-  typename rep_element,
-  int sub_group_size
->
-struct dimino_add_cosets_for_rep;
-
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename sub_group_elements,
-  typename elements,
-  typename g,
-  typename... gs,
-  typename rep_element,
-  int sub_group_size
->
-struct dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements, elements, type_list<g, gs...>, rep_element, sub_group_size>
-{
-  typedef typename Multiply<rep_element, g>::type new_coset_rep;
-  typedef contained_in_list_gf<Equality, new_coset_rep, elements> _cil;
-  constexpr static bool add_coset = !_cil::value;
-
-  typedef typename dimino_get_coset_elements<
-    Multiply,
-    sub_group_elements,
-    new_coset_rep,
-    add_coset
-  >::type coset_elements;
-
-  typedef dimino_add_cosets_for_rep<
-    Multiply,
-    Equality,
-    id,
-    sub_group_elements,
-    typename concat<elements, coset_elements>::type,
-    type_list<gs...>,
-    rep_element,
-    sub_group_size
-  > _helper;
-
-  typedef typename _helper::type type;
-  constexpr static int global_flags = _cil::global_flags | _helper::global_flags;
-
-  /* Note that we don't have to update global flags here, since
-   * we will only add these elements if they are not part of
-   * the group already. But that only happens if the coset rep
-   * is not already in the group, so the check for the coset rep
-   * will catch this.
-   */
-};
-
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename sub_group_elements,
-  typename elements
-  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, empty),
-  typename rep_element,
-  int sub_group_size
->
-struct dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements, elements, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>, rep_element, sub_group_size>
-{
-  typedef elements type;
-  constexpr static int global_flags = 0;
-};
-
-/** \internal
-  *
-  * \class dimino_add_all_coset_spaces
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Recursive template for adding all coset spaces for a new generator
-  *
-  * This template tries to go through the list of generators (with
-  * the help of the dimino_add_cosets_for_rep template) as long as
-  * it still finds elements that are not part of the group and add
-  * the corresponding cosets.
-  *
-  * \sa enumerate_group_elements, dimino_add_cosets_for_rep
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename sub_group_elements,
-  typename elements,
-  typename generators,
-  int sub_group_size,
-  int rep_pos,
-  bool stop_condition        // = false
->
-struct dimino_add_all_coset_spaces
-{
-  typedef typename get<rep_pos, elements>::type rep_element;
-  typedef dimino_add_cosets_for_rep<
-    Multiply,
-    Equality,
-    id,
-    sub_group_elements,
-    elements,
-    generators,
-    rep_element,
-    sub_group_elements::count
-  > _ac4r;
-  typedef typename _ac4r::type new_elements;
-  
-  constexpr static int new_rep_pos = rep_pos + sub_group_elements::count;
-  constexpr static bool new_stop_condition = new_rep_pos >= new_elements::count;
-
-  typedef dimino_add_all_coset_spaces<
-    Multiply,
-    Equality,
-    id,
-    sub_group_elements,
-    new_elements,
-    generators,
-    sub_group_size,
-    new_rep_pos,
-    new_stop_condition
-  > _helper;
-
-  typedef typename _helper::type type;
-  constexpr static int global_flags = _helper::global_flags | _ac4r::global_flags;
-};
-
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename sub_group_elements,
-  typename elements,
-  typename generators,
-  int sub_group_size,
-  int rep_pos
->
-struct dimino_add_all_coset_spaces<Multiply, Equality, id, sub_group_elements, elements, generators, sub_group_size, rep_pos, true>
-{
-  typedef elements type;
-  constexpr static int global_flags = 0;
-};
-
-/** \internal
-  *
-  * \class dimino_add_generator
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Enlarge the group by adding a new generator.
-  *
-  * It accepts a boolean parameter that determines if the generator is redundant,
-  * i.e. was already seen in the group. In that case, it reduces to a no-op.
-  *
-  * \sa enumerate_group_elements, dimino_add_all_coset_spaces
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename elements,
-  typename generators_done,
-  typename current_generator,
-  bool redundant          // = false
->
-struct dimino_add_generator
-{
-  /* this template is only called if the generator is not redundant
-   * => all elements of the group multiplied with the new generator
-   *    are going to be new elements of the most trivial coset space
-   */
-  typedef typename apply_op_from_right<Multiply, current_generator, elements>::type multiplied_elements;
-  typedef typename concat<elements, multiplied_elements>::type new_elements;
-
-  constexpr static int rep_pos = elements::count;
-
-  typedef dimino_add_all_coset_spaces<
-    Multiply,
-    Equality,
-    id,
-    elements, // elements of previous subgroup
-    new_elements,
-    typename concat<generators_done, type_list<current_generator>>::type,
-    elements::count, // size of previous subgroup
-    rep_pos,
-    false // don't stop (because rep_pos >= new_elements::count is always false at this point)
-  > _helper;
-  typedef typename _helper::type type;
-  constexpr static int global_flags = _helper::global_flags;
-};
-
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename elements,
-  typename generators_done,
-  typename current_generator
->
-struct dimino_add_generator<Multiply, Equality, id, elements, generators_done, current_generator, true>
-{
-  // redundant case
-  typedef elements type;
-  constexpr static int global_flags = 0;
-};
-
-/** \internal
-  *
-  * \class dimino_add_remaining_generators
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Recursive template that adds all remaining generators to a group
-  *
-  * Loop through the list of generators that remain and successively
-  * add them to the group.
-  *
-  * \sa enumerate_group_elements, dimino_add_generator
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename generators_done,
-  typename remaining_generators,
-  typename elements
->
-struct dimino_add_remaining_generators
-{
-  typedef typename get<0, remaining_generators>::type first_generator;
-  typedef typename skip<1, remaining_generators>::type next_generators;
-
-  typedef contained_in_list_gf<Equality, first_generator, elements> _cil;
-
-  typedef dimino_add_generator<
-    Multiply,
-    Equality,
-    id,
-    elements,
-    generators_done,
-    first_generator,
-    _cil::value
-  > _helper;
-
-  typedef typename _helper::type new_elements;
-
-  typedef dimino_add_remaining_generators<
-    Multiply,
-    Equality,
-    id,
-    typename concat<generators_done, type_list<first_generator>>::type,
-    next_generators,
-    new_elements
-  > _next_iter;
-
-  typedef typename _next_iter::type type;
-  constexpr static int global_flags =
-    _cil::global_flags |
-    _helper::global_flags |
-    _next_iter::global_flags;
-};
-
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename generators_done,
-  typename elements
->
-struct dimino_add_remaining_generators<Multiply, Equality, id, generators_done, type_list<>, elements>
-{
-  typedef elements type;
-  constexpr static int global_flags = 0;
-};
-
-/** \internal
-  *
-  * \class enumerate_group_elements_noid
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Helper template that implements group element enumeration
-  *
-  * This is a helper template that implements the actual enumeration
-  * of group elements. This has been split so that the list of
-  * generators can be cleansed of the identity element before
-  * performing the actual operation.
-  *
-  * \sa enumerate_group_elements
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename generators,
-  int initial_global_flags = 0
->
-struct enumerate_group_elements_noid
-{
-  typedef dimino_first_step_elements<Multiply, Equality, id, generators> first_step;
-  typedef typename first_step::type first_step_elements;
-
-  typedef dimino_add_remaining_generators<
-    Multiply,
-    Equality,
-    id,
-    typename first_step::generators_done,
-    typename first_step::next_generators, // remaining_generators
-    typename first_step::type // first_step elements
-  > _helper;
-
-  typedef typename _helper::type type;
-  constexpr static int global_flags =
-    initial_global_flags |
-    first_step::global_flags |
-    _helper::global_flags;
-};
-
-// in case when no generators are specified
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  int initial_global_flags
->
-struct enumerate_group_elements_noid<Multiply, Equality, id, type_list<>, initial_global_flags>
-{
-  typedef type_list<id> type;
-  constexpr static int global_flags = initial_global_flags;
-};
-
-/** \internal
-  *
-  * \class enumerate_group_elements
-  * \ingroup CXX11_TensorSymmetry_Module
-  *
-  * \brief Enumerate all elements in a finite group
-  *
-  * This template enumerates all elements in a finite group. It accepts
-  * the following template parameters:
-  *
-  * \tparam Multiply      The multiplication operation that multiplies two group elements
-  *                       with each other.
-  * \tparam Equality      The equality check operation that checks if two group elements
-  *                       are equal to another.
-  * \tparam id            The identity element
-  * \tparam _generators   A list of (possibly redundant) generators of the group
-  */
-template<
-  template<typename, typename> class Multiply,
-  template<typename, typename> class Equality,
-  typename id,
-  typename _generators
->
-struct enumerate_group_elements
-  : public enumerate_group_elements_noid<
-      Multiply,
-      Equality,
-      id,
-      typename strip_identities<Equality, id, _generators>::type,
-      strip_identities<Equality, id, _generators>::global_flags
-    >
-{
-};
-
-} // end namespace group_theory
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
-
-/*
- * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
- */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h
deleted file mode 100644
index e4c59dc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h
+++ /dev/null
@@ -1,67 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-// Barrier is an object that allows one or more threads to wait until
-// Notify has been called a specified number of times.
-
-#ifndef EIGEN_CXX11_THREADPOOL_BARRIER_H
-#define EIGEN_CXX11_THREADPOOL_BARRIER_H
-
-namespace Eigen {
-
-class Barrier {
- public:
-  Barrier(unsigned int count) : state_(count << 1), notified_(false) {
-    eigen_plain_assert(((count << 1) >> 1) == count);
-  }
-  ~Barrier() { eigen_plain_assert((state_ >> 1) == 0); }
-
-  void Notify() {
-    unsigned int v = state_.fetch_sub(2, std::memory_order_acq_rel) - 2;
-    if (v != 1) {
-      // Clear the lowest bit (waiter flag) and check that the original state
-      // value was not zero. If it was zero, it means that notify was called
-      // more times than the original count.
-      eigen_plain_assert(((v + 2) & ~1) != 0);
-      return;  // either count has not dropped to 0, or waiter is not waiting
-    }
-    std::unique_lock<std::mutex> l(mu_);
-    eigen_plain_assert(!notified_);
-    notified_ = true;
-    cv_.notify_all();
-  }
-
-  void Wait() {
-    unsigned int v = state_.fetch_or(1, std::memory_order_acq_rel);
-    if ((v >> 1) == 0) return;
-    std::unique_lock<std::mutex> l(mu_);
-    while (!notified_) {
-      cv_.wait(l);
-    }
-  }
-
- private:
-  std::mutex mu_;
-  std::condition_variable cv_;
-  std::atomic<unsigned int> state_;  // low bit is waiter flag
-  bool notified_;
-};
-
-// Notification is an object that allows a user to to wait for another
-// thread to signal a notification that an event has occurred.
-//
-// Multiple threads can wait on the same Notification object,
-// but only one caller must call Notify() on the object.
-struct Notification : Barrier {
-  Notification() : Barrier(1){};
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_THREADPOOL_BARRIER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h
deleted file mode 100644
index 4549aa0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h
+++ /dev/null
@@ -1,249 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H_
-#define EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H_
-
-namespace Eigen {
-
-// EventCount allows to wait for arbitrary predicates in non-blocking
-// algorithms. Think of condition variable, but wait predicate does not need to
-// be protected by a mutex. Usage:
-// Waiting thread does:
-//
-//   if (predicate)
-//     return act();
-//   EventCount::Waiter& w = waiters[my_index];
-//   ec.Prewait(&w);
-//   if (predicate) {
-//     ec.CancelWait(&w);
-//     return act();
-//   }
-//   ec.CommitWait(&w);
-//
-// Notifying thread does:
-//
-//   predicate = true;
-//   ec.Notify(true);
-//
-// Notify is cheap if there are no waiting threads. Prewait/CommitWait are not
-// cheap, but they are executed only if the preceding predicate check has
-// failed.
-//
-// Algorithm outline:
-// There are two main variables: predicate (managed by user) and state_.
-// Operation closely resembles Dekker mutual algorithm:
-// https://en.wikipedia.org/wiki/Dekker%27s_algorithm
-// Waiting thread sets state_ then checks predicate, Notifying thread sets
-// predicate then checks state_. Due to seq_cst fences in between these
-// operations it is guaranteed than either waiter will see predicate change
-// and won't block, or notifying thread will see state_ change and will unblock
-// the waiter, or both. But it can't happen that both threads don't see each
-// other changes, which would lead to deadlock.
-class EventCount {
- public:
-  class Waiter;
-
-  EventCount(MaxSizeVector<Waiter>& waiters)
-      : state_(kStackMask), waiters_(waiters) {
-    eigen_plain_assert(waiters.size() < (1 << kWaiterBits) - 1);
-  }
-
-  ~EventCount() {
-    // Ensure there are no waiters.
-    eigen_plain_assert(state_.load() == kStackMask);
-  }
-
-  // Prewait prepares for waiting.
-  // After calling Prewait, the thread must re-check the wait predicate
-  // and then call either CancelWait or CommitWait.
-  void Prewait() {
-    uint64_t state = state_.load(std::memory_order_relaxed);
-    for (;;) {
-      CheckState(state);
-      uint64_t newstate = state + kWaiterInc;
-      CheckState(newstate);
-      if (state_.compare_exchange_weak(state, newstate,
-                                       std::memory_order_seq_cst))
-        return;
-    }
-  }
-
-  // CommitWait commits waiting after Prewait.
-  void CommitWait(Waiter* w) {
-    eigen_plain_assert((w->epoch & ~kEpochMask) == 0);
-    w->state = Waiter::kNotSignaled;
-    const uint64_t me = (w - &waiters_[0]) | w->epoch;
-    uint64_t state = state_.load(std::memory_order_seq_cst);
-    for (;;) {
-      CheckState(state, true);
-      uint64_t newstate;
-      if ((state & kSignalMask) != 0) {
-        // Consume the signal and return immidiately.
-        newstate = state - kWaiterInc - kSignalInc;
-      } else {
-        // Remove this thread from pre-wait counter and add to the waiter stack.
-        newstate = ((state & kWaiterMask) - kWaiterInc) | me;
-        w->next.store(state & (kStackMask | kEpochMask),
-                      std::memory_order_relaxed);
-      }
-      CheckState(newstate);
-      if (state_.compare_exchange_weak(state, newstate,
-                                       std::memory_order_acq_rel)) {
-        if ((state & kSignalMask) == 0) {
-          w->epoch += kEpochInc;
-          Park(w);
-        }
-        return;
-      }
-    }
-  }
-
-  // CancelWait cancels effects of the previous Prewait call.
-  void CancelWait() {
-    uint64_t state = state_.load(std::memory_order_relaxed);
-    for (;;) {
-      CheckState(state, true);
-      uint64_t newstate = state - kWaiterInc;
-      // We don't know if the thread was also notified or not,
-      // so we should not consume a signal unconditionaly.
-      // Only if number of waiters is equal to number of signals,
-      // we know that the thread was notified and we must take away the signal.
-      if (((state & kWaiterMask) >> kWaiterShift) ==
-          ((state & kSignalMask) >> kSignalShift))
-        newstate -= kSignalInc;
-      CheckState(newstate);
-      if (state_.compare_exchange_weak(state, newstate,
-                                       std::memory_order_acq_rel))
-        return;
-    }
-  }
-
-  // Notify wakes one or all waiting threads.
-  // Must be called after changing the associated wait predicate.
-  void Notify(bool notifyAll) {
-    std::atomic_thread_fence(std::memory_order_seq_cst);
-    uint64_t state = state_.load(std::memory_order_acquire);
-    for (;;) {
-      CheckState(state);
-      const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
-      const uint64_t signals = (state & kSignalMask) >> kSignalShift;
-      // Easy case: no waiters.
-      if ((state & kStackMask) == kStackMask && waiters == signals) return;
-      uint64_t newstate;
-      if (notifyAll) {
-        // Empty wait stack and set signal to number of pre-wait threads.
-        newstate =
-            (state & kWaiterMask) | (waiters << kSignalShift) | kStackMask;
-      } else if (signals < waiters) {
-        // There is a thread in pre-wait state, unblock it.
-        newstate = state + kSignalInc;
-      } else {
-        // Pop a waiter from list and unpark it.
-        Waiter* w = &waiters_[state & kStackMask];
-        uint64_t next = w->next.load(std::memory_order_relaxed);
-        newstate = (state & (kWaiterMask | kSignalMask)) | next;
-      }
-      CheckState(newstate);
-      if (state_.compare_exchange_weak(state, newstate,
-                                       std::memory_order_acq_rel)) {
-        if (!notifyAll && (signals < waiters))
-          return;  // unblocked pre-wait thread
-        if ((state & kStackMask) == kStackMask) return;
-        Waiter* w = &waiters_[state & kStackMask];
-        if (!notifyAll) w->next.store(kStackMask, std::memory_order_relaxed);
-        Unpark(w);
-        return;
-      }
-    }
-  }
-
-  class Waiter {
-    friend class EventCount;
-    // Align to 128 byte boundary to prevent false sharing with other Waiter
-    // objects in the same vector.
-    EIGEN_ALIGN_TO_BOUNDARY(128) std::atomic<uint64_t> next;
-    std::mutex mu;
-    std::condition_variable cv;
-    uint64_t epoch = 0;
-    unsigned state = kNotSignaled;
-    enum {
-      kNotSignaled,
-      kWaiting,
-      kSignaled,
-    };
-  };
-
- private:
-  // State_ layout:
-  // - low kWaiterBits is a stack of waiters committed wait
-  //   (indexes in waiters_ array are used as stack elements,
-  //   kStackMask means empty stack).
-  // - next kWaiterBits is count of waiters in prewait state.
-  // - next kWaiterBits is count of pending signals.
-  // - remaining bits are ABA counter for the stack.
-  //   (stored in Waiter node and incremented on push).
-  static const uint64_t kWaiterBits = 14;
-  static const uint64_t kStackMask = (1ull << kWaiterBits) - 1;
-  static const uint64_t kWaiterShift = kWaiterBits;
-  static const uint64_t kWaiterMask = ((1ull << kWaiterBits) - 1)
-                                      << kWaiterShift;
-  static const uint64_t kWaiterInc = 1ull << kWaiterShift;
-  static const uint64_t kSignalShift = 2 * kWaiterBits;
-  static const uint64_t kSignalMask = ((1ull << kWaiterBits) - 1)
-                                      << kSignalShift;
-  static const uint64_t kSignalInc = 1ull << kSignalShift;
-  static const uint64_t kEpochShift = 3 * kWaiterBits;
-  static const uint64_t kEpochBits = 64 - kEpochShift;
-  static const uint64_t kEpochMask = ((1ull << kEpochBits) - 1) << kEpochShift;
-  static const uint64_t kEpochInc = 1ull << kEpochShift;
-  std::atomic<uint64_t> state_;
-  MaxSizeVector<Waiter>& waiters_;
-
-  static void CheckState(uint64_t state, bool waiter = false) {
-    static_assert(kEpochBits >= 20, "not enough bits to prevent ABA problem");
-    const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
-    const uint64_t signals = (state & kSignalMask) >> kSignalShift;
-    eigen_plain_assert(waiters >= signals);
-    eigen_plain_assert(waiters < (1 << kWaiterBits) - 1);
-    eigen_plain_assert(!waiter || waiters > 0);
-    (void)waiters;
-    (void)signals;
-  }
-
-  void Park(Waiter* w) {
-    std::unique_lock<std::mutex> lock(w->mu);
-    while (w->state != Waiter::kSignaled) {
-      w->state = Waiter::kWaiting;
-      w->cv.wait(lock);
-    }
-  }
-
-  void Unpark(Waiter* w) {
-    for (Waiter* next; w; w = next) {
-      uint64_t wnext = w->next.load(std::memory_order_relaxed) & kStackMask;
-      next = wnext == kStackMask ? nullptr : &waiters_[wnext];
-      unsigned state;
-      {
-        std::unique_lock<std::mutex> lock(w->mu);
-        state = w->state;
-        w->state = Waiter::kSignaled;
-      }
-      // Avoid notifying if it wasn't waiting.
-      if (state == Waiter::kWaiting) w->cv.notify_one();
-    }
-  }
-
-  EventCount(const EventCount&) = delete;
-  void operator=(const EventCount&) = delete;
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H_
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h
deleted file mode 100644
index 23a2b54..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h
+++ /dev/null
@@ -1,486 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
-#define EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
-
-namespace Eigen {
-
-template <typename Environment>
-class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
- public:
-  typedef typename Environment::Task Task;
-  typedef RunQueue<Task, 1024> Queue;
-
-  ThreadPoolTempl(int num_threads, Environment env = Environment())
-      : ThreadPoolTempl(num_threads, true, env) {}
-
-  ThreadPoolTempl(int num_threads, bool allow_spinning,
-                  Environment env = Environment())
-      : env_(env),
-        num_threads_(num_threads),
-        allow_spinning_(allow_spinning),
-        thread_data_(num_threads),
-        all_coprimes_(num_threads),
-        waiters_(num_threads),
-        global_steal_partition_(EncodePartition(0, num_threads_)),
-        blocked_(0),
-        spinning_(0),
-        done_(false),
-        cancelled_(false),
-        ec_(waiters_) {
-    waiters_.resize(num_threads_);
-    // Calculate coprimes of all numbers [1, num_threads].
-    // Coprimes are used for random walks over all threads in Steal
-    // and NonEmptyQueueIndex. Iteration is based on the fact that if we take
-    // a random starting thread index t and calculate num_threads - 1 subsequent
-    // indices as (t + coprime) % num_threads, we will cover all threads without
-    // repetitions (effectively getting a presudo-random permutation of thread
-    // indices).
-    eigen_plain_assert(num_threads_ < kMaxThreads);
-    for (int i = 1; i <= num_threads_; ++i) {
-      all_coprimes_.emplace_back(i);
-      ComputeCoprimes(i, &all_coprimes_.back());
-    }
-#ifndef EIGEN_THREAD_LOCAL
-    init_barrier_.reset(new Barrier(num_threads_));
-#endif
-    thread_data_.resize(num_threads_);
-    for (int i = 0; i < num_threads_; i++) {
-      SetStealPartition(i, EncodePartition(0, num_threads_));
-      thread_data_[i].thread.reset(
-          env_.CreateThread([this, i]() { WorkerLoop(i); }));
-    }
-#ifndef EIGEN_THREAD_LOCAL
-    // Wait for workers to initialize per_thread_map_. Otherwise we might race
-    // with them in Schedule or CurrentThreadId.
-    init_barrier_->Wait();
-#endif
-  }
-
-  ~ThreadPoolTempl() {
-    done_ = true;
-
-    // Now if all threads block without work, they will start exiting.
-    // But note that threads can continue to work arbitrary long,
-    // block, submit new work, unblock and otherwise live full life.
-    if (!cancelled_) {
-      ec_.Notify(true);
-    } else {
-      // Since we were cancelled, there might be entries in the queues.
-      // Empty them to prevent their destructor from asserting.
-      for (size_t i = 0; i < thread_data_.size(); i++) {
-        thread_data_[i].queue.Flush();
-      }
-    }
-    // Join threads explicitly (by destroying) to avoid destruction order within
-    // this class.
-    for (size_t i = 0; i < thread_data_.size(); ++i)
-      thread_data_[i].thread.reset();
-  }
-
-  void SetStealPartitions(const std::vector<std::pair<unsigned, unsigned>>& partitions) {
-    eigen_plain_assert(partitions.size() == static_cast<std::size_t>(num_threads_));
-
-    // Pass this information to each thread queue.
-    for (int i = 0; i < num_threads_; i++) {
-      const auto& pair = partitions[i];
-      unsigned start = pair.first, end = pair.second;
-      AssertBounds(start, end);
-      unsigned val = EncodePartition(start, end);
-      SetStealPartition(i, val);
-    }
-  }
-
-  void Schedule(std::function<void()> fn) EIGEN_OVERRIDE {
-    ScheduleWithHint(std::move(fn), 0, num_threads_);
-  }
-
-  void ScheduleWithHint(std::function<void()> fn, int start,
-                        int limit) override {
-    Task t = env_.CreateTask(std::move(fn));
-    PerThread* pt = GetPerThread();
-    if (pt->pool == this) {
-      // Worker thread of this pool, push onto the thread's queue.
-      Queue& q = thread_data_[pt->thread_id].queue;
-      t = q.PushFront(std::move(t));
-    } else {
-      // A free-standing thread (or worker of another pool), push onto a random
-      // queue.
-      eigen_plain_assert(start < limit);
-      eigen_plain_assert(limit <= num_threads_);
-      int num_queues = limit - start;
-      int rnd = Rand(&pt->rand) % num_queues;
-      eigen_plain_assert(start + rnd < limit);
-      Queue& q = thread_data_[start + rnd].queue;
-      t = q.PushBack(std::move(t));
-    }
-    // Note: below we touch this after making w available to worker threads.
-    // Strictly speaking, this can lead to a racy-use-after-free. Consider that
-    // Schedule is called from a thread that is neither main thread nor a worker
-    // thread of this pool. Then, execution of w directly or indirectly
-    // completes overall computations, which in turn leads to destruction of
-    // this. We expect that such scenario is prevented by program, that is,
-    // this is kept alive while any threads can potentially be in Schedule.
-    if (!t.f) {
-      ec_.Notify(false);
-    } else {
-      env_.ExecuteTask(t);  // Push failed, execute directly.
-    }
-  }
-
-  void Cancel() EIGEN_OVERRIDE {
-    cancelled_ = true;
-    done_ = true;
-
-    // Let each thread know it's been cancelled.
-#ifdef EIGEN_THREAD_ENV_SUPPORTS_CANCELLATION
-    for (size_t i = 0; i < thread_data_.size(); i++) {
-      thread_data_[i].thread->OnCancel();
-    }
-#endif
-
-    // Wake up the threads without work to let them exit on their own.
-    ec_.Notify(true);
-  }
-
-  int NumThreads() const EIGEN_FINAL { return num_threads_; }
-
-  int CurrentThreadId() const EIGEN_FINAL {
-    const PerThread* pt = const_cast<ThreadPoolTempl*>(this)->GetPerThread();
-    if (pt->pool == this) {
-      return pt->thread_id;
-    } else {
-      return -1;
-    }
-  }
-
- private:
-  // Create a single atomic<int> that encodes start and limit information for
-  // each thread.
-  // We expect num_threads_ < 65536, so we can store them in a single
-  // std::atomic<unsigned>.
-  // Exposed publicly as static functions so that external callers can reuse
-  // this encode/decode logic for maintaining their own thread-safe copies of
-  // scheduling and steal domain(s).
-  static const int kMaxPartitionBits = 16;
-  static const int kMaxThreads = 1 << kMaxPartitionBits;
-
-  inline unsigned EncodePartition(unsigned start, unsigned limit) {
-    return (start << kMaxPartitionBits) | limit;
-  }
-
-  inline void DecodePartition(unsigned val, unsigned* start, unsigned* limit) {
-    *limit = val & (kMaxThreads - 1);
-    val >>= kMaxPartitionBits;
-    *start = val;
-  }
-
-  void AssertBounds(int start, int end) {
-    eigen_plain_assert(start >= 0);
-    eigen_plain_assert(start < end);  // non-zero sized partition
-    eigen_plain_assert(end <= num_threads_);
-  }
-
-  inline void SetStealPartition(size_t i, unsigned val) {
-    thread_data_[i].steal_partition.store(val, std::memory_order_relaxed);
-  }
-
-  inline unsigned GetStealPartition(int i) {
-    return thread_data_[i].steal_partition.load(std::memory_order_relaxed);
-  }
-
-  void ComputeCoprimes(int N, MaxSizeVector<unsigned>* coprimes) {
-    for (int i = 1; i <= N; i++) {
-      unsigned a = i;
-      unsigned b = N;
-      // If GCD(a, b) == 1, then a and b are coprimes.
-      while (b != 0) {
-        unsigned tmp = a;
-        a = b;
-        b = tmp % b;
-      }
-      if (a == 1) {
-        coprimes->push_back(i);
-      }
-    }
-  }
-
-  typedef typename Environment::EnvThread Thread;
-
-  struct PerThread {
-    constexpr PerThread() : pool(NULL), rand(0), thread_id(-1) {}
-    ThreadPoolTempl* pool;  // Parent pool, or null for normal threads.
-    uint64_t rand;          // Random generator state.
-    int thread_id;          // Worker thread index in pool.
-#ifndef EIGEN_THREAD_LOCAL
-    // Prevent false sharing.
-    char pad_[128];
-#endif
-  };
-
-  struct ThreadData {
-    constexpr ThreadData() : thread(), steal_partition(0), queue() {}
-    std::unique_ptr<Thread> thread;
-    std::atomic<unsigned> steal_partition;
-    Queue queue;
-  };
-
-  Environment env_;
-  const int num_threads_;
-  const bool allow_spinning_;
-  MaxSizeVector<ThreadData> thread_data_;
-  MaxSizeVector<MaxSizeVector<unsigned>> all_coprimes_;
-  MaxSizeVector<EventCount::Waiter> waiters_;
-  unsigned global_steal_partition_;
-  std::atomic<unsigned> blocked_;
-  std::atomic<bool> spinning_;
-  std::atomic<bool> done_;
-  std::atomic<bool> cancelled_;
-  EventCount ec_;
-#ifndef EIGEN_THREAD_LOCAL
-  std::unique_ptr<Barrier> init_barrier_;
-  std::mutex per_thread_map_mutex_;  // Protects per_thread_map_.
-  std::unordered_map<uint64_t, std::unique_ptr<PerThread>> per_thread_map_;
-#endif
-
-  // Main worker thread loop.
-  void WorkerLoop(int thread_id) {
-#ifndef EIGEN_THREAD_LOCAL
-    std::unique_ptr<PerThread> new_pt(new PerThread());
-    per_thread_map_mutex_.lock();
-    bool insertOK = per_thread_map_.emplace(GlobalThreadIdHash(), std::move(new_pt)).second;
-    eigen_plain_assert(insertOK);
-    EIGEN_UNUSED_VARIABLE(insertOK);
-    per_thread_map_mutex_.unlock();
-    init_barrier_->Notify();
-    init_barrier_->Wait();
-#endif
-    PerThread* pt = GetPerThread();
-    pt->pool = this;
-    pt->rand = GlobalThreadIdHash();
-    pt->thread_id = thread_id;
-    Queue& q = thread_data_[thread_id].queue;
-    EventCount::Waiter* waiter = &waiters_[thread_id];
-    // TODO(dvyukov,rmlarsen): The time spent in NonEmptyQueueIndex() is
-    // proportional to num_threads_ and we assume that new work is scheduled at
-    // a constant rate, so we set spin_count to 5000 / num_threads_. The
-    // constant was picked based on a fair dice roll, tune it.
-    const int spin_count =
-        allow_spinning_ && num_threads_ > 0 ? 5000 / num_threads_ : 0;
-    if (num_threads_ == 1) {
-      // For num_threads_ == 1 there is no point in going through the expensive
-      // steal loop. Moreover, since NonEmptyQueueIndex() calls PopBack() on the
-      // victim queues it might reverse the order in which ops are executed
-      // compared to the order in which they are scheduled, which tends to be
-      // counter-productive for the types of I/O workloads the single thread
-      // pools tend to be used for.
-      while (!cancelled_) {
-        Task t = q.PopFront();
-        for (int i = 0; i < spin_count && !t.f; i++) {
-          if (!cancelled_.load(std::memory_order_relaxed)) {
-            t = q.PopFront();
-          }
-        }
-        if (!t.f) {
-          if (!WaitForWork(waiter, &t)) {
-            return;
-          }
-        }
-        if (t.f) {
-          env_.ExecuteTask(t);
-        }
-      }
-    } else {
-      while (!cancelled_) {
-        Task t = q.PopFront();
-        if (!t.f) {
-          t = LocalSteal();
-          if (!t.f) {
-            t = GlobalSteal();
-            if (!t.f) {
-              // Leave one thread spinning. This reduces latency.
-              if (allow_spinning_ && !spinning_ && !spinning_.exchange(true)) {
-                for (int i = 0; i < spin_count && !t.f; i++) {
-                  if (!cancelled_.load(std::memory_order_relaxed)) {
-                    t = GlobalSteal();
-                  } else {
-                    return;
-                  }
-                }
-                spinning_ = false;
-              }
-              if (!t.f) {
-                if (!WaitForWork(waiter, &t)) {
-                  return;
-                }
-              }
-            }
-          }
-        }
-        if (t.f) {
-          env_.ExecuteTask(t);
-        }
-      }
-    }
-  }
-
-  // Steal tries to steal work from other worker threads in the range [start,
-  // limit) in best-effort manner.
-  Task Steal(unsigned start, unsigned limit) {
-    PerThread* pt = GetPerThread();
-    const size_t size = limit - start;
-    unsigned r = Rand(&pt->rand);
-    // Reduce r into [0, size) range, this utilizes trick from
-    // https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
-    eigen_plain_assert(all_coprimes_[size - 1].size() < (1<<30));
-    unsigned victim = ((uint64_t)r * (uint64_t)size) >> 32;
-    unsigned index = ((uint64_t) all_coprimes_[size - 1].size() * (uint64_t)r) >> 32;
-    unsigned inc = all_coprimes_[size - 1][index];
-
-    for (unsigned i = 0; i < size; i++) {
-      eigen_plain_assert(start + victim < limit);
-      Task t = thread_data_[start + victim].queue.PopBack();
-      if (t.f) {
-        return t;
-      }
-      victim += inc;
-      if (victim >= size) {
-        victim -= size;
-      }
-    }
-    return Task();
-  }
-
-  // Steals work within threads belonging to the partition.
-  Task LocalSteal() {
-    PerThread* pt = GetPerThread();
-    unsigned partition = GetStealPartition(pt->thread_id);
-    // If thread steal partition is the same as global partition, there is no
-    // need to go through the steal loop twice.
-    if (global_steal_partition_ == partition) return Task();
-    unsigned start, limit;
-    DecodePartition(partition, &start, &limit);
-    AssertBounds(start, limit);
-
-    return Steal(start, limit);
-  }
-
-  // Steals work from any other thread in the pool.
-  Task GlobalSteal() {
-    return Steal(0, num_threads_);
-  }
-
-
-  // WaitForWork blocks until new work is available (returns true), or if it is
-  // time to exit (returns false). Can optionally return a task to execute in t
-  // (in such case t.f != nullptr on return).
-  bool WaitForWork(EventCount::Waiter* waiter, Task* t) {
-    eigen_plain_assert(!t->f);
-    // We already did best-effort emptiness check in Steal, so prepare for
-    // blocking.
-    ec_.Prewait();
-    // Now do a reliable emptiness check.
-    int victim = NonEmptyQueueIndex();
-    if (victim != -1) {
-      ec_.CancelWait();
-      if (cancelled_) {
-        return false;
-      } else {
-        *t = thread_data_[victim].queue.PopBack();
-        return true;
-      }
-    }
-    // Number of blocked threads is used as termination condition.
-    // If we are shutting down and all worker threads blocked without work,
-    // that's we are done.
-    blocked_++;
-    // TODO is blocked_ required to be unsigned?
-    if (done_ && blocked_ == static_cast<unsigned>(num_threads_)) {
-      ec_.CancelWait();
-      // Almost done, but need to re-check queues.
-      // Consider that all queues are empty and all worker threads are preempted
-      // right after incrementing blocked_ above. Now a free-standing thread
-      // submits work and calls destructor (which sets done_). If we don't
-      // re-check queues, we will exit leaving the work unexecuted.
-      if (NonEmptyQueueIndex() != -1) {
-        // Note: we must not pop from queues before we decrement blocked_,
-        // otherwise the following scenario is possible. Consider that instead
-        // of checking for emptiness we popped the only element from queues.
-        // Now other worker threads can start exiting, which is bad if the
-        // work item submits other work. So we just check emptiness here,
-        // which ensures that all worker threads exit at the same time.
-        blocked_--;
-        return true;
-      }
-      // Reached stable termination state.
-      ec_.Notify(true);
-      return false;
-    }
-    ec_.CommitWait(waiter);
-    blocked_--;
-    return true;
-  }
-
-  int NonEmptyQueueIndex() {
-    PerThread* pt = GetPerThread();
-    // We intentionally design NonEmptyQueueIndex to steal work from
-    // anywhere in the queue so threads don't block in WaitForWork() forever
-    // when all threads in their partition go to sleep. Steal is still local.
-    const size_t size = thread_data_.size();
-    unsigned r = Rand(&pt->rand);
-    unsigned inc = all_coprimes_[size - 1][r % all_coprimes_[size - 1].size()];
-    unsigned victim = r % size;
-    for (unsigned i = 0; i < size; i++) {
-      if (!thread_data_[victim].queue.Empty()) {
-        return victim;
-      }
-      victim += inc;
-      if (victim >= size) {
-        victim -= size;
-      }
-    }
-    return -1;
-  }
-
-  static EIGEN_STRONG_INLINE uint64_t GlobalThreadIdHash() {
-    return std::hash<std::thread::id>()(std::this_thread::get_id());
-  }
-
-  EIGEN_STRONG_INLINE PerThread* GetPerThread() {
-#ifndef EIGEN_THREAD_LOCAL
-    static PerThread dummy;
-    auto it = per_thread_map_.find(GlobalThreadIdHash());
-    if (it == per_thread_map_.end()) {
-      return &dummy;
-    } else {
-      return it->second.get();
-    }
-#else
-    EIGEN_THREAD_LOCAL PerThread per_thread_;
-    PerThread* pt = &per_thread_;
-    return pt;
-#endif
-  }
-
-  static EIGEN_STRONG_INLINE unsigned Rand(uint64_t* state) {
-    uint64_t current = *state;
-    // Update the internal state
-    *state = current * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
-    // Generate the random output (using the PCG-XSH-RS scheme)
-    return static_cast<unsigned>((current ^ (current >> 22)) >>
-                                 (22 + (current >> 61)));
-  }
-};
-
-typedef ThreadPoolTempl<StlThreadEnvironment> ThreadPool;
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h
deleted file mode 100644
index b572ebc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h
+++ /dev/null
@@ -1,236 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_RUNQUEUE_H_
-#define EIGEN_CXX11_THREADPOOL_RUNQUEUE_H_
-
-namespace Eigen {
-
-// RunQueue is a fixed-size, partially non-blocking deque or Work items.
-// Operations on front of the queue must be done by a single thread (owner),
-// operations on back of the queue can be done by multiple threads concurrently.
-//
-// Algorithm outline:
-// All remote threads operating on the queue back are serialized by a mutex.
-// This ensures that at most two threads access state: owner and one remote
-// thread (Size aside). The algorithm ensures that the occupied region of the
-// underlying array is logically continuous (can wraparound, but no stray
-// occupied elements). Owner operates on one end of this region, remote thread
-// operates on the other end. Synchronization between these threads
-// (potential consumption of the last element and take up of the last empty
-// element) happens by means of state variable in each element. States are:
-// empty, busy (in process of insertion of removal) and ready. Threads claim
-// elements (empty->busy and ready->busy transitions) by means of a CAS
-// operation. The finishing transition (busy->empty and busy->ready) are done
-// with plain store as the element is exclusively owned by the current thread.
-//
-// Note: we could permit only pointers as elements, then we would not need
-// separate state variable as null/non-null pointer value would serve as state,
-// but that would require malloc/free per operation for large, complex values
-// (and this is designed to store std::function<()>).
-template <typename Work, unsigned kSize>
-class RunQueue {
- public:
-  RunQueue() : front_(0), back_(0) {
-    // require power-of-two for fast masking
-    eigen_plain_assert((kSize & (kSize - 1)) == 0);
-    eigen_plain_assert(kSize > 2);            // why would you do this?
-    eigen_plain_assert(kSize <= (64 << 10));  // leave enough space for counter
-    for (unsigned i = 0; i < kSize; i++)
-      array_[i].state.store(kEmpty, std::memory_order_relaxed);
-  }
-
-  ~RunQueue() { eigen_plain_assert(Size() == 0); }
-
-  // PushFront inserts w at the beginning of the queue.
-  // If queue is full returns w, otherwise returns default-constructed Work.
-  Work PushFront(Work w) {
-    unsigned front = front_.load(std::memory_order_relaxed);
-    Elem* e = &array_[front & kMask];
-    uint8_t s = e->state.load(std::memory_order_relaxed);
-    if (s != kEmpty ||
-        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
-      return w;
-    front_.store(front + 1 + (kSize << 1), std::memory_order_relaxed);
-    e->w = std::move(w);
-    e->state.store(kReady, std::memory_order_release);
-    return Work();
-  }
-
-  // PopFront removes and returns the first element in the queue.
-  // If the queue was empty returns default-constructed Work.
-  Work PopFront() {
-    unsigned front = front_.load(std::memory_order_relaxed);
-    Elem* e = &array_[(front - 1) & kMask];
-    uint8_t s = e->state.load(std::memory_order_relaxed);
-    if (s != kReady ||
-        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
-      return Work();
-    Work w = std::move(e->w);
-    e->state.store(kEmpty, std::memory_order_release);
-    front = ((front - 1) & kMask2) | (front & ~kMask2);
-    front_.store(front, std::memory_order_relaxed);
-    return w;
-  }
-
-  // PushBack adds w at the end of the queue.
-  // If queue is full returns w, otherwise returns default-constructed Work.
-  Work PushBack(Work w) {
-    std::unique_lock<std::mutex> lock(mutex_);
-    unsigned back = back_.load(std::memory_order_relaxed);
-    Elem* e = &array_[(back - 1) & kMask];
-    uint8_t s = e->state.load(std::memory_order_relaxed);
-    if (s != kEmpty ||
-        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
-      return w;
-    back = ((back - 1) & kMask2) | (back & ~kMask2);
-    back_.store(back, std::memory_order_relaxed);
-    e->w = std::move(w);
-    e->state.store(kReady, std::memory_order_release);
-    return Work();
-  }
-
-  // PopBack removes and returns the last elements in the queue.
-  Work PopBack() {
-    if (Empty()) return Work();
-    std::unique_lock<std::mutex> lock(mutex_);
-    unsigned back = back_.load(std::memory_order_relaxed);
-    Elem* e = &array_[back & kMask];
-    uint8_t s = e->state.load(std::memory_order_relaxed);
-    if (s != kReady ||
-        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
-      return Work();
-    Work w = std::move(e->w);
-    e->state.store(kEmpty, std::memory_order_release);
-    back_.store(back + 1 + (kSize << 1), std::memory_order_relaxed);
-    return w;
-  }
-
-  // PopBackHalf removes and returns half last elements in the queue.
-  // Returns number of elements removed.
-  unsigned PopBackHalf(std::vector<Work>* result) {
-    if (Empty()) return 0;
-    std::unique_lock<std::mutex> lock(mutex_);
-    unsigned back = back_.load(std::memory_order_relaxed);
-    unsigned size = Size();
-    unsigned mid = back;
-    if (size > 1) mid = back + (size - 1) / 2;
-    unsigned n = 0;
-    unsigned start = 0;
-    for (; static_cast<int>(mid - back) >= 0; mid--) {
-      Elem* e = &array_[mid & kMask];
-      uint8_t s = e->state.load(std::memory_order_relaxed);
-      if (n == 0) {
-        if (s != kReady || !e->state.compare_exchange_strong(
-                               s, kBusy, std::memory_order_acquire))
-          continue;
-        start = mid;
-      } else {
-        // Note: no need to store temporal kBusy, we exclusively own these
-        // elements.
-        eigen_plain_assert(s == kReady);
-      }
-      result->push_back(std::move(e->w));
-      e->state.store(kEmpty, std::memory_order_release);
-      n++;
-    }
-    if (n != 0)
-      back_.store(start + 1 + (kSize << 1), std::memory_order_relaxed);
-    return n;
-  }
-
-  // Size returns current queue size.
-  // Can be called by any thread at any time.
-  unsigned Size() const { return SizeOrNotEmpty<true>(); }
-
-  // Empty tests whether container is empty.
-  // Can be called by any thread at any time.
-  bool Empty() const { return SizeOrNotEmpty<false>() == 0; }
-
-  // Delete all the elements from the queue.
-  void Flush() {
-    while (!Empty()) {
-      PopFront();
-    }
-  }
-
- private:
-  static const unsigned kMask = kSize - 1;
-  static const unsigned kMask2 = (kSize << 1) - 1;
-  struct Elem {
-    std::atomic<uint8_t> state;
-    Work w;
-  };
-  enum {
-    kEmpty,
-    kBusy,
-    kReady,
-  };
-  std::mutex mutex_;
-  // Low log(kSize) + 1 bits in front_ and back_ contain rolling index of
-  // front/back, respectively. The remaining bits contain modification counters
-  // that are incremented on Push operations. This allows us to (1) distinguish
-  // between empty and full conditions (if we would use log(kSize) bits for
-  // position, these conditions would be indistinguishable); (2) obtain
-  // consistent snapshot of front_/back_ for Size operation using the
-  // modification counters.
-  std::atomic<unsigned> front_;
-  std::atomic<unsigned> back_;
-  Elem array_[kSize];
-
-  // SizeOrNotEmpty returns current queue size; if NeedSizeEstimate is false,
-  // only whether the size is 0 is guaranteed to be correct.
-  // Can be called by any thread at any time.
-  template<bool NeedSizeEstimate>
-  unsigned SizeOrNotEmpty() const {
-    // Emptiness plays critical role in thread pool blocking. So we go to great
-    // effort to not produce false positives (claim non-empty queue as empty).
-    unsigned front = front_.load(std::memory_order_acquire);
-    for (;;) {
-      // Capture a consistent snapshot of front/tail.
-      unsigned back = back_.load(std::memory_order_acquire);
-      unsigned front1 = front_.load(std::memory_order_relaxed);
-      if (front != front1) {
-        front = front1;
-        std::atomic_thread_fence(std::memory_order_acquire);
-        continue;
-      }
-      if (NeedSizeEstimate) {
-        return CalculateSize(front, back);
-      } else {
-        // This value will be 0 if the queue is empty, and undefined otherwise.
-        unsigned maybe_zero = ((front ^ back) & kMask2);
-        // Queue size estimate must agree with maybe zero check on the queue
-        // empty/non-empty state.
-        eigen_assert((CalculateSize(front, back) == 0) == (maybe_zero == 0));
-        return maybe_zero;
-      }
-    }
-  }
-
-  EIGEN_ALWAYS_INLINE
-  unsigned CalculateSize(unsigned front, unsigned back) const {
-    int size = (front & kMask2) - (back & kMask2);
-    // Fix overflow.
-    if (size < 0) size += 2 * kSize;
-    // Order of modification in push/pop is crafted to make the queue look
-    // larger than it is during concurrent modifications. E.g. push can
-    // increment size before the corresponding pop has decremented it.
-    // So the computed size can be up to kSize + 1, fix it.
-    if (size > static_cast<int>(kSize)) size = kSize;
-    return static_cast<unsigned>(size);
-  }
-
-  RunQueue(const RunQueue&) = delete;
-  void operator=(const RunQueue&) = delete;
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_THREADPOOL_RUNQUEUE_H_
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h
deleted file mode 100644
index a05685f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h
+++ /dev/null
@@ -1,23 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
-#define EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
-
-// Try to come up with a portable way to cancel a thread
-#if EIGEN_OS_GNULINUX
-  #define EIGEN_THREAD_CANCEL(t)                  \
-    pthread_cancel(t.native_handle());
-  #define EIGEN_SUPPORTS_THREAD_CANCELLATION 1
-#else
-#define EIGEN_THREAD_CANCEL(t)
-#endif
-
-
-#endif  // EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h
deleted file mode 100644
index d94a064..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h
+++ /dev/null
@@ -1,40 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
-#define EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
-
-namespace Eigen {
-
-struct StlThreadEnvironment {
-  struct Task {
-    std::function<void()> f;
-  };
-
-  // EnvThread constructor must start the thread,
-  // destructor must join the thread.
-  class EnvThread {
-   public:
-    EnvThread(std::function<void()> f) : thr_(std::move(f)) {}
-    ~EnvThread() { thr_.join(); }
-    // This function is called when the threadpool is cancelled.
-    void OnCancel() { }
-
-   private:
-    std::thread thr_;
-  };
-
-  EnvThread* CreateThread(std::function<void()> f) { return new EnvThread(std::move(f)); }
-  Task CreateTask(std::function<void()> f) { return Task{std::move(f)}; }
-  void ExecuteTask(const Task& t) { t.f(); }
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h
deleted file mode 100644
index 4e68474..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h
+++ /dev/null
@@ -1,301 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
-#define EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
-
-#ifdef EIGEN_AVOID_THREAD_LOCAL
-
-#ifdef EIGEN_THREAD_LOCAL
-#undef EIGEN_THREAD_LOCAL
-#endif
-
-#else
-
-#if EIGEN_MAX_CPP_VER >= 11 &&                         \
-    ((EIGEN_COMP_GNUC && EIGEN_GNUC_AT_LEAST(4, 8)) || \
-     __has_feature(cxx_thread_local)                || \
-     (EIGEN_COMP_MSVC >= 1900) )
-#define EIGEN_THREAD_LOCAL static thread_local
-#endif
-
-// Disable TLS for Apple and Android builds with older toolchains.
-#if defined(__APPLE__)
-// Included for TARGET_OS_IPHONE, __IPHONE_OS_VERSION_MIN_REQUIRED,
-// __IPHONE_8_0.
-#include <Availability.h>
-#include <TargetConditionals.h>
-#endif
-// Checks whether C++11's `thread_local` storage duration specifier is
-// supported.
-#if defined(__apple_build_version__) &&     \
-    ((__apple_build_version__ < 8000042) || \
-     (TARGET_OS_IPHONE && __IPHONE_OS_VERSION_MIN_REQUIRED < __IPHONE_9_0))
-// Notes: Xcode's clang did not support `thread_local` until version
-// 8, and even then not for all iOS < 9.0.
-#undef EIGEN_THREAD_LOCAL
-
-#elif defined(__ANDROID__) && EIGEN_COMP_CLANG
-// There are platforms for which TLS should not be used even though the compiler
-// makes it seem like it's supported (Android NDK < r12b for example).
-// This is primarily because of linker problems and toolchain misconfiguration:
-// TLS isn't supported until NDK r12b per
-// https://developer.android.com/ndk/downloads/revision_history.html
-// Since NDK r16, `__NDK_MAJOR__` and `__NDK_MINOR__` are defined in
-// <android/ndk-version.h>. For NDK < r16, users should define these macros,
-// e.g. `-D__NDK_MAJOR__=11 -D__NKD_MINOR__=0` for NDK r11.
-#if __has_include(<android/ndk-version.h>)
-#include <android/ndk-version.h>
-#endif  // __has_include(<android/ndk-version.h>)
-#if defined(__ANDROID__) && defined(__clang__) && defined(__NDK_MAJOR__) && \
-    defined(__NDK_MINOR__) &&                                               \
-    ((__NDK_MAJOR__ < 12) || ((__NDK_MAJOR__ == 12) && (__NDK_MINOR__ < 1)))
-#undef EIGEN_THREAD_LOCAL
-#endif
-#endif  // defined(__ANDROID__) && defined(__clang__)
-
-#endif  // EIGEN_AVOID_THREAD_LOCAL
-
-namespace Eigen {
-
-namespace internal {
-template <typename T>
-struct ThreadLocalNoOpInitialize {
-  void operator()(T&) const {}
-};
-
-template <typename T>
-struct ThreadLocalNoOpRelease {
-  void operator()(T&) const {}
-};
-
-}  // namespace internal
-
-// Thread local container for elements of type T, that does not use thread local
-// storage. As long as the number of unique threads accessing this storage
-// is smaller than `capacity_`, it is lock-free and wait-free. Otherwise it will
-// use a mutex for synchronization.
-//
-// Type `T` has to be default constructible, and by default each thread will get
-// a default constructed value. It is possible to specify custom `initialize`
-// callable, that will be called lazily from each thread accessing this object,
-// and will be passed a default initialized object of type `T`. Also it's
-// possible to pass a custom `release` callable, that will be invoked before
-// calling ~T().
-//
-// Example:
-//
-//   struct Counter {
-//     int value = 0;
-//   }
-//
-//   Eigen::ThreadLocal<Counter> counter(10);
-//
-//   // Each thread will have access to it's own counter object.
-//   Counter& cnt = counter.local();
-//   cnt++;
-//
-// WARNING: Eigen::ThreadLocal uses the OS-specific value returned by
-// std::this_thread::get_id() to identify threads. This value is not guaranteed
-// to be unique except for the life of the thread. A newly created thread may
-// get an OS-specific ID equal to that of an already destroyed thread.
-//
-// Somewhat similar to TBB thread local storage, with similar restrictions:
-// https://www.threadingbuildingblocks.org/docs/help/reference/thread_local_storage/enumerable_thread_specific_cls.html
-//
-template <typename T,
-          typename Initialize = internal::ThreadLocalNoOpInitialize<T>,
-          typename Release = internal::ThreadLocalNoOpRelease<T>>
-class ThreadLocal {
-  // We preallocate default constructed elements in MaxSizedVector.
-  static_assert(std::is_default_constructible<T>::value,
-                "ThreadLocal data type must be default constructible");
-
- public:
-  explicit ThreadLocal(int capacity)
-      : ThreadLocal(capacity, internal::ThreadLocalNoOpInitialize<T>(),
-                    internal::ThreadLocalNoOpRelease<T>()) {}
-
-  ThreadLocal(int capacity, Initialize initialize)
-      : ThreadLocal(capacity, std::move(initialize),
-                    internal::ThreadLocalNoOpRelease<T>()) {}
-
-  ThreadLocal(int capacity, Initialize initialize, Release release)
-      : initialize_(std::move(initialize)),
-        release_(std::move(release)),
-        capacity_(capacity),
-        data_(capacity_),
-        ptr_(capacity_),
-        filled_records_(0) {
-    eigen_assert(capacity_ >= 0);
-    data_.resize(capacity_);
-    for (int i = 0; i < capacity_; ++i) {
-      ptr_.emplace_back(nullptr);
-    }
-  }
-
-  T& local() {
-    std::thread::id this_thread = std::this_thread::get_id();
-    if (capacity_ == 0) return SpilledLocal(this_thread);
-
-    std::size_t h = std::hash<std::thread::id>()(this_thread);
-    const int start_idx = h % capacity_;
-
-    // NOTE: From the definition of `std::this_thread::get_id()` it is
-    // guaranteed that we never can have concurrent insertions with the same key
-    // to our hash-map like data structure. If we didn't find an element during
-    // the initial traversal, it's guaranteed that no one else could have
-    // inserted it while we are in this function. This allows to massively
-    // simplify out lock-free insert-only hash map.
-
-    // Check if we already have an element for `this_thread`.
-    int idx = start_idx;
-    while (ptr_[idx].load() != nullptr) {
-      ThreadIdAndValue& record = *(ptr_[idx].load());
-      if (record.thread_id == this_thread) return record.value;
-
-      idx += 1;
-      if (idx >= capacity_) idx -= capacity_;
-      if (idx == start_idx) break;
-    }
-
-    // If we are here, it means that we found an insertion point in lookup
-    // table at `idx`, or we did a full traversal and table is full.
-
-    // If lock-free storage is full, fallback on mutex.
-    if (filled_records_.load() >= capacity_) return SpilledLocal(this_thread);
-
-    // We double check that we still have space to insert an element into a lock
-    // free storage. If old value in `filled_records_` is larger than the
-    // records capacity, it means that some other thread added an element while
-    // we were traversing lookup table.
-    int insertion_index =
-        filled_records_.fetch_add(1, std::memory_order_relaxed);
-    if (insertion_index >= capacity_) return SpilledLocal(this_thread);
-
-    // At this point it's guaranteed that we can access to
-    // data_[insertion_index_] without a data race.
-    data_[insertion_index].thread_id = this_thread;
-    initialize_(data_[insertion_index].value);
-
-    // That's the pointer we'll put into the lookup table.
-    ThreadIdAndValue* inserted = &data_[insertion_index];
-
-    // We'll use nullptr pointer to ThreadIdAndValue in a compare-and-swap loop.
-    ThreadIdAndValue* empty = nullptr;
-
-    // Now we have to find an insertion point into the lookup table. We start
-    // from the `idx` that was identified as an insertion point above, it's
-    // guaranteed that we will have an empty record somewhere in a lookup table
-    // (because we created a record in the `data_`).
-    const int insertion_idx = idx;
-
-    do {
-      // Always start search from the original insertion candidate.
-      idx = insertion_idx;
-      while (ptr_[idx].load() != nullptr) {
-        idx += 1;
-        if (idx >= capacity_) idx -= capacity_;
-        // If we did a full loop, it means that we don't have any free entries
-        // in the lookup table, and this means that something is terribly wrong.
-        eigen_assert(idx != insertion_idx);
-      }
-      // Atomic CAS of the pointer guarantees that any other thread, that will
-      // follow this pointer will see all the mutations in the `data_`.
-    } while (!ptr_[idx].compare_exchange_weak(empty, inserted));
-
-    return inserted->value;
-  }
-
-  // WARN: It's not thread safe to call it concurrently with `local()`.
-  void ForEach(std::function<void(std::thread::id, T&)> f) {
-    // Reading directly from `data_` is unsafe, because only CAS to the
-    // record in `ptr_` makes all changes visible to other threads.
-    for (auto& ptr : ptr_) {
-      ThreadIdAndValue* record = ptr.load();
-      if (record == nullptr) continue;
-      f(record->thread_id, record->value);
-    }
-
-    // We did not spill into the map based storage.
-    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
-
-    // Adds a happens before edge from the last call to SpilledLocal().
-    std::unique_lock<std::mutex> lock(mu_);
-    for (auto& kv : per_thread_map_) {
-      f(kv.first, kv.second);
-    }
-  }
-
-  // WARN: It's not thread safe to call it concurrently with `local()`.
-  ~ThreadLocal() {
-    // Reading directly from `data_` is unsafe, because only CAS to the record
-    // in `ptr_` makes all changes visible to other threads.
-    for (auto& ptr : ptr_) {
-      ThreadIdAndValue* record = ptr.load();
-      if (record == nullptr) continue;
-      release_(record->value);
-    }
-
-    // We did not spill into the map based storage.
-    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
-
-    // Adds a happens before edge from the last call to SpilledLocal().
-    std::unique_lock<std::mutex> lock(mu_);
-    for (auto& kv : per_thread_map_) {
-      release_(kv.second);
-    }
-  }
-
- private:
-  struct ThreadIdAndValue {
-    std::thread::id thread_id;
-    T value;
-  };
-
-  // Use unordered map guarded by a mutex when lock free storage is full.
-  T& SpilledLocal(std::thread::id this_thread) {
-    std::unique_lock<std::mutex> lock(mu_);
-
-    auto it = per_thread_map_.find(this_thread);
-    if (it == per_thread_map_.end()) {
-      auto result = per_thread_map_.emplace(this_thread, T());
-      eigen_assert(result.second);
-      initialize_((*result.first).second);
-      return (*result.first).second;
-    } else {
-      return it->second;
-    }
-  }
-
-  Initialize initialize_;
-  Release release_;
-  const int capacity_;
-
-  // Storage that backs lock-free lookup table `ptr_`. Records stored in this
-  // storage contiguously starting from index 0.
-  MaxSizeVector<ThreadIdAndValue> data_;
-
-  // Atomic pointers to the data stored in `data_`. Used as a lookup table for
-  // linear probing hash map (https://en.wikipedia.org/wiki/Linear_probing).
-  MaxSizeVector<std::atomic<ThreadIdAndValue*>> ptr_;
-
-  // Number of records stored in the `data_`.
-  std::atomic<int> filled_records_;
-
-  // We fallback on per thread map if lock-free storage is full. In practice
-  // this should never happen, if `capacity_` is a reasonable estimate of the
-  // number of threads running in a system.
-  std::mutex mu_;  // Protects per_thread_map_.
-  std::unordered_map<std::thread::id, T> per_thread_map_;
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h
deleted file mode 100644
index 25030dc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h
+++ /dev/null
@@ -1,48 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
-#define EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
-
-namespace Eigen {
-
-// This defines an interface that ThreadPoolDevice can take to use
-// custom thread pools underneath.
-class ThreadPoolInterface {
- public:
-  // Submits a closure to be run by a thread in the pool.
-  virtual void Schedule(std::function<void()> fn) = 0;
-
-  // Submits a closure to be run by threads in the range [start, end) in the
-  // pool.
-  virtual void ScheduleWithHint(std::function<void()> fn, int /*start*/,
-                                int /*end*/) {
-    // Just defer to Schedule in case sub-classes aren't interested in
-    // overriding this functionality.
-    Schedule(fn);
-  }
-
-  // If implemented, stop processing the closures that have been enqueued.
-  // Currently running closures may still be processed.
-  // If not implemented, does nothing.
-  virtual void Cancel() {}
-
-  // Returns the number of threads in the pool.
-  virtual int NumThreads() const = 0;
-
-  // Returns a logical thread index between 0 and NumThreads() - 1 if called
-  // from one of the threads in the pool. Returns -1 otherwise.
-  virtual int CurrentThreadId() const = 0;
-
-  virtual ~ThreadPoolInterface() {}
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h
deleted file mode 100644
index a859c7b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h
+++ /dev/null
@@ -1,20 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
-#define EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
-
-// Try to come up with a portable way to yield
-#if EIGEN_COMP_GNUC && EIGEN_GNUC_AT_MOST(4, 7)
-#define EIGEN_THREAD_YIELD() sched_yield()
-#else
-#define EIGEN_THREAD_YIELD() std::this_thread::yield()
-#endif
-
-#endif  // EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h
deleted file mode 100644
index 149ceaf..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h
+++ /dev/null
@@ -1,537 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11META_H
-#define EIGEN_CXX11META_H
-
-#include <vector>
-#include "EmulateArray.h"
-
-#include "CXX11Workarounds.h"
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
-  * \file CXX11/util/CXX11Meta.h
-  * This file contains generic metaprogramming classes which are not specifically related to Eigen.
-  * This file expands upon Core/util/Meta.h and adds support for C++11 specific features.
-  */
-
-template<typename... tt>
-struct type_list { constexpr static int count = sizeof...(tt); };
-
-template<typename t, typename... tt>
-struct type_list<t, tt...> { constexpr static int count = sizeof...(tt) + 1; typedef t first_type; };
-
-template<typename T, T... nn>
-struct numeric_list { constexpr static std::size_t count = sizeof...(nn); };
-
-template<typename T, T n, T... nn>
-struct numeric_list<T, n, nn...> { static const std::size_t count = sizeof...(nn) + 1; const static T first_value = n; };
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-/* numeric list constructors
- *
- * equivalencies:
- *     constructor                                              result
- *     typename gen_numeric_list<int, 5>::type                  numeric_list<int, 0,1,2,3,4>
- *     typename gen_numeric_list_reversed<int, 5>::type         numeric_list<int, 4,3,2,1,0>
- *     typename gen_numeric_list_swapped_pair<int, 5,1,2>::type numeric_list<int, 0,2,1,3,4>
- *     typename gen_numeric_list_repeated<int, 0, 5>::type      numeric_list<int, 0,0,0,0,0>
- */
-
-template<typename T, std::size_t n, T start = 0, T... ii> struct gen_numeric_list                     : gen_numeric_list<T, n-1, start, start + n-1, ii...> {};
-template<typename T, T start, T... ii>                    struct gen_numeric_list<T, 0, start, ii...> { typedef numeric_list<T, ii...> type; };
-
-template<typename T, std::size_t n, T start = 0, T... ii> struct gen_numeric_list_reversed                     : gen_numeric_list_reversed<T, n-1, start, ii..., start + n-1> {};
-template<typename T, T start, T... ii>                    struct gen_numeric_list_reversed<T, 0, start, ii...> { typedef numeric_list<T, ii...> type; };
-
-template<typename T, std::size_t n, T a, T b, T start = 0, T... ii> struct gen_numeric_list_swapped_pair                           : gen_numeric_list_swapped_pair<T, n-1, a, b, start, (start + n-1) == a ? b : ((start + n-1) == b ? a : (start + n-1)), ii...> {};
-template<typename T, T a, T b, T start, T... ii>                    struct gen_numeric_list_swapped_pair<T, 0, a, b, start, ii...> { typedef numeric_list<T, ii...> type; };
-
-template<typename T, std::size_t n, T V, T... nn> struct gen_numeric_list_repeated                 : gen_numeric_list_repeated<T, n-1, V, V, nn...> {};
-template<typename T, T V, T... nn>                struct gen_numeric_list_repeated<T, 0, V, nn...> { typedef numeric_list<T, nn...> type; };
-
-/* list manipulation: concatenate */
-
-template<class a, class b> struct concat;
-
-template<typename... as, typename... bs> struct concat<type_list<as...>,       type_list<bs...>>        { typedef type_list<as..., bs...> type; };
-template<typename T, T... as, T... bs>   struct concat<numeric_list<T, as...>, numeric_list<T, bs...> > { typedef numeric_list<T, as..., bs...> type; };
-
-template<typename... p> struct mconcat;
-template<typename a>                             struct mconcat<a>           { typedef a type; };
-template<typename a, typename b>                 struct mconcat<a, b>        : concat<a, b> {};
-template<typename a, typename b, typename... cs> struct mconcat<a, b, cs...> : concat<a, typename mconcat<b, cs...>::type> {};
-
-/* list manipulation: extract slices */
-
-template<int n, typename x> struct take;
-template<int n, typename a, typename... as> struct take<n, type_list<a, as...>> : concat<type_list<a>, typename take<n-1, type_list<as...>>::type> {};
-template<int n>                             struct take<n, type_list<>>         { typedef type_list<> type; };
-template<typename a, typename... as>        struct take<0, type_list<a, as...>> { typedef type_list<> type; };
-template<>                                  struct take<0, type_list<>>         { typedef type_list<> type; };
-
-template<typename T, int n, T a, T... as> struct take<n, numeric_list<T, a, as...>> : concat<numeric_list<T, a>, typename take<n-1, numeric_list<T, as...>>::type> {};
-template<typename T, int n>               struct take<n, numeric_list<T>>           { typedef numeric_list<T> type; };
-template<typename T, T a, T... as>        struct take<0, numeric_list<T, a, as...>> { typedef numeric_list<T> type; };
-template<typename T>                      struct take<0, numeric_list<T>>           { typedef numeric_list<T> type; };
-
-template<typename T, int n, T... ii>      struct h_skip_helper_numeric;
-template<typename T, int n, T i, T... ii> struct h_skip_helper_numeric<T, n, i, ii...> : h_skip_helper_numeric<T, n-1, ii...> {};
-template<typename T, T i, T... ii>        struct h_skip_helper_numeric<T, 0, i, ii...> { typedef numeric_list<T, i, ii...> type; };
-template<typename T, int n>               struct h_skip_helper_numeric<T, n>           { typedef numeric_list<T> type; };
-template<typename T>                      struct h_skip_helper_numeric<T, 0>           { typedef numeric_list<T> type; };
-
-template<int n, typename... tt>             struct h_skip_helper_type;
-template<int n, typename t, typename... tt> struct h_skip_helper_type<n, t, tt...> : h_skip_helper_type<n-1, tt...> {};
-template<typename t, typename... tt>        struct h_skip_helper_type<0, t, tt...> { typedef type_list<t, tt...> type; };
-template<int n>                             struct h_skip_helper_type<n>           { typedef type_list<> type; };
-template<>                                  struct h_skip_helper_type<0>           { typedef type_list<> type; };
-#endif //not EIGEN_PARSED_BY_DOXYGEN
-
-template<int n>
-struct h_skip {
-  template<typename T, T... ii>
-  constexpr static EIGEN_STRONG_INLINE typename h_skip_helper_numeric<T, n, ii...>::type helper(numeric_list<T, ii...>) { return typename h_skip_helper_numeric<T, n, ii...>::type(); }
-  template<typename... tt>
-  constexpr static EIGEN_STRONG_INLINE typename h_skip_helper_type<n, tt...>::type helper(type_list<tt...>) { return typename h_skip_helper_type<n, tt...>::type(); }
-};
-
-template<int n, typename a> struct skip { typedef decltype(h_skip<n>::helper(a())) type; };
-
-template<int start, int count, typename a> struct slice : take<count, typename skip<start, a>::type> {};
-
-/* list manipulation: retrieve single element from list */
-
-template<int n, typename x> struct get;
-
-template<int n, typename a, typename... as>               struct get<n, type_list<a, as...>>   : get<n-1, type_list<as...>> {};
-template<typename a, typename... as>                      struct get<0, type_list<a, as...>>   { typedef a type; };
-
-template<typename T, int n, T a, T... as>                        struct get<n, numeric_list<T, a, as...>>   : get<n-1, numeric_list<T, as...>> {};
-template<typename T, T a, T... as>                               struct get<0, numeric_list<T, a, as...>>   { constexpr static T value = a; };
-
-template<std::size_t n, typename T, T a, T... as> constexpr T       array_get(const numeric_list<T, a, as...>&) {
-   return get<(int)n, numeric_list<T, a, as...>>::value;
-}
-
-/* always get type, regardless of dummy; good for parameter pack expansion */
-
-template<typename T, T dummy, typename t> struct id_numeric  { typedef t type; };
-template<typename dummy, typename t>      struct id_type     { typedef t type; };
-
-/* equality checking, flagged version */
-
-template<typename a, typename b> struct is_same_gf : is_same<a, b> { constexpr static int global_flags = 0; };
-
-/* apply_op to list */
-
-template<
-  bool from_left, // false
-  template<typename, typename> class op,
-  typename additional_param,
-  typename... values
->
-struct h_apply_op_helper                                        { typedef type_list<typename op<values, additional_param>::type...> type; };
-template<
-  template<typename, typename> class op,
-  typename additional_param,
-  typename... values
->
-struct h_apply_op_helper<true, op, additional_param, values...> { typedef type_list<typename op<additional_param, values>::type...> type; };
-
-template<
-  bool from_left,
-  template<typename, typename> class op,
-  typename additional_param
->
-struct h_apply_op
-{
-  template<typename... values>
-  constexpr static typename h_apply_op_helper<from_left, op, additional_param, values...>::type helper(type_list<values...>)
-  { return typename h_apply_op_helper<from_left, op, additional_param, values...>::type(); }
-};
-
-template<
-  template<typename, typename> class op,
-  typename additional_param,
-  typename a
->
-struct apply_op_from_left { typedef decltype(h_apply_op<true, op, additional_param>::helper(a())) type; };
-
-template<
-  template<typename, typename> class op,
-  typename additional_param,
-  typename a
->
-struct apply_op_from_right { typedef decltype(h_apply_op<false, op, additional_param>::helper(a())) type; };
-
-/* see if an element is in a list */
-
-template<
-  template<typename, typename> class test,
-  typename check_against,
-  typename h_list,
-  bool last_check_positive = false
->
-struct contained_in_list;
-
-template<
-  template<typename, typename> class test,
-  typename check_against,
-  typename h_list
->
-struct contained_in_list<test, check_against, h_list, true>
-{
-  constexpr static bool value = true;
-};
-
-template<
-  template<typename, typename> class test,
-  typename check_against,
-  typename a,
-  typename... as
->
-struct contained_in_list<test, check_against, type_list<a, as...>, false> : contained_in_list<test, check_against, type_list<as...>, test<check_against, a>::value> {};
-
-template<
-  template<typename, typename> class test,
-  typename check_against
-  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, empty)
->
-struct contained_in_list<test, check_against, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>, false> { constexpr static bool value = false; };
-
-/* see if an element is in a list and check for global flags */
-
-template<
-  template<typename, typename> class test,
-  typename check_against,
-  typename h_list,
-  int default_flags = 0,
-  bool last_check_positive = false,
-  int last_check_flags = default_flags
->
-struct contained_in_list_gf;
-
-template<
-  template<typename, typename> class test,
-  typename check_against,
-  typename h_list,
-  int default_flags,
-  int last_check_flags
->
-struct contained_in_list_gf<test, check_against, h_list, default_flags, true, last_check_flags>
-{
-  constexpr static bool value = true;
-  constexpr static int global_flags = last_check_flags;
-};
-
-template<
-  template<typename, typename> class test,
-  typename check_against,
-  typename a,
-  typename... as,
-  int default_flags,
-  int last_check_flags
->
-struct contained_in_list_gf<test, check_against, type_list<a, as...>, default_flags, false, last_check_flags> : contained_in_list_gf<test, check_against, type_list<as...>, default_flags, test<check_against, a>::value, test<check_against, a>::global_flags> {};
-
-template<
-  template<typename, typename> class test,
-  typename check_against
-  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, empty),
-  int default_flags,
-  int last_check_flags
->
-struct contained_in_list_gf<test, check_against, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>, default_flags, false, last_check_flags> { constexpr static bool value = false; constexpr static int global_flags = default_flags; };
-
-/* generic reductions */
-
-template<
-  typename Reducer,
-  typename... Ts
-> struct reduce;
-
-template<
-  typename Reducer
-> struct reduce<Reducer>
-{
-  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE int run() { return Reducer::Identity; }
-};
-
-template<
-  typename Reducer,
-  typename A
-> struct reduce<Reducer, A>
-{
-  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE A run(A a) { return a; }
-};
-
-template<
-  typename Reducer,
-  typename A,
-  typename... Ts
-> struct reduce<Reducer, A, Ts...>
-{
-  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, Ts... ts) -> decltype(Reducer::run(a, reduce<Reducer, Ts...>::run(ts...))) {
-    return Reducer::run(a, reduce<Reducer, Ts...>::run(ts...));
-  }
-};
-
-/* generic binary operations */
-
-struct sum_op           {
-  template<typename A, typename B> EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a + b)   { return a + b;   }
-  static constexpr int Identity = 0;
-};
-struct product_op       {
-  template<typename A, typename B> EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a * b)   { return a * b;   }
-  static constexpr int Identity = 1;
-};
-
-struct logical_and_op   { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a && b)  { return a && b;  } };
-struct logical_or_op    { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a || b)  { return a || b;  } };
-
-struct equal_op         { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a == b)  { return a == b;  } };
-struct not_equal_op     { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a != b)  { return a != b;  } };
-struct lesser_op        { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a < b)   { return a < b;   } };
-struct lesser_equal_op  { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a <= b)  { return a <= b;  } };
-struct greater_op       { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a > b)   { return a > b;   } };
-struct greater_equal_op { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a >= b)  { return a >= b;  } };
-
-/* generic unary operations */
-
-struct not_op                { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(!a)      { return !a;      } };
-struct negation_op           { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(-a)      { return -a;      } };
-struct greater_equal_zero_op { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(a >= 0)  { return a >= 0;  } };
-
-
-/* reductions for lists */
-
-// using auto -> return value spec makes ICC 13.0 and 13.1 crash here, so we have to hack it
-// together in front... (13.0 doesn't work with array_prod/array_reduce/... anyway, but 13.1
-// does...
-template<typename... Ts>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE decltype(reduce<product_op, Ts...>::run((*((Ts*)0))...)) arg_prod(Ts... ts)
-{
-  return reduce<product_op, Ts...>::run(ts...);
-}
-
-template<typename... Ts>
-constexpr EIGEN_STRONG_INLINE decltype(reduce<sum_op, Ts...>::run((*((Ts*)0))...)) arg_sum(Ts... ts)
-{
-  return reduce<sum_op, Ts...>::run(ts...);
-}
-
-/* reverse arrays */
-
-template<typename Array, int... n>
-constexpr EIGEN_STRONG_INLINE Array h_array_reverse(Array arr, numeric_list<int, n...>)
-{
-  return {{array_get<sizeof...(n) - n - 1>(arr)...}};
-}
-
-template<typename T, std::size_t N>
-constexpr EIGEN_STRONG_INLINE array<T, N> array_reverse(array<T, N> arr)
-{
-  return h_array_reverse(arr, typename gen_numeric_list<int, N>::type());
-}
-
-
-/* generic array reductions */
-
-// can't reuse standard reduce() interface above because Intel's Compiler
-// *really* doesn't like it, so we just reimplement the stuff
-// (start from N - 1 and work down to 0 because specialization for
-// n == N - 1 also doesn't work in Intel's compiler, so it goes into
-// an infinite loop)
-template<typename Reducer, typename T, std::size_t N, std::size_t n = N - 1>
-struct h_array_reduce {
-  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(array<T, N> arr, T identity) -> decltype(Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr)))
-  {
-    return Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr));
-  }
-};
-
-template<typename Reducer, typename T, std::size_t N>
-struct h_array_reduce<Reducer, T, N, 0>
-{
-  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE T run(const array<T, N>& arr, T)
-  {
-    return array_get<0>(arr);
-  }
-};
-
-template<typename Reducer, typename T>
-struct h_array_reduce<Reducer, T, 0>
-{
-  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE T run(const array<T, 0>&, T identity)
-  {
-    return identity;
-  }
-};
-
-template<typename Reducer, typename T, std::size_t N>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_reduce(const array<T, N>& arr, T identity) -> decltype(h_array_reduce<Reducer, T, N>::run(arr, identity))
-{
-  return h_array_reduce<Reducer, T, N>::run(arr, identity);
-}
-
-/* standard array reductions */
-
-template<typename T, std::size_t N>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_sum(const array<T, N>& arr) -> decltype(array_reduce<sum_op, T, N>(arr, static_cast<T>(0)))
-{
-  return array_reduce<sum_op, T, N>(arr, static_cast<T>(0));
-}
-
-template<typename T, std::size_t N>
-EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_prod(const array<T, N>& arr) -> decltype(array_reduce<product_op, T, N>(arr, static_cast<T>(1)))
-{
-  return array_reduce<product_op, T, N>(arr, static_cast<T>(1));
-}
-
-template<typename t>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const std::vector<t>& a) {
-  eigen_assert(a.size() > 0);
-  t prod = 1;
-  for (size_t i = 0; i < a.size(); ++i) { prod *= a[i]; }
-  return prod;
-}
-
-/* zip an array */
-
-template<typename Op, typename A, typename B, std::size_t N, int... n>
-constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A(), B())),N> h_array_zip(array<A, N> a, array<B, N> b, numeric_list<int, n...>)
-{
-  return array<decltype(Op::run(A(), B())),N>{{ Op::run(array_get<n>(a), array_get<n>(b))... }};
-}
-
-template<typename Op, typename A, typename B, std::size_t N>
-constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A(), B())),N> array_zip(array<A, N> a, array<B, N> b)
-{
-  return h_array_zip<Op>(a, b, typename gen_numeric_list<int, N>::type());
-}
-
-/* zip an array and reduce the result */
-
-template<typename Reducer, typename Op, typename A, typename B, std::size_t N, int... n>
-constexpr EIGEN_STRONG_INLINE auto h_array_zip_and_reduce(array<A, N> a, array<B, N> b, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...))
-{
-  return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...);
-}
-
-template<typename Reducer, typename Op, typename A, typename B, std::size_t N>
-constexpr EIGEN_STRONG_INLINE auto array_zip_and_reduce(array<A, N> a, array<B, N> b) -> decltype(h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type()))
-{
-  return h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type());
-}
-
-/* apply stuff to an array */
-
-template<typename Op, typename A, std::size_t N, int... n>
-constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A())),N> h_array_apply(array<A, N> a, numeric_list<int, n...>)
-{
-  return array<decltype(Op::run(A())),N>{{ Op::run(array_get<n>(a))... }};
-}
-
-template<typename Op, typename A, std::size_t N>
-constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A())),N> array_apply(array<A, N> a)
-{
-  return h_array_apply<Op>(a, typename gen_numeric_list<int, N>::type());
-}
-
-/* apply stuff to an array and reduce */
-
-template<typename Reducer, typename Op, typename A, std::size_t N, int... n>
-constexpr EIGEN_STRONG_INLINE auto h_array_apply_and_reduce(array<A, N> arr, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...))
-{
-  return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...);
-}
-
-template<typename Reducer, typename Op, typename A, std::size_t N>
-constexpr EIGEN_STRONG_INLINE auto array_apply_and_reduce(array<A, N> a) -> decltype(h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type()))
-{
-  return h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type());
-}
-
-/* repeat a value n times (and make an array out of it
- * usage:
- *   array<int, 16> = repeat<16>(42);
- */
-
-template<int n>
-struct h_repeat
-{
-  template<typename t, int... ii>
-  constexpr static EIGEN_STRONG_INLINE array<t, n> run(t v, numeric_list<int, ii...>)
-  {
-    return {{ typename id_numeric<int, ii, t>::type(v)... }};
-  }
-};
-
-template<int n, typename t>
-constexpr array<t, n> repeat(t v) { return h_repeat<n>::run(v, typename gen_numeric_list<int, n>::type()); }
-
-/* instantiate a class by a C-style array */
-template<class InstType, typename ArrType, std::size_t N, bool Reverse, typename... Ps>
-struct h_instantiate_by_c_array;
-
-template<class InstType, typename ArrType, std::size_t N, typename... Ps>
-struct h_instantiate_by_c_array<InstType, ArrType, N, false, Ps...>
-{
-  static InstType run(ArrType* arr, Ps... args)
-  {
-    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, Ps..., ArrType>::run(arr + 1, args..., arr[0]);
-  }
-};
-
-template<class InstType, typename ArrType, std::size_t N, typename... Ps>
-struct h_instantiate_by_c_array<InstType, ArrType, N, true, Ps...>
-{
-  static InstType run(ArrType* arr, Ps... args)
-  {
-    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, ArrType, Ps...>::run(arr + 1, arr[0], args...);
-  }
-};
-
-template<class InstType, typename ArrType, typename... Ps>
-struct h_instantiate_by_c_array<InstType, ArrType, 0, false, Ps...>
-{
-  static InstType run(ArrType* arr, Ps... args)
-  {
-    (void)arr;
-    return InstType(args...);
-  }
-};
-
-template<class InstType, typename ArrType, typename... Ps>
-struct h_instantiate_by_c_array<InstType, ArrType, 0, true, Ps...>
-{
-  static InstType run(ArrType* arr, Ps... args)
-  {
-    (void)arr;
-    return InstType(args...);
-  }
-};
-
-template<class InstType, typename ArrType, std::size_t N, bool Reverse = false>
-InstType instantiate_by_c_array(ArrType* arr)
-{
-  return h_instantiate_by_c_array<InstType, ArrType, N, Reverse>::run(arr);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11META_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h
deleted file mode 100644
index 056736c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h
+++ /dev/null
@@ -1,88 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11WORKAROUNDS_H
-#define EIGEN_CXX11WORKAROUNDS_H
-
-/* COMPATIBILITY CHECKS
- * (so users of compilers that are too old get some realistic error messages)
- */
-#if defined(__INTEL_COMPILER) && (__INTEL_COMPILER < 1310)
-#error Intel Compiler only supports required C++ features since version 13.1.
-// note that most stuff in principle works with 13.0 but when combining
-// some features, at some point 13.0 will just fail with an internal assertion
-#elif defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 6))
-// G++ < 4.6 by default will continue processing the source files - even if we use #error to make
-// it error out. For this reason, we use the pragma to make sure G++ aborts at the first error
-// it sees. Unfortunately, that is still not our #error directive, but at least the output is
-// short enough the user has a chance to see that the compiler version is not sufficient for
-// the funky template mojo we use.
-#pragma GCC diagnostic error "-Wfatal-errors"
-#error GNU C++ Compiler (g++) only supports required C++ features since version 4.6.
-#endif
-
-/* Check that the compiler at least claims to support C++11. It might not be sufficient
- * because the compiler may not implement it correctly, but at least we'll know.
- * On the other hand, visual studio still doesn't claim to support C++11 although it's
- * compliant enugh for our purpose.
- */
-#if (EIGEN_COMP_CXXVER < 11)
-#if defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
-#pragma GCC diagnostic error "-Wfatal-errors"
-#endif
-#error This library needs at least a C++11 compliant compiler. If you use g++/clang, please enable the -std=c++11 compiler flag. (-std=c++0x on older versions.)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-/* std::get is only constexpr in C++14, not yet in C++11
- */
-
-
-template<std::size_t I_, class T> constexpr inline T&       array_get(std::vector<T>&       a) { return a[I_]; }
-template<std::size_t I_, class T> constexpr inline T&&      array_get(std::vector<T>&&      a) { return a[I_]; }
-template<std::size_t I_, class T> constexpr inline T const& array_get(std::vector<T> const& a) { return a[I_]; }
-
-/* Suppose you have a template of the form
- * template<typename T> struct X;
- * And you want to specialize it in such a way:
- *    template<typename S1, typename... SN> struct X<Foo<S1, SN...>> { ::: };
- *    template<>                            struct X<Foo<>>          { ::: };
- * This will work in Intel's compiler 13.0, but only to some extent in g++ 4.6, since
- * g++ can only match templates called with parameter packs if the number of template
- * arguments is not a fixed size (so inside the first specialization, referencing
- * X<Foo<Sn...>> will fail in g++). On the other hand, g++ will accept the following:
- *    template<typename S...> struct X<Foo<S...>> { ::: }:
- * as an additional (!) specialization, which will then only match the empty case.
- * But Intel's compiler 13.0 won't accept that, it will only accept the empty syntax,
- * so we have to create a workaround for this.
- */
-#if defined(__GNUC__) && !defined(__INTEL_COMPILER)
-#define EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)    mt... n
-#define EIGEN_TPL_PP_SPEC_HACK_DEFC(mt, n)   , EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)
-#define EIGEN_TPL_PP_SPEC_HACK_USE(n)        n...
-#define EIGEN_TPL_PP_SPEC_HACK_USEC(n)       , n...
-#else
-#define EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)
-#define EIGEN_TPL_PP_SPEC_HACK_DEFC(mt, n)
-#define EIGEN_TPL_PP_SPEC_HACK_USE(n)
-#define EIGEN_TPL_PP_SPEC_HACK_USEC(n)
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11WORKAROUNDS_H
-
-/*
- * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
- */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h
deleted file mode 100644
index 834b20b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h
+++ /dev/null
@@ -1,261 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EMULATE_ARRAY_H
-#define EIGEN_EMULATE_ARRAY_H
-
-
-
-// The array class is only available starting with cxx11. Emulate our own here
-// if needed. Beware, msvc still doesn't advertise itself as a c++11 compiler!
-// Moreover, CUDA doesn't support the STL containers, so we use our own instead.
-#if (__cplusplus <= 199711L && EIGEN_COMP_MSVC < 1900) || defined(EIGEN_GPUCC) || defined(EIGEN_AVOID_STL_ARRAY)
-
-namespace Eigen {
-template <typename T, size_t n> class array {
- public:
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T& operator[] (size_t index) { eigen_internal_assert(index < size()); return values[index]; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T& operator[] (size_t index) const { eigen_internal_assert(index < size()); return values[index]; }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T& at(size_t index) { eigen_assert(index < size()); return values[index]; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T& at(size_t index) const { eigen_assert(index < size()); return values[index]; }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T& front() { return values[0]; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T& front() const { return values[0]; }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T& back() { return values[n-1]; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T& back() const { return values[n-1]; }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-  static std::size_t size() { return n; }
-
-  T values[n];
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array() { }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(const T& v) {
-    EIGEN_STATIC_ASSERT(n==1, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(const T& v1, const T& v2) {
-    EIGEN_STATIC_ASSERT(n==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v1;
-    values[1] = v2;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3) {
-    EIGEN_STATIC_ASSERT(n==3, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v1;
-    values[1] = v2;
-    values[2] = v3;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3,
-                            const T& v4) {
-    EIGEN_STATIC_ASSERT(n==4, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v1;
-    values[1] = v2;
-    values[2] = v3;
-    values[3] = v4;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
-                            const T& v5) {
-    EIGEN_STATIC_ASSERT(n==5, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v1;
-    values[1] = v2;
-    values[2] = v3;
-    values[3] = v4;
-    values[4] = v5;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
-                            const T& v5, const T& v6) {
-    EIGEN_STATIC_ASSERT(n==6, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v1;
-    values[1] = v2;
-    values[2] = v3;
-    values[3] = v4;
-    values[4] = v5;
-    values[5] = v6;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
-                            const T& v5, const T& v6, const T& v7) {
-    EIGEN_STATIC_ASSERT(n==7, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v1;
-    values[1] = v2;
-    values[2] = v3;
-    values[3] = v4;
-    values[4] = v5;
-    values[5] = v6;
-    values[6] = v7;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(
-      const T& v1, const T& v2, const T& v3, const T& v4,
-      const T& v5, const T& v6, const T& v7, const T& v8) {
-    EIGEN_STATIC_ASSERT(n==8, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    values[0] = v1;
-    values[1] = v2;
-    values[2] = v3;
-    values[3] = v4;
-    values[4] = v5;
-    values[5] = v6;
-    values[6] = v7;
-    values[7] = v8;
-  }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array(std::initializer_list<T> l) {
-    eigen_assert(l.size() == n);
-    internal::smart_copy(l.begin(), l.end(), values);
-  }
-#endif
-};
-
-
-// Specialize array for zero size
-template <typename T> class array<T, 0> {
- public:
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T& operator[] (size_t) {
-    eigen_assert(false && "Can't index a zero size array");
-    return dummy;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T& operator[] (size_t) const {
-    eigen_assert(false && "Can't index a zero size array");
-    return dummy;
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T& front() {
-    eigen_assert(false && "Can't index a zero size array");
-    return dummy;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T& front() const {
-    eigen_assert(false && "Can't index a zero size array");
-    return dummy;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE T& back() {
-    eigen_assert(false && "Can't index a zero size array");
-    return dummy;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE const T& back() const {
-    eigen_assert(false && "Can't index a zero size array");
-    return dummy;
-  }
-
-  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::size_t size() { return 0; }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE array() : dummy() { }
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  EIGEN_DEVICE_FUNC array(std::initializer_list<T> l) : dummy() {
-    EIGEN_UNUSED_VARIABLE(l);
-    eigen_assert(l.size() == 0);
-  }
-#endif
-
- private:
-  T dummy;
-};
-
-// Comparison operator
-// Todo: implement !=, <, <=, >,  and >=
-template<class T, std::size_t N>
-EIGEN_DEVICE_FUNC bool operator==(const array<T,N>& lhs, const array<T,N>& rhs) {
-  for (std::size_t i = 0; i < N; ++i) {
-    if (lhs[i] != rhs[i]) {
-      return false;
-    }
-  }
-  return true;
-}
-
-
-namespace internal {
-template<std::size_t I_, class T, std::size_t N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& array_get(array<T,N>& a) {
-  return a[I_];
-}
-template<std::size_t I_, class T, std::size_t N>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& array_get(const array<T,N>& a) {
-  return a[I_];
-}
-
-template<class T, std::size_t N> struct array_size<array<T,N> > {
-  enum { value = N };
-};
-template<class T, std::size_t N> struct array_size<array<T,N>& > {
-  enum { value = N };
-};
-template<class T, std::size_t N> struct array_size<const array<T,N> > {
-  enum { value = N };
-};
-template<class T, std::size_t N> struct array_size<const array<T,N>& > {
-  enum { value = N };
-};
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-#else
-
-// The compiler supports c++11, and we're not targeting cuda: use std::array as Eigen::array
-#include <array>
-namespace Eigen {
-
-template <typename T, std::size_t N> using array = std::array<T, N>;
-
-namespace internal {
-/* std::get is only constexpr in C++14, not yet in C++11
- *     - libstdc++ from version 4.7 onwards has it nevertheless,
- *                                          so use that
- *     - libstdc++ older versions: use _M_instance directly
- *     - libc++ all versions so far: use __elems_ directly
- *     - all other libs: use std::get to be portable, but
- *                       this may not be constexpr
- */
-#if defined(__GLIBCXX__) && __GLIBCXX__ < 20120322
-#define STD_GET_ARR_HACK             a._M_instance[I_]
-#elif defined(_LIBCPP_VERSION)
-#define STD_GET_ARR_HACK             a.__elems_[I_]
-#else
-#define STD_GET_ARR_HACK             std::template get<I_, T, N>(a)
-#endif
-
-template<std::size_t I_, class T, std::size_t N> constexpr inline T&       array_get(std::array<T,N>&       a) { return (T&)       STD_GET_ARR_HACK; }
-template<std::size_t I_, class T, std::size_t N> constexpr inline T&&      array_get(std::array<T,N>&&      a) { return (T&&)      STD_GET_ARR_HACK; }
-template<std::size_t I_, class T, std::size_t N> constexpr inline T const& array_get(std::array<T,N> const& a) { return (T const&) STD_GET_ARR_HACK; }
-
-#undef STD_GET_ARR_HACK
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-#endif
-
-#endif  // EIGEN_EMULATE_ARRAY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h
deleted file mode 100644
index 277ab14..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h
+++ /dev/null
@@ -1,158 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FIXEDSIZEVECTOR_H
-#define EIGEN_FIXEDSIZEVECTOR_H
-
-namespace Eigen {
-
-/** \class MaxSizeVector
-  * \ingroup Core
-  *
-  * \brief The MaxSizeVector class.
-  *
-  * The %MaxSizeVector provides a subset of std::vector functionality.
-  *
-  * The goal is to provide basic std::vector operations when using
-  * std::vector is not an option (e.g. on GPU or when compiling using
-  * FMA/AVX, as this can cause either compilation failures or illegal
-  * instruction failures).
-  *
-  * Beware: The constructors are not API compatible with these of
-  * std::vector.
-  */
-template <typename T>
-class MaxSizeVector {
-  static const size_t alignment = EIGEN_PLAIN_ENUM_MAX(EIGEN_ALIGNOF(T), sizeof(void*));
- public:
-  // Construct a new MaxSizeVector, reserve n elements.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  explicit MaxSizeVector(size_t n)
-      : reserve_(n), size_(0),
-        data_(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {
-  }
-
-  // Construct a new MaxSizeVector, reserve and resize to n.
-  // Copy the init value to all elements.
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  MaxSizeVector(size_t n, const T& init)
-      : reserve_(n), size_(n),
-        data_(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {
-    size_t i = 0;
-    EIGEN_TRY
-    {
-      for(; i < size_; ++i) { new (&data_[i]) T(init); }
-    }
-    EIGEN_CATCH(...)
-    {
-      // Construction failed, destruct in reverse order:
-      for(; (i+1) > 0; --i) { data_[i-1].~T(); }
-      internal::handmade_aligned_free(data_);
-      EIGEN_THROW;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  ~MaxSizeVector() {
-    for (size_t i = size_; i > 0; --i) {
-      data_[i-1].~T();
-    }
-    internal::handmade_aligned_free(data_);
-  }
-
-  void resize(size_t n) {
-    eigen_assert(n <= reserve_);
-    for (; size_ < n; ++size_) {
-      new (&data_[size_]) T;
-    }
-    for (; size_ > n; --size_) {
-      data_[size_-1].~T();
-    }
-    eigen_assert(size_ == n);
-  }
-
-  // Append new elements (up to reserved size).
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void push_back(const T& t) {
-    eigen_assert(size_ < reserve_);
-    new (&data_[size_++]) T(t);
-  }
-
-  // For C++03 compatibility this only takes one argument
-  template<class X>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void emplace_back(const X& x) {
-    eigen_assert(size_ < reserve_);
-    new (&data_[size_++]) T(x);
-  }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const T& operator[] (size_t i) const {
-    eigen_assert(i < size_);
-    return data_[i];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  T& operator[] (size_t i) {
-    eigen_assert(i < size_);
-    return data_[i];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  T& back() {
-    eigen_assert(size_ > 0);
-    return data_[size_ - 1];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const T& back() const {
-    eigen_assert(size_ > 0);
-    return data_[size_ - 1];
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  void pop_back() {
-    eigen_assert(size_ > 0);
-    data_[--size_].~T();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  size_t size() const { return size_; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  bool empty() const { return size_ == 0; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  T* data() { return data_; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const T* data() const { return data_; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  T* begin() { return data_; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  T* end() { return data_ + size_; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const T* begin() const { return data_; }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  const T* end() const { return data_ + size_; }
-
- private:
-  size_t reserve_;
-  size_t size_;
-  T* data_;
-};
-
-}  // namespace Eigen
-
-#endif  // EIGEN_FIXEDSIZEVECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/EulerAngles b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/EulerAngles
deleted file mode 100644
index f8f1c5d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/EulerAngles
+++ /dev/null
@@ -1,43 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EULERANGLES_MODULE_H
-#define EIGEN_EULERANGLES_MODULE_H
-
-
-#include "../../Eigen/Core"
-#include "../../Eigen/Geometry"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-namespace Eigen {
-
-/**
-  * \defgroup EulerAngles_Module EulerAngles module
-  * \brief This module provides generic euler angles rotation.
-  *
-  * Euler angles are a way to represent 3D rotation.
-  *
-  * In order to use this module in your code, include this header:
-  * \code
-  * #include <unsupported/Eigen/EulerAngles>
-  * \endcode
-  *
-  * See \ref EulerAngles for more information.
-  *
-  */
-
-}
-
-#include "src/EulerAngles/EulerSystem.h"
-#include "src/EulerAngles/EulerAngles.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_EULERANGLES_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/FFT b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/FFT
deleted file mode 100644
index c8c311a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/FFT
+++ /dev/null
@@ -1,419 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra. 
-//
-// Copyright (C) 2009 Mark Borgerding mark a borgerding net
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_FFT_H
-#define EIGEN_FFT_H
-
-#include <complex>
-#include <vector>
-#include <map>
-#include "../../Eigen/Core"
-
-
-/**
-  * \defgroup FFT_Module Fast Fourier Transform module
-  *
-  * \code
-  * #include <unsupported/Eigen/FFT>
-  * \endcode
-  *
-  * This module provides Fast Fourier transformation, with a configurable backend
-  * implementation.
-  *
-  * The default implementation is based on kissfft. It is a small, free, and
-  * reasonably efficient default.
-  *
-  * There are currently two implementation backend:
-  *
-  * - fftw (http://www.fftw.org) : faster, GPL -- incompatible with Eigen in LGPL form, bigger code size.
-  * - MKL (http://en.wikipedia.org/wiki/Math_Kernel_Library) : fastest, commercial -- may be incompatible with Eigen in GPL form.
-  *
-  * \section FFTDesign Design
-  *
-  * The following design decisions were made concerning scaling and
-  * half-spectrum for real FFT.
-  *
-  * The intent is to facilitate generic programming and ease migrating code
-  * from  Matlab/octave.
-  * We think the default behavior of Eigen/FFT should favor correctness and
-  * generality over speed. Of course, the caller should be able to "opt-out" from this
-  * behavior and get the speed increase if they want it.
-  *
-  * 1) %Scaling:
-  * Other libraries (FFTW,IMKL,KISSFFT)  do not perform scaling, so there
-  * is a constant gain incurred after the forward&inverse transforms , so 
-  * IFFT(FFT(x)) = Kx;  this is done to avoid a vector-by-value multiply.  
-  * The downside is that algorithms that worked correctly in Matlab/octave 
-  * don't behave the same way once implemented in C++.
-  *
-  * How Eigen/FFT differs: invertible scaling is performed so IFFT( FFT(x) ) = x. 
-  *
-  * 2) Real FFT half-spectrum
-  * Other libraries use only half the frequency spectrum (plus one extra 
-  * sample for the Nyquist bin) for a real FFT, the other half is the 
-  * conjugate-symmetric of the first half.  This saves them a copy and some 
-  * memory.  The downside is the caller needs to have special logic for the 
-  * number of bins in complex vs real.
-  *
-  * How Eigen/FFT differs: The full spectrum is returned from the forward 
-  * transform.  This facilitates generic template programming by obviating 
-  * separate specializations for real vs complex.  On the inverse
-  * transform, only half the spectrum is actually used if the output type is real.
-  */
- 
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#ifdef EIGEN_FFTW_DEFAULT
-// FFTW: faster, GPL -- incompatible with Eigen in LGPL form, bigger code size
-#  include <fftw3.h>
-#  include "src/FFT/ei_fftw_impl.h"
-   namespace Eigen {
-     //template <typename T> typedef struct internal::fftw_impl  default_fft_impl; this does not work
-     template <typename T> struct default_fft_impl : public internal::fftw_impl<T> {};
-   }
-#elif defined EIGEN_MKL_DEFAULT
-// TODO 
-// intel Math Kernel Library: fastest, commercial -- may be incompatible with Eigen in GPL form
-#  include "src/FFT/ei_imklfft_impl.h"
-   namespace Eigen {
-     template <typename T> struct default_fft_impl : public internal::imklfft_impl {};
-   }
-#else
-// internal::kissfft_impl:  small, free, reasonably efficient default, derived from kissfft
-//
-# include "src/FFT/ei_kissfft_impl.h"
-  namespace Eigen {
-     template <typename T> 
-       struct default_fft_impl : public internal::kissfft_impl<T> {};
-  }
-#endif
-
-namespace Eigen {
-
- 
-// 
-template<typename T_SrcMat,typename T_FftIfc> struct fft_fwd_proxy;
-template<typename T_SrcMat,typename T_FftIfc> struct fft_inv_proxy;
-
-namespace internal {
-template<typename T_SrcMat,typename T_FftIfc>
-struct traits< fft_fwd_proxy<T_SrcMat,T_FftIfc> >
-{
-  typedef typename T_SrcMat::PlainObject ReturnType;
-};
-template<typename T_SrcMat,typename T_FftIfc>
-struct traits< fft_inv_proxy<T_SrcMat,T_FftIfc> >
-{
-  typedef typename T_SrcMat::PlainObject ReturnType;
-};
-}
-
-template<typename T_SrcMat,typename T_FftIfc> 
-struct fft_fwd_proxy
- : public ReturnByValue<fft_fwd_proxy<T_SrcMat,T_FftIfc> >
-{
-  typedef DenseIndex Index;
-
-  fft_fwd_proxy(const T_SrcMat& src,T_FftIfc & fft, Index nfft) : m_src(src),m_ifc(fft), m_nfft(nfft) {}
-
-  template<typename T_DestMat> void evalTo(T_DestMat& dst) const;
-
-  Index rows() const { return m_src.rows(); }
-  Index cols() const { return m_src.cols(); }
-protected:
-  const T_SrcMat & m_src;
-  T_FftIfc & m_ifc;
-  Index m_nfft;
-};
-
-template<typename T_SrcMat,typename T_FftIfc> 
-struct fft_inv_proxy
- : public ReturnByValue<fft_inv_proxy<T_SrcMat,T_FftIfc> >
-{
-  typedef DenseIndex Index;
-
-  fft_inv_proxy(const T_SrcMat& src,T_FftIfc & fft, Index nfft) : m_src(src),m_ifc(fft), m_nfft(nfft) {}
-
-  template<typename T_DestMat> void evalTo(T_DestMat& dst) const;
-
-  Index rows() const { return m_src.rows(); }
-  Index cols() const { return m_src.cols(); }
-protected:
-  const T_SrcMat & m_src;
-  T_FftIfc & m_ifc;
-  Index m_nfft;
-};
-
-
-template <typename T_Scalar,
-         typename T_Impl=default_fft_impl<T_Scalar> >
-class FFT
-{
-  public:
-    typedef T_Impl impl_type;
-    typedef DenseIndex Index;
-    typedef typename impl_type::Scalar Scalar;
-    typedef typename impl_type::Complex Complex;
-
-    enum Flag {
-      Default=0, // goof proof
-      Unscaled=1,
-      HalfSpectrum=2,
-      // SomeOtherSpeedOptimization=4
-      Speedy=32767
-    };
-
-    FFT( const impl_type & impl=impl_type() , Flag flags=Default ) :m_impl(impl),m_flag(flags) { }
-
-    inline
-    bool HasFlag(Flag f) const { return (m_flag & (int)f) == f;}
-
-    inline
-    void SetFlag(Flag f) { m_flag |= (int)f;}
-
-    inline
-    void ClearFlag(Flag f) { m_flag &= (~(int)f);}
-
-    inline
-    void fwd( Complex * dst, const Scalar * src, Index nfft)
-    {
-        m_impl.fwd(dst,src,static_cast<int>(nfft));
-        if ( HasFlag(HalfSpectrum) == false)
-          ReflectSpectrum(dst,nfft);
-    }
-
-    inline
-    void fwd( Complex * dst, const Complex * src, Index nfft)
-    {
-        m_impl.fwd(dst,src,static_cast<int>(nfft));
-    }
-
-    /*
-    inline 
-    void fwd2(Complex * dst, const Complex * src, int n0,int n1)
-    {
-      m_impl.fwd2(dst,src,n0,n1);
-    }
-    */
-
-    template <typename _Input>
-    inline
-    void fwd( std::vector<Complex> & dst, const std::vector<_Input> & src) 
-    {
-      if ( NumTraits<_Input>::IsComplex == 0 && HasFlag(HalfSpectrum) )
-        dst.resize( (src.size()>>1)+1); // half the bins + Nyquist bin
-      else
-        dst.resize(src.size());
-      fwd(&dst[0],&src[0],src.size());
-    }
-
-    template<typename InputDerived, typename ComplexDerived>
-    inline
-    void fwd( MatrixBase<ComplexDerived> & dst, const MatrixBase<InputDerived> & src, Index nfft=-1)
-    {
-      typedef typename ComplexDerived::Scalar dst_type;
-      typedef typename InputDerived::Scalar src_type;
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(InputDerived)
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived)
-      EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived,InputDerived) // size at compile-time
-      EIGEN_STATIC_ASSERT((internal::is_same<dst_type, Complex>::value),
-            YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-      EIGEN_STATIC_ASSERT(int(InputDerived::Flags)&int(ComplexDerived::Flags)&DirectAccessBit,
-            THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES)
-
-      if (nfft<1)
-        nfft = src.size();
-
-      if ( NumTraits< src_type >::IsComplex == 0 && HasFlag(HalfSpectrum) )
-        dst.derived().resize( (nfft>>1)+1);
-      else
-        dst.derived().resize(nfft);
-
-      if ( src.innerStride() != 1 || src.size() < nfft ) {
-        Matrix<src_type,1,Dynamic> tmp;
-        if (src.size()<nfft) {
-          tmp.setZero(nfft);
-          tmp.block(0,0,src.size(),1 ) = src;
-        }else{
-          tmp = src;
-        }
-        fwd( &dst[0],&tmp[0],nfft );
-      }else{
-        fwd( &dst[0],&src[0],nfft );
-      }
-    }
- 
-    template<typename InputDerived>
-    inline
-    fft_fwd_proxy< MatrixBase<InputDerived>, FFT<T_Scalar,T_Impl> >
-    fwd( const MatrixBase<InputDerived> & src, Index nfft=-1)
-    {
-      return fft_fwd_proxy< MatrixBase<InputDerived> ,FFT<T_Scalar,T_Impl> >( src, *this,nfft );
-    }
-
-    template<typename InputDerived>
-    inline
-    fft_inv_proxy< MatrixBase<InputDerived>, FFT<T_Scalar,T_Impl> >
-    inv( const MatrixBase<InputDerived> & src, Index nfft=-1)
-    {
-      return  fft_inv_proxy< MatrixBase<InputDerived> ,FFT<T_Scalar,T_Impl> >( src, *this,nfft );
-    }
-
-    inline
-    void inv( Complex * dst, const Complex * src, Index nfft)
-    {
-      m_impl.inv( dst,src,static_cast<int>(nfft) );
-      if ( HasFlag( Unscaled ) == false)
-        scale(dst,Scalar(1./nfft),nfft); // scale the time series
-    }
-
-    inline
-    void inv( Scalar * dst, const Complex * src, Index nfft)
-    {
-      m_impl.inv( dst,src,static_cast<int>(nfft) );
-      if ( HasFlag( Unscaled ) == false)
-        scale(dst,Scalar(1./nfft),nfft); // scale the time series
-    }
-
-    template<typename OutputDerived, typename ComplexDerived>
-    inline
-    void inv( MatrixBase<OutputDerived> & dst, const MatrixBase<ComplexDerived> & src, Index nfft=-1)
-    {
-      typedef typename ComplexDerived::Scalar src_type;
-      typedef typename ComplexDerived::RealScalar real_type;
-      typedef typename OutputDerived::Scalar dst_type;
-      const bool realfft= (NumTraits<dst_type>::IsComplex == 0);
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OutputDerived)
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived)
-      EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived,OutputDerived) // size at compile-time
-      EIGEN_STATIC_ASSERT((internal::is_same<src_type, Complex>::value),
-            YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-      EIGEN_STATIC_ASSERT(int(OutputDerived::Flags)&int(ComplexDerived::Flags)&DirectAccessBit,
-            THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES)
-
-      if (nfft<1) { //automatic FFT size determination
-        if ( realfft && HasFlag(HalfSpectrum) ) 
-          nfft = 2*(src.size()-1); //assume even fft size
-        else
-          nfft = src.size();
-      }
-      dst.derived().resize( nfft );
-
-      // check for nfft that does not fit the input data size
-      Index resize_input= ( realfft && HasFlag(HalfSpectrum) )
-        ? ( (nfft/2+1) - src.size() )
-        : ( nfft - src.size() );
-
-      if ( src.innerStride() != 1 || resize_input ) {
-        // if the vector is strided, then we need to copy it to a packed temporary
-        Matrix<src_type,1,Dynamic> tmp;
-        if ( resize_input ) {
-          size_t ncopy = (std::min)(src.size(),src.size() + resize_input);
-          tmp.setZero(src.size() + resize_input);
-          if ( realfft && HasFlag(HalfSpectrum) ) {
-            // pad at the Nyquist bin
-            tmp.head(ncopy) = src.head(ncopy);
-            tmp(ncopy-1) = real(tmp(ncopy-1)); // enforce real-only Nyquist bin
-          }else{
-            size_t nhead,ntail;
-            nhead = 1+ncopy/2-1; // range  [0:pi)
-            ntail = ncopy/2-1;   // range (-pi:0)
-            tmp.head(nhead) = src.head(nhead);
-            tmp.tail(ntail) = src.tail(ntail);
-            if (resize_input<0) { //shrinking -- create the Nyquist bin as the average of the two bins that fold into it
-              tmp(nhead) = ( src(nfft/2) + src( src.size() - nfft/2 ) )*real_type(.5);
-            }else{ // expanding -- split the old Nyquist bin into two halves
-              tmp(nhead) = src(nhead) * real_type(.5);
-              tmp(tmp.size()-nhead) = tmp(nhead);
-            }
-          }
-        }else{
-          tmp = src;
-        }
-        inv( &dst[0],&tmp[0], nfft);
-      }else{
-        inv( &dst[0],&src[0], nfft);
-      }
-    }
-
-    template <typename _Output>
-    inline
-    void inv( std::vector<_Output> & dst, const std::vector<Complex> & src,Index nfft=-1)
-    {
-      if (nfft<1)
-        nfft = ( NumTraits<_Output>::IsComplex == 0 && HasFlag(HalfSpectrum) ) ? 2*(src.size()-1) : src.size();
-      dst.resize( nfft );
-      inv( &dst[0],&src[0],nfft);
-    }
-
-
-    /*
-    // TODO: multi-dimensional FFTs
-    inline 
-    void inv2(Complex * dst, const Complex * src, int n0,int n1)
-    {
-      m_impl.inv2(dst,src,n0,n1);
-      if ( HasFlag( Unscaled ) == false)
-          scale(dst,1./(n0*n1),n0*n1);
-    }
-  */
-
-    inline
-    impl_type & impl() {return m_impl;}
-  private:
-
-    template <typename T_Data>
-    inline
-    void scale(T_Data * x,Scalar s,Index nx)
-    {
-#if 1
-      for (int k=0;k<nx;++k)
-        *x++ *= s;
-#else
-      if ( ((ptrdiff_t)x) & 15 )
-        Matrix<T_Data, Dynamic, 1>::Map(x,nx) *= s;
-      else
-        Matrix<T_Data, Dynamic, 1>::MapAligned(x,nx) *= s;
-         //Matrix<T_Data, Dynamic, Dynamic>::Map(x,nx) * s;
-#endif  
-    }
-
-    inline
-    void ReflectSpectrum(Complex * freq, Index nfft)
-    {
-      // create the implicit right-half spectrum (conjugate-mirror of the left-half)
-      Index nhbins=(nfft>>1)+1;
-      for (Index k=nhbins;k < nfft; ++k )
-        freq[k] = conj(freq[nfft-k]);
-    }
-
-    impl_type m_impl;
-    int m_flag;
-};
-
-template<typename T_SrcMat,typename T_FftIfc> 
-template<typename T_DestMat> inline 
-void fft_fwd_proxy<T_SrcMat,T_FftIfc>::evalTo(T_DestMat& dst) const
-{
-    m_ifc.fwd( dst, m_src, m_nfft);
-}
-
-template<typename T_SrcMat,typename T_FftIfc> 
-template<typename T_DestMat> inline 
-void fft_inv_proxy<T_SrcMat,T_FftIfc>::evalTo(T_DestMat& dst) const
-{
-    m_ifc.inv( dst, m_src, m_nfft);
-}
-
-}
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/IterativeSolvers b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/IterativeSolvers
deleted file mode 100644
index a3f58d6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/IterativeSolvers
+++ /dev/null
@@ -1,51 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ITERATIVE_SOLVERS_MODULE_H
-#define EIGEN_ITERATIVE_SOLVERS_MODULE_H
-
-#include "../../Eigen/Sparse"
-#include "../../Eigen/Jacobi"
-#include "../../Eigen/Householder"
-
-
-/**
-  * \defgroup IterativeLinearSolvers_Module Iterative solvers module
-  * This module aims to provide various iterative linear and non linear solver algorithms.
-  * It currently provides:
-  *  - a constrained conjugate gradient
-  *  - a Householder GMRES implementation
-  *  - an IDR(s) implementation
-  *  - a DGMRES implementation
-  *  - a MINRES implementation
-  *
-  * \code
-  * #include <unsupported/Eigen/IterativeSolvers>
-  * \endcode
-  */
-
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#ifndef EIGEN_MPL2_ONLY
-#include "src/IterativeSolvers/IterationController.h"
-#include "src/IterativeSolvers/ConstrainedConjGrad.h"
-#endif
-
-#include "src/IterativeSolvers/IncompleteLU.h"
-#include "src/IterativeSolvers/GMRES.h"
-#include "src/IterativeSolvers/DGMRES.h"
-//#include "src/IterativeSolvers/SSORPreconditioner.h"
-#include "src/IterativeSolvers/MINRES.h"
-#include "src/IterativeSolvers/IDRS.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-
-#endif // EIGEN_ITERATIVE_SOLVERS_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/KroneckerProduct b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/KroneckerProduct
deleted file mode 100644
index 5f5afb8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/KroneckerProduct
+++ /dev/null
@@ -1,36 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_KRONECKER_PRODUCT_MODULE_H
-#define EIGEN_KRONECKER_PRODUCT_MODULE_H
-
-#include "../../Eigen/Core"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#include "../../Eigen/src/SparseCore/SparseUtil.h"
-
-namespace Eigen {
-
-/**
-  * \defgroup KroneckerProduct_Module KroneckerProduct module
-  *
-  * This module contains an experimental Kronecker product implementation.
-  *
-  * \code
-  * #include <Eigen/KroneckerProduct>
-  * \endcode
-  */
-
-} // namespace Eigen
-
-#include "src/KroneckerProduct/KroneckerTensorProduct.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_KRONECKER_PRODUCT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/LevenbergMarquardt b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/LevenbergMarquardt
deleted file mode 100644
index 1090505..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/LevenbergMarquardt
+++ /dev/null
@@ -1,49 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LEVENBERGMARQUARDT_MODULE
-#define EIGEN_LEVENBERGMARQUARDT_MODULE
-
-// #include <vector>
-
-#include "../../Eigen/Core"
-#include "../../Eigen/Jacobi"
-#include "../../Eigen/QR"
-#include "NumericalDiff"
-
-#include "../../Eigen/SparseQR"
-
-/**
-  * \defgroup LevenbergMarquardt_Module Levenberg-Marquardt module
-  *
-  * \code
-  * #include </Eigen/LevenbergMarquardt>
-  * \endcode
-  *
-  * 
-  */
-
-#include "../../Eigen/SparseCore"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-#include "src/LevenbergMarquardt/LMqrsolv.h"
-#include "src/LevenbergMarquardt/LMcovar.h"
-#include "src/LevenbergMarquardt/LMpar.h"
-
-#endif
-
-#include "src/LevenbergMarquardt/LevenbergMarquardt.h"
-#include "src/LevenbergMarquardt/LMonestep.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_LEVENBERGMARQUARDT_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MPRealSupport b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MPRealSupport
deleted file mode 100644
index c4ea4ec..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MPRealSupport
+++ /dev/null
@@ -1,213 +0,0 @@
-// This file is part of a joint effort between Eigen, a lightweight C++ template library
-// for linear algebra, and MPFR C++, a C++ interface to MPFR library (http://www.holoborodko.com/pavel/)
-//
-// Copyright (C) 2010-2012 Pavel Holoborodko <pavel@holoborodko.com>
-// Copyright (C) 2010 Konstantin Holoborodko <konstantin@holoborodko.com>
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MPREALSUPPORT_MODULE_H
-#define EIGEN_MPREALSUPPORT_MODULE_H
-
-#include "../../Eigen/Core"
-#include <mpreal.h>
-
-namespace Eigen {
-  
-/**
-  * \defgroup MPRealSupport_Module MPFRC++ Support module
-  * \code
-  * #include <Eigen/MPRealSupport>
-  * \endcode
-  *
-  * This module provides support for multi precision floating point numbers
-  * via the <a href="http://www.holoborodko.com/pavel/mpfr">MPFR C++</a>
-  * library which itself is built upon <a href="http://www.mpfr.org/">MPFR</a>/<a href="http://gmplib.org/">GMP</a>.
-  *
-  * \warning MPFR C++ is licensed under the GPL.
-  *
-  * You can find a copy of MPFR C++ that is known to be compatible in the unsupported/test/mpreal folder.
-  *
-  * Here is an example:
-  *
-\code
-#include <iostream>
-#include <Eigen/MPRealSupport>
-#include <Eigen/LU>
-using namespace mpfr;
-using namespace Eigen;
-int main()
-{
-  // set precision to 256 bits (double has only 53 bits)
-  mpreal::set_default_prec(256);
-  // Declare matrix and vector types with multi-precision scalar type
-  typedef Matrix<mpreal,Dynamic,Dynamic>  MatrixXmp;
-  typedef Matrix<mpreal,Dynamic,1>        VectorXmp;
-
-  MatrixXmp A = MatrixXmp::Random(100,100);
-  VectorXmp b = VectorXmp::Random(100);
-
-  // Solve Ax=b using LU
-  VectorXmp x = A.lu().solve(b);
-  std::cout << "relative error: " << (A*x - b).norm() / b.norm() << std::endl;
-  return 0;
-}
-\endcode
-  *
-  */
-	
-  template<> struct NumTraits<mpfr::mpreal>
-    : GenericNumTraits<mpfr::mpreal>
-  {
-    enum {
-      IsInteger = 0,
-      IsSigned = 1,
-      IsComplex = 0,
-      RequireInitialization = 1,
-      ReadCost = HugeCost,
-      AddCost  = HugeCost,
-      MulCost  = HugeCost
-    };
-
-    typedef mpfr::mpreal Real;
-    typedef mpfr::mpreal NonInteger;
-    
-    static inline Real highest  (long Precision = mpfr::mpreal::get_default_prec()) { return  mpfr::maxval(Precision); }
-    static inline Real lowest   (long Precision = mpfr::mpreal::get_default_prec()) { return -mpfr::maxval(Precision); }
-
-    // Constants
-    static inline Real Pi      (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_pi(Precision);        }
-    static inline Real Euler   (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_euler(Precision);     }
-    static inline Real Log2    (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_log2(Precision);      }
-    static inline Real Catalan (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_catalan(Precision);   }
-
-    static inline Real epsilon (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::machine_epsilon(Precision); }
-    static inline Real epsilon (const Real& x)                                      { return mpfr::machine_epsilon(x); }
-
-#ifdef MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS
-    static inline int digits10 (long Precision = mpfr::mpreal::get_default_prec())  { return std::numeric_limits<Real>::digits10(Precision); }
-    static inline int digits10 (const Real& x)                                      { return std::numeric_limits<Real>::digits10(x); }
-    
-    static inline int digits ()               { return std::numeric_limits<Real>::digits(); }
-    static inline int digits (const Real& x)  { return std::numeric_limits<Real>::digits(x); }
-#endif
-
-    static inline Real dummy_precision()
-    {
-      mpfr_prec_t weak_prec = ((mpfr::mpreal::get_default_prec()-1) * 90) / 100;
-      return mpfr::machine_epsilon(weak_prec);
-    }
-  };
-
-  namespace internal {
-
-  template<> inline mpfr::mpreal random<mpfr::mpreal>()
-  {
-    return mpfr::random();
-  }
-
-  template<> inline mpfr::mpreal random<mpfr::mpreal>(const mpfr::mpreal& a, const mpfr::mpreal& b)
-  {
-    return a + (b-a) * random<mpfr::mpreal>();
-  }
-
-  inline bool isMuchSmallerThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
-  {
-    return mpfr::abs(a) <= mpfr::abs(b) * eps;
-  }
-
-  inline bool isApprox(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
-  {
-    return mpfr::isEqualFuzzy(a,b,eps);
-  }
-
-  inline bool isApproxOrLessThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
-  {
-    return a <= b || mpfr::isEqualFuzzy(a,b,eps);
-  }
-
-  template<> inline long double cast<mpfr::mpreal,long double>(const mpfr::mpreal& x)
-  { return x.toLDouble(); }
-
-  template<> inline double cast<mpfr::mpreal,double>(const mpfr::mpreal& x)
-  { return x.toDouble(); }
-
-  template<> inline long cast<mpfr::mpreal,long>(const mpfr::mpreal& x)
-  { return x.toLong(); }
-
-  template<> inline int cast<mpfr::mpreal,int>(const mpfr::mpreal& x)
-  { return int(x.toLong()); }
-
-  // Specialize GEBP kernel and traits for mpreal (no need for peeling, nor complicated stuff)
-  // This also permits to directly call mpfr's routines and avoid many temporaries produced by mpreal
-    template<>
-    class gebp_traits<mpfr::mpreal, mpfr::mpreal, false, false>
-    {
-    public:
-      typedef mpfr::mpreal ResScalar;
-      enum {
-        Vectorizable = false,
-        LhsPacketSize = 1,
-        RhsPacketSize = 1,
-        ResPacketSize = 1,
-        NumberOfRegisters = 1,
-        nr = 1,
-        mr = 1,
-        LhsProgress = 1,
-        RhsProgress = 1
-      };
-      typedef ResScalar LhsPacket;
-      typedef ResScalar RhsPacket;
-      typedef ResScalar ResPacket;
-      typedef LhsPacket LhsPacket4Packing;
-      
-    };
-
-
-
-    template<typename Index, typename DataMapper, bool ConjugateLhs, bool ConjugateRhs>
-    struct gebp_kernel<mpfr::mpreal,mpfr::mpreal,Index,DataMapper,1,1,ConjugateLhs,ConjugateRhs>
-    {
-      typedef mpfr::mpreal mpreal;
-
-      EIGEN_DONT_INLINE
-      void operator()(const DataMapper& res, const mpreal* blockA, const mpreal* blockB, 
-                      Index rows, Index depth, Index cols, const mpreal& alpha,
-                      Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0)
-      {
-        if(rows==0 || cols==0 || depth==0)
-          return;
-
-        mpreal  acc1(0,mpfr_get_prec(blockA[0].mpfr_srcptr())),
-                tmp (0,mpfr_get_prec(blockA[0].mpfr_srcptr()));
-
-        if(strideA==-1) strideA = depth;
-        if(strideB==-1) strideB = depth;
-
-        for(Index i=0; i<rows; ++i)
-        {
-          for(Index j=0; j<cols; ++j)
-          {
-            const mpreal *A = blockA + i*strideA + offsetA;
-            const mpreal *B = blockB + j*strideB + offsetB;
-            
-            acc1 = 0;
-            for(Index k=0; k<depth; k++)
-            {
-              mpfr_mul(tmp.mpfr_ptr(), A[k].mpfr_srcptr(), B[k].mpfr_srcptr(), mpreal::get_default_rnd());
-              mpfr_add(acc1.mpfr_ptr(), acc1.mpfr_ptr(), tmp.mpfr_ptr(),  mpreal::get_default_rnd());
-            }
-            
-            mpfr_mul(acc1.mpfr_ptr(), acc1.mpfr_srcptr(), alpha.mpfr_srcptr(), mpreal::get_default_rnd());
-            mpfr_add(res(i,j).mpfr_ptr(), res(i,j).mpfr_srcptr(), acc1.mpfr_srcptr(),  mpreal::get_default_rnd());
-          }
-        }
-      }
-    };
-  } // end namespace internal
-}
-
-#endif // EIGEN_MPREALSUPPORT_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MatrixFunctions b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MatrixFunctions
deleted file mode 100644
index 20c23d1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MatrixFunctions
+++ /dev/null
@@ -1,504 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Jitse Niesen <jitse@maths.leeds.ac.uk>
-// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_FUNCTIONS
-#define EIGEN_MATRIX_FUNCTIONS
-
-#include <cfloat>
-#include <list>
-
-#include "../../Eigen/Core"
-#include "../../Eigen/LU"
-#include "../../Eigen/Eigenvalues"
-
-/**
-  * \defgroup MatrixFunctions_Module Matrix functions module
-  * \brief This module aims to provide various methods for the computation of
-  * matrix functions. 
-  *
-  * To use this module, add 
-  * \code
-  * #include <unsupported/Eigen/MatrixFunctions>
-  * \endcode
-  * at the start of your source file.
-  *
-  * This module defines the following MatrixBase methods.
-  *  - \ref matrixbase_cos "MatrixBase::cos()", for computing the matrix cosine
-  *  - \ref matrixbase_cosh "MatrixBase::cosh()", for computing the matrix hyperbolic cosine
-  *  - \ref matrixbase_exp "MatrixBase::exp()", for computing the matrix exponential
-  *  - \ref matrixbase_log "MatrixBase::log()", for computing the matrix logarithm
-  *  - \ref matrixbase_pow "MatrixBase::pow()", for computing the matrix power
-  *  - \ref matrixbase_matrixfunction "MatrixBase::matrixFunction()", for computing general matrix functions
-  *  - \ref matrixbase_sin "MatrixBase::sin()", for computing the matrix sine
-  *  - \ref matrixbase_sinh "MatrixBase::sinh()", for computing the matrix hyperbolic sine
-  *  - \ref matrixbase_sqrt "MatrixBase::sqrt()", for computing the matrix square root
-  *
-  * These methods are the main entry points to this module. 
-  *
-  * %Matrix functions are defined as follows.  Suppose that \f$ f \f$
-  * is an entire function (that is, a function on the complex plane
-  * that is everywhere complex differentiable).  Then its Taylor
-  * series
-  * \f[ f(0) + f'(0) x + \frac{f''(0)}{2} x^2 + \frac{f'''(0)}{3!} x^3 + \cdots \f]
-  * converges to \f$ f(x) \f$. In this case, we can define the matrix
-  * function by the same series:
-  * \f[ f(M) = f(0) + f'(0) M + \frac{f''(0)}{2} M^2 + \frac{f'''(0)}{3!} M^3 + \cdots \f]
-  *
-  */
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#include "src/MatrixFunctions/MatrixExponential.h"
-#include "src/MatrixFunctions/MatrixFunction.h"
-#include "src/MatrixFunctions/MatrixSquareRoot.h"
-#include "src/MatrixFunctions/MatrixLogarithm.h"
-#include "src/MatrixFunctions/MatrixPower.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-
-/** 
-\page matrixbaseextra_page
-\ingroup MatrixFunctions_Module
-
-\section matrixbaseextra MatrixBase methods defined in the MatrixFunctions module
-
-The remainder of the page documents the following MatrixBase methods
-which are defined in the MatrixFunctions module.
-
-
-
-\subsection matrixbase_cos MatrixBase::cos()
-
-Compute the matrix cosine.
-
-\code
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const
-\endcode
-
-\param[in]  M  a square matrix.
-\returns  expression representing \f$ \cos(M) \f$.
-
-This function computes the matrix cosine. Use ArrayBase::cos() for computing the entry-wise cosine.
-
-The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cos().
-
-\sa \ref matrixbase_sin "sin()" for an example.
-
-
-
-\subsection matrixbase_cosh MatrixBase::cosh()
-
-Compute the matrix hyberbolic cosine.
-
-\code
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const
-\endcode
-
-\param[in]  M  a square matrix.
-\returns  expression representing \f$ \cosh(M) \f$
-
-This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cosh().
-
-\sa \ref matrixbase_sinh "sinh()" for an example.
-
-
-
-\subsection matrixbase_exp MatrixBase::exp()
-
-Compute the matrix exponential.
-
-\code
-const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const
-\endcode
-
-\param[in]  M  matrix whose exponential is to be computed.
-\returns    expression representing the matrix exponential of \p M.
-
-The matrix exponential of \f$ M \f$ is defined by
-\f[ \exp(M) = \sum_{k=0}^\infty \frac{M^k}{k!}. \f]
-The matrix exponential can be used to solve linear ordinary
-differential equations: the solution of \f$ y' = My \f$ with the
-initial condition \f$ y(0) = y_0 \f$ is given by
-\f$ y(t) = \exp(M) y_0 \f$.
-
-The matrix exponential is different from applying the exp function to all the entries in the matrix.
-Use ArrayBase::exp() if you want to do the latter.
-
-The cost of the computation is approximately \f$ 20 n^3 \f$ for
-matrices of size \f$ n \f$. The number 20 depends weakly on the
-norm of the matrix.
-
-The matrix exponential is computed using the scaling-and-squaring
-method combined with Pad&eacute; approximation. The matrix is first
-rescaled, then the exponential of the reduced matrix is computed
-approximant, and then the rescaling is undone by repeated
-squaring. The degree of the Pad&eacute; approximant is chosen such
-that the approximation error is less than the round-off
-error. However, errors may accumulate during the squaring phase.
-
-Details of the algorithm can be found in: Nicholas J. Higham, "The
-scaling and squaring method for the matrix exponential revisited,"
-<em>SIAM J. %Matrix Anal. Applic.</em>, <b>26</b>:1179&ndash;1193,
-2005.
-
-Example: The following program checks that
-\f[ \exp \left[ \begin{array}{ccc}
-      0 & \frac14\pi & 0 \\
-      -\frac14\pi & 0 & 0 \\
-      0 & 0 & 0
-    \end{array} \right] = \left[ \begin{array}{ccc}
-      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
-      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
-      0 & 0 & 1
-    \end{array} \right]. \f]
-This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
-the z-axis.
-
-\include MatrixExponential.cpp
-Output: \verbinclude MatrixExponential.out
-
-\note \p M has to be a matrix of \c float, \c double, `long double`
-\c complex<float>, \c complex<double>, or `complex<long double>` .
-
-
-\subsection matrixbase_log MatrixBase::log()
-
-Compute the matrix logarithm.
-
-\code
-const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const
-\endcode
-
-\param[in]  M  invertible matrix whose logarithm is to be computed.
-\returns    expression representing the matrix logarithm root of \p M.
-
-The matrix logarithm of \f$ M \f$ is a matrix \f$ X \f$ such that 
-\f$ \exp(X) = M \f$ where exp denotes the matrix exponential. As for
-the scalar logarithm, the equation \f$ \exp(X) = M \f$ may have
-multiple solutions; this function returns a matrix whose eigenvalues
-have imaginary part in the interval \f$ (-\pi,\pi] \f$.
-
-The matrix logarithm is different from applying the log function to all the entries in the matrix.
-Use ArrayBase::log() if you want to do the latter.
-
-In the real case, the matrix \f$ M \f$ should be invertible and
-it should have no eigenvalues which are real and negative (pairs of
-complex conjugate eigenvalues are allowed). In the complex case, it
-only needs to be invertible.
-
-This function computes the matrix logarithm using the Schur-Parlett
-algorithm as implemented by MatrixBase::matrixFunction(). The
-logarithm of an atomic block is computed by MatrixLogarithmAtomic,
-which uses direct computation for 1-by-1 and 2-by-2 blocks and an
-inverse scaling-and-squaring algorithm for bigger blocks, with the
-square roots computed by MatrixBase::sqrt().
-
-Details of the algorithm can be found in Section 11.6.2 of:
-Nicholas J. Higham,
-<em>Functions of Matrices: Theory and Computation</em>,
-SIAM 2008. ISBN 978-0-898716-46-7.
-
-Example: The following program checks that
-\f[ \log \left[ \begin{array}{ccc} 
-      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
-      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
-      0 & 0 & 1
-    \end{array} \right] = \left[ \begin{array}{ccc}
-      0 & \frac14\pi & 0 \\ 
-      -\frac14\pi & 0 & 0 \\
-      0 & 0 & 0 
-    \end{array} \right]. \f]
-This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
-the z-axis. This is the inverse of the example used in the
-documentation of \ref matrixbase_exp "exp()".
-
-\include MatrixLogarithm.cpp
-Output: \verbinclude MatrixLogarithm.out
-
-\note \p M has to be a matrix of \c float, \c double, `long
-double`, \c complex<float>, \c complex<double>, or `complex<long double>`.
-
-\sa MatrixBase::exp(), MatrixBase::matrixFunction(), 
-    class MatrixLogarithmAtomic, MatrixBase::sqrt().
-
-
-\subsection matrixbase_pow MatrixBase::pow()
-
-Compute the matrix raised to arbitrary real power.
-
-\code
-const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(RealScalar p) const
-\endcode
-
-\param[in]  M  base of the matrix power, should be a square matrix.
-\param[in]  p  exponent of the matrix power.
-
-The matrix power \f$ M^p \f$ is defined as \f$ \exp(p \log(M)) \f$,
-where exp denotes the matrix exponential, and log denotes the matrix
-logarithm. This is different from raising all the entries in the matrix
-to the p-th power. Use ArrayBase::pow() if you want to do the latter.
-
-If \p p is complex, the scalar type of \p M should be the type of \p
-p . \f$ M^p \f$ simply evaluates into \f$ \exp(p \log(M)) \f$.
-Therefore, the matrix \f$ M \f$ should meet the conditions to be an
-argument of matrix logarithm.
-
-If \p p is real, it is casted into the real scalar type of \p M. Then
-this function computes the matrix power using the Schur-Pad&eacute;
-algorithm as implemented by class MatrixPower. The exponent is split
-into integral part and fractional part, where the fractional part is
-in the interval \f$ (-1, 1) \f$. The main diagonal and the first
-super-diagonal is directly computed.
-
-If \p M is singular with a semisimple zero eigenvalue and \p p is
-positive, the Schur factor \f$ T \f$ is reordered with Givens
-rotations, i.e.
-
-\f[ T = \left[ \begin{array}{cc}
-      T_1 & T_2 \\
-      0   & 0
-    \end{array} \right] \f]
-
-where \f$ T_1 \f$ is invertible. Then \f$ T^p \f$ is given by
-
-\f[ T^p = \left[ \begin{array}{cc}
-      T_1^p & T_1^{-1} T_1^p T_2 \\
-      0     & 0
-    \end{array}. \right] \f]
-
-\warning Fractional power of a matrix with a non-semisimple zero
-eigenvalue is not well-defined. We introduce an assertion failure
-against inaccurate result, e.g. \code
-#include <unsupported/Eigen/MatrixFunctions>
-#include <iostream>
-
-int main()
-{
-  Eigen::Matrix4d A;
-  A << 0, 0, 2, 3,
-       0, 0, 4, 5,
-       0, 0, 6, 7,
-       0, 0, 8, 9;
-  std::cout << A.pow(0.37) << std::endl;
-  
-  // The 1 makes eigenvalue 0 non-semisimple.
-  A.coeffRef(0, 1) = 1;
-
-  // This fails if EIGEN_NO_DEBUG is undefined.
-  std::cout << A.pow(0.37) << std::endl;
-
-  return 0;
-}
-\endcode
-
-Details of the algorithm can be found in: Nicholas J. Higham and
-Lijing Lin, "A Schur-Pad&eacute; algorithm for fractional powers of a
-matrix," <em>SIAM J. %Matrix Anal. Applic.</em>,
-<b>32(3)</b>:1056&ndash;1078, 2011.
-
-Example: The following program checks that
-\f[ \left[ \begin{array}{ccc}
-      \cos1 & -\sin1 & 0 \\
-      \sin1 & \cos1 & 0 \\
-      0 & 0 & 1
-    \end{array} \right]^{\frac14\pi} = \left[ \begin{array}{ccc}
-      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
-      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
-      0 & 0 & 1
-    \end{array} \right]. \f]
-This corresponds to \f$ \frac14\pi \f$ rotations of 1 radian around
-the z-axis.
-
-\include MatrixPower.cpp
-Output: \verbinclude MatrixPower.out
-
-MatrixBase::pow() is user-friendly. However, there are some
-circumstances under which you should use class MatrixPower directly.
-MatrixPower can save the result of Schur decomposition, so it's
-better for computing various powers for the same matrix.
-
-Example:
-\include MatrixPower_optimal.cpp
-Output: \verbinclude MatrixPower_optimal.out
-
-\note \p M has to be a matrix of \c float, \c double, `long
-double`, \c complex<float>, \c complex<double>, or
-\c complex<long double> .
-
-\sa MatrixBase::exp(), MatrixBase::log(), class MatrixPower.
-
-
-\subsection matrixbase_matrixfunction MatrixBase::matrixFunction()
-
-Compute a matrix function.
-
-\code
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(typename internal::stem_function<typename internal::traits<Derived>::Scalar>::type f) const
-\endcode
-
-\param[in]  M  argument of matrix function, should be a square matrix.
-\param[in]  f  an entire function; \c f(x,n) should compute the n-th
-derivative of f at x.
-\returns  expression representing \p f applied to \p M.
-
-Suppose that \p M is a matrix whose entries have type \c Scalar. 
-Then, the second argument, \p f, should be a function with prototype
-\code 
-ComplexScalar f(ComplexScalar, int) 
-\endcode
-where \c ComplexScalar = \c std::complex<Scalar> if \c Scalar is
-real (e.g., \c float or \c double) and \c ComplexScalar =
-\c Scalar if \c Scalar is complex. The return value of \c f(x,n)
-should be \f$ f^{(n)}(x) \f$, the n-th derivative of f at x.
-
-This routine uses the algorithm described in:
-Philip Davies and Nicholas J. Higham, 
-"A Schur-Parlett algorithm for computing matrix functions", 
-<em>SIAM J. %Matrix Anal. Applic.</em>, <b>25</b>:464&ndash;485, 2003.
-
-The actual work is done by the MatrixFunction class.
-
-Example: The following program checks that
-\f[ \exp \left[ \begin{array}{ccc} 
-      0 & \frac14\pi & 0 \\ 
-      -\frac14\pi & 0 & 0 \\
-      0 & 0 & 0 
-    \end{array} \right] = \left[ \begin{array}{ccc}
-      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
-      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
-      0 & 0 & 1
-    \end{array} \right]. \f]
-This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
-the z-axis. This is the same example as used in the documentation
-of \ref matrixbase_exp "exp()".
-
-\include MatrixFunction.cpp
-Output: \verbinclude MatrixFunction.out
-
-Note that the function \c expfn is defined for complex numbers 
-\c x, even though the matrix \c A is over the reals. Instead of
-\c expfn, we could also have used StdStemFunctions::exp:
-\code
-A.matrixFunction(StdStemFunctions<std::complex<double> >::exp, &B);
-\endcode
-
-
-
-\subsection matrixbase_sin MatrixBase::sin()
-
-Compute the matrix sine.
-
-\code
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const
-\endcode
-
-\param[in]  M  a square matrix.
-\returns  expression representing \f$ \sin(M) \f$.
-
-This function computes the matrix sine. Use ArrayBase::sin() for computing the entry-wise sine.
-
-The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sin().
-
-Example: \include MatrixSine.cpp
-Output: \verbinclude MatrixSine.out
-
-
-
-\subsection matrixbase_sinh MatrixBase::sinh()
-
-Compute the matrix hyperbolic sine.
-
-\code
-MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const
-\endcode
-
-\param[in]  M  a square matrix.
-\returns  expression representing \f$ \sinh(M) \f$
-
-This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sinh().
-
-Example: \include MatrixSinh.cpp
-Output: \verbinclude MatrixSinh.out
-
-
-\subsection matrixbase_sqrt MatrixBase::sqrt()
-
-Compute the matrix square root.
-
-\code
-const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const
-\endcode
-
-\param[in]  M  invertible matrix whose square root is to be computed.
-\returns    expression representing the matrix square root of \p M.
-
-The matrix square root of \f$ M \f$ is the matrix \f$ M^{1/2} \f$
-whose square is the original matrix; so if \f$ S = M^{1/2} \f$ then
-\f$ S^2 = M \f$. This is different from taking the square root of all
-the entries in the matrix; use ArrayBase::sqrt() if you want to do the
-latter.
-
-In the <b>real case</b>, the matrix \f$ M \f$ should be invertible and
-it should have no eigenvalues which are real and negative (pairs of
-complex conjugate eigenvalues are allowed). In that case, the matrix
-has a square root which is also real, and this is the square root
-computed by this function. 
-
-The matrix square root is computed by first reducing the matrix to
-quasi-triangular form with the real Schur decomposition. The square
-root of the quasi-triangular matrix can then be computed directly. The
-cost is approximately \f$ 25 n^3 \f$ real flops for the real Schur
-decomposition and \f$ 3\frac13 n^3 \f$ real flops for the remainder
-(though the computation time in practice is likely more than this
-indicates).
-
-Details of the algorithm can be found in: Nicholas J. Highan,
-"Computing real square roots of a real matrix", <em>Linear Algebra
-Appl.</em>, 88/89:405&ndash;430, 1987.
-
-If the matrix is <b>positive-definite symmetric</b>, then the square
-root is also positive-definite symmetric. In this case, it is best to
-use SelfAdjointEigenSolver::operatorSqrt() to compute it.
-
-In the <b>complex case</b>, the matrix \f$ M \f$ should be invertible;
-this is a restriction of the algorithm. The square root computed by
-this algorithm is the one whose eigenvalues have an argument in the
-interval \f$ (-\frac12\pi, \frac12\pi] \f$. This is the usual branch
-cut.
-
-The computation is the same as in the real case, except that the
-complex Schur decomposition is used to reduce the matrix to a
-triangular matrix. The theoretical cost is the same. Details are in:
-&Aring;ke Bj&ouml;rck and Sven Hammarling, "A Schur method for the
-square root of a matrix", <em>Linear Algebra Appl.</em>,
-52/53:127&ndash;140, 1983.
-
-Example: The following program checks that the square root of
-\f[ \left[ \begin{array}{cc} 
-              \cos(\frac13\pi) & -\sin(\frac13\pi) \\
-              \sin(\frac13\pi) & \cos(\frac13\pi)
-    \end{array} \right], \f]
-corresponding to a rotation over 60 degrees, is a rotation over 30 degrees:
-\f[ \left[ \begin{array}{cc} 
-              \cos(\frac16\pi) & -\sin(\frac16\pi) \\
-              \sin(\frac16\pi) & \cos(\frac16\pi)
-    \end{array} \right]. \f]
-
-\include MatrixSquareRoot.cpp
-Output: \verbinclude MatrixSquareRoot.out
-
-\sa class RealSchur, class ComplexSchur, class MatrixSquareRoot,
-    SelfAdjointEigenSolver::operatorSqrt().
-
-*/
-
-#endif // EIGEN_MATRIX_FUNCTIONS
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MoreVectorization b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MoreVectorization
deleted file mode 100644
index 7662b47..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/MoreVectorization
+++ /dev/null
@@ -1,24 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MOREVECTORIZATION_MODULE_H
-#define EIGEN_MOREVECTORIZATION_MODULE_H
-
-#include "../../Eigen/Core"
-
-namespace Eigen {
-
-/**
-  * \defgroup MoreVectorization More vectorization module
-  */
-
-}
-
-#include "src/MoreVectorization/MathFunctions.h"
-
-#endif // EIGEN_MOREVECTORIZATION_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/NonLinearOptimization b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/NonLinearOptimization
deleted file mode 100644
index 961f192..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/NonLinearOptimization
+++ /dev/null
@@ -1,140 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NONLINEAROPTIMIZATION_MODULE
-#define EIGEN_NONLINEAROPTIMIZATION_MODULE
-
-#include <vector>
-
-#include "../../Eigen/Core"
-#include "../../Eigen/Jacobi"
-#include "../../Eigen/QR"
-#include "NumericalDiff"
-
-/**
-  * \defgroup NonLinearOptimization_Module Non linear optimization module
-  *
-  * \code
-  * #include <unsupported/Eigen/NonLinearOptimization>
-  * \endcode
-  *
-  * This module provides implementation of two important algorithms in non linear
-  * optimization. In both cases, we consider a system of non linear functions. Of
-  * course, this should work, and even work very well if those functions are
-  * actually linear. But if this is so, you should probably better use other
-  * methods more fitted to this special case.
-  *
-  * One algorithm allows to find a least-squares solution of such a system
-  * (Levenberg-Marquardt algorithm) and the second one is used to find 
-  * a zero for the system (Powell hybrid "dogleg" method).
-  *
-  * This code is a port of minpack (http://en.wikipedia.org/wiki/MINPACK).
-  * Minpack is a very famous, old, robust and well renowned package, written in
-  * fortran. Those implementations have been carefully tuned, tested, and used
-  * for several decades.
-  *
-  * The original fortran code was automatically translated using f2c (http://en.wikipedia.org/wiki/F2c) in C,
-  * then c++, and then cleaned by several different authors.
-  * The last one of those cleanings being our starting point : 
-  * http://devernay.free.fr/hacks/cminpack.html
-  * 
-  * Finally, we ported this code to Eigen, creating classes and API
-  * coherent with Eigen. When possible, we switched to Eigen
-  * implementation, such as most linear algebra (vectors, matrices, stable norms).
-  *
-  * Doing so, we were very careful to check the tests we setup at the very
-  * beginning, which ensure that the same results are found.
-  *
-  * \section Tests Tests
-  * 
-  * The tests are placed in the file unsupported/test/NonLinear.cpp.
-  * 
-  * There are two kinds of tests : those that come from examples bundled with cminpack.
-  * They guaranty we get the same results as the original algorithms (value for 'x',
-  * for the number of evaluations of the function, and for the number of evaluations
-  * of the Jacobian if ever).
-  * 
-  * Other tests were added by myself at the very beginning of the 
-  * process and check the results for Levenberg-Marquardt using the reference data 
-  * on http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml. Since then i've 
-  * carefully checked that the same results were obtained when modifying the
-  * code. Please note that we do not always get the exact same decimals as they do,
-  * but this is ok : they use 128bits float, and we do the tests using the C type 'double',
-  * which is 64 bits on most platforms (x86 and amd64, at least).
-  * I've performed those tests on several other implementations of Levenberg-Marquardt, and
-  * (c)minpack performs VERY well compared to those, both in accuracy and speed.
-  * 
-  * The documentation for running the tests is on the wiki
-  * http://eigen.tuxfamily.org/index.php?title=Tests
-  * 
-  * \section API API: overview of methods
-  * 
-  * Both algorithms needs a functor computing the Jacobian. It can be computed by
-  * hand, using auto-differentiation (see \ref AutoDiff_Module), or using numerical
-  * differences (see \ref NumericalDiff_Module). For instance:
-  *\code
-  * MyFunc func;
-  * NumericalDiff<MyFunc> func_with_num_diff(func);
-  * LevenbergMarquardt<NumericalDiff<MyFunc> > lm(func_with_num_diff);
-  * \endcode
-  * For HybridNonLinearSolver, the method solveNumericalDiff() does the above wrapping for
-  * you.
-  * 
-  * The methods LevenbergMarquardt.lmder1()/lmdif1()/lmstr1() and 
-  * HybridNonLinearSolver.hybrj1()/hybrd1() are specific methods from the original 
-  * minpack package that you probably should NOT use until you are porting a code that
-  * was previously using minpack. They just define a 'simple' API with default values 
-  * for some parameters.
-  * 
-  * All algorithms are provided using two APIs :
-  *     - one where the user inits the algorithm, and uses '*OneStep()' as much as he wants : 
-  * this way the caller have control over the steps
-  *     - one where the user just calls a method (optimize() or solve()) which will 
-  * handle the loop: init + loop until a stop condition is met. Those are provided for
-  *  convenience.
-  * 
-  * As an example, the method LevenbergMarquardt::minimize() is 
-  * implemented as follow: 
-  * \code
-  * Status LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType  &x, const int mode)
-  * {
-  *     Status status = minimizeInit(x, mode);
-  *     do {
-  *         status = minimizeOneStep(x, mode);
-  *     } while (status==Running);
-  *     return status;
-  * }
-  * \endcode
-  * 
-  * \section examples Examples
-  * 
-  * The easiest way to understand how to use this module is by looking at the many examples in the file
-  * unsupported/test/NonLinearOptimization.cpp.
-  */
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-#include "src/NonLinearOptimization/qrsolv.h"
-#include "src/NonLinearOptimization/r1updt.h"
-#include "src/NonLinearOptimization/r1mpyq.h"
-#include "src/NonLinearOptimization/rwupdt.h"
-#include "src/NonLinearOptimization/fdjac1.h"
-#include "src/NonLinearOptimization/lmpar.h"
-#include "src/NonLinearOptimization/dogleg.h"
-#include "src/NonLinearOptimization/covar.h"
-
-#include "src/NonLinearOptimization/chkder.h"
-
-#endif
-
-#include "src/NonLinearOptimization/HybridNonLinearSolver.h"
-#include "src/NonLinearOptimization/LevenbergMarquardt.h"
-
-
-#endif // EIGEN_NONLINEAROPTIMIZATION_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/NumericalDiff b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/NumericalDiff
deleted file mode 100644
index 0668f96..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/NumericalDiff
+++ /dev/null
@@ -1,56 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NUMERICALDIFF_MODULE
-#define EIGEN_NUMERICALDIFF_MODULE
-
-#include "../../Eigen/Core"
-
-namespace Eigen {
-
-/**
-  * \defgroup NumericalDiff_Module Numerical differentiation module
-  *
-  * \code
-  * #include <unsupported/Eigen/NumericalDiff>
-  * \endcode
-  *
-  * See http://en.wikipedia.org/wiki/Numerical_differentiation
-  *
-  * Warning : this should NOT be confused with automatic differentiation, which
-  * is a different method and has its own module in Eigen : \ref
-  * AutoDiff_Module.
-  *
-  * Currently only "Forward" and "Central" schemes are implemented. Those
-  * are basic methods, and there exist some more elaborated way of
-  * computing such approximates. They are implemented using both
-  * proprietary and free software, and usually requires linking to an
-  * external library. It is very easy for you to write a functor
-  * using such software, and the purpose is quite orthogonal to what we
-  * want to achieve with Eigen.
-  *
-  * This is why we will not provide wrappers for every great numerical
-  * differentiation software that exist, but should rather stick with those
-  * basic ones, that still are useful for testing.
-  *
-  * Also, the \ref NonLinearOptimization_Module needs this in order to
-  * provide full features compatibility with the original (c)minpack
-  * package.
-  *
-  */
-}
-
-//@{
-
-#include "src/NumericalDiff/NumericalDiff.h"
-
-//@}
-
-
-#endif // EIGEN_NUMERICALDIFF_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/OpenGLSupport b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/OpenGLSupport
deleted file mode 100644
index f8c2130..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/OpenGLSupport
+++ /dev/null
@@ -1,322 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_OPENGL_MODULE
-#define EIGEN_OPENGL_MODULE
-
-#include "../../Eigen/Geometry"
-
-#if defined(__APPLE_CC__)
-  #include <OpenGL/gl.h>
-#else
-  #include <GL/gl.h>
-#endif
-
-namespace Eigen {
-
-/**
-  * \defgroup OpenGLSUpport_Module OpenGL Support module
-  *
-  * This module provides wrapper functions for a couple of OpenGL functions
-  * which simplify the way to pass Eigen's object to openGL.
-  * Here is an example:
-  * 
-  * \code
-  * // You need to add path_to_eigen/unsupported to your include path.
-  * #include <Eigen/OpenGLSupport>
-  * // ...
-  * Vector3f x, y;
-  * Matrix3f rot;
-  * 
-  * glVertex(y + x * rot);
-  * 
-  * Quaternion q;
-  * glRotate(q);
-  * 
-  * // ...
-  * \endcode
-  *
-  */
-//@{
-
-#define EIGEN_GL_FUNC_DECLARATION(FUNC)                                                                             \
-namespace internal {                                                                                                \
-  template< typename XprType,                                                                                       \
-            typename Scalar = typename XprType::Scalar,                                                             \
-            int Rows = XprType::RowsAtCompileTime,                                                                  \
-            int Cols = XprType::ColsAtCompileTime,                                                                  \
-            bool IsGLCompatible = bool(internal::evaluator<XprType>::Flags&LinearAccessBit)                         \
-                              && bool(XprType::Flags&DirectAccessBit)                                               \
-                              && (XprType::IsVectorAtCompileTime || (XprType::Flags&RowMajorBit)==0)>               \
-  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl);                                                                      \
-                                                                                                                    \
-  template<typename XprType, typename Scalar, int Rows, int Cols>                                                   \
-  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType,Scalar,Rows,Cols,false> {                                     \
-    inline static void run(const XprType& p) {                                                                      \
-      EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<typename plain_matrix_type_column_major<XprType>::type>::run(p); }       \
-  };                                                                                                                \
-}                                                                                                                   \
-                                                                                                                    \
-template<typename Derived> inline void FUNC(const Eigen::DenseBase<Derived>& p) {                                   \
-  EIGEN_CAT(EIGEN_CAT(internal::gl_,FUNC),_impl)<Derived>::run(p.derived());                                        \
-}
-
-
-#define EIGEN_GL_FUNC_SPECIALIZATION_MAT(FUNC,SCALAR,ROWS,COLS,SUFFIX)                                              \
-namespace internal {                                                                                                \
-  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, ROWS, COLS, true> {      \
-    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }                                            \
-  };                                                                                                                \
-}
-
-  
-#define EIGEN_GL_FUNC_SPECIALIZATION_VEC(FUNC,SCALAR,SIZE,SUFFIX)                                                   \
-namespace internal {                                                                                                \
-  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, SIZE, 1, true> {         \
-    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }                                            \
-  };                                                                                                                \
-  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, 1, SIZE, true> {         \
-    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }                                            \
-  };                                                                                                                \
-}
-
-  
-EIGEN_GL_FUNC_DECLARATION       (glVertex)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,int,    2,2iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,short,  2,2sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,float,  2,2fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,double, 2,2dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,int,    3,3iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,short,  3,3sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,float,  3,3fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,double, 3,3dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,int,    4,4iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,short,  4,4sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,float,  4,4fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,double, 4,4dv)
-
-EIGEN_GL_FUNC_DECLARATION       (glTexCoord)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,int,    2,2iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,short,  2,2sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,float,  2,2fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,double, 2,2dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,int,    3,3iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,short,  3,3sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,float,  3,3fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,double, 3,3dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,int,    4,4iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,short,  4,4sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,float,  4,4fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,double, 4,4dv)
-
-EIGEN_GL_FUNC_DECLARATION       (glColor)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,int,    2,2iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,short,  2,2sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,float,  2,2fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,double, 2,2dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,int,    3,3iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,short,  3,3sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,float,  3,3fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,double, 3,3dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,int,    4,4iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,short,  4,4sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,float,  4,4fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,double, 4,4dv)
-
-EIGEN_GL_FUNC_DECLARATION       (glNormal)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,int,    3,3iv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,short,  3,3sv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,float,  3,3fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,double, 3,3dv)
-
-inline void glScale2fv(const float*  v) { glScalef(v[0], v[1], 1.f);  }
-inline void glScale2dv(const double* v) { glScaled(v[0], v[1], 1.0);  }
-inline void glScale3fv(const float*  v) { glScalef(v[0], v[1], v[2]); }
-inline void glScale3dv(const double* v) { glScaled(v[0], v[1], v[2]); }
-
-EIGEN_GL_FUNC_DECLARATION       (glScale)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,float,  2,2fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,double, 2,2dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,float,  3,3fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,double, 3,3dv)
-
-template<typename Scalar> void glScale(const UniformScaling<Scalar>& s)  { glScale(Matrix<Scalar,3,1>::Constant(s.factor())); }
-
-inline void glTranslate2fv(const float*  v) { glTranslatef(v[0], v[1], 0.f);  }
-inline void glTranslate2dv(const double* v) { glTranslated(v[0], v[1], 0.0);  }
-inline void glTranslate3fv(const float*  v) { glTranslatef(v[0], v[1], v[2]); }
-inline void glTranslate3dv(const double* v) { glTranslated(v[0], v[1], v[2]); }
-
-EIGEN_GL_FUNC_DECLARATION       (glTranslate)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,float,  2,2fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,double, 2,2dv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,float,  3,3fv)
-EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,double, 3,3dv)
-
-template<typename Scalar> void glTranslate(const Translation<Scalar,2>& t)  { glTranslate(t.vector()); }
-template<typename Scalar> void glTranslate(const Translation<Scalar,3>& t)  { glTranslate(t.vector()); }
-
-EIGEN_GL_FUNC_DECLARATION       (glMultMatrix)
-EIGEN_GL_FUNC_SPECIALIZATION_MAT(glMultMatrix,float,  4,4,f)
-EIGEN_GL_FUNC_SPECIALIZATION_MAT(glMultMatrix,double, 4,4,d)
-
-template<typename Scalar> void glMultMatrix(const Transform<Scalar,3,Affine>& t)        { glMultMatrix(t.matrix()); }
-template<typename Scalar> void glMultMatrix(const Transform<Scalar,3,Projective>& t)    { glMultMatrix(t.matrix()); }
-template<typename Scalar> void glMultMatrix(const Transform<Scalar,3,AffineCompact>& t) { glMultMatrix(Transform<Scalar,3,Affine>(t).matrix()); }
-
-EIGEN_GL_FUNC_DECLARATION       (glLoadMatrix)
-EIGEN_GL_FUNC_SPECIALIZATION_MAT(glLoadMatrix,float,  4,4,f)
-EIGEN_GL_FUNC_SPECIALIZATION_MAT(glLoadMatrix,double, 4,4,d)
-
-template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Affine>& t)        { glLoadMatrix(t.matrix()); }
-template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Projective>& t)    { glLoadMatrix(t.matrix()); }
-template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,AffineCompact>& t) { glLoadMatrix(Transform<Scalar,3,Affine>(t).matrix()); }
-
-inline void glRotate(const Rotation2D<float>& rot)
-{
-  glRotatef(rot.angle()*180.f/float(EIGEN_PI), 0.f, 0.f, 1.f);
-}
-inline void glRotate(const Rotation2D<double>& rot)
-{
-  glRotated(rot.angle()*180.0/double(EIGEN_PI), 0.0, 0.0, 1.0);
-}
-
-template<typename Derived> void glRotate(const RotationBase<Derived,3>& rot)
-{  
-  Transform<typename Derived::Scalar,3,Projective> tr(rot);
-  glMultMatrix(tr.matrix());
-}
-
-#define EIGEN_GL_MAKE_CONST_const const
-#define EIGEN_GL_MAKE_CONST__ 
-#define EIGEN_GL_EVAL(X) X
-
-#define EIGEN_GL_FUNC1_DECLARATION(FUNC,ARG1,CONST)                                                                             \
-namespace internal {                                                                                                            \
-  template< typename XprType,                                                                                                   \
-            typename Scalar = typename XprType::Scalar,                                                                         \
-            int Rows = XprType::RowsAtCompileTime,                                                                              \
-            int Cols = XprType::ColsAtCompileTime,                                                                              \
-            bool IsGLCompatible = bool(internal::evaluator<XprType>::Flags&LinearAccessBit)                                     \
-                              && bool(XprType::Flags&DirectAccessBit)                                                           \
-                              && (XprType::IsVectorAtCompileTime || (XprType::Flags&RowMajorBit)==0)>                           \
-  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl);                                                                                  \
-                                                                                                                                \
-  template<typename XprType, typename Scalar, int Rows, int Cols>                                                               \
-  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType,Scalar,Rows,Cols,false> {                                                 \
-    inline static void run(ARG1 a,EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) {                                      \
-      EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<typename plain_matrix_type_column_major<XprType>::type>::run(a,p); }                 \
-  };                                                                                                                            \
-}                                                                                                                               \
-                                                                                                                                \
-template<typename Derived> inline void FUNC(ARG1 a,EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) Eigen::DenseBase<Derived>& p) {   \
-  EIGEN_CAT(EIGEN_CAT(internal::gl_,FUNC),_impl)<Derived>::run(a,p.derived());                                                  \
-}
-
-
-#define EIGEN_GL_FUNC1_SPECIALIZATION_MAT(FUNC,ARG1,CONST,SCALAR,ROWS,COLS,SUFFIX)                                              \
-namespace internal {                                                                                                            \
-  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, ROWS, COLS, true> {                  \
-    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { FUNC##SUFFIX(a,p.data()); }         \
-  }; \
-}
-
-  
-#define EIGEN_GL_FUNC1_SPECIALIZATION_VEC(FUNC,ARG1,CONST,SCALAR,SIZE,SUFFIX)                                                   \
-namespace internal {                                                                                                            \
-  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, SIZE, 1, true> {                     \
-    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { FUNC##SUFFIX(a,p.data()); }         \
-  };                                                                                                                            \
-  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, 1, SIZE, true> {                     \
-    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { FUNC##SUFFIX(a,p.data()); }         \
-  };                                                                                                                            \
-}
-
-EIGEN_GL_FUNC1_DECLARATION       (glGet,GLenum,_)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet,GLenum,_,float,  4,4,Floatv)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet,GLenum,_,double, 4,4,Doublev)
-
-// glUniform API
-
-#ifdef GL_VERSION_2_0
-
-inline void glUniform2fv_ei  (GLint loc, const float* v)         { glUniform2fv(loc,1,v); }
-inline void glUniform2iv_ei  (GLint loc, const int* v)           { glUniform2iv(loc,1,v); }
-
-inline void glUniform3fv_ei  (GLint loc, const float* v)         { glUniform3fv(loc,1,v); }
-inline void glUniform3iv_ei  (GLint loc, const int* v)           { glUniform3iv(loc,1,v); }
-
-inline void glUniform4fv_ei  (GLint loc, const float* v)         { glUniform4fv(loc,1,v); }
-inline void glUniform4iv_ei  (GLint loc, const int* v)           { glUniform4iv(loc,1,v); }
-
-inline void glUniformMatrix2fv_ei  (GLint loc, const float* v)         { glUniformMatrix2fv(loc,1,false,v); }
-inline void glUniformMatrix3fv_ei  (GLint loc, const float* v)         { glUniformMatrix3fv(loc,1,false,v); }
-inline void glUniformMatrix4fv_ei  (GLint loc, const float* v)         { glUniformMatrix4fv(loc,1,false,v); }
-
-
-EIGEN_GL_FUNC1_DECLARATION       (glUniform,GLint,const)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,float,        2,2fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,int,          2,2iv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,float,        3,3fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,int,          3,3iv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,float,        4,4fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,int,          4,4iv_ei)
-
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        2,2,Matrix2fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        3,3,Matrix3fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,4,Matrix4fv_ei)
-
-#endif
-
-#ifdef GL_VERSION_2_1
-
-inline void glUniformMatrix2x3fv_ei(GLint loc, const float* v)         { glUniformMatrix2x3fv(loc,1,false,v); }
-inline void glUniformMatrix3x2fv_ei(GLint loc, const float* v)         { glUniformMatrix3x2fv(loc,1,false,v); }
-inline void glUniformMatrix2x4fv_ei(GLint loc, const float* v)         { glUniformMatrix2x4fv(loc,1,false,v); }
-inline void glUniformMatrix4x2fv_ei(GLint loc, const float* v)         { glUniformMatrix4x2fv(loc,1,false,v); }
-inline void glUniformMatrix3x4fv_ei(GLint loc, const float* v)         { glUniformMatrix3x4fv(loc,1,false,v); }
-inline void glUniformMatrix4x3fv_ei(GLint loc, const float* v)         { glUniformMatrix4x3fv(loc,1,false,v); }
-
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        2,3,Matrix2x3fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        3,2,Matrix3x2fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        2,4,Matrix2x4fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,2,Matrix4x2fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        3,4,Matrix3x4fv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,3,Matrix4x3fv_ei)
-
-#endif
-
-#ifdef GL_VERSION_3_0
-
-inline void glUniform2uiv_ei (GLint loc, const unsigned int* v)  { glUniform2uiv(loc,1,v); }
-inline void glUniform3uiv_ei (GLint loc, const unsigned int* v)  { glUniform3uiv(loc,1,v); }
-inline void glUniform4uiv_ei (GLint loc, const unsigned int* v)  { glUniform4uiv(loc,1,v); }
-
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 2,2uiv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 3,3uiv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 4,4uiv_ei)
-
-#endif
-
-#ifdef GL_ARB_gpu_shader_fp64
-inline void glUniform2dv_ei  (GLint loc, const double* v)        { glUniform2dv(loc,1,v); }
-inline void glUniform3dv_ei  (GLint loc, const double* v)        { glUniform3dv(loc,1,v); }
-inline void glUniform4dv_ei  (GLint loc, const double* v)        { glUniform4dv(loc,1,v); }
-
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       2,2dv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       3,3dv_ei)
-EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       4,4dv_ei)
-#endif
-
-
-//@}
-
-}
-
-#endif // EIGEN_OPENGL_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Polynomials b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Polynomials
deleted file mode 100644
index 32ce2a2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Polynomials
+++ /dev/null
@@ -1,137 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_POLYNOMIALS_MODULE_H
-#define EIGEN_POLYNOMIALS_MODULE_H
-
-#include "../../Eigen/Core"
-
-#include "../../Eigen/Eigenvalues"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-// Note that EIGEN_HIDE_HEAVY_CODE has to be defined per module
-#if (defined EIGEN_EXTERN_INSTANTIATIONS) && (EIGEN_EXTERN_INSTANTIATIONS>=2)
-  #ifndef EIGEN_HIDE_HEAVY_CODE
-  #define EIGEN_HIDE_HEAVY_CODE
-  #endif
-#elif defined EIGEN_HIDE_HEAVY_CODE
-  #undef EIGEN_HIDE_HEAVY_CODE
-#endif
-
-/**
-  * \defgroup Polynomials_Module Polynomials module
-  * \brief This module provides a QR based polynomial solver.
-	*
-  * To use this module, add
-  * \code
-  * #include <unsupported/Eigen/Polynomials>
-  * \endcode
-	* at the start of your source file.
-  */
-
-#include "src/Polynomials/PolynomialUtils.h"
-#include "src/Polynomials/Companion.h"
-#include "src/Polynomials/PolynomialSolver.h"
-
-/**
-	\page polynomials Polynomials defines functions for dealing with polynomials
-	and a QR based polynomial solver.
-	\ingroup Polynomials_Module
-
-	The remainder of the page documents first the functions for evaluating, computing
-	polynomials, computing estimates about polynomials and next the QR based polynomial
-	solver.
-
-	\section polynomialUtils convenient functions to deal with polynomials
-	\subsection roots_to_monicPolynomial
-	The function
-	\code
-	void roots_to_monicPolynomial( const RootVector& rv, Polynomial& poly )
-	\endcode
-	computes the coefficients \f$ a_i \f$ of
-
-	\f$ p(x) = a_0 + a_{1}x + ... + a_{n-1}x^{n-1} + x^n \f$
-
-	where \f$ p \f$ is known through its roots i.e. \f$ p(x) = (x-r_1)(x-r_2)...(x-r_n) \f$.
-
-	\subsection poly_eval
-	The function
-	\code
-	T poly_eval( const Polynomials& poly, const T& x )
-	\endcode
-	evaluates a polynomial at a given point using stabilized H&ouml;rner method.
-
-	The following code: first computes the coefficients in the monomial basis of the monic polynomial that has the provided roots;
-	then, it evaluates the computed polynomial, using a stabilized H&ouml;rner method.
-
-	\include PolynomialUtils1.cpp
-  Output: \verbinclude PolynomialUtils1.out
-
-	\subsection Cauchy bounds
-	The function
-	\code
-	Real cauchy_max_bound( const Polynomial& poly )
-	\endcode
-	provides a maximum bound (the Cauchy one: \f$C(p)\f$) for the absolute value of a root of the given polynomial i.e.
-	\f$ \forall r_i \f$ root of \f$ p(x) = \sum_{k=0}^d a_k x^k \f$,
-	\f$ |r_i| \le C(p) = \sum_{k=0}^{d} \left | \frac{a_k}{a_d} \right | \f$
-	The leading coefficient \f$ p \f$: should be non zero \f$a_d \neq 0\f$.
-
-
-	The function
-	\code
-	Real cauchy_min_bound( const Polynomial& poly )
-	\endcode
-	provides a minimum bound (the Cauchy one: \f$c(p)\f$) for the absolute value of a non zero root of the given polynomial i.e.
-	\f$ \forall r_i \neq 0 \f$ root of \f$ p(x) = \sum_{k=0}^d a_k x^k \f$,
-	\f$ |r_i| \ge c(p) = \left( \sum_{k=0}^{d} \left | \frac{a_k}{a_0} \right | \right)^{-1} \f$
-
-
-
-
-	\section QR polynomial solver class
-	Computes the complex roots of a polynomial by computing the eigenvalues of the associated companion matrix with the QR algorithm.
-	
-	The roots of \f$ p(x) = a_0 + a_1 x + a_2 x^2 + a_{3} x^3 + x^4 \f$ are the eigenvalues of
-	\f$
-	\left [
-	\begin{array}{cccc}
-	0 & 0 &  0 & a_0 \\
-	1 & 0 &  0 & a_1 \\
-	0 & 1 &  0 & a_2 \\
-	0 & 0 &  1 & a_3
-	\end{array} \right ]
-	\f$
-
-	However, the QR algorithm is not guaranteed to converge when there are several eigenvalues with same modulus.
-
-	Therefore the current polynomial solver is guaranteed to provide a correct result only when the complex roots \f$r_1,r_2,...,r_d\f$ have distinct moduli i.e.
-	
-	\f$ \forall i,j \in [1;d],~ \| r_i \| \neq \| r_j \| \f$.
-
-	With 32bit (float) floating types this problem shows up frequently.
-  However, almost always, correct accuracy is reached even in these cases for 64bit
-  (double) floating types and small polynomial degree (<20).
-
-	\include PolynomialSolver1.cpp
-	
-	In the above example:
-	
-	-# a simple use of the polynomial solver is shown;
-	-# the accuracy problem with the QR algorithm is presented: a polynomial with almost conjugate roots is provided to the solver.
-	Those roots have almost same module therefore the QR algorithm failed to converge: the accuracy
-	of the last root is bad;
-	-# a simple way to circumvent the problem is shown: use doubles instead of floats.
-
-  Output: \verbinclude PolynomialSolver1.out
-*/
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_POLYNOMIALS_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Skyline b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Skyline
deleted file mode 100644
index ebdf143..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Skyline
+++ /dev/null
@@ -1,39 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SKYLINE_MODULE_H
-#define EIGEN_SKYLINE_MODULE_H
-
-
-#include "../../Eigen/Core"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#include <map>
-#include <cstdlib>
-#include <cstring>
-#include <algorithm>
-
-/**
- *  \defgroup Skyline_Module Skyline module
- *
- *
- *
- *
- */
-
-#include "src/Skyline/SkylineUtil.h"
-#include "src/Skyline/SkylineMatrixBase.h"
-#include "src/Skyline/SkylineStorage.h"
-#include "src/Skyline/SkylineMatrix.h"
-#include "src/Skyline/SkylineInplaceLU.h"
-#include "src/Skyline/SkylineProduct.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SKYLINE_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/SparseExtra b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/SparseExtra
deleted file mode 100644
index ba5cbd6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/SparseExtra
+++ /dev/null
@@ -1,54 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_EXTRA_MODULE_H
-#define EIGEN_SPARSE_EXTRA_MODULE_H
-
-#include "../../Eigen/Sparse"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#include <vector>
-#include <map>
-#include <cstdlib>
-#include <cstring>
-#include <algorithm>
-#include <fstream>
-#include <sstream>
-
-#ifdef EIGEN_GOOGLEHASH_SUPPORT
-  #include <google/dense_hash_map>
-  #include <google/sparse_hash_map>
-#endif
-
-/**
-  * \defgroup SparseExtra_Module SparseExtra module
-  *
-  * This module contains some experimental features extending the sparse module.
-  *
-  * \code
-  * #include <Eigen/SparseExtra>
-  * \endcode
-  */
-
-
-#include "src/SparseExtra/DynamicSparseMatrix.h"
-#include "src/SparseExtra/BlockOfDynamicSparseMatrix.h"
-#include "src/SparseExtra/RandomSetter.h"
-
-#include "src/SparseExtra/MarketIO.h"
-
-#if !defined(_WIN32)
-#include <dirent.h>
-#include "src/SparseExtra/MatrixMarketIterator.h"
-#endif
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPARSE_EXTRA_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/SpecialFunctions b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/SpecialFunctions
deleted file mode 100644
index f6a2460..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/SpecialFunctions
+++ /dev/null
@@ -1,103 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Gael Guennebaud <g.gael@free.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPECIALFUNCTIONS_MODULE
-#define EIGEN_SPECIALFUNCTIONS_MODULE
-
-#include <math.h>
-
-#include "../../Eigen/Core"
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-namespace Eigen {
-
-/**
-  * \defgroup SpecialFunctions_Module Special math functions module
-  *
-  * This module features additional coefficient-wise math functions available
-  * within the numext:: namespace for the scalar version, and as method and/or free
-  * functions of Array. Those include:
-  *
-  * - erf
-  * - erfc
-  * - lgamma
-  * - igamma
-  * - igamma_der_a
-  * - gamma_sample_der_alpha
-  * - igammac
-  * - digamma
-  * - ndtri
-  * - polygamma
-  * - zeta
-  * - betainc
-  *
-  * Bessel Functions
-  * - bessel_i0
-  * - bessel_i0e
-  * - bessel_i1
-  * - bessel_i1e
-  * - bessel_j0
-  * - bessel_j1
-  * - bessel_k0
-  * - bessel_k0e
-  * - bessel_k1
-  * - bessel_k1e
-  * - bessel_y0
-  * - bessel_y1
-  *
-  * \code
-  * #include <unsupported/Eigen/SpecialFunctions>
-  * \endcode
-  */
-//@{
-
-}
-
-#include "src/SpecialFunctions/BesselFunctionsImpl.h"
-#include "src/SpecialFunctions/BesselFunctionsBFloat16.h"
-#include "src/SpecialFunctions/BesselFunctionsHalf.h"
-#include "src/SpecialFunctions/BesselFunctionsPacketMath.h"
-#include "src/SpecialFunctions/BesselFunctionsFunctors.h"
-#include "src/SpecialFunctions/BesselFunctionsArrayAPI.h"
-#include "src/SpecialFunctions/SpecialFunctionsImpl.h"
-#if defined(EIGEN_HIPCC)
-#include "src/SpecialFunctions/HipVectorCompatibility.h"
-#endif
-#include "src/SpecialFunctions/SpecialFunctionsBFloat16.h"
-#include "src/SpecialFunctions/SpecialFunctionsHalf.h"
-#include "src/SpecialFunctions/SpecialFunctionsPacketMath.h"
-#include "src/SpecialFunctions/SpecialFunctionsFunctors.h"
-#include "src/SpecialFunctions/SpecialFunctionsArrayAPI.h"
-
-#if defined EIGEN_VECTORIZE_AVX512
-  #include "src/SpecialFunctions/arch/AVX/BesselFunctions.h"
-  #include "src/SpecialFunctions/arch/AVX/SpecialFunctions.h"
-  #include "src/SpecialFunctions/arch/AVX512/BesselFunctions.h"
-  #include "src/SpecialFunctions/arch/AVX512/SpecialFunctions.h"
-#elif defined EIGEN_VECTORIZE_AVX
-  #include "src/SpecialFunctions/arch/AVX/BesselFunctions.h"
-  #include "src/SpecialFunctions/arch/AVX/SpecialFunctions.h"
-#elif defined EIGEN_VECTORIZE_NEON
-  #include "src/SpecialFunctions/arch/NEON/BesselFunctions.h"
-  #include "src/SpecialFunctions/arch/NEON/SpecialFunctions.h"
-#endif
-
-#if defined EIGEN_VECTORIZE_GPU
-  #include "src/SpecialFunctions/arch/GPU/SpecialFunctions.h"
-#endif
-
-namespace Eigen {
-//@}
-}
-
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPECIALFUNCTIONS_MODULE
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Splines b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Splines
deleted file mode 100644
index 2ca5813..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/Splines
+++ /dev/null
@@ -1,35 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPLINES_MODULE_H
-#define EIGEN_SPLINES_MODULE_H
-
-namespace Eigen 
-{
-/**
-  * \defgroup Splines_Module Spline and spline fitting module
-  *
-  * This module provides a simple multi-dimensional spline class while
-  * offering most basic functionality to fit a spline to point sets.
-  *
-  * \code
-  * #include <unsupported/Eigen/Splines>
-  * \endcode
-  */
-}
-
-#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
-
-#include "src/Splines/SplineFwd.h"
-#include "src/Splines/Spline.h"
-#include "src/Splines/SplineFitting.h"
-
-#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
-
-#endif // EIGEN_SPLINES_MODULE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
deleted file mode 100644
index 33b6c39..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
+++ /dev/null
@@ -1,108 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_AUTODIFF_JACOBIAN_H
-#define EIGEN_AUTODIFF_JACOBIAN_H
-
-namespace Eigen
-{
-
-template<typename Functor> class AutoDiffJacobian : public Functor
-{
-public:
-  AutoDiffJacobian() : Functor() {}
-  AutoDiffJacobian(const Functor& f) : Functor(f) {}
-
-  // forward constructors
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  template<typename... T>
-  AutoDiffJacobian(const T& ...Values) : Functor(Values...) {}
-#else
-  template<typename T0>
-  AutoDiffJacobian(const T0& a0) : Functor(a0) {}
-  template<typename T0, typename T1>
-  AutoDiffJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
-  template<typename T0, typename T1, typename T2>
-  AutoDiffJacobian(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2) {}
-#endif
-
-  typedef typename Functor::InputType InputType;
-  typedef typename Functor::ValueType ValueType;
-  typedef typename ValueType::Scalar Scalar;
-
-  enum {
-    InputsAtCompileTime = InputType::RowsAtCompileTime,
-    ValuesAtCompileTime = ValueType::RowsAtCompileTime
-  };
-
-  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
-  typedef typename JacobianType::Index Index;
-
-  typedef Matrix<Scalar, InputsAtCompileTime, 1> DerivativeType;
-  typedef AutoDiffScalar<DerivativeType> ActiveScalar;
-
-  typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
-  typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-  // Some compilers don't accept variadic parameters after a default parameter,
-  // i.e., we can't just write _jac=0 but we need to overload operator():
-  EIGEN_STRONG_INLINE
-  void operator() (const InputType& x, ValueType* v) const
-  {
-      this->operator()(x, v, 0);
-  }
-  template<typename... ParamsType>
-  void operator() (const InputType& x, ValueType* v, JacobianType* _jac,
-                   const ParamsType&... Params) const
-#else
-  void operator() (const InputType& x, ValueType* v, JacobianType* _jac=0) const
-#endif
-  {
-    eigen_assert(v!=0);
-
-    if (!_jac)
-    {
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-      Functor::operator()(x, v, Params...);
-#else
-      Functor::operator()(x, v);
-#endif
-      return;
-    }
-
-    JacobianType& jac = *_jac;
-
-    ActiveInput ax = x.template cast<ActiveScalar>();
-    ActiveValue av(jac.rows());
-
-    if(InputsAtCompileTime==Dynamic)
-      for (Index j=0; j<jac.rows(); j++)
-        av[j].derivatives().resize(x.rows());
-
-    for (Index i=0; i<jac.cols(); i++)
-      ax[i].derivatives() = DerivativeType::Unit(x.rows(),i);
-
-#if EIGEN_HAS_VARIADIC_TEMPLATES
-    Functor::operator()(ax, &av, Params...);
-#else
-    Functor::operator()(ax, &av);
-#endif
-
-    for (Index i=0; i<jac.rows(); i++)
-    {
-      (*v)[i] = av[i].value();
-      jac.row(i) = av[i].derivatives();
-    }
-  }
-};
-
-}
-
-#endif // EIGEN_AUTODIFF_JACOBIAN_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
deleted file mode 100644
index 0f166e3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
+++ /dev/null
@@ -1,730 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_AUTODIFF_SCALAR_H
-#define EIGEN_AUTODIFF_SCALAR_H
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename A, typename B>
-struct make_coherent_impl {
-  static void run(A&, B&) {}
-};
-
-// resize a to match b is a.size()==0, and conversely.
-template<typename A, typename B>
-void make_coherent(const A& a, const B&b)
-{
-  make_coherent_impl<A,B>::run(a.const_cast_derived(), b.const_cast_derived());
-}
-
-template<typename DerivativeType, bool Enable> struct auto_diff_special_op;
-
-} // end namespace internal
-
-template<typename DerivativeType> class AutoDiffScalar;
-
-template<typename NewDerType>
-inline AutoDiffScalar<NewDerType> MakeAutoDiffScalar(const typename NewDerType::Scalar& value, const NewDerType &der) {
-  return AutoDiffScalar<NewDerType>(value,der);
-}
-
-/** \class AutoDiffScalar
-  * \brief A scalar type replacement with automatic differentiation capability
-  *
-  * \param DerivativeType the vector type used to store/represent the derivatives. The base scalar type
-  *                 as well as the number of derivatives to compute are determined from this type.
-  *                 Typical choices include, e.g., \c Vector4f for 4 derivatives, or \c VectorXf
-  *                 if the number of derivatives is not known at compile time, and/or, the number
-  *                 of derivatives is large.
-  *                 Note that DerivativeType can also be a reference (e.g., \c VectorXf&) to wrap a
-  *                 existing vector into an AutoDiffScalar.
-  *                 Finally, DerivativeType can also be any Eigen compatible expression.
-  *
-  * This class represents a scalar value while tracking its respective derivatives using Eigen's expression
-  * template mechanism.
-  *
-  * It supports the following list of global math function:
-  *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos,
-  *  - internal::abs, internal::sqrt, numext::pow, internal::exp, internal::log, internal::sin, internal::cos,
-  *  - internal::conj, internal::real, internal::imag, numext::abs2.
-  *
-  * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
-  * in that case, the expression template mechanism only occurs at the top Matrix level,
-  * while derivatives are computed right away.
-  *
-  */
-
-template<typename DerivativeType>
-class AutoDiffScalar
-  : public internal::auto_diff_special_op
-            <DerivativeType, !internal::is_same<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar,
-                                          typename NumTraits<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar>::Real>::value>
-{
-  public:
-    typedef internal::auto_diff_special_op
-            <DerivativeType, !internal::is_same<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar,
-                       typename NumTraits<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar>::Real>::value> Base;
-    typedef typename internal::remove_all<DerivativeType>::type DerType;
-    typedef typename internal::traits<DerType>::Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real Real;
-
-    using Base::operator+;
-    using Base::operator*;
-
-    /** Default constructor without any initialization. */
-    AutoDiffScalar() {}
-
-    /** Constructs an active scalar from its \a value,
-        and initializes the \a nbDer derivatives such that it corresponds to the \a derNumber -th variable */
-    AutoDiffScalar(const Scalar& value, int nbDer, int derNumber)
-      : m_value(value), m_derivatives(DerType::Zero(nbDer))
-    {
-      m_derivatives.coeffRef(derNumber) = Scalar(1);
-    }
-
-    /** Conversion from a scalar constant to an active scalar.
-      * The derivatives are set to zero. */
-    /*explicit*/ AutoDiffScalar(const Real& value)
-      : m_value(value)
-    {
-      if(m_derivatives.size()>0)
-        m_derivatives.setZero();
-    }
-
-    /** Constructs an active scalar from its \a value and derivatives \a der */
-    AutoDiffScalar(const Scalar& value, const DerType& der)
-      : m_value(value), m_derivatives(der)
-    {}
-
-    template<typename OtherDerType>
-    AutoDiffScalar(const AutoDiffScalar<OtherDerType>& other
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    , typename internal::enable_if<
-            internal::is_same<Scalar, typename internal::traits<typename internal::remove_all<OtherDerType>::type>::Scalar>::value
-        &&  internal::is_convertible<OtherDerType,DerType>::value , void*>::type = 0
-#endif
-    )
-      : m_value(other.value()), m_derivatives(other.derivatives())
-    {}
-
-    friend  std::ostream & operator << (std::ostream & s, const AutoDiffScalar& a)
-    {
-      return s << a.value();
-    }
-
-    AutoDiffScalar(const AutoDiffScalar& other)
-      : m_value(other.value()), m_derivatives(other.derivatives())
-    {}
-
-    template<typename OtherDerType>
-    inline AutoDiffScalar& operator=(const AutoDiffScalar<OtherDerType>& other)
-    {
-      m_value = other.value();
-      m_derivatives = other.derivatives();
-      return *this;
-    }
-
-    inline AutoDiffScalar& operator=(const AutoDiffScalar& other)
-    {
-      m_value = other.value();
-      m_derivatives = other.derivatives();
-      return *this;
-    }
-
-    inline AutoDiffScalar& operator=(const Scalar& other)
-    {
-      m_value = other;
-      if(m_derivatives.size()>0)
-        m_derivatives.setZero();
-      return *this;
-    }
-
-//     inline operator const Scalar& () const { return m_value; }
-//     inline operator Scalar& () { return m_value; }
-
-    inline const Scalar& value() const { return m_value; }
-    inline Scalar& value() { return m_value; }
-
-    inline const DerType& derivatives() const { return m_derivatives; }
-    inline DerType& derivatives() { return m_derivatives; }
-
-    inline bool operator< (const Scalar& other) const  { return m_value <  other; }
-    inline bool operator<=(const Scalar& other) const  { return m_value <= other; }
-    inline bool operator> (const Scalar& other) const  { return m_value >  other; }
-    inline bool operator>=(const Scalar& other) const  { return m_value >= other; }
-    inline bool operator==(const Scalar& other) const  { return m_value == other; }
-    inline bool operator!=(const Scalar& other) const  { return m_value != other; }
-
-    friend inline bool operator< (const Scalar& a, const AutoDiffScalar& b) { return a <  b.value(); }
-    friend inline bool operator<=(const Scalar& a, const AutoDiffScalar& b) { return a <= b.value(); }
-    friend inline bool operator> (const Scalar& a, const AutoDiffScalar& b) { return a >  b.value(); }
-    friend inline bool operator>=(const Scalar& a, const AutoDiffScalar& b) { return a >= b.value(); }
-    friend inline bool operator==(const Scalar& a, const AutoDiffScalar& b) { return a == b.value(); }
-    friend inline bool operator!=(const Scalar& a, const AutoDiffScalar& b) { return a != b.value(); }
-
-    template<typename OtherDerType> inline bool operator< (const AutoDiffScalar<OtherDerType>& b) const  { return m_value <  b.value(); }
-    template<typename OtherDerType> inline bool operator<=(const AutoDiffScalar<OtherDerType>& b) const  { return m_value <= b.value(); }
-    template<typename OtherDerType> inline bool operator> (const AutoDiffScalar<OtherDerType>& b) const  { return m_value >  b.value(); }
-    template<typename OtherDerType> inline bool operator>=(const AutoDiffScalar<OtherDerType>& b) const  { return m_value >= b.value(); }
-    template<typename OtherDerType> inline bool operator==(const AutoDiffScalar<OtherDerType>& b) const  { return m_value == b.value(); }
-    template<typename OtherDerType> inline bool operator!=(const AutoDiffScalar<OtherDerType>& b) const  { return m_value != b.value(); }
-
-    inline const AutoDiffScalar<DerType&> operator+(const Scalar& other) const
-    {
-      return AutoDiffScalar<DerType&>(m_value + other, m_derivatives);
-    }
-
-    friend inline const AutoDiffScalar<DerType&> operator+(const Scalar& a, const AutoDiffScalar& b)
-    {
-      return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
-    }
-
-//     inline const AutoDiffScalar<DerType&> operator+(const Real& other) const
-//     {
-//       return AutoDiffScalar<DerType&>(m_value + other, m_derivatives);
-//     }
-
-//     friend inline const AutoDiffScalar<DerType&> operator+(const Real& a, const AutoDiffScalar& b)
-//     {
-//       return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
-//     }
-
-    inline AutoDiffScalar& operator+=(const Scalar& other)
-    {
-      value() += other;
-      return *this;
-    }
-
-    template<typename OtherDerType>
-    inline const AutoDiffScalar<CwiseBinaryOp<internal::scalar_sum_op<Scalar>,const DerType,const typename internal::remove_all<OtherDerType>::type> >
-    operator+(const AutoDiffScalar<OtherDerType>& other) const
-    {
-      internal::make_coherent(m_derivatives, other.derivatives());
-      return AutoDiffScalar<CwiseBinaryOp<internal::scalar_sum_op<Scalar>,const DerType,const typename internal::remove_all<OtherDerType>::type> >(
-        m_value + other.value(),
-        m_derivatives + other.derivatives());
-    }
-
-    template<typename OtherDerType>
-    inline AutoDiffScalar&
-    operator+=(const AutoDiffScalar<OtherDerType>& other)
-    {
-      (*this) = (*this) + other;
-      return *this;
-    }
-
-    inline const AutoDiffScalar<DerType&> operator-(const Scalar& b) const
-    {
-      return AutoDiffScalar<DerType&>(m_value - b, m_derivatives);
-    }
-
-    friend inline const AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >
-    operator-(const Scalar& a, const AutoDiffScalar& b)
-    {
-      return AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >
-            (a - b.value(), -b.derivatives());
-    }
-
-    inline AutoDiffScalar& operator-=(const Scalar& other)
-    {
-      value() -= other;
-      return *this;
-    }
-
-    template<typename OtherDerType>
-    inline const AutoDiffScalar<CwiseBinaryOp<internal::scalar_difference_op<Scalar>, const DerType,const typename internal::remove_all<OtherDerType>::type> >
-    operator-(const AutoDiffScalar<OtherDerType>& other) const
-    {
-      internal::make_coherent(m_derivatives, other.derivatives());
-      return AutoDiffScalar<CwiseBinaryOp<internal::scalar_difference_op<Scalar>, const DerType,const typename internal::remove_all<OtherDerType>::type> >(
-        m_value - other.value(),
-        m_derivatives - other.derivatives());
-    }
-
-    template<typename OtherDerType>
-    inline AutoDiffScalar&
-    operator-=(const AutoDiffScalar<OtherDerType>& other)
-    {
-      *this = *this - other;
-      return *this;
-    }
-
-    inline const AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >
-    operator-() const
-    {
-      return AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >(
-        -m_value,
-        -m_derivatives);
-    }
-
-    inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
-    operator*(const Scalar& other) const
-    {
-      return MakeAutoDiffScalar(m_value * other, m_derivatives * other);
-    }
-
-    friend inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
-    operator*(const Scalar& other, const AutoDiffScalar& a)
-    {
-      return MakeAutoDiffScalar(a.value() * other, a.derivatives() * other);
-    }
-
-//     inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
-//     operator*(const Real& other) const
-//     {
-//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
-//         m_value * other,
-//         (m_derivatives * other));
-//     }
-//
-//     friend inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
-//     operator*(const Real& other, const AutoDiffScalar& a)
-//     {
-//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
-//         a.value() * other,
-//         a.derivatives() * other);
-//     }
-
-    inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
-    operator/(const Scalar& other) const
-    {
-      return MakeAutoDiffScalar(m_value / other, (m_derivatives * (Scalar(1)/other)));
-    }
-
-    friend inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
-    operator/(const Scalar& other, const AutoDiffScalar& a)
-    {
-      return MakeAutoDiffScalar(other / a.value(), a.derivatives() * (Scalar(-other) / (a.value()*a.value())));
-    }
-
-//     inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
-//     operator/(const Real& other) const
-//     {
-//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
-//         m_value / other,
-//         (m_derivatives * (Real(1)/other)));
-//     }
-//
-//     friend inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
-//     operator/(const Real& other, const AutoDiffScalar& a)
-//     {
-//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
-//         other / a.value(),
-//         a.derivatives() * (-Real(1)/other));
-//     }
-
-    template<typename OtherDerType>
-    inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(
-        CwiseBinaryOp<internal::scalar_difference_op<Scalar> EIGEN_COMMA
-          const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) EIGEN_COMMA
-          const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename internal::remove_all<OtherDerType>::type,Scalar,product) >,Scalar,product) >
-    operator/(const AutoDiffScalar<OtherDerType>& other) const
-    {
-      internal::make_coherent(m_derivatives, other.derivatives());
-      return MakeAutoDiffScalar(
-        m_value / other.value(),
-          ((m_derivatives * other.value()) - (other.derivatives() * m_value))
-        * (Scalar(1)/(other.value()*other.value())));
-    }
-
-    template<typename OtherDerType>
-    inline const AutoDiffScalar<CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
-        const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product),
-        const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename internal::remove_all<OtherDerType>::type,Scalar,product) > >
-    operator*(const AutoDiffScalar<OtherDerType>& other) const
-    {
-      internal::make_coherent(m_derivatives, other.derivatives());
-      return MakeAutoDiffScalar(
-        m_value * other.value(),
-        (m_derivatives * other.value()) + (other.derivatives() * m_value));
-    }
-
-    inline AutoDiffScalar& operator*=(const Scalar& other)
-    {
-      *this = *this * other;
-      return *this;
-    }
-
-    template<typename OtherDerType>
-    inline AutoDiffScalar& operator*=(const AutoDiffScalar<OtherDerType>& other)
-    {
-      *this = *this * other;
-      return *this;
-    }
-
-    inline AutoDiffScalar& operator/=(const Scalar& other)
-    {
-      *this = *this / other;
-      return *this;
-    }
-
-    template<typename OtherDerType>
-    inline AutoDiffScalar& operator/=(const AutoDiffScalar<OtherDerType>& other)
-    {
-      *this = *this / other;
-      return *this;
-    }
-
-  protected:
-    Scalar m_value;
-    DerType m_derivatives;
-
-};
-
-namespace internal {
-
-template<typename DerivativeType>
-struct auto_diff_special_op<DerivativeType, true>
-//   : auto_diff_scalar_op<DerivativeType, typename NumTraits<Scalar>::Real,
-//                            is_same<Scalar,typename NumTraits<Scalar>::Real>::value>
-{
-  typedef typename remove_all<DerivativeType>::type DerType;
-  typedef typename traits<DerType>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real Real;
-
-//   typedef auto_diff_scalar_op<DerivativeType, typename NumTraits<Scalar>::Real,
-//                            is_same<Scalar,typename NumTraits<Scalar>::Real>::value> Base;
-
-//   using Base::operator+;
-//   using Base::operator+=;
-//   using Base::operator-;
-//   using Base::operator-=;
-//   using Base::operator*;
-//   using Base::operator*=;
-
-  const AutoDiffScalar<DerivativeType>& derived() const { return *static_cast<const AutoDiffScalar<DerivativeType>*>(this); }
-  AutoDiffScalar<DerivativeType>& derived() { return *static_cast<AutoDiffScalar<DerivativeType>*>(this); }
-
-
-  inline const AutoDiffScalar<DerType&> operator+(const Real& other) const
-  {
-    return AutoDiffScalar<DerType&>(derived().value() + other, derived().derivatives());
-  }
-
-  friend inline const AutoDiffScalar<DerType&> operator+(const Real& a, const AutoDiffScalar<DerivativeType>& b)
-  {
-    return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
-  }
-
-  inline AutoDiffScalar<DerivativeType>& operator+=(const Real& other)
-  {
-    derived().value() += other;
-    return derived();
-  }
-
-
-  inline const AutoDiffScalar<typename CwiseUnaryOp<bind2nd_op<scalar_product_op<Scalar,Real> >, DerType>::Type >
-  operator*(const Real& other) const
-  {
-    return AutoDiffScalar<typename CwiseUnaryOp<bind2nd_op<scalar_product_op<Scalar,Real> >, DerType>::Type >(
-      derived().value() * other,
-      derived().derivatives() * other);
-  }
-
-  friend inline const AutoDiffScalar<typename CwiseUnaryOp<bind1st_op<scalar_product_op<Real,Scalar> >, DerType>::Type >
-  operator*(const Real& other, const AutoDiffScalar<DerivativeType>& a)
-  {
-    return AutoDiffScalar<typename CwiseUnaryOp<bind1st_op<scalar_product_op<Real,Scalar> >, DerType>::Type >(
-      a.value() * other,
-      a.derivatives() * other);
-  }
-
-  inline AutoDiffScalar<DerivativeType>& operator*=(const Scalar& other)
-  {
-    *this = *this * other;
-    return derived();
-  }
-};
-
-template<typename DerivativeType>
-struct auto_diff_special_op<DerivativeType, false>
-{
-  void operator*() const;
-  void operator-() const;
-  void operator+() const;
-};
-
-template<typename BinOp, typename A, typename B, typename RefType>
-void make_coherent_expression(CwiseBinaryOp<BinOp,A,B> xpr, const RefType &ref)
-{
-  make_coherent(xpr.const_cast_derived().lhs(), ref);
-  make_coherent(xpr.const_cast_derived().rhs(), ref);
-}
-
-template<typename UnaryOp, typename A, typename RefType>
-void make_coherent_expression(const CwiseUnaryOp<UnaryOp,A> &xpr, const RefType &ref)
-{
-  make_coherent(xpr.nestedExpression().const_cast_derived(), ref);
-}
-
-// needed for compilation only
-template<typename UnaryOp, typename A, typename RefType>
-void make_coherent_expression(const CwiseNullaryOp<UnaryOp,A> &, const RefType &)
-{}
-
-template<typename A_Scalar, int A_Rows, int A_Cols, int A_Options, int A_MaxRows, int A_MaxCols, typename B>
-struct make_coherent_impl<Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols>, B> {
-  typedef Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols> A;
-  static void run(A& a, B& b) {
-    if((A_Rows==Dynamic || A_Cols==Dynamic) && (a.size()==0))
-    {
-      a.resize(b.size());
-      a.setZero();
-    }
-    else if (B::SizeAtCompileTime==Dynamic && a.size()!=0 && b.size()==0)
-    {
-      make_coherent_expression(b,a);
-    }
-  }
-};
-
-template<typename A, typename B_Scalar, int B_Rows, int B_Cols, int B_Options, int B_MaxRows, int B_MaxCols>
-struct make_coherent_impl<A, Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> > {
-  typedef Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> B;
-  static void run(A& a, B& b) {
-    if((B_Rows==Dynamic || B_Cols==Dynamic) && (b.size()==0))
-    {
-      b.resize(a.size());
-      b.setZero();
-    }
-    else if (A::SizeAtCompileTime==Dynamic && b.size()!=0 && a.size()==0)
-    {
-      make_coherent_expression(a,b);
-    }
-  }
-};
-
-template<typename A_Scalar, int A_Rows, int A_Cols, int A_Options, int A_MaxRows, int A_MaxCols,
-         typename B_Scalar, int B_Rows, int B_Cols, int B_Options, int B_MaxRows, int B_MaxCols>
-struct make_coherent_impl<Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols>,
-                          Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> > {
-  typedef Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols> A;
-  typedef Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> B;
-  static void run(A& a, B& b) {
-    if((A_Rows==Dynamic || A_Cols==Dynamic) && (a.size()==0))
-    {
-      a.resize(b.size());
-      a.setZero();
-    }
-    else if((B_Rows==Dynamic || B_Cols==Dynamic) && (b.size()==0))
-    {
-      b.resize(a.size());
-      b.setZero();
-    }
-  }
-};
-
-} // end namespace internal
-
-template<typename DerType, typename BinOp>
-struct ScalarBinaryOpTraits<AutoDiffScalar<DerType>,typename DerType::Scalar,BinOp>
-{
-  typedef AutoDiffScalar<DerType> ReturnType;
-};
-
-template<typename DerType, typename BinOp>
-struct ScalarBinaryOpTraits<typename DerType::Scalar,AutoDiffScalar<DerType>, BinOp>
-{
-  typedef AutoDiffScalar<DerType> ReturnType;
-};
-
-
-// The following is an attempt to let Eigen's known about expression template, but that's more tricky!
-
-// template<typename DerType, typename BinOp>
-// struct ScalarBinaryOpTraits<AutoDiffScalar<DerType>,AutoDiffScalar<DerType>, BinOp>
-// {
-//   enum { Defined = 1 };
-//   typedef AutoDiffScalar<typename DerType::PlainObject> ReturnType;
-// };
-//
-// template<typename DerType1,typename DerType2, typename BinOp>
-// struct ScalarBinaryOpTraits<AutoDiffScalar<DerType1>,AutoDiffScalar<DerType2>, BinOp>
-// {
-//   enum { Defined = 1 };//internal::is_same<typename DerType1::Scalar,typename DerType2::Scalar>::value };
-//   typedef AutoDiffScalar<typename DerType1::PlainObject> ReturnType;
-// };
-
-#define EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(FUNC,CODE) \
-  template<typename DerType> \
-  inline const Eigen::AutoDiffScalar< \
-  EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename Eigen::internal::remove_all<DerType>::type, typename Eigen::internal::traits<typename Eigen::internal::remove_all<DerType>::type>::Scalar, product) > \
-  FUNC(const Eigen::AutoDiffScalar<DerType>& x) { \
-    using namespace Eigen; \
-    typedef typename Eigen::internal::traits<typename Eigen::internal::remove_all<DerType>::type>::Scalar Scalar; \
-    EIGEN_UNUSED_VARIABLE(sizeof(Scalar)); \
-    CODE; \
-  }
-
-template<typename DerType>
-struct CleanedUpDerType {
-  typedef AutoDiffScalar<typename Eigen::internal::remove_all<DerType>::type::PlainObject> type;
-};
-
-template<typename DerType>
-inline const AutoDiffScalar<DerType>& conj(const AutoDiffScalar<DerType>& x)  { return x; }
-template<typename DerType>
-inline const AutoDiffScalar<DerType>& real(const AutoDiffScalar<DerType>& x)  { return x; }
-template<typename DerType>
-inline typename DerType::Scalar imag(const AutoDiffScalar<DerType>&)    { return 0.; }
-template<typename DerType, typename T>
-inline typename CleanedUpDerType<DerType>::type (min)(const AutoDiffScalar<DerType>& x, const T& y) {
-  typedef typename CleanedUpDerType<DerType>::type ADS;
-  return (x <= y ? ADS(x) : ADS(y));
-}
-template<typename DerType, typename T>
-inline typename CleanedUpDerType<DerType>::type (max)(const AutoDiffScalar<DerType>& x, const T& y) {
-  typedef typename CleanedUpDerType<DerType>::type ADS;
-  return (x >= y ? ADS(x) : ADS(y));
-}
-template<typename DerType, typename T>
-inline typename CleanedUpDerType<DerType>::type (min)(const T& x, const AutoDiffScalar<DerType>& y) {
-  typedef typename CleanedUpDerType<DerType>::type ADS;
-  return (x < y ? ADS(x) : ADS(y));
-}
-template<typename DerType, typename T>
-inline typename CleanedUpDerType<DerType>::type (max)(const T& x, const AutoDiffScalar<DerType>& y) {
-  typedef typename CleanedUpDerType<DerType>::type ADS;
-  return (x > y ? ADS(x) : ADS(y));
-}
-template<typename DerType>
-inline typename CleanedUpDerType<DerType>::type (min)(const AutoDiffScalar<DerType>& x, const AutoDiffScalar<DerType>& y) {
-  return (x.value() < y.value() ? x : y);
-}
-template<typename DerType>
-inline typename CleanedUpDerType<DerType>::type (max)(const AutoDiffScalar<DerType>& x, const AutoDiffScalar<DerType>& y) {
-  return (x.value() >= y.value() ? x : y);
-}
-
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs,
-  using std::abs;
-  return Eigen::MakeAutoDiffScalar(abs(x.value()), x.derivatives() * (x.value()<0 ? -1 : 1) );)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs2,
-  using numext::abs2;
-  return Eigen::MakeAutoDiffScalar(abs2(x.value()), x.derivatives() * (Scalar(2)*x.value()));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sqrt,
-  using std::sqrt;
-  Scalar sqrtx = sqrt(x.value());
-  return Eigen::MakeAutoDiffScalar(sqrtx,x.derivatives() * (Scalar(0.5) / sqrtx));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cos,
-  using std::cos;
-  using std::sin;
-  return Eigen::MakeAutoDiffScalar(cos(x.value()), x.derivatives() * (-sin(x.value())));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sin,
-  using std::sin;
-  using std::cos;
-  return Eigen::MakeAutoDiffScalar(sin(x.value()),x.derivatives() * cos(x.value()));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(exp,
-  using std::exp;
-  Scalar expx = exp(x.value());
-  return Eigen::MakeAutoDiffScalar(expx,x.derivatives() * expx);)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(log,
-  using std::log;
-  return Eigen::MakeAutoDiffScalar(log(x.value()),x.derivatives() * (Scalar(1)/x.value()));)
-
-template<typename DerType>
-inline const Eigen::AutoDiffScalar<
-EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename internal::remove_all<DerType>::type,typename internal::traits<typename internal::remove_all<DerType>::type>::Scalar,product) >
-pow(const Eigen::AutoDiffScalar<DerType> &x, const typename internal::traits<typename internal::remove_all<DerType>::type>::Scalar &y)
-{
-  using namespace Eigen;
-  using std::pow;
-  return Eigen::MakeAutoDiffScalar(pow(x.value(),y), x.derivatives() * (y * pow(x.value(),y-1)));
-}
-
-
-template<typename DerTypeA,typename DerTypeB>
-inline const AutoDiffScalar<Matrix<typename internal::traits<typename internal::remove_all<DerTypeA>::type>::Scalar,Dynamic,1> >
-atan2(const AutoDiffScalar<DerTypeA>& a, const AutoDiffScalar<DerTypeB>& b)
-{
-  using std::atan2;
-  typedef typename internal::traits<typename internal::remove_all<DerTypeA>::type>::Scalar Scalar;
-  typedef AutoDiffScalar<Matrix<Scalar,Dynamic,1> > PlainADS;
-  PlainADS ret;
-  ret.value() = atan2(a.value(), b.value());
-  
-  Scalar squared_hypot = a.value() * a.value() + b.value() * b.value();
-  
-  // if (squared_hypot==0) the derivation is undefined and the following results in a NaN:
-  ret.derivatives() = (a.derivatives() * b.value() - a.value() * b.derivatives()) / squared_hypot;
-
-  return ret;
-}
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(tan,
-  using std::tan;
-  using std::cos;
-  return Eigen::MakeAutoDiffScalar(tan(x.value()),x.derivatives() * (Scalar(1)/numext::abs2(cos(x.value()))));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(asin,
-  using std::sqrt;
-  using std::asin;
-  return Eigen::MakeAutoDiffScalar(asin(x.value()),x.derivatives() * (Scalar(1)/sqrt(1-numext::abs2(x.value()))));)
-  
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(acos,
-  using std::sqrt;
-  using std::acos;
-  return Eigen::MakeAutoDiffScalar(acos(x.value()),x.derivatives() * (Scalar(-1)/sqrt(1-numext::abs2(x.value()))));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(tanh,
-  using std::cosh;
-  using std::tanh;
-  return Eigen::MakeAutoDiffScalar(tanh(x.value()),x.derivatives() * (Scalar(1)/numext::abs2(cosh(x.value()))));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sinh,
-  using std::sinh;
-  using std::cosh;
-  return Eigen::MakeAutoDiffScalar(sinh(x.value()),x.derivatives() * cosh(x.value()));)
-
-EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cosh,
-  using std::sinh;
-  using std::cosh;
-  return Eigen::MakeAutoDiffScalar(cosh(x.value()),x.derivatives() * sinh(x.value()));)
-
-#undef EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY
-
-template<typename DerType> struct NumTraits<AutoDiffScalar<DerType> >
-  : NumTraits< typename NumTraits<typename internal::remove_all<DerType>::type::Scalar>::Real >
-{
-  typedef typename internal::remove_all<DerType>::type DerTypeCleaned;
-  typedef AutoDiffScalar<Matrix<typename NumTraits<typename DerTypeCleaned::Scalar>::Real,DerTypeCleaned::RowsAtCompileTime,DerTypeCleaned::ColsAtCompileTime,
-                                0, DerTypeCleaned::MaxRowsAtCompileTime, DerTypeCleaned::MaxColsAtCompileTime> > Real;
-  typedef AutoDiffScalar<DerType> NonInteger;
-  typedef AutoDiffScalar<DerType> Nested;
-  typedef typename NumTraits<typename DerTypeCleaned::Scalar>::Literal Literal;
-  enum{
-    RequireInitialization = 1
-  };
-};
-
-}
-
-namespace std {
-
-template <typename T>
-class numeric_limits<Eigen::AutoDiffScalar<T> >
-  : public numeric_limits<typename T::Scalar> {};
-
-template <typename T>
-class numeric_limits<Eigen::AutoDiffScalar<T&> >
-  : public numeric_limits<typename T::Scalar> {};
-
-}  // namespace std
-
-#endif // EIGEN_AUTODIFF_SCALAR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
deleted file mode 100644
index 8c2d048..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
+++ /dev/null
@@ -1,220 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_AUTODIFF_VECTOR_H
-#define EIGEN_AUTODIFF_VECTOR_H
-
-namespace Eigen {
-
-/* \class AutoDiffScalar
-  * \brief A scalar type replacement with automatic differentation capability
-  *
-  * \param DerType the vector type used to store/represent the derivatives (e.g. Vector3f)
-  *
-  * This class represents a scalar value while tracking its respective derivatives.
-  *
-  * It supports the following list of global math function:
-  *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos,
-  *  - internal::abs, internal::sqrt, numext::pow, internal::exp, internal::log, internal::sin, internal::cos,
-  *  - internal::conj, internal::real, internal::imag, numext::abs2.
-  *
-  * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
-  * in that case, the expression template mechanism only occurs at the top Matrix level,
-  * while derivatives are computed right away.
-  *
-  */
-template<typename ValueType, typename JacobianType>
-class AutoDiffVector
-{
-  public:
-    //typedef typename internal::traits<ValueType>::Scalar Scalar;
-    typedef typename internal::traits<ValueType>::Scalar BaseScalar;
-    typedef AutoDiffScalar<Matrix<BaseScalar,JacobianType::RowsAtCompileTime,1> > ActiveScalar;
-    typedef ActiveScalar Scalar;
-    typedef AutoDiffScalar<typename JacobianType::ColXpr> CoeffType;
-    typedef typename JacobianType::Index Index;
-
-    inline AutoDiffVector() {}
-
-    inline AutoDiffVector(const ValueType& values)
-      : m_values(values)
-    {
-      m_jacobian.setZero();
-    }
-
-
-    CoeffType operator[] (Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
-    const CoeffType operator[] (Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
-
-    CoeffType operator() (Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
-    const CoeffType operator() (Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
-
-    CoeffType coeffRef(Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
-    const CoeffType coeffRef(Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
-
-    Index size() const { return m_values.size(); }
-
-    // FIXME here we could return an expression of the sum
-    Scalar sum() const { /*std::cerr << "sum \n\n";*/ /*std::cerr << m_jacobian.rowwise().sum() << "\n\n";*/ return Scalar(m_values.sum(), m_jacobian.rowwise().sum()); }
-
-
-    inline AutoDiffVector(const ValueType& values, const JacobianType& jac)
-      : m_values(values), m_jacobian(jac)
-    {}
-
-    template<typename OtherValueType, typename OtherJacobianType>
-    inline AutoDiffVector(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
-      : m_values(other.values()), m_jacobian(other.jacobian())
-    {}
-
-    inline AutoDiffVector(const AutoDiffVector& other)
-      : m_values(other.values()), m_jacobian(other.jacobian())
-    {}
-
-    template<typename OtherValueType, typename OtherJacobianType>
-    inline AutoDiffVector& operator=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
-    {
-      m_values = other.values();
-      m_jacobian = other.jacobian();
-      return *this;
-    }
-
-    inline AutoDiffVector& operator=(const AutoDiffVector& other)
-    {
-      m_values = other.values();
-      m_jacobian = other.jacobian();
-      return *this;
-    }
-
-    inline const ValueType& values() const { return m_values; }
-    inline ValueType& values() { return m_values; }
-
-    inline const JacobianType& jacobian() const { return m_jacobian; }
-    inline JacobianType& jacobian() { return m_jacobian; }
-
-    template<typename OtherValueType,typename OtherJacobianType>
-    inline const AutoDiffVector<
-      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,ValueType,OtherValueType>::Type,
-      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,JacobianType,OtherJacobianType>::Type >
-    operator+(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
-    {
-      return AutoDiffVector<
-      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,ValueType,OtherValueType>::Type,
-      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,JacobianType,OtherJacobianType>::Type >(
-        m_values + other.values(),
-        m_jacobian + other.jacobian());
-    }
-
-    template<typename OtherValueType, typename OtherJacobianType>
-    inline AutoDiffVector&
-    operator+=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
-    {
-      m_values += other.values();
-      m_jacobian += other.jacobian();
-      return *this;
-    }
-
-    template<typename OtherValueType,typename OtherJacobianType>
-    inline const AutoDiffVector<
-      typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,ValueType,OtherValueType>::Type,
-      typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,JacobianType,OtherJacobianType>::Type >
-    operator-(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
-    {
-      return AutoDiffVector<
-        typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,ValueType,OtherValueType>::Type,
-        typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,JacobianType,OtherJacobianType>::Type >(
-          m_values - other.values(),
-          m_jacobian - other.jacobian());
-    }
-
-    template<typename OtherValueType, typename OtherJacobianType>
-    inline AutoDiffVector&
-    operator-=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
-    {
-      m_values -= other.values();
-      m_jacobian -= other.jacobian();
-      return *this;
-    }
-
-    inline const AutoDiffVector<
-      typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, ValueType>::Type,
-      typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, JacobianType>::Type >
-    operator-() const
-    {
-      return AutoDiffVector<
-        typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, ValueType>::Type,
-        typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, JacobianType>::Type >(
-          -m_values,
-          -m_jacobian);
-    }
-
-    inline const AutoDiffVector<
-      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
-      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type>
-    operator*(const BaseScalar& other) const
-    {
-      return AutoDiffVector<
-        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
-        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type >(
-          m_values * other,
-          m_jacobian * other);
-    }
-
-    friend inline const AutoDiffVector<
-      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
-      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type >
-    operator*(const Scalar& other, const AutoDiffVector& v)
-    {
-      return AutoDiffVector<
-        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
-        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type >(
-          v.values() * other,
-          v.jacobian() * other);
-    }
-
-//     template<typename OtherValueType,typename OtherJacobianType>
-//     inline const AutoDiffVector<
-//       CwiseBinaryOp<internal::scalar_multiple_op<Scalar>, ValueType, OtherValueType>
-//       CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
-//         CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>,
-//         CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, OtherJacobianType> > >
-//     operator*(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
-//     {
-//       return AutoDiffVector<
-//         CwiseBinaryOp<internal::scalar_multiple_op<Scalar>, ValueType, OtherValueType>
-//         CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
-//           CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>,
-//           CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, OtherJacobianType> > >(
-//             m_values.cwise() * other.values(),
-//             (m_jacobian * other.values()) + (m_values * other.jacobian()));
-//     }
-
-    inline AutoDiffVector& operator*=(const Scalar& other)
-    {
-      m_values *= other;
-      m_jacobian *= other;
-      return *this;
-    }
-
-    template<typename OtherValueType,typename OtherJacobianType>
-    inline AutoDiffVector& operator*=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
-    {
-      *this = *this * other;
-      return *this;
-    }
-
-  protected:
-    ValueType m_values;
-    JacobianType m_jacobian;
-
-};
-
-}
-
-#endif // EIGEN_AUTODIFF_VECTOR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h
deleted file mode 100644
index 994c8af..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h
+++ /dev/null
@@ -1,293 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BVALGORITHMS_H
-#define EIGEN_BVALGORITHMS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename BVH, typename Intersector>
-bool intersect_helper(const BVH &tree, Intersector &intersector, typename BVH::Index root)
-{
-  typedef typename BVH::Index Index;
-  typedef typename BVH::VolumeIterator VolIter;
-  typedef typename BVH::ObjectIterator ObjIter;
-
-  VolIter vBegin = VolIter(), vEnd = VolIter();
-  ObjIter oBegin = ObjIter(), oEnd = ObjIter();
-
-  std::vector<Index> todo(1, root);
-
-  while(!todo.empty()) {
-    tree.getChildren(todo.back(), vBegin, vEnd, oBegin, oEnd);
-    todo.pop_back();
-
-    for(; vBegin != vEnd; ++vBegin) //go through child volumes
-      if(intersector.intersectVolume(tree.getVolume(*vBegin)))
-        todo.push_back(*vBegin);
-
-    for(; oBegin != oEnd; ++oBegin) //go through child objects
-      if(intersector.intersectObject(*oBegin))
-        return true; //intersector said to stop query
-  }
-  return false;
-}
-#endif //not EIGEN_PARSED_BY_DOXYGEN
-
-template<typename Volume1, typename Object1, typename Object2, typename Intersector>
-struct intersector_helper1
-{
-  intersector_helper1(const Object2 &inStored, Intersector &in) : stored(inStored), intersector(in) {}
-  bool intersectVolume(const Volume1 &vol) { return intersector.intersectVolumeObject(vol, stored); }
-  bool intersectObject(const Object1 &obj) { return intersector.intersectObjectObject(obj, stored); }
-  Object2 stored;
-  Intersector &intersector;
-private:
-  intersector_helper1& operator=(const intersector_helper1&);
-};
-
-template<typename Volume2, typename Object2, typename Object1, typename Intersector>
-struct intersector_helper2
-{
-  intersector_helper2(const Object1 &inStored, Intersector &in) : stored(inStored), intersector(in) {}
-  bool intersectVolume(const Volume2 &vol) { return intersector.intersectObjectVolume(stored, vol); }
-  bool intersectObject(const Object2 &obj) { return intersector.intersectObjectObject(stored, obj); }
-  Object1 stored;
-  Intersector &intersector;
-private:
-  intersector_helper2& operator=(const intersector_helper2&);
-};
-
-} // end namespace internal
-
-/**  Given a BVH, runs the query encapsulated by \a intersector.
-  *  The Intersector type must provide the following members: \code
-     bool intersectVolume(const BVH::Volume &volume) //returns true if volume intersects the query
-     bool intersectObject(const BVH::Object &object) //returns true if the search should terminate immediately
-  \endcode
-  */
-template<typename BVH, typename Intersector>
-void BVIntersect(const BVH &tree, Intersector &intersector)
-{
-  internal::intersect_helper(tree, intersector, tree.getRootIndex());
-}
-
-/**  Given two BVH's, runs the query on their Cartesian product encapsulated by \a intersector.
-  *  The Intersector type must provide the following members: \code
-     bool intersectVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2) //returns true if product of volumes intersects the query
-     bool intersectVolumeObject(const BVH1::Volume &v1, const BVH2::Object &o2) //returns true if the volume-object product intersects the query
-     bool intersectObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2) //returns true if the volume-object product intersects the query
-     bool intersectObjectObject(const BVH1::Object &o1, const BVH2::Object &o2) //returns true if the search should terminate immediately
-  \endcode
-  */
-template<typename BVH1, typename BVH2, typename Intersector>
-void BVIntersect(const BVH1 &tree1, const BVH2 &tree2, Intersector &intersector) //TODO: tandem descent when it makes sense
-{
-  typedef typename BVH1::Index Index1;
-  typedef typename BVH2::Index Index2;
-  typedef internal::intersector_helper1<typename BVH1::Volume, typename BVH1::Object, typename BVH2::Object, Intersector> Helper1;
-  typedef internal::intersector_helper2<typename BVH2::Volume, typename BVH2::Object, typename BVH1::Object, Intersector> Helper2;
-  typedef typename BVH1::VolumeIterator VolIter1;
-  typedef typename BVH1::ObjectIterator ObjIter1;
-  typedef typename BVH2::VolumeIterator VolIter2;
-  typedef typename BVH2::ObjectIterator ObjIter2;
-
-  VolIter1 vBegin1 = VolIter1(), vEnd1 = VolIter1();
-  ObjIter1 oBegin1 = ObjIter1(), oEnd1 = ObjIter1();
-  VolIter2 vBegin2 = VolIter2(), vEnd2 = VolIter2(), vCur2 = VolIter2();
-  ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
-
-  std::vector<std::pair<Index1, Index2> > todo(1, std::make_pair(tree1.getRootIndex(), tree2.getRootIndex()));
-
-  while(!todo.empty()) {
-    tree1.getChildren(todo.back().first, vBegin1, vEnd1, oBegin1, oEnd1);
-    tree2.getChildren(todo.back().second, vBegin2, vEnd2, oBegin2, oEnd2);
-    todo.pop_back();
-
-    for(; vBegin1 != vEnd1; ++vBegin1) { //go through child volumes of first tree
-      const typename BVH1::Volume &vol1 = tree1.getVolume(*vBegin1);
-      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
-        if(intersector.intersectVolumeVolume(vol1, tree2.getVolume(*vCur2)))
-          todo.push_back(std::make_pair(*vBegin1, *vCur2));
-      }
-
-      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
-        Helper1 helper(*oCur2, intersector);
-        if(internal::intersect_helper(tree1, helper, *vBegin1))
-          return; //intersector said to stop query
-      }
-    }
-
-    for(; oBegin1 != oEnd1; ++oBegin1) { //go through child objects of first tree
-      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
-        Helper2 helper(*oBegin1, intersector);
-        if(internal::intersect_helper(tree2, helper, *vCur2))
-          return; //intersector said to stop query
-      }
-
-      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
-        if(intersector.intersectObjectObject(*oBegin1, *oCur2))
-          return; //intersector said to stop query
-      }
-    }
-  }
-}
-
-namespace internal {
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename BVH, typename Minimizer>
-typename Minimizer::Scalar minimize_helper(const BVH &tree, Minimizer &minimizer, typename BVH::Index root, typename Minimizer::Scalar minimum)
-{
-  typedef typename Minimizer::Scalar Scalar;
-  typedef typename BVH::Index Index;
-  typedef std::pair<Scalar, Index> QueueElement; //first element is priority
-  typedef typename BVH::VolumeIterator VolIter;
-  typedef typename BVH::ObjectIterator ObjIter;
-
-  VolIter vBegin = VolIter(), vEnd = VolIter();
-  ObjIter oBegin = ObjIter(), oEnd = ObjIter();
-  std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> > todo; //smallest is at the top
-
-  todo.push(std::make_pair(Scalar(), root));
-
-  while(!todo.empty()) {
-    tree.getChildren(todo.top().second, vBegin, vEnd, oBegin, oEnd);
-    todo.pop();
-
-    for(; oBegin != oEnd; ++oBegin) //go through child objects
-      minimum = (std::min)(minimum, minimizer.minimumOnObject(*oBegin));
-
-    for(; vBegin != vEnd; ++vBegin) { //go through child volumes
-      Scalar val = minimizer.minimumOnVolume(tree.getVolume(*vBegin));
-      if(val < minimum)
-        todo.push(std::make_pair(val, *vBegin));
-    }
-  }
-
-  return minimum;
-}
-#endif //not EIGEN_PARSED_BY_DOXYGEN
-
-
-template<typename Volume1, typename Object1, typename Object2, typename Minimizer>
-struct minimizer_helper1
-{
-  typedef typename Minimizer::Scalar Scalar;
-  minimizer_helper1(const Object2 &inStored, Minimizer &m) : stored(inStored), minimizer(m) {}
-  Scalar minimumOnVolume(const Volume1 &vol) { return minimizer.minimumOnVolumeObject(vol, stored); }
-  Scalar minimumOnObject(const Object1 &obj) { return minimizer.minimumOnObjectObject(obj, stored); }
-  Object2 stored;
-  Minimizer &minimizer;
-private:
-  minimizer_helper1& operator=(const minimizer_helper1&);
-};
-
-template<typename Volume2, typename Object2, typename Object1, typename Minimizer>
-struct minimizer_helper2
-{
-  typedef typename Minimizer::Scalar Scalar;
-  minimizer_helper2(const Object1 &inStored, Minimizer &m) : stored(inStored), minimizer(m) {}
-  Scalar minimumOnVolume(const Volume2 &vol) { return minimizer.minimumOnObjectVolume(stored, vol); }
-  Scalar minimumOnObject(const Object2 &obj) { return minimizer.minimumOnObjectObject(stored, obj); }
-  Object1 stored;
-  Minimizer &minimizer;
-private:
-  minimizer_helper2& operator=(const minimizer_helper2&);
-};
-
-} // end namespace internal
-
-/**  Given a BVH, runs the query encapsulated by \a minimizer.
-  *  \returns the minimum value.
-  *  The Minimizer type must provide the following members: \code
-     typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has one)
-     Scalar minimumOnVolume(const BVH::Volume &volume)
-     Scalar minimumOnObject(const BVH::Object &object)
-  \endcode
-  */
-template<typename BVH, typename Minimizer>
-typename Minimizer::Scalar BVMinimize(const BVH &tree, Minimizer &minimizer)
-{
-  return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), (std::numeric_limits<typename Minimizer::Scalar>::max)());
-}
-
-/**  Given two BVH's, runs the query on their cartesian product encapsulated by \a minimizer.
-  *  \returns the minimum value.
-  *  The Minimizer type must provide the following members: \code
-     typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has one)
-     Scalar minimumOnVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2)
-     Scalar minimumOnVolumeObject(const BVH1::Volume &v1, const BVH2::Object &o2)
-     Scalar minimumOnObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2)
-     Scalar minimumOnObjectObject(const BVH1::Object &o1, const BVH2::Object &o2)
-  \endcode
-  */
-template<typename BVH1, typename BVH2, typename Minimizer>
-typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Minimizer &minimizer)
-{
-  typedef typename Minimizer::Scalar Scalar;
-  typedef typename BVH1::Index Index1;
-  typedef typename BVH2::Index Index2;
-  typedef internal::minimizer_helper1<typename BVH1::Volume, typename BVH1::Object, typename BVH2::Object, Minimizer> Helper1;
-  typedef internal::minimizer_helper2<typename BVH2::Volume, typename BVH2::Object, typename BVH1::Object, Minimizer> Helper2;
-  typedef std::pair<Scalar, std::pair<Index1, Index2> > QueueElement; //first element is priority
-  typedef typename BVH1::VolumeIterator VolIter1;
-  typedef typename BVH1::ObjectIterator ObjIter1;
-  typedef typename BVH2::VolumeIterator VolIter2;
-  typedef typename BVH2::ObjectIterator ObjIter2;
-
-  VolIter1 vBegin1 = VolIter1(), vEnd1 = VolIter1();
-  ObjIter1 oBegin1 = ObjIter1(), oEnd1 = ObjIter1();
-  VolIter2 vBegin2 = VolIter2(), vEnd2 = VolIter2(), vCur2 = VolIter2();
-  ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
-  std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> > todo; //smallest is at the top
-
-  Scalar minimum = (std::numeric_limits<Scalar>::max)();
-  todo.push(std::make_pair(Scalar(), std::make_pair(tree1.getRootIndex(), tree2.getRootIndex())));
-
-  while(!todo.empty()) {
-    tree1.getChildren(todo.top().second.first, vBegin1, vEnd1, oBegin1, oEnd1);
-    tree2.getChildren(todo.top().second.second, vBegin2, vEnd2, oBegin2, oEnd2);
-    todo.pop();
-
-    for(; oBegin1 != oEnd1; ++oBegin1) { //go through child objects of first tree
-      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
-        minimum = (std::min)(minimum, minimizer.minimumOnObjectObject(*oBegin1, *oCur2));
-      }
-
-      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
-        Helper2 helper(*oBegin1, minimizer);
-        minimum = (std::min)(minimum, internal::minimize_helper(tree2, helper, *vCur2, minimum));
-      }
-    }
-
-    for(; vBegin1 != vEnd1; ++vBegin1) { //go through child volumes of first tree
-      const typename BVH1::Volume &vol1 = tree1.getVolume(*vBegin1);
-
-      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
-        Helper1 helper(*oCur2, minimizer);
-        minimum = (std::min)(minimum, internal::minimize_helper(tree1, helper, *vBegin1, minimum));
-      }
-
-      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
-        Scalar val = minimizer.minimumOnVolumeVolume(vol1, tree2.getVolume(*vCur2));
-        if(val < minimum)
-          todo.push(std::make_pair(val, std::make_pair(*vBegin1, *vCur2)));
-      }
-    }
-  }
-  return minimum;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_BVALGORITHMS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h
deleted file mode 100644
index 2d5b76a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h
+++ /dev/null
@@ -1,223 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef KDBVH_H_INCLUDED
-#define KDBVH_H_INCLUDED
-
-namespace Eigen { 
-
-namespace internal {
-
-//internal pair class for the BVH--used instead of std::pair because of alignment
-template<typename Scalar, int Dim>
-struct vector_int_pair
-{
-EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar, Dim)
-  typedef Matrix<Scalar, Dim, 1> VectorType;
-
-  vector_int_pair(const VectorType &v, int i) : first(v), second(i) {}
-
-  VectorType first;
-  int second;
-};
-
-//these templates help the tree initializer get the bounding boxes either from a provided
-//iterator range or using bounding_box in a unified way
-template<typename ObjectList, typename VolumeList, typename BoxIter>
-struct get_boxes_helper {
-  void operator()(const ObjectList &objects, BoxIter boxBegin, BoxIter boxEnd, VolumeList &outBoxes)
-  {
-    outBoxes.insert(outBoxes.end(), boxBegin, boxEnd);
-    eigen_assert(outBoxes.size() == objects.size());
-    EIGEN_ONLY_USED_FOR_DEBUG(objects);
-  }
-};
-
-template<typename ObjectList, typename VolumeList>
-struct get_boxes_helper<ObjectList, VolumeList, int> {
-  void operator()(const ObjectList &objects, int, int, VolumeList &outBoxes)
-  {
-    outBoxes.reserve(objects.size());
-    for(int i = 0; i < (int)objects.size(); ++i)
-      outBoxes.push_back(bounding_box(objects[i]));
-  }
-};
-
-} // end namespace internal
-
-
-/** \class KdBVH
- *  \brief A simple bounding volume hierarchy based on AlignedBox
- *
- *  \param _Scalar The underlying scalar type of the bounding boxes
- *  \param _Dim The dimension of the space in which the hierarchy lives
- *  \param _Object The object type that lives in the hierarchy.  It must have value semantics.  Either bounding_box(_Object) must
- *                 be defined and return an AlignedBox<_Scalar, _Dim> or bounding boxes must be provided to the tree initializer.
- *
- *  This class provides a simple (as opposed to optimized) implementation of a bounding volume hierarchy analogous to a Kd-tree.
- *  Given a sequence of objects, it computes their bounding boxes, constructs a Kd-tree of their centers
- *  and builds a BVH with the structure of that Kd-tree.  When the elements of the tree are too expensive to be copied around,
- *  it is useful for _Object to be a pointer.
- */
-template<typename _Scalar, int _Dim, typename _Object> class KdBVH
-{
-public:
-  enum { Dim = _Dim };
-  typedef _Object Object;
-  typedef std::vector<Object, aligned_allocator<Object> > ObjectList;
-  typedef _Scalar Scalar;
-  typedef AlignedBox<Scalar, Dim> Volume;
-  typedef std::vector<Volume, aligned_allocator<Volume> > VolumeList;
-  typedef int Index;
-  typedef const int *VolumeIterator; //the iterators are just pointers into the tree's vectors
-  typedef const Object *ObjectIterator;
-
-  KdBVH() {}
-
-  /** Given an iterator range over \a Object references, constructs the BVH.  Requires that bounding_box(Object) return a Volume. */
-  template<typename Iter> KdBVH(Iter begin, Iter end) { init(begin, end, 0, 0); } //int is recognized by init as not being an iterator type
-
-  /** Given an iterator range over \a Object references and an iterator range over their bounding boxes, constructs the BVH */
-  template<typename OIter, typename BIter> KdBVH(OIter begin, OIter end, BIter boxBegin, BIter boxEnd) { init(begin, end, boxBegin, boxEnd); }
-
-  /** Given an iterator range over \a Object references, constructs the BVH, overwriting whatever is in there currently.
-    * Requires that bounding_box(Object) return a Volume. */
-  template<typename Iter> void init(Iter begin, Iter end) { init(begin, end, 0, 0); }
-
-  /** Given an iterator range over \a Object references and an iterator range over their bounding boxes,
-    * constructs the BVH, overwriting whatever is in there currently. */
-  template<typename OIter, typename BIter> void init(OIter begin, OIter end, BIter boxBegin, BIter boxEnd)
-  {
-    objects.clear();
-    boxes.clear();
-    children.clear();
-
-    objects.insert(objects.end(), begin, end);
-    int n = static_cast<int>(objects.size());
-
-    if(n < 2)
-      return; //if we have at most one object, we don't need any internal nodes
-
-    VolumeList objBoxes;
-    VIPairList objCenters;
-
-    //compute the bounding boxes depending on BIter type
-    internal::get_boxes_helper<ObjectList, VolumeList, BIter>()(objects, boxBegin, boxEnd, objBoxes);
-
-    objCenters.reserve(n);
-    boxes.reserve(n - 1);
-    children.reserve(2 * n - 2);
-
-    for(int i = 0; i < n; ++i)
-      objCenters.push_back(VIPair(objBoxes[i].center(), i));
-
-    build(objCenters, 0, n, objBoxes, 0); //the recursive part of the algorithm
-
-    ObjectList tmp(n);
-    tmp.swap(objects);
-    for(int i = 0; i < n; ++i)
-      objects[i] = tmp[objCenters[i].second];
-  }
-
-  /** \returns the index of the root of the hierarchy */
-  inline Index getRootIndex() const { return (int)boxes.size() - 1; }
-
-  /** Given an \a index of a node, on exit, \a outVBegin and \a outVEnd range over the indices of the volume children of the node
-    * and \a outOBegin and \a outOEnd range over the object children of the node */
-  EIGEN_STRONG_INLINE void getChildren(Index index, VolumeIterator &outVBegin, VolumeIterator &outVEnd,
-                                       ObjectIterator &outOBegin, ObjectIterator &outOEnd) const
-  { //inlining this function should open lots of optimization opportunities to the compiler
-    if(index < 0) {
-      outVBegin = outVEnd;
-      if(!objects.empty())
-        outOBegin = &(objects[0]);
-      outOEnd = outOBegin + objects.size(); //output all objects--necessary when the tree has only one object
-      return;
-    }
-
-    int numBoxes = static_cast<int>(boxes.size());
-
-    int idx = index * 2;
-    if(children[idx + 1] < numBoxes) { //second index is always bigger
-      outVBegin = &(children[idx]);
-      outVEnd = outVBegin + 2;
-      outOBegin = outOEnd;
-    }
-    else if(children[idx] >= numBoxes) { //if both children are objects
-      outVBegin = outVEnd;
-      outOBegin = &(objects[children[idx] - numBoxes]);
-      outOEnd = outOBegin + 2;
-    } else { //if the first child is a volume and the second is an object
-      outVBegin = &(children[idx]);
-      outVEnd = outVBegin + 1;
-      outOBegin = &(objects[children[idx + 1] - numBoxes]);
-      outOEnd = outOBegin + 1;
-    }
-  }
-
-  /** \returns the bounding box of the node at \a index */
-  inline const Volume &getVolume(Index index) const
-  {
-    return boxes[index];
-  }
-
-private:
-  typedef internal::vector_int_pair<Scalar, Dim> VIPair;
-  typedef std::vector<VIPair, aligned_allocator<VIPair> > VIPairList;
-  typedef Matrix<Scalar, Dim, 1> VectorType;
-  struct VectorComparator //compares vectors, or more specifically, VIPairs along a particular dimension
-  {
-    VectorComparator(int inDim) : dim(inDim) {}
-    inline bool operator()(const VIPair &v1, const VIPair &v2) const { return v1.first[dim] < v2.first[dim]; }
-    int dim;
-  };
-
-  //Build the part of the tree between objects[from] and objects[to] (not including objects[to]).
-  //This routine partitions the objCenters in [from, to) along the dimension dim, recursively constructs
-  //the two halves, and adds their parent node.  TODO: a cache-friendlier layout
-  void build(VIPairList &objCenters, int from, int to, const VolumeList &objBoxes, int dim)
-  {
-    eigen_assert(to - from > 1);
-    if(to - from == 2) {
-      boxes.push_back(objBoxes[objCenters[from].second].merged(objBoxes[objCenters[from + 1].second]));
-      children.push_back(from + (int)objects.size() - 1); //there are objects.size() - 1 tree nodes
-      children.push_back(from + (int)objects.size());
-    }
-    else if(to - from == 3) {
-      int mid = from + 2;
-      std::nth_element(objCenters.begin() + from, objCenters.begin() + mid,
-                        objCenters.begin() + to, VectorComparator(dim)); //partition
-      build(objCenters, from, mid, objBoxes, (dim + 1) % Dim);
-      int idx1 = (int)boxes.size() - 1;
-      boxes.push_back(boxes[idx1].merged(objBoxes[objCenters[mid].second]));
-      children.push_back(idx1);
-      children.push_back(mid + (int)objects.size() - 1);
-    }
-    else {
-      int mid = from + (to - from) / 2;
-      nth_element(objCenters.begin() + from, objCenters.begin() + mid,
-                  objCenters.begin() + to, VectorComparator(dim)); //partition
-      build(objCenters, from, mid, objBoxes, (dim + 1) % Dim);
-      int idx1 = (int)boxes.size() - 1;
-      build(objCenters, mid, to, objBoxes, (dim + 1) % Dim);
-      int idx2 = (int)boxes.size() - 1;
-      boxes.push_back(boxes[idx1].merged(boxes[idx2]));
-      children.push_back(idx1);
-      children.push_back(idx2);
-    }
-  }
-
-  std::vector<int> children; //children of x are children[2x] and children[2x+1], indices bigger than boxes.size() index into objects.
-  VolumeList boxes;
-  ObjectList objects;
-};
-
-} // end namespace Eigen
-
-#endif //KDBVH_H_INCLUDED
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
deleted file mode 100644
index 0fbd847..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
+++ /dev/null
@@ -1,790 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 David Harmon <dharmon@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H
-#define EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H
-
-#include "../../../../Eigen/Dense"
-
-namespace Eigen { 
-
-namespace internal {
-  template<typename Scalar, typename RealScalar> struct arpack_wrapper;
-  template<typename MatrixSolver, typename MatrixType, typename Scalar, bool BisSPD> struct OP;
-}
-
-
-
-template<typename MatrixType, typename MatrixSolver=SimplicialLLT<MatrixType>, bool BisSPD=false>
-class ArpackGeneralizedSelfAdjointEigenSolver
-{
-public:
-  //typedef typename MatrixSolver::MatrixType MatrixType;
-
-  /** \brief Scalar type for matrices of type \p MatrixType. */
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::Index Index;
-
-  /** \brief Real scalar type for \p MatrixType.
-   *
-   * This is just \c Scalar if #Scalar is real (e.g., \c float or
-   * \c Scalar), and the type of the real part of \c Scalar if #Scalar is
-   * complex.
-   */
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-
-  /** \brief Type for vector of eigenvalues as returned by eigenvalues().
-   *
-   * This is a column vector with entries of type #RealScalar.
-   * The length of the vector is the size of \p nbrEigenvalues.
-   */
-  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
-
-  /** \brief Default constructor.
-   *
-   * The default constructor is for cases in which the user intends to
-   * perform decompositions via compute().
-   *
-   */
-  ArpackGeneralizedSelfAdjointEigenSolver()
-   : m_eivec(),
-     m_eivalues(),
-     m_isInitialized(false),
-     m_eigenvectorsOk(false),
-     m_nbrConverged(0),
-     m_nbrIterations(0)
-  { }
-
-  /** \brief Constructor; computes generalized eigenvalues of given matrix with respect to another matrix.
-   *
-   * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
-   *    computed. By default, the upper triangular part is used, but can be changed
-   *    through the template parameter.
-   * \param[in] B Self-adjoint matrix for the generalized eigenvalue problem.
-   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
-   *    Must be less than the size of the input matrix, or an error is returned.
-   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
-   *    respective meanings to find the largest magnitude , smallest magnitude,
-   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
-   *    value can contain floating point value in string form, in which case the
-   *    eigenvalues closest to this value will be found.
-   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
-   *    means machine precision.
-   *
-   * This constructor calls compute(const MatrixType&, const MatrixType&, Index, string, int, RealScalar)
-   * to compute the eigenvalues of the matrix \p A with respect to \p B. The eigenvectors are computed if
-   * \p options equals #ComputeEigenvectors.
-   *
-   */
-  ArpackGeneralizedSelfAdjointEigenSolver(const MatrixType& A, const MatrixType& B,
-                                          Index nbrEigenvalues, std::string eigs_sigma="LM",
-                               int options=ComputeEigenvectors, RealScalar tol=0.0)
-    : m_eivec(),
-      m_eivalues(),
-      m_isInitialized(false),
-      m_eigenvectorsOk(false),
-      m_nbrConverged(0),
-      m_nbrIterations(0)
-  {
-    compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);
-  }
-
-  /** \brief Constructor; computes eigenvalues of given matrix.
-   *
-   * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
-   *    computed. By default, the upper triangular part is used, but can be changed
-   *    through the template parameter.
-   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
-   *    Must be less than the size of the input matrix, or an error is returned.
-   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
-   *    respective meanings to find the largest magnitude , smallest magnitude,
-   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
-   *    value can contain floating point value in string form, in which case the
-   *    eigenvalues closest to this value will be found.
-   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
-   *    means machine precision.
-   *
-   * This constructor calls compute(const MatrixType&, Index, string, int, RealScalar)
-   * to compute the eigenvalues of the matrix \p A. The eigenvectors are computed if
-   * \p options equals #ComputeEigenvectors.
-   *
-   */
-
-  ArpackGeneralizedSelfAdjointEigenSolver(const MatrixType& A,
-                                          Index nbrEigenvalues, std::string eigs_sigma="LM",
-                               int options=ComputeEigenvectors, RealScalar tol=0.0)
-    : m_eivec(),
-      m_eivalues(),
-      m_isInitialized(false),
-      m_eigenvectorsOk(false),
-      m_nbrConverged(0),
-      m_nbrIterations(0)
-  {
-    compute(A, nbrEigenvalues, eigs_sigma, options, tol);
-  }
-
-
-  /** \brief Computes generalized eigenvalues / eigenvectors of given matrix using the external ARPACK library.
-   *
-   * \param[in]  A  Selfadjoint matrix whose eigendecomposition is to be computed.
-   * \param[in]  B  Selfadjoint matrix for generalized eigenvalues.
-   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
-   *    Must be less than the size of the input matrix, or an error is returned.
-   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
-   *    respective meanings to find the largest magnitude , smallest magnitude,
-   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
-   *    value can contain floating point value in string form, in which case the
-   *    eigenvalues closest to this value will be found.
-   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
-   *    means machine precision.
-   *
-   * \returns    Reference to \c *this
-   *
-   * This function computes the generalized eigenvalues of \p A with respect to \p B using ARPACK.  The eigenvalues()
-   * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
-   * then the eigenvectors are also computed and can be retrieved by
-   * calling eigenvectors().
-   *
-   */
-  ArpackGeneralizedSelfAdjointEigenSolver& compute(const MatrixType& A, const MatrixType& B,
-                                                   Index nbrEigenvalues, std::string eigs_sigma="LM",
-                                        int options=ComputeEigenvectors, RealScalar tol=0.0);
-  
-  /** \brief Computes eigenvalues / eigenvectors of given matrix using the external ARPACK library.
-   *
-   * \param[in]  A  Selfadjoint matrix whose eigendecomposition is to be computed.
-   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
-   *    Must be less than the size of the input matrix, or an error is returned.
-   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
-   *    respective meanings to find the largest magnitude , smallest magnitude,
-   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
-   *    value can contain floating point value in string form, in which case the
-   *    eigenvalues closest to this value will be found.
-   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
-   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
-   *    means machine precision.
-   *
-   * \returns    Reference to \c *this
-   *
-   * This function computes the eigenvalues of \p A using ARPACK.  The eigenvalues()
-   * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
-   * then the eigenvectors are also computed and can be retrieved by
-   * calling eigenvectors().
-   *
-   */
-  ArpackGeneralizedSelfAdjointEigenSolver& compute(const MatrixType& A,
-                                                   Index nbrEigenvalues, std::string eigs_sigma="LM",
-                                        int options=ComputeEigenvectors, RealScalar tol=0.0);
-
-
-  /** \brief Returns the eigenvectors of given matrix.
-   *
-   * \returns  A const reference to the matrix whose columns are the eigenvectors.
-   *
-   * \pre The eigenvectors have been computed before.
-   *
-   * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
-   * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
-   * eigenvectors are normalized to have (Euclidean) norm equal to one. If
-   * this object was used to solve the eigenproblem for the selfadjoint
-   * matrix \f$ A \f$, then the matrix returned by this function is the
-   * matrix \f$ V \f$ in the eigendecomposition \f$ A V = D V \f$.
-   * For the generalized eigenproblem, the matrix returned is the solution \f$ A V = D B V \f$
-   *
-   * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
-   * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out
-   *
-   * \sa eigenvalues()
-   */
-  const Matrix<Scalar, Dynamic, Dynamic>& eigenvectors() const
-  {
-    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
-    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-    return m_eivec;
-  }
-
-  /** \brief Returns the eigenvalues of given matrix.
-   *
-   * \returns A const reference to the column vector containing the eigenvalues.
-   *
-   * \pre The eigenvalues have been computed before.
-   *
-   * The eigenvalues are repeated according to their algebraic multiplicity,
-   * so there are as many eigenvalues as rows in the matrix. The eigenvalues
-   * are sorted in increasing order.
-   *
-   * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp
-   * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out
-   *
-   * \sa eigenvectors(), MatrixBase::eigenvalues()
-   */
-  const Matrix<Scalar, Dynamic, 1>& eigenvalues() const
-  {
-    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
-    return m_eivalues;
-  }
-
-  /** \brief Computes the positive-definite square root of the matrix.
-   *
-   * \returns the positive-definite square root of the matrix
-   *
-   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
-   * have been computed before.
-   *
-   * The square root of a positive-definite matrix \f$ A \f$ is the
-   * positive-definite matrix whose square equals \f$ A \f$. This function
-   * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the
-   * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$.
-   *
-   * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp
-   * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out
-   *
-   * \sa operatorInverseSqrt(),
-   *     \ref MatrixFunctions_Module "MatrixFunctions Module"
-   */
-  Matrix<Scalar, Dynamic, Dynamic> operatorSqrt() const
-  {
-    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-    return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
-  }
-
-  /** \brief Computes the inverse square root of the matrix.
-   *
-   * \returns the inverse positive-definite square root of the matrix
-   *
-   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
-   * have been computed before.
-   *
-   * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to
-   * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is
-   * cheaper than first computing the square root with operatorSqrt() and
-   * then its inverse with MatrixBase::inverse().
-   *
-   * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp
-   * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out
-   *
-   * \sa operatorSqrt(), MatrixBase::inverse(),
-   *     \ref MatrixFunctions_Module "MatrixFunctions Module"
-   */
-  Matrix<Scalar, Dynamic, Dynamic> operatorInverseSqrt() const
-  {
-    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
-    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
-    return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
-  }
-
-  /** \brief Reports whether previous computation was successful.
-   *
-   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
-   */
-  ComputationInfo info() const
-  {
-    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
-    return m_info;
-  }
-
-  size_t getNbrConvergedEigenValues() const
-  { return m_nbrConverged; }
-
-  size_t getNbrIterations() const
-  { return m_nbrIterations; }
-
-protected:
-  Matrix<Scalar, Dynamic, Dynamic> m_eivec;
-  Matrix<Scalar, Dynamic, 1> m_eivalues;
-  ComputationInfo m_info;
-  bool m_isInitialized;
-  bool m_eigenvectorsOk;
-
-  size_t m_nbrConverged;
-  size_t m_nbrIterations;
-};
-
-
-
-
-
-template<typename MatrixType, typename MatrixSolver, bool BisSPD>
-ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>&
-    ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>
-::compute(const MatrixType& A, Index nbrEigenvalues,
-          std::string eigs_sigma, int options, RealScalar tol)
-{
-    MatrixType B(0,0);
-    compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);
-    
-    return *this;
-}
-
-
-template<typename MatrixType, typename MatrixSolver, bool BisSPD>
-ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>&
-    ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>
-::compute(const MatrixType& A, const MatrixType& B, Index nbrEigenvalues,
-          std::string eigs_sigma, int options, RealScalar tol)
-{
-  eigen_assert(A.cols() == A.rows());
-  eigen_assert(B.cols() == B.rows());
-  eigen_assert(B.rows() == 0 || A.cols() == B.rows());
-  eigen_assert((options &~ (EigVecMask | GenEigMask)) == 0
-            && (options & EigVecMask) != EigVecMask
-            && "invalid option parameter");
-
-  bool isBempty = (B.rows() == 0) || (B.cols() == 0);
-
-  // For clarity, all parameters match their ARPACK name
-  //
-  // Always 0 on the first call
-  //
-  int ido = 0;
-
-  int n = (int)A.cols();
-
-  // User options: "LA", "SA", "SM", "LM", "BE"
-  //
-  char whch[3] = "LM";
-    
-  // Specifies the shift if iparam[6] = { 3, 4, 5 }, not used if iparam[6] = { 1, 2 }
-  //
-  RealScalar sigma = 0.0;
-
-  if (eigs_sigma.length() >= 2 && isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1]))
-  {
-      eigs_sigma[0] = toupper(eigs_sigma[0]);
-      eigs_sigma[1] = toupper(eigs_sigma[1]);
-
-      // In the following special case we're going to invert the problem, since solving
-      // for larger magnitude is much much faster
-      // i.e., if 'SM' is specified, we're going to really use 'LM', the default
-      //
-      if (eigs_sigma.substr(0,2) != "SM")
-      {
-          whch[0] = eigs_sigma[0];
-          whch[1] = eigs_sigma[1];
-      }
-  }
-  else
-  {
-      eigen_assert(false && "Specifying clustered eigenvalues is not yet supported!");
-
-      // If it's not scalar values, then the user may be explicitly
-      // specifying the sigma value to cluster the evs around
-      //
-      sigma = atof(eigs_sigma.c_str());
-
-      // If atof fails, it returns 0.0, which is a fine default
-      //
-  }
-
-  // "I" means normal eigenvalue problem, "G" means generalized
-  //
-  char bmat[2] = "I";
-  if (eigs_sigma.substr(0,2) == "SM" || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])) || (!isBempty && !BisSPD))
-      bmat[0] = 'G';
-
-  // Now we determine the mode to use
-  //
-  int mode = (bmat[0] == 'G') + 1;
-  if (eigs_sigma.substr(0,2) == "SM" || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])))
-  {
-      // We're going to use shift-and-invert mode, and basically find
-      // the largest eigenvalues of the inverse operator
-      //
-      mode = 3;
-  }
-
-  // The user-specified number of eigenvalues/vectors to compute
-  //
-  int nev = (int)nbrEigenvalues;
-
-  // Allocate space for ARPACK to store the residual
-  //
-  Scalar *resid = new Scalar[n];
-
-  // Number of Lanczos vectors, must satisfy nev < ncv <= n
-  // Note that this indicates that nev != n, and we cannot compute
-  // all eigenvalues of a mtrix
-  //
-  int ncv = std::min(std::max(2*nev, 20), n);
-
-  // The working n x ncv matrix, also store the final eigenvectors (if computed)
-  //
-  Scalar *v = new Scalar[n*ncv];
-  int ldv = n;
-
-  // Working space
-  //
-  Scalar *workd = new Scalar[3*n];
-  int lworkl = ncv*ncv+8*ncv; // Must be at least this length
-  Scalar *workl = new Scalar[lworkl];
-
-  int *iparam= new int[11];
-  iparam[0] = 1; // 1 means we let ARPACK perform the shifts, 0 means we'd have to do it
-  iparam[2] = std::max(300, (int)std::ceil(2*n/std::max(ncv,1)));
-  iparam[6] = mode; // The mode, 1 is standard ev problem, 2 for generalized ev, 3 for shift-and-invert
-
-  // Used during reverse communicate to notify where arrays start
-  //
-  int *ipntr = new int[11]; 
-
-  // Error codes are returned in here, initial value of 0 indicates a random initial
-  // residual vector is used, any other values means resid contains the initial residual
-  // vector, possibly from a previous run
-  //
-  int info = 0;
-
-  Scalar scale = 1.0;
-  //if (!isBempty)
-  //{
-  //Scalar scale = B.norm() / std::sqrt(n);
-  //scale = std::pow(2, std::floor(std::log(scale+1)));
-  ////M /= scale;
-  //for (size_t i=0; i<(size_t)B.outerSize(); i++)
-  //    for (typename MatrixType::InnerIterator it(B, i); it; ++it)
-  //        it.valueRef() /= scale;
-  //}
-
-  MatrixSolver OP;
-  if (mode == 1 || mode == 2)
-  {
-      if (!isBempty)
-          OP.compute(B);
-  }
-  else if (mode == 3)
-  {
-      if (sigma == 0.0)
-      {
-          OP.compute(A);
-      }
-      else
-      {
-          // Note: We will never enter here because sigma must be 0.0
-          //
-          if (isBempty)
-          {
-            MatrixType AminusSigmaB(A);
-            for (Index i=0; i<A.rows(); ++i)
-                AminusSigmaB.coeffRef(i,i) -= sigma;
-            
-            OP.compute(AminusSigmaB);
-          }
-          else
-          {
-              MatrixType AminusSigmaB = A - sigma * B;
-              OP.compute(AminusSigmaB);
-          }
-      }
-  }
- 
-  if (!(mode == 1 && isBempty) && !(mode == 2 && isBempty) && OP.info() != Success)
-      std::cout << "Error factoring matrix" << std::endl;
-
-  do
-  {
-    internal::arpack_wrapper<Scalar, RealScalar>::saupd(&ido, bmat, &n, whch, &nev, &tol, resid, 
-                                                        &ncv, v, &ldv, iparam, ipntr, workd, workl,
-                                                        &lworkl, &info);
-
-    if (ido == -1 || ido == 1)
-    {
-      Scalar *in  = workd + ipntr[0] - 1;
-      Scalar *out = workd + ipntr[1] - 1;
-
-      if (ido == 1 && mode != 2)
-      {
-          Scalar *out2 = workd + ipntr[2] - 1;
-          if (isBempty || mode == 1)
-            Matrix<Scalar, Dynamic, 1>::Map(out2, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
-          else
-            Matrix<Scalar, Dynamic, 1>::Map(out2, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
-          
-          in = workd + ipntr[2] - 1;
-      }
-
-      if (mode == 1)
-      {
-        if (isBempty)
-        {
-          // OP = A
-          //
-          Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
-        }
-        else
-        {
-          // OP = L^{-1}AL^{-T}
-          //
-          internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::applyOP(OP, A, n, in, out);
-        }
-      }
-      else if (mode == 2)
-      {
-        if (ido == 1)
-          Matrix<Scalar, Dynamic, 1>::Map(in, n)  = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
-        
-        // OP = B^{-1} A
-        //
-        Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
-      }
-      else if (mode == 3)
-      {
-        // OP = (A-\sigmaB)B (\sigma could be 0, and B could be I)
-        // The B * in is already computed and stored at in if ido == 1
-        //
-        if (ido == 1 || isBempty)
-          Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
-        else
-          Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(B * Matrix<Scalar, Dynamic, 1>::Map(in, n));
-      }
-    }
-    else if (ido == 2)
-    {
-      Scalar *in  = workd + ipntr[0] - 1;
-      Scalar *out = workd + ipntr[1] - 1;
-
-      if (isBempty || mode == 1)
-        Matrix<Scalar, Dynamic, 1>::Map(out, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
-      else
-        Matrix<Scalar, Dynamic, 1>::Map(out, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
-    }
-  } while (ido != 99);
-
-  if (info == 1)
-    m_info = NoConvergence;
-  else if (info == 3)
-    m_info = NumericalIssue;
-  else if (info < 0)
-    m_info = InvalidInput;
-  else if (info != 0)
-    eigen_assert(false && "Unknown ARPACK return value!");
-  else
-  {
-    // Do we compute eigenvectors or not?
-    //
-    int rvec = (options & ComputeEigenvectors) == ComputeEigenvectors;
-
-    // "A" means "All", use "S" to choose specific eigenvalues (not yet supported in ARPACK))
-    //
-    char howmny[2] = "A"; 
-
-    // if howmny == "S", specifies the eigenvalues to compute (not implemented in ARPACK)
-    //
-    int *select = new int[ncv];
-
-    // Final eigenvalues
-    //
-    m_eivalues.resize(nev, 1);
-
-    internal::arpack_wrapper<Scalar, RealScalar>::seupd(&rvec, howmny, select, m_eivalues.data(), v, &ldv,
-                                                        &sigma, bmat, &n, whch, &nev, &tol, resid, &ncv,
-                                                        v, &ldv, iparam, ipntr, workd, workl, &lworkl, &info);
-
-    if (info == -14)
-      m_info = NoConvergence;
-    else if (info != 0)
-      m_info = InvalidInput;
-    else
-    {
-      if (rvec)
-      {
-        m_eivec.resize(A.rows(), nev);
-        for (int i=0; i<nev; i++)
-          for (int j=0; j<n; j++)
-            m_eivec(j,i) = v[i*n+j] / scale;
-      
-        if (mode == 1 && !isBempty && BisSPD)
-          internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::project(OP, n, nev, m_eivec.data());
-
-        m_eigenvectorsOk = true;
-      }
-
-      m_nbrIterations = iparam[2];
-      m_nbrConverged  = iparam[4];
-
-      m_info = Success;
-    }
-
-    delete[] select;
-  }
-
-  delete[] v;
-  delete[] iparam;
-  delete[] ipntr;
-  delete[] workd;
-  delete[] workl;
-  delete[] resid;
-
-  m_isInitialized = true;
-
-  return *this;
-}
-
-
-// Single precision
-//
-extern "C" void ssaupd_(int *ido, char *bmat, int *n, char *which,
-    int *nev, float *tol, float *resid, int *ncv,
-    float *v, int *ldv, int *iparam, int *ipntr,
-    float *workd, float *workl, int *lworkl,
-    int *info);
-
-extern "C" void sseupd_(int *rvec, char *All, int *select, float *d,
-    float *z, int *ldz, float *sigma, 
-    char *bmat, int *n, char *which, int *nev,
-    float *tol, float *resid, int *ncv, float *v,
-    int *ldv, int *iparam, int *ipntr, float *workd,
-    float *workl, int *lworkl, int *ierr);
-
-// Double precision
-//
-extern "C" void dsaupd_(int *ido, char *bmat, int *n, char *which,
-    int *nev, double *tol, double *resid, int *ncv,
-    double *v, int *ldv, int *iparam, int *ipntr,
-    double *workd, double *workl, int *lworkl,
-    int *info);
-
-extern "C" void dseupd_(int *rvec, char *All, int *select, double *d,
-    double *z, int *ldz, double *sigma, 
-    char *bmat, int *n, char *which, int *nev,
-    double *tol, double *resid, int *ncv, double *v,
-    int *ldv, int *iparam, int *ipntr, double *workd,
-    double *workl, int *lworkl, int *ierr);
-
-
-namespace internal {
-
-template<typename Scalar, typename RealScalar> struct arpack_wrapper
-{
-  static inline void saupd(int *ido, char *bmat, int *n, char *which,
-      int *nev, RealScalar *tol, Scalar *resid, int *ncv,
-      Scalar *v, int *ldv, int *iparam, int *ipntr,
-      Scalar *workd, Scalar *workl, int *lworkl, int *info)
-  { 
-    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
-  }
-
-  static inline void seupd(int *rvec, char *All, int *select, Scalar *d,
-      Scalar *z, int *ldz, RealScalar *sigma,
-      char *bmat, int *n, char *which, int *nev,
-      RealScalar *tol, Scalar *resid, int *ncv, Scalar *v,
-      int *ldv, int *iparam, int *ipntr, Scalar *workd,
-      Scalar *workl, int *lworkl, int *ierr)
-  {
-    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
-  }
-};
-
-template <> struct arpack_wrapper<float, float>
-{
-  static inline void saupd(int *ido, char *bmat, int *n, char *which,
-      int *nev, float *tol, float *resid, int *ncv,
-      float *v, int *ldv, int *iparam, int *ipntr,
-      float *workd, float *workl, int *lworkl, int *info)
-  {
-    ssaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);
-  }
-
-  static inline void seupd(int *rvec, char *All, int *select, float *d,
-      float *z, int *ldz, float *sigma,
-      char *bmat, int *n, char *which, int *nev,
-      float *tol, float *resid, int *ncv, float *v,
-      int *ldv, int *iparam, int *ipntr, float *workd,
-      float *workl, int *lworkl, int *ierr)
-  {
-    sseupd_(rvec, All, select, d, z, ldz, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr,
-        workd, workl, lworkl, ierr);
-  }
-};
-
-template <> struct arpack_wrapper<double, double>
-{
-  static inline void saupd(int *ido, char *bmat, int *n, char *which,
-      int *nev, double *tol, double *resid, int *ncv,
-      double *v, int *ldv, int *iparam, int *ipntr,
-      double *workd, double *workl, int *lworkl, int *info)
-  {
-    dsaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);
-  }
-
-  static inline void seupd(int *rvec, char *All, int *select, double *d,
-      double *z, int *ldz, double *sigma,
-      char *bmat, int *n, char *which, int *nev,
-      double *tol, double *resid, int *ncv, double *v,
-      int *ldv, int *iparam, int *ipntr, double *workd,
-      double *workl, int *lworkl, int *ierr)
-  {
-    dseupd_(rvec, All, select, d, v, ldv, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr,
-        workd, workl, lworkl, ierr);
-  }
-};
-
-
-template<typename MatrixSolver, typename MatrixType, typename Scalar, bool BisSPD>
-struct OP
-{
-    static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out);
-    static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs);
-};
-
-template<typename MatrixSolver, typename MatrixType, typename Scalar>
-struct OP<MatrixSolver, MatrixType, Scalar, true>
-{
-  static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out)
-{
-    // OP = L^{-1} A L^{-T}  (B = LL^T)
-    //
-    // First solve L^T out = in
-    //
-    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixU().solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
-    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationPinv() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
-
-    // Then compute out = A out
-    //
-    Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(out, n);
-
-    // Then solve L out = out
-    //
-    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationP() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
-    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixL().solve(Matrix<Scalar, Dynamic, 1>::Map(out, n));
-}
-
-  static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs)
-{
-    // Solve L^T out = in
-    //
-    Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.matrixU().solve(Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k));
-    Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.permutationPinv() * Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k);
-}
-
-};
-
-template<typename MatrixSolver, typename MatrixType, typename Scalar>
-struct OP<MatrixSolver, MatrixType, Scalar, false>
-{
-  static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out)
-{
-    eigen_assert(false && "Should never be in here...");
-}
-
-  static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs)
-{
-    eigen_assert(false && "Should never be in here...");
-}
-
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_ARPACKSELFADJOINTEIGENSOLVER_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h
deleted file mode 100644
index e43cdb7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h
+++ /dev/null
@@ -1,355 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EULERANGLESCLASS_H// TODO: Fix previous "EIGEN_EULERANGLES_H" definition?
-#define EIGEN_EULERANGLESCLASS_H
-
-namespace Eigen
-{
-  /** \class EulerAngles
-    *
-    * \ingroup EulerAngles_Module
-    *
-    * \brief Represents a rotation in a 3 dimensional space as three Euler angles.
-    *
-    * Euler rotation is a set of three rotation of three angles over three fixed axes, defined by the EulerSystem given as a template parameter.
-    * 
-    * Here is how intrinsic Euler angles works:
-    *  - first, rotate the axes system over the alpha axis in angle alpha
-    *  - then, rotate the axes system over the beta axis(which was rotated in the first stage) in angle beta
-    *  - then, rotate the axes system over the gamma axis(which was rotated in the two stages above) in angle gamma
-    *
-    * \note This class support only intrinsic Euler angles for simplicity,
-    *  see EulerSystem how to easily overcome this for extrinsic systems.
-    *
-    * ### Rotation representation and conversions ###
-    *
-    * It has been proved(see Wikipedia link below) that every rotation can be represented
-    *  by Euler angles, but there is no single representation (e.g. unlike rotation matrices).
-    * Therefore, you can convert from Eigen rotation and to them
-    *  (including rotation matrices, which is not called "rotations" by Eigen design).
-    *
-    * Euler angles usually used for:
-    *  - convenient human representation of rotation, especially in interactive GUI.
-    *  - gimbal systems and robotics
-    *  - efficient encoding(i.e. 3 floats only) of rotation for network protocols.
-    *
-    * However, Euler angles are slow comparing to quaternion or matrices,
-    *  because their unnatural math definition, although it's simple for human.
-    * To overcome this, this class provide easy movement from the math friendly representation
-    *  to the human friendly representation, and vise-versa.
-    *
-    * All the user need to do is a safe simple C++ type conversion,
-    *  and this class take care for the math.
-    * Additionally, some axes related computation is done in compile time.
-    *
-    * #### Euler angles ranges in conversions ####
-    * Rotations representation as EulerAngles are not single (unlike matrices),
-    *  and even have infinite EulerAngles representations.<BR>
-    * For example, add or subtract 2*PI from either angle of EulerAngles
-    *  and you'll get the same rotation.
-    * This is the general reason for infinite representation,
-    *  but it's not the only general reason for not having a single representation.
-    *
-    * When converting rotation to EulerAngles, this class convert it to specific ranges
-    * When converting some rotation to EulerAngles, the rules for ranges are as follow:
-    * - If the rotation we converting from is an EulerAngles
-    *  (even when it represented as RotationBase explicitly), angles ranges are __undefined__.
-    * - otherwise, alpha and gamma angles will be in the range [-PI, PI].<BR>
-    *   As for Beta angle:
-    *    - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
-    *    - otherwise:
-    *      - If the beta axis is positive, the beta angle will be in the range [0, PI]
-    *      - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
-    *
-    * \sa EulerAngles(const MatrixBase<Derived>&)
-    * \sa EulerAngles(const RotationBase<Derived, 3>&)
-    *
-    * ### Convenient user typedefs ###
-    *
-    * Convenient typedefs for EulerAngles exist for float and double scalar,
-    *  in a form of EulerAngles{A}{B}{C}{scalar},
-    *  e.g. \ref EulerAnglesXYZd, \ref EulerAnglesZYZf.
-    *
-    * Only for positive axes{+x,+y,+z} Euler systems are have convenient typedef.
-    * If you need negative axes{-x,-y,-z}, it is recommended to create you own typedef with
-    *  a word that represent what you need.
-    *
-    * ### Example ###
-    *
-    * \include EulerAngles.cpp
-    * Output: \verbinclude EulerAngles.out
-    *
-    * ### Additional reading ###
-    *
-    * If you're want to get more idea about how Euler system work in Eigen see EulerSystem.
-    *
-    * More information about Euler angles: https://en.wikipedia.org/wiki/Euler_angles
-    *
-    * \tparam _Scalar the scalar type, i.e. the type of the angles.
-    *
-    * \tparam _System the EulerSystem to use, which represents the axes of rotation.
-    */
-  template <typename _Scalar, class _System>
-  class EulerAngles : public RotationBase<EulerAngles<_Scalar, _System>, 3>
-  {
-    public:
-      typedef RotationBase<EulerAngles<_Scalar, _System>, 3> Base;
-      
-      /** the scalar type of the angles */
-      typedef _Scalar Scalar;
-      typedef typename NumTraits<Scalar>::Real RealScalar;
-      
-      /** the EulerSystem to use, which represents the axes of rotation. */
-      typedef _System System;
-    
-      typedef Matrix<Scalar,3,3> Matrix3; /*!< the equivalent rotation matrix type */
-      typedef Matrix<Scalar,3,1> Vector3; /*!< the equivalent 3 dimension vector type */
-      typedef Quaternion<Scalar> QuaternionType; /*!< the equivalent quaternion type */
-      typedef AngleAxis<Scalar> AngleAxisType; /*!< the equivalent angle-axis type */
-      
-      /** \returns the axis vector of the first (alpha) rotation */
-      static Vector3 AlphaAxisVector() {
-        const Vector3& u = Vector3::Unit(System::AlphaAxisAbs - 1);
-        return System::IsAlphaOpposite ? -u : u;
-      }
-      
-      /** \returns the axis vector of the second (beta) rotation */
-      static Vector3 BetaAxisVector() {
-        const Vector3& u = Vector3::Unit(System::BetaAxisAbs - 1);
-        return System::IsBetaOpposite ? -u : u;
-      }
-      
-      /** \returns the axis vector of the third (gamma) rotation */
-      static Vector3 GammaAxisVector() {
-        const Vector3& u = Vector3::Unit(System::GammaAxisAbs - 1);
-        return System::IsGammaOpposite ? -u : u;
-      }
-
-    private:
-      Vector3 m_angles;
-
-    public:
-      /** Default constructor without initialization. */
-      EulerAngles() {}
-      /** Constructs and initialize an EulerAngles (\p alpha, \p beta, \p gamma). */
-      EulerAngles(const Scalar& alpha, const Scalar& beta, const Scalar& gamma) :
-        m_angles(alpha, beta, gamma) {}
-      
-      // TODO: Test this constructor
-      /** Constructs and initialize an EulerAngles from the array data {alpha, beta, gamma} */
-      explicit EulerAngles(const Scalar* data) : m_angles(data) {}
-      
-      /** Constructs and initializes an EulerAngles from either:
-        *  - a 3x3 rotation matrix expression(i.e. pure orthogonal matrix with determinant of +1),
-        *  - a 3D vector expression representing Euler angles.
-        *
-        * \note If \p other is a 3x3 rotation matrix, the angles range rules will be as follow:<BR>
-        *  Alpha and gamma angles will be in the range [-PI, PI].<BR>
-        *  As for Beta angle:
-        *   - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
-        *   - otherwise:
-        *     - If the beta axis is positive, the beta angle will be in the range [0, PI]
-        *     - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
-       */
-      template<typename Derived>
-      explicit EulerAngles(const MatrixBase<Derived>& other) { *this = other; }
-      
-      /** Constructs and initialize Euler angles from a rotation \p rot.
-        *
-        * \note If \p rot is an EulerAngles (even when it represented as RotationBase explicitly),
-        *  angles ranges are __undefined__.
-        *  Otherwise, alpha and gamma angles will be in the range [-PI, PI].<BR>
-        *  As for Beta angle:
-        *   - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
-        *   - otherwise:
-        *     - If the beta axis is positive, the beta angle will be in the range [0, PI]
-        *     - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
-      */
-      template<typename Derived>
-      EulerAngles(const RotationBase<Derived, 3>& rot) { System::CalcEulerAngles(*this, rot.toRotationMatrix()); }
-      
-      /*EulerAngles(const QuaternionType& q)
-      {
-        // TODO: Implement it in a faster way for quaternions
-        // According to http://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToEuler/
-        //  we can compute only the needed matrix cells and then convert to euler angles. (see ZYX example below)
-        // Currently we compute all matrix cells from quaternion.
-
-        // Special case only for ZYX
-        //Scalar y2 = q.y() * q.y();
-        //m_angles[0] = std::atan2(2*(q.w()*q.z() + q.x()*q.y()), (1 - 2*(y2 + q.z()*q.z())));
-        //m_angles[1] = std::asin( 2*(q.w()*q.y() - q.z()*q.x()));
-        //m_angles[2] = std::atan2(2*(q.w()*q.x() + q.y()*q.z()), (1 - 2*(q.x()*q.x() + y2)));
-      }*/
-
-      /** \returns The angle values stored in a vector (alpha, beta, gamma). */
-      const Vector3& angles() const { return m_angles; }
-      /** \returns A read-write reference to the angle values stored in a vector (alpha, beta, gamma). */
-      Vector3& angles() { return m_angles; }
-
-      /** \returns The value of the first angle. */
-      Scalar alpha() const { return m_angles[0]; }
-      /** \returns A read-write reference to the angle of the first angle. */
-      Scalar& alpha() { return m_angles[0]; }
-
-      /** \returns The value of the second angle. */
-      Scalar beta() const { return m_angles[1]; }
-      /** \returns A read-write reference to the angle of the second angle. */
-      Scalar& beta() { return m_angles[1]; }
-
-      /** \returns The value of the third angle. */
-      Scalar gamma() const { return m_angles[2]; }
-      /** \returns A read-write reference to the angle of the third angle. */
-      Scalar& gamma() { return m_angles[2]; }
-
-      /** \returns The Euler angles rotation inverse (which is as same as the negative),
-        *  (-alpha, -beta, -gamma).
-      */
-      EulerAngles inverse() const
-      {
-        EulerAngles res;
-        res.m_angles = -m_angles;
-        return res;
-      }
-
-      /** \returns The Euler angles rotation negative (which is as same as the inverse),
-        *  (-alpha, -beta, -gamma).
-      */
-      EulerAngles operator -() const
-      {
-        return inverse();
-      }
-      
-      /** Set \c *this from either:
-        *  - a 3x3 rotation matrix expression(i.e. pure orthogonal matrix with determinant of +1),
-        *  - a 3D vector expression representing Euler angles.
-        *
-        * See EulerAngles(const MatrixBase<Derived, 3>&) for more information about
-        *  angles ranges output.
-      */
-      template<class Derived>
-      EulerAngles& operator=(const MatrixBase<Derived>& other)
-      {
-        EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename Derived::Scalar>::value),
-         YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-        
-        internal::eulerangles_assign_impl<System, Derived>::run(*this, other.derived());
-        return *this;
-      }
-
-      // TODO: Assign and construct from another EulerAngles (with different system)
-      
-      /** Set \c *this from a rotation.
-        *
-        * See EulerAngles(const RotationBase<Derived, 3>&) for more information about
-        *  angles ranges output.
-      */
-      template<typename Derived>
-      EulerAngles& operator=(const RotationBase<Derived, 3>& rot) {
-        System::CalcEulerAngles(*this, rot.toRotationMatrix());
-        return *this;
-      }
-      
-      /** \returns \c true if \c *this is approximately equal to \a other, within the precision
-        * determined by \a prec.
-        *
-        * \sa MatrixBase::isApprox() */
-      bool isApprox(const EulerAngles& other,
-        const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
-      { return angles().isApprox(other.angles(), prec); }
-
-      /** \returns an equivalent 3x3 rotation matrix. */
-      Matrix3 toRotationMatrix() const
-      {
-        // TODO: Calc it faster
-        return static_cast<QuaternionType>(*this).toRotationMatrix();
-      }
-
-      /** Convert the Euler angles to quaternion. */
-      operator QuaternionType() const
-      {
-        return
-          AngleAxisType(alpha(), AlphaAxisVector()) *
-          AngleAxisType(beta(), BetaAxisVector())   *
-          AngleAxisType(gamma(), GammaAxisVector());
-      }
-      
-      friend std::ostream& operator<<(std::ostream& s, const EulerAngles<Scalar, System>& eulerAngles)
-      {
-        s << eulerAngles.angles().transpose();
-        return s;
-      }
-      
-      /** \returns \c *this with scalar type casted to \a NewScalarType */
-      template <typename NewScalarType>
-      EulerAngles<NewScalarType, System> cast() const
-      {
-        EulerAngles<NewScalarType, System> e;
-        e.angles() = angles().template cast<NewScalarType>();
-        return e;
-      }
-  };
-
-#define EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(AXES, SCALAR_TYPE, SCALAR_POSTFIX) \
-  /** \ingroup EulerAngles_Module */ \
-  typedef EulerAngles<SCALAR_TYPE, EulerSystem##AXES> EulerAngles##AXES##SCALAR_POSTFIX;
-
-#define EIGEN_EULER_ANGLES_TYPEDEFS(SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XYZ, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XYX, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XZY, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XZX, SCALAR_TYPE, SCALAR_POSTFIX) \
- \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YZX, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YZY, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YXZ, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YXY, SCALAR_TYPE, SCALAR_POSTFIX) \
- \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZXY, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZXZ, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZYX, SCALAR_TYPE, SCALAR_POSTFIX) \
-  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZYZ, SCALAR_TYPE, SCALAR_POSTFIX)
-
-EIGEN_EULER_ANGLES_TYPEDEFS(float, f)
-EIGEN_EULER_ANGLES_TYPEDEFS(double, d)
-
-  namespace internal
-  {
-    template<typename _Scalar, class _System>
-    struct traits<EulerAngles<_Scalar, _System> >
-    {
-      typedef _Scalar Scalar;
-    };
-    
-    // set from a rotation matrix
-    template<class System, class Other>
-    struct eulerangles_assign_impl<System,Other,3,3>
-    {
-      typedef typename Other::Scalar Scalar;
-      static void run(EulerAngles<Scalar, System>& e, const Other& m)
-      {
-        System::CalcEulerAngles(e, m);
-      }
-    };
-    
-    // set from a vector of Euler angles
-    template<class System, class Other>
-    struct eulerangles_assign_impl<System,Other,3,1>
-    {
-      typedef typename Other::Scalar Scalar;
-      static void run(EulerAngles<Scalar, System>& e, const Other& vec)
-      {
-        e.angles() = vec;
-      }
-    };
-  }
-}
-
-#endif // EIGEN_EULERANGLESCLASS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h
deleted file mode 100644
index 2a833b0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h
+++ /dev/null
@@ -1,305 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_EULERSYSTEM_H
-#define EIGEN_EULERSYSTEM_H
-
-namespace Eigen
-{
-  // Forward declarations
-  template <typename _Scalar, class _System>
-  class EulerAngles;
-  
-  namespace internal
-  {
-    // TODO: Add this trait to the Eigen internal API?
-    template <int Num, bool IsPositive = (Num > 0)>
-    struct Abs
-    {
-      enum { value = Num };
-    };
-  
-    template <int Num>
-    struct Abs<Num, false>
-    {
-      enum { value = -Num };
-    };
-
-    template <int Axis>
-    struct IsValidAxis
-    {
-      enum { value = Axis != 0 && Abs<Axis>::value <= 3 };
-    };
-    
-    template<typename System,
-            typename Other,
-            int OtherRows=Other::RowsAtCompileTime,
-            int OtherCols=Other::ColsAtCompileTime>
-    struct eulerangles_assign_impl;
-  }
-  
-  #define EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(COND,MSG) typedef char static_assertion_##MSG[(COND)?1:-1]
-  
-  /** \brief Representation of a fixed signed rotation axis for EulerSystem.
-    *
-    * \ingroup EulerAngles_Module
-    *
-    * Values here represent:
-    *  - The axis of the rotation: X, Y or Z.
-    *  - The sign (i.e. direction of the rotation along the axis): positive(+) or negative(-)
-    *
-    * Therefore, this could express all the axes {+X,+Y,+Z,-X,-Y,-Z}
-    *
-    * For positive axis, use +EULER_{axis}, and for negative axis use -EULER_{axis}.
-    */
-  enum EulerAxis
-  {
-    EULER_X = 1, /*!< the X axis */
-    EULER_Y = 2, /*!< the Y axis */
-    EULER_Z = 3  /*!< the Z axis */
-  };
-  
-  /** \class EulerSystem
-    *
-    * \ingroup EulerAngles_Module
-    *
-    * \brief Represents a fixed Euler rotation system.
-    *
-    * This meta-class goal is to represent the Euler system in compilation time, for EulerAngles.
-    *
-    * You can use this class to get two things:
-    *  - Build an Euler system, and then pass it as a template parameter to EulerAngles.
-    *  - Query some compile time data about an Euler system. (e.g. Whether it's Tait-Bryan)
-    *
-    * Euler rotation is a set of three rotation on fixed axes. (see \ref EulerAngles)
-    * This meta-class store constantly those signed axes. (see \ref EulerAxis)
-    *
-    * ### Types of Euler systems ###
-    *
-    * All and only valid 3 dimension Euler rotation over standard
-    *  signed axes{+X,+Y,+Z,-X,-Y,-Z} are supported:
-    *  - all axes X, Y, Z in each valid order (see below what order is valid)
-    *  - rotation over the axis is supported both over the positive and negative directions.
-    *  - both Tait-Bryan and proper/classic Euler angles (i.e. the opposite).
-    *
-    * Since EulerSystem support both positive and negative directions,
-    *  you may call this rotation distinction in other names:
-    *  - _right handed_ or _left handed_
-    *  - _counterclockwise_ or _clockwise_
-    *
-    * Notice all axed combination are valid, and would trigger a static assertion.
-    * Same unsigned axes can't be neighbors, e.g. {X,X,Y} is invalid.
-    * This yield two and only two classes:
-    *  - _Tait-Bryan_ - all unsigned axes are distinct, e.g. {X,Y,Z}
-    *  - _proper/classic Euler angles_ - The first and the third unsigned axes is equal,
-    *     and the second is different, e.g. {X,Y,X}
-    *
-    * ### Intrinsic vs extrinsic Euler systems ###
-    *
-    * Only intrinsic Euler systems are supported for simplicity.
-    *  If you want to use extrinsic Euler systems,
-    *   just use the equal intrinsic opposite order for axes and angles.
-    *  I.e axes (A,B,C) becomes (C,B,A), and angles (a,b,c) becomes (c,b,a).
-    *
-    * ### Convenient user typedefs ###
-    *
-    * Convenient typedefs for EulerSystem exist (only for positive axes Euler systems),
-    *  in a form of EulerSystem{A}{B}{C}, e.g. \ref EulerSystemXYZ.
-    *
-    * ### Additional reading ###
-    *
-    * More information about Euler angles: https://en.wikipedia.org/wiki/Euler_angles
-    *
-    * \tparam _AlphaAxis the first fixed EulerAxis
-    *
-    * \tparam _BetaAxis the second fixed EulerAxis
-    *
-    * \tparam _GammaAxis the third fixed EulerAxis
-    */
-  template <int _AlphaAxis, int _BetaAxis, int _GammaAxis>
-  class EulerSystem
-  {
-    public:
-    // It's defined this way and not as enum, because I think
-    //  that enum is not guerantee to support negative numbers
-    
-    /** The first rotation axis */
-    static const int AlphaAxis = _AlphaAxis;
-    
-    /** The second rotation axis */
-    static const int BetaAxis = _BetaAxis;
-    
-    /** The third rotation axis */
-    static const int GammaAxis = _GammaAxis;
-
-    enum
-    {
-      AlphaAxisAbs = internal::Abs<AlphaAxis>::value, /*!< the first rotation axis unsigned */
-      BetaAxisAbs = internal::Abs<BetaAxis>::value, /*!< the second rotation axis unsigned */
-      GammaAxisAbs = internal::Abs<GammaAxis>::value, /*!< the third rotation axis unsigned */
-      
-      IsAlphaOpposite = (AlphaAxis < 0) ? 1 : 0, /*!< whether alpha axis is negative */
-      IsBetaOpposite = (BetaAxis < 0) ? 1 : 0, /*!< whether beta axis is negative */
-      IsGammaOpposite = (GammaAxis < 0) ? 1 : 0, /*!< whether gamma axis is negative */
-
-      // Parity is even if alpha axis X is followed by beta axis Y, or Y is followed
-      // by Z, or Z is followed by X; otherwise it is odd.
-      IsOdd = ((AlphaAxisAbs)%3 == (BetaAxisAbs - 1)%3) ? 0 : 1, /*!< whether the Euler system is odd */
-      IsEven = IsOdd ? 0 : 1, /*!< whether the Euler system is even */
-
-      IsTaitBryan = ((unsigned)AlphaAxisAbs != (unsigned)GammaAxisAbs) ? 1 : 0 /*!< whether the Euler system is Tait-Bryan */
-    };
-    
-    private:
-    
-    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<AlphaAxis>::value,
-      ALPHA_AXIS_IS_INVALID);
-      
-    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<BetaAxis>::value,
-      BETA_AXIS_IS_INVALID);
-      
-    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<GammaAxis>::value,
-      GAMMA_AXIS_IS_INVALID);
-      
-    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT((unsigned)AlphaAxisAbs != (unsigned)BetaAxisAbs,
-      ALPHA_AXIS_CANT_BE_EQUAL_TO_BETA_AXIS);
-      
-    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT((unsigned)BetaAxisAbs != (unsigned)GammaAxisAbs,
-      BETA_AXIS_CANT_BE_EQUAL_TO_GAMMA_AXIS);
-
-    static const int
-      // I, J, K are the pivot indexes permutation for the rotation matrix, that match this Euler system. 
-      // They are used in this class converters.
-      // They are always different from each other, and their possible values are: 0, 1, or 2.
-      I_ = AlphaAxisAbs - 1,
-      J_ = (AlphaAxisAbs - 1 + 1 + IsOdd)%3,
-      K_ = (AlphaAxisAbs - 1 + 2 - IsOdd)%3
-    ;
-    
-    // TODO: Get @mat parameter in form that avoids double evaluation.
-    template <typename Derived>
-    static void CalcEulerAngles_imp(Matrix<typename MatrixBase<Derived>::Scalar, 3, 1>& res, const MatrixBase<Derived>& mat, internal::true_type /*isTaitBryan*/)
-    {
-      using std::atan2;
-      using std::sqrt;
-      
-      typedef typename Derived::Scalar Scalar;
-
-      const Scalar plusMinus = IsEven? 1 : -1;
-      const Scalar minusPlus = IsOdd?  1 : -1;
-
-      const Scalar Rsum = sqrt((mat(I_,I_) * mat(I_,I_) + mat(I_,J_) * mat(I_,J_) + mat(J_,K_) * mat(J_,K_) + mat(K_,K_) * mat(K_,K_))/2);
-      res[1] = atan2(plusMinus * mat(I_,K_), Rsum);
-
-      // There is a singularity when cos(beta) == 0
-      if(Rsum > 4 * NumTraits<Scalar>::epsilon()) {// cos(beta) != 0
-        res[0] = atan2(minusPlus * mat(J_, K_), mat(K_, K_));
-        res[2] = atan2(minusPlus * mat(I_, J_), mat(I_, I_));
-      }
-      else if(plusMinus * mat(I_, K_) > 0) {// cos(beta) == 0 and sin(beta) == 1
-        Scalar spos = mat(J_, I_) + plusMinus * mat(K_, J_); // 2*sin(alpha + plusMinus * gamma
-        Scalar cpos = mat(J_, J_) + minusPlus * mat(K_, I_); // 2*cos(alpha + plusMinus * gamma)
-        Scalar alphaPlusMinusGamma = atan2(spos, cpos);
-        res[0] = alphaPlusMinusGamma;
-        res[2] = 0;
-      }
-      else {// cos(beta) == 0 and sin(beta) == -1
-        Scalar sneg = plusMinus * (mat(K_, J_) + minusPlus * mat(J_, I_)); // 2*sin(alpha + minusPlus*gamma)
-        Scalar cneg = mat(J_, J_) + plusMinus * mat(K_, I_);               // 2*cos(alpha + minusPlus*gamma)
-        Scalar alphaMinusPlusBeta = atan2(sneg, cneg);
-        res[0] = alphaMinusPlusBeta;
-        res[2] = 0;
-      }
-    }
-
-    template <typename Derived>
-    static void CalcEulerAngles_imp(Matrix<typename MatrixBase<Derived>::Scalar,3,1>& res,
-                                    const MatrixBase<Derived>& mat, internal::false_type /*isTaitBryan*/)
-    {
-      using std::atan2;
-      using std::sqrt;
-
-      typedef typename Derived::Scalar Scalar;
-
-      const Scalar plusMinus = IsEven? 1 : -1;
-      const Scalar minusPlus = IsOdd?  1 : -1;
-
-      const Scalar Rsum = sqrt((mat(I_, J_) * mat(I_, J_) + mat(I_, K_) * mat(I_, K_) + mat(J_, I_) * mat(J_, I_) + mat(K_, I_) * mat(K_, I_)) / 2);
-
-      res[1] = atan2(Rsum, mat(I_, I_));
-
-      // There is a singularity when sin(beta) == 0
-      if(Rsum > 4 * NumTraits<Scalar>::epsilon()) {// sin(beta) != 0
-        res[0] = atan2(mat(J_, I_), minusPlus * mat(K_, I_));
-        res[2] = atan2(mat(I_, J_), plusMinus * mat(I_, K_));
-      }
-      else if(mat(I_, I_) > 0) {// sin(beta) == 0 and cos(beta) == 1
-        Scalar spos = plusMinus * mat(K_, J_) + minusPlus * mat(J_, K_); // 2*sin(alpha + gamma)
-        Scalar cpos = mat(J_, J_) + mat(K_, K_);                         // 2*cos(alpha + gamma)
-        res[0] = atan2(spos, cpos);
-        res[2] = 0;
-      }
-      else {// sin(beta) == 0 and cos(beta) == -1
-        Scalar sneg = plusMinus * mat(K_, J_) + plusMinus * mat(J_, K_); // 2*sin(alpha - gamma)
-        Scalar cneg = mat(J_, J_) - mat(K_, K_);                         // 2*cos(alpha - gamma)
-        res[0] = atan2(sneg, cneg);
-        res[2] = 0;
-      }
-    }
-    
-    template<typename Scalar>
-    static void CalcEulerAngles(
-      EulerAngles<Scalar, EulerSystem>& res,
-      const typename EulerAngles<Scalar, EulerSystem>::Matrix3& mat)
-    {
-      CalcEulerAngles_imp(
-        res.angles(), mat,
-        typename internal::conditional<IsTaitBryan, internal::true_type, internal::false_type>::type());
-
-      if (IsAlphaOpposite)
-        res.alpha() = -res.alpha();
-        
-      if (IsBetaOpposite)
-        res.beta() = -res.beta();
-        
-      if (IsGammaOpposite)
-        res.gamma() = -res.gamma();
-    }
-    
-    template <typename _Scalar, class _System>
-    friend class Eigen::EulerAngles;
-    
-    template<typename System,
-            typename Other,
-            int OtherRows,
-            int OtherCols>
-    friend struct internal::eulerangles_assign_impl;
-  };
-
-#define EIGEN_EULER_SYSTEM_TYPEDEF(A, B, C) \
-  /** \ingroup EulerAngles_Module */ \
-  typedef EulerSystem<EULER_##A, EULER_##B, EULER_##C> EulerSystem##A##B##C;
-  
-  EIGEN_EULER_SYSTEM_TYPEDEF(X,Y,Z)
-  EIGEN_EULER_SYSTEM_TYPEDEF(X,Y,X)
-  EIGEN_EULER_SYSTEM_TYPEDEF(X,Z,Y)
-  EIGEN_EULER_SYSTEM_TYPEDEF(X,Z,X)
-  
-  EIGEN_EULER_SYSTEM_TYPEDEF(Y,Z,X)
-  EIGEN_EULER_SYSTEM_TYPEDEF(Y,Z,Y)
-  EIGEN_EULER_SYSTEM_TYPEDEF(Y,X,Z)
-  EIGEN_EULER_SYSTEM_TYPEDEF(Y,X,Y)
-  
-  EIGEN_EULER_SYSTEM_TYPEDEF(Z,X,Y)
-  EIGEN_EULER_SYSTEM_TYPEDEF(Z,X,Z)
-  EIGEN_EULER_SYSTEM_TYPEDEF(Z,Y,X)
-  EIGEN_EULER_SYSTEM_TYPEDEF(Z,Y,Z)
-}
-
-#endif // EIGEN_EULERSYSTEM_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h
deleted file mode 100644
index 1c2cd24..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h
+++ /dev/null
@@ -1,261 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra. 
-//
-// Copyright (C) 2009 Mark Borgerding mark a borgerding net
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-namespace Eigen { 
-
-namespace internal {
-
-  // FFTW uses non-const arguments
-  // so we must use ugly const_cast calls for all the args it uses
-  //
-  // This should be safe as long as 
-  // 1. we use FFTW_ESTIMATE for all our planning
-  //       see the FFTW docs section 4.3.2 "Planner Flags"
-  // 2. fftw_complex is compatible with std::complex
-  //    This assumes std::complex<T> layout is array of size 2 with real,imag
-  template <typename T> 
-  inline 
-  T * fftw_cast(const T* p)
-  { 
-      return const_cast<T*>( p); 
-  }
-
-  inline 
-  fftw_complex * fftw_cast( const std::complex<double> * p)
-  {
-      return const_cast<fftw_complex*>( reinterpret_cast<const fftw_complex*>(p) ); 
-  }
-
-  inline 
-  fftwf_complex * fftw_cast( const std::complex<float> * p)
-  { 
-      return const_cast<fftwf_complex*>( reinterpret_cast<const fftwf_complex*>(p) ); 
-  }
-
-  inline 
-  fftwl_complex * fftw_cast( const std::complex<long double> * p)
-  { 
-      return const_cast<fftwl_complex*>( reinterpret_cast<const fftwl_complex*>(p) ); 
-  }
-
-  template <typename T> 
-  struct fftw_plan {};
-
-  template <> 
-  struct fftw_plan<float>
-  {
-      typedef float scalar_type;
-      typedef fftwf_complex complex_type;
-      fftwf_plan m_plan;
-      fftw_plan() :m_plan(NULL) {}
-      ~fftw_plan() {if (m_plan) fftwf_destroy_plan(m_plan);}
-
-      inline
-      void fwd(complex_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftwf_plan_dft_1d(nfft,src,dst, FFTW_FORWARD, FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwf_execute_dft( m_plan, src,dst);
-      }
-      inline
-      void inv(complex_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftwf_plan_dft_1d(nfft,src,dst, FFTW_BACKWARD , FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwf_execute_dft( m_plan, src,dst);
-      }
-      inline
-      void fwd(complex_type * dst,scalar_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftwf_plan_dft_r2c_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwf_execute_dft_r2c( m_plan,src,dst);
-      }
-      inline
-      void inv(scalar_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL)
-              m_plan = fftwf_plan_dft_c2r_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwf_execute_dft_c2r( m_plan, src,dst);
-      }
-
-      inline 
-      void fwd2( complex_type * dst,complex_type * src,int n0,int n1) {
-          if (m_plan==NULL) m_plan = fftwf_plan_dft_2d(n0,n1,src,dst,FFTW_FORWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwf_execute_dft( m_plan, src,dst);
-      }
-      inline 
-      void inv2( complex_type * dst,complex_type * src,int n0,int n1) {
-          if (m_plan==NULL) m_plan = fftwf_plan_dft_2d(n0,n1,src,dst,FFTW_BACKWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwf_execute_dft( m_plan, src,dst);
-      }
-
-  };
-  template <> 
-  struct fftw_plan<double>
-  {
-      typedef double scalar_type;
-      typedef fftw_complex complex_type;
-      ::fftw_plan m_plan;
-      fftw_plan() :m_plan(NULL) {}
-      ~fftw_plan() {if (m_plan) fftw_destroy_plan(m_plan);}
-
-      inline
-      void fwd(complex_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftw_plan_dft_1d(nfft,src,dst, FFTW_FORWARD, FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftw_execute_dft( m_plan, src,dst);
-      }
-      inline
-      void inv(complex_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftw_plan_dft_1d(nfft,src,dst, FFTW_BACKWARD , FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftw_execute_dft( m_plan, src,dst);
-      }
-      inline
-      void fwd(complex_type * dst,scalar_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftw_plan_dft_r2c_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftw_execute_dft_r2c( m_plan,src,dst);
-      }
-      inline
-      void inv(scalar_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL)
-              m_plan = fftw_plan_dft_c2r_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftw_execute_dft_c2r( m_plan, src,dst);
-      }
-      inline 
-      void fwd2( complex_type * dst,complex_type * src,int n0,int n1) {
-          if (m_plan==NULL) m_plan = fftw_plan_dft_2d(n0,n1,src,dst,FFTW_FORWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftw_execute_dft( m_plan, src,dst);
-      }
-      inline 
-      void inv2( complex_type * dst,complex_type * src,int n0,int n1) {
-          if (m_plan==NULL) m_plan = fftw_plan_dft_2d(n0,n1,src,dst,FFTW_BACKWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftw_execute_dft( m_plan, src,dst);
-      }
-  };
-  template <> 
-  struct fftw_plan<long double>
-  {
-      typedef long double scalar_type;
-      typedef fftwl_complex complex_type;
-      fftwl_plan m_plan;
-      fftw_plan() :m_plan(NULL) {}
-      ~fftw_plan() {if (m_plan) fftwl_destroy_plan(m_plan);}
-
-      inline
-      void fwd(complex_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftwl_plan_dft_1d(nfft,src,dst, FFTW_FORWARD, FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwl_execute_dft( m_plan, src,dst);
-      }
-      inline
-      void inv(complex_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftwl_plan_dft_1d(nfft,src,dst, FFTW_BACKWARD , FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwl_execute_dft( m_plan, src,dst);
-      }
-      inline
-      void fwd(complex_type * dst,scalar_type * src,int nfft) {
-          if (m_plan==NULL) m_plan = fftwl_plan_dft_r2c_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwl_execute_dft_r2c( m_plan,src,dst);
-      }
-      inline
-      void inv(scalar_type * dst,complex_type * src,int nfft) {
-          if (m_plan==NULL)
-              m_plan = fftwl_plan_dft_c2r_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwl_execute_dft_c2r( m_plan, src,dst);
-      }
-      inline 
-      void fwd2( complex_type * dst,complex_type * src,int n0,int n1) {
-          if (m_plan==NULL) m_plan = fftwl_plan_dft_2d(n0,n1,src,dst,FFTW_FORWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwl_execute_dft( m_plan, src,dst);
-      }
-      inline 
-      void inv2( complex_type * dst,complex_type * src,int n0,int n1) {
-          if (m_plan==NULL) m_plan = fftwl_plan_dft_2d(n0,n1,src,dst,FFTW_BACKWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
-          fftwl_execute_dft( m_plan, src,dst);
-      }
-  };
-
-  template <typename _Scalar>
-  struct fftw_impl
-  {
-      typedef _Scalar Scalar;
-      typedef std::complex<Scalar> Complex;
-
-      inline
-      void clear() 
-      {
-        m_plans.clear();
-      }
-
-      // complex-to-complex forward FFT
-      inline
-      void fwd( Complex * dst,const Complex *src,int nfft)
-      {
-        get_plan(nfft,false,dst,src).fwd(fftw_cast(dst), fftw_cast(src),nfft );
-      }
-
-      // real-to-complex forward FFT
-      inline
-      void fwd( Complex * dst,const Scalar * src,int nfft) 
-      {
-          get_plan(nfft,false,dst,src).fwd(fftw_cast(dst), fftw_cast(src) ,nfft);
-      }
-
-      // 2-d complex-to-complex
-      inline
-      void fwd2(Complex * dst, const Complex * src, int n0,int n1)
-      {
-          get_plan(n0,n1,false,dst,src).fwd2(fftw_cast(dst), fftw_cast(src) ,n0,n1);
-      }
-
-      // inverse complex-to-complex
-      inline
-      void inv(Complex * dst,const Complex  *src,int nfft)
-      {
-        get_plan(nfft,true,dst,src).inv(fftw_cast(dst), fftw_cast(src),nfft );
-      }
-
-      // half-complex to scalar
-      inline
-      void inv( Scalar * dst,const Complex * src,int nfft) 
-      {
-        get_plan(nfft,true,dst,src).inv(fftw_cast(dst), fftw_cast(src),nfft );
-      }
-
-      // 2-d complex-to-complex
-      inline
-      void inv2(Complex * dst, const Complex * src, int n0,int n1)
-      {
-        get_plan(n0,n1,true,dst,src).inv2(fftw_cast(dst), fftw_cast(src) ,n0,n1);
-      }
-
-
-  protected:
-      typedef fftw_plan<Scalar> PlanData;
-
-      typedef Eigen::numext::int64_t int64_t;
-
-      typedef std::map<int64_t,PlanData> PlanMap;
-
-      PlanMap m_plans;
-
-      inline
-      PlanData & get_plan(int nfft,bool inverse,void * dst,const void * src)
-      {
-          bool inplace = (dst==src);
-          bool aligned = ( (reinterpret_cast<size_t>(src)&15) | (reinterpret_cast<size_t>(dst)&15) ) == 0;
-          int64_t key = ( (nfft<<3 ) | (inverse<<2) | (inplace<<1) | aligned ) << 1;
-          return m_plans[key];
-      }
-
-      inline
-      PlanData & get_plan(int n0,int n1,bool inverse,void * dst,const void * src)
-      {
-          bool inplace = (dst==src);
-          bool aligned = ( (reinterpret_cast<size_t>(src)&15) | (reinterpret_cast<size_t>(dst)&15) ) == 0;
-          int64_t key = ( ( (((int64_t)n0) << 30)|(n1<<3 ) | (inverse<<2) | (inplace<<1) | aligned ) << 1 ) + 1;
-          return m_plans[key];
-      }
-  };
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
deleted file mode 100644
index 430953a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
+++ /dev/null
@@ -1,449 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Mark Borgerding mark a borgerding net
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-namespace Eigen { 
-
-namespace internal {
-
-  // This FFT implementation was derived from kissfft http:sourceforge.net/projects/kissfft
-  // Copyright 2003-2009 Mark Borgerding
-
-template <typename _Scalar>
-struct kiss_cpx_fft
-{
-  typedef _Scalar Scalar;
-  typedef std::complex<Scalar> Complex;
-  std::vector<Complex> m_twiddles;
-  std::vector<int> m_stageRadix;
-  std::vector<int> m_stageRemainder;
-  std::vector<Complex> m_scratchBuf;
-  bool m_inverse;
-
-  inline void make_twiddles(int nfft, bool inverse)
-  {
-    using numext::sin;
-    using numext::cos;
-    m_inverse = inverse;
-    m_twiddles.resize(nfft);
-    double phinc =  0.25 * double(EIGEN_PI) / nfft;
-    Scalar flip = inverse ? Scalar(1) : Scalar(-1);
-    m_twiddles[0] = Complex(Scalar(1), Scalar(0));
-    if ((nfft&1)==0)
-      m_twiddles[nfft/2] = Complex(Scalar(-1), Scalar(0));
-    int i=1;
-    for (;i*8<nfft;++i)
-    {
-      Scalar c = Scalar(cos(i*8*phinc));
-      Scalar s = Scalar(sin(i*8*phinc));
-      m_twiddles[i] = Complex(c, s*flip);
-      m_twiddles[nfft-i] = Complex(c, -s*flip);
-    }
-    for (;i*4<nfft;++i)
-    {
-      Scalar c = Scalar(cos((2*nfft-8*i)*phinc));
-      Scalar s = Scalar(sin((2*nfft-8*i)*phinc));
-      m_twiddles[i] = Complex(s, c*flip);
-      m_twiddles[nfft-i] = Complex(s, -c*flip);
-    }
-    for (;i*8<3*nfft;++i)
-    {
-      Scalar c = Scalar(cos((8*i-2*nfft)*phinc));
-      Scalar s = Scalar(sin((8*i-2*nfft)*phinc));
-      m_twiddles[i] = Complex(-s, c*flip);
-      m_twiddles[nfft-i] = Complex(-s, -c*flip);
-    }
-    for (;i*2<nfft;++i)
-    {
-      Scalar c = Scalar(cos((4*nfft-8*i)*phinc));
-      Scalar s = Scalar(sin((4*nfft-8*i)*phinc));
-      m_twiddles[i] = Complex(-c, s*flip);
-      m_twiddles[nfft-i] = Complex(-c, -s*flip);
-    }
-  }
-
-  void factorize(int nfft)
-  {
-    //start factoring out 4's, then 2's, then 3,5,7,9,...
-    int n= nfft;
-    int p=4;
-    do {
-      while (n % p) {
-        switch (p) {
-          case 4: p = 2; break;
-          case 2: p = 3; break;
-          default: p += 2; break;
-        }
-        if (p*p>n)
-          p=n;// impossible to have a factor > sqrt(n)
-      }
-      n /= p;
-      m_stageRadix.push_back(p);
-      m_stageRemainder.push_back(n);
-      if ( p > 5 )
-        m_scratchBuf.resize(p); // scratchbuf will be needed in bfly_generic
-    }while(n>1);
-  }
-
-  template <typename _Src>
-    inline
-    void work( int stage,Complex * xout, const _Src * xin, size_t fstride,size_t in_stride)
-    {
-      int p = m_stageRadix[stage];
-      int m = m_stageRemainder[stage];
-      Complex * Fout_beg = xout;
-      Complex * Fout_end = xout + p*m;
-
-      if (m>1) {
-        do{
-          // recursive call:
-          // DFT of size m*p performed by doing
-          // p instances of smaller DFTs of size m, 
-          // each one takes a decimated version of the input
-          work(stage+1, xout , xin, fstride*p,in_stride);
-          xin += fstride*in_stride;
-        }while( (xout += m) != Fout_end );
-      }else{
-        do{
-          *xout = *xin;
-          xin += fstride*in_stride;
-        }while(++xout != Fout_end );
-      }
-      xout=Fout_beg;
-
-      // recombine the p smaller DFTs 
-      switch (p) {
-        case 2: bfly2(xout,fstride,m); break;
-        case 3: bfly3(xout,fstride,m); break;
-        case 4: bfly4(xout,fstride,m); break;
-        case 5: bfly5(xout,fstride,m); break;
-        default: bfly_generic(xout,fstride,m,p); break;
-      }
-    }
-
-  inline
-    void bfly2( Complex * Fout, const size_t fstride, int m)
-    {
-      for (int k=0;k<m;++k) {
-        Complex t = Fout[m+k] * m_twiddles[k*fstride];
-        Fout[m+k] = Fout[k] - t;
-        Fout[k] += t;
-      }
-    }
-
-  inline
-    void bfly4( Complex * Fout, const size_t fstride, const size_t m)
-    {
-      Complex scratch[6];
-      int negative_if_inverse = m_inverse * -2 +1;
-      for (size_t k=0;k<m;++k) {
-        scratch[0] = Fout[k+m] * m_twiddles[k*fstride];
-        scratch[1] = Fout[k+2*m] * m_twiddles[k*fstride*2];
-        scratch[2] = Fout[k+3*m] * m_twiddles[k*fstride*3];
-        scratch[5] = Fout[k] - scratch[1];
-
-        Fout[k] += scratch[1];
-        scratch[3] = scratch[0] + scratch[2];
-        scratch[4] = scratch[0] - scratch[2];
-        scratch[4] = Complex( scratch[4].imag()*negative_if_inverse , -scratch[4].real()* negative_if_inverse );
-
-        Fout[k+2*m]  = Fout[k] - scratch[3];
-        Fout[k] += scratch[3];
-        Fout[k+m] = scratch[5] + scratch[4];
-        Fout[k+3*m] = scratch[5] - scratch[4];
-      }
-    }
-
-  inline
-    void bfly3( Complex * Fout, const size_t fstride, const size_t m)
-    {
-      size_t k=m;
-      const size_t m2 = 2*m;
-      Complex *tw1,*tw2;
-      Complex scratch[5];
-      Complex epi3;
-      epi3 = m_twiddles[fstride*m];
-
-      tw1=tw2=&m_twiddles[0];
-
-      do{
-        scratch[1]=Fout[m] * *tw1;
-        scratch[2]=Fout[m2] * *tw2;
-
-        scratch[3]=scratch[1]+scratch[2];
-        scratch[0]=scratch[1]-scratch[2];
-        tw1 += fstride;
-        tw2 += fstride*2;
-        Fout[m] = Complex( Fout->real() - Scalar(.5)*scratch[3].real() , Fout->imag() - Scalar(.5)*scratch[3].imag() );
-        scratch[0] *= epi3.imag();
-        *Fout += scratch[3];
-        Fout[m2] = Complex(  Fout[m].real() + scratch[0].imag() , Fout[m].imag() - scratch[0].real() );
-        Fout[m] += Complex( -scratch[0].imag(),scratch[0].real() );
-        ++Fout;
-      }while(--k);
-    }
-
-  inline
-    void bfly5( Complex * Fout, const size_t fstride, const size_t m)
-    {
-      Complex *Fout0,*Fout1,*Fout2,*Fout3,*Fout4;
-      size_t u;
-      Complex scratch[13];
-      Complex * twiddles = &m_twiddles[0];
-      Complex *tw;
-      Complex ya,yb;
-      ya = twiddles[fstride*m];
-      yb = twiddles[fstride*2*m];
-
-      Fout0=Fout;
-      Fout1=Fout0+m;
-      Fout2=Fout0+2*m;
-      Fout3=Fout0+3*m;
-      Fout4=Fout0+4*m;
-
-      tw=twiddles;
-      for ( u=0; u<m; ++u ) {
-        scratch[0] = *Fout0;
-
-        scratch[1]  = *Fout1 * tw[u*fstride];
-        scratch[2]  = *Fout2 * tw[2*u*fstride];
-        scratch[3]  = *Fout3 * tw[3*u*fstride];
-        scratch[4]  = *Fout4 * tw[4*u*fstride];
-
-        scratch[7] = scratch[1] + scratch[4];
-        scratch[10] = scratch[1] - scratch[4];
-        scratch[8] = scratch[2] + scratch[3];
-        scratch[9] = scratch[2] - scratch[3];
-
-        *Fout0 +=  scratch[7];
-        *Fout0 +=  scratch[8];
-
-        scratch[5] = scratch[0] + Complex(
-            (scratch[7].real()*ya.real() ) + (scratch[8].real() *yb.real() ),
-            (scratch[7].imag()*ya.real()) + (scratch[8].imag()*yb.real())
-            );
-
-        scratch[6] = Complex(
-            (scratch[10].imag()*ya.imag()) + (scratch[9].imag()*yb.imag()),
-            -(scratch[10].real()*ya.imag()) - (scratch[9].real()*yb.imag())
-            );
-
-        *Fout1 = scratch[5] - scratch[6];
-        *Fout4 = scratch[5] + scratch[6];
-
-        scratch[11] = scratch[0] +
-          Complex(
-              (scratch[7].real()*yb.real()) + (scratch[8].real()*ya.real()),
-              (scratch[7].imag()*yb.real()) + (scratch[8].imag()*ya.real())
-              );
-
-        scratch[12] = Complex(
-            -(scratch[10].imag()*yb.imag()) + (scratch[9].imag()*ya.imag()),
-            (scratch[10].real()*yb.imag()) - (scratch[9].real()*ya.imag())
-            );
-
-        *Fout2=scratch[11]+scratch[12];
-        *Fout3=scratch[11]-scratch[12];
-
-        ++Fout0;++Fout1;++Fout2;++Fout3;++Fout4;
-      }
-    }
-
-  /* perform the butterfly for one stage of a mixed radix FFT */
-  inline
-    void bfly_generic(
-        Complex * Fout,
-        const size_t fstride,
-        int m,
-        int p
-        )
-    {
-      int u,k,q1,q;
-      Complex * twiddles = &m_twiddles[0];
-      Complex t;
-      int Norig = static_cast<int>(m_twiddles.size());
-      Complex * scratchbuf = &m_scratchBuf[0];
-
-      for ( u=0; u<m; ++u ) {
-        k=u;
-        for ( q1=0 ; q1<p ; ++q1 ) {
-          scratchbuf[q1] = Fout[ k  ];
-          k += m;
-        }
-
-        k=u;
-        for ( q1=0 ; q1<p ; ++q1 ) {
-          int twidx=0;
-          Fout[ k ] = scratchbuf[0];
-          for (q=1;q<p;++q ) {
-            twidx += static_cast<int>(fstride) * k;
-            if (twidx>=Norig) twidx-=Norig;
-            t=scratchbuf[q] * twiddles[twidx];
-            Fout[ k ] += t;
-          }
-          k += m;
-        }
-      }
-    }
-};
-
-template <typename _Scalar>
-struct kissfft_impl
-{
-  typedef _Scalar Scalar;
-  typedef std::complex<Scalar> Complex;
-
-  void clear() 
-  {
-    m_plans.clear();
-    m_realTwiddles.clear();
-  }
-
-  inline
-    void fwd( Complex * dst,const Complex *src,int nfft)
-    {
-      get_plan(nfft,false).work(0, dst, src, 1,1);
-    }
-
-  inline
-    void fwd2( Complex * dst,const Complex *src,int n0,int n1)
-    {
-        EIGEN_UNUSED_VARIABLE(dst);
-        EIGEN_UNUSED_VARIABLE(src);
-        EIGEN_UNUSED_VARIABLE(n0);
-        EIGEN_UNUSED_VARIABLE(n1);
-    }
-
-  inline
-    void inv2( Complex * dst,const Complex *src,int n0,int n1)
-    {
-        EIGEN_UNUSED_VARIABLE(dst);
-        EIGEN_UNUSED_VARIABLE(src);
-        EIGEN_UNUSED_VARIABLE(n0);
-        EIGEN_UNUSED_VARIABLE(n1);
-    }
-
-  // real-to-complex forward FFT
-  // perform two FFTs of src even and src odd
-  // then twiddle to recombine them into the half-spectrum format
-  // then fill in the conjugate symmetric half
-  inline
-    void fwd( Complex * dst,const Scalar * src,int nfft) 
-    {
-      if ( nfft&3  ) {
-        // use generic mode for odd
-        m_tmpBuf1.resize(nfft);
-        get_plan(nfft,false).work(0, &m_tmpBuf1[0], src, 1,1);
-        std::copy(m_tmpBuf1.begin(),m_tmpBuf1.begin()+(nfft>>1)+1,dst );
-      }else{
-        int ncfft = nfft>>1;
-        int ncfft2 = nfft>>2;
-        Complex * rtw = real_twiddles(ncfft2);
-
-        // use optimized mode for even real
-        fwd( dst, reinterpret_cast<const Complex*> (src), ncfft);
-        Complex dc(dst[0].real() +  dst[0].imag());
-        Complex nyquist(dst[0].real() -  dst[0].imag());
-        int k;
-        for ( k=1;k <= ncfft2 ; ++k ) {
-          Complex fpk = dst[k];
-          Complex fpnk = conj(dst[ncfft-k]);
-          Complex f1k = fpk + fpnk;
-          Complex f2k = fpk - fpnk;
-          Complex tw= f2k * rtw[k-1];
-          dst[k] =  (f1k + tw) * Scalar(.5);
-          dst[ncfft-k] =  conj(f1k -tw)*Scalar(.5);
-        }
-        dst[0] = dc;
-        dst[ncfft] = nyquist;
-      }
-    }
-
-  // inverse complex-to-complex
-  inline
-    void inv(Complex * dst,const Complex  *src,int nfft)
-    {
-      get_plan(nfft,true).work(0, dst, src, 1,1);
-    }
-
-  // half-complex to scalar
-  inline
-    void inv( Scalar * dst,const Complex * src,int nfft) 
-    {
-      if (nfft&3) {
-        m_tmpBuf1.resize(nfft);
-        m_tmpBuf2.resize(nfft);
-        std::copy(src,src+(nfft>>1)+1,m_tmpBuf1.begin() );
-        for (int k=1;k<(nfft>>1)+1;++k)
-          m_tmpBuf1[nfft-k] = conj(m_tmpBuf1[k]);
-        inv(&m_tmpBuf2[0],&m_tmpBuf1[0],nfft);
-        for (int k=0;k<nfft;++k)
-          dst[k] = m_tmpBuf2[k].real();
-      }else{
-        // optimized version for multiple of 4
-        int ncfft = nfft>>1;
-        int ncfft2 = nfft>>2;
-        Complex * rtw = real_twiddles(ncfft2);
-        m_tmpBuf1.resize(ncfft);
-        m_tmpBuf1[0] = Complex( src[0].real() + src[ncfft].real(), src[0].real() - src[ncfft].real() );
-        for (int k = 1; k <= ncfft / 2; ++k) {
-          Complex fk = src[k];
-          Complex fnkc = conj(src[ncfft-k]);
-          Complex fek = fk + fnkc;
-          Complex tmp = fk - fnkc;
-          Complex fok = tmp * conj(rtw[k-1]);
-          m_tmpBuf1[k] = fek + fok;
-          m_tmpBuf1[ncfft-k] = conj(fek - fok);
-        }
-        get_plan(ncfft,true).work(0, reinterpret_cast<Complex*>(dst), &m_tmpBuf1[0], 1,1);
-      }
-    }
-
-  protected:
-  typedef kiss_cpx_fft<Scalar> PlanData;
-  typedef std::map<int,PlanData> PlanMap;
-
-  PlanMap m_plans;
-  std::map<int, std::vector<Complex> > m_realTwiddles;
-  std::vector<Complex> m_tmpBuf1;
-  std::vector<Complex> m_tmpBuf2;
-
-  inline
-    int PlanKey(int nfft, bool isinverse) const { return (nfft<<1) | int(isinverse); }
-
-  inline
-    PlanData & get_plan(int nfft, bool inverse)
-    {
-      // TODO look for PlanKey(nfft, ! inverse) and conjugate the twiddles
-      PlanData & pd = m_plans[ PlanKey(nfft,inverse) ];
-      if ( pd.m_twiddles.size() == 0 ) {
-        pd.make_twiddles(nfft,inverse);
-        pd.factorize(nfft);
-      }
-      return pd;
-    }
-
-  inline
-    Complex * real_twiddles(int ncfft2)
-    {
-      using std::acos;
-      std::vector<Complex> & twidref = m_realTwiddles[ncfft2];// creates new if not there
-      if ( (int)twidref.size() != ncfft2 ) {
-        twidref.resize(ncfft2);
-        int ncfft= ncfft2<<1;
-        Scalar pi =  acos( Scalar(-1) );
-        for (int k=1;k<=ncfft2;++k) 
-          twidref[k-1] = exp( Complex(0,-pi * (Scalar(k) / ncfft + Scalar(.5)) ) );
-      }
-      return &twidref[0];
-    }
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
deleted file mode 100644
index e7d70f3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
+++ /dev/null
@@ -1,187 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-
-/* NOTE The functions of this file have been adapted from the GMM++ library */
-
-//========================================================================
-//
-// Copyright (C) 2002-2007 Yves Renard
-//
-// This file is a part of GETFEM++
-//
-// Getfem++ is free software; you can redistribute it and/or modify
-// it under the terms of the GNU Lesser General Public License as
-// published by the Free Software Foundation; version 2.1 of the License.
-//
-// This program is distributed in the hope that it will be useful,
-// but WITHOUT ANY WARRANTY; without even the implied warranty of
-// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-// GNU Lesser General Public License for more details.
-// You should have received a copy of the GNU Lesser General Public
-// License along with this program; if not, write to the Free Software
-// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301,
-// USA.
-//
-//========================================================================
-
-#include "../../../../Eigen/src/Core/util/NonMPL2.h"
-
-#ifndef EIGEN_CONSTRAINEDCG_H
-#define EIGEN_CONSTRAINEDCG_H
-
-#include "../../../../Eigen/Core"
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \ingroup IterativeLinearSolvers_Module
-  * Compute the pseudo inverse of the non-square matrix C such that
-  * \f$ CINV = (C * C^T)^{-1} * C \f$ based on a conjugate gradient method.
-  *
-  * This function is internally used by constrained_cg.
-  */
-template <typename CMatrix, typename CINVMatrix>
-void pseudo_inverse(const CMatrix &C, CINVMatrix &CINV)
-{
-  // optimisable : copie de la ligne, precalcul de C * trans(C).
-  typedef typename CMatrix::Scalar Scalar;
-  typedef typename CMatrix::Index Index;
-  // FIXME use sparse vectors ?
-  typedef Matrix<Scalar,Dynamic,1> TmpVec;
-
-  Index rows = C.rows(), cols = C.cols();
-
-  TmpVec d(rows), e(rows), l(cols), p(rows), q(rows), r(rows);
-  Scalar rho, rho_1, alpha;
-  d.setZero();
-
-  typedef Triplet<double> T;
-  std::vector<T> tripletList;
-    
-  for (Index i = 0; i < rows; ++i)
-  {
-    d[i] = 1.0;
-    rho = 1.0;
-    e.setZero();
-    r = d;
-    p = d;
-
-    while (rho >= 1e-38)
-    { /* conjugate gradient to compute e             */
-      /* which is the i-th row of inv(C * trans(C))  */
-      l = C.transpose() * p;
-      q = C * l;
-      alpha = rho / p.dot(q);
-      e +=  alpha * p;
-      r += -alpha * q;
-      rho_1 = rho;
-      rho = r.dot(r);
-      p = (rho/rho_1) * p + r;
-    }
-
-    l = C.transpose() * e; // l is the i-th row of CINV
-    // FIXME add a generic "prune/filter" expression for both dense and sparse object to sparse
-    for (Index j=0; j<l.size(); ++j)
-      if (l[j]<1e-15)
-	tripletList.push_back(T(i,j,l(j)));
-
-	
-    d[i] = 0.0;
-  }
-  CINV.setFromTriplets(tripletList.begin(), tripletList.end());
-}
-
-
-
-/** \ingroup IterativeLinearSolvers_Module
-  * Constrained conjugate gradient
-  *
-  * Computes the minimum of \f$ 1/2((Ax).x) - bx \f$ under the constraint \f$ Cx \le f \f$
-  */
-template<typename TMatrix, typename CMatrix,
-         typename VectorX, typename VectorB, typename VectorF>
-void constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x,
-                       const VectorB& b, const VectorF& f, IterationController &iter)
-{
-  using std::sqrt;
-  typedef typename TMatrix::Scalar Scalar;
-  typedef typename TMatrix::Index Index;
-  typedef Matrix<Scalar,Dynamic,1>  TmpVec;
-
-  Scalar rho = 1.0, rho_1, lambda, gamma;
-  Index xSize = x.size();
-  TmpVec  p(xSize), q(xSize), q2(xSize),
-          r(xSize), old_z(xSize), z(xSize),
-          memox(xSize);
-  std::vector<bool> satured(C.rows());
-  p.setZero();
-  iter.setRhsNorm(sqrt(b.dot(b))); // gael vect_sp(PS, b, b)
-  if (iter.rhsNorm() == 0.0) iter.setRhsNorm(1.0);
-
-  SparseMatrix<Scalar,RowMajor> CINV(C.rows(), C.cols());
-  pseudo_inverse(C, CINV);
-
-  while(true)
-  {
-    // computation of residual
-    old_z = z;
-    memox = x;
-    r = b;
-    r += A * -x;
-    z = r;
-    bool transition = false;
-    for (Index i = 0; i < C.rows(); ++i)
-    {
-      Scalar al = C.row(i).dot(x) - f.coeff(i);
-      if (al >= -1.0E-15)
-      {
-        if (!satured[i])
-        {
-          satured[i] = true;
-          transition = true;
-        }
-        Scalar bb = CINV.row(i).dot(z);
-        if (bb > 0.0)
-          // FIXME: we should allow that: z += -bb * C.row(i);
-          for (typename CMatrix::InnerIterator it(C,i); it; ++it)
-            z.coeffRef(it.index()) -= bb*it.value();
-      }
-      else
-        satured[i] = false;
-    }
-
-    // descent direction
-    rho_1 = rho;
-    rho = r.dot(z);
-
-    if (iter.finished(rho)) break;
-    if (transition || iter.first()) gamma = 0.0;
-    else gamma = (std::max)(0.0, (rho - old_z.dot(z)) / rho_1);
-    p = z + gamma*p;
-
-    ++iter;
-    // one dimensionnal optimization
-    q = A * p;
-    lambda = rho / q.dot(p);
-    for (Index i = 0; i < C.rows(); ++i)
-    {
-      if (!satured[i])
-      {
-        Scalar bb = C.row(i).dot(p) - f[i];
-        if (bb > 0.0)
-          lambda = (std::min)(lambda, (f.coeff(i)-C.row(i).dot(x)) / bb);
-      }
-    }
-    x += lambda * p;
-    memox -= x;
-  }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_CONSTRAINEDCG_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
deleted file mode 100644
index 5ae011b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
+++ /dev/null
@@ -1,511 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DGMRES_H
-#define EIGEN_DGMRES_H
-
-#include "../../../../Eigen/Eigenvalues"
-
-namespace Eigen { 
-  
-template< typename _MatrixType,
-          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-class DGMRES;
-
-namespace internal {
-
-template< typename _MatrixType, typename _Preconditioner>
-struct traits<DGMRES<_MatrixType,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-/** \brief Computes a permutation vector to have a sorted sequence
-  * \param vec The vector to reorder.
-  * \param perm gives the sorted sequence on output. Must be initialized with 0..n-1
-  * \param ncut Put  the ncut smallest elements at the end of the vector
-  * WARNING This is an expensive sort, so should be used only 
-  * for small size vectors
-  * TODO Use modified QuickSplit or std::nth_element to get the smallest values 
-  */
-template <typename VectorType, typename IndexType>
-void sortWithPermutation (VectorType& vec, IndexType& perm, typename IndexType::Scalar& ncut)
-{
-  eigen_assert(vec.size() == perm.size());
-  bool flag; 
-  for (Index k  = 0; k < ncut; k++)
-  {
-    flag = false;
-    for (Index j = 0; j < vec.size()-1; j++)
-    {
-      if ( vec(perm(j)) < vec(perm(j+1)) )
-      {
-        std::swap(perm(j),perm(j+1)); 
-        flag = true;
-      }
-      if (!flag) break; // The vector is in sorted order
-    }
-  }
-}
-
-}
-/**
- * \ingroup IterativeLinearSolvers_Module
- * \brief A Restarted GMRES with deflation.
- * This class implements a modification of the GMRES solver for
- * sparse linear systems. The basis is built with modified 
- * Gram-Schmidt. At each restart, a few approximated eigenvectors
- * corresponding to the smallest eigenvalues are used to build a
- * preconditioner for the next cycle. This preconditioner 
- * for deflation can be combined with any other preconditioner, 
- * the IncompleteLUT for instance. The preconditioner is applied 
- * at right of the matrix and the combination is multiplicative.
- * 
- * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
- * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
- * Typical usage :
- * \code
- * SparseMatrix<double> A;
- * VectorXd x, b; 
- * //Fill A and b ...
- * DGMRES<SparseMatrix<double> > solver;
- * solver.set_restart(30); // Set restarting value
- * solver.setEigenv(1); // Set the number of eigenvalues to deflate
- * solver.compute(A);
- * x = solver.solve(b);
- * \endcode
- * 
- * DGMRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
- *
- * References :
- * [1] D. NUENTSA WAKAM and F. PACULL, Memory Efficient Hybrid
- *  Algebraic Solvers for Linear Systems Arising from Compressible
- *  Flows, Computers and Fluids, In Press,
- *  https://doi.org/10.1016/j.compfluid.2012.03.023   
- * [2] K. Burrage and J. Erhel, On the performance of various 
- * adaptive preconditioned GMRES strategies, 5(1998), 101-121.
- * [3] J. Erhel, K. Burrage and B. Pohl, Restarted GMRES 
- *  preconditioned by deflation,J. Computational and Applied
- *  Mathematics, 69(1996), 303-318. 
-
- * 
- */
-template< typename _MatrixType, typename _Preconditioner>
-class DGMRES : public IterativeSolverBase<DGMRES<_MatrixType,_Preconditioner> >
-{
-    typedef IterativeSolverBase<DGMRES> Base;
-    using Base::matrix;
-    using Base::m_error;
-    using Base::m_iterations;
-    using Base::m_info;
-    using Base::m_isInitialized;
-    using Base::m_tolerance; 
-  public:
-    using Base::_solve_impl;
-    using Base::_solve_with_guess_impl;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef _Preconditioner Preconditioner;
-    typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; 
-    typedef Matrix<RealScalar,Dynamic,Dynamic> DenseRealMatrix; 
-    typedef Matrix<Scalar,Dynamic,1> DenseVector;
-    typedef Matrix<RealScalar,Dynamic,1> DenseRealVector; 
-    typedef Matrix<std::complex<RealScalar>, Dynamic, 1> ComplexVector;
- 
-    
-  /** Default constructor. */
-  DGMRES() : Base(),m_restart(30),m_neig(0),m_r(0),m_maxNeig(5),m_isDeflAllocated(false),m_isDeflInitialized(false) {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    * 
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    * 
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit DGMRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()), m_restart(30),m_neig(0),m_r(0),m_maxNeig(5),m_isDeflAllocated(false),m_isDeflInitialized(false) {}
-
-  ~DGMRES() {}
-  
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {
-    EIGEN_STATIC_ASSERT(Rhs::ColsAtCompileTime==1 || Dest::ColsAtCompileTime==1, YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX);
-    
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-    
-    dgmres(matrix(), b, x, Base::m_preconditioner);
-  }
-
-  /** 
-   * Get the restart value
-    */
-  Index restart() { return m_restart; }
-  
-  /** 
-   * Set the restart value (default is 30)  
-   */
-  void set_restart(const Index restart) { m_restart=restart; }
-  
-  /** 
-   * Set the number of eigenvalues to deflate at each restart 
-   */
-  void setEigenv(const Index neig) 
-  {
-    m_neig = neig;
-    if (neig+1 > m_maxNeig) m_maxNeig = neig+1; // To allow for complex conjugates
-  }
-  
-  /** 
-   * Get the size of the deflation subspace size
-   */ 
-  Index deflSize() {return m_r; }
-  
-  /**
-   * Set the maximum size of the deflation subspace
-   */
-  void setMaxEigenv(const Index maxNeig) { m_maxNeig = maxNeig; }
-  
-  protected:
-    // DGMRES algorithm 
-    template<typename Rhs, typename Dest>
-    void dgmres(const MatrixType& mat,const Rhs& rhs, Dest& x, const Preconditioner& precond) const;
-    // Perform one cycle of GMRES
-    template<typename Dest>
-    Index dgmresCycle(const MatrixType& mat, const Preconditioner& precond, Dest& x, DenseVector& r0, RealScalar& beta, const RealScalar& normRhs, Index& nbIts) const; 
-    // Compute data to use for deflation 
-    Index dgmresComputeDeflationData(const MatrixType& mat, const Preconditioner& precond, const Index& it, StorageIndex& neig) const;
-    // Apply deflation to a vector
-    template<typename RhsType, typename DestType>
-    Index dgmresApplyDeflation(const RhsType& In, DestType& Out) const; 
-    ComplexVector schurValues(const ComplexSchur<DenseMatrix>& schurofH) const;
-    ComplexVector schurValues(const RealSchur<DenseMatrix>& schurofH) const;
-    // Init data for deflation
-    void dgmresInitDeflation(Index& rows) const; 
-    mutable DenseMatrix m_V; // Krylov basis vectors
-    mutable DenseMatrix m_H; // Hessenberg matrix 
-    mutable DenseMatrix m_Hes; // Initial hessenberg matrix without Givens rotations applied
-    mutable Index m_restart; // Maximum size of the Krylov subspace
-    mutable DenseMatrix m_U; // Vectors that form the basis of the invariant subspace 
-    mutable DenseMatrix m_MU; // matrix operator applied to m_U (for next cycles)
-    mutable DenseMatrix m_T; /* T=U^T*M^{-1}*A*U */
-    mutable PartialPivLU<DenseMatrix> m_luT; // LU factorization of m_T
-    mutable StorageIndex m_neig; //Number of eigenvalues to extract at each restart
-    mutable Index m_r; // Current number of deflated eigenvalues, size of m_U
-    mutable Index m_maxNeig; // Maximum number of eigenvalues to deflate
-    mutable RealScalar m_lambdaN; //Modulus of the largest eigenvalue of A
-    mutable bool m_isDeflAllocated;
-    mutable bool m_isDeflInitialized;
-    
-    //Adaptive strategy 
-    mutable RealScalar m_smv; // Smaller multiple of the remaining number of steps allowed
-    mutable bool m_force; // Force the use of deflation at each restart
-    
-}; 
-/** 
- * \brief Perform several cycles of restarted GMRES with modified Gram Schmidt, 
- * 
- * A right preconditioner is used combined with deflation.
- * 
- */
-template< typename _MatrixType, typename _Preconditioner>
-template<typename Rhs, typename Dest>
-void DGMRES<_MatrixType, _Preconditioner>::dgmres(const MatrixType& mat,const Rhs& rhs, Dest& x,
-              const Preconditioner& precond) const
-{
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-
-  RealScalar normRhs = rhs.norm();
-  if(normRhs <= considerAsZero) 
-  {
-    x.setZero();
-    m_error = 0;
-    return;
-  }
-
-  //Initialization
-  m_isDeflInitialized = false;
-  Index n = mat.rows(); 
-  DenseVector r0(n); 
-  Index nbIts = 0; 
-  m_H.resize(m_restart+1, m_restart);
-  m_Hes.resize(m_restart, m_restart);
-  m_V.resize(n,m_restart+1);
-  //Initial residual vector and initial norm
-  if(x.squaredNorm()==0) 
-    x = precond.solve(rhs);
-  r0 = rhs - mat * x; 
-  RealScalar beta = r0.norm(); 
-  
-  m_error = beta/normRhs; 
-  if(m_error < m_tolerance)
-    m_info = Success; 
-  else
-    m_info = NoConvergence;
-  
-  // Iterative process
-  while (nbIts < m_iterations && m_info == NoConvergence)
-  {
-    dgmresCycle(mat, precond, x, r0, beta, normRhs, nbIts); 
-    
-    // Compute the new residual vector for the restart 
-    if (nbIts < m_iterations && m_info == NoConvergence) {
-      r0 = rhs - mat * x;
-      beta = r0.norm();
-    }
-  }
-} 
-
-/**
- * \brief Perform one restart cycle of DGMRES
- * \param mat The coefficient matrix
- * \param precond The preconditioner
- * \param x the new approximated solution
- * \param r0 The initial residual vector
- * \param beta The norm of the residual computed so far
- * \param normRhs The norm of the right hand side vector
- * \param nbIts The number of iterations
- */
-template< typename _MatrixType, typename _Preconditioner>
-template<typename Dest>
-Index DGMRES<_MatrixType, _Preconditioner>::dgmresCycle(const MatrixType& mat, const Preconditioner& precond, Dest& x, DenseVector& r0, RealScalar& beta, const RealScalar& normRhs, Index& nbIts) const
-{
-  //Initialization 
-  DenseVector g(m_restart+1); // Right hand side of the least square problem
-  g.setZero();  
-  g(0) = Scalar(beta); 
-  m_V.col(0) = r0/beta; 
-  m_info = NoConvergence; 
-  std::vector<JacobiRotation<Scalar> >gr(m_restart); // Givens rotations
-  Index it = 0; // Number of inner iterations 
-  Index n = mat.rows();
-  DenseVector tv1(n), tv2(n);  //Temporary vectors
-  while (m_info == NoConvergence && it < m_restart && nbIts < m_iterations)
-  {    
-    // Apply preconditioner(s) at right
-    if (m_isDeflInitialized )
-    {
-      dgmresApplyDeflation(m_V.col(it), tv1); // Deflation
-      tv2 = precond.solve(tv1); 
-    }
-    else
-    {
-      tv2 = precond.solve(m_V.col(it)); // User's selected preconditioner
-    }
-    tv1 = mat * tv2; 
-   
-    // Orthogonalize it with the previous basis in the basis using modified Gram-Schmidt
-    Scalar coef; 
-    for (Index i = 0; i <= it; ++i)
-    { 
-      coef = tv1.dot(m_V.col(i));
-      tv1 = tv1 - coef * m_V.col(i); 
-      m_H(i,it) = coef; 
-      m_Hes(i,it) = coef; 
-    }
-    // Normalize the vector 
-    coef = tv1.norm(); 
-    m_V.col(it+1) = tv1/coef;
-    m_H(it+1, it) = coef;
-//     m_Hes(it+1,it) = coef; 
-    
-    // FIXME Check for happy breakdown 
-    
-    // Update Hessenberg matrix with Givens rotations
-    for (Index i = 1; i <= it; ++i) 
-    {
-      m_H.col(it).applyOnTheLeft(i-1,i,gr[i-1].adjoint());
-    }
-    // Compute the new plane rotation 
-    gr[it].makeGivens(m_H(it, it), m_H(it+1,it)); 
-    // Apply the new rotation
-    m_H.col(it).applyOnTheLeft(it,it+1,gr[it].adjoint());
-    g.applyOnTheLeft(it,it+1, gr[it].adjoint()); 
-    
-    beta = std::abs(g(it+1));
-    m_error = beta/normRhs; 
-    // std::cerr << nbIts << " Relative Residual Norm " << m_error << std::endl;
-    it++; nbIts++; 
-    
-    if (m_error < m_tolerance)
-    {
-      // The method has converged
-      m_info = Success;
-      break;
-    }
-  }
-  
-  // Compute the new coefficients by solving the least square problem
-//   it++;
-  //FIXME  Check first if the matrix is singular ... zero diagonal
-  DenseVector nrs(m_restart); 
-  nrs = m_H.topLeftCorner(it,it).template triangularView<Upper>().solve(g.head(it)); 
-  
-  // Form the new solution
-  if (m_isDeflInitialized)
-  {
-    tv1 = m_V.leftCols(it) * nrs; 
-    dgmresApplyDeflation(tv1, tv2); 
-    x = x + precond.solve(tv2);
-  }
-  else
-    x = x + precond.solve(m_V.leftCols(it) * nrs); 
-  
-  // Go for a new cycle and compute data for deflation
-  if(nbIts < m_iterations && m_info == NoConvergence && m_neig > 0 && (m_r+m_neig) < m_maxNeig)
-    dgmresComputeDeflationData(mat, precond, it, m_neig); 
-  return 0; 
-  
-}
-
-
-template< typename _MatrixType, typename _Preconditioner>
-void DGMRES<_MatrixType, _Preconditioner>::dgmresInitDeflation(Index& rows) const
-{
-  m_U.resize(rows, m_maxNeig);
-  m_MU.resize(rows, m_maxNeig); 
-  m_T.resize(m_maxNeig, m_maxNeig);
-  m_lambdaN = 0.0; 
-  m_isDeflAllocated = true; 
-}
-
-template< typename _MatrixType, typename _Preconditioner>
-inline typename DGMRES<_MatrixType, _Preconditioner>::ComplexVector DGMRES<_MatrixType, _Preconditioner>::schurValues(const ComplexSchur<DenseMatrix>& schurofH) const
-{
-  return schurofH.matrixT().diagonal();
-}
-
-template< typename _MatrixType, typename _Preconditioner>
-inline typename DGMRES<_MatrixType, _Preconditioner>::ComplexVector DGMRES<_MatrixType, _Preconditioner>::schurValues(const RealSchur<DenseMatrix>& schurofH) const
-{
-  const DenseMatrix& T = schurofH.matrixT();
-  Index it = T.rows();
-  ComplexVector eig(it);
-  Index j = 0;
-  while (j < it-1)
-  {
-    if (T(j+1,j) ==Scalar(0))
-    {
-      eig(j) = std::complex<RealScalar>(T(j,j),RealScalar(0)); 
-      j++; 
-    }
-    else
-    {
-      eig(j) = std::complex<RealScalar>(T(j,j),T(j+1,j)); 
-      eig(j+1) = std::complex<RealScalar>(T(j,j+1),T(j+1,j+1));
-      j++;
-    }
-  }
-  if (j < it-1) eig(j) = std::complex<RealScalar>(T(j,j),RealScalar(0));
-  return eig;
-}
-
-template< typename _MatrixType, typename _Preconditioner>
-Index DGMRES<_MatrixType, _Preconditioner>::dgmresComputeDeflationData(const MatrixType& mat, const Preconditioner& precond, const Index& it, StorageIndex& neig) const
-{
-  // First, find the Schur form of the Hessenberg matrix H
-  typename internal::conditional<NumTraits<Scalar>::IsComplex, ComplexSchur<DenseMatrix>, RealSchur<DenseMatrix> >::type schurofH; 
-  bool computeU = true;
-  DenseMatrix matrixQ(it,it); 
-  matrixQ.setIdentity();
-  schurofH.computeFromHessenberg(m_Hes.topLeftCorner(it,it), matrixQ, computeU); 
-  
-  ComplexVector eig(it);
-  Matrix<StorageIndex,Dynamic,1>perm(it);
-  eig = this->schurValues(schurofH);
-  
-  // Reorder the absolute values of Schur values
-  DenseRealVector modulEig(it); 
-  for (Index j=0; j<it; ++j) modulEig(j) = std::abs(eig(j)); 
-  perm.setLinSpaced(it,0,internal::convert_index<StorageIndex>(it-1));
-  internal::sortWithPermutation(modulEig, perm, neig);
-  
-  if (!m_lambdaN)
-  {
-    m_lambdaN = (std::max)(modulEig.maxCoeff(), m_lambdaN);
-  }
-  //Count the real number of extracted eigenvalues (with complex conjugates)
-  Index nbrEig = 0; 
-  while (nbrEig < neig)
-  {
-    if(eig(perm(it-nbrEig-1)).imag() == RealScalar(0)) nbrEig++; 
-    else nbrEig += 2; 
-  }
-  // Extract the  Schur vectors corresponding to the smallest Ritz values
-  DenseMatrix Sr(it, nbrEig); 
-  Sr.setZero();
-  for (Index j = 0; j < nbrEig; j++)
-  {
-    Sr.col(j) = schurofH.matrixU().col(perm(it-j-1));
-  }
-  
-  // Form the Schur vectors of the initial matrix using the Krylov basis
-  DenseMatrix X; 
-  X = m_V.leftCols(it) * Sr;
-  if (m_r)
-  {
-   // Orthogonalize X against m_U using modified Gram-Schmidt
-   for (Index j = 0; j < nbrEig; j++)
-     for (Index k =0; k < m_r; k++)
-      X.col(j) = X.col(j) - (m_U.col(k).dot(X.col(j)))*m_U.col(k); 
-  }
-  
-  // Compute m_MX = A * M^-1 * X
-  Index m = m_V.rows();
-  if (!m_isDeflAllocated) 
-    dgmresInitDeflation(m); 
-  DenseMatrix MX(m, nbrEig);
-  DenseVector tv1(m);
-  for (Index j = 0; j < nbrEig; j++)
-  {
-    tv1 = mat * X.col(j);
-    MX.col(j) = precond.solve(tv1);
-  }
-  
-  //Update m_T = [U'MU U'MX; X'MU X'MX]
-  m_T.block(m_r, m_r, nbrEig, nbrEig) = X.transpose() * MX; 
-  if(m_r)
-  {
-    m_T.block(0, m_r, m_r, nbrEig) = m_U.leftCols(m_r).transpose() * MX; 
-    m_T.block(m_r, 0, nbrEig, m_r) = X.transpose() * m_MU.leftCols(m_r);
-  }
-  
-  // Save X into m_U and m_MX in m_MU
-  for (Index j = 0; j < nbrEig; j++) m_U.col(m_r+j) = X.col(j);
-  for (Index j = 0; j < nbrEig; j++) m_MU.col(m_r+j) = MX.col(j);
-  // Increase the size of the invariant subspace
-  m_r += nbrEig; 
-  
-  // Factorize m_T into m_luT
-  m_luT.compute(m_T.topLeftCorner(m_r, m_r));
-  
-  //FIXME CHeck if the factorization was correctly done (nonsingular matrix)
-  m_isDeflInitialized = true;
-  return 0; 
-}
-template<typename _MatrixType, typename _Preconditioner>
-template<typename RhsType, typename DestType>
-Index DGMRES<_MatrixType, _Preconditioner>::dgmresApplyDeflation(const RhsType &x, DestType &y) const
-{
-  DenseVector x1 = m_U.leftCols(m_r).transpose() * x; 
-  y = x + m_U.leftCols(m_r) * ( m_lambdaN * m_luT.solve(x1) - x1);
-  return 0; 
-}
-
-} // end namespace Eigen
-#endif 
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h
deleted file mode 100644
index ff91209..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h
+++ /dev/null
@@ -1,335 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2012, 2014 Kolja Brix <brix@igpm.rwth-aaachen.de>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GMRES_H
-#define EIGEN_GMRES_H
-
-namespace Eigen {
-
-namespace internal {
-
-/**
-* Generalized Minimal Residual Algorithm based on the
-* Arnoldi algorithm implemented with Householder reflections.
-*
-* Parameters:
-*  \param mat       matrix of linear system of equations
-*  \param rhs       right hand side vector of linear system of equations
-*  \param x         on input: initial guess, on output: solution
-*  \param precond   preconditioner used
-*  \param iters     on input: maximum number of iterations to perform
-*                   on output: number of iterations performed
-*  \param restart   number of iterations for a restart
-*  \param tol_error on input: relative residual tolerance
-*                   on output: residuum achieved
-*
-* \sa IterativeMethods::bicgstab()
-*
-*
-* For references, please see:
-*
-* Saad, Y. and Schultz, M. H.
-* GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems.
-* SIAM J.Sci.Stat.Comp. 7, 1986, pp. 856 - 869.
-*
-* Saad, Y.
-* Iterative Methods for Sparse Linear Systems.
-* Society for Industrial and Applied Mathematics, Philadelphia, 2003.
-*
-* Walker, H. F.
-* Implementations of the GMRES method.
-* Comput.Phys.Comm. 53, 1989, pp. 311 - 320.
-*
-* Walker, H. F.
-* Implementation of the GMRES Method using Householder Transformations.
-* SIAM J.Sci.Stat.Comp. 9, 1988, pp. 152 - 163.
-*
-*/
-template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-bool gmres(const MatrixType & mat, const Rhs & rhs, Dest & x, const Preconditioner & precond,
-    Index &iters, const Index &restart, typename Dest::RealScalar & tol_error) {
-
-  using std::sqrt;
-  using std::abs;
-
-  typedef typename Dest::RealScalar RealScalar;
-  typedef typename Dest::Scalar Scalar;
-  typedef Matrix < Scalar, Dynamic, 1 > VectorType;
-  typedef Matrix < Scalar, Dynamic, Dynamic, ColMajor> FMatrixType;
-
-  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
-
-  if(rhs.norm() <= considerAsZero) 
-  {
-    x.setZero();
-    tol_error = 0;
-    return true;
-  }
-
-  RealScalar tol = tol_error;
-  const Index maxIters = iters;
-  iters = 0;
-
-  const Index m = mat.rows();
-
-  // residual and preconditioned residual
-  VectorType p0 = rhs - mat*x;
-  VectorType r0 = precond.solve(p0);
-
-  const RealScalar r0Norm = r0.norm();
-
-  // is initial guess already good enough?
-  if(r0Norm == 0)
-  {
-    tol_error = 0;
-    return true;
-  }
-
-  // storage for Hessenberg matrix and Householder data
-  FMatrixType H   = FMatrixType::Zero(m, restart + 1);
-  VectorType w    = VectorType::Zero(restart + 1);
-  VectorType tau  = VectorType::Zero(restart + 1);
-
-  // storage for Jacobi rotations
-  std::vector < JacobiRotation < Scalar > > G(restart);
-  
-  // storage for temporaries
-  VectorType t(m), v(m), workspace(m), x_new(m);
-
-  // generate first Householder vector
-  Ref<VectorType> H0_tail = H.col(0).tail(m - 1);
-  RealScalar beta;
-  r0.makeHouseholder(H0_tail, tau.coeffRef(0), beta);
-  w(0) = Scalar(beta);
-  
-  for (Index k = 1; k <= restart; ++k)
-  {
-    ++iters;
-
-    v = VectorType::Unit(m, k - 1);
-
-    // apply Householder reflections H_{1} ... H_{k-1} to v
-    // TODO: use a HouseholderSequence
-    for (Index i = k - 1; i >= 0; --i) {
-      v.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
-    }
-
-    // apply matrix M to v:  v = mat * v;
-    t.noalias() = mat * v;
-    v = precond.solve(t);
-
-    // apply Householder reflections H_{k-1} ... H_{1} to v
-    // TODO: use a HouseholderSequence
-    for (Index i = 0; i < k; ++i) {
-      v.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
-    }
-
-    if (v.tail(m - k).norm() != 0.0)
-    {
-      if (k <= restart)
-      {
-        // generate new Householder vector
-        Ref<VectorType> Hk_tail = H.col(k).tail(m - k - 1);
-        v.tail(m - k).makeHouseholder(Hk_tail, tau.coeffRef(k), beta);
-
-        // apply Householder reflection H_{k} to v
-        v.tail(m - k).applyHouseholderOnTheLeft(Hk_tail, tau.coeffRef(k), workspace.data());
-      }
-    }
-
-    if (k > 1)
-    {
-      for (Index i = 0; i < k - 1; ++i)
-      {
-        // apply old Givens rotations to v
-        v.applyOnTheLeft(i, i + 1, G[i].adjoint());
-      }
-    }
-
-    if (k<m && v(k) != (Scalar) 0)
-    {
-      // determine next Givens rotation
-      G[k - 1].makeGivens(v(k - 1), v(k));
-
-      // apply Givens rotation to v and w
-      v.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
-      w.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
-    }
-
-    // insert coefficients into upper matrix triangle
-    H.col(k-1).head(k) = v.head(k);
-
-    tol_error = abs(w(k)) / r0Norm;
-    bool stop = (k==m || tol_error < tol || iters == maxIters);
-
-    if (stop || k == restart)
-    {
-      // solve upper triangular system
-      Ref<VectorType> y = w.head(k);
-      H.topLeftCorner(k, k).template triangularView <Upper>().solveInPlace(y);
-
-      // use Horner-like scheme to calculate solution vector
-      x_new.setZero();
-      for (Index i = k - 1; i >= 0; --i)
-      {
-        x_new(i) += y(i);
-        // apply Householder reflection H_{i} to x_new
-        x_new.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
-      }
-
-      x += x_new;
-
-      if(stop)
-      {
-        return true;
-      }
-      else
-      {
-        k=0;
-
-        // reset data for restart
-        p0.noalias() = rhs - mat*x;
-        r0 = precond.solve(p0);
-
-        // clear Hessenberg matrix and Householder data
-        H.setZero();
-        w.setZero();
-        tau.setZero();
-
-        // generate first Householder vector
-        r0.makeHouseholder(H0_tail, tau.coeffRef(0), beta);
-        w(0) = Scalar(beta);
-      }
-    }
-  }
-
-  return false;
-
-}
-
-}
-
-template< typename _MatrixType,
-          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-class GMRES;
-
-namespace internal {
-
-template< typename _MatrixType, typename _Preconditioner>
-struct traits<GMRES<_MatrixType,_Preconditioner> >
-{
-  typedef _MatrixType MatrixType;
-  typedef _Preconditioner Preconditioner;
-};
-
-}
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief A GMRES solver for sparse square problems
-  *
-  * This class allows to solve for A.x = b sparse linear problems using a generalized minimal
-  * residual method. The vectors x and b can be either dense or sparse.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
-  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-  *
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  *
-  * This class can be used as the direct solver classes. Here is a typical usage example:
-  * \code
-  * int n = 10000;
-  * VectorXd x(n), b(n);
-  * SparseMatrix<double> A(n,n);
-  * // fill A and b
-  * GMRES<SparseMatrix<double> > solver(A);
-  * x = solver.solve(b);
-  * std::cout << "#iterations:     " << solver.iterations() << std::endl;
-  * std::cout << "estimated error: " << solver.error()      << std::endl;
-  * // update b, and solve again
-  * x = solver.solve(b);
-  * \endcode
-  *
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  * 
-  * GMRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-  *
-  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-template< typename _MatrixType, typename _Preconditioner>
-class GMRES : public IterativeSolverBase<GMRES<_MatrixType,_Preconditioner> >
-{
-  typedef IterativeSolverBase<GMRES> Base;
-  using Base::matrix;
-  using Base::m_error;
-  using Base::m_iterations;
-  using Base::m_info;
-  using Base::m_isInitialized;
-
-private:
-  Index m_restart;
-
-public:
-  using Base::_solve_impl;
-  typedef _MatrixType MatrixType;
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  typedef _Preconditioner Preconditioner;
-
-public:
-
-  /** Default constructor. */
-  GMRES() : Base(), m_restart(30) {}
-
-  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-    *
-    * This constructor is a shortcut for the default constructor followed
-    * by a call to compute().
-    *
-    * \warning this class stores a reference to the matrix A as well as some
-    * precomputed values that depend on it. Therefore, if \a A is changed
-    * this class becomes invalid. Call compute() to update it with the new
-    * matrix A, or modify a copy of A.
-    */
-  template<typename MatrixDerived>
-  explicit GMRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()), m_restart(30) {}
-
-  ~GMRES() {}
-
-  /** Get the number of iterations after that a restart is performed.
-    */
-  Index get_restart() { return m_restart; }
-
-  /** Set the number of iterations after that a restart is performed.
-    *  \param restart   number of iterations for a restarti, default is 30.
-    */
-  void set_restart(const Index restart) { m_restart=restart; }
-
-  /** \internal */
-  template<typename Rhs,typename Dest>
-  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-  {
-    m_iterations = Base::maxIterations();
-    m_error = Base::m_tolerance;
-    bool ret = internal::gmres(matrix(), b, x, Base::m_preconditioner, m_iterations, m_restart, m_error);
-    m_info = (!ret) ? NumericalIssue
-          : m_error <= Base::m_tolerance ? Success
-          : NoConvergence;
-  }
-
-protected:
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_GMRES_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h
deleted file mode 100644
index 90d20fa..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h
+++ /dev/null
@@ -1,436 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2020 Chris Schoutrop <c.e.m.schoutrop@tue.nl>
-// Copyright (C) 2020 Jens Wehner <j.wehner@esciencecenter.nl>
-// Copyright (C) 2020 Jan van Dijk <j.v.dijk@tue.nl>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_IDRS_H
-#define EIGEN_IDRS_H
-
-namespace Eigen
-{
-
-	namespace internal
-	{
-		/**     \internal Low-level Induced Dimension Reduction algoritm
-		        \param A The matrix A
-		        \param b The right hand side vector b
-		        \param x On input and initial solution, on output the computed solution.
-		        \param precond A preconditioner being able to efficiently solve for an
-		                  approximation of Ax=b (regardless of b)
-		        \param iter On input the max number of iteration, on output the number of performed iterations.
-		        \param relres On input the tolerance error, on output an estimation of the relative error.
-		        \param S On input Number of the dimension of the shadow space.
-				\param smoothing switches residual smoothing on.
-				\param angle small omega lead to faster convergence at the expense of numerical stability
-				\param replacement switches on a residual replacement strategy to increase accuracy of residual at the expense of more Mat*vec products
-		        \return false in the case of numerical issue, for example a break down of IDRS.
-		*/
-		template<typename Vector, typename RealScalar>
-		typename Vector::Scalar omega(const Vector& t, const Vector& s, RealScalar angle)
-		{
-			using numext::abs;
-			typedef typename Vector::Scalar Scalar;
-			const RealScalar ns = s.norm();
-			const RealScalar nt = t.norm();
-			const Scalar ts = t.dot(s);
-			const RealScalar rho = abs(ts / (nt * ns));
-
-			if (rho < angle) {
-				if (ts == Scalar(0)) {
-					return Scalar(0);
-				}
-				// Original relation for om is given by
-				// om = om * angle / rho;
-				// To alleviate potential (near) division by zero this can be rewritten as
-				// om = angle * (ns / nt) * (ts / abs(ts)) = angle * (ns / nt) * sgn(ts)
-  				return angle * (ns / nt) * (ts / abs(ts));
-			}
-			return ts / (nt * nt);
-		}
-
-		template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-		bool idrs(const MatrixType& A, const Rhs& b, Dest& x, const Preconditioner& precond,
-			Index& iter,
-			typename Dest::RealScalar& relres, Index S, bool smoothing, typename Dest::RealScalar angle, bool replacement)
-		{
-			typedef typename Dest::RealScalar RealScalar;
-			typedef typename Dest::Scalar Scalar;
-			typedef Matrix<Scalar, Dynamic, 1> VectorType;
-			typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> DenseMatrixType;
-			const Index N = b.size();
-			S = S < x.rows() ? S : x.rows();
-			const RealScalar tol = relres;
-			const Index maxit = iter;
-
-			Index replacements = 0;
-			bool trueres = false;
-
-			FullPivLU<DenseMatrixType> lu_solver;
-
-			DenseMatrixType P;
-			{
-				HouseholderQR<DenseMatrixType> qr(DenseMatrixType::Random(N, S));
-			    P = (qr.householderQ() * DenseMatrixType::Identity(N, S));
-			}
-
-			const RealScalar normb = b.norm();
-
-			if (internal::isApprox(normb, RealScalar(0)))
-			{
-				//Solution is the zero vector
-				x.setZero();
-				iter = 0;
-				relres = 0;
-				return true;
-			}
-			 // from http://homepage.tudelft.nl/1w5b5/IDRS/manual.pdf
-			 // A peak in the residual is considered dangerously high if‖ri‖/‖b‖> C(tol/epsilon).
-			 // With epsilon the
-             // relative machine precision. The factor tol/epsilon corresponds to the size of a
-             // finite precision number that is so large that the absolute round-off error in
-             // this number, when propagated through the process, makes it impossible to
-             // achieve the required accuracy.The factor C accounts for the accumulation of
-             // round-off errors. This parameter has beenset to 10−3.
-			 // mp is epsilon/C
-			 // 10^3 * eps is very conservative, so normally no residual replacements will take place. 
-			 // It only happens if things go very wrong. Too many restarts may ruin the convergence.
-			const RealScalar mp = RealScalar(1e3) * NumTraits<Scalar>::epsilon();
-
-
-
-			//Compute initial residual
-			const RealScalar tolb = tol * normb; //Relative tolerance
-			VectorType r = b - A * x;
-
-			VectorType x_s, r_s;
-
-			if (smoothing)
-			{
-				x_s = x;
-				r_s = r;
-			}
-
-			RealScalar normr = r.norm();
-
-			if (normr <= tolb)
-			{
-				//Initial guess is a good enough solution
-				iter = 0;
-				relres = normr / normb;
-				return true;
-			}
-
-			DenseMatrixType G = DenseMatrixType::Zero(N, S);
-			DenseMatrixType U = DenseMatrixType::Zero(N, S);
-			DenseMatrixType M = DenseMatrixType::Identity(S, S);
-			VectorType t(N), v(N);
-			Scalar om = 1.;
-
-			//Main iteration loop, guild G-spaces:
-			iter = 0;
-
-			while (normr > tolb && iter < maxit)
-			{
-				//New right hand size for small system:
-				VectorType f = (r.adjoint() * P).adjoint();
-
-				for (Index k = 0; k < S; ++k)
-				{
-					//Solve small system and make v orthogonal to P:
-					//c = M(k:s,k:s)\f(k:s);
-					lu_solver.compute(M.block(k , k , S -k, S - k ));
-					VectorType c = lu_solver.solve(f.segment(k , S - k ));
-					//v = r - G(:,k:s)*c;
-					v = r - G.rightCols(S - k ) * c;
-					//Preconditioning
-					v = precond.solve(v);
-
-					//Compute new U(:,k) and G(:,k), G(:,k) is in space G_j
-					U.col(k) = U.rightCols(S - k ) * c + om * v;
-					G.col(k) = A * U.col(k );
-
-					//Bi-Orthogonalise the new basis vectors:
-					for (Index i = 0; i < k-1 ; ++i)
-					{
-						//alpha =  ( P(:,i)'*G(:,k) )/M(i,i);
-						Scalar alpha = P.col(i ).dot(G.col(k )) / M(i, i );
-						G.col(k ) = G.col(k ) - alpha * G.col(i );
-						U.col(k ) = U.col(k ) - alpha * U.col(i );
-					}
-
-					//New column of M = P'*G  (first k-1 entries are zero)
-					//M(k:s,k) = (G(:,k)'*P(:,k:s))';
-					M.block(k , k , S - k , 1) = (G.col(k ).adjoint() * P.rightCols(S - k )).adjoint();
-
-					if (internal::isApprox(M(k,k), Scalar(0)))
-					{
-						return false;
-					}
-
-					//Make r orthogonal to q_i, i = 0..k-1
-					Scalar beta = f(k ) / M(k , k );
-					r = r - beta * G.col(k );
-					x = x + beta * U.col(k );
-					normr = r.norm();
-
-					if (replacement && normr > tolb / mp)
-					{
-						trueres = true;
-					}
-
-					//Smoothing:
-					if (smoothing)
-					{
-						t = r_s - r;
-						//gamma is a Scalar, but the conversion is not allowed
-						Scalar gamma = t.dot(r_s) / t.norm();
-						r_s = r_s - gamma * t;
-						x_s = x_s - gamma * (x_s - x);
-						normr = r_s.norm();
-					}
-
-					if (normr < tolb || iter == maxit)
-					{
-						break;
-					}
-
-					//New f = P'*r (first k  components are zero)
-					if (k < S-1)
-					{
-						f.segment(k + 1, S - (k + 1) ) = f.segment(k + 1 , S - (k + 1)) - beta * M.block(k + 1 , k , S - (k + 1), 1);
-					}
-				}//end for
-
-				if (normr < tolb || iter == maxit)
-				{
-					break;
-				}
-
-				//Now we have sufficient vectors in G_j to compute residual in G_j+1
-				//Note: r is already perpendicular to P so v = r
-				//Preconditioning
-				v = r;
-				v = precond.solve(v);
-
-				//Matrix-vector multiplication:
-				t = A * v;
-
-				//Computation of a new omega
-				om = internal::omega(t, r, angle);
-
-				if (om == RealScalar(0.0))
-				{
-					return false;
-				}
-
-				r = r - om * t;
-				x = x + om * v;
-				normr = r.norm();
-
-				if (replacement && normr > tolb / mp)
-				{
-					trueres = true;
-				}
-
-				//Residual replacement?
-				if (trueres && normr < normb)
-				{
-					r = b - A * x;
-					trueres = false;
-					replacements++;
-				}
-
-				//Smoothing:
-				if (smoothing)
-				{
-					t = r_s - r;
-					Scalar gamma = t.dot(r_s) /t.norm();
-					r_s = r_s - gamma * t;
-					x_s = x_s - gamma * (x_s - x);
-					normr = r_s.norm();
-				}
-
-				iter++;
-
-			}//end while
-
-			if (smoothing)
-			{
-				x = x_s;
-			}
-			relres=normr/normb;
-			return true;
-		}
-
-	}  // namespace internal
-
-	template <typename _MatrixType, typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
-	class IDRS;
-
-	namespace internal
-	{
-
-		template <typename _MatrixType, typename _Preconditioner>
-		struct traits<Eigen::IDRS<_MatrixType, _Preconditioner> >
-		{
-			typedef _MatrixType MatrixType;
-			typedef _Preconditioner Preconditioner;
-		};
-
-	}  // namespace internal
-
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \brief The Induced Dimension Reduction method (IDR(s)) is a short-recurrences Krylov method for sparse square problems.
-  *
-  * This class allows to solve for A.x = b sparse linear problems. The vectors x and b can be either dense or sparse.
-  * he Induced Dimension Reduction method, IDR(), is a robust and efficient short-recurrence Krylov subspace method for
-  * solving large nonsymmetric systems of linear equations.
-  *
-  * For indefinite systems IDR(S) outperforms both BiCGStab and BiCGStab(L). Additionally, IDR(S) can handle matrices
-  * with complex eigenvalues more efficiently than BiCGStab.
-  *
-  * Many problems that do not converge for BiCGSTAB converge for IDR(s) (for larger values of s). And if both methods 
-  * converge the convergence for IDR(s) is typically much faster for difficult systems (for example indefinite problems). 
-  *
-  * IDR(s) is a limited memory finite termination method. In exact arithmetic it converges in at most N+N/s iterations,
-  * with N the system size.  It uses a fixed number of 4+3s vector. In comparison, BiCGSTAB terminates in 2N iterations 
-  * and uses 7 vectors. GMRES terminates in at most N iterations, and uses I+3 vectors, with I the number of iterations. 
-  * Restarting GMRES limits the memory consumption, but destroys the finite termination property.
-  *
-  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
-  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-  *
-  * \implsparsesolverconcept
-  *
-  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-  * and NumTraits<Scalar>::epsilon() for the tolerance.
-  *
-  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
-  *
-  * \b Performance: when using sparse matrices, best performance is achied for a row-major sparse matrix format.
-  * Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
-  * See \ref TopicMultiThreading for details.
-  *
-  * By default the iterations start with x=0 as an initial guess of the solution.
-  * One can control the start using the solveWithGuess() method.
-  *
-  * IDR(s) can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-  *
-  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-  */
-	template <typename _MatrixType, typename _Preconditioner>
-	class IDRS : public IterativeSolverBase<IDRS<_MatrixType, _Preconditioner> >
-	{
-
-		public:
-			typedef _MatrixType MatrixType;
-			typedef typename MatrixType::Scalar Scalar;
-			typedef typename MatrixType::RealScalar RealScalar;
-			typedef _Preconditioner Preconditioner;
-
-		private:
-			typedef IterativeSolverBase<IDRS> Base;
-			using Base::m_error;
-			using Base::m_info;
-			using Base::m_isInitialized;
-			using Base::m_iterations;
-			using Base::matrix;
-			Index m_S;
-			bool m_smoothing;
-			RealScalar m_angle;
-			bool m_residual;
-
-		public:
-			/** Default constructor. */
-			IDRS(): m_S(4), m_smoothing(false), m_angle(RealScalar(0.7)), m_residual(false) {}
-
-			/**     Initialize the solver with matrix \a A for further \c Ax=b solving.
-
-			        This constructor is a shortcut for the default constructor followed
-			        by a call to compute().
-
-			        \warning this class stores a reference to the matrix A as well as some
-			        precomputed values that depend on it. Therefore, if \a A is changed
-			        this class becomes invalid. Call compute() to update it with the new
-			        matrix A, or modify a copy of A.
-			*/
-			template <typename MatrixDerived>
-			explicit IDRS(const EigenBase<MatrixDerived>& A) : Base(A.derived()), m_S(4), m_smoothing(false),
-															   m_angle(RealScalar(0.7)), m_residual(false) {}
-
-
-			/** \internal */
-			/**     Loops over the number of columns of b and does the following:
-			                1. sets the tolerence and maxIterations
-			                2. Calls the function that has the core solver routine
-			*/
-			template <typename Rhs, typename Dest>
-			void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-			{
-				m_iterations = Base::maxIterations();
-				m_error = Base::m_tolerance;
-
-				bool ret = internal::idrs(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error, m_S,m_smoothing,m_angle,m_residual);
-
-				m_info = (!ret) ? NumericalIssue : m_error <= Base::m_tolerance ? Success : NoConvergence;
-			}
-
-			/** Sets the parameter S, indicating the dimension of the shadow space. Default is 4*/
-			void setS(Index S)
-			{
-				if (S < 1)
-				{
-					S = 4;
-				}
-
-				m_S = S;
-			}
-
-			/** Switches off and on smoothing.
-			Residual smoothing results in monotonically decreasing residual norms at
-			the expense of two extra vectors of storage and a few extra vector
-			operations. Although monotonic decrease of the residual norms is a
-			desirable property, the rate of convergence of the unsmoothed process and
-			the smoothed process is basically the same. Default is off */
-			void setSmoothing(bool smoothing)
-			{
-				m_smoothing=smoothing;
-			}
-
-			/** The angle must be a real scalar. In IDR(s), a value for the
-			iteration parameter omega must be chosen in every s+1th step. The most
-			natural choice is to select a value to minimize the norm of the next residual.
-			This corresponds to the parameter omega = 0. In practice, this may lead to
-			values of omega that are so small that the other iteration parameters
-			cannot be computed with sufficient accuracy. In such cases it is better to
-			increase the value of omega sufficiently such that a compromise is reached
-			between accurate computations and reduction of the residual norm. The
-			parameter angle =0.7 (”maintaining the convergence strategy”)
-			results in such a compromise. */
-			void setAngle(RealScalar angle)
-			{
-				m_angle=angle;
-			}
-
-			/** The parameter replace is a logical that determines whether a
-			residual replacement strategy is employed to increase the accuracy of the
-			solution. */
-			void setResidualUpdate(bool update)
-			{
-				m_residual=update;
-			}
-
-	};
-
-}  // namespace Eigen
-
-#endif /* EIGEN_IDRS_H */
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
deleted file mode 100644
index 7d08c35..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
+++ /dev/null
@@ -1,90 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_INCOMPLETE_LU_H
-#define EIGEN_INCOMPLETE_LU_H
-
-namespace Eigen { 
-
-template <typename _Scalar>
-class IncompleteLU : public SparseSolverBase<IncompleteLU<_Scalar> >
-{
-  protected:
-    typedef SparseSolverBase<IncompleteLU<_Scalar> > Base;
-    using Base::m_isInitialized;
-    
-    typedef _Scalar Scalar;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef typename Vector::Index Index;
-    typedef SparseMatrix<Scalar,RowMajor> FactorType;
-
-  public:
-    typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
-
-    IncompleteLU() {}
-
-    template<typename MatrixType>
-    IncompleteLU(const MatrixType& mat)
-    {
-      compute(mat);
-    }
-
-    Index rows() const { return m_lu.rows(); }
-    Index cols() const { return m_lu.cols(); }
-
-    template<typename MatrixType>
-    IncompleteLU& compute(const MatrixType& mat)
-    {
-      m_lu = mat;
-      int size = mat.cols();
-      Vector diag(size);
-      for(int i=0; i<size; ++i)
-      {
-        typename FactorType::InnerIterator k_it(m_lu,i);
-        for(; k_it && k_it.index()<i; ++k_it)
-        {
-          int k = k_it.index();
-          k_it.valueRef() /= diag(k);
-
-          typename FactorType::InnerIterator j_it(k_it);
-          typename FactorType::InnerIterator kj_it(m_lu, k);
-          while(kj_it && kj_it.index()<=k) ++kj_it;
-          for(++j_it; j_it; )
-          {
-            if(kj_it.index()==j_it.index())
-            {
-              j_it.valueRef() -= k_it.value() * kj_it.value();
-              ++j_it;
-              ++kj_it;
-            }
-            else if(kj_it.index()<j_it.index()) ++kj_it;
-            else                                ++j_it;
-          }
-        }
-        if(k_it && k_it.index()==i) diag(i) = k_it.value();
-        else                        diag(i) = 1;
-      }
-      m_isInitialized = true;
-      return *this;
-    }
-
-    template<typename Rhs, typename Dest>
-    void _solve_impl(const Rhs& b, Dest& x) const
-    {
-      x = m_lu.template triangularView<UnitLower>().solve(b);
-      x = m_lu.template triangularView<Upper>().solve(x);
-    }
-
-  protected:
-    FactorType m_lu;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_INCOMPLETE_LU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h
deleted file mode 100644
index a116e09..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h
+++ /dev/null
@@ -1,154 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-
-/* NOTE The class IterationController has been adapted from the iteration
- *      class of the GMM++ and ITL libraries.
- */
-
-//=======================================================================
-// Copyright (C) 1997-2001
-// Authors: Andrew Lumsdaine <lums@osl.iu.edu> 
-//          Lie-Quan Lee     <llee@osl.iu.edu>
-//
-// This file is part of the Iterative Template Library
-//
-// You should have received a copy of the License Agreement for the
-// Iterative Template Library along with the software;  see the
-// file LICENSE.  
-//
-// Permission to modify the code and to distribute modified code is
-// granted, provided the text of this NOTICE is retained, a notice that
-// the code was modified is included with the above COPYRIGHT NOTICE and
-// with the COPYRIGHT NOTICE in the LICENSE file, and that the LICENSE
-// file is distributed with the modified code.
-//
-// LICENSOR MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED.
-// By way of example, but not limitation, Licensor MAKES NO
-// REPRESENTATIONS OR WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY
-// PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE COMPONENTS
-// OR DOCUMENTATION WILL NOT INFRINGE ANY PATENTS, COPYRIGHTS, TRADEMARKS
-// OR OTHER RIGHTS.
-//=======================================================================
-
-//========================================================================
-//
-// Copyright (C) 2002-2007 Yves Renard
-//
-// This file is a part of GETFEM++
-//
-// Getfem++ is free software; you can redistribute it and/or modify
-// it under the terms of the GNU Lesser General Public License as
-// published by the Free Software Foundation; version 2.1 of the License.
-//
-// This program is distributed in the hope that it will be useful,
-// but WITHOUT ANY WARRANTY; without even the implied warranty of
-// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-// GNU Lesser General Public License for more details.
-// You should have received a copy of the GNU Lesser General Public
-// License along with this program; if not, write to the Free Software
-// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301,
-// USA.
-//
-//========================================================================
-
-#include "../../../../Eigen/src/Core/util/NonMPL2.h"
-
-#ifndef EIGEN_ITERATION_CONTROLLER_H
-#define EIGEN_ITERATION_CONTROLLER_H
-
-namespace Eigen { 
-
-/** \ingroup IterativeLinearSolvers_Module
-  * \class IterationController
-  *
-  * \brief Controls the iterations of the iterative solvers
-  *
-  * This class has been adapted from the iteration class of GMM++ and ITL libraries.
-  *
-  */
-class IterationController
-{
-  protected :
-    double m_rhsn;        ///< Right hand side norm
-    size_t m_maxiter;     ///< Max. number of iterations
-    int m_noise;          ///< if noise > 0 iterations are printed
-    double m_resmax;      ///< maximum residual
-    double m_resminreach, m_resadd;
-    size_t m_nit;         ///< iteration number
-    double m_res;         ///< last computed residual
-    bool m_written;
-    void (*m_callback)(const IterationController&);
-  public :
-
-    void init()
-    {
-      m_nit = 0; m_res = 0.0; m_written = false;
-      m_resminreach = 1E50; m_resadd = 0.0;
-      m_callback = 0;
-    }
-
-    IterationController(double r = 1.0E-8, int noi = 0, size_t mit = size_t(-1))
-      : m_rhsn(1.0), m_maxiter(mit), m_noise(noi), m_resmax(r) { init(); }
-
-    void operator ++(int) { m_nit++; m_written = false; m_resadd += m_res; }
-    void operator ++() { (*this)++; }
-
-    bool first() { return m_nit == 0; }
-
-    /* get/set the "noisyness" (verbosity) of the solvers */
-    int noiseLevel() const { return m_noise; }
-    void setNoiseLevel(int n) { m_noise = n; }
-    void reduceNoiseLevel() { if (m_noise > 0) m_noise--; }
-
-    double maxResidual() const { return m_resmax; }
-    void setMaxResidual(double r) { m_resmax = r; }
-
-    double residual() const { return m_res; }
-
-    /* change the user-definable callback, called after each iteration */
-    void setCallback(void (*t)(const IterationController&))
-    {
-      m_callback = t;
-    }
-
-    size_t iteration() const { return m_nit; }
-    void setIteration(size_t i) { m_nit = i; }
-
-    size_t maxIterarions() const { return m_maxiter; }
-    void setMaxIterations(size_t i) { m_maxiter = i; }
-
-    double rhsNorm() const { return m_rhsn; }
-    void setRhsNorm(double r) { m_rhsn = r; }
-
-    bool converged() const { return m_res <= m_rhsn * m_resmax; }
-    bool converged(double nr)
-    {
-      using std::abs;
-      m_res = abs(nr); 
-      m_resminreach = (std::min)(m_resminreach, m_res);
-      return converged();
-    }
-    template<typename VectorType> bool converged(const VectorType &v)
-    { return converged(v.squaredNorm()); }
-
-    bool finished(double nr)
-    {
-      if (m_callback) m_callback(*this);
-      if (m_noise > 0 && !m_written)
-      {
-        converged(nr);
-        m_written = true;
-      }
-      return (m_nit >= m_maxiter || converged(nr));
-    }
-    template <typename VectorType>
-    bool finished(const MatrixBase<VectorType> &v)
-    { return finished(double(v.squaredNorm())); }
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_ITERATION_CONTROLLER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h
deleted file mode 100644
index 5db454d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h
+++ /dev/null
@@ -1,267 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Giacomo Po <gpo@ucla.edu>
-// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2018 David Hyde <dabh@stanford.edu>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_MINRES_H_
-#define EIGEN_MINRES_H_
-
-
-namespace Eigen {
-    
-    namespace internal {
-        
-        /** \internal Low-level MINRES algorithm
-         * \param mat The matrix A
-         * \param rhs The right hand side vector b
-         * \param x On input and initial solution, on output the computed solution.
-         * \param precond A right preconditioner being able to efficiently solve for an
-         *                approximation of Ax=b (regardless of b)
-         * \param iters On input the max number of iteration, on output the number of performed iterations.
-         * \param tol_error On input the tolerance error, on output an estimation of the relative error.
-         */
-        template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
-        EIGEN_DONT_INLINE
-        void minres(const MatrixType& mat, const Rhs& rhs, Dest& x,
-                    const Preconditioner& precond, Index& iters,
-                    typename Dest::RealScalar& tol_error)
-        {
-            using std::sqrt;
-            typedef typename Dest::RealScalar RealScalar;
-            typedef typename Dest::Scalar Scalar;
-            typedef Matrix<Scalar,Dynamic,1> VectorType;
-
-            // Check for zero rhs
-            const RealScalar rhsNorm2(rhs.squaredNorm());
-            if(rhsNorm2 == 0)
-            {
-                x.setZero();
-                iters = 0;
-                tol_error = 0;
-                return;
-            }
-            
-            // initialize
-            const Index maxIters(iters);  // initialize maxIters to iters
-            const Index N(mat.cols());    // the size of the matrix
-            const RealScalar threshold2(tol_error*tol_error*rhsNorm2); // convergence threshold (compared to residualNorm2)
-            
-            // Initialize preconditioned Lanczos
-            VectorType v_old(N); // will be initialized inside loop
-            VectorType v( VectorType::Zero(N) ); //initialize v
-            VectorType v_new(rhs-mat*x); //initialize v_new
-            RealScalar residualNorm2(v_new.squaredNorm());
-            VectorType w(N); // will be initialized inside loop
-            VectorType w_new(precond.solve(v_new)); // initialize w_new
-//            RealScalar beta; // will be initialized inside loop
-            RealScalar beta_new2(v_new.dot(w_new));
-            eigen_assert(beta_new2 >= 0.0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
-            RealScalar beta_new(sqrt(beta_new2));
-            const RealScalar beta_one(beta_new);
-            // Initialize other variables
-            RealScalar c(1.0); // the cosine of the Givens rotation
-            RealScalar c_old(1.0);
-            RealScalar s(0.0); // the sine of the Givens rotation
-            RealScalar s_old(0.0); // the sine of the Givens rotation
-            VectorType p_oold(N); // will be initialized in loop
-            VectorType p_old(VectorType::Zero(N)); // initialize p_old=0
-            VectorType p(p_old); // initialize p=0
-            RealScalar eta(1.0);
-                        
-            iters = 0; // reset iters
-            while ( iters < maxIters )
-            {
-                // Preconditioned Lanczos
-                /* Note that there are 4 variants on the Lanczos algorithm. These are
-                 * described in Paige, C. C. (1972). Computational variants of
-                 * the Lanczos method for the eigenproblem. IMA Journal of Applied
-                 * Mathematics, 10(3), 373-381. The current implementation corresponds 
-                 * to the case A(2,7) in the paper. It also corresponds to 
-                 * algorithm 6.14 in Y. Saad, Iterative Methods for Sparse Linear
-                 * Systems, 2003 p.173. For the preconditioned version see 
-                 * A. Greenbaum, Iterative Methods for Solving Linear Systems, SIAM (1987).
-                 */
-                const RealScalar beta(beta_new);
-                v_old = v; // update: at first time step, this makes v_old = 0 so value of beta doesn't matter
-                v_new /= beta_new; // overwrite v_new for next iteration
-                w_new /= beta_new; // overwrite w_new for next iteration
-                v = v_new; // update
-                w = w_new; // update
-                v_new.noalias() = mat*w - beta*v_old; // compute v_new
-                const RealScalar alpha = v_new.dot(w);
-                v_new -= alpha*v; // overwrite v_new
-                w_new = precond.solve(v_new); // overwrite w_new
-                beta_new2 = v_new.dot(w_new); // compute beta_new
-                eigen_assert(beta_new2 >= 0.0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
-                beta_new = sqrt(beta_new2); // compute beta_new
-                
-                // Givens rotation
-                const RealScalar r2 =s*alpha+c*c_old*beta; // s, s_old, c and c_old are still from previous iteration
-                const RealScalar r3 =s_old*beta; // s, s_old, c and c_old are still from previous iteration
-                const RealScalar r1_hat=c*alpha-c_old*s*beta;
-                const RealScalar r1 =sqrt( std::pow(r1_hat,2) + std::pow(beta_new,2) );
-                c_old = c; // store for next iteration
-                s_old = s; // store for next iteration
-                c=r1_hat/r1; // new cosine
-                s=beta_new/r1; // new sine
-                
-                // Update solution
-                p_oold = p_old;
-                p_old = p;
-                p.noalias()=(w-r2*p_old-r3*p_oold) /r1; // IS NOALIAS REQUIRED?
-                x += beta_one*c*eta*p;
-                
-                /* Update the squared residual. Note that this is the estimated residual.
-                The real residual |Ax-b|^2 may be slightly larger */
-                residualNorm2 *= s*s;
-                
-                if ( residualNorm2 < threshold2)
-                {
-                    break;
-                }
-                
-                eta=-s*eta; // update eta
-                iters++; // increment iteration number (for output purposes)
-            }
-            
-            /* Compute error. Note that this is the estimated error. The real 
-             error |Ax-b|/|b| may be slightly larger */
-            tol_error = std::sqrt(residualNorm2 / rhsNorm2);
-        }
-        
-    }
-    
-    template< typename _MatrixType, int _UpLo=Lower,
-    typename _Preconditioner = IdentityPreconditioner>
-    class MINRES;
-    
-    namespace internal {
-        
-        template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-        struct traits<MINRES<_MatrixType,_UpLo,_Preconditioner> >
-        {
-            typedef _MatrixType MatrixType;
-            typedef _Preconditioner Preconditioner;
-        };
-        
-    }
-    
-    /** \ingroup IterativeLinearSolvers_Module
-     * \brief A minimal residual solver for sparse symmetric problems
-     *
-     * This class allows to solve for A.x = b sparse linear problems using the MINRES algorithm
-     * of Paige and Saunders (1975). The sparse matrix A must be symmetric (possibly indefinite).
-     * The vectors x and b can be either dense or sparse.
-     *
-     * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
-     * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower,
-     *               Upper, or Lower|Upper in which the full matrix entries will be considered. Default is Lower.
-     * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
-     *
-     * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
-     * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
-     * and NumTraits<Scalar>::epsilon() for the tolerance.
-     *
-     * This class can be used as the direct solver classes. Here is a typical usage example:
-     * \code
-     * int n = 10000;
-     * VectorXd x(n), b(n);
-     * SparseMatrix<double> A(n,n);
-     * // fill A and b
-     * MINRES<SparseMatrix<double> > mr;
-     * mr.compute(A);
-     * x = mr.solve(b);
-     * std::cout << "#iterations:     " << mr.iterations() << std::endl;
-     * std::cout << "estimated error: " << mr.error()      << std::endl;
-     * // update b, and solve again
-     * x = mr.solve(b);
-     * \endcode
-     *
-     * By default the iterations start with x=0 as an initial guess of the solution.
-     * One can control the start using the solveWithGuess() method.
-     *
-     * MINRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
-     *
-     * \sa class ConjugateGradient, BiCGSTAB, SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
-     */
-    template< typename _MatrixType, int _UpLo, typename _Preconditioner>
-    class MINRES : public IterativeSolverBase<MINRES<_MatrixType,_UpLo,_Preconditioner> >
-    {
-        
-        typedef IterativeSolverBase<MINRES> Base;
-        using Base::matrix;
-        using Base::m_error;
-        using Base::m_iterations;
-        using Base::m_info;
-        using Base::m_isInitialized;
-    public:
-        using Base::_solve_impl;
-        typedef _MatrixType MatrixType;
-        typedef typename MatrixType::Scalar Scalar;
-        typedef typename MatrixType::RealScalar RealScalar;
-        typedef _Preconditioner Preconditioner;
-        
-        enum {UpLo = _UpLo};
-        
-    public:
-        
-        /** Default constructor. */
-        MINRES() : Base() {}
-        
-        /** Initialize the solver with matrix \a A for further \c Ax=b solving.
-         *
-         * This constructor is a shortcut for the default constructor followed
-         * by a call to compute().
-         *
-         * \warning this class stores a reference to the matrix A as well as some
-         * precomputed values that depend on it. Therefore, if \a A is changed
-         * this class becomes invalid. Call compute() to update it with the new
-         * matrix A, or modify a copy of A.
-         */
-        template<typename MatrixDerived>
-        explicit MINRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
-        
-        /** Destructor. */
-        ~MINRES(){}
-
-        /** \internal */
-        template<typename Rhs,typename Dest>
-        void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
-        {
-            typedef typename Base::MatrixWrapper MatrixWrapper;
-            typedef typename Base::ActualMatrixType ActualMatrixType;
-            enum {
-              TransposeInput  =   (!MatrixWrapper::MatrixFree)
-                              &&  (UpLo==(Lower|Upper))
-                              &&  (!MatrixType::IsRowMajor)
-                              &&  (!NumTraits<Scalar>::IsComplex)
-            };
-            typedef typename internal::conditional<TransposeInput,Transpose<const ActualMatrixType>, ActualMatrixType const&>::type RowMajorWrapper;
-            EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(MatrixWrapper::MatrixFree,UpLo==(Lower|Upper)),MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
-            typedef typename internal::conditional<UpLo==(Lower|Upper),
-                                                  RowMajorWrapper,
-                                                  typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type
-                                            >::type SelfAdjointWrapper;
-
-            m_iterations = Base::maxIterations();
-            m_error = Base::m_tolerance;
-            RowMajorWrapper row_mat(matrix());
-            internal::minres(SelfAdjointWrapper(row_mat), b, x,
-                             Base::m_preconditioner, m_iterations, m_error);
-            m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
-        }
-        
-    protected:
-        
-    };
-
-} // end namespace Eigen
-
-#endif // EIGEN_MINRES_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h
deleted file mode 100644
index 9b3eb53..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h
+++ /dev/null
@@ -1,193 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ITERSCALING_H
-#define EIGEN_ITERSCALING_H
-
-namespace Eigen {
-
-/**
-  * \ingroup IterativeSolvers_Module
-  * \brief iterative scaling algorithm to equilibrate rows and column norms in matrices
-  * 
-  * This class can be used as a preprocessing tool to accelerate the convergence of iterative methods 
-  * 
-  * This feature is  useful to limit the pivoting amount during LU/ILU factorization
-  * The  scaling strategy as presented here preserves the symmetry of the problem
-  * NOTE It is assumed that the matrix does not have empty row or column, 
-  * 
-  * Example with key steps 
-  * \code
-  * VectorXd x(n), b(n);
-  * SparseMatrix<double> A;
-  * // fill A and b;
-  * IterScaling<SparseMatrix<double> > scal; 
-  * // Compute the left and right scaling vectors. The matrix is equilibrated at output
-  * scal.computeRef(A); 
-  * // Scale the right hand side
-  * b = scal.LeftScaling().cwiseProduct(b); 
-  * // Now, solve the equilibrated linear system with any available solver
-  * 
-  * // Scale back the computed solution
-  * x = scal.RightScaling().cwiseProduct(x); 
-  * \endcode
-  * 
-  * \tparam _MatrixType the type of the matrix. It should be a real square sparsematrix
-  * 
-  * References : D. Ruiz and B. Ucar, A Symmetry Preserving Algorithm for Matrix Scaling, INRIA Research report RR-7552
-  * 
-  * \sa \ref IncompleteLUT 
-  */
-template<typename _MatrixType>
-class IterScaling
-{
-  public:
-    typedef _MatrixType MatrixType; 
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::Index Index;
-    
-  public:
-    IterScaling() { init(); }
-    
-    IterScaling(const MatrixType& matrix)
-    {
-      init();
-      compute(matrix);
-    }
-    
-    ~IterScaling() { }
-    
-    /** 
-     * Compute the left and right diagonal matrices to scale the input matrix @p mat
-     * 
-     * FIXME This algorithm will be modified such that the diagonal elements are permuted on the diagonal. 
-     * 
-     * \sa LeftScaling() RightScaling()
-     */
-    void compute (const MatrixType& mat)
-    {
-      using std::abs;
-      int m = mat.rows(); 
-      int n = mat.cols();
-      eigen_assert((m>0 && m == n) && "Please give a non - empty matrix");
-      m_left.resize(m); 
-      m_right.resize(n);
-      m_left.setOnes();
-      m_right.setOnes();
-      m_matrix = mat;
-      VectorXd Dr, Dc, DrRes, DcRes; // Temporary Left and right scaling vectors
-      Dr.resize(m); Dc.resize(n);
-      DrRes.resize(m); DcRes.resize(n);
-      double EpsRow = 1.0, EpsCol = 1.0;
-      int its = 0; 
-      do
-      { // Iterate until the infinite norm of each row and column is approximately 1
-        // Get the maximum value in each row and column
-        Dr.setZero(); Dc.setZero();
-        for (int k=0; k<m_matrix.outerSize(); ++k)
-        {
-          for (typename MatrixType::InnerIterator it(m_matrix, k); it; ++it)
-          {
-            if ( Dr(it.row()) < abs(it.value()) )
-              Dr(it.row()) = abs(it.value());
-            
-            if ( Dc(it.col()) < abs(it.value()) )
-              Dc(it.col()) = abs(it.value());
-          }
-        }
-        for (int i = 0; i < m; ++i) 
-        {
-          Dr(i) = std::sqrt(Dr(i));
-        }
-        for (int i = 0; i < n; ++i) 
-        {
-          Dc(i) = std::sqrt(Dc(i));
-        }
-        // Save the scaling factors 
-        for (int i = 0; i < m; ++i) 
-        {
-          m_left(i) /= Dr(i);
-        }
-        for (int i = 0; i < n; ++i) 
-        {
-          m_right(i) /= Dc(i);
-        }
-        // Scale the rows and the columns of the matrix
-        DrRes.setZero(); DcRes.setZero(); 
-        for (int k=0; k<m_matrix.outerSize(); ++k)
-        {
-          for (typename MatrixType::InnerIterator it(m_matrix, k); it; ++it)
-          {
-            it.valueRef() = it.value()/( Dr(it.row()) * Dc(it.col()) );
-            // Accumulate the norms of the row and column vectors   
-            if ( DrRes(it.row()) < abs(it.value()) )
-              DrRes(it.row()) = abs(it.value());
-            
-            if ( DcRes(it.col()) < abs(it.value()) )
-              DcRes(it.col()) = abs(it.value());
-          }
-        }  
-        DrRes.array() = (1-DrRes.array()).abs();
-        EpsRow = DrRes.maxCoeff();
-        DcRes.array() = (1-DcRes.array()).abs();
-        EpsCol = DcRes.maxCoeff();
-        its++;
-      }while ( (EpsRow >m_tol || EpsCol > m_tol) && (its < m_maxits) );
-      m_isInitialized = true;
-    }
-    /** Compute the left and right vectors to scale the vectors
-     * the input matrix is scaled with the computed vectors at output
-     * 
-     * \sa compute()
-     */
-    void computeRef (MatrixType& mat)
-    {
-      compute (mat);
-      mat = m_matrix;
-    }
-    /** Get the vector to scale the rows of the matrix 
-     */
-    VectorXd& LeftScaling()
-    {
-      return m_left;
-    }
-    
-    /** Get the vector to scale the columns of the matrix 
-     */
-    VectorXd& RightScaling()
-    {
-      return m_right;
-    }
-    
-    /** Set the tolerance for the convergence of the iterative scaling algorithm
-     */
-    void setTolerance(double tol)
-    {
-      m_tol = tol; 
-    }
-      
-  protected:
-    
-    void init()
-    {
-      m_tol = 1e-10;
-      m_maxits = 5;
-      m_isInitialized = false;
-    }
-    
-    MatrixType m_matrix;
-    mutable ComputationInfo m_info; 
-    bool m_isInitialized; 
-    VectorXd m_left; // Left scaling vector
-    VectorXd m_right; // m_right scaling vector
-    double m_tol; 
-    int m_maxits; // Maximum number of iterations allowed
-};
-}
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
deleted file mode 100644
index 6a9b0be..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
+++ /dev/null
@@ -1,305 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Kolja Brix <brix@igpm.rwth-aachen.de>
-// Copyright (C) 2011 Andreas Platen <andiplaten@gmx.de>
-// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef KRONECKER_TENSOR_PRODUCT_H
-#define KRONECKER_TENSOR_PRODUCT_H
-
-namespace Eigen {
-
-/*!
- * \ingroup KroneckerProduct_Module
- *
- * \brief The base class of dense and sparse Kronecker product.
- *
- * \tparam Derived is the derived type.
- */
-template<typename Derived>
-class KroneckerProductBase : public ReturnByValue<Derived>
-{
-  private:
-    typedef typename internal::traits<Derived> Traits;
-    typedef typename Traits::Scalar Scalar;
-
-  protected:
-    typedef typename Traits::Lhs Lhs;
-    typedef typename Traits::Rhs Rhs;
-
-  public:
-    /*! \brief Constructor. */
-    KroneckerProductBase(const Lhs& A, const Rhs& B)
-      : m_A(A), m_B(B)
-    {}
-
-    inline Index rows() const { return m_A.rows() * m_B.rows(); }
-    inline Index cols() const { return m_A.cols() * m_B.cols(); }
-
-    /*!
-     * This overrides ReturnByValue::coeff because this function is
-     * efficient enough.
-     */
-    Scalar coeff(Index row, Index col) const
-    {
-      return m_A.coeff(row / m_B.rows(), col / m_B.cols()) *
-             m_B.coeff(row % m_B.rows(), col % m_B.cols());
-    }
-
-    /*!
-     * This overrides ReturnByValue::coeff because this function is
-     * efficient enough.
-     */
-    Scalar coeff(Index i) const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
-      return m_A.coeff(i / m_A.size()) * m_B.coeff(i % m_A.size());
-    }
-
-  protected:
-    typename Lhs::Nested m_A;
-    typename Rhs::Nested m_B;
-};
-
-/*!
- * \ingroup KroneckerProduct_Module
- *
- * \brief Kronecker tensor product helper class for dense matrices
- *
- * This class is the return value of kroneckerProduct(MatrixBase,
- * MatrixBase). Use the function rather than construct this class
- * directly to avoid specifying template prarameters.
- *
- * \tparam Lhs  Type of the left-hand side, a matrix expression.
- * \tparam Rhs  Type of the rignt-hand side, a matrix expression.
- */
-template<typename Lhs, typename Rhs>
-class KroneckerProduct : public KroneckerProductBase<KroneckerProduct<Lhs,Rhs> >
-{
-  private:
-    typedef KroneckerProductBase<KroneckerProduct> Base;
-    using Base::m_A;
-    using Base::m_B;
-
-  public:
-    /*! \brief Constructor. */
-    KroneckerProduct(const Lhs& A, const Rhs& B)
-      : Base(A, B)
-    {}
-
-    /*! \brief Evaluate the Kronecker tensor product. */
-    template<typename Dest> void evalTo(Dest& dst) const;
-};
-
-/*!
- * \ingroup KroneckerProduct_Module
- *
- * \brief Kronecker tensor product helper class for sparse matrices
- *
- * If at least one of the operands is a sparse matrix expression,
- * then this class is returned and evaluates into a sparse matrix.
- *
- * This class is the return value of kroneckerProduct(EigenBase,
- * EigenBase). Use the function rather than construct this class
- * directly to avoid specifying template prarameters.
- *
- * \tparam Lhs  Type of the left-hand side, a matrix expression.
- * \tparam Rhs  Type of the rignt-hand side, a matrix expression.
- */
-template<typename Lhs, typename Rhs>
-class KroneckerProductSparse : public KroneckerProductBase<KroneckerProductSparse<Lhs,Rhs> >
-{
-  private:
-    typedef KroneckerProductBase<KroneckerProductSparse> Base;
-    using Base::m_A;
-    using Base::m_B;
-
-  public:
-    /*! \brief Constructor. */
-    KroneckerProductSparse(const Lhs& A, const Rhs& B)
-      : Base(A, B)
-    {}
-
-    /*! \brief Evaluate the Kronecker tensor product. */
-    template<typename Dest> void evalTo(Dest& dst) const;
-};
-
-template<typename Lhs, typename Rhs>
-template<typename Dest>
-void KroneckerProduct<Lhs,Rhs>::evalTo(Dest& dst) const
-{
-  const int BlockRows = Rhs::RowsAtCompileTime,
-            BlockCols = Rhs::ColsAtCompileTime;
-  const Index Br = m_B.rows(),
-              Bc = m_B.cols();
-  for (Index i=0; i < m_A.rows(); ++i)
-    for (Index j=0; j < m_A.cols(); ++j)
-      Block<Dest,BlockRows,BlockCols>(dst,i*Br,j*Bc,Br,Bc) = m_A.coeff(i,j) * m_B;
-}
-
-template<typename Lhs, typename Rhs>
-template<typename Dest>
-void KroneckerProductSparse<Lhs,Rhs>::evalTo(Dest& dst) const
-{
-  Index Br = m_B.rows(), Bc = m_B.cols();
-  dst.resize(this->rows(), this->cols());
-  dst.resizeNonZeros(0);
-  
-  // 1 - evaluate the operands if needed:
-  typedef typename internal::nested_eval<Lhs,Dynamic>::type Lhs1;
-  typedef typename internal::remove_all<Lhs1>::type Lhs1Cleaned;
-  const Lhs1 lhs1(m_A);
-  typedef typename internal::nested_eval<Rhs,Dynamic>::type Rhs1;
-  typedef typename internal::remove_all<Rhs1>::type Rhs1Cleaned;
-  const Rhs1 rhs1(m_B);
-    
-  // 2 - construct respective iterators
-  typedef Eigen::InnerIterator<Lhs1Cleaned> LhsInnerIterator;
-  typedef Eigen::InnerIterator<Rhs1Cleaned> RhsInnerIterator;
-  
-  // compute number of non-zeros per innervectors of dst
-  {
-    // TODO VectorXi is not necessarily big enough!
-    VectorXi nnzA = VectorXi::Zero(Dest::IsRowMajor ? m_A.rows() : m_A.cols());
-    for (Index kA=0; kA < m_A.outerSize(); ++kA)
-      for (LhsInnerIterator itA(lhs1,kA); itA; ++itA)
-        nnzA(Dest::IsRowMajor ? itA.row() : itA.col())++;
-      
-    VectorXi nnzB = VectorXi::Zero(Dest::IsRowMajor ? m_B.rows() : m_B.cols());
-    for (Index kB=0; kB < m_B.outerSize(); ++kB)
-      for (RhsInnerIterator itB(rhs1,kB); itB; ++itB)
-        nnzB(Dest::IsRowMajor ? itB.row() : itB.col())++;
-    
-    Matrix<int,Dynamic,Dynamic,ColMajor> nnzAB = nnzB * nnzA.transpose();
-    dst.reserve(VectorXi::Map(nnzAB.data(), nnzAB.size()));
-  }
-
-  for (Index kA=0; kA < m_A.outerSize(); ++kA)
-  {
-    for (Index kB=0; kB < m_B.outerSize(); ++kB)
-    {
-      for (LhsInnerIterator itA(lhs1,kA); itA; ++itA)
-      {
-        for (RhsInnerIterator itB(rhs1,kB); itB; ++itB)
-        {
-          Index i = itA.row() * Br + itB.row(),
-                j = itA.col() * Bc + itB.col();
-          dst.insert(i,j) = itA.value() * itB.value();
-        }
-      }
-    }
-  }
-}
-
-namespace internal {
-
-template<typename _Lhs, typename _Rhs>
-struct traits<KroneckerProduct<_Lhs,_Rhs> >
-{
-  typedef typename remove_all<_Lhs>::type Lhs;
-  typedef typename remove_all<_Rhs>::type Rhs;
-  typedef typename ScalarBinaryOpTraits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
-  typedef typename promote_index_type<typename Lhs::StorageIndex, typename Rhs::StorageIndex>::type StorageIndex;
-
-  enum {
-    Rows = size_at_compile_time<traits<Lhs>::RowsAtCompileTime, traits<Rhs>::RowsAtCompileTime>::ret,
-    Cols = size_at_compile_time<traits<Lhs>::ColsAtCompileTime, traits<Rhs>::ColsAtCompileTime>::ret,
-    MaxRows = size_at_compile_time<traits<Lhs>::MaxRowsAtCompileTime, traits<Rhs>::MaxRowsAtCompileTime>::ret,
-    MaxCols = size_at_compile_time<traits<Lhs>::MaxColsAtCompileTime, traits<Rhs>::MaxColsAtCompileTime>::ret
-  };
-
-  typedef Matrix<Scalar,Rows,Cols> ReturnType;
-};
-
-template<typename _Lhs, typename _Rhs>
-struct traits<KroneckerProductSparse<_Lhs,_Rhs> >
-{
-  typedef MatrixXpr XprKind;
-  typedef typename remove_all<_Lhs>::type Lhs;
-  typedef typename remove_all<_Rhs>::type Rhs;
-  typedef typename ScalarBinaryOpTraits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
-  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind, scalar_product_op<typename Lhs::Scalar, typename Rhs::Scalar> >::ret StorageKind;
-  typedef typename promote_index_type<typename Lhs::StorageIndex, typename Rhs::StorageIndex>::type StorageIndex;
-
-  enum {
-    LhsFlags = Lhs::Flags,
-    RhsFlags = Rhs::Flags,
-
-    RowsAtCompileTime = size_at_compile_time<traits<Lhs>::RowsAtCompileTime, traits<Rhs>::RowsAtCompileTime>::ret,
-    ColsAtCompileTime = size_at_compile_time<traits<Lhs>::ColsAtCompileTime, traits<Rhs>::ColsAtCompileTime>::ret,
-    MaxRowsAtCompileTime = size_at_compile_time<traits<Lhs>::MaxRowsAtCompileTime, traits<Rhs>::MaxRowsAtCompileTime>::ret,
-    MaxColsAtCompileTime = size_at_compile_time<traits<Lhs>::MaxColsAtCompileTime, traits<Rhs>::MaxColsAtCompileTime>::ret,
-
-    EvalToRowMajor = (int(LhsFlags) & int(RhsFlags) & RowMajorBit),
-    RemovedBits = ~(EvalToRowMajor ? 0 : RowMajorBit),
-
-    Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & RemovedBits)
-          | EvalBeforeNestingBit,
-    CoeffReadCost = HugeCost
-  };
-
-  typedef SparseMatrix<Scalar, 0, StorageIndex> ReturnType;
-};
-
-} // end namespace internal
-
-/*!
- * \ingroup KroneckerProduct_Module
- *
- * Computes Kronecker tensor product of two dense matrices
- *
- * \warning If you want to replace a matrix by its Kronecker product
- *          with some matrix, do \b NOT do this:
- * \code
- * A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
- * \endcode
- * instead, use eval() to work around this:
- * \code
- * A = kroneckerProduct(A,B).eval();
- * \endcode
- *
- * \param a  Dense matrix a
- * \param b  Dense matrix b
- * \return   Kronecker tensor product of a and b
- */
-template<typename A, typename B>
-KroneckerProduct<A,B> kroneckerProduct(const MatrixBase<A>& a, const MatrixBase<B>& b)
-{
-  return KroneckerProduct<A, B>(a.derived(), b.derived());
-}
-
-/*!
- * \ingroup KroneckerProduct_Module
- *
- * Computes Kronecker tensor product of two matrices, at least one of
- * which is sparse
- *
- * \warning If you want to replace a matrix by its Kronecker product
- *          with some matrix, do \b NOT do this:
- * \code
- * A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
- * \endcode
- * instead, use eval() to work around this:
- * \code
- * A = kroneckerProduct(A,B).eval();
- * \endcode
- *
- * \param a  Dense/sparse matrix a
- * \param b  Dense/sparse matrix b
- * \return   Kronecker tensor product of a and b, stored in a sparse
- *           matrix
- */
-template<typename A, typename B>
-KroneckerProductSparse<A,B> kroneckerProduct(const EigenBase<A>& a, const EigenBase<B>& b)
-{
-  return KroneckerProductSparse<A,B>(a.derived(), b.derived());
-}
-
-} // end namespace Eigen
-
-#endif // KRONECKER_TENSOR_PRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
deleted file mode 100644
index b75bea2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
+++ /dev/null
@@ -1,84 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This code initially comes from MINPACK whose original authors are:
-// Copyright Jorge More - Argonne National Laboratory
-// Copyright Burt Garbow - Argonne National Laboratory
-// Copyright Ken Hillstrom - Argonne National Laboratory
-//
-// This Source Code Form is subject to the terms of the Minpack license
-// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
-
-#ifndef EIGEN_LMCOVAR_H
-#define EIGEN_LMCOVAR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template <typename Scalar>
-void covar(
-        Matrix< Scalar, Dynamic, Dynamic > &r,
-        const VectorXi& ipvt,
-        Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) )
-{
-    using std::abs;
-    /* Local variables */
-    Index i, j, k, l, ii, jj;
-    bool sing;
-    Scalar temp;
-
-    /* Function Body */
-    const Index n = r.cols();
-    const Scalar tolr = tol * abs(r(0,0));
-    Matrix< Scalar, Dynamic, 1 > wa(n);
-    eigen_assert(ipvt.size()==n);
-
-    /* form the inverse of r in the full upper triangle of r. */
-    l = -1;
-    for (k = 0; k < n; ++k)
-        if (abs(r(k,k)) > tolr) {
-            r(k,k) = 1. / r(k,k);
-            for (j = 0; j <= k-1; ++j) {
-                temp = r(k,k) * r(j,k);
-                r(j,k) = 0.;
-                r.col(k).head(j+1) -= r.col(j).head(j+1) * temp;
-            }
-            l = k;
-        }
-
-    /* form the full upper triangle of the inverse of (r transpose)*r */
-    /* in the full upper triangle of r. */
-    for (k = 0; k <= l; ++k) {
-        for (j = 0; j <= k-1; ++j)
-            r.col(j).head(j+1) += r.col(k).head(j+1) * r(j,k);
-        r.col(k).head(k+1) *= r(k,k);
-    }
-
-    /* form the full lower triangle of the covariance matrix */
-    /* in the strict lower triangle of r and in wa. */
-    for (j = 0; j < n; ++j) {
-        jj = ipvt[j];
-        sing = j > l;
-        for (i = 0; i <= j; ++i) {
-            if (sing)
-                r(i,j) = 0.;
-            ii = ipvt[i];
-            if (ii > jj)
-                r(ii,jj) = r(i,j);
-            if (ii < jj)
-                r(jj,ii) = r(i,j);
-        }
-        wa[jj] = r(j,j);
-    }
-
-    /* symmetrize the covariance matrix in r. */
-    r.topLeftCorner(n,n).template triangularView<StrictlyUpper>() = r.topLeftCorner(n,n).transpose();
-    r.diagonal() = wa;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_LMCOVAR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
deleted file mode 100644
index 25b32ec..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
+++ /dev/null
@@ -1,202 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-//
-// This code initially comes from MINPACK whose original authors are:
-// Copyright Jorge More - Argonne National Laboratory
-// Copyright Burt Garbow - Argonne National Laboratory
-// Copyright Ken Hillstrom - Argonne National Laboratory
-//
-// This Source Code Form is subject to the terms of the Minpack license
-// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
-
-#ifndef EIGEN_LMONESTEP_H
-#define EIGEN_LMONESTEP_H
-
-namespace Eigen {
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::minimizeOneStep(FVectorType  &x)
-{
-  using std::abs;
-  using std::sqrt;
-  RealScalar temp, temp1,temp2; 
-  RealScalar ratio; 
-  RealScalar pnorm, xnorm, fnorm1, actred, dirder, prered;
-  eigen_assert(x.size()==n); // check the caller is not cheating us
-
-  temp = 0.0; xnorm = 0.0;
-  /* calculate the jacobian matrix. */
-  Index df_ret = m_functor.df(x, m_fjac);
-  if (df_ret<0)
-      return LevenbergMarquardtSpace::UserAsked;
-  if (df_ret>0)
-      // numerical diff, we evaluated the function df_ret times
-      m_nfev += df_ret;
-  else m_njev++;
-
-  /* compute the qr factorization of the jacobian. */
-  for (int j = 0; j < x.size(); ++j)
-    m_wa2(j) = m_fjac.col(j).blueNorm();
-  QRSolver qrfac(m_fjac);
-  if(qrfac.info() != Success) {
-    m_info = NumericalIssue;
-    return LevenbergMarquardtSpace::ImproperInputParameters;
-  }
-  // Make a copy of the first factor with the associated permutation
-  m_rfactor = qrfac.matrixR();
-  m_permutation = (qrfac.colsPermutation());
-
-  /* on the first iteration and if external scaling is not used, scale according */
-  /* to the norms of the columns of the initial jacobian. */
-  if (m_iter == 1) {
-      if (!m_useExternalScaling)
-          for (Index j = 0; j < n; ++j)
-              m_diag[j] = (m_wa2[j]==0.)? 1. : m_wa2[j];
-
-      /* on the first iteration, calculate the norm of the scaled x */
-      /* and initialize the step bound m_delta. */
-      xnorm = m_diag.cwiseProduct(x).stableNorm();
-      m_delta = m_factor * xnorm;
-      if (m_delta == 0.)
-          m_delta = m_factor;
-  }
-
-  /* form (q transpose)*m_fvec and store the first n components in */
-  /* m_qtf. */
-  m_wa4 = m_fvec;
-  m_wa4 = qrfac.matrixQ().adjoint() * m_fvec; 
-  m_qtf = m_wa4.head(n);
-
-  /* compute the norm of the scaled gradient. */
-  m_gnorm = 0.;
-  if (m_fnorm != 0.)
-      for (Index j = 0; j < n; ++j)
-          if (m_wa2[m_permutation.indices()[j]] != 0.)
-              m_gnorm = (std::max)(m_gnorm, abs( m_rfactor.col(j).head(j+1).dot(m_qtf.head(j+1)/m_fnorm) / m_wa2[m_permutation.indices()[j]]));
-
-  /* test for convergence of the gradient norm. */
-  if (m_gnorm <= m_gtol) {
-    m_info = Success;
-    return LevenbergMarquardtSpace::CosinusTooSmall;
-  }
-
-  /* rescale if necessary. */
-  if (!m_useExternalScaling)
-      m_diag = m_diag.cwiseMax(m_wa2);
-
-  do {
-    /* determine the levenberg-marquardt parameter. */
-    internal::lmpar2(qrfac, m_diag, m_qtf, m_delta, m_par, m_wa1);
-
-    /* store the direction p and x + p. calculate the norm of p. */
-    m_wa1 = -m_wa1;
-    m_wa2 = x + m_wa1;
-    pnorm = m_diag.cwiseProduct(m_wa1).stableNorm();
-
-    /* on the first iteration, adjust the initial step bound. */
-    if (m_iter == 1)
-        m_delta = (std::min)(m_delta,pnorm);
-
-    /* evaluate the function at x + p and calculate its norm. */
-    if ( m_functor(m_wa2, m_wa4) < 0)
-        return LevenbergMarquardtSpace::UserAsked;
-    ++m_nfev;
-    fnorm1 = m_wa4.stableNorm();
-
-    /* compute the scaled actual reduction. */
-    actred = -1.;
-    if (Scalar(.1) * fnorm1 < m_fnorm)
-        actred = 1. - numext::abs2(fnorm1 / m_fnorm);
-
-    /* compute the scaled predicted reduction and */
-    /* the scaled directional derivative. */
-    m_wa3 = m_rfactor.template triangularView<Upper>() * (m_permutation.inverse() *m_wa1);
-    temp1 = numext::abs2(m_wa3.stableNorm() / m_fnorm);
-    temp2 = numext::abs2(sqrt(m_par) * pnorm / m_fnorm);
-    prered = temp1 + temp2 / Scalar(.5);
-    dirder = -(temp1 + temp2);
-
-    /* compute the ratio of the actual to the predicted */
-    /* reduction. */
-    ratio = 0.;
-    if (prered != 0.)
-        ratio = actred / prered;
-
-    /* update the step bound. */
-    if (ratio <= Scalar(.25)) {
-        if (actred >= 0.)
-            temp = RealScalar(.5);
-        if (actred < 0.)
-            temp = RealScalar(.5) * dirder / (dirder + RealScalar(.5) * actred);
-        if (RealScalar(.1) * fnorm1 >= m_fnorm || temp < RealScalar(.1))
-            temp = Scalar(.1);
-        /* Computing MIN */
-        m_delta = temp * (std::min)(m_delta, pnorm / RealScalar(.1));
-        m_par /= temp;
-    } else if (!(m_par != 0. && ratio < RealScalar(.75))) {
-        m_delta = pnorm / RealScalar(.5);
-        m_par = RealScalar(.5) * m_par;
-    }
-
-    /* test for successful iteration. */
-    if (ratio >= RealScalar(1e-4)) {
-        /* successful iteration. update x, m_fvec, and their norms. */
-        x = m_wa2;
-        m_wa2 = m_diag.cwiseProduct(x);
-        m_fvec = m_wa4;
-        xnorm = m_wa2.stableNorm();
-        m_fnorm = fnorm1;
-        ++m_iter;
-    }
-
-    /* tests for convergence. */
-    if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1. && m_delta <= m_xtol * xnorm)
-    {
-       m_info = Success;
-      return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
-    }
-    if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1.) 
-    {
-      m_info = Success;
-      return LevenbergMarquardtSpace::RelativeReductionTooSmall;
-    }
-    if (m_delta <= m_xtol * xnorm)
-    {
-      m_info = Success;
-      return LevenbergMarquardtSpace::RelativeErrorTooSmall;
-    }
-
-    /* tests for termination and stringent tolerances. */
-    if (m_nfev >= m_maxfev) 
-    {
-      m_info = NoConvergence;
-      return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
-    }
-    if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
-    {
-      m_info = Success;
-      return LevenbergMarquardtSpace::FtolTooSmall;
-    }
-    if (m_delta <= NumTraits<Scalar>::epsilon() * xnorm) 
-    {
-      m_info = Success;
-      return LevenbergMarquardtSpace::XtolTooSmall;
-    }
-    if (m_gnorm <= NumTraits<Scalar>::epsilon())
-    {
-      m_info = Success;
-      return LevenbergMarquardtSpace::GtolTooSmall;
-    }
-
-  } while (ratio < Scalar(1e-4));
-
-  return LevenbergMarquardtSpace::Running;
-}
-
-  
-} // end namespace Eigen
-
-#endif // EIGEN_LMONESTEP_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
deleted file mode 100644
index 9a48365..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
+++ /dev/null
@@ -1,160 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This code initially comes from MINPACK whose original authors are:
-// Copyright Jorge More - Argonne National Laboratory
-// Copyright Burt Garbow - Argonne National Laboratory
-// Copyright Ken Hillstrom - Argonne National Laboratory
-//
-// This Source Code Form is subject to the terms of the Minpack license
-// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
-
-#ifndef EIGEN_LMPAR_H
-#define EIGEN_LMPAR_H
-
-namespace Eigen {
-
-namespace internal {
-  
-  template <typename QRSolver, typename VectorType>
-    void lmpar2(
-    const QRSolver &qr,
-    const VectorType  &diag,
-    const VectorType  &qtb,
-    typename VectorType::Scalar m_delta,
-    typename VectorType::Scalar &par,
-    VectorType  &x)
-
-  {
-    using std::sqrt;
-    using std::abs;
-    typedef typename QRSolver::MatrixType MatrixType;
-    typedef typename QRSolver::Scalar Scalar;
-//    typedef typename QRSolver::StorageIndex StorageIndex;
-
-    /* Local variables */
-    Index j;
-    Scalar fp;
-    Scalar parc, parl;
-    Index iter;
-    Scalar temp, paru;
-    Scalar gnorm;
-    Scalar dxnorm;
-    
-    // Make a copy of the triangular factor. 
-    // This copy is modified during call the qrsolv
-    MatrixType s;
-    s = qr.matrixR();
-
-    /* Function Body */
-    const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
-    const Index n = qr.matrixR().cols();
-    eigen_assert(n==diag.size());
-    eigen_assert(n==qtb.size());
-
-    VectorType  wa1, wa2;
-
-    /* compute and store in x the gauss-newton direction. if the */
-    /* jacobian is rank-deficient, obtain a least squares solution. */
-
-    //    const Index rank = qr.nonzeroPivots(); // exactly double(0.)
-    const Index rank = qr.rank(); // use a threshold
-    wa1 = qtb;
-    wa1.tail(n-rank).setZero();
-    //FIXME There is no solve in place for sparse triangularView
-    wa1.head(rank) = s.topLeftCorner(rank,rank).template triangularView<Upper>().solve(qtb.head(rank));
-
-    x = qr.colsPermutation()*wa1;
-
-    /* initialize the iteration counter. */
-    /* evaluate the function at the origin, and test */
-    /* for acceptance of the gauss-newton direction. */
-    iter = 0;
-    wa2 = diag.cwiseProduct(x);
-    dxnorm = wa2.blueNorm();
-    fp = dxnorm - m_delta;
-    if (fp <= Scalar(0.1) * m_delta) {
-      par = 0;
-      return;
-    }
-
-    /* if the jacobian is not rank deficient, the newton */
-    /* step provides a lower bound, parl, for the zero of */
-    /* the function. otherwise set this bound to zero. */
-    parl = 0.;
-    if (rank==n) {
-      wa1 = qr.colsPermutation().inverse() *  diag.cwiseProduct(wa2)/dxnorm;
-      s.topLeftCorner(n,n).transpose().template triangularView<Lower>().solveInPlace(wa1);
-      temp = wa1.blueNorm();
-      parl = fp / m_delta / temp / temp;
-    }
-
-    /* calculate an upper bound, paru, for the zero of the function. */
-    for (j = 0; j < n; ++j)
-      wa1[j] = s.col(j).head(j+1).dot(qtb.head(j+1)) / diag[qr.colsPermutation().indices()(j)];
-
-    gnorm = wa1.stableNorm();
-    paru = gnorm / m_delta;
-    if (paru == 0.)
-      paru = dwarf / (std::min)(m_delta,Scalar(0.1));
-
-    /* if the input par lies outside of the interval (parl,paru), */
-    /* set par to the closer endpoint. */
-    par = (std::max)(par,parl);
-    par = (std::min)(par,paru);
-    if (par == 0.)
-      par = gnorm / dxnorm;
-
-    /* beginning of an iteration. */
-    while (true) {
-      ++iter;
-
-      /* evaluate the function at the current value of par. */
-      if (par == 0.)
-        par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
-      wa1 = sqrt(par)* diag;
-
-      VectorType sdiag(n);
-      lmqrsolv(s, qr.colsPermutation(), wa1, qtb, x, sdiag);
-
-      wa2 = diag.cwiseProduct(x);
-      dxnorm = wa2.blueNorm();
-      temp = fp;
-      fp = dxnorm - m_delta;
-
-      /* if the function is small enough, accept the current value */
-      /* of par. also test for the exceptional cases where parl */
-      /* is zero or the number of iterations has reached 10. */
-      if (abs(fp) <= Scalar(0.1) * m_delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
-        break;
-
-      /* compute the newton correction. */
-      wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2/dxnorm);
-      // we could almost use this here, but the diagonal is outside qr, in sdiag[]
-      for (j = 0; j < n; ++j) {
-        wa1[j] /= sdiag[j];
-        temp = wa1[j];
-        for (Index i = j+1; i < n; ++i)
-          wa1[i] -= s.coeff(i,j) * temp;
-      }
-      temp = wa1.blueNorm();
-      parc = fp / m_delta / temp / temp;
-
-      /* depending on the sign of the function, update parl or paru. */
-      if (fp > 0.)
-        parl = (std::max)(parl,par);
-      if (fp < 0.)
-        paru = (std::min)(paru,par);
-
-      /* compute an improved estimate for par. */
-      par = (std::max)(parl,par+parc);
-    }
-    if (iter == 0)
-      par = 0.;
-    return;
-  }
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_LMPAR_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
deleted file mode 100644
index 1234858..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
+++ /dev/null
@@ -1,188 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-//
-// This code initially comes from MINPACK whose original authors are:
-// Copyright Jorge More - Argonne National Laboratory
-// Copyright Burt Garbow - Argonne National Laboratory
-// Copyright Ken Hillstrom - Argonne National Laboratory
-//
-// This Source Code Form is subject to the terms of the Minpack license
-// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
-
-#ifndef EIGEN_LMQRSOLV_H
-#define EIGEN_LMQRSOLV_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template <typename Scalar,int Rows, int Cols, typename PermIndex>
-void lmqrsolv(
-  Matrix<Scalar,Rows,Cols> &s,
-  const PermutationMatrix<Dynamic,Dynamic,PermIndex> &iPerm,
-  const Matrix<Scalar,Dynamic,1> &diag,
-  const Matrix<Scalar,Dynamic,1> &qtb,
-  Matrix<Scalar,Dynamic,1> &x,
-  Matrix<Scalar,Dynamic,1> &sdiag)
-{
-    /* Local variables */
-    Index i, j, k;
-    Scalar temp;
-    Index n = s.cols();
-    Matrix<Scalar,Dynamic,1>  wa(n);
-    JacobiRotation<Scalar> givens;
-
-    /* Function Body */
-    // the following will only change the lower triangular part of s, including
-    // the diagonal, though the diagonal is restored afterward
-
-    /*     copy r and (q transpose)*b to preserve input and initialize s. */
-    /*     in particular, save the diagonal elements of r in x. */
-    x = s.diagonal();
-    wa = qtb;
-    
-   
-    s.topLeftCorner(n,n).template triangularView<StrictlyLower>() = s.topLeftCorner(n,n).transpose();
-    /*     eliminate the diagonal matrix d using a givens rotation. */
-    for (j = 0; j < n; ++j) {
-
-        /*        prepare the row of d to be eliminated, locating the */
-        /*        diagonal element using p from the qr factorization. */
-        const PermIndex l = iPerm.indices()(j);
-        if (diag[l] == 0.)
-            break;
-        sdiag.tail(n-j).setZero();
-        sdiag[j] = diag[l];
-
-        /*        the transformations to eliminate the row of d */
-        /*        modify only a single element of (q transpose)*b */
-        /*        beyond the first n, which is initially zero. */
-        Scalar qtbpj = 0.;
-        for (k = j; k < n; ++k) {
-            /*           determine a givens rotation which eliminates the */
-            /*           appropriate element in the current row of d. */
-            givens.makeGivens(-s(k,k), sdiag[k]);
-
-            /*           compute the modified diagonal element of r and */
-            /*           the modified element of ((q transpose)*b,0). */
-            s(k,k) = givens.c() * s(k,k) + givens.s() * sdiag[k];
-            temp = givens.c() * wa[k] + givens.s() * qtbpj;
-            qtbpj = -givens.s() * wa[k] + givens.c() * qtbpj;
-            wa[k] = temp;
-
-            /*           accumulate the transformation in the row of s. */
-            for (i = k+1; i<n; ++i) {
-                temp = givens.c() * s(i,k) + givens.s() * sdiag[i];
-                sdiag[i] = -givens.s() * s(i,k) + givens.c() * sdiag[i];
-                s(i,k) = temp;
-            }
-        }
-    }
-  
-    /*     solve the triangular system for z. if the system is */
-    /*     singular, then obtain a least squares solution. */
-    Index nsing;
-    for(nsing=0; nsing<n && sdiag[nsing]!=0; nsing++) {}
-
-    wa.tail(n-nsing).setZero();
-    s.topLeftCorner(nsing, nsing).transpose().template triangularView<Upper>().solveInPlace(wa.head(nsing));
-  
-    // restore
-    sdiag = s.diagonal();
-    s.diagonal() = x;
-
-    /* permute the components of z back to components of x. */
-    x = iPerm * wa; 
-}
-
-template <typename Scalar, int _Options, typename Index>
-void lmqrsolv(
-  SparseMatrix<Scalar,_Options,Index> &s,
-  const PermutationMatrix<Dynamic,Dynamic> &iPerm,
-  const Matrix<Scalar,Dynamic,1> &diag,
-  const Matrix<Scalar,Dynamic,1> &qtb,
-  Matrix<Scalar,Dynamic,1> &x,
-  Matrix<Scalar,Dynamic,1> &sdiag)
-{
-  /* Local variables */
-  typedef SparseMatrix<Scalar,RowMajor,Index> FactorType;
-    Index i, j, k, l;
-    Scalar temp;
-    Index n = s.cols();
-    Matrix<Scalar,Dynamic,1>  wa(n);
-    JacobiRotation<Scalar> givens;
-
-    /* Function Body */
-    // the following will only change the lower triangular part of s, including
-    // the diagonal, though the diagonal is restored afterward
-
-    /*     copy r and (q transpose)*b to preserve input and initialize R. */
-    wa = qtb;
-    FactorType R(s);
-    // Eliminate the diagonal matrix d using a givens rotation
-    for (j = 0; j < n; ++j)
-    {
-      // Prepare the row of d to be eliminated, locating the 
-      // diagonal element using p from the qr factorization
-      l = iPerm.indices()(j);
-      if (diag(l) == Scalar(0)) 
-        break; 
-      sdiag.tail(n-j).setZero();
-      sdiag[j] = diag[l];
-      // the transformations to eliminate the row of d
-      // modify only a single element of (q transpose)*b
-      // beyond the first n, which is initially zero. 
-      
-      Scalar qtbpj = 0; 
-      // Browse the nonzero elements of row j of the upper triangular s
-      for (k = j; k < n; ++k)
-      {
-        typename FactorType::InnerIterator itk(R,k);
-        for (; itk; ++itk){
-          if (itk.index() < k) continue;
-          else break;
-        }
-        //At this point, we have the diagonal element R(k,k)
-        // Determine a givens rotation which eliminates 
-        // the appropriate element in the current row of d
-        givens.makeGivens(-itk.value(), sdiag(k));
-        
-        // Compute the modified diagonal element of r and 
-        // the modified element of ((q transpose)*b,0).
-        itk.valueRef() = givens.c() * itk.value() + givens.s() * sdiag(k);
-        temp = givens.c() * wa(k) + givens.s() * qtbpj; 
-        qtbpj = -givens.s() * wa(k) + givens.c() * qtbpj;
-        wa(k) = temp;
-        
-        // Accumulate the transformation in the remaining k row/column of R
-        for (++itk; itk; ++itk)
-        {
-          i = itk.index();
-          temp = givens.c() *  itk.value() + givens.s() * sdiag(i);
-          sdiag(i) = -givens.s() * itk.value() + givens.c() * sdiag(i);
-          itk.valueRef() = temp;
-        }
-      }
-    }
-    
-    // Solve the triangular system for z. If the system is 
-    // singular, then obtain a least squares solution
-    Index nsing;
-    for(nsing = 0; nsing<n && sdiag(nsing) !=0; nsing++) {}
-    
-    wa.tail(n-nsing).setZero();
-//     x = wa; 
-    wa.head(nsing) = R.topLeftCorner(nsing,nsing).template triangularView<Upper>().solve/*InPlace*/(wa.head(nsing));
-    
-    sdiag = R.diagonal();
-    // Permute the components of z back to components of x
-    x = iPerm * wa; 
-}
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_LMQRSOLV_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
deleted file mode 100644
index 62561da..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
+++ /dev/null
@@ -1,396 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-//
-// The algorithm of this class initially comes from MINPACK whose original authors are:
-// Copyright Jorge More - Argonne National Laboratory
-// Copyright Burt Garbow - Argonne National Laboratory
-// Copyright Ken Hillstrom - Argonne National Laboratory
-//
-// This Source Code Form is subject to the terms of the Minpack license
-// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LEVENBERGMARQUARDT_H
-#define EIGEN_LEVENBERGMARQUARDT_H
-
-
-namespace Eigen {
-namespace LevenbergMarquardtSpace {
-    enum Status {
-        NotStarted = -2,
-        Running = -1,
-        ImproperInputParameters = 0,
-        RelativeReductionTooSmall = 1,
-        RelativeErrorTooSmall = 2,
-        RelativeErrorAndReductionTooSmall = 3,
-        CosinusTooSmall = 4,
-        TooManyFunctionEvaluation = 5,
-        FtolTooSmall = 6,
-        XtolTooSmall = 7,
-        GtolTooSmall = 8,
-        UserAsked = 9
-    };
-}
-
-template <typename _Scalar, int NX=Dynamic, int NY=Dynamic>
-struct DenseFunctor
-{
-  typedef _Scalar Scalar;
-  enum {
-    InputsAtCompileTime = NX,
-    ValuesAtCompileTime = NY
-  };
-  typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
-  typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
-  typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
-  typedef ColPivHouseholderQR<JacobianType> QRSolver;
-  const int m_inputs, m_values;
-
-  DenseFunctor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
-  DenseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
-
-  int inputs() const { return m_inputs; }
-  int values() const { return m_values; }
-
-  //int operator()(const InputType &x, ValueType& fvec) { }
-  // should be defined in derived classes
-  
-  //int df(const InputType &x, JacobianType& fjac) { }
-  // should be defined in derived classes
-};
-
-template <typename _Scalar, typename _Index>
-struct SparseFunctor
-{
-  typedef _Scalar Scalar;
-  typedef _Index Index;
-  typedef Matrix<Scalar,Dynamic,1> InputType;
-  typedef Matrix<Scalar,Dynamic,1> ValueType;
-  typedef SparseMatrix<Scalar, ColMajor, Index> JacobianType;
-  typedef SparseQR<JacobianType, COLAMDOrdering<int> > QRSolver;
-  enum {
-    InputsAtCompileTime = Dynamic,
-    ValuesAtCompileTime = Dynamic
-  };
-  
-  SparseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
-
-  int inputs() const { return m_inputs; }
-  int values() const { return m_values; }
-  
-  const int m_inputs, m_values;
-  //int operator()(const InputType &x, ValueType& fvec) { }
-  // to be defined in the functor
-  
-  //int df(const InputType &x, JacobianType& fjac) { }
-  // to be defined in the functor if no automatic differentiation
-  
-};
-namespace internal {
-template <typename QRSolver, typename VectorType>
-void lmpar2(const QRSolver &qr, const VectorType  &diag, const VectorType  &qtb,
-	    typename VectorType::Scalar m_delta, typename VectorType::Scalar &par,
-	    VectorType  &x);
-    }
-/**
-  * \ingroup NonLinearOptimization_Module
-  * \brief Performs non linear optimization over a non-linear function,
-  * using a variant of the Levenberg Marquardt algorithm.
-  *
-  * Check wikipedia for more information.
-  * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
-  */
-template<typename _FunctorType>
-class LevenbergMarquardt : internal::no_assignment_operator
-{
-  public:
-    typedef _FunctorType FunctorType;
-    typedef typename FunctorType::QRSolver QRSolver;
-    typedef typename FunctorType::JacobianType JacobianType;
-    typedef typename JacobianType::Scalar Scalar;
-    typedef typename JacobianType::RealScalar RealScalar; 
-    typedef typename QRSolver::StorageIndex PermIndex;
-    typedef Matrix<Scalar,Dynamic,1> FVectorType;
-    typedef PermutationMatrix<Dynamic,Dynamic,int> PermutationType;
-  public:
-    LevenbergMarquardt(FunctorType& functor) 
-    : m_functor(functor),m_nfev(0),m_njev(0),m_fnorm(0.0),m_gnorm(0),
-      m_isInitialized(false),m_info(InvalidInput)
-    {
-      resetParameters();
-      m_useExternalScaling=false; 
-    }
-    
-    LevenbergMarquardtSpace::Status minimize(FVectorType &x);
-    LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
-    LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
-    LevenbergMarquardtSpace::Status lmder1(
-      FVectorType  &x, 
-      const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
-    );
-    static LevenbergMarquardtSpace::Status lmdif1(
-            FunctorType &functor,
-            FVectorType  &x,
-            Index *nfev,
-            const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
-            );
-    
-    /** Sets the default parameters */
-    void resetParameters() 
-    {
-      using std::sqrt;        
-
-      m_factor = 100.; 
-      m_maxfev = 400; 
-      m_ftol = sqrt(NumTraits<RealScalar>::epsilon());
-      m_xtol = sqrt(NumTraits<RealScalar>::epsilon());
-      m_gtol = 0. ; 
-      m_epsfcn = 0. ;
-    }
-    
-    /** Sets the tolerance for the norm of the solution vector*/
-    void setXtol(RealScalar xtol) { m_xtol = xtol; }
-    
-    /** Sets the tolerance for the norm of the vector function*/
-    void setFtol(RealScalar ftol) { m_ftol = ftol; }
-    
-    /** Sets the tolerance for the norm of the gradient of the error vector*/
-    void setGtol(RealScalar gtol) { m_gtol = gtol; }
-    
-    /** Sets the step bound for the diagonal shift */
-    void setFactor(RealScalar factor) { m_factor = factor; }    
-    
-    /** Sets the error precision  */
-    void setEpsilon (RealScalar epsfcn) { m_epsfcn = epsfcn; }
-    
-    /** Sets the maximum number of function evaluation */
-    void setMaxfev(Index maxfev) {m_maxfev = maxfev; }
-    
-    /** Use an external Scaling. If set to true, pass a nonzero diagonal to diag() */
-    void setExternalScaling(bool value) {m_useExternalScaling  = value; }
-    
-    /** \returns the tolerance for the norm of the solution vector */
-    RealScalar xtol() const {return m_xtol; }
-    
-    /** \returns the tolerance for the norm of the vector function */
-    RealScalar ftol() const {return m_ftol; }
-    
-    /** \returns the tolerance for the norm of the gradient of the error vector */
-    RealScalar gtol() const {return m_gtol; }
-    
-    /** \returns the step bound for the diagonal shift */
-    RealScalar factor() const {return m_factor; }
-    
-    /** \returns the error precision */
-    RealScalar epsilon() const {return m_epsfcn; }
-    
-    /** \returns the maximum number of function evaluation */
-    Index maxfev() const {return m_maxfev; }
-    
-    /** \returns a reference to the diagonal of the jacobian */
-    FVectorType& diag() {return m_diag; }
-    
-    /** \returns the number of iterations performed */
-    Index iterations() { return m_iter; }
-    
-    /** \returns the number of functions evaluation */
-    Index nfev() { return m_nfev; }
-    
-    /** \returns the number of jacobian evaluation */
-    Index njev() { return m_njev; }
-    
-    /** \returns the norm of current vector function */
-    RealScalar fnorm() {return m_fnorm; }
-    
-    /** \returns the norm of the gradient of the error */
-    RealScalar gnorm() {return m_gnorm; }
-    
-    /** \returns the LevenbergMarquardt parameter */
-    RealScalar lm_param(void) { return m_par; }
-    
-    /** \returns a reference to the  current vector function 
-     */
-    FVectorType& fvec() {return m_fvec; }
-    
-    /** \returns a reference to the matrix where the current Jacobian matrix is stored
-     */
-    JacobianType& jacobian() {return m_fjac; }
-    
-    /** \returns a reference to the triangular matrix R from the QR of the jacobian matrix.
-     * \sa jacobian()
-     */
-    JacobianType& matrixR() {return m_rfactor; }
-    
-    /** the permutation used in the QR factorization
-     */
-    PermutationType permutation() {return m_permutation; }
-    
-    /** 
-     * \brief Reports whether the minimization was successful
-     * \returns \c Success if the minimization was successful,
-     *         \c NumericalIssue if a numerical problem arises during the 
-     *          minimization process, for example during the QR factorization
-     *         \c NoConvergence if the minimization did not converge after 
-     *          the maximum number of function evaluation allowed
-     *          \c InvalidInput if the input matrix is invalid
-     */
-    ComputationInfo info() const
-    {
-      
-      return m_info;
-    }
-  private:
-    JacobianType m_fjac; 
-    JacobianType m_rfactor; // The triangular matrix R from the QR of the jacobian matrix m_fjac
-    FunctorType &m_functor;
-    FVectorType m_fvec, m_qtf, m_diag; 
-    Index n;
-    Index m; 
-    Index m_nfev;
-    Index m_njev; 
-    RealScalar m_fnorm; // Norm of the current vector function
-    RealScalar m_gnorm; //Norm of the gradient of the error 
-    RealScalar m_factor; //
-    Index m_maxfev; // Maximum number of function evaluation
-    RealScalar m_ftol; //Tolerance in the norm of the vector function
-    RealScalar m_xtol; // 
-    RealScalar m_gtol; //tolerance of the norm of the error gradient
-    RealScalar m_epsfcn; //
-    Index m_iter; // Number of iterations performed
-    RealScalar m_delta;
-    bool m_useExternalScaling;
-    PermutationType m_permutation;
-    FVectorType m_wa1, m_wa2, m_wa3, m_wa4; //Temporary vectors
-    RealScalar m_par;
-    bool m_isInitialized; // Check whether the minimization step has been called
-    ComputationInfo m_info; 
-};
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::minimize(FVectorType  &x)
-{
-    LevenbergMarquardtSpace::Status status = minimizeInit(x);
-    if (status==LevenbergMarquardtSpace::ImproperInputParameters) {
-      m_isInitialized = true;
-      return status;
-    }
-    do {
-//       std::cout << " uv " << x.transpose() << "\n";
-        status = minimizeOneStep(x);
-    } while (status==LevenbergMarquardtSpace::Running);
-     m_isInitialized = true;
-     return status;
-}
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::minimizeInit(FVectorType  &x)
-{
-    n = x.size();
-    m = m_functor.values();
-
-    m_wa1.resize(n); m_wa2.resize(n); m_wa3.resize(n);
-    m_wa4.resize(m);
-    m_fvec.resize(m);
-    //FIXME Sparse Case : Allocate space for the jacobian
-    m_fjac.resize(m, n);
-//     m_fjac.reserve(VectorXi::Constant(n,5)); // FIXME Find a better alternative
-    if (!m_useExternalScaling)
-        m_diag.resize(n);
-    eigen_assert( (!m_useExternalScaling || m_diag.size()==n) && "When m_useExternalScaling is set, the caller must provide a valid 'm_diag'");
-    m_qtf.resize(n);
-
-    /* Function Body */
-    m_nfev = 0;
-    m_njev = 0;
-
-    /*     check the input parameters for errors. */
-    if (n <= 0 || m < n || m_ftol < 0. || m_xtol < 0. || m_gtol < 0. || m_maxfev <= 0 || m_factor <= 0.){
-      m_info = InvalidInput;
-      return LevenbergMarquardtSpace::ImproperInputParameters;
-    }
-
-    if (m_useExternalScaling)
-        for (Index j = 0; j < n; ++j)
-            if (m_diag[j] <= 0.) 
-            {
-              m_info = InvalidInput;
-              return LevenbergMarquardtSpace::ImproperInputParameters;
-            }
-
-    /*     evaluate the function at the starting point */
-    /*     and calculate its norm. */
-    m_nfev = 1;
-    if ( m_functor(x, m_fvec) < 0)
-        return LevenbergMarquardtSpace::UserAsked;
-    m_fnorm = m_fvec.stableNorm();
-
-    /*     initialize levenberg-marquardt parameter and iteration counter. */
-    m_par = 0.;
-    m_iter = 1;
-
-    return LevenbergMarquardtSpace::NotStarted;
-}
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::lmder1(
-        FVectorType  &x,
-        const Scalar tol
-        )
-{
-    n = x.size();
-    m = m_functor.values();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || m < n || tol < 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    resetParameters();
-    m_ftol = tol;
-    m_xtol = tol;
-    m_maxfev = 100*(n+1);
-
-    return minimize(x);
-}
-
-
-template<typename FunctorType>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType>::lmdif1(
-        FunctorType &functor,
-        FVectorType  &x,
-        Index *nfev,
-        const Scalar tol
-        )
-{
-    Index n = x.size();
-    Index m = functor.values();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || m < n || tol < 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    NumericalDiff<FunctorType> numDiff(functor);
-    // embedded LevenbergMarquardt
-    LevenbergMarquardt<NumericalDiff<FunctorType> > lm(numDiff);
-    lm.setFtol(tol);
-    lm.setXtol(tol);
-    lm.setMaxfev(200*(n+1));
-
-    LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
-    if (nfev)
-        * nfev = lm.nfev();
-    return info;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LEVENBERGMARQUARDT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
deleted file mode 100644
index 02284b0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
+++ /dev/null
@@ -1,441 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009, 2010, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
-// Copyright (C) 2011, 2013 Chen-Pang He <jdh8@ms63.hinet.net>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_EXPONENTIAL
-#define EIGEN_MATRIX_EXPONENTIAL
-
-#include "StemFunction.h"
-
-namespace Eigen {
-namespace internal {
-
-/** \brief Scaling operator.
- *
- * This struct is used by CwiseUnaryOp to scale a matrix by \f$ 2^{-s} \f$.
- */
-template <typename RealScalar>
-struct MatrixExponentialScalingOp
-{
-  /** \brief Constructor.
-   *
-   * \param[in] squarings  The integer \f$ s \f$ in this document.
-   */
-  MatrixExponentialScalingOp(int squarings) : m_squarings(squarings) { }
-
-
-  /** \brief Scale a matrix coefficient.
-   *
-   * \param[in,out] x  The scalar to be scaled, becoming \f$ 2^{-s} x \f$.
-   */
-  inline const RealScalar operator() (const RealScalar& x) const
-  {
-    using std::ldexp;
-    return ldexp(x, -m_squarings);
-  }
-
-  typedef std::complex<RealScalar> ComplexScalar;
-
-  /** \brief Scale a matrix coefficient.
-   *
-   * \param[in,out] x  The scalar to be scaled, becoming \f$ 2^{-s} x \f$.
-   */
-  inline const ComplexScalar operator() (const ComplexScalar& x) const
-  {
-    using std::ldexp;
-    return ComplexScalar(ldexp(x.real(), -m_squarings), ldexp(x.imag(), -m_squarings));
-  }
-
-  private:
-    int m_squarings;
-};
-
-/** \brief Compute the (3,3)-Pad&eacute; approximant to the exponential.
- *
- *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
- *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
- */
-template <typename MatA, typename MatU, typename MatV>
-void matrix_exp_pade3(const MatA& A, MatU& U, MatV& V)
-{
-  typedef typename MatA::PlainObject MatrixType;
-  typedef typename NumTraits<typename traits<MatA>::Scalar>::Real RealScalar;
-  const RealScalar b[] = {120.L, 60.L, 12.L, 1.L};
-  const MatrixType A2 = A * A;
-  const MatrixType tmp = b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
-  U.noalias() = A * tmp;
-  V = b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
-}
-
-/** \brief Compute the (5,5)-Pad&eacute; approximant to the exponential.
- *
- *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
- *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
- */
-template <typename MatA, typename MatU, typename MatV>
-void matrix_exp_pade5(const MatA& A, MatU& U, MatV& V)
-{
-  typedef typename MatA::PlainObject MatrixType;
-  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
-  const RealScalar b[] = {30240.L, 15120.L, 3360.L, 420.L, 30.L, 1.L};
-  const MatrixType A2 = A * A;
-  const MatrixType A4 = A2 * A2;
-  const MatrixType tmp = b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
-  U.noalias() = A * tmp;
-  V = b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
-}
-
-/** \brief Compute the (7,7)-Pad&eacute; approximant to the exponential.
- *
- *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
- *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
- */
-template <typename MatA, typename MatU, typename MatV>
-void matrix_exp_pade7(const MatA& A, MatU& U, MatV& V)
-{
-  typedef typename MatA::PlainObject MatrixType;
-  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
-  const RealScalar b[] = {17297280.L, 8648640.L, 1995840.L, 277200.L, 25200.L, 1512.L, 56.L, 1.L};
-  const MatrixType A2 = A * A;
-  const MatrixType A4 = A2 * A2;
-  const MatrixType A6 = A4 * A2;
-  const MatrixType tmp = b[7] * A6 + b[5] * A4 + b[3] * A2 
-    + b[1] * MatrixType::Identity(A.rows(), A.cols());
-  U.noalias() = A * tmp;
-  V = b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
-
-}
-
-/** \brief Compute the (9,9)-Pad&eacute; approximant to the exponential.
- *
- *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
- *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
- */
-template <typename MatA, typename MatU, typename MatV>
-void matrix_exp_pade9(const MatA& A, MatU& U, MatV& V)
-{
-  typedef typename MatA::PlainObject MatrixType;
-  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
-  const RealScalar b[] = {17643225600.L, 8821612800.L, 2075673600.L, 302702400.L, 30270240.L,
-                          2162160.L, 110880.L, 3960.L, 90.L, 1.L};
-  const MatrixType A2 = A * A;
-  const MatrixType A4 = A2 * A2;
-  const MatrixType A6 = A4 * A2;
-  const MatrixType A8 = A6 * A2;
-  const MatrixType tmp = b[9] * A8 + b[7] * A6 + b[5] * A4 + b[3] * A2 
-    + b[1] * MatrixType::Identity(A.rows(), A.cols());
-  U.noalias() = A * tmp;
-  V = b[8] * A8 + b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
-}
-
-/** \brief Compute the (13,13)-Pad&eacute; approximant to the exponential.
- *
- *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
- *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
- */
-template <typename MatA, typename MatU, typename MatV>
-void matrix_exp_pade13(const MatA& A, MatU& U, MatV& V)
-{
-  typedef typename MatA::PlainObject MatrixType;
-  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
-  const RealScalar b[] = {64764752532480000.L, 32382376266240000.L, 7771770303897600.L,
-                          1187353796428800.L, 129060195264000.L, 10559470521600.L, 670442572800.L,
-                          33522128640.L, 1323241920.L, 40840800.L, 960960.L, 16380.L, 182.L, 1.L};
-  const MatrixType A2 = A * A;
-  const MatrixType A4 = A2 * A2;
-  const MatrixType A6 = A4 * A2;
-  V = b[13] * A6 + b[11] * A4 + b[9] * A2; // used for temporary storage
-  MatrixType tmp = A6 * V;
-  tmp += b[7] * A6 + b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
-  U.noalias() = A * tmp;
-  tmp = b[12] * A6 + b[10] * A4 + b[8] * A2;
-  V.noalias() = A6 * tmp;
-  V += b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
-}
-
-/** \brief Compute the (17,17)-Pad&eacute; approximant to the exponential.
- *
- *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
- *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
- *
- *  This function activates only if your long double is double-double or quadruple.
- */
-#if LDBL_MANT_DIG > 64
-template <typename MatA, typename MatU, typename MatV>
-void matrix_exp_pade17(const MatA& A, MatU& U, MatV& V)
-{
-  typedef typename MatA::PlainObject MatrixType;
-  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
-  const RealScalar b[] = {830034394580628357120000.L, 415017197290314178560000.L,
-                          100610229646136770560000.L, 15720348382208870400000.L,
-                          1774878043152614400000.L, 153822763739893248000.L, 10608466464820224000.L,
-                          595373117923584000.L, 27563570274240000.L, 1060137318240000.L,
-                          33924394183680.L, 899510451840.L, 19554575040.L, 341863200.L, 4651200.L,
-                          46512.L, 306.L, 1.L};
-  const MatrixType A2 = A * A;
-  const MatrixType A4 = A2 * A2;
-  const MatrixType A6 = A4 * A2;
-  const MatrixType A8 = A4 * A4;
-  V = b[17] * A8 + b[15] * A6 + b[13] * A4 + b[11] * A2; // used for temporary storage
-  MatrixType tmp = A8 * V;
-  tmp += b[9] * A8 + b[7] * A6 + b[5] * A4 + b[3] * A2 
-    + b[1] * MatrixType::Identity(A.rows(), A.cols());
-  U.noalias() = A * tmp;
-  tmp = b[16] * A8 + b[14] * A6 + b[12] * A4 + b[10] * A2;
-  V.noalias() = tmp * A8;
-  V += b[8] * A8 + b[6] * A6 + b[4] * A4 + b[2] * A2 
-    + b[0] * MatrixType::Identity(A.rows(), A.cols());
-}
-#endif
-
-template <typename MatrixType, typename RealScalar = typename NumTraits<typename traits<MatrixType>::Scalar>::Real>
-struct matrix_exp_computeUV
-{
-  /** \brief Compute Pad&eacute; approximant to the exponential.
-    *
-    * Computes \c U, \c V and \c squarings such that \f$ (V+U)(V-U)^{-1} \f$ is a Pad&eacute;
-    * approximant of \f$ \exp(2^{-\mbox{squarings}}M) \f$ around \f$ M = 0 \f$, where \f$ M \f$
-    * denotes the matrix \c arg. The degree of the Pad&eacute; approximant and the value of squarings
-    * are chosen such that the approximation error is no more than the round-off error.
-    */
-  static void run(const MatrixType& arg, MatrixType& U, MatrixType& V, int& squarings);
-};
-
-template <typename MatrixType>
-struct matrix_exp_computeUV<MatrixType, float>
-{
-  template <typename ArgType>
-  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings)
-  {
-    using std::frexp;
-    using std::pow;
-    const float l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
-    squarings = 0;
-    if (l1norm < 4.258730016922831e-001f) {
-      matrix_exp_pade3(arg, U, V);
-    } else if (l1norm < 1.880152677804762e+000f) {
-      matrix_exp_pade5(arg, U, V);
-    } else {
-      const float maxnorm = 3.925724783138660f;
-      frexp(l1norm / maxnorm, &squarings);
-      if (squarings < 0) squarings = 0;
-      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<float>(squarings));
-      matrix_exp_pade7(A, U, V);
-    }
-  }
-};
-
-template <typename MatrixType>
-struct matrix_exp_computeUV<MatrixType, double>
-{
-  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
-  template <typename ArgType>
-  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings)
-  {
-    using std::frexp;
-    using std::pow;
-    const RealScalar l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
-    squarings = 0;
-    if (l1norm < 1.495585217958292e-002) {
-      matrix_exp_pade3(arg, U, V);
-    } else if (l1norm < 2.539398330063230e-001) {
-      matrix_exp_pade5(arg, U, V);
-    } else if (l1norm < 9.504178996162932e-001) {
-      matrix_exp_pade7(arg, U, V);
-    } else if (l1norm < 2.097847961257068e+000) {
-      matrix_exp_pade9(arg, U, V);
-    } else {
-      const RealScalar maxnorm = 5.371920351148152;
-      frexp(l1norm / maxnorm, &squarings);
-      if (squarings < 0) squarings = 0;
-      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<RealScalar>(squarings));
-      matrix_exp_pade13(A, U, V);
-    }
-  }
-};
-  
-template <typename MatrixType>
-struct matrix_exp_computeUV<MatrixType, long double>
-{
-  template <typename ArgType>
-  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings)
-  {
-#if   LDBL_MANT_DIG == 53   // double precision
-    matrix_exp_computeUV<MatrixType, double>::run(arg, U, V, squarings);
-  
-#else
-  
-    using std::frexp;
-    using std::pow;
-    const long double l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
-    squarings = 0;
-  
-#if LDBL_MANT_DIG <= 64   // extended precision
-  
-    if (l1norm < 4.1968497232266989671e-003L) {
-      matrix_exp_pade3(arg, U, V);
-    } else if (l1norm < 1.1848116734693823091e-001L) {
-      matrix_exp_pade5(arg, U, V);
-    } else if (l1norm < 5.5170388480686700274e-001L) {
-      matrix_exp_pade7(arg, U, V);
-    } else if (l1norm < 1.3759868875587845383e+000L) {
-      matrix_exp_pade9(arg, U, V);
-    } else {
-      const long double maxnorm = 4.0246098906697353063L;
-      frexp(l1norm / maxnorm, &squarings);
-      if (squarings < 0) squarings = 0;
-      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<long double>(squarings));
-      matrix_exp_pade13(A, U, V);
-    }
-  
-#elif LDBL_MANT_DIG <= 106  // double-double
-  
-    if (l1norm < 3.2787892205607026992947488108213e-005L) {
-      matrix_exp_pade3(arg, U, V);
-    } else if (l1norm < 6.4467025060072760084130906076332e-003L) {
-      matrix_exp_pade5(arg, U, V);
-    } else if (l1norm < 6.8988028496595374751374122881143e-002L) {
-      matrix_exp_pade7(arg, U, V);
-    } else if (l1norm < 2.7339737518502231741495857201670e-001L) {
-      matrix_exp_pade9(arg, U, V);
-    } else if (l1norm < 1.3203382096514474905666448850278e+000L) {
-      matrix_exp_pade13(arg, U, V);
-    } else {
-      const long double maxnorm = 3.2579440895405400856599663723517L;
-      frexp(l1norm / maxnorm, &squarings);
-      if (squarings < 0) squarings = 0;
-      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<long double>(squarings));
-      matrix_exp_pade17(A, U, V);
-    }
-  
-#elif LDBL_MANT_DIG <= 113  // quadruple precision
-  
-    if (l1norm < 1.639394610288918690547467954466970e-005L) {
-      matrix_exp_pade3(arg, U, V);
-    } else if (l1norm < 4.253237712165275566025884344433009e-003L) {
-      matrix_exp_pade5(arg, U, V);
-    } else if (l1norm < 5.125804063165764409885122032933142e-002L) {
-      matrix_exp_pade7(arg, U, V);
-    } else if (l1norm < 2.170000765161155195453205651889853e-001L) {
-      matrix_exp_pade9(arg, U, V);
-    } else if (l1norm < 1.125358383453143065081397882891878e+000L) {
-      matrix_exp_pade13(arg, U, V);
-    } else {
-      const long double maxnorm = 2.884233277829519311757165057717815L;
-      frexp(l1norm / maxnorm, &squarings);
-      if (squarings < 0) squarings = 0;
-      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<long double>(squarings));
-      matrix_exp_pade17(A, U, V);
-    }
-  
-#else
-  
-    // this case should be handled in compute()
-    eigen_assert(false && "Bug in MatrixExponential"); 
-  
-#endif
-#endif  // LDBL_MANT_DIG
-  }
-};
-
-template<typename T> struct is_exp_known_type : false_type {};
-template<> struct is_exp_known_type<float> : true_type {};
-template<> struct is_exp_known_type<double> : true_type {};
-#if LDBL_MANT_DIG <= 113
-template<> struct is_exp_known_type<long double> : true_type {};
-#endif
-
-template <typename ArgType, typename ResultType>
-void matrix_exp_compute(const ArgType& arg, ResultType &result, true_type) // natively supported scalar type
-{
-  typedef typename ArgType::PlainObject MatrixType;
-  MatrixType U, V;
-  int squarings;
-  matrix_exp_computeUV<MatrixType>::run(arg, U, V, squarings); // Pade approximant is (U+V) / (-U+V)
-  MatrixType numer = U + V;
-  MatrixType denom = -U + V;
-  result = denom.partialPivLu().solve(numer);
-  for (int i=0; i<squarings; i++)
-    result *= result;   // undo scaling by repeated squaring
-}
-
-
-/* Computes the matrix exponential
- *
- * \param arg    argument of matrix exponential (should be plain object)
- * \param result variable in which result will be stored
- */
-template <typename ArgType, typename ResultType>
-void matrix_exp_compute(const ArgType& arg, ResultType &result, false_type) // default
-{
-  typedef typename ArgType::PlainObject MatrixType;
-  typedef typename traits<MatrixType>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef typename std::complex<RealScalar> ComplexScalar;
-  result = arg.matrixFunction(internal::stem_function_exp<ComplexScalar>);
-}
-
-} // end namespace Eigen::internal
-
-/** \ingroup MatrixFunctions_Module
-  *
-  * \brief Proxy for the matrix exponential of some matrix (expression).
-  *
-  * \tparam Derived  Type of the argument to the matrix exponential.
-  *
-  * This class holds the argument to the matrix exponential until it is assigned or evaluated for
-  * some other reason (so the argument should not be changed in the meantime). It is the return type
-  * of MatrixBase::exp() and most of the time this is the only way it is used.
-  */
-template<typename Derived> struct MatrixExponentialReturnValue
-: public ReturnByValue<MatrixExponentialReturnValue<Derived> >
-{
-  public:
-    /** \brief Constructor.
-      *
-      * \param src %Matrix (expression) forming the argument of the matrix exponential.
-      */
-    MatrixExponentialReturnValue(const Derived& src) : m_src(src) { }
-
-    /** \brief Compute the matrix exponential.
-      *
-      * \param result the matrix exponential of \p src in the constructor.
-      */
-    template <typename ResultType>
-    inline void evalTo(ResultType& result) const
-    {
-      const typename internal::nested_eval<Derived, 10>::type tmp(m_src);
-      internal::matrix_exp_compute(tmp, result, internal::is_exp_known_type<typename Derived::RealScalar>());
-    }
-
-    Index rows() const { return m_src.rows(); }
-    Index cols() const { return m_src.cols(); }
-
-  protected:
-    const typename internal::ref_selector<Derived>::type m_src;
-};
-
-namespace internal {
-template<typename Derived>
-struct traits<MatrixExponentialReturnValue<Derived> >
-{
-  typedef typename Derived::PlainObject ReturnType;
-};
-}
-
-template <typename Derived>
-const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const
-{
-  eigen_assert(rows() == cols());
-  return MatrixExponentialReturnValue<Derived>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_EXPONENTIAL
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
deleted file mode 100644
index cc12ab6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
+++ /dev/null
@@ -1,569 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009-2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_FUNCTION_H
-#define EIGEN_MATRIX_FUNCTION_H
-
-#include "StemFunction.h"
-
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \brief Maximum distance allowed between eigenvalues to be considered "close". */
-static const float matrix_function_separation = 0.1f;
-
-/** \ingroup MatrixFunctions_Module
-  * \class MatrixFunctionAtomic
-  * \brief Helper class for computing matrix functions of atomic matrices.
-  *
-  * Here, an atomic matrix is a triangular matrix whose diagonal entries are close to each other.
-  */
-template <typename MatrixType>
-class MatrixFunctionAtomic 
-{
-  public:
-
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename stem_function<Scalar>::type StemFunction;
-
-    /** \brief Constructor
-      * \param[in]  f  matrix function to compute.
-      */
-    MatrixFunctionAtomic(StemFunction f) : m_f(f) { }
-
-    /** \brief Compute matrix function of atomic matrix
-      * \param[in]  A  argument of matrix function, should be upper triangular and atomic
-      * \returns  f(A), the matrix function evaluated at the given matrix
-      */
-    MatrixType compute(const MatrixType& A);
-
-  private:
-    StemFunction* m_f;
-};
-
-template <typename MatrixType>
-typename NumTraits<typename MatrixType::Scalar>::Real matrix_function_compute_mu(const MatrixType& A)
-{
-  typedef typename plain_col_type<MatrixType>::type VectorType;
-  Index rows = A.rows();
-  const MatrixType N = MatrixType::Identity(rows, rows) - A;
-  VectorType e = VectorType::Ones(rows);
-  N.template triangularView<Upper>().solveInPlace(e);
-  return e.cwiseAbs().maxCoeff();
-}
-
-template <typename MatrixType>
-MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
-{
-  // TODO: Use that A is upper triangular
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  Index rows = A.rows();
-  Scalar avgEival = A.trace() / Scalar(RealScalar(rows));
-  MatrixType Ashifted = A - avgEival * MatrixType::Identity(rows, rows);
-  RealScalar mu = matrix_function_compute_mu(Ashifted);
-  MatrixType F = m_f(avgEival, 0) * MatrixType::Identity(rows, rows);
-  MatrixType P = Ashifted;
-  MatrixType Fincr;
-  for (Index s = 1; double(s) < 1.1 * double(rows) + 10.0; s++) { // upper limit is fairly arbitrary
-    Fincr = m_f(avgEival, static_cast<int>(s)) * P;
-    F += Fincr;
-    P = Scalar(RealScalar(1)/RealScalar(s + 1)) * P * Ashifted;
-
-    // test whether Taylor series converged
-    const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
-    const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
-    if (Fincr_norm < NumTraits<Scalar>::epsilon() * F_norm) {
-      RealScalar delta = 0;
-      RealScalar rfactorial = 1;
-      for (Index r = 0; r < rows; r++) {
-        RealScalar mx = 0;
-        for (Index i = 0; i < rows; i++)
-          mx = (std::max)(mx, std::abs(m_f(Ashifted(i, i) + avgEival, static_cast<int>(s+r))));
-        if (r != 0)
-          rfactorial *= RealScalar(r);
-        delta = (std::max)(delta, mx / rfactorial);
-      }
-      const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
-      if (mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm) // series converged
-        break;
-    }
-  }
-  return F;
-}
-
-/** \brief Find cluster in \p clusters containing some value 
-  * \param[in] key Value to find
-  * \returns Iterator to cluster containing \p key, or \c clusters.end() if no cluster in \p m_clusters
-  * contains \p key.
-  */
-template <typename Index, typename ListOfClusters>
-typename ListOfClusters::iterator matrix_function_find_cluster(Index key, ListOfClusters& clusters)
-{
-  typename std::list<Index>::iterator j;
-  for (typename ListOfClusters::iterator i = clusters.begin(); i != clusters.end(); ++i) {
-    j = std::find(i->begin(), i->end(), key);
-    if (j != i->end())
-      return i;
-  }
-  return clusters.end();
-}
-
-/** \brief Partition eigenvalues in clusters of ei'vals close to each other
-  * 
-  * \param[in]  eivals    Eigenvalues
-  * \param[out] clusters  Resulting partition of eigenvalues
-  *
-  * The partition satisfies the following two properties:
-  * # Any eigenvalue in a certain cluster is at most matrix_function_separation() away from another eigenvalue
-  *   in the same cluster.
-  * # The distance between two eigenvalues in different clusters is more than matrix_function_separation().  
-  * The implementation follows Algorithm 4.1 in the paper of Davies and Higham.
-  */
-template <typename EivalsType, typename Cluster>
-void matrix_function_partition_eigenvalues(const EivalsType& eivals, std::list<Cluster>& clusters)
-{
-  typedef typename EivalsType::RealScalar RealScalar;
-  for (Index i=0; i<eivals.rows(); ++i) {
-    // Find cluster containing i-th ei'val, adding a new cluster if necessary
-    typename std::list<Cluster>::iterator qi = matrix_function_find_cluster(i, clusters);
-    if (qi == clusters.end()) {
-      Cluster l;
-      l.push_back(i);
-      clusters.push_back(l);
-      qi = clusters.end();
-      --qi;
-    }
-
-    // Look for other element to add to the set
-    for (Index j=i+1; j<eivals.rows(); ++j) {
-      if (abs(eivals(j) - eivals(i)) <= RealScalar(matrix_function_separation)
-          && std::find(qi->begin(), qi->end(), j) == qi->end()) {
-        typename std::list<Cluster>::iterator qj = matrix_function_find_cluster(j, clusters);
-        if (qj == clusters.end()) {
-          qi->push_back(j);
-        } else {
-          qi->insert(qi->end(), qj->begin(), qj->end());
-          clusters.erase(qj);
-        }
-      }
-    }
-  }
-}
-
-/** \brief Compute size of each cluster given a partitioning */
-template <typename ListOfClusters, typename Index>
-void matrix_function_compute_cluster_size(const ListOfClusters& clusters, Matrix<Index, Dynamic, 1>& clusterSize)
-{
-  const Index numClusters = static_cast<Index>(clusters.size());
-  clusterSize.setZero(numClusters);
-  Index clusterIndex = 0;
-  for (typename ListOfClusters::const_iterator cluster = clusters.begin(); cluster != clusters.end(); ++cluster) {
-    clusterSize[clusterIndex] = cluster->size();
-    ++clusterIndex;
-  }
-}
-
-/** \brief Compute start of each block using clusterSize */
-template <typename VectorType>
-void matrix_function_compute_block_start(const VectorType& clusterSize, VectorType& blockStart)
-{
-  blockStart.resize(clusterSize.rows());
-  blockStart(0) = 0;
-  for (Index i = 1; i < clusterSize.rows(); i++) {
-    blockStart(i) = blockStart(i-1) + clusterSize(i-1);
-  }
-}
-
-/** \brief Compute mapping of eigenvalue indices to cluster indices */
-template <typename EivalsType, typename ListOfClusters, typename VectorType>
-void matrix_function_compute_map(const EivalsType& eivals, const ListOfClusters& clusters, VectorType& eivalToCluster)
-{
-  eivalToCluster.resize(eivals.rows());
-  Index clusterIndex = 0;
-  for (typename ListOfClusters::const_iterator cluster = clusters.begin(); cluster != clusters.end(); ++cluster) {
-    for (Index i = 0; i < eivals.rows(); ++i) {
-      if (std::find(cluster->begin(), cluster->end(), i) != cluster->end()) {
-        eivalToCluster[i] = clusterIndex;
-      }
-    }
-    ++clusterIndex;
-  }
-}
-
-/** \brief Compute permutation which groups ei'vals in same cluster together */
-template <typename DynVectorType, typename VectorType>
-void matrix_function_compute_permutation(const DynVectorType& blockStart, const DynVectorType& eivalToCluster, VectorType& permutation)
-{
-  DynVectorType indexNextEntry = blockStart;
-  permutation.resize(eivalToCluster.rows());
-  for (Index i = 0; i < eivalToCluster.rows(); i++) {
-    Index cluster = eivalToCluster[i];
-    permutation[i] = indexNextEntry[cluster];
-    ++indexNextEntry[cluster];
-  }
-}  
-
-/** \brief Permute Schur decomposition in U and T according to permutation */
-template <typename VectorType, typename MatrixType>
-void matrix_function_permute_schur(VectorType& permutation, MatrixType& U, MatrixType& T)
-{
-  for (Index i = 0; i < permutation.rows() - 1; i++) {
-    Index j;
-    for (j = i; j < permutation.rows(); j++) {
-      if (permutation(j) == i) break;
-    }
-    eigen_assert(permutation(j) == i);
-    for (Index k = j-1; k >= i; k--) {
-      JacobiRotation<typename MatrixType::Scalar> rotation;
-      rotation.makeGivens(T(k, k+1), T(k+1, k+1) - T(k, k));
-      T.applyOnTheLeft(k, k+1, rotation.adjoint());
-      T.applyOnTheRight(k, k+1, rotation);
-      U.applyOnTheRight(k, k+1, rotation);
-      std::swap(permutation.coeffRef(k), permutation.coeffRef(k+1));
-    }
-  }
-}
-
-/** \brief Compute block diagonal part of matrix function.
-  *
-  * This routine computes the matrix function applied to the block diagonal part of \p T (which should be
-  * upper triangular), with the blocking given by \p blockStart and \p clusterSize. The matrix function of
-  * each diagonal block is computed by \p atomic. The off-diagonal parts of \p fT are set to zero.
-  */
-template <typename MatrixType, typename AtomicType, typename VectorType>
-void matrix_function_compute_block_atomic(const MatrixType& T, AtomicType& atomic, const VectorType& blockStart, const VectorType& clusterSize, MatrixType& fT)
-{ 
-  fT.setZero(T.rows(), T.cols());
-  for (Index i = 0; i < clusterSize.rows(); ++i) {
-    fT.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i))
-      = atomic.compute(T.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i)));
-  }
-}
-
-/** \brief Solve a triangular Sylvester equation AX + XB = C 
-  *
-  * \param[in]  A  the matrix A; should be square and upper triangular
-  * \param[in]  B  the matrix B; should be square and upper triangular
-  * \param[in]  C  the matrix C; should have correct size.
-  *
-  * \returns the solution X.
-  *
-  * If A is m-by-m and B is n-by-n, then both C and X are m-by-n.  The (i,j)-th component of the Sylvester
-  * equation is
-  * \f[ 
-  *     \sum_{k=i}^m A_{ik} X_{kj} + \sum_{k=1}^j X_{ik} B_{kj} = C_{ij}. 
-  * \f]
-  * This can be re-arranged to yield:
-  * \f[ 
-  *     X_{ij} = \frac{1}{A_{ii} + B_{jj}} \Bigl( C_{ij}
-  *     - \sum_{k=i+1}^m A_{ik} X_{kj} - \sum_{k=1}^{j-1} X_{ik} B_{kj} \Bigr).
-  * \f]
-  * It is assumed that A and B are such that the numerator is never zero (otherwise the Sylvester equation
-  * does not have a unique solution). In that case, these equations can be evaluated in the order 
-  * \f$ i=m,\ldots,1 \f$ and \f$ j=1,\ldots,n \f$.
-  */
-template <typename MatrixType>
-MatrixType matrix_function_solve_triangular_sylvester(const MatrixType& A, const MatrixType& B, const MatrixType& C)
-{
-  eigen_assert(A.rows() == A.cols());
-  eigen_assert(A.isUpperTriangular());
-  eigen_assert(B.rows() == B.cols());
-  eigen_assert(B.isUpperTriangular());
-  eigen_assert(C.rows() == A.rows());
-  eigen_assert(C.cols() == B.rows());
-
-  typedef typename MatrixType::Scalar Scalar;
-
-  Index m = A.rows();
-  Index n = B.rows();
-  MatrixType X(m, n);
-
-  for (Index i = m - 1; i >= 0; --i) {
-    for (Index j = 0; j < n; ++j) {
-
-      // Compute AX = \sum_{k=i+1}^m A_{ik} X_{kj}
-      Scalar AX;
-      if (i == m - 1) {
-	AX = 0; 
-      } else {
-	Matrix<Scalar,1,1> AXmatrix = A.row(i).tail(m-1-i) * X.col(j).tail(m-1-i);
-	AX = AXmatrix(0,0);
-      }
-
-      // Compute XB = \sum_{k=1}^{j-1} X_{ik} B_{kj}
-      Scalar XB;
-      if (j == 0) {
-	XB = 0; 
-      } else {
-	Matrix<Scalar,1,1> XBmatrix = X.row(i).head(j) * B.col(j).head(j);
-	XB = XBmatrix(0,0);
-      }
-
-      X(i,j) = (C(i,j) - AX - XB) / (A(i,i) + B(j,j));
-    }
-  }
-  return X;
-}
-
-/** \brief Compute part of matrix function above block diagonal.
-  *
-  * This routine completes the computation of \p fT, denoting a matrix function applied to the triangular
-  * matrix \p T. It assumes that the block diagonal part of \p fT has already been computed. The part below
-  * the diagonal is zero, because \p T is upper triangular.
-  */
-template <typename MatrixType, typename VectorType>
-void matrix_function_compute_above_diagonal(const MatrixType& T, const VectorType& blockStart, const VectorType& clusterSize, MatrixType& fT)
-{ 
-  typedef internal::traits<MatrixType> Traits;
-  typedef typename MatrixType::Scalar Scalar;
-  static const int Options = MatrixType::Options;
-  typedef Matrix<Scalar, Dynamic, Dynamic, Options, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime> DynMatrixType;
-
-  for (Index k = 1; k < clusterSize.rows(); k++) {
-    for (Index i = 0; i < clusterSize.rows() - k; i++) {
-      // compute (i, i+k) block
-      DynMatrixType A = T.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i));
-      DynMatrixType B = -T.block(blockStart(i+k), blockStart(i+k), clusterSize(i+k), clusterSize(i+k));
-      DynMatrixType C = fT.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i))
-        * T.block(blockStart(i), blockStart(i+k), clusterSize(i), clusterSize(i+k));
-      C -= T.block(blockStart(i), blockStart(i+k), clusterSize(i), clusterSize(i+k))
-        * fT.block(blockStart(i+k), blockStart(i+k), clusterSize(i+k), clusterSize(i+k));
-      for (Index m = i + 1; m < i + k; m++) {
-        C += fT.block(blockStart(i), blockStart(m), clusterSize(i), clusterSize(m))
-          * T.block(blockStart(m), blockStart(i+k), clusterSize(m), clusterSize(i+k));
-        C -= T.block(blockStart(i), blockStart(m), clusterSize(i), clusterSize(m))
-          * fT.block(blockStart(m), blockStart(i+k), clusterSize(m), clusterSize(i+k));
-      }
-      fT.block(blockStart(i), blockStart(i+k), clusterSize(i), clusterSize(i+k))
-        = matrix_function_solve_triangular_sylvester(A, B, C);
-    }
-  }
-}
-
-/** \ingroup MatrixFunctions_Module
-  * \brief Class for computing matrix functions.
-  * \tparam  MatrixType  type of the argument of the matrix function,
-  *                      expected to be an instantiation of the Matrix class template.
-  * \tparam  AtomicType  type for computing matrix function of atomic blocks.
-  * \tparam  IsComplex   used internally to select correct specialization.
-  *
-  * This class implements the Schur-Parlett algorithm for computing matrix functions. The spectrum of the
-  * matrix is divided in clustered of eigenvalues that lies close together. This class delegates the
-  * computation of the matrix function on every block corresponding to these clusters to an object of type
-  * \p AtomicType and uses these results to compute the matrix function of the whole matrix. The class
-  * \p AtomicType should have a \p compute() member function for computing the matrix function of a block.
-  *
-  * \sa class MatrixFunctionAtomic, class MatrixLogarithmAtomic
-  */
-template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
-struct matrix_function_compute
-{  
-    /** \brief Compute the matrix function.
-      *
-      * \param[in]  A       argument of matrix function, should be a square matrix.
-      * \param[in]  atomic  class for computing matrix function of atomic blocks.
-      * \param[out] result  the function \p f applied to \p A, as
-      * specified in the constructor.
-      *
-      * See MatrixBase::matrixFunction() for details on how this computation
-      * is implemented.
-      */
-    template <typename AtomicType, typename ResultType> 
-    static void run(const MatrixType& A, AtomicType& atomic, ResultType &result);    
-};
-
-/** \internal \ingroup MatrixFunctions_Module 
-  * \brief Partial specialization of MatrixFunction for real matrices
-  *
-  * This converts the real matrix to a complex matrix, compute the matrix function of that matrix, and then
-  * converts the result back to a real matrix.
-  */
-template <typename MatrixType>
-struct matrix_function_compute<MatrixType, 0>
-{  
-  template <typename MatA, typename AtomicType, typename ResultType>
-  static void run(const MatA& A, AtomicType& atomic, ResultType &result)
-  {
-    typedef internal::traits<MatrixType> Traits;
-    typedef typename Traits::Scalar Scalar;
-    static const int Rows = Traits::RowsAtCompileTime, Cols = Traits::ColsAtCompileTime;
-    static const int MaxRows = Traits::MaxRowsAtCompileTime, MaxCols = Traits::MaxColsAtCompileTime;
-
-    typedef std::complex<Scalar> ComplexScalar;
-    typedef Matrix<ComplexScalar, Rows, Cols, 0, MaxRows, MaxCols> ComplexMatrix;
-
-    ComplexMatrix CA = A.template cast<ComplexScalar>();
-    ComplexMatrix Cresult;
-    matrix_function_compute<ComplexMatrix>::run(CA, atomic, Cresult);
-    result = Cresult.real();
-  }
-};
-
-/** \internal \ingroup MatrixFunctions_Module 
-  * \brief Partial specialization of MatrixFunction for complex matrices
-  */
-template <typename MatrixType>
-struct matrix_function_compute<MatrixType, 1>
-{
-  template <typename MatA, typename AtomicType, typename ResultType>
-  static void run(const MatA& A, AtomicType& atomic, ResultType &result)
-  {
-    typedef internal::traits<MatrixType> Traits;
-    
-    // compute Schur decomposition of A
-    const ComplexSchur<MatrixType> schurOfA(A);
-    eigen_assert(schurOfA.info()==Success);
-    MatrixType T = schurOfA.matrixT();
-    MatrixType U = schurOfA.matrixU();
-
-    // partition eigenvalues into clusters of ei'vals "close" to each other
-    std::list<std::list<Index> > clusters; 
-    matrix_function_partition_eigenvalues(T.diagonal(), clusters);
-
-    // compute size of each cluster
-    Matrix<Index, Dynamic, 1> clusterSize;
-    matrix_function_compute_cluster_size(clusters, clusterSize);
-
-    // blockStart[i] is row index at which block corresponding to i-th cluster starts 
-    Matrix<Index, Dynamic, 1> blockStart; 
-    matrix_function_compute_block_start(clusterSize, blockStart);
-
-    // compute map so that eivalToCluster[i] = j means that i-th ei'val is in j-th cluster 
-    Matrix<Index, Dynamic, 1> eivalToCluster;
-    matrix_function_compute_map(T.diagonal(), clusters, eivalToCluster);
-
-    // compute permutation which groups ei'vals in same cluster together 
-    Matrix<Index, Traits::RowsAtCompileTime, 1> permutation;
-    matrix_function_compute_permutation(blockStart, eivalToCluster, permutation);
-
-    // permute Schur decomposition
-    matrix_function_permute_schur(permutation, U, T);
-
-    // compute result
-    MatrixType fT; // matrix function applied to T
-    matrix_function_compute_block_atomic(T, atomic, blockStart, clusterSize, fT);
-    matrix_function_compute_above_diagonal(T, blockStart, clusterSize, fT);
-    result = U * (fT.template triangularView<Upper>() * U.adjoint());
-  }
-};
-
-} // end of namespace internal
-
-/** \ingroup MatrixFunctions_Module
-  *
-  * \brief Proxy for the matrix function of some matrix (expression).
-  *
-  * \tparam Derived  Type of the argument to the matrix function.
-  *
-  * This class holds the argument to the matrix function until it is assigned or evaluated for some other
-  * reason (so the argument should not be changed in the meantime). It is the return type of
-  * matrixBase::matrixFunction() and related functions and most of the time this is the only way it is used.
-  */
-template<typename Derived> class MatrixFunctionReturnValue
-: public ReturnByValue<MatrixFunctionReturnValue<Derived> >
-{
-  public:
-    typedef typename Derived::Scalar Scalar;
-    typedef typename internal::stem_function<Scalar>::type StemFunction;
-
-  protected:
-    typedef typename internal::ref_selector<Derived>::type DerivedNested;
-
-  public:
-
-    /** \brief Constructor.
-      *
-      * \param[in] A  %Matrix (expression) forming the argument of the matrix function.
-      * \param[in] f  Stem function for matrix function under consideration.
-      */
-    MatrixFunctionReturnValue(const Derived& A, StemFunction f) : m_A(A), m_f(f) { }
-
-    /** \brief Compute the matrix function.
-      *
-      * \param[out] result \p f applied to \p A, where \p f and \p A are as in the constructor.
-      */
-    template <typename ResultType>
-    inline void evalTo(ResultType& result) const
-    {
-      typedef typename internal::nested_eval<Derived, 10>::type NestedEvalType;
-      typedef typename internal::remove_all<NestedEvalType>::type NestedEvalTypeClean;
-      typedef internal::traits<NestedEvalTypeClean> Traits;
-      typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-      typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime> DynMatrixType;
-
-      typedef internal::MatrixFunctionAtomic<DynMatrixType> AtomicType;
-      AtomicType atomic(m_f);
-
-      internal::matrix_function_compute<typename NestedEvalTypeClean::PlainObject>::run(m_A, atomic, result);
-    }
-
-    Index rows() const { return m_A.rows(); }
-    Index cols() const { return m_A.cols(); }
-
-  private:
-    const DerivedNested m_A;
-    StemFunction *m_f;
-};
-
-namespace internal {
-template<typename Derived>
-struct traits<MatrixFunctionReturnValue<Derived> >
-{
-  typedef typename Derived::PlainObject ReturnType;
-};
-}
-
-
-/********** MatrixBase methods **********/
-
-
-template <typename Derived>
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(typename internal::stem_function<typename internal::traits<Derived>::Scalar>::type f) const
-{
-  eigen_assert(rows() == cols());
-  return MatrixFunctionReturnValue<Derived>(derived(), f);
-}
-
-template <typename Derived>
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const
-{
-  eigen_assert(rows() == cols());
-  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
-  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_sin<ComplexScalar>);
-}
-
-template <typename Derived>
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const
-{
-  eigen_assert(rows() == cols());
-  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
-  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_cos<ComplexScalar>);
-}
-
-template <typename Derived>
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const
-{
-  eigen_assert(rows() == cols());
-  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
-  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_sinh<ComplexScalar>);
-}
-
-template <typename Derived>
-const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const
-{
-  eigen_assert(rows() == cols());
-  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
-  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_cosh<ComplexScalar>);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_FUNCTION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
deleted file mode 100644
index e917013..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
+++ /dev/null
@@ -1,373 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
-// Copyright (C) 2011 Chen-Pang He <jdh8@ms63.hinet.net>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_LOGARITHM
-#define EIGEN_MATRIX_LOGARITHM
-
-namespace Eigen { 
-
-namespace internal { 
-
-template <typename Scalar>
-struct matrix_log_min_pade_degree 
-{
-  static const int value = 3;
-};
-
-template <typename Scalar>
-struct matrix_log_max_pade_degree 
-{
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  static const int value = std::numeric_limits<RealScalar>::digits<= 24?  5:  // single precision
-                           std::numeric_limits<RealScalar>::digits<= 53?  7:  // double precision
-                           std::numeric_limits<RealScalar>::digits<= 64?  8:  // extended precision
-                           std::numeric_limits<RealScalar>::digits<=106? 10:  // double-double
-                                                                         11;  // quadruple precision
-};
-
-/** \brief Compute logarithm of 2x2 triangular matrix. */
-template <typename MatrixType>
-void matrix_log_compute_2x2(const MatrixType& A, MatrixType& result)
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename MatrixType::RealScalar RealScalar;
-  using std::abs;
-  using std::ceil;
-  using std::imag;
-  using std::log;
-
-  Scalar logA00 = log(A(0,0));
-  Scalar logA11 = log(A(1,1));
-
-  result(0,0) = logA00;
-  result(1,0) = Scalar(0);
-  result(1,1) = logA11;
-
-  Scalar y = A(1,1) - A(0,0);
-  if (y==Scalar(0))
-  {
-    result(0,1) = A(0,1) / A(0,0);
-  }
-  else if ((abs(A(0,0)) < RealScalar(0.5)*abs(A(1,1))) || (abs(A(0,0)) > 2*abs(A(1,1))))
-  {
-    result(0,1) = A(0,1) * (logA11 - logA00) / y;
-  }
-  else
-  {
-    // computation in previous branch is inaccurate if A(1,1) \approx A(0,0)
-    RealScalar unwindingNumber = ceil((imag(logA11 - logA00) - RealScalar(EIGEN_PI)) / RealScalar(2*EIGEN_PI));
-    result(0,1) = A(0,1) * (numext::log1p(y/A(0,0)) + Scalar(0,RealScalar(2*EIGEN_PI)*unwindingNumber)) / y;
-  }
-}
-
-/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = float) */
-inline int matrix_log_get_pade_degree(float normTminusI)
-{
-  const float maxNormForPade[] = { 2.5111573934555054e-1 /* degree = 3 */ , 4.0535837411880493e-1,
-            5.3149729967117310e-1 };
-  const int minPadeDegree = matrix_log_min_pade_degree<float>::value;
-  const int maxPadeDegree = matrix_log_max_pade_degree<float>::value;
-  int degree = minPadeDegree;
-  for (; degree <= maxPadeDegree; ++degree) 
-    if (normTminusI <= maxNormForPade[degree - minPadeDegree])
-      break;
-  return degree;
-}
-
-/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = double) */
-inline int matrix_log_get_pade_degree(double normTminusI)
-{
-  const double maxNormForPade[] = { 1.6206284795015624e-2 /* degree = 3 */ , 5.3873532631381171e-2,
-            1.1352802267628681e-1, 1.8662860613541288e-1, 2.642960831111435e-1 };
-  const int minPadeDegree = matrix_log_min_pade_degree<double>::value;
-  const int maxPadeDegree = matrix_log_max_pade_degree<double>::value;
-  int degree = minPadeDegree;
-  for (; degree <= maxPadeDegree; ++degree)
-    if (normTminusI <= maxNormForPade[degree - minPadeDegree])
-      break;
-  return degree;
-}
-
-/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = long double) */
-inline int matrix_log_get_pade_degree(long double normTminusI)
-{
-#if   LDBL_MANT_DIG == 53         // double precision
-  const long double maxNormForPade[] = { 1.6206284795015624e-2L /* degree = 3 */ , 5.3873532631381171e-2L,
-            1.1352802267628681e-1L, 1.8662860613541288e-1L, 2.642960831111435e-1L };
-#elif LDBL_MANT_DIG <= 64         // extended precision
-  const long double maxNormForPade[] = { 5.48256690357782863103e-3L /* degree = 3 */, 2.34559162387971167321e-2L,
-            5.84603923897347449857e-2L, 1.08486423756725170223e-1L, 1.68385767881294446649e-1L,
-            2.32777776523703892094e-1L };
-#elif LDBL_MANT_DIG <= 106        // double-double
-  const long double maxNormForPade[] = { 8.58970550342939562202529664318890e-5L /* degree = 3 */,
-            9.34074328446359654039446552677759e-4L, 4.26117194647672175773064114582860e-3L,
-            1.21546224740281848743149666560464e-2L, 2.61100544998339436713088248557444e-2L,
-            4.66170074627052749243018566390567e-2L, 7.32585144444135027565872014932387e-2L,
-            1.05026503471351080481093652651105e-1L };
-#else                             // quadruple precision
-  const long double maxNormForPade[] = { 4.7419931187193005048501568167858103e-5L /* degree = 3 */,
-            5.8853168473544560470387769480192666e-4L, 2.9216120366601315391789493628113520e-3L,
-            8.8415758124319434347116734705174308e-3L, 1.9850836029449446668518049562565291e-2L,
-            3.6688019729653446926585242192447447e-2L, 5.9290962294020186998954055264528393e-2L,
-            8.6998436081634343903250580992127677e-2L, 1.1880960220216759245467951592883642e-1L };
-#endif
-  const int minPadeDegree = matrix_log_min_pade_degree<long double>::value;
-  const int maxPadeDegree = matrix_log_max_pade_degree<long double>::value;
-  int degree = minPadeDegree;
-  for (; degree <= maxPadeDegree; ++degree)
-    if (normTminusI <= maxNormForPade[degree - minPadeDegree])
-      break;
-  return degree;
-}
-
-/* \brief Compute Pade approximation to matrix logarithm */
-template <typename MatrixType>
-void matrix_log_compute_pade(MatrixType& result, const MatrixType& T, int degree)
-{
-  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-  const int minPadeDegree = 3;
-  const int maxPadeDegree = 11;
-  assert(degree >= minPadeDegree && degree <= maxPadeDegree);
-  // FIXME this creates float-conversion-warnings if these are enabled.
-  // Either manually convert each value, or disable the warning locally
-  const RealScalar nodes[][maxPadeDegree] = { 
-    { 0.1127016653792583114820734600217600L, 0.5000000000000000000000000000000000L,  // degree 3
-      0.8872983346207416885179265399782400L }, 
-    { 0.0694318442029737123880267555535953L, 0.3300094782075718675986671204483777L,  // degree 4
-      0.6699905217924281324013328795516223L, 0.9305681557970262876119732444464048L },
-    { 0.0469100770306680036011865608503035L, 0.2307653449471584544818427896498956L,  // degree 5
-      0.5000000000000000000000000000000000L, 0.7692346550528415455181572103501044L,
-      0.9530899229693319963988134391496965L },
-    { 0.0337652428984239860938492227530027L, 0.1693953067668677431693002024900473L,  // degree 6
-      0.3806904069584015456847491391596440L, 0.6193095930415984543152508608403560L,
-      0.8306046932331322568306997975099527L, 0.9662347571015760139061507772469973L },
-    { 0.0254460438286207377369051579760744L, 0.1292344072003027800680676133596058L,  // degree 7
-      0.2970774243113014165466967939615193L, 0.5000000000000000000000000000000000L,
-      0.7029225756886985834533032060384807L, 0.8707655927996972199319323866403942L,
-      0.9745539561713792622630948420239256L },
-    { 0.0198550717512318841582195657152635L, 0.1016667612931866302042230317620848L,  // degree 8
-      0.2372337950418355070911304754053768L, 0.4082826787521750975302619288199080L,
-      0.5917173212478249024697380711800920L, 0.7627662049581644929088695245946232L,
-      0.8983332387068133697957769682379152L, 0.9801449282487681158417804342847365L },
-    { 0.0159198802461869550822118985481636L, 0.0819844463366821028502851059651326L,  // degree 9
-      0.1933142836497048013456489803292629L, 0.3378732882980955354807309926783317L,
-      0.5000000000000000000000000000000000L, 0.6621267117019044645192690073216683L,
-      0.8066857163502951986543510196707371L, 0.9180155536633178971497148940348674L,
-      0.9840801197538130449177881014518364L },
-    { 0.0130467357414141399610179939577740L, 0.0674683166555077446339516557882535L,  // degree 10
-      0.1602952158504877968828363174425632L, 0.2833023029353764046003670284171079L,
-      0.4255628305091843945575869994351400L, 0.5744371694908156054424130005648600L,
-      0.7166976970646235953996329715828921L, 0.8397047841495122031171636825574368L,
-      0.9325316833444922553660483442117465L, 0.9869532642585858600389820060422260L },
-    { 0.0108856709269715035980309994385713L, 0.0564687001159523504624211153480364L,  // degree 11
-      0.1349239972129753379532918739844233L, 0.2404519353965940920371371652706952L,
-      0.3652284220238275138342340072995692L, 0.5000000000000000000000000000000000L,
-      0.6347715779761724861657659927004308L, 0.7595480646034059079628628347293048L,
-      0.8650760027870246620467081260155767L, 0.9435312998840476495375788846519636L,
-      0.9891143290730284964019690005614287L } };
-
-  const RealScalar weights[][maxPadeDegree] = { 
-    { 0.2777777777777777777777777777777778L, 0.4444444444444444444444444444444444L,  // degree 3
-      0.2777777777777777777777777777777778L },
-    { 0.1739274225687269286865319746109997L, 0.3260725774312730713134680253890003L,  // degree 4
-      0.3260725774312730713134680253890003L, 0.1739274225687269286865319746109997L },
-    { 0.1184634425280945437571320203599587L, 0.2393143352496832340206457574178191L,  // degree 5
-      0.2844444444444444444444444444444444L, 0.2393143352496832340206457574178191L,
-      0.1184634425280945437571320203599587L },
-    { 0.0856622461895851725201480710863665L, 0.1803807865240693037849167569188581L,  // degree 6
-      0.2339569672863455236949351719947755L, 0.2339569672863455236949351719947755L,
-      0.1803807865240693037849167569188581L, 0.0856622461895851725201480710863665L },
-    { 0.0647424830844348466353057163395410L, 0.1398526957446383339507338857118898L,  // degree 7
-      0.1909150252525594724751848877444876L, 0.2089795918367346938775510204081633L,
-      0.1909150252525594724751848877444876L, 0.1398526957446383339507338857118898L,
-      0.0647424830844348466353057163395410L },
-    { 0.0506142681451881295762656771549811L, 0.1111905172266872352721779972131204L,  // degree 8
-      0.1568533229389436436689811009933007L, 0.1813418916891809914825752246385978L,
-      0.1813418916891809914825752246385978L, 0.1568533229389436436689811009933007L,
-      0.1111905172266872352721779972131204L, 0.0506142681451881295762656771549811L },
-    { 0.0406371941807872059859460790552618L, 0.0903240803474287020292360156214564L,  // degree 9
-      0.1303053482014677311593714347093164L, 0.1561735385200014200343152032922218L,
-      0.1651196775006298815822625346434870L, 0.1561735385200014200343152032922218L,
-      0.1303053482014677311593714347093164L, 0.0903240803474287020292360156214564L,
-      0.0406371941807872059859460790552618L },
-    { 0.0333356721543440687967844049466659L, 0.0747256745752902965728881698288487L,  // degree 10
-      0.1095431812579910219977674671140816L, 0.1346333596549981775456134607847347L,
-      0.1477621123573764350869464973256692L, 0.1477621123573764350869464973256692L,
-      0.1346333596549981775456134607847347L, 0.1095431812579910219977674671140816L,
-      0.0747256745752902965728881698288487L, 0.0333356721543440687967844049466659L },
-    { 0.0278342835580868332413768602212743L, 0.0627901847324523123173471496119701L,  // degree 11
-      0.0931451054638671257130488207158280L, 0.1165968822959952399592618524215876L,
-      0.1314022722551233310903444349452546L, 0.1364625433889503153572417641681711L,
-      0.1314022722551233310903444349452546L, 0.1165968822959952399592618524215876L,
-      0.0931451054638671257130488207158280L, 0.0627901847324523123173471496119701L,
-      0.0278342835580868332413768602212743L } };
-
-  MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
-  result.setZero(T.rows(), T.rows());
-  for (int k = 0; k < degree; ++k) {
-    RealScalar weight = weights[degree-minPadeDegree][k];
-    RealScalar node = nodes[degree-minPadeDegree][k];
-    result += weight * (MatrixType::Identity(T.rows(), T.rows()) + node * TminusI)
-                       .template triangularView<Upper>().solve(TminusI);
-  }
-} 
-
-/** \brief Compute logarithm of triangular matrices with size > 2. 
-  * \details This uses a inverse scale-and-square algorithm. */
-template <typename MatrixType>
-void matrix_log_compute_big(const MatrixType& A, MatrixType& result)
-{
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  using std::pow;
-
-  int numberOfSquareRoots = 0;
-  int numberOfExtraSquareRoots = 0;
-  int degree;
-  MatrixType T = A, sqrtT;
-
-  const int maxPadeDegree = matrix_log_max_pade_degree<Scalar>::value;
-  const RealScalar maxNormForPade = RealScalar(
-                                    maxPadeDegree<= 5? 5.3149729967117310e-1L:                    // single precision
-                                    maxPadeDegree<= 7? 2.6429608311114350e-1L:                    // double precision
-                                    maxPadeDegree<= 8? 2.32777776523703892094e-1L:                // extended precision
-                                    maxPadeDegree<=10? 1.05026503471351080481093652651105e-1L:    // double-double
-                                                       1.1880960220216759245467951592883642e-1L); // quadruple precision
-
-  while (true) {
-    RealScalar normTminusI = (T - MatrixType::Identity(T.rows(), T.rows())).cwiseAbs().colwise().sum().maxCoeff();
-    if (normTminusI < maxNormForPade) {
-      degree = matrix_log_get_pade_degree(normTminusI);
-      int degree2 = matrix_log_get_pade_degree(normTminusI / RealScalar(2));
-      if ((degree - degree2 <= 1) || (numberOfExtraSquareRoots == 1)) 
-        break;
-      ++numberOfExtraSquareRoots;
-    }
-    matrix_sqrt_triangular(T, sqrtT);
-    T = sqrtT.template triangularView<Upper>();
-    ++numberOfSquareRoots;
-  }
-
-  matrix_log_compute_pade(result, T, degree);
-  result *= pow(RealScalar(2), RealScalar(numberOfSquareRoots)); // TODO replace by bitshift if possible
-}
-
-/** \ingroup MatrixFunctions_Module
-  * \class MatrixLogarithmAtomic
-  * \brief Helper class for computing matrix logarithm of atomic matrices.
-  *
-  * Here, an atomic matrix is a triangular matrix whose diagonal entries are close to each other.
-  *
-  * \sa class MatrixFunctionAtomic, MatrixBase::log()
-  */
-template <typename MatrixType>
-class MatrixLogarithmAtomic
-{
-public:
-  /** \brief Compute matrix logarithm of atomic matrix
-    * \param[in]  A  argument of matrix logarithm, should be upper triangular and atomic
-    * \returns  The logarithm of \p A.
-    */
-  MatrixType compute(const MatrixType& A);
-};
-
-template <typename MatrixType>
-MatrixType MatrixLogarithmAtomic<MatrixType>::compute(const MatrixType& A)
-{
-  using std::log;
-  MatrixType result(A.rows(), A.rows());
-  if (A.rows() == 1)
-    result(0,0) = log(A(0,0));
-  else if (A.rows() == 2)
-    matrix_log_compute_2x2(A, result);
-  else
-    matrix_log_compute_big(A, result);
-  return result;
-}
-
-} // end of namespace internal
-
-/** \ingroup MatrixFunctions_Module
-  *
-  * \brief Proxy for the matrix logarithm of some matrix (expression).
-  *
-  * \tparam Derived  Type of the argument to the matrix function.
-  *
-  * This class holds the argument to the matrix function until it is
-  * assigned or evaluated for some other reason (so the argument
-  * should not be changed in the meantime). It is the return type of
-  * MatrixBase::log() and most of the time this is the only way it
-  * is used.
-  */
-template<typename Derived> class MatrixLogarithmReturnValue
-: public ReturnByValue<MatrixLogarithmReturnValue<Derived> >
-{
-public:
-  typedef typename Derived::Scalar Scalar;
-  typedef typename Derived::Index Index;
-
-protected:
-  typedef typename internal::ref_selector<Derived>::type DerivedNested;
-
-public:
-
-  /** \brief Constructor.
-    *
-    * \param[in]  A  %Matrix (expression) forming the argument of the matrix logarithm.
-    */
-  explicit MatrixLogarithmReturnValue(const Derived& A) : m_A(A) { }
-  
-  /** \brief Compute the matrix logarithm.
-    *
-    * \param[out]  result  Logarithm of \c A, where \c A is as specified in the constructor.
-    */
-  template <typename ResultType>
-  inline void evalTo(ResultType& result) const
-  {
-    typedef typename internal::nested_eval<Derived, 10>::type DerivedEvalType;
-    typedef typename internal::remove_all<DerivedEvalType>::type DerivedEvalTypeClean;
-    typedef internal::traits<DerivedEvalTypeClean> Traits;
-    typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
-    typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime> DynMatrixType;
-    typedef internal::MatrixLogarithmAtomic<DynMatrixType> AtomicType;
-    AtomicType atomic;
-    
-    internal::matrix_function_compute<typename DerivedEvalTypeClean::PlainObject>::run(m_A, atomic, result);
-  }
-
-  Index rows() const { return m_A.rows(); }
-  Index cols() const { return m_A.cols(); }
-  
-private:
-  const DerivedNested m_A;
-};
-
-namespace internal {
-  template<typename Derived>
-  struct traits<MatrixLogarithmReturnValue<Derived> >
-  {
-    typedef typename Derived::PlainObject ReturnType;
-  };
-}
-
-
-/********** MatrixBase method **********/
-
-
-template <typename Derived>
-const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const
-{
-  eigen_assert(rows() == cols());
-  return MatrixLogarithmReturnValue<Derived>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_LOGARITHM
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
deleted file mode 100644
index d7672d7..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
+++ /dev/null
@@ -1,705 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012, 2013 Chen-Pang He <jdh8@ms63.hinet.net>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_POWER
-#define EIGEN_MATRIX_POWER
-
-namespace Eigen {
-
-template<typename MatrixType> class MatrixPower;
-
-/**
- * \ingroup MatrixFunctions_Module
- *
- * \brief Proxy for the matrix power of some matrix.
- *
- * \tparam MatrixType  type of the base, a matrix.
- *
- * This class holds the arguments to the matrix power until it is
- * assigned or evaluated for some other reason (so the argument
- * should not be changed in the meantime). It is the return type of
- * MatrixPower::operator() and related functions and most of the
- * time this is the only way it is used.
- */
-/* TODO This class is only used by MatrixPower, so it should be nested
- * into MatrixPower, like MatrixPower::ReturnValue. However, my
- * compiler complained about unused template parameter in the
- * following declaration in namespace internal.
- *
- * template<typename MatrixType>
- * struct traits<MatrixPower<MatrixType>::ReturnValue>;
- */
-template<typename MatrixType>
-class MatrixPowerParenthesesReturnValue : public ReturnByValue< MatrixPowerParenthesesReturnValue<MatrixType> >
-{
-  public:
-    typedef typename MatrixType::RealScalar RealScalar;
-
-    /**
-     * \brief Constructor.
-     *
-     * \param[in] pow  %MatrixPower storing the base.
-     * \param[in] p    scalar, the exponent of the matrix power.
-     */
-    MatrixPowerParenthesesReturnValue(MatrixPower<MatrixType>& pow, RealScalar p) : m_pow(pow), m_p(p)
-    { }
-
-    /**
-     * \brief Compute the matrix power.
-     *
-     * \param[out] result
-     */
-    template<typename ResultType>
-    inline void evalTo(ResultType& result) const
-    { m_pow.compute(result, m_p); }
-
-    Index rows() const { return m_pow.rows(); }
-    Index cols() const { return m_pow.cols(); }
-
-  private:
-    MatrixPower<MatrixType>& m_pow;
-    const RealScalar m_p;
-};
-
-/**
- * \ingroup MatrixFunctions_Module
- *
- * \brief Class for computing matrix powers.
- *
- * \tparam MatrixType  type of the base, expected to be an instantiation
- * of the Matrix class template.
- *
- * This class is capable of computing triangular real/complex matrices
- * raised to a power in the interval \f$ (-1, 1) \f$.
- *
- * \note Currently this class is only used by MatrixPower. One may
- * insist that this be nested into MatrixPower. This class is here to
- * facilitate future development of triangular matrix functions.
- */
-template<typename MatrixType>
-class MatrixPowerAtomic : internal::noncopyable
-{
-  private:
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
-    };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef std::complex<RealScalar> ComplexScalar;
-    typedef Block<MatrixType,Dynamic,Dynamic> ResultType;
-
-    const MatrixType& m_A;
-    RealScalar m_p;
-
-    void computePade(int degree, const MatrixType& IminusT, ResultType& res) const;
-    void compute2x2(ResultType& res, RealScalar p) const;
-    void computeBig(ResultType& res) const;
-    static int getPadeDegree(float normIminusT);
-    static int getPadeDegree(double normIminusT);
-    static int getPadeDegree(long double normIminusT);
-    static ComplexScalar computeSuperDiag(const ComplexScalar&, const ComplexScalar&, RealScalar p);
-    static RealScalar computeSuperDiag(RealScalar, RealScalar, RealScalar p);
-
-  public:
-    /**
-     * \brief Constructor.
-     *
-     * \param[in] T  the base of the matrix power.
-     * \param[in] p  the exponent of the matrix power, should be in
-     * \f$ (-1, 1) \f$.
-     *
-     * The class stores a reference to T, so it should not be changed
-     * (or destroyed) before evaluation. Only the upper triangular
-     * part of T is read.
-     */
-    MatrixPowerAtomic(const MatrixType& T, RealScalar p);
-    
-    /**
-     * \brief Compute the matrix power.
-     *
-     * \param[out] res  \f$ A^p \f$ where A and p are specified in the
-     * constructor.
-     */
-    void compute(ResultType& res) const;
-};
-
-template<typename MatrixType>
-MatrixPowerAtomic<MatrixType>::MatrixPowerAtomic(const MatrixType& T, RealScalar p) :
-  m_A(T), m_p(p)
-{
-  eigen_assert(T.rows() == T.cols());
-  eigen_assert(p > -1 && p < 1);
-}
-
-template<typename MatrixType>
-void MatrixPowerAtomic<MatrixType>::compute(ResultType& res) const
-{
-  using std::pow;
-  switch (m_A.rows()) {
-    case 0:
-      break;
-    case 1:
-      res(0,0) = pow(m_A(0,0), m_p);
-      break;
-    case 2:
-      compute2x2(res, m_p);
-      break;
-    default:
-      computeBig(res);
-  }
-}
-
-template<typename MatrixType>
-void MatrixPowerAtomic<MatrixType>::computePade(int degree, const MatrixType& IminusT, ResultType& res) const
-{
-  int i = 2*degree;
-  res = (m_p-RealScalar(degree)) / RealScalar(2*i-2) * IminusT;
-
-  for (--i; i; --i) {
-    res = (MatrixType::Identity(IminusT.rows(), IminusT.cols()) + res).template triangularView<Upper>()
-	.solve((i==1 ? -m_p : i&1 ? (-m_p-RealScalar(i/2))/RealScalar(2*i) : (m_p-RealScalar(i/2))/RealScalar(2*i-2)) * IminusT).eval();
-  }
-  res += MatrixType::Identity(IminusT.rows(), IminusT.cols());
-}
-
-// This function assumes that res has the correct size (see bug 614)
-template<typename MatrixType>
-void MatrixPowerAtomic<MatrixType>::compute2x2(ResultType& res, RealScalar p) const
-{
-  using std::abs;
-  using std::pow;
-  res.coeffRef(0,0) = pow(m_A.coeff(0,0), p);
-
-  for (Index i=1; i < m_A.cols(); ++i) {
-    res.coeffRef(i,i) = pow(m_A.coeff(i,i), p);
-    if (m_A.coeff(i-1,i-1) == m_A.coeff(i,i))
-      res.coeffRef(i-1,i) = p * pow(m_A.coeff(i,i), p-1);
-    else if (2*abs(m_A.coeff(i-1,i-1)) < abs(m_A.coeff(i,i)) || 2*abs(m_A.coeff(i,i)) < abs(m_A.coeff(i-1,i-1)))
-      res.coeffRef(i-1,i) = (res.coeff(i,i)-res.coeff(i-1,i-1)) / (m_A.coeff(i,i)-m_A.coeff(i-1,i-1));
-    else
-      res.coeffRef(i-1,i) = computeSuperDiag(m_A.coeff(i,i), m_A.coeff(i-1,i-1), p);
-    res.coeffRef(i-1,i) *= m_A.coeff(i-1,i);
-  }
-}
-
-template<typename MatrixType>
-void MatrixPowerAtomic<MatrixType>::computeBig(ResultType& res) const
-{
-  using std::ldexp;
-  const int digits = std::numeric_limits<RealScalar>::digits;
-  const RealScalar maxNormForPade = RealScalar(
-                                    digits <=  24? 4.3386528e-1L                            // single precision
-                                  : digits <=  53? 2.789358995219730e-1L                    // double precision
-                                  : digits <=  64? 2.4471944416607995472e-1L                // extended precision
-                                  : digits <= 106? 1.1016843812851143391275867258512e-1L    // double-double
-                                  :                9.134603732914548552537150753385375e-2L); // quadruple precision
-  MatrixType IminusT, sqrtT, T = m_A.template triangularView<Upper>();
-  RealScalar normIminusT;
-  int degree, degree2, numberOfSquareRoots = 0;
-  bool hasExtraSquareRoot = false;
-
-  for (Index i=0; i < m_A.cols(); ++i)
-    eigen_assert(m_A(i,i) != RealScalar(0));
-
-  while (true) {
-    IminusT = MatrixType::Identity(m_A.rows(), m_A.cols()) - T;
-    normIminusT = IminusT.cwiseAbs().colwise().sum().maxCoeff();
-    if (normIminusT < maxNormForPade) {
-      degree = getPadeDegree(normIminusT);
-      degree2 = getPadeDegree(normIminusT/2);
-      if (degree - degree2 <= 1 || hasExtraSquareRoot)
-	break;
-      hasExtraSquareRoot = true;
-    }
-    matrix_sqrt_triangular(T, sqrtT);
-    T = sqrtT.template triangularView<Upper>();
-    ++numberOfSquareRoots;
-  }
-  computePade(degree, IminusT, res);
-
-  for (; numberOfSquareRoots; --numberOfSquareRoots) {
-    compute2x2(res, ldexp(m_p, -numberOfSquareRoots));
-    res = res.template triangularView<Upper>() * res;
-  }
-  compute2x2(res, m_p);
-}
-  
-template<typename MatrixType>
-inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(float normIminusT)
-{
-  const float maxNormForPade[] = { 2.8064004e-1f /* degree = 3 */ , 4.3386528e-1f };
-  int degree = 3;
-  for (; degree <= 4; ++degree)
-    if (normIminusT <= maxNormForPade[degree - 3])
-      break;
-  return degree;
-}
-
-template<typename MatrixType>
-inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(double normIminusT)
-{
-  const double maxNormForPade[] = { 1.884160592658218e-2 /* degree = 3 */ , 6.038881904059573e-2, 1.239917516308172e-1,
-      1.999045567181744e-1, 2.789358995219730e-1 };
-  int degree = 3;
-  for (; degree <= 7; ++degree)
-    if (normIminusT <= maxNormForPade[degree - 3])
-      break;
-  return degree;
-}
-
-template<typename MatrixType>
-inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(long double normIminusT)
-{
-#if   LDBL_MANT_DIG == 53
-  const int maxPadeDegree = 7;
-  const double maxNormForPade[] = { 1.884160592658218e-2L /* degree = 3 */ , 6.038881904059573e-2L, 1.239917516308172e-1L,
-      1.999045567181744e-1L, 2.789358995219730e-1L };
-#elif LDBL_MANT_DIG <= 64
-  const int maxPadeDegree = 8;
-  const long double maxNormForPade[] = { 6.3854693117491799460e-3L /* degree = 3 */ , 2.6394893435456973676e-2L,
-      6.4216043030404063729e-2L, 1.1701165502926694307e-1L, 1.7904284231268670284e-1L, 2.4471944416607995472e-1L };
-#elif LDBL_MANT_DIG <= 106
-  const int maxPadeDegree = 10;
-  const double maxNormForPade[] = { 1.0007161601787493236741409687186e-4L /* degree = 3 */ ,
-      1.0007161601787493236741409687186e-3L, 4.7069769360887572939882574746264e-3L, 1.3220386624169159689406653101695e-2L,
-      2.8063482381631737920612944054906e-2L, 4.9625993951953473052385361085058e-2L, 7.7367040706027886224557538328171e-2L,
-      1.1016843812851143391275867258512e-1L };
-#else
-  const int maxPadeDegree = 10;
-  const double maxNormForPade[] = { 5.524506147036624377378713555116378e-5L /* degree = 3 */ ,
-      6.640600568157479679823602193345995e-4L, 3.227716520106894279249709728084626e-3L,
-      9.619593944683432960546978734646284e-3L, 2.134595382433742403911124458161147e-2L,
-      3.908166513900489428442993794761185e-2L, 6.266780814639442865832535460550138e-2L,
-      9.134603732914548552537150753385375e-2L };
-#endif
-  int degree = 3;
-  for (; degree <= maxPadeDegree; ++degree)
-    if (normIminusT <= maxNormForPade[degree - 3])
-      break;
-  return degree;
-}
-
-template<typename MatrixType>
-inline typename MatrixPowerAtomic<MatrixType>::ComplexScalar
-MatrixPowerAtomic<MatrixType>::computeSuperDiag(const ComplexScalar& curr, const ComplexScalar& prev, RealScalar p)
-{
-  using std::ceil;
-  using std::exp;
-  using std::log;
-  using std::sinh;
-
-  ComplexScalar logCurr = log(curr);
-  ComplexScalar logPrev = log(prev);
-  RealScalar unwindingNumber = ceil((numext::imag(logCurr - logPrev) - RealScalar(EIGEN_PI)) / RealScalar(2*EIGEN_PI));
-  ComplexScalar w = numext::log1p((curr-prev)/prev)/RealScalar(2) + ComplexScalar(0, RealScalar(EIGEN_PI)*unwindingNumber);
-  return RealScalar(2) * exp(RealScalar(0.5) * p * (logCurr + logPrev)) * sinh(p * w) / (curr - prev);
-}
-
-template<typename MatrixType>
-inline typename MatrixPowerAtomic<MatrixType>::RealScalar
-MatrixPowerAtomic<MatrixType>::computeSuperDiag(RealScalar curr, RealScalar prev, RealScalar p)
-{
-  using std::exp;
-  using std::log;
-  using std::sinh;
-
-  RealScalar w = numext::log1p((curr-prev)/prev)/RealScalar(2);
-  return 2 * exp(p * (log(curr) + log(prev)) / 2) * sinh(p * w) / (curr - prev);
-}
-
-/**
- * \ingroup MatrixFunctions_Module
- *
- * \brief Class for computing matrix powers.
- *
- * \tparam MatrixType  type of the base, expected to be an instantiation
- * of the Matrix class template.
- *
- * This class is capable of computing real/complex matrices raised to
- * an arbitrary real power. Meanwhile, it saves the result of Schur
- * decomposition if an non-integral power has even been calculated.
- * Therefore, if you want to compute multiple (>= 2) matrix powers
- * for the same matrix, using the class directly is more efficient than
- * calling MatrixBase::pow().
- *
- * Example:
- * \include MatrixPower_optimal.cpp
- * Output: \verbinclude MatrixPower_optimal.out
- */
-template<typename MatrixType>
-class MatrixPower : internal::noncopyable
-{
-  private:
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-
-  public:
-    /**
-     * \brief Constructor.
-     *
-     * \param[in] A  the base of the matrix power.
-     *
-     * The class stores a reference to A, so it should not be changed
-     * (or destroyed) before evaluation.
-     */
-    explicit MatrixPower(const MatrixType& A) :
-      m_A(A),
-      m_conditionNumber(0),
-      m_rank(A.cols()),
-      m_nulls(0)
-    { eigen_assert(A.rows() == A.cols()); }
-
-    /**
-     * \brief Returns the matrix power.
-     *
-     * \param[in] p  exponent, a real scalar.
-     * \return The expression \f$ A^p \f$, where A is specified in the
-     * constructor.
-     */
-    const MatrixPowerParenthesesReturnValue<MatrixType> operator()(RealScalar p)
-    { return MatrixPowerParenthesesReturnValue<MatrixType>(*this, p); }
-
-    /**
-     * \brief Compute the matrix power.
-     *
-     * \param[in]  p    exponent, a real scalar.
-     * \param[out] res  \f$ A^p \f$ where A is specified in the
-     * constructor.
-     */
-    template<typename ResultType>
-    void compute(ResultType& res, RealScalar p);
-    
-    Index rows() const { return m_A.rows(); }
-    Index cols() const { return m_A.cols(); }
-
-  private:
-    typedef std::complex<RealScalar> ComplexScalar;
-    typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0,
-              MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> ComplexMatrix;
-
-    /** \brief Reference to the base of matrix power. */
-    typename MatrixType::Nested m_A;
-
-    /** \brief Temporary storage. */
-    MatrixType m_tmp;
-
-    /** \brief Store the result of Schur decomposition. */
-    ComplexMatrix m_T, m_U;
-    
-    /** \brief Store fractional power of m_T. */
-    ComplexMatrix m_fT;
-
-    /**
-     * \brief Condition number of m_A.
-     *
-     * It is initialized as 0 to avoid performing unnecessary Schur
-     * decomposition, which is the bottleneck.
-     */
-    RealScalar m_conditionNumber;
-
-    /** \brief Rank of m_A. */
-    Index m_rank;
-    
-    /** \brief Rank deficiency of m_A. */
-    Index m_nulls;
-
-    /**
-     * \brief Split p into integral part and fractional part.
-     *
-     * \param[in]  p        The exponent.
-     * \param[out] p        The fractional part ranging in \f$ (-1, 1) \f$.
-     * \param[out] intpart  The integral part.
-     *
-     * Only if the fractional part is nonzero, it calls initialize().
-     */
-    void split(RealScalar& p, RealScalar& intpart);
-
-    /** \brief Perform Schur decomposition for fractional power. */
-    void initialize();
-
-    template<typename ResultType>
-    void computeIntPower(ResultType& res, RealScalar p);
-
-    template<typename ResultType>
-    void computeFracPower(ResultType& res, RealScalar p);
-
-    template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-    static void revertSchur(
-        Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
-        const ComplexMatrix& T,
-        const ComplexMatrix& U);
-
-    template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-    static void revertSchur(
-        Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
-        const ComplexMatrix& T,
-        const ComplexMatrix& U);
-};
-
-template<typename MatrixType>
-template<typename ResultType>
-void MatrixPower<MatrixType>::compute(ResultType& res, RealScalar p)
-{
-  using std::pow;
-  switch (cols()) {
-    case 0:
-      break;
-    case 1:
-      res(0,0) = pow(m_A.coeff(0,0), p);
-      break;
-    default:
-      RealScalar intpart;
-      split(p, intpart);
-
-      res = MatrixType::Identity(rows(), cols());
-      computeIntPower(res, intpart);
-      if (p) computeFracPower(res, p);
-  }
-}
-
-template<typename MatrixType>
-void MatrixPower<MatrixType>::split(RealScalar& p, RealScalar& intpart)
-{
-  using std::floor;
-  using std::pow;
-
-  intpart = floor(p);
-  p -= intpart;
-
-  // Perform Schur decomposition if it is not yet performed and the power is
-  // not an integer.
-  if (!m_conditionNumber && p)
-    initialize();
-
-  // Choose the more stable of intpart = floor(p) and intpart = ceil(p).
-  if (p > RealScalar(0.5) && p > (1-p) * pow(m_conditionNumber, p)) {
-    --p;
-    ++intpart;
-  }
-}
-
-template<typename MatrixType>
-void MatrixPower<MatrixType>::initialize()
-{
-  const ComplexSchur<MatrixType> schurOfA(m_A);
-  JacobiRotation<ComplexScalar> rot;
-  ComplexScalar eigenvalue;
-
-  m_fT.resizeLike(m_A);
-  m_T = schurOfA.matrixT();
-  m_U = schurOfA.matrixU();
-  m_conditionNumber = m_T.diagonal().array().abs().maxCoeff() / m_T.diagonal().array().abs().minCoeff();
-
-  // Move zero eigenvalues to the bottom right corner.
-  for (Index i = cols()-1; i>=0; --i) {
-    if (m_rank <= 2)
-      return;
-    if (m_T.coeff(i,i) == RealScalar(0)) {
-      for (Index j=i+1; j < m_rank; ++j) {
-        eigenvalue = m_T.coeff(j,j);
-        rot.makeGivens(m_T.coeff(j-1,j), eigenvalue);
-        m_T.applyOnTheRight(j-1, j, rot);
-        m_T.applyOnTheLeft(j-1, j, rot.adjoint());
-        m_T.coeffRef(j-1,j-1) = eigenvalue;
-        m_T.coeffRef(j,j) = RealScalar(0);
-        m_U.applyOnTheRight(j-1, j, rot);
-      }
-      --m_rank;
-    }
-  }
-
-  m_nulls = rows() - m_rank;
-  if (m_nulls) {
-    eigen_assert(m_T.bottomRightCorner(m_nulls, m_nulls).isZero()
-        && "Base of matrix power should be invertible or with a semisimple zero eigenvalue.");
-    m_fT.bottomRows(m_nulls).fill(RealScalar(0));
-  }
-}
-
-template<typename MatrixType>
-template<typename ResultType>
-void MatrixPower<MatrixType>::computeIntPower(ResultType& res, RealScalar p)
-{
-  using std::abs;
-  using std::fmod;
-  RealScalar pp = abs(p);
-
-  if (p<0) 
-    m_tmp = m_A.inverse();
-  else     
-    m_tmp = m_A;
-
-  while (true) {
-    if (fmod(pp, 2) >= 1)
-      res = m_tmp * res;
-    pp /= 2;
-    if (pp < 1)
-      break;
-    m_tmp *= m_tmp;
-  }
-}
-
-template<typename MatrixType>
-template<typename ResultType>
-void MatrixPower<MatrixType>::computeFracPower(ResultType& res, RealScalar p)
-{
-  Block<ComplexMatrix,Dynamic,Dynamic> blockTp(m_fT, 0, 0, m_rank, m_rank);
-  eigen_assert(m_conditionNumber);
-  eigen_assert(m_rank + m_nulls == rows());
-
-  MatrixPowerAtomic<ComplexMatrix>(m_T.topLeftCorner(m_rank, m_rank), p).compute(blockTp);
-  if (m_nulls) {
-    m_fT.topRightCorner(m_rank, m_nulls) = m_T.topLeftCorner(m_rank, m_rank).template triangularView<Upper>()
-        .solve(blockTp * m_T.topRightCorner(m_rank, m_nulls));
-  }
-  revertSchur(m_tmp, m_fT, m_U);
-  res = m_tmp * res;
-}
-
-template<typename MatrixType>
-template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-inline void MatrixPower<MatrixType>::revertSchur(
-    Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
-    const ComplexMatrix& T,
-    const ComplexMatrix& U)
-{ res.noalias() = U * (T.template triangularView<Upper>() * U.adjoint()); }
-
-template<typename MatrixType>
-template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
-inline void MatrixPower<MatrixType>::revertSchur(
-    Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
-    const ComplexMatrix& T,
-    const ComplexMatrix& U)
-{ res.noalias() = (U * (T.template triangularView<Upper>() * U.adjoint())).real(); }
-
-/**
- * \ingroup MatrixFunctions_Module
- *
- * \brief Proxy for the matrix power of some matrix (expression).
- *
- * \tparam Derived  type of the base, a matrix (expression).
- *
- * This class holds the arguments to the matrix power until it is
- * assigned or evaluated for some other reason (so the argument
- * should not be changed in the meantime). It is the return type of
- * MatrixBase::pow() and related functions and most of the
- * time this is the only way it is used.
- */
-template<typename Derived>
-class MatrixPowerReturnValue : public ReturnByValue< MatrixPowerReturnValue<Derived> >
-{
-  public:
-    typedef typename Derived::PlainObject PlainObject;
-    typedef typename Derived::RealScalar RealScalar;
-
-    /**
-     * \brief Constructor.
-     *
-     * \param[in] A  %Matrix (expression), the base of the matrix power.
-     * \param[in] p  real scalar, the exponent of the matrix power.
-     */
-    MatrixPowerReturnValue(const Derived& A, RealScalar p) : m_A(A), m_p(p)
-    { }
-
-    /**
-     * \brief Compute the matrix power.
-     *
-     * \param[out] result  \f$ A^p \f$ where \p A and \p p are as in the
-     * constructor.
-     */
-    template<typename ResultType>
-    inline void evalTo(ResultType& result) const
-    { MatrixPower<PlainObject>(m_A.eval()).compute(result, m_p); }
-
-    Index rows() const { return m_A.rows(); }
-    Index cols() const { return m_A.cols(); }
-
-  private:
-    const Derived& m_A;
-    const RealScalar m_p;
-};
-
-/**
- * \ingroup MatrixFunctions_Module
- *
- * \brief Proxy for the matrix power of some matrix (expression).
- *
- * \tparam Derived  type of the base, a matrix (expression).
- *
- * This class holds the arguments to the matrix power until it is
- * assigned or evaluated for some other reason (so the argument
- * should not be changed in the meantime). It is the return type of
- * MatrixBase::pow() and related functions and most of the
- * time this is the only way it is used.
- */
-template<typename Derived>
-class MatrixComplexPowerReturnValue : public ReturnByValue< MatrixComplexPowerReturnValue<Derived> >
-{
-  public:
-    typedef typename Derived::PlainObject PlainObject;
-    typedef typename std::complex<typename Derived::RealScalar> ComplexScalar;
-
-    /**
-     * \brief Constructor.
-     *
-     * \param[in] A  %Matrix (expression), the base of the matrix power.
-     * \param[in] p  complex scalar, the exponent of the matrix power.
-     */
-    MatrixComplexPowerReturnValue(const Derived& A, const ComplexScalar& p) : m_A(A), m_p(p)
-    { }
-
-    /**
-     * \brief Compute the matrix power.
-     *
-     * Because \p p is complex, \f$ A^p \f$ is simply evaluated as \f$
-     * \exp(p \log(A)) \f$.
-     *
-     * \param[out] result  \f$ A^p \f$ where \p A and \p p are as in the
-     * constructor.
-     */
-    template<typename ResultType>
-    inline void evalTo(ResultType& result) const
-    { result = (m_p * m_A.log()).exp(); }
-
-    Index rows() const { return m_A.rows(); }
-    Index cols() const { return m_A.cols(); }
-
-  private:
-    const Derived& m_A;
-    const ComplexScalar m_p;
-};
-
-namespace internal {
-
-template<typename MatrixPowerType>
-struct traits< MatrixPowerParenthesesReturnValue<MatrixPowerType> >
-{ typedef typename MatrixPowerType::PlainObject ReturnType; };
-
-template<typename Derived>
-struct traits< MatrixPowerReturnValue<Derived> >
-{ typedef typename Derived::PlainObject ReturnType; };
-
-template<typename Derived>
-struct traits< MatrixComplexPowerReturnValue<Derived> >
-{ typedef typename Derived::PlainObject ReturnType; };
-
-}
-
-template<typename Derived>
-const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(const RealScalar& p) const
-{ return MatrixPowerReturnValue<Derived>(derived(), p); }
-
-template<typename Derived>
-const MatrixComplexPowerReturnValue<Derived> MatrixBase<Derived>::pow(const std::complex<RealScalar>& p) const
-{ return MatrixComplexPowerReturnValue<Derived>(derived(), p); }
-
-} // namespace Eigen
-
-#endif // EIGEN_MATRIX_POWER
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
deleted file mode 100644
index e363e77..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
+++ /dev/null
@@ -1,368 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_SQUARE_ROOT
-#define EIGEN_MATRIX_SQUARE_ROOT
-
-namespace Eigen { 
-
-namespace internal {
-
-// pre:  T.block(i,i,2,2) has complex conjugate eigenvalues
-// post: sqrtT.block(i,i,2,2) is square root of T.block(i,i,2,2)
-template <typename MatrixType, typename ResultType>
-void matrix_sqrt_quasi_triangular_2x2_diagonal_block(const MatrixType& T, Index i, ResultType& sqrtT)
-{
-  // TODO: This case (2-by-2 blocks with complex conjugate eigenvalues) is probably hidden somewhere
-  //       in EigenSolver. If we expose it, we could call it directly from here.
-  typedef typename traits<MatrixType>::Scalar Scalar;
-  Matrix<Scalar,2,2> block = T.template block<2,2>(i,i);
-  EigenSolver<Matrix<Scalar,2,2> > es(block);
-  sqrtT.template block<2,2>(i,i)
-    = (es.eigenvectors() * es.eigenvalues().cwiseSqrt().asDiagonal() * es.eigenvectors().inverse()).real();
-}
-
-// pre:  block structure of T is such that (i,j) is a 1x1 block,
-//       all blocks of sqrtT to left of and below (i,j) are correct
-// post: sqrtT(i,j) has the correct value
-template <typename MatrixType, typename ResultType>
-void matrix_sqrt_quasi_triangular_1x1_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
-{
-  typedef typename traits<MatrixType>::Scalar Scalar;
-  Scalar tmp = (sqrtT.row(i).segment(i+1,j-i-1) * sqrtT.col(j).segment(i+1,j-i-1)).value();
-  sqrtT.coeffRef(i,j) = (T.coeff(i,j) - tmp) / (sqrtT.coeff(i,i) + sqrtT.coeff(j,j));
-}
-
-// similar to compute1x1offDiagonalBlock()
-template <typename MatrixType, typename ResultType>
-void matrix_sqrt_quasi_triangular_1x2_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
-{
-  typedef typename traits<MatrixType>::Scalar Scalar;
-  Matrix<Scalar,1,2> rhs = T.template block<1,2>(i,j);
-  if (j-i > 1)
-    rhs -= sqrtT.block(i, i+1, 1, j-i-1) * sqrtT.block(i+1, j, j-i-1, 2);
-  Matrix<Scalar,2,2> A = sqrtT.coeff(i,i) * Matrix<Scalar,2,2>::Identity();
-  A += sqrtT.template block<2,2>(j,j).transpose();
-  sqrtT.template block<1,2>(i,j).transpose() = A.fullPivLu().solve(rhs.transpose());
-}
-
-// similar to compute1x1offDiagonalBlock()
-template <typename MatrixType, typename ResultType>
-void matrix_sqrt_quasi_triangular_2x1_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
-{
-  typedef typename traits<MatrixType>::Scalar Scalar;
-  Matrix<Scalar,2,1> rhs = T.template block<2,1>(i,j);
-  if (j-i > 2)
-    rhs -= sqrtT.block(i, i+2, 2, j-i-2) * sqrtT.block(i+2, j, j-i-2, 1);
-  Matrix<Scalar,2,2> A = sqrtT.coeff(j,j) * Matrix<Scalar,2,2>::Identity();
-  A += sqrtT.template block<2,2>(i,i);
-  sqrtT.template block<2,1>(i,j) = A.fullPivLu().solve(rhs);
-}
-
-// solves the equation A X + X B = C where all matrices are 2-by-2
-template <typename MatrixType>
-void matrix_sqrt_quasi_triangular_solve_auxiliary_equation(MatrixType& X, const MatrixType& A, const MatrixType& B, const MatrixType& C)
-{
-  typedef typename traits<MatrixType>::Scalar Scalar;
-  Matrix<Scalar,4,4> coeffMatrix = Matrix<Scalar,4,4>::Zero();
-  coeffMatrix.coeffRef(0,0) = A.coeff(0,0) + B.coeff(0,0);
-  coeffMatrix.coeffRef(1,1) = A.coeff(0,0) + B.coeff(1,1);
-  coeffMatrix.coeffRef(2,2) = A.coeff(1,1) + B.coeff(0,0);
-  coeffMatrix.coeffRef(3,3) = A.coeff(1,1) + B.coeff(1,1);
-  coeffMatrix.coeffRef(0,1) = B.coeff(1,0);
-  coeffMatrix.coeffRef(0,2) = A.coeff(0,1);
-  coeffMatrix.coeffRef(1,0) = B.coeff(0,1);
-  coeffMatrix.coeffRef(1,3) = A.coeff(0,1);
-  coeffMatrix.coeffRef(2,0) = A.coeff(1,0);
-  coeffMatrix.coeffRef(2,3) = B.coeff(1,0);
-  coeffMatrix.coeffRef(3,1) = A.coeff(1,0);
-  coeffMatrix.coeffRef(3,2) = B.coeff(0,1);
-
-  Matrix<Scalar,4,1> rhs;
-  rhs.coeffRef(0) = C.coeff(0,0);
-  rhs.coeffRef(1) = C.coeff(0,1);
-  rhs.coeffRef(2) = C.coeff(1,0);
-  rhs.coeffRef(3) = C.coeff(1,1);
-
-  Matrix<Scalar,4,1> result;
-  result = coeffMatrix.fullPivLu().solve(rhs);
-
-  X.coeffRef(0,0) = result.coeff(0);
-  X.coeffRef(0,1) = result.coeff(1);
-  X.coeffRef(1,0) = result.coeff(2);
-  X.coeffRef(1,1) = result.coeff(3);
-}
-
-// similar to compute1x1offDiagonalBlock()
-template <typename MatrixType, typename ResultType>
-void matrix_sqrt_quasi_triangular_2x2_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
-{
-  typedef typename traits<MatrixType>::Scalar Scalar;
-  Matrix<Scalar,2,2> A = sqrtT.template block<2,2>(i,i);
-  Matrix<Scalar,2,2> B = sqrtT.template block<2,2>(j,j);
-  Matrix<Scalar,2,2> C = T.template block<2,2>(i,j);
-  if (j-i > 2)
-    C -= sqrtT.block(i, i+2, 2, j-i-2) * sqrtT.block(i+2, j, j-i-2, 2);
-  Matrix<Scalar,2,2> X;
-  matrix_sqrt_quasi_triangular_solve_auxiliary_equation(X, A, B, C);
-  sqrtT.template block<2,2>(i,j) = X;
-}
-
-// pre:  T is quasi-upper-triangular and sqrtT is a zero matrix of the same size
-// post: the diagonal blocks of sqrtT are the square roots of the diagonal blocks of T
-template <typename MatrixType, typename ResultType>
-void matrix_sqrt_quasi_triangular_diagonal(const MatrixType& T, ResultType& sqrtT)
-{
-  using std::sqrt;
-  const Index size = T.rows();
-  for (Index i = 0; i < size; i++) {
-    if (i == size - 1 || T.coeff(i+1, i) == 0) {
-      eigen_assert(T(i,i) >= 0);
-      sqrtT.coeffRef(i,i) = sqrt(T.coeff(i,i));
-    }
-    else {
-      matrix_sqrt_quasi_triangular_2x2_diagonal_block(T, i, sqrtT);
-      ++i;
-    }
-  }
-}
-
-// pre:  T is quasi-upper-triangular and diagonal blocks of sqrtT are square root of diagonal blocks of T.
-// post: sqrtT is the square root of T.
-template <typename MatrixType, typename ResultType>
-void matrix_sqrt_quasi_triangular_off_diagonal(const MatrixType& T, ResultType& sqrtT)
-{
-  const Index size = T.rows();
-  for (Index j = 1; j < size; j++) {
-      if (T.coeff(j, j-1) != 0)  // if T(j-1:j, j-1:j) is a 2-by-2 block
-	continue;
-    for (Index i = j-1; i >= 0; i--) {
-      if (i > 0 && T.coeff(i, i-1) != 0)  // if T(i-1:i, i-1:i) is a 2-by-2 block
-	continue;
-      bool iBlockIs2x2 = (i < size - 1) && (T.coeff(i+1, i) != 0);
-      bool jBlockIs2x2 = (j < size - 1) && (T.coeff(j+1, j) != 0);
-      if (iBlockIs2x2 && jBlockIs2x2) 
-        matrix_sqrt_quasi_triangular_2x2_off_diagonal_block(T, i, j, sqrtT);
-      else if (iBlockIs2x2 && !jBlockIs2x2) 
-        matrix_sqrt_quasi_triangular_2x1_off_diagonal_block(T, i, j, sqrtT);
-      else if (!iBlockIs2x2 && jBlockIs2x2) 
-        matrix_sqrt_quasi_triangular_1x2_off_diagonal_block(T, i, j, sqrtT);
-      else if (!iBlockIs2x2 && !jBlockIs2x2) 
-        matrix_sqrt_quasi_triangular_1x1_off_diagonal_block(T, i, j, sqrtT);
-    }
-  }
-}
-
-} // end of namespace internal
-
-/** \ingroup MatrixFunctions_Module
-  * \brief Compute matrix square root of quasi-triangular matrix.
-  *
-  * \tparam  MatrixType  type of \p arg, the argument of matrix square root,
-  *                      expected to be an instantiation of the Matrix class template.
-  * \tparam  ResultType  type of \p result, where result is to be stored.
-  * \param[in]  arg      argument of matrix square root.
-  * \param[out] result   matrix square root of upper Hessenberg part of \p arg.
-  *
-  * This function computes the square root of the upper quasi-triangular matrix stored in the upper
-  * Hessenberg part of \p arg.  Only the upper Hessenberg part of \p result is updated, the rest is
-  * not touched.  See MatrixBase::sqrt() for details on how this computation is implemented.
-  *
-  * \sa MatrixSquareRoot, MatrixSquareRootQuasiTriangular
-  */
-template <typename MatrixType, typename ResultType> 
-void matrix_sqrt_quasi_triangular(const MatrixType &arg, ResultType &result)
-{
-  eigen_assert(arg.rows() == arg.cols());
-  result.resize(arg.rows(), arg.cols());
-  internal::matrix_sqrt_quasi_triangular_diagonal(arg, result);
-  internal::matrix_sqrt_quasi_triangular_off_diagonal(arg, result);
-}
-
-
-/** \ingroup MatrixFunctions_Module
-  * \brief Compute matrix square root of triangular matrix.
-  *
-  * \tparam  MatrixType  type of \p arg, the argument of matrix square root,
-  *                      expected to be an instantiation of the Matrix class template.
-  * \tparam  ResultType  type of \p result, where result is to be stored.
-  * \param[in]  arg      argument of matrix square root.
-  * \param[out] result   matrix square root of upper triangular part of \p arg.
-  *
-  * Only the upper triangular part (including the diagonal) of \p result is updated, the rest is not
-  * touched.  See MatrixBase::sqrt() for details on how this computation is implemented.
-  *
-  * \sa MatrixSquareRoot, MatrixSquareRootQuasiTriangular
-  */
-template <typename MatrixType, typename ResultType> 
-void matrix_sqrt_triangular(const MatrixType &arg, ResultType &result)
-{
-  using std::sqrt;
-  typedef typename MatrixType::Scalar Scalar;
-
-  eigen_assert(arg.rows() == arg.cols());
-
-  // Compute square root of arg and store it in upper triangular part of result
-  // This uses that the square root of triangular matrices can be computed directly.
-  result.resize(arg.rows(), arg.cols());
-  for (Index i = 0; i < arg.rows(); i++) {
-    result.coeffRef(i,i) = sqrt(arg.coeff(i,i));
-  }
-  for (Index j = 1; j < arg.cols(); j++) {
-    for (Index i = j-1; i >= 0; i--) {
-      // if i = j-1, then segment has length 0 so tmp = 0
-      Scalar tmp = (result.row(i).segment(i+1,j-i-1) * result.col(j).segment(i+1,j-i-1)).value();
-      // denominator may be zero if original matrix is singular
-      result.coeffRef(i,j) = (arg.coeff(i,j) - tmp) / (result.coeff(i,i) + result.coeff(j,j));
-    }
-  }
-}
-
-
-namespace internal {
-
-/** \ingroup MatrixFunctions_Module
-  * \brief Helper struct for computing matrix square roots of general matrices.
-  * \tparam  MatrixType  type of the argument of the matrix square root,
-  *                      expected to be an instantiation of the Matrix class template.
-  *
-  * \sa MatrixSquareRootTriangular, MatrixSquareRootQuasiTriangular, MatrixBase::sqrt()
-  */
-template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
-struct matrix_sqrt_compute
-{
-  /** \brief Compute the matrix square root
-    *
-    * \param[in]  arg     matrix whose square root is to be computed.
-    * \param[out] result  square root of \p arg.
-    *
-    * See MatrixBase::sqrt() for details on how this computation is implemented.
-    */
-  template <typename ResultType> static void run(const MatrixType &arg, ResultType &result);    
-};
-
-
-// ********** Partial specialization for real matrices **********
-
-template <typename MatrixType>
-struct matrix_sqrt_compute<MatrixType, 0>
-{
-  typedef typename MatrixType::PlainObject PlainType;
-  template <typename ResultType>
-  static void run(const MatrixType &arg, ResultType &result)
-  {
-    eigen_assert(arg.rows() == arg.cols());
-
-    // Compute Schur decomposition of arg
-    const RealSchur<PlainType> schurOfA(arg);
-    const PlainType& T = schurOfA.matrixT();
-    const PlainType& U = schurOfA.matrixU();
-    
-    // Compute square root of T
-    PlainType sqrtT = PlainType::Zero(arg.rows(), arg.cols());
-    matrix_sqrt_quasi_triangular(T, sqrtT);
-    
-    // Compute square root of arg
-    result = U * sqrtT * U.adjoint();
-  }
-};
-
-
-// ********** Partial specialization for complex matrices **********
-
-template <typename MatrixType>
-struct matrix_sqrt_compute<MatrixType, 1>
-{
-  typedef typename MatrixType::PlainObject PlainType;
-  template <typename ResultType>
-  static void run(const MatrixType &arg, ResultType &result)
-  {
-    eigen_assert(arg.rows() == arg.cols());
-
-    // Compute Schur decomposition of arg
-    const ComplexSchur<PlainType> schurOfA(arg);
-    const PlainType& T = schurOfA.matrixT();
-    const PlainType& U = schurOfA.matrixU();
-    
-    // Compute square root of T
-    PlainType sqrtT;
-    matrix_sqrt_triangular(T, sqrtT);
-    
-    // Compute square root of arg
-    result = U * (sqrtT.template triangularView<Upper>() * U.adjoint());
-  }
-};
-
-} // end namespace internal
-
-/** \ingroup MatrixFunctions_Module
-  *
-  * \brief Proxy for the matrix square root of some matrix (expression).
-  *
-  * \tparam Derived  Type of the argument to the matrix square root.
-  *
-  * This class holds the argument to the matrix square root until it
-  * is assigned or evaluated for some other reason (so the argument
-  * should not be changed in the meantime). It is the return type of
-  * MatrixBase::sqrt() and most of the time this is the only way it is
-  * used.
-  */
-template<typename Derived> class MatrixSquareRootReturnValue
-: public ReturnByValue<MatrixSquareRootReturnValue<Derived> >
-{
-  protected:
-    typedef typename internal::ref_selector<Derived>::type DerivedNested;
-
-  public:
-    /** \brief Constructor.
-      *
-      * \param[in]  src  %Matrix (expression) forming the argument of the
-      * matrix square root.
-      */
-    explicit MatrixSquareRootReturnValue(const Derived& src) : m_src(src) { }
-
-    /** \brief Compute the matrix square root.
-      *
-      * \param[out]  result  the matrix square root of \p src in the
-      * constructor.
-      */
-    template <typename ResultType>
-    inline void evalTo(ResultType& result) const
-    {
-      typedef typename internal::nested_eval<Derived, 10>::type DerivedEvalType;
-      typedef typename internal::remove_all<DerivedEvalType>::type DerivedEvalTypeClean;
-      DerivedEvalType tmp(m_src);
-      internal::matrix_sqrt_compute<DerivedEvalTypeClean>::run(tmp, result);
-    }
-
-    Index rows() const { return m_src.rows(); }
-    Index cols() const { return m_src.cols(); }
-
-  protected:
-    const DerivedNested m_src;
-};
-
-namespace internal {
-template<typename Derived>
-struct traits<MatrixSquareRootReturnValue<Derived> >
-{
-  typedef typename Derived::PlainObject ReturnType;
-};
-}
-
-template <typename Derived>
-const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const
-{
-  eigen_assert(rows() == cols());
-  return MatrixSquareRootReturnValue<Derived>(derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_MATRIX_FUNCTION
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
deleted file mode 100644
index 7604df9..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
+++ /dev/null
@@ -1,117 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_STEM_FUNCTION
-#define EIGEN_STEM_FUNCTION
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \brief The exponential function (and its derivatives). */
-template <typename Scalar>
-Scalar stem_function_exp(Scalar x, int)
-{
-  using std::exp;
-  return exp(x);
-}
-
-/** \brief Cosine (and its derivatives). */
-template <typename Scalar>
-Scalar stem_function_cos(Scalar x, int n)
-{
-  using std::cos;
-  using std::sin;
-  Scalar res;
-
-  switch (n % 4) {
-  case 0: 
-    res = std::cos(x);
-    break;
-  case 1:
-    res = -std::sin(x);
-    break;
-  case 2:
-    res = -std::cos(x);
-    break;
-  case 3:
-    res = std::sin(x);
-    break;
-  }
-  return res;
-}
-
-/** \brief Sine (and its derivatives). */
-template <typename Scalar>
-Scalar stem_function_sin(Scalar x, int n)
-{
-  using std::cos;
-  using std::sin;
-  Scalar res;
-
-  switch (n % 4) {
-  case 0:
-    res = std::sin(x);
-    break;
-  case 1:
-    res = std::cos(x);
-    break;
-  case 2:
-    res = -std::sin(x);
-    break;
-  case 3:
-    res = -std::cos(x);
-    break;
-  }
-  return res;
-}
-
-/** \brief Hyperbolic cosine (and its derivatives). */
-template <typename Scalar>
-Scalar stem_function_cosh(Scalar x, int n)
-{
-  using std::cosh;
-  using std::sinh;
-  Scalar res;
-  
-  switch (n % 2) {
-  case 0:
-    res = std::cosh(x);
-    break;
-  case 1:
-    res = std::sinh(x);
-    break;
-  }
-  return res;
-}
-	
-/** \brief Hyperbolic sine (and its derivatives). */
-template <typename Scalar>
-Scalar stem_function_sinh(Scalar x, int n)
-{
-  using std::cosh;
-  using std::sinh;
-  Scalar res;
-  
-  switch (n % 2) {
-  case 0:
-    res = std::sinh(x);
-    break;
-  case 1:
-    res = std::cosh(x);
-    break;
-  }
-  return res;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_STEM_FUNCTION
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h
deleted file mode 100644
index 63cb28d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h
+++ /dev/null
@@ -1,95 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Rohit Garg <rpg.314@gmail.com>
-// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MOREVECTORIZATION_MATHFUNCTIONS_H
-#define EIGEN_MOREVECTORIZATION_MATHFUNCTIONS_H
-
-namespace Eigen { 
-
-namespace internal {
-
-/** \internal \returns the arcsin of \a a (coeff-wise) */
-template<typename Packet> inline static Packet pasin(Packet a) { return std::asin(a); }
-
-#ifdef EIGEN_VECTORIZE_SSE
-
-template<> EIGEN_DONT_INLINE Packet4f pasin(Packet4f x)
-{
-  _EIGEN_DECLARE_CONST_Packet4f(half, 0.5);
-  _EIGEN_DECLARE_CONST_Packet4f(minus_half, -0.5);
-  _EIGEN_DECLARE_CONST_Packet4f(3half, 1.5);
-
-  _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(sign_mask, 0x80000000);
-
-  _EIGEN_DECLARE_CONST_Packet4f(pi, 3.141592654);
-  _EIGEN_DECLARE_CONST_Packet4f(pi_over_2, 3.141592654*0.5);
-
-  _EIGEN_DECLARE_CONST_Packet4f(asin1, 4.2163199048E-2);
-  _EIGEN_DECLARE_CONST_Packet4f(asin2, 2.4181311049E-2);
-  _EIGEN_DECLARE_CONST_Packet4f(asin3, 4.5470025998E-2);
-  _EIGEN_DECLARE_CONST_Packet4f(asin4, 7.4953002686E-2);
-  _EIGEN_DECLARE_CONST_Packet4f(asin5, 1.6666752422E-1);
-
-  Packet4f a = pabs(x);//got the absolute value
-
-  Packet4f sign_bit= _mm_and_ps(x, p4f_sign_mask);//extracted the sign bit
-
-  Packet4f z1,z2;//will need them during computation    
-
-
-//will compute the two branches for asin
-//so first compare with half
-
-  Packet4f branch_mask= _mm_cmpgt_ps(a, p4f_half);//this is to select which branch to take
-//both will be taken, and finally results will be merged
-//the branch for values >0.5
-
-    {
-//the core series expansion 
-    z1=pmadd(p4f_minus_half,a,p4f_half);
-    Packet4f x1=psqrt(z1);
-    Packet4f s1=pmadd(p4f_asin1, z1, p4f_asin2);
-    Packet4f s2=pmadd(s1, z1, p4f_asin3);
-    Packet4f s3=pmadd(s2,z1, p4f_asin4);
-    Packet4f s4=pmadd(s3,z1, p4f_asin5);
-    Packet4f temp=pmul(s4,z1);//not really a madd but a mul by z so that the next term can be a madd
-    z1=pmadd(temp,x1,x1);
-    z1=padd(z1,z1);
-    z1=psub(p4f_pi_over_2,z1);
-    }
-
-    {
-//the core series expansion 
-    Packet4f x2=a;
-    z2=pmul(x2,x2);
-    Packet4f s1=pmadd(p4f_asin1, z2, p4f_asin2);
-    Packet4f s2=pmadd(s1, z2, p4f_asin3);
-    Packet4f s3=pmadd(s2,z2, p4f_asin4);
-    Packet4f s4=pmadd(s3,z2, p4f_asin5);
-    Packet4f temp=pmul(s4,z2);//not really a madd but a mul by z so that the next term can be a madd
-    z2=pmadd(temp,x2,x2);
-    }
-
-/* select the correct result from the two branch evaluations */
-  z1  = _mm_and_ps(branch_mask, z1);
-  z2  = _mm_andnot_ps(branch_mask, z2);
-  Packet4f z  = _mm_or_ps(z1,z2);
-
-/* update the sign */
-  return _mm_xor_ps(z, sign_bit);
-}
-
-#endif // EIGEN_VECTORIZE_SSE
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_MOREVECTORIZATION_MATHFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
deleted file mode 100644
index 07c5ef0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ /dev/null
@@ -1,601 +0,0 @@
-// -*- coding: utf-8
-// vim: set fileencoding=utf-8
-
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_HYBRIDNONLINEARSOLVER_H
-#define EIGEN_HYBRIDNONLINEARSOLVER_H
-
-namespace Eigen { 
-
-namespace HybridNonLinearSolverSpace { 
-    enum Status {
-        Running = -1,
-        ImproperInputParameters = 0,
-        RelativeErrorTooSmall = 1,
-        TooManyFunctionEvaluation = 2,
-        TolTooSmall = 3,
-        NotMakingProgressJacobian = 4,
-        NotMakingProgressIterations = 5,
-        UserAsked = 6
-    };
-}
-
-/**
-  * \ingroup NonLinearOptimization_Module
-  * \brief Finds a zero of a system of n
-  * nonlinear functions in n variables by a modification of the Powell
-  * hybrid method ("dogleg").
-  *
-  * The user must provide a subroutine which calculates the
-  * functions. The Jacobian is either provided by the user, or approximated
-  * using a forward-difference method.
-  *
-  */
-template<typename FunctorType, typename Scalar=double>
-class HybridNonLinearSolver
-{
-public:
-    typedef DenseIndex Index;
-
-    HybridNonLinearSolver(FunctorType &_functor)
-        : functor(_functor) { nfev=njev=iter = 0;  fnorm= 0.; useExternalScaling=false;}
-
-    struct Parameters {
-        Parameters()
-            : factor(Scalar(100.))
-            , maxfev(1000)
-            , xtol(numext::sqrt(NumTraits<Scalar>::epsilon()))
-            , nb_of_subdiagonals(-1)
-            , nb_of_superdiagonals(-1)
-            , epsfcn(Scalar(0.)) {}
-        Scalar factor;
-        Index maxfev;   // maximum number of function evaluation
-        Scalar xtol;
-        Index nb_of_subdiagonals;
-        Index nb_of_superdiagonals;
-        Scalar epsfcn;
-    };
-    typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
-    typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
-    /* TODO: if eigen provides a triangular storage, use it here */
-    typedef Matrix< Scalar, Dynamic, Dynamic > UpperTriangularType;
-
-    HybridNonLinearSolverSpace::Status hybrj1(
-            FVectorType  &x,
-            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon())
-            );
-
-    HybridNonLinearSolverSpace::Status solveInit(FVectorType  &x);
-    HybridNonLinearSolverSpace::Status solveOneStep(FVectorType  &x);
-    HybridNonLinearSolverSpace::Status solve(FVectorType  &x);
-
-    HybridNonLinearSolverSpace::Status hybrd1(
-            FVectorType  &x,
-            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon())
-            );
-
-    HybridNonLinearSolverSpace::Status solveNumericalDiffInit(FVectorType  &x);
-    HybridNonLinearSolverSpace::Status solveNumericalDiffOneStep(FVectorType  &x);
-    HybridNonLinearSolverSpace::Status solveNumericalDiff(FVectorType  &x);
-
-    void resetParameters(void) { parameters = Parameters(); }
-    Parameters parameters;
-    FVectorType  fvec, qtf, diag;
-    JacobianType fjac;
-    UpperTriangularType R;
-    Index nfev;
-    Index njev;
-    Index iter;
-    Scalar fnorm;
-    bool useExternalScaling; 
-private:
-    FunctorType &functor;
-    Index n;
-    Scalar sum;
-    bool sing;
-    Scalar temp;
-    Scalar delta;
-    bool jeval;
-    Index ncsuc;
-    Scalar ratio;
-    Scalar pnorm, xnorm, fnorm1;
-    Index nslow1, nslow2;
-    Index ncfail;
-    Scalar actred, prered;
-    FVectorType wa1, wa2, wa3, wa4;
-
-    HybridNonLinearSolver& operator=(const HybridNonLinearSolver&);
-};
-
-
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
-        FVectorType  &x,
-        const Scalar tol
-        )
-{
-    n = x.size();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || tol < 0.)
-        return HybridNonLinearSolverSpace::ImproperInputParameters;
-
-    resetParameters();
-    parameters.maxfev = 100*(n+1);
-    parameters.xtol = tol;
-    diag.setConstant(n, 1.);
-    useExternalScaling = true;
-    return solve(x);
-}
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solveInit(FVectorType  &x)
-{
-    n = x.size();
-
-    wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
-    fvec.resize(n);
-    qtf.resize(n);
-    fjac.resize(n, n);
-    if (!useExternalScaling)
-        diag.resize(n);
-    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
-
-    /* Function Body */
-    nfev = 0;
-    njev = 0;
-
-    /*     check the input parameters for errors. */
-    if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. )
-        return HybridNonLinearSolverSpace::ImproperInputParameters;
-    if (useExternalScaling)
-        for (Index j = 0; j < n; ++j)
-            if (diag[j] <= 0.)
-                return HybridNonLinearSolverSpace::ImproperInputParameters;
-
-    /*     evaluate the function at the starting point */
-    /*     and calculate its norm. */
-    nfev = 1;
-    if ( functor(x, fvec) < 0)
-        return HybridNonLinearSolverSpace::UserAsked;
-    fnorm = fvec.stableNorm();
-
-    /*     initialize iteration counter and monitors. */
-    iter = 1;
-    ncsuc = 0;
-    ncfail = 0;
-    nslow1 = 0;
-    nslow2 = 0;
-
-    return HybridNonLinearSolverSpace::Running;
-}
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
-{
-    using std::abs;
-    
-    eigen_assert(x.size()==n); // check the caller is not cheating us
-
-    Index j;
-    std::vector<JacobiRotation<Scalar> > v_givens(n), w_givens(n);
-
-    jeval = true;
-
-    /* calculate the jacobian matrix. */
-    if ( functor.df(x, fjac) < 0)
-        return HybridNonLinearSolverSpace::UserAsked;
-    ++njev;
-
-    wa2 = fjac.colwise().blueNorm();
-
-    /* on the first iteration and if external scaling is not used, scale according */
-    /* to the norms of the columns of the initial jacobian. */
-    if (iter == 1) {
-        if (!useExternalScaling)
-            for (j = 0; j < n; ++j)
-                diag[j] = (wa2[j]==0.) ? 1. : wa2[j];
-
-        /* on the first iteration, calculate the norm of the scaled x */
-        /* and initialize the step bound delta. */
-        xnorm = diag.cwiseProduct(x).stableNorm();
-        delta = parameters.factor * xnorm;
-        if (delta == 0.)
-            delta = parameters.factor;
-    }
-
-    /* compute the qr factorization of the jacobian. */
-    HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
-
-    /* copy the triangular factor of the qr factorization into r. */
-    R = qrfac.matrixQR();
-
-    /* accumulate the orthogonal factor in fjac. */
-    fjac = qrfac.householderQ();
-
-    /* form (q transpose)*fvec and store in qtf. */
-    qtf = fjac.transpose() * fvec;
-
-    /* rescale if necessary. */
-    if (!useExternalScaling)
-        diag = diag.cwiseMax(wa2);
-
-    while (true) {
-        /* determine the direction p. */
-        internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
-
-        /* store the direction p and x + p. calculate the norm of p. */
-        wa1 = -wa1;
-        wa2 = x + wa1;
-        pnorm = diag.cwiseProduct(wa1).stableNorm();
-
-        /* on the first iteration, adjust the initial step bound. */
-        if (iter == 1)
-            delta = (std::min)(delta,pnorm);
-
-        /* evaluate the function at x + p and calculate its norm. */
-        if ( functor(wa2, wa4) < 0)
-            return HybridNonLinearSolverSpace::UserAsked;
-        ++nfev;
-        fnorm1 = wa4.stableNorm();
-
-        /* compute the scaled actual reduction. */
-        actred = -1.;
-        if (fnorm1 < fnorm) /* Computing 2nd power */
-            actred = 1. - numext::abs2(fnorm1 / fnorm);
-
-        /* compute the scaled predicted reduction. */
-        wa3 = R.template triangularView<Upper>()*wa1 + qtf;
-        temp = wa3.stableNorm();
-        prered = 0.;
-        if (temp < fnorm) /* Computing 2nd power */
-            prered = 1. - numext::abs2(temp / fnorm);
-
-        /* compute the ratio of the actual to the predicted reduction. */
-        ratio = 0.;
-        if (prered > 0.)
-            ratio = actred / prered;
-
-        /* update the step bound. */
-        if (ratio < Scalar(.1)) {
-            ncsuc = 0;
-            ++ncfail;
-            delta = Scalar(.5) * delta;
-        } else {
-            ncfail = 0;
-            ++ncsuc;
-            if (ratio >= Scalar(.5) || ncsuc > 1)
-                delta = (std::max)(delta, pnorm / Scalar(.5));
-            if (abs(ratio - 1.) <= Scalar(.1)) {
-                delta = pnorm / Scalar(.5);
-            }
-        }
-
-        /* test for successful iteration. */
-        if (ratio >= Scalar(1e-4)) {
-            /* successful iteration. update x, fvec, and their norms. */
-            x = wa2;
-            wa2 = diag.cwiseProduct(x);
-            fvec = wa4;
-            xnorm = wa2.stableNorm();
-            fnorm = fnorm1;
-            ++iter;
-        }
-
-        /* determine the progress of the iteration. */
-        ++nslow1;
-        if (actred >= Scalar(.001))
-            nslow1 = 0;
-        if (jeval)
-            ++nslow2;
-        if (actred >= Scalar(.1))
-            nslow2 = 0;
-
-        /* test for convergence. */
-        if (delta <= parameters.xtol * xnorm || fnorm == 0.)
-            return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
-
-        /* tests for termination and stringent tolerances. */
-        if (nfev >= parameters.maxfev)
-            return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
-        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
-            return HybridNonLinearSolverSpace::TolTooSmall;
-        if (nslow2 == 5)
-            return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
-        if (nslow1 == 10)
-            return HybridNonLinearSolverSpace::NotMakingProgressIterations;
-
-        /* criterion for recalculating jacobian. */
-        if (ncfail == 2)
-            break; // leave inner loop and go for the next outer loop iteration
-
-        /* calculate the rank one modification to the jacobian */
-        /* and update qtf if necessary. */
-        wa1 = diag.cwiseProduct( diag.cwiseProduct(wa1)/pnorm );
-        wa2 = fjac.transpose() * wa4;
-        if (ratio >= Scalar(1e-4))
-            qtf = wa2;
-        wa2 = (wa2-wa3)/pnorm;
-
-        /* compute the qr factorization of the updated jacobian. */
-        internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
-        internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
-        internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
-
-        jeval = false;
-    }
-    return HybridNonLinearSolverSpace::Running;
-}
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solve(FVectorType  &x)
-{
-    HybridNonLinearSolverSpace::Status status = solveInit(x);
-    if (status==HybridNonLinearSolverSpace::ImproperInputParameters)
-        return status;
-    while (status==HybridNonLinearSolverSpace::Running)
-        status = solveOneStep(x);
-    return status;
-}
-
-
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
-        FVectorType  &x,
-        const Scalar tol
-        )
-{
-    n = x.size();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || tol < 0.)
-        return HybridNonLinearSolverSpace::ImproperInputParameters;
-
-    resetParameters();
-    parameters.maxfev = 200*(n+1);
-    parameters.xtol = tol;
-
-    diag.setConstant(n, 1.);
-    useExternalScaling = true;
-    return solveNumericalDiff(x);
-}
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(FVectorType  &x)
-{
-    n = x.size();
-
-    if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
-    if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
-
-    wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
-    qtf.resize(n);
-    fjac.resize(n, n);
-    fvec.resize(n);
-    if (!useExternalScaling)
-        diag.resize(n);
-    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
-
-    /* Function Body */
-    nfev = 0;
-    njev = 0;
-
-    /*     check the input parameters for errors. */
-    if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. )
-        return HybridNonLinearSolverSpace::ImproperInputParameters;
-    if (useExternalScaling)
-        for (Index j = 0; j < n; ++j)
-            if (diag[j] <= 0.)
-                return HybridNonLinearSolverSpace::ImproperInputParameters;
-
-    /*     evaluate the function at the starting point */
-    /*     and calculate its norm. */
-    nfev = 1;
-    if ( functor(x, fvec) < 0)
-        return HybridNonLinearSolverSpace::UserAsked;
-    fnorm = fvec.stableNorm();
-
-    /*     initialize iteration counter and monitors. */
-    iter = 1;
-    ncsuc = 0;
-    ncfail = 0;
-    nslow1 = 0;
-    nslow2 = 0;
-
-    return HybridNonLinearSolverSpace::Running;
-}
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType  &x)
-{
-    using std::sqrt;
-    using std::abs;
-    
-    assert(x.size()==n); // check the caller is not cheating us
-
-    Index j;
-    std::vector<JacobiRotation<Scalar> > v_givens(n), w_givens(n);
-
-    jeval = true;
-    if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
-    if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
-
-    /* calculate the jacobian matrix. */
-    if (internal::fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
-        return HybridNonLinearSolverSpace::UserAsked;
-    nfev += (std::min)(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
-
-    wa2 = fjac.colwise().blueNorm();
-
-    /* on the first iteration and if external scaling is not used, scale according */
-    /* to the norms of the columns of the initial jacobian. */
-    if (iter == 1) {
-        if (!useExternalScaling)
-            for (j = 0; j < n; ++j)
-                diag[j] = (wa2[j]==0.) ? 1. : wa2[j];
-
-        /* on the first iteration, calculate the norm of the scaled x */
-        /* and initialize the step bound delta. */
-        xnorm = diag.cwiseProduct(x).stableNorm();
-        delta = parameters.factor * xnorm;
-        if (delta == 0.)
-            delta = parameters.factor;
-    }
-
-    /* compute the qr factorization of the jacobian. */
-    HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
-
-    /* copy the triangular factor of the qr factorization into r. */
-    R = qrfac.matrixQR();
-
-    /* accumulate the orthogonal factor in fjac. */
-    fjac = qrfac.householderQ();
-
-    /* form (q transpose)*fvec and store in qtf. */
-    qtf = fjac.transpose() * fvec;
-
-    /* rescale if necessary. */
-    if (!useExternalScaling)
-        diag = diag.cwiseMax(wa2);
-
-    while (true) {
-        /* determine the direction p. */
-        internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
-
-        /* store the direction p and x + p. calculate the norm of p. */
-        wa1 = -wa1;
-        wa2 = x + wa1;
-        pnorm = diag.cwiseProduct(wa1).stableNorm();
-
-        /* on the first iteration, adjust the initial step bound. */
-        if (iter == 1)
-            delta = (std::min)(delta,pnorm);
-
-        /* evaluate the function at x + p and calculate its norm. */
-        if ( functor(wa2, wa4) < 0)
-            return HybridNonLinearSolverSpace::UserAsked;
-        ++nfev;
-        fnorm1 = wa4.stableNorm();
-
-        /* compute the scaled actual reduction. */
-        actred = -1.;
-        if (fnorm1 < fnorm) /* Computing 2nd power */
-            actred = 1. - numext::abs2(fnorm1 / fnorm);
-
-        /* compute the scaled predicted reduction. */
-        wa3 = R.template triangularView<Upper>()*wa1 + qtf;
-        temp = wa3.stableNorm();
-        prered = 0.;
-        if (temp < fnorm) /* Computing 2nd power */
-            prered = 1. - numext::abs2(temp / fnorm);
-
-        /* compute the ratio of the actual to the predicted reduction. */
-        ratio = 0.;
-        if (prered > 0.)
-            ratio = actred / prered;
-
-        /* update the step bound. */
-        if (ratio < Scalar(.1)) {
-            ncsuc = 0;
-            ++ncfail;
-            delta = Scalar(.5) * delta;
-        } else {
-            ncfail = 0;
-            ++ncsuc;
-            if (ratio >= Scalar(.5) || ncsuc > 1)
-                delta = (std::max)(delta, pnorm / Scalar(.5));
-            if (abs(ratio - 1.) <= Scalar(.1)) {
-                delta = pnorm / Scalar(.5);
-            }
-        }
-
-        /* test for successful iteration. */
-        if (ratio >= Scalar(1e-4)) {
-            /* successful iteration. update x, fvec, and their norms. */
-            x = wa2;
-            wa2 = diag.cwiseProduct(x);
-            fvec = wa4;
-            xnorm = wa2.stableNorm();
-            fnorm = fnorm1;
-            ++iter;
-        }
-
-        /* determine the progress of the iteration. */
-        ++nslow1;
-        if (actred >= Scalar(.001))
-            nslow1 = 0;
-        if (jeval)
-            ++nslow2;
-        if (actred >= Scalar(.1))
-            nslow2 = 0;
-
-        /* test for convergence. */
-        if (delta <= parameters.xtol * xnorm || fnorm == 0.)
-            return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
-
-        /* tests for termination and stringent tolerances. */
-        if (nfev >= parameters.maxfev)
-            return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
-        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
-            return HybridNonLinearSolverSpace::TolTooSmall;
-        if (nslow2 == 5)
-            return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
-        if (nslow1 == 10)
-            return HybridNonLinearSolverSpace::NotMakingProgressIterations;
-
-        /* criterion for recalculating jacobian. */
-        if (ncfail == 2)
-            break; // leave inner loop and go for the next outer loop iteration
-
-        /* calculate the rank one modification to the jacobian */
-        /* and update qtf if necessary. */
-        wa1 = diag.cwiseProduct( diag.cwiseProduct(wa1)/pnorm );
-        wa2 = fjac.transpose() * wa4;
-        if (ratio >= Scalar(1e-4))
-            qtf = wa2;
-        wa2 = (wa2-wa3)/pnorm;
-
-        /* compute the qr factorization of the updated jacobian. */
-        internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
-        internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
-        internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
-
-        jeval = false;
-    }
-    return HybridNonLinearSolverSpace::Running;
-}
-
-template<typename FunctorType, typename Scalar>
-HybridNonLinearSolverSpace::Status
-HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(FVectorType  &x)
-{
-    HybridNonLinearSolverSpace::Status status = solveNumericalDiffInit(x);
-    if (status==HybridNonLinearSolverSpace::ImproperInputParameters)
-        return status;
-    while (status==HybridNonLinearSolverSpace::Running)
-        status = solveNumericalDiffOneStep(x);
-    return status;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_HYBRIDNONLINEARSOLVER_H
-
-//vim: ai ts=4 sts=4 et sw=4
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
deleted file mode 100644
index fe3b79c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ /dev/null
@@ -1,657 +0,0 @@
-// -*- coding: utf-8
-// vim: set fileencoding=utf-8
-
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_LEVENBERGMARQUARDT__H
-#define EIGEN_LEVENBERGMARQUARDT__H
-
-namespace Eigen { 
-
-namespace LevenbergMarquardtSpace {
-    enum Status {
-        NotStarted = -2,
-        Running = -1,
-        ImproperInputParameters = 0,
-        RelativeReductionTooSmall = 1,
-        RelativeErrorTooSmall = 2,
-        RelativeErrorAndReductionTooSmall = 3,
-        CosinusTooSmall = 4,
-        TooManyFunctionEvaluation = 5,
-        FtolTooSmall = 6,
-        XtolTooSmall = 7,
-        GtolTooSmall = 8,
-        UserAsked = 9
-    };
-}
-
-
-
-/**
-  * \ingroup NonLinearOptimization_Module
-  * \brief Performs non linear optimization over a non-linear function,
-  * using a variant of the Levenberg Marquardt algorithm.
-  *
-  * Check wikipedia for more information.
-  * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
-  */
-template<typename FunctorType, typename Scalar=double>
-class LevenbergMarquardt
-{
-    static Scalar sqrt_epsilon()
-    {
-      using std::sqrt;
-      return sqrt(NumTraits<Scalar>::epsilon());
-    }
-    
-public:
-    LevenbergMarquardt(FunctorType &_functor)
-        : functor(_functor) { nfev = njev = iter = 0;  fnorm = gnorm = 0.; useExternalScaling=false; }
-
-    typedef DenseIndex Index;
-    
-    struct Parameters {
-        Parameters()
-            : factor(Scalar(100.))
-            , maxfev(400)
-            , ftol(sqrt_epsilon())
-            , xtol(sqrt_epsilon())
-            , gtol(Scalar(0.))
-            , epsfcn(Scalar(0.)) {}
-        Scalar factor;
-        Index maxfev;   // maximum number of function evaluation
-        Scalar ftol;
-        Scalar xtol;
-        Scalar gtol;
-        Scalar epsfcn;
-    };
-
-    typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
-    typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
-
-    LevenbergMarquardtSpace::Status lmder1(
-            FVectorType &x,
-            const Scalar tol = sqrt_epsilon()
-            );
-
-    LevenbergMarquardtSpace::Status minimize(FVectorType &x);
-    LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
-    LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
-
-    static LevenbergMarquardtSpace::Status lmdif1(
-            FunctorType &functor,
-            FVectorType &x,
-            Index *nfev,
-            const Scalar tol = sqrt_epsilon()
-            );
-
-    LevenbergMarquardtSpace::Status lmstr1(
-            FVectorType  &x,
-            const Scalar tol = sqrt_epsilon()
-            );
-
-    LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType  &x);
-    LevenbergMarquardtSpace::Status minimizeOptimumStorageInit(FVectorType  &x);
-    LevenbergMarquardtSpace::Status minimizeOptimumStorageOneStep(FVectorType  &x);
-
-    void resetParameters(void) { parameters = Parameters(); }
-
-    Parameters parameters;
-    FVectorType  fvec, qtf, diag;
-    JacobianType fjac;
-    PermutationMatrix<Dynamic,Dynamic> permutation;
-    Index nfev;
-    Index njev;
-    Index iter;
-    Scalar fnorm, gnorm;
-    bool useExternalScaling; 
-
-    Scalar lm_param(void) { return par; }
-private:
-    
-    FunctorType &functor;
-    Index n;
-    Index m;
-    FVectorType wa1, wa2, wa3, wa4;
-
-    Scalar par, sum;
-    Scalar temp, temp1, temp2;
-    Scalar delta;
-    Scalar ratio;
-    Scalar pnorm, xnorm, fnorm1, actred, dirder, prered;
-
-    LevenbergMarquardt& operator=(const LevenbergMarquardt&);
-};
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::lmder1(
-        FVectorType  &x,
-        const Scalar tol
-        )
-{
-    n = x.size();
-    m = functor.values();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || m < n || tol < 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    resetParameters();
-    parameters.ftol = tol;
-    parameters.xtol = tol;
-    parameters.maxfev = 100*(n+1);
-
-    return minimize(x);
-}
-
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType  &x)
-{
-    LevenbergMarquardtSpace::Status status = minimizeInit(x);
-    if (status==LevenbergMarquardtSpace::ImproperInputParameters)
-        return status;
-    do {
-        status = minimizeOneStep(x);
-    } while (status==LevenbergMarquardtSpace::Running);
-    return status;
-}
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(FVectorType  &x)
-{
-    n = x.size();
-    m = functor.values();
-
-    wa1.resize(n); wa2.resize(n); wa3.resize(n);
-    wa4.resize(m);
-    fvec.resize(m);
-    fjac.resize(m, n);
-    if (!useExternalScaling)
-        diag.resize(n);
-    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
-    qtf.resize(n);
-
-    /* Function Body */
-    nfev = 0;
-    njev = 0;
-
-    /*     check the input parameters for errors. */
-    if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    if (useExternalScaling)
-        for (Index j = 0; j < n; ++j)
-            if (diag[j] <= 0.)
-                return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    /*     evaluate the function at the starting point */
-    /*     and calculate its norm. */
-    nfev = 1;
-    if ( functor(x, fvec) < 0)
-        return LevenbergMarquardtSpace::UserAsked;
-    fnorm = fvec.stableNorm();
-
-    /*     initialize levenberg-marquardt parameter and iteration counter. */
-    par = 0.;
-    iter = 1;
-
-    return LevenbergMarquardtSpace::NotStarted;
-}
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
-{
-    using std::abs;
-    using std::sqrt;
-
-    eigen_assert(x.size()==n); // check the caller is not cheating us
-
-    /* calculate the jacobian matrix. */
-    Index df_ret = functor.df(x, fjac);
-    if (df_ret<0)
-        return LevenbergMarquardtSpace::UserAsked;
-    if (df_ret>0)
-        // numerical diff, we evaluated the function df_ret times
-        nfev += df_ret;
-    else njev++;
-
-    /* compute the qr factorization of the jacobian. */
-    wa2 = fjac.colwise().blueNorm();
-    ColPivHouseholderQR<JacobianType> qrfac(fjac);
-    fjac = qrfac.matrixQR();
-    permutation = qrfac.colsPermutation();
-
-    /* on the first iteration and if external scaling is not used, scale according */
-    /* to the norms of the columns of the initial jacobian. */
-    if (iter == 1) {
-        if (!useExternalScaling)
-            for (Index j = 0; j < n; ++j)
-                diag[j] = (wa2[j]==0.)? 1. : wa2[j];
-
-        /* on the first iteration, calculate the norm of the scaled x */
-        /* and initialize the step bound delta. */
-        xnorm = diag.cwiseProduct(x).stableNorm();
-        delta = parameters.factor * xnorm;
-        if (delta == 0.)
-            delta = parameters.factor;
-    }
-
-    /* form (q transpose)*fvec and store the first n components in */
-    /* qtf. */
-    wa4 = fvec;
-    wa4.applyOnTheLeft(qrfac.householderQ().adjoint());
-    qtf = wa4.head(n);
-
-    /* compute the norm of the scaled gradient. */
-    gnorm = 0.;
-    if (fnorm != 0.)
-        for (Index j = 0; j < n; ++j)
-            if (wa2[permutation.indices()[j]] != 0.)
-                gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
-
-    /* test for convergence of the gradient norm. */
-    if (gnorm <= parameters.gtol)
-        return LevenbergMarquardtSpace::CosinusTooSmall;
-
-    /* rescale if necessary. */
-    if (!useExternalScaling)
-        diag = diag.cwiseMax(wa2);
-
-    do {
-
-        /* determine the levenberg-marquardt parameter. */
-        internal::lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1);
-
-        /* store the direction p and x + p. calculate the norm of p. */
-        wa1 = -wa1;
-        wa2 = x + wa1;
-        pnorm = diag.cwiseProduct(wa1).stableNorm();
-
-        /* on the first iteration, adjust the initial step bound. */
-        if (iter == 1)
-            delta = (std::min)(delta,pnorm);
-
-        /* evaluate the function at x + p and calculate its norm. */
-        if ( functor(wa2, wa4) < 0)
-            return LevenbergMarquardtSpace::UserAsked;
-        ++nfev;
-        fnorm1 = wa4.stableNorm();
-
-        /* compute the scaled actual reduction. */
-        actred = -1.;
-        if (Scalar(.1) * fnorm1 < fnorm)
-            actred = 1. - numext::abs2(fnorm1 / fnorm);
-
-        /* compute the scaled predicted reduction and */
-        /* the scaled directional derivative. */
-        wa3 = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() *wa1);
-        temp1 = numext::abs2(wa3.stableNorm() / fnorm);
-        temp2 = numext::abs2(sqrt(par) * pnorm / fnorm);
-        prered = temp1 + temp2 / Scalar(.5);
-        dirder = -(temp1 + temp2);
-
-        /* compute the ratio of the actual to the predicted */
-        /* reduction. */
-        ratio = 0.;
-        if (prered != 0.)
-            ratio = actred / prered;
-
-        /* update the step bound. */
-        if (ratio <= Scalar(.25)) {
-            if (actred >= 0.)
-                temp = Scalar(.5);
-            if (actred < 0.)
-                temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred);
-            if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
-                temp = Scalar(.1);
-            /* Computing MIN */
-            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
-            par /= temp;
-        } else if (!(par != 0. && ratio < Scalar(.75))) {
-            delta = pnorm / Scalar(.5);
-            par = Scalar(.5) * par;
-        }
-
-        /* test for successful iteration. */
-        if (ratio >= Scalar(1e-4)) {
-            /* successful iteration. update x, fvec, and their norms. */
-            x = wa2;
-            wa2 = diag.cwiseProduct(x);
-            fvec = wa4;
-            xnorm = wa2.stableNorm();
-            fnorm = fnorm1;
-            ++iter;
-        }
-
-        /* tests for convergence. */
-        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
-            return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
-        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
-            return LevenbergMarquardtSpace::RelativeReductionTooSmall;
-        if (delta <= parameters.xtol * xnorm)
-            return LevenbergMarquardtSpace::RelativeErrorTooSmall;
-
-        /* tests for termination and stringent tolerances. */
-        if (nfev >= parameters.maxfev)
-            return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
-        if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
-            return LevenbergMarquardtSpace::FtolTooSmall;
-        if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
-            return LevenbergMarquardtSpace::XtolTooSmall;
-        if (gnorm <= NumTraits<Scalar>::epsilon())
-            return LevenbergMarquardtSpace::GtolTooSmall;
-
-    } while (ratio < Scalar(1e-4));
-
-    return LevenbergMarquardtSpace::Running;
-}
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
-        FVectorType  &x,
-        const Scalar tol
-        )
-{
-    n = x.size();
-    m = functor.values();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || m < n || tol < 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    resetParameters();
-    parameters.ftol = tol;
-    parameters.xtol = tol;
-    parameters.maxfev = 100*(n+1);
-
-    return minimizeOptimumStorage(x);
-}
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(FVectorType  &x)
-{
-    n = x.size();
-    m = functor.values();
-
-    wa1.resize(n); wa2.resize(n); wa3.resize(n);
-    wa4.resize(m);
-    fvec.resize(m);
-    // Only R is stored in fjac. Q is only used to compute 'qtf', which is
-    // Q.transpose()*rhs. qtf will be updated using givens rotation,
-    // instead of storing them in Q.
-    // The purpose it to only use a nxn matrix, instead of mxn here, so
-    // that we can handle cases where m>>n :
-    fjac.resize(n, n);
-    if (!useExternalScaling)
-        diag.resize(n);
-    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
-    qtf.resize(n);
-
-    /* Function Body */
-    nfev = 0;
-    njev = 0;
-
-    /*     check the input parameters for errors. */
-    if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    if (useExternalScaling)
-        for (Index j = 0; j < n; ++j)
-            if (diag[j] <= 0.)
-                return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    /*     evaluate the function at the starting point */
-    /*     and calculate its norm. */
-    nfev = 1;
-    if ( functor(x, fvec) < 0)
-        return LevenbergMarquardtSpace::UserAsked;
-    fnorm = fvec.stableNorm();
-
-    /*     initialize levenberg-marquardt parameter and iteration counter. */
-    par = 0.;
-    iter = 1;
-
-    return LevenbergMarquardtSpace::NotStarted;
-}
-
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorType  &x)
-{
-    using std::abs;
-    using std::sqrt;
-    
-    eigen_assert(x.size()==n); // check the caller is not cheating us
-
-    Index i, j;
-    bool sing;
-
-    /* compute the qr factorization of the jacobian matrix */
-    /* calculated one row at a time, while simultaneously */
-    /* forming (q transpose)*fvec and storing the first */
-    /* n components in qtf. */
-    qtf.fill(0.);
-    fjac.fill(0.);
-    Index rownb = 2;
-    for (i = 0; i < m; ++i) {
-        if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked;
-        internal::rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]);
-        ++rownb;
-    }
-    ++njev;
-
-    /* if the jacobian is rank deficient, call qrfac to */
-    /* reorder its columns and update the components of qtf. */
-    sing = false;
-    for (j = 0; j < n; ++j) {
-        if (fjac(j,j) == 0.)
-            sing = true;
-        wa2[j] = fjac.col(j).head(j).stableNorm();
-    }
-    permutation.setIdentity(n);
-    if (sing) {
-        wa2 = fjac.colwise().blueNorm();
-        // TODO We have no unit test covering this code path, do not modify
-        // until it is carefully tested
-        ColPivHouseholderQR<JacobianType> qrfac(fjac);
-        fjac = qrfac.matrixQR();
-        wa1 = fjac.diagonal();
-        fjac.diagonal() = qrfac.hCoeffs();
-        permutation = qrfac.colsPermutation();
-        // TODO : avoid this:
-        for(Index ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors
-
-        for (j = 0; j < n; ++j) {
-            if (fjac(j,j) != 0.) {
-                sum = 0.;
-                for (i = j; i < n; ++i)
-                    sum += fjac(i,j) * qtf[i];
-                temp = -sum / fjac(j,j);
-                for (i = j; i < n; ++i)
-                    qtf[i] += fjac(i,j) * temp;
-            }
-            fjac(j,j) = wa1[j];
-        }
-    }
-
-    /* on the first iteration and if external scaling is not used, scale according */
-    /* to the norms of the columns of the initial jacobian. */
-    if (iter == 1) {
-        if (!useExternalScaling)
-            for (j = 0; j < n; ++j)
-                diag[j] = (wa2[j]==0.)? 1. : wa2[j];
-
-        /* on the first iteration, calculate the norm of the scaled x */
-        /* and initialize the step bound delta. */
-        xnorm = diag.cwiseProduct(x).stableNorm();
-        delta = parameters.factor * xnorm;
-        if (delta == 0.)
-            delta = parameters.factor;
-    }
-
-    /* compute the norm of the scaled gradient. */
-    gnorm = 0.;
-    if (fnorm != 0.)
-        for (j = 0; j < n; ++j)
-            if (wa2[permutation.indices()[j]] != 0.)
-                gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
-
-    /* test for convergence of the gradient norm. */
-    if (gnorm <= parameters.gtol)
-        return LevenbergMarquardtSpace::CosinusTooSmall;
-
-    /* rescale if necessary. */
-    if (!useExternalScaling)
-        diag = diag.cwiseMax(wa2);
-
-    do {
-
-        /* determine the levenberg-marquardt parameter. */
-        internal::lmpar<Scalar>(fjac, permutation.indices(), diag, qtf, delta, par, wa1);
-
-        /* store the direction p and x + p. calculate the norm of p. */
-        wa1 = -wa1;
-        wa2 = x + wa1;
-        pnorm = diag.cwiseProduct(wa1).stableNorm();
-
-        /* on the first iteration, adjust the initial step bound. */
-        if (iter == 1)
-            delta = (std::min)(delta,pnorm);
-
-        /* evaluate the function at x + p and calculate its norm. */
-        if ( functor(wa2, wa4) < 0)
-            return LevenbergMarquardtSpace::UserAsked;
-        ++nfev;
-        fnorm1 = wa4.stableNorm();
-
-        /* compute the scaled actual reduction. */
-        actred = -1.;
-        if (Scalar(.1) * fnorm1 < fnorm)
-            actred = 1. - numext::abs2(fnorm1 / fnorm);
-
-        /* compute the scaled predicted reduction and */
-        /* the scaled directional derivative. */
-        wa3 = fjac.topLeftCorner(n,n).template triangularView<Upper>() * (permutation.inverse() * wa1);
-        temp1 = numext::abs2(wa3.stableNorm() / fnorm);
-        temp2 = numext::abs2(sqrt(par) * pnorm / fnorm);
-        prered = temp1 + temp2 / Scalar(.5);
-        dirder = -(temp1 + temp2);
-
-        /* compute the ratio of the actual to the predicted */
-        /* reduction. */
-        ratio = 0.;
-        if (prered != 0.)
-            ratio = actred / prered;
-
-        /* update the step bound. */
-        if (ratio <= Scalar(.25)) {
-            if (actred >= 0.)
-                temp = Scalar(.5);
-            if (actred < 0.)
-                temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred);
-            if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
-                temp = Scalar(.1);
-            /* Computing MIN */
-            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
-            par /= temp;
-        } else if (!(par != 0. && ratio < Scalar(.75))) {
-            delta = pnorm / Scalar(.5);
-            par = Scalar(.5) * par;
-        }
-
-        /* test for successful iteration. */
-        if (ratio >= Scalar(1e-4)) {
-            /* successful iteration. update x, fvec, and their norms. */
-            x = wa2;
-            wa2 = diag.cwiseProduct(x);
-            fvec = wa4;
-            xnorm = wa2.stableNorm();
-            fnorm = fnorm1;
-            ++iter;
-        }
-
-        /* tests for convergence. */
-        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
-            return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
-        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
-            return LevenbergMarquardtSpace::RelativeReductionTooSmall;
-        if (delta <= parameters.xtol * xnorm)
-            return LevenbergMarquardtSpace::RelativeErrorTooSmall;
-
-        /* tests for termination and stringent tolerances. */
-        if (nfev >= parameters.maxfev)
-            return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
-        if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
-            return LevenbergMarquardtSpace::FtolTooSmall;
-        if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
-            return LevenbergMarquardtSpace::XtolTooSmall;
-        if (gnorm <= NumTraits<Scalar>::epsilon())
-            return LevenbergMarquardtSpace::GtolTooSmall;
-
-    } while (ratio < Scalar(1e-4));
-
-    return LevenbergMarquardtSpace::Running;
-}
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(FVectorType  &x)
-{
-    LevenbergMarquardtSpace::Status status = minimizeOptimumStorageInit(x);
-    if (status==LevenbergMarquardtSpace::ImproperInputParameters)
-        return status;
-    do {
-        status = minimizeOptimumStorageOneStep(x);
-    } while (status==LevenbergMarquardtSpace::Running);
-    return status;
-}
-
-template<typename FunctorType, typename Scalar>
-LevenbergMarquardtSpace::Status
-LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
-        FunctorType &functor,
-        FVectorType  &x,
-        Index *nfev,
-        const Scalar tol
-        )
-{
-    Index n = x.size();
-    Index m = functor.values();
-
-    /* check the input parameters for errors. */
-    if (n <= 0 || m < n || tol < 0.)
-        return LevenbergMarquardtSpace::ImproperInputParameters;
-
-    NumericalDiff<FunctorType> numDiff(functor);
-    // embedded LevenbergMarquardt
-    LevenbergMarquardt<NumericalDiff<FunctorType>, Scalar > lm(numDiff);
-    lm.parameters.ftol = tol;
-    lm.parameters.xtol = tol;
-    lm.parameters.maxfev = 200*(n+1);
-
-    LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
-    if (nfev)
-        * nfev = lm.nfev;
-    return info;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_LEVENBERGMARQUARDT__H
-
-//vim: ai ts=4 sts=4 et sw=4
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h
deleted file mode 100644
index db8ff7d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h
+++ /dev/null
@@ -1,66 +0,0 @@
-#define chkder_log10e 0.43429448190325182765
-#define chkder_factor 100.
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename Scalar>
-void chkder(
-        const Matrix< Scalar, Dynamic, 1 >  &x,
-        const Matrix< Scalar, Dynamic, 1 >  &fvec,
-        const Matrix< Scalar, Dynamic, Dynamic > &fjac,
-        Matrix< Scalar, Dynamic, 1 >  &xp,
-        const Matrix< Scalar, Dynamic, 1 >  &fvecp,
-        int mode,
-        Matrix< Scalar, Dynamic, 1 >  &err
-        )
-{
-    using std::sqrt;
-    using std::abs;
-    using std::log;
-    
-    typedef DenseIndex Index;
-
-    const Scalar eps = sqrt(NumTraits<Scalar>::epsilon());
-    const Scalar epsf = chkder_factor * NumTraits<Scalar>::epsilon();
-    const Scalar epslog = chkder_log10e * log(eps);
-    Scalar temp;
-
-    const Index m = fvec.size(), n = x.size();
-
-    if (mode != 2) {
-        /* mode = 1. */
-        xp.resize(n);
-        for (Index j = 0; j < n; ++j) {
-            temp = eps * abs(x[j]);
-            if (temp == 0.)
-                temp = eps;
-            xp[j] = x[j] + temp;
-        }
-    }
-    else {
-        /* mode = 2. */
-        err.setZero(m); 
-        for (Index j = 0; j < n; ++j) {
-            temp = abs(x[j]);
-            if (temp == 0.)
-                temp = 1.;
-            err += temp * fjac.col(j);
-        }
-        for (Index i = 0; i < m; ++i) {
-            temp = 1.;
-            if (fvec[i] != 0. && fvecp[i] != 0. && abs(fvecp[i] - fvec[i]) >= epsf * abs(fvec[i]))
-                temp = eps * abs((fvecp[i] - fvec[i]) / eps - err[i]) / (abs(fvec[i]) + abs(fvecp[i]));
-            err[i] = 1.;
-            if (temp > NumTraits<Scalar>::epsilon() && temp < eps)
-                err[i] = (chkder_log10e * log(temp) - epslog) / epslog;
-            if (temp >= eps)
-                err[i] = 0.;
-        }
-    }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h
deleted file mode 100644
index 68260d1..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h
+++ /dev/null
@@ -1,70 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-template <typename Scalar>
-void covar(
-        Matrix< Scalar, Dynamic, Dynamic > &r,
-        const VectorXi &ipvt,
-        Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) )
-{
-    using std::abs;
-    typedef DenseIndex Index;
-
-    /* Local variables */
-    Index i, j, k, l, ii, jj;
-    bool sing;
-    Scalar temp;
-
-    /* Function Body */
-    const Index n = r.cols();
-    const Scalar tolr = tol * abs(r(0,0));
-    Matrix< Scalar, Dynamic, 1 > wa(n);
-    eigen_assert(ipvt.size()==n);
-
-    /* form the inverse of r in the full upper triangle of r. */
-    l = -1;
-    for (k = 0; k < n; ++k)
-        if (abs(r(k,k)) > tolr) {
-            r(k,k) = 1. / r(k,k);
-            for (j = 0; j <= k-1; ++j) {
-                temp = r(k,k) * r(j,k);
-                r(j,k) = 0.;
-                r.col(k).head(j+1) -= r.col(j).head(j+1) * temp;
-            }
-            l = k;
-        }
-
-    /* form the full upper triangle of the inverse of (r transpose)*r */
-    /* in the full upper triangle of r. */
-    for (k = 0; k <= l; ++k) {
-        for (j = 0; j <= k-1; ++j)
-            r.col(j).head(j+1) += r.col(k).head(j+1) * r(j,k);
-        r.col(k).head(k+1) *= r(k,k);
-    }
-
-    /* form the full lower triangle of the covariance matrix */
-    /* in the strict lower triangle of r and in wa. */
-    for (j = 0; j < n; ++j) {
-        jj = ipvt[j];
-        sing = j > l;
-        for (i = 0; i <= j; ++i) {
-            if (sing)
-                r(i,j) = 0.;
-            ii = ipvt[i];
-            if (ii > jj)
-                r(ii,jj) = r(i,j);
-            if (ii < jj)
-                r(jj,ii) = r(i,j);
-        }
-        wa[jj] = r(j,j);
-    }
-
-    /* symmetrize the covariance matrix in r. */
-    r.topLeftCorner(n,n).template triangularView<StrictlyUpper>() = r.topLeftCorner(n,n).transpose();
-    r.diagonal() = wa;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
deleted file mode 100644
index 80c5d27..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
+++ /dev/null
@@ -1,107 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-template <typename Scalar>
-void dogleg(
-        const Matrix< Scalar, Dynamic, Dynamic >  &qrfac,
-        const Matrix< Scalar, Dynamic, 1 >  &diag,
-        const Matrix< Scalar, Dynamic, 1 >  &qtb,
-        Scalar delta,
-        Matrix< Scalar, Dynamic, 1 >  &x)
-{
-    using std::abs;
-    using std::sqrt;
-    
-    typedef DenseIndex Index;
-
-    /* Local variables */
-    Index i, j;
-    Scalar sum, temp, alpha, bnorm;
-    Scalar gnorm, qnorm;
-    Scalar sgnorm;
-
-    /* Function Body */
-    const Scalar epsmch = NumTraits<Scalar>::epsilon();
-    const Index n = qrfac.cols();
-    eigen_assert(n==qtb.size());
-    eigen_assert(n==x.size());
-    eigen_assert(n==diag.size());
-    Matrix< Scalar, Dynamic, 1 >  wa1(n), wa2(n);
-
-    /* first, calculate the gauss-newton direction. */
-    for (j = n-1; j >=0; --j) {
-        temp = qrfac(j,j);
-        if (temp == 0.) {
-            temp = epsmch * qrfac.col(j).head(j+1).maxCoeff();
-            if (temp == 0.)
-                temp = epsmch;
-        }
-        if (j==n-1)
-            x[j] = qtb[j] / temp;
-        else
-            x[j] = (qtb[j] - qrfac.row(j).tail(n-j-1).dot(x.tail(n-j-1))) / temp;
-    }
-
-    /* test whether the gauss-newton direction is acceptable. */
-    qnorm = diag.cwiseProduct(x).stableNorm();
-    if (qnorm <= delta)
-        return;
-
-    // TODO : this path is not tested by Eigen unit tests
-
-    /* the gauss-newton direction is not acceptable. */
-    /* next, calculate the scaled gradient direction. */
-
-    wa1.fill(0.);
-    for (j = 0; j < n; ++j) {
-        wa1.tail(n-j) += qrfac.row(j).tail(n-j) * qtb[j];
-        wa1[j] /= diag[j];
-    }
-
-    /* calculate the norm of the scaled gradient and test for */
-    /* the special case in which the scaled gradient is zero. */
-    gnorm = wa1.stableNorm();
-    sgnorm = 0.;
-    alpha = delta / qnorm;
-    if (gnorm == 0.)
-        goto algo_end;
-
-    /* calculate the point along the scaled gradient */
-    /* at which the quadratic is minimized. */
-    wa1.array() /= (diag*gnorm).array();
-    // TODO : once unit tests cover this part,:
-    // wa2 = qrfac.template triangularView<Upper>() * wa1;
-    for (j = 0; j < n; ++j) {
-        sum = 0.;
-        for (i = j; i < n; ++i) {
-            sum += qrfac(j,i) * wa1[i];
-        }
-        wa2[j] = sum;
-    }
-    temp = wa2.stableNorm();
-    sgnorm = gnorm / temp / temp;
-
-    /* test whether the scaled gradient direction is acceptable. */
-    alpha = 0.;
-    if (sgnorm >= delta)
-        goto algo_end;
-
-    /* the scaled gradient direction is not acceptable. */
-    /* finally, calculate the point along the dogleg */
-    /* at which the quadratic is minimized. */
-    bnorm = qtb.stableNorm();
-    temp = bnorm / gnorm * (bnorm / qnorm) * (sgnorm / delta);
-    temp = temp - delta / qnorm * numext::abs2(sgnorm / delta) + sqrt(numext::abs2(temp - delta / qnorm) + (1.-numext::abs2(delta / qnorm)) * (1.-numext::abs2(sgnorm / delta)));
-    alpha = delta / qnorm * (1. - numext::abs2(sgnorm / delta)) / temp;
-algo_end:
-
-    /* form appropriate convex combination of the gauss-newton */
-    /* direction and the scaled gradient direction. */
-    temp = (1.-alpha) * (std::min)(sgnorm,delta);
-    x = temp * wa1 + alpha * x;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
deleted file mode 100644
index bb7cf26..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
+++ /dev/null
@@ -1,79 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-template<typename FunctorType, typename Scalar>
-DenseIndex fdjac1(
-        const FunctorType &Functor,
-        Matrix< Scalar, Dynamic, 1 >  &x,
-        Matrix< Scalar, Dynamic, 1 >  &fvec,
-        Matrix< Scalar, Dynamic, Dynamic > &fjac,
-        DenseIndex ml, DenseIndex mu,
-        Scalar epsfcn)
-{
-    using std::sqrt;
-    using std::abs;
-    
-    typedef DenseIndex Index;
-
-    /* Local variables */
-    Scalar h;
-    Index j, k;
-    Scalar eps, temp;
-    Index msum;
-    int iflag;
-    Index start, length;
-
-    /* Function Body */
-    const Scalar epsmch = NumTraits<Scalar>::epsilon();
-    const Index n = x.size();
-    eigen_assert(fvec.size()==n);
-    Matrix< Scalar, Dynamic, 1 >  wa1(n);
-    Matrix< Scalar, Dynamic, 1 >  wa2(n);
-
-    eps = sqrt((std::max)(epsfcn,epsmch));
-    msum = ml + mu + 1;
-    if (msum >= n) {
-        /* computation of dense approximate jacobian. */
-        for (j = 0; j < n; ++j) {
-            temp = x[j];
-            h = eps * abs(temp);
-            if (h == 0.)
-                h = eps;
-            x[j] = temp + h;
-            iflag = Functor(x, wa1);
-            if (iflag < 0)
-                return iflag;
-            x[j] = temp;
-            fjac.col(j) = (wa1-fvec)/h;
-        }
-
-    }else {
-        /* computation of banded approximate jacobian. */
-        for (k = 0; k < msum; ++k) {
-            for (j = k; (msum<0) ? (j>n): (j<n); j += msum) {
-                wa2[j] = x[j];
-                h = eps * abs(wa2[j]);
-                if (h == 0.) h = eps;
-                x[j] = wa2[j] + h;
-            }
-            iflag = Functor(x, wa1);
-            if (iflag < 0)
-                return iflag;
-            for (j = k; (msum<0) ? (j>n): (j<n); j += msum) {
-                x[j] = wa2[j];
-                h = eps * abs(wa2[j]);
-                if (h == 0.) h = eps;
-                fjac.col(j).setZero();
-                start = std::max<Index>(0,j-mu);
-                length = (std::min)(n-1, j+ml) - start + 1;
-                fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
-            }
-        }
-    }
-    return 0;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
deleted file mode 100644
index 4c17d4c..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
+++ /dev/null
@@ -1,298 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-template <typename Scalar>
-void lmpar(
-        Matrix< Scalar, Dynamic, Dynamic > &r,
-        const VectorXi &ipvt,
-        const Matrix< Scalar, Dynamic, 1 >  &diag,
-        const Matrix< Scalar, Dynamic, 1 >  &qtb,
-        Scalar delta,
-        Scalar &par,
-        Matrix< Scalar, Dynamic, 1 >  &x)
-{
-    using std::abs;
-    using std::sqrt;
-    typedef DenseIndex Index;
-
-    /* Local variables */
-    Index i, j, l;
-    Scalar fp;
-    Scalar parc, parl;
-    Index iter;
-    Scalar temp, paru;
-    Scalar gnorm;
-    Scalar dxnorm;
-
-
-    /* Function Body */
-    const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
-    const Index n = r.cols();
-    eigen_assert(n==diag.size());
-    eigen_assert(n==qtb.size());
-    eigen_assert(n==x.size());
-
-    Matrix< Scalar, Dynamic, 1 >  wa1, wa2;
-
-    /* compute and store in x the gauss-newton direction. if the */
-    /* jacobian is rank-deficient, obtain a least squares solution. */
-    Index nsing = n-1;
-    wa1 = qtb;
-    for (j = 0; j < n; ++j) {
-        if (r(j,j) == 0. && nsing == n-1)
-            nsing = j - 1;
-        if (nsing < n-1)
-            wa1[j] = 0.;
-    }
-    for (j = nsing; j>=0; --j) {
-        wa1[j] /= r(j,j);
-        temp = wa1[j];
-        for (i = 0; i < j ; ++i)
-            wa1[i] -= r(i,j) * temp;
-    }
-
-    for (j = 0; j < n; ++j)
-        x[ipvt[j]] = wa1[j];
-
-    /* initialize the iteration counter. */
-    /* evaluate the function at the origin, and test */
-    /* for acceptance of the gauss-newton direction. */
-    iter = 0;
-    wa2 = diag.cwiseProduct(x);
-    dxnorm = wa2.blueNorm();
-    fp = dxnorm - delta;
-    if (fp <= Scalar(0.1) * delta) {
-        par = 0;
-        return;
-    }
-
-    /* if the jacobian is not rank deficient, the newton */
-    /* step provides a lower bound, parl, for the zero of */
-    /* the function. otherwise set this bound to zero. */
-    parl = 0.;
-    if (nsing >= n-1) {
-        for (j = 0; j < n; ++j) {
-            l = ipvt[j];
-            wa1[j] = diag[l] * (wa2[l] / dxnorm);
-        }
-        // it's actually a triangularView.solveInplace(), though in a weird
-        // way:
-        for (j = 0; j < n; ++j) {
-            Scalar sum = 0.;
-            for (i = 0; i < j; ++i)
-                sum += r(i,j) * wa1[i];
-            wa1[j] = (wa1[j] - sum) / r(j,j);
-        }
-        temp = wa1.blueNorm();
-        parl = fp / delta / temp / temp;
-    }
-
-    /* calculate an upper bound, paru, for the zero of the function. */
-    for (j = 0; j < n; ++j)
-        wa1[j] = r.col(j).head(j+1).dot(qtb.head(j+1)) / diag[ipvt[j]];
-
-    gnorm = wa1.stableNorm();
-    paru = gnorm / delta;
-    if (paru == 0.)
-        paru = dwarf / (std::min)(delta,Scalar(0.1));
-
-    /* if the input par lies outside of the interval (parl,paru), */
-    /* set par to the closer endpoint. */
-    par = (std::max)(par,parl);
-    par = (std::min)(par,paru);
-    if (par == 0.)
-        par = gnorm / dxnorm;
-
-    /* beginning of an iteration. */
-    while (true) {
-        ++iter;
-
-        /* evaluate the function at the current value of par. */
-        if (par == 0.)
-            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
-        wa1 = sqrt(par)* diag;
-
-        Matrix< Scalar, Dynamic, 1 > sdiag(n);
-        qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
-
-        wa2 = diag.cwiseProduct(x);
-        dxnorm = wa2.blueNorm();
-        temp = fp;
-        fp = dxnorm - delta;
-
-        /* if the function is small enough, accept the current value */
-        /* of par. also test for the exceptional cases where parl */
-        /* is zero or the number of iterations has reached 10. */
-        if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
-            break;
-
-        /* compute the newton correction. */
-        for (j = 0; j < n; ++j) {
-            l = ipvt[j];
-            wa1[j] = diag[l] * (wa2[l] / dxnorm);
-        }
-        for (j = 0; j < n; ++j) {
-            wa1[j] /= sdiag[j];
-            temp = wa1[j];
-            for (i = j+1; i < n; ++i)
-                wa1[i] -= r(i,j) * temp;
-        }
-        temp = wa1.blueNorm();
-        parc = fp / delta / temp / temp;
-
-        /* depending on the sign of the function, update parl or paru. */
-        if (fp > 0.)
-            parl = (std::max)(parl,par);
-        if (fp < 0.)
-            paru = (std::min)(paru,par);
-
-        /* compute an improved estimate for par. */
-        /* Computing MAX */
-        par = (std::max)(parl,par+parc);
-
-        /* end of an iteration. */
-    }
-
-    /* termination. */
-    if (iter == 0)
-        par = 0.;
-    return;
-}
-
-template <typename Scalar>
-void lmpar2(
-        const ColPivHouseholderQR<Matrix< Scalar, Dynamic, Dynamic> > &qr,
-        const Matrix< Scalar, Dynamic, 1 >  &diag,
-        const Matrix< Scalar, Dynamic, 1 >  &qtb,
-        Scalar delta,
-        Scalar &par,
-        Matrix< Scalar, Dynamic, 1 >  &x)
-
-{
-    using std::sqrt;
-    using std::abs;
-    typedef DenseIndex Index;
-
-    /* Local variables */
-    Index j;
-    Scalar fp;
-    Scalar parc, parl;
-    Index iter;
-    Scalar temp, paru;
-    Scalar gnorm;
-    Scalar dxnorm;
-
-
-    /* Function Body */
-    const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
-    const Index n = qr.matrixQR().cols();
-    eigen_assert(n==diag.size());
-    eigen_assert(n==qtb.size());
-
-    Matrix< Scalar, Dynamic, 1 >  wa1, wa2;
-
-    /* compute and store in x the gauss-newton direction. if the */
-    /* jacobian is rank-deficient, obtain a least squares solution. */
-
-//    const Index rank = qr.nonzeroPivots(); // exactly double(0.)
-    const Index rank = qr.rank(); // use a threshold
-    wa1 = qtb;
-    wa1.tail(n-rank).setZero();
-    qr.matrixQR().topLeftCorner(rank, rank).template triangularView<Upper>().solveInPlace(wa1.head(rank));
-
-    x = qr.colsPermutation()*wa1;
-
-    /* initialize the iteration counter. */
-    /* evaluate the function at the origin, and test */
-    /* for acceptance of the gauss-newton direction. */
-    iter = 0;
-    wa2 = diag.cwiseProduct(x);
-    dxnorm = wa2.blueNorm();
-    fp = dxnorm - delta;
-    if (fp <= Scalar(0.1) * delta) {
-        par = 0;
-        return;
-    }
-
-    /* if the jacobian is not rank deficient, the newton */
-    /* step provides a lower bound, parl, for the zero of */
-    /* the function. otherwise set this bound to zero. */
-    parl = 0.;
-    if (rank==n) {
-        wa1 = qr.colsPermutation().inverse() *  diag.cwiseProduct(wa2)/dxnorm;
-        qr.matrixQR().topLeftCorner(n, n).transpose().template triangularView<Lower>().solveInPlace(wa1);
-        temp = wa1.blueNorm();
-        parl = fp / delta / temp / temp;
-    }
-
-    /* calculate an upper bound, paru, for the zero of the function. */
-    for (j = 0; j < n; ++j)
-        wa1[j] = qr.matrixQR().col(j).head(j+1).dot(qtb.head(j+1)) / diag[qr.colsPermutation().indices()(j)];
-
-    gnorm = wa1.stableNorm();
-    paru = gnorm / delta;
-    if (paru == 0.)
-        paru = dwarf / (std::min)(delta,Scalar(0.1));
-
-    /* if the input par lies outside of the interval (parl,paru), */
-    /* set par to the closer endpoint. */
-    par = (std::max)(par,parl);
-    par = (std::min)(par,paru);
-    if (par == 0.)
-        par = gnorm / dxnorm;
-
-    /* beginning of an iteration. */
-    Matrix< Scalar, Dynamic, Dynamic > s = qr.matrixQR();
-    while (true) {
-        ++iter;
-
-        /* evaluate the function at the current value of par. */
-        if (par == 0.)
-            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
-        wa1 = sqrt(par)* diag;
-
-        Matrix< Scalar, Dynamic, 1 > sdiag(n);
-        qrsolv<Scalar>(s, qr.colsPermutation().indices(), wa1, qtb, x, sdiag);
-
-        wa2 = diag.cwiseProduct(x);
-        dxnorm = wa2.blueNorm();
-        temp = fp;
-        fp = dxnorm - delta;
-
-        /* if the function is small enough, accept the current value */
-        /* of par. also test for the exceptional cases where parl */
-        /* is zero or the number of iterations has reached 10. */
-        if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
-            break;
-
-        /* compute the newton correction. */
-        wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2/dxnorm);
-        // we could almost use this here, but the diagonal is outside qr, in sdiag[]
-        // qr.matrixQR().topLeftCorner(n, n).transpose().template triangularView<Lower>().solveInPlace(wa1);
-        for (j = 0; j < n; ++j) {
-            wa1[j] /= sdiag[j];
-            temp = wa1[j];
-            for (Index i = j+1; i < n; ++i)
-                wa1[i] -= s(i,j) * temp;
-        }
-        temp = wa1.blueNorm();
-        parc = fp / delta / temp / temp;
-
-        /* depending on the sign of the function, update parl or paru. */
-        if (fp > 0.)
-            parl = (std::max)(parl,par);
-        if (fp < 0.)
-            paru = (std::min)(paru,par);
-
-        /* compute an improved estimate for par. */
-        par = (std::max)(parl,par+parc);
-    }
-    if (iter == 0)
-        par = 0.;
-    return;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
deleted file mode 100644
index 4f2f560..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
+++ /dev/null
@@ -1,91 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-// TODO : once qrsolv2 is removed, use ColPivHouseholderQR or PermutationMatrix instead of ipvt
-template <typename Scalar>
-void qrsolv(
-        Matrix< Scalar, Dynamic, Dynamic > &s,
-        // TODO : use a PermutationMatrix once lmpar is no more:
-        const VectorXi &ipvt,
-        const Matrix< Scalar, Dynamic, 1 >  &diag,
-        const Matrix< Scalar, Dynamic, 1 >  &qtb,
-        Matrix< Scalar, Dynamic, 1 >  &x,
-        Matrix< Scalar, Dynamic, 1 >  &sdiag)
-
-{
-    typedef DenseIndex Index;
-
-    /* Local variables */
-    Index i, j, k, l;
-    Scalar temp;
-    Index n = s.cols();
-    Matrix< Scalar, Dynamic, 1 >  wa(n);
-    JacobiRotation<Scalar> givens;
-
-    /* Function Body */
-    // the following will only change the lower triangular part of s, including
-    // the diagonal, though the diagonal is restored afterward
-
-    /*     copy r and (q transpose)*b to preserve input and initialize s. */
-    /*     in particular, save the diagonal elements of r in x. */
-    x = s.diagonal();
-    wa = qtb;
-
-    s.topLeftCorner(n,n).template triangularView<StrictlyLower>() = s.topLeftCorner(n,n).transpose();
-
-    /*     eliminate the diagonal matrix d using a givens rotation. */
-    for (j = 0; j < n; ++j) {
-
-        /*        prepare the row of d to be eliminated, locating the */
-        /*        diagonal element using p from the qr factorization. */
-        l = ipvt[j];
-        if (diag[l] == 0.)
-            break;
-        sdiag.tail(n-j).setZero();
-        sdiag[j] = diag[l];
-
-        /*        the transformations to eliminate the row of d */
-        /*        modify only a single element of (q transpose)*b */
-        /*        beyond the first n, which is initially zero. */
-        Scalar qtbpj = 0.;
-        for (k = j; k < n; ++k) {
-            /*           determine a givens rotation which eliminates the */
-            /*           appropriate element in the current row of d. */
-            givens.makeGivens(-s(k,k), sdiag[k]);
-
-            /*           compute the modified diagonal element of r and */
-            /*           the modified element of ((q transpose)*b,0). */
-            s(k,k) = givens.c() * s(k,k) + givens.s() * sdiag[k];
-            temp = givens.c() * wa[k] + givens.s() * qtbpj;
-            qtbpj = -givens.s() * wa[k] + givens.c() * qtbpj;
-            wa[k] = temp;
-
-            /*           accumulate the transformation in the row of s. */
-            for (i = k+1; i<n; ++i) {
-                temp = givens.c() * s(i,k) + givens.s() * sdiag[i];
-                sdiag[i] = -givens.s() * s(i,k) + givens.c() * sdiag[i];
-                s(i,k) = temp;
-            }
-        }
-    }
-
-    /*     solve the triangular system for z. if the system is */
-    /*     singular, then obtain a least squares solution. */
-    Index nsing;
-    for(nsing=0; nsing<n && sdiag[nsing]!=0; nsing++) {}
-
-    wa.tail(n-nsing).setZero();
-    s.topLeftCorner(nsing, nsing).transpose().template triangularView<Upper>().solveInPlace(wa.head(nsing));
-
-    // restore
-    sdiag = s.diagonal();
-    s.diagonal() = x;
-
-    /*     permute the components of z back to components of x. */
-    for (j = 0; j < n; ++j) x[ipvt[j]] = wa[j];
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
deleted file mode 100644
index 36ff700..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
+++ /dev/null
@@ -1,30 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-// TODO : move this to GivensQR once there's such a thing in Eigen
-
-template <typename Scalar>
-void r1mpyq(DenseIndex m, DenseIndex n, Scalar *a, const std::vector<JacobiRotation<Scalar> > &v_givens, const std::vector<JacobiRotation<Scalar> > &w_givens)
-{
-    typedef DenseIndex Index;
-
-    /*     apply the first set of givens rotations to a. */
-    for (Index j = n-2; j>=0; --j)
-        for (Index i = 0; i<m; ++i) {
-            Scalar temp = v_givens[j].c() * a[i+m*j] - v_givens[j].s() * a[i+m*(n-1)];
-            a[i+m*(n-1)] = v_givens[j].s() * a[i+m*j] + v_givens[j].c() * a[i+m*(n-1)];
-            a[i+m*j] = temp;
-        }
-    /*     apply the second set of givens rotations to a. */
-    for (Index j = 0; j<n-1; ++j)
-        for (Index i = 0; i<m; ++i) {
-            Scalar temp = w_givens[j].c() * a[i+m*j] + w_givens[j].s() * a[i+m*(n-1)];
-            a[i+m*(n-1)] = -w_givens[j].s() * a[i+m*j] + w_givens[j].c() * a[i+m*(n-1)];
-            a[i+m*j] = temp;
-        }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
deleted file mode 100644
index 09fc652..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
+++ /dev/null
@@ -1,99 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-template <typename Scalar>
-void r1updt(
-        Matrix< Scalar, Dynamic, Dynamic > &s,
-        const Matrix< Scalar, Dynamic, 1> &u,
-        std::vector<JacobiRotation<Scalar> > &v_givens,
-        std::vector<JacobiRotation<Scalar> > &w_givens,
-        Matrix< Scalar, Dynamic, 1> &v,
-        Matrix< Scalar, Dynamic, 1> &w,
-        bool *sing)
-{
-    typedef DenseIndex Index;
-    const JacobiRotation<Scalar> IdentityRotation = JacobiRotation<Scalar>(1,0);
-
-    /* Local variables */
-    const Index m = s.rows();
-    const Index n = s.cols();
-    Index i, j=1;
-    Scalar temp;
-    JacobiRotation<Scalar> givens;
-
-    // r1updt had a broader usecase, but we don't use it here. And, more
-    // importantly, we can not test it.
-    eigen_assert(m==n);
-    eigen_assert(u.size()==m);
-    eigen_assert(v.size()==n);
-    eigen_assert(w.size()==n);
-
-    /* move the nontrivial part of the last column of s into w. */
-    w[n-1] = s(n-1,n-1);
-
-    /* rotate the vector v into a multiple of the n-th unit vector */
-    /* in such a way that a spike is introduced into w. */
-    for (j=n-2; j>=0; --j) {
-        w[j] = 0.;
-        if (v[j] != 0.) {
-            /* determine a givens rotation which eliminates the */
-            /* j-th element of v. */
-            givens.makeGivens(-v[n-1], v[j]);
-
-            /* apply the transformation to v and store the information */
-            /* necessary to recover the givens rotation. */
-            v[n-1] = givens.s() * v[j] + givens.c() * v[n-1];
-            v_givens[j] = givens;
-
-            /* apply the transformation to s and extend the spike in w. */
-            for (i = j; i < m; ++i) {
-                temp = givens.c() * s(j,i) - givens.s() * w[i];
-                w[i] = givens.s() * s(j,i) + givens.c() * w[i];
-                s(j,i) = temp;
-            }
-        } else
-            v_givens[j] = IdentityRotation;
-    }
-
-    /* add the spike from the rank 1 update to w. */
-    w += v[n-1] * u;
-
-    /* eliminate the spike. */
-    *sing = false;
-    for (j = 0; j < n-1; ++j) {
-        if (w[j] != 0.) {
-            /* determine a givens rotation which eliminates the */
-            /* j-th element of the spike. */
-            givens.makeGivens(-s(j,j), w[j]);
-
-            /* apply the transformation to s and reduce the spike in w. */
-            for (i = j; i < m; ++i) {
-                temp = givens.c() * s(j,i) + givens.s() * w[i];
-                w[i] = -givens.s() * s(j,i) + givens.c() * w[i];
-                s(j,i) = temp;
-            }
-
-            /* store the information necessary to recover the */
-            /* givens rotation. */
-            w_givens[j] = givens;
-        } else
-            v_givens[j] = IdentityRotation;
-
-        /* test for zero diagonal elements in the output s. */
-        if (s(j,j) == 0.) {
-            *sing = true;
-        }
-    }
-    /* move w back into the last column of the output s. */
-    s(n-1,n-1) = w[n-1];
-
-    if (s(j,j) == 0.) {
-        *sing = true;
-    }
-    return;
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
deleted file mode 100644
index 6ebf856..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
+++ /dev/null
@@ -1,49 +0,0 @@
-namespace Eigen { 
-
-namespace internal {
-
-template <typename Scalar>
-void rwupdt(
-        Matrix< Scalar, Dynamic, Dynamic >  &r,
-        const Matrix< Scalar, Dynamic, 1>  &w,
-        Matrix< Scalar, Dynamic, 1>  &b,
-        Scalar alpha)
-{
-    typedef DenseIndex Index;
-
-    const Index n = r.cols();
-    eigen_assert(r.rows()>=n);
-    std::vector<JacobiRotation<Scalar> > givens(n);
-
-    /* Local variables */
-    Scalar temp, rowj;
-
-    /* Function Body */
-    for (Index j = 0; j < n; ++j) {
-        rowj = w[j];
-
-        /* apply the previous transformations to */
-        /* r(i,j), i=0,1,...,j-1, and to w(j). */
-        for (Index i = 0; i < j; ++i) {
-            temp = givens[i].c() * r(i,j) + givens[i].s() * rowj;
-            rowj = -givens[i].s() * r(i,j) + givens[i].c() * rowj;
-            r(i,j) = temp;
-        }
-
-        /* determine a givens rotation which eliminates w(j). */
-        givens[j].makeGivens(-r(j,j), rowj);
-
-        if (rowj == 0.)
-            continue; // givens[j] is identity
-
-        /* apply the current transformation to r(j,j), b(j), and alpha. */
-        r(j,j) = givens[j].c() * r(j,j) + givens[j].s() * rowj;
-        temp = givens[j].c() * b[j] + givens[j].s() * alpha;
-        alpha = -givens[j].s() * b[j] + givens[j].c() * alpha;
-        b[j] = temp;
-    }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
deleted file mode 100644
index ea5d8bc..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
+++ /dev/null
@@ -1,130 +0,0 @@
-// -*- coding: utf-8
-// vim: set fileencoding=utf-8
-
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_NUMERICAL_DIFF_H
-#define EIGEN_NUMERICAL_DIFF_H
-
-namespace Eigen { 
-
-enum NumericalDiffMode {
-    Forward,
-    Central
-};
-
-
-/**
-  * This class allows you to add a method df() to your functor, which will 
-  * use numerical differentiation to compute an approximate of the
-  * derivative for the functor. Of course, if you have an analytical form
-  * for the derivative, you should rather implement df() by yourself.
-  *
-  * More information on
-  * http://en.wikipedia.org/wiki/Numerical_differentiation
-  *
-  * Currently only "Forward" and "Central" scheme are implemented.
-  */
-template<typename _Functor, NumericalDiffMode mode=Forward>
-class NumericalDiff : public _Functor
-{
-public:
-    typedef _Functor Functor;
-    typedef typename Functor::Scalar Scalar;
-    typedef typename Functor::InputType InputType;
-    typedef typename Functor::ValueType ValueType;
-    typedef typename Functor::JacobianType JacobianType;
-
-    NumericalDiff(Scalar _epsfcn=0.) : Functor(), epsfcn(_epsfcn) {}
-    NumericalDiff(const Functor& f, Scalar _epsfcn=0.) : Functor(f), epsfcn(_epsfcn) {}
-
-    // forward constructors
-    template<typename T0>
-        NumericalDiff(const T0& a0) : Functor(a0), epsfcn(0) {}
-    template<typename T0, typename T1>
-        NumericalDiff(const T0& a0, const T1& a1) : Functor(a0, a1), epsfcn(0) {}
-    template<typename T0, typename T1, typename T2>
-        NumericalDiff(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2), epsfcn(0) {}
-
-    enum {
-        InputsAtCompileTime = Functor::InputsAtCompileTime,
-        ValuesAtCompileTime = Functor::ValuesAtCompileTime
-    };
-
-    /**
-      * return the number of evaluation of functor
-     */
-    int df(const InputType& _x, JacobianType &jac) const
-    {
-        using std::sqrt;
-        using std::abs;
-        /* Local variables */
-        Scalar h;
-        int nfev=0;
-        const typename InputType::Index n = _x.size();
-        const Scalar eps = sqrt(((std::max)(epsfcn,NumTraits<Scalar>::epsilon() )));
-        ValueType val1, val2;
-        InputType x = _x;
-        // TODO : we should do this only if the size is not already known
-        val1.resize(Functor::values());
-        val2.resize(Functor::values());
-
-        // initialization
-        switch(mode) {
-            case Forward:
-                // compute f(x)
-                Functor::operator()(x, val1); nfev++;
-                break;
-            case Central:
-                // do nothing
-                break;
-            default:
-                eigen_assert(false);
-        };
-
-        // Function Body
-        for (int j = 0; j < n; ++j) {
-            h = eps * abs(x[j]);
-            if (h == 0.) {
-                h = eps;
-            }
-            switch(mode) {
-                case Forward:
-                    x[j] += h;
-                    Functor::operator()(x, val2);
-                    nfev++;
-                    x[j] = _x[j];
-                    jac.col(j) = (val2-val1)/h;
-                    break;
-                case Central:
-                    x[j] += h;
-                    Functor::operator()(x, val2); nfev++;
-                    x[j] -= 2*h;
-                    Functor::operator()(x, val1); nfev++;
-                    x[j] = _x[j];
-                    jac.col(j) = (val2-val1)/(2*h);
-                    break;
-                default:
-                    eigen_assert(false);
-            };
-        }
-        return nfev;
-    }
-private:
-    Scalar epsfcn;
-
-    NumericalDiff& operator=(const NumericalDiff&);
-};
-
-} // end namespace Eigen
-
-//vim: ai ts=4 sts=4 et sw=4
-#endif // EIGEN_NUMERICAL_DIFF_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h
deleted file mode 100644
index 59a15b0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h
+++ /dev/null
@@ -1,280 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_COMPANION_H
-#define EIGEN_COMPANION_H
-
-// This file requires the user to include
-// * Eigen/Core
-// * Eigen/src/PolynomialSolver.h
-
-namespace Eigen { 
-
-namespace internal {
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-template<int Size>
-struct decrement_if_fixed_size
-{
-  enum {
-    ret = (Size == Dynamic) ? Dynamic : Size-1 };
-};
-
-#endif
-
-template< typename _Scalar, int _Deg >
-class companion
-{
-  public:
-    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Deg==Dynamic ? Dynamic : _Deg)
-
-    enum {
-      Deg = _Deg,
-      Deg_1=decrement_if_fixed_size<Deg>::ret
-    };
-
-    typedef _Scalar                                Scalar;
-    typedef typename NumTraits<Scalar>::Real       RealScalar;
-    typedef Matrix<Scalar, Deg, 1>                 RightColumn;
-    //typedef DiagonalMatrix< Scalar, Deg_1, Deg_1 > BottomLeftDiagonal;
-    typedef Matrix<Scalar, Deg_1, 1>               BottomLeftDiagonal;
-
-    typedef Matrix<Scalar, Deg, Deg>               DenseCompanionMatrixType;
-    typedef Matrix< Scalar, _Deg, Deg_1 >          LeftBlock;
-    typedef Matrix< Scalar, Deg_1, Deg_1 >         BottomLeftBlock;
-    typedef Matrix< Scalar, 1, Deg_1 >             LeftBlockFirstRow;
-
-    typedef DenseIndex Index;
-
-  public:
-    EIGEN_STRONG_INLINE const _Scalar operator()(Index row, Index col ) const
-    {
-      if( m_bl_diag.rows() > col )
-      {
-        if( 0 < row ){ return m_bl_diag[col]; }
-        else{ return 0; }
-      }
-      else{ return m_monic[row]; }
-    }
-
-  public:
-    template<typename VectorType>
-    void setPolynomial( const VectorType& poly )
-    {
-      const Index deg = poly.size()-1;
-      m_monic = -poly.head(deg)/poly[deg];
-      m_bl_diag.setOnes(deg-1);
-    }
-
-    template<typename VectorType>
-    companion( const VectorType& poly ){
-      setPolynomial( poly ); }
-
-  public:
-    DenseCompanionMatrixType denseMatrix() const
-    {
-      const Index deg   = m_monic.size();
-      const Index deg_1 = deg-1;
-      DenseCompanionMatrixType companMat(deg,deg);
-      companMat <<
-        ( LeftBlock(deg,deg_1)
-          << LeftBlockFirstRow::Zero(1,deg_1),
-          BottomLeftBlock::Identity(deg-1,deg-1)*m_bl_diag.asDiagonal() ).finished()
-        , m_monic;
-      return companMat;
-    }
-
-
-
-  protected:
-    /** Helper function for the balancing algorithm.
-     * \returns true if the row and the column, having colNorm and rowNorm
-     * as norms, are balanced, false otherwise.
-     * colB and rowB are respectively the multipliers for
-     * the column and the row in order to balance them.
-     * */
-    bool balanced( RealScalar colNorm, RealScalar rowNorm,
-        bool& isBalanced, RealScalar& colB, RealScalar& rowB );
-
-    /** Helper function for the balancing algorithm.
-     * \returns true if the row and the column, having colNorm and rowNorm
-     * as norms, are balanced, false otherwise.
-     * colB and rowB are respectively the multipliers for
-     * the column and the row in order to balance them.
-     * */
-    bool balancedR( RealScalar colNorm, RealScalar rowNorm,
-        bool& isBalanced, RealScalar& colB, RealScalar& rowB );
-
-  public:
-    /**
-     * Balancing algorithm from B. N. PARLETT and C. REINSCH (1969)
-     * "Balancing a matrix for calculation of eigenvalues and eigenvectors"
-     * adapted to the case of companion matrices.
-     * A matrix with non zero row and non zero column is balanced
-     * for a certain norm if the i-th row and the i-th column
-     * have same norm for all i.
-     */
-    void balance();
-
-  protected:
-      RightColumn                m_monic;
-      BottomLeftDiagonal         m_bl_diag;
-};
-
-
-
-template< typename _Scalar, int _Deg >
-inline
-bool companion<_Scalar,_Deg>::balanced( RealScalar colNorm, RealScalar rowNorm,
-    bool& isBalanced, RealScalar& colB, RealScalar& rowB )
-{
-  if( RealScalar(0) == colNorm || RealScalar(0) == rowNorm 
-      || !(numext::isfinite)(colNorm) || !(numext::isfinite)(rowNorm)){
-    return true;
-  }
-  else
-  {
-    //To find the balancing coefficients, if the radix is 2,
-    //one finds \f$ \sigma \f$ such that
-    // \f$ 2^{2\sigma-1} < rowNorm / colNorm \le 2^{2\sigma+1} \f$
-    // then the balancing coefficient for the row is \f$ 1/2^{\sigma} \f$
-    // and the balancing coefficient for the column is \f$ 2^{\sigma} \f$
-    const RealScalar radix = RealScalar(2);
-    const RealScalar radix2 = RealScalar(4);
-    
-    rowB = rowNorm / radix;
-    colB = RealScalar(1);
-    const RealScalar s = colNorm + rowNorm;
-
-    // Find sigma s.t. rowNorm / 2 <= 2^(2*sigma) * colNorm
-    RealScalar scout = colNorm;
-    while (scout < rowB)
-    {
-      colB *= radix;
-      scout *= radix2;
-    }
-    
-    // We now have an upper-bound for sigma, try to lower it.
-    // Find sigma s.t. 2^(2*sigma) * colNorm / 2 < rowNorm
-    scout = colNorm * (colB / radix) * colB;  // Avoid overflow.
-    while (scout >= rowNorm)
-    {
-      colB /= radix;
-      scout /= radix2;
-    }
-
-    // This line is used to avoid insubstantial balancing.
-    if ((rowNorm + radix * scout) < RealScalar(0.95) * s * colB)
-    {
-      isBalanced = false;
-      rowB = RealScalar(1) / colB;
-      return false;
-    }
-    else
-    {
-      return true;
-    }
-  }
-}
-
-template< typename _Scalar, int _Deg >
-inline
-bool companion<_Scalar,_Deg>::balancedR( RealScalar colNorm, RealScalar rowNorm,
-    bool& isBalanced, RealScalar& colB, RealScalar& rowB )
-{
-  if( RealScalar(0) == colNorm || RealScalar(0) == rowNorm ){ return true; }
-  else
-  {
-    /**
-     * Set the norm of the column and the row to the geometric mean
-     * of the row and column norm
-     */
-    const RealScalar q = colNorm/rowNorm;
-    if( !isApprox( q, _Scalar(1) ) )
-    {
-      rowB = sqrt( colNorm/rowNorm );
-      colB = RealScalar(1)/rowB;
-
-      isBalanced = false;
-      return false;
-    }
-    else{
-      return true; }
-  }
-}
-
-
-template< typename _Scalar, int _Deg >
-void companion<_Scalar,_Deg>::balance()
-{
-  using std::abs;
-  EIGEN_STATIC_ASSERT( Deg == Dynamic || 1 < Deg, YOU_MADE_A_PROGRAMMING_MISTAKE );
-  const Index deg   = m_monic.size();
-  const Index deg_1 = deg-1;
-
-  bool hasConverged=false;
-  while( !hasConverged )
-  {
-    hasConverged = true;
-    RealScalar colNorm,rowNorm;
-    RealScalar colB,rowB;
-
-    //First row, first column excluding the diagonal
-    //==============================================
-    colNorm = abs(m_bl_diag[0]);
-    rowNorm = abs(m_monic[0]);
-
-    //Compute balancing of the row and the column
-    if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
-    {
-      m_bl_diag[0] *= colB;
-      m_monic[0] *= rowB;
-    }
-
-    //Middle rows and columns excluding the diagonal
-    //==============================================
-    for( Index i=1; i<deg_1; ++i )
-    {
-      // column norm, excluding the diagonal
-      colNorm = abs(m_bl_diag[i]);
-
-      // row norm, excluding the diagonal
-      rowNorm = abs(m_bl_diag[i-1]) + abs(m_monic[i]);
-
-      //Compute balancing of the row and the column
-      if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
-      {
-        m_bl_diag[i]   *= colB;
-        m_bl_diag[i-1] *= rowB;
-        m_monic[i]     *= rowB;
-      }
-    }
-
-    //Last row, last column excluding the diagonal
-    //============================================
-    const Index ebl = m_bl_diag.size()-1;
-    VectorBlock<RightColumn,Deg_1> headMonic( m_monic, 0, deg_1 );
-    colNorm = headMonic.array().abs().sum();
-    rowNorm = abs( m_bl_diag[ebl] );
-
-    //Compute balancing of the row and the column
-    if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
-    {
-      headMonic      *= colB;
-      m_bl_diag[ebl] *= rowB;
-    }
-  }
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_COMPANION_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
deleted file mode 100644
index 5e0ecbb..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
+++ /dev/null
@@ -1,428 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_POLYNOMIAL_SOLVER_H
-#define EIGEN_POLYNOMIAL_SOLVER_H
-
-namespace Eigen { 
-
-/** \ingroup Polynomials_Module
- *  \class PolynomialSolverBase.
- *
- * \brief Defined to be inherited by polynomial solvers: it provides
- * convenient methods such as
- *  - real roots,
- *  - greatest, smallest complex roots,
- *  - real roots with greatest, smallest absolute real value,
- *  - greatest, smallest real roots.
- *
- * It stores the set of roots as a vector of complexes.
- *
- */
-template< typename _Scalar, int _Deg >
-class PolynomialSolverBase
-{
-  public:
-    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Deg==Dynamic ? Dynamic : _Deg)
-
-    typedef _Scalar                             Scalar;
-    typedef typename NumTraits<Scalar>::Real    RealScalar;
-    typedef std::complex<RealScalar>            RootType;
-    typedef Matrix<RootType,_Deg,1>             RootsType;
-
-    typedef DenseIndex Index;
-
-  protected:
-    template< typename OtherPolynomial >
-    inline void setPolynomial( const OtherPolynomial& poly ){
-      m_roots.resize(poly.size()-1); }
-
-  public:
-    template< typename OtherPolynomial >
-    inline PolynomialSolverBase( const OtherPolynomial& poly ){
-      setPolynomial( poly() ); }
-
-    inline PolynomialSolverBase(){}
-
-  public:
-    /** \returns the complex roots of the polynomial */
-    inline const RootsType& roots() const { return m_roots; }
-
-  public:
-    /** Clear and fills the back insertion sequence with the real roots of the polynomial
-     * i.e. the real part of the complex roots that have an imaginary part which
-     * absolute value is smaller than absImaginaryThreshold.
-     * absImaginaryThreshold takes the dummy_precision associated
-     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
-     *
-     * \param[out] bi_seq : the back insertion sequence (stl concept)
-     * \param[in]  absImaginaryThreshold : the maximum bound of the imaginary part of a complex
-     *  number that is considered as real.
-     * */
-    template<typename Stl_back_insertion_sequence>
-    inline void realRoots( Stl_back_insertion_sequence& bi_seq,
-        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
-    {
-      using std::abs;
-      bi_seq.clear();
-      for(Index i=0; i<m_roots.size(); ++i )
-      {
-        if( abs( m_roots[i].imag() ) < absImaginaryThreshold ){
-          bi_seq.push_back( m_roots[i].real() ); }
-      }
-    }
-
-  protected:
-    template<typename squaredNormBinaryPredicate>
-    inline const RootType& selectComplexRoot_withRespectToNorm( squaredNormBinaryPredicate& pred ) const
-    {
-      Index res=0;
-      RealScalar norm2 = numext::abs2( m_roots[0] );
-      for( Index i=1; i<m_roots.size(); ++i )
-      {
-        const RealScalar currNorm2 = numext::abs2( m_roots[i] );
-        if( pred( currNorm2, norm2 ) ){
-          res=i; norm2=currNorm2; }
-      }
-      return m_roots[res];
-    }
-
-  public:
-    /**
-     * \returns the complex root with greatest norm.
-     */
-    inline const RootType& greatestRoot() const
-    {
-      std::greater<RealScalar> greater;
-      return selectComplexRoot_withRespectToNorm( greater );
-    }
-
-    /**
-     * \returns the complex root with smallest norm.
-     */
-    inline const RootType& smallestRoot() const
-    {
-      std::less<RealScalar> less;
-      return selectComplexRoot_withRespectToNorm( less );
-    }
-
-  protected:
-    template<typename squaredRealPartBinaryPredicate>
-    inline const RealScalar& selectRealRoot_withRespectToAbsRealPart(
-        squaredRealPartBinaryPredicate& pred,
-        bool& hasArealRoot,
-        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
-    {
-      using std::abs;
-      hasArealRoot = false;
-      Index res=0;
-      RealScalar abs2(0);
-
-      for( Index i=0; i<m_roots.size(); ++i )
-      {
-        if( abs( m_roots[i].imag() ) <= absImaginaryThreshold )
-        {
-          if( !hasArealRoot )
-          {
-            hasArealRoot = true;
-            res = i;
-            abs2 = m_roots[i].real() * m_roots[i].real();
-          }
-          else
-          {
-            const RealScalar currAbs2 = m_roots[i].real() * m_roots[i].real();
-            if( pred( currAbs2, abs2 ) )
-            {
-              abs2 = currAbs2;
-              res = i;
-            }
-          }
-        }
-        else if(!hasArealRoot)
-        {
-          if( abs( m_roots[i].imag() ) < abs( m_roots[res].imag() ) ){
-            res = i;}
-        }
-      }
-      return numext::real_ref(m_roots[res]);
-    }
-
-
-    template<typename RealPartBinaryPredicate>
-    inline const RealScalar& selectRealRoot_withRespectToRealPart(
-        RealPartBinaryPredicate& pred,
-        bool& hasArealRoot,
-        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
-    {
-      using std::abs;
-      hasArealRoot = false;
-      Index res=0;
-      RealScalar val(0);
-
-      for( Index i=0; i<m_roots.size(); ++i )
-      {
-        if( abs( m_roots[i].imag() ) <= absImaginaryThreshold )
-        {
-          if( !hasArealRoot )
-          {
-            hasArealRoot = true;
-            res = i;
-            val = m_roots[i].real();
-          }
-          else
-          {
-            const RealScalar curr = m_roots[i].real();
-            if( pred( curr, val ) )
-            {
-              val = curr;
-              res = i;
-            }
-          }
-        }
-        else
-        {
-          if( abs( m_roots[i].imag() ) < abs( m_roots[res].imag() ) ){
-            res = i; }
-        }
-      }
-      return numext::real_ref(m_roots[res]);
-    }
-
-  public:
-    /**
-     * \returns a real root with greatest absolute magnitude.
-     * A real root is defined as the real part of a complex root with absolute imaginary
-     * part smallest than absImaginaryThreshold.
-     * absImaginaryThreshold takes the dummy_precision associated
-     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
-     * If no real root is found the boolean hasArealRoot is set to false and the real part of
-     * the root with smallest absolute imaginary part is returned instead.
-     *
-     * \param[out] hasArealRoot : boolean true if a real root is found according to the
-     *  absImaginaryThreshold criterion, false otherwise.
-     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
-     *  whether or not a root is real.
-     */
-    inline const RealScalar& absGreatestRealRoot(
-        bool& hasArealRoot,
-        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
-    {
-      std::greater<RealScalar> greater;
-      return selectRealRoot_withRespectToAbsRealPart( greater, hasArealRoot, absImaginaryThreshold );
-    }
-
-
-    /**
-     * \returns a real root with smallest absolute magnitude.
-     * A real root is defined as the real part of a complex root with absolute imaginary
-     * part smallest than absImaginaryThreshold.
-     * absImaginaryThreshold takes the dummy_precision associated
-     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
-     * If no real root is found the boolean hasArealRoot is set to false and the real part of
-     * the root with smallest absolute imaginary part is returned instead.
-     *
-     * \param[out] hasArealRoot : boolean true if a real root is found according to the
-     *  absImaginaryThreshold criterion, false otherwise.
-     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
-     *  whether or not a root is real.
-     */
-    inline const RealScalar& absSmallestRealRoot(
-        bool& hasArealRoot,
-        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
-    {
-      std::less<RealScalar> less;
-      return selectRealRoot_withRespectToAbsRealPart( less, hasArealRoot, absImaginaryThreshold );
-    }
-
-
-    /**
-     * \returns the real root with greatest value.
-     * A real root is defined as the real part of a complex root with absolute imaginary
-     * part smallest than absImaginaryThreshold.
-     * absImaginaryThreshold takes the dummy_precision associated
-     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
-     * If no real root is found the boolean hasArealRoot is set to false and the real part of
-     * the root with smallest absolute imaginary part is returned instead.
-     *
-     * \param[out] hasArealRoot : boolean true if a real root is found according to the
-     *  absImaginaryThreshold criterion, false otherwise.
-     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
-     *  whether or not a root is real.
-     */
-    inline const RealScalar& greatestRealRoot(
-        bool& hasArealRoot,
-        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
-    {
-      std::greater<RealScalar> greater;
-      return selectRealRoot_withRespectToRealPart( greater, hasArealRoot, absImaginaryThreshold );
-    }
-
-
-    /**
-     * \returns the real root with smallest value.
-     * A real root is defined as the real part of a complex root with absolute imaginary
-     * part smallest than absImaginaryThreshold.
-     * absImaginaryThreshold takes the dummy_precision associated
-     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
-     * If no real root is found the boolean hasArealRoot is set to false and the real part of
-     * the root with smallest absolute imaginary part is returned instead.
-     *
-     * \param[out] hasArealRoot : boolean true if a real root is found according to the
-     *  absImaginaryThreshold criterion, false otherwise.
-     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
-     *  whether or not a root is real.
-     */
-    inline const RealScalar& smallestRealRoot(
-        bool& hasArealRoot,
-        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
-    {
-      std::less<RealScalar> less;
-      return selectRealRoot_withRespectToRealPart( less, hasArealRoot, absImaginaryThreshold );
-    }
-
-  protected:
-    RootsType               m_roots;
-};
-
-#define EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES( BASE )  \
-  typedef typename BASE::Scalar                 Scalar;       \
-  typedef typename BASE::RealScalar             RealScalar;   \
-  typedef typename BASE::RootType               RootType;     \
-  typedef typename BASE::RootsType              RootsType;
-
-
-
-/** \ingroup Polynomials_Module
-  *
-  * \class PolynomialSolver
-  *
-  * \brief A polynomial solver
-  *
-  * Computes the complex roots of a real polynomial.
-  *
-  * \param _Scalar the scalar type, i.e., the type of the polynomial coefficients
-  * \param _Deg the degree of the polynomial, can be a compile time value or Dynamic.
-  *             Notice that the number of polynomial coefficients is _Deg+1.
-  *
-  * This class implements a polynomial solver and provides convenient methods such as
-  * - real roots,
-  * - greatest, smallest complex roots,
-  * - real roots with greatest, smallest absolute real value.
-  * - greatest, smallest real roots.
-  *
-  * WARNING: this polynomial solver is experimental, part of the unsupported Eigen modules.
-  *
-  *
-  * Currently a QR algorithm is used to compute the eigenvalues of the companion matrix of
-  * the polynomial to compute its roots.
-  * This supposes that the complex moduli of the roots are all distinct: e.g. there should
-  * be no multiple roots or conjugate roots for instance.
-  * With 32bit (float) floating types this problem shows up frequently.
-  * However, almost always, correct accuracy is reached even in these cases for 64bit
-  * (double) floating types and small polynomial degree (<20).
-  */
-template<typename _Scalar, int _Deg>
-class PolynomialSolver : public PolynomialSolverBase<_Scalar,_Deg>
-{
-  public:
-    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Deg==Dynamic ? Dynamic : _Deg)
-
-    typedef PolynomialSolverBase<_Scalar,_Deg>    PS_Base;
-    EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES( PS_Base )
-
-    typedef Matrix<Scalar,_Deg,_Deg>                 CompanionMatrixType;
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-                                          ComplexEigenSolver<CompanionMatrixType>,
-                                          EigenSolver<CompanionMatrixType> >::type EigenSolverType;
-    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, Scalar, std::complex<Scalar> >::type ComplexScalar;
-
-  public:
-    /** Computes the complex roots of a new polynomial. */
-    template< typename OtherPolynomial >
-    void compute( const OtherPolynomial& poly )
-    {
-      eigen_assert( Scalar(0) != poly[poly.size()-1] );
-      eigen_assert( poly.size() > 1 );
-      if(poly.size() >  2 )
-      {
-        internal::companion<Scalar,_Deg> companion( poly );
-        companion.balance();
-        m_eigenSolver.compute( companion.denseMatrix() );
-        m_roots = m_eigenSolver.eigenvalues();
-        // cleanup noise in imaginary part of real roots:
-        // if the imaginary part is rather small compared to the real part
-        // and that cancelling the imaginary part yield a smaller evaluation,
-        // then it's safe to keep the real part only.
-        RealScalar coarse_prec = RealScalar(std::pow(4,poly.size()+1))*NumTraits<RealScalar>::epsilon();
-        for(Index i = 0; i<m_roots.size(); ++i)
-        {
-          if( internal::isMuchSmallerThan(numext::abs(numext::imag(m_roots[i])),
-                                          numext::abs(numext::real(m_roots[i])),
-                                          coarse_prec) )
-          {
-            ComplexScalar as_real_root = ComplexScalar(numext::real(m_roots[i]));
-            if(    numext::abs(poly_eval(poly, as_real_root))
-                <= numext::abs(poly_eval(poly, m_roots[i])))
-            {
-              m_roots[i] = as_real_root;
-            }
-          }
-        }
-      }
-      else if(poly.size () == 2)
-      {
-        m_roots.resize(1);
-        m_roots[0] = -poly[0]/poly[1];
-      }
-    }
-
-  public:
-    template< typename OtherPolynomial >
-    inline PolynomialSolver( const OtherPolynomial& poly ){
-      compute( poly ); }
-
-    inline PolynomialSolver(){}
-
-  protected:
-    using                   PS_Base::m_roots;
-    EigenSolverType         m_eigenSolver;
-};
-
-
-template< typename _Scalar >
-class PolynomialSolver<_Scalar,1> : public PolynomialSolverBase<_Scalar,1>
-{
-  public:
-    typedef PolynomialSolverBase<_Scalar,1>    PS_Base;
-    EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES( PS_Base )
-
-  public:
-    /** Computes the complex roots of a new polynomial. */
-    template< typename OtherPolynomial >
-    void compute( const OtherPolynomial& poly )
-    {
-      eigen_assert( poly.size() == 2 );
-      eigen_assert( Scalar(0) != poly[1] );
-      m_roots[0] = -poly[0]/poly[1];
-    }
-
-  public:
-    template< typename OtherPolynomial >
-    inline PolynomialSolver( const OtherPolynomial& poly ){
-      compute( poly ); }
-
-    inline PolynomialSolver(){}
-
-  protected:
-    using                   PS_Base::m_roots;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_POLYNOMIAL_SOLVER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
deleted file mode 100644
index 394e857..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
+++ /dev/null
@@ -1,143 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_POLYNOMIAL_UTILS_H
-#define EIGEN_POLYNOMIAL_UTILS_H
-
-namespace Eigen { 
-
-/** \ingroup Polynomials_Module
- * \returns the evaluation of the polynomial at x using Horner algorithm.
- *
- * \param[in] poly : the vector of coefficients of the polynomial ordered
- *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
- *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
- * \param[in] x : the value to evaluate the polynomial at.
- *
- * \note for stability:
- *   \f$ |x| \le 1 \f$
- */
-template <typename Polynomials, typename T>
-inline
-T poly_eval_horner( const Polynomials& poly, const T& x )
-{
-  T val=poly[poly.size()-1];
-  for(DenseIndex i=poly.size()-2; i>=0; --i ){
-    val = val*x + poly[i]; }
-  return val;
-}
-
-/** \ingroup Polynomials_Module
- * \returns the evaluation of the polynomial at x using stabilized Horner algorithm.
- *
- * \param[in] poly : the vector of coefficients of the polynomial ordered
- *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
- *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
- * \param[in] x : the value to evaluate the polynomial at.
- */
-template <typename Polynomials, typename T>
-inline
-T poly_eval( const Polynomials& poly, const T& x )
-{
-  typedef typename NumTraits<T>::Real Real;
-
-  if( numext::abs2( x ) <= Real(1) ){
-    return poly_eval_horner( poly, x ); }
-  else
-  {
-    T val=poly[0];
-    T inv_x = T(1)/x;
-    for( DenseIndex i=1; i<poly.size(); ++i ){
-      val = val*inv_x + poly[i]; }
-
-    return numext::pow(x,(T)(poly.size()-1)) * val;
-  }
-}
-
-/** \ingroup Polynomials_Module
- * \returns a maximum bound for the absolute value of any root of the polynomial.
- *
- * \param[in] poly : the vector of coefficients of the polynomial ordered
- *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
- *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
- *
- *  \pre
- *   the leading coefficient of the input polynomial poly must be non zero
- */
-template <typename Polynomial>
-inline
-typename NumTraits<typename Polynomial::Scalar>::Real cauchy_max_bound( const Polynomial& poly )
-{
-  using std::abs;
-  typedef typename Polynomial::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real Real;
-
-  eigen_assert( Scalar(0) != poly[poly.size()-1] );
-  const Scalar inv_leading_coeff = Scalar(1)/poly[poly.size()-1];
-  Real cb(0);
-
-  for( DenseIndex i=0; i<poly.size()-1; ++i ){
-    cb += abs(poly[i]*inv_leading_coeff); }
-  return cb + Real(1);
-}
-
-/** \ingroup Polynomials_Module
- * \returns a minimum bound for the absolute value of any non zero root of the polynomial.
- * \param[in] poly : the vector of coefficients of the polynomial ordered
- *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
- *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
- */
-template <typename Polynomial>
-inline
-typename NumTraits<typename Polynomial::Scalar>::Real cauchy_min_bound( const Polynomial& poly )
-{
-  using std::abs;
-  typedef typename Polynomial::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real Real;
-
-  DenseIndex i=0;
-  while( i<poly.size()-1 && Scalar(0) == poly(i) ){ ++i; }
-  if( poly.size()-1 == i ){
-    return Real(1); }
-
-  const Scalar inv_min_coeff = Scalar(1)/poly[i];
-  Real cb(1);
-  for( DenseIndex j=i+1; j<poly.size(); ++j ){
-    cb += abs(poly[j]*inv_min_coeff); }
-  return Real(1)/cb;
-}
-
-/** \ingroup Polynomials_Module
- * Given the roots of a polynomial compute the coefficients in the
- * monomial basis of the monic polynomial with same roots and minimal degree.
- * If RootVector is a vector of complexes, Polynomial should also be a vector
- * of complexes.
- * \param[in] rv : a vector containing the roots of a polynomial.
- * \param[out] poly : the vector of coefficients of the polynomial ordered
- *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
- *  e.g. \f$ 3 + x^2 \f$ is stored as a vector \f$ [ 3, 0, 1 ] \f$.
- */
-template <typename RootVector, typename Polynomial>
-void roots_to_monicPolynomial( const RootVector& rv, Polynomial& poly )
-{
-
-  typedef typename Polynomial::Scalar Scalar;
-
-  poly.setZero( rv.size()+1 );
-  poly[0] = -rv[0]; poly[1] = Scalar(1);
-  for( DenseIndex i=1; i< rv.size(); ++i )
-  {
-    for( DenseIndex j=i+1; j>0; --j ){ poly[j] = poly[j-1] - rv[i]*poly[j]; }
-    poly[0] = -rv[i]*poly[0];
-  }
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_POLYNOMIAL_UTILS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
deleted file mode 100644
index 6d0370d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
+++ /dev/null
@@ -1,352 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Guillaume Saupin <guillaume.saupin@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SKYLINEINPLACELU_H
-#define EIGEN_SKYLINEINPLACELU_H
-
-namespace Eigen { 
-
-/** \ingroup Skyline_Module
- *
- * \class SkylineInplaceLU
- *
- * \brief Inplace LU decomposition of a skyline matrix and associated features
- *
- * \param MatrixType the type of the matrix of which we are computing the LU factorization
- *
- */
-template<typename MatrixType>
-class SkylineInplaceLU {
-protected:
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::Index Index;
-    
-    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
-
-public:
-
-    /** Creates a LU object and compute the respective factorization of \a matrix using
-     * flags \a flags. */
-    SkylineInplaceLU(MatrixType& matrix, int flags = 0)
-    : /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0), m_lu(matrix) {
-        m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar > ();
-        m_lu.IsRowMajor ? computeRowMajor() : compute();
-    }
-
-    /** Sets the relative threshold value used to prune zero coefficients during the decomposition.
-     *
-     * Setting a value greater than zero speeds up computation, and yields to an incomplete
-     * factorization with fewer non zero coefficients. Such approximate factors are especially
-     * useful to initialize an iterative solver.
-     *
-     * Note that the exact meaning of this parameter might depends on the actual
-     * backend. Moreover, not all backends support this feature.
-     *
-     * \sa precision() */
-    void setPrecision(RealScalar v) {
-        m_precision = v;
-    }
-
-    /** \returns the current precision.
-     *
-     * \sa setPrecision() */
-    RealScalar precision() const {
-        return m_precision;
-    }
-
-    /** Sets the flags. Possible values are:
-     *  - CompleteFactorization
-     *  - IncompleteFactorization
-     *  - MemoryEfficient
-     *  - one of the ordering methods
-     *  - etc...
-     *
-     * \sa flags() */
-    void setFlags(int f) {
-        m_flags = f;
-    }
-
-    /** \returns the current flags */
-    int flags() const {
-        return m_flags;
-    }
-
-    void setOrderingMethod(int m) {
-        m_flags = m;
-    }
-
-    int orderingMethod() const {
-        return m_flags;
-    }
-
-    /** Computes/re-computes the LU factorization */
-    void compute();
-    void computeRowMajor();
-
-    /** \returns the lower triangular matrix L */
-    //inline const MatrixType& matrixL() const { return m_matrixL; }
-
-    /** \returns the upper triangular matrix U */
-    //inline const MatrixType& matrixU() const { return m_matrixU; }
-
-    template<typename BDerived, typename XDerived>
-    bool solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x,
-            const int transposed = 0) const;
-
-    /** \returns true if the factorization succeeded */
-    inline bool succeeded(void) const {
-        return m_succeeded;
-    }
-
-protected:
-    RealScalar m_precision;
-    int m_flags;
-    mutable int m_status;
-    bool m_succeeded;
-    MatrixType& m_lu;
-};
-
-/** Computes / recomputes the in place LU decomposition of the SkylineInplaceLU.
- * using the default algorithm.
- */
-template<typename MatrixType>
-//template<typename _Scalar>
-void SkylineInplaceLU<MatrixType>::compute() {
-    const size_t rows = m_lu.rows();
-    const size_t cols = m_lu.cols();
-
-    eigen_assert(rows == cols && "We do not (yet) support rectangular LU.");
-    eigen_assert(!m_lu.IsRowMajor && "LU decomposition does not work with rowMajor Storage");
-
-    for (Index row = 0; row < rows; row++) {
-        const double pivot = m_lu.coeffDiag(row);
-
-        //Lower matrix Columns update
-        const Index& col = row;
-        for (typename MatrixType::InnerLowerIterator lIt(m_lu, col); lIt; ++lIt) {
-            lIt.valueRef() /= pivot;
-        }
-
-        //Upper matrix update -> contiguous memory access
-        typename MatrixType::InnerLowerIterator lIt(m_lu, col);
-        for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
-            typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
-            typename MatrixType::InnerUpperIterator uIt(m_lu, rrow);
-            const double coef = lIt.value();
-
-            uItPivot += (rrow - row - 1);
-
-            //update upper part  -> contiguous memory access
-            for (++uItPivot; uIt && uItPivot;) {
-                uIt.valueRef() -= uItPivot.value() * coef;
-
-                ++uIt;
-                ++uItPivot;
-            }
-            ++lIt;
-        }
-
-        //Upper matrix update -> non contiguous memory access
-        typename MatrixType::InnerLowerIterator lIt3(m_lu, col);
-        for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
-            typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
-            const double coef = lIt3.value();
-
-            //update lower part ->  non contiguous memory access
-            for (Index i = 0; i < rrow - row - 1; i++) {
-                m_lu.coeffRefLower(rrow, row + i + 1) -= uItPivot.value() * coef;
-                ++uItPivot;
-            }
-            ++lIt3;
-        }
-        //update diag -> contiguous
-        typename MatrixType::InnerLowerIterator lIt2(m_lu, col);
-        for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
-
-            typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
-            typename MatrixType::InnerUpperIterator uIt(m_lu, rrow);
-            const double coef = lIt2.value();
-
-            uItPivot += (rrow - row - 1);
-            m_lu.coeffRefDiag(rrow) -= uItPivot.value() * coef;
-            ++lIt2;
-        }
-    }
-}
-
-template<typename MatrixType>
-void SkylineInplaceLU<MatrixType>::computeRowMajor() {
-    const size_t rows = m_lu.rows();
-    const size_t cols = m_lu.cols();
-
-    eigen_assert(rows == cols && "We do not (yet) support rectangular LU.");
-    eigen_assert(m_lu.IsRowMajor && "You're trying to apply rowMajor decomposition on a ColMajor matrix !");
-
-    for (Index row = 0; row < rows; row++) {
-        typename MatrixType::InnerLowerIterator llIt(m_lu, row);
-
-
-        for (Index col = llIt.col(); col < row; col++) {
-            if (m_lu.coeffExistLower(row, col)) {
-                const double diag = m_lu.coeffDiag(col);
-
-                typename MatrixType::InnerLowerIterator lIt(m_lu, row);
-                typename MatrixType::InnerUpperIterator uIt(m_lu, col);
-
-
-                const Index offset = lIt.col() - uIt.row();
-
-
-                Index stop = offset > 0 ? col - lIt.col() : col - uIt.row();
-
-                //#define VECTORIZE
-#ifdef VECTORIZE
-                Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
-                Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
-
-
-                Scalar newCoeff = m_lu.coeffLower(row, col) - rowVal.dot(colVal);
-#else
-                if (offset > 0) //Skip zero value of lIt
-                    uIt += offset;
-                else //Skip zero values of uIt
-                    lIt += -offset;
-                Scalar newCoeff = m_lu.coeffLower(row, col);
-
-                for (Index k = 0; k < stop; ++k) {
-                    const Scalar tmp = newCoeff;
-                    newCoeff = tmp - lIt.value() * uIt.value();
-                    ++lIt;
-                    ++uIt;
-                }
-#endif
-
-                m_lu.coeffRefLower(row, col) = newCoeff / diag;
-            }
-        }
-
-        //Upper matrix update
-        const Index col = row;
-        typename MatrixType::InnerUpperIterator uuIt(m_lu, col);
-        for (Index rrow = uuIt.row(); rrow < col; rrow++) {
-
-            typename MatrixType::InnerLowerIterator lIt(m_lu, rrow);
-            typename MatrixType::InnerUpperIterator uIt(m_lu, col);
-            const Index offset = lIt.col() - uIt.row();
-
-            Index stop = offset > 0 ? rrow - lIt.col() : rrow - uIt.row();
-
-#ifdef VECTORIZE
-            Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
-            Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
-
-            Scalar newCoeff = m_lu.coeffUpper(rrow, col) - rowVal.dot(colVal);
-#else
-            if (offset > 0) //Skip zero value of lIt
-                uIt += offset;
-            else //Skip zero values of uIt
-                lIt += -offset;
-            Scalar newCoeff = m_lu.coeffUpper(rrow, col);
-            for (Index k = 0; k < stop; ++k) {
-                const Scalar tmp = newCoeff;
-                newCoeff = tmp - lIt.value() * uIt.value();
-
-                ++lIt;
-                ++uIt;
-            }
-#endif
-            m_lu.coeffRefUpper(rrow, col) = newCoeff;
-        }
-
-
-        //Diag matrix update
-        typename MatrixType::InnerLowerIterator lIt(m_lu, row);
-        typename MatrixType::InnerUpperIterator uIt(m_lu, row);
-
-        const Index offset = lIt.col() - uIt.row();
-
-
-        Index stop = offset > 0 ? lIt.size() : uIt.size();
-#ifdef VECTORIZE
-        Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
-        Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
-        Scalar newCoeff = m_lu.coeffDiag(row) - rowVal.dot(colVal);
-#else
-        if (offset > 0) //Skip zero value of lIt
-            uIt += offset;
-        else //Skip zero values of uIt
-            lIt += -offset;
-        Scalar newCoeff = m_lu.coeffDiag(row);
-        for (Index k = 0; k < stop; ++k) {
-            const Scalar tmp = newCoeff;
-            newCoeff = tmp - lIt.value() * uIt.value();
-            ++lIt;
-            ++uIt;
-        }
-#endif
-        m_lu.coeffRefDiag(row) = newCoeff;
-    }
-}
-
-/** Computes *x = U^-1 L^-1 b
- *
- * If \a transpose is set to SvTranspose or SvAdjoint, the solution
- * of the transposed/adjoint system is computed instead.
- *
- * Not all backends implement the solution of the transposed or
- * adjoint system.
- */
-template<typename MatrixType>
-template<typename BDerived, typename XDerived>
-bool SkylineInplaceLU<MatrixType>::solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x, const int transposed) const {
-    const size_t rows = m_lu.rows();
-    const size_t cols = m_lu.cols();
-
-
-    for (Index row = 0; row < rows; row++) {
-        x->coeffRef(row) = b.coeff(row);
-        Scalar newVal = x->coeff(row);
-        typename MatrixType::InnerLowerIterator lIt(m_lu, row);
-
-        Index col = lIt.col();
-        while (lIt.col() < row) {
-
-            newVal -= x->coeff(col++) * lIt.value();
-            ++lIt;
-        }
-
-        x->coeffRef(row) = newVal;
-    }
-
-
-    for (Index col = rows - 1; col > 0; col--) {
-        x->coeffRef(col) = x->coeff(col) / m_lu.coeffDiag(col);
-
-        const Scalar x_col = x->coeff(col);
-
-        typename MatrixType::InnerUpperIterator uIt(m_lu, col);
-        uIt += uIt.size()-1;
-
-
-        while (uIt) {
-            x->coeffRef(uIt.row()) -= x_col * uIt.value();
-            //TODO : introduce --operator
-            uIt += -1;
-        }
-
-
-    }
-    x->coeffRef(0) = x->coeff(0) / m_lu.coeffDiag(0);
-
-    return true;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SKYLINEINPLACELU_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h
deleted file mode 100644
index 7c7eace..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h
+++ /dev/null
@@ -1,862 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SKYLINEMATRIX_H
-#define EIGEN_SKYLINEMATRIX_H
-
-#include "SkylineStorage.h"
-#include "SkylineMatrixBase.h"
-
-namespace Eigen { 
-
-/** \ingroup Skyline_Module
- *
- * \class SkylineMatrix
- *
- * \brief The main skyline matrix class
- *
- * This class implements a skyline matrix using the very uncommon storage
- * scheme.
- *
- * \param _Scalar the scalar type, i.e. the type of the coefficients
- * \param _Options Union of bit flags controlling the storage scheme. Currently the only possibility
- *                 is RowMajor. The default is 0 which means column-major.
- *
- *
- */
-namespace internal {
-template<typename _Scalar, int _Options>
-struct traits<SkylineMatrix<_Scalar, _Options> > {
-    typedef _Scalar Scalar;
-    typedef Sparse StorageKind;
-
-    enum {
-        RowsAtCompileTime = Dynamic,
-        ColsAtCompileTime = Dynamic,
-        MaxRowsAtCompileTime = Dynamic,
-        MaxColsAtCompileTime = Dynamic,
-        Flags = SkylineBit | _Options,
-        CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    };
-};
-}
-
-template<typename _Scalar, int _Options>
-class SkylineMatrix
-: public SkylineMatrixBase<SkylineMatrix<_Scalar, _Options> > {
-public:
-    EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
-    EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
-    EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
-
-    using Base::IsRowMajor;
-
-protected:
-
-    typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix;
-
-    Index m_outerSize;
-    Index m_innerSize;
-
-public:
-    Index* m_colStartIndex;
-    Index* m_rowStartIndex;
-    SkylineStorage<Scalar> m_data;
-
-public:
-
-    inline Index rows() const {
-        return IsRowMajor ? m_outerSize : m_innerSize;
-    }
-
-    inline Index cols() const {
-        return IsRowMajor ? m_innerSize : m_outerSize;
-    }
-
-    inline Index innerSize() const {
-        return m_innerSize;
-    }
-
-    inline Index outerSize() const {
-        return m_outerSize;
-    }
-
-    inline Index upperNonZeros() const {
-        return m_data.upperSize();
-    }
-
-    inline Index lowerNonZeros() const {
-        return m_data.lowerSize();
-    }
-
-    inline Index upperNonZeros(Index j) const {
-        return m_colStartIndex[j + 1] - m_colStartIndex[j];
-    }
-
-    inline Index lowerNonZeros(Index j) const {
-        return m_rowStartIndex[j + 1] - m_rowStartIndex[j];
-    }
-
-    inline const Scalar* _diagPtr() const {
-        return &m_data.diag(0);
-    }
-
-    inline Scalar* _diagPtr() {
-        return &m_data.diag(0);
-    }
-
-    inline const Scalar* _upperPtr() const {
-        return &m_data.upper(0);
-    }
-
-    inline Scalar* _upperPtr() {
-        return &m_data.upper(0);
-    }
-
-    inline const Scalar* _lowerPtr() const {
-        return &m_data.lower(0);
-    }
-
-    inline Scalar* _lowerPtr() {
-        return &m_data.lower(0);
-    }
-
-    inline const Index* _upperProfilePtr() const {
-        return &m_data.upperProfile(0);
-    }
-
-    inline Index* _upperProfilePtr() {
-        return &m_data.upperProfile(0);
-    }
-
-    inline const Index* _lowerProfilePtr() const {
-        return &m_data.lowerProfile(0);
-    }
-
-    inline Index* _lowerProfilePtr() {
-        return &m_data.lowerProfile(0);
-    }
-
-    inline Scalar coeff(Index row, Index col) const {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-
-        if (outer == inner)
-            return this->m_data.diag(outer);
-
-        if (IsRowMajor) {
-            if (inner > outer) //upper matrix
-            {
-                const Index minOuterIndex = inner - m_data.upperProfile(inner);
-                if (outer >= minOuterIndex)
-                    return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
-                else
-                    return Scalar(0);
-            }
-            if (inner < outer) //lower matrix
-            {
-                const Index minInnerIndex = outer - m_data.lowerProfile(outer);
-                if (inner >= minInnerIndex)
-                    return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
-                else
-                    return Scalar(0);
-            }
-            return m_data.upper(m_colStartIndex[inner] + outer - inner);
-        } else {
-            if (outer > inner) //upper matrix
-            {
-                const Index maxOuterIndex = inner + m_data.upperProfile(inner);
-                if (outer <= maxOuterIndex)
-                    return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
-                else
-                    return Scalar(0);
-            }
-            if (outer < inner) //lower matrix
-            {
-                const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
-
-                if (inner <= maxInnerIndex)
-                    return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
-                else
-                    return Scalar(0);
-            }
-        }
-    }
-
-    inline Scalar& coeffRef(Index row, Index col) {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-
-        if (outer == inner)
-            return this->m_data.diag(outer);
-
-        if (IsRowMajor) {
-            if (col > row) //upper matrix
-            {
-                const Index minOuterIndex = inner - m_data.upperProfile(inner);
-                eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
-                return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
-            }
-            if (col < row) //lower matrix
-            {
-                const Index minInnerIndex = outer - m_data.lowerProfile(outer);
-                eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
-                return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
-            }
-        } else {
-            if (outer > inner) //upper matrix
-            {
-                const Index maxOuterIndex = inner + m_data.upperProfile(inner);
-                eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
-                return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
-            }
-            if (outer < inner) //lower matrix
-            {
-                const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
-                eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
-                return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
-            }
-        }
-    }
-
-    inline Scalar coeffDiag(Index idx) const {
-        eigen_assert(idx < outerSize());
-        eigen_assert(idx < innerSize());
-        return this->m_data.diag(idx);
-    }
-
-    inline Scalar coeffLower(Index row, Index col) const {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-        eigen_assert(inner != outer);
-
-        if (IsRowMajor) {
-            const Index minInnerIndex = outer - m_data.lowerProfile(outer);
-            if (inner >= minInnerIndex)
-                return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
-            else
-                return Scalar(0);
-
-        } else {
-            const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
-            if (inner <= maxInnerIndex)
-                return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
-            else
-                return Scalar(0);
-        }
-    }
-
-    inline Scalar coeffUpper(Index row, Index col) const {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-        eigen_assert(inner != outer);
-
-        if (IsRowMajor) {
-            const Index minOuterIndex = inner - m_data.upperProfile(inner);
-            if (outer >= minOuterIndex)
-                return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
-            else
-                return Scalar(0);
-        } else {
-            const Index maxOuterIndex = inner + m_data.upperProfile(inner);
-            if (outer <= maxOuterIndex)
-                return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
-            else
-                return Scalar(0);
-        }
-    }
-
-    inline Scalar& coeffRefDiag(Index idx) {
-        eigen_assert(idx < outerSize());
-        eigen_assert(idx < innerSize());
-        return this->m_data.diag(idx);
-    }
-
-    inline Scalar& coeffRefLower(Index row, Index col) {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-        eigen_assert(inner != outer);
-
-        if (IsRowMajor) {
-            const Index minInnerIndex = outer - m_data.lowerProfile(outer);
-            eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
-            return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
-        } else {
-            const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
-            eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
-            return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
-        }
-    }
-
-    inline bool coeffExistLower(Index row, Index col) {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-        eigen_assert(inner != outer);
-
-        if (IsRowMajor) {
-            const Index minInnerIndex = outer - m_data.lowerProfile(outer);
-            return inner >= minInnerIndex;
-        } else {
-            const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
-            return inner <= maxInnerIndex;
-        }
-    }
-
-    inline Scalar& coeffRefUpper(Index row, Index col) {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-        eigen_assert(inner != outer);
-
-        if (IsRowMajor) {
-            const Index minOuterIndex = inner - m_data.upperProfile(inner);
-            eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
-            return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
-        } else {
-            const Index maxOuterIndex = inner + m_data.upperProfile(inner);
-            eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
-            return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
-        }
-    }
-
-    inline bool coeffExistUpper(Index row, Index col) {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-        eigen_assert(inner != outer);
-
-        if (IsRowMajor) {
-            const Index minOuterIndex = inner - m_data.upperProfile(inner);
-            return outer >= minOuterIndex;
-        } else {
-            const Index maxOuterIndex = inner + m_data.upperProfile(inner);
-            return outer <= maxOuterIndex;
-        }
-    }
-
-
-protected:
-
-public:
-    class InnerUpperIterator;
-    class InnerLowerIterator;
-
-    class OuterUpperIterator;
-    class OuterLowerIterator;
-
-    /** Removes all non zeros */
-    inline void setZero() {
-        m_data.clear();
-        memset(m_colStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
-        memset(m_rowStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
-    }
-
-    /** \returns the number of non zero coefficients */
-    inline Index nonZeros() const {
-        return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize();
-    }
-
-    /** Preallocates \a reserveSize non zeros */
-    inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) {
-        m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize);
-    }
-
-    /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
-
-     *
-     * \warning This function can be extremely slow if the non zero coefficients
-     * are not inserted in a coherent order.
-     *
-     * After an insertion session, you should call the finalize() function.
-     */
-    EIGEN_DONT_INLINE Scalar & insert(Index row, Index col) {
-        const Index outer = IsRowMajor ? row : col;
-        const Index inner = IsRowMajor ? col : row;
-
-        eigen_assert(outer < outerSize());
-        eigen_assert(inner < innerSize());
-
-        if (outer == inner)
-            return m_data.diag(col);
-
-        if (IsRowMajor) {
-            if (outer < inner) //upper matrix
-            {
-                Index minOuterIndex = 0;
-                minOuterIndex = inner - m_data.upperProfile(inner);
-
-                if (outer < minOuterIndex) //The value does not yet exist
-                {
-                    const Index previousProfile = m_data.upperProfile(inner);
-
-                    m_data.upperProfile(inner) = inner - outer;
-
-
-                    const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
-                    //shift data stored after this new one
-                    const Index stop = m_colStartIndex[cols()];
-                    const Index start = m_colStartIndex[inner];
-
-
-                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
-                        m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
-                    }
-
-                    for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) {
-                        m_colStartIndex[innerIdx] += bandIncrement;
-                    }
-
-                    //zeros new data
-                    memset(this->_upperPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
-
-                    return m_data.upper(m_colStartIndex[inner]);
-                } else {
-                    return m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
-                }
-            }
-
-            if (outer > inner) //lower matrix
-            {
-                const Index minInnerIndex = outer - m_data.lowerProfile(outer);
-                if (inner < minInnerIndex) //The value does not yet exist
-                {
-                    const Index previousProfile = m_data.lowerProfile(outer);
-                    m_data.lowerProfile(outer) = outer - inner;
-
-                    const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
-                    //shift data stored after this new one
-                    const Index stop = m_rowStartIndex[rows()];
-                    const Index start = m_rowStartIndex[outer];
-
-
-                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
-                        m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
-                    }
-
-                    for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) {
-                        m_rowStartIndex[innerIdx] += bandIncrement;
-                    }
-
-                    //zeros new data
-                    memset(this->_lowerPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
-                    return m_data.lower(m_rowStartIndex[outer]);
-                } else {
-                    return m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
-                }
-            }
-        } else {
-            if (outer > inner) //upper matrix
-            {
-                const Index maxOuterIndex = inner + m_data.upperProfile(inner);
-                if (outer > maxOuterIndex) //The value does not yet exist
-                {
-                    const Index previousProfile = m_data.upperProfile(inner);
-                    m_data.upperProfile(inner) = outer - inner;
-
-                    const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
-                    //shift data stored after this new one
-                    const Index stop = m_rowStartIndex[rows()];
-                    const Index start = m_rowStartIndex[inner + 1];
-
-                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
-                        m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
-                    }
-
-                    for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
-                        m_rowStartIndex[innerIdx] += bandIncrement;
-                    }
-                    memset(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
-                    return m_data.upper(m_rowStartIndex[inner] + m_data.upperProfile(inner));
-                } else {
-                    return m_data.upper(m_rowStartIndex[inner] + (outer - inner));
-                }
-            }
-
-            if (outer < inner) //lower matrix
-            {
-                const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
-                if (inner > maxInnerIndex) //The value does not yet exist
-                {
-                    const Index previousProfile = m_data.lowerProfile(outer);
-                    m_data.lowerProfile(outer) = inner - outer;
-
-                    const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
-                    //shift data stored after this new one
-                    const Index stop = m_colStartIndex[cols()];
-                    const Index start = m_colStartIndex[outer + 1];
-
-                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
-                        m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
-                    }
-
-                    for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
-                        m_colStartIndex[innerIdx] += bandIncrement;
-                    }
-                    memset(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
-                    return m_data.lower(m_colStartIndex[outer] + m_data.lowerProfile(outer));
-                } else {
-                    return m_data.lower(m_colStartIndex[outer] + (inner - outer));
-                }
-            }
-        }
-    }
-
-    /** Must be called after inserting a set of non zero entries.
-     */
-    inline void finalize() {
-        if (IsRowMajor) {
-            if (rows() > cols())
-                m_data.resize(cols(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
-            else
-                m_data.resize(rows(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
-
-            //            eigen_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
-            //
-            //            Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
-            //            Index dataIdx = 0;
-            //            for (Index row = 0; row < rows(); row++) {
-            //
-            //                const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row];
-            //                //                std::cout << "nbLowerElts" << nbLowerElts << std::endl;
-            //                memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof (Scalar));
-            //                m_rowStartIndex[row] = dataIdx;
-            //                dataIdx += nbLowerElts;
-            //
-            //                const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row];
-            //                memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof (Scalar));
-            //                m_colStartIndex[row] = dataIdx;
-            //                dataIdx += nbUpperElts;
-            //
-            //
-            //            }
-            //            //todo : don't access m_data profile directly : add an accessor from SkylineMatrix
-            //            m_rowStartIndex[rows()] = m_rowStartIndex[rows()-1] + m_data.lowerProfile(rows()-1);
-            //            m_colStartIndex[cols()] = m_colStartIndex[cols()-1] + m_data.upperProfile(cols()-1);
-            //
-            //            delete[] m_data.m_lower;
-            //            delete[] m_data.m_upper;
-            //
-            //            m_data.m_lower = newArray;
-            //            m_data.m_upper = newArray;
-        } else {
-            if (rows() > cols())
-                m_data.resize(cols(), rows(), cols(), m_rowStartIndex[cols()] + 1, m_colStartIndex[cols()] + 1);
-            else
-                m_data.resize(rows(), rows(), cols(), m_rowStartIndex[rows()] + 1, m_colStartIndex[rows()] + 1);
-        }
-    }
-
-    inline void squeeze() {
-        finalize();
-        m_data.squeeze();
-    }
-
-    void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar > ()) {
-        //TODO
-    }
-
-    /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
-     * \sa resizeNonZeros(Index), reserve(), setZero()
-     */
-    void resize(size_t rows, size_t cols) {
-        const Index diagSize = rows > cols ? cols : rows;
-        m_innerSize = IsRowMajor ? cols : rows;
-
-        eigen_assert(rows == cols && "Skyline matrix must be square matrix");
-
-        if (diagSize % 2) { // diagSize is odd
-            const Index k = (diagSize - 1) / 2;
-
-            m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
-                    2 * k * k + k + 1,
-                    2 * k * k + k + 1);
-
-        } else // diagSize is even
-        {
-            const Index k = diagSize / 2;
-            m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
-                    2 * k * k - k + 1,
-                    2 * k * k - k + 1);
-        }
-
-        if (m_colStartIndex && m_rowStartIndex) {
-            delete[] m_colStartIndex;
-            delete[] m_rowStartIndex;
-        }
-        m_colStartIndex = new Index [cols + 1];
-        m_rowStartIndex = new Index [rows + 1];
-        m_outerSize = diagSize;
-
-        m_data.reset();
-        m_data.clear();
-
-        m_outerSize = diagSize;
-        memset(m_colStartIndex, 0, (cols + 1) * sizeof (Index));
-        memset(m_rowStartIndex, 0, (rows + 1) * sizeof (Index));
-    }
-
-    void resizeNonZeros(Index size) {
-        m_data.resize(size);
-    }
-
-    inline SkylineMatrix()
-    : m_outerSize(-1), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
-        resize(0, 0);
-    }
-
-    inline SkylineMatrix(size_t rows, size_t cols)
-    : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
-        resize(rows, cols);
-    }
-
-    template<typename OtherDerived>
-    inline SkylineMatrix(const SkylineMatrixBase<OtherDerived>& other)
-    : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
-        *this = other.derived();
-    }
-
-    inline SkylineMatrix(const SkylineMatrix & other)
-    : Base(), m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
-        *this = other.derived();
-    }
-
-    inline void swap(SkylineMatrix & other) {
-        //EIGEN_DBG_SKYLINE(std::cout << "SkylineMatrix:: swap\n");
-        std::swap(m_colStartIndex, other.m_colStartIndex);
-        std::swap(m_rowStartIndex, other.m_rowStartIndex);
-        std::swap(m_innerSize, other.m_innerSize);
-        std::swap(m_outerSize, other.m_outerSize);
-        m_data.swap(other.m_data);
-    }
-
-    inline SkylineMatrix & operator=(const SkylineMatrix & other) {
-        std::cout << "SkylineMatrix& operator=(const SkylineMatrix& other)\n";
-        if (other.isRValue()) {
-            swap(other.const_cast_derived());
-        } else {
-            resize(other.rows(), other.cols());
-            memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (Index));
-            memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (Index));
-            m_data = other.m_data;
-        }
-        return *this;
-    }
-
-    template<typename OtherDerived>
-            inline SkylineMatrix & operator=(const SkylineMatrixBase<OtherDerived>& other) {
-        const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
-        if (needToTranspose) {
-            //         TODO
-            //            return *this;
-        } else {
-            // there is no special optimization
-            return SkylineMatrixBase<SkylineMatrix>::operator=(other.derived());
-        }
-    }
-
-    friend std::ostream & operator <<(std::ostream & s, const SkylineMatrix & m) {
-
-        EIGEN_DBG_SKYLINE(
-        std::cout << "upper elements : " << std::endl;
-        for (Index i = 0; i < m.m_data.upperSize(); i++)
-            std::cout << m.m_data.upper(i) << "\t";
-        std::cout << std::endl;
-        std::cout << "upper profile : " << std::endl;
-        for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
-            std::cout << m.m_data.upperProfile(i) << "\t";
-        std::cout << std::endl;
-        std::cout << "lower startIdx : " << std::endl;
-        for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
-            std::cout << (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t";
-        std::cout << std::endl;
-
-
-        std::cout << "lower elements : " << std::endl;
-        for (Index i = 0; i < m.m_data.lowerSize(); i++)
-            std::cout << m.m_data.lower(i) << "\t";
-        std::cout << std::endl;
-        std::cout << "lower profile : " << std::endl;
-        for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
-            std::cout << m.m_data.lowerProfile(i) << "\t";
-        std::cout << std::endl;
-        std::cout << "lower startIdx : " << std::endl;
-        for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
-            std::cout << (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t";
-        std::cout << std::endl;
-        );
-        for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
-            for (Index colIdx = 0; colIdx < m.cols(); colIdx++) {
-                s << m.coeff(rowIdx, colIdx) << "\t";
-            }
-            s << std::endl;
-        }
-        return s;
-    }
-
-    /** Destructor */
-    inline ~SkylineMatrix() {
-        delete[] m_colStartIndex;
-        delete[] m_rowStartIndex;
-    }
-
-    /** Overloaded for performance */
-    Scalar sum() const;
-};
-
-template<typename Scalar, int _Options>
-class SkylineMatrix<Scalar, _Options>::InnerUpperIterator {
-public:
-
-    InnerUpperIterator(const SkylineMatrix& mat, Index outer)
-    : m_matrix(mat), m_outer(outer),
-    m_id(_Options == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1),
-    m_start(m_id),
-    m_end(_Options == RowMajor ? mat.m_colStartIndex[outer + 1] : mat.m_rowStartIndex[outer + 1] + 1) {
-    }
-
-    inline InnerUpperIterator & operator++() {
-        m_id++;
-        return *this;
-    }
-
-    inline InnerUpperIterator & operator+=(Index shift) {
-        m_id += shift;
-        return *this;
-    }
-
-    inline Scalar value() const {
-        return m_matrix.m_data.upper(m_id);
-    }
-
-    inline Scalar* valuePtr() {
-        return const_cast<Scalar*> (&(m_matrix.m_data.upper(m_id)));
-    }
-
-    inline Scalar& valueRef() {
-        return const_cast<Scalar&> (m_matrix.m_data.upper(m_id));
-    }
-
-    inline Index index() const {
-        return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start) :
-                m_outer + (m_id - m_start) + 1;
-    }
-
-    inline Index row() const {
-        return IsRowMajor ? index() : m_outer;
-    }
-
-    inline Index col() const {
-        return IsRowMajor ? m_outer : index();
-    }
-
-    inline size_t size() const {
-        return m_matrix.m_data.upperProfile(m_outer);
-    }
-
-    inline operator bool() const {
-        return (m_id < m_end) && (m_id >= m_start);
-    }
-
-protected:
-    const SkylineMatrix& m_matrix;
-    const Index m_outer;
-    Index m_id;
-    const Index m_start;
-    const Index m_end;
-};
-
-template<typename Scalar, int _Options>
-class SkylineMatrix<Scalar, _Options>::InnerLowerIterator {
-public:
-
-    InnerLowerIterator(const SkylineMatrix& mat, Index outer)
-    : m_matrix(mat),
-    m_outer(outer),
-    m_id(_Options == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1),
-    m_start(m_id),
-    m_end(_Options == RowMajor ? mat.m_rowStartIndex[outer + 1] : mat.m_colStartIndex[outer + 1] + 1) {
-    }
-
-    inline InnerLowerIterator & operator++() {
-        m_id++;
-        return *this;
-    }
-
-    inline InnerLowerIterator & operator+=(Index shift) {
-        m_id += shift;
-        return *this;
-    }
-
-    inline Scalar value() const {
-        return m_matrix.m_data.lower(m_id);
-    }
-
-    inline Scalar* valuePtr() {
-        return const_cast<Scalar*> (&(m_matrix.m_data.lower(m_id)));
-    }
-
-    inline Scalar& valueRef() {
-        return const_cast<Scalar&> (m_matrix.m_data.lower(m_id));
-    }
-
-    inline Index index() const {
-        return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start) :
-                m_outer + (m_id - m_start) + 1;
-        ;
-    }
-
-    inline Index row() const {
-        return IsRowMajor ? m_outer : index();
-    }
-
-    inline Index col() const {
-        return IsRowMajor ? index() : m_outer;
-    }
-
-    inline size_t size() const {
-        return m_matrix.m_data.lowerProfile(m_outer);
-    }
-
-    inline operator bool() const {
-        return (m_id < m_end) && (m_id >= m_start);
-    }
-
-protected:
-    const SkylineMatrix& m_matrix;
-    const Index m_outer;
-    Index m_id;
-    const Index m_start;
-    const Index m_end;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SKYLINEMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
deleted file mode 100644
index b0d5e10..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
+++ /dev/null
@@ -1,212 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SKYLINEMATRIXBASE_H
-#define EIGEN_SKYLINEMATRIXBASE_H
-
-#include "SkylineUtil.h"
-
-namespace Eigen {
-
-/** \ingroup Skyline_Module
- *
- * \class SkylineMatrixBase
- *
- * \brief Base class of any skyline matrices or skyline expressions
- *
- * \param Derived
- *
- */
-template<typename Derived> class SkylineMatrixBase : public EigenBase<Derived> {
-public:
-
-    typedef typename internal::traits<Derived>::Scalar Scalar;
-    typedef typename internal::traits<Derived>::StorageKind StorageKind;
-    typedef typename internal::index<StorageKind>::type Index;
-
-    enum {
-        RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
-        /**< The number of rows at compile-time. This is just a copy of the value provided
-         * by the \a Derived type. If a value is not known at compile-time,
-         * it is set to the \a Dynamic constant.
-         * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
-
-        ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
-        /**< The number of columns at compile-time. This is just a copy of the value provided
-         * by the \a Derived type. If a value is not known at compile-time,
-         * it is set to the \a Dynamic constant.
-         * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
-
-
-        SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
-        internal::traits<Derived>::ColsAtCompileTime>::ret),
-        /**< This is equal to the number of coefficients, i.e. the number of
-         * rows times the number of columns, or to \a Dynamic if this is not
-         * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
-
-        MaxRowsAtCompileTime = RowsAtCompileTime,
-        MaxColsAtCompileTime = ColsAtCompileTime,
-
-        MaxSizeAtCompileTime = (internal::size_at_compile_time<MaxRowsAtCompileTime,
-        MaxColsAtCompileTime>::ret),
-
-        IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
-        /**< This is set to true if either the number of rows or the number of
-         * columns is known at compile-time to be equal to 1. Indeed, in that case,
-         * we are dealing with a column-vector (if there is only one column) or with
-         * a row-vector (if there is only one row). */
-
-        Flags = internal::traits<Derived>::Flags,
-        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
-         * constructed from this one. See the \ref flags "list of flags".
-         */
-
-        CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
-        /**< This is a rough measure of how expensive it is to read one coefficient from
-         * this expression.
-         */
-
-        IsRowMajor = Flags & RowMajorBit ? 1 : 0
-    };
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-    /** This is the "real scalar" type; if the \a Scalar type is already real numbers
-     * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
-     * \a Scalar is \a std::complex<T> then RealScalar is \a T.
-     *
-     * \sa class NumTraits
-     */
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-
-    /** type of the equivalent square matrix */
-    typedef Matrix<Scalar, EIGEN_SIZE_MAX(RowsAtCompileTime, ColsAtCompileTime),
-                           EIGEN_SIZE_MAX(RowsAtCompileTime, ColsAtCompileTime) > SquareMatrixType;
-
-    inline const Derived& derived() const {
-        return *static_cast<const Derived*> (this);
-    }
-
-    inline Derived& derived() {
-        return *static_cast<Derived*> (this);
-    }
-
-    inline Derived& const_cast_derived() const {
-        return *static_cast<Derived*> (const_cast<SkylineMatrixBase*> (this));
-    }
-#endif // not EIGEN_PARSED_BY_DOXYGEN
-
-    /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
-    inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT {
-        return derived().rows();
-    }
-
-    /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
-    inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT {
-        return derived().cols();
-    }
-
-    /** \returns the number of coefficients, which is \a rows()*cols().
-     * \sa rows(), cols(), SizeAtCompileTime. */
-    inline EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT {
-        return rows() * cols();
-    }
-
-    /** \returns the number of nonzero coefficients which is in practice the number
-     * of stored coefficients. */
-    inline Index nonZeros() const {
-        return derived().nonZeros();
-    }
-
-    /** \returns the size of the storage major dimension,
-     * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
-    Index outerSize() const {
-        return (int(Flags) & RowMajorBit) ? this->rows() : this->cols();
-    }
-
-    /** \returns the size of the inner dimension according to the storage order,
-     * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
-    Index innerSize() const {
-        return (int(Flags) & RowMajorBit) ? this->cols() : this->rows();
-    }
-
-    bool isRValue() const {
-        return m_isRValue;
-    }
-
-    Derived& markAsRValue() {
-        m_isRValue = true;
-        return derived();
-    }
-
-    SkylineMatrixBase() : m_isRValue(false) {
-        /* TODO check flags */
-    }
-
-    inline Derived & operator=(const Derived& other) {
-        this->operator=<Derived > (other);
-        return derived();
-    }
-
-    template<typename OtherDerived>
-    inline void assignGeneric(const OtherDerived& other) {
-        derived().resize(other.rows(), other.cols());
-        for (Index row = 0; row < rows(); row++)
-            for (Index col = 0; col < cols(); col++) {
-                if (other.coeff(row, col) != Scalar(0))
-                    derived().insert(row, col) = other.coeff(row, col);
-            }
-        derived().finalize();
-    }
-
-    template<typename OtherDerived>
-            inline Derived & operator=(const SkylineMatrixBase<OtherDerived>& other) {
-        //TODO
-    }
-
-    template<typename Lhs, typename Rhs>
-            inline Derived & operator=(const SkylineProduct<Lhs, Rhs, SkylineTimeSkylineProduct>& product);
-
-    friend std::ostream & operator <<(std::ostream & s, const SkylineMatrixBase& m) {
-        s << m.derived();
-        return s;
-    }
-
-    template<typename OtherDerived>
-    const typename SkylineProductReturnType<Derived, OtherDerived>::Type
-    operator*(const MatrixBase<OtherDerived> &other) const;
-
-    /** \internal use operator= */
-    template<typename DenseDerived>
-    void evalTo(MatrixBase<DenseDerived>& dst) const {
-        dst.setZero();
-        for (Index i = 0; i < rows(); i++)
-            for (Index j = 0; j < rows(); j++)
-                dst(i, j) = derived().coeff(i, j);
-    }
-
-    Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> toDense() const {
-        return derived();
-    }
-
-    /** \returns the matrix or vector obtained by evaluating this expression.
-     *
-     * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
-     * a const reference, in order to avoid a useless copy.
-     */
-    EIGEN_STRONG_INLINE const typename internal::eval<Derived, IsSkyline>::type eval() const {
-        return typename internal::eval<Derived>::type(derived());
-    }
-
-protected:
-    bool m_isRValue;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SKYLINEMATRIXBASE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h
deleted file mode 100644
index d9eb814..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h
+++ /dev/null
@@ -1,295 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SKYLINEPRODUCT_H
-#define EIGEN_SKYLINEPRODUCT_H
-
-namespace Eigen { 
-
-template<typename Lhs, typename Rhs, int ProductMode>
-struct SkylineProductReturnType {
-    typedef const typename internal::nested_eval<Lhs, Rhs::RowsAtCompileTime>::type LhsNested;
-    typedef const typename internal::nested_eval<Rhs, Lhs::RowsAtCompileTime>::type RhsNested;
-
-    typedef SkylineProduct<LhsNested, RhsNested, ProductMode> Type;
-};
-
-template<typename LhsNested, typename RhsNested, int ProductMode>
-struct internal::traits<SkylineProduct<LhsNested, RhsNested, ProductMode> > {
-    // clean the nested types:
-    typedef typename internal::remove_all<LhsNested>::type _LhsNested;
-    typedef typename internal::remove_all<RhsNested>::type _RhsNested;
-    typedef typename _LhsNested::Scalar Scalar;
-
-    enum {
-        LhsCoeffReadCost = _LhsNested::CoeffReadCost,
-        RhsCoeffReadCost = _RhsNested::CoeffReadCost,
-        LhsFlags = _LhsNested::Flags,
-        RhsFlags = _RhsNested::Flags,
-
-        RowsAtCompileTime = _LhsNested::RowsAtCompileTime,
-        ColsAtCompileTime = _RhsNested::ColsAtCompileTime,
-        InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime),
-
-        MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime,
-        MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime,
-
-        EvalToRowMajor = (RhsFlags & LhsFlags & RowMajorBit),
-        ResultIsSkyline = ProductMode == SkylineTimeSkylineProduct,
-
-        RemovedBits = ~((EvalToRowMajor ? 0 : RowMajorBit) | (ResultIsSkyline ? 0 : SkylineBit)),
-
-        Flags = (int(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits)
-        | EvalBeforeAssigningBit
-        | EvalBeforeNestingBit,
-
-        CoeffReadCost = HugeCost
-    };
-
-    typedef typename internal::conditional<ResultIsSkyline,
-            SkylineMatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode> >,
-            MatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode> > >::type Base;
-};
-
-namespace internal {
-template<typename LhsNested, typename RhsNested, int ProductMode>
-class SkylineProduct : no_assignment_operator,
-public traits<SkylineProduct<LhsNested, RhsNested, ProductMode> >::Base {
-public:
-
-    EIGEN_GENERIC_PUBLIC_INTERFACE(SkylineProduct)
-
-private:
-
-    typedef typename traits<SkylineProduct>::_LhsNested _LhsNested;
-    typedef typename traits<SkylineProduct>::_RhsNested _RhsNested;
-
-public:
-
-    template<typename Lhs, typename Rhs>
-    EIGEN_STRONG_INLINE SkylineProduct(const Lhs& lhs, const Rhs& rhs)
-    : m_lhs(lhs), m_rhs(rhs) {
-        eigen_assert(lhs.cols() == rhs.rows());
-
-        enum {
-            ProductIsValid = _LhsNested::ColsAtCompileTime == Dynamic
-            || _RhsNested::RowsAtCompileTime == Dynamic
-            || int(_LhsNested::ColsAtCompileTime) == int(_RhsNested::RowsAtCompileTime),
-            AreVectors = _LhsNested::IsVectorAtCompileTime && _RhsNested::IsVectorAtCompileTime,
-            SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(_LhsNested, _RhsNested)
-        };
-        // note to the lost user:
-        //    * for a dot product use: v1.dot(v2)
-        //    * for a coeff-wise product use: v1.cwise()*v2
-        EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
-                INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
-                EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
-                INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
-                EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
-    }
-
-    EIGEN_STRONG_INLINE Index rows() const {
-        return m_lhs.rows();
-    }
-
-    EIGEN_STRONG_INLINE Index cols() const {
-        return m_rhs.cols();
-    }
-
-    EIGEN_STRONG_INLINE const _LhsNested& lhs() const {
-        return m_lhs;
-    }
-
-    EIGEN_STRONG_INLINE const _RhsNested& rhs() const {
-        return m_rhs;
-    }
-
-protected:
-    LhsNested m_lhs;
-    RhsNested m_rhs;
-};
-
-// dense = skyline * dense
-// Note that here we force no inlining and separate the setZero() because GCC messes up otherwise
-
-template<typename Lhs, typename Rhs, typename Dest>
-EIGEN_DONT_INLINE void skyline_row_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
-    typedef typename remove_all<Lhs>::type _Lhs;
-    typedef typename remove_all<Rhs>::type _Rhs;
-    typedef typename traits<Lhs>::Scalar Scalar;
-
-    enum {
-        LhsIsRowMajor = (_Lhs::Flags & RowMajorBit) == RowMajorBit,
-        LhsIsSelfAdjoint = (_Lhs::Flags & SelfAdjointBit) == SelfAdjointBit,
-        ProcessFirstHalf = LhsIsSelfAdjoint
-        && (((_Lhs::Flags & (UpperTriangularBit | LowerTriangularBit)) == 0)
-        || ((_Lhs::Flags & UpperTriangularBit) && !LhsIsRowMajor)
-        || ((_Lhs::Flags & LowerTriangularBit) && LhsIsRowMajor)),
-        ProcessSecondHalf = LhsIsSelfAdjoint && (!ProcessFirstHalf)
-    };
-
-    //Use matrix diagonal part <- Improvement : use inner iterator on dense matrix.
-    for (Index col = 0; col < rhs.cols(); col++) {
-        for (Index row = 0; row < lhs.rows(); row++) {
-            dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
-        }
-    }
-    //Use matrix lower triangular part
-    for (Index row = 0; row < lhs.rows(); row++) {
-        typename _Lhs::InnerLowerIterator lIt(lhs, row);
-        const Index stop = lIt.col() + lIt.size();
-        for (Index col = 0; col < rhs.cols(); col++) {
-
-            Index k = lIt.col();
-            Scalar tmp = 0;
-            while (k < stop) {
-                tmp +=
-                        lIt.value() *
-                        rhs(k++, col);
-                ++lIt;
-            }
-            dst(row, col) += tmp;
-            lIt += -lIt.size();
-        }
-
-    }
-
-    //Use matrix upper triangular part
-    for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) {
-        typename _Lhs::InnerUpperIterator uIt(lhs, lhscol);
-        const Index stop = uIt.size() + uIt.row();
-        for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) {
-
-
-            const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
-            Index k = uIt.row();
-            while (k < stop) {
-                dst(k++, rhscol) +=
-                        uIt.value() *
-                        rhsCoeff;
-                ++uIt;
-            }
-            uIt += -uIt.size();
-        }
-    }
-
-}
-
-template<typename Lhs, typename Rhs, typename Dest>
-EIGEN_DONT_INLINE void skyline_col_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
-    typedef typename remove_all<Lhs>::type _Lhs;
-    typedef typename remove_all<Rhs>::type _Rhs;
-    typedef typename traits<Lhs>::Scalar Scalar;
-
-    enum {
-        LhsIsRowMajor = (_Lhs::Flags & RowMajorBit) == RowMajorBit,
-        LhsIsSelfAdjoint = (_Lhs::Flags & SelfAdjointBit) == SelfAdjointBit,
-        ProcessFirstHalf = LhsIsSelfAdjoint
-        && (((_Lhs::Flags & (UpperTriangularBit | LowerTriangularBit)) == 0)
-        || ((_Lhs::Flags & UpperTriangularBit) && !LhsIsRowMajor)
-        || ((_Lhs::Flags & LowerTriangularBit) && LhsIsRowMajor)),
-        ProcessSecondHalf = LhsIsSelfAdjoint && (!ProcessFirstHalf)
-    };
-
-    //Use matrix diagonal part <- Improvement : use inner iterator on dense matrix.
-    for (Index col = 0; col < rhs.cols(); col++) {
-        for (Index row = 0; row < lhs.rows(); row++) {
-            dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
-        }
-    }
-
-    //Use matrix upper triangular part
-    for (Index row = 0; row < lhs.rows(); row++) {
-        typename _Lhs::InnerUpperIterator uIt(lhs, row);
-        const Index stop = uIt.col() + uIt.size();
-        for (Index col = 0; col < rhs.cols(); col++) {
-
-            Index k = uIt.col();
-            Scalar tmp = 0;
-            while (k < stop) {
-                tmp +=
-                        uIt.value() *
-                        rhs(k++, col);
-                ++uIt;
-            }
-
-
-            dst(row, col) += tmp;
-            uIt += -uIt.size();
-        }
-    }
-
-    //Use matrix lower triangular part
-    for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) {
-        typename _Lhs::InnerLowerIterator lIt(lhs, lhscol);
-        const Index stop = lIt.size() + lIt.row();
-        for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) {
-
-            const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
-            Index k = lIt.row();
-            while (k < stop) {
-                dst(k++, rhscol) +=
-                        lIt.value() *
-                        rhsCoeff;
-                ++lIt;
-            }
-            lIt += -lIt.size();
-        }
-    }
-
-}
-
-template<typename Lhs, typename Rhs, typename ResultType,
-        int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit>
-        struct skyline_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct skyline_product_selector<Lhs, Rhs, ResultType, RowMajor> {
-    typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-    static void run(const Lhs& lhs, const Rhs& rhs, ResultType & res) {
-        skyline_row_major_time_dense_product<Lhs, Rhs, ResultType > (lhs, rhs, res);
-    }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct skyline_product_selector<Lhs, Rhs, ResultType, ColMajor> {
-    typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-    static void run(const Lhs& lhs, const Rhs& rhs, ResultType & res) {
-        skyline_col_major_time_dense_product<Lhs, Rhs, ResultType > (lhs, rhs, res);
-    }
-};
-
-} // end namespace internal
-
-// template<typename Derived>
-// template<typename Lhs, typename Rhs >
-// Derived & MatrixBase<Derived>::lazyAssign(const SkylineProduct<Lhs, Rhs, SkylineTimeDenseProduct>& product) {
-//     typedef typename internal::remove_all<Lhs>::type _Lhs;
-//     internal::skyline_product_selector<typename internal::remove_all<Lhs>::type,
-//             typename internal::remove_all<Rhs>::type,
-//             Derived>::run(product.lhs(), product.rhs(), derived());
-// 
-//     return derived();
-// }
-
-// skyline * dense
-
-template<typename Derived>
-template<typename OtherDerived >
-EIGEN_STRONG_INLINE const typename SkylineProductReturnType<Derived, OtherDerived>::Type
-SkylineMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const {
-
-    return typename SkylineProductReturnType<Derived, OtherDerived>::Type(derived(), other.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SKYLINEPRODUCT_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h
deleted file mode 100644
index cc7514f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h
+++ /dev/null
@@ -1,259 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SKYLINE_STORAGE_H
-#define EIGEN_SKYLINE_STORAGE_H
-
-namespace Eigen { 
-
-/** Stores a skyline set of values in three structures :
- * The diagonal elements
- * The upper elements
- * The lower elements
- *
- */
-template<typename Scalar>
-class SkylineStorage {
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef SparseIndex Index;
-public:
-
-    SkylineStorage()
-    : m_diag(0),
-    m_lower(0),
-    m_upper(0),
-    m_lowerProfile(0),
-    m_upperProfile(0),
-    m_diagSize(0),
-    m_upperSize(0),
-    m_lowerSize(0),
-    m_upperProfileSize(0),
-    m_lowerProfileSize(0),
-    m_allocatedSize(0) {
-    }
-
-    SkylineStorage(const SkylineStorage& other)
-    : m_diag(0),
-    m_lower(0),
-    m_upper(0),
-    m_lowerProfile(0),
-    m_upperProfile(0),
-    m_diagSize(0),
-    m_upperSize(0),
-    m_lowerSize(0),
-    m_upperProfileSize(0),
-    m_lowerProfileSize(0),
-    m_allocatedSize(0) {
-        *this = other;
-    }
-
-    SkylineStorage & operator=(const SkylineStorage& other) {
-        resize(other.diagSize(), other.m_upperProfileSize, other.m_lowerProfileSize, other.upperSize(), other.lowerSize());
-        memcpy(m_diag, other.m_diag, m_diagSize * sizeof (Scalar));
-        memcpy(m_upper, other.m_upper, other.upperSize() * sizeof (Scalar));
-        memcpy(m_lower, other.m_lower, other.lowerSize() * sizeof (Scalar));
-        memcpy(m_upperProfile, other.m_upperProfile, m_upperProfileSize * sizeof (Index));
-        memcpy(m_lowerProfile, other.m_lowerProfile, m_lowerProfileSize * sizeof (Index));
-        return *this;
-    }
-
-    void swap(SkylineStorage& other) {
-        std::swap(m_diag, other.m_diag);
-        std::swap(m_upper, other.m_upper);
-        std::swap(m_lower, other.m_lower);
-        std::swap(m_upperProfile, other.m_upperProfile);
-        std::swap(m_lowerProfile, other.m_lowerProfile);
-        std::swap(m_diagSize, other.m_diagSize);
-        std::swap(m_upperSize, other.m_upperSize);
-        std::swap(m_lowerSize, other.m_lowerSize);
-        std::swap(m_allocatedSize, other.m_allocatedSize);
-    }
-
-    ~SkylineStorage() {
-        delete[] m_diag;
-        delete[] m_upper;
-        if (m_upper != m_lower)
-            delete[] m_lower;
-        delete[] m_upperProfile;
-        delete[] m_lowerProfile;
-    }
-
-    void reserve(Index size, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize) {
-        Index newAllocatedSize = size + upperSize + lowerSize;
-        if (newAllocatedSize > m_allocatedSize)
-            reallocate(size, upperProfileSize, lowerProfileSize, upperSize, lowerSize);
-    }
-
-    void squeeze() {
-        if (m_allocatedSize > m_diagSize + m_upperSize + m_lowerSize)
-            reallocate(m_diagSize, m_upperProfileSize, m_lowerProfileSize, m_upperSize, m_lowerSize);
-    }
-
-    void resize(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize, float reserveSizeFactor = 0) {
-        if (m_allocatedSize < diagSize + upperSize + lowerSize)
-            reallocate(diagSize, upperProfileSize, lowerProfileSize, upperSize + Index(reserveSizeFactor * upperSize), lowerSize + Index(reserveSizeFactor * lowerSize));
-        m_diagSize = diagSize;
-        m_upperSize = upperSize;
-        m_lowerSize = lowerSize;
-        m_upperProfileSize = upperProfileSize;
-        m_lowerProfileSize = lowerProfileSize;
-    }
-
-    inline Index diagSize() const {
-        return m_diagSize;
-    }
-
-    inline Index upperSize() const {
-        return m_upperSize;
-    }
-
-    inline Index lowerSize() const {
-        return m_lowerSize;
-    }
-
-    inline Index upperProfileSize() const {
-        return m_upperProfileSize;
-    }
-
-    inline Index lowerProfileSize() const {
-        return m_lowerProfileSize;
-    }
-
-    inline Index allocatedSize() const {
-        return m_allocatedSize;
-    }
-
-    inline void clear() {
-        m_diagSize = 0;
-    }
-
-    inline Scalar& diag(Index i) {
-        return m_diag[i];
-    }
-
-    inline const Scalar& diag(Index i) const {
-        return m_diag[i];
-    }
-
-    inline Scalar& upper(Index i) {
-        return m_upper[i];
-    }
-
-    inline const Scalar& upper(Index i) const {
-        return m_upper[i];
-    }
-
-    inline Scalar& lower(Index i) {
-        return m_lower[i];
-    }
-
-    inline const Scalar& lower(Index i) const {
-        return m_lower[i];
-    }
-
-    inline Index& upperProfile(Index i) {
-        return m_upperProfile[i];
-    }
-
-    inline const Index& upperProfile(Index i) const {
-        return m_upperProfile[i];
-    }
-
-    inline Index& lowerProfile(Index i) {
-        return m_lowerProfile[i];
-    }
-
-    inline const Index& lowerProfile(Index i) const {
-        return m_lowerProfile[i];
-    }
-
-    static SkylineStorage Map(Index* upperProfile, Index* lowerProfile, Scalar* diag, Scalar* upper, Scalar* lower, Index size, Index upperSize, Index lowerSize) {
-        SkylineStorage res;
-        res.m_upperProfile = upperProfile;
-        res.m_lowerProfile = lowerProfile;
-        res.m_diag = diag;
-        res.m_upper = upper;
-        res.m_lower = lower;
-        res.m_allocatedSize = res.m_diagSize = size;
-        res.m_upperSize = upperSize;
-        res.m_lowerSize = lowerSize;
-        return res;
-    }
-
-    inline void reset() {
-        memset(m_diag, 0, m_diagSize * sizeof (Scalar));
-        memset(m_upper, 0, m_upperSize * sizeof (Scalar));
-        memset(m_lower, 0, m_lowerSize * sizeof (Scalar));
-        memset(m_upperProfile, 0, m_diagSize * sizeof (Index));
-        memset(m_lowerProfile, 0, m_diagSize * sizeof (Index));
-    }
-
-    void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>()) {
-        //TODO
-    }
-
-protected:
-
-    inline void reallocate(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize) {
-
-        Scalar* diag = new Scalar[diagSize];
-        Scalar* upper = new Scalar[upperSize];
-        Scalar* lower = new Scalar[lowerSize];
-        Index* upperProfile = new Index[upperProfileSize];
-        Index* lowerProfile = new Index[lowerProfileSize];
-
-        Index copyDiagSize = (std::min)(diagSize, m_diagSize);
-        Index copyUpperSize = (std::min)(upperSize, m_upperSize);
-        Index copyLowerSize = (std::min)(lowerSize, m_lowerSize);
-        Index copyUpperProfileSize = (std::min)(upperProfileSize, m_upperProfileSize);
-        Index copyLowerProfileSize = (std::min)(lowerProfileSize, m_lowerProfileSize);
-
-        // copy
-        memcpy(diag, m_diag, copyDiagSize * sizeof (Scalar));
-        memcpy(upper, m_upper, copyUpperSize * sizeof (Scalar));
-        memcpy(lower, m_lower, copyLowerSize * sizeof (Scalar));
-        memcpy(upperProfile, m_upperProfile, copyUpperProfileSize * sizeof (Index));
-        memcpy(lowerProfile, m_lowerProfile, copyLowerProfileSize * sizeof (Index));
-
-
-
-        // delete old stuff
-        delete[] m_diag;
-        delete[] m_upper;
-        delete[] m_lower;
-        delete[] m_upperProfile;
-        delete[] m_lowerProfile;
-        m_diag = diag;
-        m_upper = upper;
-        m_lower = lower;
-        m_upperProfile = upperProfile;
-        m_lowerProfile = lowerProfile;
-        m_allocatedSize = diagSize + upperSize + lowerSize;
-        m_upperSize = upperSize;
-        m_lowerSize = lowerSize;
-    }
-
-public:
-    Scalar* m_diag;
-    Scalar* m_upper;
-    Scalar* m_lower;
-    Index* m_upperProfile;
-    Index* m_lowerProfile;
-    Index m_diagSize;
-    Index m_upperSize;
-    Index m_lowerSize;
-    Index m_upperProfileSize;
-    Index m_lowerProfileSize;
-    Index m_allocatedSize;
-
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_SKYLINE_STORAGE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h
deleted file mode 100644
index 75eb612..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h
+++ /dev/null
@@ -1,89 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Guillaume Saupin <guillaume.saupin@cea.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SKYLINEUTIL_H
-#define EIGEN_SKYLINEUTIL_H
-
-namespace Eigen { 
-
-#ifdef NDEBUG
-#define EIGEN_DBG_SKYLINE(X)
-#else
-#define EIGEN_DBG_SKYLINE(X) X
-#endif
-
-const unsigned int SkylineBit = 0x1200;
-template<typename Lhs, typename Rhs, int ProductMode> class SkylineProduct;
-enum AdditionalProductEvaluationMode {SkylineTimeDenseProduct, SkylineTimeSkylineProduct, DenseTimeSkylineProduct};
-enum {IsSkyline = SkylineBit};
-
-
-#define EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
-template<typename OtherDerived> \
-EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SkylineMatrixBase<OtherDerived>& other) \
-{ \
-  return Base::operator Op(other.derived()); \
-} \
-EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
-{ \
-  return Base::operator Op(other); \
-}
-
-#define EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
-template<typename Other> \
-EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
-{ \
-  return Base::operator Op(scalar); \
-}
-
-#define EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
-  EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \
-  EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \
-  EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \
-  EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \
-  EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=)
-
-#define _EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived, BaseClass) \
-  typedef BaseClass Base; \
-  typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; \
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \
-  typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \
-  typedef typename Eigen::internal::index<StorageKind>::type Index; \
-  enum {  Flags = Eigen::internal::traits<Derived>::Flags, };
-
-#define EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived) \
-  _EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived, Eigen::SkylineMatrixBase<Derived>)
-
-template<typename Derived> class SkylineMatrixBase;
-template<typename _Scalar, int _Flags = 0> class SkylineMatrix;
-template<typename _Scalar, int _Flags = 0> class DynamicSkylineMatrix;
-template<typename _Scalar, int _Flags = 0> class SkylineVector;
-template<typename _Scalar, int _Flags = 0> class MappedSkylineMatrix;
-
-namespace internal {
-
-template<typename Lhs, typename Rhs> struct skyline_product_mode;
-template<typename Lhs, typename Rhs, int ProductMode = skyline_product_mode<Lhs,Rhs>::value> struct SkylineProductReturnType;
-
-template<typename T> class eval<T,IsSkyline>
-{
-    typedef typename traits<T>::Scalar _Scalar;
-    enum {
-          _Flags = traits<T>::Flags
-    };
-
-  public:
-    typedef SkylineMatrix<_Scalar, _Flags> type;
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SKYLINEUTIL_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h
deleted file mode 100644
index e9ec746..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h
+++ /dev/null
@@ -1,122 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_BLOCKFORDYNAMICMATRIX_H
-#define EIGEN_SPARSE_BLOCKFORDYNAMICMATRIX_H
-
-namespace Eigen { 
-
-#if 0
-
-// NOTE Have to be reimplemented as a specialization of BlockImpl< DynamicSparseMatrix<_Scalar, _Options, _Index>, ... >
-// See SparseBlock.h for an example
-
-
-/***************************************************************************
-* specialisation for DynamicSparseMatrix
-***************************************************************************/
-
-template<typename _Scalar, int _Options, typename _Index, int Size>
-class SparseInnerVectorSet<DynamicSparseMatrix<_Scalar, _Options, _Index>, Size>
-  : public SparseMatrixBase<SparseInnerVectorSet<DynamicSparseMatrix<_Scalar, _Options, _Index>, Size> >
-{
-    typedef DynamicSparseMatrix<_Scalar, _Options, _Index> MatrixType;
-  public:
-
-    enum { IsRowMajor = internal::traits<SparseInnerVectorSet>::IsRowMajor };
-
-    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseInnerVectorSet)
-    class InnerIterator: public MatrixType::InnerIterator
-    {
-      public:
-        inline InnerIterator(const SparseInnerVectorSet& xpr, Index outer)
-          : MatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
-        {}
-        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
-        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
-      protected:
-        Index m_outer;
-    };
-
-    inline SparseInnerVectorSet(const MatrixType& matrix, Index outerStart, Index outerSize)
-      : m_matrix(matrix), m_outerStart(outerStart), m_outerSize(outerSize)
-    {
-      eigen_assert( (outerStart>=0) && ((outerStart+outerSize)<=matrix.outerSize()) );
-    }
-
-    inline SparseInnerVectorSet(const MatrixType& matrix, Index outer)
-      : m_matrix(matrix), m_outerStart(outer), m_outerSize(Size)
-    {
-      eigen_assert(Size!=Dynamic);
-      eigen_assert( (outer>=0) && (outer<matrix.outerSize()) );
-    }
-
-    template<typename OtherDerived>
-    inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
-    {
-      if (IsRowMajor != ((OtherDerived::Flags&RowMajorBit)==RowMajorBit))
-      {
-        // need to transpose => perform a block evaluation followed by a big swap
-        DynamicSparseMatrix<Scalar,IsRowMajor?RowMajorBit:0> aux(other);
-        *this = aux.markAsRValue();
-      }
-      else
-      {
-        // evaluate/copy vector per vector
-        for (Index j=0; j<m_outerSize.value(); ++j)
-        {
-          SparseVector<Scalar,IsRowMajor ? RowMajorBit : 0> aux(other.innerVector(j));
-          m_matrix.const_cast_derived()._data()[m_outerStart+j].swap(aux._data());
-        }
-      }
-      return *this;
-    }
-
-    inline SparseInnerVectorSet& operator=(const SparseInnerVectorSet& other)
-    {
-      return operator=<SparseInnerVectorSet>(other);
-    }
-
-    Index nonZeros() const
-    {
-      Index count = 0;
-      for (Index j=0; j<m_outerSize.value(); ++j)
-        count += m_matrix._data()[m_outerStart+j].size();
-      return count;
-    }
-
-    const Scalar& lastCoeff() const
-    {
-      EIGEN_STATIC_ASSERT_VECTOR_ONLY(SparseInnerVectorSet);
-      eigen_assert(m_matrix.data()[m_outerStart].size()>0);
-      return m_matrix.data()[m_outerStart].vale(m_matrix.data()[m_outerStart].size()-1);
-    }
-
-//     template<typename Sparse>
-//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
-//     {
-//       return *this;
-//     }
-
-    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
-    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
-
-  protected:
-
-    const typename MatrixType::Nested m_matrix;
-    Index m_outerStart;
-    const internal::variable_if_dynamic<Index, Size> m_outerSize;
-
-};
-
-#endif
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_BLOCKFORDYNAMICMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
deleted file mode 100644
index 536a0c3..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
+++ /dev/null
@@ -1,1079 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
-// Copyright (C) 2013 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSEBLOCKMATRIX_H
-#define EIGEN_SPARSEBLOCKMATRIX_H
-
-namespace Eigen { 
-/** \ingroup SparseCore_Module
-  *
-  * \class BlockSparseMatrix
-  *
-  * \brief A versatile sparse matrix representation where each element is a block
-  *
-  * This class provides routines to manipulate block sparse matrices stored in a
-  * BSR-like representation. There are two main types :
-  *
-  * 1. All blocks have the same number of rows and columns, called block size
-  * in the following. In this case, if this block size is known at compile time,
-  * it can be given as a template parameter like
-  * \code
-  * BlockSparseMatrix<Scalar, 3, ColMajor> bmat(b_rows, b_cols);
-  * \endcode
-  * Here, bmat is a b_rows x b_cols block sparse matrix
-  * where each coefficient is a 3x3 dense matrix.
-  * If the block size is fixed but will be given at runtime,
-  * \code
-  * BlockSparseMatrix<Scalar, Dynamic, ColMajor> bmat(b_rows, b_cols);
-  * bmat.setBlockSize(block_size);
-  * \endcode
-  *
-  * 2. The second case is for variable-block sparse matrices.
-  * Here each block has its own dimensions. The only restriction is that all the blocks
-  * in a row (resp. a column) should have the same number of rows (resp. of columns).
-  * It is thus required in this case to describe the layout of the matrix by calling
-  * setBlockLayout(rowBlocks, colBlocks).
-  *
-  * In any of the previous case, the matrix can be filled by calling setFromTriplets().
-  * A regular sparse matrix can be converted to a block sparse matrix and vice versa.
-  * It is obviously required to describe the block layout beforehand by calling either
-  * setBlockSize() for fixed-size blocks or setBlockLayout for variable-size blocks.
-  *
-  * \tparam _Scalar The Scalar type
-  * \tparam _BlockAtCompileTime The block layout option. It takes the following values
-  * Dynamic : block size known at runtime
-  * a numeric number : fixed-size block known at compile time
-  */
-template<typename _Scalar, int _BlockAtCompileTime=Dynamic, int _Options=ColMajor, typename _StorageIndex=int> class BlockSparseMatrix;
-
-template<typename BlockSparseMatrixT> class BlockSparseMatrixView;
-
-namespace internal {
-template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _Index>
-struct traits<BlockSparseMatrix<_Scalar,_BlockAtCompileTime,_Options, _Index> >
-{
-  typedef _Scalar Scalar;
-  typedef _Index Index;
-  typedef Sparse StorageKind; // FIXME Where is it used ??
-  typedef MatrixXpr XprKind;
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = Dynamic,
-    MaxRowsAtCompileTime = Dynamic,
-    MaxColsAtCompileTime = Dynamic,
-    BlockSize = _BlockAtCompileTime,
-    Flags = _Options | NestByRefBit | LvalueBit,
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    SupportedAccessPatterns = InnerRandomAccessPattern
-  };
-};
-template<typename BlockSparseMatrixT>
-struct traits<BlockSparseMatrixView<BlockSparseMatrixT> >
-{
-  typedef Ref<Matrix<typename BlockSparseMatrixT::Scalar, BlockSparseMatrixT::BlockSize, BlockSparseMatrixT::BlockSize> > Scalar;
-  typedef Ref<Matrix<typename BlockSparseMatrixT::RealScalar, BlockSparseMatrixT::BlockSize, BlockSparseMatrixT::BlockSize> > RealScalar;
-
-};
-
-// Function object to sort a triplet list
-template<typename Iterator, bool IsColMajor>
-struct TripletComp
-{
-  typedef typename Iterator::value_type Triplet;
-  bool operator()(const Triplet& a, const Triplet& b)
-  { if(IsColMajor)
-      return ((a.col() == b.col() && a.row() < b.row()) || (a.col() < b.col()));
-    else
-      return ((a.row() == b.row() && a.col() < b.col()) || (a.row() < b.row()));
-  }
-};
-} // end namespace internal
-
-
-/* Proxy to view the block sparse matrix as a regular sparse matrix */
-template<typename BlockSparseMatrixT>
-class BlockSparseMatrixView : public SparseMatrixBase<BlockSparseMatrixT>
-{
-  public:
-    typedef Ref<typename BlockSparseMatrixT::BlockScalar> Scalar;
-    typedef Ref<typename BlockSparseMatrixT::BlockRealScalar> RealScalar;
-    typedef typename BlockSparseMatrixT::Index Index;
-    typedef  BlockSparseMatrixT Nested;
-    enum {
-      Flags = BlockSparseMatrixT::Options,
-      Options = BlockSparseMatrixT::Options,
-      RowsAtCompileTime = BlockSparseMatrixT::RowsAtCompileTime,
-      ColsAtCompileTime = BlockSparseMatrixT::ColsAtCompileTime,
-      MaxColsAtCompileTime = BlockSparseMatrixT::MaxColsAtCompileTime,
-      MaxRowsAtCompileTime = BlockSparseMatrixT::MaxRowsAtCompileTime
-    };
-  public:
-    BlockSparseMatrixView(const BlockSparseMatrixT& spblockmat)
-     : m_spblockmat(spblockmat)
-    {}
-
-    Index outerSize() const
-    {
-      return (Flags&RowMajorBit) == 1 ? this->rows() : this->cols();
-    }
-    Index cols() const
-    {
-      return m_spblockmat.blockCols();
-    }
-    Index rows() const
-    {
-      return m_spblockmat.blockRows();
-    }
-    Scalar coeff(Index row, Index col)
-    {
-      return m_spblockmat.coeff(row, col);
-    }
-    Scalar coeffRef(Index row, Index col)
-    {
-      return m_spblockmat.coeffRef(row, col);
-    }
-    // Wrapper to iterate over all blocks
-    class InnerIterator : public BlockSparseMatrixT::BlockInnerIterator
-    {
-      public:
-      InnerIterator(const BlockSparseMatrixView& mat, Index outer)
-          : BlockSparseMatrixT::BlockInnerIterator(mat.m_spblockmat, outer)
-      {}
-
-    };
-
-  protected:
-    const BlockSparseMatrixT& m_spblockmat;
-};
-
-// Proxy to view a regular vector as a block vector
-template<typename BlockSparseMatrixT, typename VectorType>
-class BlockVectorView
-{
-  public:
-    enum {
-      BlockSize = BlockSparseMatrixT::BlockSize,
-      ColsAtCompileTime = VectorType::ColsAtCompileTime,
-      RowsAtCompileTime = VectorType::RowsAtCompileTime,
-      Flags = VectorType::Flags
-    };
-    typedef Ref<const Matrix<typename BlockSparseMatrixT::Scalar, (RowsAtCompileTime==1)? 1 : BlockSize, (ColsAtCompileTime==1)? 1 : BlockSize> >Scalar;
-    typedef typename BlockSparseMatrixT::Index Index;
-  public:
-    BlockVectorView(const BlockSparseMatrixT& spblockmat, const VectorType& vec)
-    : m_spblockmat(spblockmat),m_vec(vec)
-    { }
-    inline Index cols() const
-    {
-      return m_vec.cols();
-    }
-    inline Index size() const
-    {
-      return m_spblockmat.blockRows();
-    }
-    inline Scalar coeff(Index bi) const
-    {
-      Index startRow = m_spblockmat.blockRowsIndex(bi);
-      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
-      return m_vec.middleRows(startRow, rowSize);
-    }
-    inline Scalar coeff(Index bi, Index j) const
-    {
-      Index startRow = m_spblockmat.blockRowsIndex(bi);
-      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
-      return m_vec.block(startRow, j, rowSize, 1);
-    }
-  protected:
-    const BlockSparseMatrixT& m_spblockmat;
-    const VectorType& m_vec;
-};
-
-template<typename VectorType, typename Index> class BlockVectorReturn;
-
-
-// Proxy to view a regular vector as a block vector
-template<typename BlockSparseMatrixT, typename VectorType>
-class BlockVectorReturn
-{
-  public:
-    enum {
-      ColsAtCompileTime = VectorType::ColsAtCompileTime,
-      RowsAtCompileTime = VectorType::RowsAtCompileTime,
-      Flags = VectorType::Flags
-    };
-    typedef Ref<Matrix<typename VectorType::Scalar, RowsAtCompileTime, ColsAtCompileTime> > Scalar;
-    typedef typename BlockSparseMatrixT::Index Index;
-  public:
-    BlockVectorReturn(const BlockSparseMatrixT& spblockmat, VectorType& vec)
-    : m_spblockmat(spblockmat),m_vec(vec)
-    { }
-    inline Index size() const
-    {
-      return m_spblockmat.blockRows();
-    }
-    inline Scalar coeffRef(Index bi)
-    {
-      Index startRow = m_spblockmat.blockRowsIndex(bi);
-      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
-      return m_vec.middleRows(startRow, rowSize);
-    }
-    inline Scalar coeffRef(Index bi, Index j)
-    {
-      Index startRow = m_spblockmat.blockRowsIndex(bi);
-      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
-      return m_vec.block(startRow, j, rowSize, 1);
-    }
-
-  protected:
-    const BlockSparseMatrixT& m_spblockmat;
-    VectorType& m_vec;
-};
-
-// Block version of the sparse dense product
-template<typename Lhs, typename Rhs>
-class BlockSparseTimeDenseProduct;
-
-namespace internal {
-
-template<typename BlockSparseMatrixT, typename VecType>
-struct traits<BlockSparseTimeDenseProduct<BlockSparseMatrixT, VecType> >
-{
-  typedef Dense StorageKind;
-  typedef MatrixXpr XprKind;
-  typedef typename BlockSparseMatrixT::Scalar Scalar;
-  typedef typename BlockSparseMatrixT::Index Index;
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = Dynamic,
-    MaxRowsAtCompileTime = Dynamic,
-    MaxColsAtCompileTime = Dynamic,
-    Flags = 0,
-    CoeffReadCost = internal::traits<BlockSparseMatrixT>::CoeffReadCost
-  };
-};
-} // end namespace internal
-
-template<typename Lhs, typename Rhs>
-class BlockSparseTimeDenseProduct
-  : public ProductBase<BlockSparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs>
-{
-  public:
-    EIGEN_PRODUCT_PUBLIC_INTERFACE(BlockSparseTimeDenseProduct)
-
-    BlockSparseTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
-    {}
-
-    template<typename Dest> void scaleAndAddTo(Dest& dest, const typename Rhs::Scalar& alpha) const
-    {
-      BlockVectorReturn<Lhs,Dest> tmpDest(m_lhs, dest);
-      internal::sparse_time_dense_product( BlockSparseMatrixView<Lhs>(m_lhs),  BlockVectorView<Lhs, Rhs>(m_lhs, m_rhs), tmpDest, alpha);
-    }
-
-  private:
-    BlockSparseTimeDenseProduct& operator=(const BlockSparseTimeDenseProduct&);
-};
-
-template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _StorageIndex>
-class BlockSparseMatrix : public SparseMatrixBase<BlockSparseMatrix<_Scalar,_BlockAtCompileTime, _Options,_StorageIndex> >
-{
-  public:
-    typedef _Scalar Scalar;
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-    typedef _StorageIndex StorageIndex;
-    typedef typename internal::ref_selector<BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, _StorageIndex> >::type Nested;
-
-    enum {
-      Options = _Options,
-      Flags = Options,
-      BlockSize=_BlockAtCompileTime,
-      RowsAtCompileTime = Dynamic,
-      ColsAtCompileTime = Dynamic,
-      MaxRowsAtCompileTime = Dynamic,
-      MaxColsAtCompileTime = Dynamic,
-      IsVectorAtCompileTime = 0,
-      IsColMajor = Flags&RowMajorBit ? 0 : 1
-    };
-    typedef Matrix<Scalar, _BlockAtCompileTime, _BlockAtCompileTime,IsColMajor ? ColMajor : RowMajor> BlockScalar;
-    typedef Matrix<RealScalar, _BlockAtCompileTime, _BlockAtCompileTime,IsColMajor ? ColMajor : RowMajor> BlockRealScalar;
-    typedef typename internal::conditional<_BlockAtCompileTime==Dynamic, Scalar, BlockScalar>::type BlockScalarReturnType;
-    typedef BlockSparseMatrix<Scalar, BlockSize, IsColMajor ? ColMajor : RowMajor, StorageIndex> PlainObject;
-  public:
-    // Default constructor
-    BlockSparseMatrix()
-    : m_innerBSize(0),m_outerBSize(0),m_innerOffset(0),m_outerOffset(0),
-      m_nonzerosblocks(0),m_values(0),m_blockPtr(0),m_indices(0),
-      m_outerIndex(0),m_blockSize(BlockSize)
-    { }
-
-
-    /**
-     * \brief Construct and resize
-     *
-     */
-    BlockSparseMatrix(Index brow, Index bcol)
-      : m_innerBSize(IsColMajor ? brow : bcol),
-        m_outerBSize(IsColMajor ? bcol : brow),
-        m_innerOffset(0),m_outerOffset(0),m_nonzerosblocks(0),
-        m_values(0),m_blockPtr(0),m_indices(0),
-        m_outerIndex(0),m_blockSize(BlockSize)
-    { }
-
-    /**
-     * \brief Copy-constructor
-     */
-    BlockSparseMatrix(const BlockSparseMatrix& other)
-      : m_innerBSize(other.m_innerBSize),m_outerBSize(other.m_outerBSize),
-        m_nonzerosblocks(other.m_nonzerosblocks),m_nonzeros(other.m_nonzeros),
-        m_blockPtr(0),m_blockSize(other.m_blockSize)
-    {
-      // should we allow copying between variable-size blocks and fixed-size blocks ??
-      eigen_assert(m_blockSize == BlockSize && " CAN NOT COPY BETWEEN FIXED-SIZE AND VARIABLE-SIZE BLOCKS");
-
-      std::copy(other.m_innerOffset, other.m_innerOffset+m_innerBSize+1, m_innerOffset);
-      std::copy(other.m_outerOffset, other.m_outerOffset+m_outerBSize+1, m_outerOffset);
-      std::copy(other.m_values, other.m_values+m_nonzeros, m_values);
-
-      if(m_blockSize != Dynamic)
-        std::copy(other.m_blockPtr, other.m_blockPtr+m_nonzerosblocks, m_blockPtr);
-
-      std::copy(other.m_indices, other.m_indices+m_nonzerosblocks, m_indices);
-      std::copy(other.m_outerIndex, other.m_outerIndex+m_outerBSize, m_outerIndex);
-    }
-
-    friend void swap(BlockSparseMatrix& first, BlockSparseMatrix& second)
-    {
-      std::swap(first.m_innerBSize, second.m_innerBSize);
-      std::swap(first.m_outerBSize, second.m_outerBSize);
-      std::swap(first.m_innerOffset, second.m_innerOffset);
-      std::swap(first.m_outerOffset, second.m_outerOffset);
-      std::swap(first.m_nonzerosblocks, second.m_nonzerosblocks);
-      std::swap(first.m_nonzeros, second.m_nonzeros);
-      std::swap(first.m_values, second.m_values);
-      std::swap(first.m_blockPtr, second.m_blockPtr);
-      std::swap(first.m_indices, second.m_indices);
-      std::swap(first.m_outerIndex, second.m_outerIndex);
-      std::swap(first.m_BlockSize, second.m_blockSize);
-    }
-
-    BlockSparseMatrix& operator=(BlockSparseMatrix other)
-    {
-      //Copy-and-swap paradigm ... avoid leaked data if thrown
-      swap(*this, other);
-      return *this;
-    }
-
-    // Destructor
-    ~BlockSparseMatrix()
-    {
-      delete[] m_outerIndex;
-      delete[] m_innerOffset;
-      delete[] m_outerOffset;
-      delete[] m_indices;
-      delete[] m_blockPtr;
-      delete[] m_values;
-    }
-
-
-    /**
-      * \brief Constructor from a sparse matrix
-      *
-      */
-    template<typename MatrixType>
-    inline BlockSparseMatrix(const MatrixType& spmat) : m_blockSize(BlockSize)
-    {
-      EIGEN_STATIC_ASSERT((m_blockSize != Dynamic), THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE);
-
-      *this = spmat;
-    }
-
-    /**
-      * \brief Assignment from a sparse matrix with the same storage order
-      *
-      * Convert from a sparse matrix to block sparse matrix.
-      * \warning Before calling this function, tt is necessary to call
-      * either setBlockLayout() (matrices with variable-size blocks)
-      * or setBlockSize() (for fixed-size blocks).
-      */
-    template<typename MatrixType>
-    inline BlockSparseMatrix& operator=(const MatrixType& spmat)
-    {
-      eigen_assert((m_innerBSize != 0 && m_outerBSize != 0)
-                   && "Trying to assign to a zero-size matrix, call resize() first");
-      eigen_assert(((MatrixType::Options&RowMajorBit) != IsColMajor) && "Wrong storage order");
-      typedef SparseMatrix<bool,MatrixType::Options,typename MatrixType::Index> MatrixPatternType;
-      MatrixPatternType  blockPattern(blockRows(), blockCols());
-      m_nonzeros = 0;
-
-      // First, compute the number of nonzero blocks and their locations
-      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
-      {
-        // Browse each outer block and compute the structure
-        std::vector<bool> nzblocksFlag(m_innerBSize,false);  // Record the existing blocks
-        blockPattern.startVec(bj);
-        for(StorageIndex j = blockOuterIndex(bj); j < blockOuterIndex(bj+1); ++j)
-        {
-          typename MatrixType::InnerIterator it_spmat(spmat, j);
-          for(; it_spmat; ++it_spmat)
-          {
-            StorageIndex bi = innerToBlock(it_spmat.index()); // Index of the current nonzero block
-            if(!nzblocksFlag[bi])
-            {
-              // Save the index of this nonzero block
-              nzblocksFlag[bi] = true;
-              blockPattern.insertBackByOuterInnerUnordered(bj, bi) = true;
-              // Compute the total number of nonzeros (including explicit zeros in blocks)
-              m_nonzeros += blockOuterSize(bj) * blockInnerSize(bi);
-            }
-          }
-        } // end current outer block
-      }
-      blockPattern.finalize();
-
-      // Allocate the internal arrays
-      setBlockStructure(blockPattern);
-
-      for(StorageIndex nz = 0; nz < m_nonzeros; ++nz) m_values[nz] = Scalar(0);
-      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
-      {
-        // Now copy the values
-        for(StorageIndex j = blockOuterIndex(bj); j < blockOuterIndex(bj+1); ++j)
-        {
-          // Browse the outer block column by column (for column-major matrices)
-          typename MatrixType::InnerIterator it_spmat(spmat, j);
-          for(; it_spmat; ++it_spmat)
-          {
-            StorageIndex idx = 0; // Position of this block in the column block
-            StorageIndex bi = innerToBlock(it_spmat.index()); // Index of the current nonzero block
-            // Go to the inner block where this element belongs to
-            while(bi > m_indices[m_outerIndex[bj]+idx]) ++idx; // Not expensive for ordered blocks
-            StorageIndex idxVal;// Get the right position in the array of values for this element
-            if(m_blockSize == Dynamic)
-            {
-              // Offset from all blocks before ...
-              idxVal =  m_blockPtr[m_outerIndex[bj]+idx];
-              // ... and offset inside the block
-              idxVal += (j - blockOuterIndex(bj)) * blockOuterSize(bj) + it_spmat.index() - m_innerOffset[bi];
-            }
-            else
-            {
-              // All blocks before
-              idxVal = (m_outerIndex[bj] + idx) * m_blockSize * m_blockSize;
-              // inside the block
-              idxVal += (j - blockOuterIndex(bj)) * m_blockSize + (it_spmat.index()%m_blockSize);
-            }
-            // Insert the value
-            m_values[idxVal] = it_spmat.value();
-          } // end of this column
-        } // end of this block
-      } // end of this outer block
-
-      return *this;
-    }
-
-    /**
-      * \brief Set the nonzero block pattern of the matrix
-      *
-      * Given a sparse matrix describing the nonzero block pattern,
-      * this function prepares the internal pointers for values.
-      * After calling this function, any *nonzero* block (bi, bj) can be set
-      * with a simple call to coeffRef(bi,bj).
-      *
-      *
-      * \warning Before calling this function, tt is necessary to call
-      * either setBlockLayout() (matrices with variable-size blocks)
-      * or setBlockSize() (for fixed-size blocks).
-      *
-      * \param blockPattern Sparse matrix of boolean elements describing the block structure
-      *
-      * \sa setBlockLayout() \sa setBlockSize()
-      */
-    template<typename MatrixType>
-    void setBlockStructure(const MatrixType& blockPattern)
-    {
-      resize(blockPattern.rows(), blockPattern.cols());
-      reserve(blockPattern.nonZeros());
-
-      // Browse the block pattern and set up the various pointers
-      m_outerIndex[0] = 0;
-      if(m_blockSize == Dynamic) m_blockPtr[0] = 0;
-      for(StorageIndex nz = 0; nz < m_nonzeros; ++nz) m_values[nz] = Scalar(0);
-      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
-      {
-        //Browse each outer block
-
-        //First, copy and save the indices of nonzero blocks
-        //FIXME : find a way to avoid this ...
-        std::vector<int> nzBlockIdx;
-        typename MatrixType::InnerIterator it(blockPattern, bj);
-        for(; it; ++it)
-        {
-          nzBlockIdx.push_back(it.index());
-        }
-        std::sort(nzBlockIdx.begin(), nzBlockIdx.end());
-
-        // Now, fill block indices and (eventually) pointers to blocks
-        for(StorageIndex idx = 0; idx < nzBlockIdx.size(); ++idx)
-        {
-          StorageIndex offset = m_outerIndex[bj]+idx; // offset in m_indices
-          m_indices[offset] = nzBlockIdx[idx];
-          if(m_blockSize == Dynamic)
-            m_blockPtr[offset] = m_blockPtr[offset-1] + blockInnerSize(nzBlockIdx[idx]) * blockOuterSize(bj);
-          // There is no blockPtr for fixed-size blocks... not needed !???
-        }
-        // Save the pointer to the next outer block
-        m_outerIndex[bj+1] = m_outerIndex[bj] + nzBlockIdx.size();
-      }
-    }
-
-    /**
-      * \brief Set the number of rows and columns blocks
-      */
-    inline void resize(Index brow, Index bcol)
-    {
-      m_innerBSize = IsColMajor ? brow : bcol;
-      m_outerBSize = IsColMajor ? bcol : brow;
-    }
-
-    /**
-      * \brief set the block size at runtime for fixed-size block layout
-      *
-      * Call this only for fixed-size blocks
-      */
-    inline void setBlockSize(Index blockSize)
-    {
-      m_blockSize = blockSize;
-    }
-
-    /**
-      * \brief Set the row and column block layouts,
-      *
-      * This function set the size of each row and column block.
-      * So this function should be used only for blocks with variable size.
-      * \param rowBlocks : Number of rows per row block
-      * \param colBlocks : Number of columns per column block
-      * \sa resize(), setBlockSize()
-      */
-    inline void setBlockLayout(const VectorXi& rowBlocks, const VectorXi& colBlocks)
-    {
-      const VectorXi& innerBlocks = IsColMajor ? rowBlocks : colBlocks;
-      const VectorXi& outerBlocks = IsColMajor ? colBlocks : rowBlocks;
-      eigen_assert(m_innerBSize == innerBlocks.size() && "CHECK THE NUMBER OF ROW OR COLUMN BLOCKS");
-      eigen_assert(m_outerBSize == outerBlocks.size() && "CHECK THE NUMBER OF ROW OR COLUMN BLOCKS");
-      m_outerBSize = outerBlocks.size();
-      //  starting index of blocks... cumulative sums
-      m_innerOffset = new StorageIndex[m_innerBSize+1];
-      m_outerOffset = new StorageIndex[m_outerBSize+1];
-      m_innerOffset[0] = 0;
-      m_outerOffset[0] = 0;
-      std::partial_sum(&innerBlocks[0], &innerBlocks[m_innerBSize-1]+1, &m_innerOffset[1]);
-      std::partial_sum(&outerBlocks[0], &outerBlocks[m_outerBSize-1]+1, &m_outerOffset[1]);
-
-      // Compute the total number of nonzeros
-      m_nonzeros = 0;
-      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
-        for(StorageIndex bi = 0; bi < m_innerBSize; ++bi)
-          m_nonzeros += outerBlocks[bj] * innerBlocks[bi];
-
-    }
-
-    /**
-      * \brief Allocate the internal array of pointers to blocks and their inner indices
-      *
-      * \note For fixed-size blocks, call setBlockSize() to set the block.
-      * And For variable-size blocks, call setBlockLayout() before using this function
-      *
-      * \param nonzerosblocks Number of nonzero blocks. The total number of nonzeros is
-      * is computed in setBlockLayout() for variable-size blocks
-      * \sa setBlockSize()
-      */
-    inline void reserve(const Index nonzerosblocks)
-    {
-      eigen_assert((m_innerBSize != 0 && m_outerBSize != 0) &&
-          "TRYING TO RESERVE ZERO-SIZE MATRICES, CALL resize() first");
-
-      //FIXME Should free if already allocated
-      m_outerIndex = new StorageIndex[m_outerBSize+1];
-
-      m_nonzerosblocks = nonzerosblocks;
-      if(m_blockSize != Dynamic)
-      {
-        m_nonzeros = nonzerosblocks * (m_blockSize * m_blockSize);
-        m_blockPtr = 0;
-      }
-      else
-      {
-        // m_nonzeros  is already computed in setBlockLayout()
-        m_blockPtr = new StorageIndex[m_nonzerosblocks+1];
-      }
-      m_indices = new StorageIndex[m_nonzerosblocks+1];
-      m_values = new Scalar[m_nonzeros];
-    }
-
-
-    /**
-      * \brief Fill values in a matrix  from a triplet list.
-      *
-      * Each triplet item has a block stored in an Eigen dense matrix.
-      * The InputIterator class should provide the functions row(), col() and value()
-      *
-      * \note For fixed-size blocks, call setBlockSize() before this function.
-      *
-      * FIXME Do not accept duplicates
-      */
-    template<typename InputIterator>
-    void setFromTriplets(const InputIterator& begin, const InputIterator& end)
-    {
-      eigen_assert((m_innerBSize!=0 && m_outerBSize !=0) && "ZERO BLOCKS, PLEASE CALL resize() before");
-
-      /* First, sort the triplet list
-        * FIXME This can be unnecessarily expensive since only the inner indices have to be sorted
-        * The best approach is like in SparseMatrix::setFromTriplets()
-        */
-      internal::TripletComp<InputIterator, IsColMajor> tripletcomp;
-      std::sort(begin, end, tripletcomp);
-
-      /* Count the number of rows and column blocks,
-       * and the number of nonzero blocks per outer dimension
-       */
-      VectorXi rowBlocks(m_innerBSize); // Size of each block row
-      VectorXi colBlocks(m_outerBSize); // Size of each block column
-      rowBlocks.setZero(); colBlocks.setZero();
-      VectorXi nzblock_outer(m_outerBSize); // Number of nz blocks per outer vector
-      VectorXi nz_outer(m_outerBSize); // Number of nz per outer vector...for variable-size blocks
-      nzblock_outer.setZero();
-      nz_outer.setZero();
-      for(InputIterator it(begin); it !=end; ++it)
-      {
-        eigen_assert(it->row() >= 0 && it->row() < this->blockRows() && it->col() >= 0 && it->col() < this->blockCols());
-        eigen_assert((it->value().rows() == it->value().cols() && (it->value().rows() == m_blockSize))
-                     || (m_blockSize == Dynamic));
-
-        if(m_blockSize == Dynamic)
-        {
-          eigen_assert((rowBlocks[it->row()] == 0 || rowBlocks[it->row()] == it->value().rows()) &&
-              "NON CORRESPONDING SIZES FOR ROW BLOCKS");
-          eigen_assert((colBlocks[it->col()] == 0 || colBlocks[it->col()] == it->value().cols()) &&
-              "NON CORRESPONDING SIZES FOR COLUMN BLOCKS");
-          rowBlocks[it->row()] =it->value().rows();
-          colBlocks[it->col()] = it->value().cols();
-        }
-        nz_outer(IsColMajor ? it->col() : it->row()) += it->value().rows() * it->value().cols();
-        nzblock_outer(IsColMajor ? it->col() : it->row())++;
-      }
-      // Allocate member arrays
-      if(m_blockSize == Dynamic) setBlockLayout(rowBlocks, colBlocks);
-      StorageIndex nzblocks = nzblock_outer.sum();
-      reserve(nzblocks);
-
-       // Temporary markers
-      VectorXi block_id(m_outerBSize); // To be used as a block marker during insertion
-
-      // Setup outer index pointers and markers
-      m_outerIndex[0] = 0;
-      if (m_blockSize == Dynamic)  m_blockPtr[0] =  0;
-      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
-      {
-        m_outerIndex[bj+1] = m_outerIndex[bj] + nzblock_outer(bj);
-        block_id(bj) = m_outerIndex[bj];
-        if(m_blockSize==Dynamic)
-        {
-          m_blockPtr[m_outerIndex[bj+1]] = m_blockPtr[m_outerIndex[bj]] + nz_outer(bj);
-        }
-      }
-
-      // Fill the matrix
-      for(InputIterator it(begin); it!=end; ++it)
-      {
-        StorageIndex outer = IsColMajor ? it->col() : it->row();
-        StorageIndex inner = IsColMajor ? it->row() : it->col();
-        m_indices[block_id(outer)] = inner;
-        StorageIndex block_size = it->value().rows()*it->value().cols();
-        StorageIndex nz_marker = blockPtr(block_id[outer]);
-        memcpy(&(m_values[nz_marker]), it->value().data(), block_size * sizeof(Scalar));
-        if(m_blockSize == Dynamic)
-        {
-          m_blockPtr[block_id(outer)+1] = m_blockPtr[block_id(outer)] + block_size;
-        }
-        block_id(outer)++;
-      }
-
-      // An alternative when the outer indices are sorted...no need to use an array of markers
-//      for(Index bcol = 0; bcol < m_outerBSize; ++bcol)
-//      {
-//      Index id = 0, id_nz = 0, id_nzblock = 0;
-//      for(InputIterator it(begin); it!=end; ++it)
-//      {
-//        while (id<bcol) // one pass should do the job unless there are empty columns
-//        {
-//          id++;
-//          m_outerIndex[id+1]=m_outerIndex[id];
-//        }
-//        m_outerIndex[id+1] += 1;
-//        m_indices[id_nzblock]=brow;
-//        Index block_size = it->value().rows()*it->value().cols();
-//        m_blockPtr[id_nzblock+1] = m_blockPtr[id_nzblock] + block_size;
-//        id_nzblock++;
-//        memcpy(&(m_values[id_nz]),it->value().data(), block_size*sizeof(Scalar));
-//        id_nz += block_size;
-//      }
-//      while(id < m_outerBSize-1) // Empty columns at the end
-//      {
-//        id++;
-//        m_outerIndex[id+1]=m_outerIndex[id];
-//      }
-//      }
-    }
-
-
-    /**
-      * \returns the number of rows
-      */
-    inline Index rows() const
-    {
-//      return blockRows();
-      return (IsColMajor ? innerSize() : outerSize());
-    }
-
-    /**
-      * \returns the number of cols
-      */
-    inline Index cols() const
-    {
-//      return blockCols();
-      return (IsColMajor ? outerSize() : innerSize());
-    }
-
-    inline Index innerSize() const
-    {
-      if(m_blockSize == Dynamic) return m_innerOffset[m_innerBSize];
-      else return  (m_innerBSize * m_blockSize) ;
-    }
-
-    inline Index outerSize() const
-    {
-      if(m_blockSize == Dynamic) return m_outerOffset[m_outerBSize];
-      else return  (m_outerBSize * m_blockSize) ;
-    }
-    /** \returns the number of rows grouped by blocks */
-    inline Index blockRows() const
-    {
-      return (IsColMajor ? m_innerBSize : m_outerBSize);
-    }
-    /** \returns the number of columns grouped by blocks */
-    inline Index blockCols() const
-    {
-      return (IsColMajor ? m_outerBSize : m_innerBSize);
-    }
-
-    inline Index outerBlocks() const { return m_outerBSize; }
-    inline Index innerBlocks() const { return m_innerBSize; }
-
-    /** \returns the block index where outer belongs to */
-    inline Index outerToBlock(Index outer) const
-    {
-      eigen_assert(outer < outerSize() && "OUTER INDEX OUT OF BOUNDS");
-
-      if(m_blockSize != Dynamic)
-        return (outer / m_blockSize); // Integer division
-
-      StorageIndex b_outer = 0;
-      while(m_outerOffset[b_outer] <= outer) ++b_outer;
-      return b_outer - 1;
-    }
-    /** \returns  the block index where inner belongs to */
-    inline Index innerToBlock(Index inner) const
-    {
-      eigen_assert(inner < innerSize() && "OUTER INDEX OUT OF BOUNDS");
-
-      if(m_blockSize != Dynamic)
-        return (inner / m_blockSize); // Integer division
-
-      StorageIndex b_inner = 0;
-      while(m_innerOffset[b_inner] <= inner) ++b_inner;
-      return b_inner - 1;
-    }
-
-    /**
-      *\returns a reference to the (i,j) block as an Eigen Dense Matrix
-      */
-    Ref<BlockScalar> coeffRef(Index brow, Index bcol)
-    {
-      eigen_assert(brow < blockRows() && "BLOCK ROW INDEX OUT OF BOUNDS");
-      eigen_assert(bcol < blockCols() && "BLOCK nzblocksFlagCOLUMN OUT OF BOUNDS");
-
-      StorageIndex rsize = IsColMajor ? blockInnerSize(brow): blockOuterSize(bcol);
-      StorageIndex csize = IsColMajor ? blockOuterSize(bcol) : blockInnerSize(brow);
-      StorageIndex inner = IsColMajor ? brow : bcol;
-      StorageIndex outer = IsColMajor ? bcol : brow;
-      StorageIndex offset = m_outerIndex[outer];
-      while(offset < m_outerIndex[outer+1] && m_indices[offset] != inner)
-        offset++;
-      if(m_indices[offset] == inner)
-      {
-        return Map<BlockScalar>(&(m_values[blockPtr(offset)]), rsize, csize);
-      }
-      else
-      {
-        //FIXME the block does not exist, Insert it !!!!!!!!!
-        eigen_assert("DYNAMIC INSERTION IS NOT YET SUPPORTED");
-      }
-    }
-
-    /**
-      * \returns the value of the (i,j) block as an Eigen Dense Matrix
-      */
-    Map<const BlockScalar> coeff(Index brow, Index bcol) const
-    {
-      eigen_assert(brow < blockRows() && "BLOCK ROW INDEX OUT OF BOUNDS");
-      eigen_assert(bcol < blockCols() && "BLOCK COLUMN OUT OF BOUNDS");
-
-      StorageIndex rsize = IsColMajor ? blockInnerSize(brow): blockOuterSize(bcol);
-      StorageIndex csize = IsColMajor ? blockOuterSize(bcol) : blockInnerSize(brow);
-      StorageIndex inner = IsColMajor ? brow : bcol;
-      StorageIndex outer = IsColMajor ? bcol : brow;
-      StorageIndex offset = m_outerIndex[outer];
-      while(offset < m_outerIndex[outer+1] && m_indices[offset] != inner) offset++;
-      if(m_indices[offset] == inner)
-      {
-        return Map<const BlockScalar> (&(m_values[blockPtr(offset)]), rsize, csize);
-      }
-      else
-//        return BlockScalar::Zero(rsize, csize);
-        eigen_assert("NOT YET SUPPORTED");
-    }
-
-    // Block Matrix times vector product
-    template<typename VecType>
-    BlockSparseTimeDenseProduct<BlockSparseMatrix, VecType> operator*(const VecType& lhs) const
-    {
-      return BlockSparseTimeDenseProduct<BlockSparseMatrix, VecType>(*this, lhs);
-    }
-
-    /** \returns the number of nonzero blocks */
-    inline Index nonZerosBlocks() const { return m_nonzerosblocks; }
-    /** \returns the total number of nonzero elements, including eventual explicit zeros in blocks */
-    inline Index nonZeros() const { return m_nonzeros; }
-
-    inline BlockScalarReturnType *valuePtr() {return static_cast<BlockScalarReturnType *>(m_values);}
-//    inline Scalar *valuePtr(){ return m_values; }
-    inline StorageIndex *innerIndexPtr() {return m_indices; }
-    inline const StorageIndex *innerIndexPtr() const {return m_indices; }
-    inline StorageIndex *outerIndexPtr() {return m_outerIndex; }
-    inline const StorageIndex* outerIndexPtr() const {return m_outerIndex; }
-
-    /** \brief for compatibility purposes with the SparseMatrix class */
-    inline bool isCompressed() const {return true;}
-    /**
-      * \returns the starting index of the bi row block
-      */
-    inline Index blockRowsIndex(Index bi) const
-    {
-      return IsColMajor ? blockInnerIndex(bi) : blockOuterIndex(bi);
-    }
-
-    /**
-      * \returns the starting index of the bj col block
-      */
-    inline Index blockColsIndex(Index bj) const
-    {
-      return IsColMajor ? blockOuterIndex(bj) : blockInnerIndex(bj);
-    }
-
-    inline Index blockOuterIndex(Index bj) const
-    {
-      return (m_blockSize == Dynamic) ? m_outerOffset[bj] : (bj * m_blockSize);
-    }
-    inline Index blockInnerIndex(Index bi) const
-    {
-      return (m_blockSize == Dynamic) ? m_innerOffset[bi] : (bi * m_blockSize);
-    }
-
-    // Not needed ???
-    inline Index blockInnerSize(Index bi) const
-    {
-      return (m_blockSize == Dynamic) ? (m_innerOffset[bi+1] - m_innerOffset[bi]) : m_blockSize;
-    }
-    inline Index blockOuterSize(Index bj) const
-    {
-      return (m_blockSize == Dynamic) ? (m_outerOffset[bj+1]- m_outerOffset[bj]) : m_blockSize;
-    }
-
-    /**
-      * \brief Browse the matrix by outer index
-      */
-    class InnerIterator; // Browse column by column
-
-    /**
-      * \brief Browse the matrix by block outer index
-      */
-    class BlockInnerIterator; // Browse block by block
-
-    friend std::ostream & operator << (std::ostream & s, const BlockSparseMatrix& m)
-    {
-      for (StorageIndex j = 0; j < m.outerBlocks(); ++j)
-      {
-        BlockInnerIterator itb(m, j);
-        for(; itb; ++itb)
-        {
-          s << "("<<itb.row() << ", " << itb.col() << ")\n";
-          s << itb.value() <<"\n";
-        }
-      }
-      s << std::endl;
-      return s;
-    }
-
-    /**
-      * \returns the starting position of the block \p id in the array of values
-      */
-    Index blockPtr(Index id) const
-    {
-      if(m_blockSize == Dynamic) return m_blockPtr[id];
-      else return id * m_blockSize * m_blockSize;
-      //return blockDynIdx(id, typename internal::conditional<(BlockSize==Dynamic), internal::true_type, internal::false_type>::type());
-    }
-
-
-  protected:
-//    inline Index blockDynIdx(Index id, internal::true_type) const
-//    {
-//      return m_blockPtr[id];
-//    }
-//    inline Index blockDynIdx(Index id, internal::false_type) const
-//    {
-//      return id * BlockSize * BlockSize;
-//    }
-
-    // To be implemented
-    // Insert a block at a particular location... need to make a room for that
-    Map<BlockScalar> insert(Index brow, Index bcol);
-
-    Index m_innerBSize; // Number of block rows
-    Index m_outerBSize; // Number of block columns
-    StorageIndex *m_innerOffset; // Starting index of each inner block (size m_innerBSize+1)
-    StorageIndex *m_outerOffset; // Starting index of each outer block (size m_outerBSize+1)
-    Index m_nonzerosblocks; // Total nonzeros blocks (lower than  m_innerBSize x m_outerBSize)
-    Index m_nonzeros; // Total nonzeros elements
-    Scalar *m_values; //Values stored block column after block column (size m_nonzeros)
-    StorageIndex *m_blockPtr; // Pointer to the beginning of each block in m_values, size m_nonzeroblocks ... null for fixed-size blocks
-    StorageIndex *m_indices; //Inner block indices, size m_nonzerosblocks ... OK
-    StorageIndex *m_outerIndex; // Starting pointer of each block column in m_indices (size m_outerBSize)... OK
-    Index m_blockSize; // Size of a block for fixed-size blocks, otherwise -1
-};
-
-template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _StorageIndex>
-class BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, _StorageIndex>::BlockInnerIterator
-{
-  public:
-
-    enum{
-      Flags = _Options
-    };
-
-    BlockInnerIterator(const BlockSparseMatrix& mat, const Index outer)
-    : m_mat(mat),m_outer(outer),
-      m_id(mat.m_outerIndex[outer]),
-      m_end(mat.m_outerIndex[outer+1])
-    {
-    }
-
-    inline BlockInnerIterator& operator++() {m_id++; return *this; }
-
-    inline const Map<const BlockScalar> value() const
-    {
-      return Map<const BlockScalar>(&(m_mat.m_values[m_mat.blockPtr(m_id)]),
-          rows(),cols());
-    }
-    inline Map<BlockScalar> valueRef()
-    {
-      return Map<BlockScalar>(&(m_mat.m_values[m_mat.blockPtr(m_id)]),
-          rows(),cols());
-    }
-    // Block inner index
-    inline Index index() const {return m_mat.m_indices[m_id]; }
-    inline Index outer() const { return m_outer; }
-    // block row index
-    inline Index row() const  {return index(); }
-    // block column index
-    inline Index col() const {return outer(); }
-    // FIXME Number of rows in the current block
-    inline Index rows() const { return (m_mat.m_blockSize==Dynamic) ? (m_mat.m_innerOffset[index()+1] - m_mat.m_innerOffset[index()]) : m_mat.m_blockSize; }
-    // Number of columns in the current block ...
-    inline Index cols() const { return (m_mat.m_blockSize==Dynamic) ? (m_mat.m_outerOffset[m_outer+1]-m_mat.m_outerOffset[m_outer]) : m_mat.m_blockSize;}
-    inline operator bool() const { return (m_id < m_end); }
-
-  protected:
-    const BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, StorageIndex>& m_mat;
-    const Index m_outer;
-    Index m_id;
-    Index m_end;
-};
-
-template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _StorageIndex>
-class BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, _StorageIndex>::InnerIterator
-{
-  public:
-    InnerIterator(const BlockSparseMatrix& mat, Index outer)
-    : m_mat(mat),m_outerB(mat.outerToBlock(outer)),m_outer(outer),
-      itb(mat, mat.outerToBlock(outer)),
-      m_offset(outer - mat.blockOuterIndex(m_outerB))
-     {
-        if (itb)
-        {
-          m_id = m_mat.blockInnerIndex(itb.index());
-          m_start = m_id;
-          m_end = m_mat.blockInnerIndex(itb.index()+1);
-        }
-     }
-    inline InnerIterator& operator++()
-    {
-      m_id++;
-      if (m_id >= m_end)
-      {
-        ++itb;
-        if (itb)
-        {
-          m_id = m_mat.blockInnerIndex(itb.index());
-          m_start = m_id;
-          m_end = m_mat.blockInnerIndex(itb.index()+1);
-        }
-      }
-      return *this;
-    }
-    inline const Scalar& value() const
-    {
-      return itb.value().coeff(m_id - m_start, m_offset);
-    }
-    inline Scalar& valueRef()
-    {
-      return itb.valueRef().coeff(m_id - m_start, m_offset);
-    }
-    inline Index index() const { return m_id; }
-    inline Index outer() const {return m_outer; }
-    inline Index col() const {return outer(); }
-    inline Index row() const { return index();}
-    inline operator bool() const
-    {
-      return itb;
-    }
-  protected:
-    const BlockSparseMatrix& m_mat;
-    const Index m_outer;
-    const Index m_outerB;
-    BlockInnerIterator itb; // Iterator through the blocks
-    const Index m_offset; // Position of this column in the block
-    Index m_start; // starting inner index of this block
-    Index m_id; // current inner index in the block
-    Index m_end; // starting inner index of the next block
-
-};
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSEBLOCKMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h
deleted file mode 100644
index 42c99e4..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h
+++ /dev/null
@@ -1,404 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_DYNAMIC_SPARSEMATRIX_H
-#define EIGEN_DYNAMIC_SPARSEMATRIX_H
-
-namespace Eigen { 
-
-/** \deprecated use a SparseMatrix in an uncompressed mode
-  *
-  * \class DynamicSparseMatrix
-  *
-  * \brief A sparse matrix class designed for matrix assembly purpose
-  *
-  * \param _Scalar the scalar type, i.e. the type of the coefficients
-  *
-  * Unlike SparseMatrix, this class provides a much higher degree of flexibility. In particular, it allows
-  * random read/write accesses in log(rho*outer_size) where \c rho is the probability that a coefficient is
-  * nonzero and outer_size is the number of columns if the matrix is column-major and the number of rows
-  * otherwise.
-  *
-  * Internally, the data are stored as a std::vector of compressed vector. The performances of random writes might
-  * decrease as the number of nonzeros per inner-vector increase. In practice, we observed very good performance
-  * till about 100 nonzeros/vector, and the performance remains relatively good till 500 nonzeros/vectors.
-  *
-  * \see SparseMatrix
-  */
-
-namespace internal {
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct traits<DynamicSparseMatrix<_Scalar, _Options, _StorageIndex> >
-{
-  typedef _Scalar Scalar;
-  typedef _StorageIndex StorageIndex;
-  typedef Sparse StorageKind;
-  typedef MatrixXpr XprKind;
-  enum {
-    RowsAtCompileTime = Dynamic,
-    ColsAtCompileTime = Dynamic,
-    MaxRowsAtCompileTime = Dynamic,
-    MaxColsAtCompileTime = Dynamic,
-    Flags = _Options | NestByRefBit | LvalueBit,
-    CoeffReadCost = NumTraits<Scalar>::ReadCost,
-    SupportedAccessPatterns = OuterRandomAccessPattern
-  };
-};
-}
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
- class  DynamicSparseMatrix
-  : public SparseMatrixBase<DynamicSparseMatrix<_Scalar, _Options, _StorageIndex> >
-{
-    typedef SparseMatrixBase<DynamicSparseMatrix> Base;
-    using Base::convert_index;
-  public:
-    EIGEN_SPARSE_PUBLIC_INTERFACE(DynamicSparseMatrix)
-    // FIXME: why are these operator already alvailable ???
-    // EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(DynamicSparseMatrix, +=)
-    // EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(DynamicSparseMatrix, -=)
-    typedef MappedSparseMatrix<Scalar,Flags> Map;
-    using Base::IsRowMajor;
-    using Base::operator=;
-    enum {
-      Options = _Options
-    };
-
-  protected:
-
-    typedef DynamicSparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0), StorageIndex> TransposedSparseMatrix;
-
-    Index m_innerSize;
-    std::vector<internal::CompressedStorage<Scalar,StorageIndex> > m_data;
-
-  public:
-
-    inline Index rows() const { return IsRowMajor ? outerSize() : m_innerSize; }
-    inline Index cols() const { return IsRowMajor ? m_innerSize : outerSize(); }
-    inline Index innerSize() const { return m_innerSize; }
-    inline Index outerSize() const { return convert_index(m_data.size()); }
-    inline Index innerNonZeros(Index j) const { return m_data[j].size(); }
-
-    std::vector<internal::CompressedStorage<Scalar,StorageIndex> >& _data() { return m_data; }
-    const std::vector<internal::CompressedStorage<Scalar,StorageIndex> >& _data() const { return m_data; }
-
-    /** \returns the coefficient value at given position \a row, \a col
-      * This operation involes a log(rho*outer_size) binary search.
-      */
-    inline Scalar coeff(Index row, Index col) const
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-      return m_data[outer].at(inner);
-    }
-
-    /** \returns a reference to the coefficient value at given position \a row, \a col
-      * This operation involes a log(rho*outer_size) binary search. If the coefficient does not
-      * exist yet, then a sorted insertion into a sequential buffer is performed.
-      */
-    inline Scalar& coeffRef(Index row, Index col)
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-      return m_data[outer].atWithInsertion(inner);
-    }
-
-    class InnerIterator;
-    class ReverseInnerIterator;
-
-    void setZero()
-    {
-      for (Index j=0; j<outerSize(); ++j)
-        m_data[j].clear();
-    }
-
-    /** \returns the number of non zero coefficients */
-    Index nonZeros() const
-    {
-      Index res = 0;
-      for (Index j=0; j<outerSize(); ++j)
-        res += m_data[j].size();
-      return res;
-    }
-
-
-
-    void reserve(Index reserveSize = 1000)
-    {
-      if (outerSize()>0)
-      {
-        Index reserveSizePerVector = (std::max)(reserveSize/outerSize(),Index(4));
-        for (Index j=0; j<outerSize(); ++j)
-        {
-          m_data[j].reserve(reserveSizePerVector);
-        }
-      }
-    }
-
-    /** Does nothing: provided for compatibility with SparseMatrix */
-    inline void startVec(Index /*outer*/) {}
-
-    /** \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
-      * - the nonzero does not already exist
-      * - the new coefficient is the last one of the given inner vector.
-      *
-      * \sa insert, insertBackByOuterInner */
-    inline Scalar& insertBack(Index row, Index col)
-    {
-      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
-    }
-
-    /** \sa insertBack */
-    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
-    {
-      eigen_assert(outer<Index(m_data.size()) && inner<m_innerSize && "out of range");
-      eigen_assert(((m_data[outer].size()==0) || (m_data[outer].index(m_data[outer].size()-1)<inner))
-                && "wrong sorted insertion");
-      m_data[outer].append(0, inner);
-      return m_data[outer].value(m_data[outer].size()-1);
-    }
-
-    inline Scalar& insert(Index row, Index col)
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-
-      Index startId = 0;
-      Index id = static_cast<Index>(m_data[outer].size()) - 1;
-      m_data[outer].resize(id+2,1);
-
-      while ( (id >= startId) && (m_data[outer].index(id) > inner) )
-      {
-        m_data[outer].index(id+1) = m_data[outer].index(id);
-        m_data[outer].value(id+1) = m_data[outer].value(id);
-        --id;
-      }
-      m_data[outer].index(id+1) = inner;
-      m_data[outer].value(id+1) = 0;
-      return m_data[outer].value(id+1);
-    }
-
-    /** Does nothing: provided for compatibility with SparseMatrix */
-    inline void finalize() {}
-
-    /** Suppress all nonzeros which are smaller than \a reference under the tolerance \a epsilon */
-    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
-    {
-      for (Index j=0; j<outerSize(); ++j)
-        m_data[j].prune(reference,epsilon);
-    }
-
-    /** Resize the matrix without preserving the data (the matrix is set to zero)
-      */
-    void resize(Index rows, Index cols)
-    {
-      const Index outerSize = IsRowMajor ? rows : cols;
-      m_innerSize = convert_index(IsRowMajor ? cols : rows);
-      setZero();
-      if (Index(m_data.size()) != outerSize)
-      {
-        m_data.resize(outerSize);
-      }
-    }
-
-    void resizeAndKeepData(Index rows, Index cols)
-    {
-      const Index outerSize = IsRowMajor ? rows : cols;
-      const Index innerSize = IsRowMajor ? cols : rows;
-      if (m_innerSize>innerSize)
-      {
-        // remove all coefficients with innerCoord>=innerSize
-        // TODO
-        //std::cerr << "not implemented yet\n";
-        exit(2);
-      }
-      if (m_data.size() != outerSize)
-      {
-        m_data.resize(outerSize);
-      }
-    }
-
-    /** The class DynamicSparseMatrix is deprecated */
-    EIGEN_DEPRECATED inline DynamicSparseMatrix()
-      : m_innerSize(0), m_data(0)
-    {
-      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-      #endif
-      eigen_assert(innerSize()==0 && outerSize()==0);
-    }
-
-    /** The class DynamicSparseMatrix is deprecated */
-    EIGEN_DEPRECATED inline DynamicSparseMatrix(Index rows, Index cols)
-      : m_innerSize(0)
-    {
-      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-      #endif
-      resize(rows, cols);
-    }
-
-    /** The class DynamicSparseMatrix is deprecated */
-    template<typename OtherDerived>
-    EIGEN_DEPRECATED explicit inline DynamicSparseMatrix(const SparseMatrixBase<OtherDerived>& other)
-      : m_innerSize(0)
-    {
-      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-      #endif
-      Base::operator=(other.derived());
-    }
-
-    inline DynamicSparseMatrix(const DynamicSparseMatrix& other)
-      : Base(), m_innerSize(0)
-    {
-      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
-      #endif
-      *this = other.derived();
-    }
-
-    inline void swap(DynamicSparseMatrix& other)
-    {
-      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
-      std::swap(m_innerSize, other.m_innerSize);
-      //std::swap(m_outerSize, other.m_outerSize);
-      m_data.swap(other.m_data);
-    }
-
-    inline DynamicSparseMatrix& operator=(const DynamicSparseMatrix& other)
-    {
-      if (other.isRValue())
-      {
-        swap(other.const_cast_derived());
-      }
-      else
-      {
-        resize(other.rows(), other.cols());
-        m_data = other.m_data;
-      }
-      return *this;
-    }
-
-    /** Destructor */
-    inline ~DynamicSparseMatrix() {}
-
-  public:
-
-    /** \deprecated
-      * Set the matrix to zero and reserve the memory for \a reserveSize nonzero coefficients. */
-    EIGEN_DEPRECATED void startFill(Index reserveSize = 1000)
-    {
-      setZero();
-      reserve(reserveSize);
-    }
-
-    /** \deprecated use insert()
-      * inserts a nonzero coefficient at given coordinates \a row, \a col and returns its reference assuming that:
-      *  1 - the coefficient does not exist yet
-      *  2 - this the coefficient with greater inner coordinate for the given outer coordinate.
-      * In other words, assuming \c *this is column-major, then there must not exists any nonzero coefficient of coordinates
-      * \c i \c x \a col such that \c i >= \a row. Otherwise the matrix is invalid.
-      *
-      * \see fillrand(), coeffRef()
-      */
-    EIGEN_DEPRECATED Scalar& fill(Index row, Index col)
-    {
-      const Index outer = IsRowMajor ? row : col;
-      const Index inner = IsRowMajor ? col : row;
-      return insertBack(outer,inner);
-    }
-
-    /** \deprecated use insert()
-      * Like fill() but with random inner coordinates.
-      * Compared to the generic coeffRef(), the unique limitation is that we assume
-      * the coefficient does not exist yet.
-      */
-    EIGEN_DEPRECATED Scalar& fillrand(Index row, Index col)
-    {
-      return insert(row,col);
-    }
-
-    /** \deprecated use finalize()
-      * Does nothing. Provided for compatibility with SparseMatrix. */
-    EIGEN_DEPRECATED void endFill() {}
-    
-#   ifdef EIGEN_DYNAMICSPARSEMATRIX_PLUGIN
-#     include EIGEN_DYNAMICSPARSEMATRIX_PLUGIN
-#   endif
- };
-
-template<typename Scalar, int _Options, typename _StorageIndex>
-class DynamicSparseMatrix<Scalar,_Options,_StorageIndex>::InnerIterator : public SparseVector<Scalar,_Options,_StorageIndex>::InnerIterator
-{
-    typedef typename SparseVector<Scalar,_Options,_StorageIndex>::InnerIterator Base;
-  public:
-    InnerIterator(const DynamicSparseMatrix& mat, Index outer)
-      : Base(mat.m_data[outer]), m_outer(outer)
-    {}
-
-    inline Index row() const { return IsRowMajor ? m_outer : Base::index(); }
-    inline Index col() const { return IsRowMajor ? Base::index() : m_outer; }
-    inline Index outer() const { return m_outer; }
-
-  protected:
-    const Index m_outer;
-};
-
-template<typename Scalar, int _Options, typename _StorageIndex>
-class DynamicSparseMatrix<Scalar,_Options,_StorageIndex>::ReverseInnerIterator : public SparseVector<Scalar,_Options,_StorageIndex>::ReverseInnerIterator
-{
-    typedef typename SparseVector<Scalar,_Options,_StorageIndex>::ReverseInnerIterator Base;
-  public:
-    ReverseInnerIterator(const DynamicSparseMatrix& mat, Index outer)
-      : Base(mat.m_data[outer]), m_outer(outer)
-    {}
-
-    inline Index row() const { return IsRowMajor ? m_outer : Base::index(); }
-    inline Index col() const { return IsRowMajor ? Base::index() : m_outer; }
-    inline Index outer() const { return m_outer; }
-
-  protected:
-    const Index m_outer;
-};
-
-namespace internal {
-
-template<typename _Scalar, int _Options, typename _StorageIndex>
-struct evaluator<DynamicSparseMatrix<_Scalar,_Options,_StorageIndex> >
-  : evaluator_base<DynamicSparseMatrix<_Scalar,_Options,_StorageIndex> >
-{
-  typedef _Scalar Scalar;
-  typedef DynamicSparseMatrix<_Scalar,_Options,_StorageIndex> SparseMatrixType;
-  typedef typename SparseMatrixType::InnerIterator InnerIterator;
-  typedef typename SparseMatrixType::ReverseInnerIterator ReverseInnerIterator;
-  
-  enum {
-    CoeffReadCost = NumTraits<_Scalar>::ReadCost,
-    Flags = SparseMatrixType::Flags
-  };
-  
-  evaluator() : m_matrix(0) {}
-  evaluator(const SparseMatrixType &mat) : m_matrix(&mat) {}
-  
-  operator SparseMatrixType&() { return m_matrix->const_cast_derived(); }
-  operator const SparseMatrixType&() const { return *m_matrix; }
-  
-  Scalar coeff(Index row, Index col) const { return m_matrix->coeff(row,col); }
-  
-  Index nonZerosEstimate() const { return m_matrix->nonZeros(); }
-
-  const SparseMatrixType *m_matrix;
-};
-
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_DYNAMIC_SPARSEMATRIX_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h
deleted file mode 100644
index dd786d5..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h
+++ /dev/null
@@ -1,282 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPARSE_MARKET_IO_H
-#define EIGEN_SPARSE_MARKET_IO_H
-
-#include <iostream>
-#include <vector>
-
-namespace Eigen { 
-
-namespace internal 
-{
-  template <typename Scalar, typename StorageIndex>
-  inline void GetMarketLine (const char* line, StorageIndex& i, StorageIndex& j, Scalar& value)
-  {
-    std::stringstream sline(line);
-    sline >> i >> j >> value;
-  }
-
-  template<> inline void GetMarketLine (const char* line, int& i, int& j, float& value)
-  { std::sscanf(line, "%d %d %g", &i, &j, &value); }
-
-  template<> inline void GetMarketLine (const char* line, int& i, int& j, double& value)
-  { std::sscanf(line, "%d %d %lg", &i, &j, &value); }
-
-  template<> inline void GetMarketLine (const char* line, int& i, int& j, std::complex<float>& value)
-  { std::sscanf(line, "%d %d %g %g", &i, &j, &numext::real_ref(value), &numext::imag_ref(value)); }
-
-  template<> inline void GetMarketLine (const char* line, int& i, int& j, std::complex<double>& value)
-  { std::sscanf(line, "%d %d %lg %lg", &i, &j, &numext::real_ref(value), &numext::imag_ref(value)); }
-
-  template <typename Scalar, typename StorageIndex>
-  inline void GetMarketLine (const char* line, StorageIndex& i, StorageIndex& j, std::complex<Scalar>& value)
-  {
-    std::stringstream sline(line);
-    Scalar valR, valI;
-    sline >> i >> j >> valR >> valI;
-    value = std::complex<Scalar>(valR,valI);
-  }
-
-  template <typename RealScalar>
-  inline void  GetVectorElt (const std::string& line, RealScalar& val)
-  {
-    std::istringstream newline(line);
-    newline >> val;  
-  }
-
-  template <typename RealScalar>
-  inline void GetVectorElt (const std::string& line, std::complex<RealScalar>& val)
-  {
-    RealScalar valR, valI; 
-    std::istringstream newline(line);
-    newline >> valR >> valI; 
-    val = std::complex<RealScalar>(valR, valI);
-  }
-  
-  template<typename Scalar>
-  inline void putMarketHeader(std::string& header,int sym)
-  {
-    header= "%%MatrixMarket matrix coordinate ";
-    if(internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value)
-    {
-      header += " complex"; 
-      if(sym == Symmetric) header += " symmetric";
-      else if (sym == SelfAdjoint) header += " Hermitian";
-      else header += " general";
-    }
-    else
-    {
-      header += " real"; 
-      if(sym == Symmetric) header += " symmetric";
-      else header += " general";
-    }
-  }
-
-  template<typename Scalar, typename StorageIndex>
-  inline void PutMatrixElt(Scalar value, StorageIndex row, StorageIndex col, std::ofstream& out)
-  {
-    out << row << " "<< col << " " << value << "\n";
-  }
-  template<typename Scalar, typename StorageIndex>
-  inline void PutMatrixElt(std::complex<Scalar> value, StorageIndex row, StorageIndex col, std::ofstream& out)
-  {
-    out << row << " " << col << " " << value.real() << " " << value.imag() << "\n";
-  }
-
-
-  template<typename Scalar>
-  inline void putVectorElt(Scalar value, std::ofstream& out)
-  {
-    out << value << "\n"; 
-  }
-  template<typename Scalar>
-  inline void putVectorElt(std::complex<Scalar> value, std::ofstream& out)
-  {
-    out << value.real() << " " << value.imag()<< "\n"; 
-  }
-
-} // end namespace internal
-
-inline bool getMarketHeader(const std::string& filename, int& sym, bool& iscomplex, bool& isvector)
-{
-  sym = 0; 
-  iscomplex = false;
-  isvector = false;
-  std::ifstream in(filename.c_str(),std::ios::in);
-  if(!in)
-    return false;
-  
-  std::string line; 
-  // The matrix header is always the first line in the file 
-  std::getline(in, line); eigen_assert(in.good());
-  
-  std::stringstream fmtline(line); 
-  std::string substr[5];
-  fmtline>> substr[0] >> substr[1] >> substr[2] >> substr[3] >> substr[4];
-  if(substr[2].compare("array") == 0) isvector = true;
-  if(substr[3].compare("complex") == 0) iscomplex = true;
-  if(substr[4].compare("symmetric") == 0) sym = Symmetric;
-  else if (substr[4].compare("Hermitian") == 0) sym = SelfAdjoint;
-  
-  return true;
-}
-  
-template<typename SparseMatrixType>
-bool loadMarket(SparseMatrixType& mat, const std::string& filename)
-{
-  typedef typename SparseMatrixType::Scalar Scalar;
-  typedef typename SparseMatrixType::StorageIndex StorageIndex;
-  std::ifstream input(filename.c_str(),std::ios::in);
-  if(!input)
-    return false;
-
-  char rdbuffer[4096];
-  input.rdbuf()->pubsetbuf(rdbuffer, 4096);
-  
-  const int maxBuffersize = 2048;
-  char buffer[maxBuffersize];
-  
-  bool readsizes = false;
-
-  typedef Triplet<Scalar,StorageIndex> T;
-  std::vector<T> elements;
-  
-  Index M(-1), N(-1), NNZ(-1);
-  Index count = 0;
-  while(input.getline(buffer, maxBuffersize))
-  {
-    // skip comments   
-    //NOTE An appropriate test should be done on the header to get the  symmetry
-    if(buffer[0]=='%')
-      continue;
-
-    if(!readsizes)
-    {
-      std::stringstream line(buffer);
-      line >> M >> N >> NNZ;
-      if(M > 0 && N > 0)
-      {
-        readsizes = true;
-        mat.resize(M,N);
-        mat.reserve(NNZ);
-      }
-    }
-    else
-    { 
-      StorageIndex i(-1), j(-1);
-      Scalar value; 
-      internal::GetMarketLine(buffer, i, j, value);
-
-      i--;
-      j--;
-      if(i>=0 && j>=0 && i<M && j<N)
-      {
-        ++count;
-        elements.push_back(T(i,j,value));
-      }
-      else
-        std::cerr << "Invalid read: " << i << "," << j << "\n";        
-    }
-  }
-
-  mat.setFromTriplets(elements.begin(), elements.end());
-  if(count!=NNZ)
-    std::cerr << count << "!=" << NNZ << "\n";
-  
-  input.close();
-  return true;
-}
-
-template<typename VectorType>
-bool loadMarketVector(VectorType& vec, const std::string& filename)
-{
-   typedef typename VectorType::Scalar Scalar;
-  std::ifstream in(filename.c_str(), std::ios::in);
-  if(!in)
-    return false;
-  
-  std::string line; 
-  int n(0), col(0); 
-  do 
-  { // Skip comments
-    std::getline(in, line); eigen_assert(in.good());
-  } while (line[0] == '%');
-  std::istringstream newline(line);
-  newline  >> n >> col; 
-  eigen_assert(n>0 && col>0);
-  vec.resize(n);
-  int i = 0; 
-  Scalar value; 
-  while ( std::getline(in, line) && (i < n) ){
-    internal::GetVectorElt(line, value); 
-    vec(i++) = value; 
-  }
-  in.close();
-  if (i!=n){
-    std::cerr<< "Unable to read all elements from file " << filename << "\n";
-    return false;
-  }
-  return true;
-}
-
-template<typename SparseMatrixType>
-bool saveMarket(const SparseMatrixType& mat, const std::string& filename, int sym = 0)
-{
-  typedef typename SparseMatrixType::Scalar Scalar;
-  typedef typename SparseMatrixType::RealScalar RealScalar;
-  std::ofstream out(filename.c_str(),std::ios::out);
-  if(!out)
-    return false;
-  
-  out.flags(std::ios_base::scientific);
-  out.precision(std::numeric_limits<RealScalar>::digits10 + 2);
-  std::string header; 
-  internal::putMarketHeader<Scalar>(header, sym); 
-  out << header << std::endl; 
-  out << mat.rows() << " " << mat.cols() << " " << mat.nonZeros() << "\n";
-  int count = 0;
-  for(int j=0; j<mat.outerSize(); ++j)
-    for(typename SparseMatrixType::InnerIterator it(mat,j); it; ++it)
-    {
-      ++ count;
-      internal::PutMatrixElt(it.value(), it.row()+1, it.col()+1, out);
-    }
-  out.close();
-  return true;
-}
-
-template<typename VectorType>
-bool saveMarketVector (const VectorType& vec, const std::string& filename)
-{
- typedef typename VectorType::Scalar Scalar;
- typedef typename VectorType::RealScalar RealScalar;
- std::ofstream out(filename.c_str(),std::ios::out);
-  if(!out)
-    return false;
-  
-  out.flags(std::ios_base::scientific);
-  out.precision(std::numeric_limits<RealScalar>::digits10 + 2);
-  if(internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value)
-      out << "%%MatrixMarket matrix array complex general\n"; 
-  else
-    out << "%%MatrixMarket matrix array real general\n"; 
-  out << vec.size() << " "<< 1 << "\n";
-  for (int i=0; i < vec.size(); i++){
-    internal::putVectorElt(vec(i), out); 
-  }
-  out.close();
-  return true; 
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPARSE_MARKET_IO_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
deleted file mode 100644
index 02916ea..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
+++ /dev/null
@@ -1,247 +0,0 @@
-
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BROWSE_MATRICES_H
-#define EIGEN_BROWSE_MATRICES_H
-
-namespace Eigen {
-
-enum {
-  SPD = 0x100,
-  NonSymmetric = 0x0
-}; 
-
-/** 
- * @brief Iterator to browse matrices from a specified folder
- * 
- * This is used to load all the matrices from a folder. 
- * The matrices should be in Matrix Market format
- * It is assumed that the matrices are named as matname.mtx
- * and matname_SPD.mtx if the matrix is Symmetric and positive definite (or Hermitian)
- * The right hand side vectors are loaded as well, if they exist.
- * They should be named as matname_b.mtx. 
- * Note that the right hand side for a SPD matrix is named as matname_SPD_b.mtx
- * 
- * Sometimes a reference solution is available. In this case, it should be named as matname_x.mtx
- * 
- * Sample code
- * \code
- * 
- * \endcode
- * 
- * \tparam Scalar The scalar type 
- */
-template <typename Scalar>
-class MatrixMarketIterator 
-{
-    typedef typename NumTraits<Scalar>::Real RealScalar;
-  public:
-    typedef Matrix<Scalar,Dynamic,1> VectorType; 
-    typedef SparseMatrix<Scalar,ColMajor> MatrixType; 
-  
-  public:
-    MatrixMarketIterator(const std::string &folder)
-      : m_sym(0), m_isvalid(false), m_matIsLoaded(false), m_hasRhs(false), m_hasrefX(false), m_folder(folder)
-    {
-      m_folder_id = opendir(folder.c_str());
-      if(m_folder_id)
-        Getnextvalidmatrix();
-    }
-    
-    ~MatrixMarketIterator()
-    {
-      if (m_folder_id) closedir(m_folder_id); 
-    }
-    
-    inline MatrixMarketIterator& operator++()
-    {
-      m_matIsLoaded = false;
-      m_hasrefX = false;
-      m_hasRhs = false;
-      Getnextvalidmatrix();
-      return *this;
-    }
-    inline operator bool() const { return m_isvalid;}
-    
-    /** Return the sparse matrix corresponding to the current file */
-    inline MatrixType& matrix() 
-    { 
-      // Read the matrix
-      if (m_matIsLoaded) return m_mat;
-      
-      std::string matrix_file = m_folder + "/" + m_matname + ".mtx";
-      if ( !loadMarket(m_mat, matrix_file)) 
-      {
-        std::cerr << "Warning loadMarket failed when loading \"" << matrix_file << "\"" << std::endl;
-        m_matIsLoaded = false;
-        return m_mat;
-      }
-      m_matIsLoaded = true; 
-
-      if (m_sym != NonSymmetric) 
-      {
-        // Check whether we need to restore a full matrix:
-        RealScalar diag_norm  = m_mat.diagonal().norm();
-        RealScalar lower_norm = m_mat.template triangularView<Lower>().norm();
-        RealScalar upper_norm = m_mat.template triangularView<Upper>().norm();
-        if(lower_norm>diag_norm && upper_norm==diag_norm)
-        {
-          // only the lower part is stored
-          MatrixType tmp(m_mat);
-          m_mat = tmp.template selfadjointView<Lower>();
-        }
-        else if(upper_norm>diag_norm && lower_norm==diag_norm)
-        {
-          // only the upper part is stored
-          MatrixType tmp(m_mat);
-          m_mat = tmp.template selfadjointView<Upper>();
-        }
-      }
-      return m_mat; 
-    }
-    
-    /** Return the right hand side corresponding to the current matrix. 
-     * If the rhs file is not provided, a random rhs is generated
-     */
-    inline VectorType& rhs() 
-    { 
-       // Get the right hand side
-      if (m_hasRhs) return m_rhs;
-      
-      std::string rhs_file;
-      rhs_file = m_folder + "/" + m_matname + "_b.mtx"; // The pattern is matname_b.mtx
-      m_hasRhs = Fileexists(rhs_file);
-      if (m_hasRhs)
-      {
-        m_rhs.resize(m_mat.cols());
-        m_hasRhs = loadMarketVector(m_rhs, rhs_file);
-      }
-      if (!m_hasRhs)
-      {
-        // Generate a random right hand side
-        if (!m_matIsLoaded) this->matrix(); 
-        m_refX.resize(m_mat.cols());
-        m_refX.setRandom();
-        m_rhs = m_mat * m_refX;
-        m_hasrefX = true;
-        m_hasRhs = true;
-      }
-      return m_rhs; 
-    }
-    
-    /** Return a reference solution
-     * If it is not provided and if the right hand side is not available
-     * then refX is randomly generated such that A*refX = b 
-     * where A and b are the matrix and the rhs. 
-     * Note that when a rhs is provided, refX is not available 
-     */
-    inline VectorType& refX() 
-    { 
-      // Check if a reference solution is provided
-      if (m_hasrefX) return m_refX;
-      
-      std::string lhs_file;
-      lhs_file = m_folder + "/" + m_matname + "_x.mtx"; 
-      m_hasrefX = Fileexists(lhs_file);
-      if (m_hasrefX)
-      {
-        m_refX.resize(m_mat.cols());
-        m_hasrefX = loadMarketVector(m_refX, lhs_file);
-      }
-      else
-        m_refX.resize(0);
-      return m_refX; 
-    }
-    
-    inline std::string& matname() { return m_matname; }
-    
-    inline int sym() { return m_sym; }
-    
-    bool hasRhs() {return m_hasRhs; }
-    bool hasrefX() {return m_hasrefX; }
-    bool isFolderValid() { return bool(m_folder_id); }
-    
-  protected:
-    
-    inline bool Fileexists(std::string file)
-    {
-      std::ifstream file_id(file.c_str());
-      if (!file_id.good() ) 
-      {
-        return false;
-      }
-      else 
-      {
-        file_id.close();
-        return true;
-      }
-    }
-    
-    void Getnextvalidmatrix( )
-    {
-      m_isvalid = false;
-      // Here, we return with the next valid matrix in the folder
-      while ( (m_curs_id = readdir(m_folder_id)) != NULL) {
-        m_isvalid = false;
-        std::string curfile;
-        curfile = m_folder + "/" + m_curs_id->d_name;
-        // Discard if it is a folder
-        if (m_curs_id->d_type == DT_DIR) continue; //FIXME This may not be available on non BSD systems
-//         struct stat st_buf; 
-//         stat (curfile.c_str(), &st_buf);
-//         if (S_ISDIR(st_buf.st_mode)) continue;
-        
-        // Determine from the header if it is a matrix or a right hand side 
-        bool isvector,iscomplex=false;
-        if(!getMarketHeader(curfile,m_sym,iscomplex,isvector)) continue;
-        if(isvector) continue;
-        if (!iscomplex)
-        {
-          if(internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value)
-            continue; 
-        }
-        if (iscomplex)
-        {
-          if(internal::is_same<Scalar, float>::value || internal::is_same<Scalar, double>::value)
-            continue; 
-        }
-        
-        
-        // Get the matrix name
-        std::string filename = m_curs_id->d_name;
-        m_matname = filename.substr(0, filename.length()-4); 
-        
-        // Find if the matrix is SPD 
-        size_t found = m_matname.find("SPD");
-        if( (found!=std::string::npos) && (m_sym != NonSymmetric) )
-          m_sym = SPD;
-       
-        m_isvalid = true;
-        break; 
-      }
-    }
-    int m_sym; // Symmetry of the matrix
-    MatrixType m_mat; // Current matrix  
-    VectorType m_rhs;  // Current vector
-    VectorType m_refX; // The reference solution, if exists
-    std::string m_matname; // Matrix Name
-    bool m_isvalid; 
-    bool m_matIsLoaded; // Determine if the matrix has already been loaded from the file
-    bool m_hasRhs; // The right hand side exists
-    bool m_hasrefX; // A reference solution is provided
-    std::string m_folder;
-    DIR * m_folder_id;
-    struct dirent *m_curs_id; 
-    
-};
-
-} // end namespace Eigen
-
-#endif
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h
deleted file mode 100644
index 985702b..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h
+++ /dev/null
@@ -1,349 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_RANDOMSETTER_H
-#define EIGEN_RANDOMSETTER_H
-
-#if defined(EIGEN_GOOGLEHASH_SUPPORT)
-// Ensure the ::google namespace exists, required for checking existence of 
-// ::google::dense_hash_map and ::google::sparse_hash_map.
-namespace google {}
-#endif
-
-namespace Eigen {
-
-/** Represents a std::map
-  *
-  * \see RandomSetter
-  */
-template<typename Scalar> struct StdMapTraits
-{
-  typedef int KeyType;
-  typedef std::map<KeyType,Scalar> Type;
-  enum {
-    IsSorted = 1
-  };
-
-  static void setInvalidKey(Type&, const KeyType&) {}
-};
-
-#ifdef EIGEN_UNORDERED_MAP_SUPPORT
-/** Represents a std::unordered_map
-  *
-  * To use it you need to both define EIGEN_UNORDERED_MAP_SUPPORT and include the unordered_map header file
-  * yourself making sure that unordered_map is defined in the std namespace.
-  *
-  * For instance, with current version of gcc you can either enable C++0x standard (-std=c++0x) or do:
-  * \code
-  * #include <tr1/unordered_map>
-  * #define EIGEN_UNORDERED_MAP_SUPPORT
-  * namespace std {
-  *   using std::tr1::unordered_map;
-  * }
-  * \endcode
-  *
-  * \see RandomSetter
-  */
-template<typename Scalar> struct StdUnorderedMapTraits
-{
-  typedef int KeyType;
-  typedef std::unordered_map<KeyType,Scalar> Type;
-  enum {
-    IsSorted = 0
-  };
-
-  static void setInvalidKey(Type&, const KeyType&) {}
-};
-#endif // EIGEN_UNORDERED_MAP_SUPPORT
-
-#if defined(EIGEN_GOOGLEHASH_SUPPORT)
-
-namespace google {
-  
-// Namespace work-around, since sometimes dense_hash_map and sparse_hash_map
-// are in the global namespace, and other times they are under ::google.
-using namespace ::google;
-
-template<typename KeyType, typename Scalar>
-struct DenseHashMap {
-  typedef dense_hash_map<KeyType, Scalar> type;
-};
-
-template<typename KeyType, typename Scalar>
-struct SparseHashMap {
-  typedef sparse_hash_map<KeyType, Scalar> type;
-};
-
-} // namespace google
-
-/** Represents a google::dense_hash_map
-  *
-  * \see RandomSetter
-  */
-template<typename Scalar> struct GoogleDenseHashMapTraits
-{
-  typedef int KeyType;
-  typedef typename google::DenseHashMap<KeyType,Scalar>::type Type;
-  enum {
-    IsSorted = 0
-  };
-
-  static void setInvalidKey(Type& map, const KeyType& k)
-  { map.set_empty_key(k); }
-};
-
-/** Represents a google::sparse_hash_map
-  *
-  * \see RandomSetter
-  */
-template<typename Scalar> struct GoogleSparseHashMapTraits
-{
-  typedef int KeyType;
-  typedef typename google::SparseHashMap<KeyType,Scalar>::type Type;
-  enum {
-    IsSorted = 0
-  };
-
-  static void setInvalidKey(Type&, const KeyType&) {}
-};
-#endif
-
-/** \class RandomSetter
-  *
-  * \brief The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access
-  *
-  * \tparam SparseMatrixType the type of the sparse matrix we are updating
-  * \tparam MapTraits a traits class representing the map implementation used for the temporary sparse storage.
-  *                  Its default value depends on the system.
-  * \tparam OuterPacketBits defines the number of rows (or columns) manage by a single map object
-  *                        as a power of two exponent.
-  *
-  * This class temporarily represents a sparse matrix object using a generic map implementation allowing for
-  * efficient random access. The conversion from the compressed representation to a hash_map object is performed
-  * in the RandomSetter constructor, while the sparse matrix is updated back at destruction time. This strategy
-  * suggest the use of nested blocks as in this example:
-  *
-  * \code
-  * SparseMatrix<double> m(rows,cols);
-  * {
-  *   RandomSetter<SparseMatrix<double> > w(m);
-  *   // don't use m but w instead with read/write random access to the coefficients:
-  *   for(;;)
-  *     w(rand(),rand()) = rand;
-  * }
-  * // when w is deleted, the data are copied back to m
-  * // and m is ready to use.
-  * \endcode
-  *
-  * Since hash_map objects are not fully sorted, representing a full matrix as a single hash_map would
-  * involve a big and costly sort to update the compressed matrix back. To overcome this issue, a RandomSetter
-  * use multiple hash_map, each representing 2^OuterPacketBits columns or rows according to the storage order.
-  * To reach optimal performance, this value should be adjusted according to the average number of nonzeros
-  * per rows/columns.
-  *
-  * The possible values for the template parameter MapTraits are:
-  *  - \b StdMapTraits: corresponds to std::map. (does not perform very well)
-  *  - \b GnuHashMapTraits: corresponds to __gnu_cxx::hash_map (available only with GCC)
-  *  - \b GoogleDenseHashMapTraits: corresponds to google::dense_hash_map (best efficiency, reasonable memory consumption)
-  *  - \b GoogleSparseHashMapTraits: corresponds to google::sparse_hash_map (best memory consumption, relatively good performance)
-  *
-  * The default map implementation depends on the availability, and the preferred order is:
-  * GoogleSparseHashMapTraits, GnuHashMapTraits, and finally StdMapTraits.
-  *
-  * For performance and memory consumption reasons it is highly recommended to use one of
-  * Google's hash_map implementations. To enable the support for them, you must define
-  * EIGEN_GOOGLEHASH_SUPPORT. This will include both <google/dense_hash_map> and
-  * <google/sparse_hash_map> for you.
-  *
-  * \see https://github.com/sparsehash/sparsehash
-  */
-template<typename SparseMatrixType,
-         template <typename T> class MapTraits =
-#if defined(EIGEN_GOOGLEHASH_SUPPORT)
-          GoogleDenseHashMapTraits
-#elif defined(_HASH_MAP)
-          GnuHashMapTraits
-#else
-          StdMapTraits
-#endif
-         ,int OuterPacketBits = 6>
-class RandomSetter
-{
-    typedef typename SparseMatrixType::Scalar Scalar;
-    typedef typename SparseMatrixType::StorageIndex StorageIndex;
-
-    struct ScalarWrapper
-    {
-      ScalarWrapper() : value(0) {}
-      Scalar value;
-    };
-    typedef typename MapTraits<ScalarWrapper>::KeyType KeyType;
-    typedef typename MapTraits<ScalarWrapper>::Type HashMapType;
-    static const int OuterPacketMask = (1 << OuterPacketBits) - 1;
-    enum {
-      SwapStorage = 1 - MapTraits<ScalarWrapper>::IsSorted,
-      TargetRowMajor = (SparseMatrixType::Flags & RowMajorBit) ? 1 : 0,
-      SetterRowMajor = SwapStorage ? 1-TargetRowMajor : TargetRowMajor
-    };
-
-  public:
-
-    /** Constructs a random setter object from the sparse matrix \a target
-      *
-      * Note that the initial value of \a target are imported. If you want to re-set
-      * a sparse matrix from scratch, then you must set it to zero first using the
-      * setZero() function.
-      */
-    inline RandomSetter(SparseMatrixType& target)
-      : mp_target(&target)
-    {
-      const Index outerSize = SwapStorage ? target.innerSize() : target.outerSize();
-      const Index innerSize = SwapStorage ? target.outerSize() : target.innerSize();
-      m_outerPackets = outerSize >> OuterPacketBits;
-      if (outerSize&OuterPacketMask)
-        m_outerPackets += 1;
-      m_hashmaps = new HashMapType[m_outerPackets];
-      // compute number of bits needed to store inner indices
-      Index aux = innerSize - 1;
-      m_keyBitsOffset = 0;
-      while (aux)
-      {
-        ++m_keyBitsOffset;
-        aux = aux >> 1;
-      }
-      KeyType ik = (1<<(OuterPacketBits+m_keyBitsOffset));
-      for (Index k=0; k<m_outerPackets; ++k)
-        MapTraits<ScalarWrapper>::setInvalidKey(m_hashmaps[k],ik);
-
-      // insert current coeffs
-      for (Index j=0; j<mp_target->outerSize(); ++j)
-        for (typename SparseMatrixType::InnerIterator it(*mp_target,j); it; ++it)
-          (*this)(TargetRowMajor?j:it.index(), TargetRowMajor?it.index():j) = it.value();
-    }
-
-    /** Destructor updating back the sparse matrix target */
-    ~RandomSetter()
-    {
-      KeyType keyBitsMask = (1<<m_keyBitsOffset)-1;
-      if (!SwapStorage) // also means the map is sorted
-      {
-        mp_target->setZero();
-        mp_target->makeCompressed();
-        mp_target->reserve(nonZeros());
-        Index prevOuter = -1;
-        for (Index k=0; k<m_outerPackets; ++k)
-        {
-          const Index outerOffset = (1<<OuterPacketBits) * k;
-          typename HashMapType::iterator end = m_hashmaps[k].end();
-          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
-          {
-            const Index outer = (it->first >> m_keyBitsOffset) + outerOffset;
-            const Index inner = it->first & keyBitsMask;
-            if (prevOuter!=outer)
-            {
-              for (Index j=prevOuter+1;j<=outer;++j)
-                mp_target->startVec(j);
-              prevOuter = outer;
-            }
-            mp_target->insertBackByOuterInner(outer, inner) = it->second.value;
-          }
-        }
-        mp_target->finalize();
-      }
-      else
-      {
-        VectorXi positions(mp_target->outerSize());
-        positions.setZero();
-        // pass 1
-        for (Index k=0; k<m_outerPackets; ++k)
-        {
-          typename HashMapType::iterator end = m_hashmaps[k].end();
-          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
-          {
-            const Index outer = it->first & keyBitsMask;
-            ++positions[outer];
-          }
-        }
-        // prefix sum
-        StorageIndex count = 0;
-        for (Index j=0; j<mp_target->outerSize(); ++j)
-        {
-          StorageIndex tmp = positions[j];
-          mp_target->outerIndexPtr()[j] = count;
-          positions[j] = count;
-          count += tmp;
-        }
-        mp_target->makeCompressed();
-        mp_target->outerIndexPtr()[mp_target->outerSize()] = count;
-        mp_target->resizeNonZeros(count);
-        // pass 2
-        for (Index k=0; k<m_outerPackets; ++k)
-        {
-          const Index outerOffset = (1<<OuterPacketBits) * k;
-          typename HashMapType::iterator end = m_hashmaps[k].end();
-          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
-          {
-            const Index inner = (it->first >> m_keyBitsOffset) + outerOffset;
-            const Index outer = it->first & keyBitsMask;
-            // sorted insertion
-            // Note that we have to deal with at most 2^OuterPacketBits unsorted coefficients,
-            // moreover those 2^OuterPacketBits coeffs are likely to be sparse, an so only a
-            // small fraction of them have to be sorted, whence the following simple procedure:
-            Index posStart = mp_target->outerIndexPtr()[outer];
-            Index i = (positions[outer]++) - 1;
-            while ( (i >= posStart) && (mp_target->innerIndexPtr()[i] > inner) )
-            {
-              mp_target->valuePtr()[i+1] = mp_target->valuePtr()[i];
-              mp_target->innerIndexPtr()[i+1] = mp_target->innerIndexPtr()[i];
-              --i;
-            }
-            mp_target->innerIndexPtr()[i+1] = internal::convert_index<StorageIndex>(inner);
-            mp_target->valuePtr()[i+1] = it->second.value;
-          }
-        }
-      }
-      delete[] m_hashmaps;
-    }
-
-    /** \returns a reference to the coefficient at given coordinates \a row, \a col */
-    Scalar& operator() (Index row, Index col)
-    {
-      const Index outer = SetterRowMajor ? row : col;
-      const Index inner = SetterRowMajor ? col : row;
-      const Index outerMajor = outer >> OuterPacketBits; // index of the packet/map
-      const Index outerMinor = outer & OuterPacketMask;  // index of the inner vector in the packet
-      const KeyType key = internal::convert_index<KeyType>((outerMinor<<m_keyBitsOffset) | inner);
-      return m_hashmaps[outerMajor][key].value;
-    }
-
-    /** \returns the number of non zero coefficients
-      *
-      * \note According to the underlying map/hash_map implementation,
-      * this function might be quite expensive.
-      */
-    Index nonZeros() const
-    {
-      Index nz = 0;
-      for (Index k=0; k<m_outerPackets; ++k)
-        nz += static_cast<Index>(m_hashmaps[k].size());
-      return nz;
-    }
-
-
-  protected:
-
-    HashMapType* m_hashmaps;
-    SparseMatrixType* mp_target;
-    Index m_outerPackets;
-    unsigned char m_keyBitsOffset;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_RANDOMSETTER_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
deleted file mode 100644
index 41d2bf6..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
+++ /dev/null
@@ -1,286 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
-#define EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
-
-namespace Eigen {
-
-/** \returns an expression of the coefficient-wise i0(\a x) to the given
- * arrays.
-  *
-  * It returns the modified Bessel function of the first kind of order zero.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of i0(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_i0()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_i0_op<typename Derived::Scalar>, const Derived>
-bessel_i0(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_i0_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise i0e(\a x) to the given
- * arrays.
-  *
-  * It returns the exponentially scaled modified Bessel
-  * function of the first kind of order zero.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of i0e(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_i0e()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_i0e_op<typename Derived::Scalar>, const Derived>
-bessel_i0e(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_i0e_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise i1(\a x) to the given
- * arrays.
-  *
-  * It returns the modified Bessel function of the first kind of order one.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of i1(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_i1()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_i1_op<typename Derived::Scalar>, const Derived>
-bessel_i1(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_i1_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise i1e(\a x) to the given
- * arrays.
-  *
-  * It returns the exponentially scaled modified Bessel
-  * function of the first kind of order one.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of i1e(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_i1e()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_i1e_op<typename Derived::Scalar>, const Derived>
-bessel_i1e(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_i1e_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise k0(\a x) to the given
- * arrays.
-  *
-  * It returns the modified Bessel function of the second kind of order zero.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of k0(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_k0()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_k0_op<typename Derived::Scalar>, const Derived>
-bessel_k0(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_k0_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise k0e(\a x) to the given
- * arrays.
-  *
-  * It returns the exponentially scaled modified Bessel
-  * function of the second kind of order zero.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of k0e(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_k0e()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_k0e_op<typename Derived::Scalar>, const Derived>
-bessel_k0e(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_k0e_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise k1(\a x) to the given
- * arrays.
-  *
-  * It returns the modified Bessel function of the second kind of order one.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of k1(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_k1()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_k1_op<typename Derived::Scalar>, const Derived>
-bessel_k1(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_k1_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise k1e(\a x) to the given
- * arrays.
-  *
-  * It returns the exponentially scaled modified Bessel
-  * function of the second kind of order one.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of k1e(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_k1e()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_k1e_op<typename Derived::Scalar>, const Derived>
-bessel_k1e(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_k1e_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise j0(\a x) to the given
- * arrays.
-  *
-  * It returns the Bessel function of the first kind of order zero.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of j0(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_j0()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_j0_op<typename Derived::Scalar>, const Derived>
-bessel_j0(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_j0_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise y0(\a x) to the given
- * arrays.
-  *
-  * It returns the Bessel function of the second kind of order zero.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of y0(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_y0()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_y0_op<typename Derived::Scalar>, const Derived>
-bessel_y0(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_y0_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise j1(\a x) to the given
- * arrays.
-  *
-  * It returns the modified Bessel function of the first kind of order one.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of j1(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_j1()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_j1_op<typename Derived::Scalar>, const Derived>
-bessel_j1(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_j1_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-/** \returns an expression of the coefficient-wise y1(\a x) to the given
- * arrays.
-  *
-  * It returns the Bessel function of the second kind of order one.
-  *
-  * \param x is the argument
-  *
-  * \note This function supports only float and double scalar types. To support
-  * other scalar types, the user has to provide implementations of y1(T) for
-  * any scalar type T to be supported.
-  *
-  * \sa ArrayBase::bessel_y1()
-  */
-template <typename Derived>
-EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
-    Eigen::internal::scalar_bessel_y1_op<typename Derived::Scalar>, const Derived>
-bessel_y1(const Eigen::ArrayBase<Derived>& x) {
-  return Eigen::CwiseUnaryOp<
-      Eigen::internal::scalar_bessel_y1_op<typename Derived::Scalar>,
-      const Derived>(x.derived());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
deleted file mode 100644
index 6049cc2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
+++ /dev/null
@@ -1,68 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BESSELFUNCTIONS_BFLOAT16_H
-#define EIGEN_BESSELFUNCTIONS_BFLOAT16_H
-
-namespace Eigen {
-namespace numext {
-
-#if EIGEN_HAS_C99_MATH
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i0(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_i0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i0e(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_i0e(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i1(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_i1(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i1e(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_i1e(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_j0(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_j0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_j1(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_j1(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_y0(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_y0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_y1(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_y1(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k0(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_k0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k0e(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_k0e(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k1(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_k1(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k1e(const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::bessel_k1e(static_cast<float>(x)));
-}
-#endif
-
-}  // end namespace numext
-}  // end namespace Eigen
-
-#endif  // EIGEN_BESSELFUNCTIONS_BFLOAT16_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
deleted file mode 100644
index 8606a9f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
+++ /dev/null
@@ -1,357 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BESSELFUNCTIONS_FUNCTORS_H
-#define EIGEN_BESSELFUNCTIONS_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal
- * \brief Template functor to compute the modified Bessel function of the first
- * kind of order zero.
- * \sa class CwiseUnaryOp, Cwise::bessel_i0()
- */
-template <typename Scalar>
-struct scalar_bessel_i0_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i0_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_i0;
-    return bessel_i0(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_i0(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_i0_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=20 is computed.
-    // The cost is N multiplications and 2N additions. We also add
-    // the cost of an additional exp over i0e.
-    Cost = 28 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the exponentially scaled modified Bessel
- * function of the first kind of order zero
- * \sa class CwiseUnaryOp, Cwise::bessel_i0e()
- */
-template <typename Scalar>
-struct scalar_bessel_i0e_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i0e_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_i0e;
-    return bessel_i0e(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_i0e(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_i0e_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=20 is computed.
-    // The cost is N multiplications and 2N additions.
-    Cost = 20 * NumTraits<Scalar>::MulCost + 40 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the modified Bessel function of the first
- * kind of order one
- * \sa class CwiseUnaryOp, Cwise::bessel_i1()
- */
-template <typename Scalar>
-struct scalar_bessel_i1_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i1_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_i1;
-    return bessel_i1(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_i1(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_i1_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=20 is computed.
-    // The cost is N multiplications and 2N additions. We also add
-    // the cost of an additional exp over i1e.
-    Cost = 28 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the exponentially scaled modified Bessel
- * function of the first kind of order zero
- * \sa class CwiseUnaryOp, Cwise::bessel_i1e()
- */
-template <typename Scalar>
-struct scalar_bessel_i1e_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i1e_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_i1e;
-    return bessel_i1e(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_i1e(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_i1e_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=20 is computed.
-    // The cost is N multiplications and 2N additions.
-    Cost = 20 * NumTraits<Scalar>::MulCost + 40 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the Bessel function of the second kind of
- * order zero
- * \sa class CwiseUnaryOp, Cwise::bessel_j0()
- */
-template <typename Scalar>
-struct scalar_bessel_j0_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_j0_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_j0;
-    return bessel_j0(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_j0(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_j0_op<Scalar> > {
-  enum {
-    // 6 polynomial of order ~N=8 is computed.
-    // The cost is N multiplications and N additions each, along with a
-    // sine, cosine and rsqrt cost.
-    Cost = 63 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the Bessel function of the second kind of
- * order zero
- * \sa class CwiseUnaryOp, Cwise::bessel_y0()
- */
-template <typename Scalar>
-struct scalar_bessel_y0_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_y0_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_y0;
-    return bessel_y0(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_y0(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_y0_op<Scalar> > {
-  enum {
-    // 6 polynomial of order ~N=8 is computed.
-    // The cost is N multiplications and N additions each, along with a
-    // sine, cosine, rsqrt and j0 cost.
-    Cost = 126 * NumTraits<Scalar>::MulCost + 96 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the Bessel function of the first kind of
- * order one
- * \sa class CwiseUnaryOp, Cwise::bessel_j1()
- */
-template <typename Scalar>
-struct scalar_bessel_j1_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_j1_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_j1;
-    return bessel_j1(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_j1(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_j1_op<Scalar> > {
-  enum {
-    // 6 polynomial of order ~N=8 is computed.
-    // The cost is N multiplications and N additions each, along with a
-    // sine, cosine and rsqrt cost.
-    Cost = 63 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the Bessel function of the second kind of
- * order one
- * \sa class CwiseUnaryOp, Cwise::bessel_j1e()
- */
-template <typename Scalar>
-struct scalar_bessel_y1_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_y1_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_y1;
-    return bessel_y1(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_y1(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_y1_op<Scalar> > {
-  enum {
-    // 6 polynomial of order ~N=8 is computed.
-    // The cost is N multiplications and N additions each, along with a
-    // sine, cosine, rsqrt and j1 cost.
-    Cost = 126 * NumTraits<Scalar>::MulCost + 96 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the modified Bessel function of the second
- * kind of order zero
- * \sa class CwiseUnaryOp, Cwise::bessel_k0()
- */
-template <typename Scalar>
-struct scalar_bessel_k0_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k0_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_k0;
-    return bessel_k0(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_k0(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_k0_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=10 is computed.
-    // The cost is N multiplications and 2N additions. In addition we compute
-    // i0, a log, exp and prsqrt and sin and cos.
-    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the exponentially scaled modified Bessel
- * function of the second kind of order zero
- * \sa class CwiseUnaryOp, Cwise::bessel_k0e()
- */
-template <typename Scalar>
-struct scalar_bessel_k0e_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k0e_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_k0e;
-    return bessel_k0e(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_k0e(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_k0e_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=10 is computed.
-    // The cost is N multiplications and 2N additions. In addition we compute
-    // i0, a log, exp and prsqrt and sin and cos.
-    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the modified Bessel function of the
- * second kind of order one
- * \sa class CwiseUnaryOp, Cwise::bessel_k1()
- */
-template <typename Scalar>
-struct scalar_bessel_k1_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k1_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_k1;
-    return bessel_k1(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_k1(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_k1_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=10 is computed.
-    // The cost is N multiplications and 2N additions. In addition we compute
-    // i1, a log, exp and prsqrt and sin and cos.
-    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the exponentially scaled modified Bessel
- * function of the second kind of order one
- * \sa class CwiseUnaryOp, Cwise::bessel_k1e()
- */
-template <typename Scalar>
-struct scalar_bessel_k1e_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k1e_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
-    using numext::bessel_k1e;
-    return bessel_k1e(x);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return internal::pbessel_k1e(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_bessel_k1e_op<Scalar> > {
-  enum {
-    // On average, a Chebyshev polynomial of order N=10 is computed.
-    // The cost is N multiplications and 2N additions. In addition we compute
-    // i1, a log, exp and prsqrt and sin and cos.
-    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBessel
-  };
-};
-
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BESSELFUNCTIONS_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
deleted file mode 100644
index 8930d1a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
+++ /dev/null
@@ -1,66 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BESSELFUNCTIONS_HALF_H
-#define EIGEN_BESSELFUNCTIONS_HALF_H
-
-namespace Eigen {
-namespace numext {
-
-#if EIGEN_HAS_C99_MATH
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i0(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_i0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i0e(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_i0e(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i1(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_i1(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i1e(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_i1e(static_cast<float>(x)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_j0(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_j0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_j1(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_j1(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_y0(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_y0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_y1(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_y1(static_cast<float>(x)));
-}
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k0(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_k0(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k0e(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_k0e(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k1(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_k1(static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k1e(const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::bessel_k1e(static_cast<float>(x)));
-}
-#endif
-
-}  // end namespace numext
-}  // end namespace Eigen
-
-#endif  // EIGEN_BESSELFUNCTIONS_HALF_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
deleted file mode 100644
index 24812be..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
+++ /dev/null
@@ -1,1959 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BESSEL_FUNCTIONS_H
-#define EIGEN_BESSEL_FUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-//  Parts of this code are based on the Cephes Math Library.
-//
-//  Cephes Math Library Release 2.8:  June, 2000
-//  Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
-//
-//  Permission has been kindly provided by the original author
-//  to incorporate the Cephes software into the Eigen codebase:
-//
-//    From: Stephen Moshier
-//    To: Eugene Brevdo
-//    Subject: Re: Permission to wrap several cephes functions in Eigen
-//
-//    Hello Eugene,
-//
-//    Thank you for writing.
-//
-//    If your licensing is similar to BSD, the formal way that has been
-//    handled is simply to add a statement to the effect that you are incorporating
-//    the Cephes software by permission of the author.
-//
-//    Good luck with your project,
-//    Steve
-
-
-/****************************************************************************
- * Implementation of Bessel function, based on Cephes                       *
- ****************************************************************************/
-
-template <typename Scalar>
-struct bessel_i0e_retval {
-  typedef Scalar type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_i0e {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_i0e<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  i0ef.c
-     *
-     *  Modified Bessel function of order zero,
-     *  exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, i0ef();
-     *
-     * y = i0ef( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of order zero of the argument.
-     *
-     * The function is defined as i0e(x) = exp(-|x|) j0( ix ).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0,30        100000      3.7e-7      7.0e-8
-     * See i0f().
-     *
-     */
-
-    const float A[] = {-1.30002500998624804212E-8f, 6.04699502254191894932E-8f,
-                       -2.67079385394061173391E-7f, 1.11738753912010371815E-6f,
-                       -4.41673835845875056359E-6f, 1.64484480707288970893E-5f,
-                       -5.75419501008210370398E-5f, 1.88502885095841655729E-4f,
-                       -5.76375574538582365885E-4f, 1.63947561694133579842E-3f,
-                       -4.32430999505057594430E-3f, 1.05464603945949983183E-2f,
-                       -2.37374148058994688156E-2f, 4.93052842396707084878E-2f,
-                       -9.49010970480476444210E-2f, 1.71620901522208775349E-1f,
-                       -3.04682672343198398683E-1f, 6.76795274409476084995E-1f};
-
-    const float B[] = {3.39623202570838634515E-9f, 2.26666899049817806459E-8f,
-                       2.04891858946906374183E-7f, 2.89137052083475648297E-6f,
-                       6.88975834691682398426E-5f, 3.36911647825569408990E-3f,
-                       8.04490411014108831608E-1f};
-    T y = pabs(x);
-    T y_le_eight = internal::pchebevl<T, 18>::run(
-        pmadd(pset1<T>(0.5f), y, pset1<T>(-2.0f)), A);
-    T y_gt_eight = pmul(
-        internal::pchebevl<T, 7>::run(
-            psub(pdiv(pset1<T>(32.0f), y), pset1<T>(2.0f)), B),
-        prsqrt(y));
-    // TODO: Perhaps instead check whether all packet elements are in
-    // [-8, 8] and evaluate a branch based off of that. It's possible
-    // in practice most elements are in this region.
-    return pselect(pcmp_le(y, pset1<T>(8.0f)), y_le_eight, y_gt_eight);
-  }
-};
-
-template <typename T>
-struct generic_i0e<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  i0e.c
-     *
-     *  Modified Bessel function of order zero,
-     *  exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, i0e();
-     *
-     * y = i0e( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of order zero of the argument.
-     *
-     * The function is defined as i0e(x) = exp(-|x|) j0( ix ).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0,30        30000       5.4e-16     1.2e-16
-     * See i0().
-     *
-     */
-
-    const double A[] = {-4.41534164647933937950E-18, 3.33079451882223809783E-17,
-                        -2.43127984654795469359E-16, 1.71539128555513303061E-15,
-                        -1.16853328779934516808E-14, 7.67618549860493561688E-14,
-                        -4.85644678311192946090E-13, 2.95505266312963983461E-12,
-                        -1.72682629144155570723E-11, 9.67580903537323691224E-11,
-                        -5.18979560163526290666E-10, 2.65982372468238665035E-9,
-                        -1.30002500998624804212E-8,  6.04699502254191894932E-8,
-                        -2.67079385394061173391E-7,  1.11738753912010371815E-6,
-                        -4.41673835845875056359E-6,  1.64484480707288970893E-5,
-                        -5.75419501008210370398E-5,  1.88502885095841655729E-4,
-                        -5.76375574538582365885E-4,  1.63947561694133579842E-3,
-                        -4.32430999505057594430E-3,  1.05464603945949983183E-2,
-                        -2.37374148058994688156E-2,  4.93052842396707084878E-2,
-                        -9.49010970480476444210E-2,  1.71620901522208775349E-1,
-                        -3.04682672343198398683E-1,  6.76795274409476084995E-1};
-    const double B[] = {
-        -7.23318048787475395456E-18, -4.83050448594418207126E-18,
-        4.46562142029675999901E-17,  3.46122286769746109310E-17,
-        -2.82762398051658348494E-16, -3.42548561967721913462E-16,
-        1.77256013305652638360E-15,  3.81168066935262242075E-15,
-        -9.55484669882830764870E-15, -4.15056934728722208663E-14,
-        1.54008621752140982691E-14,  3.85277838274214270114E-13,
-        7.18012445138366623367E-13,  -1.79417853150680611778E-12,
-        -1.32158118404477131188E-11, -3.14991652796324136454E-11,
-        1.18891471078464383424E-11,  4.94060238822496958910E-10,
-        3.39623202570838634515E-9,   2.26666899049817806459E-8,
-        2.04891858946906374183E-7,   2.89137052083475648297E-6,
-        6.88975834691682398426E-5,   3.36911647825569408990E-3,
-        8.04490411014108831608E-1};
-    T y = pabs(x);
-    T y_le_eight = internal::pchebevl<T, 30>::run(
-        pmadd(pset1<T>(0.5), y, pset1<T>(-2.0)), A);
-    T y_gt_eight = pmul(
-        internal::pchebevl<T, 25>::run(
-            psub(pdiv(pset1<T>(32.0), y), pset1<T>(2.0)), B),
-        prsqrt(y));
-    // TODO: Perhaps instead check whether all packet elements are in
-    // [-8, 8] and evaluate a branch based off of that. It's possible
-    // in practice most elements are in this region.
-    return pselect(pcmp_le(y, pset1<T>(8.0)), y_le_eight, y_gt_eight);
-  }
-};
-
-template <typename T>
-struct bessel_i0e_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_i0e<T>::run(x);
-  }
-};
-
-template <typename Scalar>
-struct bessel_i0_retval {
-  typedef Scalar type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_i0 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    return pmul(
-        pexp(pabs(x)),
-        generic_i0e<T, ScalarType>::run(x));
-  }
-};
-
-template <typename T>
-struct bessel_i0_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_i0<T>::run(x);
-  }
-};
-
-template <typename Scalar>
-struct bessel_i1e_retval {
-  typedef Scalar type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type >
-struct generic_i1e {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_i1e<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /* i1ef.c
-     *
-     *  Modified Bessel function of order one,
-     *  exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, i1ef();
-     *
-     * y = i1ef( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of order one of the argument.
-     *
-     * The function is defined as i1(x) = -i exp(-|x|) j1( ix ).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       1.5e-6      1.5e-7
-     * See i1().
-     *
-     */
-    const float A[] = {9.38153738649577178388E-9f, -4.44505912879632808065E-8f,
-                       2.00329475355213526229E-7f, -8.56872026469545474066E-7f,
-                       3.47025130813767847674E-6f, -1.32731636560394358279E-5f,
-                       4.78156510755005422638E-5f, -1.61760815825896745588E-4f,
-                       5.12285956168575772895E-4f, -1.51357245063125314899E-3f,
-                       4.15642294431288815669E-3f, -1.05640848946261981558E-2f,
-                       2.47264490306265168283E-2f, -5.29459812080949914269E-2f,
-                       1.02643658689847095384E-1f, -1.76416518357834055153E-1f,
-                       2.52587186443633654823E-1f};
-
-    const float B[] = {-3.83538038596423702205E-9f, -2.63146884688951950684E-8f,
-                       -2.51223623787020892529E-7f, -3.88256480887769039346E-6f,
-                       -1.10588938762623716291E-4f, -9.76109749136146840777E-3f,
-                       7.78576235018280120474E-1f};
-
-
-    T y = pabs(x);
-    T y_le_eight = pmul(y, internal::pchebevl<T, 17>::run(
-        pmadd(pset1<T>(0.5f), y, pset1<T>(-2.0f)), A));
-    T y_gt_eight = pmul(
-        internal::pchebevl<T, 7>::run(
-            psub(pdiv(pset1<T>(32.0f), y),
-                 pset1<T>(2.0f)), B),
-        prsqrt(y));
-    // TODO: Perhaps instead check whether all packet elements are in
-    // [-8, 8] and evaluate a branch based off of that. It's possible
-    // in practice most elements are in this region.
-    y = pselect(pcmp_le(y, pset1<T>(8.0f)), y_le_eight, y_gt_eight);
-    return pselect(pcmp_lt(x, pset1<T>(0.0f)), pnegate(y), y);
-  }
-};
-
-template <typename T>
-struct generic_i1e<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  i1e.c
-     *
-     *  Modified Bessel function of order one,
-     *  exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, i1e();
-     *
-     * y = i1e( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of order one of the argument.
-     *
-     * The function is defined as i1(x) = -i exp(-|x|) j1( ix ).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       2.0e-15     2.0e-16
-     * See i1().
-     *
-     */
-    const double A[] = {2.77791411276104639959E-18, -2.11142121435816608115E-17,
-                        1.55363195773620046921E-16, -1.10559694773538630805E-15,
-                        7.60068429473540693410E-15, -5.04218550472791168711E-14,
-                        3.22379336594557470981E-13, -1.98397439776494371520E-12,
-                        1.17361862988909016308E-11, -6.66348972350202774223E-11,
-                        3.62559028155211703701E-10, -1.88724975172282928790E-9,
-                        9.38153738649577178388E-9,  -4.44505912879632808065E-8,
-                        2.00329475355213526229E-7,  -8.56872026469545474066E-7,
-                        3.47025130813767847674E-6,  -1.32731636560394358279E-5,
-                        4.78156510755005422638E-5,  -1.61760815825896745588E-4,
-                        5.12285956168575772895E-4,  -1.51357245063125314899E-3,
-                        4.15642294431288815669E-3,  -1.05640848946261981558E-2,
-                        2.47264490306265168283E-2,  -5.29459812080949914269E-2,
-                        1.02643658689847095384E-1,  -1.76416518357834055153E-1,
-                        2.52587186443633654823E-1};
-    const double B[] = {
-        7.51729631084210481353E-18,  4.41434832307170791151E-18,
-        -4.65030536848935832153E-17, -3.20952592199342395980E-17,
-        2.96262899764595013876E-16,  3.30820231092092828324E-16,
-        -1.88035477551078244854E-15, -3.81440307243700780478E-15,
-        1.04202769841288027642E-14,  4.27244001671195135429E-14,
-        -2.10154184277266431302E-14, -4.08355111109219731823E-13,
-        -7.19855177624590851209E-13, 2.03562854414708950722E-12,
-        1.41258074366137813316E-11,  3.25260358301548823856E-11,
-        -1.89749581235054123450E-11, -5.58974346219658380687E-10,
-        -3.83538038596423702205E-9,  -2.63146884688951950684E-8,
-        -2.51223623787020892529E-7,  -3.88256480887769039346E-6,
-        -1.10588938762623716291E-4,  -9.76109749136146840777E-3,
-        7.78576235018280120474E-1};
-    T y = pabs(x);
-    T y_le_eight = pmul(y, internal::pchebevl<T, 29>::run(
-        pmadd(pset1<T>(0.5), y, pset1<T>(-2.0)), A));
-    T y_gt_eight = pmul(
-        internal::pchebevl<T, 25>::run(
-            psub(pdiv(pset1<T>(32.0), y),
-                 pset1<T>(2.0)), B),
-        prsqrt(y));
-    // TODO: Perhaps instead check whether all packet elements are in
-    // [-8, 8] and evaluate a branch based off of that. It's possible
-    // in practice most elements are in this region.
-    y = pselect(pcmp_le(y, pset1<T>(8.0)), y_le_eight, y_gt_eight);
-    return pselect(pcmp_lt(x, pset1<T>(0.0)), pnegate(y), y);
-  }
-};
-
-template <typename T>
-struct bessel_i1e_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_i1e<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_i1_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_i1 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    return pmul(
-        pexp(pabs(x)),
-        generic_i1e<T, ScalarType>::run(x));
-  }
-};
-
-template <typename T>
-struct bessel_i1_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_i1<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_k0e_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_k0e {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_k0e<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  k0ef.c
-     *	Modified Bessel function, third kind, order zero,
-     *	exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, k0ef();
-     *
-     * y = k0ef( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of the third kind of order zero of the argument.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       8.1e-7      7.8e-8
-     * See k0().
-     *
-     */
-
-    const float A[] = {1.90451637722020886025E-9f, 2.53479107902614945675E-7f,
-                       2.28621210311945178607E-5f, 1.26461541144692592338E-3f,
-                       3.59799365153615016266E-2f, 3.44289899924628486886E-1f,
-                       -5.35327393233902768720E-1f};
-
-    const float B[] = {-1.69753450938905987466E-9f, 8.57403401741422608519E-9f,
-                       -4.66048989768794782956E-8f, 2.76681363944501510342E-7f,
-                       -1.83175552271911948767E-6f, 1.39498137188764993662E-5f,
-                       -1.28495495816278026384E-4f, 1.56988388573005337491E-3f,
-                       -3.14481013119645005427E-2f, 2.44030308206595545468E0f};
-    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = internal::pchebevl<T, 7>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A);
-    x_le_two = pmadd(
-        generic_i0<T, float>::run(x), pnegate(
-            plog(pmul(pset1<T>(0.5), x))), x_le_two);
-    x_le_two = pmul(pexp(x), x_le_two);
-    T x_gt_two = pmul(
-            internal::pchebevl<T, 10>::run(
-                psub(pdiv(pset1<T>(8.0), x), two), B),
-            prsqrt(x));
-    return pselect(
-        pcmp_le(x, pset1<T>(0.0)),
-        MAXNUM,
-        pselect(pcmp_le(x, two), x_le_two, x_gt_two));
-  }
-};
-
-template <typename T>
-struct generic_k0e<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  k0e.c
-     *	Modified Bessel function, third kind, order zero,
-     *	exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, k0e();
-     *
-     * y = k0e( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of the third kind of order zero of the argument.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       1.4e-15     1.4e-16
-     * See k0().
-     *
-     */
-
-    const double A[] = {
-      1.37446543561352307156E-16,
-      4.25981614279661018399E-14,
-      1.03496952576338420167E-11,
-      1.90451637722020886025E-9,
-      2.53479107902614945675E-7,
-      2.28621210311945178607E-5,
-      1.26461541144692592338E-3,
-      3.59799365153615016266E-2,
-      3.44289899924628486886E-1,
-      -5.35327393233902768720E-1};
-    const double B[] = {
-       5.30043377268626276149E-18, -1.64758043015242134646E-17,
-       5.21039150503902756861E-17, -1.67823109680541210385E-16,
-       5.51205597852431940784E-16, -1.84859337734377901440E-15,
-       6.34007647740507060557E-15, -2.22751332699166985548E-14,
-       8.03289077536357521100E-14, -2.98009692317273043925E-13,
-       1.14034058820847496303E-12, -4.51459788337394416547E-12,
-       1.85594911495471785253E-11, -7.95748924447710747776E-11,
-       3.57739728140030116597E-10, -1.69753450938905987466E-9,
-       8.57403401741422608519E-9, -4.66048989768794782956E-8,
-       2.76681363944501510342E-7, -1.83175552271911948767E-6,
-       1.39498137188764993662E-5, -1.28495495816278026384E-4,
-       1.56988388573005337491E-3, -3.14481013119645005427E-2,
-       2.44030308206595545468E0
-    };
-    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = internal::pchebevl<T, 10>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A);
-    x_le_two = pmadd(
-        generic_i0<T, double>::run(x), pmul(
-            pset1<T>(-1.0), plog(pmul(pset1<T>(0.5), x))), x_le_two);
-    x_le_two = pmul(pexp(x), x_le_two);
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
-    T x_gt_two = pmul(
-            internal::pchebevl<T, 25>::run(
-                psub(pdiv(pset1<T>(8.0), x), two), B),
-            prsqrt(x));
-    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct bessel_k0e_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_k0e<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_k0_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_k0 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_k0<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  k0f.c
-     *	Modified Bessel function, third kind, order zero
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, k0f();
-     *
-     * y = k0f( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns modified Bessel function of the third kind
-     * of order zero of the argument.
-     *
-     * The range is partitioned into the two intervals [0,8] and
-     * (8, infinity).  Chebyshev polynomial expansions are employed
-     * in each interval.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     * Tested at 2000 random points between 0 and 8.  Peak absolute
-     * error (relative when K0 > 1) was 1.46e-14; rms, 4.26e-15.
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       7.8e-7      8.5e-8
-     *
-     * ERROR MESSAGES:
-     *
-     *   message         condition      value returned
-     *  K0 domain          x <= 0          MAXNUM
-     *
-     */
-
-    const float A[] = {1.90451637722020886025E-9f, 2.53479107902614945675E-7f,
-                       2.28621210311945178607E-5f, 1.26461541144692592338E-3f,
-                       3.59799365153615016266E-2f, 3.44289899924628486886E-1f,
-                       -5.35327393233902768720E-1f};
-
-    const float B[] = {-1.69753450938905987466E-9f, 8.57403401741422608519E-9f,
-                       -4.66048989768794782956E-8f, 2.76681363944501510342E-7f,
-                       -1.83175552271911948767E-6f, 1.39498137188764993662E-5f,
-                       -1.28495495816278026384E-4f, 1.56988388573005337491E-3f,
-                       -3.14481013119645005427E-2f, 2.44030308206595545468E0f};
-    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = internal::pchebevl<T, 7>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A);
-    x_le_two = pmadd(
-        generic_i0<T, float>::run(x), pnegate(
-            plog(pmul(pset1<T>(0.5), x))), x_le_two);
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
-    T x_gt_two = pmul(
-        pmul(
-            pexp(pnegate(x)),
-            internal::pchebevl<T, 10>::run(
-                psub(pdiv(pset1<T>(8.0), x), two), B)),
-        prsqrt(x));
-    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct generic_k0<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*
-     *
-     *	Modified Bessel function, third kind, order zero,
-     *	exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, k0();
-     *
-     * y = k0( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of the third kind of order zero of the argument.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       1.4e-15     1.4e-16
-     * See k0().
-     *
-     */
-    const double A[] = {
-      1.37446543561352307156E-16,
-      4.25981614279661018399E-14,
-      1.03496952576338420167E-11,
-      1.90451637722020886025E-9,
-      2.53479107902614945675E-7,
-      2.28621210311945178607E-5,
-      1.26461541144692592338E-3,
-      3.59799365153615016266E-2,
-      3.44289899924628486886E-1,
-      -5.35327393233902768720E-1};
-    const double B[] = {
-       5.30043377268626276149E-18, -1.64758043015242134646E-17,
-       5.21039150503902756861E-17, -1.67823109680541210385E-16,
-       5.51205597852431940784E-16, -1.84859337734377901440E-15,
-       6.34007647740507060557E-15, -2.22751332699166985548E-14,
-       8.03289077536357521100E-14, -2.98009692317273043925E-13,
-       1.14034058820847496303E-12, -4.51459788337394416547E-12,
-       1.85594911495471785253E-11, -7.95748924447710747776E-11,
-       3.57739728140030116597E-10, -1.69753450938905987466E-9,
-       8.57403401741422608519E-9, -4.66048989768794782956E-8,
-       2.76681363944501510342E-7, -1.83175552271911948767E-6,
-       1.39498137188764993662E-5, -1.28495495816278026384E-4,
-       1.56988388573005337491E-3, -3.14481013119645005427E-2,
-       2.44030308206595545468E0
-    };
-    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = internal::pchebevl<T, 10>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A);
-    x_le_two = pmadd(
-        generic_i0<T, double>::run(x), pnegate(
-            plog(pmul(pset1<T>(0.5), x))), x_le_two);
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
-    T x_gt_two = pmul(
-        pmul(
-            pexp(-x),
-            internal::pchebevl<T, 25>::run(
-                psub(pdiv(pset1<T>(8.0), x), two), B)),
-        prsqrt(x));
-    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct bessel_k0_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_k0<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_k1e_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_k1e {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_k1e<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /* k1ef.c
-     *
-     *	Modified Bessel function, third kind, order one,
-     *	exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, k1ef();
-     *
-     * y = k1ef( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of the third kind of order one of the argument:
-     *
-     *      k1e(x) = exp(x) * k1(x).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       4.9e-7      6.7e-8
-     * See k1().
-     *
-     */
-
-    const float A[] = {-2.21338763073472585583E-8f, -2.43340614156596823496E-6f,
-                        -1.73028895751305206302E-4f, -6.97572385963986435018E-3f,
-                        -1.22611180822657148235E-1f, -3.53155960776544875667E-1f,
-                        1.52530022733894777053E0f};
-    const float B[] = {2.01504975519703286596E-9f, -1.03457624656780970260E-8f,
-                       5.74108412545004946722E-8f, -3.50196060308781257119E-7f,
-                       2.40648494783721712015E-6f, -1.93619797416608296024E-5f,
-                       1.95215518471351631108E-4f, -2.85781685962277938680E-3f,
-                       1.03923736576817238437E-1f, 2.72062619048444266945E0f};
-    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = pdiv(internal::pchebevl<T, 7>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A), x);
-    x_le_two = pmadd(
-        generic_i1<T, float>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
-    x_le_two = pmul(x_le_two, pexp(x));
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
-    T x_gt_two = pmul(
-        internal::pchebevl<T, 10>::run(
-            psub(pdiv(pset1<T>(8.0), x), two), B),
-        prsqrt(x));
-    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct generic_k1e<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  k1e.c
-     *
-     *	Modified Bessel function, third kind, order one,
-     *	exponentially scaled
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, k1e();
-     *
-     * y = k1e( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns exponentially scaled modified Bessel function
-     * of the third kind of order one of the argument:
-     *
-     *      k1e(x) = exp(x) * k1(x).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       7.8e-16     1.2e-16
-     * See k1().
-     *
-     */
-    const double A[] = {-7.02386347938628759343E-18, -2.42744985051936593393E-15,
-                        -6.66690169419932900609E-13, -1.41148839263352776110E-10,
-                        -2.21338763073472585583E-8, -2.43340614156596823496E-6,
-                        -1.73028895751305206302E-4, -6.97572385963986435018E-3,
-                        -1.22611180822657148235E-1, -3.53155960776544875667E-1,
-                        1.52530022733894777053E0};
-    const double B[] = {-5.75674448366501715755E-18, 1.79405087314755922667E-17,
-                        -5.68946255844285935196E-17, 1.83809354436663880070E-16,
-                        -6.05704724837331885336E-16, 2.03870316562433424052E-15,
-                        -7.01983709041831346144E-15, 2.47715442448130437068E-14,
-                        -8.97670518232499435011E-14, 3.34841966607842919884E-13,
-                        -1.28917396095102890680E-12, 5.13963967348173025100E-12,
-                        -2.12996783842756842877E-11, 9.21831518760500529508E-11,
-                        -4.19035475934189648750E-10, 2.01504975519703286596E-9,
-                        -1.03457624656780970260E-8, 5.74108412545004946722E-8,
-                        -3.50196060308781257119E-7, 2.40648494783721712015E-6,
-                        -1.93619797416608296024E-5, 1.95215518471351631108E-4,
-                        -2.85781685962277938680E-3, 1.03923736576817238437E-1,
-                        2.72062619048444266945E0};
-    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = pdiv(internal::pchebevl<T, 11>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A), x);
-    x_le_two = pmadd(
-        generic_i1<T, double>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
-    x_le_two = pmul(x_le_two, pexp(x));
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
-    T x_gt_two = pmul(
-        internal::pchebevl<T, 25>::run(
-            psub(pdiv(pset1<T>(8.0), x), two), B),
-        prsqrt(x));
-    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct bessel_k1e_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_k1e<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_k1_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_k1 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_k1<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /* k1f.c
-     *	Modified Bessel function, third kind, order one
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, k1f();
-     *
-     * y = k1f( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Computes the modified Bessel function of the third kind
-     * of order one of the argument.
-     *
-     * The range is partitioned into the two intervals [0,2] and
-     * (2, infinity).  Chebyshev polynomial expansions are employed
-     * in each interval.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       4.6e-7      7.6e-8
-     *
-     * ERROR MESSAGES:
-     *
-     *   message         condition      value returned
-     * k1 domain          x <= 0          MAXNUM
-     *
-     */
-
-    const float A[] = {-2.21338763073472585583E-8f, -2.43340614156596823496E-6f,
-                        -1.73028895751305206302E-4f, -6.97572385963986435018E-3f,
-                        -1.22611180822657148235E-1f, -3.53155960776544875667E-1f,
-                        1.52530022733894777053E0f};
-    const float B[] = {2.01504975519703286596E-9f, -1.03457624656780970260E-8f,
-                       5.74108412545004946722E-8f, -3.50196060308781257119E-7f,
-                       2.40648494783721712015E-6f, -1.93619797416608296024E-5f,
-                       1.95215518471351631108E-4f, -2.85781685962277938680E-3f,
-                       1.03923736576817238437E-1f, 2.72062619048444266945E0f};
-    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = pdiv(internal::pchebevl<T, 7>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A), x);
-    x_le_two = pmadd(
-        generic_i1<T, float>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
-    T x_gt_two = pmul(
-        pexp(pnegate(x)),
-        pmul(
-            internal::pchebevl<T, 10>::run(
-                psub(pdiv(pset1<T>(8.0), x), two), B),
-            prsqrt(x)));
-    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct generic_k1<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  k1.c
-     *	Modified Bessel function, third kind, order one
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, k1f();
-     *
-     * y = k1f( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Computes the modified Bessel function of the third kind
-     * of order one of the argument.
-     *
-     * The range is partitioned into the two intervals [0,2] and
-     * (2, infinity).  Chebyshev polynomial expansions are employed
-     * in each interval.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 30       30000       4.6e-7      7.6e-8
-     *
-     * ERROR MESSAGES:
-     *
-     *   message         condition      value returned
-     * k1 domain          x <= 0          MAXNUM
-     *
-     */
-    const double A[] = {-7.02386347938628759343E-18, -2.42744985051936593393E-15,
-                        -6.66690169419932900609E-13, -1.41148839263352776110E-10,
-                        -2.21338763073472585583E-8, -2.43340614156596823496E-6,
-                        -1.73028895751305206302E-4, -6.97572385963986435018E-3,
-                        -1.22611180822657148235E-1, -3.53155960776544875667E-1,
-                        1.52530022733894777053E0};
-    const double B[] = {-5.75674448366501715755E-18, 1.79405087314755922667E-17,
-                        -5.68946255844285935196E-17, 1.83809354436663880070E-16,
-                        -6.05704724837331885336E-16, 2.03870316562433424052E-15,
-                        -7.01983709041831346144E-15, 2.47715442448130437068E-14,
-                        -8.97670518232499435011E-14, 3.34841966607842919884E-13,
-                        -1.28917396095102890680E-12, 5.13963967348173025100E-12,
-                        -2.12996783842756842877E-11, 9.21831518760500529508E-11,
-                        -4.19035475934189648750E-10, 2.01504975519703286596E-9,
-                        -1.03457624656780970260E-8, 5.74108412545004946722E-8,
-                        -3.50196060308781257119E-7, 2.40648494783721712015E-6,
-                        -1.93619797416608296024E-5, 1.95215518471351631108E-4,
-                        -2.85781685962277938680E-3, 1.03923736576817238437E-1,
-                        2.72062619048444266945E0};
-    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
-    const T two = pset1<T>(2.0);
-    T x_le_two = pdiv(internal::pchebevl<T, 11>::run(
-        pmadd(x, x, pset1<T>(-2.0)), A), x);
-    x_le_two = pmadd(
-        generic_i1<T, double>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
-    T x_gt_two = pmul(
-        pexp(-x),
-        pmul(
-            internal::pchebevl<T, 25>::run(
-                psub(pdiv(pset1<T>(8.0), x), two), B),
-            prsqrt(x)));
-    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct bessel_k1_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_k1<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_j0_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_j0 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_j0<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /* j0f.c
-     *	Bessel function of order zero
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, j0f();
-     *
-     * y = j0f( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of order zero of the argument.
-     *
-     * The domain is divided into the intervals [0, 2] and
-     * (2, infinity). In the first interval the following polynomial
-     * approximation is used:
-     *
-     *
-     *        2         2         2
-     * (w - r  ) (w - r  ) (w - r  ) P(w)
-     *       1         2         3
-     *
-     *            2
-     * where w = x  and the three r's are zeros of the function.
-     *
-     * In the second interval, the modulus and phase are approximated
-     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
-     * and Phase(x) = x + 1/x R(1/x^2) - pi/4.  The function is
-     *
-     *   j0(x) = Modulus(x) cos( Phase(x) ).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Absolute error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0, 2        100000      1.3e-7      3.6e-8
-     *    IEEE      2, 32       100000      1.9e-7      5.4e-8
-     *
-     */
-
-    const float JP[] = {-6.068350350393235E-008f, 6.388945720783375E-006f,
-                        -3.969646342510940E-004f, 1.332913422519003E-002f,
-                        -1.729150680240724E-001f};
-    const float MO[] = {-6.838999669318810E-002f, 1.864949361379502E-001f,
-                        -2.145007480346739E-001f, 1.197549369473540E-001f,
-                        -3.560281861530129E-003f, -4.969382655296620E-002f,
-                        -3.355424622293709E-006f, 7.978845717621440E-001f};
-    const float PH[] = {3.242077816988247E+001f, -3.630592630518434E+001f,
-                        1.756221482109099E+001f, -4.974978466280903E+000f,
-                        1.001973420681837E+000f, -1.939906941791308E-001f,
-                        6.490598792654666E-002f, -1.249992184872738E-001f};
-    const T DR1 =  pset1<T>(5.78318596294678452118f);
-    const T NEG_PIO4F = pset1<T>(-0.7853981633974483096f); /* -pi / 4 */
-    T y = pabs(x);
-    T z = pmul(y, y);
-    T y_le_two = pselect(
-        pcmp_lt(y, pset1<T>(1.0e-3f)),
-        pmadd(z, pset1<T>(-0.25f), pset1<T>(1.0f)),
-        pmul(psub(z, DR1), internal::ppolevl<T, 4>::run(z, JP)));
-    T q = pdiv(pset1<T>(1.0f), y);
-    T w = prsqrt(y);
-    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO));
-    w = pmul(q, q);
-    T yn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH), NEG_PIO4F);
-    T y_gt_two = pmul(p, pcos(padd(yn, y)));
-    return pselect(pcmp_le(y, pset1<T>(2.0)), y_le_two, y_gt_two);
-  }
-};
-
-template <typename T>
-struct generic_j0<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  j0.c
-     *	Bessel function of order zero
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, j0();
-     *
-     * y = j0( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of order zero of the argument.
-     *
-     * The domain is divided into the intervals [0, 5] and
-     * (5, infinity). In the first interval the following rational
-     * approximation is used:
-     *
-     *
-     *        2         2
-     * (w - r  ) (w - r  ) P (w) / Q (w)
-     *       1         2    3       8
-     *
-     *            2
-     * where w = x  and the two r's are zeros of the function.
-     *
-     * In the second interval, the Hankel asymptotic expansion
-     * is employed with two rational functions of degree 6/6
-     * and 7/7.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Absolute error:
-     * arithmetic   domain     # trials      peak         rms
-     *    DEC       0, 30       10000       4.4e-17     6.3e-18
-     *    IEEE      0, 30       60000       4.2e-16     1.1e-16
-     *
-     */
-    const double PP[] = {7.96936729297347051624E-4, 8.28352392107440799803E-2,
-                        1.23953371646414299388E0, 5.44725003058768775090E0,
-                        8.74716500199817011941E0, 5.30324038235394892183E0,
-                        9.99999999999999997821E-1};
-    const double PQ[] = {9.24408810558863637013E-4, 8.56288474354474431428E-2,
-                         1.25352743901058953537E0, 5.47097740330417105182E0,
-                         8.76190883237069594232E0, 5.30605288235394617618E0,
-                         1.00000000000000000218E0};
-    const double QP[] = {-1.13663838898469149931E-2, -1.28252718670509318512E0,
-                         -1.95539544257735972385E1, -9.32060152123768231369E1,
-                         -1.77681167980488050595E2, -1.47077505154951170175E2,
-                         -5.14105326766599330220E1, -6.05014350600728481186E0};
-    const double QQ[] = {1.00000000000000000000E0, 6.43178256118178023184E1,
-                         8.56430025976980587198E2, 3.88240183605401609683E3,
-                         7.24046774195652478189E3, 5.93072701187316984827E3,
-                         2.06209331660327847417E3, 2.42005740240291393179E2};
-    const double RP[] = {-4.79443220978201773821E9, 1.95617491946556577543E12,
-                         -2.49248344360967716204E14, 9.70862251047306323952E15};
-    const double RQ[] = {1.00000000000000000000E0, 4.99563147152651017219E2,
-                         1.73785401676374683123E5, 4.84409658339962045305E7,
-                         1.11855537045356834862E10, 2.11277520115489217587E12,
-                         3.10518229857422583814E14, 3.18121955943204943306E16,
-                         1.71086294081043136091E18};
-    const T DR1 = pset1<T>(5.78318596294678452118E0);
-    const T DR2 = pset1<T>(3.04712623436620863991E1);
-    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
-    const T NEG_PIO4 = pset1<T>(-0.7853981633974483096); /* pi / 4 */
-
-    T y = pabs(x);
-    T z = pmul(y, y);
-    T y_le_five = pselect(
-        pcmp_lt(y, pset1<T>(1.0e-5)),
-        pmadd(z, pset1<T>(-0.25), pset1<T>(1.0)),
-        pmul(pmul(psub(z, DR1), psub(z, DR2)),
-             pdiv(internal::ppolevl<T, 3>::run(z, RP),
-                  internal::ppolevl<T, 8>::run(z, RQ))));
-    T s = pdiv(pset1<T>(25.0), z);
-    T p = pdiv(
-        internal::ppolevl<T, 6>::run(s, PP),
-        internal::ppolevl<T, 6>::run(s, PQ));
-    T q = pdiv(
-        internal::ppolevl<T, 7>::run(s, QP),
-        internal::ppolevl<T, 7>::run(s, QQ));
-    T yn = padd(y, NEG_PIO4);
-    T w = pdiv(pset1<T>(-5.0), y);
-    p = pmadd(p, pcos(yn), pmul(w, pmul(q, psin(yn))));
-    T y_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(y)));
-    return pselect(pcmp_le(y, pset1<T>(5.0)), y_le_five, y_gt_five);
-  }
-};
-
-template <typename T>
-struct bessel_j0_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_j0<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_y0_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_y0 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_y0<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /* j0f.c
-     * 	Bessel function of the second kind, order zero
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, y0f();
-     *
-     * y = y0f( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of the second kind, of order
-     * zero, of the argument.
-     *
-     * The domain is divided into the intervals [0, 2] and
-     * (2, infinity). In the first interval a rational approximation
-     * R(x) is employed to compute
-     *
-     *                  2         2         2
-     * y0(x)  =  (w - r  ) (w - r  ) (w - r  ) R(x)  +  2/pi ln(x) j0(x).
-     *                 1         2         3
-     *
-     * Thus a call to j0() is required.  The three zeros are removed
-     * from R(x) to improve its numerical stability.
-     *
-     * In the second interval, the modulus and phase are approximated
-     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
-     * and Phase(x) = x + 1/x S(1/x^2) - pi/4.  Then the function is
-     *
-     *   y0(x) = Modulus(x) sin( Phase(x) ).
-     *
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *  Absolute error, when y0(x) < 1; else relative error:
-     *
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0,  2       100000      2.4e-7      3.4e-8
-     *    IEEE      2, 32       100000      1.8e-7      5.3e-8
-     *
-     */
-
-    const float YP[] = {9.454583683980369E-008f, -9.413212653797057E-006f,
-                        5.344486707214273E-004f, -1.584289289821316E-002f,
-                        1.707584643733568E-001f};
-    const float MO[] = {-6.838999669318810E-002f, 1.864949361379502E-001f,
-                        -2.145007480346739E-001f, 1.197549369473540E-001f,
-                        -3.560281861530129E-003f, -4.969382655296620E-002f,
-                        -3.355424622293709E-006f, 7.978845717621440E-001f};
-    const float PH[] = {3.242077816988247E+001f, -3.630592630518434E+001f,
-                        1.756221482109099E+001f, -4.974978466280903E+000f,
-                        1.001973420681837E+000f, -1.939906941791308E-001f,
-                        6.490598792654666E-002f, -1.249992184872738E-001f};
-    const T YZ1 = pset1<T>(0.43221455686510834878f);
-    const T TWOOPI =  pset1<T>(0.636619772367581343075535f); /* 2 / pi */
-    const T NEG_PIO4F = pset1<T>(-0.7853981633974483096f); /* -pi / 4 */
-    const T NEG_MAXNUM = pset1<T>(-NumTraits<float>::infinity());
-    T z = pmul(x, x);
-    T x_le_two = pmul(TWOOPI, pmul(plog(x), generic_j0<T, float>::run(x)));
-    x_le_two = pmadd(
-        psub(z, YZ1), internal::ppolevl<T, 4>::run(z, YP), x_le_two);
-    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_two);
-    T q = pdiv(pset1<T>(1.0), x);
-    T w = prsqrt(x);
-    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO));
-    T u = pmul(q, q);
-    T xn = pmadd(q, internal::ppolevl<T, 7>::run(u, PH), NEG_PIO4F);
-    T x_gt_two = pmul(p, psin(padd(xn, x)));
-    return pselect(pcmp_le(x, pset1<T>(2.0)), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct generic_y0<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  j0.c
-     *	Bessel function of the second kind, order zero
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, y0();
-     *
-     * y = y0( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of the second kind, of order
-     * zero, of the argument.
-     *
-     * The domain is divided into the intervals [0, 5] and
-     * (5, infinity). In the first interval a rational approximation
-     * R(x) is employed to compute
-     *   y0(x)  = R(x)  +   2 * log(x) * j0(x) / PI.
-     * Thus a call to j0() is required.
-     *
-     * In the second interval, the Hankel asymptotic expansion
-     * is employed with two rational functions of degree 6/6
-     * and 7/7.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *  Absolute error, when y0(x) < 1; else relative error:
-     *
-     * arithmetic   domain     # trials      peak         rms
-     *    DEC       0, 30        9400       7.0e-17     7.9e-18
-     *    IEEE      0, 30       30000       1.3e-15     1.6e-16
-     *
-     */
-    const double PP[] = {7.96936729297347051624E-4, 8.28352392107440799803E-2,
-                        1.23953371646414299388E0, 5.44725003058768775090E0,
-                        8.74716500199817011941E0, 5.30324038235394892183E0,
-                        9.99999999999999997821E-1};
-    const double PQ[] = {9.24408810558863637013E-4, 8.56288474354474431428E-2,
-                         1.25352743901058953537E0, 5.47097740330417105182E0,
-                         8.76190883237069594232E0, 5.30605288235394617618E0,
-                         1.00000000000000000218E0};
-    const double QP[] = {-1.13663838898469149931E-2, -1.28252718670509318512E0,
-                         -1.95539544257735972385E1, -9.32060152123768231369E1,
-                         -1.77681167980488050595E2, -1.47077505154951170175E2,
-                         -5.14105326766599330220E1, -6.05014350600728481186E0};
-    const double QQ[] = {1.00000000000000000000E0, 6.43178256118178023184E1,
-                         8.56430025976980587198E2, 3.88240183605401609683E3,
-                         7.24046774195652478189E3, 5.93072701187316984827E3,
-                         2.06209331660327847417E3, 2.42005740240291393179E2};
-    const double YP[] = {1.55924367855235737965E4, -1.46639295903971606143E7,
-                         5.43526477051876500413E9, -9.82136065717911466409E11,
-                         8.75906394395366999549E13, -3.46628303384729719441E15,
-                         4.42733268572569800351E16, -1.84950800436986690637E16};
-    const double YQ[] = {1.00000000000000000000E0,  1.04128353664259848412E3,
-                         6.26107330137134956842E5, 2.68919633393814121987E8,
-                         8.64002487103935000337E10, 2.02979612750105546709E13,
-                         3.17157752842975028269E15, 2.50596256172653059228E17};
-    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
-    const T TWOOPI =  pset1<T>(0.636619772367581343075535); /* 2 / pi */
-    const T NEG_PIO4 = pset1<T>(-0.7853981633974483096); /* -pi / 4 */
-    const T NEG_MAXNUM = pset1<T>(-NumTraits<double>::infinity());
-
-    T z = pmul(x, x);
-    T x_le_five = pdiv(internal::ppolevl<T, 7>::run(z, YP),
-                       internal::ppolevl<T, 7>::run(z, YQ));
-    x_le_five = pmadd(
-        pmul(TWOOPI, plog(x)), generic_j0<T, double>::run(x), x_le_five);
-    x_le_five = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_five);
-    T s = pdiv(pset1<T>(25.0), z);
-    T p = pdiv(
-        internal::ppolevl<T, 6>::run(s, PP),
-        internal::ppolevl<T, 6>::run(s, PQ));
-    T q = pdiv(
-        internal::ppolevl<T, 7>::run(s, QP),
-        internal::ppolevl<T, 7>::run(s, QQ));
-    T xn = padd(x, NEG_PIO4);
-    T w = pdiv(pset1<T>(5.0), x);
-    p = pmadd(p, psin(xn), pmul(w, pmul(q, pcos(xn))));
-    T x_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(x)));
-    return pselect(pcmp_le(x, pset1<T>(5.0)), x_le_five, x_gt_five);
-  }
-};
-
-template <typename T>
-struct bessel_y0_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_y0<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_j1_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_j1 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_j1<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /* j1f.c
-     *	Bessel function of order one
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float x, y, j1f();
-     *
-     * y = j1f( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of order one of the argument.
-     *
-     * The domain is divided into the intervals [0, 2] and
-     * (2, infinity). In the first interval a polynomial approximation
-     *        2
-     * (w - r  ) x P(w)
-     *       1
-     *                     2
-     * is used, where w = x  and r is the first zero of the function.
-     *
-     * In the second interval, the modulus and phase are approximated
-     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
-     * and Phase(x) = x + 1/x R(1/x^2) - 3pi/4.  The function is
-     *
-     *   j0(x) = Modulus(x) cos( Phase(x) ).
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Absolute error:
-     * arithmetic   domain      # trials      peak       rms
-     *    IEEE      0,  2       100000       1.2e-7     2.5e-8
-     *    IEEE      2, 32       100000       2.0e-7     5.3e-8
-     *
-     *
-     */
-
-    const float JP[] = {-4.878788132172128E-009f, 6.009061827883699E-007f,
-                        -4.541343896997497E-005f, 1.937383947804541E-003f,
-                        -3.405537384615824E-002f};
-    const float MO1[] = {6.913942741265801E-002f, -2.284801500053359E-001f,
-                        3.138238455499697E-001f, -2.102302420403875E-001f,
-                        5.435364690523026E-003f, 1.493389585089498E-001f,
-                        4.976029650847191E-006f, 7.978845453073848E-001f};
-    const float PH1[] = {-4.497014141919556E+001f, 5.073465654089319E+001f,
-                        -2.485774108720340E+001f, 7.222973196770240E+000f,
-                        -1.544842782180211E+000f, 3.503787691653334E-001f,
-                        -1.637986776941202E-001f, 3.749989509080821E-001f};
-    const T Z1 = pset1<T>(1.46819706421238932572E1f);
-    const T NEG_THPIO4F = pset1<T>(-2.35619449019234492885f);    /* -3*pi/4 */
-
-    T y = pabs(x);
-    T z = pmul(y, y);
-    T y_le_two = pmul(
-        psub(z, Z1),
-        pmul(x, internal::ppolevl<T, 4>::run(z, JP)));
-    T q = pdiv(pset1<T>(1.0f), y);
-    T w = prsqrt(y);
-    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO1));
-    w = pmul(q, q);
-    T yn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH1), NEG_THPIO4F);
-    T y_gt_two = pmul(p, pcos(padd(yn, y)));
-    // j1 is an odd function. This implementation differs from cephes to
-    // take this fact in to account. Cephes returns -j1(x) for y > 2 range.
-    y_gt_two = pselect(
-        pcmp_lt(x, pset1<T>(0.0f)), pnegate(y_gt_two), y_gt_two);
-    return pselect(pcmp_le(y, pset1<T>(2.0f)), y_le_two, y_gt_two);
-  }
-};
-
-template <typename T>
-struct generic_j1<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  j1.c
-     *	Bessel function of order one
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, j1();
-     *
-     * y = j1( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of order one of the argument.
-     *
-     * The domain is divided into the intervals [0, 8] and
-     * (8, infinity). In the first interval a 24 term Chebyshev
-     * expansion is used. In the second, the asymptotic
-     * trigonometric representation is employed using two
-     * rational functions of degree 5/5.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Absolute error:
-     * arithmetic   domain      # trials      peak         rms
-     *    DEC       0, 30       10000       4.0e-17     1.1e-17
-     *    IEEE      0, 30       30000       2.6e-16     1.1e-16
-     *
-     */
-    const double PP[] = {7.62125616208173112003E-4, 7.31397056940917570436E-2,
-                         1.12719608129684925192E0, 5.11207951146807644818E0,
-                         8.42404590141772420927E0, 5.21451598682361504063E0,
-                         1.00000000000000000254E0};
-    const double PQ[] = {5.71323128072548699714E-4, 6.88455908754495404082E-2,
-                         1.10514232634061696926E0, 5.07386386128601488557E0,
-                         8.39985554327604159757E0, 5.20982848682361821619E0,
-                         9.99999999999999997461E-1};
-    const double QP[] = {5.10862594750176621635E-2, 4.98213872951233449420E0,
-                         7.58238284132545283818E1, 3.66779609360150777800E2,
-                         7.10856304998926107277E2, 5.97489612400613639965E2,
-                         2.11688757100572135698E2, 2.52070205858023719784E1};
-    const double QQ[] = {1.00000000000000000000E0, 7.42373277035675149943E1,
-                         1.05644886038262816351E3, 4.98641058337653607651E3,
-                         9.56231892404756170795E3, 7.99704160447350683650E3,
-                         2.82619278517639096600E3, 3.36093607810698293419E2};
-    const double RP[] = {-8.99971225705559398224E8, 4.52228297998194034323E11,
-                         -7.27494245221818276015E13, 3.68295732863852883286E15};
-    const double RQ[] = {1.00000000000000000000E0, 6.20836478118054335476E2,
-                         2.56987256757748830383E5, 8.35146791431949253037E7,
-                         2.21511595479792499675E10, 4.74914122079991414898E12,
-                         7.84369607876235854894E14, 8.95222336184627338078E16,
-                         5.32278620332680085395E18};
-    const T Z1 = pset1<T>(1.46819706421238932572E1);
-    const T Z2 = pset1<T>(4.92184563216946036703E1);
-    const T NEG_THPIO4 = pset1<T>(-2.35619449019234492885);    /* -3*pi/4 */
-    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
-    T y = pabs(x);
-    T z = pmul(y, y);
-    T y_le_five = pdiv(internal::ppolevl<T, 3>::run(z, RP),
-                       internal::ppolevl<T, 8>::run(z, RQ));
-    y_le_five = pmul(pmul(pmul(y_le_five, x), psub(z, Z1)), psub(z, Z2));
-    T s = pdiv(pset1<T>(25.0), z);
-    T p = pdiv(
-        internal::ppolevl<T, 6>::run(s, PP),
-        internal::ppolevl<T, 6>::run(s, PQ));
-    T q = pdiv(
-        internal::ppolevl<T, 7>::run(s, QP),
-        internal::ppolevl<T, 7>::run(s, QQ));
-    T yn = padd(y, NEG_THPIO4);
-    T w = pdiv(pset1<T>(-5.0), y);
-    p = pmadd(p, pcos(yn), pmul(w, pmul(q, psin(yn))));
-    T y_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(y)));
-    // j1 is an odd function. This implementation differs from cephes to
-    // take this fact in to account. Cephes returns -j1(x) for y > 5 range.
-    y_gt_five = pselect(
-        pcmp_lt(x, pset1<T>(0.0)), pnegate(y_gt_five), y_gt_five);
-    return pselect(pcmp_le(y, pset1<T>(5.0)), y_le_five, y_gt_five);
-  }
-};
-
-template <typename T>
-struct bessel_j1_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_j1<T>::run(x);
-  }
-};
-
-template <typename T>
-struct bessel_y1_retval {
-  typedef T type;
-};
-
-template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
-struct generic_y1 {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T&) {
-    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return ScalarType(0);
-  }
-};
-
-template <typename T>
-struct generic_y1<T, float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /* j1f.c
-     *	Bessel function of second kind of order one
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, y1();
-     *
-     * y = y1( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of the second kind of order one
-     * of the argument.
-     *
-     * The domain is divided into the intervals [0, 2] and
-     * (2, infinity). In the first interval a rational approximation
-     * R(x) is employed to compute
-     *
-     *                  2
-     * y0(x)  =  (w - r  ) x R(x^2)  +  2/pi (ln(x) j1(x) - 1/x) .
-     *                 1
-     *
-     * Thus a call to j1() is required.
-     *
-     * In the second interval, the modulus and phase are approximated
-     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
-     * and Phase(x) = x + 1/x S(1/x^2) - 3pi/4.  Then the function is
-     *
-     *   y0(x) = Modulus(x) sin( Phase(x) ).
-     *
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Absolute error:
-     * arithmetic   domain      # trials      peak         rms
-     *    IEEE      0,  2       100000       2.2e-7     4.6e-8
-     *    IEEE      2, 32       100000       1.9e-7     5.3e-8
-     *
-     * (error criterion relative when |y1| > 1).
-     *
-     */
-
-    const float YP[] = {8.061978323326852E-009f, -9.496460629917016E-007f,
-                        6.719543806674249E-005f, -2.641785726447862E-003f,
-                        4.202369946500099E-002f};
-    const float MO1[] = {6.913942741265801E-002f, -2.284801500053359E-001f,
-                        3.138238455499697E-001f, -2.102302420403875E-001f,
-                        5.435364690523026E-003f, 1.493389585089498E-001f,
-                        4.976029650847191E-006f, 7.978845453073848E-001f};
-    const float PH1[] = {-4.497014141919556E+001f, 5.073465654089319E+001f,
-                        -2.485774108720340E+001f, 7.222973196770240E+000f,
-                        -1.544842782180211E+000f, 3.503787691653334E-001f,
-                        -1.637986776941202E-001f, 3.749989509080821E-001f};
-    const T YO1 = pset1<T>(4.66539330185668857532f);
-    const T NEG_THPIO4F = pset1<T>(-2.35619449019234492885f);    /* -3*pi/4 */
-    const T TWOOPI = pset1<T>(0.636619772367581343075535f); /* 2/pi */
-    const T NEG_MAXNUM = pset1<T>(-NumTraits<float>::infinity());
-
-    T z = pmul(x, x);
-    T x_le_two = pmul(psub(z, YO1), internal::ppolevl<T, 4>::run(z, YP));
-    x_le_two = pmadd(
-       x_le_two, x,
-       pmul(TWOOPI, pmadd(
-           generic_j1<T, float>::run(x), plog(x),
-           pdiv(pset1<T>(-1.0f), x))));
-    x_le_two = pselect(pcmp_lt(x, pset1<T>(0.0f)), NEG_MAXNUM, x_le_two);
-
-    T q = pdiv(pset1<T>(1.0), x);
-    T w = prsqrt(x);
-    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO1));
-    w = pmul(q, q);
-    T xn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH1), NEG_THPIO4F);
-    T x_gt_two = pmul(p, psin(padd(xn, x)));
-    return pselect(pcmp_le(x, pset1<T>(2.0)), x_le_two, x_gt_two);
-  }
-};
-
-template <typename T>
-struct generic_y1<T, double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    /*  j1.c
-     *	Bessel function of second kind of order one
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, y1();
-     *
-     * y = y1( x );
-     *
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns Bessel function of the second kind of order one
-     * of the argument.
-     *
-     * The domain is divided into the intervals [0, 8] and
-     * (8, infinity). In the first interval a 25 term Chebyshev
-     * expansion is used, and a call to j1() is required.
-     * In the second, the asymptotic trigonometric representation
-     * is employed using two rational functions of degree 5/5.
-     *
-     *
-     *
-     * ACCURACY:
-     *
-     *                      Absolute error:
-     * arithmetic   domain      # trials      peak         rms
-     *    DEC       0, 30       10000       8.6e-17     1.3e-17
-     *    IEEE      0, 30       30000       1.0e-15     1.3e-16
-     *
-     * (error criterion relative when |y1| > 1).
-     *
-     */
-    const double PP[] = {7.62125616208173112003E-4, 7.31397056940917570436E-2,
-                         1.12719608129684925192E0, 5.11207951146807644818E0,
-                         8.42404590141772420927E0, 5.21451598682361504063E0,
-                         1.00000000000000000254E0};
-    const double PQ[] = {5.71323128072548699714E-4, 6.88455908754495404082E-2,
-                         1.10514232634061696926E0, 5.07386386128601488557E0,
-                         8.39985554327604159757E0, 5.20982848682361821619E0,
-                         9.99999999999999997461E-1};
-    const double QP[] = {5.10862594750176621635E-2, 4.98213872951233449420E0,
-                         7.58238284132545283818E1, 3.66779609360150777800E2,
-                         7.10856304998926107277E2, 5.97489612400613639965E2,
-                         2.11688757100572135698E2, 2.52070205858023719784E1};
-    const double QQ[] = {1.00000000000000000000E0, 7.42373277035675149943E1,
-                         1.05644886038262816351E3, 4.98641058337653607651E3,
-                         9.56231892404756170795E3, 7.99704160447350683650E3,
-                         2.82619278517639096600E3, 3.36093607810698293419E2};
-    const double YP[] = {1.26320474790178026440E9, -6.47355876379160291031E11,
-                         1.14509511541823727583E14, -8.12770255501325109621E15,
-                         2.02439475713594898196E17, -7.78877196265950026825E17};
-    const double YQ[] = {1.00000000000000000000E0, 5.94301592346128195359E2,
-                         2.35564092943068577943E5, 7.34811944459721705660E7,
-                         1.87601316108706159478E10, 3.88231277496238566008E12,
-                         6.20557727146953693363E14, 6.87141087355300489866E16,
-                         3.97270608116560655612E18};
-    const T SQ2OPI = pset1<T>(.79788456080286535588);
-    const T NEG_THPIO4 = pset1<T>(-2.35619449019234492885);    /* -3*pi/4 */
-    const T TWOOPI = pset1<T>(0.636619772367581343075535); /* 2/pi */
-    const T NEG_MAXNUM = pset1<T>(-NumTraits<double>::infinity());
-
-    T z = pmul(x, x);
-    T x_le_five = pdiv(internal::ppolevl<T, 5>::run(z, YP),
-                   internal::ppolevl<T, 8>::run(z, YQ));
-    x_le_five = pmadd(
-        x_le_five, x, pmul(
-            TWOOPI, pmadd(generic_j1<T, double>::run(x), plog(x),
-                          pdiv(pset1<T>(-1.0), x))));
-
-    x_le_five = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_five);
-    T s = pdiv(pset1<T>(25.0), z);
-    T p = pdiv(
-        internal::ppolevl<T, 6>::run(s, PP),
-        internal::ppolevl<T, 6>::run(s, PQ));
-    T q = pdiv(
-        internal::ppolevl<T, 7>::run(s, QP),
-        internal::ppolevl<T, 7>::run(s, QQ));
-    T xn = padd(x, NEG_THPIO4);
-    T w = pdiv(pset1<T>(5.0), x);
-    p = pmadd(p, psin(xn), pmul(w, pmul(q, pcos(xn))));
-    T x_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(x)));
-    return pselect(pcmp_le(x, pset1<T>(5.0)), x_le_five, x_gt_five);
-  }
-};
-
-template <typename T>
-struct bessel_y1_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T x) {
-    return generic_y1<T>::run(x);
-  }
-};
-
-}  // end namespace internal
-
-namespace numext {
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i0, Scalar)
-    bessel_i0(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_i0, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i0e, Scalar)
-    bessel_i0e(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_i0e, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i1, Scalar)
-    bessel_i1(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_i1, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i1e, Scalar)
-    bessel_i1e(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_i1e, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k0, Scalar)
-    bessel_k0(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_k0, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k0e, Scalar)
-    bessel_k0e(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_k0e, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k1, Scalar)
-    bessel_k1(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_k1, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k1e, Scalar)
-    bessel_k1e(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_k1e, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_j0, Scalar)
-    bessel_j0(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_j0, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_y0, Scalar)
-    bessel_y0(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_y0, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_j1, Scalar)
-    bessel_j1(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_j1, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_y1, Scalar)
-    bessel_y1(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(bessel_y1, Scalar)::run(x);
-}
-
-}  // end namespace numext
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_BESSEL_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
deleted file mode 100644
index 943d10f..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
+++ /dev/null
@@ -1,118 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_BESSELFUNCTIONS_PACKETMATH_H
-#define EIGEN_BESSELFUNCTIONS_PACKETMATH_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order zero i0(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_i0(const Packet& x) {
-  return numext::bessel_i0(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order zero i0e(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_i0e(const Packet& x) {
-  return numext::bessel_i0e(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order one i1(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_i1(const Packet& x) {
-  return numext::bessel_i1(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order one i1e(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_i1e(const Packet& x) {
-  return numext::bessel_i1e(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order zero j0(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_j0(const Packet& x) {
-  return numext::bessel_j0(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order zero j1(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_j1(const Packet& x) {
-  return numext::bessel_j1(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order one y0(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_y0(const Packet& x) {
-  return numext::bessel_y0(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order one y1(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_y1(const Packet& x) {
-  return numext::bessel_y1(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order zero k0(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_k0(const Packet& x) {
-  return numext::bessel_k0(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order zero k0e(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_k0e(const Packet& x) {
-  return numext::bessel_k0e(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order one k1e(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_k1(const Packet& x) {
-  return numext::bessel_k1(x);
-}
-
-/** \internal \returns the exponentially scaled modified Bessel function of
- * order one k1e(\a a) (coeff-wise) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pbessel_k1e(const Packet& x) {
-  return numext::bessel_k1e(x);
-}
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_BESSELFUNCTIONS_PACKETMATH_H
-
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
deleted file mode 100644
index d7b231a..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
+++ /dev/null
@@ -1,67 +0,0 @@
-#ifndef HIP_VECTOR_COMPATIBILITY_H
-#define HIP_VECTOR_COMPATIBILITY_H
-
-namespace hip_impl {
-  template <typename, typename, unsigned int> struct Scalar_accessor;
-}   // end namespace hip_impl
-
-namespace Eigen {
-namespace internal {
-
-#define HIP_SCALAR_ACCESSOR_BUILDER(NAME) \
-template <typename T, typename U, unsigned int n> \
-struct NAME <hip_impl::Scalar_accessor<T, U, n>> : NAME <T> {};
-
-#define HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(NAME) \
-template <typename T, typename U, unsigned int n> \
-struct NAME##_impl <hip_impl::Scalar_accessor<T, U, n>> : NAME##_impl <T> {}; \
-template <typename T, typename U, unsigned int n> \
-struct NAME##_retval <hip_impl::Scalar_accessor<T, U, n>> : NAME##_retval <T> {};
-
-#define HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(NAME) \
-template <typename T, typename U, unsigned int n, IgammaComputationMode mode> \
-struct NAME <hip_impl::Scalar_accessor<T, U, n>, mode> : NAME <T, mode> {};
-
-#if EIGEN_HAS_C99_MATH
-HIP_SCALAR_ACCESSOR_BUILDER(betainc_helper)
-HIP_SCALAR_ACCESSOR_BUILDER(incbeta_cfe)
-
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(erf)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(erfc)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igammac)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(lgamma)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(ndtri)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(polygamma)
-
-HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igamma_generic_impl)
-#endif
-
-HIP_SCALAR_ACCESSOR_BUILDER(digamma_impl_maybe_poly)
-HIP_SCALAR_ACCESSOR_BUILDER(zeta_impl_series)
-
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i0)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i0e)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i1)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i1e)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_j0)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_j1)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k0)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k0e)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k1)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k1e)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_y0)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_y1)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(betainc)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(digamma)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(gamma_sample_der_alpha)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igamma_der_a)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igamma)
-HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(zeta)
-
-HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igamma_series_impl)
-HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igammac_cf_impl)
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-#endif  // HIP_VECTOR_COMPATIBILITY_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
deleted file mode 100644
index 691ff4d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
+++ /dev/null
@@ -1,167 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-
-#ifndef EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
-#define EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
-
-namespace Eigen {
-
-/** \cpp11 \returns an expression of the coefficient-wise igamma(\a a, \a x) to the given arrays.
-  *
-  * This function computes the coefficient-wise incomplete gamma function.
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar
-  * type T to be supported.
-  *
-  * \sa Eigen::igammac(), Eigen::lgamma()
-  */
-template<typename Derived,typename ExponentDerived>
-EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
-igamma(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x)
-{
-  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
-    a.derived(),
-    x.derived()
-  );
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise igamma_der_a(\a a, \a x) to the given arrays.
-  *
-  * This function computes the coefficient-wise derivative of the incomplete
-  * gamma function with respect to the parameter a.
-  *
-  * \note This function supports only float and double scalar types in c++11
-  * mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations
-  * of igamma_der_a(T,T) for any scalar
-  * type T to be supported.
-  *
-  * \sa Eigen::igamma(), Eigen::lgamma()
-  */
-template <typename Derived, typename ExponentDerived>
-EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_der_a_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
-igamma_der_a(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x) {
-  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_der_a_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
-    a.derived(),
-    x.derived());
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise gamma_sample_der_alpha(\a alpha, \a sample) to the given arrays.
-  *
-  * This function computes the coefficient-wise derivative of the sample
-  * of a Gamma(alpha, 1) random variable with respect to the parameter alpha.
-  *
-  * \note This function supports only float and double scalar types in c++11
-  * mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations
-  * of gamma_sample_der_alpha(T,T) for any scalar
-  * type T to be supported.
-  *
-  * \sa Eigen::igamma(), Eigen::lgamma()
-  */
-template <typename AlphaDerived, typename SampleDerived>
-EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_gamma_sample_der_alpha_op<typename AlphaDerived::Scalar>, const AlphaDerived, const SampleDerived>
-gamma_sample_der_alpha(const Eigen::ArrayBase<AlphaDerived>& alpha, const Eigen::ArrayBase<SampleDerived>& sample) {
-  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_gamma_sample_der_alpha_op<typename AlphaDerived::Scalar>, const AlphaDerived, const SampleDerived>(
-      alpha.derived(),
-      sample.derived());
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise igammac(\a a, \a x) to the given arrays.
-  *
-  * This function computes the coefficient-wise complementary incomplete gamma function.
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar
-  * type T to be supported.
-  *
-  * \sa Eigen::igamma(), Eigen::lgamma()
-  */
-template<typename Derived,typename ExponentDerived>
-EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
-igammac(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x)
-{
-  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
-    a.derived(),
-    x.derived()
-  );
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise polygamma(\a n, \a x) to the given arrays.
-  *
-  * It returns the \a n -th derivative of the digamma(psi) evaluated at \c x.
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of polygamma(T,T) for any scalar
-  * type T to be supported.
-  *
-  * \sa Eigen::digamma()
-  */
-// * \warning Be careful with the order of the parameters: x.polygamma(n) is equivalent to polygamma(n,x)
-// * \sa ArrayBase::polygamma()
-template<typename DerivedN,typename DerivedX>
-EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_polygamma_op<typename DerivedX::Scalar>, const DerivedN, const DerivedX>
-polygamma(const Eigen::ArrayBase<DerivedN>& n, const Eigen::ArrayBase<DerivedX>& x)
-{
-  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_polygamma_op<typename DerivedX::Scalar>, const DerivedN, const DerivedX>(
-    n.derived(),
-    x.derived()
-  );
-}
-
-/** \cpp11 \returns an expression of the coefficient-wise betainc(\a x, \a a, \a b) to the given arrays.
-  *
-  * This function computes the regularized incomplete beta function (integral).
-  *
-  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
-  * or float/double in non c++11 mode, the user has to provide implementations of betainc(T,T,T) for any scalar
-  * type T to be supported.
-  *
-  * \sa Eigen::betainc(), Eigen::lgamma()
-  */
-template<typename ArgADerived, typename ArgBDerived, typename ArgXDerived>
-EIGEN_STRONG_INLINE const Eigen::CwiseTernaryOp<Eigen::internal::scalar_betainc_op<typename ArgXDerived::Scalar>, const ArgADerived, const ArgBDerived, const ArgXDerived>
-betainc(const Eigen::ArrayBase<ArgADerived>& a, const Eigen::ArrayBase<ArgBDerived>& b, const Eigen::ArrayBase<ArgXDerived>& x)
-{
-  return Eigen::CwiseTernaryOp<Eigen::internal::scalar_betainc_op<typename ArgXDerived::Scalar>, const ArgADerived, const ArgBDerived, const ArgXDerived>(
-    a.derived(),
-    b.derived(),
-    x.derived()
-  );
-}
-
-
-/** \returns an expression of the coefficient-wise zeta(\a x, \a q) to the given arrays.
-  *
-  * It returns the Riemann zeta function of two arguments \a x and \a q:
-  *
-  * \param x is the exponent, it must be > 1
-  * \param q is the shift, it must be > 0
-  *
-  * \note This function supports only float and double scalar types. To support other scalar types, the user has
-  * to provide implementations of zeta(T,T) for any scalar type T to be supported.
-  *
-  * \sa ArrayBase::zeta()
-  */
-template<typename DerivedX,typename DerivedQ>
-EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_zeta_op<typename DerivedX::Scalar>, const DerivedX, const DerivedQ>
-zeta(const Eigen::ArrayBase<DerivedX>& x, const Eigen::ArrayBase<DerivedQ>& q)
-{
-  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_zeta_op<typename DerivedX::Scalar>, const DerivedX, const DerivedQ>(
-    x.derived(),
-    q.derived()
-  );
-}
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
deleted file mode 100644
index 2d94231..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
+++ /dev/null
@@ -1,58 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
-#define EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
-
-namespace Eigen {
-namespace numext {
-
-#if EIGEN_HAS_C99_MATH
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 lgamma(const Eigen::bfloat16& a) {
-  return Eigen::bfloat16(Eigen::numext::lgamma(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 digamma(const Eigen::bfloat16& a) {
-  return Eigen::bfloat16(Eigen::numext::digamma(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 zeta(const Eigen::bfloat16& x, const Eigen::bfloat16& q) {
-  return Eigen::bfloat16(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 polygamma(const Eigen::bfloat16& n, const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 erf(const Eigen::bfloat16& a) {
-  return Eigen::bfloat16(Eigen::numext::erf(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 erfc(const Eigen::bfloat16& a) {
-  return Eigen::bfloat16(Eigen::numext::erfc(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 ndtri(const Eigen::bfloat16& a) {
-  return Eigen::bfloat16(Eigen::numext::ndtri(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igamma(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igamma_der_a(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::igamma_der_a(static_cast<float>(a), static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 gamma_sample_der_alpha(const Eigen::bfloat16& alpha, const Eigen::bfloat16& sample) {
-  return Eigen::bfloat16(Eigen::numext::gamma_sample_der_alpha(static_cast<float>(alpha), static_cast<float>(sample)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igammac(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 betainc(const Eigen::bfloat16& a, const Eigen::bfloat16& b, const Eigen::bfloat16& x) {
-  return Eigen::bfloat16(Eigen::numext::betainc(static_cast<float>(a), static_cast<float>(b), static_cast<float>(x)));
-}
-#endif
-
-}  // end namespace numext
-}  // end namespace Eigen
-
-#endif  // EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
deleted file mode 100644
index abefe99..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
+++ /dev/null
@@ -1,330 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
-#define EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
-
-namespace Eigen {
-
-namespace internal {
-
-
-/** \internal
-  * \brief Template functor to compute the incomplete gamma function igamma(a, x)
-  *
-  * \sa class CwiseBinaryOp, Cwise::igamma
-  */
-template<typename Scalar> struct scalar_igamma_op : binary_op_base<Scalar,Scalar>
-{
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_igamma_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
-    using numext::igamma; return igamma(a, x);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
-    return internal::pigamma(a, x);
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_igamma_op<Scalar> > {
-  enum {
-    // Guesstimate
-    Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasIGamma
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the derivative of the incomplete gamma
-  * function igamma_der_a(a, x)
-  *
-  * \sa class CwiseBinaryOp, Cwise::igamma_der_a
-  */
-template <typename Scalar>
-struct scalar_igamma_der_a_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_igamma_der_a_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a, const Scalar& x) const {
-    using numext::igamma_der_a;
-    return igamma_der_a(a, x);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
-    return internal::pigamma_der_a(a, x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_igamma_der_a_op<Scalar> > {
-  enum {
-    // 2x the cost of igamma
-    Cost = 40 * NumTraits<Scalar>::MulCost + 20 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasIGammaDerA
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the derivative of the sample
-  * of a Gamma(alpha, 1) random variable with respect to the parameter alpha
-  * gamma_sample_der_alpha(alpha, sample)
-  *
-  * \sa class CwiseBinaryOp, Cwise::gamma_sample_der_alpha
-  */
-template <typename Scalar>
-struct scalar_gamma_sample_der_alpha_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_gamma_sample_der_alpha_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& alpha, const Scalar& sample) const {
-    using numext::gamma_sample_der_alpha;
-    return gamma_sample_der_alpha(alpha, sample);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& alpha, const Packet& sample) const {
-    return internal::pgamma_sample_der_alpha(alpha, sample);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_gamma_sample_der_alpha_op<Scalar> > {
-  enum {
-    // 2x the cost of igamma, minus the lgamma cost (the lgamma cancels out)
-    Cost = 30 * NumTraits<Scalar>::MulCost + 15 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasGammaSampleDerAlpha
-  };
-};
-
-/** \internal
-  * \brief Template functor to compute the complementary incomplete gamma function igammac(a, x)
-  *
-  * \sa class CwiseBinaryOp, Cwise::igammac
-  */
-template<typename Scalar> struct scalar_igammac_op : binary_op_base<Scalar,Scalar>
-{
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_igammac_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
-    using numext::igammac; return igammac(a, x);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const
-  {
-    return internal::pigammac(a, x);
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_igammac_op<Scalar> > {
-  enum {
-    // Guesstimate
-    Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasIGammac
-  };
-};
-
-
-/** \internal
-  * \brief Template functor to compute the incomplete beta integral betainc(a, b, x)
-  *
-  */
-template<typename Scalar> struct scalar_betainc_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_betainc_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& x, const Scalar& a, const Scalar& b) const {
-    using numext::betainc; return betainc(x, a, b);
-  }
-  template<typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& x, const Packet& a, const Packet& b) const
-  {
-    return internal::pbetainc(x, a, b);
-  }
-};
-template<typename Scalar>
-struct functor_traits<scalar_betainc_op<Scalar> > {
-  enum {
-    // Guesstimate
-    Cost = 400 * NumTraits<Scalar>::MulCost + 400 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasBetaInc
-  };
-};
-
-
-/** \internal
- * \brief Template functor to compute the natural log of the absolute
- * value of Gamma of a scalar
- * \sa class CwiseUnaryOp, Cwise::lgamma()
- */
-template<typename Scalar> struct scalar_lgamma_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_lgamma_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
-    using numext::lgamma; return lgamma(a);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::plgamma(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_lgamma_op<Scalar> >
-{
-  enum {
-    // Guesstimate
-    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasLGamma
-  };
-};
-
-/** \internal
- * \brief Template functor to compute psi, the derivative of lgamma of a scalar.
- * \sa class CwiseUnaryOp, Cwise::digamma()
- */
-template<typename Scalar> struct scalar_digamma_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_digamma_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
-    using numext::digamma; return digamma(a);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::pdigamma(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_digamma_op<Scalar> >
-{
-  enum {
-    // Guesstimate
-    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasDiGamma
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the Riemann Zeta function of two arguments.
- * \sa class CwiseUnaryOp, Cwise::zeta()
- */
-template<typename Scalar> struct scalar_zeta_op {
-    EIGEN_EMPTY_STRUCT_CTOR(scalar_zeta_op)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& x, const Scalar& q) const {
-        using numext::zeta; return zeta(x, q);
-    }
-    typedef typename packet_traits<Scalar>::type Packet;
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x, const Packet& q) const { return internal::pzeta(x, q); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_zeta_op<Scalar> >
-{
-    enum {
-        // Guesstimate
-        Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
-        PacketAccess = packet_traits<Scalar>::HasZeta
-    };
-};
-
-/** \internal
- * \brief Template functor to compute the polygamma function.
- * \sa class CwiseUnaryOp, Cwise::polygamma()
- */
-template<typename Scalar> struct scalar_polygamma_op {
-    EIGEN_EMPTY_STRUCT_CTOR(scalar_polygamma_op)
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& n, const Scalar& x) const {
-        using numext::polygamma; return polygamma(n, x);
-    }
-    typedef typename packet_traits<Scalar>::type Packet;
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& n, const Packet& x) const { return internal::ppolygamma(n, x); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_polygamma_op<Scalar> >
-{
-    enum {
-        // Guesstimate
-        Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
-        PacketAccess = packet_traits<Scalar>::HasPolygamma
-    };
-};
-
-/** \internal
- * \brief Template functor to compute the error function of a scalar
- * \sa class CwiseUnaryOp, ArrayBase::erf()
- */
-template<typename Scalar> struct scalar_erf_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_erf_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar
-  operator()(const Scalar& a) const {
-    return numext::erf(a);
-  }
-  template <typename Packet>
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
-    return perf(x);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_erf_op<Scalar> > {
-  enum {
-    PacketAccess = packet_traits<Scalar>::HasErf,
-    Cost =
-        (PacketAccess
-#ifdef EIGEN_VECTORIZE_FMA
-             // TODO(rmlarsen): Move the FMA cost model to a central location.
-             // Haswell can issue 2 add/mul/madd per cycle.
-             // 10 pmadd, 2 pmul, 1 div, 2 other
-             ? (2 * NumTraits<Scalar>::AddCost +
-                7 * NumTraits<Scalar>::MulCost +
-                scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
-#else
-             ? (12 * NumTraits<Scalar>::AddCost +
-                12 * NumTraits<Scalar>::MulCost +
-                scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
-#endif
-             // Assume for simplicity that this is as expensive as an exp().
-             : (functor_traits<scalar_exp_op<Scalar> >::Cost))
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the Complementary Error Function
- * of a scalar
- * \sa class CwiseUnaryOp, Cwise::erfc()
- */
-template<typename Scalar> struct scalar_erfc_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_erfc_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
-    using numext::erfc; return erfc(a);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::perfc(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_erfc_op<Scalar> >
-{
-  enum {
-    // Guesstimate
-    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasErfc
-  };
-};
-
-/** \internal
- * \brief Template functor to compute the Inverse of the normal distribution
- * function of a scalar
- * \sa class CwiseUnaryOp, Cwise::ndtri()
- */
-template<typename Scalar> struct scalar_ndtri_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_ndtri_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
-    using numext::ndtri; return ndtri(a);
-  }
-  typedef typename packet_traits<Scalar>::type Packet;
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::pndtri(a); }
-};
-template<typename Scalar>
-struct functor_traits<scalar_ndtri_op<Scalar> >
-{
-  enum {
-    // On average, We are evaluating rational functions with degree N=9 in the
-    // numerator and denominator. This results in 2*N additions and 2*N
-    // multiplications.
-    Cost = 18 * NumTraits<Scalar>::MulCost + 18 * NumTraits<Scalar>::AddCost,
-    PacketAccess = packet_traits<Scalar>::HasNdtri
-  };
-};
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
deleted file mode 100644
index 2a3a531..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
+++ /dev/null
@@ -1,58 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPECIALFUNCTIONS_HALF_H
-#define EIGEN_SPECIALFUNCTIONS_HALF_H
-
-namespace Eigen {
-namespace numext {
-
-#if EIGEN_HAS_C99_MATH
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half lgamma(const Eigen::half& a) {
-  return Eigen::half(Eigen::numext::lgamma(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half digamma(const Eigen::half& a) {
-  return Eigen::half(Eigen::numext::digamma(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half zeta(const Eigen::half& x, const Eigen::half& q) {
-  return Eigen::half(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half polygamma(const Eigen::half& n, const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erf(const Eigen::half& a) {
-  return Eigen::half(Eigen::numext::erf(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erfc(const Eigen::half& a) {
-  return Eigen::half(Eigen::numext::erfc(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half ndtri(const Eigen::half& a) {
-  return Eigen::half(Eigen::numext::ndtri(static_cast<float>(a)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma(const Eigen::half& a, const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma_der_a(const Eigen::half& a, const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::igamma_der_a(static_cast<float>(a), static_cast<float>(x)));
-}
-template <>
-EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half gamma_sample_der_alpha(const Eigen::half& alpha, const Eigen::half& sample) {
-  return Eigen::half(Eigen::numext::gamma_sample_der_alpha(static_cast<float>(alpha), static_cast<float>(sample)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igammac(const Eigen::half& a, const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
-}
-template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half betainc(const Eigen::half& a, const Eigen::half& b, const Eigen::half& x) {
-  return Eigen::half(Eigen::numext::betainc(static_cast<float>(a), static_cast<float>(b), static_cast<float>(x)));
-}
-#endif
-
-}  // end namespace numext
-}  // end namespace Eigen
-
-#endif  // EIGEN_SPECIALFUNCTIONS_HALF_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
deleted file mode 100644
index f1c260e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
+++ /dev/null
@@ -1,2045 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPECIAL_FUNCTIONS_H
-#define EIGEN_SPECIAL_FUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-//  Parts of this code are based on the Cephes Math Library.
-//
-//  Cephes Math Library Release 2.8:  June, 2000
-//  Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
-//
-//  Permission has been kindly provided by the original author
-//  to incorporate the Cephes software into the Eigen codebase:
-//
-//    From: Stephen Moshier
-//    To: Eugene Brevdo
-//    Subject: Re: Permission to wrap several cephes functions in Eigen
-//
-//    Hello Eugene,
-//
-//    Thank you for writing.
-//
-//    If your licensing is similar to BSD, the formal way that has been
-//    handled is simply to add a statement to the effect that you are incorporating
-//    the Cephes software by permission of the author.
-//
-//    Good luck with your project,
-//    Steve
-
-
-/****************************************************************************
- * Implementation of lgamma, requires C++11/C99                             *
- ****************************************************************************/
-
-template <typename Scalar>
-struct lgamma_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-template <typename Scalar>
-struct lgamma_retval {
-  typedef Scalar type;
-};
-
-#if EIGEN_HAS_C99_MATH
-// Since glibc 2.19
-#if defined(__GLIBC__) && ((__GLIBC__>=2 && __GLIBC_MINOR__ >= 19) || __GLIBC__>2) \
- && (defined(_DEFAULT_SOURCE) || defined(_BSD_SOURCE) || defined(_SVID_SOURCE))
-#define EIGEN_HAS_LGAMMA_R
-#endif
-
-// Glibc versions before 2.19
-#if defined(__GLIBC__) && ((__GLIBC__==2 && __GLIBC_MINOR__ < 19) || __GLIBC__<2) \
- && (defined(_BSD_SOURCE) || defined(_SVID_SOURCE))
-#define EIGEN_HAS_LGAMMA_R
-#endif
-
-template <>
-struct lgamma_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float run(float x) {
-#if !defined(EIGEN_GPU_COMPILE_PHASE) && defined (EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__)
-    int dummy;
-    return ::lgammaf_r(x, &dummy);
-#elif defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::lgamma(x);
-#else
-    return ::lgammaf(x);
-#endif
-  }
-};
-
-template <>
-struct lgamma_impl<double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double run(double x) {
-#if !defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__)
-    int dummy;
-    return ::lgamma_r(x, &dummy);
-#elif defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::lgamma(x);
-#else
-    return ::lgamma(x);
-#endif
-  }
-};
-
-#undef EIGEN_HAS_LGAMMA_R
-#endif
-
-/****************************************************************************
- * Implementation of digamma (psi), based on Cephes                         *
- ****************************************************************************/
-
-template <typename Scalar>
-struct digamma_retval {
-  typedef Scalar type;
-};
-
-/*
- *
- * Polynomial evaluation helper for the Psi (digamma) function.
- *
- * digamma_impl_maybe_poly::run(s) evaluates the asymptotic Psi expansion for
- * input Scalar s, assuming s is above 10.0.
- *
- * If s is above a certain threshold for the given Scalar type, zero
- * is returned.  Otherwise the polynomial is evaluated with enough
- * coefficients for results matching Scalar machine precision.
- *
- *
- */
-template <typename Scalar>
-struct digamma_impl_maybe_poly {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-
-template <>
-struct digamma_impl_maybe_poly<float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float run(const float s) {
-    const float A[] = {
-      -4.16666666666666666667E-3f,
-      3.96825396825396825397E-3f,
-      -8.33333333333333333333E-3f,
-      8.33333333333333333333E-2f
-    };
-
-    float z;
-    if (s < 1.0e8f) {
-      z = 1.0f / (s * s);
-      return z * internal::ppolevl<float, 3>::run(z, A);
-    } else return 0.0f;
-  }
-};
-
-template <>
-struct digamma_impl_maybe_poly<double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double run(const double s) {
-    const double A[] = {
-      8.33333333333333333333E-2,
-      -2.10927960927960927961E-2,
-      7.57575757575757575758E-3,
-      -4.16666666666666666667E-3,
-      3.96825396825396825397E-3,
-      -8.33333333333333333333E-3,
-      8.33333333333333333333E-2
-    };
-
-    double z;
-    if (s < 1.0e17) {
-      z = 1.0 / (s * s);
-      return z * internal::ppolevl<double, 6>::run(z, A);
-    }
-    else return 0.0;
-  }
-};
-
-template <typename Scalar>
-struct digamma_impl {
-  EIGEN_DEVICE_FUNC
-  static Scalar run(Scalar x) {
-    /*
-     *
-     *     Psi (digamma) function (modified for Eigen)
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double x, y, psi();
-     *
-     * y = psi( x );
-     *
-     *
-     * DESCRIPTION:
-     *
-     *              d      -
-     *   psi(x)  =  -- ln | (x)
-     *              dx
-     *
-     * is the logarithmic derivative of the gamma function.
-     * For integer x,
-     *                   n-1
-     *                    -
-     * psi(n) = -EUL  +   >  1/k.
-     *                    -
-     *                   k=1
-     *
-     * If x is negative, it is transformed to a positive argument by the
-     * reflection formula  psi(1-x) = psi(x) + pi cot(pi x).
-     * For general positive x, the argument is made greater than 10
-     * using the recurrence  psi(x+1) = psi(x) + 1/x.
-     * Then the following asymptotic expansion is applied:
-     *
-     *                           inf.   B
-     *                            -      2k
-     * psi(x) = log(x) - 1/2x -   >   -------
-     *                            -        2k
-     *                           k=1   2k x
-     *
-     * where the B2k are Bernoulli numbers.
-     *
-     * ACCURACY (float):
-     *    Relative error (except absolute when |psi| < 1):
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0,30        30000       1.3e-15     1.4e-16
-     *    IEEE      -30,0       40000       1.5e-15     2.2e-16
-     *
-     * ACCURACY (double):
-     *    Absolute error,  relative when |psi| > 1 :
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      -33,0        30000      8.2e-7      1.2e-7
-     *    IEEE      0,33        100000      7.3e-7      7.7e-8
-     *
-     * ERROR MESSAGES:
-     *     message         condition      value returned
-     * psi singularity    x integer <=0      INFINITY
-     */
-
-    Scalar p, q, nz, s, w, y;
-    bool negative = false;
-
-    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
-    const Scalar m_pi = Scalar(EIGEN_PI);
-
-    const Scalar zero = Scalar(0);
-    const Scalar one = Scalar(1);
-    const Scalar half = Scalar(0.5);
-    nz = zero;
-
-    if (x <= zero) {
-      negative = true;
-      q = x;
-      p = numext::floor(q);
-      if (p == q) {
-        return nan;
-      }
-      /* Remove the zeros of tan(m_pi x)
-       * by subtracting the nearest integer from x
-       */
-      nz = q - p;
-      if (nz != half) {
-        if (nz > half) {
-          p += one;
-          nz = q - p;
-        }
-        nz = m_pi / numext::tan(m_pi * nz);
-      }
-      else {
-        nz = zero;
-      }
-      x = one - x;
-    }
-
-    /* use the recurrence psi(x+1) = psi(x) + 1/x. */
-    s = x;
-    w = zero;
-    while (s < Scalar(10)) {
-      w += one / s;
-      s += one;
-    }
-
-    y = digamma_impl_maybe_poly<Scalar>::run(s);
-
-    y = numext::log(s) - (half / s) - y - w;
-
-    return (negative) ? y - nz : y;
-  }
-};
-
-/****************************************************************************
- * Implementation of erf, requires C++11/C99                                *
- ****************************************************************************/
-
-/** \internal \returns the error function of \a a (coeff-wise)
-    Doesn't do anything fancy, just a 13/8-degree rational interpolant which
-    is accurate up to a couple of ulp in the range [-4, 4], outside of which
-    fl(erf(x)) = +/-1.
-
-    This implementation works on both scalars and Ts.
-*/
-template <typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_fast_erf_float(const T& a_x) {
-  // Clamp the inputs to the range [-4, 4] since anything outside
-  // this range is +/-1.0f in single-precision.
-  const T plus_4 = pset1<T>(4.f);
-  const T minus_4 = pset1<T>(-4.f);
-  const T x = pmax(pmin(a_x, plus_4), minus_4);
-  // The monomial coefficients of the numerator polynomial (odd).
-  const T alpha_1 = pset1<T>(-1.60960333262415e-02f);
-  const T alpha_3 = pset1<T>(-2.95459980854025e-03f);
-  const T alpha_5 = pset1<T>(-7.34990630326855e-04f);
-  const T alpha_7 = pset1<T>(-5.69250639462346e-05f);
-  const T alpha_9 = pset1<T>(-2.10102402082508e-06f);
-  const T alpha_11 = pset1<T>(2.77068142495902e-08f);
-  const T alpha_13 = pset1<T>(-2.72614225801306e-10f);
-
-  // The monomial coefficients of the denominator polynomial (even).
-  const T beta_0 = pset1<T>(-1.42647390514189e-02f);
-  const T beta_2 = pset1<T>(-7.37332916720468e-03f);
-  const T beta_4 = pset1<T>(-1.68282697438203e-03f);
-  const T beta_6 = pset1<T>(-2.13374055278905e-04f);
-  const T beta_8 = pset1<T>(-1.45660718464996e-05f);
-
-  // Since the polynomials are odd/even, we need x^2.
-  const T x2 = pmul(x, x);
-
-  // Evaluate the numerator polynomial p.
-  T p = pmadd(x2, alpha_13, alpha_11);
-  p = pmadd(x2, p, alpha_9);
-  p = pmadd(x2, p, alpha_7);
-  p = pmadd(x2, p, alpha_5);
-  p = pmadd(x2, p, alpha_3);
-  p = pmadd(x2, p, alpha_1);
-  p = pmul(x, p);
-
-  // Evaluate the denominator polynomial p.
-  T q = pmadd(x2, beta_8, beta_6);
-  q = pmadd(x2, q, beta_4);
-  q = pmadd(x2, q, beta_2);
-  q = pmadd(x2, q, beta_0);
-
-  // Divide the numerator by the denominator.
-  return pdiv(p, q);
-}
-
-template <typename T>
-struct erf_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE T run(const T& x) {
-    return generic_fast_erf_float(x);
-  }
-};
-
-template <typename Scalar>
-struct erf_retval {
-  typedef Scalar type;
-};
-
-#if EIGEN_HAS_C99_MATH
-template <>
-struct erf_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float run(float x) {
-#if defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::erf(x);
-#else
-    return generic_fast_erf_float(x);
-#endif
-  }
-};
-
-template <>
-struct erf_impl<double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double run(double x) {
-#if defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::erf(x);
-#else
-    return ::erf(x);
-#endif
-  }
-};
-#endif  // EIGEN_HAS_C99_MATH
-
-/***************************************************************************
-* Implementation of erfc, requires C++11/C99                               *
-****************************************************************************/
-
-template <typename Scalar>
-struct erfc_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-template <typename Scalar>
-struct erfc_retval {
-  typedef Scalar type;
-};
-
-#if EIGEN_HAS_C99_MATH
-template <>
-struct erfc_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float run(const float x) {
-#if defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::erfc(x);
-#else
-    return ::erfcf(x);
-#endif
-  }
-};
-
-template <>
-struct erfc_impl<double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double run(const double x) {
-#if defined(SYCL_DEVICE_ONLY)
-    return cl::sycl::erfc(x);
-#else
-    return ::erfc(x);
-#endif
-  }
-};
-#endif  // EIGEN_HAS_C99_MATH
-
-
-/***************************************************************************
-* Implementation of ndtri.                                                 *
-****************************************************************************/
-
-/* Inverse of Normal distribution function (modified for Eigen).
- *
- *
- * SYNOPSIS:
- *
- * double x, y, ndtri();
- *
- * x = ndtri( y );
- *
- *
- *
- * DESCRIPTION:
- *
- * Returns the argument, x, for which the area under the
- * Gaussian probability density function (integrated from
- * minus infinity to x) is equal to y.
- *
- *
- * For small arguments 0 < y < exp(-2), the program computes
- * z = sqrt( -2.0 * log(y) );  then the approximation is
- * x = z - log(z)/z  - (1/z) P(1/z) / Q(1/z).
- * There are two rational functions P/Q, one for 0 < y < exp(-32)
- * and the other for y up to exp(-2).  For larger arguments,
- * w = y - 0.5, and  x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)).
- *
- *
- * ACCURACY:
- *
- *                      Relative error:
- * arithmetic   domain        # trials      peak         rms
- *    DEC      0.125, 1         5500       9.5e-17     2.1e-17
- *    DEC      6e-39, 0.135     3500       5.7e-17     1.3e-17
- *    IEEE     0.125, 1        20000       7.2e-16     1.3e-16
- *    IEEE     3e-308, 0.135   50000       4.6e-16     9.8e-17
- *
- *
- * ERROR MESSAGES:
- *
- *   message         condition    value returned
- * ndtri domain       x <= 0        -MAXNUM
- * ndtri domain       x >= 1         MAXNUM
- *
- */
- /*
-   Cephes Math Library Release 2.2: June, 1992
-   Copyright 1985, 1987, 1992 by Stephen L. Moshier
-   Direct inquiries to 30 Frost Street, Cambridge, MA 02140
- */
-
-
-// TODO: Add a cheaper approximation for float.
-
-
-template<typename T>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T flipsign(
-    const T& should_flipsign, const T& x) {
-  typedef typename unpacket_traits<T>::type Scalar;
-  const T sign_mask = pset1<T>(Scalar(-0.0));
-  T sign_bit = pand<T>(should_flipsign, sign_mask);
-  return pxor<T>(sign_bit, x);
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double flipsign<double>(
-    const double& should_flipsign, const double& x) {
-  return should_flipsign == 0 ? x : -x;
-}
-
-template<>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float flipsign<float>(
-    const float& should_flipsign, const float& x) {
-  return should_flipsign == 0 ? x : -x;
-}
-
-// We split this computation in to two so that in the scalar path
-// only one branch is evaluated (due to our template specialization of pselect
-// being an if statement.)
-
-template <typename T, typename ScalarType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_gt_exp_neg_two(const T& b) {
-  const ScalarType p0[] = {
-    ScalarType(-5.99633501014107895267e1),
-    ScalarType(9.80010754185999661536e1),
-    ScalarType(-5.66762857469070293439e1),
-    ScalarType(1.39312609387279679503e1),
-    ScalarType(-1.23916583867381258016e0)
-  };
-  const ScalarType q0[] = {
-    ScalarType(1.0),
-    ScalarType(1.95448858338141759834e0),
-    ScalarType(4.67627912898881538453e0),
-    ScalarType(8.63602421390890590575e1),
-    ScalarType(-2.25462687854119370527e2),
-    ScalarType(2.00260212380060660359e2),
-    ScalarType(-8.20372256168333339912e1),
-    ScalarType(1.59056225126211695515e1),
-    ScalarType(-1.18331621121330003142e0)
-  };
-  const T sqrt2pi = pset1<T>(ScalarType(2.50662827463100050242e0));
-  const T half = pset1<T>(ScalarType(0.5));
-  T c, c2, ndtri_gt_exp_neg_two;
-
-  c = psub(b, half);
-  c2 = pmul(c, c);
-  ndtri_gt_exp_neg_two = pmadd(c, pmul(
-      c2, pdiv(
-          internal::ppolevl<T, 4>::run(c2, p0),
-          internal::ppolevl<T, 8>::run(c2, q0))), c);
-  return pmul(ndtri_gt_exp_neg_two, sqrt2pi);
-}
-
-template <typename T, typename ScalarType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_lt_exp_neg_two(
-    const T& b, const T& should_flipsign) {
-  /* Approximation for interval z = sqrt(-2 log a ) between 2 and 8
-   * i.e., a between exp(-2) = .135 and exp(-32) = 1.27e-14.
-   */
-  const ScalarType p1[] = {
-    ScalarType(4.05544892305962419923e0),
-    ScalarType(3.15251094599893866154e1),
-    ScalarType(5.71628192246421288162e1),
-    ScalarType(4.40805073893200834700e1),
-    ScalarType(1.46849561928858024014e1),
-    ScalarType(2.18663306850790267539e0),
-    ScalarType(-1.40256079171354495875e-1),
-    ScalarType(-3.50424626827848203418e-2),
-    ScalarType(-8.57456785154685413611e-4)
-  };
-  const ScalarType q1[] = {
-    ScalarType(1.0),
-    ScalarType(1.57799883256466749731e1),
-    ScalarType(4.53907635128879210584e1),
-    ScalarType(4.13172038254672030440e1),
-    ScalarType(1.50425385692907503408e1),
-    ScalarType(2.50464946208309415979e0),
-    ScalarType(-1.42182922854787788574e-1),
-    ScalarType(-3.80806407691578277194e-2),
-    ScalarType(-9.33259480895457427372e-4)
-  };
-  /* Approximation for interval z = sqrt(-2 log a ) between 8 and 64
-   * i.e., a between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890.
-   */
-  const ScalarType p2[] = {
-    ScalarType(3.23774891776946035970e0),
-    ScalarType(6.91522889068984211695e0),
-    ScalarType(3.93881025292474443415e0),
-    ScalarType(1.33303460815807542389e0),
-    ScalarType(2.01485389549179081538e-1),
-    ScalarType(1.23716634817820021358e-2),
-    ScalarType(3.01581553508235416007e-4),
-    ScalarType(2.65806974686737550832e-6),
-    ScalarType(6.23974539184983293730e-9)
-  };
-  const ScalarType q2[] = {
-    ScalarType(1.0),
-    ScalarType(6.02427039364742014255e0),
-    ScalarType(3.67983563856160859403e0),
-    ScalarType(1.37702099489081330271e0),
-    ScalarType(2.16236993594496635890e-1),
-    ScalarType(1.34204006088543189037e-2),
-    ScalarType(3.28014464682127739104e-4),
-    ScalarType(2.89247864745380683936e-6),
-    ScalarType(6.79019408009981274425e-9)
-  };
-  const T eight = pset1<T>(ScalarType(8.0));
-  const T one = pset1<T>(ScalarType(1));
-  const T neg_two = pset1<T>(ScalarType(-2));
-  T x, x0, x1, z;
-
-  x = psqrt(pmul(neg_two, plog(b)));
-  x0 = psub(x, pdiv(plog(x), x));
-  z = pdiv(one, x);
-  x1 = pmul(
-      z, pselect(
-          pcmp_lt(x, eight),
-          pdiv(internal::ppolevl<T, 8>::run(z, p1),
-               internal::ppolevl<T, 8>::run(z, q1)),
-          pdiv(internal::ppolevl<T, 8>::run(z, p2),
-               internal::ppolevl<T, 8>::run(z, q2))));
-  return flipsign(should_flipsign, psub(x0, x1));
-}
-
-template <typename T, typename ScalarType>
-EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
-T generic_ndtri(const T& a) {
-  const T maxnum = pset1<T>(NumTraits<ScalarType>::infinity());
-  const T neg_maxnum = pset1<T>(-NumTraits<ScalarType>::infinity());
-
-  const T zero = pset1<T>(ScalarType(0));
-  const T one = pset1<T>(ScalarType(1));
-  // exp(-2)
-  const T exp_neg_two = pset1<T>(ScalarType(0.13533528323661269189));
-  T b, ndtri, should_flipsign;
-
-  should_flipsign = pcmp_le(a, psub(one, exp_neg_two));
-  b = pselect(should_flipsign, a, psub(one, a));
-
-  ndtri = pselect(
-      pcmp_lt(exp_neg_two, b),
-      generic_ndtri_gt_exp_neg_two<T, ScalarType>(b),
-      generic_ndtri_lt_exp_neg_two<T, ScalarType>(b, should_flipsign));
-
-  return pselect(
-      pcmp_le(a, zero), neg_maxnum,
-      pselect(pcmp_le(one, a), maxnum, ndtri));
-}
-
-template <typename Scalar>
-struct ndtri_retval {
-  typedef Scalar type;
-};
-
-#if !EIGEN_HAS_C99_MATH
-
-template <typename Scalar>
-struct ndtri_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-# else
-
-template <typename Scalar>
-struct ndtri_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Scalar x) {
-    return generic_ndtri<Scalar, Scalar>(x);
-  }
-};
-
-#endif  // EIGEN_HAS_C99_MATH
-
-
-/**************************************************************************************************************
- * Implementation of igammac (complemented incomplete gamma integral), based on Cephes but requires C++11/C99 *
- **************************************************************************************************************/
-
-template <typename Scalar>
-struct igammac_retval {
-  typedef Scalar type;
-};
-
-// NOTE: cephes_helper is also used to implement zeta
-template <typename Scalar>
-struct cephes_helper {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar machep() { assert(false && "machep not supported for this type"); return 0.0; }
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar big() { assert(false && "big not supported for this type"); return 0.0; }
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar biginv() { assert(false && "biginv not supported for this type"); return 0.0; }
-};
-
-template <>
-struct cephes_helper<float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float machep() {
-    return NumTraits<float>::epsilon() / 2;  // 1.0 - machep == 1.0
-  }
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float big() {
-    // use epsneg (1.0 - epsneg == 1.0)
-    return 1.0f / (NumTraits<float>::epsilon() / 2);
-  }
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float biginv() {
-    // epsneg
-    return machep();
-  }
-};
-
-template <>
-struct cephes_helper<double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double machep() {
-    return NumTraits<double>::epsilon() / 2;  // 1.0 - machep == 1.0
-  }
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double big() {
-    return 1.0 / NumTraits<double>::epsilon();
-  }
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double biginv() {
-    // inverse of eps
-    return NumTraits<double>::epsilon();
-  }
-};
-
-enum IgammaComputationMode { VALUE, DERIVATIVE, SAMPLE_DERIVATIVE };
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC
-static EIGEN_STRONG_INLINE Scalar main_igamma_term(Scalar a, Scalar x) {
-    /* Compute  x**a * exp(-x) / gamma(a)  */
-    Scalar logax = a * numext::log(x) - x - lgamma_impl<Scalar>::run(a);
-    if (logax < -numext::log(NumTraits<Scalar>::highest()) ||
-        // Assuming x and a aren't Nan.
-        (numext::isnan)(logax)) {
-      return Scalar(0);
-    }
-    return numext::exp(logax);
-}
-
-template <typename Scalar, IgammaComputationMode mode>
-EIGEN_DEVICE_FUNC
-int igamma_num_iterations() {
-  /* Returns the maximum number of internal iterations for igamma computation.
-   */
-  if (mode == VALUE) {
-    return 2000;
-  }
-
-  if (internal::is_same<Scalar, float>::value) {
-    return 200;
-  } else if (internal::is_same<Scalar, double>::value) {
-    return 500;
-  } else {
-    return 2000;
-  }
-}
-
-template <typename Scalar, IgammaComputationMode mode>
-struct igammac_cf_impl {
-  /* Computes igamc(a, x) or derivative (depending on the mode)
-   * using the continued fraction expansion of the complementary
-   * incomplete Gamma function.
-   *
-   * Preconditions:
-   *   a > 0
-   *   x >= 1
-   *   x >= a
-   */
-  EIGEN_DEVICE_FUNC
-  static Scalar run(Scalar a, Scalar x) {
-    const Scalar zero = 0;
-    const Scalar one = 1;
-    const Scalar two = 2;
-    const Scalar machep = cephes_helper<Scalar>::machep();
-    const Scalar big = cephes_helper<Scalar>::big();
-    const Scalar biginv = cephes_helper<Scalar>::biginv();
-
-    if ((numext::isinf)(x)) {
-      return zero;
-    }
-
-    Scalar ax = main_igamma_term<Scalar>(a, x);
-    // This is independent of mode. If this value is zero,
-    // then the function value is zero. If the function value is zero,
-    // then we are in a neighborhood where the function value evalutes to zero,
-    // so the derivative is zero.
-    if (ax == zero) {
-      return zero;
-    }
-
-    // continued fraction
-    Scalar y = one - a;
-    Scalar z = x + y + one;
-    Scalar c = zero;
-    Scalar pkm2 = one;
-    Scalar qkm2 = x;
-    Scalar pkm1 = x + one;
-    Scalar qkm1 = z * x;
-    Scalar ans = pkm1 / qkm1;
-
-    Scalar dpkm2_da = zero;
-    Scalar dqkm2_da = zero;
-    Scalar dpkm1_da = zero;
-    Scalar dqkm1_da = -x;
-    Scalar dans_da = (dpkm1_da - ans * dqkm1_da) / qkm1;
-
-    for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) {
-      c += one;
-      y += one;
-      z += two;
-
-      Scalar yc = y * c;
-      Scalar pk = pkm1 * z - pkm2 * yc;
-      Scalar qk = qkm1 * z - qkm2 * yc;
-
-      Scalar dpk_da = dpkm1_da * z - pkm1 - dpkm2_da * yc + pkm2 * c;
-      Scalar dqk_da = dqkm1_da * z - qkm1 - dqkm2_da * yc + qkm2 * c;
-
-      if (qk != zero) {
-        Scalar ans_prev = ans;
-        ans = pk / qk;
-
-        Scalar dans_da_prev = dans_da;
-        dans_da = (dpk_da - ans * dqk_da) / qk;
-
-        if (mode == VALUE) {
-          if (numext::abs(ans_prev - ans) <= machep * numext::abs(ans)) {
-            break;
-          }
-        } else {
-          if (numext::abs(dans_da - dans_da_prev) <= machep) {
-            break;
-          }
-        }
-      }
-
-      pkm2 = pkm1;
-      pkm1 = pk;
-      qkm2 = qkm1;
-      qkm1 = qk;
-
-      dpkm2_da = dpkm1_da;
-      dpkm1_da = dpk_da;
-      dqkm2_da = dqkm1_da;
-      dqkm1_da = dqk_da;
-
-      if (numext::abs(pk) > big) {
-        pkm2 *= biginv;
-        pkm1 *= biginv;
-        qkm2 *= biginv;
-        qkm1 *= biginv;
-
-        dpkm2_da *= biginv;
-        dpkm1_da *= biginv;
-        dqkm2_da *= biginv;
-        dqkm1_da *= biginv;
-      }
-    }
-
-    /* Compute  x**a * exp(-x) / gamma(a)  */
-    Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a);
-    Scalar dax_da = ax * dlogax_da;
-
-    switch (mode) {
-      case VALUE:
-        return ans * ax;
-      case DERIVATIVE:
-        return ans * dax_da + dans_da * ax;
-      case SAMPLE_DERIVATIVE:
-      default: // this is needed to suppress clang warning
-        return -(dans_da + ans * dlogax_da) * x;
-    }
-  }
-};
-
-template <typename Scalar, IgammaComputationMode mode>
-struct igamma_series_impl {
-  /* Computes igam(a, x) or its derivative (depending on the mode)
-   * using the series expansion of the incomplete Gamma function.
-   *
-   * Preconditions:
-   *   x > 0
-   *   a > 0
-   *   !(x > 1 && x > a)
-   */
-  EIGEN_DEVICE_FUNC
-  static Scalar run(Scalar a, Scalar x) {
-    const Scalar zero = 0;
-    const Scalar one = 1;
-    const Scalar machep = cephes_helper<Scalar>::machep();
-
-    Scalar ax = main_igamma_term<Scalar>(a, x);
-
-    // This is independent of mode. If this value is zero,
-    // then the function value is zero. If the function value is zero,
-    // then we are in a neighborhood where the function value evalutes to zero,
-    // so the derivative is zero.
-    if (ax == zero) {
-      return zero;
-    }
-
-    ax /= a;
-
-    /* power series */
-    Scalar r = a;
-    Scalar c = one;
-    Scalar ans = one;
-
-    Scalar dc_da = zero;
-    Scalar dans_da = zero;
-
-    for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) {
-      r += one;
-      Scalar term = x / r;
-      Scalar dterm_da = -x / (r * r);
-      dc_da = term * dc_da + dterm_da * c;
-      dans_da += dc_da;
-      c *= term;
-      ans += c;
-
-      if (mode == VALUE) {
-        if (c <= machep * ans) {
-          break;
-        }
-      } else {
-        if (numext::abs(dc_da) <= machep * numext::abs(dans_da)) {
-          break;
-        }
-      }
-    }
-
-    Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a + one);
-    Scalar dax_da = ax * dlogax_da;
-
-    switch (mode) {
-      case VALUE:
-        return ans * ax;
-      case DERIVATIVE:
-        return ans * dax_da + dans_da * ax;
-      case SAMPLE_DERIVATIVE:
-      default: // this is needed to suppress clang warning
-        return -(dans_da + ans * dlogax_da) * x / a;
-    }
-  }
-};
-
-#if !EIGEN_HAS_C99_MATH
-
-template <typename Scalar>
-struct igammac_impl {
-  EIGEN_DEVICE_FUNC
-  static Scalar run(Scalar a, Scalar x) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-#else
-
-template <typename Scalar>
-struct igammac_impl {
-  EIGEN_DEVICE_FUNC
-  static Scalar run(Scalar a, Scalar x) {
-    /*  igamc()
-     *
-     *	Incomplete gamma integral (modified for Eigen)
-     *
-     *
-     *
-     * SYNOPSIS:
-     *
-     * double a, x, y, igamc();
-     *
-     * y = igamc( a, x );
-     *
-     * DESCRIPTION:
-     *
-     * The function is defined by
-     *
-     *
-     *  igamc(a,x)   =   1 - igam(a,x)
-     *
-     *                            inf.
-     *                              -
-     *                     1       | |  -t  a-1
-     *               =   -----     |   e   t   dt.
-     *                    -      | |
-     *                   | (a)    -
-     *                             x
-     *
-     *
-     * In this implementation both arguments must be positive.
-     * The integral is evaluated by either a power series or
-     * continued fraction expansion, depending on the relative
-     * values of a and x.
-     *
-     * ACCURACY (float):
-     *
-     *                      Relative error:
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0,30        30000       7.8e-6      5.9e-7
-     *
-     *
-     * ACCURACY (double):
-     *
-     * Tested at random a, x.
-     *                a         x                      Relative error:
-     * arithmetic   domain   domain     # trials      peak         rms
-     *    IEEE     0.5,100   0,100      200000       1.9e-14     1.7e-15
-     *    IEEE     0.01,0.5  0,100      200000       1.4e-13     1.6e-15
-     *
-     */
-    /*
-      Cephes Math Library Release 2.2: June, 1992
-      Copyright 1985, 1987, 1992 by Stephen L. Moshier
-      Direct inquiries to 30 Frost Street, Cambridge, MA 02140
-    */
-    const Scalar zero = 0;
-    const Scalar one = 1;
-    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
-
-    if ((x < zero) || (a <= zero)) {
-      // domain error
-      return nan;
-    }
-
-    if ((numext::isnan)(a) || (numext::isnan)(x)) {  // propagate nans
-      return nan;
-    }
-
-    if ((x < one) || (x < a)) {
-      return (one - igamma_series_impl<Scalar, VALUE>::run(a, x));
-    }
-
-    return igammac_cf_impl<Scalar, VALUE>::run(a, x);
-  }
-};
-
-#endif  // EIGEN_HAS_C99_MATH
-
-/************************************************************************************************
- * Implementation of igamma (incomplete gamma integral), based on Cephes but requires C++11/C99 *
- ************************************************************************************************/
-
-#if !EIGEN_HAS_C99_MATH
-
-template <typename Scalar, IgammaComputationMode mode>
-struct igamma_generic_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar x) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-#else
-
-template <typename Scalar, IgammaComputationMode mode>
-struct igamma_generic_impl {
-  EIGEN_DEVICE_FUNC
-  static Scalar run(Scalar a, Scalar x) {
-    /* Depending on the mode, returns
-     * - VALUE: incomplete Gamma function igamma(a, x)
-     * - DERIVATIVE: derivative of incomplete Gamma function d/da igamma(a, x)
-     * - SAMPLE_DERIVATIVE: implicit derivative of a Gamma random variable
-     * x ~ Gamma(x | a, 1), dx/da = -1 / Gamma(x | a, 1) * d igamma(a, x) / dx
-     *
-     * Derivatives are implemented by forward-mode differentiation.
-     */
-    const Scalar zero = 0;
-    const Scalar one = 1;
-    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
-
-    if (x == zero) return zero;
-
-    if ((x < zero) || (a <= zero)) {  // domain error
-      return nan;
-    }
-
-    if ((numext::isnan)(a) || (numext::isnan)(x)) {  // propagate nans
-      return nan;
-    }
-
-    if ((x > one) && (x > a)) {
-      Scalar ret = igammac_cf_impl<Scalar, mode>::run(a, x);
-      if (mode == VALUE) {
-        return one - ret;
-      } else {
-        return -ret;
-      }
-    }
-
-    return igamma_series_impl<Scalar, mode>::run(a, x);
-  }
-};
-
-#endif  // EIGEN_HAS_C99_MATH
-
-template <typename Scalar>
-struct igamma_retval {
-  typedef Scalar type;
-};
-
-template <typename Scalar>
-struct igamma_impl : igamma_generic_impl<Scalar, VALUE> {
-  /* igam()
-   * Incomplete gamma integral.
-   *
-   * The CDF of Gamma(a, 1) random variable at the point x.
-   *
-   * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample
-   * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points.
-   * The ground truth is computed by mpmath. Mean absolute error:
-   * float: 1.26713e-05
-   * double: 2.33606e-12
-   *
-   * Cephes documentation below.
-   *
-   * SYNOPSIS:
-   *
-   * double a, x, y, igam();
-   *
-   * y = igam( a, x );
-   *
-   * DESCRIPTION:
-   *
-   * The function is defined by
-   *
-   *                           x
-   *                            -
-   *                   1       | |  -t  a-1
-   *  igam(a,x)  =   -----     |   e   t   dt.
-   *                  -      | |
-   *                 | (a)    -
-   *                           0
-   *
-   *
-   * In this implementation both arguments must be positive.
-   * The integral is evaluated by either a power series or
-   * continued fraction expansion, depending on the relative
-   * values of a and x.
-   *
-   * ACCURACY (double):
-   *
-   *                      Relative error:
-   * arithmetic   domain     # trials      peak         rms
-   *    IEEE      0,30       200000       3.6e-14     2.9e-15
-   *    IEEE      0,100      300000       9.9e-14     1.5e-14
-   *
-   *
-   * ACCURACY (float):
-   *
-   *                      Relative error:
-   * arithmetic   domain     # trials      peak         rms
-   *    IEEE      0,30        20000       7.8e-6      5.9e-7
-   *
-   */
-  /*
-    Cephes Math Library Release 2.2: June, 1992
-    Copyright 1985, 1987, 1992 by Stephen L. Moshier
-    Direct inquiries to 30 Frost Street, Cambridge, MA 02140
-  */
-
-  /* left tail of incomplete gamma function:
-   *
-   *          inf.      k
-   *   a  -x   -       x
-   *  x  e     >   ----------
-   *           -     -
-   *          k=0   | (a+k+1)
-   *
-   */
-};
-
-template <typename Scalar>
-struct igamma_der_a_retval : igamma_retval<Scalar> {};
-
-template <typename Scalar>
-struct igamma_der_a_impl : igamma_generic_impl<Scalar, DERIVATIVE> {
-  /* Derivative of the incomplete Gamma function with respect to a.
-   *
-   * Computes d/da igamma(a, x) by forward differentiation of the igamma code.
-   *
-   * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample
-   * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points.
-   * The ground truth is computed by mpmath. Mean absolute error:
-   * float: 6.17992e-07
-   * double: 4.60453e-12
-   *
-   * Reference:
-   * R. Moore. "Algorithm AS 187: Derivatives of the incomplete gamma
-   * integral". Journal of the Royal Statistical Society. 1982
-   */
-};
-
-template <typename Scalar>
-struct gamma_sample_der_alpha_retval : igamma_retval<Scalar> {};
-
-template <typename Scalar>
-struct gamma_sample_der_alpha_impl
-    : igamma_generic_impl<Scalar, SAMPLE_DERIVATIVE> {
-  /* Derivative of a Gamma random variable sample with respect to alpha.
-   *
-   * Consider a sample of a Gamma random variable with the concentration
-   * parameter alpha: sample ~ Gamma(alpha, 1). The reparameterization
-   * derivative that we want to compute is dsample / dalpha =
-   * d igammainv(alpha, u) / dalpha, where u = igamma(alpha, sample).
-   * However, this formula is numerically unstable and expensive, so instead
-   * we use implicit differentiation:
-   *
-   * igamma(alpha, sample) = u, where u ~ Uniform(0, 1).
-   * Apply d / dalpha to both sides:
-   * d igamma(alpha, sample) / dalpha
-   *     + d igamma(alpha, sample) / dsample * dsample/dalpha  = 0
-   * d igamma(alpha, sample) / dalpha
-   *     + Gamma(sample | alpha, 1) dsample / dalpha = 0
-   * dsample/dalpha = - (d igamma(alpha, sample) / dalpha)
-   *                   / Gamma(sample | alpha, 1)
-   *
-   * Here Gamma(sample | alpha, 1) is the PDF of the Gamma distribution
-   * (note that the derivative of the CDF w.r.t. sample is the PDF).
-   * See the reference below for more details.
-   *
-   * The derivative of igamma(alpha, sample) is computed by forward
-   * differentiation of the igamma code. Division by the Gamma PDF is performed
-   * in the same code, increasing the accuracy and speed due to cancellation
-   * of some terms.
-   *
-   * Accuracy estimation. For each alpha in [10^-2, 10^-1...10^3] we sample
-   * 50 Gamma random variables sample ~ Gamma(sample | alpha, 1), a total of 300
-   * points. The ground truth is computed by mpmath. Mean absolute error:
-   * float: 2.1686e-06
-   * double: 1.4774e-12
-   *
-   * Reference:
-   * M. Figurnov, S. Mohamed, A. Mnih "Implicit Reparameterization Gradients".
-   * 2018
-   */
-};
-
-/*****************************************************************************
- * Implementation of Riemann zeta function of two arguments, based on Cephes *
- *****************************************************************************/
-
-template <typename Scalar>
-struct zeta_retval {
-    typedef Scalar type;
-};
-
-template <typename Scalar>
-struct zeta_impl_series {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-template <>
-struct zeta_impl_series<float> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE bool run(float& a, float& b, float& s, const float x, const float machep) {
-    int i = 0;
-    while(i < 9)
-    {
-        i += 1;
-        a += 1.0f;
-        b = numext::pow( a, -x );
-        s += b;
-        if( numext::abs(b/s) < machep )
-            return true;
-    }
-
-    //Return whether we are done
-    return false;
-  }
-};
-
-template <>
-struct zeta_impl_series<double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE bool run(double& a, double& b, double& s, const double x, const double machep) {
-    int i = 0;
-    while( (i < 9) || (a <= 9.0) )
-    {
-        i += 1;
-        a += 1.0;
-        b = numext::pow( a, -x );
-        s += b;
-        if( numext::abs(b/s) < machep )
-            return true;
-    }
-
-    //Return whether we are done
-    return false;
-  }
-};
-
-template <typename Scalar>
-struct zeta_impl {
-    EIGEN_DEVICE_FUNC
-    static Scalar run(Scalar x, Scalar q) {
-        /*							zeta.c
-         *
-         *	Riemann zeta function of two arguments
-         *
-         *
-         *
-         * SYNOPSIS:
-         *
-         * double x, q, y, zeta();
-         *
-         * y = zeta( x, q );
-         *
-         *
-         *
-         * DESCRIPTION:
-         *
-         *
-         *
-         *                 inf.
-         *                  -        -x
-         *   zeta(x,q)  =   >   (k+q)
-         *                  -
-         *                 k=0
-         *
-         * where x > 1 and q is not a negative integer or zero.
-         * The Euler-Maclaurin summation formula is used to obtain
-         * the expansion
-         *
-         *                n
-         *                -       -x
-         * zeta(x,q)  =   >  (k+q)
-         *                -
-         *               k=1
-         *
-         *           1-x                 inf.  B   x(x+1)...(x+2j)
-         *      (n+q)           1         -     2j
-         *  +  ---------  -  -------  +   >    --------------------
-         *        x-1              x      -                   x+2j+1
-         *                   2(n+q)      j=1       (2j)! (n+q)
-         *
-         * where the B2j are Bernoulli numbers.  Note that (see zetac.c)
-         * zeta(x,1) = zetac(x) + 1.
-         *
-         *
-         *
-         * ACCURACY:
-         *
-         * Relative error for single precision:
-         * arithmetic   domain     # trials      peak         rms
-         *    IEEE      0,25        10000       6.9e-7      1.0e-7
-         *
-         * Large arguments may produce underflow in powf(), in which
-         * case the results are inaccurate.
-         *
-         * REFERENCE:
-         *
-         * Gradshteyn, I. S., and I. M. Ryzhik, Tables of Integrals,
-         * Series, and Products, p. 1073; Academic Press, 1980.
-         *
-         */
-
-        int i;
-        Scalar p, r, a, b, k, s, t, w;
-
-        const Scalar A[] = {
-            Scalar(12.0),
-            Scalar(-720.0),
-            Scalar(30240.0),
-            Scalar(-1209600.0),
-            Scalar(47900160.0),
-            Scalar(-1.8924375803183791606e9), /*1.307674368e12/691*/
-            Scalar(7.47242496e10),
-            Scalar(-2.950130727918164224e12), /*1.067062284288e16/3617*/
-            Scalar(1.1646782814350067249e14), /*5.109094217170944e18/43867*/
-            Scalar(-4.5979787224074726105e15), /*8.028576626982912e20/174611*/
-            Scalar(1.8152105401943546773e17), /*1.5511210043330985984e23/854513*/
-            Scalar(-7.1661652561756670113e18) /*1.6938241367317436694528e27/236364091*/
-            };
-
-        const Scalar maxnum = NumTraits<Scalar>::infinity();
-        const Scalar zero = 0.0, half = 0.5, one = 1.0;
-        const Scalar machep = cephes_helper<Scalar>::machep();
-        const Scalar nan = NumTraits<Scalar>::quiet_NaN();
-
-        if( x == one )
-            return maxnum;
-
-        if( x < one )
-        {
-            return nan;
-        }
-
-        if( q <= zero )
-        {
-            if(q == numext::floor(q))
-            {
-                if (x == numext::floor(x) && long(x) % 2 == 0) {
-                    return maxnum;
-                }
-                else {
-                    return nan;
-                }
-            }
-            p = x;
-            r = numext::floor(p);
-            if (p != r)
-                return nan;
-        }
-
-        /* Permit negative q but continue sum until n+q > +9 .
-         * This case should be handled by a reflection formula.
-         * If q<0 and x is an integer, there is a relation to
-         * the polygamma function.
-         */
-        s = numext::pow( q, -x );
-        a = q;
-        b = zero;
-        // Run the summation in a helper function that is specific to the floating precision
-        if (zeta_impl_series<Scalar>::run(a, b, s, x, machep)) {
-            return s;
-        }
-
-        w = a;
-        s += b*w/(x-one);
-        s -= half * b;
-        a = one;
-        k = zero;
-        for( i=0; i<12; i++ )
-        {
-            a *= x + k;
-            b /= w;
-            t = a*b/A[i];
-            s = s + t;
-            t = numext::abs(t/s);
-            if( t < machep ) {
-              break;
-            }
-            k += one;
-            a *= x + k;
-            b /= w;
-            k += one;
-        }
-        return s;
-  }
-};
-
-/****************************************************************************
- * Implementation of polygamma function, requires C++11/C99                 *
- ****************************************************************************/
-
-template <typename Scalar>
-struct polygamma_retval {
-    typedef Scalar type;
-};
-
-#if !EIGEN_HAS_C99_MATH
-
-template <typename Scalar>
-struct polygamma_impl {
-    EIGEN_DEVICE_FUNC
-    static EIGEN_STRONG_INLINE Scalar run(Scalar n, Scalar x) {
-        EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                            THIS_TYPE_IS_NOT_SUPPORTED);
-        return Scalar(0);
-    }
-};
-
-#else
-
-template <typename Scalar>
-struct polygamma_impl {
-    EIGEN_DEVICE_FUNC
-    static Scalar run(Scalar n, Scalar x) {
-        Scalar zero = 0.0, one = 1.0;
-        Scalar nplus = n + one;
-        const Scalar nan = NumTraits<Scalar>::quiet_NaN();
-
-        // Check that n is a non-negative integer
-        if (numext::floor(n) != n || n < zero) {
-            return nan;
-        }
-        // Just return the digamma function for n = 0
-        else if (n == zero) {
-            return digamma_impl<Scalar>::run(x);
-        }
-        // Use the same implementation as scipy
-        else {
-            Scalar factorial = numext::exp(lgamma_impl<Scalar>::run(nplus));
-            return numext::pow(-one, nplus) * factorial * zeta_impl<Scalar>::run(nplus, x);
-        }
-  }
-};
-
-#endif  // EIGEN_HAS_C99_MATH
-
-/************************************************************************************************
- * Implementation of betainc (incomplete beta integral), based on Cephes but requires C++11/C99 *
- ************************************************************************************************/
-
-template <typename Scalar>
-struct betainc_retval {
-  typedef Scalar type;
-};
-
-#if !EIGEN_HAS_C99_MATH
-
-template <typename Scalar>
-struct betainc_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-#else
-
-template <typename Scalar>
-struct betainc_impl {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(Scalar, Scalar, Scalar) {
-    /*	betaincf.c
-     *
-     *	Incomplete beta integral
-     *
-     *
-     * SYNOPSIS:
-     *
-     * float a, b, x, y, betaincf();
-     *
-     * y = betaincf( a, b, x );
-     *
-     *
-     * DESCRIPTION:
-     *
-     * Returns incomplete beta integral of the arguments, evaluated
-     * from zero to x.  The function is defined as
-     *
-     *                  x
-     *     -            -
-     *    | (a+b)      | |  a-1     b-1
-     *  -----------    |   t   (1-t)   dt.
-     *   -     -     | |
-     *  | (a) | (b)   -
-     *                 0
-     *
-     * The domain of definition is 0 <= x <= 1.  In this
-     * implementation a and b are restricted to positive values.
-     * The integral from x to 1 may be obtained by the symmetry
-     * relation
-     *
-     *    1 - betainc( a, b, x )  =  betainc( b, a, 1-x ).
-     *
-     * The integral is evaluated by a continued fraction expansion.
-     * If a < 1, the function calls itself recursively after a
-     * transformation to increase a to a+1.
-     *
-     * ACCURACY (float):
-     *
-     * Tested at random points (a,b,x) with a and b in the indicated
-     * interval and x between 0 and 1.
-     *
-     * arithmetic   domain     # trials      peak         rms
-     * Relative error:
-     *    IEEE       0,30       10000       3.7e-5      5.1e-6
-     *    IEEE       0,100      10000       1.7e-4      2.5e-5
-     * The useful domain for relative error is limited by underflow
-     * of the single precision exponential function.
-     * Absolute error:
-     *    IEEE       0,30      100000       2.2e-5      9.6e-7
-     *    IEEE       0,100      10000       6.5e-5      3.7e-6
-     *
-     * Larger errors may occur for extreme ratios of a and b.
-     *
-     * ACCURACY (double):
-     * arithmetic   domain     # trials      peak         rms
-     *    IEEE      0,5         10000       6.9e-15     4.5e-16
-     *    IEEE      0,85       250000       2.2e-13     1.7e-14
-     *    IEEE      0,1000      30000       5.3e-12     6.3e-13
-     *    IEEE      0,10000    250000       9.3e-11     7.1e-12
-     *    IEEE      0,100000    10000       8.7e-10     4.8e-11
-     * Outputs smaller than the IEEE gradual underflow threshold
-     * were excluded from these statistics.
-     *
-     * ERROR MESSAGES:
-     *   message         condition      value returned
-     * incbet domain      x<0, x>1          nan
-     * incbet underflow                     nan
-     */
-
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    return Scalar(0);
-  }
-};
-
-/* Continued fraction expansion #1 for incomplete beta integral (small_branch = True)
- * Continued fraction expansion #2 for incomplete beta integral (small_branch = False)
- */
-template <typename Scalar>
-struct incbeta_cfe {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x, bool small_branch) {
-    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, float>::value ||
-                         internal::is_same<Scalar, double>::value),
-                        THIS_TYPE_IS_NOT_SUPPORTED);
-    const Scalar big = cephes_helper<Scalar>::big();
-    const Scalar machep = cephes_helper<Scalar>::machep();
-    const Scalar biginv = cephes_helper<Scalar>::biginv();
-
-    const Scalar zero = 0;
-    const Scalar one = 1;
-    const Scalar two = 2;
-
-    Scalar xk, pk, pkm1, pkm2, qk, qkm1, qkm2;
-    Scalar k1, k2, k3, k4, k5, k6, k7, k8, k26update;
-    Scalar ans;
-    int n;
-
-    const int num_iters = (internal::is_same<Scalar, float>::value) ? 100 : 300;
-    const Scalar thresh =
-        (internal::is_same<Scalar, float>::value) ? machep : Scalar(3) * machep;
-    Scalar r = (internal::is_same<Scalar, float>::value) ? zero : one;
-
-    if (small_branch) {
-      k1 = a;
-      k2 = a + b;
-      k3 = a;
-      k4 = a + one;
-      k5 = one;
-      k6 = b - one;
-      k7 = k4;
-      k8 = a + two;
-      k26update = one;
-    } else {
-      k1 = a;
-      k2 = b - one;
-      k3 = a;
-      k4 = a + one;
-      k5 = one;
-      k6 = a + b;
-      k7 = a + one;
-      k8 = a + two;
-      k26update = -one;
-      x = x / (one - x);
-    }
-
-    pkm2 = zero;
-    qkm2 = one;
-    pkm1 = one;
-    qkm1 = one;
-    ans = one;
-    n = 0;
-
-    do {
-      xk = -(x * k1 * k2) / (k3 * k4);
-      pk = pkm1 + pkm2 * xk;
-      qk = qkm1 + qkm2 * xk;
-      pkm2 = pkm1;
-      pkm1 = pk;
-      qkm2 = qkm1;
-      qkm1 = qk;
-
-      xk = (x * k5 * k6) / (k7 * k8);
-      pk = pkm1 + pkm2 * xk;
-      qk = qkm1 + qkm2 * xk;
-      pkm2 = pkm1;
-      pkm1 = pk;
-      qkm2 = qkm1;
-      qkm1 = qk;
-
-      if (qk != zero) {
-        r = pk / qk;
-        if (numext::abs(ans - r) < numext::abs(r) * thresh) {
-          return r;
-        }
-        ans = r;
-      }
-
-      k1 += one;
-      k2 += k26update;
-      k3 += two;
-      k4 += two;
-      k5 += one;
-      k6 -= k26update;
-      k7 += two;
-      k8 += two;
-
-      if ((numext::abs(qk) + numext::abs(pk)) > big) {
-        pkm2 *= biginv;
-        pkm1 *= biginv;
-        qkm2 *= biginv;
-        qkm1 *= biginv;
-      }
-      if ((numext::abs(qk) < biginv) || (numext::abs(pk) < biginv)) {
-        pkm2 *= big;
-        pkm1 *= big;
-        qkm2 *= big;
-        qkm1 *= big;
-      }
-    } while (++n < num_iters);
-
-    return ans;
-  }
-};
-
-/* Helper functions depending on the Scalar type */
-template <typename Scalar>
-struct betainc_helper {};
-
-template <>
-struct betainc_helper<float> {
-  /* Core implementation, assumes a large (> 1.0) */
-  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float incbsa(float aa, float bb,
-                                                            float xx) {
-    float ans, a, b, t, x, onemx;
-    bool reversed_a_b = false;
-
-    onemx = 1.0f - xx;
-
-    /* see if x is greater than the mean */
-    if (xx > (aa / (aa + bb))) {
-      reversed_a_b = true;
-      a = bb;
-      b = aa;
-      t = xx;
-      x = onemx;
-    } else {
-      a = aa;
-      b = bb;
-      t = onemx;
-      x = xx;
-    }
-
-    /* Choose expansion for optimal convergence */
-    if (b > 10.0f) {
-      if (numext::abs(b * x / a) < 0.3f) {
-        t = betainc_helper<float>::incbps(a, b, x);
-        if (reversed_a_b) t = 1.0f - t;
-        return t;
-      }
-    }
-
-    ans = x * (a + b - 2.0f) / (a - 1.0f);
-    if (ans < 1.0f) {
-      ans = incbeta_cfe<float>::run(a, b, x, true /* small_branch */);
-      t = b * numext::log(t);
-    } else {
-      ans = incbeta_cfe<float>::run(a, b, x, false /* small_branch */);
-      t = (b - 1.0f) * numext::log(t);
-    }
-
-    t += a * numext::log(x) + lgamma_impl<float>::run(a + b) -
-         lgamma_impl<float>::run(a) - lgamma_impl<float>::run(b);
-    t += numext::log(ans / a);
-    t = numext::exp(t);
-
-    if (reversed_a_b) t = 1.0f - t;
-    return t;
-  }
-
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE float incbps(float a, float b, float x) {
-    float t, u, y, s;
-    const float machep = cephes_helper<float>::machep();
-
-    y = a * numext::log(x) + (b - 1.0f) * numext::log1p(-x) - numext::log(a);
-    y -= lgamma_impl<float>::run(a) + lgamma_impl<float>::run(b);
-    y += lgamma_impl<float>::run(a + b);
-
-    t = x / (1.0f - x);
-    s = 0.0f;
-    u = 1.0f;
-    do {
-      b -= 1.0f;
-      if (b == 0.0f) {
-        break;
-      }
-      a += 1.0f;
-      u *= t * b / a;
-      s += u;
-    } while (numext::abs(u) > machep);
-
-    return numext::exp(y) * (1.0f + s);
-  }
-};
-
-template <>
-struct betainc_impl<float> {
-  EIGEN_DEVICE_FUNC
-  static float run(float a, float b, float x) {
-    const float nan = NumTraits<float>::quiet_NaN();
-    float ans, t;
-
-    if (a <= 0.0f) return nan;
-    if (b <= 0.0f) return nan;
-    if ((x <= 0.0f) || (x >= 1.0f)) {
-      if (x == 0.0f) return 0.0f;
-      if (x == 1.0f) return 1.0f;
-      // mtherr("betaincf", DOMAIN);
-      return nan;
-    }
-
-    /* transformation for small aa */
-    if (a <= 1.0f) {
-      ans = betainc_helper<float>::incbsa(a + 1.0f, b, x);
-      t = a * numext::log(x) + b * numext::log1p(-x) +
-          lgamma_impl<float>::run(a + b) - lgamma_impl<float>::run(a + 1.0f) -
-          lgamma_impl<float>::run(b);
-      return (ans + numext::exp(t));
-    } else {
-      return betainc_helper<float>::incbsa(a, b, x);
-    }
-  }
-};
-
-template <>
-struct betainc_helper<double> {
-  EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE double incbps(double a, double b, double x) {
-    const double machep = cephes_helper<double>::machep();
-
-    double s, t, u, v, n, t1, z, ai;
-
-    ai = 1.0 / a;
-    u = (1.0 - b) * x;
-    v = u / (a + 1.0);
-    t1 = v;
-    t = u;
-    n = 2.0;
-    s = 0.0;
-    z = machep * ai;
-    while (numext::abs(v) > z) {
-      u = (n - b) * x / n;
-      t *= u;
-      v = t / (a + n);
-      s += v;
-      n += 1.0;
-    }
-    s += t1;
-    s += ai;
-
-    u = a * numext::log(x);
-    // TODO: gamma() is not directly implemented in Eigen.
-    /*
-    if ((a + b) < maxgam && numext::abs(u) < maxlog) {
-      t = gamma(a + b) / (gamma(a) * gamma(b));
-      s = s * t * pow(x, a);
-    }
-    */
-    t = lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) -
-        lgamma_impl<double>::run(b) + u + numext::log(s);
-    return s = numext::exp(t);
-  }
-};
-
-template <>
-struct betainc_impl<double> {
-  EIGEN_DEVICE_FUNC
-  static double run(double aa, double bb, double xx) {
-    const double nan = NumTraits<double>::quiet_NaN();
-    const double machep = cephes_helper<double>::machep();
-    // const double maxgam = 171.624376956302725;
-
-    double a, b, t, x, xc, w, y;
-    bool reversed_a_b = false;
-
-    if (aa <= 0.0 || bb <= 0.0) {
-      return nan;  // goto domerr;
-    }
-
-    if ((xx <= 0.0) || (xx >= 1.0)) {
-      if (xx == 0.0) return (0.0);
-      if (xx == 1.0) return (1.0);
-      // mtherr("incbet", DOMAIN);
-      return nan;
-    }
-
-    if ((bb * xx) <= 1.0 && xx <= 0.95) {
-      return betainc_helper<double>::incbps(aa, bb, xx);
-    }
-
-    w = 1.0 - xx;
-
-    /* Reverse a and b if x is greater than the mean. */
-    if (xx > (aa / (aa + bb))) {
-      reversed_a_b = true;
-      a = bb;
-      b = aa;
-      xc = xx;
-      x = w;
-    } else {
-      a = aa;
-      b = bb;
-      xc = w;
-      x = xx;
-    }
-
-    if (reversed_a_b && (b * x) <= 1.0 && x <= 0.95) {
-      t = betainc_helper<double>::incbps(a, b, x);
-      if (t <= machep) {
-        t = 1.0 - machep;
-      } else {
-        t = 1.0 - t;
-      }
-      return t;
-    }
-
-    /* Choose expansion for better convergence. */
-    y = x * (a + b - 2.0) - (a - 1.0);
-    if (y < 0.0) {
-      w = incbeta_cfe<double>::run(a, b, x, true /* small_branch */);
-    } else {
-      w = incbeta_cfe<double>::run(a, b, x, false /* small_branch */) / xc;
-    }
-
-    /* Multiply w by the factor
-         a      b   _             _     _
-        x  (1-x)   | (a+b) / ( a | (a) | (b) ) .   */
-
-    y = a * numext::log(x);
-    t = b * numext::log(xc);
-    // TODO: gamma is not directly implemented in Eigen.
-    /*
-    if ((a + b) < maxgam && numext::abs(y) < maxlog && numext::abs(t) < maxlog)
-    {
-      t = pow(xc, b);
-      t *= pow(x, a);
-      t /= a;
-      t *= w;
-      t *= gamma(a + b) / (gamma(a) * gamma(b));
-    } else {
-    */
-    /* Resort to logarithms.  */
-    y += t + lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) -
-         lgamma_impl<double>::run(b);
-    y += numext::log(w / a);
-    t = numext::exp(y);
-
-    /* } */
-    // done:
-
-    if (reversed_a_b) {
-      if (t <= machep) {
-        t = 1.0 - machep;
-      } else {
-        t = 1.0 - t;
-      }
-    }
-    return t;
-  }
-};
-
-#endif  // EIGEN_HAS_C99_MATH
-
-}  // end namespace internal
-
-namespace numext {
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(lgamma, Scalar)
-    lgamma(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(lgamma, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(digamma, Scalar)
-    digamma(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(digamma, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(zeta, Scalar)
-zeta(const Scalar& x, const Scalar& q) {
-    return EIGEN_MATHFUNC_IMPL(zeta, Scalar)::run(x, q);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(polygamma, Scalar)
-polygamma(const Scalar& n, const Scalar& x) {
-    return EIGEN_MATHFUNC_IMPL(polygamma, Scalar)::run(n, x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erf, Scalar)
-    erf(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(erf, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erfc, Scalar)
-    erfc(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(erfc, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(ndtri, Scalar)
-    ndtri(const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(ndtri, Scalar)::run(x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma, Scalar)
-    igamma(const Scalar& a, const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(igamma, Scalar)::run(a, x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma_der_a, Scalar)
-    igamma_der_a(const Scalar& a, const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(igamma_der_a, Scalar)::run(a, x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(gamma_sample_der_alpha, Scalar)
-    gamma_sample_der_alpha(const Scalar& a, const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(gamma_sample_der_alpha, Scalar)::run(a, x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igammac, Scalar)
-    igammac(const Scalar& a, const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(igammac, Scalar)::run(a, x);
-}
-
-template <typename Scalar>
-EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(betainc, Scalar)
-    betainc(const Scalar& a, const Scalar& b, const Scalar& x) {
-  return EIGEN_MATHFUNC_IMPL(betainc, Scalar)::run(a, b, x);
-}
-
-}  // end namespace numext
-}  // end namespace Eigen
-
-#endif  // EIGEN_SPECIAL_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
deleted file mode 100644
index 2bb0179..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
+++ /dev/null
@@ -1,79 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
-#define EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
-
-namespace Eigen {
-
-namespace internal {
-
-/** \internal \returns the ln(|gamma(\a a)|) (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet plgamma(const Packet& a) { using numext::lgamma; return lgamma(a); }
-
-/** \internal \returns the derivative of lgamma, psi(\a a) (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pdigamma(const Packet& a) { using numext::digamma; return digamma(a); }
-
-/** \internal \returns the zeta function of two arguments (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pzeta(const Packet& x, const Packet& q) { using numext::zeta; return zeta(x, q); }
-
-/** \internal \returns the polygamma function (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet ppolygamma(const Packet& n, const Packet& x) { using numext::polygamma; return polygamma(n, x); }
-
-/** \internal \returns the erf(\a a) (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet perf(const Packet& a) { using numext::erf; return erf(a); }
-
-/** \internal \returns the erfc(\a a) (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet perfc(const Packet& a) { using numext::erfc; return erfc(a); }
-
-/** \internal \returns the ndtri(\a a) (coeff-wise) */
-template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet pndtri(const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type ScalarType;
-  using internal::generic_ndtri; return generic_ndtri<Packet, ScalarType>(a);
-}
-
-/** \internal \returns the incomplete gamma function igamma(\a a, \a x) */
-template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-Packet pigamma(const Packet& a, const Packet& x) { using numext::igamma; return igamma(a, x); }
-
-/** \internal \returns the derivative of the incomplete gamma function
- * igamma_der_a(\a a, \a x) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pigamma_der_a(const Packet& a, const Packet& x) {
-  using numext::igamma_der_a; return igamma_der_a(a, x);
-}
-
-/** \internal \returns compute the derivative of the sample
-  * of Gamma(alpha, 1) random variable with respect to the parameter a
-  * gamma_sample_der_alpha(\a alpha, \a sample) */
-template <typename Packet>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pgamma_sample_der_alpha(const Packet& alpha, const Packet& sample) {
-  using numext::gamma_sample_der_alpha; return gamma_sample_der_alpha(alpha, sample);
-}
-
-/** \internal \returns the complementary incomplete gamma function igammac(\a a, \a x) */
-template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-Packet pigammac(const Packet& a, const Packet& x) { using numext::igammac; return igammac(a, x); }
-
-/** \internal \returns the complementary incomplete gamma function betainc(\a a, \a b, \a x) */
-template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-Packet pbetainc(const Packet& a, const Packet& b,const Packet& x) { using numext::betainc; return betainc(a, b, x); }
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
deleted file mode 100644
index 2d76692..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
+++ /dev/null
@@ -1,46 +0,0 @@
-#ifndef EIGEN_AVX_BESSELFUNCTIONS_H
-#define EIGEN_AVX_BESSELFUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i0)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i0)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i0e)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i0e)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i1)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i1)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i1e)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i1e)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_j0)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_j0)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_j1)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_j1)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k0)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k0)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k0e)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k0e)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k1)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k1)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k1e)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k1e)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_y0)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_y0)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_y1)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_y1)
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_AVX_BESSELFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
deleted file mode 100644
index 35e62a8..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
+++ /dev/null
@@ -1,16 +0,0 @@
-#ifndef EIGEN_AVX_SPECIALFUNCTIONS_H
-#define EIGEN_AVX_SPECIALFUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, perf)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, perf)
-
-F16_PACKET_FUNCTION(Packet8f, Packet8h, pndtri)
-BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pndtri)
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_AVX_SPECIAL_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
deleted file mode 100644
index 7dd3c3e..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
+++ /dev/null
@@ -1,46 +0,0 @@
-#ifndef EIGEN_AVX512_BESSELFUNCTIONS_H
-#define EIGEN_AVX512_BESSELFUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i0)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i0)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i0e)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i0e)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i1)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i1)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i1e)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i1e)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_j0)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_j0)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_j1)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_j1)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k0)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k0)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k0e)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k0e)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k1)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k1)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k1e)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k1e)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_y0)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_y0)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_y1)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_y1)
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_AVX512_BESSELFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
deleted file mode 100644
index 79878f2..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
+++ /dev/null
@@ -1,16 +0,0 @@
-#ifndef EIGEN_AVX512_SPECIALFUNCTIONS_H
-#define EIGEN_AVX512_SPECIALFUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, perf)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, perf)
-
-F16_PACKET_FUNCTION(Packet16f, Packet16h, pndtri)
-BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pndtri)
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_AVX512_SPECIAL_FUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
deleted file mode 100644
index dd3bf4d..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
+++ /dev/null
@@ -1,369 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_GPU_SPECIALFUNCTIONS_H
-#define EIGEN_GPU_SPECIALFUNCTIONS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// Make sure this is only available when targeting a GPU: we don't want to
-// introduce conflicts between these packet_traits definitions and the ones
-// we'll use on the host side (SSE, AVX, ...)
-#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 plgamma<float4>(const float4& a)
-{
-  return make_float4(lgammaf(a.x), lgammaf(a.y), lgammaf(a.z), lgammaf(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 plgamma<double2>(const double2& a)
-{
-  using numext::lgamma;
-  return make_double2(lgamma(a.x), lgamma(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pdigamma<float4>(const float4& a)
-{
-  using numext::digamma;
-  return make_float4(digamma(a.x), digamma(a.y), digamma(a.z), digamma(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pdigamma<double2>(const double2& a)
-{
-  using numext::digamma;
-  return make_double2(digamma(a.x), digamma(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pzeta<float4>(const float4& x, const float4& q)
-{
-    using numext::zeta;
-    return make_float4(zeta(x.x, q.x), zeta(x.y, q.y), zeta(x.z, q.z), zeta(x.w, q.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pzeta<double2>(const double2& x, const double2& q)
-{
-    using numext::zeta;
-    return make_double2(zeta(x.x, q.x), zeta(x.y, q.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 ppolygamma<float4>(const float4& n, const float4& x)
-{
-    using numext::polygamma;
-    return make_float4(polygamma(n.x, x.x), polygamma(n.y, x.y), polygamma(n.z, x.z), polygamma(n.w, x.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 ppolygamma<double2>(const double2& n, const double2& x)
-{
-    using numext::polygamma;
-    return make_double2(polygamma(n.x, x.x), polygamma(n.y, x.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 perf<float4>(const float4& a)
-{
-  return make_float4(erff(a.x), erff(a.y), erff(a.z), erff(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 perf<double2>(const double2& a)
-{
-  using numext::erf;
-  return make_double2(erf(a.x), erf(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 perfc<float4>(const float4& a)
-{
-  using numext::erfc;
-  return make_float4(erfc(a.x), erfc(a.y), erfc(a.z), erfc(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 perfc<double2>(const double2& a)
-{
-  using numext::erfc;
-  return make_double2(erfc(a.x), erfc(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pndtri<float4>(const float4& a)
-{
-  using numext::ndtri;
-  return make_float4(ndtri(a.x), ndtri(a.y), ndtri(a.z), ndtri(a.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pndtri<double2>(const double2& a)
-{
-  using numext::ndtri;
-  return make_double2(ndtri(a.x), ndtri(a.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pigamma<float4>(const float4& a, const float4& x)
-{
-  using numext::igamma;
-  return make_float4(
-      igamma(a.x, x.x),
-      igamma(a.y, x.y),
-      igamma(a.z, x.z),
-      igamma(a.w, x.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pigamma<double2>(const double2& a, const double2& x)
-{
-  using numext::igamma;
-  return make_double2(igamma(a.x, x.x), igamma(a.y, x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pigamma_der_a<float4>(
-    const float4& a, const float4& x) {
-  using numext::igamma_der_a;
-  return make_float4(igamma_der_a(a.x, x.x), igamma_der_a(a.y, x.y),
-                     igamma_der_a(a.z, x.z), igamma_der_a(a.w, x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pigamma_der_a<double2>(const double2& a, const double2& x) {
-  using numext::igamma_der_a;
-  return make_double2(igamma_der_a(a.x, x.x), igamma_der_a(a.y, x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pgamma_sample_der_alpha<float4>(
-    const float4& alpha, const float4& sample) {
-  using numext::gamma_sample_der_alpha;
-  return make_float4(
-      gamma_sample_der_alpha(alpha.x, sample.x),
-      gamma_sample_der_alpha(alpha.y, sample.y),
-      gamma_sample_der_alpha(alpha.z, sample.z),
-      gamma_sample_der_alpha(alpha.w, sample.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pgamma_sample_der_alpha<double2>(const double2& alpha, const double2& sample) {
-  using numext::gamma_sample_der_alpha;
-  return make_double2(
-      gamma_sample_der_alpha(alpha.x, sample.x),
-      gamma_sample_der_alpha(alpha.y, sample.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pigammac<float4>(const float4& a, const float4& x)
-{
-  using numext::igammac;
-  return make_float4(
-      igammac(a.x, x.x),
-      igammac(a.y, x.y),
-      igammac(a.z, x.z),
-      igammac(a.w, x.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pigammac<double2>(const double2& a, const double2& x)
-{
-  using numext::igammac;
-  return make_double2(igammac(a.x, x.x), igammac(a.y, x.y));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-float4 pbetainc<float4>(const float4& a, const float4& b, const float4& x)
-{
-  using numext::betainc;
-  return make_float4(
-      betainc(a.x, b.x, x.x),
-      betainc(a.y, b.y, x.y),
-      betainc(a.z, b.z, x.z),
-      betainc(a.w, b.w, x.w));
-}
-
-template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-double2 pbetainc<double2>(const double2& a, const double2& b, const double2& x)
-{
-  using numext::betainc;
-  return make_double2(betainc(a.x, b.x, x.x), betainc(a.y, b.y, x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i0e<float4>(const float4& x) {
-  using numext::bessel_i0e;
-  return make_float4(bessel_i0e(x.x), bessel_i0e(x.y), bessel_i0e(x.z), bessel_i0e(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_i0e<double2>(const double2& x) {
-  using numext::bessel_i0e;
-  return make_double2(bessel_i0e(x.x), bessel_i0e(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i0<float4>(const float4& x) {
-  using numext::bessel_i0;
-  return make_float4(bessel_i0(x.x), bessel_i0(x.y), bessel_i0(x.z), bessel_i0(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_i0<double2>(const double2& x) {
-  using numext::bessel_i0;
-  return make_double2(bessel_i0(x.x), bessel_i0(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i1e<float4>(const float4& x) {
-  using numext::bessel_i1e;
-  return make_float4(bessel_i1e(x.x), bessel_i1e(x.y), bessel_i1e(x.z), bessel_i1e(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_i1e<double2>(const double2& x) {
-  using numext::bessel_i1e;
-  return make_double2(bessel_i1e(x.x), bessel_i1e(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i1<float4>(const float4& x) {
-  using numext::bessel_i1;
-  return make_float4(bessel_i1(x.x), bessel_i1(x.y), bessel_i1(x.z), bessel_i1(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_i1<double2>(const double2& x) {
-  using numext::bessel_i1;
-  return make_double2(bessel_i1(x.x), bessel_i1(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k0e<float4>(const float4& x) {
-  using numext::bessel_k0e;
-  return make_float4(bessel_k0e(x.x), bessel_k0e(x.y), bessel_k0e(x.z), bessel_k0e(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_k0e<double2>(const double2& x) {
-  using numext::bessel_k0e;
-  return make_double2(bessel_k0e(x.x), bessel_k0e(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k0<float4>(const float4& x) {
-  using numext::bessel_k0;
-  return make_float4(bessel_k0(x.x), bessel_k0(x.y), bessel_k0(x.z), bessel_k0(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_k0<double2>(const double2& x) {
-  using numext::bessel_k0;
-  return make_double2(bessel_k0(x.x), bessel_k0(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k1e<float4>(const float4& x) {
-  using numext::bessel_k1e;
-  return make_float4(bessel_k1e(x.x), bessel_k1e(x.y), bessel_k1e(x.z), bessel_k1e(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_k1e<double2>(const double2& x) {
-  using numext::bessel_k1e;
-  return make_double2(bessel_k1e(x.x), bessel_k1e(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k1<float4>(const float4& x) {
-  using numext::bessel_k1;
-  return make_float4(bessel_k1(x.x), bessel_k1(x.y), bessel_k1(x.z), bessel_k1(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_k1<double2>(const double2& x) {
-  using numext::bessel_k1;
-  return make_double2(bessel_k1(x.x), bessel_k1(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_j0<float4>(const float4& x) {
-  using numext::bessel_j0;
-  return make_float4(bessel_j0(x.x), bessel_j0(x.y), bessel_j0(x.z), bessel_j0(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_j0<double2>(const double2& x) {
-  using numext::bessel_j0;
-  return make_double2(bessel_j0(x.x), bessel_j0(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_j1<float4>(const float4& x) {
-  using numext::bessel_j1;
-  return make_float4(bessel_j1(x.x), bessel_j1(x.y), bessel_j1(x.z), bessel_j1(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_j1<double2>(const double2& x) {
-  using numext::bessel_j1;
-  return make_double2(bessel_j1(x.x), bessel_j1(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_y0<float4>(const float4& x) {
-  using numext::bessel_y0;
-  return make_float4(bessel_y0(x.x), bessel_y0(x.y), bessel_y0(x.z), bessel_y0(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_y0<double2>(const double2& x) {
-  using numext::bessel_y0;
-  return make_double2(bessel_y0(x.x), bessel_y0(x.y));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_y1<float4>(const float4& x) {
-  using numext::bessel_y1;
-  return make_float4(bessel_y1(x.x), bessel_y1(x.y), bessel_y1(x.z), bessel_y1(x.w));
-}
-
-template <>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
-pbessel_y1<double2>(const double2& x) {
-  using numext::bessel_y1;
-  return make_double2(bessel_y1(x.x), bessel_y1(x.y));
-}
-
-#endif
-
-} // end namespace internal
-
-} // end namespace Eigen
-
-#endif // EIGEN_GPU_SPECIALFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
deleted file mode 100644
index 67433b0..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
+++ /dev/null
@@ -1,54 +0,0 @@
-#ifndef EIGEN_NEON_BESSELFUNCTIONS_H
-#define EIGEN_NEON_BESSELFUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-
-#define NEON_HALF_TO_FLOAT_FUNCTIONS(METHOD)                            \
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
-Packet8hf METHOD<Packet8hf>(const Packet8hf& x) {                       \
-  const Packet4f lo = METHOD<Packet4f>(vcvt_f32_f16(vget_low_f16(x)));  \
-  const Packet4f hi = METHOD<Packet4f>(vcvt_f32_f16(vget_high_f16(x))); \
-  return vcombine_f16(vcvt_f16_f32(lo), vcvt_f16_f32(hi));              \
-}                                                                       \
-                                                                        \
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
-Packet4hf METHOD<Packet4hf>(const Packet4hf& x) {                       \
-  return vcvt_f16_f32(METHOD<Packet4f>(vcvt_f32_f16(x)));               \
-}
-
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i0)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i0e)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i1)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i1e)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_j0)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_j1)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k0)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k0e)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k1)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k1e)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_y0)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_y1)
-
-#undef NEON_HALF_TO_FLOAT_FUNCTIONS
-#endif
-
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i0)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i0e)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i1)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i1e)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_j0)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_j1)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k0)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k0e)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k1)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k1e)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_y0)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_y1)
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_NEON_BESSELFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
deleted file mode 100644
index ec92951..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
+++ /dev/null
@@ -1,34 +0,0 @@
-#ifndef EIGEN_NEON_SPECIALFUNCTIONS_H
-#define EIGEN_NEON_SPECIALFUNCTIONS_H
-
-namespace Eigen {
-namespace internal {
-
-#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-
-#define NEON_HALF_TO_FLOAT_FUNCTIONS(METHOD)                            \
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
-Packet8hf METHOD<Packet8hf>(const Packet8hf& x) {                       \
-  const Packet4f lo = METHOD<Packet4f>(vcvt_f32_f16(vget_low_f16(x)));  \
-  const Packet4f hi = METHOD<Packet4f>(vcvt_f32_f16(vget_high_f16(x))); \
-  return vcombine_f16(vcvt_f16_f32(lo), vcvt_f16_f32(hi));              \
-}                                                                       \
-                                                                        \
-template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
-Packet4hf METHOD<Packet4hf>(const Packet4hf& x) {                       \
-  return vcvt_f16_f32(METHOD<Packet4f>(vcvt_f32_f16(x)));               \
-}
-
-NEON_HALF_TO_FLOAT_FUNCTIONS(perf)
-NEON_HALF_TO_FLOAT_FUNCTIONS(pndtri)
-
-#undef NEON_HALF_TO_FLOAT_FUNCTIONS
-#endif
-
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, perf)
-BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pndtri)
-
-}  // namespace internal
-}  // namespace Eigen
-
-#endif  // EIGEN_NEON_SPECIALFUNCTIONS_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/Spline.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/Spline.h
deleted file mode 100644
index 79edd52..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/Spline.h
+++ /dev/null
@@ -1,507 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPLINE_H
-#define EIGEN_SPLINE_H
-
-#include "SplineFwd.h"
-
-namespace Eigen
-{
-    /**
-     * \ingroup Splines_Module
-     * \class Spline
-     * \brief A class representing multi-dimensional spline curves.
-     *
-     * The class represents B-splines with non-uniform knot vectors. Each control
-     * point of the B-spline is associated with a basis function
-     * \f{align*}
-     *   C(u) & = \sum_{i=0}^{n}N_{i,p}(u)P_i
-     * \f}
-     *
-     * \tparam _Scalar The underlying data type (typically float or double)
-     * \tparam _Dim The curve dimension (e.g. 2 or 3)
-     * \tparam _Degree Per default set to Dynamic; could be set to the actual desired
-     *                degree for optimization purposes (would result in stack allocation
-     *                of several temporary variables).
-     **/
-  template <typename _Scalar, int _Dim, int _Degree>
-  class Spline
-  {
-  public:
-    typedef _Scalar Scalar; /*!< The spline curve's scalar type. */
-    enum { Dimension = _Dim /*!< The spline curve's dimension. */ };
-    enum { Degree = _Degree /*!< The spline curve's degree. */ };
-
-    /** \brief The point type the spline is representing. */
-    typedef typename SplineTraits<Spline>::PointType PointType;
-    
-    /** \brief The data type used to store knot vectors. */
-    typedef typename SplineTraits<Spline>::KnotVectorType KnotVectorType;
-
-    /** \brief The data type used to store parameter vectors. */
-    typedef typename SplineTraits<Spline>::ParameterVectorType ParameterVectorType;
-    
-    /** \brief The data type used to store non-zero basis functions. */
-    typedef typename SplineTraits<Spline>::BasisVectorType BasisVectorType;
-
-    /** \brief The data type used to store the values of the basis function derivatives. */
-    typedef typename SplineTraits<Spline>::BasisDerivativeType BasisDerivativeType;
-    
-    /** \brief The data type representing the spline's control points. */
-    typedef typename SplineTraits<Spline>::ControlPointVectorType ControlPointVectorType;
-    
-    /**
-    * \brief Creates a (constant) zero spline.
-    * For Splines with dynamic degree, the resulting degree will be 0.
-    **/
-    Spline() 
-    : m_knots(1, (Degree==Dynamic ? 2 : 2*Degree+2))
-    , m_ctrls(ControlPointVectorType::Zero(Dimension,(Degree==Dynamic ? 1 : Degree+1))) 
-    {
-      // in theory this code can go to the initializer list but it will get pretty
-      // much unreadable ...
-      enum { MinDegree = (Degree==Dynamic ? 0 : Degree) };
-      m_knots.template segment<MinDegree+1>(0) = Array<Scalar,1,MinDegree+1>::Zero();
-      m_knots.template segment<MinDegree+1>(MinDegree+1) = Array<Scalar,1,MinDegree+1>::Ones();
-    }
-
-    /**
-    * \brief Creates a spline from a knot vector and control points.
-    * \param knots The spline's knot vector.
-    * \param ctrls The spline's control point vector.
-    **/
-    template <typename OtherVectorType, typename OtherArrayType>
-    Spline(const OtherVectorType& knots, const OtherArrayType& ctrls) : m_knots(knots), m_ctrls(ctrls) {}
-
-    /**
-    * \brief Copy constructor for splines.
-    * \param spline The input spline.
-    **/
-    template <int OtherDegree>
-    Spline(const Spline<Scalar, Dimension, OtherDegree>& spline) : 
-    m_knots(spline.knots()), m_ctrls(spline.ctrls()) {}
-
-    /**
-     * \brief Returns the knots of the underlying spline.
-     **/
-    const KnotVectorType& knots() const { return m_knots; }
-    
-    /**
-     * \brief Returns the ctrls of the underlying spline.
-     **/    
-    const ControlPointVectorType& ctrls() const { return m_ctrls; }
-
-    /**
-     * \brief Returns the spline value at a given site \f$u\f$.
-     *
-     * The function returns
-     * \f{align*}
-     *   C(u) & = \sum_{i=0}^{n}N_{i,p}P_i
-     * \f}
-     *
-     * \param u Parameter \f$u \in [0;1]\f$ at which the spline is evaluated.
-     * \return The spline value at the given location \f$u\f$.
-     **/
-    PointType operator()(Scalar u) const;
-
-    /**
-     * \brief Evaluation of spline derivatives of up-to given order.
-     *
-     * The function returns
-     * \f{align*}
-     *   \frac{d^i}{du^i}C(u) & = \sum_{i=0}^{n} \frac{d^i}{du^i} N_{i,p}(u)P_i
-     * \f}
-     * for i ranging between 0 and order.
-     *
-     * \param u Parameter \f$u \in [0;1]\f$ at which the spline derivative is evaluated.
-     * \param order The order up to which the derivatives are computed.
-     **/
-    typename SplineTraits<Spline>::DerivativeType
-      derivatives(Scalar u, DenseIndex order) const;
-
-    /**
-     * \copydoc Spline::derivatives
-     * Using the template version of this function is more efficieent since
-     * temporary objects are allocated on the stack whenever this is possible.
-     **/    
-    template <int DerivativeOrder>
-    typename SplineTraits<Spline,DerivativeOrder>::DerivativeType
-      derivatives(Scalar u, DenseIndex order = DerivativeOrder) const;
-
-    /**
-     * \brief Computes the non-zero basis functions at the given site.
-     *
-     * Splines have local support and a point from their image is defined
-     * by exactly \f$p+1\f$ control points \f$P_i\f$ where \f$p\f$ is the
-     * spline degree.
-     *
-     * This function computes the \f$p+1\f$ non-zero basis function values
-     * for a given parameter value \f$u\f$. It returns
-     * \f{align*}{
-     *   N_{i,p}(u), \hdots, N_{i+p+1,p}(u)
-     * \f}
-     *
-     * \param u Parameter \f$u \in [0;1]\f$ at which the non-zero basis functions 
-     *          are computed.
-     **/
-    typename SplineTraits<Spline>::BasisVectorType
-      basisFunctions(Scalar u) const;
-
-    /**
-     * \brief Computes the non-zero spline basis function derivatives up to given order.
-     *
-     * The function computes
-     * \f{align*}{
-     *   \frac{d^i}{du^i} N_{i,p}(u), \hdots, \frac{d^i}{du^i} N_{i+p+1,p}(u)
-     * \f}
-     * with i ranging from 0 up to the specified order.
-     *
-     * \param u Parameter \f$u \in [0;1]\f$ at which the non-zero basis function
-     *          derivatives are computed.
-     * \param order The order up to which the basis function derivatives are computes.
-     **/
-    typename SplineTraits<Spline>::BasisDerivativeType
-      basisFunctionDerivatives(Scalar u, DenseIndex order) const;
-
-    /**
-     * \copydoc Spline::basisFunctionDerivatives
-     * Using the template version of this function is more efficieent since
-     * temporary objects are allocated on the stack whenever this is possible.
-     **/    
-    template <int DerivativeOrder>
-    typename SplineTraits<Spline,DerivativeOrder>::BasisDerivativeType
-      basisFunctionDerivatives(Scalar u, DenseIndex order = DerivativeOrder) const;
-
-    /**
-     * \brief Returns the spline degree.
-     **/ 
-    DenseIndex degree() const;
-
-    /** 
-     * \brief Returns the span within the knot vector in which u is falling.
-     * \param u The site for which the span is determined.
-     **/
-    DenseIndex span(Scalar u) const;
-
-    /**
-     * \brief Computes the span within the provided knot vector in which u is falling.
-     **/
-    static DenseIndex Span(typename SplineTraits<Spline>::Scalar u, DenseIndex degree, const typename SplineTraits<Spline>::KnotVectorType& knots);
-    
-    /**
-     * \brief Returns the spline's non-zero basis functions.
-     *
-     * The function computes and returns
-     * \f{align*}{
-     *   N_{i,p}(u), \hdots, N_{i+p+1,p}(u)
-     * \f}
-     *
-     * \param u The site at which the basis functions are computed.
-     * \param degree The degree of the underlying spline.
-     * \param knots The underlying spline's knot vector.
-     **/
-    static BasisVectorType BasisFunctions(Scalar u, DenseIndex degree, const KnotVectorType& knots);
-
-    /**
-     * \copydoc Spline::basisFunctionDerivatives
-     * \param degree The degree of the underlying spline
-     * \param knots The underlying spline's knot vector.
-     **/    
-    static BasisDerivativeType BasisFunctionDerivatives(
-      const Scalar u, const DenseIndex order, const DenseIndex degree, const KnotVectorType& knots);
-
-  private:
-    KnotVectorType m_knots; /*!< Knot vector. */
-    ControlPointVectorType  m_ctrls; /*!< Control points. */
-
-    template <typename DerivativeType>
-    static void BasisFunctionDerivativesImpl(
-      const typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
-      const DenseIndex order,
-      const DenseIndex p, 
-      const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& U,
-      DerivativeType& N_);
-  };
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  DenseIndex Spline<_Scalar, _Dim, _Degree>::Span(
-    typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::Scalar u,
-    DenseIndex degree,
-    const typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::KnotVectorType& knots)
-  {
-    // Piegl & Tiller, "The NURBS Book", A2.1 (p. 68)
-    if (u <= knots(0)) return degree;
-    const Scalar* pos = std::upper_bound(knots.data()+degree-1, knots.data()+knots.size()-degree-1, u);
-    return static_cast<DenseIndex>( std::distance(knots.data(), pos) - 1 );
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  typename Spline<_Scalar, _Dim, _Degree>::BasisVectorType
-    Spline<_Scalar, _Dim, _Degree>::BasisFunctions(
-    typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
-    DenseIndex degree,
-    const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& knots)
-  {
-    const DenseIndex p = degree;
-    const DenseIndex i = Spline::Span(u, degree, knots);
-
-    const KnotVectorType& U = knots;
-
-    BasisVectorType left(p+1); left(0) = Scalar(0);
-    BasisVectorType right(p+1); right(0) = Scalar(0);
-
-    VectorBlock<BasisVectorType,Degree>(left,1,p) = u - VectorBlock<const KnotVectorType,Degree>(U,i+1-p,p).reverse();
-    VectorBlock<BasisVectorType,Degree>(right,1,p) = VectorBlock<const KnotVectorType,Degree>(U,i+1,p) - u;
-
-    BasisVectorType N(1,p+1);
-    N(0) = Scalar(1);
-    for (DenseIndex j=1; j<=p; ++j)
-    {
-      Scalar saved = Scalar(0);
-      for (DenseIndex r=0; r<j; r++)
-      {
-        const Scalar tmp = N(r)/(right(r+1)+left(j-r));
-        N[r] = saved + right(r+1)*tmp;
-        saved = left(j-r)*tmp;
-      }
-      N(j) = saved;
-    }
-    return N;
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  DenseIndex Spline<_Scalar, _Dim, _Degree>::degree() const
-  {
-    if (_Degree == Dynamic)
-      return m_knots.size() - m_ctrls.cols() - 1;
-    else
-      return _Degree;
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  DenseIndex Spline<_Scalar, _Dim, _Degree>::span(Scalar u) const
-  {
-    return Spline::Span(u, degree(), knots());
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  typename Spline<_Scalar, _Dim, _Degree>::PointType Spline<_Scalar, _Dim, _Degree>::operator()(Scalar u) const
-  {
-    enum { Order = SplineTraits<Spline>::OrderAtCompileTime };
-
-    const DenseIndex span = this->span(u);
-    const DenseIndex p = degree();
-    const BasisVectorType basis_funcs = basisFunctions(u);
-
-    const Replicate<BasisVectorType,Dimension,1> ctrl_weights(basis_funcs);
-    const Block<const ControlPointVectorType,Dimension,Order> ctrl_pts(ctrls(),0,span-p,Dimension,p+1);
-    return (ctrl_weights * ctrl_pts).rowwise().sum();
-  }
-
-  /* --------------------------------------------------------------------------------------------- */
-
-  template <typename SplineType, typename DerivativeType>
-  void derivativesImpl(const SplineType& spline, typename SplineType::Scalar u, DenseIndex order, DerivativeType& der)
-  {    
-    enum { Dimension = SplineTraits<SplineType>::Dimension };
-    enum { Order = SplineTraits<SplineType>::OrderAtCompileTime };
-    enum { DerivativeOrder = DerivativeType::ColsAtCompileTime };
-
-    typedef typename SplineTraits<SplineType>::ControlPointVectorType ControlPointVectorType;
-    typedef typename SplineTraits<SplineType,DerivativeOrder>::BasisDerivativeType BasisDerivativeType;
-    typedef typename BasisDerivativeType::ConstRowXpr BasisDerivativeRowXpr;    
-
-    const DenseIndex p = spline.degree();
-    const DenseIndex span = spline.span(u);
-
-    const DenseIndex n = (std::min)(p, order);
-
-    der.resize(Dimension,n+1);
-
-    // Retrieve the basis function derivatives up to the desired order...    
-    const BasisDerivativeType basis_func_ders = spline.template basisFunctionDerivatives<DerivativeOrder>(u, n+1);
-
-    // ... and perform the linear combinations of the control points.
-    for (DenseIndex der_order=0; der_order<n+1; ++der_order)
-    {
-      const Replicate<BasisDerivativeRowXpr,Dimension,1> ctrl_weights( basis_func_ders.row(der_order) );
-      const Block<const ControlPointVectorType,Dimension,Order> ctrl_pts(spline.ctrls(),0,span-p,Dimension,p+1);
-      der.col(der_order) = (ctrl_weights * ctrl_pts).rowwise().sum();
-    }
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::DerivativeType
-    Spline<_Scalar, _Dim, _Degree>::derivatives(Scalar u, DenseIndex order) const
-  {
-    typename SplineTraits< Spline >::DerivativeType res;
-    derivativesImpl(*this, u, order, res);
-    return res;
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  template <int DerivativeOrder>
-  typename SplineTraits< Spline<_Scalar, _Dim, _Degree>, DerivativeOrder >::DerivativeType
-    Spline<_Scalar, _Dim, _Degree>::derivatives(Scalar u, DenseIndex order) const
-  {
-    typename SplineTraits< Spline, DerivativeOrder >::DerivativeType res;
-    derivativesImpl(*this, u, order, res);
-    return res;
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::BasisVectorType
-    Spline<_Scalar, _Dim, _Degree>::basisFunctions(Scalar u) const
-  {
-    return Spline::BasisFunctions(u, degree(), knots());
-  }
-
-  /* --------------------------------------------------------------------------------------------- */
-  
-  
-  template <typename _Scalar, int _Dim, int _Degree>
-  template <typename DerivativeType>
-  void Spline<_Scalar, _Dim, _Degree>::BasisFunctionDerivativesImpl(
-    const typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
-    const DenseIndex order,
-    const DenseIndex p, 
-    const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& U,
-    DerivativeType& N_)
-  {
-    typedef Spline<_Scalar, _Dim, _Degree> SplineType;
-    enum { Order = SplineTraits<SplineType>::OrderAtCompileTime };
-
-    const DenseIndex span = SplineType::Span(u, p, U);
-
-    const DenseIndex n = (std::min)(p, order);
-
-    N_.resize(n+1, p+1);
-
-    BasisVectorType left = BasisVectorType::Zero(p+1);
-    BasisVectorType right = BasisVectorType::Zero(p+1);
-
-    Matrix<Scalar,Order,Order> ndu(p+1,p+1);
-
-    Scalar saved, temp; // FIXME These were double instead of Scalar. Was there a reason for that?
-
-    ndu(0,0) = 1.0;
-
-    DenseIndex j;
-    for (j=1; j<=p; ++j)
-    {
-      left[j] = u-U[span+1-j];
-      right[j] = U[span+j]-u;
-      saved = 0.0;
-
-      for (DenseIndex r=0; r<j; ++r)
-      {
-        /* Lower triangle */
-        ndu(j,r) = right[r+1]+left[j-r];
-        temp = ndu(r,j-1)/ndu(j,r);
-        /* Upper triangle */
-        ndu(r,j) = static_cast<Scalar>(saved+right[r+1] * temp);
-        saved = left[j-r] * temp;
-      }
-
-      ndu(j,j) = static_cast<Scalar>(saved);
-    }
-
-    for (j = p; j>=0; --j) 
-      N_(0,j) = ndu(j,p);
-
-    // Compute the derivatives
-    DerivativeType a(n+1,p+1);
-    DenseIndex r=0;
-    for (; r<=p; ++r)
-    {
-      DenseIndex s1,s2;
-      s1 = 0; s2 = 1; // alternate rows in array a
-      a(0,0) = 1.0;
-
-      // Compute the k-th derivative
-      for (DenseIndex k=1; k<=static_cast<DenseIndex>(n); ++k)
-      {
-        Scalar d = 0.0;
-        DenseIndex rk,pk,j1,j2;
-        rk = r-k; pk = p-k;
-
-        if (r>=k)
-        {
-          a(s2,0) = a(s1,0)/ndu(pk+1,rk);
-          d = a(s2,0)*ndu(rk,pk);
-        }
-
-        if (rk>=-1) j1 = 1;
-        else        j1 = -rk;
-
-        if (r-1 <= pk) j2 = k-1;
-        else           j2 = p-r;
-
-        for (j=j1; j<=j2; ++j)
-        {
-          a(s2,j) = (a(s1,j)-a(s1,j-1))/ndu(pk+1,rk+j);
-          d += a(s2,j)*ndu(rk+j,pk);
-        }
-
-        if (r<=pk)
-        {
-          a(s2,k) = -a(s1,k-1)/ndu(pk+1,r);
-          d += a(s2,k)*ndu(r,pk);
-        }
-
-        N_(k,r) = static_cast<Scalar>(d);
-        j = s1; s1 = s2; s2 = j; // Switch rows
-      }
-    }
-
-    /* Multiply through by the correct factors */
-    /* (Eq. [2.9])                             */
-    r = p;
-    for (DenseIndex k=1; k<=static_cast<DenseIndex>(n); ++k)
-    {
-      for (j=p; j>=0; --j) N_(k,j) *= r;
-      r *= p-k;
-    }
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::BasisDerivativeType
-    Spline<_Scalar, _Dim, _Degree>::basisFunctionDerivatives(Scalar u, DenseIndex order) const
-  {
-    typename SplineTraits<Spline<_Scalar, _Dim, _Degree> >::BasisDerivativeType der;
-    BasisFunctionDerivativesImpl(u, order, degree(), knots(), der);
-    return der;
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  template <int DerivativeOrder>
-  typename SplineTraits< Spline<_Scalar, _Dim, _Degree>, DerivativeOrder >::BasisDerivativeType
-    Spline<_Scalar, _Dim, _Degree>::basisFunctionDerivatives(Scalar u, DenseIndex order) const
-  {
-    typename SplineTraits< Spline<_Scalar, _Dim, _Degree>, DerivativeOrder >::BasisDerivativeType der;
-    BasisFunctionDerivativesImpl(u, order, degree(), knots(), der);
-    return der;
-  }
-
-  template <typename _Scalar, int _Dim, int _Degree>
-  typename SplineTraits<Spline<_Scalar, _Dim, _Degree> >::BasisDerivativeType
-  Spline<_Scalar, _Dim, _Degree>::BasisFunctionDerivatives(
-    const typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
-    const DenseIndex order,
-    const DenseIndex degree,
-    const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& knots)
-  {
-    typename SplineTraits<Spline>::BasisDerivativeType der;
-    BasisFunctionDerivativesImpl(u, order, degree, knots, der);
-    return der;
-  }
-}
-
-#endif // EIGEN_SPLINE_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h
deleted file mode 100644
index 9f6e8af..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h
+++ /dev/null
@@ -1,431 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPLINE_FITTING_H
-#define EIGEN_SPLINE_FITTING_H
-
-#include <algorithm>
-#include <functional>
-#include <numeric>
-#include <vector>
-
-#include "SplineFwd.h"
-
-#include "../../../../Eigen/LU"
-#include "../../../../Eigen/QR"
-
-namespace Eigen
-{
-  /**
-   * \brief Computes knot averages.
-   * \ingroup Splines_Module
-   *
-   * The knots are computed as
-   * \f{align*}
-   *  u_0 & = \hdots = u_p = 0 \\
-   *  u_{m-p} & = \hdots = u_{m} = 1 \\
-   *  u_{j+p} & = \frac{1}{p}\sum_{i=j}^{j+p-1}\bar{u}_i \quad\quad j=1,\hdots,n-p
-   * \f}
-   * where \f$p\f$ is the degree and \f$m+1\f$ the number knots
-   * of the desired interpolating spline.
-   *
-   * \param[in] parameters The input parameters. During interpolation one for each data point.
-   * \param[in] degree The spline degree which is used during the interpolation.
-   * \param[out] knots The output knot vector.
-   *
-   * \sa Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data
-   **/
-  template <typename KnotVectorType>
-  void KnotAveraging(const KnotVectorType& parameters, DenseIndex degree, KnotVectorType& knots)
-  {
-    knots.resize(parameters.size()+degree+1);      
-
-    for (DenseIndex j=1; j<parameters.size()-degree; ++j)
-      knots(j+degree) = parameters.segment(j,degree).mean();
-
-    knots.segment(0,degree+1) = KnotVectorType::Zero(degree+1);
-    knots.segment(knots.size()-degree-1,degree+1) = KnotVectorType::Ones(degree+1);
-  }
-
-  /**
-   * \brief Computes knot averages when derivative constraints are present.
-   * Note that this is a technical interpretation of the referenced article
-   * since the algorithm contained therein is incorrect as written.
-   * \ingroup Splines_Module
-   *
-   * \param[in] parameters The parameters at which the interpolation B-Spline
-   *            will intersect the given interpolation points. The parameters
-   *            are assumed to be a non-decreasing sequence.
-   * \param[in] degree The degree of the interpolating B-Spline. This must be
-   *            greater than zero.
-   * \param[in] derivativeIndices The indices corresponding to parameters at
-   *            which there are derivative constraints. The indices are assumed
-   *            to be a non-decreasing sequence.
-   * \param[out] knots The calculated knot vector. These will be returned as a
-   *             non-decreasing sequence
-   *
-   * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
-   * Curve interpolation with directional constraints for engineering design. 
-   * Engineering with Computers
-   **/
-  template <typename KnotVectorType, typename ParameterVectorType, typename IndexArray>
-  void KnotAveragingWithDerivatives(const ParameterVectorType& parameters,
-                                    const unsigned int degree,
-                                    const IndexArray& derivativeIndices,
-                                    KnotVectorType& knots)
-  {
-    typedef typename ParameterVectorType::Scalar Scalar;
-
-    DenseIndex numParameters = parameters.size();
-    DenseIndex numDerivatives = derivativeIndices.size();
-
-    if (numDerivatives < 1)
-    {
-      KnotAveraging(parameters, degree, knots);
-      return;
-    }
-
-    DenseIndex startIndex;
-    DenseIndex endIndex;
-  
-    DenseIndex numInternalDerivatives = numDerivatives;
-    
-    if (derivativeIndices[0] == 0)
-    {
-      startIndex = 0;
-      --numInternalDerivatives;
-    }
-    else
-    {
-      startIndex = 1;
-    }
-    if (derivativeIndices[numDerivatives - 1] == numParameters - 1)
-    {
-      endIndex = numParameters - degree;
-      --numInternalDerivatives;
-    }
-    else
-    {
-      endIndex = numParameters - degree - 1;
-    }
-
-    // There are (endIndex - startIndex + 1) knots obtained from the averaging
-    // and 2 for the first and last parameters.
-    DenseIndex numAverageKnots = endIndex - startIndex + 3;
-    KnotVectorType averageKnots(numAverageKnots);
-    averageKnots[0] = parameters[0];
-
-    int newKnotIndex = 0;
-    for (DenseIndex i = startIndex; i <= endIndex; ++i)
-      averageKnots[++newKnotIndex] = parameters.segment(i, degree).mean();
-    averageKnots[++newKnotIndex] = parameters[numParameters - 1];
-
-    newKnotIndex = -1;
-  
-    ParameterVectorType temporaryParameters(numParameters + 1);
-    KnotVectorType derivativeKnots(numInternalDerivatives);
-    for (DenseIndex i = 0; i < numAverageKnots - 1; ++i)
-    {
-      temporaryParameters[0] = averageKnots[i];
-      ParameterVectorType parameterIndices(numParameters);
-      int temporaryParameterIndex = 1;
-      for (DenseIndex j = 0; j < numParameters; ++j)
-      {
-        Scalar parameter = parameters[j];
-        if (parameter >= averageKnots[i] && parameter < averageKnots[i + 1])
-        {
-          parameterIndices[temporaryParameterIndex] = j;
-          temporaryParameters[temporaryParameterIndex++] = parameter;
-        }
-      }
-      temporaryParameters[temporaryParameterIndex] = averageKnots[i + 1];
-
-      for (int j = 0; j <= temporaryParameterIndex - 2; ++j)
-      {
-        for (DenseIndex k = 0; k < derivativeIndices.size(); ++k)
-        {
-          if (parameterIndices[j + 1] == derivativeIndices[k]
-              && parameterIndices[j + 1] != 0
-              && parameterIndices[j + 1] != numParameters - 1)
-          {
-            derivativeKnots[++newKnotIndex] = temporaryParameters.segment(j, 3).mean();
-            break;
-          }
-        }
-      }
-    }
-    
-    KnotVectorType temporaryKnots(averageKnots.size() + derivativeKnots.size());
-
-    std::merge(averageKnots.data(), averageKnots.data() + averageKnots.size(),
-               derivativeKnots.data(), derivativeKnots.data() + derivativeKnots.size(),
-               temporaryKnots.data());
-
-    // Number of knots (one for each point and derivative) plus spline order.
-    DenseIndex numKnots = numParameters + numDerivatives + degree + 1;
-    knots.resize(numKnots);
-
-    knots.head(degree).fill(temporaryKnots[0]);
-    knots.tail(degree).fill(temporaryKnots.template tail<1>()[0]);
-    knots.segment(degree, temporaryKnots.size()) = temporaryKnots;
-  }
-
-  /**
-   * \brief Computes chord length parameters which are required for spline interpolation.
-   * \ingroup Splines_Module
-   *
-   * \param[in] pts The data points to which a spline should be fit.
-   * \param[out] chord_lengths The resulting chord length vector.
-   *
-   * \sa Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data
-   **/   
-  template <typename PointArrayType, typename KnotVectorType>
-  void ChordLengths(const PointArrayType& pts, KnotVectorType& chord_lengths)
-  {
-    typedef typename KnotVectorType::Scalar Scalar;
-
-    const DenseIndex n = pts.cols();
-
-    // 1. compute the column-wise norms
-    chord_lengths.resize(pts.cols());
-    chord_lengths[0] = 0;
-    chord_lengths.rightCols(n-1) = (pts.array().leftCols(n-1) - pts.array().rightCols(n-1)).matrix().colwise().norm();
-
-    // 2. compute the partial sums
-    std::partial_sum(chord_lengths.data(), chord_lengths.data()+n, chord_lengths.data());
-
-    // 3. normalize the data
-    chord_lengths /= chord_lengths(n-1);
-    chord_lengths(n-1) = Scalar(1);
-  }
-
-  /**
-   * \brief Spline fitting methods.
-   * \ingroup Splines_Module
-   **/     
-  template <typename SplineType>
-  struct SplineFitting
-  {
-    typedef typename SplineType::KnotVectorType KnotVectorType;
-    typedef typename SplineType::ParameterVectorType ParameterVectorType;
-
-    /**
-     * \brief Fits an interpolating Spline to the given data points.
-     *
-     * \param pts The points for which an interpolating spline will be computed.
-     * \param degree The degree of the interpolating spline.
-     *
-     * \returns A spline interpolating the initially provided points.
-     **/
-    template <typename PointArrayType>
-    static SplineType Interpolate(const PointArrayType& pts, DenseIndex degree);
-
-    /**
-     * \brief Fits an interpolating Spline to the given data points.
-     *
-     * \param pts The points for which an interpolating spline will be computed.
-     * \param degree The degree of the interpolating spline.
-     * \param knot_parameters The knot parameters for the interpolation.
-     *
-     * \returns A spline interpolating the initially provided points.
-     **/
-    template <typename PointArrayType>
-    static SplineType Interpolate(const PointArrayType& pts, DenseIndex degree, const KnotVectorType& knot_parameters);
-
-    /**
-     * \brief Fits an interpolating spline to the given data points and
-     * derivatives.
-     * 
-     * \param points The points for which an interpolating spline will be computed.
-     * \param derivatives The desired derivatives of the interpolating spline at interpolation
-     *                    points.
-     * \param derivativeIndices An array indicating which point each derivative belongs to. This
-     *                          must be the same size as @a derivatives.
-     * \param degree The degree of the interpolating spline.
-     *
-     * \returns A spline interpolating @a points with @a derivatives at those points.
-     *
-     * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
-     * Curve interpolation with directional constraints for engineering design. 
-     * Engineering with Computers
-     **/
-    template <typename PointArrayType, typename IndexArray>
-    static SplineType InterpolateWithDerivatives(const PointArrayType& points,
-                                                 const PointArrayType& derivatives,
-                                                 const IndexArray& derivativeIndices,
-                                                 const unsigned int degree);
-
-    /**
-     * \brief Fits an interpolating spline to the given data points and derivatives.
-     * 
-     * \param points The points for which an interpolating spline will be computed.
-     * \param derivatives The desired derivatives of the interpolating spline at interpolation points.
-     * \param derivativeIndices An array indicating which point each derivative belongs to. This
-     *                          must be the same size as @a derivatives.
-     * \param degree The degree of the interpolating spline.
-     * \param parameters The parameters corresponding to the interpolation points.
-     *
-     * \returns A spline interpolating @a points with @a derivatives at those points.
-     *
-     * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
-     * Curve interpolation with directional constraints for engineering design. 
-     * Engineering with Computers
-     */
-    template <typename PointArrayType, typename IndexArray>
-    static SplineType InterpolateWithDerivatives(const PointArrayType& points,
-                                                 const PointArrayType& derivatives,
-                                                 const IndexArray& derivativeIndices,
-                                                 const unsigned int degree,
-                                                 const ParameterVectorType& parameters);
-  };
-
-  template <typename SplineType>
-  template <typename PointArrayType>
-  SplineType SplineFitting<SplineType>::Interpolate(const PointArrayType& pts, DenseIndex degree, const KnotVectorType& knot_parameters)
-  {
-    typedef typename SplineType::KnotVectorType::Scalar Scalar;      
-    typedef typename SplineType::ControlPointVectorType ControlPointVectorType;      
-
-    typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
-
-    KnotVectorType knots;
-    KnotAveraging(knot_parameters, degree, knots);
-
-    DenseIndex n = pts.cols();
-    MatrixType A = MatrixType::Zero(n,n);
-    for (DenseIndex i=1; i<n-1; ++i)
-    {
-      const DenseIndex span = SplineType::Span(knot_parameters[i], degree, knots);
-
-      // The segment call should somehow be told the spline order at compile time.
-      A.row(i).segment(span-degree, degree+1) = SplineType::BasisFunctions(knot_parameters[i], degree, knots);
-    }
-    A(0,0) = 1.0;
-    A(n-1,n-1) = 1.0;
-
-    HouseholderQR<MatrixType> qr(A);
-
-    // Here, we are creating a temporary due to an Eigen issue.
-    ControlPointVectorType ctrls = qr.solve(MatrixType(pts.transpose())).transpose();
-
-    return SplineType(knots, ctrls);
-  }
-
-  template <typename SplineType>
-  template <typename PointArrayType>
-  SplineType SplineFitting<SplineType>::Interpolate(const PointArrayType& pts, DenseIndex degree)
-  {
-    KnotVectorType chord_lengths; // knot parameters
-    ChordLengths(pts, chord_lengths);
-    return Interpolate(pts, degree, chord_lengths);
-  }
-  
-  template <typename SplineType>
-  template <typename PointArrayType, typename IndexArray>
-  SplineType 
-  SplineFitting<SplineType>::InterpolateWithDerivatives(const PointArrayType& points,
-                                                        const PointArrayType& derivatives,
-                                                        const IndexArray& derivativeIndices,
-                                                        const unsigned int degree,
-                                                        const ParameterVectorType& parameters)
-  {
-    typedef typename SplineType::KnotVectorType::Scalar Scalar;      
-    typedef typename SplineType::ControlPointVectorType ControlPointVectorType;
-
-    typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
-
-    const DenseIndex n = points.cols() + derivatives.cols();
-    
-    KnotVectorType knots;
-
-    KnotAveragingWithDerivatives(parameters, degree, derivativeIndices, knots);
-    
-    // fill matrix
-    MatrixType A = MatrixType::Zero(n, n);
-
-    // Use these dimensions for quicker populating, then transpose for solving.
-    MatrixType b(points.rows(), n);
-
-    DenseIndex startRow;
-    DenseIndex derivativeStart;
-
-    // End derivatives.
-    if (derivativeIndices[0] == 0)
-    {
-      A.template block<1, 2>(1, 0) << -1, 1;
-      
-      Scalar y = (knots(degree + 1) - knots(0)) / degree;
-      b.col(1) = y*derivatives.col(0);
-      
-      startRow = 2;
-      derivativeStart = 1;
-    }
-    else
-    {
-      startRow = 1;
-      derivativeStart = 0;
-    }
-    if (derivativeIndices[derivatives.cols() - 1] == points.cols() - 1)
-    {
-      A.template block<1, 2>(n - 2, n - 2) << -1, 1;
-
-      Scalar y = (knots(knots.size() - 1) - knots(knots.size() - (degree + 2))) / degree;
-      b.col(b.cols() - 2) = y*derivatives.col(derivatives.cols() - 1);
-    }
-    
-    DenseIndex row = startRow;
-    DenseIndex derivativeIndex = derivativeStart;
-    for (DenseIndex i = 1; i < parameters.size() - 1; ++i)
-    {
-      const DenseIndex span = SplineType::Span(parameters[i], degree, knots);
-
-      if (derivativeIndex < derivativeIndices.size() && derivativeIndices[derivativeIndex] == i)
-      {
-        A.block(row, span - degree, 2, degree + 1)
-          = SplineType::BasisFunctionDerivatives(parameters[i], 1, degree, knots);
-
-        b.col(row++) = points.col(i);
-        b.col(row++) = derivatives.col(derivativeIndex++);
-      }
-      else
-      {
-        A.row(row).segment(span - degree, degree + 1)
-          = SplineType::BasisFunctions(parameters[i], degree, knots);
-        b.col(row++) = points.col(i);
-      }
-    }
-    b.col(0) = points.col(0);
-    b.col(b.cols() - 1) = points.col(points.cols() - 1);
-    A(0,0) = 1;
-    A(n - 1, n - 1) = 1;
-    
-    // Solve
-    FullPivLU<MatrixType> lu(A);
-    ControlPointVectorType controlPoints = lu.solve(MatrixType(b.transpose())).transpose();
-
-    SplineType spline(knots, controlPoints);
-    
-    return spline;
-  }
-  
-  template <typename SplineType>
-  template <typename PointArrayType, typename IndexArray>
-  SplineType
-  SplineFitting<SplineType>::InterpolateWithDerivatives(const PointArrayType& points,
-                                                        const PointArrayType& derivatives,
-                                                        const IndexArray& derivativeIndices,
-                                                        const unsigned int degree)
-  {
-    ParameterVectorType parameters;
-    ChordLengths(points, parameters);
-    return InterpolateWithDerivatives(points, derivatives, derivativeIndices, degree, parameters);
-  }
-}
-
-#endif // EIGEN_SPLINE_FITTING_H
diff --git a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h b/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h
deleted file mode 100644
index 00d6b49..0000000
--- a/3rdParty/my_ceres_vc17/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h
+++ /dev/null
@@ -1,93 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_SPLINES_FWD_H
-#define EIGEN_SPLINES_FWD_H
-
-#include "../../../../Eigen/Core"
-
-namespace Eigen
-{
-    template <typename Scalar, int Dim, int Degree = Dynamic> class Spline;
-
-    template < typename SplineType, int DerivativeOrder = Dynamic > struct SplineTraits {};
-
-    /**
-     * \ingroup Splines_Module
-     * \brief Compile-time attributes of the Spline class for Dynamic degree.
-     **/
-    template <typename _Scalar, int _Dim, int _Degree>
-    struct SplineTraits< Spline<_Scalar, _Dim, _Degree>, Dynamic >
-    {
-      typedef _Scalar Scalar; /*!< The spline curve's scalar type. */
-      enum { Dimension = _Dim /*!< The spline curve's dimension. */ };
-      enum { Degree = _Degree /*!< The spline curve's degree. */ };
-
-      enum { OrderAtCompileTime = _Degree==Dynamic ? Dynamic : _Degree+1 /*!< The spline curve's order at compile-time. */ };
-      enum { NumOfDerivativesAtCompileTime = OrderAtCompileTime /*!< The number of derivatives defined for the current spline. */ };
-      
-      enum { DerivativeMemoryLayout = Dimension==1 ? RowMajor : ColMajor /*!< The derivative type's memory layout. */ };
-
-      /** \brief The data type used to store non-zero basis functions. */
-      typedef Array<Scalar,1,OrderAtCompileTime> BasisVectorType;
-
-      /** \brief The data type used to store the values of the basis function derivatives. */
-      typedef Array<Scalar,Dynamic,Dynamic,RowMajor,NumOfDerivativesAtCompileTime,OrderAtCompileTime> BasisDerivativeType;
-      
-      /** \brief The data type used to store the spline's derivative values. */
-      typedef Array<Scalar,Dimension,Dynamic,DerivativeMemoryLayout,Dimension,NumOfDerivativesAtCompileTime> DerivativeType;
-
-      /** \brief The point type the spline is representing. */
-      typedef Array<Scalar,Dimension,1> PointType;
-      
-      /** \brief The data type used to store knot vectors. */
-      typedef Array<Scalar,1,Dynamic> KnotVectorType;
-
-      /** \brief The data type used to store parameter vectors. */
-      typedef Array<Scalar,1,Dynamic> ParameterVectorType;
-      
-      /** \brief The data type representing the spline's control points. */
-      typedef Array<Scalar,Dimension,Dynamic> ControlPointVectorType;
-    };
-
-    /**
-     * \ingroup Splines_Module
-     * \brief Compile-time attributes of the Spline class for fixed degree.
-     *
-     * The traits class inherits all attributes from the SplineTraits of Dynamic degree.
-     **/
-    template < typename _Scalar, int _Dim, int _Degree, int _DerivativeOrder >
-    struct SplineTraits< Spline<_Scalar, _Dim, _Degree>, _DerivativeOrder > : public SplineTraits< Spline<_Scalar, _Dim, _Degree> >
-    {
-      enum { OrderAtCompileTime = _Degree==Dynamic ? Dynamic : _Degree+1 /*!< The spline curve's order at compile-time. */ };
-      enum { NumOfDerivativesAtCompileTime = _DerivativeOrder==Dynamic ? Dynamic : _DerivativeOrder+1 /*!< The number of derivatives defined for the current spline. */ };
-      
-      enum { DerivativeMemoryLayout = _Dim==1 ? RowMajor : ColMajor /*!< The derivative type's memory layout. */ };
-
-      /** \brief The data type used to store the values of the basis function derivatives. */
-      typedef Array<_Scalar,Dynamic,Dynamic,RowMajor,NumOfDerivativesAtCompileTime,OrderAtCompileTime> BasisDerivativeType;
-      
-      /** \brief The data type used to store the spline's derivative values. */      
-      typedef Array<_Scalar,_Dim,Dynamic,DerivativeMemoryLayout,_Dim,NumOfDerivativesAtCompileTime> DerivativeType;
-    };
-
-    /** \brief 2D float B-spline with dynamic degree. */
-    typedef Spline<float,2> Spline2f;
-    
-    /** \brief 3D float B-spline with dynamic degree. */
-    typedef Spline<float,3> Spline3f;
-
-    /** \brief 2D double B-spline with dynamic degree. */
-    typedef Spline<double,2> Spline2d;
-    
-    /** \brief 3D double B-spline with dynamic degree. */
-    typedef Spline<double,3> Spline3d;
-}
-
-#endif // EIGEN_SPLINES_FWD_H
diff --git a/3rdParty/my_ceres_vc17/include/gflags/gflags.h b/3rdParty/my_ceres_vc17/include/gflags/gflags.h
deleted file mode 100644
index 33d8cad..0000000
--- a/3rdParty/my_ceres_vc17/include/gflags/gflags.h
+++ /dev/null
@@ -1,627 +0,0 @@
-// Copyright (c) 2006, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-// ---
-// Revamped and reorganized by Craig Silverstein
-//
-// This is the file that should be included by any file which declares
-// or defines a command line flag or wants to parse command line flags
-// or print a program usage message (which will include information about
-// flags).  Executive summary, in the form of an example foo.cc file:
-//
-//    #include "foo.h"         // foo.h has a line "DECLARE_int32(start);"
-//    #include "validators.h"  // hypothetical file defining ValidateIsFile()
-//
-//    DEFINE_int32(end, 1000, "The last record to read");
-//
-//    DEFINE_string(filename, "my_file.txt", "The file to read");
-//    // Crash if the specified file does not exist.
-//    static bool dummy = RegisterFlagValidator(&FLAGS_filename,
-//                                              &ValidateIsFile);
-//
-//    DECLARE_bool(verbose); // some other file has a DEFINE_bool(verbose, ...)
-//
-//    void MyFunc() {
-//      if (FLAGS_verbose) printf("Records %d-%d\n", FLAGS_start, FLAGS_end);
-//    }
-//
-//    Then, at the command-line:
-//       ./foo --noverbose --start=5 --end=100
-//
-// For more details, see
-//    doc/gflags.html
-//
-// --- A note about thread-safety:
-//
-// We describe many functions in this routine as being thread-hostile,
-// thread-compatible, or thread-safe.  Here are the meanings we use:
-//
-// thread-safe: it is safe for multiple threads to call this routine
-//   (or, when referring to a class, methods of this class)
-//   concurrently.
-// thread-hostile: it is not safe for multiple threads to call this
-//   routine (or methods of this class) concurrently.  In gflags,
-//   most thread-hostile routines are intended to be called early in,
-//   or even before, main() -- that is, before threads are spawned.
-// thread-compatible: it is safe for multiple threads to read from
-//   this variable (when applied to variables), or to call const
-//   methods of this class (when applied to classes), as long as no
-//   other thread is writing to the variable or calling non-const
-//   methods of this class.
-
-#ifndef GFLAGS_GFLAGS_H_
-#define GFLAGS_GFLAGS_H_
-
-#include <string>
-#include <vector>
-
-#include "gflags/gflags_declare.h" // IWYU pragma: export
-
-
-// We always want to export variables defined in user code
-#ifndef GFLAGS_DLL_DEFINE_FLAG
-#  if GFLAGS_IS_A_DLL && defined(_MSC_VER)
-#    define GFLAGS_DLL_DEFINE_FLAG __declspec(dllexport)
-#  else
-#    define GFLAGS_DLL_DEFINE_FLAG
-#  endif
-#endif
-
-
-namespace GFLAGS_NAMESPACE {
-
-
-// --------------------------------------------------------------------
-// To actually define a flag in a file, use DEFINE_bool,
-// DEFINE_string, etc. at the bottom of this file.  You may also find
-// it useful to register a validator with the flag.  This ensures that
-// when the flag is parsed from the commandline, or is later set via
-// SetCommandLineOption, we call the validation function. It is _not_
-// called when you assign the value to the flag directly using the = operator.
-//
-// The validation function should return true if the flag value is valid, and
-// false otherwise. If the function returns false for the new setting of the
-// flag, the flag will retain its current value. If it returns false for the
-// default value, ParseCommandLineFlags() will die.
-//
-// This function is safe to call at global construct time (as in the
-// example below).
-//
-// Example use:
-//    static bool ValidatePort(const char* flagname, int32 value) {
-//       if (value > 0 && value < 32768)   // value is ok
-//         return true;
-//       printf("Invalid value for --%s: %d\n", flagname, (int)value);
-//       return false;
-//    }
-//    DEFINE_int32(port, 0, "What port to listen on");
-//    static bool dummy = RegisterFlagValidator(&FLAGS_port, &ValidatePort);
-
-// Returns true if successfully registered, false if not (because the
-// first argument doesn't point to a command-line flag, or because a
-// validator is already registered for this flag).
-extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const bool*        flag, bool (*validate_fn)(const char*, bool));
-extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const int32*       flag, bool (*validate_fn)(const char*, int32));
-extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const uint32*      flag, bool (*validate_fn)(const char*, uint32));
-extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const int64*       flag, bool (*validate_fn)(const char*, int64));
-extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const uint64*      flag, bool (*validate_fn)(const char*, uint64));
-extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const double*      flag, bool (*validate_fn)(const char*, double));
-extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const std::string* flag, bool (*validate_fn)(const char*, const std::string&));
-
-// Convenience macro for the registration of a flag validator
-#define DEFINE_validator(name, validator) \
-    static const bool name##_validator_registered = \
-            GFLAGS_NAMESPACE::RegisterFlagValidator(&FLAGS_##name, validator)
-
-
-// --------------------------------------------------------------------
-// These methods are the best way to get access to info about the
-// list of commandline flags.  Note that these routines are pretty slow.
-//   GetAllFlags: mostly-complete info about the list, sorted by file.
-//   ShowUsageWithFlags: pretty-prints the list to stdout (what --help does)
-//   ShowUsageWithFlagsRestrict: limit to filenames with restrict as a substr
-//
-// In addition to accessing flags, you can also access argv[0] (the program
-// name) and argv (the entire commandline), which we sock away a copy of.
-// These variables are static, so you should only set them once.
-//
-// No need to export this data only structure from DLL, avoiding VS warning 4251.
-struct CommandLineFlagInfo {
-  std::string name;            // the name of the flag
-  std::string type;            // the type of the flag: int32, etc
-  std::string description;     // the "help text" associated with the flag
-  std::string current_value;   // the current value, as a string
-  std::string default_value;   // the default value, as a string
-  std::string filename;        // 'cleaned' version of filename holding the flag
-  bool has_validator_fn;       // true if RegisterFlagValidator called on this flag
-  bool is_default;             // true if the flag has the default value and
-                               // has not been set explicitly from the cmdline
-                               // or via SetCommandLineOption
-  const void* flag_ptr;        // pointer to the flag's current value (i.e. FLAGS_foo)
-};
-
-// Using this inside of a validator is a recipe for a deadlock.
-// TODO(user) Fix locking when validators are running, to make it safe to
-// call validators during ParseAllFlags.
-// Also make sure then to uncomment the corresponding unit test in
-// gflags_unittest.sh
-extern GFLAGS_DLL_DECL void GetAllFlags(std::vector<CommandLineFlagInfo>* OUTPUT);
-// These two are actually defined in gflags_reporting.cc.
-extern GFLAGS_DLL_DECL void ShowUsageWithFlags(const char *argv0);  // what --help does
-extern GFLAGS_DLL_DECL void ShowUsageWithFlagsRestrict(const char *argv0, const char *restrict);
-
-// Create a descriptive string for a flag.
-// Goes to some trouble to make pretty line breaks.
-extern GFLAGS_DLL_DECL std::string DescribeOneFlag(const CommandLineFlagInfo& flag);
-
-// Thread-hostile; meant to be called before any threads are spawned.
-extern GFLAGS_DLL_DECL void SetArgv(int argc, const char** argv);
-
-// The following functions are thread-safe as long as SetArgv() is
-// only called before any threads start.
-extern GFLAGS_DLL_DECL const std::vector<std::string>& GetArgvs();
-extern GFLAGS_DLL_DECL const char* GetArgv();                      // all of argv as a string
-extern GFLAGS_DLL_DECL const char* GetArgv0();                     // only argv0
-extern GFLAGS_DLL_DECL uint32 GetArgvSum();                        // simple checksum of argv
-extern GFLAGS_DLL_DECL const char* ProgramInvocationName();        // argv0, or "UNKNOWN" if not set
-extern GFLAGS_DLL_DECL const char* ProgramInvocationShortName();   // basename(argv0)
-
-// ProgramUsage() is thread-safe as long as SetUsageMessage() is only
-// called before any threads start.
-extern GFLAGS_DLL_DECL const char* ProgramUsage();                 // string set by SetUsageMessage()
-
-// VersionString() is thread-safe as long as SetVersionString() is only
-// called before any threads start.
-extern GFLAGS_DLL_DECL const char* VersionString();                // string set by SetVersionString()
-
-
-
-// --------------------------------------------------------------------
-// Normally you access commandline flags by just saying "if (FLAGS_foo)"
-// or whatever, and set them by calling "FLAGS_foo = bar" (or, more
-// commonly, via the DEFINE_foo macro).  But if you need a bit more
-// control, we have programmatic ways to get/set the flags as well.
-// These programmatic ways to access flags are thread-safe, but direct
-// access is only thread-compatible.
-
-// Return true iff the flagname was found.
-// OUTPUT is set to the flag's value, or unchanged if we return false.
-extern GFLAGS_DLL_DECL bool GetCommandLineOption(const char* name, std::string* OUTPUT);
-
-// Return true iff the flagname was found. OUTPUT is set to the flag's
-// CommandLineFlagInfo or unchanged if we return false.
-extern GFLAGS_DLL_DECL bool GetCommandLineFlagInfo(const char* name, CommandLineFlagInfo* OUTPUT);
-
-// Return the CommandLineFlagInfo of the flagname.  exit() if name not found.
-// Example usage, to check if a flag's value is currently the default value:
-//   if (GetCommandLineFlagInfoOrDie("foo").is_default) ...
-extern GFLAGS_DLL_DECL CommandLineFlagInfo GetCommandLineFlagInfoOrDie(const char* name);
-
-enum GFLAGS_DLL_DECL FlagSettingMode {
-  // update the flag's value (can call this multiple times).
-  SET_FLAGS_VALUE,
-  // update the flag's value, but *only if* it has not yet been updated
-  // with SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef".
-  SET_FLAG_IF_DEFAULT,
-  // set the flag's default value to this.  If the flag has not yet updated
-  // yet (via SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef")
-  // change the flag's current value to the new default value as well.
-  SET_FLAGS_DEFAULT
-};
-
-// Set a particular flag ("command line option").  Returns a string
-// describing the new value that the option has been set to.  The
-// return value API is not well-specified, so basically just depend on
-// it to be empty if the setting failed for some reason -- the name is
-// not a valid flag name, or the value is not a valid value -- and
-// non-empty else.
-
-// SetCommandLineOption uses set_mode == SET_FLAGS_VALUE (the common case)
-extern GFLAGS_DLL_DECL std::string SetCommandLineOption        (const char* name, const char* value);
-extern GFLAGS_DLL_DECL std::string SetCommandLineOptionWithMode(const char* name, const char* value, FlagSettingMode set_mode);
-
-
-// --------------------------------------------------------------------
-// Saves the states (value, default value, whether the user has set
-// the flag, registered validators, etc) of all flags, and restores
-// them when the FlagSaver is destroyed.  This is very useful in
-// tests, say, when you want to let your tests change the flags, but
-// make sure that they get reverted to the original states when your
-// test is complete.
-//
-// Example usage:
-//   void TestFoo() {
-//     FlagSaver s1;
-//     FLAG_foo = false;
-//     FLAG_bar = "some value";
-//
-//     // test happens here.  You can return at any time
-//     // without worrying about restoring the FLAG values.
-//   }
-//
-// Note: This class is marked with GFLAGS_ATTRIBUTE_UNUSED because all
-// the work is done in the constructor and destructor, so in the standard
-// usage example above, the compiler would complain that it's an
-// unused variable.
-//
-// This class is thread-safe.  However, its destructor writes to
-// exactly the set of flags that have changed value during its
-// lifetime, so concurrent _direct_ access to those flags
-// (i.e. FLAGS_foo instead of {Get,Set}CommandLineOption()) is unsafe.
-
-class GFLAGS_DLL_DECL FlagSaver {
- public:
-  FlagSaver();
-  ~FlagSaver();
-
- private:
-  class FlagSaverImpl* impl_;   // we use pimpl here to keep API steady
-
-  FlagSaver(const FlagSaver&);  // no copying!
-  void operator=(const FlagSaver&);
-};
-
-// --------------------------------------------------------------------
-// Some deprecated or hopefully-soon-to-be-deprecated functions.
-
-// This is often used for logging.  TODO(csilvers): figure out a better way
-extern GFLAGS_DLL_DECL std::string CommandlineFlagsIntoString();
-// Usually where this is used, a FlagSaver should be used instead.
-extern GFLAGS_DLL_DECL
-bool ReadFlagsFromString(const std::string& flagfilecontents,
-                         const char* prog_name,
-                         bool errors_are_fatal);  // uses SET_FLAGS_VALUE
-
-// These let you manually implement --flagfile functionality.
-// DEPRECATED.
-extern GFLAGS_DLL_DECL bool AppendFlagsIntoFile(const std::string& filename, const char* prog_name);
-extern GFLAGS_DLL_DECL bool ReadFromFlagsFile(const std::string& filename, const char* prog_name, bool errors_are_fatal);   // uses SET_FLAGS_VALUE
-
-
-// --------------------------------------------------------------------
-// Useful routines for initializing flags from the environment.
-// In each case, if 'varname' does not exist in the environment
-// return defval.  If 'varname' does exist but is not valid
-// (e.g., not a number for an int32 flag), abort with an error.
-// Otherwise, return the value.  NOTE: for booleans, for true use
-// 't' or 'T' or 'true' or '1', for false 'f' or 'F' or 'false' or '0'.
-
-extern GFLAGS_DLL_DECL bool BoolFromEnv(const char *varname, bool defval);
-extern GFLAGS_DLL_DECL int32 Int32FromEnv(const char *varname, int32 defval);
-extern GFLAGS_DLL_DECL uint32 Uint32FromEnv(const char *varname, uint32 defval);
-extern GFLAGS_DLL_DECL int64 Int64FromEnv(const char *varname, int64 defval);
-extern GFLAGS_DLL_DECL uint64 Uint64FromEnv(const char *varname, uint64 defval);
-extern GFLAGS_DLL_DECL double DoubleFromEnv(const char *varname, double defval);
-extern GFLAGS_DLL_DECL const char *StringFromEnv(const char *varname, const char *defval);
-
-
-// --------------------------------------------------------------------
-// The next two functions parse gflags from main():
-
-// Set the "usage" message for this program.  For example:
-//   string usage("This program does nothing.  Sample usage:\n");
-//   usage += argv[0] + " <uselessarg1> <uselessarg2>";
-//   SetUsageMessage(usage);
-// Do not include commandline flags in the usage: we do that for you!
-// Thread-hostile; meant to be called before any threads are spawned.
-extern GFLAGS_DLL_DECL void SetUsageMessage(const std::string& usage);
-
-// Sets the version string, which is emitted with --version.
-// For instance: SetVersionString("1.3");
-// Thread-hostile; meant to be called before any threads are spawned.
-extern GFLAGS_DLL_DECL void SetVersionString(const std::string& version);
-
-
-// Looks for flags in argv and parses them.  Rearranges argv to put
-// flags first, or removes them entirely if remove_flags is true.
-// If a flag is defined more than once in the command line or flag
-// file, the last definition is used.  Returns the index (into argv)
-// of the first non-flag argument.
-// See top-of-file for more details on this function.
-#ifndef SWIG   // In swig, use ParseCommandLineFlagsScript() instead.
-extern GFLAGS_DLL_DECL uint32 ParseCommandLineFlags(int *argc, char*** argv, bool remove_flags);
-#endif
-
-
-// Calls to ParseCommandLineNonHelpFlags and then to
-// HandleCommandLineHelpFlags can be used instead of a call to
-// ParseCommandLineFlags during initialization, in order to allow for
-// changing default values for some FLAGS (via
-// e.g. SetCommandLineOptionWithMode calls) between the time of
-// command line parsing and the time of dumping help information for
-// the flags as a result of command line parsing.  If a flag is
-// defined more than once in the command line or flag file, the last
-// definition is used.  Returns the index (into argv) of the first
-// non-flag argument.  (If remove_flags is true, will always return 1.)
-extern GFLAGS_DLL_DECL uint32 ParseCommandLineNonHelpFlags(int *argc, char*** argv, bool remove_flags);
-
-// This is actually defined in gflags_reporting.cc.
-// This function is misnamed (it also handles --version, etc.), but
-// it's too late to change that now. :-(
-extern GFLAGS_DLL_DECL void HandleCommandLineHelpFlags();   // in gflags_reporting.cc
-
-// Allow command line reparsing.  Disables the error normally
-// generated when an unknown flag is found, since it may be found in a
-// later parse.  Thread-hostile; meant to be called before any threads
-// are spawned.
-extern GFLAGS_DLL_DECL void AllowCommandLineReparsing();
-
-// Reparse the flags that have not yet been recognized.  Only flags
-// registered since the last parse will be recognized.  Any flag value
-// must be provided as part of the argument using "=", not as a
-// separate command line argument that follows the flag argument.
-// Intended for handling flags from dynamically loaded libraries,
-// since their flags are not registered until they are loaded.
-extern GFLAGS_DLL_DECL void ReparseCommandLineNonHelpFlags();
-
-// Clean up memory allocated by flags.  This is only needed to reduce
-// the quantity of "potentially leaked" reports emitted by memory
-// debugging tools such as valgrind.  It is not required for normal
-// operation, or for the google perftools heap-checker.  It must only
-// be called when the process is about to exit, and all threads that
-// might access flags are quiescent.  Referencing flags after this is
-// called will have unexpected consequences.  This is not safe to run
-// when multiple threads might be running: the function is
-// thread-hostile.
-extern GFLAGS_DLL_DECL void ShutDownCommandLineFlags();
-
-
-// --------------------------------------------------------------------
-// Now come the command line flag declaration/definition macros that
-// will actually be used.  They're kind of hairy.  A major reason
-// for this is initialization: we want people to be able to access
-// variables in global constructors and have that not crash, even if
-// their global constructor runs before the global constructor here.
-// (Obviously, we can't guarantee the flags will have the correct
-// default value in that case, but at least accessing them is safe.)
-// The only way to do that is have flags point to a static buffer.
-// So we make one, using a union to ensure proper alignment, and
-// then use placement-new to actually set up the flag with the
-// correct default value.  In the same vein, we have to worry about
-// flag access in global destructors, so FlagRegisterer has to be
-// careful never to destroy the flag-values it constructs.
-//
-// Note that when we define a flag variable FLAGS_<name>, we also
-// preemptively define a junk variable, FLAGS_no<name>.  This is to
-// cause a link-time error if someone tries to define 2 flags with
-// names like "logging" and "nologging".  We do this because a bool
-// flag FLAG can be set from the command line to true with a "-FLAG"
-// argument, and to false with a "-noFLAG" argument, and so this can
-// potentially avert confusion.
-//
-// We also put flags into their own namespace.  It is purposefully
-// named in an opaque way that people should have trouble typing
-// directly.  The idea is that DEFINE puts the flag in the weird
-// namespace, and DECLARE imports the flag from there into the current
-// namespace.  The net result is to force people to use DECLARE to get
-// access to a flag, rather than saying "extern GFLAGS_DLL_DECL bool FLAGS_whatever;"
-// or some such instead.  We want this so we can put extra
-// functionality (like sanity-checking) in DECLARE if we want, and
-// make sure it is picked up everywhere.
-//
-// We also put the type of the variable in the namespace, so that
-// people can't DECLARE_int32 something that they DEFINE_bool'd
-// elsewhere.
-
-class GFLAGS_DLL_DECL FlagRegisterer {
- public:
-  // We instantiate this template ctor for all supported types,
-  // so it is possible to place implementation of the FlagRegisterer ctor in
-  // .cc file.
-  // Calling this constructor with unsupported type will produce linker error.
-  template <typename FlagType>
-  FlagRegisterer(const char* name,
-                 const char* help, const char* filename,
-                 FlagType* current_storage, FlagType* defvalue_storage);
-};
-
-// Force compiler to not generate code for the given template specialization.
-#if defined(_MSC_VER) && _MSC_VER < 1800 // Visual Studio 2013 version 12.0
-  #define GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(type)
-#else
-  #define GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(type)                  \
-    extern template GFLAGS_DLL_DECL FlagRegisterer::FlagRegisterer(  \
-        const char* name, const char* help, const char* filename,    \
-        type* current_storage, type* defvalue_storage)
-#endif
-
-// Do this for all supported flag types.
-GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(bool);
-GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(int32);
-GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(uint32);
-GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(int64);
-GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(uint64);
-GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(double);
-GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(std::string);
-
-#undef GFLAGS_DECLARE_FLAG_REGISTERER_CTOR
-
-// If your application #defines STRIP_FLAG_HELP to a non-zero value
-// before #including this file, we remove the help message from the
-// binary file. This can reduce the size of the resulting binary
-// somewhat, and may also be useful for security reasons.
-
-extern GFLAGS_DLL_DECL const char kStrippedFlagHelp[];
-
-
-} // namespace GFLAGS_NAMESPACE
-
-
-#ifndef SWIG  // In swig, ignore the main flag declarations
-
-#if defined(STRIP_FLAG_HELP) && STRIP_FLAG_HELP > 0
-// Need this construct to avoid the 'defined but not used' warning.
-#define MAYBE_STRIPPED_HELP(txt) \
-   (false ? (txt) : GFLAGS_NAMESPACE::kStrippedFlagHelp)
-#else
-#define MAYBE_STRIPPED_HELP(txt) txt
-#endif
-
-// Each command-line flag has two variables associated with it: one
-// with the current value, and one with the default value.  However,
-// we have a third variable, which is where value is assigned; it's a
-// constant.  This guarantees that FLAG_##value is initialized at
-// static initialization time (e.g. before program-start) rather than
-// than global construction time (which is after program-start but
-// before main), at least when 'value' is a compile-time constant.  We
-// use a small trick for the "default value" variable, and call it
-// FLAGS_no<name>.  This serves the second purpose of assuring a
-// compile error if someone tries to define a flag named no<name>
-// which is illegal (--foo and --nofoo both affect the "foo" flag).
-#define DEFINE_VARIABLE(type, shorttype, name, value, help)             \
-  namespace fL##shorttype {                                             \
-    static const type FLAGS_nono##name = value;                         \
-    /* We always want to export defined variables, dll or no */         \
-    GFLAGS_DLL_DEFINE_FLAG type FLAGS_##name = FLAGS_nono##name;        \
-    static type FLAGS_no##name = FLAGS_nono##name;                      \
-    static GFLAGS_NAMESPACE::FlagRegisterer o_##name(                   \
-      #name, MAYBE_STRIPPED_HELP(help), __FILE__,                       \
-      &FLAGS_##name, &FLAGS_no##name);                                  \
-  }                                                                     \
-  using fL##shorttype::FLAGS_##name
-
-// For DEFINE_bool, we want to do the extra check that the passed-in
-// value is actually a bool, and not a string or something that can be
-// coerced to a bool.  These declarations (no definition needed!) will
-// help us do that, and never evaluate From, which is important.
-// We'll use 'sizeof(IsBool(val))' to distinguish. This code requires
-// that the compiler have different sizes for bool & double. Since
-// this is not guaranteed by the standard, we check it with a
-// COMPILE_ASSERT.
-namespace fLB {
-struct CompileAssert {};
-typedef CompileAssert expected_sizeof_double_neq_sizeof_bool[
-                      (sizeof(double) != sizeof(bool)) ? 1 : -1];
-template<typename From> double GFLAGS_DLL_DECL IsBoolFlag(const From& from);
-GFLAGS_DLL_DECL bool IsBoolFlag(bool from);
-}  // namespace fLB
-
-// Here are the actual DEFINE_*-macros. The respective DECLARE_*-macros
-// are in a separate include, gflags_declare.h, for reducing
-// the physical transitive size for DECLARE use.
-#define DEFINE_bool(name, val, txt)                                     \
-  namespace fLB {                                                       \
-    typedef ::fLB::CompileAssert FLAG_##name##_value_is_not_a_bool[     \
-            (sizeof(::fLB::IsBoolFlag(val)) != sizeof(double))? 1: -1]; \
-  }                                                                     \
-  DEFINE_VARIABLE(bool, B, name, val, txt)
-
-#define DEFINE_int32(name, val, txt) \
-   DEFINE_VARIABLE(GFLAGS_NAMESPACE::int32, I, \
-                   name, val, txt)
-
-#define DEFINE_uint32(name,val, txt) \
-   DEFINE_VARIABLE(GFLAGS_NAMESPACE::uint32, U, \
-                   name, val, txt)
-
-#define DEFINE_int64(name, val, txt) \
-   DEFINE_VARIABLE(GFLAGS_NAMESPACE::int64, I64, \
-                   name, val, txt)
-
-#define DEFINE_uint64(name,val, txt) \
-   DEFINE_VARIABLE(GFLAGS_NAMESPACE::uint64, U64, \
-                   name, val, txt)
-
-#define DEFINE_double(name, val, txt) \
-   DEFINE_VARIABLE(double, D, name, val, txt)
-
-// Strings are trickier, because they're not a POD, so we can't
-// construct them at static-initialization time (instead they get
-// constructed at global-constructor time, which is much later).  To
-// try to avoid crashes in that case, we use a char buffer to store
-// the string, which we can static-initialize, and then placement-new
-// into it later.  It's not perfect, but the best we can do.
-
-namespace fLS {
-
-inline clstring* dont_pass0toDEFINE_string(char *stringspot,
-                                           const char *value) {
-  return new(stringspot) clstring(value);
-}
-inline clstring* dont_pass0toDEFINE_string(char *stringspot,
-                                           const clstring &value) {
-  return new(stringspot) clstring(value);
-}
-inline clstring* dont_pass0toDEFINE_string(char *stringspot,
-                                           int value);
-
-// Auxiliary class used to explicitly call destructor of string objects
-// allocated using placement new during static program deinitialization.
-// The destructor MUST be an inline function such that the explicit
-// destruction occurs in the same compilation unit as the placement new.
-class StringFlagDestructor {
-  void *current_storage_;
-  void *defvalue_storage_;
-
-public: 
-
-  StringFlagDestructor(void *current, void *defvalue)
-  : current_storage_(current), defvalue_storage_(defvalue) {}
-
-  ~StringFlagDestructor() {
-    reinterpret_cast<clstring*>(current_storage_ )->~clstring();
-    reinterpret_cast<clstring*>(defvalue_storage_)->~clstring();
-  }
-};
-
-}  // namespace fLS
-
-// We need to define a var named FLAGS_no##name so people don't define
-// --string and --nostring.  And we need a temporary place to put val
-// so we don't have to evaluate it twice.  Two great needs that go
-// great together!
-// The weird 'using' + 'extern' inside the fLS namespace is to work around
-// an unknown compiler bug/issue with the gcc 4.2.1 on SUSE 10.  See
-//    http://code.google.com/p/google-gflags/issues/detail?id=20
-#define DEFINE_string(name, val, txt)                                       \
-  namespace fLS {                                                           \
-    using ::fLS::clstring;                                                  \
-    using ::fLS::StringFlagDestructor;                                      \
-    static union { void* align; char s[sizeof(clstring)]; } s_##name[2];    \
-    clstring* const FLAGS_no##name = ::fLS::                                \
-                                   dont_pass0toDEFINE_string(s_##name[0].s, \
-                                                             val);          \
-    static GFLAGS_NAMESPACE::FlagRegisterer o_##name(                       \
-        #name, MAYBE_STRIPPED_HELP(txt), __FILE__,                          \
-        FLAGS_no##name, new (s_##name[1].s) clstring(*FLAGS_no##name));     \
-    static StringFlagDestructor d_##name(s_##name[0].s, s_##name[1].s);     \
-    extern GFLAGS_DLL_DEFINE_FLAG clstring& FLAGS_##name;                   \
-    using fLS::FLAGS_##name;                                                \
-    clstring& FLAGS_##name = *FLAGS_no##name;                               \
-  }                                                                         \
-  using fLS::FLAGS_##name
-
-#endif  // SWIG
-
-
-// Import gflags library symbols into alternative/deprecated namespace(s)
-#include "gflags_gflags.h"
-
-
-#endif  // GFLAGS_GFLAGS_H_
diff --git a/3rdParty/my_ceres_vc17/include/gflags/gflags_completions.h b/3rdParty/my_ceres_vc17/include/gflags/gflags_completions.h
deleted file mode 100644
index f951c1e..0000000
--- a/3rdParty/my_ceres_vc17/include/gflags/gflags_completions.h
+++ /dev/null
@@ -1,121 +0,0 @@
-// Copyright (c) 2008, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-// ---
-
-//
-// Implement helpful bash-style command line flag completions
-//
-// ** Functional API:
-// HandleCommandLineCompletions() should be called early during
-// program startup, but after command line flag code has been
-// initialized, such as the beginning of HandleCommandLineHelpFlags().
-// It checks the value of the flag --tab_completion_word.  If this
-// flag is empty, nothing happens here.  If it contains a string,
-// however, then HandleCommandLineCompletions() will hijack the
-// process, attempting to identify the intention behind this
-// completion.  Regardless of the outcome of this deduction, the
-// process will be terminated, similar to --helpshort flag
-// handling.
-//
-// ** Overview of Bash completions:
-// Bash can be told to programatically determine completions for the
-// current 'cursor word'.  It does this by (in this case) invoking a
-// command with some additional arguments identifying the command
-// being executed, the word being completed, and the previous word
-// (if any).  Bash then expects a sequence of output lines to be
-// printed to stdout.  If these lines all contain a common prefix
-// longer than the cursor word, bash will replace the cursor word
-// with that common prefix, and display nothing.  If there isn't such
-// a common prefix, bash will display the lines in pages using 'more'.
-//
-// ** Strategy taken for command line completions:
-// If we can deduce either the exact flag intended, or a common flag
-// prefix, we'll output exactly that.  Otherwise, if information
-// must be displayed to the user, we'll take the opportunity to add
-// some helpful information beyond just the flag name (specifically,
-// we'll include the default flag value and as much of the flag's
-// description as can fit on a single terminal line width, as specified
-// by the flag --tab_completion_columns).  Furthermore, we'll try to
-// make bash order the output such that the most useful or relevent
-// flags are the most likely to be shown at the top.
-//
-// ** Additional features:
-// To assist in finding that one really useful flag, substring matching
-// was implemented.  Before pressing a <TAB> to get completion for the
-// current word, you can append one or more '?' to the flag to do
-// substring matching.  Here's the semantics:
-//   --foo<TAB>     Show me all flags with names prefixed by 'foo'
-//   --foo?<TAB>    Show me all flags with 'foo' somewhere in the name
-//   --foo??<TAB>   Same as prior case, but also search in module
-//                  definition path for 'foo'
-//   --foo???<TAB>  Same as prior case, but also search in flag
-//                  descriptions for 'foo'
-// Finally, we'll trim the output to a relatively small number of
-// flags to keep bash quiet about the verbosity of output.  If one
-// really wanted to see all possible matches, appending a '+' to the
-// search word will force the exhaustive list of matches to be printed.
-//
-// ** How to have bash accept completions from a binary:
-// Bash requires that it be informed about each command that programmatic
-// completion should be enabled for.  Example addition to a .bashrc
-// file would be (your path to gflags_completions.sh file may differ):
-
-/*
-$ complete -o bashdefault -o default -o nospace -C                            \
- '/home/build/eng/bash/bash_completions.sh --tab_completion_columns $COLUMNS' \
-  time  env  binary_name  another_binary  [...]
-*/
-
-// This would allow the following to work:
-//   $ /path/to/binary_name --vmodule<TAB>
-// Or:
-//   $ ./bin/path/another_binary --gfs_u<TAB>
-// (etc)
-//
-// Sadly, it appears that bash gives no easy way to force this behavior for
-// all commands.  That's where the "time" in the above example comes in.
-// If you haven't specifically added a command to the list of completion
-// supported commands, you can still get completions by prefixing the
-// entire command with "env".
-//   $ env /some/brand/new/binary --vmod<TAB>
-// Assuming that "binary" is a newly compiled binary, this should still
-// produce the expected completion output.
-
-
-#ifndef GFLAGS_COMPLETIONS_H_
-#define GFLAGS_COMPLETIONS_H_
-
-namespace google {
-
-extern void HandleCommandLineCompletions(void);
-
-}
-
-#endif  // GFLAGS_COMPLETIONS_H_
diff --git a/3rdParty/my_ceres_vc17/include/gflags/gflags_declare.h b/3rdParty/my_ceres_vc17/include/gflags/gflags_declare.h
deleted file mode 100644
index 4425390..0000000
--- a/3rdParty/my_ceres_vc17/include/gflags/gflags_declare.h
+++ /dev/null
@@ -1,156 +0,0 @@
-// Copyright (c) 1999, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-// ---
-//
-// Revamped and reorganized by Craig Silverstein
-//
-// This is the file that should be included by any file which declares
-// command line flag.
-
-#ifndef GFLAGS_DECLARE_H_
-#define GFLAGS_DECLARE_H_
-
-
-// ---------------------------------------------------------------------------
-// Namespace of gflags library symbols.
-#define GFLAGS_NAMESPACE google
-
-// ---------------------------------------------------------------------------
-// Windows DLL import/export.
-
-// Whether gflags library is a DLL.
-//
-// Set to 1 by default when the shared gflags library was built on Windows.
-// Must be overwritten when this header file is used with the optionally also
-// built static library instead; set by CMake's INTERFACE_COMPILE_DEFINITIONS.
-#ifndef GFLAGS_IS_A_DLL
-#  define GFLAGS_IS_A_DLL 1
-#endif
-
-// We always want to import the symbols of the gflags library.
-#ifndef GFLAGS_DLL_DECL
-#  if GFLAGS_IS_A_DLL && defined(_MSC_VER)
-#    define GFLAGS_DLL_DECL __declspec(dllimport)
-#  elif defined(__GNUC__) && __GNUC__ >= 4
-#    define GFLAGS_DLL_DECL __attribute__((visibility("default")))
-#  else
-#    define GFLAGS_DLL_DECL
-#  endif
-#endif
-
-// We always want to import variables declared in user code.
-#ifndef GFLAGS_DLL_DECLARE_FLAG
-#  if GFLAGS_IS_A_DLL && defined(_MSC_VER)
-#    define GFLAGS_DLL_DECLARE_FLAG __declspec(dllimport)
-#  elif defined(__GNUC__) && __GNUC__ >= 4
-#    define GFLAGS_DLL_DECLARE_FLAG __attribute__((visibility("default")))
-#  else
-#    define GFLAGS_DLL_DECLARE_FLAG
-#  endif
-#endif
-
-// ---------------------------------------------------------------------------
-// Flag types
-#include <string>
-#if 1
-#  include <stdint.h>                   // the normal place uint32_t is defined
-#elif 1
-#  include <sys/types.h>                // the normal place u_int32_t is defined
-#elif 1
-#  include <inttypes.h>                 // a third place for uint32_t or u_int32_t
-#endif
-
-namespace GFLAGS_NAMESPACE {
-
-#if 0 // C99
-typedef int32_t          int32;
-typedef uint32_t         uint32;
-typedef int64_t          int64;
-typedef uint64_t         uint64;
-#elif 0 // BSD
-typedef int32_t          int32;
-typedef u_int32_t        uint32;
-typedef int64_t          int64;
-typedef u_int64_t        uint64;
-#elif 1 // Windows
-typedef __int32          int32;
-typedef unsigned __int32 uint32;
-typedef __int64          int64;
-typedef unsigned __int64 uint64;
-#else
-#  error Do not know how to define a 32-bit integer quantity on your system
-#endif
-
-} // namespace GFLAGS_NAMESPACE
-
-
-namespace fLS {
-
-// The meaning of "string" might be different between now and when the
-// macros below get invoked (e.g., if someone is experimenting with
-// other string implementations that get defined after this file is
-// included).  Save the current meaning now and use it in the macros.
-typedef std::string clstring;
-
-} // namespace fLS
-
-
-#define DECLARE_VARIABLE(type, shorttype, name) \
-  /* We always want to import declared variables, dll or no */ \
-  namespace fL##shorttype { extern GFLAGS_DLL_DECLARE_FLAG type FLAGS_##name; } \
-  using fL##shorttype::FLAGS_##name
-
-#define DECLARE_bool(name) \
-  DECLARE_VARIABLE(bool, B, name)
-
-#define DECLARE_int32(name) \
-  DECLARE_VARIABLE(::GFLAGS_NAMESPACE::int32, I, name)
-
-#define DECLARE_uint32(name) \
-  DECLARE_VARIABLE(::GFLAGS_NAMESPACE::uint32, U, name)
-
-#define DECLARE_int64(name) \
-  DECLARE_VARIABLE(::GFLAGS_NAMESPACE::int64, I64, name)
-
-#define DECLARE_uint64(name) \
-  DECLARE_VARIABLE(::GFLAGS_NAMESPACE::uint64, U64, name)
-
-#define DECLARE_double(name) \
-  DECLARE_VARIABLE(double, D, name)
-
-#define DECLARE_string(name) \
-  /* We always want to import declared variables, dll or no */ \
-  namespace fLS { \
-  extern GFLAGS_DLL_DECLARE_FLAG ::fLS::clstring& FLAGS_##name; \
-  } \
-  using fLS::FLAGS_##name
-
-
-#endif  // GFLAGS_DECLARE_H_
diff --git a/3rdParty/my_ceres_vc17/include/gflags/gflags_gflags.h b/3rdParty/my_ceres_vc17/include/gflags/gflags_gflags.h
deleted file mode 100644
index 36d0ba1..0000000
--- a/3rdParty/my_ceres_vc17/include/gflags/gflags_gflags.h
+++ /dev/null
@@ -1,102 +0,0 @@
-// Copyright (c) 2014, Andreas Schuh
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-// -----------------------------------------------------------------------------
-// Imports the gflags library symbols into an alternative/deprecated namespace.
-
-#ifndef GFLAGS_GFLAGS_H_
-#  error The internal header gflags_gflags.h may only be included by gflags.h
-#endif
-
-#ifndef GFLAGS_NS_GFLAGS_H_
-#define GFLAGS_NS_GFLAGS_H_
-
-
-namespace gflags {
-
-
-using GFLAGS_NAMESPACE::int32;
-using GFLAGS_NAMESPACE::uint32;
-using GFLAGS_NAMESPACE::int64;
-using GFLAGS_NAMESPACE::uint64;
-
-using GFLAGS_NAMESPACE::RegisterFlagValidator;
-using GFLAGS_NAMESPACE::CommandLineFlagInfo;
-using GFLAGS_NAMESPACE::GetAllFlags;
-using GFLAGS_NAMESPACE::ShowUsageWithFlags;
-using GFLAGS_NAMESPACE::ShowUsageWithFlagsRestrict;
-using GFLAGS_NAMESPACE::DescribeOneFlag;
-using GFLAGS_NAMESPACE::SetArgv;
-using GFLAGS_NAMESPACE::GetArgvs;
-using GFLAGS_NAMESPACE::GetArgv;
-using GFLAGS_NAMESPACE::GetArgv0;
-using GFLAGS_NAMESPACE::GetArgvSum;
-using GFLAGS_NAMESPACE::ProgramInvocationName;
-using GFLAGS_NAMESPACE::ProgramInvocationShortName;
-using GFLAGS_NAMESPACE::ProgramUsage;
-using GFLAGS_NAMESPACE::VersionString;
-using GFLAGS_NAMESPACE::GetCommandLineOption;
-using GFLAGS_NAMESPACE::GetCommandLineFlagInfo;
-using GFLAGS_NAMESPACE::GetCommandLineFlagInfoOrDie;
-using GFLAGS_NAMESPACE::FlagSettingMode;
-using GFLAGS_NAMESPACE::SET_FLAGS_VALUE;
-using GFLAGS_NAMESPACE::SET_FLAG_IF_DEFAULT;
-using GFLAGS_NAMESPACE::SET_FLAGS_DEFAULT;
-using GFLAGS_NAMESPACE::SetCommandLineOption;
-using GFLAGS_NAMESPACE::SetCommandLineOptionWithMode;
-using GFLAGS_NAMESPACE::FlagSaver;
-using GFLAGS_NAMESPACE::CommandlineFlagsIntoString;
-using GFLAGS_NAMESPACE::ReadFlagsFromString;
-using GFLAGS_NAMESPACE::AppendFlagsIntoFile;
-using GFLAGS_NAMESPACE::ReadFromFlagsFile;
-using GFLAGS_NAMESPACE::BoolFromEnv;
-using GFLAGS_NAMESPACE::Int32FromEnv;
-using GFLAGS_NAMESPACE::Uint32FromEnv;
-using GFLAGS_NAMESPACE::Int64FromEnv;
-using GFLAGS_NAMESPACE::Uint64FromEnv;
-using GFLAGS_NAMESPACE::DoubleFromEnv;
-using GFLAGS_NAMESPACE::StringFromEnv;
-using GFLAGS_NAMESPACE::SetUsageMessage;
-using GFLAGS_NAMESPACE::SetVersionString;
-using GFLAGS_NAMESPACE::ParseCommandLineNonHelpFlags;
-using GFLAGS_NAMESPACE::HandleCommandLineHelpFlags;
-using GFLAGS_NAMESPACE::AllowCommandLineReparsing;
-using GFLAGS_NAMESPACE::ReparseCommandLineNonHelpFlags;
-using GFLAGS_NAMESPACE::ShutDownCommandLineFlags;
-using GFLAGS_NAMESPACE::FlagRegisterer;
-
-#ifndef SWIG
-using GFLAGS_NAMESPACE::ParseCommandLineFlags;
-#endif
-
-
-} // namespace gflags
-
-
-#endif  // GFLAGS_NS_GFLAGS_H_
diff --git a/3rdParty/my_ceres_vc17/include/glog/log_severity.h b/3rdParty/my_ceres_vc17/include/glog/log_severity.h
deleted file mode 100644
index 99945a4..0000000
--- a/3rdParty/my_ceres_vc17/include/glog/log_severity.h
+++ /dev/null
@@ -1,92 +0,0 @@
-// Copyright (c) 2007, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-#ifndef BASE_LOG_SEVERITY_H__
-#define BASE_LOG_SEVERITY_H__
-
-// Annoying stuff for windows -- makes sure clients can import these functions
-#ifndef GOOGLE_GLOG_DLL_DECL
-# if defined(_WIN32) && !defined(__CYGWIN__)
-#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
-# else
-#   define GOOGLE_GLOG_DLL_DECL
-# endif
-#endif
-
-// Variables of type LogSeverity are widely taken to lie in the range
-// [0, NUM_SEVERITIES-1].  Be careful to preserve this assumption if
-// you ever need to change their values or add a new severity.
-typedef int LogSeverity;
-
-const int GLOG_INFO = 0, GLOG_WARNING = 1, GLOG_ERROR = 2, GLOG_FATAL = 3,
-  NUM_SEVERITIES = 4;
-#ifndef GLOG_NO_ABBREVIATED_SEVERITIES
-# ifdef ERROR
-#  error ERROR macro is defined. Define GLOG_NO_ABBREVIATED_SEVERITIES before including logging.h. See the document for detail.
-# endif
-const int INFO = GLOG_INFO, WARNING = GLOG_WARNING,
-  ERROR = GLOG_ERROR, FATAL = GLOG_FATAL;
-#endif
-
-// DFATAL is FATAL in debug mode, ERROR in normal mode
-#ifdef NDEBUG
-#define DFATAL_LEVEL ERROR
-#else
-#define DFATAL_LEVEL FATAL
-#endif
-
-extern GOOGLE_GLOG_DLL_DECL const char* const LogSeverityNames[NUM_SEVERITIES];
-
-// NDEBUG usage helpers related to (RAW_)DCHECK:
-//
-// DEBUG_MODE is for small !NDEBUG uses like
-//   if (DEBUG_MODE) foo.CheckThatFoo();
-// instead of substantially more verbose
-//   #ifndef NDEBUG
-//     foo.CheckThatFoo();
-//   #endif
-//
-// IF_DEBUG_MODE is for small !NDEBUG uses like
-//   IF_DEBUG_MODE( string error; )
-//   DCHECK(Foo(&error)) << error;
-// instead of substantially more verbose
-//   #ifndef NDEBUG
-//     string error;
-//     DCHECK(Foo(&error)) << error;
-//   #endif
-//
-#ifdef NDEBUG
-enum { DEBUG_MODE = 0 };
-#define IF_DEBUG_MODE(x)
-#else
-enum { DEBUG_MODE = 1 };
-#define IF_DEBUG_MODE(x) x
-#endif
-
-#endif  // BASE_LOG_SEVERITY_H__
diff --git a/3rdParty/my_ceres_vc17/include/glog/logging.h b/3rdParty/my_ceres_vc17/include/glog/logging.h
deleted file mode 100644
index ba1084a..0000000
--- a/3rdParty/my_ceres_vc17/include/glog/logging.h
+++ /dev/null
@@ -1,1662 +0,0 @@
-// Copyright (c) 1999, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: Ray Sidney
-//
-// This file contains #include information about logging-related stuff.
-// Pretty much everybody needs to #include this file so that they can
-// log various happenings.
-//
-#ifndef _LOGGING_H_
-#define _LOGGING_H_
-
-#include <errno.h>
-#include <string.h>
-#include <time.h>
-#include <iosfwd>
-#include <ostream>
-#include <sstream>
-#include <string>
-#if 0
-# include <unistd.h>
-#endif
-#include <vector>
-
-#if defined(_MSC_VER)
-#define GLOG_MSVC_PUSH_DISABLE_WARNING(n) __pragma(warning(push)) \
-                                     __pragma(warning(disable:n))
-#define GLOG_MSVC_POP_WARNING() __pragma(warning(pop))
-#else
-#define GLOG_MSVC_PUSH_DISABLE_WARNING(n)
-#define GLOG_MSVC_POP_WARNING()
-#endif
-
-// Annoying stuff for windows -- makes sure clients can import these functions
-#ifndef GOOGLE_GLOG_DLL_DECL
-# if defined(_WIN32) && !defined(__CYGWIN__)
-#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
-# else
-#   define GOOGLE_GLOG_DLL_DECL
-# endif
-#endif
-
-// We care a lot about number of bits things take up.  Unfortunately,
-// systems define their bit-specific ints in a lot of different ways.
-// We use our own way, and have a typedef to get there.
-// Note: these commands below may look like "#if 1" or "#if 0", but
-// that's because they were constructed that way at ./configure time.
-// Look at logging.h.in to see how they're calculated (based on your config).
-#if 1
-#include <stdint.h>             // the normal place uint16_t is defined
-#endif
-#if 1
-#include <sys/types.h>          // the normal place u_int16_t is defined
-#endif
-#if 1
-#include <inttypes.h>           // a third place for uint16_t or u_int16_t
-#endif
-
-#if 1
-#include <gflags/gflags.h>
-#endif
-
-namespace google {
-
-#if 1      // the C99 format
-typedef int32_t int32;
-typedef uint32_t uint32;
-typedef int64_t int64;
-typedef uint64_t uint64;
-#elif 0   // the BSD format
-typedef int32_t int32;
-typedef u_int32_t uint32;
-typedef int64_t int64;
-typedef u_int64_t uint64;
-#elif 1    // the windows (vc7) format
-typedef __int32 int32;
-typedef unsigned __int32 uint32;
-typedef __int64 int64;
-typedef unsigned __int64 uint64;
-#else
-#error Do not know how to define a 32-bit integer quantity on your system
-#endif
-
-}
-
-// The global value of GOOGLE_STRIP_LOG. All the messages logged to
-// LOG(XXX) with severity less than GOOGLE_STRIP_LOG will not be displayed.
-// If it can be determined at compile time that the message will not be
-// printed, the statement will be compiled out.
-//
-// Example: to strip out all INFO and WARNING messages, use the value
-// of 2 below. To make an exception for WARNING messages from a single
-// file, add "#define GOOGLE_STRIP_LOG 1" to that file _before_ including
-// base/logging.h
-#ifndef GOOGLE_STRIP_LOG
-#define GOOGLE_STRIP_LOG 0
-#endif
-
-// GCC can be told that a certain branch is not likely to be taken (for
-// instance, a CHECK failure), and use that information in static analysis.
-// Giving it this information can help it optimize for the common case in
-// the absence of better information (ie. -fprofile-arcs).
-//
-#ifndef GOOGLE_PREDICT_BRANCH_NOT_TAKEN
-#if 0
-#define GOOGLE_PREDICT_BRANCH_NOT_TAKEN(x) (__builtin_expect(x, 0))
-#else
-#define GOOGLE_PREDICT_BRANCH_NOT_TAKEN(x) x
-#endif
-#endif
-
-#ifndef GOOGLE_PREDICT_FALSE
-#if 0
-#define GOOGLE_PREDICT_FALSE(x) (__builtin_expect(x, 0))
-#else
-#define GOOGLE_PREDICT_FALSE(x) x
-#endif
-#endif
-
-#ifndef GOOGLE_PREDICT_TRUE
-#if 0
-#define GOOGLE_PREDICT_TRUE(x) (__builtin_expect(!!(x), 1))
-#else
-#define GOOGLE_PREDICT_TRUE(x) x
-#endif
-#endif
-
-
-// Make a bunch of macros for logging.  The way to log things is to stream
-// things to LOG(<a particular severity level>).  E.g.,
-//
-//   LOG(INFO) << "Found " << num_cookies << " cookies";
-//
-// You can capture log messages in a string, rather than reporting them
-// immediately:
-//
-//   vector<string> errors;
-//   LOG_STRING(ERROR, &errors) << "Couldn't parse cookie #" << cookie_num;
-//
-// This pushes back the new error onto 'errors'; if given a NULL pointer,
-// it reports the error via LOG(ERROR).
-//
-// You can also do conditional logging:
-//
-//   LOG_IF(INFO, num_cookies > 10) << "Got lots of cookies";
-//
-// You can also do occasional logging (log every n'th occurrence of an
-// event):
-//
-//   LOG_EVERY_N(INFO, 10) << "Got the " << google::COUNTER << "th cookie";
-//
-// The above will cause log messages to be output on the 1st, 11th, 21st, ...
-// times it is executed.  Note that the special google::COUNTER value is used
-// to identify which repetition is happening.
-//
-// You can also do occasional conditional logging (log every n'th
-// occurrence of an event, when condition is satisfied):
-//
-//   LOG_IF_EVERY_N(INFO, (size > 1024), 10) << "Got the " << google::COUNTER
-//                                           << "th big cookie";
-//
-// You can log messages the first N times your code executes a line. E.g.
-//
-//   LOG_FIRST_N(INFO, 20) << "Got the " << google::COUNTER << "th cookie";
-//
-// Outputs log messages for the first 20 times it is executed.
-//
-// Analogous SYSLOG, SYSLOG_IF, and SYSLOG_EVERY_N macros are available.
-// These log to syslog as well as to the normal logs.  If you use these at
-// all, you need to be aware that syslog can drastically reduce performance,
-// especially if it is configured for remote logging!  Don't use these
-// unless you fully understand this and have a concrete need to use them.
-// Even then, try to minimize your use of them.
-//
-// There are also "debug mode" logging macros like the ones above:
-//
-//   DLOG(INFO) << "Found cookies";
-//
-//   DLOG_IF(INFO, num_cookies > 10) << "Got lots of cookies";
-//
-//   DLOG_EVERY_N(INFO, 10) << "Got the " << google::COUNTER << "th cookie";
-//
-// All "debug mode" logging is compiled away to nothing for non-debug mode
-// compiles.
-//
-// We also have
-//
-//   LOG_ASSERT(assertion);
-//   DLOG_ASSERT(assertion);
-//
-// which is syntactic sugar for {,D}LOG_IF(FATAL, assert fails) << assertion;
-//
-// There are "verbose level" logging macros.  They look like
-//
-//   VLOG(1) << "I'm printed when you run the program with --v=1 or more";
-//   VLOG(2) << "I'm printed when you run the program with --v=2 or more";
-//
-// These always log at the INFO log level (when they log at all).
-// The verbose logging can also be turned on module-by-module.  For instance,
-//    --vmodule=mapreduce=2,file=1,gfs*=3 --v=0
-// will cause:
-//   a. VLOG(2) and lower messages to be printed from mapreduce.{h,cc}
-//   b. VLOG(1) and lower messages to be printed from file.{h,cc}
-//   c. VLOG(3) and lower messages to be printed from files prefixed with "gfs"
-//   d. VLOG(0) and lower messages to be printed from elsewhere
-//
-// The wildcarding functionality shown by (c) supports both '*' (match
-// 0 or more characters) and '?' (match any single character) wildcards.
-//
-// There's also VLOG_IS_ON(n) "verbose level" condition macro. To be used as
-//
-//   if (VLOG_IS_ON(2)) {
-//     // do some logging preparation and logging
-//     // that can't be accomplished with just VLOG(2) << ...;
-//   }
-//
-// There are also VLOG_IF, VLOG_EVERY_N and VLOG_IF_EVERY_N "verbose level"
-// condition macros for sample cases, when some extra computation and
-// preparation for logs is not needed.
-//   VLOG_IF(1, (size > 1024))
-//      << "I'm printed when size is more than 1024 and when you run the "
-//         "program with --v=1 or more";
-//   VLOG_EVERY_N(1, 10)
-//      << "I'm printed every 10th occurrence, and when you run the program "
-//         "with --v=1 or more. Present occurence is " << google::COUNTER;
-//   VLOG_IF_EVERY_N(1, (size > 1024), 10)
-//      << "I'm printed on every 10th occurence of case when size is more "
-//         " than 1024, when you run the program with --v=1 or more. ";
-//         "Present occurence is " << google::COUNTER;
-//
-// The supported severity levels for macros that allow you to specify one
-// are (in increasing order of severity) INFO, WARNING, ERROR, and FATAL.
-// Note that messages of a given severity are logged not only in the
-// logfile for that severity, but also in all logfiles of lower severity.
-// E.g., a message of severity FATAL will be logged to the logfiles of
-// severity FATAL, ERROR, WARNING, and INFO.
-//
-// There is also the special severity of DFATAL, which logs FATAL in
-// debug mode, ERROR in normal mode.
-//
-// Very important: logging a message at the FATAL severity level causes
-// the program to terminate (after the message is logged).
-//
-// Unless otherwise specified, logs will be written to the filename
-// "<program name>.<hostname>.<user name>.log.<severity level>.", followed
-// by the date, time, and pid (you can't prevent the date, time, and pid
-// from being in the filename).
-//
-// The logging code takes two flags:
-//     --v=#           set the verbose level
-//     --logtostderr   log all the messages to stderr instead of to logfiles
-
-// LOG LINE PREFIX FORMAT
-//
-// Log lines have this form:
-//
-//     Lmmdd hh:mm:ss.uuuuuu threadid file:line] msg...
-//
-// where the fields are defined as follows:
-//
-//   L                A single character, representing the log level
-//                    (eg 'I' for INFO)
-//   mm               The month (zero padded; ie May is '05')
-//   dd               The day (zero padded)
-//   hh:mm:ss.uuuuuu  Time in hours, minutes and fractional seconds
-//   threadid         The space-padded thread ID as returned by GetTID()
-//                    (this matches the PID on Linux)
-//   file             The file name
-//   line             The line number
-//   msg              The user-supplied message
-//
-// Example:
-//
-//   I1103 11:57:31.739339 24395 google.cc:2341] Command line: ./some_prog
-//   I1103 11:57:31.739403 24395 google.cc:2342] Process id 24395
-//
-// NOTE: although the microseconds are useful for comparing events on
-// a single machine, clocks on different machines may not be well
-// synchronized.  Hence, use caution when comparing the low bits of
-// timestamps from different machines.
-
-#ifndef DECLARE_VARIABLE
-#define MUST_UNDEF_GFLAGS_DECLARE_MACROS
-#define DECLARE_VARIABLE(type, shorttype, name, tn)                     \
-  namespace fL##shorttype {                                             \
-    extern GOOGLE_GLOG_DLL_DECL type FLAGS_##name;                      \
-  }                                                                     \
-  using fL##shorttype::FLAGS_##name
-
-// bool specialization
-#define DECLARE_bool(name) \
-  DECLARE_VARIABLE(bool, B, name, bool)
-
-// int32 specialization
-#define DECLARE_int32(name) \
-  DECLARE_VARIABLE(google::int32, I, name, int32)
-
-// Special case for string, because we have to specify the namespace
-// std::string, which doesn't play nicely with our FLAG__namespace hackery.
-#define DECLARE_string(name)                                            \
-  namespace fLS {                                                       \
-    extern GOOGLE_GLOG_DLL_DECL std::string& FLAGS_##name;              \
-  }                                                                     \
-  using fLS::FLAGS_##name
-#endif
-
-// Set whether log messages go to stderr instead of logfiles
-DECLARE_bool(logtostderr);
-
-// Set whether log messages go to stderr in addition to logfiles.
-DECLARE_bool(alsologtostderr);
-
-// Set color messages logged to stderr (if supported by terminal).
-DECLARE_bool(colorlogtostderr);
-
-// Log messages at a level >= this flag are automatically sent to
-// stderr in addition to log files.
-DECLARE_int32(stderrthreshold);
-
-// Set whether the log prefix should be prepended to each line of output.
-DECLARE_bool(log_prefix);
-
-// Log messages at a level <= this flag are buffered.
-// Log messages at a higher level are flushed immediately.
-DECLARE_int32(logbuflevel);
-
-// Sets the maximum number of seconds which logs may be buffered for.
-DECLARE_int32(logbufsecs);
-
-// Log suppression level: messages logged at a lower level than this
-// are suppressed.
-DECLARE_int32(minloglevel);
-
-// If specified, logfiles are written into this directory instead of the
-// default logging directory.
-DECLARE_string(log_dir);
-
-// Set the log file mode.
-DECLARE_int32(logfile_mode);
-
-// Sets the path of the directory into which to put additional links
-// to the log files.
-DECLARE_string(log_link);
-
-DECLARE_int32(v);  // in vlog_is_on.cc
-
-// Sets the maximum log file size (in MB).
-DECLARE_int32(max_log_size);
-
-// Sets whether to avoid logging to the disk if the disk is full.
-DECLARE_bool(stop_logging_if_full_disk);
-
-#ifdef MUST_UNDEF_GFLAGS_DECLARE_MACROS
-#undef MUST_UNDEF_GFLAGS_DECLARE_MACROS
-#undef DECLARE_VARIABLE
-#undef DECLARE_bool
-#undef DECLARE_int32
-#undef DECLARE_string
-#endif
-
-// Log messages below the GOOGLE_STRIP_LOG level will be compiled away for
-// security reasons. See LOG(severtiy) below.
-
-// A few definitions of macros that don't generate much code.  Since
-// LOG(INFO) and its ilk are used all over our code, it's
-// better to have compact code for these operations.
-
-#if GOOGLE_STRIP_LOG == 0
-#define COMPACT_GOOGLE_LOG_INFO google::LogMessage( \
-      __FILE__, __LINE__)
-#define LOG_TO_STRING_INFO(message) google::LogMessage( \
-      __FILE__, __LINE__, google::GLOG_INFO, message)
-#else
-#define COMPACT_GOOGLE_LOG_INFO google::NullStream()
-#define LOG_TO_STRING_INFO(message) google::NullStream()
-#endif
-
-#if GOOGLE_STRIP_LOG <= 1
-#define COMPACT_GOOGLE_LOG_WARNING google::LogMessage( \
-      __FILE__, __LINE__, google::GLOG_WARNING)
-#define LOG_TO_STRING_WARNING(message) google::LogMessage( \
-      __FILE__, __LINE__, google::GLOG_WARNING, message)
-#else
-#define COMPACT_GOOGLE_LOG_WARNING google::NullStream()
-#define LOG_TO_STRING_WARNING(message) google::NullStream()
-#endif
-
-#if GOOGLE_STRIP_LOG <= 2
-#define COMPACT_GOOGLE_LOG_ERROR google::LogMessage( \
-      __FILE__, __LINE__, google::GLOG_ERROR)
-#define LOG_TO_STRING_ERROR(message) google::LogMessage( \
-      __FILE__, __LINE__, google::GLOG_ERROR, message)
-#else
-#define COMPACT_GOOGLE_LOG_ERROR google::NullStream()
-#define LOG_TO_STRING_ERROR(message) google::NullStream()
-#endif
-
-#if GOOGLE_STRIP_LOG <= 3
-#define COMPACT_GOOGLE_LOG_FATAL google::LogMessageFatal( \
-      __FILE__, __LINE__)
-#define LOG_TO_STRING_FATAL(message) google::LogMessage( \
-      __FILE__, __LINE__, google::GLOG_FATAL, message)
-#else
-#define COMPACT_GOOGLE_LOG_FATAL google::NullStreamFatal()
-#define LOG_TO_STRING_FATAL(message) google::NullStreamFatal()
-#endif
-
-#if defined(NDEBUG) && !defined(DCHECK_ALWAYS_ON)
-#define DCHECK_IS_ON() 0
-#else
-#define DCHECK_IS_ON() 1
-#endif
-
-// For DFATAL, we want to use LogMessage (as opposed to
-// LogMessageFatal), to be consistent with the original behavior.
-#if !DCHECK_IS_ON()
-#define COMPACT_GOOGLE_LOG_DFATAL COMPACT_GOOGLE_LOG_ERROR
-#elif GOOGLE_STRIP_LOG <= 3
-#define COMPACT_GOOGLE_LOG_DFATAL google::LogMessage( \
-      __FILE__, __LINE__, google::GLOG_FATAL)
-#else
-#define COMPACT_GOOGLE_LOG_DFATAL google::NullStreamFatal()
-#endif
-
-#define GOOGLE_LOG_INFO(counter) google::LogMessage(__FILE__, __LINE__, google::GLOG_INFO, counter, &google::LogMessage::SendToLog)
-#define SYSLOG_INFO(counter) \
-  google::LogMessage(__FILE__, __LINE__, google::GLOG_INFO, counter, \
-  &google::LogMessage::SendToSyslogAndLog)
-#define GOOGLE_LOG_WARNING(counter)  \
-  google::LogMessage(__FILE__, __LINE__, google::GLOG_WARNING, counter, \
-  &google::LogMessage::SendToLog)
-#define SYSLOG_WARNING(counter)  \
-  google::LogMessage(__FILE__, __LINE__, google::GLOG_WARNING, counter, \
-  &google::LogMessage::SendToSyslogAndLog)
-#define GOOGLE_LOG_ERROR(counter)  \
-  google::LogMessage(__FILE__, __LINE__, google::GLOG_ERROR, counter, \
-  &google::LogMessage::SendToLog)
-#define SYSLOG_ERROR(counter)  \
-  google::LogMessage(__FILE__, __LINE__, google::GLOG_ERROR, counter, \
-  &google::LogMessage::SendToSyslogAndLog)
-#define GOOGLE_LOG_FATAL(counter) \
-  google::LogMessage(__FILE__, __LINE__, google::GLOG_FATAL, counter, \
-  &google::LogMessage::SendToLog)
-#define SYSLOG_FATAL(counter) \
-  google::LogMessage(__FILE__, __LINE__, google::GLOG_FATAL, counter, \
-  &google::LogMessage::SendToSyslogAndLog)
-#define GOOGLE_LOG_DFATAL(counter) \
-  google::LogMessage(__FILE__, __LINE__, google::DFATAL_LEVEL, counter, \
-  &google::LogMessage::SendToLog)
-#define SYSLOG_DFATAL(counter) \
-  google::LogMessage(__FILE__, __LINE__, google::DFATAL_LEVEL, counter, \
-  &google::LogMessage::SendToSyslogAndLog)
-
-#if defined(WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) || defined(__CYGWIN32__)
-// A very useful logging macro to log windows errors:
-#define LOG_SYSRESULT(result) \
-  if (FAILED(HRESULT_FROM_WIN32(result))) { \
-    LPSTR message = NULL; \
-    LPSTR msg = reinterpret_cast<LPSTR>(&message); \
-    DWORD message_length = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | \
-                         FORMAT_MESSAGE_FROM_SYSTEM, \
-                         0, result, 0, msg, 100, NULL); \
-    if (message_length > 0) { \
-      google::LogMessage(__FILE__, __LINE__, google::GLOG_ERROR, 0, \
-          &google::LogMessage::SendToLog).stream() \
-          << reinterpret_cast<const char*>(message); \
-      LocalFree(message); \
-    } \
-  }
-#endif
-
-// We use the preprocessor's merging operator, "##", so that, e.g.,
-// LOG(INFO) becomes the token GOOGLE_LOG_INFO.  There's some funny
-// subtle difference between ostream member streaming functions (e.g.,
-// ostream::operator<<(int) and ostream non-member streaming functions
-// (e.g., ::operator<<(ostream&, string&): it turns out that it's
-// impossible to stream something like a string directly to an unnamed
-// ostream. We employ a neat hack by calling the stream() member
-// function of LogMessage which seems to avoid the problem.
-#define LOG(severity) COMPACT_GOOGLE_LOG_ ## severity.stream()
-#define SYSLOG(severity) SYSLOG_ ## severity(0).stream()
-
-namespace google {
-
-// They need the definitions of integer types.
-#include "glog/log_severity.h"
-#include "glog/vlog_is_on.h"
-
-// Initialize google's logging library. You will see the program name
-// specified by argv0 in log outputs.
-GOOGLE_GLOG_DLL_DECL void InitGoogleLogging(const char* argv0);
-
-// Shutdown google's logging library.
-GOOGLE_GLOG_DLL_DECL void ShutdownGoogleLogging();
-
-// Install a function which will be called after LOG(FATAL).
-GOOGLE_GLOG_DLL_DECL void InstallFailureFunction(void (*fail_func)());
-
-class LogSink;  // defined below
-
-// If a non-NULL sink pointer is given, we push this message to that sink.
-// For LOG_TO_SINK we then do normal LOG(severity) logging as well.
-// This is useful for capturing messages and passing/storing them
-// somewhere more specific than the global log of the process.
-// Argument types:
-//   LogSink* sink;
-//   LogSeverity severity;
-// The cast is to disambiguate NULL arguments.
-#define LOG_TO_SINK(sink, severity) \
-  google::LogMessage(                                    \
-      __FILE__, __LINE__,                                               \
-      google::GLOG_ ## severity,                         \
-      static_cast<google::LogSink*>(sink), true).stream()
-#define LOG_TO_SINK_BUT_NOT_TO_LOGFILE(sink, severity)                  \
-  google::LogMessage(                                    \
-      __FILE__, __LINE__,                                               \
-      google::GLOG_ ## severity,                         \
-      static_cast<google::LogSink*>(sink), false).stream()
-
-// If a non-NULL string pointer is given, we write this message to that string.
-// We then do normal LOG(severity) logging as well.
-// This is useful for capturing messages and storing them somewhere more
-// specific than the global log of the process.
-// Argument types:
-//   string* message;
-//   LogSeverity severity;
-// The cast is to disambiguate NULL arguments.
-// NOTE: LOG(severity) expands to LogMessage().stream() for the specified
-// severity.
-#define LOG_TO_STRING(severity, message) \
-  LOG_TO_STRING_##severity(static_cast<string*>(message)).stream()
-
-// If a non-NULL pointer is given, we push the message onto the end
-// of a vector of strings; otherwise, we report it with LOG(severity).
-// This is handy for capturing messages and perhaps passing them back
-// to the caller, rather than reporting them immediately.
-// Argument types:
-//   LogSeverity severity;
-//   vector<string> *outvec;
-// The cast is to disambiguate NULL arguments.
-#define LOG_STRING(severity, outvec) \
-  LOG_TO_STRING_##severity(static_cast<std::vector<std::string>*>(outvec)).stream()
-
-#define LOG_IF(severity, condition) \
-  static_cast<void>(0),             \
-  !(condition) ? (void) 0 : google::LogMessageVoidify() & LOG(severity)
-#define SYSLOG_IF(severity, condition) \
-  static_cast<void>(0),                \
-  !(condition) ? (void) 0 : google::LogMessageVoidify() & SYSLOG(severity)
-
-#define LOG_ASSERT(condition)  \
-  LOG_IF(FATAL, !(condition)) << "Assert failed: " #condition
-#define SYSLOG_ASSERT(condition) \
-  SYSLOG_IF(FATAL, !(condition)) << "Assert failed: " #condition
-
-// CHECK dies with a fatal error if condition is not true.  It is *not*
-// controlled by DCHECK_IS_ON(), so the check will be executed regardless of
-// compilation mode.  Therefore, it is safe to do things like:
-//    CHECK(fp->Write(x) == 4)
-#define CHECK(condition)  \
-      LOG_IF(FATAL, GOOGLE_PREDICT_BRANCH_NOT_TAKEN(!(condition))) \
-             << "Check failed: " #condition " "
-
-// A container for a string pointer which can be evaluated to a bool -
-// true iff the pointer is NULL.
-struct CheckOpString {
-  CheckOpString(std::string* str) : str_(str) { }
-  // No destructor: if str_ is non-NULL, we're about to LOG(FATAL),
-  // so there's no point in cleaning up str_.
-  operator bool() const {
-    return GOOGLE_PREDICT_BRANCH_NOT_TAKEN(str_ != NULL);
-  }
-  std::string* str_;
-};
-
-// Function is overloaded for integral types to allow static const
-// integrals declared in classes and not defined to be used as arguments to
-// CHECK* macros. It's not encouraged though.
-template <class T>
-inline const T&       GetReferenceableValue(const T&           t) { return t; }
-inline char           GetReferenceableValue(char               t) { return t; }
-inline unsigned char  GetReferenceableValue(unsigned char      t) { return t; }
-inline signed char    GetReferenceableValue(signed char        t) { return t; }
-inline short          GetReferenceableValue(short              t) { return t; }
-inline unsigned short GetReferenceableValue(unsigned short     t) { return t; }
-inline int            GetReferenceableValue(int                t) { return t; }
-inline unsigned int   GetReferenceableValue(unsigned int       t) { return t; }
-inline long           GetReferenceableValue(long               t) { return t; }
-inline unsigned long  GetReferenceableValue(unsigned long      t) { return t; }
-inline long long      GetReferenceableValue(long long          t) { return t; }
-inline unsigned long long GetReferenceableValue(unsigned long long t) {
-  return t;
-}
-
-// This is a dummy class to define the following operator.
-struct DummyClassToDefineOperator {};
-
-}
-
-// Define global operator<< to declare using ::operator<<.
-// This declaration will allow use to use CHECK macros for user
-// defined classes which have operator<< (e.g., stl_logging.h).
-inline std::ostream& operator<<(
-    std::ostream& out, const google::DummyClassToDefineOperator&) {
-  return out;
-}
-
-namespace google {
-
-// This formats a value for a failing CHECK_XX statement.  Ordinarily,
-// it uses the definition for operator<<, with a few special cases below.
-template <typename T>
-inline void MakeCheckOpValueString(std::ostream* os, const T& v) {
-  (*os) << v;
-}
-
-// Overrides for char types provide readable values for unprintable
-// characters.
-template <> GOOGLE_GLOG_DLL_DECL
-void MakeCheckOpValueString(std::ostream* os, const char& v);
-template <> GOOGLE_GLOG_DLL_DECL
-void MakeCheckOpValueString(std::ostream* os, const signed char& v);
-template <> GOOGLE_GLOG_DLL_DECL
-void MakeCheckOpValueString(std::ostream* os, const unsigned char& v);
-
-// Build the error message string. Specify no inlining for code size.
-template <typename T1, typename T2>
-std::string* MakeCheckOpString(const T1& v1, const T2& v2, const char* exprtext)
-    ;
-
-namespace base {
-namespace internal {
-
-// If "s" is less than base_logging::INFO, returns base_logging::INFO.
-// If "s" is greater than base_logging::FATAL, returns
-// base_logging::ERROR.  Otherwise, returns "s".
-LogSeverity NormalizeSeverity(LogSeverity s);
-
-}  // namespace internal
-
-// A helper class for formatting "expr (V1 vs. V2)" in a CHECK_XX
-// statement.  See MakeCheckOpString for sample usage.  Other
-// approaches were considered: use of a template method (e.g.,
-// base::BuildCheckOpString(exprtext, base::Print<T1>, &v1,
-// base::Print<T2>, &v2), however this approach has complications
-// related to volatile arguments and function-pointer arguments).
-class GOOGLE_GLOG_DLL_DECL CheckOpMessageBuilder {
- public:
-  // Inserts "exprtext" and " (" to the stream.
-  explicit CheckOpMessageBuilder(const char *exprtext);
-  // Deletes "stream_".
-  ~CheckOpMessageBuilder();
-  // For inserting the first variable.
-  std::ostream* ForVar1() { return stream_; }
-  // For inserting the second variable (adds an intermediate " vs. ").
-  std::ostream* ForVar2();
-  // Get the result (inserts the closing ")").
-  std::string* NewString();
-
- private:
-  std::ostringstream *stream_;
-};
-
-}  // namespace base
-
-template <typename T1, typename T2>
-std::string* MakeCheckOpString(const T1& v1, const T2& v2, const char* exprtext) {
-  base::CheckOpMessageBuilder comb(exprtext);
-  MakeCheckOpValueString(comb.ForVar1(), v1);
-  MakeCheckOpValueString(comb.ForVar2(), v2);
-  return comb.NewString();
-}
-
-// Helper functions for CHECK_OP macro.
-// The (int, int) specialization works around the issue that the compiler
-// will not instantiate the template version of the function on values of
-// unnamed enum type - see comment below.
-#define DEFINE_CHECK_OP_IMPL(name, op) \
-  template <typename T1, typename T2> \
-  inline std::string* name##Impl(const T1& v1, const T2& v2,    \
-                            const char* exprtext) { \
-    if (GOOGLE_PREDICT_TRUE(v1 op v2)) return NULL; \
-    else return MakeCheckOpString(v1, v2, exprtext); \
-  } \
-  inline std::string* name##Impl(int v1, int v2, const char* exprtext) { \
-    return name##Impl<int, int>(v1, v2, exprtext); \
-  }
-
-// We use the full name Check_EQ, Check_NE, etc. in case the file including
-// base/logging.h provides its own #defines for the simpler names EQ, NE, etc.
-// This happens if, for example, those are used as token names in a
-// yacc grammar.
-DEFINE_CHECK_OP_IMPL(Check_EQ, ==)  // Compilation error with CHECK_EQ(NULL, x)?
-DEFINE_CHECK_OP_IMPL(Check_NE, !=)  // Use CHECK(x == NULL) instead.
-DEFINE_CHECK_OP_IMPL(Check_LE, <=)
-DEFINE_CHECK_OP_IMPL(Check_LT, < )
-DEFINE_CHECK_OP_IMPL(Check_GE, >=)
-DEFINE_CHECK_OP_IMPL(Check_GT, > )
-#undef DEFINE_CHECK_OP_IMPL
-
-// Helper macro for binary operators.
-// Don't use this macro directly in your code, use CHECK_EQ et al below.
-
-#if defined(STATIC_ANALYSIS)
-// Only for static analysis tool to know that it is equivalent to assert
-#define CHECK_OP_LOG(name, op, val1, val2, log) CHECK((val1) op (val2))
-#elif DCHECK_IS_ON()
-// In debug mode, avoid constructing CheckOpStrings if possible,
-// to reduce the overhead of CHECK statments by 2x.
-// Real DCHECK-heavy tests have seen 1.5x speedups.
-
-// The meaning of "string" might be different between now and
-// when this macro gets invoked (e.g., if someone is experimenting
-// with other string implementations that get defined after this
-// file is included).  Save the current meaning now and use it
-// in the macro.
-typedef std::string _Check_string;
-#define CHECK_OP_LOG(name, op, val1, val2, log)                         \
-  while (google::_Check_string* _result =                \
-         google::Check##name##Impl(                      \
-             google::GetReferenceableValue(val1),        \
-             google::GetReferenceableValue(val2),        \
-             #val1 " " #op " " #val2))                                  \
-    log(__FILE__, __LINE__,                                             \
-        google::CheckOpString(_result)).stream()
-#else
-// In optimized mode, use CheckOpString to hint to compiler that
-// the while condition is unlikely.
-#define CHECK_OP_LOG(name, op, val1, val2, log)                         \
-  while (google::CheckOpString _result =                 \
-         google::Check##name##Impl(                      \
-             google::GetReferenceableValue(val1),        \
-             google::GetReferenceableValue(val2),        \
-             #val1 " " #op " " #val2))                                  \
-    log(__FILE__, __LINE__, _result).stream()
-#endif  // STATIC_ANALYSIS, DCHECK_IS_ON()
-
-#if GOOGLE_STRIP_LOG <= 3
-#define CHECK_OP(name, op, val1, val2) \
-  CHECK_OP_LOG(name, op, val1, val2, google::LogMessageFatal)
-#else
-#define CHECK_OP(name, op, val1, val2) \
-  CHECK_OP_LOG(name, op, val1, val2, google::NullStreamFatal)
-#endif // STRIP_LOG <= 3
-
-// Equality/Inequality checks - compare two values, and log a FATAL message
-// including the two values when the result is not as expected.  The values
-// must have operator<<(ostream, ...) defined.
-//
-// You may append to the error message like so:
-//   CHECK_NE(1, 2) << ": The world must be ending!";
-//
-// We are very careful to ensure that each argument is evaluated exactly
-// once, and that anything which is legal to pass as a function argument is
-// legal here.  In particular, the arguments may be temporary expressions
-// which will end up being destroyed at the end of the apparent statement,
-// for example:
-//   CHECK_EQ(string("abc")[1], 'b');
-//
-// WARNING: These don't compile correctly if one of the arguments is a pointer
-// and the other is NULL. To work around this, simply static_cast NULL to the
-// type of the desired pointer.
-
-#define CHECK_EQ(val1, val2) CHECK_OP(_EQ, ==, val1, val2)
-#define CHECK_NE(val1, val2) CHECK_OP(_NE, !=, val1, val2)
-#define CHECK_LE(val1, val2) CHECK_OP(_LE, <=, val1, val2)
-#define CHECK_LT(val1, val2) CHECK_OP(_LT, < , val1, val2)
-#define CHECK_GE(val1, val2) CHECK_OP(_GE, >=, val1, val2)
-#define CHECK_GT(val1, val2) CHECK_OP(_GT, > , val1, val2)
-
-// Check that the input is non NULL.  This very useful in constructor
-// initializer lists.
-
-#define CHECK_NOTNULL(val) \
-  google::CheckNotNull(__FILE__, __LINE__, "'" #val "' Must be non NULL", (val))
-
-// Helper functions for string comparisons.
-// To avoid bloat, the definitions are in logging.cc.
-#define DECLARE_CHECK_STROP_IMPL(func, expected) \
-  GOOGLE_GLOG_DLL_DECL std::string* Check##func##expected##Impl( \
-      const char* s1, const char* s2, const char* names);
-DECLARE_CHECK_STROP_IMPL(strcmp, true)
-DECLARE_CHECK_STROP_IMPL(strcmp, false)
-DECLARE_CHECK_STROP_IMPL(strcasecmp, true)
-DECLARE_CHECK_STROP_IMPL(strcasecmp, false)
-#undef DECLARE_CHECK_STROP_IMPL
-
-// Helper macro for string comparisons.
-// Don't use this macro directly in your code, use CHECK_STREQ et al below.
-#define CHECK_STROP(func, op, expected, s1, s2) \
-  while (google::CheckOpString _result = \
-         google::Check##func##expected##Impl((s1), (s2), \
-                                     #s1 " " #op " " #s2)) \
-    LOG(FATAL) << *_result.str_
-
-
-// String (char*) equality/inequality checks.
-// CASE versions are case-insensitive.
-//
-// Note that "s1" and "s2" may be temporary strings which are destroyed
-// by the compiler at the end of the current "full expression"
-// (e.g. CHECK_STREQ(Foo().c_str(), Bar().c_str())).
-
-#define CHECK_STREQ(s1, s2) CHECK_STROP(strcmp, ==, true, s1, s2)
-#define CHECK_STRNE(s1, s2) CHECK_STROP(strcmp, !=, false, s1, s2)
-#define CHECK_STRCASEEQ(s1, s2) CHECK_STROP(strcasecmp, ==, true, s1, s2)
-#define CHECK_STRCASENE(s1, s2) CHECK_STROP(strcasecmp, !=, false, s1, s2)
-
-#define CHECK_INDEX(I,A) CHECK(I < (sizeof(A)/sizeof(A[0])))
-#define CHECK_BOUND(B,A) CHECK(B <= (sizeof(A)/sizeof(A[0])))
-
-#define CHECK_DOUBLE_EQ(val1, val2)              \
-  do {                                           \
-    CHECK_LE((val1), (val2)+0.000000000000001L); \
-    CHECK_GE((val1), (val2)-0.000000000000001L); \
-  } while (0)
-
-#define CHECK_NEAR(val1, val2, margin)           \
-  do {                                           \
-    CHECK_LE((val1), (val2)+(margin));           \
-    CHECK_GE((val1), (val2)-(margin));           \
-  } while (0)
-
-// perror()..googly style!
-//
-// PLOG() and PLOG_IF() and PCHECK() behave exactly like their LOG* and
-// CHECK equivalents with the addition that they postpend a description
-// of the current state of errno to their output lines.
-
-#define PLOG(severity) GOOGLE_PLOG(severity, 0).stream()
-
-#define GOOGLE_PLOG(severity, counter)  \
-  google::ErrnoLogMessage( \
-      __FILE__, __LINE__, google::GLOG_ ## severity, counter, \
-      &google::LogMessage::SendToLog)
-
-#define PLOG_IF(severity, condition) \
-  static_cast<void>(0),              \
-  !(condition) ? (void) 0 : google::LogMessageVoidify() & PLOG(severity)
-
-// A CHECK() macro that postpends errno if the condition is false. E.g.
-//
-// if (poll(fds, nfds, timeout) == -1) { PCHECK(errno == EINTR); ... }
-#define PCHECK(condition)  \
-      PLOG_IF(FATAL, GOOGLE_PREDICT_BRANCH_NOT_TAKEN(!(condition))) \
-              << "Check failed: " #condition " "
-
-// A CHECK() macro that lets you assert the success of a function that
-// returns -1 and sets errno in case of an error. E.g.
-//
-// CHECK_ERR(mkdir(path, 0700));
-//
-// or
-//
-// int fd = open(filename, flags); CHECK_ERR(fd) << ": open " << filename;
-#define CHECK_ERR(invocation)                                          \
-PLOG_IF(FATAL, GOOGLE_PREDICT_BRANCH_NOT_TAKEN((invocation) == -1))    \
-        << #invocation
-
-// Use macro expansion to create, for each use of LOG_EVERY_N(), static
-// variables with the __LINE__ expansion as part of the variable name.
-#define LOG_EVERY_N_VARNAME(base, line) LOG_EVERY_N_VARNAME_CONCAT(base, line)
-#define LOG_EVERY_N_VARNAME_CONCAT(base, line) base ## line
-
-#define LOG_OCCURRENCES LOG_EVERY_N_VARNAME(occurrences_, __LINE__)
-#define LOG_OCCURRENCES_MOD_N LOG_EVERY_N_VARNAME(occurrences_mod_n_, __LINE__)
-
-#define SOME_KIND_OF_LOG_EVERY_N(severity, n, what_to_do) \
-  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
-  ++LOG_OCCURRENCES; \
-  if (++LOG_OCCURRENCES_MOD_N > n) LOG_OCCURRENCES_MOD_N -= n; \
-  if (LOG_OCCURRENCES_MOD_N == 1) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-#define SOME_KIND_OF_LOG_IF_EVERY_N(severity, condition, n, what_to_do) \
-  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
-  ++LOG_OCCURRENCES; \
-  if (condition && \
-      ((LOG_OCCURRENCES_MOD_N=(LOG_OCCURRENCES_MOD_N + 1) % n) == (1 % n))) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-                 &what_to_do).stream()
-
-#define SOME_KIND_OF_PLOG_EVERY_N(severity, n, what_to_do) \
-  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
-  ++LOG_OCCURRENCES; \
-  if (++LOG_OCCURRENCES_MOD_N > n) LOG_OCCURRENCES_MOD_N -= n; \
-  if (LOG_OCCURRENCES_MOD_N == 1) \
-    google::ErrnoLogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-#define SOME_KIND_OF_LOG_FIRST_N(severity, n, what_to_do) \
-  static int LOG_OCCURRENCES = 0; \
-  if (LOG_OCCURRENCES <= n) \
-    ++LOG_OCCURRENCES; \
-  if (LOG_OCCURRENCES <= n) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-namespace glog_internal_namespace_ {
-template <bool>
-struct CompileAssert {
-};
-struct CrashReason;
-
-// Returns true if FailureSignalHandler is installed.
-// Needs to be exported since it's used by the signalhandler_unittest.
-GOOGLE_GLOG_DLL_DECL bool IsFailureSignalHandlerInstalled();
-}  // namespace glog_internal_namespace_
-
-#define LOG_EVERY_N(severity, n)                                        \
-  SOME_KIND_OF_LOG_EVERY_N(severity, (n), google::LogMessage::SendToLog)
-
-#define SYSLOG_EVERY_N(severity, n) \
-  SOME_KIND_OF_LOG_EVERY_N(severity, (n), google::LogMessage::SendToSyslogAndLog)
-
-#define PLOG_EVERY_N(severity, n) \
-  SOME_KIND_OF_PLOG_EVERY_N(severity, (n), google::LogMessage::SendToLog)
-
-#define LOG_FIRST_N(severity, n) \
-  SOME_KIND_OF_LOG_FIRST_N(severity, (n), google::LogMessage::SendToLog)
-
-#define LOG_IF_EVERY_N(severity, condition, n) \
-  SOME_KIND_OF_LOG_IF_EVERY_N(severity, (condition), (n), google::LogMessage::SendToLog)
-
-// We want the special COUNTER value available for LOG_EVERY_X()'ed messages
-enum PRIVATE_Counter {COUNTER};
-
-#ifdef GLOG_NO_ABBREVIATED_SEVERITIES
-// wingdi.h defines ERROR to be 0. When we call LOG(ERROR), it gets
-// substituted with 0, and it expands to COMPACT_GOOGLE_LOG_0. To allow us
-// to keep using this syntax, we define this macro to do the same thing
-// as COMPACT_GOOGLE_LOG_ERROR.
-#define COMPACT_GOOGLE_LOG_0 COMPACT_GOOGLE_LOG_ERROR
-#define SYSLOG_0 SYSLOG_ERROR
-#define LOG_TO_STRING_0 LOG_TO_STRING_ERROR
-// Needed for LOG_IS_ON(ERROR).
-const LogSeverity GLOG_0 = GLOG_ERROR;
-#else
-// Users may include windows.h after logging.h without
-// GLOG_NO_ABBREVIATED_SEVERITIES nor WIN32_LEAN_AND_MEAN.
-// For this case, we cannot detect if ERROR is defined before users
-// actually use ERROR. Let's make an undefined symbol to warn users.
-# define GLOG_ERROR_MSG ERROR_macro_is_defined_Define_GLOG_NO_ABBREVIATED_SEVERITIES_before_including_logging_h_See_the_document_for_detail
-# define COMPACT_GOOGLE_LOG_0 GLOG_ERROR_MSG
-# define SYSLOG_0 GLOG_ERROR_MSG
-# define LOG_TO_STRING_0 GLOG_ERROR_MSG
-# define GLOG_0 GLOG_ERROR_MSG
-#endif
-
-// Plus some debug-logging macros that get compiled to nothing for production
-
-#if DCHECK_IS_ON()
-
-#define DLOG(severity) LOG(severity)
-#define DVLOG(verboselevel) VLOG(verboselevel)
-#define DLOG_IF(severity, condition) LOG_IF(severity, condition)
-#define DLOG_EVERY_N(severity, n) LOG_EVERY_N(severity, n)
-#define DLOG_IF_EVERY_N(severity, condition, n) \
-  LOG_IF_EVERY_N(severity, condition, n)
-#define DLOG_ASSERT(condition) LOG_ASSERT(condition)
-
-// debug-only checking.  executed if DCHECK_IS_ON().
-#define DCHECK(condition) CHECK(condition)
-#define DCHECK_EQ(val1, val2) CHECK_EQ(val1, val2)
-#define DCHECK_NE(val1, val2) CHECK_NE(val1, val2)
-#define DCHECK_LE(val1, val2) CHECK_LE(val1, val2)
-#define DCHECK_LT(val1, val2) CHECK_LT(val1, val2)
-#define DCHECK_GE(val1, val2) CHECK_GE(val1, val2)
-#define DCHECK_GT(val1, val2) CHECK_GT(val1, val2)
-#define DCHECK_NOTNULL(val) CHECK_NOTNULL(val)
-#define DCHECK_STREQ(str1, str2) CHECK_STREQ(str1, str2)
-#define DCHECK_STRCASEEQ(str1, str2) CHECK_STRCASEEQ(str1, str2)
-#define DCHECK_STRNE(str1, str2) CHECK_STRNE(str1, str2)
-#define DCHECK_STRCASENE(str1, str2) CHECK_STRCASENE(str1, str2)
-
-#else  // !DCHECK_IS_ON()
-
-#define DLOG(severity)  \
-  static_cast<void>(0), \
-  true ? (void) 0 : google::LogMessageVoidify() & LOG(severity)
-
-#define DVLOG(verboselevel)             \
-  static_cast<void>(0),                 \
-  (true || !VLOG_IS_ON(verboselevel)) ? \
-      (void) 0 : google::LogMessageVoidify() & LOG(INFO)
-
-#define DLOG_IF(severity, condition) \
-  static_cast<void>(0),              \
-  (true || !(condition)) ? (void) 0 : google::LogMessageVoidify() & LOG(severity)
-
-#define DLOG_EVERY_N(severity, n) \
-  static_cast<void>(0),           \
-  true ? (void) 0 : google::LogMessageVoidify() & LOG(severity)
-
-#define DLOG_IF_EVERY_N(severity, condition, n) \
-  static_cast<void>(0),                         \
-  (true || !(condition))? (void) 0 : google::LogMessageVoidify() & LOG(severity)
-
-#define DLOG_ASSERT(condition) \
-  static_cast<void>(0),        \
-  true ? (void) 0 : LOG_ASSERT(condition)
-
-// MSVC warning C4127: conditional expression is constant
-#define DCHECK(condition) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK(condition)
-
-#define DCHECK_EQ(val1, val2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_EQ(val1, val2)
-
-#define DCHECK_NE(val1, val2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_NE(val1, val2)
-
-#define DCHECK_LE(val1, val2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_LE(val1, val2)
-
-#define DCHECK_LT(val1, val2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_LT(val1, val2)
-
-#define DCHECK_GE(val1, val2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_GE(val1, val2)
-
-#define DCHECK_GT(val1, val2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_GT(val1, val2)
-
-// You may see warnings in release mode if you don't use the return
-// value of DCHECK_NOTNULL. Please just use DCHECK for such cases.
-#define DCHECK_NOTNULL(val) (val)
-
-#define DCHECK_STREQ(str1, str2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_STREQ(str1, str2)
-
-#define DCHECK_STRCASEEQ(str1, str2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_STRCASEEQ(str1, str2)
-
-#define DCHECK_STRNE(str1, str2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_STRNE(str1, str2)
-
-#define DCHECK_STRCASENE(str1, str2) \
-  GLOG_MSVC_PUSH_DISABLE_WARNING(4127) \
-  while (false) \
-    GLOG_MSVC_POP_WARNING() CHECK_STRCASENE(str1, str2)
-
-#endif  // DCHECK_IS_ON()
-
-// Log only in verbose mode.
-
-#define VLOG(verboselevel) LOG_IF(INFO, VLOG_IS_ON(verboselevel))
-
-#define VLOG_IF(verboselevel, condition) \
-  LOG_IF(INFO, (condition) && VLOG_IS_ON(verboselevel))
-
-#define VLOG_EVERY_N(verboselevel, n) \
-  LOG_IF_EVERY_N(INFO, VLOG_IS_ON(verboselevel), n)
-
-#define VLOG_IF_EVERY_N(verboselevel, condition, n) \
-  LOG_IF_EVERY_N(INFO, (condition) && VLOG_IS_ON(verboselevel), n)
-
-namespace base_logging {
-
-// LogMessage::LogStream is a std::ostream backed by this streambuf.
-// This class ignores overflow and leaves two bytes at the end of the
-// buffer to allow for a '\n' and '\0'.
-class GOOGLE_GLOG_DLL_DECL LogStreamBuf : public std::streambuf {
- public:
-  // REQUIREMENTS: "len" must be >= 2 to account for the '\n' and '\0'.
-  LogStreamBuf(char *buf, int len) {
-    setp(buf, buf + len - 2);
-  }
-
-  // This effectively ignores overflow.
-  virtual int_type overflow(int_type ch) {
-    return ch;
-  }
-
-  // Legacy public ostrstream method.
-  size_t pcount() const { return pptr() - pbase(); }
-  char* pbase() const { return std::streambuf::pbase(); }
-};
-
-}  // namespace base_logging
-
-//
-// This class more or less represents a particular log message.  You
-// create an instance of LogMessage and then stream stuff to it.
-// When you finish streaming to it, ~LogMessage is called and the
-// full message gets streamed to the appropriate destination.
-//
-// You shouldn't actually use LogMessage's constructor to log things,
-// though.  You should use the LOG() macro (and variants thereof)
-// above.
-class GOOGLE_GLOG_DLL_DECL LogMessage {
-public:
-  enum {
-    // Passing kNoLogPrefix for the line number disables the
-    // log-message prefix. Useful for using the LogMessage
-    // infrastructure as a printing utility. See also the --log_prefix
-    // flag for controlling the log-message prefix on an
-    // application-wide basis.
-    kNoLogPrefix = -1
-  };
-
-  // LogStream inherit from non-DLL-exported class (std::ostrstream)
-  // and VC++ produces a warning for this situation.
-  // However, MSDN says "C4275 can be ignored in Microsoft Visual C++
-  // 2005 if you are deriving from a type in the Standard C++ Library"
-  // http://msdn.microsoft.com/en-us/library/3tdb471s(VS.80).aspx
-  // Let's just ignore the warning.
-GLOG_MSVC_PUSH_DISABLE_WARNING(4275)
-  class GOOGLE_GLOG_DLL_DECL LogStream : public std::ostream {
-GLOG_MSVC_POP_WARNING()
-  public:
-    LogStream(char *buf, int len, int ctr)
-        : std::ostream(NULL),
-          streambuf_(buf, len),
-          ctr_(ctr),
-          self_(this) {
-      rdbuf(&streambuf_);
-    }
-
-    int ctr() const { return ctr_; }
-    void set_ctr(int ctr) { ctr_ = ctr; }
-    LogStream* self() const { return self_; }
-
-    // Legacy std::streambuf methods.
-    size_t pcount() const { return streambuf_.pcount(); }
-    char* pbase() const { return streambuf_.pbase(); }
-    char* str() const { return pbase(); }
-
-  private:
-    LogStream(const LogStream&);
-    LogStream& operator=(const LogStream&);
-    base_logging::LogStreamBuf streambuf_;
-    int ctr_;  // Counter hack (for the LOG_EVERY_X() macro)
-    LogStream *self_;  // Consistency check hack
-  };
-
-public:
-  // icc 8 requires this typedef to avoid an internal compiler error.
-  typedef void (LogMessage::*SendMethod)();
-
-  LogMessage(const char* file, int line, LogSeverity severity, int ctr,
-             SendMethod send_method);
-
-  // Two special constructors that generate reduced amounts of code at
-  // LOG call sites for common cases.
-
-  // Used for LOG(INFO): Implied are:
-  // severity = INFO, ctr = 0, send_method = &LogMessage::SendToLog.
-  //
-  // Using this constructor instead of the more complex constructor above
-  // saves 19 bytes per call site.
-  LogMessage(const char* file, int line);
-
-  // Used for LOG(severity) where severity != INFO.  Implied
-  // are: ctr = 0, send_method = &LogMessage::SendToLog
-  //
-  // Using this constructor instead of the more complex constructor above
-  // saves 17 bytes per call site.
-  LogMessage(const char* file, int line, LogSeverity severity);
-
-  // Constructor to log this message to a specified sink (if not NULL).
-  // Implied are: ctr = 0, send_method = &LogMessage::SendToSinkAndLog if
-  // also_send_to_log is true, send_method = &LogMessage::SendToSink otherwise.
-  LogMessage(const char* file, int line, LogSeverity severity, LogSink* sink,
-             bool also_send_to_log);
-
-  // Constructor where we also give a vector<string> pointer
-  // for storing the messages (if the pointer is not NULL).
-  // Implied are: ctr = 0, send_method = &LogMessage::SaveOrSendToLog.
-  LogMessage(const char* file, int line, LogSeverity severity,
-             std::vector<std::string>* outvec);
-
-  // Constructor where we also give a string pointer for storing the
-  // message (if the pointer is not NULL).  Implied are: ctr = 0,
-  // send_method = &LogMessage::WriteToStringAndLog.
-  LogMessage(const char* file, int line, LogSeverity severity,
-             std::string* message);
-
-  // A special constructor used for check failures
-  LogMessage(const char* file, int line, const CheckOpString& result);
-
-  ~LogMessage();
-
-  // Flush a buffered message to the sink set in the constructor.  Always
-  // called by the destructor, it may also be called from elsewhere if
-  // needed.  Only the first call is actioned; any later ones are ignored.
-  void Flush();
-
-  // An arbitrary limit on the length of a single log message.  This
-  // is so that streaming can be done more efficiently.
-  static const size_t kMaxLogMessageLen;
-
-  // Theses should not be called directly outside of logging.*,
-  // only passed as SendMethod arguments to other LogMessage methods:
-  void SendToLog();  // Actually dispatch to the logs
-  void SendToSyslogAndLog();  // Actually dispatch to syslog and the logs
-
-  // Call abort() or similar to perform LOG(FATAL) crash.
-  static void __declspec(noreturn) Fail();
-
-  std::ostream& stream();
-
-  int preserved_errno() const;
-
-  // Must be called without the log_mutex held.  (L < log_mutex)
-  static int64 num_messages(int severity);
-
-  struct LogMessageData;
-
-private:
-  // Fully internal SendMethod cases:
-  void SendToSinkAndLog();  // Send to sink if provided and dispatch to the logs
-  void SendToSink();  // Send to sink if provided, do nothing otherwise.
-
-  // Write to string if provided and dispatch to the logs.
-  void WriteToStringAndLog();
-
-  void SaveOrSendToLog();  // Save to stringvec if provided, else to logs
-
-  void Init(const char* file, int line, LogSeverity severity,
-            void (LogMessage::*send_method)());
-
-  // Used to fill in crash information during LOG(FATAL) failures.
-  void RecordCrashReason(glog_internal_namespace_::CrashReason* reason);
-
-  // Counts of messages sent at each priority:
-  static int64 num_messages_[NUM_SEVERITIES];  // under log_mutex
-
-  // We keep the data in a separate struct so that each instance of
-  // LogMessage uses less stack space.
-  LogMessageData* allocated_;
-  LogMessageData* data_;
-
-  friend class LogDestination;
-
-  LogMessage(const LogMessage&);
-  void operator=(const LogMessage&);
-};
-
-// This class happens to be thread-hostile because all instances share
-// a single data buffer, but since it can only be created just before
-// the process dies, we don't worry so much.
-class GOOGLE_GLOG_DLL_DECL LogMessageFatal : public LogMessage {
- public:
-  LogMessageFatal(const char* file, int line);
-  LogMessageFatal(const char* file, int line, const CheckOpString& result);
-  __declspec(noreturn) ~LogMessageFatal();
-};
-
-// A non-macro interface to the log facility; (useful
-// when the logging level is not a compile-time constant).
-inline void LogAtLevel(int const severity, std::string const &msg) {
-  LogMessage(__FILE__, __LINE__, severity).stream() << msg;
-}
-
-// A macro alternative of LogAtLevel. New code may want to use this
-// version since there are two advantages: 1. this version outputs the
-// file name and the line number where this macro is put like other
-// LOG macros, 2. this macro can be used as C++ stream.
-#define LOG_AT_LEVEL(severity) google::LogMessage(__FILE__, __LINE__, severity).stream()
-
-// Check if it's compiled in C++11 mode.
-//
-// GXX_EXPERIMENTAL_CXX0X is defined by gcc and clang up to at least
-// gcc-4.7 and clang-3.1 (2011-12-13).  __cplusplus was defined to 1
-// in gcc before 4.7 (Crosstool 16) and clang before 3.1, but is
-// defined according to the language version in effect thereafter.
-// Microsoft Visual Studio 14 (2015) sets __cplusplus==199711 despite
-// reasonably good C++11 support, so we set LANG_CXX for it and
-// newer versions (_MSC_VER >= 1900).
-#if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L || \
-     (defined(_MSC_VER) && _MSC_VER >= 1900))
-// Helper for CHECK_NOTNULL().
-//
-// In C++11, all cases can be handled by a single function. Since the value
-// category of the argument is preserved (also for rvalue references),
-// member initializer lists like the one below will compile correctly:
-//
-//   Foo()
-//     : x_(CHECK_NOTNULL(MethodReturningUniquePtr())) {}
-template <typename T>
-T CheckNotNull(const char* file, int line, const char* names, T&& t) {
- if (t == nullptr) {
-   LogMessageFatal(file, line, new std::string(names));
- }
- return std::forward<T>(t);
-}
-
-#else
-
-// A small helper for CHECK_NOTNULL().
-template <typename T>
-T* CheckNotNull(const char *file, int line, const char *names, T* t) {
-  if (t == NULL) {
-    LogMessageFatal(file, line, new std::string(names));
-  }
-  return t;
-}
-#endif
-
-// Allow folks to put a counter in the LOG_EVERY_X()'ed messages. This
-// only works if ostream is a LogStream. If the ostream is not a
-// LogStream you'll get an assert saying as much at runtime.
-GOOGLE_GLOG_DLL_DECL std::ostream& operator<<(std::ostream &os,
-                                              const PRIVATE_Counter&);
-
-
-// Derived class for PLOG*() above.
-class GOOGLE_GLOG_DLL_DECL ErrnoLogMessage : public LogMessage {
- public:
-
-  ErrnoLogMessage(const char* file, int line, LogSeverity severity, int ctr,
-                  void (LogMessage::*send_method)());
-
-  // Postpends ": strerror(errno) [errno]".
-  ~ErrnoLogMessage();
-
- private:
-  ErrnoLogMessage(const ErrnoLogMessage&);
-  void operator=(const ErrnoLogMessage&);
-};
-
-
-// This class is used to explicitly ignore values in the conditional
-// logging macros.  This avoids compiler warnings like "value computed
-// is not used" and "statement has no effect".
-
-class GOOGLE_GLOG_DLL_DECL LogMessageVoidify {
- public:
-  LogMessageVoidify() { }
-  // This has to be an operator with a precedence lower than << but
-  // higher than ?:
-  void operator&(std::ostream&) { }
-};
-
-
-// Flushes all log files that contains messages that are at least of
-// the specified severity level.  Thread-safe.
-GOOGLE_GLOG_DLL_DECL void FlushLogFiles(LogSeverity min_severity);
-
-// Flushes all log files that contains messages that are at least of
-// the specified severity level. Thread-hostile because it ignores
-// locking -- used for catastrophic failures.
-GOOGLE_GLOG_DLL_DECL void FlushLogFilesUnsafe(LogSeverity min_severity);
-
-//
-// Set the destination to which a particular severity level of log
-// messages is sent.  If base_filename is "", it means "don't log this
-// severity".  Thread-safe.
-//
-GOOGLE_GLOG_DLL_DECL void SetLogDestination(LogSeverity severity,
-                                            const char* base_filename);
-
-//
-// Set the basename of the symlink to the latest log file at a given
-// severity.  If symlink_basename is empty, do not make a symlink.  If
-// you don't call this function, the symlink basename is the
-// invocation name of the program.  Thread-safe.
-//
-GOOGLE_GLOG_DLL_DECL void SetLogSymlink(LogSeverity severity,
-                                        const char* symlink_basename);
-
-//
-// Used to send logs to some other kind of destination
-// Users should subclass LogSink and override send to do whatever they want.
-// Implementations must be thread-safe because a shared instance will
-// be called from whichever thread ran the LOG(XXX) line.
-class GOOGLE_GLOG_DLL_DECL LogSink {
- public:
-  virtual ~LogSink();
-
-  // Sink's logging logic (message_len is such as to exclude '\n' at the end).
-  // This method can't use LOG() or CHECK() as logging system mutex(s) are held
-  // during this call.
-  virtual void send(LogSeverity severity, const char* full_filename,
-                    const char* base_filename, int line,
-                    const struct ::tm* tm_time,
-                    const char* message, size_t message_len) = 0;
-
-  // Redefine this to implement waiting for
-  // the sink's logging logic to complete.
-  // It will be called after each send() returns,
-  // but before that LogMessage exits or crashes.
-  // By default this function does nothing.
-  // Using this function one can implement complex logic for send()
-  // that itself involves logging; and do all this w/o causing deadlocks and
-  // inconsistent rearrangement of log messages.
-  // E.g. if a LogSink has thread-specific actions, the send() method
-  // can simply add the message to a queue and wake up another thread that
-  // handles real logging while itself making some LOG() calls;
-  // WaitTillSent() can be implemented to wait for that logic to complete.
-  // See our unittest for an example.
-  virtual void WaitTillSent();
-
-  // Returns the normal text output of the log message.
-  // Can be useful to implement send().
-  static std::string ToString(LogSeverity severity, const char* file, int line,
-                              const struct ::tm* tm_time,
-                              const char* message, size_t message_len);
-};
-
-// Add or remove a LogSink as a consumer of logging data.  Thread-safe.
-GOOGLE_GLOG_DLL_DECL void AddLogSink(LogSink *destination);
-GOOGLE_GLOG_DLL_DECL void RemoveLogSink(LogSink *destination);
-
-//
-// Specify an "extension" added to the filename specified via
-// SetLogDestination.  This applies to all severity levels.  It's
-// often used to append the port we're listening on to the logfile
-// name.  Thread-safe.
-//
-GOOGLE_GLOG_DLL_DECL void SetLogFilenameExtension(
-    const char* filename_extension);
-
-//
-// Make it so that all log messages of at least a particular severity
-// are logged to stderr (in addition to logging to the usual log
-// file(s)).  Thread-safe.
-//
-GOOGLE_GLOG_DLL_DECL void SetStderrLogging(LogSeverity min_severity);
-
-//
-// Make it so that all log messages go only to stderr.  Thread-safe.
-//
-GOOGLE_GLOG_DLL_DECL void LogToStderr();
-
-//
-// Make it so that all log messages of at least a particular severity are
-// logged via email to a list of addresses (in addition to logging to the
-// usual log file(s)).  The list of addresses is just a string containing
-// the email addresses to send to (separated by spaces, say).  Thread-safe.
-//
-GOOGLE_GLOG_DLL_DECL void SetEmailLogging(LogSeverity min_severity,
-                                          const char* addresses);
-
-// A simple function that sends email. dest is a commma-separated
-// list of addressess.  Thread-safe.
-GOOGLE_GLOG_DLL_DECL bool SendEmail(const char *dest,
-                                    const char *subject, const char *body);
-
-GOOGLE_GLOG_DLL_DECL const std::vector<std::string>& GetLoggingDirectories();
-
-// For tests only:  Clear the internal [cached] list of logging directories to
-// force a refresh the next time GetLoggingDirectories is called.
-// Thread-hostile.
-void TestOnly_ClearLoggingDirectoriesList();
-
-// Returns a set of existing temporary directories, which will be a
-// subset of the directories returned by GetLogginDirectories().
-// Thread-safe.
-GOOGLE_GLOG_DLL_DECL void GetExistingTempDirectories(
-    std::vector<std::string>* list);
-
-// Print any fatal message again -- useful to call from signal handler
-// so that the last thing in the output is the fatal message.
-// Thread-hostile, but a race is unlikely.
-GOOGLE_GLOG_DLL_DECL void ReprintFatalMessage();
-
-// Truncate a log file that may be the append-only output of multiple
-// processes and hence can't simply be renamed/reopened (typically a
-// stdout/stderr).  If the file "path" is > "limit" bytes, copy the
-// last "keep" bytes to offset 0 and truncate the rest. Since we could
-// be racing with other writers, this approach has the potential to
-// lose very small amounts of data. For security, only follow symlinks
-// if the path is /proc/self/fd/*
-GOOGLE_GLOG_DLL_DECL void TruncateLogFile(const char *path,
-                                          int64 limit, int64 keep);
-
-// Truncate stdout and stderr if they are over the value specified by
-// --max_log_size; keep the final 1MB.  This function has the same
-// race condition as TruncateLogFile.
-GOOGLE_GLOG_DLL_DECL void TruncateStdoutStderr();
-
-// Return the string representation of the provided LogSeverity level.
-// Thread-safe.
-GOOGLE_GLOG_DLL_DECL const char* GetLogSeverityName(LogSeverity severity);
-
-// ---------------------------------------------------------------------
-// Implementation details that are not useful to most clients
-// ---------------------------------------------------------------------
-
-// A Logger is the interface used by logging modules to emit entries
-// to a log.  A typical implementation will dump formatted data to a
-// sequence of files.  We also provide interfaces that will forward
-// the data to another thread so that the invoker never blocks.
-// Implementations should be thread-safe since the logging system
-// will write to them from multiple threads.
-
-namespace base {
-
-class GOOGLE_GLOG_DLL_DECL Logger {
- public:
-  virtual ~Logger();
-
-  // Writes "message[0,message_len-1]" corresponding to an event that
-  // occurred at "timestamp".  If "force_flush" is true, the log file
-  // is flushed immediately.
-  //
-  // The input message has already been formatted as deemed
-  // appropriate by the higher level logging facility.  For example,
-  // textual log messages already contain timestamps, and the
-  // file:linenumber header.
-  virtual void Write(bool force_flush,
-                     time_t timestamp,
-                     const char* message,
-                     int message_len) = 0;
-
-  // Flush any buffered messages
-  virtual void Flush() = 0;
-
-  // Get the current LOG file size.
-  // The returned value is approximate since some
-  // logged data may not have been flushed to disk yet.
-  virtual uint32 LogSize() = 0;
-};
-
-// Get the logger for the specified severity level.  The logger
-// remains the property of the logging module and should not be
-// deleted by the caller.  Thread-safe.
-extern GOOGLE_GLOG_DLL_DECL Logger* GetLogger(LogSeverity level);
-
-// Set the logger for the specified severity level.  The logger
-// becomes the property of the logging module and should not
-// be deleted by the caller.  Thread-safe.
-extern GOOGLE_GLOG_DLL_DECL void SetLogger(LogSeverity level, Logger* logger);
-
-}
-
-// glibc has traditionally implemented two incompatible versions of
-// strerror_r(). There is a poorly defined convention for picking the
-// version that we want, but it is not clear whether it even works with
-// all versions of glibc.
-// So, instead, we provide this wrapper that automatically detects the
-// version that is in use, and then implements POSIX semantics.
-// N.B. In addition to what POSIX says, we also guarantee that "buf" will
-// be set to an empty string, if this function failed. This means, in most
-// cases, you do not need to check the error code and you can directly
-// use the value of "buf". It will never have an undefined value.
-// DEPRECATED: Use StrError(int) instead.
-GOOGLE_GLOG_DLL_DECL int posix_strerror_r(int err, char *buf, size_t len);
-
-// A thread-safe replacement for strerror(). Returns a string describing the
-// given POSIX error code.
-GOOGLE_GLOG_DLL_DECL std::string StrError(int err);
-
-// A class for which we define operator<<, which does nothing.
-class GOOGLE_GLOG_DLL_DECL NullStream : public LogMessage::LogStream {
- public:
-  // Initialize the LogStream so the messages can be written somewhere
-  // (they'll never be actually displayed). This will be needed if a
-  // NullStream& is implicitly converted to LogStream&, in which case
-  // the overloaded NullStream::operator<< will not be invoked.
-  NullStream() : LogMessage::LogStream(message_buffer_, 1, 0) { }
-  NullStream(const char* /*file*/, int /*line*/,
-             const CheckOpString& /*result*/) :
-      LogMessage::LogStream(message_buffer_, 1, 0) { }
-  NullStream &stream() { return *this; }
- private:
-  // A very short buffer for messages (which we discard anyway). This
-  // will be needed if NullStream& converted to LogStream& (e.g. as a
-  // result of a conditional expression).
-  char message_buffer_[2];
-};
-
-// Do nothing. This operator is inline, allowing the message to be
-// compiled away. The message will not be compiled away if we do
-// something like (flag ? LOG(INFO) : LOG(ERROR)) << message; when
-// SKIP_LOG=WARNING. In those cases, NullStream will be implicitly
-// converted to LogStream and the message will be computed and then
-// quietly discarded.
-template<class T>
-inline NullStream& operator<<(NullStream &str, const T &) { return str; }
-
-// Similar to NullStream, but aborts the program (without stack
-// trace), like LogMessageFatal.
-class GOOGLE_GLOG_DLL_DECL NullStreamFatal : public NullStream {
- public:
-  NullStreamFatal() { }
-  NullStreamFatal(const char* file, int line, const CheckOpString& result) :
-      NullStream(file, line, result) { }
-  __declspec(noreturn) ~NullStreamFatal() throw () { _exit(1); }
-};
-
-// Install a signal handler that will dump signal information and a stack
-// trace when the program crashes on certain signals.  We'll install the
-// signal handler for the following signals.
-//
-// SIGSEGV, SIGILL, SIGFPE, SIGABRT, SIGBUS, and SIGTERM.
-//
-// By default, the signal handler will write the failure dump to the
-// standard error.  You can customize the destination by installing your
-// own writer function by InstallFailureWriter() below.
-//
-// Note on threading:
-//
-// The function should be called before threads are created, if you want
-// to use the failure signal handler for all threads.  The stack trace
-// will be shown only for the thread that receives the signal.  In other
-// words, stack traces of other threads won't be shown.
-GOOGLE_GLOG_DLL_DECL void InstallFailureSignalHandler();
-
-// Installs a function that is used for writing the failure dump.  "data"
-// is the pointer to the beginning of a message to be written, and "size"
-// is the size of the message.  You should not expect the data is
-// terminated with '\0'.
-GOOGLE_GLOG_DLL_DECL void InstallFailureWriter(
-    void (*writer)(const char* data, int size));
-
-}
-
-#endif // _LOGGING_H_
diff --git a/3rdParty/my_ceres_vc17/include/glog/raw_logging.h b/3rdParty/my_ceres_vc17/include/glog/raw_logging.h
deleted file mode 100644
index cf3f27d..0000000
--- a/3rdParty/my_ceres_vc17/include/glog/raw_logging.h
+++ /dev/null
@@ -1,180 +0,0 @@
-// Copyright (c) 2006, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: Maxim Lifantsev
-//
-// Thread-safe logging routines that do not allocate any memory or
-// acquire any locks, and can therefore be used by low-level memory
-// allocation and synchronization code.
-
-#ifndef BASE_RAW_LOGGING_H_
-#define BASE_RAW_LOGGING_H_
-
-#include <time.h>
-
-namespace google {
-
-#include "glog/log_severity.h"
-#include "glog/vlog_is_on.h"
-
-// Annoying stuff for windows -- makes sure clients can import these functions
-#ifndef GOOGLE_GLOG_DLL_DECL
-# if defined(_WIN32) && !defined(__CYGWIN__)
-#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
-# else
-#   define GOOGLE_GLOG_DLL_DECL
-# endif
-#endif
-
-// This is similar to LOG(severity) << format... and VLOG(level) << format..,
-// but
-// * it is to be used ONLY by low-level modules that can't use normal LOG()
-// * it is desiged to be a low-level logger that does not allocate any
-//   memory and does not need any locks, hence:
-// * it logs straight and ONLY to STDERR w/o buffering
-// * it uses an explicit format and arguments list
-// * it will silently chop off really long message strings
-// Usage example:
-//   RAW_LOG(ERROR, "Failed foo with %i: %s", status, error);
-//   RAW_VLOG(3, "status is %i", status);
-// These will print an almost standard log lines like this to stderr only:
-//   E0821 211317 file.cc:123] RAW: Failed foo with 22: bad_file
-//   I0821 211317 file.cc:142] RAW: status is 20
-#define RAW_LOG(severity, ...) \
-  do { \
-    switch (google::GLOG_ ## severity) {  \
-      case 0: \
-        RAW_LOG_INFO(__VA_ARGS__); \
-        break; \
-      case 1: \
-        RAW_LOG_WARNING(__VA_ARGS__); \
-        break; \
-      case 2: \
-        RAW_LOG_ERROR(__VA_ARGS__); \
-        break; \
-      case 3: \
-        RAW_LOG_FATAL(__VA_ARGS__); \
-        break; \
-      default: \
-        break; \
-    } \
-  } while (0)
-
-// The following STRIP_LOG testing is performed in the header file so that it's
-// possible to completely compile out the logging code and the log messages.
-#if STRIP_LOG == 0
-#define RAW_VLOG(verboselevel, ...) \
-  do { \
-    if (VLOG_IS_ON(verboselevel)) { \
-      RAW_LOG_INFO(__VA_ARGS__); \
-    } \
-  } while (0)
-#else
-#define RAW_VLOG(verboselevel, ...) RawLogStub__(0, __VA_ARGS__)
-#endif // STRIP_LOG == 0
-
-#if STRIP_LOG == 0
-#define RAW_LOG_INFO(...) google::RawLog__(google::GLOG_INFO, \
-                                   __FILE__, __LINE__, __VA_ARGS__)
-#else
-#define RAW_LOG_INFO(...) google::RawLogStub__(0, __VA_ARGS__)
-#endif // STRIP_LOG == 0
-
-#if STRIP_LOG <= 1
-#define RAW_LOG_WARNING(...) google::RawLog__(google::GLOG_WARNING,   \
-                                      __FILE__, __LINE__, __VA_ARGS__)
-#else
-#define RAW_LOG_WARNING(...) google::RawLogStub__(0, __VA_ARGS__)
-#endif // STRIP_LOG <= 1
-
-#if STRIP_LOG <= 2
-#define RAW_LOG_ERROR(...) google::RawLog__(google::GLOG_ERROR,       \
-                                    __FILE__, __LINE__, __VA_ARGS__)
-#else
-#define RAW_LOG_ERROR(...) google::RawLogStub__(0, __VA_ARGS__)
-#endif // STRIP_LOG <= 2
-
-#if STRIP_LOG <= 3
-#define RAW_LOG_FATAL(...) google::RawLog__(google::GLOG_FATAL,       \
-                                    __FILE__, __LINE__, __VA_ARGS__)
-#else
-#define RAW_LOG_FATAL(...) \
-  do { \
-    google::RawLogStub__(0, __VA_ARGS__);        \
-    exit(1); \
-  } while (0)
-#endif // STRIP_LOG <= 3
-
-// Similar to CHECK(condition) << message,
-// but for low-level modules: we use only RAW_LOG that does not allocate memory.
-// We do not want to provide args list here to encourage this usage:
-//   if (!cond)  RAW_LOG(FATAL, "foo ...", hard_to_compute_args);
-// so that the args are not computed when not needed.
-#define RAW_CHECK(condition, message)                                   \
-  do {                                                                  \
-    if (!(condition)) {                                                 \
-      RAW_LOG(FATAL, "Check %s failed: %s", #condition, message);       \
-    }                                                                   \
-  } while (0)
-
-// Debug versions of RAW_LOG and RAW_CHECK
-#ifndef NDEBUG
-
-#define RAW_DLOG(severity, ...) RAW_LOG(severity, __VA_ARGS__)
-#define RAW_DCHECK(condition, message) RAW_CHECK(condition, message)
-
-#else  // NDEBUG
-
-#define RAW_DLOG(severity, ...)                                 \
-  while (false)                                                 \
-    RAW_LOG(severity, __VA_ARGS__)
-#define RAW_DCHECK(condition, message) \
-  while (false) \
-    RAW_CHECK(condition, message)
-
-#endif  // NDEBUG
-
-// Stub log function used to work around for unused variable warnings when
-// building with STRIP_LOG > 0.
-static inline void RawLogStub__(int /* ignored */, ...) {
-}
-
-// Helper function to implement RAW_LOG and RAW_VLOG
-// Logs format... at "severity" level, reporting it
-// as called from file:line.
-// This does not allocate memory or acquire locks.
-GOOGLE_GLOG_DLL_DECL void RawLog__(LogSeverity severity,
-                                   const char* file,
-                                   int line,
-                                   const char* format, ...)
-   ;
-
-}
-
-#endif  // BASE_RAW_LOGGING_H_
diff --git a/3rdParty/my_ceres_vc17/include/glog/stl_logging.h b/3rdParty/my_ceres_vc17/include/glog/stl_logging.h
deleted file mode 100644
index 40a15aa..0000000
--- a/3rdParty/my_ceres_vc17/include/glog/stl_logging.h
+++ /dev/null
@@ -1,220 +0,0 @@
-// Copyright (c) 2003, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-// Stream output operators for STL containers; to be used for logging *only*.
-// Inclusion of this file lets you do:
-//
-// list<string> x;
-// LOG(INFO) << "data: " << x;
-// vector<int> v1, v2;
-// CHECK_EQ(v1, v2);
-//
-// If you want to use this header file with hash maps or slist, you
-// need to define macros before including this file:
-//
-// - GLOG_STL_LOGGING_FOR_UNORDERED     - <unordered_map> and <unordered_set>
-// - GLOG_STL_LOGGING_FOR_TR1_UNORDERED - <tr1/unordered_(map|set)>
-// - GLOG_STL_LOGGING_FOR_EXT_HASH      - <ext/hash_(map|set)>
-// - GLOG_STL_LOGGING_FOR_EXT_SLIST     - <ext/slist>
-//
-
-#ifndef UTIL_GTL_STL_LOGGING_INL_H_
-#define UTIL_GTL_STL_LOGGING_INL_H_
-
-#if !1
-# error We do not support stl_logging for this compiler
-#endif
-
-#include <deque>
-#include <list>
-#include <map>
-#include <ostream>
-#include <set>
-#include <utility>
-#include <vector>
-
-#ifdef GLOG_STL_LOGGING_FOR_UNORDERED
-# include <unordered_map>
-# include <unordered_set>
-#endif
-
-#ifdef GLOG_STL_LOGGING_FOR_TR1_UNORDERED
-# include <tr1/unordered_map>
-# include <tr1/unordered_set>
-#endif
-
-#ifdef GLOG_STL_LOGGING_FOR_EXT_HASH
-# include <ext/hash_set>
-# include <ext/hash_map>
-#endif
-#ifdef GLOG_STL_LOGGING_FOR_EXT_SLIST
-# include <ext/slist>
-#endif
-
-// Forward declare these two, and define them after all the container streams
-// operators so that we can recurse from pair -> container -> container -> pair
-// properly.
-template<class First, class Second>
-std::ostream& operator<<(std::ostream& out, const std::pair<First, Second>& p);
-
-namespace google {
-
-template<class Iter>
-void PrintSequence(std::ostream& out, Iter begin, Iter end);
-
-}
-
-#define OUTPUT_TWO_ARG_CONTAINER(Sequence) \
-template<class T1, class T2> \
-inline std::ostream& operator<<(std::ostream& out, \
-                                const Sequence<T1, T2>& seq) { \
-  google::PrintSequence(out, seq.begin(), seq.end()); \
-  return out; \
-}
-
-OUTPUT_TWO_ARG_CONTAINER(std::vector)
-OUTPUT_TWO_ARG_CONTAINER(std::deque)
-OUTPUT_TWO_ARG_CONTAINER(std::list)
-#ifdef GLOG_STL_LOGGING_FOR_EXT_SLIST
-OUTPUT_TWO_ARG_CONTAINER(__gnu_cxx::slist)
-#endif
-
-#undef OUTPUT_TWO_ARG_CONTAINER
-
-#define OUTPUT_THREE_ARG_CONTAINER(Sequence) \
-template<class T1, class T2, class T3> \
-inline std::ostream& operator<<(std::ostream& out, \
-                                const Sequence<T1, T2, T3>& seq) { \
-  google::PrintSequence(out, seq.begin(), seq.end()); \
-  return out; \
-}
-
-OUTPUT_THREE_ARG_CONTAINER(std::set)
-OUTPUT_THREE_ARG_CONTAINER(std::multiset)
-
-#undef OUTPUT_THREE_ARG_CONTAINER
-
-#define OUTPUT_FOUR_ARG_CONTAINER(Sequence) \
-template<class T1, class T2, class T3, class T4> \
-inline std::ostream& operator<<(std::ostream& out, \
-                                const Sequence<T1, T2, T3, T4>& seq) { \
-  google::PrintSequence(out, seq.begin(), seq.end()); \
-  return out; \
-}
-
-OUTPUT_FOUR_ARG_CONTAINER(std::map)
-OUTPUT_FOUR_ARG_CONTAINER(std::multimap)
-#ifdef GLOG_STL_LOGGING_FOR_UNORDERED
-OUTPUT_FOUR_ARG_CONTAINER(std::unordered_set)
-OUTPUT_FOUR_ARG_CONTAINER(std::unordered_multiset)
-#endif
-#ifdef GLOG_STL_LOGGING_FOR_TR1_UNORDERED
-OUTPUT_FOUR_ARG_CONTAINER(std::tr1::unordered_set)
-OUTPUT_FOUR_ARG_CONTAINER(std::tr1::unordered_multiset)
-#endif
-#ifdef GLOG_STL_LOGGING_FOR_EXT_HASH
-OUTPUT_FOUR_ARG_CONTAINER(__gnu_cxx::hash_set)
-OUTPUT_FOUR_ARG_CONTAINER(__gnu_cxx::hash_multiset)
-#endif
-
-#undef OUTPUT_FOUR_ARG_CONTAINER
-
-#define OUTPUT_FIVE_ARG_CONTAINER(Sequence) \
-template<class T1, class T2, class T3, class T4, class T5> \
-inline std::ostream& operator<<(std::ostream& out, \
-                                const Sequence<T1, T2, T3, T4, T5>& seq) { \
-  google::PrintSequence(out, seq.begin(), seq.end()); \
-  return out; \
-}
-
-#ifdef GLOG_STL_LOGGING_FOR_UNORDERED
-OUTPUT_FIVE_ARG_CONTAINER(std::unordered_map)
-OUTPUT_FIVE_ARG_CONTAINER(std::unordered_multimap)
-#endif
-#ifdef GLOG_STL_LOGGING_FOR_TR1_UNORDERED
-OUTPUT_FIVE_ARG_CONTAINER(std::tr1::unordered_map)
-OUTPUT_FIVE_ARG_CONTAINER(std::tr1::unordered_multimap)
-#endif
-#ifdef GLOG_STL_LOGGING_FOR_EXT_HASH
-OUTPUT_FIVE_ARG_CONTAINER(__gnu_cxx::hash_map)
-OUTPUT_FIVE_ARG_CONTAINER(__gnu_cxx::hash_multimap)
-#endif
-
-#undef OUTPUT_FIVE_ARG_CONTAINER
-
-template<class First, class Second>
-inline std::ostream& operator<<(std::ostream& out,
-                                const std::pair<First, Second>& p) {
-  out << '(' << p.first << ", " << p.second << ')';
-  return out;
-}
-
-namespace google {
-
-template<class Iter>
-inline void PrintSequence(std::ostream& out, Iter begin, Iter end) {
-  // Output at most 100 elements -- appropriate if used for logging.
-  for (int i = 0; begin != end && i < 100; ++i, ++begin) {
-    if (i > 0) out << ' ';
-    out << *begin;
-  }
-  if (begin != end) {
-    out << " ...";
-  }
-}
-
-}
-
-// Note that this is technically undefined behavior! We are adding things into
-// the std namespace for a reason though -- we are providing new operations on
-// types which are themselves defined with this namespace. Without this, these
-// operator overloads cannot be found via ADL. If these definitions are not
-// found via ADL, they must be #included before they're used, which requires
-// this header to be included before apparently independent other headers.
-//
-// For example, base/logging.h defines various template functions to implement
-// CHECK_EQ(x, y) and stream x and y into the log in the event the check fails.
-// It does so via the function template MakeCheckOpValueString:
-//   template<class T>
-//   void MakeCheckOpValueString(strstream* ss, const T& v) {
-//     (*ss) << v;
-//   }
-// Because 'glog/logging.h' is included before 'glog/stl_logging.h',
-// subsequent CHECK_EQ(v1, v2) for vector<...> typed variable v1 and v2 can only
-// find these operator definitions via ADL.
-//
-// Even this solution has problems -- it may pull unintended operators into the
-// namespace as well, allowing them to also be found via ADL, and creating code
-// that only works with a particular order of includes. Long term, we need to
-// move all of the *definitions* into namespace std, bet we need to ensure no
-// one references them first. This lets us take that step. We cannot define them
-// in both because that would create ambiguous overloads when both are found.
-namespace std { using ::operator<<; }
-
-#endif  // UTIL_GTL_STL_LOGGING_INL_H_
diff --git a/3rdParty/my_ceres_vc17/include/glog/vlog_is_on.h b/3rdParty/my_ceres_vc17/include/glog/vlog_is_on.h
deleted file mode 100644
index 02b0b86..0000000
--- a/3rdParty/my_ceres_vc17/include/glog/vlog_is_on.h
+++ /dev/null
@@ -1,129 +0,0 @@
-// Copyright (c) 1999, 2007, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: Ray Sidney and many others
-//
-// Defines the VLOG_IS_ON macro that controls the variable-verbosity
-// conditional logging.
-//
-// It's used by VLOG and VLOG_IF in logging.h
-// and by RAW_VLOG in raw_logging.h to trigger the logging.
-//
-// It can also be used directly e.g. like this:
-//   if (VLOG_IS_ON(2)) {
-//     // do some logging preparation and logging
-//     // that can't be accomplished e.g. via just VLOG(2) << ...;
-//   }
-//
-// The truth value that VLOG_IS_ON(level) returns is determined by 
-// the three verbosity level flags:
-//   --v=<n>  Gives the default maximal active V-logging level;
-//            0 is the default.
-//            Normally positive values are used for V-logging levels.
-//   --vmodule=<str>  Gives the per-module maximal V-logging levels to override
-//                    the value given by --v.
-//                    E.g. "my_module=2,foo*=3" would change the logging level
-//                    for all code in source files "my_module.*" and "foo*.*"
-//                    ("-inl" suffixes are also disregarded for this matching).
-//
-// SetVLOGLevel helper function is provided to do limited dynamic control over
-// V-logging by overriding the per-module settings given via --vmodule flag.
-//
-// CAVEAT: --vmodule functionality is not available in non gcc compilers.
-//
-
-#ifndef BASE_VLOG_IS_ON_H_
-#define BASE_VLOG_IS_ON_H_
-
-#include "glog/log_severity.h"
-
-// Annoying stuff for windows -- makes sure clients can import these functions
-#ifndef GOOGLE_GLOG_DLL_DECL
-# if defined(_WIN32) && !defined(__CYGWIN__)
-#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
-# else
-#   define GOOGLE_GLOG_DLL_DECL
-# endif
-#endif
-
-#if defined(__GNUC__)
-// We emit an anonymous static int* variable at every VLOG_IS_ON(n) site.
-// (Normally) the first time every VLOG_IS_ON(n) site is hit,
-// we determine what variable will dynamically control logging at this site:
-// it's either FLAGS_v or an appropriate internal variable
-// matching the current source file that represents results of
-// parsing of --vmodule flag and/or SetVLOGLevel calls.
-#define VLOG_IS_ON(verboselevel)                                \
-  __extension__  \
-  ({ static google::int32* vlocal__ = &google::kLogSiteUninitialized;           \
-     google::int32 verbose_level__ = (verboselevel);                    \
-     (*vlocal__ >= verbose_level__) &&                          \
-     ((vlocal__ != &google::kLogSiteUninitialized) ||                   \
-      (google::InitVLOG3__(&vlocal__, &FLAGS_v,                         \
-                   __FILE__, verbose_level__))); })
-#else
-// GNU extensions not available, so we do not support --vmodule.
-// Dynamic value of FLAGS_v always controls the logging level.
-#define VLOG_IS_ON(verboselevel) (FLAGS_v >= (verboselevel))
-#endif
-
-// Set VLOG(_IS_ON) level for module_pattern to log_level.
-// This lets us dynamically control what is normally set by the --vmodule flag.
-// Returns the level that previously applied to module_pattern.
-// NOTE: To change the log level for VLOG(_IS_ON) sites
-//	 that have already executed after/during InitGoogleLogging,
-//	 one needs to supply the exact --vmodule pattern that applied to them.
-//       (If no --vmodule pattern applied to them
-//       the value of FLAGS_v will continue to control them.)
-extern GOOGLE_GLOG_DLL_DECL int SetVLOGLevel(const char* module_pattern,
-                                             int log_level);
-
-// Various declarations needed for VLOG_IS_ON above: =========================
-
-// Special value used to indicate that a VLOG_IS_ON site has not been
-// initialized.  We make this a large value, so the common-case check
-// of "*vlocal__ >= verbose_level__" in VLOG_IS_ON definition
-// passes in such cases and InitVLOG3__ is then triggered.
-extern google::int32 kLogSiteUninitialized;
-
-// Helper routine which determines the logging info for a particalur VLOG site.
-//   site_flag     is the address of the site-local pointer to the controlling
-//                 verbosity level
-//   site_default  is the default to use for *site_flag
-//   fname         is the current source file name
-//   verbose_level is the argument to VLOG_IS_ON
-// We will return the return value for VLOG_IS_ON
-// and if possible set *site_flag appropriately.
-extern GOOGLE_GLOG_DLL_DECL bool InitVLOG3__(
-    google::int32** site_flag,
-    google::int32* site_default,
-    const char* fname,
-    google::int32 verbose_level);
-
-#endif  // BASE_VLOG_IS_ON_H_
diff --git a/3rdParty/my_ceres_vc17/lib/ceres.lib b/3rdParty/my_ceres_vc17/lib/ceres.lib
deleted file mode 100644
index 15ccd5e..0000000
Binary files a/3rdParty/my_ceres_vc17/lib/ceres.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/lib/ceres_cuda_kernels-debug.lib b/3rdParty/my_ceres_vc17/lib/ceres_cuda_kernels-debug.lib
deleted file mode 100644
index 5dce03d..0000000
Binary files a/3rdParty/my_ceres_vc17/lib/ceres_cuda_kernels-debug.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/lib/ceres_cuda_kernels.lib b/3rdParty/my_ceres_vc17/lib/ceres_cuda_kernels.lib
deleted file mode 100644
index 6372a32..0000000
Binary files a/3rdParty/my_ceres_vc17/lib/ceres_cuda_kernels.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresConfig.cmake b/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresConfig.cmake
deleted file mode 100644
index ba73951..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresConfig.cmake
+++ /dev/null
@@ -1,340 +0,0 @@
-# Ceres Solver - A fast non-linear least squares minimizer
-# Copyright 2022 Google Inc. All rights reserved.
-# http://ceres-solver.org/
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice,
-#   this list of conditions and the following disclaimer.
-# * Redistributions in binary form must reproduce the above copyright notice,
-#   this list of conditions and the following disclaimer in the documentation
-#   and/or other materials provided with the distribution.
-# * Neither the name of Google Inc. nor the names of its contributors may be
-#   used to endorse or promote products derived from this software without
-#   specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-#
-# Authors: pablo.speciale@gmail.com (Pablo Speciale)
-#          alexs.mac@gmail.com (Alex Stewart)
-#
-
-# Config file for Ceres Solver - Find Ceres & dependencies.
-#
-# This file is used by CMake when find_package(Ceres) is invoked and either
-# the directory containing this file either is present in CMAKE_MODULE_PATH
-# (if Ceres was installed), or exists in the local CMake package registry if
-# the Ceres build directory was exported.
-#
-# This module defines the following variables:
-#
-# Ceres_FOUND / CERES_FOUND: True if Ceres has been successfully
-#                            found. Both variables are set as although
-#                            FindPackage() only references Ceres_FOUND
-#                            in Config mode, given the conventions for
-#                            <package>_FOUND when FindPackage() is
-#                            called in Module mode, users could
-#                            reasonably expect to use CERES_FOUND
-#                            instead.
-#
-# CERES_VERSION: Version of Ceres found.
-#
-# CERES_LIBRARIES: Libraries for Ceres and all
-#                  dependencies against which Ceres was
-#                  compiled. This will not include any optional
-#                  dependencies that were disabled when Ceres was
-#                  compiled.
-#
-# NOTE: There is no equivalent of CERES_INCLUDE_DIRS as the exported
-#       CMake target already includes the definition of its public
-#       include directories.
-
-include(CMakeFindDependencyMacro)
-
-# Called if we failed to find Ceres or any of its required dependencies,
-# unsets all public (designed to be used externally) variables and reports
-# error message at priority depending upon [REQUIRED/QUIET/<NONE>] argument.
-macro(CERES_REPORT_NOT_FOUND REASON_MSG)
-  # FindPackage() only references Ceres_FOUND, and requires it to be
-  # explicitly set FALSE to denote not found (not merely undefined).
-  set(Ceres_FOUND FALSE)
-  set(CERES_FOUND FALSE)
-  unset(CERES_INCLUDE_DIR)
-  unset(CERES_INCLUDE_DIRS)
-  unset(CERES_LIBRARIES)
-
-  # Reset the CMake module path to its state when this script was called.
-  set(CMAKE_MODULE_PATH ${CALLERS_CMAKE_MODULE_PATH})
-
-  # Note <package>_FIND_[REQUIRED/QUIETLY] variables defined by
-  # FindPackage() use the camelcase library name, not uppercase.
-  if (Ceres_FIND_QUIETLY)
-    message(STATUS "Failed to find Ceres - " ${REASON_MSG} ${ARGN})
-  elseif (Ceres_FIND_REQUIRED)
-    message(FATAL_ERROR "Failed to find Ceres - " ${REASON_MSG} ${ARGN})
-  else()
-    # Neither QUIETLY nor REQUIRED, use SEND_ERROR which emits an error
-    # that prevents generation, but continues configuration.
-    message(SEND_ERROR "Failed to find Ceres - " ${REASON_MSG} ${ARGN})
-  endif ()
-  return()
-endmacro(CERES_REPORT_NOT_FOUND)
-
-
-# ceres_message([mode] "message text")
-#
-# Wraps the standard cmake 'message' command, but suppresses output
-# if the QUIET flag was passed to the find_package(Ceres ...) call.
-function(ceres_message)
-  if (NOT Ceres_FIND_QUIETLY)
-    message(${ARGN})
-  endif()
-endfunction()
-
-
-# ceres_pretty_print_cmake_list( OUTPUT_VAR [item1 [item2 ... ]] )
-#
-# Sets ${OUTPUT_VAR} in the caller's scope to a human-readable string
-# representation of the list passed as the remaining arguments formed
-# as: "[item1, item2, ..., itemN]".
-function(ceres_pretty_print_cmake_list OUTPUT_VAR)
-  string(REPLACE ";" ", " PRETTY_LIST_STRING "[${ARGN}]")
-  set(${OUTPUT_VAR} "${PRETTY_LIST_STRING}" PARENT_SCOPE)
-endfunction()
-
-# The list of (optional) components this version of Ceres was compiled with.
-set(CERES_COMPILED_COMPONENTS "EigenSparse;SparseLinearAlgebraLibrary;SchurSpecializations")
-
-# If Ceres was not installed, then by definition it was exported
-# from a build directory.
-set(CERES_WAS_INSTALLED TRUE)
-
-# Record the state of the CMake module path when this script was
-# called so that we can ensure that we leave it in the same state on
-# exit as it was on entry, but modify it locally.
-set(CALLERS_CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH})
-
-# Get the (current, i.e. installed) directory containing this file.
-get_filename_component(CERES_CURRENT_CONFIG_DIR
-  "${CMAKE_CURRENT_LIST_FILE}" PATH)
-
-if (CERES_WAS_INSTALLED)
-  # Reset CMake module path to the installation directory of this
-  # script, thus we will use the FindPackage() scripts shipped with
-  # Ceres to find Ceres' dependencies, even if the user has equivalently
-  # named FindPackage() scripts in their project.
-  set(CMAKE_MODULE_PATH ${CERES_CURRENT_CONFIG_DIR})
-
-  # Build the absolute root install directory as a relative path
-  # (determined when Ceres was configured & built) from the current
-  # install directory for this this file.  This allows for the install
-  # tree to be relocated, after Ceres was built, outside of CMake.
-  get_filename_component(CURRENT_ROOT_INSTALL_DIR
-    "${CERES_CURRENT_CONFIG_DIR}/../../../"
-    ABSOLUTE)
-  if (NOT EXISTS ${CURRENT_ROOT_INSTALL_DIR})
-    ceres_report_not_found(
-      "Ceres install root: ${CURRENT_ROOT_INSTALL_DIR}, "
-      "determined from relative path from CeresConfig.cmake install location: "
-      "${CERES_CURRENT_CONFIG_DIR}, does not exist. Either the install "
-      "directory was deleted, or the install tree was only partially relocated "
-      "outside of CMake after Ceres was built.")
-  endif (NOT EXISTS ${CURRENT_ROOT_INSTALL_DIR})
-
-else(CERES_WAS_INSTALLED)
-  # Ceres was exported from the build tree.
-  set(CERES_EXPORTED_BUILD_DIR ${CERES_CURRENT_CONFIG_DIR})
-  get_filename_component(CERES_EXPORTED_SOURCE_DIR
-    "${CERES_EXPORTED_BUILD_DIR}/../../../"
-    ABSOLUTE)
-  if (NOT EXISTS ${CERES_EXPORTED_SOURCE_DIR})
-    ceres_report_not_found(
-      "Ceres exported source directory: ${CERES_EXPORTED_SOURCE_DIR}, "
-      "determined from relative path from CeresConfig.cmake exported build "
-      "directory: ${CERES_EXPORTED_BUILD_DIR} does not exist.")
-  endif()
-
-  # Reset CMake module path to the cmake directory in the Ceres source
-  # tree which was exported, thus we will use the FindPackage() scripts shipped
-  # with Ceres to find Ceres' dependencies, even if the user has equivalently
-  # named FindPackage() scripts in their project.
-  set(CMAKE_MODULE_PATH ${CERES_EXPORTED_SOURCE_DIR}/cmake)
-endif(CERES_WAS_INSTALLED)
-
-# Set the version.
-set(CERES_VERSION 2.2.0)
-
-include(CMakeFindDependencyMacro)
-# Optional dependencies
-
-
-find_dependency(CUDAToolkit 12.4.99)
-find_dependency (Threads)
-
-# As imported CMake targets are not re-exported when a dependent target is
-# exported, we must invoke find_package(XXX) here to reload the definition
-# of their targets.  Without this, the dependency target names (e.g.
-# 'gflags-shared') which will be present in the ceres target would not be
-# defined, and so CMake will assume that they refer to a library name and
-# fail to link correctly.
-
-# Eigen.
-# Flag set during configuration and build of Ceres.
-set(CERES_EIGEN_VERSION 3.4.0)
-# Search quietly to control the timing of the error message if not found. The
-# search should be for an exact match, but for usability reasons do a soft
-# match and reject with an explanation below.
-find_package(Eigen3 ${CERES_EIGEN_VERSION} QUIET)
-if (Eigen3_FOUND)
-  if (NOT Eigen3_VERSION VERSION_EQUAL CERES_EIGEN_VERSION)
-    # CMake's VERSION check in FIND_PACKAGE() will accept any version >= the
-    # specified version. However, only version = is supported. Improve
-    # usability by explaining why we don't accept non-exact version matching.
-    ceres_report_not_found("Found Eigen dependency, but the version of Eigen "
-      "found (${Eigen3_VERSION}) does not exactly match the version of Eigen "
-      "Ceres was compiled with (${CERES_EIGEN_VERSION}). This can cause subtle "
-      "bugs by triggering violations of the One Definition Rule. See the "
-      "Wikipedia article http://en.wikipedia.org/wiki/One_Definition_Rule "
-      "for more details")
-  endif ()
-  ceres_message(STATUS "Found required Ceres dependency: "
-    "Eigen version ${CERES_EIGEN_VERSION} in ${Eigen3_DIR}")
-else (Eigen3_FOUND)
-  ceres_report_not_found("Missing required Ceres "
-    "dependency: Eigen version ${CERES_EIGEN_VERSION}, please set "
-    "Eigen3_DIR.")
-endif (Eigen3_FOUND)
-
-# glog (and maybe gflags).
-#
-# Flags set during configuration and build of Ceres.
-set(CERES_USES_MINIGLOG OFF)
-set(CERES_GLOG_VERSION 0.4.0)
-set(CERES_GLOG_WAS_BUILT_WITH_CMAKE 1)
-
-set(CERES_USES_GFLAGS ON)
-set(CERES_GFLAGS_VERSION 2.2.2)
-
-if (CERES_USES_MINIGLOG)
-  # Output message at standard log level (not the lower STATUS) so that
-  # the message is output in GUI during configuration to warn user.
-  ceres_message("-- Found Ceres compiled with miniglog substitute "
-    "for glog, beware this will likely cause problems if glog is later linked.")
-else(CERES_USES_MINIGLOG)
-  if (CERES_GLOG_WAS_BUILT_WITH_CMAKE)
-    find_package(glog ${CERES_GLOG_VERSION} CONFIG QUIET)
-    set(GLOG_FOUND ${glog_FOUND})
-  else()
-    # Version of glog against which Ceres was built was not built with CMake,
-    # use the exported glog find_package() module from Ceres to find it again.
-    # Append the locations of glog when Ceres was built to the search path hints.
-    list(APPEND GLOG_INCLUDE_DIR_HINTS "")
-    get_filename_component(CERES_BUILD_GLOG_LIBRARY_DIR "glog::glog" PATH)
-    list(APPEND GLOG_LIBRARY_DIR_HINTS ${CERES_BUILD_GLOG_LIBRARY_DIR})
-
-    # Search quietly s/t we control the timing of the error message if not found.
-    find_package(Glog QUIET)
-  endif()
-
-  if (GLOG_FOUND)
-    ceres_message(STATUS "Found required Ceres dependency: glog")
-  else()
-    ceres_report_not_found("Missing required Ceres dependency: glog.")
-  endif()
-
-  # gflags is only a public dependency of Ceres via glog, thus is not required
-  # if Ceres was built with MINIGLOG.
-  if (CERES_USES_GFLAGS)
-    # Search quietly s/t we control the timing of the error message if not found.
-    find_package(gflags ${CERES_GFLAGS_VERSION} QUIET)
-    if (gflags_FOUND AND TARGET gflags)
-      ceres_message(STATUS "Found required Ceres dependency: gflags")
-    else()
-      ceres_report_not_found("Missing required Ceres "
-        "dependency: gflags (not found, or not found as exported CMake target).")
-    endif()
-  endif()
-endif(CERES_USES_MINIGLOG)
-
-# Import exported Ceres targets, if they have not already been imported.
-if (NOT TARGET ceres AND NOT Ceres_BINARY_DIR)
-  include(${CERES_CURRENT_CONFIG_DIR}/CeresTargets.cmake)
-endif (NOT TARGET ceres AND NOT Ceres_BINARY_DIR)
-# Set the expected XX_LIBRARIES variable for FindPackage().
-set(CERES_LIBRARIES Ceres::ceres)
-
-# Reset CMake module path to its state when this script was called.
-set(CMAKE_MODULE_PATH ${CALLERS_CMAKE_MODULE_PATH})
-
-# Build the detected Ceres version string to correctly capture whether it
-# was installed, or exported.
-ceres_pretty_print_cmake_list(CERES_COMPILED_COMPONENTS_STRING
-  ${CERES_COMPILED_COMPONENTS})
-if (CERES_WAS_INSTALLED)
-  set(CERES_DETECTED_VERSION_STRING "Ceres version: ${CERES_VERSION} "
-    "installed in: ${CURRENT_ROOT_INSTALL_DIR} with components: "
-    "${CERES_COMPILED_COMPONENTS_STRING}")
-else (CERES_WAS_INSTALLED)
-  set(CERES_DETECTED_VERSION_STRING "Ceres version: ${CERES_VERSION} "
-    "exported from build directory: ${CERES_EXPORTED_BUILD_DIR} with "
-    "components: ${CERES_COMPILED_COMPONENTS_STRING}")
-endif()
-
-# If the user called this script through find_package() whilst specifying
-# particular Ceres components that should be found via:
-# find_package(Ceres COMPONENTS XXX YYY), check the requested components against
-# those with which Ceres was compiled.  In this case, we should only report
-# Ceres as found if all the requested components have been found.
-if (Ceres_FIND_COMPONENTS)
-  foreach (REQUESTED_COMPONENT ${Ceres_FIND_COMPONENTS})
-    list(FIND CERES_COMPILED_COMPONENTS ${REQUESTED_COMPONENT} HAVE_REQUESTED_COMPONENT)
-    # list(FIND ..) returns -1 if the element was not in the list, but CMake
-    # interprets if (VAR) to be true if VAR is any non-zero number, even
-    # negative ones, hence we have to explicitly check for >= 0.
-    if (HAVE_REQUESTED_COMPONENT EQUAL -1)
-      # Check for the presence of all requested components before reporting
-      # not found, such that we report all of the missing components rather
-      # than just the first.
-      list(APPEND MISSING_CERES_COMPONENTS ${REQUESTED_COMPONENT})
-    endif()
-  endforeach()
-  if (MISSING_CERES_COMPONENTS)
-    ceres_pretty_print_cmake_list(REQUESTED_CERES_COMPONENTS_STRING
-      ${Ceres_FIND_COMPONENTS})
-    ceres_pretty_print_cmake_list(MISSING_CERES_COMPONENTS_STRING
-      ${MISSING_CERES_COMPONENTS})
-    ceres_report_not_found("Missing requested Ceres components: "
-      "${MISSING_CERES_COMPONENTS_STRING} (components requested: "
-      "${REQUESTED_CERES_COMPONENTS_STRING}). Detected "
-      "${CERES_DETECTED_VERSION_STRING}.")
-  endif()
-endif()
-
-# As we use CERES_REPORT_NOT_FOUND() to abort, if we reach this point we have
-# found Ceres and all required dependencies.
-ceres_message(STATUS "Found " ${CERES_DETECTED_VERSION_STRING})
-
-# Set CERES_FOUND to be equivalent to Ceres_FOUND, which is set to
-# TRUE by FindPackage() if this file is found and run, and after which
-# Ceres_FOUND is not (explicitly, i.e. undefined does not count) set
-# to FALSE.
-set(CERES_FOUND TRUE)
-
-if (NOT TARGET ceres)
-  # For backwards compatibility, create a local 'alias' target with the
-  # non-namespace-qualified Ceres target name. Note that this is not a
-  # true ALIAS library in CMake terms as they cannot point to imported targets.
-  add_library(ceres INTERFACE IMPORTED)
-  set_target_properties(ceres PROPERTIES INTERFACE_LINK_LIBRARIES Ceres::ceres)
-endif()
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresConfigVersion.cmake b/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresConfigVersion.cmake
deleted file mode 100644
index dd40f26..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresConfigVersion.cmake
+++ /dev/null
@@ -1,70 +0,0 @@
-# This is a basic version file for the Config-mode of find_package().
-# It is used by write_basic_package_version_file() as input file for configure_file()
-# to create a version-file which can be installed along a config.cmake file.
-#
-# The created file sets PACKAGE_VERSION_EXACT if the current version string and
-# the requested version string are exactly the same and it sets
-# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version,
-# but only if the requested major version is the same as the current one.
-# The variable CVF_VERSION must be set before calling configure_file().
-
-
-set(PACKAGE_VERSION "2.2.0")
-
-if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION)
-  set(PACKAGE_VERSION_COMPATIBLE FALSE)
-else()
-
-  if("2.2.0" MATCHES "^([0-9]+)\\.")
-    set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}")
-    if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0)
-      string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}")
-    endif()
-  else()
-    set(CVF_VERSION_MAJOR "2.2.0")
-  endif()
-
-  if(PACKAGE_FIND_VERSION_RANGE)
-    # both endpoints of the range must have the expected major version
-    math (EXPR CVF_VERSION_MAJOR_NEXT "${CVF_VERSION_MAJOR} + 1")
-    if (NOT PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
-        OR ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX_MAJOR STREQUAL CVF_VERSION_MAJOR)
-          OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX VERSION_LESS_EQUAL CVF_VERSION_MAJOR_NEXT)))
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    elseif(PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
-        AND ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND PACKAGE_VERSION VERSION_LESS_EQUAL PACKAGE_FIND_VERSION_MAX)
-        OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION_MAX)))
-      set(PACKAGE_VERSION_COMPATIBLE TRUE)
-    else()
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    endif()
-  else()
-    if(PACKAGE_FIND_VERSION_MAJOR STREQUAL CVF_VERSION_MAJOR)
-      set(PACKAGE_VERSION_COMPATIBLE TRUE)
-    else()
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    endif()
-
-    if(PACKAGE_FIND_VERSION STREQUAL PACKAGE_VERSION)
-      set(PACKAGE_VERSION_EXACT TRUE)
-    endif()
-  endif()
-endif()
-
-
-# if the installed project requested no architecture check, don't perform the check
-if("FALSE")
-  return()
-endif()
-
-# if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
-if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "8" STREQUAL "")
-  return()
-endif()
-
-# check that the installed version has the same 32/64bit-ness as the one which is currently searching:
-if(NOT CMAKE_SIZEOF_VOID_P STREQUAL "8")
-  math(EXPR installedBits "8 * 8")
-  set(PACKAGE_VERSION "${PACKAGE_VERSION} (${installedBits}bit)")
-  set(PACKAGE_VERSION_UNSUITABLE TRUE)
-endif()
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets-debug.cmake b/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets-debug.cmake
deleted file mode 100644
index e5b749f..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets-debug.cmake
+++ /dev/null
@@ -1,29 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Debug".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "Ceres::ceres_cuda_kernels" for configuration "Debug"
-set_property(TARGET Ceres::ceres_cuda_kernels APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-set_target_properties(Ceres::ceres_cuda_kernels PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_DEBUG "CUDA"
-  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels-debug.lib"
-  )
-
-list(APPEND _cmake_import_check_targets Ceres::ceres_cuda_kernels )
-list(APPEND _cmake_import_check_files_for_Ceres::ceres_cuda_kernels "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels-debug.lib" )
-
-# Import target "Ceres::ceres" for configuration "Debug"
-set_property(TARGET Ceres::ceres APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-set_target_properties(Ceres::ceres PROPERTIES
-  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/ceres-debug.lib"
-  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/ceres-debug.dll"
-  )
-
-list(APPEND _cmake_import_check_targets Ceres::ceres )
-list(APPEND _cmake_import_check_files_for_Ceres::ceres "${_IMPORT_PREFIX}/lib/ceres-debug.lib" "${_IMPORT_PREFIX}/bin/ceres-debug.dll" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets-release.cmake b/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets-release.cmake
deleted file mode 100644
index 5e877a0..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets-release.cmake
+++ /dev/null
@@ -1,29 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Release".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "Ceres::ceres_cuda_kernels" for configuration "Release"
-set_property(TARGET Ceres::ceres_cuda_kernels APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(Ceres::ceres_cuda_kernels PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CUDA"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels.lib"
-  )
-
-list(APPEND _cmake_import_check_targets Ceres::ceres_cuda_kernels )
-list(APPEND _cmake_import_check_files_for_Ceres::ceres_cuda_kernels "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels.lib" )
-
-# Import target "Ceres::ceres" for configuration "Release"
-set_property(TARGET Ceres::ceres APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(Ceres::ceres PROPERTIES
-  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/ceres.lib"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/ceres.dll"
-  )
-
-list(APPEND _cmake_import_check_targets Ceres::ceres )
-list(APPEND _cmake_import_check_files_for_Ceres::ceres "${_IMPORT_PREFIX}/lib/ceres.lib" "${_IMPORT_PREFIX}/bin/ceres.dll" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets.cmake b/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets.cmake
deleted file mode 100644
index efbad2c..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/Ceres/CeresTargets.cmake
+++ /dev/null
@@ -1,111 +0,0 @@
-# Generated by CMake
-
-if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
-   message(FATAL_ERROR "CMake >= 2.8.0 required")
-endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.3")
-   message(FATAL_ERROR "CMake >= 2.8.3 required")
-endif()
-cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.3...3.23)
-#----------------------------------------------------------------
-# Generated CMake target import file.
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Protect against multiple inclusion, which would fail when already imported targets are added once more.
-set(_cmake_targets_defined "")
-set(_cmake_targets_not_defined "")
-set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS Ceres::ceres_cuda_kernels Ceres::ceres)
-  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
-  if(TARGET "${_cmake_expected_target}")
-    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
-  else()
-    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
-  endif()
-endforeach()
-unset(_cmake_expected_target)
-if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
-  unset(_cmake_targets_defined)
-  unset(_cmake_targets_not_defined)
-  unset(_cmake_expected_targets)
-  unset(CMAKE_IMPORT_FILE_VERSION)
-  cmake_policy(POP)
-  return()
-endif()
-if(NOT _cmake_targets_defined STREQUAL "")
-  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
-  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
-  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
-endif()
-unset(_cmake_targets_defined)
-unset(_cmake_targets_not_defined)
-unset(_cmake_expected_targets)
-
-
-# Compute the installation prefix relative to this file.
-get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-if(_IMPORT_PREFIX STREQUAL "/")
-  set(_IMPORT_PREFIX "")
-endif()
-
-# Create imported target Ceres::ceres_cuda_kernels
-add_library(Ceres::ceres_cuda_kernels STATIC IMPORTED)
-
-# Create imported target Ceres::ceres
-add_library(Ceres::ceres SHARED IMPORTED)
-
-set_target_properties(Ceres::ceres PROPERTIES
-  INTERFACE_COMPILE_FEATURES "cxx_std_17"
-  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
-  INTERFACE_LINK_LIBRARIES "glog::glog;gflags;Eigen3::Eigen"
-)
-
-if(CMAKE_VERSION VERSION_LESS 2.8.12)
-  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
-endif()
-
-# Load information for each installed configuration.
-file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/CeresTargets-*.cmake")
-foreach(_cmake_config_file IN LISTS _cmake_config_files)
-  include("${_cmake_config_file}")
-endforeach()
-unset(_cmake_config_file)
-unset(_cmake_config_files)
-
-# Cleanup temporary variables.
-set(_IMPORT_PREFIX)
-
-# Loop over all imported files and verify that they actually exist
-foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-    if(NOT EXISTS "${_cmake_file}")
-      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
-   \"${_cmake_file}\"
-but this file does not exist.  Possible reasons include:
-* The file was deleted, renamed, or moved to another location.
-* An install or uninstall procedure did not complete successfully.
-* The installation package was faulty and contained
-   \"${CMAKE_CURRENT_LIST_FILE}\"
-but not all the files it references.
-")
-    endif()
-  endforeach()
-  unset(_cmake_file)
-  unset("_cmake_import_check_files_for_${_cmake_target}")
-endforeach()
-unset(_cmake_target)
-unset(_cmake_import_check_targets)
-
-# This file does not depend on other imported targets which have
-# been exported from the same project but in a separate export set.
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
-cmake_policy(POP)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-config-version.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-config-version.cmake
deleted file mode 100644
index b1617c1..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-config-version.cmake
+++ /dev/null
@@ -1,21 +0,0 @@
-## gflags CMake configuration version file
-
-# -----------------------------------------------------------------------------
-# library version
-set (PACKAGE_VERSION "2.2.2")
-
-# -----------------------------------------------------------------------------
-# check compatibility
-
-# Perform compatibility check here using the input CMake variables.
-# See example in http://www.cmake.org/Wiki/CMake_2.6_Notes.
-
-set (PACKAGE_VERSION_COMPATIBLE TRUE)
-set (PACKAGE_VERSION_UNSUITABLE FALSE)
-
-if ("${PACKAGE_FIND_VERSION_MAJOR}" EQUAL "2" AND
-    "${PACKAGE_FIND_VERSION_MINOR}" EQUAL "2")
-  set (PACKAGE_VERSION_EXACT TRUE)
-else ()
-  set (PACKAGE_VERSION_EXACT FALSE)
-endif ()
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-config.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-config.cmake
deleted file mode 100644
index 7e25a95..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-config.cmake
+++ /dev/null
@@ -1,183 +0,0 @@
-## gflags CMake configuration file
-
-# library version information
-set (GFLAGS_VERSION_STRING "2.2.2")
-set (GFLAGS_VERSION_MAJOR  2)
-set (GFLAGS_VERSION_MINOR  2)
-set (GFLAGS_VERSION_PATCH  0)
-
-# import targets
-if (NOT DEFINED GFLAGS_USE_TARGET_NAMESPACE)
-  set (GFLAGS_USE_TARGET_NAMESPACE FALSE)
-endif ()
-if (GFLAGS_USE_TARGET_NAMESPACE)
-  include ("${CMAKE_CURRENT_LIST_DIR}/gflags-targets.cmake")
-  set (GFLAGS_TARGET_NAMESPACE gflags)
-else ()
-  include ("${CMAKE_CURRENT_LIST_DIR}/gflags-nonamespace-targets.cmake")
-  set (GFLAGS_TARGET_NAMESPACE)
-endif ()
-if (GFLAGS_TARGET_NAMESPACE)
-  set (GFLAGS_TARGET_PREFIX ${GFLAGS_TARGET_NAMESPACE}::)
-else ()
-  set (GFLAGS_TARGET_PREFIX)
-endif ()
-
-# installation prefix
-get_filename_component (CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component (_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
-
-# include directory
-#
-# Newer versions of CMake set the INTERFACE_INCLUDE_DIRECTORIES property
-# of the imported targets. It is hence not necessary to add this path
-# manually to the include search path for targets which link to gflags.
-set (GFLAGS_INCLUDE_DIR "${_INSTALL_PREFIX}/include")
-
-if (gflags_FIND_COMPONENTS)
-  foreach (gflags_FIND_COMPONENT IN LISTS gflags_FIND_COMPONENTS)
-    if (gflags_FIND_REQUIRED_${gflags_FIND_COMPONENT} AND NOT TARGET gflags_${gflags_FIND_COMPONENT})
-      message (FATAL_ERROR "Package gflags was installed without required component ${gflags_FIND_COMPONENT}!")
-    endif ()
-  endforeach ()
-  list (GET gflags_FIND_COMPONENTS 0 gflags_FIND_COMPONENT)
-else ()
-  set (gflags_FIND_COMPONENT)
-endif ()
-
-# default settings of GFLAGS_SHARED and GFLAGS_NOTHREADS
-#
-# It is recommended to use either one of the following find_package commands
-# instead of setting the GFLAGS_(SHARED|NOTHREADS) variables:
-# - find_package(gflags REQUIRED)
-# - find_package(gflags COMPONENTS nothreads_static)
-# - find_package(gflags COMPONENTS nothreads_shared)
-# - find_package(gflags COMPONENTS static)
-# - find_package(gflags COMPONENTS shared)
-if (NOT DEFINED GFLAGS_SHARED)
-  if (DEFINED gflags_SHARED)
-    set (GFLAGS_SHARED ${gflags_SHARED})
-  elseif (gflags_FIND_COMPONENT)
-    if (gflags_FIND_COMPONENT MATCHES "shared")
-      set (GFLAGS_SHARED TRUE)
-    else ()
-      set (GFLAGS_SHARED FALSE)
-    endif ()
-  elseif (TARGET ${GFLAGS_TARGET_PREFIX}gflags_shared OR TARGET ${GFLAGS_TARGET_PREFIX}gflags_nothreads_shared)
-    set (GFLAGS_SHARED TRUE)
-  else ()
-    set (GFLAGS_SHARED FALSE)
-  endif ()
-endif ()
-if (NOT DEFINED GFLAGS_NOTHREADS)
-  if (DEFINED gflags_NOTHREADS)
-    set (GFLAGS_NOTHREADS ${gflags_NOTHREADS})
-  elseif (gflags_FIND_COMPONENT)
-    if (gflags_FIND_COMPONENT MATCHES "nothreads")
-      set (GFLAGS_NOTHREADS TRUE)
-    else ()
-      set (GFLAGS_NOTHREADS FALSE)
-    endif ()
-  elseif (TARGET ${GFLAGS_TARGET_PREFIX}PACKAGE_NAME@_static OR TARGET ${GFLAGS_TARGET_PREFIX}gflags_shared)
-    set (GFLAGS_NOTHREADS FALSE)
-  else ()
-    set (GFLAGS_NOTHREADS TRUE)
-  endif ()
-endif ()
-
-# choose imported library target
-if (NOT GFLAGS_TARGET)
-  if (gflags_TARGET)
-    set (GFLAGS_TARGET ${gflags_TARGET})
-  elseif (GFLAGS_SHARED)
-    if (GFLAGS_NOTHREADS)
-      set (GFLAGS_TARGET ${GFLAGS_TARGET_PREFIX}gflags_nothreads_shared)
-    else ()
-      set (GFLAGS_TARGET ${GFLAGS_TARGET_PREFIX}gflags_shared)
-    endif ()
-  else ()
-    if (GFLAGS_NOTHREADS)
-      set (GFLAGS_TARGET ${GFLAGS_TARGET_PREFIX}gflags_nothreads_static)
-    else ()
-      set (GFLAGS_TARGET ${GFLAGS_TARGET_PREFIX}gflags_static)
-    endif ()
-  endif ()
-endif ()
-if (NOT TARGET ${GFLAGS_TARGET})
-  message (FATAL_ERROR "Your gflags installation does not contain a ${GFLAGS_TARGET} library target!"
-                       " Try a different combination of GFLAGS_SHARED and GFLAGS_NOTHREADS.")
-endif ()
-
-# add more convenient "${GFLAGS_TARGET_PREFIX}gflags" import target
-if (NOT TARGET ${GFLAGS_TARGET_PREFIX}gflags)
-  if (GFLAGS_SHARED)
-    add_library (${GFLAGS_TARGET_PREFIX}gflags SHARED IMPORTED)
-  else ()
-    add_library (${GFLAGS_TARGET_PREFIX}gflags STATIC IMPORTED)
-  endif ()
-  # copy INTERFACE_* properties
-  foreach (_GFLAGS_PROPERTY_NAME IN ITEMS
-    COMPILE_DEFINITIONS
-    COMPILE_FEATURES
-    COMPILE_OPTIONS
-    INCLUDE_DIRECTORIES
-    LINK_LIBRARIES
-    POSITION_INDEPENDENT_CODE
-  )
-    get_target_property (_GFLAGS_PROPERTY_VALUE ${GFLAGS_TARGET} INTERFACE_${_GFLAGS_PROPERTY_NAME})
-    if (_GFLAGS_PROPERTY_VALUE)
-      set_target_properties(${GFLAGS_TARGET_PREFIX}gflags PROPERTIES
-        INTERFACE_${_GFLAGS_PROPERTY_NAME} "${_GFLAGS_PROPERTY_VALUE}"
-      )
-    endif ()
-  endforeach ()
-  # copy IMPORTED_*_<CONFIG> properties
-  get_target_property (_GFLAGS_CONFIGURATIONS ${GFLAGS_TARGET} IMPORTED_CONFIGURATIONS)
-  set_target_properties (${GFLAGS_TARGET_PREFIX}gflags PROPERTIES IMPORTED_CONFIGURATIONS "${_GFLAGS_CONFIGURATIONS}")
-  foreach (_GFLAGS_PROPERTY_NAME IN ITEMS
-    IMPLIB
-    LOCATION
-    LINK_DEPENDENT_LIBRARIES
-    LINK_INTERFACE_LIBRARIES
-    LINK_INTERFACE_LANGUAGES
-    LINK_INTERFACE_MULTIPLICITY
-    NO_SONAME
-    SONAME
-  )
-    foreach (_GFLAGS_CONFIG IN LISTS _GFLAGS_CONFIGURATIONS)
-      get_target_property (_GFLAGS_PROPERTY_VALUE ${GFLAGS_TARGET} IMPORTED_${_GFLAGS_PROPERTY_NAME}_${_GFLAGS_CONFIG})
-      if (_GFLAGS_PROPERTY_VALUE)
-        set_target_properties(${GFLAGS_TARGET_PREFIX}gflags PROPERTIES
-          IMPORTED_${_GFLAGS_PROPERTY_NAME}_${_GFLAGS_CONFIG} "${_GFLAGS_PROPERTY_VALUE}"
-        )
-      endif ()
-    endforeach ()
-  endforeach ()
-  unset (_GFLAGS_CONFIGURATIONS)
-  unset (_GFLAGS_CONFIG)
-  unset (_GFLAGS_PROPERTY_NAME)
-  unset (_GFLAGS_PROPERTY_VALUE)
-endif ()
-
-# alias for default import target to be compatible with older CMake package configurations
-set (GFLAGS_LIBRARIES "${GFLAGS_TARGET}")
-
-# set gflags_* variables for backwards compatibility
-if (NOT "^gflags$" STREQUAL "^GFLAGS$")
-  foreach (_GFLAGS_VARIABLE IN ITEMS
-    VERSION_STRING
-    VERSION_MAJOR
-    VERSION_MINOR
-    VERSION_PATCH
-    INCLUDE_DIR
-    LIBRARIES
-    TARGET
-  )
-    set (gflags_${_GFLAGS_VARIABLE} "${GFLAGS_${_GFLAGS_VARIABLE}}")
-  endforeach ()
-  unset (_GFLAGS_VARIABLE)
-endif ()
-
-# unset private variables
-unset (gflags_FIND_COMPONENT)
-unset (_INSTALL_PREFIX)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
deleted file mode 100644
index b65d787..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
+++ /dev/null
@@ -1,29 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Debug".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "gflags_shared" for configuration "Debug"
-set_property(TARGET gflags_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-set_target_properties(gflags_shared PROPERTIES
-  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/gflags_debug.lib"
-  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/gflags_debug.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags_shared )
-list(APPEND _cmake_import_check_files_for_gflags_shared "${_IMPORT_PREFIX}/lib/gflags_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_debug.dll" )
-
-# Import target "gflags_nothreads_shared" for configuration "Debug"
-set_property(TARGET gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-set_target_properties(gflags_nothreads_shared PROPERTIES
-  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib"
-  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags_nothreads_shared )
-list(APPEND _cmake_import_check_files_for_gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake
deleted file mode 100644
index dc014ca..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake
+++ /dev/null
@@ -1,29 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Release".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "gflags_shared" for configuration "Release"
-set_property(TARGET gflags_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(gflags_shared PROPERTIES
-  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/gflags.lib"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/gflags.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags_shared )
-list(APPEND _cmake_import_check_files_for_gflags_shared "${_IMPORT_PREFIX}/lib/gflags.lib" "${_IMPORT_PREFIX}/bin/gflags.dll" )
-
-# Import target "gflags_nothreads_shared" for configuration "Release"
-set_property(TARGET gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(gflags_nothreads_shared PROPERTIES
-  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags_nothreads_shared )
-list(APPEND _cmake_import_check_files_for_gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets.cmake
deleted file mode 100644
index fb14f4e..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-nonamespace-targets.cmake
+++ /dev/null
@@ -1,117 +0,0 @@
-# Generated by CMake
-
-if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
-   message(FATAL_ERROR "CMake >= 2.8.0 required")
-endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.3")
-   message(FATAL_ERROR "CMake >= 2.8.3 required")
-endif()
-cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.3...3.23)
-#----------------------------------------------------------------
-# Generated CMake target import file.
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Protect against multiple inclusion, which would fail when already imported targets are added once more.
-set(_cmake_targets_defined "")
-set(_cmake_targets_not_defined "")
-set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS gflags_shared gflags_nothreads_shared)
-  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
-  if(TARGET "${_cmake_expected_target}")
-    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
-  else()
-    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
-  endif()
-endforeach()
-unset(_cmake_expected_target)
-if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
-  unset(_cmake_targets_defined)
-  unset(_cmake_targets_not_defined)
-  unset(_cmake_expected_targets)
-  unset(CMAKE_IMPORT_FILE_VERSION)
-  cmake_policy(POP)
-  return()
-endif()
-if(NOT _cmake_targets_defined STREQUAL "")
-  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
-  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
-  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
-endif()
-unset(_cmake_targets_defined)
-unset(_cmake_targets_not_defined)
-unset(_cmake_expected_targets)
-
-
-# Compute the installation prefix relative to this file.
-get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-if(_IMPORT_PREFIX STREQUAL "/")
-  set(_IMPORT_PREFIX "")
-endif()
-
-# Create imported target gflags_shared
-add_library(gflags_shared SHARED IMPORTED)
-
-set_target_properties(gflags_shared PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS "GFLAGS_IS_A_DLL=1"
-  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
-  INTERFACE_LINK_LIBRARIES "shlwapi.lib"
-)
-
-# Create imported target gflags_nothreads_shared
-add_library(gflags_nothreads_shared SHARED IMPORTED)
-
-set_target_properties(gflags_nothreads_shared PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS "GFLAGS_IS_A_DLL=1"
-  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
-  INTERFACE_LINK_LIBRARIES "shlwapi.lib"
-)
-
-if(CMAKE_VERSION VERSION_LESS 2.8.12)
-  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
-endif()
-
-# Load information for each installed configuration.
-file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/gflags-nonamespace-targets-*.cmake")
-foreach(_cmake_config_file IN LISTS _cmake_config_files)
-  include("${_cmake_config_file}")
-endforeach()
-unset(_cmake_config_file)
-unset(_cmake_config_files)
-
-# Cleanup temporary variables.
-set(_IMPORT_PREFIX)
-
-# Loop over all imported files and verify that they actually exist
-foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-    if(NOT EXISTS "${_cmake_file}")
-      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
-   \"${_cmake_file}\"
-but this file does not exist.  Possible reasons include:
-* The file was deleted, renamed, or moved to another location.
-* An install or uninstall procedure did not complete successfully.
-* The installation package was faulty and contained
-   \"${CMAKE_CURRENT_LIST_FILE}\"
-but not all the files it references.
-")
-    endif()
-  endforeach()
-  unset(_cmake_file)
-  unset("_cmake_import_check_files_for_${_cmake_target}")
-endforeach()
-unset(_cmake_target)
-unset(_cmake_import_check_targets)
-
-# This file does not depend on other imported targets which have
-# been exported from the same project but in a separate export set.
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
-cmake_policy(POP)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets-debug.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets-debug.cmake
deleted file mode 100644
index 94a1996..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets-debug.cmake
+++ /dev/null
@@ -1,29 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Debug".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "gflags::gflags_shared" for configuration "Debug"
-set_property(TARGET gflags::gflags_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-set_target_properties(gflags::gflags_shared PROPERTIES
-  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/gflags_debug.lib"
-  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/gflags_debug.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags::gflags_shared )
-list(APPEND _cmake_import_check_files_for_gflags::gflags_shared "${_IMPORT_PREFIX}/lib/gflags_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_debug.dll" )
-
-# Import target "gflags::gflags_nothreads_shared" for configuration "Debug"
-set_property(TARGET gflags::gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-set_target_properties(gflags::gflags_nothreads_shared PROPERTIES
-  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib"
-  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags::gflags_nothreads_shared )
-list(APPEND _cmake_import_check_files_for_gflags::gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets-release.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets-release.cmake
deleted file mode 100644
index abda2c0..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets-release.cmake
+++ /dev/null
@@ -1,29 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Release".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "gflags::gflags_shared" for configuration "Release"
-set_property(TARGET gflags::gflags_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(gflags::gflags_shared PROPERTIES
-  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/gflags.lib"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/gflags.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags::gflags_shared )
-list(APPEND _cmake_import_check_files_for_gflags::gflags_shared "${_IMPORT_PREFIX}/lib/gflags.lib" "${_IMPORT_PREFIX}/bin/gflags.dll" )
-
-# Import target "gflags::gflags_nothreads_shared" for configuration "Release"
-set_property(TARGET gflags::gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(gflags::gflags_nothreads_shared PROPERTIES
-  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll"
-  )
-
-list(APPEND _cmake_import_check_targets gflags::gflags_nothreads_shared )
-list(APPEND _cmake_import_check_files_for_gflags::gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets.cmake b/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets.cmake
deleted file mode 100644
index c2adf25..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/gflags/gflags-targets.cmake
+++ /dev/null
@@ -1,117 +0,0 @@
-# Generated by CMake
-
-if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
-   message(FATAL_ERROR "CMake >= 2.8.0 required")
-endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.3")
-   message(FATAL_ERROR "CMake >= 2.8.3 required")
-endif()
-cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.3...3.23)
-#----------------------------------------------------------------
-# Generated CMake target import file.
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Protect against multiple inclusion, which would fail when already imported targets are added once more.
-set(_cmake_targets_defined "")
-set(_cmake_targets_not_defined "")
-set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS gflags::gflags_shared gflags::gflags_nothreads_shared)
-  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
-  if(TARGET "${_cmake_expected_target}")
-    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
-  else()
-    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
-  endif()
-endforeach()
-unset(_cmake_expected_target)
-if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
-  unset(_cmake_targets_defined)
-  unset(_cmake_targets_not_defined)
-  unset(_cmake_expected_targets)
-  unset(CMAKE_IMPORT_FILE_VERSION)
-  cmake_policy(POP)
-  return()
-endif()
-if(NOT _cmake_targets_defined STREQUAL "")
-  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
-  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
-  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
-endif()
-unset(_cmake_targets_defined)
-unset(_cmake_targets_not_defined)
-unset(_cmake_expected_targets)
-
-
-# Compute the installation prefix relative to this file.
-get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-if(_IMPORT_PREFIX STREQUAL "/")
-  set(_IMPORT_PREFIX "")
-endif()
-
-# Create imported target gflags::gflags_shared
-add_library(gflags::gflags_shared SHARED IMPORTED)
-
-set_target_properties(gflags::gflags_shared PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS "GFLAGS_IS_A_DLL=1"
-  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
-  INTERFACE_LINK_LIBRARIES "shlwapi.lib"
-)
-
-# Create imported target gflags::gflags_nothreads_shared
-add_library(gflags::gflags_nothreads_shared SHARED IMPORTED)
-
-set_target_properties(gflags::gflags_nothreads_shared PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS "GFLAGS_IS_A_DLL=1"
-  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
-  INTERFACE_LINK_LIBRARIES "shlwapi.lib"
-)
-
-if(CMAKE_VERSION VERSION_LESS 2.8.12)
-  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
-endif()
-
-# Load information for each installed configuration.
-file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/gflags-targets-*.cmake")
-foreach(_cmake_config_file IN LISTS _cmake_config_files)
-  include("${_cmake_config_file}")
-endforeach()
-unset(_cmake_config_file)
-unset(_cmake_config_files)
-
-# Cleanup temporary variables.
-set(_IMPORT_PREFIX)
-
-# Loop over all imported files and verify that they actually exist
-foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-    if(NOT EXISTS "${_cmake_file}")
-      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
-   \"${_cmake_file}\"
-but this file does not exist.  Possible reasons include:
-* The file was deleted, renamed, or moved to another location.
-* An install or uninstall procedure did not complete successfully.
-* The installation package was faulty and contained
-   \"${CMAKE_CURRENT_LIST_FILE}\"
-but not all the files it references.
-")
-    endif()
-  endforeach()
-  unset(_cmake_file)
-  unset("_cmake_import_check_files_for_${_cmake_target}")
-endforeach()
-unset(_cmake_target)
-unset(_cmake_import_check_targets)
-
-# This file does not depend on other imported targets which have
-# been exported from the same project but in a separate export set.
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
-cmake_policy(POP)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-config-version.cmake b/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-config-version.cmake
deleted file mode 100644
index 87f98bb..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-config-version.cmake
+++ /dev/null
@@ -1,70 +0,0 @@
-# This is a basic version file for the Config-mode of find_package().
-# It is used by write_basic_package_version_file() as input file for configure_file()
-# to create a version-file which can be installed along a config.cmake file.
-#
-# The created file sets PACKAGE_VERSION_EXACT if the current version string and
-# the requested version string are exactly the same and it sets
-# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version,
-# but only if the requested major version is the same as the current one.
-# The variable CVF_VERSION must be set before calling configure_file().
-
-
-set(PACKAGE_VERSION "0.4.0")
-
-if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION)
-  set(PACKAGE_VERSION_COMPATIBLE FALSE)
-else()
-
-  if("0.4.0" MATCHES "^([0-9]+)\\.")
-    set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}")
-    if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0)
-      string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}")
-    endif()
-  else()
-    set(CVF_VERSION_MAJOR "0.4.0")
-  endif()
-
-  if(PACKAGE_FIND_VERSION_RANGE)
-    # both endpoints of the range must have the expected major version
-    math (EXPR CVF_VERSION_MAJOR_NEXT "${CVF_VERSION_MAJOR} + 1")
-    if (NOT PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
-        OR ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX_MAJOR STREQUAL CVF_VERSION_MAJOR)
-          OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX VERSION_LESS_EQUAL CVF_VERSION_MAJOR_NEXT)))
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    elseif(PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
-        AND ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND PACKAGE_VERSION VERSION_LESS_EQUAL PACKAGE_FIND_VERSION_MAX)
-        OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION_MAX)))
-      set(PACKAGE_VERSION_COMPATIBLE TRUE)
-    else()
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    endif()
-  else()
-    if(PACKAGE_FIND_VERSION_MAJOR STREQUAL CVF_VERSION_MAJOR)
-      set(PACKAGE_VERSION_COMPATIBLE TRUE)
-    else()
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    endif()
-
-    if(PACKAGE_FIND_VERSION STREQUAL PACKAGE_VERSION)
-      set(PACKAGE_VERSION_EXACT TRUE)
-    endif()
-  endif()
-endif()
-
-
-# if the installed project requested no architecture check, don't perform the check
-if("FALSE")
-  return()
-endif()
-
-# if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
-if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "8" STREQUAL "")
-  return()
-endif()
-
-# check that the installed version has the same 32/64bit-ness as the one which is currently searching:
-if(NOT CMAKE_SIZEOF_VOID_P STREQUAL "8")
-  math(EXPR installedBits "8 * 8")
-  set(PACKAGE_VERSION "${PACKAGE_VERSION} (${installedBits}bit)")
-  set(PACKAGE_VERSION_UNSUITABLE TRUE)
-endif()
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-config.cmake b/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-config.cmake
deleted file mode 100644
index dbb0e2d..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-config.cmake
+++ /dev/null
@@ -1,25 +0,0 @@
-if (CMAKE_VERSION VERSION_LESS 3.9)
-  message (FATAL_ERROR "CMake >= 3.9 required")
-endif (CMAKE_VERSION VERSION_LESS 3.9)
-
-
-####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
-####### Any changes to this file will be overwritten by the next CMake run ####
-####### The input file was glog-config.cmake.in                            ########
-
-get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
-
-macro(set_and_check _var _file)
-  set(${_var} "${_file}")
-  if(NOT EXISTS "${_file}")
-    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
-  endif()
-endmacro()
-
-####################################################################################
-
-include (CMakeFindDependencyMacro)
-
-find_dependency (gflags 2.2.2 NO_MODULE)
-
-include ("${CMAKE_CURRENT_LIST_DIR}/glog-targets.cmake")
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-targets-debug.cmake b/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-targets-debug.cmake
deleted file mode 100644
index 447e3bc..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-targets-debug.cmake
+++ /dev/null
@@ -1,19 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Debug".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "glog::glog" for configuration "Debug"
-set_property(TARGET glog::glog APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-set_target_properties(glog::glog PROPERTIES
-  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/glogd.lib"
-  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/glogd.dll"
-  )
-
-list(APPEND _cmake_import_check_targets glog::glog )
-list(APPEND _cmake_import_check_files_for_glog::glog "${_IMPORT_PREFIX}/lib/glogd.lib" "${_IMPORT_PREFIX}/bin/glogd.dll" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-targets.cmake b/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-targets.cmake
deleted file mode 100644
index 0201ac5..0000000
--- a/3rdParty/my_ceres_vc17/lib/cmake/glog/glog-targets.cmake
+++ /dev/null
@@ -1,108 +0,0 @@
-# Generated by CMake
-
-if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
-   message(FATAL_ERROR "CMake >= 2.8.0 required")
-endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.3")
-   message(FATAL_ERROR "CMake >= 2.8.3 required")
-endif()
-cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.3...3.23)
-#----------------------------------------------------------------
-# Generated CMake target import file.
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Protect against multiple inclusion, which would fail when already imported targets are added once more.
-set(_cmake_targets_defined "")
-set(_cmake_targets_not_defined "")
-set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS glog::glog)
-  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
-  if(TARGET "${_cmake_expected_target}")
-    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
-  else()
-    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
-  endif()
-endforeach()
-unset(_cmake_expected_target)
-if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
-  unset(_cmake_targets_defined)
-  unset(_cmake_targets_not_defined)
-  unset(_cmake_expected_targets)
-  unset(CMAKE_IMPORT_FILE_VERSION)
-  cmake_policy(POP)
-  return()
-endif()
-if(NOT _cmake_targets_defined STREQUAL "")
-  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
-  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
-  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
-endif()
-unset(_cmake_targets_defined)
-unset(_cmake_targets_not_defined)
-unset(_cmake_expected_targets)
-
-
-# Compute the installation prefix relative to this file.
-get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-if(_IMPORT_PREFIX STREQUAL "/")
-  set(_IMPORT_PREFIX "")
-endif()
-
-# Create imported target glog::glog
-add_library(glog::glog SHARED IMPORTED)
-
-set_target_properties(glog::glog PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS "GLOG_NO_ABBREVIATED_SEVERITIES;GOOGLE_GLOG_DLL_DECL=__declspec(dllimport);GOOGLE_GLOG_DLL_DECL_FOR_UNITTESTS=__declspec(dllimport)"
-  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
-  INTERFACE_LINK_LIBRARIES "dbghelp;gflags"
-)
-
-if(CMAKE_VERSION VERSION_LESS 2.8.12)
-  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
-endif()
-
-# Load information for each installed configuration.
-file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/glog-targets-*.cmake")
-foreach(_cmake_config_file IN LISTS _cmake_config_files)
-  include("${_cmake_config_file}")
-endforeach()
-unset(_cmake_config_file)
-unset(_cmake_config_files)
-
-# Cleanup temporary variables.
-set(_IMPORT_PREFIX)
-
-# Loop over all imported files and verify that they actually exist
-foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-    if(NOT EXISTS "${_cmake_file}")
-      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
-   \"${_cmake_file}\"
-but this file does not exist.  Possible reasons include:
-* The file was deleted, renamed, or moved to another location.
-* An install or uninstall procedure did not complete successfully.
-* The installation package was faulty and contained
-   \"${CMAKE_CURRENT_LIST_FILE}\"
-but not all the files it references.
-")
-    endif()
-  endforeach()
-  unset(_cmake_file)
-  unset("_cmake_import_check_files_for_${_cmake_target}")
-endforeach()
-unset(_cmake_target)
-unset(_cmake_import_check_targets)
-
-# This file does not depend on other imported targets which have
-# been exported from the same project but in a separate export set.
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
-cmake_policy(POP)
diff --git a/3rdParty/my_ceres_vc17/lib/gflags.lib b/3rdParty/my_ceres_vc17/lib/gflags.lib
deleted file mode 100644
index fb7153d..0000000
Binary files a/3rdParty/my_ceres_vc17/lib/gflags.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/lib/gflags_nothreads.lib b/3rdParty/my_ceres_vc17/lib/gflags_nothreads.lib
deleted file mode 100644
index f008d3e..0000000
Binary files a/3rdParty/my_ceres_vc17/lib/gflags_nothreads.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/lib/glog.lib b/3rdParty/my_ceres_vc17/lib/glog.lib
deleted file mode 100644
index 2c66140..0000000
Binary files a/3rdParty/my_ceres_vc17/lib/glog.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/lib/glogd.lib b/3rdParty/my_ceres_vc17/lib/glogd.lib
deleted file mode 100644
index 44fb071..0000000
Binary files a/3rdParty/my_ceres_vc17/lib/glogd.lib and /dev/null differ
diff --git a/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3Config.cmake b/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3Config.cmake
deleted file mode 100644
index 398a051..0000000
--- a/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3Config.cmake
+++ /dev/null
@@ -1,37 +0,0 @@
-# This file exports the Eigen3::Eigen CMake target which should be passed to the
-# target_link_libraries command.
-
-
-####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
-####### Any changes to this file will be overwritten by the next CMake run ####
-####### The input file was Eigen3Config.cmake.in                            ########
-
-get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
-
-macro(set_and_check _var _file)
-  set(${_var} "${_file}")
-  if(NOT EXISTS "${_file}")
-    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
-  endif()
-endmacro()
-
-####################################################################################
-
-if (NOT TARGET eigen)
-  include ("${CMAKE_CURRENT_LIST_DIR}/Eigen3Targets.cmake")
-endif ()
-
-# Legacy variables, do *not* use. May be removed in the future.
-
-set (EIGEN3_FOUND 1)
-set (EIGEN3_USE_FILE    "${CMAKE_CURRENT_LIST_DIR}/UseEigen3.cmake")
-
-set (EIGEN3_DEFINITIONS  "")
-set (EIGEN3_INCLUDE_DIR  "${PACKAGE_PREFIX_DIR}/include/eigen3")
-set (EIGEN3_INCLUDE_DIRS "${PACKAGE_PREFIX_DIR}/include/eigen3")
-set (EIGEN3_ROOT_DIR     "${PACKAGE_PREFIX_DIR}")
-
-set (EIGEN3_VERSION_STRING "3.4.0")
-set (EIGEN3_VERSION_MAJOR  "3")
-set (EIGEN3_VERSION_MINOR  "4")
-set (EIGEN3_VERSION_PATCH  "0")
diff --git a/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3ConfigVersion.cmake b/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3ConfigVersion.cmake
deleted file mode 100644
index 944ed77..0000000
--- a/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3ConfigVersion.cmake
+++ /dev/null
@@ -1,70 +0,0 @@
-# This is a basic version file for the Config-mode of find_package().
-# It is used by write_basic_package_version_file() as input file for configure_file()
-# to create a version-file which can be installed along a config.cmake file.
-#
-# The created file sets PACKAGE_VERSION_EXACT if the current version string and
-# the requested version string are exactly the same and it sets
-# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version,
-# but only if the requested major version is the same as the current one.
-# The variable CVF_VERSION must be set before calling configure_file().
-
-
-set(PACKAGE_VERSION "3.4.0")
-
-if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION)
-  set(PACKAGE_VERSION_COMPATIBLE FALSE)
-else()
-
-  if("3.4.0" MATCHES "^([0-9]+)\\.")
-    set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}")
-    if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0)
-      string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}")
-    endif()
-  else()
-    set(CVF_VERSION_MAJOR "3.4.0")
-  endif()
-
-  if(PACKAGE_FIND_VERSION_RANGE)
-    # both endpoints of the range must have the expected major version
-    math (EXPR CVF_VERSION_MAJOR_NEXT "${CVF_VERSION_MAJOR} + 1")
-    if (NOT PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
-        OR ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX_MAJOR STREQUAL CVF_VERSION_MAJOR)
-          OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX VERSION_LESS_EQUAL CVF_VERSION_MAJOR_NEXT)))
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    elseif(PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
-        AND ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND PACKAGE_VERSION VERSION_LESS_EQUAL PACKAGE_FIND_VERSION_MAX)
-        OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION_MAX)))
-      set(PACKAGE_VERSION_COMPATIBLE TRUE)
-    else()
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    endif()
-  else()
-    if(PACKAGE_FIND_VERSION_MAJOR STREQUAL CVF_VERSION_MAJOR)
-      set(PACKAGE_VERSION_COMPATIBLE TRUE)
-    else()
-      set(PACKAGE_VERSION_COMPATIBLE FALSE)
-    endif()
-
-    if(PACKAGE_FIND_VERSION STREQUAL PACKAGE_VERSION)
-      set(PACKAGE_VERSION_EXACT TRUE)
-    endif()
-  endif()
-endif()
-
-
-# if the installed project requested no architecture check, don't perform the check
-if("FALSE")
-  return()
-endif()
-
-# if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
-if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "" STREQUAL "")
-  return()
-endif()
-
-# check that the installed version has the same 32/64bit-ness as the one which is currently searching:
-if(NOT CMAKE_SIZEOF_VOID_P STREQUAL "")
-  math(EXPR installedBits " * 8")
-  set(PACKAGE_VERSION "${PACKAGE_VERSION} (${installedBits}bit)")
-  set(PACKAGE_VERSION_UNSUITABLE TRUE)
-endif()
diff --git a/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3Targets.cmake b/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3Targets.cmake
deleted file mode 100644
index e22f2f4..0000000
--- a/3rdParty/my_ceres_vc17/share/eigen3/cmake/Eigen3Targets.cmake
+++ /dev/null
@@ -1,106 +0,0 @@
-# Generated by CMake
-
-if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
-   message(FATAL_ERROR "CMake >= 2.8.0 required")
-endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.3")
-   message(FATAL_ERROR "CMake >= 2.8.3 required")
-endif()
-cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.3...3.23)
-#----------------------------------------------------------------
-# Generated CMake target import file.
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Protect against multiple inclusion, which would fail when already imported targets are added once more.
-set(_cmake_targets_defined "")
-set(_cmake_targets_not_defined "")
-set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS Eigen3::Eigen)
-  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
-  if(TARGET "${_cmake_expected_target}")
-    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
-  else()
-    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
-  endif()
-endforeach()
-unset(_cmake_expected_target)
-if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
-  unset(_cmake_targets_defined)
-  unset(_cmake_targets_not_defined)
-  unset(_cmake_expected_targets)
-  unset(CMAKE_IMPORT_FILE_VERSION)
-  cmake_policy(POP)
-  return()
-endif()
-if(NOT _cmake_targets_defined STREQUAL "")
-  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
-  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
-  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
-endif()
-unset(_cmake_targets_defined)
-unset(_cmake_targets_not_defined)
-unset(_cmake_expected_targets)
-
-
-# Compute the installation prefix relative to this file.
-get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-if(_IMPORT_PREFIX STREQUAL "/")
-  set(_IMPORT_PREFIX "")
-endif()
-
-# Create imported target Eigen3::Eigen
-add_library(Eigen3::Eigen INTERFACE IMPORTED)
-
-set_target_properties(Eigen3::Eigen PROPERTIES
-  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include/eigen3"
-)
-
-if(CMAKE_VERSION VERSION_LESS 3.0.0)
-  message(FATAL_ERROR "This file relies on consumers using CMake 3.0.0 or greater.")
-endif()
-
-# Load information for each installed configuration.
-file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/Eigen3Targets-*.cmake")
-foreach(_cmake_config_file IN LISTS _cmake_config_files)
-  include("${_cmake_config_file}")
-endforeach()
-unset(_cmake_config_file)
-unset(_cmake_config_files)
-
-# Cleanup temporary variables.
-set(_IMPORT_PREFIX)
-
-# Loop over all imported files and verify that they actually exist
-foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-    if(NOT EXISTS "${_cmake_file}")
-      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
-   \"${_cmake_file}\"
-but this file does not exist.  Possible reasons include:
-* The file was deleted, renamed, or moved to another location.
-* An install or uninstall procedure did not complete successfully.
-* The installation package was faulty and contained
-   \"${CMAKE_CURRENT_LIST_FILE}\"
-but not all the files it references.
-")
-    endif()
-  endforeach()
-  unset(_cmake_file)
-  unset("_cmake_import_check_files_for_${_cmake_target}")
-endforeach()
-unset(_cmake_target)
-unset(_cmake_import_check_targets)
-
-# This file does not depend on other imported targets which have
-# been exported from the same project but in a separate export set.
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
-cmake_policy(POP)
diff --git a/3rdParty/my_ceres_vc17/share/eigen3/cmake/UseEigen3.cmake b/3rdParty/my_ceres_vc17/share/eigen3/cmake/UseEigen3.cmake
deleted file mode 100644
index a38bac8..0000000
--- a/3rdParty/my_ceres_vc17/share/eigen3/cmake/UseEigen3.cmake
+++ /dev/null
@@ -1,6 +0,0 @@
-#                                               -*- cmake -*-
-#
-#  UseEigen3.cmake
-
-add_definitions     ( ${EIGEN3_DEFINITIONS} )
-include_directories ( ${EIGEN3_INCLUDE_DIRS} )
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9d25c50..cb9f49c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,19 +1,27 @@
 cmake_minimum_required(VERSION 3.15.0)
 project(stitch VERSION 0.1.0)
 
-set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD 11)  # 或更高版本
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 
 
 SET(ArithStitchDir stitch)
 
 IF(WIN32)
-    set(OpenCV_DIR "C:/Lib/opencv410_vc17/x64/vc17/lib")
+    set(OpenCV_DIR "./3rdParty")
+
+    # 设置OpenCV头文件路径
+    set(OpenCV_INCLUDE_DIRS ${OpenCV_DIR}/include)
+    set(OpenCV_LIBS_DIR ${OpenCV_DIR}/lib)
+    link_directories(${OpenCV_LIBS_DIR})
+    # 设置OpenCV库文件
+    set(OpenCV_LIBS opencv_world455)
 ELSE(WIN32)
     set(OpenCV_DIR "/home/wcw/opencv-3.4.16/install/share/OpenCV")
+    find_package(OpenCV REQUIRED)
 ENDIF(WIN32)
 
-find_package(OpenCV REQUIRED)
+
 
 include_directories(${OpenCV_INCLUDE_DIRS} public_include ${ArithStitchDir}/src)
 
@@ -22,7 +30,7 @@ SET(LIB_STITCH GuideStitch)
 
 add_subdirectory(stitch)
 
-add_executable(stitch main.cpp   "3rdParty/my_ceres_vc17/include/ceres/version.h")
+add_executable(stitch main.cpp)
 
 target_link_libraries(stitch ${OpenCV_LIBS} ${LIB_STITCH})
 
diff --git a/stitch/CMakeLists.txt b/stitch/CMakeLists.txt
index ad9a57b..32d0f25 100644
--- a/stitch/CMakeLists.txt
+++ b/stitch/CMakeLists.txt
@@ -49,7 +49,7 @@ endif()
 #set(Ceres_DIR "../3rdParty/my_ceres_vc17/lib/cmake/Ceres")
 
 IF(WIN32)
-    set(Ceres_DIR "../3rdParty/my_ceres_vc17")
+    set(Ceres_DIR "../3rdParty")
     include_directories(${Ceres_DIR}/include)
     link_directories(${Ceres_DIR}/lib)
     file(GLOB CERES_LIBRARIES ${Ceres_DIR}/lib/*.lib)
diff --git a/stitch/src/Version.h b/stitch/src/Version.h
index 822627e..21ac481 100644
--- a/stitch/src/Version.h
+++ b/stitch/src/Version.h
@@ -2,5 +2,5 @@
 
 #pragma once
 #include <string>
-std::string BUILD_TIME = "BUILD_TIME 2025_04_29-08.56.38";
+std::string BUILD_TIME = "BUILD_TIME 2025_04_29-09.38.14";
 std::string VERSION = "BUILD_VERSION 1.0.1";